diff --git a/docs/00tutorials/index.rst b/docs/00tutorials/index.rst index d3af635..3af1f97 100644 --- a/docs/00tutorials/index.rst +++ b/docs/00tutorials/index.rst @@ -5,5 +5,6 @@ TTim tutorials. .. toctree:: :maxdepth: 3 - :hidden: :glob: + + models.rst \ No newline at end of file diff --git a/docs/01howto/getting_started/models.rst b/docs/00tutorials/models.rst similarity index 100% rename from docs/01howto/getting_started/models.rst rename to docs/00tutorials/models.rst diff --git a/docs/01howto/getting_started/index.rst b/docs/01howto/getting_started/index.rst deleted file mode 100644 index 787a52a..0000000 --- a/docs/01howto/getting_started/index.rst +++ /dev/null @@ -1,8 +0,0 @@ -Getting started with TTim -========================= - -.. toctree:: - :maxdepth: 1 - - models - pathlines \ No newline at end of file diff --git a/docs/01howto/howto_pumpingtest.ipynb b/docs/01howto/howto_pumpingtest.ipynb index 15f6def..5f251e1 100755 --- a/docs/01howto/howto_pumpingtest.ipynb +++ b/docs/01howto/howto_pumpingtest.ipynb @@ -21,9 +21,10 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import ttim as ttm\n", + "\n", "#\n", - "plt.rcParams['figure.figsize'] = (4, 3) # set default figure size\n", - "plt.rcParams['font.size'] = 8 # set default font size" + "plt.rcParams[\"figure.figsize\"] = (4, 3) # set default figure size\n", + "plt.rcParams[\"font.size\"] = 8 # set default font size" ] }, { @@ -65,22 +66,26 @@ } ], "source": [ - "data = np.loadtxt('data/oudekorendijk_h30.dat')\n", + "data = np.loadtxt(\"data/oudekorendijk_h30.dat\")\n", "to1 = data[:, 0] / 60 / 24\n", "ho1 = -data[:, 1]\n", "ro1 = 30\n", - "print(f'first measurement time: {np.min(to1):.2e}, last measurement time: {np.max(to1):.2e} d')\n", + "print(\n", + " f\"first measurement time: {np.min(to1):.2e}, last measurement time: {np.max(to1):.2e} d\"\n", + ")\n", "\n", - "drawdown = np.loadtxt('data/oudekorendijk_h90.dat')\n", + "drawdown = np.loadtxt(\"data/oudekorendijk_h90.dat\")\n", "to2 = drawdown[:, 0] / 60 / 24\n", "ho2 = -drawdown[:, 1]\n", "ro2 = 90\n", - "print(f'first measurement time: {np.min(to2):.2e}, last measurement time: {np.max(to2):.2e} d')\n", + "print(\n", + " f\"first measurement time: {np.min(to2):.2e}, last measurement time: {np.max(to2):.2e} d\"\n", + ")\n", "\n", - "plt.plot(to1, ho1, 'C0.', label='r=30 m')\n", - "plt.plot(to2, ho2, 'C1.', label='r=90 m')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)')\n", + "plt.plot(to1, ho1, \"C0.\", label=\"r=30 m\")\n", + "plt.plot(to2, ho2, \"C1.\", label=\"r=90 m\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\")\n", "plt.legend()\n", "plt.grid()" ] @@ -121,17 +126,8 @@ } ], "source": [ - "ml = ttm.ModelMaq(kaq=60, \n", - " z=(-18, -25), \n", - " Saq=1e-4, \n", - " tmin=1e-5, \n", - " tmax=1)\n", - "w = ttm.Well(model=ml, \n", - " xw=0, \n", - " yw=0, \n", - " rw=0.1, \n", - " tsandQ=[(0, 788)], \n", - " layers=0)\n", + "ml = ttm.ModelMaq(kaq=60, z=(-18, -25), Saq=1e-4, tmin=1e-5, tmax=1)\n", + "w = ttm.Well(model=ml, xw=0, yw=0, rw=0.1, tsandQ=[(0, 788)], layers=0)\n", "ml.solve()" ] }, @@ -154,12 +150,12 @@ "source": [ "hm1 = ml.head(ro1, 0, to1)\n", "hm2 = ml.head(ro2, 0, to2)\n", - "plt.plot(to1, ho1, 'C0.', label='r=30 m')\n", - "plt.plot(to1, hm1[0], 'C0')\n", - "plt.plot(to2, ho2, 'C1.', label='r=90 m')\n", - "plt.plot(to2, hm2[0], 'C1')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)')\n", + "plt.plot(to1, ho1, \"C0.\", label=\"r=30 m\")\n", + "plt.plot(to1, hm1[0], \"C0\")\n", + "plt.plot(to2, ho2, \"C1.\", label=\"r=90 m\")\n", + "plt.plot(to2, hm2[0], \"C1\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\")\n", "plt.legend()\n", "plt.grid()" ] @@ -191,16 +187,17 @@ } ], "source": [ - "cal = ttm.Calibrate(model=ml) # create Calibration object\n", - "cal.set_parameter(name='kaq0', initial=20) # \n", - "cal.set_parameter(name='Saq0', initial=1e-4)\n", - "cal.series(name='obs1', # user specified name\n", - " x=ro1, # x=location of observation well\n", - " y=0, # y-location of obsevation well\n", - " layer=0, # layer number\n", - " t=to1, # observation times\n", - " h=ho1 # observed heads\n", - " )\n", + "cal = ttm.Calibrate(model=ml) # create Calibration object\n", + "cal.set_parameter(name=\"kaq0\", initial=20) #\n", + "cal.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "cal.series(\n", + " name=\"obs1\", # user specified name\n", + " x=ro1, # x=location of observation well\n", + " y=0, # y-location of obsevation well\n", + " layer=0, # layer number\n", + " t=to1, # observation times\n", + " h=ho1, # observed heads\n", + ")\n", "cal.fit(report=False)" ] }, @@ -311,8 +308,8 @@ } ], "source": [ - "print('k of model: ', ml.aq.kaq)\n", - "print('Ss of model: ', ml.aq.Saq)" + "print(\"k of model: \", ml.aq.kaq)\n", + "print(\"Ss of model: \", ml.aq.Saq)" ] }, { @@ -340,10 +337,10 @@ ], "source": [ "hm1 = ml.head(ro1, 0, to1)\n", - "plt.plot(to1, ho1, 'k.', label='r=30 m')\n", - "plt.plot(to1, hm1[0], 'C0', label='fit')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)')\n", + "plt.plot(to1, ho1, \"k.\", label=\"r=30 m\")\n", + "plt.plot(to1, hm1[0], \"C0\", label=\"fit\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\")\n", "plt.legend()\n", "plt.grid()" ] @@ -452,33 +449,31 @@ } ], "source": [ - "ml2 = ttm.ModelMaq(kaq=60, \n", - " z=(-17, -18, -25), \n", - " Saq=1e-4, \n", - " c=[1000],\n", - " Sll=0,\n", - " topboundary='semi',\n", - " tmin=1e-5, \n", - " tmax=1)\n", - "w = ttm.Well(model=ml2, \n", - " xw=0, \n", - " yw=0, \n", - " rw=0.1, \n", - " tsandQ=[(0, 788)], \n", - " layers=0)\n", + "ml2 = ttm.ModelMaq(\n", + " kaq=60,\n", + " z=(-17, -18, -25),\n", + " Saq=1e-4,\n", + " c=[1000],\n", + " Sll=0,\n", + " topboundary=\"semi\",\n", + " tmin=1e-5,\n", + " tmax=1,\n", + ")\n", + "w = ttm.Well(model=ml2, xw=0, yw=0, rw=0.1, tsandQ=[(0, 788)], layers=0)\n", "ml2.solve()\n", "#\n", - "cal2 = ttm.Calibrate(model=ml2) # create Calibration object\n", - "cal2.set_parameter(name='kaq0', initial=20) # \n", - "cal2.set_parameter(name='Saq0', initial=1e-4)\n", - "cal2.set_parameter(name='c0', initial=1000)\n", - "cal2.series(name='obs1', # user specified name\n", - " x=ro1, # x=location of observation well\n", - " y=0, # y-location of obsevation well\n", - " layer=0, # layer number\n", - " t=to1, # observation times\n", - " h=ho1 # observed heads\n", - " )\n", + "cal2 = ttm.Calibrate(model=ml2) # create Calibration object\n", + "cal2.set_parameter(name=\"kaq0\", initial=20) #\n", + "cal2.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "cal2.set_parameter(name=\"c0\", initial=1000)\n", + "cal2.series(\n", + " name=\"obs1\", # user specified name\n", + " x=ro1, # x=location of observation well\n", + " y=0, # y-location of obsevation well\n", + " layer=0, # layer number\n", + " t=to1, # observation times\n", + " h=ho1, # observed heads\n", + ")\n", "cal2.fit(report=False)\n", "display(cal2.parameters)" ] @@ -509,11 +504,11 @@ "source": [ "hm1 = ml.head(ro1, 0, to1)\n", "hm2 = ml2.head(ro1, 0, to1)\n", - "plt.plot(to1, ho1, 'k.', label='r=30 m')\n", - "plt.plot(to1, hm1[0], label='fit 2 params')\n", - "plt.plot(to1, hm2[0], label='fit 3 params')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)')\n", + "plt.plot(to1, ho1, \"k.\", label=\"r=30 m\")\n", + "plt.plot(to1, hm1[0], label=\"fit 2 params\")\n", + "plt.plot(to1, hm2[0], label=\"fit 3 params\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\")\n", "plt.legend()\n", "plt.grid()" ] @@ -540,8 +535,8 @@ } ], "source": [ - "print(f'rmse 2 params: {cal.rmse():.3f} m')\n", - "print(f'rmse 3 params: {cal2.rmse():.3f} m')" + "print(f\"rmse 2 params: {cal.rmse():.3f} m\")\n", + "print(f\"rmse 3 params: {cal2.rmse():.3f} m\")" ] }, { @@ -651,16 +646,16 @@ } ], "source": [ - "cal.series(name='obs2', # user specified name\n", - " x=ro2, # x=location of observation well\n", - " y=0, # y-location of obsevation well\n", - " layer=0, # layer number\n", - " t=to2, # observation times\n", - " h=ho2 # observed heads\n", - " )\n", + "cal.series(\n", + " name=\"obs2\", # user specified name\n", + " x=ro2, # x=location of observation well\n", + " y=0, # y-location of obsevation well\n", + " layer=0, # layer number\n", + " t=to2, # observation times\n", + " h=ho2, # observed heads\n", + ")\n", "cal.fit(report=False)\n", - "display(cal.parameters)\n", - "print" + "display(cal.parameters)" ] }, { @@ -758,13 +753,14 @@ } ], "source": [ - "cal2.series(name='obs2', # user specified name\n", - " x=ro2, # x=location of observation well\n", - " y=0, # y-location of obsevation well\n", - " layer=0, # layer number\n", - " t=to2, # observation times\n", - " h=ho2 # observed heads\n", - " )\n", + "cal2.series(\n", + " name=\"obs2\", # user specified name\n", + " x=ro2, # x=location of observation well\n", + " y=0, # y-location of obsevation well\n", + " layer=0, # layer number\n", + " t=to2, # observation times\n", + " h=ho2, # observed heads\n", + ")\n", "cal2.fit(report=False)\n", "display(cal2.parameters)" ] @@ -784,8 +780,8 @@ } ], "source": [ - "print(f'rmse 2 params: {cal.rmse():.3f} m')\n", - "print(f'rmse 3 params: {cal2.rmse():.3f} m')" + "print(f\"rmse 2 params: {cal.rmse():.3f} m\")\n", + "print(f\"rmse 3 params: {cal2.rmse():.3f} m\")" ] }, { @@ -809,24 +805,24 @@ "plt.subplot(121)\n", "hm1 = ml.head(ro1, 0, to1)\n", "hm2 = ml.head(ro2, 0, to2)\n", - "plt.plot(to1, ho1, 'C0.', label='r=30 m')\n", - "plt.plot(to2, ho2, 'C1.', label='r=90 m')\n", - "plt.plot(to1, hm1[0], 'C0', label='fit 2 params')\n", - "plt.plot(to2, hm2[0], 'C1', label='fit 2 params')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)')\n", + "plt.plot(to1, ho1, \"C0.\", label=\"r=30 m\")\n", + "plt.plot(to2, ho2, \"C1.\", label=\"r=90 m\")\n", + "plt.plot(to1, hm1[0], \"C0\", label=\"fit 2 params\")\n", + "plt.plot(to2, hm2[0], \"C1\", label=\"fit 2 params\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\")\n", "plt.legend()\n", "plt.grid()\n", "#\n", "plt.subplot(122)\n", "hm1 = ml2.head(ro1, 0, to1)\n", "hm2 = ml2.head(ro2, 0, to2)\n", - "plt.plot(to1, ho1, 'C0.', label='r=30 m')\n", - "plt.plot(to2, ho2, 'C1.', label='r=90 m')\n", - "plt.plot(to1, hm1[0], 'C0', label='fit 3 params')\n", - "plt.plot(to2, hm2[0], 'C1', label='fit 3 params')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)')\n", + "plt.plot(to1, ho1, \"C0.\", label=\"r=30 m\")\n", + "plt.plot(to2, ho2, \"C1.\", label=\"r=90 m\")\n", + "plt.plot(to1, hm1[0], \"C0\", label=\"fit 3 params\")\n", + "plt.plot(to2, hm2[0], \"C1\", label=\"fit 3 params\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\")\n", "plt.legend()\n", "plt.grid()" ] @@ -859,24 +855,24 @@ "plt.subplot(121)\n", "hm1 = ml.head(ro1, 0, to1)\n", "hm2 = ml.head(ro2, 0, to2)\n", - "plt.semilogx(to1, ho1, 'C0.', label='r=30 m')\n", - "plt.semilogx(to2, ho2, 'C1.', label='r=90 m')\n", - "plt.semilogx(to1, hm1[0], 'C0', label='fit 2 params')\n", - "plt.semilogx(to2, hm2[0], 'C1', label='fit 2 params')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)')\n", + "plt.semilogx(to1, ho1, \"C0.\", label=\"r=30 m\")\n", + "plt.semilogx(to2, ho2, \"C1.\", label=\"r=90 m\")\n", + "plt.semilogx(to1, hm1[0], \"C0\", label=\"fit 2 params\")\n", + "plt.semilogx(to2, hm2[0], \"C1\", label=\"fit 2 params\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\")\n", "plt.legend()\n", "plt.grid()\n", "#\n", "plt.subplot(122)\n", "hm1 = ml2.head(ro1, 0, to1)\n", "hm2 = ml2.head(ro2, 0, to2)\n", - "plt.semilogx(to1, ho1, 'C0.', label='r=30 m')\n", - "plt.semilogx(to2, ho2, 'C1.', label='r=90 m')\n", - "plt.semilogx(to1, hm1[0], 'C0', label='fit 3 params')\n", - "plt.semilogx(to2, hm2[0], 'C1', label='fit 3 params')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)')\n", + "plt.semilogx(to1, ho1, \"C0.\", label=\"r=30 m\")\n", + "plt.semilogx(to2, ho2, \"C1.\", label=\"r=90 m\")\n", + "plt.semilogx(to1, hm1[0], \"C0\", label=\"fit 3 params\")\n", + "plt.semilogx(to2, hm2[0], \"C1\", label=\"fit 3 params\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\")\n", "plt.legend()\n", "plt.grid()" ] diff --git a/docs/01howto/index.rst b/docs/01howto/index.rst index 8911e70..858af6d 100644 --- a/docs/01howto/index.rst +++ b/docs/01howto/index.rst @@ -7,6 +7,4 @@ This section contains how to guides on building groundwater models with TTim. .. toctree:: :maxdepth: 1 - getting_started/index.rst - elements/index.rst howto_pumpingtest \ No newline at end of file diff --git a/docs/01howto/elements/areasinks.rst b/docs/02concepts/elements/areasinks.rst similarity index 100% rename from docs/01howto/elements/areasinks.rst rename to docs/02concepts/elements/areasinks.rst diff --git a/docs/01howto/elements/index.rst b/docs/02concepts/elements/index.rst similarity index 100% rename from docs/01howto/elements/index.rst rename to docs/02concepts/elements/index.rst diff --git a/docs/01howto/elements/linedoublets.rst b/docs/02concepts/elements/linedoublets.rst similarity index 100% rename from docs/01howto/elements/linedoublets.rst rename to docs/02concepts/elements/linedoublets.rst diff --git a/docs/01howto/elements/linesinks.rst b/docs/02concepts/elements/linesinks.rst similarity index 100% rename from docs/01howto/elements/linesinks.rst rename to docs/02concepts/elements/linesinks.rst diff --git a/docs/01howto/elements/wells.rst b/docs/02concepts/elements/wells.rst similarity index 100% rename from docs/01howto/elements/wells.rst rename to docs/02concepts/elements/wells.rst diff --git a/docs/02concepts/index.rst b/docs/02concepts/index.rst index 2fa4fbf..7350e4e 100644 --- a/docs/02concepts/index.rst +++ b/docs/02concepts/index.rst @@ -7,4 +7,5 @@ This section contains explanations of some basic concepts that are used in TTim. .. toctree:: :maxdepth: 3 - \ No newline at end of file + elements/index.rst + pathlines.rst \ No newline at end of file diff --git a/docs/01howto/getting_started/pathlines.rst b/docs/02concepts/pathlines.rst similarity index 100% rename from docs/01howto/getting_started/pathlines.rst rename to docs/02concepts/pathlines.rst diff --git a/docs/03examples/aem_ttim_sol.ipynb b/docs/03examples/aem_ttim_sol.ipynb index 374d930..cbc9c9d 100644 --- a/docs/03examples/aem_ttim_sol.ipynb +++ b/docs/03examples/aem_ttim_sol.ipynb @@ -157,7 +157,7 @@ "plt.plot(to, hm[0], \"r\", label=\"modeled\")\n", "plt.legend()\n", "plt.xlabel(\"time (d)\")\n", - "plt.ylabel(\"head (m)\");" + "plt.ylabel(\"head (m)\")" ] }, { @@ -339,7 +339,7 @@ "plt.plot(to, hm[0], \"r\", label=\"modeled\")\n", "plt.legend()\n", "plt.xlabel(\"time (d)\")\n", - "plt.ylabel(\"head (m)\");" + "plt.ylabel(\"head (m)\")" ] }, { @@ -503,7 +503,7 @@ "plt.plot(to, hm[0], \"r\", label=\"modeled\")\n", "plt.legend()\n", "plt.xlabel(\"time (d)\")\n", - "plt.ylabel(\"head (m)\");" + "plt.ylabel(\"head (m)\")" ] } ], diff --git a/docs/03examples/circareasink_example.ipynb b/docs/03examples/circareasink_example.ipynb index e638523..e61fd60 100644 --- a/docs/03examples/circareasink_example.ipynb +++ b/docs/03examples/circareasink_example.ipynb @@ -85,7 +85,7 @@ "plt.axhline(-qxb, color=\"r\", ls=\"--\")\n", "plt.xlabel(\"x (m)\")\n", "plt.ylabel(\"Qx (m^2/d)\")\n", - "plt.legend(loc=\"best\");" + "plt.legend(loc=\"best\")" ] }, { @@ -168,7 +168,7 @@ "plt.semilogx(t, h[0])\n", "plt.xlabel(\"time\")\n", "plt.ylabel(\"head\")\n", - "plt.title(\"head at center of area-sink\");" + "plt.title(\"head at center of area-sink\")" ] }, { diff --git a/docs/03examples/compare_wells_linesink.ipynb b/docs/03examples/compare_wells_linesink.ipynb index 4a6ac0e..6757145 100644 --- a/docs/03examples/compare_wells_linesink.ipynb +++ b/docs/03examples/compare_wells_linesink.ipynb @@ -101,7 +101,7 @@ "plt.semilogx(t, h2b[0], \"r--\")\n", "plt.xlabel(\"time\")\n", "plt.ylabel(\"head\")\n", - "plt.legend();" + "plt.legend()" ] }, { @@ -146,7 +146,7 @@ "source": [ "plt.figure(figsize=(12, 4))\n", "ml.contour([-2, 2, -2, 2], [40, 40], t=5)\n", - "ml2.contour([-2, 2, -2, 2], [40, 40], t=5);" + "ml2.contour([-2, 2, -2, 2], [40, 40], t=5)" ] }, { diff --git a/docs/03examples/index.rst b/docs/03examples/index.rst index afcda6b..0a420be 100644 --- a/docs/03examples/index.rst +++ b/docs/03examples/index.rst @@ -6,48 +6,68 @@ TTim example notebooks. .. toctree:: :maxdepth: 4 :hidden: - :glob: - compare_wells_linesink - aem_ttim_sol - circareasink_example + well_example + well_near_wall + well_near_river_or_wall line_sink_well_sol - line-sink-ditch + wells_in_different_systems + ttim_exercise1_sol meandering_river + circareasink_example pathline_trace + aem_ttim_sol pumpingtest_hypothetical pumpingtest - theis_storage - ttim_exercise1_sol - ttim_figures - ttim_neuman_comparison ttim_pumptest_neuman ttim_slugtest - well_example - well_near_river_or_wall - well_near_wall - wells_in_different_systems + theis_storage + ttim_neuman_comparison + compare_wells_linesink + line-sink-ditch +Modeling wells +-------------- -* `Comparing linesink with row of wells`_ -* `aem_ttim_sol`_ -* `circareasink_example`_ +* `well_example`_ +* `well_near_wall`_ +* `well_near_river_or_wall`_ * `line_sink_well_sol`_ -* `line-sink-ditch`_ +* `wells_in_different_systems`_ +* `ttim_exercise1_sol`_ + +Modeling rivers +--------------- + * `meandering_river`_ + +Recharge +-------- + +* `circareasink_example`_ + +Pathlines +--------- + * `pathline_trace`_ + + +Pumping tests +------------- + +* `aem_ttim_sol`_ * `pumpingtest_hypothetical`_ * `pumpingtest`_ -* `theis_storage`_ -* `ttim_exercise1_sol`_ -* `ttim_figures`_ -* `ttim_neuman_comparison`_ * `ttim_pumptest_neuman`_ * `ttim_slugtest`_ -* `well_example`_ -* `well_near_river_or_wall`_ -* `well_near_wall`_ -* `wells_in_different_systems`_ + +Benchmarks +---------- + +* `theis_storage`_ +* `ttim_neuman_comparison`_ +* `Comparing linesink with row of wells`_ +* `line-sink-ditch`_ .. _Comparing linesink with row of wells: compare_wells_linesink.html @@ -62,7 +82,6 @@ TTim example notebooks. .. _pumpingtest: pumpingtest.html .. _theis_storage: theis_storage.html .. _ttim_exercise1_sol: ttim_exercise1_sol.html -.. _ttim_figures: ttim_figures.html .. _ttim_neuman_comparison: ttim_neuman_comparison.html .. _ttim_pumptest_neuman: ttim_pumptest_neuman.html .. _ttim_slugtest: ttim_slugtest.html diff --git a/docs/03examples/line_sink_well_sol.ipynb b/docs/03examples/line_sink_well_sol.ipynb index 0d7f8a1..f388a1a 100755 --- a/docs/03examples/line_sink_well_sol.ipynb +++ b/docs/03examples/line_sink_well_sol.ipynb @@ -111,7 +111,7 @@ "Q = ls1.discharge(t)\n", "plt.semilogx(t, Q[0])\n", "plt.ylabel(\"Q [m$^3$/d]\")\n", - "plt.xlabel(\"time [days]\");" + "plt.xlabel(\"time [days]\")" ] }, { @@ -234,7 +234,7 @@ "Q = ls1.discharge(t)\n", "plt.plot(t, Q[0])\n", "plt.ylabel(\"Q [m$^3$/d]\")\n", - "plt.xlabel(\"time [days]\");" + "plt.xlabel(\"time [days]\")" ] } ], diff --git a/docs/03examples/pathline_trace.ipynb b/docs/03examples/pathline_trace.ipynb index 560382a..022801c 100644 --- a/docs/03examples/pathline_trace.ipynb +++ b/docs/03examples/pathline_trace.ipynb @@ -498,7 +498,7 @@ "for y in [0, H, H + Hstar]:\n", " plt.axhline(y, color=\"k\")\n", "plt.xlabel(\"$x$ (m)\")\n", - "plt.ylabel(\"$z$ (m)\");" + "plt.ylabel(\"$z$ (m)\")" ] }, { @@ -611,7 +611,7 @@ "for y in [0, H, H + Hstar, H + Hstar + H]:\n", " plt.axhline(y, color=\"k\")\n", "plt.xlabel(\"$x$ (m)\")\n", - "plt.ylabel(\"$z$ (m) - VE:5\");" + "plt.ylabel(\"$z$ (m) - VE:5\")" ] }, { @@ -710,7 +710,7 @@ " plt.axhline(z, color=\"k\")\n", "plt.axis(\"scaled\")\n", "plt.xlabel(\"$x$ (m)\")\n", - "plt.ylabel(\"$z$ (m)\");" + "plt.ylabel(\"$z$ (m)\")" ] } ], diff --git a/docs/03examples/pumpingtest.ipynb b/docs/03examples/pumpingtest.ipynb index 4eb376a..6ec873a 100644 --- a/docs/03examples/pumpingtest.ipynb +++ b/docs/03examples/pumpingtest.ipynb @@ -342,7 +342,7 @@ "plt.title(\"calibrated well 2\")\n", "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"head (m)\")\n", - "plt.legend();" + "plt.legend()" ] }, { @@ -625,7 +625,7 @@ "plt.title(\"calibrated well 2\")\n", "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"head (m)\")\n", - "plt.legend();" + "plt.legend()" ] }, { @@ -770,7 +770,7 @@ "plt.title(\"two wells simulaneously\")\n", "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"head (m)\")\n", - "plt.legend();" + "plt.legend()" ] } ], diff --git a/docs/03examples/ttim_exercise1_sol.ipynb b/docs/03examples/ttim_exercise1_sol.ipynb index 6fda529..16e7929 100755 --- a/docs/03examples/ttim_exercise1_sol.ipynb +++ b/docs/03examples/ttim_exercise1_sol.ipynb @@ -97,7 +97,7 @@ "plt.semilogx(t, h[1], label=\"layer 1\")\n", "plt.semilogx(t, h[2], label=\"layer 2\")\n", "plt.legend(loc=\"best\")\n", - "plt.xlabel(\"time [days]\");" + "plt.xlabel(\"time [days]\")" ] }, { @@ -140,7 +140,7 @@ "plt.ylabel(\"head (m)\")\n", "ml.xsection(x1=-1000, x2=1000, y1=0, y2=0, npoints=100, t=1000, layers=[0, 1, 2])\n", "plt.xlabel(\"distance (m)\")\n", - "plt.ylabel(\"head (m)\");" + "plt.ylabel(\"head (m)\")" ] }, { @@ -195,7 +195,7 @@ "plt.semilogx(t, h[2], label=\"layer 2\")\n", "plt.legend(loc=\"best\")\n", "plt.ylabel(\"head [m]\")\n", - "plt.xlabel(\"time [days]\");" + "plt.xlabel(\"time [days]\")" ] }, { @@ -239,7 +239,7 @@ "plt.semilogx(t, h[0], label=\"res=0.1\")\n", "plt.legend(loc=\"best\")\n", "plt.ylabel(\"head [m]\")\n", - "plt.xlabel(\"time [days]\");" + "plt.xlabel(\"time [days]\")" ] }, { @@ -301,7 +301,7 @@ "plt.legend(loc=\"best\")\n", "plt.ylabel(\"head [m]\")\n", "plt.xlabel(\"time [days]\")\n", - "plt.xticks([tmin, 10 * tmin, 60 * tmin, 1], [\"1 min\", \"10 min\", \"1 hr\", \"1 day\"]);" + "plt.xticks([tmin, 10 * tmin, 60 * tmin, 1], [\"1 min\", \"10 min\", \"1 hr\", \"1 day\"])" ] } ], diff --git a/docs/03examples/ttim_neuman_comparison.ipynb b/docs/03examples/ttim_neuman_comparison.ipynb index 494c23d..4780fd6 100644 --- a/docs/03examples/ttim_neuman_comparison.ipynb +++ b/docs/03examples/ttim_neuman_comparison.ipynb @@ -93,7 +93,7 @@ " plt.plot(np.log10(ts), np.log10(d[-1]), \"+\", markersize=15, mew=1.5)\n", "\n", "plt.xlabel(\"$\\log[Tt/(Sr^2)]$\", fontsize=14)\n", - "plt.ylabel(\"$\\log[4\\pi Ts/Q]$\", fontsize=14);" + "plt.ylabel(\"$\\log[4\\pi Ts/Q]$\", fontsize=14)" ] }, { @@ -160,7 +160,7 @@ " plt.plot(np.log10(ts), np.log10(d[-1]), \"+\", markersize=15, mew=1.5)\n", "\n", "plt.xlabel(\"$\\log[Tt/(Sr^2)]$\", fontsize=14)\n", - "plt.ylabel(\"$\\log[4\\pi Ts/Q]$\", fontsize=14);" + "plt.ylabel(\"$\\log[4\\pi Ts/Q]$\", fontsize=14)" ] } ], diff --git a/docs/03examples/ttim_pumptest_neuman.ipynb b/docs/03examples/ttim_pumptest_neuman.ipynb index e9345ad..f691add 100755 --- a/docs/03examples/ttim_pumptest_neuman.ipynb +++ b/docs/03examples/ttim_pumptest_neuman.ipynb @@ -208,7 +208,7 @@ "plt.xlabel(\"time [min]\")\n", "plt.ylabel(\"Drawdouwn (m)\")\n", "plt.legend(loc=\"best\")\n", - "plt.suptitle(\"TTim Aquifer Test Analysis in Unconfined Aquifer\");" + "plt.suptitle(\"TTim Aquifer Test Analysis in Unconfined Aquifer\")" ] }, { diff --git a/docs/03examples/ttim_slugtest.ipynb b/docs/03examples/ttim_slugtest.ipynb index 1b50635..f423a7d 100755 --- a/docs/03examples/ttim_slugtest.ipynb +++ b/docs/03examples/ttim_slugtest.ipynb @@ -187,7 +187,7 @@ "plt.xlabel(\"time [s]\")\n", "plt.ylabel(\"h / delh\")\n", "plt.legend(loc=\"best\")\n", - "plt.title(\"TTim Slug Test Analysis\");" + "plt.title(\"TTim Slug Test Analysis\")" ] }, { diff --git a/docs/03examples/well_example.ipynb b/docs/03examples/well_example.ipynb index 09930fc..472d4f7 100644 --- a/docs/03examples/well_example.ipynb +++ b/docs/03examples/well_example.ipynb @@ -115,7 +115,7 @@ "plt.semilogx(t, Qx[0], \"r--\", label=\"ttim\")\n", "plt.xlabel(\"time (day)\")\n", "plt.ylabel(\"head (m)\")\n", - "plt.legend(loc=\"best\");" + "plt.legend(loc=\"best\")" ] }, { @@ -154,7 +154,7 @@ "enumba = test(M=10)\n", "plt.plot(t, enumba, \"C1\")\n", "plt.xlabel(\"time (d)\")\n", - "plt.ylabel(\"head difference Thies - Ttim\");" + "plt.ylabel(\"head difference Thies - Ttim\")" ] }, { @@ -178,7 +178,7 @@ "source": [ "plt.plot(t, Qrtheis - Qx[0])\n", "plt.xlabel(\"time (d)\")\n", - "plt.ylabel(\"Qx difference Thies - Ttim\");" + "plt.ylabel(\"Qx difference Thies - Ttim\")" ] }, { @@ -318,7 +318,7 @@ "source": [ "plt.plot(t2, htheis2, \"b\", label=\"theis\")\n", "plt.plot(t2, h2[0], \"r--\", label=\"ttim\")\n", - "plt.legend(loc=\"best\");" + "plt.legend(loc=\"best\")" ] }, { @@ -410,7 +410,7 @@ "h = ml.head(r, 0, t)\n", "plt.semilogx(t, hhantush, \"b\", label=\"hantush\")\n", "plt.semilogx(t, h[0], \"r--\", label=\"ttim\")\n", - "plt.legend(loc=\"best\");" + "plt.legend(loc=\"best\")" ] }, { @@ -470,7 +470,7 @@ "plt.legend(loc=\"best\")\n", "plt.xticks(\n", " [1 / (24 * 60 * 60), 1 / (24 * 60), 1 / 24, 1], [\"1 sec\", \"1 min\", \"1 hr\", \"1 d\"]\n", - ");" + ")" ] }, { @@ -523,7 +523,7 @@ "plt.xticks(\n", " [1 / (24 * 60 * 60) / 10, 1 / (24 * 60 * 60), 1 / (24 * 60), 1 / 24],\n", " [\"0.1 sec\", \"1 sec\", \"1 min\", \"1 hr\"],\n", - ");" + ")" ] }, { @@ -581,7 +581,7 @@ "plt.xticks(\n", " [1 / (24 * 60 * 60) / 10, 1 / (24 * 60 * 60), 1 / (24 * 60), 1 / 24],\n", " [\"0.1 sec\", \"1 sec\", \"1 min\", \"1 hr\"],\n", - ");" + ")" ] }, { @@ -640,7 +640,7 @@ "plt.xticks(\n", " [1 / (24 * 60 * 60) / 10, 1 / (24 * 60 * 60), 1 / (24 * 60), 1 / 24],\n", " [\"0.1 sec\", \"1 sec\", \"1 min\", \"1 hr\"],\n", - ");" + ")" ] }, { @@ -696,7 +696,7 @@ "plt.semilogx(t, dis[0], label=\"rw=0.3\")\n", "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"discharge (m3/d)\")\n", - "plt.legend();" + "plt.legend()" ] }, { diff --git a/docs/03examples/well_near_river_or_wall.ipynb b/docs/03examples/well_near_river_or_wall.ipynb index da66783..1e1be2d 100644 --- a/docs/03examples/well_near_river_or_wall.ipynb +++ b/docs/03examples/well_near_river_or_wall.ipynb @@ -63,7 +63,7 @@ "h1 = ml1.head(20, 0, t)\n", "plt.plot(t, h1[0], label=\"river modeled with image well\")\n", "plt.xlabel(\"time (d)\")\n", - "plt.ylabel(\"head (m)\");" + "plt.ylabel(\"head (m)\")" ] }, { @@ -110,7 +110,7 @@ "plt.title(\"head at (x,y)=(20,0)\")\n", "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"head (m)\")\n", - "plt.legend();" + "plt.legend()" ] }, { @@ -157,7 +157,7 @@ "h1 = ml1.head(20, 0, t)\n", "plt.plot(t, h1[0], label=\"impermeable wall modeled with image well\")\n", "plt.xlabel(\"time (d)\")\n", - "plt.ylabel(\"head (m)\");" + "plt.ylabel(\"head (m)\")" ] }, { @@ -206,7 +206,7 @@ "plt.title(\"head at (x,y)=(20,0)\")\n", "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"head (m)\")\n", - "plt.legend();" + "plt.legend()" ] } ], diff --git a/docs/03examples/wells_in_different_systems.ipynb b/docs/03examples/wells_in_different_systems.ipynb index 66c0643..17d2024 100644 --- a/docs/03examples/wells_in_different_systems.ipynb +++ b/docs/03examples/wells_in_different_systems.ipynb @@ -113,7 +113,7 @@ "for i in range(3):\n", " plt.semilogx(t, hhantush[i], label=\"c=\" + str(int(clist[i])))\n", "plt.legend(loc=\"best\")\n", - "plt.title(\"head at r=20\");" + "plt.title(\"head at r=20\")" ] }, { @@ -182,7 +182,7 @@ "for i in range(3):\n", " plt.semilogx(t, htwolayer[i], label=\"c=\" + str(int(clist[i])))\n", "plt.legend(loc=\"best\")\n", - "plt.title(\"head at r=20\");" + "plt.title(\"head at r=20\")" ] }, { @@ -236,7 +236,7 @@ "plt.semilogx(t, ht10[0], label=\"time shifted in model\")\n", "plt.semilogx(t + 10, h1[0], \"r--\", label=\"time shifted by hand\")\n", "plt.legend(loc=\"best\")\n", - "plt.title(\"note that heads are not computed at the same times\");" + "plt.title(\"note that heads are not computed at the same times\")" ] } ], diff --git a/docs/04pumpingtests/0_synthetic_data.ipynb b/docs/04pumpingtests/0_synthetic_data.ipynb old mode 100755 new mode 100644 index fa1ecfe..711a4a7 --- a/docs/04pumpingtests/0_synthetic_data.ipynb +++ b/docs/04pumpingtests/0_synthetic_data.ipynb @@ -4,19 +4,58 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Confidence Interval Test for Confined Aquifers\n", - "**Synthetic data**" + "# Example 0 - Pumping Test Analysis with Synthetic Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "### What is TTim\n", + "\n", + "TTim uses the Laplace Transform Analytic Element Method (AEM) to derive a solution to transient flow in multi-layered systems. TTim uses discrete features such as Wells and Line-elements to represent real-world aquifer features of interest.\n", + "\n", + "\n", + "In this notebook we demonstrate:\n", + "\n", + "* How to specify a simple conceptual model consisting of one confining layer and one well\n", + "* Simulate the model\n", + "* Calibrate a aquifer parameters by providing data from observation wells\n", + "* Generate Confidence Intervals from calibration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To achieve this we will create a Model, sample some observations add noise and try to find the model parameters through fitting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We begin by importing the required libraries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Import the Required Libraries" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", + "import matplotlib.pyplot as plt # Plotting library\n", + "import numpy as np # Numpy\n", + "\n", "import ttim" ] }, @@ -24,33 +63,46 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Set basic parameters for the model:" + "## Step 2: Set Model Parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We set the model parameters with dimensions in **meters** and time in **days**" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "H = 7 # aquifer thickness\n", - "k = 70 # hydraulic conductivity\n", - "S = 1e-4 # specific storage\n", - "Q = 788 # constant discharge\n", - "d1 = 30 # observation well 1\n", - "d2 = 90 # observation well 2 (positions same as for Oude Korendijk)" + "H = 7 # aquifer thickness [m]\n", + "k = 70 # hydraulic conductivity [m/d]\n", + "S = 1e-4 # specific storage fo the aquifer\n", + "Q = 788 # constant discharge [m/d]\n", + "d1 = 30 # distance of observation well 1 to pumpinq well\n", + "d2 = 90 # distance of observation well 2 to pumpinq well (positions same as for Oude Korendijk)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Load data of test site 'Oude Korendijk':" + "## Step 3: Loading Data:\n", + "\n", + "- Since we are modelling a synthetic example. We will just borrow the time interval from another pumping test\n", + "\n", + "* Data consistis of two columns:\n", + " * First column is time in minutes\n", + " * Second column is the piezometer level in meters above mean sea level [amsl]" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +114,41 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Create conceptual model:" + "As seen above, we have loaded the data as a numpy array. That is the preffered format for loading data into TTim." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Creating a Conceptual Model\n", + "\n", + "We will model our aquifer using ModelMaq, which is the 2d model interface from TTim. It assumes horizontal stacking of layers consiting of one aquifer and a leaky aquitard." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "ml = ttim.ModelMaq(\n", + " kaq=k, # Hydraulic Conductivity of the aquifer\n", + " z=[\n", + " -18,\n", + " -25,\n", + " ], # Top and bottom dimensions of the aquifer layer. The leaky aquitard can have 0 thickness\n", + " Saq=1e-4, # Specific storage of the aquifers\n", + " tmin=1e-5, # the minimum time for which heads can be computed after any change in boundary condition.\n", + " tmax=1, # The maximum time for which heads will be computed\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we add the Well element at position (0,0) with screen radius of 10 cm" ] }, { @@ -71,18 +157,20 @@ "metadata": {}, "outputs": [], "source": [ - "ml = ttim.ModelMaq(kaq=70, z=[-18, -25], Saq=1e-4, tmin=1e-5, tmax=1)\n", - "w = ttim.Well(ml, xw=0, yw=0, rw=0.1, tsandQ=[(0, 788)])\n", - "ml.solve(silent=\"True\")\n", - "h1 = ml.head(d1, 0, t)\n", - "h2 = ml.head(d2, 0, t)" + "w = ttim.Well(\n", + " ml, # Model where we add the well element\n", + " xw=0, # Position x\n", + " yw=0, # Position y\n", + " rw=0.1, # Well radius,\n", + " tsandQ=[(0, 788)], # Tuple describing starting time and discharge\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Add noises:" + "We can now 'solve' the model and compute the heads at the two observation locations" ] }, { @@ -91,9 +179,7 @@ "metadata": {}, "outputs": [], "source": [ - "np.savetxt(\"data/syn_30_0.0.txt\", h1[0])\n", - "np.savetxt(\"data/syn_90_0.0.txt\", h2[0])\n", - "# print(h2[0])" + "ml.solve(silent=\"False\")" ] }, { @@ -102,16 +188,112 @@ "metadata": {}, "outputs": [], "source": [ - "np.random.seed(5)\n", + "# To compute the heads at the specified time intervals and location, we use the 'head' method\n", + "h1 = ml.head(\n", + " x=d1, # location of observation well 1\n", + " y=0,\n", + " t=t, # Time array that the heads will be returned for\n", + ")\n", + "h2 = ml.head(\n", + " d2, 0, t\n", + ") # Computing heads at distance d2, and time array t for observation well 2" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-2.28275005e-04, -7.70478488e-03, -3.18554789e-02,\n", + " -5.15317124e-02, -7.77470940e-02, -1.06832914e-01,\n", + " -1.36233705e-01, -1.57179797e-01, -1.76793138e-01,\n", + " -1.96853417e-01, -2.16516484e-01, -2.50176995e-01,\n", + " -2.78596120e-01, -3.02578736e-01, -3.08282745e-01,\n", + " -3.25241029e-01, -3.58423647e-01, -3.97875642e-01,\n", + " -4.48679367e-01, -4.73964012e-01, -5.01394438e-01,\n", + " -5.21357145e-01, -5.47533636e-01, -5.86237506e-01,\n", + " -6.08113174e-01, -6.56622524e-01, -6.90311043e-01,\n", + " -7.28971868e-01, -7.54845212e-01, -7.78144650e-01,\n", + " -8.14919239e-01, -8.43451038e-01, -8.68180109e-01,\n", + " -8.84950599e-01]])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We can take a look at the data:\n", + "\n", + "h1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The head method output a numpy array with dimensions [number of aquifer layers, number of time data]. In this case we only have one row" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Demonstration of Calibration with TTim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To demonstrate the capability of TTim for deriving aquifer parameters with drawdown data, we will first add noise to the sampled data. We will test the model performance with two standard devitations: 0.02 and 0.05" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# np.savetxt('data/syn_30_0.0.txt', h1[0])\n", + "# np.savetxt('data/syn_90_0.0.txt', h2[0])\n", + "# print(h2[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Creating the arrays with noise for sigma = 0.02" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(5) # Adding a Random seed\n", "he12 = h1[0] - np.random.randn(len(t)) * 0.02\n", "he22 = h2[0] - np.random.randn(len(t)) * 0.02\n", "np.savetxt(\"data/syn_p30_0.02.txt\", he12)\n", "np.savetxt(\"data/syn_p90_0.02.txt\", he22)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Creating the arrays with noise for sigma = 0.05" + ] + }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -122,14 +304,21 @@ "np.savetxt(\"data/syn_p90_0.05.txt\", he25)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting and checking the noise added data:" + ] + }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -141,24 +330,26 @@ } ], "source": [ - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t, he12, \".\", label=\"obs at 30 m with sig=0.02\")\n", - "plt.semilogx(t, h1[0], label=\"ttim at 30 m\")\n", - "plt.semilogx(t, he22, \".\", label=\"obs at 90 m with sig=0.02\")\n", - "plt.semilogx(t, h2[0], label=\"ttim at 90 m\")\n", + "fig = plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, he12, \".\", label=\"obs at 30 m with $\\sigma$ =0.02\")\n", + "plt.semilogx(t, h1[0], label=\"modelled heads at 30 m (obs 1)\")\n", + "plt.semilogx(t, he22, \".\", label=\"obs at 90 m with $\\sigma$ =0.02\")\n", + "plt.semilogx(t, h2[0], label=\"modelled heads at 90 m (obs 2)\")\n", "plt.legend()\n", "plt.xlabel(\"time (d)\")\n", - "plt.ylabel(\"drawdown (m)\");" + "plt.ylabel(\"drawdown (m)\")\n", + "fig.suptitle(\"Drawdown model and observations with noise: $\\sigma$ = 0.02\")\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -170,33 +361,40 @@ } ], "source": [ - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t, he15, \".\", label=\"obs at 30 m with sig=0.05\")\n", - "plt.semilogx(t, h1[0], label=\"ttim at 30 m\")\n", - "plt.semilogx(t, he25, \".\", label=\"obs at 90 m with sig=0.05\")\n", - "plt.semilogx(t, h2[0], label=\"ttim at 90 m\")\n", + "fig = plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, he15, \".\", label=\"obs at 30 m with $\\sigma$ =0.05\")\n", + "plt.semilogx(t, h1[0], label=\"modelled heads at 30 m (obs 1)\")\n", + "plt.semilogx(t, he25, \".\", label=\"obs at 90 m with $\\sigma$ =0.05\")\n", + "plt.semilogx(t, h2[0], label=\"modelled heads at 90 m (obs 2)\")\n", "plt.legend()\n", "plt.xlabel(\"time (d)\")\n", - "plt.ylabel(\"drawdown (m)\");" + "plt.ylabel(\"drawdown (m)\")\n", + "fig.suptitle(\"Drawdown model and observations with noise: $\\sigma$ = 0.05\")\n", + "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Test if TTim finds the parameters back" + "### Step 5.1: Calibration of the model using the noisy data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Calibrate with two datasets respectively (0.02):" + "\n", + "Calibrate the model using the $\\sigma $ = 0.02 noisy data from observation well 1.\n", + "* We calibrate the model by creating a `Calibrate` object with the initial model as input.\n", + "* Then we set the parameter initial values using the `set_parameter` method\n", + "* we add the observation data with the `series` method\n", + "* And fit" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": { "scrolled": false }, @@ -214,15 +412,52 @@ " # variables = 2\n", " chi-square = 0.01117172\n", " reduced chi-square = 3.4912e-04\n", - " Akaike info crit = -268.704835\n", - " Bayesian info crit = -265.652114\n", + " Akaike info crit = -268.704829\n", + " Bayesian info crit = -265.652108\n", "[[Variables]]\n", - " kaq0: 69.9140554 +/- 1.09423921 (1.57%) (init = 10)\n", + " kaq0: 69.9141328 +/- 1.09423849 (1.57%) (init = 10)\n", " Saq0: 1.0170e-04 +/- 5.4838e-06 (5.39%) (init = 0.001)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", " C(kaq0, Saq0) = -0.852\n" ] - }, + } + ], + "source": [ + "ca23 = ttim.Calibrate(ml) # TTim class for model calibration\n", + "# Setting initial parameters values for calibration\n", + "ca23.set_parameter(name=\"kaq0\", initial=10) # Hydraulic Conductivity\n", + "ca23.set_parameter(name=\"Saq0\", initial=1e-3) # Specific Storage\n", + "\n", + "ca23.series( # Adding the observations for calibration\n", + " name=\"obs1\", # Observation well 1\n", + " x=d1, # Location\n", + " y=0,\n", + " t=t, # Time Array\n", + " h=he12, # Drawdown noisy data for well 1\n", + " layer=0, # Aquifer layer where we have the observations from\n", + ")\n", + "ca23.fit(report=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can already see the results from the calibration if we set `report = True` in the `fit` method. Besides fit statistics, we also get the Variables, with confidence interval and the correlations between variables during fitting process." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also check the calibrated parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -256,36 +491,36 @@ " \n", " \n", " kaq0\n", - " 69.9141\n", - " 1.094239\n", - " 1.56512\n", + " 69.914133\n", + " 1.094238\n", + " 1.565118\n", " -inf\n", " inf\n", " 10\n", - " [69.91405544705488]\n", + " [69.91413282570136]\n", " \n", " \n", " Saq0\n", - " 0.000101704\n", + " 0.000102\n", " 0.000005\n", " 5.39192\n", " -inf\n", " inf\n", " 0.001\n", - " [0.00010170395030742693]\n", + " [0.00010170348919178826]\n", " \n", " \n", "\n", "" ], "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 69.9141 1.094239 1.56512 -inf inf 10 \n", - "Saq0 0.000101704 0.000005 5.39192 -inf inf 0.001 \n", + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 69.914133 1.094238 1.565118 -inf inf 10 \n", + "Saq0 0.000102 0.000005 5.39192 -inf inf 0.001 \n", "\n", " parray \n", - "kaq0 [69.91405544705488] \n", - "Saq0 [0.00010170395030742693] " + "kaq0 [69.91413282570136] \n", + "Saq0 [0.00010170348919178826] " ] }, "metadata": {}, @@ -293,29 +528,38 @@ } ], "source": [ - "ca23 = ttim.Calibrate(ml)\n", - "ca23.set_parameter(name=\"kaq0\", initial=10)\n", - "ca23.set_parameter(name=\"Saq0\", initial=1e-3)\n", - "ca23.series(name=\"obs1\", x=d1, y=0, t=t, h=he12, layer=0)\n", - "ca23.fit(report=True)\n", - "display(ca23.parameters)" + "ca23.parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `display` function returns a data frame with initial and optimal values for each parameter, besides the standard deviation of the estimate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now compare the model performance for both observation wells" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "rmse: 0.018126773873208015\n" + "rmse: 0.01812677527586899\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -327,42 +571,51 @@ } ], "source": [ - "print(\"rmse:\", ca23.rmse())\n", - "h123 = ml.head(d1, 0, t)\n", - "h223 = ml.head(d2, 0, t)\n", + "print(\"rmse:\", ca23.rmse()) # Return the RMSE error for the calibration\n", + "h123 = ml.head(d1, 0, t) # Compute drawdown of the calibrated model for obs 1\n", + "h223 = ml.head(d2, 0, t) # Compute drawdown for obs 2\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t, he12, \".\", label=\"obs at 30 m with sig=0.02\")\n", - "plt.semilogx(t, h123[0], label=\"ttim at 30 m\")\n", - "plt.semilogx(t, he22, \".\", label=\"obs at 90 m with sig=0.02\")\n", - "plt.semilogx(t, h223[0], label=\"ttim at 90 m\")\n", + "plt.semilogx(t, he12, \".\", label=\"obs at 30 m with $\\sigma$ = 0.02\")\n", + "plt.semilogx(t, h123[0], label=\"modelled drawdown at 30 m\")\n", + "plt.semilogx(t, he22, \".\", label=\"obs at 90 m with $\\sigma$ = 0.02\")\n", + "plt.semilogx(t, h223[0], label=\"modelled drawdown at 90 m\")\n", "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"drawdown (m)\")\n", - "plt.title(\"ttim analysis with synthetic data, sig=0.02 errors at 30 m.\")\n", - "plt.legend();" + "plt.title(\n", + " \"Ttim analysis with synthetic data, Calibrated model with $\\sigma$ = 0.02 errors at 30 m.\"\n", + ")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we do the same procedure as [before](#sig002obs1) to calibrate the model, but now using the observation data from well 2:" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "...................................\n", + ".......................................\n", "Fit succeeded.\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", - " # function evals = 32\n", + " # function evals = 36\n", " # data points = 34\n", " # variables = 2\n", " chi-square = 0.00859548\n", " reduced chi-square = 2.6861e-04\n", - " Akaike info crit = -277.617899\n", - " Bayesian info crit = -274.565178\n", + " Akaike info crit = -277.617900\n", + " Bayesian info crit = -274.565179\n", "[[Variables]]\n", - " kaq0: 70.7905694 +/- 1.71073318 (2.42%) (init = 10)\n", + " kaq0: 70.7905238 +/- 1.71072660 (2.42%) (init = 10)\n", " Saq0: 9.3336e-05 +/- 5.1366e-06 (5.50%) (init = 0.001)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", " C(kaq0, Saq0) = -0.831\n" @@ -401,36 +654,36 @@ " \n", " \n", " kaq0\n", - " 70.7906\n", - " 1.710733\n", - " 2.41661\n", + " 70.790524\n", + " 1.710727\n", + " 2.416604\n", " -inf\n", " inf\n", " 10\n", - " [70.79056943573127]\n", + " [70.79052382809591]\n", " \n", " \n", " Saq0\n", - " 9.33358e-05\n", + " 0.000093\n", " 0.000005\n", - " 5.50339\n", + " 5.503366\n", " -inf\n", " inf\n", " 0.001\n", - " [9.333583047877941e-05]\n", + " [9.333600107013827e-05]\n", " \n", " \n", "\n", "" ], "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 70.7906 1.710733 2.41661 -inf inf 10 \n", - "Saq0 9.33358e-05 0.000005 5.50339 -inf inf 0.001 \n", + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 70.790524 1.710727 2.416604 -inf inf 10 \n", + "Saq0 0.000093 0.000005 5.503366 -inf inf 0.001 \n", "\n", " parray \n", - "kaq0 [70.79056943573127] \n", - "Saq0 [9.333583047877941e-05] " + "kaq0 [70.79052382809591] \n", + "Saq0 [9.333600107013827e-05] " ] }, "metadata": {}, @@ -446,21 +699,28 @@ "display(ca29.parameters)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can once again check the model performance with the new calibrated model" + ] + }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "rmse: 0.015899943980017286\n" + "rmse: 0.015899943725520456\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -484,26 +744,33 @@ "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"drawdown (m)\")\n", "plt.title(\"ttim analysis with synthetic data, sig=0.02 errors at 90 m.\")\n", - "plt.legend(loc=\"best\");" + "plt.legend(loc=\"best\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Calibrate with two datasets simultaneously" + "### Step 5.2: Calibrate with two datasets simultaneously" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Drawdown without errors:" + "TTim can also analyse the pumping tests using drawdown data from more than one borehole." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we calibrate the model using the data without error from both observation wells." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -517,13 +784,13 @@ " # function evals = 25\n", " # data points = 68\n", " # variables = 2\n", - " chi-square = 1.3439e-14\n", - " reduced chi-square = 2.0362e-16\n", - " Akaike info crit = -2454.88865\n", - " Bayesian info crit = -2450.44964\n", + " chi-square = 7.7331e-15\n", + " reduced chi-square = 1.1717e-16\n", + " Akaike info crit = -2492.46835\n", + " Bayesian info crit = -2488.02934\n", "[[Variables]]\n", - " kaq0: 69.9999992 +/- 6.9243e-07 (0.00%) (init = 10)\n", - " Saq0: 1.0000e-04 +/- 2.9989e-12 (0.00%) (init = 0.001)\n", + " kaq0: 69.9999996 +/- 5.2525e-07 (0.00%) (init = 10)\n", + " Saq0: 1.0000e-04 +/- 2.2749e-12 (0.00%) (init = 0.001)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", " C(kaq0, Saq0) = -0.830\n" ] @@ -561,36 +828,36 @@ " \n", " \n", " kaq0\n", - " 70\n", - " 6.924296e-07\n", - " 9.89185e-07\n", + " 70.0\n", + " 5.252545e-07\n", + " 0.000001\n", " -inf\n", " inf\n", " 10\n", - " [69.99999923293925]\n", + " [69.99999956044262]\n", " \n", " \n", " Saq0\n", " 0.0001\n", - " 2.998903e-12\n", - " 2.9989e-06\n", + " 2.274863e-12\n", + " 0.000002\n", " -inf\n", " inf\n", " 0.001\n", - " [0.00010000000167342676]\n", + " [0.00010000000009794474]\n", " \n", " \n", "\n", "" ], "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 70 6.924296e-07 9.89185e-07 -inf inf 10 \n", - "Saq0 0.0001 2.998903e-12 2.9989e-06 -inf inf 0.001 \n", + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 70.0 5.252545e-07 0.000001 -inf inf 10 \n", + "Saq0 0.0001 2.274863e-12 0.000002 -inf inf 0.001 \n", "\n", " parray \n", - "kaq0 [69.99999923293925] \n", - "Saq0 [0.00010000000167342676] " + "kaq0 [69.99999956044262] \n", + "Saq0 [0.00010000000009794474] " ] }, "metadata": {}, @@ -607,9 +874,16 @@ "display(ca0.parameters)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The obtained results match exactly the input parameters" + ] + }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "metadata": { "scrolled": true }, @@ -618,12 +892,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "rmse: 1.405814859967026e-08\n" + "rmse: 1.0664077889093849e-08\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -646,39 +920,39 @@ "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"drawdown (m)\")\n", "plt.title(\"ttim analysis with synthetic data without errors.\")\n", - "plt.legend();" + "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Drawdowns with errors with $\\sigma=0.02$." + "We do the same, but now with drawdowns with errors with $\\sigma=0.02$." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "...............................\n", + "...........................\n", "Fit succeeded.\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", - " # function evals = 28\n", + " # function evals = 24\n", " # data points = 68\n", " # variables = 2\n", " chi-square = 0.02036601\n", " reduced chi-square = 3.0858e-04\n", - " Akaike info crit = -547.710917\n", - " Bayesian info crit = -543.271902\n", + " Akaike info crit = -547.710892\n", + " Bayesian info crit = -543.271877\n", "[[Variables]]\n", - " kaq0: 70.5088065 +/- 0.85773044 (1.22%) (init = 10)\n", - " Saq0: 9.7105e-05 +/- 3.6048e-06 (3.71%) (init = 0.001)\n", + " kaq0: 70.5085864 +/- 0.85773221 (1.22%) (init = 10)\n", + " Saq0: 9.7107e-05 +/- 3.6049e-06 (3.71%) (init = 0.001)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", " C(kaq0, Saq0) = -0.830\n" ] @@ -716,36 +990,36 @@ " \n", " \n", " kaq0\n", - " 70.5088\n", - " 0.857730\n", - " 1.21649\n", + " 70.508586\n", + " 0.857732\n", + " 1.216493\n", " -inf\n", " inf\n", " 10\n", - " [70.5088065318829]\n", + " [70.50858641493859]\n", " \n", " \n", " Saq0\n", - " 9.71049e-05\n", + " 0.000097\n", " 0.000004\n", - " 3.71231\n", + " 3.712304\n", " -inf\n", " inf\n", " 0.001\n", - " [9.710489541232362e-05]\n", + " [9.71065523694604e-05]\n", " \n", " \n", "\n", "" ], "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 70.5088 0.857730 1.21649 -inf inf 10 \n", - "Saq0 9.71049e-05 0.000004 3.71231 -inf inf 0.001 \n", + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 70.508586 0.857732 1.216493 -inf inf 10 \n", + "Saq0 0.000097 0.000004 3.712304 -inf inf 0.001 \n", "\n", - " parray \n", - "kaq0 [70.5088065318829] \n", - "Saq0 [9.710489541232362e-05] " + " parray \n", + "kaq0 [70.50858641493859] \n", + "Saq0 [9.71065523694604e-05] " ] }, "metadata": {}, @@ -764,19 +1038,19 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "rmse: 0.01730607089294998\n" + "rmse: 0.017306074039873633\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -799,7 +1073,7 @@ "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"drawdown (m)\")\n", "plt.title(\"ttim analysis with synthetic data and errors with sig=0.02\")\n", - "plt.legend();" + "plt.legend()" ] }, { @@ -811,27 +1085,27 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - ".............................\n", + ".........................\n", "Fit succeeded.\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", - " # function evals = 26\n", + " # function evals = 22\n", " # data points = 68\n", " # variables = 2\n", - " chi-square = 0.16477215\n", + " chi-square = 0.16477216\n", " reduced chi-square = 0.00249655\n", - " Akaike info crit = -405.543556\n", - " Bayesian info crit = -401.104541\n", + " Akaike info crit = -405.543554\n", + " Bayesian info crit = -401.104539\n", "[[Variables]]\n", - " kaq0: 70.3178524 +/- 2.43432112 (3.46%) (init = 10)\n", - " Saq0: 9.8231e-05 +/- 1.0351e-05 (10.54%) (init = 0.001)\n", + " kaq0: 70.3176085 +/- 2.43430886 (3.46%) (init = 10)\n", + " Saq0: 9.8232e-05 +/- 1.0351e-05 (10.54%) (init = 0.001)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", " C(kaq0, Saq0) = -0.830\n" ] @@ -869,36 +1143,36 @@ " \n", " \n", " kaq0\n", - " 70.3179\n", - " 2.434321\n", - " 3.46188\n", + " 70.317608\n", + " 2.434309\n", + " 3.461877\n", " -inf\n", " inf\n", " 10\n", - " [70.3178524383838]\n", + " [70.31760849447059]\n", " \n", " \n", " Saq0\n", - " 9.82307e-05\n", + " 0.000098\n", " 0.000010\n", - " 10.5376\n", + " 10.537516\n", " -inf\n", " inf\n", " 0.001\n", - " [9.823070103722924e-05]\n", + " [9.823189067091308e-05]\n", " \n", " \n", "\n", "" ], "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 70.3179 2.434321 3.46188 -inf inf 10 \n", - "Saq0 9.82307e-05 0.000010 10.5376 -inf inf 0.001 \n", + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 70.317608 2.434309 3.461877 -inf inf 10 \n", + "Saq0 0.000098 0.000010 10.537516 -inf inf 0.001 \n", "\n", " parray \n", - "kaq0 [70.3178524383838] \n", - "Saq0 [9.823070103722924e-05] " + "kaq0 [70.31760849447059] \n", + "Saq0 [9.823189067091308e-05] " ] }, "metadata": {}, @@ -917,19 +1191,19 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "rmse: 0.04922519578206107\n" + "rmse: 0.049225196392244215\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -952,13 +1226,48 @@ "plt.xlabel(\"time (d)\")\n", "plt.ylabel(\"drawdown (m)\")\n", "plt.title(\"ttim analysis with synthetic data and errors with sig=0.05\")\n", - "plt.legend();" + "plt.legend()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Model performed also well for $\\sigma = 0.05$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Final Remarks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example we have succesfully:\n", + "\n", + "* Initiated a TTim model instance using the `Model` class\n", + "* Sampled observation data from the model and defined the synthetic data by adding noise.\n", + "* Calibrated the model with the `Calibrate` class using one and two calibration wells\n", + "* Inspected the calibration performance\n", + "\n", + "Next Example:\n", + "\n", + "* Pumping test analysis of a confined aquifer: [Example 1 - Pumping Test of Confined Aquifer](confined1_oude_korendijk.ipynb)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -972,7 +1281,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.10.12" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/docs/04pumpingtests/10_moench_test.ipynb b/docs/04pumpingtests/10_moench_test.ipynb deleted file mode 100755 index 4da1c85..0000000 --- a/docs/04pumpingtests/10_moench_test.ipynb +++ /dev/null @@ -1,833 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test for anisotropic water-table aquifer\n", - "**This test is taken from examples presented in MLU tutorial.**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import ttim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set basic parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "b = 10 # aquifer thickness in m\n", - "Q = 172.8 # constant discharge rate in m^3/d\n", - "rw = 0.1 # well radius in m\n", - "rc = 0.1 # casing radius in m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load datasets of observation wells:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "r1 = 3.16\n", - "r2 = 31.6\n", - "data0 = np.loadtxt(\"data/moench_pumped.txt\", skiprows=1)\n", - "t0 = data0[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", - "h0 = -data0[:, 1]\n", - "data1 = np.loadtxt(\"data/moench_ps1.txt\", skiprows=1)\n", - "t1 = data1[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", - "h1 = -data1[:, 1]\n", - "data2 = np.loadtxt(\"data/moench_pd1.txt\", skiprows=1)\n", - "t2 = data2[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", - "h2 = -data2[:, 1]\n", - "data3 = np.loadtxt(\"data/moench_ps2.txt\", skiprows=1)\n", - "t3 = data3[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", - "h3 = -data3[:, 1]\n", - "data4 = np.loadtxt(\"data/moench_pd2.txt\", skiprows=1)\n", - "t4 = data4[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", - "h4 = -data4[:, 1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Check how well TTim can simulate drawdowns in a vertically anisotropic water-table aquifer:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Set kaq, Saq, Sy and kzoverkh as given in Moench (1997)\n", - "kaq = 1e-4 * 60 * 60 * 24 # convert from m/s to m/d\n", - "Sy = 0.2\n", - "Saq = 2e-5\n", - "zh = 0.5 # kzoverkh" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml1 = ttim.Model3D(\n", - " kaq=kaq,\n", - " z=[0, -0.1, -2.1, -5.1, -10.1],\n", - " Saq=[Sy, Saq, Saq, Saq],\n", - " kzoverkh=zh,\n", - " tmin=1e-5,\n", - " tmax=3,\n", - ")\n", - "w1 = ttim.Well(ml1, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=3)\n", - "ml1.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm1 = ml1.head(r1, 0, t1, layers=1)[0]\n", - "hm2 = ml1.head(r1, 0, t2, layers=3)[0]\n", - "hm3 = ml1.head(r2, 0, t3, layers=1)[0]\n", - "hm4 = ml1.head(r2, 0, t4, layers=3)[0]\n", - "hm0 = ml1.head(0, 0, t0, layers=3)[0]\n", - "plt.figure(figsize=(8, 5))\n", - "plt.loglog(t0, -h0, \".\", label=\"pumped well\")\n", - "plt.loglog(t0, -hm0, label=\"ttim pumped well\")\n", - "plt.loglog(t1, -h1, \".\", label=\"PS1\")\n", - "plt.loglog(t1, -hm1, label=\"ttim PS1\")\n", - "plt.loglog(t2, -h2, \".\", label=\"PD1\")\n", - "plt.loglog(t2, -hm2, label=\"ttim PD1\")\n", - "plt.loglog(t3, -h3, \".\", label=\"PS2\")\n", - "plt.loglog(t3, -hm3, label=\"ttim PS2\")\n", - "plt.loglog(t4, -h4, \".\", label=\"PD2\")\n", - "plt.loglog(t4, -hm4, label=\"ttim PD2\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.06131800012056597\n" - ] - } - ], - "source": [ - "res1 = 0\n", - "res2 = 0\n", - "res3 = 0\n", - "res4 = 0\n", - "res0 = 0\n", - "for i in range(len(h1)):\n", - " r = (h1[i] - hm1[i]) ** 2\n", - " res1 = res1 + r\n", - "for i in range(len(h2)):\n", - " r = (h2[i] - hm2[i]) ** 2\n", - " res2 = res2 + r\n", - "for i in range(len(h3)):\n", - " r = (h3[i] - hm3[i]) ** 2\n", - " res3 = res3 + r\n", - "for i in range(len(h4)):\n", - " r = (h4[i] - hm4[i]) ** 2\n", - " res4 = res4 + r\n", - "for i in range(len(h0)):\n", - " r = (h0[i] - hm0[i]) ** 2\n", - " res0 = res0 + r\n", - "\n", - "n = len(h1) + len(h2) + len(h3) + len(h4) + len(h0)\n", - "residuals = res1 + res2 + res3 + res4 + res0\n", - "rmse = np.sqrt(residuals / n)\n", - "print(\"RMSE:\", rmse)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Try calibrating model to find the parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml2 = ttim.Model3D(\n", - " kaq=1,\n", - " z=[0, -0.1, -2.1, -5.1, -10.1],\n", - " Saq=[0.1, 1e-4, 1e-4, 1e-4],\n", - " kzoverkh=1,\n", - " tmin=1e-5,\n", - " tmax=3,\n", - ")\n", - "w2 = ttim.Well(ml2, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=3)\n", - "ml2.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".......................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 52\n", - " # data points = 70\n", - " # variables = 4\n", - " chi-square = 0.00754817\n", - " reduced chi-square = 1.1437e-04\n", - " Akaike info crit = -631.446179\n", - " Bayesian info crit = -622.452198\n", - "[[Variables]]\n", - " kaq0_3: 9.06764582 +/- 0.02235169 (0.25%) (init = 1)\n", - " Saq0: 0.17288491 +/- 0.00437293 (2.53%) (init = 0.2)\n", - " Saq1_3: 3.8697e-05 +/- 3.5516e-06 (9.18%) (init = 0.0001)\n", - " kzoverkh: 0.53505372 +/- 0.01014261 (1.90%) (init = 0.1)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0_3, kzoverkh) = -0.832\n", - " C(kaq0_3, Saq0) = -0.533\n", - " C(Saq0, kzoverkh) = 0.339\n", - " C(Saq1_3, kzoverkh) = -0.127\n" - ] - } - ], - "source": [ - "ca2 = ttim.Calibrate(ml2)\n", - "ca2.set_parameter(name=\"kaq0_3\", initial=1)\n", - "ca2.set_parameter(name=\"Saq0\", initial=0.2)\n", - "ca2.set_parameter(name=\"Saq1_3\", initial=1e-4, pmin=0)\n", - "ca2.set_parameter_by_reference(\n", - " name=\"kzoverkh\", parameter=ml2.aq.kzoverkh, initial=0.1, pmin=0\n", - ")\n", - "ca2.series(name=\"pumped\", x=0, y=0, t=t0, h=h0, layer=3)\n", - "ca2.series(name=\"PS1\", x=r1, y=0, t=t1, h=h1, layer=1)\n", - "ca2.series(name=\"PD1\", x=r1, y=0, t=t2, h=h2, layer=3)\n", - "ca2.series(name=\"PS2\", x=r2, y=0, t=t3, h=h3, layer=1)\n", - "ca2.series(name=\"PD2\", x=r2, y=0, t=t4, h=h4, layer=3)\n", - "ca2.fit()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_39.067650.0223520.246499-infinf1[9.067645818977368, 9.067645818977368, 9.06764...
Saq00.1728850.0043732.52939-infinf0.2[0.17288490900275638]
Saq1_33.86968e-050.0000049.177890.0inf0.0001[3.86968110601682e-05, 3.86968110601682e-05, 3...
kzoverkh0.5350540.0101431.895630.0inf0.1[0.5350537166002225, 0.5350537166002225, 0.535...
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0_3 9.06765 0.022352 0.246499 -inf inf 1 \n", - "Saq0 0.172885 0.004373 2.52939 -inf inf 0.2 \n", - "Saq1_3 3.86968e-05 0.000004 9.17789 0.0 inf 0.0001 \n", - "kzoverkh 0.535054 0.010143 1.89563 0.0 inf 0.1 \n", - "\n", - " parray \n", - "kaq0_3 [9.067645818977368, 9.067645818977368, 9.06764... \n", - "Saq0 [0.17288490900275638] \n", - "Saq1_3 [3.86968110601682e-05, 3.86968110601682e-05, 3... \n", - "kzoverkh [0.5350537166002225, 0.5350537166002225, 0.535... " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.010384170446842922\n" - ] - } - ], - "source": [ - "display(ca2.parameters)\n", - "print(\"RMSE:\", ca2.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm0_2 = ml2.head(0, 0, t0, layers=3)[0]\n", - "hm1_2 = ml2.head(r1, 0, t1, layers=1)[0]\n", - "hm2_2 = ml2.head(r1, 0, t2, layers=3)[0]\n", - "hm3_2 = ml2.head(r2, 0, t3, layers=1)[0]\n", - "hm4_2 = ml2.head(r2, 0, t4, layers=3)[0]\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t0, h0, \".\", label=\"pumped\")\n", - "plt.semilogx(t0, hm0_2, label=\"ttim pumped\")\n", - "plt.semilogx(t1, h1, \".\", label=\"PS1\")\n", - "plt.semilogx(t1, hm1_2, label=\"ttim PS1\")\n", - "plt.semilogx(t2, h2, \".\", label=\"PD1\")\n", - "plt.semilogx(t2, hm2_2, label=\"ttim PD1\")\n", - "plt.semilogx(t3, h3, \",\", label=\"PS2\")\n", - "plt.semilogx(t3, hm3_2, label=\"ttim PS2\")\n", - "plt.semilogx(t4, h4, \".\", label=\"PD2\")\n", - "plt.semilogx(t4, hm4_2, label=\"ttim PD2\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Try calibrating model with stratified kaq:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml3 = ttim.Model3D(\n", - " kaq=1,\n", - " z=[0, -0.1, -2.1, -5.1, -10.1],\n", - " Saq=[0.1, 1e-4, 1e-4, 1e-4],\n", - " kzoverkh=1,\n", - " tmin=1e-5,\n", - " tmax=3,\n", - ")\n", - "w3 = ttim.Well(ml3, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=3)\n", - "ml3.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".................................................................................................................................................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 206\n", - " # data points = 70\n", - " # variables = 5\n", - " chi-square = 0.00659115\n", - " reduced chi-square = 1.0140e-04\n", - " Akaike info crit = -638.936637\n", - " Bayesian info crit = -627.694160\n", - "[[Variables]]\n", - " kaq0: 1.49295291 +/- 0.49139676 (32.91%) (init = 1)\n", - " kaq1_3: 9.06775860 +/- 0.02107641 (0.23%) (init = 1)\n", - " Saq0: 0.17644488 +/- 0.00419970 (2.38%) (init = 0.2)\n", - " Saq1_3: 3.9420e-05 +/- 3.3278e-06 (8.44%) (init = 0.0001)\n", - " kzoverkh: 0.54082429 +/- 0.01008878 (1.87%) (init = 0.1)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq1_3, kzoverkh) = -0.808\n", - " C(kaq1_3, Saq0) = -0.533\n", - " C(Saq0, kzoverkh) = 0.365\n", - " C(kaq0, kzoverkh) = -0.295\n", - " C(kaq0, Saq0) = -0.153\n" - ] - } - ], - "source": [ - "ca3 = ttim.Calibrate(ml3)\n", - "ca3.set_parameter(name=\"kaq0\", initial=1, pmin=0)\n", - "ca3.set_parameter(name=\"kaq1_3\", initial=1)\n", - "ca3.set_parameter(name=\"Saq0\", initial=0.2, pmin=0)\n", - "ca3.set_parameter(name=\"Saq1_3\", initial=1e-4, pmin=0)\n", - "ca3.set_parameter_by_reference(\n", - " name=\"kzoverkh\", parameter=ml3.aq.kzoverkh, initial=0.1, pmin=0\n", - ")\n", - "ca3.series(name=\"pumped\", x=0, y=0, t=t0, h=h0, layer=3)\n", - "ca3.series(name=\"PS1\", x=r1, y=0, t=t1, h=h1, layer=1)\n", - "ca3.series(name=\"PD1\", x=r1, y=0, t=t2, h=h2, layer=3)\n", - "ca3.series(name=\"PS2\", x=r2, y=0, t=t3, h=h3, layer=1)\n", - "ca3.series(name=\"PD2\", x=r2, y=0, t=t4, h=h4, layer=3)\n", - "ca3.fit()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq01.492950.49139732.91440inf1[1.4929529078613704]
kaq1_39.067760.0210760.232432-infinf1[9.067758598328027, 9.067758598328027, 9.06775...
Saq00.1764450.0042002.380180inf0.2[0.1764448834100245]
Saq1_33.94203e-050.0000038.441750inf0.0001[3.942033806425549e-05, 3.942033806425549e-05,...
kzoverkh0.5408240.0100891.865450inf0.1[0.5408242889145249, 0.5408242889145249, 0.540...
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 1.49295 0.491397 32.9144 0 inf 1 \n", - "kaq1_3 9.06776 0.021076 0.232432 -inf inf 1 \n", - "Saq0 0.176445 0.004200 2.38018 0 inf 0.2 \n", - "Saq1_3 3.94203e-05 0.000003 8.44175 0 inf 0.0001 \n", - "kzoverkh 0.540824 0.010089 1.86545 0 inf 0.1 \n", - "\n", - " parray \n", - "kaq0 [1.4929529078613704] \n", - "kaq1_3 [9.067758598328027, 9.067758598328027, 9.06775... \n", - "Saq0 [0.1764448834100245] \n", - "Saq1_3 [3.942033806425549e-05, 3.942033806425549e-05,... \n", - "kzoverkh [0.5408242889145249, 0.5408242889145249, 0.540... " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.00970356738147155\n" - ] - } - ], - "source": [ - "display(ca3.parameters)\n", - "print(\"RMSE:\", ca3.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm0_3 = ml3.head(0, 0, t0, layers=3)[0]\n", - "hm1_3 = ml3.head(r1, 0, t1, layers=1)[0]\n", - "hm2_3 = ml3.head(r1, 0, t2, layers=3)[0]\n", - "hm3_3 = ml3.head(r2, 0, t3, layers=1)[0]\n", - "hm4_3 = ml3.head(r2, 0, t4, layers=3)[0]\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t0, h0, \".\", label=\"pumped\")\n", - "plt.semilogx(t0, hm0_3, label=\"ttim pumped\")\n", - "plt.semilogx(t1, h1, \".\", label=\"PS1\")\n", - "plt.semilogx(t1, hm1_3, label=\"ttim PS1\")\n", - "plt.semilogx(t2, h2, \".\", label=\"PD1\")\n", - "plt.semilogx(t2, hm2_3, label=\"ttim PD1\")\n", - "plt.semilogx(t3, h3, \",\", label=\"PS2\")\n", - "plt.semilogx(t3, hm3_3, label=\"ttim PS2\")\n", - "plt.semilogx(t4, h4, \".\", label=\"PD2\")\n", - "plt.semilogx(t4, hm4_3, label=\"ttim PD2\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Summary of calibrated values" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.4929529078613704, 9.067758598328027, 0.1764448834100245,\n", - " 3.942033806425549e-05, 0.5408242889145249], dtype=object)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ca3.parameters[\"optimal\"].values" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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MoenchTTimTTim-stratified
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RMSE0.0613180.01038420.00970357
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" - ], - "text/plain": [ - " Moench TTim TTim-stratified\n", - "k0[m/d] NaN NaN 1.49295\n", - "k[m/d] 8.64 9.06765 9.06776\n", - "Sy[-] 0.2 0.172885 0.176445\n", - "Ss[1/m] 2e-05 3.86968e-05 3.94203e-05\n", - "kz/kh 0.5 0.535054 0.540824\n", - "RMSE 0.061318 0.0103842 0.00970357" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ta = pd.DataFrame(\n", - " columns=[\"Moench\", \"TTim\", \"TTim-stratified\"],\n", - " index=[\"k0[m/d]\", \"k[m/d]\", \"Sy[-]\", \"Ss[1/m]\", \"kz/kh\"],\n", - ")\n", - "ta.loc[:, \"TTim-stratified\"] = ca3.parameters[\"optimal\"].values\n", - "ta.loc[1:, \"TTim\"] = ca2.parameters[\"optimal\"].values\n", - "ta.loc[1:, \"Moench\"] = [8.640, 0.2, 2e-5, 0.5]\n", - "ta.loc[\"RMSE\"] = [0.061318, ca2.rmse(), ca3.rmse()]\n", - "ta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/04pumpingtests/11_slug_test_pratt_county.ipynb b/docs/04pumpingtests/11_slug_test_pratt_county.ipynb deleted file mode 100755 index af512ec..0000000 --- a/docs/04pumpingtests/11_slug_test_pratt_county.ipynb +++ /dev/null @@ -1,590 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Slug Test at Pratt County\n", - "**This test is taken from AQTESOLV examples.**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import ttim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set basic parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "rw = 0.125 # well radius\n", - "rc = 0.064 # well casing radius\n", - "L = 1.52 # screen length\n", - "b = -47.87 # aquifer thickness\n", - "zt = -16.77 # depth to top of screen\n", - "H0 = 0.671 # initial displacement in the well\n", - "zb = zt - L # bottom of screen" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Slug:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "slug: 0.00863 m^3\n" - ] - } - ], - "source": [ - "Q = np.pi * rc**2 * H0\n", - "print(\"slug:\", round(Q, 5), \"m^3\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load data:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "data = np.loadtxt(\"data/slug.txt\", skiprows=1)\n", - "t = data[:, 0] / 60 / 60 / 24 # convert time to days\n", - "h = data[:, 1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create conceptual model:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml = ttim.Model3D(kaq=10, z=[0, zt, zb, b], Saq=1e-4, kzoverkh=1, tmin=1e-6, tmax=0.01)\n", - "w = ttim.Well(ml, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=1, wbstype=\"slug\")\n", - "ml.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "......................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 19\n", - " # data points = 61\n", - " # variables = 2\n", - " chi-square = 5.0348e-04\n", - " reduced chi-square = 8.5335e-06\n", - " Akaike info crit = -709.995370\n", - " Bayesian info crit = -705.773623\n", - "[[Variables]]\n", - " kaq0_2: 6.08820525 +/- 0.02361734 (0.39%) (init = 10)\n", - " Saq0_2: 2.0346e-04 +/- 9.3252e-06 (4.58%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0_2, Saq0_2) = -0.594\n" - ] - } - ], - "source": [ - "ca = ttim.Calibrate(ml)\n", - "ca.set_parameter(name=\"kaq0_2\", initial=10)\n", - "ca.set_parameter(name=\"Saq0_2\", initial=1e-4)\n", - "ca.series(name=\"obs\", x=0, y=0, layer=1, t=t, h=h)\n", - "ca.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_26.088210.0236170.38792-infinf10[6.088205252331587, 6.088205252331587, 6.08820...
Saq0_20.0002034550.0000094.5834-infinf0.0001[0.00020345513163869057, 0.0002034551316386905...
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0_2 6.08821 0.023617 0.38792 -inf inf 10 \n", - "Saq0_2 0.000203455 0.000009 4.5834 -inf inf 0.0001 \n", - "\n", - " parray \n", - "kaq0_2 [6.088205252331587, 6.088205252331587, 6.08820... \n", - "Saq0_2 [0.00020345513163869057, 0.0002034551316386905... " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.0028729351611905735\n" - ] - } - ], - "source": [ - "display(ca.parameters)\n", - "print(\"RMSE:\", ca.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm = ml.head(0, 0, t, layers=1)\n", - "plt.figure(figsize=(10, 5))\n", - "plt.semilogx(t, h / H0, \".\", label=\"obs\")\n", - "plt.semilogx(t, hm[-1] / H0, \"r\", label=\"ttim\")\n", - "plt.ylim([0, 1])\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"h/H0\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Conceptual model with multi-layer:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "n1 = 18\n", - "n2 = 3\n", - "n3 = 29\n", - "nlay = n1 + n2 + n3 # number of layers\n", - "zlay1 = np.linspace(0, zt, n1 + 1)\n", - "zlay2 = np.linspace(zt, zb, n2 + 1)\n", - "zlay3 = np.linspace(zb, b, n3 + 1)\n", - "layers = np.append(zlay1[:-1], zlay2[:-1])\n", - "layers = np.append(layers, zlay3) # elevation of each layer\n", - "Saq = 1e-4 * np.ones(nlay)\n", - "Saq[0] = 0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 3\n", - "solution complete\n" - ] - } - ], - "source": [ - "M_nlay = ttim.Model3D(\n", - " kaq=10, z=layers, Saq=Saq, kzoverkh=1, phreatictop=True, tmin=1e-6, tmax=0.01\n", - ")\n", - "W_nlay = ttim.Well(\n", - " M_nlay,\n", - " xw=0,\n", - " yw=0,\n", - " rw=rw,\n", - " tsandQ=[(0, -Q)],\n", - " layers=[18, 19, 20],\n", - " rc=rc,\n", - " wbstype=\"slug\",\n", - ")\n", - "M_nlay.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "......................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 51\n", - " # data points = 183\n", - " # variables = 2\n", - " chi-square = 0.00159231\n", - " reduced chi-square = 8.7973e-06\n", - " Akaike info crit = -2128.32629\n", - " Bayesian info crit = -2121.90732\n", - "[[Variables]]\n", - " kaq0_49: 4.26515924 +/- 0.01222410 (0.29%) (init = 10)\n", - " Saq0_49: 4.9276e-04 +/- 1.8069e-05 (3.67%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0_49, Saq0_49) = -0.767\n" - ] - } - ], - "source": [ - "cM = ttim.Calibrate(M_nlay)\n", - "cM.set_parameter(name=\"kaq0_49\", initial=10)\n", - "cM.set_parameter(name=\"Saq0_49\", initial=1e-4, pmin=0)\n", - "cM.series(name=\"obs\", x=0, y=0, layer=[18, 19, 20], t=t, h=h)\n", - "cM.fit()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_494.265160.0122240.286604-infinf10[4.2651592443521205, 4.2651592443521205, 4.265...
Saq0_490.0004927630.0000183.666910.0inf0.0001[0.0004927634772056155, 0.0004927634772056155,...
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0_49 4.26516 0.012224 0.286604 -inf inf 10 \n", - "Saq0_49 0.000492763 0.000018 3.66691 0.0 inf 0.0001 \n", - "\n", - " parray \n", - "kaq0_49 [4.2651592443521205, 4.2651592443521205, 4.265... \n", - "Saq0_49 [0.0004927634772056155, 0.0004927634772056155,... " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.0029497698923064536\n" - ] - } - ], - "source": [ - "display(cM.parameters)\n", - "print(\"RMSE:\", cM.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hM = M_nlay.head(0, 0, t, layers=20)\n", - "plt.figure(figsize=(10, 5))\n", - "plt.semilogx(t, h / H0, \".\", label=\"obs\")\n", - "plt.semilogx(t, hM[0] / H0, label=\"ttim\")\n", - "plt.ylim([0, 1])\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"h/H0\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Summary of values simulated by AQTESOLV:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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k [m/d]Ss [1/m]RMSE
AQTESOLV4.0340.00038340.002976
ttim-three6.088210.0002034550.002873
ttim-multi4.265160.0004927630.002950
\n", - "
" - ], - "text/plain": [ - " k [m/d] Ss [1/m] RMSE\n", - "AQTESOLV 4.034 0.0003834 0.002976\n", - "ttim-three 6.08821 0.000203455 0.002873\n", - "ttim-multi 4.26516 0.000492763 0.002950" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ta = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Ss [1/m]\"], index=[\"AQTESOLV\", \"ttim-three\", \"ttim-multi\"]\n", - ")\n", - "ta.loc[\"ttim-three\"] = ca.parameters[\"optimal\"].values\n", - "ta.loc[\"ttim-multi\"] = cM.parameters[\"optimal\"].values\n", - "ta.loc[\"AQTESOLV\"] = [4.034, 3.834e-04]\n", - "ta[\"RMSE\"] = [0.002976, ca.rmse(), cM.rmse()]\n", - "ta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/04pumpingtests/12_falling-head_slug_test.ipynb b/docs/04pumpingtests/12_falling-head_slug_test.ipynb deleted file mode 100755 index c9852c4..0000000 --- a/docs/04pumpingtests/12_falling-head_slug_test.ipynb +++ /dev/null @@ -1,817 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Slug Test \n", - "**This test is taken from examples of AQTESOLV.**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import ttim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set background parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "rw = 0.127 # well radius\n", - "rc = 0.0508 # well casing radius\n", - "L = 4.20624 # screen length\n", - "b = -9.9274 # aquifer thickness\n", - "zt = -0.1433 # depth to top of the screen\n", - "H0 = 0.4511 # initial displacement in the well\n", - "zb = zt - L # bottom of the screen" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Slug:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Slug: 0.00366 m^3\n" - ] - } - ], - "source": [ - "Q = np.pi * rc**2 * H0\n", - "print(\"Slug:\", round(Q, 5), \"m^3\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load data:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "data = np.loadtxt(\"data/falling_head.txt\", skiprows=2)\n", - "t = data[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", - "h = (10 - data[:, 1]) * 0.3048 # convert drawdown from ft to meters" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create single layer conceptual model:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_0 = ttim.Model3D(kaq=10, z=[0, zt, zb, b], Saq=1e-4, tmin=1e-5, tmax=0.01)\n", - "w_0 = ttim.Well(\n", - " ml_0, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=1, wbstype=\"slug\"\n", - ")\n", - "ml_0.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "....................................................................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 129\n", - " # data points = 27\n", - " # variables = 2\n", - " chi-square = 0.00905722\n", - " reduced chi-square = 3.6229e-04\n", - " Akaike info crit = -212.000800\n", - " Bayesian info crit = -209.409126\n", - "[[Variables]]\n", - " kaq0_2: 1.33523687 +/- 0.05906434 (4.42%) (init = 10)\n", - " Saq0_2: 2.1466e-10 +/- 2.5021e-10 (116.56%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0_2, Saq0_2) = -0.698\n" - ] - } - ], - "source": [ - "ca_0 = ttim.Calibrate(ml_0)\n", - "ca_0.set_parameter(name=\"kaq0_2\", initial=10)\n", - "ca_0.set_parameter(name=\"Saq0_2\", initial=1e-4)\n", - "ca_0.series(name=\"obs\", x=0, y=0, t=t, h=h, layer=1)\n", - "ca_0.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_21.335245.906434e-024.42351-infinf10[1.3352368684147649, 1.3352368684147649, 1.335...
Saq0_22.14664e-102.502106e-10116.559-infinf0.0001[2.1466448974593843e-10, 2.1466448974593843e-1...
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0_2 1.33524 5.906434e-02 4.42351 -inf inf 10 \n", - "Saq0_2 2.14664e-10 2.502106e-10 116.559 -inf inf 0.0001 \n", - "\n", - " parray \n", - "kaq0_2 [1.3352368684147649, 1.3352368684147649, 1.335... \n", - "Saq0_2 [2.1466448974593843e-10, 2.1466448974593843e-1... " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.018315367486363435\n" - ] - } - ], - "source": [ - "display(ca_0.parameters)\n", - "print(\"RMSE:\", ca_0.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm_0 = ml_0.head(0, 0, t, layers=1)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t, h / H0, \".\", label=\"obs\")\n", - "plt.semilogx(t, hm_0[0] / H0, label=\"ttim\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"h/H0\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Try multilayer conceptual model:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Determine elevation of each layer.\n", - "# Thickness of each layer is set to be 0.5 m.\n", - "z0 = np.arange(zt, zb, -0.5)\n", - "z1 = np.arange(zb, b, -0.5)\n", - "zlay = np.append(z0, z1)\n", - "zlay = np.append(zlay, b)\n", - "zlay = np.insert(zlay, 0, 0)\n", - "nlay = len(zlay) - 1 # number of layers\n", - "Saq_1 = 1e-4 * np.ones(nlay)\n", - "Saq_1[0] = 0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 8\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_1 = ttim.Model3D(\n", - " kaq=10, z=zlay, Saq=Saq_1, kzoverkh=1, tmin=1e-5, tmax=0.01, phreatictop=True\n", - ")\n", - "w_1 = ttim.Well(\n", - " ml_1,\n", - " xw=0,\n", - " yw=0,\n", - " rw=rw,\n", - " tsandQ=[(0, -Q)],\n", - " layers=[1, 2, 3, 4, 5, 6, 7, 8],\n", - " rc=rc,\n", - " wbstype=\"slug\",\n", - ")\n", - "ml_1.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".........................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 38\n", - " # data points = 216\n", - " # variables = 2\n", - " chi-square = 0.00868193\n", - " reduced chi-square = 4.0570e-05\n", - " Akaike info crit = -2182.30650\n", - " Bayesian info crit = -2175.55594\n", - "[[Variables]]\n", - " kaq0_21: 0.49532050 +/- 0.00771220 (1.56%) (init = 10)\n", - " Saq0_21: 4.0620e-04 +/- 3.5550e-05 (8.75%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0_21, Saq0_21) = -0.959\n" - ] - } - ], - "source": [ - "ca_1 = ttim.Calibrate(ml_1)\n", - "ca_1.set_parameter(name=\"kaq0_21\", initial=10, pmin=0)\n", - "ca_1.set_parameter(name=\"Saq0_21\", initial=1e-4, pmin=0)\n", - "ca_1.series(name=\"obs\", x=0, y=0, layer=[1, 2, 3, 4, 5, 6, 7, 8], t=t, h=h)\n", - "ca_1.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_210.4953210.0077121.557010inf10[0.4953205000476846, 0.4953205000476846, 0.495...
Saq0_210.0004061970.0000368.75190inf0.0001[0.0004061971689826027, 0.0004061971689826027,...
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0_21 0.495321 0.007712 1.55701 0 inf 10 \n", - "Saq0_21 0.000406197 0.000036 8.7519 0 inf 0.0001 \n", - "\n", - " parray \n", - "kaq0_21 [0.4953205000476846, 0.4953205000476846, 0.495... \n", - "Saq0_21 [0.0004061971689826027, 0.0004061971689826027,... " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.006339884853175206\n" - ] - } - ], - "source": [ - "display(ca_1.parameters)\n", - "print(\"RMSE:\", ca_1.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm_1 = ml_1.head(0, 0, t, layers=8)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t, h / H0, \".\", label=\"obs\")\n", - "plt.semilogx(t, hm_1[0] / H0, label=\"ttim\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"h/H0\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Try adding well screen resistance:" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 8\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_2 = ttim.Model3D(\n", - " kaq=10, z=zlay, Saq=Saq_1, kzoverkh=1, tmin=1e-5, tmax=0.01, phreatictop=True\n", - ")\n", - "w_2 = ttim.Well(\n", - " ml_2,\n", - " xw=0,\n", - " yw=0,\n", - " rw=rw,\n", - " tsandQ=[(0, -Q)],\n", - " layers=[1, 2, 3, 4, 5, 6, 7, 8],\n", - " rc=rc,\n", - " res=0.1,\n", - " wbstype=\"slug\",\n", - ")\n", - "ml_2.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "............................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 41\n", - " # data points = 216\n", - " # variables = 3\n", - " chi-square = 0.00858106\n", - " reduced chi-square = 4.0287e-05\n", - " Akaike info crit = -2182.83089\n", - " Bayesian info crit = -2172.70506\n", - "[[Variables]]\n", - " kaq0_21: 0.50749746 +/- 0.01052392 (2.07%) (init = 10)\n", - " Saq0_21: 3.4727e-04 +/- 4.4651e-05 (12.86%) (init = 0.0001)\n", - " res: 0.00233497 +/- 0.00143443 (61.43%) (init = 0)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0_21, Saq0_21) = -0.977\n", - " C(Saq0_21, res) = -0.715\n", - " C(kaq0_21, res) = 0.674\n" - ] - } - ], - "source": [ - "ca_2 = ttim.Calibrate(ml_2)\n", - "ca_2.set_parameter(name=\"kaq0_21\", initial=10, pmin=0)\n", - "ca_2.set_parameter(name=\"Saq0_21\", initial=1e-4, pmin=0)\n", - "ca_2.set_parameter_by_reference(name=\"res\", parameter=w_2.res, initial=0, pmin=0)\n", - "ca_2.series(name=\"obs\", x=0, y=0, layer=[1, 2, 3, 4, 5, 6, 7, 8], t=t, h=h)\n", - "ca_2.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_210.5074970.0105242.073690inf10[0.50749745967107, 0.50749745967107, 0.5074974...
Saq0_210.0003472660.00004512.85780inf0.0001[0.0003472655198073493, 0.0003472655198073493,...
res0.002334970.00143461.43260inf0[0.0023349682160858087]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0_21 0.507497 0.010524 2.07369 0 inf 10 \n", - "Saq0_21 0.000347266 0.000045 12.8578 0 inf 0.0001 \n", - "res 0.00233497 0.001434 61.4326 0 inf 0 \n", - "\n", - " parray \n", - "kaq0_21 [0.50749745967107, 0.50749745967107, 0.5074974... \n", - "Saq0_21 [0.0003472655198073493, 0.0003472655198073493,... \n", - "res [0.0023349682160858087] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.006302945725154117\n" - ] - } - ], - "source": [ - "display(ca_2.parameters)\n", - "print(\"RMSE:\", ca_2.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm_2 = ml_2.head(0, 0, t, layers=8)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t, h / H0, \".\", label=\"obs\")\n", - "plt.semilogx(t, hm_2[0] / H0, label=\"ttim\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"h/H0\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Optimized res is very close to the minimum limitation for res. Thus, resistance of well skin has little effect on model performance. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary of values presented in AQTESOLV:" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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k [m/d]Ss [1/m]RMSE
AQTESOLV2.6167.894e-050.001197
ttim-single1.335242.14664e-100.018315
ttim-multi0.4953210.0004061970.006340
\n", - "
" - ], - "text/plain": [ - " k [m/d] Ss [1/m] RMSE\n", - "AQTESOLV 2.616 7.894e-05 0.001197\n", - "ttim-single 1.33524 2.14664e-10 0.018315\n", - "ttim-multi 0.495321 0.000406197 0.006340" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Ss [1/m]\"], index=[\"AQTESOLV\", \"ttim-single\", \"ttim-multi\"]\n", - ")\n", - "t.loc[\"AQTESOLV\"] = [2.616, 7.894e-5]\n", - "t.loc[\"ttim-single\"] = ca_0.parameters[\"optimal\"].values\n", - "t.loc[\"ttim-multi\"] = ca_1.parameters[\"optimal\"].values\n", - "t[\"RMSE\"] = [0.001197, round(ca_0.rmse(), 6), round(ca_1.rmse(), 6)]\n", - "t" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/04pumpingtests/13_multiwell_slug_test-.ipynb b/docs/04pumpingtests/13_multiwell_slug_test-.ipynb deleted file mode 100755 index 52b1f4b..0000000 --- a/docs/04pumpingtests/13_multiwell_slug_test-.ipynb +++ /dev/null @@ -1,820 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Slug Test for Confined Aquifer\n", - "**This test is taken from examples of AQTESOLV.**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import ttim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set background parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "H0 = 2.798 # initial displacement in m\n", - "b = -6.1 # aquifer thickness\n", - "rw1 = 0.102 # well radius of Ln-2 Well\n", - "rw2 = 0.071 # well radius of observation Ln-3 Well\n", - "rc1 = 0.051 # casing radius of Ln-2 Well\n", - "rc2 = 0.025 # casing radius of Ln-3 Well\n", - "r = 6.45 # distance from observation well to test well" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Slug:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Slug: 0.02286 m^3\n" - ] - } - ], - "source": [ - "Q = np.pi * rc1**2 * H0\n", - "print(\"Slug:\", round(Q, 5), \"m^3\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load data:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "data1 = np.loadtxt(\"data/ln-2.txt\")\n", - "t1 = data1[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", - "h1 = data1[:, 1]\n", - "data2 = np.loadtxt(\"data/ln-3.txt\")\n", - "t2 = data2[:, 0] / 60 / 60 / 24\n", - "h2 = data2[:, 1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create single layer conceptual model:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_0 = ttim.ModelMaq(kaq=10, z=[0, b], Saq=1e-4, tmin=1e-5, tmax=0.01)\n", - "w_0 = ttim.Well(\n", - " ml_0, xw=0, yw=0, rw=rw1, rc=rc1, tsandQ=[(0, -Q)], layers=0, wbstype=\"slug\"\n", - ")\n", - "ml_0.solve()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calibrate with two datasets simultaneously:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".....................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 34\n", - " # data points = 162\n", - " # variables = 2\n", - " chi-square = 0.01697472\n", - " reduced chi-square = 1.0609e-04\n", - " Akaike info crit = -1480.50745\n", - " Bayesian info crit = -1474.33226\n", - "[[Variables]]\n", - " kaq0: 1.16610844 +/- 0.00292541 (0.25%) (init = 10)\n", - " Saq0: 9.3821e-06 +/- 1.1585e-07 (1.23%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.502\n" - ] - } - ], - "source": [ - "# unknown parameters: kaq, Saq\n", - "ca_0 = ttim.Calibrate(ml_0)\n", - "ca_0.set_parameter(name=\"kaq0\", initial=10)\n", - "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca_0.series(name=\"Ln-2\", x=0, y=0, layer=0, t=t1, h=h1)\n", - "ca_0.series(name=\"Ln-3\", x=r, y=0, layer=0, t=t2, h=h2)\n", - "ca_0.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq01.166112.925411e-030.25087-infinf10[1.166108439006008]
Saq09.38211e-061.158516e-071.23481-infinf0.0001[9.382108899818189e-06]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 1.16611 2.925411e-03 0.25087 -inf inf 10 \n", - "Saq0 9.38211e-06 1.158516e-07 1.23481 -inf inf 0.0001 \n", - "\n", - " parray \n", - "kaq0 [1.166108439006008] \n", - "Saq0 [9.382108899818189e-06] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.01023631976154498\n" - ] - } - ], - "source": [ - "display(ca_0.parameters)\n", - "print(\"RMSE:\", ca_0.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm1_0 = ml_0.head(0, 0, t1, layers=0)\n", - "hm2_0 = ml_0.head(r, 0, t2, layers=0)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1 / H0, \".\", label=\"obs ln-2\")\n", - "plt.semilogx(t1, hm1_0[0] / H0, label=\"ttim ln-2\")\n", - "plt.semilogx(t2, h2 / H0, \".\", label=\"obs ln-3\")\n", - "plt.semilogx(t2, hm2_0[0] / H0, label=\"ttim ln-3\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"h/H0\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Try adding well skin resistance res:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_1 = ttim.ModelMaq(kaq=10, z=[0, b], Saq=1e-4, tmin=1e-5, tmax=0.01)\n", - "w_1 = ttim.Well(\n", - " ml_1, xw=0, yw=0, rw=rw1, res=0, rc=rc1, tsandQ=[(0, -Q)], layers=0, wbstype=\"slug\"\n", - ")\n", - "ml_1.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 45\n", - " # data points = 162\n", - " # variables = 3\n", - " chi-square = 0.01690828\n", - " reduced chi-square = 1.0634e-04\n", - " Akaike info crit = -1479.14278\n", - " Bayesian info crit = -1469.87999\n", - "[[Variables]]\n", - " kaq0: 1.16580197 +/- 0.00293850 (0.25%) (init = 10)\n", - " Saq0: 9.3657e-06 +/- 1.1724e-07 (1.25%) (init = 0.0001)\n", - " res: 3.0688e-04 +/- 3.6012e-04 (117.35%) (init = 0)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.482\n", - " C(Saq0, res) = -0.157\n" - ] - } - ], - "source": [ - "# unknown parameters: kaq, Saq, res\n", - "ca_1 = ttim.Calibrate(ml_1)\n", - "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", - "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca_1.set_parameter_by_reference(name=\"res\", parameter=w_1.res, initial=0)\n", - "ca_1.series(name=\"Ln-2\", x=0, y=0, layer=0, t=t1, h=h1)\n", - "ca_1.series(name=\"Ln-3\", x=r, y=0, layer=0, t=t2, h=h2)\n", - "ca_1.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq01.16582.938501e-030.252058-infinf10[1.1658019679476392]
Saq09.36566e-061.172392e-071.2518-infinf0.0001[9.36565528100193e-06]
res0.0003068853.601222e-04117.348-infinf0[0.000306884543573789]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 1.1658 2.938501e-03 0.252058 -inf inf 10 \n", - "Saq0 9.36566e-06 1.172392e-07 1.2518 -inf inf 0.0001 \n", - "res 0.000306885 3.601222e-04 117.348 -inf inf 0 \n", - "\n", - " parray \n", - "kaq0 [1.1658019679476392] \n", - "Saq0 [9.36565528100193e-06] \n", - "res [0.000306884543573789] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.010216267246558788\n" - ] - } - ], - "source": [ - "display(ca_1.parameters)\n", - "print(\"RMSE:\", ca_1.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm1_1 = ml_1.head(0, 0, t1, layers=0)\n", - "hm2_1 = ml_1.head(r, 0, t2, layers=0)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1 / H0, \".\", label=\"obs ln-2\")\n", - "plt.semilogx(t1, hm1_1[0] / H0, label=\"ttim ln-2\")\n", - "plt.semilogx(t2, h2 / H0, \".\", label=\"obs ln-3\")\n", - "plt.semilogx(t2, hm2_1[0] / H0, label=\"ttim ln-3\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"h/H0\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Adding well screen resistance does not improve the performance obviously. While the AIC value increases. Thus, res should be removed from the model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Try multilayer conceptual model:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Determine elevations of each layer.\n", - "# Thickness of each layer is set to be 0.5 m.\n", - "z = np.arange(0, b, -0.5)\n", - "zlay = np.append(z, b)\n", - "nlay = len(zlay) - 1\n", - "Saq_2 = 1e-4 * np.ones(nlay)\n", - "n = np.arange(0, 13, 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 13\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_2 = ttim.Model3D(\n", - " kaq=10, z=zlay, Saq=Saq_2, kzoverkh=1, tmin=1e-5, tmax=0.01, phreatictop=True\n", - ")\n", - "w_2 = ttim.Well(\n", - " ml_2, xw=0, yw=0, rw=rw1, tsandQ=[(0, -Q)], layers=n, rc=rc1, wbstype=\"slug\"\n", - ")\n", - "ml_2.solve()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calibrate with two datasets simultaneously:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "...................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 32\n", - " # data points = 2106\n", - " # variables = 2\n", - " chi-square = 0.21986254\n", - " reduced chi-square = 1.0450e-04\n", - " Akaike info crit = -19302.3305\n", - " Bayesian info crit = -19291.0254\n", - "[[Variables]]\n", - " kaq0_12: 1.16570162 +/- 8.1176e-04 (0.07%) (init = 10)\n", - " Saq0_12: 8.6904e-06 +/- 2.9609e-08 (0.34%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0_12, Saq0_12) = -0.503\n" - ] - } - ], - "source": [ - "ca_2 = ttim.Calibrate(ml_2)\n", - "ca_2.set_parameter(name=\"kaq0_12\", initial=10)\n", - "ca_2.set_parameter(name=\"Saq0_12\", initial=1e-4, pmin=0)\n", - "ca_2.series(name=\"Ln-2\", x=0, y=0, layer=n, t=t1, h=h1)\n", - "ca_2.series(name=\"Ln-3\", x=r, y=0, layer=n, t=t2, h=h2)\n", - "ca_2.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_121.16578.117623e-040.0696372-infinf10[1.1657016194139789, 1.1657016194139789, 1.165...
Saq0_128.69035e-062.960946e-080.3407160.0inf0.0001[8.69035475870028e-06, 8.69035475870028e-06, 8...
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0_12 1.1657 8.117623e-04 0.0696372 -inf inf 10 \n", - "Saq0_12 8.69035e-06 2.960946e-08 0.340716 0.0 inf 0.0001 \n", - "\n", - " parray \n", - "kaq0_12 [1.1657016194139789, 1.1657016194139789, 1.165... \n", - "Saq0_12 [8.69035475870028e-06, 8.69035475870028e-06, 8... " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.010217542096555829\n" - ] - } - ], - "source": [ - "display(ca_2.parameters)\n", - "print(\"RMSE:\", ca_2.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm1_2 = ml_2.head(0, 0, t1, layers=n)\n", - "hm2_2 = ml_2.head(r, 0, t2, layers=n)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1 / H0, \".\", label=\"obs ln-2\")\n", - "plt.semilogx(t1, hm1_2[0] / H0, label=\"ttim ln-2\")\n", - "plt.semilogx(t2, h2 / H0, \".\", label=\"obs ln-3\")\n", - "plt.semilogx(t2, hm2_2[0] / H0, label=\"ttim ln-3\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"h/H0\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary of values presented by AQTESOLV & MLU" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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k [m/d]Ss [1/m]RMSE
MLU1.3118.197e-060.010373
AQTESOLV1.1669.368e-060.009151
ttim-single1.166119.38211e-060.010236
ttim-multi1.16578.69035e-060.010216
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" - ], - "text/plain": [ - " k [m/d] Ss [1/m] RMSE\n", - "MLU 1.311 8.197e-06 0.010373\n", - "AQTESOLV 1.166 9.368e-06 0.009151\n", - "ttim-single 1.16611 9.38211e-06 0.010236\n", - "ttim-multi 1.1657 8.69035e-06 0.010216" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Ss [1/m]\"],\n", - " index=[\"MLU\", \"AQTESOLV\", \"ttim-single\", \"ttim-multi\"],\n", - ")\n", - "t.loc[\"AQTESOLV\"] = [1.166, 9.368e-06]\n", - "t.loc[\"MLU\"] = [1.311, 8.197e-06]\n", - "t.loc[\"ttim-single\"] = ca_0.parameters[\"optimal\"].values\n", - "t.loc[\"ttim-multi\"] = ca_2.parameters[\"optimal\"].values\n", - "t[\"RMSE\"] = [0.010373, 0.009151, ca_0.rmse(), ca_1.rmse()]\n", - "t" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/04pumpingtests/14_dawsonville_slug_test.ipynb b/docs/04pumpingtests/14_dawsonville_slug_test.ipynb deleted file mode 100755 index de3ab70..0000000 --- a/docs/04pumpingtests/14_dawsonville_slug_test.ipynb +++ /dev/null @@ -1,773 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Slug test for confined aquifer\n", - "**This test is taken from example of MLU.**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import ttim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set background prameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "b = 98 # aquifer thickness\n", - "zt = -24\n", - "zb = zt - b\n", - "rw = 0.076 # well radius of Ln-2 Well\n", - "rc = 0.076 # casing radius of Ln-2 Well\n", - "Q = 0.01016 # slug volume in m^3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load data:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "data = np.loadtxt(\"data/dawsonville_slug.txt\")\n", - "t = data[:, 0]\n", - "h = data[:, 1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create single layer conceptual model:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml = ttim.ModelMaq(\n", - " kaq=10, z=[zt, zb], Saq=1e-4, tmin=1e-6, tmax=1e-3, topboundary=\"conf\"\n", - ")\n", - "w = ttim.Well(ml, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=0, wbstype=\"slug\")\n", - "ml.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "...............................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 28\n", - " # data points = 22\n", - " # variables = 2\n", - " chi-square = 4.2778e-04\n", - " reduced chi-square = 2.1389e-05\n", - " Akaike info crit = -234.654964\n", - " Bayesian info crit = -232.472879\n", - "[[Variables]]\n", - " kaq0: 0.42082538 +/- 0.01841831 (4.38%) (init = 10)\n", - " Saq0: 1.7028e-05 +/- 5.3141e-06 (31.21%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.985\n" - ] - } - ], - "source": [ - "# unknown parameters: kay, Saq\n", - "ca = ttim.Calibrate(ml)\n", - "ca.set_parameter(name=\"kaq0\", initial=10, pmin=0)\n", - "ca.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca.series(name=\"obs\", x=0, y=0, layer=0, t=t, h=h)\n", - "ca.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq00.4208250.0184184.376710inf10[0.4208253798974495]
Saq01.70282e-050.00000531.2075-infinf0.0001[1.702822495188348e-05]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 0.420825 0.018418 4.37671 0 inf 10 \n", - "Saq0 1.70282e-05 0.000005 31.2075 -inf inf 0.0001 \n", - "\n", - " parray \n", - "kaq0 [0.4208253798974495] \n", - "Saq0 [1.702822495188348e-05] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.004409577277491564\n" - ] - } - ], - "source": [ - "display(ca.parameters)\n", - "print(\"rmse:\", ca.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm = ml.head(0, 0, t)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t, h, \".\", label=\"obs\")\n", - "plt.semilogx(t, hm[0], label=\"ttim\")\n", - "plt.xlabel(\"times(d)\")\n", - "plt.ylabel(\"displacement(m)\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Try multilayer model:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "nlay = 49 # number of layers\n", - "zlayers = np.linspace(zt, zb, nlay + 1) # elevation of each layer\n", - "Saq = 1e-4 * np.ones(nlay)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 49\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_1 = ttim.Model3D(kaq=10, z=zlayers, Saq=Saq, tmin=1e-6, tmax=1e-3, phreatictop=True)\n", - "w_1 = ttim.Well(\n", - " ml_1, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=range(nlay), wbstype=\"slug\"\n", - ")\n", - "ml_1.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 29\n", - " # data points = 1078\n", - " # variables = 2\n", - " chi-square = 0.02094373\n", - " reduced chi-square = 1.9464e-05\n", - " Akaike info crit = -11690.9837\n", - " Bayesian info crit = -11681.0180\n", - "[[Variables]]\n", - " kaq0_48: 0.42040768 +/- 0.00252667 (0.60%) (init = 10)\n", - " Saq0_48: 1.7362e-05 +/- 7.4494e-07 (4.29%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0_48, Saq0_48) = -0.986\n" - ] - } - ], - "source": [ - "ca_1 = ttim.Calibrate(ml_1)\n", - "ca_1.set_parameter(name=\"kaq0_48\", initial=10, pmin=0)\n", - "ca_1.set_parameter(name=\"Saq0_48\", initial=1e-4)\n", - "ca_1.series(name=\"obs\", x=0, y=0, layer=range(nlay), t=t, h=h)\n", - "ca_1.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_480.4204082.526675e-030.6010060inf10[0.4204076783911592, 0.4204076783911592, 0.420...
Saq0_481.73621e-057.449361e-074.29058-infinf0.0001[1.7362144857462076e-05, 1.7362144857462076e-0...
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0_48 0.420408 2.526675e-03 0.601006 0 inf 10 \n", - "Saq0_48 1.73621e-05 7.449361e-07 4.29058 -inf inf 0.0001 \n", - "\n", - " parray \n", - "kaq0_48 [0.4204076783911592, 0.4204076783911592, 0.420... \n", - "Saq0_48 [1.7362144857462076e-05, 1.7362144857462076e-0... " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.0044077564143741275\n" - ] - } - ], - "source": [ - "display(ca_1.parameters)\n", - "print(\"RMSE:\", ca_1.rmse())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Simulate test site with multilayer model does not improve the performance much." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm_1 = ml_1.head(0, 0, t)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t, h, \".\", label=\"obs\")\n", - "plt.semilogx(t, hm_1[0], label=\"ttim\")\n", - "plt.xlabel(\"times(d)\")\n", - "plt.ylabel(\"displacement(m)\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Try if well screen resistance has an effect:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 49\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_2 = ttim.Model3D(kaq=10, z=zlayers, Saq=Saq, tmin=1e-6, tmax=1e-3, phreatictop=True)\n", - "w_2 = ttim.Well(\n", - " ml_2,\n", - " xw=0,\n", - " yw=0,\n", - " rw=rw,\n", - " rc=rc,\n", - " res=0.1,\n", - " tsandQ=[(0, -Q)],\n", - " layers=range(nlay),\n", - " wbstype=\"slug\",\n", - ")\n", - "ml_2.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "................................................................................................................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 173\n", - " # data points = 1078\n", - " # variables = 3\n", - " chi-square = 0.19769058\n", - " reduced chi-square = 1.8390e-04\n", - " Akaike info crit = -9269.02034\n", - " Bayesian info crit = -9254.07175\n", - "[[Variables]]\n", - " kaq0_48: 1.46245727 +/- 0.00987825 (0.68%) (init = 10)\n", - " Saq0_48: 1.0961e-13 +/- 1.8626e-14 (16.99%) (init = 0.0001)\n", - " res: 0.02337981 +/- 0.00160953 (6.88%) (init = 0)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0_48, Saq0_48) = -0.910\n", - " C(Saq0_48, res) = -0.184\n", - " C(kaq0_48, res) = 0.121\n" - ] - } - ], - "source": [ - "ca_2 = ttim.Calibrate(ml_2)\n", - "ca_2.set_parameter(name=\"kaq0_48\", initial=10, pmin=0)\n", - "ca_2.set_parameter(name=\"Saq0_48\", initial=1e-4)\n", - "ca_2.set_parameter_by_reference(name=\"res\", parameter=w_2.res, initial=0)\n", - "ca_2.series(name=\"obs\", x=0, y=0, layer=range(nlay), t=t, h=h)\n", - "ca_2.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_481.462469.878247e-030.6754550inf10[1.4624572716235917, 1.4624572716235917, 1.462...
Saq0_481.09612e-131.862630e-1416.993-infinf0.0001[1.0961177230839196e-13, 1.0961177230839196e-1...
res0.02337981.609533e-036.88429-infinf0[0.02337980518372843]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0_48 1.46246 9.878247e-03 0.675455 0 inf 10 \n", - "Saq0_48 1.09612e-13 1.862630e-14 16.993 -inf inf 0.0001 \n", - "res 0.0233798 1.609533e-03 6.88429 -inf inf 0 \n", - "\n", - " parray \n", - "kaq0_48 [1.4624572716235917, 1.4624572716235917, 1.462... \n", - "Saq0_48 [1.0961177230839196e-13, 1.0961177230839196e-1... \n", - "res [0.02337980518372843] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.013542024721293677\n" - ] - } - ], - "source": [ - "display(ca_2.parameters)\n", - "print(\"RMSE:\", ca_2.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm_2 = ml_2.head(0, 0, t)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t, h, \".\", label=\"obs\")\n", - "plt.semilogx(t, hm_2[0], label=\"ttim\")\n", - "plt.xlabel(\"times(d)\")\n", - "plt.ylabel(\"displacement(m)\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Adding resistance of well screen does not improve the performance. Thus, res should not be applied in the conceptual model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary of values presented by MLU" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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k [m/d]Ss [1/m]RMSE
MLU0.41331.9388e-050.004264
ttim0.4208251.70282e-050.004410
ttim-multilayer0.4204081.73621e-050.004408
ttim-res1.462461.09612e-130.013542
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" - ], - "text/plain": [ - " k [m/d] Ss [1/m] RMSE\n", - "MLU 0.4133 1.9388e-05 0.004264\n", - "ttim 0.420825 1.70282e-05 0.004410\n", - "ttim-multilayer 0.420408 1.73621e-05 0.004408\n", - "ttim-res 1.46246 1.09612e-13 0.013542" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Ss [1/m]\"],\n", - " index=[\"MLU\", \"ttim\", \"ttim-multilayer\", \"ttim-res\"],\n", - ")\n", - "tr = np.delete(ca_2.parameters[\"optimal\"].values, 2)\n", - "t.loc[\"MLU\"] = [0.4133, 1.9388e-05]\n", - "t.loc[\"ttim\"] = ca.parameters[\"optimal\"].values\n", - "t.loc[\"ttim-multilayer\"] = ca_1.parameters[\"optimal\"].values\n", - "t.loc[\"ttim-res\"] = tr\n", - "t[\"RMSE\"] = [0.004264, ca.rmse(), ca_1.rmse(), ca_2.rmse()]\n", - "t" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/04pumpingtests/1_test_of_oude_korendijk.ipynb b/docs/04pumpingtests/1_test_of_oude_korendijk.ipynb deleted file mode 100755 index c16e960..0000000 --- a/docs/04pumpingtests/1_test_of_oude_korendijk.ipynb +++ /dev/null @@ -1,1305 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Confined Aquifer Test\n", - "**This example is taken from Kruseman and de Ridder (1970)**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import ttim\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set basic parameters for the model:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "H = 7 # aquifer thickness\n", - "zt = -18 # top boundary of aquifer\n", - "zb = zt - H # bottom boundary of aquifer\n", - "Q = 788 # constant discharge" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create conceptual model:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# unkonwn parameters: kaq, Saq\n", - "ml = ttim.ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)\n", - "w = ttim.Well(ml, xw=0, yw=0, rw=0.2, tsandQ=[(0, Q)], layers=0)\n", - "ml.solve(silent=\"True\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load data of two observation wells:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# time and drawdown of piezometer 30m away from pumping well\n", - "data1 = np.loadtxt(\"data/piezometer_h30.txt\", skiprows=1)\n", - "t1 = data1[:, 0] / 60 / 24 # convert min to days\n", - "h1 = data1[:, 1]\n", - "r1 = 30\n", - "# time and drawdown of piezometer 90m away from pumping well\n", - "data2 = np.loadtxt(\"data/piezometer_h90.txt\", skiprows=1)\n", - "t2 = data2[:, 0] / 60 / 24 # convert min to days\n", - "h2 = data2[:, 1]\n", - "r2 = 90" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calibrate using only the data from observation well 1:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "...................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 32\n", - " # data points = 34\n", - " # variables = 2\n", - " chi-square = 0.03408048\n", - " reduced chi-square = 0.00106502\n", - " Akaike info crit = -230.783293\n", - " Bayesian info crit = -227.730572\n", - "[[Variables]]\n", - " kaq0: 68.6394693 +/- 1.43827759 (2.10%) (init = 10)\n", - " Saq0: 1.6071e-05 +/- 1.5823e-06 (9.85%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.891\n" - ] - }, - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq068.63951.4382782.09541-infinf10[68.63946928731693]
Saq01.60712e-050.0000029.8453-infinf0.0001[1.607118069739686e-05]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 68.6395 1.438278 2.09541 -inf inf 10 \n", - "Saq0 1.60712e-05 0.000002 9.8453 -inf inf 0.0001 \n", - "\n", - " parray \n", - "kaq0 [68.63946928731693] \n", - "Saq0 [1.607118069739686e-05] " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ca1 = ttim.Calibrate(ml)\n", - "ca1.set_parameter(name=\"kaq0\", initial=10)\n", - "ca1.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca1.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca1.fit(report=True)\n", - "display(ca1.parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.03166018156153158\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "print(\"rmse:\", ca1.rmse())\n", - "hm1 = ml.head(r1, 0, t1)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", - "plt.semilogx(t1, hm1[0], label=\"ttim at 30 m\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.title(\"ttim analysis for Oude Korendijk\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calibrate using only the data from observation well 2:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "...............................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 28\n", - " # data points = 35\n", - " # variables = 2\n", - " chi-square = 0.01806492\n", - " reduced chi-square = 5.4742e-04\n", - " Akaike info crit = -260.919609\n", - " Bayesian info crit = -257.808913\n", - "[[Variables]]\n", - " kaq0: 71.5830705 +/- 1.57403261 (2.20%) (init = 10)\n", - " Saq0: 2.9106e-05 +/- 1.9378e-06 (6.66%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.847\n" - ] - }, - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq071.58311.5740332.19889-infinf10[71.58307046727971]
Saq02.91065e-050.0000026.65777-infinf0.0001[2.9106495014403527e-05]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 71.5831 1.574033 2.19889 -inf inf 10 \n", - "Saq0 2.91065e-05 0.000002 6.65777 -inf inf 0.0001 \n", - "\n", - " parray \n", - "kaq0 [71.58307046727971] \n", - "Saq0 [2.9106495014403527e-05] " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ca2 = ttim.Calibrate(ml)\n", - "ca2.set_parameter(name=\"kaq0\", initial=10)\n", - "ca2.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca2.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", - "ca2.fit(report=True)\n", - "display(ca2.parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.022718724245662295\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "print(\"rmse:\", ca2.rmse())\n", - "hm2 = ml.head(r2, 0, t2)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t2, h2, \".\", label=\"obs at 90 m\")\n", - "plt.semilogx(t2, hm2[0], label=\"ttim at 90 m\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.title(\"ttim analysis for Oude Korendijk\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calibrate model with two datasets simultaneously:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "...............................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 28\n", - " # data points = 69\n", - " # variables = 2\n", - " chi-square = 0.17291362\n", - " reduced chi-square = 0.00258080\n", - " Akaike info crit = -409.245804\n", - " Bayesian info crit = -404.777591\n", - "[[Variables]]\n", - " kaq0: 66.0884336 +/- 1.65496581 (2.50%) (init = 10)\n", - " Saq0: 2.5410e-05 +/- 2.4017e-06 (9.45%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.855\n" - ] - }, - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq066.08841.6549662.50417-infinf10[66.0884335879812]
Saq02.54102e-050.0000029.45178-infinf0.0001[2.5410188529330492e-05]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 66.0884 1.654966 2.50417 -inf inf 10 \n", - "Saq0 2.54102e-05 0.000002 9.45178 -inf inf 0.0001 \n", - "\n", - " parray \n", - "kaq0 [66.0884335879812] \n", - "Saq0 [2.5410188529330492e-05] " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ca = ttim.Calibrate(ml)\n", - "ca.set_parameter(name=\"kaq0\", initial=10)\n", - "ca.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", - "ca.fit(report=True)\n", - "display(ca.parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.05005990899693861\n" - ] - }, - { - "data": { - "image/png": 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4+Phw4sR/k8lOnjxJiRIlYl/nyJEDABcXl9jnMa+joqLu6rp3QxN9CnYdu8z2w6HU8/Wm9gPt4YH2YIuGk3/AgTVW4v9xqPUoVt1K+g+0s57rfNP7QkCRgLtqak/s5qCiV8W7ullQKjuoXcaLeX3r/fd/aBqa7QEeeughevfuzYgRIzDGsHTpUubOnQvA8ePH2bZtG/Xr1+f777+nUaNGXL9+nfDwcNq1a0e9evWoUKFCgnOGhYVRsqQ1s/vrr7+OfT9v3rxcu3Yt0TiOHDlCqVKlcHNz49ixYxw4cICyZctSoEABDh48yJEjRyhZsiTz58/nu+++S9N3dgRN9MlIsn/JxRVK17MeLcfAxYNW0j/wI/wyHn4ZB/l8rFp+5bZQtjG4eTj76ygHin9zkJqbheDzwXozoLKd2mW80pzgY9SqVYvevXtTp04dAPr27UvNmjU5evQoVapUYc6cObzwwgtUrFiR/v37ExYWRufOnWOb1SdNmpTgnMOGDaNXr15MnDiRhx9+OPb9Zs2aMW7cOAICAnjjjTd4/PHHYz/bunUr48aNw93dHRcXF6ZPn06hQoUAmDZtGq1btyY6Opo+ffpQrVo1h3z3tJCMHsSe3gIDA01QkGP6SD/ddIiP1x3AZsBV4LVWlRnQLOEd4R1uXIR/f7ISf8jPEBkOHnmhYguo3N76M5dj/tIrx4i6dAlbeDgePj7pdo34A/h0wJ66H/3f//0fVapUcXYY2V5ivwcR2WWMSbTfUGv0ybin/qU8haBmT+sReRMO/wIHVsOBtbBvKbi4QZkG9n79duBVJv2/iErWlYULuTD5E3LVrEm+jh3I17Ytbl6OvRlLanS/1vKVUulNa/QpuKOPPi3NTzYbnNplb+JfAxf+sd4vUs3q06/cForXBBenToTIliJPnyZs1WqurlzJ7YMHwc0Nz4YNydehA3mbP4xLIiN171ZMjT5mwN6MVjMAtJav7itao88c7rZGr4neWUJDrD79A2vg+DYwNshbHCq1sQb8lXsI3HKkfB7lULcOHODqqlWErVpN1JkzSO7c5G3enPwdO5CnQQPE7d4bweLX3mfumcnU3VOxYcNVXBlYcyB9/fs68Nso5Via6DMHbbq/X3iXhwYDrUf4pf/69f9eCLu+Ag9PKP+wlfQrtoLcBZ0dcbaQs3JlclauTOFXX+Xmrl2ErVzF1Z9+4urKlbgWLEi+tm3J16E9uQIC7noXqfgD9uJP09NpeUqp9KA1+swm8hYc3QL/rLZq/NfPgrhC6fpW8/4D7aCgr7OjzFZsERHc2LKFsJWruL5pE+b2bdxLlSJ/x47k79oVD5/4my6mnvbRq/uJ1ugzB226v98TfVw2G5z500r4/6yB8/YVmwo/8N9gvpK1tV8/A0Vfv8619Ru4unIlN7ZtAyBPw4YU6N6dvM0fRtwdtzyy3gSozEYTfeZwt4leM0Rm5uJiJfKHR8JLv8Mrf0GbceBZBH77BGa1gI8rc+G7F1iy7AeHbRqhkubq6UmBLo9QevYsKmzcQKGXXuL2oUOcGjyYQ81bcGH6dKIuXkzzde51sx2lsrIrV64wffr02NdHjx69Y0GaoKAgBg0a5PDrLlu2LMld6D7//HP8/f0JCAigUaNGd5QbO3YsFSpUoHLlyvz0008Ojyu1NNHfT7zKQr3+0GslDAuBrjO5VPhBch1YTtfgvpjZrTn42xLdbS+DuJcoQeGXB1Jh4wZ8pk8nR8WKXJwylYPNHubU0GHc/Ouvez53YtPxlMruUkr0gYGBTJkyxeHXTS7RP/nkk+zZs4fg4GCGDRvGa6+9BsD+/fuZP38++/btY+3atbz00ktER0c7PLbU0ER/v8rlBdUf5fsyY6gTMZ13IntRgotUXP8sfNEY9i6xlupV6U5cXcn7cDNKz5qJ75o1eD3+ONd//pmjj/fgyKOPcWXZMmwREXd1zuQ229EtcVV2NWLECEJCQggICGDo0KGMGDGCLVu2EBAQwKRJk9i8eTMdOnQA4N1336VXr160atWKsmXLsmTJEoYNG4a/vz9t2rQhMjIywflnzJjBgw8+SI0aNejWrRvh4eH8/vvvrFixgqFDhxIQEEBISMgdx+TLly/2+Y0bN2IH6S5fvpwePXqQI0cOypUrR4UKFdi5c2eCa3p6ejJ8+HBq165NixYt2LlzJ02bNsXX15cVK+Lv3H5vdNT9fa6erzdT3XLxbVRrlthasrLJGcr+35ew6FkoWB4aDYbqPXQJ3gySw7ccxUa+ReHBgwlbvozL877jzIg3OP/hBAo89iheTzyBe9GiKZ4nqc12dIU9lWn8OALO7nHsOYv5Q9txSX48btw49u7dS3CwdZO7efNmPvroI1atWhX7Oq6QkBA2bdrE/v37qV+/PosXL+bDDz+kS5curF69mkceeeSO8l27dqVfv34AjBw5klmzZvHyyy/TqVMnOnToQPfu3RON69NPP2XixIlERETEbppz6tQp6tWrF1vGx8eHU6dOJTj2xo0bNG3alPHjx9OlSxdGjhzJ+vXr2b9/P7169Yrd8S4ttEZ/n4vZNOK1VpX5um8jyrZ4HgbsgMe+gRyesOJl+KQGbJtu7cCnMoSrZx4K9uyJ7+pVlJo1k1w1ahD6xZccat6C08NHcOvAgRTPEVAkgL7+fe9I5Nqkr1TqtW3bFnd3d/z9/YmOjqZNmzaAtU3s0aNHE5Tfu3cvjRs3xt/fn3nz5qV6y9oBAwYQEhLC+PHj+d///gckvtNeYlNyPTw87oirSZMmsTEnFuO90Bp9FpBg0wgXV6jaGap0stbb3zIRfnoDfp1g9fHX6afr7WcQEcGzYUM8GzYk4sQJLn0zlyuLFhG2fDl5GjXC+7k+5K5XL9Vz8nXuvco0kql5ZxZxt4V1d3eP/XeW1LawvXv3ZtmyZdSoUYOvv/46QQtBSnr06EH//v0BUtyyNkb8uOLG7Kita7VGn5WJQIXm8Oxq6LMOStWBTe/DJD9Y9zZcO+vsCLMVj1KlKPbWm1Tc9DOFB7/CrX/+4fizfTjSrRtX167FpGKgTkyT/sCaA2Ob7bXPXmUX8beOTW4r2Xtx7do1ihcvTmRkJPPmzUvVdQ4ePBj7fPXq1VSsWBGATp06MX/+fG7fvs2RI0c4ePBg7K57GU1r9NlF6brw5AI4uxe2ToJt02DHF9bmOw0GQcFyzo4w23AtUIBCL75IwWefJWzFCi7Nms2pwa/i4euLd79+5O/QPtn5+HFX2NM+e5WdeHt707BhQ/z8/Gjbti0ffPABbm5u1KhRg969e1OzZs00nf+9996jbt26lClTBn9//9jk3qNHD/r168eUKVNYtGgR5cuXjz1m2rRpbNiwAXd3d7y8vJgzZw4A1apV47HHHqNq1aq4ubnx6aef4urqmqb47pUumJNdhYbA71Mg+DtrdL5fN2j0KhSt6uzIsh0THc21deu4+MWX3P7nH9xLlsS773Pk79oVlxzJ73eg6+WrjKQL5mQOumCOSh3v8tDxE3jlb6vf/p/V8Fl9+P4JOPGHs6PLVsTVlXxt21Ju6RJ8PpuOW6FCnB09hpAWLQmd/RW28PAkj01uGp5SSoHW6FWM8Euw80vY/hncugJlG0Pj18C3mdXXrzKMMYbwHTu5+MXnhG/bjqu3N97PPYfXEz1wyZUrQXldKldlFK3RZw661r0m+rS5fR12fQ2/T7U21ClRExq/DpXbJ7qm/q5jl9l+OJR6vt53jvxXDhG+ezcXp33Kjd9/x7VQIbz7PofX448nmvCVSm+a6DMHbbpXaZPD09o6d/DfVtP+zSuw4CmYXg+Cv4fo/1aT2nXsMj1nbufjdQfoOXO7rrWfDnLXqkXp2bMoM+9bclSswPlx4znUqhWX5szBdvv2XZ9PR+grlf1ooleJc8sBtXvDwCDoNgtc3WHZizClFuycAZE32X44lIgoGzYDkVE2th8OdXbUWVbu2rUp89VXlPl2Ljl8y3Nu7DhCWrXm8vz5mFQur6sb5SiVPWmiV8lzdQP/7vDiVnhyIeQrDmuGwGR/Ol9fQEG3W7gKuLu5UM/X29nRZnm5AwMpM+drSs+Zg3uJEpx9dzQh7doTtnx5ivPwdVU9pbInTfQqdUSgUmvo8xP0XgPFquOz60N25HyF+ZU28X0vP+2jz0B56tahzHfzKPXF57jky8vp4SM40rUb13/9NdGlN0FH6Kus6+jRo/j5+Tn0nMHBwaxZsybRzyIiInj22Wfx9/enRo0ad6ygt2vXLvz9/alQoQKDBg1K8t9jRtJEr+6OCJRtCE8vged/wbVCMx48NoOaS5vBHzPv6MNX6UtE8GzShHKLFlHi44+whYdz4vkXON6rNzf//jtB+cRW1VNKJS65RD9jxgwA9uzZw/r163n99dex2WwA9O/fny+//JKDBw9y8OBB1q5dm2ExJ0UTvbp3JQLg8bnQdyN4V4TVr8P0+vB/qyAT3MVmF+LiQv727Sm/ehVFR47k9qFDHH3scU6+MpjbR47cUTb+Rjk6OE+lN0f/HZs4cSJ+fn74+fkxefLk2PejoqLo1asX1atXp3v37oTb158YMWIEVatWpXr16gwZMiTB+Xbu3EmDBg2oWbMmDRo04MCBA0RERDBq1CgWLFhAQEAACxYsuOOY/fv307x5cwCKFClCgQIFCAoK4syZM1y9epX69esjIjzzzDMsW7YMgKZNm/Lqq6/y0EMPUaVKFf744w+6du1KxYoVGTlypEN+NkkyxmSpR+3atY1yApvNmP9bbczUQGPeyWfMrNbGHN/p7Kiypahr1835KVPN/9WsZfZXrWZOv/OOiTh3LkG5P8/9aQLnBprqX1c3gXMDzZ/n/nRCtOp+sn///rsq7+i/Y0FBQcbPz89cv37dXLt2zVStWtXs3r3bHDlyxABm69atxhhjnn32WTNhwgQTGhpqKlWqZGw2mzHGmMuXLyc4Z1hYmImMjDTGGLN+/XrTtWtXY4wxX331lRkwYECicXzxxReme/fuJjIy0hw+fNjkz5/fLFq0yPzxxx+mefPmseV+/fVX0759e2OMMU2aNDHDhg0zxhgzefJkU7x4cXP69Glz69YtU7JkSXPx4sVU/xwS+z0AQSaJvKg1euUYIvBAO+i/DTpMspbYndUCFvaCS4edHV224uqZh8IvD6TCup/w6tGDK4sWE9K6Dec/+YTo69djy+ngPJXeHP13bOvWrXTp0oU8efLg6elJ165d2bJlCwClSpWiYcOGADz11FNs3bqVfPnykTNnTvr27cuSJUvInTt3gnOGhYXx6KOP4ufnx6uvvpqqrWn79OmDj48PgYGBDB48mAYNGuDm5pbi1rQxe8v7+/tTrVo1ihcvTo4cOfD19b1jpztH00SvHMvVDQL7wKA/ockIOLgOptWBH4fDDZ1+l5HcChWi2NsjKb96FXmbNSP0s88JadGSS3O/xURG6uA8le4c/XcssUQaI/5WzyKCm5sbO3fupFu3bixbtix23/e43n77bZo1a8bevXtZuXIlt27dSjEONzc3Jk2aRHBwMMuXL+fKlStUrFgRHx8fTp48GVsu/ta0cbegzRFnHwtHbkmbGE30Kn3k8IRmb1gJv2ZPa3ndKQGwZSJE3nR2dNmKR5kylJz4MWUXLSLHAw9w7v33OdyxE+X3hDKj5Zc6OE+lG0cPAH3ooYdYtmwZ4eHh3Lhxg6VLl9K4cWMAjh8/zrZt2wD4/vvvadSoEdevXycsLIx27doxefJkgoMTjhMICwujZMmSAHz99dex7ye3NW3M9QHWr1+Pm5sbVatWpXjx4uTNm5ft27djjOGbb76hc+fOafrOjqCJXqWvvMWsFfb6b4MyDWHjaJha+79d81SGyeVXjdJfzcbns+ng4sLJAQPxGjqZp1wbpPgfsA7aU/cq/gDQtKhVqxa9e/emTp061K1bl759+8ZuTVulShXmzJlD9erVuXTpEv379+fatWt06NCB6tWr06RJEyZNmpTgnMOGDeONN96gYcOGRMdZi6JZs2bs378/0cF458+fp1atWlSpUoXx48czd+7c2M8+++wz+vbtS4UKFShfvjxt27ZN8/dOK13rXmWso1th3dtwejfhBauwvuRAfALb6xz8DGYiI7n8w57S+N4AACAASURBVA9cnDqN6CtXKNC9G4VfeQW3QoUSlNU971UMXes+c9C17lXmVrYR9N3I4SZTCQ0NpfOeAVye3Z09e/9ydmTZiri7U/DJJyn/01oK9urFlaXLCGndhtCZM7HFW1JXB+0pdX/TRK8ynosLP9KAlhETGBfZg/rsocriFrBxjLV7nsowrvnyUXTEcHxXriD3gw9y/qOPOdyhI9c2bIgd+KSD9pS6vzkl0YtIQRFZLyIH7X8maLcVkQAR2SYi+0TkbxF53BmxqvRRz9cb3HIww9aJtrZJhPm2hy0fw7QH4e8fdMGdDJajXDlKff4ZpWbORDzcOTnwZY4/24dbB/7VFfWUus85pY9eRD4ELhljxonICMDLGDM8XplKgDHGHBSREsAuoIox5kpy59Y++vtHgr3sj++AH4fBmWAoXR/ajofiNZwdZrZjoqK4vGABF6dMJfraNQo89iiFBw3CrWBBZ4emnEz76DOH+6WPvjMwx/58DvBI/ALGmH+NMQftz08D54HCGRahSne1y3gxoFmF/wbila4L/TZBp6lw8SB80QRWvgI3Ljo30GxG3Nwo2LMn5X9ai1fPnlz5YREhrdtw6ZtvMEnM9dVR+UplXs5K9EWNMWcA7H8WSa6wiNQBPICQJD5/XkSCRCTowoULDg9WZSAXF6j1DLy8C+q9BH9+C1NrwfbPdcOcDOZaoADF3noT3xXLyVW9Ouc+GMuRLl25sWPnHeV0n3ulMrd0S/QiskFE9ibyuKvVA0SkODAXeNYYY0usjDHmS2NMoDEmsHBhrfRnCbkKQJsPoP/vULI2rB0OnzeGw5udHVm2k6N8eUrNnIHPtKnYwsM53qsXJ199lcgzZwAdla8yzpUrV5g+fXrs66NHj/Ldd9/Fvg4KCmLQoEEOv+6yZcvYv39/op8dO3aM5s2bU716dZo2bXrHynhz5syhYsWKVKxYkTlz5iR6fEZIt0RvjGlhjPFL5LEcOGdP4DGJ/Hxi5xCRfMBqYKQxZnt6xaoyscKV4akl0OM7iLoJ33SGBU/B5aPOjixbERHytmiB7+pVFBo4kOs/byKkXXsufv4FgV41dFS+yhApJfrAwECmTJni8Osml+iHDBnCM888w99//82oUaN44403ALh06RKjR49mx44d7Ny5k9GjR3P58mWHx5Yazmq6XwH0sj/vBSyPX0BEPIClwDfGmB8yMDaV2YjAA+3hpR3w8NtwaKO1fv7P70PEDWdHl6245MxJ4YED8F29Gs9GDbkweTKefUYyK29/HZWv0t2IESMICQkhICCAoUOHMmLECLZs2UJAQACTJk1i8+bNdOjQAYB3332XXr160apVK8qWLcuSJUsYNmwY/v7+tGnThsjIhF2BM2bM4MEHH6RGjRp069aN8PBwfv/9d1asWMHQoUMJCAggJOTOHuS4W9Y2a9aM5cutdPbTTz/RsmVLChYsiJeXFy1btozdm97T05Phw4dTu3ZtWrRowc6dO2natCm+vr6sWLHC4T83N4efMXXGAQtF5DngOPAogIgEAi8aY/oCjwEPAd4i0tt+XG9jjHYAZlfuOeGhIVDjCdjwDvz6obWUbttx8EAH64ZAZQgPn5L4TJ3K9a2/ce7993EfPoHWzZpR9C3nL/epMsbZDz7g9v/949Bz5qjyAMXefDPJz8eNG8fevXtj16zfvHkzH330EatWrYp9HVdISAibNm1i//791K9fn8WLF/Phhx/SpUsXVq9ezSOP3DkOvGvXrvTr1w+AkSNHMmvWLF5++WU6depEhw4d6N69e4KYatSoweLFi3nllVdYunQp165dIzQ0lFOnTlGqVKnYcj4+Ppw6dQqAGzdu0LRpU8aPH0+XLl0YOXIk69evZ//+/fTq1St2lztHcUqN3hgTaoxpboypaP/zkv39IHuSxxjzrTHG3RgTEOehSV5B/pLQbSb/tPuBi9E5rab87x6HS0ecHVm249moIb7Ll1FkyOvc2L6dwx06cPHLGZh4q+sp5Qxt27bF3d0df39/oqOjY3ev8/f35+jRownK7927l8aNG+Pv78+8efNStWXtRx99xC+//ELNmjX55ZdfKFmyZIpb1np4eNwRS5MmTWLjTCyutHJWjV6pNNl17DI9V0YTHfUOfdzXMezIElyn14PGQ6DhIHDLkfJJlEOIhwfeffuSr107zo0dy4WJEwlbtoxio0aRp15dZ4en0klyNe/MIu62sO7u7rGJNqltYXv37s2yZcuoUaMGX3/9dYIWgsSUKFGCJUuWAHD9+nUWL15M/vz58fHxueP4kydP0rRpU4AEscSNMz22q9UlcNV9afvhUCKibEQaV2ZGtmVu4A9QqQ1s+h981gBCNjk7xGzHvUQJfKZOxeez6Zjbtzneuzenhg0j6qKug6AcI/7WscltJXsvrl27RvHixYmMjGTevHmpus7Fixex2awJYWPHjqVPnz4AtG7dmnXr1nH58mUuX77MunXraN26tcNivRua6NV9qZ6vNx5uLrgKuLu54F+lKjw2B55aDMYGcx+BH56Fq2ecHWq2k7dZM3xXrcT7xRe4+uNaQtq249J332GidVtilTbe3t40bNgQPz8/hg4dSvXq1XFzc6NGjRqJbkF7t9577z3q1q1Ly5YteeCBB2Lf79GjBxMmTKBmzZoJBuNt3ryZypUrU6lSJc6dO8dbb70FQMGCBXn77bd58MEHefDBBxk1ahQFnbS6pG5Tq+5bCZbQjRF5C377xFo739UDHn4LHuwHrtpTldFuHz7C2TFjCN++nZx+fhR75x1y+fslKBd8Ppigc0EEFg3UUfuZmC6Bmznc7RK4muhV1hUaAmuGQshGKOoPHSZCqTrOjirbMcZwdfUazo0fR/TFULye6EHhwYNxzZcP0P3u7yea6DOH+2Wte6XSn3d5qyn/sW8gPBRmtYQVL0P4JWdHlq2ICPk7tKf8mjV49ezJ5fkLCGnbjrAVKzDG6Mp6SqUzTfQqaxOBqp1h4E6oPxD+nAdTa8PuuWBLdEVllU5c8+al2Mi3KPvDQtxLluT0sOEc79WbB28W15X17iNZrRX4fnMvP39tulfZy7l9sOo1OLEdStWFDpOhaFVnR5XtmOhorvzwA+cnTsJ28yZRj7fjjzZlqFW6vjbbZ2JHjhwhb968eHt7x04PUxnHGENoaCjXrl2jXLlyd3ymffRKxWWzwV/fwbq34fZVaPgKPDQU3HM5O7JsJyo0lPMTPiJs2TLcS5Sg2Duj8GzSxNlhqSRERkZy8uRJbt265exQsq2cOXPi4+ODu7v7He9rolcqMTdCYd1IK+kX9IX2E6F8M2dHlS2F//EHZ94dTURICHnbtKHom2/gXsTavVpH5CuVMk30SsVzx9S86L9h1WC4dBiqPw6tP4A8hZwdYrZji4jg0qxZXPzsc8TDgyKvv8axZpXpt+EFHZGvVAp01L1Scew6dpmeM7fz8boD9Jy5nV2u1aH/Nqv5fu8SmBYIf34LWewmOLNz8fCgUP/++K5YTk5/P86OHsPtfkModuaWjshXKg000atsJ2b5XJuByCgb2w+HWjvjPTwSXtwKhSrD8gEwpyNcPOTscLMdj7JlKT17NiU+HE/e89cZOzuKnpsNuaPddES+UvdAE73KduIvn1vP1/u/D4s8AM/+CB0/gbN/w2f1YfN4iLrtvICzIREhf6dOVFr7E7RtQudt0cz81pOKh8KdHZpS9x3to1fZUpLL58Z17Rz89AbsXQyFKlnJv0yDjA1UAXBj+w7OvvMOEceOkb9zZ4qMGI6bVxK/N6WyIR2Mp1RaHFwPq1+DK8eh1jPQYjTkds7mFNmZ7fZtLn7+OaEzZuKaNy9F3xhBvo4ddT63UmiiVyrtIm7A5nGw7VMrybf9EKp1sVbeUxnq1r//cvbtUdz86y/yNGjApUGPEeR6QqffqWxNE71SjnLmb1g5CE7/CZXaQvuPIL+Ps6PKdozNxuX58zn70UdERNxkUWNX1tfPyRdtZmqyV9mSTq9TylGKV4fnNkCr9+HIL/BpXdg5Q9fNz2Di4kLBJ58kaOLT/OUr9NwUzajZ4fzf9tXODk2pTEcTvVJ3y9UNGgyEl7ZZ296uGQJftYHz/zg7smynetVmTHs0N5O6ulHwGtR44zvOf/wxNl2iValY2nSvVFoYA38vhLUj4PY1aPw6NH4N3HI4O7JsI3aJ3NwPUGT2j4QtXoJ7mdIUH/MeeerWcXZ4SmUI7aNXykGSnJZ34yKsfQP2LLQW3Ok0BUrXc16g2diNbds4M+odIk+coMCjj1Jk6BBc8+VzdlhKpSvto1fKARIsnXvs8n8f5ikE3WZAz0UQeRNmt4E1Q61avspQeerXx3fFcgo+14crixdzuH0Hrq5f7+ywlHIaTfRKpVKiS+fGV7Gl1Xdf90VrkN6n9ax5+CpDueTKRdGhQym7cCGu3t6cenkQJwe9QtSFC84OTakMp4leqVRKduncuHJ4Qttx8Nw68MgD87rDkuetbXFVhsrlV41yPyyk8GuvcX3zZkLad+DKokVktS5LpZKjffRK3YVULZ0bV9Rt2PIxbJkIOfNZC+34ddOFdpzg9pEjnH17FOFBQeSuV4/iY0azP+cl3eteZQk6GE8pZzu3H1YMhFO7oFIbaP+xLrTjBMZm48oPizg/YQLRkREsaAQrAg1u7jl0r3t1X9PBeEo5W9Gq8Nx6aP0BHPnV6rv/Y5YutJPBxMUFr8cfw3f1Ki5VL0WPjRGMmRNJ8bO3da97lWVpolfKwXYdu8ynmw7dOSofwMUV6g+A/r9DyVrWRjlzOkJoiHMCzcbcixbF86P3mNo1J4WvwvuzI6m7+gi2iAhnh6aUw2nTvVIOFDMFLyLKhoebC/P61ku8L98Y+HMu/DQSom9Ds7eg3kvWqnsqwwSfDyb40BYCF+zB9acteFQoT4n33ydXjRqxn2sfvrofJNd0r/+rKOVAiU3BSzTRi1hb3lZoadXs178N+5ZC52lQtFrGB55NBRQJsBJ4A7j+yy+ceeddjvZ4goLPPMOZJ5vSb8vLRERH4OHqoX346r6lTfdKOVCqp+DFyFccenwH3Wdb+91/0QQ2jYUobULOaJ5NmuC7aiUFejzOpTlzsD0zmIoht7BhI9IWqX346r6lTfdKOdhdT8GLcSMU1g6HPT9A4SrQ+VPwqZ1+gaok3di5k6NvDMfl1Fk2BLjwQ4tcTOmkW+CqzEun1yl1PzmwFla9CtfPQv2B0OxNcM/l7KiyHdvNm+wbPwrXBauhkBel3x+L50MPOTsspRKl0+uUup9UbgMDtkPNp+H3KfBZQzi2zdlRZTsuuXLh/+4Eys3/nhz5vTjx/AucHj6C6CtXnB2aUndFE71SGSzJ6Xdx5cxv7YD39DKwRcJXbe2b5FzPuEAVALlq1KDckiV493+RsFWrCOnQUTfJUfcVpyR6ESkoIutF5KD9zyQ7MkUkn4icEpFpGRmjUukh2R3wElO+GfTfBnWetzbJ+aw+hGzKmGBVLBcPD4q88grlfliIW6FC1iY5r75KVKjuX6AyP2fV6EcAG40xFYGN9tdJeQ/4JUOiUiqdpWoHvPhyeEK7D+HZH8HVA+Y+Aitehlth6R+wukPOqlWtTXJeGcS1DRs53KEjYatX6yY5KlNzVqLvDMyxP58DPJJYIRGpDRQF1mVQXEqlq7uefhdXmfrw4lZoMAj+/NZaRvdf/aeR0cTdnUL9++O7ZDHuPj6cfn0IJwe+TOT584C1yM7MPTMJPh/s5EiVsjhl1L2IXDHGFIjz+rIxxiteGRfgZ+BpoDkQaIwZmMT5ngeeByhdunTtY8eOpVvsSqXVPU+/i+vkLlj+Elz4B2o8AW3GQq57PJe6ZyYqiktzvuHClClIjhzcHvAkz7nMJcIWqYvsqAzllFH3IrJBRPYm8uicylO8BKwxxpxIqaAx5ktjTKAxJrBw4cJpC1ypdFa7jBcDmlW49yQP1vz6F36FxkPg74XwaV34Z43jglSpIm5ueD/Xh3LLlpKjYkXcx37Oa9+F4xUWrYvsqEzDWTX6A0BTY8wZESkObDbGVI5XZh7QGLABnoAHMN0Yk1x/vs6jV9nP6WBYPgDO7QX/R60973MXdHZU2Y6x2djzxQSip3+NTWB+ixz0HP4VAUVrOjs0lQ1kxnn0K4Be9ue9gOXxCxhjehpjShtjygJDgG9SSvJKZQWpmn4XV4kA6LcJmr5hrZf/aR3Yn+CflEpn4uJC9f7DkbmfEFGxFH3W3KbgG1OJOHnK2aGpbM5ZiX4c0FJEDgIt7a8RkUARmemkmJRyuruefhfDzQOajoDnf4F8JWDhM7CwF1y/kL4BqwQCAlpR74e1FHv3HW4G/8WRTp24/P33GJvN2aGpbMopid4YE2qMaW6MqWj/85L9/SBjTN9Eyn+d1EA8pbKSe5p+F1cxP+i7ER5+Gw6sgel1Yc8ia1tclWHExQWvHj3wXbmCXAE1ODt6DMd7P0vEiYRDjnSUvkpvqU70IuIlItVExNc+Il4p5WBpmn4Xw9UdHhpiDdbzKguLn4MFT8G1cw6PVyXPvWRJSs2aRbH3xnBr3z4Od+rMpbnfxtbug88H029dP6bunkq/df002at0kWzCFpH8IvKmiOwBtgNfAAuBYyLyg4g0y4gglcouapfxYl7ferzWqjLz+tZL28j8IlWgzzpoOQYOrrf67v9aoLX7DCYieD36KL6rVpI7MJBz77/P8Wd6EXHsGEHngoiIjtCtcFW6SnbUvYisB74BVhpjrsT7rDbWHPc9xphZ6RrlXdBR90ol4uJBa2T+iR1QqS10mAT5ijs7qmzHGEPY0mWcGzsWExlJZN9HeS7fEiKIwt3F/Y5598Hngwk6F0Rg0UCdi69SpNvUKpWF3POCO7Zo2PE5bBwDbjmgzXio0QNE0i9YlajIc+c4O+odrv/yCza/SgT3bUi1Wq3uSPL91vUjIjpCF95RqeKQ6XUiUl1EOolI15iH40JUSqXGPY/KB3BxhfoD4MXfoHAVWPYifPc4XD2dfgGrRLkXLYrP559RYvw43E6co/bw7yi16k9MdDSANukrh0pVoheR2cBsoBvQ0f7okI5xKaUSkeZR+QCFKsCza6DNODjyq7Vm/p/ztO8+g4kI+Tt3xnflCvI0bMj5Dz/k2JM9uX34MIFFA/Fw9cBVXHF3cSewaKIVNaVSJVVN9yKy3xhTNQPiSTNtuldZWUyNPjLKhrubS9oH7IWGWH33x7dBxVbQ8RNrHr7KUMYYrq5azbn//Q/bzZsUfuUVTrQLIOjibu2jV6mS5j56EZkFfGyM2e/o4BxNE73K6hyyKU5cNhvs/BI2vGttg9vmAwjoqX33ThB14QJnRo/m+oaN5KpRg+JjPyCHr2+yx+igPQWOSfQPASuBs8BtQABjjKnuyEAdQRO9UvcoNMTa5/7Yb1ChBXScAvlLOjuqbMcYw9U1azg35j177X4QBXv3RlxdE5TVQXsqhiMG483GmkrXhv/65zs6JjylVKbgXR56rbI2xTn2O0yvB7vnat99BhMR8rdvj+/qVXg2eYjzEz6K7buPTwftqdRIbaI/boxZYYw5Yow5FvNI18iUUhnPxQXqvgD9f4Ni/rBiIMzrDmG6MUtGcytUiJJTplDio4+IOHqUI490IXTWrNiR+YAO2lOpktqm++lAAazm+9sx7xtjlqRfaPdGm+6VchCbDf6YYfXdu7hB6w+g5lPad+8EURcvcnb0aK6t35Cg71776BU4po/+q0TeNsaYPmkNztE00SvlYJcOw/KB9r77ltbIfO27z3DGGK6uXsO591Luu1fZj66Mp5RKmztq9+46Mt+J7nZkvtb4s4d7HownIiNFpGAynz8sIrpwjlKZ3K5jl/l006G7W0kvrrh990WrWXPv5z2qffdO4Fa4MD5Tpybbdx9Dd8dTkPJgvD3AShHZKCITRGSYiIwSkbn2He06AjvSP0yl1L1K07K58RX0hd6rrXXyj26F6fXhz291ZH4GExHyd2iP76qV5HmosTUyv+dT3D585I5yOipfQQqJ3hiz3BjTEHgR2Ae4AleBb4E6xphXjTEX0j9MpdS9csiyuXG5uEC9F++s3X/3mK6Z7wSxtfsJE7h95AhHunQhdPZXsbX7lEblB58PZuaemVrTz+K0j16pLM7hy+bGZbPBzi9gw2hw89Ad8Zwo6sIFzrw7musbN5KrZk2Kf/A+OcqVS7KPXhfbyVocMeq+EjAEKAu4xbxvjHnYQTE6jCZ6pRJy+LK58YWGwLKX4MR2qNQGOkzW/e6dwBjD1ZUrOfv+B5hbtyj86mAKPv10oiPzZ+6ZydTdU7Fhw1VcGVhzIH39+zohauUIjkj0fwGfA7uA2BEfxphdjgrSUTTRK+Uk8fe7bzsBqj+mtXsniDx/nrPvvMv1TZvIVbs2JT54H48yZe4oE1Ojj7RF4u7irjX6+5wjEv0uY0xth0eWDjTRK+VkFw/B8pfgxA6o3B46TIK8RZ0dVbZjjCFs+XLOfTAWExFBkddew+upnojLf0OzdOpd1uGIRP8ucB5Yyp0r411yUIwOo4leqUzAFg3bp8PG98AjN7T7CPy6ae3eCSLPnePMqFHc+OVXcgcGUvyD9/EoXTrZY/QG4P7jiER/JJG3jTEm+f0TnUATvVKZyIV/YVl/OBUEVTpC+0ngWdjZUWU7xhjCli7j3AcfYKKjKfL663g9+cQdtfsYOkjv/pTm3euMMeUSeWS6JK+USrs0L64TV+FK0OcnaDEa/v0JpteFvZlui4wsT0Qo0LULvqtWkjswkHP/+x/Hez9LxMmTCcrq3PusJ1WJXkS2iMj7ItJGRPKmd1BKKedw6OI6MVzdoNFgeGELFCgDi56Fhb3gxsW0n1vdFfdixSj15RcU/9973Nq3j8OdOnP5++8xNltsGd0RL+tJ7Ta1vYADQDfgdxEJEpFJ6ReWUsoZHL64TlxFHoDn1kPzUfDPavi0Luxf7rjzq1QREQp0747vyhXkDgjg7OgxHH/uOSJPWcsZBxQJYEarGQysOVCb7bOI1DbdHwbWAxuBX4HcQJV0jEsp5QT1fL3xcHPBVcDdzYV6vt6OvYCrGzR+HV74xdoBb+EzsKgPhGe6cb1ZnnuJEpSaNZNio0dz66+/rdr9woUYYwgoEkBf/74pJnldWe/+kNrBeCHAReA7YAsQbIyxJX+Uc+hgPKXSJt0X14kRHQlbJ8Mv4yGXlzUNr4rukeUMESdPcWbkSMK3bydPo0YUf28M7sWTX/BIB+1lLmkejAdMAY4DTwCDgF4iUt5B8SmlMpHaZbwY0KxC+iZ5AFd3aDIUnt9kzbNf0BMW99PavRN4+JSk9OxZFB31NuG7d3O4YyeuLF5CchVBHbR3/0ht0/0nxphHgRZYq+O9C/ybjnEppbKLYv7Q92doMgL2LbF2xDuw1tlRZTvi4kLBJ5/Ed/kyclapwpm33uLEiy8See58ouV10N79I7VN9x8DjQBPYDtWP/0We999pqJN90qlv3Rr3j8dbM27P78fAnpC6w8gVwHHnV+lirHZuPztPM5PnIh4eFBs5Fvk69gRibfgUUoL6+jCOxnHEQvmPAr8aow55+jgHE0TvVLpK2YKXkSUDQ9H74YHEHXb6rffOgnyFodOU6BCC8edX6VaxNGjnH7zLW7u3o1n8+YUf/cd3AqnbsEj7cPPWI5YMOcHoK6IfGR/dHRohEqp+0bcKXgRkTYmb/jXMfPtY7jlsKbg9d0AHp7wbTdYMQhuXXXcNVSqeJQtS5m531Bk+HBubNnC4Q4dCVu9Otm++xjah595pHbBnLHAK8B++2OQ/T2lVDYTMwXPBbABvx266LjFdeIqWRte+BUavgJ/zoXPGsDhzY69hkqRuLri/Wxvyi1binvZMpx+fQinXhlMVGjyayxoH37mkdqm+7+BgJgpdSLiCvxpjKmezvHdNW26Vyr97Tp2mckb/uW3QxexGXAVeK1VZQY0q5A+Fzyx0+q7Dz0ED/a1ltTN4Zk+11JJMlFRhH71FRenTMXF05Ni77xDvjatkyyvffQZxxHT6wDijojJn7aQlFL3s9plvBjcolL6Lq4TV6k68OJWqDcA/phl1e6Pbk2/66lEiZsbhfr1o9ySxbiXKMGpwYM59dprRF1OvDUntQvvJEYX43Gc1NbonwDGAZsAAR4C3jDGzL+ni4oUBBYAZYGjwGPGmAR/U0SkNDATKAUYoJ0x5mhy59YavVIZJ8MW14nr2O+w7CW4fATq9rf68z1yZ8y1VSwTGUnozJlcmP4ZrvnzU3zMaPI+/LBDzq0D+e6eIwbjfQ/UA5bYH/XvNcnbjQA2GmMqYi2rOyKJct8AE4wxVYA6QOITOpVSTpHU4joO3QEvvjINoP9vUOd52PEZfN4Qjm93/HVUssTdnUL9+1Puh4W4FSrEyZcGcHr4cKLDwtJ8bh3I51jJJnoRqRXzAIoDJ4ETQAn7e/eqMzDH/nwO8Egi164KuBlj1gMYY64bY8LTcE2lVAZIlx3w4vPIA+0mQK+VYIuC2W1g3UiIvOX4a6lk5XzgAcotXEChl14ibNVqDnfsxPVff03TOXUgn2Ml23QvIpvsT3MCgcBfWE331YEdxphG93RRkSvGmAJxXl82xnjFK/MI0BeIAMoBG4ARxpjoRM73PPA8QOnSpWsfO3bsXsJSSjnAp5sO8fG6AxkzSA/g9jVY9zbs+goKVYJHPgef2ul3PZWkm3v3ceaNEdw+eIj83btRdPhwXPPe287mOpDv7txz070xppkxphlwDKhljAk0xtQGagKHUrjoBhHZm8ijcyrjdgMaA0OABwFfoHcScX5pjy2wcCoXc1BKpY903wEvvhx5oeNkeGoJRNyAWS1g4xhr4R2VoXL5VaPs4sV4P/88YUuWcrhTZ278/vs9nSulgXw6WC/1UjsYL9gYE5DSe6m+qMgBoKkx5oyI6MxORAAAHfFJREFUFAc2G2MqxytTDxhnjGlqf/00UM8YMyC5c+tgPKWczymD9ABuhcFPb8Kf/9/efYdHVaZ9HP/eSWjSexMpIq6+oCAqKCCCgI26omJDRUBAxL6CqIu6go3dtVAERNkVC8oCoSioi4CrIIIdxBJAkN67IeR5/zhBMaRMkjNzJjO/z3XlcuacM+e5cz2Ge85TX4Uq/wfdxkD1MyNXvvzm4JdfsmHwEFJXr6Zcj6upet99JJQs6cu9NVjveH5Mr1tpZhPM7EIza21m44GVBYgpGbgx4/WNwIwsrlkKlDezo4/obfEW6xGRKBexHfAyK14WuoyCa6fAge0wvi18+IS3Ja5EVIkzz6TutP9Q4eab2fXmFFK6dGX/p5/6cm8N1subUBP9zcC3eKvj3YmXcG8uQLlPAO3N7AegfcZ7zOxsM5sAkNEXfy/wgZl9jTc2YHwByhSReNHgYhjwCTS8Aj4c4SX8zd8GHVXcSShenKr3/4Xar/4bEhL4ueeNbBo+nPSDBwt0Xw3Wy5tQm+7bAosLw6h3Nd2LyB+snAmz7oKDu6DNEDj/DkhMCjqquJN+4ABbRv6dnZMnU7R2baqPGMEJZzXJ9/00WO+P/Ni97l948+i3A4syfj7KapGboCnRi8hx9m+D2ffAiuneGvpdx0DlU3P/nPhu/+IlbBw6lMMbN1Lh5puoPGgQCcWKhaWsePoyUOBEf8yNagDd8ZrUazjnou5rsRK9SOEUkQF83/zHS/ip+6Htg3DebZCQGJ6yJFtH9u1ny1NPsWvKFIqefDI1nhhBiUaNfC0j3gbsFXgwnpldb2YvAm8D7YAX8Ka+iYgUWEQW2QFo+Ge4bYm3v/17D8HLl8H2n8JTlmQrsVRJqj/6CLXGjyd93z7W9LiGLf/8J+mpqb6VoQF7vwt1MN4/gcZ4g+EGOeeecs59Er6wRCSeHLvH/eG0dBan5LwFaoGUqgI9JkO3cbB1JYxpAYvHQnp6+MqULJVq1ZJ6M5Mp27kz28e+yJruV3JoZUEmdP1OA/Z+F+pa95WAXngr5D1uZp+a2b/DGpmIxI2IL7JjBmdeDQMWQ52W8O798K/OsHNNeMuV4ySWKUONEcM5ccxo0nbuYPWVV7F11Cjc4YJNiWxcpTHjO4xnYJOBMd9sn5tQB+OVAVoArfGa7CvhjcK/MccPBkB99CKFU2CL7DjnLbDz7hBw6dDhMTi7l/dlQCLqyK5dbPrb4+yZNYvip59O9SdGULxBA9/LicVBen6Muv8K+CjjZ6Fzbr2/IfpHiV5E8mXXOkgeCCkfQr020OUFKHti0FHFpT3z5rFp2COk791Lpdtvp2Kvm7Ekf8Z+x+ogPT+2qT3DOTfAOfdaNCd5EZF8K1cLbpgOl4+EdZ/C6PO8J/08zEwSf5Tp0IF6s2ZSqk0btv7976y57jp+TVnty73jcZBeqKPuK5vZ02Y2x8z+e/Qn3MGJiESUGZzT29vvvlojmHEbvN4D9m4KOrK4k1ShAjWf/Sc1Rj7D4TVrWd2tG9tfeQVXwEGT8ThIL9Sm+3nAm3jz5/vhrU+/1Tl3f3jDyzs13YuIL9LTYclY+OARSCoOlz0Djbqr7z4AaVu3svHhv7Jv/nxKnN2UGsOHU/Skk/J9P/XRZ3+Dpmb2lXPujIxjC5xzrX2OtcCU6EXiS9gH8W37Aab3h/VL4bROcPk/oJS2w4405xy7p89g8/DhuLQ0qtx7D+WvuQZLCHWWeP4Vhi8GOSX6UEc3HJ3nsNHMLgc2ABqlIiKBOrrQTmpaOkWTEpjcu7n/yb7SKdBrLnz8HMwfDmubQcd/wOld/C1HcmRmlOvWlZLnNWfjgw+x+bG/sfe996nx+N8oUrNm2MqNhcF7oX4V+puZlQXuwWu+nwDcFbaoRERCELGFdhISoeVd0HeBNxJ/Sk94+xY4sCM85Um2ilSrRq3x46j26CMc+uorUjp3Yedbb5GX5dzzIqvBe19s+YIJX0/giy1fhKVMv+Wa6M0sETjFObfbOfeNc66Nc66pcy45AvGJiGQr4gvtVD0den8AFz7gbZAzujmseje8ZcpxzIzyV11F3eRkijdsyKaHHmbdrbdyePNm38vKPHivbNGy9JnXh+eXP0+feX0KRbIPtY9+vnOuTQTiKTD10YvEl8AW2tn4JUzrD1u+hcbXwSUjoHjZyJUvALj0dHZOfo0tI0diRYtS7cGhlOnUCfNx0OSxffSfbf6M55c/TzrpJFoiA5sMpHej3r6VlV9+DMZ7HCiLN/J+/9HjzrnlfgXpFyV6EYmYtF9hwZPw0T+gdHXo/DzUvyjoqOJS6po1bBjyAAc//5xS7S6i+rBhJFWq5Hs5R/vsD6cfpkhCkT/02Qc5aM+PRD8/4+XRiw1wzrm2/oToHyV6EYm49ctgej/Y9j00vdlbRrdY6aCjijvuyBF2vDKJrc8+S8IJJ1Bt2F8pc8klvpeTVUIPetBevkfdm9ndGS9n4SX5Y9tCtFyUiAjAiU3h1oXw37/BJ6Pgpw+g6xhvwxyJGEtMpOItvSjV+gI2DB7CL3fexd7L5lH1oYdIKu9ft07jKo2PS+JZDdqLltH5uQ3GK53x0xToD1QHagC3AqeHNzQRkUKkSAm4+HHo9S4kJMErl8M7gyH1QNCRxZ1i9etT543XqXzHIPa89z4pnTqz97/hXcw1pxX3Mo/Sj/So/bysjHeFc25vxvvSwFvOOf/bRApITfciErjU/fD+MPh0HFQ4GbqNhVrnBh1VXDr03XdsGDyEX7/7jrJdulB16AMklikTlrJCadL/yzl/4amlT/nexF/gTW2Ak4DUY96nAnUKGJeISGwqWhIuexp6JsORwzDxYnjvYTh8KOjI4k7xP/2JulPepGL/fuyeNYuUTp3Zt+ijsJTVuEpjejfq/YfEnblJ//2f34/4pjqhJvp/A5+a2TAz+yuwBJgUvrBERGJAvdbeBjlNboD/PQvjWsOGz4OOKu5Y0aJUueMO6rzxOgmlSrGuTx82PvQwR/btz/3DBZS5Sb/dSe0ivqlOSE33AGZ2FtAq4+1C51xU/t+qpnsRiUo/vAfJt8O+LdDqHrjgPkgqGnRUcSf911/Z+txz7Jj4MkVq1KD68OGUbBbebpXMTfrhmIZX4Ol1hYkSvYhErYM7vQF6X73hbYPbdSxUaxh0VHHpwPLP2TBkMIfX/kz566+nyj13k1CiRNBh5ZsfffQiIjFt2dqdjJr/I8vW7gxfISXKw59fhB6veXvcj7sQFj4DR9LCV6Zk6YSzmlBv+nTK33ADO199ldVdu3FgeVQ2VBeYnuhFJO5FZBe8zPZvh9l3e2vm12zqPd1XbhDeMiVL+xcvYeMDD3B40yYq3HwTlQcNIqFYsaDDyhM90YuI5CDUXfB8feovWRGumgTdJ8KOFHixFXz8AqQfKfi9JU9KNm9G3eRkynXvzo6XJrL6iis4+M23QYflGyV6EYl7oeyCd/Spf+S8VVw3YbF/TfwNr4ABS+DktjBvqLfQzo4Uf+4tIUssVZLqjz5CrfHjSN+7jzVXX83W557Dpabm/uEop0QvInGvae3yTO7dnLs7nJpts32oT/35Urqq12/fdQxsXgFjWsCn4yE93b8yJCSlWrWi3sxkynbsyLbRY1h9dQ8OrVoVdFgFokQvIoKX7G9rUz/bvvlQnvoLxAwaXwsDPoGTmsOce+HVbrBrnb/lSK4Sy5ShxpNPcOKoF0jbsoXV3a9k29gXcWmFc9CkBuOJiIRo2dqdLE7ZTvN6FcM7WM85WPYyzH0QEhLh4uHQ5Hrvy4BEVNrOnWx69FH2vvMuxc84gxpPjKBYvXpBh3UczaMXESmMdq6B6bfB2o/glIuh07NQpnrQUcWlPe+8w6ZHHiX94EEq33knFXregCUmBh3WbzTqXkQkgnwbnV++Dtw4Ey55ElYvhNHN4au3vCd+iagyl15KvZnJlGzRgi1PPsnanjeS+vPPQYcVEj3Ri4j4KGxz8rf9CNP7wfqlcFonuPwfUKpywe8reeKcY/eMGWx+fDguLY0q991L+R49sIRgn5v1RC8iEiFhG51fqT70mgvtHoHv53pP9yuS/bm3hMzMKNe1K/VmJnPCWWex+dHH+PmWWzi8YUPQoWVLiV5ExEe5jc4vULN+QiK0vBP6LoCyNWHKDTC1NxzY4VP0Eqoi1apRa8J4qj3yCAe//IqUTp3ZNXUq0dhKHkjTvZlVAN7E29N+DXCVc+64/+vN7CngcrwvJO8Bd7hcAlbTvYgELbvR+b426x85DIv+DgufghMqQefnoMHFPv0Gkhep69ax8YGhHFi6lJKtL6D6o49RpGqViMYQjU33g4EPnHOnAB9kvP8DMzsfaAGcATQEzgFaRzJIEZH8yG5Ovq/N+olF4ML7oc9/4YQK8NpV3gj9Q7sLGL3kVdFatThp0itUfWAIBxYvIaVzZ3bPnBU1T/dBJfouwKSM15OArllc44DiQFGgGFAE2ByR6EREwiAsi+5UPxP6fggt74YvX4PR58NP8wt+X8kTS0igQs+e1J02jWJ16rDhvvv4ZdAdpG33cQXF/MYWUNP9LudcuWPe73TOHdd+ZWbPAL0BA15wzg3N5n59gb4AJ510UtO1a9eGJ3ARkQIK66I76z+Daf1g+w9wTm9o/ygULelvGZIrd+QI2ydOZNtzz5NQujTVhv2VMh06hLXMQBbMMbP3gWpZnBoKTMot0ZtZfeBZ4OqMQ+8B9zvnFuZUrvroRSSuHT4IHzwGi0d78/C7joHa5wUdVVw69P33bBw8hEMrVlCmY0eqPTiUxHLlcv9gPgTSR++ca+eca5jFzwxgs5lVzwiuOrAli1t0AxY75/Y55/YB7wDNwxWviEi0KNDI/CIl4JLhcNNscOnw8qUwd6j3BUAiqniDBtR58w0qDRzInnffJaVTZ/YtWBDxOILqo08Gbsx4fSMwI4trfgZam1mSmRXBG4i3MkLxiYgEwrftcOu0gP4fw9m94JMX4MUL4Jdl/gYrubIiRag88DbqvPkGieXKse7WfmwYOpQj+/ZFLIagEv0TQHsz+wFon/EeMzvbzCZkXPM28BPwNfAl8KVzbmYQwYqIRIqvI/OLlYKOf4fr/wOp+2FCe69ZP63w77Fe2JT4v/+jztS3qdinD7unTWf/okURK1tL4IqIRJGjT/SH09Ip4ucSugd3wdwH4IvJULURdBsD1RoV/L6SZ7+mpFC0bl3Mx90ItXudiEghEtaR+avegeRBcHCnNw+/xV2QmORvGRJxSvQiIvK7Aztgzr3wzVSocRZ0GwuVTw06KimAaFwZT0REgnJCBeg+Ebq/7O15P7YVfPw8pB8JOjIJAyV6EZF41fDPcNsSqN8O5j0IL18G238KOirxmRK9iEiMyNf8+1JVoMdk6PYibFkJY1vCp+MhPT18gUpEaQSGiEgMKNDOeGZwZg+o0wqSb/f671fOhC6joFyt8AYuYacnehGRGODL/PuyNeH6qdDpWW9xndHnwfJ/Q4wN2o43SvQiIjHAt53xzKDpTd6qejUaQ/JAbwvcPRt9jVciR9PrRERiRE7z7/M1Nz89HT4dB+8Pg6RicNkz0Ki792VAoorm0YuIxLEC9d8DbPsRpveD9UvhtM7Q8R9QslL4ApY80zx6EZE4VuD++0r1oddcaPcIfP8ujGoGK5LDE6z4ToleRCTG+dJ/n5AILe+Evgu8QXtTboCpfbyldCWqqeleRCQO+Lp+/pHDsGgkLHwaTqgEnZ+HBh38CVTyRX30IiLivw1fwLR+sHUlnNUTOjwOxcsEHVVcUh+9iIj4r0ZjuHUBtLwLPn8VxpwPKQuCjkoyUaIXEZH8SyoG7YZBr3ne6391hjn3Qer+oCOTDEr0IiJScLXOgVsXQbP+3tz7sS3h58VBRyUo0YuIiF+KngCXPgE3zoL0NJh4ibcr3uFDQUcW15ToRUQkZCHtkFe3lbeEbtObvH3ux7WGX5ZHLEb5IyV6EREJydEV9kbOW8V1ExbnnOyLlYZO//Q2yTm0Bya0g/8+DmmpkQtYACV6EREJUb5W2KvfDgZ8AmdcBQufggltYdM34Q9WfqNELyIiIcn3CnslykG3sdDjNdi7CcZd6C24cyQtrPGKRwvmiIhIyPKywl6W1+7fDrPvhhXToWZT6DoWKjeIQOSxTSvjiYhIROW6Y943U2H2PXD4IFz0sDctL0GNzPmllfFERCSicu3Pb3gFDFgC9drA3AdgUkfYsTqYYGOcEr2IiPgupP780lXhmteh6xjY9DWMaQFLX4IYa2kOmpruRUQkLPK0Y97u9TBjIKTM957yu7wAZU+MTKAxQH30IiIS/ZyDzybCvIcgIREueQIaXwtmQUcW9dRHLyIi0c8MzrkF+v8PqjaEGQPg9Wtg7+agIyvUlOhFRCSqLNtbjlF1nmXduQ96Tfmjm3mj9CVflOhFRCRq/LbM7ns/0P6ThnzTaRZUqAdv94K3bvLm4UueKNGLiEjUyDwtb8GO8t5e9xc9DCtneU/3380JOsxCRYleRESiRpbT8hKToNU90PdDKF0N3rgGpvWDg7uCDrdQ0Kh7ERGJKjlOy0tLhYVPe2vll6rqTcOrf1EwgUYRTa8TEZHY8sty76l+2ypoejN0eMzbGjdOaXqdiIjElGVpdRl72stsatgXlr3iraq35qOgw4pKgSR6M7vSzL41s3Qzy/IbSMZ1l5jZKjP70cwGRzJGERGJTkdH5j/1/hou/LIt3132FlgCvNIR3h3ibZQjvwnqif4b4M/AwuwuMLNEYBRwKXA6cI2ZnR6Z8EREJFplHpn/wf663iI75/aBxaNhbCtYry7cowJJ9M65lc65Vblcdi7wo3MuxTmXCrwBdAl/dCIiEs2yHJlftCRc9jT0nAFph3AvtWfZS3eyPGVT0OEGLpr76GsC6455vz7j2HHMrK+ZfWZmn23dujUiwYmISDCa1i7P5N7NubvDqcfvc1/vQj7vOIepRy6g6bqXKTmpPSuWLwos1miQFK4bm9n7QLUsTg11zs0I5RZZHMtyioBzbhwwDrxR9yEHKSIihVLT2uWz3RHv4/WpjDzcl9l2Dk8WGU/FmV1gz/3Q6m5ILBLhSIMXtkTvnGtXwFusB2od8/5EYEMB7ykiIjHuaNP+wrQmdEp/hjknz6Tih8Nh1RzoNhaqnBZ0iBEVtkTvg6XAKWZWF/gF6AFcG2xIIiIS7Y427R9ddKdi7SthxQyYdRe8eAG0fRDOG+hthRsHgppe183M1gPnAbPNbG7G8RpmNgfAOZcGDATmAiuBKc65b4OIV0RECpemtctzW5v6vzfvn94FBiyBUzrAew/Dy5fC9p+CDTJCtDKeiIjED+fg67dgzr3ecrrtH4VzekNCNI9Nz51WxhMREQEwgzOuggGLoU4LeOc++Fdn2Lk26MjCRoleRETiT5kacN3b0Ok52PA5jDkflk3ynvhjjBK9iIjEJzNoeiP0/xhqNIGZg2DylbAntiZ4KdGLiEh8K18beibzc7NhHE5ZSNoLzeCrKTHzdK9ELyIicW/Zut10+PhPXHJoOF//WhX+0wfevB72Hb/a6rK1Oxk1/0eWrd0ZQKR5p0QvIiJx7+hGOT+lV+eq1L/ycd1B8MM8GN0MViT/dt3RnfNGzlvFdRMWF4pkr0QvIiJx79iNchKTkih24d3QdwGUPRGm3ABTe8OBHcftnLc4ZXvQoecqmlfGExERiYjMq+l5C+2Uh94fwKKRsPBpWL2IDs1H8HxSCQ6npf++c16U04I5IiIiudnwBUzrB1tXsrVBD6ZV7k/TBrWz3Vhn2dqdmb40hFdOC+boiV5ERCQ3NRrDrQvgwxFU/t+z9N38Pzh5FND6uEuP9uOnpqVTNCnh+K10I0x99CIiIqFIKgbthkGvuZBY1FtRb859kLr/D5dFWz++Er2IiEhe1DoX+n0EzfrDp+NgbEv4ecl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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "print(\"rmse:\", ca.rmse())\n", - "hs1 = ml.head(r1, 0, t1)\n", - "hs2 = ml.head(r2, 0, t2)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"obs at 30m\")\n", - "plt.semilogx(t1, hs1[0], label=\"ttim at 30 m\")\n", - "plt.semilogx(t2, h2, \".\", label=\"obs at 90m\")\n", - "plt.semilogx(t2, hs2[0], label=\"ttim at 90m\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.title(\"ttim analysis for Oude Korendijk\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Investigate whether adding well bore storage improves the fit:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create conceptual model:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# unknown parameters: kaq, Saq and rc\n", - "ml1 = ttim.ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)\n", - "w1 = ttim.Well(ml1, xw=0, yw=0, rw=0.2, rc=0.2, tsandQ=[(0, Q)], layers=0)\n", - "ml1.solve(silent=\"True\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calibrate using only the data from observation well 1:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "..............................................................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 123\n", - " # data points = 34\n", - " # variables = 3\n", - " chi-square = 0.00793373\n", - " reduced chi-square = 2.5593e-04\n", - " Akaike info crit = -278.341728\n", - " Bayesian info crit = -273.762646\n", - "[[Variables]]\n", - " kaq0: 80.8711512 +/- 1.71386696 (2.12%) (init = 10)\n", - " Saq0: 5.4851e-06 +/- 7.9030e-07 (14.41%) (init = 0.0001)\n", - " rc: 0.30300286 +/- 0.01743120 (5.75%) (init = 0.2)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.975\n", - " C(Saq0, rc) = -0.870\n", - " C(kaq0, rc) = 0.829\n" - ] - }, - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq080.87121.713867e+002.11926-infinf10[80.87115115684033]
Saq05.48506e-067.903050e-0714.4083-infinf0.0001[5.485055599340013e-06]
rc0.3030031.743120e-025.752820.01inf0.2[0.30300285668761484]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 80.8712 1.713867e+00 2.11926 -inf inf 10 \n", - "Saq0 5.48506e-06 7.903050e-07 14.4083 -inf inf 0.0001 \n", - "rc 0.303003 1.743120e-02 5.75282 0.01 inf 0.2 \n", - "\n", - " parray \n", - "kaq0 [80.87115115684033] \n", - "Saq0 [5.485055599340013e-06] \n", - "rc [0.30300285668761484] " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ca3 = ttim.Calibrate(ml1)\n", - "ca3.set_parameter(name=\"kaq0\", initial=10)\n", - "ca3.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca3.set_parameter_by_reference(name=\"rc\", parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", - "ca3.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca3.fit(report=True)\n", - "display(ca3.parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.015275638126121028\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "print(\"rmse:\", ca3.rmse())\n", - "hm3 = ml1.head(r1, 0, t1)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", - "plt.semilogx(t1, hm3[0], label=\"ttim at 30 m\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.title(\"ttim analysis for Oude Korendijk\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calibrate using only the data from observation well 2:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "...................................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 96\n", - " # data points = 35\n", - " # variables = 3\n", - " chi-square = 0.00135387\n", - " reduced chi-square = 4.2308e-05\n", - " Akaike info crit = -349.604764\n", - " Bayesian info crit = -344.938720\n", - "[[Variables]]\n", - " kaq0: 88.4253341 +/- 1.46366354 (1.66%) (init = 10)\n", - " Saq0: 1.1271e-05 +/- 9.2075e-07 (8.17%) (init = 0.0001)\n", - " rc: 0.67776972 +/- 0.02992366 (4.42%) (init = 0.2)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.981\n", - " C(Saq0, rc) = -0.940\n", - " C(kaq0, rc) = 0.912\n" - ] - }, - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq088.42531.463664e+001.65525-infinf10[88.42533414084704]
Saq01.12708e-059.207533e-078.16939-infinf0.0001[1.1270766843523035e-05]
rc0.677772.992366e-024.415020.01inf0.2[0.6777697177678981]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 88.4253 1.463664e+00 1.65525 -inf inf 10 \n", - "Saq0 1.12708e-05 9.207533e-07 8.16939 -inf inf 0.0001 \n", - "rc 0.67777 2.992366e-02 4.41502 0.01 inf 0.2 \n", - "\n", - " parray \n", - "kaq0 [88.42533414084704] \n", - "Saq0 [1.1270766843523035e-05] \n", - "rc [0.6777697177678981] " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ca4 = ttim.Calibrate(ml1)\n", - "ca4.set_parameter(name=\"kaq0\", initial=10)\n", - "ca4.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca4.set_parameter_by_reference(name=\"rc\", parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", - "ca4.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", - "ca4.fit(report=True)\n", - "display(ca4.parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.006219485714670683\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "print(\"rmse:\", ca4.rmse())\n", - "hm4 = ml1.head(r2, 0, t2)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t2, h2, \".\", label=\"obs at 90 m\")\n", - "plt.semilogx(t2, hm4[0], label=\"ttim at 90 m\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.title(\"ttim analysis for Oude Korendijk\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calibrate model with two datasets simultaneously:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".....................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 66\n", - " # data points = 69\n", - " # variables = 3\n", - " chi-square = 0.17294914\n", - " reduced chi-square = 0.00262044\n", - " Akaike info crit = -407.231630\n", - " Bayesian info crit = -400.529311\n", - "[[Variables]]\n", - " kaq0: 66.0820095 +/- 1.71591047 (2.60%) (init = 10)\n", - " Saq0: 2.5413e-05 +/- 2.4540e-06 (9.66%) (init = 0.0001)\n", - " rc: 0.01002663 +/- 0.23362725 (2330.07%) (init = 0.2)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.859\n", - " C(kaq0, rc) = 0.236\n", - " C(Saq0, rc) = -0.165\n" - ] - }, - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq066.0821.7159102.59664-infinf10[66.08200945032905]
Saq02.54132e-050.0000029.65654-infinf0.0001[2.541322353896833e-05]
rc0.01002660.2336272330.070.01inf0.2[0.010026632267619018]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 66.082 1.715910 2.59664 -inf inf 10 \n", - "Saq0 2.54132e-05 0.000002 9.65654 -inf inf 0.0001 \n", - "rc 0.0100266 0.233627 2330.07 0.01 inf 0.2 \n", - "\n", - " parray \n", - "kaq0 [66.08200945032905] \n", - "Saq0 [2.541322353896833e-05] \n", - "rc [0.010026632267619018] " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ca0 = ttim.Calibrate(ml1)\n", - "ca0.set_parameter(name=\"kaq0\", initial=10)\n", - "ca0.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca0.set_parameter_by_reference(name=\"rc\", parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", - "ca0.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca0.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", - "ca0.fit(report=True)\n", - "display(ca0.parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.05006505082044996\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "print(\"rmse:\", ca0.rmse())\n", - "hs1 = ml1.head(r1, 0, t1)\n", - "hs2 = ml1.head(r2, 0, t2)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"obs at 30m\")\n", - "plt.semilogx(t1, hs1[0], label=\"ttim at 30 m\")\n", - "plt.semilogx(t2, h2, \".\", label=\"obs at 90m\")\n", - "plt.semilogx(t2, hs2[0], label=\"ttim at 90m\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.title(\"ttim analysis for Oude Korendijk\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary of values presented in Kruseman and de Ridder (1970)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compare effect of rc:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE of two conceptual models:\n" - ] - }, - { - "data": { - "text/html": [ - "
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obs 30 mobs 90 mobs simultaneously
without rc0.03166020.02271870.0500599
with rc0.01527560.006219490.0500651
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" - ], - "text/plain": [ - " obs 30 m obs 90 m obs simultaneously\n", - "without rc 0.0316602 0.0227187 0.0500599\n", - "with rc 0.0152756 0.00621949 0.0500651" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t0 = pd.DataFrame(\n", - " columns=[\"obs 30 m\", \"obs 90 m\", \"obs simultaneously\"],\n", - " index=[\"without rc\", \"with rc\"],\n", - ")\n", - "t0.loc[\"without rc\", \"obs 30 m\"] = ca1.rmse()\n", - "t0.loc[\"without rc\", \"obs 90 m\"] = ca2.rmse()\n", - "t0.loc[\"without rc\", \"obs simultaneously\"] = ca.rmse()\n", - "t0.loc[\"with rc\", \"obs 30 m\"] = ca3.rmse()\n", - "t0.loc[\"with rc\", \"obs 90 m\"] = ca4.rmse()\n", - "t0.loc[\"with rc\", \"obs simultaneously\"] = ca0.rmse()\n", - "print(\"RMSE of two conceptual models:\")\n", - "t0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Adding wellbore storage improve the performance when use drawdown data of two observation wells respectively. However, when calibrate model with two datasets simultaneously, rc approaches minimum value. Adding rc does not improve the performance much." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compare ttim to results of K&dR, AQTEOLV and MLU:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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k [m/d]Ss [1/m]RMSE
K&dR55.71430.00017-
ttim66.08842.54102e-050.0500599
AQTESOLV66.0862.541e-050.05006
MLU66.852.4e-050.05083
\n", - "
" - ], - "text/plain": [ - " k [m/d] Ss [1/m] RMSE\n", - "K&dR 55.7143 0.00017 -\n", - "ttim 66.0884 2.54102e-05 0.0500599\n", - "AQTESOLV 66.086 2.541e-05 0.05006\n", - "MLU 66.85 2.4e-05 0.05083" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE\"], index=[\"K&dR\", \"ttim\", \"AQTESOLV\", \"MLU\"]\n", - ")\n", - "t.loc[\"ttim\"] = np.append(ca.parameters[\"optimal\"].values, ca.rmse())\n", - "t.loc[\"AQTESOLV\"] = [66.086, 2.541e-05, 0.05006]\n", - "t.loc[\"MLU\"] = [66.850, 2.400e-05, 0.05083]\n", - "t.loc[\"K&dR\"] = [55.71429, 1.7e-4, \"-\"]\n", - "t" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - }, - "latex_envs": { - "LaTeX_envs_menu_present": true, - "autoclose": false, - "autocomplete": true, - "bibliofile": "biblio.bib", - "cite_by": "apalike", - "current_citInitial": 1, - "eqLabelWithNumbers": true, - "eqNumInitial": 1, - "hotkeys": { - "equation": "Ctrl-E", - "itemize": "Ctrl-I" - }, - "labels_anchors": false, - "latex_user_defs": false, - "report_style_numbering": false, - "user_envs_cfg": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/04pumpingtests/2_test_of_dalem.ipynb b/docs/04pumpingtests/2_test_of_dalem.ipynb deleted file mode 100755 index f25f9e2..0000000 --- a/docs/04pumpingtests/2_test_of_dalem.ipynb +++ /dev/null @@ -1,1971 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Leaky Aquifer Test\n", - "**This example is taken from Kruseman and de Ridder (1970)**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import ttim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set basic parameters for the model:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "H = 37 # aquifer thickness [m]\n", - "zt = -8 # top boundary of aquifer\n", - "zb = zt - H\n", - "Q = 761 # constant pumping rate [m^3/d]\n", - "t = 0.34 # time start pumping [d]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create concptual model:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# unkonwn parameters: kaq, Saq, c\n", - "ml = ttim.ModelMaq(\n", - " kaq=10, z=[0, zt, zb], c=500, Saq=0.001, topboundary=\"semi\", tmin=0.001, tmax=0.5\n", - ")\n", - "w = ttim.Well(ml, xw=0, yw=0, tsandQ=[(0, Q), (0.34, 0)])\n", - "ml.solve(silent=\"True\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load data of four observation wells:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# data of observation well 30 m away from pumping well\n", - "data1 = np.loadtxt(\"data/dalem_p30.txt\", skiprows=1)\n", - "t1 = data1[:, 0]\n", - "h1 = data1[:, 1]\n", - "r1 = 30\n", - "# data of observation well 60 m away from pumping well\n", - "data2 = np.loadtxt(\"data/dalem_p60.txt\", skiprows=1)\n", - "t2 = data2[:, 0]\n", - "h2 = data2[:, 1]\n", - "r2 = 60\n", - "# data of observation well 90 m away from pumping well\n", - "data3 = np.loadtxt(\"data/dalem_p90.txt\", skiprows=1)\n", - "t3 = data3[:, 0]\n", - "h3 = data3[:, 1]\n", - "r3 = 90\n", - "# data of observation well 120 m away from pumping well\n", - "data4 = np.loadtxt(\"data/dalem_p120.txt\", skiprows=1)\n", - "t4 = data4[:, 0]\n", - "h4 = data4[:, 1]\n", - "r4 = 120" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Try calibrate with three datasets:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "....................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 49\n", - " # data points = 37\n", - " # variables = 3\n", - " chi-square = 3.8607e-04\n", - " reduced chi-square = 1.1355e-05\n", - " Akaike info crit = -418.405059\n", - " Bayesian info crit = -413.572305\n", - "[[Variables]]\n", - " kaq0: 57.5582489 +/- 1.29402212 (2.25%) (init = 10)\n", - " Saq0: 3.2824e-05 +/- 2.1684e-06 (6.61%) (init = 0.0001)\n", - " c0: 998486.574 +/- 1.4257e+09 (142786.17%) (init = 1000)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.919\n", - " C(kaq0, c0) = -0.429\n", - " C(Saq0, c0) = 0.166\n" - ] - }, - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq057.55821.294022e+002.2482110010[57.5582489070078]
Saq03.2824e-052.168422e-066.606221e-050.0010.0001[3.282396928052271e-05]
c09984871.425701e+091427861001e+061000[998486.5739264318]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 57.5582 1.294022e+00 2.2482 1 100 10 \n", - "Saq0 3.2824e-05 2.168422e-06 6.60622 1e-05 0.001 0.0001 \n", - "c0 998487 1.425701e+09 142786 100 1e+06 1000 \n", - "\n", - " parray \n", - "kaq0 [57.5582489070078] \n", - "Saq0 [3.282396928052271e-05] \n", - "c0 [998486.5739264318] " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ca1 = ttim.Calibrate(ml)\n", - "ca1.set_parameter(name=\"kaq0\", initial=10, pmin=1, pmax=100)\n", - "ca1.set_parameter(name=\"Saq0\", initial=1e-4, pmin=1e-5, pmax=1e-3)\n", - "ca1.set_parameter(name=\"c0\", initial=1000, pmin=100, pmax=1e6)\n", - "ca1.series(name=\"obs2\", x=r2, y=0, layer=0, t=t2, h=h2)\n", - "ca1.series(name=\"obs3\", x=r3, y=0, layer=0, t=t3, h=h3)\n", - "ca1.series(name=\"obs4\", x=r4, y=0, layer=0, t=t4, h=h4)\n", - "ca1.fit()\n", - "display(ca1.parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.003230224955833457\n" - ] - }, - { - "data": { - "image/png": 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WEfy+ACLg9we42jqyZoWBMYbsPQUknfKw15vPbJyP0aMpdA5d4dKlS5w5cyboqzAvFDZt2hTtsBVFUe4LFQbKsthZsglnnAO/P4DT6WBnydr+IJz3S5ifyeDOS6ecSvx+P1euXKG1tZXW1la++93v8t3vfpesrKygSMjOzsbpdEb7KSiKooRFlxLQpYTlcrVlkO4LvbgPuNdstuB+GRoaCoqEnp4eAoEAycnJQWMlt9tNcnJytMNUFGUdo0sJyoox4+mk/p9+m/aT+RQeOIi7/BDbikpwONbvt+PMzEwOHz7M4cOHmZmZoaOjI1jAeP78eYwx5OXlBbMJmZmZ6pmgKEpMoBkDNGOwXCaGb9Lyxmt0Np2m7/JFJBAgOX0DhfsrKDxwkLw9B0hMSYl2mDHB/Ajp+WzC9evXAcjIyAiKhNzcXPVMUBRlxdGuhDCoMIgcM5Meus810dl4iq4zDcxMenA448h+eBfu8kMUHjjExq3boh1mzDA6OhoUCV1dXfj9fhITE3G73UFzpdTU1GiHqSjKGiSmhIExJgP4RyAf6AbeJyIjixz3U8Cn7Ye/IyJfNca4gNdCDssG/k5E/psx5kPAHwBX7X1/JiJfvFs8KgxWhoDfT3/rZToaT9HZdJrhq70AZGbnUnjgIIXlh9hRXIZDC/IAyzOhs7MzKBQ8Hg8A2dnZlJSUUFpaypYtW3TJQVGUiBBrwuD3gWERedEY80lgk4h8YsExGUADUAEI0AiULxQQxphG4GMicsIWBhUi8tH7iUeFwYNh9NoAnU2n6Gg8Rd/liwT8fpLSXBTYSw4F+8pJTNFvx2AtOVy7di0oEvr7+wHYsGFDcMkhPz+f+Ph4ZnvGg10S6sSoKMq9EmvCoAU4IiIDxpjtwHERKV1wzAfsY37OfvzX9nHfCDmmGKgDckVEVBisHmanJuk+d4bOplN0nmlgZmIch9PJzrJH7CWHg2zavjPaYcYMExMTQZvmjo4OvF4v8fHx5G/LZVtPAtneTNLiktn8wm4VB4qi3BOxJgxGRWRjyOMREdm04JiPA0ki8jv2418DpkXkD0OO+Z9Auoh83H78IeAzwCDQipVJ6L1DDB8BPgKQm5tb3tPTE8FnqNwPgYCfgbZWOhtP0tF4iqG+KwBs2pEd7HLYUfIQTi3IA8Dr9QZtmpvPX2JidhKAzQEXxQXF7HnXQbZt26ZLDoqihOWBCwNjzCvAYlVmnwK+eg/C4H8AiQuEwZSI/FHIMZeAnxSRRvtxJuARkVljzM9j1S5U3y1WzRjEFmM3rtHZdNpacrh0Ab/PR2JqKgX75pccKkhKSwPW9pyGe2Gme4y2L71JjwxyxWnZNAOkp6cHlxwKCgqIj4+PcqSKosQasZYxWPZSgjFmL/BPIlJyh3s4seoY7vppocIgdpmbnqLn/FmrgPHMaabHxzAOBztLHyYrfw8tJxMJyEbi4p1rek5DOEJrDLyZDtra2mhpabltyaGwsJDS0lKKi4txuVzRDllRlBgg1oTBHwBDIcWHGSLyywuOycAqODxgb2rCKj4ctve/CMyKyK+HnLNdRAbs3/8T8AkRqbpbPCoMVgcSCDDQ3kpn02k6G08yeKUbAOPYiCOhkF1HHufoT1brkoNN6JJDS0sL4+PjAOzcuTPY5bB161ZdclCUdUqsCYNM4JtALnAFeK+IDBtjKoCfF5EX7ON+BvhV+7TfFZEvh1yjE3hWRJpDtn0GeDfgA4aBXwjdfydUGKxOOpo6+O6f/xve2Q4C3l7AT2JqKvl7y3GXH7ptyWG9IyJcv36dlpYWWltbuXrV6ugN7XIoKChQYyVFWUfElDCINVQYrF7mawy25CUzM9ER9EwILjmUPYy7vBJ3+SHtcghhvsuhpaWFzs7O4JKD2+0OLjmkqahSlDWNCoMwrGZhcPbGWRquN1CxtYJ9W/ZFO5yYIBDwc6291RIJjae42Wt1nGzakY27/BDuA4fYUfqQGivZeL1eurq6gksOExMTgGWsVFpaSklJiRorKcoaRIVBGFarMDh74yw/+/2fZc4/R4IzgS888wUVB4swduO6nUk4Re9bFwj4fZax0r5y3BWV5O89oMZKNiLCwMBAUCQMDAwAsHHjxmBdQl5eni45KMoaQIVBGFarMPjihS/yuabPESCA0zj56P6P8sLuF6IdVkwzOzVFz/kmOhpO0nm2MWislP2QPcuhvFJnOYQwPj4edF/s7OzE5/ORkJBAUVGRznJQlFWOCoMwrFZhMJ8x8Aa8xDviNWNwnwQCfvpbm+lstGyab5vlYC85bC8pDY6PXu+eCXNzc3R1dQULGD0eD8aY25YcsrKydMlBUVYJKgzCsFqFAWiNQSQZvTZgLzmcpO/yWwT8fpJd6RQeOEhG9i6avicEAnE44xzr1jNhnkAgcNuSw7Vr1wDYtGnTbUsOTq3jUJSYRYVBGFazMFBWhplJD91nG+loPEX32UZmJj2AE0dcNs5EN+XPHuGxHztw1+usF8bGxm5bcpgfHx265JCSkhLtMBVFCUGFQRhUGCjhCPj9XDh2mlf/7mV8s+1IYBSArLwC3BWVuMsr2VrgxjgcUY40NpgfHz2/5DA5OYkxhtzcXEpLSyktLSUzMzPaYSrKukeFQRhUGCj3wnyNgWvTNGPXL9HReIr+lsuIBEjdlIH7wCHcFZXk7NpDfEJitMONCQKBAP39/cElh+vXrwOwefPm4JJDTk4ODhVVivLAUWEQBhUGylKZGh+j60wDnY2n6DrXhHdmmrjERPJ278ddcYjC/QdJ3bjp7hdaw4TOcphK9wdFQnd3N4FAgJSUFIqLiyktLcXtdpOYqKJKUR4EKgzCsJqFwfTFt0h6qAyjRV5Rx+f10vfWeTqaTtHRcIqJoUEwhu1FJZb7YkUlmdm566pqf7ZnnJtfvID4Apg4B5tf2E1iXjoAMzMztLe309LSQltbGzMzMzidTgoKCoJdDhs2rN8CT0VZaVQYhGG1CoO5vj46nn4HzowM0o4cwVV9lNRHH8WhRV5RR0QY7Omio/EkHQ2nuN7ZBsCGLVuDImFn2SNrfuDT+LFexr/fDQIYSH8mn/SjOW87zu/309vbS0tLC83NzYyMjACwbdu2YF3C9u3b15WoUpSVRoVBGFarMAhMTjJx/DieumN4TpwgMDGBSUgg9fBh0qqrSTtyhPitW6IdpgJ4hofobDpNR+NJrlw4h887R2JKKvn71vbAp3AZgzshIty8eZOWlhZaWlro7bX8JVwuVzCTUFBQQHx8/IN4CoqyZlFhEIbVKgxCEa+XqcZGJurq8NQdw9vXB0DS7t2kHT2Cq7qaxNLSiH3jUv+EpeOdmaHnwlk6Gk/S2XSaqbFRjMMRdF90l1eycdv2aIcZMUJrDO4mChZjcnIy2ArZ3t7+toFPJSUl6r6oKEtAhUEY1oIwCEVEmG1rszIJx44xff48iBC3Yzuuo9WkVR8l9eBBTELCkq6vMxoihwQCDLS30tl0io6Gk8GBT0H3xfJKtheXBN0X1zter5fu7u5gNmF+4FNOTk5wyWHz5s265KAo94AKgzCsNWGwEN/gIJ5XX2Wi7hiTb7yBzMzgSE0l9ckncFVXk/bEEzg3brzn6+mMhpVj7MY1OhotkdB3+aLlvpi+gcIDB62BT7v3E5+UFO0wY4L5gU/zIiHUfXFeJOTm5qr7oqLcARUGYVjrwiCUwPQ0k2/W4zlWx8Tx4/gHb4LTSUp5OWnVR3EdPUpCXl7Ya+iMhgfDzKSHs99/nbZT9Yz0X8I7M0VcfAK5u/firqik8MAh0jZlRDvMmGHefbGlpYWuri78fj9JSUnBVsiioiKSVFQpShAVBmFYT8IgFAkEmLl4MViXMNvaCkCC242r+ihpR6tJ3rtn0VZIrTFYea51jvGtPz6D3xfA4RSq3pPMyNWLtDecZHzQMgraVlRCUUUV7vJDZObkaQrdZnZ2lo6OjqD74vT0NA6Hg/z8fKt4MW0nyYMsue5BUdYCKgzCsF6FwULm+vrw1B1j4lgdU6cbwOfTVsgo0vhyNye/1YkIGAdUvruQ8nflIyIM9fbQ3nCSjsaTXGu3BN16a4W8VwKBAH19fcElh5s3bwKwKZBGHlnsfXcVeQeK1X1RWXeoMAiDCoO34x8fx/Paa3iOHbdaIcfHtRXyARPMGPgDOJ13nujoGRkOFi8GWyFTUynYV4G7opKCfeUkpmjV/jw9L13k0pvn6HEMct2MIgbS0tKCdQnaCqmsF1QYhEGFQXisVsgmqy6htu5WK+SuXbhqqkmrriaxpOSBpbHX0zLG/HyGnSWb7mnMs3dmhu4LZ+hosFohp8fHcDidZD+8G3d5JUUVlaRnrW9BF+qtMBvnY+RIMp1DV2hvb2dubk5bIZV1gwqDMKgwuHdEhLn2diZq625rhYzfuZO06mpc1UdJqajArNA3Lm2VvHcCAT8Dba2W++Lpeob7LUGnUyEX91bw+Xx0dXXd1gppjHlbK6SirBVUGIRBhcHS8Q0OBt0XJ994A5mdxeFykfbkk6RVH7VaIdMjV9ylrZJLZ7j/Kp2NJ+loPMXV5kuIBEjblEFh+SGKKqrIeWQPcUv0tlhr3KkVMjMzMygSdCqkstpRYRAGFQaRITA9zeQbb1hdDseO4x8ehrg4Ug5W4KquIe3oURKydy7rHtoqGRmmJ8bpOtNAe0M93Web8M7OEJ+YRP7eA1Zdwv4KUtJ1gNE8o6OjQZEQOhVyfnR0YWGhToVUVh0qDMKgwiDyiN/P9PnzeOrqmKg7xlxHBwCJpaVWXcLRapIeeXhJaez1VGPwIPDNzdF76QIddpeDZ3gIYxzsKC3DXVGFu7ySjB3LE3RriTtNhSwsLAzWJaRHMEumKCuFCoMwqDBYeea6u5k4dhxPbS1TTU0QCBC3ZQtpR4/iqqkmpbISh37jijoiwo2ujmAr5GB3JwCbdmRbcxwqKtlRUqYWzTZ+v58rV64Ep0KOjo4CsGPHDsrKyigtLWXLli3qL6HEJCoMwqDC4MHiGxlh8sQJq4Dx9deRqSlMSgppjz1mt0I+RdymTdEOUwHGb94IWjT3vnWBgN9HsiudwgOHcFccIm/PfhKSkqMdZkwgIty4cSO45HD16lUANm7cGKxLyMvLU4tmJWZQYRAGFQbRIzA7y9SpU0H3Rd/16+BwkHxgP66j1bhqqknIz492mAowOzVJ97kmqxXyzGlmJydxxseTu2svRRVVFB44SFpGZrTDjBkmJiaCFs0dHR34/X4SExMpLi6mrKxMLZqVqBNzwsAYkwH8I5APdAPvE5GRRY57GagCXheR50O2FwD/AGQATcBPisicMSYR+BpQDgwBPy4i3eFiUWEQG4gIM29dsusS6phtbgYgobDQsmiuriZ5795FLZqVB4vf56O/5ZK15NBQz9gN26LZXRx0X9ycm68pdJu5ubnbLJqnpqaCFs3z2YSN9zHITFEiQSwKg98HhkXkRWPMJ4FNIvKJRY6rAVKAn1sgDL4J/KuI/IMx5q+AcyLyl8aY/wzsEZGfN8a8H/hPIvLj4WJRYRCbeK9eteoS6uqYPHVKLZpjFBFhqO9KsHhxoL0VREjP2sr24n0kuUp4+IkKdhRrNgHubNG8detWSktLKSsrY/v27SqqlBUnFoVBC3BERAaMMduB4yJSeodjjwAfnxcGxvo/ZhDYJiI+Y8xh4DdE5J3GmO/Zv79pjIkDrgFZEuaJqjCIffwTE0y+9hoTdcduWTQnJloWzTXVuI4eJW4FzGe0A+L+mRwdobPpNG+deJ2rl88DPjCJ5O05wK4jT6hF8wJu3rwZFAm9vb2ICC6X6zaL5jide6GsALEoDEZFZGPI4xERWbTibBFhsBmoF5Ei+3EO8F0R2WWMuQi8S0T67H0dQKWI3FxwzY8AHwHIzc0t7+npifhzVFYGy6K50SperK3F298PxpC8Zw9pNTW4qo+S4HYv+xuXuiwuj8aXu6n//1vwz/UQ8HXgMD14Zzw4nHHkPLI72OWQvnl9WzSHMjk5SVtbGy0tLbS3t+P1eklISMDtdlNWVkZxcTEpmiVTIsSdhMGKylBjzCvAtkV2fWq5l15km9zDvlsbRD4PfB6sjMEy41EeICY+ntSqKlKrqpBf/RVmW3gVPhMAACAASURBVFutuoTaOgY/+1kGP/tZ4vNycVVbIiF5/37MEr5xNVxvYM4/R4AA3oCXhusNKgzug50lm4iLT8Q4inA6S/iRX9yL+AZob6ino/EUdV/+a+q+/NdsyXfjrjiEu6KKLfmF6zqFnpqayr59+9i3bx9er5fu7m6am5tpaWnh8uXLGGPIzc0NtkJmZGREO2RlDaJLCehSwlrCe/06nmPHmKitY6q+HvF6cW7cSNpTT5FWU03aY4/huMehOOqyuHzCDYEa7u+jo+Ek7Q0n6W+9DCK4MrOCIiHn4V0443TKIVh1CQMDA0GRcOPGDQCysrKCImHHjh1q0azcF7G4lPAHwFBI8WGGiPzyHY49QogwsLf9E/AvIcWH50XkL4wx/wXYHVJ8+KMi8r5wsagwWJv4PZNMvv46E3W1eF49QWBsDJOQQMrhKtui+QjxW8KnsbXG4MEwNTZKZ9Np2htO0nP+DL65WRKSUyjYV477YBUF+8pJSk2Ldpgxw/DwcLAuoaenBxHR0dHKfROLwiAT+CaQC1wB3isiw8aYCuDnReQF+7jXgDIgDav98MMi8j1jTCG32hXPAD8hIrPGmCTgb4H9wDDwfhHpDBeLCoO1j/h81ujoutrbR0fv2RNshUwsLl7XaexYwTs7w5WL52g/fZLOplNMjY1ao6Mf2oW7opKiiqp1Pzo6lKmpqdvqEuZHRxcVFVFaWkpxcbGOjlYWJeaEQSyhwmB9ISLMtrXhqTvGRF0dM+fPAxCfk2OLhBpSyg8sqS5BiSyBgJ9r7a22X8JJhq/2ArdGRxdVVLGlYPmFprHMYiOi78SdRkfn5uYGswmZmdo2qlioMAiDCoP1jffGDTzHjjNRV8vUm/XI3ByODRtIe+pJXNXVpD7+BM40/cYVC4wMXA2KhP6Wy9bo6IxM3OWVFFVUkv3IHuLWUAp9tmecm1+8gPgCmDgHm1/YfVdxMI+I0N/fHxQJ169bJlRZWVlBkbBz506tS1jHqDAIgwoDZZ7A5CSeH/4QT90xPMeP4x8dxcTHk1JVZU+FPEr81q3RDlMBpsbH6Gw6TUfDSbrPN+GbnSUhOZn8veUUVVRSsP8gSWmruy5h/Fgv49/vtvqqDKQ/k0/60ZwlXWtkZOS20dHzdQmho6O1LmF9ocIgDCoMlMUQn4/pM2eYqDvGRF0t3p4rACTt2mWJhOoaEku0LiEW8M7NcuXCOToarWzC1NgoxuGw6hLKKyk6WMmGLYt1Tsc2y8kYhGN6ejpYl9DW1hasSwj1S9C6hLWPCoMwqDBQ7oaIMNfRwURtHRN1tcycs+sSsrNJqz6Kq7qGlIpyrUuIASQQYKC9NSgShvosQbc5Nz+45LC1sAizSlLo91NjsBR8Pt9tfgnzdQk5OTnBVkitS1ibqDAIgwoD5X6Zr0vw1NUx+eabC+oSakh9/HGtS4gRRq71W3McGk5ytfmSVZewKYPC8kMUVVSRs2vvmqpLWA4icptfwnxdwubNm4MiQesS1g4qDMKgwkBZDsG6hNo6qy5hbMyqSzhchau6mrSj1cRvXXp7nXopRI7piXHOfO8EbafeZHSgGd/cLPFJyRTsPYD7YBWFa6AuIZKE1iX09PQQCARITU0NFi9qXcLqRoVBGFQYKJEiWJdQa42O9l6x6xJ277brEu7PL0HnNUSWa51jfOuPz+D3BXA4Axx8No6hvgtvq0soqqjEXVHFhi1aaDpPaF1Ce3s7s7OzwbqE0tJSSkpKtC5hlaHCIAwqDJSVQESYa28PioSl+CV88cIX+VzT5wgQwGmcfHT/R3lh9wsP6imsORpf7ubktzoRAeOAyncXUv6u/Ft1CfYch/m6hKzcfNwHq9aFX8L9MF+XMJ9NGB8fD9YlzI+O1rqE2EeFQRhUGCgPAu/1G9Ych2N1Qb8E54YNpB15irTqGtIef/scB53XEFmCGQN/AKfTwXs+tv9tMxzA8ksIznGY90vI3GwVL5YfImfXHp3jYKN1CasXFQZhUGGgPGgCk5N4Xv8hnrpaPMdfteoS5uc4HK0mrfpocI6D1hhElnCDnRbjll9CPd3n5uc4JJO/r8L2S6jQOQ4hhPNLKCsr0zkOMYQKgzCoMFCiifh8TDU14ZmvS+i1bH+tOQ7VuGqqSSgq0jR2DGD5JZy9wxyHKooOVpK+Wec4zDNfl9Dc3LzoHIeSkhJSUlKiHea6RYVBGFQYKLHCrTkOdUzU1jFz4QIA8bm5uI4exfV0Dcn796tfQgwQCPgZaLPqEtobTjLSbw3mysovDBYvbskvXLOC7n79Fe42x6GsrIyMjIwHELkyjwqDMKgwUGKVYF3C/BwHrxfnxo2kHTlCWk01aY89hkO/ccUEw/19t+oSWi+DCK7NWbapUhXZD+/CuUYE3XIdGQOBwG11CTdu3ACsOQ7zdQk7duzQuoQVRoVBGFQYKKsBv2eSyddfY6K2Ds+rrxIYH8ckJpJ6+DBpNdW4jh4lbvPmaIepAFNjo3Q0naKj4SQ958/im5slMSWVgv0VuCsqKdhXQeIqFnSRnOEAMDw8TEtLC83NzVy5cgURweVy3VaXELdGRFUsocIgDCoMlNWGeL1MNTZaIqG2Fm9/PxhD8t69lkioqSGxsDDaYSqAd3aGnvNnaW+op7PxFNMT4zicceQ8sht3RSXu8krSN2dFO8z7YqVmOABMTU3dVpfg9XpJSEgI1iUUFxdrXUKEUGEQBhUGympGRJhtaWGithZPbR0zly4BkJCfj+vpGtKqa0jeuwfjdEY5UiUQ8NPf2mxbNNczMtAPwJYCN0UVVbgrKsnKK1gVdQkrPcMBwOv13laX4PF4MMaQl5cXXHLYtGnTitx7PaDCIAwqDJS1hHdggIm6Ojy1dUyeOgU+H87MTNKOHrHmODx6GEdSUrTDVIChq712XUI9A20tIEJ61lbcFdYch51lj6yZuoTlEggE6O/vD9YlDA4OArB169Zg8eL27dtXhaiKFVQYhEGFgbJW8Y+P4znxmuWXcOI1Ah4PJjmZ1McexVVdQ9rRI8TpN66YYHJ0hI7GU3Q01HPlwjl83jkSU1Mp3H8Qd0UVBfsOkJCsKfR5hoaGgnUJvb29iAjp6enBOQ75+flal3AXVBiEQYWBsh6QuTkmT53GU1fLRG0dvuvXweEg+cB+XNU1ll9CXl60w1QA78wM3eebrCWHptPMTIzjjIsjZ9deqxWyvJK0DLUcnmdycpLW1tbgHAefz0diYiJFRUWUlZVRXFxMkmbJ3oYKgzCoMFDWGyLCzFuXgiJhtqUFgIQiN66ap3HVVJO0axdG28WiTsDvp7/lMu0N9XQ0nGT0+gAA24pKgnUJmdm5mkK38Xq9dHZ20tzcTGtrK5OTkzgcDvLz84N1CRs23N3xcj2gwiAMKgyU9c5cX59lqvRKLVONjeD3E5eVRZrtvJhSVYUjISHaYa57RIShvitWXcLpN7nW0QbAxq3b7WFPlewofQiHQwtNwapL6OvrCy45DA0NAbB9+/ZgXcLWrVvXrahSYRCGiAqD6RFo+BsofQ6ySmGdvuGU1Yt/dBTPq69arZCvv45MTeFISSH1iSes0dFPPYXzLt+4dL5D5Ag328EzPERHo2Wq1HvxHH6fj2RXOoUHDuE+WEn+nv3EJ2oKfZ7BwcGgSOjrs5wqN27cGBQJubm5ONdR944KgzBEVBi0fg/+/n3W7xluKHvWEgk5h0BVvLLKCMzOMlVfb42OPlaHf/AmOJ2kVFRYIqG6hoTsnbedMz8Rcs4/R4IzQSdCLoPgNEhfAGfcnadBAsxOTdF9rpH20/V0nWlgdmqSuPgE8vbut/wSDhwiZcPGB/wMYpeJiQlaW1tpbm6ms7MTv99PUlJS0FTJ7XaTmJgY7TBXFBUGYYj4UsJ4P7R8B5q/A10nIOCFlM1Q8i4oew4Kj0CCVhcrqwsJBJi5cMESCXW1zLV3AJBYVoarupq0mmqSHn6YL138Ep9r+hwBAjiNk4/u/ygv7H4hytGvThpf7ubktzoRAeOAyncXUv6u/Lue5/f56Lt8MdgKOXFzEIxhR8lDFFVUUnSwik3bd971OuuF2dlZOjo6aGlpobW1lenpaZxOJ4WFhcEuB5fLFe0wI86yhYExZhOwA5gGukUkENkQo8eK1hjMjEH7K5ZIaPsBzI5BXDK4q61sQsm7IFVtbJXVx1x3ty0S6pg+cwYCAeK2b2e2ajd/mHyCCzkBHPGaMVgOwYyBP4DTGT5jcCdEhMGeLtpPv0l7w0kGuzsByNiZExz2tL2oZFUUmj4IUyW/309vby/Nzc00NzczOjoKwM6dOykrK6OsrIzNmzevibqEJQkDY8wG4L8AHwASgEEgCdgK1AN/ISLHViTiB8gDKz70zUHPD29lE8b7rK8BOVX2ksOzkOle+TgUJcL4hofxHDvORF0dkz/8ITIzgy8lEXP4ALnP/RipTz6JMy0t2mGuSsLVGCyF8cEbtDecpKPhTXovXUQCAVI3bsJdXon7YCW5j+wlLgYLTVfShvlOiAg3btwImir191tOlRkZGcEOh5ycnFU77GmpwuAHwNeAfxeR0QX7yoGfBC6IyJciHO8DJSpdCSIwcO6WSLhujdcl66FbdQk79sMqfcMp65fA9DSTb75pWTQfO45/eBji40k9dMi2aK4mfuvWaIepADMeD11nTtPecJKus414Z6aJT0wif98BiiqqKDhwkOS02EihR3pw01IYGxsL1iV0dXURCARISUkJLjcUFhaSEIOi6k7EVI2BMSYD+EcgH+gG3iciI4sc9zJQBbwuIs+HbP86UAF4gVPAz4mI1xhzBPgW0GUf+q8i8lt3iycm2hVHemyR8BL0vAHih7RtUPofoOx5KHgC4tZ2IYyy9hC/n+mzZ4PDnuZ6egBI2rUrKBISi4vXRFp2tePzeum9eM7yS2g8xeTIMMbhIPuhXcElhw1boifoopExCMfMzAzt7e00NzfT1tbG7OwscXFxuN1uysrKKCkpITU1NWrx3QuRqDHYg/VBHvSYFJF/XWIwvw8Mi8iLxphPAptE5BOLHFcDpGB98IcKg2eB79oP/x44ISJ/aQuDj4ceey/EhDAIZWrYqkdoeQnaXgHvJCS4oKjGKl4sfgckq42tsroQEeY6O5l4pZaJulpmzp0HID4nB1d1Na6na0jevx+jNrZRRwIBrnW22X4J9Qz1XQEgKzff9kuoYkuB+4ELuqXWGKx0bYLP56OnpyfYCjk+Po4xhpycnOCSQ2Zm7DlVLksYGGP+BtgDvAXMFx2KiPzMEoNpAY6IyIAxZjtwXERK73DsEcJ82BtjPgZsFpFPrRlhEIp3xupsaHkJWr4LnuvgiIO8xyyRUPosbHyw6TRFiQTe6zfwHDvGRF0tU2/WI14vzo0bSTtyBNfTNaQ++igOHa8bE4xc66fjdD3tDSfpb7mMSABXZhbuikO4K6rIeXgXzrj4aIe5KA860yAiDAwMBEXC9evXAcjKygqKhB07dsREXcJyhcElEXk4gsGMisjGkMcjIrLoV+BwH/bGmHjgJPCLIvKafey/AH1Av33eW3e47keAjwDk5uaW99gpzpgmEID+Jmj+tlWXcNOysWXbHksklD0HW3epqZKy6vB7Jpl8/XWrLuHVVwmMj2MSE0l99FFryeHIEeJi8BvXemRqfIzOptN0NNTTfe4MvrlZElNSKdhfgbuikoJ9FSTGkKCLdm3CyMhIUCT09PQgIrhcrmBdQkFBQdSGPS1XGHwJ+CMRuXQfN3wF2LbIrk8BX42QMPgCMCki/81+nA4ERMRjLzf8qYgU3y3WmM4YhONmu5VJaP4O9J4EBDbkWsWLZc9B7qPg1LSssroQr5epxkarFbL2FXz9A2AMyfv346qxhz3l59/3ddWNMfJ4Z2fouXCODnuOw/TEOA5nHLm79uCuqMJdcQhXRnTbsaNdmxC6jOHPiqOtrY2Wlhba2trwer0kJCRQXFxMaWkpxcXFJCcnP7DYlisMngT+HbgGzAIGaylhzxKDWfZSgjHm14H9wI/eyVPBGNMNVIjIzXDxrFphEIrnBrS+bImEzmPgm4GkjVDyTkskuGsgUdvFlNWFiDDb3Bw0VZq9dBmABLc7WJeQtHv3XXvw1Y1x5QkE/PS3NgfnOIxes4c9uYtxV1hzHDJz8qJSaPog/A/udN87iRKv10tXVxctLS20tLTg8XhwOBzk5eUFlxw2blxZp8rlCoN24JeAC9yqMUBElpR/N8b8ATAUUnyYISK/fIdjj7BAGBhjXgB+BqgRkemQ7duA6yIixphDwD8DeXKXJ7kmhEEoc5PQUWeJhNaXYXoYnIlQ+JQlEkr+A7i0XUxZfXivXmWizq5LOHX61rCno0dxPV1zx2FPX7zwRXVjfICICMNXe2k/bWUSBtqtZc+NW7fjrqikqKKKHWVrf9jTvS5jBAIBrl69GlxyuHnT+i67bdu2oKnSSgx7Wq4wqBOR6ggGkwl8E8gFrgDvFZFhY0wF8PMi8oJ93GtAGZAGDAEfFpHvGWN8QA8wYV/yX0Xkt4wxHwV+AfBhOTT+koi8cbd41pwwCMXvg956SyS0vAQj3YCB7AqrcLHsecgqiXaUinLf+MfG8Jw4wURtHZMnThAIM+xpPmPgDXiJd8RrxiAC3I/xkjXs6RQdDfVcsYc9JbnSca/xYU9LXca4efNmUCT09vYCkJ6Uxv/77g+w+eHIWVkvVxj8BbARazlhdn77UtsVY401LQxCEYEbly2vhJaXoP+MtT2z6JZIyK7QYU/KqiMwNxcc9uSpq8M3OGgNezp40FpyqKnmrfhBrTGIEPcz3Gkhc9NTdJ1toqOhns6m028f9lReSUr68h0eY4XlLmMMNw9w5u9f45oM8xS7yHphT8SWQ5YrDL68yOYltyvGGutGGCxk7KplqtTyHeh6zRr2lJplmSqVPmctPcQ/uEIYRYkEEggwc/Fi0C8hOOzpoYeCdQmJZWVqqrQMljrcaSG3DXs6Xc/EkDXsaWfpQ8G6hPU+7Gkluypiyvkw1li3wiCUmTHbVGl+2NM4xKfYw56et4oYUzKiHaWi3DdzPT12h0Mt001NIEL8jh2k2R0OKeXlmPjY7MGPVSIx3GkhIsKN7k46GuppP13PYI9lYJuZnRusS9jmLl4Vw54iyUp2VSx1VsKnsQYlDd9hfzWQIiLfjkiUUUKFwQJ8c9D92q05DhP9YJyQe9j2S3gWNuVHO0pFuW98Q0N4jh+36hJ++ENkdhbHhg2kPfUkruoaUh9/HGdabNvYxgqRHu60kLEb1+lotDIJfZftYU+bMnCXH6KoooqcXXuJWyeCbqW6KpYqDN4D/DIwAzRxa7piMbAPeAX4PREZjFikUUCFQRhErFqEeZFww/aL2rrLrkt4DrbvVVMlZdURmJpi8o03rLqEY8fwj45i4uNJefQwruoaXNVHicvKinaYCjDtmaDrTAMdp+utYU+zM8QnJVOwr5yig1UU7K8gKVXbse+X5dYYFAOPAduxqv0vY80nmA574ipBhcF9MNx1a9jTlTdBApCebQ97eg7yHwfn+lDxytpBfD6mz5yx6xLq8Pb2WqZKe/aQ9nQNrpoaEgsLox2mAvjm5rjy1rlgK+TU2CgOp9Ma9nSwCndFJembt0Q7zFWB1hiEQYXBEpkcsnwSWr4D7bXgm4bEDVDyjJVNKHoakqI3/UxRloKIMNvWhqe2lonaOmYuXgQgIT/fnghZQ/K+veturTsWkUCAgfYW2htO0nG6nuH+PgC25LuDIiErr0ALTe/AcjMGJcDHeft0xYh5G0QTFQYRYG4KOo/fGvY0NQTOBCh48tawJ9diDtmKEtt4r11joq4Ozyu1TJ46BT4fzs2bcR09Qlp1tTXsKVFHoscCw/19wUxCf1sziJCetZWiikqKDlaxs+wRHE5tx55nucLgHPBXQCPgn98uIo2RDDJaqDCIMAE/9J6y5zi8BMOd1vad5bZIeA6ySrUuQVl1+Ccm8Jw4gae2Fs+rJwhMTmJSUkh77DErm/DUUzhX2MZWuTcmR0eCpko9F87i93pJSnNRuL+CooOHydu7n4Sk9d2OvVxh0Cgi5SsSWQygwmAFEYHBFmsiZMt34KqtJTMKb4mEnENqqqSsOgJzc0ydPMVEXS2e2jp8N25Ypkrl5cElh4Ts9d2DH2mW2gkxNzNN97kmOk5bpkozkx6c8fHk7d5nDXsqP0TqxkXn+K1plisMfgO4Afwfbnc+XLSNcbWhwuABMj5wy1Sp81XLVCllM5S+y/JLKDyipkrKqkMCAWbeessaG11by2xbOwCJZWW3TJUeemhJa906FdJiOW6LoQT8fvouv2X5JTTUMz54A4xhR3GZ5Zdw8DAZO9aHoFuuMOhaZLOIyJoo01VhECVmxqH9FWu5oe0HMDsWYqr0HJS8S02VlFVJ0FSprpbppjMQCBC3Yzuuo5ZISKmouCdTJZ0KeYtIuS2GIiIM9nQFnRdvdFsumRk7su3ixSq2F5Ws2UJT7UoIQySFQWPPCPWdQ1QVZlKet/5SU0vGNwc9r9vDnr4D41et//tzH7UMlUqfhYyCaEepKPeNb3gYz7HjTNTZpkozMzjS00l76ilcNdWkPv7EHU2VdCrkLVbCbXEh4zdv0H76JB0NlqlSwO8ndeMm3OWVuA9WkvvIXuIWmd65WlluxuA14ATwGvBDEZm4yymrikgJg8aeET74xXrmfAES4hx8/YUqFQdLQQQGzloiofmlW6ZKWx655by4fZ8WLyqrjsD0tGWq9Ert7aZKh6twVdeQVn2U+C23evB1KuTtrLTbYigzHg9dZ07T3nDSMlWambZMlfYesE2VDpKUtrpNlZYrDAqBx4EngCqsOoPXRORjkQ40GkRKGPz5sXb+6PstBAScBn7pmVL+y9GiCES4zgmaKn0HrrxhmyrttJ0Xn4W8xyFu7ah4ZX0QNFWy5zh47fG6SXv3WM6LT9eQUFjIucFzWmMQZXxeL70Xz9HeYLVCTo6OBE2V3BVVFB1cnaZKy15KMMZsB57CEgdHgSsi8q6IRhklIp0x8PoCxGvGYGWYHIK271mZhFBTpeJ3WNkENVVSViFBU6W6OiZeqb1lqpSXZw17erqG5L17MdqDH3UkEOBaRxvtp9+kveEkw1ctQbcaTZWWmzHoAG4Cf4+1nHBWRAIRjzJKrJYaA61fWIB32jJVav42tLwMUzdvmSqV2nUJ6dujHaWi3DdBU6XaOstUyevFmZlJ2tEjuGpqSD18GEdSUrTDXJPc73LFcP/V4ETI1WaqtFxh8ItYSwk5QDPwKtashI5IBxoNVkNXgtYv3IV5U6Xmb1vZhBG7kUZNlZRVzi1TpTo8r75qmSolJ5P2+OOk1VST9tRTxG3SfwsiwXJbIidHR+hsOk376TeDpkoJyWls2vEwJVWPsv+Zx4mPIUEXka4EY0wa8NNY9sjZIhKbMug+WQ3CQOsX7gMRGGy2BELzS9DfZG3PcFs1CWXPQ/ZBNVVSVh1qqrSyRLIl0jszw9kfvM4b//x9fLOdIDM44uLJ3xM7pkrLzRj8EVbGIA2ox+5QEJHOSAcaDVaDMND6hWUw3n+reLHrhJoqKWuCoKnSK7V46iJrqrReiXRL5LzQCAQCSOAqWdmDTAxeZnzwumWqVPJQcMlh0/Y7C7qV6sZYrjB4L9bSwfWIRRRDrAZhAFpjEBFmxhaYKo2rqZKyJgiaKtXWMt3UBCKWqVJ1Da6a6ns2VVrvRPJDeDGhsbUgfXFTpZ05tkg4zDZ3cdBUKVKOj4sRia6EdwNP2g9fFZF/j0hkMcBqEQZKhPHNQfdrt7IJE/1gnJB7+JZfwqb8aEepKPeNb2gIz/HjTNTapkqzsyGmSjWkPv74HU2VlMhyN6ExPnjDGhvdUE/vpQtIIEDqpgzc5YcoOniYm1fTOf3t3og6Ps6z3IzBZ4BDwNftTR8AGkTkVyISXZRRYaAgAv1nrExCy3fgxiVr+9Zdt8ZGb9+rxYvKqiMwNXXLVOn4cctUKSHBMlWqqcF19ChxWVnRDlMhxFTpdD1d55rwzkwTl5gE5GLi3SQkl/Aff+lQbGQMjDHngX3zLYrGGCdwRkT2RCS6KKPCQHkbw523nBd7621TpWy7ePE5yHsMnJqWVVYX4vMx1dSEZ95Uqa8PjCF5717Saqpx1TxNYqFaj8cCvrk5rrx1jvbT9bSdrGdmcoIf+/RfkrcrcsWlkRAGR+anKRpjMoDjKgyUdcHkTWh92RIJHXXgm4GkDVD8TttUqQYSXdGOUlHuCxFhtrWNidpX8NTWMfOWZT2eUFBgdzhUW6ZKa3SA0GpCAgGG+q6wOTc/otddrjD4APAicAwwWLUGvyIi/xDRKKOECgPlnpmbhI5j9ujo78L0sGWqVHjklqmSa2u0o1SU+8bb389E3TE8dbVMnjoNPh/OrM24jhy1JkJWVeFITIx2mEoEiZQl8kEsYXBSRK5FNsToocJAWRJ+H/SetP0Svg2jPYCB7Aq7ePF52Fwc7SgV5b7xj4/jefUEE7W1TJ44QWBqCkdKCqlPPGFlE558kguzXTrDYZWzJGFgjDkQ7qIi0hSB2KKOCgNl2YhYBYvzpkoDZ63tmcW2SHgOdlaApmWVVUZgbo6p+nqrFbKuFv/gTcTp5FIOnCqG82WJvPjeL6k4WIUsVRgcs39NAiqAc1gZgz1YWYPHlxhMBvCPQD7QDbxPREYWOe5lrGmOr4vI8yHbv4I10GnM3vQhETlrLCePPwWeBabs7XcVL4sJA6/XS19fHzMzM/f9/JSVJSkpiezsbOJjuSd7tNdaamh5Cbpfh4AP0rZaPgllz1vzHOJjxxpVUe4FCQSYuXCB17/xWXjtFNlD1nZPwRbyn38frpoaEktL1VRplbDcGoN/AH5XRC7Yj3cBHxeRDy0xmN8HhkXkRWPMJ4FNIvKJRY6rAVKAn1tEGHxbRP55wfHPAo+cbwAAIABJREFUAv8VSxhUAn8qIpV3i2cxYdDV1YXL5SIzM1Pf5MDkrI/JWR+piXGkJsZFLQ4RYWhoiImJCQoKVkn19PSoZabU/G3LXGnOAwlpVtFi2fPWZMhkNaxSVg9nb5zlZ7//s2QOzlLZbnjf9Xwcb7WBCPE7dwbtmVPKD2DiovfvhRKeOwmDe/2Llc2LAgARuWiMWU7e6D3AEfv3rwLHgbcJAxGpNcYcWbj9Ltf9mlhqp94Ys9EYs11EBu43wJmZGfLz81UUYImCrpuTiAjGGAo2p0ZNHBhjyMzMZHBwMCr3XxLJG2HPe60f36xlyzzvl3DpW+CIs9of5/0SNuZEO2JFCcu+Lfv4wjNfsGoMPljBI1v24bt5k4ljx/C8UsvIN/6B4a9+DeeGDaQdOULa0zWkPfYYjpSUaIeu3AP3mjH4BjAJ/B0gwE8AaSLygSXd1JhREdkY8nhERBb9ymQLg48vkjE4DMwCtcAnRWTWGPNt4EURed0+rhb4hIi8rYDAGPMR4CMAubm55T09Pbftv3z5Mg899NBSnt6a48b4DNfHZxCsdaSt6UlsSY9uGnxN/H0CAbjaaC03NL8EN1ut7dv2WJmEsudg6yNqqqSsOgKTk3he/6HVCvnqCQJjY5jERFIffdTKJhw5QlxmZrTDXPcsN2Pw08AvAL9oPz4B/OVdbvgKsG2RXZ+6x3uG41eAa0AC8HmsbMNvYX1uLWRR5SMin7fPpaKi4t5HTK5DUhPjrMyJnTGI5lLCmsLhgJyD1s/TvwE3224VLx7/DBz/PdiYd6t4MacKnPraK7GPIzWV9Hc+Q/o7n0G8XqYaG+05Dq/gOXbMMlU6cCA47CkhLy/aISsh3GvGoBqoF5GpiNzUmBYsw6QBuw3yuIiU3uHYIyzIGNxpvzHmr+1rfWPhfcLFs1iNQax+I+3u7ub555/n4sWLEbvm2bNn6e/v59lnn33bvlOnTvGRj3yEgAj+gPDpX/uffOB9/7e9O4+rssz/P/664KCkoqKmqbjEooisbgiYiuZOlmblNE2QYeVYpo061Jg/W6ax8qvWmNOYTlJjZaXibimKommIDhXiQioqapkoCJKyXb8/7uMRZRHlwAH9PB+P+wHnXq/DKflw3dd9vUcBsGHDBl588UUKCwuJjIwkKirKam26kZr6+VhN9q9waL0x++KROCi8DHc1MQ9eHGaEPtWRbllRu2itubx/vyXs6fKBAwDUcXfDqf/9ON3fH8fOnWVSpWpS2R6DCOBDpVQGEG9etpf2JEEFrQLCMSZNCgdW3szBV8YNmJ9CeAi48ltyFfC8ebBkIJB1K+ML7jRJSUkkJiaWWhh4e3uTmJiIyWTi9OnT+Pn58cjIh1BKMX78eDZu3IiLiwvdu3dn+PDheHl52eAd3IacWkDXCGO5nA0/x5onVVoLP3wGJsdrEyHrN7N1i4W4IaUUjl5eOHp5cfcLz5OXfpKczbFkx24mY+FCMv79b0zNmxvTM/frT/3AHqg6dWzd7DtOhcoyrfWTWusOwMNAOvABUJnRXzOBAUqpVGCA+TVKqW5KqYVXdlJKxQNfAf2VUulKqUHmTUuUUj8BPwHNgDfN69cBR4CfgY+AP1eijTdtz7HzfLDlZ/Ycu9V66VqzZ8/G29sbb29v5s6da1lfUFBAeHg4vr6+jBo1itxcoyMnKioKLy8vfH19mTx5conzJSQkEBwcTEBAAMHBwRw8eJC8vDymT5/O0qVL8ff3Z+nSpdccU69ePUzmUcWXLl2yDMZMSEjA3d0dV1dX6tSpw+jRo1m5smR917dvXyZNmkTv3r3p1KkTu3fvZuTIkXh4eDBt2jSr/Jxue3WdoPNDMHIBTDkMT66ELuFw+kdYOR5mecB/hsB3/zQyHoSoJeq4tKbJk0/SLnoxHtvjaTnzH9zl50dWzEpOjB3LoeAQTr70F7LWrqUwO9vWzb1zaK1vuGAMNvw38B3GX+VTgaCKHFsblq5du+rrpaSklFhXnsS0c7rjtHX63qg1uuO0dTox7dxNHV/ifImJ2tvbW+fk5Ojs7Gzt5eWl9+7dq48ePaoBvX37dq211k899ZR+9913dUZGhu7QoYMuKirSWmt9/vz5EufMysrS+fn5WmutN27cqEeOHKm11vrjjz/W48ePL7Mtu3bt0l5eXrp+/fp6+fLlWmutv/rqK/30009b9vnkk09KPUefPn301KlTtdZaz507V7ds2VKfOnVKX7p0Sbdu3VqfPXv2Vn48N/353JaKirQ+laT15r9rPT9Y6//X0Fg+6Kn1pte1Tt9j7CNELVP4++/6wubN+uTf/qYPBgXrlI6eOsXbRyc/8ahe/e54/b/kWFs38baAkZJc4ndiRW8lzAUOAx8CW7TWadYuUGq7XUcyyCsookhDfkERu45k0LXdrT+bvn37dkaMGEH9+kZm+siRI4mPj2f48OG0adOGkJAQAJ544gnef/99Jk6ciKOjI5GRkQwbNoywsJJDMrKysggPDyc1NRWlFPn5+RVqS2BgIPv27WP//v2Eh4czZMiQKwXjNcp6tHP48OEA+Pj40LlzZ1q2bAmAq6srJ06coKmMTr41ShlR0C39IPQVOJ92NRFy+2yInwUNW0PHIeZEyF5gkm5ZUfPZOTriFBqKU2gourCQ33/4gcOrPufsN2tx261hYSz7PN1oMfgBY/Cim5s8Wm5FFSoMtNbNlFKdMcKT/q6U8gAOaq3/VKWtq0V6ujaljsmO/IIiHEx29HSt3C+70n7xXnH9/wBKKUwmEwkJCcTGxvLFF18wb948Nm/efM1+r776KqGhoaxYsYK0tDT69u17U23q1KkT9evXJzk5GRcXF06cOGHZlp6eTqtWrUo9rq45eMXOzs7y/ZXXBQUFFbp2TZlgqUZzbg9BfzaWixmQ+o1RJPxvCexeCHUbGZMpeQ4D9/vBsaGtWyzEDSl7e+p16cL3Dnv5Z/tvaJlRRGAqDEu/yG9z5/Lb3Lk4tGtrGbx4l58fyt7e1s2u1Sr0L6xSqiHQFmiHMY1xI6Co6ppV+3Rt58ySyJ7sOpJBT9emleotAOjduzcRERFERUWhtWbFihV8+umnABw/fpydO3cSFBTE559/Tq9evcjJySE3N5ehQ4fSs2dP3N3dS5wzKyuL1q2NLO/Fixdb1js5OZFdxv27o0eP0qZNG0wmE8eOHePgwYO0b9+exo0bk5qaytGjR2ndujVffPEFn332WaXec1lKm2BJ3ED9puD/uLHk/2482XBgjTFNc/LXRiLkvb2vTqrkVNqTxULUHN1adKOOqS6/3J3PuhYOjBg4B3fdipwtm8neFMu5Tz/l3H/+g32TJjToF2oMXgwOws5Rph6/WRX902t7sWWe1jq96ppUe3Vt51zpguCKLl26EBERQY8ePQCIjIwkICCAtLQ0OnXqRHR0NM8++yweHh6MGzeOrKwsHnzwQS5duoTWmjlz5pQ459SpUwkPD2f27Nn069fPsj40NJSZM2fi7+/Pyy+/zGOPPWbZtn37dmbOnImDgwN2dnbMnz+fZs2MEfDz5s1j0KBBFBYWMmbMGDp37myV9369i5cLjHtfAFpz8XLFehmEmcNdxu2EjkOgqBBOJBhFwoG1sGaSsbTudnW+hGYdZFIlUeNcM9tisURH59GjcR49msLsbC7Gx5O9KZbsDd+Q9fUy1F130aBXL2NSpT59sG/c+AZXEXATscu3s9o0j8GdqLQeg+NHUuXzqSyt4bcD5iJhHZwy5401dTd6ETzDjAhpO+mWFbWLzsvjYsJuY0Kl2M0UnDkD9vbU69YNp/79cerfDwdz7+n1ks4k3TFx0pUNUbob40mEzhhJiwBorfuVeVAtIoVBzXf9GAP5fKpA1knzXAnrjDyHogKof7d58GIY3NtHEiFFraOLiri0bx/ZsbHkxMZyOfVnAOp26mSZebGupydKKUs4VF5hHnXs6/DRwI9u6+KgshMcLcGISQ4DnsOYlKgWpdiI2k4GHVaDRq2hx1hj+T3TSII8sBaSV8DeT8Ch/tVEyA4DJRFS1ArKzo67fHy4y8eH5hMnknfsmGXmxbPz53P2gw9waNWKBv37k+p6iYL8yxTZafKL8kn8NbHShUFt7IGo6L+0TbXWi5RSL2qttwJblVJbq7JhQggbuqsx+IwyloLLcDTeHPa0DvavAmUP7UOg4zDwHAqN29q6xUJUSJ127Wg65imajnmKgowMcuLiyN4US+aXX+J9+TILHGGvux1Jnia69fGu1LVqaw9ERQuDKw+8n1ZKDQNOAS5V0yQhRI1iqgse9xvL0P+DU/+7Onhxw1+N5R7fq4MXW3jL4EVRK5iaNqXxww/T+OGHKcrNJWfHDo6v+YqgHbvpnXwJtepZTlxJhAwNvelEyMRfE8krzKOIIqv1QFSHihYGbyqlGgF/Af4JNAQmVVmrhBA1k50duHQ1lvv/H5z9+WpsdNxMIxWycVtzT8IwaBskiZCiVrCrV4+GAwbgPWAAuqCA3D17jRyHTbHkxMUZiZABAZbBi3Xat7/hObu16EYd+zrkF+XjYOdAtxYlbufXSDccfKiUsgcmaK1LPv92m5DBh7WPfD41UM4ZY56EA2uLJUI6X5cIKXNQiNpFa83lgwfJjo01EiFT9gPmRMh+/Y1ESG/vMhMha/IYg7IGH94wRElrXQgMr5JWiTJlZmYyf/58y+u0tLRrJhBKTExkwoQJVr9uTEwMKSkppW778MMP8fHxwd/fn169el2z3z/+8Q/c3d3p2LEj33zzjdXbVd2sHYh1R2jQHLqGwx+/hKlH4NFPwGOgUSwsfQLecYXPRsPeT+HiWVu3VogKUUrh6OnJ3ePH47p8Oe6xm2jxt79hanY3GYsWkfboY/zcN5TTM2aQE78dnZd3zfH+zf2J9ImscUVBeSr6uOLfMWY7XApcvLJea7236ppWfWpij0FaWhphYWEkJxuJ0nFxccyaNYs1a9ZU6XUjIiIICwtj1KhRJbZduHCBhg2NaXRXrVrF/Pnz2bBhAykpKfzhD38gISGBU6dOcf/993Po0CHsq3Ba0qr8fPYcO88fF+4ir6CIOiY7lkT2tNrEVXekwnw49p3Rk3BwHWSdAGUHbXoaAxc7DoWmbrZupRA3rTAzk5xt24zbDdu3o3NzsatfnwZ9etOgf38a9O6NvZNTpa9TVb0OlX1cMdj89bUr5wM0cFvMY1ATRUVFcfjwYfz9/RkwYADx8fHs378ff39/wsPDCQgIsBQKM2bM4OjRo5w+fZpDhw4xe/Zsdu3axfr162ndujWrV6/GwcHhmvN/9NFHLFiwgLy8PNzd3fn0009JSkpi1apVbN26lTfffJNly5bh5nb1H+wrRQHAxYsXLZkNK1euZPTo0dStW5d7770Xd3d3EhISCAoKuuaaDRo0YPz48WzatAlnZ2feeustpk6dyvHjx5k7d64lbMnWrB2IdcezdwDXPsYy5G345UejSDiwDr6dZizNvcyTKg2DVgEyeFHUCvaNG9No+HAaDR9O0eXLXNy5k5zYWLI3b+HCuvXg4ED9Hj1o0L8fTv3749CixU1fwxZPNpRbGCilXjJ/uwajECj+f+udM2Xi+ij45SfrnvMeHxgys8zNM2fOJDk5maSkJKBkj0FcXNw1+x8+fJgtW7aQkpJCUFAQy5Yt45133mHEiBGsXbuWhx566Jr9R44cydixYwGYNm0aixYt4oUXXmD48OFl9hgAfPDBB8yePZu8vDxLSNPJkyfp2bOnZR8XFxdOnjxZ4tiLFy/St29f3n77bUaMGMG0adPYuHEjKSkphIeH15jCwNqBWKKYshIhD66TREhRq9nVrYtT37449e3LPTMK+f2HH42ZFzfF8uvrb/Dr62/g6ONzdfCiu3uFEiFt8WTDjXoMrvSBdAS6AysxioMHgG1V2C5xk4YMGYKDgwM+Pj4UFhYyePBgwIg6TktLK7F/cnIy06ZNIzMzk5ycHAYNGlSh64wfP57x48fz2Wef8eabbxIdHV3hCOY6depc0666deta2lxaG23F2oFYohySCCluQ0YiZAD1ugTQfPJk8o4cMTIcYmNLJkL278dd/v5lJkLa4smGcgsDrfVrAEqpb4EuWuts8+sZwFdV3rqaopy/7GuK4tHGDg4Oll/MZUUbR0REEBMTg5+fH4sXLy7RA3Ejo0ePZty4cQAVjmC+vl3F21zR+OXqYs1ALFFBxRMh83KNJxsOrpVESFGrKaWo6+ZGXTc3mj37DPm/nik9ETK0L0797y+RCFlWeFRVqugYg7ZA8aGWeRjxy6KKXB+FXF408q3Izs6mZcuW5Ofns2TJEkscc3nXSU1NxcPDA4C1a9davh8+fDiPP/44L730EqdOnSI1NdWSCinELalTzxiY6DnUnAj5vXlcwppSEiHD4O4Otm6xEBXi0KJ56YmQ33xL1rLl5kTIEGPwYp8+mJyd8W/uX61PNVS0MPgUSFBKrcAYWzACiK6yVgmaNm1KSEgI3t7eDBkyhLfeeguTyYSfnx8REREEBARU6vxvvPEGgYGBtGvXDh8fH0sxMHr0aMaOHcv777/P119/fc3gw3nz5rFp0yYcHBxwdnYmOtr4T6Bz5848+uijeHl5YTKZ+OCDD6r0iQRxh7Gzh3bBxjLwTTiz/+qkSrGvGUtT96tFQutuxkRMQtRw9k5ONBw6lIZDh5ZIhMzeuMlIhOza1Zh5sV9/6riUnghpbRWOXVZKdQHuM7/cprX+X5W1qprVxMcVRfnk8xHA1UTIA2shLd6cCNm8WCJkb0mEFLWOJRFyUyw5m68mQrrHbcHhHuvdQqtU7PLtTgqD2kc+H1HC75mQutHoTUjdCHk5UKfB1URIjwGSCClqpby0NHITE2lcxtNit6qy8xgIIUTNdldj8H3EWAouw9FtVydVSlkJdiZoF2IUCZ5DoZHkwInaoU779hXKZrAWKQyEqGH2HDsvj0pWlqmu0UPgMQCGzYaTe66OS1g/xVha+pmLhGHGBEsyqZIQgBQGQtQoMh1zFbCzgzbdjeX+GfDbIXORsA62/N1YnNsXS4TsaQx4FOIOJYWBEDWITMdcDe7uYCy9JkH2L1cTIXd/BLs+gHpNryZCuoYaj04KcQeRwkCIGkSmY65mTvdAt6eM5XI2/LzJKBL2r4GkJWC6yxi82HGoUSzUl89D3P6kMKhlrk9dtIakpCROnTrF0KFDS93+448/8uyzz3LhwgXs7OzYvXs3jo6O7Nmzh4iICH7//XeGDh3Ke++9V6G5v0XZZDpmG6rrBJ1HGEthPqRtvzp48cAaIxGybbB5voShxu0HIW5DNpkFRCnVRCm1USmVav5a6r9+SqkNSqlMpdSa69bHK6WSzMsppVSMeX1fpVRWsW3Tq+P91HZJSUmsW7eu1G0FBQU88cQTfPjhh+zbt4+4uDhLUuO4ceNYsGABqamppKamsmHDhups9m2raztnxoe6S1FgS/YO4BYKw2bBpH3wTBzc9xf4/Tx88zK85wf/CoEtb8GpJJDHvsVtxFbTg0UBsVprDyDW/Lo07wJ/un6l1vo+rbW/1tof2AksL7Y5/so2rfXr1m54eZLOJLHwp4UknUmyyvlmz56Nt7c33t7ezJ0717K+oKCA8PBwfH19GTVqFLm5uYAR1ezl5YWvry+TJ08ucb6EhASCg4MJCAggODiYgwcPkpeXx/Tp01m6dCn+/v4sXbr0mmO+/fZbfH198fPzA4wZGe3t7Tl9+jQXLlwgKCgIpRRPPvkkMTExJa4ZERHBuHHjCA0NxdXVla1btzJmzBg6depERESEVX5OQlQppYwo6H7T4M/fwYT/wcC/Q92GsO1dWNAH5vrAuqlGvkNhvq1bLESl2OpWwoNAX/P30UAc8Nfrd9Jaxyql+l6//gqllBPQD3jK6i28SdbOzN6zZw8ff/wx33//PVprAgMD6dOnD87Ozhw8eJBFixYREhLCmDFjmD9/PmPGjGHFihUcOHAApRSZmZklzunp6cm2bdswmUxs2rSJV155hWXLlvH666+TmJjIvHnzShxz6NAhlFIMGjSI3377jdGjRzN16lROnjyJi8vV58DLiloGOH/+PJs3b2bVqlU88MAD7Nixg4ULF9K9e3eSkpLw96++OcCFqLQmrhD8vLFcPAuHNhi3HPZGQ8K/wbHR1cGLbv2hbgNbt1iIm2KrwqCF1vo0gNb6tFKq+S2eZwRGz8OFYuuClFI/AKeAyVrrfaUdqJR6BngGoG3btrd4+ausnZm9fft2RowYQf369QEYOXIk8fHxDB8+nDZt2hASEgLAE088wfvvv8/EiRNxdHQkMjKSYcOGERYWVuKcWVlZhIeHk5qailKK/Pwb/2VTUFDA9u3b2b17N/Xq1aN///507dqVhg1Lxt+WNb7ggQceQCmFj48PLVq0wMfHBzAyFtLS0qQwELVX/WYQ8ISx5F2Ew1uMIuHQevhxKdjXBde+5kTIIdDgVv+pE6L6VNmtBKXUJqVUcinLg1a8zB+Az4u93gu001r7Af8ESvZtm2mtF2itu2mtu919992VbsiVzGx7ZW+VzOzypqq+/hewUgqTyURCQgIPP/wwMTExDB48uMRxr776KqGhoSQnJ7N69WouXbp0w3a4uLjQp08fmjVrRr169Rg6dCh79+7FxcWF9PR0y35lRS3DtZHQV76/8rqmxS0Lccvq1IdOYTDiXzD5Z4hYC92fht/2w+oJMKsDLBoIO96DjMO2bq0QZaqywkBrfb/W2ruUZSXwq1KqJYD565mbPb9SqinQA1hb7JoXtNY55u/XAQ5KqWZWeUM3cCUz+/mA5yt9GwGgd+/exMTEkJuby8WLF1mxYgX33WdkWB0/fpydO3cC8Pnnn9OrVy9ycnLIyspi6NChzJ07l6SkkuMcsrKyLPHKixcvtqwvL2p50KBB/Pjjj+Tm5lJQUMDWrVvx8vKiZcuWODk5sWvXLrTWfPLJJzz4oDVrPiFqMXsTtO8Fg/8BL/4Iz22Hvi9D/u+wcTr8swvM6wGbXoP0PVBUZOsWC2Fhq8GHq4Bw8/fhwMpbOMcjwBqtteXPXqXUPcr857RSqgfG+8uoZFsrzL+5P5E+kVbJze7SpQsRERH06NGDwMBAIiMjLVHLnTp1Ijo6Gl9fX86dO8e4cePIzs4mLCwMX19f+vTpw5w5c0qcc+rUqbz88suEhIRQWFhoWR8aGkpKSkqpgw+dnZ156aWX6N69O/7+/nTp0oVhw4YB8K9//YvIyEjc3d1xc3NjyJAhlX7fQtx2lIJ7fKDvX+G5eJj4Ewx5B5xaGL0HC/vBHC9YM8mYR6Egz9YtFnc4m6Qrmv/a/xJoCxwHHtFan1NKdQOe01pHmveLBzyBBhi/4J/WWn9j3hYHzNRabyh23ueBcUAB8Dvwktb6uxu1R9IVax/5fMRtIfeckQR5YA38HAv5F42nHdzvN8YleAwwBjMKUQUkdrkcUhjUPvL51FwSAnWL8n+HI1uNHIeD6+Hib2DnAPfeZx68OBQalj6OR4hbIbHLQogqJyFQleBwF3QcbCxFhZC+2+hJOLAW1v7FWFp3NRcJw+DujpIIKaqEFAZCCKuRECgrsbM3Uh7b9oQBb8BvB40i4eA6iH3dWJq4madnHgYu3SURUliNFAZCCKuREKgqoBQ09zSW3pPhwilzfsNa2PUv+O59qH+3MU+CZxjc2wccHG3dalGLSWEghLAaCYGqBg1bQfdIY7mUZR68uBaSV8DeT8ChvpEI6RkGHQbCXfIZiJsjhYEQwqq6tnOWgqC6ODYCn1HGUnAZ0uKNIuHAOti/CpQ9tA8xioSOQ6FxG1u3WNQCtprHQNxAZmYm8+fPt7xOS0vjs88+s7xOTExkwoQJVr9uTEwMKSkpZW7/8ssv8fLyonPnzjz++OOW9dHR0Xh4eODh4UF0dLTV2yWEuAFTXeMxx7A58NJ+iNwMIS9C9q+wfirM9YYP74O4t+GXZEmEFGWSxxWpmY8rpqWlERYWRnJyMgBxcXHMmjWLNWvW3ODIyomIiCAsLIxRo0aV2Jaamsqjjz7K5s2bcXZ25syZMzRv3pxz587RrVs3EhMTUUrRtWtX9uzZg7Nz1f3VaOvPR4ha5ezPxmOQB9bCiQRAQ+N2VwcvtulpzNYo7ihlPa4oPQY1VFRUFIcPH8bf358pU6YQFRVFfHw8/v7+zJkzh7i4OEtQ0owZMwgPD2fgwIG0b9+e5cuXM3XqVHx8fBg8eHCpYUkfffQR3bt3x8/Pj4cffpjc3Fy+++47Vq1axZQpU/D39+fw4cMljhk/frzlF37z5kYgzDfffMOAAQNo0qQJzs7ODBgwgA0bNpS4Zvv27XnllVcICgqiW7du7N27l0GDBuHm5saHH35o7R+hEOKKZu5G78HT38JfDsID78HdnrB7ESweBrM8IObPsH8N5OXaurXCxqRErIBf3nqLy/sPWPWcdTt5cs8rr5S5febMmSQnJ1syD67vMYiLi7tm/8OHD7NlyxZSUlIICgpi2bJlvPPOO4wYMYK1a9fy0EMPXbP/yJEjGTt2LADTpk1j0aJFvPDCCwwfPrzMHoNDhw4BWKZUnjFjBoMHD+bkyZO0aXP13mV5Ecxt2rRh586dTJo0iYiICHbs2MGlS5fo3Lkzzz333A1+akKISnNqAV0jjOVytjHj4oG1xuOQSUvAdBe49QPPoUZ8dP1qiZsRNYgUBreJIUOG4ODggI+PD4WFhZZ0RR8fH9LS0krsn5yczLRp08jMzCQnJ4dBgwbd8BoFBQWkpqYSFxdHeno69913H8nJyaUmQZYVwTx8+HBLu3JycnBycsLJyQlHR0cyMzNp3LjxTbxrIUSl1HWCzg8ZS2E+HNthLhLWGrcelB20Dbo682KTe23dYlENpDCogPL+sq8pikcbOzg4WH4xlxVtHBERQUxMDH5+fixevLhED0RpXFxc6NkoUgyXAAAegUlEQVSzJw4ODtx777107NiR1NRUXFxcrjk+PT2dvn373rCdEsEsRA1i7wCufY1lyDtw+oerRcI3rxhL885XxyW09JOZF29TMsaghro+Crm8aORbkZ2dTcuWLcnPz2fJkiUVus5DDz3Eli1bADh79iyHDh3C1dWVQYMG8e2333L+/HnOnz/Pt99+W6EeCCFEDaUUtPKHfn+DP38HE5Jg0FtwV2OInwUL+sAcb1g3BY7EGb0N4rYhPQY1VNOmTQkJCcHb25shQ4bw1ltvYTKZ8PPzIyIiwhLBfKveeOMNAgMDadeuHT4+PpZiYPTo0YwdO5b333+fr7/+Gjc3N8sxVwoALy8v7O3teffdd2na1JjZ7tVXX6V79+4ATJ8+nSZNmlSqfUKIGqTJvRA03lgunoVD3xg9CXs/gYQFxnwKHoOMngT3+6FuA1u3WFSCPK5IzXxcUZRPPh8haoC8i3B4izFF88H18Ps5sK9r3I7wHGqMS2jQ3NatFGWQdEUhxB1JYqCrUJ360CnMWAoL4MSuq084pH4DqydCmx5XEyGbudu6xaICpDAQQty2JAa6GtmboH0vYxn0Fvy672qRsHG6sTTraB68GAatAsBOhrnVRFIYCCFuWxIDbSNKwT3extL3r5B53LjVcGAN7HgPts8Gp5bmRMhh0L43mOrYutXCTAoDIcRtS2Kga4jGbSHwWWPJPWdOhFwDPyyFxP9A3YbGoEXPYeAxwBjMKGxGCgMhxG1LYqBroHpNwO8xY8n/HY5sNec4rIN9y8HOAe7tfXVSpYYtbd3iO448lYA8lVAbyecjxG2mqBDSdxs9CQfWwrkjxvrWXa+OS2jWQSZVsiIJUbpNpKWl4e3tbdVzJiUlsW7dulK35eXl8dRTT+Hj44Ofn981Mxzu2bMHHx8f3N3dmTBhQqlTIwshRIXY2UPbnjDwTXhhL/z5e+j3qhEPHfs6fNAD/tkVvn0Vjn9vFBKiSkhhIMotDD766CMAfvrpJzZu3Mhf/vIXioqKABg3bhwLFiwgNTWV1NTUUhMVhRDipikFzT2h92R4Zgu8tB+G/R84t4Nd8+E/A+H/PGHVC8ZkS/mXbN3i24oUBlb0y5Es9mxI45cjWVY53+zZs/H29sbb25u5c+da1hcUFBAeHo6vry+jRo0iN9eISY2KisLLywtfX18mT55c4nwJCQkEBwcTEBBAcHAwBw8eJC8vj+nTp7N06VL8/f1ZunTpNcekpKTQv39/wIhZbty4MYmJiZw+fZoLFy4QFBSEUoonn3ySmJiYEteMiIhg3LhxhIaG4urqytatWxkzZgydOnUiIiLCKj8nIcRtrmEr6B4Jf1oBU4/Aw4uMxyKTV8Bnj8I7rvDlk8Zgxt/P27q1tZ4MPrSSX45ksXLO/ygsKMLeZMeDkwK4x/XWR9bu2bOHjz/+mO+//x6tNYGBgfTp0wdnZ2cOHjzIokWLCAkJYcyYMcyfP58xY8awYsUKDhw4gFKKzMzMEuf09PRk27ZtmEwmNm3axCuvvMKyZct4/fXXSUxMZN68eSWO8fPzY+XKlYwePZoTJ06wZ88eTpw4gZ2dHS4uLpb9yotaPn/+PJs3b2bVqlU88MAD7Nixg4ULF9K9e3eSkpLw9/e/5Z+TEOIO49gIfEYZS8FlOBpvjEs4uB5SVoKdCdqFGGMSPIdCI5cbn1NcQ3oMrOTkofMUFhShNRQWFnHyUOWq1u3btzNixAjq169PgwYNGDlyJPHx8QC0adOGkJAQAJ544gm2b99Ow4YNcXR0JDIykuXLl1OvXr0S58zKyuKRRx7B29ubSZMmsW/fvhu2Y8yYMbi4uNCtWzcmTpxIcHAwJpPppqKWH3jgAZRS+Pj40KJFC3x8fLCzs6Nz586lRkILIUSFmOqCx/3wwFzjdkNkLAS/ANmnYf0UmNMZ/t0btr5jTLgk46AqRHoMrKR1B2fsTXYUFhZhb29H6w6VeyyqvIF81/8CVkphMplISEggNjaWL774gnnz5rF58+Zr9nv11VcJDQ1lxYoVpKWllRmNXJzJZGLOnDmW18HBwXh4eODs7Ex6erplfXp6Oq1atSr1HBK1LISocnZ24NLNWO6fAWdTr8ZGb3kLtvwdnNsbUzN7DjMGOtrZ27jRNZP0GFjJPa6NeHBSAIHDXSt9GwGgd+/exMTEkJuby8WLF1mxYgX33XcfAMePH2fnzp0AfP755/Tq1YucnByysrIYOnQoc+fOJSkpqcQ5s7KyaN26NQCLFy+2rC8vavnK9QE2btyIyWTCy8uLli1b4uTkxK5du9Ba88knn/Dggw9W6j0LIYTVNPOAXhMhciP85SA88J7xuOPuj2DxUJjlATHjjfkT8nJt3doaxWaFgVKqiVJqo1Iq1fy1xJ/YSil/pdROpdQ+pdSPSqnHim27Vyn1vfn4pUqpOub1dc2vfzZvb19d7+ke10Z0Hdy+0kUBQJcuXYiIiKBHjx4EBgYSGRlpiVru1KkT0dHR+Pr6cu7cOcaNG0d2djZhYWH4+vrSp0+fa/7Kv2Lq1Km8/PLLhISEUFh49VGf0NBQUlJSSh18eObMGbp06UKnTp14++23+fTTTy3b/vWvfxEZGYm7uztubm4MGTKk0u9bCCGszqkFdI2AP35lDF58JBrc+sP+1fDFH4zBi1/8EZI+M2ZmvMPZbIIjpdQ7wDmt9UylVBTgrLX+63X7dAC01jpVKdUK2AN00lpnKqW+BJZrrb9QSn0I/KC1/pdS6s+Ar9b6OaXUaGCE1voxyiETHNU+8vkIISqtMB/Sthux0QfWwoWToOygbbB5UqWhxu2H21RZExzZsjA4CPTVWp9WSrUE4rTWHW9wzA/AKOBn4DfgHq11gVIqCJihtR6klPrG/P1OpZQJ+AW4W5fzRqUwqH3k8xFCWJXWcDrJPC5hHZwxD85u4WMUCJ7D4B7f22rmxbIKA1sOPmyhtT4NYC4Ompe3s1KqB1AHOAw0BTK11ldGrqUDrc3ftwZOmM9boJTKMu9/9rrzPQM8A9C2bVurvCEhhLC2PcfOS9ZDdVDKiIJuFQD9phlTMh8w9yRsexe2vg2N2hj5DZ7DoF0w2DvYutVVokoLA6XUJuCeUjb97SbP0xL4FAjXWhep0p+Lu9IjUN62qyu0XgAsAKPH4GbaI4QQ1WHPsfP8ceEu8gqKqGOyY0lkTykOqksTVwh+3lgunoVDG4wiYW80JPwbHBtDh0FGkeDWH+o2sHWLraZKCwOt9f1lbVNK/aqUalnsVsKZMvZrCKwFpmmtd5lXnwUaK6VM5l4DF+CUeVs60AZIN99KaATIaBIhRK2z60gGeQVFFGnILyhi15EMKQxsoX4zCHjCWPIuwuEtRpFwaD38uBTs64JbqNGb0HEINCi3A7zGs+WthFVAODDT/HXl9TuYnzRYAXyitf7qynqttVZKbcEYb/DFdcdfOe9O8/bN5Y0vEEKImqqna1PqmOzILyjCwWRHT9emtm6SqFMfOoUZS2EBHN9pHry4xuhVWK2gTaB58OIwaOpm6xbfNFsOPmwKfAm0BY4Dj2itzymlugHPaa0jlVJPAB8Dxafoi9BaJymlXDGKgibA/4AntNaXlVKOGLcdAjB6CkZrrY+U1xYZfFj7yOcj7hQyxqCW0Bp+Tb46qdIvPxrr7/Y0CoSOw4zxC3Y1Z/qgGhe7rLXO0Fr311p7mL+eM69P1FpHmr//r9baQWvtX2xJMm87orXuobV211o/orW+bF5/yfza3by93KKgpsrMzGT+/PmW12lpaXz22WeW14mJiUyYMMHq142JiSElJaXUbceOHaN///74+vrSt2/fa2Y+jI6OxsPDAw8PD6Kjo63eLiHuVF3bOTM+1F2KgppOKbjHB/pGwXPxMPEnGPw21L8bts+Fhf1gjheseQl+joWCPFu3uEw1p3QR17hRYdCtWzfef/99q1+3vMJg8uTJPPnkk/z4449Mnz6dl19+GYBz587x2muv8f3335OQkMBrr73G+fOScCaEuIM1bgs9n4OINTDlZxjxb2O65h8+h/+OhHfd4OsxkLwMLl2wdWuvIYVBDRUVFcXhw4fx9/dnypQpREVFER8fj7+/P3PmzCEuLo6wsDAAZsyYQXh4OAMHDqR9+/YsX76cqVOn4uPjw+DBg8nPzy9x/o8++oju3bvj5+fHww8/TG5uLt999x2rVq1iypQp+Pv7c/jw4WuOKR7BHBoaysqVxrCOb775hgEDBtCkSROcnZ0ZMGAAGzZsKHHN9u3b88orrxAUFES3bt3Yu3cvgwYNws3NjQ8//NDaP0IhhKgZ6jUBv9Hw2H+NmRf/8AV4PQhHthrFwTuu8N+HYfciuHDa1q2VEKWK2LJ4AWeOWfeORPN2roRGPFPm9pkzZ5KcnGzJPIiLi2PWrFmsWbPG8rq4w4cPs2XLFlJSUggKCmLZsmW88847jBgxgrVr1/LQQw9ds//IkSMZO3YsANOmTWPRokW88MILDB8+nLCwMEaNGlWiTX5+fixbtowXX3yRFStWkJ2dTUZGBidPnqRNmzaW/cqLYG7Tpg07d+5k0qRJREREsGPHDi5dukTnzp157rnnbvyDE0KI2szhLuPJhY5DoKgQTiQYAxcPrIW1LxlL627mwYthcHeHam+iFAa3iSFDhuDg4ICPjw+FhYUMHjwYAB8fn1KjjZOTk5k2bRqZmZnk5OQwaNCgG15j1qxZPP/88yxevJjevXvTunXrm45gHj58uKVdOTk5ODk54eTkhKOjI5mZmTRu3Pgm3rUQQtRidvbQLshYBr4Jvx24WiTEvmYsTd2vFgmtu1XL4EUpDCqgvL/sa4ri0cYODg6WX8xlRRtHREQQExODn58fixcvLtEDUZpWrVqxfPlyAHJycli2bBmNGjXCxcXlmuPT09PLjHSWCGYhhCiFUtC8k7H0ngJZJ69mOOz8AHa8By8dgIYtq7wpMsaghro+Crm8aORbkZ2dTcuWLcnPz2fJkiUVus7Zs2cpKioC4B//+AdjxowBYNCgQXz77becP3+e8+fP8+2331aoB0IIIUQZGrWGHmPhyRiYchge/6paigKQwqDGatq0KSEhIXh7ezNlyhR8fX0xmUz4+fmVGql8s9544w0CAwMZMGAAnp6elvWjR4/m3XffJSAgoMTgw7i4ODp27EiHDh349ddf+dvfjJmtmzRpwquvvkr37t3p3r0706dPp0mTJpVuoxBCCOCuxtBhYLVdzmYTHNUkMsFR7SOfjxC2JRMv1X41MV1RCCFELSThTrc3uZUghBDippQW7iRuH1IYCCGEuClXwp3sFRLudBuSWwlCCCFuStd2ziyJ7CljDG5TUhgIIYS4aV3bOUtBcJuSWwlCCCGEsJDCoJZJS0vD29vbqudMSkpi3bp1pW7LyMggNDSUBg0a8Pzzz1vW5+bmMmzYMDw9PencuTNRUVGWbZcvX+axxx7D3d2dwMDAUqdkFkIIUTNJYSDKLQwcHR154403mDVrVoltkydP5sCBA/zvf/9jx44drF+/HoBFixbh7OzMzz//zKRJk/jrX/9ape0XQghhPVIYWNHlYxe4sOUEl49ZJ1t79uzZeHt74+3tzdy5cy3rCwoKCA8Px9fXl1GjRpGbmwsYUc1eXl74+voyefLkEudLSEggODiYgIAAgoODOXjwIHl5eUyfPp2lS5fi7+/P0qVLrzmmfv369OrVC0dHx2vW16tXj9DQUADq1KlDly5dSE9PB2DlypWEh4cDMGrUKGJjY0sELcXFxdGnTx8effRROnToQFRUFEuWLKFHjx74+PiUmHVRCCFE9ZDBh1Zy+dgFzi78CV1QhDLZ0SzSh7rtGt7y+fbs2cPHH3/M999/j9aawMBA+vTpg7OzMwcPHmTRokWEhIQwZswY5s+fz5gxY1ixYgUHDhxAKUVmZmaJc3p6erJt2zZMJhObNm3ilVdeYdmyZbz++uskJiYyb968W2prZmYmq1ev5sUXXwS4JobZZDLRqFEjMjIyaNas2TXH/fDDD+zfv58mTZrg6upKZGQkCQkJvPfee/zzn/+8phgSQgiZbbF6SI+BlVw+koUuKAINuqCIy0eyKnW+7du3M2LECOrXr0+DBg0YOXIk8fHxALRp04aQkBAAnnjiCbZv307Dhg1xdHQkMjKS5cuXU69evRLnzMrK4pFHHsHb25tJkyaxb9++SrURjN6LP/zhD0yYMAFXV1eACscwd+/enZYtW1K3bl3c3NwYONCYC7ysqGghxJ3rymyL//ftQf64cBd7jp23dZNuW1IYWEld10Yokx0oUCY76ro2qtT5ysuwuP6XrFIKk8lEQkICDz/8MDExMQwePLjEca+++iqhoaEkJyezevVqLl26VKk2AjzzzDN4eHgwceJEyzoXFxdOnDgBGIVDVlZWqaFK18cuF49klghmIURxMtti9ZHCwErqtmtIs0gfGg5sX+nbCAC9e/cmJiaG3NxcLl68yIoVK7jvvvsAOH78ODt37gTg888/p1evXuTk5JCVlcXQoUOZO3cuSUlJJc6ZlZVF69atAVi8eLFl/a1GOk+bNo2srKwSXf7Dhw8nOjoagK+//pp+/fqV2mMghBAVJbMtVh8pDKyobruGNAxtU+miAKBLly5ERETQo0cPAgMDiYyMJCAgAIBOnToRHR2Nr68v586dY9y4cWRnZxMWFoavry99+vQpNZp56tSpvPzyy4SEhFBYWGhZHxoaSkpKSqmDDwHat2/PSy+9xOLFi3FxcSElJYX09HT+/ve/k5KSQpcuXfD392fhwoUAPP3002RkZODu7s7s2bOZOXNmpX8eQog725XZFl8a2LFKQ5v2HDvPB1t+vqNvVUjsMhK7XBvJ5yOEsLY7LTWyrNhl6TEQQgghkHEMV0hhIIQQQiDjGK6QeQyEEEIIJDXyCikMhBBCCDNJjbTRrQSlVBOl1EalVKr5a4lPQSnlr5TaqZTap5T6USn1WLFtS5RSB5VSyUqp/yilHMzr+yqlspRSSeZlenW+LyGEEKK42viUg63GGEQBsVprDyDW/Pp6ucCTWuvOwGBgrlKqsXnbEsAT8AHuAiKLHRevtfY3L69X2TsQQgghylFbZ2u0VWHwIBBt/j4aeOj6HbTWh7TWqebvTwFngLvNr9dpMyABcKmWVlejzMxM5s+fb3mdlpbGZ599ZnmdmJjIhAkTrH7dmJgYUlJSSt22bds2unTpgslk4uuvv7asT0pKIigoiM6dO+Pr63vNXAhHjx4lMDAQDw8PHnvsMfLy8qzeZiGEqIlq61MOtioMWmitTwOYvzYvb2elVA+gDnD4uvUOwJ+ADcVWBymlflBKrVdKdS7nnM8opRKVUom//fbbrb6PKnOjwqBbt268//77Vr9ueYVB27ZtWbx4MY8//vg16+vVq8cnn3zCvn372LBhAxMnTrSEOP31r39l0qRJpKam4uzszKJFi6zeZiGEqIlq61MOVTb4UCm1CbinlE1/u8nztAQ+BcK11kXXbZ4PbNNax5tf7wXaaa1zlFJDgRjAo7Tzaq0XAAvAmODoZtpUHaKiojh8+DD+/v4MGDCA+Ph49u/fj7+/P+Hh4QQEBDBr1izWrFnDjBkzOHr0KKdPn+bQoUPMnj2bXbt2sX79elq3bs3q1atxcHC45vwfffQRCxYsIC8vD3d3dz799FOSkpJYtWoVW7du5c0332TZsmW4ublZjmnfvj1gZBkU16FDB8v3rVq1onnz5vz22280atSIzZs3Wwqa8PBwZsyYwbhx4645/lbaL4QQNV1tfcqhygoDrfX9ZW1TSv2qlGqptT5t/sV/poz9GgJrgWla613Xbft/GLcWni12zQvFvl+nlJqvlGqmtT5bmfeyfv16fvnll8qcooR77rmHIUOGlLl95syZJCcnWzIP4uLiLIXAldfFHT58mC1btpCSkkJQUBDLli3jnXfeYcSIEaxdu5aHHrr2bs3IkSMZO3YsYGQeLFq0iBdeeIHhw4cTFhbGqFGjbul9JSQkkJeXh5ubGxkZGTRu3BiTyfjPzMXFhZMnT5Z63M22XwghaoPa+JSDrW4lrALCzd+HAyuv30EpVQdYAXyitf7qum2RwCDgD8V7EZRS9yhzWo/59oMdUDtu6lTSkCFDcHBwwMfHh8LCQku6YlkRxsnJydx33334+PiwZMkSq0Qwnz59mj/96U98/PHH2NnZVTh++VbaL4QQd4rqfrLBVvMYzAS+VEo9DRwHHgFQSnUDntNaRwKPAr2BpkqpCPNxEVrrJOBD4Biw0/yLZrn5CYRRwDilVAHwOzBaWyEMory/7GuK4pHFDg4Oll/AZUUYR0REEBMTg5+fH4sXLy7RA3GzLly4wLBhw3jzzTfp2bMnAM2aNSMzM5OCggJMJhPp6em0atXKKu0XQog7gS3yG2xSGGitM4D+paxPxPzoodb6v8B/yzi+1HZrrecB86zXUtu5Pgr5VqORy5KdnU3Lli3Jz89nyZIlljjmW7lOXl4eI0aM4Mknn+SRRx6xrFdKERoaytdff83o0aOJjo7mwQcftNp7EEKI211pTzZUdWEgWQk1VNOmTQkJCcHb25spU6bg6+uLyWTCz8+v1Ejlm/XGG28QGBjIgAED8PT0tKwfPXo07777LgEBARw+fM1DIOzevRsXFxe++uornn32WTp3Nh76+PLLL9m2bRuLFy/G398ff39/y9iIt99+m9mzZ+Pu7k5GRgZPP/10pdsuhBB3Cls82SCxy0jscm0kn48Q4k6x59j5KnmyoazYZclKEEIIIWqw6n6yQW4lCCGEEMJCCoNyyG2Wmkk+FyGEqDpSGJTB0dGRjIwM+SVUw2itycjIwNHR0dZNEUKI25KMMSiDi4sL6enp1MQchTudo6MjLi63XW6WEELUCFIYlMHBwYF7773X1s0QQgghqpXcShBCCCGEhRQGQgghhLCQwkAIIYQQFjLzIaCU+g0jlOl21QjIsnUjrKymvidbtauqr2vt81vjfJU9x60e3wyoVJS7qLCa+v95ZdWU99VOa3339SulMLgDKKUWaK2fsXU7rKmmvidbtauqr2vt81vjfJU9x60er5RKLG0aWWF9NfX/88qq6e9LbiXcGVbbugFVoKa+J1u1q6qva+3zW+N8lT1HTf1vSFx1u35GNfp9SY+BEELcBOkxELc76TEQQoibs8DWDRCiKkmPgRBCCCEspMdACCGEEBZSGAghhBDCQgoDIYQQQlhIYSCEEEIICykMhBDCSpRSrkqpRUqpr23dFiFulRQGQggBKKX+o5Q6o5RKvm79YKXUQaXUz0qpqPLOobU+orV+umpbKkTVMtm6AUIIUUMsBuYBn1xZoZSyBz4ABgDpwG6l1CrAHvjHdceP0VqfqZ6mClF1pDAQQghAa71NKdX+utU9gJ+11kcAlFJfAA9qrf8BhFVvC4WoHnIrQQghytYaOFHsdbp5XamUUk2VUh8CAUqpl6u6cUJUBekxEEKIsqlS1pU5XazWOgN4ruqaI0TVkx4DIYQoWzrQpthrF+CUjdoiRLWQwkAIIcq2G/BQSt2rlKoDjAZW2bhNQlQpKQyEEAJQSn0O7AQ6KqXSlVJPa60LgOeBb4D9wJda6322bKcQVU3SFYUQQghhIT0GQgghhLCQwkAIIYQQFlIYCCGEEMJCCgMhhBBCWEhhIIQQQggLKQyEEEIIYSGFgRDipimlGiul/mz+vpVS6msrnnuiUurJUta3vxKJrJTyUUotttY1hRBXSWEghLgVjYE/A2itT2mtR1njpEopEzAG+Ky8/bTWPwEuSqm21riuEOIqCVESQtyKmYCbUioJSAU6aa29lVIRwEOAPeAN/B9QB/gTcBkYqrU+p5RyAz4A7gZygbFa6wNAP2CvecZBlFJdgf+Y99l+XRtWY0xR/E5VvlEh7jTSYyCEuBVRwGGttT8w5bpt3sDjQA/g70Cu1joAY7rhK7cIFgAvaK27ApOB+eb1IcCeYuf6GJigtQ4qpQ2JwH1WeC9CiGKkx0AIYW1btNbZQLZSKgvjL3uAnwBfpVQDIBj4SilLqnFd89eWGJkEKKUaAY211lvN2z4FhhS7zhmgVZW9CyHuUFIYCCGs7XKx74uKvS7C+DfHDsg09zZc73fA0fy9AsoLc3E07y+EsCK5lSCEuBXZgNOtHKi1vgAcVUo9AqAMfubN+wF3836ZQJZSqpd52x+vO1UHIPlW2iCEKJsUBkKIm6a1zgB2mB8ffPcWTvFH4Gml1A/APuBB8/r1QO9i+z0FfKCU2knJ3oFQYO0tXFsIUQ6JXRZC1ChKqRXAVK11ajn71AW2Ar2uPMEghLAOKQyEEDWKUqoj0EJrva2cfTyA1lrruGprmBB3CCkMhBBCCGEhYwyEEEIIYSGFgRBCCCEspDAQQgghhIUUBkIIIYSwkMJACCGEEBb/HxuP6jF55I5LAAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "print(\"rmse:\", ca1.rmse())\n", - "plt.figure(figsize=(8, 5))\n", - "ha1 = ml.head(r1, 0, t1)\n", - "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", - "plt.semilogx(t1, ha1[0], label=\"ttim at 30 m\")\n", - "ha2 = ml.head(r2, 0, t2)\n", - "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", - "plt.semilogx(t2, ha2[0], label=\"ttim at 60 m\")\n", - "ha3 = ml.head(r3, 0, t3)\n", - "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", - "plt.semilogx(t3, ha3[0], label=\"ttim at 90 m\")\n", - "ha4 = ml.head(r4, 0, t4)\n", - "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", - "plt.semilogx(t4, ha4[0], label=\"ttim at 120 m\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.title(\"ttim fit exceppt for data of obs1\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".....................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 50\n", - " # data points = 38\n", - " # variables = 3\n", - " chi-square = 2.6352e-04\n", - " reduced chi-square = 7.5293e-06\n", - " Akaike info crit = -445.400159\n", - " Bayesian info crit = -440.487401\n", - "[[Variables]]\n", - " kaq0: 45.0264163 +/- 0.52738242 (1.17%) (init = 10)\n", - " Saq0: 4.4092e-05 +/- 1.4055e-06 (3.19%) (init = 0.0001)\n", - " c0: 349.133120 +/- 26.4954569 (7.59%) (init = 1000)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.714\n", - " C(kaq0, c0) = 0.711\n", - " C(Saq0, c0) = -0.155\n" - ] - }, - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq045.02640.5273821.17127110010[45.02641630853667]
Saq04.40921e-050.0000013.187541e-050.0010.0001[4.409211881296062e-05]
c0349.13326.4954577.588931001e+061000[349.1331197231116]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 45.0264 0.527382 1.17127 1 100 10 \n", - "Saq0 4.40921e-05 0.000001 3.18754 1e-05 0.001 0.0001 \n", - "c0 349.133 26.495457 7.58893 100 1e+06 1000 \n", - "\n", - " parray \n", - "kaq0 [45.02641630853667] \n", - "Saq0 [4.409211881296062e-05] \n", - "c0 [349.1331197231116] " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ca2 = ttim.Calibrate(ml)\n", - "ca2.set_parameter(name=\"kaq0\", initial=10, pmin=1, pmax=100)\n", - "ca2.set_parameter(name=\"Saq0\", initial=1e-4, pmin=1e-5, pmax=1e-3)\n", - "ca2.set_parameter(name=\"c0\", initial=1000, pmin=100, pmax=1e6)\n", - "ca2.series(name=\"obs1\", x=r1, y=0, layer=0, t=t1, h=h1)\n", - "ca2.series(name=\"obs3\", x=r3, y=0, layer=0, t=t3, h=h3)\n", - "ca2.series(name=\"obs4\", x=r4, y=0, layer=0, t=t4, h=h4)\n", - "ca2.fit()\n", - "display(ca2.parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.0026334097681187055\n" - ] - }, - { - "data": { - "image/png": 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SDy2LXLJkCUuWLMHtdlNdXc2VK1c4d+4cp0+fDoSEJUuW6C6QSqlJT4OBGpXU/NmER4Th8/kJDw8jNX92qJs07qIzHUPWSYiKinogJPRtFd0XEvq2il6yZAnp6ekaEpRSk45OPkQnH47W9epWmq5cJ2tZ+rTuLRgNl8sV6Eno2yo6MTExMCdBQ4JSaqLp5EM1blxd9Rz/0R9wo/JxCtdvYvHqtUTFxIa6WZNKdHQ0JSUllJSU4HK5Aj0JZ8+e5dSpUyQmJgZ6EtLS0jQkKKVCRnsM0B6D0bp38wYXDx+govQIHS13iIiKJmflGgrXbyR7xSoioqJC3cRJq39IqK6uxufzkZiYGBiOSE1N1ZCglBoXk6qOgTEmGXgDyAIagI+JSNsg570K/JZ98w9F5HvGmETgaL/T0oB/EZH/ZYz5JPCnwDX7vr8Wkdcf1h4NBmND/H6uV1VQfuw9qk68T4+znei4eHJXr6NwwyYySpYTpuv8h9Tb2xsICTU1Nfh8PhwOxwM9CcaYUDdTKTVNTLZg8CdAq4h8zRjzJWC2iHxxwDnJwBlgFSDAWWDlwABhjDkLfE5EjtjBYJWIfHYk7dFgMPb8Ph9Nly9QcewI1adKcfd0E5c0i/y1Gyhc/xSL8gsx+kl4SL29vVRWVlJWVhYICUlJSYGQkJqaGggJI908SimlYPIFg0pgs4jcMMakAO+KSMGAcz5un/PL9u2/s8/7Yb9z8oB3gAwREQ0Gk5PX7ab+/Bkqjh2h7uwpvB43iXPnUbh+E4UbnmJeZrZ+Eg6iLyT09ST4/X6SkpJYsmQJecmZRL55B7yCiQhj7mtLNRwopYZlsgWDeyIyq9/tNhGZPeCczwMxIvKH9u3fBnpE5M/6nfM7gENEPm/f/iTwVeAOUIXVk3B1iDZ8CvgUQEZGxsrGxsYxfIVqKO6ebmrOnKTi2Hs0XjyH3+cjeVEahRueonDDJmanpIa6iZNaT09PICTU1tbi9/tJkBiyffPJ8S8g57mlJD2TEepmKqWmgAkPBsaYt4GFg9z1ZeB7wwgGXwCiBwSDbhH5er9zyoCfE5Gz9u05QKeIuIwxn8aau/DMw9qqPQah0e1sp/pkKRWl79FcfgVEmJ+9mMINT1GwbiOOufMeeo2ZUo55MD09PVw+dp6LR89yjbv4jZCU4GDJshKKi4sfGG5QSqmBJluPwaiHEowxy4F/E5H8IZ4jHGsew0PfLTQYhF5HawuVpUepOHaEW3XVAKQWLqFww1Pkr91AnOPDP8aZVo55KK5GJ+2Vt2kMu0PVrbpAT0LfcIOGBKXUYCZbMPhT4G6/yYfJIvIbA85Jxppw+Lh96AOsyYet9v1fA1wi8rv9HpMiIjfsv/8k8EURWfuw9mgwmFzablyjovQIFceO0HrtKiYsjMxlj1G4fhO5q9cRHRcHwNkDDZx8sw4RMGHwxM4cVr6YFdrGTwKDDTcMNXFRKTVzTbZgMAf4EZABNAEfFZFWY8wq4NMi8pp93i8A/9t+2FdE5B/7XaMO2CoiFf2OfRXYCXiBVuBX+t8/FA0Gk5OIcKexnorSI1SWHsF55zYRkVFkP76Kwg1PETcrn71/fSVQjnmm9hgEoyFBKTWUSRUMJhsNBpOfiHC9qoIKu0ZCd/s9omJjSS1cScLcZZQ89QSL8uaEupmTmoYEpVR/GgyC0GAwtfh9PpquXKTi2HvUnDqOq7uL2EQH+WufpHDDJlILirVGwkNoSFBKaTAIYioHg/O3z3Pm1hlWLVjFivkrQt2cCef1eB6skeB2kThnHgXrN1K44SnmZ+XoG9xDBAsJxcXFWnFRqWlKg0EQUzUYnL99nl86+Eu4fW6iwqP49pZvz8hw0Mfd003tmZNUlB6h4cIH+H0+Zi9KCxRSSl6kNRIepi8k9FVc9Pv9OByOwOoGDQlKTR8aDIKYqsHg9Uuv840PvoEfP+EmnM8+9lleW/paqJs1KfR0OKk6ceyBGgkLcnIpXL+JgvWbSJwzN9RNnPT6h4Ta2lrdu0GpaUaDQRBTNRj09Rh4/B4iwyJnfI/BUD5UI8EY0oqWULjeqpEQmzi8EsIzvZhSsJCgu0AqNfVoMAhiqgYD0DkGI9V24xoVx45Qcew9Wq83ExYebtVI2PAUuaueICo2btDHaTGl+/rv3aAhQampS4NBEFM5GKhHE6iRcOw9KkqP0NFyh4ioaHJWrqFwwyayV6wiIjIycL4WUxqchgSlpi4NBkFoMJjZxO/nelUF5XaNhB5nO9Fx8eSuWUfRhs2klyzldkOn1WOgxZSGFCwk9E1c1JCg1OShwSAIDQaqj9/no+nSeSpKj1B9qhR3Tw9xSbMoWLeReVkrcfXOIa0gWUPBQzwQEmpq8fl9JMTFU1xirW7IyMjQkKBUiGkwCEKDgRqM1+2m/twZyo+9S90Hp/F5PDjmLaDQrpEwNyNLZ+U/hKvRyfXXP6DRf4f6iNtci2jF6/MRHx9PYWEhxcXFZGVlER4eHuqmKjXjaDAIQoOBehhXdzc1p49TUXqExovnEL+fOWkZ1vLHDZuYvXBRqJs4KTl/fBXnwQYQwEDMs6ncXNhNWVkZ1dXVeDweYmNjAyEhOzubiIiIUDdbqRlBg0EQGgzUSHQ7260aCcfe41rFFQAWLs6jYP0mCtZvJDFZayT0cTU6aXn9EuL1YyLCmPvaUqIzreWhbreb2tpaysrKqKysxO12Ex0dTUFBAcXFxSxevJjIfhNAlVJjS4NBEBoMRk6XSVqcLXeoPH6UimPvcbu+9pFrJExnrkYnrrp2onOSAqFgIK/XS11dHWVlZVRUVNDb20tUVBT5+fkUFRWRl5dHVFTUBLdcqelNg0EQGgxGRksxD671+jUqS0deI0E9yOfzUV9fHwgJ3d3dREREkJeXR3FxMXl5ecTExIS6mUpNeRoMgtBgMDJaijm4IWskPL76fo0E/fQ7LD6fj6amJsrKyigvL6ezs5Pw8HAWL15McXExBQUFxMbGhrqZSk1JGgyCmMrBQDwezASPw2op5uHrq5FQUfoelcetGglRsXHkrVlP4YZNZJQsJyzIjPyZXIZ5IL/fT3NzM2VlZZSVleF0OgkLCyMnJycQEuLj40PdTKWmDA0GQUzVYOBrb6f2hRdJeOopkl7eSdwTT2AmaNmXzjEYOb/PR9PlC1Qc66uR0E2sI4n8tU9SuGETqflFmH5r+7UM89BEhGvXrgV6Etra2jDGkJWVRXFxMYWFhSQmJg75+OHMe1BqutNgEMRUDQaeW7e4841v0HHgLfydnUTMn49jx3aSdu4kpqAg1M1TQXjdburPn6Hi2BHqzp7C63GTOHceBeusGgnzs3L44K1GLcM8DCLCzZs3Az0Jd+/eBSAzM5OioiKKiopISrofqIKtlFBqJtFgEMRUDQZ9/L29dL77Lu1v7qLz6FHweokuKCBp504c27cTuWB+qJuognD3dFNz5iQVx96j8eI5/D4fsxelkVa0htpzsxAzS8swD5OIcOfOnUBIuH37NgBpaWkUFxdTVFRE+PnOB2orOLZk4Xg6PaTtVioUNBgEMdWDQX/e1lac+/bTvmsXvRcvgjHEr1uLY+dOHM8/T9g0GIOdzsMYPR1Oqk+WUnHsPa6WXwYREuakk7t6A6u3b8ExT0PeSLS0tASGG27cuAHAwjkLSL+TSJZ3HrPCE7THQM1YGgyCmE7BoD9XXT3OPbtp37UbT3MzJjaWxOeeI2nnTuLXrcVMwQpzM2mpZEdrC1XH36ei9Ag3a6oASMkvpHD9JvLXPknC7OQQt3BqaW1tpby8nLKyMq5duwbA3FlzKF62hMLCQlJSUrTEtZpRNBgEMV2DQR8RoeeDD2h/cxfOAwfwO52Ez5tL0tZtJL28k+iioinzH+JMXSp579ZNKo8fpbL0CHca68EY0otKKFi/ibwn1hPn0CGGkWhvb6e8vJyKigoaGxsRERwOB4WFhRQVFZGRkaH7N6hpT4NBENM9GPTnd7ut+Qi7dtH53hHweIjOy8WxYydJO7YTmZIS6iYGpUsl4e61q1SWHqWi9Aht15sxYWFkLl1BwfpN5K5eS0x8QqibOKV0dXVRVVVFRUUFtbW1eL1eYmNjyc/Pp7CwkMWLF2vVRTUtaTAIYiYFg/68bW10HDhA+5u76Dl/Howhbs0aknbuJPGFLYQnTM43mOk8x2Ak+gopVZYeofL4Udpv3yI8IoKsFasoWL+RxSvXEBWjxX9Gom//hvLycqqqqujt7SUiIoLc3FwKCwvJz88nLk4rWKrpQYNBEDM1GPTnbmqifddu2nftwtPUhImOJvHZZ3Ds3EnChg0TXkRJjYyIcLO2ygoJpUfpbGu1qi2uXEPhuo1kPbaSyKjoUDdzSvH5fDQ2NlJRUUFFRQVOpzNQK6GwsJDCwsIHlkEqNdVoMAhCg8F9IkLvhQu079qFc+8+fO3thCcn49i6laSXdxJTUjJl5iPMVOL3c62yjIrSo1Sd6Ku2GEvuqrUUbNhE5tIVhEdo0BsJEeH69euBkHDnzh0AUlJSKCoqorCwkHnz5um/DTWlaDAIQoPB4MTtpvPoUas+wo9/jHg8RGVnk/TyThzbdxCVlhrqJqqH8Pt8NF25SGXpUapPHcPV1UVMfAJ5T6ynYP0m0pcsJSxMJ9mNVEtLSyAkNDc3A5CcnByYvJiamkpYvyqWSk1Gky4YGGOSgTeALKAB+JiItA1y3gFgLfC+iGzvdzwb+FcgGfgA+DkRcRtjooF/AlYCd4GfFpGGYG3RYPBwvvZ2nG+9RfuuXfScOQtA7KqVVhGlF18k3KHrwCc7n9dDw4VzVB4/Ss3pE3h6e4hLmkX+2g0UrP9wSWY1PB0dHYGQUF9fj9/vJyEhgYKCAgoLC8nOziZiCi4NVtPfZAwGfwK0isjXjDFfAmaLyBcHOe9ZIA745QHB4EfAf4rIvxpj/ha4ICJ/Y4z5VWCZiHzaGPMK8JMi8tPB2qLBYGTczc04d++m/c1duBsaMJGRJDz9NEkv7yRh40aMzuCe9DxuF/XnzlB57Ah1H5zG63GTMGeuVZJ53UYWLM4bk27xmbYJVE9PDzU1NZSXl1NTU4Pb7SY6Opq8vDwKCwvJy8sjOlrneqjJYTIGg0pgs4jcMMakAO+KyKAF/o0xm4HP9wUDY/2PdQdYKCJeY8w64PdE5AVjzFv2348bYyKAm8A8CfJCNRg8GhGh9/Jlqz7Cvn34WlsJT0oicetLJO3cSeyKFVN+zHUmrIBw93RTe/YUFaVHaDj/AX6fl6QFCylcv4mCdRuZm5H1SD/Hmb4JlMfjob6+PtCb0N3dTXh4ODk5ORQWFlJQUEDCJF35o2aGyRgM7onIrH6320Rk9hDnbubBYDAXOCEiufbtdGC/iJQYYy4DL4pIs31fLfCEiLQMuOangE8BZGRkrGxsbBzz1ziTiMdD57FjOHftpuPwYcTlIjIjg6QdO0jauYOozMxQN3HEZlKVxT69nZ1Uny6lsvQoTZcvIH4/yanpVkhYv5HkRWnDvtbZAw26CZTN7/dz9epVKioqKC8v5969ewCkp6cHJi8mJ2slSzWxQhIMjDFvAwsHuevLwPdGEQzmAccHBIN9IrLUGHMFeGFAMFgjIneHaqf2GIwtX2cnHW8dpH33brpPngQRYleswLFzB46XXiJi9qA/5klnplZZ7NPdfo+qk6VUlh6hueIKiDAvKyfQk5A0f0HQxwd6DHx+3QSqHxHh1q1bgZ6EmzdvAjB//vzA5MWFCxdO+d42NflNxh4DHUqYATw3btC+Zw/OXbtwVddAZCQJmzaRtGMHCU9vJmwSj7dqlcX7rH0bjlFZeoQbNZUApOQVULBuE/nrNpCYPHfQx820OQaPoq2tLRASmpqaEBGSkpICtRK0PLMaL5MxGPwpcLff5MNkEfmNIc7dTL9gYB/7N+A/+k0+vCgi3zLGfAZY2m/y4U+JyMeCtUWDwfgTEVwVFbTv2o1zzx68d+4QlpiI48UXSdq5g9iVKyfljPiZMMdgpNpv36TS3tzpTkMdGENa0RIK128i74kNum/DKHR1dVFZWRkoz+zz+QLlmQsKCsjJySEmJibUzVTTxGQMBnOAHwEZQBPwURFpNcasAj4tIq/Z5x0FCoEErH0a7/AAACAASURBVOWHvygibxljcri/XPEc8LMi4jLGxAD/DDwGtAKviEhdsLZoMJhY4vPRdfwEzt27cB56G+nuJnLRIhw7d5C0cyfROTmhbqIaptbrzda+Dcfeo9XetyGjZDmF6zeRu2ad7tswCi6XK1Ceubq6mt7eXsLCwsjKyiI/P5/8/Hydl6BGZdIFg8lEg0Ho+Lu66Dh8mPZdu+kqLQW/n5iSEqs+wratRMyZE+omqmEQEVqaGqjo27fh1k3CwiPIWvE4hes2krPyCaJ1j4FH4mp00lPbxp34Lurbm6mqqqKlxZpLPXfu3EBISE9P1yEHNSIaDILQYDA5eG7fxrl3H+27d+EqK4fwcOKf3GBt6vTMM4TF6oZAU4GIcKu22goJJ96n824L4ZGRZC1fScHaDRoSRsDV6KTl9UuI14+JCGPua0uJznTQ2tpKVVUVVVVVNDQ04Pf7iYmJITc3l/z8fHJzc3WzJ/VQGgyC0GAw+biqq61NnfbswXvjBmHx8SRu2ULSyzuJW7NmUs5HUB8mfj/XqyqoOvE+VSeP0dl6V0PCCDh/fBXnwQYQwIBjSxaOp9MfOMflclFXVxcICl1dXRhjSE9PD/Qm6D4OajAaDILQYDB5id9P96nTtO/eRceBt/B3dRGxcCFJ27fh2LGTmIL8UDdRDZOGhJEbqsdgKH6/nxs3bgRCwo0bNwCYNWtWICRkZmYSqbulKjQYBKXBYGrw9/bS+c471qZOx46B10t0YSFJO3bg2L6NyAXB19WryUNDwvC5Gp246tqJzkkKGgoG43Q6qa6upqqqitraWrxeL5GRkeTk5ASCQmJi4ji1XE12GgyC0GAw9XhbW3Hu20/77l30XrgIxhC39gmSduwkccvzhGup2SlDQ8LE8Hg8NDQ0BHoT2tvbAWvr6L6QkJKSortCziAaDILQYDC1uRsaaN+9h/bdu/E0NWFiYkh85hkcO3eQsGEDZop3m86kWgoTERK06JI1QfT27duBkHD16lUAEhISyMvLIz8/n5ycHN3waZrTYBCEBoPpQUToOX8e5+7dOPftx3fvHuGzZ+PYupWknTuIWbZsyk3Amon7NfQZj5Aw0zd2GkpXVxc1NTVUVVVRU1ODy+UiPDz8gZoJs6dIKXM1fBoMgtBgMP2I203n+8do37WLznfeQdxuojIzrSJKO3YQlZER6iYOy0zfr6HPWIUE3djp4Xw+H01NTYHehLt3rW1m5s2bFwgJaWlpWjNhGtBgEIQGg+nN19FBx8GDtO/aTfepU4FNnZJe3kniiy9O6k2ddL+GD3sgJJx4n8621vshYd2TLF65hqjYwUOCbuw0ci0tLYEJjI2Njfj9fmJjYwM1ExYvXqw1E6YoDQZBaDCYOT60qVNEhLWp084dJGzeTNgkrEM/k+YYjNSjhASdY/Doent7qa2tpaqqiurqarq7uwFYtGgRubm5LF68WHsTphANBkFoMJh5RARXZSXtb+66v6lTQgKJL75A0o6dxK1epUWUppjR9CSokeupv0fTxVqaaaGhpZnm5mZEhOjoaLKzswNBQecmTF4aDILQYDCzic9H98mTtO/aTcfBg/i7u60iSju249ixg5h8LaI01WhIGF+DFV7yz4+kvr6empoaamtrA8shk5OTAyEhKytLVzpMIhoMgtBgoPr4e3roOPwO7bt30fX+MfD57CJK23Fs20bkwoWhbqIaoaFCQubSFeStWU/OyjW6VfQIPaxUs4jQ0tJCbW0tNTU1NDQ04PV6CQsLIyMjIxAUFi5cOOVWCk0nGgyC0GCgBuO9e9fa1GnPHnov2kWU1qwhacd2ErdsIdwxsip0KvQCIeHkMapPldLRcgcTFkZaUQl5a9aRu3odiXPmhrqZk95ISzV7PB6ampoCQeH27dsAxMfHs3jxYnJzc8nJySFBC5NNKA0GQWgwUA8TKKK0ZzeexiZMVBQJmzeTtHMH8Zs2ERYVFeomqhESEW7X11J96jjVp0ppvWYV+UnJLSB3zTry1qxjdkpqiFs5eY22VHNtbW3gq6enB7CqMPYFhbS0NCIiIsaj6cqmwSAIDQZquESE3kuXaN+1G+e+ffhaWwlzOHC88AKOHduJW6WTFqequ9euUnPqONWnjnOrrhqAuemZ5K5ZT96adczLzNZu73HQt/FT39yEq1evIiJERUWRnZ0dCArJycmhbuq0o8EgCA0G6lGI10vX8ePWpMW330Z6eohISbF3ftRJi1OZs+U2NadPUH2qlGvlZYj4SZq/wA4J61mUV6ABcJz09vY+MInx3r17AMyePfuBSYwxk3Bp8VSjwSAIDQZqtPzd3fcnLR4rtSYtFhTcn7SYkhLqJqpH1N1+j5ozJ6k5VUrjpQv4fV7iZ80md/VactesJ714KeHa5T0uRIS7d+8+MInR4/FgjCE1NZXs7GxycnJIT0/XYYdHoMEgCA0Gaix5797Fuf/Agzs/rl6NY8d2HC+8oJMWpzBXdxd1585Qc7KUuvNn8LpcRMfHs/jxNeQ+sZ6sZY8RGa2fZMeL1+vl6tWr1NfXU1dXx7Vr1xARIiIiyMjIICcnh5ycHBYuXKi7RA6DBoMgNBio8eJubLQrLe7G3diIiYwkYfNmHDu2W5UWddLilOVxu2i8eJ6aU6XUnjlJb1cnEdHRZK9YSd7qdWQ/vpqYeJ1lP556e3tpbGykrq6Ouro67ty5A0BMTEygNyE7O5s5c+bo/JBBaDAIQoOBGm8iQu/ly7Tv3o1z7z58d+/akxa34Ni+Y9pVWpxpZZx9Xi/NZZepPn2cmtPH6WprJSw8goyly61lkKvWEpc065GurSWch6+joyPQm1BfXx8osuRwOMjKyiIzM5OsrCySk5M1KDAGwcAYMxtYBPQADSLiH9smho4GAzWRrEmLJ3Du2Y3z0NtId7c1aXHbVmvSYkFBqJs4KjN5q2iwaiXcqKkMLINsv3UTjCG1oJg8e4WDY978YV1Lt4l+dCJCa2trICg0NjbS1dUFQEJCQiAkZGZmMm/evBkZFB4pGBhjkoDPAB8HooA7QAywADgBfEtEfjwuLZ5AGgxUqPi7u+l458c4d++m8/33rUmL+fk4dmwnafv2KTlpUbeKvk9EaGlqoPpUKdWnjtPS1ADA/OzFdkhYz5y09CEfr9tEj52+aoyNjY00NjbS0NBAR0cHAHFxcWRmZgbCwvz582fEHIVHDQaHgH8CdovIvQH3rQR+DrgkIv8wxu2dUBoM1GTgbW3FuX8/zt176Dl/HuDBSYtJU+OTom4VPbS2m9ftWgml3KiuBCB5UZpdUGk9C3JyH/jkqttEP2g0RZUGEhHa2tpoaGgIhIW+pZExMTFkZGQEehQWLlw4LXeM1DkGQWgwUJONu6nJmrS4ew/u+nqIjCRh40aStm8j4emnCYuNDXUTg5ppcwweRUdrC7WnT1J9qpSrZZcQv5/EOfOsOQlr1pFaWExYWLjOMbCNtAzzo7h3716gN6GxsZHW1lYAoqKiyMjICPQoLFq0aFoEhbGYY7AMyAICi0VF5D/HqoGhpMFATVbWpMUrOPfuxblvH97btzFxcSQ++yyObVtJ2LABExkZ6maqUerpcFJ79hQ1p4/TcOEDfB4PsY4kclc9Qd6a9aSXLCdihv+cH7Zx07g8p9MZ6E1obGwMrHqIjIwkLS0t0KOQmppK5BT8+YwqGBhjvgMsA64AfZMORUR+4REbkwy8gRU0GoCPiUjbIOcdANYC74vI9n7Hvw+sAjzAKeCXRcRjjNkMvAnU26f+p4j8wcPao8FATQXi89F95izOPXtwHjyIv72d8FmzSHzhBZK2byN25cpptbJhpnL39lB/7izVp0qpP3cad08PUbGxZC57jMUrnyD7sVUzcjfIiegxeJjOzk6ampoCPQq3bt0CIDw8nLS0tECPQlpaGlFTYCnyaINBmYgUj2Fj/gRoFZGvGWO+BMwWkS8Oct6zQBzWG3//YLAV2G/f/AFwRET+xg4Gn+9/7nBoMFBTjbjddL5/DOfevXS8845VjnnhQhxbt+LYtpWY4uIZOct6uvF6PDRdOk/NmRPUfXCarrZWMIZFeYXkrFzD4sdXMyc9c8b8rB91jsFYzk3or7u7m6ampsDww82bNxERwsLCWLRoUaBHIT09fVKWcB5tMPgH4OsiUjZGjakENovIDWNMCvCuiAy6Ruthb/bGmM8Bc0XkyxoM1EwUWNmwZ4+1ssHrJSo7G8e2bTi2bSU6OzvUTVRjQPx+bjfUUXv2JLVnT3G7vhaApPkLyHl8DTkr15BeXEJ4xNTr0h5PE9nT0Nvby9WrVwM9CtevX8fvtzrZFyxYQFpaGunp6aSnp0+KWgqjDQabgN3ATcAFGKyhhGWP2Jh7IjKr3+02EZk9xLmbGeLN3hgTCZwE/qeIHLXP/Q+gGbhuP+7KENf9FPApgIyMjJWNjY2P8lKUmlS8bW10HDyEc+9euk+fBhFilizBsX07jq0vEblgQaibqMZIR2sL9R+cofbsSZouXcDrceuQwyBCMTehj9vt5urVq4Gv5uZmXC4XYC2R7B8UFi1aNOHDD6MNBjXArwGXuD/HABEZ8t3UGPM2sHCQu74MfG+MgsG3gS4R+V/2bQfgF5FOe7jhr0Qk72GvT3sM1HTkuXUL5779OPfsoffKlft7NmzfhmPLFsJnPVolvqlqOq+U8Lh6abp8gbqzp6n74BSd/YccHl/N4pVrZtSQQ3+hnpvQfxgjMj2BO3fu0NzcHAgLd+/eBcAYw8KFCwNBIS0tjVmzZo3rz2y0weAdEXlmDBsz6qEEY8zvAo8BPzVUFUZjTAOwSkRagrVHg4Ga7lz19Tj37sO5d+/95Y8bNuDYvp3EZ54mLC4u1E0cVzOpGqOIcLu+9kNDDo55C1i8cg05j68mrXjpjFrlMF5zDIbzvA8LJd3d3Q8EhWvXruHxeACrQmNfSEhPTyc1NXVMl0kOFQyGu09lhTHmB1jDCa6+g6NYrrgLeBX4mv3nmyN5sDHmNeAF4Nn+ocAYsxC4JSJijFkDhAF3H7GNSk0b0dnZzPvsZ5j7mV+lt6wsEBI6330XExtL4jPP4Ni2jYQnN2CmwGzqkTpz6wxunxs/fjx+D2dunZm2wcAYw4KcXBbk5LL+o594YMjh0uG3OHdgN5ExsWQtnzlDDtGZjglfwQDgqmtHvH4QEK/fCicD2hEXF0d+fj75+fkA+Hw+bt++HRh6uHr1KuXl5QB84QtfID4+ftzbPdweg38c5PBolivOAX4EZABNwEdFpNUYswr4tIi8Zp93FCgEErDe4H9RRN4yxniBRqDDvuR/isgfGGM+C/wK4MXa0+HXRKT0Ye3RHgM1E4nfT8/Zs7Tv2UvHgQP42tsJS0rCsWULju3bp9XGTlqN0WINOVyk7uypB4YcUvIKWPz4mhENOWjhpYcbq2GM1oobNF9poGDVkjENOFr5MAgNBmqmE7ebztJSnHv30XH4sLWx0/z51vLH7duJWTL1lz9O5zkGj+L+kIMVEm7V1QDWkEPfvIShhhx0c6fhG+0wxnjOkXjUvRJ+C2ujpNYh7n8GiBORPWPSyhDRYKDUff7ubjrffZf2PXvpPHoUPB6iMjOtlQ3bthGdo8sfp6PBVjn0DTnkPG7NTegbctDNnSbOeK6qeNQ5BpeA3caYXuAD7u+umAesAN4G/mhMWqiUmhTC4uKsnoKtW/G1t+M8eBDn3n20fOtbtHzzm8QUF1s1Era+NCV3f1SDS0yey7LnXmTZcy9+aMih+mTpA0MOSQuKCQs3+P1CeHgYqfmDLipTYyA6JwkTERboMYiegJ6Z4c4xyAM2AClYY/flWNUGe8a3eRNDewyUejjPrdt0HNhP+5699F66BEDcqlU4tm8j8YUXiJitbw7T0VBDDvGz5zFrYRF5a9aw7Jm1RE7Cyn7TxXitqtA5BkFoMFBqZNyNjbTv3Ytzz17cdXUQEWEvf9xG4jPPEDYBM6dVaHS23qXu3Glqz56yhhzcLsIjIkgtXELWipVkL398xtZMmGpGW8cgH/g8H95dccxqG4TSmAaDXiec/DtY/tMwK2NsrqnUJCUiuCoqcO7dS/vefXhv3MDExJD4zNM4tm0jfuNGwqbh8kdl8brdNFdcoeHCBzScP8vd5iYAEpLnkLX8cbKWP07G0hXEJiSGuKVqMKMNBheAvwXOAr6+4yJydiwbGSpjGgwqD8APf9r6e/YmWPEJKNoBUfoJSk1v4vfTc+4c7Xv20HHgLXxtbYQlJFhbRG99ifh166ZljQR1X8fdFiskXPiAxkvncHV1YUwYC3PzyFq+kqzlj7MwN4+wsLEr0qMe3WiDwVkRWTkuLZsExnwooa0RLvwrnP8+3GuEqERY8hNWSMhYC9rFpqY58XjoOnEC5779dLz9Nv6ODsKSkkh87lkcL20lfu0TmIjh1ldTU5Hf5+NGTZUdFM5ys7ba2rsjIZHMpSsCPQoJyXNC3dQZa7TB4PeA28B/8WDlw0GXMU414zbHwO+HpuNw/gdw5b/A0wXJObD8Z2D5KzBrYjbyUCqU/G43XceO4dy/n87D7+Dv6iJ89mwSt2zB8dKLxK1ejRnDMq9qcurpcNJ48RwNF87RcOEsXffaAJibkUXW8sfJXrGSRQXFM6pUc6iNNhjUD3JYRCRnLBoXahMy+dDVCeW7rJDQcBQwkPOU1YtQuB2ipneteqUA/C4XXUePWj0J776LdHcTPneuVW1x60vEPv74tKm2qIYmItxprA8MO1yrKMPv8xIRHU3GkmVWb8KKlcxeuOiBx2m1xbGlqxKCmPBVCW0N/YYamiDaAUt+0goJ6Wt0qEHNCP6eHjrfO2L1JLz3HtLbS8T8+SS++AKOl14idvlyDQnMjIqN7t4erl65aE9i/IB7t24AkLRgYWBuQnR8Jvu+VaHVFsfQaHsMjgJHgKPAMRHpeMhDppSQLVf0+6HxmNWLUPbf4OmG5MWw4mdg+cchKXXi26RUCPi7uuh4912c+/fTdeQo4nYTkZKC48UXcWx9iZiSkhm5/G0m7QrZX9vN64GVDlevXMLj6sWEhWPCUgiLzCI8KoO1P7GWVVunRad1yIw2GOQATwIbgbVY8wyOisjnxrqhoTCWweBsYxsn6u6yNmcOKzNHUPDF1QFl9lBD4/uAgcVP20MN2yAydkzap9Rk5+vspPOdd3Du20/nsWPg8RCZlobjpRdxvPQS0UVFMyYkvH7pdb7xwTfw4yfchPPZxz7La0tfC3WzJpTX4+F6ZRlX3jtORelJ/N47AETFxJGxdBkZJcvJKFlOcmr6jPm9GCujHkowxqQAT2GFg6eBJhF5cUxbGSJjFQzONrbxiddP4Pb6iYoI4/uvrR1ZOOjTWmcPNfwQ2psgOglKfhJW/CykrdKhBjVj+Nrb6Tj8jtWTcPw4eL1EZWaS+NKLOF7aSnR+3rR+M9BdIR90s66duvONhHGN9tvVNF2+gPPOLQDiZyeTseR+UHDMmx/i1k5+o+0xqAVagB9gDSecFxH/mLcyRMYqGHzzxzV8/WAlfoFwA7+2pYDPPJ376Bf0+63eg/M/gLI3raGGObnWUMOyV3SoQc0o3rY2Ot5+m479++k6cRL8fqIWLw4MN0QvXhzqJo6LmTDHYDTab9+k8dIFmi5f4OqVi3S33wNg1oIUMkqWk15ihYW+DaDUfaMNBv8TayghHagA3sPaK6F2rBsaCmPdY+Dx+okcTY/BYFwdcOW/4cIPrXkJOtSgZjDv3bt0HDyIc/8Buk+fBhGi8/MDww1RWVmhbqIKARHh7tVGmi5foOnKRa5euYS7pxuAeRlZpNu9CWlFJUTH6UqwMVmVYIxJAH4eqzxymohMi8XHk2KOwQiuvWluJ0tb9vUbatBVDWrm8ty+TcdbB3Hu30/PBx8AEF1chOPFl3BsfYmotLQQt1CFit/n41ZdjRUULl/gemU5Xo8bExbGwsV5ZJSsIKNkOYvyC4mwq3LOpCWRo+0x+DpWj0ECcAJ7hYKI1I11Q0NhKmyiNOj8hfSkDw81BFY1vAJJ+h+imlk8N2/iPHAA5/799F64CEDM0qU4XnoJx4svELlo0UOuoKYzr9vN9aoKOyic52ZtNeL3ExEZxaKCImanFlB1KhJhHhGREWOyJHIyB43RBoOPYg0d3BqPxoXaVAgGD52/MNiqBi2gpGYwd/M1Ot46gHPffnqvXAEgdsUKErdsIXHL89qToHB1d9Ncftman3D5AneaGux7IgmLXETm0qWs2fkkCxfnB3oURuJmXTtv/sW5SVt7YSxWJewENtk33xOR3WPYvpCaCsFgRPMXWuutVQ0XfmAVUIpKtFc1fALSn9ChBjXjuJuacO4/gPOtA7jKygF7uGHLFhK3bCE6R9fDK2i42MSeb+zD625GvNcCSyPDIyNJyS0grWgJqUUlLMovJCrm4fO6zh5o4OSbdYiACYMnduaw8sWsEbdrvHodRttj8FVgDfB9+9DHgTMi8ptj1sIQmgrBAB5h/oLfD02l9l4N/617NSgFuK9epePQ23QcPEjP+fMARC1eTOKW53Fs2UJ0YeG0XgKpguv/Jpw0P4zrlWU0l1+hufwyt+pqEL8fExbGguzFpBaVkFZUQmph8aBbSwd6DHx+wsMfrcdgPHsdRhsMLgIr+pYoGmPCgXMismxMWhdiUyUYjMpgezUEtoXerttCqxnJc+uWFRIOHbJWN/j9RKank/j88zi2PE/MsmVallkFuHt7uF5VwbXyyzSXX+FGTSU+jwewNoNKK1piB4UlJMxOBkb/aX+seh0GMxbBYHPfborGmGTgXQ0GU9Sg20K/bG8LvU6HGtSM5G1tpePwYToOHqLrxAnweIhYsIDE554jccsW4lat1F0g1QO8bjc3a6sCPQrXK8vxuHoBmJ2yiNTCEjssLMExb8Ej9USNRa/DUEYbDD4OfA34MWCw5hr8poj865i0LsRmXDDoM9i20LOz769qmJUR6hYqFRI+p5POd9/FefAgXUffR1wuwpOTSXz2WRK3PE/8E09gHmEymprefF4vdxrqaC6/THPFFa6VX6G3qxOAuKRZLMovYlFBEYvyClmQkzvsCY2Tco6BfYEUYDVWMDgpIjfHrHUhNmODQX+uTijfbU1YrD9iHcvaaPUiFO/UoQY1Y/m7u+k8cpSOgwfpfPdd/N3dhCUmkvjM0yQ+/zzxTz5JWExMqJupJiHx+2m52sj1qnKuV5ZzvaoisHNkWHgEC3IWW2Ehv5BF+UUkJM+Z0PY9UjAwxjwe7KIi8sEYtC3kNBgM0NYIF9+whhraGiAqAYp/AlZ8HDLWg465qhnK73LRVVpKx8FDdLzzDv72dkxcHAmbNuHY8jzxm54iPEFDtBpad/s9rldVWGGhqpxbtTV4PW4AHPPmk5JnhYTUgiLmZmQRHhExbm151GDwY/uvMcAq4AJWj8EyrF6DJ8ehrRNusGDg8Xhobm6mt7c3RK2aJLwucHdZxZPED2ERVu9BVLz19xCIiYkhLS2NyMjIkDy/UgDi8dB16hQdhw7R8fZhfC0tmKgo4jdssGolPL2Z8FmzQt3McaN7OIwNn9fD7YY6rldWcL26guuVZXS23gUgIjqahYvz7F6FIjKXrnikegpDGe0cg38FviIil+zbJcDnReSTY9bCEBosGNTX15OYmMicOXN06RKA3we97dDdCu4O61hUPMQmQ+ysCQsJIsLdu3fp6OggOzt7Qp5TqYcRn4+ec+foOHQI58FDeG/cgIgI4tessULCc88SMXduqJs5Zvp2fXT73ESFR834XR/HmrPlDjeqK+zhh3JuN9Th9/n41X/44aDLIh/VaIPBeRFZ8bBjI2hMMvAGkAU0AB8TkbZBzjsArAXeF5Ht/Y5/F2sL6Hb70CdF5Lyx3sH/CtgKdNvHHzrcMVgwKC8vp1DXMw/O64aeVisk+FyAgZhZEJcM0YnjvqpBRKioqKCoqGhcn0epRyEi9F6+bG3ydPAgnsYmMIbYlY9bBZWef57IlJRQN3NUXr/0Ot/44Bv48RNuwvnsY5/ltaWvhbpZ05bH1UvL1UZScgvG9LpDBYPhfswrN8a8DvwLIMDPAuWjaM+XgMMi8jVjzJfs218c5Lw/BeKAXx7kvi+IyL8POPYSkGd/PQH8jf3nI9FQcF+Xy0uXy0t8dATx0VGQuBASFlhDDN2t0NMGvW0QFglxsyF2DkSOz4Qs/bmoycwYQ+zSpcQuXcq8X/s1XFXVdBw8SMfBg9z6o69y64++SszSpYGCSlGZmaFu8oitWrCKqPAoPH4PkWGRrFrwofcWNYYio2PGPBQEM9wegxjgV7hfEvkI8Dci8kgD8MaYSqy6CDfs1Q7visigr9oYsxlr2GJgj8GegcHAGPN39rV+OPB5grVnqB4D/URq6XJ5qW/pQkQwxpA9N5746AGZUvz3hxpcTutYZJzVixAzG8LHdqhBfz5qKnLV1weqLvZevgxAdH5+YP+G6Ly8KRN8dY7B1DdUj8Fwp5evB/5ORH7S/vqLRw0FtgV9b9b2n/Mf4RpfMcZcNMb8hTEm2j6WClztd06zfexDjDGfMsacMcacuXPnziM8fWg0NDRQUlIyptc8f/48+/btG/S+U6dO8cTqlXxky5N8ZMuTvL1vN10uLwAHDhygoKCA3NxcvvbHfwKxs2HOYlhQAo5UEIH2Zrh1GVrrrOBgFc9UakaKzs5m7qd+iex//zdyD7/Ngt/8EmGJibR885vU73yZupe2cvvrf07PpcsMdyl5qKyYv4LXlr6moWAaGu7HuE8Cf2uMuQsctb/eH2xeQB9jzNvAwkHu+vJIGzmI3wRuAlHA32MNQ/wB1oqJgQb91yUif28/llWrVk3uf4Hj7Pz585w5c4atW7d+6L6SkhKOHT/J1Xsubt+8wUdf2MjP/8xH8Pl8fOYzn+HQoUOkpaWxevVqdu7cSXFxMYRHQsJ868vd/unVGwAAIABJREFUbc1H6GmzgkFYhBUgYpN1x0c1o0WmppL86qskv/oqntu36Tx8mI5Dh7j7ne9w99vfJmJRCo7nnydxyxZiH3tMSzOrCTOs3zQR+X9FJB/4f7A+hX8TCPoxW0SeE5GSQb7eBG7ZQwh9hZNuj6TRInJDLC7gH7E2eMJuW/+dgdKA6yO59micbWzjmz+u4WzjkHlpRP78z/+ckpISSkpK+Mu//MvAca/Xy6uvvsqyZcv4yEc+Qnd3NwBf+tKXKC4uZtmyZXz+85//0PVOnTrF+vXreeyxx1i/fj2VlZW43W5+53d+hzfeeIMVK1bwxhtvPPCYuLg4kuJjyJ4bT2IkhIeHER8dwalTp8jNzSUnJ4eoqCheeeUV3nzzzQ895+YtW/nc732dTR/7DEXPvMLpy7X81MdfJa+gkN/63Keh8zb4PGPy/VJqqoqcP5/ZH/84Gd/5DnnvHyXlK18hJi///2fvzuOqLNPHj39uOCAiqIC54g5u7G6AuKEpouSWpTUWZJQ5lWNNGjXW11/LZNrYMuY0mgk6LpULmablhqK5oZISLrjgniYKgqhs9++P53hcAFcQ0Ov9ej0vz3mW+7kfjnou7u3i3Jy5HP7LUFI6d+HE2LFkrlpFgfnfuxCl5bZaDJRSQ4FOgBdwBpiM0WpwtxYD4RjLLIcDhb9Rbl6fOubxCQroDyRdU+4r5umV/kDGrcYXlJQraZFz8gqwvVVa5Nspb9s2ZsyYwebNm9Fa4+/vT5cuXXBycmLv3r1Mnz6doKAghg0bxpQpUxg2bBiLFi1iz549KKVIT08vVGaLFi1Yt24dJpOJlStX8vbbb7NgwQLee+89EhISmDx5cpF12bx5M8OGDePw4cPMmjULk8nE8ePHqV//agzm6urK5s2bi7ze1taWdevW8fnnn9Pv2b+ybctmnO0VTT3b8trzg3Fxrg6VqppnNVSTBZTEQ83k5ET1xwdS/fGB5GdlkRW3lsxVK8lc/jMZ8xegKlWiSkAADt264dC1Kza17qYnVoji3W5XwmfAAeArYI3WOvUe7zse+E4p9TxwBHgCQCnVFnhJax1pfh8PtAAclFLHgOe11j8Ds5VSj2B0HSQCL5nL/QljquJ+jOmKz91jPW/bpoNp5OQVUKAhN6+ATQfT7ikwWL9+PQMGDKBKFWMVtYEDBxIfH0/fvn2pX78+QUFBAAwdOpQvvviCUaNGYWdnR2RkJH369CEsLKxQmRkZGYSHh5OSkoJSitzc2/tN3d/fn99//53du3cTHh5OaGhokf2fxQ2a6tu3LwBeXl54eHhQx9UIKJq4NePoRXtcHGpC9jljlUVlbXQ12DsbgxcryEAsIUqDtYMD1cL6UC2sDzonh+yEBDLXxJG1Zg1Za9cCYOfhgUO3YByDg6nUsmWFGbwoyq/bCgy01jWUUh4YsxI+VEq5A3u11s/czU211mlA9yL2JwCR17zvVMz13YrZr4GX76ZO9yqgiQu2Jity8wqwMVkR0OTe1ry+xYqUhd6bTEbz/qpVq5g3bx6TJ09m9erV1533zjvvEBwczKJFi0hNTaVr1653VKeWLVtSpUoVkpKScHV15ejRq+M8jx07Rt26dYu8rlIlY2yolZWV5fWV93nK2hio6FgXLmdeXR8h+wxYVzIChMrOYJKENeLhpmxtqdKhA1U6dEC//RaXU1LIMgcJZyZ/yZl/T8ZUuzYOwV1xDA7G3t8fq2v+vT0IZCbE/XG7XQlVgQZAQ4xFiaoBMrz8Gm0aOjE7MoBNB9MIaOJyT60FAJ07dyYiIoKoqCi01ixatIhZs2YBcOTIETZu3EhgYCBz586lY8eOZGVlkZ2dTe/evQkICMDNza1QmRkZGdSrZ0zSiI6Otux3dHQkMzOzyHocOnSI+vXrYzKZOHz4MHv37qVRo0ZUr16dlJQUDh06RL169Zg3bx5z5sy5+wdWCuyqGlu1fGOw4sWzkHkSMk+Sb+PABeuqmOydsLeTIEE83JRS2DVrhl2zZtQY/iJ5aWlkxa0lK24NGbE/kD53npHDIagDDl2DcejaBZPL/U3QU9JktcX753a7EtZfs03WWh8rvSpVXG0aOt1zQHBF69atiYiIoH17Y1xlZGQkfn5+pKam0rJlS2JiYhg+fDju7u6MGDGCjIwM+vXrx6VLl9Ba8+mnnxYqc8yYMYSHhzNp0iS6dbva6BIcHMz48ePx9fXlrbfeYvDgwZZj69evZ/z48djY2GBlZcWUKVOoYV7adfLkyYSEhJCfn8+wYcPw8PAokWfHyhqq1DC2vMvkZJ6B7LNUVVnkXzxJrl11yLtkpI2W8QhCYHJxsYxLKLh8mezNm8lcs4as1WvIXLHSWHnRxweH4GAcuwVj6+ZW4bocEk4lkJOfQwEF5BbkknAqQQKDUnLbaZcfZLLAUfl2+vwlTp2/hD2XcFJZVFcX2Hv4D1pufB18BoP3EHikWVlXU4hyR2vN5d27LUHCpd9/B8DG1dUyLsG+bVtUBUhIdqXF4Mpqi9JicO/uNVfCI8AYwAMj0yJQfF9/RSOBQflWaOVFl8oc2buLlrs+ggOrjEWT6rUBn6fA83FjXIIQopDcU6cs4xIubNqEvnwZKwcHHDp3wiE4GIdOncp1Rsj7McbgYRrHcK+BwS8YSY/ewJgBEA78qbUuKr9BhSOBQfl3fa4G09XPJ/MU7PoefpsHp3YZuRqahRhBgntPGbQoRDEKsrO5sHGj0ZoQt5b8M2fA2hr71q2vdjk0alTW1byvHrZxDPeaRMlFaz1dKfU3rfVaYK1Sam3JVlGI4l0JCApxrAUdXjG2P3YZAcLO72DPEmPao+cgI0io11qmPgpxDSt7exy7d8exe3d0QQGXdu0ygoQ1cZyeMIHTEyZg27ixJUio7OuLMt2f9OplRcYxGG73U74y4f2kUqoPxmqCrqVTJSHuUm0vY3v0/8HBNfDbXNgxC7ZOAxd38BkC3oOhev1blyXEQ0RZWVHZx4fKPj7UHDWK3OPHLeslnJ01i7PffIN1tWpU6dIZx27dqNKxI9YODmVd7RJXGlkjK2LXxO12JYRhrHRYH/g3UBX4f1rrxaVbvftDuhIqntv+fC5lQPIPRkvC4Q2AgsadjFaElo9BJcdSr6sQFVl+VhYX1m+wLKqUn54ONjZUadfOGJfQuVOFTB1dnJL8Ii/vXRN33ZWglLIG3LXWS4AMILgU6idE6bCrBq2fNbazh4xuht/mQuwIWPp3IzjwGQKNuxjTJIUQ17F2cKBqrxCq9gpB5+dzMTGRrDVryFy9hlMffsipD8GmQQMcOnakSqeOVGnfHivziq0VkW9N3xL78q6oXRO3nASutc4H+t6HuohrpKenM2XKFMv71NTU6xYQSkhIYOTIkSV+39jYWJKTk4s89tVXX+Hl5YWvry8dO3a87ryPPvoINzc3mjdvzs8//1zi9SoRzo2h65swcgcM+8XoVti3HGYNgE89YcX/wek9ZV1LIcotZW2NfZs21HzjDZr+tJSmv/xMrXffoVLTpqTHxnJsxF/ZFxDI4eeeI236N1zat6/cp48uTVe6JqyVdYl1TdwPt9uV8CHGaoffAheu7Ndaby+9qt0/5bErITU1lbCwMJKSjPxQcXFxfPLJJyxZsqRU7xsREUFYWBiDBg0qdOz8+fNUrVoVgMWLFzNlyhSWL19OcnIyTz31FFu2bOHEiRM8+uij7Nu3D2vr0vsNvMQ+n9xLsG+Z0dWQsgJ0PtTx5UiDfqy06ohPC/cSW7RKiAdZQU4OF7dvJys+ngvx67m8bx8Aplq1qNKpIw4dO1GlQyDW5v9DHhbleYzBvU5XXGN+eeVkhZGaQNYxKCVX0hg3b96cHj16EB8fz+7du2ncuDHh4eH4+flZAoVx48Zx6NAhTp48yb59+5g0aRKbNm1i2bJl1KtXjx9//BGbGxYwmTZtGlOnTiUnJwc3NzdmzZpFYmIiYWFhVKtWjWrVqrFgwQKaNm1aZP3mzp3LzJkzWbZsGR999BEAb731FgAhISGMGzeOwMDA665xcHDg5ZdfZuXKlTg5OfHPf/6TMWPGcOTIET777DNLsqXbUSqfT9afkDSf7C2zsD/7O3naivX40KDrMJp0fAJsKpfs/YR4gOX+8QcXNmwgK349F379lYLz58Ha2liBsVNHqnTsiJ2HB0pWLy0zdzXGQCn1uvnlEoyg4Nr5Xg9P+9CyKGMqXEmq7QWh44s9PH78eJKSkkhMTAQKtxjExcVdd/6BAwdYs2YNycnJBAYGsmDBAiZMmMCAAQNYunQp/fv3v+78gQMH8sILLwAwduxYpk+fzquvvkrfvn2LbTEA+PLLL5k0aRI5OTmWJE3Hjx8nICDAco6rqyvHjx8vdO2FCxfo2rUrH3/8MQMGDGDs2LGsWLGC5ORkwsPD7ygwKBUOj0DACGZc7MGPK1bS32o9/aw3UGftq7DpH9Cqr7HKYsMgWYpZiFuwqV2b6o8/TvXHH0fn5XFx5y4urI8nK349f37xb/78/AusnZyoEhSEQ+dOVAkKqvD5HErL/W51uNXgwytDtpsD7YAfMIKDx4B1pVgvcYdCQ0OxsbHBy8uL/Px8evXqBRipjlNTUwudn5SUxNixY0lPTycrK4uQkJDbus/LL7/Myy+/zJw5c/jggw+IiYm57RTMtra219WrUqVKljoXVceyEtDEhX9bN2RiXn2+UE8R21vT7I+l8Hss7PgfVKsPXk8YgxYfaV7W1RWi3FMmE/at/bBv7ccjI0eSd/YsFzb8agkUzpt/4bHz8DC6HTp1orKPzwO/bsLtKIuZDTf9qWut/x9YVj5srbXONL8fB3xfqjUrT27ym315cW1qYxsbG8sXs5WVFXl5eYXOj4iIIDY2Fh8fH6Kjowu1QNzKkCFDGDFiBMBtp2C+sV7X1rmoOpaVGzNlNmvoBIRBn3/BnqWwcx5s+AzWT4I6vkaA4DnIaHEQQtySydmZao+FUe2xMGNxpd27uRC/nqz18aRN+5q0r/6LlaMjVQICLIGCTZ06ZV3tMlEWMxtuNxxrAORc8z4HI/2yKCU3pkK+WWrku5GZmUmdOnXIzc1l9uzZlnTMN7tPSkoK7u7uACxdutTyum/fvjz99NO8/vrrnDhxgpSUFEtWyIqqyEyZtvbg/YSxZZ6CpPnGoMXlUfDzP8CtuzHToXlv41whxC0pKysqe3hQ2cODGi8NJ//8eS5s2sSFeKM1IXPFCgBs3ZoaAxg7dsS+TWusKj8cY35KY9GlW7ndwGAWsEUptQhjbMEAIKbUaiVwcXEhKCgIT09PQkND+ec//4nJZMLHx4eIiAj8/Pzuqfz3338ff39/GjZsiJeXlyUYGDJkCC+88AJffPEF8+fPv27w4eTJk1m5ciU2NjY4OTkRE2P8FfDw8ODJJ5+kVatWmEwmvvzyy1KdkVAuONaCwJeN7fRu2PmtsUbCgufB1tE8HmEwNOok4xGEuAPWVatStWdPqvbsidaanP37jQGM6+M5N3s2Z6OjUTY2VPbzo0pgAPYBAVT28npgux18a/oyree0+zrG4LbTLiulWgOdzG/Xaa13lFqt7rPyOCtB3Fy5/HwKCuDwevjtW2O1xZxMqFrv6niEmuWsvkJUMAXZ2WQnJHBh4yYubNrE5d27AbCqUgX7du3MgUIglZq5FznOSVzvnqYrPugkMKh4yv3nk5MNe38yWhL2rzLWR6jtfXU8gmOtsq6hEBVe3rlzZG/ebA4UNpJ7+AgA1i4uVPH3xz4wgCqBgdi6SmqfokhgcBMSGFQ8FerzyToNSQuM8QgnE0FZQ9NgY+pjiz4yHkGIEpJ74oSlNeHCpo3k/3kGABtXV+wD/KkSEEiVAH9MNWqUcU3LBwkMbkICg4qnwn4+f+69mhr6/DGwdYCWfcHnyniEB3xshhD3idaanAMHLIFC9pYtFJjHUlVydzdaEwICsW/f7oHMFHk7JDC4CQkMKp4K//kUFBjZHnfOg+TFcPk8ONYFr0FGd0Mtj7KuoRAPFJ2Xx6XkZEu3w8XtO9CXLxurMXp6WgKFyn6+WJmnUj/oJDC4CQkMKp4H6vPJvWiMR/jtW9i/EnQ+Z6o041LLx3Ht9AxUq1fWNRTigVNw+TIXd+ywBAqXdiVBQQGqUiUq+/pi37Yt9u3aUtnH54GdGimBwU1IYFDxPKifz297Ulgy59/0YT2+VgfQKFTDIGPthFb9oLIkdBKiNORnZpK9davR7ZCQwOXde0BrsLExWhSuBAqtWz8wXQ8SGNxERQoMbsy6WBISExM5ceIEvXv3LvL4zp07GT58OOfPn8fKyoqtW7diZ2fHtm3biIiI4OLFi/Tu3ZvPP//8vk0RKq+fz736cs1+/vXLXgo0NLU6yT/d9+GftQrS9oO1Lbj3NLobmvWSpE5ClKL88+e5uGMH2Vu3kr01gYu//w55eWBlhV3LllcDhTZtMDlVzID9rpIoiYdDYmIiCQkJRQYGeXl5DB06lFmzZuHj40NaWpolU+OIESOYOnUqAQEB9O7dm+XLlxMaGnq/q/9ACWjigq3Jity8Ao5b18PU7XFo8DGc2AG75hurLe5ZcnURJa9B0LiLDFoUooRZV62KQ5cuOHTpAhhrKFz87TdLoHBu7lzOmhd5q+Tujn27tti3bUvltm2xqVmzLKt+z8qkxUAp5Qx8i7GscirwpNb6XBHnLQcCgPVa67Br9sdzNcFTTWCL1rq/UqorRqKnQ+ZjC7XW792qPiXVYlDSGbAmTZrEN998A0BkZCSjRo0iNTWVXr164e/vz44dO2jWrBkzZ87E3t6eqKgoFi9ejMlkomfPnnzyySfXlbdlyxZGjRrFxYsXqVy5MjNmzKBx48a4ublx8eJF6tWrx1tvvcXgwYMt1/z000/MmTOH//3vf9eVdfLkSYKDg9mzZw9gpGGOi4vjv//973XnRUREULlyZfbs2cPhw4eZMWMGMTExbNy4EX9/f6Kjo+/qZ/OgthgAbDt8zpKnodCyzAX5cGidESTsNg9adKgFno8bCynV9QNZ2EWIUleQk8OlXbvI3ppgBAs7dqCzswGwbdiQyuZAoUq7dtjUK5/jhIprMUBrfd83YAIQZX4dBXxczHndMTI5LrlJWQuAZ82vu97s3OK2Nm3a6BslJycX2nczO07t0G1ntdXe0d667ay2esepHXd0/Y0SEhK0p6enzsrK0pmZmbpVq1Z6+/bt+tChQxrQ69ev11pr/dxzz+mJEyfqtLQ03axZM11QUKC11vrcuXOFyszIyNC5ublaa61XrFihBw4cqLXWesaMGfrll18ush6ffvqpHjp0qO7Zs6f28/PTH3/8sdZa661bt+ru3btbzlu3bp3u06dPoevDw8P14MGDdUFBgY6NjdWOjo56586dOj8/X7du3Vrv2HF3P6c7/XweSDnZWict0nru01q/V0Pr/6uq9RettV7zkdZn9pd17YR4qBTk5ursnTv1menf6CMvjdB72rXXyc1b6OTmLfS+Ll310VGjdFpMjM7euVMX5OSUdXW11loDCbqI78Sy6kroZ/4SByPnQhzw5o0naa1XmVsBiqSUcgS6Ac+VeA3vUElnwFq/fj0DBgygSpUqAAwcOJD4+Hj69u1L/fr1CQoKAmDo0KF88cUXjBo1Cjs7OyIjI+nTpw9hYWGFyszIyCA8PJyUlBSUUuTm5t6yHnl5eaxfv56tW7dib29P9+7dadOmDVWrVi10bnHjCx577DGUUnh5eVGrVi28vLwAI8dCamoqvr6lv/b3A8mmMnj0N7aL54xpj7u+h7jxEPcR1GsDXk+C50BwqNhNm0KUd8pkorKXF5W9vHAZ9hy6oIDLKSlkb9lqjFVI3EHmsuXGuXZ2VPb0pLKfn3nzLVfjFMoqMKiltT4JoLU+qZS62/+1BgCrtNbnr9kXqJT6DTgBvKG1/r2oC5VSLwIvAjRo0OAub39VSWfA0jfp4rnxC1gphclkYsuWLaxatYp58+YxefJkVq9efd1577zzDsHBwSxatIjU1FS6du16y3q4urrSpUsXaphXCuvduzfbt29n6NChHDt2zHJecamW4fqU0JWumR9c3tItV2iVnaBNuLFlHDdWWtz1HSx/E35+C5p0NYKElmFQyfFWpQkh7pGyssKueXPsmjeHZ4YCkPvHH1xMTDQChR2JpM2YAdOmAWDbqBGVfX0tgUIlNzdUGSVgK7XAQCm1EqhdxKF/lOBtngK+vub9dqCh1jpLKdUbiAXci7pQaz0VmArGGIN7rUhJZ8Dq3LkzERERREVFobVm0aJFzJo1C4AjR46wceNGAgMDmTt3Lh07diQrK4vs7Gx69+5NQEAAbm5uhcrMyMiwpFe+tm//ZqmWQ0JCmDBhAtnZ2dja2rJ27Vpee+016tSpg6OjI5s2bcLf35+ZM2fy6quv3tMzixJSrR4EjTS203uMVoRd30HsS7DEDpqHGkGC26Ngsi3r2grx0LCpXRubXr2o2qsXAAWXLnEpKYnsHTu4uCORrLVryYiNBcDK0ZHKPj5U9vPF3s8PO2/v+zZNstQCA631o8UdU0qdUkrVMbcW1AFO32n5SikXoD1Gq8GVe56/5vVPSqkpSqkaWuszd1r+3fCt6VtiKTFbt25NREQE7du3B4zBh35+fqSmptKyZUtiYmIYPnw47u7ujBgxgoyMDPr168elS5fQWvPpp58WKnPMmDGEh4czadIkunXrZtkfHBzM+PHj8fX1LTT40MnJiddff5127dqhlKJ379706dMHgP/85z+W6YqhoaEyI6E8qtkCur8D3cbC0S1GgPD7ImOr7ASt+huDFhsESnpoIe4zKzs7Y9pjW6OFWWtN7pEjlkDh4o4dnJn8pbGegpUVbmvWYFOr9LsFy2pWwkQgTWs9XikVBThrrccUc25XjC6BsBv2vwQEaq3Dr9lXGziltdZKqfbAfIwWhJs+ZEVax0AY5PO5B/m5cGCNESTsWQq52VCtvjGzwftJWY5ZiHIkPzOTi7/t5NLuZFwiI0t0rZjyto7BeOA7pdTzwBHgCQClVFvgJa11pPl9PNACcFBKHQOe11r/bC5jiLmcaw0CRiil8oCLwJBbBQVCPHSsbaBZT2O7nAV7lxlBwq//hg2fQU0PY30Er0FQ/d7H3wgh7p61oyMOHYNw6Bh03+4pKx8iLQYVkXw+peDCGaOLYdf3cHSzsa9BByNA8BgA9s63VcxN12EQQpQb5a3FQAhR3lSpAe1fMLZzqUaAsPN7WPo6LHvTGKzo/QQ0CwVb+yKL2Hb4HH/5ehM5eQXYmqyYHRkgwYEQFYwEBkKIwpwaQefR0OkN+GOX0dWwawHsWwa2DtAizAgSGncF66v/jWw6mEZOXgEFGnLzCth0ME0CAyEqGAkMhBDFUwrqeBvbo/8PDv9qBAnJP8DOeVDlEfAYaAxarNfmulwPNiYrApq4lPUTCCHukAQGQojbY2UNjTsZW+9PIOUXo7thWzRs+S84N6GN1xPMfyKEtWnVZIyBEBWUTFwup9LT05kyZYrlfWpqKnPmzLG8T0hIYOTIkSV+39jYWJKTk4s9/t1339GqVSs8PDx4+umnLftjYmJwd3fH3d2dGHPGMfEAM1WClo/BkzNhdAr0+9KYwbBuIp4Lu/Hyvudpc2wmpB8p65oKIe6QzEqgfM5KSE1NJSwsjKSkJADi4uL45JNPWLJkSaneNyIigrCwMAYNGlToWEpKCk8++SSrV6/GycmJ06dPU7NmTc6ePUvbtm1JSEhAKUWbNm3Ytm0bTqW49ndZfz6iGJl/GMsxJy2A49uMffX9je4Gj/7gWNRiqEKIslDcrARpMSinoqKiOHDgAL6+vowePZqoqCji4+Px9fXl008/JS4uzpIoady4cYSHh9OzZ08aNWrEwoULGTNmDF5eXvTq1avIZEnTpk2jXbt2+Pj48Pjjj5Odnc2vv/7K4sWLGT16NL6+vhw4cKDQNS+//LLlC7+mOef4zz//TI8ePXB2dsbJyYkePXqwfPnyQvds1KgRb7/9NoGBgbRt25bt27cTEhJC06ZN+eqrr0r6RyjKgmNtCHwZXlgNIxOh+7uQk23kbPhXC4gOg4Rv4EJaWddUCFEMGWNwG/745z+5vHtPiZZZqWULar/9drHHx48fT1JSEomJiUDhFoO4uLjrzj9w4ABr1qwhOTmZwMBAFixYwIQJExgwYABLly6lf//+150/cOBAXnjhBQDGjh3L9OnTefXVV+nbt2+xLQb79u0DICgoiPz8fMaNG0evXr04fvw49evXt5zn6urK8ePHi3yu+vXrs3HjRl577TUiIiLYsGEDly5dwsPDg5deeukWPzVRoTg3hk5/N7Y/98HvC42WhCWvwdI3jMROno9Diz5QuXpZ11YIYSaBwQMiNDQUGxsbvLy8yM/Pp5c5SYeXlxepqamFzk9KSmLs2LGkp6eTlZVFSEjILe+Rl5dHSkoKcXFxHDt2jE6dOpGUlFRkJsjilu3s27evpV5ZWVk4Ojri6OiInZ0d6enpVK8uXxAPpEeaQdco6PImnEqCJHOQ8MNfYYmtsUaC5+PQrBdUuj+JYoQQRZPA4Dbc7Df78uLa1MY2NjaWL+biUhtHREQQGxuLj48P0dHRhVogiuLq6kpAQAA2NjY0btyY5s2bk5KSgqur63XXHzt2rNiUzpKC+SGnFNT2Mrbu78KJ7eYgYSHs/QlMlaFZiBEkuPcAm8plXWMhHjoyxqCcujEV8s1SI9+NzMxM6tSpQ25uLrNnz76t+/Tv3581a9YAcObMGfbt20eTJk0ICQnhl19+4dy5c5w7d45ffvnltlogxENOKajXBkI+hNd+h+eWg99QOLwBvnsGJrrBwhdh73LIyynr2grx0JDAoJxycXEhKCgIT09PRo8ejbe3NyaTCR8fnyJTKt+p999/H39/f3r06EGLFi0s+4cMGcLEiRPx8/MrNPjI6pBiAAAgAElEQVQwJCQEFxcXWrVqRXBwMBMnTsTFxQVnZ2feeecd2rVrR7t27Xj33Xdxdr69dfWFAIyUzw0Doc8n8PoeePYH8BwI+36GuYPhE3f44WU4sBrypWVJiNIk0xUpn9MVxc3J5/OQyMuBg3HGeIQ9SyEnE+xrQKt+RndDg0AjqBBC3DFJoiSEqHhMtldTROdegv0rjPEIiXMgYTo41jEyP3o+bnRLlGCueiEeVhIYCCEqBhs7Y7XFlo/B5SzYt9xIE731a9g0xVh50WOg0QVR29sSJEgaaCHujAQGQoiKp5IDeA0ytksZsOcno7th42TY8Bm4uIHn4/zu/Ch/mZ8maaCFuAMSGAghKja7auD7lLFln4Xdi40gYd1EPPTHxKr6/GgVyC/57dh00F0CAyFuQQIDIcSDw94Z2kQYW+Ypjqyfy4VNcxht8x2j+Y6Lv7kDA6FlX6jlIWMShCiCBAZCiAeTYy0ahI7iz1bhRO/eTTe9mQZ/rIR1E2Htx+DcFFr1NYKEun4SJAhhJvN8KpjU1FQ8PT1LtMzExER++umnIo/l5OTw3HPP4eXlhY+Pz3UrHG7btg0vLy/c3NwYOXJkkUsjC1HW2jR0IqJXBxqEvgbPLYW/74WwT43Bihu+gGnB8Jk3/PwPOLoFCgrKuspClCkJDMRNA4Np06YBsGvXLlasWMHf//53Csz/cY4YMYKpU6eSkpJCSkpKkRkVhSh3HGpC22HwbCyM3g/9voSaLWHLVJjeAz5tBT+NhkPxUJBf1rUV4r6TwKAE/XEwg23LU/njYEaJlDdp0iQ8PT3x9PTks88+s+zPy8sjPDwcb29vBg0aRHZ2NmCkam7VqhXe3t688cYbhcrbsmULHTp0wM/Pjw4dOrB3715ycnJ49913+fbbb/H19eXbb7+97prk5GS6d+8OGGmWq1evTkJCAidPnuT8+fMEBgailOLZZ58lNja20D0jIiIYMWIEwcHBNGnShLVr1zJs2DBatmxJREREifychLhr9s7GMsx/+c4IEgZOM9ZD2D4TYsLgX83hx7/B/lWQXzh9uRAPIhljUEL+OJjBD5/uID+vAGuTFf1e86N2k2p3Xd62bduYMWMGmzdvRmuNv78/Xbp0wcnJib179zJ9+nSCgoIYNmwYU6ZMYdiwYSxatIg9e/aglCI9Pb1QmS1atGDdunWYTCZWrlzJ22+/zYIFC3jvvfdISEhg8uTJha7x8fHhhx9+YMiQIRw9epRt27Zx9OhRrKyscHV1tZx3s1TL586dY/Xq1SxevJjHHnuMDRs28PXXX9OuXTsSExPx9fW965+TECXGrhp4P2lsl7OMxZSSF8PO72FbNNhVN1JEt+pnpIw2VbpFgUJUTBIYlJDj+86Rn1eA1pCfX8DxfefuKTBYv349AwYMoEqVKgAMHDiQ+Ph4+vbtS/369QkKCgJg6NChfPHFF4waNQo7OzsiIyPp06cPYWFhhcrMyMggPDyclJQUlFLk5t76N6Bhw4axe/du2rZtS8OGDenQoQMmk+mOUi0/9thjKKXw8vKiVq1aeHl5AeDh4UFqaqoEBqL8qeRgrKjoMQByLxo5GpIXw+4lkDgbKlU1skC26gdNu4OtfVnXWIgSI4FBCanXzAlrkxX5+QVYW1tRr9m9zZW+2UC+G7+AlVKYTCa2bNnCqlWrmDdvHpMnT2b16tXXnffOO+8QHBzMokWLSE1NLTY18rVMJtN1SZs6dOiAu7s7Tk5OHDt2zLL/2LFj1K1bt8gyJNWyqNBsKhstBS36GLkbDq2F5B+M3A27vgcbeyNFdKt+4N4TKjmWdY2FuCcyxqCE1G5SjX6v+eHft8k9dyMAdO7cmdjYWLKzs7lw4QKLFi2iU6dOABw5coSNGzcCMHfuXDp27EhWVhYZGRn07t2bzz77jMTExEJlZmRkUK9ePQCio6Mt+2+WavnK/QFWrFiByWSiVatW1KlTB0dHRzZt2oTWmpkzZ9KvX797emYhyj2TrREE9JsMb6QYWSB9noLDG2H+MJjQFOY+Bb/Ng4uFu/OEqAikxaAE1W5S7Z4Dgitat25NREQE7du3ByAyMhI/Pz9SU1Np2bIlMTExDB8+HHd3d0aMGEFGRgb9+vXj0qVLaK2LTM08ZswYwsPDmTRpEt26dbPsDw4OZvz48fj6+vLWW28xePBgy7HTp08TEhKClZUV9erVY9asWZZj//nPf4iIiODixYuEhoYSGhpaIs8uRIVgbTLGGjTpCr0nwtHN5u6GxbD3J7CygSZdjJaE5n2gikvZ1leI21RmaZeVUs7At0AjIBV4Umt97oZzfIH/AFWBfOBDrfW35mONgXmAM7AdeEZrnaOUqgTMBNoAacBgrXXqzeoiaZcrHvl8RLlVUAAnthvdDck/QPphUNbQqKOxoFKLx8CxVlnXUohi0y6XZVdCFLBKa+0OrDK/v1E28KzW2gPoBXymlKpuPvYx8Kn5+nPA8+b9zwPntNZuwKfm84QQ4v6wsgLXttDzffjbbzB8HXR8Dc6fgKV/N6ZAfhMKm/4DGcduXZ4Q91lZBgb9gBjz6xig/40naK33aa1TzK9PAKeBR5Qx+q4bML+I668tdz7QXRU3XF4IIUqTUlDHB7q/A69shb9ugq5RRkbI5VHwqQdM6w4bPoezh4osYtvhc3y5Zj/bDp8r8rgQJa0sxxjU0lqfBNBan1RK1bzZyUqp9oAtcABwAdK11leGtB8D6plf1wOOmsvNU0plmM8/c0N5LwIvAjRo0KBEHkgIIYqllLHCYs2WRnBwZj/s/sEYl7DiXWOr7W3O39APHmnGtsPn+MvXmyRttLivSjUwUEqtBGoXcegfd1hOHWAWEK61LiimBeDKYImbHbu6Q+upwFQwxhjcSX2EEOKe1XCDTn83tnOHYfePxpiE1R8Y2yMtKbDvSOP8puzW9cnNK2DTwTQJDESpK9XAQGv9aHHHlFKnlFJ1zK0FdTC6CYo6ryqwFBirtd5k3n0GqK6UMplbDVyBE+Zjx4D6wDGllAmoBpwtmScSQohS4NQQOrxibOdPmIOExbQ9Mp1ltgWkFtRiDW3pYP8M5DcyZkQIUUrKcozBYiDc/Doc+OHGE5RStsAiYKbW+vsr+7UxlWINMKiI668tdxCwWkvaPyFERVG1LvgPh+eWov6+l8OBH6JcmhJuWkHzZUNgYlNY+CIkLYRL58u6tuIBVJaBwXigh1IqBehhfo9Sqq1S6mvzOU8CnYEIpVSiebuyfu6bwOtKqf0YYwimm/dPB1zM+1+n6NkO5V56ejpTpkyxvE9NTWXOnDmW9wkJCYwcObLE7xsbG0tycnKRxw4fPkz37t3x9vama9eu1618GBMTg7u7O+7u7sTExBR5vRDiDjnUpGHIKzT82zKs3jwIT86E5r0hZQXMfw4mNIGZ/WHzVEg/Uta1FQ+IMlvHoDwpj+sYpKamEhYWRlJSEgBxcXF88sknLFmypFTvGxERQVhYGIMGDSp07IknniAsLIzw8HBWr17NjBkzmDVrFmfPnqVt27YkJCSglKJNmzZs27YNJ6fS6wst689HiDJVkA9Ht8C+ZbB3GZzZZ+yv5QnNQ42tjp8xdVKIYpTHdQzETURFRXHgwAF8fX0ZPXo0UVFRxMfH4+vry6effkpcXJwlUdK4ceMIDw+nZ8+eNGrUiIULFzJmzBi8vLzo1atXkcmSpk2bRrt27fDx8eHxxx8nOzubX3/9lcWLFzN69Gh8fX05cODAdddcm4I5ODiYH34wem9+/vlnevTogbOzM05OTvTo0YPly5cXumejRo14++23CQwMpG3btmzfvp2QkBCaNm3KV199VdI/QiEeXFbW0DAQerxnTIN8ZRv0/MDIABn/L5jWDSa1gMUjjcAhJ7usaywqEBnBchvWRE/l9OGDJVpmzYZNCI54sdjj48ePJykpyZLz4MYWg7i4uOvOP3DgAGvWrCE5OZnAwEAWLFjAhAkTGDBgAEuXLqV//+uXiRg4cCAvvPACAGPHjmX69Om8+uqr9O3bt9gWAx8fHxYsWMDf/vY3Fi1aRGZmJmlpaRw/fpz69etbzrtZCub69euzceNGXnvtNSIiItiwYQOXLl3Cw8ODl1566dY/OCFEYTXcoMar0OFVyD5rdDXs/ckYh7A9BkyVoWmw0ZLgHiIrL4qbksDgAREaGoqNjQ1eXl7k5+fTq1cvALy8vEhNTS10flJSEmPHjiU9PZ2srCxCQkJueY9PPvmEV155hejoaDp37ky9evXuOAVz3759LfXKysrC0dERR0dH7OzsSE9Pp3r16kVeJ4S4TfbO4DPY2PJy4PB6o9Vg7zIjWACo19bc5dDbWFdB1oAT15DA4Dbc7Df78uLa1MY2NjaWL+biUhtHREQQGxuLj48P0dHRhVogilK3bl0WLlwIQFZWFgsWLKBatWq4urped/2xY8eKTeksKZiFuI9MttC0m7GFToBTv18NEFa/b2zVGxgBQvNQaBgE1jZlXWtRxmSMQTl1Yyrkm6VGvhuZmZnUqVOH3NxcZs+efVv3OXPmDAUFBQB89NFHDBs2DICQkBB++eUXzp07x7lz5/jll19uqwVCCHEfKQW1PaHLaHhxDby+Bx77HGq2gm3RMLOfkTb6++dg5/dwUZZgflhJYFBOubi4EBQUhKenJ6NHj8bb2xuTyYSPj0+RKZXv1Pvvv4+/vz89evSgRYsWlv1Dhgxh4sSJ+Pn5FRp8GBcXR/PmzWnWrBmnTp3iH/8wFrB0dnbmnXfeoV27drRr1453330XZ2fne66jEKIUVa0DbSLg6W9hzEEYMsdYjjk1HhZGGkFCdBhs/BLOluwYK1G+yXRFyud0RXFz8vkIUUoKCuD4NqO7Yd9yOG1e16RG86vjElzbsu3oeTYdTCOgiYss01xBFTddUcYYCCGEuMrKCuq3M7ZH/8/I+rhvuREobJwMGz4j186Fw9meJOX78Y2VN1Mju0lw8ACRwEAIIUTxnBtDwAhju5gO+1dyKP47ul+MZ6DNWnK1NacX+kK7x8DtUWORJZnlUKFJYCCEEOL2VK4OXoPIrNqdgV+vxzt/D11NOxlqnQIrxxmbQ21w624ECU26GtMnRYUigYEQQog70qahEzGRHdl0sCVtmkRi39AJzp+EA6th/wrYswQSZ4OyMtZMcHsU3B+VZZorCAkMhBBC3LE2DZ2uH1dQtQ74/cXY8vPgxHbYv9JYhTHuI4j7J9i7GGsquD0KTbuDwyNl9wCiWBIYCCGEKFnWJqjf3tiC34YLZ+DAGiNQ2L8Sdn1vnFfH1wgS3B4F13bGdaLMSZtOBZOamoqnp2eJlpmYmMhPP/1U5LG0tDSCg4NxcHDglVdesezPzs6mT58+tGjRAg8PD6Kirma3vnz5MoMHD8bNzQ1/f/8il2QWQjxEqtQA7ydg4H/hjRR4MQ66jQWTHayfBDN6GSmkv3sWts+EjKJzrYj7Q8IzQWJiIgkJCfTu3bvQMTs7O95//32SkpIsKaCveOONNwgODiYnJ4fu3buzbNkyQkNDmT59Ok5OTuzfv5958+bx5ptv8u23396vxxFClGdWVlDXz9g6jzZWWDy41tyasAqSjayt1PS4OoixQQCYKt28XFFipMWgBF0+fJ7za45y+fD5Eilv0qRJeHp64unpyWeffWbZn5eXR3h4ON7e3gwaNIjsbCOlalRUFK1atcLb25s33nijUHlbtmyhQ4cO+Pn50aFDB/bu3UtOTg7vvvsu3377Lb6+voW+wKtUqULHjh2xs7O7br+9vT3BwcEA2Nra0rp1a44dOwbADz/8QHh4OACDBg1i1apVhRItxcXF0aVLF5588kmaNWtGVFQUs2fPpn379nh5eRVadVEI8YCq7AQe/aHfZHg9GUb8aqSTruICm/4DM/vCx41h7lOw9WtjXQVRqqTFoIRcPnyeM1/vQucVoExW1Ij0olLDqndd3rZt25gxYwabN29Ga42/vz9dunTBycmJvXv3Mn36dIKCghg2bBhTpkxh2LBhLFq0iD179qCUIj09vVCZLVq0YN26dZhMJlauXMnbb7/NggULeO+990hISGDy5Ml3Vdf09HR+/PFH/va3vwFcl4bZZDJRrVo10tLSqFGjxnXX/fbbb+zevRtnZ2eaNGlCZGQkW7Zs4fPPP+ff//73dcGQEOIhoBTU8jC2oL/B5Uw4FG9uTVhhyQ552aE+lZoFG9MhG3cxuipEiZEWgxJy+WAGOq8ANOi8Ai4fzLin8tavX8+AAQOoUqUKDg4ODBw4kPj4eADq169PUFAQAEOHDmX9+vVUrVoVOzs7IiMjWbhwIfb29oXKzMjI4IknnsDT05PXXnuN33///Z7qCEbrxVNPPcXIkSNp0qQJwG2nYW7Xrh116tShUqVKNG3alJ49ewLFp4oWQjxkKjlCi94QNolt/dfSK38S43LDic+sRV7SIpg/DCY2hf8EwfK3Yd/PRjAh7okEBiWkUpNqKJMVKFAmKyo1qXZP5d0sh8WNX7JKKUwmE1u2bOHxxx8nNjaWXr16FbrunXfeITg4mKSkJH788UcuXbp0T3UEePHFF3F3d2fUqFGWfa6urhw9ehQwAoeMjIwikyrdmHb52pTMkoJZCHGtTYfOsi+vNtH5IQzPeZ2pASshcjV0e8dYRGnr1zDnSfi4EUzvCWv+CakbIC+nrKte4UhXQgmp1LAqNSK9uHwwg0pNqt1TNwJA586diYiIICoqCq01ixYtYtasWQAcOXKEjRs3EhgYyNy5c+nYsSNZWVlkZ2fTu3dvAgICcHNzK1RmRkYG9erVAyA6Otqy/25TOo8dO5aMjAy+/vrr6/b37duXmJgYAgMDmT9/Pt26dSuyxUAIIW5XQBMXbE1W5OYVYGOywr9pLXB1Atc20PkNyL0IRzcbAxkPrYV1E2Htx2BjDw0CoUkXo+uhlpcssnQLEhiUoEoNq95zQHBF69atiYiIoH379gBERkbi5+dHamoqLVu2JCYmhuHDh+Pu7s6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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "print(\"rmse:\", ca2.rmse())\n", - "plt.figure(figsize=(8, 5))\n", - "hb1 = ml.head(r1, 0, t1)\n", - "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", - "plt.semilogx(t1, hb1[0], label=\"ttim at 30 m\")\n", - "hb2 = ml.head(r2, 0, t2)\n", - "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", - "plt.semilogx(t2, hb2[0], label=\"ttim at 60 m\")\n", - "hb3 = ml.head(r3, 0, t3)\n", - "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", - "plt.semilogx(t3, hb3[0], label=\"ttim at 90 m\")\n", - "hb4 = ml.head(r4, 0, t4)\n", - "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", - "plt.semilogx(t4, hb4[0], label=\"ttim at 120 m\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.title(\"ttim fit exceppt for data of obs2\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".....................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 50\n", - " # data points = 39\n", - " # variables = 3\n", - " chi-square = 0.00176424\n", - " reduced chi-square = 4.9007e-05\n", - " Akaike info crit = -384.140218\n", - " Bayesian info crit = -379.149533\n", - "[[Variables]]\n", - " kaq0: 45.2049588 +/- 1.46508149 (3.24%) (init = 10)\n", - " Saq0: 4.7842e-05 +/- 4.1025e-06 (8.57%) (init = 0.0001)\n", - " c0: 318.727713 +/- 67.0042107 (21.02%) (init = 1000)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.765\n", - " C(kaq0, c0) = 0.763\n", - " C(Saq0, c0) = -0.289\n" - ] - }, - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq045.2051.4650813.24098110010[45.20495883227782]
Saq04.78424e-050.0000048.574961e-050.0010.0001[4.784240411045677e-05]
c0318.72867.00421121.02241001e+061000[318.72771295766415]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 45.205 1.465081 3.24098 1 100 10 \n", - "Saq0 4.78424e-05 0.000004 8.57496 1e-05 0.001 0.0001 \n", - "c0 318.728 67.004211 21.0224 100 1e+06 1000 \n", - "\n", - " parray \n", - "kaq0 [45.20495883227782] \n", - "Saq0 [4.784240411045677e-05] \n", - "c0 [318.72771295766415] " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ca3 = ttim.Calibrate(ml)\n", - "ca3.set_parameter(name=\"kaq0\", initial=10, pmin=1, pmax=100)\n", - "ca3.set_parameter(name=\"Saq0\", initial=1e-4, pmin=1e-5, pmax=1e-3)\n", - "ca3.set_parameter(name=\"c0\", initial=1000, pmin=100, pmax=1e6)\n", - "ca3.series(name=\"obs1\", x=r1, y=0, layer=0, t=t1, h=h1)\n", - "ca3.series(name=\"obs3\", x=r2, y=0, layer=0, t=t2, h=h2)\n", - "ca3.series(name=\"obs4\", x=r4, y=0, layer=0, t=t4, h=h4)\n", - "ca3.fit()\n", - "display(ca3.parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.0067258453079875775\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "print(\"rmse:\", ca3.rmse())\n", - "plt.figure(figsize=(8, 5))\n", - "hc1 = ml.head(r1, 0, t1)\n", - "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", - "plt.semilogx(t1, hc1[0], label=\"ttim at 30 m\")\n", - "hc2 = ml.head(r2, 0, t2)\n", - "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", - "plt.semilogx(t2, hc2[0], label=\"ttim at 60 m\")\n", - "hc3 = ml.head(r3, 0, t3)\n", - "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", - "plt.semilogx(t3, hc3[0], label=\"ttim at 90 m\")\n", - "hc4 = ml.head(r4, 0, t4)\n", - "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", - "plt.semilogx(t4, hc4[0], label=\"ttim at 120 m\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.title(\"ttim fit exceppt for data of obs3\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "...............................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 28\n", - " # data points = 39\n", - " # variables = 3\n", - " chi-square = 0.00113973\n", - " reduced chi-square = 3.1659e-05\n", - " Akaike info crit = -401.180633\n", - " Bayesian info crit = -396.189948\n", - "[[Variables]]\n", - " kaq0: 41.7208045 +/- 1.22792264 (2.94%) (init = 10)\n", - " Saq0: 5.7838e-05 +/- 3.9836e-06 (6.89%) (init = 0.0001)\n", - " c0: 180.961892 +/- 38.2627543 (21.14%) (init = 1000)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, c0) = 0.844\n", - " C(kaq0, Saq0) = -0.794\n", - " C(Saq0, c0) = -0.452\n" - ] - }, - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq041.72081.2279232.94319110010[41.720804507406896]
Saq05.78382e-050.0000046.887471e-050.0010.0001[5.7838177631703926e-05]
c0180.96238.26275421.14411001e+061000[180.96189235248528]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 41.7208 1.227923 2.94319 1 100 10 \n", - "Saq0 5.78382e-05 0.000004 6.88747 1e-05 0.001 0.0001 \n", - "c0 180.962 38.262754 21.1441 100 1e+06 1000 \n", - "\n", - " parray \n", - "kaq0 [41.720804507406896] \n", - "Saq0 [5.7838177631703926e-05] \n", - "c0 [180.96189235248528] " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ca4 = ttim.Calibrate(ml)\n", - "ca4.set_parameter(name=\"kaq0\", initial=10, pmin=1, pmax=100)\n", - "ca4.set_parameter(name=\"Saq0\", initial=1e-4, pmin=1e-5, pmax=1e-3)\n", - "ca4.set_parameter(name=\"c0\", initial=1000, pmin=100, pmax=1e6)\n", - "ca4.series(name=\"obs1\", x=r1, y=0, layer=0, t=t1, h=h1)\n", - "ca4.series(name=\"obs3\", x=r2, y=0, layer=0, t=t2, h=h2)\n", - "ca4.series(name=\"obs4\", x=r3, y=0, layer=0, t=t3, h=h3)\n", - "ca4.fit()\n", - "display(ca4.parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.005405898926519027\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "print(\"rmse:\", ca4.rmse())\n", - "plt.figure(figsize=(8, 5))\n", - "hd1 = ml.head(r1, 0, t1)\n", - "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", - "plt.semilogx(t1, hd1[0], label=\"ttim at 30 m\")\n", - "hd2 = ml.head(r2, 0, t2)\n", - "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", - "plt.semilogx(t2, hd2[0], label=\"ttim at 60 m\")\n", - "hd3 = ml.head(r3, 0, t3)\n", - "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", - "plt.semilogx(t3, hd3[0], label=\"ttim at 90 m\")\n", - "hd4 = ml.head(r4, 0, t4)\n", - "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", - "plt.semilogx(t4, hd4[0], label=\"ttim at 120 m\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.title(\"ttim fit exceppt for data of obs4\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Summary of test with three datasets:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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k [m/d]Ss [1/m]c [d]RMSE
Data at 30 m removed57.55823.2824e-059984870.003230
Data at 60 m removed45.02644.40921e-05349.1330.002633
Data at 90 m removed57.55823.2824e-059984870.006726
Data at 120 m removed41.72085.78382e-05180.9620.005406
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" - ], - "text/plain": [ - " k [m/d] Ss [1/m] c [d] RMSE\n", - "Data at 30 m removed 57.5582 3.2824e-05 998487 0.003230\n", - "Data at 60 m removed 45.0264 4.40921e-05 349.133 0.002633\n", - "Data at 90 m removed 57.5582 3.2824e-05 998487 0.006726\n", - "Data at 120 m removed 41.7208 5.78382e-05 180.962 0.005406" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\"],\n", - " index=[\n", - " \"Data at 30 m removed\",\n", - " \"Data at 60 m removed\",\n", - " \"Data at 90 m removed\",\n", - " \"Data at 120 m removed\",\n", - " ],\n", - ")\n", - "t.loc[\"Data at 30 m removed\"] = ca1.parameters[\"optimal\"].values\n", - "t.loc[\"Data at 60 m removed\"] = ca2.parameters[\"optimal\"].values\n", - "t.loc[\"Data at 90 m removed\"] = ca1.parameters[\"optimal\"].values\n", - "t.loc[\"Data at 120 m removed\"] = ca4.parameters[\"optimal\"].values\n", - "rmse = [ca1.rmse(), ca2.rmse(), ca3.rmse(), ca4.rmse()]\n", - "t[\"RMSE\"] = rmse\n", - "t" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calibrate with four datasets simultaneously:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Model with pervious top — leakage only:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# unkonwn parameters: kaq, Saq, c\n", - "m_1 = ttim.ModelMaq(\n", - " kaq=10, z=[0, zt, zb], c=500, Saq=0.001, topboundary=\"semi\", tmin=0.001, tmax=0.5\n", - ")\n", - "w_1 = ttim.Well(m_1, xw=0, yw=0, tsandQ=[(0, Q), (0.34, 0)])\n", - "m_1.solve(silent=\"True\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "........................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 37\n", - " # data points = 51\n", - " # variables = 3\n", - " chi-square = 0.00178546\n", - " reduced chi-square = 3.7197e-05\n", - " Akaike info crit = -517.255143\n", - " Bayesian info crit = -511.459666\n", - "[[Variables]]\n", - " kaq0: 45.3320015 +/- 1.18524849 (2.61%) (init = 10)\n", - " Saq0: 4.7622e-05 +/- 3.1043e-06 (6.52%) (init = 0.0001)\n", - " c0: 331.170978 +/- 76.1898424 (23.01%) (init = 500)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.771\n", - " C(kaq0, c0) = 0.762\n", - " C(Saq0, c0) = -0.299\n" - ] - }, - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq045.3321.1852482.6146-infinf10[45.332001524257045]
Saq04.76224e-050.0000036.51863-infinf0.0001[4.762239136215571e-05]
c0331.17176.18984223.00620.0inf500[331.170977660899]
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" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 45.332 1.185248 2.6146 -inf inf 10 \n", - "Saq0 4.76224e-05 0.000003 6.51863 -inf inf 0.0001 \n", - "c0 331.171 76.189842 23.0062 0.0 inf 500 \n", - "\n", - " parray \n", - "kaq0 [45.332001524257045] \n", - "Saq0 [4.762239136215571e-05] \n", - "c0 [331.170977660899] " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "c0 = ttim.Calibrate(m_1)\n", - "c0.set_parameter(name=\"kaq0\", initial=10)\n", - "c0.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "c0.set_parameter(name=\"c0\", initial=500, pmin=0)\n", - "c0.series(name=\"obs1\", x=30, y=0, t=t1, h=h1, layer=0)\n", - "c0.series(name=\"obs2\", x=60, y=0, t=t2, h=h2, layer=0)\n", - "c0.series(name=\"obs3\", x=90, y=0, t=t3, h=h3, layer=0)\n", - "c0.series(name=\"obs4\", x=120, y=0, t=t4, h=h4, layer=0)\n", - "c0.fit(report=True)\n", - "display(c0.parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.005916842209512141\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm_11 = m_1.head(r1, 0, t1)\n", - "hm_12 = m_1.head(r2, 0, t2)\n", - "hm_13 = m_1.head(r3, 0, t3)\n", - "hm_14 = m_1.head(r4, 0, t4)\n", - "print(\"rmse:\", c0.rmse())\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", - "plt.semilogx(t1, hm_11[0], label=\"ttim at 30 m\")\n", - "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", - "plt.semilogx(t2, hm_12[0], label=\"ttim at 60 m\")\n", - "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", - "plt.semilogx(t3, hm_13[0], label=\"ttim at 90 m\")\n", - "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", - "plt.semilogx(t4, hm_14[0], label=\"ttim at 120 m\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.title(\"model with leakage only\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Model with pervious top — leakage & storage:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# unkonwn parameters: kaq, Saq, c, Sll\n", - "m_2 = ttim.ModelMaq(\n", - " kaq=10,\n", - " z=[0, zt, zb],\n", - " c=500,\n", - " Saq=0.001,\n", - " Sll=0.001,\n", - " topboundary=\"semi\",\n", - " tmin=0.001,\n", - " tmax=0.5,\n", - ")\n", - "w_2 = ttim.Well(m_2, xw=0, yw=0, tsandQ=[(0, Q), (0.34, 0)])\n", - "m_2.solve(silent=\"True\")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "........................................................................................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 149\n", - " # data points = 51\n", - " # variables = 4\n", - " chi-square = 0.00175221\n", - " reduced chi-square = 3.7281e-05\n", - " Akaike info crit = -516.213733\n", - " Bayesian info crit = -508.486431\n", - "[[Variables]]\n", - " kaq0: 45.1608873 +/- 1.19625233 (2.65%) (init = 10)\n", - " Saq0: 4.1015e-05 +/- 5.1016e-06 (12.44%) (init = 0.0001)\n", - " c0: 367.718757 +/- 146.861596 (39.94%) (init = 500)\n", - " Sll: 1.3255e-04 +/- 1.5174e-04 (114.48%) (init = 1e-05)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(Saq0, Sll) = -0.809\n", - " C(c0, Sll) = 0.699\n", - " C(Saq0, c0) = -0.594\n", - " C(kaq0, c0) = 0.382\n", - " C(kaq0, Sll) = -0.209\n", - " C(kaq0, Saq0) = -0.189\n" - ] - }, - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq045.16091.1962522.64887-infinf10[45.160887348164884]
Saq04.10148e-050.00000512.4384-infinf0.0001[4.10148093882865e-05]
c0367.719146.86159639.93860.0inf500[367.71875740092133]
Sll0.0001325460.000152114.479-infinf1e-05[0.00013254587256535368]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 45.1609 1.196252 2.64887 -inf inf 10 \n", - "Saq0 4.10148e-05 0.000005 12.4384 -inf inf 0.0001 \n", - "c0 367.719 146.861596 39.9386 0.0 inf 500 \n", - "Sll 0.000132546 0.000152 114.479 -inf inf 1e-05 \n", - "\n", - " parray \n", - "kaq0 [45.160887348164884] \n", - "Saq0 [4.10148093882865e-05] \n", - "c0 [367.71875740092133] \n", - "Sll [0.00013254587256535368] " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "c1 = ttim.Calibrate(m_2)\n", - "c1.set_parameter(name=\"kaq0\", initial=10)\n", - "c1.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "c1.set_parameter(name=\"c0\", initial=500, pmin=0)\n", - "c1.set_parameter_by_reference(name=\"Sll\", parameter=m_2.aq.Sll[:], initial=1e-5)\n", - "c1.series(name=\"obs1\", x=30, y=0, t=t1, h=h1, layer=0)\n", - "c1.series(name=\"obs2\", x=60, y=0, t=t2, h=h2, layer=0)\n", - "c1.series(name=\"obs3\", x=90, y=0, t=t3, h=h3, layer=0)\n", - "c1.series(name=\"obs4\", x=120, y=0, t=t4, h=h4, layer=0)\n", - "c1.fit(report=True)\n", - "display(c1.parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.005861496548401953\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm_21 = m_2.head(r1, 0, t1)\n", - "hm_22 = m_2.head(r2, 0, t2)\n", - "hm_23 = m_2.head(r3, 0, t3)\n", - "hm_24 = m_2.head(r4, 0, t4)\n", - "print(\"rmse:\", c1.rmse())\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", - "plt.semilogx(t1, hm_21[0], label=\"ttim at 30 m\")\n", - "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", - "plt.semilogx(t2, hm_22[0], label=\"ttim at 60 m\")\n", - "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", - "plt.semilogx(t3, hm_23[0], label=\"ttim at 90 m\")\n", - "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", - "plt.semilogx(t4, hm_24[0], label=\"ttim at 120 m\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.title(\"model with both leakage and storage\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Model with impervious top — storage only:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\DELL\\Anaconda3\\lib\\site-packages\\ttim\\aquifer.py:60: RuntimeWarning: divide by zero encountered in true_divide\n", - " self.D = self.T / self.Scoefaq\n" - ] - } - ], - "source": [ - "# unkonwn parameters: kaq1, Saq1, c, Sll\n", - "m_3 = ttim.ModelMaq(\n", - " kaq=[0.01, 10],\n", - " z=[0, -0.001, -8.001, -45.001],\n", - " c=500,\n", - " Saq=[0, 0.001],\n", - " Sll=1e-4,\n", - " topboundary=\"conf\",\n", - " tmin=0.001,\n", - " tmax=0.5,\n", - ")\n", - "w_3 = ttim.Well(m_3, xw=0, yw=0, tsandQ=[(0, 761), (0.34, 0)], layers=1)\n", - "m_3.solve(silent=\"True\")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "..............................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 91\n", - " # data points = 51\n", - " # variables = 4\n", - " chi-square = 0.00177210\n", - " reduced chi-square = 3.7704e-05\n", - " Akaike info crit = -515.638239\n", - " Bayesian info crit = -507.910936\n", - "[[Variables]]\n", - " kaq1: 45.1865884 +/- 1.21577651 (2.69%) (init = 10)\n", - " Saq1: 3.9424e-05 +/- 5.1577e-06 (13.08%) (init = 0.0001)\n", - " c1: 6670474.95 +/- 2.4647e+10 (369498.49%) (init = 500)\n", - " Sll: 3.12840553 +/- 11683.2831 (373458.08%) (init = 1e-05)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(c1, Sll) = 1.000\n", - " C(Saq1, Sll) = -0.784\n", - " C(Saq1, c1) = -0.784\n", - " C(kaq1, Sll) = -0.139\n", - " C(kaq1, c1) = -0.139\n" - ] - }, - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq145.18661.215777e+002.69057-infinf10[45.1865883789621]
Saq13.94235e-055.157737e-0613.0829-infinf0.0001[3.9423547312967535e-05]
c16.67047e+062.464730e+103694980.0inf500[6670474.949823412]
Sll3.128411.168328e+043734580.0inf1e-05[3.1284055340533667, 3.1284055340533667]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq1 45.1866 1.215777e+00 2.69057 -inf inf 10 \n", - "Saq1 3.94235e-05 5.157737e-06 13.0829 -inf inf 0.0001 \n", - "c1 6.67047e+06 2.464730e+10 369498 0.0 inf 500 \n", - "Sll 3.12841 1.168328e+04 373458 0.0 inf 1e-05 \n", - "\n", - " parray \n", - "kaq1 [45.1865883789621] \n", - "Saq1 [3.9423547312967535e-05] \n", - "c1 [6670474.949823412] \n", - "Sll [3.1284055340533667, 3.1284055340533667] " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "c2 = ttim.Calibrate(m_3)\n", - "c2.set_parameter(name=\"kaq1\", initial=10)\n", - "c2.set_parameter(name=\"Saq1\", initial=1e-4)\n", - "c2.set_parameter(name=\"c1\", initial=500, pmin=0)\n", - "c2.set_parameter_by_reference(name=\"Sll\", parameter=m_3.aq.Sll[:], initial=1e-5, pmin=0)\n", - "c2.series(name=\"obs1\", x=30, y=0, t=t1, h=h1, layer=1)\n", - "c2.series(name=\"obs2\", x=60, y=0, t=t2, h=h2, layer=1)\n", - "c2.series(name=\"obs3\", x=90, y=0, t=t3, h=h3, layer=1)\n", - "c2.series(name=\"obs4\", x=120, y=0, t=t4, h=h4, layer=1)\n", - "c2.fit(report=True)\n", - "display(c2.parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.005894661176988822\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm_31 = m_3.head(r1, 0, t1)\n", - "hm_32 = m_3.head(r2, 0, t2)\n", - "hm_33 = m_3.head(r3, 0, t3)\n", - "hm_34 = m_3.head(r4, 0, t4)\n", - "print(\"rmse:\", c2.rmse())\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", - "plt.semilogx(t1, hm_31[-1], label=\"ttim at 30 m\")\n", - "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", - "plt.semilogx(t2, hm_32[-1], label=\"ttim at 60 m\")\n", - "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", - "plt.semilogx(t3, hm_33[-1], label=\"ttim at 90 m\")\n", - "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", - "plt.semilogx(t4, hm_34[-1], label=\"ttim at 120 m\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.title(\"model with storage only\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since the correlation coefficient between Sll and c is 1.00, which indicates a fully positive correlation, the model is overparameterized. Thus, the uncertainties for parameter Sll and c are unavailabel and are larger for Saq than MLU gives. The calibration is repeated with Sll removed from parameters to be optimized:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\DELL\\Anaconda3\\lib\\site-packages\\ttim\\aquifer.py:60: RuntimeWarning: divide by zero encountered in true_divide\n", - " self.D = self.T / self.Scoefaq\n" - ] - } - ], - "source": [ - "# unkonwn parameters: kaq1, Saq1, c, Sll\n", - "m_4 = ttim.ModelMaq(\n", - " kaq=[0.01, 10],\n", - " z=[0, -0.001, -8.001, -45.001],\n", - " c=500,\n", - " Saq=[0, 0.001],\n", - " Sll=0.1,\n", - " topboundary=\"conf\",\n", - " tmin=0.001,\n", - " tmax=0.5,\n", - ")\n", - "w_4 = ttim.Well(m_4, xw=0, yw=0, tsandQ=[(0, 761), (0.34, 0)], layers=1)\n", - "m_4.solve(silent=\"True\")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".............................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 58\n", - " # data points = 51\n", - " # variables = 3\n", - " chi-square = 0.00177210\n", - " reduced chi-square = 3.6919e-05\n", - " Akaike info crit = -517.638239\n", - " Bayesian info crit = -511.842762\n", - "[[Variables]]\n", - " kaq1: 45.1861189 +/- 1.19134389 (2.64%) (init = 10)\n", - " Saq1: 3.9424e-05 +/- 3.1685e-06 (8.04%) (init = 0.0001)\n", - " c1: 213243.195 +/- 98612.5988 (46.24%) (init = 500)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq1, c1) = 0.765\n", - " C(Saq1, c1) = 0.330\n", - " C(kaq1, Saq1) = -0.283\n" - ] - }, - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq145.18611.1913442.63653-infinf10[45.18611885600881]
Saq13.94243e-050.0000038.03701-infinf0.0001[3.9424253603649176e-05]
c121324398612.59875246.24420.0inf500[213243.19515519644]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq1 45.1861 1.191344 2.63653 -inf inf 10 \n", - "Saq1 3.94243e-05 0.000003 8.03701 -inf inf 0.0001 \n", - "c1 213243 98612.598752 46.2442 0.0 inf 500 \n", - "\n", - " parray \n", - "kaq1 [45.18611885600881] \n", - "Saq1 [3.9424253603649176e-05] \n", - "c1 [213243.19515519644] " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "c3 = ttim.Calibrate(m_4)\n", - "c3.set_parameter(name=\"kaq1\", initial=10)\n", - "c3.set_parameter(name=\"Saq1\", initial=1e-4)\n", - "c3.set_parameter(name=\"c1\", initial=500, pmin=0)\n", - "c3.series(name=\"obs1\", x=30, y=0, t=t1, h=h1, layer=1)\n", - "c3.series(name=\"obs2\", x=60, y=0, t=t2, h=h2, layer=1)\n", - "c3.series(name=\"obs3\", x=90, y=0, t=t3, h=h3, layer=1)\n", - "c3.series(name=\"obs4\", x=120, y=0, t=t4, h=h4, layer=1)\n", - "c3.fit(report=True)\n", - "display(c3.parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.005894661176430443\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm_r1 = m_4.head(r1, 0, t1)\n", - "hm_r2 = m_4.head(r2, 0, t2)\n", - "hm_r3 = m_4.head(r3, 0, t3)\n", - "hm_r4 = m_4.head(r4, 0, t4)\n", - "print(\"rmse:\", c3.rmse())\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", - "plt.semilogx(t1, hm_r1[-1], label=\"ttim at 30 m\")\n", - "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", - "plt.semilogx(t2, hm_r2[-1], label=\"ttim at 60 m\")\n", - "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", - "plt.semilogx(t3, hm_r3[-1], label=\"ttim at 90 m\")\n", - "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", - "plt.semilogx(t4, hm_r4[-1], label=\"ttim at 120 m\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.title(\"model with storage only\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary of values simulated by different models" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since values for parameters presented in Kruseman and de Ridder (1970) are determined by Hantush family of type curves, the results are approximate, not accurate. Values presented in following table are calculated by Hantush well function." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Table of methods only considered storage:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\DELL\\Anaconda3\\lib\\site-packages\\ttim\\aquifer.py:60: RuntimeWarning: divide by zero encountered in true_divide\n", - " self.D = self.T / self.Scoefaq\n" - ] - }, - { - "data": { - "text/html": [ - "
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k [m/d]Ss [1/m]c [d]Sll [1/m]RMSE
Hantush45.3324.762e-05331.141-0.005917
ttim45.18613.94243e-05213243-0.005895
MLU45.1863.941e-05769.20.00036110.005941
AQTESOLV49.2864.559e-05745.156-0.007245
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" - ], - "text/plain": [ - " k [m/d] Ss [1/m] c [d] Sll [1/m] RMSE\n", - "Hantush 45.332 4.762e-05 331.141 - 0.005917\n", - "ttim 45.1861 3.94243e-05 213243 - 0.005895\n", - "MLU 45.186 3.941e-05 769.2 0.0003611 0.005941\n", - "AQTESOLV 49.286 4.559e-05 745.156 - 0.007245" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t1 = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\", \"Sll [1/m]\"],\n", - " index=[\"Hantush\", \"ttim\", \"MLU\", \"AQTESOLV\"],\n", - ")\n", - "t1.loc[\"Hantush\"] = [45.332, 4.762e-5, 331.141, \"-\"]\n", - "t1.loc[\"ttim\"] = np.append(c3.parameters[\"optimal\"].values, \"-\")\n", - "t1.loc[\"MLU\"] = [45.186, 3.941e-05, 769.200, 3.611e-04]\n", - "t1.loc[\"AQTESOLV\"] = [49.286, 4.559e-05, 745.156, \"-\"]\n", - "rmse = [0.005917, c3.rmse(), 0.005941, 0.007245]\n", - "t1[\"RMSE\"] = rmse\n", - "t1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Table of methods considered both storage and leakage:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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k [m/d]Ss [1/m]c [d]Sll [1/m]RMSE
ttim45.18663.94235e-056.67047e+063.128410.005895
MLU45.3354.668e-05331.41.284e-050.004941
AQTESOLV45.1594.1e-05367.5772.868e-050.005861
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" - ], - "text/plain": [ - " k [m/d] Ss [1/m] c [d] Sll [1/m] RMSE\n", - "ttim 45.1866 3.94235e-05 6.67047e+06 3.12841 0.005895\n", - "MLU 45.335 4.668e-05 331.4 1.284e-05 0.004941\n", - "AQTESOLV 45.159 4.1e-05 367.577 2.868e-05 0.005861" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t2 = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\", \"Sll [1/m]\"],\n", - " index=[\"ttim\", \"MLU\", \"AQTESOLV\"],\n", - ")\n", - "t2.loc[\"MLU\"] = [45.335, 4.668e-05, 331.400, 1.284e-05]\n", - "t2.loc[\"AQTESOLV\"] = [45.159, 4.100e-05, 367.577, 2.868e-05]\n", - "t2.loc[\"ttim\"] = c2.parameters[\"optimal\"].values\n", - "t2[\"RMSE\"] = [c2.rmse(), 0.004941, 0.005861]\n", - "t2" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/04pumpingtests/3_test_of_vennebulten.ipynb b/docs/04pumpingtests/3_test_of_vennebulten.ipynb deleted file mode 100755 index b3a0f5e..0000000 --- a/docs/04pumpingtests/3_test_of_vennebulten.ipynb +++ /dev/null @@ -1,1122 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Unconfined Aquifer Test\n", - "**This example is taken from Kruseman and de Ridder (1970).**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import ttim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set basic parameters for the model:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "b = -21 # aquifer thickness in m\n", - "r = 90 # distance from observation wells to pumping well in m\n", - "Q = 873 # constant discharge in m^3/d" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load data of two piezometers:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "data1 = np.loadtxt(\"data/venne_shallow.txt\", skiprows=1)\n", - "ts = data1[:, 0] / 60 / 24 # convert min to days\n", - "hs = data1[:, 1]\n", - "\n", - "data2 = np.loadtxt(\"data/venne_deep.txt\", skiprows=1)\n", - "td = data2[:, 0] / 60 / 24 # convert min to days\n", - "hd = data2[:, 1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create conceptual one-layer model:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_1 = ttim.Model3D(kaq=10, z=[0, b], Saq=1e-4, tmin=1e-4, tmax=1.1)\n", - "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=0.1, tsandQ=[(0, Q)])\n", - "ml_1.solve()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calibrate with data of two piezometers respectively:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".............................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 42\n", - " # data points = 19\n", - " # variables = 2\n", - " chi-square = 2.3691e-04\n", - " reduced chi-square = 1.3936e-05\n", - " Akaike info crit = -210.553192\n", - " Bayesian info crit = -208.664314\n", - "[[Variables]]\n", - " kaq0: 136.469285 +/- 5.82623528 (4.27%) (init = 10)\n", - " Saq0: 0.01672407 +/- 0.00131400 (7.86%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.809\n" - ] - } - ], - "source": [ - "# calibrate with data of shallow piezometer\n", - "# unknown parameters: kaq, Saq\n", - "ca_1 = ttim.Calibrate(ml_1)\n", - "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", - "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca_1.series(name=\"obs\", x=r, y=0, t=ts, h=hs, layer=0)\n", - "ca_1.fit()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq0136.4695.8262354.26926-infinf10[136.46928505810396]
Saq00.01672410.0013147.85692-infinf0.0001[0.01672406931266408]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial parray\n", - "kaq0 136.469 5.826235 4.26926 -inf inf 10 [136.46928505810396]\n", - "Saq0 0.0167241 0.001314 7.85692 -inf inf 0.0001 [0.01672406931266408]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.003531132513897005\n" - ] - } - ], - "source": [ - "display(ca_1.parameters)\n", - "print(\"RMSE:\", ca_1.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hs_1 = ml_1.head(r, 0, ts)\n", - "hd_1 = ml_1.head(r, 0, td)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(ts, hs, \".\", label=\"shallow obs\")\n", - "plt.semilogx(ts, hs_1[0], label=\"shallow ttim\")\n", - "plt.semilogx(td, hd, \".\", label=\"deep obs\")\n", - "plt.semilogx(td, hd_1[0], label=\"deep ttim\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.title(\"ttim analysis of shallow piezometer\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "..................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 31\n", - " # data points = 29\n", - " # variables = 2\n", - " chi-square = 0.00294675\n", - " reduced chi-square = 1.0914e-04\n", - " Akaike info crit = -262.636144\n", - " Bayesian info crit = -259.901553\n", - "[[Variables]]\n", - " kaq0: 116.576664 +/- 4.33980927 (3.72%) (init = 10)\n", - " Saq0: 3.4576e-04 +/- 5.1114e-05 (14.78%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.873\n" - ] - } - ], - "source": [ - "# Calibrate with deep piezometer\n", - "# unknown parameters: kay, Saq\n", - "ca_2 = ttim.Calibrate(ml_1)\n", - "ca_2.set_parameter(name=\"kaq0\", initial=10)\n", - "ca_2.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca_2.series(name=\"obs\", x=r, y=0, t=td, h=hd, layer=0)\n", - "ca_2.fit()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq0116.5774.3398093.72271-infinf10[116.57666407791346]
Saq00.0003457610.00005114.783-infinf0.0001[0.00034576112845901126]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 116.577 4.339809 3.72271 -inf inf 10 \n", - "Saq0 0.000345761 0.000051 14.783 -inf inf 0.0001 \n", - "\n", - " parray \n", - "kaq0 [116.57666407791346] \n", - "Saq0 [0.00034576112845901126] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.010080273329140315\n" - ] - } - ], - "source": [ - "display(ca_2.parameters)\n", - "print(\"RMSE:\", ca_2.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hd_2 = ml_1.head(r, 0, td)\n", - "hs_2 = ml_1.head(r, 0, ts)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(td, hd, \".\", label=\"deep obs\")\n", - "plt.semilogx(td, hd_2[0], label=\"deep ttim\")\n", - "plt.semilogx(ts, hs, \".\", label=\"shallow obs\")\n", - "plt.semilogx(ts, hs_2[0], label=\"shallow ttim\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.title(\"ttim analysis of deep piezometer\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create conceptual model with n-layers:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "nlay = 21 # number of layers\n", - "zlayers = np.linspace(0, b, nlay + 1) # elevation of each layer\n", - "Saq = 1e-4 * np.ones(nlay)\n", - "Saq[0] = 0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 21\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_2 = ttim.Model3D(\n", - " kaq=10, z=zlayers, Saq=Saq, kzoverkh=0.1, phreatictop=True, tmin=1e-4, tmax=1.1\n", - ")\n", - "w_2 = ttim.Well(ml_2, xw=0, yw=0, rw=0.1, tsandQ=[(0, Q)], layers=range(nlay))\n", - "ml_2.solve()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calibrate with two piezometers simultaneously:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "aquifers with same kaq and Saq \n", - "unknown parameters: kaq, Saq, kzoverkh" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "............................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 57\n", - " # data points = 48\n", - " # variables = 4\n", - " chi-square = 0.00474318\n", - " reduced chi-square = 1.0780e-04\n", - " Akaike info crit = -434.667919\n", - " Bayesian info crit = -427.183115\n", - "[[Variables]]\n", - " kaq0_20: 31.5792604 +/- 0.75573096 (2.39%) (init = 10)\n", - " Saq0: 0.05541542 +/- 0.00380254 (6.86%) (init = 0.2)\n", - " Saq1_20: 3.4725e-05 +/- 2.4525e-06 (7.06%) (init = 0.0001)\n", - " kzoverkh: 0.01001515 +/- 1.1683e-04 (1.17%) (init = 0.1)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0_20, kzoverkh) = -0.517\n", - " C(kaq0_20, Saq0) = -0.326\n", - " C(kaq0_20, Saq1_20) = -0.279\n", - " C(Saq0, kzoverkh) = -0.126\n", - " C(Saq1_20, kzoverkh) = 0.123\n" - ] - } - ], - "source": [ - "ca_3 = ttim.Calibrate(ml_2)\n", - "ca_3.set_parameter(name=\"kaq0_20\", initial=10)\n", - "ca_3.set_parameter(name=\"Saq0\", initial=0.2)\n", - "ca_3.set_parameter(name=\"Saq1_20\", initial=1e-4)\n", - "ca_3.set_parameter_by_reference(\n", - " name=\"kzoverkh\", parameter=ml_2.aq.kzoverkh[:], initial=0.1, pmin=0.01\n", - ")\n", - "ca_3.series(name=\"obs1\", x=r, y=0, layer=1, t=ts, h=hs)\n", - "ca_3.series(name=\"obs2\", x=r, y=0, layer=15, t=td, h=hd)\n", - "ca_3.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_2031.57930.7557312.39312-infinf10[31.579260407774143, 31.579260407774143, 31.57...
Saq00.05541540.0038036.86188-infinf0.2[0.05541542451273692]
Saq1_203.47254e-050.0000027.06255-infinf0.0001[3.47254241180992e-05, 3.47254241180992e-05, 3...
kzoverkh0.01001510.0001171.166580.01inf0.1[0.01001514532231007, 0.01001514532231007, 0.0...
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0_20 31.5793 0.755731 2.39312 -inf inf 10 \n", - "Saq0 0.0554154 0.003803 6.86188 -inf inf 0.2 \n", - "Saq1_20 3.47254e-05 0.000002 7.06255 -inf inf 0.0001 \n", - "kzoverkh 0.0100151 0.000117 1.16658 0.01 inf 0.1 \n", - "\n", - " parray \n", - "kaq0_20 [31.579260407774143, 31.579260407774143, 31.57... \n", - "Saq0 [0.05541542451273692] \n", - "Saq1_20 [3.47254241180992e-05, 3.47254241180992e-05, 3... \n", - "kzoverkh [0.01001514532231007, 0.01001514532231007, 0.0... " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.009940637191248936\n" - ] - } - ], - "source": [ - "display(ca_3.parameters)\n", - "print(\"RMSE:\", ca_3.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hs_3 = ml_2.head(x=r, y=0, t=ts, layers=1)\n", - "hd_3 = ml_2.head(x=r, y=0, t=td, layers=15)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(ts, hs, \".\", label=\"shallow obs\")\n", - "plt.semilogx(td, hd, \".\", label=\"deep obs\")\n", - "plt.semilogx(ts, hs_3[0], label=\"shallow ttim\")\n", - "plt.semilogx(td, hd_3[0], label=\"deep ttim\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "aquifer with stratified Saq \n", - "unknown parameters: kaq, Saq, kzoverkh" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 21\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_3 = ttim.Model3D(\n", - " kaq=10, z=zlayers, Saq=Saq, kzoverkh=0.1, phreatictop=True, tmin=1e-4, tmax=1.1\n", - ")\n", - "w_3 = ttim.Well(ml_3, xw=0, yw=0, rw=0.1, tsandQ=[(0, Q)], layers=range(nlay))\n", - "ml_3.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".............................................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 106\n", - " # data points = 48\n", - " # variables = 5\n", - " chi-square = 5.6239e-04\n", - " reduced chi-square = 1.3079e-05\n", - " Akaike info crit = -535.016761\n", - " Bayesian info crit = -525.660756\n", - "[[Variables]]\n", - " kaq0_20: 74.9208569 +/- 2.27496608 (3.04%) (init = 50)\n", - " Saq0: 0.02057828 +/- 0.00155901 (7.58%) (init = 0.1)\n", - " Saq1_7: 4.5145e-04 +/- 5.8381e-05 (12.93%) (init = 0.0001)\n", - " Saq7_20: 2.3115e-05 +/- 1.1161e-06 (4.83%) (init = 0.0001)\n", - " kzoverkh: 3.7487e-04 +/- 7.8565e-05 (20.96%) (init = 0.1)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0_20, kzoverkh) = -0.965\n", - " C(kaq0_20, Saq7_20) = -0.755\n", - " C(Saq1_7, kzoverkh) = -0.736\n", - " C(Saq7_20, kzoverkh) = 0.709\n", - " C(kaq0_20, Saq1_7) = 0.629\n", - " C(kaq0_20, Saq0) = -0.609\n", - " C(Saq0, kzoverkh) = 0.597\n", - " C(Saq0, Saq1_7) = -0.596\n", - " C(Saq1_7, Saq7_20) = -0.556\n", - " C(Saq0, Saq7_20) = 0.486\n" - ] - } - ], - "source": [ - "ca_4 = ttim.Calibrate(ml_3)\n", - "ca_4.set_parameter(name=\"kaq0_20\", initial=50)\n", - "ca_4.set_parameter(name=\"Saq0\", initial=0.1)\n", - "ca_4.set_parameter(name=\"Saq1_7\", initial=1e-4, pmin=0)\n", - "ca_4.set_parameter(name=\"Saq7_20\", initial=1e-4, pmin=0)\n", - "ca_4.set_parameter_by_reference(\n", - " name=\"kzoverkh\", parameter=ml_3.aq.kzoverkh[:], initial=0.1, pmin=0\n", - ")\n", - "ca_4.series(name=\"obs1\", x=r, y=0, layer=1, t=ts, h=hs)\n", - "ca_4.series(name=\"obs2\", x=r, y=0, layer=15, t=td, h=hd)\n", - "ca_4.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_2074.92092.2749663.03649-infinf50[74.92085686643718, 74.92085686643718, 74.9208...
Saq00.02057830.0015597.576-infinf0.1[0.02057828028722851]
Saq1_70.0004514520.00005812.93190.0inf0.0001[0.0004514519384575255, 0.0004514519384575255,...
Saq7_202.31146e-050.0000014.828690.0inf0.0001[2.3114632324849893e-05, 2.3114632324849893e-0...
kzoverkh0.0003748730.00007920.95760.0inf0.1[0.00037487333092922626, 0.0003748733309292262...
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0_20 74.9209 2.274966 3.03649 -inf inf 50 \n", - "Saq0 0.0205783 0.001559 7.576 -inf inf 0.1 \n", - "Saq1_7 0.000451452 0.000058 12.9319 0.0 inf 0.0001 \n", - "Saq7_20 2.31146e-05 0.000001 4.82869 0.0 inf 0.0001 \n", - "kzoverkh 0.000374873 0.000079 20.9576 0.0 inf 0.1 \n", - "\n", - " parray \n", - "kaq0_20 [74.92085686643718, 74.92085686643718, 74.9208... \n", - "Saq0 [0.02057828028722851] \n", - "Saq1_7 [0.0004514519384575255, 0.0004514519384575255,... \n", - "Saq7_20 [2.3114632324849893e-05, 2.3114632324849893e-0... \n", - "kzoverkh [0.00037487333092922626, 0.0003748733309292262... " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.003422931514726531\n" - ] - } - ], - "source": [ - "display(ca_4.parameters)\n", - "print(\"RMSE:\", ca_4.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hs_4 = ml_3.head(x=r, y=0, t=ts, layers=1)\n", - "hd_4 = ml_3.head(x=r, y=0, t=td, layers=15)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(ts, hs, \".\", label=\"shallow obs\")\n", - "plt.semilogx(td, hd, \".\", label=\"deep obs\")\n", - "plt.semilogx(ts, hs_4[0], label=\"shallow ttim\")\n", - "plt.semilogx(td, hd_4[0], label=\"deep ttim\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary of values presented in Kruseman and de Ridder (1970)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "K&dR applies graphical analysis. Only the drawdown data of deep piezometer is used to estimate unknown parameters.AQTESOLV simulates this pumping test in the same way. The table shown below sumarrizes results of different methods applied to data of deep piezometer only." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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k [m/d]Ss [1/m]Sy [-]kz/khRMSE
K&dR732.476e-050.0050.000548-
AQTESOLV63.8052.663e-050.0110.000690.003041
MLU74.6572.767e-050.0050.0007370.003216
ttim116.5770.000345761NaNNaN0.0100803
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" - ], - "text/plain": [ - " k [m/d] Ss [1/m] Sy [-] kz/kh RMSE\n", - "K&dR 73 2.476e-05 0.005 0.000548 -\n", - "AQTESOLV 63.805 2.663e-05 0.011 0.00069 0.003041\n", - "MLU 74.657 2.767e-05 0.005 0.000737 0.003216\n", - "ttim 116.577 0.000345761 NaN NaN 0.0100803" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t1 = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Ss [1/m]\", \"Sy [-]\", \"kz/kh\"],\n", - " index=[\"K&dR\", \"AQTESOLV\", \"MLU\", \"ttim\"],\n", - ")\n", - "t1.loc[\"K&dR\"] = [73, 2.476e-05, 0.005, 0.000548]\n", - "t1.loc[\"AQTESOLV\"] = [63.805, 2.663e-05, 0.011, 0.000690]\n", - "t1.loc[\"MLU\"] = [74.657, 2.767e-05, 0.005, 0.000737]\n", - "t1.iloc[3, 0:2] = ca_2.parameters[\"optimal\"].values\n", - "t1[\"RMSE\"] = [\"-\", 0.003041, 0.003216, ca_2.rmse()]\n", - "t1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To optimize the conceptual model to be more realistic, multilayer model is applied. Table shown below sumarrizes results of MLU and ttim simulated with both piezometers." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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k [m/d]Sy [-]Ss [1/m]kzoverkhRMSE
MLU62.6570.00122.79e-050.0025950.013540
ttim-multilayer31.57930.05541543.47254e-050.01001510.009941
ttim-stratified Ss74.92090.02057832.31146e-050.0003748730.003423
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" - ], - "text/plain": [ - " k [m/d] Sy [-] Ss [1/m] kzoverkh RMSE\n", - "MLU 62.657 0.0012 2.79e-05 0.002595 0.013540\n", - "ttim-multilayer 31.5793 0.0554154 3.47254e-05 0.0100151 0.009941\n", - "ttim-stratified Ss 74.9209 0.0205783 2.31146e-05 0.000374873 0.003423" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t2 = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Sy [-]\", \"Ss [1/m]\", \"kzoverkh\"],\n", - " index=[\"MLU\", \"ttim-multilayer\", \"ttim-stratified Ss\"],\n", - ")\n", - "t2.loc[\"MLU\"] = [62.657, 0.0012, 2.790e-05, 0.002595]\n", - "t2.loc[\"ttim-multilayer\"] = ca_3.parameters[\"optimal\"].values\n", - "t2.iloc[2, 0:2] = ca_4.parameters[\"optimal\"].values[0:2]\n", - "t2.iloc[2, 2:4] = ca_4.parameters[\"optimal\"].values[3:5]\n", - "t2[\"RMSE\"] = [0.013540, ca_3.rmse(), ca_4.rmse()]\n", - "t2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/04pumpingtests/4_test_of_gridley.ipynb b/docs/04pumpingtests/4_test_of_gridley.ipynb deleted file mode 100755 index 3415b66..0000000 --- a/docs/04pumpingtests/4_test_of_gridley.ipynb +++ /dev/null @@ -1,928 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Confined Aquifer Test\n", - "**This test is taken from AQTESOLV examples.**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import ttim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set basic parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "b = -5.4846 # aquifer thickness in m\n", - "Q = 1199.218 # constant discharge in m^3/d\n", - "r = 251.1552 # distance between observation well to test well in m\n", - "rw = 0.1524 # screen radius of test well in m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "data1 = np.loadtxt(\"data/gridley_well_1.txt\")\n", - "t1 = data1[:, 0]\n", - "h1 = data1[:, 1]\n", - "data2 = np.loadtxt(\"data/gridley_well_3.txt\")\n", - "t2 = data2[:, 0]\n", - "h2 = data2[:, 1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create conceptual model:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml = ttim.ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\")\n", - "w = ttim.Well(ml, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", - "ml.solve()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calibrate with two datasets simultaneously:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "..................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 31\n", - " # data points = 22\n", - " # variables = 2\n", - " chi-square = 0.01702153\n", - " reduced chi-square = 8.5108e-04\n", - " Akaike info crit = -153.615012\n", - " Bayesian info crit = -151.432927\n", - "[[Variables]]\n", - " kaq0: 22.4340745 +/- 0.22268844 (0.99%) (init = 10)\n", - " Saq0: 3.8208e-06 +/- 7.4239e-08 (1.94%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.883\n" - ] - } - ], - "source": [ - "# unknown parameters: kaq, Saq\n", - "ca_0 = ttim.Calibrate(ml)\n", - "ca_0.set_parameter(name=\"kaq0\", initial=10)\n", - "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca_0.series(name=\"obs1\", x=r, y=0, t=t1, h=h1, layer=0)\n", - "ca_0.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq022.43412.226884e-010.992635-infinf10[22.43407446037401]
Saq03.82077e-067.423947e-081.94305-infinf0.0001[3.8207731812808345e-06]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 22.4341 2.226884e-01 0.992635 -inf inf 10 \n", - "Saq0 3.82077e-06 7.423947e-08 1.94305 -inf inf 0.0001 \n", - "\n", - " parray \n", - "kaq0 [22.43407446037401] \n", - "Saq0 [3.8207731812808345e-06] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.02781556960189975\n" - ] - } - ], - "source": [ - "display(ca_0.parameters)\n", - "print(\"rmse:\", ca_0.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm_0 = ml.head(r, 0, t1)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, hm_0[0], label=\"ttim\")\n", - "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "..............................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 27\n", - " # data points = 14\n", - " # variables = 2\n", - " chi-square = 0.04383792\n", - " reduced chi-square = 0.00365316\n", - " Akaike info crit = -76.7283879\n", - " Bayesian info crit = -75.4502733\n", - "[[Variables]]\n", - " kaq0: 27.9004460 +/- 0.73222849 (2.62%) (init = 10)\n", - " Saq0: 1.7009e-04 +/- 5.8287e-05 (34.27%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.997\n" - ] - } - ], - "source": [ - "# unknown parameters: kaq, Saq\n", - "ca_1 = ttim.Calibrate(ml)\n", - "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", - "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca_1.series(name=\"obs2\", x=0, y=0, t=t2, h=h2, layer=0)\n", - "ca_1.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq027.90040.7322282.62443-infinf10[27.90044604680429]
Saq00.0001700880.00005834.2689-infinf0.0001[0.00017008779752072242]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 27.9004 0.732228 2.62443 -inf inf 10 \n", - "Saq0 0.000170088 0.000058 34.2689 -inf inf 0.0001 \n", - "\n", - " parray \n", - "kaq0 [27.90044604680429] \n", - "Saq0 [0.00017008779752072242] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.05595784100028279\n" - ] - } - ], - "source": [ - "display(ca_1.parameters)\n", - "print(\"rmse:\", ca_1.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm_1 = ml.head(0, 0, t2)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t2, hm_1[0], label=\"ttim\")\n", - "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calibrate with two datasets simultaneously:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_1 = ttim.ModelMaq(\n", - " kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\"\n", - ")\n", - "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", - "ml_1.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "..........................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 23\n", - " # data points = 36\n", - " # variables = 2\n", - " chi-square = 2.65994309\n", - " reduced chi-square = 0.07823362\n", - " Akaike info crit = -89.7877116\n", - " Bayesian info crit = -86.6206737\n", - "[[Variables]]\n", - " kaq0: 38.0492587 +/- 0.52461607 (1.38%) (init = 10)\n", - " Saq0: 1.2468e-06 +/- 2.0176e-07 (16.18%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.769\n" - ] - } - ], - "source": [ - "ca_2 = ttim.Calibrate(ml_1)\n", - "ca_2.set_parameter(name=\"kaq0\", initial=10)\n", - "ca_2.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", - "ca_2.series(name=\"obs1\", x=r, y=0, t=t1, h=h1, layer=0)\n", - "ca_2.series(name=\"obs2\", x=0, y=0, t=t2, h=h2, layer=0)\n", - "ca_2.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq038.04935.246161e-011.37878-infinf10[38.049258732018906]
Saq01.24679e-062.017582e-0716.18230.0inf0.0001[1.2467860428522215e-06]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 38.0493 5.246161e-01 1.37878 -inf inf 10 \n", - "Saq0 1.24679e-06 2.017582e-07 16.1823 0.0 inf 0.0001 \n", - "\n", - " parray \n", - "kaq0 [38.049258732018906] \n", - "Saq0 [1.2467860428522215e-06] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.27182219922005885\n" - ] - } - ], - "source": [ - "display(ca_2.parameters)\n", - "print(\"rmse:\", ca_2.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm1_2 = ml.head(r, 0, t1)\n", - "hm2_2 = ml.head(0, 0, t2)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, hm1_2[0], label=\"ttim1\")\n", - "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", - "plt.semilogx(t2, hm2_2[0], label=\"ttim3\")\n", - "plt.semilogx(t2, h2, \".\", label=\"obs3\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ty adding well skin resistance and wellbore storage:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_2 = ttim.ModelMaq(\n", - " kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\"\n", - ")\n", - "w_2 = ttim.Well(ml_2, xw=0, yw=0, rw=rw, rc=0.2, res=0.2, tsandQ=[(0, Q)], layers=0)\n", - "ml_2.solve()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If adding wellbore sotrage to the parameters to be optimized, the fit gives extremely large values of each parameter which is imposiible. However, when remove rc from well function, the fit cannot be completed with uncertainties. Thus, the rc value is determined as 0.2 by trial-and-error procedure." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "..........................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 39\n", - " # data points = 36\n", - " # variables = 3\n", - " chi-square = 1.32970403\n", - " reduced chi-square = 0.04029406\n", - " Akaike info crit = -112.748252\n", - " Bayesian info crit = -107.997695\n", - "[[Variables]]\n", - " kaq0: 38.4235138 +/- 0.39672514 (1.03%) (init = 10)\n", - " Saq0: 8.9513e-07 +/- 1.1638e-07 (13.00%) (init = 0.0001)\n", - " res: 0.12326394 +/- 0.02036696 (16.52%) (init = 0.2)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.761\n", - " C(Saq0, res) = -0.246\n" - ] - } - ], - "source": [ - "ca_3 = ttim.Calibrate(ml_2)\n", - "ca_3.set_parameter(name=\"kaq0\", initial=10)\n", - "ca_3.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", - "ca_3.set_parameter_by_reference(name=\"res\", parameter=w_2.res, initial=0.2)\n", - "ca_3.series(name=\"obs1\", x=r, y=0, t=t1, h=h1, layer=0)\n", - "ca_3.series(name=\"obs3\", x=0, y=0, t=t2, h=h2, layer=0)\n", - "ca_3.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq038.42353.967251e-011.03251-infinf10[38.423513816802576]
Saq08.95129e-071.163837e-0713.00190.0inf0.0001[8.951294596659665e-07]
res0.1232642.036696e-0216.523-infinf0.2[0.1232639435087151]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 38.4235 3.967251e-01 1.03251 -inf inf 10 \n", - "Saq0 8.95129e-07 1.163837e-07 13.0019 0.0 inf 0.0001 \n", - "res 0.123264 2.036696e-02 16.523 -inf inf 0.2 \n", - "\n", - " parray \n", - "kaq0 [38.423513816802576] \n", - "Saq0 [8.951294596659665e-07] \n", - "res [0.1232639435087151] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse: 0.19218798899587736\n" - ] - } - ], - "source": [ - "display(ca_3.parameters)\n", - "print(\"rmse:\", ca_3.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hw1 = ml_2.head(r, 0, t1)\n", - "hw2 = ml_2.head(0, 0, t2)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, hw1[0], label=\"ttim1\")\n", - "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", - "plt.semilogx(t2, hw2[0], label=\"ttim3\")\n", - "plt.semilogx(t2, h2, \".\", label=\"obs3\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"drawdown(m)\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary of values simulated by AQTESOLV and MLU" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The results simulated by different methods with two datasets simultaneously are presented below. In the example of AQTESOLV, result simulated with only observation well is presented. The comparision of results when only observation well is included can be found in the report related to this test." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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k [m/d]Ss [1/m]resrcRMSE
MLU38.0941.193e-06--0.259
AQTESOLV37.8031.356e-06--0.270
ttim38.04931.24679e-06--0.272
ttim-res&rc38.42358.95129e-070.1232640.20.192
\n", - "
" - ], - "text/plain": [ - " k [m/d] Ss [1/m] res rc RMSE\n", - "MLU 38.094 1.193e-06 - - 0.259\n", - "AQTESOLV 37.803 1.356e-06 - - 0.270\n", - "ttim 38.0493 1.24679e-06 - - 0.272\n", - "ttim-res&rc 38.4235 8.95129e-07 0.123264 0.2 0.192" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Ss [1/m]\", \"res\"],\n", - " index=[\"MLU\", \"AQTESOLV\", \"ttim\", \"ttim-res&rc\"],\n", - ")\n", - "t.loc[\"MLU\"] = [38.094, 1.193e-06, \"-\"]\n", - "t.loc[\"AQTESOLV\"] = [37.803, 1.356e-06, \"-\"]\n", - "t.loc[\"ttim\"] = np.append(ca_2.parameters[\"optimal\"].values, \"-\")\n", - "t.loc[\"ttim-res&rc\"] = ca_3.parameters[\"optimal\"].values\n", - "t[\"rc\"] = [\"-\", \"-\", \"-\", 0.2]\n", - "t[\"RMSE\"] = [0.259, 0.270, 0.272, 0.192]\n", - "t" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/04pumpingtests/5_test_of_sioux.ipynb b/docs/04pumpingtests/5_test_of_sioux.ipynb deleted file mode 100755 index e000ee1..0000000 --- a/docs/04pumpingtests/5_test_of_sioux.ipynb +++ /dev/null @@ -1,633 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Confined Aquifer Test\n", - "**This test is taken from AQTESOLV examples.**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import ttim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set basic parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "Q = 6605.754 # constant discharge in m^3/d\n", - "b = -15.24 # aquifer thickness in m\n", - "rw = 0.1524 # well radius in m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load data of three observation wells:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "data1 = np.loadtxt(\"data/sioux100.txt\")\n", - "t1 = data1[:, 0]\n", - "h1 = data1[:, 1]\n", - "r1 = 30.48 # distance between obs1 to pumping well\n", - "\n", - "data2 = np.loadtxt(\"data/sioux200.txt\")\n", - "t2 = data2[:, 0]\n", - "h2 = data2[:, 1]\n", - "r2 = 60.96 # distance between obs2 to pumping well\n", - "\n", - "data3 = np.loadtxt(\"data/sioux400.txt\")\n", - "t3 = data3[:, 0]\n", - "h3 = data3[:, 1]\n", - "r3 = 121.92 # distance between obs3 to pumping well" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create conceptual model:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_0 = ttim.ModelMaq(\n", - " kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=10, topboundary=\"conf\"\n", - ")\n", - "w_0 = ttim.Well(ml_0, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", - "ml_0.solve()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calibrate with three datasets simultaneously:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "........................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 37\n", - " # data points = 77\n", - " # variables = 2\n", - " chi-square = 0.00121634\n", - " reduced chi-square = 1.6218e-05\n", - " Akaike info crit = -847.289957\n", - " Bayesian info crit = -842.602346\n", - "[[Variables]]\n", - " kaq0: 282.795193 +/- 1.13788959 (0.40%) (init = 10)\n", - " Saq0: 0.00420855 +/- 3.3461e-05 (0.80%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.811\n" - ] - } - ], - "source": [ - "# unknown parameters: k, Saq\n", - "ca_0 = ttim.Calibrate(ml_0)\n", - "ca_0.set_parameter(name=\"kaq0\", initial=10)\n", - "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca_0.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca_0.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", - "ca_0.series(name=\"obs3\", x=r3, y=0, t=t3, h=h3, layer=0)\n", - "ca_0.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq0282.7951.1378900.402372-infinf10[282.7951928965306]
Saq00.004208550.0000330.795071-infinf0.0001[0.004208548777783135]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 282.795 1.137890 0.402372 -inf inf 10 \n", - "Saq0 0.00420855 0.000033 0.795071 -inf inf 0.0001 \n", - "\n", - " parray \n", - "kaq0 [282.7951928965306] \n", - "Saq0 [0.004208548777783135] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.003974497855253861\n" - ] - } - ], - "source": [ - "display(ca_0.parameters)\n", - "print(\"RMSE:\", ca_0.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm1_0 = ml_0.head(r1, 0, t1)\n", - "hm2_0 = ml_0.head(r2, 0, t2)\n", - "hm3_0 = ml_0.head(r3, 0, t3)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", - "plt.semilogx(t1, hm1_0[0], label=\"ttim1\")\n", - "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", - "plt.semilogx(t2, hm2_0[0], label=\"ttim2\")\n", - "plt.semilogx(t3, h3, \".\", label=\"obs3\")\n", - "plt.semilogx(t3, hm3_0[0], label=\"ttim3\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"head(m)\")\n", - "plt.legend()\n", - "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/siouxsfit1.eps\")\n", - "plt.show();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Try adding res and rc:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_1 = ttim.ModelMaq(\n", - " kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=10, topboundary=\"conf\"\n", - ")\n", - "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=rw, rc=0, res=0, tsandQ=[(0, Q)], layers=0)\n", - "ml_1.solve()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calibrate with three datasets simultaneously:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When adding both res and rc into calibration, the optimized res value is bout 1.2e-12, which is close to the minimum limitation. Thus, adding res has nearly no effect on improving conceptual model's performance. res is removed from the calibration." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".........................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 54\n", - " # data points = 77\n", - " # variables = 3\n", - " chi-square = 0.00116245\n", - " reduced chi-square = 1.5709e-05\n", - " Akaike info crit = -848.779350\n", - " Bayesian info crit = -841.747934\n", - "[[Variables]]\n", - " kaq0: 283.922753 +/- 1.28518297 (0.45%) (init = 10)\n", - " Saq0: 0.00415476 +/- 4.3874e-05 (1.06%) (init = 0.0001)\n", - " rc: 0.79000886 +/- 0.21255267 (26.91%) (init = 0)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.852\n", - " C(Saq0, rc) = -0.669\n", - " C(kaq0, rc) = 0.487\n" - ] - } - ], - "source": [ - "# unknown parameters: k, Saq, res, rc\n", - "ca_1 = ttim.Calibrate(ml_1)\n", - "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", - "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "# ca_1.set_parameter_by_reference(name='res', parameter=w_1.res, initial=0, pmin = 0)\n", - "ca_1.set_parameter_by_reference(name=\"rc\", parameter=w_1.rc, initial=0)\n", - "ca_1.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca_1.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", - "ca_1.series(name=\"obs3\", x=r3, y=0, t=t3, h=h3, layer=0)\n", - "ca_1.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq0283.9231.2851830.452652-infinf10[283.92275254894827]
Saq00.004154760.0000441.056-infinf0.0001[0.004154762618152215]
rc0.7900090.21255326.9051-infinf0[0.7900088595416288]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 283.923 1.285183 0.452652 -inf inf 10 \n", - "Saq0 0.00415476 0.000044 1.056 -inf inf 0.0001 \n", - "rc 0.790009 0.212553 26.9051 -inf inf 0 \n", - "\n", - " parray \n", - "kaq0 [283.92275254894827] \n", - "Saq0 [0.004154762618152215] \n", - "rc [0.7900088595416288] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.0038854547009272195\n" - ] - } - ], - "source": [ - "display(ca_1.parameters)\n", - "print(\"RMSE:\", ca_1.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm1_1 = ml_1.head(r1, 0, t1)\n", - "hm2_1 = ml_1.head(r2, 0, t2)\n", - "hm3_1 = ml_1.head(r3, 0, t3)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", - "plt.semilogx(t1, hm1_1[0], label=\"ttim1\")\n", - "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", - "plt.semilogx(t2, hm2_1[0], label=\"ttim2\")\n", - "plt.semilogx(t3, h3, \".\", label=\"obs3\")\n", - "plt.semilogx(t3, hm3_1[0], label=\"ttim3\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"head(m)\")\n", - "plt.legend()\n", - "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/siouxfit2.eps\")\n", - "plt.show();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary of values simulated by AQTESOLV" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
k [m/d]Ss [1/m]rcRMSE
AQTESOLV282.6590.004211-0.003925
MLU282.6840.004209-0.003897
ttim282.7950.00420855-0.003974
ttim-rc283.9230.004154760.7900090.003885
\n", - "
" - ], - "text/plain": [ - " k [m/d] Ss [1/m] rc RMSE\n", - "AQTESOLV 282.659 0.004211 - 0.003925\n", - "MLU 282.684 0.004209 - 0.003897\n", - "ttim 282.795 0.00420855 - 0.003974\n", - "ttim-rc 283.923 0.00415476 0.790009 0.003885" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Ss [1/m]\", \"rc\"], index=[\"AQTESOLV\", \"MLU\", \"ttim\", \"ttim-rc\"]\n", - ")\n", - "t.loc[\"AQTESOLV\"] = [282.659, 4.211e-03, \"-\"]\n", - "t.loc[\"ttim\"] = np.append(ca_0.parameters[\"optimal\"].values, \"-\")\n", - "t.loc[\"ttim-rc\"] = ca_1.parameters[\"optimal\"].values\n", - "t.loc[\"MLU\"] = [282.684, 4.209e-03, \"-\"]\n", - "t[\"RMSE\"] = [0.003925, 0.003897, ca_0.rmse(), ca_1.rmse()]\n", - "t" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/04pumpingtests/6_test_of_schroth.ipynb b/docs/04pumpingtests/6_test_of_schroth.ipynb deleted file mode 100755 index b9df833..0000000 --- a/docs/04pumpingtests/6_test_of_schroth.ipynb +++ /dev/null @@ -1,1500 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Confined Aquifer Test\n", - "**This test is taken from examples presented in MLU tutorial.**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import ttim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The test is condected at a fully confined two-aquifer system. Both the pumping well and the observation piezometer are screened at the second aquifer." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set basic parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "Q = 82.08 # constant discharge in m^3/d\n", - "zt0 = -46 # top boundary of upper aquifer in m\n", - "zb0 = -49 # bottom boundary of upper aquifer in m\n", - "zt1 = -52 # top boundary of lower aquifer in m\n", - "zb1 = -55 # bottom boundary of lower aquifer in m\n", - "rw = 0.05 # well radius in m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load data of two observation wells:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "data1 = np.loadtxt(\"data/schroth_obs1.txt\", skiprows=1)\n", - "t1 = data1[:, 0]\n", - "h1 = data1[:, 1]\n", - "r1 = 0\n", - "data2 = np.loadtxt(\"data/schroth_obs2.txt\", skiprows=1)\n", - "t2 = data2[:, 0]\n", - "h2 = data2[:, 1]\n", - "r2 = 46 # distance between observation well2 and pumping well" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create single layer model (overlying aquifer and aquitard are excluded):" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_0 = ttim.ModelMaq(z=[zt1, zb1], kaq=10, Saq=1e-4, tmin=1e-4, tmax=1)\n", - "w_0 = ttim.Well(ml_0, xw=0, yw=0, rw=rw, tsandQ=[(0, Q), (1e08, 0)])\n", - "ml_0.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "................................................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 109\n", - " # data points = 40\n", - " # variables = 2\n", - " chi-square = 111.249432\n", - " reduced chi-square = 2.92761662\n", - " Akaike info crit = 44.9158143\n", - " Bayesian info crit = 48.2935732\n", - "[[Variables]]\n", - " kaq0: 1.03194176 +/- 0.10473587 (10.15%) (init = 10)\n", - " Saq0: 0.04015834 +/- 0.02030818 (50.57%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.931\n" - ] - } - ], - "source": [ - "ca_0 = ttim.Calibrate(ml_0)\n", - "ca_0.set_parameter(name=\"kaq0\", initial=10)\n", - "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca_0.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca_0.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", - "ca_0.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq01.031940.10473610.1494-infinf10[1.031941763294424]
Saq00.04015830.02030850.5702-infinf0.0001[0.04015834492968417]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial parray\n", - "kaq0 1.03194 0.104736 10.1494 -inf inf 10 [1.031941763294424]\n", - "Saq0 0.0401583 0.020308 50.5702 -inf inf 0.0001 [0.04015834492968417]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 1.6677037475808145\n" - ] - } - ], - "source": [ - "display(ca_0.parameters)\n", - "print(\"RMSE:\", ca_0.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm1_0 = ml_0.head(r1, 0, t1)\n", - "hm2_0 = ml_0.head(r2, 0, t2)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", - "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", - "plt.semilogx(t1, hm1_0[-1], label=\"ttim1\")\n", - "plt.semilogx(t2, hm2_0[-1], label=\"ttim2\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"head(m)\")\n", - "plt.legend()\n", - "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/schroth_one1.eps\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To improve model's performance, rc & res are adding:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_1 = ttim.ModelMaq(z=[zt1, zb1], kaq=10, Saq=1e-4, tmin=1e-4, tmax=1)\n", - "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=rw, rc=0, res=5, tsandQ=[(0, Q), (1e08, 0)])\n", - "ml_1.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".....................................................................................................................................................................................................................................................................................................................................................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 402\n", - " # data points = 40\n", - " # variables = 4\n", - " chi-square = 13.6490004\n", - " reduced chi-square = 0.37913890\n", - " Akaike info crit = -35.0085267\n", - " Bayesian info crit = -28.2530089\n", - "[[Variables]]\n", - " kaq0: 1.95212728 +/- 0.05269125 (2.70%) (init = 10)\n", - " Saq0: 1.1462e-04 +/- 3.3116e-05 (28.89%) (init = 0.0001)\n", - " rc: 0.00272166 +/- 0.02445717 (898.61%) (init = 0.2)\n", - " res: 35.9712200 +/- 648.602536 (1803.12%) (init = 3)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(rc, res) = -1.000\n", - " C(kaq0, Saq0) = -0.864\n", - " C(Saq0, rc) = -0.105\n", - " C(Saq0, res) = 0.104\n" - ] - } - ], - "source": [ - "ca_1 = ttim.Calibrate(ml_1)\n", - "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", - "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca_1.set_parameter_by_reference(name=\"rc\", parameter=w_1.rc[:], initial=0.2)\n", - "ca_1.set_parameter_by_reference(name=\"res\", parameter=w_1.res[:], initial=3)\n", - "ca_1.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca_1.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", - "ca_1.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq01.952130.0526912.69917-infinf10[1.9521272762456596]
Saq00.0001146150.00003328.8936-infinf0.0001[0.00011461547019049991]
rc0.002721660.024457898.614-infinf0.2[0.0027216554033511415]
res35.9712648.6025361803.12-infinf3[35.97121995862126]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 1.95213 0.052691 2.69917 -inf inf 10 \n", - "Saq0 0.000114615 0.000033 28.8936 -inf inf 0.0001 \n", - "rc 0.00272166 0.024457 898.614 -inf inf 0.2 \n", - "res 35.9712 648.602536 1803.12 -inf inf 3 \n", - "\n", - " parray \n", - "kaq0 [1.9521272762456596] \n", - "Saq0 [0.00011461547019049991] \n", - "rc [0.0027216554033511415] \n", - "res [35.97121995862126] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.5841446816200618\n" - ] - } - ], - "source": [ - "display(ca_1.parameters)\n", - "print(\"RMSE:\", ca_1.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm1_1 = ml_1.head(r1, 0, t1)\n", - "hm2_1 = ml_1.head(r2, 0, t2)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", - "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", - "plt.semilogx(t1, hm1_1[-1], label=\"ttim1\")\n", - "plt.semilogx(t2, hm2_1[-1], label=\"ttim2\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"head(m)\")\n", - "plt.legend()\n", - "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/schroth_one2.eps\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create three-layer conceptual model:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_2 = ttim.ModelMaq(\n", - " kaq=[17.28, 2],\n", - " z=[zt0, zb0, zt1, zb1],\n", - " c=200,\n", - " Saq=[1.2e-4, 1e-5],\n", - " Sll=3e-5,\n", - " topboundary=\"conf\",\n", - " tmin=1e-4,\n", - " tmax=0.5,\n", - ")\n", - "w_2 = ttim.Well(ml_2, xw=0, yw=0, rw=rw, tsandQ=[(0, Q), (1e08, 0)], layers=1)\n", - "ml_2.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".......................................................................................................................................................................................................................................................................................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 340\n", - " # data points = 40\n", - " # variables = 6\n", - " chi-square = 20.7597828\n", - " reduced chi-square = 0.61058185\n", - " Akaike info crit = -14.2344744\n", - " Bayesian info crit = -4.10119767\n", - "[[Variables]]\n", - " kaq0: 7121.54738 +/- 4391140.16 (61659.92%) (init = 20)\n", - " kaq1: 0.05228819 +/- 0.24082470 (460.57%) (init = 1)\n", - " Saq0: 1.71840155 +/- 3299.14117 (191988.95%) (init = 0.0001)\n", - " Saq1: 3.47007395 +/- 0.00372657 (0.11%) (init = 1e-05)\n", - " Sll: 0.08926901 +/- 51.2204008 (57377.58%) (init = 0.0001)\n", - " c1: 6.5485e-04 +/- 0.00365259 (557.77%) (init = 100)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq1, Sll) = -1.000\n", - " C(kaq1, c1) = 0.999\n", - " C(Sll, c1) = -0.999\n", - " C(kaq0, c1) = 0.704\n", - " C(kaq0, kaq1) = 0.681\n", - " C(kaq0, Sll) = -0.673\n", - " C(kaq0, Saq0) = -0.628\n", - " C(Saq0, c1) = -0.590\n", - " C(kaq1, Saq0) = -0.583\n", - " C(Saq0, Sll) = 0.579\n", - " C(kaq0, Saq1) = -0.189\n" - ] - } - ], - "source": [ - "ca_2 = ttim.Calibrate(ml_2)\n", - "ca_2.set_parameter(name=\"kaq0\", initial=20, pmin=0)\n", - "ca_2.set_parameter(name=\"kaq1\", initial=1, pmin=0)\n", - "ca_2.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", - "ca_2.set_parameter(name=\"Saq1\", initial=1e-5, pmin=0)\n", - "ca_2.set_parameter_by_reference(\n", - " name=\"Sll\", parameter=ml_2.aq.Sll[:], initial=1e-4, pmin=0\n", - ")\n", - "ca_2.set_parameter(name=\"c1\", initial=100, pmin=0)\n", - "ca_2.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=1)\n", - "ca_2.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=1)\n", - "ca_2.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq07121.554.391140e+0661659.90inf20[7121.547379372198]
kaq10.05228822.408247e-01460.5720inf1[0.052288192295638636]
Saq01.71843.299141e+031919890inf0.0001[1.718401550088255]
Saq13.470073.726568e-030.1073920inf1e-05[3.470073949617663]
Sll0.0892695.122040e+0157377.60inf0.0001[0.0892690109689882, 0.0892690109689882]
c10.0006548513.652587e-03557.7740inf100[0.0006548508260826313]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 7121.55 4.391140e+06 61659.9 0 inf 20 \n", - "kaq1 0.0522882 2.408247e-01 460.572 0 inf 1 \n", - "Saq0 1.7184 3.299141e+03 191989 0 inf 0.0001 \n", - "Saq1 3.47007 3.726568e-03 0.107392 0 inf 1e-05 \n", - "Sll 0.089269 5.122040e+01 57377.6 0 inf 0.0001 \n", - "c1 0.000654851 3.652587e-03 557.774 0 inf 100 \n", - "\n", - " parray \n", - "kaq0 [7121.547379372198] \n", - "kaq1 [0.052288192295638636] \n", - "Saq0 [1.718401550088255] \n", - "Saq1 [3.470073949617663] \n", - "Sll [0.0892690109689882, 0.0892690109689882] \n", - "c1 [0.0006548508260826313] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.720412776980667\n" - ] - } - ], - "source": [ - "display(ca_2.parameters)\n", - "print(\"RMSE:\", ca_2.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm1_2 = ml_2.head(r1, 0, t1)\n", - "hm2_2 = ml_2.head(r2, 0, t2)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", - "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", - "plt.semilogx(t1, hm1_2[-1], label=\"ttim1\")\n", - "plt.semilogx(t2, hm2_2[-1], label=\"ttim2\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"head(m)\")\n", - "plt.legend()\n", - "# plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/schroth_three1.eps\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Try adding res & rc:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_3 = ttim.ModelMaq(\n", - " kaq=[19, 2],\n", - " z=[zt0, zb0, zt1, zb1],\n", - " c=200,\n", - " Saq=[4e-4, 1e-5],\n", - " Sll=1e-4,\n", - " topboundary=\"conf\",\n", - " tmin=1e-4,\n", - " tmax=0.5,\n", - ")\n", - "w_3 = ttim.Well(\n", - " ml_3, xw=0, yw=0, rw=rw, rc=None, res=0, tsandQ=[(0, Q), (1e08, 0)], layers=1\n", - ")\n", - "ml_3.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "............................................................................................................................................................................................................................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 281\n", - " # data points = 40\n", - " # variables = 8\n", - " chi-square = 1.31482276\n", - " reduced chi-square = 0.04108821\n", - " Akaike info crit = -120.607103\n", - " Bayesian info crit = -107.096068\n", - "[[Variables]]\n", - " kaq0: 5.35396479 +/- 9.58019344 (178.94%) (init = 20)\n", - " kaq1: 1.24301176 +/- 9.18218767 (738.70%) (init = 1)\n", - " Saq0: 4.3148e-05 +/- 4.7634e-04 (1103.96%) (init = 0.0001)\n", - " Saq1: 3.3501e-06 +/- 4.1173e-04 (12290.20%) (init = 1e-05)\n", - " Sll: 2.0607e-06 +/- 1.4925e-04 (7242.43%) (init = 0.0001)\n", - " c1: 0.68271013 +/- 39.8053513 (5830.49%) (init = 100)\n", - " res: 9.4847e-06 +/- 0.02700515 (284722.84%) (init = 0)\n", - " rc: 0.05542009 +/- 0.00690208 (12.45%) (init = 0.2)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq1, c1) = 1.000\n", - " C(kaq0, kaq1) = -0.997\n", - " C(kaq0, c1) = -0.997\n", - " C(kaq1, Saq1) = -0.988\n", - " C(Saq1, c1) = -0.988\n", - " C(kaq0, Saq1) = 0.984\n", - " C(Saq0, Saq1) = -0.952\n", - " C(kaq0, Saq0) = -0.910\n", - " C(kaq1, Saq0) = 0.909\n", - " C(Saq0, c1) = 0.909\n", - " C(res, rc) = -0.873\n", - " C(Saq1, rc) = -0.761\n", - " C(Saq0, rc) = 0.740\n", - " C(c1, rc) = 0.738\n", - " C(kaq1, rc) = 0.738\n", - " C(kaq0, rc) = -0.718\n", - " C(Saq0, Sll) = -0.484\n", - " C(Saq1, res) = 0.367\n", - " C(c1, res) = -0.355\n", - " C(kaq1, res) = -0.355\n", - " C(Saq0, res) = -0.339\n", - " C(kaq0, res) = 0.333\n", - " C(Sll, rc) = -0.209\n", - " C(Saq1, Sll) = 0.194\n" - ] - } - ], - "source": [ - "ca_3 = ttim.Calibrate(ml_3)\n", - "ca_3.set_parameter(name=\"kaq0\", initial=20, pmin=0)\n", - "ca_3.set_parameter(name=\"kaq1\", initial=1, pmin=0)\n", - "ca_3.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", - "ca_3.set_parameter(name=\"Saq1\", initial=1e-5, pmin=0)\n", - "ca_3.set_parameter_by_reference(\n", - " name=\"Sll\", parameter=ml_3.aq.Sll[:], initial=1e-4, pmin=0\n", - ")\n", - "ca_3.set_parameter(name=\"c1\", initial=100, pmin=0)\n", - "ca_3.set_parameter_by_reference(name=\"res\", parameter=w_3.res[:], initial=0, pmin=0)\n", - "ca_3.set_parameter_by_reference(name=\"rc\", parameter=w_3.rc[:], initial=0.2, pmin=0)\n", - "ca_3.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=1)\n", - "ca_3.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=1)\n", - "ca_3.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq05.353969.580193178.9360inf20[5.353964789384363]
kaq11.243019.182188738.7050inf1[1.2430117621749774]
Saq04.31484e-050.0004761103.960inf0.0001[4.314844196295908e-05]
Saq13.35008e-060.00041212290.20inf1e-05[3.3500846787770655e-06]
Sll2.06071e-060.0001497242.430inf0.0001[2.0607078541345913e-06, 2.0607078541345913e-06]
c10.6827139.8053515830.490inf100[0.6827101318045474]
res9.48472e-060.0270052847230inf0[9.484716873453536e-06]
rc0.05542010.00690212.45410inf0.2[0.0554200877661315]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 5.35396 9.580193 178.936 0 inf 20 \n", - "kaq1 1.24301 9.182188 738.705 0 inf 1 \n", - "Saq0 4.31484e-05 0.000476 1103.96 0 inf 0.0001 \n", - "Saq1 3.35008e-06 0.000412 12290.2 0 inf 1e-05 \n", - "Sll 2.06071e-06 0.000149 7242.43 0 inf 0.0001 \n", - "c1 0.68271 39.805351 5830.49 0 inf 100 \n", - "res 9.48472e-06 0.027005 284723 0 inf 0 \n", - "rc 0.0554201 0.006902 12.4541 0 inf 0.2 \n", - "\n", - " parray \n", - "kaq0 [5.353964789384363] \n", - "kaq1 [1.2430117621749774] \n", - "Saq0 [4.314844196295908e-05] \n", - "Saq1 [3.3500846787770655e-06] \n", - "Sll [2.0607078541345913e-06, 2.0607078541345913e-06] \n", - "c1 [0.6827101318045474] \n", - "res [9.484716873453536e-06] \n", - "rc [0.0554200877661315] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.18130242391611345\n" - ] - } - ], - "source": [ - "display(ca_3.parameters)\n", - "print(\"RMSE:\", ca_3.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm1_3 = ml_3.head(r1, 0, t1)\n", - "hm2_3 = ml_3.head(r2, 0, t2)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", - "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", - "plt.semilogx(t1, hm1_3[-1], label=\"ttim1\")\n", - "plt.semilogx(t2, hm2_3[-1], label=\"ttim2\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"head(m)\")\n", - "plt.legend()\n", - "# plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/schroth_three2.eps\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calibrate with fitted characters for upper aquifer:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_4 = ttim.ModelMaq(\n", - " kaq=[17.28, 2],\n", - " z=[zt0, zb0, zt1, zb1],\n", - " c=200,\n", - " Saq=[1.2e-4, 1e-5],\n", - " Sll=3e-5,\n", - " topboundary=\"conf\",\n", - " tmin=1e-4,\n", - " tmax=0.5,\n", - ")\n", - "w_4 = ttim.Well(\n", - " ml_4, xw=0, yw=0, rw=rw, rc=None, res=0, tsandQ=[(0, Q), (1e08, 0)], layers=1\n", - ")\n", - "ml_4.solve()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The optimized value of res is very close to the minimum limitation, thus res has little effect on the performance of the model. res is removed in this calibration." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".......................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 68\n", - " # data points = 40\n", - " # variables = 4\n", - " chi-square = 0.56419255\n", - " reduced chi-square = 0.01567202\n", - " Akaike info crit = -162.449565\n", - " Bayesian info crit = -155.694048\n", - "[[Variables]]\n", - " kaq1: 1.93432822 +/- 0.01232555 (0.64%) (init = 1)\n", - " Saq1: 1.3177e-05 +/- 2.3128e-06 (17.55%) (init = 1e-05)\n", - " c1: 239.777472 +/- 20.5042386 (8.55%) (init = 100)\n", - " rc: 0.05268634 +/- 4.0803e-04 (0.77%) (init = 0.2)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq1, c1) = 0.839\n", - " C(Saq1, rc) = -0.582\n", - " C(kaq1, Saq1) = -0.503\n", - " C(Saq1, c1) = -0.214\n", - " C(c1, rc) = -0.111\n" - ] - } - ], - "source": [ - "ca_4 = ttim.Calibrate(ml_4)\n", - "ca_4.set_parameter(name=\"kaq1\", initial=1, pmin=0)\n", - "ca_4.set_parameter(name=\"Saq1\", initial=1e-5, pmin=0)\n", - "ca_4.set_parameter(name=\"c1\", initial=100, pmin=0)\n", - "ca_4.set_parameter_by_reference(name=\"rc\", parameter=w_4.rc[:], initial=0.2, pmin=0)\n", - "ca_4.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=1)\n", - "ca_4.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=1)\n", - "ca_4.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq11.934330.0123260.6372010inf1[1.9343282166578555]
Saq11.31765e-050.00000217.55250inf1e-05[1.3176530509806383e-05]
c1239.77720.5042398.551360inf100[239.7774720841064]
rc0.05268630.0004080.7744530inf0.2[0.05268634438990305]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq1 1.93433 0.012326 0.637201 0 inf 1 \n", - "Saq1 1.31765e-05 0.000002 17.5525 0 inf 1e-05 \n", - "c1 239.777 20.504239 8.55136 0 inf 100 \n", - "rc 0.0526863 0.000408 0.774453 0 inf 0.2 \n", - "\n", - " parray \n", - "kaq1 [1.9343282166578555] \n", - "Saq1 [1.3176530509806383e-05] \n", - "c1 [239.7774720841064] \n", - "rc [0.05268634438990305] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.11876368880039383\n" - ] - } - ], - "source": [ - "display(ca_4.parameters)\n", - "print(\"RMSE:\", ca_4.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm1_4 = ml_4.head(r1, 0, t1)\n", - "hm2_4 = ml_4.head(r2, 0, t2)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", - "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", - "plt.semilogx(t1, hm1_4[-1], label=\"ttim1\")\n", - "plt.semilogx(t2, hm2_4[-1], label=\"ttim2\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"head(m)\")\n", - "plt.legend()\n", - "# plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/schroth_three3.eps\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary of values simulated by MLU" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Results of calibrations done with three-layer model of ttim are presented below." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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k0[m/d]k1[m/d]Ss0[1/m]Ss1[1/m]Sll[1/m]c[d]resrcRMSE
MLU17.4246.027e-051.7476.473e-063.997e-05216NaNNaN0.023452
MLU-fixed k10.0002020.0009113.4566.214e-057.286e-05453.5NaNNaN0.162596
ttim7121.550.05228821.71843.470070.0892690.000654851NaNNaN0.720413
ttim-rc5.353961.243014.31484e-053.35008e-062.06071e-060.682719.48472e-060.05542010.181302
ttim-fixed upper17.281.934330.000121.31765e-053e-05239.777NaN0.05268630.118764
\n", - "
" - ], - "text/plain": [ - " k0[m/d] k1[m/d] Ss0[1/m] Ss1[1/m] Sll[1/m] \\\n", - "MLU 17.424 6.027e-05 1.747 6.473e-06 3.997e-05 \n", - "MLU-fixed k1 0.000202 0.000911 3.456 6.214e-05 7.286e-05 \n", - "ttim 7121.55 0.0522882 1.7184 3.47007 0.089269 \n", - "ttim-rc 5.35396 1.24301 4.31484e-05 3.35008e-06 2.06071e-06 \n", - "ttim-fixed upper 17.28 1.93433 0.00012 1.31765e-05 3e-05 \n", - "\n", - " c[d] res rc RMSE \n", - "MLU 216 NaN NaN 0.023452 \n", - "MLU-fixed k1 453.5 NaN NaN 0.162596 \n", - "ttim 0.000654851 NaN NaN 0.720413 \n", - "ttim-rc 0.68271 9.48472e-06 0.0554201 0.181302 \n", - "ttim-fixed upper 239.777 NaN 0.0526863 0.118764 " - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t = pd.DataFrame(\n", - " columns=[\n", - " \"k0[m/d]\",\n", - " \"k1[m/d]\",\n", - " \"Ss0[1/m]\",\n", - " \"Ss1[1/m]\",\n", - " \"Sll[1/m]\",\n", - " \"c[d]\",\n", - " \"res\",\n", - " \"rc\",\n", - " ],\n", - " index=[\"MLU\", \"MLU-fixed k1\", \"ttim\", \"ttim-rc\", \"ttim-fixed upper\"],\n", - ")\n", - "t.loc[\"ttim-rc\"] = ca_3.parameters[\"optimal\"].values\n", - "t.iloc[2, 0:6] = ca_2.parameters[\"optimal\"].values\n", - "t.iloc[4, 5] = ca_4.parameters[\"optimal\"].values[2]\n", - "t.iloc[4, 7] = ca_4.parameters[\"optimal\"].values[3]\n", - "t.iloc[4, 0] = 17.28\n", - "t.iloc[4, 1] = ca_4.parameters[\"optimal\"].values[0]\n", - "t.iloc[4, 2] = 1.2e-4\n", - "t.iloc[4, 3] = ca_4.parameters[\"optimal\"].values[1]\n", - "t.iloc[4, 4] = 3e-5\n", - "t.iloc[0, 0:6] = [17.424, 6.027e-05, 1.747, 6.473e-06, 3.997e-05, 216]\n", - "t.iloc[1, 0:6] = [2.020e-04, 9.110e-04, 3.456, 6.214e-05, 7.286e-05, 453.5]\n", - "t[\"RMSE\"] = [0.023452, 0.162596, ca_2.rmse(), ca_3.rmse(), ca_4.rmse()]\n", - "t" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/04pumpingtests/7_test_of_neveda_double-porosity.ipynb b/docs/04pumpingtests/7_test_of_neveda_double-porosity.ipynb deleted file mode 100755 index fb2e3c1..0000000 --- a/docs/04pumpingtests/7_test_of_neveda_double-porosity.ipynb +++ /dev/null @@ -1,737 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Double Porosity Test\n", - "**This test is taken from MLU examples.**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import ttim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set basic parameters for the model:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "H = 400 # aquifer thickness [m]\n", - "Q = 3093.12 # constant pumping rate [m^3/d]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load data:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Pumped well UE-25b#1\n", - "data1 = np.loadtxt(\"data/double-porosity-pumpingwell.txt\", skiprows=1)\n", - "t1 = data1[:, 0]\n", - "h1 = data1[:, 1]\n", - "\n", - "# Observation well UE-25a#1\n", - "data2 = np.loadtxt(\"data/double-porosity-110m.txt\", skiprows=1)\n", - "t2 = data2[:, 0]\n", - "h2 = data2[:, 1]\n", - "r = 110 # distance from obs to pumped well" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create conceptual model:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "km = 0.1 / H # hydraulic conductivity of matrix calculated by K&dR\n", - "Sm = 3.85e-4 # specific storage of matrix calculated by" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml = ttim.ModelMaq(\n", - " kaq=[km, 1],\n", - " z=[0, -400, -401, -801],\n", - " c=5,\n", - " Saq=[Sm, 1e-3],\n", - " Sll=0,\n", - " topboundary=\"conf\",\n", - " tmin=1e-5,\n", - " tmax=3,\n", - ")\n", - "w = ttim.Well(ml, xw=0, yw=0, rw=0.11, rc=0, tsandQ=[0, 3093.12], layers=1)\n", - "ml.solve()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calibrate with two datasets simultaneously:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "...............................................................................................................................................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 204\n", - " # data points = 138\n", - " # variables = 4\n", - " chi-square = 5.47351448\n", - " reduced chi-square = 0.04084712\n", - " Akaike info crit = -437.371923\n", - " Bayesian info crit = -425.662908\n", - "[[Variables]]\n", - " kaq1: 0.87697420 +/- 0.00699008 (0.80%) (init = 10)\n", - " Saq1: 5.0872e-06 +/- 5.0669e-07 (9.96%) (init = 0.0001)\n", - " c1: 13.0062814 +/- 1.59753939 (12.28%) (init = 10)\n", - " rc: 0.10560388 +/- 0.00320828 (3.04%) (init = 0)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq1, c1) = 0.858\n", - " C(kaq1, Saq1) = -0.731\n", - " C(Saq1, c1) = -0.546\n", - " C(Saq1, rc) = -0.401\n", - " C(kaq1, rc) = 0.101\n" - ] - }, - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq10.8769746.990084e-030.797068-infinf10[0.8769742034500458]
Saq15.08718e-065.066946e-079.960220.0inf0.0001[5.0871806069885395e-06]
c113.00631.597539e+0012.2828-infinf10[13.006281425119226]
rc0.1056043.208283e-033.03803-infinf0[0.10560388038543249]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq1 0.876974 6.990084e-03 0.797068 -inf inf 10 \n", - "Saq1 5.08718e-06 5.066946e-07 9.96022 0.0 inf 0.0001 \n", - "c1 13.0063 1.597539e+00 12.2828 -inf inf 10 \n", - "rc 0.105604 3.208283e-03 3.03803 -inf inf 0 \n", - "\n", - " parray \n", - "kaq1 [0.8769742034500458] \n", - "Saq1 [5.0871806069885395e-06] \n", - "c1 [13.006281425119226] \n", - "rc [0.10560388038543249] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.199156090620443\n" - ] - } - ], - "source": [ - "ca = ttim.Calibrate(ml)\n", - "ca.set_parameter(name=\"kaq1\", initial=10)\n", - "ca.set_parameter(name=\"Saq1\", initial=1e-4, pmin=0)\n", - "ca.set_parameter(name=\"c1\", initial=10)\n", - "ca.set_parameter_by_reference(name=\"rc\", parameter=w.rc, initial=0)\n", - "ca.series(name=\"UE-25b#1\", x=0, y=0, t=t1, h=h1, layer=1)\n", - "ca.series(name=\"UE-25a#1\", x=110, y=0, t=t2, h=h2, layer=1)\n", - "ca.fit(report=True)\n", - "display(ca.parameters)\n", - "print(\"RMSE:\", ca.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm1 = ml.head(0, 0, t1)\n", - "hm2 = ml.head(110, 0, t2)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"obs UE-25b#1\")\n", - "plt.semilogx(t1, hm1[-1], label=\"TTim UE-25b#1\")\n", - "plt.semilogx(t2, h2, \".\", label=\"obs UE-25a#1\")\n", - "plt.semilogx(t2, hm2[-1], label=\"TTim UE-25a#1\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"head(m)\")\n", - "plt.legend()\n", - "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/neveda_1.eps\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Simulate parameters of both fracture and matrix:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml1 = ttim.ModelMaq(\n", - " kaq=[1, 1],\n", - " z=[0, -400, -401, -801],\n", - " c=5,\n", - " Saq=[1e-3, 1e-3],\n", - " Sll=0,\n", - " topboundary=\"conf\",\n", - " tmin=1e-5,\n", - " tmax=3,\n", - ")\n", - "w1 = ttim.Well(ml1, xw=0, yw=0, rw=0.11, rc=0, tsandQ=[0, 3093.12], layers=1)\n", - "ml1.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "...........................................................................................................................................................................................................................................................................................................................................................................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 424\n", - " # data points = 138\n", - " # variables = 6\n", - " chi-square = 3.50593034\n", - " reduced chi-square = 0.02656008\n", - " Akaike info crit = -494.846092\n", - " Bayesian info crit = -477.282570\n", - "[[Variables]]\n", - " kaq0: 7.2084e-07 +/- 2.6714e-04 (37058.99%) (init = 1)\n", - " Saq0: 1.4416e-04 +/- 1.4738e-05 (10.22%) (init = 0.0001)\n", - " kaq1: 0.90900946 +/- 0.00603635 (0.66%) (init = 1)\n", - " Saq1: 3.3885e-06 +/- 3.0026e-07 (8.86%) (init = 0.0001)\n", - " c1: 15.5698007 +/- 1.43992968 (9.25%) (init = 100)\n", - " rc: 0.10856639 +/- 0.00253333 (2.33%) (init = 0)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq1, Saq1) = -0.742\n", - " C(kaq1, c1) = 0.721\n", - " C(Saq0, kaq1) = -0.661\n", - " C(Saq0, Saq1) = 0.532\n", - " C(Saq1, rc) = -0.408\n", - " C(Saq1, c1) = -0.403\n", - " C(Saq0, c1) = -0.264\n", - " C(kaq0, c1) = -0.167\n", - " C(kaq0, Saq0) = -0.165\n", - " C(kaq1, rc) = 0.116\n" - ] - }, - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq07.20841e-072.671362e-04370590inf1[7.208405168324106e-07]
Saq00.0001441621.473768e-0510.2230inf0.0001[0.00014416209370748945]
kaq10.9090096.036350e-030.6640580inf1[0.9090094568168918]
Saq13.38847e-063.002560e-078.86110inf0.0001[3.388472165299916e-06]
c115.56981.439930e+009.248220inf100[15.569800695761781]
rc0.1085662.533330e-032.333440inf0[0.10856639204347074]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 7.20841e-07 2.671362e-04 37059 0 inf 1 \n", - "Saq0 0.000144162 1.473768e-05 10.223 0 inf 0.0001 \n", - "kaq1 0.909009 6.036350e-03 0.664058 0 inf 1 \n", - "Saq1 3.38847e-06 3.002560e-07 8.8611 0 inf 0.0001 \n", - "c1 15.5698 1.439930e+00 9.24822 0 inf 100 \n", - "rc 0.108566 2.533330e-03 2.33344 0 inf 0 \n", - "\n", - " parray \n", - "kaq0 [7.208405168324106e-07] \n", - "Saq0 [0.00014416209370748945] \n", - "kaq1 [0.9090094568168918] \n", - "Saq1 [3.388472165299916e-06] \n", - "c1 [15.569800695761781] \n", - "rc [0.10856639204347074] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.1593903771061828\n" - ] - } - ], - "source": [ - "ca1 = ttim.Calibrate(ml1)\n", - "ca1.set_parameter(name=\"kaq0\", initial=1, pmin=0)\n", - "ca1.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", - "ca1.set_parameter(name=\"kaq1\", initial=1, pmin=0)\n", - "ca1.set_parameter(name=\"Saq1\", initial=1e-4, pmin=0)\n", - "ca1.set_parameter(name=\"c1\", initial=100, pmin=0)\n", - "ca1.set_parameter_by_reference(name=\"rc\", parameter=w1.rc, initial=0, pmin=0)\n", - "ca1.series(name=\"UE-25b#1\", x=0, y=0, t=t1, h=h1, layer=1)\n", - "ca1.series(name=\"UE-25a#1\", x=110, y=0, t=t2, h=h2, layer=1)\n", - "ca1.fit(report=True)\n", - "display(ca1.parameters)\n", - "print(\"RMSE:\", ca1.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ht1 = ml1.head(0, 0, t1)\n", - "ht2 = ml1.head(110, 0, t2)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"obs UE-25b#1\")\n", - "plt.semilogx(t1, ht1[-1], label=\"TTim UE-25b#1\")\n", - "plt.semilogx(t2, h2, \".\", label=\"obs UE-25a#1\")\n", - "plt.semilogx(t2, ht2[-1], label=\"TTim UE-25a#1\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"head(m)\")\n", - "plt.legend()\n", - "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/neveda_2.eps\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Summary of values simulate by different methods:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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km [m/d]Sm [1/m]kf [m/d]Sf [1/m]crcRMSE
K&dR0.83250.0003750.83254e-06---
Moench0.17280.00030.8641.5e-06---
AQTESOLV0.1490.00055120.9375.533e-06-0.110.031736
MLU0.000250.0003850.8748.053e-0612.380.10.434638
TTim10.000250.0003850.8769745.08718e-0613.00630.1056040.199156
TTim27.20841e-070.0001441620.9090093.38847e-0615.56980.1085660.15939
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" - ], - "text/plain": [ - " km [m/d] Sm [1/m] kf [m/d] Sf [1/m] c rc \\\n", - "K&dR 0.8325 0.000375 0.8325 4e-06 - - \n", - "Moench 0.1728 0.0003 0.864 1.5e-06 - - \n", - "AQTESOLV 0.149 0.0005512 0.937 5.533e-06 - 0.11 \n", - "MLU 0.00025 0.000385 0.874 8.053e-06 12.38 0.1 \n", - "TTim1 0.00025 0.000385 0.876974 5.08718e-06 13.0063 0.105604 \n", - "TTim2 7.20841e-07 0.000144162 0.909009 3.38847e-06 15.5698 0.108566 \n", - "\n", - " RMSE \n", - "K&dR - \n", - "Moench - \n", - "AQTESOLV 0.031736 \n", - "MLU 0.434638 \n", - "TTim1 0.199156 \n", - "TTim2 0.15939 " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t = pd.DataFrame(\n", - " columns=[\"km [m/d]\", \"Sm [1/m]\", \"kf [m/d]\", \"Sf [1/m]\", \"c\", \"rc\"],\n", - " index=[\"K&dR\", \"Moench\", \"AQTESOLV\", \"MLU\", \"TTim1\", \"TTim2\"],\n", - ")\n", - "t.loc[\"TTim2\"] = ca1.parameters[\"optimal\"].values\n", - "t.loc[\"K&dR\"] = [0.8325, 3.750e-4, 0.8325, 4.000e-6, \"-\", \"-\"]\n", - "t.loc[\"Moench\"] = [0.1728, 3.000e-4, 0.864, 1.500e-6, \"-\", \"-\"]\n", - "t.loc[\"AQTESOLV\"] = [0.149, 5.512e-4, 0.937, 5.533e-6, \"-\", 0.11]\n", - "t.loc[\"MLU\"] = [0.00025, 3.850e-04, 0.874, 8.053e-6, 12.380, 0.1]\n", - "t.iloc[4, 0:2] = [km, Sm]\n", - "t.iloc[4, 2:6] = ca.parameters[\"optimal\"].values\n", - "t[\"RMSE\"] = [\"-\", \"-\", 0.031736, 0.434638, ca.rmse(), ca1.rmse()]\n", - "t" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/04pumpingtests/8_test_of_hardinxveld_recovery.ipynb b/docs/04pumpingtests/8_test_of_hardinxveld_recovery.ipynb deleted file mode 100755 index fd102d2..0000000 --- a/docs/04pumpingtests/8_test_of_hardinxveld_recovery.ipynb +++ /dev/null @@ -1,1047 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Recovery Test\n", - "**This test is taken from MLU examples.**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import ttim\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set basic parameters for the model:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "H = 27 # aquifer thickness [m]\n", - "zt = -10 # upper boundary of aquifer\n", - "zb = zt - H\n", - "rw = 0.155 # well screen radius [m]\n", - "Q = 1848 # constant discharge rate [m^3/d]\n", - "t0 = 0.013889 # time stop pumping [d]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load data:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "data = np.loadtxt(\"data/recovery.txt\", skiprows=1)\n", - "t = data[:, 0]\n", - "h = data[:, 1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Conceptual model:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml1 = ttim.ModelMaq(\n", - " kaq=[50, 40],\n", - " z=[0, zt, zb, -68, -88],\n", - " c=[1000, 1000],\n", - " Saq=[1e-4, 5e-5],\n", - " topboundary=\"semi\",\n", - " tmin=1e-4,\n", - " tmax=0.04,\n", - ")\n", - "w1 = ttim.Well(ml1, xw=0, yw=0, rw=rw, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0)\n", - "ml1.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".....................................................................................................................................................................................................................................................................................................................................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 386\n", - " # data points = 35\n", - " # variables = 3\n", - " chi-square = 0.00106334\n", - " reduced chi-square = 3.3229e-05\n", - " Akaike info crit = -358.059061\n", - " Bayesian info crit = -353.393016\n", - "[[Variables]]\n", - " kaq0: 44.5282566 +/- 0.64521411 (1.45%) (init = 50)\n", - " Saq0: 6.3906e-06 +/- 9.7664e-07 (15.28%) (init = 0.0001)\n", - " res: 0.01620422 +/- 5.7744e-04 (3.56%) (init = 1)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, res) = 0.976\n", - " C(Saq0, res) = 0.951\n", - " C(kaq0, Saq0) = 0.863\n" - ] - } - ], - "source": [ - "ca1 = ttim.Calibrate(ml1)\n", - "ca1.set_parameter(name=\"kaq0\", initial=50, pmin=0)\n", - "ca1.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", - "ca1.set_parameter_by_reference(name=\"res\", parameter=w1.res[:], initial=1, pmin=0)\n", - "ca1.seriesinwell(name=\"obs\", element=w1, t=t, h=h)\n", - "ca1.fit()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq044.52836.452141e-011.4490inf50[44.52825664300624]
Saq06.39061e-069.766419e-0715.28250inf0.0001[6.390608494610817e-06]
res0.01620425.774437e-043.563540inf1[0.016204215932634325]
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" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 44.5283 6.452141e-01 1.449 0 inf 50 \n", - "Saq0 6.39061e-06 9.766419e-07 15.2825 0 inf 0.0001 \n", - "res 0.0162042 5.774437e-04 3.56354 0 inf 1 \n", - "\n", - " parray \n", - "kaq0 [44.52825664300624] \n", - "Saq0 [6.390608494610817e-06] \n", - "res [0.016204215932634325] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.005511911936638888\n" - ] - } - ], - "source": [ - "display(ca1.parameters)\n", - "print(\"RMSE:\", ca1.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm1 = w1.headinside(t)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.loglog(t, -h, \".\", label=\"obs\")\n", - "plt.loglog(t, -hm1[0], label=\"ttim\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"head(m)\")\n", - "plt.legend()\n", - "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/recovery-double.eps\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Add wellbore storage:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml2 = ttim.ModelMaq(\n", - " kaq=[50, 40],\n", - " z=[0, zt, zb, -68, -88],\n", - " c=[1000, 1000],\n", - " Saq=[1e-4, 5e-5],\n", - " topboundary=\"semi\",\n", - " tmin=1e-4,\n", - " tmax=0.04,\n", - ")\n", - "w2 = ttim.Well(\n", - " ml2, xw=0, yw=0, rw=rw, rc=0.155, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0\n", - ")\n", - "ml2.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 725\n", - " # data points = 35\n", - " # variables = 4\n", - " chi-square = 0.00106334\n", - " reduced chi-square = 3.4301e-05\n", - " Akaike info crit = -356.059080\n", - " Bayesian info crit = -349.837687\n", - "[[Variables]]\n", - " kaq0: 44.5298968 +/- 0.66771885 (1.50%) (init = 50)\n", - " Saq0: 6.3909e-06 +/- 1.0101e-06 (15.80%) (init = 0.0001)\n", - " rc: 0.00353647 +/- 0.22114709 (6253.32%) (init = 0.1)\n", - " res: 0.01620525 +/- 5.9232e-04 (3.66%) (init = 1)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, res) = 0.974\n", - " C(Saq0, res) = 0.942\n", - " C(kaq0, Saq0) = 0.844\n", - " C(Saq0, rc) = -0.198\n" - ] - } - ], - "source": [ - "ca2 = ttim.Calibrate(ml2)\n", - "ca2.set_parameter(name=\"kaq0\", initial=50, pmin=0)\n", - "ca2.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", - "ca2.set_parameter_by_reference(name=\"rc\", parameter=w2.rc[:], initial=0.1, pmin=0)\n", - "ca2.set_parameter_by_reference(name=\"res\", parameter=w2.res[:], initial=1, pmin=0)\n", - "ca2.seriesinwell(name=\"obs\", element=w2, t=t, h=h)\n", - "ca2.fit()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq044.52990.6677191.499480inf50[44.52989684168029]
Saq06.39092e-060.00000115.80490inf0.0001[6.390921652332793e-06]
rc0.003536470.2211476253.320inf0.1[0.003536472392082546]
res0.01620520.0005923.65510inf1[0.016205248228838176]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 44.5299 0.667719 1.49948 0 inf 50 \n", - "Saq0 6.39092e-06 0.000001 15.8049 0 inf 0.0001 \n", - "rc 0.00353647 0.221147 6253.32 0 inf 0.1 \n", - "res 0.0162052 0.000592 3.6551 0 inf 1 \n", - "\n", - " parray \n", - "kaq0 [44.52989684168029] \n", - "Saq0 [6.390921652332793e-06] \n", - "rc [0.003536472392082546] \n", - "res [0.016205248228838176] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.005511910430700859\n" - ] - } - ], - "source": [ - "display(ca2.parameters)\n", - "print(\"RMSE:\", ca2.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm2 = w2.headinside(t)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.loglog(t, -h, \".\", label=\"obs\")\n", - "plt.loglog(t, -hm2[0], label=\"ttim\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"head(m)\")\n", - "plt.legend()\n", - "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/recovery-double rc.eps\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Simulation with rc has a worse performance. RMSE increases, and the Akaike criteria is much larger than the former model. Thus, rc should be removed." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Single aquifer model:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml0 = ttim.ModelMaq(\n", - " kaq=50, z=[0, zt, zb], c=1000, Saq=1e-4, topboundary=\"semi\", tmin=1e-4, tmax=0.04\n", - ")\n", - "w0 = ttim.Well(ml0, xw=0, yw=0, rw=rw, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0)\n", - "ml0.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "....................................................................................................................................................................................................................................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 289\n", - " # data points = 35\n", - " # variables = 3\n", - " chi-square = 0.00106439\n", - " reduced chi-square = 3.3262e-05\n", - " Akaike info crit = -358.024604\n", - " Bayesian info crit = -353.358560\n", - "[[Variables]]\n", - " kaq0: 44.5517010 +/- 0.65622200 (1.47%) (init = 50)\n", - " Saq0: 3.2314e-06 +/- 4.9354e-07 (15.27%) (init = 0.0001)\n", - " res: 0.01502958 +/- 5.9551e-04 (3.96%) (init = 1)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, res) = 0.977\n", - " C(Saq0, res) = 0.949\n", - " C(kaq0, Saq0) = 0.862\n" - ] - } - ], - "source": [ - "ca0 = ttim.Calibrate(ml0)\n", - "ca0.set_parameter(name=\"kaq0\", initial=50, pmin=0)\n", - "ca0.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", - "ca0.set_parameter_by_reference(name=\"res\", parameter=w0.res[:], initial=1)\n", - "ca0.seriesinwell(name=\"obs\", element=w0, t=t, h=h)\n", - "ca0.fit()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq044.55176.562220e-011.472940inf50[44.551700962690695]
Saq03.23139e-064.935356e-0715.27320inf0.0001[3.2313908613357256e-06]
res0.01502965.955128e-043.96227-infinf1[0.015029581626088763]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 44.5517 6.562220e-01 1.47294 0 inf 50 \n", - "Saq0 3.23139e-06 4.935356e-07 15.2732 0 inf 0.0001 \n", - "res 0.0150296 5.955128e-04 3.96227 -inf inf 1 \n", - "\n", - " parray \n", - "kaq0 [44.551700962690695] \n", - "Saq0 [3.2313908613357256e-06] \n", - "res [0.015029581626088763] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.005514625729462559\n" - ] - } - ], - "source": [ - "display(ca0.parameters)\n", - "print(\"RMSE:\", ca0.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "data": { - "image/png": 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optimalstdperc_stdpminpmaxinitialparray
kaq045.29927.206879e-011.590950inf50[45.29920969773198]
Saq07.24786e-066.922490e-079.551080inf0.0001[7.247863323955883e-06]
res0.01681856.077721e-043.613710inf1[0.016818522068650976]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 45.2992 7.206879e-01 1.59095 0 inf 50 \n", - "Saq0 7.24786e-06 6.922490e-07 9.55108 0 inf 0.0001 \n", - "res 0.0168185 6.077721e-04 3.61371 0 inf 1 \n", - "\n", - " parray \n", - "kaq0 [45.29920969773198] \n", - "Saq0 [7.247863323955883e-06] \n", - "res [0.016818522068650976] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.01237621536126682\n" - ] - } - ], - "source": [ - "display(ca3.parameters)\n", - "print(\"RMSE:\", ca3.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm3 = w3.headinside(t)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.loglog(t, -h, \".\", label=\"obs\")\n", - "plt.loglog(t, -hm3[0], label=\"ttim\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"head(m)\")\n", - "plt.legend()\n", - "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/recovery-double log.eps\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "According to rmse and the Akaike criteria, log curve fitting solution performs worse than linear curve fitting. The results reported in following table are from models calibrated by linear curve fitting solution." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Summary of values modeled by different methods:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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k [m/d]Ss [1/m]resRMSE [m]
MLU-log51.530.0008160.0220.007560
TTim-single layer44.55173.23139e-060.01502960.005515
TTim-two layers44.52836.39061e-060.01620420.005512
\n", - "
" - ], - "text/plain": [ - " k [m/d] Ss [1/m] res RMSE [m]\n", - "MLU-log 51.53 0.000816 0.022 0.007560\n", - "TTim-single layer 44.5517 3.23139e-06 0.0150296 0.005515\n", - "TTim-two layers 44.5283 6.39061e-06 0.0162042 0.005512" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ta = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Ss [1/m]\", \"res\"],\n", - " index=[\"MLU-log\", \"TTim-single layer\", \"TTim-two layers\"],\n", - ")\n", - "ta.loc[\"TTim-single layer\"] = ca0.parameters[\"optimal\"].values\n", - "ta.loc[\"TTim-two layers\"] = ca1.parameters[\"optimal\"].values\n", - "ta.loc[\"MLU-log\"] = [51.530, 8.16e-04, 0.022]\n", - "ta[\"RMSE [m]\"] = [0.00756, ca0.rmse(), ca1.rmse()]\n", - "ta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/04pumpingtests/9_test_of_texas_hill.ipynb b/docs/04pumpingtests/9_test_of_texas_hill.ipynb deleted file mode 100755 index 12ed555..0000000 --- a/docs/04pumpingtests/9_test_of_texas_hill.ipynb +++ /dev/null @@ -1,889 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Leaky Aquifer Test\n", - "**This example is taken from AQTESOLV examples.**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import ttim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set basic parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "Q = 24464.06 # constant discharge in m^3/d\n", - "b1 = 6.096 # overlying aquitard thickness in m\n", - "b2 = 15.24 # aquifer thickness in m\n", - "zt = -b1 # top boundary of aquifer\n", - "zb = -b1 - b2 # bottom boundary of aquifer\n", - "rw = 0.1524 # well radius in m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load dataset of observation wells:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "data1 = np.loadtxt(\"data/texas40.txt\")\n", - "t1 = data1[:, 0]\n", - "h1 = data1[:, 1]\n", - "r1 = 12.191 # distance between obs1 to pumping well in m\n", - "\n", - "data2 = np.loadtxt(\"data/texas80.txt\")\n", - "t2 = data2[:, 0]\n", - "h2 = data2[:, 1]\n", - "r2 = 24.383 # distance between obs2 to pumping well in m\n", - "\n", - "data3 = np.loadtxt(\"data/texas160.txt\")\n", - "t3 = data3[:, 0]\n", - "h3 = data3[:, 1]\n", - "r3 = 48.766 # distance between obs3 to pumping well in m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create conceptual model:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_0 = ttim.ModelMaq(\n", - " kaq=10,\n", - " z=[0, zt, zb],\n", - " Saq=0.001,\n", - " Sll=0,\n", - " c=10,\n", - " tmin=0.001,\n", - " tmax=1,\n", - " topboundary=\"semi\",\n", - ")\n", - "w_0 = ttim.Well(ml_0, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", - "ml_0.solve()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calibrate with three datasets simultaneously：" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "...............................................................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 124\n", - " # data points = 78\n", - " # variables = 4\n", - " chi-square = 0.28305607\n", - " reduced chi-square = 0.00382508\n", - " Akaike info crit = -430.267891\n", - " Bayesian info crit = -420.841055\n", - "[[Variables]]\n", - " kaq0: 224.580728 +/- 2.48429386 (1.11%) (init = 10)\n", - " Saq0: 2.1316e-04 +/- 7.0488e-05 (33.07%) (init = 0.0001)\n", - " Sll0: 1.7482e-06 +/- 5.3118e-04 (30383.82%) (init = 0.0001)\n", - " c0: 43.8231417 +/- 3.15115666 (7.19%) (init = 100)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(Saq0, Sll0) = -0.994\n", - " C(kaq0, c0) = 0.890\n" - ] - } - ], - "source": [ - "# unknown parameters: kaq, Saq, c, Sll\n", - "ca_0 = ttim.Calibrate(ml_0)\n", - "ca_0.set_parameter(name=\"kaq0\", initial=10)\n", - "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca_0.set_parameter_by_reference(\n", - " name=\"Sll0\", parameter=ml_0.aq.Sll, initial=1e-4, pmin=0\n", - ")\n", - "ca_0.set_parameter(name=\"c0\", initial=100)\n", - "ca_0.series(name=\"OW1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca_0.series(name=\"OW2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", - "ca_0.series(name=\"OW3\", x=r3, y=0, t=t3, h=h3, layer=0)\n", - "ca_0.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq0224.5812.4842941.10619-infinf10[224.58072771909585]
Saq00.0002131590.00007033.0681-infinf0.0001[0.00021315894214556682]
Sll01.74823e-060.00053130383.80.0inf0.0001[1.7482336305274515e-06]
c043.82313.1511577.19062-infinf100[43.823141666697225]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 224.581 2.484294 1.10619 -inf inf 10 \n", - "Saq0 0.000213159 0.000070 33.0681 -inf inf 0.0001 \n", - "Sll0 1.74823e-06 0.000531 30383.8 0.0 inf 0.0001 \n", - "c0 43.8231 3.151157 7.19062 -inf inf 100 \n", - "\n", - " parray \n", - "kaq0 [224.58072771909585] \n", - "Saq0 [0.00021315894214556682] \n", - "Sll0 [1.7482336305274515e-06] \n", - "c0 [43.823141666697225] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.06024055063827496\n" - ] - } - ], - "source": [ - "display(ca_0.parameters)\n", - "print(\"RMSE:\", ca_0.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm1_0 = ml_0.head(r1, 0, t1)\n", - "hm2_0 = ml_0.head(r2, 0, t2)\n", - "hm3_0 = ml_0.head(r3, 0, t3)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"OW1\")\n", - "plt.semilogx(t1, hm1_0[0], label=\"ttim OW1\")\n", - "plt.semilogx(t2, h2, \".\", label=\"OW2\")\n", - "plt.semilogx(t2, hm2_0[0], label=\"ttim OW2\")\n", - "plt.semilogx(t3, h3, \".\", label=\"OW3\")\n", - "plt.semilogx(t3, hm3_0[0], label=\"ttim OW3\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"head(m)\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since the value of Sll is very close to the minimum limit (zero), Sll is set to 0 and removed from the calibration of the following model." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_1 = ttim.ModelMaq(\n", - " kaq=10,\n", - " z=[0, zt, zb],\n", - " Saq=0.001,\n", - " Sll=0,\n", - " c=10,\n", - " tmin=0.001,\n", - " tmax=1,\n", - " topboundary=\"semi\",\n", - ")\n", - "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", - "ml_1.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "............................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 41\n", - " # data points = 78\n", - " # variables = 3\n", - " chi-square = 0.28305319\n", - " reduced chi-square = 0.00377404\n", - " Akaike info crit = -432.268684\n", - " Bayesian info crit = -425.198558\n", - "[[Variables]]\n", - " kaq0: 224.635209 +/- 2.46690651 (1.10%) (init = 10)\n", - " Saq0: 2.1325e-04 +/- 7.5821e-06 (3.56%) (init = 0.0001)\n", - " c0: 43.8842130 +/- 3.13582327 (7.15%) (init = 100)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, c0) = 0.890\n", - " C(kaq0, Saq0) = -0.815\n", - " C(Saq0, c0) = -0.595\n" - ] - } - ], - "source": [ - "# unknown parameters: kaq, Saq, c\n", - "ca_1 = ttim.Calibrate(ml_1)\n", - "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", - "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca_1.set_parameter(name=\"c0\", initial=100)\n", - "ca_1.series(name=\"OW1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca_1.series(name=\"OW2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", - "ca_1.series(name=\"OW3\", x=r3, y=0, t=t3, h=h3, layer=0)\n", - "ca_1.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq0224.6352.4669071.09818-infinf10[224.63520868741116]
Saq00.0002132480.0000083.55556-infinf0.0001[0.0002132479209285246]
c043.88423.1358237.14568-infinf100[43.884212958982246]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 224.635 2.466907 1.09818 -inf inf 10 \n", - "Saq0 0.000213248 0.000008 3.55556 -inf inf 0.0001 \n", - "c0 43.8842 3.135823 7.14568 -inf inf 100 \n", - "\n", - " parray \n", - "kaq0 [224.63520868741116] \n", - "Saq0 [0.0002132479209285246] \n", - "c0 [43.884212958982246] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.06024024415946245\n" - ] - } - ], - "source": [ - "display(ca_1.parameters)\n", - "print(\"RMSE:\", ca_1.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm1_1 = ml_1.head(r1, 0, t1)\n", - "hm2_1 = ml_1.head(r2, 0, t2)\n", - "hm3_1 = ml_1.head(r3, 0, t3)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"OW1\")\n", - "plt.semilogx(t1, hm1_1[0], label=\"ttim OW1\")\n", - "plt.semilogx(t2, h2, \".\", label=\"OW2\")\n", - "plt.semilogx(t2, hm2_1[0], label=\"ttim OW2\")\n", - "plt.semilogx(t3, h3, \".\", label=\"OW3\")\n", - "plt.semilogx(t3, hm3_1[0], label=\"ttim OW3\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"head(m)\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Model with fixed Sll has similar performance with the former model. The second model has an AIC value of -432.269, which is two units lower than that of the former model (-430.268). Thus, Sll should set to zero (default value) and keep removed from the calibration." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Try adding res & rc:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], - "source": [ - "ml_2 = ttim.ModelMaq(\n", - " kaq=10,\n", - " z=[0, zt, zb],\n", - " Sll=0,\n", - " Saq=0.001,\n", - " c=10,\n", - " tmin=0.001,\n", - " tmax=1,\n", - " topboundary=\"semi\",\n", - ")\n", - "w_2 = ttim.Well(ml_2, xw=0, yw=0, rw=rw, res=0, rc=None, tsandQ=[(0, Q)], layers=0)\n", - "ml_2.solve()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calibrate with three datasets simultaneously:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When adding both res and rc into calibration and set the minimum limits as zero, the optimized res value is about 2.8e-08, which means adding res in the conceptual model has little effect on improving the performance. Thus, res is removed from the calibration." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "........................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 53\n", - " # data points = 78\n", - " # variables = 4\n", - " chi-square = 0.22837262\n", - " reduced chi-square = 0.00308612\n", - " Akaike info crit = -447.011870\n", - " Bayesian info crit = -437.585035\n", - "[[Variables]]\n", - " kaq0: 227.477290 +/- 2.38398138 (1.05%) (init = 10)\n", - " Saq0: 1.9189e-04 +/- 7.9503e-06 (4.14%) (init = 0.0001)\n", - " c0: 45.1694641 +/- 2.92677590 (6.48%) (init = 10)\n", - " rc: 0.58831979 +/- 0.06177175 (10.50%) (init = 0)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, c0) = 0.885\n", - " C(kaq0, Saq0) = -0.799\n", - " C(Saq0, rc) = -0.619\n", - " C(Saq0, c0) = -0.552\n", - " C(kaq0, rc) = 0.321\n", - " C(c0, rc) = 0.143\n" - ] - } - ], - "source": [ - "# unknown parameters: kaq, Saq, c, rc\n", - "ca_2 = ttim.Calibrate(ml_2)\n", - "ca_2.set_parameter(name=\"kaq0\", initial=10)\n", - "ca_2.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "ca_2.set_parameter(name=\"c0\", initial=10)\n", - "ca_2.set_parameter_by_reference(name=\"rc\", parameter=w_2.rc, initial=0)\n", - "ca_2.series(name=\"OW1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca_2.series(name=\"OW2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", - "ca_2.series(name=\"OW3\", x=r3, y=0, t=t3, h=h3, layer=0)\n", - "ca_2.fit(report=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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optimalstdperc_stdpminpmaxinitialparray
kaq0227.4772.3839811.04801-infinf10[227.4772896898763]
Saq00.0001918930.0000084.14307-infinf0.0001[0.00019189341127877495]
c045.16952.9267766.47955-infinf10[45.16946409782755]
rc0.588320.06177210.4997-infinf0[0.5883197891848962]
\n", - "
" - ], - "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 227.477 2.383981 1.04801 -inf inf 10 \n", - "Saq0 0.000191893 0.000008 4.14307 -inf inf 0.0001 \n", - "c0 45.1695 2.926776 6.47955 -inf inf 10 \n", - "rc 0.58832 0.061772 10.4997 -inf inf 0 \n", - "\n", - " parray \n", - "kaq0 [227.4772896898763] \n", - "Saq0 [0.00019189341127877495] \n", - "c0 [45.16946409782755] \n", - "rc [0.5883197891848962] " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.05410964893943807\n" - ] - } - ], - "source": [ - "display(ca_2.parameters)\n", - "print(\"RMSE:\", ca_2.rmse())" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hm1_2 = ml_2.head(r1, 0, t1)\n", - "hm2_2 = ml_2.head(r2, 0, t2)\n", - "hm3_2 = ml_2.head(r3, 0, t3)\n", - "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, \".\", label=\"OW1\")\n", - "plt.semilogx(t1, hm1_2[0], label=\"ttim OW1\")\n", - "plt.semilogx(t2, h2, \".\", label=\"OW2\")\n", - "plt.semilogx(t2, hm2_2[0], label=\"ttim OW2\")\n", - "plt.semilogx(t3, h3, \".\", label=\"OW3\")\n", - "plt.semilogx(t3, hm3_2[0], label=\"ttim OW3\")\n", - "plt.xlabel(\"time(d)\")\n", - "plt.ylabel(\"head(m)\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary of values simulated by AQTESOLV" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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k [m/d]Ss [1/m]c [d]rcRMSE
AQTESOLV224.7260.000212543.964-0.059627
ttim224.6350.00021324843.8842-0.060240
ttim-rc227.4770.00019189345.16950.588320.054110
\n", - "
" - ], - "text/plain": [ - " k [m/d] Ss [1/m] c [d] rc RMSE\n", - "AQTESOLV 224.726 0.0002125 43.964 - 0.059627\n", - "ttim 224.635 0.000213248 43.8842 - 0.060240\n", - "ttim-rc 227.477 0.000191893 45.1695 0.58832 0.054110" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\", \"rc\"],\n", - " index=[\"AQTESOLV\", \"ttim\", \"ttim-rc\"],\n", - ")\n", - "t.loc[\"AQTESOLV\"] = [224.726, 2.125e-4, 43.964, \"-\"]\n", - "t.loc[\"ttim\"] = np.append(ca_1.parameters[\"optimal\"].values, \"-\")\n", - "t.loc[\"ttim-rc\"] = ca_2.parameters[\"optimal\"].values\n", - "t[\"RMSE\"] = [0.059627, ca_1.rmse(), ca_2.rmse()]\n", - "t" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/04pumpingtests/confined1_oude_korendijk.ipynb b/docs/04pumpingtests/confined1_oude_korendijk.ipynb index 8265f20..0c71b08 100644 --- a/docs/04pumpingtests/confined1_oude_korendijk.ipynb +++ b/docs/04pumpingtests/confined1_oude_korendijk.ipynb @@ -5,8 +5,7 @@ "metadata": {}, "source": [ "# 1. Confined Aquifer Test - Oude Korendijk\n", - "\n", - "**This example is taken from Kruseman and de Ridder (1970)**" + "**This example is taken from Kruseman et al. 1970**" ] }, { @@ -15,16 +14,138 @@ "source": [ "## Introduction and Conceptual Model\n", "\n", - "In this example we will analyse the pumping test data from Oude Korendijk (Kruseman and de Ridder, 1970). This is a polder area south of Rotterdam, the Netherlands. The stratigraphy can be summarised by:\n", - "* the presence in the first 18 m depth of a impermeable layer,\n", + "TTim is a semi-analytical model of transient groundwater flow systems (Bakker, 2013). It applies the Laplace-transform analytic element method to solve for groundwater flow in a variety of hydrogeological features. One of the many applications of TTim is the analysis of aquifer tests. In this series of Jupyter Notebooks, we demonstrate the capabilities to model and calibrate aquifer tests in different hydrogeological conditions.\n", + "\n", + "In this example, we will use the pumping test data from Oude Korendijk (Kruseman et al. 1970) to demonstrate how TTim can be used to model and analyze pumping tests. Furthermore, we reproduce the work of Yang (2020) and compare the performance of TTim with other transient well hydraulics software AQTESOLV (Duffield, 2007) and MLU (Carlson and Randall, 2012).\n", + "\n", + "Oude Korendijk is a polder area south of Rotterdam, the Netherlands. The stratigraphy can be summarised by:\n", + "* the presence in the first 18 m depth of an impermeable layer,\n", "* followed by a 7 m succession of coarse gravel and sands, which are considered as the aquifer layer,\n", - "* and finally a layer of fine sands and clayey sediments that are deemed impermeable.\n", + "* and finally, a layer of fine sands and clayey sediments that are deemed impermeable.\n", "\n", - "The well screen was placed at the whole thickness of the aquifer. Drawdowns were taken from piezometers installed 30 and 90 m away from the well. Pumping at the well has been taken with constant discharge of 788 m3/d for almost 14 hours.\n", + "The well screens the whole thickness of the aquifer. Drawdowns were taken from piezometers installed 30 and 90 m away from the well. Pumping at the well has been taken with a constant discharge of 788 m3/d for almost 14 hours.\n", "\n", - "The conceptual model of the area is a single layer confined aquifer located at 18 m below surface and 7 m thickness. At $t=0$ pumping starts at constant discharge of 788 m3/d and drawdowns are recorded at two piezometers, 30 and 90 meters away, respectively. The figure below summarises the conceptualization of the problem\n", + "The conceptual model of the area is a single layer confined aquifer located at 18 m below surface and 7 m thickness. At $t=0$, the pumping starts at a constant discharge of 788 m3/d and drawdowns are recorded at two piezometers, 30 and 90 meters away, respectively. The figure below summarises the conceptualization of the problem\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(8, 5))\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "# sky\n", + "sky = plt.Rectangle((-10, 0), width=110, height=3, fc=\"b\", zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "# Aquifer:\n", + "ground = plt.Rectangle(\n", + " (-10, -25),\n", + " width=110,\n", + " height=25,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + ")\n", + "ax.add_patch(ground)\n", + "\n", + "# Confining bed:\n", + "confining_unit = plt.Rectangle(\n", + " (-10, -18),\n", + " width=110,\n", + " height=18,\n", + " fc=np.array([100, 100, 100]) / 255,\n", + " zorder=0,\n", + " alpha=0.7,\n", + ")\n", + "ax.add_patch(confining_unit)\n", + "well = plt.Rectangle(\n", + " (-2, -25), width=4, height=25, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "ax.add_patch(well)\n", + "\n", + "# Wellhead\n", + "wellhead = plt.Rectangle(\n", + " (-2.5, 0), width=5, height=1.5, fc=np.array([200, 200, 200]) / 255, zorder=2, ec=\"k\"\n", + ")\n", + "ax.add_patch(wellhead)\n", "\n", - "![Conceptual Model](figures/nb_01_conc_model.png)" + "# Screen for the well:\n", + "screen = plt.Rectangle(\n", + " (-2, -25),\n", + " width=4,\n", + " height=7,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x=2.5, y=0.75, dx=4, dy=0, color=\"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x=6.5, y=0.75, s=r\"$ Q = 788 \\frac{m^3}{d}$\", fontsize=\"large\")\n", + "# Piezometers\n", + "piez1 = plt.Rectangle(\n", + " (29, -25), width=2, height=25, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "piez2 = plt.Rectangle(\n", + " (89, -25), width=2, height=25, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "screen_piez_1 = plt.Rectangle(\n", + " (29, -25),\n", + " width=2,\n", + " height=7,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_1.set_linewidth(2)\n", + "screen_piez_2 = plt.Rectangle(\n", + " (89, -25),\n", + " width=2,\n", + " height=7,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_2.set_linewidth(2)\n", + "ax.add_patch(piez1)\n", + "ax.add_patch(piez2)\n", + "ax.add_patch(screen_piez_1)\n", + "ax.add_patch(screen_piez_2)\n", + "# last line\n", + "line = plt.Line2D(xdata=[-10, 100], ydata=[0, 0], color=\"k\")\n", + "ax.add_line(line)\n", + "ax.text(x=30, y=0.75, s=\"P30\", fontsize=\"large\")\n", + "ax.text(x=90, y=0.75, s=\"P90\", fontsize=\"large\")\n", + "ax.set_xlim([-10, 100])\n", + "ax.set_ylim([-25, 3])" ] }, { @@ -36,14 +157,16 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "\n", + "plt.rcParams[\"figure.figsize\"] = (5, 3) # default figure size\n", "import pandas as pd\n", - "import ttim" + "import ttim as ttm" ] }, { @@ -55,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -69,39 +192,68 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", "## Step 3: Creating a TTim conceptual model\n", "\n", "In this example, we are using the ModelMaq model to conceptualize our aquifer. ModelMaq defines the aquifer system as a stacked vertical sequence of aquifers and leaky layers (aquifer-leaky layer, aquifer-leaky layer, etc). Aquifers are conceptualized as having no vertical resistance (Dupuit approximation), with constant hydraulic conductivity and storage. Aquitard layers are approximated as having only vertical flow. They are characterized by the parameter resistance to vertical flow and by having no storage.\n", "\n", "In the model construction, we have to set the parameters for each layer (which consists of an aquifer layer and an aquitard layer).\n", "\n", - "For our one layer model we have to set:\n", + "For our one-layer model we have to set:\n", "\n", - "- The hydraulic conductivity: ```kaq``` (We will first assume a value of 60 m/d, but will later calibrate it with the pumping data). This can be a float or a list/array, depending on the number of layers, see notebook (***multi-aquifer notebook reference***).\n", - "- The top and bottom of the aquifer: ```z``` defined by a list ```[zt,zb]```. This construction is slightly different in multi-aquifer configurations, as can be seen in notebook (***multi-aquifer notebook reference***).\n", - "- The specific storage: ```Saq```, a float or list/array which will also be calibrated later. Again, defined for each aquifer layer.\n", + "- The hydraulic conductivity: ```kaq``` this is a list/array with a float element for every aquifer, for example: ```[kaq0,kaq1]```. We can also set a float value, in this case, the same ```kaq``` is assumed for every layer.\n", + "- The top and bottom of each aquifer: ```z``` defined by a list/array ```[zt0,zb0,zt1,zb1,...]```, where the inputs are a sequence of top and bottoms of the aquifer layers.\n", + "- The specific storage: ```Saq```. The input is a list/array with a float element for every aquifer, for example: ```[Saq0, Saq1]```. We can also set a float value. In this case, the same ```Saq``` is assumed for every layer.\n", "- The minimum time for which TTim solve the groundwater flow: ```tmin```, a float.\n", "- And the maximum time: ```tmax```, float.\n", "\n", - "In case of more than one layer model we would also have to set the resistance to vertical flow ```c``` of the aquitard portions. An example of this configuration can be seen in the notebook (***Insert the reference to multi-layer benchmark***)\n", + "Optional parameters:\n", + "\n", + "- TTim automatically assumes the ```topboundary``` is confined (```topboundary = 'conf'```). If we assign: ```topboundary = 'semi'``` This means that we assume the layer on top of the uppermost aquifer is a leaky layer, and we must also characterize it. Thus, even though we have only one aquifer, we have to set an additional element to the ```z``` array, which is the top of the aquitard formation:\n", + "\n", + " * For example: ```z = [0,zt,zb]```. 0 is the depth of the aquitard overlying the aquifer, ```zt``` and ```zb``` are the top and bottom of the aquifer. \n", + "\n", + " * We would also specify the leaky-layer parameters ```c``` and ```Sll``` (see below). \n", + "\n", + "- ```phreatictop```: Is a boolean (True/False). If ```True```, the first element in ```Saq``` is considered phreatic storage (Specific Yield) and it is not multiplied by the layer thickness. The default value is ```False``` in ```ModelMaq```. Generally, this parameter is set to ```True``` only in unconfined aquifers.\n", + "\n", + "In case of more than one layer model, or when ```topboundary = 'semi'```, we would also have to set the resistance to vertical flow ```c```, and the storage ```Sll``` of the aquitard portions. An example of this configuration can be seen in the notebook [Confined 4 - Schroth](confined4_schroth.ipynb)\n", "\n", "To represent our pumping well, we will use the ```Well``` feature.\n", + "\n", + "Wells, in TTim, are features with specified discharge. The well may be screened in multiple layers. In case the screen is in more than one layer, TTim distributes the discharge across the layers such that the head inside the well is the same in all screened layers. The wellbore storage and skin effect may be taken into account.\n", + "\n", + "The discharge of the well acting on layer $n$ is computed inside TTim with the expression (Bakker, 2013):\n", + "\n", + "$$ Q_n = 2\\pi r_wH_n\\frac{h_n-h_w}{c_e} $$\n", + "\n", + "where, $Q_n$ is the discharge at layer $n$, which is a positive value for water being pumped, $r_w$ is the radius of the well, $H_n$ is the layer thickness, $h_n$ is the head just outside the well, and $h_w$ is the head inside the well. $c_e$ is the entry resistance that can be defined in TTim by the skin resistance of the well (see notebook [Confined 2 - Grindley](confined2_grindley.ipynb) for more details)\n", + "\n", "For the well we have to set:\n", - "- The TTim model: ```ml``` where it is placed\n", + "- The TTim model: ```ml``` where the well is added to\n", "- The x and y location: ```xw, yw```, floats\n", - "- The pumping scheme defined by a list (```tsandQ```) where each element is a tuple representing a new stress condition with the starting time and the discharge rate, in our case: ```(0,Q)``` meaning that pumping begins at t = 0 with pumping rate Q\n", - "- The layers where it is screened: ```layers```, which can be set as an integer (one layer) or a list of integers (multi-screen well)." + "- The pumping scheme is defined by a list (```tsandQ```) where each element is a tuple representing a new stress condition with the starting time and the discharge rate, in our case: ```(0, Q)``` meaning that pumping begins at t = 0 with pumping rate Q\n", + "- The layers where it is screened: ```layers```. This argument can be set as an integer (one layer) or a list/array of integers (multi-screen well).\n", + "\n", + "Optional parameters for the ```Well``` object are:\n", + "- The well radius: ```rw```, a float, if not specified a value of 0.1 m is assumed.\n", + "- the skin resistance of the well: ```res```. If not specified, it is set to 0. An example of setting up the skin resistance is seen in the notebook : [Confined 2 - Grindley](confined2_grindley.ipynb).\n", + "- The radius of the caisson: ```rc```. The radius of the caisson is the parameter used to account for wellbore storage in the simulation. If not specified, this value is set to ```None``` and TTim will ignore wellbore storage. TTim considers the wellbore storage by solving the water balance inside the well with the expression (Bakker, 2013):\n", + "$$\\pi r_c^2 \\frac{dh_w}{dt} = \\sum_nQ_n-Q_w $$\n", + "where: $Q_n$ and $Q_w$ are the inflows and outflows in the well, $h_w$ is the head inside the well and $r_c$ is the radius of the caisson.\n", + "\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# unkonwn parameters: kaq, Saq\n", - "ml = ttim.ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)\n", - "w = ttim.Well(ml, xw=0, yw=0, rw=0.2, tsandQ=[(0, Q)], layers=0)\n", + "ml = ttm.ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)\n", + "w = ttm.Well(ml, xw=0, yw=0, rw=0.2, tsandQ=[(0, Q)], layers=0)\n", "\n", "# Here we are setting everything in meters for length and days for time" ] @@ -115,11 +267,20 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ - "ml.solve(silent=\"True\")" + "ml.solve()" ] }, { @@ -137,12 +298,12 @@ "\n", "The data is in a text file where the first column is the time data in ***minutes*** and the second column is the drawdown in ***meters***\n", "\n", - "For each piezometer we will load the data as a numpy array and create separate time from drawdown into two different 1d arrays. Time data will also be converted from minutes to days" + "We load the data as a numpy array for each piezometer. We then separate time and drawdown into two different 1d arrays. We also convert time data from minutes to days" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -169,7 +330,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For the calibration of our groundwater model we proceed by creating a calibration object with the ```Calibrate``` class. The ```Calibrate``` object takes the model ```ml``` as argument.\n", + "Model Calibration is done in TTim using the ```Calibrate``` object. TTim calibrates the parameters by minimizing an objective function using a non-linear least-squares fitting algorithm. The objective function used is the sum of the squares of the residuals calculated as:\n", + "\n", + "$$\\sum_n (h_o - h_c)^2$$,\n", + "\n", + "where $h_0$ is the observed heads and $h_c$ is the calculated heads by the model.\n", + "\n", + "and TTim uses ```lmfit```, a python package for non-linear least-squares minimization (Newville et al. 2014), to find the optimal parameters that minimize the residuals.\n", + "\n", + "For the calibration of our groundwater model, we proceed by creating a calibration object with the ```Calibrate``` class. The ```Calibrate``` object takes the model ```ml``` as argument.\n", "We then set the parameters we are adjusting:\n", "- Hydraulic conductivity: ```kaq0``` (Hydraulic conductivity of layer 0)\n", "- Specific Storage ```Saq0``` (Specific Storage of layer 0)\n", @@ -177,11 +346,13 @@ "with the ```.set_parameter``` method.\n", "\n", "- ```.set_parameter``` takes two arguments:\n", - "- ```name``` is the parameter name, a string, where the letters define the parameter. The possible values are \"kaq\", \"Saq\" or 'c', and they represent hydraulic conductivity, Specific storage and resistance to vertical flow, respectively. The letters are followed by a number, that define the layer of that parameter. For the example ```\"kaq0\"``` means the hydraulic conductivity of the layer 0.\n", - " - ```initial```is the initial guess value for the fitting algorithmn.\n", + "- ```name``` is the parameter name, a string, where the letters define the parameter. The possible values are \"kaq\", \"Saq\" or 'c', and they represent hydraulic conductivity, Specific storage and resistance to vertical flow, respectively. The letters are followed by a number, that define the layer of that parameter. For the example ```\"kaq0\"``` means the hydraulic conductivity of the layer 0. In our multilayer model we can extend the numbering to adjust one parameters for various layers in that case, we write the number of the first layer followed by a underline \"_\" and the number of the last layer, for example ```kaq0_1```, which means the hydraulic conductivity for layers 0 to 1\n", + " - ```initial```is the initial guess value for the fitting algorithm.\n", "\n", "We can also add the optional parameters:\n", - "- ```pmin``` and ```pmax```, which are floats that define the minimum and maximum possible values for the parameter.\n", + "- ```pmin``` and ```pmax```, which are floats that define the minimum and maximum possible values for the parameter. If not set, TTim assume their values are -inf and inf, respectively.\n", + "\n", + "The other method for adjusting parameters, ```.set_parameter_by_reference``` is later explained in [step 6.3](#step_6_3).\n", "\n", "We add the observation data using the ```.series``` method. The arguments are:\n", " - ```name```: string with the observation name\n", @@ -195,6 +366,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", "### Step 5.1: Calibration with Observation from Piezometer 1 (30 m from well)\n", "\n", "We begin calibrating using only the data from observation 1:" @@ -202,11 +374,11 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "ca1 = ttim.Calibrate(ml) # Calibrate object\n", + "ca1 = ttm.Calibrate(ml) # Calibrate object\n", "ca1.set_parameter(name=\"kaq0\", initial=10) # Setting parameters\n", "ca1.set_parameter(name=\"Saq0\", initial=1e-4)\n", "ca1.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0) # Adding observations" @@ -216,23 +388,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The ```fit``` method is used to run the least-squares algorithmn for finding the optimal parameter values:" + "The ```fit``` method is used to run the least-squares algorithm (```lmfit```) for finding the optimal parameter values:" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - ".........................\n", + "..................................\n", "Fit succeeded.\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", - " # function evals = 22\n", + " # function evals = 31\n", " # data points = 34\n", " # variables = 2\n", " chi-square = 0.03408049\n", @@ -240,10 +412,10 @@ " Akaike info crit = -230.783289\n", " Bayesian info crit = -227.730568\n", "[[Variables]]\n", - " kaq0: 68.6394868 +/- 1.43827068 (2.10%) (init = 10)\n", + " kaq0: 68.6394052 +/- 1.43826777 (2.10%) (init = 10)\n", " Saq0: 1.6072e-05 +/- 1.5823e-06 (9.85%) (init = 0.0001)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.891\n" + " C(kaq0, Saq0) = -0.8911\n" ] } ], @@ -262,7 +434,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -298,39 +470,39 @@ " \n", " \n", " kaq0\n", - " 68.6395\n", - " 1.438271\n", - " 2.0954\n", + " 68.639405\n", + " 1.438268\n", + " 2.095397\n", " -inf\n", " inf\n", - " 10\n", - " [68.63948675671419]\n", + " 10.0000\n", + " [68.6394051606159]\n", " \n", " \n", " Saq0\n", - " 1.60717e-05\n", + " 0.000016\n", " 0.000002\n", - " 9.84519\n", + " 9.845150\n", " -inf\n", " inf\n", " 0.0001\n", - " [1.6071655613732773e-05]\n", + " [1.607160696374144e-05]\n", " \n", " \n", "\n", "" ], "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 68.6395 1.438271 2.0954 -inf inf 10 \n", - "Saq0 1.60717e-05 0.000002 9.84519 -inf inf 0.0001 \n", + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 68.639405 1.438268 2.095397 -inf inf 10.0000 \n", + "Saq0 0.000016 0.000002 9.845150 -inf inf 0.0001 \n", "\n", - " parray \n", - "kaq0 [68.63948675671419] \n", - "Saq0 [1.6071655613732773e-05] " + " parray \n", + "kaq0 [68.6394051606159] \n", + "Saq0 [1.607160696374144e-05] " ] }, - "execution_count": 42, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -348,16 +520,14 @@ }, { "cell_type": "code", - "execution_count": 43, - "metadata": { - "scrolled": true - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "rmse: 0.031660183647463674\n" + "rmse: 0.03166018365112287\n" ] } ], @@ -369,22 +539,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Finally, we can access the model drawdowns by asking for the calibrated model to compute the heads at the well location and time intervals specified by the sampled data.\n", + "Finally, we can access the model drawdowns by asking the calibrated model to compute the heads at the well location and time intervals specified by the sampled data.\n", "\n", - "For this we use the ```.head``` method in the model object, in our case, ```ml```.\n", + "For this, we use the ```.head``` method in the model object, in our case, ```ml```.\n", "\n", "The arguments are:\n", "* the positions ```x``` and ```y``` of the piezometric well (or any other point of interest). In our case, our well is located at position ```x= r1``` and ```y = 0```.\n", "* the time intervals, defined by the numpy array ```t```, for the computation of the heads. In our case, this is defined by the variable ```t1```.\n", "\n", - "* Another optional inputs is ```layers```, that can be a list, integer or an array defining the model layers that shall be computed. For our case, we will not define anything, and TTim will compute for all layers (just one layer model).\n", + "* Another optional input is ```layers```, which can be a list, integer or an array defining the model layers. When we do not assign anything, the head is computed for all layers.\n", "\n", - "The output is a numpy array with dimensions ```(nl,nt)```, where ```nl``` is the number of layers and ```nt``` is the number of time intervals" + "The output is a numpy array with dimensions ```(nl,nt)```, where ```nl``` is the number of layers and ```nt``` is the number of time intervals.\n" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -393,7 +563,7 @@ "(1, 34)" ] }, - "execution_count": 44, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -407,78 +577,75 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Plotting the model Results" + "### Plotting the model Results" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "# matplotlib plot for calibration,\n", - "plt.figure(figsize=(10, 7))\n", "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\") # Plotting the observed drawdown\n", "plt.semilogx(t1, hm1[0], label=\"ttim at 30 m\") # Simulated drawdown\n", "plt.xlabel(\"time [d]\")\n", "plt.ylabel(\"drawdown [m]\")\n", "plt.title(\"ttim analysis for Oude Korendijk - Piezometer 30 m\")\n", - "plt.legend();" + "plt.legend()\n", + "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Step 5.2. Calibrate model Parameters with Observation Well 2 (90 m distance)" + "## Step 5.2. Calibrate model Parameters with Observation Well 2 (90 m distance)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - " We proceed to calibrate using only the data from observation well 2.\n", - " This time we will rush foward through the calibration steps. If the user feels confused, one can go back and check the inputs in [***step 5.1***](#step-51-calibration-with-observation-from-piezometer-1-30-m-from-well)" + "We proceed to calibrate using only the data from observation well 2. Details on the procedures can be reviewed in [***step 5.1***](#step_5_1)" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "...............................\n", + ".........................................\n", "Fit succeeded.\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", - " # function evals = 28\n", + " # function evals = 38\n", " # data points = 35\n", " # variables = 2\n", - " chi-square = 0.01806492\n", + " chi-square = 0.01806491\n", " reduced chi-square = 5.4742e-04\n", - " Akaike info crit = -260.919608\n", - " Bayesian info crit = -257.808912\n", + " Akaike info crit = -260.919619\n", + " Bayesian info crit = -257.808923\n", "[[Variables]]\n", - " kaq0: 71.5831423 +/- 1.57402551 (2.20%) (init = 10)\n", - " Saq0: 2.9106e-05 +/- 1.9379e-06 (6.66%) (init = 0.0001)\n", + " kaq0: 71.5821661 +/- 1.57398171 (2.20%) (init = 10)\n", + " Saq0: 2.9107e-05 +/- 1.9379e-06 (6.66%) (init = 0.0001)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.847\n" + " C(kaq0, Saq0) = -0.8473\n" ] }, { @@ -514,45 +681,45 @@ " \n", " \n", " kaq0\n", - " 71.5831\n", - " 1.574026\n", - " 2.19888\n", + " 71.582166\n", + " 1.573982\n", + " 2.198846\n", " -inf\n", " inf\n", - " 10\n", - " [71.58314231775141]\n", + " 10.0000\n", + " [71.58216612015345]\n", " \n", " \n", " Saq0\n", - " 2.91065e-05\n", + " 0.000029\n", " 0.000002\n", - " 6.65787\n", + " 6.657679\n", " -inf\n", " inf\n", " 0.0001\n", - " [2.910648177493867e-05]\n", + " [2.910742855968684e-05]\n", " \n", " \n", "\n", "" ], "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 71.5831 1.574026 2.19888 -inf inf 10 \n", - "Saq0 2.91065e-05 0.000002 6.65787 -inf inf 0.0001 \n", + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 71.582166 1.573982 2.198846 -inf inf 10.0000 \n", + "Saq0 0.000029 0.000002 6.657679 -inf inf 0.0001 \n", "\n", " parray \n", - "kaq0 [71.58314231775141] \n", - "Saq0 [2.910648177493867e-05] " + "kaq0 [71.58216612015345] \n", + "Saq0 [2.910742855968684e-05] " ] }, - "execution_count": 46, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ca2 = ttim.Calibrate(ml)\n", + "ca2 = ttm.Calibrate(ml)\n", "ca2.set_parameter(name=\"kaq0\", initial=10)\n", "ca2.set_parameter(name=\"Saq0\", initial=1e-4)\n", "ca2.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", @@ -562,82 +729,80 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "rmse: 0.022718724430908815\n" + "rmse: 0.022718720768954242\n" ] }, { "data": { - "image/png": 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"rmse:\", ca2.rmse())\n", "hm2 = ml.head(r2, 0, t2)\n", - "plt.figure(figsize=(10, 7))\n", "plt.semilogx(t2, h2, \".\", label=\"obs at 90 m\")\n", "plt.semilogx(t2, hm2[0], label=\"ttim at 90 m\")\n", "plt.xlabel(\"time [d]\")\n", "plt.ylabel(\"drawdown [m]\")\n", "plt.title(\"ttim analysis for Oude Korendijk - Piezometer 90 m\")\n", - "plt.legend();" + "plt.legend()\n", + "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Step 5.3. Calibrate model with two datasets simultaneously" + "## Step 5.3. Calibrate model with two datasets simultaneously" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Here we explore the hability of TTim to calibrate the model using more than one observation location.\n", + "Here we explore the ability of TTim to calibrate the model using more than one observation location.\n", "\n", - "This can be done simply by calling the method ```.series``` multiple times to the ```Calibrate``` object:" + "We achieve this by calling the method ```.series``` multiple times to the ```Calibrate``` object:" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "..................................\n", + ".................................\n", "Fit succeeded.\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", - " # function evals = 31\n", + " # function evals = 30\n", " # data points = 69\n", " # variables = 2\n", - " chi-square = 0.17291362\n", + " chi-square = 0.17291364\n", " reduced chi-square = 0.00258080\n", - " Akaike info crit = -409.245802\n", - " Bayesian info crit = -404.777589\n", + " Akaike info crit = -409.245796\n", + " Bayesian info crit = -404.777583\n", "[[Variables]]\n", - " kaq0: 66.0893566 +/- 1.65498708 (2.50%) (init = 10)\n", + " kaq0: 66.0892907 +/- 1.65498894 (2.50%) (init = 10)\n", " Saq0: 2.5409e-05 +/- 2.4016e-06 (9.45%) (init = 0.0001)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.855\n" + " C(kaq0, Saq0) = -0.8553\n" ] }, { @@ -673,45 +838,45 @@ " \n", " \n", " kaq0\n", - " 66.0894\n", - " 1.654987\n", - " 2.50417\n", + " 66.089291\n", + " 1.654989\n", + " 2.504171\n", " -inf\n", " inf\n", - " 10\n", - " [66.0893566210241]\n", + " 10.0000\n", + " [66.08929067179805]\n", " \n", " \n", " Saq0\n", - " 2.54086e-05\n", + " 0.000025\n", " 0.000002\n", - " 9.45189\n", + " 9.451956\n", " -inf\n", " inf\n", " 0.0001\n", - " [2.5408624326744147e-05]\n", + " [2.540871621501991e-05]\n", " \n", " \n", "\n", "" ], "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 66.0894 1.654987 2.50417 -inf inf 10 \n", - "Saq0 2.54086e-05 0.000002 9.45189 -inf inf 0.0001 \n", + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 66.089291 1.654989 2.504171 -inf inf 10.0000 \n", + "Saq0 0.000025 0.000002 9.451956 -inf inf 0.0001 \n", "\n", - " parray \n", - "kaq0 [66.0893566210241] \n", - "Saq0 [2.5408624326744147e-05] " + " parray \n", + "kaq0 [66.08929067179805] \n", + "Saq0 [2.540871621501991e-05] " ] }, - "execution_count": 48, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ca = ttim.Calibrate(ml)\n", + "ca = ttm.Calibrate(ml)\n", "ca.set_parameter(name=\"kaq0\", initial=10)\n", "ca.set_parameter(name=\"Saq0\", initial=1e-4)\n", "ca.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0) # Adding well 1\n", @@ -722,26 +887,24 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "rmse: 0.050059909702252964\n" + "rmse: 0.050059911747508554\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -749,7 +912,6 @@ "print(\"rmse:\", ca.rmse())\n", "hs1 = ml.head(r1, 0, t1)\n", "hs2 = ml.head(r2, 0, t2)\n", - "plt.figure(figsize=(8, 5))\n", "plt.semilogx(t1, h1, \".\", label=\"obs at 30m\")\n", "plt.semilogx(t1, hs1[0], label=\"ttim at 30 m\")\n", "plt.semilogx(t2, h2, \".\", label=\"obs at 90m\")\n", @@ -757,7 +919,8 @@ "plt.xlabel(\"time(d)\")\n", "plt.ylabel(\"drawdown(m)\")\n", "plt.title(\"ttim analysis for Oude Korendijk - Piezometers at 30 and 90 m\")\n", - "plt.legend();" + "plt.legend()\n", + "plt.grid()" ] }, { @@ -766,7 +929,7 @@ "source": [ "## Step 6. Calibrate Model with Wellbore Storage\n", "\n", - "In this continuation, we investigate whether adding well bore storage improves the fit." + "In this continuation, we investigate whether adding wellbore storage improves the fit." ] }, { @@ -778,12 +941,12 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# unknown parameters: kaq, Saq and rc\n", - "ml1 = ttim.ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)" + "ml1 = ttm.ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)" ] }, { @@ -797,23 +960,33 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, besides the parameters explained in [Step 3](#step-3-creating-a-ttim-conceptual-model), we have to add the radius of the caisson (```rc```)" + "Now, besides the parameters explained in [Step 3](#step_3), we have to add the radius of the caisson (```rc```)" ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ - "w1 = ttim.Well(ml1, xw=0, yw=0, rw=0.2, rc=0.3, tsandQ=[(0, Q)], layers=0)\n", - "ml1.solve(silent=\"True\")" + "w1 = ttm.Well(ml1, xw=0, yw=0, rw=0.2, rc=0.2, tsandQ=[(0, Q)], layers=0)\n", + "ml1.solve()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "\n", "### Step 6.3. Calibrate using only the data from observation well 1" ] }, @@ -821,40 +994,45 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here we use the method ```.set_parameter_by_reference``` to calibrate the rc parameter in our well.\n", + "The parameters not cited in the ```.set_paramater``` method, must be calibrated with the ```.set_parameter_by_reference``` method.\n", + "\n", + "Here we use the method ```.set_parameter_by_reference``` to calibrate the ```rc``` parameter in our well.\n", "\n", "```.set_parameter_by_reference``` takes the following arguments:\n", - "* ```name```: string of the parameter name\n", + "* ```name```: a string of the parameter name\n", "* ```parameter```: numpy-array with the parameter to be optimized. It should be specified as a reference, for example, in our case: ```w1.rc[0:]``` referencing to the parameter ```rc``` in object ```w1```.\n", "* ```initial```: float with the initial guess for the parameter value.\n", - "* ```pmin```and ```pmax```: floats with the minimum and maximum values allowed for the parameter to be optimized. If not specified these will be defined as ```-np.inf``` and ```np.inf```." + "* ```pmin``` and ```pmax```: floats with the minimum and maximum values allowed. If not specified, these will be defined as ```-np.inf``` and ```np.inf```.\n" ] }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "................................\n", + ".......................................................................................................................................\n", "Fit succeeded.\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", - " # function evals = 29\n", + " # function evals = 132\n", " # data points = 34\n", - " # variables = 2\n", - " chi-square = 0.00794623\n", - " reduced chi-square = 2.4832e-04\n", - " Akaike info crit = -280.288200\n", - " Bayesian info crit = -277.235479\n", + " # variables = 3\n", + " chi-square = 0.00793373\n", + " reduced chi-square = 2.5593e-04\n", + " Akaike info crit = -278.341725\n", + " Bayesian info crit = -273.762644\n", "[[Variables]]\n", - " kaq0: 80.6405644 +/- 0.93948623 (1.17%) (init = 10)\n", - " Saq0: 5.5994e-06 +/- 3.8942e-07 (6.95%) (init = 0.0001)\n", + " kaq0: 80.8710496 +/- 1.71390065 (2.12%) (init = 10)\n", + " Saq0: 5.4851e-06 +/- 7.9032e-07 (14.41%) (init = 0.0001)\n", + " rc: 0.30300204 +/- 0.01743105 (5.75%) (init = 0.2)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.921\n" + " C(kaq0, Saq0) = -0.9754\n", + " C(Saq0, rc) = -0.8698\n", + " C(kaq0, rc) = +0.8288\n" ] }, { @@ -890,48 +1068,60 @@ " \n", " \n", " kaq0\n", - " 80.6406\n", - " 9.394862e-01\n", - " 1.16503\n", + " 80.871050\n", + " 1.713901e+00\n", + " 2.119301\n", " -inf\n", " inf\n", - " 10\n", - " [80.64056435863938]\n", + " 10.0000\n", + " [80.87104964374493]\n", " \n", " \n", " Saq0\n", - " 5.5994e-06\n", - " 3.894218e-07\n", - " 6.9547\n", + " 0.000005\n", + " 7.903184e-07\n", + " 14.408459\n", " -inf\n", " inf\n", " 0.0001\n", - " [5.599401724943624e-06]\n", + " [5.485100333679987e-06]\n", + " \n", + " \n", + " rc\n", + " 0.303002\n", + " 1.743105e-02\n", + " 5.752783\n", + " 0.01\n", + " inf\n", + " 0.2000\n", + " [0.3030020413797092]\n", " \n", " \n", "\n", "" ], "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 80.6406 9.394862e-01 1.16503 -inf inf 10 \n", - "Saq0 5.5994e-06 3.894218e-07 6.9547 -inf inf 0.0001 \n", + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 80.871050 1.713901e+00 2.119301 -inf inf 10.0000 \n", + "Saq0 0.000005 7.903184e-07 14.408459 -inf inf 0.0001 \n", + "rc 0.303002 1.743105e-02 5.752783 0.01 inf 0.2000 \n", "\n", " parray \n", - "kaq0 [80.64056435863938] \n", - "Saq0 [5.599401724943624e-06] " + "kaq0 [80.87104964374493] \n", + "Saq0 [5.485100333679987e-06] \n", + "rc [0.3030020413797092] " ] }, - "execution_count": 67, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ca3 = ttim.Calibrate(ml1)\n", + "ca3 = ttm.Calibrate(ml1)\n", "ca3.set_parameter(name=\"kaq0\", initial=10)\n", "ca3.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "# ca3.set_parameter_by_reference(name='rc', parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", + "ca3.set_parameter_by_reference(name=\"rc\", parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", "ca3.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", "ca3.fit(report=True)\n", "ca3.parameters" @@ -939,39 +1129,37 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "rmse: 0.015287667397425852\n" + "rmse: 0.01527563869271272\n" ] }, { "data": { - "image/png": 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"rmse:\", ca3.rmse())\n", "hm3 = ml1.head(r1, 0, t1)\n", - "plt.figure(figsize=(10, 7))\n", "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", "plt.semilogx(t1, hm3[0], label=\"ttim at 30 m\")\n", "plt.xlabel(\"time(d)\")\n", "plt.ylabel(\"drawdown(m)\")\n", "plt.title(\"ttim analysis for Oude Korendijk - Piezometer 30 m and Wellbore Storage\")\n", - "plt.legend();" + "plt.legend()\n", + "plt.grid()" ] }, { @@ -985,29 +1173,32 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - ".................................\n", + "....................................................................................\n", "Fit succeeded.\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", - " # function evals = 30\n", + " # function evals = 81\n", " # data points = 35\n", - " # variables = 2\n", - " chi-square = 0.00976276\n", - " reduced chi-square = 2.9584e-04\n", - " Akaike info crit = -282.458485\n", - " Bayesian info crit = -279.347789\n", + " # variables = 3\n", + " chi-square = 0.00135389\n", + " reduced chi-square = 4.2309e-05\n", + " Akaike info crit = -349.604373\n", + " Bayesian info crit = -344.938328\n", "[[Variables]]\n", - " kaq0: 75.0893629 +/- 1.21674620 (1.62%) (init = 10)\n", - " Saq0: 2.3917e-05 +/- 1.2586e-06 (5.26%) (init = 0.0001)\n", + " kaq0: 88.2878230 +/- 1.48491787 (1.68%) (init = 10)\n", + " Saq0: 1.1361e-05 +/- 9.4427e-07 (8.31%) (init = 0.0001)\n", + " rc: 0.67465915 +/- 0.03064973 (4.54%) (init = 0.2)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.851\n" + " C(kaq0, Saq0) = -0.9820\n", + " C(Saq0, rc) = -0.9421\n", + " C(kaq0, rc) = +0.9154\n" ] }, { @@ -1043,48 +1234,60 @@ " \n", " \n", " kaq0\n", - " 75.0894\n", - " 1.216746\n", - " 1.6204\n", + " 88.287823\n", + " 1.484918e+00\n", + " 1.681906\n", " -inf\n", " inf\n", - " 10\n", - " [75.08936291305277]\n", + " 10.0000\n", + " [88.28782298814623]\n", " \n", " \n", " Saq0\n", - " 2.39169e-05\n", - " 0.000001\n", - " 5.26246\n", + " 0.000011\n", + " 9.442731e-07\n", + " 8.311813\n", " -inf\n", " inf\n", " 0.0001\n", - " [2.391691367998678e-05]\n", + " [1.1360615243112807e-05]\n", + " \n", + " \n", + " rc\n", + " 0.674659\n", + " 3.064973e-02\n", + " 4.542995\n", + " 0.01\n", + " inf\n", + " 0.2000\n", + " [0.6746591458251263]\n", " \n", " \n", "\n", "" ], "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 75.0894 1.216746 1.6204 -inf inf 10 \n", - "Saq0 2.39169e-05 0.000001 5.26246 -inf inf 0.0001 \n", + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 88.287823 1.484918e+00 1.681906 -inf inf 10.0000 \n", + "Saq0 0.000011 9.442731e-07 8.311813 -inf inf 0.0001 \n", + "rc 0.674659 3.064973e-02 4.542995 0.01 inf 0.2000 \n", "\n", - " parray \n", - "kaq0 [75.08936291305277] \n", - "Saq0 [2.391691367998678e-05] " + " parray \n", + "kaq0 [88.28782298814623] \n", + "Saq0 [1.1360615243112807e-05] \n", + "rc [0.6746591458251263] " ] }, - "execution_count": 69, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ca4 = ttim.Calibrate(ml1)\n", + "ca4 = ttm.Calibrate(ml1)\n", "ca4.set_parameter(name=\"kaq0\", initial=10)\n", "ca4.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "# ca4.set_parameter_by_reference(name='rc', parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", + "ca4.set_parameter_by_reference(name=\"rc\", parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", "ca4.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", "ca4.fit(report=True)\n", "ca4.parameters" @@ -1092,39 +1295,37 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "rmse: 0.01670137754442732\n" + "rmse: 0.006219520494372699\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"rmse:\", ca4.rmse())\n", "hm4 = ml1.head(r2, 0, t2)\n", - "plt.figure(figsize=(10, 7))\n", "plt.semilogx(t2, h2, \".\", label=\"obs at 90 m\")\n", "plt.semilogx(t2, hm4[0], label=\"ttim at 90 m\")\n", "plt.xlabel(\"time [d]\")\n", "plt.ylabel(\"drawdown [m]\")\n", "plt.title(\"ttim analysis for Oude Korendijk - Piezometer 90 m and Wellbore Storage\")\n", - "plt.legend();" + "plt.legend()\n", + "plt.grid()" ] }, { @@ -1133,34 +1334,36 @@ "source": [ "### Step 6.5. Calibrate model with two datasets simultaneously\n", "\n", - "Following the same logic from steps 6.3 to 6.4 and the calibration from step 5.3 we can now check the calibration using both wells and including wellbore storage." + "Following the same logic from steps 6.3 to 6.4 and the calibration from step 5.3, we can now check the calibration using both wells and wellbore storage." ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - ".........................\n", + "........................................................................\n", "Fit succeeded.\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", - " # function evals = 22\n", + " # function evals = 69\n", " # data points = 69\n", - " # variables = 2\n", - " chi-square = 0.24681339\n", - " reduced chi-square = 0.00368378\n", - " Akaike info crit = -384.692818\n", - " Bayesian info crit = -380.224605\n", + " # variables = 3\n", + " chi-square = 0.17294881\n", + " reduced chi-square = 0.00262044\n", + " Akaike info crit = -407.231761\n", + " Bayesian info crit = -400.529442\n", "[[Variables]]\n", - " kaq0: 71.4301741 +/- 2.20521581 (3.09%) (init = 10)\n", - " Saq0: 1.7118e-05 +/- 2.2331e-06 (13.05%) (init = 0.0001)\n", + " kaq0: 66.0880240 +/- 1.67012200 (2.53%) (init = 10)\n", + " Saq0: 2.5406e-05 +/- 2.4565e-06 (9.67%) (init = 0.0001)\n", + " rc: 0.01000311 +/- 0.01580396 (157.99%) (init = 0.2)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0, Saq0) = -0.866\n" + " C(kaq0, Saq0) = -0.8508\n", + " C(Saq0, rc) = -0.1722\n" ] }, { @@ -1196,48 +1399,60 @@ " \n", " \n", " kaq0\n", - " 71.4302\n", - " 2.205216\n", - " 3.08723\n", + " 66.088024\n", + " 1.670122\n", + " 2.527117\n", " -inf\n", " inf\n", - " 10\n", - " [71.43017412811331]\n", + " 10.0000\n", + " [66.0880240001346]\n", " \n", " \n", " Saq0\n", - " 1.71184e-05\n", + " 0.000025\n", " 0.000002\n", - " 13.0452\n", + " 9.669129\n", " -inf\n", " inf\n", " 0.0001\n", - " [1.7118434516330002e-05]\n", + " [2.5405869311639107e-05]\n", + " \n", + " \n", + " rc\n", + " 0.010003\n", + " 0.015804\n", + " 157.990429\n", + " 0.01\n", + " inf\n", + " 0.2000\n", + " [0.010003111933319042]\n", " \n", " \n", "\n", "" ], "text/plain": [ - " optimal std perc_std pmin pmax initial \\\n", - "kaq0 71.4302 2.205216 3.08723 -inf inf 10 \n", - "Saq0 1.71184e-05 0.000002 13.0452 -inf inf 0.0001 \n", + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 66.088024 1.670122 2.527117 -inf inf 10.0000 \n", + "Saq0 0.000025 0.000002 9.669129 -inf inf 0.0001 \n", + "rc 0.010003 0.015804 157.990429 0.01 inf 0.2000 \n", "\n", " parray \n", - "kaq0 [71.43017412811331] \n", - "Saq0 [1.7118434516330002e-05] " + "kaq0 [66.0880240001346] \n", + "Saq0 [2.5405869311639107e-05] \n", + "rc [0.010003111933319042] " ] }, - "execution_count": 71, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ca0 = ttim.Calibrate(ml1)\n", + "ca0 = ttm.Calibrate(ml1)\n", "ca0.set_parameter(name=\"kaq0\", initial=10)\n", "ca0.set_parameter(name=\"Saq0\", initial=1e-4)\n", - "# ca0.set_parameter_by_reference(name='rc', parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", + "ca0.set_parameter_by_reference(name=\"rc\", parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", "ca0.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", "ca0.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", "ca0.fit(report=True)\n", @@ -1246,26 +1461,24 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "rmse: 0.05980807316640966\n" + "rmse: 0.050065003351326486\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1273,7 +1486,6 @@ "print(\"rmse:\", ca0.rmse())\n", "hs1 = ml1.head(r1, 0, t1)\n", "hs2 = ml1.head(r2, 0, t2)\n", - "plt.figure(figsize=(10, 7))\n", "plt.semilogx(t1, h1, \".\", label=\"obs at 30m\")\n", "plt.semilogx(t1, hs1[0], label=\"ttim at 30 m\")\n", "plt.semilogx(t2, h2, \".\", label=\"obs at 90m\")\n", @@ -1281,7 +1493,8 @@ "plt.xlabel(\"time [d]\")\n", "plt.ylabel(\"drawdown [m]\")\n", "plt.title(\"ttim analysis for Oude Korendijk\")\n", - "plt.legend();" + "plt.legend()\n", + "plt.grid()" ] }, { @@ -1309,38 +1522,50 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The following table summarises the rmse values of the obtained models with and without well wellbore storage consideration." + "The following table summarises the RMSE values of the obtained models with and without wellbore storage." ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
RMSE of two conceptual models
obs 30 m obs 90 m obs simultaneously
without rc0.0316600.0227190.050060
with rc0.0152730.0062190.050065
" + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
RMSE of two conceptual models
 obs 30 mobs 90 mobs simultaneously
without rc0.0316600.0227190.050060
with rc0.0152760.0062200.050065
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 30, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1378,36 +1603,32 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can see the summaries of the hydraulic conductivities in the next table.\n", + "We can see the summaries of the hydraulic conductivities in the following table.\n", "\n", - "We will access the parameter values by accessing the ```.parameters``` attribute of each ```Calibrate``` object" + "We will access the parameter values by accessing the ```.parameters``` attribute of each ```Calibrate``` object." ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 26, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "Text(0.5, 0, 'Calibration Dataset')" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ky/8r_kg9w91ld3b898xn53q9wm0000gn/T/ipykernel_32090/2457231165.py:11: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " t1 = pd.concat((t1, tab))\n" + ] }, { "data": { - "image/png": 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1450,11 +1671,10 @@ " ],\n", " columns=[\"kaq - opt\", \"kaq - min\", \"kaq - max\", \"W. Storage\", \"Calib. Dataset\"],\n", " )\n", - " t1 = t1.append(tab)\n", + " t1 = pd.concat((t1, tab))\n", "\n", "# Plotting\n", "groups = t1.groupby(\"W. Storage\")\n", - "plt.figure(figsize=(10, 7))\n", "for name, group in groups:\n", " plt.errorbar(\n", " x=group[\"Calib. Dataset\"],\n", @@ -1468,7 +1688,8 @@ "plt.legend()\n", "plt.suptitle(\"Error bar plot of calibrated hydraulic conductivities\")\n", "plt.ylabel(\"K [m/d]\")\n", - "plt.xlabel(\"Calibration Dataset\")" + "plt.xlabel(\"Calibration Dataset\")\n", + "plt.grid()" ] }, { @@ -1479,8 +1700,7 @@ "\n", "As for the dataset using both obs wells at the same time, the calibration results have no significant differences.\n", "\n", - "Both scenarios with and without wellbore storage showed lower values for the calibrated model using both observations, calibration with a single well are overestimated.\n", - "\n" + "Both scenarios with and without wellbore storage showed lower values for the calibrated model using both observations, calibration with a single well are overestimated." ] }, { @@ -1494,52 +1714,62 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The final important step is to compare the data obtained from this model with the data from other Aquifer Analysis software. Xinzhu Y. (2020) compared TTim results with the published results in Kruseman and de Ridder (1970), here abbreviated to K&dR, and with the results obtained from the software AQTESOLV (Duffield, 2007) and MLU (Carlson & Randall, 2012)." + "The final important step is to compare the data obtained from this model with the data from other Aquifer Analysis software. Yang (2020) compared TTim results with the published results in Kruseman and de Ridder (1990), here abbreviated to K&dR, and with the results obtained from the software AQTESOLV (Duffield, 2007) and MLU (Carlson & Randall, 2012)." ] }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "scrolled": true - }, + "execution_count": 27, + "metadata": {}, "outputs": [ { "data": { "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Comparison of Model Results with different Softwares
k [m/d] Ss [1/m] RMSE
K&dR55.7142900.000170-
TTim66.0893570.0000250.050060
AQTESOLV66.0860000.0000250.050060
MLU66.8500000.0000240.050830
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Comparison of Model Results with different Softwares
 k [m/d]Ss [1/m]RMSE
K&dR55.7142900.000170-
TTim66.0892910.0000250.050060
AQTESOLV66.0860000.0000250.050060
MLU66.8500000.0000240.050830
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 32, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1559,7 +1789,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Results show good agreement between different analysis programs, including TTim. The values from Kruseman and de Ridder (1970) were obtained through Thiem's approximation, and seems to have been underestimated as the pumping never reached steady-state conditions" + "Results show good agreement between different analysis programs, including TTim. The values from Kruseman and de Ridder (1990) were obtained through Thiem's approximation. They seem to be an underestimation, as the pumping never reached steady-state conditions." ] }, { @@ -1568,8 +1798,10 @@ "source": [ "## References\n", "\n", + "* Bakker, M. Semi-analytic modeling of transient multi-layer flow with TTim. Hydrogeol J 21, 935–943 (2013). https://doi.org/10.1007/s10040-013-0975-2\n", "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Newville, M.,Stensitzki, T., Allen, D.B., Ingargiola, A. (2014) LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python.https://dx.doi.org/10.5281/zenodo.11813. https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021).\n", "* Kruseman, G.P., De Ridder, N.A., Verweij, J.M., 1970. Analysis and evaluationof pumping test data. volume 11. International institute for land reclamation and improvement The Netherlands.\n", "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." ] @@ -1577,7 +1809,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1591,27 +1823,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" - }, - "latex_envs": { - "LaTeX_envs_menu_present": true, - "autoclose": false, - "autocomplete": true, - "bibliofile": "biblio.bib", - "cite_by": "apalike", - "current_citInitial": 1, - "eqLabelWithNumbers": true, - "eqNumInitial": 1, - "hotkeys": { - "equation": "Ctrl-E", - "itemize": "Ctrl-I" - }, - "labels_anchors": false, - "latex_user_defs": false, - "report_style_numbering": false, - "user_envs_cfg": false + "version": "3.11.4" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/04pumpingtests/confined2_grindley.ipynb b/docs/04pumpingtests/confined2_grindley.ipynb new file mode 100644 index 0000000..0b613d3 --- /dev/null +++ b/docs/04pumpingtests/confined2_grindley.ipynb @@ -0,0 +1,1605 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Confined Aquifer Test - Grindley\n", + "**This test is taken from AQTESOLV examples.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we reproduce the work of Yang (2020) to use the pumping test data to demonstrate how TTim can be used to model and analyze pumping tests in a single layer, confined setting. Furthermore, we compare the performance of TTim with other transient well hydraulics software AQTESOLV (Duffield, 2007) and MLU (Carlson and Randall, 2012).\n", + "\n", + "This example is a pumping test conducted in 1953 in Grindley, Illinois, US. It was reported by Walton (1962). The aquifer is an 18 ft thickness sand and gravel layer under confined conditions. The pumping well fully penetrates the formation, and pumping was conducted for 8 hours at a rate of 220 gallons per minute. The effect of pumping was observed at observation well 1, located 824 ft away from the well.\n", + "\n", + "The time-drawdown data for the observation well was obtained from AQTESOLV documentation (Duffield, 2007), while data from the pumping well was obtained from the original paper from Walton (1962). Following AQTESOLV documentation (Duffield, 2007), we have assumed that both well and observation well radii are 0.5 ft.\n", + "\n", + "A simplified cross-section of the model area can be seen below:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "# sky\n", + "sky = plt.Rectangle((-200, 0), width=1400, height=3, fc=\"b\", zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "# Aquifer:\n", + "ground = plt.Rectangle(\n", + " (-200, -20),\n", + " width=1400,\n", + " height=18,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + ")\n", + "ax.add_patch(ground)\n", + "\n", + "# Confining bed:\n", + "confining_unit = plt.Rectangle(\n", + " (-200, -2),\n", + " width=1400,\n", + " height=2,\n", + " fc=np.array([100, 100, 100]) / 255,\n", + " zorder=0,\n", + " alpha=0.7,\n", + ")\n", + "ax.add_patch(confining_unit)\n", + "well = plt.Rectangle(\n", + " (-8, -20), width=16, height=20, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "ax.add_patch(well)\n", + "\n", + "# Wellhead\n", + "wellhead = plt.Rectangle(\n", + " (-10, 0), width=20, height=1.5, fc=np.array([200, 200, 200]) / 255, zorder=2, ec=\"k\"\n", + ")\n", + "ax.add_patch(wellhead)\n", + "\n", + "# Screen for the well:\n", + "screen = plt.Rectangle(\n", + " (-8, -20),\n", + " width=16,\n", + " height=18,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x=10, y=0.75, dx=16, dy=0, color=\"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x=29, y=0.75, s=r\"$ Q = 220 \\frac{gal}{min}$\", fontsize=\"large\")\n", + "# Piezometers\n", + "piez1 = plt.Rectangle(\n", + " (820, -20), width=8, height=20, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "screen_piez_1 = plt.Rectangle(\n", + " (820, -20),\n", + " width=8,\n", + " height=18,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_1.set_linewidth(2)\n", + "\n", + "ax.add_patch(piez1)\n", + "\n", + "ax.add_patch(screen_piez_1)\n", + "\n", + "# last line\n", + "line = plt.Line2D(xdata=[-200, 1200], ydata=[0, 0], color=\"k\")\n", + "ax.add_line(line)\n", + "ax.text(x=780, y=0.75, s=\"Obs Well 1\", fontsize=\"large\")\n", + "\n", + "ax.set_xlim([-200, 1050])\n", + "ax.set_ylim([-20, 3])\n", + "ax.set_xlabel(\"Distance [ft]\")\n", + "ax.set_ylabel(\"Relative height [ft]\")\n", + "ax.set_title(\"Conceptual Model - Grindley Example\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Load required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import ttim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters\n", + "\n", + "- We will work with time in days and length in meters from this step onwards\n", + "- The parameters below have already been converted to m and days." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "b = -5.4846 # aquifer thickness in m (Converted from 18 ft)\n", + "Q = 1199.218 # constant discharge in m^3/d (Converted from 220 gallons/minute)\n", + "r = 251.1552 # distance between observation well to test well in m (Converted from 824 ft)\n", + "rw = 0.1524 # screen radius of test well in m (Converted from 0.5 ft)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Load data of observation and pumping well\n", + "\n", + "The preferred method of loading data into TTim is to use numpy arrays.\n", + "\n", + "The data is in a text file where the first column is the time data in ***days*** and the second column is the drawdown in ***meters***\n", + "\n", + "The observation well is referred to as ***Well 1*** and the pumping well as ***Well 3***.\n", + "\n", + "For each piezometer, we will load the data as a numpy array and split time and drawdown into two different 1d arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Loading Observation well (Well 1)\n", + "data1 = np.loadtxt(\"data/gridley_well_1.txt\")\n", + "t1 = data1[:, 0]\n", + "h1 = data1[:, 1]\n", + "\n", + "# Loading Pumping Well data (Well 3)\n", + "data2 = np.loadtxt(\"data/gridley_well_3.txt\")\n", + "t2 = data2[:, 0]\n", + "h2 = data2[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Step 4. Creating a TTim conceptual model\n", + "\n", + "In this example, we are using the ModelMaq model to conceptualize our aquifer. ModelMaq defines the aquifer system as a stacked vertical sequence of aquifers and leaky layers (aquifer-leaky layer, aquifer-leaky layer etc). A thorough explanation of the ModelMaq and TTim one-layer modelling conceptualization is given in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml = ttim.ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\")\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Calibration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The calibration workflow has been described in detail in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Step 5.1. Calibration Using the Observation Well" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We begin the calibration using the data from observation well (well 1) as our data set." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 31\n", + " # data points = 22\n", + " # variables = 2\n", + " chi-square = 0.01702168\n", + " reduced chi-square = 8.5108e-04\n", + " Akaike info crit = -153.614816\n", + " Bayesian info crit = -151.432731\n", + "[[Variables]]\n", + " kaq0: 22.4340266 +/- 0.22268654 (0.99%) (init = 10)\n", + " Saq0: 3.8208e-06 +/- 7.4239e-08 (1.94%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.8829\n" + ] + } + ], + "source": [ + "# unknown parameters: kaq, Saq\n", + "ca_0 = ttim.Calibrate(\n", + " ml\n", + ") # Create the Calibrate object, calling the model to the object\n", + "ca_0.set_parameter(name=\"kaq0\", initial=10) # Setting the parameters for calibration\n", + "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_0.series(name=\"obs1\", x=r, y=0, t=t1, h=h1, layer=0) # Setting the observation data\n", + "ca_0.fit(\n", + " report=True\n", + ") # Fitting the model. We can hide the message below setting report = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results from calibration are stored in the ```.parameters``` attribute of the calibration object.\n", + "We can also call the ```.rmse``` method to check the fitting error (RMSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq022.4340272.226865e-010.992628-infinf10.0000[22.43402657500395]
Saq00.0000047.423925e-081.943042-infinf0.0001[3.8207741931644555e-06]
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" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 22.434027 2.226865e-01 0.992628 -inf inf 10.0000 \n", + "Saq0 0.000004 7.423925e-08 1.943042 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0 [22.43402657500395] \n", + "Saq0 [3.8207741931644555e-06] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.027815693150943354\n" + ] + } + ], + "source": [ + "display(ca_0.parameters)\n", + "print(\"rmse:\", ca_0.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we plot the model with our observation data:\n", + "\n", + "* First, we have to compute the model calculated heads at the observation location. For this, we use the ```.head```method in the model object (```ml```). This method takes the following arguments\n", + " * the positions ```x``` and ```y``` of the piezometric well (or any other point of interest). In our case, our well is located at position ```x= r1``` and ```y = 0```.\n", + " * the time intervals, defined by the numpy array ```t```, for the computation of the heads. In our case, this is defined by the variable ```t1```.\n", + "\n", + " * Another optional input is ```layers```, which can be a list, integer or an array defining the model layers. When we do not assign anything, the head is computed for all layers.\n", + "\n", + "The output is a numpy array with dimensions ```(nl,nt)```, where ```nl``` is the number of layers and ```nt``` is the number of time intervals.\n", + "\n", + "* Now, we can compare both observations and predictions in a plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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tt9/UtGlTlS9f/r7277+SnvP/dvKqXbu2ypcvn2okxs/PT7Vr104xrUqVKsmHVt5JwYIFFRISohkzZiQfZnjt2jUtWLBA3bt3l729ffJ2w8PD9fTTT2vBggWpRtQehGEYkpTq0MkOHTrI2dk5+X7+/PkVEhKiNWvWJH9TX7t2bU2fPl3vvfeeNm3alKqD4YYNG3T16lX16NEjxetvsVjUqlUrbdmyJdWo3H9/hqTEkb+zZ8+mOLRu2rRp8vPzSzGye/HiRfXt21dBQUGyt7eXg4ODihYtKkk6cODA/Tw96f49TdK0adMUjW98fX3l4+OT4mfhXs/bndz+HMbHxye/dtYQHx+v999/XxUqVJCjo6Ps7e3l6OioI0eO3Pdzlz9/fj366KMppnXp0kUWi0Vr1qxJMf3RRx9N83yxn3/+WcHBwXJzc0t+Tb/55pt017R48WI1bdpUAQEBKZ67pJ+b1atXS0r8nb9TvRlVvHhxNWnSRN9++62uXLmiBQsW3PXvgJT6vaZTp07Kly/fPf8OPPnkk8nvExndZwDpR9ACsgGTyaRevXpp1qxZmjx5ssqUKZPmOThSYsttPz+/VB9wfXx8ZG9vn3wY0ZUrV2Rvb6+CBQummM/Pzy/V+uLj4/XFF1/IwcEhxS0pEN3vB/TBgwdry5Yt2rZtm44dO6bz58/r+eefTzXff7uOXbhwQZL00EMPpappzpw5yfUk7et/9+lO0/7r0qVLVm1mkVRPWl3UAgICUh3i9d/XRpKcnJx069ate26rd+/eCg0NTT7Mb/bs2YqJiUnxwatbt2769ttvderUKT3xxBPy8fFRnTp1kpe5kyJFikhK7Hh2J0nn1QQFBaWYfqfXIjY2Vjdu3JCUeO5djx499PXXX6tevXry8vJS9+7dk89rSXr9O3bsmOr1//DDD2UYhq5evZpiG2k9561bt5a/v3/y4Z/Xrl3TwoUL1b17d9nZ2UlKPA+mRYsW+vXXX/Xaa6/pzz//1N9//5385UJ6Xou0pPf3NEl6fhbu9byl5eTJk6mew7t9YE7Pa3+7oUOHasSIEXr88ce1aNEibd68WVu2bFHVqlXv+7nz9fVNNS3p5+q/z1tar/uvv/6qJ598UoGBgZo1a5Y2btyY/OVVdHR0umq4cOGCFi1alOq5q1ixoiSleA+6W70Z9eyzz2rRokX69NNP5eLikiqoJ0l6f//vYdgmk0l+fn4p/g6kVU9afxvSu88A0o+ug0A20bNnT7311luaPHmyRo8efcf5ChYsqM2bN8swjBQf4i5evKj4+Pjk804KFiyo+Ph4XblyJcUf1P9+KCtQoIDs7OzUrVs3DRgwIM1tFi9e/L72qXDhwqpVq9Y95/vvh9GkfZg7d27yyEJakvYrrQ+a6TkZvVChQqmaVDyIpHrOnz+fKsCdO3cueb+soWXLlgoICNC0adPUsmVLTZs2TXXq1EnV/a5Xr17q1auXbt68qTVr1ujtt99Wu3btdPjw4Ts+t82bN9fUqVM1f/58vf7662nOM3/+fNnb26dqAHGn18LR0VFubm6SEl/fcePGady4cTp9+rQWLlyo119/XRcvXtTvv/+e/Dx98cUXd+wU+d8Pt2l1qUz6uf78888VHh6uH374QTExMSnOedm7d6927dql6dOnq0ePHsnT02qikRHp/T3NiHs9b2kJCAjQli1bUkwrW7bsHbfRsmXLe772t5s1a5a6d++u999/P8X0y5cvy9PT8947lYakoH27pJ+r/4aDtF73WbNmqXjx4pozZ06Kx//bhORuvL29VaVKlTu+FwcEBCTXk1ZjiYw2w0jSoUMHDRgwQB988IH69OkjFxeXNOdLen+/dOlSirBlGIbCwsKSm/jc/h4ZGBiYPF/S34bbpXefAaQfI1pANhEYGKhXX31VISEhKT7w/dcjjzyiGzdupLr2S9JJ3o888oikxEORJOn7779PMd8PP/yQ4r6rq2vyta2qVKmiWrVqpbql9W17ZmrZsqXs7e117NixNOtJCm9ly5aVv7+/Zs+eneJwqFOnTmnDhg333E7r1q21cuXKu560n9QqOj3fzj/88MOSlKqZxZYtW3TgwIHk18YakkLE/PnztXbtWm3duvWOhxlJUr58+dS6dWu98cYbio2N1b59++44b/v27VWhQgV98MEHaXapnDNnjpYvX67nnnsu1Tflv/76a4pRg+vXr2vRokVq2LBh8ijS7YoUKaKBAweqefPmyRfCDQ4Olqenp/bv33/H19/R0fGez5GUGDSjo6M1e/ZsTZ8+XfXq1VO5cuWSH0/6IP7fluBTpkxJta6M/Cyk9/f0fqX1vKXF0dEx1XN3t2vzPfbYY6pcubLGjBlzx1bcy5YtS76GmslkSvXcLVmyRKGhofexV4muX7+uhQsXppj2ww8/yGw2q1GjRvdc3mQyydHRMUXICgsLS9V1ULrzCHJSK/KSJUum+fOXFDqaNm16x3rvh4uLi9566y2FhISoX79+d5wv6efnv+81v/zyi27evJn8eNIXIf/9O/DTTz+l6iSY3n0GkH6MaAHZyAcffHDPebp3766JEyeqR48eOnnypCpXrqx169bp/fffV5s2bdSsWTNJUosWLdSoUSO99tprunnzpmrVqqX169fru+++S7XO8ePHq0GDBmrYsKH69eunYsWK6fr16zp69KgWLVqUrg5+1lSsWDG98847euONN3T8+HG1atVKBQoU0IULF/T3338rX758GjVqlMxms959910999xzat++vfr06aPw8HCNHDkyXYfuvPPOO/rtt9/UqFEj/e9//1PlypUVHh6u33//XUOHDlW5cuVUsmRJubi46Pvvv1f58uXl5uamgICAND90lC1bVs8//7y++OKL5G57J0+e1IgRIxQUFKSXXnrJqs9T79699eGHH6pLly5ycXFJ1eks6Rvx4OBg+fv7KywsTGPGjJGHh0fyN95psbOz0y+//KLmzZurXr16evnll1WvXj3FxMRo0aJFmjp1qho3bqxPPvkkzWWbN2+uoUOHymKx6MMPP1RkZGRy98qIiAg1bdpUXbp0Ubly5ZQ/f35t2bJFv//+e3I3Pjc3N33xxRfq0aOHrl69qo4dO8rHx0eXLl3Srl27dOnSJU2aNCldz1G5cuVUr149jRkzRmfOnNHUqVNTPV6yZEm9/vrrMgxDXl5eWrRoUZqHV1auXFlS4u9Ljx495ODgoLJly6YZXNL7e5pe6XnerMHOzk7z5s1TixYtVK9ePfXr109NmzZVvnz5dOrUKc2dO1eLFi1K7ljXrl07TZ8+XeXKlVOVKlW0bds2ffzxxw90SG7BggXVr18/nT59WmXKlNHSpUv11VdfqV+/fsmHNt5Nu3bt9Ouvv6p///7q2LGjzpw5o3fffVf+/v6pOnVWrlxZq1at0qJFi+Tv76/8+fOrbNmyeuedd7RixQrVr19fgwYNUtmyZRUdHa2TJ09q6dKlmjx5sgoXLqzu3bvrs88+U/fu3TV69GiVLl1aS5cu1bJly+57/4cOHaqhQ4fedZ7mzZurZcuWGjZsmCIjIxUcHKzdu3fr7bffVvXq1dWtWzdJUvny5dW1a1eNGzdODg4Oatasmfbu3auxY8emughyevc5I/bv35/cJTYsLExRUVGaO3eupMRrz+WW688Bd2S7PhxA3nZ718G7+W/XQcMwjCtXrhh9+/Y1/P39DXt7e6No0aLG8OHDU3UKCw8PN3r37m14enoarq6uRvPmzY2DBw+m2THqxIkTRu/evY3AwEDDwcHBKFSokFG/fn3jvffeSzGPMtB18OOPP36g52D+/PlG06ZNDXd3d8PJyckoWrSo0bFjR+OPP/5IMd/XX39tlC5d2nB0dDTKlCljfPvtt0aPHj3u2XXQMAzjzJkzRu/evQ0/Pz/DwcHBCAgIMJ588knjwoULyfPMnj3bKFeunOHg4JBiHf/tOmgYiZ3xPvzwQ6NMmTKGg4OD4e3tbXTt2tU4c+ZMivkaN25sVKxYMdU+p1X33dSvX9+QZDzzzDOpHpsxY4bRtGlTw9fX13B0dEzet927d6dr3ZcvXzZef/11o1y5coazs7Ph5uZm1K5d25gwYUKqTm+3d5ocNWqUUbhwYcPR0dGoXr26sWzZsuT5oqOjjb59+xpVqlQx3N3dDRcXF6Ns2bLG22+/bdy8eTPFOlevXm20bdvW8PLyMhwcHIzAwECjbdu2xs8//5w8T9JrcOnSpTvux9SpUw1JhouLS6rOmoZhGPv37zeaN29u5M+f3yhQoIDRqVMn4/Tp02n+vAwfPtwICAgwzGazIclYuXKlYRipuw4aRvp/TyUZAwYMSFVX0aJFk7vCZeR5s4bw8HDj3XffNWrUqGG4ubkZDg4ORpEiRYyuXbsa69evT57v2rVrxrPPPmv4+PgYrq6uRoMGDYy1a9emej4y0nWwYsWKxqpVq4xatWoZTk5Ohr+/v/G///0vuZvm7eu703vMBx98YBQrVsxwcnIyypcvb3z11Vdp/r7u3LnTCA4ONlxdXQ1JKWq+dOmSMWjQIKN48eKGg4OD4eXlZdSsWdN44403jBs3biTPd/bsWeOJJ54w3NzcjPz58xtPPPGEsWHDhgx3HbybtDr23bp1yxg2bJhRtGhRw8HBwfD39zf69etnXLt2LcV8MTExxssvv2z4+PgYzs7ORt26dY2NGzem+PnK6D6n9buRlqTnPK1bRrsWAjmRyTCs2H4IAAAAAMA5WgAAAABgbQQtAAAAALAyghYAAAAAWBlBCwAAAACsjKAFAAAAAFZG0AIAAAAAKyNoAQAAAICVEbQAAAAAwMoIWgAAAABgZQQtAAAAALAyghYAAAAAWBlBCwAAAACsjKAFAAAAAFZG0AIAAAAAKyNoAQAAAICVEbQAAAAAwMoIWgAAAABgZQQtAAAAALAyghYAAAAAWBlBCwAAAACsjKAFAAAAAFZG0AIAAAAAKyNoAQAAAICVEbQAAAAAwMoIWgAAAABgZQQtAAAAALAyghYAAAAAWBlBCwAAAACsjKAFAAAAAFZG0AIAAAAAKyNoAQAAAICVEbQAAAAAwMoIWgAAAABgZQQtAAAAALAyghYAAAAAWBlBCwAAAACsjKAFAAAAAFZG0AIAAAAAKyNoAQAAAICVEbQAAAAAwMoIWgAAAABgZQQtAAAAALAyghYAAAAAWBlBCwAAAACsjKAFAAAAAFZG0AIAAAAAKyNoAQAAAICVEbQAAAAAwMoIWgAAAABgZQQtAAAAALAyghYAAAAAWBlBCwAAAACsjKAFAAAAAFZG0AIAAAAAKyNoAQAAAICV2du6gOzOYrHo3Llzyp8/v0wmk63LAQAAAGAjhmHo+vXrCggIkNl89zErgtY9nDt3TkFBQbYuAwAAAEA2cebMGRUuXPiu8xC07iF//vySEp9Md3d3G1cDAAAAwFYiIyMVFBSUnBHuhqB1D0mHC7q7uxO0AAAAAKTrlCKaYQAAAACAlRG0AAAAAMDKCFoAAAAAYGWcowUAAACkISEhQXFxcbYuA1nIwcFBdnZ2VllXjgtaX375pT7++GOdP39eFStW1Lhx49SwYcM7zr969WoNHTpU+/btU0BAgF577TX17ds3CysGAABATmIYhsLCwhQeHm7rUmADnp6e8vPze+Br6OaooDVnzhwNGTJEX375pYKDgzVlyhS1bt1a+/fvV5EiRVLNf+LECbVp00Z9+vTRrFmztH79evXv31+FChXSE088YYM9AAAAQHaXFLJ8fHzk6ur6wB+4kTMYhqGoqChdvHhRkuTv7/9A6zMZhmFYo7CsUKdOHdWoUUOTJk1Knla+fHk9/vjjGjNmTKr5hw0bpoULF+rAgQPJ0/r27atdu3Zp48aN6dpmZGSkPDw8FBERQXt3AACAXC4hIUGHDx+Wj4+PChYsaOtyYANXrlzRxYsXVaZMmVSHEWYkG+SYZhixsbHatm2bWrRokWJ6ixYttGHDhjSX2bhxY6r5W7Zsqa1bt97xeNuYmBhFRkamuAEAACBvSPqM6OrqauNKYCtJr/2Dnp+XY4LW5cuXlZCQIF9f3xTTfX19FRYWluYyYWFhac4fHx+vy5cvp7nMmDFj5OHhkXwLCgqyzg4AAAAgx+BwwbzLWq99jglaSf6744Zh3PXJSGv+tKYnGT58uCIiIpJvZ86cecCKAQAAAOQ1OaYZhre3t+zs7FKNXl28eDHVqFUSPz+/NOe3t7e/4zG3Tk5OcnJysk7RAAAAAPKkHDOi5ejoqJo1a2rFihUppq9YsUL169dPc5l69eqlmn/58uWqVauWHBwcMq1WAAAAILtq0qSJhgwZYusyrOLkyZMymUzauXOnrUtJJccELUkaOnSovv76a3377bc6cOCAXnrpJZ0+fTr5uljDhw9X9+7dk+fv27evTp06paFDh+rAgQP69ttv9c033+iVV16x1S4AAAAAVpdWeFq1apVMJlOq64H9+uuvevfdd7OuuCx0p322hRxz6KAkPfXUU7py5YreeecdnT9/XpUqVdLSpUtVtGhRSdL58+d1+vTp5PmLFy+upUuX6qWXXtLEiRMVEBCgzz//nGtoAQAAIM/y8vLKku3ExcXl6aPIctSIliT1799fJ0+eVExMjLZt26ZGjRolPzZ9+nStWrUqxfyNGzfW9u3bFRMToxMnTiSPfgEAAADpYRiGomLjs/yW3svd9uzZU6tXr9b48eNlMplkMpl08uRJNW3aVJJUoEABmUwm9ezZU1Lq0a9ixYrpvffeU/fu3eXm5qaiRYtqwYIFunTpkh577DG5ubmpcuXK2rp1613rMJlMmjx5sh577DHly5dP7733niRp0aJFqlmzppydnVWiRAmNGjVK8fHxycuNHDlSRYoUkZOTkwICAjRo0KAU65w/f36K7Xh6emr69Omptn+3fZ47d64qV64sFxcXFSxYUM2aNdPNmzfT8/Tetxw1ogUAAABktVtxCarw1rIs3+7+d1rK1fHeH9fHjx+vw4cPq1KlSnrnnXckSYUKFdIvv/yiJ554QocOHZK7u7tcXFzuuI7PPvtM77//vkaMGKHPPvtM3bp1U3BwsHr37q2PP/5Yw4YNU/fu3bVv3767dvx+++23NWbMGH322Weys7PTsmXL1LVrV33++edq2LChjh07pueffz553rlz5+qzzz7Tjz/+qIoVKyosLEy7du3K4DOVKCgoKM19Pn/+vJ5++ml99NFHat++va5fv661a9emO8jeL4IWAAAAkIN5eHjI0dFRrq6u8vPzS56edIigj4+PPD0977qONm3a6IUXXpAkvfXWW5o0aZIeeughderUSZI0bNgw1atXTxcuXEixjf/q0qWLevfunXy/W7duev3119WjRw9JUokSJfTuu+/qtdde09tvv63Tp0/Lz89PzZo1k4ODg4oUKaLatWvf1/NgZ2eX5j4fO3ZM8fHx6tChQ/IpR5UrV76vbWQEQQsAAAC4CxcHO+1/p6VNtptVqlSpkvz/pEsn3R5GkqZdvHjxrkGrVq1aKe5v27ZNW7Zs0ejRo5OnJSQkKDo6WlFRUerUqZPGjRunEiVKqFWrVmrTpo1CQkJkb2+9mFK1alU98sgjqly5slq2bKkWLVqoY8eOKlCggNW2kRaCFgAAAHAXJpMpXYfw5WS3N61IOjQwrWkWi+Wu68mXL1+K+xaLRaNGjVKHDh1Szevs7KygoCAdOnRIK1as0B9//KH+/fvr448/1urVq+Xg4CCTyZTqEL+4uLgM7ZudnZ1WrFihDRs2aPny5friiy/0xhtvaPPmzSpevHiG1pUROa4ZRp4WESqdWJP4LwAAAPAPR0dHJSQkpJomKdX0rFSjRg0dOnRIpUqVSnUzmxOjiIuLix599FF9/vnnWrVqlTZu3Kg9e/ZISjzX7Pz588nrO3LkiKKiou64vTvts8lkUnBwsEaNGqUdO3bI0dFR8+bNs/buppC7o3lusn2mtGiwZFgkk1kKGS/V6H7v5QAAAJDrFStWTJs3b9bJkyfl5uYmLy8vFS1aVCaTSYsXL1abNm3k4uIiNze3LK3rrbfeUrt27RQUFKROnTrJbDZr9+7d2rNnj9577z1Nnz5dCQkJqlOnjlxdXfXdd9/JxcUl+Vyqhx9+WBMmTFDdunVlsVg0bNiwu7aMT2uf9+3bpz///FMtWrSQj4+PNm/erEuXLql8+fKZuu+MaOUEEaH/hiwp8d9FQxjZAgAAgCTplVdekZ2dnSpUqKBChQrp9OnTCgwM1KhRo/T666/L19dXAwcOzPK6WrZsqcWLF2vFihV66KGHVLduXX366afJQcrT01NfffWVgoODVaVKFf35559atGiRChYsKEn65JNPFBQUpEaNGqlLly565ZVX5OrqesftpbXP7u7uWrNmjdq0aaMyZcrozTff1CeffKLWrVtn6r6bjMzua5jDRUZGysPDQxEREXJ3d7dNESfWSDNCUk/vsVgq3jDr6wEAAMiloqOjdeLECRUvXlzOzs62Lgc2cLefgYxkA0a0cgKvkomHC97GIrPC7ANsVBAAAACAuyFo5QQegbK0G6eEf16ueMOs1+OeVd2JBxXyxTp98ecRHb5wPdMvugYAAAAgfWiGkUOYa/aQSjVT2Mn9+uuCm04cN2Q6dU17QiO0JzRCn6w4rGIFXdWiop9aVvRV9aACMpvvfNVuAAAAAJmHoJWTeATKr2qgukjqIunS9Rj9eeCClu+/oHVHLuvklShNXXNcU9ccl7ebk5pX8FGLin6qX7KgnOyz7oJ3AAAAQF5H0MrBCuV3UufaRdS5dhHdiInX6kOXtHx/mP46eFGXb8Ro9t9nNPvvM8rnaKcm5XzUsqKfmpQtJHfnO7fEBAAAAPDgCFq5hJuTvdpW8VfbKv6Kjbdo84krWrYvTCv2X9CFyBgt2X1eS3afl4OdSfVKeqtFBV+1qOArH3e66QAAAADWRnv3e8gW7d0fgMViaHdohJbtC9PyfWE6dulmiserF/FUiwqJ53WVKJS1F7ADAADIbmjvDmu1d2dEK5czm02qFuSpakGeGtaqnI5evKHl+8O0fN8F7TwTrh2nE28f/n5QpXzc1KKCr1pW9FPlQA+aaQAAAAD3ifbueUwpHzf1b1JK8wcEa/P/HtF7j1dSw9LesjebdPTiDX256pgem7he9T/4SyPm79W6I5cVl2CxddkAAAB4AKtWrZLJZFJ4eLitS7kjk8mk+fPnS5JOnjwpk8mknTt32rSmB8GIVh7m6+6srnWLqmvdooq4FadVhy5q+b4LWnXoosIio/XdplP6btMpuTvb6+F/mmk0KlNI+Zz4sQEAAIBt/frrr5oyZYq2bdumK1euaMeOHapWrZqty0rGJ2ZIkjxcHPRYtUA9Vi1Q0XEJ2ngssZnGHwcu6PKNWM3feU7zd56To71ZDUt5q0VFXzUr76uCbk62Lh0AAAB50M2bNxUcHKxOnTqpT58+ti4nFQ4dRCrODnZqWs5HHzxRRZv/10xz+9ZTn4bFVcTLVbHxFv158KKG/bJHD43+Q09O3qiv1x7X6StRti4bAAAge4kIlU6sSfw3k8XExGjQoEHy8fGRs7OzGjRooC1btqSab/369apataqcnZ1Vp04d7dmzJ/mxU6dOKSQkRAUKFFC+fPlUsWJFLV26NM3tffHFF6pcuXLy/fnz58tkMmnixInJ01q2bKnhw4cn31+0aJFq1qwpZ2dnlShRQqNGjVJ8fPx973O3bt301ltvqVmzZve9jsxE0MJd2ZlNqlXMS2+0raDVrzbRsiGNNLR5GVUKdJfFkP4+eVXvLTmgRh+vVKtxa/TpisPady5CNLMEAAB52vaZ0rhK0oyQxH+3z8zUzb322mv65ZdfNGPGDG3fvl2lSpVSy5YtdfXq1RTzvfrqqxo7dqy2bNkiHx8fPfroo4qLi5MkDRgwQDExMVqzZo327NmjDz/8UG5uaXelbtKkifbt26fLly9LklavXi1vb2+tXr1akhQfH68NGzaocePGkqRly5apa9euGjRokPbv368pU6Zo+vTpGj16dGY9JTZH0EK6mUwmlfXLr0GPlNbiFxtq/esPa2RIBdUrUVB2ZpMOhl3X538eUdvP16nBhys1atE+bTx2RfE00wAAAHlJRKi0aLBk/PMZyLBIi4Zk2sjWzZs3NWnSJH388cdq3bq1KlSooK+++kouLi765ptvUsz79ttvq3nz5qpcubJmzJihCxcuaN68eZKk06dPKzg4WJUrV1aJEiXUrl07NWrUKM1tVqpUSQULFkwOVqtWrdLLL7+cfH/Lli2Kjo5WgwYNJEmjR4/W66+/rh49eqhEiRJq3ry53n33XU2ZMiVTnpPsgHO0cN8CPV3UM7i4egYX17Wbsfrr4EUt2xemNUcuKTT8lqatP6lp60+qgKuDHimfeIHkRmUKydnBzvrFRIRKV49JXiUlj0Drrx8AACC9rh77N2QlMRKkq8cz5XPKsWPHFBcXp+Dg4ORpDg4Oql27tg4cOJBi3nr16iX/38vLS2XLlk2eZ9CgQerXr5+WL1+uZs2a6YknnlCVKlXS3KbJZFKjRo20atUqPfLII9q3b5/69u2rsWPH6sCBA1q1apVq1KiRPCK2bds2bdmyJcUIVkJCgqKjoxUVFSVXV1erPR/ZBUELVlEgn6OeqFlYT9QsrFuxCVp75JKW7bugPw9e0LWoOM3ddlZzt52Vi4OdGpXxVosKfnqkvI88XR0ffOPbZ/77rZHJLIWMl2p0f/D1AgAA3A+vkomfSW4PWyY7yatEpmwu6ZQNk8mUavp/p6UlaZ7nnntOLVu21JIlS7R8+XKNGTNGn3zyiV588cU0l2vSpImmTp2qtWvXqmrVqvL09FSjRo20evVqrVq1Sk2aNEme12KxaNSoUerQoUOq9eTWC0Nz6CCszsXRTi0q+umTJ6tq6xvNNLtPXfWsX0yBni66FZegZfsu6OWfd6nme3+oy1ebNH39CYWG37q/jWXx0DwAAMA9eQQmfvFr+ucoHpOdFDIu0466KVWqlBwdHbVu3brkaXFxcdq6davKly+fYt5NmzYl///atWs6fPiwypUrlzwtKChIffv21a+//qqXX35ZX3311R23m3Se1ty5c5NDVePGjfXHH3+kOD9LkmrUqKFDhw6pVKlSqW5mc+6MJIxoIVPZ25lVr2RB1StZUG+HVNC+c5Favi9My/df0MGw69pw7Io2HLuikYv2q3Kgh1pU8FWLin4q4+uWrm9gsnpoHgAAIF1qdJdKPpL4mcSrRKZ+LsmXL5/69eunV199VV5eXipSpIg++ugjRUVF6dlnn00x7zvvvKOCBQvK19dXb7zxhry9vfX4449LkoYMGaLWrVurTJkyunbtmv76669UQe12Sedpff/991qwYIGkxPD18ssvS1Ly+VmS9NZbb6ldu3YKCgpSp06dZDabtXv3bu3Zs0fvvffefe331atXdfr0aZ07d06SdOjQIUmSn5+f/Pz87mud1kTQQpYxmUyqFOihSoEeGtqirE5duakV+y9o2b4wbT11TXtCI7QnNEKfrDisYgVd1aKin1pU8FX1IgVkZ75D6MrioXkAAIB08wjMsi9+P/jgA1ksFnXr1k3Xr19XrVq1tGzZMhUoUCDVfIMHD9aRI0dUtWpVLVy4UI6OiadyJCQkaMCAATp79qzc3d3VqlUrffbZZ3fcpslkUuPGjTV//nw1bNhQklSlShV5eHioRIkScnd3T563ZcuWWrx4sd555x199NFHcnBwULly5fTcc8/d9z4vXLhQvXr1Sr7fuXNnSYkNP0aOHHnf67UWk0Ef7ruKjIyUh4eHIiIiUvywwLou34jRnwcuaNm+C1p39LJi4/8NTt5uTmpewUctKvipfqmCcrL/TzON7TMTDxc0Ev4dmuccLQAAcB+io6N14sQJFS9ePNeeO4S7u9vPQEayAUHrHghaWe9GTLzWHL6kZfvC9NfBi7oe/e+F7PI52qlJOR+1qOCrpuV85O7skPhARGiWDM0DAIDcjaAFawUtDh1EtuPmZK82lf3VprK/YuMt2nziipbtC9OK/Rd0ITJGS3af15Ld5+VgZ1K9kt5qUcFXzSv4yrc4AQsAAADZAyNa98CIVvZhsRjaHRqhZfvCtHxfmI5dupni8cZlCumFxiVUr0TB9DXSAAAA+A9GtMCIFvIcs9mkakGeqhbkqWGtyunoxRtavj9My/dd0M4z4Vp9+JJWH76kqoU99ELjkmpZ0e/OTTQAAACATETQQo5VysdNpXxKqX+TUjp9JUpfrzuuOVvOaNfZCPX/fruKFnRVn4Yl1LFmYTk72N17hQAAAP/goK+8y1qvfe68OhjynCIFXfXOY5W04fWHNeiR0vJ0ddCpK1F6c/5eBX/wl77484jCo2JtXSYAAMjmHBwSG21FRUXZuBLYStJrn/SzcL84R+seOEcrZ4qKjddPW87oq7UnFBp+S5Lk6minpx4K0nMNSyjQ08XGFQIAgOzq/PnzCg8Pl4+Pj1xdXTn3O48wDENRUVG6ePGiPD095e/vn2oe2rtbEUErZ4tPsGjJnvOasvq49p+PlCTZmU16tGqAnm9UQuX9eU0BAEBKhmEoLCxM4eHhti4FNuDp6Sk/P780AzZBy4oIWrmDYRhad/SyJq8+pvVHryRPp1MhAAC4k4SEBMXFxdm6DGQhBwcH2dnd+dx+gpYVEbRynz1nIzRlzTEt3XNeln9++ulUCAAAgHshaFkRQSv3SupU+NPWM4qOs0gSnQoBAABwRwQtKyJo5X5XbsRo5sZTmrHxpMKjEg8PKJjPUT3rF1O3ekXl6epo4woBAACQHRC0rIiglXfcrVPhsw2Kq3ABVxtXCAAAAFsiaFkRQSvvoVMhAAAA0kLQsiKCVt6V1KlwyurjWnf0cvJ0OhUCAADkTQQtKyJoQUq7U2GVwh56oVFJtapEp0IAAIC8gKBlRQQt3O5OnQqfa1hCnehUCAAAkKsRtKyIoIW00KkQAAAg7yFoWRFBC3cTFRuvn7ee1Vdrj+vsNToVAgAA5GYELSsiaCE97tSpMKSKv15oXJJOhQAAALkAQcuKCFrICDoVAgAA5F4ELSsiaOF+7Q2N0JQ1x7Vk9zk6FQIAAOQCBC0rImjhQWVZp8KIUOnqMcmrpOQRaJ11AgAAIBlBy4oIWrCWTO1UuH2mtGiwZFgkk1kKGS/V6G6lygEAACARtKyKoAVrs3qnwohQaVylxJCVxGQnDdnDyBYAAIAVZSQbmLOoJgD/cHW0V4/6xbTqlSYa37maKvi7Kyo2QdPWn1Tjj1dpyI87tP9cZPpXePVYypAlSUaCdPW4dQsHAABAutnbugAgr7K3M+uxaoF6tGpAik6F83ee0/yd59SoTCH1bVRC9Ureo1OhV8nEwwX/O6LlVSLzdwIAAABp4tDBe+DQQWSl++5UuH2mtGhI4kiWyU4KGcc5WgAAAFbGOVpWRNCCLdxXp8KI0MTDBb1KcG4WAABAJiBoWRFBC7aU1Klw5saTunZbp8Ie9YupW92iKpDvAToVAgAAIEMIWlZE0EJ2kFanQhcHO3WufZ+dCgEAAJBhBC0rImghO4lPsGjJnvOasvq49p9P7ExoZzYppIq/nm9UUhUC+BkFAADILAQtKyJoITsyDCNFp8IkrSr66X9tyqtIQUa4AAAArI2gZUUELWR3/+1U6Ghn1rMNi2tA01Jyc+IKDgAAANZC0LIighZyikNh1/Xu4v3JI1yF8jvp1ZZl1bFGYZnv1BYeAAAA6UbQsiKCFnISwzD0x4GLGr1kv05eiZIkVQp019shFfVQMS8bVwcAAJCzEbSsiKCFnCg23qIZG07q8z+P6HpMvCSpbRV/DW9djg6FAAAA9ykj2cCcRTU9sGvXrqlbt27y8PCQh4eHunXrpvDw8Lsu07NnT5lMphS3unXrZk3BgA052pvVp1EJrXy1iZ6uXUQmk7Rk93k98slqfbL8kG7+E74AAACQOXLMiFbr1q119uxZTZ06VZL0/PPPq1ixYlq0aNEdl+nZs6cuXLigadOmJU9zdHSUl1f6D6FiRAu5wf5zkXpn8T5tOn5VkuTr7qRhrcrp8WqBnL8FAACQTrnu0MEDBw6oQoUK2rRpk+rUqSNJ2rRpk+rVq6eDBw+qbNmyaS7Xs2dPhYeHa/78+fe9bYIWcgvDMLRs3wW9v/SATl9NPH+rapCn3mpXQTWLFrBxdQAAANlfrjt0cOPGjfLw8EgOWZJUt25deXh4aMOGDXdddtWqVfLx8VGZMmXUp08fXbx48a7zx8TEKDIyMsUNyA1MJpNaVfLT8pcaaVircsrnaKddZ8L1xKQNGvzjDp0Lv2XrEgEAAHKNHBG0wsLC5OPjk2q6j4+PwsLC7rhc69at9f333+uvv/7SJ598oi1btujhhx9WTEzMHZcZM2ZM8nlgHh4eCgoKsso+ANmFs4Od+jUpqZWvNtGTtQrLZJIW7Dynhz9ZpXF/HNat2ARblwgAAJDj2TRojRw5MlWziv/etm7dKinx2/j/MgwjzelJnnrqKbVt21aVKlVSSEiIfvvtNx0+fFhLliy54zLDhw9XRERE8u3MmTMPvqNANuST31kfdayqRQMb6KFiBRQdZ9G4P47o4U9WacHOUOWAo4oBAACyLXtbbnzgwIHq3LnzXecpVqyYdu/erQsXLqR67NKlS/L19U339vz9/VW0aFEdOXLkjvM4OTnJyckp3esEcrpKgR766YV6WronTO8vPaDQ8Fsa/ONOzdhwUm+HVFTVIE9blwgAAJDj2DRoeXt7y9vb+57z1atXTxEREfr7779Vu3ZtSdLmzZsVERGh+vXrp3t7V65c0ZkzZ+Tv73/fNQO5kclkUtsq/nqkvI++XntcX646pu2nw/XYxPXqUCNQw1qVk6+7s63LBAAAyDFyxDla5cuXV6tWrdSnTx9t2rRJmzZtUp8+fdSuXbsUHQfLlSunefPmSZJu3LihV155RRs3btTJkye1atUqhYSEyNvbW+3bt7fVrgDZmrODnQY+XForX2miDjUCJUm/bg9V07GrNOGvI4qO4/wtAACA9MgRQUuSvv/+e1WuXFktWrRQixYtVKVKFX333Xcp5jl06JAiIiIkSXZ2dtqzZ48ee+wxlSlTRj169FCZMmW0ceNG5c+f3xa7AOQYvu7O+vTJapo/IFg1ingqKjZBY5cf1iOfrNbi3ec4fwsAAOAecsR1tGyJ62ghrzMMQwt3ndMHvx3U+YhoSVLtYl56K6SCKgV62Lg6AACArJPrLlhsSwQtINGt2ARNWXNMk1cfU3ScRSaT1KlmYb3Ssqx88nP+FgAAyP0IWlZE0AJSOhd+Sx/+flALdp6TJLk52WtA01LqFVxMzg52Nq4OAAAg8xC0rIigBaRt26lremfxfu06Ey5JCvJy0RttyqtlRb+7Xt8OAAAgpyJoWRFBC7gzi8XQ/J2h+vD3g7oQGSNJqlvCS2+1q6gKAfy+AACA3IWgZUUELeDebsbEa/LqY5q65rhi4i0ym6SnHiqil1uUkbcbFwAHAAC5A0HLighaQPqdvRalD347qMW7z0uS8jvZ68VHSqln/eJytM8xV5MAAABIE0HLighaQMZtOXlV7yzarz2hide1K1bQVW+0raBm5X04fwsAAORYBC0rImgB98diMTR3+1l9vOyQLl1PPH+rQSlvjWhXQWX9uGg4AADIeQhaVkTQAh7MjZh4fbnyqL5ed0Kx/5y/1aVOEQ1tXlZe+RxtXR4AAEC6EbSsiKAFWMeZq1F6f+kB/bY3TJLk7myvwc3KqHu9onKw4/wtAACQ/RG0rIigBVjXxmNX9M7i/TpwPlKSVKJQPo1oW0FNy/nYuDIAAIC7I2hZEUELsL4Ei6Gftp7R2GWHdOVmrCSpcZlCGtGuvEr5pHH+VkSodPWY5FVS8gjM4moBAAASEbSsiKAFZJ7I6DhN/Ouovl1/QnEJhuzMJnWrW1RDmpWWp+s/529tnyktGiwZFslklkLGSzW627ZwAACQJxG0rIigBWS+k5dvavTSA1qx/4IkydPVQS81K6NnytvJ/vMqiSEriclOGrKHkS0AAJDlMpINOAMdgM0V886nr7rX0vfP1VFZ3/wKj4rT2wv36fWv5qcMWZJkJEhXj9ukTgAAgPQiaAHINoJLeWvJoAZ67/FKKuDqoHVXPZRg/OcCxyY7yauEbQoEAABIJ4IWgGzF3s6srnWLatWrTdW2QS29mdBH8UbiW5VFZsW3/YzDBgEAQLbHOVr3wDlagG0du3RDE+ev1rkT+3XS4isv/+L6uFMVVQzwsHVpAAAgj6EZhhURtADbMwxDi3af19sL9upaVJzszSb1b1pKA5uWkqM9A/MAACBr0AwDQK5iMpn0aNUALX+psVpX8lO8xdDnfx7RoxPWaW9ohK3LAwAASIWgBSDHKJTfSZO61tTELjXklc9RB8Ou67GJ6zV22SHFxCfYujwAAIBkBC0AOU7bKv5a8VIjta3irwSLoQkrjyrki3XafTbc1qUBAABIImgByKEKujlpYpcamvRMDXm7OerwhRtq/+UGffj7QUXHMboFAABsi6AFIEdrXdlfy19qrMeqBSjBYmjSqmNq98U67Th9zdalAQCAPIygBSDH88rnqPGdq2tKt5rydnPS0Ys39MSkDRqz9ACjWwAAwCYIWgByjZYV/fTH0EZqXz1QFkOasua42ny+VttOMboFAACyFkELQK7i6eqoz56qpq+715JPficdv3RTHSdv0HuL9+tWLKNbAAAgaxC0AORKzSr4asVLjdWxZmEZhvT1uhNq8/labTl51dalAQCAPICgBSDX8nB10NhOVTWt50Pyc3fWics39eSUjRq1aJ+iYuNtXR4AAMjFCFoAcr2m5Xy07KVGerJW4ujWtPUn1Xr8Wm06fsXWpQEAgFyKoAUgT/BwcdBHHatqeq+H5O/hrFNXotR56ia9vWCvbsYwugUAAKyLoAUgT2lS1kfLX2qkp2sXkSTN2HhKrcav0YZjl21cGQAAyE0IWgDynPzODhrTobK+e7a2Aj1ddObqLXX5arPenL9HNxjdAgAAVkDQApBnNSxdSMteaqRn6iSObs3adFotP1ujdUcY3QIAAA+GoAUgT3Nzstfo9pX1w3N1VLiAi0LDb6nrN5s1/Nc9uh4dZ+vyAABADkXQAgBJ9Ut5a9mQRuper6gkafbfiaNbaw5fsnFlAAAgJyJoAcA/8jnZ653HKml2n7oq4uWqcxHR6v7t3xo2d7ciGd0CAAAZQNACgP+oV7Kgfh/SUD3rF5Mkzdl6Ri0/W6OVhy7atjAAAJBjELQAIA2ujvYa+WhF/fRCPRUr6KrzEdHqNW2LXvl5lyKiGN0CAAB3R9ACgLuoXdxLvw1upGcbFJfJJM3ddlYtxq3Wnwcu2Lo0AACQjRG0AOAeXBztNKJdBc3tW08lvPPpQmSMnp2xVUPn7FR4VKytywMAANkQQQsA0qlmUS8tHdxQzzcqIbNJ+nVHqJp/tkbL94XZujQAAJDNELQAIAOcHez0vzblNbdffZUslE+Xrsfo+e+2afCPO3TtJqNbAAAgEUELAO5DjSIFtGRQQ/VtXFJmk7Rg5zk1/2y1ft973talAQCAbICgBQD3ydnBTq+3Lqdf+wertI+bLt+IVd9Z2zXwh+26ciPG1uUBAAAbImgBwAOqFuSpxYMaaEDTkrIzm7R493m1+GyNluxmdAsAgLyKoAUAVuBkb6dXW5bT/P7BKuubX1duxmrAD9vV//ttuszoFgAAeQ5BCwCsqHJhDy16sYEGPVxK9maTlu4JU/NPV2vRrnMyDMPW5QEAgCxC0AIAK3O0N2toi7KaPyBY5f3ddS0qTi/O3qG+s7bp4vVoW5cHAACyAEELADJJpUAPLRgQrCHNSsvebNKyfRfU4rM1mr8jlNEtAAByOYIWAGQiR3uzhjQro4UDG6higLvCo+I0ZM5O9Zm5TRcjGd0CACC3ImgBQBaoEOCu+QOC9XLzMnKwM+mPAxfU7NPV+nX7WUa3AADIhQhaAJBFHOzMevGR0lr0YgNVDvRQZHS8hv60S8/O2KqwCEa3AADITQhaAJDFyvm5a17/+nq1ZVk52pn118GLav7Zav209QyjWwAA5BIELQCwAXs7swY0LaXFgxqoapCnrkfH67W5u9Vz2hadC79l6/IAAMADImgBgA2V8c2vX/rW0+uty8nR3qzVhy+p5WdrNGfLaUa3AADIwQhaAGBj9nZm9W1cUksHNVT1Ip66HhOvYb/sUfdv/1Yoo1sAAORIBC0AyCZK+bhpbt/6eqNNeTnZm7X2yGW1+HS1vt98itEtAAByGIIWAGQjdmaT+jQqod8GN1StogV0MzZBb8zbq67fbNaZq1G2Lg8AAKQTQQsAsqEShdw054V6GtGugpwdzFp/9Ipajluj7zadksXC6BYAANkdQQsAsik7s0nPNiiu3wc3Uu1iXoqKTdCI+XvV5etNOn2F0S0AALIzghYAZHPFvPPpx+framRIBbk42GnT8atqOW6Npq8/IUv4WenEGiki1NZlAgCA29jbugAAwL2ZzSb1DC6upuV8NOyX3dp0/Kr2L50orfhakiGZzFLIeKlGd1uXCgAAxIgWAOQoRQvm0w/P1dUnLb01xv5rmfXP+VqGRcaiIYxsAQCQTRC0ACCHMZtNeqJYjOxMKZtimIwE3Th/2EZVAQCA2+WYoDV69GjVr19frq6u8vT0TNcyhmFo5MiRCggIkIuLi5o0aaJ9+/ZlbqEAkBW8SiYeLnibeMOsZ369qM3Hr9ioKAAAkCTHBK3Y2Fh16tRJ/fr1S/cyH330kT799FNNmDBBW7ZskZ+fn5o3b67r169nYqUAkAU8AhPPyTLZSZIMk50+ceqnXZFuevqrTRq77JDiEiw2LhIAgLzLZBhGjrogy/Tp0zVkyBCFh4ffdT7DMBQQEKAhQ4Zo2LBhkqSYmBj5+vrqww8/1AsvvJDmcjExMYqJiUm+HxkZqaCgIEVERMjd3d1q+wEAVhERKl09LnmV0A1nX41cuE9zt52VJFUL8tT4ztVUtGA+GxcJAEDuEBkZKQ8Pj3RlgxwzopVRJ06cUFhYmFq0aJE8zcnJSY0bN9aGDRvuuNyYMWPk4eGRfAsKCsqKcgHg/ngESsUbSh6BcnOy19hOVTWhS3Xld7bXzjPhajN+reZuO6sc9p0aAAA5Xq4NWmFhYZIkX1/fFNN9fX2TH0vL8OHDFRERkXw7c+ZMptYJANbWrkqAfh/SSLWLe+lmbIJe+XmXXpy9QxG34mxdGgAAeYZNg9bIkSNlMpnuetu6desDbcNkMqW4bxhGqmm3c3Jykru7e4obAOQ0gZ4umt2nrl5tWVZ2ZpMW7z6vNuPX0igDAIAsYtMLFg8cOFCdO3e+6zzFihW7r3X7+flJShzZ8vf3T55+8eLFVKNcAJAb2ZlNGtC0lIJLeWvwjzt06kqUnv5qk/o3KaXBzUrLwS7XHtQAAIDN2TRoeXt7y9vbO1PWXbx4cfn5+WnFihWqXr26pMTOhatXr9aHH36YKdsEgOyoWpCnlgxqmNwoY8LKo1p39DKNMgAAyEQ55uvM06dPa+fOnTp9+rQSEhK0c+dO7dy5Uzdu3Eiep1y5cpo3b56kxEMGhwwZovfff1/z5s3T3r171bNnT7m6uqpLly622g0AsAkaZQAAkLVsOqKVEW+99ZZmzJiRfD9plGrlypVq0qSJJOnQoUOKiIhInue1117TrVu31L9/f127dk116tTR8uXLlT9//iytHQCyi3ZVAlS9SAG9NGen/j5xVa/8vEurDl3U6PaV5eHiYOvyAADINXLcdbSyWkZ65QNATpFgMTR59TF9uuKwEiyGAj1d9OmTVVWnREFblwYAQLbFdbQAAHeV1Cjjl371VbSgq0LDb+nprzZp7LJDikuw2Lo8AAByPIIWAORhSY0yOtYsLIshTVh5VJ0mb9SpKzdtXRoAADkaQQsA8jgaZQAAYH0ELQCApMRGGb8PaaTaxbx0MzZBr/y8Sy/O3qGIW3G2Lg0AgByHoAUASBbo6aLZz9fVqy3Lys5s0uLd59Vj3Dwd2LBEigi1dXkAAOQYdB28B7oOAsirdp4J17LvPtIrMV/KzmTIIrMUMk7mmj1sXRoAADZB10EAwAOr5n5Tr8VNkp0p8fs4sywyFg3R5dDjNq4MAIDsj6AFAEjb1WMyGSlbvdvJov99s0CrDl20UVEAAOQMBC0AQNq8SkqmlH8mEmTW7qiC6jlti8b8doBrbgEAcAcELQBA2jwCpZDxksku8b7JTpa2n6l53RqSpCmrj6vT5I06czXKhkUCAJA90QzjHmiGASDPiwiVrh6XvEokhi9Jv+05r9d+2a3r0fHK72yvj56ootaV/W1cKAAAmSsj2YCgdQ8ELQBI25mrURr04w7tOB0uSepat4jebFtBzg52ti0MAIBMQtdBAECmC/Jy1U8v1FPfxiUlSbM2nVb7Lzfo2KUbNq4MAADbI2gBAO6bg51Zr7cupxm9a6tgPkcdOB+pkC/Wae62s7YuDQAAmyJoAQAeWOMyhfTb4IaqX7KgomIT9MrPuzR0zk7djIm3dWkAANgEQQsAYBU+7s767tk6erl5GZlN0q87QhXyxTrtOxdh69IAAMhyBC0AgNXYmU168ZHS+vH5evL3cNbxyzfV/ssNmrnxpOi9BADISwhaAACrq13cS0sHNVSz8j6KjbforQX71HfWNkVExdm6NAAAsgRBCwCQKQrkc9RX3WtpRLsKcrAzadm+C2rz+VptO3XN1qUBAJDpCFoAgExjMpn0bIPi+qVffRUt6KrQ8Ft6cspGfbnqqCwWDiUEAOReBC0AQKarUthTi19soJCqAUqwGPro90PqMe1vXboeY+vSAADIFAQtAECWyO/soM87V9MHHSrL2cGstUcuq83na7X+6GVblwYAgNURtAAAWcZkMqlz7SJaOLCByvi66dL1GHX9ZrPGLjuk+ASLrcsDAMBqCFoAgCxXxje/FgxooKdrB8kwpAkrj6rz1E06F37L1qUBAGAVBC0AgE24ONppTIcq+uLp6nJzstfWU9fUevxardh/wdalAQDwwAhaAACbCqkaoCWDGqhKYQ9F3IpTn5lbNXLhPsXEJ9i6NAAA7htBCwBgc0UL5tPcvvX1bIPikqTpG07qiUkbdOLyTRtXBgDA/SFoAQCyBUd7s0a0q6BvetRSAVcHXQ49oXc+n6Tlm7bbujQAADKMoAUAyFYeKe+rlY+c0QbnQZpmfleP/Paw5n0zhkMJAQA5CkELAJC9RITK849XZJYhSbIzGQo5/ZFemLBIZ65G2bg4AADSh6AFAMherh6TjJTX1LI3WRR98Yjafk5XQgBAzkDQAgBkL14lJVPKP0+GyU75/MooMjpefWZu1ZilBxTHBY4BANkYQQsAkL14BEoh4yWTXeJ9k51MIeM0qX+IegcndiWcsua4uny1SWER0TYsFACAOzMZhmHYuojsLDIyUh4eHoqIiJC7u7utywGAvCMiVLp6XPIqkRi+/vHbnvN6be5uXY+Jl1c+R43vXE0NSxeyYaEAgLwiI9mAES0AQPbkESgVb5giZElS68r+WvRiA1Xwd9fVm7Hq/u3f+mzFYSVY+N4QAJB9ELQAADlOMe98+rV/fT1du4gMQxr/5xH1+PZvXb4RY+vSAACQRNACAORQzg52GtOhsj57qqpcHOy07uhltRm/Vn+fuGrr0gAAIGgBAHK29tULa+HAYJXycdPF6zF6+qtNmrz6mCwcSggAsCGCFgAgxyvtm18LBgTr8WoBSrAY+uC3g3r+u60Kj4q1dWkAgDyKoAUAyBXyOdnrs6eq6f32leVob9YfBy6q7efrtOtMuK1LAwDkQQQtAECuYTKZ1KVOEf3ar76KeLkqNPyWOk7eoBkbToqrmQAAshJBCwCQ61QK9NDiQQ3UsqKv4hIMvb1wnwbO3qHr0XG2Lg0AkEcQtAAAuZK7s4Mmd62pEe0qyN5s0pLd5/XohPU6cD7S1qUBAPIAghYAINcymUx6tkFxzXmhnvw9nHXi8k09PnG9ftpyxtalAQByOYIWACDXq1m0gJYMaqgmZQspJt6i137ZrVd+3qVbsQm2Lg0AkEsRtAAAeYJXPkd92+MhvdqyrMwmae62s3p84nodu3TD1qUBAHIhghYAIM8wm00a0LSUZj1XR95uTjp04boe/WKdFu06Z+vSAAC5DEELAJDn1C/praWDGqhOcS/djE3Qi7N3aMT8vYqJ51BCAIB1ELQAAHmSj7uzvn+ujgY0LSlJ+m7TKXWctFFnrkbZuDIAQG5A0AIA5Fn2dma92rKcpvV6SJ6uDtoTGqG2n6/Viv0XbF0aACCHI2gBAPK8pmV9tGRQQ1Uv4qnI6Hj1mblV7y89oLgEi61LAwDkUAQtAAAkBXq6aM7z9dQ7uLgkaeqa4+ry1SaFRUTbuDIAQE5kMgzDuNdMNWrUyNhKTSYtXLhQgYGB911YdhEZGSkPDw9FRETI3d3d1uUAALLA73vP69Wfd+t6TLy88jnqi6erK7iUt63LAgDYWEaygX16Vrhz5069/PLLcnNzu+e8hmHogw8+UExMTPqqBQAgm2lVyV/l/NzV//vt2n8+Ut2+2axXWpZVv8YlZTKZbF0eACAHSNeIltlsVlhYmHx8fNK10vz582vXrl0qUaLEAxdoa4xoAUDeFR2XoBHz9+rnbWclSc0r+OqTJ6vK3dnBxpUBAGwhI9kgXedonThxQoUKFUp3Afv371fRokXTPT8AANmRc1SYPqoZrnGtC8nRzqwV+y/o0S/W6cD5SFuXBgDI5tI1opWXMaIFAHnU9pnSosGSYZFMZp1pMEadt5RRaPgtOTuYNaZDZbWvXtjWVQIAslBGssF9Ba3o6Gjt3r1bFy9elMWSsvXto48+mtHVZWsELQDIgyJCpXGVEkNWEpOdrr2wXYOWXNDaI5clSd3qFtWIdhXkaE8TXwDIC6zeDON2v//+u7p3767Lly+nesxkMikhISGjqwQAIHu5eixlyJIkI0EFbp3R9F4NNP6Pw/r8r6P6btMp7QmN0KSuNeTv4WKbWgEA2VKGv4IbOHCgOnXqpPPnz8tisaS4EbIAALmCV0nJ9J8/kSY7yauE7MwmDW1RVt/2rCV3Z3vtPBOudp+v04ajqb+ABADkXRkOWhcvXtTQoUPl6+ubGfUAAGB7HoFSyPjEcCUl/hsyLnH6Px4u56vFLzZUBX93XbkZq67fbNaXq46KU58BANJ9nKPVu3dvBQcH69lnn82smrIVztECgDwsIlS6elzyKpEiZN2OFvAAkHdkajOMqKgoderUSYUKFVLlypXl4JDyD8mgQYMyXnE2RtACANyLYRj6ccsZvb1gn2ITLCpW0FWTutZUeX/+bgBAbpKpQevrr79W37595eLiooIFC8pkMv27MpNJx48fv7+qsymCFgAgvXafDVe/WdtpAQ8AuZTVL1h8uzfffFPvvPOOIiIidPLkSZ04cSL5lpkha/To0apfv75cXV3l6emZrmV69uwpk8mU4la3bt1MqxEAkLdVKeypRS82UMPS3oqOs+ilObs0Yv5excZb7r0wACBXyXDQio2N1VNPPSWzOWuvGRIbG6tOnTqpX79+GVquVatWOn/+fPJt6dKlmVQhAACSVz5HTe9VW4MeLiVJ+m7TKT05ZaPOR9yycWUAgKyU4bTUo0cPzZkzJzNquatRo0bppZdeUuXKlTO0nJOTk/z8/JJvXl5emVQhAACJaAEPAMjwBYsTEhL00UcfadmyZapSpUqqZhiffvqp1YqzhlWrVsnHx0eenp5q3LixRo8eLR8fnzvOHxMTo5iYmOT7kZGRWVEmACAXSmoB33fWNu0/H6mu32zWKy3Lql/jkinOcQYA5D4ZDlp79uxR9erVJUl79+5N8Vh2+6PRunVrderUSUWLFtWJEyc0YsQIPfzww9q2bZucnJzSXGbMmDEaNWpUFlcKAMitihR01a/96+vN+Xs1d9tZffT7Ie04HU4LeADI5TLcddCaRo4cec9Qs2XLFtWqVSv5/vTp0zVkyBCFh4dneHvnz59X0aJF9eOPP6pDhw5pzpPWiFZQUBBdBwEAD4QW8ACQ82Wk62CGR7SsaeDAgercufNd5ylWrJjVtufv76+iRYvqyJEjd5zHycnpjqNdAADcL5PJpKdrF1HFAHf1m7VdJ69Eqf2X62kBDwC5VLqaYXTo0CFD5yo988wzunjx4j3n8/b2Vrly5e56c3Z2Tvd27+XKlSs6c+aM/P39rbZOAAAyghbwAJA3pCtoLViwQJcuXVJkZOQ9bxEREVq0aJFu3Lhh1UJPnz6tnTt36vTp00pISNDOnTu1c+fOFNspV66c5s2bJ0m6ceOGXnnlFW3cuFEnT57UqlWrFBISIm9vb7Vv396qtQEAkBG0gAeA3C9dhw4ahqEyZcpkdi139dZbb2nGjBnJ95MacqxcuVJNmjSRJB06dEgRERGSJDs7O+3Zs0czZ85UeHi4/P391bRpU82ZM0f58+fP8voBALhdUgv4akU8NeTHnckt4Kc86qda7tckr5KSR6CtywQA3Kd0NcNYvXp1hldct27dXHGuU0ZOeAMA4H6cvhKlvrO2qdLFBRpj/7XsTIYMk1mmkPFSje62Lg8A8I+MZAObdh3MCQhaAICsEH3ltBy/qCqz/j1XyzDZyTRkDyNbAJBNZCQbpOscLQAAkLmcI0+mCFmSZDISdPbY3jssAQDIzghaAABkB14lJVPKP8vxhlnd51/Wb3vO26goAMD9ImgBAJAdeARKIeMlk52kxMMGv/EcrOOxnur3/XaN+e2A4hNoAQ8AOQXnaN0D52gBALJURKh09bjkVULxbv76aNkhTV1zXJIUXKqgPu9cXQXdcn6zKQDIiThHCwCAnMojUCreUPIIlL2dWf9rU14Tu9SQq6Od1h+9opAv1mn32XBbVwkAuIcMB60LFy6oW7duCggIkL29vezs7FLcAACAdbWt4q/5A4JVwjufzkVEq+PkjfppyxlblwUAuIt0XbD4dj179tTp06c1YsQI+fv7y2QyZUZdAADgNmV882v+wGC9/NMu7dm/X/Pmzdbx4zX10hNN5GTPF50AkN1kOGitW7dOa9euVbVq1TKhHAAAcCfuzg6aUnGfdHyIzLIoYb9Jk88OUsc+/5Ovu7OtywMA3CbDhw4GBQWJ/hkAANhARKjMi4ckX2/LzmTohcgv1PvzBdp68qqNiwMA3C7DQWvcuHF6/fXXdfLkyUwoBwAA3NHVY5KRssW7vcmi/FGn1XnqJn238SRfhgJANpHhQwefeuopRUVFqWTJknJ1dZWDg0OKx69e5Rs1AAAyRdJFjW8LW4bJTqXKVtGmA/EasWCfdp2N0HuPV5KzA+dtAYAtZThoffbZZzTAAADAFpIuarxoiGQkSCY7mULG6d3qLVRk7XF98NtBzd12VocvXNekrjUV6Oli64oBIM/igsX3wAWLAQDZzm0XNZZHYPLk9Ucva+AP23UtKk5e+Rw1oUt11S/pbcNCASB3ydQLFj/zzDP66quvdPjw4fsuEAAAPIDbLmp8u+BS3lr0YgNVDHDX1Zux6vbN3/p67XHO2wIAG8hw0HJzc9Mnn3yicuXKKSAgQE8//bQmT56sgwcPZkZ9AAAgAwoXcNUv/eqrQ41AJVgMvbfkgAb/uFO3YhNsXRoA5Cn3fehgWFiYVq1apVWrVmn16tU6fPiwfHx8dP78eWvXaFMcOggAyIkMw9CMDSf13pIDircYKueXX1O71VKRgq62Lg0AcqxMPXQwSf78+VWgQAEVKFBAnp6esre3l5+f3/2uDgAAWJHJZFLP4OL6oU9debs56mDYdYVMWKfVhy/ZujQAyBMyHLSGDRumunXrytvbW2+++aZiY2M1fPhwXbhwQTt27MiMGgEAwH2qXdxLi19sqGpBnoq4Faee0/7WxJVHOW8LADJZhg8dNJvNKlSokF566SU99thjKl++fGbVli1w6CAAIDeIiU/QyIX7NPvvM5KkVhX9NPbJqnJzyvCVXgAgz8pINshw0Nq1a5dWr16tVatWae3atbKzs1Pjxo3VpEkTNWnSJNcFL4IWACA3mf33ab29YJ9iEywq5eOmKd1qqmQhN1uXBQA5QqYGrf/atWuXxo0bp1mzZslisSghIXd1NSJoAQBym+2nr6nfrG26EBmj/E72+vSpampewdfWZQFAtpeRbHBfxwvs2LEjuePg2rVrFRkZqWrVqqlp06b3VTAAAMg6NYoU0KIXG2jA99u15eQ19Zm5VYMeKa0hj5SW2WyydXkAkCtkeESrQIECunHjhqpWrZp8uGCjRo1y7WgPI1oAgNwqNt6i0Uv2a8bGU5KkR8r56NOnqsnDxcHGlQFA9pSphw4uXrw4Vwer/yJoAQByu1+2ndX/5u1RTLxFxb3zaUq3mirjm9/WZQFAtpNl52idPXtWJpNJgYGB97uKbI+gBQDIC/acjVDfWdsUGn5Lro52GtupqtpU9rd1WQCQrWTqBYstFoveeecdeXh4qGjRoipSpIg8PT317rvvymKx3HfRAADAdioX9tDCgcGqX7KgomIT9M73K/TDj7OUEH7W1qUBQI6U4WYYb7zxhr755ht98MEHCg4OlmEYWr9+vUaOHKno6GiNHj06M+oEAACZrKCbk2b2rq1lsz5Wq+NjZHfQkOWgWVGtPpVr3V62Lg8AcpQMHzoYEBCgyZMn69FHH00xfcGCBerfv79CQ0OtWqCtceggACBPiQiVxlWSjH+PUkmQWSe7blLJUmVtWBgA2F6mHjp49epVlStXLtX0cuXK6erVqxldHQAAyE6uHksRsiTJTha9M3Oxfttz3kZFAUDOk+GgVbVqVU2YMCHV9AkTJqhq1apWKQoAANiIV0nJlPLjQYLMOhRbSP2+366xyw4pwXLffbQAIM/I8DlaH330kdq2bas//vhD9erVk8lk0oYNG3TmzBktXbo0M2oEAABZxSNQChkvLRoiGQmSyU5q+5nahdXS1+tOaMLKo9p/PlKfcb0tALir+2rvfu7cOU2cOFEHDx6UYRiqUKGC+vfvr4CAgMyo0aY4RwsAkCdFhEpXj0teJRLDl6R5O87q9V/+vd7WV91rqpQP19sCkHdk2XW08gKCFgAA/9obGqHnZ27VuYhouTnZ69Mnq6pFRT9blwUAWcLqQWv37t3p3niVKlXSPW9OQNACACClyzdiNOD77dp8IrEJ1uBHSmvwI6VlNptsXBkAZC6rBy2z2SyTySTDMGQy/fsmmrTo7dMSEhLut+5siaAFAEBqcQkWjV5yQNM3nJQkNSvvq8+eqqr8zpy3BSD3snp79xMnTuj48eM6ceKEfvnlFxUvXlxffvmldu7cqZ07d+rLL79UyZIl9csvv1hlBwAAQPbmYGfWyEcramynqnK0N+uPAxf0+MT1Onbphq1LA4BsIcPnaNWuXVsjR45UmzZtUkxfunSpRowYoW3btlm1QFtjRAsAgLvbdSZcfWdt0/mIaOV3ste4ztX0SHlfW5cFAFaXqRcs3rNnj4oXL55qevHixbV///6Mrg4AAORwVYM8tXBgAz1UrICux8TruZlb9cWfR2ThelsA8rAMB63y5cvrvffeU3R0dPK0mJgYvffeeypfvrxViwMAADlDofxO+v65uupWt6gMQ/pkxWH1/367bsTE27o0ALCJDB86+PfffyskJEQWi0VVq1aVJO3atUsmk0mLFy9W7dq1M6VQW+HQQQAAMmbOltMaMX+fYhMsKu3jpq+611Ix73y2LgsAHlimX0crKipKs2bNSnHB4i5duihfvtz3JkrQAgAg47afvqZ+s7bpQmSM3J3t9fnT1dWkrI+tywKAB8IFi62IoAUAwP25GBmtft9v17ZT12QySa+2LKt+jUumuCwMAOQkmdoMIyAgQF26dNHUqVN1+PDh+y4SAADkbj7uzprdp6661Ckiw5A++v2QBv6wQ1GxnLcFIPfLcND65JNP5O7urk8//VTlypWTv7+/OnfurMmTJ+vAgQOZUSMAAMihHO3Ner99ZY1uX0lBdld1dd8fen7CQp25GmXr0gAgUz3QoYMXLlzQypUrtXjxYs2ZM0cWi0UJCQnWrM/mOHQQAAAr2D5TxqLBMhkWJRgmvWd+Qc2feVX1S3nbujIASLeMZAP7+9nAjRs3tG7dOq1evVqrVq3Sjh07VLlyZTVu3Pi+CgYAALlYRKj0T8iSJDuToTcsU9X420p6rm0D9axfjPO2AOQ6GQ5aderU0e7du1WpUiU1adJE//vf/9SwYUN5enpmQnkAACDHu3pM+idkJbE3WRSkMI1atF/7zkXqvccrydnBzkYFAoD1ZfgcrSNHjsjV1VUlSpRQiRIlVKpUKUIWAAC4M6+SkinlRw7DZKfHHm4os0mau+2snpq6SRcio21UIABYX4aD1tWrV7Vy5UoFBwfrjz/+UOPGjeXn56ennnpKkydPzowaAQBATuYRKIWMl0z/jFiZ7GQKGaenm9XVzN515OHioF1nwtXui3XaduqabWsFACt54Otobdu2TRMmTNCsWbNohgEAAO4sIlS6elzyKpEYvv5x6spNPT9zmw5duC5HO7Pee7ySnnwoyIaFAkDaMvWCxTt27NCqVau0atUqrV27VtevX1fVqlXVpEkTNW3aVG3btn2g4rMbghYAAJnvZky8Xv5pl37fFyZJ6lGvqN5sV0EOdhk++AYAMk2mBi17e3tVr15djRs3VpMmTdSoUaNcHUAIWgAAZA2LxdCElUf16YrDkqS6Jbw0sUsNFXRzsnFlAJAoU4NWZGRkngocBC0AALLWiv0X9NKcnboRE69ATxdN7V5TFQM8Us4UEZrYzdCrZIrDEAEgM2UkG2R4PJ6wAQAAMlPzCr6a17++ihV0VWj4LT0xaYMW7Tr37wzbZ0rjKkkzQhL/3T7TdsUCwB1kOGglJCRo7Nixql27tvz8/OTl5ZXiBgAA8KBK++bXggEN1LhMIUXHWfTi7B368PeDSgg/Ky0a/O91uQyLtGhI4ggXAGQjGQ5ao0aN0qeffqonn3xSERERGjp0qDp06CCz2ayRI0dmQokAACAv8nB10Lc9H9ILjUtIkiatOqaPZy9NdfFjGQmJ3QwBIBvJcND6/vvv9dVXX+mVV16Rvb29nn76aX399dd66623tGnTpsyoEQAA5FF2ZpOGty6v8Z2rycnerPmnnJUgU8qZTHaJLeMBIBvJcNAKCwtT5cqVJUlubm6KiIiQJLVr105LliyxbnUAAACSHqsWqF/61ZfZI1DD455TgvHPRxiTnRQyjoYYALKdDAetwoUL6/z585KkUqVKafny5ZKkLVu2yMmJ9qsAACBzVAr00MIXG+hk0BMKjhmvzrFvanqdRTKqd7N1aQCQSoaDVvv27fXnn39KkgYPHqwRI0aodOnS6t69u3r37m31AgEAAJJ4uznp+z511KxudW2yVNDIVeF6cfYO3YpNsHVpAJBChq+j9V+bN2/W+vXrVapUKT366KPWqivb4DpaAABkT99vPqW3F+xTvMVQxQB3Te1eS4GeLrYuC0AulmkXLI6Li9Pzzz+vESNGqESJvHHSKUELAIDs6+8TV9Vv1jZduRmrgvkcNalrTdUuzuVmAGSOTLtgsYODg+bNm/dAxQEAAFhL7eJeWjAwWBX83XXlZqy6fLVJ328+ZeuyAOD+ztGaP39+JpQCAACQcYULuGpuv3pqW8Vf8RZDb8zbqzfn71FsvOXeCwNAJrHP6AKlSpXSu+++qw0bNqhmzZrKly9fiscHDRpkteKSnDx5Uu+++67++usvhYWFKSAgQF27dtUbb7whR0fHOy5nGIZGjRqlqVOn6tq1a6pTp44mTpyoihUrWr1GAABgO66O9prwdHVV8HfX2OWHNGvTaR2+cEOTnqmhgm50RQaQ9TLcDKN48eJ3XpnJpOPHrX9l9t9//11z5szR008/rVKlSmnv3r3q06ePunXrprFjx95xuQ8//FCjR4/W9OnTVaZMGb333ntas2aNDh06pPz586dr25yjBQBAzvLH/gsaMmenbsTEK9DTRVO711TFAA9blwUgF8i0ZhjZyccff6xJkybdMdgZhqGAgAANGTJEw4YNkyTFxMTI19dXH374oV544YU0l4uJiVFMTEzy/cjISAUFBRG0AADIQY5cuK4+M7fq5JUouTjYaWynqmpbxd/WZQHI4TKtGUZ2EhERIS+vO3cVOnHihMLCwtSiRYvkaU5OTmrcuLE2bNhwx+XGjBkjDw+P5FtQUJBV6wYAAJmvtG9+LRjQQA1Le+tWXIIG/LBdY5cdksWSI79fBpADpescraFDh6Z7hZ9++ul9F5Nex44d0xdffKFPPvnkjvOEhYVJknx9fVNM9/X11alTd+5GNHz48BT7mzSiBQAAchYPVwdN6/mQPvjtoL5ed0ITVh7VwbBIffZUNeV3drB1eQByuXQFrR07dqS4v23bNiUkJKhs2bKSpMOHD8vOzk41a9bM0MZHjhypUaNG3XWeLVu2qFatWsn3z507p1atWqlTp0567rnn7rkNk8mU4r5hGKmm3c7JyUlOTpw0CwBAbmBvZ9ab7SqovL+7hs/boz8OXFSHLzfoq+61VMw7371XAAD3KV1Ba+XKlcn///TTT5U/f37NmDFDBQoUkCRdu3ZNvXr1UsOGDTO08YEDB6pz5853nadYsWLJ/z937pyaNm2qevXqaerUqXddzs/PT1LiyJa//7/HZF+8eDHVKBcAAMjdnqhZWCV93PT8zK06cvGGHp2wThOfqaGGpQvZujQAuVSGm2EEBgZq+fLlqVqk7927Vy1atNC5c+esWmCS0NBQNW3aVDVr1tSsWbNkZ2d31/mTmmG89NJLeu211yRJsbGx8vHxuWszjP+i6yAAALnHhchovfDdNoWdOaYS5jCFPNxInR+pc9ejXQAgSaY2w4iMjNSFCxdSTb948aKuX7+e0dWly7lz59SkSRMFBQVp7NixunTpksLCwpLPw0pSrlw5zZs3T1LiIYNDhgzR+++/r3nz5mnv3r3q2bOnXF1d1aVLl0ypEwAAZG++7s76ufYRbXAerB8cR+vJta3081fvKyY+wdalAchlMnzB4vbt26tXr1765JNPVLduXUnSpk2b9Oqrr6pDhw5WL1CSli9frqNHj+ro0aMqXLhwisduH5A7dOiQIiIiku+/9tprunXrlvr37598weLly5en+xpaAAAgl4kIlcPSlyRZJEl2JkMdQseq36RKGt2zpXzyO9u2PgC5RoYPHYyKitIrr7yib7/9VnFxcZIke3t7Pfvss/r444+VL1/uOrGUQwcBAMhFTqyRZoSkmtw59k2ddKuhqd1rqkphz6yvC0COkCUXLL5586aOHTsmwzBUqlSpXBewkhC0AADIRSJCpXGVJMOSPMkw2ekp16n6+4qLnOzN+qhjFT1WLdCGRQLIrrLkgsX58uVTlSpVVLVq1VwbsgAAQC7jESiFjJdM/zTVMtnJFDJOX7/4mJqWLaSYeIsG/7hTH/5+UAlc3BjAA7jvEa28ghEtAAByoYhQ6epxyatEYviSlGAx9PGyQ5q8+pgk6ZFyPhrXmYsbA/hXloxoAQAA5FgegVLxhskhS5LszCa93rqcxj1VTU72Zv158KLaf7lBJy/ftGGhAHIqghYAAMBtHq8eqJ9eqCdfdycdvXhDj01cr3VHLtu6LAA5DEELAADgP6oGeWrRwAaqXsRTEbfi1P3bzfp23QlxxgWA9CJoAQAApMHH3Vmz+9RVx5qFZTGkdxbv17BfdnNxYwDpQtACAAC4A2cHO33csYrebFteZpP009az6vLVZl28Hm3r0gBkcwQtAACAuzCZTHquYQlN61Vb7s722nbqmh6bsF57QyNsXRqAbIygBQAAkA6NyxTS/AHBKlEon85HRKvj5A1asvu8rcsCkE0RtAAAANKphGOEFra16PESUnScRQN+2K7PVhyWhYsbA/gPghYAAEB6bJ8pjasktx/b67PzXTWh7B5J0vg/j2jAD9sVFRtv4wIBZCcELQAAgHuJCJUWDZYMiyTJZFjU7vRHmtDWR452Zv22N0xPTNqos9eibFwogOyCoAUAAHAvV48lh6xkRoLaFY7W7OfryNvNUQfOR+qxCeu19eRV29QIIFshaAEAANyLV0nJ9J+PTSY7yauEahb10oKBDVTB311Xbsbq6a826actZ2xTJ4Bsg6AFAABwLx6BUsj4xHAlJf4bMi5xuqRATxfN7VdPbSr7KS7B0Gu/7NY7i/YrPsFy53UCyNVMhmHQJucuIiMj5eHhoYiICLm7u9u6HAAAYEsRodLV45JXieSQdTuLxdDnfx3RuD+OSJIalvbWhC415OHikNWVAsgEGckGjGgBAACkl0egVLxhmiFLksxmk4Y0K6NJz9SQi4Od1h65rPYT1+vYpRtZXCgAWyNoAQAAWFnryv6a26+eAj1ddPzyTT0+cb1WH75k67IAZCGCFgAAQCaoGOChBQODVatoAV2PjlevaX/r67XHxVkbQN5A0AIAAMgk3m5O+r5PHT1Zq7AshvTekgN6be5uxcQn2Lo0AJmMoAUAAJCJnOzt9OETVTSiXQWZTdLP287qma8269L1GFuXBiATEbQAAAAymclk0rMNimtar9rK72yvraeu6bEJ67TvXIStSwOQSQhaAAAAWaRxmUKaPyBYJbzz6VxEtDpO2qjf9py3dVkAMgFBCwAAIAuVLOSmef2D1bC0t27FJajf99s17o/DslhokgHkJgQtAACALObh6qBpPR9S7+DikqRxfxzRwNnbFRUbb+PKAFgLQQsAAMAG7O3Meiukgj56oooc7ExauidMHSdtVGj4LVuXBsAKCFoAAAA29ORDQfqhT10VzOeo/ecj9diEddp26qqtywLwgAhaAAAANvZQMS8tGBis8v7uunwjVk9P3ayft56xdVkAHgBBCwAAIBsoXMBVc/vWU6uKfopNsOjVubv17uL9ik+w2Lo0APeBoAUAAJBN5HOy15fP1NDgR0pLkr5Zd0K9Z2xVxK04G1cGIKMIWgAAANmI2WzSS83LaGKXGnJ2MGvN4Utq/+V6Hb90w9alAcgAghYAAEA21LaKv+b2rS9/D2cdv3RTj09crzWHL9m6LADpRNACAADIpioFemjhwAaqUcRTkdHx6jntb3277oQM45+LG0eESifWJP4LIFsxGcm/qUhLZGSkPDw8FBERIXd3d1uXAwAA8qCY+AS9MW+v5m47K0l6qlaQRhfdIfulQyTDIpnMUsh4qUZ32xYK5HIZyQaMaAEAAGRzTvZ2+rhjFb3ZtrzMJmn11p0yLxmcGLKkxH8XDWFkC8hGCFoAAAA5gMlk0nMNS+ibng+pgtMlmfWfg5KMBOnqcdsUByAVghYAAEAO0rSsj0b0CFGCTCkfMNlJXiVsUxSAVAhaAAAAOUzxEmUU0+ozJfzzUS7eMOvPUsNluAfYuDIASQhaAAAAOZBr3V4yBu/W9NIT1CBmvJ7dU0GDf9yp6LgEW5cGQAQtAACAHMu+QJB6PtNNL7ZvLHuzSQt3ndNTUzbqQmS0rUsD8jyCFgAAQA73TJ2imvlsbXm6OmjX2Qg9NmG99pyNsHVZQJ5G0AIAAMgF6pf01oIBwSrl46awyGh1mrJBi3efs3VZQJ5F0AIAAMglihbMp1/711eTsoUUHWfRwB926LMVh2WxGPdeGIBVEbQAAAByEXdnB33T4yH1aVhckjT+zyN6cfYO3YqlSQaQlQhaAAAAuYyd2aQ32lbQR09UkYOdSUv2nFenKRt0PuKWrUsD8gyCFgAAQC715ENB+qFPXXnlc9Te0Eg9OmG9dpy+ZuuygDyBoAUAAJCLPVTMSwsGBKusb35duh6jp6Zu0oKdobYuC8j1CFoAAAC5XJCXq37pX1/NyvsqNt6iwT/u1MfLDtIkA8hEBC0AAIA8wM3JXlO71VS/JiUlSRNXHlPfWdt0MybexpUBuRNBCwAAII8wm00a1qqcPn2yqhztzFq+/4KemLRBZ69F2bo0INchaAEAAOQxHWoU1uzn68rbzUkHw67r8Ynrte3UVVuXBeQqBC0AAIA8qGbRAlowMFgV/N11+Uasnp66WXO3nbV1WUCuQdACAADIowI9XTS3Xz21quin2ASLXvl5l8YsPaAEmmQAD4ygBQAAkIe5Otrry2dqaNDDpSRJU9Yc1/Mzt+p6dJyNKwNyNoIWAABAHmc2mzS0RVl9/nR1Odmb9efBi3pi0gadvkKTDOB+EbQAAAAgSXq0aoB+eqGefPI76fCFG3ps4jptOn7F1mUBORJBCwAAAMmqBnlq4cAGqlLYQ9ei4tT168368e/Tti4LyHEIWgAAAEjBz8NZc56vp3ZV/BVvMfT6r3s0atE+xSdYbF0akGMQtAAAAJCKi6Odvni6uoY2LyNJmrb+pHrP2KpImmQA6ULQAgAAQJpMJpMGPVJaXz5TQ84OZq05fEntJ67Xics3bV0akO0RtAAAAHBXbSr7a27f+vL3cNaxSzf1+MT12nD0sq3LArI1ghYAAADuqVKghxYMDFa1IE9F3IpT92//1vebT9m6LCDbImgBAAAgXXzyO+vH5+vqsWoBircYemPeXo1cSJMMIC0ELQAAAKSbs4Odxj1VTa+2LCtJmr7hpHpN36KIWzTJAG6XI4LWyZMn9eyzz6p48eJycXFRyZIl9fbbbys2Nvauy/Xs2VMmkynFrW7dullUNQAAQO5kMpk0oGkpTe5aQy4Odlp75LI6fLleJ2mSASSzt3UB6XHw4EFZLBZNmTJFpUqV0t69e9WnTx/dvHlTY8eOveuyrVq10rRp05LvOzo6Zna5AAAAeUKrSv4qXMBVfWZuTWyS8eV6fflMDdUv6W3r0gCbMxmGYdi6iPvx8ccfa9KkSTp+/Pgd5+nZs6fCw8M1f/78dK83JiZGMTExyfcjIyMVFBSkiIgIubu7P0jJAAAAudLFyGg9/9027TwTLnuzSe88Vkld6hSxdVmA1UVGRsrDwyNd2SBHHDqYloiICHl5ed1zvlWrVsnHx0dlypRRnz59dPHixbvOP2bMGHl4eCTfgoKCrFUyAABAruTjntgk49GqiU0y/jdvj0YtokkG8rYcOaJ17Ngx1ahRQ5988omee+65O843Z84cubm5qWjRojpx4oRGjBih+Ph4bdu2TU5OTmkuw4gWAADA/TEMQxNXHtXY5YclSY3KFNKELtXl7uxg48oA68jIiJZNg9bIkSM1atSou86zZcsW1apVK/n+uXPn1LhxYzVu3Fhff/11hrZ3/vx5FS1aVD/++KM6dOiQrmUy8mQCAADkehGh0tVjkldJySMwzVl+23NeQ3/apVtxCSrl46ZvetRS0YL5srhQwPoykg1s2gxj4MCB6ty5813nKVasWPL/z507p6ZNm6pevXqaOnVqhrfn7++vokWL6siRIxleFgAAIM/bPlNaNFgyLJLJLIWMl2p0TzVb68r+CvJy1XMzturoxRt6bOJ6TXqmpuqVLGiDogHbsGnQ8vb2lrd3+rrShIaGqmnTpqpZs6amTZsmsznjp5dduXJFZ86ckb+/f4aXBQAAyNMiQv8NWVLiv4uGSCUfSXNkq1KghxYODFaf77Zp15lwdftms959vJKerk2TDOQNOaIZxrlz59SkSRMFBQVp7NixunTpksLCwhQWFpZivnLlymnevHmSpBs3buiVV17Rxo0bdfLkSa1atUohISHy9vZW+/btbbEbAAAAOdfVY/+GrCRGgnT1zh2gfdydNef5ugr5p0nG8F/36J1F+2mSgTwhR1xHa/ny5Tp69KiOHj2qwoULp3js9lPMDh06pIiICEmSnZ2d9uzZo5kzZyo8PFz+/v5q2rSp5syZo/z582dp/QAAADmeV8nEwwVvD1smO8mrxF0Xc3aw0+edq6m0j5s+XXFY364/oWOXbugLmmQgl8uRXQezEs0wAAAA/rF9ZuLhgkZCYsgKGZfmOVp3snTPeQ39aaei4yw0yUCOlGO6DuYEBC0AAIDbRIQmHi7oVeKOXQfvZs/ZCPWZuVVhkdHydHXQ5K41VbcETTKQM+SJCxYDAADABjwCpeIN7ytkSVLlwh5aMDBYVQt7KDwqTl2/3qwf/z5t5SIB2yNoAQAAIEv5ujtrzgv11K6Kv+Ithl7/dY/eXbxfCRYOtELuQdACAABAlnN2sNMXT1fXS83KSJK+WXdCz83YouvRcXdeKCJUOrEm8V8gmyNoAQAAwCZMJpMGNyutiV1qyNnBrJWHLqnDlxt0+kpU6pm3z5TGVZJmhCT+u31m1hcMZABBCwAAADbVtoq/fn6hvnzdnXTk4g09NnGdNh+/8u8Md7pYMiNbyMYIWgAAALC5yoU9tHBgA1Up7KFrUXHq+s1mzdnyT5OM+7hYMmBrBC0AAABkC77uzprzfD21reKvuARDw37Zo/cW71dCgRKJF0u+XToulgzYEkELAAAA2YaLo50mPF1dQ5qVliR9ve6Enpt3TtGtPk0MV9K/F0u+zxbzQFbggsX3wAWLAQAAbGPJ7vN6+eedio6zqIyvm6Z1CFSg5fx9XywZeFBcsBgAAAA5Xtsq/vrphXrydXfS4Qs31G7Gcf2tioQs5AgELQAAAGRbVQp7asGABqocmNgk45mvN+nnrWdsXRZwTwQtAAAAZGt+Hs766YV6als5sUnGq3N3a8zSA0qwcAYMsi+CFgAAALI9F0c7ffF0dQ16JLFJxpQ1x/XCd9t0MybexpUBaSNoAQAAIEcwm00a2ryMxneuJkd7s/44cEFPTNqg0PBbti4NSIWgBQAAgBzlsWqBmvN8XXm7Oelg2HU9NmG9tp++ZuuygBQIWgAAAMhxqhcpoAUDg1Xe312Xb8So89RNWrAz1NZlAckIWgAAAMiRAj1dNLdvPTUr76vYeIsG/7hTny4/JAtNMpANELQAAACQY+VzsteUbjX1QuMSkqTP/zqqF2fv0K3YBBtXhryOoAUAAIAczc5s0vDW5fVxxypysDNpyZ7zemrqRl2IjLZ1acjDCFoAAADIFTrVCtL3z9VVAVcH7T4boUcnrNOesxG2Lgt5FEELAAAAuUbt4l5aMKCBSvu46UJkjDpN2aDf9py3dVnIgwhaAAAAyFWKFHTVL/3rq3GZQoqOs6jf99s14a8jMgyaZCDrELQAAACQ67g7O+ibHrXUK7iYJGns8sN6ac5ORcfRJANZg6AFAACAXMnezqy3QypqdPtKsjebNH/nOXX5apMuXY+xdWnIAwhaAAAAyNWeqVNUM3vXlruzvbafDtfjE9frwPlIW5eFXI6gBQAAgFyvfilvzR8QrOLe+RQafksdJ23QH/sv2Los5GIELQAAAOQJJQq5aV7/+qpfsqBuxiaoz3db9dWa4zTJQKYgaAEAACDP8HR11IzetdWlThEZhjR66QEN+2W3YuMtti4NuQxBCwAAAHmKg51Zox+vpLdDKshskn7aelZdv9msqzdjbV0achGCFgAAAPIck8mkXsHF9U3Ph+TmZK+/T1zV4xPX6+jF67YuDbkEQQsAAAB5VtOyPvq1f30Febno9NUotZ+4QasPX7J1WcgFCFoAAADI08r45tf8/sF6qFgBXY+JV69pf2vGhpO2Lgs5HEELAAAAeV5BNyfNeq6OOtYsLIshvb1wn0bM36u4BJpk4P4QtAAAAABJTvZ2+rhjFQ1vXU4mk/TdplPqNW2LIqLibF0aciCCFgAAAPAPk8mkFxqX1JSuNeXqaKd1Ry+r/aT1OnH5ZuqZI0KlE2sS/wX+g6AFAAAA/EeLin6a27e+AjycdfzSTT0+cb02HLv87wzbZ0rjKkkzQhL/3T7TdsUiWyJoAQAAAGmoEOCu+QODVS3IUxG34tT9m781++/TiSNYiwZLxj/nbxkWadEQRraQAkELAAAAuAOf/M768fm6erRqgOIthob/ukczl/z1b8hKYiRIV4/bpkhkSwQtAAAA4C6cHew0vnM1DW1eRpL05W5Dlv9+jDbZSV4lbFAdsiuCFgAAAHAPJpNJgx4prYldaijcoZBej3tWCUkfpU12Usg4ySPQpjUie7G3dQEAAABATtG2ir+CvFz03AwHBV+voiquVzTgiRaqWrGirUtDNsOIFgAAAJABVQp7auHABvIOLK7lUWXU6YfTmrfjrK3LQjZD0AIAAMD/27v3sKrqRI3j72IDIreNeEEUPKjl/YKCGWildbTb0ShxrBzRNMdbJdNFTzOn0fOcsmymGZ0UzWZGzSxvBdaMafaMWEGa10wryiteUEJGwBsge58/GJkhb1zW3msD38/z7Gc/e+11eZf6/B5ffmstUE0t7X5aNSFOd3cNU0mZQ79c+ZV+u+E7ORxOq6PBQ1C0AAAAgBrw9/XWgpExmjygvSRp/qYDmvLOTl0oKbM4GTwBRQsAAACoIS8vQ9Pu6aTXhveUj83QR3tP6mdvfKFThRetjgaLUbQAAACAWhoWE6F3xt+q0ABffX28QEPnfa69xwusjgULUbQAAAAAE/SJClXa5H66uUWgThUWa/jCL7R+b47VsWARihYAAABgkjZN/fXe5Hjd3qG5LpSWaeLbOzV/0345nTwko6GhaAEAAAAmCvbz0V9Gx2pMfJQk6bcbsvTM6q9UfImHZDQkFC0AAADAZN42L80c2lX/90BX2bwMvb/zuH7+p606fbbY6mhwE4oWAAAA4CKj4qK05LE+CvLz1rbD/1BCSoa+P1VkdSy4AUULAAAAcKHbbm6u1MnxahPqr6P5FzQsJVPpWblWx4KLUbQAAAAAF7upRZDSpvTTLW1DVVR8SWOXbNPSzMNWx4ILUbQAAAAANwgN8NXb4/pqeEyEHE5pxgf79ELaXpWWOayOBhegaAEAAABu4uvtpVcTe+i/7+0kw5CWbTmisUu2qeBCqdXRYDKKFgAAAOBGhmFo4h3ttfDnMWrsY9NnP+TpoZQMHc47Z3U0mIiiBQAAAFjg7q4ttXpinMLtfjrw4zklpGRoy8HTVseCSShaAAAAgEW6tbZr7ZR+6hlh15nzpRr1561atf3o1VcuOC4d+rT8HR6PogUAAABYqEWwn1ZOiNP9PcJVWubUtDV79PK6b1XmcP5rpZ1vSXO6SUuHlL/vfMu6wKgSihYAAABgMT8fm15/uJeeuutmSdIbnx7UxLd36FzxpfIZrA+nSs5/Pp3Q6ZA+TGZmy8NRtAAAAAAP4OVl6OlBHTT34Wj5entp4zenlLjwC+Vlf/OvknWZs0zKP2hNUFQJRQsAAADwIA9Et9a7429Vs0BffZtTqNFr8+U0fvLfdsMmhbazJiCqhKIFAAAAeJiY/2iitCn91DEsSPvOBup/Lj0ux+WyZdikIXMke2tLM+L66kzRGjp0qNq0aSM/Pz+Fh4dr1KhROnHixHW3cTqdmjlzplq1aqXGjRtrwIAB2rdvn5sSAwAAADUX0cRf702O152dWmh56QDFX5irNd0Wypm8R+qdZHU83ECdKVoDBw7UqlWrlJWVpffee08HDhxQYmLidbd59dVX9fvf/17z5s3Ttm3b1LJlSw0aNEhFRUVuSg0AAADUXGAjb72ZFKvH+7fVSTXVs9uDNXXdj7pYWmZ1NNyA4XQ6nTdezfN88MEHSkhIUHFxsXx8fK743ul0qlWrVkpOTtb06dMlScXFxQoLC9Ps2bM1YcKEKh2nsLBQdrtdBQUFCg4ONvUcAAAAgKp698tsvZC2V5ccTvVuE6I3RsWqeVAjq2M1KNXpBnVmRuvf5efna/ny5YqPj79qyZKkQ4cO6eTJkxo8eHDFskaNGumOO+5QZmbmNfddXFyswsLCSi8AAADAao/c0kZvjb1FwX7e2pl9RgnzM5R1kiu1PFWdKlrTp09XQECAmjZtquzsbK1du/aa6548eVKSFBYWVml5WFhYxXdX8/LLL8tut1e8IiMjzQkPAAAA1FL8Tc2UNqWfopr66/iZCxq2IFObvsu1OhauwtKiNXPmTBmGcd3X9u3bK9Z/7rnntGvXLn388cey2WxKSkrSja58NAyj0men03nFsn/3/PPPq6CgoOJ19OjR2p0kAAAAYKJ2zQOVOrmfbm0XqrPFlzRu6Tb95fNDN/x/MdzL0nu08vLylJeXd911oqKi5Ofnd8XyY8eOKTIyUpmZmYqLi7vi+4MHD6p9+/bauXOnevXqVbH8gQceUEhIiJYuXVqljNyjBQAAAE9UcsmhF9L2auX28omBkX3baObQrvKx1amL1uqU6nQDbzdluqpmzZqpWbNmNdr2cj8sLi6+6vdt27ZVy5YttXHjxoqiVVJSos2bN2v27Nk1CwwAAAB4CF9vL70yrLtuahGoWR99q+Vbs3Xk9HnNH9lb9sZXf44B3KdO1N0vv/xS8+bN0+7du3XkyBFt2rRJjz76qNq3b19pNqtTp05KTU2VVH7JYHJysmbNmqXU1FTt3btXY8aMkb+/vx599FGrTgUAAAAwjWEYGn97Oy0aFSt/X5s+35+nh1IydDjvnNXRGrw6UbQaN26s999/X3fddZc6duyosWPHqlu3btq8ebMaNfrXIy2zsrJUUFBQ8XnatGlKTk7W5MmTFRsbq+PHj+vjjz9WUFCQFacBAAAAuMSgLmFaPTFO4XY/HfjxnBJSMrT14GmrYzVodfb3aLkL92gBAACgrsgtvKjxb23XV8cK5GMzNOvB7hoey1O0zVLvf48WAAAAgCu1CPbTil/E6f7u4Sotc+q5NXv0ykffyeFgbsXdKFoAAABAPdLY16bXH+mlp+68SZK0cPMBTVq+Q+dLLlmcrGGhaAEAAAD1jJeXoacHd9ScEdHytXlpw75TGr7wC50suGh1tAaDogUAAADUUwm9Wuud8X3VNMBX+04U6oH5n+vrYwU33hC1RtECAAAA6rHYqFClTemnDmGBOlVYrOFvZGr93hyrY9V7FC0AAACgnosM9dd7k+J1R4fmuljq0MS3d2r+pv3iAeSuQ9ECAAAAGoAgPx/9eXSsxsRHSZJ+uyFLz6z+SsWXyspXKDguHfq0/B215m11AAAAAADu4W3z0syhXdW+eYBmfviN3t95XEfzz2txz+8U+PEzktMhGV7SkLlS7ySr49ZpzGgBAAAADcyouCgtHtNHQY28dfTwfjXe8HR5yZLK3z9MZmarlihaAAAAQAN0e4fmen9yvPraz8imn9yr5SyT8g9aE6yeoGgBAAAADdTNYUGa+dhQOWRU/sKwSaHtrAlVT1C0AAAAgAasScsold0/R45/VoNLTi+tjZymS4HhFier2yhaAAAAQAPn02eMjF9+rbSeb6h/8VxN/b67Hn9ru4oullodrc6iaAEAAACQYY9QwoMPa8bIQfLz8VJ61o9KXPCFjuaftzpanUTRAgAAAFDh3u7hWjUhTi2CGinrVJEeTMnQjiP/sDpWnUPRAgAAAFBJj4gQrX2in7qEByvvbIkeeXOL1u7mce/VQdECAAAAcIVwe2Otnhin/+wcppJLDk1dsVtzPvleTqfzxhuDogUAAADg6gIaeeuNUTGacHv5o97nfPKDpq7YrYulZRYn83wULQAAAADXZPMy9Px9nTV7WHd5exn64KsTeuTNLfqxqNjqaB6NogUAAADghkb0aaO3xt0ie2Mf7co+o4T5Gco6WWR1LI9F0QIAAABQJfHtmyl1crzaNgvQ8TMXNGxBpjZ9l2t1LI9E0QIAAABQZe2aByp1crxubReqs8WXNG7pNi3OOMRDMn6CogUAAACgWkL8ffXW2L76WWyEHE7pfz/8Rr9Zu0+XyhxWR/MYFC0AAAAA1ebr7aXZw3roV/d1kmFIy7Yc0WNLtqngQqnV0TwCRQsAAABAjRiGoV/c3l4Lfx6jxj42ffZDnoYtyFT26fNWR7McRQsAAABArdzdtaVWT4xTy2A/7c89q4SUDG07nG91LEtRtAAAAADUWrfWdq19op+6t7Yr/1yJRr65Ve/vPGZ1LMtQtAAAAACYIizYT6smxOmeri1VUubQ06u+0u82ZMnhaHhPJKRoAQAAADBNY1+bUkb21uQB7SVJ8zbt15Pv7tKFkjKLk7kXRQsAAACAqby8DE27p5N+N7ynfGyG/vZ1jh5e9IVyCy9aHc1tKFoAAAAAXCIxJkJvj+urEH8ffXWsQAnzM/TNiUKrY7kFRQsAAACAy/Rt11Rpk/upXfMAnSi4qMSFmfrkm1NWx3I5ihYAAAAAl4pqFqDUSf3U76amOl9SpvHLtutPnx2U01l/H5JB0QIAAADgcnZ/Hy157BY92reNnE7pxb99q1+l7lVpmcPqaC5B0QIAAADgFj42L72U0E0v/FcXGYb07pfZGv2XL1VwvtTqaKajaAEAAABwG8MwNK5/W/0pKVYBvjZlHjitB1MydDjvnNXRTEXRAgAAAOB2d3UO05pJ8Wpl99PBvHNKSMnQloOnrY5lGooWAAAAAEt0Dg9W2hP91DMyRGfOl2rUn7dq1fajVscyBUULAAAAgGVaBPlp5S9u1f09wlVa5tS0NXv0ykffyeGo208kpGgBAAAAsJSfj02vP9xLT915kyRp4eYDmrR8h86XXJIKjkuHPi1/r0MMZ31+eL0JCgsLZbfbVVBQoODgYKvjAAAAAPVa2q7jmrZmj0rKHPpl0y166vw8GU6HZHhJQ+ZKvZMsy1adbsCMFgAAAACPkdCrtd4Z31ed/Yv0xNnXy0uWJDkd0ofJdWZmi6IFAAAAwKPERoVq6QNNZTN+cvGds0zKP2hNqGqiaAEAAADwOC2iushp/KSuGDYptJ01gaqJogUAAADA89hbyxgyV07DVv7ZsElD5kj21pbGqipvqwMAAAAAwFX1TpLR/q7yywVD29WZkiVRtAAAAAB4MnvrOlWwLuPSQQAAAAAwGUULAAAAAExG0QIAAAAAk1G0AAAAAMBkFC0AAAAAMBlFCwAAAABMRtECAAAAAJNRtAAAAADAZBQtAAAAADAZRQsAAAAATEbRAgAAAACTUbQAAAAAwGQULQAAAAAwGUULAAAAAExG0QIAAAAAk1G0AAAAAMBkFC0AAAAAMBlFCwAAAABMRtECAAAAAJN5Wx3A0zmdTklSYWGhxUkAAAAAWOlyJ7jcEa6HonUDRUVFkqTIyEiLkwAAAADwBEVFRbLb7dddx3BWpY41YA6HQydOnFBQUJAMw3Drsfv06aNt27a59Zju5MnnZ2U2dxzbFccwc5+13VdNti8sLFRkZKSOHj2q4ODgGh8bNefJY4IZPPn8GPOs3SdjXsPkyWOCGTz5/GqTzel0qqioSK1atZKX1/XvwmJG6wa8vLwUERFhybFtNlu9Hvw8+fyszOaOY7viGGbus7b7qs32wcHBHvvvsr7z5DHBDJ58fox51u6TMa9h8uQxwQyefH61zXajmazLeBiGB5syZYrVEVzKk8/PymzuOLYrjmHmPmu7L0/+t4Vrq+9/b558fox51u6TMa9hqu9/b558fu7KxqWDAKDyy2jsdrsKCgo89idwAGAWxjzA9ZjRAgBJjRo10owZM9SoUSOrowCAyzHmAa7HjBYAAAAAmIwZLQAAAAAwGUULAAAAAExG0QIAAAAAk1G0AAAAAMBkFC0AAAAAMBlFCwCq6ejRoxowYIC6dOmiHj16aPXq1VZHAgCXevDBB9WkSRMlJiZaHQWoM3i8OwBUU05Ojk6dOqXo6Gjl5uaqd+/eysrKUkBAgNXRAMAlNm3apLNnz2rp0qVas2aN1XGAOoEZLQCopvDwcEVHR0uSWrRoodDQUOXn51sbCgBcaODAgQoKCrI6BlCnULQA1DuffvqphgwZolatWskwDKWlpV2xTkpKitq2bSs/Pz/FxMTos88+q9Gxtm/fLofDocjIyFqmBoCaceeYB6DqKFoA6p1z586pZ8+emjdv3lW/X7lypZKTk/XrX/9au3bt0m233aZ7771X2dnZFevExMSoW7duV7xOnDhRsc7p06eVlJSkRYsWufycAOBa3DXmAage7tECUK8ZhqHU1FQlJCRULOvbt6969+6tBQsWVCzr3LmzEhIS9PLLL1dpv8XFxRo0aJDGjx+vUaNGmR0bAGrEVWOeJKWnp2vevHncowVUETNaABqUkpIS7dixQ4MHD660fPDgwcrMzKzSPpxOp8aMGaM777yTkgXAo5kx5gGoGYoWgAYlLy9PZWVlCgsLq7Q8LCxMJ0+erNI+MjIytHLlSqWlpSk6OlrR0dH6+uuvXREXAGrFjDFPku6++24NHz5c69atU0REhLZt22Z2VKDe8bY6AABYwTCMSp+dTucVy66lf//+cjgcrogFAC5RmzFPkjZs2GB2JKDeY0YLQIPSrFkz2Wy2K36Sm5ube8VPfAGgrmPMA6xD0QLQoPj6+iomJkYbN26stHzjxo2Kj4+3KBUAuAZjHmAdLh0EUO+cPXtW+/fvr/h86NAh7d69W6GhoWrTpo2efvppjRo1SrGxsYqLi9OiRYuUnZ2tiRMnWpgaAGqGMQ/wTDzeHUC9k56eroEDB16xfPTo0VqyZImk8l/e+eqrryonJ0fdunXTH/7wB91+++1uTgoAtceYB3gmihYAAAAAmIx7tAAAAADAZBQtAAAAADAZRQsAAAAATEbRAgAAAACTUbQAAAAAwGQULQAAAAAwGUULAAAAAExG0QIAAAAAk1G0AAAAAMBkFC0AQL2Tnp4uwzB05swZtx/bMAwZhqGQkJDrrjdz5kxFR0dX+nx52zlz5rg0IwDA9ShaAIA6bcCAAUpOTq60LD4+Xjk5ObLb7ZZkWrx4sb7//vtqbfPss88qJydHERERLkoFAHAnb6sDAABgNl9fX7Vs2dKy44eEhKhFixbV2iYwMFCBgYGy2WwuSgUAcCdmtAAAddaYMWO0efNmzZ07t+Kyu8OHD19x6eCSJUsUEhKiv/71r+rYsaP8/f2VmJioc+fOaenSpYqKilKTJk305JNPqqysrGL/JSUlmjZtmlq3bq2AgAD17dtX6enpNcr6yiuvKCwsTEFBQRo3bpwuXrxowp8AAMBTUbQAAHXW3LlzFRcXp/HjxysnJ0c5OTmKjIy86rrnz5/XH//4R61YsULr169Xenq6HnroIa1bt07r1q3TsmXLtGjRIq1Zs6Zim8cee0wZGRlasWKF9uzZo+HDh+uee+7RDz/8UK2cq1at0owZM/TSSy9p+/btCg8PV0pKSq3OHQDg2bh0EABQZ9ntdvn6+srf3/+GlwqWlpZqwYIFat++vSQpMTFRy5Yt06lTpxQYGKguXbpo4MCB2rRpk0aMGKEDBw7o3Xff1bFjx9SqVStJ5fdRrV+/XosXL9asWbOqnHPOnDkaO3asHn/8cUnSiy++qE8++YRZLQCox5jRAgA0CP7+/hUlS5LCwsIUFRWlwMDASstyc3MlSTt37pTT6VSHDh0q7p8KDAzU5s2bdeDAgWod+9tvv1VcXFylZT/9DACoX5jRAgA0CD4+PpU+G4Zx1WUOh0OS5HA4ZLPZtGPHjiseUPHv5QwAgKuhaAEA6jRfX99KD7AwS69evVRWVqbc3FzddttttdpX586dtWXLFiUlJVUs27JlS20jAgA8GEULAFCnRUVFaevWrTp8+LACAwMVGhpqyn47dOigkSNHKikpSa+99pp69eqlvLw8/f3vf1f37t113333VXlfU6dO1ejRoxUbG6v+/ftr+fLl2rdvn9q1a2dKVgCA5+EeLQBAnfbss8/KZrOpS5cuat68ubKzs03b9+LFi5WUlKRnnnlGHTt21NChQ7V169ZrPtnwWkaMGKHf/OY3mj59umJiYnTkyBFNmjTJtJwAAM9jOJ1Op9UhAACoLwzDUGpqqhISEmq0fVRUlJKTk5WcnGxqLgCAezGjBQCAyR555BFFRERUa5tZs2YpMDDQ1Bk5AIB1mNECAMBE+/fvlyTZbDa1bdu2ytvl5+crPz9fktS8eXPZ7XaX5AMAuAdFCwAAAABMxqWDAAAAAGAyihYAAAAAmIyiBQAAAAAmo2gBAAAAgMkoWgAAAABgMooWAAAAAJiMogUAAAAAJqNoAQAAAIDJ/h/Uhmb9UanjRwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm_0 = ml.head(x=r, y=0, t=t1) # Compute heads at observation well location\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(t1, hm_0[0], label=\"ttim results\") # Plotting TTim model Results\n", + "plt.semilogx(t1, h1, \".\", label=\"obs well 1\") # Plotting Observed points\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.suptitle(\"Model Prediction vs Observations - Calibrated Model 1\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's check how it performs with the Pumping Well data:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm_0_2 = ml.head(x=0, y=0, t=t2) # Compute heads at observation well location\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t2, hm_0_2[0], label=\"ttim results\") # Plotting TTim model Results\n", + "plt.semilogx(t2, h2, \".\", label=\"well 3\") # Plotting Observed points\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.suptitle(\n", + " \"Model Prediction vs Observations - Calibrated Model 1, Results in the Pumping Well\"\n", + ")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, although the model presented an initial good fit, when we challenged it with an outside sample, it performed poorly." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.2. Calibration using the Pumping Well data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We proceed to calibrate using only the data from the pumping well (Well 3).\n", + "The initial inputs can be checked in [***step 5.1***](#step_5_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...........................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 24\n", + " # data points = 14\n", + " # variables = 2\n", + " chi-square = 0.04383685\n", + " reduced chi-square = 0.00365307\n", + " Akaike info crit = -76.7287306\n", + " Bayesian info crit = -75.4506160\n", + "[[Variables]]\n", + " kaq0: 27.8987411 +/- 0.73011666 (2.62%) (init = 10)\n", + " Saq0: 1.7023e-04 +/- 5.8161e-05 (34.17%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.9971\n" + ] + } + ], + "source": [ + "# unknown parameters: kaq, Saq\n", + "ca_1 = ttim.Calibrate(ml)\n", + "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_1.series(name=\"well3\", x=0, y=0, t=t2, h=h2, layer=0)\n", + "ca_1.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.05595715606295086\n" + ] + } + ], + "source": [ + "ca_1.parameters\n", + "print(\"rmse:\", ca_1.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm_1 = ml.head(0, 0, t2)\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(t2, hm_1[0], label=\"ttim results\")\n", + "plt.semilogx(t2, h2, \".\", label=\"well 3 observations\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.suptitle(\"Model Prediction vs Observations - Calibration 2\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.3. Model Calibration with both datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now will proceed to calibrate the model using both datasets at the same time. We begin by creating a new model so we can compare the different results later:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_1 = ttim.ModelMaq(\n", + " kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\"\n", + ")\n", + "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", + "ml_1.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now create a new ```Calibrate``` object. The difference from the previous calibration objects is that now we add a second observation series to the object:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "............................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 25\n", + " # data points = 36\n", + " # variables = 2\n", + " chi-square = 2.65994093\n", + " reduced chi-square = 0.07823356\n", + " Akaike info crit = -89.7877408\n", + " Bayesian info crit = -86.6207029\n", + "[[Variables]]\n", + " kaq0: 38.0492316 +/- 0.52463395 (1.38%) (init = 10)\n", + " Saq0: 1.2468e-06 +/- 2.0176e-07 (16.18%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.7694\n" + ] + } + ], + "source": [ + "ca_2 = ttim.Calibrate(ml_1)\n", + "ca_2.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_2.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "ca_2.series(name=\"obs1\", x=r, y=0, t=t1, h=h1, layer=0) # Adding observation Well 1\n", + "ca_2.series(name=\"well3\", x=0, y=0, t=t2, h=h2, layer=0) # Adding Pumping Well (Well 3)\n", + "ca_2.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq038.0492325.246339e-011.378829-infinf10.0000[38.049231613692605]
Saq00.0000012.017624e-0716.1823820.0inf0.0001[1.2468025740730582e-06]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 38.049232 5.246339e-01 1.378829 -inf inf 10.0000 \n", + "Saq0 0.000001 2.017624e-07 16.182382 0.0 inf 0.0001 \n", + "\n", + " parray \n", + "kaq0 [38.049231613692605] \n", + "Saq0 [1.2468025740730582e-06] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.2718220890129378\n" + ] + } + ], + "source": [ + "display(ca_2.parameters)\n", + "print(\"rmse:\", ca_2.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The parameters are quite different from the first two models. We can also see that the errors have increased significantly. Let's plot the model results with the observations and check why we have large errors:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_2 = ml.head(r, 0, t1)\n", + "hm2_2 = ml.head(0, 0, t2)\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(t1, hm1_2[0], label=\"ttim model results - obs 1\")\n", + "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", + "plt.semilogx(t2, hm2_2[0], label=\"ttim model results - well 3\")\n", + "plt.semilogx(t2, h2, \".\", label=\"well3\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.suptitle(\"Model Prediction vs Observations - Calibration 3\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As seen in the Figure above, TTim could not adjust both curves simultaneously and ended up fitting only Well 3.\n", + "\n", + "Fortunately, in TTim, we can improve this fit by simulating skin resistance and wellbore storage." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.4. Model Calibration with skin resistance and wellbore storage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this, we create a new model and add two extra parameters to the ```Well``` object:\n", + "\n", + "* The radius of the caisson ```rc```, which we use to simulate wellbore storage. In this case, we use a value in meters (float). The function of the radius of the caisson to account for wellbore storage is explained in the previous notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk);\n", + "* The skin resistance ```res```, a float value unit of time (in our case days). The effect of the skin resistance is explained in [Confined 1 - Oude Korendijk](confined1_oude_korendijk)." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_2 = ttim.ModelMaq(\n", + " kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\"\n", + ")\n", + "w_2 = ttim.Well(ml_2, xw=0, yw=0, rw=rw, rc=0.2, res=0.2, tsandQ=[(0, Q)], layers=0)\n", + "ml_2.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we use the method ```.set_parameter_by_reference``` to calibrate the ```rc``` and ```res``` parameters in our well.\n", + "\n", + "```.set_parameter_by_reference``` takes the following arguments:\n", + "* ```name```: a string of the parameter name\n", + "* ```parameter```: numpy-array with the parameter to be optimized. It should be specified as a reference, for example, in our case: ```w1.rc[0:]``` referencing to the parameter ```rc``` in object ```w1```.\n", + "* ```initial```: float with the initial guess for the parameter value.\n", + "* ```pmin``` and ```pmax```: floats with the minimum and maximum values allowed. If not specified, these will be ```-np.inf``` and ```np.inf```." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..............................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 188\n", + " # data points = 36\n", + " # variables = 4\n", + " chi-square = 1.29522940\n", + " reduced chi-square = 0.04047592\n", + " Akaike info crit = -111.693920\n", + " Bayesian info crit = -105.359844\n", + "[[Variables]]\n", + " kaq0: 38.2995471 +/- 0.40273920 (1.05%) (init = 10)\n", + " Saq0: 8.9358e-07 +/- 1.1726e-07 (13.12%) (init = 0.0001)\n", + " res: 6.0492e-06 +/- 0.01070643 (176989.84%) (init = 0.2)\n", + " rc: 0.42247557 +/- 0.07071625 (16.74%) (init = 0.2)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(res, rc) = -0.9172\n", + " C(kaq0, Saq0) = -0.7472\n", + " C(Saq0, rc) = -0.1770\n", + " C(kaq0, res) = -0.1645\n", + " C(kaq0, rc) = +0.1393\n" + ] + } + ], + "source": [ + "ca_3 = ttim.Calibrate(ml_2)\n", + "ca_3.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_3.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "ca_3.set_parameter_by_reference(\n", + " name=\"res\", parameter=w_2.res, initial=0.2, pmin=0\n", + ") # Here we add pmin = 0 to avoid unrealistic values\n", + "ca_3.set_parameter_by_reference(name=\"rc\", parameter=w_2.rc, initial=0.2)\n", + "ca_3.series(name=\"obs1\", x=r, y=0, t=t1, h=h1, layer=0)\n", + "ca_3.series(name=\"obs3\", x=0, y=0, t=t2, h=h2, layer=0)\n", + "ca_3.fit(report=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
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rc4.224756e-017.071625e-0216.738542-infinf0.2000[0.42247557130811325]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 3.829955e+01 4.027392e-01 1.051551 -inf inf 10.0000 \n", + "Saq0 8.935817e-07 1.172566e-07 13.122087 0.0 inf 0.0001 \n", + "res 6.049176e-06 1.070643e-02 176989.839759 0.0 inf 0.2000 \n", + "rc 4.224756e-01 7.071625e-02 16.738542 -inf inf 0.2000 \n", + "\n", + " parray \n", + "kaq0 [38.29954705235744] \n", + "Saq0 [8.935816591115753e-07] \n", + "res [6.04917587931908e-06] \n", + "rc [0.42247557130811325] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.18968024268277686\n" + ] + } + ], + "source": [ + "display(ca_3.parameters)\n", + "print(\"rmse:\", ca_3.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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aGmr3tXC25+10Tn4OS0tLba9dXSgtLdULL7ygpKQkubq6ytnZWa6urvrzzz9r/dz5+Pjoqquusjs2cuRIWSwWLVu2zO74VVddVeX6rv/973/q37+/vL29ba/pu+++W+s+Vfysufnmm2U2n3g75u3treuuu06rVq2qVLjl1MfQpUsXFRYWKjMz84zXeuyxx+Ti4qKnnnqqVn0FcHaEKqCFMplMGjt2rD788EPNmjVLHTp0qHLNjGQtcx0eHl7pzWxoaKicnZ1tU4EOHz4sZ2dnBQUF2bULDw+vdL7S0lL985//lIuLi91HRfip7Zvx8ePHa/Xq1VqzZo12796tAwcO6I477qjU7tTqXwcPHpQk9e7du1KfPvvsM1t/Kh7rqY/pdMdOdejQoTotNFHRn6qqmUVGRlaapnXqayNJbm5uOn78+FmvNW7cOKWlpdmm6n3yyScqKiqyC3Q333yz/vOf/2jv3r267rrrFBoaqr59+9ruczoxMTGSpOTk5NO2qVgHEx0dbXf8dK9FcXGxjhw5Ism6Vu6WW27RO++8o379+ikwMFCjR4+2rc+qeP1HjBhR6fV/6aWXZBiGsrOz7a5R1XN+6aWXKiIiwjaFMycnR1999ZVGjx5tm1JlsVh0ySWX6IsvvtA//vEP/fTTT/r9999tf0iozmtRlep+n1aoztfC2Z63qqSkpFR6DpcuXXra9tV57U/24IMP6sknn9Tw4cO1YMEC/fbbb1q9erW6du1a6+cuLCys0rGKr6tTn7eqXvcvvvhCN9xwg6KiovThhx9q5cqVtj9UFRYW1qpPZ/vetlgsysnJsTt+6mtaUUDjTM/L77//rhkzZujll19WYWGhcnNzlZubK4vFotLSUuXm5lZakweg5qj+B7RgY8aM0VNPPaVZs2bp+eefP227oKAg/fbbbzIMw+4NW2ZmpkpLS23rRIKCglRaWqrDhw/b/XI/9Q1YQECAnJycdPPNN+uee+6p8pqtW7eu1WNq1aqVevXqddZ2p77xrHgMn3/+uW3EoCoVj6uqN5VneqNZISQkpFIBCUdU9OfAgQOVwlp6errtcdWFoUOHKjIyUrNnz9bQoUM1e/Zs9e3bt1IVurFjx2rs2LE6evSoli1bpqefflpXXHGFdu7cedrn9uKLL9a///1vzZ8/X4899liVbebPny9nZ+dKxRlO91q4urrK29tbkvX1nTZtmqZNm6bU1FR99dVXeuyxx5SZmanvv//e9jz985//PG0FtlPfeFdVLbLi6/qNN95Qbm6uPv74YxUVFdnWnEnS5s2btWHDBs2ZM0e33HKL7XhVBS5qorrfpzVxtuetKpGRkVq9erXdsfj4+NNeY+jQoWd97U/24YcfavTo0XrhhRfsjmdlZcnf3//sD6oKFaH6ZBVfV6cGlape9w8//FCtW7fWZ599Zne7I2Hk5O/tU6Wnp8tsNisgIKDW56+wdetWGYaha665ptJt+/btU0BAgF5//XVNmDDB4WsB5zJGqoAWLCoqSo888oiuvPJKuzd3p7rwwgt15MiRSnurVCzAvvDCCyVZpxNJ0kcffWTX7uOPP7b73NPT07Z3VJcuXdSrV69KH1X9Fb0+DR06VM7Oztq9e3eV/akIavHx8YqIiNAnn3xiN6Vp7969WrFixVmvc+mll2rx4sVnXFBfnb8uV/jLX/4iSZUKTaxevVrbtm2zvTZ1oSIwzJ8/X8uXL9cff/xx2imjkuTl5aVLL71UTzzxhIqLi7Vly5bTtr3mmmuUlJSkF198scpqkZ999pkWLlyo2267rdLI1BdffGE3GlBQUKAFCxZowIABVS64j4mJ0b333quLL77Ytqls//795e/vr61bt5729Xd1dT3rcyRZQ2VhYaE++eQTzZkzR/369VNCQoLt9oo33aeW4X7rrbcqnasmXwvV/T6traqet6q4urpWeu7OtPfd1Vdfrc6dO2vKlCnavHlzlW1++OEH21Q3k8lU6bn75ptvlJaWVotHZVVQUKCvvvrK7tjHH38ss9msgQMHnvX+JpNJrq6udoEqIyOjUvU/qfojw/Hx8YqKitLHH39s97Pm6NGjmjt3rq0ioKOGDRumxYsXV/oICwvTeeedp8WLF1eaUgqg5hipAlq4F1988axtRo8erX/961+65ZZblJKSos6dO+uXX37RCy+8oMsuu0wXXXSRJOmSSy7RwIED9Y9//ENHjx5Vr1699Ouvv+qDDz6odM7p06frggsu0IABA3TXXXcpLi5OBQUF2rVrlxYsWFCtSnp1KS4uTs8884yeeOIJ7dmzR8OGDVNAQIAOHjyo33//XV5eXpo8ebLMZrOeffZZ3Xbbbbrmmmt0++23Kzc3V5MmTarW9L9nnnlG3333nQYOHKjHH39cnTt3Vm5urr7//ns9+OCDSkhIUNu2beXh4aGPPvpIiYmJ8vb2VmRkpCIjIyudLz4+XnfccYf++c9/2qrepaSk6Mknn1R0dHS1K39V17hx4/TSSy9p5MiR8vDwqFQt8vbbb5eHh4f69++viIgIZWRkaMqUKfLz87OViq+Kk5OT5s6dq4svvlj9+vXTQw89pH79+qmoqEgLFizQv//9bw0aNEivvvpqlfe9+OKL9eCDD8piseill15Sfn6+rYpkXl6ehgwZopEjRyohIUE+Pj5avXq1vv/+e1tVPG9vb/3zn//ULbfcouzsbI0YMUKhoaE6dOiQNmzYoEOHDmnmzJnVeo4SEhLUr18/TZkyRfv27dO///3vSre3bdtWjz32mAzDUGBgoBYsWFDlFMnOnTtLsn6/3HLLLXJxcVF8fHyVIaW636fVVZ3nrS44OTlp3rx5uuSSS9SvXz/dddddGjJkiLy8vLR37159/vnnWrBggW2q2xVXXKE5c+YoISFBXbp00Zo1azR16lSHptUGBQXprrvuUmpqqjp06KBvv/1Wb7/9tu666y7b9MQzueKKK/TFF1/o7rvv1ogRI7Rv3z49++yzioiIqFQxs3PnzlqyZIkWLFigiIgI+fj4VDmSZzab9fLLL2vUqFG64oor9Pe//11FRUWaOnWqcnNzq/WzuzrCw8Or/Nnl7u6uoKCgSiPDAGqp0UpkAKhzJ1f/O5NTq/8ZhmEcPnzYuPPOO42IiAjD2dnZiI2NNSZOnFipYldubq4xbtw4w9/f3/D09DQuvvhiY/v27ZWq4BmGtZrWuHHjjKioKMPFxcUICQkxzj//fOO5556za6MaVP+bOnWqQ8/B/PnzjSFDhhi+vr6Gm5ubERsba4wYMcJYtGiRXbt33nnHaN++veHq6mp06NDB+M9//mPccsstZ63+ZxiGsW/fPmPcuHFGeHi44eLiYkRGRho33HCDcfDgQVubTz75xEhISDBcXFzsznFq9T/DsFYJe+mll4wOHToYLi4uRnBwsHHTTTcZ+/bts2s3aNAgo2PHjpUec1X9PpPzzz/fkGSMGjWq0m3vvfeeMWTIECMsLMxwdXW1PbaNGzdW69xZWVnGY489ZiQkJBju7u6Gt7e30adPH+PNN9+sVHHt5IqPkydPNlq1amW4uroa3bt3N3744Qdbu8LCQuPOO+80unTpYvj6+hoeHh5GfHy88fTTTxtHjx61O+fSpUuNyy+/3AgMDDRcXFyMqKgo4/LLLzf+97//2dpUvAaHDh067eP497//bUgyPDw8KlW4NAzD2Lp1q3HxxRcbPj4+RkBAgHH99dcbqampVX69TJw40YiMjDTMZrNddbhTq/8ZRvW/TyUZ99xzT6V+xcbG2irT1eR5qwu5ubnGs88+a/To0cPw9vY2XFxcjJiYGOOmm24yfv31V1u7nJwc49ZbbzVCQ0MNT09P44ILLjCWL19e6fmoSfW/jh07GkuWLDF69epluLm5GREREcbjjz9uq2p58vlO9zPmxRdfNOLi4gw3NzcjMTHRePvtt6v8fl2/fr3Rv39/w9PT05Bk6/Op1f8qzJ8/3+jbt6/h7u5ueHl5GRdeeKHd82EYp/+arOrxVhfV/4C6ZTKMOizZAwAAAADnGNZUAQAAAIADCFUAAAAA4ABCFQAAAAA4gFAFAAAAAA4gVAEAAACAAwhVAAAAAOAAQhUAAAAAOIBQBQAAAAAOIFQBAAAAgAMIVQAAAADgAEIVAAAAADiAUAUAAAAADiBUAQAAAIADCFUAAAAA4ABCFQAAAAA4gFAFAAAAAA4gVAEAAACAAwhVAAAAAOAAQhUAAAAAOIBQBQAAAAAOIFQBAAAAgAMIVQAAAADgAEIVAAAAADiAUAUAAAAADiBUAQAAAIADCFUAAAAA4ABCFQAAAAA4gFAFAAAAAA4gVAEAAACAAwhVAAAAAOAAQhUAAAAAOIBQBQAAAAAOIFQBAAAAgAMIVQAAAADgAEIVAAAAADiAUAUAAAAADiBUAQAAAIADCFUAAAAA4ABCFQAAAAA4gFAFAAAAAA4gVAEAAACAAwhVAAAAAOAAQhUAAAAAOIBQBQAAAAAOIFQBAAAAgAMIVQAAAADgAEIVAAAAADiAUAUAAAAADiBUAQAAAIADCFUAAAAA4ABCFQAAAAA4gFAFAAAAAA4gVAEAAACAAwhVAAAAAOAAQhUAAAAAOMC5sTvQ1FgsFqWnp8vHx0cmk6mxuwMAAACgkRiGoYKCAkVGRspsPv14FKHqFOnp6YqOjm7sbgAAAABoIvbt26dWrVqd9nZC1Sl8fHwkWZ84X1/fRu4NAAAAgMaSn5+v6OhoW0Y4HULVKSqm/Pn6+hKqAAAAAJx1WRCFKgAAAADAAYQqAAAAAHAAoQoAAAAAHMCaKgAAgAZUVlamkpKSxu4GAEkuLi5ycnJy+DyEKgAAgAZgGIYyMjKUm5vb2F0BcBJ/f3+Fh4c7tEctoQoAAKABVASq0NBQeXp6OvQGDoDjDMPQsWPHlJmZKUmKiIio9bkIVQAAAPWsrKzMFqiCgoIauzsAynl4eEiSMjMzFRoaWuupgBSqAAAAqGcVa6g8PT0buScATlXxfenIWkdCFQAAQANhyh/Q9NTF9yWhCgAAAAAcQKgCAAAAAAcQqgAAAFAnBg8erAkTJjR2NxxiMpk0f/78arcfM2aMhg8fXm/9qa1JkyapW7dujd2NKsXFxWnatGmN3Y06RagCAADAaVUVlJYsWSKTyVRpz60vvvhCzz77bMN1DtXWVMNfXThw4IBGjhyp+Ph4mc3mRgn2LTJUzZgxQ61bt5a7u7t69uyp5cuXN3aXAAAAWrzAwED5+Pg0djeatLKyMlkslsbuRotSVFSkkJAQPfHEE+ratWuj9KHFharPPvtMEyZM0BNPPKF169ZpwIABuvTSS5WamtrYXQMAALAxDEPHiksb5cMwjGr1ccyYMVq6dKmmT58uk8kkk8mklJQUDRkyRJIUEBAgk8mkMWPGSKo8qhUXF6fnnntOo0ePlre3t2JjY/Xll1/q0KFDuvrqq+Xt7a3OnTvrjz/+OGM/TCaT3nrrLV1xxRXy9PRUYmKiVq5cqV27dmnw4MHy8vJSv379tHv3brv7zZw5U23btpWrq6vi4+P1wQcf2N3+559/auDAgXJ3d1dSUpJ+/PHHStdOS0vTjTfeqICAAAUFBenqq69WSkpKtZ4/SZozZ478/f319ddfKykpSW5ubtq7d6+Ki4v1j3/8Q1FRUfLy8lLfvn21ZMkS2/327t2rK6+8UgEBAfLy8lLHjh317bff2p3zZPPnzz9tFbtJkybpvffe05dffml7HZcsWaLi4mLde++9ioiIkLu7u+Li4jRlypRqP7bTmTt3rjp27Cg3NzfFxcXp1VdfrdSmoKBAI0eOlLe3tyIjI/XPf/6zUp9jYmLk5uamyMhI3X///ae9XlxcnKZPn67Ro0fLz8/P4f7XRovb/Pe1117Trbfeqttuu02SNG3aNP3www+aOXNmnXyRAAAA1IXjJWVKeuqHRrn21meGytP17G8Dp0+frp07d6pTp0565plnJEkhISGaO3eurrvuOu3YsUO+vr62DVSr8vrrr+uFF17Qk08+qddff10333yz+vfvr3Hjxmnq1Kl69NFHNXr0aG3ZsuWMpa2fffZZvfbaa3rttdf06KOPauTIkWrTpo0mTpyomJgYjRs3Tvfee6++++47SdK8efM0fvx4TZs2TRdddJG+/vprjR07Vq1atdKQIUNksVh07bXXKjg4WKtWrVJ+fn6laWPHjh3TkCFDNGDAAC1btkzOzs567rnnNGzYMG3cuFGurq7VeLat55kyZYreeecdBQUFKTQ0VGPHjlVKSoo+/fRTRUZGat68eRo2bJg2bdqk9u3b65577lFxcbGWLVsmLy8vbd26Vd7e3tW63qkefvhhbdu2Tfn5+Zo9e7Yk66jiG2+8oa+++kr//e9/FRMTo3379mnfvn21ukaFNWvW6IYbbtCkSZN04403asWKFbr77rsVFBRkC9+SNHXqVD3++OOaNGmSfvjhBz3wwANKSEjQxRdfrM8//1yvv/66Pv30U3Xs2FEZGRnasGGDQ/2qby0qVBUXF2vNmjV67LHH7I5fcsklWrFiRZX3KSoqUlFRke3z/Pz8eu0jAABAc+Hn5ydXV1d5enoqPDzcdjwwMFCSFBoaWmnE5FSXXXaZ/v73v0uSnnrqKc2cOVO9e/fW9ddfL0l69NFH1a9fPx08eNDuGqcaO3asbrjhBrv7PPnkkxo6dKgkafz48Ro7dqyt/SuvvKIxY8bo7rvvliQ9+OCDWrVqlV555RUNGTJEixYt0rZt25SSkqJWrVpJkl544QVdeumltnN8+umnMpvNeuedd2yBb/bs2fL399eSJUt0ySWXnP1JlHVT2RkzZtimpu3evVuffPKJ9u/fr8jISEnW4PP9999r9uzZeuGFF5SamqrrrrtOnTt3liS1adOmWteqire3tzw8PFRUVGT3HKempqp9+/a64IILZDKZFBsbW+trVHjttdd04YUX6sknn5QkdejQQVu3btXUqVPtQlX//v1t79k7dOigX3/9Va+//rouvvhipaamKjw8XBdddJFcXFwUExOjPn36ONy3+tSiQlVWVpbKysoUFhZmdzwsLEwZGRlV3mfKlCmaPHlyQ3QPAADAxsPFSVufGdpo124oXbp0sf2/4j1aRVA4+VhmZuYZQ1V1zlNYWKj8/Hz5+vpq27ZtuuOOO+zO0b9/f02fPl2StG3bNsXExNgClST169fPrv2aNWu0a9euSuvECgsLK001PBNXV1e7/q9du1aGYahDhw527YqKihQUFCRJuv/++3XXXXdp4cKFuuiii3TdddfZnaMujBkzRhdffLHi4+M1bNgwXXHFFacNisuXL7cLnG+99ZZGjRpVqd22bdt09dVX2x3r37+/pk2bprKyMjk5Wb/2Tn2u+/XrZ6sIeP3112vatGlq06aNhg0bpssuu0xXXnmlnJ2bbnRpuj1zwKlDx4ZhnHY4eeLEiXrwwQdtn+fn5ys6Orpe+wcAAGAymao1Ba+5c3Fxsf2/4v1YVcfOVryhNuc503vCqtaVndreYrGoZ8+e+uijjyq1DQkJOWN/T+bh4WF3bovFIicnJ61Zs8YWMipUTPG77bbbNHToUH3zzTdauHChpkyZoldffVX33XefzGZzpf6XlJRUuz8VevTooeTkZH333XdatGiRbrjhBl100UX6/PPPK7Xt1auX1q9fb/v81EGMClW9767uGr6K+0VHR2vHjh368ccftWjRIt19992aOnWqli5daveaNyUt6js5ODhYTk5OlUalMjMzT/vCu7m5yc3NrSG6V3N5aVL2bimwreQX1di9AQAA5yBXV1eVlZVVOiap0vGmJDExUb/88otGjx5tO7ZixQolJiZKkpKSkpSamqr09HTbFLyVK1fanaNHjx767LPPFBoaKl9f3zrrW/fu3VVWVqbMzEwNGDDgtO2io6N155136s4779TEiRP19ttv67777lNISIgKCgp09OhReXl5SZJd4KlKVa+jJPn6+urGG2/UjTfeqBEjRmjYsGHKzs62TfGs4OHhoXbt2p31sSUlJemXX36xO7ZixQp16NDBLkCuWrXKrs2qVauUkJBgd72rrrpKV111le655x4lJCRo06ZN6tGjx1n70BhaVPU/V1dX9ezZs1Lllh9//FHnn39+I/Wqlta+L03rJL13pfXfte83do8AAMA5KC4uTr/99ptSUlKUlZUli8Wi2NhYmUwmff311zp06JCOHDnS2N2s5JFHHtGcOXM0a9Ys/fnnn3rttdf0xRdf6OGHH5YkXXTRRYqPj9fo0aO1YcMGLV++XE888YTdOUaNGqXg4GBdffXVWr58uZKTk7V06VKNHz9e+/fvr3XfOnTooFGjRmn06NH64osvlJycrNWrV+ull16yVfibMGGCfvjhByUnJ2vt2rX6+eefbYGwb9++8vT01OOPP65du3bp448/1pw5c854zbi4OG3cuFE7duxQVlaWSkpKbMUgtm/frp07d+p///ufwsPDz7pO7kweeugh/fTTT3r22We1c+dOvffee3rzzTdtz3uFX3/9VS+//LJ27typf/3rX/rf//6n8ePHS7JWN3z33Xe1efNm7dmzRx988IE8PDzOuOZr/fr1Wr9+vY4cOaJDhw5p/fr12rp1a60fR021qFAlWRchvvPOO/rPf/6jbdu26YEHHlBqaqruvPPOxu5a9eWlSQvGS0b58LVhkRZMsB4HAABoQA8//LCcnJyUlJSkkJAQpaamKioqSpMnT9Zjjz2msLAw3XvvvY3dzUqGDx+u6dOna+rUqerYsaPeeustzZ49W4MHD5Ykmc1mzZs3T0VFRerTp49uu+02Pf/883bn8PT01LJlyxQTE6Nrr71WiYmJGjdunI4fP+7wyNXs2bM1evRoPfTQQ4qPj9dVV12l3377zbYMpaysTPfcc48SExM1bNgwxcfHa8aMGZKshUI+/PBDffvtt+rcubM++eQTTZo06YzXu/322xUfH69evXopJCREv/76q7y9vfXSSy+pV69e6t27t1JSUvTtt9/KbK59ROjRo4f++9//6tNPP1WnTp301FNP6ZlnnrErUiFZw9eaNWvUvXt3Pfvss3r11VdtRUf8/f319ttvq3///urSpYt++uknLViwwLberCrdu3dX9+7dtWbNGn388cfq3r27Lrvsslo/jpoyGdWd5NiMzJgxQy+//LIOHDigTp066fXXX9fAgQOrdd/8/Hz5+fkpLy+vTod5ayR5mXWE6lS3fC21Pv0QMQAAaJoKCwuVnJys1q1by93dvbG7A+AkZ/r+rG42aFFrqircfffdtvKZzVJgW8lkPjFSJUkmJymw9qU0AQAAANSPFjf9r0Xwi5LlimkyTOWL+UxO0pXTKFYBAAAANEEtcqSqJchsd6OGHzcp3vWQyvxby2NzjGLTtyomyFPRgZ6KCfRUqwAPuTk33D4TTQZVEQEAANCEEKqaqAN5x3XQFKSM4iApU1LmwUptTCYp3NfdFrJiAz3tQleQl+tp9+dqtta+f6KIh8ksXTld6jH67PcDAAAA6gmhqonqHhOg7c8OU1rOcaVmH9O+7GNKtX0cV+rhozpaXKYDeYU6kFeo35OzK53D09VJMYEnQlZMeeiKCfRUlL+H3BtwN/U6cbqqiG0vZMQKAAAAjYZQ1YS5OTupTYi32oR4V7rNMAxlHy22Ba2TQ9e+7ONKzzuuY8Vl2p5RoO0ZBZXuf+ooV8VHdKCnYoOa6ChX9m774h2SZJRJ2XsIVQAAAGg0hKpmymQyKcjbTUHebuoeE1Dp9qLSsrof5Sr/vFVAI41yURURAAAATRChqoVqrFGumEBPBXvX0yiXX5R1DdWCCdYRKqoiAgAAoAkgVJ2DmvUoV4/R1jVU2XusI1QEKgAAGtWSJUs0ZMgQ5eTkyN/fv7G7AzQKQhUqqc9RLsk6ynXy+q0aj3L5RRGmAABoQcaPH69ffvlFmzdvVmJiotavX9/YXQJqhFCFGqmLUa6M/EJl5Bfq95TKo1weLqeOcnkoNsircddyAQCAemUYhsaNG6fffvtNGzdubOzuADVGqEKdcnSU63hJmXYcLNCOg2ce5YoN8lRSpK86R/kpKdJXnq7n2JcyGyADwLmtgX8PFBUV6ZFHHtGnn36q/Px89erVS6+//rp69+5ta/Prr7/q8ccf144dO9S1a1e988476ty5syRp7969uvfee/XLL7+ouLhYcXFxmjp1qi677DJJ0htvvCFJOnToEKEKzdI59k4UjanOR7nWVJxXahvirc5Rfup4UtDycXdp4EfYQNgAGQDObY3we+Af//iH5s6dq/fee0+xsbF6+eWXNXToUO3atcvW5pFHHtH06dMVHh6uxx9/XFdddZV27twpFxcX3XPPPSouLtayZcvk5eWlrVu3ytu78h9ggeaKUIUmoyajXLsPHdWWtDxtSstTZkGRdmUe0a7MI5q3Ls12nzbBXuoY5afOUb7qFOmnjlF+8vNo5kGLDZAB4NzWCL8Hjh49qpkzZ2rOnDm69NJLJUlvv/22fvzxR7377ru20aqnn35aF198sSTpvffeU6tWrTRv3jzdcMMNSk1N1XXXXWcbuWrThu1Q0LIQqtAsnGmUKzO/UJvT87Q5LV+b0vK0JS1P6XmF2pN1VHuyjmrBhnRb25hAT+uIVpR1RKtTpJ8CvFwb+uHUHhsgA8C5rRF+D+zevVslJSXq37+/7ZiLi4v69Omjbdu22UJVv379bLcHBgYqPj5e27ZtkyTdf//9uuuuu7Rw4UJddNFFuu6669SlS5d66S/QGAhVaPZCfd31F193/SUhzHYs60iRtqTna3NanjaXj2jtL59amJp9TN9sOmBrG+XvoU7lIcs6suWnYG+3xngoZ8cGyABwbmuE3wOGYVgvc0p1XsMwzlqxt+L22267TUOHDtU333yjhQsXasqUKXr11Vd133331U+ngQZGqEKLFOztpkEdQjSoQ4jtWO6xYm1Oy9fm9DzbiFbK4WNKyz2utNzj+mHLQVvbcF93dYrys4WtTlF+CvN1b4yHYo8NkAHg3NYIvwfatWsnV1dX/fLLLxo5cqQkqaSkRH/88YcmTJhga7dq1SrFxMRIknJycrRz504lJCTYbo+Ojtadd96pO++8UxMnTtTbb79NqEKLQajCOcPf01UXtA/WBe2Dbcfyjpdoa8WIVnnYSs46aiuIsWjbiaAV4uOmTpH2I1oRfu5n31errrEBMgCc2xr494CXl5fuuusuPfLIIwoMDFRMTIxefvllHTt2TLfeeqs2bNggSXrmmWcUFBSksLAwPfHEEwoODtbw4cMlSRMmTNCll16qDh06KCcnRz///LMSExNt19i1a5eOHDmijIwMHT9+3LZPVVJSklxdm9E0fZyzCFU4p/l5uKhf2yD1axtkO3akqPRE0CoPW7syj+hQQZEW7zikxTsO2doGebmqY5SfLWx1ivJTqwCP+g9abIAMAOe2Bv498OKLL8pisejmm29WQUGBevXqpR9++EEBAQF2bcaPH68///xTXbt21VdffWULRGVlZbrnnnu0f/9++fr6atiwYXr99ddt973tttu0dOlS2+fdu3eXJCUnJysuLq5hHiTgAJNRMVEWkqT8/Hz5+fkpLy9Pvr6+jd0dNBHHiku17UCB3RqtPzOPqMxS+dvHz8NFnaJ8rdMHI60jWjGBnjKbG3hECwDQZBQWFio5OVmtW7eWu3sTmE4OwOZM35/VzQaMVAHV4OnqrJ6xAeoZe+IvcoUlZdqeUWA3orUjo0B5x0v0667D+nXXYVtbH3dndYy0lnbv3MpPHSP91CbYi6AFAADQAhCqgFpyd3FSt2h/dYv2tx0rKi3TnwePaFNF0ErL07aMAhUUlmrVnmyt2pNta+vl6qSkyJNGtFr5qW2It5wIWgAAAM0KoQqoQ27OTuVVA/1sx0rKLPrz4JHyvbSsH1sP5OtocZlWp+RodUqOra27i1lJESeKYXSK9FP7MG+5OJkb4+EAAACgGghVQD1zcTIrKdJXSZG+uqFXtCSptMyiPVlHtWl/ni1sbUnP17HiMq1NzdXa1Fzb/V2dzUqM8LUrhtEhzEeuzgQtAACApoBQBTQCZyezOoT5qEOYj67r2UqSVGYxlJx1VFvS82xha0tavgqKSrVhX6427Mu13d/FyaSEcF/1jgtUn9YB6h0XqKCmumFxbeSlSdm7rZtcUuUQAAA0cYQqoIlwMpvULtRb7UK9dXU3a5CwWAylZh+zrtGyTR/MV97xEm0qr0L4n1+TJUltQ7zUp3Wg+rQOVO+4QLUK8GzMh1N7a9+XFoyXDItkMls3uewxurF7BQAAcFqEKqAJM5tNigv2Ulywl67sGilJMgxD+3OOa21qjlanZGt1co52HCzQ7kNHtfvQUX3y+z5JUqSfuzVgtQ5Un7hAtQv1bviNimsqL+1EoJKs/y6YYN3kkhErAADQRBGqgGbGZDIpOtBT0YGethGtnKPF+mOvNWT9npytzWl5Ss8r1Pz16Zq/Pl2SFOjlql6xAbbRrKQIXzk3tQIY2btPBKoKRpmUvYdQBQAAmixCFdACBHi56uKkMF2cFCbJulnxutRc/ZacrdXJ2Vq3L0fZR4u1cOtBLdx6UJK1pHuP2AD1ibOOZnWL9pe7i1NjPgzrGiqT2T5YmZykwDaN1ycAAICzIFQBLZCnq7P6twtW/3bBkqTiUos2peWVTxfM1uqUbOUXlmr5n1la/meWJMnVyawurfys0wVbB6pnbIB83V0atuN+UdY1VAsmWEeoTE7SldMYpQKAZmLw4MHq1q2bpk2b1thdqTWTyaR58+Zp+PDh1Wo/ZswY5ebmav78+fXar5qaNGmS5s+fr/Xr1zd2Vxxy6uNoqs83oQo4B7g6m9UzNkA9YwN056C2slgM7ThYoNUp2bbRrMyCIv2xN0d/7M3RzCW7ZTZJCeG+dsUvQnwaoMJgj9HWNVTZe6wjVAQqAGhUVQWlJUuWaMiQIcrJyZG/v7/t+BdffCEXlwb+gxyqpamGkbq2Y8cO3Xnnndq6davy8vIUGRmpkSNH6umnn67Xr01CFXAOMptNSozwVWKEr0b3i5NhWKsMVgSs1SnZSjl8TFsP5GvrgXzNWZEiSWoT7KXe5dMF+7YOVKsAj/opfuEXRZgCgGYoMDCwsbvQ5JWVlclkMslsbmLrmlsIFxcXjR49Wj169JC/v782bNig22+/XRaLRS+88EK9XZdXE4BMJpNig7x0Q69oTb2+q5Y8MkS/P36h3hzZXbf0i1VCuI9MJmlP1lF99sc+Pfy/DRrw8mL1m/Kz7vtknT5YtVc7MgpksRiN/VAAAHVozJgxWrp0qaZPny6TySSTyaSUlBQNGTJEkhQQECCTyaQxY8ZIso5qTZgwwXb/uLg4Pffccxo9erS8vb0VGxurL7/8UocOHdLVV18tb29vde7cWX/88ccZ+2EymfTWW2/piiuukKenpxITE7Vy5Urt2rVLgwcPlpeXl/r166fdu3fb3W/mzJlq27atXF1dFR8frw8++MDu9j///FMDBw6Uu7u7kpKS9OOPP1a6dlpamm688UYFBAQoKChIV199tVJSUqr9HM6ZM0f+/v76+uuvlZSUJDc3N+3du1fFxcX6xz/+oaioKHl5ealv375asmSJ7X579+7VlVdeqYCAAHl5ealjx4769ttv7c55svnz55/2D52TJk3Se++9py+//NL2Oi5ZskTFxcW69957FRERIXd3d8XFxWnKlCnVfmynMgxDISEhmjt3ru1Yt27dFBoaavt85cqVcnFx0ZEjRyRJeXl5uuOOOxQaGipfX1/95S9/0YYNG2rdhzZt2mjs2LHq2rWrYmNjddVVV2nUqFFavnx5rc9ZHYxUAahSqK+7rugSqSu6WEu55x0r0R97s/V7+bqsjfvzlJFfqAUb0rVgg7XCoL+ni3rFntiQuFOUn1yaWoVBAGgiDMPQ8dLjjXJtD+fqzTSYPn26du7cqU6dOumZZ56RJNub5uuuu047duyQr6+vPDw8TnuO119/XS+88IKefPJJvf7667r55pvVv39/jRs3TlOnTtWjjz6q0aNHa8uWLWfs07PPPqvXXntNr732mh599FGNHDlSbdq00cSJExUTE6Nx48bp3nvv1XfffSdJmjdvnsaPH69p06bpoosu0tdff62xY8eqVatWGjJkiCwWi6699loFBwdr1apVys/PtwuEknTs2DENGTJEAwYM0LJly+Ts7KznnntOw4YN08aNG+Xq6lqNZ9t6nilTpuidd95RUFCQQkNDNXbsWKWkpOjTTz9VZGSk5s2bp2HDhmnTpk1q37697rnnHhUXF2vZsmXy8vLS1q1b5e3tXa3rnerhhx/Wtm3blJ+fr9mzZ0uyjiq+8cYb+uqrr/Tf//5XMTEx2rdvn/bt21era0jW8Dtw4EAtWbJE1113nXJycrR161Zb/5OSkrRkyRL17NlT3t7eMgxDl19+uQIDA/Xtt9/Kz89Pb731li688ELt3LmzTkY+d+3ape+//17XXnutw+c6E0IVgGrx83TRhYlhujDRWmHweHGZ1u3L0erkHP2eclhr9+Yq91iJFm07qEXbrBUGPVyc1CPWX33igtS7dYC6RwfIw7WRKwwCQBNxvPS4+n7ct1Gu/dvI3+TpcvZN4v38/OTq6ipPT0+Fh4fbjle82Q0NDa00YnKqyy67TH//+98lSU899ZRmzpyp3r176/rrr5ckPfroo+rXr58OHjxod41TjR07VjfccIPdfZ588kkNHTpUkjR+/HiNHTvW1v6VV17RmDFjdPfdd0uSHnzwQa1atUqvvPKKhgwZokWLFmnbtm1KSUlRq1atJEkvvPCCLr30Uts5Pv30U5nNZr3zzju2wDd79mz5+/tryZIluuSSS876HEpSSUmJZsyYoa5du0qSdu/erU8++UT79+9XZKT1j5cPP/ywvv/+e82ePVsvvPCCUlNTdd1116lz586SrCMwteXt7S0PDw8VFRXZPcepqalq3769LrjgAuusldjYWl+jwuDBg/Xvf/9bkrRs2TJ17dpVMTExWrJkiS1UDR48WJK0ePFibdq0SZmZmXJzs67bfuWVVzR//nx9/vnnuuOOO2rdj/PPP19r165VUVGR7rjjDtsfBeoLoQpArXi4Oun8tsE6v22wpPYqKbNoS3q+fk8+rN+Tc/TH3mzlHivRr7sO69ddhyVJLk4mdY7ys21I3Cs2UH6eLGgGgJasS5cutv+HhVn/MFcRFE4+lpmZecZQVZ3zFBYWKj8/X76+vtq2bVulN+X9+/fX9OnTJUnbtm1TTEyMLVBJUr9+/ezar1mzRrt27ZKPj4/d8cLCwkpTDc/E1dXVrv9r166VYRjq0KGDXbuioiIFBQVJku6//37dddddWrhwoS666CJdd911dueoC2PGjNHFF1+s+Ph4DRs2TFdcccVpg+Ly5cvtAudbb72lUaNGVWo3ePBgjR8/XllZWVq6dKkGDx6smJgYLV26VHfccYdWrFhhGxFcs2aNjhw5YnvMFY4fP16j57cqn332mQoKCrRhwwY98sgjeuWVV/SPf/zDoXOeCaEKQJ1wcTKrW7S/ukX7646BksVi6M/MI7bpgr8nZysjv1BrU3O1NjVXby3dI5NJig/zsVUY7BMXqFBf98Z+KADQIDycPfTbyN8a7doN5eSKaxWjPVUds1hO2fy9Ds5z6nRCwzBsxwyj8jrgU9tbLBb17NlTH330UaW2ISEhZ+zvyTw87KdbWiwWOTk5ac2aNXJysp/BUTHF77bbbtPQoUP1zTffaOHChZoyZYpeffVV3XfffTKbzZX6X1JSUu3+VOjRo4eSk5P13XffadGiRbrhhht00UUX6fPPP6/UtlevXnbl2SuC7ak6deqkoKAgLV26VEuXLtUzzzyj6OhoPf/881q9erWOHz+uCy64wPY8RERE2K0lq3C2EdCziY6OliQlJSWprKxMd9xxhx566KFKz3ddIVQBqBdms0nx4T6KD/fRzefFyjAM7c85bldhcE/WUW3PKND2jAK9v3KvJCk2yFN94qwh67w2QYoOPPv0FABojkwmU7Wm4DU2V1dXlZWVVTomqdLxpiQxMVG//PKLRo8ebTu2YsUKJSYmSrK+2U5NTVV6erptCt7KlSvtztGjRw999tlntiIKdaV79+4qKytTZmamBgwYcNp20dHRuvPOO3XnnXdq4sSJevvtt3XfffcpJCREBQUFOnr0qLy8vCTprPtRVfU6SpKvr69uvPFG3XjjjRoxYoSGDRum7OzsSuuZPDw81K5du7M+top1VV9++aU2b96sAQMGyMfHRyUlJZo1a5Z69OhhG/nr0aOHMjIy5OzsrLi4uLOeu7YMw1BJSUmVQbquEKoANAiTyaToQE9FB3pqRE/rVIvMgkL9kZKj38tHsrZl5Gvv4WPae/iY/rdmvyQpyt9DfdtYA1a/NkH1V8YdAFCluLg4/fbbb0pJSZG3t7cCAwMVGxsrk8mkr7/+Wpdddpk8PDxqXUShvjzyyCO64YYb1KNHD1144YVasGCBvvjiCy1atEiSdNFFFyk+Pl6jR4/Wq6++qvz8fD3xxBN25xg1apSmTp2qq6++Ws8884xatWql1NRUffHFF3rkkUfspg7WRIcOHTRq1Cjbtbt3766srCz9/PPP6ty5sy677DJNmDBBl156qTp06KCcnBz9/PPPtkDYt29feXp66vHHH9d9992n33//XXPmzDnjNePi4vTDDz9ox44dCgoKkp+fn958801FRESoW7duMpvN+t///qfw8HCHR4kGDx6sBx54QN27d7eF0YEDB+qjjz7Sgw8+aGt30UUXqV+/fho+fLheeuklxcfHKz09Xd9++62GDx+uXr161fjaH330kVxcXNS5c2e5ublpzZo1mjhxom688UY5O9df9KEsF4BGE+rjrss6R2jSVR317fgB2vD0JZo9trfuGtxWPWL85Ww2KS33uL5Ym6Z/fL5RA15erAteWqwHP1uv/67ep9TDx+r1r04AAGsBBScnJyUlJSkkJESpqamKiorS5MmT9dhjjyksLEz33ntvY3ezkuHDh2v69OmaOnWqOnbsqLfeekuzZ8+2FUkwm82aN2+eioqK1KdPH9122216/vnn7c7h6empZcuWKSYmRtdee60SExM1btw4HT9+3OGRq9mzZ2v06NF66KGHFB8fr6uuukq//fabbdpaWVmZ7rnnHiUmJmrYsGGKj4/XjBkzJFkLhXz44Yf69ttv1blzZ33yySeaNGnSGa93++23Kz4+Xr169VJISIh+/fVXeXt766WXXlKvXr3Uu3dvpaSk6Ntvv3V4D60hQ4aorKzM9lxL0qBBg1RWVqZBgwbZjplMJn377bcaOHCgxo0bpw4dOuivf/2rUlJSTju98GycnZ310ksvqU+fPurSpYsmTZqke+65R++8845Dj+lsTAbvSOzk5+fLz89PeXl5dTrMC6DmjhWXas3eHK3ac1ir9mRrw75clZ6yF1akn7v6tgnSeeWjWTGBnoxkAWhyCgsLlZycrNatW8vdnbWjQFNypu/P6mYDpv8BaLI8XZ01oH2IBrS3LgY+VlyqtXtzy0PWYW3Yn6v0vELNW5emeevSJEkRfu7qW74e67w2QYoNakEhKy9Nyt4tBbaV/KIauzcAAKAcoQpAs+Hp6qwL2gfrgvbBkqx7Za1NzbGFrPX7cnUgr1Dz16dr/nrrhsThvu62NVnntQlSXHMNWWvflxaMlwyLZDJLV06Xeow++/0AAEC9I1QBaLY8XJ3Uv12w+rc7EbLWpZ6YLrh+X64y8gv15fp0fVkessJ83dS3dVB5yApU62Cvph+y8tJOBCrJ+u+CCVLbCxmxAgCgCSBUAWgxPFyddH67YJ1fHrIKSypGsrKtI1mpuTqYX6SvNqTrqw3WkBXq42a3JqtNUwxZ2btPBKoKRpmUvYdQBQBAE0CoAtBiubs46fy2wTq/7YmQtS71xJqsdftylVlQpAUb0rWgPGSF+LjZrclqG9IEQlZgW+uUv5ODlclJCmzTeH0CAAA2hCoA5wx3Fyf1axukfm2DJFlD1vp9J0LW2tRcHSoo0tcbD+jrjQckScHebiftkxWotiHeDR+y/KKsa6gWTLCOUJmcpCunMUoFAEATQagCcM5yd3GyjUhJ1pC1YV+ubbrg2tQcZR0p0jcbD+gbW8hyLV+TZQ1a7UIbKGT1GG1dQ5W9xzpCVV+BigqDAADUGKEKAMq5uzipb5sg9W0TpPFqr6LSMm3Yl2cbyVqzN0dZR4r1zaYD+maTNWQFebnaVRdsX58hyy+qfoMOFQYBAKgVNv89BZv/AjidotIybdyfp1W7D2tVsjVkFZbYF5AI9HK1W5PVPtRbZnMTK3xRlbw0aVqnyuu2JmxixAqoA2z+CzRdbP4LAA3IzdlJveMC1TsuUPepvYpLLdq4P9dWwn3N3hxlHy3Wd5sz9N3mDElSgKfLiemCbYPUIdSnaYYsKgwCaABxcXGaMGGCJkyYIEkymUyaN2+ehg8f3qj9AhxFqAKAWnJ1NqtXXKB6xQXq3r9IxaUWbUo7sSbrj5Qc5Rwr0fdbMvT9FmvI8vd0sRvJig9rIiGLCoMAmoCrrrpK69evV2ZmpgICAnTRRRfppZdeUmRkZGN3DTgjQhUA1BFXZ7N6xgaqZ2yg7hnSTiVlFut0wT2H9Vtytv5IyVbusRL9sOWgfthyUJI1ZPWJOxGyEsIbKWRRYRBAEzBkyBA9/vjjioiIUFpamh5++GGNGDFCK1asaOyuAWdEqAKAeuLiZFbP2AD1jA3QPUOkkjKLNqWVh6w92VpdHrIWbj2ohVutIcvPw0V9bCNZgUoM9224kNVQFQYBOKwkI0PFKXvlGhcrl/DwervOggULdPPNNys7O1tms1nr169X9+7d9fDDD2vq1KmSpL///e/Kz8/XJ598ohUrVuixxx7T6tWrFRwcrGuuuUZTpkyRl5dXta73wAMP2P4fGxurxx57TMOHD1dJSYlcXFzq5TECdYFQBQANxMXJrB4xAeoRE6C7B1tD1ua0PK3ak63fkg9rdXK28o6X6MetB/VjecjydXdWn5NKuCdG+MqpPkNWfVcYBOCw3M8/14GnnpYsFslsVsQzk+U/YkS9XGvgwIEqKCjQunXr1LNnTy1dulTBwcFaunSprc2SJUv0wAMPaNOmTRo6dKieffZZvfvuuzp06JDuvfde3XvvvZo9e3aNr52dna2PPvpI559/PoEKTR6hCgAaiYuTWd1jAtQ9JkB3DW6r0jKLNqfnl49kHdbqlBzlF5Zq0baDWrTNGrJ83J3VKzZAveIC1ad1oDpH+cndxamRH0kdYY8s4KxKMjJOBCpJslh04Kmn5XXBBfUyYuXn56du3bppyZIl6tmzpy1ATZ48WQUFBTp69Kh27typwYMH64UXXtDIkSNtRSjat2+vN954Q4MGDdLMmTOrXfXw0Ucf1Ztvvqljx47pvPPO09dff13njwuoa4QqAGginJ3M6hbtr27R/rpzkDVkbakIWcnZ+j05WwWFpVq845AW7zgkybqOq2srP2vIigtUj9gA+Xk0w7/oskcWUC3FKXtPBKoKFouK96bW2zTAwYMHa8mSJXrwwQe1fPlyPffcc5o7d65++eUX5ebmKiwsTAkJCVqzZo127dqljz76yHZfwzBksViUnJysxMTEal3vkUce0a233qq9e/dq8uTJGj16tL7++uuG2WgdqCVCFQA0Uc5OZnWN9lfXaH/9vTxkbTtQoNUp2baPrCPFWp2So9UpOZqp3TKZpPgwH/VpHWgLWuF+TXxPnLy0E4FKsv67YIJ1fRcjVoAd17hYyWy2D1Zms1xjY+rtmoMHD9a7776rDRs2yGw2KykpSYMGDdLSpUuVk5OjQYMGSZIsFov+/ve/6/777690jpiY6vcvODhYwcHB6tChgxITExUdHa1Vq1apX79+dfaYgLpGqAKAZsLZyazOrfzUuZWfxl3QWoZhKOXwMa1OPhGyUg4f0/aMAm3PKND7K/dKkloFeKhPeen3Pq0D1DbEu2n9xZc9soBqcwkPV8QzkyutqarPYhUV66qmTZumQYMGyWQyadCgQZoyZYpycnI0fvx4SVKPHj20ZcsWtWvXrs6ubRiGJKmoqKjOzgnUB0IVADRTJpNJrYO91DrYSzf0jpYkZRYU6o+UHFvI2pqer/05x7U/J01frEuTZN2QuGIUq1dcgDpF+cnFydx4D4Q9soAa8R8xQl4XXKDivalyjY2p10AlnVhX9eGHH2r69OmSrEHr+uuvV0lJiQYPHizJuhbqvPPO0z333KPbb79dXl5e2rZtm3788Uf985//POt1fv/9d/3++++64IILFBAQoD179uipp55S27ZtGaVCk0eoAoAWJNTHXZd1jtBlnSMkSUeKSrV274mQtS41VznH7CsMuruY1T06QL1bB6p3nLU6oZdbA/56YI8soMZcwsPrPUydbMiQIVq7dq0tQAUEBCgpKUnp6em2tVJdunTR0qVL9cQTT2jAgAEyDENt27bVjTfeWK1reHh46IsvvtDTTz+to0ePKiIiQsOGDdOnn34qNze3+npoQJ0wGRXjqpAk5efny8/PT3l5efL19W3s7gBAnSoutWhzel75lMEc/bHXulfWycwmKTHCVz1iAmz7bLUK8Kj/KYN5aeyRhRarsLBQycnJat26dbWr4AFoGGf6/qxuNmCkCgDOIa7OJ/bK+vsgyWIxtOvQEetIVnnQSss9ri3p+dqSnq8PVlnXZQV7u6lnrL8taHWqj1Lu7JEFAGimCFUAcA4zm03qEOajDmE+GtU3VpJ0IO+41u7N1Zq9OVqbmqMt6XnKOlKkH7Yc1A9brFMGXZxM6hjpZxvJ6hET0PSrDAIAUE8IVQAAOxF+Hrq8i4cu72Jdl1VYUqZNaXlauzenPGjlKutIkdbvy9X6fbl695dkSVKUv4e6x/jbglZihG/jFsAAAKCBEKoAAGfk7uKk3nGB6h0XKMla4nhf9nGtTbWGrDV7c7Q9I19puceVlntcX288UH4/s7q0OjFlsEeMv4K8m/hi87w0a4n3wLZMRQQAVBuhCgBQIyaTSTFBnooJ8tTw7tbgcbSoVBv25dqC1trUXOUdL9Hvydn6PTnbdt+4IE/1KJ8u2DM2QB3CfORkbiJ7Zq19/8QmxCaztSJhj9GN3Su0MNQHA5qeuvi+JFQBABzm5eas89sF6/x2wZKsBTD2ZB3V2vJ1WWv25ujPzCNKOXxMKYeP6Yu11j2zvN2c1S3avzxo+at7TID8PFwa/gHkpZ0IVJL13wUTpLYXMmKFOuHiYv26PnbsmDw8PBq5NwBOduzYMUknvk9rg1AFAKhzZrNJ7UK91S7U27Yxcd6xEq3bl1MetHK1LjVHR4pK9cuuLP2yK8t23zYhXurWyl9do/3VLdpfCRE+cnOu40qDp8rebb/5sGTdMyt7D6EKdcLJyUn+/v7KzMyUJHl6etb/NgUAzsgwDB07dkyZmZny9/eXk1Ptf9cQqgAADcLP00WD40M1OD5UklRmMbTzYIF1uuDeHK1JzdHew8e059BR7Tl0VF+ss45muTqZlRjpq+7R/uoa7aeurfzVOtirbt+QBra1Tvk7OViZnKx7ZgF1JLx8s96KYAWgafD397d9f9YWm/+egs1/AaDxZB8t1ob9udpQXllww75c5ZyyObEk+Xm4qEsrv/KgZf0IdrQIxtr3rVP+jDJroLpyGmuqUC/KyspUUlL56xpAw3NxcTnjCFV1swGh6hSEKgBoOgzDUGr2sfKAlaf1+3K0OT1fxaWWSm1bBXhYpwy28le3GH91ivSTh2sNp3LkpVmn/AW2abhpf1QcBIAmi1BVS4QqAGjaSsos2pFRoHXlI1kb9uVq16EjOvW3mVP5xsbdov3VLdpPXaP91T60CVUblKg4CABNHKGqlghVAND85BeWaPP+PK3fn6v1qdapg5kFRZXaebo6qXOUX3nQsk4bjPBzb5yCAXlp0rROlddxTdjEiBUANBHVzQYUqgAANHu+7i52Jd0l6UDe8fK1WdZpg5v25+locZl+S87WbyftnRXq42arNNgt2l+dovwapqx7dSsOMj0QAJo8QhUAoEWK8PNQhJ+HhnWKkGStNrj70BHrSFZ5MYztGQXKLCjSj1sP6setB233jQvyVOdW/uoS5adOUX7qFOUrH/c6DlrVqTjI9EAAaBaY/ncKpv8BwLnjeHGZtqTnaX1FtcH9udqXfbzKtm1CvGwhq0srf3WM9JWXm4N/mzxTxUGmBwJAo2P6HwAAZ+Hh6qRecYHqFRdoO5ZztFib0vKsH/ut/6blHrftnzV/fbokyWSS2oV4q3OUnzq38lOXVn5KiqhhxcEeo6W2F1ZdcZANiQGg2WCk6hSMVAEATnX4SJEtZG0s/zcjv7BSO7NJah/qYwtZnaP8lBjhK3eXGpZ2lxipAoAmgOp/tUSoAgBUR2ZBoTan5Wnj/hNh61AVFQedzSa1D/NRl/IRrc5RfkqI8JGbczWCFhsSA0CjIlTVEqEKAFBbB/MLy0NWrjaVB67DR4srtXNxMik+3Eedo/zVOco6qtU+zLvqoNVQGxJTZRAAKiFU1RKhCgBQVwzD0IE8a9DanFYxdTBXOcdKKrWtCFqdIisqDvopIdyndlMHa4oqgwBQJUJVLRGqAAD1yTAM7c85flLIshbDyDteOWg5mU1qH+ptDVmRvupUvkbL4aqDJ2PtFgCcFtX/AABogkwmk6IDPRUd6KlLO1v30Do5aG1Oz9PmtHxtTrNOHdyeUaDtGQX6fE3F/aXWwV7qGGkNWh0j/dQx0lcBXq616xBVBgHAYYQqAAAa2emCVkZ+oTan5WtTWp62loetjPxCW3n3BRvSbeeI8vdQx/KQ1SnK+m+Yr5tMJtOZL16dTYgBAGfE9L9TMP0PANCUZR0p0pb0fG1Jz9OWtHxtTs/T3sPHqmwb7O2qpJNGtDpF+Som0LNy0KLKIABUiTVVtUSoAgA0N/mFJdqanm8NW2l52pKerz8zC2Sp4je8j7uzkiKs67M6lq/TahPsJecjBxqmyiAANCOEqloiVAEAWoLCkjJtzyjQ5rQ866hWer62HyhQcZmlUls3Z7MSI3yVFOmrjpG+SorwVUK4rzxcHaw8SJl2AM0coaqWCFUAgJaqpMyiXZlHyoOWdQrh1vR8HS0uq9TWbJLahHjbQlbHSD8lRfoqsLoFMSjTDqAFIFTVEqEKAHAusVgMpRw+qs3p+dqanq+tB/K1NT1PWUcqb1osSRF+7uUhq2Jky0+tAjzs12lRph1AC0FJdQAAcFZms0ltQrzVJsRbV3WNlGStPHiowFoQY+uBEyNaKYeP6UBeoQ7kFeqn7Zm2c1Ss06oIWb2MzYqryzLtTCME0MQRqgAAgB2TyaRQX3eF+rprSEKo7XhBYYm2ZxRoS1qedUTrQL52ZhxRQWGpfkvO1m/J2ZKkcB3Wr24mOZlOTIYxTE464hUjn5p2hmmEAJoBpv+dgul/AABUX3GpRbsPHbGOalWs0zqQr0tLftQLzu/K2WRRqWHW46W36r9lQ9QqwMM2qpUYYV2vVWn6YAWmEQJoZOfc9L+UlBQ9++yz+vnnn5WRkaHIyEjddNNNeuKJJ+TqWstd5gEAwBm5llcOTIzwlXpajxmGof05A7R81ygdSt2u1Xn+WnHITco9rv051o+FWw/azlExfbCiAmFShK/ah3nLLXu3faCSHJtGCAD1pMWEqu3bt8tiseitt95Su3bttHnzZt1+++06evSoXnnllcbuHgAA5wyTyaToQE9F9+ku9emuG8qP5x0rsU0b3Jqer20HrPtpnTp9UJKczSadF1yo92WWWaeMVAW2adgHBABn0aKn/02dOlUzZ87Unj17qn0fpv8BANBwikutZd63HrCGrIoKhHnHSyRJNzgttptG+JLzndrV6hrb6FhihK9aB3vJyVzF9EEAcNA5N/2vKnl5eQoMDDxjm6KiIhUVFdk+z8/Pr+9uAQCAcq7OZuuUv8gTb1YMw1B6XqG2pedr64EOeiL1Qh3P+FO/5/kroyhI2nFIi3ccsrV3dzErPtxXSRE+tqCVEO4jH3cXawOqBwKoZy12pGr37t3q0aOHXn31Vd12222nbTdp0iRNnjy50nFGqgAAaFoKCku0I6PAOqJ1IF9bDxRoR0a+CkssVbaPCfTUbV7LddOh12WWRYbJLF0xTaaetzRwzwE0Vy1m89/ThZ6TrV69Wr169bJ9np6erkGDBmnQoEF65513znjfqkaqoqOjCVUAADQDZeWbF287aZ3WtgMFysgvLC/tfr9dafcyw6y7Q+corFVb26hWfJiPPFydGvFRAGiqWkyoysrKUlZW1hnbxMXFyd3dXZI1UA0ZMkR9+/bVnDlzZDaba3Q91lQBAND8ZR8tVvq6H9Rp0U2Vbvtr8f9plSXJ9rnJJLUO8ioPWSemEEb4uVdd6h3AOaPFhKqaSEtL05AhQ9SzZ099+OGHcnKq+V+dCFUAALQQVexzZZic9P3FC7Uu16t8VCtfWUeKq7y7n4eLEsJ97MJW+1BGtYBzyTkXqiqm/MXExOj999+3C1Th4eHVPg+hCgCAFmTt+9KCCdb9rUxO0pXTpB6j7ZocKiiyBaxtB/K1PaNAuzKPqNRiKFyH1dqcoWRLuDIUJLNJigv2UmK4ry1wJUT4KMr/NBsYA2jWzrlQNWfOHI0dO7bK22ryEAlVAAC0MHlp1g2DA9tUu/pfUWmZspa9o8hlj8kkiywy6znT3/Wf4wOqbO/j5qyE8tGshHBr0IoP85GXW4sutAy0eOdcqKorhCoAAFDV1EGZnHT49jXacsRb2zPytf1AgbZlFGhXZoFKyiq/nTKZpNhAT1vISgi3TiOMDvCUmX21gGaBfaoAAABqK3u3faCSJKNMQUX7NbDDAA3sEGI7XFxq0Z6sI+Uhy1p9cPuBfGUWFCnl8DGlHD6m77dk2Np7uTopPtxHCRG+SiyfQhh/8r5aAJodQhUAAMCpAttKJnOlkSoFtqnU1NXZbB2NCvfVcJ2YXnj4SJF2ZBRoa/k6re0Z+dp58IiOFpdpbWqu1qbm2p2nVYCHbTSrYgPj2CAvOTGqBTR5TP87BdP/AACApGoVuaip0jKLkrOOaluGdTRre/lmxgfyCqts7+5iVnzYiamD1tEtX/l5MqoFNATWVNUSoQoAANjUoshFbeQeK7aOZpVvXrw9I187DhaosMRSZfsIP3frFMLysBUf7qM2wd5yda7Z/pwAzoxQVUuEKgAA0BSUWQztPXzUNpq17YD137Tc41W2d3EyqW2It22NVkXJ91AfN8q9A7VEqKolQhUAAGiy8tJ0NGOHdpWGaVPBiSqE2zMKdKSotMq7+HtaNzFOKN9bKyHCVx3CvOXpytJ64GwIVbVEqAIAAE3S2velBeOtxTNMZunK6bY1XoZhKC33eHnAqiiMUaA9h47IUsU7PZNJigvysq7XijgRuGICKfcOnIxQVUuEKgAA0OScZt8sTdh0xrVehSVl2pV5xLZea8fBAm07UKCsI0VVtvd0dVKHMJ/ykS3rqFZCuI/8PV3r+hEBzQL7VAEAALQUp9k3S9l7zhiq3F2c1CnKT52i/OyOZ5WXe992Srn3Y8VlWr8vV+v35dq1D/d1t63TqiiQ0TbUS27OTnX1CIFmjVAFAADQ1NVg36zqCPZ2U3A7N/VvF2w7VlpmUcrhY9bKgxkFtiqE+3OOKyO/UBn5hVq685CtvZPZpDbBXoqv2MA4zBq4WgV4UBgD5xym/52C6X8AAKBJqod9s6qjoLBEOw9a12jtKF+rtf1AvvILqy6M4e3mrHjbiJaPbZ8t9tZCc8SaqloiVAEAgCargfbNOhvDMJSRX1gesAq0o7w4xu5DR1RSVvVby4q9tSrCVkK4r9qGsLcWmjZCVS0RqgAAAGqnpMyiPYeO2qYQVoxsnW5vLWezSW1CvBRfUe69PHRF+TOFEE0DoaqWCFUAAAB1K7+wRDszTp5CaB3ZKjjNFEKfU6cQhlvXbDGFEA2NUFVLhCoAAID6ZxiGDuQVWotinDSydbYphBUl3ytCV7tQb6oQot4QqmqJUAUAANB4ikst2pN1xDZ1sCJsnW4KoZPZpNblVQgTwk6UfG8V4MFGxnAYoaqWCFUAAABNT8UUwh0HC+wCV97xkirbe7o6qX3YyUHL+m+Qt1sD9xzNGaGqlghVAAAAJ8lLs24+HNi2USsOVsUwDB3ML6pUGGPXoSMqLrVUeZ9gbze76YMJ4T5qH+ojD1emEKIyQlUtEaoAAADKrX1fWjDeuumwySxdOb1B9sZyVMVGxtagZS2KseNggVKzj6mqd74mkxQb6FketHxtoSsuyEtOTCE8pxGqaolQBQAAIOsI1bRO1kBVweQkTdjU5EasqutYcal2HjxyImiVfxw+Wlxlezdns9qHeSs+zBq0OpSPbIX6uFHy/RxR3Wzg3IB9AgAAQHORvds+UEmSUWbdfLghQlU9TDv0dHVWt2h/dYv2tzuedaTopHVa1qmEOw8e0fGSMm1Oy9fmtHy79v6eLvZVCMOsgcvXnZLv5ypCFQAAACoLbGud8nfqSFVgm/q/dgNPOwz2dlNwOzf1bxdsO2axGErNPnZiROugdXQrJeuoco+V6PfkbP2enG13nih/D3UI87buqxVuHeFqG+pFyfdzANP/TsH0PwAAgHJr35cWTLCOUJmcpCun1f+aqiY+7bCwpEy7Dx2xTR2sqEZ4IK+wyvYnl3yPP6kSYXSAJyXfmwGm/wEAAMAxPUZLbS+0TvkLbNMwoaaxpx2ehbuLkzpG+qljpJ/d8bzjJdp50DqFcKetEmG+8gtLtSvziHZlHtE3OmBr7+HipA5h3upw0t5aHcK9FeLNeq3miFAFAACA0/OLatgw05jTDh3g5+Gi3nGB6h0XaDt2csn3isC1I6NAf2Za12tt2J+nDfvz7M4T6OWqDmHeSgj3VXy4jy10ebvxtr0pY/rfKZj+BwAA0MgaY9phAyqzGEo5fFQ7T9rEeOfBAqUcPirLad6ZR/l72CoQVkwjbBPCeq36Rkn1WiJUAQAANAF5aQ077bAJKCwp067MI7YqhNvLw9bB/KIq2zuXr9fqEO6jhLATgSsmkPVadYVQVUuEKgAAADQluceKbaNZ20/6t6CwtMr2Hi5O5ftr+dimECaE+yiE/bVqjFBVS4QqAAAANHWGYSgjv7BSFcI/M4+ouNRS5X38PV0qBa32YT7y82B/rdMhVNUSoQoAAADN1anrtXYetAaulKzTr9eK9HO3TR2sKIzRLtRb7i6s1yJU1RKhCgAAAC1NxXqtk6cR7sgoUEZ+1ftrmU1SXJCXXQXCDmE+igvylLOTuYF733gIVbVEqAIAAEAleWnWPbQC27aowhkV+2tVhK2KqYS5x0qqbO/qbFa7EG+7KYQdwn0U6efeItdrEapqiVAFAAAAO2vflxaMt+6dZTJLV05vUSXeT2UYhg4VFNnWaVUErp0HrftrVcXHzVkdKka1wrytFQnDfRXo5drAva9bhKpaIlQBAADAJi9Nmtap8mbEEza1qBGr6rBYDO3POW7bzHjHwSPamVGg3YeOqPQ0C7aCvd0UH+59YlSrfN2WVzPZzLi62aB5PBoAAACgMWTvtg9UknVT4uw951yoMptNignyVEyQpy7pGG47XlxqUXLWUe04WKCdJ1UiTM0+pqwjRcraVaRfdx22O1d0oIddYYz4cB+1CfaWq3PzXK9FqAIAAABOJ7CtdcrfqSNVgW0ar09NjKuz2RaM1PXE8aNFpdbiGKes2cosKNK+7OPal31ci7Zl2tqfvJnx1V0j7YJbU0eoAgAAAE7HL8q6hmrBBOsIlclJunLaOTdKVRtebs7qGu2vrtH+dsdzjhZbR7VOClsVmxn/mXlEf2YeUecov8bpdC2xpuoUrKkCAABAJXlp1il/gW0IVPXg5M2Mdx4sUP92weoY2fjBijVVAAAAQF3xiyJM1SOTyaQIPw9F+HlocHxoY3enxprnSjAAAACgpctLk5KXWf9Fk8ZIFQAAANDUnGN7YzV3jFQBAAAATUle2olAJVn/XTCBEasmjFAFAAAANCVn2hsLTRKhCgAAAGhKKvbGOhl7YzVphCoAAACgKanYG8vkZP2cvbGaPApVAAAAAE1Nj9FS2wvZG6uZIFQBAAAATRF7YzUbTP8DAAAAzkXsg1VnGKkCAAAAzjXsg1WnGKkCAAAAziXsg1XnCFUAAADAuYR9sOocoQoAAAA4l7APVp0jVAEAAADnEvbBqnMUqgAAAADONeyDVacIVQAAAMC5iH2w6gzT/wAAAADAAYQqAAAAAHAAoQoAAAAAHECoAgAAANA05KVJycua3UbEFKoAAAAA0PjWvi8tGG/dmNhktpZ97zG6sXtVLYxUAQAAAGhceWknApVk/XfBhGYzYkWoAgAAANC4snefCFQVjDLrPlrNAKEKAAAAQOMKbGud8ncyk5N1Y+JmgFAFAAAAoHH5RVnXUJmcrJ+bnKQrpzWbzYkpVAEAAACg8fUYLbW90DrlL7BNswlUEqEKAAAAQFPhF9WswlQFpv8BAAAAgAMIVQAAAADgAEIVAAAAADiAUAUAAAAADiBUAQAAAIADCFUAAAAA4ABCFQAAAAA4gFAFAAAAAA4gVAEAAACAAwhVAAAAAOAAQhUAAAAAOIBQBQAAAAAOIFQBAAAAgAMIVQAAAADgAEIVAAAAADiAUAUAAAAADiBUAQAAAIADCFUAAAAA4ABCFQAAAAA4gFAFAAAAAA4gVAEAAACAAwhVAAAAAOAAQhUAAAAAOIBQBQAAAAAOIFQBAAAAgAMIVQAAAADgAOfqNOrRo0eNTmoymfTVV18pKiqqVp1yVFFRkfr27asNGzZo3bp16tatW6P0AwAAAEDLV61QtX79ej300EPy9vY+a1vDMPTiiy+qqKjI4c7V1j/+8Q9FRkZqw4YNjdYHAAAAAOeGaoUqSXrkkUcUGhparbavvvpqrTvkqO+++04LFy7U3Llz9d133zVaPwAAAACcG6oVqpKTkxUSElLtk27dulWRkZG17lRtHTx4ULfffrvmz58vT0/Pat2nqKjIblQtPz+/vroHAAAAoAWqVqGK2NhYmUymap80OjpaTk5Ote5UbRiGoTFjxujOO+9Ur169qn2/KVOmyM/Pz/YRHR1dj70EAAAA0NJUe/rfyQoLC7Vx40ZlZmbKYrHY3XbVVVfVSccqTJo0SZMnTz5jm9WrV2vFihXKz8/XxIkTa3T+iRMn6sEHH7R9np+fT7ACAAAAUG0mwzCMmtzh+++/1+jRo5WVlVX5ZCaTysrK6qxzkpSVlVXltU4WFxenv/71r1qwYIHdiFpZWZmcnJw0atQovffee9W6Xn5+vvz8/JSXlydfX1+H+g4AAACg+apuNqhxqGrXrp2GDh2qp556SmFhYQ53tK6kpqbarYdKT0/X0KFD9fnnn6tv375q1apVtc5DqAIAAAAgVT8b1Hj6X2Zmph588MEmFagkKSYmxu7zivLvbdu2rXagAgAAAICaqlahipONGDFCS5YsqYeuAAAAAEDzU+Ppf8eOHdP111+vkJAQde7cWS4uLna333///XXawYbG9D8AAAAAUj1O//v444/1ww8/yMPDQ0uWLLErDGEymZp9qAIAAACAmqhxqPq///s/PfPMM3rsscdkNtd49iAAAAAAtCg1TkXFxcW68cYbCVQAAAAAoFqEqltuuUWfffZZffQFAAAAAJqdGk//Kysr08svv6wffvhBXbp0qVSo4rXXXquzzgEAAABAU1fjULVp0yZ1795dkrR582a7204uWgEAAAAA54Iah6rFixfXRz8AAAAAoFmi2gQAAAAAOKBaoeraa69Vfn5+tU86atQoZWZm1rpTAAAAANBcmAzDMM7WyMnJSTt37lRISMhZT2gYhqKjo7V+/Xq1adOmTjrZkKq7azIAAACAlq262aBaa6oMw1CHDh3qrHMAAAAA0FJUK1TVpjhFVFRUje8DAAAAAM1NtULVoEGD6rsfAAAAANAsUf0PAAAAABxAqAIAAAAABxCqAAAAAMABhCoAAAAAcAChCgAAAAAcUONQdfDgQd18882KjIyUs7OznJyc7D4AAAAA4FxSrZLqJxszZoxSU1P15JNPKiIiQiaTqT76BQAAAADNQo1D1S+//KLly5erW7du9dAdAAAAAGheajz9Lzo6WoZh1EdfAAAAAKDZqXGomjZtmh577DGlpKTUQ3cAAAAAoHmp8fS/G2+8UceOHVPbtm3l6ekpFxcXu9uzs7PrrHMAAAAA0NTVOFS9/vrrFKcAAAAAgHK1qv4HAAAAALCq8ZqqUaNG6e2339bOnTvroz8AAAAA0KzUOFR5e3vr1VdfVUJCgiIjI/W3v/1Ns2bN0vbt2+ujfwAAAADQpJmMWtZHz8jI0JIlS7RkyRItXbpUO3fuVGhoqA4cOFDXfWxQ+fn58vPzU15ennx9fRu7OwAAAAAaSXWzQY1Hqir4+PgoICBAAQEB8vf3l7Ozs8LDw2t7OgAAAABolmocqh599FGdd955Cg4O1v/93/+puLhYEydO1MGDB7Vu3br66CMAAAAANFk1nv5nNpsVEhKiBx54QFdffbUSExPrq2+Ngul/AAAAAKTqZ4Mal1Rft26dli5dqiVLlujVV1+Vk5OTBg0apMGDB2vw4MEtLmQBAAAAwJnUulBFhQ0bNmjatGn68MMPZbFYVFZWVld9axSMVAEAAACQ6nGkSrKOVlVU/lu+fLny8/PVrVs3DRkypNYdBgAAAIDmqMahKiAgQEeOHFHXrl01ePBg3X777Ro4cCCjOgAAAADOSTUOVR988AEhCgAAAADK1ThUXXHFFbb/79+/XyaTSVFRUXXaKQAAAABoLmq8T5XFYtEzzzwjPz8/xcbGKiYmRv7+/nr22WdlsVjqo48AAAAA0GTVeKTqiSee0LvvvqsXX3xR/fv3l2EY+vXXXzVp0iQVFhbq+eefr49+AgAAAECTVOOS6pGRkZo1a5auuuoqu+Nffvml7r77bqWlpdVpBxsaJdUBAAAASNXPBjWe/pedna2EhIRKxxMSEpSdnV3T0wEAAABAs1bjUNW1a1e9+eablY6/+eab6tq1a510CgAAAACaixqvqXr55Zd1+eWXa9GiRerXr59MJpNWrFihffv26dtvv62PPgIAAABAk1XjkapBgwZp586duuaaa5Sbm6vs7Gxde+212rFjhwYMGFAffQQAAACAJqvGhSpaOgpVAAAAAJCqnw2qNf1v48aN1b5wly5dqt0WAAAAAJq7aoWqbt26yWQyyTAMmUwm2/GKQa6Tj5WVldVxFwEAAACg6arWmqrk5GTt2bNHycnJmjt3rlq3bq0ZM2Zo/fr1Wr9+vWbMmKG2bdtq7ty59d1fAAAAAGhSqjVSFRsba/v/9ddfrzfeeEOXXXaZ7ViXLl0UHR2tJ598UsOHD6/zTgIAAABAU1Xj6n+bNm1S69atKx1v3bq1tm7dWiedAgAAAIDmosahKjExUc8995wKCwttx4qKivTcc88pMTGxTjsHAAAAAE1djTf/nTVrlq688kpFR0era9eukqQNGzbIZDLp66+/rvMOAgAAAEBTVqt9qo4dO6YPP/xQ27dvl2EYSkpK0siRI+Xl5VUffWxQ7FMFAAAAQKrjfapO5enpqTvuuKPWnQMAAACAlqLGa6oiIyM1cuRI/fvf/9bOnTvro08AAAAA0GzUOFS9+uqr8vX11WuvvaaEhARFRETor3/9q2bNmqVt27bVRx8BAAAAoMmq1ZqqCgcPHtTixYv19ddf67PPPpPFYlFZWVld9q/BsaYKAAAAgFTPa6qOHDmiX375RUuXLtWSJUu0bt06de7cWYMGDap1hwEAAACgOapxqOrbt682btyoTp06afDgwXr88cc1YMAA+fv710P3AAAAAKBpq/Gaqj///FOenp5q06aN2rRpo3bt2hGoAAAAAJyzahyqsrOztXjxYvXv31+LFi3SoEGDFB4erhtvvFGzZs2qjz4CAAAAQJPlUKEKSVqzZo3efPNNffjhhxSqAAAAANBi1FuhinXr1mnJkiVasmSJli9froKCAnXt2lXjx4/XkCFDHOo0AAAAADQ3NQ5VvXv3Vvfu3TVo0CDdfvvtGjhwICM6AAAAAM5ZNQ5V2dnZhCgAAAAAKFfjQhUEKgAAAAA4ocYjVWVlZXr99df13//+V6mpqSouLra7PTs7u846BwAAAABNXY1HqiZPnqzXXntNN9xwg/Ly8vTggw/q2muvldls1qRJk+qhiwAAAADQdNU4VH300Ud6++239fDDD8vZ2Vl/+9vf9M477+ipp57SqlWr6qOPAAAAANBk1ThUZWRkqHPnzpIkb29v5eXlSZKuuOIKffPNN3XbOwAAAABo4mocqlq1aqUDBw5Iktq1a6eFCxdKklavXi03N7e67R0AAAAANHE1DlXXXHONfvrpJ0nS+PHj9eSTT6p9+/YaPXq0xo0bV+cdBAAAAICmzGQYhuHICX777Tf9+uuvateuna666qq66lejyc/Pl5+fn/Ly8igfDwAAAJzDqpsNalRSvaSkRHfccYeefPJJtWnTRpLUt29f9e3b17HeAgAAAEAzVaPpfy4uLpo3b1599QUAAAAAmp1aramaP39+PXQFAAAAAJqfGk3/k6wV/5599lmtWLFCPXv2lJeXl93t999/f511DgAAAACauhoXqmjduvXpT2Yyac+ePQ53qjFRqAIAAACAVE+FKiQpOTnZoY4BAAAAQEtS4zVVAAAAAIATqjVS9eCDD1b7hK+99lqtOwMAAAAAzU21QtW6devsPl+zZo3KysoUHx8vSdq5c6ecnJzUs2fPuu8hAAAAADRh1QpVixcvtv3/tddek4+Pj9577z0FBARIknJycjR27FgNGDCgfnoJAAAAAE1Ujav/RUVFaeHCherYsaPd8c2bN+uSSy5Renp6nXawoVH9DwAAAIBU/WxQ40IV+fn5OnjwYKXjmZmZKigoqOnpAAAAAKBZq3GouuaaazR27Fh9/vnn2r9/v/bv36/PP/9ct956q6699tr66CMAAAAANFk13qdq1qxZevjhh3XTTTeppKTEehJnZ916662aOnVqnXcQAAAAAJqyGq+pqnD06FHt3r1bhmGoXbt28vLyquu+NQrWVAEAAACQqp8NajxSVcHLy0tdunSp7d0BAAAAoEWo8ZoqAAAAAMAJhCoAAAAAcECLC1XffPON+vbtKw8PDwUHB1OREAAAAEC9qvWaqqZo7ty5uv322/XCCy/oL3/5iwzD0KZNmxq7WwAAAABasBYTqkpLSzV+/HhNnTpVt956q+14fHx8I/YKAAAAQEvXYqb/rV27VmlpaTKbzerevbsiIiJ06aWXasuWLWe8X1FRkfLz8+0+AAAAAKC6Wkyo2rNnjyRp0qRJ+r//+z99/fXXCggI0KBBg5SdnX3a+02ZMkV+fn62j+jo6IbqMgAAAIAWoMmHqkmTJslkMp3x448//pDFYpEkPfHEE7ruuuvUs2dPzZ49WyaTSf/73/9Oe/6JEycqLy/P9rFv376GemgAAAAAWoAmv6bq3nvv1V//+tcztomLi1NBQYEkKSkpyXbczc1Nbdq0UWpq6mnv6+bmJjc3t7rpLAAAAIBzTpMPVcHBwQoODj5ru549e8rNzU07duzQBRdcIEkqKSlRSkqKYmNj67ubAAAAAM5RTT5UVZevr6/uvPNOPf3004qOjlZsbKymTp0qSbr++usbuXcAAAAAWqoWE6okaerUqXJ2dtbNN9+s48ePq2/fvvr5558VEBDQ2F0DAAAA0EKZDMMwGrsTTUl+fr78/PyUl5cnX1/fxu4OAAAAgEZS3WzQ5Kv/AQAAAEBTRqgCAAAAAAcQqgAAAADAAYQqAAAAAHAAoQoAAAAAHECoAgAAAAAHEKoAAAAAwAGEKgAAAABwAKEKAAAAABxAqAIAAAAABxCqAAAAAMABhCoAAAAAcAChCgAAAAAcQKgCAAAAAAcQqgAAAADAAYQqAAAAAHAAoQoAAAAAHECoAgAAAAAHEKoAAAAAwAGEKgAAAABwAKEKAAAAABxAqAIAAAAABxCqAAAAAMABhCoAAAAAcAChCgAAAAAcQKgCAAAAAAcQqgAAAADAAYQqAAAAAHAAoQoAAAAAHECoAgAAAAAHEKoAAAAAwAGEKgAAAABwAKEKAAAAABxAqAIAAAAABxCqAAAAAMABhCoAAAAAcAChCgAAAAAcQKgCAAAAAAcQqgAAAADAAYQqAAAAAHAAoQoAAAAAHECoAgAAAAAHEKoAAAAAwAGEKgAAAABwAKEKAAAAABxAqAIAAAAABxCqAAAAAMABhCoAAAAAcAChCgAAAAAcQKgCAAAAAAcQqgAAAADAAYQqAAAAAHAAoQoAAAAAHECoAoBypZZSvb3xbeUU5jR2VwAAQDNCqAKAcl/v+VpvrHtDQ+cO1bQ10whXAACgWghVAFAuwitCSUFJOl56XO9ufpdwBQAAqsVkGIbR2J1oSvLz8+Xn56e8vDz5+vo2dncANDDDMLRs/zLN2DBDWw9vlSR5OntqZOJIjU4arQD3gEbuIQAAaCjVzQaEqlMQqgBI1nC1dP9SzVg/Q9uyt0k6Ea5uSbpF/u7+jdtBAABQ7whVtUSoAnCy04WrUYmjNDppNOEKAIAWjFBVS4QqAFUxDENL9i3RzA0zCVcAAJwjCFW1RKgCcCZVhSsvFy+NTBipWzreIj83v8btIAAAqDOEqloiVAGoDsMwtHjfYs3cMFPbs7dLIlwBANDSEKpqiVAFoCZOF64qpgUSrgAAaL4IVbVEqAJQG4Zh6Od9P2vm+pnakbNDEuEKAIDmjlBVS4QqAI6wGBbryBXhCgCAZo9QVUuEKgB1wWJYtDh1sWZsmKGdOTslSd4u3hqVOEo3J91MuAIAoBkgVNUSoQpAXSJcAQDQfBGqaolQBaA+WAyLfk79WTM3zLQLVzcl3aSbEm8iXAEA0AQRqmqJUAWgPlWEqxkbZujPnD8lEa4AAGiqCFW1RKgC0BAshkU/pf6kmRtm2sKVj4uPNVwl3SRfV37+AADQ2AhVtUSoAs5tJRkZKk7ZK9e4WLmEh9f79SrC1Yz1M7Qrd5ckwhUAAE0FoaqWCFXAuSv388914KmnJYtFMpsV8cxk+Y8Y0SDXthgWLdq7SDM3zLQLVzcn3axRSaMIVwAANAJCVS0RqoBzU0lGhnb95UJroKpgNqvdzz81yIhVBcIVAABNR3WzgbkB+wQATVZxyl77QCVJFouK96Y2aD/MJrMuibtEc6+aq1cGvaJ2/u1UUFKgGRtmaNjcYZq5YaYKigsatE8AAODMCFUAIMk1LlYyn/Ij0WyWa2xMo/THbDJraNxQ+3BVXKAZ62do6NyhhCsAAJoQQhUASHIJD1fEM5NPBKvyNVUNOfWvKieHq6mDpqqtX1u7cDVrwyzCFQAAjYw1VadgTRVwbivJyFDx3lS5xsY0eqCqisWwaOHehZq1fpZ25+2WJPm4+mh00miNShwlH1efRu4hAAAtB4UqaolQBaA5KLOU6ce9P2rmhpnak7dHEuEKAIC6RqiqJUIVgOakzFJmHbnaMMsWrnxdfW3hytvVu5F7CABA80WoqiVCFYDmqCJczdwwU8l5yZIIVwAAOIpQVUuEKgDNWZmlTD+k/KBZG2fZhatbOt6ikQkjCVcAANQAoaqWCFUAWoK6DlclGRkqTtkr17jYJlnAAwCA+kCoqiVCFYCWpMxSpu9TvtesDbOUkp8iSfJz89MtSbfobwl/q1a4yv38cx146mnr5sjlpeb9R4yo554DAND4CFW1RKgC0BKdKVyNTBwpLxevKu9XkpGhXX+50BqoKpjNavfzT4xYAQBavOpmAzb/BYBzgJPZSZe3uVzzr56vKQOmKM43TnlFeXpj3RsaOneo3tn0jo6WHK10v+KUvfaBSpIsFhXvTW2gngMA0PQRqgDgHOJkdtIVba6oFK6mr51eZbhyjYuVzKf8qjCb5Rob08A9BwCg6SJUAcA5qCJczbt6nl644AXF+sbawtWwucNs4colPFwRz0w+EazK11Qx9Q8AgBNYU3UK1lQBOBeVWkr1XfJ3emvjW9qbv1eS5O/mr1s6WgtauB4uUPHeVLnGxhCoAADnDApV1BKhCsC57HThakzHMfpbwt/k6eLZyD0EAKDhEKpqiVAFACfC1awNs5RaYC1K4e7krr4RfTWw1UANbDVQ4V6MWAEAWjZCVS0RqgDghFJLqb5N/lb/3vhv28hVhQ4BHWwBq0twFzmZnRqplwAA1A9CVS0RqgCgMsMwtDNnp5btX6Zl+5dpY9ZGWYwTpdb93PzUP7K/BrYaqAuiLpCfm18j9hYAgLpBqKolQhUAnF1uYa5+Sf9Fy/Yv069pvyq/ON92m9lkVteQrrZRrPb+7WUymRqxtwAA1M45Gap27typRx55RL/++quKi4vVuXNnPffccxoyZEi1z0GoAoCaKbWUauOhjVq6f6mW7V+mXbm77G4P9wrXwChrwOoT0Ucezh6N1FMAAGrmnAxV7du3V4cOHTRlyhR5eHho2rRpmjNnjnbv3q3wapYAJlQBgGPSj6Rr+f7lWpa2TL8d+E1FZUW229yc3NQ7vLdtFCvKO6oRewoAwJmdc6EqKytLISEhWrZsmQYMGCBJKigokK+vrxYtWqQLL7ywyvsVFRWpqOjEL/z8/HxFR0cTqgCgDhSWFur3jN9ta7EOHD1gd3s7/3Ya0GqABkYNVLfQbnI2OzdSTwEAqOycC1WGYahjx47q37+/pk2bJjc3N02bNk2vvPKKtm/fLn9//yrvN2nSJE2ePLnScUIVANQtwzC0K3eXLWBtOLRBZUaZ7XYfVx+7YhcB7gGN2FsAAM7BUCVJaWlpuvrqq7V27VqZzWaFhYXpm2++Ubdu3U57H0aqAKBx5BXlaUX6Ci3bv0y/pP2i3KJc220mmdQlpIttmmB8QDzFLgAADa7FhKrTjSSdbPXq1erZs6eGDx+ukpISPfHEE/Lw8NA777yjr776SqtXr1ZERES1rseaKgBoeGWWMm3K2mQbxdqRs8Pu9lDPUA2IGqCBrQbqvIjz5Oni2Ug9BQCcS1pMqMrKylJWVtYZ28TFxenXX3/VJZdcopycHLsH3L59e91666167LHHqnU9QhUANL6MoxlanrZcy/Zbi10cLz1uu83F7HKi2EXUQEX7RjdiT6uvJCNDxSl75RoXK5dqFk8CADSu6maDJr8iODg4WMHBwWdtd+zYMUmS2Wy2O242m2WxWKq6CwCgiQr3Ctf1Ha7X9R2uV1FZkf7I+EPL9i/T0v1LlXYkTSvSV2hF+gq9qBfV2q+1rWR797DucjG7NHb3K8n9/HMdeOppyWKRzGZFPDNZ/iNGNHa3AAB1pMmPVFVXVlaWEhISNGjQID311FPy8PDQ22+/renTp2v16tXq2rVrtc7DSBUANF2GYSg5L9k6TTBtmdYdXKdSo9R2u7eLt/pF9rMVuwj2OPsf5epbSUaGdv3lQmugqmA2q93PPzFiBQBNXIsZqaqu4OBgff/993riiSf0l7/8RSUlJerYsaO+/PLLagcqAEDTZjKZ1Ma/jdr4t9GYTmNUUFxgV+wiuzBbP+79UT/u/VEmmdQpuJO1ZHurgUoMTJTZZD77RepYccpe+0AlSRaLivemEqoAoIVoMSNVdYWRKgBoniyGRZuzNtuKXWzL3mZ3e7BHsK3YRb/IfvJy8WqQfjFSBQDNV4spVNHQCFUA0DIcOnbIVuxiZfpKHSs9ZrvN2eysnmE9NajVIA1sNVCxvrH12hfWVAFA80SoqiVCFQC0PMVlxVpzcI1tFCu1INXu9ljfWNsoVq+wXnJxqvtiFyUZGSremyrX2BhGqACgmSBU1RKhCgBavpS8FFuxizUH16jUcqLYhaezp63YxYCoAQrxDGnEngIAGhOhqpYIVQBwbjlSfESrDqzSsv3LtDxtubKO2++NmBSUZNsTq2Nwx0YpdgEAaByEqloiVAHAuctiWLTt8DbbNMHNhzfb3R7oHqgLoi7QwFYDdX7k+fJx9WmkngIAGgKhqpYIVQCAClnHs/RL2i+2YhdHSo7YbnM2Oat7WHcNajVIA1oNUGvf1jKZTI3YWwBAXSNU1RKhCgBQlZKyEq3LXGdbi5Wcl2x3eyvvVtZpgq0Gqld4L7k5uTVST2unJCNDxSl75RoXSyENAChHqKolQhUAoDr25e/TsjTrNMHVGatVYimx3ebh7KG+EX1ta7HCvMIasadnR8l3AKgaoaqWCFUAgJo6VnLsRLGL/cuVeTzT7vaEwAQNiBqgfpH91Cm4kzycPRqpp5WxOTEAnF51s4FzA/YJAIAWydPFU3+J+Yv+EvMXGYahHTk7tHTfUi1LW6ZNhzZpe/Z2bc/errc3vS1nk7PiA+PVNaSruoV2U9eQrorwimi09VjFKXvtA5UkWSwq3ptKqAKAamKk6hSMVAEA6lJ2YbZ+TftVy/cv15qDayqNYklSqEeouoZ2tQWtxMBEuTq5Nkj/GKkCgNNj+l8tEaoAAPXFMAxlHM3QhkMbtP7Qem3I3KDt2dtVapTatXMxu6hjUEe70az63ISYNVUAUDVCVS0RqgAADel46XFtydpiC1obD21UdmF2pXZR3lHqEtJF3UK6qWtoV3UI6CAXs0ud9aMkI0PFe1PlGhvDCBUAlCNU1RKhCgDQmAzD0L6CfdaQlbleGw5t0J+5f8pi2K978nD2UKfgTtbRrJBu6hLSRQHuAY3UawBomQhVtUSoAgA0NUeKj2hT1ia70ayC4oJK7eJ846yjWeVTBtv6tZWT2akRegwALQOhqpYIVQCAps5iWJScl2w3mrUnb0+ldt4u3uoS0sU2mtU5pLN8XH0aoccA0DwRqmqJUAUAaI7yivK08dBGWwGMjVkbdbz0uF0bk0xq69/WrgBGnG9co5VzB4CmjlBVS4QqAEBLUGop1a7cXdqQWV5p8NAG7SvYV6mdv5u/rQBGp+BO6hjcUb6uDf/7ryQjQ8Upe+UaF0uhDABNBqGqlghVAICWKut4ljYc2mD9yNygLYe3qKisqFK7WN9YdQzqqM7BndUpuJMSAhPk7uxeb/2ipDuApopQVUuEKgDAuaKkrETbs7fbgtbmrM3af2R/pXZOJie1829nG8nqHNxZbf3b1klJdzYfBtCUVTcbODdgnwAAQBPi4uSiziGd1Tmks27STZKknMIcbTm8RZuzNmtL1hZtPrxZWceztCNnh3bk7NDcP+dKktyc3JQQmGANWkEd1Sm4k2J9Y2U2mWvUh+KUvfaBSpIsFhXvTa2zUMXUQgD1jVAFAABsAtwDdEHUBbog6gJJ1n2zDh47aAtYm7I2aWvWVhWUFNhGuCr4uPgoKThJnYI6qVOw9SPMM+yMhTBc42Ils7nSSJVrbEydPB6mFgJoCEz/OwXT/wAAODOLYVFqfqo2Hy4fzcrarG3Z26pcnxXkHqTOwZ3VMdg6mtUpqJP83f3t2tRX8GFqIQBHsaaqlghVAADUXImlRLtzd2tz1mbr1MHDW/Rnzp8qM8oqtY3yjrIFrE7BnZQUlCSXw/kq3psq19iYOgs8R1f9ptQxYyodj3nvPXn17fP/7d15bJT3ncfxzzNjj7E99swYGx/gg8M2dinh6kYmUHEo0LRKlnRhk6bi6EGPpG1QglpVlTiikFRtoyZtlGiRusBGUZKGVbJSSpOmCU7btEGBlpJy+CI+Ah6MPT7wgT14Zv8wHjPYJvYcnhn7/ZJG1jz+ze/5Phg98se/4wnJOQBMboSqABGqAAAIjavXruqc65xOt5zWR80f6XTzadV21A5rZzJMmmObo9LppSqdXqrPTP+MitOKlRiXGNT5GakCECxCVYAIVQAAhE9HX4fOtJzx2wjD2eUc1u7moFU6vVTFjmIlxSeN63ysqQIQDEJVgAhVAABMrOaeZv2r+V8603LG97rcc3lYO5Nh0uzU2X5Ba37a/E8NWm6nM+RTCwFMDYSqABGqAACIvMvdl/1C1pmWM2rqaRrWzpChAlvBQMhKGwhaJdNLlByfHIGqAUw2hKoAEaoAAIhOzT3NOtNyRqdbTg8Fre6Rg1Z+ar7fiFZJWomsFmsEqgYQywhVASJUAQAQOwaD1o2vS92XRmybl5Kn+WnzVTK9RPPT5mt+2nylJ6ZPcMUAYgmhKkCEKgAAYltLT4t/0HKdGXEzDEnKSMzwBaz5afNVklaiWSmzbvnAYgBTB6EqQIQqAAAmH9dVl865zg28Ws7prOus6jrq5NXwX4Os8VYVpxWrJG1oRGuOfY7iTfERqBxAJBGqAkSoAgBgauh2d6uytVJnXWd1znVOZ1vOqrqtWm6Pe1hbi8mieY55fkGryFE07i3eQ8XtdKqvtk6Wgnx2NATCiFAVIEIVAABTl9vj1vm2876gdc51ThWuCnW6O4e1NRkm5afm+6YNDn61T7OHtUaevQVMHEJVgAhVAADgRh6vRxeuXBga0br+tbmnecT2WclZvoA1OI0wOzk7JOu03E6nqtesHQhUg0wmzXv3HUasgDAYazaIm8CaAAAAYo7JMCk3NVe5qblaV7DOd7y5p1lnW/yDVsOVBjm7nHJ2OVXeUO5ra0uwab7j+oYY0wcCV0Fqgcwm87hq6aut8w9UkuTxqK+unlAFRBChCgAAIADpielaOWulVs5a6Tt2pe+KKlwVfkHrfNt5tfe265jzmI45j/naTjNPU5GjyC9oFToKlWBOGPWcloJ8yWQaNlJlyc8LyzUCGBum/92E6X8AACCU+vr7VN1W7dsM45zrnCpaK9RzrWdYW7Nh1mzb7KE1WtMHphCmWoZ+J2FNFTBxWFMVIEIVAAAIt35Pv+qv1A+NaLUMbIrR2ts6YvuZ1pn+Ow+605RyuVsJ+ez+B4QToSpAhCoAABAJXq9Xl7ovDQtaF7sujtg+bVqaih3FKk4r9k0jLLAV8DwtIIQIVQEiVAEAgGjS3ts+9ODi66/z7efl8XqGtY03xWuefZ6K04r9ApctwRbSmnhOFqYKQlWACFUAACDa9VzrUU1bjW9TjMrWSlW0VqjL3TVi++zkbL+gNd8xXzNTZspkmMZ9btZ0YSohVAWIUAUAAGKRx+vRhc4LqnRV6lzrwEOLK1srdaHzwojtk+KSVOQoGghb1wNXoaNQiXGJo56D52RhquE5VQAAAFOIyTApNyVXuSm5Wpu/1ne8o69Dla6BkawKV4UqWitU3Vqt7mvdOnn5pE5ePunXR15K3sBoVtr8gdDlKNaMpBkyDIPnZAGjIFQBAABMYqmWVC3LWqZlWct8x655rqm2vXYgaA2GLVeFWq62qLajVrUdtXqr9i1fe0eCQ0VpRVrkzdUaw5Bx40QnnpMFMP3vZkz/AwAAU1VzT7NvNGswaNV21Krf2+9rs/qfHn3r9x6ZvZLHkI5tXizzv69XkaNIRY4iTU+cHsErAEKLNVUBIlQBAAAM6e3vVXVbtS9kVbRWqKn2rFKaOuV0GHKlGn7t0xPTVewY2HWwKG1g+iBbvSNWEaoCRKgCAAC4Na/Xq4tdF31rtSpbK1XZWqn6jnp5NfxXy3hTvOba5/pGswY3xnBMc0SgemDsCFUBIlQBAAAEptvdraq2qoEt3l1DYWu0rd4zEjN8o1mDm2Lk2/IZ1ULUIFQFiFAFAAAQOl6vVxc6LwyNaF0f3Wq40jBi+5tHtVirhUgiVAWIUAUAABB+3e5u30jWjSNb3de6R2w/fdr0oZCVNvB1jm2OLGbLBFeOqYRQFSBCFQAAQGT4HmB8PWhVtVbdcq2W2TBrtm22Ch2FfqNamUmZMgxjhDMA40OoChChCgAAILp0u7tV01bjN7JV2Vqpjr6OEdunWlKHTR+ca5+rpPikCa4csY5QFSBCFQAAQGS4nU711dbJUpCv+KysW7b1er261H3JL2RVtVbp4/aP/Z6rNciQobzUPBU5ivxGtmZaZ8pkmMJ1SYhxhKoAEaoAAAAmXtvhw2rctVvyeCSTSdmP7ZV948Zx99PX36fz7ed9m2IMBq6Wqy0jtk+KSxo2fbDQUagUS0qwl4RJgFAVIEIVAADAxHI7napes3YgUA0ymTTv3Xc+dcRqrJp7mn1rtAZHtarbquX2uEdsn5OcMzSqlVakInuR8lLzFGeKC0k9iA1jzQb8rwAAAEBE9dXW+QcqSfJ41FdXH7JQlZ6YrvTEdJXllPmOXfNcU11H3bC1Ws4upy52XdTFros6deaoslu9anQY6rQnaK59rm9ka/BremJ6SGpE7CJUAQAAIKIsBfmSyTRspMqSnxfW88aZ4jTXPldz7XN11+y7fMfbe9tV1Vql5ldfUd5zb8jwSh5D+q+7rurobWd11nXWr5+0aWlDQcs+MLKV32OV8YlzTOvDEPuY/ncTpv8BAABMvFCtqQqV0aYkOv9nr87FX1ZVa5WqWqtU11E3bLv31f/06Nu/98h0PYxVfmutUv/jXhXZizQzhY0xYgnT/wAAABAz7Bs3KnnFCvXV1cuSnxfx0Z3RpiT+W3+eVi8bCns913pU01bjW6/V+PG/tP33x2W6nrNMXqlw/zt6KK5crlRDiXGJKrQXqtBR6De6ZZ9mlzS+HRARPQhVAAAAiArxWVlREyTGOiUxMS5RC9IXaEH6AklSl+eY6r3b/NqYvdLt/Xl6x3RJPdd6dKr5lE41n/JrMyNxhjacs2rtS1UyvF7JZChjzy6l/+f9Ybk+hBahCgAAALhJfFaWsh/bO2xK4qeFvtHC2L77/1tPzEhXfUe9KtsGtnuvahuYQnih84KuXbqkNS9dlDE4k9DjlXP3Xj3S84IyC0oHRraur9fKSc6RYRifeg2Mek0c1lTdhDVVAAAAGOR2Osc9JXG868M6+zpV8+7/ybLj8WHf2/OASWfy/ddgJccna559ni9oDU4jtCXYAq4BI+M5VQEiVAEAACBY4w1jo22M4XrxSVXEDz1j63z7eV3zXBuxjxlJM1ToKNTC/hyt2vHywDTCG/oK5XO/pgo2qgAAAAAiZLzrw0abbliy+B7dcUM7t8etuvY639TBwbB1seuimrqb1NTdpLY6j1bfPG7i8eh/331WM1asUaGjUDOt7EIYSoxU3YSRKgAAAERKINMNpYEphNVt1apsrdQnNSe1/tHXfDsQSlK/IT30oFmu1IG1WElxSUNTCG+YRuiY5gj1JcU0pv8FiFAFAACAWHfjmiqvyaTz375T7y9JVHVbtWraauT2uEf83PRp0/2CVpGjSHPsc5QYlzjBVxAdCFUBIlQBAABgMhht1Oua55pvF8LBKYRVrVX6pPOTEfsxZCgvNc/v+VqF9kLlpuTKbDJP1OVEBKEqQIQqAAAATEXd7m5Vt1UPhKwb1my19raO2H6aeZrm2Odoec5yPbzk4QmudmKwUQUAAACAMUuKT9LCjIVamLHQd8zr9arlaosqW28Y1WqrUk1bja72X9WZljPKSmJHQUIVAAAAgBEZhqH0xHSlJ6Zrec5y3/F+T78arjSoqq1KNovtFj1MDYQqAAAAAONiNplVYCtQga0g0qVEBTanBwAAABAQt9Oprg+Oye10RrqUiCJUAQAAABi3tsOHVb1mreq3bVP1mrVqO3w40iVFDKEKAAAAwLi4nU7fc7AkSR6PGnftnrIjVoQqAAAAAOPSV1s3FKgGeTzqq6uPTEERRqgCAAAAMC6WgnzJdFOUMJlkyc+LTEERRqgCAAAAMC7xWVnKfmzvULAymZT92F7FZ03NZ1axpToAAACAcbNv3KjkFSvUV1cvS37elA1UEqEKAAAAQIDis7KmdJgaxPQ/AAAAAAgCoQoAAAAAgkCoAgAAAIAgEKoAAAAAIAiEKgAAAAAIAqEKAAAAAIJAqAIAAACAIBCqAAAAACAIhCoAAAAACAKhCgAAAACCQKgCAAAAgCAQqgAAAAAgCDETqvbt26fly5crKSlJdrt9xDb19fW6++67lZycrPT0dP3gBz9QX1/fxBYKAAAAYEqJi3QBY9XX16dNmzaprKxMv/nNb4Z9v7+/X1/60peUkZGhv/zlL2ppadHWrVvl9Xr161//OgIVAwAAAJgKYiZU7d27V5J08ODBEb//hz/8QWfOnFFDQ4NycnIkSU899ZS2bdumffv2KTU1dcTP9fb2qre31/e+o6MjtIUDAAAAmNRiZvrfp/nb3/6mBQsW+AKVJK1fv169vb06ceLEqJ978sknZbPZfK/c3NyJKBcAAADAJDFpQpXT6VRmZqbfMYfDIYvFIqfTOernfvzjH6u9vd33amhoCHepAAAAACaRiIaqPXv2yDCMW76OHz8+5v4Mwxh2zOv1jnh8UEJCglJTU/1eAAAAADBWEV1T9b3vfU/333//LdsUFBSMqa+srCwdO3bM71hra6vcbvewEaxb8Xq9klhbBQAAAEx1g5lgMCOMJqKhKj09Xenp6SHpq6ysTPv27VNjY6Oys7MlDWxekZCQoKVLl465nytXrkgSa6sAAAAASBrICDabbdTvx8zuf/X19XK5XKqvr1d/f79OnjwpSZo3b56sVqvWrVun0tJSbd68WT//+c/lcrm0c+dObd++fVxT+nJyctTQ0KCUlJRbThsMtc997nP68MMPJ+x8Ey3ary+S9U3EucNxjlD1GYp+Aumjo6NDubm5amhoYNpvBET7PSFY0X593PMi1yf3vKkp2u8JwYr26wumPq/XqytXrvhthjeSmAlVu3bt0qFDh3zvFy9eLEk6evSoVq1aJbPZrN/97nd68MEHdccddygxMVEPPPCAfvGLX4zrPCaTSbNmzQpp7WNhNpsn9U0u2q8vkvVNxLnDcY5Q9RmKfoLpg7WUkRHt94RgRfv1cc+LXJ/c86amaL8nBCvary/Y+m41QjUoZkLVwYMHR31G1aC8vDy98cYbE1NQiD300EORLiGsov36IlnfRJw7HOcIVZ+h6Cf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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_3 = ml_2.head(r, 0, t1)\n", + "hm2_3 = ml_2.head(0, 0, t2)\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(t1, hm1_3[0], label=\"ttim model results - obs 1\")\n", + "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", + "plt.semilogx(t2, hm2_3[0], label=\"ttim model results - well 3\")\n", + "plt.semilogx(t2, h2, \".\", label=\"well3\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.suptitle(\"Model Prediction vs Observations - Calibration 4\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we see in the picture that we have significantly improved both models by adding the resistance and the wellbore storage. We can make a critique of our current model that the adjusted resistance value is too low and with a very high standard deviation ([adjusted parameters](#adjusted_pars_ca_3)). Let's now disregard the skin resistance and check our model performance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5.5. Model Calibration with wellbore storage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We start by creating a model and adding a well with no resistance:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_3 = ttim.ModelMaq(\n", + " kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\"\n", + ")\n", + "w_3 = ttim.Well(ml_3, xw=0, yw=0, rw=rw, rc=0.2, res=0, tsandQ=[(0, Q)], layers=0)\n", + "ml_3.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we calibrate without changing the resistance parameter" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 47\n", + " # data points = 36\n", + " # variables = 3\n", + " chi-square = 1.29522429\n", + " reduced chi-square = 0.03924922\n", + " Akaike info crit = -113.694062\n", + " Bayesian info crit = -108.943506\n", + "[[Variables]]\n", + " kaq0: 38.2995706 +/- 0.39115142 (1.02%) (init = 10)\n", + " Saq0: 8.9400e-07 +/- 1.1516e-07 (12.88%) (init = 0.0001)\n", + " rc: 0.42216584 +/- 0.02778676 (6.58%) (init = 0.2)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.7468\n", + " C(Saq0, rc) = -0.2667\n" + ] + } + ], + "source": [ + "ca_4 = ttim.Calibrate(ml_3)\n", + "ca_4.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_4.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "ca_4.set_parameter_by_reference(name=\"rc\", parameter=w_3.rc, initial=0.2)\n", + "ca_4.series(name=\"obs1\", x=r, y=0, t=t1, h=h1, layer=0)\n", + "ca_4.series(name=\"obs3\", x=0, y=0, t=t2, h=h2, layer=0)\n", + "ca_4.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq03.829957e+013.911514e-011.021295-infinf10.0000[38.299570565924455]
Saq08.940022e-071.151644e-0712.8818880.0inf0.0001[8.940022488967969e-07]
rc4.221658e-012.778676e-026.581955-infinf0.2000[0.42216583686189674]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 3.829957e+01 3.911514e-01 1.021295 -inf inf 10.0000 \n", + "Saq0 8.940022e-07 1.151644e-07 12.881888 0.0 inf 0.0001 \n", + "rc 4.221658e-01 2.778676e-02 6.581955 -inf inf 0.2000 \n", + "\n", + " parray \n", + "kaq0 [38.299570565924455] \n", + "Saq0 [8.940022488967969e-07] \n", + "rc [0.42216583686189674] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.18967986812378132\n" + ] + } + ], + "source": [ + "display(ca_4.parameters)\n", + "print(\"rmse:\", ca_4.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we can see that we got very similar results from the previous models. The standard deviations are also in a reasonable range. As pointed out by Yang (2020), without skin resistance, we have a lower AIC (-113 versus -111). Thus, the skin resistance does not add information to the model, and the current model is preferred." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Comparison of Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6.1. Error comparison and model selection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some of the fit statistics are stored in the ```fitresult``` attribute of the calibration object. This object is a lmfit ```MinimizerResult``` object. ```lmfit``` is the python library doing the calibration for TTim behind the scenes. We accessed below the AIC and BIC values of this object.\n", + "\n", + "Check the lmfit documentation (Newville et al. 2014) to learn more about this object and lmfit." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Fit statistics for the tested models
 RMSEAICBICCalibration scheme
Model 10.027816-153.614816-151.432731Obs 1
Model 20.055957-76.728731-75.450616Well 3
Model 30.271822-89.787741-86.620703Obs 1 + Well 3
Model 40.189680-111.693920-105.359844Obs 1 + Well 3, res + rc
Model 50.189680-113.694062-108.943506Obs 1 + Well 3, rc
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\"RMSE\", \"AIC\", \"BIC\", \"Calibration scheme\"],\n", + " index=[\"Model 1\", \"Model 2\", \"Model 3\", \"Model 4\", \"Model 5\"],\n", + ")\n", + "\n", + "t[\"RMSE\"] = [ca_0.rmse(), ca_1.rmse(), ca_2.rmse(), ca_3.rmse(), ca_4.rmse()]\n", + "\n", + "t[\"AIC\"] = [\n", + " ca_0.fitresult.aic,\n", + " ca_1.fitresult.aic,\n", + " ca_2.fitresult.aic,\n", + " ca_3.fitresult.aic,\n", + " ca_4.fitresult.aic,\n", + "]\n", + "\n", + "t[\"BIC\"] = [\n", + " ca_0.fitresult.bic,\n", + " ca_1.fitresult.bic,\n", + " ca_2.fitresult.bic,\n", + " ca_3.fitresult.bic,\n", + " ca_4.fitresult.bic,\n", + "]\n", + "\n", + "t[\"Calibration scheme\"] = [\n", + " \"Obs 1\",\n", + " \"Well 3\",\n", + " \"Obs 1 + Well 3\",\n", + " \"Obs 1 + Well 3, res + rc\",\n", + " \"Obs 1 + Well 3, rc\",\n", + "]\n", + "t.style.set_caption(\"Fit statistics for the tested models\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first model had overall better statistics with lower RMSE and AIC, BIC. However, it does not fit with the data in the pumping well. Therefore if we were to update the statistics with the residuals from the pumping well, this result would be worse.\n", + "Comparing the models estimated with both drawdowns, the last model performed best. It has a larger RMSE than the one-well models, however less bias as it fits well both datasets. Another highlight is the lower AIC and BIC values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6.2. Comparison of TTim model performance with values simulated by AQTESOLV and MLU" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results simulated by different methods with two datasets simultaneously are presented below. Furthermore, Yang (2020) compared TTim results with the results obtained from the software AQTESOLV (Duffield, 2007) and MLU (Carlson & Randall, 2012). In both software, the model was calibrated with both the pumping well and observation well data." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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k [m/d]Ss [1/m]rcRMSE
MLU38.0940.000001-0.26
AQTESOLV37.8030.000001-0.27
ttim38.0492316136926051.2468025740730582e-06-0.27
ttim-rc38.2995710.0000010.4221660.19
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" + ], + "text/plain": [ + " k [m/d] Ss [1/m] rc RMSE\n", + "MLU 38.094 0.000001 - 0.26\n", + "AQTESOLV 37.803 0.000001 - 0.27\n", + "ttim 38.049231613692605 1.2468025740730582e-06 - 0.27\n", + "ttim-rc 38.299571 0.000001 0.422166 0.19" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"rc\"], index=[\"MLU\", \"AQTESOLV\", \"ttim\", \"ttim-rc\"]\n", + ")\n", + "t.loc[\"MLU\"] = [38.094, 1.193e-06, \"-\"]\n", + "t.loc[\"AQTESOLV\"] = [37.803, 1.356e-06, \"-\"]\n", + "t.loc[\"ttim\"] = np.append(ca_2.parameters[\"optimal\"].values, \"-\")\n", + "t.loc[\"ttim-rc\"] = ca_4.parameters[\"optimal\"].values\n", + "t[\"RMSE\"] = [0.259, 0.270, ca_2.rmse(), ca_4.rmse()]\n", + "t.round(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see good agreement between model results in both hydraulic conductivity and specific storage values. TTim was able to calculate a better fit with wellbore storage added." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Preparing the DataFrame:\n", + "t1 = pd.DataFrame(\n", + " columns=[\"kaq - opt\", \"kaq - 95%\"], index=[\"MLU\", \"AQTESOLV\", \"TTim\", \"TTim - rc\"]\n", + ")\n", + "simulation = [\"MLU\", \"AQTESOLV\", \"TTim\", \"TTim - rc\"]\n", + "t1.loc[\"MLU\"] = [38.094, 2.622 * 1e-2 * 38.094]\n", + "t1.loc[\"AQTESOLV\"] = [37.803, 2.745 * 1e-2 * 37.803]\n", + "t1.loc[\"TTim\"] = [\n", + " ca_2.parameters.loc[\"kaq0\", \"optimal\"],\n", + " 2 * ca_2.parameters.loc[\"kaq0\", \"std\"],\n", + "]\n", + "t1.loc[\"TTim - rc\"] = [\n", + " ca_4.parameters.loc[\"kaq0\", \"optimal\"],\n", + " 2 * ca_4.parameters.loc[\"kaq0\", \"std\"],\n", + "]\n", + "\n", + "# Plotting\n", + "\n", + "plt.figure(figsize=(10, 7))\n", + "\n", + "plt.errorbar(\n", + " x=t1.index,\n", + " y=t1[\"kaq - opt\"],\n", + " yerr=[t1[\"kaq - 95%\"], t1[\"kaq - 95%\"]],\n", + " marker=\"o\",\n", + " linestyle=\"\",\n", + " markersize=12,\n", + ")\n", + "# plt.legend()\n", + "plt.suptitle(\"Error bar plot of calibrated hydraulic conductivities\")\n", + "plt.ylabel(\"K [m/d]\")\n", + "plt.ylim([36, 40])\n", + "plt.xlabel(\"Model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Error bar plot shows that TTim has similar confidence intervals to the other models. The model with wellbore storage has a slightly smaller error range." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Newville, M.,Stensitzki, T., Allen, D.B., Ingargiola, A. (2014) LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python.https://dx.doi.org/10.5281/zenodo.11813. https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021).\n", + "* Walton, W.C., 1962. Selected analytical methods for well and aquifer evaluation. Illinois.department of Registration & Education.bulletin 49.\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/04pumpingtests/confined3_sioux.ipynb b/docs/04pumpingtests/confined3_sioux.ipynb new file mode 100644 index 0000000..0ecefdc --- /dev/null +++ b/docs/04pumpingtests/confined3_sioux.ipynb @@ -0,0 +1,1170 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Confined Aquifer Test - Sioux Example\n", + "**This test is taken from AQTESOLV examples.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model\n", + "\n", + "In this example, we reproduce the work of Yang (2020) to use the pumping test data to demonstrate how TTim can be used to model and analyze pumping tests in a single layer, confined setting, in multiple piezometers. Furthermore, we compare the performance of TTim with other transient well hydraulics software AQTESOLV (Duffield, 2007) and MLU (Carlson and Randall, 2012).\n", + "\n", + "This example is a pumping test done in Sioux Flats, South Dakota, USA. The data comes from the AQTESOLV documentation (Duffield, 2007).\n", + "The aquifer is 50 ft thick and is bounded by impermeable layers. The test was conducted for 2045 minutes (~34 hours), with a constant pumping rate of 2.7 $ft^3/s$. Drawdown data has been collected at three piezometers located 100, 200 and 400 ft away, respectively. The well radius is 0.5 ft.\n", + "\n", + "We can resume the conceptual model in the picture below:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "# sky\n", + "sky = plt.Rectangle((-50, 0), width=500, height=3, fc=\"b\", zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "# Aquifer:\n", + "ground = plt.Rectangle(\n", + " (-50, -55),\n", + " width=500,\n", + " height=50,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + ")\n", + "ax.add_patch(ground)\n", + "\n", + "# Confining bed:\n", + "confining_unit = plt.Rectangle(\n", + " (-50, -5),\n", + " width=500,\n", + " height=5,\n", + " fc=np.array([100, 100, 100]) / 255,\n", + " zorder=0,\n", + " alpha=0.7,\n", + ")\n", + "ax.add_patch(confining_unit)\n", + "well = plt.Rectangle(\n", + " (-4, -55), width=8, height=55, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "ax.add_patch(well)\n", + "\n", + "# Wellhead\n", + "wellhead = plt.Rectangle(\n", + " (-5, 0), width=10, height=1.5, fc=np.array([200, 200, 200]) / 255, zorder=2, ec=\"k\"\n", + ")\n", + "ax.add_patch(wellhead)\n", + "\n", + "# Screen for the well:\n", + "screen = plt.Rectangle(\n", + " (-4, -55),\n", + " width=8,\n", + " height=50,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x=5, y=0.75, dx=8, dy=0, color=\"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x=13, y=0.75, s=r\"$ Q = 2.7 \\frac{ft^3}{s}$\", fontsize=\"x-large\")\n", + "# Piezometers\n", + "piez1 = plt.Rectangle(\n", + " (98, -55), width=4, height=55, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "piez2 = plt.Rectangle(\n", + " (198, -55), width=4, height=55, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "piez3 = plt.Rectangle(\n", + " (398, -55), width=4, height=55, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "\n", + "screen_piez_1 = plt.Rectangle(\n", + " (98, -55),\n", + " width=4,\n", + " height=50,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_1.set_linewidth(2)\n", + "screen_piez_2 = plt.Rectangle(\n", + " (198, -55),\n", + " width=4,\n", + " height=50,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_2.set_linewidth(2)\n", + "screen_piez_3 = plt.Rectangle(\n", + " (398, -55),\n", + " width=4,\n", + " height=50,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_3.set_linewidth(2)\n", + "ax.add_patch(piez1)\n", + "ax.add_patch(piez2)\n", + "ax.add_patch(piez3)\n", + "ax.add_patch(screen_piez_1)\n", + "ax.add_patch(screen_piez_2)\n", + "ax.add_patch(screen_piez_3)\n", + "# last line\n", + "line = plt.Line2D(xdata=[-50, 500], ydata=[0, 0], color=\"k\")\n", + "ax.add_line(line)\n", + "ax.text(x=100, y=0.75, s=\"P100\", fontsize=\"large\")\n", + "ax.text(x=200, y=0.75, s=\"P200\", fontsize=\"large\")\n", + "ax.text(x=400, y=0.75, s=\"P400\", fontsize=\"large\")\n", + "ax.set_xlim([-50, 450])\n", + "ax.set_ylim([-55, 3])\n", + "ax.set_title(\"Conceptual Model - Sioux Example\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Load Required Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import ttim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters\n", + "\n", + "- We will work with time in days and length in meters from this step onwards\n", + "- The parameters below have already been converted to m and days." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "Q = 6605.754 # constant discharge in m^3/d\n", + "b = -15.24 # aquifer thickness in m\n", + "rw = 0.1524 # well radius in m\n", + "r1 = 30.48 # distance between obs1 to pumping well\n", + "r2 = 60.96 # distance between obs2 to pumping well\n", + "r3 = 121.92 # distance between obs3 to pumping well" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Load data of observation and pumping well\n", + "\n", + "The preferred method of loading data into TTim is to use numpy arrays.\n", + "\n", + "The data is in a text file where the first column is the time data in ***days*** and the second column is the drawdown in ***meters***\n", + "\n", + "For each piezometer, we will load the data as a numpy array. We further split the data into two different 1d arrays, one for time and another for drawdown." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data1 = np.loadtxt(\"data/sioux100.txt\")\n", + "t1 = data1[:, 0]\n", + "h1 = data1[:, 1]\n", + "\n", + "\n", + "data2 = np.loadtxt(\"data/sioux200.txt\")\n", + "t2 = data2[:, 0]\n", + "h2 = data2[:, 1]\n", + "\n", + "data3 = np.loadtxt(\"data/sioux400.txt\")\n", + "t3 = data3[:, 0]\n", + "h3 = data3[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Step 4. Creating a TTim conceptual model\n", + "\n", + "In this example, we are using the ModelMaq model to conceptualize our aquifer. ModelMaq defines the aquifer system as a stacked vertical sequence of aquifers and leaky layers (aquifer-leaky layer, aquifer-leaky layer, etc). A thorough explanation of the ModelMaq and TTim one-layer modelling conceptualization is given in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_0 = ttim.ModelMaq(\n", + " kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=10, topboundary=\"conf\"\n", + ")\n", + "w_0 = ttim.Well(ml_0, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", + "ml_0.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Calibration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The calibration workflow has been described in detail in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.1. Model Calibration with all observation wells\n", + "\n", + "We calibrate the model with all observation wells.\n", + "We begin by assuming no wellbore storage or skin resistance, and we only calibrate the hydraulic conductivity and specific storage" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".....................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 34\n", + " # data points = 77\n", + " # variables = 2\n", + " chi-square = 0.00121634\n", + " reduced chi-square = 1.6218e-05\n", + " Akaike info crit = -847.289943\n", + " Bayesian info crit = -842.602332\n", + "[[Variables]]\n", + " kaq0: 282.794907 +/- 1.13788442 (0.40%) (init = 10)\n", + " Saq0: 0.00420857 +/- 3.3461e-05 (0.80%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.8113\n" + ] + } + ], + "source": [ + "# unknown parameters: k, Saq\n", + "ca_0 = ttim.Calibrate(ml_0)\n", + "ca_0.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_0.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_0.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_0.series(name=\"obs3\", x=r3, y=0, t=t3, h=h3, layer=0)\n", + "ca_0.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0282.7949071.1378840.402371-infinf10.0000[282.7949071561833]
Saq00.0042090.0000330.795069-infinf0.0001[0.004208570397720751]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 282.794907 1.137884 0.402371 -inf inf 10.0000 \n", + "Saq0 0.004209 0.000033 0.795069 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0 [282.7949071561833] \n", + "Saq0 [0.004208570397720751] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.0039744982325343485\n" + ] + } + ], + "source": [ + "display(ca_0.parameters)\n", + "print(\"RMSE:\", ca_0.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Model has achieved a good fit and parameters with a low confidence interval" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_0 = ml_0.head(x=r1, y=0, t=t1)\n", + "hm2_0 = ml_0.head(x=r2, y=0, t=t2)\n", + "hm3_0 = ml_0.head(x=r3, y=0, t=t3)\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", + "plt.semilogx(t1, hm1_0[0], label=\"ttim result 1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", + "plt.semilogx(t2, hm2_0[0], label=\"ttim result 2\")\n", + "plt.semilogx(t3, h3, \".\", label=\"obs3\")\n", + "plt.semilogx(t3, hm3_0[0], label=\"ttim result 3\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.legend()\n", + "plt.suptitle(\"Calibration Results vs Observations - Model 1\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visually, the model seems to have a good fit with the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.2. Model Calibration with skin resistance and wellbore storage\n", + "\n", + "In this new model, we investigate whether the well parameters are relevant in the fit.\n", + "\n", + "We begin by reloading the model and creating a ```Well``` object with two extra parameters:\n", + "\n", + "* The radius of the caisson ```rc```, which we use to simulate wellbore storage. In this case, we use a value in meters (float);\n", + "* The skin resistance ```res```, a float value with the unit in days." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_1 = ttim.ModelMaq(\n", + " kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=10, topboundary=\"conf\"\n", + ")\n", + "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=rw, rc=0, res=0, tsandQ=[(0, Q)], layers=0)\n", + "ml_1.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we use the method ```.set_parameter_by_reference``` to calibrate the ```rc``` and ```res``` parameters in our well.\n", + "\n", + "```.set_parameter_by_reference``` takes the following arguments:\n", + "* ```name```: a string of the parameter name\n", + "* ```parameter```: numpy-array with the parameter to be optimized. It should be specified as a reference, for example, in our case: ```w1.rc[0:]``` referencing to the parameter ```rc``` in object ```w1```.\n", + "* ```initial```: float with the initial guess for the parameter value.\n", + "* ```pmin``` and ```pmax```: floats with the minimum and maximum values allowed. If not specified, these will be ```-np.inf``` and ```np.inf```." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...............................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 172\n", + " # data points = 77\n", + " # variables = 4\n", + " chi-square = 0.12810129\n", + " reduced chi-square = 0.00175481\n", + " Akaike info crit = -484.702933\n", + " Bayesian info crit = -475.327711\n", + "[[Variables]]\n", + " kaq0: 275.758881 +/- 13.7289200 (4.98%) (init = 10)\n", + " Saq0: 0.00261417 +/- 3.6976e-04 (14.14%) (init = 0.0001)\n", + " res: 9.5352e-10 +/- 6.8790e-06 (721434.55%) (init = 0)\n", + " rc: 5.60604682 +/- 0.60256208 (10.75%) (init = 0)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.8605\n", + " C(Saq0, rc) = -0.7868\n", + " C(kaq0, rc) = +0.5822\n", + " C(kaq0, res) = -0.3280\n", + " C(Saq0, res) = +0.1666\n" + ] + } + ], + "source": [ + "# unknown parameters: k, Saq, res, rc\n", + "ca_1 = ttim.Calibrate(ml_1)\n", + "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_1.set_parameter_by_reference(name=\"res\", parameter=w_1.res, initial=0, pmin=0)\n", + "ca_1.set_parameter_by_reference(name=\"rc\", parameter=w_1.rc, initial=0)\n", + "ca_1.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_1.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_1.series(name=\"obs3\", x=r3, y=0, t=t3, h=h3, layer=0)\n", + "ca_1.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq02.757589e+0213.7289204.978596-infinf10.0000[275.7588811873042]
Saq02.614165e-030.00037014.144592-infinf0.0001[0.0026141651677158927]
res9.535202e-100.000007721434.5484330.0inf0.0000[9.53520151725229e-10]
rc5.606047e+000.60256210.748431-infinf0.0000[5.606046819073966]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 2.757589e+02 13.728920 4.978596 -inf inf 10.0000 \n", + "Saq0 2.614165e-03 0.000370 14.144592 -inf inf 0.0001 \n", + "res 9.535202e-10 0.000007 721434.548433 0.0 inf 0.0000 \n", + "rc 5.606047e+00 0.602562 10.748431 -inf inf 0.0000 \n", + "\n", + " parray \n", + "kaq0 [275.7588811873042] \n", + "Saq0 [0.0026141651677158927] \n", + "res [9.53520151725229e-10] \n", + "rc [5.606046819073966] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.04078790466973798\n" + ] + } + ], + "source": [ + "display(ca_1.parameters)\n", + "print(\"RMSE:\", ca_1.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When adding both res and rc into calibration, the optimized res value is very close to 0. Moreover, the standard deviation is way above any reasonable limit. Thus, adding res has nearly no effect on improving the conceptual model's performance. Therefore, ```res``` is removed from the calibration." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 48\n", + " # data points = 77\n", + " # variables = 3\n", + " chi-square = 0.00116245\n", + " reduced chi-square = 1.5709e-05\n", + " Akaike info crit = -848.779358\n", + " Bayesian info crit = -841.747942\n", + "[[Variables]]\n", + " kaq0: 283.922147 +/- 1.28530612 (0.45%) (init = 10)\n", + " Saq0: 0.00415480 +/- 4.3877e-05 (1.06%) (init = 0.0001)\n", + " rc: 0.78983676 +/- 0.21260242 (26.92%) (init = 0)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.8522\n", + " C(Saq0, rc) = -0.6690\n", + " C(kaq0, rc) = +0.4874\n" + ] + } + ], + "source": [ + "# unknown parameters: k, Saq, res, rc\n", + "ca_1 = ttim.Calibrate(ml_1)\n", + "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "# ca_1.set_parameter_by_reference(name='res', parameter=w_1.res, initial=0, pmin = 0)\n", + "ca_1.set_parameter_by_reference(name=\"rc\", parameter=w_1.rc, initial=0)\n", + "ca_1.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_1.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_1.series(name=\"obs3\", x=r3, y=0, t=t3, h=h3, layer=0)\n", + "ca_1.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0283.9221471.2853060.452697-infinf10.0000[283.9221470098433]
Saq00.0041550.0000441.056061-infinf0.0001[0.004154797612454171]
rc0.7898370.21260226.917261-infinf0.0000[0.7898367647324968]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 283.922147 1.285306 0.452697 -inf inf 10.0000 \n", + "Saq0 0.004155 0.000044 1.056061 -inf inf 0.0001 \n", + "rc 0.789837 0.212602 26.917261 -inf inf 0.0000 \n", + "\n", + " parray \n", + "kaq0 [283.9221470098433] \n", + "Saq0 [0.004154797612454171] \n", + "rc [0.7898367647324968] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.003885454501818169\n" + ] + } + ], + "source": [ + "display(ca_1.parameters)\n", + "print(\"RMSE:\", ca_1.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The new model has better statistics: lower AIC and BIC, lower RMSE and lower standard deviations for hydraulic conductivities and specific storage. We proceed with plotting the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_1 = ml_1.head(r1, 0, t1)\n", + "hm2_1 = ml_1.head(r2, 0, t2)\n", + "hm3_1 = ml_1.head(r3, 0, t3)\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", + "plt.semilogx(t1, hm1_1[0], label=\"ttim1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", + "plt.semilogx(t2, hm2_1[0], label=\"ttim2\")\n", + "plt.semilogx(t3, h3, \".\", label=\"obs3\")\n", + "plt.semilogx(t3, hm3_1[0], label=\"ttim3\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.legend()\n", + "plt.suptitle(\"Model Calibration Results - Model 2\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the plot above, we can see that there is good agreement between the model calculated heads and the observed ones for all observation wells" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Analysis of Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6.1. Analysis of Fit Statistics\n", + "\n", + "Some of the fit statistics are stored in the ```fitresult``` attribute of the calibration object. This object is a lmfit ```MinimizerResult``` object. ```lmfit``` is the python library doing the calibration for TTim behind the scenes. We accessed below the AIC and BIC values of this object.\n", + "\n", + "Check the lmfit documentation (Newville et al. 2014) to learn more about this object and lmfit." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Fit statistics for the tested models
 RMSEAICBICCalibration scheme
Model 10.003974-847.289943-842.602332All Obs Wells
Model 20.003885-848.779358-841.747942All Obs Wells + wellbore storage
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\"RMSE\", \"AIC\", \"BIC\", \"Calibration scheme\"], index=[\"Model 1\", \"Model 2\"]\n", + ")\n", + "\n", + "t[\"RMSE\"] = [ca_0.rmse(), ca_1.rmse()]\n", + "\n", + "t[\"AIC\"] = [ca_0.fitresult.aic, ca_1.fitresult.aic]\n", + "\n", + "t[\"BIC\"] = [ca_0.fitresult.bic, ca_1.fitresult.bic]\n", + "\n", + "t[\"Calibration scheme\"] = [\"All Obs Wells\", \"All Obs Wells + wellbore storage\"]\n", + "t.style.set_caption(\"Fit statistics for the tested models\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The fit statistics show that the models have very similar performance as all indicators are closely related. By RMSE and AIC criteria, we should pick Model 2, while by BIC, we should pick Model 1. The result means that we cannot exclude one model in favour of the other." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6.2. Summary of Calibrated Parameters and comparison with different Software solutions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We present the results simulated in TTim under different configurations below. Furthermore, Yang (2020) compared TTim results with the results obtained from the software AQTESOLV (Duffield, 2007) and MLU (Carlson & Randall, 2012). In both software, the model was calibrated with observations." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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k [m/d]Ss [1/m]rcRMSE
AQTESOLV282.6590.004211-0.003925
MLU282.6840.004209-0.003897
ttim282.79490715618330.004208570397720751-0.003974
ttim-rc283.9221470.0041550.7898370.003885
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" + ], + "text/plain": [ + " k [m/d] Ss [1/m] rc RMSE\n", + "AQTESOLV 282.659 0.004211 - 0.003925\n", + "MLU 282.684 0.004209 - 0.003897\n", + "ttim 282.7949071561833 0.004208570397720751 - 0.003974\n", + "ttim-rc 283.922147 0.004155 0.789837 0.003885" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"rc\"], index=[\"AQTESOLV\", \"MLU\", \"ttim\", \"ttim-rc\"]\n", + ")\n", + "t.loc[\"AQTESOLV\"] = [282.659, 4.211e-03, \"-\"]\n", + "t.loc[\"ttim\"] = np.append(ca_0.parameters[\"optimal\"].values, \"-\")\n", + "t.loc[\"ttim-rc\"] = ca_1.parameters[\"optimal\"].values\n", + "t.loc[\"MLU\"] = [282.684, 4.209e-03, \"-\"]\n", + "t[\"RMSE\"] = [0.003925, 0.003897, ca_0.rmse(), ca_1.rmse()]\n", + "t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TTim achieved similar results with the other software. The TTim model with wellbore storage had a slightly better RMSE error." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Preparing the DataFrame:\n", + "t1 = pd.DataFrame(\n", + " columns=[\"kaq - opt\", \"kaq - 95%\"], index=[\"MLU\", \"AQTESOLV\", \"TTim\", \"TTim - rc\"]\n", + ")\n", + "simulation = [\"MLU\", \"AQTESOLV\", \"TTim\", \"TTim - rc\"]\n", + "t1.loc[\"MLU\"] = [282.684, 0.783 * 1e-2 * 282.6842]\n", + "t1.loc[\"AQTESOLV\"] = [282.659, 0.394 * 1e-2 * 282.659]\n", + "t1.loc[\"TTim\"] = [\n", + " ca_0.parameters.loc[\"kaq0\", \"optimal\"],\n", + " 2 * ca_0.parameters.loc[\"kaq0\", \"std\"],\n", + "]\n", + "t1.loc[\"TTim - rc\"] = [\n", + " ca_1.parameters.loc[\"kaq0\", \"optimal\"],\n", + " 2 * ca_0.parameters.loc[\"kaq0\", \"std\"],\n", + "]\n", + "\n", + "# Plotting\n", + "\n", + "plt.figure(figsize=(10, 7))\n", + "\n", + "plt.errorbar(\n", + " x=t1.index,\n", + " y=t1[\"kaq - opt\"],\n", + " yerr=[t1[\"kaq - 95%\"], t1[\"kaq - 95%\"]],\n", + " marker=\"o\",\n", + " linestyle=\"\",\n", + " markersize=12,\n", + ")\n", + "# plt.legend()\n", + "plt.suptitle(\"Error bar plot of calibrated hydraulic conductivities\")\n", + "plt.ylabel(\"K [m/d]\")\n", + "plt.ylim([278, 289])\n", + "plt.xlabel(\"Model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Error bar plot shows AQTESOLV with the lowest confidence interval. The models in TTim have larger confidence intervals. However, they are still small. All models seem to agree, and there is a wide overlap in the confidence intervals for hydraulic conductivity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Newville, M.,Stensitzki, T., Allen, D.B., Ingargiola, A. (2014) LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python.https://dx.doi.org/10.5281/zenodo.11813. https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021).\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/04pumpingtests/confined4_schroth.ipynb b/docs/04pumpingtests/confined4_schroth.ipynb new file mode 100644 index 0000000..ab6fe1e --- /dev/null +++ b/docs/04pumpingtests/confined4_schroth.ipynb @@ -0,0 +1,1950 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Confined Aquifer Test - Schroth\n", + "**This test is taken from examples presented in MLU tutorial.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This test example was a pumping test conducted near San Francisco, US, and reported by Brian et al. (1997).\n", + "The system consists of a confined system of two aquifers separated by an aquitard layer. The upper aquifer layer is located from 46 to 49 m depth, followed by an aquitard layer from 49 to 52 m depth and the second aquifer at 52 to 55 m depth.\n", + "\n", + "The lower aquifer is pumped by a well, named EW-712 that fully penetrates the formation. An observation well is placed 46 m away from the well, in the lower aquifer formation, and it is named MW-616. The last observation well is placed in the upper aquifer right on top of the pumping well. However, data was not available for this well. The radius of all wells was 0.05 m.\n", + "\n", + "All wells before pumping had water levels around 20 m depth, which means that the system can be characterized as confined.\n", + "\n", + "The time-drawdown data for the observation well MW-616 and pumping well EW-712 was obtained from MLU documentation (Duffield, 2007).\n", + "\n", + "In this example, we are reproducing the results obtained by Yang (2020). We will use TTim to test two hypotheses: the first is that the lower aquifer is, by confinement, disconnected to the upper aquifer, and the second is there is enough leakage from the upper aquifer to consider a leaky aquifer relation between them.\n", + "\n", + "A simplified cross-section of the model area can be seen below:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "# sky\n", + "sky = plt.Rectangle((-20, 0), width=100, height=3, fc=\"b\", zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "# Aquifer 1:\n", + "ground = plt.Rectangle(\n", + " (-20, -55),\n", + " width=100,\n", + " height=3,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + ")\n", + "ax.add_patch(ground)\n", + "\n", + "# Aquifer 2:\n", + "\n", + "ground = plt.Rectangle(\n", + " (-20, -49),\n", + " width=100,\n", + " height=3,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + ")\n", + "ax.add_patch(ground)\n", + "\n", + "# Confining bed:\n", + "confining_unit = plt.Rectangle(\n", + " (-20, -52),\n", + " width=100,\n", + " height=3,\n", + " fc=np.array([100, 100, 100]) / 255,\n", + " zorder=0,\n", + " alpha=0.7,\n", + ")\n", + "ax.add_patch(confining_unit)\n", + "\n", + "well = plt.Rectangle(\n", + " (-1.5, -55), width=3, height=55, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "ax.add_patch(well)\n", + "\n", + "# Wellhead\n", + "wellhead = plt.Rectangle(\n", + " (-2.5, 0), width=5, height=1.5, fc=np.array([200, 200, 200]) / 255, zorder=2, ec=\"k\"\n", + ")\n", + "ax.add_patch(wellhead)\n", + "\n", + "# Screen for the well:\n", + "screen = plt.Rectangle(\n", + " (-1.5, -55),\n", + " width=3,\n", + " height=3,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x=5, y=0.75, dx=3, dy=0, color=\"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x=9, y=0.75, s=r\"$ Q = 220 \\frac{gal}{min}$\", fontsize=\"large\")\n", + "# Piezometers\n", + "piez1 = plt.Rectangle(\n", + " (45, -55), width=2, height=55, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "screen_piez_1 = plt.Rectangle(\n", + " (45, -55),\n", + " width=2,\n", + " height=3,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_1.set_linewidth(2)\n", + "\n", + "ax.add_patch(piez1)\n", + "\n", + "ax.add_patch(screen_piez_1)\n", + "\n", + "# last line\n", + "line = plt.Line2D(xdata=[-20, 100], ydata=[0, 0], color=\"k\")\n", + "ax.add_line(line)\n", + "ax.text(x=45, y=0.75, s=\"Obs Well 1\", fontsize=\"large\")\n", + "\n", + "ax.text(\n", + " x=-17,\n", + " y=-40,\n", + " s=\"Upper Formations\",\n", + " fontsize=\"large\",\n", + " bbox=dict(facecolor=\"w\", alpha=0.9),\n", + ")\n", + "ax.text(x=-17, y=-48, s=\"Upper Aquifer\", fontsize=\"large\")\n", + "ax.text(x=-17, y=-51, s=\"Aquitard\", fontsize=\"large\")\n", + "ax.text(x=-17, y=-54, s=\"Lower Aquifer\", fontsize=\"large\")\n", + "# Complete the figure:\n", + "\n", + "upper_formations = plt.Rectangle(\n", + " (-20, -46), width=100, height=46, fc=\"white\", hatch=\"-/\", zorder=0, alpha=0.9\n", + ")\n", + "ax.add_patch(upper_formations)\n", + "\n", + "ax.set_xlim([-20, 80])\n", + "ax.set_ylim([-55, 3])\n", + "ax.set_xlabel(\"Distance [m]\")\n", + "ax.set_ylabel(\"Relative height [m]\")\n", + "ax.set_title(\"Conceptual Model - Schroth Example\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import ttim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "Q = 82.08 # constant discharge in m^3/d\n", + "zt0 = -46 # top boundary of upper aquifer in m\n", + "zb0 = -49 # bottom boundary of upper aquifer in m\n", + "zt1 = -52 # top boundary of lower aquifer in m\n", + "zb1 = -55 # bottom boundary of lower aquifer in m\n", + "rw = 0.05 # well radius in m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Load data of the pumping and observation well\n", + "\n", + "The preferred method of loading data into TTim is to use numpy arrays.\n", + "\n", + "The data is in a text file where the first column is the time data in ***days*** and the second column is the drawdown in ***meters***\n", + "\n", + "The observation well is referred to as ***Well 1*** and the pumping well as ***Well 3***.\n", + "\n", + "For each piezometer, we will load the data as a numpy array. We further split the data into two different 1d arrays, one for time and another for drawdown. Finally, we convert the time data from minutes to days" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Loading data for the pumping well\n", + "data1 = np.loadtxt(\"data/schroth_obs1.txt\", skiprows=1)\n", + "t1 = data1[:, 0]\n", + "h1 = data1[:, 1]\n", + "r1 = 0 # Pumping well is at distance 0 to pumping\n", + "\n", + "# Loading data for the observation well\n", + "data2 = np.loadtxt(\"data/schroth_obs2.txt\", skiprows=1)\n", + "t2 = data2[:, 0]\n", + "h2 = data2[:, 1]\n", + "r2 = 46 # distance between observation well2 and pumping well" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Step 4. Create TTim model\n", + "\n", + "We begin by considering the underlying aquifer as a single confined aquifer (overlying aquifer and aquitard are excluded).\n", + "\n", + "In this example, we are using the ModelMaq model to conceptualize our aquifer. ModelMaq defines the aquifer system as a stacked vertical sequence of aquifers and leaky layers (aquifer-leaky layer, aquifer-leaky layer, etc). A thorough explanation of the ModelMaq and TTim one-layer modelling conceptualization is given in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_0 = ttim.ModelMaq(z=[zt1, zb1], kaq=10, Saq=1e-4, tmin=1e-4, tmax=1)\n", + "w_0 = ttim.Well(ml_0, xw=0, yw=0, rw=rw, tsandQ=[(0, Q), (1e08, 0)])\n", + "ml_0.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Calibration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The calibration workflow has been described in detail in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 119\n", + " # data points = 40\n", + " # variables = 2\n", + " chi-square = 111.249393\n", + " reduced chi-square = 2.92761561\n", + " Akaike info crit = 44.9158005\n", + " Bayesian info crit = 48.2935594\n", + "[[Variables]]\n", + " kaq0: 1.03195576 +/- 0.10473795 (10.15%) (init = 10)\n", + " Saq0: 0.04015499 +/- 0.02030043 (50.56%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.9309\n" + ] + } + ], + "source": [ + "ca_0 = ttim.Calibrate(ml_0)\n", + "ca_0.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_0.series(name=\"well\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_0.series(name=\"obs_well\", x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_0.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq01.0319560.10473810.149461-infinf10.0000[1.0319557609869272]
Saq00.0401550.02030050.555188-infinf0.0001[0.04015499415257017]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 1.031956 0.104738 10.149461 -inf inf 10.0000 \n", + "Saq0 0.040155 0.020300 50.555188 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0 [1.0319557609869272] \n", + "Saq0 [0.04015499415257017] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 1.6677034592607083\n" + ] + } + ], + "source": [ + "display(ca_0.parameters)\n", + "print(\"RMSE:\", ca_0.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's plot the model with our observation data:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_0 = ml_0.head(r1, 0, t1)\n", + "hm2_0 = ml_0.head(r2, 0, t2)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"well\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs_well\")\n", + "plt.semilogx(t1, hm1_0[-1], label=\"ttim model - well\")\n", + "plt.semilogx(t2, hm2_0[-1], label=\"ttim model - obs_well\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"head [m]\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The figure shows a poor fit specially for the well. Probably well effects might be relevant in this case, so we will try to calibrate them next." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5.2. Model Calibration with skin resistance and wellbore storage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To improve the fit, we will try to calibrate the model adding wellbore storage and skin resistance to the pumping well.\n", + "\n", + "For this, we create a new model and add two extra parameters to the ```Well``` object:\n", + "\n", + "* The radius of the caisson ```rc```, which we use to simulate wellbore storage. In this case, we use a value in meters (float);\n", + "* The skin resistance ```res```, a float value with the unit in days." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_1 = ttim.ModelMaq(z=[zt1, zb1], kaq=10, Saq=1e-4, tmin=1e-4, tmax=1)\n", + "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=rw, rc=0, res=5, tsandQ=[(0, Q), (1e08, 0)])\n", + "ml_1.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we use the method ```.set_parameter_by_reference``` to calibrate the ```rc``` and ```res``` parameters in our well.\n", + "\n", + "```.set_parameter_by_reference``` takes the following arguments:\n", + "* ```name```: a string of the parameter name\n", + "* ```parameter```: numpy-array with the parameter to be optimized. It should be specified as a reference, for example, in our case: ```w1.rc[0:]``` referencing to the parameter ```rc``` in object ```w1```.\n", + "* ```initial```: float with the initial guess for the parameter value.\n", + "* ```pmin``` and ```pmax```: floats with the minimum and maximum values allowed. If not specified, these will be ```-np.inf``` and ```np.inf```." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".....................................................................................................................................................................................................................................................................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 423\n", + " # data points = 40\n", + " # variables = 4\n", + " chi-square = 13.6479966\n", + " reduced chi-square = 0.37911102\n", + " Akaike info crit = -35.0114685\n", + " Bayesian info crit = -28.2559507\n", + "[[Variables]]\n", + " kaq0: 1.95214232 +/- 0.05267396 (2.70%) (init = 10)\n", + " Saq0: 1.1461e-04 +/- 3.3111e-05 (28.89%) (init = 0.0001)\n", + " rc: 0.00247716 +/- 0.02267359 (915.31%) (init = 0.2)\n", + " res: 43.4469287 +/- 797.527160 (1835.64%) (init = 3)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(rc, res) = -1.0000\n", + " C(kaq0, Saq0) = -0.8652\n", + " C(Saq0, rc) = -0.1040\n", + " C(Saq0, res) = +0.1027\n" + ] + } + ], + "source": [ + "ca_1 = ttim.Calibrate(ml_1)\n", + "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_1.set_parameter_by_reference(name=\"rc\", parameter=w_1.rc[:], initial=0.2)\n", + "ca_1.set_parameter_by_reference(name=\"res\", parameter=w_1.res[:], initial=3)\n", + "ca_1.series(name=\"well\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_1.series(name=\"obs_well\", x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_1.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq01.9521420.0526742.698265-infinf10.0000[1.952142324081202]
Saq00.0001150.00003328.889289-infinf0.0001[0.00011461313147626868]
rc0.0024770.022674915.307610-infinf0.2000[0.0024771553087048403]
res43.446929797.5271601835.635300-infinf3.0000[43.4469287097652]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 1.952142 0.052674 2.698265 -inf inf 10.0000 \n", + "Saq0 0.000115 0.000033 28.889289 -inf inf 0.0001 \n", + "rc 0.002477 0.022674 915.307610 -inf inf 0.2000 \n", + "res 43.446929 797.527160 1835.635300 -inf inf 3.0000 \n", + "\n", + " parray \n", + "kaq0 [1.952142324081202] \n", + "Saq0 [0.00011461313147626868] \n", + "rc [0.0024771553087048403] \n", + "res [43.4469287097652] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.5841232018739387\n" + ] + } + ], + "source": [ + "display(ca_1.parameters)\n", + "print(\"RMSE:\", ca_1.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_1 = ml_1.head(r1, 0, t1)\n", + "hm2_1 = ml_1.head(r2, 0, t2)\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(t1, h1, \".\", label=\"well\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs_well\")\n", + "plt.semilogx(t1, hm1_1[-1], label=\"ttim model - well\")\n", + "plt.semilogx(t2, hm2_1[-1], label=\"ttim model - obs_well\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.legend()\n", + "plt.suptitle(\"Model Results - One Aquifer + Well Parameters\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model has improved significantly. However, we can visually see that the fit is not very good for late time data. Let us investigate if we can do better with a three-layer model such as the one defined in our conceptual model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Step 6. Create a two-aquifer conceptual model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Until so far, we have only considered horizontal flow in our TTim models. The assumption is sufficient in fully-penetrated wells in confined aquifers. However, it might not represent the situation so well in a more complex scenario, where the vertical flow is a relevant component of the groundwater flow. In TTim, this is done by assigning more layers to the model.\n", + "\n", + "An advantage of TTim is the ability to create a model with more than one aquifer layer. We will explore this feature under the ```ModelMaq``` model as in [Step4](#step_4).\n", + "\n", + "When we use ModelMaq, the leaky layers are located in between the aquifers. These leaky layers only have vertical flow and are characterized by the parameters resistance to vertical flow (```c```) and storage (```Sll```). The specific flux is computed as (Bakker, 2013):\n", + "\n", + "$$q_n = \\frac{h_n-h_{n-1}}{c_n}$$\n", + "\n", + "where $q_n$ is the vertical flux from layer $n$ to layer $n-1$, $h_n$ is the head in layer $n$ and $c_n$ is the vertical resistance to flow. $c_n$ is computed as: $H_n/k_n$ where, $H_n$ is the leaky-layer thickness and $k_n$ the vertical hydraulic conductance. $c_n$ is the inverse of the parameter Leakance ($L_n = 1/c_n$), that is used in MODFLOW (Harbaugh, 2005) or analytical solutions of leaky-layers, such as in Hantush (1955).\n", + "\n", + "Alternatively, we can also model the interface of two aquifers that are not separated by a leaky layer. In that case, the leaky layer is set to 0 m thickness, and the model calculates the resistance to vertical flow by a finite differences scheme (Bakker, 2013).\n", + "\n", + "In the model construction, we have to set the parameters for each layer (which consists of an aquifer layer and an aquitard layer).\n", + "\n", + "For our two-layer model, we have to set:\n", + "\n", + "- The hydraulic conductivity: ```kaq```. It is a list/array with a float element for every aquifer, for example: ```[kaq0,kaq1]```.\n", + "- The top and bottom of each aquifer: ```z``` defined by a list/array ```[zt0,zb0,zt1,zb1,...]```, where the inputs are a sequence of top and bottoms of the aquifer layers. The aquitard layers are defined by the space between the aquifer layers. The top of the first aquitard is the bottom of the first aquifer, the bottom of the first aquitard is the top of the second aquifer, and so on. But they can also have 0 thickness. In case the parameter ```topboundary``` is set to ```semi```, where we assume we have semi-confined conditions, we have to add one more element to the beginning of the list, which is the top of the aquitard layer that is above of the first aquifer.\n", + "- The specific storage: ```Saq```. It is a list/array with a float element for every aquifer, for example: ```[Saq0, Saq1]```.\n", + "- The minimum time for which TTim solve the groundwater flow: ```tmin```, a float.\n", + "- And the maximum time: ```tmax```, float.\n", + "- ```topboundary``` is the parameter that sets whether the upper layer is a confined unit or a semi-confined unit. We can assign this parameter as:\n", + " * ```'conf'```: This means that the upper layer is sealed from above in a confined condition;\n", + " *```'semi'```: This means that the upper layer receives leakage from phreatic storage (```phreatictop = True```) or from a fixed head above the upper leaky-layer (```phreatictop = False```).\n", + "- TTim automatically assumes the ```topboundary``` is confined. In this case, we assume the ```topboundary``` is confined, so we do not need to set this parameter.\n", + "- The resistance to vertical flow: ```c``` of the aquitard layers, which is a list with length n-1 where n is the number of aquifer layers, setting the resistance of each aquitard layer. In the case of semi-confined conditions (```topboundary = \" semi\"```), we also add a first element representing the resistance of the aquitard above the first aquifer.\n", + "- The storage ```Sll``` of the aquitard layers: float/list/array with the specific storage values for each aquitard layer. In case a float is defined, the same storage is assumed for every layer. In case ```topboundary = semi``` and ```phreatictop = True```, the first element of ```Sll``` is the specific yield (see example [Unconfined - 1 - Vennebulten](unconfined1_vennebulten.ipynb)). \n", + "- ```phreatictop```: Is a boolean (True/False). If ```True```, the first element in ```Saq``` is considered phreatic storage (Specific Yield), and it is not multiplied by the layer thickness. The default value is ```False``` in ```ModelMaq```. This parameter is normally ```True``` only in unconfined aquifers." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_2 = ttim.ModelMaq(\n", + " kaq=[17.28, 2],\n", + " z=[zt0, zb0, zt1, zb1],\n", + " c=200,\n", + " Saq=[1.2e-4, 1e-5],\n", + " Sll=3e-5,\n", + " topboundary=\"conf\",\n", + " tmin=1e-4,\n", + " tmax=0.5,\n", + ")\n", + "w_2 = ttim.Well(ml_2, xw=0, yw=0, rw=rw, tsandQ=[(0, Q), (1e08, 0)], layers=1)\n", + "ml_2.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Calibrate Multi-Aquifer Model\n", + "\n", + "Now we follow the procedures in step 5 again, but calibrating the parameters of both aquifers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7.1. Calibrate model without wellstorage and skin resistance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Complementing the prior calibration, we add the hydraulic parameters of both layers 0 and 1 and the parameters of the aquitard layer in between. The procedure is the same as explained in Step 5." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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+ "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 6662\n", + " # data points = 40\n", + " # variables = 6\n", + " chi-square = 22.1596023\n", + " reduced chi-square = 0.65175301\n", + " Akaike info crit = -11.6243416\n", + " Bayesian info crit = -1.49106486\n", + "[[Variables]]\n", + " kaq0: 199.161550 +/- 13053.6284 (6554.29%) (init = 20)\n", + " kaq1: 0.09746439 +/- 0.32364902 (332.07%) (init = 1)\n", + " Saq0: 5.61034653 +/- 2477.77332 (44164.35%) (init = 0.0001)\n", + " Saq1: 1.73735848 +/- 5.46149021 (314.36%) (init = 1e-05)\n", + " Sll: 0.06354292 +/- 15.7399790 (24770.62%) (init = 0.0001)\n", + " c1: 0.00142507 +/- 0.00681141 (477.97%) (init = 100)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq1, c1) = +0.9963\n", + " C(Saq1, c1) = -0.6697\n", + " C(kaq1, Saq1) = -0.6366\n", + " C(kaq1, Sll) = -0.5740\n", + " C(Saq0, Saq1) = -0.5698\n", + " C(Sll, c1) = -0.5326\n", + " C(Saq0, c1) = +0.4449\n", + " C(kaq0, kaq1) = -0.4012\n", + " C(kaq0, Saq0) = +0.3894\n", + " C(kaq0, c1) = -0.3775\n", + " C(kaq1, Saq0) = +0.3736\n", + " C(kaq0, Sll) = +0.2948\n", + " C(Saq1, Sll) = -0.2655\n", + " C(kaq0, Saq1) = +0.1940\n", + " C(Saq0, Sll) = +0.1522\n" + ] + } + ], + "source": [ + "ca_2 = ttim.Calibrate(ml_2)\n", + "ca_2.set_parameter(name=\"kaq0\", initial=20, pmin=0, pmax=200)\n", + "ca_2.set_parameter(name=\"kaq1\", initial=1, pmin=0)\n", + "ca_2.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "ca_2.set_parameter(name=\"Saq1\", initial=1e-5, pmin=0)\n", + "ca_2.set_parameter_by_reference(\n", + " name=\"Sll\", parameter=ml_2.aq.Sll[:], initial=1e-4, pmin=0\n", + ")\n", + "ca_2.set_parameter(name=\"c1\", initial=100, pmin=0)\n", + "ca_2.series(name=\"well\", x=r1, y=0, t=t1, h=h1, layer=1)\n", + "ca_2.series(name=\"obs_well\", x=r2, y=0, t=t2, h=h2, layer=1)\n", + "ca_2.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": 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optimalstdperc_stdpminpmaxinitialparray
kaq0199.16155013053.6284306554.2914380200.020.00000[199.1615501616947]
kaq10.0974640.323649332.0689980inf1.00000[0.09746438855592698]
Saq05.6103472477.77331744164.3542560inf0.00010[5.610346530366302]
Saq11.7373585.461490314.3559770inf0.00001[1.737358478821435]
Sll0.06354315.73997924770.6241160inf0.00010[0.06354292456986621, 0.06354292456986621]
c10.0014250.006811477.9702440inf100.00000[0.0014250704284297644]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 199.161550 13053.628430 6554.291438 0 200.0 20.00000 \n", + "kaq1 0.097464 0.323649 332.068998 0 inf 1.00000 \n", + "Saq0 5.610347 2477.773317 44164.354256 0 inf 0.00010 \n", + "Saq1 1.737358 5.461490 314.355977 0 inf 0.00001 \n", + "Sll 0.063543 15.739979 24770.624116 0 inf 0.00010 \n", + "c1 0.001425 0.006811 477.970244 0 inf 100.00000 \n", + "\n", + " parray \n", + "kaq0 [199.1615501616947] \n", + "kaq1 [0.09746438855592698] \n", + "Saq0 [5.610346530366302] \n", + "Saq1 [1.737358478821435] \n", + "Sll [0.06354292456986621, 0.06354292456986621] \n", + "c1 [0.0014250704284297644] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.744305083413874\n" + ] + } + ], + "source": [ + "display(ca_2.parameters)\n", + "print(\"RMSE:\", ca_2.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The more complex model has improved the fit significantly from the first model. However, it showed worse RMSE, AIC and BIC values than the second model, with wellbore storage and skin resistance. We also have to note the unrealistic values found for the hydraulic conductivity of the upper layer and the storage parameters.\n", + "\n", + "Next, we plot this model results." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0.98, 'Model Results - Two Aquifer Model')" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_2 = ml_2.head(r1, 0, t1)\n", + "hm2_2 = ml_2.head(r2, 0, t2)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"well\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs_well\")\n", + "plt.semilogx(t1, hm1_2[-1], label=\"ttim model - well\")\n", + "plt.semilogx(t2, hm2_2[-1], label=\"ttim model - obs_well\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.legend()\n", + "plt.suptitle(\"Model Results - Two Aquifer Model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7.2. Calibrate the two-layer model with wellparameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will add the skinresistance and wellbore storage parameters to our well and resume calibration." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_3 = ttim.ModelMaq(\n", + " kaq=[19, 2],\n", + " z=[zt0, zb0, zt1, zb1],\n", + " c=200,\n", + " Saq=[4e-4, 1e-5],\n", + " Sll=1e-4,\n", + " topboundary=\"conf\",\n", + " tmin=1e-4,\n", + " tmax=0.5,\n", + ")\n", + "w_3 = ttim.Well(\n", + " ml_3, xw=0, yw=0, rw=rw, rc=0.2, res=0, tsandQ=[(0, Q), (1e08, 0)], layers=1\n", + ")\n", + "ml_3.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....................................................................................................................................................................................................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 354\n", + " # data points = 40\n", + " # variables = 8\n", + " chi-square = 1.27784500\n", + " reduced chi-square = 0.03993266\n", + " Akaike info crit = -121.748175\n", + " Bayesian info crit = -108.237140\n", + "[[Variables]]\n", + " kaq0: 5.49229632 +/- 1.29840540 (23.64%) (init = 20)\n", + " kaq1: 1.25160350 +/- 1.39258206 (111.26%) (init = 1)\n", + " Saq0: 1.3810e-07 +/- 1.1375e-05 (8236.99%) (init = 0.0001)\n", + " Saq1: 4.5101e-05 +/- 1.0052e-04 (222.89%) (init = 1e-05)\n", + " Sll: 2.6917e-06 +/- 9.3437e-05 (3471.25%) (init = 0.0001)\n", + " c1: 0.74550570 +/- 6.69233955 (897.69%) (init = 100)\n", + " res: 1.7857e-05 +/- 0.02326246 (130271.52%) (init = 0)\n", + " rc: 0.05512719 +/- 0.00498230 (9.04%) (init = 0.2)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq1, c1) = +0.9998\n", + " C(Saq1, Sll) = -0.9894\n", + " C(res, rc) = -0.9187\n", + " C(Saq0, res) = +0.8677\n", + " C(Saq0, Saq1) = -0.8584\n", + " C(Saq0, Sll) = +0.8454\n", + " C(Saq0, rc) = -0.7826\n", + " C(kaq0, rc) = +0.7584\n", + " C(kaq0, kaq1) = -0.7474\n", + " C(kaq0, c1) = -0.7369\n", + " C(Saq1, rc) = +0.7163\n", + " C(Saq1, res) = -0.7158\n", + " C(Sll, res) = +0.7115\n", + " C(Sll, rc) = -0.7006\n", + " C(kaq1, rc) = -0.6468\n", + " C(c1, rc) = -0.6408\n", + " C(kaq0, res) = -0.5345\n", + " C(kaq1, Saq1) = -0.4994\n", + " C(Saq1, c1) = -0.4989\n", + " C(kaq0, Sll) = -0.4739\n", + " C(kaq0, Saq1) = +0.4623\n", + " C(kaq1, Sll) = +0.4565\n", + " C(kaq0, Saq0) = -0.4562\n", + " C(Sll, c1) = +0.4555\n", + " C(kaq1, res) = +0.3210\n", + " C(c1, res) = +0.3151\n", + " C(kaq1, Saq0) = +0.3104\n", + " C(Saq0, c1) = +0.3072\n" + ] + } + ], + "source": [ + "ca_3 = ttim.Calibrate(ml_3)\n", + "ca_3.set_parameter(name=\"kaq0\", initial=20, pmin=0)\n", + "ca_3.set_parameter(name=\"kaq1\", initial=1, pmin=0)\n", + "ca_3.set_parameter(name=\"Saq0\", initial=1e-4, pmin=1e-7)\n", + "ca_3.set_parameter(name=\"Saq1\", initial=1e-5, pmin=1e-7)\n", + "ca_3.set_parameter_by_reference(\n", + " name=\"Sll\", parameter=ml_3.aq.Sll[:], initial=1e-4, pmin=1e-7\n", + ")\n", + "ca_3.set_parameter(name=\"c1\", initial=100, pmin=0)\n", + "ca_3.set_parameter_by_reference(name=\"res\", parameter=w_3.res[:], initial=0, pmin=0)\n", + "ca_3.set_parameter_by_reference(name=\"rc\", parameter=w_3.rc[:], initial=0.2, pmin=0)\n", + "ca_3.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=1)\n", + "ca_3.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=1)\n", + "ca_3.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq05.492296e+001.29840523.6404830.000000e+00inf20.00000[5.492296320117666]
kaq11.251603e+001.392582111.2638350.000000e+00inf1.00000[1.2516034968134555]
Saq01.380963e-070.0000118236.9944801.000000e-07inf0.00010[1.3809634069605892e-07]
Saq14.510125e-050.000101222.8855101.000000e-07inf0.00001[4.510124987577857e-05]
Sll2.691725e-060.0000933471.2521761.000000e-07inf0.00010[2.6917248675539795e-06, 2.6917248675539795e-06]
c17.455057e-016.692340897.6912640.000000e+00inf100.00000[0.7455057006178796]
res1.785690e-050.023262130271.5189800.000000e+00inf0.00000[1.785690417777097e-05]
rc5.512719e-020.0049829.0378300.000000e+00inf0.20000[0.05512718682053297]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 5.492296e+00 1.298405 23.640483 0.000000e+00 inf 20.00000 \n", + "kaq1 1.251603e+00 1.392582 111.263835 0.000000e+00 inf 1.00000 \n", + "Saq0 1.380963e-07 0.000011 8236.994480 1.000000e-07 inf 0.00010 \n", + "Saq1 4.510125e-05 0.000101 222.885510 1.000000e-07 inf 0.00001 \n", + "Sll 2.691725e-06 0.000093 3471.252176 1.000000e-07 inf 0.00010 \n", + "c1 7.455057e-01 6.692340 897.691264 0.000000e+00 inf 100.00000 \n", + "res 1.785690e-05 0.023262 130271.518980 0.000000e+00 inf 0.00000 \n", + "rc 5.512719e-02 0.004982 9.037830 0.000000e+00 inf 0.20000 \n", + "\n", + " parray \n", + "kaq0 [5.492296320117666] \n", + "kaq1 [1.2516034968134555] \n", + "Saq0 [1.3809634069605892e-07] \n", + "Saq1 [4.510124987577857e-05] \n", + "Sll [2.6917248675539795e-06, 2.6917248675539795e-06] \n", + "c1 [0.7455057006178796] \n", + "res [1.785690417777097e-05] \n", + "rc [0.05512718682053297] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.17873478985914232\n" + ] + } + ], + "source": [ + "display(ca_3.parameters)\n", + "print(\"RMSE:\", ca_3.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we see a much better fit and AIC BIC values from the previous simulation. One thing to consider is the tiny storage values of the first aquifer and the aquitard. It might mean that it is troubling to fit all these parameters together. The small result of the skin resistance and its very high standard deviation might also mean it is irrelevant." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_3 = ml_3.head(r1, 0, t1)\n", + "hm2_3 = ml_3.head(r2, 0, t2)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"well\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs_well\")\n", + "plt.semilogx(t1, hm1_3[-1], label=\"ttim model - well\")\n", + "plt.semilogx(t2, hm2_3[-1], label=\"ttim model - observation well\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.suptitle(\"Model Results - Two Aquifers + Well Parameters\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plot showed the significant fit improvement with the new model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7.3. Calibrate Model with some fixed parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this new model, we will fix some of the parameters. Thus, we expect to have better confidence in estimating the others.\n", + "\n", + "We will fix the hydraulic parameters of the first aquifer layer, the storage coefficient of the leaky layer and the skin resistance of the well. The fixed parameters were defined from the estimates of Brian et al. (1997)." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_4 = ttim.ModelMaq(\n", + " kaq=[17.28, 2],\n", + " z=[zt0, zb0, zt1, zb1],\n", + " c=200,\n", + " Saq=[1.2e-4, 1e-5],\n", + " Sll=3e-5,\n", + " topboundary=\"conf\",\n", + " tmin=1e-4,\n", + " tmax=0.5,\n", + ")\n", + "w_4 = ttim.Well(\n", + " ml_4, xw=0, yw=0, rw=rw, rc=None, res=0, tsandQ=[(0, Q), (1e08, 0)], layers=1\n", + ")\n", + "ml_4.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 63\n", + " # data points = 40\n", + " # variables = 4\n", + " chi-square = 0.56419184\n", + " reduced chi-square = 0.01567200\n", + " Akaike info crit = -162.449616\n", + " Bayesian info crit = -155.694098\n", + "[[Variables]]\n", + " kaq1: 1.93431452 +/- 0.01232404 (0.64%) (init = 1)\n", + " Saq1: 1.3170e-05 +/- 2.3128e-06 (17.56%) (init = 1e-05)\n", + " c1: 239.719879 +/- 20.4969876 (8.55%) (init = 100)\n", + " rc: 0.05268806 +/- 4.0809e-04 (0.77%) (init = 0.2)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq1, c1) = +0.8393\n", + " C(Saq1, rc) = -0.5821\n", + " C(kaq1, Saq1) = -0.5032\n", + " C(Saq1, c1) = -0.2138\n", + " C(c1, rc) = -0.1113\n" + ] + } + ], + "source": [ + "ca_4 = ttim.Calibrate(ml_4)\n", + "ca_4.set_parameter(name=\"kaq1\", initial=1, pmin=0)\n", + "ca_4.set_parameter(name=\"Saq1\", initial=1e-5, pmin=0)\n", + "ca_4.set_parameter(name=\"c1\", initial=100, pmin=0)\n", + "ca_4.set_parameter_by_reference(name=\"rc\", parameter=w_4.rc[:], initial=0.2, pmin=0)\n", + "ca_4.series(name=\"well\", x=r1, y=0, t=t1, h=h1, layer=1)\n", + "ca_4.series(name=\"obs_well\", x=r2, y=0, t=t2, h=h2, layer=1)\n", + "ca_4.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq11.9343150.0123240.6371270inf1.00000[1.934314522419533]
Saq10.0000130.00000217.5613620inf0.00001[1.3170044361299205e-05]
c1239.71987920.4969888.5503910inf100.00000[239.71987879221703]
rc0.0526880.0004080.7745400inf0.20000[0.05268806073287169]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq1 1.934315 0.012324 0.637127 0 inf 1.00000 \n", + "Saq1 0.000013 0.000002 17.561362 0 inf 0.00001 \n", + "c1 239.719879 20.496988 8.550391 0 inf 100.00000 \n", + "rc 0.052688 0.000408 0.774540 0 inf 0.20000 \n", + "\n", + " parray \n", + "kaq1 [1.934314522419533] \n", + "Saq1 [1.3170044361299205e-05] \n", + "c1 [239.71987879221703] \n", + "rc [0.05268806073287169] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.11876361374572386\n" + ] + } + ], + "source": [ + "display(ca_4.parameters)\n", + "print(\"RMSE:\", ca_4.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The new estimates had much better confidence intervals when we used part of the parameters fixed. The RMSE, BIC and AIC indicators also showed better results." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_4 = ml_4.head(r1, 0, t1)\n", + "hm2_4 = ml_4.head(r2, 0, t2)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"well\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs_well\")\n", + "plt.semilogx(t1, hm1_4[-1], label=\"ttim model - well\")\n", + "plt.semilogx(t2, hm2_4[-1], label=\"ttim model - observation well\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.suptitle(\"Model Results - Two Aquifer Model + Well Parameters\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Comparison of Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results simulated with the two aquifer models are present below. Furthermore, Yang (2020) compared TTim results with the results obtained from MLU (Carlson & Randall, 2012) and the published results obtained by Brian et al. (1997). The authors of the original paper applied a finite-difference model of radial flow with a trial and error approach to find the optimal parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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k0[m/d]k1[m/d]Ss0[1/m]Ss1[1/m]Sll[1/m]c[d]resrcRMSE
MLU17.4240.000061.7470.0000060.00004216NaNNaN0.023452
Brian et al.17.283.4560.000120.0000150.00003NaNNaNNaNNaN
ttim199.161550.0974645.6103471.7373580.0635430.001425NaNNaN0.744305
ttim-rc5.4922961.2516030.00.0000450.0000030.7455060.0000180.0551270.178735
ttim-fixed upper17.281.9343150.000120.0000130.00003239.719879NaN0.0526880.118764
\n", + "
" + ], + "text/plain": [ + " k0[m/d] k1[m/d] Ss0[1/m] Ss1[1/m] Sll[1/m] \\\n", + "MLU 17.424 0.00006 1.747 0.000006 0.00004 \n", + "Brian et al. 17.28 3.456 0.00012 0.000015 0.00003 \n", + "ttim 199.16155 0.097464 5.610347 1.737358 0.063543 \n", + "ttim-rc 5.492296 1.251603 0.0 0.000045 0.000003 \n", + "ttim-fixed upper 17.28 1.934315 0.00012 0.000013 0.00003 \n", + "\n", + " c[d] res rc RMSE \n", + "MLU 216 NaN NaN 0.023452 \n", + "Brian et al. NaN NaN NaN NaN \n", + "ttim 0.001425 NaN NaN 0.744305 \n", + "ttim-rc 0.745506 0.000018 0.055127 0.178735 \n", + "ttim-fixed upper 239.719879 NaN 0.052688 0.118764 " + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\n", + " \"k0[m/d]\",\n", + " \"k1[m/d]\",\n", + " \"Ss0[1/m]\",\n", + " \"Ss1[1/m]\",\n", + " \"Sll[1/m]\",\n", + " \"c[d]\",\n", + " \"res\",\n", + " \"rc\",\n", + " ],\n", + " index=[\"MLU\", \"Brian et al.\", \"ttim\", \"ttim-rc\", \"ttim-fixed upper\"],\n", + ")\n", + "t.loc[\"ttim-rc\"] = ca_3.parameters[\"optimal\"].values\n", + "t.iloc[2, 0:6] = ca_2.parameters[\"optimal\"].values\n", + "t.iloc[4, 5] = ca_4.parameters[\"optimal\"].values[2]\n", + "t.iloc[4, 7] = ca_4.parameters[\"optimal\"].values[3]\n", + "t.iloc[4, 0] = 17.28\n", + "t.iloc[4, 1] = ca_4.parameters[\"optimal\"].values[0]\n", + "t.iloc[4, 2] = 1.2e-4\n", + "t.iloc[4, 3] = ca_4.parameters[\"optimal\"].values[1]\n", + "t.iloc[4, 4] = 3e-5\n", + "t.iloc[0, 0:6] = [17.424, 6.027e-05, 1.747, 6.473e-06, 3.997e-05, 216]\n", + "t.iloc[1, 0:6] = [17.28, 3.456, 1.2e-04, 1.5e-5, 3e-05, np.nan]\n", + "t[\"RMSE\"] = [0.023452, np.nan, ca_2.rmse(), ca_3.rmse(), ca_4.rmse()]\n", + "t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first TTim model showed unrealistic results. The second model, although improving the fit, had wide standard deviations (and confidence intervals). That indicates that this solution, with more parameters, in multi-aquifer configuration, is non-unique. \n", + "\n", + " When we kept some of the data fixed with the values from Brian et al., we improved the fit and the confidence interval of the estimates of the model.\n", + "\n", + "MLU results have very low RMSE. However, the specific storage of the upper aquifer (Ss0) is probably too high." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8.1. Comparison of estimates and model error\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Preparing the DataFrame:\n", + "t1 = pd.DataFrame(\n", + " columns=[\"kaq - opt -l1\", \"kaq - 95% -l1\", \"kaq - opt -l2\", \"kaq - 95% -l2\"],\n", + " index=[\"MLU\", \"Brian\", \"TTim - rc\", \"TTim - fixed upper\"],\n", + ")\n", + "simulation = [\"MLU\", \"Brian\", \"TTim - rc\", \"TTim - fixed upper\"]\n", + "t1.loc[\"MLU\"] = [17.424, 124.160 * 1e-2 * 17.424, 1.747, 4.521 * 1e-2 * 1.747]\n", + "t1.loc[\"Brian\"] = [17.28, np.nan, 3.456, np.nan]\n", + "t1.loc[\"TTim - fixed upper\"] = [\n", + " 17.28,\n", + " np.nan,\n", + " ca_4.parameters.loc[\"kaq1\", \"optimal\"],\n", + " 2 * ca_4.parameters.loc[\"kaq1\", \"std\"],\n", + "]\n", + "t1.loc[\"TTim - rc\"] = [\n", + " ca_3.parameters.loc[\"kaq0\", \"optimal\"],\n", + " 2 * ca_3.parameters.loc[\"kaq0\", \"std\"],\n", + " ca_3.parameters.loc[\"kaq1\", \"optimal\"],\n", + " 2 * ca_3.parameters.loc[\"kaq1\", \"std\"],\n", + "]\n", + "\n", + "# Plotting\n", + "\n", + "plt.figure(figsize=(10, 7))\n", + "\n", + "plt.errorbar(\n", + " x=t1.index,\n", + " y=t1[\"kaq - opt -l1\"],\n", + " yerr=[t1[\"kaq - 95% -l1\"], t1[\"kaq - 95% -l1\"]],\n", + " marker=\"o\",\n", + " linestyle=\"\",\n", + " markersize=12,\n", + " label=\"upper aquifer\",\n", + ")\n", + "plt.errorbar(\n", + " x=t1.index,\n", + " y=t1[\"kaq - opt -l2\"],\n", + " yerr=[t1[\"kaq - 95% -l2\"], t1[\"kaq - 95% -l2\"]],\n", + " marker=\"o\",\n", + " linestyle=\"\",\n", + " markersize=12,\n", + " label=\"lower aquifer\",\n", + ")\n", + "\n", + "plt.legend()\n", + "plt.suptitle(\"Error bar plot of calibrated hydraulic conductivities\")\n", + "plt.ylabel(\"K [m/d]\")\n", + "# plt.ylim([278,289])\n", + "plt.xlabel(\"Model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MLU model has a larger confidence interval for the first aquifer layer, while TTim got lower values for the generic model (TTim - rc) and a relatively small range. The model with some fixed values has a small confidence interval. Confidence intervals were not reported in Brian et al. (1997)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Bakker, M. Semi-analytic modeling of transient multi-layer flow with TTim. Hydrogeol J 21, 935–943 (2013). https://doi.org/10.1007/s10040-013-0975-2\n", + "* Brian, Schroth, T., N., Narasimhan, 1997. Application of a numerical model in the interpretation of a leaky aquifer test. Ground Water .\n", + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Newville, M.,Stensitzki, T., Allen, D.B., Ingargiola, A. (2014) LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python.https://dx.doi.org/10.5281/zenodo.11813. https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021).\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/04pumpingtests/confined5_nevada.ipynb b/docs/04pumpingtests/confined5_nevada.ipynb new file mode 100644 index 0000000..f6a71c0 --- /dev/null +++ b/docs/04pumpingtests/confined5_nevada.ipynb @@ -0,0 +1,1187 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Confined - Double Porosity Test, Nevada\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this following benchmark example, we demonstrate by reproducing Yang (2020) how we can apply TTim in fractured media. We further compare the values obtained in TTim with the ones simulated in AQTESOLV and MLU by Yang (2020)\n", + "\n", + "The example is taken from Kruseman and de Ridder (1990). It is based on a pumping test conducted in a fractured tertiary volcanic aquifer near Yucca Mountains, Nevada, US. Although conventional Darcy's flow is not applied to fracture flow, in this case, we can assume that fracturing is pervasive enough to flow occur as Darcy's flow at the aquifer scale. Flow to the well comes from fractures, while the matrix exchanges water with the fractures.\n", + "\n", + "The borehole has 1219 m depth and penetrated a 400 m thick sequence of fractured volcanic rocks with water entry points. At every entry point, the head is more or less the same, so they have good hydraulic connection. The water table is about 470 m below depth, which indicates confined conditions.\n", + "\n", + "Drawdown data was sampled at the well, named ***UE-25b#1*** and at an observation well, named ***UE-25a#1***, 110 m away. Three pumping tests were conducted at the site and will be the object of our investigation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "\n", + "def conc_confined5():\n", + " ##Now printing the conceptual model figure:\n", + "\n", + " fig = plt.figure(figsize=(14, 9))\n", + " ax = fig.add_subplot(1, 1, 1)\n", + " # sky\n", + " sky = plt.Rectangle((-20, 0), width=170, height=150, fc=\"b\", zorder=0, alpha=0.1)\n", + " ax.add_patch(sky)\n", + "\n", + " # Aquifer:\n", + " ground = plt.Rectangle(\n", + " (-20, -1219),\n", + " width=170,\n", + " height=400,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + " hatch=\"xx\",\n", + " )\n", + " ax.add_patch(ground)\n", + "\n", + " # Confining bed:\n", + " confining_unit = plt.Rectangle(\n", + " (-20, -1219 + 400),\n", + " width=170,\n", + " height=1219 - 400,\n", + " fc=np.array([100, 100, 100]) / 255,\n", + " zorder=0,\n", + " alpha=0.7,\n", + " )\n", + " ax.add_patch(confining_unit)\n", + " well = plt.Rectangle(\n", + " (-2, -1219), width=4, height=1219, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + " )\n", + " ax.add_patch(well)\n", + "\n", + " # Wellhead\n", + " wellhead = plt.Rectangle(\n", + " (-4, 0),\n", + " width=8,\n", + " height=55,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " )\n", + " ax.add_patch(wellhead)\n", + "\n", + " # Screen for the well:\n", + " screen = plt.Rectangle(\n", + " (-2, -1219),\n", + " width=4,\n", + " height=400,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + " )\n", + " screen.set_linewidth(2)\n", + " ax.add_patch(screen)\n", + " pumping_arrow = plt.Arrow(x=4, y=25, dx=20, dy=0, color=\"#00035b\", zorder=3)\n", + " ax.add_patch(pumping_arrow)\n", + " ax.text(x=28, y=55, s=r\"$ Q = 3093 \\frac{m^3}{d}$\", fontsize=\"large\")\n", + " # Piezometers\n", + " piez1 = plt.Rectangle(\n", + " (99, -1219), width=2, height=1219, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + " )\n", + " screen_piez_1 = plt.Rectangle(\n", + " (99, -1219),\n", + " width=2,\n", + " height=400,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + " )\n", + " screen_piez_1.set_linewidth(2)\n", + "\n", + " ax.add_patch(piez1)\n", + "\n", + " ax.add_patch(screen_piez_1)\n", + "\n", + " # last line\n", + " line = plt.Line2D(xdata=[-20, 150], ydata=[0, 0], color=\"k\")\n", + " ax.add_line(line)\n", + " ax.text(x=100, y=55, s=\"Obs Well 1\", fontsize=\"large\")\n", + "\n", + " ax.set_xlim([-20, 150])\n", + " ax.set_ylim([-1219, 150])\n", + " ax.set_xlabel(\"Distance [m]\")\n", + " ax.set_ylabel(\"Relative height [m]\")\n", + " ax.set_title(\"Conceptual Model - Nevada Example\")\n", + " plt.show()\n", + "\n", + "\n", + "conc_confined5()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Step 1. Import the Required Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import ttim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set the Basic Parameters for the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "H = 400 # aquifer thickness [m]\n", + "Q = 3093.12 # constant pumping rate [m^3/d]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Load Data\n", + "\n", + "Dataset is stored in a text-file for each well, where the first column is the time in days and the second is the drawdown in meters." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Pumped well UE-25b#1\n", + "data1 = np.loadtxt(\"data/double-porosity-pumpingwell.txt\", skiprows=1)\n", + "t1 = data1[:, 0]\n", + "h1 = data1[:, 1]\n", + "\n", + "# Observation well UE-25a#1\n", + "data2 = np.loadtxt(\"data/double-porosity-110m.txt\", skiprows=1)\n", + "t2 = data2[:, 0]\n", + "h2 = data2[:, 1]\n", + "r = 110 # distance from obs to pumped well" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Create conceptual model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To conceptualize the model in TTim, we have to approximate the fractured system to a possible ModelMaq configuration. We can do this by creating a first layer that represents the matrix of the aquifer, followed by a 1 m thick aquitard and a second layer that represents the fractures." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def conc_mmaq_confined5():\n", + " ##Now printing the conceptual model figure:\n", + "\n", + " fig = plt.figure(figsize=(14, 9))\n", + " ax = fig.add_subplot(1, 1, 1)\n", + " # sky\n", + " sky = plt.Rectangle((-20, 0), width=170, height=150, fc=\"b\", zorder=0, alpha=0.1)\n", + " ax.add_patch(sky)\n", + "\n", + " # Aquifer:\n", + " ground_2 = plt.Rectangle(\n", + " (-20, -1219 + 401),\n", + " width=170,\n", + " height=400,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + " hatch=\"oo\",\n", + " )\n", + " ax.add_patch(ground_2)\n", + " ground = plt.Rectangle(\n", + " (-20, -1219),\n", + " width=170,\n", + " height=400,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + " hatch=\"xx\",\n", + " )\n", + " ax.add_patch(ground)\n", + "\n", + " # Confining bed:\n", + " confining_unit_1 = plt.Rectangle(\n", + " (-20, -1219 + 400),\n", + " width=170,\n", + " height=1,\n", + " fc=np.array([100, 100, 100]) / 255,\n", + " zorder=0,\n", + " alpha=0.7,\n", + " )\n", + " ax.add_patch(confining_unit_1)\n", + " confining_unit = plt.Rectangle(\n", + " (-20, -1219 + 801),\n", + " width=170,\n", + " height=1219 - 801,\n", + " fc=np.array([100, 100, 100]) / 255,\n", + " zorder=0,\n", + " alpha=0.7,\n", + " )\n", + " ax.add_patch(confining_unit)\n", + " well = plt.Rectangle(\n", + " (-2, -1219), width=4, height=1219, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + " )\n", + " ax.add_patch(well)\n", + "\n", + " # Wellhead\n", + " wellhead = plt.Rectangle(\n", + " (-4, 0),\n", + " width=8,\n", + " height=55,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " )\n", + " ax.add_patch(wellhead)\n", + "\n", + " # Screen for the well:\n", + " screen = plt.Rectangle(\n", + " (-2, -1219),\n", + " width=4,\n", + " height=400,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + " )\n", + " screen.set_linewidth(2)\n", + " ax.add_patch(screen)\n", + " pumping_arrow = plt.Arrow(x=4, y=25, dx=22, dy=0, color=\"#00035b\")\n", + " ax.add_patch(pumping_arrow)\n", + " ax.text(x=29, y=55, s=r\"$ Q = 3093 \\frac{m^3}{d}$\", fontsize=\"large\")\n", + " # Piezometers\n", + " piez1 = plt.Rectangle(\n", + " (99, -1219), width=2, height=1219, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + " )\n", + " screen_piez_1 = plt.Rectangle(\n", + " (99, -1219),\n", + " width=2,\n", + " height=400,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + " )\n", + " screen_piez_1.set_linewidth(2)\n", + "\n", + " ax.add_patch(piez1)\n", + "\n", + " ax.add_patch(screen_piez_1)\n", + "\n", + " # last line\n", + " line = plt.Line2D(xdata=[-20, 150], ydata=[0, 0], color=\"k\")\n", + " ax.add_line(line)\n", + " ax.text(x=100, y=55, s=\"Obs Well 1\", fontsize=\"large\")\n", + "\n", + " ax.set_xlim([-20, 150])\n", + " ax.set_ylim([-1219, 150])\n", + " ax.set_xlabel(\"Distance [m]\")\n", + " ax.set_ylabel(\"Relative height [m]\")\n", + " ax.set_title(\"Conceptual ModelMaq Model - Nevada Example\")\n", + " plt.show()\n", + "\n", + "\n", + "conc_mmaq_confined5()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, for the TTim model, we will adopt the parameters for the first layer from the results obtained from Kruseman and de Ridder (1970):" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "km = 0.1 / H # hydraulic conductivity of matrix calculated by K&dR\n", + "Sm = 3.85e-4 # specific storage of matrix calculated by K&dR" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can construct the two-layer model:\n", + "Instructions on how to construct this model are in:\n", + "* [Confined 4 - Schroth](confined4_schroty.ipynb)\n", + "* [Confined 1 - Oude Korendijk](confined1_oude_korendijk.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml = ttim.ModelMaq(\n", + " kaq=[km, 1],\n", + " z=[0, -400, -401, -801],\n", + " c=5,\n", + " Saq=[Sm, 1e-3],\n", + " Sll=0,\n", + " topboundary=\"conf\",\n", + " tmin=1e-5,\n", + " tmax=3,\n", + ")\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=0.11, rc=0, tsandQ=[0, 3093.12], layers=1)\n", + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Calibrate the model using both Datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this calibration procedure, we only calibrate the parameters of the fractured system and keep the matrix values fixed. We also calibrate the wellbore storage parameter ```rc``` that is the radius of the caisson considered for storage.\n", + "Instructions on how to set this calibration are in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".........................................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 198\n", + " # data points = 138\n", + " # variables = 4\n", + " chi-square = 5.47351190\n", + " reduced chi-square = 0.04084710\n", + " Akaike info crit = -437.371988\n", + " Bayesian info crit = -425.662973\n", + "[[Variables]]\n", + " kaq1: 0.87696320 +/- 0.00699050 (0.80%) (init = 10)\n", + " Saq1: 5.0877e-06 +/- 5.0675e-07 (9.96%) (init = 0.0001)\n", + " c1: 13.0035244 +/- 1.59723924 (12.28%) (init = 10)\n", + " rc: 0.10560361 +/- 0.00320824 (3.04%) (init = 0)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq1, c1) = +0.8580\n", + " C(kaq1, Saq1) = -0.7312\n", + " C(Saq1, c1) = -0.5463\n", + " C(Saq1, rc) = -0.4011\n", + " C(kaq1, rc) = +0.1007\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq10.8769636.990504e-030.797126-infinf10.0000[0.8769632032421197]
Saq10.0000055.067510e-079.9603050.0inf0.0001[5.0877062902632275e-06]
c113.0035241.597239e+0012.283126-infinf10.0000[13.003524396048254]
rc0.1056043.208241e-033.038003-infinf0.0000[0.1056036071201357]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq1 0.876963 6.990504e-03 0.797126 -inf inf 10.0000 \n", + "Saq1 0.000005 5.067510e-07 9.960305 0.0 inf 0.0001 \n", + "c1 13.003524 1.597239e+00 12.283126 -inf inf 10.0000 \n", + "rc 0.105604 3.208241e-03 3.038003 -inf inf 0.0000 \n", + "\n", + " parray \n", + "kaq1 [0.8769632032421197] \n", + "Saq1 [5.0877062902632275e-06] \n", + "c1 [13.003524396048254] \n", + "rc [0.1056036071201357] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.19915604354014974\n" + ] + } + ], + "source": [ + "ca = ttim.Calibrate(ml)\n", + "ca.set_parameter(name=\"kaq1\", initial=10)\n", + "ca.set_parameter(name=\"Saq1\", initial=1e-4, pmin=0)\n", + "ca.set_parameter(name=\"c1\", initial=10)\n", + "ca.set_parameter_by_reference(name=\"rc\", parameter=w.rc, initial=0)\n", + "ca.series(name=\"UE-25b#1\", x=0, y=0, t=t1, h=h1, layer=1)\n", + "ca.series(name=\"UE-25a#1\", x=r, y=0, t=t2, h=h2, layer=1)\n", + "ca.fit(report=True)\n", + "display(ca.parameters)\n", + "print(\"RMSE:\", ca.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Overall, a good fit has been obtained. Reasonable standard deviations of the estimates and a low value for AIC and BIC gives us confidence in the adopted parameters. Now we can check how the results plot with the observations:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1 = ml.head(0, 0, t1)\n", + "hm2 = ml.head(110, 0, t2)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs UE-25b#1\")\n", + "plt.semilogx(t1, hm1[-1], label=\"TTim UE-25b#1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs UE-25a#1\")\n", + "plt.semilogx(t2, hm2[-1], label=\"TTim UE-25a#1\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.suptitle(\"Model Results - Simulation 1\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Simulate parameters of both fracture and matrix\n", + "\n", + "Now we will repeat the procedures of steps 5 and 6, but this time we will let TTim find the parameters for both the matrix and the fractures" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml1 = ttim.ModelMaq(\n", + " kaq=[1, 1],\n", + " z=[0, -400, -401, -801],\n", + " c=5,\n", + " Saq=[1e-3, 1e-3],\n", + " Sll=0,\n", + " topboundary=\"conf\",\n", + " tmin=1e-5,\n", + " tmax=3,\n", + ")\n", + "w1 = ttim.Well(ml1, xw=0, yw=0, rw=0.11, rc=0, tsandQ=[0, 3093.12], layers=1)\n", + "ml1.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".....................................................................................................................................................................................................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 354\n", + " # data points = 138\n", + " # variables = 6\n", + " chi-square = 3.50592808\n", + " reduced chi-square = 0.02656006\n", + " Akaike info crit = -494.846181\n", + " Bayesian info crit = -477.282659\n", + "[[Variables]]\n", + " kaq0: 8.3798e-08 +/- 2.7038e-05 (32265.92%) (init = 1)\n", + " Saq0: 1.4424e-04 +/- 1.4640e-05 (10.15%) (init = 0.0001)\n", + " kaq1: 0.90902373 +/- 0.00603961 (0.66%) (init = 1)\n", + " Saq1: 3.3892e-06 +/- 3.0267e-07 (8.93%) (init = 0.0001)\n", + " c1: 15.5844403 +/- 1.43538189 (9.21%) (init = 100)\n", + " rc: 0.10855545 +/- 0.00253751 (2.34%) (init = 0)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq1, Saq1) = -0.7423\n", + " C(kaq1, c1) = +0.7105\n", + " C(Saq0, kaq1) = -0.6759\n", + " C(Saq0, Saq1) = +0.5464\n", + " C(Saq1, rc) = -0.4120\n", + " C(Saq1, c1) = -0.3831\n", + " C(Saq0, c1) = -0.2784\n", + " C(kaq0, c1) = +0.1418\n", + " C(kaq0, Saq1) = +0.1270\n", + " C(kaq1, rc) = +0.1182\n", + " C(kaq0, Saq0) = +0.1102\n", + " C(Saq0, rc) = -0.1095\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq08.379800e-082.703820e-0532265.9236021.000000e-08inf1.0000[8.379799754099082e-08]
Saq01.442428e-041.463980e-0510.1494141.000000e-08inf0.0001[0.00014424278513025524]
kaq19.090237e-016.039612e-030.6644061.000000e-08inf1.0000[0.9090237295301099]
Saq13.389227e-063.026748e-078.9304951.000000e-08inf0.0001[3.3892272003344104e-06]
c11.558444e+011.435382e+009.2103531.000000e-08inf100.0000[15.584440297512415]
rc1.085555e-012.537508e-032.3375230.000000e+00inf0.0000[0.10855545326039162]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 8.379800e-08 2.703820e-05 32265.923602 1.000000e-08 inf 1.0000 \n", + "Saq0 1.442428e-04 1.463980e-05 10.149414 1.000000e-08 inf 0.0001 \n", + "kaq1 9.090237e-01 6.039612e-03 0.664406 1.000000e-08 inf 1.0000 \n", + "Saq1 3.389227e-06 3.026748e-07 8.930495 1.000000e-08 inf 0.0001 \n", + "c1 1.558444e+01 1.435382e+00 9.210353 1.000000e-08 inf 100.0000 \n", + "rc 1.085555e-01 2.537508e-03 2.337523 0.000000e+00 inf 0.0000 \n", + "\n", + " parray \n", + "kaq0 [8.379799754099082e-08] \n", + "Saq0 [0.00014424278513025524] \n", + "kaq1 [0.9090237295301099] \n", + "Saq1 [3.3892272003344104e-06] \n", + "c1 [15.584440297512415] \n", + "rc [0.10855545326039162] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.1593903257365926\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/Caskroom/miniforge/base/envs/ttim-env/lib/python3.10/site-packages/ttim/well.py:35: RuntimeWarning: divide by zero encountered in divide\n", + " laboverrwk1 = self.aq.lab / (self.rw * kv(1, self.rw/self.aq.lab))\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/ttim-env/lib/python3.10/site-packages/ttim/well.py:35: RuntimeWarning: invalid value encountered in divide\n", + " laboverrwk1 = self.aq.lab / (self.rw * kv(1, self.rw/self.aq.lab))\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/ttim-env/lib/python3.10/site-packages/ttim/well.py:38: RuntimeWarning: invalid value encountered in multiply\n", + " self.term = -1.0 / (2 * np.pi) * laboverrwk1 * self.flowcoef * coef\n" + ] + } + ], + "source": [ + "ca1 = ttim.Calibrate(ml1)\n", + "ca1.set_parameter(name=\"kaq0\", initial=1, pmin=1e-8)\n", + "ca1.set_parameter(name=\"Saq0\", initial=1e-4, pmin=1e-8)\n", + "ca1.set_parameter(name=\"kaq1\", initial=1, pmin=1e-8)\n", + "ca1.set_parameter(name=\"Saq1\", initial=1e-4, pmin=1e-8)\n", + "ca1.set_parameter(name=\"c1\", initial=100, pmin=1e-8)\n", + "ca1.set_parameter_by_reference(name=\"rc\", parameter=w1.rc, initial=0, pmin=0)\n", + "ca1.series(name=\"UE-25b#1\", x=0, y=0, t=t1, h=h1, layer=1)\n", + "ca1.series(name=\"UE-25a#1\", x=110, y=0, t=t2, h=h2, layer=1)\n", + "ca1.fit(report=True)\n", + "display(ca1.parameters)\n", + "print(\"RMSE:\", ca1.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the fit has slightly improved, as the AIC and BIC values. However, from the table above, one can see that we have low confidence in the hydraulic conductivity of the matrix. Nevertheless, we can see it is a small value." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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3d3ejYcOGRr9+/Yx//vOfTudQgVkXZs+eXezxG264wXBzczMOHDhgGIZ9xoOHH37YiI6ONtzd3Y2QkBCjZ8+exjPPPGOkpaUZhmEYH3zwgTFkyBAjLCzM8PDwMBo3bmyMHTvW2Llzp9O9N2/ebPTr18/w9fU1mjRpYkyZMsV45513ypx1wTAM48cffzS6d+9ueHp6GoBx2223GVlZWcakSZOMLl26GAEBAYa3t7fRtm1bY8qUKUZ6enqp30NF7Nmzx/i///s/o1evXkbDhg0NNzc3Izg42Bg0aJDx4YcfOp1b0qwLHTt2LHLf5s2bGyNHjiyyHzAeeOABp33ffvut0a1bN8Pb29to0aKF8cYbb5R71oVZs2YZUVFRhqenp9G+fXtjwYIFxV4bExNj9O/f3/Dx8TEAx/8HhWddyPe///3PuPjiiw0vLy/D19fXuOyyy4z169c7nZP/OSdPnizzeypO/u/X4rbiZqIQkcozGUYZw3JFRERERGoh9dEVERERkTpJQVdERERE6iQFXRERERGpkxR0RURERKROUtAVERERkTpJQVdERERE6iQFXRERERGpkxR0RURERKROUtAVERERkTpJQVdERERE6iQFXRERERGpkxR0RURERKROUtAVERERkTpJQVdERERE6iQFXRERERGpkxR0RURERKROUtAVERERkTpJQVdERERE6iQFXRERERGpkxR0RURERKROUtAVERERkTpJQVdERERE6iQFXRERERGpkxR0RURERKROUtAVERERkTpJQVdERERE6iQFXRERERGpkxR0RURERKROUtAVERERkTpJQVdERERE6iQFXRERERGpkxR0RURERKROUtAVERERkTpJQVdERERE6iQFXRERERGpk9xcXcCFxmazcezYMfz9/TGZTK4uR0REREQKMQyD1NRUGjdujNlccrutgm4hx44dIzIy0tVliIiIiEgZDh8+TNOmTUs8rqBbiL+/P2D/4gICAlxcjYiIiIgUlpKSQmRkpCO3lURBt5D87goBAQEKuiIiIiIXsLK6mWowmoiIiIjUSQq6IiIiIlInKeiKiIiISJ2koCsiIiIidZKCroiIiIjUSQq6IiIiIlInKeiKiIiISJ2koCsiIiIidZKCroiIiIjUSQq6IiIiIlInKeiKiIiISJ1UJ4Pum2++SXR0NF5eXvTs2ZO1a9e6uiQRERERqWF1LuguXryYRx99lGeeeYYdO3YwYMAARowYQVxcnKtLkzosIT2BzfGbSUhPqPQ5JR2vyP7y1FHWeZU5Vt5aCu+r6HsREZGKMBmGYbi6iKp08cUX06NHD9566y3Hvvbt23Pttdcyc+bMMq9PSUkhMDCQ5ORkAgICqrNUqSO+2PUJGxb8EwMDEyaubHElXRt2dTrn15O/8u2f35Z4TknHK7IfKPUzylNLZY6Vt5bC+zqFdmJ34u5i32My0Tm0E7sSd2PDwIyJS5tdSoRvBCHeDQjwDABMYDLZH8oEKdkpJGUnEeLdgECvYPsxs4nknBROZSUR6tuQIO9gMJvBZOZMTjKJWacI9QsjxLcBmC2YLGb7r24WTG5uYHGzv7ZYMLm7Y3J3Bzd3TB7umNw9MHu4g7s7pvw6RESkRpQ3r9WpoJuTk4OPjw+fffYZ1113nWP/I488QkxMDKtXry5yTXZ2NtnZ2Y73KSkpREZGKuhKuSSkJzD2g6G8/Xquq0sRVzGZMHl6YvLwwOTpgdndA5OXF2YvL0ze3md/9cLs7YPZywuzjzdmX9+im58fFn9/zP7+WPz8MPv52cO2iIgUUd6gW6f+Fk1MTMRqtRIWFua0PywsjISE4n/0OXPmTKZNm1YT5UkdFJcSR7bFxsZ2zi16vcJ7E+IVAkBS5im2Ht9a5Nr8c0o63ja4LXtP7y33/uIUrKOsWjCMCh+rSC2VYSr0z3ATgGH/tX/j/nhaPMnOy2LjsQ2O/fm/dmvYjZ0nYsAA89n7mA3oGNyeP079jskwMNvs+8yGidYBLUjPTiMx7ThmG1gM8DN7k5OdicUGbjbwMtwx5Vkx2WznijIMjKwsjKwsAKxV+PxmX18sQUFYAgPtvwbZfzUHBuKW/2twMJaQBrg1CMESEoLZy6sKKxARqd3qVNDNV/jHiIZhlPijxcmTJ/P444873ue36IqUR7OAZmR5W3j1unO/v8wmM8vHvEi4bzgAbukJvLZkGDbDVuw5JR3/aMR0XvvulnLtN539n43iPyNfabUAFT5W3lqK23e+Ogy7h97hvdkcv5mXVvxS5PgTvYYze+uuYvZfw+yt+4rsnzPoIZ5c8yQ2o+Bfi7kU/GvSBJhwwzCsuOWBuxU8rWYWXfEfQi2B/HjgW97dPh+3XAPvPBO3RN/ARcFdsWVmYcvMJCX5BMnJJwjIc8Mrx8Cano6RkUFW8mlyUpNxS8+G9AxHaLalp2NLTyf36NFyfy9mX18sISG4hYRgadAAt9BQ+9YwFLeGDXELDcUS2hC3hqGYPT3LfV8RkdqoTgXd0NBQLBZLkdbbEydOFGnlzefp6Ymn/rKXSgr3DWdK3ylM2zgNm2HDbDIzpe8Up3BZ1jklHe/csHOF9gOl1lGeWip6rCK1FN43qsUovv7z63K9L8xsMhPpb/8HabOAZphN5iIhvHuj7hXabxhGsZ9VkHH2f5hM5LpDrjtkYHDMPxeLvzdPr/s3tgg427ZMjGkJyy+/h3DfcJbuX8q0ja9iCz73nYxuPfrsfufv6rrmo4g/fpCj8fuJsPoSmG3BeuYMtuRkrMnJWM+cwXrG/mvemdNYTyVhTUrCyM09F44PHy71WQAsgYG4NWp0bmvY0P5rWCPcw8JwCwvDrUEDdaEQkVqrTvXRBftgtJ49e/Lmm2869nXo0IFrrrlGg9Gk2iSkJ3A49TCR/pFFwmV5zynpeEX2l6eOss6rzLHy1lJ4X3nf707czdztc53C4OjWox2fX1xYLClElrS/X+N+DCvUal1YyS3ny4lLiWPiiolFrnlv2HtE+kcWube9Rfwjbimm1f7RHo+W+rwFv/e4lDiaBTQjzCcMW1oa1lOnyEtKIu/UKaynksg7lUjeyZPkJSZiPXnutZGTU+JzOjGb7S3CYWG4h4fh1iis0Gt7KDb7+JTvfiIiVaBeDkYD+/RiEyZMYP78+fTt25d///vfLFiwgN9++43mzZuXeb2CrsiFqSb+oVA4ABduZS6ptXp069EkpCcUG2ZLC8FP9HqC2VtnF9lvwmRvOS50n4L1lxTiC38n+UG44LWGYWBLSbGH3hMnyD1xgrwT9td5x4+Te+I4ecdPkHfyJFjL1+vYHBBwrhU4v0W4USOnMGwJCcFkrnOzWoqIC9TboAv2BSNeeukl4uPj6dSpE6+++ioDBw4s17UKuiL1W1mtzMWdk6+k8FlSCC6uRbdwyM333rD36B3e2/H5JYXqkkJ7wSBcUgAuvN+wWsk7dcoeek8cJ/f4cfISjtvD8PFzvxoZGeX7ct3dcWsYinvDgl0lGp7rMtHQ/toSHKxALCKlqtdB93wo6IrI+ahoCC68v2C3hXyFQ+zm+M0ldpPoHd671CC84diGCnXzKCkU5zMMA1taGnkJCeQeP0He8QR7K/Hxs63Cx4+Te+IE1lOnoLz/uXFzs88mERqKW4MGuDVogCW0AW4hDbA0ODvQLjgEt5Bg+0wT3t7l/b9HROqIejm9mIiIq4X7hhcbCEe3Hk2/xv2KhODi9gd6BpY6sLCkAXj5A/TiUuKK9DW2GTZ+Pfmr4775+6ZtnEbroNbF7k/OTi6zr7DJZMLi74/F3x/P1q2dgnFkwe4SubnkJSaebQW2d4sosp04gTUpCfLyHPuyKZvJ2/vs9GvnpmCzBAZiCQzCEnB2buKAAMevFn9/zGd/Nbm7l+MTRKS2UtAVEakhJYXgwvtLCsUFzy9thoySgnBxM0vYDBs7Tuwodv+r2151dKPID7/9GvcrcaBjad0lTO7uuEdE4B4RQbIjDF9G40L3MnJz7YPpEhPtA+sST5F3KhFr4in7ALukU+QlncaalETe6dOQm4uRmUleZiZ58fGlfv/FMXl5nVu0w8fn7Gufc/u8vO0LgXh6YvL0KuG1fXO89vDE7OW8Hzc3raAn4gIKuiIiF6CSQnG+0sJwSUG4W6Nu5Z5yrbi+wjbDxuHUwyUOBiyuVbhwMC5rEN3xnFPE2eJoFtWM8I4di3zG0YJ9iA0DW3o61qSks1OunbFPv3b67OuUFGypKVhTUrGmpmBLTbP/mpKKLS0NACMrC2tWlr1rRXUym+2h18O+cp49AHucDcxnX7t7nF1e2r3Qa3f7ynulvfbwKLDf49x7D3d7AM9/f7YGLV0t9YWCrohILVVaGC4pCJd3PuSS+grnd48orKTuEgWDcVlhuLQQXNIxi58fJ01pxLln0axlG6fPyu9CUbjVGMCwWok/8SdH4/fR2BxMsM3bMQdx/pZ85jjJKScIwBsfmxu27CyM7ByM7OxCr7Mxzm4FXztN4WazYWRmYs3MhOTk8vzfW70KBm9Hq7QHZg/Pc0tYF/7V2wuzp30Za5OXN2Zvb/uS1j4+9uWufc62ip/dZ/b1xWSxuPpJpZ5T0BURqaOKC8JV2Ve4oLL6DUPpYRgoMQSXdqy4wXUFzy+pb/EXf35Z9Jwehedm/hBbYOlzGZfGsNkwckoPw7bsbIysbE6nnuBUynEauAXgb/LGyM3ByM3FyMklLeMMKWmn8Dd74224n92fU+RXW24ORk6u/dqcXMdnGzk5jvPOffEFgnc1Mnl5Yfbzc3QHsfj62buE+Plh9vPF4n+277S/H2b/ACwBZ/tTBwTY+1MHBGDy8KjWGqVu06wLhWjWBRGRc8q7CAmU3S2hMnMNvzfsPQzDKPbYnEFzzi7bXJ7lsJc7tfaWNj1beaZvq0qVacnOV96p4gDiU49xOOlPmnqG08gSiC07ByMnGyMr62z4ziEpJYGTSUdoYAkgwPC0H8vMwsi2/5qeeoq01CR8rBaMzCyyUs/gmWtgyc7DlpHh2MjLq7Lvx+TldW6wYXBQgYGHQfbZOfLfhzTArYF96WuzwnGdp1kXRETkvJXVV7ig8x1EV1qLcHkH1zmWaS6gcBeKsrpZlKcbRlUprTsHlNySXVpXj+L2F7xXSS3US/cvZdq+wufc6nx843+xGTZMZ5e5NjCKn6c58SBNLaGE4ostLe1cl5C0NKzp6SQnxXPm1DECct3wyrKRcSaRzDOJeGbkYU7LwJqaii01FQwDIyuLvIQE8hISyv29mv397dPSNWhgn44utAFuDRviXmi5a83ZXPcp6IqISJWp7CC6skJweQfXldSiW7ALRVndLMrTDaOqlBaqS5olo7SuHsVNFTd1w1Sn76S4gYJl9Z8ufLzgPybK6koyul0xgXrju9iaFFyB8JciIdywWrGlpZGQcJD4Y/sIy/MhIMuE9fTpc4MPz5wh7/Rpsk+dJDfpFObTqWC1YktNJSc1FWJjS/8/wGLB1iAQ9/AIfBpH4h4ejlt4GOlBXpzwN2jcqisRzdpr4F4tpqArIiI1qqJzDZd2rLgADEVbL8szK0V5Q3dVKitUl3SspIBc3FRx1dXKXfjckuZpLitQLzu4zOk+Ba/58sRPTPul5FZmKNS9AzPTOj/FiKB+WE+dYv3ub1mx4zMC02wEp5u4yL0VDdIt9nmaT53CZLViPpGE9UQSqTt/c7qvL5AMnHZ3w6tJU2xhoWSE+hLYoi0hrTri0SwS98hmWPx8S/xexPUUdEVE5IJRWotwRQbXldaForTrynu8qlSmJbu0rh4lTRVXHa3cBZU2T3NZgbqw8gxQLDE4YzBl92z6jFkOYT787fel2HqaAPvsD/82xbJ8zHIArvx0KP5pNoLTIDTFIDTVxITQEWz69VtCUmyEpkBIKphz88iJjYXYWDyATFZztEC9lpAQPCIjcW/ejJzwEE6HetKofQ8ad+iF2dc5BFekT3Vl9pd1rD5S0BURkVqtuABcnr7FZZ1Tkf7J56MyLdklBeTipoqrjlbu4vroljRPc1mBurCyWq3L2xJdWvA2DINcs0FSgImkADjY2P48HXp1Zm7094C9367FatAgBRqlQINkg0ZnDMLPQPhpaJMRgHEmGWtSEplJSWT++isA3kAqsBdwCw/Hs0U0HtEt+N0/hQUp3xHXwCDVz8yUflNLXX67ovuhagcv1hWadaEQzbogIiK1RUmzYhS3vzwzaJR1TsHjQJFzywpaxZ1j76P7dZFryjP7RWnnABU+9tGIj7jlu1vKXDwF7DOC9PBrT25cHCf272LB98/T6LSNiCSDJqcgKL3YrxiAFG+Ia2SiQ+/hLMhYwV+hcLghZHuYSqyjtP1lPW9FBy+WFI6BCyYQlzevKegWoqArIiJSeZUJ1CVdU5ngXN4WzvIGv5IWTykYuDfHby4yBZ5vpsG/Wj9NyzNexO5az85t39Ek0d4ibC4medmAhBA4FGaiWa/BLMxdTWyYiVSfcwPhnuj1BLO3zi5ybWnT8L037D0i/SPLHexLCselzbThCppeTERERGpcZbqNVGaAYnnOqcyxyiyeUlyXjEwfC036XEqQbzjhw/tx25IfsRk23HMNmiZCVCLc6zuC3Zu/IfKEQXA6NE6CxkkG/P4zz569T2IAHIwwcbCxmR5h7vjmmEj3OJeUq3rwYkl9pEuaacPVLbtlUdAVERGRC9b59reuzLHC+893juiCx3PdbfzV2MztY6bQsfVo9u7vy30bp+GXZqXFCRN3eVxKy5NmEn/dguexU4Sm2AfLXbzXCj9P5z2TiWMhBvuamNjX1MzlI+8jzLsRJrO5SgYvlhaOC6quuaWrmrouFKKuCyIiIlIZFenjXFafaoD44wc5tmMDDQ4l4bYvlqydu8g9erTIfc0BAXh364pP9+5ktmtOQnQAkQ1blasbSEn7i+v/7PSZ1bhaYHmoj24lKeiKiIjIhSrv7AwPmTG/krljB5m7dmFkZjqf5O6Od5cu+FzUG9+LLsK7WzfM3t4VDtp1oY+ugm4hCroiIiJSWxi5uWTt3WcPvTt2kLF9e9HlkvODb+9e9uDbowdmL69y3b+smTZcRUG3khR0RUREpLYyDIPcI0fI2LyZjM2bSf9lc5Hga/LwwKdXT3z79cO3Xz8827XDZDa7qOLKUdCtJAVdERERqSuKBN9Nv5B3/LjTOZaQEHz798dv0CD8LumPJSjINcVWgIJuJSnoioiISF1lGAY5f/5J+voNpG/YQMbmzdgyMs6dYDbj3bWrPfQOGmhv7TWZSr6hiyjoVpKCroiIiNQXRk4Omb/+StqaNaStXkP2vn1Ox90aNcJv0ED8hgzBt29fzN7eLqrUmYJuJSnoioiISH2VGx9P2uo1pK1ZQ/rGjU4zOpg8PfHt14/Gs2ZiCQx0YZVaGU1EREREKsg9IoLgG8cRfOM4bNnZZGzZStqqVaStXEnusWNk/fYb5lrUEKgW3ULUoisiIiLizDAMsvftIy8hAb9Bg1xdjlp0RURERKRqmEwmvNq2hbZtXV1KhdSuSdNERERERMpJQVdERERE6iQFXRERERGpkxR0RURERKROUtAVERERkTpJQVdERERE6iQFXRERERGpkxR0RURERKROUtAVERERkTpJQVdERERE6iQFXRERERGpkxR0RURERKROUtAVERERkTpJQVdERERE6iQFXRERERGpkxR0RURERKROUtAVERERkTpJQVdERERE6iQFXRERERGpkxR0RURERKROUtAVERERkTpJQVdERERE6iQFXRERERGpkxR0RURERKROUtAVERERkTpJQVdERERE6iQFXRERERGpkxR0RURERKROUtAVERERkTpJQVdERERE6qQ6E3RjY2OZOHEi0dHReHt707JlS6ZMmUJOTo6rSxMRERERF3BzdQFV5Y8//sBms/H222/TqlUrdu/ezd133016ejpz5sxxdXkiIiIiUsNMhmEYri6iusyePZu33nqLP//8s9zXpKSkEBgYSHJyMgEBAdVYnYiIiIhURnnzWp1p0S1OcnIyISEhpZ6TnZ1Ndna2431KSkp1lyUiIiIiNaDO9NEt7ODBg8ybN49JkyaVet7MmTMJDAx0bJGRkTVUoYiIiIhUpws+6E6dOhWTyVTqtnXrVqdrjh07xvDhw7nhhhu46667Sr3/5MmTSU5OdmyHDx+uzscRERERkRpywffRTUxMJDExsdRzoqKi8PLyAuwhd8iQIVx88cUsXLgQs7liWb5G++hac2H5M9CkJ3QdV72fJSIiIlJH1Jk+uqGhoYSGhpbr3KNHjzJkyBB69uzJ+++/X+GQW+N+XQSb3waLJzRoBU17uroiERERkTrjAk+C5Xfs2DEGDx5MZGQkc+bM4eTJkyQkJJCQkODq0krW7RZoMwKs2fDJeEiJd3VFIiIiInXGBd+iW14rVqzgwIEDHDhwgKZNmzodu2B7Z5jNMPrf8O4VcPIPWHwz3P4tuHu5ujIRERGRWq/OtOjefvvtGIZR7HZB8wqAmxaBVxAc3QZfPQIXes0iIiIitUCdCbq1WkgLGPsBmCyw8xPYMM/VFYmIiIjUegq6F4oWg2H4TPvrH6fA/h9cWo6IiIhIbaegeyG56B7ocSsYNvj8Tji5z9UViYiIiNRaCroXEpMJrnwZmvWF7BT45CbIPOPqqkRERERqJQXdC42bB4z9EAKawqkD9pZdm9XVVYmIiIjUOgq6FyK/hnDTx+DmDQd/gh+ec3VFIiIiIrWOgu6FKqIrXPeW/fXGNyBmkWvrEREREallFHRdLD45kw0HE4lPzix6sON1MPAJ++uvHobDW2q2OBEREZFarM6sjFYbLd4Sx+Slu7AZYDbBzNGdGde7mfNJg5+G43tg7zf2ldPuWQUBjV1Sr4iIiEhtohZdF4lPznSEXACbAU8v3V20ZddshtFvQ6MOkHYcPrkZcotp/RURERERJwq6LnIoMd0RcvNZDYPYxIyiJ3v6w40fg3cwHNsOyx7WMsEiIiIiZVDQdZHoUF/MJud9FpOJqFCf4i8IiYax/7EvE7zrU1j/WvUXKSIiIlKLKei6SESgNzNHd8Zisqddi8nEjNGdiAj0Lvmi6IEw4kX76x+nwr4V1V+oiIiISC1lMgz9DLyglJQUAgMDSU5OJiAgoNo/Lz45k9jEDKJCfUoPufkMA75+FLYtBM8AuOtHaNi2ussUERERuWCUN6+pRdfFIgK96duyQflCLtiXCR4xG5r1sy8TvOhGyDxdvUWKiIiI1EIKurWRmweM+xACm0HSn/DZHWDNc3VVIiIiIhcUBd3ayjfUvkywuw/8+bOWCRYREREpREG3NgvvDNfNt7/e9C/Y8V/X1iMiIiJyAVHQre06XAODnrK//vpROLzZpeWIiIiIXCgUdOuCQX+HdqPAmmNfOS35qKsrEhEREXE5Bd26wGyG696GRh0h/QR8Ml7LBIuIiEi9p6BbV3j6wU2LwKcBxMfAlw9qmWARERGp1xR065Lg5vZlgs1usPtzWPeqqysSERERcRkF3bom6hIY8ZL99U/TYe93rq1HRERExEUUdOui3hOh10TAgCV3w4k/XF2RiIiISI1T0K2rRrwIUQMgJ9W+THBGkqsrEhEREalRCrp1lcUdbvgAgprB6UPwuZYJFhERkfpFQbcu820ANy4Cd1/4cxWs+IerKxIRERGpMQq6dV14Jxj9tv31L2/B9v+4th4RERGRGqKgWx+0vwoGP21//fXjELfJtfWIiIiI1AAF3fpi4BPQ4Rqw5cLiW+DMYVdXJCIiIlKtFHTrC7MZrn0LwjpD+kn7MsE5Ga6uSkRERKTaKOjWJx6+cNPH4BMKCTvhywe0TLCIiIjUWQq69U1QMxj3oX2Z4N+WwtqXXV2RiIiISLVQ0K2PmveDkWcD7srn4Y9vXFuPiIiISDVQ0K2vet4Ove+2v156Dxzf49JyRERERKqagm59Nnzm2WWC0+CTm7RMsIiIiNQpCrr1mcUdxv4HgprD6VhYchfYbK6uSkRERKRKKOjWdz4hcNMicPOCgz/BxjdcXZGIiIhIlVDQFQjrCMNn2V//NA2ObnNtPSIiIiJVQEFX7HreDh2uBVsefH4nZKW4uiIRERGR86KgK3YmE1z1GgQ2s/fX/fpRLSYhIiIitZqCrpzjHQTXvwcmC+xeAjs+cnVFIiIiIpWmoCvOInvDZc/aX3/7BJzc69p6RERERCpJQVeK6vcItBgCeZn2/rq5ma6uSERERKTCFHSlKLMZrnsbfBvC8d2w4h+urkhERESkwhR0pXj+YfawC7DlHdizzLX1iIiIiFSQgq6UrNVl0P8R++tlD8KZONfWIyIiIlIBCrpSukufhSa9ICsZltwN1jxXVyQiIiJSLgq6AkB8ciYbDiYSn1xo4JnFHa5/FzwD4PAmWD3LNQWKiIiIVJCCrrB4Sxz9Z61k/IJf6D9rJYu3FOqiEBwFV821v14zB/5cXdMlioiIiFSYgm49F5+cyeSlu7CdXQTNZsDTS3cXbdntNAZ63AYYsPQeSE+s8VpFREREKkJBt547lJjuCLn5rIZBbGJG0ZOHz4KG7SAtAb6YBDZbzRQpIiIiUglu5TmpR48eFbqpyWRi2bJlNGnSpFJFSc2JDvXFbMIp7FpMJqJCfYqe7OFjXyJ4waVw4AfY9Cb0e7DmihURERGpgHIF3ZiYGP7v//4PPz+/Ms81DINZs2aRnZ193sVJ9YsI9Gbm6M48vXQ3VsPAYjIxY3QnIgK9i78grCMMmwHfPA4/ToXm/aBJxf4hJCIiIlITTIZhGGWdZDabSUhIoFGjRuW6qb+/P7/++istWrQ47wJrWkpKCoGBgSQnJxMQEODqcmpMfHImsYkZRIX6lBxy8xkGfHor/L4MgqPh3jXgVX++KxEREXGt8ua1cvXRPXToEA0bNiz3h+/Zs4fmzZuX+/yqlp2dTbdu3TCZTMTExLisjtokItCbvi0blB1yAUwmuPp1CGwGpw/B14/Zw6+IiIjIBaRcQbd58+aYTKZy3zQyMhKLxVLpos7Xk08+SePGjV32+fWCd7B9fl2TBXZ/DjH/dXVFIiIiIk7K1Ue3sKysLHbu3MmJEyewFRp5f/XVV1dJYZX13XffsWLFCpYsWcJ3333n0lrqvMiL4NJn4Kfp8O0T0PQiaNjG1VWJiIiIAJUIut9//z233noriYlF51E1mUxYrdYqKawyjh8/zt13383//vc/fHyKmTWgGNnZ2U4D51JSUqqrvLqp/2P2BSQOrYbP74C7fgJ3L1dXJSIiIlLxeXQffPBBbrjhBuLj47HZbE6bK0OuYRjcfvvtTJo0iV69epX7upkzZxIYGOjYIiMjq7HKOshshtH/Bp9QOL4bfnjW1RWJiIiIAJUIuidOnODxxx8nLCysOuopYurUqZhMplK3rVu3Mm/ePFJSUpg8eXKF7j958mSSk5Md2+HDh6vpSeow/3C47m37683/ht+/dm09IiIiIpRzerGC7rzzTvr378/EiROrqyYniYmJxXaTKCgqKoobb7yRr776ymnQnNVqxWKxcPPNN/PBBx+U6/Pq6/RiVWLFP2DDPPAKgknrIEit4yIiIlL1ypvXKhx0MzIyuOGGG2jYsCGdO3fG3d3d6fjDDz9cuYrPU1xcnFP/2mPHjjFs2DA+//xzLr74Ypo2bVqu+yjonoe8HHhvGBzbDs36wm1fg6VS4x1FRERESlTevFbhFPLxxx+zfPlyvL29WbVqlVMLqslkclnQbdasmdP7/FXcWrZsWe6QK+fJzcO+RPDbAyFuI6yeBZf+w9VViYiISD1V4T66//jHP5g+fTrJycnExsZy6NAhx/bnn39WR41Sm4REw6hX7a/XzIE/V7m0HBEREam/Khx0c3JyGDduHGZzhS+tUVFRURiGQbdu3VxdSv3T+XrocStgwJK7IDXB1RWJiIhIPVThtHrbbbexePHi6qhF6pIRL0GjjpB+0h52rXmurkhERETqmQr30bVarbz00kssX76cLl26FBmM9sorr1RZcVKLuXvD2A/g34Mhdi2sftG+ipqIiIhIDalw0N21axfdu3cHYPfu3U7HCg5MEyG0NVz1GiyZCGtmQ7M+0OoyV1clIiIi9USFpxer6zS9WDX46hHYttC+etqkdRAQ4eqKREREpBYrb167sEeUSd0wfBaEdYaMRHvrrvrrioiISA0oV9AdPXq002IMZbn55ps5ceJEpYuSOia/v66HH/y1HlbNcHVFIiIiUg+Uq+uCxWJh3759NGzYsMwbGoZBZGQkMTExtGjRokqKrEnqulCNdi+Bz++0v75lCbS63LX1iIiISK1UpSujGYZBmzZtqqw4qac6jYHY9bD1XVh6D9y7FgKbuLoqERERqaPKFXR//vnnCt+4SRMFGCnGsBlwZAsk7LT3173ta7BUePIPERERkTJp1oVC1HWhBpw6CG8PgpxUuOQxuHyqqysSERGRWkSzLohLxSdnsuFgIvHJmUUPNmgJ18yzv173KuxbUbPFiYiISL2goCtVbvGWOPrPWsn4Bb/Qf9ZKFm+JK3pSx+ug993211/cC8lHarZIERERqfMUdKVKxSdnMnnpLmxnO8TYDHh66e7iW3aHvQARXSEzyT4bgzW3ZosVERGROk1BV6rUocR0R8jNZzUMYhMzip7s5gk3LATPADj8C6x8vkZqFBERkfpBQVeqVHSoL2aT8z6LyURUqE/xF4S0gGvesL9e/xrs/b56CxQREZF6o8JB9/jx40yYMIHGjRvj5uaGxWJx2qR+iwj0ZubozlhM9rRrMZmYMboTEYHeJV/U4Rq46F776/9NgjOHa6BSERERqesqPIHp7bffTlxcHM8++ywRERGYTKayL5J6ZVzvZgxs05DYxAyiQn1KD7n5hj4PRzbDsR3w+R1wx3dgca/+YkVERKTOqvA8uv7+/qxdu5Zu3bpVU0mupXl0Xeh0LMwfCNnJ9hbeK19ydUUiIiJyAaq2eXQjIyPRGhNSLYKj4Lq37K83vw07PnJpOSIiIlK7VTjozp07l6eeeorY2NhqKEfqu/iISznc5WH7m68fgyNbXVuQiIiI1FoV7roQHBxMRkYGeXl5+Pj44O7u3I8yKSmpSgusaeq64DqLt8QxeekuDMPGfPe5DLNsBf8IuGcV+Ie7ujwRERG5QJQ3r1V4MNqrr76qAWhS5ZwXmjDzeO59fGF6jjapR2HxBLj9a/u8uyIiIiLlVKlZF0SqWuGFJtLx5p7cx/nRfxpuRzbDt3+Dq14H/SNLREREyqnCfXRvvvlmFixYwL59+6qjHqmnilto4jCNSRk5H0xm2P4f2PKOa4oTERGRWqnCQdfPz4+XX36Zdu3a0bhxY2666Sbmz5/PH3/8UR31ST1R0kITIV1HwmVT7Cd9/xTErne6Lj45kw0HE4lPzqzpkkVEROQCV+HBaPkSEhJYtWoVq1atYvXq1ezbt49GjRoRHx9f1TXWKA1Gc6345MyiC00YBiy5C3Z/Dj6h9sFpQZGOwWs2A8wmmDm6M+N6N3Np/SIiIlL9qm0e3Xz+/v4EBwcTHBxMUFAQbm5uhIdrZLycn4hAb/q2bOC8mprJBFfPg/DOkJEIn4wnPvFUgcFrYDPg6aW71bIrIiIiDhUOun//+9/p06cPoaGh/OMf/yAnJ4fJkydz/PhxduzYUR01ioCHD9z4Mfg0gISduH39KLZCP4ywGgaxiRkuKlBEREQuNBWedWH27Nk0bNiQKVOmcM0119C+ffvqqEukqKBmcMMH8J9raBi7jHvc/Ph33ijHYYvJRFSoj9Ml8cmZHEpMJzrU17mVWEREROq8CgfdHTt2sHr1alatWsXLL7+MxWJh0KBBDB48mMGDByv4SvWKHgDDZ8F3T/CU2yfsszVjla2LY/BawTCrPrwiIiL1W6UHo+X79ddfmTt3Lh999BE2mw2r1VpVtbmEBqPVAoYByx6EHR9h8wzk1+FfEN6ig1PIjU/OpP+slU5z81pMJtY9NUQtuyIiIrVcta2MBvZW3fwZF9auXUtKSgrdunVjyJAhlS5YpNxMJhj5Cpzci/nIFrqvuwfa/gCcC7CFF6CAc314CwdidW0QERGpmyocdIODg0lLS6Nr164MHjyYu+++m4EDB6r1U2qWmyeM+wjeuRxOHYBFN8KtX4K7PazmL0BRuEW3YB9edW0QERGp2yrcdeHrr7+u08FWXRdqmRO/w7vDIDsZ2l8NNywEswWwB9mnl+7GahiOPrz5QVZdG0RERGqvauu6MGrUuVHuR44cwWQy0aRJk8pVKXK+GrWHG/8LH42G35fBin/A8JkAjOvdjIFtGhZdgILyd20QERGR2qvC8+jabDamT59OYGAgzZs3p1mzZgQFBfH8889js9mqo0aR0kUPgGvfsr/e9CZsfNNxqNgFKDjXtaGg4qYnExERkdqrwkH3mWee4Y033mDWrFns2LGD7du3M2PGDObNm8ezzz5bHTWKlK3z9XD5NPvr5U/Db/8r9fSIQG9mju6MxWRPu8VNTyYiIiK1W4X76DZu3Jj58+dz9dVXO+3/8ssvuf/++zl69GiVFljT1Ee3FjMM+PZvsOUdsHjaB6c171vqJfHJmcV2bcg/phkZRERELjzV1kc3KSmJdu3aFdnfrl07kpKSKno7kapjMsGIlyAlHvZ+A5/cBBN/gNDWJV4SEehdbIjVjAwiIiK1X4W7LnTt2pU33nijyP433niDrl27VklRIpVmtsCYd6BJL8g8bR+klnq8QreIT850hFywT1H29NLdxCdnVkPBIiIiUl0q3KL70ksvMXLkSH788Uf69u2LyWRiw4YNHD58mG+//bY6ahSpGA8fGL/YPsfu6UPw8Vi4/Rvw9CvX5ZqRQUREpG6ocIvuoEGD2LdvH9dddx1nzpwhKSmJ0aNHs3fvXgYMGFAdNYpUnG8o3LIEfBpAfAwsvhlys8p1qWZkEBERqRsqPBitrtNgtDrmyFb44GrITYc2w2Hsh+DmUeZlZS02oUFqIiIirlPevFauoLtz585yf3CXLl3Kfe6FSEG3Djq0Fv57PeRlQYdrYcy7YCm7105xMzJokJqIiIjrVWnQNZvNmEwmDMPAZDr3M938Swvus1qt51O3yyno1lH7f4RFN4ItF7reBNe8CeaK9dzRssEiIiIXhvLmtXL9l/7QoUP8+eefHDp0iCVLlhAdHc2bb75JTEwMMTExvPnmm7Rs2ZIlS5ZU2QOIVKnWl8MN74PJAr8ugm//zz7vbgWUNkhNRERELjzlmnWhefPmjtc33HADr7/+OldeeaVjX5cuXYiMjOTZZ5/l2muvrfIiRapE+6tg9L9hyV2w9T1w94Gh/7TPv1sO+YPUCrfoapCaiIjIhanCsy7s2rWL6OjoIvujo6PZs2dPlRQlUm06Xw9Xv25/vfENWDWz3JeWtmxwfHImGw4maq5dERGRC0iFZ13o0aMH7du3591338XLywuA7Oxs7rzzTn7//Xe2b99eLYXWFPXRrSd+eRu+e9L++vKpcMlj5b608CA1DVATERGpWdW2BPD8+fO56qqriIyMdKyE9uuvv2Iymfj6668rX7FITbr4XsjNgB+n2jcod9gtuGxwSauoDWzTUAPUREREXKzCQfeiiy7i0KFDfPTRR/zxxx8YhsG4ceMYP348vr6+1VGjSPW45DHIy7Z3X/hxKuRmwuDJ5e6zC1pFTURE5EJW4aAL4OPjwz333FPVtYjUvMFPgcUDfpoGq1+0h90rpmuAmoiISB1Q4cFojRs3Zvz48fz73/9m37591VGTSM0a8DgMf9H+esPr8O0TYLOV69LSBqiJiIiIa1V4MNqiRYtYvXo1q1atYt++fYSFhTFo0CAGDx7MoEGDaN++fXXVWiM0GK0e27YQvnoUMKD7LXDV62C2lOvS4lZRExERkepRpSujleT48eP8/PPPfP311yxevBibzaaV0aR2+3Ux/G8SGDbodD1cNx8s7q6uSkRERAqotlkXANLS0li3bp2jZXfHjh107tyZQYMGVbpgkQtC13Hg5glLJsLuzyEvC8a8A+5qpRUREaltKtyie/HFF7Nz5046derE4MGDGThwIAMGDCAoKKiaSqxZatEVAPYth8UTwJoNzfrCjR+DT4irqxIRERHKn9cqPBht//79+Pj40KJFC1q0aEGrVq0uqJD7zTffcPHFF+Pt7U1oaCijR492dUlSG7UZBhOWgmcgxG2E94bDmcPndUutniYiIlKzKhx0k5KS+Pnnn+nfvz8//vgjgwYNIjw8nHHjxjF//vzqqLHclixZwoQJE7jjjjv49ddfWb9+PePHj3dpTVKLRV0Cd34PAU0gcS+8ewUk7K7UrRZviaP/rJWMX/AL/WetZPGWuCouVkRERAo7r8FoANu2beONN97go48+culgtLy8PKKiopg2bRoTJ04s93XZ2dlkZ2c73qekpBAZGamuC3JO8hH46Ho4+Tt4BsC4j6BF+fujxydn0n/WyiJz7a57aohmaBAREamEauu6sGPHDl599VWuueYaQkJC6NOnD7t27eKRRx5h2bJl51X0+di+fTtHjx7FbDbTvXt3IiIiGDFiBL/99lup182cOZPAwEDHFhkZWUMVS60R2BTu/A6a94fsFPhoDOz6vNyXl7Z6moiIiFSfCrfourm50b17d8fcuQMHDrwgWj4/+eQTbrrpJpo1a8Yrr7xCVFQUL7/8MitWrGDfvn2EhBQ/kEgtulJuuVnwxT2w50v7+0FPwaC/g7n0fy+qRVdERKRqVVuLblJSElu2bGHOnDmMGjWq2sPg1KlTMZlMpW5bt27FdnYlq2eeeYYxY8bQs2dP3n//fUwmE5999lmJ9/f09CQgIMBpEymWuxdc/z70fdD+fvUsWHIn5JTeMqvV00RERFyjwvPo1nQQfPDBB7nxxhtLPScqKorU1FQAOnTo4Njv6elJixYtiIvTwB+pImYLDHsBGraDrx+D376A07Fw4yIIiCjxsnG9mzGwTUOtniYiIlKDKhx0rVYrr776Kp9++ilxcXHk5OQ4HU9KSqqy4gBCQ0MJDQ0t87yePXvi6enJ3r17ueSSSwDIzc0lNjaW5s2bV2lNIvSYACEtYPEtcGwHLBgCNy2Cxt1LvCQi0LvUgBufnMmhxHSiQ30VhEVERKpAhbsuTJs2jVdeeYWxY8eSnJzM448/zujRozGbzUydOrUaSiyfgIAAJk2axJQpU1ixYgV79+7lvvvuA+CGG25wWV1Sh0X1h7tX2lt3U+Ptc+1u/7BSt9L0YyIiIlWvwoPRWrZsyeuvv87IkSPx9/cnJibGsW/Tpk18/PHH1VVrmXJzc5k8eTIffvghmZmZXHzxxcydO5eOHTuW+x5aGU0qLCsFlt4N+763v+9+C1w5p9zLBmuwmoiISMVU22C0hIQEOnfuDICfnx/JyckAjBo1im+++aaS5VYNd3d35syZw/Hjx0lJSeGHH36oUMgVqRSvAHsf3UufBZMZdnxkX1wi6VC5Ltf0YyIiItWjwkG3adOmxMfHA9CqVStWrFgBwJYtW/D09Kza6kRqC7MZBv4NJnwBPqGQsAveHgR/fFvmpdGhvphNzvssJhNRoT7VVKyIiEj9UOGge9111/HTTz8B8Mgjj/Dss8/SunVrbr31Vu68884qL1CkVmkxGCathciLITsZPrkJfpgC1rwSL9H0YyIiItXjvJcA/uWXX1i/fj2tWrXi6quvrqq6XEZ9dKVKWHPhh+dg05v291EDYMy74B9W4iXxyZmafkxERKQcypvXKhR0c3Nzueeee3j22Wdp0aJFlRR6oVHQlSr12xfw5YOQkwZ+4XDD+9C8X4VuoWnHREREnFXLYDR3d3e++OKL8y5OpN7oeB3cswoatoe0BFg4Cn6eWWpXhoI07ZiIiEjlVaqP7v/+979qKEWkjgptDXf/BF1uBMNqXzr4vWFw6mCpl8UnZzJ56S7HjAw2A55eupv45MwaKFpERKT2q/DKaK1ateL5559nw4YN9OzZE19fX6fjDz/8cJUVJ1JnePjC6Leh9RXwzeNwdCvMHwDDZ0KPW8FkKnJJadOOqQuDiIhI2So8GC06Orrkm5lM/Pnnn+ddlCupj65Uu+Qj8MUkiF1rf992JFz9Ovg6L3WthSRERESKVy2D0eoDBV2pETYbbHwDfpoOtlzwC4Nr3oTWlzudtnhLHE8v3Y3VMBzTjo3r3cxFRYuIiFwYFHQrSUFXalT8TvvywSf/sL/vNRGumAae/udOKWHaMc3GICIi9VWVBt3HH3+83B/8yiuvlPvcC5GCrtS43Ez4cSr8Mt/+PjASrnoNWl1W4iWLt8Q5BqqZTTBzdGe19IqISL1R3rxWrsFoO3bscHq/bds2rFYrbdu2BWDfvn1YLBZ69ux5HiWL1FPu3jDiRWh7JSx7EM7EwUejofsEGPpP8A5yOr2k2RgGtmmoll0REZECyhV0f/75Z8frV155BX9/fz744AOCg4MBOH36NHfccQcDBgyonipF6oMWg+C+jfZ+u5vfhh0fwoGfYNSr0Ha44zTNxiAiIlI+Fe6j26RJE1asWEHHjh2d9u/evZuhQ4dy7NixKi2wpqnrglwQ/tpgX1Et6excu13GwbCZ4NtAszGIiEi9Vy0ro+Xf+Pjx40X2nzhxgtTU1IreTkSK07wfTFoH/R4Ckxl2LoY3ekHMx0QEeDFzdGcsZ+fezZ+NISLQm/jkTDYcTNSiEiIiIlSiRffWW29l9erVvPzyy/Tp0weATZs28cQTTzBw4EA++OCDaim0pqhFVy44R7bCsofhxG/291EDYOQrxHtEOs3GoAFqIiJSX1Tb9GIZGRn87W9/47333iM3NxcANzc3Jk6cyOzZs4uslFbbKOjKBcmaC5vehJ9nQl4mWDzgksfgksfB3UvdGUREpF6p9nl009PTOXjwIIZh0KpVq1ofcPMp6MoF7fRf8O3fYP8K+/uQlnDlS2wwdWP8gl+KnL7o7j70bdmghosUERGpXlU6vVhxfH196dKlS2UvF5HKCG4O4z+FPV/Cd3+3D1b7aAzdW42kqWkYR4xzywhbTCaiQn1cWKyIiIhrVXgwmoi4mMkEHa+FB7dAnwfAZMH7wDes8n6SB92+xINcpwFqIiIi9ZWWAC5EXRek1jn+G3z7BPy1HoBM/ygyL51OSLer7aFYRESkjqm26cVE5AIT1hFu/wZGvwN+4XinxhLy5a321dVO/O7q6kRERFxGQVekLjCZoMsN9u4M/R+1z8pwcCW81Q+++T9IP+V0uubbFRGR+kBdFwpR1wWpE5IOwQ/Pwe/L7O89A2Hw36H33SzekaD5dkVEpFar9unF6ioFXalTDq2F5ZMhYRcAeUEtmHTyOn609gDOraym+XZFRKQ2UR9dEYHoAXDParh6Hvg2wu3Mn7zj/jIfus+knSkOAKthEJuY4eJCRUREqp6CrkhdZ7ZAj1vhoW2k9X6IbMONAZbdfOsxmZfd36Kp6ZTm2xURkTpJQVekvvAKwG/kP/nh0mV8Y+2D2WQwxrKW1d7/R8QvMyDztKsrFBERqVLqo1uI+uhKfRCfnMnJPzbSdtdsPI9ssO/0CoIB/wcX3QPuXi6tT0REpDQajFZJCrpSrxgG7P/BPkPDSfucu3n+TTjU5TH8et1ERLCfiwsUEREpSoPRRKRsJhO0GQr3rYdr/kWGVyPcUo/Sev3fSHm1D2u/WmgPwyIiIrWQgq6IgNlCfIsx9Ep+kRdzbyTZ8KGt+TADtj1CztuXwp+rXV2hiIhIhSnoiggAhxLTyTA8ect6NQOy5/JG3jVkGJ54JGyH/1wNH1wNR7a6ukwREZFyU9AVEQCiQ30x29eQIAU/5uSNY0jOXNK7TQSzOxxaDe9cBovGOxagEBERuZAp6IoIABGB3swc3RmL6dyKaY+PvgTfa1+Bh7ZBt5sxTGbY+w3MvwQ+vRVO/O7iqkVEREqmWRcK0awLUt/FJ2cSm5hBVKiP07LAi7fEseCL73nEsoSR5l8wmwzABJ2vh0F/h9DWritaRETqFU0vVkkKuiJFxSdn0n/WSmxn/7Zoa4rjMbelDLdstu8wmaHLOBj4BDRo6bpCRUSkXtD0YiJSZQ4lpjtCLsBeoxmTch/l15FfQZsRYNjg10XwRi9Yei8kHnBdsSIiImcp6IpImQoOVMtnMZlo1KY3jP+ExJu+J6nJpfbAu/MT+FdvWHoPJO53TcEiIiIo6IpIORQ3UG3G6E5EBHqzeEscFy1MosfBu7gm558cbTT4bOBdDG/0hs/ugITdrn0AERGpl9RHtxD10RUpWeGBaoX77oI9BG+6I4SGW+fCvu/OHWh7JQz4GzTtWeN1i4hI3aI+uiJS5SICvenbsoFjNobCfXcBrIbBAUtrGP8JTFoHHa8DTLD3W3jnUvjPtRC7vsZrFxGR+kdBV0QqraS+u1GhPsQnZ7IhPYL4oW/BA5uh63gwWeDPn2HhlfDecDjwI+iHSiIiUk3UdaEQdV0QqZjFW+J4eulurIbh6LsLMHnpLmwGmE0wc3RnxvVuBqdjYf1rsOMjsObYbxDRzT4tWdsrwax/e4uISNk0j24lKeiKVFzBvrtAsf121z015NwCFCnHYMMbsPU9yMu072vYHvo/Yl+AwuJew08gIiK1ifroikiNKdh3t6R+u7GJGed2BDSG4TPgsd0w4P/AMwBO/g7/mwSvdYONb0J2Wo0+g4iI1D0KuiJSpUrrt1uEbyhc9hw8ugsumwK+jSDlCCyfDK92hJUvQHpizRQuIiJ1joKuiFSp0ubcLZF3EAx43B54R82FkBaQdQbWvASvdoJv/mbv3ysiIlIB6qNbiProilSNwnPuVojNCr8vg3VzIT7Gvs9ksU9V1u9BaNy9qssVEZFaRIPRKklBV6TmxSdncigxnehQX+dQbBhwaA2snwsHV57bHzUA+j0Era7QTA0iIvWQgm4lKeiK1KzFW+KKn4qssPhf7TM1/LYUbHn2faFtoO+D0GUcuHvVbOEiIuIyCrqVpKArUnNKWkLYaSqywpKPwC/zYdsHkJ1i3+fbEHrfDb3vAt8G1V+4iIi4lKYXE5ELXrmmIisssCkM/Sc89hsMmwGBkZB+ElbNgFc7wLKH4MTv1Vu4iIjUCgq6IuIyFZqKrDCvAOj7ADy8A8a8a19hLS8Ltv8H3uwD/7kW9q0Am606ShcRkVpAQVdEXKYiU5HFJ2ey4WAi8cmZzgcs7vbV1O5ZBXcuhw7XgMkMf/4MH98A/7oItryjBShEROoh9dEtRH10RWpeWVORlXvAWr7Tf8Hmf9tbd/P78XoGQveb7f14G7SspicREZGaUC/76O7bt49rrrmG0NBQAgIC6N+/Pz///LOryxKRMhRcQriw+ORMR8gFsBnw9NLdRVt2CwpuDsNegMf3wIjZENISspNh05swrwd8dL26NYiI1AN1KuiOHDmSvLw8Vq5cybZt2+jWrRujRo0iISHB1aWJSCVVasBaPk9/uPgeeHAr3LIEWg8DTHDgB3u3hnk9YOO/IPNMdZQuIiIuVme6LiQmJtKwYUPWrFnDgAEDAEhNTSUgIIAff/yRyy67rFz3UdcFkQtLeacgK3HRicKS/oQt78KODyEr2b7P3Qe6jIWL7oGwjtX0JCIiUlXqXdeFBg0a0L59e/7zn/+Qnp5OXl4eb7/9NmFhYfTs2bPE67Kzs0lJSXHaROTCUZ4Ba4u3xNF/1krGL/iF/rNWsnhLXMk3DGlxtlvD7zBqLjTqALkZsG0hvNUP3h8Ju5dCXk71PpiIiFS7OtOiC3D06FGuueYatm/fjtlsJiwsjG+++YZu3bqVeM3UqVOZNm1akf1q0RW5sJQ0YK1Si04UZBjw13r74LXfvwbDat/v2xC6T4Cet0FwVNU+jIiInJc606I7depUTCZTqdvWrVsxDIP777+fRo0asXbtWjZv3sw111zDqFGjiI+PL/H+kydPJjk52bEdPny4Bp9ORMqrpAFrZfXhLXFasnwmE0RdAmP/A4/uhIFPgF+4fRGKda/Aa93gozHwxzdgzauGJxMRkepywbfoJiYmkpiYWOo5UVFRrF+/nqFDh3L69GmnZN+6dWsmTpzIU089Va7PK8+/EGw2Gzk5+rGm1Ax3d3csFoury7hgldaiu2bfyYpNS5bPmgt7v4Ot79nn483n39jewtvjVghoXPUPIyIi5VLeFl23GqypUkJDQwkNDS3zvIwMe+uN2ezcSG02m7FV4RRCOTk5HDp0qErvKVKWoKAgwsPDMZlMZZ9cz+T34X166W6shuHowwsUOy3ZwDYNy+7SYHGHDlfbt1MH7f13Y/4Lqcdg1UxY/RK0HmoPvK2HguWC/6tURKReqjN/O/ft25fg4GBuu+02nnvuOby9vVmwYAGHDh1i5MiRVfIZhmEQHx+PxWIhMjKySKgWqWqGYZCRkcGJEycAiIiIcHFFF6ZxvZsxsE1Dpz68Gw4mltiloVx9d/M1aAlDn4dL/wG/f2Vv5f1rPez7zr75hUO38dD9Fi1EISJygakzQTc0NJTvv/+eZ555hksvvZTc3Fw6duzIl19+SdeuXavkM/Ly8sjIyKBx48b4+PhUyT1FyuLtbQ9lJ06coFGjRurGUIKIQG+nABsd6ovZRJEuDVGhpf/ZLXGaMjdP+1LDna+Hk3vt05PFLIK0BHtf3nWvQNQAeytv+6vAvQJhWkREqsUF30e3ppXW5yMrK4tDhw4RFRXlCB8iNSEzM5PY2Fiio6Px8vJydTm1xuItcUW6NJTWR7fCSw3n5dhbdbd/CAd+BM7+deoVCJ3GQLeboUlP+4A3ERGpMuXto6ugW0h5gq7ChtQ0/d6rvJKmJSvuvPJOU1Zsq++ZwxDzMez4CJILzOMb2tbetaHLOAhQ1xMRkapQZwajiYicj8JdGkpS2jRlhRenKLbVNygSBv/dPj1Z7Bp76N2zDBL3wo9T4Kdp0PIy6HYTtL1SXRtERGqAgq6ICOXr0xufnFn2TA5mM7QYbN+unAN7/mcPvXEb4cAP9s3DHzpcA13HQfNL7NeIiEiV09+uwqpVqzCZTJw5c8bVpdSo+vrcUrzyLDVc1uIUUGiBCq8A++C0O7+Hh7bDgL9BYDPISYWYj+CDq2BuJ/hhChz/rUaeU0SkPlHQlWoRFRXF3Llzi+yfO3cuUVFRjvclrXzXrl27Eu+dlJTEQw89RNu2bfHx8aFZs2Y8/PDDJCcnF6mh8H3Lu3BISUaOHMm///1vAO6++26mT5/udPy3335jzJgxjs8u7juQC9e43s1Y99QQFt3dh3VPDSkyEC2/1beggq2+i7fE0X/WSsYv+IX+s1ayeEuBvroNWsJlz8Ijv8Id30GP28AzEFKOwvq58FY/eLMvrJkDp2Or90FFROoJBV0XKXNZ0nqkY8eOxMfHO23r1q0r8fxjx45x7Ngx5syZw65du1i4cCHff/89EydOLHLu9OnTne77j3/8o9J1GobBpk2b6N+/PwDr1q1zvM6XkZFBixYtmDVrFuHh4ZX+LHGdkpYazj9WUqtvSd0aCv4Zj0/OZMOhJOKDusPVr8Pf9tmXHm47EiwecGIPrHweXusK71wOm96ClGM18twiInWRgq4LlNrqUw2ys7N5+OGHadSoEV5eXlxyySVs2bKlyHnr16+na9eueHl5cfHFF7Nr1y7Hsb/++ourrrqK4OBgfH196dixI99++22V1Ofm5kZ4eLjTVtpqeJ06dWLJkiVcddVVtGzZkksvvZQXXniBr776iry8PKdz/f39ne7r5+dXoecuaO/evdhsNjp06MCpU6c4cOAAF198sdM5vXv3Zvbs2dx44414enpW4tuQC11Jrb5ldWso9s+9u5e9r+5NH9tD79XzIHoQmMxwZAt8/xS80h7eHWYPvclHa/pxRURqNQXdGlaeVp+q9uSTT7JkyRI++OADtm/fTqtWrRg2bBhJSUlO5z3xxBPMmTOHLVu20KhRI66++mpyc3MBeOCBB8jOzmbNmjXs2rWLF198sdjQ6Cr504u4uTmPr3zxxRdp0KAB3bp144UXXiAnJ6fItaU9N8CoUaMICgqiV69epKSkEBwcTFRUFFarlaZNmxIUFFTdjycXmOJafUvr1lCuP/fewfb+vLctg8d/h+GzILKP/djhTfbQ+2oHeHcobHxToVdEpBwUdGtYeQazVKX09HTeeustZs+ezYgRI+jQoQMLFizA29ubd9991+ncKVOmcMUVV9C5c2c++OADjh8/zhdffAFAXFwc/fv3p3PnzrRo0YJRo0YxcODAKqlx165d+Pn5OW133XVXua8/deoUzz//PPfee6/T/kceeYRPPvmEn3/+mQcffJC5c+dy//33F7m+tOcGeOedd4iJieGSSy7h2WefJSYmhuuuu44HH3yQmJgYYmJiKv3sUneU1q2hrD/3Rboy+YdDn/tg4vKzofdFaNYXMMHhX2D5ZHvofecK2PAGJP1Zg08qIlJ7aHqxGlbZZUkr6+DBg+Tm5jr1JXV3d+eiiy7i999/dzq3b9++jtchISG0bdvWcc7DDz/Mfffdx4oVK7j88ssZM2YMXbp0qZIa27Zty7Jly5z2+fv7AzBjxgxmzJjh2L9nzx6aNTs3QCglJYWRI0fSoUMHpkyZ4nSPxx57zPG6S5cuBAcHc/311ztaefOV9twA4eHh5ObmsmnTJubNm0dUVBQbN27k/fffdxpYJzKudzMGtmlYZIGK0v7cl7kaW0Bj6DPJvqXEw+/L4Lf/2acrO7LZvq14Bhp1sM/P224kNO6u1dhERFCLbo0rzxRGVSl/4TtTof/oGYZRZF9x8s+56667+PPPP5kwYQK7du2iV69ezJs3r8TrAgICisyCAHDmzBkCAwOd9nl4eNCqVSunLSwsDIBJkyY5Wk1jYmJo3Lix47rU1FSGDx+On58fX3zxBe7u7qU+S58+9h8DHzhwoNzPPWPGDPz8/AgKCiI5OZnu3bvj5+fHgQMHGDZsGH5+fqxdu7bM+0n9UVy3hpL+3AMldmkodsBqQARcfC/c+Z29pXfE7LN9ei32gWxr58CCIfBKB/j6cTjwk32ZYhGRekotui5QUqtPdWjVqhUeHh6sW7eO8ePHA5Cbm8vWrVt59NFHnc7dtGmTo7X09OnT7Nu3z2mar8jISCZNmsSkSZOYPHkyCxYs4KGHHir2c9u1a1fsgLctW7bQtm3bctcfEhJCSEhIkf0pKSkMGzYMT09Pli1bVq5lcXfs2AFARITzMqylPfekSZMYO3Ysb775JkeOHGHGjBl89tlnrFixggULFgDQpEmTcj+P1F/F/bnfcDCx2C4N76+L5Z11f5bcygtnQ+899i3zNOz/Af742h5uU4/B1nftm4efffGK1ldA66H2FmIRkXpCQddFyrss6fny9fXlvvvu44knniAkJIRmzZrx0ksvkZGRUWQ6runTp9OgQQPCwsJ45plnCA0N5dprrwXg0UcfZcSIEbRp04bTp0+zcuVK2rdvX+LnPv744/Tv35/p06dz/fXXA7BkyRK+//57NmzY4HRuXl4eCQkJTvtMJpOjVbew1NRUhg4dSkZGBh999BEpKSmkpKQA0LBhQywWCxs3bmTTpk0MGTKEwMBAtmzZwmOPPcbVV1/t1PWhrOfOD9p79uxh3LhxtGrViv379zN06FBatWpVpLacnBz27NnjeH306FFiYmLw8/Mr9nypXwr/uS+uS4MZHCEXSlh97az45EwOJaYTHepLRJex0GUs5GbBoTWw9xvY+x2kHbcH4D++tl8U1vlc6G3aCyyl/yRERKQ2U9CtB2bNmoXNZmPChAmkpqbSq1cvli9fTnBwcJHzHnnkEfbv30/Xrl1ZtmwZHh4eAFitVh544AGOHDlCQEAAw4cP59VXXy3xM/v06cPy5cuZPn26Y9GEjh07snz58iJTcv32229FWlk9PT3Jysoq9t7btm3jl19+ASgSHg8dOkRUVBSenp4sXryYadOmkZ2dTfPmzbn77rt58skni/1+SnpusAfx9evX869//QuA1atXFztnL9jn+O3evbvj/Zw5c5gzZw6DBg1i1apVxV4j9Vd+l4anl+7GahhYTCYmXhLFv9cecjovf+BawaBbYt9edy9oMxTaDCV+wAxO7NtMi9Mb8D/8MxzZCsd32bd1r9iXIo4eCC2HQMtLIaSF+vaKSJ1iMvI7cQpg/5F4YGCgY7qqgrKysjh06BDR0dHl+lG5SFXR7726LT4509GlAaD/rJVFBq6te2qII+jGJ2eWeU6xQbiDLxz8icw93+F26Gfcs087FxLUDFoMsYffqAHgX/xPVUREXK20vFaQWnRFRFyscJeGwq28hQesljZdWWmrtA1sM4Q12X2Y/KsvhjGazuZY/tn5JF2yt0PcJjgTB9s/sG8AoW1Jb9KXI4G9CGo3kLDGzav7qxARqVIKuiIiF5iyBqyWNU1hSUF4W+zpAgHYzE5bC67b2ZJ1T01m/Z6/+Pbrz+lr+o2+5j10NP+FKXEvvol7actCWANpPk3xa3UJRF4EzfpAw3ZgtlTrdyEicj4UdEVELkClDVgtrm9vwVbfkoIwhfbBuQD85LKD2IzurMTexzyYNC62/M7Fpj30Mf9OW9Nh/DKOwM5P7BuAZwA07Q2RF0Ozi6FJT+Kz3M4NkKuBAbciIqVR0BURqYVKa/UtKQj3bB5c7gB8Gj++t/bme3oD4E8G3cwHmNErnci0XfaBbdkpcPAn+wbYMHPK1oxYWwu+MaIZPPhyrhh8qX2AXAFOs0WUNpOEgrKInCcFXRGRWqq0Vt+SgnB5A7AZnAJwKj5sMLridtkQCPQGa559kYrDv8DhX8j7ayNuKUfoZI6lkznWftH6dzE2WDA1ag8RXSGiKz+dCeeRVXmkGV5F5ggua5W4ioRgBWYRAc26UIRmXZALkX7vSVUqOMtDwVkaCgdgoMi+IgtXnLXhYCKPLfiO7uYDdDb/SSdTLJ3Mh2hgSi1yrs0wEWc0Yr/RlP1GU24aNZS80HYMePcwWca5qf0KziRR5lLJBZT33LLCsMKyyIWrvLMuKOgWoqArFyL93pOaUFwALm5fSdcWnfIMNjzQjrC0vRD/K0kHt5B9eAcRpqRi72E1TMQa4RwwmnDIiOCQEc6tIy8ltHkH+r6xG5thKnBv5+nUSq+j6LllheHSjpcUgBWMRWqOphcTEZEKKa4rRHlXcSypX3BY02ZAS2h3Jdk97CE02EimjfkIbUxHaGs+wvWRabid2osl+wwtTfG0JP7cjVfYl9re6eHFX0YYcUYjjhqhHDVCSdl+hoi2HSAwEnxCwGQqc+o1oJTp1xqWMT1bQ9bsO1lsAK5MMC5IIVmkeijoiohIlShrWrSCYXijLZDNpk7MuLYTHr2bgWHw5brtfP7dj7Q0HaGFOYErwtOJyDuKcSYOP7LoaPqLjvx17oarP4TVZ1+7+0BgU3r4NmGmOxyxNSTBCOEkgZwimBbencEWBGZLmWG4fNOznQvA7cL9KxyMCzqfvslqXRYpnYKulCk2Npbo6Gh27NhBt27dXF1OjRs8eDDdunVzLGUsIiUrqwW4xDBsMnHNgJ5c1KVDkWOmvBy+XbuBL35cS2NOEmlKZFjTHCItSXDmMKSfgNwMSNyHV+I+brIAhaf3/fdTYDKDTyi9fRrygYeFRCOQk0YgZww/kvGn7ZksiA2ntcmXRqZkkgxf8s7+Z7K06dm2xJ6uUDDObzmGsluXSwvBJR0rab/Cr9RHCrp1nKmMdetvu+02Pvjgg1LPycvLIz4+ntDQ0KosjYULF/Loo49y5syZIseCgoKYO3cut99+O1DycyxatIgbb7yx2GNLly7lrbfeIiYmhuzsbDp27MjUqVMZNmyYUw133HFHkWszMzMr3Rd2y5YtXHPNNRw7doxjx47RsmVLkpOT8fA4N8jmhRde4JtvviEmJgYPD49ivwORuqqsOYKLHHPz4Mohg+ne4+LiW4tzsyDlKCQftgff5CNknDxEzumj+OYm4Z55EtITwbBB+gnc008wyFzMh39l7ybRENjsad+VaniTig8+ASF4/9KABe65pOBDiuFDCj5k4MOwrFh+tcTbzzW8ScebTLxxzzmNxcjDVuA/tYW7UZTWugyUGIJLOlZS6/KZzFxe/O6P8x6gJ1LbKOjWcfHx5/q6LV68mOeee469e/c69nl7ezNr1izH+4iICN5//32GDx/u2GexWAgPD6+ZgktRuC6wB+KSrFmzhiuuuIIZM2YQFBTE+++/z1VXXcUvv/xC9+7dHecFBAQ4fSfAeQ342rhxI/379wdg7dq19OrVyynkAuTk5HDDDTfQt29f3n333Up/lkh9UmJAdveCBi3t21k+ZzcHax5kJELaibPbcVISj5KWFE8QqfjkpUBmEmScgowkyDoDgL8pE38yIfUUpO7niuIWglsP89yL2f8N7PeCbMONdLzIwIsMw4uoH8NgvT94+NHN5MXz7smkG16kGV6k40Um3rQ7lUr8UTd68BcZJi9S8Sbd8CbN8CY2MQMDo0Kty7O++wOjlJZlKP9sFfkUiqU2UNA9H4Zh/3GZK7j7QBmttYBTQA0MDMRkMhUJrYGBgU7vg4KCnM4p3HVh1apVDBkyhO+//56nnnqKP/74g759+/LJJ5+wbds2Hn/8cY4ePcrIkSN599138fFx+s9NpRWuqyyFuxrMmDGDL7/8kq+++sop6Bb3nRSWl5fHgw8+yEcffYTFYuG+++7j+eefL7alecOGDY6gu27dOsfrgqZNmwbYW5RFpAZY3MA/3L6dFXB2K5Y1zx52s5IL/GrfUs4kknI6kWBLFr5GJmSnQnYKuRnJ5GUm42HNwJKTBnmZAHia8vAkjRDSwATEH3F8jA8wobjw/C0EA597Fj1k/Ncdw8OftZ4W0gxvUvEm1fAhHW/6HY0mzy3F0eKcYviSig9nDF9STD4kG34k40uu4VahAXqFVTQUF6aQLDVFQfd85GbAjMau+eynj4GHr2s++6ypU6fyxhtv4OPjw9ixYxk7diyenp58/PHHpKWlcd111zFv3jz+/ve/u7TOfDabjdTUVEJCQpz2p6Wl0bx5c6xWK926deP55593CsIAH3zwARMnTuSXX35h69at3HPPPTRv3py7774bsAfaUaNGOe731VdfMXXqVNLT03F3d2f+/Pk89dRTPPXUUzXzsCJyfixu4Btq3wopKSC7n90crHmQk8bxU4nEH0+kqZ+VUPdcyEmH7DTIyd/SSUs9Q3rKGQLMWXgbWfb92amkppwhKz0ZX7LwMWUDYLLlYspKItKEPTgX9McGJpXjv+xphhdeX4baZ6vwDsbd5st0SzZn8OO04U+iEcApAjmxL4CIdq3ApwFY7E9X0VBcmFqOpSYp6Eql/fOf/3S0Vk6cOJHJkydz8OBBWrRoAcD111/Pzz//XGVB96abbsJicW762Llzp+PzyvLyyy+Tnp7O2LFjHfvatWvHwoUL6dy5MykpKbz22mv079+fX3/9ldatWzvOi4yM5NVXX8VkMtG2bVt27drFq6++6gi6vXr1IiYmhj/++IPx48ezbds2kpKS6NevH9u3b8fLy6vUbhYiUgdZ3MA7iLCmQYQ1bVXqqX5nt8L8gbTkTH5NzCAqxIMIL6sjBJOdxqnTiSQmJhLmmUOQ6VzrckZqEpmpp/Ez0vDMTSUt+RTWjDP4k4HZZOBnyoKUI/YNCAVuKS4RfHN2A/AOBt+G+JiDmOcGp4xAEo1A4gnhuBHM8QPBRHRoD16BJf7EsbpajrWstJREQfd8uPvYW1Zd9dku1qVLF8frsLAwfHx8nEJnWFgYmzdvrrLPe/XVV7n88sud9kVGRgLg53fuPxG33HIL8+fPdzpv0aJFTJ06lS+//JJGjRo59vfp04c+ffo43vfv358ePXowb948Xn/9dafzCnZT6Nu3Ly+//DJWqxWLxYKXlxdRUVF8+umnjBgxgujoaDZs2MCAAQNo165d1XwBIlIvFemb7B3keNkgEhoUc03hPsp+2APfnhOpRAfkEe6WCZmnz20ZSew+GMu23/8k2JRCA1MqHQOzCbIl2/s2GzbHuYHAyOK6W3z1InwFuPtCQAQENAb/xvZfz24nUnxoYJwhkQAM+0LTRQbo5StvKK7MzBQFP0MhuG5T0D0fJpPLuw+4krv7uR/SmUwmp/f5+2w2W4nXBwQEkJaW5giL+axWK2lpaUX6DoeHh9OqVfGtIjExMU73LWjx4sVMnDiRzz77rEhQLsxsNtO7d2/2799f6nmF5Qft7OxszGYzX375JTk5ORiGgZ+fHwMGDOC7776r0D1FRKpSWVO/deoDDQqshBeUf67tbMhNP+nYtv2+jw2/7iWU00SYTtMtKIOgvJP283LT4dQB+1ZIV2CLF+QYFo4aoRw2GhFHGB1j/4Ds1hASDUHNwSvgvBf/gJJnrajostJSeynoisu0a9cOq9XKjh076NWrl2P/9u3bsVqttG3bttz3KikAL1q0iDvvvJNFixYxcuTIMu9jGAYxMTF07tzZaf+mTZuKvG/durUjoMfExJCXl0e3bt348ccfCQ8PZ8CAAbz55pt07twZb2+1FIjIha/YMGw2g28D+4b9J1Q9O0HjocWE4txMSDkGqfH2X1OOQkq8/dez+2ypx/EwWYk2HSea48AuWPuj82f6NKBXQDPmuXsRa4RxyGZfGvovGhMVeq6turQwXNLMFGVN3Za/Op5aeusGBV1xmQ4dOjBixAjuvPNOXnnlFVq2bMnBgwd5/PHHGTFiBB06dHA6/8yZMyQkJDjt8/f3x9e3+Fb1RYsWceutt/Laa6/Rp08fx7Xe3t6O1uJp06bRp08fWrduTUpKCq+//joxMTH861//crrX4cOHefzxx7n33nvZvn078+bN4+WXX3Ycb9WqFZs2bSIsLIxLLrmEuLg4UlNTGTVqVJGWboC4uDiSkpKIi4vDarU6WqRbtWrl1A1DRORCVWwodvcuMtVbYWZrHsePHSLx8H6akEBQ1jFIOgSnY+1bRiJknMIj4xRXFddF4p3GENoaQtvQ0S+aS8xp7Lc15jjBgAmLyeQIw+ZCi3zkHystIJe0mp3Cb+2koCsu9cknnzB16lTuu+8+jhw5QtOmTRk1ahRTp04tcm5xCzvMnDmzxJkM3n77bfLy8njggQd44IEHHPtvu+02x7ReZ86c4Z577iEhIYHAwEC6d+/OmjVruOiii5zudeutt5KZmclFF12ExWLhoYce4p577nE6Z9WqVQwcOBCA1atX07dv32JDLsBzzz3ntFBH/iwPP//8M4MHDy72GhGROsHiRlhka8IiWxd/PDv1XOg9HUt6wj7yju/HL/UglowTkHrMvh1aTSDw0dlpys8YvvxhNCOkRXciDpyEsE68dE0r/v7lQayGgcVkYsboTo6QWlwI9vEwn9eCG3LhMRmGYZR9Wv2RkpJCYGAgycnJRfp6ZmVlcejQIaKjo89rQQGRitLvPRERIPMMJO6HxH1Om5F0CJNhLeYCE3nBLUj2b4N7ZA8CWvSCiG7gE8LiLXE8vXS3UwiODPFh/IJfit7FBEahULzuqSFq2XWh0vJaQWrRFRERkdrBOwgie9u3Akx52XByLxz/DY7vPvdr+kncTh+kwemDEPcdrD97QVBzxjXuzojLOnLYqz0N2vYhvGFD4pMzi7T0mqHMQXFy4VLQFRERkdrNzRMiuti3gtJO2ANv/E6Ij4FjMXD6EJz5C878RQD/oyPAT2Zo1IGIpr1Y1Lsl/9jqzQFbOGaThSeHt+XF7/8otq+vXPgUdEVERKRu8msEfpdCy0vP7cs8DfG/wrEd9uB7dBskHz7bErybi4EfPCDXIwhr04vxcruElkOieeBng2zDUqSvb0EasHbhUdAVERGR+sM7GFoMtm/5UuLhyJaz21Y4tgP3nDO4/7kc/lzO5cAfvt4kh3bH3HIQARGNwNrYvvrdWZqX98KkoCsiIiL1W0AEdLjavgFYc+2tvn9tgLhNELcRU2YSQQkbIGEDrJ8JngEQdQlED+Rkw4uZvPQYNsO+gmZZSxtLzVHQFRERESnI4g5Ne9m3/g/bV4dL3Aexa+HPVfZfs5Jh77ew91saAr94BLLB1pF1tk6ssnblpBGsAWsXAAVdERERkdKYzdConX276G6wWSFhJ/y5Gg6txvhrIw3zkrnGsoFrLBvAHXbbomi+91pwv9IemM0W9eF1AQVdERERkYowW6Bxd/t2yaOY8rJZ+ePX/LbuawaYf6WL6U86mWNh81z75h3MX8F9mPtXND9bu5Ji8lcf3hqioCsiIiJyPtw8uXT4GNr3vZLYxAxO+qQTdmID7F8BB36CzNM0z/yOV90hz83MFls7fvyyFyfCHqRRs7aurr5OU9AVVq1axZAhQzh9+jRBQUGuLkdERKRWigj0PtsloQFENIOuN4I1j12bf2T1Nx9zqTmGDua/6GvZQ1/2wHv/gfAupEYP42CDwYS16kFEkObnrUpmVxcgdVNUVBRz584tsn/u3LlERUU53k+dOhWTyVRka9euXYn3TkpK4qGHHqJt27b4+PjQrFkzHn74YZKTk4vUUPi+Tz31VKWfyWazERAQwL59+wBo3bo1a9ascTpn6dKlDBs2jNDQUEwmEzExMZX+PBERqQMsboR2GMQr1nFcmTOTS7LnMj13Apts7TFMZkjYif/G2XT7eiRZr3Rnz4d/g4RdzmsOS6WpRddFEtITiEuJo1lAM8J9w11djkt17NiRH3/80Wmfm1vJvzWPHTvGsWPHmDNnDh06dOCvv/5i0qRJHDt2jM8//9zp3OnTp3P33Xc73vv5+VW6zt27d+Pp6UmbNm04ceIEcXFx9O7tvAxleno6/fv354YbbnD6XBERqb8iAr2ZObozTy/dzRGjER/YrqTttU8S3dTEy2+8zhXmbQw07yTanAAHF9i3kJbQ8Tr7FtYRTCZXP0atpKDrAkv3L2XaxmnYDBtmk5kpfacwuvXoavu87OxsnnjiCT755BNSUlLo1asXr776apGQtn79ep5++mn27t1L165deeedd+jcuTMAf/31Fw8++CDr1q0jJyeHqKgoZs+ezZVXXnne9bm5uREeXv6w36lTJ5YsWeJ437JlS1544QVuueUW8vLynEKyv79/ife2Wq3cc889rFy5koSEBJo1a8b999/PI488Uuz5GzZsoH///gCsXbuW7t274+3tPGp2woQJAMTGxpb7eUREpO4b17sZA9s0JDYxg6hQHyICvdlwMJFPrYP51DoYXzK5zLyDkZZNXOG+E3PSQVg7B9bOITekDe7dxkHn6yE4ytWPUqso6NawhPQER8gFsBk2pm2cRr/G/aqtZffJJ59kyZIlfPDBBzRv3pyXXnqJYcOGceDAAUJCQhznPfHEE7z22muEh4fz9NNPc/XVV7Nv3z7c3d154IEHyMnJYc2aNfj6+rJnz57zah2tasnJyQQEBBRpCX7xxRd5/vnniYyM5IYbbuCJJ57Aw8MDsHdFaNq0KZ9++imhoaFs2LCBe+65h4iICMaOHeu4R36/5aysLAzDICgoiOzsbKxWK0FBQVxyySV8/fXXNfasIiJSO53rw2sXHeqL2WRfYCIdb5bZ+vGN0Z/1D/Xi0IYlpG77jEHmX/FM2gcrn7dvTS8iufU17A29gsimzTRNWRkUdGtYXEqcI+Tmsxk2Dqcerpagm56ezltvvcXChQsZMWIEAAsWLOCHH37g3Xff5YknnnCcO2XKFK644goAPvjgA5o2bcoXX3zB2LFjiYuLY8yYMY4W3hYtWlRZjbt27SoSmm+88Ubeeeedcl1/6tQpnn/+ee69916n/Y888gg9evQgODiYzZs3M3nyZA4dOuS4r7u7O9OmTXOcHx0dzYYNG/j000+dgm5MTAyGYdCzZ08+/vhj2rVrx9ChQ5k6dSr9+vXDy8urso8uIiL1WMEuDVbDwGIyMWN0JwxPf27+pRk24/8IIJ1hli1cY9lAf8seTEc2E3hkM92N51ht68qfvW+mRf8xHDpj1fy8xVDQrWHNApphNpmdwq7ZZCbSP7JaPu/gwYPk5uY6fuQO9oB30UUX8fvvvzud27dvX8frkJAQ2rZt6zjn4Ycf5r777mPFihVcfvnljBkzhi5dulRJjW3btmXZsmVO+/z9/QGYMWMGM2bMcOzfs2cPzZqdm3cwJSWFkSNH0qFDB6ZMmeJ0j8cee8zxukuXLgQHB3P99dfz4osv0qBBAwDmz5/PO++8w19//UVmZiY5OTl069bN6T5RUVFs3rwZHx8fhg8fztGjRzl27BhjxozB09OzSr4DERGpn0rq0mA7OxYtBV8+sw7mM+tgFlzXhI1fvcM15vV0Nf/J5ZbtsH07ydue5ZC1L3NsAxl37XWMu6i5Fqc4S0G3hoX7hjOl75QifXSrq9uCcXbUpqlQJ3bDMIrsK07+OXfddRfDhg3jm2++YcWKFcycOZOXX36Zhx56qNjrAgICisyCAHDmzBkCAwOd9nl4eNCqVati7zNp0iSn1tXGjRs7XqempjJ8+HD8/Pz44osvcHd3L/VZ+vTpA8CBAwdo0KABn376KY899hgvv/wyffv2xd/fn9mzZ/PLL784rhkxYgRr164lLy+PvLw8/Pz8sFqtZGdnO8JyWlpaqZ8rIiJSmtK6NOSzmExkezfivbwRvMcIWpqOMtqyluss62hsSuJmt5+4mZ/Y//W/2bT3eh7e05YTRhBmE/V6cQoFXRcY3Xo0/Rr343DqYSL9I6t11oVWrVrh4eHBunXrGD9+PAC5ubls3bqVRx991OncTZs2OVpLT58+zb59+5ym+YqMjGTSpElMmjSJyZMns2DBghKDbrt27diyZUuR/Vu2bKFt2/JPjh0SEuLUjzhfSkoKw4YNw9PTk2XLlpWr+8COHTsAiIiIAOwDyvr168f999/vOOfgwYNO17zzzjtkZmZy2223MXr0aK655hr+9re/0a5dO+66665yP4eIiEh5ldSloWfzYEcAPmg0YXbejczJG0tf8x7GWNZwpXkzrc1HaX3wNTZ4mPnZ1p1PrIN5dqmNgW0a1suWXQVdFwn3Da+RacV8fX257777eOKJJwgJCaFZs2a89NJLZGRkMHHiRKdzp0+fToMGDQgLC+OZZ54hNDSUa6+9FoBHH32UESNG0KZNG06fPs3KlStp3759iZ/7+OOP079/f6ZPn871118PwJIlS/j+++/ZsGGD07l5eXkkJCQ47TOZTISFhRV779TUVIYOHUpGRgYfffQRKSkppKSkANCwYUMsFgsbN25k06ZNDBkyhMDAQLZs2cJjjz3G1Vdf7QjzrVq14j//+Q/Lly8nOjqaDz/8kC1bthAdHe34rCZNmpCXl8fOnTv56KOPiI6OZufOnfz9738vthU6KSmJuLg4jh07BsDevXsBCA8Pr9DMEiIiUr8V16UBcArAZsDAzAZbJzbYOjGV2xll2cT1ltX0NO/nCss2rrBs47gRRMo349jW4SYaR7erV4FXQbcemDVrFjabjQkTJpCamkqvXr1Yvnw5wcHBRc575JFH2L9/P127dmXZsmWOGQqsVisPPPAAR44cISAggOHDh/Pqq6+W+Jl9+vRh+fLlTJ8+3bFwRMeOHVm+fDkXX3yx07m//fabo5U1n6enJ1lZWcXee9u2bY7uBYXD5qFDh4iKisLT05PFixczbdo0srOzad68OXfffTdPPvmk49xJkyYRExPDuHHjMJlM3HTTTdx///189913TvfcunUrQUFBREdHc+TIEY4fP06vXr2KrW3ZsmXccccdjvc33ngjYB/oN3Xq1BK+LRERkaIKd2mAogF4zb6TjuCbYfIlauj93PD9pURzlHGWVYyxrCHMdIawfW/DvrdZY+vMn73uoP/IW8FSepe/usBkGLVj6Y0XXniBb775hpiYGDw8PDhz5kyRc+Li4njggQdYuXIl3t7ejB8/njlz5jjCWnmkpKQQGBjomK6qoKysLA4dOkR0dLRG2kuN0u89EREpSXxyplPL7+ItcY7w60kel5m3caNlJQMtuxzXWH0aYel5K/S8HYKqZ0B8dSotrxVUa1p0c3JyuOGGG+jbty/vvvtukeNWq5WRI0fSsGFD1q1bx6lTp7jtttswDIN58+a5oGIRERGR6le45bdgq++p9Gwe/NiNb20XE5l3nBstPzPWspqGGSfsC1KsewVaD4Xed0HLy8BsduGTVL1aE3Tz5ztduHBhscdXrFjBnj17OHz4sGNk/ssvv8ztt9/OCy+8UGraFxEREalL8sNvfHKmYwDbYSOM2Xk38nre9Xw88BSd4pfgeXgd7PvevgVH2wNv95vBO7jsD6kF6kxs37hxI506dXKafmrYsGFkZ2ezbdu2Eq/Lzs52DGYqOKhJREREpLbLn8HBcna6UBOQgxtj1oTR/sD9fDtoGfS5HzwD4fQhWPEMvNwelj0ECbtKv3ktUGeCbkJCQpFR+sHBwXh4eBQZ0V/QzJkzCQwMdGyRkbWvn4qIiIhIScb1bsa6p4bwxk3dMZkgf3CWzYCHVqTza8cn2XTdOs5cNhvCOkFeJmz/D8y/BN4bDrs+B2uuS5+hslwadKdOnYrJZCp127p1a7nvV9wCCGUtjDB58mSSk5Md2+HDhyv1LCIiIiIXqohAb0L8PJwWoQCwGgbXvrmBGxfuose3TVjc82O44zvoOBrMbhC3EZZMhLmdYfVsSE90zQNUkkv76D744IOO6ZdKEhUVVa57hYeHO61oBfZFD3Jzc0ucjxXs01hpGVcRERGp64pbcQ0gf/4tmwFPf/EbA58aQsQN70NqAmxbCFvfg9R4+PmfsGY2dL4eLp4EEV1q/BkqyqVBNzQ0lNDQ0Cq5V9++fXnhhReIj493zMm6YsUKPD096dmzZ5V8hoiIiEhtVXjFNTNgK3SO1TCITcywz+LgHw6Dn4JLHoc9/4NNb8Gx7RDzX/vWrB/0mQRtR4Llwpzf4MKsqhhxcXGOVaesVisxMTGAfcEAPz8/hg4dSocOHZgwYQKzZ88mKSmJv/3tb9x9992acUFEREQE56nHfDzMXPfmBqcWXovJRFSoj/NFbh7QZax9O7wFfplvD75xG+xbYDO4+F7oMQG8Amv0ecpSawajPffcc3Tv3p0pU6aQlpZG9+7d6d69u6MPr8Vi4ZtvvsHLy4v+/fszduxYrr32WubMmePiykVEREQuHBGB3vRt2YCukcFOMzJYTCZmjO5U+hLBkb3h+nfh0V0w4G/g0wCS4+yzNXz1SA09QfnVmpXRaopWRisqNjaW6OhoduzYQbdu3VxdTr1UX3/viYhI9Su8slrhY4cS04kO9S0+AOdmws5P7d0aRs6BqEtqpObyroxWa1p0pXLKmtXi9ttvL/OcyMhI4uPj6dSpU5XWtnDhQoKCgoo9FhQU5LQ4SEm1ffLJJyXef+nSpVxxxRU0bNiQgIAA+vbty/Lly4vUUNx9s7KyKv1cs2fPZvz48QD897//5dJLL3U6npWVxe23307nzp1xc3Pj2muvrfRniYiInK/8Ft7CQXbxljj6z1rJ+AW/0H/WShZviXM6Hp+cyYa4dOJbjYX7N0Lz/jVZdrnUmj66Ujnx8fGO14sXL+a5555j7969jn3e3t7MmjXL8T4iIoL333+f4cOHO/ZZLBbCw8NrpuBSFK4LKDEoA6xZs4YrrriCGTNmEBQUxPvvv89VV13FL7/8Qvfu3R3nBQQEOH0nwHm1mm7cuJHLLrsMgHXr1tG/v/MffKvVire3Nw8//DBLliyp9OeIiIhUl/jkTCYv3eXov2sz4OmluxnYpiERgd4s3hLnOG42wczRnRnXu5lriy6GWnTPg2EY2DIyXLKVt8dJeHi4YwsMDMRkMhXZV/A92MNjwX2xsbGYTCbHAMBVq1ZhMplYvnw53bt3x9vbm0svvZQTJ07w3Xff0b59ewICArjpppvIyMiosu+7cF3h4eGlBtK5c+fy5JNP0rt3b1q3bs2MGTNo3bo1X331ldN5hb+TwqH++++/55JLLiEoKIgGDRowatQoDh48WOLnbty40RFuiwu6vr6+vPXWW9x9990XxD8gRERECjuUmF7snLuxiRklhuD45MyaL7QMatE9D0ZmJnt7uGbqsrbbt2Hy8Sn7xGo0depU3njjDXx8fBg7dixjx47F09OTjz/+mLS0NK677jrmzZvH3//+d5fWmc9ms5GamkpISIjT/rS0NJo3b47VaqVbt248//zzTi2+6enpPP7443Tu3Jn09HSee+45rrvuOmJiYjCb7f9WnDVrlqNlPDk5mUGDBmEymUhOTmbs2LGYzWa+/vprLrmkZvouiYiInI/i5tzNn5GhtBBc6kA2F1DQlUr75z//6WitnDhxIpMnT+bgwYO0aNECgOuvv56ff/65yoLuTTfdhMVicdq3c+dOx+eV5eWXXyY9PZ2xY8c69rVr146FCxfSuXNnUlJSeO211+jfvz+//vorrVu3BmDMmDFO93n33Xdp1KgRe/bscfRbnjRpEjfeeCMLFy5k06ZNzJ8/n2+//ZaFCxfy6aefAqj1VkREao3Cc+4WnpGhcAg2Az4eF15HAQXd82Dy9qbt9m0u+2xX69Ll3IooYWFh+Pj4OIXOsLAwNm/eXGWf9+qrr3L55Zc77YuMjATAz8/Pse+WW25h/vz5TuctWrSIqVOn8uWXX9KoUSPH/j59+tCnTx/H+/79+9OjRw/mzZvH66+/DsDBgwd59tln2bRpE4mJidhs9um14+LiHEE3KCiIoKAgNm/ezJgxY4iKimLHjh1cffXV5V7dT0RE5EJScM7dgjMyFA7BYF944ro3N1xwfXUVdM+DyWRyefcBV3J3d3e8NplMTu/z9+WHwuIEBASQlpaG1Wp1aqm1Wq2kpaURGOg86XR4eDitWrUq9l75/Yfz71vQ4sWLmThxIp999lmRoFyY2Wymd+/e7N+/37HvqquuIjIykgULFtC4cWNsNhudOnUiJycHgLVr1zJixAgAMjIyWLVqFY899hiZmZm4u7sza9Ysnn76aZ5++ulSP1tERORCExHoXWx3hHG9m9Eu3J9r39zgvIRwgQFrFwIFXXGZdu3aYbVa2bFjB7169XLs3759O1arlbZt25b7XiUF4EWLFnHnnXeyaNEiRo4cWeZ9DMMgJiaGzp07A3Dq1Cl+//133n77bQYMGADYB5gV1KtXL2JiYti2bRtPPvkkP/30E3FxcVx99dVs374ds9lcpF+wiIhIbZeeY6Xw2PgLra+ugq64TIcOHRgxYgR33nknr7zyCi1btuTgwYM8/vjjjBgxgg4dOjidf+bMGRISEpz2+fv74+vrW+z9Fy1axK233sprr71Gnz59HNd6e3s7WounTZtGnz59aN26NSkpKbz++uvExMTwr3/9C4Dg4GAaNGjAv//9byIiIoiLi+Opp55y+hxvb29atWrF559/zuDBg2nVqhUbNmygf//+tGnTptja9uzZQ05ODklJSaSmpjpapLUgh4iI1BalDVi7UFx4vYalXvnkk0+4/PLLue++++jQoQP33Xcfl112GYsWLSpy7h133EFERITTNm/evBLv/fbbb5OXl8cDDzzgdM0jj5xbovDMmTPcc889tG/fnqFDh3L06FHWrFnDRRddBNi7MnzyySds27aNTp068dhjjzF79uxiP2/VqlUMHDgQgNWrVzteF+fKK6+ke/fufPXVV6xatcqxpLWIiEhtkd9Xt0JLCNcwLQFciJYAlguRfu+JiMiFqrQlhKtLeZcAVtcFEREREam0kgasXQjUdUFERERE6iQFXRERERGpkxR0K0Hdmv+/vfuPqar+4zj+ul65tuuFe8UfyC/BmlAkiCA2nGzSH1psMSuaa02kzI3WL1aM3Ny0ttL+0E1b2UZtZMxSc6M2x2y2ABtNVMxarR/oQFAJMmYgjCzu+f7xnXy/yA+5l3vvuR6fj40/7jnnc+4bXhz2vh8+9x6EGr9zAAD4jkbXBzduanDjRgFAqAwMDEjSqJtyAACA8fFmNB9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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht1 = ml1.head(0, 0, t1)\n", + "ht2 = ml1.head(110, 0, t2)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs UE-25b#1\")\n", + "plt.semilogx(t1, ht1[-1], label=\"TTim UE-25b#1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs UE-25a#1\")\n", + "plt.semilogx(t2, ht2[-1], label=\"TTim UE-25a#1\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.suptitle(\"Model Results - Simulation 1\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Overall the curves follow the trends of the drawdown and the fit is good in general." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Analysis and Comparison of the Results under different methods" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The final important step is to compare the data obtained from this model with the data from other Aquifer Analysis software. Yang (2020) compared TTim results with the published results in Kruseman and de Ridder (1990), here abbreviated to K&dR, and with the results obtained from the software AQTESOLV (Duffield, 2007) and MLU (Carlson & Randall, 2012).\n", + "\n", + "Kruseman et al. (1970) solved the problem using a graphical method, where the transmissivity was calculated as one aquifer and the storativity was separated between matrix and fractures. The MLU (Carlson & Randall, 2012) solution used a similar approach to our TTim model by simulating the matrix as a very-low transmissivity aquifer on top of the fractured aquifer and separated by a zero-storage aquitard. Yang (2020) solved the problem in AQTESOLV using a double-porosity analytical solution proposed by Moench (1984).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/Caskroom/miniforge/base/envs/ttim-env/lib/python3.10/site-packages/ttim/well.py:35: RuntimeWarning: divide by zero encountered in divide\n", + " laboverrwk1 = self.aq.lab / (self.rw * kv(1, self.rw/self.aq.lab))\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/ttim-env/lib/python3.10/site-packages/ttim/well.py:35: RuntimeWarning: invalid value encountered in divide\n", + " laboverrwk1 = self.aq.lab / (self.rw * kv(1, self.rw/self.aq.lab))\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/ttim-env/lib/python3.10/site-packages/ttim/well.py:38: RuntimeWarning: invalid value encountered in multiply\n", + " self.term = -1.0 / (2 * np.pi) * laboverrwk1 * self.flowcoef * coef\n" + ] + }, + { + "data": { + "text/html": [ + "
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TTim20.00.0001440.9090240.00000315.584440.1085550.15939
\n", + "
" + ], + "text/plain": [ + " km [m/d] Sm [1/m] kf [m/d] Sf [1/m] c rc RMSE\n", + "K&dR 0.8325 0.000375 0.8325 0.000004 - - -\n", + "AQTESOLV 0.149 0.000551 0.937 0.000006 - 0.11 0.031736\n", + "MLU 0.00025 0.000385 0.874 0.000008 12.38 0.1 0.434638\n", + "TTim1 0.00025 0.000385 0.876963 0.000005 13.003524 0.105604 0.199156\n", + "TTim2 0.0 0.000144 0.909024 0.000003 15.58444 0.108555 0.15939" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\"km [m/d]\", \"Sm [1/m]\", \"kf [m/d]\", \"Sf [1/m]\", \"c\", \"rc\"],\n", + " index=[\n", + " \"K&dR\", #'Moench',\n", + " \"AQTESOLV\",\n", + " \"MLU\",\n", + " \"TTim1\",\n", + " \"TTim2\",\n", + " ],\n", + ")\n", + "t.loc[\"TTim1\"] = np.concatenate(\n", + " (np.array([0.00025, 3.850e-04]), ca.parameters[\"optimal\"].values)\n", + ")\n", + "t.loc[\"TTim2\"] = ca1.parameters[\"optimal\"].values\n", + "t.loc[\"K&dR\"] = [0.8325, 3.750e-4, 0.8325, 4.000e-6, \"-\", \"-\"]\n", + "# t.loc['Moench'] = [0.1728, 3.000e-4, 0.864, 1.500e-6, '-', '-'] # I don't know where these values for Moench come from\n", + "t.loc[\"AQTESOLV\"] = [0.149, 5.512e-4, 0.937, 5.533e-6, \"-\", 0.11]\n", + "t.loc[\"MLU\"] = [0.00025, 3.850e-04, 0.874, 8.053e-6, 12.380, 0.1]\n", + "t[\"RMSE\"] = [\"-\", 0.031736, 0.434638, ca.rmse(), ca1.rmse()]\n", + "t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Overall, TTim model 1 showed similar results to MLU but with a slightly better fit. AQTESOLV obtained the best fit using Moench's analytical solution." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8.1. Comparison of estimates and model error" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Preparing the DataFrame:\n", + "t1 = pd.DataFrame(\n", + " columns=[\"kaq - opt -l1\", \"kaq - 95% -l1\", \"kaq - opt -l2\", \"kaq - 95% -l2\"],\n", + " index=[\"AQTESOLV\", \"MLU\", \"K&dR\", \"TTim - all params\", \"TTim - fixed upper\"],\n", + ")\n", + "simulation = [\"AQTESOLV\", \"MLU\", \"K&dR\", \"TTim - rc\", \"TTim - fixed upper\"]\n", + "t1.loc[\"MLU\"] = [0.00025, np.nan, 0.874, 1.221 * 1e-2 * 0.874]\n", + "t1.loc[\"KdR\"] = [0.8325, np.nan, 0.8325, np.nan]\n", + "t1.loc[\"AQTESOLV\"] = [0.149, 291.236 * 1e-2 * 0.149, 0.937, 1.946 * 1e-2 * 0.937]\n", + "t1.loc[\"TTim - fixed upper\"] = [\n", + " 0.00025,\n", + " np.nan,\n", + " ca.parameters.loc[\"kaq1\", \"optimal\"],\n", + " 2 * ca.parameters.loc[\"kaq1\", \"std\"],\n", + "]\n", + "t1.loc[\"TTim - all params\"] = [\n", + " ca1.parameters.loc[\"kaq0\", \"optimal\"],\n", + " 2 * ca1.parameters.loc[\"kaq0\", \"std\"],\n", + " ca1.parameters.loc[\"kaq1\", \"optimal\"],\n", + " 2 * ca1.parameters.loc[\"kaq1\", \"std\"],\n", + "]\n", + "\n", + "# Plotting\n", + "\n", + "# plt.figure(figsize = (10,7))\n", + "fig, ax = plt.subplots(2, 1, figsize=(10, 7), sharex=True)\n", + "ax[0].errorbar(\n", + " x=t1.index,\n", + " y=t1[\"kaq - opt -l1\"],\n", + " yerr=[t1[\"kaq - 95% -l1\"], t1[\"kaq - 95% -l1\"]],\n", + " marker=\"o\",\n", + " linestyle=\"\",\n", + " markersize=12,\n", + " label=\"aquifer matrix\",\n", + " color=\"red\",\n", + ")\n", + "ax[0].legend()\n", + "ax[1].errorbar(\n", + " x=t1.index,\n", + " y=t1[\"kaq - opt -l2\"],\n", + " yerr=[t1[\"kaq - 95% -l2\"], t1[\"kaq - 95% -l2\"]],\n", + " marker=\"o\",\n", + " linestyle=\"\",\n", + " markersize=12,\n", + " label=\"fractures\",\n", + ")\n", + "\n", + "plt.legend()\n", + "plt.suptitle(\"Error bar plot of calibrated hydraulic conductivities\")\n", + "plt.ylabel(\"K [m/d]\")\n", + "# plt.ylim([278,289])\n", + "plt.xlabel(\"Model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hydraulic conductivities varied between simulations. When estimated, the matrix conductivity had higher uncertainty. Uncertainty ranges are similar for the fractured portion. However, the solutions do not always overlap each other." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reference\n", + "\n", + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Kruseman GP, De Ridder NA (1990) Analysis and evaluation of pumping test data, 2nd edn. ILRI Publ. 47, ILRI, Wageningen, The Netherlands\n", + "* Moench, A. F. (1984), Double-Porosity Models for a Fissured Groundwater Reservoir With Fracture Skin, Water Resour. Res., 20( 7), 831– 846, doi:10.1029/WR020i007p00831.\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/04pumpingtests/index.rst b/docs/04pumpingtests/index.rst index 04eacd9..73ec46f 100644 --- a/docs/04pumpingtests/index.rst +++ b/docs/04pumpingtests/index.rst @@ -1,24 +1,85 @@ Pumping tests ============= -TTim pumping test benchmark notebooks. +This series of benchmark problems demonstrate the features and capabilities of TTim for +simulating and analyzing transient groundwater hydraulic problems such as pumping tests +and slug tests. + + +Synthetic +--------- + +1. `Synthetic pumping test`_ - a synthetic pumping test + +.. _Synthetic pumping test: 0_synthetic_data.html + +Confined Pumping Tests +---------------------- + +1. `Oude Korendijk`_ - One layer confined pumping test with two observation wells +2. `Grindley`_ - One layer confined pumping test with data from both observation well and pumping well. +3. `Sioux`_ - One Layer confined aquifer test with three observation wells +4. `Schroth`_ - Three layers (Aquifer - Aquitard - Aquifer) confined pumping test with one observation well. +5. `Nevada`_ - One layer, fractured confined aquifer test with data from both observation well and pumping well. + +.. _Oude Korendijk: confined1_oude_korendijk.html +.. _Grindley: confined2_grindley.html +.. _Sioux: confined3_sioux.html +.. _Schroth: confined4_schroth.html +.. _Nevada: confined5_nevada.html + + +Leaky Pumping Tests (Semi-confined) +----------------------------------- + + +1. `Dalem`_ - One layer, semi-confined aquifer test with four observation wells. +2. `Hardixveld`_ - Four layers (Aquitard - Aquifer - Aquitard - Aquifer) semi-confined pumping test with data from the pumping well. +3. `Texas Hill`_ - One layer, semi-confined aquifer test with three observation wells. + +.. _Dalem: leaky1_dalem.html +.. _Hardixveld: leaky2_hardixveld.html +.. _Texas Hill: leaky3_texashill.html + +Unconfined Pumping Tests +------------------------ + +1. `Vennebulten`_ - Two layers, unconfined aquifer test with data two observation wells screened at different depths. +2. `Moench`_ - Solving the Analytical model from Moench in TTim. Pumping test in unconfined aquifer with four piezometers screened at two different depths and two different distances + +.. _Vennebulten: unconfined1_vennebulten.html +.. _Moench: unconfined2_moench.html + +Slug Tests +---------- + +1. `Pratt County`_ - One layer partially penetrated slug test with data from the test well. +2. `Falling Head`_ - One layer partially penetrated slug test with data from the test well. +3. `Multi-Well`_ - One layer confined slug test with data from the test well and from an observation well. +4. `Dawsonville`_ - One layer fully-penetrated confined slug test with data from the test well. + +.. _Pratt County: slug1_pratt_county.html +.. _Falling Head: slug2_falling_head.html +.. _Multi-Well: slug3_multiwell.html +.. _Dawsonville: slug4_dawsonville.html + .. toctree:: :maxdepth: 1 + :hidden: 0_synthetic_data - 1_test_of_oude_korendijk - 2_test_of_dalem - 3_test_of_vennebulten - 4_test_of_gridley - 5_test_of_sioux - 6_test_of_schroth - 7_test_of_neveda_double-porosity - 8_test_of_hardinxveld_recovery - 9_test_of_texas_hill - 10_moench_test - 11_slug_test_pratt_county - 12_falling-head_slug_test - 13_multiwell_slug_test- - 14_dawsonville_slug_test confined1_oude_korendijk + confined2_grindley + confined3_sioux + confined4_schroth + confined5_nevada + leaky1_dalem + leaky2_hardixveld + leaky3_texashill + unconfined1_vennebulten + unconfined2_moenc + slug1_pratt_county + slug2_falling_head + slug3_multiwell + slug4_dawsonville diff --git a/docs/04pumpingtests/leaky1_dalem.ipynb b/docs/04pumpingtests/leaky1_dalem.ipynb new file mode 100644 index 0000000..8bbf4e5 --- /dev/null +++ b/docs/04pumpingtests/leaky1_dalem.ipynb @@ -0,0 +1,2383 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Leaky Aquifer Test - Dalem Example\n", + "**This example is taken from Kruseman et al. (1970)**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model\n", + "\n", + "In many situations, we cannot ignore the leakage potential of overlying and underlying formations to aquifers, and we cannot conceptualize them as confined. TTim is capable of modelling and adjusting parameters to leaky, semi-confined aquifers.\n", + "\n", + "The current is the pumping test from Dalem (Kruseman et al., 1970), the Netherlands. The hydrogeological cross-section is composed of the following elements: an initial 8 m deep aquitard layer, followed by an aquifer from 8 m to 45 m depth. The layer underlying the aquifer is considered an aquiclude. The pumping well is placed at the aquifer, and drawdown is recorded at four different piezometers, 30, 60, 90 and 120 m away from the well. The pumping lasted 8 hours in total at a rate of 761 m3/d. There is a river 1500 m away from the well. The tide affects both river and well levels. Data has been previously corrected for the tide effect.\n", + "\n", + "In this benchmarking exercise, we will simulate two different conceptual models. The first model assumes no storage in the aquitard. That matches most analytical solutions for leaky-aquitard (Kruseman et al., 1970). In the second model, we will explore TTim's flexibility for modelling and add storage as an additional parameter to be adjusted. Finally, we compare the results of the models with other software.\n", + "\n", + "The figures below resume the conceptual models:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Conceptual Model 1\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "# sky\n", + "sky = plt.Rectangle((-20, 0), width=170, height=3, fc=\"b\", zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "# Aquifer:\n", + "ground = plt.Rectangle(\n", + " (-20, -45),\n", + " width=170,\n", + " height=37,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + ")\n", + "ax.add_patch(ground)\n", + "\n", + "# Confining bed:\n", + "confining_unit = plt.Rectangle(\n", + " (-20, -8),\n", + " width=170,\n", + " height=8,\n", + " fc=np.array([100, 100, 100]) / 255,\n", + " zorder=0,\n", + " alpha=0.7,\n", + ")\n", + "ax.add_patch(confining_unit)\n", + "\n", + "well = plt.Rectangle(\n", + " (-2, -45), width=4, height=45, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "ax.add_patch(well)\n", + "\n", + "# Wellhead\n", + "wellhead = plt.Rectangle(\n", + " (-2.5, 0), width=5, height=1.5, fc=np.array([200, 200, 200]) / 255, zorder=2, ec=\"k\"\n", + ")\n", + "ax.add_patch(wellhead)\n", + "\n", + "# Screen for the well:\n", + "screen = plt.Rectangle(\n", + " (-2, -45),\n", + " width=4,\n", + " height=37,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x=2.5, y=0.75, dx=5, dy=0, color=\"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x=7.5, y=0.75, s=r\"$ Q = 761 \\frac{m^3}{d}$\", fontsize=\"large\")\n", + "# Piezometers\n", + "piez1 = plt.Rectangle(\n", + " (29, -45), width=2, height=45, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "screen_piez_1 = plt.Rectangle(\n", + " (29, -45),\n", + " width=2,\n", + " height=37,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_1.set_linewidth(2)\n", + "\n", + "piez2 = plt.Rectangle(\n", + " (59, -45), width=2, height=45, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "screen_piez_2 = plt.Rectangle(\n", + " (59, -45),\n", + " width=2,\n", + " height=37,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_2.set_linewidth(2)\n", + "\n", + "piez3 = plt.Rectangle(\n", + " (89, -45), width=2, height=45, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "screen_piez_3 = plt.Rectangle(\n", + " (89, -45),\n", + " width=2,\n", + " height=37,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_3.set_linewidth(2)\n", + "\n", + "piez4 = plt.Rectangle(\n", + " (119, -45), width=2, height=45, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "screen_piez_4 = plt.Rectangle(\n", + " (119, -45),\n", + " width=2,\n", + " height=37,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_4.set_linewidth(2)\n", + "\n", + "ax.add_patch(piez1)\n", + "ax.add_patch(screen_piez_1)\n", + "ax.add_patch(piez2)\n", + "ax.add_patch(screen_piez_2)\n", + "ax.add_patch(piez3)\n", + "ax.add_patch(screen_piez_3)\n", + "ax.add_patch(piez4)\n", + "ax.add_patch(screen_piez_4)\n", + "\n", + "# last line\n", + "line = plt.Line2D(xdata=[-200, 1200], ydata=[0, 0], color=\"k\")\n", + "ax.add_line(line)\n", + "\n", + "ax.text(x=29, y=0.75, s=\"P30\", fontsize=\"large\")\n", + "ax.text(x=59, y=0.75, s=\"P60\", fontsize=\"large\")\n", + "ax.text(x=89, y=0.75, s=\"P90\", fontsize=\"large\")\n", + "ax.text(x=119, y=0.75, s=\"P120\", fontsize=\"large\")\n", + "\n", + "\n", + "ax.set_xlim([-20, 150])\n", + "ax.set_ylim([-45, 3])\n", + "ax.set_xlabel(\"Distance [m]\")\n", + "ax.set_ylabel(\"Relative height [m]\")\n", + "ax.set_title(\"Conceptual Model - Dalem Example\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Load Required Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from ttim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters for the model." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "H = 37 # aquifer thickness [m]\n", + "zt = -8 # top boundary of aquifer\n", + "zb = zt - H # bottom boundary of the aquifer\n", + "Q = 761 # constant pumping rate [m^3/d]\n", + "t = 0.34 # total pumping time [d]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Create conceptual model\n", + "\n", + "Until so far, we have only considered impermeable upper boundaries in our model. This assumption, however, is not sufficient in many situations where there is enough leakage from above to influence flow results. TTim can simulate such semi-confined conditions in ```ModelMaq``` setup with the parameter: ```topboundary = 'semi'```.\n", + "\n", + "When we do this, ModelMaq assumes a leaky layer is on top of the uppermost aquifer. A leaky layer in TTim only has vertical flow and is characterized by the parameters resistance to vertical flow (```c```) and storage (```Sll```). The specific flux is computed as (Bakker, 2013):\n", + "\n", + "$$q_n = \\frac{h_n-h_{n-1}}{c_n}$$\n", + "\n", + "where $q_n$ is the vertical flux from layer $n$ to layer $n-1$, $h_n$ is the head in layer $n$ and $c_n$ is the vertical resistance to flow. $c_n$ is computed as: $H_n/k_n$ where, $H_n$ is the leaky-layer thickness and $k_n$ the vertical hydraulic conductance. $c_n$ is the inverse of the parameter Leakance ($L_n = 1/c_n$), that is used in MODFLOW (Harbaugh, 2005) or analytical solutions of leaky-layers, such as in Hantush (1955).\n", + "\n", + "Specifying ```topboundary = 'semi'``` means that we also have to set the parameters for the aquitard overlying the aquifer formation. Thus, even though we have only one aquifer, we have to set an additional element to the ```z``` array, which is the top of the aquitard formation:\n", + "* ```z = [0,zt,zb]```: 0 is the depth of the aquitard overlying the aquifer, zt and zb are the top and bottom of the aquifer\n", + "\n", + "In this first example, we also have to set the resistance of the aquitard:\n", + "* ```c = 500```: We will calibrate this value later.\n", + "\n", + "For now, we are ignoring the storage of this leaky layer. In this case, TTim will consider the head remains fixed above the leaky layer.\n", + "\n", + "\n", + "More explanations over how TTim sets up the ModelMaq model can be seen in the notebooks:\n", + "- [Confined 1 - Oude Korendijk](confined1_oude_korendijk)\n", + "- [Confined 4 - Schroth](confined4_schroth.ipynb)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# unkonwn parameters: kaq, Saq, c\n", + "ml = ModelMaq(\n", + " kaq=10, z=[0, zt, zb], c=500, Saq=0.001, topboundary=\"semi\", tmin=0.001, tmax=0.5\n", + ")\n", + "w = Well(ml, xw=0, yw=0, tsandQ=[(0, Q), (0.34, 0)])\n", + "ml.solve(silent=\"True\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Load data of four observation wells.\n", + "\n", + "The data for each observation well is organized in text files where the first column is the time data in days and the second is the drawdown in meters, corrected for the tide effect. Here we are also declaring the distance from the pumping well:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# data of observation well 30 m away from pumping well\n", + "data1 = np.loadtxt(\"data/dalem_p30.txt\", skiprows=1)\n", + "t1 = data1[:, 0]\n", + "h1 = data1[:, 1]\n", + "r1 = 30\n", + "# data of observation well 60 m away from pumping well\n", + "data2 = np.loadtxt(\"data/dalem_p60.txt\", skiprows=1)\n", + "t2 = data2[:, 0]\n", + "h2 = data2[:, 1]\n", + "r2 = 60\n", + "# data of observation well 90 m away from pumping well\n", + "data3 = np.loadtxt(\"data/dalem_p90.txt\", skiprows=1)\n", + "t3 = data3[:, 0]\n", + "h3 = data3[:, 1]\n", + "r3 = 90\n", + "# data of observation well 120 m away from pumping well\n", + "data4 = np.loadtxt(\"data/dalem_p120.txt\", skiprows=1)\n", + "t4 = data4[:, 0]\n", + "h4 = data4[:, 1]\n", + "r4 = 120" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Model Calibration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this step, we proceed with the model calibration in TTim's framework. This procedure has been extensively described in the following notebook:\n", + "* [Confined 1 - Oude Korendijk](confined1_oude_korendijk)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.1. Calibration with three datasets (excluding one piezometer at a time)\n", + "\n", + "We begin investigating the model calibration if we exclude one piezometer at a time. Hence, we look into the influence of each piezometer on parameter calibration." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.1.1. Calibration without obs1" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 49\n", + " # data points = 37\n", + " # variables = 3\n", + " chi-square = 3.8607e-04\n", + " reduced chi-square = 1.1355e-05\n", + " Akaike info crit = -418.405079\n", + " Bayesian info crit = -413.572325\n", + "[[Variables]]\n", + " kaq0: 57.5581602 +/- 1.53705301 (2.67%) (init = 10)\n", + " Saq0: 3.2824e-05 +/- 2.2610e-06 (6.89%) (init = 0.0001)\n", + " c0: 999468.865 +/- 2.8952e+08 (28967.53%) (init = 1000)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.895\n", + " C(kaq0, c0) = -0.650\n", + " C(Saq0, c0) = 0.325\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq057.558161.537053e+002.670435110010[57.55816021034674]
Saq00.0000332.261039e-066.8883460.000010.0010.0001[3.2824121238720075e-05]
c0999468.8648482.895215e+0828967.5314171001000000.01000[999468.8648475128]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 57.55816 1.537053e+00 2.670435 1 100 10 \n", + "Saq0 0.000033 2.261039e-06 6.888346 0.00001 0.001 0.0001 \n", + "c0 999468.864848 2.895215e+08 28967.531417 100 1000000.0 1000 \n", + "\n", + " parray \n", + "kaq0 [57.55816021034674] \n", + "Saq0 [3.2824121238720075e-05] \n", + "c0 [999468.8648475128] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ca1 = Calibrate(ml)\n", + "ca1.set_parameter(name=\"kaq0\", initial=10, pmin=1, pmax=100)\n", + "ca1.set_parameter(name=\"Saq0\", initial=1e-4, pmin=1e-5, pmax=1e-3)\n", + "ca1.set_parameter(name=\"c0\", initial=1000, pmin=100, pmax=1e6)\n", + "ca1.series(name=\"obs2\", x=r2, y=0, layer=0, t=t2, h=h2)\n", + "ca1.series(name=\"obs3\", x=r3, y=0, layer=0, t=t3, h=h3)\n", + "ca1.series(name=\"obs4\", x=r4, y=0, layer=0, t=t4, h=h4)\n", + "ca1.fit()\n", + "display(ca1.parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.0032302240706974304\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(\"rmse:\", ca1.rmse())\n", + "plt.figure(figsize=(8, 5))\n", + "ha1 = ml.head(r1, 0, t1)\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, ha1[0], label=\"ttim at 30 m\")\n", + "ha2 = ml.head(r2, 0, t2)\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", + "plt.semilogx(t2, ha2[0], label=\"ttim at 60 m\")\n", + "ha3 = ml.head(r3, 0, t3)\n", + "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t3, ha3[0], label=\"ttim at 90 m\")\n", + "ha4 = ml.head(r4, 0, t4)\n", + "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", + "plt.semilogx(t4, ha4[0], label=\"ttim at 120 m\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"drawdown (m)\")\n", + "plt.title(\"ttim fit except for data of obs1\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.1.2. Calibration without obs2" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".........................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 54\n", + " # data points = 38\n", + " # variables = 3\n", + " chi-square = 2.6352e-04\n", + " reduced chi-square = 7.5293e-06\n", + " Akaike info crit = -445.400171\n", + " Bayesian info crit = -440.487412\n", + "[[Variables]]\n", + " kaq0: 45.0264920 +/- 0.52739715 (1.17%) (init = 10)\n", + " Saq0: 4.4092e-05 +/- 1.4055e-06 (3.19%) (init = 0.0001)\n", + " c0: 349.139013 +/- 26.4963699 (7.59%) (init = 1000)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.714\n", + " C(kaq0, c0) = 0.711\n", + " C(Saq0, c0) = -0.155\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq045.0264920.5273971.171304110010[45.0264919508138]
Saq00.0000440.0000013.1875540.000010.0010.0001[4.409195656319494e-05]
c0349.13901326.4963707.589061001000000.01000[349.1390127093816]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 45.026492 0.527397 1.171304 1 100 10 \n", + "Saq0 0.000044 0.000001 3.187554 0.00001 0.001 0.0001 \n", + "c0 349.139013 26.496370 7.58906 100 1000000.0 1000 \n", + "\n", + " parray \n", + "kaq0 [45.0264919508138] \n", + "Saq0 [4.409195656319494e-05] \n", + "c0 [349.1390127093816] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ca2 = Calibrate(ml)\n", + "ca2.set_parameter(name=\"kaq0\", initial=10, pmin=1, pmax=100)\n", + "ca2.set_parameter(name=\"Saq0\", initial=1e-4, pmin=1e-5, pmax=1e-3)\n", + "ca2.set_parameter(name=\"c0\", initial=1000, pmin=100, pmax=1e6)\n", + "ca2.series(name=\"obs1\", x=r1, y=0, layer=0, t=t1, h=h1)\n", + "ca2.series(name=\"obs3\", x=r3, y=0, layer=0, t=t3, h=h3)\n", + "ca2.series(name=\"obs4\", x=r4, y=0, layer=0, t=t4, h=h4)\n", + "ca2.fit()\n", + "display(ca2.parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.0026334093716488772\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(\"rmse:\", ca2.rmse())\n", + "plt.figure(figsize=(8, 5))\n", + "hb1 = ml.head(r1, 0, t1)\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, hb1[0], label=\"ttim at 30 m\")\n", + "hb2 = ml.head(r2, 0, t2)\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", + "plt.semilogx(t2, hb2[0], label=\"ttim at 60 m\")\n", + "hb3 = ml.head(r3, 0, t3)\n", + "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t3, hb3[0], label=\"ttim at 90 m\")\n", + "hb4 = ml.head(r4, 0, t4)\n", + "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", + "plt.semilogx(t4, hb4[0], label=\"ttim at 120 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"ttim fit except for data of obs2\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.1.3. Calibration without obs3" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 51\n", + " # data points = 39\n", + " # variables = 3\n", + " chi-square = 0.00176424\n", + " reduced chi-square = 4.9007e-05\n", + " Akaike info crit = -384.140220\n", + " Bayesian info crit = -379.149535\n", + "[[Variables]]\n", + " kaq0: 45.2048754 +/- 1.46499595 (3.24%) (init = 10)\n", + " Saq0: 4.7843e-05 +/- 4.1024e-06 (8.57%) (init = 0.0001)\n", + " c0: 318.726085 +/- 67.0025787 (21.02%) (init = 1000)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.765\n", + " C(kaq0, c0) = 0.763\n", + " C(Saq0, c0) = -0.289\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq045.2048751.4649963.240792110010[45.20487543361927]
Saq00.0000480.0000048.5748380.000010.0010.0001[4.7842575268505704e-05]
c0318.72608567.00257921.0219941001000000.01000[318.7260846231769]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 45.204875 1.464996 3.240792 1 100 10 \n", + "Saq0 0.000048 0.000004 8.574838 0.00001 0.001 0.0001 \n", + "c0 318.726085 67.002579 21.021994 100 1000000.0 1000 \n", + "\n", + " parray \n", + "kaq0 [45.20487543361927] \n", + "Saq0 [4.7842575268505704e-05] \n", + "c0 [318.7260846231769] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ca3 = Calibrate(ml)\n", + "ca3.set_parameter(name=\"kaq0\", initial=10, pmin=1, pmax=100)\n", + "ca3.set_parameter(name=\"Saq0\", initial=1e-4, pmin=1e-5, pmax=1e-3)\n", + "ca3.set_parameter(name=\"c0\", initial=1000, pmin=100, pmax=1e6)\n", + "ca3.series(name=\"obs1\", x=r1, y=0, layer=0, t=t1, h=h1)\n", + "ca3.series(name=\"obs3\", x=r2, y=0, layer=0, t=t2, h=h2)\n", + "ca3.series(name=\"obs4\", x=r4, y=0, layer=0, t=t4, h=h4)\n", + "ca3.fit()\n", + "display(ca3.parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.006725845157213259\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(\"rmse:\", ca3.rmse())\n", + "plt.figure(figsize=(8, 5))\n", + "hc1 = ml.head(r1, 0, t1)\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, hc1[0], label=\"ttim at 30 m\")\n", + "hc2 = ml.head(r2, 0, t2)\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", + "plt.semilogx(t2, hc2[0], label=\"ttim at 60 m\")\n", + "hc3 = ml.head(r3, 0, t3)\n", + "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t3, hc3[0], label=\"ttim at 90 m\")\n", + "hc4 = ml.head(r4, 0, t4)\n", + "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", + "plt.semilogx(t4, hc4[0], label=\"ttim at 120 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"ttim fit except for data of obs3\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.1.4. Calibration without obs4" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...............................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 28\n", + " # data points = 39\n", + " # variables = 3\n", + " chi-square = 0.00113973\n", + " reduced chi-square = 3.1659e-05\n", + " Akaike info crit = -401.180660\n", + " Bayesian info crit = -396.189975\n", + "[[Variables]]\n", + " kaq0: 41.7209084 +/- 1.22793564 (2.94%) (init = 10)\n", + " Saq0: 5.7838e-05 +/- 3.9836e-06 (6.89%) (init = 0.0001)\n", + " c0: 180.964217 +/- 38.2629064 (21.14%) (init = 1000)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, c0) = 0.844\n", + " C(kaq0, Saq0) = -0.794\n", + " C(Saq0, c0) = -0.452\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq041.7209081.2279362.943214110010[41.72090838864663]
Saq00.0000580.0000046.8875610.000010.0010.0001[5.78378831790338e-05]
c0180.96421738.26290621.1439071001000000.01000[180.96421744249864]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 41.720908 1.227936 2.943214 1 100 10 \n", + "Saq0 0.000058 0.000004 6.887561 0.00001 0.001 0.0001 \n", + "c0 180.964217 38.262906 21.143907 100 1000000.0 1000 \n", + "\n", + " parray \n", + "kaq0 [41.72090838864663] \n", + "Saq0 [5.78378831790338e-05] \n", + "c0 [180.96421744249864] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ca4 = Calibrate(ml)\n", + "ca4.set_parameter(name=\"kaq0\", initial=10, pmin=1, pmax=100)\n", + "ca4.set_parameter(name=\"Saq0\", initial=1e-4, pmin=1e-5, pmax=1e-3)\n", + "ca4.set_parameter(name=\"c0\", initial=1000, pmin=100, pmax=1e6)\n", + "ca4.series(name=\"obs1\", x=r1, y=0, layer=0, t=t1, h=h1)\n", + "ca4.series(name=\"obs3\", x=r2, y=0, layer=0, t=t2, h=h2)\n", + "ca4.series(name=\"obs4\", x=r3, y=0, layer=0, t=t3, h=h3)\n", + "ca4.fit()\n", + "display(ca4.parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.005405897042964738\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(\"rmse:\", ca4.rmse())\n", + "plt.figure(figsize=(8, 5))\n", + "hd1 = ml.head(r1, 0, t1)\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, hd1[0], label=\"ttim at 30 m\")\n", + "hd2 = ml.head(r2, 0, t2)\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", + "plt.semilogx(t2, hd2[0], label=\"ttim at 60 m\")\n", + "hd3 = ml.head(r3, 0, t3)\n", + "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t3, hd3[0], label=\"ttim at 90 m\")\n", + "hd4 = ml.head(r4, 0, t4)\n", + "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", + "plt.semilogx(t4, hd4[0], label=\"ttim at 120 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"ttim fit except for data of obs4\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.1.4. Summary of results of the simulations missing one observation" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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k [m/d]Ss [1/m]c [d]RMSE
Data at 30 m removed57.558160.000033999468.8648480.003230
Data at 60 m removed45.0264920.000044349.1390130.002633
Data at 90 m removed57.558160.000033999468.8648480.006726
Data at 120 m removed41.7209080.000058180.9642170.005406
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" + ], + "text/plain": [ + " k [m/d] Ss [1/m] c [d] RMSE\n", + "Data at 30 m removed 57.55816 0.000033 999468.864848 0.003230\n", + "Data at 60 m removed 45.026492 0.000044 349.139013 0.002633\n", + "Data at 90 m removed 57.55816 0.000033 999468.864848 0.006726\n", + "Data at 120 m removed 41.720908 0.000058 180.964217 0.005406" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\"],\n", + " index=[\n", + " \"Data at 30 m removed\",\n", + " \"Data at 60 m removed\",\n", + " \"Data at 90 m removed\",\n", + " \"Data at 120 m removed\",\n", + " ],\n", + ")\n", + "t.loc[\"Data at 30 m removed\"] = ca1.parameters[\"optimal\"].values\n", + "t.loc[\"Data at 60 m removed\"] = ca2.parameters[\"optimal\"].values\n", + "t.loc[\"Data at 90 m removed\"] = ca1.parameters[\"optimal\"].values\n", + "t.loc[\"Data at 120 m removed\"] = ca4.parameters[\"optimal\"].values\n", + "rmse = [ca1.rmse(), ca2.rmse(), ca3.rmse(), ca4.rmse()]\n", + "t[\"RMSE\"] = rmse\n", + "t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The values for hydraulic conductivity and specific storage changed slightly for every simulation. However, the resistance of the aquitard layer varied significantly, indicating that this parameter is not uniform in the region investigated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.2. Calibrate with four datasets simultaneously:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we calibrate the same model but with all four observation wells considered." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# unkonwn parameters: kaq, Saq, c\n", + "m_1 = ModelMaq(\n", + " kaq=10, z=[0, zt, zb], c=500, Saq=0.001, topboundary=\"semi\", tmin=0.001, tmax=0.5\n", + ")\n", + "w_1 = Well(m_1, xw=0, yw=0, tsandQ=[(0, Q), (0.34, 0)])\n", + "m_1.solve(silent=\"True\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 33\n", + " # data points = 51\n", + " # variables = 3\n", + " chi-square = 0.00178546\n", + " reduced chi-square = 3.7197e-05\n", + " Akaike info crit = -517.255140\n", + " Bayesian info crit = -511.459663\n", + "[[Variables]]\n", + " kaq0: 45.3318581 +/- 1.18521248 (2.61%) (init = 10)\n", + " Saq0: 4.7623e-05 +/- 3.1043e-06 (6.52%) (init = 0.0001)\n", + " c0: 331.164465 +/- 76.1857064 (23.01%) (init = 500)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.771\n", + " C(kaq0, c0) = 0.762\n", + " C(Saq0, c0) = -0.299\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq045.3318581.1852122.614524-infinf10[45.33185809252601]
Saq00.0000480.0000036.518546-infinf0.0001[4.762268497957192e-05]
c0331.16446576.18570623.0053990.0inf500[331.16446453346026]
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" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 45.331858 1.185212 2.614524 -inf inf 10 \n", + "Saq0 0.000048 0.000003 6.518546 -inf inf 0.0001 \n", + "c0 331.164465 76.185706 23.005399 0.0 inf 500 \n", + "\n", + " parray \n", + "kaq0 [45.33185809252601] \n", + "Saq0 [4.762268497957192e-05] \n", + "c0 [331.16446453346026] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "c0 = Calibrate(ml)\n", + "c0.set_parameter(name=\"kaq0\", initial=10)\n", + "c0.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "c0.set_parameter(name=\"c0\", initial=500, pmin=0)\n", + "c0.series(name=\"obs1\", x=30, y=0, t=t1, h=h1, layer=0)\n", + "c0.series(name=\"obs2\", x=60, y=0, t=t2, h=h2, layer=0)\n", + "c0.series(name=\"obs3\", x=90, y=0, t=t3, h=h3, layer=0)\n", + "c0.series(name=\"obs4\", x=120, y=0, t=t4, h=h4, layer=0)\n", + "c0.fit(report=True)\n", + "display(c0.parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.0059168424104503155\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm_11 = ml.head(r1, 0, t1)\n", + "hm_12 = ml.head(r2, 0, t2)\n", + "hm_13 = ml.head(r3, 0, t3)\n", + "hm_14 = ml.head(r4, 0, t4)\n", + "print(\"rmse:\", c0.rmse())\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, hm_11[0], label=\"ttim at 30 m\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", + "plt.semilogx(t2, hm_12[0], label=\"ttim at 60 m\")\n", + "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t3, hm_13[0], label=\"ttim at 90 m\")\n", + "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", + "plt.semilogx(t4, hm_14[0], label=\"ttim at 120 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"model with leakage only\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The overall fit is relatively good. Comparing the new model to the three previous models, the adjusted parameters seem to be in between the previously computed values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Alternative Model Aquitard with leakage & storage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second conceptualization for the Dalem test is to consider the storage in the aquitard. Hence, we define the ```Sll``` parameter in the model building class ModelMaq:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# unkonwn parameters: kaq, Saq, c, Sll\n", + "m_2 = ModelMaq(\n", + " kaq=10,\n", + " z=[0, zt, zb],\n", + " c=500,\n", + " Saq=0.001,\n", + " Sll=0.001,\n", + " topboundary=\"semi\",\n", + " tmin=0.001,\n", + " tmax=0.5,\n", + ")\n", + "w_2 = Well(m_2, xw=0, yw=0, tsandQ=[(0, Q), (0.34, 0)])\n", + "m_2.solve(silent=\"True\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Calibration of Alternative Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We follow the same previous steps for calibration, but now we add the additional `Sll` parameter with the ```set_parameter_by_reference``` method: ***I think I have found a bug***" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 79\n", + " # data points = 51\n", + " # variables = 4\n", + " chi-square = 0.00206484\n", + " reduced chi-square = 4.3933e-05\n", + " Akaike info crit = -507.840896\n", + " Bayesian info crit = -500.113593\n", + "[[Variables]]\n", + " kaq0: 44.8361465 +/- 1.37005719 (3.06%) (init = 10)\n", + " Saq0: 4.3738e-05 +/- 9.0241e-06 (20.63%) (init = 0.0001)\n", + " c0: 934.126739 +/- 8774.46957 (939.32%) (init = 500)\n", + " Sll: 2.2042e-04 +/- 0.00332575 (1508.80%) (init = 0.001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(c0, Sll) = 0.987\n", + " C(Saq0, Sll) = 0.922\n", + " C(Saq0, c0) = 0.893\n", + " C(kaq0, Sll) = 0.361\n", + " C(kaq0, c0) = 0.246\n", + " C(kaq0, Saq0) = 0.202\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq044.8361471.3700573.055698-infinf10[44.836146524596224]
Saq00.0000440.00000920.6323210.000000e+00inf0.0001[4.373790659051302e-05]
c0934.1267398774.469571939.3232420.000000e+001000.00500[934.1267393695258]
Sll0.000220.0033261508.7969561.000000e-080.010.001[0.00022042417906498941]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 44.836147 1.370057 3.055698 -inf inf 10 \n", + "Saq0 0.000044 0.000009 20.632321 0.000000e+00 inf 0.0001 \n", + "c0 934.126739 8774.469571 939.323242 0.000000e+00 1000.00 500 \n", + "Sll 0.00022 0.003326 1508.796956 1.000000e-08 0.01 0.001 \n", + "\n", + " parray \n", + "kaq0 [44.836146524596224] \n", + "Saq0 [4.373790659051302e-05] \n", + "c0 [934.1267393695258] \n", + "Sll [0.00022042417906498941] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## Check why the errors in the pmin of the Sll adjustment\n", + "c1 = Calibrate(m_2)\n", + "c1.set_parameter(name=\"kaq0\", initial=10)\n", + "c1.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "c1.set_parameter(name=\"c0\", initial=500, pmin=0, pmax=1000)\n", + "c1.set_parameter_by_reference(\n", + " name=\"Sll\", parameter=m_2.aq.Sll[:], initial=1e-3, pmin=1e-8, pmax=0.01\n", + ")\n", + "c1.series(name=\"obs1\", x=30, y=0, t=t1, h=h1, layer=0)\n", + "c1.series(name=\"obs2\", x=60, y=0, t=t2, h=h2, layer=0)\n", + "c1.series(name=\"obs3\", x=90, y=0, t=t3, h=h3, layer=0)\n", + "c1.series(name=\"obs4\", x=120, y=0, t=t4, h=h4, layer=0)\n", + "c1.fit(report=True)\n", + "display(c1.parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.006362946741751943\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm_21 = m_2.head(r1, 0, t1)\n", + "hm_22 = m_2.head(r2, 0, t2)\n", + "hm_23 = m_2.head(r3, 0, t3)\n", + "hm_24 = m_2.head(r4, 0, t4)\n", + "print(\"rmse:\", c1.rmse())\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, hm_21[0], label=\"ttim at 30 m\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", + "plt.semilogx(t2, hm_22[0], label=\"ttim at 60 m\")\n", + "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t3, hm_23[0], label=\"ttim at 90 m\")\n", + "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", + "plt.semilogx(t4, hm_24[0], label=\"ttim at 120 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"model with both leakage and storage\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The adjusted parameter did not improve the fit and the new model has higher values of AIC and BIC compared with the previous model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Alternative model with Impervious top.\n", + "\n", + "We test the final alternative model that the surface layer is impervious at the top. Hence, we adjust a ModelMaq with an additional 1 mm thick aquifer at the top and the configuration ```topboundary = 'conf'```.\n", + "We then test this configuration considering a model with both storage and resistance in the aquitard layer and a model with just the resistance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8.1. Model with storage and leakage" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vcant\\anaconda3\\envs\\ttim\\lib\\site-packages\\ttim\\aquifer.py:73: RuntimeWarning: divide by zero encountered in true_divide\n", + " self.D = self.T / self.Scoefaq\n" + ] + } + ], + "source": [ + "# unkonwn parameters: kaq1, Saq1, c, Sll\n", + "m_3 = ModelMaq(\n", + " kaq=[0.01, 10],\n", + " z=[0, -0.001, -8.001, -45.001],\n", + " c=500,\n", + " Saq=[0, 0.001],\n", + " Sll=1e-4,\n", + " topboundary=\"conf\",\n", + " tmin=0.001,\n", + " tmax=0.5,\n", + ")\n", + "w_3 = Well(m_3, xw=0, yw=0, tsandQ=[(0, 761), (0.34, 0)], layers=1)\n", + "m_3.solve(silent=\"True\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 94\n", + " # data points = 51\n", + " # variables = 4\n", + " chi-square = 0.00177210\n", + " reduced chi-square = 3.7704e-05\n", + " Akaike info crit = -515.638082\n", + " Bayesian info crit = -507.910779\n", + "[[Variables]]\n", + " kaq1: 45.1862811 +/- 1.22692771 (2.72%) (init = 10)\n", + " Saq1: 3.9423e-05 +/- 6.9685e-06 (17.68%) (init = 0.0001)\n", + " c1: 888.317499 +/- 207898.722 (23403.65%) (init = 500)\n", + " Sll: 4.1670e-04 +/- 0.11253785 (27006.75%) (init = 1e-05)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(c1, Sll) = 1.000\n", + " C(Saq1, c1) = 0.888\n", + " C(Saq1, Sll) = 0.888\n", + " C(kaq1, c1) = 0.192\n", + " C(kaq1, Sll) = 0.191\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq145.1862811.2269282.715266-infinf10[45.18628110628006]
Saq10.0000390.00000717.676049-infinf0.0001[3.942336849842717e-05]
c1888.317499207898.72232823403.6504440.01000.0500[888.3174991342955]
Sll0.0004170.11253827006.752060.0inf0.00001[0.0004167026352754899, 0.0004167026352754899]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq1 45.186281 1.226928 2.715266 -inf inf 10 \n", + "Saq1 0.000039 0.000007 17.676049 -inf inf 0.0001 \n", + "c1 888.317499 207898.722328 23403.650444 0.0 1000.0 500 \n", + "Sll 0.000417 0.112538 27006.75206 0.0 inf 0.00001 \n", + "\n", + " parray \n", + "kaq1 [45.18628110628006] \n", + "Saq1 [3.942336849842717e-05] \n", + "c1 [888.3174991342955] \n", + "Sll [0.0004167026352754899, 0.0004167026352754899] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "c2 = Calibrate(m_3)\n", + "c2.set_parameter(name=\"kaq1\", initial=10)\n", + "c2.set_parameter(name=\"Saq1\", initial=1e-4)\n", + "c2.set_parameter(name=\"c1\", initial=500, pmin=0, pmax=1000)\n", + "c2.set_parameter_by_reference(name=\"Sll\", parameter=m_3.aq.Sll[:], initial=1e-5, pmin=0)\n", + "c2.series(name=\"obs1\", x=30, y=0, t=t1, h=h1, layer=1)\n", + "c2.series(name=\"obs2\", x=60, y=0, t=t2, h=h2, layer=1)\n", + "c2.series(name=\"obs3\", x=90, y=0, t=t3, h=h3, layer=1)\n", + "c2.series(name=\"obs4\", x=120, y=0, t=t4, h=h4, layer=1)\n", + "c2.fit(report=True)\n", + "display(c2.parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model adjusted unrealistic values for vertical resistance and aquitard storage. Moreover, their correlation coefficient shows a perfect correlation, which means this is a problem without determination. We must fix or remove one of these parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.005894670238538926\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vcant\\anaconda3\\envs\\ttim\\lib\\site-packages\\ttim\\aquifer.py:73: RuntimeWarning: divide by zero encountered in true_divide\n", + " self.D = self.T / self.Scoefaq\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm_31 = m_3.head(r1, 0, t1)\n", + "hm_32 = m_3.head(r2, 0, t2)\n", + "hm_33 = m_3.head(r3, 0, t3)\n", + "hm_34 = m_3.head(r4, 0, t4)\n", + "print(\"rmse:\", c2.rmse())\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, hm_31[-1], label=\"ttim at 30 m\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", + "plt.semilogx(t2, hm_32[-1], label=\"ttim at 60 m\")\n", + "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t3, hm_33[-1], label=\"ttim at 90 m\")\n", + "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", + "plt.semilogx(t4, hm_34[-1], label=\"ttim at 120 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"model with storage only\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8.2. Model with vertical leakage only (without aquitard storage)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We reload and calibrate a model without storage in the aquitard layer" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vcant\\anaconda3\\envs\\ttim\\lib\\site-packages\\ttim\\aquifer.py:73: RuntimeWarning: divide by zero encountered in true_divide\n", + " self.D = self.T / self.Scoefaq\n" + ] + } + ], + "source": [ + "# unkonwn parameters: kaq1, Saq1, c, Sll\n", + "m_4 = ModelMaq(\n", + " kaq=[0.01, 10],\n", + " z=[0, -0.001, -8.001, -45.001],\n", + " c=500,\n", + " Saq=[0, 0.001],\n", + " Sll=0.1,\n", + " topboundary=\"conf\",\n", + " tmin=0.001,\n", + " tmax=0.5,\n", + ")\n", + "w_4 = Well(m_4, xw=0, yw=0, tsandQ=[(0, 761), (0.34, 0)], layers=1)\n", + "m_4.solve(silent=\"True\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".......................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 52\n", + " # data points = 51\n", + " # variables = 3\n", + " chi-square = 0.02308536\n", + " reduced chi-square = 4.8095e-04\n", + " Akaike info crit = -386.719493\n", + " Bayesian info crit = -380.924016\n", + "[[Variables]]\n", + " kaq1: 27.4923071 +/- 1.79949992 (6.55%) (init = 10)\n", + " Saq1: 1.0001e-07 +/- 1.3554e-07 (135.52%) (init = 0.001)\n", + " c1: 1999.96913 +/- 83514.7282 (4175.80%) (init = 500)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(Saq1, c1) = -0.877\n", + " C(kaq1, c1) = 0.839\n", + " C(kaq1, Saq1) = -0.788\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq127.4923071.799500e+006.545467-infinf10[27.492307129448434]
Saq10.01.355366e-07135.5218391.000000e-07inf0.001[1.0001088546207626e-07]
c11999.9691278.351473e+044175.8008680.000000e+002000.0500[1999.9691266874966]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq1 27.492307 1.799500e+00 6.545467 -inf inf 10 \n", + "Saq1 0.0 1.355366e-07 135.521839 1.000000e-07 inf 0.001 \n", + "c1 1999.969127 8.351473e+04 4175.800868 0.000000e+00 2000.0 500 \n", + "\n", + " parray \n", + "kaq1 [27.492307129448434] \n", + "Saq1 [1.0001088546207626e-07] \n", + "c1 [1999.9691266874966] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "c3 = Calibrate(m_4)\n", + "c3.set_parameter(name=\"kaq1\", initial=10)\n", + "c3.set_parameter(name=\"Saq1\", initial=1e-3, pmin=1e-7)\n", + "c3.set_parameter(name=\"c1\", initial=500, pmin=0, pmax=2000)\n", + "c3.series(name=\"obs1\", x=30, y=0, t=t1, h=h1, layer=1)\n", + "c3.series(name=\"obs2\", x=60, y=0, t=t2, h=h2, layer=1)\n", + "c3.series(name=\"obs3\", x=90, y=0, t=t3, h=h3, layer=1)\n", + "c3.series(name=\"obs4\", x=120, y=0, t=t4, h=h4, layer=1)\n", + "c3.fit(report=True)\n", + "display(c3.parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.021275670123861237\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vcant\\anaconda3\\envs\\ttim\\lib\\site-packages\\ttim\\aquifer.py:73: RuntimeWarning: divide by zero encountered in true_divide\n", + " self.D = self.T / self.Scoefaq\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm_r1 = m_4.head(r1, 0, t1)\n", + "hm_r2 = m_4.head(r2, 0, t2)\n", + "hm_r3 = m_4.head(r3, 0, t3)\n", + "hm_r4 = m_4.head(r4, 0, t4)\n", + "print(\"rmse:\", c3.rmse())\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, hm_r1[-1], label=\"ttim at 30 m\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", + "plt.semilogx(t2, hm_r2[-1], label=\"ttim at 60 m\")\n", + "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t3, hm_r3[-1], label=\"ttim at 90 m\")\n", + "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", + "plt.semilogx(t4, hm_r4[-1], label=\"ttim at 120 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"model with storage only\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The resulting model shows a large value for the resistance to vertical flow in the aquitard layer (```c0```). Fit error and AIC, BIC values were similar to the statistics encountered in the semi-confined model without storage (model 2). However, the large vertical resistance for this model is a key result, as it indicates a confined condition. Thus, just with a curve fitting approach, we cannot discard confined conditions in this aquifer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 9. Analysis and summary of values simulated by different models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we compare the simulations done with TTim with other software and the values reported in Kruseman and de Ridder (1970). The published values were determined by graphical adjustment to the Hantush family of type curves (Hantush, 1955). To compare the results reported in the literature with different models, we begin by analysing the values obtained without storage.\n", + "Alongside with TTim and literature values, we report the values MLU (Carlson & Randall, 2012) and AQTESOLV (Duffield, 2007) models, reported by Xinzhu (2020)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9.1. Comparison of results of models without storage" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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k [m/d]Ss [1/m]c [d]Sll [1/m]RMSE
Hantush45.3320.000048331.141-0.005917
ttim - impervious top27.4923070.01999.969127-NaN
ttim - semi-confined45.3318580.000048331.164465-0.005917
MLU45.1860.000039769.20.0003610.005941
AQTESOLV49.2860.000046745.156-0.007245
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" + ], + "text/plain": [ + " k [m/d] Ss [1/m] c [d] Sll [1/m] RMSE\n", + "Hantush 45.332 0.000048 331.141 - 0.005917\n", + "ttim - impervious top 27.492307 0.0 1999.969127 - NaN\n", + "ttim - semi-confined 45.331858 0.000048 331.164465 - 0.005917\n", + "MLU 45.186 0.000039 769.2 0.000361 0.005941\n", + "AQTESOLV 49.286 0.000046 745.156 - 0.007245" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t1 = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\", \"Sll [1/m]\"],\n", + " index=[\n", + " \"Hantush\",\n", + " \"ttim - impervious top\",\n", + " \"ttim - semi-confined\",\n", + " \"MLU\",\n", + " \"AQTESOLV\",\n", + " ],\n", + ")\n", + "t1.loc[\"Hantush\"] = [45.332, 4.762e-5, 331.141, \"-\"]\n", + "t1.loc[\"ttim - impervious top\"] = np.append(c3.parameters[\"optimal\"].values, \"-\")\n", + "t1.loc[\"ttim - semi-confined\"] = np.append(c0.parameters[\"optimal\"].values, \"-\")\n", + "t1.loc[\"MLU\"] = [45.186, 3.941e-05, 769.200, 3.611e-04]\n", + "t1.loc[\"AQTESOLV\"] = [49.286, 4.559e-05, 745.156, \"-\"]\n", + "rmse = [0.005917, np.nan, c0.rmse(), 0.005941, 0.007245]\n", + "t1[\"RMSE\"] = rmse\n", + "t1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9.2. Comparison of results of models with storage\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vcant\\anaconda3\\envs\\ttim\\lib\\site-packages\\ttim\\aquifer.py:73: RuntimeWarning: divide by zero encountered in true_divide\n", + " self.D = self.T / self.Scoefaq\n" + ] + }, + { + "data": { + "text/html": [ + "
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k [m/d]Ss [1/m]c [d]Sll [1/m]RMSE
ttim - impervious top45.1862810.000039888.3174990.0004170.005895
ttim - semi-confined44.8361470.000044934.1267390.000220.006363
MLU45.3350.000047331.40.0000130.004941
AQTESOLV45.1590.000041367.5770.0000290.005861
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" + ], + "text/plain": [ + " k [m/d] Ss [1/m] c [d] Sll [1/m] RMSE\n", + "ttim - impervious top 45.186281 0.000039 888.317499 0.000417 0.005895\n", + "ttim - semi-confined 44.836147 0.000044 934.126739 0.00022 0.006363\n", + "MLU 45.335 0.000047 331.4 0.000013 0.004941\n", + "AQTESOLV 45.159 0.000041 367.577 0.000029 0.005861" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t2 = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\", \"Sll [1/m]\"],\n", + " index=[\"ttim - impervious top\", \"ttim - semi-confined\", \"MLU\", \"AQTESOLV\"],\n", + ")\n", + "t2.loc[\"MLU\"] = [45.335, 4.668e-05, 331.400, 1.284e-05]\n", + "t2.loc[\"AQTESOLV\"] = [45.159, 4.100e-05, 367.577, 2.868e-05]\n", + "t2.loc[\"ttim - impervious top\"] = c2.parameters[\"optimal\"].values\n", + "t2.loc[\"ttim - semi-confined\"] = c1.parameters[\"optimal\"].values\n", + "t2[\"RMSE\"] = [c2.rmse(), c1.rmse(), 0.004941, 0.005861]\n", + "t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Overall, all models found similar K values for the aquifer. TTim models differed significantly from the MLU and AQTESOLV solutions for the aquitard storage and resistance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Bakker, M. Semi-analytic modeling of transient multi-layer flow with TTim. Hydrogeol J 21, 935–943 (2013). https://doi.org/10.1007/s10040-013-0975-2\n", + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Newville, M.,Stensitzki, T., Allen, D.B., Ingargiola, A. (2014) LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python.https://dx.doi.org/10.5281/zenodo.11813. https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021).\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Next Notebook: [Leaky 2 - Hardixveld](leaky2_hardixveld.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/04pumpingtests/leaky2_hardixveld.ipynb b/docs/04pumpingtests/leaky2_hardixveld.ipynb new file mode 100644 index 0000000..aaec0ef --- /dev/null +++ b/docs/04pumpingtests/leaky2_hardixveld.ipynb @@ -0,0 +1,1351 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Leaky Aquifer Recovery Test - Hardixveld Example\n", + "**This test is taken from MLU examples.**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Introduction and Conceptual Model\n", + "\n", + "This test was taken in Hardinxveld-Giessendam, Netherlands, in 1981 to quantify the head-loss at each pumping well by clogging and to assess the transmissivity variation in the area.\n", + "\n", + "The hydrogeological conceptualization can be described as the following:\n", + "* The first ten meters depth is an aquitard\n", + "* Followed by the first aquifer from 10 to 37 m depth, this is also the test aquifer.\n", + "* A new aquitard is present from 37 m depth to 68 m depth\n", + "* A final aquifer is from 68 to 88 m depth.\n", + "* Below 88 m depth the formations are considered an aquiclude\n", + "\n", + "Five pumping wells are screened in the first aquifer. The drawdown of one of them is available in the MLU documentation (Carlson & Randall, 2012).\n", + "The provided pumping well was operated for 20 minutes at 1848 m3/d. Drawdown was recorded for a total of 50 minutes during and after pumping. The radius of the pumped well is 0.155 m.\n", + "\n", + "In this notebook, we reproduce the work of Xinzhu (2020) and demonstrate the use of TTim to fit a recovery test and quantify the skin resistance in the well and the hydraulic parameters of the aquifer.\n", + "\n", + "The following figure summarises the hydrogeological conceptual model." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "# sky\n", + "sky = plt.Rectangle((-20, 0), width=50, height=20, fc=\"b\", zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "# Aquifer:\n", + "ground = plt.Rectangle(\n", + " (-20, -37),\n", + " width=50,\n", + " height=27,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + ")\n", + "ax.add_patch(ground)\n", + "\n", + "# Aquifer 2:\n", + "ground2 = plt.Rectangle(\n", + " (-20, -88),\n", + " width=50,\n", + " height=20,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + ")\n", + "ax.add_patch(ground2)\n", + "\n", + "# Confining bed:\n", + "confining_unit = plt.Rectangle(\n", + " (-20, -10),\n", + " width=50,\n", + " height=10,\n", + " fc=np.array([100, 100, 100]) / 255,\n", + " zorder=0,\n", + " alpha=0.7,\n", + ")\n", + "ax.add_patch(confining_unit)\n", + "\n", + "confining_unit2 = plt.Rectangle(\n", + " (-20, -68),\n", + " width=50,\n", + " height=31,\n", + " fc=np.array([100, 100, 100]) / 255,\n", + " zorder=0,\n", + " alpha=0.7,\n", + ")\n", + "ax.add_patch(confining_unit2)\n", + "\n", + "well = plt.Rectangle(\n", + " (-1.5, -37), width=3, height=37, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "ax.add_patch(well)\n", + "\n", + "# Wellhead\n", + "wellhead = plt.Rectangle(\n", + " (-2, 0), width=4, height=10, fc=np.array([200, 200, 200]) / 255, zorder=2, ec=\"k\"\n", + ")\n", + "ax.add_patch(wellhead)\n", + "\n", + "# Screen for the well:\n", + "screen = plt.Rectangle(\n", + " (-1.5, -37),\n", + " width=3,\n", + " height=27,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x=2, y=7, dx=5, dy=0, color=\"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x=7, y=7, s=r\"$ Q = 1848 \\frac{m^3}{d}$\", fontsize=\"large\")\n", + "\n", + "# last line\n", + "line = plt.Line2D(xdata=[-200, 1200], ydata=[0, 0], color=\"k\")\n", + "ax.add_line(line)\n", + "\n", + "\n", + "ax.set_xlim([-20, 30])\n", + "ax.set_ylim([-88, 20])\n", + "ax.set_xlabel(\"Distance [m]\")\n", + "ax.set_ylabel(\"Relative height [m]\")\n", + "ax.set_title(\"Conceptual Model - Hardixveld Example\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Import required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from ttim import *\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters for the model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "H = 27 # aquifer thickness [m]\n", + "zt = -10 # upper boundary of aquifer\n", + "zb = zt - H # lower boundary of the aquifer\n", + "rw = 0.155 # well screen radius [m]\n", + "Q = 1848 # constant discharge rate [m^3/d]\n", + "t0 = 0.013889 # time stop pumping [d]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Load data for the recovery test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data is saved in a text file where the first column is the time data in days and in the second is the drawdown in m" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data = np.loadtxt(\"data/recovery.txt\", skiprows=1)\n", + "t = data[:, 0]\n", + "h = data[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Create the first conceptual model\n", + "\n", + "Here we create a two aquifer leaky model in ModelMaq, so we have to define the top of the first aquitard layer, followed by the tops and bottoms of the aquifer layers. Here we ignore storage (```Sll```) of the aquitard layers.\n", + "\n", + "The well is defined with skin resistance (```res```) and the pumping schedule with the start and shutdown of the pump.\n", + "\n", + "More details on model construction and theory can be seen in:\n", + "\n", + "- [Confined 1 - Oude Korendijk](confined1_oude_korendijk)\n", + "- [Confined 4 - Schroth](confined4_schroth.ipynb)\n", + "- [Leaky 1 - Dalem](leaky1_dalem.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml1 = ModelMaq(\n", + " kaq=[50, 40],\n", + " z=[0, zt, zb, -68, -88],\n", + " c=[1000, 1000],\n", + " Saq=[1e-4, 5e-5],\n", + " topboundary=\"semi\",\n", + " tmin=1e-4,\n", + " tmax=0.04,\n", + ")\n", + "w1 = Well(ml1, xw=0, yw=0, rw=rw, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0)\n", + "ml1.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Calibration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The parameters to be calibrated are the hydraulic conductivity and specific storage of the first layer, and the skin resistance of the well. The parameters of the aquitards and the second aquifer are kept fixed.\n", + "\n", + "More details on the calibration procedure and theory can be seen in:\n", + "\n", + "- [Confined 1 - Oude Korendijk](confined1_oude_korendijk)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..............................................................................................................................................................................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 331\n", + " # data points = 35\n", + " # variables = 3\n", + " chi-square = 0.00106334\n", + " reduced chi-square = 3.3229e-05\n", + " Akaike info crit = -358.059006\n", + " Bayesian info crit = -353.392962\n", + "[[Variables]]\n", + " kaq0: 44.5287657 +/- 0.66007100 (1.48%) (init = 50)\n", + " Saq0: 6.3907e-06 +/- 9.5299e-07 (14.91%) (init = 0.0001)\n", + " res: 0.01620452 +/- 5.7491e-04 (3.55%) (init = 1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, res) = 0.977\n", + " C(Saq0, res) = 0.950\n", + " C(kaq0, Saq0) = 0.864\n" + ] + } + ], + "source": [ + "ca1 = Calibrate(ml1)\n", + "ca1.set_parameter(name=\"kaq0\", initial=50, pmin=0)\n", + "ca1.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "ca1.set_parameter_by_reference(name=\"res\", parameter=w1.res[:], initial=1, pmin=0)\n", + "ca1.seriesinwell(name=\"obs\", element=w1, t=t, h=h)\n", + "ca1.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq044.5287666.600710e-011.4823470inf50[44.52876567003543]
Saq00.0000069.529879e-0714.9121820inf0.0001[6.3906674174774025e-06]
res0.0162055.749070e-043.5478180inf1[0.016204524089252326]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 44.528766 6.600710e-01 1.482347 0 inf 50 \n", + "Saq0 0.000006 9.529879e-07 14.912182 0 inf 0.0001 \n", + "res 0.016205 5.749070e-04 3.547818 0 inf 1 \n", + "\n", + " parray \n", + "kaq0 [44.52876567003543] \n", + "Saq0 [6.3906674174774025e-06] \n", + "res [0.016204524089252326] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.005511916215804842\n" + ] + } + ], + "source": [ + "display(ca1.parameters)\n", + "print(\"RMSE:\", ca1.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm1 = w1.headinside(t)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.loglog(t, -h, \".\", label=\"obs - pumping well\")\n", + "plt.loglog(t, -hm1[0], label=\"ttim results\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"head [m]\")\n", + "plt.legend()\n", + "plt.title(\"Model Results - Model 1\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Overall a good fit has been observed. We will test the results with wellbore storage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Alternative model with wellbore storage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally to the parameters in the previous model, we add the ```rc``` parameter in the well. It is the radius of the caisson of the well, and TTim uses it to calculate the water storage in the well" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml2 = ModelMaq(\n", + " kaq=[50, 40],\n", + " z=[0, zt, zb, -68, -88],\n", + " c=[1000, 1000],\n", + " Saq=[1e-4, 5e-5],\n", + " topboundary=\"semi\",\n", + " tmin=1e-4,\n", + " tmax=0.04,\n", + ")\n", + "w2 = Well(ml2, xw=0, yw=0, rw=rw, rc=0.155, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0)\n", + "ml2.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Calibrate the alternative model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally, we also calibrate the rc parameter in the well:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 573\n", + " # data points = 35\n", + " # variables = 4\n", + " chi-square = 0.00106334\n", + " reduced chi-square = 3.4301e-05\n", + " Akaike info crit = -356.058972\n", + " Bayesian info crit = -349.837580\n", + "[[Variables]]\n", + " kaq0: 44.5322775 +/- 0.99969014 (2.24%) (init = 50)\n", + " Saq0: 6.3928e-06 +/- 1.0629e-06 (16.63%) (init = 0.0001)\n", + " rc: 0.00458531 +/- 0.45478829 (9918.37%) (init = 0.1)\n", + " res: 0.01620699 +/- 7.8457e-04 (4.84%) (init = 1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, res) = 0.982\n", + " C(Saq0, res) = 0.902\n", + " C(kaq0, Saq0) = 0.806\n", + " C(kaq0, rc) = 0.743\n", + " C(rc, res) = 0.664\n", + " C(Saq0, rc) = 0.359\n" + ] + } + ], + "source": [ + "ca2 = Calibrate(ml2)\n", + "ca2.set_parameter(name=\"kaq0\", initial=50, pmin=0)\n", + "ca2.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "ca2.set_parameter_by_reference(name=\"rc\", parameter=w2.rc[:], initial=0.1, pmin=0)\n", + "ca2.set_parameter_by_reference(name=\"res\", parameter=w2.res[:], initial=1, pmin=0)\n", + "ca2.seriesinwell(name=\"obs\", element=w2, t=t, h=h)\n", + "ca2.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq044.5322770.9996902.2448660inf50[44.5322774989901]
Saq00.0000060.00000116.6257560inf0.0001[6.392843512337265e-06]
rc0.0045850.4547889918.3673320inf0.1[0.0045853140190308395]
res0.0162070.0007854.8409220inf1[0.016206990038743596]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 44.532277 0.999690 2.244866 0 inf 50 \n", + "Saq0 0.000006 0.000001 16.625756 0 inf 0.0001 \n", + "rc 0.004585 0.454788 9918.367332 0 inf 0.1 \n", + "res 0.016207 0.000785 4.840922 0 inf 1 \n", + "\n", + " parray \n", + "kaq0 [44.5322774989901] \n", + "Saq0 [6.392843512337265e-06] \n", + "rc [0.0045853140190308395] \n", + "res [0.016206990038743596] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.005511918881910666\n" + ] + } + ], + "source": [ + "display(ca2.parameters)\n", + "print(\"RMSE:\", ca2.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm2 = w2.headinside(t)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.loglog(t, -h, \".\", label=\"obs - pumping well\")\n", + "plt.loglog(t, -hm2[0], label=\"ttim model results\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"head [m]\")\n", + "plt.legend()\n", + "plt.title(\"Model Results - Model 2\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simulation with rc has slightly worse performance. The Akaike and Bayes criteria are somewhat worse than with the previous model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Testing single layer model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this final model, we will test the assumption that we can ignore the underlying aquifer. Thus, we only worry about the upper aquifer, where water is pumped.\n", + "\n", + "Therefore the last model is a single layer semi-confined aquifer that only includes the top aquitard and the first aquifer. In the well, we are not considering the ```rc``` parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml0 = ModelMaq(\n", + " kaq=50, z=[0, zt, zb], c=1000, Saq=1e-4, topboundary=\"semi\", tmin=1e-4, tmax=0.04\n", + ")\n", + "w0 = Well(ml0, xw=0, yw=0, rw=rw, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0)\n", + "ml0.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8.1. Calibration\n", + "\n", + "In the calibration we repeat the procedure for model 1." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......................................................................................................................................................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 307\n", + " # data points = 35\n", + " # variables = 3\n", + " chi-square = 0.00106438\n", + " reduced chi-square = 3.3262e-05\n", + " Akaike info crit = -358.024761\n", + " Bayesian info crit = -353.358717\n", + "[[Variables]]\n", + " kaq0: 44.5516140 +/- 0.65533645 (1.47%) (init = 50)\n", + " Saq0: 3.2314e-06 +/- 4.9282e-07 (15.25%) (init = 0.0001)\n", + " res: 0.01502951 +/- 5.9465e-04 (3.96%) (init = 1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, res) = 0.977\n", + " C(Saq0, res) = 0.948\n", + " C(kaq0, Saq0) = 0.861\n" + ] + } + ], + "source": [ + "ca0 = Calibrate(ml0)\n", + "ca0.set_parameter(name=\"kaq0\", initial=50, pmin=0)\n", + "ca0.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "ca0.set_parameter_by_reference(name=\"res\", parameter=w0.res[:], initial=1)\n", + "ca0.seriesinwell(name=\"obs\", element=w0, t=t, h=h)\n", + "ca0.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq044.5516146.553365e-011.470960inf50[44.55161399621271]
Saq00.0000034.928237e-0715.2513080inf0.0001[3.231353431054629e-06]
res0.015035.946511e-043.956557-infinf1[0.015029508743142891]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 44.551614 6.553365e-01 1.47096 0 inf 50 \n", + "Saq0 0.000003 4.928237e-07 15.251308 0 inf 0.0001 \n", + "res 0.01503 5.946511e-04 3.956557 -inf inf 1 \n", + "\n", + " parray \n", + "kaq0 [44.55161399621271] \n", + "Saq0 [3.231353431054629e-06] \n", + "res [0.015029508743142891] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.005514613406867893\n" + ] + } + ], + "source": [ + "display(ca0.parameters)\n", + "print(\"RMSE:\", ca0.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Model Results - Model 3')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm = w0.headinside(t)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.loglog(t, -h, \".\", label=\"obs - pumping well\")\n", + "plt.loglog(t, -hm[0], label=\"ttim model results\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.legend()\n", + "plt.title(\"Model Results - Model 3\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 9. Consider Log-curve-fitting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The example reported in the MLU documentation (Carslon & Randall, 2012) uses curve-fitting of log-drawdowns, besides linear curve-fitting. The use of log-curve-fitting can help the fitting when observed errors are not independent of the observations. We will apply log-curve-fitting in TTim to reproduce the methodology from MLU.\n", + "\n", + "For the new calibration, we use the ```fitm``` script, which has an implementation of the log-curve fitting. The original objective function is ```h_observed - h_predicted```, while for the log curve fitting, the objective function has been changed to ```log(abs(h_observed-h_predicted))```." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from fitm import Calibrate" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml3 = ModelMaq(\n", + " kaq=[50, 40],\n", + " z=[0, zt, zb, -68, -88],\n", + " c=[1000, 1000],\n", + " Saq=[1e-4, 5e-5],\n", + " topboundary=\"semi\",\n", + " tmin=1e-4,\n", + " tmax=0.04,\n", + ")\n", + "w3 = Well(ml3, xw=0, yw=0, rw=rw, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0)\n", + "ml3.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...............................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 44\n", + " # data points = 35\n", + " # variables = 3\n", + " chi-square = 423.133174\n", + " reduced chi-square = 13.2229117\n", + " Akaike info crit = 93.2318615\n", + " Bayesian info crit = 97.8979057\n", + "[[Variables]]\n", + " kaq0: 46.5775700 +/- 5.25395341 (11.28%) (init = 50)\n", + " Saq0: 6.7883e-05 +/- 7.5775e-04 (1116.27%) (init = 0.0001)\n", + " res: 0.00723010 +/- 0.02440774 (337.58%) (init = 1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = 0.936\n", + " C(Saq0, res) = 0.842\n", + " C(kaq0, res) = 0.799\n" + ] + } + ], + "source": [ + "ca3 = Calibrate(ml3)\n", + "ca3.set_parameter(name=\"kaq0\", initial=50, pmin=0)\n", + "ca3.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "ca3.set_parameter_by_reference(name=\"res\", parameter=w3.res[:], initial=1, pmin=0)\n", + "ca3.seriesinwell(name=\"obs\", element=w3, t=t, h=h)\n", + "ca3.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq046.577575.25395311.2800080inf50[46.57756996233315]
Saq00.0000680.0007581116.2656280inf0.0001[6.788282070635532e-05]
res0.007230.024408337.5849420inf1[0.00723010336653207]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 46.57757 5.253953 11.280008 0 inf 50 \n", + "Saq0 0.000068 0.000758 1116.265628 0 inf 0.0001 \n", + "res 0.00723 0.024408 337.584942 0 inf 1 \n", + "\n", + " parray \n", + "kaq0 [46.57756996233315] \n", + "Saq0 [6.788282070635532e-05] \n", + "res [0.00723010336653207] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 3.4769986006316445\n" + ] + } + ], + "source": [ + "display(ca3.parameters)\n", + "print(\"RMSE:\", ca3.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm3 = w3.headinside(t)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.loglog(t, -h, \".\", label=\"obs\")\n", + "plt.loglog(t, -hm3[0], label=\"ttim\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "According to RMSE and the Akaike criteria, the log curve fitting solution performs worse than linear curve fitting. The results reported in the following table are from models calibrated by linear curve fitting solution." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 10. Analysis and summary of values modeled by different methods" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
k [m/d]Ss [1/m]resRMSE [m]
MLU-log51.530.0008160.0220.007560
TTim-single layer44.5516140.0000030.015030.005515
TTim-two layers44.5287660.0000060.0162050.005512
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" + ], + "text/plain": [ + " k [m/d] Ss [1/m] res RMSE [m]\n", + "MLU-log 51.53 0.000816 0.022 0.007560\n", + "TTim-single layer 44.551614 0.000003 0.01503 0.005515\n", + "TTim-two layers 44.528766 0.000006 0.016205 0.005512" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ta = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"res\"],\n", + " index=[\"MLU-log\", \"TTim-single layer\", \"TTim-two layers\"],\n", + ")\n", + "ta.loc[\"TTim-single layer\"] = ca0.parameters[\"optimal\"].values\n", + "ta.loc[\"TTim-two layers\"] = ca1.parameters[\"optimal\"].values\n", + "ta.loc[\"MLU-log\"] = [51.530, 8.16e-04, 0.022]\n", + "ta[\"RMSE [m]\"] = [0.00756, ca0.rmse(), ca1.rmse()]\n", + "ta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Both TTim models agree with each other with similar parameters. MLU adjusted parameters are higher than the ones in TTim." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Preparing the DataFrame:\n", + "t1 = pd.DataFrame(\n", + " columns=[\"kaq - opt\", \"kaq - 95%\"],\n", + " index=[\"MLU\", \"TTim - single layer\", \"TTim - two layers\"],\n", + ")\n", + "\n", + "t1.loc[\"MLU\"] = [51.53, 4 * 1e-2 * 51.53]\n", + "t1.loc[\"TTim - single layer\"] = [\n", + " ca0.parameters.loc[\"kaq0\", \"optimal\"],\n", + " 2 * ca0.parameters.loc[\"kaq0\", \"std\"],\n", + "]\n", + "t1.loc[\"TTim - two layers\"] = [\n", + " ca1.parameters.loc[\"kaq0\", \"optimal\"],\n", + " 2 * ca1.parameters.loc[\"kaq0\", \"std\"],\n", + "]\n", + "\n", + "# Plotting\n", + "\n", + "plt.figure(figsize=(10, 7))\n", + "\n", + "plt.errorbar(\n", + " x=t1.index,\n", + " y=t1[\"kaq - opt\"],\n", + " yerr=[t1[\"kaq - 95%\"], t1[\"kaq - 95%\"]],\n", + " marker=\"o\",\n", + " linestyle=\"\",\n", + " markersize=12,\n", + ")\n", + "# plt.legend()\n", + "plt.suptitle(\"Error bar plot of calibrated hydraulic conductivities\")\n", + "plt.ylabel(\"K [m/d]\")\n", + "# plt.ylim([36,40])\n", + "plt.xlabel(\"Model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comparing the adjusted error bars for hydraulic conductivities for our models, we see that the TTim models do not overlap the MLU range." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Newville, M.,Stensitzki, T., Allen, D.B., Ingargiola, A. (2014) LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python.https://dx.doi.org/10.5281/zenodo.11813. https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021).\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Next Notebook: [Leaky 3 - Texas Hill](leaky3_texashill.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/04pumpingtests/leaky3_texashill.ipynb b/docs/04pumpingtests/leaky3_texashill.ipynb new file mode 100644 index 0000000..3666c7c --- /dev/null +++ b/docs/04pumpingtests/leaky3_texashill.ipynb @@ -0,0 +1,1293 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Leaky Aquifer Test - Texas Hill Example\n", + "**This example is taken from AQTESOLV examples.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model\n", + "\n", + "This pumping test, taken from the AQTESOLV examples, was done in the location of 'Texas Hill'. A pumping well was screened at an aquifer located between 20 ft and 70 ft depths. The aquifer is overlain by an aquitard. The formation at the base of the aquifer is considered an aquiclude.\n", + "\n", + "Three observation wells are located at 40, 80 160 ft distance. They are denominated OW1, OW2 and OW3, respectively. Pumping lasted for 420 minutes at a rate of 4488 gallons per minute. The hydrogeological conceptual model can be seen in the figure below.\n", + "\n", + "In this example, we will reproduce the work of Xinzhu (2020). We compare the aquifer test results using Hantush's solution (Hantush 1955) in the software AQTESOLV (Duffield, 2007) and TTim. We also explore the capabilities of TTim to account for aquitard storage and wellbore effects such as wellbore storage and skin effect, which cannot be simulated with Hantush's solution." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Conceptual Model 1\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "# sky\n", + "sky = plt.Rectangle((-20, 0), width=220, height=8, fc=\"b\", zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "# Aquifer:\n", + "ground = plt.Rectangle(\n", + " (-20, -70),\n", + " width=220,\n", + " height=50,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + ")\n", + "ax.add_patch(ground)\n", + "\n", + "# Confining bed:\n", + "confining_unit = plt.Rectangle(\n", + " (-20, -20),\n", + " width=220,\n", + " height=20,\n", + " fc=np.array([100, 100, 100]) / 255,\n", + " zorder=0,\n", + " alpha=0.7,\n", + ")\n", + "ax.add_patch(confining_unit)\n", + "\n", + "well = plt.Rectangle(\n", + " (-2, -70), width=4, height=70, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "ax.add_patch(well)\n", + "\n", + "# Wellhead\n", + "wellhead = plt.Rectangle(\n", + " (-2.5, 0), width=5, height=1.5, fc=np.array([200, 200, 200]) / 255, zorder=2, ec=\"k\"\n", + ")\n", + "ax.add_patch(wellhead)\n", + "\n", + "# Screen for the well:\n", + "screen = plt.Rectangle(\n", + " (-2, -70),\n", + " width=4,\n", + " height=50,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x=2.5, y=0.75, dx=5, dy=0, color=\"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x=7.5, y=2, s=r\"$ Q = 4488 \\frac{gal}{min}$\", fontsize=\"large\")\n", + "# Piezometers\n", + "piez1 = plt.Rectangle(\n", + " (39, -70), width=2, height=70, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "screen_piez_1 = plt.Rectangle(\n", + " (39, -70),\n", + " width=2,\n", + " height=50,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_1.set_linewidth(2)\n", + "\n", + "piez2 = plt.Rectangle(\n", + " (79, -70), width=2, height=70, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "screen_piez_2 = plt.Rectangle(\n", + " (79, -70),\n", + " width=2,\n", + " height=50,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_2.set_linewidth(2)\n", + "\n", + "piez3 = plt.Rectangle(\n", + " (159, -70), width=2, height=70, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "screen_piez_3 = plt.Rectangle(\n", + " (159, -70),\n", + " width=2,\n", + " height=50,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_3.set_linewidth(2)\n", + "\n", + "\n", + "ax.add_patch(piez1)\n", + "ax.add_patch(screen_piez_1)\n", + "ax.add_patch(piez2)\n", + "ax.add_patch(screen_piez_2)\n", + "ax.add_patch(piez3)\n", + "ax.add_patch(screen_piez_3)\n", + "\n", + "# last line\n", + "line = plt.Line2D(xdata=[-200, 1200], ydata=[0, 0], color=\"k\")\n", + "ax.add_line(line)\n", + "\n", + "ax.text(x=39, y=2, s=\"P40\", fontsize=\"large\")\n", + "ax.text(x=79, y=2, s=\"P80\", fontsize=\"large\")\n", + "ax.text(x=159, y=2, s=\"P160\", fontsize=\"large\")\n", + "\n", + "\n", + "ax.set_xlim([-20, 200])\n", + "ax.set_ylim([-70, 8])\n", + "ax.set_xlabel(\"Distance [ft]\")\n", + "ax.set_ylabel(\"Relative height [ft]\")\n", + "ax.set_title(\"Conceptual Model - Texas Hill Example\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Load Required Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from ttim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters\n", + "\n", + "Here we have previously converted the exercise values to ***meters*** and ***days***." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "Q = 24464.06 # constant discharge in m^3/d\n", + "b1 = 6.096 # overlying aquitard thickness in m\n", + "b2 = 15.24 # aquifer thickness in m\n", + "zt = -b1 # top boundary of aquifer\n", + "zb = -b1 - b2 # bottom boundary of aquifer\n", + "rw = 0.1524 # well radius in m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Load dataset of observation wells\n", + "\n", + "Each observation well is located in a text file, where the first column is time data in days and the second column is drawdown in meters.\n", + "We also declare the distance of each observation to the wells: (```r1```:```r3```)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data1 = np.loadtxt(\"data/texas40.txt\")\n", + "t1 = data1[:, 0]\n", + "h1 = data1[:, 1]\n", + "r1 = 12.191 # distance between obs1 to pumping well in m\n", + "\n", + "data2 = np.loadtxt(\"data/texas80.txt\")\n", + "t2 = data2[:, 0]\n", + "h2 = data2[:, 1]\n", + "r2 = 24.383 # distance between obs2 to pumping well in m\n", + "\n", + "data3 = np.loadtxt(\"data/texas160.txt\")\n", + "t3 = data3[:, 0]\n", + "h3 = data3[:, 1]\n", + "r3 = 48.766 # distance between obs3 to pumping well in m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Create a conceptual model\n", + "\n", + "We define a Modelmaq model for the semi-confined aquifer using the methods described in previous notebooks (see below).\n", + "In this initial model, we will model the overlying layer as an aquitard with storage (```Sll```, the storage parameter, is defined in ModelMaq).\n", + "\n", + "More details on model construction and theory can be seen in:\n", + "\n", + "- [Confined 1 - Oude Korendijk](confined1_oude_korendijk)\n", + "- [Confined 4 - Schroth](confined4_schroth.ipynb)\n", + "- [Leaky 1 - Dalem](leaky1_dalem.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_0 = ModelMaq(\n", + " kaq=10,\n", + " z=[0, zt, zb],\n", + " Saq=0.001,\n", + " Sll=0,\n", + " c=10,\n", + " tmin=0.001,\n", + " tmax=1,\n", + " topboundary=\"semi\",\n", + ")\n", + "w_0 = Well(ml_0, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", + "ml_0.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Model Calibration\n", + "\n", + "Using all three observation wells, we calibrate for the hydraulic parameters of the aquifer (```kaq``` and ```Saq```) and for the aquitard (```c``` and ```Sll```)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 126\n", + " # data points = 78\n", + " # variables = 4\n", + " chi-square = 0.28305542\n", + " reduced chi-square = 0.00382507\n", + " Akaike info crit = -430.268069\n", + " Bayesian info crit = -420.841234\n", + "[[Variables]]\n", + " kaq0: 224.589795 +/- 2.48600891 (1.11%) (init = 10)\n", + " Saq0: 2.1313e-04 +/- 3.7534e-05 (17.61%) (init = 0.0001)\n", + " Sll0: 1.7304e-06 +/- 2.7782e-04 (16055.52%) (init = 0.0001)\n", + " c0: 43.8331237 +/- 3.15335028 (7.19%) (init = 100)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(Saq0, Sll0) = -0.979\n", + " C(kaq0, c0) = 0.887\n", + " C(Saq0, c0) = -0.149\n", + " C(kaq0, Saq0) = -0.118\n" + ] + } + ], + "source": [ + "# unknown parameters: kaq, Saq, c, Sll\n", + "ca_0 = Calibrate(ml_0)\n", + "ca_0.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_0.set_parameter_by_reference(\n", + " name=\"Sll0\", parameter=ml_0.aq.Sll, initial=1e-4, pmin=0\n", + ")\n", + "ca_0.set_parameter(name=\"c0\", initial=100)\n", + "ca_0.series(name=\"OW1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_0.series(name=\"OW2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_0.series(name=\"OW3\", x=r3, y=0, t=t3, h=h3, layer=0)\n", + "ca_0.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0224.5897952.4860091.106911-infinf10[224.5897947124587]
Saq00.0002130.00003817.610508-infinf0.0001[0.0002131340798389554]
Sll00.0000020.00027816055.5232830.0inf0.0001[1.730365937868683e-06]
c043.8331243.1533507.193989-infinf100[43.833123697435646]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 224.589795 2.486009 1.106911 -inf inf 10 \n", + "Saq0 0.000213 0.000038 17.610508 -inf inf 0.0001 \n", + "Sll0 0.000002 0.000278 16055.523283 0.0 inf 0.0001 \n", + "c0 43.833124 3.153350 7.193989 -inf inf 100 \n", + "\n", + " parray \n", + "kaq0 [224.5897947124587] \n", + "Saq0 [0.0002131340798389554] \n", + "Sll0 [1.730365937868683e-06] \n", + "c0 [43.833123697435646] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.06024048173207126\n" + ] + } + ], + "source": [ + "display(ca_0.parameters)\n", + "print(\"RMSE:\", ca_0.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm1_0 = ml_0.head(r1, 0, t1)\n", + "hm2_0 = ml_0.head(r2, 0, t2)\n", + "hm3_0 = ml_0.head(r3, 0, t3)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"OW1\")\n", + "plt.semilogx(t1, hm1_0[0], label=\"ttim OW1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"OW2\")\n", + "plt.semilogx(t2, hm2_0[0], label=\"ttim OW2\")\n", + "plt.semilogx(t3, h3, \".\", label=\"OW3\")\n", + "plt.semilogx(t3, hm3_0[0], label=\"ttim OW3\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the specific storage of the aquitard (```Sll```) is very close to the minimum limit (zero), we will test to set Sll to 0 and remove it from the calibration:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Model calibration without aquitard storage" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_1 = ModelMaq(\n", + " kaq=10,\n", + " z=[0, zt, zb],\n", + " Saq=0.001,\n", + " Sll=0,\n", + " c=10,\n", + " tmin=0.001,\n", + " tmax=1,\n", + " topboundary=\"semi\",\n", + ")\n", + "w_1 = Well(ml_1, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", + "ml_1.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".....................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 50\n", + " # data points = 78\n", + " # variables = 3\n", + " chi-square = 0.28305317\n", + " reduced chi-square = 0.00377404\n", + " Akaike info crit = -432.268689\n", + " Bayesian info crit = -425.198562\n", + "[[Variables]]\n", + " kaq0: 224.635193 +/- 2.46701615 (1.10%) (init = 10)\n", + " Saq0: 2.1325e-04 +/- 7.5822e-06 (3.56%) (init = 0.0001)\n", + " c0: 43.8841639 +/- 3.13605878 (7.15%) (init = 100)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, c0) = 0.890\n", + " C(kaq0, Saq0) = -0.815\n", + " C(Saq0, c0) = -0.595\n" + ] + } + ], + "source": [ + "# unknown parameters: kaq, Saq, c\n", + "ca_1 = Calibrate(ml_1)\n", + "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_1.set_parameter(name=\"c0\", initial=100)\n", + "ca_1.series(name=\"OW1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_1.series(name=\"OW2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_1.series(name=\"OW3\", x=r3, y=0, t=t3, h=h3, layer=0)\n", + "ca_1.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0224.6351932.4670161.098232-infinf10[224.63519273946454]
Saq00.0002130.0000083.555577-infinf0.0001[0.00021324780404289543]
c043.8841643.1360597.14622-infinf100[43.884163861907204]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 224.635193 2.467016 1.098232 -inf inf 10 \n", + "Saq0 0.000213 0.000008 3.555577 -inf inf 0.0001 \n", + "c0 43.884164 3.136059 7.14622 -inf inf 100 \n", + "\n", + " parray \n", + "kaq0 [224.63519273946454] \n", + "Saq0 [0.00021324780404289543] \n", + "c0 [43.884163861907204] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.06024024248774193\n" + ] + } + ], + "source": [ + "display(ca_1.parameters)\n", + "print(\"RMSE:\", ca_1.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm1_1 = ml_1.head(r1, 0, t1)\n", + "hm2_1 = ml_1.head(r2, 0, t2)\n", + "hm3_1 = ml_1.head(r3, 0, t3)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"OW1\")\n", + "plt.semilogx(t1, hm1_1[0], label=\"ttim OW1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"OW2\")\n", + "plt.semilogx(t2, hm2_1[0], label=\"ttim OW2\")\n", + "plt.semilogx(t3, h3, \".\", label=\"OW3\")\n", + "plt.semilogx(t3, hm3_1[0], label=\"ttim OW3\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model with fixed Sll has a similar performance to the former model. The second model has an AIC value of -432.269, two units lower than the former model (-430.268). Thus, Sll should be set to zero (default value) and keep removed from the calibration." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Model Calibration with well parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We redo the model with the well parameters skin-resistance (```res```) and wellbore storage (```rc```)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_2 = ModelMaq(\n", + " kaq=10,\n", + " z=[0, zt, zb],\n", + " Sll=0,\n", + " Saq=0.001,\n", + " c=10,\n", + " tmin=0.001,\n", + " tmax=1,\n", + " topboundary=\"semi\",\n", + ")\n", + "w_2 = Well(ml_2, xw=0, yw=0, rw=rw, res=0, rc=None, tsandQ=[(0, Q)], layers=0)\n", + "ml_2.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calibrate with the additional well paramenters" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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+ "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 4670\n", + " # data points = 78\n", + " # variables = 5\n", + " chi-square = 6.23156762\n", + " reduced chi-square = 0.08536394\n", + " Akaike info crit = -187.112310\n", + " Bayesian info crit = -175.328766\n", + "[[Variables]]\n", + " kaq0: 186.901532 +/- 10.2767557 (5.50%) (init = 10)\n", + " Saq0: 9.7363e-11 +/- 1.7605e-07 (180814.64%) (init = 0.0001)\n", + " c0: 12.2172420 +/- 2.43597544 (19.94%) (init = 10)\n", + " rc: 2.31172969 +/- 0.09075350 (3.93%) (init = 0)\n", + " res: 2.0686e-10 +/- 5.8060e-07 (280673.69%) (init = 0.1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, c0) = 0.951\n", + " C(c0, rc) = 0.464\n", + " C(kaq0, rc) = 0.382\n", + " C(Saq0, res) = -0.354\n", + " C(rc, res) = -0.229\n", + " C(Saq0, rc) = 0.186\n" + ] + } + ], + "source": [ + "# unknown parameters: kaq, Saq, c, rc\n", + "ca_2 = Calibrate(ml_2)\n", + "ca_2.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_2.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "ca_2.set_parameter(name=\"c0\", initial=10)\n", + "ca_2.set_parameter_by_reference(name=\"rc\", parameter=w_2.rc, initial=0)\n", + "ca_2.set_parameter_by_reference(name=\"res\", parameter=w_2.res, initial=0.1, pmin=0)\n", + "ca_2.series(name=\"OW1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_2.series(name=\"OW2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_2.series(name=\"OW3\", x=r3, y=0, t=t3, h=h3, layer=0)\n", + "ca_2.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0186.9015321.027676e+015.498487-infinf10[186.90153170230298]
Saq00.01.760474e-07180814.6443930.0inf0.0001[9.736345063515728e-11]
c012.2172422.435975e+0019.938833-infinf10[12.217242017113444]
rc2.311739.075350e-023.925784-infinf0[2.311729685846916]
res0.05.806043e-07280673.6852850.0inf0.1[2.0686097279565274e-10]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 186.901532 1.027676e+01 5.498487 -inf inf 10 \n", + "Saq0 0.0 1.760474e-07 180814.644393 0.0 inf 0.0001 \n", + "c0 12.217242 2.435975e+00 19.938833 -inf inf 10 \n", + "rc 2.31173 9.075350e-02 3.925784 -inf inf 0 \n", + "res 0.0 5.806043e-07 280673.685285 0.0 inf 0.1 \n", + "\n", + " parray \n", + "kaq0 [186.90153170230298] \n", + "Saq0 [9.736345063515728e-11] \n", + "c0 [12.217242017113444] \n", + "rc [2.311729685846916] \n", + "res [2.0686097279565274e-10] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.2826515392059663\n" + ] + } + ], + "source": [ + "display(ca_2.parameters)\n", + "print(\"RMSE:\", ca_2.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When adding both res and rc in the calibration, the model fit is poor. The ```res``` value is being adjusted to very close to the minimum value 0. Thus, we repeat the calibration procedure, where res is removed from it." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".........................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 54\n", + " # data points = 78\n", + " # variables = 4\n", + " chi-square = 0.22837259\n", + " reduced chi-square = 0.00308612\n", + " Akaike info crit = -447.011880\n", + " Bayesian info crit = -437.585045\n", + "[[Variables]]\n", + " kaq0: 227.476745 +/- 2.38403194 (1.05%) (init = 10)\n", + " Saq0: 1.9189e-04 +/- 7.9503e-06 (4.14%) (init = 0.0001)\n", + " c0: 45.1685665 +/- 2.92674386 (6.48%) (init = 10)\n", + " rc: 0.58831953 +/- 0.06177017 (10.50%) (init = 0)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, c0) = 0.885\n", + " C(kaq0, Saq0) = -0.799\n", + " C(Saq0, rc) = -0.619\n", + " C(Saq0, c0) = -0.552\n", + " C(kaq0, rc) = 0.321\n", + " C(c0, rc) = 0.143\n" + ] + } + ], + "source": [ + "# unknown parameters: kaq, Saq, c, rc\n", + "ca_2 = Calibrate(ml_2)\n", + "ca_2.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_2.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_2.set_parameter(name=\"c0\", initial=10)\n", + "ca_2.set_parameter_by_reference(name=\"rc\", parameter=w_2.rc, initial=0)\n", + "ca_2.series(name=\"OW1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_2.series(name=\"OW2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_2.series(name=\"OW3\", x=r3, y=0, t=t3, h=h3, layer=0)\n", + "ca_2.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0227.4767452.3840321.048033-infinf10[227.4767448937833]
Saq00.0001920.0000084.143071-infinf0.0001[0.00019189384485118635]
c045.1685662.9267446.479603-infinf10[45.16856645283155]
rc0.588320.06177010.499425-infinf0[0.5883195294792068]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 227.476745 2.384032 1.048033 -inf inf 10 \n", + "Saq0 0.000192 0.000008 4.143071 -inf inf 0.0001 \n", + "c0 45.168566 2.926744 6.479603 -inf inf 10 \n", + "rc 0.58832 0.061770 10.499425 -inf inf 0 \n", + "\n", + " parray \n", + "kaq0 [227.4767448937833] \n", + "Saq0 [0.00019189384485118635] \n", + "c0 [45.16856645283155] \n", + "rc [0.5883195294792068] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.05410964530240864\n" + ] + } + ], + "source": [ + "display(ca_2.parameters)\n", + "print(\"RMSE:\", ca_2.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm1_2 = ml_2.head(r1, 0, t1)\n", + "hm2_2 = ml_2.head(r2, 0, t2)\n", + "hm3_2 = ml_2.head(r3, 0, t3)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"OW1\")\n", + "plt.semilogx(t1, hm1_2[0], label=\"ttim OW1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"OW2\")\n", + "plt.semilogx(t2, hm2_2[0], label=\"ttim OW2\")\n", + "plt.semilogx(t3, h3, \".\", label=\"OW3\")\n", + "plt.semilogx(t3, hm3_2[0], label=\"ttim OW3\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown (m)\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have a much better fit. We also have better AIC and BIC values, which indicates that the extra parameter ```rc``` improved the model performance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Analysis and summary of values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we compare the simulations done with TTim with the one done in AQTESOLV by Xinzhu (2020). TTim has found very similar values to AQTESOLV. When we consider the ```rc``` parameter in TTim, the fit is slightly better." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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k [m/d]Ss [1/m]c [d]rcRMSE
AQTESOLV224.7260.00021243.964-0.059627
ttim224.6351930.00021343.884164-0.060240
ttim-rc227.4767450.00019245.1685660.588320.054110
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" + ], + "text/plain": [ + " k [m/d] Ss [1/m] c [d] rc RMSE\n", + "AQTESOLV 224.726 0.000212 43.964 - 0.059627\n", + "ttim 224.635193 0.000213 43.884164 - 0.060240\n", + "ttim-rc 227.476745 0.000192 45.168566 0.58832 0.054110" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\", \"rc\"],\n", + " index=[\"AQTESOLV\", \"ttim\", \"ttim-rc\"],\n", + ")\n", + "t.loc[\"AQTESOLV\"] = [224.726, 2.125e-4, 43.964, \"-\"]\n", + "t.loc[\"ttim\"] = np.append(ca_1.parameters[\"optimal\"].values, \"-\")\n", + "t.loc[\"ttim-rc\"] = ca_2.parameters[\"optimal\"].values\n", + "t[\"RMSE\"] = [0.059627, ca_1.rmse(), ca_2.rmse()]\n", + "t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Newville, M.,Stensitzki, T., Allen, D.B., Ingargiola, A. (2014) LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python.https://dx.doi.org/10.5281/zenodo.11813. https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021).\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Next Notebook: [Unconfined 1 - Vennebulten](unconfined1_vennebulten.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/04pumpingtests/slug1_pratt_county.ipynb b/docs/04pumpingtests/slug1_pratt_county.ipynb new file mode 100644 index 0000000..0b7258d --- /dev/null +++ b/docs/04pumpingtests/slug1_pratt_county.ipynb @@ -0,0 +1,940 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Slug Test - Pratt County\n", + "**This test is taken from AQTESOLV examples.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we reproduce the work of Yang (2020) to check the TTim performance in analysing slug tests. We later compare the solution in TTim with the KGS analytical model (Hyder et al. 1994) implemented in AQTESOLV (Duffield, 2007).\n", + "\n", + "This slug test was conducted in Pratt County Monitoring Site, US, and reported by Butler (1998). A partially penetrating well is screened in unconsolidated alluvial deposits, consisting of sand and gravel interbedded by clay. The total thickness of the aquifer is 47.87 m. The screen is located at 16.77 m depth and has a screen length 1.52 m the well radius is 0.125 m, and the casing radius 0.064 m.\n", + "\n", + "The slug displacement is 0.671 m. Head change has been recorded at the slug well.\n", + "\n", + "The conceptual model can be seen in the figure below." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "# sky\n", + "sky = plt.Rectangle((-20, 0), width=50, height=20, fc=\"b\", zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "# Aquifer:\n", + "ground = plt.Rectangle(\n", + " (-20, -47.87),\n", + " width=50,\n", + " height=47.87,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + ")\n", + "ax.add_patch(ground)\n", + "\n", + "well = plt.Rectangle(\n", + " (-1, -(16.77 + 1.52)),\n", + " width=2,\n", + " height=(16.77 + 1.52),\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " zorder=1,\n", + ")\n", + "ax.add_patch(well)\n", + "\n", + "# Wellhead\n", + "wellhead = plt.Rectangle(\n", + " (-1.25, 0),\n", + " width=2.5,\n", + " height=10,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " zorder=2,\n", + " ec=\"k\",\n", + ")\n", + "ax.add_patch(wellhead)\n", + "\n", + "# Screen for the well:\n", + "screen = plt.Rectangle(\n", + " (-1, -(16.77 + 1.52)),\n", + " width=2,\n", + " height=1.52,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x=0, y=10, dx=0, dy=3, color=\"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x=0.5, y=11, s=r\"$ D = 0.671 m $\", fontsize=\"large\")\n", + "\n", + "# last line\n", + "line = plt.Line2D(xdata=[-200, 1200], ydata=[0, 0], color=\"k\")\n", + "ax.add_line(line)\n", + "\n", + "\n", + "ax.set_xlim([-20, 30])\n", + "ax.set_ylim([-47, 20])\n", + "ax.set_xlabel(\"Distance [m]\")\n", + "ax.set_ylabel(\"Relative height [m]\")\n", + "ax.set_title(\"Conceptual Model - Pratt County Example\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Load required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from ttim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "rw = 0.125 # well radius\n", + "rc = 0.064 # well casing radius\n", + "L = 1.52 # screen length\n", + "b = -47.87 # aquifer thickness\n", + "zt = -16.77 # depth to top of screen\n", + "H0 = 0.671 # initial displacement in the well\n", + "zb = zt - L # bottom of screen" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Calculate the added volume\n", + "\n", + "As we will see later, the input for the slug test in TTim is the added or removed volume. Therefore we must first convert our measured displacement into volume." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "slug: 0.00863 m^3\n" + ] + } + ], + "source": [ + "Q = np.pi * rc**2 * H0\n", + "print(\"slug:\", round(Q, 5), \"m^3\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Load data from well" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data from the slug well is loaded from a text file, where the first column is the time in seconds and the second column is the head." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data = np.loadtxt(\"data/slug.txt\", skiprows=1)\n", + "t = data[:, 0] / 60 / 60 / 24 # convert time to days\n", + "h = data[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Create a conceptual model in TTim\n", + "\n", + "We conceptualize the aquifer as a three-layer model, one layer above the screen, one layer at the screen top and bottom and another layer just below it.\n", + "\n", + "We use ```Model3D``` method to build this model. Details on how to set the model can be seen in notebook: [Unconfined - 1 - Vennebulten](unconfined1_vennebulten.ipynb).\n", + "\n", + "The setting of the slug well is slightly different from the pumping well. We detail the differences below:\n", + "* the ```tsandQ``` argument in the ```Well``` object has a different meaning. Instead of meaning the time of start or shutdown and the pumping rate of the pumping well, it means the time a volume is instantaneously added or removed from the well. In our case, we defined it as ```[(0, -Q)]``` where ```0``` is the moment in time when we added the slug and ```Q``` is the added volume. A negative sign means a volume is added. Otherwise, it would mean an extracted volume.\n", + "* the ```wbstype``` argument is set to ```' slug'```, so TTim knows the ```tsandQ``` argument means time and instant volumes, instead of pumping rates." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml = Model3D(kaq=10, z=[0, zt, zb, b], Saq=1e-4, kzoverkh=1, tmin=1e-6, tmax=0.01)\n", + "w = Well(ml, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=1, wbstype=\"slug\")\n", + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Model calibration\n", + "\n", + "We calibrate both hydraulic conductivity and specific storage, considering uniform parameters in all layers." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...........................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 24\n", + " # data points = 61\n", + " # variables = 2\n", + " chi-square = 5.0379e-04\n", + " reduced chi-square = 8.5388e-06\n", + " Akaike info crit = -709.958078\n", + " Bayesian info crit = -705.736330\n", + "[[Variables]]\n", + " kaq0_2: 6.08971640 +/- 0.02515114 (0.41%) (init = 10)\n", + " Saq0_2: 2.0365e-04 +/- 1.0023e-05 (4.92%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_2, Saq0_2) = -0.654\n" + ] + } + ], + "source": [ + "ca = Calibrate(ml)\n", + "ca.set_parameter(name=\"kaq0_2\", initial=10)\n", + "ca.set_parameter(name=\"Saq0_2\", initial=1e-4)\n", + "ca.series(name=\"obs\", x=0, y=0, layer=1, t=t, h=h)\n", + "ca.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Saq0_20.0002040.0000104.921381-infinf0.0001[0.0002036532699278222, 0.0002036532699278222,...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_2 6.089716 0.025151 0.41301 -inf inf 10 \n", + "Saq0_2 0.000204 0.000010 4.921381 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0_2 [6.089716397344325, 6.089716397344325, 6.08971... \n", + "Saq0_2 [0.0002036532699278222, 0.0002036532699278222,... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.002873813488818492\n" + ] + } + ], + "source": [ + "display(ca.parameters)\n", + "print(\"RMSE:\", ca.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we plot the results in heads over initial heads, as done for the graphical solution." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm = ml.head(0, 0, t, layers=1)\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(t, h / H0, \".\", label=\"obs\")\n", + "plt.semilogx(t, hm[-1] / H0, \"r\", label=\"ttim\")\n", + "plt.ylim([0, 1])\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"h/H0\")\n", + "plt.title(\"Model Results - Three layers\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. A conceptual model with more layers\n", + "\n", + "Now we check the model performance in a model with more layers.\n", + "We use the same division that we set before, but now we divide the first layers into 18 layers, the second layer into 3 layers and the third layer into 29 layers. So each layer is roughly 1 m thick." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "n1 = 18\n", + "n2 = 3\n", + "n3 = 29\n", + "nlay = n1 + n2 + n3 # number of layers\n", + "zlay1 = np.linspace(0, zt, n1 + 1)\n", + "zlay2 = np.linspace(zt, zb, n2 + 1)\n", + "zlay3 = np.linspace(zb, b, n3 + 1)\n", + "layers = np.append(zlay1[:-1], zlay2[:-1])\n", + "layers = np.append(layers, zlay3) # elevation of each layer\n", + "Saq = 1e-4 * np.ones(nlay)\n", + "Saq[0] = 0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 3\n", + "solution complete\n" + ] + } + ], + "source": [ + "M_nlay = Model3D(\n", + " kaq=10, z=layers, Saq=Saq, kzoverkh=1, phreatictop=True, tmin=1e-6, tmax=0.01\n", + ")\n", + "W_nlay = Well(\n", + " M_nlay,\n", + " xw=0,\n", + " yw=0,\n", + " rw=rw,\n", + " tsandQ=[(0, -Q)],\n", + " layers=[18, 19, 20],\n", + " rc=rc,\n", + " wbstype=\"slug\",\n", + ")\n", + "M_nlay.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Calibration of multi-layer model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..............................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 27\n", + " # data points = 183\n", + " # variables = 2\n", + " chi-square = 0.00159219\n", + " reduced chi-square = 8.7966e-06\n", + " Akaike info crit = -2128.33971\n", + " Bayesian info crit = -2121.92074\n", + "[[Variables]]\n", + " kaq0_49: 4.26554464 +/- 0.01214302 (0.28%) (init = 10)\n", + " Saq0_49: 4.9196e-04 +/- 1.7924e-05 (3.64%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_49, Saq0_49) = -0.763\n" + ] + } + ], + "source": [ + "cM = Calibrate(M_nlay)\n", + "cM.set_parameter(name=\"kaq0_49\", initial=10)\n", + "cM.set_parameter(name=\"Saq0_49\", initial=1e-4, pmin=1e-7)\n", + "cM.series(name=\"obs\", x=0, y=0, layer=[18, 19, 20], t=t, h=h)\n", + "cM.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_494.2655450.0121430.284677-infinf10[4.265544642582153, 4.265544642582153, 4.26554...
Saq0_490.0004920.0000183.6434111.000000e-07inf0.0001[0.0004919581741910095, 0.0004919581741910095,...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_49 4.265545 0.012143 0.284677 -inf inf 10 \n", + "Saq0_49 0.000492 0.000018 3.643411 1.000000e-07 inf 0.0001 \n", + "\n", + " parray \n", + "kaq0_49 [4.265544642582153, 4.265544642582153, 4.26554... \n", + "Saq0_49 [0.0004919581741910095, 0.0004919581741910095,... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.002949661707868684\n" + ] + } + ], + "source": [ + "display(cM.parameters)\n", + "print(\"RMSE:\", cM.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hM = M_nlay.head(0, 0, t, layers=20)\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(t, h / H0, \".\", label=\"obs\")\n", + "plt.semilogx(t, hM[0] / H0, label=\"ttim\")\n", + "plt.ylim([0, 1])\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"h/H0\")\n", + "plt.title(\"Model Results - more layers\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Parameters varied significantly, but the AIC value has dropped sharply." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 9. Final Model calibration with well skin resistance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we test if the skin resistance of the well has an impact on model calibration. For this, we add the ```res``` parameter in the calibration settings. We use the same multi-layer model." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".......................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 52\n", + " # data points = 183\n", + " # variables = 3\n", + " chi-square = 0.00136417\n", + " reduced chi-square = 7.5787e-06\n", + " Akaike info crit = -2154.62552\n", + " Bayesian info crit = -2144.99706\n", + "[[Variables]]\n", + " kaq0_49: 4.29785100 +/- 0.01340379 (0.31%) (init = 10)\n", + " Saq0_49: 4.0896e-04 +/- 2.1915e-05 (5.36%) (init = 0.0001)\n", + " res: 1.7233e-04 +/- 3.2195e-05 (18.68%) (init = 0.2)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_49, Saq0_49) = -0.834\n", + " C(Saq0_49, res) = -0.730\n", + " C(kaq0_49, res) = 0.544\n" + ] + } + ], + "source": [ + "cR = Calibrate(M_nlay)\n", + "cR.set_parameter(name=\"kaq0_49\", initial=10)\n", + "cR.set_parameter(name=\"Saq0_49\", initial=1e-4, pmin=1e-7)\n", + "cR.set_parameter_by_reference(name=\"res\", parameter=W_nlay.res, initial=0.2, pmin=0)\n", + "cR.series(name=\"obs\", x=0, y=0, layer=[18, 19, 20], t=t, h=h)\n", + "cR.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_494.2978510.0134040.311872-infinf10[4.297851003743346, 4.297851003743346, 4.29785...
Saq0_490.0004090.0000225.3588031.000000e-07inf0.0001[0.0004089557630334584, 0.0004089557630334584,...
res0.0001720.00003218.6820860.000000e+00inf0.2[0.00017233339191813357]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_49 4.297851 0.013404 0.311872 -inf inf 10 \n", + "Saq0_49 0.000409 0.000022 5.358803 1.000000e-07 inf 0.0001 \n", + "res 0.000172 0.000032 18.682086 0.000000e+00 inf 0.2 \n", + "\n", + " parray \n", + "kaq0_49 [4.297851003743346, 4.297851003743346, 4.29785... \n", + "Saq0_49 [0.0004089557630334584, 0.0004089557630334584,... \n", + "res [0.00017233339191813357] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.002730287389920131\n" + ] + } + ], + "source": [ + "display(cR.parameters)\n", + "print(\"RMSE:\", cR.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The RMSE has improved slightly, and AIC has also decreased, which means adding skin resistance improved the model. However, the improvement is minimal, as the ```res``` calibrated value is tiny. Therefore, one can justify a calibration without ```res```." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 10. Analysis and comparison of simulated values\n", + "\n", + "We now compare the values in TTim and add the results of the AQTESOLV modelling reported by Yang (2020)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Comparison of parameter values and error under different models
 k [m/d]Ss [1/m]res [1/d]RMSE
AQTESOLV4.0340000.000383nan0.002976
ttim-three6.0897160.000204nan0.002874
ttim-multi4.2655450.000492nan0.002955
ttim-res4.2978510.0004090.0001720.002730
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ta = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"res [1/d]\"],\n", + " index=[\"AQTESOLV\", \"ttim-three\", \"ttim-multi\", \"ttim-res\"],\n", + ")\n", + "ta.loc[\"ttim-three\"] = np.concatenate((ca.parameters[\"optimal\"].values, [np.nan]))\n", + "ta.loc[\"ttim-multi\"] = np.concatenate((cM.parameters[\"optimal\"].values, [np.nan]))\n", + "ta.loc[\"ttim-res\"] = cR.parameters[\"optimal\"].values\n", + "ta.loc[\"AQTESOLV\"] = [4.034, 3.834e-04] + [np.nan]\n", + "ta[\"RMSE\"] = [0.002976, ca.rmse(), cM.rmse(), cR.rmse()]\n", + "ta.style.set_caption(\"Comparison of parameter values and error under different models\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All models had similar RMSE performance. However, there was a significant variation in parameter values between the three-layer model and the multi-layered model. Multi-layered model parameters were closer to AQTESOLV values. The best RMSE model was the last model, the multi-layered with skin resistance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "* Butler, J.J., Jr., 1998. The Design, Performance, and Analysis of Slug Tests, Lewis Publishers, Boca Raton, Florida, 252p.\n", + "* Hyder, Z., Butler Jr, J.J., McElwee, C.D., Liu, W., 1994. Slug tests in partially penetrating wells. Water Resources Research 30, 2945–2957.\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Next Example: [Slug 2 - Falling Head](slug2_falling_head.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/04pumpingtests/slug2_falling_head.ipynb b/docs/04pumpingtests/slug2_falling_head.ipynb new file mode 100644 index 0000000..a3da603 --- /dev/null +++ b/docs/04pumpingtests/slug2_falling_head.ipynb @@ -0,0 +1,950 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Slug Test - Falling Head\n", + "**This test is taken from examples of AQTESOLV.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model\n", + "\n", + "In this notebook, we reproduce the work of Yang (2020) to check the TTim performance in analysing slug-test. We later compare the solution in TTim with the KGS analytical model (Hyder et al. 1994) implemented in AQTESOLV (Duffield, 2007).\n", + "\n", + "This slug test was reported in Batu (1998). A well partially penetrates a sandy unconfined aquifer that has a saturated depth of 32.57 ft. The top of the screen is located 0.47 ft below the water table and has 13.8 ft in length. The well and casing radii are 5 and 2 inches, respectively.\n", + "\n", + "The slug displacement is 1.48 ft. Head change has been recorded at the slug well.\n", + "\n", + "The conceptual model is seen in the figure below." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "# sky\n", + "sky = plt.Rectangle((-20, 2), width=50, height=20, fc=\"b\", zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "# Aquifer:\n", + "ground = plt.Rectangle(\n", + " (-20, -32.57),\n", + " width=50,\n", + " height=34.57,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + ")\n", + "ax.add_patch(ground)\n", + "\n", + "well = plt.Rectangle(\n", + " (-1, -(0.47 + 13.8)),\n", + " width=2,\n", + " height=(0.47 + 13.8) + 2,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " zorder=1,\n", + ")\n", + "ax.add_patch(well)\n", + "\n", + "# Wellhead\n", + "wellhead = plt.Rectangle(\n", + " (-1.25, 2),\n", + " width=2.5,\n", + " height=10,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " zorder=2,\n", + " ec=\"k\",\n", + ")\n", + "ax.add_patch(wellhead)\n", + "\n", + "# Screen for the well:\n", + "screen = plt.Rectangle(\n", + " (-1, -(0.47 + 13.8)),\n", + " width=2,\n", + " height=13.8,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x=0, y=10, dx=0, dy=6, color=\"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x=0.5, y=13, s=r\"$ D = 1.48 ft $\", fontsize=\"large\")\n", + "\n", + "# last line\n", + "line = plt.Line2D(xdata=[-200, 1200], ydata=[2, 2], color=\"k\")\n", + "ax.add_line(line)\n", + "\n", + "# water table\n", + "line = plt.Line2D(xdata=[-200, 1200], ydata=[0, 0], color=\"blue\")\n", + "ax.add_line(line)\n", + "\n", + "\n", + "ax.set_xlim([-20, 20])\n", + "ax.set_ylim([-32, 20])\n", + "ax.set_xlabel(\"Distance [ft]\")\n", + "ax.set_ylabel(\"Relative height [ft]\")\n", + "ax.set_title(\"Conceptual Model - Falling Head Example\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Import required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from ttim import *\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters\n", + "\n", + "Parameters here declared are already converted from feet and inches to meters" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "rw = 0.127 # well radius\n", + "rc = 0.0508 # well casing radius\n", + "L = 4.20624 # screen length\n", + "b = -9.9274 # aquifer thickness\n", + "zt = -0.1433 # depth to top of the screen\n", + "H0 = 0.4511 # initial displacement in the well\n", + "zb = zt - L # bottom of the screen" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Converting slug displacement to volume" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Slug: 0.00366 m^3\n" + ] + } + ], + "source": [ + "Q = np.pi * rc**2 * H0\n", + "print(\"Slug:\", round(Q, 5), \"m^3\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Load data\n", + "\n", + "Drawdown data is available in feet and seconds and are converted to meters and days" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data = np.loadtxt(\"data/falling_head.txt\", skiprows=2)\n", + "t = data[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h = (10 - data[:, 1]) * 0.3048 # convert drawdown from ft to meters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Create First Model - three layers\n", + "\n", + "We begin with a model with just three layers. We arranged the layers to match the screen length. The first layer is located just above the screen, the second layer is located at the screen depths, and the last layer is just below the screen, up to the total aquifer depth.\n", + "\n", + "We set the model in the same manner as in [Slug 1 - Pratt County](slug1_pratt_county.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_0 = Model3D(kaq=10, z=[0, zt, zb, b], Saq=1e-4, tmin=1e-5, tmax=0.01)\n", + "w_0 = Well(ml_0, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=1, wbstype=\"slug\")\n", + "ml_0.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Model calibration\n", + "\n", + "The procedures for calibration can be seen in [Unconfined 1 - Vennebulten](unconfined1_vennebulten.ipynb)\n", + "\n", + "We calibrate hydraulic conductivity and specific storage, as in the KGS model (Hyder et al. 1994)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 51\n", + " # data points = 27\n", + " # variables = 2\n", + " chi-square = 0.00113000\n", + " reduced chi-square = 4.5200e-05\n", + " Akaike info crit = -268.197122\n", + " Bayesian info crit = -265.605448\n", + "[[Variables]]\n", + " kaq0_2: 0.59645054 +/- 0.03142467 (5.27%) (init = 10)\n", + " Saq0_2: 2.1306e-04 +/- 6.2567e-05 (29.37%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_2, Saq0_2) = -0.968\n" + ] + } + ], + "source": [ + "ca_0 = Calibrate(ml_0)\n", + "ca_0.set_parameter(name=\"kaq0_2\", initial=10)\n", + "ca_0.set_parameter(name=\"Saq0_2\", initial=1e-4, pmin=1e-7)\n", + "ca_0.series(name=\"obs\", x=0, y=0, t=t, h=h, layer=1)\n", + "ca_0.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_20.5964510.0314255.268613-infinf10[0.5964505364271898, 0.5964505364271898, 0.596...
Saq0_20.0002130.00006329.366631.000000e-07inf0.0001[0.00021305549571903892, 0.0002130554957190389...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_2 0.596451 0.031425 5.268613 -inf inf 10 \n", + "Saq0_2 0.000213 0.000063 29.36663 1.000000e-07 inf 0.0001 \n", + "\n", + " parray \n", + "kaq0_2 [0.5964505364271898, 0.5964505364271898, 0.596... \n", + "Saq0_2 [0.00021305549571903892, 0.0002130554957190389... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.00646929950106304\n" + ] + } + ], + "source": [ + "display(ca_0.parameters)\n", + "print(\"RMSE:\", ca_0.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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HvPLKKz+XLoBXXnllnXNnzZrFyy+/zGeffcZee+3FI488wr///W+OP/54nnzySY477rgtjqsS1hjSMoJS1bFPsOasthWLghGz9UfQZr0Eq1etPa95u+ibAfquO8XZoXcwOhdH2jtNRESamiOOOIKMjAx22mknVq9ezeGHHw7ATjvtxOzZsxvkNVTCwtaiPXTLD2611ayGxXPWrjlbU9K+fhU+ur/WiQbtu6/7pgBtrSEiIomujhGrRNCsWTMAIpEIGRkZWPTf00gkQnV1dV0PjZlKWKKKpAVTk1k9oe8h6x5btSz6xoCZa0fOyr+C94qg6qe15zXxrTVERERqa9OmDUuXLv3Fx2FRCUtGzVpDlwHBrTZ3ba0hIiKyEdnZ2QwcOJD+/fszaNAg0tLS2GWXXRg1ahS77rpro+cxd6//rASSn5/vxcXFYcdIPj9vrVFr3dmasrZy8drzNrq1Rt9g6lRERGQzzZgxgx122CHsGHGzoa/PzErcfYN7X2gkrKmoa2uNnxb+8p2b82f8cmuNDr2h+x7Brdse0HkHjZqJiIhsJpWwps4MWncKbjl7r3tsdRVUzA7K2YIZwbTmV8/Dh1OC45ltoOtua0tZt3xo2aHRvwQREZFkpBImG5eWEUxDduwL2x8Z3OcOFV/DnHdh7jsw52147XrwmuB4x22DQtZ99+C/nbaHSCS8r0FERBKKu//8TsNUsjnLu1TCZNOYBWvGOuTCLsOC+1Ytg2/fgznvwNx34Yun4INJwbFmbaFr3tppzK75WlsmItJENW/enPLycrKzs1OqiLk75eXlNG++aft2qoTJlmvWGnrtF9wgGC37sTRayt4J/vvqtWtHyzptD912XzuN2XFbjZaJiDQB3bp1Y+7cuSxYsCDsKA2uefPmdOvWbZMeo3dHSuNYtRTmlaydxpz7LqyoCI41bxeMkHXfM5jG7JoPzduGm1dERKQB6N2REr5mbSD3gOAGwWhZ+cxgTdmaacxX/kWwl5kF77xcZ7Ssr3b/FxGRlKISJuEwW7vof9eRwX0rF687WvbZY/DehOBYi6yglK1Z9N81Lyh2IiIiSUolTBJH83bQ+6DgBlBTE+xbVnu07KvngmMWgc791o6Udd8jeLOARstERCRJaE2YJJcVi2Beca21ZcWwaklwrE0X6HMw9B0UTHs2bxdmUhEREa0JkxTSoj30OSS4QTBatvAL+KYISl+Gz6bB+xPB0oKF/n0PgT6DYOudNEomIiIJRSNhklpWVwfTljOfD3b3//6j4P7WW60tb70PDNaYiYiIxFldI2EqYZLalv4As14MCtmsl2DlomA9WbfdgxGyvofA1rtonzIREYkLlTBJeSVlFRSVllOQm01ezkZGuVZXB+++nPlCMFL27fvB/a06Qe/oWrLeB+n6lyIi0mBUwiSllZRVMGJcEZXVNWSmRygcXbDxIlbbsgXB6NjM52Hmi7DiR8CC7S/6DgpGyroMgEhavL8EERFJUVqYLymtqLScyuoaahyqqmsoKi2PrYS17hRc/3KXYVCzOhgZm/lCMHX5ytXB5rEts4PRsT7RUbLWneL/BYmISJOgEiZJryA3m8z0CFXVNWSkRyjIzd70J4mkQbf84HbAGPipPDpK9kJw+/ghwIKRsT6DgpGyrnkaJRMRkc2m6UhJCTGtCdtcNTXw3QdrC9ncd4OLkbfIgu2Pgh2HBBcvT8to2NcVEZGkpzVhIg1p+Y/BnmRfPANfPA2VS6FFB+g3OChkPffRCJmIiAAqYSLxU7UyGB379NGglFX9BK06Q79jYcfjocde2v5CRKQJ08J8kXjJaA47HB3cKpcH17b89FF4fxK8exe02Qb6HRcUsm67q5CJiMjPNBImEg+rlsGXz8CnU4N3W65eBW27wY7HQf8h0GU3XUZJRKQJ0HSkSJhWLgnWjn36aLAfWU0VtM8JRsf6D4Gtd1YhExFJUSphIoliRQV8/lRQyEpfgZpq6NB7bSHr3E+FTEQkhaiEiSSi5T/CjGnBlOXXrwbbXnTcLihjA0ZA++5hJxQRkS2kEiaS6JYtgBmPwydToeyNYIuL/kNh4K9gq35hpxMRkc2kEiaSTBbNgaLboGR8sOVF30Nh4KWQs7emKkVEkkxdJUzvlxdJNO27w+H/hF9/Agf+H8x7D8YfCeMOgRlPBDv4i4hI0lMJE0lULTvA/r8LythR18PyhfDASBi7O5RMgOpVYScUEZEtoBImkugyWsDuo+Hi92DovZDZCp64BG7aCV6/EVYuDjuhiIhsBpUwkQRTUlbB2JdnUlJWse6BSFrwzslz/genPQ5b7QgvXA437AjP/QWWfBdKXhER2Ty6bJFIAikpq2DEuCIqq2vITI9QOLqAvJysdU8yg9wDgtt3H8IbN8Nb/w0W8+8yDPb+FXTaNoz4IiKyCTQSJpJAikrLqayuocahqrqGotLyuh+wzS4w9G645H3IGwUfPxKsGZsyHOa80yiZRURk86iEiSSQgtxsMtMjpBlkpEcoyM2O7YFZPeGo64JF/Pv/Ab55E+4eBPccDl88o3dUiogkIO0TJpJgSsoqKCotpyA3+5dTkbGq/AnemxhMUy6eA512gIGXBBvApmc2bGAREdkobdYq0lStrgoui/TGf+CHT6BtVyi4APJOh2Ztwk4nIpLytFmrSFOVlgE7nwTnvQ4jHoEOufDcn+HGHeHFK2DZ/LATiog0WSphIk2BGfQ9BEZNh9EvQa/94bUb4Mb+8MSlUD4r7IQiIk2OSphIU9MtD4ZNhItLYMAp8MFkuCUPHjwN5pWEnU5EpMlQCRNpqrJ7wzH/gUs/hn1+DbNegbsOgvFHw1cvQJKtFxURSTYqYSJNXZut4JC/BdtbHHpVMDVZeALcvi989BCsrg47oYhISlIJE9kMG720UDJr3hb2vhh+9SEceyusroRHR8PNu8LbdwTbXoiISIPRFhUimyimSwulgpoa+OpZeP0mmFMELTrAHucEt1YxbiIrItLEaYsKkQa0yZcWSlaRCGx3BJz1LJz5LPQogP9dHWxv8dTvoGJ22AlFRJKaLuAtsonWXFqoqrpm0y4tlMx6FAS3+Z/Dm7dA8b3w7t2w4/Ew8Fewzc5hJxQRSTqajhTZDA1yaaFktngevH0bFI+HyqXQ+yAYeCn02i/Yk0xERABdtkhE4mXFIii+B4pug5/mwzYDYJ9LYYfBEEkLOZyISPi0JkxE4qNFe9j3smCvsWP+A6uWwkOjgs1f370bqlaEnVBEJGGphInIlstoDnmj4KJ34aSJ0LIDPHkZ3LQTvHotLP8x7IQiIglHJUxEGk4kDfoNhtEvwunTg+nJl64KrlH5zJ9g8dywE4qIJAy9O1JEGp4Z9No3uH3/Cbx5M7x9O7xzB+x0Iux9CWzVL+yUIiKh0kiYiMTX1v1hyJ3wqw9g97Phs8fhtr2g8CQoe1PXqBSRJiuuJczMDjezL8xsppmN2cDxHmb2spm9b2YfmdmR8cwjIiFq3wOOuBp+/Skc+GeYVwz3HgF3D4IZ04Md+kVEmpC4lTAzSwPGAkcA/YBTzGz9+Yf/Ax50912Bk4Fb45VHRBJEyw6w/+/h0k/gyOtg2Xx4YASM3QPeuw+qV4WdUESkUcRzJGwPYKa7l7p7JXA/cOx65zjQNvpxO+DbOOYRkUSS2RL2OBsufg9OuBsyWsC0i+GmnYPrVa5cHHZCEZG4imcJ6wrMqfX53Oh9tV0OjDSzucBTwMVxzCMiiSgtHXYaCue+CqdOhc7bwwt/C95R+fxfYcl3YScUEYmLsBfmnwKMd/duwJHARDP7RSYzO8fMis2seMGCBY0eUkQagVlw+aPTHodzXoE+hwTXqfzPzvD4RbDom7ATiog0qHiWsHlA91qfd4veV9tZwIMA7v4W0BzouP4Tufud7p7v7vmdOnWKU1wRSRhddoUT74WLS2C30+Djh2BsAbx9B9SsDjudiEiDiGcJexfoa2a9zCyTYOH9tPXO+QY4GMDMdiAoYRrqEpFAh1w46no+Pv4FylrvAk//Hu45HBZ8EXYyEZEtFrcS5u7VwEXAs8AMgndBfmpmV5jZ4OhpvwHONrMPgSnAKE+2K4qLSFyVlFVw4v1zOfD7C/l9zQVUL/gSbt8H/nctVFeGHU9EZLPFdcd8d3+KYMF97fv+Wuvjz4CB8cwgIsmtqLScyuoaatx4pGofth94HGcuuR1evgo+ewwG3wJddws7pojIJgt7Yb6ISJ0KcrPJTI+QZpCRHmGX7bcN1oudPBmWl8O4g+G5/4PK5WFHFRHZJJZss3/5+fleXFwcdgwRaUQlZRUUlZZTkJtNXk7W2gMrFgXbWLw3AbJ6BaNivfYNLaeIyPrMrMTd8zd4TCVMRJLe16/CtEug4mvIGwWDroDm7cJOJSJSZwnTdKSIJL9e+8H5b8LeFweXPhq7J3z+VP2PExEJkUqYiKSGzJZw6FUw+gVo0QHuPwUeOgOWadcbEUlMKmEiklq65gU77h/4Z/h8OozdHT58AJJs6YWIpD6VMBFJPemZsP/v4dzXILsvTD0HCk+ERXPqf6yISCNRCROR1NV5ezjzGTj8Gih7A24tgHfugpqasJOJiKiEiUiKi6RBwXlwQRF03wOe+i2MPxIWfhV2MhFp4lTCRKRpyMqBkY/CcbfB/Blw20B47XpYXRV2MhFpolTCRKTpMIMBw+HCd2C7w+HFK+CuA+HbD8JOJiJNkEqYiDQ9bbaCk+6DYZNg2Xy46yB4/m9QtSLsZCLShKiEiUjTtcMxcOHbwejYGzcFU5Sz3wg7lYg0ESphItK0tciCY/8Lpz0ONdXBov3pv4aVS8JOJiIpTiVMRAQg9wC44C3Y6yIoGR9sZ/Hls2GnEpEUphImIrJGZis47B9w1vPQrC1MPgkeGQ0/LQw7mYikIJUwEZH1dcuHc1+FA/4Inz4GY/eAjx/WpY9EpEGphImIbEh6JhwwBs57DbJ6wSNnweRhsHhu2MlEJEWohImI1KXzDnDWc3DYv2D2azC2AN69W5c+EpEtphImIlKfSBrsdUGwcL9bHjx5GUw4GhbOBKCkrIKxL8+kpKwi5KAikkzSww4gIpI0snrCqY/BB4Xw7J/g9oHMHXApp729Eyuqjcz0CIWjC8jLyQo7qYgkAY2EiYhsCjPYdWRw6aO+g+hWfDX325/ZntlUVddQVFoedkIRSRIqYSIim6PN1jBsErMOvI2trYKpmX9leMbLFORmh51MRJKESpiIyBbovf9w5g1/he877M6VkbvI++AvULUy7FgikgRUwkREttCA7XLJufhJ2O938P5EuOcwWPRN2LFEJMGphImINIRIGhz0f3DyFPixFO7YH2a9FHYqEUlgKmEiIg1p+yPhnFeCNWMTh8Cr12lPMRHZIJUwEZGGlt0bRr8AOw2Fl66EB0bCysVhpxKRBKMSJiISD5mtYMhdcPg18NWzcOcB8MNnYacSkQSiEiYiEi9mUHAenD4dKn+CcQcHFwIXEUElTEQk/nL2gnNfhW12CS4E/swfYXVV2KlEJGQqYSIijaHN1nD6E7Dn+VB0K0wYDEt/CDuViIRIJUxEpLGkZcARV8MJd8N3H8Ad+8E3RWGnEpGQqISJiDS2nYYG757MbAnjj4Ki28E97FQi0shUwkREwrDVjnD2y9BnEDzzB3j07GDxvog0GSphIiJhadEeTp4c7LT/8cMwbhCUzwo7lYg0EpUwEZEwRSLBNSdHPgJLv4U7D4Qvng47lYg0ApUwEZFE0OdgOOd/0KEnTDkZXroKalaHnUpE4iimEmZmETPb1cyOMrODzKxzvIOJiDQ5WTlw5rMwYCS8ei0UngjLfww7lYjESXpdB82sN/AH4BDgK2AB0BzY1syWA3cAE9xdV6cVEWkIGS3g2P9C993hqd/BnfvDSROhy4Cwk4lIA6tvJOwqYBLQ290Pc/eR7j7U3XcGBgPtgFPjHVJEpEkxg7xRcMYzUFMD9xwG7xeGnUpEGph5ku1Nk5+f78XFxWHHEBFpHD8thIfPhK//B3lnwBHXQHqzsFOJSIzMrMTd8zd0rM7pyOiDtweOBbpG75oHTHP3GQ0XUURENqhVRxj5KLx0JbxxE3z/EZx0HyWLWlFUWk5BbjZ5OVlhpxSRzVDfmrA/AKcA9wPvRO/uBkwxs/vd/eo45xMRkbR0GPR36JoHj11A1a37cvPyC3ituh+Z6REKRxeoiIkkofrWhJ0F7O7uV7v7pOjtamCP6DEREWks/QbD2S+xNK0d90T+wemRp6mqrqGotDzsZCKyGeorYTVAlw3cv030mIiINKZO21I2ZDovks/fMiby54xCCnppFEwkGdW3JuxS4EUz+wqYE72vB9AHuCiOuUREZCN27dONklH389Hzf+LMbx+A4ubQ7TYt2BdJMnWWMHd/xsy2JZh+rL0w/11311bOIiIhyevVEc6+A97oBy/8DZb+ACcXBtejFJGkUN/C/A7RD7+M3tZoZ2a4u7ZyFhEJixnscym07QqPnQ/3HA4jH4Z23cJOJiIxqG86sgRwwAjWgX0bvd+i9+fGL5qIiMRk5xOhdWd4YCSMGxQUsa12DDuViNSjzoX57t7L3XPdvRcwI/px7pr7GymjiIjUJ3d/OONpwIMRsa9fDTuRiNQjpgt4RyXX1voiIk3N1v3hrOehbReYOAQ+fjjsRCJSh00pYSIikujad4czn4Hue8AjZ8EbN0OSXZ5OpKmob2H+ZbU+7bze57j7DXFJJSIim69FVnCpo8fOg+f/AkvmwWH/hEha2MlEpJb6Fua3qfXxXet9LiIiiSqjOZxwD7TpAkVjYcm3MOROyGgRdjIRiaqvhH0FPOvuuiaGiEiyiUTg8H9Cu67w7J9g4gI4eTK07FD/Y0Uk7upbE9YdeMjMXjOzy81sTzOzxggmIiINZK8LYei9MK8keOfkom/CTiQi1L9FxTXufhBwJPAhcCbwnplNNrPTzGyrxggpIiJbqP8QOHUqLPsexh0C330UdiKRJi+md0e6+1J3n+ru57r7rsBVQCfgvrimExGRhtNzHzjzWYikw71HwqyXwk4k0qTFvEWFmXU1s73NbD+gI8H1Iw+LXzQREWlwnXeA0S9A+x5QeCJ8eP86h0vKKhj78kxKyipCCijSdNS3MB8AM7sGGAZ8Bqy5cLcD2pJZRCTZtO0CZz4dXOZo6rnBFhb7XEbJN4sYMa6IyuoaMtMjFI4uIC8nK+y0IikrphIGHAds5+6r4phFREQaS/N2MOIRePwCePEKWDyPt1ueS2V1DTUOVdU1FJWWq4SJxFGsJawUyABUwkREUkV6Jhx/ZzAy9sZ/GN7jG+5MH8nS6gwy0iMU5GaHnVAkpdW3Y/4tBNOOy4EPzOxFahUxd78kvvFERCSuIhEYdAW07Ub7p3/P61sv5IG+1zJguz4aBROJs/pGwoqj/y0BpsU5i4iIhGXPc6DN1rR+9GzO+vICKHgcUAkTiac6S5i7T2isICIiErJ+g6FlNkw+KdjC4vRpwbsoRSQu6tyiwsyeMLNjzCxjA8dyzewKMzszfvFERKRR9RwIpz4Gy38MitiPpWEnEklZ9e0TdjawL/C5mb1rZk+Z2Utm9jVwB1Di7vfEPaWIiDSe7rsHo2CVPwVFbMGXYScSSUnm7rGdaNYT2AZYAXzp7svjmGuj8vPzvbi4uP4TRURky/zwKdx3bPDxadNgq37h5hFJQmZW4u75GzoW84757j7b3d9y9w/CKmAiItKIttoRRj0VXOZo/FHw7QdhJxJJKTGXMBERaYI6bQtnPAWZrWDCYJirmQiRhqISJiIideuQGxSxlh2C6cmyN8NOJJISVMJERKR+7XsERaxtF5h0ApS+EnYikaRX3xYVH5vZRxu7NVZIERFJAG27wKgnIasXFJ4EXz4XdiKRpFbfSNjRwDHAM9HbiOjtqehNRESaktadYdR06Lw93D8cZjwRdiKRpFVnCXP3MncvAwa5++/d/ePobQxwaH1PbmaHm9kXZjbTzMZs5JyTzOwzM/vUzCZv3pchIiKNpmWHYMuKLgPgwdPh44d/cUpJWQVjX55JSVlF4+cTSRL1XTtyDTOzge7+RvSTval/KjMNGAsMAuYC75rZNHf/rNY5fYE/AgPdvcLMOm/OFyEiIo2sRXs4dSpMHgaPng2rK2HAcCAoYCPGFVFZXUNmeoTC0QW6GLjIBsS6MP8s4FYzm21mZcCtQH2XK9oDmOnupe5eCdwPHLveOWcDY929AsDd58ceXUREQtWsDYx4GHrtB4+dD8XBBVSKSsuprK6hxqGquoai0vKQg4okpphGwty9BNjFzNpFP18cw8O6AnNqfT4X2HO9c7YFMLM3gDTgcnd/Zv0nMrNzgHMAevTQxWRFRBJGZks45QF48FSY/muoXkVB7slkpkeoqq4hIz1CQW522ClFElKs05GY2VHAjkBzMwPA3a9ogNfvCxwAdANeNbOd3H1R7ZPc/U7gTgguW7SFrykiIg0pozkMK4SHz4BnxpB38AoKR59BUWk5BbnZmooU2YiYSpiZ3Q60BA4ExgFDgXfqedg8oHutz7tF76ttLvC2u1cBX5vZlwSl7N1YcomISIJIz4QTx8PUc+HFv5O3/yryDhgD0f/TLiK/FOuasL3d/TSgwt3/DuxFdCqxDu8Cfc2sl5llAicD09Y75zGCUTDMrGP0OUtjzCQiIokkLQOG3AUDRsD/roYXLgfX5IXIxsQ6Hbki+t/lZtYFKAe2qesB7l5tZhcBzxKs97rH3T81syuAYnefFj12qJl9BqwGfufuWsEpIpKsImkw+L+Q3gzeuCm4+PfBfwk7lUhCirWETTez9sC1wHuAE0xL1sndf7Gpq7v/tdbHDlwWvYmISCqIRODI66GmGl67Lihk+/8+7FQiCSfWd0deGf3wETObDjSP8R2SIiLSFEUicPR/YHUVvPyPYKpyn1+HnUokocS6ML8l8Bugh7ufbWY9zGxfd58e33giIpK0IhE4dmywkesLl0NaJux1YdipRBJGrNOR9wIlBAvyIXiX40OASpiIiGxcJA2OvyMoYs/+KShie5wddiqRhBDruyN7u/u/gSoAd18O6H3HIiJSv7QMOOEe2PYIeOq3UDIh7EQiCSHWElZpZi0IFuRjZr2BVXFLJSIiqSU9E06aAH0OgSd+BR9MCTuRSOhiLWF/A54BuptZIfAioLe6iIhI7NKbwbBJwbUmH78APnkk7EQioYr13ZHPm9l7QAHBNOSv3H1hXJOJiEjqyWgBp0yBSUPhkbMhkgH9BoedSiQUsY6EATQHKoAlQD8z2y8+kUREJKVltoIRD0LXvOB6k188HXYikVDEukXFNcAw4FOgJnq3A6/GKZeIiKSyZm1g5MNw37Hw4Glw8hToe8jPh0vKKnQBcEl5sW5RcRywnbtrMb6IiDSM5u3g1Kkw4Rh4YAQMfwByD6CkrIIR44qorK4hMz1C4egCFTFJSbFOR5YCGfEMIiIiTVCLLDj1ceiQC1NOgbI3KSotp7K6hhqHquoaikp1SWFJTXWOhJnZLQTTjsuBD8zsRWptTeHul8Q3noiIpLxW2XDa4zD+KCg8kYMHTeCW9AhV1TVkpEcoyM0OO6FIXNQ3HVkc/W8JMC3OWUREpKlq3RlOmwbjj2T7F85g6nGTeGlJV60Jk5Rm7h52hk2Sn5/vxcXF9Z8oIiLJZ/FcuPdIWLkYRj0JW/cPO5HIFjGzEnfP39CxTdmiQkREJL7adYPTn4CMljBpCPxYGnYikbhRCRMRkcSSlRO8a3J1JUw8HpZ+H3YikbhQCRMRkcTTeXsY8TAsWwATh8CKirATiTS4+t4d+QTRi3ZviLvrWhMiIhIf3fLh5ElQeBJMPjkYHctsGXYqkQZT30jYdcD1wNfACuCu6G0ZMCu+0UREpMnrfRCcMA7mvA0PnQ6rq8JOJNJg6hwJc/f/AZjZ9eut7H/CzPQWRRERib8dj4MVN8L0S+Gx8+H4OyGi1TSS/GK9bFErM8t191IAM+sFtIpfLBERkVryz4AVP8KLVwS77B/xbzALO5XIFom1hP0aeMXMSgEDcoBz45ZKRERkfftcBst/hLf+Cy2z4YAxYScS2SIxlTB3f8bM+gLbR+/6XBfzFhGRRmUGh14VvFPylX9Biw6w5zlhpxLZbDGVMDNrCVwG5Lj72WbW18y2c/fp8Y0nIiJSixkcc3NQxJ7+XTA1ufOJYacS2Syxrmy8F6gE9op+Pg+4Ki6JRERE6pKWDkPvhZx94LHz4Mvnwk4kslliLWG93f3fQBWAuy8nWBsmIiLS+DKawylTYKsd4cHT4JuisBOJbLJYS1ilmbUgunGrmfUGtCZMRETC07wtjHgE2naBySfB95+EnUhkk8Rawi4HngG6m1kh8CLw+3iFEhERiUnrTnDaY5DRKnrB76/DTiQSs5hKmLs/BwwBRgFTgHx3fyV+sURERGLUvketC34fpwt+S9KIqYSZ2YvAnu7+pLtPd/eFZnZnnLOJiIjERhf8liQU63RkL+APZva3Wvflb+xkERGRRrfmgt8Lvwwu+F25POxEInWKtYQtAg4GtjKzJ8ysXfwiiYiIbKbeB8EJd/3igt8lZRWMfXkmJWUaIZPEEetli8zdq4ELzGwU8DqQFbdUIiIim2vH42HFop8v+F2Sdw0j7n6HyuoaMtMjFI4uIC9H/4RJ+GItYbev+cDdx5vZx8CF8YkkIiKyhWpd8DujPEJl9WBq3KiqrqGotFwlTBJCndORZtY2+uFDZtZhzQ34Gvht3NOJiIhsrn0ug70uYudvH+DSjMdIM8hIj1CQmx12MhGg/pGwycDRQAnBRq21d8l3IDdOuURERLaMGQy6Epb/yCUfTmaXHXNpve/5GgWThFFnCXP3o6P/7dU4cURERBpQJAKDb4GVi9j/i3/Dbv2BwWGnEgHqKWFmtltdx939vYaNIyIi0sDS0uGEu+G+wfDo2dC6M/QoCDuVSL3TkdfXccyBgxowi4iISHxktoRTHoC7B8HkYXDW89Bp27BTSRNX33TkgY0VREREJK5aZcPIR4IiNukEGP08tNk67FTShMW6WStm1t/MTjKz09bc4hlMRESkwXXoBcMfhOXlUHgirFoadiJpwmK9duTfgFuitwOBf6OVjSIikoy67gYnTYAfPoUHT/t5V32RxhbrSNhQgssWfe/uZwC7ALp0kYiIJKe+g+CY/8Csl2DaxeAediJpgmLdMX+Fu9eYWXV0A9f5QPc45hIREYmv3U6FJfPglX9B265w8F/CTiRNTKwlrNjM2gN3EWzcugx4K16hREREGsX+fwiK2GvXQbuukH9m2ImkCYmphLn7BdEPbzezZ4C27v5R/GKJiIg0AjM46kZY+j08+RtovTVsf2TYqaSJ2JR3R+5sZoOB3YA+ZjYkfrFEREQaSVo6DL0XttkFHj4T5rwbdiJpImJ9d+Q9wD3ACcAx0dvRccwlIiLSeJq1huEPQZutYMowKJ8VdiJpAmJdE1bg7v3imkRERCRMrTvByEejm7kOCXbVb90ZgJKyCopKyynIzdYFwKXBxDod+ZaZqYSJiEhqy+4dbOa69AeYfBKsWkZJWQUjxhVx/XNfMGJcESVlFWGnlBQRawm7j6CIfWFmH5nZx2amhfkiIpJ6uuXDiffCdx/Cw2fw9qwfqKyuocahqrqGotLysBNKioh1OvJu4FTgY6AmfnFEREQSwHZHwFHXw/RfcxJZ3Jx+HFXVTkZ6hILc7LDTSYqItYQtcPdpcU0iIiKSSPLPhMXz6PjadbyUtw1T252qNWHSoGItYe+b2WTgCWDVmjvd/dG4pBIREUkEB/0fLJlHlw9u4sLBfSDn1LATSQqJtYS1IChfh9a6zwGVMBERSV1mMPgWWPYDPPEraNsF+hwcdipJEfWWMDNLA8rd/beNkEdERCSxpGXAiRPg3iPgwdPhzKdh653CTiUpoN53R7r7amBgI2QRERFJTM3bBltXNGsDhSfBkm/DTiQpINYtKj4ws2lmdqqZDVlzi2syERGRRNKuK4x4EFYtCYrYyiVhJ5IkF2sJaw6UAwehyxaJiEhTtfVOcNIEmP8ZPDQKVleFnUiSWEwL8939jHgHERERSQp9DoGjb4QnLoEnfwPH/CdYwC+yiWK9gHc3M5tqZvOjt0fMrFu8w4mIiCSkvNNh39/AexPg9RvCTiNJKtbpyHuBaUCX6O2J6H0iIiJN00F/gZ1OhBevgI8fDjuNJKFYS1gnd7/X3aujt/FApzjmEhERSWxmcOxYyBkIj50Ps98IO5EkmVhLWLmZjTSztOhtJMFCfRERkaYrvRkMmwTtc+D+4bDwq7ATSRKJtYSdCZwEfA98BwwFtFhfRESkZQcY+XCwqeukE2DZgrATSZKIqYS5e5m7D3b3Tu7e2d2Pc/dv4h1OREQkKWT1ZMaBd1G15HuWTRgKVSvCTiRJoM4tKszsr3Ucdne/soHziIiIJJ2SsgpGPL6S/Wsu4Lb5N1Ex6QyyTp8MkVgnnKQpqu+346cN3ADOAv4Qx1wiIiJJo6i0nMrqGp5dvTv/XD2CrLKn4YW6xjFE6hkJc/fr13xsZm2AXxGsBbsfuH5jjxMREWlKCnKzyUyPUFVdwyQ7inO2j9D5zVsgqxfsflbY8SRB1btjvpl1AC4DRgATgN3cvSLewURERJJFXk4WhaMLKCotpyA3m87dDoX7f4Cnfgvte0DfQWFHlARU53SkmV0LvAssBXZy98tVwERERH4pLyeLCw/sQ15OFqSlw9B7YKv+wTUmv/8YCNaOjX15JiVl+qdUwNx94wfNaoBVQDVQ+0QjWJjfNr7xfik/P9+Li4sb+2VFREQ23ZLvYNzB4M5HRzzCSVPKqKyuITM9QuHogqCwSUozsxJ3z9/QsTpHwtw94u4t3L2Nu7etdWsTRgETERFJKm23geEPwqqldHnydDKqf6LGoaq6hqJS7Xne1Om9syIiIvG0dX84aTzZy2fx38xbyLTVZKRHKMjNDjuZhCyuJczMDjezL8xsppmNqeO8E8zMzWyDw3UiIiJJrc8h2FHXs799wKM9H6PwrD01FSnxK2FmlgaMBY4A+gGnmFm/DZy3ZuuLt+OVRUREJHT5Z8DAX9H/u0fI+7Yw7DSSAOI5ErYHMNPdS929kmBvsWM3cN6VwDXAyjhmERERCd/Bl0O/Y+G5v8Bn08JOIyGLZwnrCsyp9fnc6H0/M7PdgO7u/mRdT2Rm55hZsZkVL1igC6OKiEiSikTg+DugWz48eg7MLQk7kYQotIX5ZhYBbgB+U9+57n6nu+e7e36nTp3iH05ERCReMlrAyVOgdWeYMgwqysJOJCGJZwmbB3Sv9Xm36H1rtAH6A6+Y2WygAJimxfkiIpLyWneCEQ/B6kqYfBKsWBR2IglBPEvYu0BfM+tlZpnAycDPE+DuvtjdO7p7T3fvCRQBg91dO7GKiEjq67QdDCuE8lnw4GlQXRl2ImlkcSth7l4NXAQ8C8wAHnT3T83sCjMbHK/XFRERSRq99oXBN8PX/4Mnfw11XMVGUk+9F/DeEu7+FPDUevf9dSPnHhDPLCIiIglpwHComA3/uwayesF+vw07kTSSuJYwERERicEBfwyK2EtXQlZP2Glo2ImkEaiEiYiIhM0MBt8Ci+fCYxdAu27QoyDsVBJnunakiIhIIkhvBsMmQfvuMOWUYMG+pDSVMBERkUTRsgMMfzAYGSs8EZb/GHYiiSOVMBERkUSS3RtOngyL57J0wjBue/EzSsoqwk4lcaASJiIikmh6FFC6z3W0+eEdurzyG0aMe0tFLAVpYb6IiEgCepq9WV49jN+lP8A31VtRVLoteTlZYceSBqQSJiIikoAKcrMZ8dJx9Fj9AxenT2X26r2APmHHkgak6UgREZEElJeTReHovfjxgKtZ0mVfer75Jyh9JexY0oBUwkRERBJUXk4W5x+8A21PK4SO28IDp8H8GWHHkgaiEiYiIpLomrcLtq7IaB5sXbH0h7ATSQNQCRMREUkG7bsHRWz5jzBlGFT+FHYi2UIqYSIiIsmiywAYeg989yE8MhpqVoedSLaASpiIiEgy2e5wOPwa+OIpePbPYaeRLaAtKkRERJLNnudAxWwoGgtZPaHgvLATyWZQCRMREUlGh14Ji8rgmTHMrMzi2dV5FORma0PXJKLpSBERkWQUSYMhd/FTx53p8uLFPPf804wYV6TLGyURlTAREZFkldmSB/peSzltGZdxLZ2rf6CotDzsVBIjlTAREZEktsv223JuzRgyqeLezGsY2FUrjZKFSpiIiEgSy8vJ4srRJ/DSgJvolbaQAW9cCNWrwo4lMVAJExERSXJ5OVkcf/wwIsfdCmWvw2MXQE1N2LGkHhqzFBERSRU7nwiL58CLf4f2PeCQv4WdSOqgEiYiIpJK9vk1LPoGXr8huNRR/plhJ5KNUAkTERFJJWZw5HWwZB48+Rto2w22PTTsVLIBWhMmIiKSatLSYei9sPVO8NAo+Pb9sBPJBqiEiYiIpKJmrWH4g9CyA0weFkxRSkJRCRMREUlVbbaGEQ9D1UqYNBRWaDf9RKISJiIikso6bw8nF1LzYynzbj+B90q/DzuRRKmEiYiIpLiSSH9+X30uXReX8N34MyiZrUsbJQKVMBERkRRXVFrOo1V786+qUzgq8iaRF/4adiRBJUxERCTlFeRmk5keYVzN0UysOZxd506Ct8aGHavJ0z5hIiIiKS4vJ4vC0QUUlZbTr+dYeOfX8OyfgoX7/U8IO16TpRImIiLSBOTlZJGXkxV80u0umLgQpp4HrTpDr33DDddEaTpSRESkqcloDqdMhg65cP8I+OHTsBM1SSphIiIiTVGLrGAPscyWMOkEWDQn7ERNjkqYiIhIU9W+e1DEKn+CQm3m2thUwkRERJqyrfvDyYVQPgumDA9215dGoRImIiLS1PXaD46/Hb55E6aeAzWrw07UJKiEiYiICOw0FA79B3z2ODzzR3APO1HK0xYVIiIiEtj7IljyLRSNhXZdYeCvwk6U0lTCREREZK1Dr4Kl38Hzf4XWW8Muw8JOlLI0HSkiIiJrRSJw/O0s2XovVj92Pl+98WjYiVKWSpiIiIiso2Tecg6cdzYzVnen23Pn8vm7L4QdKSWphImIiMg6ikrLqahuzqjKPzCfLHo+ewbMnxF2rJSjEiYiIiLrKMjNJjM9QoW146yaP5GW0QwmDtGu+g1MC/NFRERkHXk5WRSOLqCotJyC3L3JaJYP9x4JE4+HM5+FVtlhR0wJGgkTERGRX8jLyeLCA/uQl5MV7Ko//H5YPCe4vNGqZZSUVTD25ZmUlOlSR5tLI2EiIiJSv5y9Yei98MBIFk84mVFzzuWn6giZ6REKRxcEZU02iUbCREREJDbbHwmDb6bdt6/xT8biXkNVdQ1FpeVhJ0tKKmEiIiISu11HMjd/DMekvcWVGePJSDcKcrVGbHNoOlJEREQ2SbejxvD9ygpGfnIHB+/Ul21yjgw7UlJSCRMREZFNY8bWJ1wDzSrZpuQ26NwZ9r0s7FRJRyVMRERENp0ZHHU9VC6DF/8OzdvC7qPDTpVUVMJERERk80TS4LjbYNUyePK3kNlGF/zeBFqYLyIiIpsvLQNOHA8994HHzofPnww7UdJQCRMREZEtk9EcTpkCXQbAQ6Og9JWQAyUHlTARERHZcs3awIiHIbsPTBkOc94NO1HCUwkTERGRhtGyA5w6FVp3hsIT4PtPwk6U0FTCREREpOG02RpOexwyW1M14VgmPfmSri+5ESphIiIi0rCycvjk4AksXb6Kg94Zze/HTVMR2wCVMBEREWlw//sxi1Or/kgrVnBP5Co+njEj7EgJRyVMREREGlxBbjaz0nIZVTWGbJZy8owLYdn8sGMlFJUwERERaXB5OVkUji7gkEFHMfeo+2i+/Hu471j4qTzsaAlDO+aLiIhIXOTlZJGXkwX0gY73w+STYOJxcPo0aJEVdrzQaSRMRERE4i93fxhWCAs+h4nHw4pFYScKnUqYiIiINI6+h8CwScH+YROPh5WLw04UKpUwERERaRQlZRWMndebmQfeBt9/DBOHwMolYccKjUqYiIiIxF1JWQUjxhVx/XNfcPRzrZl5wFj47gOYdEKTLWIqYSIiIhJ3RaXlVFbXUONQVV3Ds6vz4MTx8O17UDgUVi0NO2KjUwkTERGRuCvIzSYzPUKaQUZ6hILcbNjhGBh6D8wtZtndx3HnCx81qZ31VcJEREQk7tbsG3bZodtROLogunUF0O9YZu1/M81/eI/dXj2Lc8c1nWtNqoSJiIhIo8jLyeLCA/usLWBRz3gBl1Rfwi42i3vsKt7/ojSkhI1LJUxERERCVZCbzUuRAi6ovpTt7RuGf34R/LQw7FhxpxImIiIioVozVTngkOGUHXY3LZeUwvijYekPYUeLq7iWMDM73My+MLOZZjZmA8cvM7PPzOwjM3vRzHLimUdEREQS05qpyr57HwcjHoZF38D4I2HxvLCjxU3cSpiZpQFjgSOAfsApZtZvvdPeB/LdfWfgYeDf8cojIiIiSaLXvnDqo8FI2L1HQEVZ2IniIp4jYXsAM9291N0rgfuBY2uf4O4vu/vy6KdFQLc45hEREZFk0aMATnscVi6C8UdB+aywEzW4eJawrsCcWp/Pjd63MWcBT8cxj4iIiCSTbnlw+hNQ+VNQxBZ8GXaiBpUQC/PNbCSQD1y7kePnmFmxmRUvWLCgccOJiIhIeLbZBUY9CTWrgzViP3wadqIGE88SNg/oXuvzbtH71mFmhwB/Bga7+6oNPZG73+nu+e6e36lTp7iEFRERkQS1VT844ymIpAcjYnOLw07UIOJZwt4F+ppZLzPLBE4GptU+wcx2Be4gKGDz45hFREREklnHvnx82AMs9pasHn80zHwh7ERbLG4lzN2rgYuAZ4EZwIPu/qmZXWFmg6OnXQu0Bh4ysw/MbNpGnk5ERESasJKyCk584FsGLf4zX1Z1xguHwccPhx1ri6TH88nd/SngqfXu+2utjw+J5+uLiIhIaigqLaeyuob53p6TK//CU51vo+sjZwU76xecF3a8zZIQC/NFRERE6lKQm01meoQ0g1XprflhcCFsfzQ88wd48UpwDzviJovrSJiIiIhIQ1hzaaOi0nIKcrPZLScLek6AJ38Nr10HPy2Ao26AtOSpNsmTVERERJq0vJws8nKy1t6Rlg7H3AytOgdFbHk5nHA3ZDQPL+Qm0HSkiIiIJC8zOPgvcPg18Pl0mHQCrFwcdqqYqISJiIhI8is4D4aMgzlFcO9RwXUnE5xKmIiIiKSGnU+E4Q/Aj7NYdechTHrqZUrKKsJOtVEqYSIiIpI6+hzCjMMKWb6kgiPePpV/j5uYsEVMJUxERERSyktLe3BC1eUs9ZZMiFzB/LcfCjvSBqmEiYiISEopyM3m27SunFj1d2bQk8M/+z2v33c5JbN/DDvaOlTCREREJKWs2VNs1KG78+Vhk3nW92Cf0huZec9oSr5OnEtVq4SJiIhIysnLyeLCA/uwcFWECysvZmz1YIZFXqTz4yNgRWKsEVMJExERkZRVkJtNRno6N6w+mTE159N1yfsw7hAonxV2NMyT7FpL+fn5XlxcHHYMERERSRIlZRU/X+4ozz+DB0YEB4ZNgp77xPW1zazE3fM3dEyXLRIREZGUtu7ljgbC6Bdhysmw5NtQc6mEiYiISNOS3RvOex3Sm4UaQ2vCREREpOkJuYCBSpiIiIhIKFTCREREpMkpKatg7MszQ72kkdaEiYiISJNSUlbBiHFFVFbXkJkeoXB0Qa2F+41HI2EiIiLSpBSVllNZXUONQ1V1DUWl5aHkUAkTERGRJqUgN5vM9AhpBhnpEQpys0PJoelIERERaVLWXFvy5w1cQ5iKBJUwERERaYLW3cA1HJqOFBEREQmBSpiIiIhICFTCREREREKgEiYiIiISApUwERERkRCohImIiIiEQCVMREREJAQqYSIiIiIhUAkTERERCYFKmIiIiEgIVMJEREREQqASJiIiIhIClTARERGREJi7h51hk5jZAqAsxtPbAYvjGGdLhZEvXq/ZUM+7pc+zOY/f1MfEen5HYOEmZkl1if43CY2fMdH/Jrf0ufQ3mdj0Nxn/18xx904bPOLuKXsD7gw7Q6Lli9drNtTzbunzbM7jN/UxsZ4PFDf2zzfRb4n+NxlGxkT/m9zS59LfZGLf9DcZ7mum+nTkE2EHqEcY+eL1mg31vFv6PJvz+E19TKL/XiWyZPjeNXbGRP+b3NLn0t9kYkuG710q/Vu5jqSbjhRJFmZW7O75YecQkYD+JiXRpPpImEiY7gw7gIisQ3+TklA0EiYiIiISAo2EiYiIiIRAJUxEREQkBCphIiIiIiFQCRMREREJgUqYSEjMrJWZFZvZ0WFnEWnqzGwHM7vdzB42s/PDziNNg0qYyCYys3vMbL6ZfbLe/Yeb2RdmNtPMxsTwVH8AHoxPSpGmoyH+Jt19hrufB5wEDIxnXpE1tEWFyCYys/2AZcB97t4/el8a8CUwCJgLvAucAqQB/1rvKc4EdgGygebAQnef3jjpRVJPQ/xNuvt8MxsMnA9MdPfJjZVfmq70sAOIJBt3f9XMeq539x7ATHcvBTCz+4Fj3f1fwC+mG83sAKAV0A9YYWZPuXtNPHOLpKqG+JuMPs80YJqZPQmohEncqYSJNIyuwJxan88F9tzYye7+ZwAzG0UwEqYCJtKwNulvMvp/jIYAzYCn4hlMZA2VMJEQufv4sDOICLj7K8ArIceQJkYL80Uaxjyge63Pu0XvE5Fw6G9SEp5KmEjDeBfoa2a9zCwTOBmYFnImkaZMf5OS8FTCRDaRmU0B3gK2M7O5ZnaWu1cDFwHPAjOAB9390zBzijQV+puUZKUtKkRERERCoJEwERERkRCohImIiIiEQCVMREREJAQqYSIiIiIhUAkTERERCYFKmIiIiEgIVMJEJCmZWXszu6DW513M7OE4vM7lZjbPzK7YyPHZZtbRzFqY2QdmVmlmHRs6h4ikHpUwEUlW7YGfS5i7f+vuQ+P0Wje6+1/rOsHdV7j7AODbOGUQkRSjC3iLSLK6GuhtZh8AzwNjgenu3t/MRgHHAa2AvsB1QCZwKrAKONLdfzSz3tHHdQKWA2e7++d1vaiZZQNTgK4Eu7Rbg39lItIkaCRMRJLVGGCWuw9w999t4Hh/YAiwO/APYLm770pQnE6LnnMncLG75wG/BW6N4XX/Brzu7jsCU4EeW/ZliEhTpZEwEUlVL7v7UmCpmS0Gnoje/zGws5m1BvYGHjL7eTCrWQzPux9BucPdnzSzioaNLSJNhUqYiKSqVbU+rqn1eQ3B//ZFgEXRdVwiIo1O05EikqyWAm0298HuvgT42sxOBLDALjE89FVgePQxRwBZm5tBRJo2lTARSUruXg68YWafmNm1m/k0I4CzzOxD4FPg2Bge83dgPzP7lGBa8pvNfG0RaeLM3cPOICKSsMzscmCZu18X4/mzgXx3XxjPXCKS/DQSJiJSt2XAORvbrHWNNZu1AhkE685EROqkkTARERGREGgkTERERCQEKmEiIiIiIVAJExEREQmBSpiIiIhICFTCRERERELw/z41Uqc2CuskAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm_0 = ml_0.head(0, 0, t, layers=1)\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(t, h / H0, \".\", label=\"obs\")\n", + "plt.semilogx(t, hm_0[0] / H0, label=\"ttim\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"Normalized head (h/H0)\")\n", + "plt.title(\"Model results - three layers model\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Create Second Model - multi-layer model\n", + "\n", + "To investigate whether we can improve the model performance, we will create a multi-layer model. For this, we divide the previous second and third layers into 0.5 m thick layers:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Determine elevation of each layer.\n", + "# Thickness of each layer is set to be 0.5 m.\n", + "z0 = np.arange(zt, zb, -0.5)\n", + "z1 = np.arange(zb, b, -0.5)\n", + "zlay = np.append(z0, z1)\n", + "zlay = np.append(zlay, b)\n", + "zlay = np.insert(zlay, 0, 0)\n", + "nlay = len(zlay) - 1 # number of layers\n", + "Saq_1 = 1e-4 * np.ones(nlay)\n", + "Saq_1[0] = 0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 8\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_1 = Model3D(\n", + " kaq=10, z=zlay, Saq=Saq_1, kzoverkh=1, tmin=1e-5, tmax=0.01, phreatictop=True\n", + ")\n", + "w_1 = Well(\n", + " ml_1,\n", + " xw=0,\n", + " yw=0,\n", + " rw=rw,\n", + " tsandQ=[(0, -Q)],\n", + " layers=[1, 2, 3, 4, 5, 6, 7, 8],\n", + " rc=rc,\n", + " wbstype=\"slug\",\n", + ")\n", + "ml_1.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Calibration of multi-layer model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".....................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 34\n", + " # data points = 216\n", + " # variables = 2\n", + " chi-square = 0.00868197\n", + " reduced chi-square = 4.0570e-05\n", + " Akaike info crit = -2182.30557\n", + " Bayesian info crit = -2175.55502\n", + "[[Variables]]\n", + " kaq0_21: 0.49534587 +/- 0.00771304 (1.56%) (init = 10)\n", + " Saq0_21: 4.0608e-04 +/- 3.5535e-05 (8.75%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_21, Saq0_21) = -0.959\n" + ] + } + ], + "source": [ + "ca_1 = Calibrate(ml_1)\n", + "ca_1.set_parameter(name=\"kaq0_21\", initial=10, pmin=0)\n", + "ca_1.set_parameter(name=\"Saq0_21\", initial=1e-4, pmin=0)\n", + "ca_1.series(name=\"obs\", x=0, y=0, layer=[1, 2, 3, 4, 5, 6, 7, 8], t=t, h=h)\n", + "ca_1.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_210.4953460.0077131.5571020inf10[0.49534586991870566, 0.49534586991870566, 0.4...
Saq0_210.0004060.0000368.7506830inf0.0001[0.0004060803540884006, 0.0004060803540884006,...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_21 0.495346 0.007713 1.557102 0 inf 10 \n", + "Saq0_21 0.000406 0.000036 8.750683 0 inf 0.0001 \n", + "\n", + " parray \n", + "kaq0_21 [0.49534586991870566, 0.49534586991870566, 0.4... \n", + "Saq0_21 [0.0004060803540884006, 0.0004060803540884006,... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.006339898421521682\n" + ] + } + ], + "source": [ + "display(ca_1.parameters)\n", + "print(\"RMSE:\", ca_1.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "RMSE has just slightly improved, and the parameter values are more or less similar to the previous values. However, AIC has improved significantly." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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W6W3Ia/cnTtv5KGOLelFr2WT2/rszfPgoFO32uzoRCaGwhbhzrgi4BXgLWAK87JxbZGb3mFmfYLPbgV+Z2RfAC8BgF20T10UiUM7KzWwuqsq9RdfQs/BB1tTsAu/8DUadCktmaJ12kRgR1rXTnXMzgZmljt1V4vlioFs4axCJRyXXl1+f2IgtfSbQvPBzeOvP8NIgaNYdej4Ax7f3u1QROQZasU0kRpW5vvzeIsgbB7Pvg13boMv1cO6dUO0XQ1FEJEJo2VUROdiOLfD+P+CzpyG5Opz1R+g6whsgJyIRxbdlV0UkQh1XB3r9A276BJqcAm//BR4/DZa+qd/LRaKIQlwknqW3gWtehUGTAYMXroDn+8MPS/yuTETKQSEuItDqAq9X3vMBWJ8Hj3eDN+7wbruLSMRSiIuIJzEJsm+EW+dD1hDIHQuPdYacJ2Bvod/ViUgZFOIicrBqaXDRv+CGj6BhJ3jzT17P/Jt3/K5MREpRiItI2eq3g2unwFUvQnEhTLwMJl4OG5f5XZmIBCnEReTQzKBNL7jpU+hxL6zJ8UaxzxoJOwv8rk4k7inEReTIAslw+q1w6+fQ+Rr49Al4rIs3z3xvkd/VicQthbiIlF/1dLjk33DDXKh/Esy8A57sDitm+12ZSFxSiItIxR3fAa6fDlc8D4U74LlL4YWrYPMKvysTiSsKcRE5OmbQ9hLv9/Lz74ZVH3i7pL19J+zaRl5+AaNmLycvX7+di4RLWHcxE5E4kJQCZ/weOl4N790DH/+Hws8nMXVHPyYVnk0gEGDi8OwDm7CISMioJy4ioVGjPvQdBSNmszG5CfckjOH1pDvJ2LuGnJWb/a5OJCYpxEUktBp25tv+r/P74t/Q0DYzLekv9Nk5FYqL/a5MJOYoxEUk5DKb1eGaYbcx9fRX2dn0TJp8do83+G3ber9LE4kpCnERCYvMjFSGXngqtYe+Cpc8ButyvYVivprsd2kiMUMhLiLhZQaZ13tzy+u2gVeHweShWvFNJAQU4iJSOdJOgCGz4Nw7YfFUGH26FokROUYKcRGpPIkBOPMPMPwdqFLd+5181p+gcKfflYlEJYW4iFS+hp3h1x/AqTd467A/eSZsmO93VSJRRyEuIv5Iqgq9/gHXvg67t8OY8+GDf2pDFZEKUIiLiL9OOBdu/Bja9YX37oVxvWDLSr+rEokKCnER8d9xdWDAM3DZWNi0FB4/A/LGg3N+VyYS0RTiIhI5OgzweuWNs2D6b+GFK+GnH/yuSiRiKcRFJLLUagzXToGeD3hT0EafBktm+F2VSERSiItI5ElIgOwbvRHsNRvCS4Ng6s3eADgR2U8hLiKRq96JMPxd6H47LJgEj3eD/E+0V7lIkEJcRCJbIBnOu8tb7c0MN64XeWN/y2NvL2LQmBwFucQ1hbiIRIem2XDDhyw+vi8jEqbyStLfSC/6XnuVS1xTiItI9KhSg129HuXmvbfT3L5jWvKdXJCyxO+qRHyjEBeRqJKZkcrQ4bcyteskUlIb0Prt6+DDRzSnXOJSwO8CREQqKjMjlcyMc2H3bJh2K7xzN6z/HC4dDVVq+F2eSKVRT1xEoleV6t5Kbz3ug6/fgKfPhY3L/K5KpNIoxEUkupnB6bfAdVNgxxYvyJdM97sqkUqhEBeR2ND8TPj1+5DeGl66Bt75OxTv9bsqkbBSiItI7KjV2JtPnjkYPnwYnr/M652LxCiFuIjElkAVuOTfcMljkP8RPHkWbFjgd1UiYaEQF5HYlHk9DH0T3F545kJv2VaRGKMQF5HY1SgTRrwPjU+BKTfCG7dD0Z79p7UGu0Q7zRMXkdhWPd3b2vTdv8PHj8G3X8LACeQVpDBoTA57iopJDiQwcXg2mRmpflcrUiHqiYtI7EsMQI//hcvHw/eL4Mkzyf/8HfYUFVPsoLCoWGuwS1RSiItI/DipH/zqXUipSb8vb2Bo0tskmiMpkEB2izS/qxOpMIW4iMSXem3hV+9hrXtwZ8J4ZjR9kUlDuuhWukQlhbiIxJ+UWnDFRDhrJG2/n06XD4bBTg1uk+ijEBeR+JSQAOf8D/R7CtZ+CmN7wJZVflclUiEKcRGJbx2v8Eav/7wRxpwPa+f5XZFIuSnERUSadYNh70BKTXj2Ylj0ut8ViZSLQlxEBKBuSy/IG3SCVwbDh4+Ac35XJXJYYQ1xM+tpZkvNbLmZjTxEm4FmttjMFpmZ1kUUEf9US4PrpkL7y+Cdu2H6b2Bvod9ViRxS2FZsM7NEYBRwAbAOmGdm05xzi0u0aQX8D9DNOVdgZvXCVY+ISLkkpUD/MVCnBXzwT9i6FgY+641oF4kw4eyJdwWWO+dWOuf2AC8CfUu1+RUwyjlXAOCc+yGM9YiIlE9CApx7J/QdBavnwtgLYeua/ae15rpEinCund4IWFvi9Trg1FJtWgOY2UdAInC3c+7NMNYkIlJ+na+BWk3gpWvh6fPg6hfJK2qhNdclYvg9sC0AtALOBq4Cnjaz2qUbmdkIM8s1s9yNGzdWboUiEt9anAXD/+vdZh93Ed9/9orWXJeIEc4QXw80KfG6cfBYSeuAac65QufcKmAZXqgfxDn3lHMuyzmXlZ6eHraCRUTKlN4Ghr8H9U+i16I/MiJpltZcl4gQzhCfB7Qys+ZmlgxcCUwr1WYKXi8cM6uLd3t9ZRhrEhE5OtXTYfAMrF0fRiY8x5TmrzNxaJZupYuvwhbizrki4BbgLWAJ8LJzbpGZ3WNmfYLN3gI2m9liYDbwB+ec7k2JSGRKqgoDxkO339Jhw2QyP/0dFO7yuyqJY+YOs5iBmQWAYUA/oGHw8HpgKjDWOVfpEyizsrJcbm5uZX+siMjBcp6AN/8EzbrDlZO81d5EwsDM8pxzWWWdO1JP/DmgE3A30Dv4+DvQEXg+dCWKiESZ7Bug/9Ow5hMYfxH8pBmyUvmONMUs0znXutSxdUCOmS0LU00iItHh5IFQtQ68fC08cyFc+zqkNvO7KokjR+qJbzGzy81sfzszSzCzKwCtciAi0up8b6nWHVu87Uy/W+h3RRJHjhTiVwIDgO/NbJmZfQN8B/QPnhMRkSZdYeibYIkwrjfkf+x3RRInDhvizrnVzrkrnHPpwGlAtnOuXvDYqsopUUQkCtRrC8Pehur14Ll+sHSW3xVJHDjsb+Jm1r+MY/ufO+deC0NNIiLRqXYTGPoWTBwALw6CPv8POg8CvPXWc1ZuJrtFmuaWS8gcaWDbJaWeTy/x2gEKcRGRkqqlwfXT4KVrYOpNsGMzeY2v1XrrEhaHDXHn3JB9z81sfsnXIiJyCFVqwNUvw+u/hv/+FWv8DXuKLqTY2f711hXiEgoVWbHt0KvCiIjIwQJV4LKxcMpwuqybwENJT5Fse7XeuoRUOLciFRGJbwmJ0PshqJZO/zn307FRItt6P0EX9cIlRI40sG06B3rgLczsoA1MnHN9fvkuERHZzwzOHgkptTjhzZHwya3QZIK3tanIMTpST/yhEs//Fc5CRERiWvaNkJgMb9wGL1zprbeefJzfVUmUO1KIDwJmAe8457ZXQj0iIrHrlGEQSIFpt3jT0K5+yRsEJ3KUjjSwbSzeZiczzexdM/uTmXWshLpERGJT50HBjVNyvEVhdm71uyKJYkdase1T59zdzrnuwEBgDXC7mc03s2fMbGClVCkiEks6DICBz8KGBTChj7fuushRKNcUMzOrAlwINAdW4O0nng60DF9pIiIxrO0lcOVE+OFrGH+xtjKVo1LeeeJTgb5AEfATsB2Y45z7v3AVJiIS81pf6P0uvmWltyf5jxv8rkiiTHnniTd2zvUMayUiIvHohHPgmldh0kBvB7Trp3trsIuUQ3l74h+bWYewViIiEq+adYNrp3i/jY/r7fXMRcrhsCFuZl+Z2ZfAGcDnZrbUzL4scVxEREKhySnexil7tntBvukbvyuSKHCk2+kXV0oVIiICDTvB4DdgQl8vyK+bCvXb+V2VRLAjTTHLP9yjsooUEYkb9U+CwTO9ddfHX+RNQ8Pbj3zU7OXk5Rf4W59EFG2AIiISadJbw5CZ8GwfeLYPSy4Yz6Cpu7UfufxCRbYiFRGRylKnhRfkx9XhhFmD6Lh3McWO/fuRi4BCXEQkctVuCkNmsrd6A8Yl/YPuCQu1H7kcRCEuIhLJajak6q/exFKbMS7lX0zrvVe30mU/hbiISKSrXo+qv5pJoG5LWr87HFa+73dFEiEU4iIi0aBaXW8eeZ3mMOkKWPWB3xVJBFCIi4hEi2p14bppkJoBEwfCqrl+VyQ+U4iLiEST6unB9dWbeuutr/7I74rERwpxEZFoU72eF+S1GsPEyyH/Y78rEp8oxEVEolGN+l6Q12wIzw+A/E/8rkh8oBAXEYlWNY6HwTOgZgOYOADWfOp3RVLJFOIiItGsxvFw/QyoXh+evwzWfrb/lNZbj30KcRGRaFezgdcjr54Oz/WHtfPIyy9g0Jgc/vX2UgaNyVGQxyiFuIhILKjZ0OuRV6sLz/dn1YI57Ckq1nrrMU4hLiISK2o18nrkx9Wh38JbyQysJNHQeusxTCEuIhJLajWGwW+QWD2NF6r+g/tP26utS2OYQlxEJNbUagzXzyBwXCoDF99CZvJavyuSMFGIi4jEotpNvN/Ik2vAhL7w/WK/K5IwUIiLiMSq1Axv05RAFZjQBzYu87siCTGFuIhILEs7wVvZDYNnL4HNK/yuSEJIIS4iEuvqtvJ65MWFXpAXrPa7IgkRhbiISDyo1xaumwp7fvaCfKsGu8UChbiISLw4vgNcNwV2bvOC/McNflckx0ghLiISTxp2hmteZe/2Hyh4vCdffK3BbtFMIS4iEmfyilty7e4/UGXHd1R9oT8LlmqwW7RSiIuIxJmclZvJKWrN8MLbyeA7Gk2/CnZu9bssOQoKcRGROJPdIo3kQAKfuvbcUnwbaTtWevuR797ud2lSQQG/CxARkcqVmZHKxOHZ5KzcTHaL00nYcTK8fB1MugIGTSbv293Bc2lacz3CmXPO7xoqJCsry+Xm5vpdhohIbFn4Krw6nB8bnM4Za0fwU1GA5ECCNk+JAGaW55zLKutcWG+nm1lPM1tqZsvNbORh2l1mZs7MyixSRETCrP1l0HcUNTd8yKM8QqIr0j7kUSBsIW5micAooBfQDrjKzNqV0a4G8Fvg03DVIiIi5dDpavJPu49zE+fzWNJ/SAk47UMe4cLZE+8KLHfOrXTO7QFeBPqW0e5/gX8Au8JYi4iIlEPGhbewtutd9Er8jA9avUJmk5p+lySHEc4QbwSUXNdvXfDYfmbWBWjinHsjjHWIiEgFNOl9O5z3N9JWToEZv4PiYr9LkkPwbXS6mSUADwODy9F2BDACoGnTpuEtTEREoPttULgTPngQAinQ60Ew87sqKSWcIb4eaFLidePgsX1qAO2BOeZ9MY4HpplZH+fcQcPPnXNPAU+BNzo9jDWLiMg+5/wZCnfAJ//xgvyCexTkESacIT4PaGVmzfHC+0rg6n0nnXPbgLr7XpvZHOCO0gEuIiI+MYMe93o98o8fg+RqcPYhJxqJD8IW4s65IjO7BXgLSASecc4tMrN7gFzn3LRwfbaIiISIGfR+CIp2w5z7vR75Gb/zuyoJCutv4s65mcDMUsfuOkTbs8NZi4iIHKWEBOjzGBTthHf+BklV4dRf+12VoGVXRUSkPBISod+TXo981h8hUAUyB/tdVdxTiIuISPkkJsGAZ+DFq2H671i1rZiZdqbWWPeRdjETEZHyC1SBK57nx+Ozafr+bSx+ZwKDxuSQl1/gd2VxSSEuIiIVk1SVF1s+yHzXikcD/6F78Tytse4ThbiIiFRYZqsm3OBGssRlMCrwby6ossjvkuKSQlxERCosMyOVJ4efy2dnjKEorRWt3/s1rP7Q77LijkJcRESOSmZGKsN7ZHLcsBlQuylMHAhrP/O7rLiiEBcRkWNTrS5cPw1q1IfnL4MN8/2uKG4oxEVE5NjVOB6umwYpteG5fvDdQr8rigsKcRERCY3aTbweeaAqTOjLwi/mMWr2ck0/CyOFuIiIhE6d5nD9dAod1Hvtcia//b7mkYeRQlxEREKrbksmnzSKAIU8n3wf9Yq+1zzyMFGIi4hIyLXucCpDi++kGruYmHwf3evt9rukmKQQFxGRkMvMSOWvw6/k7cwnaJi8k5PfvQZ+/NbvsmKOQlxERMIiMyOVgX36kHjda/DTDzChj/e/EjIKcRERCa8mXWHQK7BtHTzbB37e5HdFMUMhLiIi4ZdxOlz1IhSsggmXwo4tflcUExTiIiJSOVqcBVdOhE1L+XlsH57+73xNPTtGCnEREak8Lc9n+TmPk7RpMVlzhzNizHsK8mOgEBcRkUr1VmEnbi36De1tFU/b/eQtW+N3SVFLIS4iIpUqu0Ua7yecym+LbuVkW8HV39wGu7f7XVZUUoiLiEilysxIZeLwbE46/1ryz/0P1TfOh4mXK8iPQsDvAkREJP5kZqSSmZEKtIS6x8HkYd5+5INegSrV/S4vaqgnLiIi/jqpH1z2NKzNgUkDYc/PflcUNRTiIiLiv/aXQf+nYc0nMOkKBXk5KcRFRCQydBgA/Z6C/I+CQb7D74oinkJcREQix8mXQ78nvSB/QUF+JApxERGJLCcPhEufwK2ay9rRffl8xQa/K4pYCnEREYk4ebV7MLL4RhoVzGP3hAHMX77e75IikkJcREQiTs7KzbxSeAa3Fd5IVxZTb/q1sPsnv8uKOApxERGJONkt0kgOJDDdncEf3S00/HEBTBygBWFK0WIvIiIScfat6pazcjPZLU7Hfsr0FoR5rj9cM5m874uD59KCi8bEJ4W4iIhEpAOrugH0A0uEyUP4ecwl3PD9b9hcVJXkQAITh2fHbZDrdrqIiESHdn1g4ARSNi/iGbuXGu4nCou8Hnm8UoiLiEj0OPEiVp33JG1sLS8m30v9wHayW6T5XZVvFOIiIhJVWp4xgNUXPkPLwPe8W+dBMlN3+l2SbxTiIiISdVqf3pek66dQdecP8ExPKFjtd0m+UIiLiEh0yjgdrpsKu7bCuN6w6Ru/K6p0CnEREYlejTNh8BtQtBvG9YLvFvpdUaVSiIuISHQ7vgMMmQUJARh/Eaz/3O+KKo1CXEREol96ay/IU2qyd/wlvDblFfLyC/yuKuwU4iIiEhvqNOfLHi+xZk8Nes2/icfHPBnzQa4QFxGRmDH3+2QG7rmLla4BoxMeZFPOC36XFFYKcRERiRnZLdLYHkhlUOGdfElLeiz5M+SN97ussNHa6SIiEjNKbpyS0HQK9slvYPpvYccWOOP3YOZ3iSGlEBcRkZhy0MYpGZNgyo3w7t/h543Q4z5IiJ2b0ApxERGJXYFk8rIeJFCQRMec0V6Q9x0NgWS/KwsJhbiIiMSsvPwCBo39jD1FF3NzEtz+1QuwYzMMfA6qVPe7vGMWO/cURERESslZuZk9RcUUO2N04SW81/ouWDmHn5/uzdi35kX9FDSFuIiIxKzsFmkkBxJINEgKJFCr21CWn/skiRsXc95Hg/jzmNejOsjDGuJm1tPMlprZcjMbWcb528xssZl9aWbvmllGOOsREZH4sm+0+m092jBxeDaZGam8VdSFQYV/oYbt4IWEv7J6/nt+l3nUwhbiZpYIjAJ6Ae2Aq8ysXalm84Es59zJwGTgwXDVIyIi8SkzI5Wbz2m5f8R6dos0FiWeyOV7/s6PVKffVzfAoin+FnmUwtkT7wosd86tdM7tAV4E+pZs4Jyb7ZzbEXyZAzQOYz0iIiL7e+eX9TiLrVfPJKFBJ3hlMHz8H3DO7/IqJJyj0xsBa0u8Xgecepj2w4BZYaxHREQEKDWXvMU0eG0EvP0X2JoPPR+AhER/CyyniBjYZmbXAFnAPw9xfoSZ5ZpZ7saNGyu3OBERiW1JVeHyZ+G0W+Czp+Cla2DPz35XVS7hDPH1QJMSrxsHjx3EzM4H/gL0cc7tLutCzrmnnHNZzrms9PT0sBQrIiJxLCEBLrwPev0Tlr0J4y+G7d/5XdURhTPE5wGtzKy5mSUDVwLTSjYws87Ak3gB/kMYaxERETmyU0ew/NwnKfxuMXsePwvWf+53RYcVthB3zhUBtwBvAUuAl51zi8zsHjPrE2z2T6A68IqZLTCzaYe4nIiISNjl5Rdw8ds16Lvrb2z8uYjiZ3rCV5P9LuuQwrrsqnNuJjCz1LG7Sjw/P5yfLyIiUhH7Vnhb7DLot+d/mVL7KRq+Ogx+WAzn3Blxm6dEVjUiIiI+KrnC24+BVL7r+xJ0uQ7m/gtevBp2/eh3iQfRBigiIiJBJfcjz26RRpeMVGj+GNTvAG+OhLE94KoXoE5zv0sFwFyUTWzPyspyubm5fpchIiLxZuUcePl6MIOBE6D5mZXysWaW55zLKuucbqeLiIiUR4uzYcRsqF4fN+FSPnj+XvJWb/G1JIW4iIhIedVpwfwerzC7uBNnLv8n6565ls9XbPCtHIW4iIhIBXy8bg+/2vN7Hiq8nEvsI+q+eBELv/RnPrlCXEREpAKyW6SRFAgwem8/hhT+kRp7fqDZq71Z8f6kSq9FIS4iIlIB+0awd2tVl7muIxfvvo9vXCNOmH0jvPk/ULSn0mpRiIuIiFRQZkYqvzu/NcmBBL6zdK5zd/N928GQMxrG94Zd2yqlDs0TFxEROQql55TXz7gEFp0N37wDVWpWSg2aJy4iIhLBNE9cRESkEuTlFzBq9nLy8gsq5fN0O11ERCQE8vILGDQmhz1FxSQHEpg4PJvMjNSwfqZ64iIiIiGwbwe0YgeFRcXkrNwc9s9UiIuIiIRAyR3QkgIJZLdIC/tn6na6iIhICJQerR7uW+mgEBcREQmZzIzUSgnvfXQ7XUREJEopxEVERKKUQlxERCRKKcRFRESilEJcREQkSinERUREopRCXEREJEopxEVERKJU1G1FamYbgfwjNKsFVM6O7JX/+aG69tFe52jeV973hKpdXWBTOa4TjfTdDu91KvreirQvT9t4/m6Dvt+HkuGcSy/zjHMu5h7AU7H6+aG69tFe52jeV973hKodkOvnP/9wPvTdDu91KvreirQvT9t4/m6H8jsQiZ8frmvH6u306TH8+aG69tFe52jeV973hLpdLPL7b4/l7/bRvLci7cvT1u9/vn7z+++Phu/3QaLudrrIkZhZrnMuy+86REJN320pLVZ74hLfnvK7AJEw0XdbDqKeuIiISJRST1xERCRKKcRFRESilEJcREQkSinEJe6YWTUzyzWzi/2uRSRUzKytmT1hZpPN7Ea/65HKoRCXqGFmz5jZD2a2sNTxnma21MyWm9nIclzqT8DL4alSpOJC8d12zi1xzt0ADAS6hbNeiRwanS5Rw8zOBH4CJjjn2gePJQLLgAuAdcA84CogEbi/1CWGAh2BNCAF2OScm1E51YscWii+2865H8ysD3Aj8JxzblJl1S/+CfhdgEh5Oec+MLNmpQ53BZY751YCmNmLQF/n3P3AL26Xm9nZQDWgHbDTzGY654rDWbfIkYTiux28zjRgmpm9ASjE44BCXKJdI2BtidfrgFMP1dg59xcAMxuM1xNXgEukqtB3O/gfqP2BKsDMcBYmkUMhLnHJOTfe7xpEQsk5NweY43MZUsk0sE2i3XqgSYnXjYPHRKKdvttyRApxiXbzgFZm1tzMkoErgWk+1yQSCvpuyxEpxCVqmNkLwCdAGzNbZ2bDnHNFwC3AW8AS4GXn3CI/6xSpKH235WhpipmIiEiUUk9cREQkSinERUREopRCXEREJEopxEVERKKUQlxERCRKKcRFRESilEJcJMaZWW0zuyn4vKGZTQ7htX9nZteVcbzZvm01zayDmY0P1WeKyAEKcZHYVxu4CcA5t8E5NyAUFzWzAN72rofdLcs59xXQ2MyahuJzReQAhbhI7HsAOMHMFpjZKyV6yIPNbIqZ/dfMVpvZLWZ2m5nNN7McM6sTbHeCmb1pZnlmNtfMTgxe91zg8+DKYphZppl9YWZ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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm_1 = ml_1.head(0, 0, t, layers=8)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, h / H0, \".\", label=\"obs\")\n", + "plt.semilogx(t, hm_1[0] / H0, label=\"ttim\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"h/H0\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 9. Final Model calibration with well skin resistance\n", + "\n", + "Now we test if the skin resistance of the well has an impact on model calibration. For this, we add the ```res``` parameter in the calibration settings. We use the same multi-layer model." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...........................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 40\n", + " # data points = 216\n", + " # variables = 3\n", + " chi-square = 0.00858103\n", + " reduced chi-square = 4.0287e-05\n", + " Akaike info crit = -2182.83157\n", + " Bayesian info crit = -2172.70573\n", + "[[Variables]]\n", + " kaq0_21: 0.50869799 +/- 0.01009968 (1.99%) (init = 10)\n", + " Saq0_21: 3.4204e-04 +/- 4.2853e-05 (12.53%) (init = 0.0001)\n", + " res: 0.00247891 +/- 0.00142206 (57.37%) (init = 0.1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_21, Saq0_21) = -0.976\n", + " C(Saq0_21, res) = -0.709\n", + " C(kaq0_21, res) = 0.667\n" + ] + } + ], + "source": [ + "ca_2 = Calibrate(ml_1)\n", + "ca_2.set_parameter(name=\"kaq0_21\", initial=10, pmin=0)\n", + "ca_2.set_parameter(name=\"Saq0_21\", initial=1e-4, pmin=0)\n", + "ca_2.set_parameter_by_reference(name=\"res\", parameter=w_1.res, initial=0.1, pmin=0)\n", + "ca_2.series(name=\"obs\", x=0, y=0, layer=[1, 2, 3, 4, 5, 6, 7, 8], t=t, h=h)\n", + "ca_2.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_210.5086980.0101001.9853990inf10[0.5086979931631646, 0.5086979931631646, 0.508...
Saq0_210.0003420.00004312.5286950inf0.0001[0.00034203719744052563, 0.0003420371974405256...
res0.0024790.00142257.3663260inf0.1[0.0024789096083315254]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_21 0.508698 0.010100 1.985399 0 inf 10 \n", + "Saq0_21 0.000342 0.000043 12.528695 0 inf 0.0001 \n", + "res 0.002479 0.001422 57.366326 0 inf 0.1 \n", + "\n", + " parray \n", + "kaq0_21 [0.5086979931631646, 0.5086979931631646, 0.508... \n", + "Saq0_21 [0.00034203719744052563, 0.0003420371974405256... \n", + "res [0.0024789096083315254] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.006302935863326607\n" + ] + } + ], + "source": [ + "display(ca_2.parameters)\n", + "print(\"RMSE:\", ca_2.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model has only improved slightly with the addition of the skin resistance, with a tiny improvement in AIC and RMSE. That indicates that skin resistance can be ignored in this situation." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm_2 = ml_1.head(0, 0, t, layers=8)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, h / H0, \".\", label=\"obs\")\n", + "plt.semilogx(t, hm_2[0] / H0, label=\"ttim\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"h/H0\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 10. Analysis and comparison of simulated values\n", + "\n", + "We now compare the values in TTim and add the results of the AQTESOLV modelling reported by Yang (2020)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Comparison of parameter values and error under different models
 k [m/d]Ss [1/m]res [1/d]RMSE
AQTESOLV2.6160000.000079nan0.001197
ttim-three0.5964510.000213nan0.006469
ttim-multi0.4953460.000406nan0.006358
ttim-res0.5086980.0003420.0024790.006303
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ta = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"res [1/d]\"],\n", + " index=[\"AQTESOLV\", \"ttim-three\", \"ttim-multi\", \"ttim-res\"],\n", + ")\n", + "ta.loc[\"ttim-three\"] = np.concatenate((ca_0.parameters[\"optimal\"].values, [np.nan]))\n", + "ta.loc[\"ttim-multi\"] = np.concatenate((ca_1.parameters[\"optimal\"].values, [np.nan]))\n", + "ta.loc[\"ttim-res\"] = ca_2.parameters[\"optimal\"].values\n", + "ta.loc[\"AQTESOLV\"] = [2.616, 7.894e-5] + [np.nan]\n", + "ta[\"RMSE\"] = [\n", + " 0.001197,\n", + " round(ca_0.rmse(), 6),\n", + " round(ca_1.rmse(), 6),\n", + " round(ca_2.rmse(), 6),\n", + "]\n", + "ta.style.set_caption(\"Comparison of parameter values and error under different models\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "AQTESOLV parameters are quite different from the set parameters in TTim. It also has a better RMSE performance. All TTim models are very similar to each other. However, the multi-layer models performed better." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Batu, V., 1998. Aquifer hydraulics: a comprehensive guide to hydrogeologic data analysis. John Wiley & Sons\n", + "* Hyder, Z., Butler Jr, J.J., McElwee, C.D., Liu, W., 1994. Slug tests in partially penetrating wells. Water Resources Research 30, 2945–2957.\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Next Notebook: [Slug Test 3 - Multiwell](slug3_multiwell.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/04pumpingtests/slug3_multiwell.ipynb b/docs/04pumpingtests/slug3_multiwell.ipynb new file mode 100644 index 0000000..74806e7 --- /dev/null +++ b/docs/04pumpingtests/slug3_multiwell.ipynb @@ -0,0 +1,985 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Slug Test for Confined Aquifer - Multi-well Example\n", + "**This test is taken from examples of AQTESOLV.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model\n", + "\n", + "In this notebook, we reproduce the work of Yang (2020) to check the TTim performance in analysing slug-test. We later compare the solution in TTim with the KGS analytical model (Hyder et al. 1994) implemented in AQTESOLV (Duffield, 2007) and to the MLU model (Carlson & Randall, 2012).\n", + "\n", + "This Slug Test was reported in Butler (1998). A well (Ln-2) fully penetrates a sandy confined aquifer, with 6.1 m thickness. Additionally, an observation well (Ln-3) is placed 6.45 m away from the test well. The observation well is also fully penetrated.\n", + "\n", + "The slug displacement is 2.798 m. Head change has been recorded at the slug well and the observation well. The well and casing radii of the slug well are 0.102 and 0.051 m, respectively. For the observation well, they are 0.051 and 0.025 m, respectively.\n", + "\n", + "The conceptual model can be seen in the figure below.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "# sky\n", + "sky = plt.Rectangle((-5, 1), width=15, height=3, fc=\"b\", zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "# Aquifer:\n", + "ground = plt.Rectangle(\n", + " (-5, -6.1),\n", + " width=15,\n", + " height=6.1,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + ")\n", + "ax.add_patch(ground)\n", + "\n", + "well = plt.Rectangle(\n", + " (-0.5, -(6.1)), width=1, height=(7.1), fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "ax.add_patch(well)\n", + "\n", + "# Confining Unit\n", + "conf = plt.Rectangle(\n", + " (-5, 0), width=15, height=1, fc=np.array([100, 100, 100]) / 255, zorder=0, alpha=0.9\n", + ")\n", + "ax.add_patch(conf)\n", + "\n", + "# Wellhead\n", + "wellhead = plt.Rectangle(\n", + " (-0.6, 1),\n", + " width=1.2,\n", + " height=1.5,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " zorder=2,\n", + " ec=\"k\",\n", + ")\n", + "ax.add_patch(wellhead)\n", + "\n", + "# Screen for the well:\n", + "screen = plt.Rectangle(\n", + " (-0.5, -(6.1)),\n", + " width=1,\n", + " height=6.1,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x=1, y=1.5, dx=0, dy=1, color=\"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x=1.2, y=1.5, s=r\"$ D = 2.798 m $\", fontsize=\"large\")\n", + "\n", + "# Piezometer\n", + "piez = plt.Rectangle(\n", + " (6.20, -(6.1)),\n", + " width=0.5,\n", + " height=(7.1),\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " zorder=1,\n", + ")\n", + "ax.add_patch(piez)\n", + "screen_piez = plt.Rectangle(\n", + " (6.2, -(6.1)),\n", + " width=0.5,\n", + " height=6.1,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez.set_linewidth(2)\n", + "ax.add_patch(screen_piez)\n", + "\n", + "# last line\n", + "line = plt.Line2D(xdata=[-200, 1200], ydata=[1, 1], color=\"k\")\n", + "ax.add_line(line)\n", + "\n", + "# Water table\n", + "# wt = plt.Line2D(xdata= [-200,1200], ydata = [0,0], color = \"b\")\n", + "# ax.add_line(wt)\n", + "\n", + "ax.text(0.6, -0.5, s=\"Ln-2\", fontsize=\"large\")\n", + "ax.text(6.9, -0.5, \"Ln-3\", fontsize=\"large\")\n", + "ax.set_xlim([-5, 10])\n", + "ax.set_ylim([-6.1, 3])\n", + "ax.set_xlabel(\"Distance [m]\")\n", + "ax.set_ylabel(\"Relative height [m]\")\n", + "ax.set_title(\"Conceptual Model - Multi-Well Example\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Import required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from ttim import *\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "H0 = 2.798 # initial displacement in m\n", + "b = -6.1 # aquifer thickness\n", + "rw1 = 0.102 # well radius of Ln-2 Well\n", + "rw2 = 0.071 # well radius of observation Ln-3 Well\n", + "rc1 = 0.051 # casing radius of Ln-2 Well\n", + "rc2 = 0.025 # casing radius of Ln-3 Well\n", + "r = 6.45 # distance from observation well to test well" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Converting slug displacement to volume" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Slug: 0.02286 m^3\n" + ] + } + ], + "source": [ + "Q = np.pi * rc1**2 * H0\n", + "print(\"Slug:\", round(Q, 5), \"m^3\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data1 = np.loadtxt(\"data/ln-2.txt\")\n", + "t1 = data1[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h1 = data1[:, 1]\n", + "data2 = np.loadtxt(\"data/ln-3.txt\")\n", + "t2 = data2[:, 0] / 60 / 60 / 24\n", + "h2 = data2[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Create First Model - single layer\n", + "\n", + "We begin with a single layer model built in ```ModelMaq```.\n", + "Details on setting up the model can be seen in: [Confined 1 - Oude Korendijk](confined1_oude_korendijk.ipynb).\n", + "\n", + "The slug well is set accordingly. Details on setting up the ```Well``` object can be seen in: [Slug 1 - Pratt County](slug1_pratt_county.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_0 = ModelMaq(kaq=10, z=[0, b], Saq=1e-4, tmin=1e-5, tmax=0.01)\n", + "w_0 = Well(ml_0, xw=0, yw=0, rw=rw1, rc=rc1, tsandQ=[(0, -Q)], layers=0, wbstype=\"slug\")\n", + "ml_0.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Model calibration both simultaneous wells\n", + "\n", + "\n", + "The procedures for calibration can be seen in [Unconfined 1 - Vennebulten](unconfined1_vennebulten.ipynb)\n", + "\n", + "We calibrate hydraulic conductivity and specific storage, as in the KGS model (Hyder et al. 1994)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 33\n", + " # data points = 162\n", + " # variables = 2\n", + " chi-square = 0.01697483\n", + " reduced chi-square = 1.0609e-04\n", + " Akaike info crit = -1480.50639\n", + " Bayesian info crit = -1474.33119\n", + "[[Variables]]\n", + " kaq0: 1.16611071 +/- 0.00292597 (0.25%) (init = 10)\n", + " Saq0: 9.3822e-06 +/- 1.1585e-07 (1.23%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.502\n" + ] + } + ], + "source": [ + "# unknown parameters: kaq, Saq\n", + "ca_0 = Calibrate(ml_0)\n", + "ca_0.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_0.series(name=\"Ln-2\", x=0, y=0, layer=0, t=t1, h=h1)\n", + "ca_0.series(name=\"Ln-3\", x=r, y=0, layer=0, t=t2, h=h2)\n", + "ca_0.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq01.1661112.925968e-030.250917-infinf10[1.166110712749139]
Saq00.0000091.158486e-071.234774-infinf0.0001[9.38217155340542e-06]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 1.166111 2.925968e-03 0.250917 -inf inf 10 \n", + "Saq0 0.000009 1.158486e-07 1.234774 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0 [1.166110712749139] \n", + "Saq0 [9.38217155340542e-06] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.01023635339478882\n" + ] + } + ], + "source": [ + "display(ca_0.parameters)\n", + "print(\"RMSE:\", ca_0.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm1_0 = ml_0.head(0, 0, t1, layers=0)\n", + "hm2_0 = ml_0.head(r, 0, t2, layers=0)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1 / H0, \".\", label=\"obs ln-2\")\n", + "plt.semilogx(t1, hm1_0[0] / H0, label=\"ttim ln-2\")\n", + "plt.semilogx(t2, h2 / H0, \".\", label=\"obs ln-3\")\n", + "plt.semilogx(t2, hm2_0[0] / H0, label=\"ttim ln-3\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"Normalized Head: h/H0\")\n", + "plt.title(\"Model Results - Single layer model\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In general, the single-layer model seems to be performing well, with a good visual fit between observations and the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Calibration with well skin resistance\n", + "\n", + "Now we test if the skin resistance of the well has an impact on model calibration. Therefore, we add the ```res``` parameter in the calibration settings. We use the one-layer model." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 51\n", + " # data points = 162\n", + " # variables = 3\n", + " chi-square = 0.01690851\n", + " reduced chi-square = 1.0634e-04\n", + " Akaike info crit = -1479.14056\n", + " Bayesian info crit = -1469.87777\n", + "[[Variables]]\n", + " kaq0: 1.16581441 +/- 0.00296305 (0.25%) (init = 10)\n", + " Saq0: 9.3669e-06 +/- 1.1770e-07 (1.26%) (init = 0.0001)\n", + " res: 2.8663e-04 +/- 3.8599e-04 (134.66%) (init = 0)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.463\n", + " C(Saq0, res) = -0.179\n", + " C(kaq0, res) = -0.149\n" + ] + } + ], + "source": [ + "# unknown parameters: kaq, Saq, res\n", + "ca_1 = Calibrate(ml_0)\n", + "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_1.set_parameter_by_reference(name=\"res\", parameter=w_0.res, initial=0)\n", + "ca_1.series(name=\"Ln-2\", x=0, y=0, layer=0, t=t1, h=h1)\n", + "ca_1.series(name=\"Ln-3\", x=r, y=0, layer=0, t=t2, h=h2)\n", + "ca_1.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq01.1658142.963047e-030.254161-infinf10[1.1658144145327587]
Saq00.0000091.176977e-071.256531-infinf0.0001[9.366880025069768e-06]
res0.0002873.859882e-04134.664369-infinf0[0.0002866298026861447]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 1.165814 2.963047e-03 0.254161 -inf inf 10 \n", + "Saq0 0.000009 1.176977e-07 1.256531 -inf inf 0.0001 \n", + "res 0.000287 3.859882e-04 134.664369 -inf inf 0 \n", + "\n", + " parray \n", + "kaq0 [1.1658144145327587] \n", + "Saq0 [9.366880025069768e-06] \n", + "res [0.0002866298026861447] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.010216337267978508\n" + ] + } + ], + "source": [ + "display(ca_1.parameters)\n", + "print(\"RMSE:\", ca_1.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm1_1 = ml_0.head(0, 0, t1, layers=0)\n", + "hm2_1 = ml_0.head(r, 0, t2, layers=0)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1 / H0, \".\", label=\"obs ln-2\")\n", + "plt.semilogx(t1, hm1_1[0] / H0, label=\"ttim ln-2\")\n", + "plt.semilogx(t2, h2 / H0, \".\", label=\"obs ln-3\")\n", + "plt.semilogx(t2, hm2_1[0] / H0, label=\"ttim ln-3\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"Normalized Head: h/H0\")\n", + "plt.title(\"Model Results - Single layer model with res\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding well screen resistance does not improve the performance significantly, while the AIC value increases. Thus, it is recommended to leave it out of the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 9. Create Second Model - multi-layer model\n", + "\n", + "We will create a multi-layer model to investigate whether we can improve the model performance if we account for the vertical flow component. We carry this out by dividing the previous aquifer into 0.5 m thick layers." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Determine elevations of each layer.\n", + "# Thickness of each layer is set to be 0.5 m.\n", + "z = np.arange(0, b, -0.5)\n", + "zlay = np.append(z, b)\n", + "nlay = len(zlay) - 1\n", + "Saq_2 = 1e-4 * np.ones(nlay)\n", + "n = np.arange(0, 13, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we use the ```Model3D``` object to model multi-layer aquifer:\n", + "\n", + "Details on how to set it up can be seen in the notebook: [Unconfined - 1 - Vennebulten](unconfined1_vennebulten.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 13\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_2 = Model3D(\n", + " kaq=10, z=zlay, Saq=Saq_2, kzoverkh=1, tmin=1e-5, tmax=0.01, phreatictop=True\n", + ")\n", + "w_2 = Well(ml_2, xw=0, yw=0, rw=rw1, tsandQ=[(0, -Q)], layers=n, rc=rc1, wbstype=\"slug\")\n", + "ml_2.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 10. Calibration of multi-layer model" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 30\n", + " # data points = 2106\n", + " # variables = 2\n", + " chi-square = 0.21986745\n", + " reduced chi-square = 1.0450e-04\n", + " Akaike info crit = -19302.2835\n", + " Bayesian info crit = -19290.9784\n", + "[[Variables]]\n", + " kaq0_12: 1.16574643 +/- 8.0333e-04 (0.07%) (init = 10)\n", + " Saq0_12: 8.6961e-06 +/- 2.9670e-08 (0.34%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_12, Saq0_12) = -0.490\n" + ] + } + ], + "source": [ + "ca_2 = Calibrate(ml_2)\n", + "ca_2.set_parameter(name=\"kaq0_12\", initial=10)\n", + "ca_2.set_parameter(name=\"Saq0_12\", initial=1e-4, pmin=0)\n", + "ca_2.series(name=\"Ln-2\", x=0, y=0, layer=n, t=t1, h=h1)\n", + "ca_2.series(name=\"Ln-3\", x=r, y=0, layer=n, t=t2, h=h2)\n", + "ca_2.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_121.1657468.033304e-040.068911-infinf10[1.1657464303972085, 1.1657464303972085, 1.165...
Saq0_120.0000092.967009e-080.341190.0inf0.0001[8.69605115960681e-06, 8.69605115960681e-06, 8...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_12 1.165746 8.033304e-04 0.068911 -inf inf 10 \n", + "Saq0_12 0.000009 2.967009e-08 0.34119 0.0 inf 0.0001 \n", + "\n", + " parray \n", + "kaq0_12 [1.1657464303972085, 1.1657464303972085, 1.165... \n", + "Saq0_12 [8.69605115960681e-06, 8.69605115960681e-06, 8... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.010217656154254037\n" + ] + } + ], + "source": [ + "display(ca_2.parameters)\n", + "print(\"RMSE:\", ca_2.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm1_2 = ml_2.head(0, 0, t1, layers=n)\n", + "hm2_2 = ml_2.head(r, 0, t2, layers=n)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1 / H0, \".\", label=\"obs ln-2\")\n", + "plt.semilogx(t1, hm1_2[0] / H0, label=\"ttim ln-2\")\n", + "plt.semilogx(t2, h2 / H0, \".\", label=\"obs ln-3\")\n", + "plt.semilogx(t2, hm2_2[0] / H0, label=\"ttim ln-3\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"Normalized Head: h/H0\")\n", + "plt.title(\"Model Results - Multi-layer model\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The new model showed similar parameters and RMSE values compared to the previous single-layer model. However, the AIC value was much smaller." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 11. Analysis and comparison of simulated values\n", + "\n", + "We now compare the values in TTim and also add the results of the modelling done in AQTESOLV and MLU by Yang (2020)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Comparison of parameter values and error under different models
 k [m/d]Ss [1/m]RMSE
MLU1.3110000.0000080.010373
AQTESOLV1.1660000.0000090.009151
ttim-single1.1661110.0000090.010218
ttim-multi1.1657460.0000090.010218
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ta = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\"],\n", + " index=[\"MLU\", \"AQTESOLV\", \"ttim-single\", \"ttim-multi\"],\n", + ")\n", + "ta.loc[\"AQTESOLV\"] = [1.166, 9.368e-06]\n", + "ta.loc[\"MLU\"] = [1.311, 8.197e-06]\n", + "ta.loc[\"ttim-single\"] = ca_0.parameters[\"optimal\"].values\n", + "ta.loc[\"ttim-multi\"] = ca_2.parameters[\"optimal\"].values\n", + "ta[\"RMSE\"] = [0.010373, 0.009151, ca_0.rmse(), ca_2.rmse()]\n", + "ta.style.set_caption(\"Comparison of parameter values and error under different models\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The parameters in every model closely match each other. The error was also very similar." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Butler, J.J., Jr., 1998. The Design, Performance, and Analysis of Slug Tests, Lewis Publishers, Boca Raton, Florida, 252p.\n", + "* Hyder, Z., Butler Jr, J.J., McElwee, C.D., Liu, W., 1994. Slug tests in partially penetrating wells. Water Resources Research 30, 2945–2957.\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Next Notebook: [Slug Test 4 - Dawsonville](slug4_dawsonville.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/04pumpingtests/slug4_dawsonville.ipynb b/docs/04pumpingtests/slug4_dawsonville.ipynb new file mode 100644 index 0000000..4c8f449 --- /dev/null +++ b/docs/04pumpingtests/slug4_dawsonville.ipynb @@ -0,0 +1,912 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Slug test for confined aquifer - Dawsonville Example\n", + "**This test is taken from example of MLU.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model\n", + "\n", + "In this notebook, we reproduce the work of Yang (2020) to check the TTim performance in analysing slug-test. We later compare the solution in TTim with the MLU model (Carlson & Randall, 2012).\n", + "\n", + "This Slug Test was reported in Cooper Jr et al. (1967), and it was performed in Dawsonville, Georgia, USA. A fully penetrated well (Ln-2) is screened in a confined aquifer, located between depths 24 and 122 (98 m thick).\n", + "\n", + "The volume of the slug is 10.16 litres. Head change has been recorded at the slug well. Both the well and the casing radii of the slug well is 0.076 m.\n", + "\n", + "The conceptual model can be seen in the figure below:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "# sky\n", + "sky = plt.Rectangle((-5, 0), width=15, height=10, fc=\"b\", zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "# Aquifer:\n", + "ground = plt.Rectangle(\n", + " (-5, -122),\n", + " width=15,\n", + " height=98,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + ")\n", + "ax.add_patch(ground)\n", + "\n", + "well = plt.Rectangle(\n", + " (-0.5, -(122)), width=1, height=122, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "ax.add_patch(well)\n", + "\n", + "# Confining Unit\n", + "conf = plt.Rectangle(\n", + " (-5, -24),\n", + " width=15,\n", + " height=24,\n", + " fc=np.array([100, 100, 100]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + ")\n", + "ax.add_patch(conf)\n", + "\n", + "# Wellhead\n", + "wellhead = plt.Rectangle(\n", + " (-0.6, 0), width=1.2, height=4, fc=np.array([200, 200, 200]) / 255, zorder=2, ec=\"k\"\n", + ")\n", + "ax.add_patch(wellhead)\n", + "\n", + "# Screen for the well:\n", + "screen = plt.Rectangle(\n", + " (-0.5, -(122)),\n", + " width=1,\n", + " height=98,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "# pumping_arrow = plt.Arrow(x = 1,y = 1.5, dx = 0, dy = 1, color = \"#00035b\")\n", + "# ax.add_patch(pumping_arrow)\n", + "ax.text(x=1, y=2.5, s=r\"$ Q = 10.16 L $\", fontsize=\"large\")\n", + "\n", + "\n", + "# last line\n", + "line = plt.Line2D(xdata=[-200, 1200], ydata=[0, 0], color=\"k\")\n", + "ax.add_line(line)\n", + "\n", + "# Water table\n", + "# wt = plt.Line2D(xdata= [-200,1200], ydata = [0,0], color = \"b\")\n", + "# ax.add_line(wt)\n", + "\n", + "ax.text(0.6, -35, s=\"Ln-2\", fontsize=\"large\")\n", + "# ax.text(6.9, -0.5, \"Ln-3\", fontsize = 'large')\n", + "ax.set_xlim([-5, 10])\n", + "ax.set_ylim([-122, 10])\n", + "ax.set_xlabel(\"Distance [m]\")\n", + "ax.set_ylabel(\"Relative height [m]\")\n", + "ax.set_title(\"Conceptual Model - Dawsonville Example\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Load required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from ttim import *\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "b = 98 # aquifer thickness\n", + "zt = -24\n", + "zb = zt - b\n", + "rw = 0.076 # well radius of Ln-2 Well\n", + "rc = 0.076 # casing radius of Ln-2 Well\n", + "Q = 0.01016 # slug volume in m^3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Load data\n", + "\n", + "Data for the Dawsonville test is available in a text file, where the first column is the time data, in days and in the second column is the head displacement in meters" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data = np.loadtxt(\"data/dawsonville_slug.txt\")\n", + "t = data[:, 0]\n", + "h = data[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Create First Model - single layer\n", + "\n", + "We begin with a single layer model built in ModelMaq.\n", + "Details on setting up the model can be seen in: [Confined 1 - Oude Korendijk](confined1_oude_korendijk.ipynb).\n", + "\n", + "The slug well is set accordingly. Details on setting up the ```Well``` object can be seen in: [Slug 1 - Pratt County](slug1_pratt_county.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml = ModelMaq(kaq=10, z=[zt, zb], Saq=1e-4, tmin=1e-6, tmax=1e-3, topboundary=\"conf\")\n", + "w = Well(ml, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=0, wbstype=\"slug\")\n", + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Model calibration both simultaneous wells\n", + "\n", + "\n", + "The procedures for calibration can be seen in [Unconfined 1 - Vennebulten](unconfined1_vennebulten.ipynb)\n", + "\n", + "We calibrate hydraulic conductivity and specific storage, as in the KGS model (Hyder et al. 1994)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "............................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 25\n", + " # data points = 22\n", + " # variables = 2\n", + " chi-square = 4.2779e-04\n", + " reduced chi-square = 2.1389e-05\n", + " Akaike info crit = -234.654488\n", + " Bayesian info crit = -232.472403\n", + "[[Variables]]\n", + " kaq0: 0.42113030 +/- 0.01842476 (4.38%) (init = 10)\n", + " Saq0: 1.6938e-05 +/- 5.2880e-06 (31.22%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.985\n" + ] + } + ], + "source": [ + "# unknown parameters: kay, Saq\n", + "ca = Calibrate(ml)\n", + "ca.set_parameter(name=\"kaq0\", initial=10, pmin=0)\n", + "ca.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca.series(name=\"obs\", x=0, y=0, layer=0, t=t, h=h)\n", + "ca.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq00.421130.0184254.3750740inf10[0.42113029595796414]
Saq00.0000170.00000531.219119-infinf0.0001[1.6938294179330807e-05]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 0.42113 0.018425 4.375074 0 inf 10 \n", + "Saq0 0.000017 0.000005 31.219119 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0 [0.42113029595796414] \n", + "Saq0 [1.6938294179330807e-05] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.004409624944092217\n" + ] + } + ], + "source": [ + "display(ca.parameters)\n", + "print(\"rmse:\", ca.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm = ml.head(0, 0, t)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, h, \".\", label=\"obs\")\n", + "plt.semilogx(t, hm[0], label=\"ttim\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"displacement [m]\")\n", + "plt.title(\"Model Results - Single-layer model\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In general, the single-layer model seems to be performing well, with a good visual fit between observations and the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Create Second Model - multi-layer model\n", + "\n", + "To investigate whether we need to account for the vertical flow component or not, we will create a multi-layer model. Consequently, we divide the previous aquifer into 49 layers (2 m thick each)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "nlay = 49 # number of layers\n", + "zlayers = np.linspace(zt, zb, nlay + 1) # elevation of each layer\n", + "Saq = 1e-4 * np.ones(nlay)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we use the ```Model3D``` object to model multi-layer aquifer:\n", + "\n", + "Details on how to set it up can be seen in the notebook: [Unconfined - 1 - Vennebulten](unconfined1_vennebulten.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 49\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_1 = Model3D(kaq=10, z=zlayers, Saq=Saq, tmin=1e-6, tmax=1e-3, phreatictop=False)\n", + "w_1 = Well(\n", + " ml_1, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=range(nlay), wbstype=\"slug\"\n", + ")\n", + "ml_1.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Calibration of multi-layer model" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".............................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 26\n", + " # data points = 1078\n", + " # variables = 2\n", + " chi-square = 0.02096017\n", + " reduced chi-square = 1.9480e-05\n", + " Akaike info crit = -11690.1377\n", + " Bayesian info crit = -11680.1720\n", + "[[Variables]]\n", + " kaq0_48: 0.42104101 +/- 0.00252524 (0.60%) (init = 10)\n", + " Saq0_48: 1.6968e-05 +/- 7.2665e-07 (4.28%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_48, Saq0_48) = -0.986\n" + ] + } + ], + "source": [ + "ca_1 = Calibrate(ml_1)\n", + "ca_1.set_parameter(name=\"kaq0_48\", initial=10, pmin=0)\n", + "ca_1.set_parameter(name=\"Saq0_48\", initial=1e-4)\n", + "ca_1.series(name=\"obs\", x=0, y=0, layer=range(nlay), t=t, h=h)\n", + "ca_1.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_480.4210412.525240e-030.5997610inf10[0.42104101396365423, 0.42104101396365423, 0.4...
Saq0_480.0000177.266545e-074.282481-infinf0.0001[1.6968074939135146e-05, 1.6968074939135146e-0...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_48 0.421041 2.525240e-03 0.599761 0 inf 10 \n", + "Saq0_48 0.000017 7.266545e-07 4.282481 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0_48 [0.42104101396365423, 0.42104101396365423, 0.4... \n", + "Saq0_48 [1.6968074939135146e-05, 1.6968074939135146e-0... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.004409486363804946\n" + ] + } + ], + "source": [ + "display(ca_1.parameters)\n", + "print(\"RMSE:\", ca_1.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The multi-layer model does not improve the calibration by much." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm_1 = ml_1.head(0, 0, t)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, h, \".\", label=\"obs\")\n", + "plt.semilogx(t, hm_1[0], label=\"ttim\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"displacement [m]\")\n", + "plt.title(\"Model Results - Multi-layer model\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Final Model calibration with well skin resistance\n", + "\n", + "Now we test if the skin resistance of the well has an impact on model calibration. We thus add the ```res``` parameter in the calibration settings. We use the same multi-layer model." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 48\n", + " # data points = 1078\n", + " # variables = 3\n", + " chi-square = 0.02096895\n", + " reduced chi-square = 1.9506e-05\n", + " Akaike info crit = -11687.6864\n", + " Bayesian info crit = -11672.7378\n", + "[[Variables]]\n", + " kaq0_48: 0.41949032 +/- 0.00255131 (0.61%) (init = 10)\n", + " Saq0_48: 1.7431e-05 +/- 7.4956e-07 (4.30%) (init = 0.0001)\n", + " res: 4.1889e-06 +/- 5.9899e-06 (142.99%) (init = 0.1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_48, Saq0_48) = -0.986\n", + " C(Saq0_48, res) = -0.210\n", + " C(kaq0_48, res) = 0.188\n" + ] + } + ], + "source": [ + "ca_2 = Calibrate(ml_1)\n", + "ca_2.set_parameter(name=\"kaq0_48\", initial=10, pmin=0)\n", + "ca_2.set_parameter(name=\"Saq0_48\", initial=1e-4, pmin=1e-7)\n", + "ca_2.set_parameter_by_reference(name=\"res\", parameter=w_1.res, initial=0.1, pmin=0)\n", + "ca_2.series(name=\"obs\", x=0, y=0, layer=range(nlay), t=t, h=h)\n", + "ca_2.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_480.419492.551309e-030.6081930inf10[0.41949032003442377, 0.41949032003442377, 0.4...
Saq0_480.0000177.495585e-074.3001590.0inf0.0001[1.743095019002272e-05, 1.743095019002272e-05,...
res0.0000045.989853e-06142.9940930inf0.1[4.1888812432056e-06]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_48 0.41949 2.551309e-03 0.608193 0 inf 10 \n", + "Saq0_48 0.000017 7.495585e-07 4.300159 0.0 inf 0.0001 \n", + "res 0.000004 5.989853e-06 142.994093 0 inf 0.1 \n", + "\n", + " parray \n", + "kaq0_48 [0.41949032003442377, 0.41949032003442377, 0.4... \n", + "Saq0_48 [1.743095019002272e-05, 1.743095019002272e-05,... \n", + "res [4.1888812432056e-06] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.004410409508316654\n" + ] + } + ], + "source": [ + "display(ca_2.parameters)\n", + "print(\"RMSE:\", ca_2.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm_2 = ml_1.head(0, 0, t)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, h, \".\", label=\"obs\")\n", + "plt.semilogx(t, hm_2[0], label=\"ttim\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"displacement [m]\")\n", + "plt.title(\"Model Results - Multi-layer with res\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding resistance of the well screen does not improve the performance. Thus, res should not be applied in the conceptual model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 9. Analysis and comparison of simulated values\n", + "\n", + "We now compare the values in TTim and add the results of the modelling done in MLU by Yang (2020)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Comparison of parameter values and error under different models
 k [m/d]Ss [1/m]RMSE
MLU0.4133000.0000190.004264
ttim0.4211300.0000170.004410
ttim-multilayer0.4210410.0000170.004410
ttim-res0.4194900.0000170.004410
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ta = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\"],\n", + " index=[\"MLU\", \"ttim\", \"ttim-multilayer\", \"ttim-res\"],\n", + ")\n", + "tr = np.delete(ca_2.parameters[\"optimal\"].values, 2)\n", + "ta.loc[\"MLU\"] = [0.4133, 1.9388e-05]\n", + "ta.loc[\"ttim\"] = ca.parameters[\"optimal\"].values\n", + "ta.loc[\"ttim-multilayer\"] = ca_1.parameters[\"optimal\"].values\n", + "ta.loc[\"ttim-res\"] = tr\n", + "ta[\"RMSE\"] = [0.004264, ca.rmse(), ca_1.rmse(), ca_2.rmse()]\n", + "ta.style.set_caption(\"Comparison of parameter values and error under different models\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Results are similar between all models. The RMSE of MLU is slightly better than the one from TTim. The one-layer model has accomplished the same results as more complex ones. Hence, we note the importance of trying simpler models to avoid adding unnecessary complexity." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Cooper Jr, H.H., Bredehoeft, J.D., Papadopulos, I.S., 1967. Response of a finite diameter well to an instantaneous charge of water. Water Resources Research 3, 263–269\n", + "* Hyder, Z., Butler Jr, J.J., McElwee, C.D., Liu, W., 1994. Slug tests in partially penetrating wells. Water Resources Research 30, 2945–2957.\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/04pumpingtests/unconfined1_vennebulten.ipynb b/docs/04pumpingtests/unconfined1_vennebulten.ipynb new file mode 100644 index 0000000..15df63e --- /dev/null +++ b/docs/04pumpingtests/unconfined1_vennebulten.ipynb @@ -0,0 +1,1848 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Unconfined Aquifer Test - Vennebulten\n", + "**This example is taken from Kruseman et al. (1970).**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In aquifer tests in unconfined aquifers, there is also the vertical component to flow to the well. The drawdown data shows the delayed water table response, a distinguishable S-shape in the log-log plot. In the early times of the drawdown, the drawdown behaves as a confined aquifer: when the aquifer releases the elastic storage. However, as pumping continues, the water table storage begins to be released, generating further drawdown and the S-shape.\n", + "\n", + "This test conducted in Vennebulten, the Netherlands, is reported in Kruseman et al. (1970). The cross-section consists of a first layer up to 6 m depth of very fine and loamy sands, followed by coarse sands until 21 m deep.\n", + "\n", + "In this example, we will reproduce the work of Xinzhu (2020) that compared different conceptualizations in TTim to various solutions presented in the original report (Kruseman et al., 1970) and in other software, MLU (Carson & Randall, 2012) and AQTESOLV (Duffield, 2007).\n", + "\n", + "The screen of the pumping well is placed between 10 and 21 meters depth, and pumping has taken place for 25 hours at a rate of 873 m3/d. The available drawdown data comes from two piezometers, a shallow one, screened at 3 m depth, and a deeper one, screened in the depths between 12 to 19 m. Both wells are located 90 m from the pumping well.\n", + "\n", + "The conceptual model of the aquifer is displayed below:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "# sky\n", + "sky = plt.Rectangle((-20, 2), width=150, height=5, fc=\"b\", zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "# Aquifer:\n", + "ground = plt.Rectangle(\n", + " (-20, -6),\n", + " width=150,\n", + " height=8,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + " hatch=\".\",\n", + ")\n", + "ax.add_patch(ground)\n", + "\n", + "# Aquifer 2:\n", + "ground2 = plt.Rectangle(\n", + " (-20, -21),\n", + " width=150,\n", + " height=15,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + " hatch=\"o\",\n", + ")\n", + "ax.add_patch(ground2)\n", + "\n", + "\n", + "well = plt.Rectangle(\n", + " (-1.5, -21), width=3, height=23, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "ax.add_patch(well)\n", + "\n", + "# Wellhead\n", + "wellhead = plt.Rectangle(\n", + " (-2, 2), width=4, height=2.5, fc=np.array([200, 200, 200]) / 255, zorder=2, ec=\"k\"\n", + ")\n", + "ax.add_patch(wellhead)\n", + "\n", + "# Screen for the well:\n", + "screen = plt.Rectangle(\n", + " (-1.5, -21),\n", + " width=3,\n", + " height=11,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x=2, y=3.5, dx=5, dy=0, color=\"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x=7, y=3.5, s=r\"$ Q = 873 \\frac{m^3}{d}$\", fontsize=\"large\")\n", + "\n", + "# Piezometers\n", + "piez1 = plt.Rectangle(\n", + " (89, -21), width=2, height=23, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "screen_piez_1 = plt.Rectangle(\n", + " (89, -19),\n", + " width=2,\n", + " height=7,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_1.set_linewidth(2)\n", + "screen_piez_2 = plt.Rectangle(\n", + " (89, -3),\n", + " width=2,\n", + " height=0.5,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_2.set_linewidth(2)\n", + "\n", + "ax.add_patch(piez1)\n", + "ax.add_patch(screen_piez_1)\n", + "ax.add_patch(screen_piez_2)\n", + "\n", + "\n", + "# last line\n", + "line = plt.Line2D(xdata=[-200, 1200], ydata=[2, 2], color=\"k\")\n", + "ax.add_line(line)\n", + "\n", + "# Water table\n", + "line2 = plt.Line2D(xdata=[-200, 1200], ydata=[0, 0], color=\"b\")\n", + "ax.add_line(line2)\n", + "\n", + "ax.text(-18, 0.5, s=\"Water Table\", fontsize=\"large\", color=\"b\", bbox={\"fc\": \"w\"})\n", + "ax.text(93, -3, s=\"Shallow piezometer\", bbox={\"fc\": \"w\"})\n", + "ax.text(93, -16, s=\"Deeper piezometer\", bbox={\"fc\": \"w\"})\n", + "\n", + "ax.set_xlim([-20, 130])\n", + "ax.set_ylim([-21, 7])\n", + "ax.set_xlabel(\"Distance [m]\")\n", + "ax.set_ylabel(\"Relative height [m]\")\n", + "ax.set_title(\"Conceptual Model - Vennebulten Example\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Load the required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from ttim import *\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters for the model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "b = -21 # aquifer thickness in m\n", + "r = 90 # distance from observation wells to pumping well in m\n", + "Q = 873 # constant discharge in m^3/d" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Load data of the two piezometers" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data1 = np.loadtxt(\"data/venne_shallow.txt\", skiprows=1)\n", + "ts = data1[:, 0] / 60 / 24 # convert min to days\n", + "hs = data1[:, 1]\n", + "\n", + "data2 = np.loadtxt(\"data/venne_deep.txt\", skiprows=1)\n", + "td = data2[:, 0] / 60 / 24 # convert min to days\n", + "hd = data2[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Create a conceptual one-layer model\n", + "\n", + "Both Kruseman et al. (1970) and AQTESOLV solutions that use the Neuman method (Neumann, 1969) assume a one layer unconfined model.\n", + "To compare TTim with both, we begin by modelling a one-layer aquifer.\n", + "\n", + "For the unconfined test, the preferred method for modelling is to use the ```Model3D``` class. ```Model3D``` assumes the system is a vertical stacking of aquifer layers. Vertical flow is computed between layers by calculating the vertical resistance between layers. Vertical resistance between the aquifer layers is determined as the resistance from the middle of one layer to the middle of the next layer. The vertical anisotropy can be specified for each layer.\n", + "\n", + "Model construction is similar to the ```ModelMaq``` class. We detail it below:\n", + "\n", + "For our Model3D model, we have to set:\n", + "\n", + "- The hydraulic conductivity: ```kaq```. It is a list/array with a float element for every aquifer, for example: ```[kaq0,kaq1]```. We can also set a float value. In this case, the same ```kaq``` is assumed for every layer.\n", + "- The top and bottom of each aquifer: ```z``` defined by a list/array ```[zt0,zb0,zt1,zb1,...]```, where the inputs are a sequence of top and bottoms of the aquifer layers.\n", + "- The specific storage: ```Saq```. It is a list/array with a float element for every aquifer, for example: ```[Saq0, Saq1]```. We can also set a float value. In this case, the same ```Saq``` is assumed for every layer.\n", + "- The minimum time for which TTim solve the groundwater flow: ```tmin```, a float.\n", + "- And the maximum time: ```tmax```, float.\n", + "- TTim automatically assumes the ```topboundary``` is confined. In this case, we also assume the ```topboundary``` is confined, so we do not need to set this parameter. In the code example, the parameter is set for clarity.\n", + "- The vertical anisotropy, defined by the parameter: ```kzoverkh```, which means the vertical hydraulic conductivity divided by the horizontal conductivity. This parameter is a list/array with a float element for every aquifer, for example: ```[kzoverkh0,kzoverkh1]```. We can also set a float value. In this case, the same ```kzoverkh``` is assumed for every layer. If one does not set this parameter, a isotropic model is considered: ```kzoverkh = 1```\n", + "- ```phreatictop```: Is a boolean (True/False). If ```True```, the first element in ```Saq``` is considered phreatic storage (Specific Yield) and is not multiplied by the layer thickness. The default value is ```True```. This parameter is relevant for the unconfined aquifer test, and we will show underneath how to set it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To reproduce the one-layer aquifer model in Kruseman et al. (1970), we will build a two-layer Model3D model. The first layer is a very thin (0.1 m thick) layer with phreatic storage, followed by the 21 m thick aquifer layer. This thin layer is how TTim accounts for the water table storage in the unconfined situation. The first conceptual model is represented in the image below.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Model Figure - One - layer model\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "\n", + "\n", + "# Aquifer:\n", + "ground = plt.Rectangle(\n", + " (-20, -21), width=150, height=23, fc=\"w\", zorder=0, alpha=0.9, hatch=\".\"\n", + ")\n", + "ax.add_patch(ground)\n", + "\n", + "well = plt.Rectangle((-1.5, -21), width=3, height=23, fc=\"w\", zorder=1, ec=\"k\")\n", + "ax.add_patch(well)\n", + "\n", + "# Wellhead\n", + "wellhead = plt.Rectangle((-2, 2), width=4, height=2.5, fc=\"w\", zorder=2, ec=\"k\")\n", + "ax.add_patch(wellhead)\n", + "\n", + "# Screen for the well:\n", + "screen = plt.Rectangle(\n", + " (-1.5, -21), width=3, height=11, fc=\"w\", alpha=1, zorder=2, ec=\"k\", ls=\"--\"\n", + ")\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "\n", + "# Piezometers\n", + "piez1 = plt.Rectangle((89, -21), width=2, height=23, fc=\"w\", zorder=1, ec=\"k\")\n", + "screen_piez_1 = plt.Rectangle(\n", + " (89, -19), width=2, height=7, fc=\"w\", alpha=1, zorder=2, ec=\"k\", ls=\"--\"\n", + ")\n", + "screen_piez_1.set_linewidth(2)\n", + "screen_piez_2 = plt.Rectangle(\n", + " (89, -3), width=2, height=0.5, fc=\"w\", alpha=1, zorder=2, ec=\"k\", ls=\"--\"\n", + ")\n", + "screen_piez_2.set_linewidth(2)\n", + "\n", + "ax.add_patch(piez1)\n", + "ax.add_patch(screen_piez_1)\n", + "ax.add_patch(screen_piez_2)\n", + "\n", + "\n", + "# last line\n", + "line = plt.Line2D(xdata=[-200, 1200], ydata=[2, 2], color=\"k\")\n", + "ax.add_line(line)\n", + "\n", + "# Water table\n", + "line2 = plt.Line2D(xdata=[-200, 1200], ydata=[0, 0], color=\"b\")\n", + "ax.add_line(line2)\n", + "\n", + "ax.text(-18, 0.5, s=\"Water Table\", fontsize=\"large\", color=\"b\", bbox={\"fc\": \"w\"})\n", + "ax.text(93, -3, s=\"Shallow piezometer\", bbox={\"fc\": \"w\"})\n", + "ax.text(93, -16, s=\"Deeper piezometer\", bbox={\"fc\": \"w\"})\n", + "\n", + "ax.set_xlim([-20, 130])\n", + "ax.set_ylim([-21, 7])\n", + "ax.set_xlabel(\"Distance [m]\")\n", + "ax.set_ylabel(\"Relative height [m]\")\n", + "ax.set_title(\"Conceptual Model 1 - Vennebulten Example\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_1 = Model3D(\n", + " kaq=10,\n", + " z=[0, -0.1, b],\n", + " Saq=[0.1, 1e-4],\n", + " tmin=1e-4,\n", + " tmax=1.1,\n", + " kzoverkh=1,\n", + " phreatictop=True,\n", + ")\n", + "w_1 = Well(ml_1, xw=0, yw=0, rw=0.1, tsandQ=[(0, Q)])\n", + "ml_1.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Model calibration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Model Calibration is done in TTim using the ```Calibrate``` object. TTim calibrates the parameters by minimizing an objective function using a non-linear least-squares fitting algorithm. The objective function used is the sum of the squares of the residuals calculated as:\n", + "\n", + "$$\\sum_n (h_o - h_c)^2$$,\n", + "\n", + "where $h_0$ is the observed heads and $h_c$ is the calculated heads by the model.\n", + "\n", + "TTim uses ```lmfit```, a python package for non-linear least-squares minimization (Newville et al. 2014), to find the optimal parameters that minimize the residuals.\n", + "\n", + "For calibrating our groundwater model, we proceed by creating a calibration object with the ```Calibrate``` class. The ```Calibrate``` object takes the model ```ml``` as an argument.\n", + "We then set the parameters we are adjusting:\n", + "- Hydraulic conductivity: ```kaq0_1``` (Hydraulic conductivity of layer 0 and 1)\n", + "- Specific Yield ```Saq0``` (Phreatic Storage in our case (Check Step 4 - Model Construction))\n", + "- Specific Storage ```Saq1``` of layer 1.\n", + "\n", + "with the ```.set_parameter``` method.\n", + "\n", + "- ```.set_parameter``` takes two arguments:\n", + "- ```name``` is the parameter name, a string, where the letters define the parameter. The possible values are \"kaq\", \"Saq\" or 'c', and they represent hydraulic conductivity, Specific storage and resistance to vertical flow, respectively. The letters come before a number, which defines the layer of that parameter. For the example ```\"kaq0\"``` means the hydraulic conductivity of layer 0. In our multilayer model, we can extend the numbering to adjust one parameter for various layers. In that case, we write the number of the first layer followed by an underline \"_\" and the number of the last layer, for example, in our first parameter ```kaq0_1```, which means the hydraulic conductivity for layers 0 to 1\n", + " - ```initial``` is the initial guess value for the fitting algorithm.\n", + "\n", + "We can also add the optional parameters:\n", + "- ```pmin``` and ```pmax```, which are floats that define the parameter´s minimum and maximum possible values.\n", + "\n", + "In TTim, parameters other than hydraulic conductivity, specific storage of aquifers and the resistance of leaky layers are calibrated with the ```.set_parameter_by_reference``` method.\n", + "\n", + "Here we use the method ```.set_parameter_by_reference``` to calibrate the ```kzoverkh``` parameter in our aquifer.\n", + "\n", + "```.set_parameter_by_reference``` takes the following arguments:\n", + "* ```name```: a string of the parameter name\n", + "* ```parameter```: a numpy-array with the parameter to be optimized. It is specified as a reference, for example, in our case: ```ml_1.aq.kzoverkh[0:]``` referencing the parameter ```kzoverkh``` in object ```ml_1```.\n", + "* ```initial```: float with the initial guess for the parameter value.\n", + "* ```pmin``` and ```pmax```: floats with the minimum and maximum values allowed for the parameter to be optimized. If not specified, these are set as ```-np.inf``` and ```np.inf```.\n", + "\n", + "We add the observation data using the ```.series``` method. The arguments are:\n", + " - ```name```: a string with the observation name\n", + " - ```x``` and ```y```: floats with the x and y coordinates of the observation\n", + " - ```t```: the array of observation times\n", + " - ```h```: the array of observed drawdowns\n", + " - ```layer```: an integer. The layer of the observation (0 indexed)\n", + "\n", + " \n", + "In the end, we call the ```.fit``` method to compute the calibration." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.1. Calibrating the one layer model with the shallow piezometer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We begin the initial model by adding the shallow observation well as the observation for the residuals calibration. And we calibrate hydraulic conductivity, specific yield and specific storage of our one layer unconfined aquifer:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 110\n", + " # data points = 19\n", + " # variables = 4\n", + " chi-square = 2.4407e-04\n", + " reduced chi-square = 1.6272e-05\n", + " Akaike info crit = -205.987092\n", + " Bayesian info crit = -202.209336\n", + "[[Variables]]\n", + " kaq0_1: 138.511974 +/- 8.26913024 (5.97%) (init = 10)\n", + " Saq0: 3.9294e-04 +/- 0.00204391 (520.16%) (init = 0.2)\n", + " Saq1: 7.8824e-04 +/- 1.1386e-04 (14.44%) (init = 0.0001)\n", + " kzoverkh: 39.3273819 +/- 1021.81271 (2598.22%) (init = 1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(Saq0, kzoverkh) = -0.978\n", + " C(Saq1, kzoverkh) = 0.792\n", + " C(Saq0, Saq1) = -0.771\n", + " C(kaq0_1, Saq1) = -0.432\n" + ] + } + ], + "source": [ + "# calibrate with data of shallow piezometer\n", + "# unknown parameters: kaq, Saq\n", + "ca_1 = Calibrate(ml_1)\n", + "ca_1.set_parameter(name=\"kaq0_1\", initial=10)\n", + "ca_1.set_parameter(name=\"Saq0\", initial=0.2)\n", + "ca_1.set_parameter(name=\"Saq1\", initial=1e-4, pmin=0)\n", + "ca_1.set_parameter_by_reference(\n", + " name=\"kzoverkh\", parameter=ml_1.aq.kzoverkh[:], initial=1, pmin=1e-5\n", + ")\n", + "ca_1.series(name=\"obs\", x=r, y=0, t=ts, h=hs, layer=0)\n", + "ca_1.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_1138.5119748.2691305.969975-infinf10[138.51197446385456, 138.51197446385456]
Saq00.0003930.002044520.159374-infinf0.2[0.00039293922956682207]
Saq10.0007880.00011414.4448330.00000inf0.0001[0.0007882381862700516]
kzoverkh39.3273821021.8127102598.2220530.00001inf1[39.32738191322426, 39.32738191322426]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_1 138.511974 8.269130 5.969975 -inf inf 10 \n", + "Saq0 0.000393 0.002044 520.159374 -inf inf 0.2 \n", + "Saq1 0.000788 0.000114 14.444833 0.00000 inf 0.0001 \n", + "kzoverkh 39.327382 1021.812710 2598.222053 0.00001 inf 1 \n", + "\n", + " parray \n", + "kaq0_1 [138.51197446385456, 138.51197446385456] \n", + "Saq0 [0.00039293922956682207] \n", + "Saq1 [0.0007882381862700516] \n", + "kzoverkh [39.32738191322426, 39.32738191322426] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.003584130910426842\n" + ] + } + ], + "source": [ + "display(ca_1.parameters)\n", + "print(\"RMSE:\", ca_1.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hs_1 = ml_1.head(r, 0, ts)\n", + "hd_1 = ml_1.head(r, 0, td)\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(ts, hs, \".\", label=\"shallow obs\")\n", + "plt.semilogx(ts, hs_1[0], label=\"shallow ttim\")\n", + "plt.semilogx(td, hd, \".\", label=\"deep obs\")\n", + "plt.semilogx(td, hd_1[0], label=\"deep ttim\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.title(\"TTim Unconfined Model Results - Shallow Piezometer\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.2. Calibrating the one layer model with the deeper piezometer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this second approach, we adjust the model to the deeper piezometer, as done by Kruseman and de Ridder (1970)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".............................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 106\n", + " # data points = 29\n", + " # variables = 4\n", + " chi-square = 0.00294184\n", + " reduced chi-square = 1.1767e-04\n", + " Akaike info crit = -258.684496\n", + " Bayesian info crit = -253.215313\n", + "[[Variables]]\n", + " kaq0_1: 116.805699 +/- 5.09590024 (4.36%) (init = 10)\n", + " Saq1: 1.0010e-05 +/- 1.2461e-07 (1.24%) (init = 0.0001)\n", + " Saq0: 1.3198e-04 +/- 5.3435e-05 (40.49%) (init = 0.2)\n", + " kzoverkh: 9.87951240 +/- 12.5230444 (126.76%) (init = 0.1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_1, Saq0) = -0.857\n", + " C(Saq1, kzoverkh) = -0.610\n", + " C(kaq0_1, Saq1) = -0.493\n", + " C(Saq1, Saq0) = 0.265\n", + " C(kaq0_1, kzoverkh) = 0.121\n" + ] + } + ], + "source": [ + "# calibrate with data of deeper piezometer\n", + "# unknown parameters: kaq, Saq, kzoverkh\n", + "ca_2 = Calibrate(ml_1)\n", + "ca_2.set_parameter(name=\"kaq0_1\", initial=10, pmin=1e-8)\n", + "ca_2.set_parameter(name=\"Saq1\", initial=1e-4, pmin=1e-5)\n", + "ca_2.set_parameter(name=\"Saq0\", initial=0.2, pmin=1e-8)\n", + "ca_2.set_parameter_by_reference(\n", + " name=\"kzoverkh\", parameter=ml_1.aq.kzoverkh[:], initial=0.1, pmin=1e-8, pmax=10\n", + ")\n", + "ca_2.series(name=\"obs\", x=r, y=0, t=td, h=hd, layer=0)\n", + "ca_2.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_1116.8056995.095900e+004.3627151.000000e-08inf10[116.80569944533225, 116.80569944533225]
Saq10.000011.246126e-071.2448491.000000e-05inf0.0001[1.0010260699355733e-05]
Saq00.0001325.343454e-0540.4879681.000000e-08inf0.2[0.00013197635356376747]
kzoverkh9.8795121.252304e+01126.7577171.000000e-0810.00.1[9.879512396185161, 9.879512396185161]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_1 116.805699 5.095900e+00 4.362715 1.000000e-08 inf 10 \n", + "Saq1 0.00001 1.246126e-07 1.244849 1.000000e-05 inf 0.0001 \n", + "Saq0 0.000132 5.343454e-05 40.487968 1.000000e-08 inf 0.2 \n", + "kzoverkh 9.879512 1.252304e+01 126.757717 1.000000e-08 10.0 0.1 \n", + "\n", + " parray \n", + "kaq0_1 [116.80569944533225, 116.80569944533225] \n", + "Saq1 [1.0010260699355733e-05] \n", + "Saq0 [0.00013197635356376747] \n", + "kzoverkh [9.879512396185161, 9.879512396185161] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.010071873403913098\n" + ] + } + ], + "source": [ + "display(ca_2.parameters)\n", + "print(\"RMSE:\", ca_2.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hd_2 = ml_1.head(r, 0, td)\n", + "hs_2 = ml_1.head(r, 0, ts)\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(td, hd, \".\", label=\"deep obs\")\n", + "plt.semilogx(td, hd_2[0], label=\"deep ttim\")\n", + "plt.semilogx(ts, hs, \".\", label=\"shallow obs\")\n", + "plt.semilogx(ts, hs_2[0], label=\"shallow ttim\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.title(\"TTim Unconfined Model Results - Deeper Piezometer\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Create a conceptual model with n-layers\n", + "\n", + "As we can see in the examples of step 5, the single-layer simplification does not represent the system well as we have a vertical component to flow, shown in the head difference between both piezometers.\n", + "\n", + "We now explore the feature of TTim to create a multi-layer model to represent better the unconfined system and simulate the vertical flow component. We will discretize the aquifer in a 21 layer model, with 1 m thick each." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Model Figure - One - layer model\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "\n", + "\n", + "# Aquifer:\n", + "for i in range(21, -2, -1):\n", + " ground = plt.Rectangle(\n", + " (-20, -i),\n", + " width=150,\n", + " height=1,\n", + " fc=\"w\",\n", + " zorder=0,\n", + " alpha=0.9,\n", + " hatch=\"..\",\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + " )\n", + " ax.add_patch(ground)\n", + "\n", + "\n", + "well = plt.Rectangle((-1.5, -21), width=3, height=23, fc=\"w\", zorder=1, ec=\"k\")\n", + "ax.add_patch(well)\n", + "\n", + "# Wellhead\n", + "wellhead = plt.Rectangle((-2, 2), width=4, height=2.5, fc=\"w\", zorder=2, ec=\"k\")\n", + "ax.add_patch(wellhead)\n", + "\n", + "# Screen for the well:\n", + "screen = plt.Rectangle(\n", + " (-1.5, -21),\n", + " width=3,\n", + " height=11,\n", + " fc=\"w\",\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + " hatch=\"-\",\n", + ")\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "\n", + "# Piezometers\n", + "piez1 = plt.Rectangle((89, -21), width=2, height=23, fc=\"w\", zorder=1, ec=\"k\")\n", + "screen_piez_1 = plt.Rectangle(\n", + " (89, -19), width=2, height=7, fc=\"w\", alpha=1, zorder=2, ec=\"k\", ls=\"--\", hatch=\"-\"\n", + ")\n", + "screen_piez_1.set_linewidth(2)\n", + "screen_piez_2 = plt.Rectangle(\n", + " (89, -3), width=2, height=0.5, fc=\"w\", alpha=1, zorder=2, ec=\"k\", ls=\"--\", hatch=\"-\"\n", + ")\n", + "screen_piez_2.set_linewidth(2)\n", + "\n", + "ax.add_patch(piez1)\n", + "ax.add_patch(screen_piez_1)\n", + "ax.add_patch(screen_piez_2)\n", + "\n", + "\n", + "# last line\n", + "line = plt.Line2D(xdata=[-200, 1200], ydata=[2, 2], color=\"k\")\n", + "ax.add_line(line)\n", + "\n", + "# Water table\n", + "line2 = plt.Line2D(xdata=[-200, 1200], ydata=[0, 0], color=\"b\")\n", + "ax.add_line(line2)\n", + "\n", + "ax.text(-18, 0.5, s=\"Water Table\", fontsize=\"large\", color=\"b\", bbox={\"fc\": \"w\"})\n", + "ax.text(93, -3, s=\"Shallow piezometer\", bbox={\"fc\": \"w\"})\n", + "ax.text(93, -16, s=\"Deeper piezometer\", bbox={\"fc\": \"w\"})\n", + "\n", + "ax.set_xlim([-20, 130])\n", + "ax.set_ylim([-21, 7])\n", + "ax.set_xlabel(\"Distance [m]\")\n", + "ax.set_ylabel(\"Relative height [m]\")\n", + "ax.set_title(\"Conceptual Model 2 - Multi-layer Model 3D - Vennebulten Example\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "nlay = 21 # number of layers\n", + "zlayers = np.linspace(0, b, nlay + 1) # elevation of each layer\n", + "Saq = 1e-4 * np.ones(nlay)\n", + "Saq[0] = 0.1 # Setting the first storage as specific yield" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model is created just as in the previous step, however with the new parameters defined above:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 21\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_2 = Model3D(\n", + " kaq=10, z=zlayers, Saq=Saq, kzoverkh=0.1, phreatictop=True, tmin=1e-4, tmax=1.1\n", + ")\n", + "w_2 = Well(ml_2, xw=0, yw=0, rw=0.1, tsandQ=[(0, Q)], layers=range(nlay))\n", + "ml_2.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Calibrate multi-layer model with the two piezometers simultaneously" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the TTim multi-layer model, we can fit the parameters using data from both piezometers simultaneously.\n", + "For this initial assumption, we assume the aquifer has one hydraulic conductivity and storage parameter.\n", + "\n", + "The unknown parameters are kaq, Saq, kzoverkh.\n", + "\n", + "Now, on the ```series``` method, we have to remember to set a different layer for each piezometer, corresponding to the depth of the screen." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".......................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 68\n", + " # data points = 48\n", + " # variables = 4\n", + " chi-square = 0.00474083\n", + " reduced chi-square = 1.0775e-04\n", + " Akaike info crit = -434.691739\n", + " Bayesian info crit = -427.206935\n", + "[[Variables]]\n", + " kaq0_20: 31.6346602 +/- 0.67601473 (2.14%) (init = 10)\n", + " Saq0: 0.05527954 +/- 0.00391719 (7.09%) (init = 0.2)\n", + " Saq1_20: 3.4687e-05 +/- 2.4467e-06 (7.05%) (init = 0.0001)\n", + " kzoverkh: 0.01000252 +/- 1.2914e-05 (0.13%) (init = 0.1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_20, Saq0) = -0.346\n", + " C(kaq0_20, kzoverkh) = -0.284\n", + " C(kaq0_20, Saq1_20) = -0.269\n", + " C(Saq0, kzoverkh) = -0.269\n" + ] + } + ], + "source": [ + "ca_3 = Calibrate(ml_2)\n", + "ca_3.set_parameter(name=\"kaq0_20\", initial=10)\n", + "ca_3.set_parameter(name=\"Saq0\", initial=0.2)\n", + "ca_3.set_parameter(name=\"Saq1_20\", initial=1e-4)\n", + "ca_3.set_parameter_by_reference(\n", + " name=\"kzoverkh\", parameter=ml_2.aq.kzoverkh[:], initial=0.1, pmin=0.01\n", + ")\n", + "ca_3.series(name=\"obs1\", x=r, y=0, layer=1, t=ts, h=hs)\n", + "ca_3.series(name=\"obs2\", x=r, y=0, layer=15, t=td, h=hd)\n", + "ca_3.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_2031.634660.6760152.136943-infinf10[31.63466020198202, 31.63466020198202, 31.6346...
Saq00.055280.0039177.086154-infinf0.2[0.05527953775592172]
Saq1_200.0000350.0000027.053639-infinf0.0001[3.468735269338616e-05, 3.468735269338616e-05,...
kzoverkh0.0100030.0000130.1291030.01inf0.1[0.010002522487706056, 0.010002522487706056, 0...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_20 31.63466 0.676015 2.136943 -inf inf 10 \n", + "Saq0 0.05528 0.003917 7.086154 -inf inf 0.2 \n", + "Saq1_20 0.000035 0.000002 7.053639 -inf inf 0.0001 \n", + "kzoverkh 0.010003 0.000013 0.129103 0.01 inf 0.1 \n", + "\n", + " parray \n", + "kaq0_20 [31.63466020198202, 31.63466020198202, 31.6346... \n", + "Saq0 [0.05527953775592172] \n", + "Saq1_20 [3.468735269338616e-05, 3.468735269338616e-05,... \n", + "kzoverkh [0.010002522487706056, 0.010002522487706056, 0... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.009938171014409325\n" + ] + } + ], + "source": [ + "display(ca_3.parameters)\n", + "print(\"RMSE:\", ca_3.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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Sp0+fA/5MB8rrW4qtBRKKvI/3LytumxQzCwHqAFtL2LekY1ao7ORVrDjjDMJbtiS8bVvC27QhvE1rItq2JaRJE03+KCIi1Ua3FnXLHOgKLFq0iNtuu42goCBCQ0N58cUX97v97bffztChQ3nooYc49dRTSzz++PHjufzyy3n88cdp2LAhr732GuBrDUxISKBXr16AL7y9/fbbdOjQ4R/HGD58ONdeey0dOnQgJCSE119/vbA1sUePHpx77rmkpKRw8cUX0717d7788ssD+kwHypxz5XrAAzq5L6j9BRyHL3z9D7jQOfdbkW2uAzo4567xD544xzk32MyOAN7Cd01dM+BboDVgJR1zb927d3dz584NxEcEIGf9elLfnkrWkiVkLv2L3HXrC9cF1a7tC3lt2hDepg0RRxxBxOGHY8HBAatHRESkNP744w8OO+wwr8uo0Yr7MzCzec657sVt72mLnXMu18yuB74EgoFXnXO/mdkYYK5zbgbwCvCmmS0DtuEbCYt/u3eB34Fc4DrnXB5Acces6M9WVGjTpjS65ebC93k7dpC1dClZf/1F5l9/kbXkL9JmfEx+ejoAwXXqENX7KGL69iW6Tx9CmzTxqnQRERGpQjxtsassAt1iB8CaOZCcBIn9IKHHP1Y758hdt45dCxaQMfMnMn76idxNmwAIO/QQYvr0IbpvX6K6dycoMjKwtYqIiKAWu8qgSrXY1Rhr5sDkMyAvG4LDYOiMf4Q7MyM0Lo46cXHUOfVUnHNkLV1aGPJSp77DtslvYGFhRHXvRnSfvkT37UN4mza6Rq+CzVuVyuwVW+nVqn65XUciIiJSHhTsKkJyki/UuTzfc3JSsa12RZkZEW3aENGmDfWvuJz8zEx2zZ1HxsyZZPz0E5sefxwef5zghg2I6e1rzYvufRQh9cs2tFz2r7wn3xQRESlPCnYVIbGfr6WuoMUu8cDmwQEIioggpm8fYvr2ASBn40YyfppFxsyZpP/f/5H20UcARBx+OLVOPpm6F5xPcK1a5foxpPjJNysi2KmVUERESkPBriIk9PB1v+7nGrsDFdq4MbHnnE3sOWfj8vLI/P0PMn6aSfqPSWx+6im2TppEvYsvpt6llxAcG1v2zyDA35Nv5uTml8vkm6WhVkIRESmtGjtBcYVL6AH9bi2XULc3Cw4mskN7GlxzDYlvTSFx+nSie/ZgywsvsOy449n05FPkbttW7uetibq1qMud5wTTv+cCrjttB8GRq9iyewuBHIRU3rfoERGpqu6//36eeOKJCj9vcnIy7du3r/DzHgy12FVDke2PIH78eDKX/MXWlyawddIktv3nP9QdMoR6V1xOaKNGXpdYpaW5P5mbNpW5afDyn75lkSGRxNeKJyEmwfdc6+/nZtHNCA0OPejzedFKWJS6gUVEqg5Nd0IFTXfioawVK9j60kukffIpFhxM7KBB1L9qGKFNm3pdWpWVlZfF2p1rSUlPYc3ONaTsTCFlp/91egpZeVmF2wZZEE2imuwR+IqGwDrhJd8A2qtwpW5gkZrtoKY7KWF6rwM1duxYJk+eTKNGjUhISKBbt26MHDmS5cuXc91117F582aioqKYOHEi7dq1Y/PmzVxzzTWsXr0agGeeeYY+ffpw//33s3z5cpYtW8aWLVu4/fbbueqqq/5xvqeeeopXX30VgGHDhnHTTTeRnJzMySefTLdu3Zg/fz5HHHEEb7zxBlFRUYwaNYoZM2YQEhLCiSeeWO4tiprupCor578MBcJbtaLZY4/R4Lrr2DpxIqnvvkvqtGnEnnUW9a++irCEhJIPInsIDw6nVWwrWsW2+se6fJfPlt1b9gh6BeHvhzU/sDVzz67UWmG1fIEvZs+Wvvha8TSOakxIUEi53qLnQHg1WEREqqhSTO91IObNm8fUqVNZsGABubm5dO3alW7dugFw9dVXM2HCBFq3bs3PP//M8OHD+e6777jxxhu5+eab6du3L6tXr+akk07ijz/+AGDhwoXMnj2bjIwMunTpwqmnnkqzZs32ON9rr73Gzz//jHOOnj17cvTRR1O3bl2WLFnCK6+8Qp8+fbjiiit44YUXuPzyy/nggw/4888/MTO2b99eph9feVCwqyzK+S9DccKaN6fpgw/S4Npr2TppEtunTWf7++9T5/TTqX/11YS3almu56upgiyIRlGNaBTViK6Nu/5j/a6cXXuEvYLwtyR1Cd+t+Y7c/NzCbUMshGYxzQrDXlxMnK/FLyaeuFpx1A6rHdDP4nU3sIhUMQcxvdf+JCUlcfbZZxMVFQXAGWecAUB6ejqzZs3ivPPOK9w2K8vXU/LNN9/w+++/Fy7fsWMH6f47O5155plERkYSGRnJgAEDmDNnDmeddVbhtjNnzuTss88mOjoagHPOOYekpCTOOOMMEhIS6NPHNzPFxRdfzLhx47jpppuIiIjgyiuv5LTTTuO000476M9aXhTsKoty/suwP6HNmtHk3nup/69r2PbqK6S+8y5pM2ZQ++STqX/Nv4ho0yYg5xWfqNAo2tRtQ5u6//w55+XnsXHXRl/X7l7h77etv5GWlbbH9rXDau8R9OJj/u7mbRLThNCgg7+2D3yDRaYM61Uu3cC6Vk+kBiiH6b1KIz8/n9jYWBYsWFDsutmzZxMREfGPdXtP6H8gE/wXt29ISAhz5szh22+/Zfr06Tz33HN89913pT5mIOgaOyrJNXYV0GK3L7lbt7Lt9ddJnfIW+bt2UeuEE2hw7TVEHH54hZxfSm9n9k7Wpq8tvKYvJd33WLtzLWvT15KTn1O4bdFr+4q29BW8rxdRr8LuWlKR1+opQIqUH6+vsZs/fz6XXXYZP//8c2FX7L/+9S9GjhxJ7969ufnmmznvvPNwzrFw4UI6derEhRdeSJcuXbjtttsAWLBgAZ07d+b+++/nww8/3KMrdvbs2Xt0xRacb/bs2YVdsW+++SZ169alZcuWzJo1i6OOOophw4Zx2GGH8a9//Ytdu3bRqFEj0tLSaNWqFVu3lu/MBbrGrqoKwFx3pRVSvz6Nbr2VeldcQeqbb7Ltzf+w8+uviTnmGBpcew2RnTpVWC2yf7XCatGuXjva1Wv3j3X5Lp9Nuzb9Hfh2phSGwKS1SWzZvWWP7QtG8sbFFGnp83f3xsXEERHyz//tHqyKulZPgz1EKoGEHuX2O6xr164MGTKETp060ahRI4488sjCdVOmTOHaa6/loYceIicnh/PPP59OnToxbtw4rrvuOjp27Ehubi79+/dnwoQJAHTs2JEBAwawZcsWRo8evUeoKzjfZZddRo8evvqHDRtGly5dSE5Opm3btjz//PNcccUVHH744Vx77bWkpaVx5plnkpmZiXOOp556qlw+d1moxY5K0mJXieTt2EHqlClse30yeWlpRPfuTYPh1xLVvdj/HEgVsTt3N+vS1+0R/IoGwN25u/fYvmFkw8KWvoKwFxcTR7OYZjSJbkJIUOn/X1gQuAqu1QtU4Hr++2U8+dUS8h0EG9xyYluuG3BouZ9HpKY4qBa7Sur+++8nJiaGkSNHel3KAVGLnZRZcO3aNLj2WupdeimpU6ey9dXXWHXxJUR17+4LeEcdVWFdeFJ+IkMiOST2EA6JPeQf65xzbMvc9nfgK2jtS09h3sZ5fLriUxx//ycw2IJpHNWYZjHNaBbTbI/QFxcTR6OoRnsEv/K8Vm9/NNhDRGo6tdihFruS5O/ezfZp09g66RVyN20islMnGgy/luj+/RXwaoicvBw27NrAuvR1rE33Xc+3Ln2drwUwPYXNuzbvEfxCLITG0Y0Lw16zmGbEx8TTNLopTWOa0iiqUZkHduyLrrETKT/VqcWuqjrQFjsFOxTsSis/O5u0999n68sTyVm3jogjjqDB9ddRa8AAr0sTj2XnZbMhY0Nh4Ns7/G3avWmP7YMsiIaRDQu7dZtGN6VZdDOaxjT1hb/opsSExXj0aUSkgIKd99QVKwETFBZG3fPPJ/bcc0mbMYMtL71MyrXDqTd0KI1uG4mF6OtUU4UFh9G8dnOa125e7PqsvCzWp69nXcY6NmT4Wv7WZ6xnfcZ6Fm1exNervt5j/j6AWqG19gh6e7yObkrDqIYEWdlvd60WPhGpTvSbuLoJ0N0rirLQUGLPPZc6Z5zBxn8/zrbJk8lavpy4p54kuHZgJ8yVqik8OJzEOokk1kksdn3B3TrWZ6xnfbov8K1L94fAjHXM3zSfndk799gnJCiExlGNfa19/pa/ZtHNaBrdlCYxTWgS1YSo0Kj91qVRtCJS3SjYVScVPBeehYbS5O67CG/Tmg1jHiR58BDiX3hBd7CQA1b0bh2dGhY/vU56dnphK19h+PO3AM7ZMIdNuzaR7/L32KdWWC2aRPtCXpPoIg//+5nL03XLNBGpVhTsqpMKvHtFUXXPO4/wli1JGXEjyUOGEPfUU8T06xvw80rNEhMWQ+uw1rSu27rY9Tn5OWzetdnX0rdrAxsyfI+NGRvZsGsDi7YsYnvW9n/sF3VoNC43FnLrsDS3LZMWNadxVGOaRDehcVRjGkU1Ktc5/USk7BITE5k7dy4NGjQo1fY//PADTzzxBJ988gmvv/46c+fO5bnnniuXWhYsWMC6des45ZRTCs8VFhZG7969AZgwYQJRUVFceuml5XK+kijYVScVdCuX4kR1707Lae+yZvh1rPnXv2h0+23UGzpUo2alwoQGhRaOwN2XzNxMNu7aWBj6NmRsYPHGVSxPXQvBafxvy7d8u27HP/arG16XxtGNaRzlf0T//dwkqgmNohqV2O0rItXTggULmDt37h7BLiYmpjDYXXPNNRVaj4JddeLh3SsAQuPiSHxrCutGjWLTo4+R9ddSmtx/H0FhYRVah8i+RIRE0KJ2C1rUbrHPbXbl7GLTrk1s3LWxMARuzNhY+H7h5oWkZqX+Y7/aYbX3CH8FLX6NoxsXdv8q/IkcmIyMDAYPHkxKSgp5eXmMHj2aIUOGADB+/Hg+/vhjcnJymDZtGu3atWPOnDnceOONZGZmEhkZyWuvvUbbtm33efzk5GSuuOIKtmzZQsOGDXnttdeIi4vj0EMPZcWKFaSlpVG/fn2+//57+vfvT//+/XnllVdo3drXc5Cdnc29997L7t27mTlzJhdccAETJkwgODiY//znP4wfP55vv/22cGLkY445hi5dupCUlERGRgZvvPEGjzzyCIsWLWLIkCE89NBDZf6ZKdhVN+V4K5eDERQdTdyzz7LluefZ8sILZK9cSfz4cYSUsrlcxGtRoVH7HegBvpa/gvC3IWPDHs8rU9cxd/1CMvP/2fJXK7SWL/z5W/qKe44OjQ7gpxM5eI/NeYw/t/1ZrsdsV68dd/S4Y5/rv/jiC5o1a8ann34KQFpaWuG6Bg0aMH/+fF544QWeeOIJJk2aRLt27UhKSiIkJIRvvvmGu+66i/fee2+fx7/hhhsYOnQoQ4cO5dVXX2XEiBF8+OGHtG3blt9//52VK1fStWtXkpKS6NmzJ2vWrCkMdQBhYWGMGTNmj67d3bt373GHi2+//XaPc4aFhTF37lyeffZZzjzzTObNm0e9evU45JBDuPnmm6lfv2wTqyvYSbmzoCAajriB8DatWTfqTlaeN5iE558j4vDDvS5NpFxEhEQUO73LHqNsQ/N4+sJDaBi7mw27irT6+a/5+3Prn2zN/OfNwgvCX7MY/whf/zx/Rad5OZDbuYlUZR06dODWW2/ljjvu4LTTTqNfv78vMTrnnHMA6NatG++//z7gC35Dhw5l6dKlmBk5OTn7Pf5///vfwn0vueQSbr/9dgD69evHjz/+yMqVK7nzzjuZOHEiRx999B73qj1YZ5xxRuFnO+KII2jatCkArVq1Ys2aNQp2UnnVPvlkQhMSSLnuepIvvIhmjz5C7ZNP9roskYCZvWLr36Nsc4JZsT6cge2O2Of2OXk5hV28BcFvfcb6wuv/Fm5e+I8BHwUjiJtFN9sz9MX8HQJrhdUK8CeVmmh/LWuB0qZNG+bPn89nn33GPffcw3HHHce9994LQHh4OADBwcHk5vrmwRw9ejQDBgzggw8+IDk5mWOOOeagztu/f39efPFF1q1bx5gxY3j88cf54Ycf9giWB6ug7qCgoMLXBe8LPkdZKNhJQEUecQQtp08j5fobWHvTzWQNX0qD66/Dgso+saxIZXOg96oNDQ4lvlY88bXi97nNrpxdvlG+6b45/QqC3/qM9SzcvJCvVn31j8mdY0JjiIuJ8x07Jp6EWgmF52kW3YzQ4MDczk2kvK1bt4569epx8cUXExsby6RJk/a7fVpaGnFxcQC8/vrrJR6/d+/eTJ06lUsuuYQpU6YUBrcePXpwySWX0KpVKyIiIujcuTMvvfQSn3zyyT+OUatWLXbu3LnH+x07/nkpRkVRsJOAC2nQgOZvTGbDffez5YUXyFq6lGaPPkJQtK4lkuqlW4u6TBnWq1zvZBEVGkWrOq1oVadVsevzXT5bd2/9O/T5A+C69HWsTFtJUkoS2fnZhdsHWRBNopoQX6tI4CsS/mqH1dZodqk0Fi1axG233UZQUBChoaG8+OKL+93+9ttvZ+jQoTz00EOceuqpJR5//PjxXH755Tz++OOFgyfA16qWkJBAr169AF/X7Ntvv02HDh3+cYwBAwbw6KOP0rlzZ+68805OP/10Bg0axEcffcT48eMP4lOXje4Vi+4VW1Gcc2ybPJlN/36c8NatSXjheUL9/7MSkcAouKvHmp1rSNmZQkp6yt+vd6b84zq/WqG1Clv3CkJfyzotOST2EOpF1PPoU4hXdK9Y7+lesVJpmRn1L7uM8EMOZe0tt7DyvMHEjx9HVLduXpcmUm0VvatHt8b//Lu2K2cXKem+kFc0/C1NXcr3a77fo5u3XkQ9WtVpxSGxh3Bo7KEcEnuIAp9IJaNgJxUupl9fEt95h5Thw1l12eU0uXc0dc87z+uyRCqFeatSy7UrtyRRoVG0qduGNnXb/GNdXn4em3ZtYkXaCpZvX87ytOUs276MT1d8SnpOeuF2dcPrFoa8oqFPgU+k4inYiSfCW7Uk8Z2prL3lVjaMvpesv5bS+I7bsRB9JaXm2mO6lJAgpgzr5em9a4ODgmka4xtx2yeuT+Fy5xwbd21kxfYVLNu+jOVpy1m+fXmpAl+rOq2oH1m26RxEZN/0W1Q8E1ynDgkvTWDT40+wbfJkspcvI+6ppwiOjfW6NBFP7DFdSm4+s1ds9TTY7YuZ+e6mEd2E3nG9C5c759i0axPLt/ta9lakrdhnC1+bem3o3LAzXRt1pWPDjsSExXjxUUSqHQU78ZSFhND4zlGEt2nN+vsfYOWQISS88ALhhxzidWkiFe5Ap0upbMys8M4a+wp8Ba17v2/9nYmLJpLv8gmyINrUbUOXRl0KH02im3j4SUSqLk9GxZpZPeAdIBFIBgY75/5x80UzGwrc43/7kHNusplFAdOAQ4A84GPn3Cj/9pcBjwNr/fs855zb/6Q3aFRsZbFr/nxSbhiBy8oi7skniDn6aK9LEqlwFX2NnZcycjL4dfOvLNi0gPmb5rNw80J25+4GoGl0U7o06kLXRl3p3Kgzh8YeSnBQsMcV1zwaFeu9Ax0V61Ww+zewzTn3qJmNAuo65+7Ya5t6wFygO+CAeUA3IAvo6Zz73szCgG+Bh51zn/uDXXfn3PUHUo+CXeWRs24da667nqw//6TRyJHUu+JyzaklUkPk5ueyJHUJv2z8hV82+R6bd28GfNOwdGrUqbBFr32D9kSGRHpccfVX2YLd/fffv8d9WMvb9u3beeuttxg+fDgAycnJzJo1iwsvvBCAuXPn8sYbbzBu3LiAnL84VWW6kzOBY/yvJwM/AHvfq+Qk4Gvn3DYAM/saONk59zbwPYBzLtvM5gP7nrZdqpTQZs1InPIf1t15F5sef5ysv/6iyZgHCCpy2xURqZ5CgkI4ov4RHFH/CC4+/GKcc6SkpxS26C3YtIDxv/gmfA2xEA6vf3hh0OvcqLMGZUiZbd++nRdeeGGPYPfWW28VBrvu3bvTvXuxearS8CrYNXbOrfe/3gA0LmabOGBNkfcp/mWFzCwWOB14tsjic82sP/AXcLNzrugxiu57NXA1QPPmzYvbRDwSFBVF3NNPseXFF9ky/jmyk5OJGz+O0EaNvC5NpEqoLt25ZkZCrQQSaiVw+iGnA5CWlcaCTQsKW/Te/vNtJv8+GYAWtVvs0X2bWDtRLf7VwNixY5k8eTKNGjUiISGBbv65T5cvX851113H5s2biYqKYuLEibRr147NmzdzzTXXsHr1agCeeeYZ+vTpw/3338/y5ctZtmwZW7Zs4fbbb+eqq67a41yjRo1i+fLldO7cmRNOOIGkpCT++OMPOnfuzNChQ+nSpQtPPPEEn3zyCffffz8rV65kxYoVrF69mqeffprZs2fz+eefExcXx8cff0xoaMXfvi9gwc7MvgGKu/r17qJvnHPOzA64P9jMQoC3gXHOuRX+xR8DbzvnsszsX/haA48tbn/n3MvAy+Drij3Q80tgWVAQDa+7jvBDW7Nu1CiSzxtM/HPPEdmhvdeliVRqlW3KlPJWJ7wORycczdEJvmtws/Oy+X3r78zfNJ9fNv3CD2t+4MNlHwK+0bedG3UuDHpH1D9C98ktgw0PP0zWH3+W6zHDD2tHk7vu2uf6efPmMXXqVBYsWEBubi5du3YtDHZXX301EyZMoHXr1vz8888MHz6c7777jhtvvJGbb76Zvn37snr1ak466ST++OMPABYuXMjs2bPJyMigS5cunHrqqTRr1qzwfI8++iiLFy9mwYIFAPzwww+FQa7gfVHLly/n+++/5/fff+eoo47ivffe49///jdnn302n376KWeddVb5/bBKKWDBzjl3/L7WmdlGM2vqnFtvZk2BTcVstpa/u2vB1936Q5H3LwNLnXPPFDln0XvjTAL+feCVS2VS+6QTCWvRnDXDh7Pq4otp+vBY6pTi/n8iNVVVmTKlvIQFh9G5UWc6N+oM+Ebgrtyxkl82/lLYffv9mu8BCA8Op0eTHgxsOZBjmx9LdKjuV13ZJSUlcfbZZxMVFQXAGWecAUB6ejqzZs3ivCKT22dlZQHwzTff8Pvvvxcu37FjB+npvul2zjzzTCIjI4mMjGTAgAHMmTOnTOFr4MCBhIaG0qFDB/Ly8jj55JMB6NChA8nJyQd93LLwqit2BjAUeNT//FEx23wJPGxmBf8inQjcCWBmDwF1gGFFdygIi/63ZwB/lH/pUtEi2rWj5bRppIy4kXW3jiTrr6U0vHEEFhTkdWkilU55T5lS1bp1zYxWdVrRqk4rzm1zLgBbdm/hl02/MH/jfL5d/S13zbyLiOAI+sf355SWp9A3vi/hwbqOtyT7a1mraPn5+cTGxha2rO29bvbs2URERPxj3d5d82Xtqg/3X/8dFBREaGho4fGCgoLIzc3d364B49VvxkeBE8xsKXC8/z1m1t3MJgH4B008CPzP/xjjnNtmZvH4unMPB+ab2QIzKwh4I8zsNzP7FRgBXFaRH0oCJ6R+fVq89ip1Bp3L1pdeIuWGEeSlZ3hdlkil061FXaYM68UtJ7YtczdsQbfuk18t4aJJs5m36h+zUlUJDSIbcEKLE7ijxx18ce4XvDHwDc489EzmbpzLTT/cxIB3BjD6p9HMWjdrj3vjivf69+/Phx9+yO7du9m5cycff/wxALVr16Zly5ZMmzYN8LXU/vrrrwCceOKJjB8/vvAYRcPfRx99RGZmJlu3buWHH37gyCOP3ON8tWrVYufOnft8XxV40mLn7zI9rpjlcynSCuecexV4da9tUoBiI7Zz7k78rXpS/VhYGE0ffJCINm3Z+OijrLrgAuJffIGweA2KFimqW4u65dK6Vh27dYMsqHAk7ageo/h5/c98tvIzvln1DR8u+5B6EfU4KfEkTml5Cp0adtLgC4917dqVIUOG0KlTJxo1arRHEJsyZQrXXnstDz30EDk5OZx//vl06tSJcePGcd1119GxY0dyc3Pp378/EyZMAKBjx44MGDCALVu2MHr06D2urwOoX78+ffr0oX379gwcOJCHH36Y4OBgOnXqxGWXXUaXLl0q9PMfDE/msatsNI9d1ZP+00+svfkWLCiIuHHPEt2jh9cliVQ7BS12Bd261W0gRlFZeVkkpSTx2crP+DHlR7LysmgW3YyTW57MKS1PoU3dNjUy5FW2eezKItBz4AVKVZnHTqqDNXMgOQkS+0FCxQarmD59SHxnKinDr2P1FVcS9/RT1D7hhAqtQaS6K+jWrUrX2B2s8OBwjm9xPMe3OJ707HS+X/M9n638jMm/TebVxa9ySJ1DGNhyIANbDqR5bU2RJZWXWuxQi91BWTMHJp8BedkQHAZDZ1R4uAPI27mTNcOuIvP330l4+SWijzqqwmsQkeprW+Y2vk7+ms9Wfsb8TfMBaF+/PQNbDuTklifTKKp6z69ZnVrsqqoDbbHTsEI5OMlJvlDn8nzPyUmelBFcqxYJL00gLDGRNdddz+6FCz2pQ0Sqp3oR9RjSbgiTB07m60Ffc2u3W8lzeTw+93GOn3Y8V3x5BdP+msb2zO1elxowagDyzsH87NVih1rsDkolabErkLNpE6suvIj8nTtpMeU/hB96qGe1iEj1tzJtJV+s/ILPVn5G8o5kQiyE3nG9OaXlKQxIGEBUaJTXJZaLlStXUqtWLerXr18jrzH0knOOrVu3snPnTlq2bLnHuv212CnYoWB30Dy8xq442atXk3zRRVhQMC2mTCEsPq7knUREysA5xx/b/uDzlZ/z+crP2bhrIxHBERyTcAwDWw6kX1y/Kn23i5ycHFJSUsjMzPS6lBopIiKC+Pj4f9yaTMGuBAp21Ufmkr9YdcklBNeNJXHKFEIaNPC6JBHZS1Wb9Li08l0+v2z6hc9Xfs5XyV+RmpVKw8iGXHjYhZzX5jzqhNfxukSpJhTsSqBgV73s+uUXVl9xJWEtWtDijckE167tdUki4lfd72VbICc/h/+u+y9T/pjCrHWziAyJ5NzW53Lx4RcTF6PeBCkbDZ6QGiWqSxfix40ja/ly1lw7nPzdu70uSUT8ipv0uDoKDQqlf3x/XjrhJaafPp0TWpzA1D+ncsr7p3Db/93Gb1t+87pEqaYU7KRaiunXl7h/P8bu+fNJufFGXHa21yWJCH/fyzbYKJd72VYFbeu1ZWzfsXx+7ucMPXwoM9fO5PxPz+fyLy7n/9b8H/ku3+sSpRpRVyzqiq3OUt95lw333UftU0+l2eP/xoL0fxkRr1XXa+xKKz07nfeWvsd//vgPGzI20LJOS67qcBWntTpNI0+lVHSNXQkU7Kq3LRMnsvnJp6h74QU0Hj1a/3CKSKWQk5/DV8lf8dri11iSuoQjmxzJ6F6jaVmnZck7S42ma+ykRmtw1VXUu/IKUt96m83jxnldjogI4LsO79RWp/Lu6e8yutdo/tz6J+fOOJcXFrxAVl6W1+VJFaVgJzVCo5EjqTPoXLa+OIGtr7/udTkiIoWCLIjBbQcz4+wZHN/ieF789UUGzRjEnPVzvC5NqiAFO6kRzIymDzxArRNPZNOjj7H9/Q+8LklEZA8NIhvw7/7/ZsLxE8jNz+XKr67k7pl3k5qZ6nVpUoUo2EmNYcHBNHvicaJ7H8X6e+5h5zffeF2SiMg/9InrwwdnfsBVHa7isxWfcfqHp/PB0g90z1YpFQU7qVGCwsKIHz+eiA7tWXvzLWTMnu11SSIi/xAREsGIriOYdvo0WtVpxb2z7uWKL69gRdoKr0uTSk7BTmqcoOhomr/0EmGJLUgZfh27Fy3yuiQRkWIdWvdQXj/5de476j6WpC7h3Bnn8twvz2lwheyTgp3USMGxsSRMeoXgunVZc9XVZK9a5XVJIiLFCrIgBrUZxIyzZnBS4km8tPAlzp1xLrPXq8dB/knBTmqs0MaNaP7qK+Aca64dTt6OHV6XJCKyTw0iG/Bov0d56YSXyHf5XPXVVdyZdCdbd1fP27LJwVGwkxotrEUL4saPI3v1atbefAsuN9frkkRE9qt3s968f8b7XN3xar5I/oIzPjyD95e+r1uTCaBgJ0J0jx40vf8+Mn76iY2PPOp1OSIiJYoIieCGLjcw/fTpHBp7KPfNuo/Lv7ic5duXe12aeEzBTgSIHTSIepddRuqUKWx76y2vyxERKZVDYg/htZNfY0zvMSzbvoxBHw9i3PxxZOZmel2aeETBTsSv0W0jiTn6aDaOfZiMWbO8LkdEpFSCLIizW5/NjLNmMDBxIBMXTeScGecwa53+HauJFOxE/Cw4mGZPPkF4q1ak3HQzWStWel2SiJSjeatSef77ZcxbVT3v5FA/sj4P93uYiSdOJMiC+NfX/+KOH+9gy+4tXpcmFcg0kzV0797dzZ071+sypJLITllL8uDBBNeqReI7UwmOjfW6JBEpo3mrUrlo0myyc/MJCwliyrBedGtR1+uyAiYrL4tJiyYxadEkIkMiuaXbLZzT+hyCTO051YGZzXPOdS9unf6ERfYSFh9H/HPjyVm3jpQbb8Ll5HhdkoiU0ewVW8nOzSffQU5uPrNXVO8pQsKDw7mu83W8d8Z7tKnbhgf++wCXfXEZS1OXel2aBJiCnUgxorp2pcmDY9j1889sePAh3aNRpIrr1ao+YSFBBBuEhgTRq1V9r0uqEK3qtOK1k17jwT4PsjJtJYM/Hsyz859ld+5ur0uTAFFXLOqKlX3b9ORTbJ04kcZ33UW9Sy/xuhwRKYN5q1KZvWIrvVrVr9bdsPuSmpnKk3Of5KPlHxEfE889ve6hT1wfr8uSg7C/rlgFOxTsqrw1cyA5CRL7QUKPcj20y88nZcQI0r/7noQJLxLTv3+5Hl9EpKLNWT+HB2c/SPKOZAYmDuT2HrfTILKB12XJAVCwK4GCXRW2Zg5MPgPysiE4DIbOKPdwl5+RQfLFl5CzejWJU98mvHXrcj2+iEhFy87L5pVFrzBx0UQigiO4qdtNDGozSIMrqggNnpDqKznJF+pcnu85OancTxEUHU3CC89jkZGsuXY4uanVc6oEEak5woLDuLbztbx3xnscVv8wHpz9IJd+finLUpd5XZqUkYKdVG2J/XwtdRbse07sF5DThDZtSsLzz5G7aRMpN9yAy84OyHlERCpSyzotmXTiJMb2HcuqHau46LOLmL1+ttdlSRl4FuzMrJ6ZfW1mS/3PxV7JamZD/dssNbOhRZb/YGZLzGyB/9HIvzzczN4xs2Vm9rOZJVbQRxIvJPTwdb8ee3dAumGLiuzUiaaPPMzuufNYf/8DGikrItWCmXHGIWfw3hnv0SymGcO/Gc5XyV95XZYcJC9b7EYB3zrnWgPf+t/vwczqAfcBPYEewH17BcCLnHOd/Y9N/mVXAqnOuUOBp4HHAvkhpBJI6AH9bg1oqCtQ59RTaTD8WtLef5/tU6cG/HwiIhWlUVQjXj/5ddo3aM/I/xvJu0ve9bokOQheBrszgcn+15OBs4rZ5iTga+fcNudcKvA1cPIBHHc6cJyZWdnLFfFpcP31RB/dnw0PP8LuBQu8LkdEKqmqeAuzOuF1eOmEl+gX348HZz/IhF8nqHeiivEy2DV2zq33v94ANC5mmzhgTZH3Kf5lBV7zd8OOLhLeCvdxzuUCaUDNmIlSKoQFBRH32GOENm5Myo03kbu1es9gLyIHruAWZk9+tYSLJs2uUuEuMiSSZwY8wxmHnMHzC57nkTmPkO/yvS5LSimgwc7MvjGzxcU8ziy6nfP9d+BA/0twkXOuA9DP/zig2WPN7Gozm2tmczdv3nyAp5aaLjg2lvhxz5K3fTtrb7kVl5vrdUkiUolU9VuYhQaF8mCfBxl6+FDe/vNtRv04ipw83V6xKghosHPOHe+ca1/M4yNgo5k1BfA/byrmEGuBhCLv4/3LcM4VPO8E3sJ3Dd4e+5hZCFAH+MffKOfcy8657s657g0bNiyPjys1TMThh9Pk/vvZ9fPPbH7mGa/LEZFKpDrcwizIghh55Ehu6XYLnyd/zvXfXc+unF1elyUl8LIrdgZQMMp1KPBRMdt8CZxoZnX9gyZOBL40sxAzawBgZqHAacDiYo47CPjO6QIBCZDYs88i9vwhbJ30Cju+1CgyEfHp1qIuU4b14pYT2zJlWK8qfQuzy9tfzpjeY5i9fjbDvhpGambV6VauiTy784SZ1QfeBZoDq4DBzrltZtYduMY5N8y/3RXAXf7dxjrnXjOzaOBHIBQIBr4BbnHO5ZlZBPAm0AXYBpzvnFuxv1p05wkpi/zsbFZdfAnZy5eTOO1dwlu18rokEZFy9/3q77ntx9toGt2Ul094maYxTb0uqcbSLcVKoGBXg5XTfWZz1q9n5TnnElyvHi3ffYeg6OhyLFJEpHKYu2EuI74bQVRoFC+f8DKtYvUfWS/olmIixSm4z+x3Y33Pa+Yc9KFCmzYl7qknyV65kvWjR2t6ABGplro36c5rJ79Gnsvj0i8uZeHmhV6XJHtRsJOaq5zvMxt91FE0vPkmdnz2OalvvFFORYpIdVfV5rtrW68tbwx8g9phtRn21TB+WvuT1yVJEQp2UnMF4D6z9YcNo9YJx7Px34+zS937IlKCqjrfXUKtBN4Y+AYtarfg+m+v57MVn3ldkvgp2EnNFYD7zJoZTR9+mLCEBFJuvpmcTcXN4iMi4lOV57trENmAV096lc6NOnNH0h1M+WOK1yUJCnZS0wXgPrPBtWoRP34c+ekZrL3pZlyOJvUUkeJV9fnuaoXVYsIJEzg24VgenfMo438Zr2uMPaZgJxIA4a1b0/ShB9k9fz4bH3/c63JEpJKqDvPdhQeH8+QxT3Ju63N5eeHLjJk9hrz8PK/LqrFCvC5ApLqqc+qpZC5cyLbJbxDZsRN1TjvV65JEpBLq1qJulQx0RYUEhXDfUfdRL6IeExdNZHvmdh7t/yjhweFel1bjqMVOJIAajRxJZLdurB89msy//vK6HBGRgDEzRnQdwe1H3s43q79h+DfDSc9O97qsGkfBTiSALDSUuKefIigmmrU3jCBv506vSxIRCahLDr+ER/o9wvyN87niyyvYsnuL1yXVKAp2IgEW2qgR8c88Q/batawbdScuP9/rkkREAuq0Vqcx7thxrExbydDPh5KyM8XrkmoMBTuRChDVrRuNb7+N9G+/ZeukV7wuR0Qk4PrF92PiiRPZnrWdoZ8PZfOuzV6XVCMo2IkcqDVzIOnJA74FWd1LLqH2Kaew+ZlnyJg1K0DFiYgcnEDcAaNzo868etKrpGWncd+s+zQVSgVQsBM5EGW4v6yZ0fTBMYQf0oq1t44kZ+PGABYqIlJ6gbwDRtt6bbm5280krU1i2l/Tyu24UjwFO5EDUcb7ywZFRxP37LPkZ2Wx9tZbcbm5ASpURKT0An0HjAvaXcBRTY/iiblPsHrH6nI9tuxJwU7kQJTD/WXDW7Wi6f33sXvuPDaPGx+AIkVEDkyg74ARZEE82OdBQoJCuHPmneTm6z+1gWLq74bu3bu7ubphu5TWmjm+lrrEfmW6Fdn60aPZPm06CRNfJqbfgQdEEZHyNG9VKrNXbKVXq/oBmzD585Wfc/uPt3NDlxu4uuPVATlHTWBm85xz3Ytdp2CnYCfeyM/MJHnwEHI3baLlhx8Q2qTJvjcupzApIuK12//vdr5e9TVTTp3C4fUP97qcKml/wU5dsSIeCYqIIO6ZZ8jPzmbtLfu53q4MAzZERCqbu3vdTb3IetyZdCeZuZlel1PtKNiJeCi8VUuaPvAAu+fPZ/Ozzxa/URkHbIiIVCZ1wuvwYJ8HWZG2gmfn7+PfPTloCnYiHqtz+mnEDh7M1omTSP+///vnBuUwYENEpDLp3aw3F7a7kP/88R9mr5/tdTnViq6xQ9fYiffyMzNJPv8Cctev911v17TpnhvoGjsRqWZ25+5m8MeD2Z27m/fPfJ/aYbW9LqnK0DV2IpVcUEQEcU8/hcvJ8V1vl5Oz5wYJPaDfrQp1IlJtRIZE8mi/R9m6eysP//yw1+VUGwp2IpVEeMuWNHlwDLt/+YVNzzzjdTkiIgF3RIMjuLrT1Xy64lO+SP7C63KqBQU7kYq2n3vN1jn1VGLPH8K2V15l5/ffe1CciEjFuqrDVXRo0IEH//sgm3Zt8rqcKk/BTqQilWLqksZ33kn4YYexftSd5Kxb50GRIiIVJyQohIf7Pkx2Xjb3/nQvuva/bBTsRCpSKaYuCQoPJ/6Zp3G5uay9+RZcdrYHhYqIVJzEOomM7D6Sn9b9xDtL3vG6nCpNwU6kIpVy6pKwFi1o+tCD7P71VzY9/UzF1igi4oHBbQfTJ64PT859kuS0ZK/LqbIU7EQqUkIPGDoDjr3b97yfUa61Bw6k7oUXsO2119j53XcVWKSISMUzM8b0HkN4SDh3zbyL3Px93I1H9kvBTqSiHcDUJY3uuIOIww9n3ag7yU5ZWwHFiYiUr3mrUnn++2XMW5Va4raNohoxutdoFm1ZxMRFEyuguupHwU6kEgsKDyfumachP5+1t+h6OxGpWuatSuWiSbN58qslXDRpdqnC3UmJJ3Fqq1N56deXWLxlcQVUWb0o2IlUcmHNm9N07FgyFy5k05NPel2OiEipzV6xlezcfPId5OTmM3vF1lLtd1fPu2gQ2YA7k+5kd+7uAFdZvSjYiVQBtU86kbqXXMK2yW+w46uvvC5HRKRUerWqT1hIEMEGoSFB9GpVv1T71Q6rzUN9HyJ5RzJPz3s6wFVWLwp2IlVE49tGEtGhA+vvvofs1au9LkdEpETdWtRlyrBe3HJiW6YM60W3FnVLvW+vpr24+LCLefvPt5m1dlYAq6xeTBMBQvfu3d3cuXO9LkOkRNkpa1l5zjmExcfT4u23CAoP97okEZGAyczNZMgnQ0jPTuf9M9+nTngdr0uqFMxsnnOue3HrPGmxM7N6Zva1mS31Pxcb4c1sqH+bpWY21L+slpktKPLYYmbP+NddZmabi6wbVoEfSyTgwuLjaPboI2T+/jubHnvM63JERAIqIiSCR/o9wrbMbYydPdbrcqoEr7piRwHfOudaA9/63+/BzOoB9wE9gR7AfWZW1zm30znXueABrALeL7LrO0XWTwr4JxGpYLWOPZZ6l19O6ltvs+Ozz7wuR0QkoA6vfzjXdr6Wz5M/57MV+jevJF4FuzOByf7Xk4GzitnmJOBr59w251wq8DVwctENzKwN0Aj4532ZRKqxRrfcTGTnzqy/ZzRZK1d6XY6ISEBd0f4KOjXsxEM/P8SGjA1el1OpeRXsGjvn1vtfbwAaF7NNHLCmyPsU/7KizsfXQlf0QsFzzWyhmU03s4R9FWBmV5vZXDObu3nz5oP4CCLesdBQ4p5+CgsLY+1NN5Ofmel1SSIiARMSFMLDfR8mNz+X0T+NJt/le11SpRWwYGdm35jZ4mIeZxbdzh/KDnYEx/nA20XefwwkOuc64mvhm1zsXr7zvuyc6+6c696wYcODPL2Id0KbNqXZvx8ja8kSNo592OtyREQCqnnt5tx25G3MXj+bt/98u+QdaqiABTvn3PHOufbFPD4CNppZUwD/86ZiDrEWKNriFu9fhn+/TkCIc25ekXNudc5l+d9OArqV88cSqVRi+ven/tVXs33aNNJmzPC6HBGRgBrUehD94/vz9LynWZG2wutyKiWvumJnAEP9r4cCHxWzzZfAiWZW1z9q9kT/sgIXsGdrXUFILHAG8Ee5VSxSSTUccQNR3buz/r77yVq2zOtyREQCxsx4oPcDRIZEcmfSneTk53hdUqXjVbB7FDjBzJYCx/vfY2bdzWwSgHNuG/Ag8D//Y4x/WYHB7BXsgBFm9puZ/QqMAC4L6KcQqQQsJIRmTz5JUGQkKTfdRP6uXV6XJCISMA0iG3DfUffx+9bfeXnhy16XU+logmI0QbFUDxmzZrH6ymHUOfNMmj36iNfliIgE1N0z7+bTFZ/yxsA36Niwo9flVKhKN0GxiJS/6N69aXDttaR9+CHb33u/5B1ERKqwUT1G0SiqEXfNvItdOeqpKKBgJ1KNNLhuOFG9erFhzBgyl/zldTkiIgFTK6wWY/uOZfWO1Tw17ymvy6k0FOxEqhELDibuiccJql2LtTfdRF56htcliYgEzJFNjuTSwy/lnSXvkJSiexWAgp1ItRPSoAFxTzxJ9qpVbLj/fnQdrYhUZzd0vYFDYw/l3ln3sj1zu9fleE7BTqQaiu7Zg4YjbmDHJ5+w/Z13vS5HRCRgwoPDeaTfI2zP2s6Y2WNq/H9mFexEqqn6V19NdN++bHz4YTJ//93rckREAqZdvXZc1/k6vl71NZ+s+MTrcjylYCdSTVlQEM3+/RjBdeuSctPN5O3c6XVJIiIBc/kRl9OlURce+fkR1qevL3mHakrBTqQaC6lXj7innyJn7VrW3zO6xndRiEj1FRwUzNi+Y8lzedzz0z3ku3yvS/KEgp1INRfVtSuNbr6JnV9+SeqUt7wuR0QkYBJqJXBHjzuYs2EOU/6Y4nU5ngjZ30ozu6UUx8hwzr1UTvWISADUu+IKds2dx8bHHiOyU0ciO3TwuiQRkYA4+9Cz+X719zwz7xmOanoUh9Y91OuSKlRJLXa3ATFArf08bg1kgSJSdhYURLNHHyGkYQPW3nQzeWlpXpckIhIQZsZ9ve8jJiyGu2beRU5ejtclVaj9ttgBbzrnxuxvAzOLLsd6RCRAgmNjiX/6aZIvvoR1d91N/HPjMTOvyxIRKXcNIhtw31H3ceP3N/Liry8yousIr0uqMPttsXPO3V7SAUqzjYhUDpGdOtF45K2kf/st216f7HU5IiIBc2zzYzn70LN5ZfErLNi0wOtyKoyVZpScmcUClwKJFGnlc85ViwjcvXt3N3fuXK/LEKkQzjnWjhjBzu9/IPE/bxLZubPXJYmIBER6djqDPh6EYbx3xntEhUZ5XVK5MLN5zrnuxa0r7ajYz/CFukXAvCIPEalizIymY8cS2qQJKTffQm5qqtcliYgERExYDGP7jmVt+loen/u41+VUiNIGuwjn3C3Oudecc5MLHgGtTEQCJrh2beKeeYa8LVtYN2oULr9mzvckItVft8bduKz9ZUz/azr/t+b/vC4n4Eob7N40s6vMrKmZ1St4BLQyEQmoyPZH0GjUHWT8349sfeUVr8sREQmY6ztfT5u6bbhv1n1sy9zmdTkBVdpglw08DvyXv7thdVGaSBVX98ILqTXwZDY/8yy79nWd6Zo5kPSk71lEpAoKCw7j4b4PsyN7B2P+O6Za34WntMHuVuBQ51yic66l/9EqkIWJSOCZGU0ffJCw+HjW3nIruVu37rnBmjkw+Qz4bqzvWeFORKqotvXackOXG/h29bfMWD7D63ICprTBbhmwK5CFiIg3gmNiiHv2GfK2b2fd7Xfg8vL+XpmcBHnZ4PJ8z8lJ3hUqIlJGlx5+Kd0ad+OROY+wNn2t1+UERGmDXQawwMxeMrNxBY9AFiYiFSeiXTsa33M3GT/9xJaXitwhMLEfBIeBBfueE/t5V6SISBkFBwUztu9YAO6ZeQ/5rvoNHCttsPsQGAvMQtOdiFRLseedR+3TT2fLc8+TMftn38KEHjB0Bhx7t+85oYe3RYqIlFFcTByjeoxi7sa5vPn7m16XU+5KNUFxdacJikV88jMyWHneYPJ27KDVB+8T0rCh1yWJiJQ75xw3fX8TSWuTmHraVNrUbeN1SQfkoCcoNrOXS3HwErcRkaohKDqauGeeJj89nbUjb9vzejsRkWrCzLiv933UCqvFXUl3kZ2X7XVJ5SakhPVnmVnmftYbMKAc6xERj0W0aUOT++5j/Z13suX552k4olrcOVBEZA/1IuoxpvcYrv/uep5f8Dw3d7vZ65LKRUnB7rZSHEPD5ESqmdizz2LX//7HlhcnENm1GzF9+3hdkohIuTs64WjObX0ury1+jaPjj6Zr465el1RmusYOXWMnUpz83btJHjyE3C1baPnhB4Q2bux1SSIi5W5Xzi7OnXEuDsd7Z7xHdGi01yWV6KCvsRORmisoMpK4Z58hPyuLtTffgsuuPtegiIgUiAqN4uF+D7M+Yz3//t+/97ndvFWpPP/9MuatSq3A6g6cgp2I7FN4q1Y0fXAMu+fPZ8PDD3tdjohIQHRp1IUr2l/B+0vf57vV3/1j/bxVqVw0aTZPfrWEiybNrtThTsFORParzqmnUv+qYWyf+g6pU6d6XY6ISEAM7zScdvXa8cB/H2Dr7j1vrzh7xVayc/PJd5CTm8/sFVv3cRTvlSrYmVkbM5toZl+Z2XcFj0AXJyKVQ8ObbiLm6KPZ8NBYMubofrEiUv2EBofySN9HSM9O5/7/3k/RMQi9WtUnLCSIYIPQkCB6tarvYaX7V6rBE2b2KzAB390mCie2cs5Vi7tPaPCESMnydu4kecj55KWmkjhtGmHxcQd2gDVzfPeaTeynO1iISKX1xm9v8PjcxxnTewxntz67cPm8VanMXrGVXq3q061FXQ8r3P/gidIGu3nOuW7lXlkloWAnUjpZK1eSPHgIoc2akfj2WwRFRZVuxzVzYPIZkJftu+esbk8mIpVUvsvnqq+uYvGWxUw/YzoJtRK8LukfymNU7MdmNtzMmppZvYJHGYuqZ2Zfm9lS/3Ox8dfMvjCz7Wb2yV7LW5rZz2a2zMzeMbMw//Jw//tl/vWJZalTRP4W3rIlcU89RdbSpay78y5KPV1ScpIv1Lk833Oypr8UkcopyIJ4qM9DBFkQ98y8h7z8ku/AUzBi9q2fV3s+cra0wW4ovsmKZ+Hrjp0HlLWJaxTwrXOuNfCt/31xHgcuKWb5Y8DTzrlDgVTgSv/yK4FU//Kn/duJSDmJ6deXRrfeys4vv2TLiy+WbqfEfr6WOgv2PSf2C2yRIiJl0DSmKXf1vIv5m+bz+m+v73fbghGzT3y5hLs+WOT5yNlSBTvnXMtiHq3KeO4zgcn+15OBs/Zx7m+BnUWXmZkBxwLTi9m/6HGnA8f5txeRclLvisupfcbpbBk3np3ffFPyDgk9fN2vx96tblgRqRJOa3UaJ7Q4gecWPMeSbUv2uV3BiNmC/guvR86WdlTsTDMba2Ynm1mtcjp3Y+fcev/rDcCBTGtfH9junMv1v08BCq7kjgPWAPjXp/m334OZXW1mc81s7ubNmw+mfpEay8xoOmYMER06sO72O8j866+Sd0roAf1uVagTkSrBzBjdazSx4bGMShpFVl5WsdsVjJgtCFRBHo+cLW1X7CXAEuBcYJY/ED1d0k5m9o2ZLS7mcWbR7ZzvQp0KvbeZc+5l51x351z3hg0bVuSpRaqFoIgI4p8bT1B0NCnXXU9uauWdsFNE5GDUjajLmN5jWLZ9Gc/98lyx23RrUZcpw3px60ltefjsDtx6YlumDOvl2cjZkNJs5JxbaWaZQLb/MQA4rBT7Hb+vdWa20cyaOufWm1lTYFMpawbYCsSaWYi/VS4eWOtftxZIAFLMLASo499eRMpZaOPGxI8fx6pLLmXtLbfQfOJELKRU/6yIiFQJ/eL7MbjNYCb/Npn+8f05ssmR/9imW4u6nk+BUqC0XbHLgQ/xdZe+ArR3zp1cxnPPwDcoA//zR6Xd0d/C9z0wqJj9ix53EPCdK/XQPRE5UJGdO9NkzBh2/Xc2Gx/b930WD8iaOZD0pO9ZRMRjt3a/lfha8YydPbZUo2S9VNqu2HHAauACYAQw1MwOKeO5HwVOMLOlwPH+95hZdzObVLCRmSUB0/ANgkgxs5P8q+4AbjGzZfiuoXvFv/wVoL5/+S3se7StiJST2LPPot7QoaS++Sbb33uvbAcrmPPuu7G+Z4U7EfFYVGgUI7qOYHnacj5b+ZnX5exXqSYoLtzYLAa4HBgJxDvnggNVWEXSBMUiZedyc1lz9b/I+N//aDF5MlFduxzcgZKe9IU6l+ebHuXYu32DLkREPJTv8hnyyRDSs9OZcfYMQoNCPaulzBMUm9mTZvYz8DPQCbgXaF1+JYpIVWchIcQ9/RShzZqSMmIEOevXl7xTcTTnnYhUQkEWxA1dbiAlPYUPln7gdTn7VNpbig0CkpxzGwNfUsVTi51I+clatozkIecTlphIiyn/ISgi4sAPovvKikgl5Jzjks8vYX3Gej49+1MiQg7i37dyUOYWO+fcdKCnmT3hf5xerhWKSLURfuihNHv8cTJ//53194wu/W3HitKcdyJSCZkZN3a9kU27NvHOkne8LqdYpe2KfQS4Efjd/xhhZg8HsjARqbpqHTuAhjfeyI5PPmHbK6+UvIOISBVxZJMj6dW0F68seoWMnAyvy/mH0o6KPRU4wTn3qnPuVeBk4LTAlSUiVV39f11N7VMGsunJp0j/v//zuhwRkXIzossIUrNS+c/v//G6lH8obbADiC3yuk451yEi1YyZ0XTsWMIPa8faW0eStWKF1yWJiJSLDg07MCBhAK//9jppWWlel7OH0ga7R4BfzOx1M5sMzAPGBq4sEakOgiIjSXjuOSw8nJTh15G3Y4fXJYmIlIvru1xPRk4Gry1+zetS9lDawRNvA72A94H3gKOcc5XzqkERqVRCmzUjftyzZK9dy9qbbsZlZ3tdkohImbWp24aBLQcy5Y8pbNm9xetyCu032JlZ14IH0BRI8T+a+ZeJiJQoqls3mj7wABmzZrF+9EGOlBURqWSu63wdOfk5TFw40etSCpV0t+4n/c8RQHfgV8CAjsBc4KjAlSYi1UnsOWeTu3EDm58dR0jjJjS65ebAnEhz4IlIBWleuzlnHXoW0/6axtAjhtIsppnXJe2/xc45N8A5NwBYD3R1znV3znUDugBrK6JAEak+6l9zDbFDhrD15ZfZNmVK+Z9A95kVkQp2TadrAJjw6wSPK/Ep7eCJts65RQVvnHOLgcMCU5KIVFdmRpPR9xBz7LFsfGgsO77+unxPkJwEedm++8zmZfvei4gEUJPoJgxpO4SPln9Eclqy1+WUOtgtNLNJZnaM/zERWBjIwkSkerKQEOKefILIjh1ZN/I2ds2fX34H131mRcQDV3a4krCgMF5e+LLXpZQ62F0O/Ibv7hMFd6C4PFBFiUj1FhQZSfyEFwlt2pQ11w4na/ny8jlwQg8YOgOOvdv3rGvsRKQCNIhswPntzufTlZ+yMm2lp7VYaUanmdlxwCzn3O7Al1Txunfv7ubOnet1GSI1TnZKCsnnX4CFhZL49lRCGzfyuiQRkYOydfdWBr4/kBNanMDYvoGd6tfM5jnnuhe3rrQtdpcCv5rZbDN73MxON7O65VeiiNREYfHxJLw0gfztaaz517/IS0/3uiQRkYNSP7I+j/R7hBu63OBpHaWdoHioc64NcA6wBnge2BzIwkSkZog84gjixo0ja9kyUm64QRMYi0iVdVzz42gS3cTTGkoV7MzsYjN7CZgOHA88B+iqZBEpFzF9+9Bs7EPs+u9s1t19Dy4/3+uSRESqpJImKC7wDLAcmAB875xLDlRBIlIz1TnzTHI2bmLzU08R2rgRjUaOrJgTa0JjEalGShXsnHMNzOwIoD8w1sxaA0ucc5cEtDoRqVHqXzWM3A0b2DrpFYLrN6D+5ZcF9oQFExrnZfumR9FIWhGp4koV7MysNtAcaAEkAnUA9ZWISLkyMxrffRe5W7ey6bHHsJAQ6l1yceBOWNyExgp2IlKFlbYrdmaRx3POuZTAlSQiNZkFBxP3xOOszc9j49ixEGTUu+iiwJysYELjghY7TWgsIlVcabtiOwa6EBGRAhYaStyTT5Jyyy1sfPAhMKPehReW/4kKJjTWNXYiUk2Utiu2IXA7cAQQUbDcOXdsgOoSkRrOwsKIf+opUm66mY1jHsSCgqh7/vnlf6KEHgp0IlJtlHaC4inAn0BL4AEgGfhfgGoSEQH84e6Zp4kZMIAN9z9A6jvvel2SiEilVtpgV9859wqQ45z7P+fcFYBa60Qk4CwsjLhnnyHm6KPZcN99pE6b5nVJIiKVVmmDXY7/eb2ZnWpmXYB6AapJRGQPQWFhxI0fR/TR/dkw+l62v/ee1yWJiFRKpQ12D5lZHeBWYCQwCbg5YFWJiOwlKCyM+HHjiO7Xj/X3jGb7e+97XZKISKVTYrAzs2CgtXMuzTm32Dk3wDnXzTk3owLqExEpFBQeTvxz44nu3Zv199zD9g8+9LokEZFKpcRg55zLAy6ogFpEREoUFB5O/PPPEX3UUay/6y7SPvrI65JERCqN0nbF/mRmz5lZPzPrWvAIaGUiIvsQFBFB/AvPE31UL9aNupO0GepAEBGB0t95orP/eUyRZQ6NjBURjwRFRBD//POsuXY460bdCWbUOf10r8sSEfFUae88MSDQhYiIHKigyEgSXnyBNddcy7o7RoEFUee0U70uS0TEM/sNdmZ2y/7WO+eeOpiTmlk94B0gEd9kx4Odc6nFbPcF0AuY6Zw7rcjyKUB3fNOwzAH+5ZzLMbNjgI+Alf5N33fOjUFEqq3CcPeva1h3++1YkFH7lFO8LktExBMlXWNXy//oDlwLxPkf1wBlucZuFPCtc6418K3/fXEeBy4pZvkUoB3QAYgEhhVZl+Sc6+x/KNSJ1ABBUVEkvDSBqK5dWXvb7aR9+qnXJYmIeGK/wc4594Bz7gEgHujqnLvVOXcr0A1oXobznglM9r+eDJy1j/N/C+wsZvlnzg9fi118GWoRkWqgMNx16cK6kbex9dXX8P0TISJSc5R2VGxjILvI+2z/soPV2Dm33v96w8Eey8xC8bXofVFk8VFm9quZfW5mR5ShRhGpYoKio0mYNJFaJ57Ipn//m40PPoTLy/O6LBGRClPaUbFvAHPM7AP/+7OA1/e3g5l9AzQpZtXdRd8455yZHex/q18AfnTOJfnfzwdaOOfSzewU4EOg9T7quxq4GqB587I0PopIZRIUEUHc00+x6Ykn2fbqq+SsX0/ck08QFBXldWkiIgFnpe2q8M9b18//9kfn3C8HfVKzJcAxzrn1ZtYU+ME513Yf2x4DjCw6eMK//D6gC3COcy5/H/smA92dc1v2V0/37t3d3LlzD/hziEjltu2tt9j40FgiDj+chBdfIKRhQ69LEhEpMzOb55zrXty60nbF4pyb75x71v846FDnNwMY6n89FN9I1lIzs2HAScAFRUOdmTUxM/O/7oHv820tY60iUkXVu/BC4p97jqzly0k+/wKyli/3uiQRkYAqdbArZ48CJ5jZUuB4/3vMrLuZTSrYyMySgGnAcWaWYmYn+VdNwHdd3n/NbIGZ3etfPghYbGa/AuOA852unhap0WodO4AWb7xBflYWyRdcSMacOYE94Zo5kPSk71lEpIKVuiu2OlNXrEj1l52yljX/+hfZq1fT7OGxgblLxZo5MPkMyMuG4DAYOgMSepT/eUSkRiuXrlgRkaosLD6OxLemENW5M+tuu50tEyaU/3QoyUm+UOfyfM/JSSXvIyJSjhTsRKTGCK5Th4RXJlH79NPZ/MyzbLj3XlxOTvmdILGfr6XOgn3Pif1K3kdEpByVdroTEZFqISgsjGb/fozQ+Di2vjiBnPUbiHvmaYJjYsp+8IQevu7X5CRfqFM3rIhUMLXYiUiNY2Y0uvFGmj70IBn//S+rLrqY7OTk8jl4Qg/od6tCnYh4QsFORGqs2EGDSJgwgZwNG1hxzrmkTpum25CJSJWmYCciNVpMv760+uhDIjt2ZMPoe0m5/gZyt23zuiwRkYOiYCciNV5okyY0f/UVGt1xBxk//siKM88kPcmjEa2aB09EykDBTkQEsKAg6l9+GYnT3iUkNpY1V13NhgcfIj8zs+KKKJgH77uxvmeFOxE5QAp2IiJFRLRrR+L06dQbeimpU6aw8txBZP7+e8WcXPPgiUgZKdiJiOwlKDycxnfeScIrk8jfsYOVQ85n66RJuLy8wJ5Y8+CJSBnplmLolmIism+5qalsuPc+dn79NVFHHkmzxx4ltFmzwJ1wzRzNgyci+7W/W4op2KFgJyL755wj7YMP2fjQQxAcTJP77qPOaad6XZaI1FC6V6yISBmYGbHnnE3Ljz4k/JBDWDdyJGtH3kbejh1elyYisgcFOxGRUgpLSKDFf96kwYgb2PH556w46ywy5mjkqohUHgp2IiIHwEJCaDh8OIlvv4WFhrJ66GVsevJJXHa216WJiCjYiYgcjMiOHWn1/vvEDhrE1omTWHn++WQtX+51WSJSwynYiYgcpKDoaJo+OIb4558jd/0GVp5zLtumTNH9ZkXEMwp2IiJlVOu442g14yOievZg44MPsebKYWQu+cvrskSkBlKwExEpByENG5Lw0ks0vnc0uxctYuVZZ7H29tvJXrPG69JEpAZRsBMRKSdmRr0LL+TQr7+i/pVXsPPLr1h+yqlsePAhcrds8bo8EakBFOxERMpZcGwsjUaO5JCvviT2nHNInTqVZSecyKZnniFv587AF7BmDiQ96XsWkRpFd55Ad54QkcDKTk5m87jx7PjsM4Lq1KHB1VdR96KLCIqIKP+TrZkDk8+AvGzf/WaHztCtyUSqGd15QkTEQ2GJicQ99SQt33+PyI4d2fT4Eyw/8SRS33kXl5NTvidLTvKFOpfne05OKt/ji0ilpmAnIlJBIg4/nOYTX6b5G5MJbdaMDffdx4rTTmfHZ5/h8vPL5ySJ/XwtdRbse07sVz7HFZEqQV2xqCtWRCqec470779n89PPkLV0KeGHH0ajm28hum8fzKxsB18zx9dSl9hP3bAi1dD+umIV7FCwExHvuLw8dnzyCZvHjSdn7VqievSg3hWXE9OvHxYc7HV5IlIJKdiVQMFORLzmsrNJfXcaW16aQN7mLYQ0bkzsuedQ55xzCYuP87o8EalEFOxKoGAnIpWFy85m5/c/sH3aNDJ++gmA6N69iT3vPGodOwALC/O4QhHxmoJdCRTsRKQyylm7lu3vf8D2998nd/16guvVo85ZZxE76FzCW7WqmCJ0vZ5IpaNgVwIFOxGpzFxeHhk//cT2adPY+f0PkJtLZPduxA4aRO2TTiIoMjIwJ9aceCKVkuaxExGpwiw4mJj+/YkfP57W339Ho5G3krd5C+tH3cnS/kezYcwYMv/4o/xPrDnxRKocBTsRkSokpGFD6g8bRqsvPqf5G5OJOeYYtk9/j5Vnn8PKcweROvUd8tLTy+dkmhNPpMpRVyzqihWRqi0vLY20GR+zfdo0sv76C4uMJKZ/f2L69SW6b19CmzQ5+IPrGjuRSkfX2JVAwU5EqgPnHJmLF7N9+nuk//ADuRs3AhDeujXR/foR068vkd26EaSRtSJVWqULdmZWD3gHSASSgcHOudRitvsC6AXMdM6dVmT568DRQJp/0WXOuQXmm679WeAUYJd/+fyS6lGwE5HqxjlH1tKlZCTNJH1mErvnzsPl5GCRkUT37El0v77E9OtHWPPmXpcqIgdof8EupKKL8RsFfOuce9TMRvnf31HMdo8DUcC/ill3m3Nu+l7LBgKt/Y+ewIv+ZxGRGsXMiGjThog2bah/5RXkZ2SQMWeOL+glJZH+ww9sBEJbNCembz+i+/UlukcPgqKivC5dRMrAq2B3JnCM//Vk4AeKCXbOuW/N7Ji9l5dw3DecrxlytpnFmllT59z6MlUrIlLFBUVHU2vAAGoNGABA9qpVpCfNJCMpie3vvUfqlClYWBhR3bsXdtuGHXJI2e9bKyIVyquu2O3OuVj/awNSC94Xs+0xwMhiumKPArKAb4FRzrksM/sEeNQ5N9O/3bfAHc65f/SzmtnVwNUAzZs377Zq1ary+ngiIlVKflYWu+fN8wW9mUlkLV0GQEjTpsT07UPUkUcS2bEjoS1aKOiJVAKedMWa2TdAcUOx7i76xjnnzOxA0+WdwAYgDHgZX2vfmAM5gHPuZf++dO/eXSNIRKTGCgoPJ7p3b6J794Y7bidn/XrSZ84kI2kmOz7/gu3TfFe9BNepQ0THjkR27Ehkp45EdOhASN26HlcvIkUFLNg5547f1zoz21jQRWpmTYFNB3jsgq7VLDN7DRjpf78WSCiyabx/mYiIlFJo06bUPe886p53Hi43l6zly9n966/sXriQzF8XsmXmTPD39oS2aE5kx05/h7127XQ/WxEPeXWN3QxgKPCo//mjA9m5SCg04CxgcZHjXm9mU/ENmkjT9XUiIgfPQkKIaNuWiLZtqTt4MAB56RlkLl7M7oUL2b3wV3bNns2Ojz/2bR8aSvjhh+0R9kITEtSFK1JBvLrGrj7wLtAcWIVvupNtZtYduMY5N8y/XRLQDogBtgJXOue+NLPvgIaAAQv8+6T7g95zwMn4pju5vLjr6/am6U5ERA6ec47cDRvY/evCwrCXufg3XGYmAMF16xLRsYMv6HXsRGTHDgTXqeNx1SJVV6Wbx66yUbATESlfLjeXrKVL9wh72ctXFHbhhiUmEtG+PZEd2hPRoQMR7dppqhWRUlKwK4GCnYhI4OXt3Onrwv11IbsXLSJz8eLCu2MQFET4oYcS0aE9ke3bE9G+AxFt2+h6PZFiKNiVQMFORMQbORs3kfnbYl/gW7SYzEWLyNu+HfBfr9e27R5hL/yQVliIV5eHi1QOCnYlULATEakcnHPkrF1H5uJFf4e9334jPz0dAIuMJOKww3xduO07ENH+CMJatMCCgjyuXKTiKNiVQMFORKTycvn5ZCevInPxInYvXkzmosVk/vFH4eCMoFq1iDjiiMKwF9n+CEKaNdNIXKm2FOxKoGAnIlK1FMyvl7moSNj76y/IyQEguF49XxfuEe19zx06ENKggcdVi5QPBbsSKNiJiFR9+dnZZC1Z4h+Y8RuZixaRtXw55OcDENKkCZFdOhPdsxdRPXsQlpioVj2pkjy5pZiIiEhFCgoLI7JDByI7dChclp+RQeaff/rC3qLF7Jo7l52ffwFASKNGRPXqSXTPnkT17ElYfLxXpYuUGwU7ERGptoKio4nq1o2obt0A/+CMVavImP0zu+b8TMZPs9gxw3fXjNC4OKJ69iS6Zw+ievYktElxtzsXqdzUFYu6YkVEairnHNnLlpHx8xx2/fwzu+bMIS8tDYCwFi2I6tmTqJ49iO7ZU9foSaWha+xKoGAnIiLgG4GbtWQJGT//zK7ZP7Nr7tzCqVbCDj2E6B49ierVk6gjjySkbl2Pq5WaSsGuBAp2IiJSHJebS+Yff5Axeza7fp7DrnnzcLt3gxnhbdsWXp8XdWR3gmvV8rpcqSEU7EqgYCciUoHWzIHkJEjsBwk9qtR5XXY2uxcvZtfPP5Px8xx2z5+Py86GoCAijjii8Pq8qK5dCYqOLucPIOKjYFcCBTsRkQqyZg5MPgPysiE4DIbOqJhwF6Dz5mdlsXvBr/6g9zO7Fy70zaUXEkJkhw5E9+1DreOOI7xtW02tIuVG052IiEjlkJzkC1cuz/ecnFQxwS5A5w0KDye6Zw+ie/agITeQv2sXu375hV2zfUFvy3PPs2X8c4Q2a0bMccdR67hjierWDQsNLYcPJfJPCnYiIlJxEvv5WswKWs4S+1Wr8wZFRRHTpw8xffoAkLtlCzu//570b79j+7vvkvrmmwTVrk3M0UdT67hjie7bj+AYddlK+VFXLOqKFRGpUFX4GruyyN+1i/SffiL92+9I/+EH8rZvx0JDierVi1onHE/tE08kODa2wuuSqkfX2JVAwU5ERCqSy81l9y+/sPPb79j53XfkrF4NoaHE9O9PndNOJeaYYwiKjPS6TKmkFOxKoGAnIiJecc6R+dvv7PjkE3Z8+im5mzcTFBVFrRNOoPZppxF9VC8sRFdOyd8U7EqgYCciIpWBy8tj1//+R9onn7Dzy6/I37mT4Pr1qX3yydQ5/TQiOnXS6FpRsCuJgp2IiFQ2+dnZpP/f/7Hjk09J//57XHY2ofHx1D7tVOqcdhrhhx7qdYniEQW7EijYiYhIZZa3cyc7v/6GHZ98Qsbs2ZCfT/hhh1HntFOpfdrphDZu5HWJUoEU7EqgYCciIlVF7ubN7Pj8C9I++YTMhQshOJiYAcdQd8j5RPfpjQUFeV2iBJiCXQkU7EREpCrKTk5m+/TpbH/vffJSUwmNjyd28GBizzmbkAYNvC5PAkTBrgQKdiIiUpXlZ2ez8+uv2T71HXb9738QGkqt44+j7pAhRPXsqQEX1YyCXQkU7EREpLrIWrGC7e+8y/YPPyQ/LY2wxERiBw+mztlnEVK3rtflSTlQsCuBgp2IiFQ3+ZmZ7PzyS1KnvsPuX37BwsKofcbp1L/sMo2oreIU7EqgYCciItVZ5pK/SH3rLdI+/BCXlUV0/37Uv+wyoo46St20VZCCXQkU7EREpCbITU1l+9SpbJvyFnlbthDeti31LruMOqeegoWFeV2elJKCXQkU7EREpCbJz8pixyefsu3118laupSQhg2pe/HF1D1/CMF16nhdnpRgf8FOk92IiIgEwpo5kPSk77mSCQoPJ/bcc2g54yMSJk4kvE0bNj/9NMuOO54tL75IfkaG1yXKQVKLHWqxExGRcrZmDkw+A/KyITgMhs6AhB5eV7VfmUuWsHn8eNK/+Zbg+vVpcM01xA4ZTJC6aCsdtdiJiIhUpOQkX6hzeb7n5KR9b1uWlr1ybBWMaNuWhOeeI3Hq24Qfcggbx45lxcBTSPvoI1xeXpmPLxVDwU5ERKS8JfbztdRZsO85sV/x2xW07H031vd8IAGtLPvuR2TnzjSf/DoJkyYRXKcO6+4YxcqzzmLnt9+iXr7Kz5NgZ2b1zOxrM1vqfy52xkQz+8LMtpvZJ3stTzKzBf7HOjP70L/8GDNLK7Lu3gr4OCIiIntK6OHrfj327v13wx5Iy1557lsCMyOmbx8Sp08j7pmncTm5pFx3PavOv4CMnyvfNYPyN69a7EYB3zrnWgPf+t8X53Hgkr0XOuf6Oec6O+c6A/8F3i+yOqlgnXNuTDnXLSIiUjoJPaDfrfu/tq60LXvlvW8pWVAQtU8+mVaffEyTB8eQs2EDq4cOZfWwq9j922/lfj4pO08GT5jZEuAY59x6M2sK/OCca7uPbY8BRjrnTitmXW1gFdDCObdjf9vujwZPiIiIZ9bM8bW2JfY78AEWZdn3IORnZpL61ttsfekl8tLSqHXyyTQcMYLwVi0Dfm75W6Wbx87MtjvnYv2vDUgteF/Mtsew72B3KXCGc25QkW3fA1KAdf79iv0vhZldDVwN0Lx5826rVq0qy0cSERGpMfJ27mTba6+x9fXJuKwsYs85mwbXXUdokyZel1YjeDIq1sy+MbPFxTzOLLqd8yXLg02XFwBvF3k/H1/rXSdgPPDhvnZ0zr3snOvunOvesGHDgzy9iIhIzRNcqxYNR4zg0K+/ou6FF7L9w49YfuJJbHzs3+SmpnpdXo0WsGDnnDveOde+mMdHwEZ/Fyz+500HenwzawD0AD4tcs4dzrl0/+vPgFD/diIiIlLOQurXp8ndd3HI559T+5RT2DZ5MstPOJHNL7ygSY494tXgiRnAUP/rocBHB3GMQcAnzrnMggVm1sTftYuZ9cD3+baWsVYRERHZj7D4OJo9+gitPvqQqF492TJuPMtOPIltb7xJfna21+XVKF4Fu0eBE8xsKXC8/z1m1t3MJhVsZGZJwDTgODNLMbOTihzjfPbshgVf2FtsZr8C44DznSbdERERqRDhrVv/PcnxoYey8eGHWXHyQN8kx/p1XCF0SzE0KlZERKq5Ch49C+CcI2PWLDY/9TSZv/1GrYEn0/TBhwiOia6Q81dn+xs8EVLRxYiIiEgF8ui+tWZGTJ8+RB91FFtfeYXNTz9D1pK/iB/3LOGHHhrw89dUuqWYiIhIdRbAO1SUhgUF0eCqq2j+6qvkpaWxcvAQ0j79tOQd5aAo2ImIiFRnFXCHitKI7tWTlu+/R0Tbtqy7dSQbxj6M08CKcqdgJyIiUp2V9r61FSC0cWNavDGZekMvJfXNN1l16VByNmzwrJ7qSIMn0OAJERGRgNnHwI0dn3/O+rvvwcLDiXvqSaKPOsrDIqsWT+48ISIiIjVcwcCN78b6ntfMKVxVe+BAEqdPI7h+PVZfOYwtE17C5ed7WGz1oGAnIiIivtCV9OQe4avMShi4Ed6qFS3feYfaAwey+ZlnSBl+HXlpaeV3/hpIwU5ERKSm20/LWpmUYuBGUHQ0zZ54nMb33EP6Tz+x8txB7P7tt/I5fw2kYCciIlLTBWpKlFIO3DAz6l18EYlvvoHLzWXVBReyffr08qmhhlGwExERqekCOSVKQg/od2upRuNGdu5My/ffI6p7N9bfM5p1d99NfmZmifvJ3zQqFo2KFRER8eK2Y/vi8vLY/NxzbH1xAuGHHUb8s88Q1ry5pzVVJhoVKyIiIvt3AC1rgWbBwTS68UbiJ7xIzrp1rDx3EDu/+97rsqoEBTsRERGplGodcwwt35tOWEICKcOHs+mpp3G5uV6XVakp2ImIiEilFRYfT4u33yL2vPPY+vLLrB52Fblbt3pdVqWlYCciIiKVWlB4OE0fHEPThx9m9y+/sPLsc9g1/xevy6qUFOxERESkSog952wSp76NRUSw6tJL2fbGG2gQ6J4U7ERERKTKiDjsMFpOn0ZM//5sfPgR1t5yC3npGV6XVWko2ImIiEiVEly7NvHPjafhrbew88uvSB48mKxly7wuq1JQsBMREZEqx4KCaHDVVTR/9VXy0tJYOXgIaZ9+6nVZnlOwExERkSoruldPWr7/HhHt2rHu1pFseGgsLjvb67I8o2AnIiIiVVpo48a0mPw69YYOJfU//2HVJZeSs2GD12V5QsFOREREqjwLDaXxnaOIe+ZpspYuZeXZ55Axa5bXZVU4BTsRERGpNmqffDKJ06cRXL8eq68cxpYJE3D5+V6XVWEU7ERERKRaCW/VipbvvEPtU05h8zPPknLtcPLS0rwuq0Io2ImIiEi1ExQdTbMnHqfx6HtInzWLlecOYvdvv3ldVsAp2ImIiEi1ZGbUu+giEt98A5eby6oLLmT79OlelxVQCnYiIiJSrUV27kzL998jqnt31t8zmnV33U1+ZqbXZQWEgp2IiIhUeyH16pEw8WUaDL+WtPffJ/mCC8levdrrssqdgp2IiIjUCBYcTMMRI4if8CI569ax8txB7PzuO6/LKlcKdiIiIlKj1DrmGFq+9x5hzZuTMvw6Nj31NC431+uyyoWCnYiIiNQ4YfFxtHhrCrGDB7P15ZdZPewqcrdu9bqsMlOwExERkRopKDycpmMeoOnDD7P7l19YefY57Jo/3+uyykTBTkRERGq02HPOJvGdqVhEBKsuHcq2N97AOed1WQfFs2BnZvXM7GszW+p/rlvMNp3N7L9m9puZLTSzIUXWtTSzn81smZm9Y2Zh/uXh/vfL/OsTK/BjiYiISBUU0a4dLadPI6Z/fzY+/Ahrb7mFvPQMr8s6YF622I0CvnXOtQa+9b/f2y7gUufcEcDJwDNmFutf9xjwtHPuUCAVuNK//Eog1b/8af92IiIiIvsVXLs28c+Np+Gtt7Dzy69IHjyYrGXLvC7rgHgZ7M4EJvtfTwbO2nsD59xfzrml/tfrgE1AQzMz4FhgejH7Fz3udOA4//YiIiIi+2VBQTS46iqav/YaeWlprBw8hLRPPoU1cyDpSd9zJRbi4bkbO+fW+19vABrvb2Mz6wGEAcuB+sB251zB2OQUIM7/Og5YA+CcyzWzNP/2W8q3fBEREamuonv2oOX777P25ptZN3Iku9tm0rhTGhYWBkNnQEIPr0ssVkBb7MzsGzNbXMzjzKLbOd8Vivu8StHMmgJvApc75/LLqbarzWyumc3dvHlzeRxSREREqpHQxo1oMfl16h3fntQlEaz+IZb8rGxITvK6tH0KaIudc+74fa0zs41m1tQ5t94f3DbtY7vawKfA3c652f7FW4FYMwvxt9rFA2v969YCCUCKmYUAdfzb713by8DLAN27d6+aQ19EREQkoCw0lMZ33Eb4rkGs/28Ua5LqkXBpj0o7rYiXdc0AhvpfDwU+2nsD/0jXD4A3nHMF19MVtPB9DwwqZv+ixx0EfOeq6phlERER8V5CD2IfmE6zK45h18Yw1ox9lfzdu72uqlheBrtHgRPMbClwvP89ZtbdzCb5txkM9AcuM7MF/kdn/7o7gFvMbBm+a+he8S9/BajvX34LxY+2FRERESm9hB7UuW0CzR57lF0//8yaa4f/He4q0cAKU2OWryt27ty5XpchIiIiVUDaRx+xbtSdRPXqScJdVxD0znmQlw1BwdDlYuh0QUAHV5jZPOdc9+LWeTkqVkRERKTKqXOmbwzoulF3sub21SS0zyYoOA/y8mDua7Dgbc9GzlbWa/9EREREKq06Z55Js0cfYdef61mTVI/83IIpc52v9c6jkbMKdiIiIiIHoc6ZZ9L0kYd9Ayp+PYJ8Fw4WDMFhkNjPk5rUFSsiIiJykGLPOguA9XfeRUrsABKu6IYdeoxnExgr2ImIiIiUQexZZ+Gystlw332sb9GaZgO8uyuFgp2IiIhIGdUdMhjy84js2tXTOhTsRERERMpB3Qsu8LoEDZ4QERERqS4U7ERERESqCQU7ERERkWpCwU5ERESkmlCwExEREakmFOxEREREqgkFOxEREZFqQsFOREREpJpQsBMRERGpJhTsRERERKoJBTsRERGRakLBTkRERKSaULATERERqSYU7ERERESqCQU7ERERkWrCnHNe1+A5M9sMrCqyqA6QVsrdS7NtWbdpAGwpZT2V1YH8TCvrOct6vIPZX9/F8qfvor6LlYW+i/ouHqwWzrmGxa5xzumx1wN4uTy3Les2wFyvfyYV+TOtrOcs6/EOZn99Fyv/98KLc+q7qO9iZTmnvouV77uortjifVzO25bXNlWZF5+vvM9Z1uMdzP76LpY/fRf1Xaws9F3Ud7HcqSu2CjCzuc657l7XIaLvolQW+i5KZVHZvotqsasaXva6ABE/fRelstB3USqLSvVdVIudiIiISDWhFjsRERGRakLBTkRERKSaULATERERqSYU7ERERESqCQW7Ks7MDjOzCWY23cyu9boeqbnM7Cwzm2hm75jZiV7XIzWXmbUys1fMbLrXtUjNY2bRZjbZ/+/hRRV9fgU7D5nZq2a2ycwW77X8ZDNbYmbLzGzU/o7hnPvDOXcNMBjoE8h6pfoqp+/ih865q4BrgCGBrFeqr3L6Lq5wzl0Z2EqlJjnA7+U5wHT/v4dnVHStCnbeeh04uegCMwsGngcGAocDF5jZ4WbWwcw+2evRyL/PGcCnwGcVW75UI69TDt9Fv3v8+4kcjNcpv++iSHl5nVJ+L4F4YI1/s7wKrBGAkIo+ofzNOfejmSXutbgHsMw5twLAzKYCZzrnHgFO28dxZgAzzOxT4K0AlizVVHl8F83MgEeBz51z8wNcslRT5fXvokh5OpDvJZCCL9wtwIMGNLXYVT5x/J30wfcFidvXxmZ2jJmNM7OXUIudlK8D+i4CNwDHA4PM7JpAFiY1zoH+u1jfzCYAXczszkAXJzXWvr6X7wPnmtmLeHCPWbXYVXHOuR+AHzwuQwTn3DhgnNd1iDjntuK71lOkwjnnMoDLvTq/Wuwqn7VAQpH38f5lIhVN30WpLPRdlMqoUn4vFewqn/8Brc2spZmFAecDMzyuSWomfRelstB3USqjSvm9VLDzkJm9DfwXaGtmKWZ2pXMuF7ge+BL4A3jXOfebl3VK9afvolQW+i5KZVSVvpfmnPO6BhEREREpB2qxExEREakmFOxEREREqgkFOxEREZFqQsFOREREpJpQsBMRERGpJhTsRERERKoJBTsRET8zizWz4UXeNzOz6QE4z/1mttbMxuxjfbKZNTCzSDNbYGbZZtagvOsQkepHwU5E5G+xQGGwc86tc84NCtC5nnbO3bu/DZxzu51znYF1AapBRKqZEK8LEBGpRB4FDjGzBcDXwPPAJ8659mZ2GXAWEA20Bp4AwoBLgCzgFOfcNjM7xL9fQ2AXcJVz7s/9ndTM6gNvA3H4Zre3cv9kIlIjqMVORORvo4DlzrnOzrnbilnfHjgHOBIYC+xyznXBF8Yu9W/zMnCDc64bMBJ4oRTnvQ+Y6Zw7AvgAaF62jyEiNZVa7ERESu9759xOYKeZpQEf+5cvAjqaWQzQG5hmVtjoFl6K4/bHFxhxzn1qZqnlW7aI1BQKdiIipZdV5HV+kff5+P49DQK2+6+LExGpcOqKFRH5206g1sHu7JzbAaw0s/MAzKdTKXb9EbjQv89AoO7B1iAiNZuCnYiIn3NuK/CTmS02s8cP8jAXAVea2a/Ab8CZpdjnAaC/mf2Gr0t29UGeW0RqOHPOeV2DiEiNYmb3A+nOuSdKuX0y0N05tyWQdYlI1acWOxGRipcOXL2vCYoLFExQDITiu45PRGS/1GInIiIiUk2oxU5ERESkmlCwExEREakmFOxEREREqgkFOxEREZFqQsFOREREpJr4f612pCUOmOvNAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hs_3 = ml_2.head(x=r, y=0, t=ts, layers=1)\n", + "hd_3 = ml_2.head(x=r, y=0, t=td, layers=15)\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(ts, hs, \".\", label=\"shallow obs\")\n", + "plt.semilogx(td, hd, \".\", label=\"deep obs\")\n", + "plt.semilogx(ts, hs_3[0], label=\"shallow ttim\")\n", + "plt.semilogx(td, hd_3[0], label=\"deep ttim\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.title(\"TTim Multi - Layer Unconfined Model Results\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We already see significant improvement from the previous single-layer model. The fit is better, and we have a much better confidence interval on the parameters. The AIC and BIC indicators have also significantly improved.\n", + "\n", + "What if we take into account the described stratification of the aquifer? In that case, we could try to stratify our model into two: The first 6 m and the deeper layers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Calibration of the Stratified Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this final example, we will assume the storage is distributed according to the sediment stratification in the aquifer. We will adjust two different ```Saq```values, one for the first 6 m of the aquifer and another for the deeper layers. We assume the hydraulic conductivity and the anisotropy is constant." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Model Figure - One - layer model\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "\n", + "\n", + "# Aquifer 1:\n", + "for i in range(21, 6, -1):\n", + " ground = plt.Rectangle(\n", + " (-20, -i),\n", + " width=150,\n", + " height=1,\n", + " fc=\"w\",\n", + " zorder=0,\n", + " alpha=0.9,\n", + " hatch=\"oo\",\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + " )\n", + " ax.add_patch(ground)\n", + "# Aquifer 2:\n", + "for i in range(6, -2, -1):\n", + " ground = plt.Rectangle(\n", + " (-20, -i),\n", + " width=150,\n", + " height=1,\n", + " fc=\"w\",\n", + " zorder=0,\n", + " alpha=0.9,\n", + " hatch=\"..\",\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + " )\n", + " ax.add_patch(ground)\n", + "\n", + "\n", + "well = plt.Rectangle((-1.5, -21), width=3, height=23, fc=\"w\", zorder=1, ec=\"k\")\n", + "ax.add_patch(well)\n", + "\n", + "# Wellhead\n", + "wellhead = plt.Rectangle((-2, 2), width=4, height=2.5, fc=\"w\", zorder=2, ec=\"k\")\n", + "ax.add_patch(wellhead)\n", + "\n", + "# Screen for the well:\n", + "screen = plt.Rectangle(\n", + " (-1.5, -21),\n", + " width=3,\n", + " height=11,\n", + " fc=\"w\",\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + " hatch=\"-\",\n", + ")\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "\n", + "# Piezometers\n", + "piez1 = plt.Rectangle((89, -21), width=2, height=23, fc=\"w\", zorder=1, ec=\"k\")\n", + "screen_piez_1 = plt.Rectangle(\n", + " (89, -19), width=2, height=7, fc=\"w\", alpha=1, zorder=2, ec=\"k\", ls=\"--\", hatch=\"-\"\n", + ")\n", + "screen_piez_1.set_linewidth(2)\n", + "screen_piez_2 = plt.Rectangle(\n", + " (89, -3), width=2, height=0.5, fc=\"w\", alpha=1, zorder=2, ec=\"k\", ls=\"--\", hatch=\"-\"\n", + ")\n", + "screen_piez_2.set_linewidth(2)\n", + "\n", + "ax.add_patch(piez1)\n", + "ax.add_patch(screen_piez_1)\n", + "ax.add_patch(screen_piez_2)\n", + "\n", + "\n", + "# last line\n", + "line = plt.Line2D(xdata=[-200, 1200], ydata=[2, 2], color=\"k\")\n", + "ax.add_line(line)\n", + "\n", + "# Water table\n", + "line2 = plt.Line2D(xdata=[-200, 1200], ydata=[0, 0], color=\"b\")\n", + "ax.add_line(line2)\n", + "\n", + "ax.text(-18, 0.5, s=\"Water Table\", fontsize=\"large\", color=\"b\", bbox={\"fc\": \"w\"})\n", + "ax.text(93, -3, s=\"Shallow piezometer\", bbox={\"fc\": \"w\"})\n", + "ax.text(93, -16, s=\"Deeper piezometer\", bbox={\"fc\": \"w\"})\n", + "\n", + "ax.set_xlim([-20, 130])\n", + "ax.set_ylim([-21, 7])\n", + "ax.set_xlabel(\"Distance [m]\")\n", + "ax.set_ylabel(\"Relative height [m]\")\n", + "ax.set_title(\"Conceptual Model 3 - Multi-layer Model 3D - Vennebulten Example\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 21\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_3 = Model3D(\n", + " kaq=10, z=zlayers, Saq=Saq, kzoverkh=0.1, phreatictop=True, tmin=1e-4, tmax=1.1\n", + ")\n", + "w_3 = Well(ml_3, xw=0, yw=0, rw=0.1, tsandQ=[(0, Q)], layers=range(nlay))\n", + "ml_3.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 119\n", + " # data points = 48\n", + " # variables = 5\n", + " chi-square = 5.6235e-04\n", + " reduced chi-square = 1.3078e-05\n", + " Akaike info crit = -535.020588\n", + " Bayesian info crit = -525.664583\n", + "[[Variables]]\n", + " kaq0_20: 74.7835895 +/- 2.28391453 (3.05%) (init = 50)\n", + " Saq0: 0.02068784 +/- 0.00156494 (7.56%) (init = 0.1)\n", + " Saq1_7: 4.4944e-04 +/- 5.8482e-05 (13.01%) (init = 0.0001)\n", + " Saq7_20: 2.3179e-05 +/- 1.1194e-06 (4.83%) (init = 0.0001)\n", + " kzoverkh: 3.7920e-04 +/- 7.9863e-05 (21.06%) (init = 0.1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_20, kzoverkh) = -0.966\n", + " C(kaq0_20, Saq7_20) = -0.755\n", + " C(Saq1_7, kzoverkh) = -0.738\n", + " C(Saq7_20, kzoverkh) = 0.710\n", + " C(kaq0_20, Saq1_7) = 0.632\n", + " C(kaq0_20, Saq0) = -0.614\n", + " C(Saq0, kzoverkh) = 0.602\n", + " C(Saq0, Saq1_7) = -0.598\n", + " C(Saq1_7, Saq7_20) = -0.559\n", + " C(Saq0, Saq7_20) = 0.489\n" + ] + } + ], + "source": [ + "ca_4 = Calibrate(ml_3)\n", + "ca_4.set_parameter(name=\"kaq0_20\", initial=50)\n", + "ca_4.set_parameter(name=\"Saq0\", initial=0.1)\n", + "ca_4.set_parameter(name=\"Saq1_7\", initial=1e-4, pmin=0)\n", + "ca_4.set_parameter(name=\"Saq7_20\", initial=1e-4, pmin=0)\n", + "ca_4.set_parameter_by_reference(\n", + " name=\"kzoverkh\", parameter=ml_3.aq.kzoverkh[:], initial=0.1, pmin=0\n", + ")\n", + "ca_4.series(name=\"obs1\", x=r, y=0, layer=1, t=ts, h=hs)\n", + "ca_4.series(name=\"obs2\", x=r, y=0, layer=15, t=td, h=hd)\n", + "ca_4.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_2074.7835892.2839153.054032-infinf50[74.7835894785934, 74.7835894785934, 74.783589...
Saq00.0206880.0015657.564524-infinf0.1[0.02068783521410092]
Saq1_70.0004490.00005813.0121630.0inf0.0001[0.00044944008673608593, 0.0004494400867360859...
Saq7_200.0000230.0000014.8290720.0inf0.0001[2.3179487157021228e-05, 2.3179487157021228e-0...
kzoverkh0.0003790.00008021.0612110.0inf0.1[0.0003791957224930087, 0.0003791957224930087,...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_20 74.783589 2.283915 3.054032 -inf inf 50 \n", + "Saq0 0.020688 0.001565 7.564524 -inf inf 0.1 \n", + "Saq1_7 0.000449 0.000058 13.012163 0.0 inf 0.0001 \n", + "Saq7_20 0.000023 0.000001 4.829072 0.0 inf 0.0001 \n", + "kzoverkh 0.000379 0.000080 21.061211 0.0 inf 0.1 \n", + "\n", + " parray \n", + "kaq0_20 [74.7835894785934, 74.7835894785934, 74.783589... \n", + "Saq0 [0.02068783521410092] \n", + "Saq1_7 [0.00044944008673608593, 0.0004494400867360859... \n", + "Saq7_20 [2.3179487157021228e-05, 2.3179487157021228e-0... \n", + "kzoverkh [0.0003791957224930087, 0.0003791957224930087,... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.003422795065626066\n" + ] + } + ], + "source": [ + "display(ca_4.parameters)\n", + "print(\"RMSE:\", ca_4.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hs_4 = ml_3.head(x=r, y=0, t=ts, layers=1)\n", + "hd_4 = ml_3.head(x=r, y=0, t=td, layers=15)\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(ts, hs, \".\", label=\"shallow obs\")\n", + "plt.semilogx(td, hd, \".\", label=\"deep obs\")\n", + "plt.semilogx(ts, hs_4[0], label=\"shallow ttim\")\n", + "plt.semilogx(td, hd_4[0], label=\"deep ttim\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.title(\"TTim Stratified Unconfined Model Results\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we see that the model fit has significantly improved. AIC and BIC indicators are lower than the previous multi-layer model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 9. Analysis and comparison of model results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9.1. Analysis of models with single layer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Kruseman and de Ridder (K&dR) applied graphical analysis using the Neuman method considering a single layer unconfined aquifer and the drawdown of the deeper well. The same conceptualization and data have been used by Xinzhu (2020) to model the aquifer parameters in AQTESOLV (Duffield, 2007) and MLU (Carlson and Randall, 2012). However, for the latter, since MLU is a similar model method as in TTim, Xinzhu used the same approach in this notebook by representing the one-layer unconfined aquifer in a two-layer configuration: a shallow 0.1 m thick with phreatic storage and the aquifer thickness with elastic storage.\n", + "\n", + "The table below summarises the results obtained by each approach." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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k [m/d]Ss [1/m]Sy [-]kz/khRMSE
K&dR730.0000250.0050.000548-
AQTESOLV63.8050.0000270.0110.000690.003041
MLU74.6570.0000280.0050.0007370.003216
ttim116.8056990.000010.0001329.8795120.010072
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" + ], + "text/plain": [ + " k [m/d] Ss [1/m] Sy [-] kz/kh RMSE\n", + "K&dR 73 0.000025 0.005 0.000548 -\n", + "AQTESOLV 63.805 0.000027 0.011 0.00069 0.003041\n", + "MLU 74.657 0.000028 0.005 0.000737 0.003216\n", + "ttim 116.805699 0.00001 0.000132 9.879512 0.010072" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t1 = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"Sy [-]\", \"kz/kh\"],\n", + " index=[\"K&dR\", \"AQTESOLV\", \"MLU\", \"ttim\"],\n", + ")\n", + "t1.loc[\"K&dR\"] = [73, 2.476e-05, 0.005, 0.000548]\n", + "t1.loc[\"AQTESOLV\"] = [63.805, 2.663e-05, 0.011, 0.000690]\n", + "t1.loc[\"MLU\"] = [74.657, 2.767e-05, 0.005, 0.000737]\n", + "t1.loc[\"ttim\"] = ca_2.parameters[\"optimal\"].values\n", + "t1[\"RMSE\"] = [\"-\", 0.003041, 0.003216, ca_2.rmse()]\n", + "t1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TTim has overall found a different solution than the other presented results. It also could not reach the RMSE of the other solvers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9.2. Analysis of multi-layer models\n", + "\n", + "Xinzhu (2020) also applied the multi-layer approach to MLU model and the different results are presented in the table below:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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k [m/d]Sy [-]Ss [1/m]kzoverkhRMSE
MLU62.6570.00120.0000280.0025950.013540
ttim-multilayer31.634660.055280.0000350.0100030.009938
ttim-stratified Ss74.7835890.0206880.0000230.0003790.003423
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" + ], + "text/plain": [ + " k [m/d] Sy [-] Ss [1/m] kzoverkh RMSE\n", + "MLU 62.657 0.0012 0.000028 0.002595 0.013540\n", + "ttim-multilayer 31.63466 0.05528 0.000035 0.010003 0.009938\n", + "ttim-stratified Ss 74.783589 0.020688 0.000023 0.000379 0.003423" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t2 = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Sy [-]\", \"Ss [1/m]\", \"kzoverkh\"],\n", + " index=[\"MLU\", \"ttim-multilayer\", \"ttim-stratified Ss\"],\n", + ")\n", + "t2.loc[\"MLU\"] = [62.657, 0.0012, 2.790e-05, 0.002595]\n", + "t2.loc[\"ttim-multilayer\"] = ca_3.parameters[\"optimal\"].values\n", + "t2.iloc[2, 0:2] = ca_4.parameters[\"optimal\"].values[0:2]\n", + "t2.iloc[2, 2:4] = ca_4.parameters[\"optimal\"].values[3:5]\n", + "t2.loc[:, \"RMSE\"] = pd.Series([0.013540, ca_3.rmse(), ca_4.rmse()], index=t2.index)\n", + "t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The multi-layer approach allowed us to fit both piezometers and better represent the vertical component of flow. However, the parameters were sensitive to the conceptualization applied. The stratified model had much larger hydraulic conductivity in comparison to the multi-layer model. In the stratified approach, the fit has significantly improved." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Kruseman, G.P., De Ridder, N.A., Verweij, J.M., 1970. Analysis and evaluationof pumping test data. volume 11. International institute for land reclamation and improvement The Netherlands.\n", + "* Neuman, S.P., Witherspoon, P.A., 1969. Applicability of current theories of flow in leaky aquifers. Water Resources Research 5, 817–829.\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Next Notebook: [Unconfined 2 - Moench](unconfined2_moench.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/04pumpingtests/unconfined2_moench.ipynb b/docs/04pumpingtests/unconfined2_moench.ipynb new file mode 100644 index 0000000..cf32ce6 --- /dev/null +++ b/docs/04pumpingtests/unconfined2_moench.ipynb @@ -0,0 +1,1186 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2.Test for an anisotropic water-table aquifer - Moench Example\n", + "**This test is taken from examples presented in MLU tutorial.**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Introduction and Conceptual Model\n", + "\n", + "This test is based on a synthetic example reported by Barlow and Moench (1999), utilizing an analytical solution developed by Moench and Allen (1997) for the transient flow of partially-penetrating wells in unconfined aquifers. The data reported has been used in MLU (Carlson and Randall, 2012) to check the model performance.\n", + "\n", + "We will reproduce the work of Yang (2020) to explore the performance of anisotropic water table aquifer modelling with TTim and compare the results with the published values and the MLU solution.\n", + "\n", + "The conceptual model of the test is of an aquifer, partially saturated with water (10 m water table). A pumping well is screened from 5 to 10 m depth. The well and the well-casing radius is 0.1 m. Drawdown is recorded at the pumping well and four piezometers located at two different distances and two different depths. Two piezometers, PS1 and PS2, are located at one-meter depth below the water table and 3.16 and 31.6 m distance, respectively. Another two (PD1 and PD2) piezometers are at 7.5 m depth below the water table and the same distances, directly below the previous piezometers. The figure below shows the location of the well and the piezometers\n", + "\n", + "Pumping starts at time t = 0 at a constant rate of 172.8 m3/d. Drawdown is recorded until t = 3 days." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1, 1, 1)\n", + "# sky\n", + "sky = plt.Rectangle((-20, 2), width=70, height=5, fc=\"b\", zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "# Aquifer:\n", + "ground = plt.Rectangle(\n", + " (-20, -10),\n", + " width=70,\n", + " height=12,\n", + " fc=np.array([209, 179, 127]) / 255,\n", + " zorder=0,\n", + " alpha=0.9,\n", + ")\n", + "ax.add_patch(ground)\n", + "\n", + "\n", + "well = plt.Rectangle(\n", + " (-0.5, -10), width=1, height=12, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "ax.add_patch(well)\n", + "\n", + "# Wellhead\n", + "wellhead = plt.Rectangle(\n", + " (-0.75, 2),\n", + " width=1.5,\n", + " height=1.5,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " zorder=2,\n", + " ec=\"k\",\n", + ")\n", + "ax.add_patch(wellhead)\n", + "\n", + "# Screen for the well:\n", + "screen = plt.Rectangle(\n", + " (-0.5, -10),\n", + " width=1,\n", + " height=5,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x=1.1, y=3, dx=3, dy=0, color=\"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x=4.2, y=3, s=r\"$ Q = 172.8 \\frac{m^3}{d}$\", fontsize=\"large\")\n", + "\n", + "# Piezometers\n", + "piez1 = plt.Rectangle(\n", + " (31.4, -10), width=0.5, height=12, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "screen_piez_1 = plt.Rectangle(\n", + " (31.4, -8),\n", + " width=0.5,\n", + " height=1,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_1.set_linewidth(2)\n", + "screen_piez_2 = plt.Rectangle(\n", + " (31.4, -1.5),\n", + " width=0.5,\n", + " height=1,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_2.set_linewidth(2)\n", + "\n", + "ax.add_patch(piez1)\n", + "ax.add_patch(screen_piez_1)\n", + "ax.add_patch(screen_piez_2)\n", + "\n", + "piez2 = plt.Rectangle(\n", + " (2.75, -10), width=0.5, height=12, fc=np.array([200, 200, 200]) / 255, zorder=1\n", + ")\n", + "screen_piez_3 = plt.Rectangle(\n", + " (2.75, -8),\n", + " width=0.5,\n", + " height=1,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_3.set_linewidth(2)\n", + "screen_piez_4 = plt.Rectangle(\n", + " (2.75, -1.5),\n", + " width=0.5,\n", + " height=1,\n", + " fc=np.array([200, 200, 200]) / 255,\n", + " alpha=1,\n", + " zorder=2,\n", + " ec=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "screen_piez_4.set_linewidth(2)\n", + "\n", + "ax.add_patch(piez2)\n", + "ax.add_patch(screen_piez_3)\n", + "ax.add_patch(screen_piez_4)\n", + "\n", + "# last line\n", + "line = plt.Line2D(xdata=[-200, 1200], ydata=[2, 2], color=\"k\")\n", + "ax.add_line(line)\n", + "\n", + "# Water table\n", + "line2 = plt.Line2D(xdata=[-200, 1200], ydata=[0, 0], color=\"b\")\n", + "ax.add_line(line2)\n", + "\n", + "ax.text(-9, 0.5, s=\"Water Table\", fontsize=\"large\", color=\"b\", bbox={\"fc\": \"w\"})\n", + "ax.text(4.5, -1, s=\"PS1\", bbox={\"fc\": \"w\"})\n", + "ax.text(34, -1, s=\"PS2\", bbox={\"fc\": \"w\"})\n", + "ax.text(4.5, -7.5, s=\"PD1\", bbox={\"fc\": \"w\"})\n", + "ax.text(34, -7.5, s=\"PD2\", bbox={\"fc\": \"w\"})\n", + "\n", + "ax.set_xlim([-10, 50])\n", + "ax.set_ylim([-10, 4])\n", + "ax.set_xlabel(\"Distance [m]\")\n", + "ax.set_ylabel(\"Relative height [m]\")\n", + "ax.set_title(\"Conceptual Model - Vennebulten Example\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Import required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from ttim import *\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "b = 10 # aquifer thickness in m\n", + "Q = 172.8 # constant discharge rate in m^3/d\n", + "rw = 0.1 # well radius in m\n", + "rc = 0.1 # casing radius in m\n", + "r1 = 3.16 # distance of closer wells in m\n", + "r2 = 31.6 # distance of wells more far away in m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Load datasets of observation wells\n", + "\n", + "The dataset for each well consists of a column with the time data in seconds and drawdown in meters. We are loading it and converting it to days and meters." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data0 = np.loadtxt(\"data/moench_pumped.txt\", skiprows=1)\n", + "t0 = data0[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h0 = -data0[:, 1] # converting drawdown to heads\n", + "data1 = np.loadtxt(\"data/moench_ps1.txt\", skiprows=1)\n", + "t1 = data1[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h1 = -data1[:, 1]\n", + "data2 = np.loadtxt(\"data/moench_pd1.txt\", skiprows=1)\n", + "t2 = data2[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h2 = -data2[:, 1]\n", + "data3 = np.loadtxt(\"data/moench_ps2.txt\", skiprows=1)\n", + "t3 = data3[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h3 = -data3[:, 1]\n", + "data4 = np.loadtxt(\"data/moench_pd2.txt\", skiprows=1)\n", + "t4 = data4[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h4 = -data4[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Creating a TTim model\n", + "\n", + "We will create an initial model divided in the same way as in the MLU documentation. A first layer with 0.1 m thick and phreatic storage, followed by a 2 m thick layer where the shallow piezometers are located, a 3 m layer and finally, a 5 m layer where the pump is placed and the last piezometers are screened. Additionally, we will set the model parameters with the given ones in Barlow and Moench (1999) and compare the results with the heads given in the paper." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Set kaq, Saq, Sy and kzoverkh as given in Moench (1997)\n", + "kaq = 1e-4 * 60 * 60 * 24 # convert from m/s to m/d\n", + "Sy = 0.2\n", + "Saq = 2e-5\n", + "zh = 0.5 # kzoverkh" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml1 = Model3D(\n", + " kaq=kaq,\n", + " z=[0, -0.1, -2.1, -5.1, -10.1],\n", + " Saq=[Sy, Saq, Saq, Saq],\n", + " kzoverkh=zh,\n", + " tmin=1e-5,\n", + " tmax=3,\n", + ")\n", + "w1 = Well(ml1, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=3)\n", + "ml1.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Check the TTim model with values obtained by Moench" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm1 = ml1.head(r1, 0, t1, layers=1)[0]\n", + "hm2 = ml1.head(r1, 0, t2, layers=3)[0]\n", + "hm3 = ml1.head(r2, 0, t3, layers=1)[0]\n", + "hm4 = ml1.head(r2, 0, t4, layers=3)[0]\n", + "hm0 = ml1.head(0, 0, t0, layers=3)[0]\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t0, h0, \".\", label=\"pumped\")\n", + "plt.semilogx(t0, hm0, label=\"ttim pumped\")\n", + "plt.semilogx(t1, h1, \".\", label=\"PS1\")\n", + "plt.semilogx(t1, hm1, label=\"ttim PS1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"PD1\")\n", + "plt.semilogx(t2, hm2, label=\"ttim PD1\")\n", + "plt.semilogx(t3, h3, \".\", label=\"PS2\")\n", + "plt.semilogx(t3, hm3, label=\"ttim PS2\")\n", + "plt.semilogx(t4, h4, \".\", label=\"PD2\")\n", + "plt.semilogx(t4, hm4, label=\"ttim PD2\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"head [m]\")\n", + "plt.title(\"Model Results - Simulation 1\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visually, TTim's solution is in good agreement with the model, but we can see that the simulated values in the pumping well and PD1 are a little below the values obtained in the analytical solution." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.1. Calculate RMSE of TTim model" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.06131792435676353\n" + ] + } + ], + "source": [ + "rmse = np.sqrt(\n", + " np.sum(\n", + " np.sum((h1 - hm1) ** 2)\n", + " + np.sum((h2 - hm2) ** 2)\n", + " + np.sum((h3 - hm3) ** 2)\n", + " + np.sum((h4 - hm4) ** 2)\n", + " + np.sum((h0 - hm0) ** 2)\n", + " )\n", + " / (len(h1) + len(h2) + len(h3) + len(h4) + len(h0))\n", + ")\n", + "\n", + "print(\"RMSE:\", rmse)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model has obtained a very close result with a good approximation to the analytical solution." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Model Calibration\n", + "\n", + "Now, instead of using the given values, we will try to find them through the TTim optimization framework. One can learn more about the calibration procedure and how to set the parameters in the following notebook: [Unconfined Test - Vennebulten](unconfined1_vennebulten.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml2 = Model3D(\n", + " kaq=1,\n", + " z=[0, -0.1, -2.1, -5.1, -10.1],\n", + " Saq=[0.1, 1e-4, 1e-4, 1e-4],\n", + " kzoverkh=1,\n", + " tmin=1e-5,\n", + " tmax=3,\n", + ")\n", + "w2 = Well(ml2, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=3)\n", + "ml2.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "............................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 57\n", + " # data points = 70\n", + " # variables = 4\n", + " chi-square = 0.00754821\n", + " reduced chi-square = 1.1437e-04\n", + " Akaike info crit = -631.445846\n", + " Bayesian info crit = -622.451865\n", + "[[Variables]]\n", + " kaq0_3: 9.06766484 +/- 0.02235216 (0.25%) (init = 1)\n", + " Saq0: 0.17287565 +/- 0.00437375 (2.53%) (init = 0.2)\n", + " Saq1_3: 3.8699e-05 +/- 3.5515e-06 (9.18%) (init = 0.0001)\n", + " kzoverkh: 0.53504852 +/- 0.01014208 (1.90%) (init = 0.1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_3, kzoverkh) = -0.832\n", + " C(kaq0_3, Saq0) = -0.533\n", + " C(Saq0, kzoverkh) = 0.339\n", + " C(Saq1_3, kzoverkh) = -0.127\n" + ] + } + ], + "source": [ + "ca2 = Calibrate(ml2)\n", + "ca2.set_parameter(name=\"kaq0_3\", initial=1)\n", + "ca2.set_parameter(name=\"Saq0\", initial=0.2)\n", + "ca2.set_parameter(name=\"Saq1_3\", initial=1e-4, pmin=0)\n", + "ca2.set_parameter_by_reference(\n", + " name=\"kzoverkh\", parameter=ml2.aq.kzoverkh, initial=0.1, pmin=0\n", + ")\n", + "ca2.series(name=\"pumped\", x=0, y=0, t=t0, h=h0, layer=3)\n", + "ca2.series(name=\"PS1\", x=r1, y=0, t=t1, h=h1, layer=1)\n", + "ca2.series(name=\"PD1\", x=r1, y=0, t=t2, h=h2, layer=3)\n", + "ca2.series(name=\"PS2\", x=r2, y=0, t=t3, h=h3, layer=1)\n", + "ca2.series(name=\"PD2\", x=r2, y=0, t=t4, h=h4, layer=3)\n", + "ca2.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_39.0676650.0223520.246504-infinf1[9.067664842599923, 9.067664842599923, 9.06766...
Saq00.1728760.0043742.529999-infinf0.2[0.17287564535992025]
Saq1_30.0000390.0000049.1772340.0inf0.0001[3.869900362540868e-05, 3.869900362540868e-05,...
kzoverkh0.5350490.0101421.8955440.0inf0.1[0.5350485185874931, 0.5350485185874931, 0.535...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_3 9.067665 0.022352 0.246504 -inf inf 1 \n", + "Saq0 0.172876 0.004374 2.529999 -inf inf 0.2 \n", + "Saq1_3 0.000039 0.000004 9.177234 0.0 inf 0.0001 \n", + "kzoverkh 0.535049 0.010142 1.895544 0.0 inf 0.1 \n", + "\n", + " parray \n", + "kaq0_3 [9.067664842599923, 9.067664842599923, 9.06766... \n", + "Saq0 [0.17287564535992025] \n", + "Saq1_3 [3.869900362540868e-05, 3.869900362540868e-05,... \n", + "kzoverkh [0.5350485185874931, 0.5350485185874931, 0.535... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.010384195093738518\n" + ] + } + ], + "source": [ + "display(ca2.parameters)\n", + "print(\"RMSE:\", ca2.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The values are close to the values in Moench." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm0_2 = ml2.head(0, 0, t0, layers=3)[0]\n", + "hm1_2 = ml2.head(r1, 0, t1, layers=1)[0]\n", + "hm2_2 = ml2.head(r1, 0, t2, layers=3)[0]\n", + "hm3_2 = ml2.head(r2, 0, t3, layers=1)[0]\n", + "hm4_2 = ml2.head(r2, 0, t4, layers=3)[0]\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t0, h0, \".\", label=\"pumped\")\n", + "plt.semilogx(t0, hm0_2, label=\"ttim pumped\")\n", + "plt.semilogx(t1, h1, \".\", label=\"PS1\")\n", + "plt.semilogx(t1, hm1_2, label=\"ttim PS1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"PD1\")\n", + "plt.semilogx(t2, hm2_2, label=\"ttim PD1\")\n", + "plt.semilogx(t3, h3, \",\", label=\"PS2\")\n", + "plt.semilogx(t3, hm3_2, label=\"ttim PS2\")\n", + "plt.semilogx(t4, h4, \".\", label=\"PD2\")\n", + "plt.semilogx(t4, hm4_2, label=\"ttim PD2\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"head [m]\")\n", + "plt.title(\"Model Results - Calibration 1\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Creating and calibrating a model with more layers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The calibration resulted in a lower error than the simulated model, which could mean that we could improve the conceptualization of the model in TTim. In this next step, we will increase the number of layers to better account for the vertical flow component. We will create an 18-layers model with 0.5 m thick layers, except at the layers in the piezometers where a 1 m thickness has been established to make sure the piezometers lie at the centre of the layer:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. -0.5 -1.5 -2. -2.5 -3. -3.5 -4. -4.5 -5. -5.5 -6.\n", + " -6.5 -7. -8. -8.5 -9. -9.5 -10. ]\n", + "2e-05\n" + ] + } + ], + "source": [ + "## Model Configuration:\n", + "\n", + "nlay = 18\n", + "zlayers = np.concatenate(\n", + " (\n", + " np.array([0, -0.5, -1.5]),\n", + " np.linspace(-2, -7, 11),\n", + " np.array([-8, -8.5, -9, -9.5, -10]),\n", + " )\n", + ") # elevation of each layer\n", + "Saq_n = 1e-4 * np.ones(nlay)\n", + "Saq_n[0] = 0.1 # Setting the first storage as specific yield\n", + "print(zlayers)\n", + "print(Saq)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 9\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml3 = Model3D(kaq=1, z=zlayers, Saq=Saq_n, kzoverkh=1, tmin=1e-5, tmax=3)\n", + "w3 = Well(ml3, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=range(9, 18))\n", + "ml3.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..............................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 59\n", + " # data points = 70\n", + " # variables = 4\n", + " chi-square = 0.00667651\n", + " reduced chi-square = 1.0116e-04\n", + " Akaike info crit = -640.035843\n", + " Bayesian info crit = -631.041862\n", + "[[Variables]]\n", + " kaq0_19: 8.69563509 +/- 0.02074132 (0.24%) (init = 1)\n", + " Saq0: 0.19607178 +/- 0.00461632 (2.35%) (init = 0.2)\n", + " Saq1_19: 3.8968e-05 +/- 3.3211e-06 (8.52%) (init = 0.0001)\n", + " kzoverkh: 0.48796230 +/- 0.00856631 (1.76%) (init = 0.1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_19, kzoverkh) = -0.842\n", + " C(kaq0_19, Saq0) = -0.549\n", + " C(Saq0, kzoverkh) = 0.364\n", + " C(Saq1_19, kzoverkh) = -0.135\n" + ] + } + ], + "source": [ + "ca3 = Calibrate(ml3)\n", + "ca3.set_parameter(name=\"kaq0_19\", initial=1, pmin=0)\n", + "ca3.set_parameter(name=\"Saq0\", initial=0.2, pmin=0)\n", + "ca3.set_parameter(name=\"Saq1_19\", initial=1e-4, pmin=0)\n", + "ca3.set_parameter_by_reference(\n", + " name=\"kzoverkh\", parameter=ml3.aq.kzoverkh, initial=0.1, pmin=0\n", + ")\n", + "ca3.series(name=\"pumped\", x=0, y=0, t=t0, h=h0, layer=15)\n", + "ca3.series(name=\"PS1\", x=r1, y=0, t=t1, h=h1, layer=1)\n", + "ca3.series(name=\"PD1\", x=r1, y=0, t=t2, h=h2, layer=13)\n", + "ca3.series(name=\"PS2\", x=r2, y=0, t=t3, h=h3, layer=1)\n", + "ca3.series(name=\"PD2\", x=r2, y=0, t=t4, h=h4, layer=13)\n", + "ca3.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_198.6956350.0207410.2385260inf1[8.695635092973196, 8.695635092973196, 8.69563...
Saq00.1960720.0046162.3544020inf0.2[0.19607177947520582]
Saq1_190.0000390.0000038.5224390inf0.0001[3.896832991934218e-05, 3.896832991934218e-05,...
kzoverkh0.4879620.0085661.7555260inf0.1[0.4879623030841507, 0.4879623030841507, 0.487...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_19 8.695635 0.020741 0.238526 0 inf 1 \n", + "Saq0 0.196072 0.004616 2.354402 0 inf 0.2 \n", + "Saq1_19 0.000039 0.000003 8.522439 0 inf 0.0001 \n", + "kzoverkh 0.487962 0.008566 1.755526 0 inf 0.1 \n", + "\n", + " parray \n", + "kaq0_19 [8.695635092973196, 8.695635092973196, 8.69563... \n", + "Saq0 [0.19607177947520582] \n", + "Saq1_19 [3.896832991934218e-05, 3.896832991934218e-05,... \n", + "kzoverkh [0.4879623030841507, 0.4879623030841507, 0.487... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.009766203739979508\n" + ] + } + ], + "source": [ + "display(ca3.parameters)\n", + "print(\"RMSE:\", ca3.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm0_3 = ml3.head(0, 0, t0, layers=15)[0]\n", + "hm1_3 = ml3.head(r1, 0, t1, layers=1)[0]\n", + "hm2_3 = ml3.head(r1, 0, t2, layers=13)[0]\n", + "hm3_3 = ml3.head(r2, 0, t3, layers=1)[0]\n", + "hm4_3 = ml3.head(r2, 0, t4, layers=13)[0]\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t0, h0, \".\", label=\"pumped\")\n", + "plt.semilogx(t0, hm0_3, label=\"ttim pumped\")\n", + "plt.semilogx(t1, h1, \".\", label=\"PS1\")\n", + "plt.semilogx(t1, hm1_3, label=\"ttim PS1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"PD1\")\n", + "plt.semilogx(t2, hm2_3, label=\"ttim PD1\")\n", + "plt.semilogx(t3, h3, \",\", label=\"PS2\")\n", + "plt.semilogx(t3, hm3_3, label=\"ttim PS2\")\n", + "plt.semilogx(t4, h4, \".\", label=\"PD2\")\n", + "plt.semilogx(t4, hm4_3, label=\"ttim PD2\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Increasing the number of layers has not improved the model calibration performance. Let's see how the model behaves with the same parameters from the analytical solution:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7.1. Simulating multi-layered model with Moench's parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 9\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml4 = Model3D(\n", + " kaq=kaq,\n", + " z=zlayers,\n", + " Saq=[Sy] + list(np.repeat(Saq, nlay - 1)),\n", + " kzoverkh=zh,\n", + " tmin=1e-5,\n", + " tmax=3,\n", + ")\n", + "w4 = Well(ml4, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=range(9, 18))\n", + "ml4.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm0_4 = ml4.head(0, 0, t0, layers=16)[0]\n", + "hm1_4 = ml4.head(r1, 0, t1, layers=1)[0]\n", + "hm2_4 = ml4.head(r1, 0, t2, layers=13)[0]\n", + "hm3_4 = ml4.head(r2, 0, t3, layers=1)[0]\n", + "hm4_4 = ml4.head(r2, 0, t4, layers=13)[0]\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(t0, h0, \".\", label=\"pumped\")\n", + "plt.semilogx(t0, hm0_4, label=\"ttim pumped\")\n", + "plt.semilogx(t1, h1, \".\", label=\"PS1\")\n", + "plt.semilogx(t1, hm1_4, label=\"ttim PS1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"PD1\")\n", + "plt.semilogx(t2, hm2_4, label=\"ttim PD1\")\n", + "plt.semilogx(t3, h3, \",\", label=\"PS2\")\n", + "plt.semilogx(t3, hm3_4, label=\"ttim PS2\")\n", + "plt.semilogx(t4, h4, \".\", label=\"PD2\")\n", + "plt.semilogx(t4, hm4_4, label=\"ttim PD2\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 7.1.1. Checking RMSE performance improvement" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.013118477167630852\n" + ] + } + ], + "source": [ + "rmse_n = np.sqrt(\n", + " np.sum(\n", + " np.sum((h1 - hm1_4) ** 2)\n", + " + np.sum((h2 - hm2_4) ** 2)\n", + " + np.sum((h3 - hm3_4) ** 2)\n", + " + np.sum((h4 - hm4_4) ** 2)\n", + " + np.sum((h0 - hm0_4) ** 2)\n", + " )\n", + " / (len(h1) + len(h2) + len(h3) + len(h4) + len(h0))\n", + ")\n", + "\n", + "print(\"RMSE:\", rmse_n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Summary of and analysis of calibrated values and model errors" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MoenchMoench - more layersTTimTTim - more layers
k[m/d]8.6400008.6400009.0676658.695635
Sy[-]0.2000000.2000000.1728760.196072
Ss[1/m]0.0000200.0000200.0000390.000039
kz/kh0.5000000.5000000.5350490.487962
RMSE0.0613180.0131180.0103840.009766
\n", + "
" + ], + "text/plain": [ + " Moench Moench - more layers TTim TTim - more layers\n", + "k[m/d] 8.640000 8.640000 9.067665 8.695635\n", + "Sy[-] 0.200000 0.200000 0.172876 0.196072\n", + "Ss[1/m] 0.000020 0.000020 0.000039 0.000039\n", + "kz/kh 0.500000 0.500000 0.535049 0.487962\n", + "RMSE 0.061318 0.013118 0.010384 0.009766" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ta = pd.DataFrame(\n", + " columns=[\"Moench\", \"Moench - more layers\", \"TTim\", \"TTim - more layers\"],\n", + " index=[\"k[m/d]\", \"Sy[-]\", \"Ss[1/m]\", \"kz/kh\"],\n", + ")\n", + "ta.loc[:, \"TTim - more layers\"] = ca3.parameters[\"optimal\"].values\n", + "ta.loc[:, \"TTim\"] = ca2.parameters[\"optimal\"].values\n", + "ta.loc[:, \"Moench\"] = [kaq, Sy, Saq, zh]\n", + "ta.loc[:, \"Moench - more layers\"] = [kaq, Sy, Saq, zh]\n", + "ta.loc[\"RMSE\"] = [rmse, rmse_n, ca2.rmse(), ca3.rmse()]\n", + "ta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The table above shows the TTim model results, with the first two columns showing the model simulated using Moench's parameters (Barlow and Moench, 1999). The first column is the original Model 1, and the second is the last model (Model 4) with more layers. Columns 3 and 4 show the results for the calibration with Conceptual Models 1 and 4.\n", + "\n", + "Overall, the model accuracy improved when we added more layers, so the added complexity resulted in added accuracy. However, when we look at the calibrated data, we see that it did not improve calibration performance, and both the simple model and the more complex one had similar performance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Barlow, P.M., Moench, A.F., 1999. WTAQ, a computer program for calculating drawdowns and estimating hydraulic properties for confined and water-table aquifers. 99-4225, US Dept. of the Interior, US Geological Survey\n", + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Kruseman, G.P., De Ridder, N.A., Verweij, J.M., 1970. Analysis and evaluationof pumping test data. volume 11. International institute for land reclamation and improvement The Netherlands.\n", + "* Moench, Allen, F., 1997. Flow to a well of finite diameter in a homogeneous, anisotropic water table aquifer. Water Resources Research 34, 2431–2432.\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Next Notebook: [Slug 1 - Pratt County](slug1_pratt_county.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/06about/publications.bib b/docs/06about/publications.bib index bbbd74e..278e270 100644 --- a/docs/06about/publications.bib +++ b/docs/06about/publications.bib @@ -8,7 +8,6 @@ @article{bakker_semi-analytic_2013 abstract = {TTim is a free code for the semi-analytic simulation of transient flow in multi-layer systems consisting of an arbitrary number of layers. No grid or time-stepping is required, nor does a closed model boundary need to be specified in any of the layers. Currently, TTim includes multi-layer wells and line-sinks, which may be used to simulate transient flow to a variety of hydrogeologic features, including wells with a skin and wellbore storage, incompletely sealed abandoned wells, streams with leaky beds, vertical faults, and horizontal wells; transient forcing needs to be represented by a step function. Other features that may be simulated include vertical anisotropy and the delayed response of the water table. Behind the scenes of TTim, the Laplace-transform analytic element method is applied. TTim is written in Python, with Python scripts used as input files. TTim has many practical applications, including the design of riverbank filtration systems, analysis of aquifer tests near surface-water bodies, design and evaluation of recirculation wells, and modeling of the transient pressure response of proposed carbon geologic sequestration projects. In addition, the short and simple input files and the one-to-one link between analytic elements and hydrogeologic features make TTim well suited for education.}, language = {en}, number = {4}, - urldate = {2023-03-17}, journal = {Hydrogeology Journal}, author = {Bakker, Mark}, month = jun, @@ -25,7 +24,6 @@ @incollection{bakker_analytic_2013 url = {https://doi.org/10.1007/978-1-4614-6479-2_5}, abstract = {An approach is presented for the semi-analytic simulation of transient flow in systems consisting of an arbitrary number of layers. Storage in both aquifer layers and leaky layers is taken into account. Flow in the system is generated by wells and line-sinks. Wells and line-sinks may be open to an arbitrary number of layers, which allows for the simulation of multi-aquifer wells, abandoned wells, partially penetrating streams, and linear fractures that provide a hydraulic connection between aquifer layers.}, language = {en}, - urldate = {2023-03-17}, booktitle = {Advances in {Hydrogeology}}, publisher = {Springer}, author = {Bakker, Mark}, diff --git a/docs/conf.py b/docs/conf.py index e161e80..233b867 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -102,6 +102,8 @@ nb_execution_allow_errors = True # Allow errors in notebooks, to see the error online nb_execution_mode = "auto" nb_merge_streams = True +myst_enable_extensions = ["dollarmath", "amsmath"] +myst_dmath_double_inline = True # -- bibtex options ------------------------------------------------------------------ diff --git a/pumpingtests/00_Reference.ipynb b/pumpingtests/00_Reference.ipynb new file mode 100644 index 0000000..482933d --- /dev/null +++ b/pumpingtests/00_Reference.ipynb @@ -0,0 +1,74 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e94ddbf2", + "metadata": {}, + "source": [ + "# TTim Benchmarks" + ] + }, + { + "cell_type": "markdown", + "id": "dcd52e70", + "metadata": {}, + "source": [ + "In this series of benchmark problems, we will demonstrate through Jupyter Notebooks the features and capabilities of TTim to simulate and analyze transient groundwater hydraulic problems such as pumping tests and slug tests.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "7960279f", + "metadata": {}, + "source": [ + "## Confined Pumping Tests\n", + "\n", + "1. [Oude Korendijk](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/confined1_oude_korendijk.ipynb) - One layer confined pumping test with two observation wells\n", + "2. [Grindley](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/confined2_grindley.ipynb) - One layer confined pumping test with data from both observation well and pumping well.\n", + "3. [Sioux](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/confined3_sioux.ipynb) - One Layer confined aquifer test with three observation wells\n", + "4. [Schroth](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/confined4_schroth.ipynb) - Three layers (Aquifer - Aquitard - Aquifer) confined pumping test with one observation well.\n", + "5. [Nevada](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/confined5_nevada.ipynb) - One layer, fractured confined aquifer test with data from both observation well and pumping well.\n", + "\n", + "## Leaky Pumping Tests (Semi-confined)\n", + "\n", + "1. [Dalem](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/leaky1_dalem.ipynb) - One layer, semi-confined aquifer test with four observation wells.\n", + "2. [Hardixveld](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/leaky2_hardixveld.ipynb) - Four layers (Aquitard - Aquifer - Aquitard - Aquifer) semi-confined pumping test with data from the pumping well.\n", + "3. [Texas Hill](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/leaky3_texashill.ipynb) - One layer, semi-confined aquifer test with three observation wells.\n", + "\n", + "## Unconfined Pumping Tests\n", + "\n", + "1. [Vennebulten](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/unconfined1_vennebulten.ipynb) - Two layers, unconfined aquifer test with data two observation wells screened at different depths.\n", + "2. [Moench](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/unconfined2_moench.ipynb) - Solving the Analytical model from Moench in TTim. Pumping test in unconfined aquifer with four piezometers screened at two different depths and two different distances\n", + "\n", + "## Slug Tests\n", + "\n", + "1. [Pratt County](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/slug1_pratt_county.ipynb) - One layer partially penetrated slug test with data from the test well.\n", + "2. [Falling Head](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/slug2_falling_head.ipynb) - One layer partially penetrated slug test with data from the test well.\n", + "3. [Multi-Well](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/slug3_multiwell.ipynb) - One layer confined slug test with data from the test well and from an observation well.\n", + "4. [Dawsonville](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/slug4_dawsonville.ipynb) - One layer fully-penetrated confined slug test with data from the test well." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pumpingtests/0_synthetic_data.ipynb b/pumpingtests/0_synthetic_data.ipynb new file mode 100644 index 0000000..c0328fc --- /dev/null +++ b/pumpingtests/0_synthetic_data.ipynb @@ -0,0 +1,1294 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example 0 - Pumping Test Analysis with Synthetic Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "### What is TTim\n", + "***Improve***\n", + "TTim uses the Laplace Transform Analytic Element Method (AEM) to derive a solution to transient flow in multi-layered systems. TTim uses discrete features such as Wells and Line-elements to represent real-world aquifer features of interest.\n", + "***Improve***\n", + "\n", + "In this notebook we demonstrate:\n", + "\n", + "* How to specify a simple conceptual model consisting of one confining layer and one Well\n", + "\n", + "* Simulate the model\n", + "\n", + "* Calibrate a aquifer parameters by providing data from observation wells\n", + "\n", + "- Generate Confidence Intervals from Calibration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To achieve this we will create a Model, sample some observations add noise and try to find the model parameters through fitting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We begin by importing the required libraries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Import the Required Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np # Numpy\n", + "import matplotlib.pyplot as plt # Plotting library\n", + "from ttim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Set Model Parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We set the model parameters with dimensions in **meters** and time in **days**" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "H = 7 #aquifer thickness [m]\n", + "k = 70 #hydraulic conductivity [m/d]\n", + "S = 1e-4 #specific storage fo the aquifer \n", + "Q = 788 #constant discharge [m/d]\n", + "d1 = 30 #distance of observation well 1 to pumpinq well\n", + "d2 = 90 #distance of observation well 2 to pumpinq well (positions same as for Oude Korendijk)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Loading Data:\n", + "\n", + "- Since we are modelling a synthetic example. We will just borrow the time interval from another pumping test\n", + "\n", + "* Data consistis of two columns:\n", + " * First column is time in minutes\n", + " * Second column is the piezometer level in meters above mean sea level [amsl]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data1 = np.loadtxt('data/piezometer_h30.txt', skiprows = 1)\n", + "t = data1[:, 0] / 60 / 24 # convert min to days" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As seen above, we have loaded the data as a numpy array. That is the preffered format for loading data into TTim." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Creating a Conceptual Model\n", + "\n", + "We will model our aquifer using ModelMaq, which is the 2d model interface from TTim. It assumes horizontal stacking of layers consiting of one aquifer and a leaky aquitard." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "ml = ModelMaq(kaq= k, # Hydraulic Conductivity of the aquifer\n", + " z =[-18, -25], # Top and bottom dimensions of the aquifer layer. The leaky aquitard can have 0 thickness\n", + " Saq=1e-4, # Specific storage of the aquifers\n", + " tmin=1e-5, #the minimum time for which heads can be computed after any change in boundary condition.\n", + " tmax=1, # The maximum time for which heads will be computed\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we add the Well element at position (0,0) with screen radius of 10 cm" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "w = Well(ml, #Model where we add the well element\n", + " xw=0, # Position x\n", + " yw=0, # Position y\n", + " rw=0.1, #Well radius,\n", + " tsandQ=[(0, 788)], # Tuple describing starting time and discharge\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now 'solve' the model and compute the heads at the two observation locations" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "ml.solve(silent='False')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# To compute the heads at the specified time intervals and location, we use the 'head' method\n", + "h1 = ml.head(x = d1, # location of observation well 1\n", + " y = 0,\n", + " t = t, # Time array that the heads will be returned for\n", + " ) \n", + "h2 = ml.head(d2, 0, t) #Computing heads at distance d2, and time array t for observation well 2" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-2.28275005e-04, -7.70478488e-03, -3.18554789e-02,\n", + " -5.15317124e-02, -7.77470940e-02, -1.06832914e-01,\n", + " -1.36233705e-01, -1.57179797e-01, -1.76793138e-01,\n", + " -1.96853417e-01, -2.16516484e-01, -2.50176995e-01,\n", + " -2.78596120e-01, -3.02578736e-01, -3.08282745e-01,\n", + " -3.25241029e-01, -3.58423647e-01, -3.97875642e-01,\n", + " -4.48679367e-01, -4.73964012e-01, -5.01394438e-01,\n", + " -5.21357145e-01, -5.47533636e-01, -5.86237506e-01,\n", + " -6.08113174e-01, -6.56622524e-01, -6.90311043e-01,\n", + " -7.28971868e-01, -7.54845212e-01, -7.78144650e-01,\n", + " -8.14919239e-01, -8.43451038e-01, -8.68180109e-01,\n", + " -8.84950599e-01]])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We can take a look at the data:\n", + "\n", + "h1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The head method output a numpy array with dimensions [number of aquifer layers, number of time data]. In this case we only have one row" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Demonstration of Calibration with TTim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To demonstrate the capability of TTim for deriving aquifer parameters with drawdown data, we will first add noise to the sampled data. We will test the model performance with two standard devitations: 0.02 and 0.05" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "#np.savetxt('data/syn_30_0.0.txt', h1[0])\n", + "#np.savetxt('data/syn_90_0.0.txt', h2[0])\n", + "#print(h2[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Creating the arrays with noise for sigma = 0.02" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(5) # Adding a Random seed \n", + "he12 = h1[0] - np.random.randn(len(t)) * 0.02\n", + "he22 = h2[0] - np.random.randn(len(t)) * 0.02\n", + "np.savetxt('data/syn_p30_0.02.txt', he12)\n", + "np.savetxt('data/syn_p90_0.02.txt', he22)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Creating the arrays with noise for sigma = 0.05" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(4)\n", + "he15 = h1[0] - np.random.randn(len(t)) * 0.05\n", + "he25 = h2[0] - np.random.randn(len(t)) * 0.05\n", + "np.savetxt('data/syn_p30_0.05.txt', he15)\n", + "np.savetxt('data/syn_p90_0.05.txt', he25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting and checking the noise added data:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, he12, '.', label='obs at 30 m with $\\sigma$ =0.02')\n", + "plt.semilogx(t, h1[0], label='modelled heads at 30 m (obs 1)')\n", + "plt.semilogx(t, he22, '.', label='obs at 90 m with $\\sigma$ =0.02')\n", + "plt.semilogx(t, h2[0], label='modelled heads at 90 m (obs 2)')\n", + "plt.legend()\n", + "plt.xlabel('time (d)')\n", + "plt.ylabel('drawdown (m)')\n", + "fig.suptitle(\"Drawdown model and observations with noise: $\\sigma$ = 0.02\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, he15, '.', label='obs at 30 m with $\\sigma$ =0.05')\n", + "plt.semilogx(t, h1[0], label='modelled heads at 30 m (obs 1)')\n", + "plt.semilogx(t, he25, '.', label='obs at 90 m with $\\sigma$ =0.05')\n", + "plt.semilogx(t, h2[0], label='modelled heads at 90 m (obs 2)')\n", + "plt.legend()\n", + "plt.xlabel('time (d)')\n", + "plt.ylabel('drawdown (m)')\n", + "fig.suptitle(\"Drawdown model and observations with noise: $\\sigma$ = 0.05\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.1: Calibration of the model using the noisy data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Calibrate the model using the $\\sigma $ = 0.02 noisy data from observation well 1.\n", + "* We calibrate the model by creating a `Calibrate` object with the initial model as input.\n", + "* Then we set the parameter initial values using the `set_parameter` method\n", + "* we add the observation data with the `series` method\n", + "* And fit" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...............................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 28\n", + " # data points = 34\n", + " # variables = 2\n", + " chi-square = 0.01117172\n", + " reduced chi-square = 3.4912e-04\n", + " Akaike info crit = -268.704829\n", + " Bayesian info crit = -265.652108\n", + "[[Variables]]\n", + " kaq0: 69.9141328 +/- 1.09423849 (1.57%) (init = 10)\n", + " Saq0: 1.0170e-04 +/- 5.4838e-06 (5.39%) (init = 0.001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.852\n" + ] + } + ], + "source": [ + "ca23 = Calibrate(ml) # TTim class for model calibration\n", + "# Setting initial parameters values for calibration\n", + "ca23.set_parameter(name='kaq0', initial=10) # Hydraulic Conductivity\n", + "ca23.set_parameter(name='Saq0', initial=1e-3) # Specific Storage\n", + "\n", + "ca23.series( # Adding the observations for calibration\n", + " name='obs1', #Observation well 1\n", + " x=d1, # Location\n", + " y=0,\n", + " t=t, # Time Array\n", + " h=he12, # Drawdown noisy data for well 1\n", + " layer=0, # Aquifer layer where we have the observations from\n", + " )\n", + "ca23.fit(report=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can already see the results from the calibration if we set `report = True` in the `fit` method. Besides fit statistics, we also get the Variables, with confidence interval and the correlations between variables during fitting process." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also check the calibrated parameters with the `display` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq069.9141331.0942381.565118-infinf10[69.91413282570136]
Saq00.0001020.0000055.39192-infinf0.001[0.00010170348919178826]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 69.914133 1.094238 1.565118 -inf inf 10 \n", + "Saq0 0.000102 0.000005 5.39192 -inf inf 0.001 \n", + "\n", + " parray \n", + "kaq0 [69.91413282570136] \n", + "Saq0 [0.00010170348919178826] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(ca23.parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `display` function returns a data frame with initial and optimal values for each parameter, besides the standard deviation of the estimate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now compare the model performance for both observation wells" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.01812677527586899\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print('rmse:', ca23.rmse()) # Return the RMSE error for the calibration\n", + "h123 = ml.head(d1, 0, t) # Compute drawdown of the calibrated model for obs 1\n", + "h223 = ml.head(d2, 0 ,t) # Compute drawdown for obs 2\n", + "plt.figure(figsize = (8, 5))\n", + "plt.semilogx(t, he12, '.', label='obs at 30 m with $\\sigma$ = 0.02')\n", + "plt.semilogx(t, h123[0], label='modelled drawdown at 30 m')\n", + "plt.semilogx(t, he22, '.', label='obs at 90 m with $\\sigma$ = 0.02')\n", + "plt.semilogx(t, h223[0], label='modelled drawdown at 90 m')\n", + "plt.xlabel('time (d)')\n", + "plt.ylabel('drawdown (m)')\n", + "plt.title('Ttim analysis with synthetic data, Calibrated model with $\\sigma$ = 0.02 errors at 30 m.')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we do the same procedure as [before](#sig002obs1) to calibrate the model, but now using the observation data from well 2:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".......................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 36\n", + " # data points = 34\n", + " # variables = 2\n", + " chi-square = 0.00859548\n", + " reduced chi-square = 2.6861e-04\n", + " Akaike info crit = -277.617900\n", + " Bayesian info crit = -274.565179\n", + "[[Variables]]\n", + " kaq0: 70.7905238 +/- 1.71072660 (2.42%) (init = 10)\n", + " Saq0: 9.3336e-05 +/- 5.1366e-06 (5.50%) (init = 0.001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.831\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq070.7905241.7107272.416604-infinf10[70.79052382809591]
Saq00.0000930.0000055.503366-infinf0.001[9.333600107013827e-05]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 70.790524 1.710727 2.416604 -inf inf 10 \n", + "Saq0 0.000093 0.000005 5.503366 -inf inf 0.001 \n", + "\n", + " parray \n", + "kaq0 [70.79052382809591] \n", + "Saq0 [9.333600107013827e-05] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ca29 = Calibrate(ml)\n", + "ca29.set_parameter(name='kaq0', initial=10)\n", + "ca29.set_parameter(name='Saq0', initial=1e-3)\n", + "ca29.series(name='obs2', x=d2, y=0, t=t, h=he22, layer=0)\n", + "ca29.fit(report=True)\n", + "display(ca29.parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can once again check the model performance with the new calibrated model" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.015899943725520456\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print('rmse:', ca29.rmse())\n", + "h129 = ml.head(d1, 0, t)\n", + "h229 = ml.head(d2, 0, t)\n", + "plt.figure(figsize = (8, 5))\n", + "plt.semilogx(t, he15, '.', label='obs at 30 m with sig=0.02')\n", + "plt.semilogx(t, h129[0], label='ttim at 30 m')\n", + "plt.semilogx(t, he25, '.', label='obs at 90 m with sig=0.02')\n", + "plt.semilogx(t, h229[0], label='ttim at 90 m')\n", + "plt.legend()\n", + "plt.xlabel('time (d)')\n", + "plt.ylabel('drawdown (m)');\n", + "plt.title('ttim analysis with synthetic data, sig=0.02 errors at 90 m.')\n", + "plt.legend(loc = 'best');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.2: Calibrate with two datasets simultaneously" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TTim can also analyse the pumping tests using drawdown data from more than one borehole." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we calibrate the model using the data without error from both observation wells." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "............................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 25\n", + " # data points = 68\n", + " # variables = 2\n", + " chi-square = 7.7331e-15\n", + " reduced chi-square = 1.1717e-16\n", + " Akaike info crit = -2492.46835\n", + " Bayesian info crit = -2488.02934\n", + "[[Variables]]\n", + " kaq0: 69.9999996 +/- 5.2525e-07 (0.00%) (init = 10)\n", + " Saq0: 1.0000e-04 +/- 2.2749e-12 (0.00%) (init = 0.001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.830\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq070.05.252545e-070.000001-infinf10[69.99999956044262]
Saq00.00012.274863e-120.000002-infinf0.001[0.00010000000009794474]
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" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 70.0 5.252545e-07 0.000001 -inf inf 10 \n", + "Saq0 0.0001 2.274863e-12 0.000002 -inf inf 0.001 \n", + "\n", + " parray \n", + "kaq0 [69.99999956044262] \n", + "Saq0 [0.00010000000009794474] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ca0 = Calibrate(ml)\n", + "ca0.set_parameter(name='kaq0', initial=10)\n", + "ca0.set_parameter(name='Saq0', initial=1e-3)\n", + "ca0.series(name='obs1', x=d1, y=0, t=t, h=h1[0], layer=0)\n", + "ca0.series(name='obs2', x=d2, y=0, t=t, h=h2[0], layer=0)\n", + "ca0.fit(report=True)\n", + "display(ca0.parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The obtained results match exactly the input parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 1.0664077889093849e-08\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print('rmse:', ca0.rmse())\n", + "h1n = ml.head(d1, 0, t)\n", + "h2n = ml.head(d2, 0, t)\n", + "plt.figure(figsize = (7, 4))\n", + "plt.semilogx(t, h1[0], 'b.', label='obs at 30 m no errors')\n", + "plt.semilogx(t, h1n[0], color = 'r', label = 'ttim at 30 m')\n", + "plt.semilogx(t, h2[0], 'g.', label='obs at 90 m no errors')\n", + "plt.semilogx(t, h2n[0], color='orange', label = 'ttim at 90 m')\n", + "plt.xlabel('time (d)')\n", + "plt.ylabel('drawdown (m)')\n", + "plt.title('ttim analysis with synthetic data without errors.')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We do the same, but now with drawdowns with errors with $\\sigma=0.02$." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...........................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 24\n", + " # data points = 68\n", + " # variables = 2\n", + " chi-square = 0.02036601\n", + " reduced chi-square = 3.0858e-04\n", + " Akaike info crit = -547.710892\n", + " Bayesian info crit = -543.271877\n", + "[[Variables]]\n", + " kaq0: 70.5085864 +/- 0.85773221 (1.22%) (init = 10)\n", + " Saq0: 9.7107e-05 +/- 3.6049e-06 (3.71%) (init = 0.001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.830\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq070.5085860.8577321.216493-infinf10[70.50858641493859]
Saq00.0000970.0000043.712304-infinf0.001[9.71065523694604e-05]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 70.508586 0.857732 1.216493 -inf inf 10 \n", + "Saq0 0.000097 0.000004 3.712304 -inf inf 0.001 \n", + "\n", + " parray \n", + "kaq0 [70.50858641493859] \n", + "Saq0 [9.71065523694604e-05] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ca2 = Calibrate(ml)\n", + "ca2.set_parameter(name='kaq0', initial=10)\n", + "ca2.set_parameter(name='Saq0', initial=1e-3)\n", + "ca2.series(name='obs1', x=d1, y=0, t=t, h=he12, layer=0)\n", + "ca2.series(name='obs2', x=d2, y=0, t=t, h=he22, layer=0)\n", + "ca2.fit()\n", + "display(ca2.parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.017306074039873633\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print('rmse:', ca2.rmse())\n", + "h12 = ml.head(d1, 0, t)\n", + "h22 = ml.head(d2, 0, t)\n", + "plt.figure(figsize = (8, 5))\n", + "plt.semilogx(t, he12, 'b.', label='obs at 30 m, sig=0.02')\n", + "plt.semilogx(t, h12[0], color = 'r', label = 'ttim at 30 m')\n", + "plt.semilogx(t, he22, 'g.', label='obs at 90 m, sig=0.02')\n", + "plt.semilogx(t, h22[0], color='orange', label = 'ttim at 90 m')\n", + "plt.xlabel('time (d)')\n", + "plt.ylabel('drawdown (m)')\n", + "plt.title('ttim analysis with synthetic data and errors with sig=0.02')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Drawdowns with errors with $\\sigma=0.05$." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".........................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 22\n", + " # data points = 68\n", + " # variables = 2\n", + " chi-square = 0.16477216\n", + " reduced chi-square = 0.00249655\n", + " Akaike info crit = -405.543554\n", + " Bayesian info crit = -401.104539\n", + "[[Variables]]\n", + " kaq0: 70.3176085 +/- 2.43430886 (3.46%) (init = 10)\n", + " Saq0: 9.8232e-05 +/- 1.0351e-05 (10.54%) (init = 0.001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.830\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq070.3176082.4343093.461877-infinf10[70.31760849447059]
Saq00.0000980.00001010.537516-infinf0.001[9.823189067091308e-05]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 70.317608 2.434309 3.461877 -inf inf 10 \n", + "Saq0 0.000098 0.000010 10.537516 -inf inf 0.001 \n", + "\n", + " parray \n", + "kaq0 [70.31760849447059] \n", + "Saq0 [9.823189067091308e-05] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ca5 = Calibrate(ml)\n", + "ca5.set_parameter(name='kaq0', initial=10)\n", + "ca5.set_parameter(name='Saq0', initial=1e-3)\n", + "ca5.series(name='obs1', x=d1, y=0, t=t, h=he15, layer=0)\n", + "ca5.series(name='obs2', x=d2, y=0, t=t, h=he25, layer=0)\n", + "ca5.fit()\n", + "display(ca5.parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.049225196392244215\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print('rmse:', ca5.rmse())\n", + "h15 = ml.head(d1, 0, t)\n", + "h25 = ml.head(d2, 0, t)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, he15, 'b.', label='obs at 30 m')\n", + "plt.semilogx(t, h15[0], color = 'r', label = 'ttim at 30 m')\n", + "plt.semilogx(t, he25, 'g.', label='obs at 90 m')\n", + "plt.semilogx(t, h25[0], color='orange', label = 'ttim at 90 m')\n", + "plt.xlabel('time (d)')\n", + "plt.ylabel('drawdown (m)')\n", + "plt.title('ttim analysis with synthetic data and errors with sig=0.05')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Model performed also well for $\\sigma = 0.05$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Final Remarks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example we have succesfully:\n", + "\n", + "* Initiated a TTim model instance using the `Model` class\n", + "* Sampled observation data from the model and defined the synthetic data by adding noise.\n", + "* Calibrated the model with the `Calibrate` class using one and two calibration wells\n", + "* Inspected the calibration performance\n", + "\n", + "Next Example:\n", + "\n", + "* Pumping test analysis of a confined aquifer: [Example 1 - Pumping Test of Confined Aquifer](1_test_of_oude_korendijk.ipynb)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pumpingtests/README.md b/pumpingtests/README.md new file mode 100644 index 0000000..b5933a1 --- /dev/null +++ b/pumpingtests/README.md @@ -0,0 +1,31 @@ +# TTim Benchmarks + +In this series of benchmark problems, we will demonstrate through Jupyter Notebooks the features and capabilities of TTim to simulate and analyze transient groundwater hydraulic problems such as pumping tests and slug tests. + + + +## Confined Pumping Tests + +1. [Oude Korendijk](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/confined1_oude_korendijk.ipynb) - One layer confined pumping test with two observation wells +2. [Grindley](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/confined2_grindley.ipynb) - One layer confined pumping test with data from both observation well and pumping well. +3. [Sioux](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/confined3_sioux.ipynb) - One Layer confined aquifer test with three observation wells +4. [Schroth](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/confined4_schroth.ipynb) - Three layers (Aquifer - Aquitard - Aquifer) confined pumping test with one observation well. +5. [Nevada](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/confined5_nevada.ipynb) - One layer, fractured confined aquifer test with data from both observation well and pumping well. + +## Leaky Pumping Tests (Semi-confined) + +1. [Dalem](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/leaky1_dalem.ipynb) - One layer, semi-confined aquifer test with four observation wells. +2. [Hardixveld](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/leaky2_hardixveld.ipynb) - Four layers (Aquitard - Aquifer - Aquitard - Aquifer) semi-confined pumping test with data from the pumping well. +3. [Texas Hill](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/leaky3_texashill.ipynb) - One layer, semi-confined aquifer test with three observation wells. + +## Unconfined Pumping Tests + +1. [Vennebulten](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/unconfined1_vennebulten.ipynb) - Two layers, unconfined aquifer test with data two observation wells screened at different depths. +2. [Moench](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/unconfined2_moench.ipynb) - Solving the Analytical model from Moench in TTim. Pumping test in unconfined aquifer with four piezometers screened at two different depths and two different distances + +## Slug Tests + +1. [Pratt County](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/slug1_pratt_county.ipynb) - One layer partially penetrated slug test with data from the test well. +2. [Falling Head](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/slug2_falling_head.ipynb) - One layer partially penetrated slug test with data from the test well. +3. [Multi-Well](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/slug3_multiwell.ipynb) - One layer confined slug test with data from the test well and from an observation well. +4. [Dawsonville](https://github.com/vcantarella/ttim/blob/master/pumpingtest_benchmarks/slug4_dawsonville.ipynb) - One layer fully-penetrated confined slug test with data from the test well. diff --git a/pumpingtests/confined1_oude_korendijk.ipynb b/pumpingtests/confined1_oude_korendijk.ipynb new file mode 100644 index 0000000..45bad34 --- /dev/null +++ b/pumpingtests/confined1_oude_korendijk.ipynb @@ -0,0 +1,1740 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Confined Aquifer Test - Oude Korendijk\n", + "**This example is taken from Kruseman et al. 1970**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model\n", + "\n", + "TTim is a semi-analytical model of transient groundwater flow systems (Bakker, 2013). It applies the Laplace-transform analytic element method to solve for groundwater flow in a variety of hydrogeological features. One of the many applications of TTim is the analysis of aquifer tests. In this series of Jupyter Notebooks, we demonstrate the capabilities to model and calibrate aquifer tests in different hydrogeological conditions.\n", + "\n", + "In this example, we will use the pumping test data from Oude Korendijk (Kruseman et al. 1970) to demonstrate how TTim can be used to model and analyze pumping tests. Furthermore, we reproduce the work of Yang (2020) and compare the performance of TTim with other transient well hydraulics software AQTESOLV (Duffield, 2007) and MLU (Carlson and Randall, 2012).\n", + "\n", + "Oude Korendijk is a polder area south of Rotterdam, the Netherlands. The stratigraphy can be summarised by:\n", + "* the presence in the first 18 m depth of an impermeable layer,\n", + "* followed by a 7 m succession of coarse gravel and sands, which are considered as the aquifer layer,\n", + "* and finally, a layer of fine sands and clayey sediments that are deemed impermeable.\n", + "\n", + "The well screens the whole thickness of the aquifer. Drawdowns were taken from piezometers installed 30 and 90 m away from the well. Pumping at the well has been taken with a constant discharge of 788 m3/d for almost 14 hours.\n", + "\n", + "The conceptual model of the area is a single layer confined aquifer located at 18 m below surface and 7 m thickness. At $t=0$, the pumping starts at a constant discharge of 788 m3/d and drawdowns are recorded at two piezometers, 30 and 90 meters away, respectively. The figure below summarises the conceptualization of the problem\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(8, 5))\n", + "ax = fig.add_subplot(1,1,1)\n", + "#sky\n", + "sky = plt.Rectangle((-10,0), width = 110, height = 3, fc = 'b', zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "#Aquifer:\n", + "ground = plt.Rectangle((-10,-25), width = 110, height = 25, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9)\n", + "ax.add_patch(ground)\n", + "\n", + "#Confining bed:\n", + "confining_unit = plt.Rectangle((-10,-18), width = 110, height = 18, fc = np.array([100,100,100])/255, zorder=0, alpha=0.7)\n", + "ax.add_patch(confining_unit)\n", + "well = plt.Rectangle((-2,-25), width = 4, height = 25, fc = np.array([200,200,200])/255, zorder=1)\n", + "ax.add_patch(well)\n", + "\n", + "#Wellhead\n", + "wellhead = plt.Rectangle((-2.5,0),width = 5, height = 1.5, fc = np.array([200,200,200])/255, zorder=2, ec='k')\n", + "ax.add_patch(wellhead)\n", + "\n", + "#Screen for the well:\n", + "screen = plt.Rectangle((-2,-25), width = 4, height = 7, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x = 2.5,y = 0.75, dx = 4, dy = 0, color = \"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x = 6.5, y = 0.75, s = r'$ Q = 788 \\frac{m^3}{d}$', fontsize = 'large')\n", + "#Piezometers\n", + "piez1 = plt.Rectangle((29,-25), width = 2, height = 25,fc = np.array([200,200,200])/255, zorder=1)\n", + "piez2 = plt.Rectangle((89,-25), width = 2, height = 25,fc = np.array([200,200,200])/255, zorder=1)\n", + "screen_piez_1 = plt.Rectangle((29,-25), width = 2, height = 7, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_1.set_linewidth(2)\n", + "screen_piez_2 = plt.Rectangle((89,-25), width = 2, height = 7, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_2.set_linewidth(2)\n", + "ax.add_patch(piez1)\n", + "ax.add_patch(piez2)\n", + "ax.add_patch(screen_piez_1)\n", + "ax.add_patch(screen_piez_2)\n", + "#last line\n", + "line = plt.Line2D(xdata= [-10,100], ydata = [0,0], color = \"k\")\n", + "ax.add_line(line)\n", + "ax.text(x = 30, y = 0.75, s = 'P30', fontsize = 'large' )\n", + "ax.text(x = 90, y = 0.75, s = 'P90', fontsize = 'large' )\n", + "ax.set_xlim([-10,100])\n", + "ax.set_ylim([-25,3]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Loading libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams['figure.figsize'] = (5, 3) # default figure size\n", + "import pandas as pd\n", + "import ttim as ttm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Setting basic parameters for the model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "H = 7 #aquifer thickness in meters\n", + "zt = -18 #top boundary of aquifer (m)\n", + "zb = zt - H #bottom boundary of aquifer (m)\n", + "Q = 788 #constant discharge m3/d" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Step 3: Creating a TTim conceptual model\n", + "\n", + "In this example, we are using the ModelMaq model to conceptualize our aquifer. ModelMaq defines the aquifer system as a stacked vertical sequence of aquifers and leaky layers (aquifer-leaky layer, aquifer-leaky layer, etc). Aquifers are conceptualized as having no vertical resistance (Dupuit approximation), with constant hydraulic conductivity and storage. Aquitard layers are approximated as having only vertical flow. They are characterized by the parameter resistance to vertical flow and by having no storage.\n", + "\n", + "In the model construction, we have to set the parameters for each layer (which consists of an aquifer layer and an aquitard layer).\n", + "\n", + "For our one-layer model we have to set:\n", + "\n", + "- The hydraulic conductivity: ```kaq``` this is a list/array with a float element for every aquifer, for example: ```[kaq0,kaq1]```. We can also set a float value, in this case, the same ```kaq``` is assumed for every layer.\n", + "- The top and bottom of each aquifer: ```z``` defined by a list/array ```[zt0,zb0,zt1,zb1,...]```, where the inputs are a sequence of top and bottoms of the aquifer layers.\n", + "- The specific storage: ```Saq```. The input is a list/array with a float element for every aquifer, for example: ```[Saq0, Saq1]```. We can also set a float value. In this case, the same ```Saq``` is assumed for every layer.\n", + "- The minimum time for which TTim solve the groundwater flow: ```tmin```, a float.\n", + "- And the maximum time: ```tmax```, float.\n", + "\n", + "Optional parameters:\n", + "\n", + "- TTim automatically assumes the ```topboundary``` is confined (```topboundary = 'conf'```). If we assign: ```topboundary = 'semi'``` This means that we assume the layer on top of the uppermost aquifer is a leaky layer, and we must also characterize it. Thus, even though we have only one aquifer, we have to set an additional element to the ```z``` array, which is the top of the aquitard formation:\n", + "\n", + " * For example: ```z = [0,zt,zb]```. 0 is the depth of the aquitard overlying the aquifer, ```zt``` and ```zb``` are the top and bottom of the aquifer. \n", + "\n", + " * We would also specify the leaky-layer parameters ```c``` and ```Sll``` (see below). \n", + "\n", + "- ```phreatictop```: Is a boolean (True/False). If ```True```, the first element in ```Saq``` is considered phreatic storage (Specific Yield) and it is not multiplied by the layer thickness. The default value is ```False``` in ```ModelMaq```. Generally, this parameter is set to ```True``` only in unconfined aquifers.\n", + "\n", + "In case of more than one layer model, or when ```topboundary = 'semi'```, we would also have to set the resistance to vertical flow ```c```, and the storage ```Sll``` of the aquitard portions. An example of this configuration can be seen in the notebook [Confined 4 - Schroth](confined4_schroth.ipynb)\n", + "\n", + "To represent our pumping well, we will use the ```Well``` feature.\n", + "\n", + "Wells, in TTim, are features with specified discharge. The well may be screened in multiple layers. In case the screen is in more than one layer, TTim distributes the discharge across the layers such that the head inside the well is the same in all screened layers. The wellbore storage and skin effect may be taken into account.\n", + "\n", + "The discharge of the well acting on layer $n$ is computed inside TTim with the expression (Bakker, 2013):\n", + "\n", + "$$ Q_n = 2\\pi r_wH_n\\frac{h_n-h_w}{c_e} $$\n", + "\n", + "where, $Q_n$ is the discharge at layer $n$, which is a positive value for water being pumped, $r_w$ is the radius of the well, $H_n$ is the layer thickness, $h_n$ is the head just outside the well, and $h_w$ is the head inside the well. $c_e$ is the entry resistance that can be defined in TTim by the skin resistance of the well (see notebook [Confined 2 - Grindley](confined2_grindley.ipynb) for more details)\n", + "\n", + "For the well we have to set:\n", + "- The TTim model: ```ml``` where the well is added to\n", + "- The x and y location: ```xw, yw```, floats\n", + "- The pumping scheme is defined by a list (```tsandQ```) where each element is a tuple representing a new stress condition with the starting time and the discharge rate, in our case: ```(0, Q)``` meaning that pumping begins at t = 0 with pumping rate Q\n", + "- The layers where it is screened: ```layers```. This argument can be set as an integer (one layer) or a list/array of integers (multi-screen well).\n", + "\n", + "Optional parameters for the ```Well``` object are:\n", + "- The well radius: ```rw```, a float, if not specified a value of 0.1 m is assumed.\n", + "- the skin resistance of the well: ```res```. If not specified, it is set to 0. An example of setting up the skin resistance is seen in the notebook : [Confined 2 - Grindley](confined2_grindley.ipynb).\n", + "- The radius of the caisson: ```rc```. The radius of the caisson is the parameter used to account for wellbore storage in the simulation. If not specified, this value is set to ```None``` and TTim will ignore wellbore storage. TTim considers the wellbore storage by solving the water balance inside the well with the expression (Bakker, 2013):\n", + "$$\\pi r_c^2 \\frac{dh_w}{dt} = \\sum_nQ_n-Q_w $$\n", + "where: $Q_n$ and $Q_w$ are the inflows and outflows in the well, $h_w$ is the head inside the well and $r_c$ is the radius of the caisson.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#unkonwn parameters: kaq, Saq\n", + "ml = ttm.ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)\n", + "w = ttm.Well(ml, xw=0, yw=0, rw=0.2, tsandQ=[(0, Q)], layers=0)\n", + "\n", + "# Here we are setting everything in meters for length and days for time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The last step in our model creation is to \"solve\" the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Load data of two observation wells:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The preferred method of loading data into TTim is to use numpy arrays.\n", + "\n", + "The data is in a text file where the first column is the time data in ***minutes*** and the second column is the drawdown in ***meters***\n", + "\n", + "We load the data as a numpy array for each piezometer. We then separate time and drawdown into two different 1d arrays. We also convert time data from minutes to days" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "#time and drawdown of piezometer 30m away from pumping well\n", + "data1 = np.loadtxt('data/piezometer_h30.txt', skiprows = 1)\n", + "t1 = data1[:, 0] / 60 / 24 #convert min to days\n", + "h1 = data1[:, 1]\n", + "r1 = 30\n", + "#time and drawdown of piezometer 90m away from pumping well\n", + "data2 = np.loadtxt('data/piezometer_h90.txt', skiprows = 1)\n", + "t2 = data2[:, 0] / 60 / 24 #convert min to days\n", + "h2 = data2[:, 1]\n", + "r2 = 90" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Calibration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Model Calibration is done in TTim using the ```Calibrate``` object. TTim calibrates the parameters by minimizing an objective function using a non-linear least-squares fitting algorithm. The objective function used is the sum of the squares of the residuals calculated as:\n", + "\n", + "$$\\sum_n (h_o - h_c)^2$$,\n", + "\n", + "where $h_0$ is the observed heads and $h_c$ is the calculated heads by the model.\n", + "\n", + "and TTim uses ```lmfit```, a python package for non-linear least-squares minimization (Newville et al. 2014), to find the optimal parameters that minimize the residuals.\n", + "\n", + "For the calibration of our groundwater model, we proceed by creating a calibration object with the ```Calibrate``` class. The ```Calibrate``` object takes the model ```ml``` as argument.\n", + "We then set the parameters we are adjusting:\n", + "- Hydraulic conductivity: ```kaq0``` (Hydraulic conductivity of layer 0)\n", + "- Specific Storage ```Saq0``` (Specific Storage of layer 0)\n", + "\n", + "with the ```.set_parameter``` method.\n", + "\n", + "- ```.set_parameter``` takes two arguments:\n", + "- ```name``` is the parameter name, a string, where the letters define the parameter. The possible values are \"kaq\", \"Saq\" or 'c', and they represent hydraulic conductivity, Specific storage and resistance to vertical flow, respectively. The letters are followed by a number, that define the layer of that parameter. For the example ```\"kaq0\"``` means the hydraulic conductivity of the layer 0. In our multilayer model we can extend the numbering to adjust one parameters for various layers in that case, we write the number of the first layer followed by a underline \"_\" and the number of the last layer, for example ```kaq0_1```, which means the hydraulic conductivity for layers 0 to 1\n", + " - ```initial```is the initial guess value for the fitting algorithm.\n", + "\n", + "We can also add the optional parameters:\n", + "- ```pmin``` and ```pmax```, which are floats that define the minimum and maximum possible values for the parameter. If not set, TTim assume their values are -inf and inf, respectively.\n", + "\n", + "The other method for adjusting parameters, ```.set_parameter_by_reference``` is later explained in [step 6.3](#step_6_3).\n", + "\n", + "We add the observation data using the ```.series``` method. The arguments are:\n", + " - ```name```: string with the observation name\n", + " - ```x``` and ```y```: float positions of the observation\n", + " - ```t```: the array of observation times\n", + " - ```h```: the array of observed drawdowns\n", + " - ```layer```: integer. The layer of the observation (0 indexed)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Step 5.1: Calibration with Observation from Piezometer 1 (30 m from well)\n", + "\n", + "We begin calibrating using only the data from observation 1:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "ca1 = ttm.Calibrate(ml) # Calibrate object\n", + "ca1.set_parameter(name = 'kaq0', initial=10) # Setting parameters\n", + "ca1.set_parameter(name = 'Saq0', initial=1e-4)\n", + "ca1.series(name = 'obs1', x=r1, y=0, t=t1, h=h1, layer=0) # Adding observations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ```fit``` method is used to run the least-squares algorithm (```lmfit```) for finding the optimal parameter values:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 31\n", + " # data points = 34\n", + " # variables = 2\n", + " chi-square = 0.03408049\n", + " reduced chi-square = 0.00106502\n", + " Akaike info crit = -230.783289\n", + " Bayesian info crit = -227.730568\n", + "[[Variables]]\n", + " kaq0: 68.6394052 +/- 1.43826777 (2.10%) (init = 10)\n", + " Saq0: 1.6072e-05 +/- 1.5823e-06 (9.85%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.8911\n" + ] + } + ], + "source": [ + "ca1.fit(report=True) # Fitting the model. We can hide the message below setting report = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The optimal parameters and their related fit statistics are saved inside the ```Calibrate``` object as a DataFrame in the ```.parameters``` attribute:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 68.639405 1.438268 2.095397 -inf inf 10.0000 \n", + "Saq0 0.000016 0.000002 9.845150 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0 [68.6394051606159] \n", + "Saq0 [1.607160696374144e-05] " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ca1.parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The calibration RMSE can be accessed with the ```.rmse``` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.03166018365112287\n" + ] + } + ], + "source": [ + "print('rmse:', ca1.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can access the model drawdowns by asking the calibrated model to compute the heads at the well location and time intervals specified by the sampled data.\n", + "\n", + "For this, we use the ```.head``` method in the model object, in our case, ```ml```.\n", + "\n", + "The arguments are:\n", + "* the positions ```x``` and ```y``` of the piezometric well (or any other point of interest). In our case, our well is located at position ```x= r1``` and ```y = 0```.\n", + "* the time intervals, defined by the numpy array ```t```, for the computation of the heads. In our case, this is defined by the variable ```t1```.\n", + "\n", + "* Another optional input is ```layers```, which can be a list, integer or an array defining the model layers. When we do not assign anything, the head is computed for all layers.\n", + "\n", + "The output is a numpy array with dimensions ```(nl,nt)```, where ```nl``` is the number of layers and ```nt``` is the number of time intervals.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 34)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hm1 = ml.head(x = r1, y = 0, t = t1) #Using the head method to calculate model resuts\n", + "hm1.shape #Demonstration of the output shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting the model Results" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#matplotlib plot for calibration, \n", + "plt.semilogx(t1, h1, '.', label='obs at 30 m') #Plotting the observed drawdown\n", + "plt.semilogx(t1, hm1[0], label='ttim at 30 m') #Simulated drawdown\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.title('ttim analysis for Oude Korendijk - Piezometer 30 m')\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5.2. Calibrate model Parameters with Observation Well 2 (90 m distance)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We proceed to calibrate using only the data from observation well 2. Details on the procedures can be reviewed in [***step 5.1***](#step_5_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".........................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 38\n", + " # data points = 35\n", + " # variables = 2\n", + " chi-square = 0.01806491\n", + " reduced chi-square = 5.4742e-04\n", + " Akaike info crit = -260.919619\n", + " Bayesian info crit = -257.808923\n", + "[[Variables]]\n", + " kaq0: 71.5821661 +/- 1.57398171 (2.20%) (init = 10)\n", + " Saq0: 2.9107e-05 +/- 1.9379e-06 (6.66%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.8473\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq071.5821661.5739822.198846-infinf10.0000[71.58216612015345]
Saq00.0000290.0000026.657679-infinf0.0001[2.910742855968684e-05]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 71.582166 1.573982 2.198846 -inf inf 10.0000 \n", + "Saq0 0.000029 0.000002 6.657679 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0 [71.58216612015345] \n", + "Saq0 [2.910742855968684e-05] " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ca2 = ttm.Calibrate(ml)\n", + "ca2.set_parameter(name='kaq0', initial=10)\n", + "ca2.set_parameter(name='Saq0', initial=1e-4)\n", + "ca2.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca2.fit(report=True)\n", + "ca2.parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.022718720768954242\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print('rmse:', ca2.rmse())\n", + "hm2 = ml.head(r2, 0, t2)\n", + "plt.semilogx(t2, h2, '.', label='obs at 90 m')\n", + "plt.semilogx(t2, hm2[0], label='ttim at 90 m')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.title('ttim analysis for Oude Korendijk - Piezometer 90 m')\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5.3. Calibrate model with two datasets simultaneously" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we explore the ability of TTim to calibrate the model using more than one observation location.\n", + "\n", + "We achieve this by calling the method ```.series``` multiple times to the ```Calibrate``` object:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 30\n", + " # data points = 69\n", + " # variables = 2\n", + " chi-square = 0.17291364\n", + " reduced chi-square = 0.00258080\n", + " Akaike info crit = -409.245796\n", + " Bayesian info crit = -404.777583\n", + "[[Variables]]\n", + " kaq0: 66.0892907 +/- 1.65498894 (2.50%) (init = 10)\n", + " Saq0: 2.5409e-05 +/- 2.4016e-06 (9.45%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.8553\n" + ] + }, + { + "data": { + "text/html": [ + "
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Saq00.0000250.0000029.451956-infinf0.0001[2.540871621501991e-05]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 66.089291 1.654989 2.504171 -inf inf 10.0000 \n", + "Saq0 0.000025 0.000002 9.451956 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0 [66.08929067179805] \n", + "Saq0 [2.540871621501991e-05] " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ca = ttm.Calibrate(ml)\n", + "ca.set_parameter(name='kaq0', initial=10)\n", + "ca.set_parameter(name='Saq0', initial=1e-4)\n", + "ca.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=0) # Adding well 1\n", + "ca.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0) # Adding well 2\n", + "ca.fit(report=True)\n", + "ca.parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.050059911747508554\n" + ] + }, + { + "data": { + "image/png": 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ZYvQeZUZycrJITEwUpUqV0nv9ZbUbZerUqbr3oEKhEF9//bXe8//++68AxNSpUw32Xbt2rQDEsWPHMny+lODm8OHDAhB//fWX7rnu3bsLQPzyyy96+zRv3lyUKVNG9/OPP/4oALFt2za9cn369MnQPWjbtq1wcHAQz54909seEBAgADFlyhQhRM5cu5eXl7C0tNTrskl5nbq7u4sXL17otm/dulUAYvv27Wke82V5OvX11RHtvr6+KBQKmjVrpttmampKyZIldU2maTl79iytW7fG2dkZExMTVCoV3bp1Q61Wc+3aNb2ybm5u1KhRQ2+bn5+fwXk2btxInTp1sLGxwdTUFJVKxdKlS7l8+XKadSlevDgtW7ZkwYIFCCEAWLt2LY8ePWLQoEG6cjVq1GDPnj2MGDGCQ4cOERcXl+51Vq5cGTMzM/r27cvKlSv1moaz4sCBAwAGXRcffPAB1tbWBl0Nfn5+lC5dOlvnfJkQIkuzbQ4ePEj58uWpVKmS3vYuXbro/Xzs2DEeP35M9+7dSU5O1j00Gg1NmzYlNDTUoJnUmMDAQMzNzRk6dCgPHz40eP7AgQOUK1fO4HXVo0cPhBC6+5yidevWqFQq3c///PMPV65coWvXrgB6dW3evDkREREGA71at26t97Ofnx/w/y6GlK6ZlGOm6NixI6ampulec2oePXrEu+++y6lTpzh69CjvvfdeuvtoNBq9a1Kr1Rk6V61atVCpVNja2tKyZUvc3NzYs2cPrq6uRsvv3LkThULBRx99pHc+Nzc3KlWqZHSW0osXL2jRogXx8fHs2bMHBwcH4P/379X3Ro0aNfD19TV4b7i7u1O1alXdz05OThQuXJjKlStTpEgR3XZfX1/g/7+n+Ph4fvvtN9q1a4eVlZXB7z4+Pp4TJ06keZ+y8vrp0KGDwXFq1KjBihUrmDRpEidOnDDohk5NcnIyU6ZMoVy5cpiZmWFqaoqZmRnXr19P9/MyI3r06EFoaCh79+7lyy+/ZObMmXz66acG5dL6LEnvc+bmzZt06dIFNzc33XdI/fr1AQyuQaFQ0KpVK71tr36HHDx4EFtbW4P36aufUakZNGgQ0dHRdOvWjZs3b3L//n3GjBnDsWPHAFAq9b/Cs3PtoP1uebnLJuV12qBBA6ysrAy2Z+R7OUWeBhtOTk56P5uZmWFlZYWFhYXB9vj4+DSPFR4eTkBAAP/++y/fffcdR44cITQ0VNff+eqXuLOzs8ExzM3N9coFBwfTsWNHihYtyurVqzl+/DihoaH06tUr3foAfPbZZ1y/fp39+/cD2jEl/v7+eqP9582bx1dffcXWrVtp2LAhTk5OtG3bluvXr6d63BIlSvDrr79SuHBhBg4cSIkSJShRogTfffddunUy5tGjR5iamlKoUCG97QqFAjc3Nx49eqS33d3dPUPH9fT0BCAsLCzVMi9evCAqKgoPD49M1lpbbzc3N4Ptr267f/8+AO+//z4qlUrvMX36dIQQPH78ON3zNWrUiC1btnD9+nUaNmzIgwcPDOpj7N6kfMmkdx9T6jls2DCDeg4YMACAqKgovX1efR2bm5sD/3+9p5zz1Xtiampq9D2QUdeuXePkyZM0a9aMChUqZGifCRMm6F1TiRIlMrTfzz//TGhoKGfPnuXevXucP3+eOnXqpFr+/v37CCFwdXU1uI8nTpwwuIfJycm8//77XLt2jd27d+u9FlPuX2q/11d/p69+poH288vYZx2g+xx59OgRycnJzJ8/36DOzZs3Bwx/98auGzL3+jF2XRs2bKB79+4sWbIEf39/nJyc6NatW7rjDIYOHcqYMWNo27YtO3bs4OTJk4SGhlKpUqUM/RGVHjc3N6pVq0aTJk2YNm0aEyZM4Pvvv+fs2bMAODo6olAoDH4ngO79bez3k+L58+cEBARw8uRJJk2axKFDhwgNDSU4OBgw/A4x9l1lbm6u993w6NEjo0Gxsc8tY9577z2WL1/O77//TokSJXBzcyM4OJiJEycC6AKD7F57itRep+m9fjMi63/avGG2bt3KixcvCA4O1hsgk53pYatXr8bHx4cNGzboRYWvDgBKzbvvvkuFChX4/vvvsbGx4cyZM6xevVqvjLW1NePHj2f8+PHcv39f18rRqlUrrly5kuqxAwICCAgIQK1Wc/r0aebPn8+QIUNwdXU1GLSYHmdnZ5KTk3n48KFewCGEIDIyUjegK0VGWyGqVq2Ko6Mj27dvZ+rUqUb32759OxqNhsaNG+u2WVhYGL3HUVFRuLi46NXb2Afgq9tS9pk/f75ucOGrUvsr+VXNmjVj27ZttG3bloYNG3LgwAHdvs7OzkRERBjskzJg8+W6g+F9THl+5MiRtG/f3uj5y5Qpk6F6pkgJKCIjI/X+YklOTjb6wZRR/v7+fPDBB/Tu3RvQTql+9a+sV/Xt21evNTMlMEqPr68v1apVy3DdXFxcUCgUHDlyxOg5Xt3Wt29ffvvtN3bv3m3QSpZy/yIiIihWrJjec/fu3TP4nWaVo6MjJiYmBAUFMXDgQKNlfHx80jxGVl4/xt6TLi4uzJ07l7lz5xIeHs727dsZMWIEDx48MDqoNcXq1avp1q0bU6ZM0dseFRWlaynKSSktiNeuXdMNaCxZsqTegNQUFy5cwNLSkuLFi6d6vAMHDnDv3j0OHTqka80AjKZ6yChnZ2ejg7EzOkAUoHv37nTt2pXr16+jUqkoWbKk7vM0ICAAINvX/jpkqmXj1b+aXn0uJ6LXrEp507z8QSKEYPHixdk6ppmZmd4bMjIyMt3ZKC8bPHgwu3btYuTIkbi6uvLBBx+kWtbV1ZUePXrw4YcfcvXq1TRnlqQwMTGhZs2auhacrCRwSWkCfzUQ2rx5My9evMhQE7kxZmZmDB8+nMuXL+tm37zswYMHuvvy8ccf67Z7e3tz/vx5vbLXrl0zaAJu2LAhf//9N3/99Zfe9rVr1+r9XKdOHRwcHLh06RLVqlUz+kiJ1DMiMDCQbdu2cfPmTRo2bKj74Hjvvfe4dOmSwe/g559/RqFQ0LBhwzSPW6ZMGUqVKsVff/2Vaj1tbW0zXE9AN3tnzZo1ett/+eWXdEenp6d79+6sX7+e5cuX67or01KkSBG9a6lYsWK2zp+ali1bIoTg33//NXoPXz7v6NGjWb58OUuWLKFRo0YGx3r33XcBw/dGaGgoly9fzvJ741VWVlY0bNiQs2fP4ufnZ7TeKYFPap/DufH68fT0ZNCgQRlKDqVQKAwCuV27duklYEur/pmV0sVVsmRJ3bZ27dpx4MABvWSBz549Izg4mNatW6fZdWjsOwRg0aJFWa5jw4YNefbsmUESrlc/o9JjamqKr68vJUuWJDo6mp9++ok2bdro/WGdnWt/HTJ19pQ36XfffUf37t1RqVSUKVMGW1tbKlasyPr169mwYQPFixfHwsIi1z5MjGncuDFmZmZ8+OGHfPnll8THx/Pjjz/y5MmTLB8zZYrngAEDeP/997lz5w4TJ07E3d09zW6Ol3300UeMHDmS33//ndGjRxt8qdWsWZOWLVvi5+eHo6Mjly9fZtWqVfj7++v1kb1s4cKFHDhwgBYtWuDp6Ul8fDzLli0DMPqBmZ7GjRsTGBjIV199RUxMDHXq1OH8+fOMHTuWKlWqEBQUlOljpvjqq6/466+/dP926tQJe3t7zp8/z8yZM3n27Bk7d+7E3t5et09QUBAfffQRAwYMoEOHDty+fZsZM2YYdPMMGTKEZcuW0aJFCyZNmoSrqytr1qwxaBGysbFh/vz5dO/encePH/P+++9TuHBhHj58yF9//cXDhw/58ccfM3VdTZo0Yfv27bRp00bXwvH555/z888/06JFCyZMmICXlxe7du1iwYIF9O/fP0PjXBYtWkSzZs0IDAykR48eFC1alMePH3P58mXOnDnDxo0bM1VPX19fPvroI+bOnYtKpaJRo0ZcvHiRWbNmYWdnl6ljGfP+++9jZWXF+++/T1xcHOvWrctU4JYb6tSpQ9++fenZsyenT5+mXr16WFtbExERwdGjR6lYsSL9+/dn48aNTJ48mffff5/SpUvrjYkwNzenSpUqlClThr59+zJ//nyUSiXNmjXj1q1bjBkzBg8PDz7//PMcq/d3331H3bp1CQgIoH///nh7e/Ps2TP++ecfduzYoRvzU6JECSwtLVmzZg2+vr7Y2NhQpEgRihQpku3XT3R0NA0bNqRLly6ULVsWW1tbQkNDCQkJSbW1JEXLli1ZsWIFZcuWxc/Pjz///JOZM2catAilVX9jxo4dy/3796lXrx5Fixbl6dOnhISEsHjxYj744AO9MTLDhg1j1apVuvegubk506ZNIz4+Pt3ptbVr18bR0ZF+/foxduxYVCoVa9asMfhjJjO6devGt99+S7du3Zg8eTKlSpVi9+7d7N27N0P7P3jwgNmzZ1OnTh1sbW25cuUKM2bMQKlU6k2Jhuxd+2uR4aGk/xk5cqQoUqSIUCqVeiPXb926JZo0aSJsbW0FoBsdntZslJenjQlhfKaEENpR3uXLl0+3bjt27BCVKlUSFhYWomjRomL48OFiz549BiPsUzuesSl006ZNE97e3sLc3Fz4+vqKxYsXpzpCP7UEST169BCmpqZ6o3xTjBgxQlSrVk04OjoKc3NzUbx4cfH555/rpsYKYTgj4Pjx46Jdu3bCy8tLmJubC2dnZ1G/fv0MjQxO7R7HxcWJr776Snh5eQmVSiXc3d1F//79DabSZiWhk0ajEWvWrBENGjQQDg4OwszMTPj4+Ij+/fuL27dvGy0/Y8YMUbx4cWFhYSGqVasmDhw4YDAbRQghLl26JBo3biwsLCyEk5OT6N27t9i2bZvRpF6HDx8WLVq0EE5OTkKlUomiRYuKFi1aiI0bN6ZZ/1eTer3s119/FZaWlqJMmTLi33//Fbdv3xZdunQRzs7OQqVSiTJlyoiZM2fqJd9J63hCCPHXX3+Jjh07isKFCwuVSiXc3NzEu+++qzerJ7VpocZmlCQkJIgvvvhCFC5cWFhYWIhatWqJ48ePG7xmszL19eV9bWxsRNOmTTM1HS4tqV3jq4y9b4UQYtmyZaJmzZrC2tpaWFpaihIlSohu3brpZoukXJuxx8vHU6vVYvr06aJ06dJCpVIJFxcX8dFHHxlM10ztcyW19wwgBg4cqLctLCxM9OrVSxQtWlSoVCpRqFAhUbt2bTFp0iS9cuvWrRNly5YVKpVKAGLs2LG657Lz+omPjxf9+vUTfn5+ws7OTvfaHjt2rN5sBGOePHkievfuLQoXLiysrKxE3bp1xZEjR4y+b9Oq/6u2b98uGjVqJFxdXYWpqamwsbERNWrUEPPmzTOYdSOEEP/8849o27atsLOzE1ZWVuK9994Tf/75Z5p1T3Hs2DHh7+8vrKysRKFChcTHH38szpw5Y/AdltrnqLH3y927d0WHDh2EjY2NsLW1FR06dNClBUhvNsqjR49EkyZNRKFChYRKpRKenp7i008/NfjuzIlrz+zrNK3PMGMU/x1MyiWJiYl4e3tTt25dg8Q6kvSm+/zzz1m1alW6gxMlSZLS8tYMEH3TPHz4kKtXr7J8+XLu37/PiBEj8rpKkpRhDx484Pjx4wQHB+Pv75/X1ZEkKZ+TS8znkl27dhEQEMCePXtYsGCB3nRXSXrT7d69m65du1KqVKksT6mWJElKIbtRJEmSJEnKVbJlQ5IkSZKkXCWDDUmSJEmScpUMNiRJkiRJylVyNkomaDQa7t27h62tbZYWDpMkSZLeDkIInj17RpEiRdJN1S/JYCNT7t27l6UFwyRJkqS30507dwyypEqGZLCRCSnrCty5cydHUj1nVlJSEvv27aNJkyZ6S5NLOUve59wn7/HrIe9z7omJicHDwyPT680UVDLYyISUrhM7O7s8CzasrKyws7OTHxy5SN7n3Cfv8esh73Puk13qGZOvO5oWLFiAj48PFhYWVK1alSNHjqRZ/vDhw1StWhULCwuKFy/OwoULX1NNJUmSJKngyrfBxoYNGxgyZAhff/01Z8+eJSAggGbNmhEeHm60fFhYGM2bNycgIICzZ88yatQoBg8ezObNm19zzSVJkiSpYMm3wcacOXPo3bs3H3/8Mb6+vsydOxcPD49UlwpfuHAhnp6ezJ07F19fXz7++GN69erFrFmzXnPNJUmSJKlgyZfBRmJiIn/++SdNmjTR296kSROOHTtmdJ/jx48blA8MDOT06dMkJSXlWl0lSZIkqaDLlwNEo6KiUKvVuLq66m13dXUlMjLS6D6RkZFGyycnJxMVFYW7u7vBPgkJCSQkJOh+jomJAbSDrvIiQEk5pwyOcpe8z7lP3uPXQ97n3CPvaebky2AjxaujgIUQaY4MNlbe2PYUU6dOZfz48Qbb9+3bh5WVVWarm2P279+fZ+cuKKI10SzavQhnE2fslfZ5XZ23lnwtvx7yPue82NjYvK5CvpIvgw0XFxdMTEwMWjEePHhg0HqRws3NzWh5U1NTnJ2dje4zcuRIhg4dqvs5ZV51kyZN8mzq6/79+2ncuLGcxpaLNl/bzKzTsxAIlCgZXXM0bUu0TXOf+7H3CX8WjqetJ4Du/65Wxl+PBZ18Lb8e8j7nnpSWbilj8mWwYWZmRtWqVdm/fz/t2rXTbd+/fz9t2rQxuo+/vz87duzQ27Zv3z6qVauW6pvQ3Nwcc3Nzg+0qlSrrb9zkRNg7Eko2Ap/6YJb5FpJsnV9KU+SLSKaenopA2+qlQcOkU5MI8AjAzdrN6D7B14MZf3w8GqFBgbaVTCBQKpSM9R9L+1LtX1v98xv5Wn495H3OefJ+Zk6+HCAKMHToUJYsWcKyZcu4fPkyn3/+OeHh4fTr1w/Qtkp069ZNV75fv37cvn2boUOHcvnyZZYtW8bSpUsZNmzY66347T8gdAms6wwzfGBtJzi9DGLuvd56SEaFx4SjQaO3TSM03Hl2x2j5yBeRukADtEGGLlARGsYfH0/kC+PjiCRJkgqKfNmyAdCpUycePXrEhAkTiIiIoEKFCuzevRsvLy8AIiIi9HJu+Pj4sHv3bj7//HN++OEHihQpwrx58+jQocPrrbitOy8q98Lk+l4sXvwL10K0Dz4H90pQuhmUaQrulUFmpnvtPO08UaLUCziUCiUetsbXxAmPCdcFGsakBCqptYpIkiQVBPk22AAYMGAAAwYMMPrcihUrDLbVr1+fM2fO5HKt0rbhthUjTzZCI96jrPIuMyrew+/FcbgbChF/aR+Hp4GtO5QO1AYfxeuDyjJP611QuFm7MbrmaCaenKjXFZJasOBp54lSoUw14DAWqES+iCQ8JhxPO08ZhEiSVCDk62Ajv4mIjmNk8AU0AkDBFY0H7c57cnTESNxNnsP1fXBtD9w4CM8i4M8V2oepJRSvj6JEYyyS5K8st7Ut0Za4y3GUql4KH0efNAMCN2s3xvqPTXPMxsv7vzy+Q47pkCSpoJDfXK9RWNSL/wKN/1MLwa2oWNxLFIIqXbWP5AS4dQSu/tfFEn0HroVgei2EQEATtQzKNofSTbVdL7K7JccVuXyXkk/VOLQoAdZpl21fqj21i9TmzrM7ulaMlP+/HGi8Or4jZUxHKYdSxCXHyZYOSZLeWjLYeI18XKxRKtALOEwUCrxdXpmRYmquna1SshE0nwn3/4Zre9BcDUHx758oI/+CyL/g0FSwLaLtbinTDHzqye6WHOL4+xGibt0iauYsrGrUwK5Fc+yaNMHE3njODTdrN71AwVjQYGx8h0Zo6Lq7q5y9IknSWy3fzkbJj9ztLZnaviIm/7VEmCgUTGlfAXf7NAIEhQLcKkC94ah7hLC3wjySW86Dsi1BZQ3P7sGfy2FtR5juA2s7a7tenskZEFklhOB5JT/M/fxAoyH2xAkix3zDtboB3Ok/gOidu9BkIaFPyvgOg/PJ2SuSJL3lZMvGa9apuif1ShfiVlQs3i5WaQcaRiSo7BGVmkO17pAUD7eOasd5XA2BmLva/1/boy1cpMr/Z7e4+cnulgxSKBQ8rV2b2pMmIe7fJ2bXbmJ27SLh2jWeHzzI84MHUVhaYtuwIXYtW2Bdty5KM7N0j2tsfEdKoJFCzl6RJOltJIONPOBub5npIMMolQWUaqR9NJ8F9y/+N85jD/z7J9w7q30cmgJ2Rf8/u8UnQHa3ZJBZsWK4fNIXl0/6knD9OtG7dhGzazdJd+4Qs3s3Mbt3o7Szw7ZJYxzatsWyatU0U+a/PL7DwsSCj/Z8pNe18vLsFTlrRZKkt4UMNt4WCgW4VdQ+6g+HZ/fh+l64thduHICYf7XJw04vA5UVFG8ApZty370BN+Ks8XGxzpkA6C1mXqoUhYcModBnnxF/4QIxu3YRs3sPyQ8fEr1pM9GbNmPm7Y19h/Y4tG+PaSpp8F8e3/FyS8fLs1fkrBVJkt4mMth4W9m6wjvdtI+k+P9mt+zRzm6J+Reu7oaru3EFIjQl+E79LlVb9eODWiXzuuZvPIVCgaWfH5Z+fhT+8ktiQ08TvWM7MXtCSLx1i4ez5xA1bz62TZvi+OGHWFapnGprx6szWdys3VKdtVK7SG3ZwiFJUr4kg42CQGUBpRprH2I2RF7g2fkd3PxjE5WUN6msvEFl5Q0i92wi5sUg7Or0BYvXv9BcfqQwMcG6Vk2sa9XEdeQonoXs4ckvG4k/f56YHTuI2bEDc19fHD/sjH3LliiNrBb86kyW1GatpIzlkN0rkiTlN3I2SkGjUIC7HxdKfkKbxElUj/+ByUldiBBOuCmeYHdkInxbAX4dp+2KkTLMxMYah/ffx+eXDXhv3Ih9+/YozM1JuHyZyG/Gcr1+AyKnTCHhZliaxzE2ayVlLEfw9WACNwfSe19vAjcHEnw9ODcvSZIkKUfIYKOASsn58RBHFqtbUi9hLl8mfUKyUylIiIaj38LcirDjM3h0I6+rm+9YVqxAkSmTKXX4EIW//BKVpyeaZ8948vMqbjZvTvjHfXh+9A+EEAb7psxaSQk4UsZsAEa7V+RUWUmS3nQy2CigXs35oVGoqNp2EKaDTkHntVCsBqgTtDk75leFX7rBv3m7rkx+ZOLggHOvnpQI2YPH4p+wadgQFApeHD3KnY8/Jqx1G55uDkaTmKi3X/tS7dnbYS/LApext8Ne2pdqn2r3yr5b+2TAIUnSG02O2SjAUs35UbYFlGkO4cfh6FztrJZL27QPn3pQZwiUeFfm7cgEhVKJTUAANgEBJIaH83jVap5u3kzC9etEfP01D779Fqdu3XD8sDMmtraA4ViO1BZ9m3l6JrP/nC1nrEiS9MaSLRsFnLu9Jf4lnA2nvSoU4FUbuv4C/Y+BX2dQmkLY77C6PSyqBxc2gTo5byqej5l5euL29ShKHTpI4eHDMHVzQx0VxcM5c/in4bs8mD2H5IcPDfZ7tXvlZbJLRZKkN5kMNqT0uZaH9otg8Dmo2V+bpyPyPGzuDfPfgVOLISkur2uZ75jY2eHcuzcl9++jyPRpmJUsgeb5cx4tXsw/7zUiYvx4Eu/c0dsnpXtleLXhBsdLmbEiSZL0ppHBhpRxDh7QbBp8/jc0GAVWzvD0Nuwepp3BcngGxD7O61rmOwqVCvs2bSi+fTvFFvyAZaVKiMREnq5bz42mzbj31Qi9GSxu1m408W6S6owVSZKkN40MNqTMs3KCBl/BkIvQbCY4eEJsFBycrA06QkZC9F2ju0ZEx3HsRhQR0bIl5FUKpRLbd9/Fa/06vFb9jHVAAKjVRG/bxs2WLfl32HAS/vkHSH3GirG8G5EvIjkVcUp2sUiSlGfkAFEp68ysoGZfqNYL/t4Cf3wH9y/AiQVw6ieo+AHU+QwK+wKwITSckcEX0AhQKmBq+4p0qu6Zxxfx5lEoFFhVr45n9erEXbhI1I8/8vzAAWJ27iRm1y5sAwNxGdCf9qUNs4++SqY9lyTpTSBbNqTsMzEFvw+g3xHouhm8A0CTDH+tgwW1YG0noi4d0gUaABoBo4IvyhaOdFhWrIDHgh/wCd6MbePGIATPQkIIa9OWf4cOxTHyBdXdqqfaoiHzckiS9CaQwYaUcxQK7Qq0PXbCxwfAtzWggGshuPzShl9U42ik/BMF2i8/tRDciorN2zrnExblylFs/jx8tm3DNjAQhCBm9x5utmzFv19+aTCQFNJOey5JkvQ6yWBDyh3FqkKnVTDoNLzTHWFiRjXlNZaYzWab2RhqKy9iolDg7WK4VoiUOosypSn23Vx8tm3FtnEjbdCxfQc3mrcgcsIEkh480JU1lvZcgYJHcY9k64YkSa+VDDak3OVSElrPQzHkApdL9OK5sMBPGcZasyn8UXQe7i+u5nUN8yWLMmUoNn8+3ps2YV2nDiQl8WTtOm40CeTBnG9RR0cbDCJVoE3CNvz34XJdFUmSXisZbEivh60bvkHf8qJfKBFleyCUKtyijsNP9WFTb3h8M69rmC9ZViiP59IleK5cqZ0yGx/Po59+4p/GTYj6aTFtizVjb4e9zKo/CwUKBNpBM3L8hiRJr5MMNqTXytXdE/fO36EYFAoVO2o3XtwE31eH3cPhuWHmTCl91jVr4LV+HcV++B7zUiXRxMRoM5I2aYLZtgM4Km3RIMdvSJKUN2SwIeUNJx/osBg++R1KvKedvXLqJ5hXGQ5OhYRneV3DfEehUGD73nv4bN1KkenTUBUtivphFPcnTMS+5xhqXxbw0iqzMgmYJEmviww2pLzlXgmCgqHbdihSBRKfw+Fp8F1lOLkIkhNlIrBMUpiYYN+mDSX27MZ1zGhMXFzQ/HuPIVvVTPlZg2+4kEnAJEl6rWRSL+nNULw+9DkIl7bCbxPh8Q3Y8yXPD89jenQbtqn9USiUMhFYJijMzHDq2hWHtm15tGIFj5cuo+S9WMavUaOqXwuPgCoG+8gkYJIk5QbZsiG9ORQKKN8OBp6EFnNQWxXCJvYuc1U/sNXsG6pwVSYCywKltTWFBg6kxL69OHzYGUxMSDr8BzdbtSZyyhTUT58CMgmYJEm5J18GG0+ePCEoKAh7e3vs7e0JCgri6X8fmMYkJSXx1VdfUbFiRaytrSlSpAjdunXj3r17r6/SUsaZqKB6b0JbH2BmUkeeCUsqKW+y2Xw835rO496t63ldw3zJ1MUF97FjKb59Gzb160NyMk9+XsU/TQJ5tGIF4Y9uyiRgkiTlinwZbHTp0oVz584REhJCSEgI586dIygoKNXysbGxnDlzhjFjxnDmzBmCg4O5du0arVu3fo21ljLLy70QP2ra0jBhDuuSG6IRClqbHOed7Y3hwCRIeJ7XVcyXzEuUwGPRQjyWLsG8dGk0MTE8mDYd+97fUPMachCpJEk5Lt+N2bh8+TIhISGcOHGCmjVrArB48WL8/f25evUqZcqUMdjH3t6e/fv3622bP38+NWrUIDw8HE9POQbgTeRub8nU9hUZFXyRkcl9WKNpwjK3zRR+fBp+nwlnV8N7Y8GvEyjzZdycp2zq1MF6SzBPg4N5+N081Hf+5Ys7cNlDwcr3lNwqYmIwiDTyRSThMeF42nkaHVwqSZJkTL4LNo4fP469vb0u0ACoVasW9vb2HDt2zGiwYUx0dDQKhQIHB4dUyyQkJJCQkKD7OSYmBtB2yyQlJWXtArIh5Zx5ce680r6yO/4+joQ/jsXTqR6Odh+TfHUXJr+NRfH0Nmzth+bkIjRNJiOK1ciRcxa0+2zTti1WjRvzZNlynq5cie+dBKatUGPashHFGvvr7sPWG1uZdHISGjQoUTK65mjalmibpXMWtHucV+R9zj3ynmaOQoiX2kzzgSlTprBixQquXbumt7106dL07NmTkSNHpnuM+Ph46tatS9myZVm9enWq5caNG8f48eMNtq9duxYrK7mmR15SahIp/nAfpSO3o9LEA3DXsRaXinQkzswlj2uXf5k+fYpLSAh2Z88BoDY35/F773LbvwIzY+fqMpCCNv35MLth2Cvt86i2kpR3YmNj6dKlC9HR0djZ2eV1dd54b0zLRmpf7C8LDQ0FtMmLXiWEMLr9VUlJSXTu3BmNRsOCBQvSLDty5EiGDh2q+zkmJgYPDw+aNGmSJy+upKQk9u/fT+PGjVGpVK/9/G+etvB8LJrDU1GcW0OxJyco+uwcmloD0fgPBjPrLB21wN/nLl2IO/cXUdOnk3DxIoV278Hprz+p4q/mTEmFdtYQIBCUql6Kaq7VMn2KAn+PXxN5n3NPSku3lDFvTLAxaNAgOnfunGYZb29vzp8/z/379w2ee/jwIa6urmnun5SURMeOHQkLC+PAgQPpBgzm5uaYm5sbbFepVHn6xs3r879RHItB2x+gZl8IGYni9h+YHJ2NyV9rodE4bUr0LI7nKMj3WVW9Gra/bCB623YezJkN/z5gxCY456NgZSMl/7ooUCqU+Dj6ZOseFeR7/DrJ+5zz5P3MnDcm2HBxccHFJf3mb39/f6Kjozl16hQ1amj76E+ePEl0dDS1a9dOdb+UQOP69escPHgQZ2fnHKu79AZwrwQ9dsHl7bBvDDy9DVs+IeboQhIaT6dQ6ZrpH0PSo1AqcWjXFtvGjXm0aCEPly+ncpiaCkvV7KmupPTQUXKQqCRJGZLvhvD7+vrStGlT+vTpw4kTJzhx4gR9+vShZcuWeoNDy5Yty5YtWwBITk7m/fff5/Tp06xZswa1Wk1kZCSRkZEkJibm1aVIOU2hgHJtYOAp/irzGc+FBXYPz+K8JpB/lvWF2Md5XcN8ycTGmsJffEGpXbtQ1a+NqQZandRQ/tOFRO/cRT4b9iVJUh7Id8EGwJo1a6hYsSJNmjShSZMm+Pn5sWrVKr0yV69eJTo6GoC7d++yfft27t69S+XKlXF3d9c9jh07lheXIOWiiFhBu/M1eS9hFtvUtVEqBCXDN6CeXxX+XAkaTfoHkQyYeXlRctFSPBYtROXpSfKDB9wbNozw7j1IuC4TrUmSlLo3phslM5ycnNKcRQLo/bXl7e0t//oqQMKiXqARcB8nPksaxDr1u4w3XUGZuLuwYzCcWQnNZ0HRd/K6qvmSTf36FK9Vi8fLlhG16CdiT53iZtt2OAUF4TJoICY2Ngb7yPwcklSw5cuWDUlKi4+LNcqXJiad0JSjddJUYuqPBzNb+PdPWPwu7PhMdq1kkdLcHJf+/Smxaye2jRuBWs3jFSu40awZ0Tt26AX3wdeDCdwcSO99vQncHEjw9eA8rLkkSXlBBhvSWycl86jJf1M0TRQKJrSvjF3DIfDpaW3GUQT8uQLmvwOnl4FGnZdVzrdURYtSbP58PBb/hMrLE/XDKO4N/5LwoG7EX72W6uJu92MNZ5RJkvT2ypfdKJKUnk7VPalXuhC3omLxdrHC3d5S+4StG7T/Car2gF3D4MHfsPNz7ViOFrOhWOZzRkhgExBA8R07eLxsOVELFxJ7+jRh7duT2PY9zD3VxFn8v6lJLu4mSQWPbNmQ3lru9pb4l3D+f6DxMq/a8Mnv0HQ6mNtBxDlY8h5sGwgvol57Xd8GSjMzXPp9Qondu7Bt0gTUasw27+O7n9QEXNDoFniTi7tJUsEjgw2p4DIxhVr94NM/oVIX7bazqzFdWAvPqEMg5KyVrFAVKUKxed/hsWQJZt7eOLyAT3dqGL9ajfdDGOs/FlcrbQK++7H3ORVxisgXkXlca0mScpMMNiTJpjC0+xF67SOpUHkU8U+pcmcZJitbQOTFvK5dvmVTtw4+27dRaOhQsLDA9y7MWK6hTvA/aGJjOZ1wmhZbW8iBo5JUAMhgQ5L+s+G+O+XujmBi0kc8FxYo/w2FRfVg79eQ8Dyvq5cvKc3McOnbh5J7dv/XtaLh8fLlhLVqRcS5LWiEdmBuysBR2cIhSW8nGWxIEhARHcfI4AskCROWqpvTKGEme9Q1QKjh+PfwQw24vEM37kDKHJW7u7ZrZdFCVMWKIR485ItgNSM2aij8RHtP5cBRSXp7yWBDkvh/IrAUkTjTP2kIl99dCg5eEPMvbPgI1naCJ7fyrJ75nU39+hTfuQOznl1JVsI7NwRzlqhpd0yDSqOQA0cl6S0lgw1JwjARGIBSAQ6VWsCAExAwDJQquL4XfqgFR2ZDslxXJyuUFhZ4Dv2KPYMCueilwCwZPjysYdlaR+wu383r6kmSlAtksCFJGCYCUyCY1KacdtqsmRW8Nwb6/wHeAZAcB79NgIV1IexIHtc8/ypTtCG1NuwlftQnKBwdML/zgNsfBXFv1NckP3lC5ItIOVNFkt4SMqmXJP0nJRHYjfsx3Dh3gg+qFtMvUKgMdN8B5zdoB41GXYWVLbXTZptMAmvnvKl4PuZm7YZHtyGoW/fgwew5PN24kejgYB7tD2Fx/QQOVgCl0oSx/mNpX6p9XldXkqQski0bkvQSd3tLavo44WCeSgGFAip11qY9r9YLUMBfa+H7anBurRxAmkUmDg64T5yA19q1KEv4YPoslv471Yxbo8YtSi1nqkhSPieDDUnKCktHaPkt9N4HhctD3GPY2h9WtoIoudx6Vlm9U4XohaNZ3VBJgimUuwMzl6rp8HsSdx7dzOvqSZKURTLYkKTs8KgBnxyGRuPA1BJuHYEfa8Oh6ZCckNe1y5c8nYqz09+UoX1MOFNcgUoNHxwV2PUdT2xoaF5XT5KkLJDBhiRlQ0R0HMduRRNRsR8MOA4lG4E6EQ5NgR/rwK2jeV3FfMfN2o2x/mN55GjCtI5KvmtrQrKDDZpb4dwO6sa90aNRP32a19WUJCkT5ABRScqiDaHhjAy+gEZop8lObV+RTl03wd/BsGcEPLoOK1pAlY+g8USwcsrrKucb7Uu1p3aR2tx5dgePDzwoNNJSO4D0l1+I3rSZ5wcP4TpyJHYtmqNQKNI/oCRJeSpHWjbu3LnDkSNH2Lt3L2fOnCEhQTYfS2+3lIyjKYnANAJGBV8kIiYeKnSAQaFQtaf2ybOrtRlIL2ySA0gzwc3ajepu1XGzdsPE3h73CePxWrMasxIlUD96xL1hw7jTpy+Jd2VuDkl602U52Lh9+zYjR47E29sbb29v6tevT7NmzahWrRr29vY0btyYjRs3otHIlTOlt8+rGUcB1EJwKypW+4OlA7SaC732QqGy8OIhbO4Na96XGUizwapqVXy2BOMy+FMUKhUvjh7lZstWPFq6FJGcLHNzSNIbKkvBxmeffUbFihW5fv06EyZM4O+//yY6OprExEQiIyPZvXs3devWZcyYMfj5+REqB3VJbxljGUdNFAq8Xaz0N3rWgk+OQMPRYGIG//wKC/zhj3mgTn59FX6LKM3MKDRgAD7bt2FVowYiPp4HM2dxrlVj+s9vLFeRlaQ3UJaCDTMzM27cuMGmTZvo1q0bZcuWxdbWFlNTUwoXLsy7777L2LFjuXLlCjNmzOD27ds5XW9JylOvZhw1USiY0r6CNuPofyKi4zh2I4qIF2qoPxz6HwOvupAUC/vHwOKGcO9sXl1Cvmfu44PnyhW4T5mCwt4Oi7BIJq1Ipsd+NWbxMjeHJL1JsjRAdObMmRku27x586ycQpLeeCkZR29FxeLtYqUXaBgdPFq9FPTYqR3DsW80RJ6Hxe9CrQHQcBSYWefh1eRPCoUCh/btuO5ry5nRg6n3t6D5aUGNa2oWBwruPLuDm7UbAJEvIgmPCcfTzlO3TZKk10NOfZWkbHC3t8S/hLNBi4bRwaPRcdoMpO8EaQeQVugAQqNdwn5BLfjntzy6ivzPw7MCC9qomNhZyX0HcImBkRs1OE5ZTnJUFMHXgwncHCi7WCQpj2Q72Hj06BEDBw6kXLlyuLi44OTkpPeQpIIm3cGjADaF4f1l0OUXsCsGT8NhdXsI7gsvHr3eCr8FUnJz/F3clGG9TdhRU4lQKkjad5B/mjfn8MJv0GjUAGiERnaxSNJrlu08Gx999BE3btygd+/euLq6yjnvUoGXMnj05YDD6OBRgNKBMPAEHJgEJxdpF3m7vh+aTgO/jtqWEClD9HJzdPHA/tYjIsaMIeHSZfrtgroXFfzUVEmkkwKN0Oh1sUiSlLuyHWwcPXqUo0ePUqlSpZyojyTleymDR0cFX0QthNHBo3rMbaHZdKj4AWz/FB5cgi19tYFHy2/B0ev1XkA+5mbt9v8AorwbPr/8wu3F3/P0h0VUuC2YtVTNxrpKdtc0wcPWI28rK0kFSLaDjbJlyxIXF5cTdZGkt0Zag0dTRETHERb1Ah8Xa+3zxapB38Nw7Ds4PBNu/KYdy/HuGKj5CShN8uBK8jeFqSne/Yewo7w1sVO+xe+WoOshDR3CXbGv8QjKy5YNSXodsj1mY8GCBXz99dccPnyYR48eERMTo/fIDU+ePCEoKAh7e3vs7e0JCgriaSbWSvjkk09QKBTMnTs3V+onSWB88GiKDaHh1Jl2gC6LT1Jn2gE2hIZrnzA1g3rDof8f4FVHO01270hY2hju//2ar+Dt0apeH+pt+o34r/qgsLPF4mYEtzp24sGsWWjkH0uSlOuyHWw4ODgQHR3Nu+++S+HChXF0dMTR0REHBwccHR1zoo4GunTpwrlz5wgJCSEkJIRz584RFBSUoX23bt3KyZMnKVKkSK7UTZLSk+ZslRQupaD7Tmg5F8zt4N8/YVE97dgOuZpslrjbuFOl51BK7t6NbbOmoFbzaMlSbrZpy4sTJ/O6epL0Vst2N0rXrl0xMzNj7dq1r2WA6OXLlwkJCeHEiRPUrFkTgMWLF+Pv78/Vq1cpU6ZMqvv++++/DBo0iL1799KiRYtcrackpSat2Sp6rSBKJVTrqR1EumsYXN0Fv8+ES9ug9XxtdlIp00xdXCj27bc8a9WKyPETSAoPJ7xHDxw+eJ/Cw4djYmcnc3JIUg7LdrBx8eJFzp49m+aXfE46fvw49vb2ukADoFatWtjb23Ps2LFU66HRaAgKCmL48OGUL18+Q+dKSEjQW1QupVsoKSmJpKSkbFxF1qScMy/OXZDk9n0uZm9uMFtFqYCi9mbGz2lZCDqsQHF1JyYhX6GIugbLAlFX7Y2m4WjtANN85k14LVsEBOCxJZhH331HzIZfeLpxE88OHiKsdyO+VAajQYMSJaNrjqZtibZ5Vs/seBPu89tK3tPMyXawUa1aNe7cufPago3IyEgKFy5ssL1w4cJERqY+b3769OmYmpoyePDgDJ9r6tSpjB8/3mD7vn37sLIyMo3xNdm/f3+enbsgyc373NFHwYabSgQKFAg6+mg4+8cB0k5eboKq+HjK31uP16PDmPy5lITzW/jLoycP7PPnbLA34rX8zjtYOjriumkzZlFReE5fz+dlFCxtouSpjYaJJycSdzkOe6V9Xtc0y96I+/yWiY2NTb+QpJPtYOPTTz/ls88+Y/jw4VSsWBGVSqX3vJ+fX4aOM27cOKNf7C9LWdDNWFeNECLVLpw///yT7777jjNnzmSqm2fkyJEMHTpU93NMTAweHh40adIEOzu7DB8npyQlJbF//34aN25scJ+lnPM67nNzYEB0POGPY/F0ssLd3kLv+YjoeG4/isXL2fA5+IDksN8x2T0Uq6e38L85G02F91E3ngxWzrlS35z2Jr6WNR9/zN9zxqFav4uaVwUVbqn5+T0lB/0UlKpeimqu1fK6ipn2Jt7nt0VuTYB4W2U72OjUqRMAvXr10m1TKBS6L3+1Wp2h4wwaNIjOnTunWcbb25vz589z//59g+cePnyIq6ur0f2OHDnCgwcP8PT01G1Tq9V88cUXzJ07l1u3bhndz9zcHHNzc4PtKpUqT9+4eX3+giK377OniwpPF8MuEOPrqnjqFyr9Hngfg4NT4MQClBc3obx5EJrN0KZBzyfJwN6o17JKRZEvhtHHai99dyVTMgL679YQ8LcCT3/zN6eeWfBG3ee3hLyfmZPtYCMsLCwn6oGLiwsuLi7plvP39yc6OppTp05Ro0YNAE6ePEl0dDS1a9c2uk9QUBCNGjXS2xYYGEhQUBA9e/bMfuUlKYekNlOlXulChlNozawhcDKUbw/bB2mTgW3uDRc2QovZYF/s9V9APudm7UbPNuMZ4zKOpqFqOh/WUOG24Fmn3jwa8hlOQUEoTPTzncjBpJKUvmwHG15erze7oa+vL02bNqVPnz4sWrQIgL59+9KyZUu9cSNly5Zl6tSptGvXDmdnZ5yd9ZuXVSoVbm5ur22siSRlRIZnqrysWFVtMrA/5sLhGXAtBG79AY3HQdVe2lktUobp0p43vUOhT5UkT51P7MmTPJg2nZjde3CfNBGL0qUBCL4ezPjj49EIDUqFkrH+Y2lfqn0eX4EkvXmy9Cl0/PjxDJd98eIFf/+ds8mI1qxZQ8WKFWnSpAlNmjTBz8+PVatW6ZW5evUq0dHROXpeScptKeuqvCzVdVVeZmoG9b+EfkehWHVIfAa7voAVLSDqeu5V+C3lZu1GdbfqFC1bFc8Vy3GbOAGljQ3x588T1uF9Hs7/noind3SBBsgF3iQpLVkKNrp160bjxo355ZdfeP78udEyly5dYtSoUZQsWZIzZ85kq5KvcnJyYvXq1bospatXr8bBwUGvjBCCHj16pHqMW7duMWTIkBytlyRlV8q6Kib/jblId12VVxUuC732QtPpoLKC8GPwYx04+i2ok3Ox5m8vhUKB4wcfUHzXTmzefReSkoj64Qcede5J8bv6Y9JSFniLfBHJqYhTMvCQpP9kqRvl0qVLLFq0iG+++YauXbtSunRpihQpgoWFBU+ePOHKlSu8ePGC9u3bs3//fipUqJDT9Zakt1Z666oYrKnyKqUJ1OoHZZrBjs/g5kH4dRz8vQXa/ABuFV/PhbxlVK6uFPvhe56FhBA5cRLc+pdJt2B3dQUb6ilJMFOgVCi5GHWRj/d9LLtWJOklWQo2VCoVgwYNYtCgQZw5c4YjR45w69Yt4uLiqFSpEp9//jkNGzbEyckpp+srSQWCu71lqmuqpDtTJYWjFwRtgXNrteurRPwFPzWAOkO0XS6mhjOtpLQpFArsmjXDqlYtHkybRvS27bQMFVS/rmZxM1MadRjC3DNzDbpWahepLQePSgVatgeIvvPOO7zzzjs5URdJktKQqZkqKRQKqNIVSjaC3V/A5R1wZJb23zbfg0eN13cBbxFTR0eKTJ+OXcuW/DtmDK6R9xm9LpnEmJNYlFYTa/H/gTcpXSsy2JAKMjlMXZLyibRmqqTL1hU6rYaOP4N1YYi6CkubwJ4RkPgidypcANgEBFBy5y4cu3QBwGzXYeYsVlP1ukZXRqlQ4mHrYbCvHNchFSTZDjbu379PUFAQRYoUwdTUFBMTE72HJEk5I8szVV5Wrg0MPAmVugACTv4IC2rBjYM5WteCxMTGGrdvxuC1ehVmXl44PYevNmn4bKsa+1gFY/3HGrRqBF8PJnBzIL339SZwcyDB14PzqPaS9HpkuxulR48ehIeHM2bMGNzd3XN91VdJKqhSZqqMCr6IWojMz1RJYeUE7X6Eih1gxxB4Gg6r2kKVIGgyCSwdcqH2bz+ratXw2baVqB8W8GjZUupc1lA3wgp3D3NEyf8vpxD5ItLolFk5rkN6m2U72Dh69ChHjhyhcuXKOVAdSZLSkt5MlUwp2QgGHIdfx0PoYji7Cv75FVp+q53JImWa0sKCwl8MxTYwkIivvybh6lXuDRtGzK5duI0bi8rVlfCYcF2gkUKO65DedtnuRvHw8EAIkX5BSZJyhLu9Jf4lnLMXaKQwt4UWs6DnHnAqAc8iYF1n2NQbXkRl//gFlGWF8vhs/AWXwZ+CSsXzgwe52bIVTzZuxMPWA6VC/6M3tXEdkvS2yHawMXfuXEaMGJHqYmaSJOUDXrWh/x9Q5zNQKOHiJvihBlzYBPKPiSxRmJlRaMAAfDZvwsLPD82zZ0SO+YbET0cxyedTXcCRkovj5VYNOXhUetvkyKqvsbGxlChRAisrK4OV8B4/fpzdU0iSlEXpJgB7mcoSGk+Acm1h2yB48Ld2YbeLm6HFHLBzfy11fttYlC6N97q1PF75Mw+/+47Y4ycode4vtg3ow8Pm1fFw8NILNOR6K9LbKNvBxrfffisHhUrSGyhTCcBeVvQd6HsIjs6B32fB1d3ahd0CJ0OVj/LN8vVvEoWJCc69emL73rtEjB5DbGgocbN/oNCBKjhOngTFteXk4FHpbZUjs1EkSXqzZCkB2MtMzaDBCPBtBdsGwr2z2mXsL26G1vPAIQNBi2TAzMsLz5UrePrLLzyYMZO4s2cJa9sOl08H4dyzZ7qDR+Vy9lJ+le0xG127dmXx4sVcu3YtJ+ojSVIOyFYCsJe5lofev2q7V0wttOusLPCHU4tBo0l/f8mAQqnEsXNniu/cgXXduojERB7OnsOtTp0pcj8p1cGjMjeHlJ9lO9iwsbFh9uzZlC1bliJFivDhhx+ycOFCrly5khP1kyQpC3IkAZhuR1PtwNF+f4CnPyQ+h93DYGVLeHQjZypcAKmKFMFj8U+4T52K0s6O+L//5tlH/ZgfVheVRvvLSxmzAcjl7KV8LdvBxqJFi7hy5Qr37t1jzpw52Nvb891331G+fHnc3eWAMknKC9leqt4Yl5LQYzc0mwkqa7j9B/xYG47NB406/f0lAwqFAod2bSm+cwc2770HyckUWneAdZuKscJrNHs77KV9qfZpdq9IUn6QY2uj2Nra4ujoiKOjIw4ODpiamuLmJvsUJSmvdKruydERDVnXpxZHRzTM2ODQ9CiVULMvDDgGPvUhOR72jdaus/JAtmZmlapwYYp9P5+ic2Zj4uiI5kYYVgMmoFy4Bk18PJ52nql2r8hpslJ+kO1g46uvvqJWrVq4uLgwevRoEhMTGTlyJPfv3+fs2bM5UUdJkrIoRxOAvczRG7ptg1bzwNwO/j0NiwLg95mgTsrZcxUQCoUCu+bNKb5rJ3YtWoBGw6PFSwhr1x67q/cY6z/WIDfHsXvH5DgOKV/I9myUmTNnUqhQIcaOHUubNm3w9fXNiXpJkvSaZCoXx8sUCqjaXZv2fOfncH0vHJgEl7ZBmwXg7pd7lX6LmTo5UXT2LOyaNyNy3HgSw8K43fUjagd9REjfrdxNjtJlGw3cHCinyUr5QrZbNs6ePcvXX3/NqVOnqFevHm5ubnTq1Ikff/yRy5cv50QdJUnKJRtCw6kz7QBdFp+kzrQDbAgNz/xB7ItClw3QfjFYOkLkBVjcEA5MhuSEnK90AWH73nsU37kD+/btQQie/LyKF537Uu6WGjdrNzmOQ8pXsh1sVKpUicGDBxMcHMzDhw/Zu3cvVlZWDB48mAoVKuREHSVJygWp5eKIiI7L/MEUCvDrCANPgW9r0CTD7zNgUX24+2fOVrwAMbG3p8iUyXgsXoypuztJd+8S3qMnEWPH4aF0zvAaK/dj78txHVKeypEBomfPnuXbb7+lTZs2NGzYkFWrVlGpUiWGDh2aE4eXJCkX5FgujpfZFIZOq+CDlWDlAg8vw9JGsG8MJGUhiJEAsAmoS/Ed23Ho3AmApxs28LxzH2aYfZjmGisApxNO02JrCzmuQ8pT2R6z4ejoyPPnz6lUqRINGjSgT58+1KtXDzs7u5yonyRJuSQlF8fLAUeWc3G8qnxb8A6AkBFw4Rc4Ng+u7II2P4CXf/aPXwCZ2NjgPm4cdk2bETFmDEl37uA5biXBrZryuG8bPIqUNQg07sfeZ1vcNgTaX7Ic1yHllWy3bKxatYpHjx5x+vRpZs2aRcuWLWWgIUn5QK7k4niZtTN0WAwfrgdbd3h8A5Y3gz1fQeKLnDlHAWRdqybFt23FqXs3UChI3BGCQ69vsD55yaBs+LNwXaCR4tVxHXLqrPQ6ZLtlo2XLlrr/3717F4VCQdGiRbN7WEmSXoNO1T2pV7oQt6Ji8XaxMhpoZHm2SooyzbSZR/d9DWdXw8mFmF7dg4vLh0Dz7F9EAaS0ssJ15EhsA5sS8fXXJIaFcXfAQOxatsT161GYOjoC4GnriQKFXsDx8rgOucKs9Lpku2VDo9EwYcIE7O3t8fLywtPTEwcHByZOnIhGrp0gSW+8tHJxvDxbpfbUA0zZdSlrA0gtHbRdKB8Fg70Hiqe3qfPPNJR7hkHCs+xfRAFl9U4VfLZuwbnPx6BUErNzJzdbtiImZC8ArlautLFsY3RcR2orzMoWDik3ZDvY+Prrr/n++++ZNm0aZ8+e5cyZM0yZMoX58+czZsyYnKijJEl54NXZKgL46UhY1qfIApR8D/ofQ/1ODwBMzqzQLuz2z685UeUCSWluTuEvvsB7w3rMS5VC/egR/w4Zwt3Bn5Ec9Yhq5tXY1WYXywKX6dKfA3LqrPRaZTvYWLlyJUuWLKF///74+flRqVIlBgwYwOLFi1mxYkUOVFGSpLxgbLYKZHOKLICFHZpms/ij5AiEgxdE34HVHbRL2cc9zVadCzLLihXx3rwJlwH9wdSUZ/v2Ed6uHbZnz1HYsjDV3arrDQpNKwW6JOW0bAcbjx8/pmzZsgbby5Yty+PHj7N7eKOePHlCUFAQ9vb22NvbExQUxNOnT9Pd7/Lly7Ru3Rp7e3tsbW2pVasW4eFZ/AtNkt5yxlaOTZHtKbJAlG05kvv8DjX7AQrteI4FteBqSLaOW5ApzcwoNHgwPht/wdzXF83Tp7ivX0/E4MEk3X+gV9bN2s1oCvT0ZqnIAaVSVuRIUq/vv//eYPv3339PpUqVsnt4o7p06cK5c+cICQkhJCSEc+fOERQUlOY+N27coG7dupQtW5ZDhw7x119/MWbMGCwsLHKljpKU36XMVjH2IZEyRTYiOo5jN6Ky3sphZg3NpkPPPeBUAp5FwLpOENwXYnPnj5WCwMLXF59fNuD06SCEiQmxhw5zs2VLnm4ORoj/N1e1L9WevR32GnSxpCb4erBci0XKEoV4+ZWXBYcPH6ZFixZ4enri7++PQqHg2LFj3Llzh927dxMQEJBTdQW0rRPlypXjxIkT1KxZE4ATJ07g7+/PlStXKFOmjNH9OnfujEqlYtWqVVk+d0xMDPb29kRHR+fJ9N6kpCR2795N8+bNUalUr/38BYW8z/oiouNYfvQWS47eRCP+P0UW0I3pUCpgavuKGV5Z1ug9ToqDg5Ph+A8gNGBdGFrMhnKtc+vS3npJSUn8umw5ZfbvJ+HiRQCsAwJwnzAelbt7po4V+SJSby0W0LaG7O2wt0Dm7Mjr74P8JttTX+vXr8+1a9f44YcfuHLlCkII2rdvz4ABAyhSpEhO1FHP8ePHsbe31wUaALVq1cLe3p5jx44ZDTY0Gg27du3iyy+/JDAwkLNnz+Lj48PIkSNp27ZtqudKSEggIeH/azvExMQA2jdwUtLrX9ky5Zx5ce6CRN5nfS5WpgxvUpKPahYj/HEsnk7apF8NZv+ul+p8ZPAF/H0ccbdPv7XQ+D02hYZjUZRuicnOwSiirsIvQWh826AOnA7WLjl9aW+9pKQkEt1ccV22lBfr1vH4hwW8OHKEGy1b4fLFF9i93wGFIpW+slfcfHLT6IDSsCdhOJs550b132jy8yFzst2y8bpNmTKFFStWcO3aNb3tpUuXpmfPnowcOdJgn8jISNzd3bGysmLSpEk0bNiQkJAQRo0axcGDB6lfv77Rc40bN47x48cbbF+7di1WVjmQZVGS8qnr0Qq+v2RisH1QOTWl7LP/kaLUJFE6chul7u9EiYYEU1vOFwvinkNN7TosUpaoHjzAbeMmLP8bqxZbogSR73cg2ckp3X2jNdHMipmll7NDgYJhdsOwV9rrlXukfoSzibPe9rdNbGwsXbp0kS0bGZSllo3z589nuKyfX8aWmU7ti/1loaGhAEYjcSFEqhF6Sr6PNm3a8PnnnwNQuXJljh07xsKFC1MNNkaOHKm3vktMTAweHh40adIkz7pR9u/fT+PGjWXzfi6S9zl9EdHxLLj8u95sFaUCOjZvmOGWjfTvcRvUEX+h2DkY8wd/U/3WAjRlbqNuOgNsXHPmQt5yxu6zCAoieu1aHs2bj9WNG5SYNx/nIUOw79wJhTLtYXyWNyyZdGqSLgnY6BqjaVuire75rTe2MvvkbDRoUKJkdE39598mKS3dUsZkKdioXLkyCoXC4As+pZHk5W1qtTpDxxw0aBCdO3dOs4y3tzfnz5/n/v37Bs89fPgQV1fjH0AuLi6YmppSrlw5ve2+vr4cPXo01fOZm5tjbm5usF2lUuXpl1Ben7+gkPc5dZ4uKqa2r8io4IuohdCN4/B0sc3UcdK9x57VoO8hODoHfp+J8uoulOHHoOl07SqzspUjQ/Tus0pFoV69sH/vPSK+Hk3s6dNETZ1K7P79uE+ehJmXV6rH+aDsBwR4BHDn2R08bD30xmpEvojUBiL8lyQMDZNOTSLAI+CtHNMhPxsyJ0vBRlhYmO7/Z8+eZdiwYQwfPhx/f+0CS8ePH2f27NnMmDEjw8d0cXHBxSX9Pll/f3+io6M5deoUNWrUAODkyZNER0dTu3Zto/uYmZlRvXp1rl69qrf92rVreKXxxpIkKXUZSXWeI0zNoMEIKNsCtg6AyPOwpS/8HQwt54Jd5gY6SlpmXl54/rySJ+vW8WD2HGJPn+Zmm7YUGvIZTkFBKEwMu8lAO2XWWPCQVpKwtzHYkDInS8HGy1/QH3zwAfPmzaN58/+vceDn54eHhwdjxoxJcwBmVvj6+tK0aVP69OnDokWLAOjbty8tW7bUGxxatmxZpk6dSrt27QAYPnw4nTp1ol69eroxGzt27ODQoUM5Wj9JKkjc7S1zL8h4lVtF6HMA/pgLh2fAtRD4oSY0nQKVu8pWjixQKJU4de2KTf36RIweQ+yJEzyYNp1ne/fhPnky5sV9MnyslCRhr85WeTlJWOSLSMJjwvG085QBSAGT7TwbFy5cwMfH8AXp4+PDpUuGqxDmhDVr1lCxYkWaNGlCkyZN8PPzM5jSevXqVaKjo3U/t2vXjoULFzJjxgwqVqzIkiVL2Lx5M3Xr1s2VOkqSpC/bOTkATFRQbzh88jsUeQcSorWZR9e8D9F3c66yBYxZsWJ4Ll+G2/jxKK2tiTt7lrC2bXm0ZAkiOTlDx0gvSZjM0VGwZXs2yjvvvIOvry9Lly7VJchKSEigV69eXL58mTNnzuRIRd8EeT2vWuZ/eD3kfc55G0LD9XJyTGpTDuv757N3j9XJcPx7ODgF1AlgZgtNJkLVHrKV4z9ZeS0n3btHxDdjefHfeDaLihUpMmUy5qVKZWj/yBeRBmM63sYcHXn9fZDfZLtlY+HChfz66694eHjQqFEjGjVqRLFixdi/fz8LFy7MiTpKkpSPvbqgm0bA6G2XeJqQ9n7pMjGFukOg31EoVgMSn8HOIfBzG3hyO5sHL7hURYrgsfgn3CdPQmlrS/yFC4S170DUwkWIDOSWcLN2M1iHRS76JmU72KhRowZhYWFMnjwZPz8/KlasyJQpUwgLC9MN4JQkqeAytqCbRsDD+BxqfShUGnqFQOAUMLWEsMPalWRPLQaNJv39JQMKhQKHDh0ovnMHNvXrI5KSeDh3Lrc6dSb+lYH2GZHeom9yvZW3X7YziAJYWVnRt2/fnDiUJElvmZQF3V7NyVHIIgfzCSpNwH8glG4K2wZB+DHYPQz+3gpt5oNT8Zw7VwGicnWl2MIfidmxg8jJU4i/dImw9z/A5ZNPcOnbB4WZWYaOkzKeY/zx8bocHSnjOYKvBxtsT2+NFin/yXbLRpEiRejSpQs//fSTQVZPSZKklAXdTP4bR2GiUDCpTTkcDFPYZJ9zCeixC5rNBJU13D4KP9aBEz/KVo4sUigU2LduTfEd27Fp9B4kJRH1/feEfdCR+ExMAjC26Fvki0hdoAHarpXxx8fLFo63ULaDjdmzZ2NnZ8ecOXMoW7Ys7u7udO7cmYULF3L58uWcqKMkSflcp+qeHB3RkHV9anF0REM+qFos906mVELNvjDgGHgHQFIshIyA5c0g6p/cO+9bTlW4MMXmz6fI7FmYODiQcPUqYR905MF336FJTMzQMV4dzyHHchQc2Q42PvzwQxYuXMiVK1eIiIjg22+/xdTUlE8//ZQKFSrkRB0lSXoLuNtb4l/C+fXl5XD0hm7bocUcMLOBOydgYR04Nh80GctsLOlTKBTYt2hB8V07sW3aFNRqHv24kFsdOhB34WKmj5feWI5XybEd+VeOjNl4/vw5R48e5fDhwxw6dIizZ89SsWLFVNccedup1epcWREwKSkJU1NT4uPjM5wGXsq83LjPKpUKk1QyMkrGRUTHERb1Ah8X66wHKEolVO8NpRrD9sFw8yDsGw2XtkGbH6CQ4SrRUvpMnZ0pNvdbYkKaEjlhAgnX/+FW58449+qFy6CBKI0s82BMWmM5XiXHduRv2c6zUbNmTc6fP0+FChVo0KAB9erVIyAgAAcHhxyq4psjvXnVQggiIyN5+vRprpxfCEFcXByWlpYZXhZayrzcus8ODg64ubnJ3x3p5394NS/H1PYV6VTdM3snFQLO/KwNNhJiwMQcGo4E/0+102jfQq8jZ0zykyfcnziJmN27ATArUYIikydhWblyho9hLDfHq88by9Oxutlq4pLj8iQjqcyzkTnZfoddv34dKysrihcvTvHixSlZsuRbGWhkREqgUbhwYaysrHL8S0Wj0fD8+XNsbGxQprM6o5R1OX2fhRDExsby4MEDANzd5VoeaTGWl2NU8EXqlS6UvS4YhQKqdoeS78GOIfDPfvh1HFzaDm0XQGHfnKh+gWPq6EjRObOxa96MiHHjSbxxg1tduuLUoweFBn+K0iL9VYBTW28lRWpjO7ru7opAyJaOfCDbwcbjx485f/48hw4d4tdff2Xs2LEolUrq169Pw4YN6devX07U842nVqt1gYazs3OunEOj0ZCYmIiFhYUMNnJRbtxnS0vtl+SDBw8oXLiw7FJJg7G8HGohuBUVmzPjPeyLQdeNcG4thIyEe2dgUT2o/yXUGaJNiS5lmm2jRlhVq0bklCnEbN/B42XLeH7gAO5TJmP1zjvZOraxdVcABNoXSsosltpFaufbjKRvuxz5JPXz82Pw4MFs3ryZPXv20KxZM4KDgxk4cGBOHD5fSBmjYWVllcc1kd5UKa+N3BjP8zZJycvxMhOFAm+XHHxvKRRQpSsMPKnNzaFOhAOTYMl7EJn5gY6SlomDA0VnzKDYjwswLVyYxFu3uN31I+5PnYomLutr4ry67ooCw1bjlFkschDpmynbwcbZs2f59ttvadOmDU5OTtSqVYsLFy7w2WefsX379pyoY74i++Ol1MjXRsYYy8sxpX2F3JnFYucOH66Hdj+BhQNE/AU/NYBD0yA5Y9M5JUO2DRtSfMd27Nu1AyF4vPJnbrZtS2xoaJaP+XKejjXN1xidxXIx6qJc7O0Nle1ulOrVq1OlShXq169Pnz59qFevnhwsI0lStnSq7km90oW4FRWLt4tV7k6XVSigUico3gB2DYUrO+HQVLi8UzuWw90v9879FjOxt6fI1CnYNWtKxDdjSbodzu2gbjh27UrhoZ+jtLbO9DFfHtvx6iyWIe8MYe6ZuQYJwl7tWpHL3OeNHBmzIYOLt9uhQ4do2LAhT548KbCDf6XXz93e8vXl5ACwdYVOq+HiZtg9HO5fgMUNoe5Q7bL2phlLzS3ps6lXj+I7tvNgxgyebtzEkzVreH74MO6TJmFdq2aWj9u+VHtqF6mtm8WSXoKw8Jhw/n70ty4gkYNKX69sd6PIQEPKDYcOHUKhUKQ7jfjq1as0bNgQV1dXLCwsKF68OKNHjzYYF3H48GGqVq2qKyNXJJaMUiig4vsw8BSUawOaZPh9hrZr5d7ZvK5dvmVia4v7xIl4LF2CaRF3ku7eJbxHDyLGj0f9/EWWj/tyRtLUEoS93LUy5885MjV6Hsl2sKFWq5k1axY1atTAzc0NJycnvYck5SaVSkW3bt3Yt28fV69eZe7cuSxevJixY8fqyoSFhdG8eXMCAgI4e/Yso0aN0g1oliSjbApBx5/hgxVg5QIP/obF78FvEyA5Ia9rl2/Z1KlD8e3bcejcCYCn69YT1ro1L44fz/axXx1Eaqxr5VUyNfrrk+1gY/z48cyZM4eOHTsSHR3N0KFDad++PUqlknHjxuVAFQumiOg4jt2IIiI66yO4MyohIYHBgwdTuHBhLCwsqFu3LqFGBnL98ccfVKpUCQsLC2rWrMmFCxd0z92+fZtWrVrh6OiItbU15cuXZ/d/SX6MWb16NdWqVcPW1hY3Nze6dOmiy0Nx69YtGjZsCICjoyMKhYIePXoYPU7x4sXp2bMnlSpVwsvLi9atW9O1a1eOHDmiK7Nw4UI8PT2ZO3cuvr6+fPzxx/Tq1YtZs2bpyvTo0YO2bdsyZcoU3N3d8fLyYsKECSQnJzN8+HCcnJwoVqwYy5Yty9S9lfK58u20M1YqdAChhiOztdNk7/6Z1zXLt0xsbHAfNw7PFctRFS1K0r17hPfsRcQ3Y1E/f56tY7+62Ft55/KpBhqQdmp0KWdlO9hYs2YNixcvZtiwYZiamvLhhx+yZMkSvvnmG06cOJETdSxwNoSGU2faAbosPkmdaQfYEBqeq+f78ssv2bx5MytXruTMmTOULFmSwMBAHj9+rFdu+PDhzJo1i9DQUAoXLkzr1q113RUDBw4kISGB33//nQsXLjB9+nRsbGxSPWdiYiITJ07kr7/+YuvWrYSFhekCCg8PD12rw9WrV4mIiOC7777L0LX8888/hISE6KXKP378OE2aNNErFxgYyOnTp/W6Ww4cOMC9e/c4dOgQkydPZvz48bRs2RJHR0dOnjxJv3796NevH3fuyL+E8oMcC9itXeD9ZdBxFVgXgodXYGkj2P8NJMXnTGULIOtatSi+fRuOXboA8PSXX7jZqjXPj/6RreOm17WSIq3U6FIuENlkZWUlbt++LYQQws3NTfz5559CCCFu3Lgh7Ozssnv4N0p0dLQARHR0tMFzcXFx4tKlSyIuLi5b57j3NFb4jNgpvL76/6P4iF3i3tNYoVarxZMnT4Rarc7WOV72/PlzoVKpxJo1a3TbEhMTRZEiRcSMGTOEEEIcPHhQAGL9+vW6Mo8ePRKWlpZiw4YNQgghKlasKMaNG5flepw6dUoA4tmzZ3rnfPLkSYb29/f3F+bm5gIQffv21btHpUqVEpMnT9Yr/8cffwhA3Lt3TwghRPfu3YWXl5dQq9W6+1ymTBkREBCg2yc5OVlYW1uLdevWZekac+o18jZITEwUW7duFYmJibly/PWnbuveRz4jdor1p27nzIFfPBJiU28hxtppH/OrCRF+MmeOnQty+z7nlOcnTorrjRqLS2XKiktlyop/v/5aJMfE5MixN1/bLPxW+okKKyoIv5V+YtmFZeJUxCkR8TwiW8dN6/tAMpTtlo1ixYoREREBQMmSJdm3bx8AoaGhmGdwMR7p/9LKnpgbbty4QVJSEnXq1NFtU6lU1KhRg8uXL+uV9ff31/3fycmJMmXK6MoMHjyYSZMmUadOHcaOHcv58+fTPO/Zs2dp06YNXl5e2Nra0qBBAwDCw7PWirNhwwbOnDnD2rVr2bVrl14XCRjmuBD/LQn08vby5cvrZQx1dXWlYsWKup9NTExwdnbWdfdIb6bU0p2/2sKRpZYPKyfosAQ6rwUbV4i6BkubwN6vISn3uzzfVtY1a1B821Ycg4JAoSB602ZtK8fvv2f72K92rfSs0FNvmXvp9ch2sNGuXTt+++03AD777DPGjBlDqVKl6NatG7169cp2BQua15I98SXGvnRTtmckCVVKmY8//pibN28SFBTEhQsXqFatGvPnzze6z4sXL2jSpAk2NjasXr2a0NBQtmzZAmi7V7LCw8ODcuXK8eGHHzJt2jTGjRunW7HVzc2NyEj9EecPHjzA1NRUL7X8qwtVKRQKo9s0mtT7gKW8l5GAPdtdlWVbwIATUOlDQMDx72FhXQiXXcdZpbSywu3rUXit+hmVlyfJkZHc6fsJ90aOQh0dna1jv9y1IuWNbAcb06ZNY9SoUQC8//77HD16lP79+7Nx40amTZuW7QoWNK81eyLa1igzMzOOHj2q25aUlMTp06fx9dVfmOrlMThPnjzh2rVrlC1bVrfNw8ODfv36ERwczBdffMHixYuNnvPKlStERUUxbdo0AgICKFu2rEFrgZmZNqdBVpZ4F0KQlJSkC6T8/f3Zv3+/Xpl9+/ZRrVq1XFsJU8o76QXsGW35SJeVE7RbCF1+AVt3ePQPLGuqXW8lMXdaIgsCq2rVKL51K049emhbObZs4War1jw7eDCvqyZlQ7aCjaSkJHr27MnNmzd122rWrMnQoUNp3bp1titXUHWq7snREQ1Z16cWR0c0zP7S2mmwtramf//+DB8+nJCQEC5dukSfPn2IjY2ld+/eemUnTJjAb7/9xsWLF+nRowcuLi60bdsWgCFDhrB3717CwsI4c+YMBw4cMAhWUnh6emJmZsb8+fO5efMm27dvZ+LEiXplvLy8UCgU7Ny5k4cPH/I8lVHqa9as4ZdffuHy5cvcvHmTjRs3MnLkSDp16oSpqTZnXb9+/bh9+zZDhw7l8uXLLFu2jKVLlzJs2LBs3j3pTZRewJ7jXZWlA7WtHJU/AgScWAA/1oZb2RvoWJApLS1xHfEVXmvWYObtTfKDB9ztP4B7X32FOp3cO9KbKVvBhkql0jV/SznL3d4S/xLOryWD4rRp0+jQoQNBQUG88847/PPPP+zduxdHR0eDcp999hlVq1YlIiKC7du367VADBw4EF9fX5o2bUqZMmVYsGCB0fMVKlSIFStWsHHjRsqVK8e0adMMxlgULVqU8ePHM2LECFxdXRk0aJDRY5mamjJ9+nRq1KiBn58f48aNY+DAgSxZskRXxsfHh927d3Po0CEqV67MxIkTmTdvHh06dMjObZPeYGkF7LnSVWnpAG1/gK6bwK4oPAmDFc21mUgTsjedsyCzeqcKPlu34NS7FyiVRG/bzo1WrXj2X9e9lH8oREpbcxb17NmTihUrMnTo0Jyq0xsrJiYGe3t7oqOjDTKnxsfHExYWho+PDxYWFrlyfo1GQ0xMDHZ2dnKJ+VyUW/f5dbxG8oukpCR2795N8+bN86Qra0NoOKOCL6IWQtfy8WoLYkR0HGFRL/Bxsc5c0B8fDftGw5mftT87eEGb78GnXg5eQcbk9X3OSXF//cW9UV+TeOMGAHYtW+L69ShMX/mj6HVJ6/tAMpTttVFKlizJxIkTOXbsGFWrVsX6lcV1Bg8enN1TSJIk5aj0FnrbEBquG9ehVMDU9hUz3p1pYQ+t50O5trB9MDy9DStbQbXe0Hg8mNvm/AUVAJaVKuETvJmo73/g0dKlxOzcyYvjx3Eb+w12r+TRkd482W7Z8PHxSf3gCoXeeI78TrZsFAyyZSP3vcl/cUdEx1Fn2gG9cR0mCgVHRzTMfLdmfIw2+defy7U/23tCm/naFWZfgzf5PmdH3IULRIwaRcL1fwCwa94M19GjMX2NS2TIlo3MyfYnaVhYWKqP3Ao0njx5QlBQEPb29tjb2xMUFJTugl3Pnz9n0KBBFCtWDEtLS3x9ffnxxx9zpX6SJOVf6Q0gzVR+Dgs7aDUXum3TBhrR4fBzG9gxRBuISFliWbEi3ps349zvEzAxIWb3Hm62bEVMSEheV01KRZa6UTI6PkOhUDB79uysnCJNXbp04e7du4T898Lq27cvQUFB7NixI9V9Pv/8cw4ePMjq1avx9vZm3759DBgwgCJFitCmTZscr6MkSflTygDSV1s2vF2sst69UrwBDDgGv46D0CXalo7r+6H1PCj5Xm5dyltNaWZG4SFDsG3UWNvKce0a/w75nJgme3D7ZgymLi55XUXpJVkKNs6e1V9q+c8//0StVlOmTBkArl27homJCVWrVs1+DV9x+fJlQkJCOHHiBDVr1gRg8eLF+Pv7c/XqVV0dXnX8+HG6d++uy1TZt29fFi1axOnTp2WwIUmSTsrU2VcHkAJG83PUK10oY90r5rbQYrZ26fptg7RjOVa3h3e6QZNJ2rEeUqZZViiPz6aNRC1cRNRPP/Fs3z5iT53Cdcxo7Jo3z1ByQin3ZSnYOPhScpU5c+Zga2vLypUrdVMlnzx5Qs+ePQkICMiZWr7k+PHj2Nvb6wINgFq1amFvb8+xY8dSDTbq1q3L9u3b6dWrF0WKFOHQoUNcu3YtzQW+EhISSEj4/3LSMTHaZs+kpCS9BbxStgkh0Gg0uZZhMmV4Tcp5pNyRW/dZo9HoEo6ZmJjk2HHzo5T3z6vvozdF+8ru+Ps4Ev44Fk8nK9ztLThx87HR7pUb92NwscrER2kxf+hzGOXByZicXgxnfkZc/xV18zmIko1y9Dre9PucYxQKHPr3w7JBfe6P+YbEq1e598UwonfvodDor3OlleOtv6c5LNsDRIsWLcq+ffsoX7683vaLFy/SpEkT7t27l60KvmrKlCmsWLGCa9eu6W0vXbo0PXv2ZOTIkUb3S0xMpE+fPvz888+YmpqiVCpZsmQJQUFBqZ5r3LhxjB8/3mD72rVrsbLSn5NvamqKm5sbHh4eutwTkvSyxMRE7ty5Q2RkJMnJyXldHSmTnibAuDMmCP7/l7ICwbh31DhkcRko5+dXqHx7CTaJ2gy6t50CuFi0C8mm1unsKaUqORmng4dwPnAAhUaD2sqKB61b8axyZcjBVo7Y2Fi6dOkiB4hmULanvsbExHD//n2DYOPBgwc8e/Ysw8dJ7Yv9ZaGhoYDhOh6Q/loe8+bN48SJE2zfvh0vLy9+//13BgwYgLu7O40aGf9rYuTIkXrjU2JiYvDw8KBJkyZGZ6PcuXMHGxubXJtpIITg2bNn2NrayqbBXJRb9zk+Ph5LS0vq1asnZ6MkJbF//34aN26cr2ZJqDzvMnrbJd2YjUltyvNB1WJ6ZSKi47n9KBYvZ22LSNqaQ1I/1Icmozz1E16Pj+CZeF3bylEq+9M58+t9zrbWrUm4epX7o8eQeOUK7us3UDLyPoW+GYNpoUI5coqUlm4pY7IdbLRr146ePXsye/ZsatWqBWjX0Bg+fDjt27fP8HEGDRpE586d0yzj7e3N+fPnuX//vsFzDx8+xNXV1eh+cXFxjBo1ii1bttCiRQsA/Pz8OHfuHLNmzUo12DA3Nze6cq1KpTJ446rVahQKBUqlMtempaY06aecR8oduXWflUqlbnG3AvXBn4b8di+61PKhoa9bzubnUNlD8xlQvh1sG4ji8Q1Mf+kClbpA0ylgmf2kVfntPucEVYUKWG/8hUdLlvBwwY+8OHSIuDNncPt6FHatW2f7D4mCdj+zK9vBxsKFCxk2bBgfffSRrg/L1NSU3r17M3PmzAwfx8XFBZcM9Kv5+/sTHR3NqVOnqFGjBgAnT54kOjqa2rVrG90nZYzFq18cJiYmcuxDJjVo0IDKlSszd+7cvK6KJOUJd3tLowNCU1vgLcMDSL38od9RODgZjv8Af62FGwe0U2fLNMvZiyggFCoVLv37Y/Pue0SMHEn8pUvc+2oEMbv34DZhPKpU/kCVcl62/2yzsrJiwYIFPHr0iLNnz3LmzBkeP37MggULDLKJ5oSUtTf69OnDiRMnOHHiBH369KFly5Z6g0PLli2rW7fFzs6O+vXrM3z4cA4dOkRYWBgrVqzg559/pl27djlex/ymQYMGDBkyRG/boUOHUCgUBvlLgoODDRZNex1WrFiBg4NDuuWOHj1KnTp1cHZ2xtLSkrJly/Ltt98alNu8eTPlypXD3NyccuXKyTV+pGzLkQXezKwgcDL02gvOJeF5JKzrDMF9IfZxzla4ALEoUxrvDespNGQICpWK53/8gfrRo7yuVoGS7ZaNFNbW1vj5+eXU4dK0Zs0aBg8eTJP/UtS2bt2a77//Xq/M1atXiY6O1v28fv16Ro4cSdeuXXn8+DFeXl5MnjyZfv36vZY6vy2cXmOGvqywtrZm0KBB+Pn5YW1tzdGjR/nkk0+wtramb9++gHZGU6dOnZg4cSLt2rVjy5YtdOzYkaNHj+rNcpKkzEgrP0emedbUb+U4vwFuHISW34Jvy5yrdAGiUKlw6fcJtu+9S9z581iUK5fXVSpYhJRh0dHRAhDR0dEGz8XFxYlLly6JuLi4XDu/Wq0WT548EWq1OseO2b17dwHoPcLCwgy2de/eXQghRP369cVnn32m29/Ly0tMnDhRBAUFCWtra+Hp6Sm2bt0qHjx4IFq3bi2sra1FhQoVRGhoaJr1mD17tqhQoYKwsrISxYoVE/379xfPnj0TQghx8OBBg/qMHTs2w9fYrl078dFHH+l+7tixo2jatKlemcDAQNG5c2chhPH7vHz5cmFvby927NghSpcuLSwtLUWHDh3E8+fPxYoVK4SXl5dwcHAQgwYNEsnJyUbr8TpeI/lFYmKi2Lp1q0hMTMzrquSo9adui+Ijdgmvr3aK4iN2ifWnbmf/oOGnhJhfTYixdtrHxl5CPI/K0K5v631+E6T1fSAZkqMMc5MQkPgiZx9JsemXycRs5u+++w5/f3/69OlDREQEEREReHh4sHnzZkDbQhQREZFmPpJvv/2WOnXqcPbsWVq0aEFQUBDdunXjo48+4syZM5QsWZJu3brp8lcYo1QqmTdvHhcvXmTlypUcOHCAL7/8EoDatWszd+5c7OzsdHUcNmxYhq7v7NmzHDt2jPr16+u2HT9+XNcqliIwMJBjx46leazY2FjmzZvH+vXrCQkJ4dChQ7Rv357du3eze/duVq1axU8//cSmTZsyVDfp7ZPW0vZZ5lEdPjkCdYaAQgkXN8GCmnBpe/aPLUmvSY51o0hGJMXClCI5djgl4JCRgqPugVnGxsvY29tjZmaGlZUVbm5uuu0p3SWFCxdOd6xE8+bN+eSTTwD45ptv+PHHH6levToffPABAF999RX+/v7cv39f7xwve3nMiI+PDxMnTqR///4sWLAAMzMz7O3tUSgUqe7/qmLFivHw4UOSk5MZN24cH3/8se65yMhIg5lLrq6uREZGpnnMpKQkfvzxR0qUKAHA+++/z6pVq7h//z42NjaUK1eOhg0bcvDgQTp16pShekpvn9QGkKYmQ0vZqyy0K8b6toZtA+DhFfglCMq3h+YzwVqm5pbebDLYkLLt5bE6KV/iFStWNNj24MGDVIOFgwcPMmXKFC5dukRMTAzJycnEx8fz4sWLLA00PnLkCM+fP+fEiROMGDGCkiVL8uGHH+qef3Xam0gnTwtoB0OnBBop1+Xt7Y2NjY3etgcPHmS6vlLBlOmpssWqwie/w+HpcHQu/B0MYb9Di1naqbOS9IaSwUZuUllpWxlyiEajIebZM+xsbdPO/6DKwoC0bHh5vnnKF7axbalNM759+zbNmzenX79+TJw4EScnJ44ePUrv3r2znBLYx8cH0AY99+/fZ9y4cbpgw83NzaAV48GDB6nmaUnx6rz6lJwZr26T06mljMjyVFlTc3jvG/BtBVsHwINLsLEH/L0Fms8Gm5xJWiVJOUmO2chNCoW2OyMnHyqr9MtkMlmNmZkZarXaYBtgsD03nD59muTkZF1iuNKlSxukuTdWx4wSQuitcePv78/+/fv1yuzbty/VPC2SlBsyMlU2zeXsi1SBvoeg3pegMIFL2+CHGnBxc6bGbUnS6yBbNiS8vb05efIkt27dwsbGBicnJ7y8vFAoFOzcuZPmzZtjaWmp112Qk0qUKEFycjLz58+nVatW/PHHHyxcuNCgjs+fP+e3336jUqVKWFlZGaxPA/DDDz/g6elJ2bJlAW3ejVmzZvHpp5/qynz22WfUq1eP6dOn06ZNG7Zt28avv/7K0aNHc+X6JMmY9KbKZqiLxdQc3v0ayraAbQPh/kXY1EvbytFiDphnP/uoJOUE2bIhMWzYMExMTChXrhyFChUiPDycokWLMn78eEaMGIGrqyuDBg3KtfNXrlyZOXPmMH36dCpUqMCaNWuYOnWqXpnatWvTr18/OnXqRKFChZgxY4bRY2k0GkaOHEnlypWpVq0a8+fPZ9q0aUyYMEHvWOvXr2f58uX4+fmxYsUKNmzYIHNsSK9VylL2Jv+1RKYsZe9ub5lqF4vRFg6AIpWhz0GoPwKUpnB5B/xQA8XFTbKVQ3ojZHvV14IkJiYGe3t7o6v8xcfHExYWho+PT64tsqXRaIiJicHOzk6ujZKLcus+v47XSH6RlJTE7t27ad68eYFfYyIiOs5grZVjN6LosvikQdl1fWrhX8I5nQOe147luH9B+6P9O7h0/xmVk0eO170gS+v7QDIkv7EkSZLykLu9Jf4lnPUGhaZ0sbwsw9lI3f2g70FoMAqhVOEefQbTn+rCXxtkK4eUZ2SwIUmS9IZJq4slQ0xU0OArknv9ylNLbxTxT2FLX1j3IcRE5F7FJSkVcoCoJEnSG6hTdU/qlS6U6nL2GeJant/LfEML+38wOTITru2BBceg6XSo1DnTM9ckKatky4YkSdIbylgXS2YJhSmaukO1ycDcK0N8NGztB2s7QUzO5QGSpLTIYEOSJOktFBEdx4mbj3makmLGtRx8/Bu8NxZMzOD6XvihFpxdI8dySLlOdqNIkiS9ZV7O0aHABJXnXbrU8gETUwgYCmWaa9dY+fdP7b9/b4FW34F90byuuvSWki0bkiRJb5FXc3QIFIzedkk/R0fhstBrHzQaDybm8M9+WFALzqySrRxSrpDBhiRJ0lvEWBp0jUAvDTqgbeWoOwT6HYVi1SEhBrYPgtUdIPrua6uvVDDIYEOSJOktYixHh1JBqjk6Isw8OFZ/DTH1xoKpBdz4TTuW488VspVDyjEy2JDSdejQIRQKBU+fPs3rqkiSlI5Xc3QoEExqU87ojJYNoeHUmXaALktPU3l/GXbX+QU8akLiM9jxGaxqB0/DX/clSG8hGWxIb6TMBDi//PILlStXxsrKCi8vL2bOnGlQ5vDhw1StWhULCwuKFy9usNCbJL1NOlX35OiIhqzuVY1x76j5oGoxgzLG1l/5dN8LItoHQ+AUbSvHzYOwwB9OL5OtHFK2yGBDytf27NlD165d6devHxcvXmTBggXMmTOH77//XlcmLCyM5s2bExAQwNmzZxk1ahSDBw9m8+bNeVhzScpd7vaW1PRxwsHc+POpLnH/OAH8B0L/Y+DpD4nPYefn8HNreHI79ysuvZVksPGGinwRyamIU0S+iMz1cyUkJDB48GAKFy6MhYUFdevWJTQ01KDcH3/8QaVKlbCwsKBmzZpcuHBB99zt27dp1aoVjo6OWFtbU758eXbv3p3qOVevXk21atWwtbXFzc2NLl268ODBAwBu3bpFw4YNAXB0dEShUNCjRw+jx1m1ahVt27alX79+FC9enBYtWvDVV18xffp0UtYYXLhwIZ6ensydOxdfX18+/vhjevXqxaxZs3TH6dGjB23btmXKlCm4u7vj5eXFhAkTSE5OZvjw4Tg5OVGsWDGWLVuW6fsrSW+idNdfcS4BPXZD02lgaglhv2tbOUKXgEbz+iss5Wsy2HgDBV8PJnBzIL339SZwcyDB14Nz9XxffvklmzdvZuXKlZw5c4aSJUsSGBjI48eP9coNHz6cWbNmERoaSuHChWndujVJSUkADBw4kISEBH7//XcuXLjA9OnTsbGxSfWciYmJTJw4kb/++outW7cSFhamCyg8PDx0rQ5Xr14lIiKC7777zuhxEhISDFZQtbS05O7du9y+rf0r7Pjx4zRp0kSvTGBgIKdPn9bVH+DAgQPcu3ePQ4cOMXnyZMaPH0/Lli1xdHTk5MmT9OvXj379+nHnzp0M3FVJerNlaP0VpRJq9Yf+f4BnbUh6Abu+0LZyPA7Lo5pL+ZKQMiw6OloAIjo62uC5uLg4cenSJREXF5etc0Q8jxB+K/1EhRUVdA+/lX4i4nmEUKvV4smTJ0KtVmfrHC97/vy5UKlUYs2aNbptiYmJokiRImLGjBlCCCEOHjwoALF+/XpdmUePHglLS0uxYcMGIYQQFStWFOPGjctyPU6dOiUA8ezZM71zPnnyJM39Fi1aJKysrMSvv/4q1Gq1uHr1qihbtqwAxLFjx4QQQpQqVUpMnjxZb78//vhDAOLevXtCCCG6d+8uvLy8hFqt1t3nMmXKiICAAN0+ycnJwtraWqxbty5L15hTr5G3QWJioti6datITEzM66q81TJyn+89jRXH/okS957Gpn0wtVqIE4uEeqKrEGPttP+eWKTdXgCl9X0gGZItG2+Y8JhwNEK/iVIjNNx5ljt/Td+4cYOkpCTq1Kmj26ZSqahRowaXL1/WK+vv76/7v5OTE2XKlNGVGTx4MJMmTaJOnTqMHTuW8+fPp3nes2fP0qZNG7y8vLC1taVBgwYAhIdnbuR7nz59GDRoEC1btsTMzIxatWrRuXNnAExMTHTlFK8sOCX+62J5eXv58uVRKv//lnB1daVixYq6n01MTHB2dtZ190jS2yDD668olWxQNqXhiymc0PiiTI6DPcNhZSt4fPP1VFbKt2Sw8YbxtPNEqdD/tSgVSjxsPXLlfMa+dFO2v7rNmJQyH3/8MTdv3iQoKIgLFy5QrVo15s+fb3SfFy9e0KRJE2xsbFi9ejWhoaFs2bIF0HavZIZCoWD69Ok8f/6c27dvExkZSY0aNQDw9vYGwM3NjchI/bEvDx48wNTUFGdnZ902lUplcGxj2zSyv1oqgFJmr9wWrnyY+DWjk3ryQpjD7aPwYx04sVCO5ZBSJYONN4ybtRtj/cfqAg6lQslY/7G4WbvlyvlKliyJmZkZR48e1W1LSkri9OnT+Pr66pU9ceKE7v9Pnjzh2rVrlC1bVrfNw8ODfv36ERwczBdffMHixYuNnvPKlStERUUxbdo0AgICKFu2rEFrgZmZGQBqtTpD12FiYkLRokUxMzNj3bp1+Pv7U7hwYUDbIrN//3698vv27aNatWoGwYQkSca9PHtFoGS1ujGBidOJdvWHpFgI+QpWtIBHN/K2otIbSS7E9gZqX6o9tYvU5s6zO3jYeuRaoAFgbW1N//79dTMuPD09mTFjBrGxsfTu3Vuv7IQJE3B2dsbV1ZWvv/4aFxcX2rZtC8CQIUNo1qwZpUuX5smTJxw4cMAgWEnh6emJmZkZ8+fP101ZnThxol4ZLy8vFAoFO3fupHnz5lhaWhodcBoVFcWmTZto0KAB8fHxLF++nI0bN3L48GFdmX79+vH9998zdOhQ+vTpw/Hjx1m6dCnr1q3L5t2TpIIjZfbKy9NlI3Al9sPN2F9fD/u/gfBj2laO976Bmp+A0iT1A0oFSr5s2Zg8eTK1a9fGysoKBweHDO0jhGDcuHEUKVIES0tLGjRowN9//527Fc0GN2s3qrtVz9VAI8W0adPo0KEDQUFBvPPOO/zzzz/s3bsXR0dHg3KfffYZVatWJSIigu3bt+u1QAwcOBBfX1+aNm1KmTJlWLBggdHzFSpUiBUrVrBx40bKlSvHtGnT9KahAhQtWpTx48czYsQIXF1dGTRoUKr1X7lyJdWqVaNOnTr8/fffHDp0SNeVAuDj48Pu3bs5dOgQlStXZuLEicybN48OHTpk9ZZJUoGT6uwVB2uo3lubl8OnPiTHwd6RJC5pClH/5HGtpTeFQoj8lxZu7NixODg4cPfuXZYuXZqhLJPTp09n8uTJrFixgtKlSzNp0iR+//13rl69iq2tbYbOGxMTg729PdHR0djZ2ek9Fx8fT1hYGD4+PgZTMXOKRqMhJiYGOzs7vYGMUs7Krfv8Ol4j+UVSUhK7d++mefPmsisrF+XGfY6IjuNWVCzeLlYGg0o3nLrN+W3fMdJ0DTaKeJKV5pg2+kY7ffYta+VI6/tAMpQvv7HGjx/P559/rjdTIC1CCObOncvXX39N+/btqVChAitXriQ2Npa1a9fmcm0lSZLeHqnNXomIjmPklousUb9HYMJ0fldXxFSTAPu+hmVNIep6HtVYehPky2Ajs8LCwoiMjNRL7GRubk79+vU5duxYHtZMkiTp7fDyANJ/KUS3pBF8ldSHZJUN3D0FP9Yh5rfZHLt+n4jouLytrPTaFYgBoinTHl1dXfW2u7q66rJMGpOQkEBCQoLu55iYGEDbNPly5smUbUIINBpNrk2NTOnxSjmPlDty6z5rNBqEECQlJenlACmIUt4/r76PpJz1Ou9zMXvzVwaQKtioacjgLn1wPzIC5c0D2B2ZgKVmLd2SP6Fn68ZGF4jLL+RrN3PemGBj3LhxjB8/Ps0yoaGhVKtWLcvnyGwuialTpxqt0759+7CystLbZmpqipubG8+fP890rojMevbsWa4eX9LK6fucmJhIXFwcv//+O8nJyTl67Pzq1SnJUu54Xfe5o4+CDTeVCBQoEHT00XD6/BWemnfn76QSjDZdTRXlP+xUjeLbHX+y7nYg9hb5M/COjY3N6yrkK2/MANGoqCiioqLSLOPt7a03sG7FihUMGTIk3QGiN2/epESJEpw5c4YqVarotrdp0wYHBwdWrlxpdD9jLRseHh5ERUUZHSB6584dgzrmJCEEz549w9bWNkMJt6Ssya37HB8fz61bt/Dw8JADRJOS2L9/P40bN5YDRHNRXtzniOh4wh/H4ulkhbu99nV+4uZjgpafxo1HTFUtoaHJXwA8c6qIxfsLoVCZ11K3nBQTE4OLi4scIJpBb0zLhouLCy4uLrlybB8fH9zc3Ni/f78u2EhMTOTw4cNMnz491f3Mzc0xNzdcn1mlUhm8cdVqNQqFAqVSmWszRVKa9FPOI+WO3LrPSqVSl5VUfsFqyXvxerzO++zposLTRX+GX0k3O5QKiBTO9Ez6kvc1v/ON6SrsHl+ApQ2hwQio/RmYvDFfSemSr9vMyZffWOHh4Zw7d47w8HDUajXnzp3j3LlzPH/+XFembNmyuhTYCoWCIUOGMGXKFLZs2cLFixfp0aMHVlZWdOnSJa8uQ5IkqUDQz9GhYIumAYcabYdSgaBOhN8mwNJGcP9SXldVyiX5J4x8yTfffKPX9ZHSWnHw4EHdgl5Xr14lOjpaV+bLL78kLi6OAQMG8OTJE2rWrMm+ffsynGNDkiRJyrpO1T2pV7qQfo4OsQHOb4A9X8K9s7CoHjT4CuoMARPZcvA2yZfBxooVK1ixYkWaZV4diqJQKBg3bhzjxv2vvXsPq6rO9zj+Xm7dCshFLiIqCmMjSgLeDcxGm6OW3b2EcxwYPGbRmGbFMa1pyqlHs1OONqZlF29TYU1aU2lleU9DxdI0b5WEnRAij6IgCux1/nBg2m1U1L32hu3n9Tw8D6z9W2t91/fZsL/81m/9fo9ZF5iIiJxVVLCf8/wchgFJI8/MPPrefbB/Jax+Ava8C7fMhVZdvBesuFWDvI0i3tO/f38mTpzo7TBExJcERcHvXoehL0KzECjYgTm/P/nLH6XgSIm3oxM3ULEhtRYQa9euxTAMlyd9li1b5rJomicsXLiwzuvgPPfcc3Tu3Bk/Pz/i4uJYvHixS5u33nqL+Ph4mjZtSnx8fM34HhHxEsOAxNth3Ba+jxyA4aig3Y5ZHJl1NR9+UvujuwXHTrLpm2JNEtYAqNiQCxIaGlqvx7nMmzePKVOm8Nhjj7F7926mTp3KuHHjePfdd2vabN68mdTUVNLS0tixYwdpaWncfvvt5OTkeDFyEQEocARxTf4dTDh9D/9nNufKRt9x7fpUjn/wOFT+ew6jpVvz6fvkav7zxRz6PrmapVvzvRi1nI+KjctcRkYG69atY/bs2RiGgWEY5OXlMWDAAABatGiBYRhkZGQArr0gMTExPPHEE6Snp9O8eXPat2/PO++8w48//sgtt9xC8+bNSUhIYNu2beeMY+bMmSQkJBAQEEB0dDR//OMfa54uWrt2LaNHj+bYsWM1MZ5t7M2SJUu46667SE1N5Ve/+hUjR45kzJgxTo84z5o1i4EDBzJlyhQ6derElClT+O1vf8usWbNq2tx4441MmDCBiRMn0qJFCyIjI5k/fz6lpaWMHj2awMBAOnTowMqVKy886SJyVmemPTf4pyOFgaf+hw+qetHEqCLws6fhxWuhYMeZdViWfVkzW6nDhIeW7VIPRz2mYsNCpmniKCtz79fJk+dtcyHztM2ePZvk5GTGjh1LQUEBBQUFREdH89ZbbwFnnuopKChg9uzZZz3GX//6V/r27cvnn3/ODTfcQFpaGunp6fz+979n+/btXHHFFaSnp58zrkaNGvHss8+ya9cuFi1axOrVq5k0aRIAKSkpzJo1i6CgoJoYs7Kyaj3OqVOnXCbM8vPzY8uWLTXTC2/evNlpnRyAwYMHu6yTs3jxYsLDw9myZQvjx4/n7rvvZsSIEaSkpLB9+3YGDx5MWlqaZhIUcaPY8AAa/WsuvWKCyayYyISKCTiahULhl/DitVR+/AQ203kW3irTJK/437+LusVSvzTIp1EaCvPkSfZ17+H24xae5/W47bkYv5hO/WyCg4Ox2+34+/vTqlWrmu2hoaEAtGzZ8rxjJYYMGcJdd90FnHksed68efTq1YsRI0YA8OCDD5KcnExhYaHTOX7u570lsbGxPP7449x9993MnTsXu91OcHAwhmGcdf9qgwcP5qWXXuLWW2+le/fu5Obm8sorr1BRUUFxcTFRUVEcPny41nVyqtfQqZaUlMSf/vQnAKZMmcKTTz5JeHg4Y8eOdbrWnTt3ctVVV50zLhGpm+o5OR5atosq08RmNKLvrWNp1HkCvH8/7Pkn0V/O4V17NA9UZLLbjAXAZhjEhJ/5u7d0a35Nz0cjA6YPTSC1VztvXtZlT8WGXLLExMSa76s/xBMSEly2FRUVnbVYWLNmDdOmTeOrr76ipKSEyspKysvLKS0tJSAgoM6xPPLIIxw+fJirrroK0zSJjIwkIyODp556ymnxs7qsk/Pza7DZbISFhZ31ukTEfWqdkwMgdQnsXg7vP0CnskO8Y3+EeVU3M7dqKI8N7UZUsN9Zb7Fc0zHC+bFb8SgVGxYy/PyI257rtuM5HA5Kjh8nKDDwnNNoG36e/YX6+bS91R/YtW072wqq3333HUOGDCEzM5PHH3+c0NBQNm7cyJgxYy54ZUU/Pz9eeeUVXnjhBQoLC4mKimL+/PkEBgbWTIffqlUrl16MoqIil96OX05HXD3VeF2vS0QunsucHNWuvA1i+sGKLBrvXs74xm+TGbmXJq3nAe2clrqv9vNbLAeLS4kND1Dh4WEqNixkGEadb2fUicNBo8pKGvn7u3XNDrvdTlVVlcs2wGW7FbZt20ZlZSXPPPNMzXW98cYb543xXJo0aULbtmeWr87OzubGG2+sOXZycjKrVq3ivvvuq2n/0UcfkZKScqmXIiKeEBAOIxZC/K3w/gM0+WkvvPQf0PdeYrvd+4ul7s/cYtn5v0cZ9dJnurXiJRogKsTExJCTk0NeXh7FxcU4HA7at2+PYRi89957/Pjjj07rzrhbhw4dqKys5G9/+xvffvstS5Ys4fnnn3eJ8cSJE3zyyScUFxefdVDm/v37+fvf/86BAwfYsmULI0eOZNeuXUybNq2mzb333stHH33EjBkz2Lt3LzNmzODjjz/WZGUiDc2Vt8K4LdBlGJhVsHEmUdmDeeFa41/rsJwpNCZdF8eMlXv19IoXqdgQsrKysNlsxMfHExERQX5+Pm3atGHq1KlMnjyZyMhI7rnnHsvO37VrV2bOnMmMGTPo0qULr776KtOnT3dqk5KSQmZmJqmpqURERPDUU0/VeqyqqiqeeeYZkpKSGDhwIOXl5WzatImYmBinY2VnZ7NgwQISExNZuHAhS5cupU+fPpZdo4hYJCAMhr8Cty+BgAj4cS8DN41iR99Pyf6vbmycPICEtsHnvLUi1jPMC3lO8jJXUlJCcHAwx44dIygoyOm18vJyDh48SGxsrMujl+7icDgoKSkhKChIS8xbyKo8e+I90lBUVFSwYsUKhgwZoqW6LXTZ5bnsyJlF3b5888zP4XFw61wKAq+k75OrXW6tbJw84KLHbpzr80Bc6RNLRER8g38oDHsJRr4GzSOheB+8PJConGnMuKWj062VaUO7aJCoB2mAqIiI+JZON0C7ZPhg8pkl7Dc9y4jwDxiQPpMD9njnx2nFI9SzISIivsc/FIbOh99lQ/NWULyf8KU3kfz1TKLc+JCg1I2KDRER8V1x18O4zyDpd4AJm+fAhw95O6rLjooNN9N4WzkbvTdEvMSvBdz2PPznGxDRCa75b29HdNnRmA03qR7pXVZWhp+HZ/CUhqF6bpDL4qkAkfqo42C4YiDoaT6PU7HhJjabjZCQkJp1Mvz9/V3W2rhUDoeD06dPU15erkdfLeTuPJumSVlZGUVFRYSEhDit0SIiHqa/nV6hYsONqhcZs2phLtM0OXnyJH5+fm4vZOTfrMpzSEjIeVetFRHxRSo23MgwDKKiomjZsuUFLyBWFxUVFaxfv55rrrlGXfEWsiLPTZo0UY+GiFy2VGxYwGazWfLBYrPZqKyspFmzZio2LKQ8i4i4l25eiYiIiKVUbIiIiIilVGyIiIiIpTRm4wJUT8pUUlLilfNXVFRQVlZGSUmJxhJYSHm2nnLsGcqzdao/BzRZX92o2LgAx48fByA6OtrLkYiISH1w/PhxgoODvR1GvWeYKsvqzOFw8MMPPxAYGOg0/0KvXr3YunVrrfuc7bXatp9vW0lJCdHR0Rw6dIigoKBLvZzzOtd1uXv/urRVni99//O1vdjX65Jn5bhubfRebhh57tmzJ6tXr6Z169aaZLEO1LNxARo1akTbtm1dtttstrP+Ip/ttdq213VbUFCQR/5wnOu63L1/Xdoqz5e+//naXuzrdcmpcly3NnovN4w8N27cuNbPA6mdyjE3GDdu3AW/Vtv2um7zlEs994XsX5e2yvOl73++thf7el1yqhzXrY3ey+5r66t5boh0G6UBKSkpITg4mGPHjnnkv5TLlfJsPeXYM5RnqS/Us9GANG3alEcffZSmTZt6OxSfpjxbTzn2DOVZ6gv1bIiIiIil1LMhIiIillKxISIiIpZSsSEiIiKWUrEhIiIillKxISIiIpZSseHDysrKaN+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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print('rmse:', ca.rmse())\n", + "hs1 = ml.head(r1, 0, t1)\n", + "hs2 = ml.head(r2, 0 ,t2)\n", + "plt.semilogx(t1, h1, '.', label='obs at 30m')\n", + "plt.semilogx(t1, hs1[0], label='ttim at 30 m')\n", + "plt.semilogx(t2, h2, '.', label='obs at 90m')\n", + "plt.semilogx(t2, hs2[0], label = 'ttim at 90m')\n", + "plt.xlabel('time(d)')\n", + "plt.ylabel('drawdown(m)')\n", + "plt.title('ttim analysis for Oude Korendijk - Piezometers at 30 and 90 m')\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Calibrate Model with Wellbore Storage\n", + "\n", + "In this continuation, we investigate whether adding wellbore storage improves the fit." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6.1. Reload the model" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "#unknown parameters: kaq, Saq and rc\n", + "ml1 = ttm.ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6.2. Define new Well object with wellbore storage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, besides the parameters explained in [Step 3](#step_3), we have to add the radius of the caisson (```rc```)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "w1 = ttm.Well(ml1, xw=0, yw=0, rw=0.2, rc=0.2, tsandQ=[(0, Q)], layers=0)\n", + "ml1.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Step 6.3. Calibrate using only the data from observation well 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The parameters not cited in the ```.set_paramater``` method, must be calibrated with the ```.set_parameter_by_reference``` method.\n", + "\n", + "Here we use the method ```.set_parameter_by_reference``` to calibrate the ```rc``` parameter in our well.\n", + "\n", + "```.set_parameter_by_reference``` takes the following arguments:\n", + "* ```name```: a string of the parameter name\n", + "* ```parameter```: numpy-array with the parameter to be optimized. It should be specified as a reference, for example, in our case: ```w1.rc[0:]``` referencing to the parameter ```rc``` in object ```w1```.\n", + "* ```initial```: float with the initial guess for the parameter value.\n", + "* ```pmin``` and ```pmax```: floats with the minimum and maximum values allowed. If not specified, these will be defined as ```-np.inf``` and ```np.inf```.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".......................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 132\n", + " # data points = 34\n", + " # variables = 3\n", + " chi-square = 0.00793373\n", + " reduced chi-square = 2.5593e-04\n", + " Akaike info crit = -278.341725\n", + " Bayesian info crit = -273.762644\n", + "[[Variables]]\n", + " kaq0: 80.8710496 +/- 1.71390065 (2.12%) (init = 10)\n", + " Saq0: 5.4851e-06 +/- 7.9032e-07 (14.41%) (init = 0.0001)\n", + " rc: 0.30300204 +/- 0.01743105 (5.75%) (init = 0.2)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.9754\n", + " C(Saq0, rc) = -0.8698\n", + " C(kaq0, rc) = +0.8288\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq080.8710501.713901e+002.119301-infinf10.0000[80.87104964374493]
Saq00.0000057.903184e-0714.408459-infinf0.0001[5.485100333679987e-06]
rc0.3030021.743105e-025.7527830.01inf0.2000[0.3030020413797092]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 80.871050 1.713901e+00 2.119301 -inf inf 10.0000 \n", + "Saq0 0.000005 7.903184e-07 14.408459 -inf inf 0.0001 \n", + "rc 0.303002 1.743105e-02 5.752783 0.01 inf 0.2000 \n", + "\n", + " parray \n", + "kaq0 [80.87104964374493] \n", + "Saq0 [5.485100333679987e-06] \n", + "rc [0.3030020413797092] " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ca3 = ttm.Calibrate(ml1)\n", + "ca3.set_parameter(name='kaq0', initial=10)\n", + "ca3.set_parameter(name='Saq0', initial=1e-4)\n", + "ca3.set_parameter_by_reference(name='rc', parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", + "ca3.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca3.fit(report=True)\n", + "ca3.parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.01527563869271272\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print('rmse:', ca3.rmse())\n", + "hm3 = ml1.head(r1, 0, t1)\n", + "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", + "plt.semilogx(t1, hm3[0], label='ttim at 30 m')\n", + "plt.xlabel('time(d)')\n", + "plt.ylabel('drawdown(m)')\n", + "plt.title('ttim analysis for Oude Korendijk - Piezometer 30 m and Wellbore Storage')\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6.4. Calibrate using only the data from observation well 2\n", + "\n", + "Here we repeat the step 6.3 for well 2" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 81\n", + " # data points = 35\n", + " # variables = 3\n", + " chi-square = 0.00135389\n", + " reduced chi-square = 4.2309e-05\n", + " Akaike info crit = -349.604373\n", + " Bayesian info crit = -344.938328\n", + "[[Variables]]\n", + " kaq0: 88.2878230 +/- 1.48491787 (1.68%) (init = 10)\n", + " Saq0: 1.1361e-05 +/- 9.4427e-07 (8.31%) (init = 0.0001)\n", + " rc: 0.67465915 +/- 0.03064973 (4.54%) (init = 0.2)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.9820\n", + " C(Saq0, rc) = -0.9421\n", + " C(kaq0, rc) = +0.9154\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq088.2878231.484918e+001.681906-infinf10.0000[88.28782298814623]
Saq00.0000119.442731e-078.311813-infinf0.0001[1.1360615243112807e-05]
rc0.6746593.064973e-024.5429950.01inf0.2000[0.6746591458251263]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 88.287823 1.484918e+00 1.681906 -inf inf 10.0000 \n", + "Saq0 0.000011 9.442731e-07 8.311813 -inf inf 0.0001 \n", + "rc 0.674659 3.064973e-02 4.542995 0.01 inf 0.2000 \n", + "\n", + " parray \n", + "kaq0 [88.28782298814623] \n", + "Saq0 [1.1360615243112807e-05] \n", + "rc [0.6746591458251263] " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ca4 = ttm.Calibrate(ml1)\n", + "ca4.set_parameter(name='kaq0', initial=10)\n", + "ca4.set_parameter(name='Saq0', initial=1e-4)\n", + "ca4.set_parameter_by_reference(name='rc', parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", + "ca4.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca4.fit(report=True)\n", + "ca4.parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.006219520494372699\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print('rmse:', ca4.rmse())\n", + "hm4 = ml1.head(r2, 0, t2)\n", + "plt.semilogx(t2, h2, '.', label='obs at 90 m')\n", + "plt.semilogx(t2, hm4[0], label='ttim at 90 m')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.title('ttim analysis for Oude Korendijk - Piezometer 90 m and Wellbore Storage')\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6.5. Calibrate model with two datasets simultaneously\n", + "\n", + "Following the same logic from steps 6.3 to 6.4 and the calibration from step 5.3, we can now check the calibration using both wells and wellbore storage." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "........................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 69\n", + " # data points = 69\n", + " # variables = 3\n", + " chi-square = 0.17294881\n", + " reduced chi-square = 0.00262044\n", + " Akaike info crit = -407.231761\n", + " Bayesian info crit = -400.529442\n", + "[[Variables]]\n", + " kaq0: 66.0880240 +/- 1.67012200 (2.53%) (init = 10)\n", + " Saq0: 2.5406e-05 +/- 2.4565e-06 (9.67%) (init = 0.0001)\n", + " rc: 0.01000311 +/- 0.01580396 (157.99%) (init = 0.2)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.8508\n", + " C(Saq0, rc) = -0.1722\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq066.0880241.6701222.527117-infinf10.0000[66.0880240001346]
Saq00.0000250.0000029.669129-infinf0.0001[2.5405869311639107e-05]
rc0.0100030.015804157.9904290.01inf0.2000[0.010003111933319042]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 66.088024 1.670122 2.527117 -inf inf 10.0000 \n", + "Saq0 0.000025 0.000002 9.669129 -inf inf 0.0001 \n", + "rc 0.010003 0.015804 157.990429 0.01 inf 0.2000 \n", + "\n", + " parray \n", + "kaq0 [66.0880240001346] \n", + "Saq0 [2.5405869311639107e-05] \n", + "rc [0.010003111933319042] " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ca0 = ttm.Calibrate(ml1)\n", + "ca0.set_parameter(name='kaq0', initial=10)\n", + "ca0.set_parameter(name='Saq0', initial=1e-4)\n", + "ca0.set_parameter_by_reference(name='rc', parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", + "ca0.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca0.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca0.fit(report=True)\n", + "ca0.parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.050065003351326486\n" + ] + }, + { + "data": { + "image/png": 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NzfI1k5aVDBs2jC5duvDkyROmTZuGi4tLmkO1b/voo4+YMGECx48fZ9KkScl+gdeuXZs2bdpQqVIlChUqxK1bt1i3bh3e3t6pzu5dvnw5hw8fpnXr1ri7uxMTE6Od9dy0adNMv89mzZrh4+PDuHHjCA8Pp169etpZwVWrVsXX1zfT10wybtw4rl27pv3/7t27o1KpuH79OvPmzePNmzfs3r1bZ+a0r68vH330EcOGDaNz5848fvyYuXPnJhuqHjlyJCtXrqR169ZMnz5dOyv43Z6+lZUVS5cupW/fvrx69YouXbrg6OjIixcvuHbtGi9evOCHH37I1Ptq3rw5O3fupH379tqe66hRo1i7di2tW7fm22+/xcPDgz179uDn58fQoUMz9Fz6xx9/pGXLlvj4+NCvXz+KFCnCq1evuHXrFpcvX+b333/PVD3Lli3LRx99xKJFizA2NqZp06bcuHFDOxP5fXXp0gULCwu6dOlCdHQ0v/76a6b+SClQcnLmlJC7TZgwQXZ1dZUVCoXODMJHjx7JzZs3l62trWVA9vDwkGU57VnBL1680Ll2SjNWZVkzG7R8+fLp1m3Xrl1y5cqVZTMzM7lIkSLy2LFj5T///DPZTMfUrte3b19tvZPMnj1b9vT0lE1NTeWyZcvKK1asSHWmZEozWWVZlvv16ycbGRnJT58+Tfba+PHj5Ro1asiFChWSTU1N5WLFismjRo2SQ0JCtGXevd+ZM2fkjh07yh4eHrKpqalsb28vN2rUSN65c2e6bZRaG0dHR8vjxo2TPTw8ZGNjY9nFxUUeOnSoHBoamux9tm7dOt37vE2tVssbNmyQGzduLNva2somJiayl5eXPHToUPnx48cplp87d65crFgx2czMTK5Ro4Z8+PDhZLOCZVmWb968KTdr1kw2MzOT7ezs5IEDB8o7duxI9jOXZVk+duyY3Lp1a9nOzk42NjaWixQpIrdu3Vr+/fff06x/0md43rx5yV47dOiQbG5uLpcuXVr+999/5cePH8u9evWS7e3tZWNjY7l06dLyvHnztLN507ueLMvytWvX5G7dusmOjo6ysbGx7OzsLH/wwQc6s6uTZgVfuHBB59yUZvbGxsbKX3zxhezo6CibmZnJderUkc+cOZPsM5vZWcHv3tfKykpu0aKFHBUVlWpbFmSSLP9/GqMgCO8lLi4OT09P6tevn2yRvyDkdqNGjWLdunXJJk0JmSeGggXhPb148YI7d+6watUqnj9/zvjx43O6SoKQYcHBwZw5c4atW7fi7e2d09XJF8TkJUF4T3v27KFBgwb8+eef+Pn56SyxEYTcbu/evfTu3ZuSJUtmeRmXoEsMBQuCIAiCHokeqyAIgiDokQisgiAIgqBHIrAKgiAIgh6JWcHpUKvVPHv2DGtr6ywlDxcEQRDyB1mWefPmDa6urmmmdRSBNR3Pnj3LUtJwQRAEIX968uQJRYsWTfV1EVjTkZSv88mTJ3pJC5ZZ8fHxHDhwgObNm+tsOyXol2hnwxNtnD1EOxtOeHg4bm5u6eZxFoE1HUnDvzY2NjkWWC0sLLCxsRH/SAxItLPhiTbOHqKdDS+9x4J5bvKSn58fXl5emJmZUb16dU6cOJFm+WPHjlG9enXMzMwoVqwYy5cvz6aaCoIgCAVRngqsmzdvZuTIkUycOJErV67QoEEDWrZsSUBAQIrl/f39adWqFQ0aNODKlSt89dVXjBgxgi1btmRzzQVBEISCIk8F1gULFjBw4EAGDRpE2bJlWbRoEW5ubqluA7V8+XLc3d1ZtGgRZcuWZdCgQQwYMID58+dnc80FQRCEgiLPBNa4uDguXbpE8+bNdY43b96c06dPp3jOmTNnkpX38fHh4sWLxMfHG6yugiAIQsGVZyYvhYSEkJiYiJOTk85xJycngoKCUjwnKCgoxfIJCQmEhITg4uKS7JzY2FhiY2O134eHhwOaCQE5EYyT7in+EDAs0c6GJ9o4e4h2NpyMtmmeCaxJ3p2NJctymjO0Uiqf0vEks2bNYurUqcmOHzhwAAsLi8xWV28OHjyYY/cuKMLUYfy490fslfaoFKqcrk6+JT7L2UO0s/5FRUVlqFyeCawODg4olcpkvdPg4OBkvdIkzs7OKZY3MjLC3t4+xXMmTJjA6NGjtd8nrVtq3rx5ji23OXjwIM2aNRNT5w1oy90tzL84HxkZBQom1Z5Eh+IdUi3/POo5AW8CcLd2B9D+t5NFyp9FQXyWs4toZ8NJGsFMT54JrCYmJlSvXp2DBw/SsWNH7fGDBw/Svn37FM/x9vZm165dOscOHDhAjRo1Uv3AmZqaYmpqmuy4sbFx1j+kr/zh5AIo1RKKNQaTzPd83+v+QpqCIoOYdXEWMprRDDVqpp+fTgO3BjhbOicrv/XeVqaemYpaViOhGfmQkVFICiZ7T6ZTyU7ZWv+8RnyWs4doZ/3LaHvmmclLAKNHj+bnn39m5cqV3Lp1i1GjRhEQEMCQIUMATW+zT58+2vJDhgzh8ePHjB49mlu3brFy5Up++eUXxowZk70Vv7MXLq+FTT1hrhds6AYXV0L4s+yth5CigPAA1Kh1jqllNU/ePElWNigySBtUQRNQtQFZVjP1zFSCIlN+5i8IQsGQZ3qsAN27d+fly5d8++23BAYGUqFCBfbu3YuHhwcAgYGBOmtavby82Lt3L6NGjWLZsmW4urqyZMkSOnfunL0Vd69DZJUBKO/txyzyX7i3X/PFKHCuBKVbQqkW4FIF0kjsLBiGu407ChQ6wVUhKXCzTp4jOiA8QBtUU5IUkFPq6QqCUDDkqcAKMGzYMIYNG5bia6tXr052rFGjRly+fNnAtUrb5meFmXCuKWr5Q8oqnjCn4jMqRZ6Bpxch6Lrm69gcsHKGUj6aQOvVKEtDxkLmOVs6M6n2JKadm6YzpJtScHS3cUchKVINrikF5KDIIALCA3C3cRcBVxAKgDwXWPOawLBoJmz9G7UMIHFL7U7H6x6cHP8VLsoIuHcA7v4J9w9DRBBcXqP5MjKDYo1RFG+KWZz4MRlah+IdiL4VTcmaJfEq5JVqAHS2dGay9+Q0n7G+fe7bz2PFM1hBKBjEb2wD8w+J/H9Q/U+iLPMoJAqX4oWham/NV0IsPDoBd/bB3X0Q9gTu7kN5dx8+gPxyJZRuBaX/P2Qs9obVO/dztygVb45ti/JplutUshN1Xevy5M0Tbe806b/fDqrvPo9NegZb17UugOjFCkI+JQKrgXk5WKKQ0AmuSknC0+GdYV4jUyjRVPPVah48/wfu/on6zj6kfy8haYeMZ4O1i2bIuFRLKNYIjM2z903lQ3JCAvaHDhG8fTsvpk3DsmFDVK1bYdWkCQrz5O3rbOmsExBTCo4pPY9Vy2o23NrA2ptrRS9WEPIpEVgNzEVlzqxOFflq6w0SZRmlJDGzUwVcVGkEQ0kC5wrgXIFE75H8teNXmnrKGN0/AA+OwJtAuLRa82VkrlnCU7oFlPQBm+TZpIT0yfHxvK5XD9cHD4i7f5+Iv/4i4q+/kCwssP7gA2xat8KqXj0kE5MMXzOl57ESEmv/WaudKPV2L1b0XAUhfxCBNRt0r+lOw1KFeRQShaeDRdpBNQWxxirkyq2gRl+Ij4FHJzXPZe/sg/Cnmv+++6emsEuVt2YZVxZDxhmkMDfn1QdNqDN/Hon+/oTv2Uv4nj3EP31K+O7dhO/ejUKlwqZ5c2xat8KiZk0kpTLNa777PFYhKehTrg+r/1mtU07MJBaE/EUE1mziojLPdEBNkbEZlGyq+Wo1H57f0DyTvbMP/r0EgVc1X0dngbXrW7OMG4oh4wwyK1UKs1KlKDzyc2KuXydszx7C//yTxBchvP79d17//jtGhQtj06olqo4dMStTJtVrpfQ8NmkYOMnbM4nFDGJByPtEYM3LJAmcK2q+Go6FiGC4u18TaB8chjfP4NIqzZeRORRvAqVa8Ny5EQ9irPBysNRPsM+nJEnCvHJlzCtXxmncOKIuXCB8zx7CDxwk4cULXq1Zy6s1azGrUAHbLl2wadMGpZVlsuu8+zz23V5s0kxiMYNYEPIHEVjzEytHqOar+YqP+f8s4z81wTb8qSYD1J29OAGB6uLMTfTBu90gutUuntM1z/UkpRLLOnWwrFMH56+/JuLkKcJ27ODN4cPE3LhB0I0bBM+di6pDewr17IlpyZKpXuvdXqyzpXOaM4hFz1UQ8hYRWPMrYzMo2UzzJcvw/AZvru/iwck/qKJ48P8vP/7d+xth0SNR1R0AJsl7W0JykokJ1h80wfqDJiS8ekXYjp28/u034vz9Cd34K6Ebf8WiVi0K9eqJ9YcfIqWQX/TdXmxqM4iT0iqK4WFByDtEYC0I/j9k/HekC70OV6Ywr+mqPEZ/oz8pIoXA0UlwbgHU+ljzZZnyzj9CckZ2dtj374ddv75EnT1L6MaNvPnrMFHnzxN1/jxGhQtj260btt26YpzKLkyQ8gxihaTgRsgNBh0YJIaHBSEPEYlpC5CkNbUvsMUvsT31Y5cwKX4ACSpPiH6lWSO7qALs/RJeB6R7PeE/kiRh6e1N0aVLKfHXIeyHDkHp4EDCixeELFvG/Q8+5N/RXxD9940Uz0+aQayQNP8kFZKCkdVGsujyomTDwyLJvyDkbiKwFiBJa2qV/1+CkyCZUrHDKIw+vwxdVmmW58RHwfkfYXEV2PqxJlGFkCnGLi44fv45JQ//RZEF32FeozokJhK+dy+Punbl0Ucf8ebQIeTERJ3zOpXsxP7O+1nps5L9nfdT3r58isPDBx4dEMFVEHIxMRRcwKS6prZCJyjfER4ehVOLNP9/fbPmq2RzqDcSPOqKdbGZIJmYYNOqFTatWhFz8yav1qwhbM9eoi9e4unFSxh7uGM/YCCqjh1Q/D/xxLvPXlNK+D/v4jy+u/SdGBYWhFxK9FgLIBeVOd7F7ZMvtZEkzZKcPjvg46OaQCspNBsFrG4FvzSDW7tBnfq2aULKzMqVw3XOHM0w8eDBKGxsiH8cQNDkyTz4sCkvf/mFxIgInXPeHR5+mxgWFoTcSwRWIWWuVaHrahh+Ear3B6UpPL0Am3uDX224vA4S4nK6lnmOsZMTjl+MpuSRwzhNGI+RszMJL14QPG8+9z/4kOBFi0h4+VJbPml4eGyNscmuldpm7IIg5CwRWIW02ReHtotg5N9QfzSYqiDkLuwcDosrwaklEBOe07XMcxSWltj17UuJA/txmTkTk2LFUIeH83L5j9z/sCnPZ80mPjgY0PRcm3s2T9ZzTW0zdkEQcpYIrELGWDtB08kw6gY0m6bZYedNIBz8GhZWgENTNZmf3hEYFs3pByEEhkXnQKVzP8nEBNtOHSm2exdFli7BrGJF5JgYXq1Zw4OmzQiaPoP4oKAUZw2nthk7aFIjng88L4aKBSEHiMlLQuaY2UC9EVD7E7j+G5xaDC/vwckFcGaZZm/Zup+BXTE2XwjQbvKukGBWp4p0r+me0+8gV5IUCmyaNcO6aVMiT50mZNkyoq9cIXT9el5v3oyqS2fafvwxdTvvT3Hv17eJ1IiCkLNEj1XIGiNTTerET89D9w1QpAYkxsLFlbC0OtEbfVm/bad2H1q1DF9tvSF6rumQJAmr+vXw2LgB99WrsKhZU7Ol3a+beNDcB75bQRUp9QxMqaVGFD1XQcg+IrAK70ehgLJtYNAh6LcHSjQDWY353Z3sMpnIOuOZ1FXcAGQSZZlHIVE5XeM8QZIkLOvUwWPdWtzXrsGidm3k+HhCN27kQbPmPJ89h4TQ0GTnpZcaURAEwxOBVdAPSQLP+vDRHzDkFNFlOpMgK2igvMFGk5lsMplONcUDPB0scrqmeY5lrVp4rFmN++rVmNeojhwXx6vVq3nQtBkvvl9GYkSktmxSasS3SUi8jH4peq2CkE1EYBX0z7kC5j1W8ucHe1mT4EOsbEwdxS22mnyNy76PIeReTtcwT7KsUxuPdetwW7ECs3LlUEdGEvL99zxo1oyXq1ejjo1NNslJQpPQY+zxsfhs8WHrva05+RYEoUAQgVUwmLaNvGk+dg03Oh8mqnwPTbKJWzthWW3Y9TmEB+Z0FfMcSZKwalAfzz9+p8iihZh4epIYGkrw7Dk8aNGS13/8QUevduzvvJ/5jeYjISGjedAtnrcKQvYQgVUwKBeVOdUrVcKi648w5BSUaglyIlxaDUuqwl/fQkxYTlczz5EUCmxatKDY7l24TJ+mSTQRGEjgpK952LYdFieuUshYhRrxvFUQspsIrEL2cSoHvTZB/z+haC1IiIYT38HiynD6e83m7EKmSEZG2HbpQvH9+3AcPw6lrS1x/v78O3IUtsNnUeGxbnmRVEIQDE8EViH7edSFgQegx0ZwKA3RoXBgInxfA67+SmBohEgqkUkKU1Ps+/Wj+KGDOHz6KQoLCxJv3eWbjQmM/y0RtxdymkklREIJQdAfEViFnCFJUKY1DD0N7ZaCtSuEPYHtQwhfWIcVvyyn3uy/2HxB7AubGUorKwp/NpziBw9QqFcvMDKi2gOZ+SvVbL3VjHa2DZKds/XeVny2+DDwwEAxwUkQ9EAEViFnKY2gWh/47BLh9ScSJltQWvGEVSbzWGs0k9Xb9oqeaxYY2dvj/M3XFN+9C+vmzZHUMnHb9vDApwUhy39EHaMZdhcJJQRB//JMYA0NDcXX1xeVSoVKpcLX15fXr1+nWj4+Pp5x48ZRsWJFLC0tcXV1pU+fPjx79iz7Ki1knIkFN7wG0DB2ET8mtCZWNqa+8h92G49H2jMaIkNyuoZ5komnJ0WXLMZjw3rMKlZEHRXFi0WLeNCyFWG7dhPw+pFIKCEIepZnAmuvXr24evUq+/btY9++fVy9ehVfX99Uy0dFRXH58mW+/vprLl++zNatW7l79y7t2rXLxloLmeHlYMkbyYpZCb35MG4eexJroZRknO9uhCXVNBOcxFZ1WWJRvTqemzfhOm8eRi4uJAQG8mzsWGxHzKHsU92yYoKTILyfPJGE/9atW+zbt4+zZ89Su3ZtAFasWIG3tzd37tyhdOnSyc5RqVQcPHhQ59jSpUupVasWAQEBuLuLZPC5jYvKnFmdKvLV1hs8lR0ZkTAK83rRfPBoIQT9rZngdHEl+MyAUi00z2mFDJMUClRt22DdrCmv1qzl5Y8/kvjPbab+A2fLSKxvrCDETplsglNQZBAB4QG426Seo1gQhP/kicB65swZVCqVNqgC1KlTB5VKxenTp1MMrCkJCwtDkiRsbW1TLRMbG0tsbKz2+/BwzV6j8fHxxMfHZ+0NvIeke+bEvXNCpyoueHsVIuBVFO52FriozIhXt0a6/ivKozOQXj2AX3ug9mpEYtPp4FhWL/ctUO2sVKIa0B/Ldu14tWwZ4Vu3Uue2mtoPJEx7daJo4Q+07bD9wXamn5uOGjUKFEyqPYkOxTtk6bYFqo1zkGhnw8lom0qyLMsGrst7mzlzJqtXr+bu3bs6x0uVKkX//v2ZMGFCuteIiYmhfv36lClThvXr16dabsqUKUydOjXZ8Y0bN2JhIfLc5iSjxGhKBe2k2Iv9KOUEZCQeOTThtktn4oysc7p6eZZJUBCFd+/B8p4m1WSCtTUvWrbgaeXizI/4Tpu5CTQpEsfYjEGlUOVUdQUhx0RFRdGrVy/CwsKwsbFJtVyO9lhTC2Jvu3DhAqBJ5fYuWZZTPP6u+Ph4evTogVqtxs/PL82yEyZMYPTo0drvw8PDcXNzo3nz5mk2pKHEx8dz8OBBmjVrhrGxcbbfP/fpjDrUH+mvqSju7MYr5DCeEZdQNxiLuvoAUJpk6aoFvZ3l/v2JOnqUkHnz4ckTXH77ncLXi1G8lpr7Rf77NyYjU7JmSWo41cj0PQp6G2cX0c6GkzSCmZ4cDazDhw+nR48eaZbx9PTk+vXrPH/+PNlrL168wMnJKc3z4+Pj6datG/7+/hw+fDjd4GhqaoqpqWmy48bGxjn6Ic3p++cqjqWg5wbwPwH7JiA9/xvlwUkoL68Gn5lQsnmWn78W5HY2ad4cm8aNCV27lhC/HzC6/ZCZt+FYBYmNjRWEWksoJAVehbzeq40KchtnJ9HO+pfR9szRwOrg4ICDg0O65by9vQkLC+P8+fPUqlULgHPnzhEWFkbdunVTPS8pqN67d48jR45gb2+vt7oLuYBXA/jkGFxZB39Ng5f3YWM3Yrw+xKzNPLAvntM1zHMUJibYDxqETbt2vFi4iLBt22h0Q6bW3UT+aKCkxvBJYgKTIKQjTyy3KVu2LC1atGDw4MGcPXuWs2fPMnjwYNq0aaMzcalMmTJs27YNgISEBLp06cLFixfZsGEDiYmJBAUFERQURFycWLKRbyiUUL0fW+vv5MeENsTJSsz8/yJxWW04PB3ixMbqWWHs6IjrrJl4/rYZZYWymMeB71+JVB67hsiz53K6eoKQq+WJwAqwYcMGKlasSPPmzWnevDmVKlVi3bp1OmXu3LlDWJhmp5SnT5+yc+dOnj59SpUqVXBxcdF+nT59OifegmAggWHRjNn1iFkJvWgRN4fjiRVRquPh+DzNFnW3dkPun6OXK5lXqkTJ3/7AZcZ0lIUKEXf/AQH9+vHv6NHEp/B4RhCEPLLcBsDOzi7N2bygmcyUxNPTkzww4VnQA/+QSNT//1E/lF3pEz+eFokXWGy7GdOwANjcG0o0hRZzwKFEzlY2D5IUCmw7d8a6aVNeLF5C6KZNhO/9kzdHj1F42FDs+vRBMkk+aUysfxUKqjzTYxWE1Hg5WKLQmaskcVCuTeiAk9DgC81M4fuH4AdvODQV4iJzqqp5mlKlwvmbr/H643fMq1RBjooieP53POzQkcgzZ3TKisT+QkEmAquQ5yVlbFL+fyawUpKY2akCzg4O8OE3MOyspseaGAcnF8D3teDmDjE8nEVm5crhsXEDLjNnorSzI+7hQwL6D+DpyFHEBwWlmtj/eZQYOhYKhjwzFCwIaele052GpQrzKCQKTwcLXFTm/71oXxx6/wG398C+CRAWAL/1gWJNoNU8cCiZcxXPoySFAttOHbFu+iEvliwldONG3uzbR8Tx48R81AbJNhGU/w0jiMT+QkEieqxCvuGiMse7uL1uUE0iSVC2DXx6Dhp+CUpTeHgE/Lzh4GQxPJxFShsbnCdNxGvrFsyrVUOOisL0p9+Y/3MiFf3/2zVHJPYXChIRWIWCxcQCPpgIw85ASR9Qx8OpRRj9WBfn15dyunZ5llmZMnhsWI/L7FkoHRwo8gq+3qRm9NZECr+RmOw9GScLTTKX51HPOR94Xuz5KuRbIrAKBZN9cej9G6/arSXGsihS+L/U9l+M8rfe8Dogp2uXJ0mShG2HDhT/cy+F+viCUkGdOzLLflHS6EQYcnw8F2Mv0np7azGpScjXRGAVCqzNFwKo8bsRVV5Owy+hHYkoUdzbr1n7enIhJIrdQbJCaW2N81df4bV1K+bVqkF0DMFz5+LfrQu3721Dje6kJtFzFfIbEViFAikwLJoJW/9GLUMMpsxN6EGL2JnEuNaG+Cg4NAWWN4DHIplIVpmVLo3H+nWa5BK2tqjv+/PtukQ+2ZuIdZRmRraY1CTkRyKwCgXS20klktyT3bjYaC10+AEs7OHFLVjVErZ/CpEvc6aieVxScolif+7FuF0LAD68JrPwp0QaXVejQBKTmoR8RwRWoUBKnlQCJGTc7S2hSi8YfhGq99O8cHU9fF8dLq8FtTrZtYT0GRUqhMeMuRwY2JjHhSVsouHTPWp+2VmEQkFiRraQv4jAKhRI7yaVUEjQvZgaF5WZpoCFHbRdDAMPglMFiA6FnZ/Bqhbw/J8crHne5lmqBeW37yV2SHcwM8Pyn0c87NCR4EWLUMfEEBQZJGYMC3meSBAhFFhvJ5UoojLhyqnDyQu51YKPj8G55XBkJjw5p3n2Wnc4NBoHJpbZX/E8ztmmCG4jpxDXZTDPp08n4uhRXi7/kcDtvzO/cThXvTTrXid7T6ZTyU45XV1ByDTRYxUKtP+SSpilXkhppAmkwy9A2bYgJ8KpxeBXB+4dzL7K5jMmRYtQ9Ac/iixZjOTogEnQK77alMDn2xOxfpMoZgwLeZYIrIKQUaoi0H099NwEKjfNetcNXeD3/vBGBICskCQJm+bNCV81nT01JdQS1Lsls3BFIk0uJ/Ak7HFOV1EQMi1DQ8HVqlXL1EUlSWLnzp0UKVIkS5UShFytdEvwbABHZ8FZP/hnK9z/C5pOhur9QSH+Xs0sd+fSrGtmzPEKiXz8ZyLFg+CTfWqMgr4jdvpMTEuI7f6EvCNDgfXq1at88cUXWFlZpVtWlmVmz55NbGzse1dOEHKbwLBo/EMi8XKwxMVnBlTsCrtHwrMrsGc0XNukmfTkVC6nq5qnOFs6M9l7MlPPTGViX2h1CT46qSTh6t887NgJh8GDsP/kExSmpjldVUFIV4YnL40dOxZHR8cMlf3uu++yXCFByK02XwjQJpVQSDCrU0W616wCg/6C8yvg8DR4eh5+bAB1R0CjL8E4hQ0BhBR1KtmJuq51efLmCW7d3LB/nUjQNM3kphC/Hwjf+yfOU6ZgWad2TldVENKUoTErf39/ChcunOGL3rx5Ew8PjyxXShBym7czNQGoZfhq6w0Cw6JBoYQ6QzQ755RuDeoEzb6vP9SFh0dztN55jbOlMzWda+Js6Yxxkf9Pblq0CKPChYl79IiAfv149tVEEkJDc7qqgpCqDAVWDw8PJElKv+D/ubm5oVQqs1wpQchtUsrUlCjLPAqJ+u+Aqij03KiZ4GTtAq8ewtr2sG2oyNyURZIkYdPCh2J792DbswcAYVu38rBVa8J27kSWZbH2Vch1srSONSYmhuvXrxMcHIz6nUw07dq100vFBCE3ScrU9HZwVUoSng4WyQuXbQteDeGvaXDhZ7i2Ee7tB59ZUKmbZm9YIVOU1ta4TJ6Mqm07giZ/Q+y9+zz7chx3N65govdjAm1lsfZVyDUyHVj37dtHnz59CAkJSfaaJEkkJibqpWKCkJskZWr6ausNEmUZpSQxs1OFZJuq60xuaj1fE0h3fQ7BN2Hbx3DtV2izEOy8cuid5G0W1aritWULL1eu4oXfMqyu3mfeDfi9voLdtWSmnplKXde6OFs653RVhQIs04F1+PDhdO3alW+++QYnJydD1EkQcqW3MzV5OlgkC6opT276f+am00vg2Fx4eAT8vKHJV1BnmCb5hJApkokJDkM+wb+6C/6TxlPxsUzvo2rq3YTlreDJmyfawBoUGURAeADuNu4i2ArZJtML7oKDgxk9erQIqkKB9F+mpuQ91VQnNxmZQMMxMOyMZv1rQjQc/Bp+/gCeXc3+N5FPuJWrxYxeRixrreCNGXgGw8w1idiv2Ik6Koqt97bis8VHbKouZLtMB9YuXbpw9OhRA1RFEPKuDE1usi8OfXdB+2VgZguB12DFB3Dga4iLQsgcZ0tnJtedwonKRoz6WMnJ8goUMsRu+IN7bVqzbcNk1LLYVF3Ifpkeh/r+++/p2rUrJ06coGLFihgbG+u8PmLECL1VThDyigxPbpIkqPoRlGwOf47TZG06vQRu7YQ2i6B4k2ytd16ns/a1rxtWl+4SNGUq8c+e8dUmOFFeYnVTBW8sJO2m6mJIWDC0TAfWjRs3sn//fszNzTl69KjOMhxJkkRgFQqkjE5u0rJyhK6roFJ3Tcam0EewrgNU7gU+MzTb1gkZ4mzp/F+wbOhMsV07efTdbKJ//YMG/8hUfpjImqYKTlVQik3VhWyR6cA6adIkvv32W8aPH49C5EQVBK30JjfpzBhOeq10C/CsB399q8nedG0j3D8ILedA+U5iaU4WKCwtKfbNNPZWL4xiznI8gmU+26Wm/7/FsW+aCGKnP8HAMh0Z4+Li6N69e7YH1dDQUHx9fVGpVKhUKnx9fXn9+nWGz//kk0+QJIlFixYZrI6CkNrkps0XAqg3+zC9Vpyj3uzDbL4Q8N+LptbQah4MPAAOpSHyBfwxAH7tAWFPs/kd5B+tWo+g8s4DxA7qAibGWF2+x4O27Xi1dh2yWBYoGFCmo2Pfvn3ZvHmzIeqSpl69enH16lX27dvHvn37uHr1Kr6+vhk6d/v27Zw7dw5XV1cD11IQkktzxvDb3GrBkBPQeAIojOHuPlhWR9OTfScRi5AxLrZFqTJmGsW278C8RnXkqCiez5zJ4169ib13L6erJ+RTmR4KTkxMZO7cuezfv59KlSolm7y0YMECvVUuya1bt9i3bx9nz56ldm1NAu4VK1bg7e3NnTt3KF26dKrn/vvvvwwfPpz9+/fTunVrvddNENKT1ozhZM9gjUyh8Xgo1x52fgZPL8DeMfD379BuKRRO/bMupM60mBcea9fy+rffCJ43n+hr13jYqTMOgwdjP+QTFCYmYs2roDeZDqx///03VatWBeDGjRs6r2Umn3BmnDlzBpVKpQ2qAHXq1EGlUnH69OlUA6tarcbX15exY8dSvnz5DN0rNjZWZ8u78PBwAOLj44mPj3+Pd5E1SffMiXsXJIZs56Iq02QzhhUSFFGZpH6/QiXAdzeKS6tQHJ2G9OQc8vL6qOt/gdp7BCiNUz4vF8sNn2Wrzp0xq1+fF9NnEHn0KCF+foTt28ftjz9gYtha1KhRoGBS7Ul0KN4hx+r5PnJDO+dXGW3TTAfWI0eOZLoy7ysoKCjFLescHR0JCkp9XdqcOXMwMjLK1EzlWbNmMXXq1GTHDxw4gIVFCnlhs8nBgwdz7N4FiaHauZuXxOaHCmQkJGS6eam5cuowV9I90xXzktOo/GQ1TuHXUR6bRcS59Vz1GMhri2IGqauh5YrPcgsfrIq44rhjJzx8iNf4h/StLvFrIwUxpmqmnZtG9K1oVApVTtc0y3JFO+czUVEZW2+eo/nUpkyZkmIQe9uFCxeAlHvDsiyn2ku+dOkSixcv5vLly5nqSU+YMIHRo0drvw8PD8fNzY3mzZtjY2OT4evoS3x8PAcPHqRZs2bJht0F/TF0O7cChoXFEPAqCnc7C1xUZsnKBIbF8PhlFB72Kbwu+5Lwzx8oD0xEFf2Ehne/RV17KOqG48A45/7gy4xc91lu3ZrEoUO5PW08pvtP0fKSTM17ifzUQsHV4gpK1ixJDacaOV3LTMt17ZyPJI1gpidDgbVTp06sXr06w4Gld+/eLFy4MN2N0YcPH06PHj3SLOPp6cn169d5/vx5stdevHiRamrFEydOEBwcjLu7u/ZYYmIiX3zxBYsWLeLRo0cpnmdqaoqpqWmy48bGxjn6Ic3p+xcUhmxndwdj3B2sU3wt5TzD7rqFqvaCUprEEtKNP1CeXYbyzl5ot0Szm04ekZs+y8YODjjPnM4Xds0Y/GcCTq/hq9/UnKgAHh/a5Zp6ZkVuauf8IqPtmaHAumPHDl68eJGhC8qyzK5du5g2bVq6gdXBwQEHB4d0r+nt7U1YWBjnz5+nVq1aAJw7d46wsDDq1q2b4jm+vr40bdpU55iPjw++vr70798/Q+9FELJDarOGG5YqnHxyk6UDdPkFKnaF3aMg1B/WtIVqfaDZNDC3zfb653XOls507jWVL4tMocvxBFpfkGlwQ01EtwGET5qIdcuWyUa9xEQnIS0ZCqyyLFOqVClD1yVVZcuWpUWLFgwePJgff/wRgI8//pg2bdroTFwqU6YMs2bNomPHjtjb22Nvb69zHWNjY5ydndOcRSwI2S1Ts4aTlG4BHnXh0BS4+AtcXgt3D0Dr76BsG4PXOb/RpkZs8wTVozfEzVhI7L37/Dv6C6x278F58jcY/390bOu9rUw9MxW1rBZ7wAopylBgzcqEpSJFimT6nLRs2LCBESNG0Lx5c0Czofr333+vU+bOnTuEhYXp9b6CYGiZ2kT9bWY20GYBVOisWZrz6gFs7g3lOmgSTlilPWIk6NKmRnQGeUt9Qn5aQciPPxJx+DAPz5/HcexYYlrV1wZV+C+5v9gDVnhbhgJro0aNDF2PdNnZ2bF+/fo0y8iynObrqT1XFYSclOk8w+/yrAdDT8GxOXBqCdzcDv7HoMVsTS5ikRYx0yQTEwoP/xQbn+Y8mzSJmGvXCZo8mYRtZShcO5Hndv+1aVJyf0AMDwtADs8KFgRBI608wynmGH6XsTk0nQLlO8KOTyHob9j2Cfz9B7RZCLYi+XxWmJYsiefGjYSuX0/wosUYXb3Ndzdgc0MFe2pKqBUSCknBjZAbDDowSAwPC0AWUhoKgmAYKeUZTjPHcIoXqQyDj8CH34DSVJPQ30+kRXwfklKJXd++FNu5AwvvOpgkgO9hNdPXJOL5AkZWG8miy4vE3q+ClgisgpBLZTjH8LuUxtDgCxhyEtzqQFyEJi3imjbw8oHhK55Pmbi54b5yJS4zpiNZWVIiCOaukqm68w5Sgm5S/7eHh4WCRwRWQcil0potnCGFS0H/P6HlPDC2hMen4Ie6cHIRJCbovb4FgSRJ2HbuTPG9e7Fq+iEkJmK6dgdzV6op/uy/H5ZCUqS492tQZBDnA8+L3mw+JwKrIORSSbOF35ah2cJvUyig9scw7AwUawIJMXBoMvz8IQTdSP98IUXGjo4UXbqUIgsXoLSzwy1EZsbaRHz/SsQsQWKy9+RkE5i23tuKzxYfBh4YiM8WH7be25pDtRcMLdOB9fnz5/j6+uLq6oqRkRFKpVLnSxAE/UiaLaz8/6zeTM8WflshD/DdBu39wEwFgVfhp0ZweAYkxKZ7upCcJEnYtGxJsT27sWnXFoUMbc/LrP+1MD6hRXXKBkUGpbhMR/Rc86dMzwru168fAQEBfP3117i4uBhsRxtBENKeLZxpkgRVe0OJprD3C7i1C47P1fx/+2VQtLr+Kl6AGBUqRJG5c7Fp1YqgKVNJePqMgD59se3RHccxY1BaWREQHqANqkmSnsOKpTn5T6YD68mTJzlx4gRVqlQxQHUEQXiXi8r8/QLqu6ydoPt6+Ge7ZlLTi1vwS1OoMwyaTASTvJHUP7exbtwYi13VCZ43n9e//cbrTZuJOHYcl6lTcK9eCoWk0AmuqT2HFfK+TA8Fu7m5pZuIQRCEPKB8B/j0vCaJhKyGM99rJjc9OpnTNcuzlNbWuHw7FffVqzF2cyMhMJAnH3+C+tuFfFt+LApJ8ys3aa3r271VMbEp/8h0YF20aBHjx48XWYwEIZcKDIvm9IOQ9JflAFjYQaefoNdvYO2qSeq/urUmwX9MxrbIEpKzrFObYju2Y9e3L0gSYTt2UnbEcnapvmKlz0r2d96vk0BCTGzKXzIdWLt3787Ro0cpXrw41tbW2NnZ6XwJgpBzMp1QIkkpH/j0LFTvp/n+4krw84Z7hwxW1/xOYWGB04TxeGzcgEnx4iS+fEnkuCm4zFyPQ9R/Ez3FxKb8J9PPWBcuXCgmLAlCLpSp7edSYqaCtov/S+of+gg2dIbKvcBnhqZ3K2SaRdWqeG3bSoifHy9//oU3Bw4Qde4cThO/wqZt23QnNokt6vKeLM0KFgQh98nS9nMp8WoIQ0/D4elw9ge4thEe/AWtF4gt6bJIYWKC48iR2DRvzrOJk4i9dYtnX44jbM8eio4bnurEJrFFXd6U6aHg3r17s2LFCu7evWuI+giCkEV6SSiRxMQSWsyCgQfAoRREPNdsSfd7f4gM0U+FCyCzcuXw+m0zhUeORDI2JvLYcd50G8DCsJYo0PzwkgIoIIaI86hMB1YrKyu+++47ypQpg6urKz179mT58uXcvn3bEPUTBCGD9JpQIolbLfjkBNQfDZIS/tkK39fU7JojVgdkiWRsjMOQT/DathWzypVQR0TgsmwHfxyqwKqKs7UTm9IaIhZyt0wH1h9//JHbt2/z7NkzFixYgEqlYvHixZQvXx4XFxdD1FEQhAzqXtOdk+Ob8OvgOpwc34TuNd3f/6LGZtB0Mgw+DE4VIPoVbBkIm3pDeOD7X7+AMi1RAs+NG3EcNw7JzIyEC1ewGvg1JlsPIavVuNu4a5fnJEkaIhZLc3K3LOcKtra2plChQhQqVAhbW1uMjIxwdhYP1gUhp6W0/ZxeuFbRbEnXZCIojOHOHvCrDVc2iN5rFklKJfb9+1Fsx3YsatZEjo7m+YwZPP7Il0LB0Uz2npxs7evpZ6fF0pxcLtOBddy4cdSpUwcHBwcmTZpEXFwcEyZM4Pnz51y5csUQdRQEQc8ytdb1bUYm0OhL+OQ4uFaDmDDYMQzWd4bXYogyq0w8PHBfsxrnyd+gsLAg+vJl/Dt0pNHxUPa136td+1rXta547poHZHpW8Lx58yhcuDCTJ0+mffv2lC1b1hD1EgTBQDZfCNAuy1FIMKtTxcwPGTuVg4EHNdmajszUzBr2qwPNvoXq/TW76giZIikUFOrZE6uGDQn8ZjKRp04RPG8+Zvv2U3HGdMycnTkfeF7kHM4DMv3pv3LlChMnTuT8+fM0bNgQZ2dnunfvzg8//MCtW7cMUUdBEPQky5unp0RpBPVHwtBT4FZbs6H6ntGwth28eqjXehckxkWK4PbzClxmzEBhbU3M33/j37kLL/z8cDN3TfW567ueRz0Xz2FzSKYDa+XKlRkxYgRbt27lxYsX7N+/HwsLC0aMGEGFChUMUUdBEPTkvTdPT4lDSc2G6i3mgLEFPDoBP9TTrIFVJ75fhQsozYbqnSi2ezdWH3wA8fGELFlKdL/PmOk4OM2cwwAXYy/Sentr8Rw2h2R6KBg0vdajR49y9OhRTpw4QXh4OFWqVKFJkyb6rp8gCHqUtNb17eCa5bWub1Mooc4QTWrEnZ9pguu+8fDPNmj3PRQu9X7XL6CMnRwpuux7wvfs5fn06cTevk2xL+6xvV9PQro3wc2+WLKg+jzqOTuidyCj+SEnPYet61pXDBdnk0z3WAsVKkStWrXYsGEDJUuWZO3atbx69YqLFy8yb948Q9RREAQ9Mcha17fZeUHfXdBmEZhYw5NzsLw+nFwE6gT93KOAkSQJVZvWFNu9C+sWLSAxkZhf1mM3bCaqB8HJyge8CdAG1STvrn8Vy3UMK9M91nXr1tGwYUNsbGwMUR9BEAwsI5unB4ZF4x8SiZeDZeaDriRBjf5Qshns+hzuH4JDk1H+sx1rVVc9vYuCx8jBgaKLFhK+vyVB335L3P0HPOrRE7v+/Sj82WcozMwAcLd2R0LSCa5vP4cVaRINL9M91jZt2miD6tOnT/n333/1XilBEAwrrbWub++QU3fWYWbuuZm1yU2qotD7D2jvB2YqFIFXaHznaxQnv4PEeD28i4LJxqc5xXbvwqZtW1CrefXLSvw7dCTq8mUAnCycaG/ePsXnsGInneyR6cCqVqv59ttvUalUeHh44O7ujq2tLdOmTUOtVqd/AUEQcq13Zw3LwE8n/DO3Bd3bJAmq9oZh51CX9EEhJ6I8NgtWNIHAa3qte0FiVKgQRebNpaifH0aOjsQ9esTj3h8RNGMm6qgoapjWYE/7Pcn2fhVpErNHpgPrxIkT+f7775k9ezZXrlzh8uXLzJw5k6VLl/L1118boo6CIGSTlGYNw3suywGwcSGx63ouegxBNi8EQX/Dig80O+gkxL5fpQsw6w+aUGz3LlSdO4EsE7puHQGdu2D+4AFOFk7UdK6pM2EprTSJgv5kOrCuWbOGn3/+maFDh1KpUiUqV67MsGHDWLFiBatXrzZAFTVCQ0Px9fVFpVKhUqnw9fXl9evX6Z5369Yt2rVrh0qlwtramjp16hAQkIW/vAWhAEhph5wk770sR5L4164uCR+fgrLtNJOZjs+DHxvBv5eyft0CTmljg+uMGbj9/DNGri4kPH2K208rCJ42jcSISJ2yzpbOKaZJTG+2sJjslDmZDqyvXr2iTJkyyY6XKVOGV69e6aVSKenVqxdXr15l37597Nu3j6tXr+Lr65vmOQ8ePKB+/fqUKVOGo0ePcu3aNb7++mvM/v+QXxAEXUmzhlP6xZC0LCfL6RCTWDlC93XQdQ1YOMCLW/BzUzj4DcRn8ZoCVvXrUWznTmy6dwMg/LffediuLREnT+mU61SyE/s77082TJyarfe2itzEmSTJcuayZ9euXZvatWuzZMkSneOfffYZFy5c4OzZs3qtIGh6neXKlePs2bPUrl0bgLNnz+Lt7c3t27cpXbp0iuf16NEDY2Nj1q1bl+V7h4eHo1KpCAsLy5GZ0PHx8ezdu5dWrVphbGyc7fcvKEQ76woMi2bVyUf8fPIhavm/ZTlAltMhptjGkS/hzy/hxh+a7+1LQvtl4F7bEG+rQIiPj+fI0qV47dlLwv8nl6o6d8Jp3DiUmfwdFhQZhM8Wn2SbsO/vvL9AronNaDzI9HKbuXPn0rp1aw4dOoS3tzeSJHH69GmePHnC3r1736vSqTlz5gwqlUobVAHq1KmDSqXi9OnTKQZWtVrNnj17+PLLL/Hx8eHKlSt4eXkxYcIEOnTokOq9YmNjiY3975lPeHg4oPmwxsdn/0zGpHvmxL0LEtHOuhwsjBjbvAQf1S5KwKso3O00CSQaf3dcJx3ihK1/4+1VCBdV+qNAKbaxiQ20X45Uph3KfWORXt5DXumDutbHqBtP1GRyEjIlPj6e6OLFcfltM+F+foRt/JWwLVuJOHESx8nfYNmwYYav9TD0YYqTnfxD/bE3sdd31XO9jP5+yHSPFeDZs2csW7aM27dvI8sy5cqVY9iwYbi6uma6ohkxc+ZMVq9ezd27d3WOlypViv79+zNhwoRk5wQFBeHi4oKFhQXTp0+nSZMm7Nu3j6+++oojR47QqFGjFO81ZcoUpk6dmuz4xo0bsbAQ/8iFgutemMT3N5XJjg8vl0hJ1ftvG2ecEEmFfzfg/uokABEmjlz1GMRLq+SPnoSMM/N/hPMff2ASEgJAeNWqBLdrizoDv8/C1GHMD5+vsyZWQmKMzRhUCpVOuZeJL7FX2uscz2+ioqLo1atXuj3WLAVWfUktiL3twoULHDhwgDVr1nDnzh2d10qWLMnAgQMZP358svOePXtGkSJF6NmzJxs3btQeb9euHZaWlvz6668p3i+lHqubmxshISE5NhR88OBBmjVrJoYoDUi0c/oCw2J0eqygGQ4++kXDDPdYM9LG0v1DKPeORnrzDIDEGoNQN5kEJlbv/R4KgpTaWR0Tw6tly3i9dh2o1Sjt7Sn89SSsPvww3ettf7Cd6eenaxNKTKo1iQ7FO+i+fm46atQoUDCptu7r+Ul4eDgODg76GQq+fv16hm9cqVKlDJcdPnw4PXr0SLOMp6cn169f5/nz58lee/HiBU5OTime5+DggJGREeXKldM5XrZsWU6ePJnq/UxNTTE1NU123NjYOEd/4eb0/QsK0c6pc3cwZlaniny19QaJsqx97uruYJ2p66TbxmVbglddODAJLq9FefFnlPcPQLulUKzx+72JAkSnnY2NcRk/HtuWLXn21UTiHjwgaOQobFq1xGnSJIzs7FK9TtcyXWng1oAnb57gZu2m82w1KDJIE3T5f8IJ1Ew/P50Gbg3y5TPYjP5uyFBgrVKlCpIkIcsykvTfXPykzu7bxxITM76bhYODAw4ODumW8/b2JiwsjPPnz1OrVi0Azp07R1hYGHXr1k3xHBMTE2rWrJmsl3v37l08PDwyXEdBEP6TkXSIemGm0gTS8h1h5wh4HQBr22v2em32LZiJlKpZYV65Ml5btxCyzI+Xv/xC+N4/iTx7DudvvsamRYtUz3O2dE4xUKaVcCI/BtaMytByG39/fx4+fIi/vz9btmzBy8sLPz8/rl69ytWrV/Hz86N48eJs2bLFIJUsW7YsLVq0YPDgwZw9e5azZ88yePBg2rRpozNxqUyZMmzbtk37/dixY9m8eTMrVqzg/v37fP/99+zatYthw4YZpJ6CUBCklQ5R74p/AMPOQI2Bmu8vrQI/b03+YSFLFKamOI4ehefmzZiWLEniq1f8O3IUT0d8TsL/n8NmVEYSThTINbByJtWsWVPes2dPsuN79uyRq1WrltnLZdjLly/l3r17y9bW1rK1tbXcu3dvOTQ0VKcMIK9atUrn2C+//CKXKFFCNjMzkytXrixv3749U/cNCwuTATksLOw930HWxMXFydu3b5fj4uJy5P4FhWhnw3j2Oko+df+F/Ox11Pu38cNjsrywoixPttF8bR8my1Gheq1vfpCZdlbHxsrBi5fIN8tXkG+WLiPfqV1Hfr1zl6xWqzN8vy13t8iV1lSSK6yuIFdaU0necndLhl7LizIaDzI9ecnc3JzLly9TtmxZneO3bt2iWrVqREfnrwXeYh1rwSDaWf82XwjQWfM6vX05LJ9ff782jouEv76Fcz8CMli7QttFmn1gBSBrn+WYW7d49tVEYm/dAsDqgw9wnjIZY0fHDJ0fFBmU7BlsflwDm9F4kOnMS2XLlmX69OnExMRoj8XGxjJ9+vRkwVYQhILp3WT+ahkm7bjJ6/dNC2xiCS3nQP8/wa44vHkGG7vB1k8gynCZ3/I7s7Jl8fptMw4jPgNjYyIOH+Zhm7a83radjPS9nC2dk+UlLsgJ/zMdWJcvX86hQ4dwc3OjadOmNG3alKJFi3Lw4EGWL19uiDoKgpDHpJTMXy3Di5hUEhFnloc3DDkJ3sMBCa5vAr86cGu3fq5fAEnGxhQeNgyvP/7ArHx51OHhBE6YwJMhQ4gPyvzz0fSev+bnZ6+ZDqy1atXC39+fGTNmUKlSJSpWrMjMmTPx9/fXztgVBKFgSymZv0KCwmZ6XDZvYgE+M2DgQXAoBRHPYXNv+GOgJlWikCVmpUvhuXkThUePRjI2JvLYcU3v9Y8/MtR7TZJWwv/8nn84RxNE5AXiGWvBINpZ/zZfCNBZ8zqtfdn3f8aamvgYODYbTi0GWQ2WhaHVfCjfQb/3yQP0+VmOvX+fZxMnEnNNk8vAsl49XKZ9i3Emsuy9+/w1Lz97NdgzVldXV3r16sVPP/2ULMWgIAhCku413Tk5vgm/Dq7DyfFN6Fq9qOFuZmwGTafAoL/AsRxEvoDf+8JvfSDiheHum8+ZliiB58aNOI4di2RqSuSpUzxs05bQTZsz3Ht99/lrQXj2munA+t1332FjY8OCBQsoU6YMLi4u9OjRg+XLl3Pr/zPKBEEQIJvXvAIUqQYfH4WGY0FSws0dsKwW/P0HiMG5LJGUSuwHDsBr+zbMq1VDHRVF0JQpBPQfQNzTp5m+XmY3W8+Lz2IzvbtNz5496dmzJwDPnz/nyJEj7N69m88++wy1Wp2pzEv5SWJiokF2RomPj8fIyIiYmJgC27bZwRDtbGxsjFKZPGm9kLrAsGj8QyLxcrDMejA2MoUPJkHZtrD9U3j+N2wZCP9sg9YLwDrlNKhC2ky9vPBYt5bQ9esJXriIqLNnediuPY6jR1OoV08kRcb6aUnPXqeemarNP5zaZutb721NVi69/WNzgyw9Y42IiODkyZMcO3aMo0ePcuXKFcqVK0ejRo1YuHChIeqZY9IbU5dlmaCgIF6/fm2Q+8uyTHR0NObm5jqpIwX9MlQ729ra4uzsLH52pP/s7911r5nZ6zVVCXFwcgEcnwfqBDCzhZZzoVI3yKc/k+yYLxD3+DGBEycRdfEiABY1auAyYzommUgXm9La13dfT+lZ7PqW64lOiMbdxj3bn8kabD/W2rVrc/36dSpUqEDjxo356quvaNCgAba2tu9T3zwrKag6OjpiYWGh91+garWaiIgIrKysUGTwL0Ih8/TdzrIsExUVRXBwMAAuLi7vfc38LKV1r19tvUHDUoXfbxjZyAQaj4cyrWH7MAi6Dts+1vRe2ywEG/FzyQoTDw/c164h9NdfCf5uAVEXL/KwfQccR4+i0EcfZaj3mlr+4SSpPYvtvbc3MnKu7sFmOrDeu3cPCwsLihUrRrFixShRokSBDaqJiYnaoGpvb5hNf9VqNXFxcZiZmYnAakCGaGdzc01ACA4OxtHRUQwLpyGlda+JssyjkCj9PJ91rgiDD8OpRXB0Dtz9E/xOQ4vZULlnvu29GpKkUGDXuzdWjRoROOlros6e5fnMWYTv24/LjOmYenm91/WTnsW+G1yT9oZVy2qmnplKXde6uW42caZ/g7x69YojR45Qr149Dh06RKNGjXB2dqZ79+4FLkFE0jNVsQG6kJqkz4Yhnr/nJymte1VKEp4Oevy3pTTWTGr65Di4VoWYMNg+FDZ0hbB/9XefAsakaFHcV63EecoUFBYWRF++jH+HjrxcuQr5PeYrvLsOViL5Hz9qWc21F9dy3eSm917HeunSJb7//nvWr1+fLycvpTWmHhMTg7+/P15eXpiZpb/Rc1ao1WrCw8OxsbERPVYDMlQ7Z8dnJK/IyDPWd/d6fe9nrKlJTIAzS+HILEiMBVMbTbKJqr55vveak2uy4//9l8CvvyHy9GlAs02dy6yZmBYrluVrJj2LNVOa8dGfH+n0YKX//09N9kxuMtg61itXrrBw4ULat2+PnZ0dderU4e+//+bzzz9n586d71VpQRAKrnfXvRosqAIojaD+KBhyAorWhNhw2PkZrO8Er/PPesrsZlykCG6//IzL9GkorKyIvnYN/w4dCVmxAjkhIUvXTFoHW7FwxRR7sNpN1v8/NJxSzzW7l+xk+hlrzZo1qVq1Ko0aNWLw4ME0bNgwRzISCYZz9OhRmjRpQmhoaIF9fi7kDBeVefateQUoXBoG7IezfnB4Ojw4rMk53HyaZlP1PN57zQmSJGHbpQuW9esT+M03RB4/wYvvFvDmwEFcZ87AtGTJLF+7U8lO1HWty5M3T3gZ/ZKxx8fqvP72JutBkUEEhAfwz8t/WHR5UbYu2cnSM9YLFy4wf/582rRpI4KqoBdHjx5FkqR0ly3duXOHJk2a4OTkhJmZGcWKFWPSpEnJnmEeO3aM6tWra8sUtOf/QiYolFD3MxhyCtzqQFwE7B4Fa9tD6OOcrl2eZezsjNuPP+IycyYKa2ti/v4b/06dCVn+Y5Z7r/BfD7aKY5VUE028nYt4waUF2uHjtHq1+pTpwCoCqZCTjI2N6dOnDwcOHODOnTssWrSIFStWMHnyZG0Zf39/WrVqRYMGDbhy5QpfffUVI0aMYMuWLTlYcyHXcygB/fdqZgobmYP/MfDzhvMrQK1O/3whGUmSsO3UkWK7d2PVuDFyfDwvFi3iUbfuxNy5817XTi3JP6BNKpGS7EifmOnAmpiYyPz586lVqxbOzs7Y2dnpfAlZExgWzekHIQSGGX6j+NjYWEaMGIGjoyNmZmbUr1+fCxcuJCt36tQpKleujJmZGbVr1+bvv//Wvvb48WPatm1LoUKFsLS0pHz58uzduzfVe65fv54aNWpgbW2Ns7MzvXr10q7xfPToEU2aNAGgUKFCSJJEv379UrxOsWLF6N+/P5UrV8bDw4N27drRu3dvTpw4oS2zfPly3N3dWbRoEWXLlmXQoEEMGDCA+fPna8v069ePDh06MHPmTJycnLCzs2POnDkkJCQwduxY7OzsKFq0KCtXrsxU2wp5nEIJdYbC0FPgXhfiI2HvGFjbDl49zOna5VnGTo4U/cEP17lzUKhUxNy8iX+XrrxYtgz5PWbMdyrZif2d97PSZyX7O++nU8lOKa5/fVta6RP1JdOBderUqSxYsIBu3boRFhbG6NGj6dSpEwqFgilTphigivnf5gsB1Jt9mF4rzlFv9mE2Xwgw6P2+/PJLtmzZwpo1a7h8+TIlSpTAx8eHV690N4oeO3Ys8+fP58KFCzg6OtKuXTvtkOunn35KbGwsx48f5++//2bOnDlYWVmles+4uDimTZvGtWvX2L59O/7+/trg6ebmpu1N3rlzh8DAQBYvXpyh93L//n327dtHo0aNtMfOnDlD8+bNdcr5+Phw8eJFnSHjw4cP8+zZM44fP878+fOZPXu29o+Fc+fOMWTIEIYMGcKTJ2IyS16htz9Q7YtDvz3Qch4YW8CjE/BDPTj3o+i9ZpEkSajataPYrp1YffghxMcTsvR7/Lt1J+Y98sy/m+Q/pVzESdJKn6hXciYVK1ZM3r17tyzLsmxlZSXfv39flmVZXrx4sdyzZ8/MXi7XCwsLkwE5LCws2WvR0dHyzZs35ejo6Cxf/9nrKNlr/G7ZY9x/X8XG75GfvY6SZVmWExMT5dDQUDkxMTHL93hbRESEbGxsLG/YsEF7LC4uTnZ1dZXnzp0ry7IsHzlyRAbkTZs2acu8fPlSNjc3lzdv3izLsixXrFhRnjJlSpbrcf78eRmQ37x5o3PP0NDQDJ3v7e0tm5qayoD88ccf67RPyZIl5RkzZuiUP3XqlAzIz549k2VZlvv27St7eHhoz0tMTJRLliwpN2jQQHtOQkKCbGlpKf/6669Zfp/6+IzkF3FxcfL27dvluLg4g1x/0/nH2n9LXuN3y5vOP9bPhV8+lOVVrWV5so3m65cWshxyXz/XNgBDt7M+qNVq+fWu3fKd2nXkm6XLyDfLV5CDFy+R1bGxern+lrtb5EprKskVVleQK62pJK/8e6V8PvC8HBgR+F7XTSsevC3TPdagoCAqVqwIgJWVFWFhYQC0adOGPXv26CncFxxpZZwxhAcPHhAfH0+9evW0x4yNjalVq1ay3Ym8vb21/21nZ0fp0qW1ZUaMGMH06dOpV68ekydP5vr162ne98qVK7Rv3x4PDw+sra1p3LgxAAEBWeudb968mcuXL7Nx40b27NmjM8wLJEstKf9/ufbbx8uXL6+zZrVw4cJUqFBB+71SqcTe3l47ZC3kXqmlRHy355qlHq2dF/TZCa2/AxMrCDit6b2eWQbq/LVuP7tIkoSqTWuK7d6FdbNmkJBAiJ8f/l27Ef3PP+99/XeHiPtX6K/TqzW0TAfWokWLEhgYCECJEiU4cOAAABcuXMDU1FS/tSsAsiXjzFtSCjBJxzOS5zipzKBBg3j48CG+vr78/fff1KhRg6VLl6Z4TmRkJM2bN8fKyor169dz4cIFtm3bBmiGiLPCzc2NcuXK0bNnT2bPns2UKVO0yUmcnZ0JCtKd9RccHIyRkZFO6sl3F89LkpTiMbUY+sv1MvIH6ns9clEooOYgGHoavBpBQjTs/wpWtYSQe3p6FwWPkYMDRZYspsjCBSgLFSL2zh0edetO8KJFqLP4uyHJu0PE2SnTgbVjx4789ddfAHz++ed8/fXXlCxZkj59+jBgwAC9VzC/c1GZM6tTRZT/D1hJGWcMtZavRIkSmJiYcPLkSe2x+Ph4Ll68SNmyZXXKnj17VvvfoaGh3L17lzJlymiPubm5MWTIELZu3coXX3zBihUrUrzn7du3CQkJYfbs2TRo0IAyZcok6wWamJgAZClzlyzLxMfHa/9o8Pb25uDBgzplDhw4QI0aNbI9E42QPdL7AzWjPdp0FfKAPjugzSIwsYYn52B5fTi1RPRes0iSJGxatqTYnt1Yt2wBiYm8XP4jjzp3JvrvGzldvSzJdIKI2bNna/+7S5cuuLm5cerUKUqUKEG7du30WrmContNdxqWKsyjkCg8HSwMukDe0tKSoUOHame+uru7M3fuXKKiohg4cKBO2W+//RZ7e3ucnJyYOHEiDg4OdOjQAYCRI0fSsmVLSpUqRWhoKIcPH04WmJO4u7tjYmLC0qVLGTJkCDdu3GDatGk6ZTw8PJAkid27d9OqVSvMzc1TnAy1YcMGjI2NqVixIqamply6dIkJEybQvXt3jIw0H+chQ4bw/fffM3r0aAYPHsyZM2f45Zdf+PXXX/XQgkJulPQH6rspEZP+Lek1yb8kQY3+UKIp7BqhSSpx8GvNpuod/DRJJ4RMM7Kzo+jChYS3aEnQt98Se+8+j3r0wH7AAByGf4oiL42IZubBbVxcnNyvXz/5wYMHWX32m+cYevJSevQ9eUmWNfX+7LPPZAcHB9nU1FSuV6+efP78ee3rSROJdu3aJZcvX142MTGRa9asKV+9elVbZvjw4XLx4sVlU1NTuXDhwrKvr68cEhKS6j03btwoe3p6yqamprK3t7e8c+dOGZCvXLmiLfPtt9/Kzs7OsiRJct++fVO8zqZNm+Rq1arJVlZWsqWlpVyuXDl55syZyX4GR48elatWrSqbmJjInp6e8g8//KDzet++feX27dtrv09MTJTr1asnjxgxQqech4eHvHDhwlTfV3rE5KX/ZMekmmevo+TT90O0k//ePp7WJMEsU6tl+dJaWZ5ZVDOx6dvCsnxigSwnxL/fdd9DXpi8lJ74V6/kp6O/0ExsKl1Gvt+qtRz11u+fnJLRyUuZTsJva2vL5cuXKfYeSZXzEpGEv2AQSfgNLyeTw0PGkvwHhkXjHxKJl4Nl5nqyYf/C7pFwTzPnBNdqmt6rY8qjOIaU0+2sT28OHSJwylQSQ0JAocCufz8Kf/YZihz6t2SwJPwdO3Zk+/bt71M3QRCEbJdekv/3mtykKgK9foMOP4CpCp5dhh8bwvH5mp10hCyxbtqU4rt3YdOuLajVvPplJf4dOxF15UpOVy1NmX7GWqJECaZNm8bp06epXr06lpaWOq+PGDFCb5UTBEHQp9SS/Kc2ualhqcIZ77lKElTpBcUaa3IN390Hh6fBrV2a3qtTef29kQJEaWtLkblzsWnRkqDJk4nz9+dxr97Y9e1L4c9HoDDPxk0bMijTPdaff/4ZW1tbLl26xE8//cTChQu1X4sWLTJAFTVCQ0Px9fVFpVKhUqnw9fVNN2F7REQEw4cPp2jRopibm1O2bFl++OEHg9VREIS8Kb3lOpla/2rjCj03QcefwMwWAq/Cj43g2FxIFBveZ5X1B00otnsXqvbtQZZ5tXo1/h06EnXpUk5XLZlM91j9/f0NUY909erVi6dPn7Jv3z4APv74Y3x9fdm1a1eq54waNYojR46wfv16PD09OXDgAMOGDcPV1ZX27dtnV9UFQcjlkpbrvB1ck5brbL4QoO3NKiSY1ali+nvFShJU7g7FGml6r3f2wpEZ//VenSsa9g3lU0qVCtc5s7Fu2YKgyVOIe/yYxx/5Usj3IxxHjkRhYZj1/5mVJ2bD3Lp1i3379vHzzz/j7e2Nt7c3K1asYPfu3dxJY4eEM2fO0LdvXxo3boynpycff/wxlStX5uLFi9lYe0EQcrvU1pMD77f+1doZemyETj+DeSEIug4/NYajsyHh/RIgFGTWjRtTbNdOVJ07gSwTunYdDzt0JCqFzURyQoZ6rKNHj87wBRcsWJDlyqTmzJkzqFQqateurT1Wp04dVCoVp0+fpnTplNeN1a9fn507dzJgwABcXV05evQod+/eTTPBe2xsLLGxsdrvw8PDAc1Mu3f3/ExKSqBWqw2WnSdp0nbSfQTDMFQ7q9VqbQILpVKpt+vmRUn/ft79d5RbdKrigrdXIQJeReFuZ4GLyoyzD1+lOET84Hk4DhaZGPAr2wHcvFHu+xLFnT1wdBbyzZ0ktF0KzpX0+j5yezvrjbk5hadMwaJpU15MmUp8QACPffug6tkT+5GfG6T3mtE2zdAn48o7M7AuXbpEYmKiNqDdvXsXpVJJ9erVM1nNjAkKCsLR0THZcUdHx2Sp6962ZMkSBg8eTNGiRTEyMkKhUPDzzz9Tv379VM+ZNWsWU6dOTXb8wIEDWLzzgzIyMsLZ2ZmIiIgsp+bLqDdv3hj0+oKGvts5Li6O6Ohojh8/TsJ7bO6cn7ybFSs3eglcAV7HgoQSmf/SOknIPLh6lpdZ2ZDFvBuunh5UeroO0+B/UP7SjHvObbjj1B5Zkeknc2nKC+2sL4qhQ3HYuxfb8+cJ+/VXXuzfz/MunYkuXlyv94mKylgO9wz9JI8cOaL97wULFmBtbc2aNWsoVKgQoJlY1L9/fxo0aJCpSk6ZMiXFIPa2pH1CU8pjK6eT33bJkiWcPXuWnTt34uHhwfHjxxk2bBguLi40bdo0xXMmTJig00MPDw/Hzc2N5s2bp7iO9cmTJ1hZWRlsjaIsy7x58wZra+sM5fIVssZQ7RwTE4O5uTkNGzYU61jj4zl48CDNmjXLU+srjd2fMmnHTe0z1unty9O1elGdMoFhMTx+GYWHvaanm7bWEPkZ6n3jUNzeSemgHZRS3yOhzRJwqfLe9c2r7fzeOnci6vRpgqdMhcBA3H5agU33bjiMGoXindUrWZU0gpmeTCeIKFKkCAcOHKB8ed2p4zdu3KB58+Y8e/Ysw9cKCQkhJCQkzTKenp5s3LiR0aNHJ5sFbGtry8KFC+nfv3+y86Kjo1GpVGzbto3WrVtrjw8aNEhnElR6RIKIgkEkiDC8vJy4IDAsOtWUo1ma3JTkn22wZwxEhYCkhPojodE4MMp6+r683M76kBgRQfD8+bzetBkAY1dXXGZMx/Kt3bqyymAJIsLDw3n+/Hmy48HBwZkeRnNwcKBMmTJpfpmZmeHt7U1YWBjnz5/Xnnvu3DnCwsKoW7duitdOeib67i9JpVIpnlVmQuPGjRk5cmROV0MQcpSLyhzv4vbJgup7J/cv3xE+PQflO4GcCCe+0ySWeJr7lpDkFUorK1ymTMF91UqMXV2Jf/aMgP4DCPxmMokREdlShyxlXurfvz9//PEHT58+5enTp/zxxx8MHDiQTp06GaKOlC1blhYtWjB48GDOnj3L2bNnGTx4MG3atNGZuFSmTBntdmQ2NjY0atSIsWPHcvToUfz9/Vm9ejVr166lY8eOBqlnXpFSsDx69CiSJCUbFdi6dWuyhPnZYfXq1dja2qZb7uTJk9SrVw97e3vMzc0pU6YMCxcuTFZuy5YtlCtXDlNTU8qVK6f9nAjC+9DLfsqWDtB1FXRbC5aF4cVt+KUpHJwM8TH6rXABYuntjdfOnRTq1ROANwcOIMdkT3tm+mn58uXLGTNmDB999JF2hpSRkREDBw5k3rx5eq9gkg0bNjBixAiaN28OQLt27fj+++91yty5c0e78TrApk2bmDBhAr179+bVq1d4eHgwY8YMhgwZYrB65jd2dnY5XYU0WVpaMnz4cCpVqoSlpSUnT57kk08+wdLSko8//hjQzCrv3r0706ZNo2PHjmzbto1u3bpx8uRJnZnmgpBZaa1/zbRy7cGjPvz5Jdz4A04t0qx/be8HbjX1VueCRGllifM332Dt0wJ1ZCRGDg7Zc+OsZvmPiIiQr127Jl+9elWOiIjI6mVyvfy2u03fvn1lQOfL398/2bGk3WUaNWokf/7559rzPTw85GnTpsm+vr6ypaWl7O7uLm/fvl0ODg6W27VrJ1taWsoVKlSQL1y4kGY9vvvuO7lChQqyhYWFXLRoUXno0KHymzdvZFn+b3edt78mT56c4ffYsWNH+aOPPtJ+361bN7lFixY6ZXx8fOQePXpov3+3nVetWiWrVCp5165dcqlSpWRzc3O5c+fOckREhLx69WrZw8NDtrW1lYcPHy4nJCSkWhexu81/8sOuKynZdP6xXGz8Hu2OOZvOP37/i97cJctzS2h2zJliK8v7J8pyXMZ24smv7ZwbZHR3myzP0rC0tKRSpUpUrlw5Wb7gAk2WIS5Sv1/xUemXyeActMWLF+Pt7c3gwYMJDAwkMDAQNzc3tmzZAmh6/YGBgWmu9V24cCH16tXjypUrtG7dGl9fX/r06cNHH33E5cuXKVGiBH369NGuDU2JQqFgyZIl3LhxgzVr1nD48GG+/PJLAOrWrcuiRYuwsbHR1nHMmDEZen9Xrlzh9OnTNGrUSHvszJkz2pGOJD4+Ppw+fTrNa0VFRbFkyRI2bdrEvn37OHr0KJ06dWLv3r3s3buXdevW8dNPP/HHH39kqG5C/pRecv8sKdtG8+y1UneQ1XB6qWZD9YBz739tweD0u3BK0ATBma56u5wCsM1Iwa+egUn6f+CoVCpMTEywsLDA2dlZezxpyNfR0THdZ5utWrXik08+AeCbb77hhx9+oGbNmnTt2hWAcePG4e3tzfPnz3Xu8ba3n/F6eXkxbdo0hg4dip+fHyYmJqhUKiRJSvX8dxUtWpQXL16QkJDAlClTGDRokPa1oKAgnJycdMo7OTmluQYaNBPgfvjhB4r/fy1cly5dWLduHc+fP8fKyopy5crRpEkTjhw5Qvfu3TNUTyF/Si25f2oytD2dhR10+kkzwWnXSHh5H1b6gPen0GQimOSO9H1CciKwCplWqdJ/mWKSAlbFihWTHQsODk41MB45coSZM2dy8+ZNwsPDSUhIICYmhsjIyCyNgJw4cYKIiAjOnj3L+PHjKVGiBD179tS+/u7aVDmdNdAAFhYW2qCa9L48PT2xsrLSORYcHJzp+goFV6aX55RuCe51YN9XcG0jnPke7vwJ7ZeBx/svIRH0TwRWfTO20PQe9UStVhP+5g021tZpr680zr6/Xt9eG5cUnFI6ltqypsePH9OqVSuGDBnCtGnTsLOz4+TJkwwcODDLadi8vLwATYB//vw5U6ZM0QZWZ2fnZL3T4ODgZL3Yd727BlCSpBSPieVbQkZleXs680LQ8Qco3wF2fQ6vHsCqllB7CHz4dYZGq4TsIzIO6JskaT7k+vwytki/TCayBZmYmJCYmJjsGJDsuCFcvHiRhIQEvvvuO+rUqUOpUqWSJRZJqY4ZJcuyTr5nb2/vZOndDhw4kOoaaEEwlIwsz0lzi7pSPjDsLFT9CJDh3A/wQz14dMqwFRcyRfRYCyBPT0/OnTvHo0ePsLKyws7ODg8PDyRJYvfu3bRq1Qpzc3OdIU99Kl68OAkJCSxdupS2bdty6tQpli9fnqyOERER/PXXX1SuXBkLC4tkuZoBli1bhru7O2XKlAE061rnz5/PZ599pi3z+eef07BhQ+bMmUP79u3ZsWMHhw4d4uTJkwZ5f4KQmvSW52RomNjcVjMMXK4j7BoBof6wuhXU+gSaTgbJJPvekJAi0WMtgMaMGYNSqaRcuXIULlyYgIAAihQpwtSpUxk/fjxOTk4MHz7cYPevUqUKCxYsYM6cOVSoUIENGzYwa9YsnTJ169ZlyJAhdO/encKFCzN37twUr6VWq5kwYQJVqlShRo0aLF26lNmzZ/Ptt9/qXGvTpk2sWrWKSpUqsXr1ajZv3izWsArZLrXt6VxU5pnP4lSyKQw7A9X6aL4//yP4eSM9Fn8w5rRM5wouaESu4IJB5Ao2vIKew/ZtKeUePv0ghF4rki+n+XVwHbyL26d9wft/wc4REP4UAH+HDyna/xeMLQvpve4FmcFyBQuCIAjvJ6Xcw0nDxG/LcBanEh9qeq/V+2muFfIXRj81hIfH9FhrIaNEYBUEQcgF0homzhAzG2i7mIReW4gycUAKC4C17WD3KIgV+zlnJzF5SRAEIZfoXtOdhqUKp7pFXUbIXo04UmYGLYzOory8Ci6uhHsHod1SKN7EALUW3iV6rIIgCLlIalvUZUaC0hx1y3nQZyfYukPYE1jXQbMGNiZjm3ULWScCqyAIQj4RGBbN2YeveJ20jLtYIxh6BmoO1nx/aTX4eWsmOwkGI4aCBUEQ8oG318BKKDF2f0qvOl5gagWt52u2pds5HEIfwfpOUNUXfGaAmSqnq57viB6rIAhCHvfuGlgZiUk7buqugfVqAENPa9IgAlxZp+m93juY/ILCexGBVRAEIY9LKVWiWkYnVSKgSX/acg70/xPsikH4v7ChC2z/FKJfZ1t98zsRWAVBEPK4lNbAKiRSXQMbaFuVsz47iaj2CSDB1fXgVwfu7jd8ZQsAEViFZI4ePYokSbx+/TqnqyIIQga8uwZWQmZ6+3IpzizefCGAerMP02PVdSqdacRf3mvArji8CYSN3WDbUIgOze63kK+IwCrkCpkJ5r/99htVqlTBwsICDw8P5s2bl6zMsWPHqF69OmZmZhQrVixZkn9ByG+613Tn5PgmrB9QgynVEulavWiyMinlI/74qDGBvQ6C93BA0uz5uqyOZs9XIUtEYBXylD///JPevXszZMgQbty4gZ+fHwsWLOD777/XlvH396dVq1Y0aNCAK1eu8NVXXzFixAi2bNmSgzUXBMNzUZlT28sOW9OUX09127owNDOEB+wH+5IQEQS/9oCtn0DUK4PXO78RgTWXCIoM4nzgeYIig9Iv/J5iY2MZMWIEjo6OmJmZUb9+fS5cuJCs3KlTp6hcuTJmZmbUrl2bv//+W/va48ePadu2LYUKFcLS0pLy5cuzd+/eVO+5fv16atSogbW1Nc7OzvTq1Yvg4GAAHj16RJMmmowwhQoVQpIk+vXrl+J11q1bR4cOHRgyZAjFihWjdevWjBs3jjlz5pC0n8Ty5ctxd3dn0aJFlC1blkGDBjFgwADmz5+vvU6/fv3o0KEDM2fOxMnJCTs7O+bMmUNCQgJjx47Fzs6OokWLsnLlyky3ryDkVunmI3avDUNOQN0RICng+ibNs9fbqf/bFpITgTUX2HpvKz5bfBh4YCA+W3zYem+rQe/35ZdfsmXLFtasWcPly5cpUaIEPj4+vHql+5fp2LFjmT9/PhcuXMDR0ZF27doRHx8PwKeffkpsbCzHjx/n77//Zs6cOWnu3xoXF8e0adO4du0a27dvx9/fXxs83dzctL3JO3fuEBgYyOLFi1O8TmxsbLJdYszNzXn69CmPHz8G4MyZMzRv3lynjI+PDxcvXtTWH+Dw4cM8e/aM48ePM3/+fGbPnq39Y+HcuXMMGTKEIUOG8OTJkwy0qiDkfhnKR2xsDs2nwYAD4FAKIp7Dpp6wZZDovWaULKQpLCxMBuSwsLBkr0VHR8s3b96Uo6Ojs3z9wIhAudKaSnKF1RW0X5XWVJIDIwJlWZblxMREOTQ0VE5MTMzyPd4WEREhGxsbyxs2bNAei4uLk11dXeW5c+fKsizLR44ckQF506ZN2jIvX76Uzc3N5c2bN8uyLMsVK1aUp0yZkuV6nD9/XgbkN2/e6NwzNDQ0zfN+/PFH2cLCQj506JCcmJgo37lzRy5TpowMyKdPn5ZlWZZLliwpz5gxQ+e8U6dOyYD87NkzWZZluW/fvrKHh4e2XRMTE+WSJUvKDRo00J6TkJAgW1payr/++muW36c+PiP5RVxcnLx9+3Y5Li4up6uSr2WknZ+9jpJP3w+Rn72OSudi0bJ84BtZPcVWlifbyAlzisvyzV16rnHekVY8eJvoseawgPAA1LJa55haVvPkjWF6SQ8ePCA+Pp569eppjxkbG1OrVi1u3bqlU9bb21v733Z2dpQuXVpbZsSIEUyfPp169eoxefJkrl+/nuZ9r1y5Qvv27fHw8MDa2prGjRsDEBAQkKn6Dx48mOHDh9OmTRtMTEyoU6cOPXr0AECpVGrLSZLueJf8/2Hit4+XL19eZ+/VwoULU6FCBe33SqUSe3t77ZC1IOQXGc5HbGzGZtuBdIydwl11EZRRL2Bzb/hjIES+zJ7K5kEisOYwdxt3FJLuj0EhKXCzdjPI/VIKMEnH3z2WkqQygwYN4uHDh/j6+vL3339To0YNli5dmuI5kZGRNG/eHCsrK9avX8+FCxfYtm0boBkizgxJkpgzZw4RERE8fvyYoKAgatWqBYCnpycAzs7OBAXpPqsODg7GyMgIe/v/Nox+d7NtSZJSPKZW6/7hIwgFRdIs4qvqErSNm8GyhHYkyAq48Qf41YabO3K6irmSCKw5zNnSmcnek7XBVSEpmOw9GWdLZ4Pcr0SJEpiYmHDy5Entsfj4eC5evEjZsmV1yp49e1b736Ghody9e5cyZcpoj7m5uTFkyBC2bt3KF198wYoVK1K85+3btwkJCWH27Nk0aNCAMmXKJOsFmpiYAJCYmJih96FUKilSpAgmJib8+uuveHt74+joCGh62gcP6qZpO3DgADVq1EgWOAVBSN3bs4hjMWFeQg86xn1LlKoURL6A3/rA7/0gMiRH65nbiCT8uUCnkp2o61qXJ2+e4GbtZrCgCmBpacnQoUO1M1/d3d2ZO3cuUVFRDBw4UKfst99+i729PU5OTkycOBEHBwc6dOgAwMiRI2nZsiWlSpUiNDSUw4cPJwvMSdzd3TExMWHp0qXaZTLTpk3TKePh4YEkSezevZtWrVphbm6e4mSokJAQ/vjjDxo3bkxMTAyrVq3i999/59ixY9oyQ4YM4fvvv2f06NEMHjyYM2fO8Msvv/Drr7++Z+sJQsGSNIv47SU6NylOWJ+DWFxdCicXwj/bwP+EJtF/+Y45V9lcJM/0WGfMmEHdunWxsLDA1tY2Q+fIssyUKVNwdXXF3Nycxo0b888//xi2olnkbOlMTeeaBg2qSWbPnk3nzp3x9fWlWrVq3L9/n/3791OoUKFk5T7//HOqV69OYGAgO3fu1OlZfvrpp5QtW5YWLVpQunRp/Pz8Urxf4cKFWb16Nb///jvlypVj9uzZOktfAIoUKcLUqVMZP348Tk5ODB8+PNX6r1mzhho1alCvXj3++ecfjh49qh0OBvDy8mLv3r0cPXqUKlWqMG3aNJYsWULnzp2z2mSCUCClOovY3hY+/BoG/wWO5SEqBH7vR/SG3hDxImcrnQtIctJDt1xu8uTJ2Nra8vTpU3755ZcMZeiZM2cOM2bMYPXq1ZQqVYrp06dz/Phx7ty5g7W1dYbuGx4ejkqlIiwsDBsbG53XYmJi8Pf3x8vLK9kSEH1Rq9WEh4djY2OjM9FG0C9DtXN2fEbyivj4ePbu3UurVq3EkLwBGaKdA8OieRQShaeDRbIJT7+fe0DgrukMVe7AWEok1tgW0/YLoHwnyMC8jbwkrXjwtjzzm3rq1KmMGjWKihUrZqi8LMssWrSIiRMn0qlTJypUqMCaNWuIiopi48aNBq6tIAhC/pHaLOLAsGjGbb/NgoQudIibxk21B6bxr+GPAfCbL0QUzBn1eSawZpa/vz9BQUE6iQJMTU1p1KgRp0+fzsGaCYIg5A9vT276R/akfdw0FsZ3Ri0Zwa1dsKwWoec2cvr+C929YfO5fDt5KWm5hZOTk85xJycnbYaelMTGxhIbG6v9Pjw8HNAMr7ydtSfpmCzLqNVqgy3JSBqpT7qPYBiGame1Wo0sy8THx+ussy2Ikv79vPvvSNCv7GznoipTnclN8RixVN2ZHt0/wfnIF0jP/6bQn0OJSKxOh4QBfN6+XoqbA+QVGW3THA2sU6ZMYerUqWmWuXDhAjVq1MjyPTK7XnPWrFkp1unAgQNYWOjubWhkZISzszMRERGZXo+ZWW/evDHo9QUNfbdzXFwc0dHRHD9+nISEBL1eO696dymUYBjZ1c7dvCQ2P1QgIyEh081Lzfk7QYTZjeLF0718ZrSN5spL1FLcZurOPsQ/roOtWd589hoVFZV+IXJ48lJISAghIWmvf/L09NSZ9LF69WpGjhyZ7uSlhw8fUrx4cS5fvkzVqlW1x9u3b4+trS1r1qxJ8byUeqxubm6EhISkOHnpyZMnyeqoT7Is8+bNG6ytrTOUwEHIGkO1c0xMDI8ePcLNzU1MXoqP5+DBgzRr1kxMXjKgnGjnwLAYAl5F4W5ngYtK8zk/+/AVvqsuUloKYL7xcioqHgHwssgH2HReDNYu2VI3fQoPD8fBwSHdyUs52mN1cHDAwcHBINf28vLC2dmZgwcPagNrXFwcx44dY86cOameZ2pqiqlp8j2XjI2Nk31IExMTkSQJhUJhsBm7ScOSSfcRDMNQ7axQKLQZnUQw0RBtkT2ys53dHYxxd9BdaVHC2QaFBHdkdzrGfcvHyt2MNNqC/b+H4af60GIOVO6Rp2YOZ7Q988xv6oCAAK5evUpAQACJiYlcvXqVq1evEhERoS1TpkwZbao8SZIYOXIkM2fOZNu2bdy4cYN+/fphYWFBr169cuptCIIgFAhvr4FNwIgf1R053Oh3cK0KMWGwfQhs7A7hz3K6qnqXZyYvffPNNzrDt0m90CNHjmgTut+5c4ewsDBtmS+//JLo6GiGDRtGaGgotWvX5sCBAxlewyoIgiBkXfea7jQsVVh3DWyjRnB6CRydBff2w7I60GIWVOmVp3qvackzgXX16tWsXr06zTLvPi6WJIkpU6YwZcoUw1VMEARBSJWLylx3/avSCBqMhtKtYPtQeHYZdgzTpEZsuxhURXKusnqSZ4aChZzRuHFjRo4cmdPVEAQhv3EsAwMPQtOpoDSF+wdRL6vN/f0/EPg6Y7NvcysRWAuglILl0aNHkSQp2WzrrVu3JkuYnx1Wr16d4ZzQy5Yto2zZspibm1O6dGnWrl2brMyWLVsoV64cpqamlCtXTvssXhCEHKQ0gvojYcgJXtpWQhH3hhJnxnN3gQ87j59P8ZTAsGhOPwjJ1QknRGAV0mRnZ5ern0n/8MMPTJgwgSlTpvDPP/8wdepUPv30U3bt2qUtc+bMGbp3746vry/Xrl3D19eXbt26ce7cuRysuSAISQJN3Kn9/EtmxvckVjamkeI6Tf5qx+uTP8Nbj/g2Xwig3uzD9FpxjnqzD7P5QkAO1jp1IrAWMP369ePYsWMsXrwYSZKQJIlHjx7RpEkTAAoVKoQkSfTr1w9I3rv19PRk+vTp9OnTBysrKzw8PNixYwcvXrygffv2WFlZUbFiRS5evJhmPRYsWEDFihWxtLTEzc2NYcOGaWd4Hz16lP79+xMWFqatY2rPydetW8cnn3xC9+7dKVasGD169GDgwIE6S6oWLVpEs2bNmDBhAmXKlGHChAl8+OGHLFq0SFvmgw8+4Msvv2TUqFEUKlQIJycnfvrpJyIjI+nfvz/W1tYUL16cP//8M/ONLghCmvxDIkmQFfyU2JZWcTO5pC6JtRSN7aEvYF1HeP1Eu+l6UpYntQxfbb2RK3uuIrDqmSzLqKOi9PsVHZ1umYzm+Vi8eDHe3t4MHjyYwMBAAgMDcXNzY8uWLYBmZnVgYCCLFy9O9RoLFy6kXr16XLlyhdatW+Pr60ufPn346KOPuHz5MiVKlKBPnz5p1kmhULBkyRJu3LjBmjVrOHz4MF9++SUAdevWZdGiRdjY2GjrOGbMmBSvExsbmyzxgrm5OefPn9emHztz5oxOzmgAHx+fZDmjN23ahIODA+fPn+ezzz5j6NChdO3albp163L58mV8fHzw9fXNcPYVQRAyJmnfV4AHchG6xk1mRsJHyEozeHgE/LyJOv0z6nd+pyTKMo9C/vv3mFuGifPMrOC8Qo6O5k616nq/7vN0Xi99+RLSOykXU6JSqTAxMcHCwgJn5//2frWzswPA0dEx3WebrVq14pNPPgE0y6B++OEHatasSdeuXQEYN24c3t7ePH/+XOceb3u7F+zl5cW0adMYOnQofn5+mJiYoFKpkCQp1fOT+Pj48PPPP9OhQweqVavGpUuXWLlyJfHx8YSEhODi4kJQUFCKOaOT8kknKV++PBMnTkShUDBhwgRmz56Ng4MDgwcP1nmv169fp06dOmnWSxCEjEta8/rV1hskyjKSpKRE+3FIXqM0M4afnKP4uUmsN6nA+PjBPJULA5r9YT0dNL/3Nl8I0PZoFRLM6lSR7jXdc+T9iMAqZFqlSpW0/50UsN7ezi/pWHBwcKqB8ciRI8ycOZObN28SHh5OQkICMTExREZGYmlpmeG6fP311wQFBVGnTh1kWcbJyYl+/foxd+5cnaT3GckZXb58ee1/K5VK7O3tU31fgiDoV4prXgH6/wnnlsNf31I/4Qb7TMYxO6Enm9RNmdGpIi4q81SHiRuWKpxsq7vsIAKrnknm5pS+fElv11Or1YS/eYONtXWaqfYk8+z78Lyd1ispOKV0LLVdYh4/fkyrVq0YMmQI06ZNw87OjpMnTzJw4MBM78hhbm7OypUr+fHHH3n+/DkuLi789NNPWFtba9NlOjs7J+udBgcHJ+vFvpuuLCkVYUbflyAI7yfZmlcAhRK8P4VSLWDHp1gFnGG68Sq+druHaYllgO72dUneHib2D4nEy8Ey24KsCKx6JklShoZkM0ytRpGQgMLCQm85bE1MTEhMTEx2DEh23BAuXrxIQkIC3333nfY9/fbbb+nWMS3GxsYULarZjmrTpk20adNGe21vb28OHjzIqFGjtOUPHDhA3bp13/etCIKQXeyLQ7+9cP4nODQF0ycnwa8uNJuKV6leOtvXgWaY+Pq/r+n989lsHx4Wk5cKIE9PT86dO8ejR48ICQlBrVbj4eGBJEns3r2bFy9e6ORg1rfixYuTkJDA0qVLefjwIevWrWP58uXJ6hgREcFff/1FSEhIqhOG7t69y/r167l37x7nz5+nR48e3Lhxg5kzZ2rLfP755xw4cIA5c+Zw+/Zt5syZw6FDh0TiC0HIaxQKqDMEhp4Cj3oQHwl7x+CyvTtLWtii/P+oklKS+LJFaeb8eTtHZhGLwFoAjRkzBqVSSbly5ShcuDABAQEUKVKEqVOnMn78eJycnBg+fLjB7l+lShUWLFjAnDlzqFChAhs2bGDWrFk6ZerWrcuQIUPo3r07hQsXZu7cuSleKzExke+++47KlSvTrFkzYmJiOH36NJ6enjrX2rRpE6tWraJSpUqsXr2azZs3U7t2bYO9R0EQDMi+OPTdDS3ngbEFPDpBm5NduNziIb8OqsXJ8U2oWFSV5vCwIeXofqx5QXh4OCqVKsX992JiYvD398fLy8tge22q1WrCw8OxsbER28YZkKHaOTs+I3lFfHw8e/fupVWrVmLbOAMqcO38yh92fgaPTmi+96gH7ZYSaORKvdmHkw0PnxzfJMvPWtOKB28Tv6kFQRCEvMvOC/rshFbzwdgSHp+CH+rhcmsNszqW1xkentmpQrZMYBKTlwRBEIS8TaGAWoOhZDNN79X/OOwbR3f3HTT5ZD4PEp11l/AYujrZchdBEARBMLRCnuC7A1ovABMrCDiN4/oP8Q7ejIu1SbZVQwRWQRAEIf9QKKDmQBh6GrwaQUI07J8A51dkXxWy7U75mJj/JaRGfDYEIYcU8oA+O6DNInCtCtX7ZtutxTPW95A04y4qKgrzbMx8JOQdSetvC8TsTEHIbSQJavSHan01PdlsIgLre1Aqldja2mpzx1pYWCTLP/u+1Go1cXFxxMTEiOU2BqTvdpZlmaioKIKDg7G1tdXJWywIQjbL5t+dIrC+p6Qk84ZKzC7LMtHR0Zibm+s9aAv/MVQ729raprtDjyAI+YsIrO9JkiRcXFxwdHTMdAL5jIiPj+f48eM0bNhQDCcakCHa2djYWPRUBaEAEoFVT5RKpUF+iSqVShISEjAzMxOB1YBEOwuCoC/ioZ0gCIIg6JEIrIIgCIKgRyKwCoIgCIIeiWes6Uha4B8eHp4j94+PjycqKorw8HDx7M+ARDsbnmjj7CHa2XCS4kB6iV9EYE3HmzdvAHBzc8vhmgiCIAi5wZs3b1CpVKm+LvZjTYdarebZs2dYW1vrrG+sWbMmFy5cSPGc1F5L6Xh6x8LDw3Fzc+PJkydp7v+nL2m9L32fn5Gyhmrnd7/PS+2c2XPTK6+vNk7puPgsZ6yM+J2RN9q5Ro0aHD58GFdX1zQTyYgeazoUCgVFixZNdlypVKb6oU3ttZSOZ/SYjY1NtvwjSet96fv8jJQ1VDundm5eaOfMnpteeX21cUrHxWc5Y2XE74y80c5GRkYpxoN3iclLWfTpp59m+rWUjmf0WHZ533tn5vyMlDVUO+dkG7/v/TN7bnrl9dXGKR0Xn+WMlRG/M/RXNje0sxgKzuXCw8NRqVSEhYVly1+fBZVoZ8MTbZw9RDvnPNFjzeVMTU2ZPHkypqamOV2VfE20s+GJNs4eop1znuixCoIgCIIeiR6rIAiCIOiRCKyCIAiCoEcisAqCIAiCHonAKgiCIAh6JAKrIAiCIOiRCKz5TFRUFB4eHowZMyanq5IvvXnzhpo1a1KlShUqVqzIihUrcrpK+dKTJ09o3Lgx5cqVo1KlSvz+++85XaV8qWPHjhQqVIguXbrkdFXyFbHcJp+ZOHEi9+7dw93dnfnz5+d0dfKdxMREYmNjsbCwICoqigoVKnDhwgXs7e1zumr5SmBgIM+fP6dKlSoEBwdTrVo17ty5g6WlZU5XLV85cuQIERERrFmzhj/++COnq5NviB5rPnLv3j1u375Nq1atcroq+ZZSqcTCwgKAmJgYEhMT091CSsg8FxcXqlSpAoCjoyN2dna8evUqZyuVDzVp0gRra+ucrka+IwJrNjl+/Dht27bF1dUVSZLYvn17sjJ+fn54eXlhZmZG9erVOXHiRKbuMWbMGGbNmqWnGudN2dHOr1+/pnLlyhQtWpQvv/wSBwcHPdU+78iOdk5y8eJF1Gp1gdu6MTvbWNAvEVizSWRkJJUrV+b7779P8fXNmzczcuRIJk6cyJUrV2jQoAEtW7YkICBAW6Z69epUqFAh2dezZ8/YsWMHpUqVolSpUtn1lnIlQ7czgK2tLdeuXcPf35+NGzfy/PnzbHlvuUl2tDPAy5cv6dOnDz/99JPB31Nuk11tLBiALGQ7QN62bZvOsVq1aslDhgzROVamTBl5/PjxGbrm+PHj5aJFi8oeHh6yvb29bGNjI0+dOlVfVc6TDNHO7xoyZIj822+/ZbWK+YKh2jkmJkZu0KCBvHbtWn1UM08z5Gf5yJEjcufOnd+3isJbRI81F4iLi+PSpUs0b95c53jz5s05ffp0hq4xa9Ysnjx5wqNHj5g/fz6DBw/mm2++MUR18yx9tPPz588JDw8HNLuIHD9+nNKlS+u9rnmZPtpZlmX69evHBx98gK+vryGqmafpo40FwxEbnecCISEhJCYm4uTkpHPcycmJoKCgHKpV/qOPdn769CkDBw5ElmVkWWb48OFUqlTJENXNs/TRzqdOnWLz5s1UqlRJ+2xx3bp1VKxYUd/VzZP09TvDx8eHy5cvExkZSdGiRdm2bRs1a9bUd3ULHBFYcxFJknS+l2U52bGM6Nevn55qlD+9TztXr16dq1evGqBW+c/7tHP9+vVRq9WGqFa+8r6/M/bv36/vKgmIyUu5goODA0qlMtlfmsHBwcn+IhWyTrRz9hDtbHiijXM3EVhzARMTE6pXr87Bgwd1jh88eJC6devmUK3yH9HO2UO0s+GJNs7dxFBwNomIiOD+/fva7/39/bl69Sp2dna4u7szevRofH19qVGjBt7e3vz0008EBAQwZMiQHKx13iPaOXuIdjY80cZ5WA7OSC5Qjhw5IgPJvvr27asts2zZMtnDw0M2MTGRq1WrJh87diznKpxHiXbOHqKdDU+0cd4lcgULgiAIgh6JZ6yCIAiCoEcisAqCIAiCHonAKgiCIAh6JAKrIAiCIOiRCKyCIAiCoEcisAqCIAiCHonAKgiCIAh6JAKrIAiCIOiRCKyCkM8cPXoUSZJ4/fp1tt9bkiQkScLW1jbNclOmTKFKlSo63yedu2jRIoPWURAMTQRWQcjDGjduzMiRI3WO1a1bl8DAQFQqVY7UadWqVdy9ezdT54wZM4bAwECKFi1qoFoJQvYRSfgFIZ8xMTHB2dk5x+5va2uLo6Njps6xsrLCysoKpVJpoFoJQvYRPVZByKP69evHsWPHWLx4sXYY9dGjR8mGglevXo2trS27d++mdOnSWFhY8L927ieU/TCOA/j72zc7zCNRayH1RU1WDovSioObHCRNS+or/w4OoogbF5aLwoFyWVqypNzWDmIOaisclBxYIbUs7Uai7fs7/Gr99uPXz5+nbLxft+/Tnmef7fLu8zzfHpfLhfv7e6ytrUHTNBQVFWF4eBjJZDK9/tPTEyYmJlBWVob8/Hw0NDQgFAp9qNa5uTlYrVYUFBSgv78fj4+PEv4BouzEYCXKUYuLi3A6nRgcHEQsFkMsFkN5efmrn314eMDS0hL8fj+CwSBCoRA6OjoQCAQQCATg8/mwurqKra2t9Jze3l4cHBzA7/fj5OQEnZ2daGlpwfn5+bvq3NzcxPT0NGZnZ3F4eIiSkhIsLy9/6rcTZTNuBRPlqMLCQphMJpjN5v9u/T4/P2NlZQVVVVUAAJfLBZ/Ph9vbWwghYLfb0dzcjL29PbjdbkSjUWxsbODm5galpaUAfp+DBoNBeL1eeDyeN9e5sLCAvr4+DAwMAABmZmaws7PDrpW+LXasRD+A2WxOhyoAWK1WaJoGIUTGWDweBwAcHx/DMAzYbLb0+acQAvv7+4hGo+/67rOzMzidzoyxv5+JvhN2rEQ/QF5eXsazoiivjqVSKQBAKpWCqqo4Ojp68ULRn2FMRC8xWIlymMlkynjhSBaHw4FkMol4PI6mpqZPrVVTU4NwOAxd19Nj4XD4syUSZS0GK1EO0zQNkUgEl5eXEEKguLhYyro2mw3d3d3QdR3z8/NwOBy4u7vD7u4uamtr0dra+ua1RkZG0NPTg/r6ejQ2NmJ9fR2np6eorKyUUitRtuEZK1EOGx8fh6qqsNvtsFgsuL6+lra21+uFrusYGxtDdXU12traEIlE/vnm8b+43W5MTU1hcnISdXV1uLq6wtDQkLQ6ibKNYhiG8dVFENH3oCgKtre30d7e/qH5mqZhdHT0xW1SRLmEHSsRSdXV1fXuqwk9Hg+EEFI7bqKvwo6ViKS5uLgAAKiqioqKijfPSyQSSCQSAACLxfJl9xwTycBgJSIikohbwURERBIxWImIiCRisBIREUnEYCUiIpKIwUpERCQRg5WIiEgiBisREZFEDFYiIiKJGKxEREQS/QJYNxc5oqH1JwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print('rmse:', ca0.rmse())\n", + "hs1 = ml1.head(r1, 0, t1)\n", + "hs2 = ml1.head(r2, 0 ,t2)\n", + "plt.semilogx(t1, h1, '.', label='obs at 30m')\n", + "plt.semilogx(t1, hs1[0], label='ttim at 30 m')\n", + "plt.semilogx(t2, h2, '.', label='obs at 90m')\n", + "plt.semilogx(t2, hs2[0], label = 'ttim at 90m')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.title('ttim analysis for Oude Korendijk')\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Comparison of Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7.1. Comparison of model performance and Results with and without wellbore storage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 7.1.1. RMSE of the two conceptual models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following table summarises the RMSE values of the obtained models with and without wellbore storage." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
RMSE of two conceptual models
 obs 30 mobs 90 mobs simultaneously
without rc0.0316600.0227190.050060
with rc0.0152760.0062200.050065
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t0 = pd.DataFrame(columns=['obs 30 m', 'obs 90 m', 'obs simultaneously'], index=['without rc', 'with rc'])\n", + "t0.loc['without rc', 'obs 30 m'] = ca1.rmse()\n", + "t0.loc['without rc', 'obs 90 m'] = ca2.rmse()\n", + "t0.loc['without rc', 'obs simultaneously'] = ca.rmse()\n", + "t0.loc['with rc', 'obs 30 m'] = ca3.rmse()\n", + "t0.loc['with rc', 'obs 90 m'] = ca4.rmse()\n", + "t0.loc['with rc', 'obs simultaneously'] = ca0.rmse()\n", + "\n", + "t0.style.set_caption('RMSE of two conceptual models')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding wellbore storage improved the fit performance when used drawdown data of the individual observation wells. However, when calibrated the model with both datasets simultaneously, ```rc``` was adjusted to the minimum value. Adding rc did not improve the performance much in this case." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 7.1.2. Model comparisons" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see the summaries of the hydraulic conductivities in the following table.\n", + "\n", + "We will access the parameter values by accessing the ```.parameters``` attribute of each ```Calibrate``` object." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ky/8r_kg9w91ld3b898xn53q9wm0000gn/T/ipykernel_32090/2457231165.py:11: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " t1 = pd.concat((t1, tab))\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Preparing the DataFrame:\n", + "t1 = pd.DataFrame(columns=['kaq - opt', 'kaq - min', 'kaq - max', 'W. Storage', 'Calib. Dataset']) \n", + "w_storage = ['without rc','without rc','without rc','with rc','with rc','with rc',]\n", + "obs_dataset = ['obs 30 m','obs 90 m','obs simultaneously','obs 30 m','obs 90 m','obs simultaneously']\n", + "\n", + "# Looping through all calibration objects and fetching the desired values\n", + "for calib,w_sto,obs_dts in zip([ca1,ca2,ca,ca3,ca4,ca0],w_storage,obs_dataset):\n", + " p = calib.parameters #Accessing the parameters Dataframe inside the Calibrate object\n", + " tab = pd.DataFrame([[p.loc['kaq0','optimal'], 2*p.loc['kaq0', 'std'], 2*p.loc['kaq0', 'std'],w_sto,obs_dts]],\n", + " columns=['kaq - opt', 'kaq - min', 'kaq - max', 'W. Storage', 'Calib. Dataset'])\n", + " t1 = pd.concat((t1, tab))\n", + "\n", + "# Plotting\n", + "groups = t1.groupby('W. Storage')\n", + "for name, group in groups:\n", + " plt.errorbar(x = group['Calib. Dataset'], y = group['kaq - opt'], yerr = [group['kaq - min'], group['kaq - max']],\n", + " marker='o', linestyle='', markersize=12, label=name)\n", + "plt.legend()\n", + "plt.suptitle(\"Error bar plot of calibrated hydraulic conductivities\")\n", + "plt.ylabel('K [m/d]')\n", + "plt.xlabel('Calibration Dataset')\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Errorbar plot shows that the Hydraulic Conductivities calculated are significantly higher with wellbore storage than without when considering the individual wells datasets for calibration.\n", + "\n", + "As for the dataset using both obs wells at the same time, the calibration results have no significant differences.\n", + "\n", + "Both scenarios with and without wellbore storage showed lower values for the calibrated model using both observations, calibration with a single well are overestimated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7.2. Compare TTim to results of K&dR, AQTEOLV and MLU:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The final important step is to compare the data obtained from this model with the data from other Aquifer Analysis software. Yang (2020) compared TTim results with the published results in Kruseman and de Ridder (1990), here abbreviated to K&dR, and with the results obtained from the software AQTESOLV (Duffield, 2007) and MLU (Carlson & Randall, 2012)." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Comparison of Model Results with different Softwares
 k [m/d]Ss [1/m]RMSE
K&dR55.7142900.000170-
TTim66.0892910.0000250.050060
AQTESOLV66.0860000.0000250.050060
MLU66.8500000.0000240.050830
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'RMSE'], \\\n", + " index=['K&dR', 'TTim', 'AQTESOLV', 'MLU'])\n", + "t.loc['TTim'] = np.append(ca.parameters['optimal'].values, ca.rmse())\n", + "t.loc['AQTESOLV'] = [66.086, 2.541e-05, 0.05006]\n", + "t.loc['MLU'] = [66.850, 2.400e-05, 0.05083]\n", + "t.loc['K&dR'] = [55.71429, 1.7E-4, '-']\n", + "t.style.set_caption('Comparison of Model Results with different Softwares')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Results show good agreement between different analysis programs, including TTim. The values from Kruseman and de Ridder (1990) were obtained through Thiem's approximation. They seem to be an underestimation, as the pumping never reached steady-state conditions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Bakker, M. Semi-analytic modeling of transient multi-layer flow with TTim. Hydrogeol J 21, 935–943 (2013). https://doi.org/10.1007/s10040-013-0975-2\n", + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Newville, M.,Stensitzki, T., Allen, D.B., Ingargiola, A. (2014) LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python.https://dx.doi.org/10.5281/zenodo.11813. https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021).\n", + "* Kruseman, G.P., De Ridder, N.A., Verweij, J.M., 1970. Analysis and evaluationof pumping test data. volume 11. International institute for land reclamation and improvement The Netherlands.\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pumpingtests/confined2_grindley.ipynb b/pumpingtests/confined2_grindley.ipynb new file mode 100644 index 0000000..847d7a1 --- /dev/null +++ b/pumpingtests/confined2_grindley.ipynb @@ -0,0 +1,1517 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Confined Aquifer Test - Grindley\n", + "**This test is taken from AQTESOLV examples.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we reproduce the work of Yang (2020) to use the pumping test data to demonstrate how TTim can be used to model and analyze pumping tests in a single layer, confined setting. Furthermore, we compare the performance of TTim with other transient well hydraulics software AQTESOLV (Duffield, 2007) and MLU (Carlson and Randall, 2012).\n", + "\n", + "This example is a pumping test conducted in 1953 in Grindley, Illinois, US. It was reported by Walton (1962). The aquifer is an 18 ft thickness sand and gravel layer under confined conditions. The pumping well fully penetrates the formation, and pumping was conducted for 8 hours at a rate of 220 gallons per minute. The effect of pumping was observed at observation well 1, located 824 ft away from the well.\n", + "\n", + "The time-drawdown data for the observation well was obtained from AQTESOLV documentation (Duffield, 2007), while data from the pumping well was obtained from the original paper from Walton (1962). Following AQTESOLV documentation (Duffield, 2007), we have assumed that both well and observation well radii are 0.5 ft.\n", + "\n", + "A simplified cross-section of the model area can be seen below:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1,1,1)\n", + "#sky\n", + "sky = plt.Rectangle((-200,0), width = 1400, height = 3, fc = 'b', zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "#Aquifer:\n", + "ground = plt.Rectangle((-200,-20), width = 1400, height = 18, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9)\n", + "ax.add_patch(ground)\n", + "\n", + "#Confining bed:\n", + "confining_unit = plt.Rectangle((-200,-2), width = 1400, height = 2, fc = np.array([100,100,100])/255, zorder=0, alpha=0.7)\n", + "ax.add_patch(confining_unit)\n", + "well = plt.Rectangle((-8,-20), width = 16, height = 20, fc = np.array([200,200,200])/255, zorder=1)\n", + "ax.add_patch(well)\n", + "\n", + "#Wellhead\n", + "wellhead = plt.Rectangle((-10,0),width = 20, height = 1.5, fc = np.array([200,200,200])/255, zorder=2, ec='k')\n", + "ax.add_patch(wellhead)\n", + "\n", + "#Screen for the well:\n", + "screen = plt.Rectangle((-8,-20), width = 16, height = 18, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x = 10,y = 0.75, dx = 16, dy = 0, color = \"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x = 29, y = 0.75, s = r'$ Q = 220 \\frac{gal}{min}$', fontsize = 'large' )\n", + "#Piezometers\n", + "piez1 = plt.Rectangle((820,-20), width = 8, height = 20,fc = np.array([200,200,200])/255, zorder=1)\n", + "screen_piez_1 = plt.Rectangle((820,-20), width = 8, height = 18, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_1.set_linewidth(2)\n", + "\n", + "ax.add_patch(piez1)\n", + "\n", + "ax.add_patch(screen_piez_1)\n", + "\n", + "#last line\n", + "line = plt.Line2D(xdata= [-200,1200], ydata = [0,0], color = \"k\")\n", + "ax.add_line(line)\n", + "ax.text(x = 780, y = 0.75, s = 'Obs Well 1', fontsize = 'large' )\n", + "\n", + "ax.set_xlim([-200,1050])\n", + "ax.set_ylim([-20,3])\n", + "ax.set_xlabel('Distance [ft]')\n", + "ax.set_ylabel('Relative height [ft]')\n", + "ax.set_title('Conceptual Model - Grindley Example');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Load required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import ttim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters\n", + "\n", + "- We will work with time in days and length in meters from this step onwards\n", + "- The parameters below have already been converted to m and days." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "b = -5.4846 #aquifer thickness in m (Converted from 18 ft)\n", + "Q = 1199.218 #constant discharge in m^3/d (Converted from 220 gallons/minute)\n", + "r = 251.1552 #distance between observation well to test well in m (Converted from 824 ft)\n", + "rw = 0.1524 #screen radius of test well in m (Converted from 0.5 ft)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Load data of observation and pumping well\n", + "\n", + "The preferred method of loading data into TTim is to use numpy arrays.\n", + "\n", + "The data is in a text file where the first column is the time data in ***days*** and the second column is the drawdown in ***meters***\n", + "\n", + "The observation well is referred to as ***Well 1*** and the pumping well as ***Well 3***.\n", + "\n", + "For each piezometer, we will load the data as a numpy array and split time and drawdown into two different 1d arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Loading Observation well (Well 1)\n", + "data1 = np.loadtxt('data/gridley_well_1.txt')\n", + "t1 = data1[:, 0]\n", + "h1 = data1[:, 1]\n", + "\n", + "# Loading Pumping Well data (Well 3)\n", + "data2 = np.loadtxt('data/gridley_well_3.txt')\n", + "t2 = data2[:, 0]\n", + "h2 = data2[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Step 4. Creating a TTim conceptual model\n", + "\n", + "In this example, we are using the ModelMaq model to conceptualize our aquifer. ModelMaq defines the aquifer system as a stacked vertical sequence of aquifers and leaky layers (aquifer-leaky layer, aquifer-leaky layer etc). A thorough explanation of the ModelMaq and TTim one-layer modelling conceptualization is given in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml = ttim.ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary='conf')\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Calibration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The calibration workflow has been described in detail in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Step 5.1. Calibration Using the Observation Well" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We begin the calibration using the data from observation well (well 1) as our data set." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 31\n", + " # data points = 22\n", + " # variables = 2\n", + " chi-square = 0.01702168\n", + " reduced chi-square = 8.5108e-04\n", + " Akaike info crit = -153.614816\n", + " Bayesian info crit = -151.432731\n", + "[[Variables]]\n", + " kaq0: 22.4340266 +/- 0.22268654 (0.99%) (init = 10)\n", + " Saq0: 3.8208e-06 +/- 7.4239e-08 (1.94%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.8829\n" + ] + } + ], + "source": [ + "#unknown parameters: kaq, Saq\n", + "ca_0 = ttim.Calibrate(ml) # Create the Calibrate object, calling the model to the object\n", + "ca_0.set_parameter(name='kaq0', initial=10) # Setting the parameters for calibration\n", + "ca_0.set_parameter(name='Saq0', initial=1e-4)\n", + "ca_0.series(name='obs1', x=r, y=0, t=t1, h=h1, layer=0) # Setting the observation data\n", + "ca_0.fit(report=True) # Fitting the model. We can hide the message below setting report = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results from calibration are stored in the ```.parameters``` attribute of the calibration object.\n", + "We can also call the ```.rmse``` method to check the fitting error (RMSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq022.4340272.226865e-010.992628-infinf10.0000[22.43402657500395]
Saq00.0000047.423925e-081.943042-infinf0.0001[3.8207741931644555e-06]
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" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 22.434027 2.226865e-01 0.992628 -inf inf 10.0000 \n", + "Saq0 0.000004 7.423925e-08 1.943042 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0 [22.43402657500395] \n", + "Saq0 [3.8207741931644555e-06] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.027815693150943354\n" + ] + } + ], + "source": [ + "display(ca_0.parameters)\n", + "print('rmse:', ca_0.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we plot the model with our observation data:\n", + "\n", + "* First, we have to compute the model calculated heads at the observation location. For this, we use the ```.head```method in the model object (```ml```). This method takes the following arguments\n", + " * the positions ```x``` and ```y``` of the piezometric well (or any other point of interest). In our case, our well is located at position ```x= r1``` and ```y = 0```.\n", + " * the time intervals, defined by the numpy array ```t```, for the computation of the heads. In our case, this is defined by the variable ```t1```.\n", + "\n", + " * Another optional input is ```layers```, which can be a list, integer or an array defining the model layers. When we do not assign anything, the head is computed for all layers.\n", + "\n", + "The output is a numpy array with dimensions ```(nl,nt)```, where ```nl``` is the number of layers and ```nt``` is the number of time intervals.\n", + "\n", + "* Now, we can compare both observations and predictions in a plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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tt9/UtGlTlS9f/r7277+SnvP/dvKqXbu2ypcvn2okxs/PT7Vr104xrUqVKsmHVt5JwYIFFRISohkzZiQfZnjt2jUtWLBA3bt3l729ffJ2w8PD9fTTT2vBggWpRtQehGEYkpTq0MkOHTrI2dk5+X7+/PkVEhKiNWvWJH9TX7t2bU2fPl3vvfeeNm3alKqD4YYNG3T16lX16NEjxetvsVjUqlUrbdmyJdWo3H9/hqTEkb+zZ8+mOLRu2rRp8vPzSzGye/HiRfXt21dBQUGyt7eXg4ODihYtKkk6cODA/Tw96f49TdK0adMUjW98fX3l4+OT4mfhXs/bndz+HMbHxye/dtYQHx+v999/XxUqVJCjo6Ps7e3l6OioI0eO3Pdzlz9/fj366KMppnXp0kUWi0Vr1qxJMf3RRx9N83yxn3/+WcHBwXJzc0t+Tb/55pt017R48WI1bdpUAQEBKZ67pJ+b1atXS0r8nb9TvRlVvHhxNWnSRN9++62uXLmiBQsW3PXvgJT6vaZTp07Kly/fPf8OPPnkk8nvExndZwDpR9ACsgGTyaRevXpp1qxZmjx5ssqUKZPmOThSYsttPz+/VB9wfXx8ZG9vn3wY0ZUrV2Rvb6+CBQummM/Pzy/V+uLj4/XFF1/IwcEhxS0pEN3vB/TBgwdry5Yt2rZtm44dO6bz58/r+eefTzXff7uOXbhwQZL00EMPpappzpw5yfUk7et/9+lO0/7r0qVLVm1mkVRPWl3UAgICUh3i9d/XRpKcnJx069ate26rd+/eCg0NTT7Mb/bs2YqJiUnxwatbt2769ttvderUKT3xxBPy8fFRnTp1kpe5kyJFikhK7Hh2J0nn1QQFBaWYfqfXIjY2Vjdu3JCUeO5djx499PXXX6tevXry8vJS9+7dk89rSXr9O3bsmOr1//DDD2UYhq5evZpiG2k9561bt5a/v3/y4Z/Xrl3TwoUL1b17d9nZ2UlKPA+mRYsW+vXXX/Xaa6/pzz//1N9//5385UJ6Xou0pPf3NEl6fhbu9byl5eTJk6mew7t9YE7Pa3+7oUOHasSIEXr88ce1aNEibd68WVu2bFHVqlXv+7nz9fVNNS3p5+q/z1tar/uvv/6qJ598UoGBgZo1a5Y2btyY/OVVdHR0umq4cOGCFi1alOq5q1ixoiSleA+6W70Z9eyzz2rRokX69NNP5eLikiqoJ0l6f//vYdgmk0l+fn4p/g6kVU9afxvSu88A0o+ug0A20bNnT7311luaPHmyRo8efcf5ChYsqM2bN8swjBQf4i5evKj4+Pjk804KFiyo+Ph4XblyJcUf1P9+KCtQoIDs7OzUrVs3DRgwIM1tFi9e/L72qXDhwqpVq9Y95/vvh9GkfZg7d27yyEJakvYrrQ+a6TkZvVChQqmaVDyIpHrOnz+fKsCdO3cueb+soWXLlgoICNC0adPUsmVLTZs2TXXq1EnV/a5Xr17q1auXbt68qTVr1ujtt99Wu3btdPjw4Ts+t82bN9fUqVM1f/58vf7662nOM3/+fNnb26dqAHGn18LR0VFubm6SEl/fcePGady4cTp9+rQWLlyo119/XRcvXtTvv/+e/Dx98cUXd+wU+d8Pt2l1qUz6uf78888VHh6uH374QTExMSnOedm7d6927dql6dOnq0ePHsnT02qikRHp/T3NiHs9b2kJCAjQli1bUkwrW7bsHbfRsmXLe772t5s1a5a6d++u999/P8X0y5cvy9PT8947lYakoH27pJ+r/4aDtF73WbNmqXjx4pozZ06Kx//bhORuvL29VaVKlTu+FwcEBCTXk1ZjiYw2w0jSoUMHDRgwQB988IH69OkjFxeXNOdLen+/dOlSirBlGIbCwsKSm/jc/h4ZGBiYPF/S34bbpXefAaQfI1pANhEYGKhXX31VISEhKT7w/dcjjzyiGzdupLr2S9JJ3o888oikxEORJOn7779PMd8PP/yQ4r6rq2vyta2qVKmiWrVqpbql9W17ZmrZsqXs7e117NixNOtJCm9ly5aVv7+/Zs+eneJwqFOnTmnDhg333E7r1q21cuXKu560n9QqOj3fzj/88MOSlKqZxZYtW3TgwIHk18YakkLE/PnztXbtWm3duvWOhxlJUr58+dS6dWu98cYbio2N1b59++44b/v27VWhQgV98MEHaXapnDNnjpYvX67nnnsu1Tflv/76a4pRg+vXr2vRokVq2LBh8ijS7YoUKaKBAweqefPmyRfCDQ4Olqenp/bv33/H19/R0fGez5GUGDSjo6M1e/ZsTZ8+XfXq1VO5cuWSH0/6IP7fluBTpkxJta6M/Cyk9/f0fqX1vKXF0dEx1XN3t2vzPfbYY6pcubLGjBlzx1bcy5YtS76GmslkSvXcLVmyRKGhofexV4muX7+uhQsXppj2ww8/yGw2q1GjRvdc3mQyydHRMUXICgsLS9V1ULrzCHJSK/KSJUum+fOXFDqaNm16x3rvh4uLi9566y2FhISoX79+d5wv6efnv+81v/zyi27evJn8eNIXIf/9O/DTTz+l6iSY3n0GkH6MaAHZyAcffHDPebp3766JEyeqR48eOnnypCpXrqx169bp/fffV5s2bdSsWTNJUosWLdSoUSO99tprunnzpmrVqqX169fru+++S7XO8ePHq0GDBmrYsKH69eunYsWK6fr16zp69KgWLVqUrg5+1lSsWDG98847euONN3T8+HG1atVKBQoU0IULF/T3338rX758GjVqlMxms959910999xzat++vfr06aPw8HCNHDkyXYfuvPPOO/rtt9/UqFEj/e9//1PlypUVHh6u33//XUOHDlW5cuVUsmRJubi46Pvvv1f58uXl5uamgICAND90lC1bVs8//7y++OKL5G57J0+e1IgRIxQUFKSXXnrJqs9T79699eGHH6pLly5ycXFJ1eks6Rvx4OBg+fv7KywsTGPGjJGHh0fyN95psbOz0y+//KLmzZurXr16evnll1WvXj3FxMRo0aJFmjp1qho3bqxPPvkkzWWbN2+uoUOHymKx6MMPP1RkZGRy98qIiAg1bdpUXbp0Ubly5ZQ/f35t2bJFv//+e3I3Pjc3N33xxRfq0aOHrl69qo4dO8rHx0eXLl3Srl27dOnSJU2aNCldz1G5cuVUr149jRkzRmfOnNHUqVNTPV6yZEm9/vrrMgxDXl5eWrRoUZqHV1auXFlS4u9Ljx495ODgoLJly6YZXNL7e5pe6XnerMHOzk7z5s1TixYtVK9ePfXr109NmzZVvnz5dOrUKc2dO1eLFi1K7ljXrl07TZ8+XeXKlVOVKlW0bds2ffzxxw90SG7BggXVr18/nT59WmXKlNHSpUv11VdfqV+/fsmHNt5Nu3bt9Ouvv6p///7q2LGjzpw5o3fffVf+/v6pOnVWrlxZq1at0qJFi+Tv76/8+fOrbNmyeuedd7RixQrVr19fgwYNUtmyZRUdHa2TJ09q6dKlmjx5sgoXLqzu3bvrs88+U/fu3TV69GiVLl1aS5cu1bJly+57/4cOHaqhQ4fedZ7mzZurZcuWGjZsmCIjIxUcHKzdu3fr7bffVvXq1dWtWzdJUvny5dW1a1eNGzdODg4Oatasmfbu3auxY8emughyevc5I/bv35/cJTYsLExRUVGaO3eupMRrz+WW688Bd2S7PhxA3nZ718G7+W/XQcMwjCtXrhh9+/Y1/P39DXt7e6No0aLG8OHDU3UKCw8PN3r37m14enoarq6uRvPmzY2DBw+m2THqxIkTRu/evY3AwEDDwcHBKFSokFG/fn3jvffeSzGPMtB18OOPP36g52D+/PlG06ZNDXd3d8PJyckoWrSo0bFjR+OPP/5IMd/XX39tlC5d2nB0dDTKlCljfPvtt0aPHj3u2XXQMAzjzJkzRu/evQ0/Pz/DwcHBCAgIMJ588knjwoULyfPMnj3bKFeunOHg4JBiHf/tOmgYiZ3xPvzwQ6NMmTKGg4OD4e3tbXTt2tU4c+ZMivkaN25sVKxYMdU+p1X33dSvX9+QZDzzzDOpHpsxY4bRtGlTw9fX13B0dEzet927d6dr3ZcvXzZef/11o1y5coazs7Ph5uZm1K5d25gwYUKqTm+3d5ocNWqUUbhwYcPR0dGoXr26sWzZsuT5oqOjjb59+xpVqlQx3N3dDRcXF6Ns2bLG22+/bdy8eTPFOlevXm20bdvW8PLyMhwcHIzAwECjbdu2xs8//5w8T9JrcOnSpTvux9SpUw1JhouLS6rOmoZhGPv37zeaN29u5M+f3yhQoIDRqVMn4/Tp02n+vAwfPtwICAgwzGazIclYuXKlYRipuw4aRvp/TyUZAwYMSFVX0aJFk7vCZeR5s4bw8HDj3XffNWrUqGG4ubkZDg4ORpEiRYyuXbsa69evT57v2rVrxrPPPmv4+PgYrq6uRoMGDYy1a9emej4y0nWwYsWKxqpVq4xatWoZTk5Ohr+/v/G///0vuZvm7eu703vMBx98YBQrVsxwcnIyypcvb3z11Vdp/r7u3LnTCA4ONlxdXQ1JKWq+dOmSMWjQIKN48eKGg4OD4eXlZdSsWdN44403jBs3biTPd/bsWeOJJ54w3NzcjPz58xtPPPGEsWHDhgx3HbybtDr23bp1yxg2bJhRtGhRw8HBwfD39zf69etnXLt2LcV8MTExxssvv2z4+PgYzs7ORt26dY2NGzem+PnK6D6n9buRlqTnPK1bRrsWAjmRyTCs2H4IAAAAAMA5WgAAAABgbQQtAAAAALAyghYAAAAAWBlBCwAAAACsjKAFAAAAAFZG0AIAAAAAKyNoAQAAAICVEbQAAAAAwMoIWgAAAABgZQQtAAAAALAyghYAAAAAWBlBCwAAAACsjKAFAAAAAFZG0AIAAAAAKyNoAQAAAICVEbQAAAAAwMoIWgAAAABgZQQtAAAAALAyghYAAAAAWBlBCwAAAACsjKAFAAAAAFZG0AIAAAAAKyNoAQAAAICVEbQAAAAAwMoIWgAAAABgZQQtAAAAALAyghYAAAAAWBlBCwAAAACsjKAFAAAAAFZG0AIAAAAAKyNoAQAAAICVEbQAAAAAwMoIWgAAAABgZQQtAAAAALAyghYAAAAAWBlBCwAAAACsjKAFAAAAAFZG0AIAAAAAKyNoAQAAAICVEbQAAAAAwMoIWgAAAABgZQQtAAAAALAyghYAAAAAWBlBCwAAAACsjKAFAAAAAFZG0AIAAAAAKyNoAQAAAICVEbQAAAAAwMoIWgAAAABgZQQtAAAAALAyghYAAAAAWBlBCwAAAACsjKAFAAAAAFZG0AIAAAAAKyNoAQAAAICV2du6gOzOYrHo3Llzyp8/v0wmk63LAQAAAGAjhmHo+vXrCggIkNl89zErgtY9nDt3TkFBQbYuAwAAAEA2cebMGRUuXPiu8xC07iF//vySEp9Md3d3G1cDAAAAwFYiIyMVFBSUnBHuhqB1D0mHC7q7uxO0AAAAAKTrlCKaYQAAAACAlRG0AAAAAMDKCFoAAAAAYGWcowUAAACkISEhQXFxcbYuA1nIwcFBdnZ2VllXjgtaX375pT7++GOdP39eFStW1Lhx49SwYcM7zr969WoNHTpU+/btU0BAgF577TX17ds3CysGAABATmIYhsLCwhQeHm7rUmADnp6e8vPze+Br6OaooDVnzhwNGTJEX375pYKDgzVlyhS1bt1a+/fvV5EiRVLNf+LECbVp00Z9+vTRrFmztH79evXv31+FChXSE088YYM9AAAAQHaXFLJ8fHzk6ur6wB+4kTMYhqGoqChdvHhRkuTv7/9A6zMZhmFYo7CsUKdOHdWoUUOTJk1Knla+fHk9/vjjGjNmTKr5hw0bpoULF+rAgQPJ0/r27atdu3Zp48aN6dpmZGSkPDw8FBERQXt3AACAXC4hIUGHDx+Wj4+PChYsaOtyYANXrlzRxYsXVaZMmVSHEWYkG+SYZhixsbHatm2bWrRokWJ6ixYttGHDhjSX2bhxY6r5W7Zsqa1bt97xeNuYmBhFRkamuAEAACBvSPqM6OrqauNKYCtJr/2Dnp+XY4LW5cuXlZCQIF9f3xTTfX19FRYWluYyYWFhac4fHx+vy5cvp7nMmDFj5OHhkXwLCgqyzg4AAAAgx+BwwbzLWq99jglaSf6744Zh3PXJSGv+tKYnGT58uCIiIpJvZ86cecCKAQAAAOQ1OaYZhre3t+zs7FKNXl28eDHVqFUSPz+/NOe3t7e/4zG3Tk5OcnJysk7RAAAAAPKkHDOi5ejoqJo1a2rFihUppq9YsUL169dPc5l69eqlmn/58uWqVauWHBwcMq1WAAAAILtq0qSJhgwZYusyrOLkyZMymUzauXOnrUtJJccELUkaOnSovv76a3377bc6cOCAXnrpJZ0+fTr5uljDhw9X9+7dk+fv27evTp06paFDh+rAgQP69ttv9c033+iVV16x1S4AAAAAVpdWeFq1apVMJlOq64H9+uuvevfdd7OuuCx0p322hRxz6KAkPfXUU7py5YreeecdnT9/XpUqVdLSpUtVtGhRSdL58+d1+vTp5PmLFy+upUuX6qWXXtLEiRMVEBCgzz//nGtoAQAAIM/y8vLKku3ExcXl6aPIctSIliT1799fJ0+eVExMjLZt26ZGjRolPzZ9+nStWrUqxfyNGzfW9u3bFRMToxMnTiSPfgEAAADpYRiGomLjs/yW3svd9uzZU6tXr9b48eNlMplkMpl08uRJNW3aVJJUoEABmUwm9ezZU1Lq0a9ixYrpvffeU/fu3eXm5qaiRYtqwYIFunTpkh577DG5ubmpcuXK2rp1613rMJlMmjx5sh577DHly5dP7733niRp0aJFqlmzppydnVWiRAmNGjVK8fHxycuNHDlSRYoUkZOTkwICAjRo0KAU65w/f36K7Xh6emr69Omptn+3fZ47d64qV64sFxcXFSxYUM2aNdPNmzfT8/Tetxw1ogUAAABktVtxCarw1rIs3+7+d1rK1fHeH9fHjx+vw4cPq1KlSnrnnXckSYUKFdIvv/yiJ554QocOHZK7u7tcXFzuuI7PPvtM77//vkaMGKHPPvtM3bp1U3BwsHr37q2PP/5Yw4YNU/fu3bVv3767dvx+++23NWbMGH322Weys7PTsmXL1LVrV33++edq2LChjh07pueffz553rlz5+qzzz7Tjz/+qIoVKyosLEy7du3K4DOVKCgoKM19Pn/+vJ5++ml99NFHat++va5fv661a9emO8jeL4IWAAAAkIN5eHjI0dFRrq6u8vPzS56edIigj4+PPD0977qONm3a6IUXXpAkvfXWW5o0aZIeeughderUSZI0bNgw1atXTxcuXEixjf/q0qWLevfunXy/W7duev3119WjRw9JUokSJfTuu+/qtdde09tvv63Tp0/Lz89PzZo1k4ODg4oUKaLatWvf1/NgZ2eX5j4fO3ZM8fHx6tChQ/IpR5UrV76vbWQEQQsAAAC4CxcHO+1/p6VNtptVqlSpkvz/pEsn3R5GkqZdvHjxrkGrVq1aKe5v27ZNW7Zs0ejRo5OnJSQkKDo6WlFRUerUqZPGjRunEiVKqFWrVmrTpo1CQkJkb2+9mFK1alU98sgjqly5slq2bKkWLVqoY8eOKlCggNW2kRaCFgAAAHAXJpMpXYfw5WS3N61IOjQwrWkWi+Wu68mXL1+K+xaLRaNGjVKHDh1Szevs7KygoCAdOnRIK1as0B9//KH+/fvr448/1urVq+Xg4CCTyZTqEL+4uLgM7ZudnZ1WrFihDRs2aPny5friiy/0xhtvaPPmzSpevHiG1pUROa4ZRp4WESqdWJP4LwAAAPAPR0dHJSQkpJomKdX0rFSjRg0dOnRIpUqVSnUzmxOjiIuLix599FF9/vnnWrVqlTZu3Kg9e/ZISjzX7Pz588nrO3LkiKKiou64vTvts8lkUnBwsEaNGqUdO3bI0dFR8+bNs/buppC7o3lusn2mtGiwZFgkk1kKGS/V6H7v5QAAAJDrFStWTJs3b9bJkyfl5uYmLy8vFS1aVCaTSYsXL1abNm3k4uIiNze3LK3rrbfeUrt27RQUFKROnTrJbDZr9+7d2rNnj9577z1Nnz5dCQkJqlOnjlxdXfXdd9/JxcUl+Vyqhx9+WBMmTFDdunVlsVg0bNiwu7aMT2uf9+3bpz///FMtWrSQj4+PNm/erEuXLql8+fKZuu+MaOUEEaH/hiwp8d9FQxjZAgAAgCTplVdekZ2dnSpUqKBChQrp9OnTCgwM1KhRo/T666/L19dXAwcOzPK6WrZsqcWLF2vFihV66KGHVLduXX366afJQcrT01NfffWVgoODVaVKFf35559atGiRChYsKEn65JNPFBQUpEaNGqlLly565ZVX5OrqesftpbXP7u7uWrNmjdq0aaMyZcrozTff1CeffKLWrVtn6r6bjMzua5jDRUZGysPDQxEREXJ3d7dNESfWSDNCUk/vsVgq3jDr6wEAAMiloqOjdeLECRUvXlzOzs62Lgc2cLefgYxkA0a0cgKvkomHC97GIrPC7ANsVBAAAACAuyFo5QQegbK0G6eEf16ueMOs1+OeVd2JBxXyxTp98ecRHb5wPdMvugYAAAAgfWiGkUOYa/aQSjVT2Mn9+uuCm04cN2Q6dU17QiO0JzRCn6w4rGIFXdWiop9aVvRV9aACMpvvfNVuAAAAAJmHoJWTeATKr2qgukjqIunS9Rj9eeCClu+/oHVHLuvklShNXXNcU9ccl7ebk5pX8FGLin6qX7KgnOyz7oJ3AAAAQF5H0MrBCuV3UufaRdS5dhHdiInX6kOXtHx/mP46eFGXb8Ro9t9nNPvvM8rnaKcm5XzUsqKfmpQtJHfnO7fEBAAAAPDgCFq5hJuTvdpW8VfbKv6Kjbdo84krWrYvTCv2X9CFyBgt2X1eS3afl4OdSfVKeqtFBV+1qOArH3e66QAAAADWRnv3e8gW7d0fgMViaHdohJbtC9PyfWE6dulmiserF/FUiwqJ53WVKJS1F7ADAADIbmjvDmu1d2dEK5czm02qFuSpakGeGtaqnI5evKHl+8O0fN8F7TwTrh2nE28f/n5QpXzc1KKCr1pW9FPlQA+aaQAAAAD3ifbueUwpHzf1b1JK8wcEa/P/HtF7j1dSw9LesjebdPTiDX256pgem7he9T/4SyPm79W6I5cVl2CxddkAAAB4AKtWrZLJZFJ4eLitS7kjk8mk+fPnS5JOnjwpk8mknTt32rSmB8GIVh7m6+6srnWLqmvdooq4FadVhy5q+b4LWnXoosIio/XdplP6btMpuTvb6+F/mmk0KlNI+Zz4sQEAAIBt/frrr5oyZYq2bdumK1euaMeOHapWrZqty0rGJ2ZIkjxcHPRYtUA9Vi1Q0XEJ2ngssZnGHwcu6PKNWM3feU7zd56To71ZDUt5q0VFXzUr76uCbk62Lh0AAAB50M2bNxUcHKxOnTqpT58+ti4nFQ4dRCrODnZqWs5HHzxRRZv/10xz+9ZTn4bFVcTLVbHxFv158KKG/bJHD43+Q09O3qiv1x7X6StRti4bAAAge4kIlU6sSfw3k8XExGjQoEHy8fGRs7OzGjRooC1btqSab/369apataqcnZ1Vp04d7dmzJ/mxU6dOKSQkRAUKFFC+fPlUsWJFLV26NM3tffHFF6pcuXLy/fnz58tkMmnixInJ01q2bKnhw4cn31+0aJFq1qwpZ2dnlShRQqNGjVJ8fPx973O3bt301ltvqVmzZve9jsxE0MJd2ZlNqlXMS2+0raDVrzbRsiGNNLR5GVUKdJfFkP4+eVXvLTmgRh+vVKtxa/TpisPady5CNLMEAAB52vaZ0rhK0oyQxH+3z8zUzb322mv65ZdfNGPGDG3fvl2lSpVSy5YtdfXq1RTzvfrqqxo7dqy2bNkiHx8fPfroo4qLi5MkDRgwQDExMVqzZo327NmjDz/8UG5uaXelbtKkifbt26fLly9LklavXi1vb2+tXr1akhQfH68NGzaocePGkqRly5apa9euGjRokPbv368pU6Zo+vTpGj16dGY9JTZH0EK6mUwmlfXLr0GPlNbiFxtq/esPa2RIBdUrUVB2ZpMOhl3X538eUdvP16nBhys1atE+bTx2RfE00wAAAHlJRKi0aLBk/PMZyLBIi4Zk2sjWzZs3NWnSJH388cdq3bq1KlSooK+++kouLi765ptvUsz79ttvq3nz5qpcubJmzJihCxcuaN68eZKk06dPKzg4WJUrV1aJEiXUrl07NWrUKM1tVqpUSQULFkwOVqtWrdLLL7+cfH/Lli2Kjo5WgwYNJEmjR4/W66+/rh49eqhEiRJq3ry53n33XU2ZMiVTnpPsgHO0cN8CPV3UM7i4egYX17Wbsfrr4EUt2xemNUcuKTT8lqatP6lp60+qgKuDHimfeIHkRmUKydnBzvrFRIRKV49JXiUlj0Drrx8AACC9rh77N2QlMRKkq8cz5XPKsWPHFBcXp+Dg4ORpDg4Oql27tg4cOJBi3nr16iX/38vLS2XLlk2eZ9CgQerXr5+WL1+uZs2a6YknnlCVKlXS3KbJZFKjRo20atUqPfLII9q3b5/69u2rsWPH6sCBA1q1apVq1KiRPCK2bds2bdmyJcUIVkJCgqKjoxUVFSVXV1erPR/ZBUELVlEgn6OeqFlYT9QsrFuxCVp75JKW7bugPw9e0LWoOM3ddlZzt52Vi4OdGpXxVosKfnqkvI88XR0ffOPbZ/77rZHJLIWMl2p0f/D1AgAA3A+vkomfSW4PWyY7yatEpmwu6ZQNk8mUavp/p6UlaZ7nnntOLVu21JIlS7R8+XKNGTNGn3zyiV588cU0l2vSpImmTp2qtWvXqmrVqvL09FSjRo20evVqrVq1Sk2aNEme12KxaNSoUerQoUOq9eTWC0Nz6CCszsXRTi0q+umTJ6tq6xvNNLtPXfWsX0yBni66FZegZfsu6OWfd6nme3+oy1ebNH39CYWG37q/jWXx0DwAAMA9eQQmfvFr+ucoHpOdFDIu0466KVWqlBwdHbVu3brkaXFxcdq6davKly+fYt5NmzYl///atWs6fPiwypUrlzwtKChIffv21a+//qqXX35ZX3311R23m3Se1ty5c5NDVePGjfXHH3+kOD9LkmrUqKFDhw6pVKlSqW5mc+6MJIxoIVPZ25lVr2RB1StZUG+HVNC+c5Favi9My/df0MGw69pw7Io2HLuikYv2q3Kgh1pU8FWLin4q4+uWrm9gsnpoHgAAIF1qdJdKPpL4mcSrRKZ+LsmXL5/69eunV199VV5eXipSpIg++ugjRUVF6dlnn00x7zvvvKOCBQvK19dXb7zxhry9vfX4449LkoYMGaLWrVurTJkyunbtmv76669UQe12Sedpff/991qwYIGkxPD18ssvS1Ly+VmS9NZbb6ldu3YKCgpSp06dZDabtXv3bu3Zs0fvvffefe331atXdfr0aZ07d06SdOjQIUmSn5+f/Pz87mud1kTQQpYxmUyqFOihSoEeGtqirE5duakV+y9o2b4wbT11TXtCI7QnNEKfrDisYgVd1aKin1pU8FX1IgVkZ75D6MrioXkAAIB08wjMsi9+P/jgA1ksFnXr1k3Xr19XrVq1tGzZMhUoUCDVfIMHD9aRI0dUtWpVLVy4UI6OiadyJCQkaMCAATp79qzc3d3VqlUrffbZZ3fcpslkUuPGjTV//nw1bNhQklSlShV5eHioRIkScnd3T563ZcuWWrx4sd555x199NFHcnBwULly5fTcc8/d9z4vXLhQvXr1Sr7fuXNnSYkNP0aOHHnf67UWk0Ef7ruKjIyUh4eHIiIiUvywwLou34jRnwcuaNm+C1p39LJi4/8NTt5uTmpewUctKvipfqmCcrL/TzON7TMTDxc0Ev4dmuccLQAAcB+io6N14sQJFS9ePNeeO4S7u9vPQEayAUHrHghaWe9GTLzWHL6kZfvC9NfBi7oe/e+F7PI52qlJOR+1qOCrpuV85O7skPhARGiWDM0DAIDcjaAFawUtDh1EtuPmZK82lf3VprK/YuMt2nziipbtC9OK/Rd0ITJGS3af15Ld5+VgZ1K9kt5qUcFXzSv4yrc4AQsAAADZAyNa98CIVvZhsRjaHRqhZfvCtHxfmI5dupni8cZlCumFxiVUr0TB9DXSAAAA+A9GtMCIFvIcs9mkakGeqhbkqWGtyunoxRtavj9My/dd0M4z4Vp9+JJWH76kqoU99ELjkmpZ0e/OTTQAAACATETQQo5VysdNpXxKqX+TUjp9JUpfrzuuOVvOaNfZCPX/fruKFnRVn4Yl1LFmYTk72N17hQAAAP/goK+8y1qvfe68OhjynCIFXfXOY5W04fWHNeiR0vJ0ddCpK1F6c/5eBX/wl77484jCo2JtXSYAAMjmHBwSG21FRUXZuBLYStJrn/SzcL84R+seOEcrZ4qKjddPW87oq7UnFBp+S5Lk6minpx4K0nMNSyjQ08XGFQIAgOzq/PnzCg8Pl4+Pj1xdXTn3O48wDENRUVG6ePGiPD095e/vn2oe2rtbEUErZ4tPsGjJnvOasvq49p+PlCTZmU16tGqAnm9UQuX9eU0BAEBKhmEoLCxM4eHhti4FNuDp6Sk/P780AzZBy4oIWrmDYRhad/SyJq8+pvVHryRPp1MhAAC4k4SEBMXFxdm6DGQhBwcH2dnd+dx+gpYVEbRynz1nIzRlzTEt3XNeln9++ulUCAAAgHshaFkRQSv3SupU+NPWM4qOs0gSnQoBAABwRwQtKyJo5X5XbsRo5sZTmrHxpMKjEg8PKJjPUT3rF1O3ekXl6epo4woBAACQHRC0rIiglXfcrVPhsw2Kq3ABVxtXCAAAAFsiaFkRQSvvoVMhAAAA0kLQsiKCVt6V1KlwyurjWnf0cvJ0OhUCAADkTQQtKyJoQUq7U2GVwh56oVFJtapEp0IAAIC8gKBlRQQt3O5OnQqfa1hCnehUCAAAkKsRtKyIoIW00KkQAAAg7yFoWRFBC3cTFRuvn7ee1Vdrj+vsNToVAgAA5GYELSsiaCE97tSpMKSKv15oXJJOhQAAALkAQcuKCFrICDoVAgAA5F4ELSsiaOF+7Q2N0JQ1x7Vk9zk6FQIAAOQCBC0rImjhQWVZp8KIUOnqMcmrpOQRaJ11AgAAIBlBy4oIWrCWTO1UuH2mtGiwZFgkk1kKGS/V6G6lygEAACARtKyKoAVrs3qnwohQaVylxJCVxGQnDdnDyBYAAIAVZSQbmLOoJgD/cHW0V4/6xbTqlSYa37maKvi7Kyo2QdPWn1Tjj1dpyI87tP9cZPpXePVYypAlSUaCdPW4dQsHAABAutnbugAgr7K3M+uxaoF6tGpAik6F83ee0/yd59SoTCH1bVRC9Ureo1OhV8nEwwX/O6LlVSLzdwIAAABp4tDBe+DQQWSl++5UuH2mtGhI4kiWyU4KGcc5WgAAAFbGOVpWRNCCLdxXp8KI0MTDBb1KcG4WAABAJiBoWRFBC7aU1Klw5saTunZbp8Ie9YupW92iKpDvAToVAgAAIEMIWlZE0EJ2kFanQhcHO3WufZ+dCgEAAJBhBC0rImghO4lPsGjJnvOasvq49p9P7ExoZzYppIq/nm9UUhUC+BkFAADILAQtKyJoITsyDCNFp8IkrSr66X9tyqtIQUa4AAAArI2gZUUELWR3/+1U6Ghn1rMNi2tA01Jyc+IKDgAAANZC0LIighZyikNh1/Xu4v3JI1yF8jvp1ZZl1bFGYZnv1BYeAAAA6UbQsiKCFnISwzD0x4GLGr1kv05eiZIkVQp019shFfVQMS8bVwcAAJCzEbSsiKCFnCg23qIZG07q8z+P6HpMvCSpbRV/DW9djg6FAAAA9ykj2cCcRTU9sGvXrqlbt27y8PCQh4eHunXrpvDw8Lsu07NnT5lMphS3unXrZk3BgA052pvVp1EJrXy1iZ6uXUQmk7Rk93k98slqfbL8kG7+E74AAACQOXLMiFbr1q119uxZTZ06VZL0/PPPq1ixYlq0aNEdl+nZs6cuXLigadOmJU9zdHSUl1f6D6FiRAu5wf5zkXpn8T5tOn5VkuTr7qRhrcrp8WqBnL8FAACQTrnu0MEDBw6oQoUK2rRpk+rUqSNJ2rRpk+rVq6eDBw+qbNmyaS7Xs2dPhYeHa/78+fe9bYIWcgvDMLRs3wW9v/SATl9NPH+rapCn3mpXQTWLFrBxdQAAANlfrjt0cOPGjfLw8EgOWZJUt25deXh4aMOGDXdddtWqVfLx8VGZMmXUp08fXbx48a7zx8TEKDIyMsUNyA1MJpNaVfLT8pcaaVircsrnaKddZ8L1xKQNGvzjDp0Lv2XrEgEAAHKNHBG0wsLC5OPjk2q6j4+PwsLC7rhc69at9f333+uvv/7SJ598oi1btujhhx9WTEzMHZcZM2ZM8nlgHh4eCgoKsso+ANmFs4Od+jUpqZWvNtGTtQrLZJIW7Dynhz9ZpXF/HNat2ARblwgAAJDj2TRojRw5MlWziv/etm7dKinx2/j/MgwjzelJnnrqKbVt21aVKlVSSEiIfvvtNx0+fFhLliy54zLDhw9XRERE8u3MmTMPvqNANuST31kfdayqRQMb6KFiBRQdZ9G4P47o4U9WacHOUOWAo4oBAACyLXtbbnzgwIHq3LnzXecpVqyYdu/erQsXLqR67NKlS/L19U339vz9/VW0aFEdOXLkjvM4OTnJyckp3esEcrpKgR766YV6WronTO8vPaDQ8Fsa/ONOzdhwUm+HVFTVIE9blwgAAJDj2DRoeXt7y9vb+57z1atXTxEREfr7779Vu3ZtSdLmzZsVERGh+vXrp3t7V65c0ZkzZ+Tv73/fNQO5kclkUtsq/nqkvI++XntcX646pu2nw/XYxPXqUCNQw1qVk6+7s63LBAAAyDFyxDla5cuXV6tWrdSnTx9t2rRJmzZtUp8+fdSuXbsUHQfLlSunefPmSZJu3LihV155RRs3btTJkye1atUqhYSEyNvbW+3bt7fVrgDZmrODnQY+XForX2miDjUCJUm/bg9V07GrNOGvI4qO4/wtAACA9MgRQUuSvv/+e1WuXFktWrRQixYtVKVKFX333Xcp5jl06JAiIiIkSXZ2dtqzZ48ee+wxlSlTRj169FCZMmW0ceNG5c+f3xa7AOQYvu7O+vTJapo/IFg1ingqKjZBY5cf1iOfrNbi3ec4fwsAAOAecsR1tGyJ62ghrzMMQwt3ndMHvx3U+YhoSVLtYl56K6SCKgV62Lg6AACArJPrLlhsSwQtINGt2ARNWXNMk1cfU3ScRSaT1KlmYb3Ssqx88nP+FgAAyP0IWlZE0AJSOhd+Sx/+flALdp6TJLk52WtA01LqFVxMzg52Nq4OAAAg8xC0rIigBaRt26lremfxfu06Ey5JCvJy0RttyqtlRb+7Xt8OAAAgpyJoWRFBC7gzi8XQ/J2h+vD3g7oQGSNJqlvCS2+1q6gKAfy+AACA3IWgZUUELeDebsbEa/LqY5q65rhi4i0ym6SnHiqil1uUkbcbFwAHAAC5A0HLighaQPqdvRalD347qMW7z0uS8jvZ68VHSqln/eJytM8xV5MAAABIE0HLighaQMZtOXlV7yzarz2hide1K1bQVW+0raBm5X04fwsAAORYBC0rImgB98diMTR3+1l9vOyQLl1PPH+rQSlvjWhXQWX9uGg4AADIeQhaVkTQAh7MjZh4fbnyqL5ed0Kx/5y/1aVOEQ1tXlZe+RxtXR4AAEC6EbSsiKAFWMeZq1F6f+kB/bY3TJLk7myvwc3KqHu9onKw4/wtAACQ/RG0rIigBVjXxmNX9M7i/TpwPlKSVKJQPo1oW0FNy/nYuDIAAIC7I2hZEUELsL4Ei6Gftp7R2GWHdOVmrCSpcZlCGtGuvEr5pHH+VkSodPWY5FVS8gjM4moBAAASEbSsiKAFZJ7I6DhN/Ouovl1/QnEJhuzMJnWrW1RDmpWWp+s/529tnyktGiwZFslklkLGSzW627ZwAACQJxG0rIigBWS+k5dvavTSA1qx/4IkydPVQS81K6NnytvJ/vMqiSEriclOGrKHkS0AAJDlMpINOAMdgM0V886nr7rX0vfP1VFZ3/wKj4rT2wv36fWv5qcMWZJkJEhXj9ukTgAAgPQiaAHINoJLeWvJoAZ67/FKKuDqoHVXPZRg/OcCxyY7yauEbQoEAABIJ4IWgGzF3s6srnWLatWrTdW2QS29mdBH8UbiW5VFZsW3/YzDBgEAQLbHOVr3wDlagG0du3RDE+ev1rkT+3XS4isv/+L6uFMVVQzwsHVpAAAgj6EZhhURtADbMwxDi3af19sL9upaVJzszSb1b1pKA5uWkqM9A/MAACBr0AwDQK5iMpn0aNUALX+psVpX8lO8xdDnfx7RoxPWaW9ohK3LAwAASIWgBSDHKJTfSZO61tTELjXklc9RB8Ou67GJ6zV22SHFxCfYujwAAIBkBC0AOU7bKv5a8VIjta3irwSLoQkrjyrki3XafTbc1qUBAABIImgByKEKujlpYpcamvRMDXm7OerwhRtq/+UGffj7QUXHMboFAABsi6AFIEdrXdlfy19qrMeqBSjBYmjSqmNq98U67Th9zdalAQCAPIygBSDH88rnqPGdq2tKt5rydnPS0Ys39MSkDRqz9ACjWwAAwCYIWgByjZYV/fTH0EZqXz1QFkOasua42ny+VttOMboFAACyFkELQK7i6eqoz56qpq+715JPficdv3RTHSdv0HuL9+tWLKNbAAAgaxC0AORKzSr4asVLjdWxZmEZhvT1uhNq8/labTl51dalAQCAPICgBSDX8nB10NhOVTWt50Pyc3fWics39eSUjRq1aJ+iYuNtXR4AAMjFCFoAcr2m5Xy07KVGerJW4ujWtPUn1Xr8Wm06fsXWpQEAgFyKoAUgT/BwcdBHHatqeq+H5O/hrFNXotR56ia9vWCvbsYwugUAAKyLoAUgT2lS1kfLX2qkp2sXkSTN2HhKrcav0YZjl21cGQAAyE0IWgDynPzODhrTobK+e7a2Aj1ddObqLXX5arPenL9HNxjdAgAAVkDQApBnNSxdSMteaqRn6iSObs3adFotP1ujdUcY3QIAAA+GoAUgT3Nzstfo9pX1w3N1VLiAi0LDb6nrN5s1/Nc9uh4dZ+vyAABADkXQAgBJ9Ut5a9mQRuper6gkafbfiaNbaw5fsnFlAAAgJyJoAcA/8jnZ653HKml2n7oq4uWqcxHR6v7t3xo2d7ciGd0CAAAZQNACgP+oV7Kgfh/SUD3rF5Mkzdl6Ri0/W6OVhy7atjAAAJBjELQAIA2ujvYa+WhF/fRCPRUr6KrzEdHqNW2LXvl5lyKiGN0CAAB3R9ACgLuoXdxLvw1upGcbFJfJJM3ddlYtxq3Wnwcu2Lo0AACQjRG0AOAeXBztNKJdBc3tW08lvPPpQmSMnp2xVUPn7FR4VKytywMAANkQQQsA0qlmUS8tHdxQzzcqIbNJ+nVHqJp/tkbL94XZujQAAJDNELQAIAOcHez0vzblNbdffZUslE+Xrsfo+e+2afCPO3TtJqNbAAAgEUELAO5DjSIFtGRQQ/VtXFJmk7Rg5zk1/2y1ft973talAQCAbICgBQD3ydnBTq+3Lqdf+wertI+bLt+IVd9Z2zXwh+26ciPG1uUBAAAbImgBwAOqFuSpxYMaaEDTkrIzm7R493m1+GyNluxmdAsAgLyKoAUAVuBkb6dXW5bT/P7BKuubX1duxmrAD9vV//ttuszoFgAAeQ5BCwCsqHJhDy16sYEGPVxK9maTlu4JU/NPV2vRrnMyDMPW5QEAgCxC0AIAK3O0N2toi7KaPyBY5f3ddS0qTi/O3qG+s7bp4vVoW5cHAACyAEELADJJpUAPLRgQrCHNSsvebNKyfRfU4rM1mr8jlNEtAAByOYIWAGQiR3uzhjQro4UDG6higLvCo+I0ZM5O9Zm5TRcjGd0CACC3ImgBQBaoEOCu+QOC9XLzMnKwM+mPAxfU7NPV+nX7WUa3AADIhQhaAJBFHOzMevGR0lr0YgNVDvRQZHS8hv60S8/O2KqwCEa3AADITQhaAJDFyvm5a17/+nq1ZVk52pn118GLav7Zav209QyjWwAA5BIELQCwAXs7swY0LaXFgxqoapCnrkfH67W5u9Vz2hadC79l6/IAAMADImgBgA2V8c2vX/rW0+uty8nR3qzVhy+p5WdrNGfLaUa3AADIwQhaAGBj9nZm9W1cUksHNVT1Ip66HhOvYb/sUfdv/1Yoo1sAAORIBC0AyCZK+bhpbt/6eqNNeTnZm7X2yGW1+HS1vt98itEtAAByGIIWAGQjdmaT+jQqod8GN1StogV0MzZBb8zbq67fbNaZq1G2Lg8AAKQTQQsAsqEShdw054V6GtGugpwdzFp/9Ipajluj7zadksXC6BYAANkdQQsAsik7s0nPNiiu3wc3Uu1iXoqKTdCI+XvV5etNOn2F0S0AALIzghYAZHPFvPPpx+framRIBbk42GnT8atqOW6Npq8/IUv4WenEGiki1NZlAgCA29jbugAAwL2ZzSb1DC6upuV8NOyX3dp0/Kr2L50orfhakiGZzFLIeKlGd1uXCgAAxIgWAOQoRQvm0w/P1dUnLb01xv5rmfXP+VqGRcaiIYxsAQCQTRC0ACCHMZtNeqJYjOxMKZtimIwE3Th/2EZVAQCA2+WYoDV69GjVr19frq6u8vT0TNcyhmFo5MiRCggIkIuLi5o0aaJ9+/ZlbqEAkBW8SiYeLnibeMOsZ369qM3Hr9ioKAAAkCTHBK3Y2Fh16tRJ/fr1S/cyH330kT799FNNmDBBW7ZskZ+fn5o3b67r169nYqUAkAU8AhPPyTLZSZIMk50+ceqnXZFuevqrTRq77JDiEiw2LhIAgLzLZBhGjrogy/Tp0zVkyBCFh4ffdT7DMBQQEKAhQ4Zo2LBhkqSYmBj5+vrqww8/1AsvvJDmcjExMYqJiUm+HxkZqaCgIEVERMjd3d1q+wEAVhERKl09LnmV0A1nX41cuE9zt52VJFUL8tT4ztVUtGA+GxcJAEDuEBkZKQ8Pj3RlgxwzopVRJ06cUFhYmFq0aJE8zcnJSY0bN9aGDRvuuNyYMWPk4eGRfAsKCsqKcgHg/ngESsUbSh6BcnOy19hOVTWhS3Xld7bXzjPhajN+reZuO6sc9p0aAAA5Xq4NWmFhYZIkX1/fFNN9fX2TH0vL8OHDFRERkXw7c+ZMptYJANbWrkqAfh/SSLWLe+lmbIJe+XmXXpy9QxG34mxdGgAAeYZNg9bIkSNlMpnuetu6desDbcNkMqW4bxhGqmm3c3Jykru7e4obAOQ0gZ4umt2nrl5tWVZ2ZpMW7z6vNuPX0igDAIAsYtMLFg8cOFCdO3e+6zzFihW7r3X7+flJShzZ8vf3T55+8eLFVKNcAJAb2ZlNGtC0lIJLeWvwjzt06kqUnv5qk/o3KaXBzUrLwS7XHtQAAIDN2TRoeXt7y9vbO1PWXbx4cfn5+WnFihWqXr26pMTOhatXr9aHH36YKdsEgOyoWpCnlgxqmNwoY8LKo1p39DKNMgAAyEQ55uvM06dPa+fOnTp9+rQSEhK0c+dO7dy5Uzdu3Eiep1y5cpo3b56kxEMGhwwZovfff1/z5s3T3r171bNnT7m6uqpLly622g0AsAkaZQAAkLVsOqKVEW+99ZZmzJiRfD9plGrlypVq0qSJJOnQoUOKiIhInue1117TrVu31L9/f127dk116tTR8uXLlT9//iytHQCyi3ZVAlS9SAG9NGen/j5xVa/8vEurDl3U6PaV5eHiYOvyAADINXLcdbSyWkZ65QNATpFgMTR59TF9uuKwEiyGAj1d9OmTVVWnREFblwYAQLbFdbQAAHeV1Cjjl371VbSgq0LDb+nprzZp7LJDikuw2Lo8AAByPIIWAORhSY0yOtYsLIshTVh5VJ0mb9SpKzdtXRoAADkaQQsA8jgaZQAAYH0ELQCApMRGGb8PaaTaxbx0MzZBr/y8Sy/O3qGIW3G2Lg0AgByHoAUASBbo6aLZz9fVqy3Lys5s0uLd59Vj3Dwd2LBEigi1dXkAAOQYdB28B7oOAsirdp4J17LvPtIrMV/KzmTIIrMUMk7mmj1sXRoAADZB10EAwAOr5n5Tr8VNkp0p8fs4sywyFg3R5dDjNq4MAIDsj6AFAEjb1WMyGSlbvdvJov99s0CrDl20UVEAAOQMBC0AQNq8SkqmlH8mEmTW7qiC6jlti8b8doBrbgEAcAcELQBA2jwCpZDxksku8b7JTpa2n6l53RqSpCmrj6vT5I06czXKhkUCAJA90QzjHmiGASDPiwiVrh6XvEokhi9Jv+05r9d+2a3r0fHK72yvj56ootaV/W1cKAAAmSsj2YCgdQ8ELQBI25mrURr04w7tOB0uSepat4jebFtBzg52ti0MAIBMQtdBAECmC/Jy1U8v1FPfxiUlSbM2nVb7Lzfo2KUbNq4MAADbI2gBAO6bg51Zr7cupxm9a6tgPkcdOB+pkC/Wae62s7YuDQAAmyJoAQAeWOMyhfTb4IaqX7KgomIT9MrPuzR0zk7djIm3dWkAANgEQQsAYBU+7s767tk6erl5GZlN0q87QhXyxTrtOxdh69IAAMhyBC0AgNXYmU168ZHS+vH5evL3cNbxyzfV/ssNmrnxpOi9BADISwhaAACrq13cS0sHNVSz8j6KjbforQX71HfWNkVExdm6NAAAsgRBCwCQKQrkc9RX3WtpRLsKcrAzadm+C2rz+VptO3XN1qUBAJDpCFoAgExjMpn0bIPi+qVffRUt6KrQ8Ft6cspGfbnqqCwWDiUEAOReBC0AQKarUthTi19soJCqAUqwGPro90PqMe1vXboeY+vSAADIFAQtAECWyO/soM87V9MHHSrL2cGstUcuq83na7X+6GVblwYAgNURtAAAWcZkMqlz7SJaOLCByvi66dL1GHX9ZrPGLjuk+ASLrcsDAMBqCFoAgCxXxje/FgxooKdrB8kwpAkrj6rz1E06F37L1qUBAGAVBC0AgE24ONppTIcq+uLp6nJzstfWU9fUevxardh/wdalAQDwwAhaAACbCqkaoCWDGqhKYQ9F3IpTn5lbNXLhPsXEJ9i6NAAA7htBCwBgc0UL5tPcvvX1bIPikqTpG07qiUkbdOLyTRtXBgDA/SFoAQCyBUd7s0a0q6BvetRSAVcHXQ49oXc+n6Tlm7bbujQAADKMoAUAyFYeKe+rlY+c0QbnQZpmfleP/Paw5n0zhkMJAQA5CkELAJC9RITK849XZJYhSbIzGQo5/ZFemLBIZ65G2bg4AADSh6AFAMherh6TjJTX1LI3WRR98Yjafk5XQgBAzkDQAgBkL14lJVPKP0+GyU75/MooMjpefWZu1ZilBxTHBY4BANkYQQsAkL14BEoh4yWTXeJ9k51MIeM0qX+IegcndiWcsua4uny1SWER0TYsFACAOzMZhmHYuojsLDIyUh4eHoqIiJC7u7utywGAvCMiVLp6XPIqkRi+/vHbnvN6be5uXY+Jl1c+R43vXE0NSxeyYaEAgLwiI9mAES0AQPbkESgVb5giZElS68r+WvRiA1Xwd9fVm7Hq/u3f+mzFYSVY+N4QAJB9ELQAADlOMe98+rV/fT1du4gMQxr/5xH1+PZvXb4RY+vSAACQRNACAORQzg52GtOhsj57qqpcHOy07uhltRm/Vn+fuGrr0gAAIGgBAHK29tULa+HAYJXycdPF6zF6+qtNmrz6mCwcSggAsCGCFgAgxyvtm18LBgTr8WoBSrAY+uC3g3r+u60Kj4q1dWkAgDyKoAUAyBXyOdnrs6eq6f32leVob9YfBy6q7efrtOtMuK1LAwDkQQQtAECuYTKZ1KVOEf3ar76KeLkqNPyWOk7eoBkbToqrmQAAshJBCwCQ61QK9NDiQQ3UsqKv4hIMvb1wnwbO3qHr0XG2Lg0AkEcQtAAAuZK7s4Mmd62pEe0qyN5s0pLd5/XohPU6cD7S1qUBAPIAghYAINcymUx6tkFxzXmhnvw9nHXi8k09PnG9ftpyxtalAQByOYIWACDXq1m0gJYMaqgmZQspJt6i137ZrVd+3qVbsQm2Lg0AkEsRtAAAeYJXPkd92+MhvdqyrMwmae62s3p84nodu3TD1qUBAHIhghYAIM8wm00a0LSUZj1XR95uTjp04boe/WKdFu06Z+vSAAC5DEELAJDn1C/praWDGqhOcS/djE3Qi7N3aMT8vYqJ51BCAIB1ELQAAHmSj7uzvn+ujgY0LSlJ+m7TKXWctFFnrkbZuDIAQG5A0AIA5Fn2dma92rKcpvV6SJ6uDtoTGqG2n6/Viv0XbF0aACCHI2gBAPK8pmV9tGRQQ1Uv4qnI6Hj1mblV7y89oLgEi61LAwDkUAQtAAAkBXq6aM7z9dQ7uLgkaeqa4+ry1SaFRUTbuDIAQE5kMgzDuNdMNWrUyNhKTSYtXLhQgYGB911YdhEZGSkPDw9FRETI3d3d1uUAALLA73vP69Wfd+t6TLy88jnqi6erK7iUt63LAgDYWEaygX16Vrhz5069/PLLcnNzu+e8hmHogw8+UExMTPqqBQAgm2lVyV/l/NzV//vt2n8+Ut2+2axXWpZVv8YlZTKZbF0eACAHSNeIltlsVlhYmHx8fNK10vz582vXrl0qUaLEAxdoa4xoAUDeFR2XoBHz9+rnbWclSc0r+OqTJ6vK3dnBxpUBAGwhI9kgXedonThxQoUKFUp3Afv371fRokXTPT8AANmRc1SYPqoZrnGtC8nRzqwV+y/o0S/W6cD5SFuXBgDI5tI1opWXMaIFAHnU9pnSosGSYZFMZp1pMEadt5RRaPgtOTuYNaZDZbWvXtjWVQIAslBGssF9Ba3o6Gjt3r1bFy9elMWSsvXto48+mtHVZWsELQDIgyJCpXGVEkNWEpOdrr2wXYOWXNDaI5clSd3qFtWIdhXkaE8TXwDIC6zeDON2v//+u7p3767Lly+nesxkMikhISGjqwQAIHu5eixlyJIkI0EFbp3R9F4NNP6Pw/r8r6P6btMp7QmN0KSuNeTv4WKbWgEA2VKGv4IbOHCgOnXqpPPnz8tisaS4EbIAALmCV0nJ9J8/kSY7yauE7MwmDW1RVt/2rCV3Z3vtPBOudp+v04ajqb+ABADkXRkOWhcvXtTQoUPl6+ubGfUAAGB7HoFSyPjEcCUl/hsyLnH6Px4u56vFLzZUBX93XbkZq67fbNaXq46KU58BANJ9nKPVu3dvBQcH69lnn82smrIVztECgDwsIlS6elzyKpEiZN2OFvAAkHdkajOMqKgoderUSYUKFVLlypXl4JDyD8mgQYMyXnE2RtACANyLYRj6ccsZvb1gn2ITLCpW0FWTutZUeX/+bgBAbpKpQevrr79W37595eLiooIFC8pkMv27MpNJx48fv7+qsymCFgAgvXafDVe/WdtpAQ8AuZTVL1h8uzfffFPvvPOOIiIidPLkSZ04cSL5lpkha/To0apfv75cXV3l6emZrmV69uwpk8mU4la3bt1MqxEAkLdVKeypRS82UMPS3oqOs+ilObs0Yv5excZb7r0wACBXyXDQio2N1VNPPSWzOWuvGRIbG6tOnTqpX79+GVquVatWOn/+fPJt6dKlmVQhAACSVz5HTe9VW4MeLiVJ+m7TKT05ZaPOR9yycWUAgKyU4bTUo0cPzZkzJzNquatRo0bppZdeUuXKlTO0nJOTk/z8/JJvXl5emVQhAACJaAEPAMjwBYsTEhL00UcfadmyZapSpUqqZhiffvqp1YqzhlWrVsnHx0eenp5q3LixRo8eLR8fnzvOHxMTo5iYmOT7kZGRWVEmACAXSmoB33fWNu0/H6mu32zWKy3Lql/jkinOcQYA5D4ZDlp79uxR9erVJUl79+5N8Vh2+6PRunVrderUSUWLFtWJEyc0YsQIPfzww9q2bZucnJzSXGbMmDEaNWpUFlcKAMitihR01a/96+vN+Xs1d9tZffT7Ie04HU4LeADI5TLcddCaRo4cec9Qs2XLFtWqVSv5/vTp0zVkyBCFh4dneHvnz59X0aJF9eOPP6pDhw5pzpPWiFZQUBBdBwEAD4QW8ACQ82Wk62CGR7SsaeDAgercufNd5ylWrJjVtufv76+iRYvqyJEjd5zHycnpjqNdAADcL5PJpKdrF1HFAHf1m7VdJ69Eqf2X62kBDwC5VLqaYXTo0CFD5yo988wzunjx4j3n8/b2Vrly5e56c3Z2Tvd27+XKlSs6c+aM/P39rbZOAAAyghbwAJA3pCtoLViwQJcuXVJkZOQ9bxEREVq0aJFu3Lhh1UJPnz6tnTt36vTp00pISNDOnTu1c+fOFNspV66c5s2bJ0m6ceOGXnnlFW3cuFEnT57UqlWrFBISIm9vb7Vv396qtQEAkBG0gAeA3C9dhw4ahqEyZcpkdi139dZbb2nGjBnJ95MacqxcuVJNmjSRJB06dEgRERGSJDs7O+3Zs0czZ85UeHi4/P391bRpU82ZM0f58+fP8voBALhdUgv4akU8NeTHnckt4Kc86qda7tckr5KSR6CtywQA3Kd0NcNYvXp1hldct27dXHGuU0ZOeAMA4H6cvhKlvrO2qdLFBRpj/7XsTIYMk1mmkPFSje62Lg8A8I+MZAObdh3MCQhaAICsEH3ltBy/qCqz/j1XyzDZyTRkDyNbAJBNZCQbpOscLQAAkLmcI0+mCFmSZDISdPbY3jssAQDIzghaAABkB14lJVPKP8vxhlnd51/Wb3vO26goAMD9ImgBAJAdeARKIeMlk52kxMMGv/EcrOOxnur3/XaN+e2A4hNoAQ8AOQXnaN0D52gBALJURKh09bjkVULxbv76aNkhTV1zXJIUXKqgPu9cXQXdcn6zKQDIiThHCwCAnMojUCreUPIIlL2dWf9rU14Tu9SQq6Od1h+9opAv1mn32XBbVwkAuIcMB60LFy6oW7duCggIkL29vezs7FLcAACAdbWt4q/5A4JVwjufzkVEq+PkjfppyxlblwUAuIt0XbD4dj179tTp06c1YsQI+fv7y2QyZUZdAADgNmV882v+wGC9/NMu7dm/X/Pmzdbx4zX10hNN5GTPF50AkN1kOGitW7dOa9euVbVq1TKhHAAAcCfuzg6aUnGfdHyIzLIoYb9Jk88OUsc+/5Ovu7OtywMA3CbDhw4GBQWJ/hkAANhARKjMi4ckX2/LzmTohcgv1PvzBdp68qqNiwMA3C7DQWvcuHF6/fXXdfLkyUwoBwAA3NHVY5KRssW7vcmi/FGn1XnqJn238SRfhgJANpHhQwefeuopRUVFqWTJknJ1dZWDg0OKx69e5Rs1AAAyRdJFjW8LW4bJTqXKVtGmA/EasWCfdp2N0HuPV5KzA+dtAYAtZThoffbZZzTAAADAFpIuarxoiGQkSCY7mULG6d3qLVRk7XF98NtBzd12VocvXNekrjUV6Oli64oBIM/igsX3wAWLAQDZzm0XNZZHYPLk9Ucva+AP23UtKk5e+Rw1oUt11S/pbcNCASB3ydQLFj/zzDP66quvdPjw4fsuEAAAPIDbLmp8u+BS3lr0YgNVDHDX1Zux6vbN3/p67XHO2wIAG8hw0HJzc9Mnn3yicuXKKSAgQE8//bQmT56sgwcPZkZ9AAAgAwoXcNUv/eqrQ41AJVgMvbfkgAb/uFO3YhNsXRoA5Cn3fehgWFiYVq1apVWrVmn16tU6fPiwfHx8dP78eWvXaFMcOggAyIkMw9CMDSf13pIDircYKueXX1O71VKRgq62Lg0AcqxMPXQwSf78+VWgQAEVKFBAnp6esre3l5+f3/2uDgAAWJHJZFLP4OL6oU9debs56mDYdYVMWKfVhy/ZujQAyBMyHLSGDRumunXrytvbW2+++aZiY2M1fPhwXbhwQTt27MiMGgEAwH2qXdxLi19sqGpBnoq4Faee0/7WxJVHOW8LADJZhg8dNJvNKlSokF566SU99thjKl++fGbVli1w6CAAIDeIiU/QyIX7NPvvM5KkVhX9NPbJqnJzyvCVXgAgz8pINshw0Nq1a5dWr16tVatWae3atbKzs1Pjxo3VpEkTNWnSJNcFL4IWACA3mf33ab29YJ9iEywq5eOmKd1qqmQhN1uXBQA5QqYGrf/atWuXxo0bp1mzZslisSghIXd1NSJoAQBym+2nr6nfrG26EBmj/E72+vSpampewdfWZQFAtpeRbHBfxwvs2LEjuePg2rVrFRkZqWrVqqlp06b3VTAAAMg6NYoU0KIXG2jA99u15eQ19Zm5VYMeKa0hj5SW2WyydXkAkCtkeESrQIECunHjhqpWrZp8uGCjRo1y7WgPI1oAgNwqNt6i0Uv2a8bGU5KkR8r56NOnqsnDxcHGlQFA9pSphw4uXrw4Vwer/yJoAQByu1+2ndX/5u1RTLxFxb3zaUq3mirjm9/WZQFAtpNl52idPXtWJpNJgYGB97uKbI+gBQDIC/acjVDfWdsUGn5Lro52GtupqtpU9rd1WQCQrWTqBYstFoveeecdeXh4qGjRoipSpIg8PT317rvvymKx3HfRAADAdioX9tDCgcGqX7KgomIT9M73K/TDj7OUEH7W1qUBQI6U4WYYb7zxhr755ht98MEHCg4OlmEYWr9+vUaOHKno6GiNHj06M+oEAACZrKCbk2b2rq1lsz5Wq+NjZHfQkOWgWVGtPpVr3V62Lg8AcpQMHzoYEBCgyZMn69FHH00xfcGCBerfv79CQ0OtWqCtceggACBPiQiVxlWSjH+PUkmQWSe7blLJUmVtWBgA2F6mHjp49epVlStXLtX0cuXK6erVqxldHQAAyE6uHksRsiTJTha9M3Oxfttz3kZFAUDOk+GgVbVqVU2YMCHV9AkTJqhq1apWKQoAANiIV0nJlPLjQYLMOhRbSP2+366xyw4pwXLffbQAIM/I8DlaH330kdq2bas//vhD9erVk8lk0oYNG3TmzBktXbo0M2oEAABZxSNQChkvLRoiGQmSyU5q+5nahdXS1+tOaMLKo9p/PlKfcb0tALir+2rvfu7cOU2cOFEHDx6UYRiqUKGC+vfvr4CAgMyo0aY4RwsAkCdFhEpXj0teJRLDl6R5O87q9V/+vd7WV91rqpQP19sCkHdk2XW08gKCFgAA/9obGqHnZ27VuYhouTnZ69Mnq6pFRT9blwUAWcLqQWv37t3p3niVKlXSPW9OQNACACClyzdiNOD77dp8IrEJ1uBHSmvwI6VlNptsXBkAZC6rBy2z2SyTySTDMGQy/fsmmrTo7dMSEhLut+5siaAFAEBqcQkWjV5yQNM3nJQkNSvvq8+eqqr8zpy3BSD3snp79xMnTuj48eM6ceKEfvnlFxUvXlxffvmldu7cqZ07d+rLL79UyZIl9csvv1hlBwAAQPbmYGfWyEcramynqnK0N+uPAxf0+MT1Onbphq1LA4BsIcPnaNWuXVsjR45UmzZtUkxfunSpRowYoW3btlm1QFtjRAsAgLvbdSZcfWdt0/mIaOV3ste4ztX0SHlfW5cFAFaXqRcs3rNnj4oXL55qevHixbV///6Mrg4AAORwVYM8tXBgAz1UrICux8TruZlb9cWfR2ThelsA8rAMB63y5cvrvffeU3R0dPK0mJgYvffeeypfvrxViwMAADlDofxO+v65uupWt6gMQ/pkxWH1/367bsTE27o0ALCJDB86+PfffyskJEQWi0VVq1aVJO3atUsmk0mLFy9W7dq1M6VQW+HQQQAAMmbOltMaMX+fYhMsKu3jpq+611Ix73y2LgsAHlimX0crKipKs2bNSnHB4i5duihfvtz3JkrQAgAg47afvqZ+s7bpQmSM3J3t9fnT1dWkrI+tywKAB8IFi62IoAUAwP25GBmtft9v17ZT12QySa+2LKt+jUumuCwMAOQkmdoMIyAgQF26dNHUqVN1+PDh+y4SAADkbj7uzprdp6661Ckiw5A++v2QBv6wQ1GxnLcFIPfLcND65JNP5O7urk8//VTlypWTv7+/OnfurMmTJ+vAgQOZUSMAAMihHO3Ner99ZY1uX0lBdld1dd8fen7CQp25GmXr0gAgUz3QoYMXLlzQypUrtXjxYs2ZM0cWi0UJCQnWrM/mOHQQAAAr2D5TxqLBMhkWJRgmvWd+Qc2feVX1S3nbujIASLeMZAP7+9nAjRs3tG7dOq1evVqrVq3Sjh07VLlyZTVu3Pi+CgYAALlYRKj0T8iSJDuToTcsU9X420p6rm0D9axfjPO2AOQ6GQ5aderU0e7du1WpUiU1adJE//vf/9SwYUN5enpmQnkAACDHu3pM+idkJbE3WRSkMI1atF/7zkXqvccrydnBzkYFAoD1ZfgcrSNHjsjV1VUlSpRQiRIlVKpUKUIWAAC4M6+SkinlRw7DZKfHHm4os0mau+2snpq6SRcio21UIABYX4aD1tWrV7Vy5UoFBwfrjz/+UOPGjeXn56ennnpKkydPzowaAQBATuYRKIWMl0z/jFiZ7GQKGaenm9XVzN515OHioF1nwtXui3XaduqabWsFACt54Otobdu2TRMmTNCsWbNohgEAAO4sIlS6elzyKpEYvv5x6spNPT9zmw5duC5HO7Pee7ySnnwoyIaFAkDaMvWCxTt27NCqVau0atUqrV27VtevX1fVqlXVpEkTNW3aVG3btn2g4rMbghYAAJnvZky8Xv5pl37fFyZJ6lGvqN5sV0EOdhk++AYAMk2mBi17e3tVr15djRs3VpMmTdSoUaNcHUAIWgAAZA2LxdCElUf16YrDkqS6Jbw0sUsNFXRzsnFlAJAoU4NWZGRkngocBC0AALLWiv0X9NKcnboRE69ATxdN7V5TFQM8Us4UEZrYzdCrZIrDEAEgM2UkG2R4PJ6wAQAAMlPzCr6a17++ihV0VWj4LT0xaYMW7Tr37wzbZ0rjKkkzQhL/3T7TdsUCwB1kOGglJCRo7Nixql27tvz8/OTl5ZXiBgAA8KBK++bXggEN1LhMIUXHWfTi7B368PeDSgg/Ky0a/O91uQyLtGhI4ggXAGQjGQ5ao0aN0qeffqonn3xSERERGjp0qDp06CCz2ayRI0dmQokAACAv8nB10Lc9H9ILjUtIkiatOqaPZy9NdfFjGQmJ3QwBIBvJcND6/vvv9dVXX+mVV16Rvb29nn76aX399dd66623tGnTpsyoEQAA5FF2ZpOGty6v8Z2rycnerPmnnJUgU8qZTHaJLeMBIBvJcNAKCwtT5cqVJUlubm6KiIiQJLVr105LliyxbnUAAACSHqsWqF/61ZfZI1DD455TgvHPRxiTnRQyjoYYALKdDAetwoUL6/z585KkUqVKafny5ZKkLVu2yMmJ9qsAACBzVAr00MIXG+hk0BMKjhmvzrFvanqdRTKqd7N1aQCQSoaDVvv27fXnn39KkgYPHqwRI0aodOnS6t69u3r37m31AgEAAJJ4uznp+z511KxudW2yVNDIVeF6cfYO3YpNsHVpAJBChq+j9V+bN2/W+vXrVapUKT366KPWqivb4DpaAABkT99vPqW3F+xTvMVQxQB3Te1eS4GeLrYuC0AulmkXLI6Li9Pzzz+vESNGqESJvHHSKUELAIDs6+8TV9Vv1jZduRmrgvkcNalrTdUuzuVmAGSOTLtgsYODg+bNm/dAxQEAAFhL7eJeWjAwWBX83XXlZqy6fLVJ328+ZeuyAOD+ztGaP39+JpQCAACQcYULuGpuv3pqW8Vf8RZDb8zbqzfn71FsvOXeCwNAJrHP6AKlSpXSu+++qw0bNqhmzZrKly9fiscHDRpkteKSnDx5Uu+++67++usvhYWFKSAgQF27dtUbb7whR0fHOy5nGIZGjRqlqVOn6tq1a6pTp44mTpyoihUrWr1GAABgO66O9prwdHVV8HfX2OWHNGvTaR2+cEOTnqmhgm50RQaQ9TLcDKN48eJ3XpnJpOPHrX9l9t9//11z5szR008/rVKlSmnv3r3q06ePunXrprFjx95xuQ8//FCjR4/W9OnTVaZMGb333ntas2aNDh06pPz586dr25yjBQBAzvLH/gsaMmenbsTEK9DTRVO711TFAA9blwUgF8i0ZhjZyccff6xJkybdMdgZhqGAgAANGTJEw4YNkyTFxMTI19dXH374oV544YU0l4uJiVFMTEzy/cjISAUFBRG0AADIQY5cuK4+M7fq5JUouTjYaWynqmpbxd/WZQHI4TKtGUZ2EhERIS+vO3cVOnHihMLCwtSiRYvkaU5OTmrcuLE2bNhwx+XGjBkjDw+P5FtQUJBV6wYAAJmvtG9+LRjQQA1Le+tWXIIG/LBdY5cdksWSI79fBpADpescraFDh6Z7hZ9++ul9F5Nex44d0xdffKFPPvnkjvOEhYVJknx9fVNM9/X11alTd+5GNHz48BT7mzSiBQAAchYPVwdN6/mQPvjtoL5ed0ITVh7VwbBIffZUNeV3drB1eQByuXQFrR07dqS4v23bNiUkJKhs2bKSpMOHD8vOzk41a9bM0MZHjhypUaNG3XWeLVu2qFatWsn3z507p1atWqlTp0567rnn7rkNk8mU4r5hGKmm3c7JyUlOTpw0CwBAbmBvZ9ab7SqovL+7hs/boz8OXFSHLzfoq+61VMw7371XAAD3KV1Ba+XKlcn///TTT5U/f37NmDFDBQoUkCRdu3ZNvXr1UsOGDTO08YEDB6pz5853nadYsWLJ/z937pyaNm2qevXqaerUqXddzs/PT1LiyJa//7/HZF+8eDHVKBcAAMjdnqhZWCV93PT8zK06cvGGHp2wThOfqaGGpQvZujQAuVSGm2EEBgZq+fLlqVqk7927Vy1atNC5c+esWmCS0NBQNW3aVDVr1tSsWbNkZ2d31/mTmmG89NJLeu211yRJsbGx8vHxuWszjP+i6yAAALnHhchovfDdNoWdOaYS5jCFPNxInR+pc9ejXQAgSaY2w4iMjNSFCxdSTb948aKuX7+e0dWly7lz59SkSRMFBQVp7NixunTpksLCwpLPw0pSrlw5zZs3T1LiIYNDhgzR+++/r3nz5mnv3r3q2bOnXF1d1aVLl0ypEwAAZG++7s76ufYRbXAerB8cR+vJta3081fvKyY+wdalAchlMnzB4vbt26tXr1765JNPVLduXUnSpk2b9Oqrr6pDhw5WL1CSli9frqNHj+ro0aMqXLhwisduH5A7dOiQIiIiku+/9tprunXrlvr37598weLly5en+xpaAAAgl4kIlcPSlyRZJEl2JkMdQseq36RKGt2zpXzyO9u2PgC5RoYPHYyKitIrr7yib7/9VnFxcZIke3t7Pfvss/r444+VL1/uOrGUQwcBAMhFTqyRZoSkmtw59k2ddKuhqd1rqkphz6yvC0COkCUXLL5586aOHTsmwzBUqlSpXBewkhC0AADIRSJCpXGVJMOSPMkw2ekp16n6+4qLnOzN+qhjFT1WLdCGRQLIrrLkgsX58uVTlSpVVLVq1VwbsgAAQC7jESiFjJdM/zTVMtnJFDJOX7/4mJqWLaSYeIsG/7hTH/5+UAlc3BjAA7jvEa28ghEtAAByoYhQ6epxyatEYviSlGAx9PGyQ5q8+pgk6ZFyPhrXmYsbA/hXloxoAQAA5FgegVLxhskhS5LszCa93rqcxj1VTU72Zv158KLaf7lBJy/ftGGhAHIqghYAAMBtHq8eqJ9eqCdfdycdvXhDj01cr3VHLtu6LAA5DEELAADgP6oGeWrRwAaqXsRTEbfi1P3bzfp23QlxxgWA9CJoAQAApMHH3Vmz+9RVx5qFZTGkdxbv17BfdnNxYwDpQtACAAC4A2cHO33csYrebFteZpP009az6vLVZl28Hm3r0gBkcwQtAACAuzCZTHquYQlN61Vb7s722nbqmh6bsF57QyNsXRqAbIygBQAAkA6NyxTS/AHBKlEon85HRKvj5A1asvu8rcsCkE0RtAAAANKphGOEFra16PESUnScRQN+2K7PVhyWhYsbA/gPghYAAEB6bJ8pjasktx/b67PzXTWh7B5J0vg/j2jAD9sVFRtv4wIBZCcELQAAgHuJCJUWDZYMiyTJZFjU7vRHmtDWR452Zv22N0xPTNqos9eibFwogOyCoAUAAHAvV48lh6xkRoLaFY7W7OfryNvNUQfOR+qxCeu19eRV29QIIFshaAEAANyLV0nJ9J+PTSY7yauEahb10oKBDVTB311Xbsbq6a826actZ2xTJ4Bsg6AFAABwLx6BUsj4xHAlJf4bMi5xuqRATxfN7VdPbSr7KS7B0Gu/7NY7i/YrPsFy53UCyNVMhmHQJucuIiMj5eHhoYiICLm7u9u6HAAAYEsRodLV45JXieSQdTuLxdDnfx3RuD+OSJIalvbWhC415OHikNWVAsgEGckGjGgBAACkl0egVLxhmiFLksxmk4Y0K6NJz9SQi4Od1h65rPYT1+vYpRtZXCgAWyNoAQAAWFnryv6a26+eAj1ddPzyTT0+cb1WH75k67IAZCGCFgAAQCaoGOChBQODVatoAV2PjlevaX/r67XHxVkbQN5A0AIAAMgk3m5O+r5PHT1Zq7AshvTekgN6be5uxcQn2Lo0AJmMoAUAAJCJnOzt9OETVTSiXQWZTdLP287qma8269L1GFuXBiATEbQAAAAymclk0rMNimtar9rK72yvraeu6bEJ67TvXIStSwOQSQhaAAAAWaRxmUKaPyBYJbzz6VxEtDpO2qjf9py3dVkAMgFBCwAAIAuVLOSmef2D1bC0t27FJajf99s17o/DslhokgHkJgQtAACALObh6qBpPR9S7+DikqRxfxzRwNnbFRUbb+PKAFgLQQsAAMAG7O3Meiukgj56oooc7ExauidMHSdtVGj4LVuXBsAKCFoAAAA29ORDQfqhT10VzOeo/ecj9diEddp26qqtywLwgAhaAAAANvZQMS8tGBis8v7uunwjVk9P3ayft56xdVkAHgBBCwAAIBsoXMBVc/vWU6uKfopNsOjVubv17uL9ik+w2Lo0APeBoAUAAJBN5HOy15fP1NDgR0pLkr5Zd0K9Z2xVxK04G1cGIKMIWgAAANmI2WzSS83LaGKXGnJ2MGvN4Utq/+V6Hb90w9alAcgAghYAAEA21LaKv+b2rS9/D2cdv3RTj09crzWHL9m6LADpRNACAADIpioFemjhwAaqUcRTkdHx6jntb3277oQM45+LG0eESifWJP4LIFsxGcm/qUhLZGSkPDw8FBERIXd3d1uXAwAA8qCY+AS9MW+v5m47K0l6qlaQRhfdIfulQyTDIpnMUsh4qUZ32xYK5HIZyQaMaAEAAGRzTvZ2+rhjFb3ZtrzMJmn11p0yLxmcGLKkxH8XDWFkC8hGCFoAAAA5gMlk0nMNS+ibng+pgtMlmfWfg5KMBOnqcdsUByAVghYAAEAO0rSsj0b0CFGCTCkfMNlJXiVsUxSAVAhaAAAAOUzxEmUU0+ozJfzzUS7eMOvPUsNluAfYuDIASQhaAAAAOZBr3V4yBu/W9NIT1CBmvJ7dU0GDf9yp6LgEW5cGQAQtAACAHMu+QJB6PtNNL7ZvLHuzSQt3ndNTUzbqQmS0rUsD8jyCFgAAQA73TJ2imvlsbXm6OmjX2Qg9NmG99pyNsHVZQJ5G0AIAAMgF6pf01oIBwSrl46awyGh1mrJBi3efs3VZQJ5F0AIAAMglihbMp1/711eTsoUUHWfRwB926LMVh2WxGPdeGIBVEbQAAAByEXdnB33T4yH1aVhckjT+zyN6cfYO3YqlSQaQlQhaAAAAuYyd2aQ32lbQR09UkYOdSUv2nFenKRt0PuKWrUsD8gyCFgAAQC715ENB+qFPXXnlc9Te0Eg9OmG9dpy+ZuuygDyBoAUAAJCLPVTMSwsGBKusb35duh6jp6Zu0oKdobYuC8j1CFoAAAC5XJCXq37pX1/NyvsqNt6iwT/u1MfLDtIkA8hEBC0AAIA8wM3JXlO71VS/JiUlSRNXHlPfWdt0MybexpUBuRNBCwAAII8wm00a1qqcPn2yqhztzFq+/4KemLRBZ69F2bo0INchaAEAAOQxHWoU1uzn68rbzUkHw67r8Ynrte3UVVuXBeQqBC0AAIA8qGbRAlowMFgV/N11+Uasnp66WXO3nbV1WUCuQdACAADIowI9XTS3Xz21quin2ASLXvl5l8YsPaAEmmQAD4ygBQAAkIe5Otrry2dqaNDDpSRJU9Yc1/Mzt+p6dJyNKwNyNoIWAABAHmc2mzS0RVl9/nR1Odmb9efBi3pi0gadvkKTDOB+EbQAAAAgSXq0aoB+eqGefPI76fCFG3ps4jptOn7F1mUBORJBCwAAAMmqBnlq4cAGqlLYQ9ei4tT168368e/Tti4LyHEIWgAAAEjBz8NZc56vp3ZV/BVvMfT6r3s0atE+xSdYbF0akGMQtAAAAJCKi6Odvni6uoY2LyNJmrb+pHrP2KpImmQA6ULQAgAAQJpMJpMGPVJaXz5TQ84OZq05fEntJ67Xics3bV0akO0RtAAAAHBXbSr7a27f+vL3cNaxSzf1+MT12nD0sq3LArI1ghYAAADuqVKghxYMDFa1IE9F3IpT92//1vebT9m6LCDbImgBAAAgXXzyO+vH5+vqsWoBircYemPeXo1cSJMMIC0ELQAAAKSbs4Odxj1VTa+2LCtJmr7hpHpN36KIWzTJAG6XI4LWyZMn9eyzz6p48eJycXFRyZIl9fbbbys2Nvauy/Xs2VMmkynFrW7dullUNQAAQO5kMpk0oGkpTe5aQy4Odlp75LI6fLleJ2mSASSzt3UB6XHw4EFZLBZNmTJFpUqV0t69e9WnTx/dvHlTY8eOveuyrVq10rRp05LvOzo6Zna5AAAAeUKrSv4qXMBVfWZuTWyS8eV6fflMDdUv6W3r0gCbMxmGYdi6iPvx8ccfa9KkSTp+/Pgd5+nZs6fCw8M1f/78dK83JiZGMTExyfcjIyMVFBSkiIgIubu7P0jJAAAAudLFyGg9/9027TwTLnuzSe88Vkld6hSxdVmA1UVGRsrDwyNd2SBHHDqYloiICHl5ed1zvlWrVsnHx0dlypRRnz59dPHixbvOP2bMGHl4eCTfgoKCrFUyAABAruTjntgk49GqiU0y/jdvj0YtokkG8rYcOaJ17Ngx1ahRQ5988omee+65O843Z84cubm5qWjRojpx4oRGjBih+Ph4bdu2TU5OTmkuw4gWAADA/TEMQxNXHtXY5YclSY3KFNKELtXl7uxg48oA68jIiJZNg9bIkSM1atSou86zZcsW1apVK/n+uXPn1LhxYzVu3Fhff/11hrZ3/vx5FS1aVD/++KM6dOiQrmUy8mQCAADkehGh0tVjkldJySMwzVl+23NeQ3/apVtxCSrl46ZvetRS0YL5srhQwPoykg1s2gxj4MCB6ty5813nKVasWPL/z507p6ZNm6pevXqaOnVqhrfn7++vokWL6siRIxleFgAAIM/bPlNaNFgyLJLJLIWMl2p0TzVb68r+CvJy1XMzturoxRt6bOJ6TXqmpuqVLGiDogHbsGnQ8vb2lrd3+rrShIaGqmnTpqpZs6amTZsmsznjp5dduXJFZ86ckb+/f4aXBQAAyNMiQv8NWVLiv4uGSCUfSXNkq1KghxYODFaf77Zp15lwdftms959vJKerk2TDOQNOaIZxrlz59SkSRMFBQVp7NixunTpksLCwhQWFpZivnLlymnevHmSpBs3buiVV17Rxo0bdfLkSa1atUohISHy9vZW+/btbbEbAAAAOdfVY/+GrCRGgnT1zh2gfdydNef5ugr5p0nG8F/36J1F+2mSgTwhR1xHa/ny5Tp69KiOHj2qwoULp3js9lPMDh06pIiICEmSnZ2d9uzZo5kzZyo8PFz+/v5q2rSp5syZo/z582dp/QAAADmeV8nEwwVvD1smO8mrxF0Xc3aw0+edq6m0j5s+XXFY364/oWOXbugLmmQgl8uRXQezEs0wAAAA/rF9ZuLhgkZCYsgKGZfmOVp3snTPeQ39aaei4yw0yUCOlGO6DuYEBC0AAIDbRIQmHi7oVeKOXQfvZs/ZCPWZuVVhkdHydHXQ5K41VbcETTKQM+SJCxYDAADABjwCpeIN7ytkSVLlwh5aMDBYVQt7KDwqTl2/3qwf/z5t5SIB2yNoAQAAIEv5ujtrzgv11K6Kv+Ithl7/dY/eXbxfCRYOtELuQdACAABAlnN2sNMXT1fXS83KSJK+WXdCz83YouvRcXdeKCJUOrEm8V8gmyNoAQAAwCZMJpMGNyutiV1qyNnBrJWHLqnDlxt0+kpU6pm3z5TGVZJmhCT+u31m1hcMZABBCwAAADbVtoq/fn6hvnzdnXTk4g09NnGdNh+/8u8Md7pYMiNbyMYIWgAAALC5yoU9tHBgA1Up7KFrUXHq+s1mzdnyT5OM+7hYMmBrBC0AAABkC77uzprzfD21reKvuARDw37Zo/cW71dCgRKJF0u+XToulgzYEkELAAAA2YaLo50mPF1dQ5qVliR9ve6Enpt3TtGtPk0MV9K/F0u+zxbzQFbggsX3wAWLAQAAbGPJ7vN6+eedio6zqIyvm6Z1CFSg5fx9XywZeFBcsBgAAAA5Xtsq/vrphXrydXfS4Qs31G7Gcf2tioQs5AgELQAAAGRbVQp7asGABqocmNgk45mvN+nnrWdsXRZwTwQtAAAAZGt+Hs766YV6als5sUnGq3N3a8zSA0qwcAYMsi+CFgAAALI9F0c7ffF0dQ16JLFJxpQ1x/XCd9t0MybexpUBaSNoAQAAIEcwm00a2ryMxneuJkd7s/44cEFPTNqg0PBbti4NSIWgBQAAgBzlsWqBmvN8XXm7Oelg2HU9NmG9tp++ZuuygBQIWgAAAMhxqhcpoAUDg1Xe312Xb8So89RNWrAz1NZlAckIWgAAAMiRAj1dNLdvPTUr76vYeIsG/7hTny4/JAtNMpANELQAAACQY+VzsteUbjX1QuMSkqTP/zqqF2fv0K3YBBtXhryOoAUAAIAczc5s0vDW5fVxxypysDNpyZ7zemrqRl2IjLZ1acjDCFoAAADIFTrVCtL3z9VVAVcH7T4boUcnrNOesxG2Lgt5FEELAAAAuUbt4l5aMKCBSvu46UJkjDpN2aDf9py3dVnIgwhaAAAAyFWKFHTVL/3rq3GZQoqOs6jf99s14a8jMgyaZCDrELQAAACQ67g7O+ibHrXUK7iYJGns8sN6ac5ORcfRJANZg6AFAACAXMnezqy3QypqdPtKsjebNH/nOXX5apMuXY+xdWnIAwhaAAAAyNWeqVNUM3vXlruzvbafDtfjE9frwPlIW5eFXI6gBQAAgFyvfilvzR8QrOLe+RQafksdJ23QH/sv2Los5GIELQAAAOQJJQq5aV7/+qpfsqBuxiaoz3db9dWa4zTJQKYgaAEAACDP8HR11IzetdWlThEZhjR66QEN+2W3YuMtti4NuQxBCwAAAHmKg51Zox+vpLdDKshskn7aelZdv9msqzdjbV0achGCFgAAAPIck8mkXsHF9U3Ph+TmZK+/T1zV4xPX6+jF67YuDbkEQQsAAAB5VtOyPvq1f30Febno9NUotZ+4QasPX7J1WcgFCFoAAADI08r45tf8/sF6qFgBXY+JV69pf2vGhpO2Lgs5HEELAAAAeV5BNyfNeq6OOtYsLIshvb1wn0bM36u4BJpk4P4QtAAAAABJTvZ2+rhjFQ1vXU4mk/TdplPqNW2LIqLibF0aciCCFgAAAPAPk8mkFxqX1JSuNeXqaKd1Ry+r/aT1OnH5ZuqZI0KlE2sS/wX+g6AFAAAA/EeLin6a27e+AjycdfzSTT0+cb02HLv87wzbZ0rjKkkzQhL/3T7TdsUiWyJoAQAAAGmoEOCu+QODVS3IUxG34tT9m781++/TiSNYiwZLxj/nbxkWadEQRraQAkELAAAAuAOf/M768fm6erRqgOIthob/ukczl/z1b8hKYiRIV4/bpkhkSwQtAAAA4C6cHew0vnM1DW1eRpL05W5Dlv9+jDbZSV4lbFAdsiuCFgAAAHAPJpNJgx4prYldaijcoZBej3tWCUkfpU12Usg4ySPQpjUie7G3dQEAAABATtG2ir+CvFz03AwHBV+voiquVzTgiRaqWrGirUtDNsOIFgAAAJABVQp7auHABvIOLK7lUWXU6YfTmrfjrK3LQjZD0AIAAMD/27v3sKrqRI3j72IDIreNeEEUPKjl/YKCGWildbTb0ShxrBzRNMdbJdNFTzOn0fOcsmymGZ0UzWZGzSxvBdaMafaMWEGa10wryiteUEJGwBsge58/GJkhb1zW3msD38/z7Gc/e+11eZf6/B5ffmstUE0t7X5aNSFOd3cNU0mZQ79c+ZV+u+E7ORxOq6PBQ1C0AAAAgBrw9/XWgpExmjygvSRp/qYDmvLOTl0oKbM4GTwBRQsAAACoIS8vQ9Pu6aTXhveUj83QR3tP6mdvfKFThRetjgaLUbQAAACAWhoWE6F3xt+q0ABffX28QEPnfa69xwusjgULUbQAAAAAE/SJClXa5H66uUWgThUWa/jCL7R+b47VsWARihYAAABgkjZN/fXe5Hjd3qG5LpSWaeLbOzV/0345nTwko6GhaAEAAAAmCvbz0V9Gx2pMfJQk6bcbsvTM6q9UfImHZDQkFC0AAADAZN42L80c2lX/90BX2bwMvb/zuH7+p606fbbY6mhwE4oWAAAA4CKj4qK05LE+CvLz1rbD/1BCSoa+P1VkdSy4AUULAAAAcKHbbm6u1MnxahPqr6P5FzQsJVPpWblWx4KLUbQAAAAAF7upRZDSpvTTLW1DVVR8SWOXbNPSzMNWx4ILUbQAAAAANwgN8NXb4/pqeEyEHE5pxgf79ELaXpWWOayOBhegaAEAAABu4uvtpVcTe+i/7+0kw5CWbTmisUu2qeBCqdXRYDKKFgAAAOBGhmFo4h3ttfDnMWrsY9NnP+TpoZQMHc47Z3U0mIiiBQAAAFjg7q4ttXpinMLtfjrw4zklpGRoy8HTVseCSShaAAAAgEW6tbZr7ZR+6hlh15nzpRr1561atf3o1VcuOC4d+rT8HR6PogUAAABYqEWwn1ZOiNP9PcJVWubUtDV79PK6b1XmcP5rpZ1vSXO6SUuHlL/vfMu6wKgSihYAAABgMT8fm15/uJeeuutmSdIbnx7UxLd36FzxpfIZrA+nSs5/Pp3Q6ZA+TGZmy8NRtAAAAAAP4OVl6OlBHTT34Wj5entp4zenlLjwC+Vlf/OvknWZs0zKP2hNUFQJRQsAAADwIA9Et9a7429Vs0BffZtTqNFr8+U0fvLfdsMmhbazJiCqhKIFAAAAeJiY/2iitCn91DEsSPvOBup/Lj0ux+WyZdikIXMke2tLM+L66kzRGjp0qNq0aSM/Pz+Fh4dr1KhROnHixHW3cTqdmjlzplq1aqXGjRtrwIAB2rdvn5sSAwAAADUX0cRf702O152dWmh56QDFX5irNd0Wypm8R+qdZHU83ECdKVoDBw7UqlWrlJWVpffee08HDhxQYmLidbd59dVX9fvf/17z5s3Ttm3b1LJlSw0aNEhFRUVuSg0AAADUXGAjb72ZFKvH+7fVSTXVs9uDNXXdj7pYWmZ1NNyA4XQ6nTdezfN88MEHSkhIUHFxsXx8fK743ul0qlWrVkpOTtb06dMlScXFxQoLC9Ps2bM1YcKEKh2nsLBQdrtdBQUFCg4ONvUcAAAAgKp698tsvZC2V5ccTvVuE6I3RsWqeVAjq2M1KNXpBnVmRuvf5efna/ny5YqPj79qyZKkQ4cO6eTJkxo8eHDFskaNGumOO+5QZmbmNfddXFyswsLCSi8AAADAao/c0kZvjb1FwX7e2pl9RgnzM5R1kiu1PFWdKlrTp09XQECAmjZtquzsbK1du/aa6548eVKSFBYWVml5WFhYxXdX8/LLL8tut1e8IiMjzQkPAAAA1FL8Tc2UNqWfopr66/iZCxq2IFObvsu1OhauwtKiNXPmTBmGcd3X9u3bK9Z/7rnntGvXLn388cey2WxKSkrSja58NAyj0men03nFsn/3/PPPq6CgoOJ19OjR2p0kAAAAYKJ2zQOVOrmfbm0XqrPFlzRu6Tb95fNDN/x/MdzL0nu08vLylJeXd911oqKi5Ofnd8XyY8eOKTIyUpmZmYqLi7vi+4MHD6p9+/bauXOnevXqVbH8gQceUEhIiJYuXVqljNyjBQAAAE9UcsmhF9L2auX28omBkX3baObQrvKx1amL1uqU6nQDbzdluqpmzZqpWbNmNdr2cj8sLi6+6vdt27ZVy5YttXHjxoqiVVJSos2bN2v27Nk1CwwAAAB4CF9vL70yrLtuahGoWR99q+Vbs3Xk9HnNH9lb9sZXf44B3KdO1N0vv/xS8+bN0+7du3XkyBFt2rRJjz76qNq3b19pNqtTp05KTU2VVH7JYHJysmbNmqXU1FTt3btXY8aMkb+/vx599FGrTgUAAAAwjWEYGn97Oy0aFSt/X5s+35+nh1IydDjvnNXRGrw6UbQaN26s999/X3fddZc6duyosWPHqlu3btq8ebMaNfrXIy2zsrJUUFBQ8XnatGlKTk7W5MmTFRsbq+PHj+vjjz9WUFCQFacBAAAAuMSgLmFaPTFO4XY/HfjxnBJSMrT14GmrYzVodfb3aLkL92gBAACgrsgtvKjxb23XV8cK5GMzNOvB7hoey1O0zVLvf48WAAAAgCu1CPbTil/E6f7u4Sotc+q5NXv0ykffyeFgbsXdKFoAAABAPdLY16bXH+mlp+68SZK0cPMBTVq+Q+dLLlmcrGGhaAEAAAD1jJeXoacHd9ScEdHytXlpw75TGr7wC50suGh1tAaDogUAAADUUwm9Wuud8X3VNMBX+04U6oH5n+vrYwU33hC1RtECAAAA6rHYqFClTemnDmGBOlVYrOFvZGr93hyrY9V7FC0AAACgnosM9dd7k+J1R4fmuljq0MS3d2r+pv3iAeSuQ9ECAAAAGoAgPx/9eXSsxsRHSZJ+uyFLz6z+SsWXyspXKDguHfq0/B215m11AAAAAADu4W3z0syhXdW+eYBmfviN3t95XEfzz2txz+8U+PEzktMhGV7SkLlS7ySr49ZpzGgBAAAADcyouCgtHtNHQY28dfTwfjXe8HR5yZLK3z9MZmarlihaAAAAQAN0e4fmen9yvPraz8imn9yr5SyT8g9aE6yeoGgBAAAADdTNYUGa+dhQOWRU/sKwSaHtrAlVT1C0AAAAgAasScsold0/R45/VoNLTi+tjZymS4HhFier2yhaAAAAQAPn02eMjF9+rbSeb6h/8VxN/b67Hn9ru4oullodrc6iaAEAAACQYY9QwoMPa8bIQfLz8VJ61o9KXPCFjuaftzpanUTRAgAAAFDh3u7hWjUhTi2CGinrVJEeTMnQjiP/sDpWnUPRAgAAAFBJj4gQrX2in7qEByvvbIkeeXOL1u7mce/VQdECAAAAcIVwe2Otnhin/+wcppJLDk1dsVtzPvleTqfzxhuDogUAAADg6gIaeeuNUTGacHv5o97nfPKDpq7YrYulZRYn83wULQAAAADXZPMy9Px9nTV7WHd5exn64KsTeuTNLfqxqNjqaB6NogUAAADghkb0aaO3xt0ie2Mf7co+o4T5Gco6WWR1LI9F0QIAAABQJfHtmyl1crzaNgvQ8TMXNGxBpjZ9l2t1LI9E0QIAAABQZe2aByp1crxubReqs8WXNG7pNi3OOMRDMn6CogUAAACgWkL8ffXW2L76WWyEHE7pfz/8Rr9Zu0+XyhxWR/MYFC0AAAAA1ebr7aXZw3roV/d1kmFIy7Yc0WNLtqngQqnV0TwCRQsAAABAjRiGoV/c3l4Lfx6jxj42ffZDnoYtyFT26fNWR7McRQsAAABArdzdtaVWT4xTy2A/7c89q4SUDG07nG91LEtRtAAAAADUWrfWdq19op+6t7Yr/1yJRr65Ve/vPGZ1LMtQtAAAAACYIizYT6smxOmeri1VUubQ06u+0u82ZMnhaHhPJKRoAQAAADBNY1+bUkb21uQB7SVJ8zbt15Pv7tKFkjKLk7kXRQsAAACAqby8DE27p5N+N7ynfGyG/vZ1jh5e9IVyCy9aHc1tKFoAAAAAXCIxJkJvj+urEH8ffXWsQAnzM/TNiUKrY7kFRQsAAACAy/Rt11Rpk/upXfMAnSi4qMSFmfrkm1NWx3I5ihYAAAAAl4pqFqDUSf3U76amOl9SpvHLtutPnx2U01l/H5JB0QIAAADgcnZ/Hy157BY92reNnE7pxb99q1+l7lVpmcPqaC5B0QIAAADgFj42L72U0E0v/FcXGYb07pfZGv2XL1VwvtTqaKajaAEAAABwG8MwNK5/W/0pKVYBvjZlHjitB1MydDjvnNXRTEXRAgAAAOB2d3UO05pJ8Wpl99PBvHNKSMnQloOnrY5lGooWAAAAAEt0Dg9W2hP91DMyRGfOl2rUn7dq1fajVscyBUULAAAAgGVaBPlp5S9u1f09wlVa5tS0NXv0ykffyeGo208kpGgBAAAAsJSfj02vP9xLT915kyRp4eYDmrR8h86XXJIKjkuHPi1/r0MMZ31+eL0JCgsLZbfbVVBQoODgYKvjAAAAAPVa2q7jmrZmj0rKHPpl0y166vw8GU6HZHhJQ+ZKvZMsy1adbsCMFgAAAACPkdCrtd4Z31ed/Yv0xNnXy0uWJDkd0ofJdWZmi6IFAAAAwKPERoVq6QNNZTN+cvGds0zKP2hNqGqiaAEAAADwOC2iushp/KSuGDYptJ01gaqJogUAAADA89hbyxgyV07DVv7ZsElD5kj21pbGqipvqwMAAAAAwFX1TpLR/q7yywVD29WZkiVRtAAAAAB4MnvrOlWwLuPSQQAAAAAwGUULAAAAAExG0QIAAAAAk1G0AAAAAMBkFC0AAAAAMBlFCwAAAABMRtECAAAAAJNRtAAAAADAZBQtAAAAADAZRQsAAAAATEbRAgAAAACTUbQAAAAAwGQULQAAAAAwGUULAAAAAExG0QIAAAAAk1G0AAAAAMBkFC0AAAAAMBlFCwAAAABMRtECAAAAAJN5Wx3A0zmdTklSYWGhxUkAAAAAWOlyJ7jcEa6HonUDRUVFkqTIyEiLkwAAAADwBEVFRbLb7dddx3BWpY41YA6HQydOnFBQUJAMw3Drsfv06aNt27a59Zju5MnnZ2U2dxzbFccwc5+13VdNti8sLFRkZKSOHj2q4ODgGh8bNefJY4IZPPn8GPOs3SdjXsPkyWOCGTz5/GqTzel0qqioSK1atZKX1/XvwmJG6wa8vLwUERFhybFtNlu9Hvw8+fyszOaOY7viGGbus7b7qs32wcHBHvvvsr7z5DHBDJ58fox51u6TMa9h8uQxwQyefH61zXajmazLeBiGB5syZYrVEVzKk8/PymzuOLYrjmHmPmu7L0/+t4Vrq+9/b558fox51u6TMa9hqu9/b558fu7KxqWDAKDyy2jsdrsKCgo89idwAGAWxjzA9ZjRAgBJjRo10owZM9SoUSOrowCAyzHmAa7HjBYAAAAAmIwZLQAAAAAwGUULAAAAAExG0QIAAAAAk1G0AAAAAMBkFC0AAAAAMBlFCwCq6ejRoxowYIC6dOmiHj16aPXq1VZHAgCXevDBB9WkSRMlJiZaHQWoM3i8OwBUU05Ojk6dOqXo6Gjl5uaqd+/eysrKUkBAgNXRAMAlNm3apLNnz2rp0qVas2aN1XGAOoEZLQCopvDwcEVHR0uSWrRoodDQUOXn51sbCgBcaODAgQoKCrI6BlCnULQA1DuffvqphgwZolatWskwDKWlpV2xTkpKitq2bSs/Pz/FxMTos88+q9Gxtm/fLofDocjIyFqmBoCaceeYB6DqKFoA6p1z586pZ8+emjdv3lW/X7lypZKTk/XrX/9au3bt0m233aZ7771X2dnZFevExMSoW7duV7xOnDhRsc7p06eVlJSkRYsWufycAOBa3DXmAage7tECUK8ZhqHU1FQlJCRULOvbt6969+6tBQsWVCzr3LmzEhIS9PLLL1dpv8XFxRo0aJDGjx+vUaNGmR0bAGrEVWOeJKWnp2vevHncowVUETNaABqUkpIS7dixQ4MHD660fPDgwcrMzKzSPpxOp8aMGaM777yTkgXAo5kx5gGoGYoWgAYlLy9PZWVlCgsLq7Q8LCxMJ0+erNI+MjIytHLlSqWlpSk6OlrR0dH6+uuvXREXAGrFjDFPku6++24NHz5c69atU0REhLZt22Z2VKDe8bY6AABYwTCMSp+dTucVy66lf//+cjgcrogFAC5RmzFPkjZs2GB2JKDeY0YLQIPSrFkz2Wy2K36Sm5ube8VPfAGgrmPMA6xD0QLQoPj6+iomJkYbN26stHzjxo2Kj4+3KBUAuAZjHmAdLh0EUO+cPXtW+/fvr/h86NAh7d69W6GhoWrTpo2efvppjRo1SrGxsYqLi9OiRYuUnZ2tiRMnWpgaAGqGMQ/wTDzeHUC9k56eroEDB16xfPTo0VqyZImk8l/e+eqrryonJ0fdunXTH/7wB91+++1uTgoAtceYB3gmihYAAAAAmIx7tAAAAADAZBQtAAAAADAZRQsAAAAATEbRAgAAAACTUbQAAAAAwGQULQAAAAAwGUULAAAAAExG0QIAAAAAk1G0AAAAAMBkFC0AQL2Tnp4uwzB05swZtx/bMAwZhqGQkJDrrjdz5kxFR0dX+nx52zlz5rg0IwDA9ShaAIA6bcCAAUpOTq60LD4+Xjk5ObLb7ZZkWrx4sb7//vtqbfPss88qJydHERERLkoFAHAnb6sDAABgNl9fX7Vs2dKy44eEhKhFixbV2iYwMFCBgYGy2WwuSgUAcCdmtAAAddaYMWO0efNmzZ07t+Kyu8OHD19x6eCSJUsUEhKiv/71r+rYsaP8/f2VmJioc+fOaenSpYqKilKTJk305JNPqqysrGL/JSUlmjZtmlq3bq2AgAD17dtX6enpNcr6yiuvKCwsTEFBQRo3bpwuXrxowp8AAMBTUbQAAHXW3LlzFRcXp/HjxysnJ0c5OTmKjIy86rrnz5/XH//4R61YsULr169Xenq6HnroIa1bt07r1q3TsmXLtGjRIq1Zs6Zim8cee0wZGRlasWKF9uzZo+HDh+uee+7RDz/8UK2cq1at0owZM/TSSy9p+/btCg8PV0pKSq3OHQDg2bh0EABQZ9ntdvn6+srf3/+GlwqWlpZqwYIFat++vSQpMTFRy5Yt06lTpxQYGKguXbpo4MCB2rRpk0aMGKEDBw7o3Xff1bFjx9SqVStJ5fdRrV+/XosXL9asWbOqnHPOnDkaO3asHn/8cUnSiy++qE8++YRZLQCox5jRAgA0CP7+/hUlS5LCwsIUFRWlwMDASstyc3MlSTt37pTT6VSHDh0q7p8KDAzU5s2bdeDAgWod+9tvv1VcXFylZT/9DACoX5jRAgA0CD4+PpU+G4Zx1WUOh0OS5HA4ZLPZtGPHjiseUPHv5QwAgKuhaAEA6jRfX99KD7AwS69evVRWVqbc3FzddttttdpX586dtWXLFiUlJVUs27JlS20jAgA8GEULAFCnRUVFaevWrTp8+LACAwMVGhpqyn47dOigkSNHKikpSa+99pp69eqlvLw8/f3vf1f37t113333VXlfU6dO1ejRoxUbG6v+/ftr+fLl2rdvn9q1a2dKVgCA5+EeLQBAnfbss8/KZrOpS5cuat68ubKzs03b9+LFi5WUlKRnnnlGHTt21NChQ7V169ZrPtnwWkaMGKHf/OY3mj59umJiYnTkyBFNmjTJtJwAAM9jOJ1Op9UhAACoLwzDUGpqqhISEmq0fVRUlJKTk5WcnGxqLgCAezGjBQCAyR555BFFRERUa5tZs2YpMDDQ1Bk5AIB1mNECAMBE+/fvlyTZbDa1bdu2ytvl5+crPz9fktS8eXPZ7XaX5AMAuAdFCwAAAABMxqWDAAAAAGAyihYAAAAAmIyiBQAAAAAmo2gBAAAAgMkoWgAAAABgMooWAAAAAJiMogUAAAAAJqNoAQAAAIDJ/h/Uhmb9UanjRwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm_0 = ml.head(x = r, y = 0, t = t1) # Compute heads at observation well location\n", + "plt.figure(figsize = (10, 7))\n", + "plt.semilogx(t1, hm_0[0], label = 'ttim results') # Plotting TTim model Results\n", + "plt.semilogx(t1, h1, '.', label = 'obs well 1') # Plotting Observed points\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.suptitle('Model Prediction vs Observations - Calibrated Model 1')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's check how it performs with the Pumping Well data:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm_0_2 = ml.head(x = 0, y = 0, t = t2) # Compute heads at observation well location\n", + "plt.figure(figsize = (8, 5))\n", + "plt.semilogx(t2, hm_0_2[0], label = 'ttim results') # Plotting TTim model Results\n", + "plt.semilogx(t2, h2, '.', label = 'well 3') # Plotting Observed points\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.suptitle('Model Prediction vs Observations - Calibrated Model 1, Results in the Pumping Well')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, although the model presented an initial good fit, when we challenged it with an outside sample, it performed poorly." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.2. Calibration using the Pumping Well data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We proceed to calibrate using only the data from the pumping well (Well 3).\n", + "The initial inputs can be checked in [***step 5.1***](#step_5_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...........................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 24\n", + " # data points = 14\n", + " # variables = 2\n", + " chi-square = 0.04383685\n", + " reduced chi-square = 0.00365307\n", + " Akaike info crit = -76.7287306\n", + " Bayesian info crit = -75.4506160\n", + "[[Variables]]\n", + " kaq0: 27.8987411 +/- 0.73011666 (2.62%) (init = 10)\n", + " Saq0: 1.7023e-04 +/- 5.8161e-05 (34.17%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.9971\n" + ] + } + ], + "source": [ + "#unknown parameters: kaq, Saq\n", + "ca_1 = ttim.Calibrate(ml)\n", + "ca_1.set_parameter(name='kaq0', initial=10)\n", + "ca_1.set_parameter(name='Saq0', initial=1e-4)\n", + "ca_1.series(name='well3', x=0, y=0, t=t2, h=h2, layer=0)\n", + "ca_1.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.05595715606295086\n" + ] + } + ], + "source": [ + "ca_1.parameters\n", + "print('rmse:', ca_1.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm_1 = ml.head(0, 0, t2)\n", + "plt.figure(figsize = (10, 7))\n", + "plt.semilogx(t2, hm_1[0], label = 'ttim results')\n", + "plt.semilogx(t2, h2, '.', label = 'well 3 observations')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.suptitle('Model Prediction vs Observations - Calibration 2')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.3. Model Calibration with both datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now will proceed to calibrate the model using both datasets at the same time. We begin by creating a new model so we can compare the different results later:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_1 = ttim.ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary='conf')\n", + "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", + "ml_1.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now create a new ```Calibrate``` object. The difference from the previous calibration objects is that now we add a second observation series to the object:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "............................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 25\n", + " # data points = 36\n", + " # variables = 2\n", + " chi-square = 2.65994093\n", + " reduced chi-square = 0.07823356\n", + " Akaike info crit = -89.7877408\n", + " Bayesian info crit = -86.6207029\n", + "[[Variables]]\n", + " kaq0: 38.0492316 +/- 0.52463395 (1.38%) (init = 10)\n", + " Saq0: 1.2468e-06 +/- 2.0176e-07 (16.18%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.7694\n" + ] + } + ], + "source": [ + "ca_2 = ttim.Calibrate(ml_1)\n", + "ca_2.set_parameter(name='kaq0', initial=10)\n", + "ca_2.set_parameter(name='Saq0', initial=1e-4, pmin=0)\n", + "ca_2.series(name='obs1', x=r, y=0, t=t1, h=h1, layer=0) # Adding observation Well 1\n", + "ca_2.series(name='well3', x=0, y=0, t=t2, h=h2, layer=0)# Adding Pumping Well (Well 3)\n", + "ca_2.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq038.0492325.246339e-011.378829-infinf10.0000[38.049231613692605]
Saq00.0000012.017624e-0716.1823820.0inf0.0001[1.2468025740730582e-06]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 38.049232 5.246339e-01 1.378829 -inf inf 10.0000 \n", + "Saq0 0.000001 2.017624e-07 16.182382 0.0 inf 0.0001 \n", + "\n", + " parray \n", + "kaq0 [38.049231613692605] \n", + "Saq0 [1.2468025740730582e-06] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.2718220890129378\n" + ] + } + ], + "source": [ + "display(ca_2.parameters)\n", + "print('rmse:', ca_2.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The parameters are quite different from the first two models. We can also see that the errors have increased significantly. Let's plot the model results with the observations and check why we have large errors:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_2 = ml.head(r, 0, t1)\n", + "hm2_2 = ml.head(0, 0, t2)\n", + "plt.figure(figsize = (10, 7))\n", + "plt.semilogx(t1, hm1_2[0], label = 'ttim model results - obs 1')\n", + "plt.semilogx(t1, h1, '.', label = 'obs1')\n", + "plt.semilogx(t2, hm2_2[0], label = 'ttim model results - well 3')\n", + "plt.semilogx(t2, h2, '.', label = 'well3')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.suptitle('Model Prediction vs Observations - Calibration 3')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As seen in the Figure above, TTim could not adjust both curves simultaneously and ended up fitting only Well 3.\n", + "\n", + "Fortunately, in TTim, we can improve this fit by simulating skin resistance and wellbore storage." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.4. Model Calibration with skin resistance and wellbore storage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this, we create a new model and add two extra parameters to the ```Well``` object:\n", + "\n", + "* The radius of the caisson ```rc```, which we use to simulate wellbore storage. In this case, we use a value in meters (float). The function of the radius of the caisson to account for wellbore storage is explained in the previous notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk);\n", + "* The skin resistance ```res```, a float value unit of time (in our case days). The effect of the skin resistance is explained in [Confined 1 - Oude Korendijk](confined1_oude_korendijk)." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_2 = ttim.ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary='conf')\n", + "w_2 = ttim.Well(ml_2, xw=0, yw=0, rw=rw, rc=0.2, res=0.2, tsandQ=[(0, Q)], layers=0)\n", + "ml_2.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we use the method ```.set_parameter_by_reference``` to calibrate the ```rc``` and ```res``` parameters in our well.\n", + "\n", + "```.set_parameter_by_reference``` takes the following arguments:\n", + "* ```name```: a string of the parameter name\n", + "* ```parameter```: numpy-array with the parameter to be optimized. It should be specified as a reference, for example, in our case: ```w1.rc[0:]``` referencing to the parameter ```rc``` in object ```w1```.\n", + "* ```initial```: float with the initial guess for the parameter value.\n", + "* ```pmin``` and ```pmax```: floats with the minimum and maximum values allowed. If not specified, these will be ```-np.inf``` and ```np.inf```." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..............................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 188\n", + " # data points = 36\n", + " # variables = 4\n", + " chi-square = 1.29522940\n", + " reduced chi-square = 0.04047592\n", + " Akaike info crit = -111.693920\n", + " Bayesian info crit = -105.359844\n", + "[[Variables]]\n", + " kaq0: 38.2995471 +/- 0.40273920 (1.05%) (init = 10)\n", + " Saq0: 8.9358e-07 +/- 1.1726e-07 (13.12%) (init = 0.0001)\n", + " res: 6.0492e-06 +/- 0.01070643 (176989.84%) (init = 0.2)\n", + " rc: 0.42247557 +/- 0.07071625 (16.74%) (init = 0.2)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(res, rc) = -0.9172\n", + " C(kaq0, Saq0) = -0.7472\n", + " C(Saq0, rc) = -0.1770\n", + " C(kaq0, res) = -0.1645\n", + " C(kaq0, rc) = +0.1393\n" + ] + } + ], + "source": [ + "ca_3 = ttim.Calibrate(ml_2)\n", + "ca_3.set_parameter(name = 'kaq0', initial = 10)\n", + "ca_3.set_parameter(name = 'Saq0', initial = 1e-4, pmin=0)\n", + "ca_3.set_parameter_by_reference(name='res', parameter=w_2.res, initial =0.2, pmin = 0) # Here we add pmin = 0 to avoid unrealistic values\n", + "ca_3.set_parameter_by_reference(name='rc', parameter=w_2.rc, initial = 0.2)\n", + "ca_3.series(name='obs1', x=r, y=0, t=t1, h=h1, layer=0)\n", + "ca_3.series(name='obs3', x=0, y=0, t=t2, h=h2, layer=0)\n", + "ca_3.fit(report=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq03.829955e+014.027392e-011.051551-infinf10.0000[38.29954705235744]
Saq08.935817e-071.172566e-0713.1220870.0inf0.0001[8.935816591115753e-07]
res6.049176e-061.070643e-02176989.8397590.0inf0.2000[6.04917587931908e-06]
rc4.224756e-017.071625e-0216.738542-infinf0.2000[0.42247557130811325]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 3.829955e+01 4.027392e-01 1.051551 -inf inf 10.0000 \n", + "Saq0 8.935817e-07 1.172566e-07 13.122087 0.0 inf 0.0001 \n", + "res 6.049176e-06 1.070643e-02 176989.839759 0.0 inf 0.2000 \n", + "rc 4.224756e-01 7.071625e-02 16.738542 -inf inf 0.2000 \n", + "\n", + " parray \n", + "kaq0 [38.29954705235744] \n", + "Saq0 [8.935816591115753e-07] \n", + "res [6.04917587931908e-06] \n", + "rc [0.42247557130811325] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.18968024268277686\n" + ] + } + ], + "source": [ + "display(ca_3.parameters)\n", + "print('rmse:', ca_3.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_3 = ml_2.head(r, 0, t1)\n", + "hm2_3 = ml_2.head(0, 0, t2)\n", + "plt.figure(figsize = (10, 7))\n", + "plt.semilogx(t1, hm1_3[0], label = 'ttim model results - obs 1')\n", + "plt.semilogx(t1, h1, '.', label = 'obs1')\n", + "plt.semilogx(t2, hm2_3[0], label = 'ttim model results - well 3')\n", + "plt.semilogx(t2, h2, '.', label = 'well3')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.suptitle('Model Prediction vs Observations - Calibration 4')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we see in the picture that we have significantly improved both models by adding the resistance and the wellbore storage. We can make a critique of our current model that the adjusted resistance value is too low and with a very high standard deviation ([adjusted parameters](#adjusted_pars_ca_3)). Let's now disregard the skin resistance and check our model performance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5.5. Model Calibration with wellbore storage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We start by creating a model and adding a well with no resistance:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_3 = ttim.ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary='conf')\n", + "w_3 = ttim.Well(ml_3, xw=0, yw=0, rw=rw, rc=0.2, res=0, tsandQ=[(0, Q)], layers=0)\n", + "ml_3.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we calibrate without changing the resistance parameter" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 47\n", + " # data points = 36\n", + " # variables = 3\n", + " chi-square = 1.29522429\n", + " reduced chi-square = 0.03924922\n", + " Akaike info crit = -113.694062\n", + " Bayesian info crit = -108.943506\n", + "[[Variables]]\n", + " kaq0: 38.2995706 +/- 0.39115142 (1.02%) (init = 10)\n", + " Saq0: 8.9400e-07 +/- 1.1516e-07 (12.88%) (init = 0.0001)\n", + " rc: 0.42216584 +/- 0.02778676 (6.58%) (init = 0.2)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.7468\n", + " C(Saq0, rc) = -0.2667\n" + ] + } + ], + "source": [ + "ca_4 = ttim.Calibrate(ml_3)\n", + "ca_4.set_parameter(name = 'kaq0', initial = 10)\n", + "ca_4.set_parameter(name = 'Saq0', initial = 1e-4, pmin=0)\n", + "ca_4.set_parameter_by_reference(name='rc', parameter=w_3.rc, initial = 0.2)\n", + "ca_4.series(name='obs1', x=r, y=0, t=t1, h=h1, layer=0)\n", + "ca_4.series(name='obs3', x=0, y=0, t=t2, h=h2, layer=0)\n", + "ca_4.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq03.829957e+013.911514e-011.021295-infinf10.0000[38.299570565924455]
Saq08.940022e-071.151644e-0712.8818880.0inf0.0001[8.940022488967969e-07]
rc4.221658e-012.778676e-026.581955-infinf0.2000[0.42216583686189674]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 3.829957e+01 3.911514e-01 1.021295 -inf inf 10.0000 \n", + "Saq0 8.940022e-07 1.151644e-07 12.881888 0.0 inf 0.0001 \n", + "rc 4.221658e-01 2.778676e-02 6.581955 -inf inf 0.2000 \n", + "\n", + " parray \n", + "kaq0 [38.299570565924455] \n", + "Saq0 [8.940022488967969e-07] \n", + "rc [0.42216583686189674] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.18967986812378132\n" + ] + } + ], + "source": [ + "display(ca_4.parameters)\n", + "print('rmse:', ca_4.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we can see that we got very similar results from the previous models. The standard deviations are also in a reasonable range. As pointed out by Yang (2020), without skin resistance, we have a lower AIC (-113 versus -111). Thus, the skin resistance does not add information to the model, and the current model is preferred." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Comparison of Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6.1. Error comparison and model selection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some of the fit statistics are stored in the ```fitresult``` attribute of the calibration object. This object is a lmfit ```MinimizerResult``` object. ```lmfit``` is the python library doing the calibration for TTim behind the scenes. We accessed below the AIC and BIC values of this object.\n", + "\n", + "Check the lmfit documentation (Newville et al. 2014) to learn more about this object and lmfit." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Fit statistics for the tested models
 RMSEAICBICCalibration scheme
Model 10.027816-153.614816-151.432731Obs 1
Model 20.055957-76.728731-75.450616Well 3
Model 30.271822-89.787741-86.620703Obs 1 + Well 3
Model 40.189680-111.693920-105.359844Obs 1 + Well 3, res + rc
Model 50.189680-113.694062-108.943506Obs 1 + Well 3, rc
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(columns = ['RMSE', 'AIC', 'BIC','Calibration scheme'], index = ['Model 1', 'Model 2', 'Model 3', 'Model 4', 'Model 5'])\n", + "\n", + "t['RMSE'] = [ca_0.rmse(), ca_1.rmse(), ca_2.rmse(), ca_3.rmse(), ca_4.rmse()]\n", + "\n", + "t['AIC'] = [ca_0.fitresult.aic, ca_1.fitresult.aic, ca_2.fitresult.aic, ca_3.fitresult.aic, ca_4.fitresult.aic]\n", + "\n", + "t['BIC'] = [ca_0.fitresult.bic, ca_1.fitresult.bic, ca_2.fitresult.bic, ca_3.fitresult.bic, ca_4.fitresult.bic]\n", + "\n", + "t['Calibration scheme'] = [\"Obs 1\", \"Well 3\",\"Obs 1 + Well 3\", \"Obs 1 + Well 3, res + rc\", \"Obs 1 + Well 3, rc\"]\n", + "t.style.set_caption(\"Fit statistics for the tested models\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first model had overall better statistics with lower RMSE and AIC, BIC. However, it does not fit with the data in the pumping well. Therefore if we were to update the statistics with the residuals from the pumping well, this result would be worse.\n", + "Comparing the models estimated with both drawdowns, the last model performed best. It has a larger RMSE than the one-well models, however less bias as it fits well both datasets. Another highlight is the lower AIC and BIC values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6.2. Comparison of TTim model performance with values simulated by AQTESOLV and MLU" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results simulated by different methods with two datasets simultaneously are presented below. Furthermore, Yang (2020) compared TTim results with the results obtained from the software AQTESOLV (Duffield, 2007) and MLU (Carlson & Randall, 2012). In both software, the model was calibrated with both the pumping well and observation well data." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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k [m/d]Ss [1/m]rcRMSE
MLU38.0940.000001-0.26
AQTESOLV37.8030.000001-0.27
ttim38.0492316136926051.2468025740730582e-06-0.27
ttim-rc38.2995710.0000010.4221660.19
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" + ], + "text/plain": [ + " k [m/d] Ss [1/m] rc RMSE\n", + "MLU 38.094 0.000001 - 0.26\n", + "AQTESOLV 37.803 0.000001 - 0.27\n", + "ttim 38.049231613692605 1.2468025740730582e-06 - 0.27\n", + "ttim-rc 38.299571 0.000001 0.422166 0.19" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'rc'], \\\n", + " index=['MLU', 'AQTESOLV', 'ttim', 'ttim-rc'])\n", + "t.loc['MLU'] = [38.094, 1.193E-06, '-']\n", + "t.loc['AQTESOLV'] = [37.803, 1.356E-06, '-']\n", + "t.loc['ttim'] = np.append(ca_2.parameters['optimal'].values, '-')\n", + "t.loc['ttim-rc'] = ca_4.parameters['optimal'].values \n", + "t['RMSE'] = [0.259, 0.270, ca_2.rmse(), ca_4.rmse()]\n", + "t.round(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see good agreement between model results in both hydraulic conductivity and specific storage values. TTim was able to calculate a better fit with wellbore storage added." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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X28/Z6+Tar4vVapUk+/nnbV/Y+30jU8zfzHtQlGHDhunpp5/Wjz/+qIYNG2rRokWyWq3q37+/acdIT0/XmTNn5OnpWeD6//73v5Ku3Hq7YcMGvfnmm3r66aeVmZmpunXrauzYsRo3bpxp9QB3CsIVcAcq6q/I167L+4UkNTU13y8I//nPfxyet7pe3wW5fPmyfv31V4dffNLS0hyO/fe//12ZmZlauXKlgoKC7NulpKQU6xzMqKEgfn5+Sk1Nzdf+n//8R5LyvTfFVRL9Vq1aVTk5OUpLSys0fG7atEn/+c9/lJiYaB+tklTgZ+I4I++9vLbN09Mz3+ecXfu1O3v2rL744gu98soreumll+ztWVlZOn36dLGOn/d+zZkzp9DZ0/z9/XXp0iVZLJZC6zVbhw4ddO+99+q9995ThQoV9N133+njjz922MbPz8+pegr63nfm+vHy8lJWVla+9v/+979Of9/lPVd47cQ3xd33evuZfZ3kXevFeb/zRqGufa/yAkeem3kPitK/f39NmDBBixcv1tSpU/XRRx+pV69ehd6RcCOqVKkiPz8/JSQkFLi+YsWK9n+3bt1arVu3Vk5Ojnbt2qU5c+bo2Weflb+/v/r162daTcCdgNsCgTIu7/a6a3/pS05O1oEDB+wTAdyMTz75xOF13sP6eTNX5f3CmPeXZunKFPJ/+ctfbvrYxa2hIB07dtT+/fv13XffObQvXbpUFotF7du3l5T/L+TXU9x+ndG9e3dJUlxcXKHbFPQ+S9L777/v9PGutnLlSl28eNH++ty5c/r888/VunXrfJOtFFSTYRj5avrwww/z3VJY2Pv80EMP6a677tL+/fvVtGnTAhdPT0+VL19ezZs3L7TekjB27FitXbtWMTEx8vf3t894mKd9+/bauHGjw4QaOTk5Wr58ebGP4cz1U6dOHX3//fcObYcOHdLBgweLfbw8LVu2lK+vr+bPn2+/9bG4unfvrs2bNxd5XLOvk5CQEFWvXl3Lli1zqPfYsWPavn27w7Z5I2bXvldXz7wpScHBwapXr54WLlxYYGjN4+zPiEqVKqlXr15aunSpvvjiC6WlpRXrlkBnjvPII4/o119/VU5OToHXTEhISL593N3dFR4errlz50pSvq8NAEaugNvKDz/8kG+2QOnKbRs3+vkzISEhGjlypObMmSM3Nzd1797dPltgYGCgxo8ff1M1e3p66u2339b58+fVrFkz+0x93bt3V6tWrSRJnTt3lqenp/r3768XXnhBFy9eVFxcnH777bebOrYzNRRk/PjxWrp0qXr06KFXX31VQUFBWrt2rebNm6ennnrK/uxLxYoVFRQUpNWrV6tjx46qXLmyqlSpUugH3Ra3X2e0bt1aUVFRev3115Wenq5HHnlEVqtVu3fvlo+Pj5555hm1bNlSlSpV0qhRo/TKK6/Iw8NDn3zyifbs2eP08a7m7u6uzp07a8KECcrNzdX06dOVkZFhnya8KDabTW3atNGMGTPs79mWLVu0YMGCfB+Ceu+990qSPvjgA1WsWFFeXl66++675efnpzlz5ig6OlqnT59Wnz59VK1aNZ06dUp79uzRqVOn7KHztddeU7du3dS5c2c999xzysnJ0fTp01W+fPlij5Q5Y9CgQYqJidHWrVv18ssv57sF6+WXX9aaNWvUoUMH/fnPf5aPj4/mzp1brKnx8zhz/URFRWnQoEEaPXq0Hn/8cR07dkxvvvnmDf38qFChgt5++22NGDFCnTp10h//+Ef5+/vrp59+0p49e/Tee+8Vuu+rr76qdevWqU2bNpo4caLuu+8+nTlzRgkJCZowYYIaNmxo+nXi5uam1157TSNGjNBjjz2mP/7xjzpz5owmT56c71bBgIAAderUSbGxsapUqZKCgoK0ceNGrVy5Ml+/c+fOVc+ePdWiRQuNHz9etWvX1vHjx/Xll1/a/6hz3333SZJmzZql6OhoeXh4KCQkxGF06FrDhg3T8uXLNWbMGNWqVUudOnW67jk6c5x+/frpk08+0cMPP6xx48apefPm8vDw0L///W9t3rxZkZGReuyxxzR//nxt2rRJPXr0UO3atXXx4kX7DJTFqQkoc1w2lQaAYitqtkBJxl/+8hf7tpKMp59+utA+kpOT863Lyckxpk+fbgQHBxseHh5GlSpVjEGDBhknTpxw2K5t27bGPffcU+y6o6OjjfLlyxvff/+90a5dO8Pb29uoXLmy8dRTTxnnz5932Pbzzz837r//fsPLy8uoWbOm8fzzzxvr1q3LN2NXSdZQ0Exqx44dMwYMGGD4+fkZHh4eRkhIiDFjxgwjJyfHYbsNGzYYjRs3NqxWqyGpwBnZbqTf4s4WaBhXvo7vvvuuce+99xqenp6Gr6+vERERYXz++ef2bbZv325EREQYPj4+RtWqVY0RI0YY3333Xb5Zx5yZLXD69OnGlClTjFq1ahmenp5G48aNjS+//NJh37z+Tp06la/uf//738bjjz9uVKpUyahYsaLRrVs344cffijw6zFz5kzj7rvvNtzd3fPVvGXLFqNHjx5G5cqVDQ8PD6NmzZpGjx49jM8++8yhjzVr1hhhYWGGp6enUbt2bWPatGkFnm9BCvv+i46Odpht7mpDhgwxypUrZ/z73/8ucP0333xjtGjRwrBarUZAQIDx/PPPGx988EGBswUW9r1Q3OsnNzfXePPNN426desaXl5eRtOmTY1Nmzbd0GyBef7xj38Ybdu2NcqXL2/4+PgYoaGhxvTp0wus82onTpwwhg0bZgQEBBgeHh5GjRo1jCeeeMJIT0+3b1Oc6ySv1hkzZuQ7hgqYxfPDDz80GjRoYHh6ehrBwcHGwoULC/z6paamGn369DEqV65s+Pr6GoMGDTJ27dqV730xDMPYsWOH0b17d8PX19ewWq1GvXr1jPHjxztsExMTY9SoUcNwc3Nz+Lpc+97nycnJMQIDAwudBbSgr5Gzx7l06ZLx1ltv2b93KlSoYDRs2NB48sknjcOHD9vP7bHHHjOCgoIMq9Vq+Pn5GW3btjXWrFmTryYAhmExDCfH8gHgNjJkyBD97W9/0/nz511dCsqg7Oxs1alTR61atdJf//pXV5cDAChh3BYIAIDJTp06pYMHD2rRokVKT093mKwDAHDnIlwBAGCytWvXaujQoapevbrmzZvnMP06AODOxW2BAAAAAGACpmIHAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABKUmXMXGxspisejZZ5+1txmGocmTJ6tGjRry9vZWu3bttG/fvuv2tWLFCoWGhspqtSo0NFSrVq0qwcoBAAAAoJSEq+TkZH3wwQcKCwtzaH/zzTf1zjvv6L333lNycrICAgLUuXNnnTt3rtC+duzYob59+yoqKkp79uxRVFSUnnjiCe3cubOkTwMAAABAGWYxDMNwZQHnz59XkyZNNG/ePL3++ut64IEHNHPmTBmGoRo1aujZZ5/Viy++KEnKysqSv7+/pk+frieffLLA/vr27auMjAytW7fO3tatWzdVqlRJy5YtuyXnBAAAAKDsKefqAp5++mn16NFDnTp10uuvv25v/9e//qW0tDR16dLF3ma1WtW2bVtt37690HC1Y8cOjR8/3qGta9eumjlzZqE1ZGVlKSsry/46NzdXp0+flp+fnywWyw2eGQAAAIDbnWEYOnfunGrUqCE3t6Jv/HNpuIqPj9d3332n5OTkfOvS0tIkSf7+/g7t/v7+OnbsWKF9pqWlFbhPXn8FiY2N1ZQpU5wpHQAAAEAZcuLECdWqVavIbVwWrk6cOKFx48Zp/fr18vLyKnS7a0eODMO47miSs/vExMRowoQJ9tdnz55V7dq1deLECdlstiKPBQAAAODOlZGRocDAQFWsWPG627osXP3zn//UyZMn9eCDD9rbcnJytHXrVr333ns6ePCgpCsjUdWrV7dvc/LkyXwjU1cLCAjIN0p1vX2sVqusVmu+dpvNRrgCAAAAUKzHhVw2W2DHjh21d+9epaSk2JemTZtq4MCBSklJUd26dRUQEKCvvvrKvk92dra2bNmili1bFtpvRESEwz6StH79+iL3AQAAAICb5bKRq4oVK+ree+91aCtfvrz8/Pzs7c8++6zeeOMNNWjQQA0aNNAbb7whHx8fDRgwwL7P4MGDVbNmTcXGxkqSxo0bpzZt2mj69OmKjIzU6tWrtWHDBm3btu3WnRwAAACAMsflswUW5YUXXtCFCxc0evRo/fbbbwoPD9f69esd7nc8fvy4w6wdLVu2VHx8vF5++WVNmjRJ9erV0/LlyxUeHu6KUwAAAABQRrj8c65Ko4yMDPn6+urs2bM8cwUAAACUYc5kA5c9cwUAAAAAdxLCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAlcGq7i4uIUFhYmm80mm82miIgIrVu3zr4+PT1dQ4YMUY0aNeTj46Nu3brp8OHDRfa5ePFiWSyWfMvFixdL+nQAAAAAlGEuDVe1atXStGnTtGvXLu3atUsdOnRQZGSk9u3bJ8Mw1KtXL/38889avXq1du/eraCgIHXq1EmZmZlF9muz2ZSamuqweHl53aKzAgAAAFAWlXPlwXv27OnweurUqYqLi1NSUpI8PDyUlJSkH374Qffcc48kad68eapWrZqWLVumESNGFNqvxWJRQEBAidYOAAAAAFcrNc9c5eTkKD4+XpmZmYqIiFBWVpYkOYw4ubu7y9PTU9u2bSuyr/PnzysoKEi1atXSI488ot27dxe5fVZWljIyMhwWAAAAAHCGy8PV3r17VaFCBVmtVo0aNUqrVq1SaGioGjZsqKCgIMXExOi3335Tdna2pk2bprS0NKWmphbaX8OGDbV48WKtWbNGy5Ytk5eXlx566KEin9WKjY2Vr6+vfQkMDCyJUwUAAABwB7MYhmG4soDs7GwdP35cZ86c0YoVK/Thhx9qy5YtCg0N1T//+U8NHz5ce/bskbu7uzp16iQ3tyt58B//+Eex+s/NzVWTJk3Upk0bzZ49u8BtsrKy7CNlkpSRkaHAwECdPXtWNpvt5k8SAAAAwG0pIyNDvr6+xcoGLn3mSpI8PT1Vv359SVLTpk2VnJysWbNm6f3339eDDz6olJQUnT17VtnZ2apatarCw8PVtGnTYvfv5uamZs2aFTlyZbVaZbVab/pcAAAAAJRdLr8t8FqGYTiMIkmSr6+vqlatqsOHD2vXrl2KjIx0qr+UlBRVr17d7FIBAAAAwM6lI1cTJ05U9+7dFRgYqHPnzik+Pl6JiYlKSEiQJH322WeqWrWqateurb1792rcuHHq1auXunTpYu9j8ODBqlmzpmJjYyVJU6ZMUYsWLdSgQQNlZGRo9uzZSklJ0dy5c11yjgAAAADKBpeGq/T0dEVFRSk1NVW+vr4KCwtTQkKCOnfuLElKTU3VhAkTlJ6erurVq2vw4MGaNGmSQx/Hjx+3P4clSWfOnNHIkSOVlpYmX19fNW7cWFu3blXz5s1v6bkBAAAAKFtcPqFFaeTMQ2sAAAAA7lzOZINS98wVAAAAANyOCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAMqE37Mvq85La1XnpbX6Pfuyq8sBcAciXJVy/EcAAAAA3B4IVwAAAABKjdt5cIFwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJXBqu4uLiFBYWJpvNJpvNpoiICK1bt86+Pj09XUOGDFGNGjXk4+Ojbt266fDhw9ftd8WKFQoNDZXValVoaKhWrVpVkqcBAAAAAK4NV7Vq1dK0adO0a9cu7dq1Sx06dFBkZKT27dsnwzDUq1cv/fzzz1q9erV2796toKAgderUSZmZmYX2uWPHDvXt21dRUVHas2ePoqKi9MQTT2jnzp238MwAAAAAlDXlXHnwnj17OryeOnWq4uLilJSUJA8PDyUlJemHH37QPffcI0maN2+eqlWrpmXLlmnEiBEF9jlz5kx17txZMTExkqSYmBht2bJFM2fO1LJly0r2hAAAAACUWaXmmaucnBzFx8crMzNTERERysrKkiR5eXnZt3F3d5enp6e2bdtWaD87duxQly5dHNq6du2q7du3F7pPVlaWMjIyHBYAAAAAcIbLw9XevXtVoUIFWa1WjRo1SqtWrVJoaKgaNmyooKAgxcTE6LffflN2dramTZumtLQ0paamFtpfWlqa/P39Hdr8/f2VlpZW6D6xsbHy9fW1L4GBgaadHwAAAICyweXhKiQkRCkpKUpKStJTTz2l6Oho7d+/Xx4eHlqxYoUOHTqkypUry8fHR4mJierevbvc3d2L7NNisTi8NgwjX9vVYmJidPbsWfty4sQJU84NAAAAQNnh0meuJMnT01P169eXJDVt2lTJycmaNWuW3n//fT344INKSUnR2bNnlZ2drapVqyo8PFxNmzYttL+AgIB8o1QnT57MN5p1NavVKqvVas4JAQAAACiTXD5ydS3DMOzPW+Xx9fVV1apVdfjwYe3atUuRkZGF7h8REaGvvvrKoW39+vVq2bJlidQLAAAAAJKLR64mTpyo7t27KzAwUOfOnVN8fLwSExOVkJAgSfrss89UtWpV1a5dW3v37tW4cePUq1cvhwkrBg8erJo1ayo2NlaSNG7cOLVp00bTp09XZGSkVq9erQ0bNhQ5CQYAAAAA3CyXhqv09HRFRUUpNTVVvr6+CgsLU0JCgjp37ixJSk1N1YQJE5Senq7q1atr8ODBmjRpkkMfx48fl5vb/wbgWrZsqfj4eL388suaNGmS6tWrp+XLlys8PPyWnhsAAACAssWl4WrBggVFrh87dqzGjh1b5DaJiYn52vr06aM+ffrcTGkAAAAA4JRS98wVAAAAANyOCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFADfp9+zLqvPSWtV5aa1+z77s6nIAAICLEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAELg1XcXFxCgsLk81mk81mU0REhNatW2dff/78eY0ZM0a1atWSt7e3GjVqpLi4uCL7XLx4sSwWS77l4sWLJX06AAAAAMqwcq48eK1atTRt2jTVr19fkrRkyRJFRkZq9+7duueeezR+/Hht3rxZH3/8serUqaP169dr9OjRqlGjhiIjIwvt12az6eDBgw5tXl5eJXouAAAAAMo2l45c9ezZUw8//LCCg4MVHBysqVOnqkKFCkpKSpIk7dixQ9HR0WrXrp3q1KmjkSNH6v7779euXbuK7NdisSggIMBhAQAAAICSVGqeucrJyVF8fLwyMzMVEREhSWrVqpXWrFmjX375RYZhaPPmzTp06JC6du1aZF/nz59XUFCQatWqpUceeUS7d+8ucvusrCxlZGQ4LAAAAADgDJeHq71796pChQqyWq0aNWqUVq1apdDQUEnS7NmzFRoaqlq1asnT01PdunXTvHnz1KpVq0L7a9iwoRYvXqw1a9Zo2bJl8vLy0kMPPaTDhw8Xuk9sbKx8fX3tS2BgoOnnCQAAAODO5tJnriQpJCREKSkpOnPmjFasWKHo6Ght2bJFoaGhmj17tpKSkrRmzRoFBQVp69atGj16tKpXr65OnToV2F+LFi3UokUL++uHHnpITZo00Zw5czR79uwC94mJidGECRPsrzMyMghYAAAAAJzi8nDl6elpn9CiadOmSk5O1qxZszRz5kxNnDhRq1atUo8ePSRJYWFhSklJ0VtvvVVouLqWm5ubmjVrVuTIldVqldVqvfmTAQAAAFBmufy2wGsZhqGsrCxdunRJly5dkpubY4nu7u7Kzc11qr+UlBRVr17d7FIBAAAAwM6lI1cTJ05U9+7dFRgYqHPnzik+Pl6JiYlKSEiQzWZT27Zt9fzzz8vb21tBQUHasmWLli5dqnfeecfex+DBg1WzZk3FxsZKkqZMmaIWLVqoQYMGysjI0OzZs5WSkqK5c+e66jQBAAAAlAEuDVfp6emKiopSamqqfH19FRYWpoSEBHXu3FmSFB8fr5iYGA0cOFCnT59WUFCQpk6dqlGjRtn7OH78uMPo1pkzZzRy5EilpaXJ19dXjRs31tatW9W8efNbfn4AAAAAyg6XhqsFCxYUuT4gIECLFi0qcpvExESH1++++67efffdmy0NAAAAAJxS6p65AgAAAIDbEeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAlAqXcnK14UC6/fVftv6sL/el6VJOrgurKr5yri4AAAAAQNmWnnFRn+w8ro+Tjul0Zra9ffamn5STa6hyeU8NahGkgeG15W/zcmGlRSNcAQAAAHCZHUd+1YglybpwKUe5huO6nP/fcDozW+9tOqwFX/+sD6ObKaKenwsqvT5uCwQAAADgEjuO/KqoBTsLDFbXyjWkC5dyFLVgp3Yc+fXWFOgkwhUAAACAWy4946JGLElWrmFcN1jlyTWkXMPQiKXJSs+4WLIF3gDCFQAAAIBb7pOdx4s1YnWtXEO6kJ2jT3ceL5nCbgLhCgAAAMAtdSknVx8nHXM6WOXJNaSPk46VulkECVcAAAAAbqlNP550mBXwRvyama3NP540qSJzEK4AAAAA3FJHTp2Xu5vlpvpwd7PoyKlMkyoyB+EKAAAAwC31e1aObi5aSRZJmVmXzSjHNIQrAAAAALeUj9VdN/i4lZ0hqby1dH1sL+EKAAAAwC1Vr2oF+wcE36icXEP1qpY3qSJzEK4AAAAA3FIdGlZT5fKeN9WHX3lPtW9YzaSKzEG4AgAAAHBLebi7aVCLIN3onBZuFmlQiyB5uJeuOFO6qgEAAABQJgwMry1vD3enA5abRfL2dNeA8NolU9hNIFwBAAAAuOX8bV76MLqZ3CyWYgcsN4vkZrFoQXQz+du8SrbAG0C4AgAAAOASEfX89NHwcHl7Xn8EK2/E6uMR4WpR1+/WFOik0jV3IQAAAIAyJaKenzY9106f7jyuj5KO6XRmtn2du8WiHMOQX3lPDWoRpAHhtUvliFUewhUAAAAAl/K3eWl852CN6VBfCT+k6ZlluyVJ4zrVV8MAm9o3rFbqJq8oCOEKAAAAQKng4e6mjo3+N736iNZ15eN5+0SW0h//AAAAAOA2QLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATFCsqTfWrFnjdMedO3eWt7e30/sBAAAAwO2oWOGqV69eTnVqsVh0+PBh1a1b90ZqAgAAAIDbTrFvC0xLS1Nubm6xFh8fn5KsGQAAAABKnWKFq+joaKdu8Rs0aJBsNtsNF4UrLuXkasOBdPvrv2z9WV/uS9OlnFwXVgUAAACgIMW6LXDRokVOdRoXF3dDxeCK9IyL+mTncX2cdEynM7Pt7bM3/aScXEOVy3tqUIsgDQyvLX+blwsrBQAAAJCnWOEKt86OI79qxJJkXbiUo1zDcV3O/284nZmt9zYd1oKvf9aH0c0UUc/PBZUCAAAAuFqxwlXv3r2L3eHKlStvuJiybseRXxW1YKdyDSNfsLpWriFduJSjqAU79dHwcAIWAAAA4GLFeubK19fXvthsNm3cuFG7du2yr//nP/+pjRs3ytfXt8QKvdOlZ1zUiCXJxQpWeXINKdcwNGJpstIzLpZsgQAAAACK5PQzVy+++KKeeOIJzZ8/X+7u7pKknJwcjR49mkksbsInO48XeCvg9eQa0oXsHH2687jGdw4umeIAAAAAXFexp2LPs3DhQv3pT3+yBytJcnd314QJE7Rw4UJTiysrLuXk6uOkY04Hqzy5hvRx0jFmEQQAoBDMwAvgVnB6QovLly/rwIEDCgkJcWg/cOCAcnP5AXUjNv140mFWwBvxa2a2Nv94Ul3uCTCpKgAAbn/MwAvgVnI6XA0dOlTDhg3TTz/9pBYtWkiSkpKSNG3aNA0dOtT0AsuCI6fOy93NYp8N8Ea4u1l05FSmiVUBAHB7YwZeALea0+HqrbfeUkBAgN59912lpqZKkqpXr64XXnhBzz33nOkFlgW/Z+XIcpN9WCRlZl02oxwAAG57zMALwBWK/czV+fPnr+zg5qYXXnhBv/zyi86cOaMzZ87ol19+0QsvvODwHBaKz8fqrhsfs7rCkFTeyseWAQDADLwAXKXY4apKlSrq3r274uLi9Msvv0iSbDYbMwSaoF7VCjd1S6B05faGelXLm1QRAAC3LzNm4AWAG1HscHXw4EE9/PDDWrFiherWratmzZrptdde0/fff1+S9ZUJHRpWU+XynjfVh195T7VvWM2kigAAuD0xAy8AVyp2uAoKCtIzzzyjDRs26OTJk5owYYL27dunNm3a6O6779a4ceO0adMm5eTklGS9dyQPdzcNahEktxt88MrNIg1qESQPd6dn1gcA4I5i5gy8AOCsG/pt3NfXV/3791d8fLz++9//6v3331dubq6GDh2qqlWr6pNPPjG7zjvewPDa8vZwdzpguVkkb093DQivXTKFAQBwG8mbgfdmMAMvgBt100Md5cqVU5cuXTRnzhwdO3ZMGzduVHBwsBm1lSn+Ni99GN1MbhZLsQOWm0Vys1i0ILoZn80BAICYgReAa93Q9HIXL17U999/r5MnTzp8cLDFYlHPnj1NK66siajnp4+Gh2vE0mRdyC76Qdy8EasF0c3Uoi5TxgIAIDEDLwDXcnrkKiEhQbVr11aLFi306KOPqlevXg6LM+Li4hQWFmafdTAiIkLr1q2zrz9//rzGjBmjWrVqydvbW40aNVJcXNx1+12xYoVCQ0NltVoVGhqqVatWOXuaLhNRz0+bnmunZzo0yDfJhbvlyt/i/Mp76pkODbTpuXYEKwAArsIMvABcyelwNWbMGP3hD39QamqqcnNzHRZnJ7OoVauWpk2bpl27dmnXrl3q0KGDIiMjtW/fPknS+PHjlZCQoI8//lgHDhzQ+PHj9cwzz2j16tWF9rljxw717dtXUVFR2rNnj6KiovTEE09o586dzp6qy/jbvDS+c7B2TuyoOf0b29vHdaqvD6IeVNLEjhrfOZhbAQEAuAYz8AJwJafDVd5Mgf7+/jd98J49e+rhhx9WcHCwgoODNXXqVFWoUEFJSUmSrgSl6OhotWvXTnXq1NHIkSN1//33a9euXYX2OXPmTHXu3FkxMTFq2LChYmJi1LFjR82cOfOm673VPNzd1LHR/364j2hdV13uCWBWQAAACsEMvABcyemfHH369FFiYqLpheTk5Cg+Pl6ZmZmKiIiQJLVq1Upr1qzRL7/8IsMwtHnzZh06dEhdu3YttJ8dO3aoS5cuDm1du3bV9u3bC90nKytLGRkZDgsAALg9MQMvAFdx+mnN9957T3/4wx/09ddf67777pOHh4fD+rFjxzrV3969exUREaGLFy+qQoUKWrVqlUJDQyVJs2fP1h//+EfVqlVL5cqVk5ubmz788EO1atWq0P7S0tLyjar5+/srLS2t0H1iY2M1ZcoUp+oGAAClU94MvFELdkoyivWBwszAC8AMToerTz/9VF9++aW8vb2VmJgoi+V/fxayWCxOh6uQkBClpKTozJkzWrFihaKjo7VlyxaFhoZq9uzZSkpK0po1axQUFKStW7dq9OjRql69ujp16lRon1fXJEmGYeRru1pMTIwmTJhgf52RkaHAwECnzgMAAJQezMALwBWcDlcvv/yyXn31Vb300ktyc7v5+5E9PT1Vv359SVLTpk2VnJysWbNmaebMmZo4caJWrVqlHj16SJLCwsKUkpKit956q9BwFRAQkG+U6uTJk0U+I2a1WmW1Wm/6XAAAQOmRNwPvpzuP66OkYzqdmW1f526xKMcw5FfeU4NaBGlAeG1GrADcNKfDVXZ2tvr27WtKsCqIYRjKysrSpUuXdOnSpXzHcXd3d/hsrWtFREToq6++0vjx4+1t69evV8uWLUukXgAAUHrlzcA7pkN9JfyQpmeW7ZZ0ZQbehgE2tW9YjckrAJjG6XAVHR2t5cuXa+LEiTd98IkTJ6p79+4KDAzUuXPnFB8fr8TERCUkJMhms6lt27Z6/vnn5e3traCgIG3ZskVLly7VO++8Y+9j8ODBqlmzpmJjYyVJ48aNU5s2bTR9+nRFRkZq9erV2rBhg7Zt23bT9QIAgNtTQTPw+njyQcEAzOX0T5WcnBy9+eab+vLLLxUWFpZvQourg8/1pKenKyoqSqmpqfL19VVYWJgSEhLUuXNnSVJ8fLxiYmI0cOBAnT59WkFBQZo6dapGjRpl7+P48eMOo1stW7ZUfHy8Xn75ZU2aNEn16tXT8uXLFR4e7uypAgAAAECxOR2u9u7dq8aNr3yw7Q8//OCwrqhJIwqyYMGCItcHBARo0aJFRW5T0LTwffr0UZ8+fZyqBQAAAABuhtPhavPmzSVRBwAAAADc1niCEwAAAABMUKxw1bt3b2VkZBS704EDB+rkyZM3XBQAAAAA3G6KdVvg6tWrderUqWJ1aBiGPv/8c7322muqVq3a9XcAAAAAgDtAscKVYRgKDg4u6VoAAAAA4LZVrHB1I5NY1KxZ0+l9AAAAAOB2Vaxw1bZt25KuAwAAAABua8wWCAAAAAAmIFwBAAAAgAkIVwAAAABgAlPDlTOfhQUAAAAAd5Jih6u33nqryPUZGRnq0qXLTRcEALeTSzm52nAg3f76L1t/1pf70nQpJ9eFVQEAAFco1myBkjRp0iT5+flp6NCh+dadP39eXbt2ZeQKQJmRnnFRn+w8ro+Tjul0Zra9ffamn5STa6hyeU8NahGkgeG15W/zcmGlAADgVil2uProo48UFRWlSpUqqVevXvb28+fPq0uXLjp9+rS2bt1aEjUCQKmy48ivGrEkWRcu5SjXcFyX8/8bTmdm671Nh7Xg65/1YXQzRdTzc0GlAADgVir2bYF9+vTRnDlzNGDAAPuHCp8/f17dunXTf//7XyUmJsrf37/ECgWA0mDHkV8VtWBngcHqWrmGdOFSjqIW7NSOI7/emgIBAIDLODWhxYgRIzR58mT16tVLiYmJ6t69u9LS0rR582ZVr169pGoEgFIhPeOiRixJVq5hXDdY5ck1pFzD0IilyUrPuFiyBQIAAJcq9m2BeV544QX99ttv6tixo+rUqaMtW7aoZs2aJVEbAJQqn+w8XqwRq2vlGtKF7Bx9uvO4xncOLpniAACAyxU7XPXu3dvhtYeHh6pUqaKxY8c6tK9cudKcygCgFLmUk6uPk445Hazy5BrSx0nHNKZDfXm48xGDAADciYodrnx9fR1e9+/f3/RiAKC02vTjSYdZAW/Er5nZ2vzjSXW5J8CkqgAAQGlS7HC1aNGikqwDAEq1I6fOy93NYp8N8Ea4u1l05FSmiVUBAIDShHtTAKAYfs/KkeUm+7BIysy6bEY5AACgFCJcAUAx+FjddeNjVlcYkspbnZ5HCAAA3CYIVwBQDPWqVripWwKlKx8wXK9qeZMqAgAApQ3hCgCKoUPDaqpc3vOm+vAr76n2DauZVBEAAChtCFcAUAwe7m4a1CJIbjf44JWbRRrUIohp2AEAuIPxvzwAFNPA8Nry9nB3OmC5WSRvT3cNCK9dMoUBAIBSgXAFAMXkb/PSh9HN5GaxFDtguVkkN4tFC6Kbyd/mVbIFAgAAlyJcAYATIur56aPh4fL2vP4IVt6I1ccjwtWirt+tKRAAALgMcwIDgJMi6vlp03Pt9OnO4/oo6ZhOZ2bb17lbLMoxDPmV99SgFkEaEF6bESsAAMoIwhUA3AB/m5fGdw7WmA71lfBDmp5ZtluSNK5TfTUMsKl9w2pMXgEAQBlDuAKAm+Dh7qaOjf43vfqI1nXl48mPVgAAyiL+rAoAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJnBpuIqLi1NYWJhsNptsNpsiIiK0bt06+3qLxVLgMmPGjEL7XLx4cYH7XLx48VacEgAAAIAyqpwrD16rVi1NmzZN9evXlyQtWbJEkZGR2r17t+655x6lpqY6bL9u3ToNHz5cjz/+eJH92mw2HTx40KHNy8vL3OIBAAAA4CouDVc9e/Z0eD116lTFxcUpKSlJ99xzjwICAhzWr169Wu3bt1fdunWL7NdiseTbFwAAAEDp5+NZTken9XB1GTek1DxzlZOTo/j4eGVmZioiIiLf+vT0dK1du1bDhw+/bl/nz59XUFCQatWqpUceeUS7d+8uiZIBAAAAwM6lI1eStHfvXkVEROjixYuqUKGCVq1apdDQ0HzbLVmyRBUrVlTv3r2L7K9hw4ZavHix7rvvPmVkZGjWrFl66KGHtGfPHjVo0KDAfbKyspSVlWV/nZGRcXMnBQAAAKDMcfnIVUhIiFJSUpSUlKSnnnpK0dHR2r9/f77tFi5cqIEDB1732akWLVpo0KBBuv/++9W6dWv99a9/VXBwsObMmVPoPrGxsfL19bUvgYGBN31eAAAAAMoWl4crT09P1a9fX02bNlVsbKzuv/9+zZo1y2Gbr7/+WgcPHtSIESOc7t/NzU3NmjXT4cOHC90mJiZGZ8+etS8nTpxw+jgAAAAAyjaX3xZ4LcMwHG7Rk6QFCxbowQcf1P33339D/aWkpOi+++4rdBur1Sqr1ep03wAAAACQx6XhauLEierevbsCAwN17tw5xcfHKzExUQkJCfZtMjIy9Nlnn+ntt98usI/BgwerZs2aio2NlSRNmTJFLVq0UIMGDZSRkaHZs2crJSVFc+fOvSXnBAAAAKBscmm4Sk9PV1RUlFJTU+Xr66uwsDAlJCSoc+fO9m3i4+NlGIb69+9fYB/Hjx+Xm9v/7m48c+aMRo4cqbS0NPn6+qpx48baunWrmjdvXuLnAwAAAKDscmm4WrBgwXW3GTlypEaOHFno+sTERIfX7777rt59992bLQ0AAAAAnOLyCS0AAAAA4E5AuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwQTlXF4Ci+XiW09FpPVxdBgAAAIDrYOQKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADCBS8NVXFycwsLCZLPZZLPZFBERoXXr1tnXWyyWApcZM2YU2e+KFSsUGhoqq9Wq0NBQrVq1qqRPBQAAAEAZ59JwVatWLU2bNk27du3Srl271KFDB0VGRmrfvn2SpNTUVIdl4cKFslgsevzxxwvtc8eOHerbt6+ioqK0Z88eRUVF6YknntDOnTtv1WkBAAAAKIMshmEYri7iapUrV9aMGTM0fPjwfOt69eqlc+fOaePGjYXu37dvX2VkZDiMgHXr1k2VKlXSsmXLilVDRkaGfH19dfbsWdlsNudPAkCZ8nv2ZYX++UtJ0v5Xu8rHs5yLKwJQEK5VADfCmWxQap65ysnJUXx8vDIzMxUREZFvfXp6utauXVtg6Lrajh071KVLF4e2rl27avv27YXuk5WVpYyMDIcFAAAAAJzh8j/Z7N27VxEREbp48aIqVKigVatWKTQ0NN92S5YsUcWKFdW7d+8i+0tLS5O/v79Dm7+/v9LS0grdJzY2VlOmTLmxEwAAALcFH89yOjqth6vLAHAHc/nIVUhIiFJSUpSUlKSnnnpK0dHR2r9/f77tFi5cqIEDB8rLy+u6fVosFofXhmHka7taTEyMzp49a19OnDjh/IkAAAAAKNNcPnLl6emp+vXrS5KaNm2q5ORkzZo1S++//759m6+//loHDx7U8uXLr9tfQEBAvlGqkydP5hvNuprVapXVar3BMwAAAACAUjBydS3DMJSVleXQtmDBAj344IO6//77r7t/RESEvvrqK4e29evXq2XLlqbWCQAAAABXc+nI1cSJE9W9e3cFBgbq3Llzio+PV2JiohISEuzbZGRk6LPPPtPbb79dYB+DBw9WzZo1FRsbK0kaN26c2rRpo+nTpysyMlKrV6/Whg0btG3btltyTgAAAADKJpeGq/T0dEVFRSk1NVW+vr4KCwtTQkKCOnfubN8mPj5ehmGof//+BfZx/Phxubn9bwCuZcuWio+P18svv6xJkyapXr16Wr58ucLDw0v8fAAAAACUXaXuc65KAz7nCoAz+OwcAADuXLfl51wBAAAAwO2McAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJignKsLAIDbnY9nOR2d1sPVZQAAABdj5AoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAE7g0XMXFxSksLEw2m002m00RERFat26dwzYHDhzQo48+Kl9fX1WsWFEtWrTQ8ePHC+1z8eLFslgs+ZaLFy+W9OkAAAAAKMPKufLgtWrV0rRp01S/fn1J0pIlSxQZGandu3frnnvu0ZEjR9SqVSsNHz5cU6ZMka+vrw4cOCAvL68i+7XZbDp48KBD2/X2AQAAAICbYTEMw3B1EVerXLmyZsyYoeHDh6tfv37y8PDQRx99VOz9Fy9erGeffVZnzpy54RoyMjLk6+urs2fPymaz3XA/AAAAAG5vzmSDUvPMVU5OjuLj45WZmamIiAjl5uZq7dq1Cg4OVteuXVWtWjWFh4fr73//+3X7On/+vIKCglSrVi098sgj2r17d5HbZ2VlKSMjw2EBAAAAAGe4PFzt3btXFSpUkNVq1ahRo7Rq1SqFhobq5MmTOn/+vKZNm6Zu3bpp/fr1euyxx9S7d29t2bKl0P4aNmyoxYsXa82aNVq2bJm8vLz00EMP6fDhw4XuExsbK19fX/sSGBhYEqcKAAAA4A7m8tsCs7Ozdfz4cZ05c0YrVqzQhx9+qC1btuiuu+5SzZo11b9/f3366af27R999FGVL19ey5YtK1b/ubm5atKkidq0aaPZs2cXuE1WVpaysrLsrzMyMhQYGMhtgQAAAEAZ58xtgS6d0EKSPD097RNaNG3aVMnJyZo1a5bmzJmjcuXKKTQ01GH7Ro0aadu2bcXu383NTc2aNSty5Mpqtcpqtd7YCQAAAACASsFtgdcyDENZWVny9PRUs2bN8s36d+jQIQUFBTnVX0pKiqpXr252qQAAAABg59KRq4kTJ6p79+4KDAzUuXPnFB8fr8TERCUkJEiSnn/+efXt21dt2rRR+/btlZCQoM8//1yJiYn2PgYPHqyaNWsqNjZWkjRlyhS1aNFCDRo0UEZGhmbPnq2UlBTNnTvXFacIAAAAoIxwabhKT09XVFSUUlNT5evrq7CwMCUkJKhz586SpMcee0zz589XbGysxo4dq5CQEK1YsUKtWrWy93H8+HG5uf1vAO7MmTMaOXKk0tLS5Ovrq8aNG2vr1q1q3rz5LT8/AAAAAGWHyye0KI34nCsAAAAA0m36OVcAAAAAcDsjXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJnBpuIqLi1NYWJhsNptsNpsiIiK0bt06h20OHDigRx99VL6+vqpYsaJatGih48ePF9nvihUrFBoaKqvVqtDQUK1ataokTwMAAAAAXBuuatWqpWnTpmnXrl3atWuXOnTooMjISO3bt0+SdOTIEbVq1UoNGzZUYmKi9uzZo0mTJsnLy6vQPnfs2KG+ffsqKipKe/bsUVRUlJ544gnt3LnzVp0WAAAAgDLIYhiG4eoirla5cmXNmDFDw4cPV79+/eTh4aGPPvqo2Pv37dtXGRkZDiNg3bp1U6VKlbRs2bJi9ZGRkSFfX1+dPXtWNpvN6XMAAAAAcGdwJhuUu0U1XVdOTo4+++wzZWZmKiIiQrm5uVq7dq1eeOEFde3aVbt379bdd9+tmJgY9erVq9B+duzYofHjxzu0de3aVTNnzix0n6ysLGVlZdlfnz17VtKVNxIAAABA2ZWXCYo1JmW42Pfff2+UL1/ecHd3N3x9fY21a9cahmEYqamphiTDx8fHeOedd4zdu3cbsbGxhsViMRITEwvtz8PDw/jkk08c2j755BPD09Oz0H1eeeUVQxILCwsLCwsLCwsLC0uBy4kTJ66bbVw+chUSEqKUlBSdOXNGK1asUHR0tLZs2aK77rpLkhQZGWkfiXrggQe0fft2zZ8/X23bti20T4vF4vDaMIx8bVeLiYnRhAkT7K9zc3N1+vRp+fn5FbnfrZKRkaHAwECdOHGC2xSBUorrFLg9cK0Ct4fSdK0ahqFz586pRo0a193W5eHK09NT9evXlyQ1bdpUycnJmjVrlubMmaNy5copNDTUYftGjRpp27ZthfYXEBCgtLQ0h7aTJ0/K39+/0H2sVqusVqtDW164K03yZlUEUHpxnQK3B65V4PZQWq5VX1/fYm1X6j7nyjAMZWVlydPTU82aNdPBgwcd1h86dEhBQUGF7h8REaGvvvrKoW39+vVq2bJlidQLAAAAAJKLR64mTpyo7t27KzAwUOfOnVN8fLwSExOVkJAgSXr++efVt29ftWnTRu3bt1dCQoI+//xzJSYm2vsYPHiwatasqdjYWEnSuHHj1KZNG02fPl2RkZFavXq1NmzYUORoFwAAAADcLJeGq/T0dEVFRSk1NVW+vr4KCwtTQkKCOnfuLEl67LHHNH/+fMXGxmrs2LEKCQnRihUr1KpVK3sfx48fl5vb/wbgWrZsqfj4eL388suaNGmS6tWrp+XLlys8PPyWn59ZrFarXnnllXy3LgIoPbhOgdsD1ypwe7hdr9VS9zlXAAAAAHA7KnXPXAEAAADA7YhwBQAAAAAmIFwBAAAAgAkIVwAAoEw5evSoLBaLUlJSXF0KUGaUleuOcHWLDRkyRBaLRaNGjcq3bvTo0bJYLBoyZIh92169ehXYT7t27fTss8/ma//73/8ui8ViYsXA7WH79u1yd3dXt27d8q27cOGCXnnlFYWEhMhqtapKlSrq06eP9u3bZ9+mTp06slgshS7t2rUrcrtp06bZ+1qxYoXCw8Pl6+urihUr6p577tFzzz3ndE2SNHnyZD3wwAMObStWrJC7u7uOHz9e4HvRsGFDjR071pm3D7htFHWd5v0fer1tAgMDlZqaqnvvvdfVpwPcFrjuio9w5QKBgYGKj4/XhQsX7G0XL17UsmXLVLt2bRdWBty+Fi5cqGeeeUbbtm1zCB1ZWVnq1KmTFi5cqNdee02HDh3SP/7xD+Xk5Cg8PFxJSUmSpOTkZKWmpio1NVUrVqyQJB08eNDetnLlSnufr776qr09b3nmmWckSRs2bFC/fv3Up08fffvtt/rnP/+pqVOnKjs72+maCvPoo4/Kz89PS5Ysybfum2++0cGDBzV8+PAbfzOBUuzq627mzJmy2WwObbNmzXJ4LUmLFi1yaHN3d1dAQIDKlXPpJ9IAt4078bq7dOlSifRLuHKBJk2aqHbt2g6/rK1cuVKBgYFq3LixCysDbk+ZmZn661//qqeeekqPPPKIFi9ebF83c+ZM7dixQ1988YWeeOIJBQUFqXnz5lqxYoUaNWqk4cOHyzAMVa1aVQEBAQoICFDlypUlSdWqVcvXJkkVK1a0t+ct5cuXlyR98cUXatWqlZ5//nmFhIQoODhYvXr10pw5c5yuqTAeHh6KiorS4sWL8223cOFCPfjgg7r//vvNeGuBUufq687X11cWiyVf29WvJemuu+5yaLv29qTExERZLBZ9+eWXaty4sby9vdWhQwedPHlS69atU6NGjWSz2dS/f3/9/vvvLjx7wDVu9+su79h//etf1a5dO3l5eenjjz+WdOX/zXvuuUdWq1XVq1fXmDFjbupYhCsXGTp0qBYtWmR/vXDhQg0bNsyFFQG3r+XLlyskJEQhISEaNGiQFi1aZA8dn376qTp37pwvbLi5uWn8+PHav3+/9uzZY1otAQEB2rdvn3744YdCtzGjpuHDh+vnn3/Wli1b7G15IZNRK+DGTJ48We+99562b9+uEydO6IknntDMmTP16aefau3atfrqq68c/lAC4ObdyuvuxRdf1NixY3XgwAF17dpVcXFxevrppzVy5Ejt3btXa9asUf369W/qGIQrF4mKitK2bdt09OhRHTt2TN98840GDRrk6rKA29KCBQvs10+3bt10/vx5bdy4UZJ06NAhNWrUqMD98toPHTrk1PFefPFFVahQwWFJTEyUJD3zzDNq1qyZ7rvvPtWpU0f9+vXTwoULlZWVZd/fjJpCQ0MVHh7u8Eeav/71r8rJyVH//v2dOh8AV7z++ut66KGH1LhxYw0fPlxbtmxRXFycGjdurNatW6tPnz7avHmzq8sE7ii38rp79tln1bt3b919992qUaOGXn/9dT333HMaN26cgoOD1axZswLnNHAG4cpFqlSpoh49emjJkiVatGiRevTooSpVqri6LOC2c/DgQX377bfq16+fJKlcuXLq27evFi5ceN1980a3PD09nTrm888/r5SUFIclPDxcklS+fHmtXbtWP/30k15++WVVqFBBzz33nJo3b16s2xryairOxDTDhw/X3/72N507d07SlRHw3r1766677nLqfABcERYWZv+3v7+/fHx8VLduXYe2kydPuqI04I51s9fd1X/oLGjCuKs1bdrU/u+TJ0/qP//5jzp27HgT1edXOp4oK6OGDRtmv69z7ty5Tu1rs9l09uzZfO1nzpyRzWYzpT7gdrBgwQJdvnxZNWvWtLcZhiEPDw/99ttvatCggfbv31/gvj/++KMkKTg42KljVqlS5bq3DdSrV0/16tXTiBEj9H//938KDg7W8uXLNXToUAUHB1+3pgYNGly3jn79+mn8+PFavny52rVrp23btunVV1916lwA/I+Hh4f93xaLxeF1Xltubu6tLgu4o93sdXf11O7X+x047/loSfL29nay0uJh5MqFunXrpuzsbGVnZ6tr165O7duwYUPt2rUrX3tycrJCQkLMKhEo1S5fvqylS5fq7bffdhhF2rNnj4KCgvTJJ5+of//+2rBhQ75nmHJzc/Xuu++qadOmCg0NLdE669SpIx8fH2VmZkq6EoqKqik0NLRYE1JUrFhRf/jDH7Ro0SItXLhQdevWtU8ZDwBAWVC/fn37Uq1atWLvV7FiRdWpU8f+GIFZGLlyIXd3dx04cMD+74KcPXs234etVa5cWaNHj9Z7771nfwjP29tbX331lRYsWKCPPvqopEsHSoUvvvhCv/32m4YPHy5fX1+HdX369NGCBQu0Y8cOrV69Wj179tTbb7+t8PBwpaen64033tDhw4f1zTffOH3cc+fOKS0tzaHNx8dHNptNkydP1u+//66HH35YQUFBOnPmjGbPnq1Lly6pc+fOkqTx48cXWtOBAwe0YcMGh9sCL1y4kO/nQIUKFVS/fn0NHz5crVu31v79+/WnP/2Jz7kDAKCYJk+erFGjRqlatWrq3r27zp07p2+++cb+8So3gnDlYtcbvkxMTMw3PXt0dLQWL16sr7/+Wv/3f/+nLl266OLFiwoODtbixYv1hz/8oSRLBkqNBQsWqFOnTvmClSQ9/vjjeuONN7R//35t3LhRsbGxiomJ0bFjx3T58mXVr19fP/zwg2rVquX0cf/85z/rz3/+s0Pbk08+qfnz56tt27aaO3euBg8erPT0dFWqVEmNGzfW+vXr7aPKXl5e2rRpk2JjYzVx4kQdO3ZMFStWVPv27ZWUlJTvAxYPHTqU7+dA27ZtlZiYqFatWikkJESHDx9WdHS00+cCAEBZFR0drYsXL+rdd9/Vn/70J1WpUkV9+vS5qT4tRlEfpgIAd6B169bpscce01tvvXXTn2cBAACQh2euAJQ53bt317p163T69Gn997//dXU5AADgDsHIFQAAAACYgJErAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAnJCYmymKx6MyZM8Xep06dOpo5c2aJ1QQAKB0IVwCAO8qQIUNksVg0atSofOtGjx4ti8WiIUOG3PrCAAB3PMIVAOCOExgYqPj4eF24cMHedvHiRS1btky1a9d2YWUAgDsZ4QoAcMdp0qSJateurZUrV9rbVq5cqcDAQDVu3NjelpWVpbFjx6patWry8vJSq1atlJyc7NDXP/7xDwUHB8vb21vt27fX0aNH8x1v+/btatOmjby9vRUYGKixY8cqMzOzxM4PAFA6Ea4AAHekoUOHatGiRfbXCxcu1LBhwxy2eeGFF7RixQotWbJE3333nerXr6+uXbvq9OnTkqQTJ06od+/eevjhh5WSkqIRI0bopZdecuhj79696tq1q3r37q3vv/9ey5cv17Zt2zRmzJiSP0kAQKlCuAIA3JGioqK0bds2HT16VMeOHdM333yjQYMG2ddnZmYqLi5OM2bMUPfu3RUaGqq//OUv8vb21oIFCyRJcXFxqlu3rt59912FhIRo4MCB+Z7XmjFjhgYMGKBnn31WDRo0UMuWLTV79mwtXbpUFy9evJWnDABwsXKuLgAAgJJQpUoV9ejRQ0uWLJFhGOrRo4eqVKliX3/kyBFdunRJDz30kL3Nw8NDzZs314EDByRJBw4cUIsWLWSxWOzbREREOBznn//8p3766Sd98skn9jbDMJSbm6t//etfatSoUUmdIgCglCFcAQDuWMOGDbPfnjd37lyHdYZhSJJDcMprz2vL26Youbm5evLJJzV27Nh865g8AwDKFm4LBADcsbp166bs7GxlZ2era9euDuvq168vT09Pbdu2zd526dIl7dq1yz7aFBoaqqSkJIf9rn3dpEkT7du3T/Xr18+3eHp6ltCZAQBKI8IVAOCO5e7urgMHDujAgQNyd3d3WFe+fHk99dRTev7555WQkKD9+/frj3/8o37//XcNHz5ckjRq1CgdOXJEEyZM0MGDB/Xpp59q8eLFDv28+OKL2rFjh55++mmlpKTo8OHDWrNmjZ555plbdZoAgFKCcAUAuKPZbDbZbLYC102bNk2PP/64oqKi1KRJE/3000/68ssvValSJUlXbutbsWKFPv/8c91///2aP3++3njjDYc+wsLCtGXLFh0+fFitW7dW48aNNWnSJFWvXr3Ezw0AULpYjOLcUA4AAAAAKBIjVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAn+H/m5fW78ECBhAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Preparing the DataFrame:\n", + "t1 = pd.DataFrame(columns=['kaq - opt', 'kaq - 95%'], index = ['MLU','AQTESOLV','TTim','TTim - rc']) \n", + "simulation = ['MLU','AQTESOLV','TTim','TTim - rc']\n", + "t1.loc['MLU'] = [38.094, 2.622*1e-2*38.094]\n", + "t1.loc['AQTESOLV'] = [37.803, 2.745*1e-2*37.803]\n", + "t1.loc['TTim'] = [ca_2.parameters.loc['kaq0','optimal'],2*ca_2.parameters.loc['kaq0','std']]\n", + "t1.loc['TTim - rc'] = [ca_4.parameters.loc['kaq0','optimal'],2*ca_4.parameters.loc['kaq0','std']]\n", + "\n", + "# Plotting\n", + "\n", + "plt.figure(figsize = (10,7))\n", + "\n", + "plt.errorbar(x = t1.index, y = t1['kaq - opt'], yerr = [t1['kaq - 95%'], t1['kaq - 95%']],\n", + " marker='o', linestyle='', markersize=12)\n", + "#plt.legend()\n", + "plt.suptitle(\"Error bar plot of calibrated hydraulic conductivities\")\n", + "plt.ylabel('K [m/d]')\n", + "plt.ylim([36,40])\n", + "plt.xlabel('Model');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Error bar plot shows that TTim has similar confidence intervals to the other models. The model with wellbore storage has a slightly smaller error range." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Newville, M.,Stensitzki, T., Allen, D.B., Ingargiola, A. (2014) LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python.https://dx.doi.org/10.5281/zenodo.11813. https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021).\n", + "* Walton, W.C., 1962. Selected analytical methods for well and aquifer evaluation. Illinois.department of Registration & Education.bulletin 49.\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pumpingtests/confined3_sioux.ipynb b/pumpingtests/confined3_sioux.ipynb new file mode 100644 index 0000000..3eb0f8d --- /dev/null +++ b/pumpingtests/confined3_sioux.ipynb @@ -0,0 +1,1089 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Confined Aquifer Test - Sioux Example\n", + "**This test is taken from AQTESOLV examples.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model\n", + "\n", + "In this example, we reproduce the work of Yang (2020) to use the pumping test data to demonstrate how TTim can be used to model and analyze pumping tests in a single layer, confined setting, in multiple piezometers. Furthermore, we compare the performance of TTim with other transient well hydraulics software AQTESOLV (Duffield, 2007) and MLU (Carlson and Randall, 2012).\n", + "\n", + "This example is a pumping test done in Sioux Flats, South Dakota, USA. The data comes from the AQTESOLV documentation (Duffield, 2007).\n", + "The aquifer is 50 ft thick and is bounded by impermeable layers. The test was conducted for 2045 minutes (~34 hours), with a constant pumping rate of 2.7 $ft^3/s$. Drawdown data has been collected at three piezometers located 100, 200 and 400 ft away, respectively. The well radius is 0.5 ft.\n", + "\n", + "We can resume the conceptual model in the picture below:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1,1,1)\n", + "#sky\n", + "sky = plt.Rectangle((-50,0), width = 500, height = 3, fc = 'b', zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "#Aquifer:\n", + "ground = plt.Rectangle((-50,-55), width = 500, height = 50, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9)\n", + "ax.add_patch(ground)\n", + "\n", + "#Confining bed:\n", + "confining_unit = plt.Rectangle((-50,-5), width = 500, height = 5, fc = np.array([100,100,100])/255, zorder=0, alpha=0.7)\n", + "ax.add_patch(confining_unit)\n", + "well = plt.Rectangle((-4,-55), width = 8, height = 55, fc = np.array([200,200,200])/255, zorder=1)\n", + "ax.add_patch(well)\n", + "\n", + "#Wellhead\n", + "wellhead = plt.Rectangle((-5,0),width = 10, height = 1.5, fc = np.array([200,200,200])/255, zorder=2, ec='k')\n", + "ax.add_patch(wellhead)\n", + "\n", + "#Screen for the well:\n", + "screen = plt.Rectangle((-4,-55), width = 8, height = 50, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x = 5,y = 0.75, dx = 8, dy = 0, color = \"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x = 13, y = 0.75, s = r'$ Q = 2.7 \\frac{ft^3}{s}$', fontsize = 'x-large' )\n", + "#Piezometers\n", + "piez1 = plt.Rectangle((98,-55), width = 4, height = 55,fc = np.array([200,200,200])/255, zorder=1)\n", + "piez2 = plt.Rectangle((198,-55), width = 4, height = 55,fc = np.array([200,200,200])/255, zorder=1)\n", + "piez3 = plt.Rectangle((398,-55), width = 4, height = 55,fc = np.array([200,200,200])/255, zorder=1)\n", + "\n", + "screen_piez_1 = plt.Rectangle((98,-55), width = 4, height = 50, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_1.set_linewidth(2)\n", + "screen_piez_2 = plt.Rectangle((198,-55), width = 4, height = 50, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_2.set_linewidth(2)\n", + "screen_piez_3 = plt.Rectangle((398,-55), width = 4, height = 50, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_3.set_linewidth(2)\n", + "ax.add_patch(piez1)\n", + "ax.add_patch(piez2)\n", + "ax.add_patch(piez3)\n", + "ax.add_patch(screen_piez_1)\n", + "ax.add_patch(screen_piez_2)\n", + "ax.add_patch(screen_piez_3)\n", + "#last line\n", + "line = plt.Line2D(xdata= [-50,500], ydata = [0,0], color = \"k\")\n", + "ax.add_line(line)\n", + "ax.text(x = 100, y = 0.75, s = 'P100', fontsize = 'large' )\n", + "ax.text(x = 200, y = 0.75, s = 'P200', fontsize = 'large' )\n", + "ax.text(x = 400, y = 0.75, s = 'P400', fontsize = 'large' )\n", + "ax.set_xlim([-50,450])\n", + "ax.set_ylim([-55,3])\n", + "ax.set_title('Conceptual Model - Sioux Example');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Load Required Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import ttim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters\n", + "\n", + "- We will work with time in days and length in meters from this step onwards\n", + "- The parameters below have already been converted to m and days." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "Q = 6605.754 #constant discharge in m^3/d\n", + "b = -15.24 #aquifer thickness in m\n", + "rw = 0.1524 #well radius in m\n", + "r1 = 30.48 #distance between obs1 to pumping well\n", + "r2 = 60.96 #distance between obs2 to pumping well\n", + "r3 = 121.92 #distance between obs3 to pumping well" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Load data of observation and pumping well\n", + "\n", + "The preferred method of loading data into TTim is to use numpy arrays.\n", + "\n", + "The data is in a text file where the first column is the time data in ***days*** and the second column is the drawdown in ***meters***\n", + "\n", + "For each piezometer, we will load the data as a numpy array. We further split the data into two different 1d arrays, one for time and another for drawdown." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data1 = np.loadtxt('data/sioux100.txt')\n", + "t1 = data1[:, 0]\n", + "h1 = data1[:, 1]\n", + "\n", + "\n", + "data2 = np.loadtxt('data/sioux200.txt')\n", + "t2 = data2[:, 0]\n", + "h2 = data2[:, 1]\n", + "\n", + "data3 = np.loadtxt('data/sioux400.txt')\n", + "t3 = data3[:, 0]\n", + "h3 = data3[:, 1]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Step 4. Creating a TTim conceptual model\n", + "\n", + "In this example, we are using the ModelMaq model to conceptualize our aquifer. ModelMaq defines the aquifer system as a stacked vertical sequence of aquifers and leaky layers (aquifer-leaky layer, aquifer-leaky layer, etc). A thorough explanation of the ModelMaq and TTim one-layer modelling conceptualization is given in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_0 = ttim.ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=10, topboundary='conf')\n", + "w_0 = ttim.Well(ml_0, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers = 0)\n", + "ml_0.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Calibration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The calibration workflow has been described in detail in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.1. Model Calibration with all observation wells\n", + "\n", + "We calibrate the model with all observation wells.\n", + "We begin by assuming no wellbore storage or skin resistance, and we only calibrate the hydraulic conductivity and specific storage" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".....................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 34\n", + " # data points = 77\n", + " # variables = 2\n", + " chi-square = 0.00121634\n", + " reduced chi-square = 1.6218e-05\n", + " Akaike info crit = -847.289943\n", + " Bayesian info crit = -842.602332\n", + "[[Variables]]\n", + " kaq0: 282.794907 +/- 1.13788442 (0.40%) (init = 10)\n", + " Saq0: 0.00420857 +/- 3.3461e-05 (0.80%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.8113\n" + ] + } + ], + "source": [ + "#unknown parameters: k, Saq\n", + "ca_0 = ttim.Calibrate(ml_0)\n", + "ca_0.set_parameter(name='kaq0', initial=10)\n", + "ca_0.set_parameter(name='Saq0', initial=1e-4)\n", + "ca_0.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_0.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_0.series(name='obs3', x=r3, y=0, t=t3, h=h3, layer=0)\n", + "ca_0.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0282.7949071.1378840.402371-infinf10.0000[282.7949071561833]
Saq00.0042090.0000330.795069-infinf0.0001[0.004208570397720751]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 282.794907 1.137884 0.402371 -inf inf 10.0000 \n", + "Saq0 0.004209 0.000033 0.795069 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0 [282.7949071561833] \n", + "Saq0 [0.004208570397720751] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.0039744982325343485\n" + ] + } + ], + "source": [ + "display(ca_0.parameters)\n", + "print('RMSE:', ca_0.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Model has achieved a good fit and parameters with a low confidence interval" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_0 = ml_0.head(x = r1, y = 0,t = t1)\n", + "hm2_0 = ml_0.head(x = r2, y = 0, t = t2)\n", + "hm3_0 = ml_0.head(x = r3, y = 0, t = t3)\n", + "plt.figure(figsize = (10, 7))\n", + "plt.semilogx(t1, h1, '.', label='obs1')\n", + "plt.semilogx(t1, hm1_0[0], label='ttim result 1')\n", + "plt.semilogx(t2, h2, '.', label='obs2')\n", + "plt.semilogx(t2, hm2_0[0], label='ttim result 2')\n", + "plt.semilogx(t3, h3, '.', label='obs3')\n", + "plt.semilogx(t3, hm3_0[0], label='ttim result 3')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.legend()\n", + "plt.suptitle('Calibration Results vs Observations - Model 1');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visually, the model seems to have a good fit with the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.2. Model Calibration with skin resistance and wellbore storage\n", + "\n", + "In this new model, we investigate whether the well parameters are relevant in the fit.\n", + "\n", + "We begin by reloading the model and creating a ```Well``` object with two extra parameters:\n", + "\n", + "* The radius of the caisson ```rc```, which we use to simulate wellbore storage. In this case, we use a value in meters (float);\n", + "* The skin resistance ```res```, a float value with the unit in days." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_1 = ttim.ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=10, topboundary='conf')\n", + "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=rw, rc=0, res=0, tsandQ=[(0, Q)], layers=0)\n", + "ml_1.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we use the method ```.set_parameter_by_reference``` to calibrate the ```rc``` and ```res``` parameters in our well.\n", + "\n", + "```.set_parameter_by_reference``` takes the following arguments:\n", + "* ```name```: a string of the parameter name\n", + "* ```parameter```: numpy-array with the parameter to be optimized. It should be specified as a reference, for example, in our case: ```w1.rc[0:]``` referencing to the parameter ```rc``` in object ```w1```.\n", + "* ```initial```: float with the initial guess for the parameter value.\n", + "* ```pmin``` and ```pmax```: floats with the minimum and maximum values allowed. If not specified, these will be ```-np.inf``` and ```np.inf```." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...............................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 172\n", + " # data points = 77\n", + " # variables = 4\n", + " chi-square = 0.12810129\n", + " reduced chi-square = 0.00175481\n", + " Akaike info crit = -484.702933\n", + " Bayesian info crit = -475.327711\n", + "[[Variables]]\n", + " kaq0: 275.758881 +/- 13.7289200 (4.98%) (init = 10)\n", + " Saq0: 0.00261417 +/- 3.6976e-04 (14.14%) (init = 0.0001)\n", + " res: 9.5352e-10 +/- 6.8790e-06 (721434.55%) (init = 0)\n", + " rc: 5.60604682 +/- 0.60256208 (10.75%) (init = 0)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.8605\n", + " C(Saq0, rc) = -0.7868\n", + " C(kaq0, rc) = +0.5822\n", + " C(kaq0, res) = -0.3280\n", + " C(Saq0, res) = +0.1666\n" + ] + } + ], + "source": [ + "#unknown parameters: k, Saq, res, rc\n", + "ca_1 = ttim.Calibrate(ml_1)\n", + "ca_1.set_parameter(name='kaq0', initial=10)\n", + "ca_1.set_parameter(name='Saq0', initial=1e-4)\n", + "ca_1.set_parameter_by_reference(name='res', parameter=w_1.res, initial=0, pmin = 0)\n", + "ca_1.set_parameter_by_reference(name='rc', parameter=w_1.rc, initial=0)\n", + "ca_1.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_1.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_1.series(name='obs3', x=r3, y=0, t=t3, h=h3, layer=0)\n", + "ca_1.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq02.757589e+0213.7289204.978596-infinf10.0000[275.7588811873042]
Saq02.614165e-030.00037014.144592-infinf0.0001[0.0026141651677158927]
res9.535202e-100.000007721434.5484330.0inf0.0000[9.53520151725229e-10]
rc5.606047e+000.60256210.748431-infinf0.0000[5.606046819073966]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 2.757589e+02 13.728920 4.978596 -inf inf 10.0000 \n", + "Saq0 2.614165e-03 0.000370 14.144592 -inf inf 0.0001 \n", + "res 9.535202e-10 0.000007 721434.548433 0.0 inf 0.0000 \n", + "rc 5.606047e+00 0.602562 10.748431 -inf inf 0.0000 \n", + "\n", + " parray \n", + "kaq0 [275.7588811873042] \n", + "Saq0 [0.0026141651677158927] \n", + "res [9.53520151725229e-10] \n", + "rc [5.606046819073966] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.04078790466973798\n" + ] + } + ], + "source": [ + "display(ca_1.parameters)\n", + "print('RMSE:', ca_1.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When adding both res and rc into calibration, the optimized res value is very close to 0. Moreover, the standard deviation is way above any reasonable limit. Thus, adding res has nearly no effect on improving the conceptual model's performance. Therefore, ```res``` is removed from the calibration." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 48\n", + " # data points = 77\n", + " # variables = 3\n", + " chi-square = 0.00116245\n", + " reduced chi-square = 1.5709e-05\n", + " Akaike info crit = -848.779358\n", + " Bayesian info crit = -841.747942\n", + "[[Variables]]\n", + " kaq0: 283.922147 +/- 1.28530612 (0.45%) (init = 10)\n", + " Saq0: 0.00415480 +/- 4.3877e-05 (1.06%) (init = 0.0001)\n", + " rc: 0.78983676 +/- 0.21260242 (26.92%) (init = 0)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.8522\n", + " C(Saq0, rc) = -0.6690\n", + " C(kaq0, rc) = +0.4874\n" + ] + } + ], + "source": [ + "#unknown parameters: k, Saq, res, rc\n", + "ca_1 = ttim.Calibrate(ml_1)\n", + "ca_1.set_parameter(name='kaq0', initial=10)\n", + "ca_1.set_parameter(name='Saq0', initial=1e-4)\n", + "#ca_1.set_parameter_by_reference(name='res', parameter=w_1.res, initial=0, pmin = 0)\n", + "ca_1.set_parameter_by_reference(name='rc', parameter=w_1.rc, initial=0)\n", + "ca_1.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_1.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_1.series(name='obs3', x=r3, y=0, t=t3, h=h3, layer=0)\n", + "ca_1.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0283.9221471.2853060.452697-infinf10.0000[283.9221470098433]
Saq00.0041550.0000441.056061-infinf0.0001[0.004154797612454171]
rc0.7898370.21260226.917261-infinf0.0000[0.7898367647324968]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 283.922147 1.285306 0.452697 -inf inf 10.0000 \n", + "Saq0 0.004155 0.000044 1.056061 -inf inf 0.0001 \n", + "rc 0.789837 0.212602 26.917261 -inf inf 0.0000 \n", + "\n", + " parray \n", + "kaq0 [283.9221470098433] \n", + "Saq0 [0.004154797612454171] \n", + "rc [0.7898367647324968] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.003885454501818169\n" + ] + } + ], + "source": [ + "display(ca_1.parameters)\n", + "print('RMSE:', ca_1.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The new model has better statistics: lower AIC and BIC, lower RMSE and lower standard deviations for hydraulic conductivities and specific storage. We proceed with plotting the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_1 = ml_1.head(r1, 0, t1)\n", + "hm2_1 = ml_1.head(r2, 0, t2)\n", + "hm3_1 = ml_1.head(r3, 0, t3)\n", + "plt.figure(figsize = (10, 7))\n", + "plt.semilogx(t1, h1, '.', label='obs1')\n", + "plt.semilogx(t1, hm1_1[0], label='ttim1')\n", + "plt.semilogx(t2, h2, '.', label='obs2')\n", + "plt.semilogx(t2, hm2_1[0], label='ttim2')\n", + "plt.semilogx(t3, h3, '.', label='obs3')\n", + "plt.semilogx(t3, hm3_1[0], label='ttim3')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.legend()\n", + "plt.suptitle('Model Calibration Results - Model 2');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the plot above, we can see that there is good agreement between the model calculated heads and the observed ones for all observation wells" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Analysis of Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6.1. Analysis of Fit Statistics\n", + "\n", + "Some of the fit statistics are stored in the ```fitresult``` attribute of the calibration object. This object is a lmfit ```MinimizerResult``` object. ```lmfit``` is the python library doing the calibration for TTim behind the scenes. We accessed below the AIC and BIC values of this object.\n", + "\n", + "Check the lmfit documentation (Newville et al. 2014) to learn more about this object and lmfit." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Fit statistics for the tested models
 RMSEAICBICCalibration scheme
Model 10.003974-847.289943-842.602332All Obs Wells
Model 20.003885-848.779358-841.747942All Obs Wells + wellbore storage
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(columns = ['RMSE', 'AIC', 'BIC','Calibration scheme'], index = ['Model 1', 'Model 2'])\n", + "\n", + "t['RMSE'] = [ca_0.rmse(), ca_1.rmse()]\n", + "\n", + "t['AIC'] = [ca_0.fitresult.aic, ca_1.fitresult.aic]\n", + "\n", + "t['BIC'] = [ca_0.fitresult.bic, ca_1.fitresult.bic]\n", + "\n", + "t['Calibration scheme'] = [\"All Obs Wells\", \"All Obs Wells + wellbore storage\"]\n", + "t.style.set_caption(\"Fit statistics for the tested models\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The fit statistics show that the models have very similar performance as all indicators are closely related. By RMSE and AIC criteria, we should pick Model 2, while by BIC, we should pick Model 1. The result means that we cannot exclude one model in favour of the other." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6.2. Summary of Calibrated Parameters and comparison with different Software solutions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We present the results simulated in TTim under different configurations below. Furthermore, Yang (2020) compared TTim results with the results obtained from the software AQTESOLV (Duffield, 2007) and MLU (Carlson & Randall, 2012). In both software, the model was calibrated with observations." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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k [m/d]Ss [1/m]rcRMSE
AQTESOLV282.6590.004211-0.003925
MLU282.6840.004209-0.003897
ttim282.79490715618330.004208570397720751-0.003974
ttim-rc283.9221470.0041550.7898370.003885
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" + ], + "text/plain": [ + " k [m/d] Ss [1/m] rc RMSE\n", + "AQTESOLV 282.659 0.004211 - 0.003925\n", + "MLU 282.684 0.004209 - 0.003897\n", + "ttim 282.7949071561833 0.004208570397720751 - 0.003974\n", + "ttim-rc 283.922147 0.004155 0.789837 0.003885" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'rc'], \\\n", + " index=['AQTESOLV', 'MLU', 'ttim', 'ttim-rc'])\n", + "t.loc['AQTESOLV'] = [282.659, 4.211E-03, '-']\n", + "t.loc['ttim'] = np.append(ca_0.parameters['optimal'].values, '-')\n", + "t.loc['ttim-rc'] = ca_1.parameters['optimal'].values\n", + "t.loc['MLU'] = [282.684, 4.209e-03, '-']\n", + "t['RMSE'] = [0.003925, 0.003897, ca_0.rmse(), ca_1.rmse()]\n", + "t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TTim achieved similar results with the other software. The TTim model with wellbore storage had a slightly better RMSE error." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Preparing the DataFrame:\n", + "t1 = pd.DataFrame(columns=['kaq - opt', 'kaq - 95%'], index = ['MLU','AQTESOLV','TTim','TTim - rc']) \n", + "simulation = ['MLU','AQTESOLV','TTim','TTim - rc']\n", + "t1.loc['MLU'] = [282.684, 0.783*1e-2*282.6842]\n", + "t1.loc['AQTESOLV'] = [282.659, 0.394*1e-2*282.659]\n", + "t1.loc['TTim'] = [ca_0.parameters.loc['kaq0','optimal'],2*ca_0.parameters.loc['kaq0','std']]\n", + "t1.loc['TTim - rc'] = [ca_1.parameters.loc['kaq0','optimal'],2*ca_0.parameters.loc['kaq0','std']]\n", + "\n", + "# Plotting\n", + "\n", + "plt.figure(figsize = (10,7))\n", + "\n", + "plt.errorbar(x = t1.index, y = t1['kaq - opt'], yerr = [t1['kaq - 95%'], t1['kaq - 95%']],\n", + " marker='o', linestyle='', markersize=12)\n", + "#plt.legend()\n", + "plt.suptitle(\"Error bar plot of calibrated hydraulic conductivities\")\n", + "plt.ylabel('K [m/d]')\n", + "plt.ylim([278,289])\n", + "plt.xlabel('Model');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Error bar plot shows AQTESOLV with the lowest confidence interval. The models in TTim have larger confidence intervals. However, they are still small. All models seem to agree, and there is a wide overlap in the confidence intervals for hydraulic conductivity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Newville, M.,Stensitzki, T., Allen, D.B., Ingargiola, A. (2014) LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python.https://dx.doi.org/10.5281/zenodo.11813. https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021).\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pumpingtests/confined4_schroth.ipynb b/pumpingtests/confined4_schroth.ipynb new file mode 100644 index 0000000..f8faee1 --- /dev/null +++ b/pumpingtests/confined4_schroth.ipynb @@ -0,0 +1,1832 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Confined Aquifer Test - Schroth\n", + "**This test is taken from examples presented in MLU tutorial.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This test example was a pumping test conducted near San Francisco, US, and reported by Brian et al. (1997).\n", + "The system consists of a confined system of two aquifers separated by an aquitard layer. The upper aquifer layer is located from 46 to 49 m depth, followed by an aquitard layer from 49 to 52 m depth and the second aquifer at 52 to 55 m depth.\n", + "\n", + "The lower aquifer is pumped by a well, named EW-712 that fully penetrates the formation. An observation well is placed 46 m away from the well, in the lower aquifer formation, and it is named MW-616. The last observation well is placed in the upper aquifer right on top of the pumping well. However, data was not available for this well. The radius of all wells was 0.05 m.\n", + "\n", + "All wells before pumping had water levels around 20 m depth, which means that the system can be characterized as confined.\n", + "\n", + "The time-drawdown data for the observation well MW-616 and pumping well EW-712 was obtained from MLU documentation (Duffield, 2007).\n", + "\n", + "In this example, we are reproducing the results obtained by Yang (2020). We will use TTim to test two hypotheses: the first is that the lower aquifer is, by confinement, disconnected to the upper aquifer, and the second is there is enough leakage from the upper aquifer to consider a leaky aquifer relation between them.\n", + "\n", + "A simplified cross-section of the model area can be seen below:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1,1,1)\n", + "#sky\n", + "sky = plt.Rectangle((-20,0), width = 100, height = 3, fc = 'b', zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "#Aquifer 1:\n", + "ground = plt.Rectangle((-20,-55), width = 100, height = 3, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9)\n", + "ax.add_patch(ground)\n", + "\n", + "#Aquifer 2:\n", + "\n", + "ground = plt.Rectangle((-20,-49), width = 100, height = 3, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9)\n", + "ax.add_patch(ground)\n", + "\n", + "#Confining bed:\n", + "confining_unit = plt.Rectangle((-20,-52), width = 100, height = 3, fc = np.array([100,100,100])/255, zorder=0, alpha=0.7)\n", + "ax.add_patch(confining_unit)\n", + "\n", + "well = plt.Rectangle((-1.5,-55), width = 3, height = 55, fc = np.array([200,200,200])/255, zorder=1)\n", + "ax.add_patch(well)\n", + "\n", + "#Wellhead\n", + "wellhead = plt.Rectangle((-2.5,0),width = 5, height = 1.5, fc = np.array([200,200,200])/255, zorder=2, ec='k')\n", + "ax.add_patch(wellhead)\n", + "\n", + "#Screen for the well:\n", + "screen = plt.Rectangle((-1.5,-55), width = 3, height = 3, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x = 5,y = 0.75, dx = 3, dy = 0, color = \"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x = 9, y = 0.75, s = r'$ Q = 220 \\frac{gal}{min}$', fontsize = 'large' )\n", + "#Piezometers\n", + "piez1 = plt.Rectangle((45,-55), width = 2, height = 55,fc = np.array([200,200,200])/255, zorder=1)\n", + "screen_piez_1 = plt.Rectangle((45,-55), width = 2, height = 3, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_1.set_linewidth(2)\n", + "\n", + "ax.add_patch(piez1)\n", + "\n", + "ax.add_patch(screen_piez_1)\n", + "\n", + "#last line\n", + "line = plt.Line2D(xdata= [-20,100], ydata = [0,0], color = \"k\")\n", + "ax.add_line(line)\n", + "ax.text(x = 45, y = 0.75, s = 'Obs Well 1', fontsize = 'large' )\n", + "\n", + "ax.text(x = -17, y = -40, s = 'Upper Formations', fontsize = 'large', bbox=dict(facecolor='w', alpha=0.9) )\n", + "ax.text(x = -17, y = -48, s = 'Upper Aquifer', fontsize = 'large' )\n", + "ax.text(x = -17, y = -51, s = 'Aquitard', fontsize = 'large' )\n", + "ax.text(x = -17, y = -54, s = 'Lower Aquifer', fontsize = 'large' )\n", + "# Complete the figure:\n", + "\n", + "upper_formations = plt.Rectangle((-20,-46), width = 100, height = 46, fc = 'white', hatch = '-/', zorder=0, alpha=0.9)\n", + "ax.add_patch(upper_formations)\n", + "\n", + "ax.set_xlim([-20,80])\n", + "ax.set_ylim([-55,3])\n", + "ax.set_xlabel('Distance [m]')\n", + "ax.set_ylabel('Relative height [m]')\n", + "ax.set_title('Conceptual Model - Schroth Example');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import ttim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "Q = 82.08 #constant discharge in m^3/d\n", + "zt0 = -46 #top boundary of upper aquifer in m\n", + "zb0 = -49 #bottom boundary of upper aquifer in m\n", + "zt1 = -52 #top boundary of lower aquifer in m\n", + "zb1 = -55 #bottom boundary of lower aquifer in m\n", + "rw = 0.05 #well radius in m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Load data of the pumping and observation well\n", + "\n", + "The preferred method of loading data into TTim is to use numpy arrays.\n", + "\n", + "The data is in a text file where the first column is the time data in ***days*** and the second column is the drawdown in ***meters***\n", + "\n", + "The observation well is referred to as ***Well 1*** and the pumping well as ***Well 3***.\n", + "\n", + "For each piezometer, we will load the data as a numpy array. We further split the data into two different 1d arrays, one for time and another for drawdown. Finally, we convert the time data from minutes to days" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Loading data for the pumping well\n", + "data1 = np.loadtxt('data/schroth_obs1.txt', skiprows = 1)\n", + "t1 = data1[:, 0]\n", + "h1 = data1[:, 1]\n", + "r1 = 0 #Pumping well is at distance 0 to pumping\n", + "\n", + "# Loading data for the observation well\n", + "data2 = np.loadtxt('data/schroth_obs2.txt', skiprows = 1)\n", + "t2 = data2[:, 0]\n", + "h2 = data2[:, 1]\n", + "r2 = 46 #distance between observation well2 and pumping well" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Step 4. Create TTim model\n", + "\n", + "We begin by considering the underlying aquifer as a single confined aquifer (overlying aquifer and aquitard are excluded).\n", + "\n", + "In this example, we are using the ModelMaq model to conceptualize our aquifer. ModelMaq defines the aquifer system as a stacked vertical sequence of aquifers and leaky layers (aquifer-leaky layer, aquifer-leaky layer, etc). A thorough explanation of the ModelMaq and TTim one-layer modelling conceptualization is given in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_0 = ttim.ModelMaq(z=[zt1, zb1], kaq=10, Saq=1e-4, tmin=1e-4, tmax=1)\n", + "w_0 = ttim.Well(ml_0, xw=0, yw=0, rw=rw, tsandQ = [(0, Q), (1e+08, 0)])\n", + "ml_0.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Calibration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The calibration workflow has been described in detail in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 119\n", + " # data points = 40\n", + " # variables = 2\n", + " chi-square = 111.249393\n", + " reduced chi-square = 2.92761561\n", + " Akaike info crit = 44.9158005\n", + " Bayesian info crit = 48.2935594\n", + "[[Variables]]\n", + " kaq0: 1.03195576 +/- 0.10473795 (10.15%) (init = 10)\n", + " Saq0: 0.04015499 +/- 0.02030043 (50.56%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.9309\n" + ] + } + ], + "source": [ + "ca_0 = ttim.Calibrate(ml_0)\n", + "ca_0.set_parameter(name='kaq0', initial=10)\n", + "ca_0.set_parameter(name='Saq0', initial=1e-4)\n", + "ca_0.series(name='well', x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_0.series(name='obs_well', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_0.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq01.0319560.10473810.149461-infinf10.0000[1.0319557609869272]
Saq00.0401550.02030050.555188-infinf0.0001[0.04015499415257017]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 1.031956 0.104738 10.149461 -inf inf 10.0000 \n", + "Saq0 0.040155 0.020300 50.555188 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0 [1.0319557609869272] \n", + "Saq0 [0.04015499415257017] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 1.6677034592607083\n" + ] + } + ], + "source": [ + "display(ca_0.parameters)\n", + "print('RMSE:', ca_0.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's plot the model with our observation data:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_0 = ml_0.head(r1, 0, t1)\n", + "hm2_0 = ml_0.head(r2, 0, t2)\n", + "plt.figure(figsize = (8, 5))\n", + "plt.semilogx(t1, h1, '.', label='well')\n", + "plt.semilogx(t2, h2, '.', label='obs_well')\n", + "plt.semilogx(t1, hm1_0[-1], label='ttim model - well')\n", + "plt.semilogx(t2, hm2_0[-1], label='ttim model - obs_well')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('head [m]')\n", + "plt.legend()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The figure shows a poor fit specially for the well. Probably well effects might be relevant in this case, so we will try to calibrate them next." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5.2. Model Calibration with skin resistance and wellbore storage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To improve the fit, we will try to calibrate the model adding wellbore storage and skin resistance to the pumping well.\n", + "\n", + "For this, we create a new model and add two extra parameters to the ```Well``` object:\n", + "\n", + "* The radius of the caisson ```rc```, which we use to simulate wellbore storage. In this case, we use a value in meters (float);\n", + "* The skin resistance ```res```, a float value with the unit in days." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_1 = ttim.ModelMaq(z=[zt1, zb1], kaq=10, Saq=1e-4, tmin=1e-4, tmax=1)\n", + "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=rw, rc=0, res=5, tsandQ = [(0, Q), (1e+08, 0)])\n", + "ml_1.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we use the method ```.set_parameter_by_reference``` to calibrate the ```rc``` and ```res``` parameters in our well.\n", + "\n", + "```.set_parameter_by_reference``` takes the following arguments:\n", + "* ```name```: a string of the parameter name\n", + "* ```parameter```: numpy-array with the parameter to be optimized. It should be specified as a reference, for example, in our case: ```w1.rc[0:]``` referencing to the parameter ```rc``` in object ```w1```.\n", + "* ```initial```: float with the initial guess for the parameter value.\n", + "* ```pmin``` and ```pmax```: floats with the minimum and maximum values allowed. If not specified, these will be ```-np.inf``` and ```np.inf```." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".....................................................................................................................................................................................................................................................................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 423\n", + " # data points = 40\n", + " # variables = 4\n", + " chi-square = 13.6479966\n", + " reduced chi-square = 0.37911102\n", + " Akaike info crit = -35.0114685\n", + " Bayesian info crit = -28.2559507\n", + "[[Variables]]\n", + " kaq0: 1.95214232 +/- 0.05267396 (2.70%) (init = 10)\n", + " Saq0: 1.1461e-04 +/- 3.3111e-05 (28.89%) (init = 0.0001)\n", + " rc: 0.00247716 +/- 0.02267359 (915.31%) (init = 0.2)\n", + " res: 43.4469287 +/- 797.527160 (1835.64%) (init = 3)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(rc, res) = -1.0000\n", + " C(kaq0, Saq0) = -0.8652\n", + " C(Saq0, rc) = -0.1040\n", + " C(Saq0, res) = +0.1027\n" + ] + } + ], + "source": [ + "ca_1 = ttim.Calibrate(ml_1)\n", + "ca_1.set_parameter(name='kaq0', initial=10)\n", + "ca_1.set_parameter(name='Saq0', initial=1e-4)\n", + "ca_1.set_parameter_by_reference(name='rc', parameter=w_1.rc[:], initial=0.2)\n", + "ca_1.set_parameter_by_reference(name='res', parameter=w_1.res[:], initial=3)\n", + "ca_1.series(name='well', x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_1.series(name='obs_well', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_1.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq01.9521420.0526742.698265-infinf10.0000[1.952142324081202]
Saq00.0001150.00003328.889289-infinf0.0001[0.00011461313147626868]
rc0.0024770.022674915.307610-infinf0.2000[0.0024771553087048403]
res43.446929797.5271601835.635300-infinf3.0000[43.4469287097652]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 1.952142 0.052674 2.698265 -inf inf 10.0000 \n", + "Saq0 0.000115 0.000033 28.889289 -inf inf 0.0001 \n", + "rc 0.002477 0.022674 915.307610 -inf inf 0.2000 \n", + "res 43.446929 797.527160 1835.635300 -inf inf 3.0000 \n", + "\n", + " parray \n", + "kaq0 [1.952142324081202] \n", + "Saq0 [0.00011461313147626868] \n", + "rc [0.0024771553087048403] \n", + "res [43.4469287097652] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.5841232018739387\n" + ] + } + ], + "source": [ + "display(ca_1.parameters)\n", + "print('RMSE:', ca_1.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_1 = ml_1.head(r1, 0, t1)\n", + "hm2_1 = ml_1.head(r2, 0, t2)\n", + "plt.figure(figsize = (10, 7))\n", + "plt.semilogx(t1, h1, '.', label='well')\n", + "plt.semilogx(t2, h2, '.', label='obs_well')\n", + "plt.semilogx(t1, hm1_1[-1], label='ttim model - well')\n", + "plt.semilogx(t2, hm2_1[-1], label='ttim model - obs_well')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.legend()\n", + "plt.suptitle('Model Results - One Aquifer + Well Parameters');\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model has improved significantly. However, we can visually see that the fit is not very good for late time data. Let us investigate if we can do better with a three-layer model such as the one defined in our conceptual model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Step 6. Create a two-aquifer conceptual model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Until so far, we have only considered horizontal flow in our TTim models. The assumption is sufficient in fully-penetrated wells in confined aquifers. However, it might not represent the situation so well in a more complex scenario, where the vertical flow is a relevant component of the groundwater flow. In TTim, this is done by assigning more layers to the model.\n", + "\n", + "An advantage of TTim is the ability to create a model with more than one aquifer layer. We will explore this feature under the ```ModelMaq``` model as in [Step4](#step_4).\n", + "\n", + "When we use ModelMaq, the leaky layers are located in between the aquifers. These leaky layers only have vertical flow and are characterized by the parameters resistance to vertical flow (```c```) and storage (```Sll```). The specific flux is computed as (Bakker, 2013):\n", + "\n", + "$$q_n = \\frac{h_n-h_{n-1}}{c_n}$$\n", + "\n", + "where $q_n$ is the vertical flux from layer $n$ to layer $n-1$, $h_n$ is the head in layer $n$ and $c_n$ is the vertical resistance to flow. $c_n$ is computed as: $H_n/k_n$ where, $H_n$ is the leaky-layer thickness and $k_n$ the vertical hydraulic conductance. $c_n$ is the inverse of the parameter Leakance ($L_n = 1/c_n$), that is used in MODFLOW (Harbaugh, 2005) or analytical solutions of leaky-layers, such as in Hantush (1955).\n", + "\n", + "Alternatively, we can also model the interface of two aquifers that are not separated by a leaky layer. In that case, the leaky layer is set to 0 m thickness, and the model calculates the resistance to vertical flow by a finite differences scheme (Bakker, 2013).\n", + "\n", + "In the model construction, we have to set the parameters for each layer (which consists of an aquifer layer and an aquitard layer).\n", + "\n", + "For our two-layer model, we have to set:\n", + "\n", + "- The hydraulic conductivity: ```kaq```. It is a list/array with a float element for every aquifer, for example: ```[kaq0,kaq1]```.\n", + "- The top and bottom of each aquifer: ```z``` defined by a list/array ```[zt0,zb0,zt1,zb1,...]```, where the inputs are a sequence of top and bottoms of the aquifer layers. The aquitard layers are defined by the space between the aquifer layers. The top of the first aquitard is the bottom of the first aquifer, the bottom of the first aquitard is the top of the second aquifer, and so on. But they can also have 0 thickness. In case the parameter ```topboundary``` is set to ```semi```, where we assume we have semi-confined conditions, we have to add one more element to the beginning of the list, which is the top of the aquitard layer that is above of the first aquifer.\n", + "- The specific storage: ```Saq```. It is a list/array with a float element for every aquifer, for example: ```[Saq0, Saq1]```.\n", + "- The minimum time for which TTim solve the groundwater flow: ```tmin```, a float.\n", + "- And the maximum time: ```tmax```, float.\n", + "- ```topboundary``` is the parameter that sets whether the upper layer is a confined unit or a semi-confined unit. We can assign this parameter as:\n", + " * ```'conf'```: This means that the upper layer is sealed from above in a confined condition;\n", + " *```'semi'```: This means that the upper layer receives leakage from phreatic storage (```phreatictop = True```) or from a fixed head above the upper leaky-layer (```phreatictop = False```).\n", + "- TTim automatically assumes the ```topboundary``` is confined. In this case, we assume the ```topboundary``` is confined, so we do not need to set this parameter.\n", + "- The resistance to vertical flow: ```c``` of the aquitard layers, which is a list with length n-1 where n is the number of aquifer layers, setting the resistance of each aquitard layer. In the case of semi-confined conditions (```topboundary = \" semi\"```), we also add a first element representing the resistance of the aquitard above the first aquifer.\n", + "- The storage ```Sll``` of the aquitard layers: float/list/array with the specific storage values for each aquitard layer. In case a float is defined, the same storage is assumed for every layer. In case ```topboundary = semi``` and ```phreatictop = True```, the first element of ```Sll``` is the specific yield (see example [Unconfined - 1 - Vennebulten](unconfined1_vennebulten.ipynb)). \n", + "- ```phreatictop```: Is a boolean (True/False). If ```True```, the first element in ```Saq``` is considered phreatic storage (Specific Yield), and it is not multiplied by the layer thickness. The default value is ```False``` in ```ModelMaq```. This parameter is normally ```True``` only in unconfined aquifers." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_2 = ttim.ModelMaq(kaq=[17.28, 2], z=[zt0, zb0, zt1, zb1], c=200, Saq=[1.2e-4, 1e-5],\\\n", + " Sll=3e-5, topboundary='conf', tmin=1e-4, tmax=0.5)\n", + "w_2 = ttim.Well(ml_2, xw=0, yw=0, rw=rw, tsandQ = [(0, Q), (1e+08, 0)], layers=1)\n", + "ml_2.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Calibrate Multi-Aquifer Model\n", + "\n", + "Now we follow the procedures in step 5 again, but calibrating the parameters of both aquifers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7.1. Calibrate model without wellstorage and skin resistance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Complementing the prior calibration, we add the hydraulic parameters of both layers 0 and 1 and the parameters of the aquitard layer in between. The procedure is the same as explained in Step 5." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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+ "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 6662\n", + " # data points = 40\n", + " # variables = 6\n", + " chi-square = 22.1596023\n", + " reduced chi-square = 0.65175301\n", + " Akaike info crit = -11.6243416\n", + " Bayesian info crit = -1.49106486\n", + "[[Variables]]\n", + " kaq0: 199.161550 +/- 13053.6284 (6554.29%) (init = 20)\n", + " kaq1: 0.09746439 +/- 0.32364902 (332.07%) (init = 1)\n", + " Saq0: 5.61034653 +/- 2477.77332 (44164.35%) (init = 0.0001)\n", + " Saq1: 1.73735848 +/- 5.46149021 (314.36%) (init = 1e-05)\n", + " Sll: 0.06354292 +/- 15.7399790 (24770.62%) (init = 0.0001)\n", + " c1: 0.00142507 +/- 0.00681141 (477.97%) (init = 100)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq1, c1) = +0.9963\n", + " C(Saq1, c1) = -0.6697\n", + " C(kaq1, Saq1) = -0.6366\n", + " C(kaq1, Sll) = -0.5740\n", + " C(Saq0, Saq1) = -0.5698\n", + " C(Sll, c1) = -0.5326\n", + " C(Saq0, c1) = +0.4449\n", + " C(kaq0, kaq1) = -0.4012\n", + " C(kaq0, Saq0) = +0.3894\n", + " C(kaq0, c1) = -0.3775\n", + " C(kaq1, Saq0) = +0.3736\n", + " C(kaq0, Sll) = +0.2948\n", + " C(Saq1, Sll) = -0.2655\n", + " C(kaq0, Saq1) = +0.1940\n", + " C(Saq0, Sll) = +0.1522\n" + ] + } + ], + "source": [ + "ca_2 = ttim.Calibrate(ml_2)\n", + "ca_2.set_parameter(name= 'kaq0', initial=20, pmin=0, pmax = 200)\n", + "ca_2.set_parameter(name='kaq1', initial=1, pmin=0)\n", + "ca_2.set_parameter(name='Saq0', initial=1e-4, pmin=0)\n", + "ca_2.set_parameter(name='Saq1', initial=1e-5, pmin=0)\n", + "ca_2.set_parameter_by_reference(name='Sll', parameter=ml_2.aq.Sll[:],\\\n", + " initial=1e-4, pmin=0)\n", + "ca_2.set_parameter(name='c1', initial=100, pmin=0)\n", + "ca_2.series(name='well', x=r1, y=0, t=t1, h=h1, layer=1)\n", + "ca_2.series(name='obs_well', x=r2, y=0, t=t2, h=h2, layer=1)\n", + "ca_2.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + 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optimalstdperc_stdpminpmaxinitialparray
kaq0199.16155013053.6284306554.2914380200.020.00000[199.1615501616947]
kaq10.0974640.323649332.0689980inf1.00000[0.09746438855592698]
Saq05.6103472477.77331744164.3542560inf0.00010[5.610346530366302]
Saq11.7373585.461490314.3559770inf0.00001[1.737358478821435]
Sll0.06354315.73997924770.6241160inf0.00010[0.06354292456986621, 0.06354292456986621]
c10.0014250.006811477.9702440inf100.00000[0.0014250704284297644]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 199.161550 13053.628430 6554.291438 0 200.0 20.00000 \n", + "kaq1 0.097464 0.323649 332.068998 0 inf 1.00000 \n", + "Saq0 5.610347 2477.773317 44164.354256 0 inf 0.00010 \n", + "Saq1 1.737358 5.461490 314.355977 0 inf 0.00001 \n", + "Sll 0.063543 15.739979 24770.624116 0 inf 0.00010 \n", + "c1 0.001425 0.006811 477.970244 0 inf 100.00000 \n", + "\n", + " parray \n", + "kaq0 [199.1615501616947] \n", + "kaq1 [0.09746438855592698] \n", + "Saq0 [5.610346530366302] \n", + "Saq1 [1.737358478821435] \n", + "Sll [0.06354292456986621, 0.06354292456986621] \n", + "c1 [0.0014250704284297644] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.744305083413874\n" + ] + } + ], + "source": [ + "display(ca_2.parameters)\n", + "print('RMSE:',ca_2.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The more complex model has improved the fit significantly from the first model. However, it showed worse RMSE, AIC and BIC values than the second model, with wellbore storage and skin resistance. We also have to note the unrealistic values found for the hydraulic conductivity of the upper layer and the storage parameters.\n", + "\n", + "Next, we plot this model results." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0.98, 'Model Results - Two Aquifer Model')" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_2 = ml_2.head(r1, 0, t1)\n", + "hm2_2 = ml_2.head(r2, 0, t2)\n", + "plt.figure(figsize = (8, 5))\n", + "plt.semilogx(t1, h1, '.', label='well')\n", + "plt.semilogx(t2, h2, '.', label='obs_well')\n", + "plt.semilogx(t1, hm1_2[-1], label='ttim model - well')\n", + "plt.semilogx(t2, hm2_2[-1], label='ttim model - obs_well')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.legend()\n", + "plt.suptitle('Model Results - Two Aquifer Model')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7.2. Calibrate the two-layer model with wellparameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will add the skinresistance and wellbore storage parameters to our well and resume calibration." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_3 = ttim.ModelMaq(kaq=[19, 2], z=[zt0, zb0, zt1, zb1], c=200, Saq=[4e-4, 1e-5],\\\n", + " Sll=1e-4, topboundary='conf', tmin=1e-4, tmax=0.5)\n", + "w_3 = ttim.Well(ml_3, xw=0, yw=0, rw=rw, rc=0.2, res=0, tsandQ = [(0, Q), (1e+08, 0)], \\\n", + " layers=1)\n", + "ml_3.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....................................................................................................................................................................................................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 354\n", + " # data points = 40\n", + " # variables = 8\n", + " chi-square = 1.27784500\n", + " reduced chi-square = 0.03993266\n", + " Akaike info crit = -121.748175\n", + " Bayesian info crit = -108.237140\n", + "[[Variables]]\n", + " kaq0: 5.49229632 +/- 1.29840540 (23.64%) (init = 20)\n", + " kaq1: 1.25160350 +/- 1.39258206 (111.26%) (init = 1)\n", + " Saq0: 1.3810e-07 +/- 1.1375e-05 (8236.99%) (init = 0.0001)\n", + " Saq1: 4.5101e-05 +/- 1.0052e-04 (222.89%) (init = 1e-05)\n", + " Sll: 2.6917e-06 +/- 9.3437e-05 (3471.25%) (init = 0.0001)\n", + " c1: 0.74550570 +/- 6.69233955 (897.69%) (init = 100)\n", + " res: 1.7857e-05 +/- 0.02326246 (130271.52%) (init = 0)\n", + " rc: 0.05512719 +/- 0.00498230 (9.04%) (init = 0.2)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq1, c1) = +0.9998\n", + " C(Saq1, Sll) = -0.9894\n", + " C(res, rc) = -0.9187\n", + " C(Saq0, res) = +0.8677\n", + " C(Saq0, Saq1) = -0.8584\n", + " C(Saq0, Sll) = +0.8454\n", + " C(Saq0, rc) = -0.7826\n", + " C(kaq0, rc) = +0.7584\n", + " C(kaq0, kaq1) = -0.7474\n", + " C(kaq0, c1) = -0.7369\n", + " C(Saq1, rc) = +0.7163\n", + " C(Saq1, res) = -0.7158\n", + " C(Sll, res) = +0.7115\n", + " C(Sll, rc) = -0.7006\n", + " C(kaq1, rc) = -0.6468\n", + " C(c1, rc) = -0.6408\n", + " C(kaq0, res) = -0.5345\n", + " C(kaq1, Saq1) = -0.4994\n", + " C(Saq1, c1) = -0.4989\n", + " C(kaq0, Sll) = -0.4739\n", + " C(kaq0, Saq1) = +0.4623\n", + " C(kaq1, Sll) = +0.4565\n", + " C(kaq0, Saq0) = -0.4562\n", + " C(Sll, c1) = +0.4555\n", + " C(kaq1, res) = +0.3210\n", + " C(c1, res) = +0.3151\n", + " C(kaq1, Saq0) = +0.3104\n", + " C(Saq0, c1) = +0.3072\n" + ] + } + ], + "source": [ + "ca_3 = ttim.Calibrate(ml_3)\n", + "ca_3.set_parameter(name= 'kaq0', initial=20, pmin=0)\n", + "ca_3.set_parameter(name='kaq1', initial=1, pmin=0)\n", + "ca_3.set_parameter(name='Saq0', initial=1e-4, pmin=1e-7)\n", + "ca_3.set_parameter(name='Saq1', initial=1e-5, pmin=1e-7)\n", + "ca_3.set_parameter_by_reference(name='Sll', parameter=ml_3.aq.Sll[:],\\\n", + " initial=1e-4, pmin=1e-7)\n", + "ca_3.set_parameter(name='c1', initial=100, pmin=0)\n", + "ca_3.set_parameter_by_reference(name='res', parameter=w_3.res[:], initial=0, pmin=0)\n", + "ca_3.set_parameter_by_reference(name='rc', parameter=w_3.rc[:], initial=0.2, pmin=0)\n", + "ca_3.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=1)\n", + "ca_3.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=1)\n", + "ca_3.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq05.492296e+001.29840523.6404830.000000e+00inf20.00000[5.492296320117666]
kaq11.251603e+001.392582111.2638350.000000e+00inf1.00000[1.2516034968134555]
Saq01.380963e-070.0000118236.9944801.000000e-07inf0.00010[1.3809634069605892e-07]
Saq14.510125e-050.000101222.8855101.000000e-07inf0.00001[4.510124987577857e-05]
Sll2.691725e-060.0000933471.2521761.000000e-07inf0.00010[2.6917248675539795e-06, 2.6917248675539795e-06]
c17.455057e-016.692340897.6912640.000000e+00inf100.00000[0.7455057006178796]
res1.785690e-050.023262130271.5189800.000000e+00inf0.00000[1.785690417777097e-05]
rc5.512719e-020.0049829.0378300.000000e+00inf0.20000[0.05512718682053297]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 5.492296e+00 1.298405 23.640483 0.000000e+00 inf 20.00000 \n", + "kaq1 1.251603e+00 1.392582 111.263835 0.000000e+00 inf 1.00000 \n", + "Saq0 1.380963e-07 0.000011 8236.994480 1.000000e-07 inf 0.00010 \n", + "Saq1 4.510125e-05 0.000101 222.885510 1.000000e-07 inf 0.00001 \n", + "Sll 2.691725e-06 0.000093 3471.252176 1.000000e-07 inf 0.00010 \n", + "c1 7.455057e-01 6.692340 897.691264 0.000000e+00 inf 100.00000 \n", + "res 1.785690e-05 0.023262 130271.518980 0.000000e+00 inf 0.00000 \n", + "rc 5.512719e-02 0.004982 9.037830 0.000000e+00 inf 0.20000 \n", + "\n", + " parray \n", + "kaq0 [5.492296320117666] \n", + "kaq1 [1.2516034968134555] \n", + "Saq0 [1.3809634069605892e-07] \n", + "Saq1 [4.510124987577857e-05] \n", + "Sll [2.6917248675539795e-06, 2.6917248675539795e-06] \n", + "c1 [0.7455057006178796] \n", + "res [1.785690417777097e-05] \n", + "rc [0.05512718682053297] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.17873478985914232\n" + ] + } + ], + "source": [ + "display(ca_3.parameters)\n", + "print('RMSE:', ca_3.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we see a much better fit and AIC BIC values from the previous simulation. One thing to consider is the tiny storage values of the first aquifer and the aquitard. It might mean that it is troubling to fit all these parameters together. The small result of the skin resistance and its very high standard deviation might also mean it is irrelevant." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_3 = ml_3.head(r1, 0, t1)\n", + "hm2_3 = ml_3.head(r2, 0, t2)\n", + "plt.figure(figsize = (8, 5))\n", + "plt.semilogx(t1, h1, '.', label='well')\n", + "plt.semilogx(t2, h2, '.', label='obs_well')\n", + "plt.semilogx(t1, hm1_3[-1], label='ttim model - well')\n", + "plt.semilogx(t2, hm2_3[-1], label='ttim model - observation well')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.suptitle('Model Results - Two Aquifers + Well Parameters')\n", + "plt.legend()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plot showed the significant fit improvement with the new model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7.3. Calibrate Model with some fixed parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this new model, we will fix some of the parameters. Thus, we expect to have better confidence in estimating the others.\n", + "\n", + "We will fix the hydraulic parameters of the first aquifer layer, the storage coefficient of the leaky layer and the skin resistance of the well. The fixed parameters were defined from the estimates of Brian et al. (1997)." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_4 = ttim.ModelMaq(kaq=[17.28, 2], z=[zt0, zb0, zt1, zb1], c=200, Saq=[1.2e-4, 1e-5],\\\n", + " Sll=3e-5, topboundary='conf', tmin=1e-4, tmax=0.5)\n", + "w_4 = ttim.Well(ml_4, xw=0, yw=0, rw=rw, rc=None, res=0, tsandQ = [(0, Q), (1e+08, 0)], \\\n", + " layers=1)\n", + "ml_4.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 63\n", + " # data points = 40\n", + " # variables = 4\n", + " chi-square = 0.56419184\n", + " reduced chi-square = 0.01567200\n", + " Akaike info crit = -162.449616\n", + " Bayesian info crit = -155.694098\n", + "[[Variables]]\n", + " kaq1: 1.93431452 +/- 0.01232404 (0.64%) (init = 1)\n", + " Saq1: 1.3170e-05 +/- 2.3128e-06 (17.56%) (init = 1e-05)\n", + " c1: 239.719879 +/- 20.4969876 (8.55%) (init = 100)\n", + " rc: 0.05268806 +/- 4.0809e-04 (0.77%) (init = 0.2)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq1, c1) = +0.8393\n", + " C(Saq1, rc) = -0.5821\n", + " C(kaq1, Saq1) = -0.5032\n", + " C(Saq1, c1) = -0.2138\n", + " C(c1, rc) = -0.1113\n" + ] + } + ], + "source": [ + "ca_4 = ttim.Calibrate(ml_4)\n", + "ca_4.set_parameter(name='kaq1', initial=1, pmin=0)\n", + "ca_4.set_parameter(name='Saq1', initial=1e-5, pmin=0)\n", + "ca_4.set_parameter(name='c1', initial=100, pmin=0)\n", + "ca_4.set_parameter_by_reference(name='rc', parameter=w_4.rc[:], initial=0.2, pmin=0)\n", + "ca_4.series(name='well', x=r1, y=0, t=t1, h=h1, layer=1)\n", + "ca_4.series(name='obs_well', x=r2, y=0, t=t2, h=h2, layer=1)\n", + "ca_4.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq11.9343150.0123240.6371270inf1.00000[1.934314522419533]
Saq10.0000130.00000217.5613620inf0.00001[1.3170044361299205e-05]
c1239.71987920.4969888.5503910inf100.00000[239.71987879221703]
rc0.0526880.0004080.7745400inf0.20000[0.05268806073287169]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq1 1.934315 0.012324 0.637127 0 inf 1.00000 \n", + "Saq1 0.000013 0.000002 17.561362 0 inf 0.00001 \n", + "c1 239.719879 20.496988 8.550391 0 inf 100.00000 \n", + "rc 0.052688 0.000408 0.774540 0 inf 0.20000 \n", + "\n", + " parray \n", + "kaq1 [1.934314522419533] \n", + "Saq1 [1.3170044361299205e-05] \n", + "c1 [239.71987879221703] \n", + "rc [0.05268806073287169] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.11876361374572386\n" + ] + } + ], + "source": [ + "display(ca_4.parameters)\n", + "print('RMSE:', ca_4.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The new estimates had much better confidence intervals when we used part of the parameters fixed. The RMSE, BIC and AIC indicators also showed better results." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_4 = ml_4.head(r1, 0, t1)\n", + "hm2_4 = ml_4.head(r2, 0, t2)\n", + "plt.figure(figsize = (8, 5))\n", + "plt.semilogx(t1, h1, '.', label='well')\n", + "plt.semilogx(t2, h2, '.', label='obs_well')\n", + "plt.semilogx(t1, hm1_4[-1], label='ttim model - well')\n", + "plt.semilogx(t2, hm2_4[-1], label='ttim model - observation well')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.suptitle('Model Results - Two Aquifer Model + Well Parameters')\n", + "plt.legend()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Comparison of Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results simulated with the two aquifer models are present below. Furthermore, Yang (2020) compared TTim results with the results obtained from MLU (Carlson & Randall, 2012) and the published results obtained by Brian et al. (1997). The authors of the original paper applied a finite-difference model of radial flow with a trial and error approach to find the optimal parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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k0[m/d]k1[m/d]Ss0[1/m]Ss1[1/m]Sll[1/m]c[d]resrcRMSE
MLU17.4240.000061.7470.0000060.00004216NaNNaN0.023452
Brian et al.17.283.4560.000120.0000150.00003NaNNaNNaNNaN
ttim199.161550.0974645.6103471.7373580.0635430.001425NaNNaN0.744305
ttim-rc5.4922961.2516030.00.0000450.0000030.7455060.0000180.0551270.178735
ttim-fixed upper17.281.9343150.000120.0000130.00003239.719879NaN0.0526880.118764
\n", + "
" + ], + "text/plain": [ + " k0[m/d] k1[m/d] Ss0[1/m] Ss1[1/m] Sll[1/m] \\\n", + "MLU 17.424 0.00006 1.747 0.000006 0.00004 \n", + "Brian et al. 17.28 3.456 0.00012 0.000015 0.00003 \n", + "ttim 199.16155 0.097464 5.610347 1.737358 0.063543 \n", + "ttim-rc 5.492296 1.251603 0.0 0.000045 0.000003 \n", + "ttim-fixed upper 17.28 1.934315 0.00012 0.000013 0.00003 \n", + "\n", + " c[d] res rc RMSE \n", + "MLU 216 NaN NaN 0.023452 \n", + "Brian et al. NaN NaN NaN NaN \n", + "ttim 0.001425 NaN NaN 0.744305 \n", + "ttim-rc 0.745506 0.000018 0.055127 0.178735 \n", + "ttim-fixed upper 239.719879 NaN 0.052688 0.118764 " + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(columns=['k0[m/d]','k1[m/d]','Ss0[1/m]','Ss1[1/m]','Sll[1/m]','c[d]',\\\n", + " 'res', 'rc'], \\\n", + " index=['MLU', 'Brian et al.','ttim','ttim-rc','ttim-fixed upper'])\n", + "t.loc['ttim-rc'] = ca_3.parameters['optimal'].values\n", + "t.iloc[2,0:6] = ca_2.parameters['optimal'].values\n", + "t.iloc[4,5] = ca_4.parameters['optimal'].values[2]\n", + "t.iloc[4,7] = ca_4.parameters['optimal'].values[3]\n", + "t.iloc[4,0] = 17.28\n", + "t.iloc[4,1] = ca_4.parameters['optimal'].values[0]\n", + "t.iloc[4,2] = 1.2e-4\n", + "t.iloc[4,3] = ca_4.parameters['optimal'].values[1]\n", + "t.iloc[4,4] = 3e-5\n", + "t.iloc[0, 0:6] = [17.424, 6.027e-05, 1.747, 6.473e-06, 3.997e-05, 216]\n", + "t.iloc[1, 0:6] = [17.28, 3.456, 1.2e-04, 1.5e-5, 3e-05, np.nan]\n", + "t['RMSE'] = [0.023452, np.nan, ca_2.rmse(), ca_3.rmse(), ca_4.rmse()]\n", + "t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first TTim model showed unrealistic results. The second model, although improving the fit, had wide standard deviations (and confidence intervals). That indicates that this solution, with more parameters, in multi-aquifer configuration, is non-unique. \n", + "\n", + " When we kept some of the data fixed with the values from Brian et al., we improved the fit and the confidence interval of the estimates of the model.\n", + "\n", + "MLU results have very low RMSE. However, the specific storage of the upper aquifer (Ss0) is probably too high." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8.1. Comparison of estimates and model error\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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e76v5GYCCvAaX079/fw0aNEg7d+5U9erVNXv2bAUEBKh79+4e28fhw4f1xx9/yN/fP9f1x44dk5Q1XPjrr7/WhAkTNGjQIKWlpalixYoaMmSIHnvsMY/VA+DqEcwAL3a5s9cXr8v+MpOcnJzjy8Vvv/3mdn3Zlbadm/Pnz+v48eNuX5pSUlLc9v3JJ58oLS1NixYtUvny5V3ttm3blqfn4IkachMREaHk5OQcy3/77TdJyvHa5FVhbDcqKkoOh0MpKSmXDK4rV67Ub7/9ptWrV7t6ySTl+ptH+ZH9Wl68zN/fP8fv2F383p08eVKff/65Ro0apaefftq1/Ny5czpx4kSe9p/9er311luXnKUuOjpamZmZstlsl6zX01q2bKlatWpp8uTJCg0N1datW/X++++7tYmIiMhXPbn97efn+AkMDNS5c+dyLD927Fi+/+6yr6O8eJKgvD72So/z9HGSfazn5fXO7v26+LXKDivZCvIaXE737t01bNgwzZkzR6+88oree+89denS5ZIjIa5GZGSkIiIitGTJklzXlyhRwvXvpk2bqmnTpnI4HNq8ebPeeustDR06VNHR0br33ns9VhOAq8NQRuA6kT0k8OIvjImJiUpKSnJNmlAQ8+bNc7ufPbFB9gxh2V82s89wS1nT/L/zzjsF3ndea8hNq1attGPHDm3dutVt+bvvviubzaYWLVpIynlm/kryut38aN++vSRp2rRpl2yT2+ssSW+//Xa+93ehRYsW6ezZs677p06d0meffaamTZvmmJgmt5osy8pR07/+9a8cwyAv9Tr/5S9/UcmSJbVjxw7Vr18/15u/v79CQkJ02223XbLewjBkyBB98cUXGjlypKKjo10zS2Zr0aKFVqxY4Tb5iMPh0MKFC/O8j/wcP/Hx8frhhx/clu3evVu7du3K8/6yNWnSROHh4Zo+fbpruGZetW/fXqtWrbrsfj19nFSrVk2xsbFasGCBW70HDhzQ+vXr3dpm99Rd/FpdOMOpJFWtWlWVKlXSrFmzcg282fL7f0SpUqXUpUsXvfvuu/r888+VkpKSp2GM+dnPnXfeqePHj8vhcOR6zFSrVi3HY+x2uxo2bKgpU6ZIUo73BoAZ9JgBBvz00085ZmWUsoaaXO3vC1WrVk0PPvig3nrrLfn4+Kh9+/auWRnj4uL0+OOPF6hmf39/TZw4UadPn1aDBg1cMyK2b99et99+uySpdevW8vf3V/fu3TV8+HCdPXtW06ZN0++//16gfeenhtw8/vjjevfdd9WxY0e9+OKLKl++vL744gtNnTpVjzzyiOtanxIlSqh8+fL69NNP1apVK5UuXVqRkZGX/BHkvG43P5o2barevXvr5Zdf1uHDh3XnnXcqICBA33//vYKDg/Xoo4+qSZMmKlWqlB5++GGNGjVKfn5+mjdvnrZv357v/V3IbrerdevWGjZsmJxOp8aPH6/U1FTXVO6XExYWpmbNmum1115zvWZr1qzRzJkzc/xAbq1atSRJM2bMUIkSJRQYGKgKFSooIiJCb731lvr27asTJ07o7rvvVpkyZXT06FFt375dR48edQXWl156Se3atVPr1q31xBNPyOFwaPz48QoJCclzD11+9OrVSyNHjtTatWv13HPP5Rg29txzz2nx4sVq2bKlXnjhBQUHB2vKlCl5+vmCbPk5fnr37q1evXpp4MCBuuuuu3TgwAFNmDDhqv7/CA0N1cSJEzVgwADdcccdeuCBBxQdHa1ffvlF27dv1+TJky/52BdffFFfffWVmjVrpmeeeUY333yz/vjjDy1ZskTDhg1T9erVPX6c+Pj46KWXXtKAAQP0t7/9TQ888ID++OMPjR49OsfwxpiYGN1xxx0aN26cSpUqpfLly2vFihVatGhRju1OmTJFnTp1UqNGjfT444+rXLlyOnjwoJYuXeo6IXTzzTdLkv7xj3+ob9++8vPzU7Vq1dx6pS7Wv39/LVy4UIMHD9aNN96oO+6444rPMT/7uffeezVv3jx16NBBjz32mG677Tb5+fnp119/1apVq9S5c2f97W9/0/Tp07Vy5Up17NhR5cqV09mzZ10zfealJgDXgLFpR4Bi6HKzMkqy3nnnHVdbSdagQYMuuY3ExMQc6xwOhzV+/HiratWqlp+fnxUZGWn16tXLOnTokFu75s2bWzfddFOe6+7bt68VEhJi/fDDD1ZCQoIVFBRklS5d2nrkkUes06dPu7X97LPPrFtuucUKDAy0brjhBuupp56yvvrqqxwzoxVmDbnNWHfgwAGrR48eVkREhOXn52dVq1bNeu211yyHw+HW7uuvv7ZuvfVWKyAgwJKU68x3V7PdvM7KaFlZ7+Obb75p1apVy/L397fCw8Otxo0bW5999pmrzfr1663GjRtbwcHBVlRUlDVgwABr69atOWZ3y8+sjOPHj7fGjBlj3XjjjZa/v7916623WkuXLnV7bPb2jh49mqPuX3/91brrrrusUqVKWSVKlLDatWtn/fTTT7m+H5MmTbIqVKhg2e32HDWvWbPG6tixo1W6dGnLz8/PuuGGG6yOHTtaH374ods2Fi9ebNWuXdvy9/e3ypUrZ7366qu5Pt/cXOrvr2/fvm6z+l2oX79+lq+vr/Xrr7/muv7bb7+1GjVqZAUEBFgxMTHWU089Zc2YMSPXWRkv9beQ1+PH6XRaEyZMsCpWrGgFBgZa9evXt1auXHlVszJm+/LLL63mzZtbISEhVnBwsFWzZk1r/PjxudZ5oUOHDln9+/e3YmJiLD8/P6ts2bLW3//+d+vw4cOuNnk5TrJrfe2113LsQ7nMlvqvf/3LqlKliuXv729VrVrVmjVrVq7vX3JysnX33XdbpUuXtsLDw61evXpZmzdvzvG6WJZlbdiwwWrfvr0VHh5uBQQEWJUqVbIef/xxtzYjR460ypYta/n4+Li9Lxe/9tkcDocVFxd3ydlWc3uP8rufzMxM6/XXX3f97YSGhlrVq1e3HnroIWvPnj2u5/a3v/3NKl++vBUQEGBFRERYzZs3txYvXpyjJgBm2Cwrn+MWAMCAfv366d///rdOnz5tuhQUQxkZGYqPj9ftt9+u//u//zNdDgDgOsRQRgAALuHo0aPatWuXZs+ercOHD7tNbAIAgCcRzAAAuIQvvvhC9913n2JjYzV16lS3KfIBAPAkhjICAAAAgGFMlw8AAAAAhhHMAAAAAMAwghkAAAAAGEYwAwAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIYRzAAAAADAMIIZAAAAABhGMAMAAAAAwwhmAAAAAGAYwQwAAAAADCOYAQAAAIBhBDMAAAAAMIxgBgAAAACGEcwAAAAAwDCCGQAAAAAYRjADAAAAAMMIZgAAAABgGMEMAAAAAAwjmAEAAACAYQQzAAAAADCMYAYAAAAAhhHMAAAAAMAwghkAAAAAGEYwAwAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIYRzAAAAADAMIIZAAAAABhGMAMAAAAAwwhmAAAAAGAYwQwAAAAADCOYAQAAAIBhBDMAAAAAMIxgBgAAAACGEcwAAAAAwDCCGQAAAAAYRjADAAAAAMMIZgAAAABgGMEMAAAAAAwjmAEAAACAYQQzAAAAADCMYAYAAAAAhhHMAAAAAMAwghkAAAAAGEYwAwAAAADDCGYAAAAAYJiv6QIKm9Pp1G+//aYSJUrIZrOZLgcAAACAIZZl6dSpUypbtqx8fLyrj+q6D2a//fab4uLiTJcBAAAAwEscOnRIN954o+ky3Fz3waxEiRKSsl78sLAww9UAAAAAMCU1NVVxcXGujOBNrvtglj18MSwsjGAGAAAAwCsvcfKugZUAAAAAUAwRzAAAAADAMIIZAAAAABh23V9jBgAAgKLJ4XAoMzPTdBkoQux2u3x9fb3yGrIrIZgBAADA65w+fVq//vqrLMsyXQqKmODgYMXGxsrf3990KflCMAMAAIBXcTgc+vXXXxUcHKyoqKgi2fuBa8+yLGVkZOjo0aPat2+fqlSp4nU/In05XhPMxo0bp2eeeUaPPfaYJk2aJCnrxR0zZoxmzJih33//XQ0bNtSUKVN00003mS0WAAAAhSYzM1OWZSkqKkpBQUFXtw2HUyt3HtHeo6eVfs6h4AC7KkWFqmX1MvKzF50v68ifoKAg+fn56cCBA8rIyFBgYKDpkvLMK4JZYmKiZsyYodq1a7stnzBhgt544w3NmTNHVatW1csvv6zWrVtr165dXvmjcAAAAPCcq+kpO5x6VvM2HdT7Gw/oRFqG7D422SRZkhxOS6VD/NWrUXn1bFhO0WFF50s78q4o9ZJdyHjVp0+fVs+ePfXOO++oVKlSruWWZWnSpEl69tln1bVrV9WqVUtz585Venq65s+fb7BiAAAAeKMNe4+r5eurNXnlHp1Iy5CUFcbOOy05nFnXqp1Iy9DklXvU8vXV2rD3uMlyATfGg9mgQYPUsWNH3XHHHW7L9+3bp5SUFLVp08a1LCAgQM2bN9f69euvdZkAAADwYhv2HlfvmZt0JtMh5xXmC3Fa0plMh3rP3EQ4u0bmzJmjkiVLui2bMWOG4uLi5OPj47qUqTgzOpTxgw8+0NatW5WYmJhjXUpKiiQpOjrabXl0dLQOHDhwyW2eO3dO586dc91PTU31ULUAAADwRodTz2rA3EQ5LeuKoSxbVjtLA95N1MonEhjWWMi6deumDh06uO6npqZq8ODBeuONN3TXXXcpPDzcYHXewViP2aFDh/TYY4/p/fffv+xFeRePLbYs67LjjceNG6fw8HDXLS4uzmM1AwAAwPvM23QwTz1lF3Na0pkMh+ZvOlg4hXmRjIwMo/sPCgpSmTJlXPcPHjyozMxMdezYUbGxsQoODr6q7V5Pv3NnLJht2bJFR44cUb169eTr6ytfX1+tWbNG//znP+Xr6+vqKcvuOct25MiRHL1oFxo5cqROnjzpuh06dKhQnwcAAADMyXQ49f7GA/kOZdmclvT+xgPKdDgLXEt8fHyOIXl16tTR6NGjXfdtNpumTZum9u3bKygoSBUqVNCHH37oWr9//37ZbDZ98MEHatKkiQIDA3XTTTdp9erVbtvdsWOHOnTooNDQUEVHR6t37946duyYa31CQoIGDx6sYcOGKTIyUq1bt8615sTERLVu3VqRkZEKDw9X8+bNtXXrVrc2e/bsUbNmzRQYGKiaNWtq+fLlstls+uSTTyRJq1evls1m0x9//OF6zLZt22Sz2bR//35J7kMZ58yZo5tvvlmSVLFiRbd2n332merVq6fAwEBVrFhRY8aM0fnz591ev+nTp6tz584KCQnRyy+/nOvzKoqMBbNWrVrpxx9/1LZt21y3+vXrq2fPntq2bZsqVqyomJgYLV++3PWYjIwMrVmzRk2aNLnkdgMCAhQWFuZ2AwAAwPVp5c4jrok+rtbxtAyt2nnEQxVd2fPPP6+77rpL27dvV69evdS9e3clJSW5tXnqqaf0xBNP6Pvvv1eTJk3017/+VcePZ10Pl5ycrObNm6tOnTravHmzlixZosOHD+vvf/+72zbmzp0rX19fffvtt3r77bdzreXUqVPq27ev1q1bp40bN6pKlSrq0KGDTp06JUlyOp3q2rWr7Ha7Nm7cqOnTp2vEiBEFev7dunXT119/LUn67rvvlJycrLi4OC1dulS9evXSkCFDtGPHDr399tuaM2eOXnnlFbfHjxo1Sp07d9aPP/6o/v37F6gWb2LsGrMSJUqoVq1abstCQkIUERHhWj506FCNHTtWVapUUZUqVTR27FgFBwerR48eJkoGAACAl9l79LTsPjbXrItXw+5j096jaR6s6vLuueceDRgwQJL00ksvafny5Xrrrbc0depUV5vBgwfrrrvukiRNmzZNS5Ys0cyZMzV8+HBNmzZNdevW1dixY13tZ82apbi4OO3evVtVq1aVJFWuXFkTJky4bC0tW7Z0u//222+rVKlSWrNmje688059/fXXSkpK0v79+3XjjTdKksaOHav27dtf9fMPCgpSRESEJCkqKkoxMTGSpFdeeUVPP/20+vbtKymrN+2ll17S8OHDNWrUKNfje/TocV0Fsmxe8TtmlzJ8+HCdOXNGAwcOdP3A9LJly/gNMwAAAEiS0s85lP9fO3Nnk5R27vwV23lK48aNc9zftm3bJdv4+vqqfv36rl61LVu2aNWqVQoNDc2x7b1797qCWf369a9Yy5EjR/TCCy9o5cqVOnz4sBwOh9LT03XwYNZ1d0lJSSpXrpwrlOVWv6ds2bJFiYmJbj1kDodDZ8+eVXp6uus6tLw8r6LIq4LZxWNnbTabRo8e7TYuFwAAAMgWHGDX1feVZbEkhQQU/Guxj4+PLMu9mrxOTpGXH9PObuN0OtWpUyeNHz8+R5vY2FjXv0NCQq64zX79+uno0aOaNGmSypcvr4CAADVu3Ng1WcjFzye3WrN/0PnCtlczKYfT6dSYMWPUtWvXHOsunCwwL8+rKPKqYAYAAADkR6Wo0AINY5SyfoS6UlTBv+xHRUUpOTnZdT81NVX79u3L0W7jxo3q06eP2/1bb701R5tmzZpJks6fP68tW7Zo8ODBkqS6devqo48+Unx8vHx9C/Z1ft26dZo6daprKvtDhw65TSJSs2ZNHTx4UL/99pvKli0rSdqwYYPbNqKioiRlXftWqlQpScrRA5gXdevW1a5du1S5cuWreSpFnvEfmAYAAACuVsvqZVQ6xL9A24gI8VeL6mWu3PBKtbRsqffee0/r1q3TTz/9pL59+8put+do9+GHH2rWrFnavXu3Ro0ape+++84VurJNmTJFH3/8sXbu3KlBgwbp999/d11XNWjQIJ04cULdu3fXd999p//85z9atmyZ+vfvL4fDka+aK1eurPfee09JSUnatGmTevbsqaCgINf6O+64Q9WqVVOfPn20fft2rVu3Ts8++2yObcTFxWn06NHavXu3vvjiC02cODFfdUjSCy+8oHfffVejR4/Wzz//rKSkJC1cuFDPPfdcvrdVFBHMAAAAUGT52X3Uq1F5+VzlhWY+NqlXo/Lysxf8a/HIkSPVrFkz3XnnnerQoYO6dOmiSpUq5Wg3ZswYffDBB6pdu7bmzp2refPmqWbNmm5tXn31VY0fP1633HKL1q1bp08//VSRkZGSpLJly+rbb7+Vw+FQ27ZtVatWLT322GMKDw93DSvMq1mzZun333/Xrbfeqt69e2vIkCFuvzfm4+Ojjz/+WOfOndNtt92mAQMG5Jgl0c/PTwsWLNDOnTt1yy23aPz48Vc1jX3btm31+eefa/ny5WrQoIEaNWqkN954Q+XLl8/3tooim5XbwNHrSGpqqsLDw3Xy5EmmzgcAACgCzp49q3379qlChQpu1xZdyuHUs2r5+up8/8i0j00K8rdr5RMJig678n48wWaz6eOPP1aXLl1yXb9//35VqFBB33//verUqXNNaroaV3oeJl3u78ebswE9ZtdIesZ5xT/9heKf/kLpGddu1h8AAIDrXXRYoP7Vt4F8bLY895z52CQfm00z+za4ZqEMuByCGQAAAIq8xpUi9N79DRXkb79iOMvuKXt/QEM1qhhxbQoEroBZGQEAAHBdaFwpQiufSND8TQf13sYDOpGWIbuPTTZlTYnvcFqKCPFXr0bl1aNhOSM9ZVe6iig+Pv6KbbxBUaixqCGYAQAA4LoRHRaox1tX1eCWlbVq5xHtPZqmtHPnFRLgq0pRIWpRvYxHJvoAPI1gBgAAgOuOn91HbW6KMV0GkGecLgAAAAAAw+gxAwAAwPXHkSntXiod2y1lpEn+IVJkValqW8nuZ7o6IAeCGQAAAK4fqcnSltlS4kwp/ZjkY1fWIDGn5HRIwZFSg/ulevdJYbGmqwVcCGYAAAC4PuxbJy3oJmWelSxH1jKnQ5Ljzzbpx6S1r0sbJkvdF0oVmhopFbgY15gBAACg6Nu3Tnqvi5R55s9QdimWI6vde12yHuchCQkJGjp0qMe2V9Tt379fNptN27Ztcy379ttvdfPNN8vPz09dunQxVps3oscMAAAARVtqclZPmeXMuuVFdrsF90qDNzOssRDExcUpOTlZkZGRrmXDhg1TnTp19NVXXyk0NNRgdd6HHjMAAAAUbVtm/2/4Yh5DWTbLKWWmS1vmFEpZpmVmZhrdv91uV0xMjHx9/+wL2rt3r1q2bKkbb7xRJUuWvKrtZmRkeKhC70IwAwAAQNHlyMya6ONKwxcvxXJKm2dmbcfDfv/9d/Xp00elSpVScHCw2rdvrz179mTt1rIUFRWljz76yNW+Tp06KlOmjOv+hg0b5Ofnp9OnT0uSTp48qQcffFBlypRRWFiYWrZsqe3bt7vajx49WnXq1NGsWbNUsWJFBQQEyLKsHHUdP35c3bt314033qjg4GDdfPPNWrBggVubtLQ09enTR6GhoYqNjdXEiRNzDNW02Wz65JNP3B5XsmRJzZkzR5L7UMbsfx8/flz9+/eXzWZztduxY4c6dOig0NBQRUdHq3fv3jp27JhrmwkJCRo8eLCGDRumyMhItW7dOu9vQhFCMAMAAEDRtXtp1oQeBZF2VNqzzDP1XKBfv37avHmzFi9erA0bNsiyLHXo0EGZmZmy2Wxq1qyZVq9eLSkrxO3YsUOZmZnasWOHJGn16tWqV6+eQkNDZVmWOnbsqJSUFH355ZfasmWL6tatq1atWunEiROuff7yyy/6v//7P3300Udu13Zd6OzZs6pXr54+//xz/fTTT3rwwQfVu3dvbdq0ydXmqaee0qpVq/Txxx9r2bJlWr16tbZs2XLVr0X2sMawsDBNmjRJycnJ6tatm5KTk9W8eXPVqVNHmzdv1pIlS3T48GH9/e9/d3v83Llz5evrq2+//VZvv/32VdfhzbjGDAAAAEXXsd1ZU+I7r7LHTJJs9qztqKPHytqzZ48WL16sb7/9Vk2aNJEkzZs3T3Fxcfrkk090zz33KCEhQTNmzJAkrV27VrfccovKlSun1atXq2bNmlq9erUSEhIkSatWrdKPP/6oI0eOKCAgQJL0+uuv65NPPtG///1vPfjgg5Kyhvm99957ioqKumRtN9xwg5588knX/UcffVRLlizRhx9+qIYNG+r06dOaOXOm3n33XVfv1Ny5c3XjjTde9euRPazRZrMpPDxcMTExkqRx48apbt26Gjt2rKvtrFmzFBcXp927d6tq1aqSpMqVK2vChAlXvf+igB4zAAAAFF0ZaSrwV1qbj3TutEfKyZaUlCRfX181bNjQtSwiIkLVqlVTUlKSpKwhej///LOOHTumNWvWKCEhQQkJCVqzZo3Onz+v9evXq3nz5pKkLVu26PTp04qIiFBoaKjrtm/fPu3du9e1j/Lly182lEmSw+HQK6+8otq1a7u2t2zZMh08eFBS1nVgGRkZaty4sesxpUuXVrVq1Tz2+mTbsmWLVq1a5facqlev7qojW/369T2+b29DjxkAAACKLv8QSfmc9ONillMK8OwMgbld25W93GazSZJq1aqliIgIrVmzRmvWrNGLL76ouLg4vfLKK0pMTNSZM2d0++23S5KcTqdiY2NdQx8vdOEkGiEhIVesbeLEiXrzzTc1adIk3XzzzQoJCdHQoUNdk2pcqvaL2Wy2HG3zO+GI0+lUp06dNH78+BzrYmP/nCkzL8+rqCOYAQAAoOiKrFqwYYxS1sQhkVU9U8//1KxZU+fPn9emTZtcQxmPHz+u3bt3q0aNGpLkus7s008/1U8//aSmTZuqRIkSyszM1PTp01W3bl2VKFFCklS3bl2lpKTI19dX8fHxBapt3bp16ty5s3r16iUpKxzt2bPHVVflypXl5+enjRs3qly5cpKyroHbvXu3qwdPkqKiopScnOy6v2fPHqWnp+erlrp16+qjjz5SfHy82+yNxRFDGQEAAFB0VW0rBUdeud3lhERJVdp4pp7/qVKlijp37qwHHnhA33zzjbZv365evXrphhtuUOfOnV3tEhISNH/+fNWuXVthYWGusDZv3jzX9WWSdMcdd6hx48bq0qWLli5dqv3792v9+vV67rnntHnz5nzVVrlyZS1fvlzr169XUlKSHnroIaWkpLjWh4aG6v7779dTTz2lFStW6KefflK/fv3k4+MeHVq2bKnJkydr69at2rx5sx5++GH5+fnlq5ZBgwbpxIkT6t69u7777jv95z//0bJly9S/f385HAUM3EUMwQwAAABFl91PanB/1gQeV8PmI9W/P2s7HjZ79mzVq1dPd955pxo3bizLsvTll1+6hZcWLVrI4XC4hbDmzZvL4XC49U7ZbDZ9+eWXatasmfr376+qVavq3nvv1f79+xUdHZ2vup5//nnVrVtXbdu2VUJCgmJiYtSlSxe3Nq+99pqaNWumv/71r7rjjjt0++23q169em5tJk6cqLi4ODVr1kw9evTQk08+qeDg4HzVUrZsWX377bdyOBxq27atatWqpccee0zh4eE5guD1zmbldRBpEZWamqrw8HCdPHlSYWFhxupIzzivmi8slSTteLGtgv2Ld1ctAADApZw9e1b79u1ThQoVFBgYeOUHpCZLk+tJmWfy9yPTNh/JL1gavFkKi71y+2IuISFBderU0aRJk0yXclmX+/vxlmyQm+IVQwEAAHD9CYuVui/MClq2PH69zW7bYyGhDF6BYAYAAICir0JTqfcnWT1gVxrWmN1T1udTKf72a1IecCWMpwMAAMD1oULTrGGJW+ZIif+S0o9lhTSbT9YQR8uRNdFH/fulev3oKcun3Kbqh+cQzAAAAHD9CIuVWoyUmj0p7VkmHdud9ePRAaFZU+JXaVMoE30ABUUwAwAAwPXH7idV7yipo+lKgDzhGjMAAAB4pet88nAUkqL6d0MwAwAAgFex27Mm78jIyDBcCYqi9PR0Scr3j12bxlBGAAAAeBVfX18FBwfr6NGj8vPzK3Y/NIyrY1mW0tPTdeTIEZUsWdIV8IsKghkAAAC8is1mU2xsrPbt26cDBw6YLgdFTMmSJRUTE2O6jHwjmAEAAMDr+Pv7q0qVKgxnRL74+fkVuZ6ybAQzAAAAeCUfHx8FBgaaLgO4JhiwCwAAAACGEcwAAAAAwDCCGQAAAAAYRjADAAAAAMMIZgAAAABgGMEMAAAAAAwjmAEAAACAYQQzAAAAADCMYAYAAAAAhhHMAAAAAMAwghkAAAAAGEYwAwAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIYRzAAAAADAMIIZAAAAABhGMAMAAAAAwwhmAAAAAGAYwQwAAAAADCOYAQAAAIBhBDMAAAAAMIxgBgAAAACGEcwAAAAAwDCCGQAAAAAYRjADAAAAAMMIZgAAAABgGMEMAAAAAAwjmAEAAACAYQQzAAAAADCMYAYAAAAAhhHMAAAAAMAwghkAAAAAGEYwAwAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIYRzAAAAADAMIIZAAAAABhGMAMAAAAAwwhmAAAAAGAYwQwAAAAADCOYAQAAAIBhBDMAAAAAMIxgBgAAAACGEcwAAAAAwDCCGQAAAAAYRjADAAAAAMMIZgAAAABgGMEMAAAAAAwjmAEAAACAYQQzAAAAADCMYAYAAAAAhhHMAAAAAMAwghkAAAAAGEYwAwAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIYRzAAAAADAMIIZAAAAABhmNJhNmzZNtWvXVlhYmMLCwtS4cWN99dVXrvWWZWn06NEqW7asgoKClJCQoJ9//tlgxQAAAADgeUaD2Y033qhXX31Vmzdv1ubNm9WyZUt17tzZFb4mTJigN954Q5MnT1ZiYqJiYmLUunVrnTp1ymTZAAAAAOBRRoNZp06d1KFDB1WtWlVVq1bVK6+8otDQUG3cuFGWZWnSpEl69tln1bVrV9WqVUtz585Venq65s+fb7JsAAAAAPAor7nGzOFw6IMPPlBaWpoaN26sffv2KSUlRW3atHG1CQgIUPPmzbV+/fpLbufcuXNKTU11uwEAAACANzMezH788UeFhoYqICBADz/8sD7++GPVrFlTKSkpkqTo6Gi39tHR0a51uRk3bpzCw8Ndt7i4uEKtHwAAAAAKyngwq1atmrZt26aNGzfqkUceUd++fbVjxw7XepvN5tbesqwcyy40cuRInTx50nU7dOhQodUOAAAAAJ7ga7oAf39/Va5cWZJUv359JSYm6h//+IdGjBghSUpJSVFsbKyr/ZEjR3L0ol0oICBAAQEBhVs0AAAAAHiQ8R6zi1mWpXPnzqlChQqKiYnR8uXLXesyMjK0Zs0aNWnSxGCFAAAAAOBZRnvMnnnmGbVv315xcXE6deqUPvjgA61evVpLliyRzWbT0KFDNXbsWFWpUkVVqlTR2LFjFRwcrB49epgsGwAAAAA8ymgwO3z4sHr37q3k5GSFh4erdu3aWrJkiVq3bi1JGj58uM6cOaOBAwfq999/V8OGDbVs2TKVKFHCZNkAAAAA4FE2y7Is00UUptTUVIWHh+vkyZMKCwszVkd6xnnVfGGpJGnHi20V7G/88j4AAACgWPGWbJAbr7vGDAAAAACKG4IZAAAAABhGMAMAAAAAwwhmAAAAAGAYwQwAAAAADCOYAQAAAIBhBDMAAAAAMIxgBgAAAACGEcwAAAAAwDCCGQAAAAAYRjADAAAAAMMIZgAAAABgGMEMAAAAAAwjmAEAAACAYQQzAAAAADCMYAYAAAAAhhHMAAAAAMAwghkAAAAAGEYwAwAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIYRzAAAAADAMIIZAAAAABhGMAMAAAAAwwhmAAAAAGAYwQwAAAAADCOYAQAAAIBhBDMAAAAAMIxgBgAAAACGEcwAAAAAwDCCGQAAAAAYRjADAAAAAMMIZgAAAABgGMEMAAAAAAwjmAEAAACAYQQzAAAAADCMYAYAAAAAhhHMAAAAAMAwghkAAAAAGEYwAwAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIYRzAAAAADAMIIZAAAAABhGMAMAAAAAwwhmAAAAAGAYwQwAAAAADCOYAQAAAIBhBDMAAAAAMIxgBgAAAACGEcwAAAAAwDCCGQAAAAAYRjADAAAAAMMIZgAAAABgGMEMAAAAAAwjmAEAAACAYQQzAAAAADCMYAYAAAAAhhHMAAAAAMAwghkAAAAAGEYwAwAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIYRzAAAAADAMIIZAAAAABhGMAMAAAAAwwhmAAAAAGAYwQwAAAAADCOYAQAAAIBhBDMAAAAAMIxgBgAAAACGEcwAAAAAwDCCGQAAAAAYRjADAAAAAMMIZgAAAABgGMEMAAAAAAwjmAEAAACAYQQzAAAAADCMYAYAAAAAhhHMAAAAAMAwghkAAAAAGEYwAwAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIYRzAAAAADAMIIZAAAAABhGMAMAAAAAwwhmAAAAAGAYwQwAAAAADCOYAQAAAIBhRoPZuHHj1KBBA5UoUUJlypRRly5dtGvXLrc2lmVp9OjRKlu2rIKCgpSQkKCff/7ZUMUAAAAA4HlGg9maNWs0aNAgbdy4UcuXL9f58+fVpk0bpaWludpMmDBBb7zxhiZPnqzExETFxMSodevWOnXqlMHKAQAAAMBzfE3ufMmSJW73Z8+erTJlymjLli1q1qyZLMvSpEmT9Oyzz6pr166SpLlz5yo6Olrz58/XQw89ZKJsAAAAAPAor7rG7OTJk5Kk0qVLS5L27dunlJQUtWnTxtUmICBAzZs31/r163Pdxrlz55Samup2AwAAAABv5jXBzLIsDRs2TLfffrtq1aolSUpJSZEkRUdHu7WNjo52rbvYuHHjFB4e7rrFxcUVbuEAAAAAUEBeE8wGDx6sH374QQsWLMixzmazud23LCvHsmwjR47UyZMnXbdDhw4VSr0AAAAA4ClGrzHL9uijj2rx4sVau3atbrzxRtfymJgYSVk9Z7Gxsa7lR44cydGLli0gIEABAQGFWzAAAAAAeJDRHjPLsjR48GAtWrRIK1euVIUKFdzWV6hQQTExMVq+fLlrWUZGhtasWaMmTZpc63IBAAAAoFAY7TEbNGiQ5s+fr08//VQlSpRwXTcWHh6uoKAg2Ww2DR06VGPHjlWVKlVUpUoVjR07VsHBwerRo4fJ0gEAAADAY/IUzBYvXpzvDbdu3VpBQUGXbTNt2jRJUkJCgtvy2bNnq1+/fpKk4cOH68yZMxo4cKB+//13NWzYUMuWLVOJEiXyXRMAAAAAeCObZVnWlRr5+ORvxKPNZtOePXtUsWLFqy7MU1JTUxUeHq6TJ08qLCzMWB3pGedV84WlkqQdL7ZVsL9XXN4HAAAAFBvekg1yk+fElZKSIqfTmadbcHBwYdYMAAAAANeVPAWzvn37XnFY4oV69erldQkUAAAAALxVnsbTzZ49O18bzb52DAAAAABwZV7zA9MAAAAAUFzlqcesa9eued7gokWLrroYAAAAACiO8tRjFh4e7rqFhYVpxYoV2rx5s2v9li1btGLFCoWHhxdaoQAAAABwvcr3NWYjRozQ3//+d02fPl12u12S5HA4NHDgQCb8AAAAAICrkO9rzGbNmqUnn3zSFcokyW63a9iwYZo1a5ZHiwMAAACA4iDfwez8+fNKSkrKsTwpKUlOp9MjRQEAAABAcZKnoYwXuu+++9S/f3/98ssvatSokSRp48aNevXVV3Xfffd5vEAAAAAAuN7lO5i9/vrriomJ0Ztvvqnk5GRJUmxsrIYPH64nnnjC4wUCAAAAwPUuz8Hs9OnTCg0NlY+Pj4YPH67hw4crNTVVkpj0AwAAAAAKIM/XmEVGRqp9+/aaNm2a/vvf/0rKCmSEMgAAAAAomDwHs127dqlDhw766KOPVLFiRTVo0EAvvfSSfvjhh8Ks77qQ6XDq66TDrvvvrP2Plv6cokwHk6UAAAAAkGyWZVn5fdDJkyf15Zdf6tNPP9WSJUtUqlQp/fWvf1Xnzp3VvHlzt6n0TUtNTVV4eLhOnjx5zXv3Dqee1bxNB/X+xgM6kZbhWm73scnhtFQ6xF+9GpVXz4blFB0WeE1rAwAAAIobk9ngSq4qmF3o/PnzWrlypT777DMtXrxYp06d0ltvvaWePXt6qsYCMfXib9h7XAPmJupMpkPOy7zCPjYpyM+uf/VtoMaVIq5ZfQByynQ4tXLnEe09elrp5xwKDrCrUlSoWlYvIz97vn9dBACAYs0bP1ev62B2se+//17nz59XgwYNPLnZq2bixd+w97h6z9wkp2VdNpRl87FJPjab3ru/IeEMMODi3m27j002SZZE7zYAAPnkzZ+r110wO3v2rH744QcdOXLE7UelbTabOnXq5NECC+pav/iHU8+q5eurr9hTdjEfmxTkb9fKJxL44gdcQ/RuAwDgOd7+uerNwSzfv2O2ZMkS9enTR8eOHcuxzmazyeFweKSwomrepoP5DmWS5LSkMxkOzd90UI+3rlo4xQFwk5/ebaclncl0qPfMTfRuAwCQCz5XCybfgzsHDx6se+65R8nJyXI6nW634h7KMh1Ovb/xQL5DWTanJb2/8QCzNQLXwOHUsxowNzHPQ46lrGPUaVka8G6iDqeeLdwCAQAoQvhcLbh8B7MjR45o2LBhio6OLox6irSVO4+4zb54NY6nZWjVziMeqgjApXiidxsAAGThc7Xg8h3M7r77bq1evboQSin69h49LbuPrUDbsPvYtPdomocqApAbercBAPAcPlc9I9/XmE2ePFn33HOP1q1bp5tvvll+fn5u64cMGeKx4oqa9HMOFSyWSTZJaefOe6IcAJfgyd7tNjfFeKgqAACKJj5XPSPfwWz+/PlaunSpgoKCtHr1atlsf0YRm81WrINZcIBdBf3tAUtSSEC+3xYA+ZDdu+242lN7oncbAIBsfK56Rr4TwHPPPacXX3xRTz/9tHx8+MHVC1WKCi3QH6SU9dsOlaJCPFQRgNzQuw0AgOfwueoZ+U5WGRkZ6tatG6EsFy2rl1HpEP8CbSMixF8tqpfxUEUAckPvNgAAnsPnqmfkO1317dtXCxcuLIxaijw/u496NSqvq53/w8cm9WpUXn52Qi9QmOjdBgDAc/hc9Yx8x1KHw6EJEyZo6dKlql27do7JP9544w2PFVcU9WxYTjPX/Sff04X62KQgf7t6NCxXeMUBkPRn73ZBLlSmdxsAgCx8rnpGvrtmfvzxR916663y8fHRTz/9pO+//95127ZtWyGUWLREhwXqX30byMdmy3PPmY9N8rHZNLNvA0WHBRZugQDo3QYAwIP4XPUMm2VZBR0S6tVSU1MVHh6ukydPKiws7Jrtd8Pe4xrwbqLOZFy+5yy7p2xm3wZqVDHimtUHFHeHU8+q5eurr7p3e+UTCZxIAQDgf4rK56qpbJAXxTuWFqLGlSK08okEPdqySo4JQez/+4mBiBB/PdqyilY+kUAoA64xercBAPAcPlcLLk/BrGvXrkpNTc3zRnv27KkjR45cdVHXi+iwQD3euqo2PdNKb3W/1bX8sTsqa0bvetr4TCs93roqf4iAIY0rRei9+xsqyN9+xQ+R7DN67w9oyIkUAABywedqweRpKKPdbtfu3bsVFRV1xQ1alqW4uDht27ZNFStW9EiRBeEt3ZXpGedV84WlkqQdL7ZVsH/xng4U8CaHU89q/qaDem/jAZ1Iy5DdxyabsqbudTgtRYT4q1ej8urRsBwnUgAAuAJv/lz1lmyQmzylA8uyVLVq1cKuBQCMyO7dHtyyslbtPKK9R9OUdu68QgJ8VSkqRC2qlyn2FyQDAJBXfK5enTwFs1WrVuV7wzfccEO+HwMAJvnZfdTmphjTZQAAcF3gczV/8hTMmjdvXth1AAAAAECxRR8iAAAAABhGMAMAAAAAwwhmAAAAAGCYR4NZfn7rDAAAAACQJc/B7PXXX7/s+tTUVLVp06bABQEAAABAcZPnYPb8889r9uzZua47ffq02rZtS48ZAAAAAFyFPAez9957TwMHDtQnn3zitvz06dNq06aNTpw4cVW/dwYAAAAAxV2efsdMku6++2798ccf6tGjh7744gu1aNFCp0+fVrt27XTs2DGtWbNG0dHRhVkrAAAAAFyX8hzMJGnAgAE6ceKEunTpok8//VTPP/+8UlJStGbNGsXGxhZWjQAAAABwXctXMJOk4cOH6/fff1erVq0UHx+vNWvW6IYbbiiM2gAAAACgWMhzMOvatavbfT8/P0VGRmrIkCFuyxctWuSZygAAAACgmMhzMAsPD3e73717d48XAwAAAADFUZ6D2aWmygcAAAAAFEyep8sHAAAAABQOghkAAAAAGEYwAwAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIYRzAAAAADAMIIZAAAAABhGMAMAAAAAwwhmAAAAAGAYwQwAAAAADCOYAQAAAIBhBDMAAAAAMIxgBgAAAACGEcwAAAAAwDCCGQAAAAAYRjADAAAAAMMIZgAAAABgGMEMAAAAAAwjmAEAAACAYQQzAAAAADCMYAYAAAAAhhHMAAAAAMAwghkAAAAAGEYwAwAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIYRzAAAAADAMIIZAAAAABhGMAMAAAAAwwhmAAAAAGAYwQwAAAAADCOYAQAAAIBhBDMAAAAAMIxgBgAAAACGEcwAAAAAwDCCGQAAAAAYRjADAAAAAMMIZgAAAABgGMEMAAAAAAwjmAEAAACAYQQzAAAAADCMYAYAAAAAhhHMAAAAAMAwghkAAAAAGEYwAwAAAADDCGYAAAAAYBjBDAAAAAAMMxrM1q5dq06dOqls2bKy2Wz65JNP3NZblqXRo0erbNmyCgoKUkJCgn7++WczxQIAAABAITEazNLS0nTLLbdo8uTJua6fMGGC3njjDU2ePFmJiYmKiYlR69atderUqWtcKQAAAAAUHl+TO2/fvr3at2+f6zrLsjRp0iQ9++yz6tq1qyRp7ty5io6O1vz58/XQQw9dy1IBAAAAoNB47TVm+/btU0pKitq0aeNaFhAQoObNm2v9+vWXfNy5c+eUmprqdgMAAAAAb+a1wSwlJUWSFB0d7bY8OjratS4348aNU3h4uOsWFxdXqHUCAAAAQEF5bTDLZrPZ3O5blpVj2YVGjhypkydPum6HDh0q7BIBAAAAoECMXmN2OTExMZKyes5iY2Ndy48cOZKjF+1CAQEBCggIKPT6AAAAAMBTvLbHrEKFCoqJidHy5ctdyzIyMrRmzRo1adLEYGUAAAAA4FlGe8xOnz6tX375xXV/37592rZtm0qXLq1y5cpp6NChGjt2rKpUqaIqVapo7NixCg4OVo8ePQxWDQAAAACeZTSYbd68WS1atHDdHzZsmCSpb9++mjNnjoYPH64zZ85o4MCB+v3339WwYUMtW7ZMJUqUMFUyAAAAAHiczbIsy3QRhSk1NVXh4eE6efKkwsLCjNWRnnFeNV9YKkna8WJbBft77eV9AAAAwHXJW7JBbrz2GjMAAAAAKC4IZgAAAABgGMEMAAAAAAwjmAEAAACAYQQzAAAAADCMYAYAAAAAhhHMAAAAAMAwghkAAAAAGEYwAwAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIYRzAAAAADAMIIZAAAoctIzziv+6S8U//QXSs84b7ocACgwghkAAAAAGEYwAwAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIYRzAAAAADAMIIZAAAAABhGMAMAAAAAwwhmAAAAAGAYwQwAAAAADCOYAQAAAIBhBDMAAAAAMIxgBgAAAACGEcwAAAAAwDCCGQAAAAAYRjADAAAAAMMIZgAAAABgGMEMAAAUKZkOp75OOuy6/87a/2jpzynKdDgNVgUABeNrugAAAIC8OJx6VvM2HdT7Gw/oRFqGa/k/V/4ih9NS6RB/9WpUXj0bllN0WKDBSgEg/whmAADA623Ye1wD5ibqTKZDTst9neN/C06kZWjyyj2aue4/+lffBmpcKcJApQBwdRjKCAAAvNqGvcfVe+amXEPZxZyWdCbTod4zN2nD3uPXpkAA8ACCGQAA8FqHU89qwNxEOS3riqEsm9OSnJalAe8m6nDq2cItEAA8hGAGAAC81rxNB/PUU3YxpyWdyXBo/qaDhVMYAHgYwQwAAHilTIdT7288kO9Qls1pSe9vPMBsjQCKBIIZAADwSit3HnGbffFqHE/L0KqdRzxUEQAUHoIZAADwSnuPnpbdx1agbdh9bNp7NM1DFQFA4SGYAQAAr5R+zqGCxTLJJint3HlPlAMAhYpgBgAAvFJwgF1XeXmZiyUpJICfbQXg/QhmAADAK1WKCnX9ePTVcjgtVYoK8VBFAFB4CGYAAMArtaxeRqVD/Au0jYgQf7WoXsZDFQFA4SGYAQAAr+Rn91GvRuV1tfN/+NikXo3Ky8/O1x0A3o//qQAAgNfq2bCcgvzs+Q5nPjYpyN+uHg3LFU5hAOBhBDMAAOC1osMC9a++DeRjs+U5nPnYJB+bTTP7NlB0WGDhFggAHkIwAwAAXq1xpQi9d39DBflfuecsu6fs/QEN1ahixLUpEAA8gPljAQCA12tcKUIrn0jQ/E0H9d7GAzqRluFaZ7fZ5LAsRYT4q1ej8urRsBw9ZQCKHIIZAAAoEqLDAvV466oa3LKylvyUokcXfC9JeuyOyqoeE6YW1csw0QeAIotgBgAAihQ/u49a1fhzCvwBTSsq2J+vNACKNk4rAQAAAIBhBDMAAAAAMIxgBgAAAACGMSAbALI5MqXdS6Vju6WMNMk/RIqsKlVtK9n9TFcHAACuYwQzAEhNlrbMlhJnSunHJB+7sgYUOCWnQwqOlBrcL9W7TwqLNV0tAAC4DhHMABRv+9ZJC7pJmWcly5G1zOmQ5PizTfoxae3r0obJUveFUoWmRkoFAADXL64xA1B87VsnvddFyjzzZyi7FMuR1e69LlmPA2BWRpr2B/bQ/sAeWUOPAaCII5gBKJ5Sk7N6yixn1i0vstsuuDfr8QAAAB5CMANQPG2Z/b/hi3kMZdksp5SZLm2ZUyhlAQCA4olgBqD4cWRmTfRxpeGLl2I5pc0zs7YDAADgAQQzAMXP7qVZE3oURNpRac8yz9QDAACKPYIZgOLn2O7/TYlfADZ71nYAAAA8gGAGoPjJSFOB//uz+UjnTnukHAAAAIIZgOLHP0RSPif9uJjllAJCPVIOAAAAwQxA8RNZ9X8/Il0AliNrOwAAAB5AMANQ/FRtKwVHFmwbIVFSlTaeqQcAABR7BDMAxY/dT2pwf9YEHlfD5iPVvz9rOwAAAB5AMANQPNW7T/ILzApZ+WHzkfyCpXr9CqUsAABQPBHMABRPYbFS94VZQSuv4Sy7bY+FWY8HAADwEIIZgOKrQlOp9ydZPWBXGtaY3VPW51Mp/vZrUh6ASwv298313wBQVBHMABRvFZpKgzdLzZ76c0IQm13y8fszrIVESc2GZ7UjlAEAgELAKSYACIuVWoyUmj0p7VkmHdud9ePRAaFZU+JXacNEHwAA5JcjU9q9NOtzNSMt63dEI6tmzY7M52oOBDMAyGb3k6p3lNTRdCUAABRdqcnSltlS4kwp/ZjkY1fWQD1n1u+IBkdmzY5c7z6u2b4AwexacGTKvutLPWL/UsG2s/JdnyRFV+dsAQAAAK4v+9ZJC7pJmWcly5G1zOmQ5PizTfoxae3r0obJWRNxVWhqpFRvQzArTBecLQhIP6YnfH1kySbfNZ9l/aFytgAAAADXi33rpPe6SJYz63Y5lkPKPJPVvvcnhDMx+Ufh2bdOmlwv62xA+jFJkq/NKT+bQ7bsswfZZwsm18tqDwAAABRFqclZPWV5CWXZstsuuDfr8cUcwawwZJ8tyDzzZxfupVx4toBwBgAAgKJoy+z/DV/MYyjLZjmlzHRpy5xCKasoIZh5GmcLAAAAUJw4MrMm+rhSh8SlWE5p88ys7RRjBDNP42wBAACFy5Ep7frqz/vr35KSPi/2X+oAY3YvdV26c9XSjmb9ZE0xxuQfnuSpswXNnmS2RgAALnbxFNzZ1kxgUi3ApGO7s6bEd17ld2BJstmztlOMf7KGHjNP4mwBAACFI5dJtVyYVAswKyNNBY4VNh/p3GmPlFNUEcw8KftsQUG4zhYAAABJTKoFeDv/EEn5vIznYpZTCgj1SDlFFcHMkzhbAACAZzGpFuD9IqsWbBijlHVSJbKqZ+opoghmnsTZAgAAPItJtQDvV7Vt1jWeBRESJVVp45l6iiiCmSdxtgAAAM9hCm6gaLD7ZU28Y7vKS3psPlL9+4v95HcEM0/ibAEAAJ7DpFpA0VHvPskvMCtk5YfNR/ILlur1K5SyihKCmSdxtgAAAM9hUi2g6AiLlbovzPo+m9dwlt22x0J+4kIEM8/jbAEAAJ7BpFpA0VKhqdT7k6zvtFfqqMj+7tvnUyn+9mtSnrcjmHkaZwsAAPAMJtUCip4KTaXBm6VmT/15iY/NLvn4/RnWQqKkZsOz2hHKXHxNF3Bdyj5bsODeK//mSvbZgh4L+cMEAOBCTKoFFE1hsVKLkVKzJ7Ou8Ty2O6vnOiA063is0oZLd3JBMCss2WcLtsyREv8lpR/TectHlmzy9ZFsliPrbEH9+7OGL9JTBgCAu+xJtQoyAQiTagHm2P2k6h0ldTRdSZFAMCtMF5wtOJf0ld5c8IVCbGf1UMva8o+uztkCAAAuJ3tSrbWvX92U+UyqBaAIIZhdC3Y/Oap20HRH1rja+xu3lb8/Lz0AAFdU7z5pw+T/XRqQj+vNmFQLQBHD5B8AAMB7MakWgGKCYAYAALwbU3ADKAYIZgAAwPvlNgV3NqbgBnAd4EInAABQNFw4BXfSYunf/bOWJ4yQomsxqRaAIq1I9JhNnTpVFSpUUGBgoOrVq6d169aZLgkAAJhi95OqtvvzfuPBWVNyE8oAFGFeH8wWLlyooUOH6tlnn9X333+vpk2bqn379jp48KDp0gAAAADAI7w+mL3xxhu6//77NWDAANWoUUOTJk1SXFycpk2bZro0AAAAAPAIrw5mGRkZ2rJli9q0aeO2vE2bNlq/fr2hqgAAAADAs7x68o9jx47J4XAoOjrabXl0dLRSUlJyfcy5c+d07tw51/3U1NRCrREAAAAACsqre8yy2Ww2t/uWZeVYlm3cuHEKDw933eLi4q5FiQAAAABw1bw6mEVGRsput+foHTty5EiOXrRsI0eO1MmTJ123Q4cOXYtSAQAAAOCqeXUw8/f3V7169bR8+XK35cuXL1eTJk1yfUxAQIDCwsLcbgAAAADgzbz6GjNJGjZsmHr37q369eurcePGmjFjhg4ePKiHH37YdGkAAAAA4BFeH8y6deum48eP68UXX1RycrJq1aqlL7/8UuXLlzddGgAAAAB4hNcHM0kaOHCgBg4caLoMAAAAACgUXn2NGQAAAAAUBwQzAAAAADCMYAYAAAAAhhHMAAAAAMAwghkAAAAAGEYwAwAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIb5mi4AAAAg3/xDpNEnTVcBAB5DjxkAAAAAGEYwAwAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIYRzAAAAADAMIIZAAAAABhGMAMAAAAAwwhmAAAAAGAYwQwAAAAADCOYAQAAAIBhBDMAAAAAMIxgBgAAAACGEcwAAAAAwDCCGQAAAAAYRjADAAAAAMMIZgAAAABgGMEMAAAAAAwjmAEAAACAYQQzAAAAADCMYAYAAAAAhhHMAAAAAMAwghkAAAAAGEYwAwAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGG+pgsoLoL9fbX/1Y6mywAAAADghegxAwAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIYRzAAAAADAMIIZAAAAABhGMAMAAAAAwwhmAAAAAGAYwQwAAAAADCOYAQAAAIBhBDMAAAAAMIxgBgAAAACGEcwAAAAAwDCCGQAAAAAYRjADAAAAAMMIZgAAAABgGMEMAAAAAAwjmAEAAACAYQQzAAAAADCMYAYAAAAAhhHMAAAAAMAwX9MFFDbLsiRJqamphisBAAAAYFJ2JsjOCN7kug9mp06dkiTFxcUZrgQAAACANzh16pTCw8NNl+HGZnljXPQgp9Op3377TSVKlJDNZjNaS2pqquLi4nTo0CGFhYUZrQXApXGsAkUDxypQNHjTsWpZlk6dOqWyZcvKx8e7ruq67nvMfHx8dOONN5ouw01YWJjxP0oAV8axChQNHKtA0eAtx6q39ZRl866YCAAAAADFEMEMAAAAAAwjmF1DAQEBGjVqlAICAkyXAuAyOFaBooFjFSgaOFbz5rqf/AMAAAAAvB09ZgAAAABgGMEMAAAAAAwjmAEAAACAYQQzAPifOXPmqGTJkqbLAIqt/fv3y2azadu2baZLAbyS6WNk586datSokQIDA1WnTp1rVk98fLwmTZpUqPvwBgSzAujXr59sNpsefvjhHOsGDhwom82mfv36udp26dIl1+0kJCRo6NChOZZ/8sknstlsHqwYKB6yj83sW0REhNq1a6cffvjhso/r1q2bdu/efY2qBK4vFx5zud0uPi5zu8XFxSk5OVm1atUy/XQAj7sejpFRo0YpJCREu3bt0ooVK4zXc70hmBVQXFycPvjgA505c8a17OzZs1qwYIHKlStnsDKgeGvXrp2Sk5OVnJysFStWyNfXV3feeecl22dmZiooKEhlypS5hlUC14/s4y05OVmTJk1SWFiY27J//OMfbvclafbs2W7L7Ha7YmJi5Ovra/jZZMnMzDRdAq4j18MxsnfvXt1+++0qX768IiIijNdTFGRkZOS5LcGsgOrWraty5cpp0aJFrmWLFi1SXFycbr31VoOVAcVbQECAYmJiFBMTozp16mjEiBE6dOiQjh496hp68X//939KSEhQYGCg3n///RxDGffu3avOnTsrOjpaoaGhatCggb7++mu3/cTHx2vs2LHq37+/SpQooXLlymnGjBnX+NkC5mUfbzExMQoPD5fNZsux7ML7klSyZEm3ZRcPi1q9erVsNpuWLl2qW2+9VUFBQWrZsqWOHDmir776SjVq1FBYWJi6d++u9PT0AtV/qf8XJGnWrFm66aabFBAQoNjYWA0ePLhA+0LxVNSPEZvNpi1btujFF1+UzWbT6NGjc9Tz4osvqmzZsjp+/LjrcX/961/VrFkzOZ1OSdL69evVrFkzBQUFKS4uTkOGDFFaWpqr/ZEjR9SpUycFBQWpQoUKmjdv3hVry230WZcuXVwj16Ssz+uXXnpJPXr0UGhoqMqWLau33norx3OcNm2a2rdv79r/hx9+6Nbmv//9r7p166ZSpUopIiJCnTt31v79+13rs0fJjRs3TmXLllXVqlWvWH82gpkH3HfffZo9e7br/qxZs9S/f3+DFQG40OnTpzVv3jxVrlxZERERruUjRozQkCFDlJSUpLZt2+b6uA4dOujrr7/W999/r7Zt26pTp046ePCgW7uJEyeqfv36+v777zVw4EA98sgj2rlzZ6E/L6C4GD16tCZPnqz169fr0KFD+vvf/65JkyZp/vz5+uKLL7R8+fIcX7Cu1sX/L0ybNk2DBg3Sgw8+qB9//FGLFy9W5cqVPbIvwFOuxTGSnJysm266SU888YSSk5P15JNP5mjz7LPPKj4+XgMGDJAkTZ8+XWvXrtV7770nHx8f/fjjj2rbtq26du2qH374QQsXLtQ333zjdrKjX79+2r9/v1auXKl///vfmjp1qo4cOVKg2rO99tprql27trZu3aqRI0fq8ccf1/Lly93aPP/887rrrru0fft29erVS927d1dSUpIkKT09XS1atFBoaKjWrl2rb775RqGhoWrXrp1bz9iKFSuUlJSk5cuX6/PPP897gRauWt++fa3OnTtbR48etQICAqx9+/ZZ+/fvtwIDA62jR49anTt3tvr27evWNjfNmze3HnvssRzLP/74Y4u3CMi/vn37Wna73QoJCbFCQkIsSVZsbKy1ZcsWy7Isa9++fZYka9KkSW6Pmz17thUeHn7ZbdesWdN66623XPfLly9v9erVy3Xf6XRaZcqUsaZNm+a5JwQUMXk5liRZH3/8sduy7GPz+++/tyzLslatWmVJsr7++mtXm3HjxlmSrL1797qWPfTQQ1bbtm0LVPOl/l8oW7as9eyzzxZo28DFiuIxYlmWdcstt1ijRo26ZD2WZVl79+61SpQoYY0YMcIKDg623n//fde63r17Ww8++KDbNtetW2f5+PhYZ86csXbt2mVJsjZu3Ohan5SUZEmy3nzzzUvWldt36Qu/h1tW1ud1u3bt3Np069bNat++veu+JOvhhx92a9OwYUPrkUcesSzLsmbOnGlVq1bNcjqdrvXnzp2zgoKCrKVLl1qWlfUdJDo62jp37twl670Uesw8IDIyUh07dtTcuXM1e/ZsdezYUZGRkabLAoq1Fi1aaNu2bdq2bZs2bdqkNm3aqH379jpw4ICrTf369S+7jbS0NA0fPlw1a9ZUyZIlFRoaqp07d+boMatdu7br39lDUzx1dg+A+zEWHR2t4OBgVaxY0W3Z5Y650NBQ1y23CbsudOH/C0eOHNFvv/2mVq1aFaB6oPBdy2PkSipWrKjXX39d48ePV6dOndSzZ0/Xui1btmjOnDlu+2vbtq2cTqf27dunpKQk+fr6uh2H1atX99iMyY0bN85xP7s3LC9ttmzZol9++UUlSpRw1V+6dGmdPXtWe/fudT3m5ptvlr+/f77r40o9D+nfv7+rG3bKlCn5emxYWJhOnjyZY/kff/yhsLAwj9QHFDchISFuw43q1aun8PBwvfPOO64hFiEhIZfdxlNPPaWlS5fq9ddfV+XKlRUUFKS77747x4W8fn5+bvdtNptrLD2AgrvwGLPZbPk+5i6cyvtKn6sX/r8QFBSUz0oBM67lMZIXa9euld1u1/79+3X+/HnX5CBOp1MPPfSQhgwZkuMx5cqV065du1z15oePj4+yOrz+lNfJe/Kyr+w2TqdT9erVy/W6t6ioKNe/r/T94lIIZh5y4djS3K5VuZzq1avrq6++yrE8MTFR1apV80h9QHFns9nk4+PjNoPqlaxbt079+vXT3/72N0lZ15xdeIEvgKLhaq8JK1GihOLj47VixQq1aNHCw1UB3sOT100uXLhQixYt0urVq9WtWze99NJLGjNmjKSsSfN+/vnnS+6vRo0aOn/+vDZv3qzbbrtNkrRr1y798ccfl91nVFSUayZLSXI4HPrpp59yHLcbN27Mcb969eo5lvXp08ftfvaEfnXr1tXChQtVpkyZQuk8YSijh9jtdiUlJSkpKUl2uz3XNidPnnQNrcq+HTx4UAMHDtTevXs1aNAgbd++Xbt379aUKVM0c+ZMPfXUU9f4mQDXh3PnziklJUUpKSlKSkrSo48+qtOnT6tTp0553kblypW1aNEibdu2Tdu3b1ePHj3oCQOKmdGjR2vixIn65z//qT179mjr1q0em2gEuN78+uuveuSRRzR+/HjdfvvtmjNnjsaNG+cKRCNGjNCGDRs0aNAgbdu2TXv27NHixYv16KOPSpKqVaumdu3a6YEHHtCmTZu0ZcsWDRgw4Iq91y1bttQXX3yhL774Qjt37tTAgQNzDXPffvutJkyY4Pqu/eGHH+qxxx5za/Phhx9q1qxZ2r17t0aNGqXvvvvONSquZ8+eioyMVOfOnbVu3Trt27dPa9as0WOPPaZff/21wK8fPWYedKXkvHr16hxT6Pft21dz5szRunXr9Oyzz6pNmzY6e/asqlatqjlz5uiee+4pzJKB69aSJUsUGxsrKeusd/Xq1fXhhx8qISEhz71eb775pvr3768mTZooMjJSI0aMUGpqaiFWDcDb9O3bV2fPntWbb76pJ598UpGRkbr77rtNlwV4Hcuy1K9fP912222uINO6dWsNHjxYvXr10rZt21S7dm2tWbNGzz77rJo2bSrLslSpUiV169bNtZ3Zs2drwIABat68uaKjo/Xyyy/r+eefv+y++/fvr+3bt6tPnz7y9fXV448/nmsv9xNPPKEtW7ZozJgxKlGihCZOnJhjpNuYMWP0wQcfaODAgYqJidG8efNUs2ZNSVJwcLDWrl2rESNGqGvXrjp16pRuuOEGtWrVyiM9aDbr4gGZAAAAAHAdiY+P19ChQ3P83tmFbDabPv74Y3Xp0uWa1XUhhjICAAAAgGEEMwAAAAAwjKGMAAAAAGAYPWYAAAAAYBjBDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGACi2Vq9eLZvNpj/++CPPj4mPj9ekSZMKrSYAQPFEMAMAeK1+/frJZrPp4YcfzrFu4MCBstls6tev37UvDAAADyOYAQC8WlxcnD744AOdOXPGtezs2bNasGCBypUrZ7AyAAA8h2AGAPBqdevWVbly5bRo0SLXskWLFikuLk633nqra9m5c+c0ZMgQlSlTRoGBgbr99tuVmJjotq0vv/xSVatWVVBQkFq0aKH9+/fn2N/69evVrFkzBQUFKS4uTkOGDFFaWlqhPT8AACSCGQCgCLjvvvs0e/Zs1/1Zs2apf//+bm2GDx+ujz76SHPnztXWrVtVuXJltW3bVidOnJAkHTp0SF27dlWHDh20bds2DRgwQE8//bTbNn788Ue1bdtWXbt21Q8//KCFCxfqm2++0eDBgwv/SQIAijWCGQDA6/Xu3VvffPON9u/frwMHDujbb79Vr169XOvT0tI0bdo0vfbaa2rfvr1q1qypd955R0FBQZo5c6Ykadq0aapYsaLefPNNVatWTT179sxxfdprr72mHj16aOjQoapSpYqaNGmif/7zn3r33Xd19uzZa/mUAQDFjK/pAgAAuJLIyEh17NhRc+fOlWVZ6tixoyIjI13r9+7dq8zMTP3lL39xLfPz89Ntt92mpKQkSVJSUpIaNWokm83matO4cWO3/WzZskW//PKL5s2b51pmWZacTqf27dunGjVqFNZTBAAUcwQzAECR0L9/f9eQwilTpritsyxLktxCV/by7GXZbS7H6XTqoYce0pAhQ3KsY6IRAEBhYigjAKBIaNeunTIyMpSRkaG2bdu6ratcubL8/f31zTffuJZlZmZq8+bNrl6umjVrauPGjW6Pu/h+3bp19fPPP6ty5co5bv7+/oX0zAAAIJgBAIoIu92upKQkJSUlyW63u60LCQnRI488oqeeekpLlizRjh079MADDyg9PV3333+/JOnhhx/W3r17NWzYMO3atUvz58/XnDlz3LYzYsQIbdiwQYMGDdK2bdu0Z88eLV68WI8++ui1epoAgGKKYAYAKDLCwsIUFhaW67pXX31Vd911l3r37q26devql19+0dKlS1WqVClJWUMRP/roI3322We65ZZbNH36dI0dO9ZtG7Vr19aaNWu0Z88eNW3aVLfeequef/55xcbGFvpzAwAUbzYrL4PuAQAAAACFhh4zAAAAADCMYAYAAAAAhhHMAAAAAMAwghkAAAAAGEYwAwAAAADDCGYAAAAAYBjBDAAAAAAMI5gBAAAAgGEEMwAAAAAwjGAGAAAAAIYRzAAAAADAMIIZAAAAABj2/0LtGhCvrahGAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Preparing the DataFrame:\n", + "t1 = pd.DataFrame(columns=['kaq - opt -l1', 'kaq - 95% -l1','kaq - opt -l2', 'kaq - 95% -l2' ], index = ['MLU','Brian','TTim - rc', 'TTim - fixed upper']) \n", + "simulation = ['MLU','Brian','TTim - rc', 'TTim - fixed upper']\n", + "t1.loc['MLU'] = [17.424, 124.160*1e-2*17.424,1.747,4.521*1e-2*1.747]\n", + "t1.loc['Brian'] = [17.28, np.nan, 3.456, np.nan]\n", + "t1.loc['TTim - fixed upper'] = [17.28,np.nan,ca_4.parameters.loc['kaq1','optimal'],2*ca_4.parameters.loc['kaq1','std']]\n", + "t1.loc['TTim - rc'] = [ca_3.parameters.loc['kaq0','optimal'],2*ca_3.parameters.loc['kaq0','std'],ca_3.parameters.loc['kaq1','optimal'],2*ca_3.parameters.loc['kaq1','std']]\n", + "\n", + "# Plotting\n", + "\n", + "plt.figure(figsize = (10,7))\n", + "\n", + "plt.errorbar(x = t1.index, y = t1['kaq - opt -l1'], yerr = [t1['kaq - 95% -l1'], t1['kaq - 95% -l1']],\n", + " marker='o', linestyle='', markersize=12, label = 'upper aquifer')\n", + "plt.errorbar(x = t1.index, y = t1['kaq - opt -l2'], yerr = [t1['kaq - 95% -l2'], t1['kaq - 95% -l2']],\n", + " marker='o', linestyle='', markersize=12, label = 'lower aquifer')\n", + "\n", + "plt.legend()\n", + "plt.suptitle(\"Error bar plot of calibrated hydraulic conductivities\")\n", + "plt.ylabel('K [m/d]')\n", + "#plt.ylim([278,289])\n", + "plt.xlabel('Model');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MLU model has a larger confidence interval for the first aquifer layer, while TTim got lower values for the generic model (TTim - rc) and a relatively small range. The model with some fixed values has a small confidence interval. Confidence intervals were not reported in Brian et al. (1997)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Bakker, M. Semi-analytic modeling of transient multi-layer flow with TTim. Hydrogeol J 21, 935–943 (2013). https://doi.org/10.1007/s10040-013-0975-2\n", + "* Brian, Schroth, T., N., Narasimhan, 1997. Application of a numerical model in the interpretation of a leaky aquifer test. Ground Water .\n", + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Newville, M.,Stensitzki, T., Allen, D.B., Ingargiola, A. (2014) LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python.https://dx.doi.org/10.5281/zenodo.11813. https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021).\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pumpingtests/confined5_nevada.ipynb b/pumpingtests/confined5_nevada.ipynb new file mode 100644 index 0000000..e6cf7c1 --- /dev/null +++ b/pumpingtests/confined5_nevada.ipynb @@ -0,0 +1,1028 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Confined - Double Porosity Test, Nevada\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this following benchmark example, we demonstrate by reproducing Yang (2020) how we can apply TTim in fractured media. We further compare the values obtained in TTim with the ones simulated in AQTESOLV and MLU by Yang (2020)\n", + "\n", + "The example is taken from Kruseman and de Ridder (1990). It is based on a pumping test conducted in a fractured tertiary volcanic aquifer near Yucca Mountains, Nevada, US. Although conventional Darcy's flow is not applied to fracture flow, in this case, we can assume that fracturing is pervasive enough to flow occur as Darcy's flow at the aquifer scale. Flow to the well comes from fractures, while the matrix exchanges water with the fractures.\n", + "\n", + "The borehole has 1219 m depth and penetrated a 400 m thick sequence of fractured volcanic rocks with water entry points. At every entry point, the head is more or less the same, so they have good hydraulic connection. The water table is about 470 m below depth, which indicates confined conditions.\n", + "\n", + "Drawdown data was sampled at the well, named ***UE-25b#1*** and at an observation well, named ***UE-25a#1***, 110 m away. Three pumping tests were conducted at the site and will be the object of our investigation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "def conc_confined5():\n", + " ##Now printing the conceptual model figure:\n", + "\n", + " fig = plt.figure(figsize=(14, 9))\n", + " ax = fig.add_subplot(1,1,1)\n", + " #sky\n", + " sky = plt.Rectangle((-20,0), width = 170, height = 150, fc = 'b', zorder=0, alpha=0.1)\n", + " ax.add_patch(sky)\n", + "\n", + " #Aquifer:\n", + " ground = plt.Rectangle((-20,-1219), width = 170, height = 400, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9, hatch = 'xx')\n", + " ax.add_patch(ground)\n", + "\n", + " #Confining bed:\n", + " confining_unit = plt.Rectangle((-20,-1219+400), width = 170, height = 1219-400, fc = np.array([100,100,100])/255, zorder=0, alpha=0.7)\n", + " ax.add_patch(confining_unit)\n", + " well = plt.Rectangle((-2,-1219), width = 4, height = 1219, fc = np.array([200,200,200])/255, zorder=1)\n", + " ax.add_patch(well)\n", + "\n", + " #Wellhead\n", + " wellhead = plt.Rectangle((-4,0),width = 8, height = 55, fc = np.array([200,200,200])/255, zorder=2, ec='k')\n", + " ax.add_patch(wellhead)\n", + "\n", + " #Screen for the well:\n", + " screen = plt.Rectangle((-2,-1219), width = 4, height = 400, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + " screen.set_linewidth(2)\n", + " ax.add_patch(screen)\n", + " pumping_arrow = plt.Arrow(x = 4,y = 25, dx = 20, dy = 0, color = \"#00035b\", zorder = 3)\n", + " ax.add_patch(pumping_arrow)\n", + " ax.text(x = 28, y = 55, s = r'$ Q = 3093 \\frac{m^3}{d}$' , fontsize = 'large')\n", + " #Piezometers\n", + " piez1 = plt.Rectangle((99,-1219), width = 2, height = 1219,fc = np.array([200,200,200])/255, zorder=1)\n", + " screen_piez_1 = plt.Rectangle((99,-1219), width = 2, height = 400, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + " screen_piez_1.set_linewidth(2)\n", + "\n", + " ax.add_patch(piez1)\n", + "\n", + " ax.add_patch(screen_piez_1)\n", + "\n", + " #last line\n", + " line = plt.Line2D(xdata= [-20,150], ydata = [0,0], color = \"k\")\n", + " ax.add_line(line)\n", + " ax.text(x = 100, y = 55, s = 'Obs Well 1', fontsize = 'large' )\n", + "\n", + " ax.set_xlim([-20,150])\n", + " ax.set_ylim([-1219,150])\n", + " ax.set_xlabel('Distance [m]')\n", + " ax.set_ylabel('Relative height [m]')\n", + " ax.set_title('Conceptual Model - Nevada Example')\n", + " plt.show()\n", + "\n", + "conc_confined5()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Step 1. Import the Required Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import ttim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set the Basic Parameters for the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "H = 400 #aquifer thickness [m]\n", + "Q = 3093.12 #constant pumping rate [m^3/d]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Load Data\n", + "\n", + "Dataset is stored in a text-file for each well, where the first column is the time in days and the second is the drawdown in meters." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#Pumped well UE-25b#1\n", + "data1 = np.loadtxt('data/double-porosity-pumpingwell.txt', skiprows = 1)\n", + "t1 = data1[:, 0]\n", + "h1 = data1[:, 1]\n", + "\n", + "#Observation well UE-25a#1\n", + "data2 = np.loadtxt('data/double-porosity-110m.txt', skiprows = 1)\n", + "t2 = data2[:, 0]\n", + "h2 = data2[:, 1]\n", + "r = 110 #distance from obs to pumped well" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Create conceptual model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To conceptualize the model in TTim, we have to approximate the fractured system to a possible ModelMaq configuration. We can do this by creating a first layer that represents the matrix of the aquifer, followed by a 1 m thick aquitard and a second layer that represents the fractures." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def conc_mmaq_confined5():\n", + " ##Now printing the conceptual model figure:\n", + "\n", + " fig = plt.figure(figsize=(14, 9))\n", + " ax = fig.add_subplot(1,1,1)\n", + " #sky\n", + " sky = plt.Rectangle((-20,0), width = 170, height = 150, fc = 'b', zorder=0, alpha=0.1)\n", + " ax.add_patch(sky)\n", + "\n", + " #Aquifer:\n", + " ground_2 = plt.Rectangle((-20,-1219+401), width = 170, height = 400, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9, hatch = 'oo')\n", + " ax.add_patch(ground_2)\n", + " ground = plt.Rectangle((-20,-1219), width = 170, height = 400, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9, hatch = 'xx')\n", + " ax.add_patch(ground)\n", + "\n", + " #Confining bed:\n", + " confining_unit_1 = plt.Rectangle((-20,-1219+400), width = 170, height = 1, fc = np.array([100,100,100])/255, zorder=0, alpha=0.7)\n", + " ax.add_patch(confining_unit_1)\n", + " confining_unit = plt.Rectangle((-20,-1219+801), width = 170, height = 1219-801, fc = np.array([100,100,100])/255, zorder=0, alpha=0.7)\n", + " ax.add_patch(confining_unit)\n", + " well = plt.Rectangle((-2,-1219), width = 4, height = 1219, fc = np.array([200,200,200])/255, zorder=1)\n", + " ax.add_patch(well)\n", + "\n", + " #Wellhead\n", + " wellhead = plt.Rectangle((-4,0),width = 8, height = 55, fc = np.array([200,200,200])/255, zorder=2, ec='k')\n", + " ax.add_patch(wellhead)\n", + "\n", + " #Screen for the well:\n", + " screen = plt.Rectangle((-2,-1219), width = 4, height = 400, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + " screen.set_linewidth(2)\n", + " ax.add_patch(screen)\n", + " pumping_arrow = plt.Arrow(x = 4,y = 25, dx = 22, dy = 0, color = \"#00035b\")\n", + " ax.add_patch(pumping_arrow)\n", + " ax.text(x = 29, y = 55, s = r'$ Q = 3093 \\frac{m^3}{d}$', fontsize = 'large' )\n", + " #Piezometers\n", + " piez1 = plt.Rectangle((99,-1219), width = 2, height = 1219,fc = np.array([200,200,200])/255, zorder=1)\n", + " screen_piez_1 = plt.Rectangle((99,-1219), width = 2, height = 400, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + " screen_piez_1.set_linewidth(2)\n", + "\n", + " ax.add_patch(piez1)\n", + "\n", + " ax.add_patch(screen_piez_1)\n", + "\n", + " #last line\n", + " line = plt.Line2D(xdata= [-20,150], ydata = [0,0], color = \"k\")\n", + " ax.add_line(line)\n", + " ax.text(x = 100, y = 55, s = 'Obs Well 1', fontsize = 'large' )\n", + "\n", + " ax.set_xlim([-20,150])\n", + " ax.set_ylim([-1219,150])\n", + " ax.set_xlabel('Distance [m]')\n", + " ax.set_ylabel('Relative height [m]')\n", + " ax.set_title('Conceptual ModelMaq Model - Nevada Example')\n", + " plt.show()\n", + " \n", + "conc_mmaq_confined5()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, for the TTim model, we will adopt the parameters for the first layer from the results obtained from Kruseman and de Ridder (1970):" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "km = 0.1 / H #hydraulic conductivity of matrix calculated by K&dR\n", + "Sm = 3.85e-4 #specific storage of matrix calculated by K&dR" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can construct the two-layer model:\n", + "Instructions on how to construct this model are in:\n", + "* [Confined 4 - Schroth](confined4_schroty.ipynb)\n", + "* [Confined 1 - Oude Korendijk](confined1_oude_korendijk.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml = ttim.ModelMaq(kaq=[km, 1], z=[0, -400, -401, -801], c=5, Saq=[Sm, 1e-3],\\\n", + " Sll=0, topboundary='conf', tmin=1e-5, tmax=3)\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=0.11, rc=0, tsandQ=[0, 3093.12], layers=1)\n", + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Calibrate the model using both Datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this calibration procedure, we only calibrate the parameters of the fractured system and keep the matrix values fixed. We also calibrate the wellbore storage parameter ```rc``` that is the radius of the caisson considered for storage.\n", + "Instructions on how to set this calibration are in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".........................................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 198\n", + " # data points = 138\n", + " # variables = 4\n", + " chi-square = 5.47351190\n", + " reduced chi-square = 0.04084710\n", + " Akaike info crit = -437.371988\n", + " Bayesian info crit = -425.662973\n", + "[[Variables]]\n", + " kaq1: 0.87696320 +/- 0.00699050 (0.80%) (init = 10)\n", + " Saq1: 5.0877e-06 +/- 5.0675e-07 (9.96%) (init = 0.0001)\n", + " c1: 13.0035244 +/- 1.59723924 (12.28%) (init = 10)\n", + " rc: 0.10560361 +/- 0.00320824 (3.04%) (init = 0)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq1, c1) = +0.8580\n", + " C(kaq1, Saq1) = -0.7312\n", + " C(Saq1, c1) = -0.5463\n", + " C(Saq1, rc) = -0.4011\n", + " C(kaq1, rc) = +0.1007\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq10.8769636.990504e-030.797126-infinf10.0000[0.8769632032421197]
Saq10.0000055.067510e-079.9603050.0inf0.0001[5.0877062902632275e-06]
c113.0035241.597239e+0012.283126-infinf10.0000[13.003524396048254]
rc0.1056043.208241e-033.038003-infinf0.0000[0.1056036071201357]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq1 0.876963 6.990504e-03 0.797126 -inf inf 10.0000 \n", + "Saq1 0.000005 5.067510e-07 9.960305 0.0 inf 0.0001 \n", + "c1 13.003524 1.597239e+00 12.283126 -inf inf 10.0000 \n", + "rc 0.105604 3.208241e-03 3.038003 -inf inf 0.0000 \n", + "\n", + " parray \n", + "kaq1 [0.8769632032421197] \n", + "Saq1 [5.0877062902632275e-06] \n", + "c1 [13.003524396048254] \n", + "rc [0.1056036071201357] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.19915604354014974\n" + ] + } + ], + "source": [ + "ca = ttim.Calibrate(ml)\n", + "ca.set_parameter(name='kaq1', initial=10)\n", + "ca.set_parameter(name='Saq1', initial=1e-4, pmin=0)\n", + "ca.set_parameter(name='c1', initial=10)\n", + "ca.set_parameter_by_reference(name='rc', parameter=w.rc, initial=0)\n", + "ca.series(name='UE-25b#1', x=0, y=0, t=t1, h=h1, layer=1)\n", + "ca.series(name='UE-25a#1', x=r, y=0, t=t2, h=h2, layer=1)\n", + "ca.fit(report=True)\n", + "display(ca.parameters)\n", + "print('RMSE:', ca.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Overall, a good fit has been obtained. Reasonable standard deviations of the estimates and a low value for AIC and BIC gives us confidence in the adopted parameters. Now we can check how the results plot with the observations:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ubHh7exsNGjQw+vfvbzz++ONOfajAqgvPPPNMieevvvpqw8vLy9i7d69hGPYVD+666y6jefPmhre3txEeHm706NHDePjhh42TJ08ahmEY77zzjjF48GAjMjLS8PHxMRo1amSMHj3a+O2335zuvXHjRqN///5GYGCg0bhxY2PatGnGG2+8Ue6qC4ZhGD/++KPRvXt3w9fX1wCM8ePHG9nZ2caUKVOMLl26GCEhIYa/v7/Rtm1bY9q0aUZGRkaZ34eK2Llzp/Gvf/3L6Nmzp9GgQQPDy8vLqFevnjFw4EDj3Xffdepb2qoLHTt2LHbfZs2aGZdeemmxdsC4/fbbndq+/fZbo1u3boa/v7/RokUL46WXXnJ51YXZs2cbsbGxhq+vr9G+fXtjwYIFJV4bHx9vxMXFGQEBAQbg+AyKrrpQ4IsvvjD69Olj+Pn5GYGBgcYFF1xg/PTTT059Cl7nn3/+Kff7VJKCf68lHSWtRCEilWcyjHKm5YqIiIiI1EGq0RURERERj6REV0REREQ8khJdEREREfFISnRFRERExCMp0RURERERj6REV0REREQ8khJdEREREfFISnRFRERExCMp0RURERERj6REV0REREQ8khJdEREREfFISnRFRERExCMp0RURERERj6REV0REREQ8khJdEREREfFISnRFRERExCMp0RURERERj6REV0REREQ8khJdEREREfFISnRFRERExCMp0RURERERj6REV0REREQ8khJdEREREfFISnRFRERExCMp0RURERERj6REV0REREQ8khJdEREREfFISnRFRERExCMp0RURERERj6REV0REREQ8khJdEREREfFISnRFRERExCMp0RURERERj6REV0REREQ8khJdEREREfFIXu4OoLax2WwcPnyY4OBgTCaTu8MRERERkSIMwyA9PZ1GjRphNpc+bqtEt4jDhw8TExPj7jBEREREpBwHDx6kSZMmpZ5XoltEcHAwYP/GhYSEuDkaERERESkqLS2NmJgYR95WGiW6RRSUK4SEhCjRFREREanFyisz1WQ0EREREfFISnRFRERExCMp0RURERERj6REV0REREQ8khJdEREREfFISnRFRERExCMp0RURERERj6REV0REREQ8khJdEREREfFISnRFRERExCMp0RURERERj+SRie4rr7xC8+bN8fPzo0ePHqxZs8bdIYmIiIhIDfO4RHfRokXcc889PPzww2zdupXzzjuPYcOGkZCQ4O7QxIMlZSSxMXEjSRlJle5T2vmKtLsSR3n9KnPO1ViKtlX0uYiISEWYDMMw3B1EVerTpw/nnHMOr776qqOtffv2XHnllTz55JPlXp+WlkZoaCipqamEhIRUZ6jiIT7f9hHrFjyOgYEJE5e0uISuDbo69fn1n1/59s9vS+1T2vmKtANlvoYrsVTmnKuxFG3rFNGJ7cnbXX4+OGYw0YHR1POvR4hP8Z/NtNw0jmcdL3b+dHs4Ib6F2nPSOJ59nPCCdpMJMNlPmkz2hybTqacmMJlPtdvPmcxme5vZVPzxqcNk8cJkMYPZYv9q8cLkZcHk5WV/7O2FyevU4e1tP3x8MHl7g5eX/XVFRKQYV/M1j0p0c3NzCQgI4JNPPmHEiBGO9rvvvpv4+HhWrVpV7JqcnBxycnIcz9PS0oiJiVGiKy5Jykhi9DtDeO3FPHeHIp7GZLInvX5+mH19S/xq8vPF7B+AOaDQERh46mvB8yDMwUFYgoMxBwdjCQqyJ9IiInWYq4muVw3GVO2Sk5OxWq1ERkY6tUdGRpKUVPKfPp988klmzJhRE+GJB0pISyDHYmN9O+eRt55RvQj3CwcgJesYvxz5pdi1BX1KO9+2Xlt2Hd/lcntJCsdRXiwYRoXPVSSWqmViQJPz8PPyJzs/i9V/rwEMp/N9onvzc+JGp3YT0DuyN5uSNmLgGL/FZEBc43M5ln2MP479jsmwn4sMjOJIRpLjeZvQ1mAY7D2xFwwwG9AypDkN/CLAZsMwDHLzs8nNzcbH5IUXZsi3YtisYLVhWK2Qn4+Rn49htWLk50Nenv15Xh4UHncwDIycHIycHGxV/d3z98cSFIQlLBRzSCiW0FAsISH2r2GhWMLCsNSrZ/966rFXvXqYfHyqOBIRkerlUYlugaJ/7jMMo9Q/AU6dOpX77rvP8bxgRFfEFU1DmpLtb+H5Eaf/fZlNZpaOeoqowCgAvDKSeOGzodgMW4l9Sjv/3rCZvPDd9S61m079z0bJr1GgrFiACp9zNZaS2s5U56GT6BXVi42JG3l+2U/Fzt/fczDP/1I8Ob+/5/k8+8vmYu1RA0fxwOoHsBmWQq3/AKefm9h/6n2cbjOb/mbpqDeJCoxi8Z7FzFg/A5thw2wyM63fNEa2Hunom5SRREJaAk1Dmjp9LkkZSSSk/kVMYBMiveth5OVh5OZiy8nFyMnGlp1tT3qzs7EVJL/Z2RjZOdiysrBlZmDLzLQfGfbHRmYm1owMbCczsKWnYz15EiMzEwAjK4v8rCzy//mnIt9yzCEheNWrh6V+fbzqh2MJP/W1Xrjz84gILKGh9jIOERE38qhENyIiAovFUmz09ujRo8VGeQv4+vri6+tbE+GJB4oKjGJav2nFkpvCSUx5fUo737lB5wq1A2XG4UosFT1XkViKtg1vMZyv//zapedFmU1mYoLtv5A2DWmK2WQuloR3b9i9Qu2GYZT4WoUZp/5XmM2wcTD9oNP7K2ifsX4G/Rv1LzMJLq09KSOJhPxDNI1qSlRgyzLjcpWRl4f15ElsJ0/ak9+0NKwnUrGmpWJNTcWWlob1xAn7cfwE+SeO288fPw5WK7a0NHLT0uCvv8p/MS8vvMLD8YqIwNIgAq/6EXhFnDoa2L9aIiLwatAAc2Cg6pFFpFp4VI0u2Cej9ejRg1deecXR1qFDB6644gpNRpNqk5SRxMH0g8QExxRLLl3tU9r5irS7Ekd5/SpzztVYira5+nx78nbmbplb6khpRZPIktr7N+rP0CKj1kWVPnK+lIS0BCYtm1TsmreGvkVMcEyxe9tHxN/j+hJG7e85554y32/h73tJI8RVzbDZsKWlkZ+SgvXYMfJTjmNNOUb+sZTTX48dIz8lhfxjx7Clplbo/iY/P0cSbImob39c354QW+rXP50g16+POSCgmt6liNQlZ+VkNLAvLzZu3Djmz59Pv379eP3111mwYAE7duygWbNm5V6vRFekdqqJXxSKJsBFR5lLG60uGIEtKZktKwm+v+f9PPPLM8XaTZicRo4L7lM4/vLKJAreY00kwkUZubn2pPefZPKPJWNNTiY/OZn85GOnvv6D9dRj28mTFbq3KSAAr/r18QoPP1VCUR9L/XD783rhWMLr2dvqheNVL0x1xSIe6qxNdMG+YcTTTz9NYmIinTp14vnnn2fAgAEuXatEV+TsVt4oc0l9CpRVhuDqiG7RJLfAW0PfoldUL8frl5ZUl5a0F06Ey6wVruHE2JaVRf6xY+T/8w/5yaeS4n/+sSfFx46dTpKPHcPIzq7w/c0BAZhPTbDzCgvDHHpqsp3jaxiWkGDMQcH2r8EhWIKDMAcFYbJYyn8BEXGLszrRPRNKdEXkTFQ0CS7aXrhsoUDRJHZj4sZSyyR6RfUqMxFed3hdxWuF3TAqXJRhGNgyMrEeSz5dMpF8jPyUY/bR4eMpWFOOY01JIf/4cUdd8ZkwBwVhDgiwL+nm54fJ3w+zn799WTc//9NtvqfPmf39MPn62b8We+6H2d8fk68vZv9T12upN5FKOSuXFxMRcbeowKgSE8KRrUfSv1H/YklwSe2hvqFlTiwsbQJewQS9hLSEYrXGNsPGr//8WuKEudZhrUtsT81JdalWuLDqSoxNJhOWoEAsQYH4uFCGZths9gl3qamnJ9ilpmI9fsL5eWqqvV96Otb0NGxp6Rin1la3nZq4V628vE6vi+zjc3oDES8v8D61kYiX9+k2L4t9IxIv++YjWLzsI88F7QWPzRb7+YKvFov9nKXg+qLnvJw2NXG0eZ+6Z8FmJwWPC9oLxWuPr9DmJ15eGhUXt1OiKyJSQ0pLgou2l5YUF+5f1goZpSXCJa0sYTNsbD26tcT25zc/7yijKLqKRElcqRuGmimRMJnN9vKE0FBo2rRC19pycx2rUhhZWaeWcrMv82bLynI8NrKzsWVlY2RnYcvOwZadhZGVjS0n2/41O9t+fU6O4z4FbY41k/PzseXnQ0ZGNXwXagGz+XTiWzgB9vbG5OONydvHeVfAwoev/avZxweTrx8mXx/7LwUlPfYrMnLu5zyCrlU9zl5KdEVEaqHSkuICZSXDpSXC3Rp2c3nJtZJqhQuWUittMmBZy6sVqOxaw+Wdq0pmHx/Mpya6VQfDMOxrJWfZE2R7opyNkZuHkZ93elMRx2Yi+fZ2qxUj33r6cZ69H7ZT7TarfYMSqxWs+RhWG4Y1v9CmJfZ+jv7WU21Wq3O/vFMbmhRscFL4vGOzkzx7v/zTB/n5xd+szebY+MSdTP7+9nIRf3/MAf6YAgLsuwr6+5/eUdCxq+Cpx0GBWILs9drmoCDMgUGq366DlOiKiNRRZSXDpSXCrq6HXFqtcEF5RFGllUsUTozLS4bLSoIrOrnOlaS4vD7VWYZh8vEBHx8soVV2W7czDON0kl5w5OZBfp49aS+0C6CRm+v8uOBrbi623FyMnFzHcyM3x96efWqzlJxs+/nCjws2Uyk0Am/knd6a3cjKwpqVxZlVbZ9mDgrCHBKMJSTUvr12SMipr8FYgkNO7yro2F3Q/lVrRtc8JboiIh6qpES4KmuFCyuvbhjKToah9A03yjpX0uS6wv1LK6Eob2TZ1TKMqlIbRrLPlMlkglNlCLWBkZ9/esQ8y34YmZmOx7aMTGxZzjsK2jIy7O0ZGfbjVJ229dTjgpU/CtrzDydWLCgvLyz1wvAKr29fCi+8Ppbw8NM7DDaIKLSmdARmLY93xrTqQhFadUFE5DRXNyEB18oSKrrW8FtD38IwjBLPzRk459S2za5sh73UabS3rOXZXFm+rSpVdiS74L24ulRcVY9yA3UiAa9KRm4u1pMnT09iTEvHlp7m9NVpAmShozLlG+bQULwiIvCObIhXg4Z4RUbiFdkQ78hIvKKi8Y6OwhIeflaOEmvVBREROWPl1QoXdqaT6MoaEXZ1cl1Z2zQXvE55ZRaulGFUlbLKOaDqtpUufK+qGOU2YU+sDAyX12ku+r4L96kzo9Y+PvatrcPDK3ytLSvr1Pbax+1L5B1PObXbYIp9ebxTa0fnn1o7mrw8bKmp5KamkrtvX+kx+friFRWJd3QjvKOi8G4UjXfjJnjHNMGnSRO8IiPP6ppiJboiIlJlKjuJrrwk2NXJdaWN6BYuoSivzMKVMoyqUlZSXdoqGWWVepS0VNz0ddOdviclTRQsr3666PnCv0yUV0pSXkJd0g6Erk5QdKVPZc5VR+JdMBnOOzq63L6GYWBLTbUnvUePknf0KPlH/yH/yBHyjx4h78hR8hMTyU9OxsjJIe+vBPL+Sij5Zt7eeDeKxqdJDN5NmuAT0wTvZs3wadYMn6ZNMfv5Vcn7q62U6IqISI2q6FrDZZ0rKQGG4qOXrqxK4WrSXZXKS6pLO1daglzSUnHVNcpdtG9p6zSXl1Av2bfE6T6uTlAsUNnSj4qMiNf05ikmk8kxkc23VatS+xm5ufYkODGRvMRE8g6f+vr33+T+fZC8w4mQl1d6Imwy4RUdhW9sLD7NW+DTsgW+LVvh26qlx5REKNEVEZFao6wR4YpMriurhKKs61w9X1UqM5JdVqlHaUvFVccod2FlrdNcXkJdlCsTFF0ZiS7r+tLOnenmKZVdyeOM2pvYSxQKn0s6dS7SrwH5R46Q+/ff5B38m7xDf5ObcJDcv/4i98ABbOnp5B9OJP9wIhnr1jvFagkNxadVK3xbtsS3VUt8W7fGt02balt2r7oo0RURkTqtpATYldri8vpUpD75TFRmJLu0BLmkpeKqY5S7pBrd0tZpLi+hLqq8UWtXR6LLSryrY/OUyq7kUVXtZb2Gd6NG0Ls3SRlJHDqVBMcGRGI9fpzcA3/xz654ju/aTtCh4/DXIfIOHsSamkrW5s1kbd7s9P2wRETQ6n8/Yvb1LfUzrE206kIRWnVBRETqitJWxSip3ZUVNMrrU/g8UKxvZUoNSqvRdWX1i7L6ABU+996w97j+u+uLjYgXLf0A+4ogvaJ6lRtHWSt5lPR6lWkv7/1WdPLilTGXkHvgADl795Hy+6+k796J74EkbH8fxtawPvW//cTtkwa16oKIiIiHc3Vb6bL6VqRP0fOVKfkoqc+d3e+s8ARFV/pU9FxlN0+p7EoepY0gV7S9KicvOkar27XjW8tOZqQswtbQhulcE765FsJOHufoZ0OrfW3pqqJEV0RERKpMZRLqykxQdKVPZc5VZvOUyq7kUVJNdWXaq3LyYmnJsYFBtg8khZuglPKN2sjs7gBEREREShMVGEWvqF7ljjSX1qcy54q2j2w9kqWjlvLW0LdYOmppsZHMgpFls8meVpVW41z0fMEI8pm2F/yiUNq5gkS7sMKJc9H20pLjwgonxLWZanSLUI2uiIiIVEZFapzLq6muTHtZ5yqznFrRmt/CqnO3QFe4mq8p0S1Cia6IiIh4ooomzq7uhucOSnQrSYmuiIiIiF15K224i1ZdEBEREZEzUt5KG7WdJqOJiIiIiEdSoisiIiIiHkmJroiIiIh4JCW6IiIiIuKRlOiKiIiIiEdSoisiIiIiHkmJroiIiIh4JCW6IiIiIuKRlOiKiIiIiEdSoisiIiIiHkmJroiIiIh4JCW6IiIiIuKRlOiKiIiIiEdSoisiIiIiHkmJroiIiIh4JCW6IiIiIuKRlOiKiIiIiEdSoisiIiIiHkmJroiIiIh4JCW6IiIiIuKRlOiKiIiIiEdSoisiIiIiHkmJroiIiIh4JCW6IiIiIuKRlOiKiIiIiEdSoisiIiIiHkmJroiIiIh4JCW6IiIiIuKRlOiKiIiIiEdSoisiIiIiHkmJroiIiIh4JCW6IiIiIuKRlOiKiIiIiEdSoisiIiIiHkmJroiIiIh4JCW6IiIiIuKRlOiKiIiIiEfymET3wIEDTJo0iebNm+Pv70/Lli2ZNm0aubm57g5NRERERNzAy90BVJU//vgDm83Ga6+9RqtWrdi+fTuTJ08mIyODOXPmuDs8EREREalhJsMwDHcHUV2eeeYZXn31Vf7880+Xr0lLSyM0NJTU1FRCQkKqMToRERERqQxX8zWPGdEtSWpqKuHh4WX2ycnJIScnx/E8LS2tusMSERERkRrgMTW6Re3bt4958+YxZcqUMvs9+eSThIaGOo6YmJgailBEREREqlOtT3SnT5+OyWQq8/jll1+crjl8+DAXX3wxV199NTfddFOZ9586dSqpqamO4+DBg9X5dkRERESkhtT6Gt3k5GSSk5PL7BMbG4ufnx9gT3IHDx5Mnz59WLhwIWZzxXJ51eiKiIiI1G4eU6MbERFBRESES30PHTrE4MGD6dGjB2+//XaFk1y3MAwwmdwdhYiIiIjHqQOZoGsOHz7MoEGDiImJYc6cOfzzzz8kJSWRlJTk7tBKl58DX94Ov7zt7khEREREPE6tH9F11bJly9i7dy979+6lSZMmTudqbXXGbx9D/Pvw2yKIaA2x57o7IhERERGP4TEjuhMmTMAwjBKPWqv79dBxJNjyYdE4OH7A3RGJiIiIeAyPSXTrJJMJrngZortBVgp8eC3kpLs7KhERERGPoETX3XwC4JoPICgSju6ExbeAzebuqERERETqPCW6tUFoYxjzPlh8Ydc3sOIJd0ckIiIiUucp0a0tYnrB5S/aH6+ZA9s+dW88IiIiInWcEt3apOs10P8u++Mvb4dDW9wbj4iIiEgdpkS3trlwOrQeCvnZ8NF1kF6L1wEWERERqcWU6NY2ZguMegMi2kL6YfhoLORluzsqERERkTpHia6bJaZmsW5fMompWacb/ULg2g/BLwwObYav7rJvFSwiIiIiLlOi60aLNiUQN3s5Yxf8TNzs5SzalHD6ZP2WMPodMFnsO6ete9F9gYqIiIjUQUp03SQxNYupi7dhOzVQazPgocXbnUd2WwyCi2fbH/8wDXYvrfE4RUREROoqJbpusj85w5HkFrAaBgeSM50be0+GHhMAAz6dBEf/qKkQRUREROo0Jbpu0jwiELPJuc1iMhEbEeDcaDLBsGegWRzkpsOH10BmSs0FKiIiIlJHKdF1k+hQf54c2RmLyZ7tWkwmZo3sRHSof/HOXj4w+l0IawrH98Mn48GaV8MRi4iIiNQtJsPQdP7C0tLSCA0NJTU1lZCQkGp/vcTULA4kZxIbEVByklvYkR3w5hDIPQm9JsOlc6o9PhEREZHaxtV8TSO6bhYd6k+/lvXLT3IBIjvCyNcBE2xaAL+8Ve3xiYiIiNRVSnTrmnaXwvn/sT/+9n7Yv8a98YiIiIjUUkp066Lz/gWdRoEtHz6+AY4fcHdEIiIiIrWOEt26yGSCy1+C6G6QlQIfXgs56e6OSkRERKRWUaJbV/kE2LcJDoqEozth8S1gs7k7KhEREZFaQ4luXRbSCK75ACy+sOsbWPG4uyMSERERqTWU6NZ1TXrC5fPsj9c8C9s+dW88IiIiIrWEEl1P0HUMxN1tf/zl7XBos3vjEREREakFlOh6igumQeuhkJ8NH10HaYnujkhERETErZToegqzBUa9ARFtIT0RFl0HednujkpERETEbZToehK/EPtKDH5h9vKFr+4C7fAsIiIiZyklup6mfksY/V8wWeC3RfDTC+6OSERERMQtlOh6ohYDYdhT9sc/Todd37s1HBERERF3UKLrqXrdBD1uBAz47CY4+ru7IxIRERGpUUp0PZXJBJc8A83Ohdx0+PAayExxd1QiIiIiNUaJriezeNvrdcOawfED8PENYM1zd1QiIiIiNUKJrqcLrA/XfgQ+QXBgDXz/oLsjEhEREakRSnTPBpEdYOQCwASb3oBNb7o7IhEREZFqp0T3bNHuErjgEfvj7x6A/WvcG4+IiIhINVOiezY59z7odBXY8u31uin73R2RiIiISLVRons2MZngipegUXfISoGPxkJOurujEhEREakWSnTPNt7+cM0HEBQFR3fC4pvBZnN3VCIiIiJVTonu2SikkT3ZtfjCrm9h+WPujkhERESkyinRPVs16WEvYwBY+xz89ol74xERERGpYkp0z2ZdRkPcPfbHS+6AQ5vdGo6IiIhIVVKie7a74FFoczHkZ8PHE7RNsIiIiHgMJbpnO7PFvplEveaQmgBf3g6G4e6oRERERM6YEl0BvxC4eiFYfOyT0za84u6IRERERM6YEl2xa9QNLn7S/viHR+HgJreGIyIiInKmlOjKaT0nQccR9p3TPr1R9boiIiJSpynRldNMJrjsxVP1ugfhi9tUrysiIiJ1lhJdceYXAqPfsW8msfs7WP+yuyMSERERqRQlulJcdNfT9bo/TlO9roiIiNRJSnSlZD0nQseR9nrdTyaoXldERETqHCW6UjKTCS57AcJbQNrf8MWtqtcVERGROkWJrgCQmJrFun3JJKZmnW50rK/rC7u/h/UvuS0+ERERkYpSoiss2pRA3OzljF3wM3Gzl7NoU8Lpk071utPh4Ea3xCgiIiJSUUp0z3KJqVlMXbwN26mqBJsBDy3e7jyy23MidBp1ql5X6+uKiIhI3aBE9yy3PznDkeQWsBoGB5IzTzeYTDB8LoS3tNfrfj4FbLYajVNERESkorxc6XTOOedU6KYmk4klS5bQuHHjSgUlNad5RCBmE07JrsVkIjYiwLljQb3uGxfCnqX2et24u2o0VhEREZGKcCnRjY+P51//+hdBQUHl9jUMg9mzZ5OTk3PGwUn1iw7158mRnXlo8XashoHFZGLWyE5Eh/qX0LkLDJsNX99rr9eN6QNN+9R4zCIiIiKuMBlG+WtGmc1mkpKSaNiwoUs3DQ4O5tdff6VFixZnHGBNS0tLIzQ0lNTUVEJCQtwdTo1JTM3iQHImsREBJSe5BQwDPpsE2z+DkCYwZQ0EhNdcoCIiInLWczVfc6lGd//+/TRo0MDlF9+5cyfNmjVzuX9Vy8nJoVu3bphMJuLj490WR10SHepPv5b1y05yodD6uqrXFRERkdrNpUS3WbNmmEwml28aExODxWKpdFBn6oEHHqBRo0Zue32P5xsMo9+xr6+7Zymsn+fuiERERESKcalGt6js7Gx+++03jh49iq3IaN7ll19eJYFV1nfffceyZcv47LPP+O6779wai0eL6gzDnoKv74EfZ0BMX9XrioiISK1S4UT3+++/54YbbiA5ObnYOZPJhNVqrZLAKuPIkSNMnjyZL774goCAgPIvwF7mUHjiXFpaWnWF53l6TIADa2H7p/DpjXDLGgis7+6oRERERIBKrKN7xx13cPXVV5OYmIjNZnM63JnkGobBhAkTmDJlCj179nT5uieffJLQ0FDHERMTU41RehiTCS6bC/VbQdoh+EL1uiIiIlJ7VDjRPXr0KPfddx+RkZHVEU8x06dPx2QylXn88ssvzJs3j7S0NKZOnVqh+0+dOpXU1FTHcfDgwWp6Jx7KNxiufge8/GDPMlj3orsjEhEREQFcXF6ssIkTJxIXF8ekSZOqKyYnycnJJZZJFBYbG8s111zDV1995TRpzmq1YrFYuO6663jnnXdcer2zdXmxM7Z5IXx1N5gscOO30LSvuyMSERERD+VqvlbhRDczM5Orr76aBg0a0LlzZ7y9vZ3O33WXe3bLSkhIcKqvPXz4MEOHDuXTTz+lT58+NGnSxKX7KNGtJMOAxZNh2ycQ3AimrFW9roiIiFQLV/O1Ck9G++CDD1i6dCn+/v6sXLnSaQTVZDK5LdFt2rSp0/OCXdxatmzpcpIrZ8BkguHPw+GtcGwvfH4LjP0YzBWujhERERGpEhXOQv7zn/8wc+ZMUlNTOXDgAPv373ccf/75Z3XEKHVF4XrdvT/AT3PdHZGIiIicxSqc6Obm5jJmzBjMtXykLjY2FsMw6Natm7tDObtEdYJhT9sfL3/cvvyYiIiIiBtUOFsdP348ixYtqo5YxFOccwN0uQYMK3wyAdIOuzsiEREROQtVuEbXarXy9NNPs3TpUrp06VJsMtpzzz1XZcFJHVVQr3tku/34ZAKM/xq8fNwdmYiIiJxFKpzobtu2je7duwOwfft2p3OFJ6bJWc4nAEb/F14fDAd/hh8esW8ZLCIiIlJDKry8mKfT8mJV7I9v4aNr7Y9HvQmdr3JvPCIiIlLnuZqv1e4ZZVL3tbsEzvuX/fGSO+HITvfGIyIiImcNlxLdkSNHOm3GUJ7rrruOo0ePVjoo8TCDH4YWgyAvExZdD9mp7o5IREREzgIulS5YLBZ2795NgwYNyr2hYRjExMQQHx9PixYtqiTImqTShWqSkQyvDYS0v6HdcBjznn3SmoiIiEgFVenOaIZh0KZNmyoLTs5CgRH2yWlvXwx/fA0/vQDn3uPuqERERMSDuZTorlixosI3bty4cYWvEQ/XpId95YWv74X/zYBG3aHFQHdHJSIiIh5Kqy4UodKFamYY8MVt8OsHEBABt6yGUP1SJCIiIq7TqgviVompWazbl0xiapbzCZMJLn0WIjtDZjJ8Mh7yc90TpIiIiHg0JbpS5RZtSiBu9nLGLviZuNnLWbQpwbmDTwCM+S/4hcLfm2DpQ+4JVERERDyaEl2pUompWUxdvA3bqYIYmwEPLd5efGQ3vAWMeN3+eNMC+HVRzQYqIiIiHk+JrlSp/ckZjiS3gNUwOJCcWbxz24thwAP2x1/dDUnbi/cRERERqSQlulKlmkcEYi6yPK7FZCI2IqDkCwY9CC3Ph/ws+HgcZJ2o9hhFRETk7FDhRPfIkSOMGzeORo0a4eXlhcVicTrk7BYd6s+TIztjObUZhMVkYtbITkSH+pd8gdkCo96E0BhI+RO+uBVsthqMWERERDyVS+voFjZhwgQSEhJ45JFHiI6OxqTdraSIMb2aMqBNAw4kZxIbEVB6klsgINy+mcRbQ2HXt/DT83Dev2omWBEREfFYFV5HNzg4mDVr1tCtW7dqCsm9tI6uG21eaK/VNZnhuk+h1QXujkhERERqoWpbRzcmJgbtMSHV4pzx0P16MGzwyY3wz253RyQiIiJ1WIUT3blz5/Lggw9y4MCBaghHzmomE4nnPkFagx6QkwofjIbMFHdHJSIiInVUhUsX6tWrR2ZmJvn5+QQEBODt7e10PiWlbicmKl1wn0WbEpi6eBthRhpf+j5CjOkfaBYH474ALx93hyciIiK1hKv5WoUnoz3//POagCZVrvBGEymEMDH3fhb7TCP4r5/g63vhipfs2weLiIiIuKhSqy6IVLWiG03sMZpwR95dLPR9BlP8e9CgDcTd7b4ARUREpM6pcI3uddddx4IFC9i9WxOFpOqUtNHEWqMbaQNn2p/8MA3++KbmAxMREZE6q8KJblBQEM8++yzt2rWjUaNGXHvttcyfP58//vijOuKTs0RpG02EDrwDek4EDPjsJkj8zem6xNQs1u1LJjE1yw1Ri4iISG1W4cloBZKSkli5ciUrV65k1apV7N69m4YNG5KYmFjVMdYoTUZzr8TUrOIbTVjz4P2r4M+VENIYJi+H4CjH5DWbAWYTPDmyM2N6NXVr/CIiIlL9qm0d3QLBwcHUq1ePevXqERYWhpeXF1FRUZW9nQhgH9nt17K+825qFm+4+h2o3xrSDsFHY0k8luJIcgFsBjy0eLtGdkVERMShwonuv//9b/r27UtERAT/+c9/yM3NZerUqRw5coStW7dWR4wi4B8GYxeBfz04tBnvJbdjGDanLlbD4EBypnviExERkVqnwqsuPPPMMzRo0IBp06ZxxRVX0L59++qIS6S4+i1hzHvw3yuJ+Otb7vXy47n8qxynLSYTsREBTpckpmaxPzmD5hGBzqPEIiIi4vEqnOhu3bqVVatWsXLlSp599lksFgsDBw5k0KBBDBo0SImvVK/Yc2H487DkDu7yWsyfRiO+sPZ3TF4rnMyqhldEROTsVunJaAV+/fVX5s6dy3vvvYfNZsNqtVZVbG6hyWh1xLL/wLp5GBZftl/0PhHtz3VKchNTs4ibvdxpbV6LycTaBwdrZFdERKSOq7ad0cA+qluw4sKaNWtIS0ujW7duDB48uNIBi1TIhTPg2D5Mu76l86qboeX3QFvH6aIbUMDpGt6iCbFKG0RERDxThRPdevXqcfLkSbp27cqgQYOYPHkyAwYM0Oin1CyzBUYugP9eDoc2w7sjYNIyCG0CnN6AouiIbuEaXpU2iIiIeLYKly58/fXXHp3YqnShjsk4Bm9fDMm7IaINTFwKAeGAPZF9aPF2rIbhqOEtSGRV2iAiIlJ3VVvpwvDhwx2P//77b0wmE40bN65clCJnKrA+XL8Y3hpqT3bfvwpuWAK+QYzp1ZQBbRoU34AC10sbREREpO6q8Dq6NpuNmTNnEhoaSrNmzWjatClhYWE89thj2Gy28m8gUtXCYmDc5441dvl4HOTnAqVsQMHp0obCSlqeTEREROquCie6Dz/8MC+99BKzZ89m69atbNmyhVmzZjFv3jweeeSR6ohRpHwN2sJ1n4J3AOxbDl9MgTJ+8YoO9efJkZ2xmOzZbknLk4mIiEjdVuEa3UaNGjF//nwuv/xyp/Yvv/yS2267jUOHDlVpgDVNNbp13N4f4YNrwJYHvW+GYU+DyVRq98TUrBJLGwrOaUUGERGR2qfaanRTUlJo165dsfZ27dqRkpJS0duJVK1WF8KI+fDZTbDxdQhsAAMfKLV7dKh/iUmsVmQQERGp+ypcutC1a1deeumlYu0vvfQSXbt2rZKgRM5I56tg2FP2xyuegE1vVujyxNQsR5IL9iXKHlq8ncTUrCoOVERERKpThUd0n376aS699FJ+/PFH+vXrh8lkYt26dRw8eJBvv/22OmIUqbg+t0BGMqx+Gr75F/iF2hNgF2hFBhEREc9Q4RHdgQMHsnv3bkaMGMGJEydISUlh5MiR7Nq1i/POO686YhSpnMEPQc+JgAGLJ8P2z1y6TCsyiIiIeIYKT0bzdJqM5mFsNlhyJ8S/ByYLjHoDOo0s97LyNpvQJDURERH3qdLJaL/99pvLL9ylSxeX+4pUO7MZLp8HGBD/vn2SmskEHUeUeVlpm01okpqIiEjd4dKIrtlsxmQyYRgGpkJLNRVcWrjNarVWQ5g1RyO6HspmhS/vgF8/sI/sXvVmucluUdo2WEREpHZwNV9zqUZ3//79/Pnnn+zfv5/PPvuM5s2b88orrxAfH098fDyvvPIKLVu25LPPXKuBFKlxZgtc8RJ0vRYMK3w6CXZ8UaFblDVJTURERGofl0oXmjVr5nh89dVX8+KLL3LJJZc42rp06UJMTAyPPPIIV155ZZUHKVIlzBa44mUwDPjtI/h0or2MocMVLl1eMEmt6IiuJqmJiIjUThVedWHbtm00b968WHvz5s3ZuXNnlQQlUm3MFrjyFegy5tTI7kTYucSlS8vaNjgxNYt1+5K11q6IiEgtUuFVF8455xzat2/Pm2++iZ+fHwA5OTlMnDiR33//nS1btlRLoDVFNbpnCZsVPp8C2z4GsxeMeM3ldXaLbhusCWoiIiI1q9q2AJ4/fz6XXXYZMTExjp3Qfv31V0wmE19//XXlIxapSWaLfatgkwl+W2RfjSH7BPS6qdxLC28bXNouagPaNNAENRERETercKLbu3dv9u/fz3vvvccff/yBYRiMGTOGsWPHEhgYWB0xilQPswWunA++wbDpDfsOalkn4Lx/2RNgF2gXNRERkdqrwokuQEBAADfffHNVxyJS88xmuGQO+NeD1c/A8scg6zgMedylZFcT1ERERGqvCk9Ga9SoEWPHjuX1119n9+7d1RGTSM0ymeD8/8DQWfbn61+CJXeANb/cS8uaoCYiIiLuVeHJaB9++CGrVq1i5cqV7N69m8jISAYOHMigQYMYOHAg7du3r65Ya4Qmo53ltr5n3zLYsEH7y2DUm+DlW+5lRSeoiYiISPVxNV+rcKJb2JEjR1ixYgVff/01ixYtwmazaWc0qft+/8q+7Jg1F5oPgDHvgV+ou6MSERGRU6pt1QWAkydPsnbtWsfI7tatW+ncuTMDBw6sdMAitUb7y+C6T+Cj62D/anhzKFz3MYRpyTAREZG6pMIjun369OG3336jU6dODBo0iAEDBnDeeecRFhZWTSHWLI3oikPir/DBGEhPhKBIGLsIGnV3d1QiIiJnPVfztQpPRtuzZw8BAQG0aNGCFi1a0KpVq1qV5H7zzTf06dMHf39/IiIiGDlypLtDkroquivc9CM07Agnj8Dbl8Cu7yp9O+2eJiIiUrMqnOimpKSwYsUK4uLi+PHHHxk4cCBRUVGMGTOG+fPnV0eMLvvss88YN24cN954I7/++is//fQTY8eOdWtMUseFNoGJ30PL8yEvEz4aCxsXVPg2izYlEDd7OWMX/Ezc7OUs2pRQDcGKiIhIYWc0GQ1g8+bNvPTSS7z33ntunYyWn59PbGwsM2bMYNKkSS5fl5OTQ05OjuN5WloaMTExKl0QZ9Y8+OY+2PJf+/O+t8FFj4Gl/DL3xNQs4mYvL7bW7toHB2uFBhERkUqottKFrVu38vzzz3PFFVcQHh5O37592bZtG3fffTdLliw5o6DPxJYtWzh06BBms5nu3bsTHR3NsGHD2LFjR5nXPfnkk4SGhjqOmJiYGopY6hSLN1z2Ilwwzf58wyvw/lX2zSXKUdbuaSIiIlJ9Kjyi6+XlRffu3R1r5w4YMKBWjHx+9NFHXHvttTRt2pTnnnuO2NhYnn32WZYtW8bu3bsJDw8v8TqN6EqF7fwSPp9iL2UIbwHXfgQN2pbaXSO6IiIiVavaRnRTUlLYtGkTc+bMYfjw4dWeDE6fPh2TyVTm8csvv2Cz2QB4+OGHGTVqFD169ODtt9/GZDLxySeflHp/X19fQkJCnA6RMnW4AiYtg9CmkPInLLgAdn1fanftniYiIuIeFV5Ht6YTwTvuuINrrrmmzD6xsbGkp6cD0KFDB0e7r68vLVq0ICFBE3+kikV1hptXwMfj4a+18OE19m2Ez70PzMV/fxzTqykD2jTQ7mkiIiI1qMKJrtVq5fnnn+fjjz8mISGB3Nxcp/MpKSlVFhxAREQEERER5fbr0aMHvr6+7Nq1i3PPPReAvLw8Dhw4QLNmzao0JhEAAiPghi/gu3/DL2/C8sfg4EYYMR8CipfKRIf6l5ngJqZmsT85g+YRgUqERUREqkCFSxdmzJjBc889x+jRo0lNTeW+++5j5MiRmM1mpk+fXg0huiYkJIQpU6Ywbdo0li1bxq5du7j11lsBuPrqq90Wl3g4izcMf84+Uc3iC3uWwmsD4O9fKnQbLT8mIiJS9So8Ga1ly5a8+OKLXHrppQQHBxMfH+9o27BhAx988EF1xVquvLw8pk6dyrvvvktWVhZ9+vRh7ty5dOzY0eV7aGc0qbTE3+CT8fa6XbM3DHkM+kyBU7W5pV6myWoiIiIVUm2T0ZKSkujcuTMAQUFBpKamAjB8+HC++eabSoZbNby9vZkzZw5HjhwhLS2NH374oUJJrsgZie4CN6+CDleCLQ++fxA+HgfZqWVepuXHREREqkeFE90mTZqQmJgIQKtWrVi2bBkAmzZtwtfXt2qjE6lr/ELg6oUw7Bn7qO7vX8FrAyHx11IvaR4RiLnIoK/FZCI2IqB6YxUREfFwFU50R4wYwf/+9z8A7r77bh555BFat27NDTfcwMSJE6s8QJE6x2SCPjfDxKX2JciO74c3LoJf3oISKoW0/JiIiEj1OOMtgH/++Wd++uknWrVqxeWXX15VcbmNanSlSmWmwBe3we7v7M87Xw2XPgt+ocW6JqZmafkxERERF7iar1Uo0c3Ly+Pmm2/mkUceoUWLFlUSaG2jRFeqnGHAunnw43QwrPZR3pGvQbP+Ll2uZcdEREScVctkNG9vbz7//PMzDk7krGIyQdxdMPF7qBcLqQnw9iXwwzTIzy3zUi07JiIiUnmVqtH94osvqiEUEQ8X0xumrIXu1wMG/DQX3jgfjv5RYvfE1CymLt7mWJHBZsBDi7eTmJpVYyGLiIjUZRXeGa1Vq1Y89thjrFu3jh49ehAYGOh0/q677qqy4EQ8jm8wXPEytLkYltwFSdvg9YFw4QzofbPT9sFlLTumEgYREZHyVXgyWvPmzUu/mcnEn3/+ecZBuZNqdKXGpCfBl7fD3h/tz1sMhitfgZBGgDaSEBERKU21TEY7GyjRlRplGLDpDVj2H8jPBt9QuHgWdLsOTCYWbUrgocXbsRqGY9mxMb2aujtqERERt1KiW0lKdMUt/tkNn98Ch7fYn7e8AC57AcJiSl12TKsxiIjI2apKE9377rvP5Rd+7rnnXO5bGynRFbex5sP6l2DFLLDmgE8wDJkJPW60r9xQyKJNCY6JamYTPDmys0Z6RUTkrOFqvubSZLStW7c6Pd+8eTNWq5W2bdsCsHv3biwWCz169DiDkEXOchYvOPceaHuJvXb3743w9b2w43O4fJ59aTJKX41hQJsGGtkVEREpxKVEd8WKFY7Hzz33HMHBwbzzzjvUq1cPgOPHj3PjjTdy3nnnVU+UImeTBm3sa+7+PB/+9xjsXw2v9Ifz/wN9btFqDCIiIi6qcI1u48aNWbZsGR07dnRq3759O0OGDOHw4cNVGmBNU+mC1CrH9sGSO+Gvn+zPo7vxz+Cn6fN2slZjEBGRs1a17IxWcOMjR44Uaz969Cjp6ekVvZ2IlKV+Sxj/NQx/3r4iQ2I8DT68mO/afU+wKQfAsRpDdKg/ialZrNuXrE0lREREqMSGESNGjODGG2/k2WefpW/fvgBs2LCB+++/n5EjR1Z5gCJnPbMZek601+5+/yDs+Jy2+//L1oj/safnDMK6DSc61F8T1ERERIqocOlCZmYm//d//8dbb71FXl4eAF5eXkyaNIlnnnmm2E5pdY1KF6TW270MvvkXpCbYn7e/jCNx0+n38i6VM4iIyFmh2tfRzcjIYN++fRiGQatWrep8gltAia7UCbkZ9mXINrwKhhWrxZ9nsq/gTesl5BX6Q82Hk/vSr2V9NwYqIiJS9bRhRCUp0ZU65cgO++huwnoA9tga80j+jWywddCIroiIeKxqm4wmIrVIZEe48Tu48lWyfcJpbT7ERz6P85L3PJ4fFqEkV0REzmpKdEXqOpMJuo3F794tZHQZj4GJ4Zb1XL76clj5FORpBQYRETk7KdEV8RT+9Qgc+SKmW1ZB036QnwUrZ8FLvWHHF6AqJREROcso0RXxNNFd7eUMo96EkMb21Rk+GQ/vXGav6QWttysiImcFTUYrQpPRxKPkZsDaubDuRcjPBpOZPU1HM3r3+Rw3grTeroiI1EmajCYi4BMI5z8Mt2+E9peDYaP1Xx+x3Oc+xluWYjbyeWjxdo3sioiIR1KiK3I2qNcMxrzL9ove4w9bDPVMJ5nh/Q5Lff7N+aZNHPgnw90RioiIVDkluiJnkfqdLuSyvFk8nDeRZCOEluZEFvg8xzkrr4fDW90dnoiISJVSoityFokO9efxkd34yHYRg3Ke45X8K8g3++L793p4fRAsvgVS/3Z3mCIiIlVCk9GK0GQ0ORskpmZxIDmT2IgAoo1kWP4Y/LbIftLLD/rdDnH3gJ9+BkREpPbRFsCVpERXzlqHtsCy/8BfPwFg9Q/HMvAB6DkRvHzdHJyIiMhpWnVBRCqm8Tks6vgqt+Tdyz5bNJasFPj+QXipF/z2Cdhs7o5QRESkQpToighgL2eY+vl2llp7MST3aabmTeKIEQYn/oLFN8GCQbBvhbvDFBERcZkSXREBYH9yBrZThUxWLHxovYBBOc+R0O1f4BsCib/Cu1fCf6+AQ5vdGquIiIgrlOiKCADNIwIxm5zbck3+eA++H+6Kh763gdkb/lwJC86HRdfDP7vcEaqIiIhLlOiKCGBfeuzJkZ2xmOzZrsVkYtbITkSH+kNgfbj4SY5O+ImjLUZiYILfv4JX+sIXt8GJBDdHLyIiUpxWXShCqy7I2c5p6bFQf0f7ok0JTF28DZsBbc1/82bM9zQ5stx+0uJjX53h3PsgONJNkYuIyNlCy4tVkhJdkeISU7OIm73cUcML9hHfn8eHEvHzbNi/2t7o5Q99bravwRsQ7pZYRUTE82l5MRGpMoUnqhWwGgZ7vNvB+K/ghi+hcU/Iz4KfXoC5XWDFLMhOdU/AIiIiKNEVEReUNFHNYjIRGxEAQGL9Pqwb/BEpV7wLUZ0hNx1WPWVPeFc9A9lpbohaRETOdkp0RaRcZU1UW7QpgbjZyxn7xkZ6fmxh0TnvwdXvQERbyD4BKx6HuZ1htRJeERGpWarRLUI1uiKlKzpRrbTa3bUPDiY62Ad2fG4f2U3ebT/pXw/63QF9bgHfYPe8CRERqfNUoysiVS461J9+Les7VmMorXb3QHImmC3Q+Sq4bQOMfAPqt4as47D8MXtJw5rnICfdDe9CRETOFkp0RaTSyqrdTUzNYt2+ZBLTc6HL1XD7zzDidQhvCVkp8L8Z9oR37fOQc9I9b0BERDyaSheKUOmCSMUs2pTAQ4u3YzUMR+0u4Fhz12yCJ0d2ZkyvpvYLrPmw/VN7SUPKn/a2gPrQ/y7oNUklDSIiUi6to1tJSnRFKq5w7S5Qet1uoQ0osObDtk9g9dOnE17/etD3dug9GfzDau4NiIhInaIaXRGpMYVrd8us2y3M4gXdroXbN8EVr5wqaTh+epWG5Y9DxrGaexMiIuJxlOiKSJUqb83dYixe0P06uGMTjHoTGrSHnDT7cmRzO8Oy/0D6keoPXEREPI4SXRGpUmWtuVumglUabl0Ho9+FqC6QlwHr5sELXeC7f0PqoRp4ByIi4ilUo1uEanRFqkbRNXcrzDBgzw/2Gt6/N9nbzN7Q9Ro4916o37JqAxYRkTpDk9EqSYmuSM1LTM1if3IGzSMCiyfFhgH7V9m3Ev5r7alGE3S80p7wRnet6XBFRMTNlOhWkhJdkZq1aFNC6UuRFZXwM6x9DnZ/f7qt1YVw7n3QrD+YTCVfJyIiHkWJbiUp0RWpOWVuIVxWuUPSdvtGEzsWg2Gzt8X0sY/wth4KZk0/EBHxZFpeTERqPZeXIisqqhNc9SbcuRl63AgWHzj4M3x4DbzaD+I/gPzc6gtcRETqBCW6IuI2FV6KrKjwFnDZXLhnm31nNZ9g+OcP+OJWeLEbrHsJctKrOmwREakjlOiKiNtUZCmyxNQs1u1LJjE1q/iNgqNgyGNw3w64cDoERULaIVj2MDzfEf43U2vxioichVSjW4RqdEVqXnlLkVVowhpAfg78+hGsexGO7bW3WXyg82jodztEdqimdyIiIjXhrKzR3b17N1dccQURERGEhIQQFxfHihUr3B2WiJSj8BbCRSWmZjmSXACbAQ8t3l7yyG4BL1/oMd6+vfCY9+wT1ay5EP+evYb33ZGwb7l96TIREfFYHpXoXnrppeTn57N8+XI2b95Mt27dGD58OElJSe4OTUQqqdIT1sC++kL7y2DSMpj0A7S/HExm2Pc/eHcEzD9XE9dERDyYxyS6ycnJ7N27lwcffJAuXbrQunVrZs+eTWZmJjt27HB3eCJSSa5OWCuzhhcgpjeMeRfu3AK9bwHvQDiy3T5xbW5nWPMsZKZU07sQERF38JhEt379+rRv357//ve/ZGRkkJ+fz2uvvUZkZCQ9evQo9bqcnBzS0tKcDhGpPVyZsLZoUwJxs5czdsHPxM1ezqJNCaXfMLw5XPL06YlrwdFwMsk+Ye35jvDt/XBsXzW/KxERqQkeNRnt0KFDXHHFFWzZsgWz2UxkZCTffPMN3bp1K/Wa6dOnM2PGjGLtmowmUruUNmGt0ptOFMjPtW88se4lOLLtVKMJ2gyFPrdAi8HacU1EpJbxmMlo06dPx2QylXn88ssvGIbBbbfdRsOGDVmzZg0bN27kiiuuYPjw4SQmJpZ6/6lTp5Kamuo4Dh48WIPvTkRcVdqEtfJqeMstafDyga7XwJQ1cMOX0HoIYNi3GX53BLzSF355G3JdqAkWEZFapdaP6CYnJ5OcnFxmn9jYWH766SeGDBnC8ePHnTL71q1bM2nSJB588EGXXs+V3xBsNhu5uZq8IjXD29sbi8Xi7jBqrbJGdFfv/qdiy5IVSN4LG1+Dre9DXoa9zS/MvpJDr8kQFlMt70VERFzj6oiuVw3GVCkRERFERESU2y8z0z7aYi6yx73ZbMZms1VZPLm5uezfv79K7ylSnrCwMKKiojDpT+jFFNTwPrR4O1bDcNTwAiUuSzagTYPySxoiWsElz8D5/4Gt78HPr8GJv+CnF+wlDu2HQ58p0LSfyhpERGqxWp/ouqpfv37Uq1eP8ePH8+ijj+Lv78+CBQvYv38/l156aZW8hmEYJCYmYrFYiImJKZZUi1Q1wzDIzMzk6NGjAERHR7s5otppTK+mDGjTwKmGd92+5FJLGlyq3QXwC7VvMNFnir2UYcOrcGAN7PzSfjTsCL0nQ5fR4BNY9W9MRETOiMckuhEREXz//fc8/PDDnH/++eTl5dGxY0e+/PJLunbtWiWvkZ+fT2ZmJo0aNSIgIKD8C0SqgL+/PSk7evQoDRs2VBlDKaJD/Z0S2IJlyYqWNBRdlqyoxNQs9idn0Dwi8PT9zBZod6n9SNpuL2v47RM4ugO+vgd+mAbdr4NeN0H9ltXw7kREpDJqfY1uTSur5iM7O5v9+/cTGxvrSD5EakJWVhYHDhygefPm+Pn5uTucOmPRpoRiJQ1l1ehWaKvhrOP2Gt5Nb8Dx/afbW55vr+NtM9SeIIuISJXzmBrd2kh1klLT9G+uckoqaShNaVsNl1TTax/1tdK84ySi+95m30544+uwZ5n98b7lENIEekyAc8ZBcFQ1vksRESmNEl0R8WhFSxpKU9YyZUU3pyg+6nshtL4QUvbDL2/aR3rT/oYVj8Oq2dD2Eug5EZoPtG9LLCIiNUL/xRURwbWthksb9XWs0RveHIY8Dvf9DiNeh5i+YMuH35fAu1fCSz3sKzec/Kdm3pSIyFlOia6wcuVKTCYTJ06ccHcoNepsfd9SMle2Gi5vcwo4tUFFwkkSYy+HSUvh1nX2ml2fYEj5E354FJ5rDx/fAHv/B1qqUESk2ijRlWoRGxvL3Llzi7XPnTuX2NhYx/PSdr5r165dqfdOSUnhzjvvpG3btgQEBNC0aVPuuusuUlNTi8VQ9L6ubhxSmksvvZTXX38dgMmTJzNz5kyn8zt27GDUqFGO1y7peyC115heTVn74GA+nNyXtQ8OLjYRrbxR30WbEoibvZyxC34mbvZyFm1KgMiOcOkc+NcfcNmL0LgH2PLsy5O9NxJe7AqrnoG0wzX1NkVEzhpKdN2k3G1JzyIdO3YkMTHR6Vi7dm2p/Q8fPszhw4eZM2cO27ZtY+HChXz//fdMmjSpWN+ZM2c63fc///lPpeM0DIMNGzYQFxcHwNq1ax2PC2RmZtKiRQtmz55NVJQmINVFpW01XHCutFHfcssafINIbDWaded/zD/X/w9632xfp/dEgr2W9/mO8P7V9gQ4XzsviohUBSW6blDiqE81ysnJ4a677qJhw4b4+flx7rnnsmnTpmL9fvrpJ7p27Yqfnx99+vRh27ZtjnN//fUXl112GfXq1SMwMJCOHTvy7bffVkl8Xl5eREVFOR1l7YbXqVMnPvvsMy677DJatmzJ+eefzxNPPMFXX31Ffn6+U9/g4GCn+wYFBVXofRe2a9cubDYbHTp04NixY+zdu5c+ffo49enVqxfPPPMM11xzDb6+vpX4bkhtV9qob3llDYV/7vu8eYRFDe6Ef+2CEa9B0/5g2OyrNnx8AzzXDr6fal+zV0REKk2Jbg0rd9SnGjzwwAN89tlnvPPOO2zZsoVWrVoxdOhQUlJSnPrdf//9zJkzh02bNtGwYUMuv/xy8vLyALj99tvJyclh9erVbNu2jaeeeqrEpNFdCtbR8/JyXkjkqaeeon79+nTr1o0nnniC3NziI2VlvW+A4cOHExYWRs+ePUlLS6NevXrExsZitVpp0qQJYWFh1f32pJYpadS3rLKGUn/uM4Gu18DE7+DOLXDevyA4GjKPwYZXYH4czD8X1r8M6Udq7g2KiHgIJbo1zJXJLFUpIyODV199lWeeeYZhw4bRoUMHFixYgL+/P2+++aZT32nTpnHRRRfRuXNn3nnnHY4cOcLnn38OQEJCAnFxcXTu3JkWLVowfPhwBgwYUCUxbtu2jaCgIKfjpptucvn6Y8eO8dhjj3HLLbc4td9999189NFHrFixgjvuuIO5c+dy2223Fbu+rPcN8MYbbxAfH8+5557LI488Qnx8PCNGjOCOO+4gPj6e+Pj4Sr938RxllTWU93OfmJrFuhOhJPa8H+7ZDtd9Cu0vB4sPJG2DpQ/ZJ7C9fzVs/wzyVPIkIuIKraNbwyq7LWll7du3j7y8PKdaUm9vb3r37s3vv//u1Ldfv36Ox+Hh4bRt29bR56677uLWW29l2bJlXHjhhYwaNYouXbpUSYxt27ZlyZIlTm3BwcEAzJo1i1mzZjnad+7cSdOmpycIpaWlcemll9KhQwemTZvmdI97773X8bhLly7Uq1ePq666yjHKW6Cs9w0QFRVFXl4eGzZsYN68ecTGxrJ+/Xrefvttp4l1IqVtUFHWz33J6/JeBK0vgswU2PE5/Poh/L3JXtqwZxn4hkDHK6HrtdC0H2hDERGREmlEt4a5soRRVSrY4bnozlqGYbi021ZBn5tuuok///yTcePGsW3bNnr27Mm8efNKvS4kJKTYKggAJ06cIDQ01KnNx8eHVq1aOR2RkZEATJkyxTFqGh8fT6NGjRzXpaenc/HFFxMUFMTnn3+Ot7d3me+lb9++AOzdu9fl9z1r1iyCgoIICwsjNTWV7t27ExQUxN69exk6dChBQUGsWbOm3PvJ2aOksobSfu6BUkuZElOzWJdoI7HNWLjpR7hjMwy4H0KbQk4abPkvvD0MXugCP06HIztq+q2KiNR6GtF1g4psS3qmWrVqhY+PD2vXrmXs2LEA5OXl8csvv3DPPfc49d2wYYNjtPT48ePs3r3baZmvmJgYpkyZwpQpU5g6dSoLFizgzjvvLPF127VrV+KEt02bNtG2bVuX4w8PDyc8PLxYe1paGkOHDsXX15clS5bg5+dX7r22bt0KQHR0tFN7We97ypQpjB49mldeeYW///6bWbNm8cknn7Bs2TIWLFgAQOPGjV1+P3L2Kunnft2+5BJLGt5ee4A31v5ZZJS3FZz/Hxj0ECSss4/y7vjSvmrD2uftR4P20GkUdB4F4S3c80ZFRGoRJbpu4uq2pGcqMDCQW2+9lfvvv5/w8HCaNm3K008/TWZmZrHluGbOnEn9+vWJjIzk4YcfJiIigiuvvBKAe+65h2HDhtGmTRuOHz/O8uXLad++famve9999xEXF8fMmTO56qqrAPjss8/4/vvvWbdunVPf/Px8kpKSnNpMJpNjVLeo9PR0hgwZQmZmJu+99x5paWmkpaUB0KBBAywWC+vXr2fDhg0MHjyY0NBQNm3axL333svll1/uVPpQ3vsuSLR37tzJmDFjaNWqFXv27GHIkCG0atWqWGy5ubns3LnT8fjQoUPEx8cTFBRUYn85uxT9uS+ppMEMjiQXTo/yDmjTwH6t2Qyx50LsuSTGzSTt169pdvg7/A78D/753b5U2YrHodE50Pkq6DgCQhohInI2UqJ7Fpg9ezY2m41x48aRnp5Oz549Wbp0KfXq1SvW7+6772bPnj107dqVJUuW4OPjA4DVauX222/n77//JiQkhIsvvpjnn3++1Nfs27cvS5cuZebMmY5NEzp27MjSpUuLLcm1Y8eOYqOsvr6+ZGdnl3jvzZs38/PPPwMUSx73799PbGwsvr6+LFq0iBkzZpCTk0OzZs2YPHkyDzzwQInfn9LeN9gT8Z9++omXX34ZgFWrVpW4Zi/Y1/jt3r274/mcOXOYM2cOAwcOZOXKlSVeI2evgpKGhxZvx2oYWEwmJp0by+tr9jv1K5i4VjhJPl3bG4HZNI45l/2Hkf7xsO1T2L8KDm+xH0sfIje6Fz5dRkCHKyC0SQ2/SxER9zEZBUWcAtj/JB4aGupYrqqw7Oxs9u/fT/PmzV36U7lIVdG/Pc+WmJrlKGkAiJu9vNjEtbUPDnYkuompWWX3OXmULd++iW37Ynqadzu/WOOepLW4hD0RF9Aotm2N/GVJRKSqlZWvFaYRXRERNyta0lB0lLfohNWyliuLDvUn0RrMVVu7YDO6EEkKwywbucSykV7mXZgO/ULIoV/owUy222JJbncJnc+/DiI7kpiWzf7kDJpHBDol1UXbRETqCiW6IiK1THkTVstbprBwInyEcBZaL2ah9WIWjGjMmiULudj8M33Mv9PJfAB2vwK7X+Gkf2O+S+/CMlsPthhteGykvQSn+NJnzjXuIiK1mRJdEZFaqKwJqyXV9hYe9S0tEc7xb8h/rRfxX+tFhJPGBZYtXGTezAXe2wnKOsREr0NM5DtSjQBWL+nKclt3QoyunCC4+KS4UmgEWERqEyW6IiJ1UFmjvqUlwj2a1XMkwCmE8Il1EIttg3lpRBs+//Q9hlg2M9i8lfqmdC6zrOcyy3qsXia2GK1Zbe3CWltnDhztUWoCW/LmF8VHgMtKhpUoi0hV0mS0IjQZTWoj/duTyig8ya0gaVy0KaFYAjygTQPH5DYzNrqZ9nKBZSvnm7fS3pzgdE+bbyjmFgOgxWBoeT6EN3e8VnmT6Apev7RkuLxEuSJJsBJmEc+myWgiIme5ksofShsJPj0CbOZX2jLmilH8BkxevJIB5njOM2/jAt/f8clJhd+/sh8A9WKhxWBSA3sSZHiTRpDjtYoui5aYmlXiTnAD2jQASt4lrqBUwtXRYqiakWVXzotI7adEV0TkLFORBHhAm6s5kHwpsREB+AT7wOGtsG8F/LkCDv4Mxw/A5rdpx9ts9TXxm9GSn23t2Whry1ajrWOCHJS9WoSBUeo5KDsJLqysZNrVkeXyzpeWACsxFql9lOiKiAhQcgJcrK1JT/sx8H7ISYcDP9mT3n0rsCTvortpL93Ne5nCVxiYML3fAZr1g6b9aBl+TpmrRZR2rrzl1ApzpW95yXBZ51fv/qfEBLgyiXFhSpJFqocSXRERqRzfYGh7sf0ASD3EiR3LyN33E+HHNuN14k84usN+bHqDSOC3sMb8cLIFm22t2G605LorLimhfKL4ShJlJciFlbf0GpSfDJd2fvOB4yUmwO2igiucGBd2JrXJGl0WKZsSXSnXgQMHaN68OVu3bqVbt27uDqfGDRo0iG7dujm2MhaRUoQ2Jqz/jdD/Rvvz9CNwcAP8tR4S1kHSNoKyDjHCcogRljX2Pstmwm+doXEPxjQ6h8E3d2SfNZrYBkGOBK285dQKc6Vveclwaecp0gb2BHjTgeMVSowLl1GUN7pcmcl7pbUr+ZWzkRJdD2cymco8P378eN55550y++Tn55OYmEhERERVhsbChQu55557OHHiRLFzYWFhzJ07lwkTJgClv48PP/yQa665psRzixcv5tVXXyU+Pp6cnBw6duzI9OnTGTp0qFMMN954Y7Frs7KyKr26waZNm7jiiis4fPgwhw8fpmXLlqSmpuLj4+Po88QTT/DNN98QHx+Pj49Pid8DkTovOBI6XGE/ALLT4O+NkLABDm2BQ5sh+4T966HNADQEGvoEQ2RHiOoEUZ0hsjNjurZnQJvBpW6iUVh5G26Ulwy7sjxbAYvJRK/YkttLS4wLl1GUNboMpdcml3autNHlE1l5PPXdH2c8QU+krlGi6+ESExMdjxctWsSjjz7Krl27HG3+/v7Mnj3b8Tw6Opq3336biy++2NFmsViIioqqmYDLUDQusCfEpVm9ejUXXXQRs2bNIiwsjLfffpvLLruMn3/+me7duzv6hYSEOH1PgDNawmv9+vXExcUBsGbNGnr27OmU5ALk5uZy9dVX069fP958881Kv5ZIneIXAq0utB8AhgEpf9onuB3abE9+E3+F3HT7SPDBDaevNZmJDm9JdGQHqN8aItpARCv7Y7/iSwuVteEGlJ8Ml786xekEuGtMvQolxoXLKMoaXa7M5L3SRpdnf/cHRhkjy+D6ahUFlBRLXaBE90wYBuRluue1vQOgnNFawClBDQ0NxWQyFUtaQ0NDnZ6HhYU59SlaurBy5UoGDx7M999/z4MPPsgff/xBv379+Oijj9i8eTP33Xcfhw4d4tJLL+XNN98kIKB4HV1lFI2rPEVLDWbNmsWXX37JV1995ZTolvQ9KSo/P5877riD9957D4vFwq233spjjz1W4kjzunXrHInu2rVrHY8LmzFjBmAfURY5a5lMUL+l/eh8lb3Nmg/JuyBpOxzZdurrdsj4B47tsR9FBUVBROtTR5tTiXArCG0KZnOpL19eMlyR1Skqkhi7srmHK7XJro4umyl/ZNnV1SoKVDQpLkpJstQUJbpnIi8TZjVyz2s/dBh8At3z2qdMnz6dl156iYCAAEaPHs3o0aPx9fXlgw8+4OTJk4wYMYJ58+bx73//261xFrDZbKSnpxMeHu7UfvLkSZo1a4bVaqVbt2489thjTokwwDvvvMOkSZP4+eef+eWXX7j55ptp1qwZkydPBuwJ7fDhwx33++qrr5g+fToZGRl4e3szf/58HnzwQR588MGaebMidZXFy162ENkRGHO6Pf2IPfH9Zxck77Efx/bAySNwMsl+HFjjfC+zN4Q2htAYCGt66mvM6a8hTcDL+a8trigtQa5IYuxKn/KSYFdHlx+4uC1Pff/HGU3QK6yiSXFRGjmWmqREVyrt8ccfd4xWTpo0ialTp7Jv3z5atGgBwFVXXcWKFSuqLNG99tprsVgsTm2//fab4/XK8+yzz5KRkcHo0aMdbe3atWPhwoV07tyZtLQ0XnjhBeLi4vj1119p3bq1o19MTAzPP/88JpOJtm3bsm3bNp5//nlHotuzZ0/i4+P5448/GDt2LJs3byYlJYX+/fuzZcsW/Pz8yiyzEJFyBEfaj4KyhwLZqZC8F5J32xPfgiQ4ZR9Yc+3r/B4/UMpNTRDUEIIiITjK/rXo46AGEFAffENc+itaScobOS6rT1mJckVGl8MCvM9ogl5hFUmKi6qukWNtKy2lUaJ7JrwD7COr7nptN+vSpYvjcWRkJAEBAU5JZ2RkJBs3bqyy13v++ee58ELn/5OLiYkBICjo9G5M119/PfPnz3fq9+GHHzJ9+nS+/PJLGjZs6Gjv27cvffv2dTyPi4vjnHPOYd68ebz44otO/QqXKfTr149nn30Wq9WKxWLBz8+P2NhYPv74Y4YNG0bz5s1Zt24d5513Hu3atauab4CIFOcXCk162I/CbFZIOwypB+HEQUhNOPX14Omv+dmnRoSPQNJvZb+O2Qv8w+1Jb0B9CKgHfmHgH2aPwS/s1BFqX3at6GHxrvRbLCtRdnV0+Uwn6BVWkaS4qOoYOa6pbaWlblKieyZMJreXD7iTt/fp/3CbTCan5wVtNput1OtDQkI4efKkI1ksYLVaOXnyZLHa4aioKFq1alXiveLj453uW9iiRYuYNGkSn3zySbFEuSiz2UyvXr3Ys6eEOsAyFCTaOTk5mM1mvvzyS3JzczEMg6CgIM477zy+++67Ct1TRM6A2WIvTwiLgWYlnDcMyEiGtENw8qi99CG9oAziyOnHmSmQexJs+ZBx1H5UKh5v+/9f+ATaByp8AsAn6PRj70Dw9j99ePkV+hoA3n72xwWH47kvePnbv3r7g8W31LrkM52gV/g+ribFRVX1yHFNbSstdZcSXXGbdu3aYbVa2bp1Kz179nS0b9myBavVStu2bV2+V2kJ8IcffsjEiRP58MMPufTSS8u9j2EYxMfH07lzZ6f2DRs2FHveunVrR4IeHx9Pfn4+3bp148cffyQqKorzzjuPV155hc6dO+Pvr5ECkVrFZLKXJQQ1KL9vXjZkpUDmsUJHin1ptKwT9vKJ7FT78+xUyDlp3zUuJx3ys+z3sOWdOn+i2t6Sg8WnUPJbUoJcQkLt7Q9e/kR7+xHt7Q/pgacTcJ+AU8l2gOPxmO6RDGgdwYFjWeUu91ZYVY8cV9e20hrp9RxKdMVtOnTowLBhw5g4cSLPPfccLVu2ZN++fdx3330MGzaMDh06OPU/ceIESUlJTm3BwcEEBpY8qv7hhx9yww038MILL9C3b1/Htf7+/o7R4hkzZtC3b19at25NWloaL774IvHx8bz88stO9zp48CD33Xcft9xyC1u2bGHevHk8++yzjvOtWrViw4YNREZGcu6555KQkEB6ejrDhw8vNtINkJCQQEpKCgkJCVitVseIdKtWrZzKMESkFvD2A+9GEFKJycfWPHvCm5cJuZn20eGCx3kZkJtx+nFetv1cfjbkZZ3+Wvhxfo49ec7POd2enw1Gob+eWXPtR07VfQtKEm2yEO0TVKhEI6hIyUaI/atTnxDGNAzm/EmRJGT60Dg6mqiI+iXfvwo2/6jM0m2l7Wan5LduUqIrbvXRRx8xffp0br31Vv7++2+aNGnC8OHDmT59erG+JW3s8OSTT5a6ksFrr71Gfn4+t99+O7fffrujffz48Y5lvU6cOMHNN99MUlISoaGhdO/endWrV9O7d2+ne91www1kZWXRu3dvLBYLd955JzfffLNTn5UrVzJgwAAAVq1aRb9+/UpMcgEeffRRp406ClZ5WLFiBYMGDSrxGhGpgyzeEBAOhJfbtdIMw15akZ99KhHOtifN+YWOvGx7glzsa1bxpDovs0gynnmq/dRjW96p17VCTqr9qKAGpw7AXtbhH3aq5rme/bG/vQZ6jH89Lh4SyNH8QMIjG1G/wXFItUBgBHj5VmplirKWbgvwMZ/RhhtS+5gMwzDK73b2SEtLIzQ0lNTU1GK1ntnZ2ezfv5/mzZuf0YYCIhWlf3siUmtY8+wj0XmZ9q8FZRoFR+5JyEkr0l5CW9bx00lzZfgEQ2B9CIgg2zecVHM9Auo3Irh+Y6eVMxJtIRw4YStWYrFoU0KxJDgmPICxC34u9lImExhFkuK1Dw7WyK4blZWvFaYRXREREXGd5dQIrH/Ymd2nYNOlrBP2pDf71Ndiz4/b66IzjkFmsv2xLd++g15uOhw/gB/gB7C3+MtEA9F+oRB8qvzk1DEmpBEXjYngYH49Ipu2JqphQxJTsyq14YbUXkp0RUREpOYVrFzkE2jf2MNVhmFPggsS34xk+855Gf9AetLpJeMKVs+w5pyeMPjP7063cioq8QslOrQpa5rW58dDviQYDfibSC7o34cZa0+SYZz+a5qry6mJ+ynRFRERkbrDZDpVy1sPKHnFHQfDsCe46UmQfti+tnJaon1ZubTDp9daLlgxI3sbjYHxhbOjTTDaF/4xQvnLiOSAEU2bDt2IPpQP2a0gvIV9wiJal7c2UqIrIiIinslkOl1m0bCMzXty0gttKJJw+uvxA5CyH7JP0MCUSgNTKj3ZDbtWwS7Hi0BYDIleTVh2JIi9tkbsMWIYc+lFjIjrWu1vUcqmRFdERETObr7BENnBfpQk6/jppPfY3tNH8l77qhMnEogmgfEWoGD/ox8ew/pTQyyR7aFhB2hY6KuvlpGsKUp0RURERMpSUCrRqLtz+6kd9rZt28y7X/9IC1MiLU2HaWs6SFPzP1gyj8L+o7B/VaGLTFC/JUR1geiuEN0ForraV5CQKqdEV0RERKQyTu2wF9FhIJ8usTqtzhBsymHFhEgiMv6Eo7/D0Z1Yk3bYk9+CEeEdi09fENLYnkg36QmNe0B0N/ArfdkscY0SXREREZEzUNLGFf8Z2ZOItk2BOMC+bu/U37cRbqTSyXyA+zpn08XyFyT9Bil/npogdwj++PrUXU3QoK096Y3pDTF9IKItmM1ue591kRJdERERkTM0pldTBrRpwIHkzGKbUySmZjl2XEsmlJW2rqz5zcTaB6fa+2Wn2RPeQ1vg0Gb719QE+OcP+xH/vv1GfqHQpDc07QNN+9uTYG9tIlQWJbrCypUrGTx4MMePHycsLMzd4YiIiNRJ0aH+JS4rtj85o8xNJxJzvNlvbUfzTj2IjrvL3uHkUXvC+/dGOLjRngBnp8LeH+wHgMXXXurQLA5i4+yjvt5a1qwwjX9LtYiNjWXu3LnF2ufOnUtsbKzj+fTp0zGZTMWOdu1KXwYmJSWFO++8k7Zt2xIQEEDTpk256667SE113m89Nja22H0ffPDBSr8nm81GSEgIu3fvBqB169asXr3aqc/ixYsZOnQoERERmEwm4uPjK/16IiLiGZpHBGI2ObcVbDqxaFMCcbOXM3bBz8TNXs6iTQn2DkENoe3FcMGjMOFreDABbl4JFz8FHa6EwIb2zTD++glWPw3/vQJmN4WFw2HVM5DwM1jza/qt1joa0XWTpIwkEtISaBrSlKjAKHeH41YdO3bkxx9/dGrz8ir9n+bhw4c5fPgwc+bMoUOHDvz1119MmTKFw4cP8+mnnzr1nTlzJpMnT3Y8Dwqq/JIu27dvx9fXlzZt2nD06FESEhLo1auXU5+MjAzi4uK4+uqrnV5XRETOXiXV8M4a2QnAUdIA9q2GH1q8nQFtGhQfGbZ42yerNeoOfafYV3w4tg/+Wgt/rYP9a+ybYhxYYz9WAL4h0HwAtDzffoQ3r9k3Xgso0XWDxXsWM2P9DGyGDbPJzLR+0xjZemS1vV5OTg73338/H330EWlpafTs2ZPnn3++WJL2008/8dBDD7Fr1y66du3KG2+8QefOnQH466+/uOOOO1i7di25ubnExsbyzDPPcMkll5xxfF5eXkRFuZ7sd+rUic8++8zxvGXLljzxxBNcf/315OfnOyXJwcHBpd7barVy8803s3z5cpKSkmjatCm33XYbd999d4n9161bR1ycfVLBmjVr6N69O/7+zv8hGjduHAAHDhxw+f2IiIjnK6mGd92+5LJLGsraac1kgohW9qPHhFOJ7177UmZ/roL9q+07vv3x9ekJbvVbQesh0Poie7mDl29NvHW3UqJbw5IykhxJLoDNsDFj/Qz6N+pfbSO7DzzwAJ999hnvvPMOzZo14+mnn2bo0KHs3buX8HDHLt/cf//9vPDCC0RFRfHQQw9x+eWXs3v3bry9vbn99tvJzc1l9erVBAYGsnPnzjMaHa1qqamphISEFBsJfuqpp3jssceIiYnh6quv5v7778fHxwewlyI0adKEjz/+mIiICNatW8fNN99MdHQ0o0ePdtyjoG45OzsbwzAICwsjJycHq9VKWFgY5557Ll9//TUiIiJlKVrDW1DSUDjZLVzSUDDaazbBkyM7M6ZXU6CUrYZNJohobT963QQ2KxyOh33L7cffG08va7bhFfAOhJaDoc3F9uQ3OLIGvxM1R4luDUtIS3AkuQVsho2D6QerJdHNyMjg1VdfZeHChQwbNgyABQsW8MMPP/Dmm29y//33O/pOmzaNiy66CIB33nmHJk2a8PnnnzN69GgSEhIYNWqUY4S3RYsWVRbjtm3biiXN11xzDW+88YZL1x87dozHHnuMW265xan97rvv5pxzzqFevXps3LiRqVOnsn//fsd9vb29mTFjhqN/8+bNWbduHR9//LFTohsfH49hGPTo0YMPPviAdu3aMWTIEKZPn07//v3x89OMVxERqbjKlDSs3v1PsQR4QJsGxRNfswWa9LAfA++3T2T7cyXs+cF+nExyHu1t0hvaXwbth0N41f1/vLsp0a1hTUOaYjaZnZJds8lMTHBMtbzevn37yMvLc/zJHewJXu/evfn999+d+vbr18/xODw8nLZt2zr63HXXXdx6660sW7aMCy+8kFGjRtGlS5cqibFt27YsWbLEqS04OBiAWbNmMWvWLEf7zp07adq0qeN5Wloal156KR06dGDatGlO97j33nsdj7t06UK9evW46qqreOqpp6hf374Dzfz583njjTf466+/yMrKIjc3l27dujndJzY2lo0bNxIQEMDFF1/MoUOHOHz4MKNGjcLX1/P/7CMiItWnIiUNmw8cL5YAP/jZNkynRoULj/wWG/X1C4UOV9gPw7AvZ7bre9j9HRzeah/x/Xsj/PAIRHY6lfReZt+22GQqHngdoUS3hkUFRjGt37RiNbrVVbZgGPafBlORf6SGYRRrK0lBn5tuuomhQ4fyzTffsGzZMp588kmeffZZ7rzzzhKvCwkJKbYKAsCJEycIDQ11avPx8aFVq1Yl3mfKlClOo6uNGjVyPE5PT+fiiy8mKCiIzz//HG9v7zLfS9++fQHYu3cv9evX5+OPP+bee+/l2WefpV+/fgQHB/PMM8/w888/O64ZNmwYa9asIT8/n/z8fIKCgrBareTk5DiS5ZMnT5b5uiIiImVxtaSBIm0ABva8FU6P/J7IyuOp7/4osewBsCeu0V3tx6B/Q1ri6dHd/WvgyHb7sfJJ++hu+8ug4wj7bm11LOnV8mJuMLL1SJaOWspbQ99i6ail1ToRrVWrVvj4+LB27VpHW15eHr/88gvt27d36rthwwbH4+PHj7N7926nZb5iYmKYMmUKixcv5l//+hcLFiwo9XXbtWvHpk2birVv2rSJtm3buhx/eHg4rVq1chwFNbhpaWkMGTIEHx8flixZ4lL5wNatWwGIjo4G7BPK+vfvz2233Ub37t1p1aoV+/btc7rmjTfeID4+nh49evDUU08RHx/P0KFDeeCBB4iPj9fyYSIiUuUKShosp5LKgpKGHs3qFVumrCirYTD7VJILp5PfxNSs0i8KiYbek+GGL+H+vXDlq9D2Evs6vSl/wk8vwOuDYN45sPxx+5bGdYRGdN0kKjCqRpYVCwwM5NZbb+X+++8nPDycpk2b8vTTT5OZmcmkSZOc+s6cOZP69esTGRnJww8/TEREBFdeeSUA99xzD8OGDaNNmzYcP36c5cuXF0uUC7vvvvuIi4tj5syZXHXVVQB89tlnfP/996xbt86pb35+PklJSU5tJpOJyMiSC+PT09MZMmQImZmZvPfee6SlpZGWlgZAgwYNsFgsrF+/ng0bNjB48GBCQ0PZtGkT9957L5dffrmj9KFVq1b897//ZenSpTRv3px3332XTZs20bz56eVXGjduTH5+Pr/99hvvvfcezZs357fffuPf//53iaPQKSkpJCQkcPjwYQB27doFQFRUVIVWlhARkbNbaTutFa7pNXNqRLfQdWaKj/oWlD2EB5WygkNhAeHQbaz9yDlp35xixxewe6k96V39jP1o2AE6jYROo2p1Ta8S3bPA7NmzsdlsjBs3jvT0dHr27MnSpUupV69esX533303e/bsoWvXrixZssSxQoHVauX222/n77//JiQkhIsvvpjnn3++1Nfs27cvS5cuZebMmY6NIzp27MjSpUvp06ePU98dO3Y4RlkL+Pr6kp2dXeK9N2/e7CgvKJps7t+/n9jYWHx9fVm0aBEzZswgJyeHZs2aMXnyZB544AFH3ylTphAfH8+YMWMwmUxce+213HbbbXz33XdO9/zll18ICwujefPm/P333xw5coSePXuWGNuSJUu48cYbHc+vueYawD7Rb/r06aV8t0RERIoraae1ognw6t3/OE1me+Ditjz1/R9Oya4JuOujraWXMpTGN8hestBxhD3p3f09bP/MPpnt6E5YvtM+wtukN3QZDR1HQmD9qv0mnCGTUVDEWcs98cQTfPPNN8THx+Pj48OJEyeK9UlISOD2229n+fLl+Pv7M3bsWObMmeNI1lyRlpZGaGioY7mqwrKzs9m/fz/NmzfXTHupUfq3JyIipUlMzXIa+V20KaHMUV+LycTaBweXPbJblqzj8Mc3sO1T+7q9BRPsO4+GUaWXNValsvK1wurMiG5ubi5XX301/fr148033yx23mq1cumll9KgQQPWrl3LsWPHGD9+PIZhMG/ePDdELCIiIlL9io78Fh71PZaRwx0fbHXqX3hTikrxrwfdr7cf6Udg+6fw2yLoMuZM3ka1qDOJbsF6pwsXLizx/LJly9i5cycHDx50zMx/9tlnmTBhAk888USZ2b6IiIiIJylIfhNTs4qt4GAGjmXkkJiaVflkt0BwJPS73X7UwiIBj1l1Yf369XTq1Mlp+amhQ4eSk5PD5s2bS70uJyfHMZmp8KQmERERkbqu6AoOJuxlDHd8sJW42ctZtCmh6l6sFi495jGJblJSUrFZ+vXq1cPHx6fYjP7CnnzySUJDQx1HTEz1bNwgIiIi4g5jejVl7YODeena7phMp+t1C5Ye+/XgcdbtSy57CbI6yq2J7vTp0zGZTGUev/zyi8v3K2kDhPI2Rpg6dSqpqamO4+DBg5V6LyIiIiK1VXSoP+FBPiUuPXblK+sYu+Dnqh/hrQXcWqN7xx13OJZfKk1sbKxL94qKinLa0Qrsmx7k5eWVuh4r2Jex0jauIiIi4ulK2nENiu+sNqBNgzOv3a0l3JroRkREEBERUSX36tevH0888QSJiYmONVmXLVuGr68vPXr0qJLXEBEREamrCup1Cy89ZivS54xXZKhl6syqCwkJCY5dp6xWq2Pr1VatWhEUFMSQIUPo0KED48aN45lnniElJYX/+7//Y/LkyVpxQURERATnpccCfMyMeGWd0wivxWQiNiLAfQFWsTqT6D766KO88847jufdu3cHYMWKFQwaNAiLxcI333zDbbfdRlxcnNOGESIiIiJiV3jd3cIjvBaTiVkjO3nMaC7UoZ3Raop2RivuwIEDNG/enK1bt9KtWzd3h3NWOlv/7YmISPUrurNa0XP7kzNoHhFYqxJgV3dG85jlxaRk5a1qMWHChHL7xMTEkJiYSKdOnao0toULFxIWFlbiubCwMKfNQUqL7aOPPir1/osXL+aiiy6iQYMGhISE0K9fP5YuXVoshpLum52dXen39cwzzzB27FgA3n//fc4//3yn89nZ2UyYMIHOnTvj5eXFlVdeWenXEhEROVPRof70a1m/WCK7aFMCcbOXl7oiQ2JqVq1flqzOlC5I5SQmJjoeL1q0iEcffZRdu3Y52vz9/Zk9e7bjeXR0NG+//TYXX3yxo81isRAVFVUzAZehaFxAqYkywOrVq7nooouYNWsWYWFhvP3221x22WX8/PPPjtIXgJCQEKfvCXBGo6br16/nggsuAGDt2rXExcU5nbdarfj7+3PXXXfx2WefVfp1REREqktiahZTF29z1O8WXZFh0aYEx3mzyV4CMaZXU/cGXQKN6J4BwzCwZWa65XC14iQqKspxhIaGYjKZirUVfg725LFw24EDBzCZTI4JgCtXrsRkMrF06VK6d++Ov78/559/PkePHuW7776jffv2hISEcO2115KZmVll3++icUVFRZWZkM6dO5cHHniAXr160bp1a2bNmkXr1q356quvnPoV/Z4UTeq///57zj33XMLCwqhfvz7Dhw9n3759pb7u+vXrHcltSYluYGAgr776KpMnT64Vv0CIiIgUtT85o8Q1dw8kZ5aaBNfGkV2N6J4BIyuLXee4Z+mytls2Ywpw76zI6dOn89JLLxEQEMDo0aMZPXo0vr6+fPDBB5w8eZIRI0Ywb948/v3vf7s1zgI2m4309HTCw8Od2k+ePEmzZs2wWq1069aNxx57zGnENyMjg/vuu4/OnTuTkZHBo48+yogRI4iPj8dstv+uOHv2bMfIeGpqKgMHDsRkMpGamsro0aMxm818/fXXnHvuuTX3hkVERCqppDV3C1ZkKCsJrk11vKBEV87A448/7hitnDRpElOnTmXfvn20aNECgKuuuooVK1ZUWaJ77bXXYrFYnNp+++03x+uV59lnnyUjI4PRo0c72tq1a8fChQvp3LkzaWlpvPDCC8TFxfHrr7/SunVrAEaNGuV0nzfffJOGDRuyc+dOR93ylClTuOaaa1i4cCEbNmxg/vz5fPvttyxcuJCPP/4YQKO3IiJSZxRdc7foigxFk2AzEOBT+woFlOieAZO/P223bHbba7tbly5dHI8jIyMJCAhwSjojIyPZuHFjlb3e888/z4UXXujUFhMTA0BQUJCj7frrr2f+/PlO/T788EOmT5/Ol19+ScOGDR3tffv2pW/fvo7ncXFxnHPOOcybN48XX3wRgH379vHII4+wYcMGkpOTsdnsy2snJCQ4Et2wsDDCwsLYuHEjo0aNIjY2lq1bt3L55Ze7vLufiIhIbVJ4zd3CKzIUTYLBvvHEiFfW1bpaXSW6Z8BkMrm9fMCdvL29HY9NJpPT84K2gqSwJCEhIZw8eRKr1eo0Umu1Wjl58iShoaFO/aOiomjVqlWJ9yqoHy64b2GLFi1i0qRJfPLJJ8US5aLMZjO9evViz549jrbLLruMmJgYFixYQKNGjbDZbHTq1Inc3FwA1qxZw7BhwwDIzMxk5cqV3HvvvWRlZeHt7c3s2bN56KGHeOihh8p8bRERkdqm8Jq7hY3p1ZR2UcFc+cq6Wr2FsBJdcZt27dphtVrZunUrPXv2dLRv2bIFq9VK27ZtXb5XaQnwhx9+yMSJE/nwww+59NJLy72PYRjEx8fTuXNnAI4dO8bvv//Oa6+9xnnnnQfYJ5gV1rNnT+Lj49m8eTMPPPAA//vf/0hISODyyy9ny5YtmM3mYnXBIiIidV1GrpWic+NrW62uEl1xmw4dOjBs2DAmTpzIc889R8uWLdm3bx/33Xcfw4YNo0OHDk79T5w4QVJSklNbcHAwgYGBJd7/ww8/5IYbbuCFF16gb9++jmv9/f0do8UzZsygb9++tG7dmrS0NF588UXi4+N5+eWXAahXrx7169fn9ddfJzo6moSEBB588EGn1/H396dVq1Z8+umnDBo0iFatWrFu3Tri4uJo06ZNibHt3LmT3NxcUlJSSE9Pd4xIa0MOERGpK8qasFZb1L6qYTmrfPTRR1x44YXceuutdOjQgVtvvZULLriADz/8sFjfG2+8kejoaKdj3rx5pd77tddeIz8/n9tvv93pmrvvvtvR58SJE9x88820b9+eIUOGcOjQIVavXk3v3r0BeynDRx99xObNm+nUqRP33nsvzzzzTImvt3LlSgYMGADAqlWrHI9Lcskll9C9e3e++uorVq5cSffu3Z1WehAREantCmp1LSYTQK3cQlhbABehLYClNtK/PRERqa3K2kK4uri6BbBKF0RERESk0kqbsFYbqHRBRERERDySEl0RERER8UhKdCtBZc1S0/RvTkREpOKU6FZAwaYGBRsFiNSUzMxMgGKbcoiIiEjpNBmtAry8vAgICOCff/7B29sbs1m/J0j1MgyDzMxMjh49SlhYmNMOciIiIlI2JboVYDKZiI6OZv/+/fz111/uDkfOImFhYURFRbk7DBERkTpFiW4F+fj40Lp1a5UvSI3x9vbWSK6IiEglKNGtBLPZrEX7RURERGo5FZmKiIiIiEdSoisiIiIiHkmJroiIiIh4JNXoFlGwMH9aWpqbIxERERGRkhTkaeVtqKREt4j09HQAYmJi3ByJiIiIiJQlPT2d0NDQUs+bDO0t6sRms9GmTRs2b96MyWSqknv26tWLTZs2Vft1rvQvq09lzhVtT0tLIyYmhoMHDxISEuJy7NWlst/7qr5fRa4708+xrPMVaa9Nn2Vt+Rwreq1+JourLZ9lbfmZLO2cfiar51r9TBZXWz7Lin6OGzduJD09nUaNGpW5gZdGdIswm834+PiU+dtBRVkslkr9Y67oda70L6tPZc6V1h4SElIrfoAr+72v6vtV5Loz/RzLOl/Rdqgdn2Vt+Rwreq1+JourLZ9lbfmZLO2cfiar51r9TBZXWz7Lin6OoaGhLuVqmoxWgttvv71W3K+i17nSv6w+lTlX1d+rqlYXP8sz/RzLOq/P8czvV5OfpX4mq+9+teVnsrRz+hyr51r9TBZXWz7Lqv4cC6h0QapUWloaoaGhpKam1orfVKXy9Fl6Bn2OnkOfpWfQ51izNKIrVcrX15dp06bh6+vr7lDkDOmz9Az6HD2HPkvPoM+xZmlEV0REREQ8kkZ0RURERMQjKdEVEREREY+kRFdEREREPJISXRERERHxSEp0RURERMQjKdEVt8rMzKRZs2b83//9n7tDkUpKT0+nV69edOvWjc6dO7NgwQJ3hySVdPDgQQYNGkSHDh3o0qULn3zyibtDkkoaMWIE9erV46qrrnJ3KFJBX3/9NW3btqV169a88cYb7g6nztPyYuJWDz/8MHv27KFp06bMmTPH3eFIJVitVnJycggICCAzM5NOnTqxadMm6tev7+7QpIISExM5cuQI3bp14+jRo5xzzjns2rWLwMBAd4cmFbRixQpOnjzJO++8w6effurucMRF+fn5dOjQgRUrVhASEsI555zDzz//THh4uLtDq7M0oitus2fPHv744w8uueQSd4ciZ8BisRAQEABAdnY2VqsV/f5cN0VHR9OtWzcAGjZsSHh4OCkpKe4NSipl8ODBBAcHuzsMqaCNGzfSsWNHGjduTHBwMJdccglLly51d1h1mhJdKdHq1au57LLLaNSoESaTiS+++KJYn1deeYXmzZvj5+dHjx49WLNmTYVe4//+7/948sknqyhiKU1NfJYnTpyga9euNGnShAceeICIiIgqil4Kq4nPssAvv/yCzWYjJibmDKOWomryc5Sadaaf7eHDh2ncuLHjeZMmTTh06FBNhO6xlOhKiTIyMujatSsvvfRSiecXLVrEPffcw8MPP8zWrVs577zzGDZsGAkJCY4+PXr0oFOnTsWOw4cP8+WXX9KmTRvatGlTU2/prFXdnyVAWFgYv/76K/v37+eDDz7gyJEjNfLezjY18VkCHDt2jBtuuIH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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1 = ml.head(0, 0, t1)\n", + "hm2 = ml.head(110, 0, t2)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, '.', label='obs UE-25b#1')\n", + "plt.semilogx(t1, hm1[-1], label='TTim UE-25b#1')\n", + "plt.semilogx(t2, h2, '.', label='obs UE-25a#1')\n", + "plt.semilogx(t2, hm2[-1], label='TTim UE-25a#1')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.suptitle('Model Results - Simulation 1')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Simulate parameters of both fracture and matrix\n", + "\n", + "Now we will repeat the procedures of steps 5 and 6, but this time we will let TTim find the parameters for both the matrix and the fractures" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml1 = ttim.ModelMaq(kaq=[1, 1], z=[0, -400, -401, -801], c=5, Saq=[1e-3, 1e-3],\\\n", + " Sll=0, topboundary='conf', tmin=1e-5, tmax=3)\n", + "w1 = ttim.Well(ml1, xw=0, yw=0, rw=0.11, rc=0, tsandQ=[0, 3093.12], layers=1)\n", + "ml1.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".....................................................................................................................................................................................................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 354\n", + " # data points = 138\n", + " # variables = 6\n", + " chi-square = 3.50592808\n", + " reduced chi-square = 0.02656006\n", + " Akaike info crit = -494.846181\n", + " Bayesian info crit = -477.282659\n", + "[[Variables]]\n", + " kaq0: 8.3798e-08 +/- 2.7038e-05 (32265.92%) (init = 1)\n", + " Saq0: 1.4424e-04 +/- 1.4640e-05 (10.15%) (init = 0.0001)\n", + " kaq1: 0.90902373 +/- 0.00603961 (0.66%) (init = 1)\n", + " Saq1: 3.3892e-06 +/- 3.0267e-07 (8.93%) (init = 0.0001)\n", + " c1: 15.5844403 +/- 1.43538189 (9.21%) (init = 100)\n", + " rc: 0.10855545 +/- 0.00253751 (2.34%) (init = 0)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq1, Saq1) = -0.7423\n", + " C(kaq1, c1) = +0.7105\n", + " C(Saq0, kaq1) = -0.6759\n", + " C(Saq0, Saq1) = +0.5464\n", + " C(Saq1, rc) = -0.4120\n", + " C(Saq1, c1) = -0.3831\n", + " C(Saq0, c1) = -0.2784\n", + " C(kaq0, c1) = +0.1418\n", + " C(kaq0, Saq1) = +0.1270\n", + " C(kaq1, rc) = +0.1182\n", + " C(kaq0, Saq0) = +0.1102\n", + " C(Saq0, rc) = -0.1095\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq08.379800e-082.703820e-0532265.9236021.000000e-08inf1.0000[8.379799754099082e-08]
Saq01.442428e-041.463980e-0510.1494141.000000e-08inf0.0001[0.00014424278513025524]
kaq19.090237e-016.039612e-030.6644061.000000e-08inf1.0000[0.9090237295301099]
Saq13.389227e-063.026748e-078.9304951.000000e-08inf0.0001[3.3892272003344104e-06]
c11.558444e+011.435382e+009.2103531.000000e-08inf100.0000[15.584440297512415]
rc1.085555e-012.537508e-032.3375230.000000e+00inf0.0000[0.10855545326039162]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 8.379800e-08 2.703820e-05 32265.923602 1.000000e-08 inf 1.0000 \n", + "Saq0 1.442428e-04 1.463980e-05 10.149414 1.000000e-08 inf 0.0001 \n", + "kaq1 9.090237e-01 6.039612e-03 0.664406 1.000000e-08 inf 1.0000 \n", + "Saq1 3.389227e-06 3.026748e-07 8.930495 1.000000e-08 inf 0.0001 \n", + "c1 1.558444e+01 1.435382e+00 9.210353 1.000000e-08 inf 100.0000 \n", + "rc 1.085555e-01 2.537508e-03 2.337523 0.000000e+00 inf 0.0000 \n", + "\n", + " parray \n", + "kaq0 [8.379799754099082e-08] \n", + "Saq0 [0.00014424278513025524] \n", + "kaq1 [0.9090237295301099] \n", + "Saq1 [3.3892272003344104e-06] \n", + "c1 [15.584440297512415] \n", + "rc [0.10855545326039162] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.1593903257365926\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/Caskroom/miniforge/base/envs/ttim-env/lib/python3.10/site-packages/ttim/well.py:35: RuntimeWarning: divide by zero encountered in divide\n", + " laboverrwk1 = self.aq.lab / (self.rw * kv(1, self.rw/self.aq.lab))\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/ttim-env/lib/python3.10/site-packages/ttim/well.py:35: RuntimeWarning: invalid value encountered in divide\n", + " laboverrwk1 = self.aq.lab / (self.rw * kv(1, self.rw/self.aq.lab))\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/ttim-env/lib/python3.10/site-packages/ttim/well.py:38: RuntimeWarning: invalid value encountered in multiply\n", + " self.term = -1.0 / (2 * np.pi) * laboverrwk1 * self.flowcoef * coef\n" + ] + } + ], + "source": [ + "ca1 = ttim.Calibrate(ml1)\n", + "ca1.set_parameter(name='kaq0', initial=1, pmin=1e-8)\n", + "ca1.set_parameter(name='Saq0', initial=1e-4, pmin=1e-8)\n", + "ca1.set_parameter(name='kaq1', initial=1, pmin=1e-8)\n", + "ca1.set_parameter(name='Saq1', initial=1e-4, pmin=1e-8)\n", + "ca1.set_parameter(name='c1', initial=100, pmin=1e-8)\n", + "ca1.set_parameter_by_reference(name='rc', parameter=w1.rc, initial=0, pmin=0)\n", + "ca1.series(name='UE-25b#1', x=0, y=0, t=t1, h=h1, layer=1)\n", + "ca1.series(name='UE-25a#1', x=110, y=0, t=t2, h=h2, layer=1)\n", + "ca1.fit(report=True)\n", + "display(ca1.parameters)\n", + "print('RMSE:', ca1.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the fit has slightly improved, as the AIC and BIC values. However, from the table above, one can see that we have low confidence in the hydraulic conductivity of the matrix. Nevertheless, we can see it is a small value." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht1 = ml1.head(0, 0, t1)\n", + "ht2 = ml1.head(110, 0, t2)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, '.', label = 'obs UE-25b#1')\n", + "plt.semilogx(t1, ht1[-1], label = 'TTim UE-25b#1')\n", + "plt.semilogx(t2, h2, '.', label = 'obs UE-25a#1')\n", + "plt.semilogx(t2, ht2[-1], label = 'TTim UE-25a#1')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.suptitle('Model Results - Simulation 1')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Overall the curves follow the trends of the drawdown and the fit is good in general." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Analysis and Comparison of the Results under different methods" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The final important step is to compare the data obtained from this model with the data from other Aquifer Analysis software. Yang (2020) compared TTim results with the published results in Kruseman and de Ridder (1990), here abbreviated to K&dR, and with the results obtained from the software AQTESOLV (Duffield, 2007) and MLU (Carlson & Randall, 2012).\n", + "\n", + "Kruseman et al. (1970) solved the problem using a graphical method, where the transmissivity was calculated as one aquifer and the storativity was separated between matrix and fractures. The MLU (Carlson & Randall, 2012) solution used a similar approach to our TTim model by simulating the matrix as a very-low transmissivity aquifer on top of the fractured aquifer and separated by a zero-storage aquitard. Yang (2020) solved the problem in AQTESOLV using a double-porosity analytical solution proposed by Moench (1984).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/Caskroom/miniforge/base/envs/ttim-env/lib/python3.10/site-packages/ttim/well.py:35: RuntimeWarning: divide by zero encountered in divide\n", + " laboverrwk1 = self.aq.lab / (self.rw * kv(1, self.rw/self.aq.lab))\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/ttim-env/lib/python3.10/site-packages/ttim/well.py:35: RuntimeWarning: invalid value encountered in divide\n", + " laboverrwk1 = self.aq.lab / (self.rw * kv(1, self.rw/self.aq.lab))\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/ttim-env/lib/python3.10/site-packages/ttim/well.py:38: RuntimeWarning: invalid value encountered in multiply\n", + " self.term = -1.0 / (2 * np.pi) * laboverrwk1 * self.flowcoef * coef\n" + ] + }, + { + "data": { + "text/html": [ + "
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TTim20.00.0001440.9090240.00000315.584440.1085550.15939
\n", + "
" + ], + "text/plain": [ + " km [m/d] Sm [1/m] kf [m/d] Sf [1/m] c rc RMSE\n", + "K&dR 0.8325 0.000375 0.8325 0.000004 - - -\n", + "AQTESOLV 0.149 0.000551 0.937 0.000006 - 0.11 0.031736\n", + "MLU 0.00025 0.000385 0.874 0.000008 12.38 0.1 0.434638\n", + "TTim1 0.00025 0.000385 0.876963 0.000005 13.003524 0.105604 0.199156\n", + "TTim2 0.0 0.000144 0.909024 0.000003 15.58444 0.108555 0.15939" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(columns=['km [m/d]', 'Sm [1/m]', 'kf [m/d]', 'Sf [1/m]', 'c', 'rc'], \\\n", + " index=['K&dR', #'Moench',\n", + " 'AQTESOLV', 'MLU', 'TTim1', 'TTim2'])\n", + "t.loc['TTim1'] = np.concatenate((np.array([0.00025, 3.850e-04]),ca.parameters['optimal'].values))\n", + "t.loc['TTim2'] = ca1.parameters['optimal'].values\n", + "t.loc['K&dR'] = [0.8325, 3.750e-4, 0.8325, 4.000e-6, '-', '-']\n", + "#t.loc['Moench'] = [0.1728, 3.000e-4, 0.864, 1.500e-6, '-', '-'] # I don't know where these values for Moench come from\n", + "t.loc['AQTESOLV'] = [0.149, 5.512e-4, 0.937, 5.533e-6, '-', 0.11]\n", + "t.loc['MLU'] = [0.00025, 3.850e-04, 0.874, 8.053e-6, 12.380, 0.1]\n", + "t['RMSE'] = ['-', 0.031736,\n", + " 0.434638, ca.rmse(), ca1.rmse()]\n", + "t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Overall, TTim model 1 showed similar results to MLU but with a slightly better fit. AQTESOLV obtained the best fit using Moench's analytical solution." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8.1. Comparison of estimates and model error" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Preparing the DataFrame:\n", + "t1 = pd.DataFrame(columns=['kaq - opt -l1', 'kaq - 95% -l1','kaq - opt -l2', 'kaq - 95% -l2' ], index = ['AQTESOLV','MLU','K&dR','TTim - all params', 'TTim - fixed upper']) \n", + "simulation = ['AQTESOLV','MLU','K&dR','TTim - rc', 'TTim - fixed upper']\n", + "t1.loc['MLU'] = [0.00025, np.nan,0.874, 1.221*1e-2*0.874]\n", + "t1.loc['KdR'] = [0.8325, np.nan, 0.8325, np.nan]\n", + "t1.loc['AQTESOLV'] = [0.149,291.236*1e-2*0.149,0.937,1.946*1e-2*0.937]\n", + "t1.loc['TTim - fixed upper'] = [0.00025,np.nan,ca.parameters.loc['kaq1','optimal'],2*ca.parameters.loc['kaq1','std']]\n", + "t1.loc['TTim - all params'] = [ca1.parameters.loc['kaq0','optimal'],2*ca1.parameters.loc['kaq0','std'],ca1.parameters.loc['kaq1','optimal'],2*ca1.parameters.loc['kaq1','std']]\n", + "\n", + "# Plotting\n", + "\n", + "#plt.figure(figsize = (10,7))\n", + "fig,ax = plt.subplots(2,1, figsize = (10,7), sharex = True)\n", + "ax[0].errorbar(x = t1.index, y = t1['kaq - opt -l1'], yerr = [t1['kaq - 95% -l1'], t1['kaq - 95% -l1']],\n", + " marker='o', linestyle='', markersize=12, label = 'aquifer matrix', color = 'red')\n", + "ax[0].legend()\n", + "ax[1].errorbar(x = t1.index, y = t1['kaq - opt -l2'], yerr = [t1['kaq - 95% -l2'], t1['kaq - 95% -l2']],\n", + " marker='o', linestyle='', markersize=12, label = 'fractures')\n", + "\n", + "plt.legend()\n", + "plt.suptitle(\"Error bar plot of calibrated hydraulic conductivities\")\n", + "plt.ylabel('K [m/d]')\n", + "#plt.ylim([278,289])\n", + "plt.xlabel('Model');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hydraulic conductivities varied between simulations. When estimated, the matrix conductivity had higher uncertainty. Uncertainty ranges are similar for the fractured portion. However, the solutions do not always overlap each other." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reference\n", + "\n", + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Kruseman GP, De Ridder NA (1990) Analysis and evaluation of pumping test data, 2nd edn. ILRI Publ. 47, ILRI, Wageningen, The Netherlands\n", + "* Moench, A. F. (1984), Double-Porosity Models for a Fissured Groundwater Reservoir With Fracture Skin, Water Resour. Res., 20( 7), 831– 846, doi:10.1029/WR020i007p00831.\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pumpingtests/data/dalem_p120.txt b/pumpingtests/data/dalem_p120.txt new file mode 100644 index 0000000..dfd8e65 --- /dev/null +++ b/pumpingtests/data/dalem_p120.txt @@ -0,0 +1,13 @@ +# time (d), head (m) +0.0250 -0.057 +0.0313 -0.063 +0.0382 -0.068 +0.0500 -0.075 +0.0681 -0.086 +0.0903 -0.092 +0.1250 -0.105 +0.1670 -0.113 +0.2080 -0.122 +0.2300 -0.125 +0.2920 -0.127 +0.3330 -0.129 \ No newline at end of file diff --git a/pumpingtests/data/dalem_p30.txt b/pumpingtests/data/dalem_p30.txt new file mode 100644 index 0000000..9538143 --- /dev/null +++ b/pumpingtests/data/dalem_p30.txt @@ -0,0 +1,15 @@ +# time (d), head (m) +0.0153 -0.138 +0.0181 -0.141 +0.0229 -0.150 +0.0292 -0.156 +0.0361 -0.163 +0.0458 -0.171 +0.0660 -0.180 +0.0868 -0.190 +0.1250 -0.201 +0.1670 -0.210 +0.2080 -0.217 +0.2500 -0.220 +0.2920 -0.224 +0.3330 -0.228 \ No newline at end of file diff --git a/pumpingtests/data/dalem_p60.txt b/pumpingtests/data/dalem_p60.txt new file mode 100644 index 0000000..907bbde --- /dev/null +++ b/pumpingtests/data/dalem_p60.txt @@ -0,0 +1,14 @@ +# time (d), head (m) +0.0188 -0.081 +0.0236 -0.089 +0.0299 -0.094 +0.0368 -0.101 +0.0472 -0.109 +0.0667 -0.120 +0.0882 -0.127 +0.1250 -0.137 +0.1670 -0.148 +0.2080 -0.155 +0.2500 -0.158 +0.2920 -0.160 +0.3330 -0.164 \ No newline at end of file diff --git a/pumpingtests/data/dalem_p90.txt b/pumpingtests/data/dalem_p90.txt new file mode 100644 index 0000000..9760587 --- /dev/null +++ b/pumpingtests/data/dalem_p90.txt @@ -0,0 +1,13 @@ +# time (d), head (m) +0.0243 -0.069 +0.0306 -0.077 +0.0375 -0.083 +0.0468 -0.091 +0.0674 -0.100 +0.0896 -0.109 +0.1250 -0.120 +0.1670 -0.129 +0.2080 -0.136 +0.2500 -0.141 +0.2920 -0.142 +0.3330 -0.143 \ No newline at end of file diff --git a/pumpingtests/data/dawsonville_slug.txt b/pumpingtests/data/dawsonville_slug.txt new file mode 100644 index 0000000..25546ba --- /dev/null +++ b/pumpingtests/data/dawsonville_slug.txt @@ -0,0 +1,22 @@ +1.157407E-06 0.56 +0.000035 0.457 +0.000069 0.392 +0.000104 0.345 +0.000139 0.308 +0.000174 0.28 +0.000208 0.252 +0.000243 0.224 +0.000278 0.205 +0.000313 0.187 +0.000347 0.168 +0.000382 0.149 +0.000417 0.14 +0.000451 0.131 +0.000486 0.112 +0.000521 0.108 +0.000556 0.093 +0.00059 0.089 +0.000625 0.082 +0.00066 0.075 +0.000694 0.071 +0.000729 0.065 \ No newline at end of file diff --git a/pumpingtests/data/double-porosity-110m.txt b/pumpingtests/data/double-porosity-110m.txt new file mode 100644 index 0000000..e0d119c --- /dev/null +++ b/pumpingtests/data/double-porosity-110m.txt @@ -0,0 +1,67 @@ +# time (d), head (m) +0.000347 -0.002 +0.000417 -0.002 +0.000486 -0.002 +0.000556 -0.002 +0.000625 -0.002 +0.000694 -0.005 +0.000833 -0.005 +0.000972 -0.007 +0.001111 -0.007 +0.001250 -0.012 +0.001389 -0.015 +0.001528 -0.015 +0.001667 -0.020 +0.001806 -0.022 +0.001944 -0.025 +0.002083 -0.027 +0.002431 -0.037 +0.002778 -0.045 +0.003125 -0.052 +0.003472 -0.059 +0.003819 -0.069 +0.004167 -0.079 +0.004861 -0.097 +0.005556 -0.116 +0.006250 -0.134 +0.006944 -0.151 +0.008333 -0.186 +0.009722 -0.213 +0.011111 -0.238 +0.012500 -0.260 +0.013889 -0.285 +0.017361 -0.32 +0.020833 -0.342 +0.024306 -0.359 +0.027778 -0.374 +0.031250 -0.384 +0.034722 -0.392 +0.041667 -0.401 +0.048611 -0.411 +0.083333 -0.434 +0.097222 -0.439 +0.111111 -0.444 +0.125000 -0.451 +0.138889 -0.453 +0.166667 -0.461 +0.194444 -0.468 +0.208333 -0.471 +0.236111 -0.478 +0.277778 -0.491 +0.305556 -0.498 +0.347222 -0.506 +0.416667 -0.518 +0.486111 -0.525 +0.555556 -0.528 +0.625000 -0.528 +0.694444 -0.538 +0.833333 -0.563 +0.972222 -0.577 +1.111111 -0.577 +1.250000 -0.577 +1.388889 -0.59 +1.597222 -0.587 +1.875000 -0.615 +2.083333 -0.615 +2.43056 -0.627 +2.55556 -0.639 diff --git a/pumpingtests/data/double-porosity-pumpingwell.txt b/pumpingtests/data/double-porosity-pumpingwell.txt new file mode 100644 index 0000000..792979e --- /dev/null +++ b/pumpingtests/data/double-porosity-pumpingwell.txt @@ -0,0 +1,73 @@ +# time (d), head (m) +0.000035 -2.513 +0.000069 -3.769 +0.000104 -4.583 +0.000139 -4.858 +0.000174 -5.003 +0.000208 -5.119 +0.000243 -5.23 +0.000278 -5.39 +0.000313 -5.542 +0.000347 -5.69 +0.000417 -5.99 +0.000486 -6.19 +0.000556 -6.42 +0.000625 -6.59 +0.000694 -6.74 +0.000833 -6.96 +0.000972 -7.17 +0.001111 -7.33 +0.001250 -7.45 +0.001389 -7.56 +0.001736 -7.76 +0.002083 -7.93 +0.002431 -8.03 +0.002778 -8.12 +0.003472 -8.24 +0.004167 -8.32 +0.004861 -8.41 +0.005556 -8.46 +0.006250 -8.54 +0.006944 -8.62 +0.008333 -8.67 +0.009722 -8.7 +0.011111 -8.74 +0.012500 -8.76 +0.013889 -8.770001 +0.017361 -8.81 +0.020833 -8.84 +0.024306 -8.84 +0.027778 -8.86 +0.034722 -8.86 +0.041667 -8.9 +0.048611 -8.91 +0.055556 -8.92 +0.062500 -8.93 +0.069444 -8.95 +0.083333 -8.97 +0.097222 -8.979999 +0.111111 -8.99 +0.125000 -9 +0.138889 -9.020001 +0.166667 -9.04 +0.208333 -9.07 +0.277778 -9.11 +0.347222 -9.14 +0.416667 -9.17 +0.486111 -9.18 +0.555556 -9.21 +0.625000 -9.25 +0.694444 -9.3 +0.833333 -9.44 +0.972222 -9.55 +1.111111 -9.64 +1.250000 -9.74 +1.388889 -9.78 +1.527778 -9.8 +1.666667 -9.84 +1.805556 -9.93 +1.944444 -10.03 +2.083333 -10.08 +2.430556 -10.26 +2.777778 -10.3 +2.916667 -10.41 \ No newline at end of file diff --git a/pumpingtests/data/falling_head.txt b/pumpingtests/data/falling_head.txt new file mode 100644 index 0000000..760b582 --- /dev/null +++ b/pumpingtests/data/falling_head.txt @@ -0,0 +1,29 @@ +# time (s), head (ft) +0 8.52 +2 8.56 +4 8.59 +6 8.67 +8 8.77 +10 8.73 +20 8.83 +30 8.91 +40 8.99 +50 9.05 +60 9.11 +70 9.17 +86 9.25 +101 9.31 +111 9.37 +121 9.39 +136 9.45 +151 9.49 +166 9.53 +186 9.58 +201 9.60 +221 9.64 +231 9.67 +246 9.68 +271 9.72 +301 9.75 +311 9.76 +326 9.79 \ No newline at end of file diff --git a/pumpingtests/data/gridley_well_1.txt b/pumpingtests/data/gridley_well_1.txt new file mode 100644 index 0000000..14c933c --- /dev/null +++ b/pumpingtests/data/gridley_well_1.txt @@ -0,0 +1,22 @@ +0.00208 -0.091 +0.00347 -0.213 +0.00556 -0.396 +0.00833 -0.640 +0.01389 -0.975 +0.01667 -1.097 +0.02083 -1.250 +0.02639 -1.433 +0.03264 -1.554 +0.03472 -1.615 +0.04167 -1.737 +0.04861 -1.859 +0.05556 -1.920 +0.06250 -2.042 +0.06944 -2.134 +0.09028 -2.286 +0.11111 -2.530 +0.13889 -2.591 +0.18056 -2.804 +0.22222 -2.957 +0.26389 -3.109 +0.34722 -3.322 diff --git a/pumpingtests/data/gridley_well_3.txt b/pumpingtests/data/gridley_well_3.txt new file mode 100644 index 0000000..03a1f14 --- /dev/null +++ b/pumpingtests/data/gridley_well_3.txt @@ -0,0 +1,15 @@ +0.010417 -7.55904 +0.017361 -7.7724 +0.03125 -8.10768 +0.041667 -8.32104 +0.052778 -8.5344 +0.06250 -8.59536 +0.091667 -8.8392 +0.115278 -8.9916 +0.135417 -9.23544 +0.177778 -9.2964 +0.195833 -9.32688 +0.218056 -9.35736 +0.250000 -9.38784 +0.298611 -9.6012 + diff --git a/pumpingtests/data/ln-2.txt b/pumpingtests/data/ln-2.txt new file mode 100644 index 0000000..980699b --- /dev/null +++ b/pumpingtests/data/ln-2.txt @@ -0,0 +1,81 @@ +1.4 2.661 +2 2.689 +2.6 2.628 +3.2 2.614 +3.8 2.584 +4.4 2.56 +5 2.536 +5.6 2.513 +6.2 2.488 +6.8 2.468 +7.4 2.445 +8 2.425 +8.6 2.405 +9.2 2.383 +9.8 2.36 +10.4 2.34 +13.4 2.245 +16.4 2.154 +19.4 2.065 +22.4 1.984 +25.4 1.909 +28.4 1.835 +31.4 1.767 +34.4 1.703 +37.4 1.638 +40.4 1.58 +43.4 1.523 +46.4 1.469 +49.4 1.418 +52.4 1.367 +55.4 1.319 +58.4 1.276 +61.4 1.231 +64.4 1.191 +67.4 1.15 +70.4 1.113 +73.4 1.076 +82.4 0.997 +85.4 0.947 +88.4 0.92 +91.4 0.89 +94.4 0.862 +97.4 0.839 +100.4 0.812 +115.4 0.703 +130.4 0.611 +145.4 0.534 +160.4 0.469 +175.4 0.414 +190.4 0.368 +205.4 0.327 +220.4 0.293 +235.4 0.265 +250.4 0.238 +265.4 0.218 +280.4 0.198 +295.4 0.181 +310.4 0.163 +325.4 0.15 +340.4 0.136 +355.4 0.13 +370.4 0.12 +385.4 0.113 +400.4 0.103 +415.4 0.1 +430.4 0.093 +445.4 0.082 +460.4 0.082 +475.4 0.075 +490.4 0.072 +506.4 0.068 +525.4 0.065 +541.4 0.062 +557 0.058 +578.6 0.055 +598.4 0.052 +613.4 0.051 +628.4 0.048 +643.4 0.045 +666.2 0.042 +681.2 0.041 diff --git a/pumpingtests/data/ln-3.txt b/pumpingtests/data/ln-3.txt new file mode 100644 index 0000000..b2780c5 --- /dev/null +++ b/pumpingtests/data/ln-3.txt @@ -0,0 +1,81 @@ +1.4 0.004 +2 0.011 +2.6 0.022 +3.2 0.034 +3.8 0.044 +4.4 0.058 +5 0.066 +5.6 0.08 +6.2 0.088 +6.8 0.099 +7.4 0.109 +8 0.117 +8.6 0.123 +9.2 0.131 +9.8 0.139 +10.4 0.145 +13.4 0.179 +16.4 0.2 +19.4 0.222 +22.4 0.236 +25.4 0.251 +28.4 0.262 +31.4 0.268 +34.4 0.279 +37.4 0.284 +40.4 0.287 +43.4 0.29 +46.4 0.294 +49.4 0.298 +52.4 0.298 +55.4 0.298 +58.4 0.298 +61.4 0.294 +64.4 0.294 +67.4 0.29 +70.4 0.29 +73.4 0.287 +82.4 0.284 +85.4 0.279 +88.4 0.279 +91.4 0.268 +94.4 0.263 +97.4 0.262 +100.4 0.258 +115.4 0.239 +130.4 0.222 +145.4 0.207 +160.4 0.189 +175.4 0.174 +190.4 0.159 +205.4 0.145 +220.4 0.131 +235.4 0.123 +250.4 0.112 +265.4 0.108 +280.4 0.102 +295.4 0.094 +310.4 0.09 +325.4 0.08 +340.4 0.071 +355.4 0.068 +370.4 0.065 +385.4 0.058 +400.4 0.057 +415.4 0.05 +430.4 0.05 +445.4 0.047 +460.4 0.043 +475.4 0.043 +490.4 0.039 +506.4 0.039 +525.4 0.036 +541.4 0.036 +557 0.036 +578.6 0.036 +598.4 0.032 +613.4 0.028 +628.4 0.028 +643.4 0.028 +666.2 0.025 +681.2 0.025 \ No newline at end of file diff --git a/pumpingtests/data/moench_pd1.txt b/pumpingtests/data/moench_pd1.txt new file mode 100644 index 0000000..4a9be56 --- /dev/null +++ b/pumpingtests/data/moench_pd1.txt @@ -0,0 +1,15 @@ +#time(s) drawdown(m) +9.28 0.090 +20 0.204 +43.1 0.384 +92.8 0.568 +200 0.662 +431 0.680 +928 0.683 +2000 0.690 +4310 0.703 +9280 0.729 +20000 0.775 +43100 0.847 +92800 0.942 +200000 1.052 \ No newline at end of file diff --git a/pumpingtests/data/moench_pd2.txt b/pumpingtests/data/moench_pd2.txt new file mode 100644 index 0000000..3232206 --- /dev/null +++ b/pumpingtests/data/moench_pd2.txt @@ -0,0 +1,15 @@ +#time(s) drawdown(m) +9.28 0.000 +20 0.0005 +43.1 0.0028 +92.8 0.0071 +200 0.0099 +431 0.0105 +928 0.0110 +2000 0.0119 +4310 0.0140 +9280 0.019 +20000 0.029 +43100 0.052 +92800 0.098 +200000 0.172 \ No newline at end of file diff --git a/pumpingtests/data/moench_ps1.txt b/pumpingtests/data/moench_ps1.txt new file mode 100644 index 0000000..560a216 --- /dev/null +++ b/pumpingtests/data/moench_ps1.txt @@ -0,0 +1,15 @@ +#time(s) drawdown(m) +9.28 0.007 +20 0.019 +43.1 0.039 +92.8 0.060 +200 0.072 +431 0.077 +928 0.084 +2000 0.098 +4310 0.126 +9280 0.177 +20000 0.258 +43100 0.365 +92800 0.487 +200000 0.612 \ No newline at end of file diff --git a/pumpingtests/data/moench_ps2.txt b/pumpingtests/data/moench_ps2.txt new file mode 100644 index 0000000..daecbb2 --- /dev/null +++ b/pumpingtests/data/moench_ps2.txt @@ -0,0 +1,15 @@ +#time(s) drawdown(m) +9.28 0.000 +20 0.0001 +43.1 0.0005 +92.8 0.0012 +200 0.0017 +431 0.0019 +928 0.0022 +2000 0.0028 +4310 0.0042 +9280 0.0075 +20000 0.016 +43100 0.038 +92800 0.084 +200000 0.162 \ No newline at end of file diff --git a/pumpingtests/data/moench_pumped.txt b/pumpingtests/data/moench_pumped.txt new file mode 100644 index 0000000..47efe4f --- /dev/null +++ b/pumpingtests/data/moench_pumped.txt @@ -0,0 +1,15 @@ +#time(s) drawdown(m) +9.28 0.508 +20 0.971 +43.1 1.64 +92.8 2.31 +200 2.65 +431 2.71 +928 2.71 +2000 2.72 +4310 2.74 +9280 2.76 +20000 2.81 +43100 2.88 +92800 2.98 +200000 3.12 \ No newline at end of file diff --git a/pumpingtests/data/piezometer_h30.txt b/pumpingtests/data/piezometer_h30.txt new file mode 100644 index 0000000..4382e2b --- /dev/null +++ b/pumpingtests/data/piezometer_h30.txt @@ -0,0 +1,35 @@ +# time (min), head (m) +0.1 -0.040 +0.25 -0.080 +0.50 -0.130 +0.70 -0.180 +1.0 -0.230 +1.40 -0.280 +1.90 -0.330 +2.33 -0.360 +2.80 -0.390 +3.36 -0.420 +4.00 -0.450 +5.35 -0.500 +6.80 -0.540 +8.3 -0.570 +8.7 -0.580 +10.0 -0.600 +13.1 -0.640 +18 -0.680 +27 -0.742 +33 -0.753 +41 -0.779 +48 -0.793 +59 -0.819 +80 -0.855 +95 -0.873 +139 -0.915 +181 -0.935 +245 -0.966 +300 -0.990 +360 -1.007 +480 -1.050 +600 -1.053 +728 -1.072 +830 -1.088 \ No newline at end of file diff --git a/pumpingtests/data/piezometer_h90.txt b/pumpingtests/data/piezometer_h90.txt new file mode 100644 index 0000000..e2feb96 --- /dev/null +++ b/pumpingtests/data/piezometer_h90.txt @@ -0,0 +1,36 @@ +# time (min), head (m) +1.5 -0.015 +2.0 -0.021 +2.16 -0.023 +2.66 -0.044 +3 -0.054 +3.5 -0.075 +4 -0.090 +4.33 -0.104 +5.5 -0.133 +6 -0.153 +7.5 -0.178 +9 -0.206 +13 -0.250 +15 -0.275 +18 -0.305 +25 -0.348 +30 -0.364 +40 -0.404 +53 -0.429 +60 -0.444 +75 -0.467 +90 -0.494 +105 -0.507 +120 -0.528 +150 -0.550 +180 -0.569 +248 -0.593 +301 -0.614 +363 -0.636 +422 -0.657 +542 -0.679 +602 -0.688 +680 -0.701 +785 -0.718 +845 -0.716 \ No newline at end of file diff --git a/pumpingtests/data/recovery.txt b/pumpingtests/data/recovery.txt new file mode 100644 index 0000000..c8e7253 --- /dev/null +++ b/pumpingtests/data/recovery.txt @@ -0,0 +1,36 @@ +# time (d), head (m) +0.000694 -2.746 +0.001389 -2.807 +0.002083 -2.869 +0.002778 -2.9 +0.003472 -2.923 +0.004167 -2.944 +0.004861 -2.96 +0.005556 -2.977 +0.006250 -2.993 +0.006944 -3.004 +0.008333 -3.019 +0.009722 -3.031 +0.011111 -3.058 +0.012500 -3.064 +0.013889 -3.084 +0.014583 -0.364 +0.015278 -0.282 +0.015972 -0.241 +0.016667 -0.19 +0.017361 -0.174 +0.018056 -0.162 +0.018750 -0.146 +0.019444 -0.13 +0.020139 -0.12 +0.020833 -0.114 +0.022222 -0.102 +0.023611 -0.086 +0.025000 -0.08 +0.026389 -0.075 +0.027778 -0.068 +0.029167 -0.067 +0.030556 -0.061 +0.031944 -0.056 +0.033333 -0.05 +0.034722 -0.044 \ No newline at end of file diff --git a/pumpingtests/data/schroth_obs1.txt b/pumpingtests/data/schroth_obs1.txt new file mode 100644 index 0000000..df6081a --- /dev/null +++ b/pumpingtests/data/schroth_obs1.txt @@ -0,0 +1,25 @@ +# time (d), head (m) +0.000104 -0.8 +0.000139 -1.2 +0.000208 -1.8 +0.000278 -2.4 +0.000347 -3 +0.000486 -4 +0.000694 -5 +0.001042 -6.5 +0.001389 -8 +0.002083 -9.5 +0.002778 -11 +0.003472 -12 +0.004861 -13 +0.006944 -14 +0.010417 -14.5 +0.013889 -14.5 +0.020833 -15 +0.027778 -15 +0.048611 -15 +0.069444 -15 +0.104167 -15.5 +0.138889 -15.5 +0.208333 -15.5 +0.277778 -15.5 diff --git a/pumpingtests/data/schroth_obs2.txt b/pumpingtests/data/schroth_obs2.txt new file mode 100644 index 0000000..e408bc3 --- /dev/null +++ b/pumpingtests/data/schroth_obs2.txt @@ -0,0 +1,17 @@ +# time (d), head (m) +0.002083 -0.02 +0.002778 -0.04 +0.003472 -0.07 +0.004861 -0.13 +0.006944 -0.22 +0.010417 -0.33 +0.013889 -0.42 +0.020833 -0.5 +0.027778 -0.6 +0.041667 -0.7 +0.055556 -0.75 +0.069444 -0.8 +0.104167 -0.85 +0.138889 -0.9 +0.208333 -0.93 +0.277778 -0.95 \ No newline at end of file diff --git a/pumpingtests/data/sioux100.txt b/pumpingtests/data/sioux100.txt new file mode 100644 index 0000000..d4c1530 --- /dev/null +++ b/pumpingtests/data/sioux100.txt @@ -0,0 +1,42 @@ +0.003472 -0.024384 +0.006944 -0.067056 +0.010417 -0.097536 +0.013889 -0.124968 +0.017361 -0.146304 +0.020833 -0.164592 +0.027778 -0.195072 +0.034722 -0.219456 +0.041667 -0.237744 +0.048611 -0.25908 +0.055556 -0.27432 +0.062500 -0.286512 +0.069444 -0.298704 +0.076389 -0.310896 +0.083333 -0.32004 +0.125000 -0.36576 +0.166667 -0.399288 +0.208333 -0.429768 +0.250000 -0.451104 +0.296667 -0.469392 +0.333333 -0.484632 +0.375000 -0.496824 +0.416667 -0.509016 +0.458333 -0.524256 +0.500000 -0.5334 +0.583333 -0.560832 +0.666667 -0.576072 +1.420139 -0.661416 + + + + + + + + + + + + + + diff --git a/pumpingtests/data/sioux200.txt b/pumpingtests/data/sioux200.txt new file mode 100644 index 0000000..18b26ad --- /dev/null +++ b/pumpingtests/data/sioux200.txt @@ -0,0 +1,64 @@ +0.008333333 -0.009144 +0.011805556 -0.021336 +0.015277778 -0.033528 +0.018750000 -0.04572 +0.022222222 -0.054864 +0.029166667 -0.0762 +0.036111111 -0.094488 +0.043055556 -0.109728 +0.050000000 -0.12192 +0.056944444 -0.134112 +0.063888889 -0.146304 +0.070833333 -0.155448 +0.077777778 -0.164592 +0.084722222 -0.173736 +0.126388889 -0.216408 +0.209722222 -0.271272 +0.251388889 -0.295656 +0.293055556 -0.313944 +0.334722222 -0.329184 +0.376388889 -0.341376 +0.418055556 -0.353568 +0.459722222 -0.36576 +0.501388889 -0.374904 +0.584722222 -0.402336 +0.668055556 -0.414528 +1.420138889 -0.50292 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/pumpingtests/data/sioux400.txt b/pumpingtests/data/sioux400.txt new file mode 100644 index 0000000..9e3356c --- /dev/null +++ b/pumpingtests/data/sioux400.txt @@ -0,0 +1,81 @@ +0.024305556 -0.003048 +0.027777778 -0.006096 +0.034722222 -0.009144 +0.041666667 -0.01524 +0.048611111 -0.021336 +0.055555556 -0.024384 +0.062500000 -0.03048 +0.069444444 -0.036576 +0.076388889 -0.042672 +0.083333333 -0.04572 +0.090277778 -0.051816 +0.131944444 -0.079248 +0.173611111 -0.103632 +0.256944444 -0.140208 +0.298611111 -0.155448 +0.340277778 -0.16764 +0.381944444 -0.179832 +0.423611111 -0.192024 +0.465277778 -0.19812 +0.506944444 -0.207264 +0.590277778 -0.2286 +0.673611111 -0.24384 +1.420138889 -0.326136 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/pumpingtests/data/slug.txt b/pumpingtests/data/slug.txt new file mode 100644 index 0000000..6ac3c68 --- /dev/null +++ b/pumpingtests/data/slug.txt @@ -0,0 +1,62 @@ +Time(sec) Displacement(m) +0.1 0.663 +0.2 0.664 +0.3 0.656 +0.4 0.656 +0.5 0.656 +0.6 0.656 +0.7 0.653 +0.8 0.649 +0.9 0.649 +1.1 0.649 +1.2 0.645 +1.3 0.642 +1.5 0.653 +1.6 0.648 +1.8 0.642 +2 0.638 +2.3 0.63 +2.6 0.627 +2.9 0.619 +3.2 0.619 +3.6 0.616 +4 0.608 +4.5 0.605 +5.1 0.596 +5.7 0.59 +6.4 0.587 +7.1 0.579 +8 0.568 +9 0.56 +10 0.553 +11.3 0.539 +12.6 0.531 +14.2 0.517 +15.9 0.501 +17.8 0.486 +20 0.469 +22.4 0.45 +25.2 0.435 +28.2 0.413 +31.7 0.39 +35.5 0.368 +39.9 0.346 +44.7 0.321 +50.2 0.295 +56.3 0.273 +63.1 0.244 +70.8 0.221 +79.5 0.191 +89.2 0.166 +100.1 0.14 +112.3 0.118 +125.9 0.099 +141.3 0.081 +158.5 0.059 +177.9 0.051 +199.6 0.037 +223.9 0.025 +251.2 0.019 +281.9 0.014 +316.3 0.008 +354.9 0.008 \ No newline at end of file diff --git a/pumpingtests/data/syn_30_0.0.txt b/pumpingtests/data/syn_30_0.0.txt new file mode 100644 index 0000000..74e6c81 --- /dev/null +++ b/pumpingtests/data/syn_30_0.0.txt @@ -0,0 +1,34 @@ +-2.282750045284432999e-04 +-7.704784845990329985e-03 +-3.185547887588566218e-02 +-5.153171240076842574e-02 +-7.774709401474182158e-02 +-1.068329142243158031e-01 +-1.362336956776154517e-01 +-1.571798004272764726e-01 +-1.767931387467296667e-01 +-1.968534166201827140e-01 +-2.165164836467547149e-01 +-2.501769946698436109e-01 +-2.785961197443417858e-01 +-3.025787358490645840e-01 +-3.082827447787410691e-01 +-3.252410285140134860e-01 +-3.584236465637882230e-01 +-3.978756424309923823e-01 +-4.486793675237064072e-01 +-4.739640128808013109e-01 +-5.013944374916838864e-01 +-5.213571454729150068e-01 +-5.475336363355971514e-01 +-5.862375058498485725e-01 +-6.081131735625954216e-01 +-6.566225243060911376e-01 +-6.903110782750575547e-01 +-7.289718651248350278e-01 +-7.548452108486574108e-01 +-7.781446506111728834e-01 +-8.149192390334780711e-01 +-8.434510379128740132e-01 +-8.681801086233337239e-01 +-8.849505993053231601e-01 diff --git a/pumpingtests/data/syn_90_0.0.txt b/pumpingtests/data/syn_90_0.0.txt new file mode 100644 index 0000000..4ccdb6d --- /dev/null +++ b/pumpingtests/data/syn_90_0.0.txt @@ -0,0 +1,34 @@ +-3.828309802576449966e-14 +-4.722523771551754832e-10 +-3.338189641453456631e-06 +-4.877024247328960439e-05 +-3.960344335050562572e-04 +-1.722989004307982864e-03 +-4.808662898581723792e-03 +-8.426175639424184766e-03 +-1.303603405956227541e-02 +-1.903985489995950811e-02 +-2.620381449998462203e-02 +-4.132206987913787655e-02 +-5.669824942430446574e-02 +-7.134385437620797965e-02 +-7.503303811998220108e-02 +-8.643301584301035789e-02 +-1.104299125495191713e-01 +-1.414071266372295410e-01 +-1.843163899368466530e-01 +-2.066473349678304983e-01 +-2.314465765321050972e-01 +-2.498092520564259289e-01 +-2.742235364960921573e-01 +-3.108894785483351519e-01 +-3.318544065069818916e-01 +-3.788167765900235517e-01 +-4.117259855898281473e-01 +-4.497101974960640569e-01 +-4.752320358591946570e-01 +-4.982700613199744777e-01 +-5.347174193638666306e-01 +-5.630526284753810673e-01 +-5.876433247801334803e-01 +-6.043340617137795689e-01 diff --git a/pumpingtests/data/syn_p30_0.02.txt b/pumpingtests/data/syn_p30_0.02.txt new file mode 100644 index 0000000..cd0d80b --- /dev/null +++ b/pumpingtests/data/syn_p30_0.02.txt @@ -0,0 +1,34 @@ +-9.052824742229272081e-03 +-1.087381846000991595e-03 +-8.047090261555786550e-02 +-4.648986980869800056e-02 +-7.993929084630237158e-02 +-1.384825365655469043e-01 +-1.180490567955005121e-01 +-1.453470640247159773e-01 +-1.805452027611058607e-01 +-1.902560178836633087e-01 +-1.926611913393961040e-01 +-2.460794644731121306e-01 +-2.714195408017513467e-01 +-3.146481679017283373e-01 +-2.749869741893496977e-01 +-3.112374477601553280e-01 +-3.814514667535215398e-01 +-4.350222618611732095e-01 +-4.184557754844994149e-01 +-4.868609625115816186e-01 +-4.817822799702990988e-01 +-5.042200823515674557e-01 +-5.300960526731867128e-01 +-5.777873472655193909e-01 +-6.280419701011356048e-01 +-6.708709497235985086e-01 +-6.914939276888146802e-01 +-7.217056504171938114e-01 +-7.549109891798470029e-01 +-7.760260412441044586e-01 +-8.307803054985312130e-01 +-8.308196053029990313e-01 +-8.680562104566795778e-01 +-8.829292470686931349e-01 diff --git a/pumpingtests/data/syn_p30_0.05.txt b/pumpingtests/data/syn_p30_0.05.txt new file mode 100644 index 0000000..24aa026 --- /dev/null +++ b/pumpingtests/data/syn_p30_0.05.txt @@ -0,0 +1,34 @@ +-2.756360361675422050e-03 +-3.270235154577419423e-02 +1.793996767994098657e-02 +-8.621163781532512060e-02 +-5.683201801339320819e-02 +-2.760405246870942242e-02 +-1.038483665365328157e-01 +-1.871085558816906191e-01 +-1.934056398748633665e-01 +-1.394795853919764961e-01 +-2.474499680391762935e-01 +-2.457776482678500907e-01 +-2.998497395661273157e-01 +-3.191913931181551978e-01 +-2.504419317328264727e-01 +-3.427908861680809549e-01 +-3.280792824089080462e-01 +-4.752246081673333622e-01 +-4.848464470756596589e-01 +-4.762707906552342552e-01 +-4.522448550037448944e-01 +-5.240787823891471797e-01 +-5.555282830919311410e-01 +-5.257900978923213398e-01 +-7.192811844084268103e-01 +-6.763372850417866955e-01 +-7.749289285863415477e-01 +-6.733312602941434744e-01 +-8.366325894112411898e-01 +-7.100963704934282195e-01 +-7.823579474428018488e-01 +-8.705736033190097922e-01 +-8.705804209859291376e-01 +-7.670469176543002199e-01 diff --git a/pumpingtests/data/syn_p90_0.02.txt b/pumpingtests/data/syn_p90_0.02.txt new file mode 100644 index 0000000..3074e39 --- /dev/null +++ b/pumpingtests/data/syn_p90_0.02.txt @@ -0,0 +1,34 @@ +1.046163016998891571e-03 +-4.984353643550517958e-03 +-3.956540010491400861e-03 +-2.674574172730492458e-02 +1.341477692047653766e-03 +-3.295363487328579338e-02 +1.308397634571356605e-03 +1.128452592129236542e-03 +-1.505079780464806290e-02 +-2.614862434405295158e-02 +-3.159606263346673855e-02 +-6.716133754696450298e-02 +-7.948510900071199814e-02 +-8.123266233865565622e-02 +-6.830631293726227571e-02 +-8.442072891686350222e-02 +-1.386978729079542116e-01 +-1.458322096905850240e-01 +-1.581009273210319810e-01 +-1.928560303652065988e-01 +-2.198963118551405804e-01 +-2.728533474666501313e-01 +-2.720802567991417176e-01 +-3.560916140227831095e-01 +-3.449867959121835637e-01 +-3.813129130966280700e-01 +-4.030118910222660888e-01 +-4.691537848972390790e-01 +-4.704178137740058219e-01 +-4.817875919884139213e-01 +-5.460800737569914132e-01 +-5.633077948023250681e-01 +-6.114245392982063931e-01 +-6.028621953385241428e-01 diff --git a/pumpingtests/data/syn_p90_0.05.txt b/pumpingtests/data/syn_p90_0.05.txt new file mode 100644 index 0000000..a1bbd4f --- /dev/null +++ b/pumpingtests/data/syn_p90_0.05.txt @@ -0,0 +1,34 @@ +5.527920219093176296e-02 +-4.189181816825900823e-02 +-1.043968816037973840e-01 +-4.579081813250742566e-02 +1.341413327974683850e-02 +-4.154858394055453014e-02 +5.238126580354997286e-02 +-3.392216490194586986e-02 +5.433698072498228254e-02 +-1.857184986557051595e-02 +-1.966858257512471911e-02 +-8.142640056067314280e-02 +-4.155005106778267981e-02 +-1.314439838668357563e-01 +-6.519577419607644475e-02 +-1.282594509495364465e-01 +-1.497600266872264330e-01 +-4.936333389363729840e-02 +-1.861937643141556298e-01 +-2.084437375769631062e-01 +-1.925095802919220001e-01 +-2.587797877769957844e-01 +-2.014468201358464439e-01 +-3.386987396958988095e-01 +-3.573433492338933259e-01 +-3.938390537374355516e-01 +-5.355551774753285477e-01 +-4.673273685310599390e-01 +-4.786055866468206244e-01 +-4.616568260682945568e-01 +-5.495744799163742034e-01 +-5.149637884130895404e-01 +-6.512342556480108513e-01 +-5.719518350647768701e-01 diff --git a/pumpingtests/data/texas160.txt b/pumpingtests/data/texas160.txt new file mode 100644 index 0000000..f50e793 --- /dev/null +++ b/pumpingtests/data/texas160.txt @@ -0,0 +1,26 @@ +0.0014 -0.3383 +0.0028 -0.6553 +0.0042 -0.8717 +0.0056 -1.0546 +0.0069 -1.1521 +0.0104 -1.3959 +0.0139 -1.5514 +0.0174 -1.6733 +0.0208 -1.7830 +0.0278 -1.9415 +0.0347 -2.0238 +0.0417 -2.0725 +0.0486 -2.1213 +0.0556 -2.1823 +0.0625 -2.2432 +0.0694 -2.2676 +0.0764 -2.2920 +0.0833 -2.3042 +0.1042 -2.3286 +0.1250 -2.4017 +0.1458 -2.4139 +0.1667 -2.4261 +0.1875 -2.4261 +0.2083 -2.4261 +0.2500 -2.4230 +0.2917 -2.4261 \ No newline at end of file diff --git a/pumpingtests/data/texas40.txt b/pumpingtests/data/texas40.txt new file mode 100644 index 0000000..1b8ed41 --- /dev/null +++ b/pumpingtests/data/texas40.txt @@ -0,0 +1,26 @@ +0.0014 -1.7220 +0.0028 -2.1213 +0.0042 -2.3529 +0.0056 -2.4383 +0.0069 -2.6547 +0.0104 -2.8863 +0.0139 -3.0448 +0.0174 -3.1545 +0.0208 -3.2612 +0.0278 -3.3953 +0.0347 -3.4928 +0.0417 -3.5416 +0.0486 -3.6148 +0.0556 -3.6635 +0.0625 -3.7367 +0.0694 -3.7580 +0.0764 -3.7702 +0.0833 -3.7824 +0.1042 -3.8677 +0.1250 -3.9165 +0.1458 -3.9896 +0.1667 -4.0018 +0.1875 -4.0384 +0.2083 -4.0628 +0.2500 -4.0750 +0.2917 -4.0872 diff --git a/pumpingtests/data/texas80.txt b/pumpingtests/data/texas80.txt new file mode 100644 index 0000000..ba08242 --- /dev/null +++ b/pumpingtests/data/texas80.txt @@ -0,0 +1,27 @@ +0.0014 -0.9448 +0.0028 -1.2252 +0.0042 -1.5392 +0.0056 -1.6123 +0.0069 -1.8196 +0.0104 -2.0482 +0.0139 -2.1823 +0.0174 -2.3164 +0.0208 -2.4261 +0.0278 -2.5480 +0.0347 -2.6303 +0.0417 -2.7156 +0.0486 -2.8010 +0.0556 -2.8375 +0.0625 -2.8863 +0.0694 -2.9107 +0.0764 -2.9351 +0.0833 -2.9717 +0.1042 -3.0326 +0.1250 -3.0692 +0.1458 -3.1058 +0.1667 -3.1301 +0.1875 -3.1545 +0.2083 -3.1667 +0.2500 -3.1515 +0.2917 -3.1759 + diff --git a/pumpingtests/data/venne_deep.txt b/pumpingtests/data/venne_deep.txt new file mode 100644 index 0000000..f8fdf8b --- /dev/null +++ b/pumpingtests/data/venne_deep.txt @@ -0,0 +1,30 @@ +# time (min), head (m) +1.17 -0.004 +1.34 -0.009 +1.7 -0.015 +2.5 -0.030 +4.0 -0.047 +5.0 -0.054 +6.0 -0.061 +7.5 -0.068 +9 -0.064 +14 -0.090 +18 -0.098 +21 -0.103 +26 -0.110 +31 -0.115 +41 -0.128 +51 -0.133 +65 -0.141 +85 -0.146 +115 -0.161 +175 -0.161 +260 -0.172 +300 -0.173 +370 -0.173 +430 -0.179 +485 -0.183 +665 -0.182 +1340 -0.200 +1490 -0.203 +1520 -0.204 \ No newline at end of file diff --git a/pumpingtests/data/venne_shallow.txt b/pumpingtests/data/venne_shallow.txt new file mode 100644 index 0000000..7cb29c1 --- /dev/null +++ b/pumpingtests/data/venne_shallow.txt @@ -0,0 +1,20 @@ +# time (min), head (m) +6.0 -0.005 +9 -0.006 +14 -0.008 +18 -0.010 +26 -0.011 +31 -0.014 +41 -0.018 +51 -0.022 +65 -0.026 +85 -0.028 +115 -0.033 +175 -0.044 +260 -0.050 +300 -0.055 +485 -0.061 +665 -0.071 +1340 -0.096 +1490 -0.099 +1520 -0.099 \ No newline at end of file diff --git a/pumpingtests/info.txt b/pumpingtests/info.txt new file mode 100644 index 0000000..8feb4de --- /dev/null +++ b/pumpingtests/info.txt @@ -0,0 +1 @@ +This directory contains notebooks of pumping tests used to benchmark. \ No newline at end of file diff --git a/pumpingtests/leaky1_dalem.ipynb b/pumpingtests/leaky1_dalem.ipynb new file mode 100644 index 0000000..041199b --- /dev/null +++ b/pumpingtests/leaky1_dalem.ipynb @@ -0,0 +1,2268 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Leaky Aquifer Test - Dalem Example\n", + "**This example is taken from Kruseman et al. (1970)**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model\n", + "\n", + "In many situations, we cannot ignore the leakage potential of overlying and underlying formations to aquifers, and we cannot conceptualize them as confined. TTim is capable of modelling and adjusting parameters to leaky, semi-confined aquifers.\n", + "\n", + "The current is the pumping test from Dalem (Kruseman et al., 1970), the Netherlands. The hydrogeological cross-section is composed of the following elements: an initial 8 m deep aquitard layer, followed by an aquifer from 8 m to 45 m depth. The layer underlying the aquifer is considered an aquiclude. The pumping well is placed at the aquifer, and drawdown is recorded at four different piezometers, 30, 60, 90 and 120 m away from the well. The pumping lasted 8 hours in total at a rate of 761 m3/d. There is a river 1500 m away from the well. The tide affects both river and well levels. Data has been previously corrected for the tide effect.\n", + "\n", + "In this benchmarking exercise, we will simulate two different conceptual models. The first model assumes no storage in the aquitard. That matches most analytical solutions for leaky-aquitard (Kruseman et al., 1970). In the second model, we will explore TTim's flexibility for modelling and add storage as an additional parameter to be adjusted. Finally, we compare the results of the models with other software.\n", + "\n", + "The figures below resume the conceptual models:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Conceptual Model 1\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1,1,1)\n", + "#sky\n", + "sky = plt.Rectangle((-20,0), width = 170, height = 3, fc = 'b', zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "#Aquifer:\n", + "ground = plt.Rectangle((-20,-45), width = 170, height = 37, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9)\n", + "ax.add_patch(ground)\n", + "\n", + "#Confining bed:\n", + "confining_unit = plt.Rectangle((-20,-8), width = 170, height = 8, fc = np.array([100,100,100])/255, zorder=0, alpha=0.7)\n", + "ax.add_patch(confining_unit)\n", + "\n", + "well = plt.Rectangle((-2,-45), width = 4, height = 45, fc = np.array([200,200,200])/255, zorder=1)\n", + "ax.add_patch(well)\n", + "\n", + "#Wellhead\n", + "wellhead = plt.Rectangle((-2.5,0),width = 5, height = 1.5, fc = np.array([200,200,200])/255, zorder=2, ec='k')\n", + "ax.add_patch(wellhead)\n", + "\n", + "#Screen for the well:\n", + "screen = plt.Rectangle((-2,-45), width = 4, height = 37, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x = 2.5,y = 0.75, dx = 5, dy = 0, color = \"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x = 7.5, y = 0.75, s = r'$ Q = 761 \\frac{m^3}{d}$', fontsize = 'large' )\n", + "#Piezometers\n", + "piez1 =plt.Rectangle((29,-45), width = 2, height = 45, fc = np.array([200,200,200])/255, zorder=1)\n", + "screen_piez_1 = plt.Rectangle((29,-45), width = 2, height = 37, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_1.set_linewidth(2)\n", + "\n", + "piez2 =plt.Rectangle((59,-45), width = 2, height = 45, fc = np.array([200,200,200])/255, zorder=1)\n", + "screen_piez_2 = plt.Rectangle((59,-45), width = 2, height = 37, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_2.set_linewidth(2)\n", + "\n", + "piez3 =plt.Rectangle((89,-45), width = 2, height = 45, fc = np.array([200,200,200])/255, zorder=1)\n", + "screen_piez_3 = plt.Rectangle((89,-45), width = 2, height = 37, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_3.set_linewidth(2)\n", + "\n", + "piez4 =plt.Rectangle((119,-45), width = 2, height = 45, fc = np.array([200,200,200])/255, zorder=1)\n", + "screen_piez_4 = plt.Rectangle((119,-45), width = 2, height = 37, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_4.set_linewidth(2)\n", + "\n", + "ax.add_patch(piez1)\n", + "ax.add_patch(screen_piez_1)\n", + "ax.add_patch(piez2)\n", + "ax.add_patch(screen_piez_2)\n", + "ax.add_patch(piez3)\n", + "ax.add_patch(screen_piez_3)\n", + "ax.add_patch(piez4)\n", + "ax.add_patch(screen_piez_4)\n", + "\n", + "#last line\n", + "line = plt.Line2D(xdata= [-200,1200], ydata = [0,0], color = \"k\")\n", + "ax.add_line(line)\n", + "\n", + "ax.text(x = 29, y = 0.75, s = 'P30', fontsize = 'large' )\n", + "ax.text(x = 59, y = 0.75, s = 'P60', fontsize = 'large' )\n", + "ax.text(x = 89, y = 0.75, s = 'P90', fontsize = 'large' )\n", + "ax.text(x = 119, y = 0.75, s = 'P120', fontsize = 'large' )\n", + "\n", + "\n", + "ax.set_xlim([-20,150])\n", + "ax.set_ylim([-45,3])\n", + "ax.set_xlabel('Distance [m]')\n", + "ax.set_ylabel('Relative height [m]')\n", + "ax.set_title('Conceptual Model - Dalem Example');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Load Required Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from ttim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters for the model." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "H = 37 #aquifer thickness [m]\n", + "zt = - 8 #top boundary of aquifer\n", + "zb = zt - H #bottom boundary of the aquifer\n", + "Q = 761 #constant pumping rate [m^3/d]\n", + "t = 0.34 #total pumping time [d]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Create conceptual model\n", + "\n", + "Until so far, we have only considered impermeable upper boundaries in our model. This assumption, however, is not sufficient in many situations where there is enough leakage from above to influence flow results. TTim can simulate such semi-confined conditions in ```ModelMaq``` setup with the parameter: ```topboundary = 'semi'```.\n", + "\n", + "When we do this, ModelMaq assumes a leaky layer is on top of the uppermost aquifer. A leaky layer in TTim only has vertical flow and is characterized by the parameters resistance to vertical flow (```c```) and storage (```Sll```). The specific flux is computed as (Bakker, 2013):\n", + "\n", + "$$q_n = \\frac{h_n-h_{n-1}}{c_n}$$\n", + "\n", + "where $q_n$ is the vertical flux from layer $n$ to layer $n-1$, $h_n$ is the head in layer $n$ and $c_n$ is the vertical resistance to flow. $c_n$ is computed as: $H_n/k_n$ where, $H_n$ is the leaky-layer thickness and $k_n$ the vertical hydraulic conductance. $c_n$ is the inverse of the parameter Leakance ($L_n = 1/c_n$), that is used in MODFLOW (Harbaugh, 2005) or analytical solutions of leaky-layers, such as in Hantush (1955).\n", + "\n", + "Specifying ```topboundary = 'semi'``` means that we also have to set the parameters for the aquitard overlying the aquifer formation. Thus, even though we have only one aquifer, we have to set an additional element to the ```z``` array, which is the top of the aquitard formation:\n", + "* ```z = [0,zt,zb]```: 0 is the depth of the aquitard overlying the aquifer, zt and zb are the top and bottom of the aquifer\n", + "\n", + "In this first example, we also have to set the resistance of the aquitard:\n", + "* ```c = 500```: We will calibrate this value later.\n", + "\n", + "For now, we are ignoring the storage of this leaky layer. In this case, TTim will consider the head remains fixed above the leaky layer.\n", + "\n", + "\n", + "More explanations over how TTim sets up the ModelMaq model can be seen in the notebooks:\n", + "- [Confined 1 - Oude Korendijk](confined1_oude_korendijk)\n", + "- [Confined 4 - Schroth](confined4_schroth.ipynb)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#unkonwn parameters: kaq, Saq, c\n", + "ml = ModelMaq(kaq=10, z=[0, zt, zb], c=500, Saq=0.001, topboundary='semi', \\\n", + " tmin=0.001, tmax=0.5)\n", + "w = Well(ml, xw=0, yw=0, tsandQ=[(0, Q), (0.34, 0)])\n", + "ml.solve(silent = 'True')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Load data of four observation wells.\n", + "\n", + "The data for each observation well is organized in text files where the first column is the time data in days and the second is the drawdown in meters, corrected for the tide effect. Here we are also declaring the distance from the pumping well:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#data of observation well 30 m away from pumping well\n", + "data1 = np.loadtxt('data/dalem_p30.txt', skiprows = 1)\n", + "t1 = data1[:, 0]\n", + "h1 = data1[:, 1]\n", + "r1 = 30\n", + "#data of observation well 60 m away from pumping well\n", + "data2 = np.loadtxt('data/dalem_p60.txt', skiprows = 1)\n", + "t2 = data2[:, 0]\n", + "h2 = data2[:, 1]\n", + "r2 = 60\n", + "#data of observation well 90 m away from pumping well\n", + "data3 = np.loadtxt('data/dalem_p90.txt', skiprows = 1)\n", + "t3 = data3[:, 0]\n", + "h3 = data3[:, 1]\n", + "r3 = 90\n", + "#data of observation well 120 m away from pumping well\n", + "data4 = np.loadtxt('data/dalem_p120.txt', skiprows = 1)\n", + "t4 = data4[:, 0]\n", + "h4 = data4[:, 1]\n", + "r4 = 120" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Model Calibration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this step, we proceed with the model calibration in TTim's framework. This procedure has been extensively described in the following notebook:\n", + "* [Confined 1 - Oude Korendijk](confined1_oude_korendijk)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.1. Calibration with three datasets (excluding one piezometer at a time)\n", + "\n", + "We begin investigating the model calibration if we exclude one piezometer at a time. Hence, we look into the influence of each piezometer on parameter calibration." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.1.1. Calibration without obs1" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 49\n", + " # data points = 37\n", + " # variables = 3\n", + " chi-square = 3.8607e-04\n", + " reduced chi-square = 1.1355e-05\n", + " Akaike info crit = -418.405079\n", + " Bayesian info crit = -413.572325\n", + "[[Variables]]\n", + " kaq0: 57.5581602 +/- 1.53705301 (2.67%) (init = 10)\n", + " Saq0: 3.2824e-05 +/- 2.2610e-06 (6.89%) (init = 0.0001)\n", + " c0: 999468.865 +/- 2.8952e+08 (28967.53%) (init = 1000)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.895\n", + " C(kaq0, c0) = -0.650\n", + " C(Saq0, c0) = 0.325\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq057.558161.537053e+002.670435110010[57.55816021034674]
Saq00.0000332.261039e-066.8883460.000010.0010.0001[3.2824121238720075e-05]
c0999468.8648482.895215e+0828967.5314171001000000.01000[999468.8648475128]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 57.55816 1.537053e+00 2.670435 1 100 10 \n", + "Saq0 0.000033 2.261039e-06 6.888346 0.00001 0.001 0.0001 \n", + "c0 999468.864848 2.895215e+08 28967.531417 100 1000000.0 1000 \n", + "\n", + " parray \n", + "kaq0 [57.55816021034674] \n", + "Saq0 [3.2824121238720075e-05] \n", + "c0 [999468.8648475128] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ca1 = Calibrate(ml)\n", + "ca1.set_parameter(name='kaq0', initial=10, pmin=1, pmax=100)\n", + "ca1.set_parameter(name='Saq0', initial=1e-4, pmin=1e-5, pmax=1e-3)\n", + "ca1.set_parameter(name='c0', initial=1000, pmin=100, pmax=1e6)\n", + "ca1.series(name='obs2', x=r2, y=0, layer=0, t=t2, h=h2)\n", + "ca1.series(name='obs3', x=r3, y=0, layer=0, t=t3, h=h3)\n", + "ca1.series(name='obs4', x=r4, y=0, layer=0, t=t4, h=h4)\n", + "ca1.fit()\n", + "display(ca1.parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.0032302240706974304\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print('rmse:', ca1.rmse())\n", + "plt.figure(figsize=(8, 5))\n", + "ha1 = ml.head(r1, 0, t1)\n", + "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", + "plt.semilogx(t1, ha1[0], label='ttim at 30 m')\n", + "ha2 = ml.head(r2, 0, t2)\n", + "plt.semilogx(t2, h2, '.', label='obs at 60 m')\n", + "plt.semilogx(t2, ha2[0], label='ttim at 60 m')\n", + "ha3 = ml.head(r3, 0, t3)\n", + "plt.semilogx(t3, h3, '.', label='obs at 90 m')\n", + "plt.semilogx(t3, ha3[0], label='ttim at 90 m')\n", + "ha4 = ml.head(r4, 0, t4)\n", + "plt.semilogx(t4, h4, '.', label='obs at 120 m')\n", + "plt.semilogx(t4, ha4[0], label='ttim at 120 m')\n", + "plt.xlabel('time (d)')\n", + "plt.ylabel('drawdown (m)')\n", + "plt.title('ttim fit except for data of obs1')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.1.2. Calibration without obs2" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".........................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 54\n", + " # data points = 38\n", + " # variables = 3\n", + " chi-square = 2.6352e-04\n", + " reduced chi-square = 7.5293e-06\n", + " Akaike info crit = -445.400171\n", + " Bayesian info crit = -440.487412\n", + "[[Variables]]\n", + " kaq0: 45.0264920 +/- 0.52739715 (1.17%) (init = 10)\n", + " Saq0: 4.4092e-05 +/- 1.4055e-06 (3.19%) (init = 0.0001)\n", + " c0: 349.139013 +/- 26.4963699 (7.59%) (init = 1000)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.714\n", + " C(kaq0, c0) = 0.711\n", + " C(Saq0, c0) = -0.155\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq045.0264920.5273971.171304110010[45.0264919508138]
Saq00.0000440.0000013.1875540.000010.0010.0001[4.409195656319494e-05]
c0349.13901326.4963707.589061001000000.01000[349.1390127093816]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 45.026492 0.527397 1.171304 1 100 10 \n", + "Saq0 0.000044 0.000001 3.187554 0.00001 0.001 0.0001 \n", + "c0 349.139013 26.496370 7.58906 100 1000000.0 1000 \n", + "\n", + " parray \n", + "kaq0 [45.0264919508138] \n", + "Saq0 [4.409195656319494e-05] \n", + "c0 [349.1390127093816] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ca2 = Calibrate(ml)\n", + "ca2.set_parameter(name='kaq0', initial=10, pmin=1, pmax=100)\n", + "ca2.set_parameter(name='Saq0', initial=1e-4, pmin=1e-5, pmax=1e-3)\n", + "ca2.set_parameter(name='c0', initial=1000, pmin=100, pmax=1e6)\n", + "ca2.series(name='obs1', x=r1, y=0, layer=0, t=t1, h=h1)\n", + "ca2.series(name='obs3', x=r3, y=0, layer=0, t=t3, h=h3)\n", + "ca2.series(name='obs4', x=r4, y=0, layer=0, t=t4, h=h4)\n", + "ca2.fit()\n", + "display(ca2.parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.0026334093716488772\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print('rmse:', ca2.rmse())\n", + "plt.figure(figsize=(8, 5))\n", + "hb1 = ml.head(r1, 0, t1)\n", + "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", + "plt.semilogx(t1, hb1[0], label='ttim at 30 m')\n", + "hb2 = ml.head(r2, 0, t2)\n", + "plt.semilogx(t2, h2, '.', label='obs at 60 m')\n", + "plt.semilogx(t2, hb2[0], label='ttim at 60 m')\n", + "hb3 = ml.head(r3, 0, t3)\n", + "plt.semilogx(t3, h3, '.', label='obs at 90 m')\n", + "plt.semilogx(t3, hb3[0], label='ttim at 90 m')\n", + "hb4 = ml.head(r4, 0, t4)\n", + "plt.semilogx(t4, h4, '.', label='obs at 120 m')\n", + "plt.semilogx(t4, hb4[0], label='ttim at 120 m')\n", + "plt.xlabel('time(d)')\n", + "plt.ylabel('drawdown(m)')\n", + "plt.title('ttim fit except for data of obs2')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.1.3. Calibration without obs3" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 51\n", + " # data points = 39\n", + " # variables = 3\n", + " chi-square = 0.00176424\n", + " reduced chi-square = 4.9007e-05\n", + " Akaike info crit = -384.140220\n", + " Bayesian info crit = -379.149535\n", + "[[Variables]]\n", + " kaq0: 45.2048754 +/- 1.46499595 (3.24%) (init = 10)\n", + " Saq0: 4.7843e-05 +/- 4.1024e-06 (8.57%) (init = 0.0001)\n", + " c0: 318.726085 +/- 67.0025787 (21.02%) (init = 1000)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.765\n", + " C(kaq0, c0) = 0.763\n", + " C(Saq0, c0) = -0.289\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq045.2048751.4649963.240792110010[45.20487543361927]
Saq00.0000480.0000048.5748380.000010.0010.0001[4.7842575268505704e-05]
c0318.72608567.00257921.0219941001000000.01000[318.7260846231769]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 45.204875 1.464996 3.240792 1 100 10 \n", + "Saq0 0.000048 0.000004 8.574838 0.00001 0.001 0.0001 \n", + "c0 318.726085 67.002579 21.021994 100 1000000.0 1000 \n", + "\n", + " parray \n", + "kaq0 [45.20487543361927] \n", + "Saq0 [4.7842575268505704e-05] \n", + "c0 [318.7260846231769] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ca3 = Calibrate(ml)\n", + "ca3.set_parameter(name='kaq0', initial=10, pmin=1, pmax=100)\n", + "ca3.set_parameter(name='Saq0', initial=1e-4, pmin=1e-5, pmax=1e-3)\n", + "ca3.set_parameter(name='c0', initial=1000, pmin=100, pmax=1e6)\n", + "ca3.series(name='obs1', x=r1, y=0, layer=0, t=t1, h=h1)\n", + "ca3.series(name='obs3', x=r2, y=0, layer=0, t=t2, h=h2)\n", + "ca3.series(name='obs4', x=r4, y=0, layer=0, t=t4, h=h4)\n", + "ca3.fit()\n", + "display(ca3.parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.006725845157213259\n" + ] + }, + { + "data": { + "image/png": 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KYcOGER8fT3x8PAEBATZ6J0IIYV/SY4D0GPQXPbWx0tmzZ609CSUlJQAMGTKEuLg44uPjGTx4cLe9thBC2IoMJXRAgoG4UuXl5daJi0eOHAGMrZtbQoLUbxBC9FYSDDogwUDYQlVVlbUnoaioCK01fn5+1jkJgYGBEhKEEL2GBIMOSDAQl6MzQxY1NTUcOHCAnJwcDh8+jNlsxtfX19qTIEWehBD2JsGgAxIMuuZEQQUlB8sIjPZjqA2rKfZGV7Issra21lq/IT8/n+bmZry8vKwhITQ0VEKCEKLHyaoE0S1OFFTw6QuraKhcg7NbLHMf/h5hiSH2bla3aSioQJvMoEGbzEbPwSWCgYeHB0lJSSQlJVmLPO3fv5+dO3eyfft2PDw8rPskhIWFSSVIIYRdSTAQXVJysIzmphq0uY6G6rV8+vzXhIwcTUzaZCLHp+Hh0796ELq6LNLNzY3Ro0czevRoa5GnnJwcdu/eTVZWFm5ubtZy0eHh4VIJUgjR42QoARlK6IoTBRV8/vJOTKZmFGeJSKyg5MA2yo4fQzk4EJqQZISEcWm4eXnZu7k20R3LIpuamsjPz7cWeWpoaMDV1ZXo6Gji4+OJjIyUSpBCCJuSOQYdkGDQNRfOMdBac6qwgNzN6zm4eT0Vp07i4OjEiMRkYtImE5GSiquH1B9oj8lk4vDhw+Tk5HDgwAHq6upwdnYmKiqK+Ph4qQQphLAJCQYdkGDQfbTWnMzP48Dm9RzcvIGq0tM4OjszInEsMRMnEzF2PC5uUsmwPc3NzRQWFlpDQk1NzXmVIGNiYnBzc7N3M4UQfZAEgw5IMOgZ2mzm+KFccjet5+CWDVSXncXJ2YWwMSnEpF1D+JgUnF3lQ649LZUgW7ZmrqqqwsHBgYiICGslSA8PD3s3UwjRR/SqYKCU8gc+AkYAhcBtWuuyNs6bDzxtuftbrfU7SilvYH2r04KAf2qtH1FKLQBeAEosj72itX7jUu2RYNDztNlMSW6OMdywZSO1FeU4u7oRPnY8MWmTCEtKwcnFxd7N7LXMZjMlJSXk5OSQk5NDRUUFSilGjBhBfHw8sbGxeHt727uZQoherLcFgz8AZ7XWi5VSiwA/rfUvLjjHH8gEUjBq5mUBYy8MEEqpLOBRrfV3lmCQorV+6HLaI8HAvszmZopz9pK7eT15WzdRV1WJi7s7ESkTiEmbTOjoZJxk4l276gsrOLq7gALTcXKLD3H27FkAQkJCrLsu+vr2r9UhQoiu623BIBeYqrU+rpQaBqzTWsdccM6dlnN+bLn//1nO+1erc6KBtUCI1lpLMOj7zM3NHNm7i9zN6zm0bTP1NdW4engSOS6NmImTCRmViKMs4bO6cMOlgPtGUeFebx1uOHXqFACBgYHWDZX8/f3t3GohRG/Q24JBudZ6gOVnBZS13G91zmOAm9b6t5b7zwB1WusXW53zLOCjtX7Mcn8B8DxwGjiI0ZNwtJ023A/cDxASEjK2qKjIlm9R2ECzqYmiPdnkblrPoe1baKyrxc3Lm6jxacSkXUPwyAQcrvLNgCq/OUrlqkKjT02Bz4wR+EwLtj5+5swZa/2G48ePAzB06FBrSBg0aJB9Gi6EsLseDwZKqTXA0DYe+iXwTusgoJQq01r7XfD8zgSDHOAHWussy/0AoFpr3aCU+jFwu9b62ku1VXoMej9TUxOFu3aQu+k78rO20VRfh7uPL0FxKXj4jSR+8jiGR/pd+kL9zOVs0VxWVmYNCcXFxYBRCTI+Pp74+HiGDBkiRZ6EuIr0th6DLg8lKKUSgf9oraPbeQ1HjHkMlxxclWDQtzQ1NlC4M4vs1Ws5sicLMKEcPImeMInkWdMZHhWDuopqD1zJhkuVlZXWctFSCVKIq1NvCwYvAKWtJh/6a62fuOAcf4wJh2Msh3ZgTD48a3l8MdCgtf5Vq+cM01oft/x8M/ALrfWES7VHgkHflLWikC1LcmluLKC5KRfdXIhuNuEdMIjotEnEpk1mSESUfMBdQnV1NQcOHGD//v3WSpA+Pj7W4Ybg4GAp8iREP9TbgkEA8G8gBCjCWK54VimVAvxEa73Qct69wFOWp/1Oa/1Wq2sUAHO01gdaHXsemAeYgLPAA60fb48Eg76pZTvm5mYzjo4OzHkghqrSA+Ru+o7CXTsxN5vwHTKUmAmTiJl4DYNCwyQkXEJdXR25ubkXVYJsqd8QGhoqRZ6E6Cd6VTDobSQY9F3tlXyur67m0PbN5G5eT9GebLTZjN+wQGImTiYmbTIDg0Pt2Oq+oaGhwVoJMi8vj6amJtzd3a0hISwsTIo8CdGHSTDogASD/q22soK8rZvI3bye4py9aG0mICjEEhKuwX94oL2b2Os1NjZy6NAh9u/fT25uLo2Njbi6uhITE0NcXJwUeRKiD5Jg0IG+HAyyT2WTeTKTlCEpJA1Osndzer2a8jIObtlA7ub1lBzIAWDQiHBi0iYTO3EyvoPbWkgjWjOZTBQUFFgrQUqRJyH6JgkGHeirwSD7VDY/WvUjGpsbcXF04fUZr0s4uAxVpWeMkLBpPccP5QIwNCKKmLTJRKdNxmegrPG/lEsVeYqOjsbdXYpkCdEbSTDoQF8NBm/seYO/7vgrZsw4KkceSn6IhQkL7d2sPqni1ElyN68nd/N6Th3OB2B4TLwREiak4+UnuwVeSkuRp5ZlkJWVlTg4OBAeHm6tBOnpKeW2hegtJBh0oK8Gg5YegyZzE84OztJjYCNlx0vI3WwMN5w5UghKERw3iqFRKTi7RxOWGHLeREdxMbPZzLFjx6xbM5eVlaGUIiQkxFoJcsCAAfZuphBXNQkGHeirwQBkjkF3Ky0+Su7m79j37bdUnj4GKBydQ0i54XrGzr0Ody+pYHgpWmtOnjxp7Uloqd8wfPhwYmNjiYuLk62ZhbADCQYd6MvBQPSMzK8Os+XTbZgaczE35qLNFTg4OhKakETMxGuIHDcBVw/pJu+M0tJSa0goKTEqpA8cOJC4uDji4uIYNmyY7DchRA+QYNABCQbiUlpvpuTgoJh8ux9ninZwcMsGKk+fwtHJiRFJY4lJm0zE2PG4uHvYu8l9QmVlpXXXxcLCQrTW+Pr6WnsSQkJCZNdFIbqJBIMOSDAQndHWZkpaa47n5ZK7eT0Ht2yg+mwpTs4uhI1JISZtMuHJ43B2c7Nzy+2rs7Ucamtryc3NZf/+/dZdFz08PKwhQTZUEsK2JBh0QIKBsAVtNlNycD+5m4yQUFtRjpOrKxFjxhMzcTJhSSk4ubjYu5k96nKqP573vIYG64ZKBw8epLGxERcXF6Kjo60bKsleCUJ0jQSDDkgwELZmNjdTnLOP3M3fkbd1E3VVlbi4uxORMoGYtMmMSEzG0an/7xRY+c1RKlcVggYU+MwYgc+04Mu6RsuGSgcOHODAgQPU1tbi6OhIZGQksbGxxMTE4OEhQzdCXC4JBh3oy8Gg+ttv8Rg/HgfZRKbXMjc3c2TvLnI3b+DQtk3U11Tj6ulJ5Lg0YtImEzIqEcd+2kV+pT0G7WlrrwSlFCNGjLAug/TxufLrC3E1kWDQgb4aDBry8ymYm4GDhwfe11+Pzw034DkhFdUDHzKyTPLKNJuaKNqTTe6m9RzavoXGulrcvH2IGm+EhOD4BBz6WfXCzs4xuFxaa44fP24NCWfOnAEgMDDQusIhICDAZq8nRH8jwaADfTUY6OZmardvp2LpUqpWrsJcVYXjwIH4zJ6N7w0ZuCUkdMuyL9mK2TZMjY0U7tpB7ub15GdupamhHg/fAUSlphObNpnA2HiUzMjvtNOnT1tDwvHjxwEYPHiwtSdh6NChsgxSiFYkGHSgrwaD1swNDVR/+y2VS5dRvW4durER59AQfOdm4HNDBq5hYTZ7LdmK2faaGuo5vDOT3M0bKNixHVNjA55+/kRPSCcm7RqGR8VISLgM5eXl1mWQR44cQWvNgAEDrD0JQUFBsgxSXPUkGHSgPwSD1porK6lavZqKpUup3bIVtMZt5Eh8bsjAZ84cnAcP7tL1ZSvm7tVYX0dB1jZyN6+nYGcWZlMTHr7+xE2aTPSESQyLlJBwOaqrq8nNzeXAgQPk5+djNpvx9PQkNjaW2NhYWQYprloSDDrQ34JBa00nT1G5fDmVS5dSv28fODjgkToe34wb8J5xPY7eV7alr8wx6H4nCipY8sctNNYdwtx0EN1chLnZhHfAIEtPwmSGRkZL9/hlqK+vJy8vj31Ze8g/UkCT2YSLiwsRERHExsYSFRUlKxzEVUOCQQf6czBoraGggMqlS6lYuoymI0dQLi54TZ2Kzw0ZeE2ZgsNVtsa+t8taUcjWzwvQGpQDjJ01DO8Bx8ndvJ7CXTuNkDBwENETJhGTNomhERISOqNlpUSTqYnjTuWcijeTV1JAdXW1tdBTyzJIf3+pqin6LwkGHbhagkELrTX1u3dT8eVSKr/6iubSUhy8vfGeOQPfjBvwGD9Ouqp7gdbbMDs6OnDjo8nWHRfra6rJz9zKwS0brCHBZ9BgIyRMmMSQiCgJCe1oa28FrymBHDt2jNzcXHJzc62FngYNGmQNCcOHD5d5CaJfkWDQgastGLSmTSZqNm+hcumXVK1eg7m2FqchQ/CZMwffGzJwjYvrdR8wV9MwRlvbMF+ovrqa/Kyt5G5eT9HunZibm/EZNISYtElET5jEkPDIXvc3tKfO7K1w9uxZa0goKipCa42XlxcxMTHExMQQFhaGs3P/36BK9G8SDDpwNQeD1sx1dVR/8w0VXy6lev16MJlwiYjAN2MuPhkZuARf3o513UGWSnasrrqK/O1byN2ygSN7sjE3N+M7eAjRaZOJmTCJwWEREhK4vL0VamtrycvLIzc3l0OHDtHY2IizszORkZHExMQQFRWFp6dU1hR9jwSDDkgwuJiprIyqlauoWPoldZlZALgnJeGTkYHP7Fk42WnjGFkq2Xl11VUc2r6Zg5s3cGTvLszNzQwYMozoCelEp01m8IhwCQmXyWQycfjwYWtvQlVVlXVeQktvgmyqJPoKCQYdkGDQsaZjx6hYtozKpctoyM0FR0c8J07E94YMvK+7Doce/LYkSyWvTF1VJYe2byF383qO7N2FNpsZMHQYMWnGEshBoWESEi6T1vq8eQknT54EjHkJLSEhMDBQ5iWIXkuCQQckGHRe/cGDVH65lIplSzEdO45yc8P72muNlQ2TJqF6YNz1appj0B1qKys4tH0LB7dssIYEv2HDiZ4wmZi0SQwMGSEh4QqUlZVZQ0JhYaF1XkJ0dDQxMTGEh4fLvATRq0gw6IAEg8unzWbqdu6k4ssvqfpqBc0VFTgOGID3rJn43nAD7snJsrKhD6itrODQts3kbl7P0X170NqM37BAY+Ji2mQGBodKSLgCdXV11nkJeXl51nkJERERxMTEEB0dLfMShN1JMOiABIOu0Y2NVG/cSOWXS6n6+mt0fT3Ow4fjM3cuPjdk4BYdbe8mik6orSgnb9tmDm5Zz9F9e9HajP/wIKLTjCWQARISrojJZKKwsNDam9BSETI4OJiYmBhiY2NlXoKwi14XDJRS/sBHwAigELhNa13WxnkrgAnABq11RqvjYcCHQACQBfxAa92olHIF3gXGAqXA7Vrrwo7aIsHAdpqra6j+ei0VXy6lZtMmaG7GNSYGn4y5+M6di/Pw4fZuouiEmvIyDm3fTO7mDRTnWEJCYLB1CeTA4FB7N7FPaqkI2bJFc8u8hIEDB1pDgsxLED2lNwaDPwBntdaLlVKLAD+t9S/aOO86wAP48QXB4N/Ap1rrD5VSfwN2aa3/Tyn138BorfVPlFJ3ADdrrW/vqC0SDLqHqbSUyq9WUPnll9Tt2gWAR0oKPjfcgM/MGTgOGGDfBopOqSkvI2/rJnK3rKd4/z7QmoCgEMuOi5MJCGp/GWtn9mG4mpWVlXHw4EEOHDhAUVGRtY5DdHQ0sbGxMi9BdKveGAxygala6+NKqWHAOq11TDvnTgUeawkGyujPPA0M1VqblFJpwHNa65lKqZWWnzcrpZyAE8Ag3cEblWDQ/RqPHjW2Y/5yKY0FBeDsjNfkyfhmzMVr2jQc3N3t3UTRCdVlZ8nbtomDmzdQfMAICQODQ4meMInotEkEBJ4LCdadG01mHJ3O37lRXKyuro5Dhw5x4MABDh06RENDA05OTtZ5CVFRUXhfYW0TIdrSG4NBudZ6gOVnBZS13G/j3KmcHwwGAlu01pGW+8HAV1rrUUqpvcAsrXWx5bF8IFVrfeaCa94P3A8QEhIytqioyObvUVxMa03D/v3GdszLlmE6dQoHDw+8r78en4wMPNMmoHpRpTtZAdG+6rKz5G3dSO7mDZTk5hghIWQEMZaQcHi36bxaD6nzwhk7a4S9m90nmEwmioqKOHDggHVeAsCwYcOIiooiKipKhhxEl9klGCil1gBD23jol8A7rYOAUqpMa+3XznWmYuNg0Jr0GNiHbm6mdnsmFUu/pGrlKsxVVTgOHIjP7Nn4ZszFbfRou052k10WO6/q7Bnytm7i4JYNlBzIAWDA0BBqq0JQzlE4uwRIj8EV0lpz8uRJ8vLyyMvL4+jRo2itcXd3JzIykqioKCIjI6UqpLhs7QWDbv1qprWe3kGDTiqlhrUaSjh1GZcuBQYopZy01iYgCCixPFYCBAPFlqEEX8v5opdRjo54TkjFc0Iq5meeofq776j8cinlH31E2Xvv4Rwagu/cDHwyMnAND+vx9mWezKSxuREzZprMTWSezJRg0A5v/4GMmT2PMbPnUVV6xtqTUH5iA7ABtyFBFGSV4OSULqsbLpNSiqFDhzJ06FAmT55MbW0tBQUFHDx4kEOHDrFnzx6UUgQGBhIVFUV0dDRDhw6V37G4YvYcSngBKG01+dBfa/1EO+dOpVWPgeXYf4BPWk0+3K21fk0p9SCQ0Gry4S1a69s6aov0GPQuzVVVVK1aTcXSL6ndshW0xm3kSHxuyMBn9hychwzukXbILotdV1V6xpiTsGWjdbjBb1gg0RPSiRo/UWo3dJHZbObYsWPW3oRjx44B4OXlZR1yCA8Px83Nzc4tFb1Rb5xjEAD8GwgBijCWK55VSqUAP9FaL7Sctx6IBbwwvvnfp7VeqZQKx1iu6A/sBO7WWjcopdyA94Bk4Cxwh9a6oKO2SDDovZpOnqLyq+VUfrmU+n37QCk8JqTim5GB94wZOHbzZCyZY2A7LUsgD27ZyNGcPWizGd/BQ4hKTSc6NZ2hkdESErqourqaQ4cOkZeXZ53A6ODgQEhIiDUoDBo0SH7PAuiFwaA3kWDQNzQUFFC5dBkVS5fSdOQIysUFrylT8MnIwGvqFBxcXe3dRNFJtZUV5GduJW/rRor27MLcbMIrYCDR4ycSlTqR4TFxODg42ruZvcLlVIJsrbm5meLiYg4ePEheXh6nThmjtQMGDLCGhBEjRuDi4tJdTRe9nASDDkgw6Fu01tTv2UPF0qVULv+K5jNncPDywnvGDHwz5uKRmopylA+VvqK+ppqCrG0c3LqRwl07aG5qwnOAH5Hj0ohKnUhwfAIOV+nfs6GokjNv7EGbzCgnBwYuTLiscNBaRUWFdcihoKCApqYmHB0dCQsLswYFf39/G78D0ZtJMOiABIO+S5tM1GzdSuXSZVStWoW5pgbHQZaVDTfcgNuoUdJt2oc01tVSsDOTvC0bKcjOxNTQgJu3D5EpE4iekE7IqNE4Ol09G/5UfnOUylWFoAEFPjNG4DOt/Q2lOqtlOWRLUCgtNeZnBwQEWENCaGgoTr1o6bCwPQkGHZBg0D+Y6+upXvctlcuWUr3uW3RTk91XNogr19RQT2H2Dg5u3UjBjm001tXh6ulJxNhUolLTGTE6Gad+3g1uyx6DjpSWllpDQmFhIc3Nzbi4uBAeHm5dDunrK0tN+xsJBh2QYND/NFdWUrV6NRVLl56/siEjA585s3EeMsTeTRSXwdTYSNGebPK2biI/cwv1NdU4u7kTPmYc0RPSCUsci3M/nXl/pXMMrlRjYyOHDx+2BoWKigoAhgwZYu1NCAoKwvEqHd7pTyQYdECCQf9mXdmwdBn1e/caKxvGj8cnYy4+M2bgKN+E+pRmk4mj+3ZzcOtGDm3fQl1lBU4uroQljyUqNZ3w5HG4ymY/NqG15vTp09aQcOTIEcxmM25ubkRERBAVFUVERIRs1dxHSTDogASDq0fD4cNULl1G5dKlNBYVoZyd8ZxyDb4ZGXhNnYpDL/zWKUsm22dubqZ4/z7ytm0kb9tmasrO4ujkRGjiGKJT04kYm4qbl9d5z5HCTleuvr6e/Px8a1CoqakBYPDgwYSHhxMREUFoaKisdOgjJBh0QILB1UdrTf3efVQuXUrl8uWYTp/GwdPzXM2GCam9omaDbMvcedps5tjBA+Rt28jBLZuoKj2Ng6MjIaMSiUpNJ3LcBCrPIIWdbMRsNnPy5Eny8/PJz8/nyJEjNDc34+joSHBwMBEREURERDB06FCp6dBLSTDogASDq5tubqZ22zYqli6latVqo2ZDQMC5mg2JiXZb2fDGnjf4646/YsaMo3LkoeSHWJiw0C5t6Uu01pzMz+Pg1o3kbd1E+cnjKOWA79BIaiqCcHCOxMHJSwo72VBjYyNHjhwhPz+fgoICTp48CYC7u7u1NyE8PJwBUm6915Bg0AEJBqKFuaHBWrOhet06dGMjzsHB+Mydg+/cubhGRfVoe2Rb5q7TWnO66DB5WzeSs349laeNbYMdnIYzevo1JM+chv/wQDu3sv+pqqqioKCAgoIC8vPzqa6uBowlkS0hYcSIEbJdsx1JMOiABAPRlpaaDZXLllGzZQuYzbjGxOAzdy4+c+bgEtQzHyYyx8C2cjfvY++366k8tY+zJYcB8A8MJnLcBCLHTWBoeBRKur5tqmUSY8uwQ1FREU1NTTg4OBAUFGTtURg+fLisduhBEgw6IMFAXIrp9GkqV6ykctky6rKzAXBPTjZCwuxZOAUE2LeB4opUnjlNfuYWDmVupThnD+bmZrz8/IlIMUJC8MiEq2pDpZ5iMpk4evSoNSgcP34cAFdXV8LCwqzzE2Qnxu4lwaADEgzE5WgsLqZy2XIqly2j4eBBcHTEc8IEfDIy8L5+Oo4XzIIXfUN9dTWHd27n0PYtHM7OoqmhHhd3D8KSU4gcN4GwpBRZBtmKLfdXqKmp4fDhw9b5CS17JwwYMMA67BAWFoaH/P5tSoJBByQYiCtVf/CgERKWLqWppOT8wk5TrumVyx/FpZkaGzmydxeHtm/mUOZW6iorcHB0IiQhkciUCUSkpOLld/V+m+3OHRm11pSWllpDwuHDh2lsbEQpxfDhw60hITg4GGdn6c3pCgkGHZBgILpKa039rl1ULFtO5VeWwk4tyx/nzsUzbUKvWP4oLp/Z3Mzxg7kcytzCoW2bKT9pdHsPi4ohclwakeMm4D88yM6t7FndVcOhLc3NzZSUlFiHHUpKStBaW5dFhoWFERYWxvDhw6W2w2WSYNABCQbClrTJZFn+uIyq1Zblj/7++MyahU9GBu7JSVLYqY/SWlNafIRD27dwaPsWThbkAeA/PMgyeTGNoRH9f/JiT9VwaEt9fT1FRUUUFhZy+PBhTpw4AYCzszOhoaGMGDGCsLAwhg0bJvsnXIIEgw5IMBDdxdzYSM1331GxdBnV33yDbmjAefhwfObOMQo7RUdLSOjDKs+cJj9rK4e2b7FOXvT08ycyJZXIlAkE9+NqkD1dw6E9tbW11pBQWFjI6dOnAWMiY0tICAsLY9CgQRIULiDBoAMSDERPaK6uofrrtVQsXUrNxk3Q3IxLZAS+GRn4zJ2LS3D3dMWKnlFfU83hnZnG5MWdmTJ50U6qqqqsQeHw4cOUlZUBxkZLwcHBhIaGEhoayrBhw676pZESDDogwUD0NNPZs1StXEnFsmXUZWYB4DZ6NL4Zc/GeNQvnwYPt3ELRFabGRo7s28Wh7VvIz9xKbUV5q8mLqUSMTcXLX5a49oTy8nIKCwspKiriyJEjlJaWAsbQQ1BQEKGhoYSEhBAUFHTV1XiQYNABCQbCnpqOHaPyq6+oWLaMhpz94OCAR+p4fOfOxfv666X6Yx9nNjdzPO+gscJh+2bKT1gmL0bGEGHZVCkgUHqLekpVVRVHjhzhyJEjFBUVWecoODg4MHz4cEJCQqxhwd3d3c6t7V5dDgZKKT9gOFAHFGqtzbZtov1IMBC9RUNBgVH9cdmyc9Ufr7kG37lzjOqP0hXdp2mtOVty1DJ5cTMn8o3Ji34tkxdTJjAsMrrfT17sTerr6zl69Ki1R6GkpITm5mbAqBrZEhJCQ0Px8bHfXIrucEXBQCnlCzwI3Am4AKcBN2AIsAV4TWv9Tbe0uAdJMBC9jdaa+n0556o/njqFcnfHe9pUfObMwXPyZBxcXe3dzHbJNs6dU1V6hvzMrRzK3MLRfbuNyYsD/IhISSVyXBrBI0dz5mitlInuQU1NTZSUlFh7FI4ePUpjYyMAfn5+1pAQGhqKv79/n548fKXBYDXwLvCl1rr8gsfGAj8A9mit37Rtc3uWBAPRm2mzmbqsLCqWL6dq5Sqaz57FwcsL7+uuw2fuHDzT0lC9aKMXKRV9Zc6bvJidRVN9HU6ubkAoyikCF/cwbvqfiRIOelhzczMnT56kqKjI2qtQW1sLgKen53k9CkOGDOlTKx9kjkEHJBiIvkKbTNRs3Url8uVUrV6DubISR19fvGfMwGfObDzGj0fZeaa1lIruupbJi1s+XcPxvJ2gawGF75BwEq6dRPiYcQwMDu3T31b7Kq01Z86csYaEoqIi6xbOrq6u56186O2bLtlijsFoYARgfZda609t1UB7kmAg+iLd2Ej1xo1ULv+K6rVrMdfW4jhwID4zZuAzdw7uycl2GauWUtG2c6KggiUv7cDUcAyz6TDeA05w9lghAN4DBxGenEJY8jhCRo3G2bX/b7/dW/ZOuFB5ebk1JBw5csS6l4KTkxOBgYHWVQ+BgYF49aJaKl0KBkqpfwCjgX1Ay6RDrbW+16attBMJBqKvM9fXU/3td1R+9ZV1IyWnoUON3RbnzsFt1Kge/XYpcwxs50RBxXlzDKrPllKwM5PDO7dTtDubpoZ6nJxdCB6ZQNiYcYQnj8N38BB7N9vm7Lnb4uWqqam5aOWD2Wx8dPr5+REUFGS9DRkyxG69Cl0NBjla6/huaVkvIMFA9CfN1TVUf/ONERLWr4emJpyDg/GZPRufuXNkt8V+xNTURPH+vRzesZ2CndutSyEDgkIIt4SE4TFxOPSDjXx6sj6DrTU2NnL8+HGKi4utt6qqKgAcHR0ZPnz4eWHBx8enR/4b7WoweBP4o9Y6x0aN8Qc+whiaKARu01qXtXHeCmACsEFrndHq+PtACtAEbAN+rLVuUkpNBT4HDltO/VRr/etLtUeCgeivmisqqFqzlsqvvqJm82Zjt8XwcHzmzMFnzmxcw8Pt3URhQ2ePlXB453YKdmyneP8+zM0mXD09GTF6DOFjxjEiaSwePn1z8mJf6jHojIqKivOCwvHjxzGZTAB4e3sTGBhoDQrDhw/vls2XuhoMpgBfACeABkBhDCWMvsLG/AE4q7VerJRaBPhprX/RxnnXAR4YH/ytg8Ec4CvL3Q+A77TW/2cJBo+1PrczJBiIq4Hp7FmqVq2mcvlyardvB61xjY21hgSXoKurQmB/11Bby5E92RRYgkJtRTkoxbDIaMKTxxE2ZhyDR4T3qd6jK51j0FvnJrRmMpk4efIkxcXFlJSUUFxczNmzZwFQSjFkyBDuvvtum85R6GowOAT8HNjDuTkGaK2LrrAxucBUrfVxpdQwYJ3WOqadc6fSwYe9UupRYKDW+pcSDITonKaTp6hauZLK5cupy84GjC2ZfebMxmfWLJyHDrVvA4VNabOZk4fzKdixncM7t1s3VvLy8ycsOYWwMeMITUjCxa3/7fTXl3saampqrCHhxIkT3HHHHTZdDtnVYLBZa51mw8aUa60HWH5WQFnL/TbOnUo7H/ZKKWdgK/AzrfV6y7mfAMXAMcvz9rVz3fuB+wFCQkLGFhVdUcYRos9rKimhcsUKKpctpz7HGC10Txlr9CTMnIlTgOzp39/UlJdxODuLwzu2U7h7J411tTg6OREUn2CdmzBg6DB7N9Mm+vLchO7W1WDwGjAA+BJjKAHoeLmiUmoN0NbXjl8C77QOAkqpMq21XzvXmUr7weB1oEZr/Yjlvg9g1lpXW4Yb/qy1jrrE25MeAyEsGgsLqfzqKyqXL6ch7xA4OOA5IRXv2bPxuf56HAcMsHcTr5islGhbs8lEyYEcCnZu5/CO7Zw9VgwY2zSHJ6cQPmYcgbHxfbZ8tL17DHrzMEZXg8FbbRy+4uWKthhKUEr9CkgGbmmvboNSqhBI0Vqf6ag9EgyEuFj9wYPWkNBUdAScnPBKT8dnzmy8rrsOx160HvtSZDfGzis/ecI65HB0326aTSZc3N0JHZ1szE1ITsFzQJvf43ote3042zuUXEp7waBTiye11vfYuD1fAPOBxZZ/fn45T1ZKLQRmAte1DgVKqaHASa21VkqNBxyAUpu1WoiriFt0NG7R0Qx6+GHqc3KoXL7cWAL5i29RLi54TbkGnzmW4k69vApd5slMGpsbMWOmydxE5slMCQbtGDBkKGNm38CY2TfQWF/HkT27jN6EnZnkbd0EwJDwKMLHpBCePI4h4ZG9vuiTa6iPXT6QGwoq0CYzaNAmsxFOelEwaM+laiU8jVEo6Ww7j18LeGitl17WiyoVAPwbCAGKMJYrnlVKpQA/0VovtJy3HogFvDA+4O/TWq9USpksz6uyXPJTrfWvlVIPAQ8AJowqkD/XWm+6VHukx0CIztFmM3W7dlG5/CuqVqzAdPo0ysMD76lTjboNkyfj0Atr2stujF2nteZ00WEKLHsmHM/LNVa2eHgxIjGZ0MRkQhOS8Rk4yN5N7TVs1WPQXT0eV1pE6UbgCaAe2MG56opRQBKwBvi91vq0zVpqBzYNBlpD1Qnw6R8Td4Roj25upjYzy6jbsHIlzeXlOHh7nyvuNGFCryvuJHMMbONEQQVL/riRxrrD6OYjODuXUFdVDoB/YDAjRhtBITguAWe3/r9Vc0e6+qHencMRXZ1jEAWkA8Mwvonvx9g7oM4mrbMzmwaDos3w1mwInwpJ/wWxc8HF0zbXFqKX0k1N1GzZSuVXX1G1ejXmqiocBwzAa/p1+MycheeE1F4VEkTXZK0oZOvnBWgNygHG3xDGiJGKwt07Kdq9k+KcvZiaGnFwdCIwNp7Q0cmMGJ1s7JvQy4cdepvuXFUh1RU7YNNgUFECWW/D7g+h/Ai4eEH8jZB4J4Smg/xHIfo5c2MjNRs2GMWdvv7aKO7k62uEhFmzel1Pgrh8Jwoq+PzlnTQ3m3F0dODGR5PPKwdtamyk5EAOhbt3ULR7J6eLjM1o3b19CB2dbLkl4e0/0F5voc/ozT0G0cBjXFxd8VqbtM7OumWOgdkMRzbDrn/BviXQWAW+wTD6diMkDIy07esJ0QuZGxqMkLBipRESampw8PU1hhtmzTRCQi+ckyAu7cLiTh2pKS+jaE82Rbt2ULh7p7ELI0ZNh5behKC4UVf9sEN7etUcg1ZP3gX8DcgCmluOa62zbNZCO+r2yYeNtZC73AgJ+V+DNkPQOEi8A0beAh7+3ffaQvQS5oYGajZupGrlSqrWfo25uhoHHx+8r7sO75kz8Jo4UULCVUBrzZkjheeGHfbvpbmpCUenlmGHMYSOTmZwaJgMO3SzrgaDLK312G5pWS/Qo6sSqk7A7n8bIeFUDji6QPQsoxch6npwlC5W0f+ZGxuNkLBiJVVff425qsqYuHjttXjPmolnenqvXN0gbK+psYGS/fusQeHMkUIA3H18CU1IYkTiGEITkvDylx04ba2rweA54BTwGefvfNjmMsa+xi7LFbWGE3uMgLDnP1BzGjwCIOH7Rk/CsCToQ8VNhLhS5sZGajZtOhcSKistIWEa3jNn4Zk+EQdXV3s3U/SQ6rKzFFlCQtGebOuww8DgUOv8hMDY+H5Z16GndTUYHG7jsNZa94uarXbfx6C5yRhi2PUvOLAcmhtgUKwREEbfDj7D7dc2IXqQbmykZvNmKlespGrtWiMkeHride21xpyESZMkJFxFtNnM6SOFFO7aQdGebEoO7KO5qQkHR0eGREQRMjKR4JEJDI+Jw9lF/r24XLIqoQN2Dwat1ZUZkxV3fQhHtwDKWPqYeCfEZcjSR3HV0I2N1GzZQuXKlVStWYu5osIICdOmnQsJMlntqtLUUE/JgRyO7tvN0X17OFGQhzabcXRyYlhULMEjEwgeOZphUbE4ycqXS+pqj8EG4FtgPbBRa111iaf0Kb0qGLRWmg+7PzJ6Es5b+ngHhE6SpY/iqmHdJ2HlCqpXr6G5ogIHDw+8pk0zJi5ec42EhKvAhSshGmprKcndx5G9uzm6bzenCgtAa5ycXRgeE0tw/GiCRyUyNCIKR6dOVQC4qnQ1GIQBky23CRjzDNZrrR+1dUPtodcGgxZms9F7kP0B5HwODZXgEwSjbzN6EgZF27uFQvQY3dREzdZtVK1cQdXqNTSXl1u2ZZ6C98xZeF0zudfXbrgcsmOjwbp3gsmMo9PFeycA1FdXU7x/r6VHYTenLRMZnV3dCIyNJ3jkaIJHJjAkLBIHR0c7vIvepctDCZYqiFMwwsE04IjWepZNW2knvT4YtNZUZ1n6+CEcWgu6GYaPMQLCqO+Bp8zcFVcP3dREzbZtVK1cRdXq1TSXlaE8PIwCTzNn4TXlmj4dEqQq5DkX7raYOi+csbNGdPic2sqKVkFhD6XFRwBwcfcgKG4kwfHG0MOgEWE4OFx9QaGrPQb5wBngA4zhhOz2Sh33RbYMBllFZWwpKGVCeABjQ7u5NGnVSdj7sTHUcGIPODhB1ExjqCF6JjjJZBxx9dAmE7XbtxsTF1evpvnsWZS7O15TpuAzcwZeU6bg4OFh72Zeljf2vMFfd/wVM2YclSMPJT/EwoSF9m6WXVxqt8XOqCkv42jOHmuPQtnxYwC4eXoRFD/K0qMwmoFBIVfFHgpdDQY/AyYBwcABjPkG32mt823dUHuwVTDIKirjrje20Ggy4+LkwPsLJ3R/OGhxYq+xDfPuf0P1SXAbAKNuMXoSgsbJ0kdxVdEmE7WZmVSusAw3lJai3NzwuuYavK+fjteUKTj69P7yt1IV8nyXs9tiZ1SdPcPRfeeCQsWpk4ARFIbHxBEYO5LA2JEMjYjE0an/TWa0yaoEpZQXcA/G9shBWut+0fdiq2Dw6jeH+OOqXMwaHBX8fEYMD07r4a2Pm01weJ0x1LB/KZjqwD/cCAijbwO/ET3bHiHsTDc3U7s9k8qVK6has4bm02fA2RnP8eONkHDttTgPHmzvZrZL5hj0nMrTpzias4fi/fsoObCPsuMlADg5uzA0MtoSFOIZHh2Hax/rfWpLV3sM/ojRY+AFbMYYTlivtS6wdUPtwdY9Bk0mM8493WPQlvpK2P+lMdRQuN44FppuDDXE3whuXU/cQvQl2mymLnsXVWvWULVmDU1HjoBSuCcm4n39dLynT8clNNTezRS9RG1FOSUHcijJ3Ufx/hxOFeajzWaUcmBQaBiBsfHWsODl1/e2tu9qMLgVIwic7I7G2VtfmWPQpWuXH7FsxfwhlOaBk5tREnr0HRBxLTjKUh5xddFa03Awj6o1q6las5aG/fsBcI2KsoYE17g4lAzDXVU6Gq5orK/j+MFcSnKNHoVjebmYGozNgAcMGXZeUPAbFtjr/92xxaqEecA1lrvfaq2/tGH77KovrEqw2fwFraFkh9GLsPdjY0Mlz8HntmIemiDzEcRVqbG4mOq1a6lavYbaHTvAbMY5MBDv6dfhPX067mPGoGSJW7/WmSWRrTWbTJwqzDd6FQ7so+RADnVVlYBR6yEwJh6fwRGghhKdmkBgdO9aNdbVHoPngfHA+5ZDdwLbtdZP2bSVdtIXgkG3zF8wNULeKmPSYu4KMDfB4JFGQEj4PvgMs03jhehjTKWlVH/zDVWr11CzaRO6qQlHf3+8rp2G9/TpeKalydbM/dCVLIlsTWvN2WPF1qBwZO9eqs+esjzqyMDQcEJHjWRYVCzDo2PxDhjYLe+js7oaDHYDSS1LFJVSjsBOrfVom7fUDvpCMOj2+Qu1Z2Hfp8ZQQ/F247+Klq2YY+fKVsziqtVcXUPN+u+oWr2G6m+/xVxTg4OHB55TrsF7umWFg5eXvZspbMAWSyJby1pRyJbPdtHcdBzdfBwPnzJqyo7S3NQEgJd/AMOiYoygEBXL4PCINms+2Ho1RgtbBIOpLdUUlVL+wDoJBj2rx/ZIOHPI6EXY9RFUtNqKefTtMGKybMUsrlrmxkZqt2yhavUaqr7+2lgG6eyMR9oEvKdPx/vaa3EaaN9vgaJrbPkh3FbQGBTiwenCwxzLy+V43gGOH8ql4uQJABwcHRkUGs7w6FhrYKirduOLP2V3enjjcnQ1GNwJLAa+ARTGXINFWuuPbNI6O+srwaDHmc1wZJMxH2Hf59BYZdmK+fvGpMXBsfZuoRB2o5ubqcvONkLCmjU0FRcbKxzGjDFCwvXTcQkKsnczhZ11JmjUlJdx/NBBIygcPMCJ/DyaGuoBcHbzprl5MA6Ow3ByT2TCTXGXNbzREVttiTzOcneb1vqETVrWC0gw6ITGWmMr5t0fnduKeViiERASbgWv3rsOXIjuprWmITfXGhIacnMBcI2NtYYE1+joXj9LXfQO5uZmzhwt4nheLgU7dnN41z50czmegx7ipp+n2rfHQCk1pqOLaq132KBtdifB4DJVn4I9HxvDDcd3gXKEyOuMoYbYueDcd/emF8IWGo8etYaEup07QWucg4Pxvu46vK+fjntSkqxwEJ12oqCCor3HCB013P5zDJRS31h+dANSgF0YQwmjgUytdZrNWmhHbQWDpqYmiouLqa+vt1Or+ojmJmiqMXoUzCZj0qKzB7h4gKNrtyx9dHNzIygoCGepty76ANOZM1R9/TVVa9ZQs3kLNDXhOHAg3tOm4X39dDwmTMDBxcXezRRXoa7OMfgU+JXWeo/l/ijgOa31rTZvqR20FQwOHz6Mt7c3AQEB0v3XGVpDY7WxuqG+HLQZHF3A3Q/c/cHZzUYvoyktLaWqqoqwsDCbXFOIntJcXU31t98aIeHb7zDX1uLg5WWt4eA5+RocvWQFkOgZ7QWDzm53F9MSCgC01nuVUnE2a10vVF9fz4gRIyQUdJZS4Opt3MxBUF8BdWeNgk7VJ41eBHc/4+Z45d/0lVIEBARw+vRpGzZeiJ7h6OWF79y5+M6di7mhgZrNm6las4bqr7+hcvlylIsLnmlp1hoOTv69d5tdqeHQf3U2GOxWSr0B/NNy/y5g95W+qGW540fACKAQuE1rXdbGeSuACcAGrXVGq+NvA1OACsuhBVrrbGV8iv8ZmAPUWo5f8TwICQVXyMERPPyNW3OTsbti7VmoLDFurj7GY66+V7T0Uf4uoj9wcHXFe+pUvKdONVY47Nhh1HCw7JeAw6/wGDPGCAnXTcclKNDeTbZqqfrY2NyIi6PLVV/1sb/p7P+V7wH2AT+z3HIsx67UImCt1joKWGu535YXgB+089jjWuskyy3bcmw2EGW53Q/8XxfaKFqpaTBxqrKemgbT5T3R0dlYsTA4FgbFGj831UFZIZzca9RwaKg2hiKEuEopR0c8xo1jyJNPErF2DWGffsLAn/yY5ooKTj6/mPzp0ym4YR6n/vgStVlZaNNl/ndoY5knM2lsbsSMmSZzE5knZfJ2f9LZYJAO/E1rfbPl9rLWuiuz8m4E3rH8/A5wU1snaa3XAlWXed13tWELMMCyzLLfKCwsZNSoUTa9ZnZ2NsuXL2/zsW3btjE6MZGxY5KZPCGFN9770BoOVqxYQUxMDJGRkSxevPjSL+TsDj6BMGQkBEQa1R3ryoyiTqdyoPIYNMlkT3F1U0rhFh/PoIcfJvzLL4hYuYLBv/gFjgEBlL71FkV33U1e+iRKHn+CiqXLaK6ouPRFbSxlSAouji44KkecHZxJGXLRMLXowzo7lPBD4P+UUmcxSi5/h9G9f1H3fycN0Voft/x8AhhyBdf4nVLqWSw9DlrrBiAQONrqnGLLseMXPlkpdT9GrwIhISFX8PL9R3Z2NpmZmcyZM+eix0aNGsWKbzZSWmvi1MkTfH/mZL5/y024Obnz4IMPsnr1aoKCghg3bhzz5s0jPj7+0i/YqfkI/pb5CFL1UVzdXEJDCbhnAQH3LKC5qoqajRupXvct1d9+S+WXX4KjIx7JyXhNm4rXlCm4RER0+3Bb0uAkXp/xuswx6Kc61WOgtZ6vtY4GbsH44H0V6HD2l1JqjVJqbxu3Gy+4tgYutx/5SSAWY8Mlf+AXl/l8tNZ/11qnaK1TBg0adLlPb1NWURmvfnOIrKIrzUvne+mllxg1ahSjRo3iT3/6k/W4yWTirrvuIi4ujltvvZXa2loAFi1aRHx8PKNHj+axxx676Hrbtm0jLS2N5ORkJk6cSG5uLo2NjTz77LN89NFHJCUl8dFH529m6eHhga+nG0opGhsaUErh6erEtm3biIyMJDw8HBcXF+644w4+//zzi15z6tSpPProo6SkpBAXF8f27du55ZZbiIqK4umnnz43HyEgEoaMMnoUtIbKYmOooTTf6FUwm23yOxWiL3P09sZn1iyGL36eqA3rGfHhvwj40UKaa2o49cKLFGTcQP71Mzjx299RvX4DZktJ4O6QNDiJhQkLJRT0Q536OqaUuhuYDCQAZ4BXMHoO2qW1nt7B9U4qpYZprY9buvpPtXduO9du6QFoUEq9BbR8CpYAwa1ODbIc63Y2K4vccr2sLN566y22bt2K1prU1FSmTJmCn58fubm5vPnmm6Snp3Pvvffy2muvcc899/DZZ59x4MABlFKUl5dfdM3Y2FjWr1+Pk5MTa9as4amnnuKTTz7h17/+NZmZmbzyyitttmVvdhYL7rmXI0eKeOMfb+Pr6UZJSQnBwed+1UFBQWzdurXN57u4uJCZmcmf//xnbrzxRrKysvD39yciIoJHH32UgABLKdKW+Qgt8xBqzxqhoKzQ2ETJfYDRkyAFnYRAOTrinpSEe1ISgx95hKYTJ6w9CeUff0zZP/+J8vDAc2IaXlOm4HXNFJyHyA6l4tI620/7JyAf+Bvwjda6sIuv+wUwH6P+wnzg4q+aHWgVKhTG/IS9ra77kFLqQyAVqGgVIrrVloJSGk1mzBqaTGa2FJR2KRhs2LCBm2++GU9P40PwlltuYf369cybN4/g4GDS09MBuPvuu/nLX/7CI488gpubG/fddx8ZGRlkZGRcdM2Kigrmz59PXl4eSimaLBW+LiU1NZX9OfvYv38/8+fP5+Z5F1+7I/PmzQMgISGBkSNHMmyYMe0jPDyco0ePngsGrTm7g28g+AyHhiojINSVQW2psT9CfSWczoVBMZfVFiH6K+ehQ/G743b87rgdc309tVu3GnsmrFtH9Zq1ALiNHGmEhGlTcRs5EiUF0UQbOjuUMBC4F2MHxN8ppbYppd7rwusuBq5XSuUB0y33UUqlWJZFYrm/HvgPcJ1SqlgpNdPy0PtKqT3AHmAg8FvL8eVAAXAIeB347y608bJMCA/AxckBRwXOTg5MCG/jw85GLhw/VErh5GR07996660sXbqUWbNmXfS8Z555hmnTprF3716+/PLLy97VMS4uDi8vL/bu3UtgYCBHj56bzlFcXExgYNvLqVwtdesdHBysP7fcN11qdrVS4OYDfqHGUMOAUHByg/oqeHU8/H/XwOZXoerkZb0XIfozBzc3vKZMYeizzxK5di1hn3/OoEcfRbm6cuZvf6Pw+7eRd80Ujv3yl1SuWkVzdY29myx6kc4OJfgAIUAoxt4DvsAVD/pqrUuB69o4ngksbHV/cjvPv7ad4xp48Erb1RVjQ/14f+EEm5VFnjx5MgsWLGDRokVorfnss8947z0jix05coTNmzeTlpbGBx98wKRJk6iurqa2tpY5c+aQnp5OeHj4RdesqKiwfni//fbb1uPe3t5UVbW9+OPw4cMEBwfj5OREUVERBw4cYMSIEQwYMIC8vDwOHz5MYGAgH374IR988EGX3vMltd4fwacOZi02ijqtfApWPQ3h087Va3D16t62CNFHKKVwi4nGLSaagT++H1NZGTXr11O97luqVq+h4pNPwdkZz3Hj8Jo6Ba+pU3HppROyZVOlntHZoYQNrW6vaK2Lu69JfdfYUL8uB4IWY8aMYcGCBYwfPx6AhQsXkpycTGFhITExMbz66qvce++9xMfH88ADD1BRUcGNN95IfX09Wmteeumli675xBNPMH/+fH77298yd+5c6/Fp06axePFikpKSePLJJ7n99tutj23YsIHFixfj7OyMg4MDr732GgMt9eZfeeUVZs6cSXNzM/feey8jR460yXvvFAdHmPCAcTt90AgIu/8Nn90Pzp4QlwGjb4OwqbKyQYhWnPz88J03D99589BNTdTu3En1t99Sve5bTv7+eU7+/nlcwsONIYepU/EYk4zqBXVJZFOlntPpssv9WVu1Evbv309cXL/e9blPqWkwUdNgwtPVCU9Xp7b/PmYzHN1qVH3c95mxDNJzsFEWevTtRplo2TVRiHY1Hj1qTGBct47abdvQTU04eHvjOSkd76lT8bzmGpz8bPPl53K9secN/rrjr5gx46gceSj5IRYmLLz0E0W7ulQrQSk1CHgCGIkxzwBov0tfCFuqaTBx+EwNWmuUUoQNbGdVgoMDhKYZt9l/gLxVsOtD2PY6bHkNBsYYvQijb4MBvbOrVAh7cgkOxv8Hd+P/g7sx19QYtRzWrTMmMX61ApTCPSnJOoHRNTq6x7Yob9lUqcncJJsqdbPOVldchVHb4DHgJxgrCU5rrS97/4DeSHoMerdTlfWcrKxHY9T8HuLjRmnJ4c7/fWrPQs7nxnDDkc3GsdB0IyDE32hspCSEaJc2m6nfl2MZclhH/V5jIZjTsGF4pk/Ea9IkPCdMwHHAgG5th8wxsK2ull3O0lqPVUrt1lqPthzbrrUe1w1t7XESDHq3tnoMjhTkXdnfp6wQ9vwHdn1kbMXs6ALRs4yhhqjrwcn1kpcQ4mrXdOqUdQJjzZYtmKuqwMEBt4RReKWn45mejvvo0b1ibsLluprCR1eDwRat9QSl1ErgL8Ax4GOtdYTtm9rzJBj0fp2aY3A5tIZjO40Ji3s/hprT4DYARt1ihITgVJmPIEQnaJOJuj17qNmwkZqNG6nbvRvMZhy8vPCYkGr0JqSn4xIcfOmL2dnVNsGxS3MMgN8qpXyB/wH+CvgAj9qwfUJ0qCUQ2IxSEDjGuM34LRR8Yww1ZP8LMv9h7JeQ8H1juEE2URKiXcrJCY/kZDySkxn004dorqykZvMWajZupGbDBuvmSs4hIXhNMnoTPFJTcfTqfUuK26oa2dVg0Bd7IC75f1qllCMQpbVeClQA07q9VUL0JEcnYxgh6npjl8X9S42QsOElWP+isZoh4TYY9T3w6VfFOoWwOUcfH3xmzsBn5gy01jQWFlKzcRM1GzZQvuRzyj74Fzg54Z6UaAw7TJqEW3w8ytHR3k23+QTHvtoDccmdD7XWzcCdPdAW0Up5eTmvvfaa9X5hYeF5GwhlZmby8MMP2/x1lyxZQk5OTpuP/e1vfyMhIYGkpCQmTZp03nnPP/88kZGRxMTEsHLlSpu3q8e4ekPSnfDDJfDz/TDzeVAOsOqX8FIcvDMPdv7TWAophOiQUgrXsDD8776L4L/9HzFbNhPy7jsE3Hsvuq6e03/+i7EL48R0Sn7+c8o/+YSmEyfs1t6WqpEPJT9kkw/xtnog+oLOzjF4GXDGWJlg3TtTa72j+5rWc3rjHIPCwkIyMjLYa5n9u27dOl588UWWLl3ara+7YMECMjIyuPXWWy96rLKyEh8fHwC++OILXnvtNVasWEFOTg533nkn27Zt49ixY0yfPp2DBw/i2I3fAHr873Mmz5i0uPvfUHYYHF0hZpbRkyCTFoW4IqazZ6nZtNk67GA6bRTtdYmMsPYmeKSk4ODubueWXpmWHoOWHoje1mPQ1cmH31h+bDlZYexA3C/2MeiNwaCljHFMTAzXX38969evZ//+/YSFhTF//nySk5OtQeG5557j8OHDFBQUcOTIEV5++WW2bNnCV199RWBgIF9++SXOF8wOfv311/n73/9OY2MjkZGRvPfee2RnZ5ORkYGvry++vr588sknRES0Pb/0X//6F++++y5fffUVzz//PABPPvkkADNnzuS5554jLS3tvOd4eXnxwAMPsHz5coYNG8bvf/97nnjiCY4cOcKf/vQna7GlzrDb30drKMkyhhr2fgq1Z4xJi/E3GvMRQiYa+ykIIS6L1pqGvDzrJMbazEx0QwPK2Rn3lLHWSYyuMTE9tneCLfTmOQZXNPlQKfVzy49LwbqMvMXVs2XiV4vgxB7bXnNoAsxe3O7DixcvZu/evWRnZwMX9xisW7fuvPPz8/P55ptvyMnJIS0tjU8++YQ//OEP3HzzzSxbtoybbrrpvPNvueUWfvSjHwHw9NNP8+abb/LTn/6UefPmtdtjAPDqq6/y0ksv0djYyNdffw1ASUkJEyZMsJ4TFBREScnF1a5ramq49tpreeGFF7j55pt5+umnWb16NTk5OcyfP/+ygkF3yyoqa7vuhVIQlGLcZv4eCtYZvQh7PoYd74BPkGWnxdtgSA9uES1EH6eUwi06GrfoaALuvceoEJmZZe1NOPXCi/DCizgOHIjn+PF4pKbiOSEV55CQXh0UkgYn9bpAcCmXmnzobflnDDAOozyyAm4AtnVju8Rlmj17Ns7OziQkJNDc3GytrpiQkEBhYeFF5+/du5enn36a8vJyqqurmTlz5kXntOXBBx/kwQcf5IMPPuC3v/0t77zzTqfb6OLicl67XF1drW1uq432klVUxl1vbKHRZMbFyYH3F05ouwaGo/O5SYuNNXBgOez5N2z6K2z8EwweCaO/D6NuhQG9f6mWEL2Jg5sbXpPS8ZqUDr94gqaTp6jZtImaTZuo3bqVyuXLAXAaOhTP1NRzQWH4cDu33PZ6utehw2Cgtf5/AEqp74AxWusqy/3ngGXd3rreooNv9r1F69LGzs7O1gTdXmnjBQsWsGTJEhITE3n77bcv6oG4lDvuuIMHHngAoNMlmC9sV+s2X7L8cg/aUlBKo8mMWUOTycyWgtJLF8dy8TRCwOjvQ80Zo1bD7n/DmueMW2i6sfxx5E2y06IQV8B5yGAG3HwTA26+yVjtcLiQ2m1bqdmylervvqPi88+N84KD8ZyQisf4VDxSx+M8eLCdW9419ljZ0NmF4UOAxlb3Gy3HRDe5sBRyR6WRr0RVVRXDhg2jqamJ999/3/pB3tHr5OXlERUVBcCyZcusP8+bN4//+q//4uc//znHjh0jLy/PWhWyL5oQHoCLkwNNJjPOTg5MCA+4vAt4DoTxPzJuZw8bwwx7/g1LH4Hlj0PUDCNARM8C5745qUoIe1JK4Roehmt4GH533IE2m2nIO0Tt1q3UbN1K5cpVlP/nYwBcwsPPCwr2KgJ1pbpjb4VL6WwweBfYppT6zHL/JuDt7miQMAQEBJCens6oUaOYPXs2v//973F0dCQxMZEFCxaQnJzcpev/5je/ITU1lUGDBpGammoNA3fccQc/+tGP+Mtf/sLHH3983uTDV155hTVr1uDs7Iyfn591GGHkyJHcdtttxMfH4+TkxKuvvtqtKxK629hQP95fOKHtOQaXyz8MpjwO1zwGx3dZdlr8BHKXgasPxM0zQsKIyUYpaSHEZVMODrjFROMWE43/D3+Abm6mfv8BS1DYQkXL/gmAa0yMERRSU/FIScHRstKqt7JH8ahOl11WSo0BJlvufqe13tltrephvXFVguhYn/77mJvh8HfG8secL6CxCryHGRsoJXxfykMLYWO6qYm6vXup3bqN2m1bqc3agW5oMOo7xMefCwpjxuDg2U71VjvqrjkGXVqu2N9JMOh7+s3fp6kODq6A3f8xykSbm2BgtGWnxVsgoF+UIxGiVzE3NlK/axc1W7ZSu3Urtbt2QVOTsSNjQgIeKSm4jx2Dx5gxvb5HoSskGHRAgkHf0y//Pi3loff8B4o2GseGjzF6EkbdAj79b7a1EL2Bua6Oup07rUGhbt8+MJlAKVyjo/EYO9YICikpOA/pP9PrJBh0QIJB39Pv/z4VxcYGSns/NuYmoCwrG74H8TeBh7+9WyhEv2Wuq6Nu9x5qszKpy8yiLjsbc20tAM5BQecFBZewsF69j0JHJBh0QIJB33NV/X3OHDImLO79GM4cBAcniLjW2B8hdo5R30EI0W20yUT9gVzqsjKpzcyiNiuL5rNnAXD08zNCwtgUPFLG4hYXh3KyYSXYbiTBoAMSDPqeq/Lvo7WxA+fej43ehIqj4OQO0TON3RYjrwdnN3u3Uoh+z7qPQlYmdVk7qM3Kosmyl4vy8MA9cbQ1KLiPHo2Dh4edW9y2K9oSWQjR8zrcjnnYaON23XNQvM3YI2HfZ5CzxLL88QZjTkLYFKOctBDC5s7bR+H73weg6eRJ6rKyjB6FHTs48+qrRph3csItLg73pCTcExNxT0rCOXB4rx5+kB4D+laPwYVVF20hOzubY8eOMWfOnDYf3717Nz/+8Y+prKzEwcGB7du34+bmRlZWFgsWLKCuro45c+bw5z//ucf+Ze+tf5+u6vR2zK01m+Dwt8Zww/4voaESPAYauyyOuhWCU6WwkxA9rLmykrqdO6nNzKJu507q9u5F19cD4DhwIO5JibgnJuKRlITbyJF26VWQHgPRruzsbDIzM9sMBiaTibvvvpv33nuPxMRESktLrZUaH3jgAV5//XVSU1OZM2cOK1asYPbs2T3d/H7lirZjdnSCyOuM29yX4NBqIyTsfB+2vwG+wTDyZmO4Yeho2SNBiB7g6OOD15QpeE2ZAhh7KdQfPEjdrl3U79pFbXY21WvWWk52xC0mxggLlp4FexaHkmBgQ7behOKll17iH//4BwALFy7kkUceAYwP67vuuosdO3YwcuRI3n33XTw8PFi0aBFffPEFTk5OzJgxgxdffPG8623bto2f/exn1NfX4+7uzltvvUVYWBjPPvssdXV1bNiwgSeffJLbb7/d+pxVq1YxevRoEhMTAWNHRoDjx49TWVlprar4wx/+kCVLllwUDBYsWIC7uzs7d+7k1KlT/OMf/+Ddd99l8+bNpKam8vbbb3f599SfdHk7Zmc3Yzgh7gZoqILcr4zhhi2vwaa/QECUERBG3QoDI7vnTQghLqKcnXEfORL3kSPhv/4LAFNZGXXZ2dTt2kVd9q7zdmh09POzDj24JyXiNioBR68e2nxJa93jN8AfWA3kWf7p1855K4ByYOkFx9cD2ZbbMWCJ5fhUoKLVY892pj1jx47VF8rJybnoWEd2ntypU95L0aPfHq1T3kvRO0/uvKznXygzM1OPGjVKV1dX66qqKh0fH6937NihDx8+rAG9YcMGrbXW99xzj37hhRf0mTNndHR0tDabzVprrcvKyi66ZkVFhW5qatJaa7169Wp9yy23aK21fuutt/SDDz7YZjtefvllfffdd+sZM2bo5ORk/b//+79aa623b9+ur7vuOut53333nZ47d+5Fz58/f76+/fbbtdls1kuWLNHe3t569+7durm5WY8ZM0bv3Lnzin4/l/v36UsyC8/qV77O05mFZ2130ZpSrbf/Q+u35mr9K1+tf+Wj9d8ma73hz1qXH7Xd6wghrpjZZNJ1Bw7osx9+pEsWPakPzZ6jc2JijVtcvG48edKmrwdk6jY+E+3VY7AIWKu1XqyUWmS5/4s2znsB8AB+3Pqg1rpla2aUUp9glINusV5rnWH7JnfM1oUuNmzYwM0334ynZXvOW265hfXr1zNv3jyCg4NJT08H4O677+Yvf/kLjzzyCG5ubtx3331kZGSQkXHxr6CiooL58+eTl5eHUoqmpqZLtsNkMrFhwwa2b9+Oh4cH1113HWPHjsXX17fT7+WGG25AKUVCQgJDhgwhISEBMGosFBYWkpSU1OlrXQ3Ghvp1rT5DWzz8IeUe41Z5zJiwuOdjWP2McQuZaGyiNPJmowiUEKLHKcuQgltMDH633wZAc3k5dXv2UL//AE6DBvVIO+w1I+lG4B3Lz+9gFGW6iNZ6LdBuSUGllA9wLbDEts27fC2FLhyVY7cXurhw3EkphZOTE9u2bePWW29l6dKlzJo166LnPfPMM0ybNo29e/fy5ZdfUm+ZCNORoKAgrrnmGgYOHIiHhwdz5sxhx44dBAYGUlxcbD2vvVLLcH5J6JafW+73pnLLVw2f4ZD2INz/Dfx0B0x7GurOwvLH4MVoeO8WyP4A6ivt3VIhrnqOAwbgNXkyA+//UY/NObBXMBiitT5u+fkEV17C+SaMnofW/wdLU0rtUkp9pZQa2d4TlVL3K6UylVKZp0+fvsKXPydpcBKvz3idh5Ifskm97MmTJ7NkyRJqa2upqanhs88+Y/Jko6PkyJEjbN68GYAPPviASZMmUV1dTUVFBXPmzOHll19m165dF12zoqLC+uHdemy/o1LLM2fOZM+ePdTW1mIymfj222+Jj49n2LBh+Pj4sGXLFrTWvPvuu9x4441des/CDgIijOqP/70FHtgE6T+D0jxY8gC8EAkf3Q37lhg1HYQQV4VuCwZKqTVKqb1t3M779LCMc1zpmsk7gX+1ur8DCNVaJwJ/pYOeBK3137XWKVrrlEE26p5JGpzEwoSFNpl4OGbMGBYsWMD48eNJTU1l4cKF1lLLMTExvPrqq8TFxVFWVsYDDzxAVVUVGRkZjB49mkmTJvHSSy9ddM0nnniCJ598kuTk5PO+qU+bNo2cnBySkpL46KOPznuOn58fP//5zxk3bhxJSUmMGTOGuXPnAvDaa6+xcOFCIiMjiYiIkBUJfZlSMGQkTP8V/Gw33LfGGHY4shX+Mx9eiIJPfwx5q6H50kNQQoi+yy77GCilcoGpWuvjSqlhwDqtdUw7504FHrtw3oBSaiCQCwRqrdvsE1dKFQIpWuszHbWnL+1jIAzy9+kh5mYoXG/MR9j/BdRXgLv/uT0SQtJkjwQh+qjeto/BF8B8YLHln593fHqbbsVYrWANBUqpocBJrbVWSo3H6BEptUF7hbg6OThC+FTjNvePcGitsSXzrg8h8x/gPdyYtDjqezA8WfZIEKIfsFcwWAz8Wyl1H1AE3AaglEoBfqK1Xmi5vx6IBbyUUsXAfVrrlZZr3GG5Tmu3Ag8opUxAHXCHtkeXiBD9kZOrUbQpdg401hh7JOz9BLb+f7D5FfCPMAJCwq0wqM0OQCFEHyBbIiNDCX2R/H16kboyYyvmPf+Bw+sBzRnPaBpibiQw/U5jgqMQotdpbyhBBgeFEF3j7gdjfgjzv2TX7Vv5nXk+hVUQuOMF+OsY+NskWP9HKM23d0uFEJ0gWyILIWxmwwlH3myayet6JkGqlMXxh5nUsAHW/tq4DUs0NlGKvwn8w+zdXCFEGyQYCCFspnWthzNOg3C/5gYI/RWUH4Wcz40dF9c8Z9yGJRkhYeRN4DfCru0WQpwjQwm9VHl5Oa+99pr1fmFhIR988IH1fmZmJg8//LDNX3fJkiXk5OS0+/i///1v4uPjGTlyJP9lKQQC8M477xAVFUVUVBTvvPNOu88X/dvYUD/eXziBn8+IOb9k9IBgmPgQ/GitsU/C9b8xVjys+RX8ORH+Pg02/gXKj9j3DQghZPIh9M7Jh4WFhWRkZLB3714A1q1bx4svvsjSpUu79XUXLFhARkYGt95660WP5eXlcdttt/H111/j5+fHqVOnGDx4MGfPniUlJYXMzEyUUowdO5asrCz8/Gy8338r9v77CBspKzzXk3Bsp3EscOy54YYBwfZsnRD9mkw+7GMWLVpEfn4+SUlJPP744yxatIj169eTlJTEyy+/zLp166yFkp577jnmz5/P5MmTCQ0N5dNPP+WJJ54gISGBWbNmtVks6fXXX2fcuHEkJibyve99j9raWjZt2sQXX3zB448/TlJSEvn5+Rc958EHH7R+4A8ePBiAlStXcv311+Pv74+fnx/XX389K1asuOg1R4wYwZNPPklSUhIpKSns2LGDmTNnEhERwd/+9jdb/wpFX+A3wtiG+f518HA2TH/O2FRp1dPwp1HwxnTY/CpUFHd8HSGEzcgcg0448fvf07D/gE2v6RoXy9Cnnmr38cWLF7N3716ys7OBi3sM1q1bd975+fn5fPPNN+Tk5JCWlsYnn3zCH/7wB26++WaWLVvGTTfddN75t9xyCz/60Y8AePrpp3nzzTf56U9/yrx589rtMTh48CAA6enpNDc389xzzzFr1ixKSkoIDj73zS4oKIiSkpI231dISAjZ2dk8+uijLFiwgI0bN1JfX8+oUaP4yU9+0uHvTPRz/mEw6VHjdrbAqNGw7zNY+ZRxCxpv6Um4EXzbLtglhOg6CQb9xOzZs3F2diYhIYHm5mZrdcWEhAQKCwsvOn/v3r08/fTTlJeXU11dzcyZMy/5GiaTiby8PNatW0dxcTHXXHMNe/bsuax2zps3z9qu6upqvL298fb2xtXVlfLycgYMGHBZ1xP9lH84TP65cSvNNwJCzhJY+aRxC55gCQnzjGqRQgibkWDQCR19s+8tWpc2dnZ2tpbnbK+08YIFC1iyZAmJiYm8/fbbF/VAtCUoKIjU1FScnZ0JCwsjOjqavLw8AgMDz3t+cXExU6dOvWQ7pQSz6JSACLjmMeN25hDkfGb0Jqz4BaxYZNRrGHkTxM0Dn2H2bq0QfZ7MMeilLiyF3FFp5CtRVVXFsGHDaGpq4v333+/U69x0003WAHDmzBkOHjxIeHg4M2fOZNWqVZSVlVFWVsaqVas61QMhxGUbGAnXPA4PbIQHt8O0p4zCTl89AS/FwVtzYNvrUHXS3i0Vos+SYNBLBQQEkJ6ezqhRo3j88ccZPXo0jo6OJCYm8vLLL3f5+r/5zW9ITU0lPT2d2NhY6/E77riDF154geTk5IsmH86cOZOAgADi4+OZNm0aL7zwAgEBAfj7+/PMM88wbtw4xo0bx7PPPou/v3+X2yhEhwZFw5Qn4L83wYPbYOqTUHsWlj8Gf4yBt+YaIaH6lL1bKkSfIssV6Z3LFUXH5O8j2nVq/7mJi2dyQTlAaDrE3QCxc8E3yN4tFKJX6G1ll4UQonsMjjNuUxdZQsJnsP8LY7jhqydg+BgjJMTNM4YmhBDnkWAghOiflIIh8WTVD2OLup0pAeWMqvjOqAS59v8Zt0GxlpBwAwwdbTxHiKucBAMhRL+VVVTGXW9sodFk5q9ODry/8B7GTv65sWHSgWVGSFj/R/juBfANsYSEDAhONbZsFuIqJMFACNFvbSkopdFkxqyhyWRmS0GpUb/BNwhSf2zcas5A7ldwYClsfx22vAqeg4z5CLE3QNg14ORi77ciRI+RYCCE6LdaV3t0dnJgQnjAxSd5DoQxPzBu9ZVwaLXRk7DnY8h6G1x9IXqm0ZMQOR1cPHv8fQjRkyQYCCH6rZZqj1sKSpkQHnCu2mN73Hxg1PeMW1M9FKwzQkLuctjzb3ByM8JBbAbEzAL37isUJoS9yD4GfUxhYSGjRo2y6TWzs7NZvnx5m481NjZyzz33kJCQQGJi4nk7HGZlZZGQkEBkZCQPP/wwsvRV9EZjQ/14cFrkpUPBhZzdjA//m16Fx/Jg/pcwZj6U7IAlP4EXIuHdm2D7G1B1olvaLoQ9SDAQHQaD119/HYA9e/awevVq/ud//gez2QzAAw88wOuvv05eXh55eXltVlQUol9wdDLmGsz5Azy6DxZ+DRN/ChVHYdn/wB9j4c0ZsPEvRgEoIfowCQY2dKKggqwVhZwoqLDJ9V566SVGjRrFqFGj+NOf/mQ9bjKZuOuuu4iLi+PWW2+ltrYWMEo1x8fHM3r0aB577LGLrrdt2zbS0tJITk5m4sSJ5Obm0tjYyLPPPstHH31EUlISH3300XnPycnJ4dprrwWMMssDBgwgMzOT48ePU1lZyYQJE1BK8cMf/pAlS5Zc9JoLFizggQceYMKECYSHh7Nu3Truvfde4uLiWLBggU1+T0L0KAcHCBprlIh+KBP+eytM+yU01cHqZ+AvyfB/6bBuMZzcB9KTJvoYmWNgIycKKvj85Z00m8w4Ojlw46PJDA33veLrZWVl8dZbb7F161a01qSmpjJlyhT8/PzIzc3lzTffJD09nXvvvZfXXnuNe+65h88++4wDBw6glKK8vPyia8bGxrJ+/XqcnJxYs2YNTz31FJ988gm//vWvyczM5JVXXrnoOYmJiXzxxRfceeedHD16lKysLI4ePYqDgwNBQed2kOuo1HJZWRmbN2/miy++YN68eWzcuJE33niDcePGkZ2dTVJS0hX/noSwK6VgcKxxm/I4lBWeWwa5bjGsex78ws5tqBQ41ggWQvRi8m+ojZQcLKPZZEZraG42U3KwrEvX27BhAzfffDOenp54eXlxyy23sH79egCCg4NJT08H4O6772bDhg34+vri5ubGfffdx6effoqHh8dF16yoqOD73/8+o0aN4tFHH2Xfvn2XbMe9995LUFAQKSkpPPLII0ycOBFHx8tb333DDTeglCIhIYEhQ4aQkJCAg4MDI0eObLMktBB9lt8ISHsQ7l0Bjx2EjD8ZJaS3/B+8Od0o9LTsf4xJjc1Ndm6sEG2THgMbCYz2w9HJgeZmM46ODgRGd99sZXXB7mxKKZycnNi2bRtr167l448/5pVXXuHrr78+77xnnnmGadOm8dlnn1FYWNhuaeTWnJyczivaNHHiRKKjo/Hz86O4uNh6vLi4mMDAwDavIaWWxVXJazCk3GPc6sohb5WxNXP2B8aERbcBEDPH6E2ImAbO7vZusRCABAObGRruy42PJlNysIzAaL8uDSMATJ48mQULFrBo0SK01nz22We89957ABw5coTNmzeTlpbGBx98wKRJk6iurqa2tpY5c+aQnp5OeHj4RdesqKiwfni//fbb1uMdlVqura1Fa42npyerV6/GycmJ+Ph4AHx8fNiyZQupqam8++67/PSnP+3Sexai33IfAKNvM26NtZD/tWUZ5DLY9QE4e0LUdGNDpegZ4Na1/38I0RUSDGxoaLhvlwNBizFjxrBgwQLGjx8PwMKFC0lOTqawsJCYmBheffVV7r33XuLj43nggQeoqKjgxhtvpL6+Hq01L7300kXXfOKJJ5g/fz6//e1vmTt3rvX4tGnTWLx4MUlJSTz55JPcfvvt1sdOnTrFzJkzcXBwIDAw0BpOAF577TUWLFhAXV0ds2fPZvbs2TZ570L0ay4exmZJcRnGcELheti/1Nh5MedzcHCG8KnG4zFzwWuQvVssrjJ2K7uslPIHPgJGAIXAbVrrsgvOSQL+D/ABmoHfaa0/sjwWBnwIBABZwA+01o1KKVfgXWAsUArcrrUu7KgtUna575G/j+h3zGYo3g4HvjR6E8oKjZLRwROMkBA9CwIi7N1K0Y+0V3bZnpMPFwFrtdZRwFrL/QvVAj/UWo8EZgF/UkoNsDz2v8DLWutIoAy4z3L8PqDMcvxly3lCCNG7OThASCrM+C08nA0/2QDXPAH1FbDyKfjrGPhrCqz8JRxeL5MXRbexZ49BLjBVa31cKTUMWKe1jrnEc3YBtwKHgNPAUK21SSmVBjyntZ6plFpp+XmzUsoJOAEM0h28Uekx6Hvk7yOuKmcPG5MXD66Awg3Q3GjMQ4icbvQkRE4HD397t1L0Me31GNhzjsEQrfVxy88ngCEdnayUGg+4APkYwwflWuuWKe3FQMuU+EDgKIAlNFRYzj9zwfXuB+4HCAkJ6fKbEUKI7pBVVMaWgmYmhN/G2NQfQ0OVsdzx4Ao4uAr2fmIZckg1ij1Fz4JBscYeC0JcgW4NBkqpNcDQNh76Zes7WmutlGr3G72lR+E9YL7W2nzhcr0robX+O/B3MHoMunxBIYSwsayiMu56YwuNJjMuTg68v3CCUfMh7gbjZjbD8Z1wcKURFNY8Z9wGhBgBIXomhE4y6j4I0UndGgy01tPbe0wpdVIpNazVUMKpds7zAZYBv9Rab7EcLgUGKKWcLL0GQUDLtnslQDBQbBlK8LWcL4QQfcqWglIaTWbMGppMZrYUlJ5fDMrBwdhNMXAsTHsKKo9ZQsJK2PEebPu7sRQyYpoREqJmgHdb39WEOMeeQwlfAPOBxZZ/fn7hCUopF+Az4F2t9cctxy09DN9gzDf48ILnt1x3s+XxrzuaXyCEEL3VhPAAXJwcaDKZcXZyYEJ4QMdP8Bl+blOlpjpjkuLBFUZQOLDUOGf4mHO9CcMSZchBXMSeqxIWA9crpfKA6Zb7KKVSlFJvWM65DbgGWKCUyrbckiyP/QL4uVLqEMYcgjctx98EAizHf07bqx16vfLycl577TXr/cLCQj744APr/czMTB5++GGbv+6SJUvIyclp87GioiKuu+46Ro8ezdSpU8/b+fCdd94hKiqKqKgo3nnnHZu3S4ir0dhQP95fOIGfz4g5N4zQWc7uxmZJGS/Bo3vhJxvh2mfAwcmo4fD3KcYWzV88DAeWQ2NN970R0afYbVVCb9IbVyUUFhaSkZHB3r17AVi3bh0vvvgiS5cu7dbXXbBgARkZGdx6660XPfb973+fjIwM5s+fz9dff81bb73Fe++9x9mzZ0lJSSEzMxOlFGPHjiUrKws/v+7bFtrefx8h+rSaM5C32uhNOLQWGqvA0dUoLR0907gNkEnZ/V1v3MdAdGDRokXk5+eTlJTE448/zqJFi1i/fj1JSUm8/PLLrFu3joyMDACee+455s+fz+TJkwkNDeXTTz/liSeeICEhgVmzZtHUdPF659dff51x48aRmJjI9773PWpra9m0aRNffPEFjz/+OElJSeTn55/3nNYlmKdNm8bnnxujNytXruT666/H398fPz8/rr/+elasWHHRa44YMYInn3ySpKQkUlJS2LFjBzNnziQiIoK//e1vtv4VCiHa4zkQku6E296BJwrgh1/AuIVwNh+WPwZ/SoDXJsKa/wdHtoK52d4tFj1ItkTuhG/e/junigpses3BoeFMW3B/u48vXryYvXv3kp2dDVzcY7Bu3brzzs/Pz+ebb74hJyeHtLQ0PvnkE/7whz9w8803s2zZMm666abzzr/lllv40Y9+BMDTTz/Nm2++yU9/+lPmzZvXbo9BYmIin376KT/72c/47LPPqKqqorS0lJKSEoKDg63ndVSCOSQkhOzsbB599FEWLFjAxo0bqa+vZ9SoUfzkJz+51K9NCGFrTi4QPsW4zfo9nMk7Ny9h459hw0vgEWBMXIyeCRHXSi2Hfk6CQT8xe/ZsnJ2dSUhIoLm5mVmzZgGQkJDQZmnjvXv38vTTT1NeXk51dTUzZ8685Gu8+OKLPPTQQ7z99ttcc801BAYGXnYJ5nnz5lnbVV1djbe3N97e3ri6ulJeXs6AAQMu63pCCBsbGGXcJv7UqAqZv/bccshd/zLmKIRONCYwRs00tmmWCYz9igSDTujom31v0bq0sbOzs7U0c3uljRcsWMCSJUtITEzk7bffvqgHoi3Dhw/n008/BaC6uppPPvmEAQMGEBgYeN7zi4uL2y3pLCWYhehD3AfAqO8ZN3OzUcuhpTdh5VPGbUCo0YsQeZ0xR0F6E/o8CQa91IWlkDsqjXwlqqqqGDZsGE1NTbz//vvWcswdvc6ZM2fw9/fHwcGB559/nnvvvReAmTNn8tRTT1FWZtTAWrVqFc8//7zN2iqE6AUcHCFkgnGb/hyUFRnbNOd/DXv+A1lvgXKEoHFGSIi4DoYnGc8TfYpMPuylAgICSE9PZ9SoUTz++OOMHj0aR0dHEhMTefnll7t8/d/85jekpqaSnp5ObGys9fgdd9zBCy+8QHJy8kWTD9etW0dMTAzR0dGcPHmSX/7S2MDS39+fZ555hnHjxjFu3DieffZZ/P1l33Yh+jW/UBj/I7jzX/CLQliwHCY9As0N8M3v4Y1r4YUI+M8CY7OlirbnHYneR5Yr0juXK4qOyd9HCPsyajiUMiE84OL9FWpKoeAbozfh0FqoPmEcHxRr9CREXGvMU3Dx6PmGC6veWERJCCFEH9RuDYcWngGQcKtx0xpO5ZwLCdvfgC2vGvsmhE48Nz9hcLxMYuwlJBgIIYS4LJes4dCaUjBkpHGb+FNjq+aijXDoayMsrH7GuHkNPRcSwqcZ4ULYhQQDIYQQl+Wyazi05uwOkdONGxhzD/ItIeHgV7DrA0AZdRxaJjEGjTP2WxA9QoKBEEKIy9JSw6HdOQaXwzcQxvzAuJmb4Vi2JSishQ1/gvV/BBcvYylkxLXGzT9chh26kQQDIYQQl21sqF/XAkFbHBwhaKxxm/I41FcYFSLz1xrzE3KXG+f5BBlBIWyy8U/fINu24yonwUAIIUTv5OYLcRnGTWs4W2Csdji8HvJWWoYdMHoQRlhCQtg14DXYvu3u42Qfgz6msLCQUaNG2fSa2dnZLF++vM3HSktLmTZtGl5eXjz00EPW47W1tcydO5fY2FhGjhzJokXnqls3NDRw++23ExkZSWpqaptbMgshxGVRyth+edxCo/jTY4eMUtIznzeWQe77DD65D16MgldTYfnjsP9LqD1r75b3OdJjIMjOziYzM5M5c+Zc9Jibmxu/+c1v2Lt3r7UEdIvHHnuMadOm0djYyHXXXcdXX33F7NmzefPNN/Hz8+PQoUN8+OGH/OIXv+Cjjz7qqbcjhLgaODjA0FHGLe2/odkEJ3YZvQmHv4Od/4RtfwcUDE0415sQkgZuPvZufa8mPQY21FBUSeU3R2koqrTJ9V566SVGjRrFqFGj+NOf/mQ9bjKZuOuuu4iLi+PWW2+ltrYWMEo1x8fHM3r0aB577LGLrrdt2zbS0tJITk5m4sSJ5Obm0tjYyLPPPstHH31EUlLSRR/gnp6eTJo0CTc3t/OOe3h4MG3aNABcXFwYM2YMxcXFAHz++efMnz8fgFtvvZW1a9dy4UZa69atY8qUKdx4442Eh4ezaNEi3n//fcaPH09CQsJFuy4KIUSHHJ0gcKyx++IPPoVfFMG9K2HaU8aQxLbX4YPb4H9HwBvTjZLS+d9AY629W97rSI+BjTQUVXLmjT1okxnl5MDAhQm4hl55Ks3KyuKtt95i69ataK1JTU1lypQp+Pn5kZuby5tvvkl6ejr33nsvr732Gvfccw+fffYZBw4cQClFeXn5RdeMjY1l/fr1ODk5sWbNGp566ik++eQTfv3rX5OZmckrr7xyRW0tLy/nyy+/5Gc/+xnAeWWYnZyc8PX1pbS0lIEDB573vF27drF//378/f0JDw9n4cKFbNu2jT//+c/89a9/PS8MCSFEh7stXsjJ5VxthylPGPsnHN1m9CYUrodNfzFKSju6GMshW3oUAlOu+qWREgxspKGgAm0ygwZtMtNQUNGlYLBhwwZuvvlmPD09AbjllltYv3498+bNIzg4mPT0dADuvvtu/vKXv/DII4/g5ubGfffdR0ZGBhkZGRdds6Kigvnz55OXl4dSiqampituXwuTycSdd97Jww8/THh4+GU9d9y4cQwbNgyAiIgIZsyYARglmb/55psut00I0X9ccrfFS3F2h/Apxg2goQqObDGCwuHvYN1iWPc8OLlDUIqxK2NImhEaXL265031UhIMbMQ13Bfl5GDtMXAN777So+qC9btKKZycnNi2bRtr167l448/5pVXXuHrr78+77xnnnmGadOm8dlnn1FYWNhuaeTLcf/99xMVFcUjjzxiPRYYGMjRo0cJCgrCZDJRUVFBQMDFG6BcWHa5dUlmKcEshGjtsnZb7AxXb4i63rgB1JVB0SYjJBRtgu9eAG02KkYOSzwXFELS+v2ujBIMbMQ11IeBCxOMnoJw3y71FgBMnjyZBQsWsGjRIrTWfPbZZ7z33nsAHDlyhM2bN5OWlsYHH3zApEmTqK6upra2ljlz5pCent7mt/eKigpreeW3337bevxKSzo//fTTVFRU8MYbb5x3fN68ebzzzjukpaXx8ccfc+21114UZoQQ4nJ0abfFznD3g9i5xg2gvtIYejiy2bhtex02W4ZbB8ZAaBqETDT+OSDEtm2xMwkGNuQa6tPlQNBizJgxLFiwgPHjxwOwcOFCkpOTKSwsJCYmhldffZV7772X+Ph4HnjgASoqKrjxxhupr69Ha81LL7100TWfeOIJ5s+fz29/+1vmzp1rPT5t2jQWL15MUlISTz75JLfffvt5zxsxYgSVlZU0NjayZMkSVq1ahY+PD7/73e+IjY1lzJgxADz00EMsXLiQ++67jx/84AdERkbi7+/Phx9+aJPfiRDi6mXT3RY7cN48hqjpEGXZutnUAMd2Gr0JRzbD3s8g623jMZ8gYy5DS1gYFGusmuijpOwyUna5L5K/jxDC1i5rHoO52agaWbQZjmwy/tlSXtrdD4JbBYXhSeDo3GPvo7Ok7LIQQgjRgcuax+DgaOyPMDQBUu83dmYsO3x+UDj4lXGukxsMTzYmNQaNh+Dx4D20597YZZJgIIQQQtDFeQxKGVsz+4dD8l3GsaqTxrDD0W1QvB22/n+w6a/GY77BxoqHoHFGUBg6utcsk5RgIIQQQtAN8xi8h8DIm4wbGPMUju82QkLxNiMw7PvUeMzR1Vj9EDz+XM+Cb2DXXv8KSTAQQgghLLqlamQLJ1cIHmfc+G/jWOUxS1DYDke3n7/6wSfwXEgIGgeBY3pkroJdgoFSyh/4CBgBFAK3aa3LLjgnCfg/wAdoBn6ntf7I8tj7QArQBGwDfqy1blJKTQU+Bw5bLvOp1vrX3ftuhBBCiLZdcrdGn+EQf6NxAzA1wsk9RkgotgxB5HxuPPZ4PngOvPgaNmavHoNFwFqt9WKl1CLL/V9ccE4t8EOtdZ5SajiQpZRaqbUuB94H7rac9wGwECNEAKzXWl+87Z8QQgjRg65ot0YnF6PmQ+BY4CfGsaoTcGJPj4QCsF8RpRuBdyw/vwPcdOEJWuuDWus8y8/HgFPAIMv95doCo8cgqCca3ZPKy8t57bXXrPcLCwv54IMPrPczMzN5+OGHbf66S5YsIScnp83HvvvuO8aMGYOTkxMff/yx9Xh2djZpaWmMHDmS0aNHn1eI6fDhw6SmphIZGcntt99OY2OjzdsshBC9UVurHK6I99BzOzT2AHsFgyFa6+OWn08AQzo6WSk1HnAB8i847gz8AFjR6nCaUmqXUuorpdTIDq55v1IqUymVefr06St6E93pUsEgJSWFv/zlLzZ/3Y6CQUhICG+//Tb/9V//dd5xDw8P3n33Xfbt28eKFSt45JFHrEWcfvGLX/Doo49y6NAh/Pz8ePPNN23eZiGE6I1aVjk4Krpnt8buorXulhuwBtjbxu1GoPyCc8s6uM4wIBeY0MZjrwN/anXfB/Cy/DwHyOtMW8eOHasvlJOTc9GxnnT77bdrNzc3nZiYqB977DGdmpqqfXx8dGJion7ppZf0N998o+fOnau11vpXv/qV/uEPf6gnTZqkQ0JC9CeffKIff/xxPWrUKD1z5kzd2Nh40fX//ve/65SUFD169Gh9yy236JqaGr1x40bt5+enR4wYoRMTE/WhQ4fabNv8+fP1f/7zn3bbPnr0aH3w4EFtNpt1QECAbmpq0lprvWnTJj1jxoyLzr+S9tv77yOEEJ2RWXhWv/J1ns4sPGvvplwEyNRtfCZ22xwDrfX09h5TSp1USg3TWh9XSg3DGCZo6zwfYBnwS631lgse+xXG0MKPW71mZauflyulXlNKDdRan+nKe/nqq684ceJEVy5xkaFDhzJ79ux2H1+8eDF79+4lOzsbgHXr1vHiiy+ydOlS6/3W8vPz+eabb8jJySEtLY1PPvmEP/zhD9x8880sW7aMm2666bzzb7nlFn70ox8BRs2DN998k5/+9KfMmzePjIwMbr311it6X9u2baOxsZGIiAhKS0sZMGAATk7Gv2ZBQUGUlJS0+bzLbb8QQvQF3brKoZvYayjhC2C+5ef5GCsJzqOUcgE+A97VWn98wWMLgZnAnVprc6vjQ5WlWo9l+MEBuMJBnb5l9uzZODs7k5CQQHNzM7NmzQKMEsaFhYUXnb93714mT55MQkIC77//Pvv27etyG44fP84PfvAD3nrrLRwuc5/wy22/EEJcLbKKynj1m0NkFZVd+mQbsNeqhMXAv5VS9wFFwG0ASqkU4Cda64WWY9cAAUqpBZbnLdBaZwN/szxvsyUHtCxLvBV4QCllAuqAOyzdJV3S0Tf73qJ1yWJnZ2drNcP2ShgvWLCAJUuWkJiYyNtvv31RD8TlqqysZO7cufzud79jwoQJAAQEBFBeXo7JZMLJyYni4mJrdceutl8IIa4GV7SyoYvsEgy01qXAdW0cz8RYeojW+p/AP9t5fpvt1lq/Arxiu5baz4WlkK+0NHJ7qqqqGDZsGE1NTbz//vvWD+wreZ3GxkZuvvlmfvjDH543BKGUYtq0aXz88cfccccdvPPOO9x44402ew9CCNHfXVb9Bhvpu3Uh+7mAgADS09MZNWoUjz/+OKNHj8bR0ZHExERefvnlLl//N7/5DampqaSnpxMbG2s9fscdd/DCCy+QnJxMfv55i0DYvn07QUFB/Oc//+HHP/4xI0caiz7+/e9/89133/H222+TlJREUlKSdW7E//7v//LSSy8RGRlJaWkp9913X5fbLoQQVwt7rGyQsstI2eW+SP4+QoirxSV3T7xCUnZZCCGE6IN6emWDDCUIIYQQwkqCQQdkmKV3kr+LEEJ0HwkG7XBzc6O0tFQ+hHoZrTWlpaW4ubnZuylCCNEvyRyDdgQFBVFcXExvrKNwtXNzcyMoqN/VzRJCiF5BgkE7nJ2dCQsLs3czhBBCiB4lQwlCCCGEsJJgIIQQQggrCQZCCCGEsJKdDwGl1GmMokz9lS9QYe9G2FhvfU/2ald3v66tr2+L63X1Glf6/IFAl0q5i07rrf+dd1VveV+hWutBFx6UYHAVUEr9XWt9v73bYUu99T3Zq13d/bq2vr4trtfVa1zp85VSmW1tIytsr7f+d95Vvf19yVDC1eFLezegG/TW92SvdnX369r6+ra4Xlev0Vv/HRLn9Ne/Ua9+X9JjIIQQl0F6DER/Jz0GQghxef5u7wYI0Z2kx0AIIYQQVtJjIIQQQggrCQZCCCGEsJJgIIQQQggrCQZCCCGEsJJgIIQQNqKUCldKvamU+tjebRHiSkkwEEIIQCn1D6XUKaXU3guOz1JK5SqlDimlFnV0Da11gdb6vu5tqRDdy8neDRBCiF7ibeAV4N2WA0opR+BV4HqgGNiulPoCcASev+D592qtT/VMU4XoPhIMhBAC0Fp/p5QaccHh8cAhrXUBgFLqQ+BGrfXzQEYPN1GIHiFDCUII0b5A4Gir+8WWY21SSgUopf4GJCulnuzuxgnRHaTHQAghbERrXQr8xN7tEKIrpMdACCHaVwIEt7ofZDkmRL8lwUAIIdq3HYhSSoUppVyAO4Av7NwmIbqVBAMhhACUUv8CNgMxSqlipdR9WmsT8BCwEtgP/Ftrvc+e7RSiu0l1RSGEEEJYSY+BEEIIIawkGAghhBDCSoKBEEIIIawkGAghhBDCSoKBEEIIIawkGAghhBDCSoKBEOKyKaUGKKX+2/LzcKXUxza89iNKqR+2cXxES0lkpVSCUuptW72mEOIcCQZCiCsxAPhvAK31Ma31rba4qFLKCbgX+KCj87TWe4AgpVSILV5XCHGOBAMhxJVYDEQopbKVUv9p9U1+gVJqiVJqtVKqUCn1kFLq50qpnUqpLUopf8t5EUr9/+3dvY9MURjH8e9Ps1uQbCdovRXiJRsNS0K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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print('rmse:', ca3.rmse())\n", + "plt.figure(figsize=(8, 5))\n", + "hc1 = ml.head(r1, 0, t1)\n", + "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", + "plt.semilogx(t1, hc1[0], label='ttim at 30 m')\n", + "hc2 = ml.head(r2, 0, t2)\n", + "plt.semilogx(t2, h2, '.', label='obs at 60 m')\n", + "plt.semilogx(t2, hc2[0], label='ttim at 60 m')\n", + "hc3 = ml.head(r3, 0, t3)\n", + "plt.semilogx(t3, h3, '.', label='obs at 90 m')\n", + "plt.semilogx(t3, hc3[0], label='ttim at 90 m')\n", + "hc4 = ml.head(r4, 0, t4)\n", + "plt.semilogx(t4, h4, '.', label='obs at 120 m')\n", + "plt.semilogx(t4, hc4[0], label='ttim at 120 m')\n", + "plt.xlabel('time(d)')\n", + "plt.ylabel('drawdown(m)')\n", + "plt.title('ttim fit except for data of obs3')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.1.4. Calibration without obs4" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...............................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 28\n", + " # data points = 39\n", + " # variables = 3\n", + " chi-square = 0.00113973\n", + " reduced chi-square = 3.1659e-05\n", + " Akaike info crit = -401.180660\n", + " Bayesian info crit = -396.189975\n", + "[[Variables]]\n", + " kaq0: 41.7209084 +/- 1.22793564 (2.94%) (init = 10)\n", + " Saq0: 5.7838e-05 +/- 3.9836e-06 (6.89%) (init = 0.0001)\n", + " c0: 180.964217 +/- 38.2629064 (21.14%) (init = 1000)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, c0) = 0.844\n", + " C(kaq0, Saq0) = -0.794\n", + " C(Saq0, c0) = -0.452\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq041.7209081.2279362.943214110010[41.72090838864663]
Saq00.0000580.0000046.8875610.000010.0010.0001[5.78378831790338e-05]
c0180.96421738.26290621.1439071001000000.01000[180.96421744249864]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 41.720908 1.227936 2.943214 1 100 10 \n", + "Saq0 0.000058 0.000004 6.887561 0.00001 0.001 0.0001 \n", + "c0 180.964217 38.262906 21.143907 100 1000000.0 1000 \n", + "\n", + " parray \n", + "kaq0 [41.72090838864663] \n", + "Saq0 [5.78378831790338e-05] \n", + "c0 [180.96421744249864] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ca4 = Calibrate(ml)\n", + "ca4.set_parameter(name='kaq0', initial=10, pmin=1, pmax=100)\n", + "ca4.set_parameter(name='Saq0', initial=1e-4, pmin=1e-5, pmax=1e-3)\n", + "ca4.set_parameter(name='c0', initial=1000, pmin=100, pmax=1e6)\n", + "ca4.series(name='obs1', x=r1, y=0, layer=0, t=t1, h=h1)\n", + "ca4.series(name='obs3', x=r2, y=0, layer=0, t=t2, h=h2)\n", + "ca4.series(name='obs4', x=r3, y=0, layer=0, t=t3, h=h3)\n", + "ca4.fit()\n", + "display(ca4.parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.005405897042964738\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print('rmse:', ca4.rmse())\n", + "plt.figure(figsize=(8, 5))\n", + "hd1 = ml.head(r1, 0, t1)\n", + "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", + "plt.semilogx(t1, hd1[0], label='ttim at 30 m')\n", + "hd2 = ml.head(r2, 0, t2)\n", + "plt.semilogx(t2, h2, '.', label='obs at 60 m')\n", + "plt.semilogx(t2, hd2[0], label='ttim at 60 m')\n", + "hd3 = ml.head(r3, 0, t3)\n", + "plt.semilogx(t3, h3, '.', label='obs at 90 m')\n", + "plt.semilogx(t3, hd3[0], label='ttim at 90 m')\n", + "hd4 = ml.head(r4, 0, t4)\n", + "plt.semilogx(t4, h4, '.', label='obs at 120 m')\n", + "plt.semilogx(t4, hd4[0], label='ttim at 120 m')\n", + "plt.xlabel('time(d)')\n", + "plt.ylabel('drawdown(m)')\n", + "plt.title('ttim fit except for data of obs4')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.1.4. Summary of results of the simulations missing one observation" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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k [m/d]Ss [1/m]c [d]RMSE
Data at 30 m removed57.558160.000033999468.8648480.003230
Data at 60 m removed45.0264920.000044349.1390130.002633
Data at 90 m removed57.558160.000033999468.8648480.006726
Data at 120 m removed41.7209080.000058180.9642170.005406
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" + ], + "text/plain": [ + " k [m/d] Ss [1/m] c [d] RMSE\n", + "Data at 30 m removed 57.55816 0.000033 999468.864848 0.003230\n", + "Data at 60 m removed 45.026492 0.000044 349.139013 0.002633\n", + "Data at 90 m removed 57.55816 0.000033 999468.864848 0.006726\n", + "Data at 120 m removed 41.720908 0.000058 180.964217 0.005406" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'c [d]'], \\\n", + " index=['Data at 30 m removed', 'Data at 60 m removed', \\\n", + " 'Data at 90 m removed', 'Data at 120 m removed'])\n", + "t.loc['Data at 30 m removed'] = ca1.parameters['optimal'].values\n", + "t.loc['Data at 60 m removed'] = ca2.parameters['optimal'].values\n", + "t.loc['Data at 90 m removed'] = ca1.parameters['optimal'].values\n", + "t.loc['Data at 120 m removed'] = ca4.parameters['optimal'].values\n", + "rmse = [ca1.rmse(), ca2.rmse(), ca3.rmse(), ca4.rmse()]\n", + "t['RMSE'] = rmse\n", + "t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The values for hydraulic conductivity and specific storage changed slightly for every simulation. However, the resistance of the aquitard layer varied significantly, indicating that this parameter is not uniform in the region investigated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.2. Calibrate with four datasets simultaneously:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we calibrate the same model but with all four observation wells considered." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "#unkonwn parameters: kaq, Saq, c\n", + "m_1 = ModelMaq(kaq=10, z=[0, zt, zb], c=500, Saq=0.001, topboundary='semi', \\\n", + " tmin=0.001, tmax=0.5)\n", + "w_1 = Well(m_1, xw=0, yw=0, tsandQ=[(0, Q), (0.34, 0)])\n", + "m_1.solve(silent = 'True')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 33\n", + " # data points = 51\n", + " # variables = 3\n", + " chi-square = 0.00178546\n", + " reduced chi-square = 3.7197e-05\n", + " Akaike info crit = -517.255140\n", + " Bayesian info crit = -511.459663\n", + "[[Variables]]\n", + " kaq0: 45.3318581 +/- 1.18521248 (2.61%) (init = 10)\n", + " Saq0: 4.7623e-05 +/- 3.1043e-06 (6.52%) (init = 0.0001)\n", + " c0: 331.164465 +/- 76.1857064 (23.01%) (init = 500)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.771\n", + " C(kaq0, c0) = 0.762\n", + " C(Saq0, c0) = -0.299\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq045.3318581.1852122.614524-infinf10[45.33185809252601]
Saq00.0000480.0000036.518546-infinf0.0001[4.762268497957192e-05]
c0331.16446576.18570623.0053990.0inf500[331.16446453346026]
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" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 45.331858 1.185212 2.614524 -inf inf 10 \n", + "Saq0 0.000048 0.000003 6.518546 -inf inf 0.0001 \n", + "c0 331.164465 76.185706 23.005399 0.0 inf 500 \n", + "\n", + " parray \n", + "kaq0 [45.33185809252601] \n", + "Saq0 [4.762268497957192e-05] \n", + "c0 [331.16446453346026] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "c0 = Calibrate(ml)\n", + "c0.set_parameter(name='kaq0', initial=10)\n", + "c0.set_parameter(name='Saq0', initial=1e-4)\n", + "c0.set_parameter(name='c0', initial=500, pmin=0)\n", + "c0.series(name='obs1', x=30, y=0, t=t1, h=h1, layer=0)\n", + "c0.series(name='obs2', x=60, y=0, t=t2, h=h2, layer=0)\n", + "c0.series(name='obs3', x=90, y=0, t=t3, h=h3, layer=0)\n", + "c0.series(name='obs4', x=120, y=0, t=t4, h=h4, layer=0)\n", + "c0.fit(report=True)\n", + "display(c0.parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.0059168424104503155\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm_11 = ml.head(r1, 0, t1)\n", + "hm_12 = ml.head(r2, 0, t2)\n", + "hm_13 = ml.head(r3, 0, t3)\n", + "hm_14 = ml.head(r4, 0, t4)\n", + "print('rmse:', c0.rmse())\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", + "plt.semilogx(t1, hm_11[0], label='ttim at 30 m')\n", + "plt.semilogx(t2, h2, '.', label='obs at 60 m')\n", + "plt.semilogx(t2, hm_12[0], label='ttim at 60 m')\n", + "plt.semilogx(t3, h3, '.', label='obs at 90 m')\n", + "plt.semilogx(t3, hm_13[0], label='ttim at 90 m')\n", + "plt.semilogx(t4, h4, '.', label='obs at 120 m')\n", + "plt.semilogx(t4, hm_14[0], label='ttim at 120 m')\n", + "plt.xlabel('time(d)')\n", + "plt.ylabel('drawdown(m)')\n", + "plt.title('model with leakage only')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The overall fit is relatively good. Comparing the new model to the three previous models, the adjusted parameters seem to be in between the previously computed values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Alternative Model Aquitard with leakage & storage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second conceptualization for the Dalem test is to consider the storage in the aquitard. Hence, we define the ```Sll``` parameter in the model building class ModelMaq:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "#unkonwn parameters: kaq, Saq, c, Sll\n", + "m_2 = ModelMaq(kaq=10, z=[0, zt, zb], c=500, Saq=0.001, Sll=0.001, \\\n", + " topboundary='semi', tmin=0.001, tmax=0.5)\n", + "w_2 = Well(m_2, xw=0, yw=0, tsandQ=[(0, Q), (0.34, 0)])\n", + "m_2.solve(silent = 'True')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Calibration of Alternative Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We follow the same previous steps for calibration, but now we add the additional `Sll` parameter with the ```set_parameter_by_reference``` method: ***I think I have found a bug***" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 79\n", + " # data points = 51\n", + " # variables = 4\n", + " chi-square = 0.00206484\n", + " reduced chi-square = 4.3933e-05\n", + " Akaike info crit = -507.840896\n", + " Bayesian info crit = -500.113593\n", + "[[Variables]]\n", + " kaq0: 44.8361465 +/- 1.37005719 (3.06%) (init = 10)\n", + " Saq0: 4.3738e-05 +/- 9.0241e-06 (20.63%) (init = 0.0001)\n", + " c0: 934.126739 +/- 8774.46957 (939.32%) (init = 500)\n", + " Sll: 2.2042e-04 +/- 0.00332575 (1508.80%) (init = 0.001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(c0, Sll) = 0.987\n", + " C(Saq0, Sll) = 0.922\n", + " C(Saq0, c0) = 0.893\n", + " C(kaq0, Sll) = 0.361\n", + " C(kaq0, c0) = 0.246\n", + " C(kaq0, Saq0) = 0.202\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq044.8361471.3700573.055698-infinf10[44.836146524596224]
Saq00.0000440.00000920.6323210.000000e+00inf0.0001[4.373790659051302e-05]
c0934.1267398774.469571939.3232420.000000e+001000.00500[934.1267393695258]
Sll0.000220.0033261508.7969561.000000e-080.010.001[0.00022042417906498941]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 44.836147 1.370057 3.055698 -inf inf 10 \n", + "Saq0 0.000044 0.000009 20.632321 0.000000e+00 inf 0.0001 \n", + "c0 934.126739 8774.469571 939.323242 0.000000e+00 1000.00 500 \n", + "Sll 0.00022 0.003326 1508.796956 1.000000e-08 0.01 0.001 \n", + "\n", + " parray \n", + "kaq0 [44.836146524596224] \n", + "Saq0 [4.373790659051302e-05] \n", + "c0 [934.1267393695258] \n", + "Sll [0.00022042417906498941] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## Check why the errors in the pmin of the Sll adjustment\n", + "c1 = Calibrate(m_2)\n", + "c1.set_parameter(name='kaq0', initial=10)\n", + "c1.set_parameter(name='Saq0', initial=1e-4, pmin = 0)\n", + "c1.set_parameter(name='c0', initial=500, pmin=0, pmax = 1000)\n", + "c1.set_parameter_by_reference(name='Sll', parameter=m_2.aq.Sll[:], initial=1e-3, pmin = 1e-8, pmax = 0.01)\n", + "c1.series(name='obs1', x=30, y=0, t=t1, h=h1, layer=0)\n", + "c1.series(name='obs2', x=60, y=0, t=t2, h=h2, layer=0)\n", + "c1.series(name='obs3', x=90, y=0, t=t3, h=h3, layer=0)\n", + "c1.series(name='obs4', x=120, y=0, t=t4, h=h4, layer=0)\n", + "c1.fit(report=True)\n", + "display(c1.parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.006362946741751943\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm_21 = m_2.head(r1, 0, t1)\n", + "hm_22 = m_2.head(r2, 0, t2)\n", + "hm_23 = m_2.head(r3, 0, t3)\n", + "hm_24 = m_2.head(r4, 0, t4)\n", + "print('rmse:', c1.rmse())\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", + "plt.semilogx(t1, hm_21[0], label='ttim at 30 m')\n", + "plt.semilogx(t2, h2, '.', label='obs at 60 m')\n", + "plt.semilogx(t2, hm_22[0], label='ttim at 60 m')\n", + "plt.semilogx(t3, h3, '.', label='obs at 90 m')\n", + "plt.semilogx(t3, hm_23[0], label='ttim at 90 m')\n", + "plt.semilogx(t4, h4, '.', label='obs at 120 m')\n", + "plt.semilogx(t4, hm_24[0], label='ttim at 120 m')\n", + "plt.xlabel('time(d)')\n", + "plt.ylabel('drawdown(m)')\n", + "plt.title('model with both leakage and storage')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The adjusted parameter did not improve the fit and the new model has higher values of AIC and BIC compared with the previous model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Alternative model with Impervious top.\n", + "\n", + "We test the final alternative model that the surface layer is impervious at the top. Hence, we adjust a ModelMaq with an additional 1 mm thick aquifer at the top and the configuration ```topboundary = 'conf'```.\n", + "We then test this configuration considering a model with both storage and resistance in the aquitard layer and a model with just the resistance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8.1. Model with storage and leakage" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vcant\\anaconda3\\envs\\ttim\\lib\\site-packages\\ttim\\aquifer.py:73: RuntimeWarning: divide by zero encountered in true_divide\n", + " self.D = self.T / self.Scoefaq\n" + ] + } + ], + "source": [ + "#unkonwn parameters: kaq1, Saq1, c, Sll\n", + "m_3 = ModelMaq(kaq=[0.01, 10], z=[0, -0.001, -8.001, -45.001], c = 500, \\\n", + " Saq = [0, 0.001], Sll = 1e-4, topboundary = 'conf', tmin=0.001, tmax=0.5)\n", + "w_3 = Well(m_3, xw = 0, yw = 0, tsandQ = [(0, 761), (0.34, 0)], layers = 1)\n", + "m_3.solve(silent = 'True')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 94\n", + " # data points = 51\n", + " # variables = 4\n", + " chi-square = 0.00177210\n", + " reduced chi-square = 3.7704e-05\n", + " Akaike info crit = -515.638082\n", + " Bayesian info crit = -507.910779\n", + "[[Variables]]\n", + " kaq1: 45.1862811 +/- 1.22692771 (2.72%) (init = 10)\n", + " Saq1: 3.9423e-05 +/- 6.9685e-06 (17.68%) (init = 0.0001)\n", + " c1: 888.317499 +/- 207898.722 (23403.65%) (init = 500)\n", + " Sll: 4.1670e-04 +/- 0.11253785 (27006.75%) (init = 1e-05)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(c1, Sll) = 1.000\n", + " C(Saq1, c1) = 0.888\n", + " C(Saq1, Sll) = 0.888\n", + " C(kaq1, c1) = 0.192\n", + " C(kaq1, Sll) = 0.191\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq145.1862811.2269282.715266-infinf10[45.18628110628006]
Saq10.0000390.00000717.676049-infinf0.0001[3.942336849842717e-05]
c1888.317499207898.72232823403.6504440.01000.0500[888.3174991342955]
Sll0.0004170.11253827006.752060.0inf0.00001[0.0004167026352754899, 0.0004167026352754899]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq1 45.186281 1.226928 2.715266 -inf inf 10 \n", + "Saq1 0.000039 0.000007 17.676049 -inf inf 0.0001 \n", + "c1 888.317499 207898.722328 23403.650444 0.0 1000.0 500 \n", + "Sll 0.000417 0.112538 27006.75206 0.0 inf 0.00001 \n", + "\n", + " parray \n", + "kaq1 [45.18628110628006] \n", + "Saq1 [3.942336849842717e-05] \n", + "c1 [888.3174991342955] \n", + "Sll [0.0004167026352754899, 0.0004167026352754899] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "c2 = Calibrate(m_3)\n", + "c2.set_parameter(name='kaq1', initial=10)\n", + "c2.set_parameter(name='Saq1', initial=1e-4)\n", + "c2.set_parameter(name='c1', initial=500, pmin=0, pmax = 1000)\n", + "c2.set_parameter_by_reference(name='Sll', parameter=m_3.aq.Sll[:], initial=1e-5, pmin=0)\n", + "c2.series(name='obs1', x=30, y=0, t=t1, h=h1, layer=1)\n", + "c2.series(name='obs2', x=60, y=0, t=t2, h=h2, layer=1)\n", + "c2.series(name='obs3', x=90, y=0, t=t3, h=h3, layer=1)\n", + "c2.series(name='obs4', x=120, y=0, t=t4, h=h4, layer=1)\n", + "c2.fit(report=True)\n", + "display(c2.parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model adjusted unrealistic values for vertical resistance and aquitard storage. Moreover, their correlation coefficient shows a perfect correlation, which means this is a problem without determination. We must fix or remove one of these parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.005894670238538926\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vcant\\anaconda3\\envs\\ttim\\lib\\site-packages\\ttim\\aquifer.py:73: RuntimeWarning: divide by zero encountered in true_divide\n", + " self.D = self.T / self.Scoefaq\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm_31 = m_3.head(r1, 0, t1)\n", + "hm_32 = m_3.head(r2, 0, t2)\n", + "hm_33 = m_3.head(r3, 0, t3)\n", + "hm_34 = m_3.head(r4, 0, t4)\n", + "print('rmse:', c2.rmse())\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", + "plt.semilogx(t1, hm_31[-1], label='ttim at 30 m')\n", + "plt.semilogx(t2, h2, '.', label='obs at 60 m')\n", + "plt.semilogx(t2, hm_32[-1], label='ttim at 60 m')\n", + "plt.semilogx(t3, h3, '.', label='obs at 90 m')\n", + "plt.semilogx(t3, hm_33[-1], label='ttim at 90 m')\n", + "plt.semilogx(t4, h4, '.', label='obs at 120 m')\n", + "plt.semilogx(t4, hm_34[-1], label='ttim at 120 m')\n", + "plt.xlabel('time(d)')\n", + "plt.ylabel('drawdown(m)')\n", + "plt.title('model with storage only')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8.2. Model with vertical leakage only (without aquitard storage)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We reload and calibrate a model without storage in the aquitard layer" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vcant\\anaconda3\\envs\\ttim\\lib\\site-packages\\ttim\\aquifer.py:73: RuntimeWarning: divide by zero encountered in true_divide\n", + " self.D = self.T / self.Scoefaq\n" + ] + } + ], + "source": [ + "#unkonwn parameters: kaq1, Saq1, c, Sll\n", + "m_4 = ModelMaq(kaq=[0.01, 10], z=[0, -0.001, -8.001, -45.001], c = 500, \\\n", + " Saq = [0, 0.001], Sll = 0.1, topboundary = 'conf', tmin=0.001, tmax=0.5)\n", + "w_4 = Well(m_4, xw = 0, yw = 0, tsandQ = [(0, 761), (0.34, 0)], layers = 1)\n", + "m_4.solve(silent = 'True')" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".......................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 52\n", + " # data points = 51\n", + " # variables = 3\n", + " chi-square = 0.02308536\n", + " reduced chi-square = 4.8095e-04\n", + " Akaike info crit = -386.719493\n", + " Bayesian info crit = -380.924016\n", + "[[Variables]]\n", + " kaq1: 27.4923071 +/- 1.79949992 (6.55%) (init = 10)\n", + " Saq1: 1.0001e-07 +/- 1.3554e-07 (135.52%) (init = 0.001)\n", + " c1: 1999.96913 +/- 83514.7282 (4175.80%) (init = 500)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(Saq1, c1) = -0.877\n", + " C(kaq1, c1) = 0.839\n", + " C(kaq1, Saq1) = -0.788\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq127.4923071.799500e+006.545467-infinf10[27.492307129448434]
Saq10.01.355366e-07135.5218391.000000e-07inf0.001[1.0001088546207626e-07]
c11999.9691278.351473e+044175.8008680.000000e+002000.0500[1999.9691266874966]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq1 27.492307 1.799500e+00 6.545467 -inf inf 10 \n", + "Saq1 0.0 1.355366e-07 135.521839 1.000000e-07 inf 0.001 \n", + "c1 1999.969127 8.351473e+04 4175.800868 0.000000e+00 2000.0 500 \n", + "\n", + " parray \n", + "kaq1 [27.492307129448434] \n", + "Saq1 [1.0001088546207626e-07] \n", + "c1 [1999.9691266874966] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "c3 = Calibrate(m_4)\n", + "c3.set_parameter(name='kaq1', initial=10)\n", + "c3.set_parameter(name='Saq1', initial=1e-3, pmin = 1e-7)\n", + "c3.set_parameter(name='c1', initial=500, pmin=0, pmax = 2000)\n", + "c3.series(name='obs1', x=30, y=0, t=t1, h=h1, layer=1)\n", + "c3.series(name='obs2', x=60, y=0, t=t2, h=h2, layer=1)\n", + "c3.series(name='obs3', x=90, y=0, t=t3, h=h3, layer=1)\n", + "c3.series(name='obs4', x=120, y=0, t=t4, h=h4, layer=1)\n", + "c3.fit(report=True)\n", + "display(c3.parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.021275670123861237\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vcant\\anaconda3\\envs\\ttim\\lib\\site-packages\\ttim\\aquifer.py:73: RuntimeWarning: divide by zero encountered in true_divide\n", + " self.D = self.T / self.Scoefaq\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm_r1 = m_4.head(r1, 0, t1)\n", + "hm_r2 = m_4.head(r2, 0, t2)\n", + "hm_r3 = m_4.head(r3, 0, t3)\n", + "hm_r4 = m_4.head(r4, 0, t4)\n", + "print('rmse:', c3.rmse())\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", + "plt.semilogx(t1, hm_r1[-1], label='ttim at 30 m')\n", + "plt.semilogx(t2, h2, '.', label='obs at 60 m')\n", + "plt.semilogx(t2, hm_r2[-1], label='ttim at 60 m')\n", + "plt.semilogx(t3, h3, '.', label='obs at 90 m')\n", + "plt.semilogx(t3, hm_r3[-1], label='ttim at 90 m')\n", + "plt.semilogx(t4, h4, '.', label='obs at 120 m')\n", + "plt.semilogx(t4, hm_r4[-1], label='ttim at 120 m')\n", + "plt.xlabel('time(d)')\n", + "plt.ylabel('drawdown(m)')\n", + "plt.title('model with storage only')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The resulting model shows a large value for the resistance to vertical flow in the aquitard layer (```c0```). Fit error and AIC, BIC values were similar to the statistics encountered in the semi-confined model without storage (model 2). However, the large vertical resistance for this model is a key result, as it indicates a confined condition. Thus, just with a curve fitting approach, we cannot discard confined conditions in this aquifer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 9. Analysis and summary of values simulated by different models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we compare the simulations done with TTim with other software and the values reported in Kruseman and de Ridder (1970). The published values were determined by graphical adjustment to the Hantush family of type curves (Hantush, 1955). To compare the results reported in the literature with different models, we begin by analysing the values obtained without storage.\n", + "Alongside with TTim and literature values, we report the values MLU (Carlson & Randall, 2012) and AQTESOLV (Duffield, 2007) models, reported by Xinzhu (2020)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9.1. Comparison of results of models without storage" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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k [m/d]Ss [1/m]c [d]Sll [1/m]RMSE
Hantush45.3320.000048331.141-0.005917
ttim - impervious top27.4923070.01999.969127-NaN
ttim - semi-confined45.3318580.000048331.164465-0.005917
MLU45.1860.000039769.20.0003610.005941
AQTESOLV49.2860.000046745.156-0.007245
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" + ], + "text/plain": [ + " k [m/d] Ss [1/m] c [d] Sll [1/m] RMSE\n", + "Hantush 45.332 0.000048 331.141 - 0.005917\n", + "ttim - impervious top 27.492307 0.0 1999.969127 - NaN\n", + "ttim - semi-confined 45.331858 0.000048 331.164465 - 0.005917\n", + "MLU 45.186 0.000039 769.2 0.000361 0.005941\n", + "AQTESOLV 49.286 0.000046 745.156 - 0.007245" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t1 = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'c [d]', 'Sll [1/m]'], \\\n", + " index=['Hantush', 'ttim - impervious top', 'ttim - semi-confined', 'MLU', 'AQTESOLV'])\n", + "t1.loc['Hantush'] = [45.332, 4.762E-5, 331.141, '-']\n", + "t1.loc['ttim - impervious top'] = np.append(c3.parameters['optimal'].values, '-')\n", + "t1.loc['ttim - semi-confined'] = np.append(c0.parameters['optimal'].values, '-')\n", + "t1.loc['MLU'] = [45.186, 3.941e-05, 769.200, 3.611e-04]\n", + "t1.loc['AQTESOLV'] = [49.286, 4.559e-05, 745.156, '-']\n", + "rmse = [0.005917, np.nan,c0.rmse(), 0.005941, 0.007245]\n", + "t1['RMSE'] = rmse\n", + "t1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9.2. Comparison of results of models with storage\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vcant\\anaconda3\\envs\\ttim\\lib\\site-packages\\ttim\\aquifer.py:73: RuntimeWarning: divide by zero encountered in true_divide\n", + " self.D = self.T / self.Scoefaq\n" + ] + }, + { + "data": { + "text/html": [ + "
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k [m/d]Ss [1/m]c [d]Sll [1/m]RMSE
ttim - impervious top45.1862810.000039888.3174990.0004170.005895
ttim - semi-confined44.8361470.000044934.1267390.000220.006363
MLU45.3350.000047331.40.0000130.004941
AQTESOLV45.1590.000041367.5770.0000290.005861
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" + ], + "text/plain": [ + " k [m/d] Ss [1/m] c [d] Sll [1/m] RMSE\n", + "ttim - impervious top 45.186281 0.000039 888.317499 0.000417 0.005895\n", + "ttim - semi-confined 44.836147 0.000044 934.126739 0.00022 0.006363\n", + "MLU 45.335 0.000047 331.4 0.000013 0.004941\n", + "AQTESOLV 45.159 0.000041 367.577 0.000029 0.005861" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t2 = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'c [d]', 'Sll [1/m]'],\\\n", + " index=['ttim - impervious top','ttim - semi-confined', 'MLU', 'AQTESOLV'])\n", + "t2.loc['MLU'] = [45.335, 4.668e-05, 331.400, 1.284e-05]\n", + "t2.loc['AQTESOLV'] = [45.159, 4.100e-05, 367.577, 2.868e-05]\n", + "t2.loc['ttim - impervious top'] = c2.parameters['optimal'].values\n", + "t2.loc['ttim - semi-confined'] = c1.parameters['optimal'].values\n", + "t2['RMSE'] = [c2.rmse(),c1.rmse(), 0.004941, 0.005861]\n", + "t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Overall, all models found similar K values for the aquifer. TTim models differed significantly from the MLU and AQTESOLV solutions for the aquitard storage and resistance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Bakker, M. Semi-analytic modeling of transient multi-layer flow with TTim. Hydrogeol J 21, 935–943 (2013). https://doi.org/10.1007/s10040-013-0975-2\n", + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Newville, M.,Stensitzki, T., Allen, D.B., Ingargiola, A. (2014) LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python.https://dx.doi.org/10.5281/zenodo.11813. https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021).\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Next Notebook: [Leaky 2 - Hardixveld](leaky2_hardixveld.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pumpingtests/leaky2_hardixveld.ipynb b/pumpingtests/leaky2_hardixveld.ipynb new file mode 100644 index 0000000..c1d4c91 --- /dev/null +++ b/pumpingtests/leaky2_hardixveld.ipynb @@ -0,0 +1,1271 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Leaky Aquifer Recovery Test - Hardixveld Example\n", + "**This test is taken from MLU examples.**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Introduction and Conceptual Model\n", + "\n", + "This test was taken in Hardinxveld-Giessendam, Netherlands, in 1981 to quantify the head-loss at each pumping well by clogging and to assess the transmissivity variation in the area.\n", + "\n", + "The hydrogeological conceptualization can be described as the following:\n", + "* The first ten meters depth is an aquitard\n", + "* Followed by the first aquifer from 10 to 37 m depth, this is also the test aquifer.\n", + "* A new aquitard is present from 37 m depth to 68 m depth\n", + "* A final aquifer is from 68 to 88 m depth.\n", + "* Below 88 m depth the formations are considered an aquiclude\n", + "\n", + "Five pumping wells are screened in the first aquifer. The drawdown of one of them is available in the MLU documentation (Carlson & Randall, 2012).\n", + "The provided pumping well was operated for 20 minutes at 1848 m3/d. Drawdown was recorded for a total of 50 minutes during and after pumping. The radius of the pumped well is 0.155 m.\n", + "\n", + "In this notebook, we reproduce the work of Xinzhu (2020) and demonstrate the use of TTim to fit a recovery test and quantify the skin resistance in the well and the hydraulic parameters of the aquifer.\n", + "\n", + "The following figure summarises the hydrogeological conceptual model." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1,1,1)\n", + "#sky\n", + "sky = plt.Rectangle((-20,0), width = 50, height = 20, fc = 'b', zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "#Aquifer:\n", + "ground = plt.Rectangle((-20,-37), width = 50, height = 27, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9)\n", + "ax.add_patch(ground)\n", + "\n", + "#Aquifer 2:\n", + "ground2 = plt.Rectangle((-20,-88), width = 50, height = 20, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9)\n", + "ax.add_patch(ground2)\n", + "\n", + "#Confining bed:\n", + "confining_unit = plt.Rectangle((-20,-10), width = 50, height = 10, fc = np.array([100,100,100])/255, zorder=0, alpha=0.7)\n", + "ax.add_patch(confining_unit)\n", + "\n", + "confining_unit2 = plt.Rectangle((-20,-68), width = 50, height = 31, fc = np.array([100,100,100])/255, zorder=0, alpha=0.7)\n", + "ax.add_patch(confining_unit2)\n", + "\n", + "well = plt.Rectangle((-1.5,-37), width = 3, height = 37, fc = np.array([200,200,200])/255, zorder=1)\n", + "ax.add_patch(well)\n", + "\n", + "#Wellhead\n", + "wellhead = plt.Rectangle((-2,0),width = 4, height = 10, fc = np.array([200,200,200])/255, zorder=2, ec='k')\n", + "ax.add_patch(wellhead)\n", + "\n", + "#Screen for the well:\n", + "screen = plt.Rectangle((-1.5,-37), width = 3, height = 27, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x = 2,y = 7, dx = 5, dy = 0, color = \"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x = 7, y = 7, s = r'$ Q = 1848 \\frac{m^3}{d}$', fontsize = 'large' )\n", + "\n", + "#last line\n", + "line = plt.Line2D(xdata= [-200,1200], ydata = [0,0], color = \"k\")\n", + "ax.add_line(line)\n", + "\n", + "\n", + "ax.set_xlim([-20,30])\n", + "ax.set_ylim([-88,20])\n", + "ax.set_xlabel('Distance [m]')\n", + "ax.set_ylabel('Relative height [m]')\n", + "ax.set_title('Conceptual Model - Hardixveld Example');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Import required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from ttim import *\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters for the model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "H = 27 #aquifer thickness [m]\n", + "zt = -10 #upper boundary of aquifer\n", + "zb = zt - H #lower boundary of the aquifer\n", + "rw = 0.155 #well screen radius [m]\n", + "Q = 1848 #constant discharge rate [m^3/d]\n", + "t0 = 0.013889 #time stop pumping [d]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Load data for the recovery test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data is saved in a text file where the first column is the time data in days and in the second is the drawdown in m" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data = np.loadtxt('data/recovery.txt', skiprows=1)\n", + "t = data[:, 0]\n", + "h = data[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Create the first conceptual model\n", + "\n", + "Here we create a two aquifer leaky model in ModelMaq, so we have to define the top of the first aquitard layer, followed by the tops and bottoms of the aquifer layers. Here we ignore storage (```Sll```) of the aquitard layers.\n", + "\n", + "The well is defined with skin resistance (```res```) and the pumping schedule with the start and shutdown of the pump.\n", + "\n", + "More details on model construction and theory can be seen in:\n", + "\n", + "- [Confined 1 - Oude Korendijk](confined1_oude_korendijk)\n", + "- [Confined 4 - Schroth](confined4_schroth.ipynb)\n", + "- [Leaky 1 - Dalem](leaky1_dalem.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml1 = ModelMaq(kaq=[50, 40], z=[0, zt, zb, -68, -88], c=[1000, 1000], Saq=[1e-4, 5e-5],\\\n", + " topboundary='semi', tmin=1e-4, tmax=0.04)\n", + "w1 = Well(ml1, xw=0, yw=0, rw=rw, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0)\n", + "ml1.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Calibration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The parameters to be calibrated are the hydraulic conductivity and specific storage of the first layer, and the skin resistance of the well. The parameters of the aquitards and the second aquifer are kept fixed.\n", + "\n", + "More details on the calibration procedure and theory can be seen in:\n", + "\n", + "- [Confined 1 - Oude Korendijk](confined1_oude_korendijk)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..............................................................................................................................................................................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 331\n", + " # data points = 35\n", + " # variables = 3\n", + " chi-square = 0.00106334\n", + " reduced chi-square = 3.3229e-05\n", + " Akaike info crit = -358.059006\n", + " Bayesian info crit = -353.392962\n", + "[[Variables]]\n", + " kaq0: 44.5287657 +/- 0.66007100 (1.48%) (init = 50)\n", + " Saq0: 6.3907e-06 +/- 9.5299e-07 (14.91%) (init = 0.0001)\n", + " res: 0.01620452 +/- 5.7491e-04 (3.55%) (init = 1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, res) = 0.977\n", + " C(Saq0, res) = 0.950\n", + " C(kaq0, Saq0) = 0.864\n" + ] + } + ], + "source": [ + "ca1 = Calibrate(ml1)\n", + "ca1.set_parameter(name='kaq0', initial=50, pmin=0)\n", + "ca1.set_parameter(name='Saq0', initial=1e-4, pmin=0)\n", + "ca1.set_parameter_by_reference(name='res', parameter=w1.res[:], initial=1, pmin=0)\n", + "ca1.seriesinwell(name='obs', element=w1, t=t, h=h)\n", + "ca1.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq044.5287666.600710e-011.4823470inf50[44.52876567003543]
Saq00.0000069.529879e-0714.9121820inf0.0001[6.3906674174774025e-06]
res0.0162055.749070e-043.5478180inf1[0.016204524089252326]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 44.528766 6.600710e-01 1.482347 0 inf 50 \n", + "Saq0 0.000006 9.529879e-07 14.912182 0 inf 0.0001 \n", + "res 0.016205 5.749070e-04 3.547818 0 inf 1 \n", + "\n", + " parray \n", + "kaq0 [44.52876567003543] \n", + "Saq0 [6.3906674174774025e-06] \n", + "res [0.016204524089252326] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.005511916215804842\n" + ] + } + ], + "source": [ + "display(ca1.parameters)\n", + "print('RMSE:', ca1.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm1 = w1.headinside(t)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.loglog(t, -h, '.', label='obs - pumping well')\n", + "plt.loglog(t, -hm1[0], label='ttim results')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('head [m]')\n", + "plt.legend()\n", + "plt.title(\"Model Results - Model 1\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Overall a good fit has been observed. We will test the results with wellbore storage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Alternative model with wellbore storage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally to the parameters in the previous model, we add the ```rc``` parameter in the well. It is the radius of the caisson of the well, and TTim uses it to calculate the water storage in the well" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml2 = ModelMaq(kaq=[50, 40], z=[0, zt, zb, -68, -88], c=[1000, 1000], Saq=[1e-4, 5e-5],\\\n", + " topboundary='semi', tmin=1e-4, tmax=0.04)\n", + "w2 = Well(ml2, xw=0, yw=0, rw=rw, rc=0.155, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0)\n", + "ml2.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Calibrate the alternative model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally, we also calibrate the rc parameter in the well:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 573\n", + " # data points = 35\n", + " # variables = 4\n", + " chi-square = 0.00106334\n", + " reduced chi-square = 3.4301e-05\n", + " Akaike info crit = -356.058972\n", + " Bayesian info crit = -349.837580\n", + "[[Variables]]\n", + " kaq0: 44.5322775 +/- 0.99969014 (2.24%) (init = 50)\n", + " Saq0: 6.3928e-06 +/- 1.0629e-06 (16.63%) (init = 0.0001)\n", + " rc: 0.00458531 +/- 0.45478829 (9918.37%) (init = 0.1)\n", + " res: 0.01620699 +/- 7.8457e-04 (4.84%) (init = 1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, res) = 0.982\n", + " C(Saq0, res) = 0.902\n", + " C(kaq0, Saq0) = 0.806\n", + " C(kaq0, rc) = 0.743\n", + " C(rc, res) = 0.664\n", + " C(Saq0, rc) = 0.359\n" + ] + } + ], + "source": [ + "ca2 = Calibrate(ml2)\n", + "ca2.set_parameter(name='kaq0', initial=50, pmin=0)\n", + "ca2.set_parameter(name='Saq0', initial=1e-4, pmin=0)\n", + "ca2.set_parameter_by_reference(name='rc', parameter=w2.rc[:], initial=0.1, pmin=0)\n", + "ca2.set_parameter_by_reference(name='res', parameter=w2.res[:], initial=1, pmin=0)\n", + "ca2.seriesinwell(name='obs', element=w2, t=t, h=h)\n", + "ca2.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq044.5322770.9996902.2448660inf50[44.5322774989901]
Saq00.0000060.00000116.6257560inf0.0001[6.392843512337265e-06]
rc0.0045850.4547889918.3673320inf0.1[0.0045853140190308395]
res0.0162070.0007854.8409220inf1[0.016206990038743596]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 44.532277 0.999690 2.244866 0 inf 50 \n", + "Saq0 0.000006 0.000001 16.625756 0 inf 0.0001 \n", + "rc 0.004585 0.454788 9918.367332 0 inf 0.1 \n", + "res 0.016207 0.000785 4.840922 0 inf 1 \n", + "\n", + " parray \n", + "kaq0 [44.5322774989901] \n", + "Saq0 [6.392843512337265e-06] \n", + "rc [0.0045853140190308395] \n", + "res [0.016206990038743596] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.005511918881910666\n" + ] + } + ], + "source": [ + "display(ca2.parameters)\n", + "print('RMSE:', ca2.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm2 = w2.headinside(t)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.loglog(t, -h, '.', label='obs - pumping well')\n", + "plt.loglog(t, -hm2[0], label='ttim model results')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('head [m]')\n", + "plt.legend()\n", + "plt.title(\"Model Results - Model 2\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simulation with rc has slightly worse performance. The Akaike and Bayes criteria are somewhat worse than with the previous model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Testing single layer model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this final model, we will test the assumption that we can ignore the underlying aquifer. Thus, we only worry about the upper aquifer, where water is pumped.\n", + "\n", + "Therefore the last model is a single layer semi-confined aquifer that only includes the top aquitard and the first aquifer. In the well, we are not considering the ```rc``` parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml0 = ModelMaq(kaq=50, z=[0, zt, zb], c=1000, Saq=1e-4, topboundary='semi', \\\n", + " tmin=1e-4, tmax=0.04)\n", + "w0 = Well(ml0, xw=0, yw=0, rw=rw, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0)\n", + "ml0.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8.1. Calibration\n", + "\n", + "In the calibration we repeat the procedure for model 1." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......................................................................................................................................................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 307\n", + " # data points = 35\n", + " # variables = 3\n", + " chi-square = 0.00106438\n", + " reduced chi-square = 3.3262e-05\n", + " Akaike info crit = -358.024761\n", + " Bayesian info crit = -353.358717\n", + "[[Variables]]\n", + " kaq0: 44.5516140 +/- 0.65533645 (1.47%) (init = 50)\n", + " Saq0: 3.2314e-06 +/- 4.9282e-07 (15.25%) (init = 0.0001)\n", + " res: 0.01502951 +/- 5.9465e-04 (3.96%) (init = 1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, res) = 0.977\n", + " C(Saq0, res) = 0.948\n", + " C(kaq0, Saq0) = 0.861\n" + ] + } + ], + "source": [ + "ca0 = Calibrate(ml0)\n", + "ca0.set_parameter(name='kaq0', initial=50, pmin=0)\n", + "ca0.set_parameter(name='Saq0', initial=1e-4, pmin=0)\n", + "ca0.set_parameter_by_reference(name='res', parameter=w0.res[:], initial=1)\n", + "ca0.seriesinwell(name='obs', element=w0, t=t, h=h)\n", + "ca0.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq044.5516146.553365e-011.470960inf50[44.55161399621271]
Saq00.0000034.928237e-0715.2513080inf0.0001[3.231353431054629e-06]
res0.015035.946511e-043.956557-infinf1[0.015029508743142891]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 44.551614 6.553365e-01 1.47096 0 inf 50 \n", + "Saq0 0.000003 4.928237e-07 15.251308 0 inf 0.0001 \n", + "res 0.01503 5.946511e-04 3.956557 -inf inf 1 \n", + "\n", + " parray \n", + "kaq0 [44.55161399621271] \n", + "Saq0 [3.231353431054629e-06] \n", + "res [0.015029508743142891] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.005514613406867893\n" + ] + } + ], + "source": [ + "display(ca0.parameters)\n", + "print('RMSE:', ca0.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Model Results - Model 3')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm = w0.headinside(t)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.loglog(t, -h, '.', label='obs - pumping well')\n", + "plt.loglog(t, -hm[0], label='ttim model results')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.legend()\n", + "plt.title(\"Model Results - Model 3\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 9. Consider Log-curve-fitting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The example reported in the MLU documentation (Carslon & Randall, 2012) uses curve-fitting of log-drawdowns, besides linear curve-fitting. The use of log-curve-fitting can help the fitting when observed errors are not independent of the observations. We will apply log-curve-fitting in TTim to reproduce the methodology from MLU.\n", + "\n", + "For the new calibration, we use the ```fitm``` script, which has an implementation of the log-curve fitting. The original objective function is ```h_observed - h_predicted```, while for the log curve fitting, the objective function has been changed to ```log(abs(h_observed-h_predicted))```." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from fitm import Calibrate" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml3 = ModelMaq(kaq=[50, 40], z=[0, zt, zb, -68, -88], c=[1000, 1000], Saq=[1e-4, 5e-5],\\\n", + " topboundary='semi', tmin=1e-4, tmax=0.04)\n", + "w3 = Well(ml3, xw=0, yw=0, rw=rw, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0)\n", + "ml3.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...............................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 44\n", + " # data points = 35\n", + " # variables = 3\n", + " chi-square = 423.133174\n", + " reduced chi-square = 13.2229117\n", + " Akaike info crit = 93.2318615\n", + " Bayesian info crit = 97.8979057\n", + "[[Variables]]\n", + " kaq0: 46.5775700 +/- 5.25395341 (11.28%) (init = 50)\n", + " Saq0: 6.7883e-05 +/- 7.5775e-04 (1116.27%) (init = 0.0001)\n", + " res: 0.00723010 +/- 0.02440774 (337.58%) (init = 1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = 0.936\n", + " C(Saq0, res) = 0.842\n", + " C(kaq0, res) = 0.799\n" + ] + } + ], + "source": [ + "ca3 = Calibrate(ml3)\n", + "ca3.set_parameter(name='kaq0', initial=50, pmin=0)\n", + "ca3.set_parameter(name='Saq0', initial=1e-4, pmin=0)\n", + "ca3.set_parameter_by_reference(name='res', parameter=w3.res[:], initial=1, pmin=0)\n", + "ca3.seriesinwell(name='obs', element=w3, t=t, h=h)\n", + "ca3.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq046.577575.25395311.2800080inf50[46.57756996233315]
Saq00.0000680.0007581116.2656280inf0.0001[6.788282070635532e-05]
res0.007230.024408337.5849420inf1[0.00723010336653207]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 46.57757 5.253953 11.280008 0 inf 50 \n", + "Saq0 0.000068 0.000758 1116.265628 0 inf 0.0001 \n", + "res 0.00723 0.024408 337.584942 0 inf 1 \n", + "\n", + " parray \n", + "kaq0 [46.57756996233315] \n", + "Saq0 [6.788282070635532e-05] \n", + "res [0.00723010336653207] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 3.4769986006316445\n" + ] + } + ], + "source": [ + "display(ca3.parameters)\n", + "print('RMSE:', ca3.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm3 = w3.headinside(t)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.loglog(t, -h, '.', label='obs')\n", + "plt.loglog(t, -hm3[0], label='ttim')\n", + "plt.xlabel('time(d)')\n", + "plt.ylabel('head(m)')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "According to RMSE and the Akaike criteria, the log curve fitting solution performs worse than linear curve fitting. The results reported in the following table are from models calibrated by linear curve fitting solution." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 10. Analysis and summary of values modeled by different methods" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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k [m/d]Ss [1/m]resRMSE [m]
MLU-log51.530.0008160.0220.007560
TTim-single layer44.5516140.0000030.015030.005515
TTim-two layers44.5287660.0000060.0162050.005512
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" + ], + "text/plain": [ + " k [m/d] Ss [1/m] res RMSE [m]\n", + "MLU-log 51.53 0.000816 0.022 0.007560\n", + "TTim-single layer 44.551614 0.000003 0.01503 0.005515\n", + "TTim-two layers 44.528766 0.000006 0.016205 0.005512" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ta = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'res'], \\\n", + " index=['MLU-log', 'TTim-single layer', 'TTim-two layers'])\n", + "ta.loc['TTim-single layer'] = ca0.parameters['optimal'].values\n", + "ta.loc['TTim-two layers'] = ca1.parameters['optimal'].values\n", + "ta.loc['MLU-log'] = [51.530, 8.16E-04, 0.022]\n", + "ta['RMSE [m]'] = [0.00756, ca0.rmse(), ca1.rmse()]\n", + "ta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Both TTim models agree with each other with similar parameters. MLU adjusted parameters are higher than the ones in TTim." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Preparing the DataFrame:\n", + "t1 = pd.DataFrame(columns=['kaq - opt', 'kaq - 95%'], index = ['MLU','TTim - single layer','TTim - two layers']) \n", + "\n", + "t1.loc['MLU'] = [51.53, 4*1e-2*51.53]\n", + "t1.loc['TTim - single layer'] = [ca0.parameters.loc['kaq0','optimal'],2*ca0.parameters.loc['kaq0','std']]\n", + "t1.loc['TTim - two layers'] = [ca1.parameters.loc['kaq0','optimal'],2*ca1.parameters.loc['kaq0','std']]\n", + "\n", + "# Plotting\n", + "\n", + "plt.figure(figsize = (10,7))\n", + "\n", + "plt.errorbar(x = t1.index, y = t1['kaq - opt'], yerr = [t1['kaq - 95%'], t1['kaq - 95%']],\n", + " marker='o', linestyle='', markersize=12)\n", + "#plt.legend()\n", + "plt.suptitle(\"Error bar plot of calibrated hydraulic conductivities\")\n", + "plt.ylabel('K [m/d]')\n", + "#plt.ylim([36,40])\n", + "plt.xlabel('Model');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comparing the adjusted error bars for hydraulic conductivities for our models, we see that the TTim models do not overlap the MLU range." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Newville, M.,Stensitzki, T., Allen, D.B., Ingargiola, A. (2014) LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python.https://dx.doi.org/10.5281/zenodo.11813. https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021).\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Next Notebook: [Leaky 3 - Texas Hill](leaky3_texashill.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pumpingtests/leaky3_texashill.ipynb b/pumpingtests/leaky3_texashill.ipynb new file mode 100644 index 0000000..4e1c9ec --- /dev/null +++ b/pumpingtests/leaky3_texashill.ipynb @@ -0,0 +1,1207 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Leaky Aquifer Test - Texas Hill Example\n", + "**This example is taken from AQTESOLV examples.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model\n", + "\n", + "This pumping test, taken from the AQTESOLV examples, was done in the location of 'Texas Hill'. A pumping well was screened at an aquifer located between 20 ft and 70 ft depths. The aquifer is overlain by an aquitard. The formation at the base of the aquifer is considered an aquiclude.\n", + "\n", + "Three observation wells are located at 40, 80 160 ft distance. They are denominated OW1, OW2 and OW3, respectively. Pumping lasted for 420 minutes at a rate of 4488 gallons per minute. The hydrogeological conceptual model can be seen in the figure below.\n", + "\n", + "In this example, we will reproduce the work of Xinzhu (2020). We compare the aquifer test results using Hantush's solution (Hantush 1955) in the software AQTESOLV (Duffield, 2007) and TTim. We also explore the capabilities of TTim to account for aquitard storage and wellbore effects such as wellbore storage and skin effect, which cannot be simulated with Hantush's solution." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Conceptual Model 1\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1,1,1)\n", + "#sky\n", + "sky = plt.Rectangle((-20,0), width = 220, height = 8, fc = 'b', zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "#Aquifer:\n", + "ground = plt.Rectangle((-20,-70), width = 220, height = 50, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9)\n", + "ax.add_patch(ground)\n", + "\n", + "#Confining bed:\n", + "confining_unit = plt.Rectangle((-20,-20), width = 220, height = 20, fc = np.array([100,100,100])/255, zorder=0, alpha=0.7)\n", + "ax.add_patch(confining_unit)\n", + "\n", + "well = plt.Rectangle((-2,-70), width = 4, height = 70, fc = np.array([200,200,200])/255, zorder=1)\n", + "ax.add_patch(well)\n", + "\n", + "#Wellhead\n", + "wellhead = plt.Rectangle((-2.5,0),width = 5, height = 1.5, fc = np.array([200,200,200])/255, zorder=2, ec='k')\n", + "ax.add_patch(wellhead)\n", + "\n", + "#Screen for the well:\n", + "screen = plt.Rectangle((-2,-70), width = 4, height = 50, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x = 2.5,y = 0.75, dx = 5, dy = 0, color = \"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x = 7.5, y = 2, s = r'$ Q = 4488 \\frac{gal}{min}$', fontsize = 'large' )\n", + "#Piezometers\n", + "piez1 =plt.Rectangle((39,-70), width = 2, height = 70, fc = np.array([200,200,200])/255, zorder=1)\n", + "screen_piez_1 = plt.Rectangle((39,-70), width = 2, height = 50, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_1.set_linewidth(2)\n", + "\n", + "piez2 =plt.Rectangle((79,-70), width = 2, height = 70, fc = np.array([200,200,200])/255, zorder=1)\n", + "screen_piez_2 = plt.Rectangle((79,-70), width = 2, height = 50, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_2.set_linewidth(2)\n", + "\n", + "piez3 =plt.Rectangle((159,-70), width = 2, height = 70, fc = np.array([200,200,200])/255, zorder=1)\n", + "screen_piez_3 = plt.Rectangle((159,-70), width = 2, height = 50, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_3.set_linewidth(2)\n", + "\n", + "\n", + "ax.add_patch(piez1)\n", + "ax.add_patch(screen_piez_1)\n", + "ax.add_patch(piez2)\n", + "ax.add_patch(screen_piez_2)\n", + "ax.add_patch(piez3)\n", + "ax.add_patch(screen_piez_3)\n", + "\n", + "#last line\n", + "line = plt.Line2D(xdata= [-200,1200], ydata = [0,0], color = \"k\")\n", + "ax.add_line(line)\n", + "\n", + "ax.text(x = 39, y = 2, s = 'P40', fontsize = 'large' )\n", + "ax.text(x = 79, y = 2, s = 'P80', fontsize = 'large' )\n", + "ax.text(x = 159, y = 2, s = 'P160', fontsize = 'large' )\n", + "\n", + "\n", + "\n", + "ax.set_xlim([-20,200])\n", + "ax.set_ylim([-70,8])\n", + "ax.set_xlabel('Distance [ft]')\n", + "ax.set_ylabel('Relative height [ft]')\n", + "ax.set_title('Conceptual Model - Texas Hill Example');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Load Required Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from ttim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters\n", + "\n", + "Here we have previously converted the exercise values to ***meters*** and ***days***." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "Q = 24464.06 #constant discharge in m^3/d\n", + "b1 = 6.096 #overlying aquitard thickness in m\n", + "b2 = 15.24 #aquifer thickness in m\n", + "zt = -b1 #top boundary of aquifer\n", + "zb = -b1 - b2 #bottom boundary of aquifer\n", + "rw = 0.1524 #well radius in m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Load dataset of observation wells\n", + "\n", + "Each observation well is located in a text file, where the first column is time data in days and the second column is drawdown in meters.\n", + "We also declare the distance of each observation to the wells: (```r1```:```r3```)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data1 = np.loadtxt('data/texas40.txt')\n", + "t1 = data1[:, 0]\n", + "h1 = data1[:, 1]\n", + "r1 = 12.191 #distance between obs1 to pumping well in m\n", + "\n", + "data2 = np.loadtxt('data/texas80.txt')\n", + "t2 = data2[:, 0]\n", + "h2 = data2[:, 1]\n", + "r2 = 24.383 #distance between obs2 to pumping well in m\n", + "\n", + "data3 = np.loadtxt('data/texas160.txt')\n", + "t3 = data3[:, 0]\n", + "h3 = data3[:, 1]\n", + "r3 = 48.766 #distance between obs3 to pumping well in m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Create a conceptual model\n", + "\n", + "We define a Modelmaq model for the semi-confined aquifer using the methods described in previous notebooks (see below).\n", + "In this initial model, we will model the overlying layer as an aquitard with storage (```Sll```, the storage parameter, is defined in ModelMaq).\n", + "\n", + "More details on model construction and theory can be seen in:\n", + "\n", + "- [Confined 1 - Oude Korendijk](confined1_oude_korendijk)\n", + "- [Confined 4 - Schroth](confined4_schroth.ipynb)\n", + "- [Leaky 1 - Dalem](leaky1_dalem.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_0 = ModelMaq(kaq=10, z=[0, zt, zb], Saq=0.001, Sll=0, c=10, tmin=0.001, \\\n", + " tmax=1, topboundary='semi')\n", + "w_0 = Well(ml_0, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", + "ml_0.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Model Calibration\n", + "\n", + "Using all three observation wells, we calibrate for the hydraulic parameters of the aquifer (```kaq``` and ```Saq```) and for the aquitard (```c``` and ```Sll```)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 126\n", + " # data points = 78\n", + " # variables = 4\n", + " chi-square = 0.28305542\n", + " reduced chi-square = 0.00382507\n", + " Akaike info crit = -430.268069\n", + " Bayesian info crit = -420.841234\n", + "[[Variables]]\n", + " kaq0: 224.589795 +/- 2.48600891 (1.11%) (init = 10)\n", + " Saq0: 2.1313e-04 +/- 3.7534e-05 (17.61%) (init = 0.0001)\n", + " Sll0: 1.7304e-06 +/- 2.7782e-04 (16055.52%) (init = 0.0001)\n", + " c0: 43.8331237 +/- 3.15335028 (7.19%) (init = 100)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(Saq0, Sll0) = -0.979\n", + " C(kaq0, c0) = 0.887\n", + " C(Saq0, c0) = -0.149\n", + " C(kaq0, Saq0) = -0.118\n" + ] + } + ], + "source": [ + "#unknown parameters: kaq, Saq, c, Sll\n", + "ca_0 = Calibrate(ml_0)\n", + "ca_0.set_parameter(name='kaq0', initial=10)\n", + "ca_0.set_parameter(name='Saq0', initial=1e-4)\n", + "ca_0.set_parameter_by_reference(name='Sll0', parameter=ml_0.aq.Sll, \\\n", + " initial=1e-4, pmin=0)\n", + "ca_0.set_parameter(name='c0', initial=100)\n", + "ca_0.series(name='OW1', x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_0.series(name='OW2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_0.series(name='OW3', x=r3, y=0, t=t3, h=h3, layer=0)\n", + "ca_0.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0224.5897952.4860091.106911-infinf10[224.5897947124587]
Saq00.0002130.00003817.610508-infinf0.0001[0.0002131340798389554]
Sll00.0000020.00027816055.5232830.0inf0.0001[1.730365937868683e-06]
c043.8331243.1533507.193989-infinf100[43.833123697435646]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 224.589795 2.486009 1.106911 -inf inf 10 \n", + "Saq0 0.000213 0.000038 17.610508 -inf inf 0.0001 \n", + "Sll0 0.000002 0.000278 16055.523283 0.0 inf 0.0001 \n", + "c0 43.833124 3.153350 7.193989 -inf inf 100 \n", + "\n", + " parray \n", + "kaq0 [224.5897947124587] \n", + "Saq0 [0.0002131340798389554] \n", + "Sll0 [1.730365937868683e-06] \n", + "c0 [43.833123697435646] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.06024048173207126\n" + ] + } + ], + "source": [ + "display(ca_0.parameters)\n", + "print('RMSE:', ca_0.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm1_0 = ml_0.head(r1, 0, t1)\n", + "hm2_0 = ml_0.head(r2, 0, t2)\n", + "hm3_0 = ml_0.head(r3, 0, t3)\n", + "plt.figure(figsize = (8, 5))\n", + "plt.semilogx(t1, h1, '.', label = 'OW1')\n", + "plt.semilogx(t1, hm1_0[0], label = 'ttim OW1')\n", + "plt.semilogx(t2, h2, '.', label = 'OW2')\n", + "plt.semilogx(t2, hm2_0[0], label = 'ttim OW2')\n", + "plt.semilogx(t3, h3, '.', label = 'OW3')\n", + "plt.semilogx(t3, hm3_0[0], label = 'ttim OW3')\n", + "plt.xlabel('time(d)')\n", + "plt.ylabel('head(m)')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the specific storage of the aquitard (```Sll```) is very close to the minimum limit (zero), we will test to set Sll to 0 and remove it from the calibration:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Model calibration without aquitard storage" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_1 = ModelMaq(kaq=10, z=[0, zt, zb], Saq=0.001, Sll=0, c=10, tmin=0.001, \\\n", + " tmax=1, topboundary='semi')\n", + "w_1 = Well(ml_1, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", + "ml_1.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".....................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 50\n", + " # data points = 78\n", + " # variables = 3\n", + " chi-square = 0.28305317\n", + " reduced chi-square = 0.00377404\n", + " Akaike info crit = -432.268689\n", + " Bayesian info crit = -425.198562\n", + "[[Variables]]\n", + " kaq0: 224.635193 +/- 2.46701615 (1.10%) (init = 10)\n", + " Saq0: 2.1325e-04 +/- 7.5822e-06 (3.56%) (init = 0.0001)\n", + " c0: 43.8841639 +/- 3.13605878 (7.15%) (init = 100)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, c0) = 0.890\n", + " C(kaq0, Saq0) = -0.815\n", + " C(Saq0, c0) = -0.595\n" + ] + } + ], + "source": [ + "#unknown parameters: kaq, Saq, c\n", + "ca_1 = Calibrate(ml_1)\n", + "ca_1.set_parameter(name='kaq0', initial=10)\n", + "ca_1.set_parameter(name='Saq0', initial=1e-4)\n", + "ca_1.set_parameter(name='c0', initial=100)\n", + "ca_1.series(name='OW1', x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_1.series(name='OW2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_1.series(name='OW3', x=r3, y=0, t=t3, h=h3, layer=0)\n", + "ca_1.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0224.6351932.4670161.098232-infinf10[224.63519273946454]
Saq00.0002130.0000083.555577-infinf0.0001[0.00021324780404289543]
c043.8841643.1360597.14622-infinf100[43.884163861907204]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 224.635193 2.467016 1.098232 -inf inf 10 \n", + "Saq0 0.000213 0.000008 3.555577 -inf inf 0.0001 \n", + "c0 43.884164 3.136059 7.14622 -inf inf 100 \n", + "\n", + " parray \n", + "kaq0 [224.63519273946454] \n", + "Saq0 [0.00021324780404289543] \n", + "c0 [43.884163861907204] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.06024024248774193\n" + ] + } + ], + "source": [ + "display(ca_1.parameters)\n", + "print('RMSE:', ca_1.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm1_1 = ml_1.head(r1, 0, t1)\n", + "hm2_1 = ml_1.head(r2, 0, t2)\n", + "hm3_1 = ml_1.head(r3, 0, t3)\n", + "plt.figure(figsize = (8, 5))\n", + "plt.semilogx(t1, h1, '.', label = 'OW1')\n", + "plt.semilogx(t1, hm1_1[0], label = 'ttim OW1')\n", + "plt.semilogx(t2, h2, '.', label = 'OW2')\n", + "plt.semilogx(t2, hm2_1[0], label = 'ttim OW2')\n", + "plt.semilogx(t3, h3, '.', label = 'OW3')\n", + "plt.semilogx(t3, hm3_1[0], label = 'ttim OW3')\n", + "plt.xlabel('time(d)')\n", + "plt.ylabel('head(m)')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model with fixed Sll has a similar performance to the former model. The second model has an AIC value of -432.269, two units lower than the former model (-430.268). Thus, Sll should be set to zero (default value) and keep removed from the calibration." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Model Calibration with well parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We redo the model with the well parameters skin-resistance (```res```) and wellbore storage (```rc```)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_2 = ModelMaq(kaq=10, z=[0, zt, zb], Sll=0, Saq=0.001, c=10, tmin=0.001, \\\n", + " tmax=1, topboundary='semi')\n", + "w_2 = Well(ml_2, xw=0, yw=0, rw=rw, res=0, rc=None, tsandQ=[(0, Q)], layers=0)\n", + "ml_2.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calibrate with the additional well paramenters" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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+ "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 4670\n", + " # data points = 78\n", + " # variables = 5\n", + " chi-square = 6.23156762\n", + " reduced chi-square = 0.08536394\n", + " Akaike info crit = -187.112310\n", + " Bayesian info crit = -175.328766\n", + "[[Variables]]\n", + " kaq0: 186.901532 +/- 10.2767557 (5.50%) (init = 10)\n", + " Saq0: 9.7363e-11 +/- 1.7605e-07 (180814.64%) (init = 0.0001)\n", + " c0: 12.2172420 +/- 2.43597544 (19.94%) (init = 10)\n", + " rc: 2.31172969 +/- 0.09075350 (3.93%) (init = 0)\n", + " res: 2.0686e-10 +/- 5.8060e-07 (280673.69%) (init = 0.1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, c0) = 0.951\n", + " C(c0, rc) = 0.464\n", + " C(kaq0, rc) = 0.382\n", + " C(Saq0, res) = -0.354\n", + " C(rc, res) = -0.229\n", + " C(Saq0, rc) = 0.186\n" + ] + } + ], + "source": [ + "#unknown parameters: kaq, Saq, c, rc\n", + "ca_2 = Calibrate(ml_2)\n", + "ca_2.set_parameter(name='kaq0', initial=10)\n", + "ca_2.set_parameter(name='Saq0', initial=1e-4, pmin = 0)\n", + "ca_2.set_parameter(name='c0', initial=10)\n", + "ca_2.set_parameter_by_reference(name='rc', parameter=w_2.rc, initial=0)\n", + "ca_2.set_parameter_by_reference(name='res', parameter=w_2.res, initial = 0.1, pmin = 0)\n", + "ca_2.series(name='OW1', x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_2.series(name='OW2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_2.series(name='OW3', x=r3, y=0, t=t3, h=h3, layer=0)\n", + "ca_2.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0186.9015321.027676e+015.498487-infinf10[186.90153170230298]
Saq00.01.760474e-07180814.6443930.0inf0.0001[9.736345063515728e-11]
c012.2172422.435975e+0019.938833-infinf10[12.217242017113444]
rc2.311739.075350e-023.925784-infinf0[2.311729685846916]
res0.05.806043e-07280673.6852850.0inf0.1[2.0686097279565274e-10]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 186.901532 1.027676e+01 5.498487 -inf inf 10 \n", + "Saq0 0.0 1.760474e-07 180814.644393 0.0 inf 0.0001 \n", + "c0 12.217242 2.435975e+00 19.938833 -inf inf 10 \n", + "rc 2.31173 9.075350e-02 3.925784 -inf inf 0 \n", + "res 0.0 5.806043e-07 280673.685285 0.0 inf 0.1 \n", + "\n", + " parray \n", + "kaq0 [186.90153170230298] \n", + "Saq0 [9.736345063515728e-11] \n", + "c0 [12.217242017113444] \n", + "rc [2.311729685846916] \n", + "res [2.0686097279565274e-10] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.2826515392059663\n" + ] + } + ], + "source": [ + "display(ca_2.parameters)\n", + "print('RMSE:', ca_2.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When adding both res and rc in the calibration, the model fit is poor. The ```res``` value is being adjusted to very close to the minimum value 0. Thus, we repeat the calibration procedure, where res is removed from it." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".........................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 54\n", + " # data points = 78\n", + " # variables = 4\n", + " chi-square = 0.22837259\n", + " reduced chi-square = 0.00308612\n", + " Akaike info crit = -447.011880\n", + " Bayesian info crit = -437.585045\n", + "[[Variables]]\n", + " kaq0: 227.476745 +/- 2.38403194 (1.05%) (init = 10)\n", + " Saq0: 1.9189e-04 +/- 7.9503e-06 (4.14%) (init = 0.0001)\n", + " c0: 45.1685665 +/- 2.92674386 (6.48%) (init = 10)\n", + " rc: 0.58831953 +/- 0.06177017 (10.50%) (init = 0)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, c0) = 0.885\n", + " C(kaq0, Saq0) = -0.799\n", + " C(Saq0, rc) = -0.619\n", + " C(Saq0, c0) = -0.552\n", + " C(kaq0, rc) = 0.321\n", + " C(c0, rc) = 0.143\n" + ] + } + ], + "source": [ + "#unknown parameters: kaq, Saq, c, rc\n", + "ca_2 = Calibrate(ml_2)\n", + "ca_2.set_parameter(name='kaq0', initial=10)\n", + "ca_2.set_parameter(name='Saq0', initial=1e-4)\n", + "ca_2.set_parameter(name='c0', initial=10)\n", + "ca_2.set_parameter_by_reference(name='rc', parameter=w_2.rc, initial=0)\n", + "ca_2.series(name='OW1', x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_2.series(name='OW2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_2.series(name='OW3', x=r3, y=0, t=t3, h=h3, layer=0)\n", + "ca_2.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0227.4767452.3840321.048033-infinf10[227.4767448937833]
Saq00.0001920.0000084.143071-infinf0.0001[0.00019189384485118635]
c045.1685662.9267446.479603-infinf10[45.16856645283155]
rc0.588320.06177010.499425-infinf0[0.5883195294792068]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 227.476745 2.384032 1.048033 -inf inf 10 \n", + "Saq0 0.000192 0.000008 4.143071 -inf inf 0.0001 \n", + "c0 45.168566 2.926744 6.479603 -inf inf 10 \n", + "rc 0.58832 0.061770 10.499425 -inf inf 0 \n", + "\n", + " parray \n", + "kaq0 [227.4767448937833] \n", + "Saq0 [0.00019189384485118635] \n", + "c0 [45.16856645283155] \n", + "rc [0.5883195294792068] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.05410964530240864\n" + ] + } + ], + "source": [ + "display(ca_2.parameters)\n", + "print('RMSE:', ca_2.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm1_2 = ml_2.head(r1, 0, t1)\n", + "hm2_2 = ml_2.head(r2, 0, t2)\n", + "hm3_2 = ml_2.head(r3, 0, t3)\n", + "plt.figure(figsize = (8, 5))\n", + "plt.semilogx(t1, h1, '.', label='OW1')\n", + "plt.semilogx(t1, hm1_2[0], label='ttim OW1')\n", + "plt.semilogx(t2, h2, '.', label='OW2')\n", + "plt.semilogx(t2, hm2_2[0], label='ttim OW2')\n", + "plt.semilogx(t3, h3, '.', label='OW3')\n", + "plt.semilogx(t3, hm3_2[0], label='ttim OW3')\n", + "plt.xlabel('time(d)')\n", + "plt.ylabel('drawdown (m)')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have a much better fit. We also have better AIC and BIC values, which indicates that the extra parameter ```rc``` improved the model performance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Analysis and summary of values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we compare the simulations done with TTim with the one done in AQTESOLV by Xinzhu (2020). TTim has found very similar values to AQTESOLV. When we consider the ```rc``` parameter in TTim, the fit is slightly better." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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k [m/d]Ss [1/m]c [d]rcRMSE
AQTESOLV224.7260.00021243.964-0.059627
ttim224.6351930.00021343.884164-0.060240
ttim-rc227.4767450.00019245.1685660.588320.054110
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" + ], + "text/plain": [ + " k [m/d] Ss [1/m] c [d] rc RMSE\n", + "AQTESOLV 224.726 0.000212 43.964 - 0.059627\n", + "ttim 224.635193 0.000213 43.884164 - 0.060240\n", + "ttim-rc 227.476745 0.000192 45.168566 0.58832 0.054110" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'c [d]', 'rc'], \\\n", + " index=['AQTESOLV', 'ttim', 'ttim-rc'])\n", + "t.loc['AQTESOLV'] = [224.726, 2.125e-4, 43.964, '-']\n", + "t.loc['ttim'] = np.append(ca_1.parameters['optimal'].values, '-')\n", + "t.loc['ttim-rc'] = ca_2.parameters['optimal'].values\n", + "t['RMSE'] = [0.059627, ca_1.rmse(), ca_2.rmse()]\n", + "t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Newville, M.,Stensitzki, T., Allen, D.B., Ingargiola, A. (2014) LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python.https://dx.doi.org/10.5281/zenodo.11813. https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021).\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Next Notebook: [Unconfined 1 - Vennebulten](unconfined1_vennebulten.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pumpingtests/slug1_pratt_county.ipynb b/pumpingtests/slug1_pratt_county.ipynb new file mode 100644 index 0000000..2934d2d --- /dev/null +++ b/pumpingtests/slug1_pratt_county.ipynb @@ -0,0 +1,900 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Slug Test - Pratt County\n", + "**This test is taken from AQTESOLV examples.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we reproduce the work of Yang (2020) to check the TTim performance in analysing slug tests. We later compare the solution in TTim with the KGS analytical model (Hyder et al. 1994) implemented in AQTESOLV (Duffield, 2007).\n", + "\n", + "This slug test was conducted in Pratt County Monitoring Site, US, and reported by Butler (1998). A partially penetrating well is screened in unconsolidated alluvial deposits, consisting of sand and gravel interbedded by clay. The total thickness of the aquifer is 47.87 m. The screen is located at 16.77 m depth and has a screen length 1.52 m the well radius is 0.125 m, and the casing radius 0.064 m.\n", + "\n", + "The slug displacement is 0.671 m. Head change has been recorded at the slug well.\n", + "\n", + "The conceptual model can be seen in the figure below." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1,1,1)\n", + "#sky\n", + "sky = plt.Rectangle((-20,0), width = 50, height = 20, fc = 'b', zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "#Aquifer:\n", + "ground = plt.Rectangle((-20,-47.87), width = 50, height = 47.87, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9)\n", + "ax.add_patch(ground)\n", + "\n", + "well = plt.Rectangle((-1,-(16.77+1.52)), width = 2, height = (16.77+1.52), fc = np.array([200,200,200])/255, zorder=1)\n", + "ax.add_patch(well)\n", + "\n", + "#Wellhead\n", + "wellhead = plt.Rectangle((-1.25,0),width = 2.5, height = 10, fc = np.array([200,200,200])/255, zorder=2, ec='k')\n", + "ax.add_patch(wellhead)\n", + "\n", + "#Screen for the well:\n", + "screen = plt.Rectangle((-1,-(16.77+1.52)), width = 2, height = 1.52, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x = 0,y = 10, dx = 0, dy = 3, color = \"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x = 0.5, y = 11, s = r'$ D = 0.671 m $', fontsize = 'large' )\n", + "\n", + "#last line\n", + "line = plt.Line2D(xdata= [-200,1200], ydata = [0,0], color = \"k\")\n", + "ax.add_line(line)\n", + "\n", + "\n", + "ax.set_xlim([-20,30])\n", + "ax.set_ylim([-47,20])\n", + "ax.set_xlabel('Distance [m]')\n", + "ax.set_ylabel('Relative height [m]')\n", + "ax.set_title('Conceptual Model - Pratt County Example');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Load required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from ttim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "rw = 0.125 # well radius\n", + "rc = 0.064 # well casing radius\n", + "L = 1.52 # screen length\n", + "b = -47.87 # aquifer thickness\n", + "zt = -16.77 # depth to top of screen\n", + "H0 = 0.671 # initial displacement in the well\n", + "zb = zt - L # bottom of screen" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Calculate the added volume\n", + "\n", + "As we will see later, the input for the slug test in TTim is the added or removed volume. Therefore we must first convert our measured displacement into volume." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "slug: 0.00863 m^3\n" + ] + } + ], + "source": [ + "Q = np.pi * rc ** 2 * H0\n", + "print('slug:', round(Q, 5), 'm^3')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Load data from well" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data from the slug well is loaded from a text file, where the first column is the time in seconds and the second column is the head." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data = np.loadtxt('data/slug.txt', skiprows = 1)\n", + "t = data[:, 0] / 60 / 60 / 24 #convert time to days\n", + "h = data[:, 1] " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Create a conceptual model in TTim\n", + "\n", + "We conceptualize the aquifer as a three-layer model, one layer above the screen, one layer at the screen top and bottom and another layer just below it.\n", + "\n", + "We use ```Model3D``` method to build this model. Details on how to set the model can be seen in notebook: [Unconfined - 1 - Vennebulten](unconfined1_vennebulten.ipynb).\n", + "\n", + "The setting of the slug well is slightly different from the pumping well. We detail the differences below:\n", + "* the ```tsandQ``` argument in the ```Well``` object has a different meaning. Instead of meaning the time of start or shutdown and the pumping rate of the pumping well, it means the time a volume is instantaneously added or removed from the well. In our case, we defined it as ```[(0, -Q)]``` where ```0``` is the moment in time when we added the slug and ```Q``` is the added volume. A negative sign means a volume is added. Otherwise, it would mean an extracted volume.\n", + "* the ```wbstype``` argument is set to ```' slug'```, so TTim knows the ```tsandQ``` argument means time and instant volumes, instead of pumping rates." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml = Model3D(kaq=10, z=[0, zt, zb, b], Saq=1e-4, kzoverkh=1, tmin=1e-6, tmax=0.01)\n", + "w = Well(ml, xw=0, yw=0, rw=rw,rc=rc, tsandQ=[(0, -Q)], layers=1, wbstype='slug')\n", + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Model calibration\n", + "\n", + "We calibrate both hydraulic conductivity and specific storage, considering uniform parameters in all layers." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...........................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 24\n", + " # data points = 61\n", + " # variables = 2\n", + " chi-square = 5.0379e-04\n", + " reduced chi-square = 8.5388e-06\n", + " Akaike info crit = -709.958078\n", + " Bayesian info crit = -705.736330\n", + "[[Variables]]\n", + " kaq0_2: 6.08971640 +/- 0.02515114 (0.41%) (init = 10)\n", + " Saq0_2: 2.0365e-04 +/- 1.0023e-05 (4.92%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_2, Saq0_2) = -0.654\n" + ] + } + ], + "source": [ + "ca = Calibrate(ml)\n", + "ca.set_parameter(name='kaq0_2', initial=10)\n", + "ca.set_parameter(name='Saq0_2', initial=1e-4)\n", + "ca.series(name='obs', x=0, y=0, layer=1, t=t, h=h)\n", + "ca.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_26.0897160.0251510.41301-infinf10[6.089716397344325, 6.089716397344325, 6.08971...
Saq0_20.0002040.0000104.921381-infinf0.0001[0.0002036532699278222, 0.0002036532699278222,...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_2 6.089716 0.025151 0.41301 -inf inf 10 \n", + "Saq0_2 0.000204 0.000010 4.921381 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0_2 [6.089716397344325, 6.089716397344325, 6.08971... \n", + "Saq0_2 [0.0002036532699278222, 0.0002036532699278222,... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.002873813488818492\n" + ] + } + ], + "source": [ + "display(ca.parameters)\n", + "print('RMSE:', ca.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we plot the results in heads over initial heads, as done for the graphical solution." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm = ml.head(0, 0, t, layers=1)\n", + "plt.figure(figsize = (10, 7))\n", + "plt.semilogx(t, h/H0, '.', label='obs')\n", + "plt.semilogx(t, hm[-1]/H0, 'r', label='ttim')\n", + "plt.ylim([0, 1])\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('h/H0')\n", + "plt.title('Model Results - Three layers')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. A conceptual model with more layers\n", + "\n", + "Now we check the model performance in a model with more layers.\n", + "We use the same division that we set before, but now we divide the first layers into 18 layers, the second layer into 3 layers and the third layer into 29 layers. So each layer is roughly 1 m thick." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "n1 = 18\n", + "n2 = 3\n", + "n3 = 29\n", + "nlay = n1 + n2 + n3 #number of layers\n", + "zlay1 = np.linspace(0, zt, n1 + 1)\n", + "zlay2 = np.linspace(zt, zb, n2 + 1)\n", + "zlay3 = np.linspace(zb, b, n3 + 1)\n", + "layers = np.append(zlay1[:-1], zlay2[:-1])\n", + "layers = np.append(layers, zlay3) #elevation of each layer\n", + "Saq = 1e-4 * np.ones(nlay)\n", + "Saq[0] = 0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 3\n", + "solution complete\n" + ] + } + ], + "source": [ + "M_nlay = Model3D(kaq=10, z=layers, Saq=Saq, kzoverkh=1, phreatictop=True,\\\n", + " tmin=1e-6, tmax=0.01)\n", + "W_nlay = Well(M_nlay, xw=0, yw=0, rw=rw, tsandQ=[(0, -Q)], layers=[18,19,20], \\\n", + " rc=rc, wbstype='slug')\n", + "M_nlay.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Calibration of multi-layer model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..............................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 27\n", + " # data points = 183\n", + " # variables = 2\n", + " chi-square = 0.00159219\n", + " reduced chi-square = 8.7966e-06\n", + " Akaike info crit = -2128.33971\n", + " Bayesian info crit = -2121.92074\n", + "[[Variables]]\n", + " kaq0_49: 4.26554464 +/- 0.01214302 (0.28%) (init = 10)\n", + " Saq0_49: 4.9196e-04 +/- 1.7924e-05 (3.64%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_49, Saq0_49) = -0.763\n" + ] + } + ], + "source": [ + "cM = Calibrate(M_nlay)\n", + "cM.set_parameter(name='kaq0_49', initial=10)\n", + "cM.set_parameter(name='Saq0_49', initial=1e-4, pmin=1e-7)\n", + "cM.series(name='obs', x=0, y=0, layer=[18,19,20], t=t, h=h)\n", + "cM.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_494.2655450.0121430.284677-infinf10[4.265544642582153, 4.265544642582153, 4.26554...
Saq0_490.0004920.0000183.6434111.000000e-07inf0.0001[0.0004919581741910095, 0.0004919581741910095,...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_49 4.265545 0.012143 0.284677 -inf inf 10 \n", + "Saq0_49 0.000492 0.000018 3.643411 1.000000e-07 inf 0.0001 \n", + "\n", + " parray \n", + "kaq0_49 [4.265544642582153, 4.265544642582153, 4.26554... \n", + "Saq0_49 [0.0004919581741910095, 0.0004919581741910095,... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.002949661707868684\n" + ] + } + ], + "source": [ + "display(cM.parameters)\n", + "print('RMSE:', cM.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hM = M_nlay.head(0, 0, t, layers = 20)\n", + "plt.figure(figsize = (10, 7))\n", + "plt.semilogx(t, h / H0, '.', label = 'obs')\n", + "plt.semilogx(t, hM[0] / H0, label = 'ttim')\n", + "plt.ylim([0, 1])\n", + "plt.xlabel('time(d)')\n", + "plt.ylabel('h/H0')\n", + "plt.title(\"Model Results - more layers\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Parameters varied significantly, but the AIC value has dropped sharply." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 9. Final Model calibration with well skin resistance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we test if the skin resistance of the well has an impact on model calibration. For this, we add the ```res``` parameter in the calibration settings. We use the same multi-layer model." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".......................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 52\n", + " # data points = 183\n", + " # variables = 3\n", + " chi-square = 0.00136417\n", + " reduced chi-square = 7.5787e-06\n", + " Akaike info crit = -2154.62552\n", + " Bayesian info crit = -2144.99706\n", + "[[Variables]]\n", + " kaq0_49: 4.29785100 +/- 0.01340379 (0.31%) (init = 10)\n", + " Saq0_49: 4.0896e-04 +/- 2.1915e-05 (5.36%) (init = 0.0001)\n", + " res: 1.7233e-04 +/- 3.2195e-05 (18.68%) (init = 0.2)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_49, Saq0_49) = -0.834\n", + " C(Saq0_49, res) = -0.730\n", + " C(kaq0_49, res) = 0.544\n" + ] + } + ], + "source": [ + "cR = Calibrate(M_nlay)\n", + "cR.set_parameter(name='kaq0_49', initial=10)\n", + "cR.set_parameter(name='Saq0_49', initial=1e-4, pmin=1e-7)\n", + "cR.set_parameter_by_reference(name='res', parameter = W_nlay.res, initial = 0.2, pmin = 0)\n", + "cR.series(name='obs', x=0, y=0, layer=[18,19,20], t=t, h=h)\n", + "cR.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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kaq0_494.2978510.0134040.311872-infinf10[4.297851003743346, 4.297851003743346, 4.29785...
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res0.0001720.00003218.6820860.000000e+00inf0.2[0.00017233339191813357]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_49 4.297851 0.013404 0.311872 -inf inf 10 \n", + "Saq0_49 0.000409 0.000022 5.358803 1.000000e-07 inf 0.0001 \n", + "res 0.000172 0.000032 18.682086 0.000000e+00 inf 0.2 \n", + "\n", + " parray \n", + "kaq0_49 [4.297851003743346, 4.297851003743346, 4.29785... \n", + "Saq0_49 [0.0004089557630334584, 0.0004089557630334584,... \n", + "res [0.00017233339191813357] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.002730287389920131\n" + ] + } + ], + "source": [ + "display(cR.parameters)\n", + "print('RMSE:', cR.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The RMSE has improved slightly, and AIC has also decreased, which means adding skin resistance improved the model. However, the improvement is minimal, as the ```res``` calibrated value is tiny. Therefore, one can justify a calibration without ```res```." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 10. Analysis and comparison of simulated values\n", + "\n", + "We now compare the values in TTim and add the results of the AQTESOLV modelling reported by Yang (2020)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Comparison of parameter values and error under different models
 k [m/d]Ss [1/m]res [1/d]RMSE
AQTESOLV4.0340000.000383nan0.002976
ttim-three6.0897160.000204nan0.002874
ttim-multi4.2655450.000492nan0.002955
ttim-res4.2978510.0004090.0001720.002730
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ta = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'res [1/d]'],\\\n", + " index=['AQTESOLV', 'ttim-three', 'ttim-multi', 'ttim-res'])\n", + "ta.loc['ttim-three'] = np.concatenate((ca.parameters['optimal'].values,[np.nan]))\n", + "ta.loc['ttim-multi'] = np.concatenate((cM.parameters['optimal'].values,[np.nan]))\n", + "ta.loc['ttim-res'] = cR.parameters['optimal'].values\n", + "ta.loc['AQTESOLV'] = [4.034, 3.834E-04]+[np.nan]\n", + "ta['RMSE'] = [0.002976, ca.rmse(), cM.rmse(), cR.rmse()]\n", + "ta.style.set_caption('Comparison of parameter values and error under different models')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All models had similar RMSE performance. However, there was a significant variation in parameter values between the three-layer model and the multi-layered model. Multi-layered model parameters were closer to AQTESOLV values. The best RMSE model was the last model, the multi-layered with skin resistance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "* Butler, J.J., Jr., 1998. The Design, Performance, and Analysis of Slug Tests, Lewis Publishers, Boca Raton, Florida, 252p.\n", + "* Hyder, Z., Butler Jr, J.J., McElwee, C.D., Liu, W., 1994. Slug tests in partially penetrating wells. Water Resources Research 30, 2945–2957.\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Next Example: [Slug 2 - Falling Head](slug2_falling_head.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pumpingtests/slug2_falling_head.ipynb b/pumpingtests/slug2_falling_head.ipynb new file mode 100644 index 0000000..bfe5ea6 --- /dev/null +++ b/pumpingtests/slug2_falling_head.ipynb @@ -0,0 +1,906 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Slug Test - Falling Head\n", + "**This test is taken from examples of AQTESOLV.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model\n", + "\n", + "In this notebook, we reproduce the work of Yang (2020) to check the TTim performance in analysing slug-test. We later compare the solution in TTim with the KGS analytical model (Hyder et al. 1994) implemented in AQTESOLV (Duffield, 2007).\n", + "\n", + "This slug test was reported in Batu (1998). A well partially penetrates a sandy unconfined aquifer that has a saturated depth of 32.57 ft. The top of the screen is located 0.47 ft below the water table and has 13.8 ft in length. The well and casing radii are 5 and 2 inches, respectively.\n", + "\n", + "The slug displacement is 1.48 ft. Head change has been recorded at the slug well.\n", + "\n", + "The conceptual model is seen in the figure below." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1,1,1)\n", + "#sky\n", + "sky = plt.Rectangle((-20,2), width = 50, height = 20, fc = 'b', zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "#Aquifer:\n", + "ground = plt.Rectangle((-20,-32.57), width = 50, height = 34.57, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9)\n", + "ax.add_patch(ground)\n", + "\n", + "well = plt.Rectangle((-1,-(0.47+13.8)), width = 2, height = (0.47+13.8)+2, fc = np.array([200,200,200])/255, zorder=1)\n", + "ax.add_patch(well)\n", + "\n", + "#Wellhead\n", + "wellhead = plt.Rectangle((-1.25,2),width = 2.5, height = 10, fc = np.array([200,200,200])/255, zorder=2, ec='k')\n", + "ax.add_patch(wellhead)\n", + "\n", + "#Screen for the well:\n", + "screen = plt.Rectangle((-1,-(0.47+13.8)), width = 2, height = 13.8, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x = 0,y = 10, dx = 0, dy = 6, color = \"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x = 0.5, y = 13, s = r'$ D = 1.48 ft $', fontsize = 'large' )\n", + "\n", + "#last line\n", + "line = plt.Line2D(xdata= [-200,1200], ydata = [2,2], color = \"k\")\n", + "ax.add_line(line)\n", + "\n", + "#water table\n", + "line = plt.Line2D(xdata= [-200,1200], ydata = [0,0], color = \"blue\")\n", + "ax.add_line(line)\n", + "\n", + "\n", + "\n", + "ax.set_xlim([-20,20])\n", + "ax.set_ylim([-32,20])\n", + "ax.set_xlabel('Distance [ft]')\n", + "ax.set_ylabel('Relative height [ft]')\n", + "ax.set_title('Conceptual Model - Falling Head Example');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Import required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from ttim import *\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters\n", + "\n", + "Parameters here declared are already converted from feet and inches to meters" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "rw = 0.127 # well radius\n", + "rc = 0.0508 # well casing radius\n", + "L = 4.20624 # screen length\n", + "b = -9.9274 # aquifer thickness\n", + "zt = -0.1433 # depth to top of the screen\n", + "H0 = 0.4511 # initial displacement in the well\n", + "zb = zt - L # bottom of the screen" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Converting slug displacement to volume" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Slug: 0.00366 m^3\n" + ] + } + ], + "source": [ + "Q = np.pi * rc ** 2 * H0\n", + "print('Slug:', round(Q, 5), 'm^3')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Load data\n", + "\n", + "Drawdown data is available in feet and seconds and are converted to meters and days" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data = np.loadtxt('data/falling_head.txt', skiprows = 2)\n", + "t = data[:, 0] / 60 / 60 / 24 #convert time from seconds to days\n", + "h = (10 - data[:, 1]) * 0.3048 #convert drawdown from ft to meters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Create First Model - three layers\n", + "\n", + "We begin with a model with just three layers. We arranged the layers to match the screen length. The first layer is located just above the screen, the second layer is located at the screen depths, and the last layer is just below the screen, up to the total aquifer depth.\n", + "\n", + "We set the model in the same manner as in [Slug 1 - Pratt County](slug1_pratt_county.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_0 = Model3D(kaq=10, z=[0, zt, zb, b], Saq=1e-4, tmin=1e-5, tmax=0.01)\n", + "w_0 = Well(ml_0, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=1, wbstype='slug')\n", + "ml_0.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Model calibration\n", + "\n", + "The procedures for calibration can be seen in [Unconfined 1 - Vennebulten](unconfined1_vennebulten.ipynb)\n", + "\n", + "We calibrate hydraulic conductivity and specific storage, as in the KGS model (Hyder et al. 1994)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 51\n", + " # data points = 27\n", + " # variables = 2\n", + " chi-square = 0.00113000\n", + " reduced chi-square = 4.5200e-05\n", + " Akaike info crit = -268.197122\n", + " Bayesian info crit = -265.605448\n", + "[[Variables]]\n", + " kaq0_2: 0.59645054 +/- 0.03142467 (5.27%) (init = 10)\n", + " Saq0_2: 2.1306e-04 +/- 6.2567e-05 (29.37%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_2, Saq0_2) = -0.968\n" + ] + } + ], + "source": [ + "ca_0 = Calibrate(ml_0)\n", + "ca_0.set_parameter(name='kaq0_2', initial=10)\n", + "ca_0.set_parameter(name='Saq0_2', initial=1e-4, pmin = 1e-7)\n", + "ca_0.series(name='obs', x=0, y=0, t=t, h=h, layer=1)\n", + "ca_0.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_20.5964510.0314255.268613-infinf10[0.5964505364271898, 0.5964505364271898, 0.596...
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" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_2 0.596451 0.031425 5.268613 -inf inf 10 \n", + "Saq0_2 0.000213 0.000063 29.36663 1.000000e-07 inf 0.0001 \n", + "\n", + " parray \n", + "kaq0_2 [0.5964505364271898, 0.5964505364271898, 0.596... \n", + "Saq0_2 [0.00021305549571903892, 0.0002130554957190389... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.00646929950106304\n" + ] + } + ], + "source": [ + "display(ca_0.parameters)\n", + "print('RMSE:', ca_0.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm_0 = ml_0.head(0, 0, t, layers=1)\n", + "plt.figure(figsize = (10, 7))\n", + "plt.semilogx(t, h/H0, '.', label='obs')\n", + "plt.semilogx(t, hm_0[0]/H0, label='ttim')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('Normalized head (h/H0)')\n", + "plt.title('Model results - three layers model')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Create Second Model - multi-layer model\n", + "\n", + "To investigate whether we can improve the model performance, we will create a multi-layer model. For this, we divide the previous second and third layers into 0.5 m thick layers:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "#Determine elevation of each layer. \n", + "#Thickness of each layer is set to be 0.5 m.\n", + "z0 = np.arange(zt, zb, -0.5)\n", + "z1 = np.arange(zb, b, -0.5)\n", + "zlay = np.append(z0, z1)\n", + "zlay = np.append(zlay, b)\n", + "zlay = np.insert(zlay, 0, 0)\n", + "nlay = len(zlay) - 1 #number of layers\n", + "Saq_1 = 1e-4 * np.ones(nlay)\n", + "Saq_1[0] = 0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 8\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_1 = Model3D(kaq=10, z=zlay, Saq=Saq_1, kzoverkh=1, \\\n", + " tmin=1e-5, tmax=0.01, phreatictop=True)\n", + "w_1 = Well(ml_1, xw=0, yw=0, rw=rw, tsandQ=[(0, -Q)], layers=[1,2,3,4,5,6,7,8], rc=rc, \\\n", + " wbstype='slug')\n", + "ml_1.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Calibration of multi-layer model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".....................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 34\n", + " # data points = 216\n", + " # variables = 2\n", + " chi-square = 0.00868197\n", + " reduced chi-square = 4.0570e-05\n", + " Akaike info crit = -2182.30557\n", + " Bayesian info crit = -2175.55502\n", + "[[Variables]]\n", + " kaq0_21: 0.49534587 +/- 0.00771304 (1.56%) (init = 10)\n", + " Saq0_21: 4.0608e-04 +/- 3.5535e-05 (8.75%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_21, Saq0_21) = -0.959\n" + ] + } + ], + "source": [ + "ca_1 = Calibrate(ml_1)\n", + "ca_1.set_parameter(name='kaq0_21', initial=10, pmin=0)\n", + "ca_1.set_parameter(name='Saq0_21', initial=1e-4, pmin=0)\n", + "ca_1.series(name='obs', x=0, y=0, layer=[1,2,3,4,5,6,7,8], t=t, h=h)\n", + "ca_1.fit(report = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_210.4953460.0077131.5571020inf10[0.49534586991870566, 0.49534586991870566, 0.4...
Saq0_210.0004060.0000368.7506830inf0.0001[0.0004060803540884006, 0.0004060803540884006,...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_21 0.495346 0.007713 1.557102 0 inf 10 \n", + "Saq0_21 0.000406 0.000036 8.750683 0 inf 0.0001 \n", + "\n", + " parray \n", + "kaq0_21 [0.49534586991870566, 0.49534586991870566, 0.4... \n", + "Saq0_21 [0.0004060803540884006, 0.0004060803540884006,... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.006339898421521682\n" + ] + } + ], + "source": [ + "display(ca_1.parameters)\n", + "print('RMSE:', ca_1.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "RMSE has just slightly improved, and the parameter values are more or less similar to the previous values. However, AIC has improved significantly." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm_1 = ml_1.head(0, 0, t, layers=8)\n", + "plt.figure(figsize = (8, 5))\n", + "plt.semilogx(t, h/H0, '.', label='obs')\n", + "plt.semilogx(t, hm_1[0]/H0, label='ttim')\n", + "plt.xlabel('time(d)')\n", + "plt.ylabel('h/H0')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 9. Final Model calibration with well skin resistance\n", + "\n", + "Now we test if the skin resistance of the well has an impact on model calibration. For this, we add the ```res``` parameter in the calibration settings. We use the same multi-layer model." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...........................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 40\n", + " # data points = 216\n", + " # variables = 3\n", + " chi-square = 0.00858103\n", + " reduced chi-square = 4.0287e-05\n", + " Akaike info crit = -2182.83157\n", + " Bayesian info crit = -2172.70573\n", + "[[Variables]]\n", + " kaq0_21: 0.50869799 +/- 0.01009968 (1.99%) (init = 10)\n", + " Saq0_21: 3.4204e-04 +/- 4.2853e-05 (12.53%) (init = 0.0001)\n", + " res: 0.00247891 +/- 0.00142206 (57.37%) (init = 0.1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_21, Saq0_21) = -0.976\n", + " C(Saq0_21, res) = -0.709\n", + " C(kaq0_21, res) = 0.667\n" + ] + } + ], + "source": [ + "ca_2 = Calibrate(ml_1)\n", + "ca_2.set_parameter(name='kaq0_21', initial=10, pmin=0)\n", + "ca_2.set_parameter(name='Saq0_21', initial=1e-4, pmin=0)\n", + "ca_2.set_parameter_by_reference(name='res', parameter=w_1.res, initial=0.1, pmin=0)\n", + "ca_2.series(name='obs', x=0, y=0, layer=[1,2,3,4,5,6,7,8], t=t, h=h)\n", + "ca_2.fit(report = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_210.5086980.0101001.9853990inf10[0.5086979931631646, 0.5086979931631646, 0.508...
Saq0_210.0003420.00004312.5286950inf0.0001[0.00034203719744052563, 0.0003420371974405256...
res0.0024790.00142257.3663260inf0.1[0.0024789096083315254]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_21 0.508698 0.010100 1.985399 0 inf 10 \n", + "Saq0_21 0.000342 0.000043 12.528695 0 inf 0.0001 \n", + "res 0.002479 0.001422 57.366326 0 inf 0.1 \n", + "\n", + " parray \n", + "kaq0_21 [0.5086979931631646, 0.5086979931631646, 0.508... \n", + "Saq0_21 [0.00034203719744052563, 0.0003420371974405256... \n", + "res [0.0024789096083315254] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.006302935863326607\n" + ] + } + ], + "source": [ + "display(ca_2.parameters)\n", + "print('RMSE:', ca_2.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model has only improved slightly with the addition of the skin resistance, with a tiny improvement in AIC and RMSE. That indicates that skin resistance can be ignored in this situation." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm_2 = ml_1.head(0, 0, t, layers=8)\n", + "plt.figure(figsize = (8, 5))\n", + "plt.semilogx(t, h/H0, '.', label='obs')\n", + "plt.semilogx(t, hm_2[0]/H0, label='ttim')\n", + "plt.xlabel('time(d)')\n", + "plt.ylabel('h/H0')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 10. Analysis and comparison of simulated values\n", + "\n", + "We now compare the values in TTim and add the results of the AQTESOLV modelling reported by Yang (2020)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Comparison of parameter values and error under different models
 k [m/d]Ss [1/m]res [1/d]RMSE
AQTESOLV2.6160000.000079nan0.001197
ttim-three0.5964510.000213nan0.006469
ttim-multi0.4953460.000406nan0.006358
ttim-res0.5086980.0003420.0024790.006303
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ta = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'res [1/d]'],\\\n", + " index=['AQTESOLV', 'ttim-three', 'ttim-multi', 'ttim-res'])\n", + "ta.loc['ttim-three'] = np.concatenate((ca_0.parameters['optimal'].values,[np.nan]))\n", + "ta.loc['ttim-multi'] = np.concatenate((ca_1.parameters['optimal'].values,[np.nan]))\n", + "ta.loc['ttim-res'] = ca_2.parameters['optimal'].values\n", + "ta.loc['AQTESOLV'] = [2.616, 7.894E-5]+[np.nan]\n", + "ta['RMSE'] = [0.001197, round(ca_0.rmse(), 6), round(ca_1.rmse(), 6), round(ca_2.rmse(),6)]\n", + "ta.style.set_caption('Comparison of parameter values and error under different models')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "AQTESOLV parameters are quite different from the set parameters in TTim. It also has a better RMSE performance. All TTim models are very similar to each other. However, the multi-layer models performed better." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Batu, V., 1998. Aquifer hydraulics: a comprehensive guide to hydrogeologic data analysis. John Wiley & Sons\n", + "* Hyder, Z., Butler Jr, J.J., McElwee, C.D., Liu, W., 1994. Slug tests in partially penetrating wells. Water Resources Research 30, 2945–2957.\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Next Notebook: [Slug Test 3 - Multiwell](slug3_multiwell.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pumpingtests/slug3_multiwell.ipynb b/pumpingtests/slug3_multiwell.ipynb new file mode 100644 index 0000000..9a925bf --- /dev/null +++ b/pumpingtests/slug3_multiwell.ipynb @@ -0,0 +1,942 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Slug Test for Confined Aquifer - Multi-well Example\n", + "**This test is taken from examples of AQTESOLV.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model\n", + "\n", + "In this notebook, we reproduce the work of Yang (2020) to check the TTim performance in analysing slug-test. We later compare the solution in TTim with the KGS analytical model (Hyder et al. 1994) implemented in AQTESOLV (Duffield, 2007) and to the MLU model (Carlson & Randall, 2012).\n", + "\n", + "This Slug Test was reported in Butler (1998). A well (Ln-2) fully penetrates a sandy confined aquifer, with 6.1 m thickness. Additionally, an observation well (Ln-3) is placed 6.45 m away from the test well. The observation well is also fully penetrated.\n", + "\n", + "The slug displacement is 2.798 m. Head change has been recorded at the slug well and the observation well. The well and casing radii of the slug well are 0.102 and 0.051 m, respectively. For the observation well, they are 0.051 and 0.025 m, respectively.\n", + "\n", + "The conceptual model can be seen in the figure below.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1,1,1)\n", + "#sky\n", + "sky = plt.Rectangle((-5,1), width = 15, height = 3, fc = 'b', zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "#Aquifer:\n", + "ground = plt.Rectangle((-5,-6.1), width = 15, height = 6.1, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9)\n", + "ax.add_patch(ground)\n", + "\n", + "well = plt.Rectangle((-0.5,-(6.1)), width = 1, height = (7.1), fc = np.array([200,200,200])/255, zorder=1)\n", + "ax.add_patch(well)\n", + "\n", + "#Confining Unit\n", + "conf = plt.Rectangle((-5,0), width = 15, height = 1, fc = np.array([100,100,100])/255, zorder=0, alpha=0.9)\n", + "ax.add_patch(conf)\n", + "\n", + "#Wellhead\n", + "wellhead = plt.Rectangle((-0.6,1),width = 1.2, height = 1.5, fc = np.array([200,200,200])/255, zorder=2, ec='k')\n", + "ax.add_patch(wellhead)\n", + "\n", + "#Screen for the well:\n", + "screen = plt.Rectangle((-0.5,-(6.1)), width = 1, height = 6.1, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x = 1,y = 1.5, dx = 0, dy = 1, color = \"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x = 1.2, y = 1.5, s = r'$ D = 2.798 m $', fontsize = 'large' )\n", + "\n", + "#Piezometer\n", + "piez = plt.Rectangle((6.20,-(6.1)), width = 0.5, height = (7.1), fc = np.array([200,200,200])/255, zorder=1)\n", + "ax.add_patch(piez)\n", + "screen_piez = plt.Rectangle((6.2,-(6.1)), width = 0.5, height = 6.1, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez.set_linewidth(2)\n", + "ax.add_patch(screen_piez)\n", + "\n", + "#last line\n", + "line = plt.Line2D(xdata= [-200,1200], ydata = [1,1], color = \"k\")\n", + "ax.add_line(line)\n", + "\n", + "#Water table\n", + "#wt = plt.Line2D(xdata= [-200,1200], ydata = [0,0], color = \"b\")\n", + "#ax.add_line(wt)\n", + "\n", + "ax.text(0.6,-0.5, s = \"Ln-2\", fontsize = 'large')\n", + "ax.text(6.9, -0.5, \"Ln-3\", fontsize = 'large')\n", + "ax.set_xlim([-5,10])\n", + "ax.set_ylim([-6.1,3])\n", + "ax.set_xlabel('Distance [m]')\n", + "ax.set_ylabel('Relative height [m]')\n", + "ax.set_title('Conceptual Model - Multi-Well Example');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Import required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from ttim import *\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "H0 = 2.798 #initial displacement in m\n", + "b = -6.1 #aquifer thickness\n", + "rw1 = 0.102 #well radius of Ln-2 Well\n", + "rw2 = 0.071 #well radius of observation Ln-3 Well\n", + "rc1 = 0.051 #casing radius of Ln-2 Well\n", + "rc2 = 0.025 #casing radius of Ln-3 Well\n", + "r = 6.45 #distance from observation well to test well" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Converting slug displacement to volume" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Slug: 0.02286 m^3\n" + ] + } + ], + "source": [ + "Q = np.pi * rc1 ** 2 * H0\n", + "print('Slug:', round(Q, 5), 'm^3')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data1 = np.loadtxt('data/ln-2.txt')\n", + "t1 = data1[:, 0] / 60 / 60 / 24 #convert time from seconds to days\n", + "h1 = data1[:, 1]\n", + "data2 = np.loadtxt('data/ln-3.txt')\n", + "t2 = data2[:, 0] / 60 / 60 / 24\n", + "h2 = data2[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Create First Model - single layer\n", + "\n", + "We begin with a single layer model built in ```ModelMaq```.\n", + "Details on setting up the model can be seen in: [Confined 1 - Oude Korendijk](confined1_oude_korendijk.ipynb).\n", + "\n", + "The slug well is set accordingly. Details on setting up the ```Well``` object can be seen in: [Slug 1 - Pratt County](slug1_pratt_county.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_0 = ModelMaq(kaq=10, z=[0, b], Saq=1e-4, \\\n", + " tmin=1e-5, tmax=0.01)\n", + "w_0 = Well(ml_0, xw=0, yw=0, rw=rw1, rc=rc1, tsandQ=[(0, -Q)], layers=0, wbstype='slug')\n", + "ml_0.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Model calibration both simultaneous wells\n", + "\n", + "\n", + "The procedures for calibration can be seen in [Unconfined 1 - Vennebulten](unconfined1_vennebulten.ipynb)\n", + "\n", + "We calibrate hydraulic conductivity and specific storage, as in the KGS model (Hyder et al. 1994)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 33\n", + " # data points = 162\n", + " # variables = 2\n", + " chi-square = 0.01697483\n", + " reduced chi-square = 1.0609e-04\n", + " Akaike info crit = -1480.50639\n", + " Bayesian info crit = -1474.33119\n", + "[[Variables]]\n", + " kaq0: 1.16611071 +/- 0.00292597 (0.25%) (init = 10)\n", + " Saq0: 9.3822e-06 +/- 1.1585e-07 (1.23%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.502\n" + ] + } + ], + "source": [ + "#unknown parameters: kaq, Saq\n", + "ca_0 = Calibrate(ml_0)\n", + "ca_0.set_parameter(name='kaq0', initial=10)\n", + "ca_0.set_parameter(name='Saq0', initial=1e-4)\n", + "ca_0.series(name='Ln-2', x=0, y=0, layer=0, t=t1, h=h1)\n", + "ca_0.series(name='Ln-3', x=r, y=0, layer=0, t=t2, h=h2)\n", + "ca_0.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq01.1661112.925968e-030.250917-infinf10[1.166110712749139]
Saq00.0000091.158486e-071.234774-infinf0.0001[9.38217155340542e-06]
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" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 1.166111 2.925968e-03 0.250917 -inf inf 10 \n", + "Saq0 0.000009 1.158486e-07 1.234774 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0 [1.166110712749139] \n", + "Saq0 [9.38217155340542e-06] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.01023635339478882\n" + ] + } + ], + "source": [ + "display(ca_0.parameters)\n", + "print('RMSE:', ca_0.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm1_0 = ml_0.head(0, 0, t1, layers=0)\n", + "hm2_0 = ml_0.head(r, 0, t2, layers=0)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1/H0, '.', label='obs ln-2')\n", + "plt.semilogx(t1, hm1_0[0]/H0, label='ttim ln-2')\n", + "plt.semilogx(t2, h2/H0, '.', label='obs ln-3')\n", + "plt.semilogx(t2, hm2_0[0]/H0, label='ttim ln-3')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('Normalized Head: h/H0')\n", + "plt.title('Model Results - Single layer model')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In general, the single-layer model seems to be performing well, with a good visual fit between observations and the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Calibration with well skin resistance\n", + "\n", + "Now we test if the skin resistance of the well has an impact on model calibration. Therefore, we add the ```res``` parameter in the calibration settings. We use the one-layer model." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 51\n", + " # data points = 162\n", + " # variables = 3\n", + " chi-square = 0.01690851\n", + " reduced chi-square = 1.0634e-04\n", + " Akaike info crit = -1479.14056\n", + " Bayesian info crit = -1469.87777\n", + "[[Variables]]\n", + " kaq0: 1.16581441 +/- 0.00296305 (0.25%) (init = 10)\n", + " Saq0: 9.3669e-06 +/- 1.1770e-07 (1.26%) (init = 0.0001)\n", + " res: 2.8663e-04 +/- 3.8599e-04 (134.66%) (init = 0)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.463\n", + " C(Saq0, res) = -0.179\n", + " C(kaq0, res) = -0.149\n" + ] + } + ], + "source": [ + "#unknown parameters: kaq, Saq, res\n", + "ca_1 = Calibrate(ml_0)\n", + "ca_1.set_parameter(name='kaq0', initial=10)\n", + "ca_1.set_parameter(name='Saq0', initial=1e-4)\n", + "ca_1.set_parameter_by_reference(name='res', parameter=w_0.res, initial=0)\n", + "ca_1.series(name='Ln-2', x=0, y=0, layer=0, t=t1, h=h1)\n", + "ca_1.series(name='Ln-3', x=r, y=0, layer=0, t=t2, h=h2)\n", + "ca_1.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq01.1658142.963047e-030.254161-infinf10[1.1658144145327587]
Saq00.0000091.176977e-071.256531-infinf0.0001[9.366880025069768e-06]
res0.0002873.859882e-04134.664369-infinf0[0.0002866298026861447]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 1.165814 2.963047e-03 0.254161 -inf inf 10 \n", + "Saq0 0.000009 1.176977e-07 1.256531 -inf inf 0.0001 \n", + "res 0.000287 3.859882e-04 134.664369 -inf inf 0 \n", + "\n", + " parray \n", + "kaq0 [1.1658144145327587] \n", + "Saq0 [9.366880025069768e-06] \n", + "res [0.0002866298026861447] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.010216337267978508\n" + ] + } + ], + "source": [ + "display(ca_1.parameters)\n", + "print('RMSE:', ca_1.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm1_1 = ml_0.head(0, 0, t1, layers=0)\n", + "hm2_1 = ml_0.head(r, 0, t2, layers=0)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1/H0, '.', label='obs ln-2')\n", + "plt.semilogx(t1, hm1_1[0]/H0, label='ttim ln-2')\n", + "plt.semilogx(t2, h2/H0, '.', label='obs ln-3')\n", + "plt.semilogx(t2, hm2_1[0]/H0, label='ttim ln-3')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('Normalized Head: h/H0')\n", + "plt.title('Model Results - Single layer model with res')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding well screen resistance does not improve the performance significantly, while the AIC value increases. Thus, it is recommended to leave it out of the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 9. Create Second Model - multi-layer model\n", + "\n", + "We will create a multi-layer model to investigate whether we can improve the model performance if we account for the vertical flow component. We carry this out by dividing the previous aquifer into 0.5 m thick layers." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "#Determine elevations of each layer.\n", + "#Thickness of each layer is set to be 0.5 m.\n", + "z = np.arange(0, b, -0.5)\n", + "zlay = np.append(z, b)\n", + "nlay = len(zlay) - 1\n", + "Saq_2 = 1e-4 * np.ones(nlay)\n", + "n = np.arange(0, 13,1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we use the ```Model3D``` object to model multi-layer aquifer:\n", + "\n", + "Details on how to set it up can be seen in the notebook: [Unconfined - 1 - Vennebulten](unconfined1_vennebulten.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 13\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_2 = Model3D(kaq=10, z=zlay, Saq=Saq_2, kzoverkh=1, tmin=1e-5, tmax=0.01, \\\n", + " phreatictop=True)\n", + "w_2 = Well(ml_2, xw=0, yw=0, rw=rw1, tsandQ=[(0, -Q)], layers=n, rc=rc1, \\\n", + " wbstype='slug')\n", + "ml_2.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 10. Calibration of multi-layer model" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 30\n", + " # data points = 2106\n", + " # variables = 2\n", + " chi-square = 0.21986745\n", + " reduced chi-square = 1.0450e-04\n", + " Akaike info crit = -19302.2835\n", + " Bayesian info crit = -19290.9784\n", + "[[Variables]]\n", + " kaq0_12: 1.16574643 +/- 8.0333e-04 (0.07%) (init = 10)\n", + " Saq0_12: 8.6961e-06 +/- 2.9670e-08 (0.34%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_12, Saq0_12) = -0.490\n" + ] + } + ], + "source": [ + "ca_2 = Calibrate(ml_2)\n", + "ca_2.set_parameter(name='kaq0_12', initial=10)\n", + "ca_2.set_parameter(name='Saq0_12', initial=1e-4, pmin=0)\n", + "ca_2.series(name='Ln-2', x=0, y=0, layer=n, t=t1, h=h1)\n", + "ca_2.series(name='Ln-3', x=r, y=0, layer=n, t=t2, h=h2)\n", + "ca_2.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_121.1657468.033304e-040.068911-infinf10[1.1657464303972085, 1.1657464303972085, 1.165...
Saq0_120.0000092.967009e-080.341190.0inf0.0001[8.69605115960681e-06, 8.69605115960681e-06, 8...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_12 1.165746 8.033304e-04 0.068911 -inf inf 10 \n", + "Saq0_12 0.000009 2.967009e-08 0.34119 0.0 inf 0.0001 \n", + "\n", + " parray \n", + "kaq0_12 [1.1657464303972085, 1.1657464303972085, 1.165... \n", + "Saq0_12 [8.69605115960681e-06, 8.69605115960681e-06, 8... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.010217656154254037\n" + ] + } + ], + "source": [ + "display(ca_2.parameters)\n", + "print('RMSE:', ca_2.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm1_2 = ml_2.head(0, 0, t1, layers=n)\n", + "hm2_2 = ml_2.head(r, 0, t2, layers=n)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1/H0, '.', label='obs ln-2')\n", + "plt.semilogx(t1, hm1_2[0]/H0, label='ttim ln-2')\n", + "plt.semilogx(t2, h2/H0, '.', label='obs ln-3')\n", + "plt.semilogx(t2, hm2_2[0]/H0, label='ttim ln-3')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('Normalized Head: h/H0')\n", + "plt.title('Model Results - Multi-layer model')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The new model showed similar parameters and RMSE values compared to the previous single-layer model. However, the AIC value was much smaller." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 11. Analysis and comparison of simulated values\n", + "\n", + "We now compare the values in TTim and also add the results of the modelling done in AQTESOLV and MLU by Yang (2020)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Comparison of parameter values and error under different models
 k [m/d]Ss [1/m]RMSE
MLU1.3110000.0000080.010373
AQTESOLV1.1660000.0000090.009151
ttim-single1.1661110.0000090.010218
ttim-multi1.1657460.0000090.010218
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ta = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]'], \\\n", + " index=['MLU', 'AQTESOLV', 'ttim-single', 'ttim-multi'])\n", + "ta.loc['AQTESOLV'] = [1.166, 9.368E-06]\n", + "ta.loc['MLU'] = [1.311, 8.197E-06]\n", + "ta.loc['ttim-single'] = ca_0.parameters['optimal'].values\n", + "ta.loc['ttim-multi'] = ca_2.parameters['optimal'].values\n", + "ta['RMSE'] = [0.010373, 0.009151, ca_0.rmse(), ca_2.rmse()]\n", + "ta.style.set_caption('Comparison of parameter values and error under different models')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The parameters in every model closely match each other. The error was also very similar." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Butler, J.J., Jr., 1998. The Design, Performance, and Analysis of Slug Tests, Lewis Publishers, Boca Raton, Florida, 252p.\n", + "* Hyder, Z., Butler Jr, J.J., McElwee, C.D., Liu, W., 1994. Slug tests in partially penetrating wells. Water Resources Research 30, 2945–2957.\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Next Notebook: [Slug Test 4 - Dawsonville](slug4_dawsonville.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pumpingtests/slug4_dawsonville.ipynb b/pumpingtests/slug4_dawsonville.ipynb new file mode 100644 index 0000000..2b6d4f3 --- /dev/null +++ b/pumpingtests/slug4_dawsonville.ipynb @@ -0,0 +1,882 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Slug test for confined aquifer - Dawsonville Example\n", + "**This test is taken from example of MLU.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model\n", + "\n", + "In this notebook, we reproduce the work of Yang (2020) to check the TTim performance in analysing slug-test. We later compare the solution in TTim with the MLU model (Carlson & Randall, 2012).\n", + "\n", + "This Slug Test was reported in Cooper Jr et al. (1967), and it was performed in Dawsonville, Georgia, USA. A fully penetrated well (Ln-2) is screened in a confined aquifer, located between depths 24 and 122 (98 m thick).\n", + "\n", + "The volume of the slug is 10.16 litres. Head change has been recorded at the slug well. Both the well and the casing radii of the slug well is 0.076 m.\n", + "\n", + "The conceptual model can be seen in the figure below:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1,1,1)\n", + "#sky\n", + "sky = plt.Rectangle((-5,0), width = 15, height = 10, fc = 'b', zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "#Aquifer:\n", + "ground = plt.Rectangle((-5,-122), width = 15, height = 98, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9)\n", + "ax.add_patch(ground)\n", + "\n", + "well = plt.Rectangle((-0.5,-(122)), width = 1, height = 122, fc = np.array([200,200,200])/255, zorder=1)\n", + "ax.add_patch(well)\n", + "\n", + "#Confining Unit\n", + "conf = plt.Rectangle((-5,-24), width = 15, height = 24, fc = np.array([100,100,100])/255, zorder=0, alpha=0.9)\n", + "ax.add_patch(conf)\n", + "\n", + "#Wellhead\n", + "wellhead = plt.Rectangle((-0.6,0),width = 1.2, height = 4, fc = np.array([200,200,200])/255, zorder=2, ec='k')\n", + "ax.add_patch(wellhead)\n", + "\n", + "#Screen for the well:\n", + "screen = plt.Rectangle((-0.5,-(122)), width = 1, height = 98, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "#pumping_arrow = plt.Arrow(x = 1,y = 1.5, dx = 0, dy = 1, color = \"#00035b\")\n", + "#ax.add_patch(pumping_arrow)\n", + "ax.text(x = 1, y = 2.5, s = r'$ Q = 10.16 L $', fontsize = 'large' )\n", + "\n", + "\n", + "#last line\n", + "line = plt.Line2D(xdata= [-200,1200], ydata = [0,0], color = \"k\")\n", + "ax.add_line(line)\n", + "\n", + "#Water table\n", + "#wt = plt.Line2D(xdata= [-200,1200], ydata = [0,0], color = \"b\")\n", + "#ax.add_line(wt)\n", + "\n", + "ax.text(0.6,-35, s = \"Ln-2\", fontsize = 'large')\n", + "#ax.text(6.9, -0.5, \"Ln-3\", fontsize = 'large')\n", + "ax.set_xlim([-5,10])\n", + "ax.set_ylim([-122,10])\n", + "ax.set_xlabel('Distance [m]')\n", + "ax.set_ylabel('Relative height [m]')\n", + "ax.set_title('Conceptual Model - Dawsonville Example');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Load required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from ttim import *\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "b = 98 #aquifer thickness\n", + "zt = -24\n", + "zb = zt - b\n", + "rw = 0.076 #well radius of Ln-2 Well\n", + "rc = 0.076 #casing radius of Ln-2 Well\n", + "Q = 0.01016 #slug volume in m^3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Load data\n", + "\n", + "Data for the Dawsonville test is available in a text file, where the first column is the time data, in days and in the second column is the head displacement in meters" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data = np.loadtxt('data/dawsonville_slug.txt')\n", + "t = data[:, 0]\n", + "h = data[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Create First Model - single layer\n", + "\n", + "We begin with a single layer model built in ModelMaq.\n", + "Details on setting up the model can be seen in: [Confined 1 - Oude Korendijk](confined1_oude_korendijk.ipynb).\n", + "\n", + "The slug well is set accordingly. Details on setting up the ```Well``` object can be seen in: [Slug 1 - Pratt County](slug1_pratt_county.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml = ModelMaq(kaq=10, z=[zt, zb], Saq=1e-4, tmin=1e-6, tmax=1e-3, topboundary='conf')\n", + "w = Well(ml, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=0, wbstype='slug')\n", + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Model calibration both simultaneous wells\n", + "\n", + "\n", + "The procedures for calibration can be seen in [Unconfined 1 - Vennebulten](unconfined1_vennebulten.ipynb)\n", + "\n", + "We calibrate hydraulic conductivity and specific storage, as in the KGS model (Hyder et al. 1994)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "............................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 25\n", + " # data points = 22\n", + " # variables = 2\n", + " chi-square = 4.2779e-04\n", + " reduced chi-square = 2.1389e-05\n", + " Akaike info crit = -234.654488\n", + " Bayesian info crit = -232.472403\n", + "[[Variables]]\n", + " kaq0: 0.42113030 +/- 0.01842476 (4.38%) (init = 10)\n", + " Saq0: 1.6938e-05 +/- 5.2880e-06 (31.22%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0, Saq0) = -0.985\n" + ] + } + ], + "source": [ + "#unknown parameters: kay, Saq\n", + "ca = Calibrate(ml)\n", + "ca.set_parameter(name='kaq0', initial=10, pmin=0)\n", + "ca.set_parameter(name='Saq0', initial=1e-4)\n", + "ca.series(name='obs', x=0, y=0, layer=0, t=t, h=h)\n", + "ca.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq00.421130.0184254.3750740inf10[0.42113029595796414]
Saq00.0000170.00000531.219119-infinf0.0001[1.6938294179330807e-05]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 0.42113 0.018425 4.375074 0 inf 10 \n", + "Saq0 0.000017 0.000005 31.219119 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0 [0.42113029595796414] \n", + "Saq0 [1.6938294179330807e-05] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.004409624944092217\n" + ] + } + ], + "source": [ + "display(ca.parameters)\n", + "print('rmse:', ca.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm = ml.head(0, 0, t)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, h, '.', label='obs')\n", + "plt.semilogx(t, hm[0], label='ttim')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('displacement [m]')\n", + "plt.title('Model Results - Single-layer model')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In general, the single-layer model seems to be performing well, with a good visual fit between observations and the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Create Second Model - multi-layer model\n", + "\n", + "To investigate whether we need to account for the vertical flow component or not, we will create a multi-layer model. Consequently, we divide the previous aquifer into 49 layers (2 m thick each)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "nlay = 49 #number of layers\n", + "zlayers = np.linspace(zt, zb, nlay + 1) #elevation of each layer\n", + "Saq = 1e-4 * np.ones(nlay)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we use the ```Model3D``` object to model multi-layer aquifer:\n", + "\n", + "Details on how to set it up can be seen in the notebook: [Unconfined - 1 - Vennebulten](unconfined1_vennebulten.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 49\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_1 = Model3D(kaq=10, z=zlayers, Saq=Saq, tmin=1e-6, tmax=1e-3, phreatictop=False)\n", + "w_1 = Well(ml_1, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=range(nlay), \\\n", + " wbstype='slug')\n", + "ml_1.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Calibration of multi-layer model" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".............................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 26\n", + " # data points = 1078\n", + " # variables = 2\n", + " chi-square = 0.02096017\n", + " reduced chi-square = 1.9480e-05\n", + " Akaike info crit = -11690.1377\n", + " Bayesian info crit = -11680.1720\n", + "[[Variables]]\n", + " kaq0_48: 0.42104101 +/- 0.00252524 (0.60%) (init = 10)\n", + " Saq0_48: 1.6968e-05 +/- 7.2665e-07 (4.28%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_48, Saq0_48) = -0.986\n" + ] + } + ], + "source": [ + "ca_1 = Calibrate(ml_1)\n", + "ca_1.set_parameter(name='kaq0_48', initial=10, pmin=0)\n", + "ca_1.set_parameter(name='Saq0_48', initial=1e-4)\n", + "ca_1.series(name='obs', x=0, y=0, layer=range(nlay), t=t, h=h)\n", + "ca_1.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_480.4210412.525240e-030.5997610inf10[0.42104101396365423, 0.42104101396365423, 0.4...
Saq0_480.0000177.266545e-074.282481-infinf0.0001[1.6968074939135146e-05, 1.6968074939135146e-0...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_48 0.421041 2.525240e-03 0.599761 0 inf 10 \n", + "Saq0_48 0.000017 7.266545e-07 4.282481 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0_48 [0.42104101396365423, 0.42104101396365423, 0.4... \n", + "Saq0_48 [1.6968074939135146e-05, 1.6968074939135146e-0... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.004409486363804946\n" + ] + } + ], + "source": [ + "display(ca_1.parameters)\n", + "print('RMSE:', ca_1.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The multi-layer model does not improve the calibration by much." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm_1 = ml_1.head(0, 0, t)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, h, '.', label='obs')\n", + "plt.semilogx(t, hm_1[0], label='ttim')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('displacement [m]')\n", + "plt.title('Model Results - Multi-layer model')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Final Model calibration with well skin resistance\n", + "\n", + "Now we test if the skin resistance of the well has an impact on model calibration. We thus add the ```res``` parameter in the calibration settings. We use the same multi-layer model." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 48\n", + " # data points = 1078\n", + " # variables = 3\n", + " chi-square = 0.02096895\n", + " reduced chi-square = 1.9506e-05\n", + " Akaike info crit = -11687.6864\n", + " Bayesian info crit = -11672.7378\n", + "[[Variables]]\n", + " kaq0_48: 0.41949032 +/- 0.00255131 (0.61%) (init = 10)\n", + " Saq0_48: 1.7431e-05 +/- 7.4956e-07 (4.30%) (init = 0.0001)\n", + " res: 4.1889e-06 +/- 5.9899e-06 (142.99%) (init = 0.1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_48, Saq0_48) = -0.986\n", + " C(Saq0_48, res) = -0.210\n", + " C(kaq0_48, res) = 0.188\n" + ] + } + ], + "source": [ + "ca_2 = Calibrate(ml_1)\n", + "ca_2.set_parameter(name='kaq0_48', initial=10, pmin=0)\n", + "ca_2.set_parameter(name='Saq0_48', initial=1e-4, pmin = 1e-7)\n", + "ca_2.set_parameter_by_reference(name='res', parameter=w_1.res, initial=0.1, pmin = 0)\n", + "ca_2.series(name='obs', x=0, y=0, layer=range(nlay), t=t, h=h)\n", + "ca_2.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_480.419492.551309e-030.6081930inf10[0.41949032003442377, 0.41949032003442377, 0.4...
Saq0_480.0000177.495585e-074.3001590.0inf0.0001[1.743095019002272e-05, 1.743095019002272e-05,...
res0.0000045.989853e-06142.9940930inf0.1[4.1888812432056e-06]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_48 0.41949 2.551309e-03 0.608193 0 inf 10 \n", + "Saq0_48 0.000017 7.495585e-07 4.300159 0.0 inf 0.0001 \n", + "res 0.000004 5.989853e-06 142.994093 0 inf 0.1 \n", + "\n", + " parray \n", + "kaq0_48 [0.41949032003442377, 0.41949032003442377, 0.4... \n", + "Saq0_48 [1.743095019002272e-05, 1.743095019002272e-05,... \n", + "res [4.1888812432056e-06] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.004410409508316654\n" + ] + } + ], + "source": [ + "display(ca_2.parameters)\n", + "print('RMSE:', ca_2.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm_2 = ml_1.head(0, 0, t)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, h, '.', label='obs')\n", + "plt.semilogx(t, hm_2[0], label='ttim')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('displacement [m]')\n", + "plt.title('Model Results - Multi-layer with res')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding resistance of the well screen does not improve the performance. Thus, res should not be applied in the conceptual model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 9. Analysis and comparison of simulated values\n", + "\n", + "We now compare the values in TTim and add the results of the modelling done in MLU by Yang (2020)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Comparison of parameter values and error under different models
 k [m/d]Ss [1/m]RMSE
MLU0.4133000.0000190.004264
ttim0.4211300.0000170.004410
ttim-multilayer0.4210410.0000170.004410
ttim-res0.4194900.0000170.004410
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ta = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]'], \\\n", + " index = ['MLU', 'ttim', 'ttim-multilayer', 'ttim-res'])\n", + "tr = np.delete(ca_2.parameters['optimal'].values, 2)\n", + "ta.loc['MLU'] = [0.4133, 1.9388E-05]\n", + "ta.loc['ttim'] = ca.parameters['optimal'].values\n", + "ta.loc['ttim-multilayer'] = ca_1.parameters['optimal'].values\n", + "ta.loc['ttim-res'] = tr\n", + "ta['RMSE'] = [0.004264, ca.rmse(), ca_1.rmse(), ca_2.rmse()]\n", + "ta.style.set_caption('Comparison of parameter values and error under different models')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Results are similar between all models. The RMSE of MLU is slightly better than the one from TTim. The one-layer model has accomplished the same results as more complex ones. Hence, we note the importance of trying simpler models to avoid adding unnecessary complexity." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Cooper Jr, H.H., Bredehoeft, J.D., Papadopulos, I.S., 1967. Response of a finite diameter well to an instantaneous charge of water. Water Resources Research 3, 263–269\n", + "* Hyder, Z., Butler Jr, J.J., McElwee, C.D., Liu, W., 1994. Slug tests in partially penetrating wells. Water Resources Research 30, 2945–2957.\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pumpingtests/unconfined1_vennebulten.ipynb b/pumpingtests/unconfined1_vennebulten.ipynb new file mode 100644 index 0000000..85f7f6c --- /dev/null +++ b/pumpingtests/unconfined1_vennebulten.ipynb @@ -0,0 +1,1715 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Unconfined Aquifer Test - Vennebulten\n", + "**This example is taken from Kruseman et al. (1970).**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In aquifer tests in unconfined aquifers, there is also the vertical component to flow to the well. The drawdown data shows the delayed water table response, a distinguishable S-shape in the log-log plot. In the early times of the drawdown, the drawdown behaves as a confined aquifer: when the aquifer releases the elastic storage. However, as pumping continues, the water table storage begins to be released, generating further drawdown and the S-shape.\n", + "\n", + "This test conducted in Vennebulten, the Netherlands, is reported in Kruseman et al. (1970). The cross-section consists of a first layer up to 6 m depth of very fine and loamy sands, followed by coarse sands until 21 m deep.\n", + "\n", + "In this example, we will reproduce the work of Xinzhu (2020) that compared different conceptualizations in TTim to various solutions presented in the original report (Kruseman et al., 1970) and in other software, MLU (Carson & Randall, 2012) and AQTESOLV (Duffield, 2007).\n", + "\n", + "The screen of the pumping well is placed between 10 and 21 meters depth, and pumping has taken place for 25 hours at a rate of 873 m3/d. The available drawdown data comes from two piezometers, a shallow one, screened at 3 m depth, and a deeper one, screened in the depths between 12 to 19 m. Both wells are located 90 m from the pumping well.\n", + "\n", + "The conceptual model of the aquifer is displayed below:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1,1,1)\n", + "#sky\n", + "sky = plt.Rectangle((-20,2), width = 150, height = 5, fc = 'b', zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "#Aquifer:\n", + "ground = plt.Rectangle((-20,-6), width = 150, height = 8, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9, hatch = '.')\n", + "ax.add_patch(ground)\n", + "\n", + "#Aquifer 2:\n", + "ground2 = plt.Rectangle((-20,-21), width = 150, height = 15, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9, hatch = 'o')\n", + "ax.add_patch(ground2)\n", + "\n", + "\n", + "well = plt.Rectangle((-1.5,-21), width = 3, height = 23, fc = np.array([200,200,200])/255, zorder=1)\n", + "ax.add_patch(well)\n", + "\n", + "#Wellhead\n", + "wellhead = plt.Rectangle((-2,2),width = 4, height = 2.5, fc = np.array([200,200,200])/255, zorder=2, ec='k')\n", + "ax.add_patch(wellhead)\n", + "\n", + "#Screen for the well:\n", + "screen = plt.Rectangle((-1.5,-21), width = 3, height = 11, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x = 2,y = 3.5, dx = 5, dy = 0, color = \"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x = 7, y = 3.5, s = r'$ Q = 873 \\frac{m^3}{d}$', fontsize = 'large' )\n", + "\n", + "#Piezometers\n", + "piez1 =plt.Rectangle((89,-21), width = 2, height = 23, fc = np.array([200,200,200])/255, zorder=1)\n", + "screen_piez_1 = plt.Rectangle((89,-19), width = 2, height = 7, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_1.set_linewidth(2)\n", + "screen_piez_2 = plt.Rectangle((89,-3), width = 2, height = 0.5, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_2.set_linewidth(2)\n", + "\n", + "ax.add_patch(piez1)\n", + "ax.add_patch(screen_piez_1)\n", + "ax.add_patch(screen_piez_2)\n", + "\n", + "\n", + "#last line\n", + "line = plt.Line2D(xdata= [-200,1200], ydata = [2,2], color = \"k\")\n", + "ax.add_line(line)\n", + "\n", + "#Water table\n", + "line2 = plt.Line2D(xdata = [-200,1200], ydata = [0,0], color = 'b')\n", + "ax.add_line(line2)\n", + "\n", + "ax.text(-18,0.5, s = 'Water Table', fontsize = 'large', color = 'b',bbox = {'fc' : 'w'})\n", + "ax.text(93, -3, s = 'Shallow piezometer', bbox = {'fc' : 'w'})\n", + "ax.text(93, -16, s = 'Deeper piezometer', bbox = {'fc' : 'w'})\n", + "\n", + "ax.set_xlim([-20,130])\n", + "ax.set_ylim([-21,7])\n", + "ax.set_xlabel('Distance [m]')\n", + "ax.set_ylabel('Relative height [m]')\n", + "ax.set_title('Conceptual Model - Vennebulten Example');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Load the required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from ttim import *\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters for the model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "b = -21 #aquifer thickness in m\n", + "r = 90 #distance from observation wells to pumping well in m\n", + "Q = 873 #constant discharge in m^3/d" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Load data of the two piezometers" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data1 = np.loadtxt('data/venne_shallow.txt', skiprows=1)\n", + "ts = data1[:, 0] / 60 / 24 #convert min to days\n", + "hs = data1[:, 1]\n", + "\n", + "data2 = np.loadtxt('data/venne_deep.txt', skiprows=1)\n", + "td = data2[:, 0] / 60 / 24 #convert min to days\n", + "hd = data2[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Create a conceptual one-layer model\n", + "\n", + "Both Kruseman et al. (1970) and AQTESOLV solutions that use the Neuman method (Neumann, 1969) assume a one layer unconfined model.\n", + "To compare TTim with both, we begin by modelling a one-layer aquifer.\n", + "\n", + "For the unconfined test, the preferred method for modelling is to use the ```Model3D``` class. ```Model3D``` assumes the system is a vertical stacking of aquifer layers. Vertical flow is computed between layers by calculating the vertical resistance between layers. Vertical resistance between the aquifer layers is determined as the resistance from the middle of one layer to the middle of the next layer. The vertical anisotropy can be specified for each layer.\n", + "\n", + "Model construction is similar to the ```ModelMaq``` class. We detail it below:\n", + "\n", + "For our Model3D model, we have to set:\n", + "\n", + "- The hydraulic conductivity: ```kaq```. It is a list/array with a float element for every aquifer, for example: ```[kaq0,kaq1]```. We can also set a float value. In this case, the same ```kaq``` is assumed for every layer.\n", + "- The top and bottom of each aquifer: ```z``` defined by a list/array ```[zt0,zb0,zt1,zb1,...]```, where the inputs are a sequence of top and bottoms of the aquifer layers.\n", + "- The specific storage: ```Saq```. It is a list/array with a float element for every aquifer, for example: ```[Saq0, Saq1]```. We can also set a float value. In this case, the same ```Saq``` is assumed for every layer.\n", + "- The minimum time for which TTim solve the groundwater flow: ```tmin```, a float.\n", + "- And the maximum time: ```tmax```, float.\n", + "- TTim automatically assumes the ```topboundary``` is confined. In this case, we also assume the ```topboundary``` is confined, so we do not need to set this parameter. In the code example, the parameter is set for clarity.\n", + "- The vertical anisotropy, defined by the parameter: ```kzoverkh```, which means the vertical hydraulic conductivity divided by the horizontal conductivity. This parameter is a list/array with a float element for every aquifer, for example: ```[kzoverkh0,kzoverkh1]```. We can also set a float value. In this case, the same ```kzoverkh``` is assumed for every layer. If one does not set this parameter, a isotropic model is considered: ```kzoverkh = 1```\n", + "- ```phreatictop```: Is a boolean (True/False). If ```True```, the first element in ```Saq``` is considered phreatic storage (Specific Yield) and is not multiplied by the layer thickness. The default value is ```True```. This parameter is relevant for the unconfined aquifer test, and we will show underneath how to set it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To reproduce the one-layer aquifer model in Kruseman et al. (1970), we will build a two-layer Model3D model. The first layer is a very thin (0.1 m thick) layer with phreatic storage, followed by the 21 m thick aquifer layer. This thin layer is how TTim accounts for the water table storage in the unconfined situation. The first conceptual model is represented in the image below.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Model Figure - One - layer model\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1,1,1)\n", + "\n", + "\n", + "#Aquifer:\n", + "ground = plt.Rectangle((-20,-21), width = 150, height = 23, fc = 'w', zorder=0, alpha=0.9, hatch = '.')\n", + "ax.add_patch(ground)\n", + "\n", + "well = plt.Rectangle((-1.5,-21), width = 3, height = 23, fc = 'w', zorder=1, ec = 'k')\n", + "ax.add_patch(well)\n", + "\n", + "#Wellhead\n", + "wellhead = plt.Rectangle((-2,2),width = 4, height = 2.5, fc = 'w', zorder=2, ec='k')\n", + "ax.add_patch(wellhead)\n", + "\n", + "#Screen for the well:\n", + "screen = plt.Rectangle((-1.5,-21), width = 3, height = 11, fc = 'w', alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "\n", + "#Piezometers\n", + "piez1 =plt.Rectangle((89,-21), width = 2, height = 23, fc = 'w', zorder=1, ec = 'k')\n", + "screen_piez_1 = plt.Rectangle((89,-19), width = 2, height = 7, fc = 'w', alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_1.set_linewidth(2)\n", + "screen_piez_2 = plt.Rectangle((89,-3), width = 2, height = 0.5, fc = 'w', alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_2.set_linewidth(2)\n", + "\n", + "ax.add_patch(piez1)\n", + "ax.add_patch(screen_piez_1)\n", + "ax.add_patch(screen_piez_2)\n", + "\n", + "\n", + "#last line\n", + "line = plt.Line2D(xdata= [-200,1200], ydata = [2,2], color = \"k\")\n", + "ax.add_line(line)\n", + "\n", + "#Water table\n", + "line2 = plt.Line2D(xdata = [-200,1200], ydata = [0,0], color = 'b')\n", + "ax.add_line(line2)\n", + "\n", + "ax.text(-18,0.5, s = 'Water Table', fontsize = 'large', color = 'b',bbox = {'fc' : 'w'})\n", + "ax.text(93, -3, s = 'Shallow piezometer', bbox = {'fc' : 'w'})\n", + "ax.text(93, -16, s = 'Deeper piezometer', bbox = {'fc' : 'w'})\n", + "\n", + "ax.set_xlim([-20,130])\n", + "ax.set_ylim([-21,7])\n", + "ax.set_xlabel('Distance [m]')\n", + "ax.set_ylabel('Relative height [m]')\n", + "ax.set_title('Conceptual Model 1 - Vennebulten Example');" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_1 = Model3D(kaq=10, z=[0,-0.1, b], Saq=[0.1,1e-4], tmin=1e-4, tmax=1.1, kzoverkh = 1, phreatictop = True)\n", + "w_1 = Well(ml_1, xw=0, yw=0, rw=0.1, tsandQ=[(0, Q)])\n", + "ml_1.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Model calibration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Model Calibration is done in TTim using the ```Calibrate``` object. TTim calibrates the parameters by minimizing an objective function using a non-linear least-squares fitting algorithm. The objective function used is the sum of the squares of the residuals calculated as:\n", + "\n", + "$$\\sum_n (h_o - h_c)^2$$,\n", + "\n", + "where $h_0$ is the observed heads and $h_c$ is the calculated heads by the model.\n", + "\n", + "TTim uses ```lmfit```, a python package for non-linear least-squares minimization (Newville et al. 2014), to find the optimal parameters that minimize the residuals.\n", + "\n", + "For calibrating our groundwater model, we proceed by creating a calibration object with the ```Calibrate``` class. The ```Calibrate``` object takes the model ```ml``` as an argument.\n", + "We then set the parameters we are adjusting:\n", + "- Hydraulic conductivity: ```kaq0_1``` (Hydraulic conductivity of layer 0 and 1)\n", + "- Specific Yield ```Saq0``` (Phreatic Storage in our case (Check Step 4 - Model Construction))\n", + "- Specific Storage ```Saq1``` of layer 1.\n", + "\n", + "with the ```.set_parameter``` method.\n", + "\n", + "- ```.set_parameter``` takes two arguments:\n", + "- ```name``` is the parameter name, a string, where the letters define the parameter. The possible values are \"kaq\", \"Saq\" or 'c', and they represent hydraulic conductivity, Specific storage and resistance to vertical flow, respectively. The letters come before a number, which defines the layer of that parameter. For the example ```\"kaq0\"``` means the hydraulic conductivity of layer 0. In our multilayer model, we can extend the numbering to adjust one parameter for various layers. In that case, we write the number of the first layer followed by an underline \"_\" and the number of the last layer, for example, in our first parameter ```kaq0_1```, which means the hydraulic conductivity for layers 0 to 1\n", + " - ```initial``` is the initial guess value for the fitting algorithm.\n", + "\n", + "We can also add the optional parameters:\n", + "- ```pmin``` and ```pmax```, which are floats that define the parameter´s minimum and maximum possible values.\n", + "\n", + "In TTim, parameters other than hydraulic conductivity, specific storage of aquifers and the resistance of leaky layers are calibrated with the ```.set_parameter_by_reference``` method.\n", + "\n", + "Here we use the method ```.set_parameter_by_reference``` to calibrate the ```kzoverkh``` parameter in our aquifer.\n", + "\n", + "```.set_parameter_by_reference``` takes the following arguments:\n", + "* ```name```: a string of the parameter name\n", + "* ```parameter```: a numpy-array with the parameter to be optimized. It is specified as a reference, for example, in our case: ```ml_1.aq.kzoverkh[0:]``` referencing the parameter ```kzoverkh``` in object ```ml_1```.\n", + "* ```initial```: float with the initial guess for the parameter value.\n", + "* ```pmin``` and ```pmax```: floats with the minimum and maximum values allowed for the parameter to be optimized. If not specified, these are set as ```-np.inf``` and ```np.inf```.\n", + "\n", + "We add the observation data using the ```.series``` method. The arguments are:\n", + " - ```name```: a string with the observation name\n", + " - ```x``` and ```y```: floats with the x and y coordinates of the observation\n", + " - ```t```: the array of observation times\n", + " - ```h```: the array of observed drawdowns\n", + " - ```layer```: an integer. The layer of the observation (0 indexed)\n", + "\n", + " \n", + "In the end, we call the ```.fit``` method to compute the calibration." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.1. Calibrating the one layer model with the shallow piezometer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We begin the initial model by adding the shallow observation well as the observation for the residuals calibration. And we calibrate hydraulic conductivity, specific yield and specific storage of our one layer unconfined aquifer:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 110\n", + " # data points = 19\n", + " # variables = 4\n", + " chi-square = 2.4407e-04\n", + " reduced chi-square = 1.6272e-05\n", + " Akaike info crit = -205.987092\n", + " Bayesian info crit = -202.209336\n", + "[[Variables]]\n", + " kaq0_1: 138.511974 +/- 8.26913024 (5.97%) (init = 10)\n", + " Saq0: 3.9294e-04 +/- 0.00204391 (520.16%) (init = 0.2)\n", + " Saq1: 7.8824e-04 +/- 1.1386e-04 (14.44%) (init = 0.0001)\n", + " kzoverkh: 39.3273819 +/- 1021.81271 (2598.22%) (init = 1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(Saq0, kzoverkh) = -0.978\n", + " C(Saq1, kzoverkh) = 0.792\n", + " C(Saq0, Saq1) = -0.771\n", + " C(kaq0_1, Saq1) = -0.432\n" + ] + } + ], + "source": [ + "#calibrate with data of shallow piezometer\n", + "#unknown parameters: kaq, Saq\n", + "ca_1 = Calibrate(ml_1)\n", + "ca_1.set_parameter(name='kaq0_1', initial=10)\n", + "ca_1.set_parameter(name='Saq0', initial=0.2)\n", + "ca_1.set_parameter(name='Saq1', initial=1e-4, pmin = 0)\n", + "ca_1.set_parameter_by_reference(name = 'kzoverkh', parameter=ml_1.aq.kzoverkh[:], initial=1, pmin = 1e-5)\n", + "ca_1.series(name='obs', x=r, y=0, t=ts, h=hs, layer=0)\n", + "ca_1.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_1138.5119748.2691305.969975-infinf10[138.51197446385456, 138.51197446385456]
Saq00.0003930.002044520.159374-infinf0.2[0.00039293922956682207]
Saq10.0007880.00011414.4448330.00000inf0.0001[0.0007882381862700516]
kzoverkh39.3273821021.8127102598.2220530.00001inf1[39.32738191322426, 39.32738191322426]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_1 138.511974 8.269130 5.969975 -inf inf 10 \n", + "Saq0 0.000393 0.002044 520.159374 -inf inf 0.2 \n", + "Saq1 0.000788 0.000114 14.444833 0.00000 inf 0.0001 \n", + "kzoverkh 39.327382 1021.812710 2598.222053 0.00001 inf 1 \n", + "\n", + " parray \n", + "kaq0_1 [138.51197446385456, 138.51197446385456] \n", + "Saq0 [0.00039293922956682207] \n", + "Saq1 [0.0007882381862700516] \n", + "kzoverkh [39.32738191322426, 39.32738191322426] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.003584130910426842\n" + ] + } + ], + "source": [ + "display(ca_1.parameters)\n", + "print('RMSE:', ca_1.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hs_1 = ml_1.head(r, 0, ts)\n", + "hd_1 = ml_1.head(r, 0, td)\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(ts, hs, '.', label='shallow obs')\n", + "plt.semilogx(ts, hs_1[0], label='shallow ttim')\n", + "plt.semilogx(td, hd, '.', label='deep obs')\n", + "plt.semilogx(td, hd_1[0], label='deep ttim')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.title('TTim Unconfined Model Results - Shallow Piezometer')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.2. Calibrating the one layer model with the deeper piezometer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this second approach, we adjust the model to the deeper piezometer, as done by Kruseman and de Ridder (1970)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".............................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 106\n", + " # data points = 29\n", + " # variables = 4\n", + " chi-square = 0.00294184\n", + " reduced chi-square = 1.1767e-04\n", + " Akaike info crit = -258.684496\n", + " Bayesian info crit = -253.215313\n", + "[[Variables]]\n", + " kaq0_1: 116.805699 +/- 5.09590024 (4.36%) (init = 10)\n", + " Saq1: 1.0010e-05 +/- 1.2461e-07 (1.24%) (init = 0.0001)\n", + " Saq0: 1.3198e-04 +/- 5.3435e-05 (40.49%) (init = 0.2)\n", + " kzoverkh: 9.87951240 +/- 12.5230444 (126.76%) (init = 0.1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_1, Saq0) = -0.857\n", + " C(Saq1, kzoverkh) = -0.610\n", + " C(kaq0_1, Saq1) = -0.493\n", + " C(Saq1, Saq0) = 0.265\n", + " C(kaq0_1, kzoverkh) = 0.121\n" + ] + } + ], + "source": [ + "#calibrate with data of deeper piezometer\n", + "#unknown parameters: kaq, Saq, kzoverkh\n", + "ca_2 = Calibrate(ml_1)\n", + "ca_2.set_parameter(name='kaq0_1', initial=10, pmin = 1e-8)\n", + "ca_2.set_parameter(name='Saq1', initial=1e-4, pmin = 1e-5)\n", + "ca_2.set_parameter(name='Saq0', initial=0.2, pmin = 1e-8)\n", + "ca_2.set_parameter_by_reference(name = 'kzoverkh', parameter=ml_1.aq.kzoverkh[:], initial=0.1, pmin = 1e-8, pmax = 10)\n", + "ca_2.series(name='obs', x=r, y=0, t=td, h=hd, layer=0)\n", + "ca_2.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_1116.8056995.095900e+004.3627151.000000e-08inf10[116.80569944533225, 116.80569944533225]
Saq10.000011.246126e-071.2448491.000000e-05inf0.0001[1.0010260699355733e-05]
Saq00.0001325.343454e-0540.4879681.000000e-08inf0.2[0.00013197635356376747]
kzoverkh9.8795121.252304e+01126.7577171.000000e-0810.00.1[9.879512396185161, 9.879512396185161]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_1 116.805699 5.095900e+00 4.362715 1.000000e-08 inf 10 \n", + "Saq1 0.00001 1.246126e-07 1.244849 1.000000e-05 inf 0.0001 \n", + "Saq0 0.000132 5.343454e-05 40.487968 1.000000e-08 inf 0.2 \n", + "kzoverkh 9.879512 1.252304e+01 126.757717 1.000000e-08 10.0 0.1 \n", + "\n", + " parray \n", + "kaq0_1 [116.80569944533225, 116.80569944533225] \n", + "Saq1 [1.0010260699355733e-05] \n", + "Saq0 [0.00013197635356376747] \n", + "kzoverkh [9.879512396185161, 9.879512396185161] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.010071873403913098\n" + ] + } + ], + "source": [ + "display(ca_2.parameters)\n", + "print('RMSE:', ca_2.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hd_2 = ml_1.head(r, 0, td)\n", + "hs_2 = ml_1.head(r, 0, ts)\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(td, hd, '.', label='deep obs')\n", + "plt.semilogx(td, hd_2[0], label='deep ttim')\n", + "plt.semilogx(ts, hs, '.', label='shallow obs')\n", + "plt.semilogx(ts, hs_2[0], label='shallow ttim')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.title('TTim Unconfined Model Results - Deeper Piezometer')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Create a conceptual model with n-layers\n", + "\n", + "As we can see in the examples of step 5, the single-layer simplification does not represent the system well as we have a vertical component to flow, shown in the head difference between both piezometers.\n", + "\n", + "We now explore the feature of TTim to create a multi-layer model to represent better the unconfined system and simulate the vertical flow component. We will discretize the aquifer in a 21 layer model, with 1 m thick each." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Model Figure - One - layer model\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1,1,1)\n", + "\n", + "\n", + "#Aquifer:\n", + "for i in range(21,-2,-1):\n", + " ground = plt.Rectangle((-20,-i), width = 150, height = 1, fc = 'w', zorder=0, alpha=0.9, hatch = '..', ec = 'k', ls = '--')\n", + " ax.add_patch(ground)\n", + " \n", + "\n", + "\n", + "well = plt.Rectangle((-1.5,-21), width = 3, height = 23, fc = 'w', zorder=1, ec = 'k')\n", + "ax.add_patch(well)\n", + "\n", + "#Wellhead\n", + "wellhead = plt.Rectangle((-2,2),width = 4, height = 2.5, fc = 'w', zorder=2, ec='k')\n", + "ax.add_patch(wellhead)\n", + "\n", + "#Screen for the well:\n", + "screen = plt.Rectangle((-1.5,-21), width = 3, height = 11, fc = 'w', alpha=1, zorder = 2, ec = \"k\", ls = '--', hatch = \"-\")\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "\n", + "#Piezometers\n", + "piez1 =plt.Rectangle((89,-21), width = 2, height = 23, fc = 'w', zorder=1, ec = 'k')\n", + "screen_piez_1 = plt.Rectangle((89,-19), width = 2, height = 7, fc = 'w', alpha=1, zorder = 2, ec = \"k\", ls = '--', hatch = \"-\")\n", + "screen_piez_1.set_linewidth(2)\n", + "screen_piez_2 = plt.Rectangle((89,-3), width = 2, height = 0.5, fc = 'w', alpha=1, zorder = 2, ec = \"k\", ls = '--', hatch = \"-\")\n", + "screen_piez_2.set_linewidth(2)\n", + "\n", + "ax.add_patch(piez1)\n", + "ax.add_patch(screen_piez_1)\n", + "ax.add_patch(screen_piez_2)\n", + "\n", + "\n", + "#last line\n", + "line = plt.Line2D(xdata= [-200,1200], ydata = [2,2], color = \"k\")\n", + "ax.add_line(line)\n", + "\n", + "#Water table\n", + "line2 = plt.Line2D(xdata = [-200,1200], ydata = [0,0], color = 'b')\n", + "ax.add_line(line2)\n", + "\n", + "ax.text(-18,0.5, s = 'Water Table', fontsize = 'large', color = 'b',bbox = {'fc' : 'w'})\n", + "ax.text(93, -3, s = 'Shallow piezometer', bbox = {'fc' : 'w'})\n", + "ax.text(93, -16, s = 'Deeper piezometer', bbox = {'fc' : 'w'})\n", + "\n", + "ax.set_xlim([-20,130])\n", + "ax.set_ylim([-21,7])\n", + "ax.set_xlabel('Distance [m]')\n", + "ax.set_ylabel('Relative height [m]')\n", + "ax.set_title('Conceptual Model 2 - Multi-layer Model 3D - Vennebulten Example');" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "nlay = 21 #number of layers\n", + "zlayers = np.linspace(0, b, nlay + 1) #elevation of each layer\n", + "Saq = 1e-4 * np.ones(nlay)\n", + "Saq[0] = 0.1 # Setting the first storage as specific yield" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model is created just as in the previous step, however with the new parameters defined above:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 21\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_2 = Model3D(kaq=10, z=zlayers, Saq=Saq, kzoverkh=0.1, phreatictop=True, \\\n", + " tmin=1e-4, tmax=1.1)\n", + "w_2 = Well(ml_2, xw=0, yw=0, rw=0.1, tsandQ=[(0, Q)], layers=range(nlay))\n", + "ml_2.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Calibrate multi-layer model with the two piezometers simultaneously" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the TTim multi-layer model, we can fit the parameters using data from both piezometers simultaneously.\n", + "For this initial assumption, we assume the aquifer has one hydraulic conductivity and storage parameter.\n", + "\n", + "The unknown parameters are kaq, Saq, kzoverkh.\n", + "\n", + "Now, on the ```series``` method, we have to remember to set a different layer for each piezometer, corresponding to the depth of the screen." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".......................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 68\n", + " # data points = 48\n", + " # variables = 4\n", + " chi-square = 0.00474083\n", + " reduced chi-square = 1.0775e-04\n", + " Akaike info crit = -434.691739\n", + " Bayesian info crit = -427.206935\n", + "[[Variables]]\n", + " kaq0_20: 31.6346602 +/- 0.67601473 (2.14%) (init = 10)\n", + " Saq0: 0.05527954 +/- 0.00391719 (7.09%) (init = 0.2)\n", + " Saq1_20: 3.4687e-05 +/- 2.4467e-06 (7.05%) (init = 0.0001)\n", + " kzoverkh: 0.01000252 +/- 1.2914e-05 (0.13%) (init = 0.1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_20, Saq0) = -0.346\n", + " C(kaq0_20, kzoverkh) = -0.284\n", + " C(kaq0_20, Saq1_20) = -0.269\n", + " C(Saq0, kzoverkh) = -0.269\n" + ] + } + ], + "source": [ + "ca_3 = Calibrate(ml_2)\n", + "ca_3.set_parameter(name='kaq0_20', initial=10)\n", + "ca_3.set_parameter(name='Saq0', initial=0.2)\n", + "ca_3.set_parameter(name='Saq1_20', initial=1e-4)\n", + "ca_3.set_parameter_by_reference(name='kzoverkh', parameter=ml_2.aq.kzoverkh[:], \\\n", + " initial=0.1, pmin=0.01)\n", + "ca_3.series(name='obs1', x=r, y=0, layer=1,t=ts, h=hs)\n", + "ca_3.series(name='obs2', x=r, y=0, layer=15, t=td, h=hd)\n", + "ca_3.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_2031.634660.6760152.136943-infinf10[31.63466020198202, 31.63466020198202, 31.6346...
Saq00.055280.0039177.086154-infinf0.2[0.05527953775592172]
Saq1_200.0000350.0000027.053639-infinf0.0001[3.468735269338616e-05, 3.468735269338616e-05,...
kzoverkh0.0100030.0000130.1291030.01inf0.1[0.010002522487706056, 0.010002522487706056, 0...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_20 31.63466 0.676015 2.136943 -inf inf 10 \n", + "Saq0 0.05528 0.003917 7.086154 -inf inf 0.2 \n", + "Saq1_20 0.000035 0.000002 7.053639 -inf inf 0.0001 \n", + "kzoverkh 0.010003 0.000013 0.129103 0.01 inf 0.1 \n", + "\n", + " parray \n", + "kaq0_20 [31.63466020198202, 31.63466020198202, 31.6346... \n", + "Saq0 [0.05527953775592172] \n", + "Saq1_20 [3.468735269338616e-05, 3.468735269338616e-05,... \n", + "kzoverkh [0.010002522487706056, 0.010002522487706056, 0... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.009938171014409325\n" + ] + } + ], + "source": [ + "display(ca_3.parameters)\n", + "print('RMSE:', ca_3.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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Sp0+fA/5MB8rrW4qtBRKKvI/3LytumxQzCwHqAFtL2LekY1ao7ORVrDjjDMJbtiS8bVvC27QhvE1rItq2JaRJE03+KCIi1Ua3FnXLHOgKLFq0iNtuu42goCBCQ0N58cUX97v97bffztChQ3nooYc49dRTSzz++PHjufzyy3n88cdp2LAhr732GuBrDUxISKBXr16AL7y9/fbbdOjQ4R/HGD58ONdeey0dOnQgJCSE119/vbA1sUePHpx77rmkpKRw8cUX0717d7788ssD+kwHypxz5XrAAzq5L6j9BRyHL3z9D7jQOfdbkW2uAzo4567xD544xzk32MyOAN7Cd01dM+BboDVgJR1zb927d3dz584NxEcEIGf9elLfnkrWkiVkLv2L3HXrC9cF1a7tC3lt2hDepg0RRxxBxOGHY8HBAatHRESkNP744w8OO+wwr8uo0Yr7MzCzec657sVt72mLnXMu18yuB74EgoFXnXO/mdkYYK5zbgbwCvCmmS0DtuEbCYt/u3eB34Fc4DrnXB5Acces6M9WVGjTpjS65ebC93k7dpC1dClZf/1F5l9/kbXkL9JmfEx+ejoAwXXqENX7KGL69iW6Tx9CmzTxqnQRERGpQjxtsassAt1iB8CaOZCcBIn9IKHHP1Y758hdt45dCxaQMfMnMn76idxNmwAIO/QQYvr0IbpvX6K6dycoMjKwtYqIiKAWu8qgSrXY1Rhr5sDkMyAvG4LDYOiMf4Q7MyM0Lo46cXHUOfVUnHNkLV1aGPJSp77DtslvYGFhRHXvRnSfvkT37UN4mza6Rq+CzVuVyuwVW+nVqn65XUciIiJSHhTsKkJyki/UuTzfc3JSsa12RZkZEW3aENGmDfWvuJz8zEx2zZ1HxsyZZPz0E5sefxwef5zghg2I6e1rzYvufRQh9cs2tFz2r7wn3xQRESlPCnYVIbGfr6WuoMUu8cDmwQEIioggpm8fYvr2ASBn40YyfppFxsyZpP/f/5H20UcARBx+OLVOPpm6F5xPcK1a5foxpPjJNysi2KmVUERESkPBriIk9PB1v+7nGrsDFdq4MbHnnE3sOWfj8vLI/P0PMn6aSfqPSWx+6im2TppEvYsvpt6llxAcG1v2zyDA35Nv5uTml8vkm6WhVkIRESmtGjtBcYVL6AH9bi2XULc3Cw4mskN7GlxzDYlvTSFx+nSie/ZgywsvsOy449n05FPkbttW7uetibq1qMud5wTTv+cCrjttB8GRq9iyewuBHIRU3rfoERGpqu6//36eeOKJCj9vcnIy7du3r/DzHgy12FVDke2PIH78eDKX/MXWlyawddIktv3nP9QdMoR6V1xOaKNGXpdYpaW5P5mbNpW5afDyn75lkSGRxNeKJyEmwfdc6+/nZtHNCA0OPejzedFKWJS6gUVEqg5Nd0IFTXfioawVK9j60kukffIpFhxM7KBB1L9qGKFNm3pdWpWVlZfF2p1rSUlPYc3ONaTsTCFlp/91egpZeVmF2wZZEE2imuwR+IqGwDrhJd8A2qtwpW5gkZrtoKY7KWF6rwM1duxYJk+eTKNGjUhISKBbt26MHDmS5cuXc91117F582aioqKYOHEi7dq1Y/PmzVxzzTWsXr0agGeeeYY+ffpw//33s3z5cpYtW8aWLVu4/fbbueqqq/5xvqeeeopXX30VgGHDhnHTTTeRnJzMySefTLdu3Zg/fz5HHHEEb7zxBlFRUYwaNYoZM2YQEhLCiSeeWO4tiprupCor578MBcJbtaLZY4/R4Lrr2DpxIqnvvkvqtGnEnnUW9a++irCEhJIPInsIDw6nVWwrWsW2+se6fJfPlt1b9gh6BeHvhzU/sDVzz67UWmG1fIEvZs+Wvvha8TSOakxIUEi53qLnQHg1WEREqqhSTO91IObNm8fUqVNZsGABubm5dO3alW7dugFw9dVXM2HCBFq3bs3PP//M8OHD+e6777jxxhu5+eab6du3L6tXr+akk07ijz/+AGDhwoXMnj2bjIwMunTpwqmnnkqzZs32ON9rr73Gzz//jHOOnj17cvTRR1O3bl2WLFnCK6+8Qp8+fbjiiit44YUXuPzyy/nggw/4888/MTO2b99eph9feVCwqyzK+S9DccKaN6fpgw/S4Npr2TppEtunTWf7++9T5/TTqX/11YS3almu56upgiyIRlGNaBTViK6Nu/5j/a6cXXuEvYLwtyR1Cd+t+Y7c/NzCbUMshGYxzQrDXlxMnK/FLyaeuFpx1A6rHdDP4nU3sIhUMQcxvdf+JCUlcfbZZxMVFQXAGWecAUB6ejqzZs3ivPPOK9w2K8vXU/LNN9/w+++/Fy7fsWMH6f47O5155plERkYSGRnJgAEDmDNnDmeddVbhtjNnzuTss88mOjoagHPOOYekpCTOOOMMEhIS6NPHNzPFxRdfzLhx47jpppuIiIjgyiuv5LTTTuO000476M9aXhTsKoty/suwP6HNmtHk3nup/69r2PbqK6S+8y5pM2ZQ++STqX/Nv4ho0yYg5xWfqNAo2tRtQ5u6//w55+XnsXHXRl/X7l7h77etv5GWlbbH9rXDau8R9OJj/u7mbRLThNCgg7+2D3yDRaYM61Uu3cC6Vk+kBiiH6b1KIz8/n9jYWBYsWFDsutmzZxMREfGPdXtP6H8gE/wXt29ISAhz5szh22+/Zfr06Tz33HN89913pT5mIOgaOyrJNXYV0GK3L7lbt7Lt9ddJnfIW+bt2UeuEE2hw7TVEHH54hZxfSm9n9k7Wpq8tvKYvJd33WLtzLWvT15KTn1O4bdFr+4q29BW8rxdRr8LuWlKR1+opQIqUH6+vsZs/fz6XXXYZP//8c2FX7L/+9S9GjhxJ7969ufnmmznvvPNwzrFw4UI6derEhRdeSJcuXbjtttsAWLBgAZ07d+b+++/nww8/3KMrdvbs2Xt0xRacb/bs2YVdsW+++SZ169alZcuWzJo1i6OOOophw4Zx2GGH8a9//Ytdu3bRqFEj0tLSaNWqFVu3lu/MBbrGrqoKwFx3pRVSvz6Nbr2VeldcQeqbb7Ltzf+w8+uviTnmGBpcew2RnTpVWC2yf7XCatGuXjva1Wv3j3X5Lp9Nuzb9Hfh2phSGwKS1SWzZvWWP7QtG8sbFFGnp83f3xsXEERHyz//tHqyKulZPgz1EKoGEHuX2O6xr164MGTKETp060ahRI4488sjCdVOmTOHaa6/loYceIicnh/PPP59OnToxbtw4rrvuOjp27Ehubi79+/dnwoQJAHTs2JEBAwawZcsWRo8evUeoKzjfZZddRo8evvqHDRtGly5dSE5Opm3btjz//PNcccUVHH744Vx77bWkpaVx5plnkpmZiXOOp556qlw+d1moxY5K0mJXieTt2EHqlClse30yeWlpRPfuTYPh1xLVvdj/HEgVsTt3N+vS1+0R/IoGwN25u/fYvmFkw8KWvoKwFxcTR7OYZjSJbkJIUOn/X1gQuAqu1QtU4Hr++2U8+dUS8h0EG9xyYluuG3BouZ9HpKY4qBa7Sur+++8nJiaGkSNHel3KAVGLnZRZcO3aNLj2WupdeimpU6ey9dXXWHXxJUR17+4LeEcdVWFdeFJ+IkMiOST2EA6JPeQf65xzbMvc9nfgK2jtS09h3sZ5fLriUxx//ycw2IJpHNWYZjHNaBbTbI/QFxcTR6OoRnsEv/K8Vm9/NNhDRGo6tdihFruS5O/ezfZp09g66RVyN20islMnGgy/luj+/RXwaoicvBw27NrAuvR1rE33Xc+3Ln2drwUwPYXNuzbvEfxCLITG0Y0Lw16zmGbEx8TTNLopTWOa0iiqUZkHduyLrrETKT/VqcWuqjrQFjsFOxTsSis/O5u0999n68sTyVm3jogjjqDB9ddRa8AAr0sTj2XnZbMhY0Nh4Ns7/G3avWmP7YMsiIaRDQu7dZtGN6VZdDOaxjT1hb/opsSExXj0aUSkgIKd99QVKwETFBZG3fPPJ/bcc0mbMYMtL71MyrXDqTd0KI1uG4mF6OtUU4UFh9G8dnOa125e7PqsvCzWp69nXcY6NmT4Wv7WZ6xnfcZ6Fm1exNervt5j/j6AWqG19gh6e7yObkrDqIYEWdlvd60WPhGpTvSbuLoJ0N0rirLQUGLPPZc6Z5zBxn8/zrbJk8lavpy4p54kuHZgJ8yVqik8OJzEOokk1kksdn3B3TrWZ6xnfbov8K1L94fAjHXM3zSfndk799gnJCiExlGNfa19/pa/ZtHNaBrdlCYxTWgS1YSo0Kj91qVRtCJS3SjYVScVPBeehYbS5O67CG/Tmg1jHiR58BDiX3hBd7CQA1b0bh2dGhY/vU56dnphK19h+PO3AM7ZMIdNuzaR7/L32KdWWC2aRPtCXpPoIg//+5nL03XLNBGpVhTsqpMKvHtFUXXPO4/wli1JGXEjyUOGEPfUU8T06xvw80rNEhMWQ+uw1rSu27rY9Tn5OWzetdnX0rdrAxsyfI+NGRvZsGsDi7YsYnvW9n/sF3VoNC43FnLrsDS3LZMWNadxVGOaRDehcVRjGkU1Ktc5/USk7BITE5k7dy4NGjQo1fY//PADTzzxBJ988gmvv/46c+fO5bnnniuXWhYsWMC6des45ZRTCs8VFhZG7969AZgwYQJRUVFceuml5XK+kijYVScVdCuX4kR1707Lae+yZvh1rPnXv2h0+23UGzpUo2alwoQGhRaOwN2XzNxMNu7aWBj6NmRsYPHGVSxPXQvBafxvy7d8u27HP/arG16XxtGNaRzlf0T//dwkqgmNohqV2O0rItXTggULmDt37h7BLiYmpjDYXXPNNRVaj4JddeLh3SsAQuPiSHxrCutGjWLTo4+R9ddSmtx/H0FhYRVah8i+RIRE0KJ2C1rUbrHPbXbl7GLTrk1s3LWxMARuzNhY+H7h5oWkZqX+Y7/aYbX3CH8FLX6NoxsXdv8q/IkcmIyMDAYPHkxKSgp5eXmMHj2aIUOGADB+/Hg+/vhjcnJymDZtGu3atWPOnDnceOONZGZmEhkZyWuvvUbbtm33efzk5GSuuOIKtmzZQsOGDXnttdeIi4vj0EMPZcWKFaSlpVG/fn2+//57+vfvT//+/XnllVdo3drXc5Cdnc29997L7t27mTlzJhdccAETJkwgODiY//znP4wfP55vv/22cGLkY445hi5dupCUlERGRgZvvPEGjzzyCIsWLWLIkCE89NBDZf6ZKdhVN+V4K5eDERQdTdyzz7LluefZ8sILZK9cSfz4cYSUsrlcxGtRoVH7HegBvpa/gvC3IWPDHs8rU9cxd/1CMvP/2fJXK7SWL/z5W/qKe44OjQ7gpxM5eI/NeYw/t/1ZrsdsV68dd/S4Y5/rv/jiC5o1a8ann34KQFpaWuG6Bg0aMH/+fF544QWeeOIJJk2aRLt27UhKSiIkJIRvvvmGu+66i/fee2+fx7/hhhsYOnQoQ4cO5dVXX2XEiBF8+OGHtG3blt9//52VK1fStWtXkpKS6NmzJ2vWrCkMdQBhYWGMGTNmj67d3bt373GHi2+//XaPc4aFhTF37lyeffZZzjzzTObNm0e9evU45JBDuPnmm6lfv2wTqyvYSbmzoCAajriB8DatWTfqTlaeN5iE558j4vDDvS5NpFxEhEQUO73LHqNsQ/N4+sJDaBi7mw27irT6+a/5+3Prn2zN/OfNwgvCX7MY/whf/zx/Rad5OZDbuYlUZR06dODWW2/ljjvu4LTTTqNfv78vMTrnnHMA6NatG++//z7gC35Dhw5l6dKlmBk5OTn7Pf5///vfwn0vueQSbr/9dgD69evHjz/+yMqVK7nzzjuZOHEiRx999B73qj1YZ5xxRuFnO+KII2jatCkArVq1Ys2aNQp2UnnVPvlkQhMSSLnuepIvvIhmjz5C7ZNP9roskYCZvWLr36Nsc4JZsT6cge2O2Of2OXk5hV28BcFvfcb6wuv/Fm5e+I8BHwUjiJtFN9sz9MX8HQJrhdUK8CeVmmh/LWuB0qZNG+bPn89nn33GPffcw3HHHce9994LQHh4OADBwcHk5vrmwRw9ejQDBgzggw8+IDk5mWOOOeagztu/f39efPFF1q1bx5gxY3j88cf54Ycf9giWB6ug7qCgoMLXBe8LPkdZKNhJQEUecQQtp08j5fobWHvTzWQNX0qD66/Dgso+saxIZXOg96oNDQ4lvlY88bXi97nNrpxdvlG+6b45/QqC3/qM9SzcvJCvVn31j8mdY0JjiIuJ8x07Jp6EWgmF52kW3YzQ4MDczk2kvK1bt4569epx8cUXExsby6RJk/a7fVpaGnFxcQC8/vrrJR6/d+/eTJ06lUsuuYQpU6YUBrcePXpwySWX0KpVKyIiIujcuTMvvfQSn3zyyT+OUatWLXbu3LnH+x07/nkpRkVRsJOAC2nQgOZvTGbDffez5YUXyFq6lGaPPkJQtK4lkuqlW4u6TBnWq1zvZBEVGkWrOq1oVadVsevzXT5bd2/9O/T5A+C69HWsTFtJUkoS2fnZhdsHWRBNopoQX6tI4CsS/mqH1dZodqk0Fi1axG233UZQUBChoaG8+OKL+93+9ttvZ+jQoTz00EOceuqpJR5//PjxXH755Tz++OOFgyfA16qWkJBAr169AF/X7Ntvv02HDh3+cYwBAwbw6KOP0rlzZ+68805OP/10Bg0axEcffcT48eMP4lOXje4Vi+4VW1Gcc2ybPJlN/36c8NatSXjheUL9/7MSkcAouKvHmp1rSNmZQkp6yt+vd6b84zq/WqG1Clv3CkJfyzotOST2EOpF1PPoU4hXdK9Y7+lesVJpmRn1L7uM8EMOZe0tt7DyvMHEjx9HVLduXpcmUm0VvatHt8b//Lu2K2cXKem+kFc0/C1NXcr3a77fo5u3XkQ9WtVpxSGxh3Bo7KEcEnuIAp9IJaNgJxUupl9fEt95h5Thw1l12eU0uXc0dc87z+uyRCqFeatSy7UrtyRRoVG0qduGNnXb/GNdXn4em3ZtYkXaCpZvX87ytOUs276MT1d8SnpOeuF2dcPrFoa8oqFPgU+k4inYiSfCW7Uk8Z2prL3lVjaMvpesv5bS+I7bsRB9JaXm2mO6lJAgpgzr5em9a4ODgmka4xtx2yeuT+Fy5xwbd21kxfYVLNu+jOVpy1m+fXmpAl+rOq2oH1m26RxEZN/0W1Q8E1ynDgkvTWDT40+wbfJkspcvI+6ppwiOjfW6NBFP7DFdSm4+s1ds9TTY7YuZ+e6mEd2E3nG9C5c759i0axPLt/ta9lakrdhnC1+bem3o3LAzXRt1pWPDjsSExXjxUUSqHQU78ZSFhND4zlGEt2nN+vsfYOWQISS88ALhhxzidWkiFe5Ap0upbMys8M4a+wp8Ba17v2/9nYmLJpLv8gmyINrUbUOXRl0KH02im3j4SUSqLk9GxZpZPeAdIBFIBgY75/5x80UzGwrc43/7kHNusplFAdOAQ4A84GPn3Cj/9pcBjwNr/fs855zb/6Q3aFRsZbFr/nxSbhiBy8oi7skniDn6aK9LEqlwFX2NnZcycjL4dfOvLNi0gPmb5rNw80J25+4GoGl0U7o06kLXRl3p3Kgzh8YeSnBQsMcV1zwaFeu9Ax0V61Ww+zewzTn3qJmNAuo65+7Ya5t6wFygO+CAeUA3IAvo6Zz73szCgG+Bh51zn/uDXXfn3PUHUo+CXeWRs24da667nqw//6TRyJHUu+JyzaklUkPk5ueyJHUJv2z8hV82+R6bd28GfNOwdGrUqbBFr32D9kSGRHpccfVX2YLd/fffv8d9WMvb9u3beeuttxg+fDgAycnJzJo1iwsvvBCAuXPn8sYbbzBu3LiAnL84VWW6kzOBY/yvJwM/AHvfq+Qk4Gvn3DYAM/saONk59zbwPYBzLtvM5gP7nrZdqpTQZs1InPIf1t15F5sef5ysv/6iyZgHCCpy2xURqZ5CgkI4ov4RHFH/CC4+/GKcc6SkpxS26C3YtIDxv/gmfA2xEA6vf3hh0OvcqLMGZUiZbd++nRdeeGGPYPfWW28VBrvu3bvTvXuxearS8CrYNXbOrfe/3gA0LmabOGBNkfcp/mWFzCwWOB14tsjic82sP/AXcLNzrugxiu57NXA1QPPmzYvbRDwSFBVF3NNPseXFF9ky/jmyk5OJGz+O0EaNvC5NpEqoLt25ZkZCrQQSaiVw+iGnA5CWlcaCTQsKW/Te/vNtJv8+GYAWtVvs0X2bWDtRLf7VwNixY5k8eTKNGjUiISGBbv65T5cvX851113H5s2biYqKYuLEibRr147NmzdzzTXXsHr1agCeeeYZ+vTpw/3338/y5ctZtmwZW7Zs4fbbb+eqq67a41yjRo1i+fLldO7cmRNOOIGkpCT++OMPOnfuzNChQ+nSpQtPPPEEn3zyCffffz8rV65kxYoVrF69mqeffprZs2fz+eefExcXx8cff0xoaMXfvi9gwc7MvgGKu/r17qJvnHPOzA64P9jMQoC3gXHOuRX+xR8DbzvnsszsX/haA48tbn/n3MvAy+Drij3Q80tgWVAQDa+7jvBDW7Nu1CiSzxtM/HPPEdmhvdeliVRqlW3KlPJWJ7wORycczdEJvmtws/Oy+X3r78zfNJ9fNv3CD2t+4MNlHwK+0bedG3UuDHpH1D9C98ktgw0PP0zWH3+W6zHDD2tHk7vu2uf6efPmMXXqVBYsWEBubi5du3YtDHZXX301EyZMoHXr1vz8888MHz6c7777jhtvvJGbb76Zvn37snr1ak466ST++OMPABYuXMjs2bPJyMigS5cunHrqqTRr1qzwfI8++iiLFy9mwYIFAPzwww+FQa7gfVHLly/n+++/5/fff+eoo47ivffe49///jdnn302n376KWeddVb5/bBKKWDBzjl3/L7WmdlGM2vqnFtvZk2BTcVstpa/u2vB1936Q5H3LwNLnXPPFDln0XvjTAL+feCVS2VS+6QTCWvRnDXDh7Pq4otp+vBY6pTi/n8iNVVVmTKlvIQFh9G5UWc6N+oM+Ebgrtyxkl82/lLYffv9mu8BCA8Op0eTHgxsOZBjmx9LdKjuV13ZJSUlcfbZZxMVFQXAGWecAUB6ejqzZs3ivCKT22dlZQHwzTff8Pvvvxcu37FjB+npvul2zjzzTCIjI4mMjGTAgAHMmTOnTOFr4MCBhIaG0qFDB/Ly8jj55JMB6NChA8nJyQd93LLwqit2BjAUeNT//FEx23wJPGxmBf8inQjcCWBmDwF1gGFFdygIi/63ZwB/lH/pUtEi2rWj5bRppIy4kXW3jiTrr6U0vHEEFhTkdWkilU55T5lS1bp1zYxWdVrRqk4rzm1zLgBbdm/hl02/MH/jfL5d/S13zbyLiOAI+sf355SWp9A3vi/hwbqOtyT7a1mraPn5+cTGxha2rO29bvbs2URERPxj3d5d82Xtqg/3X/8dFBREaGho4fGCgoLIzc3d364B49VvxkeBE8xsKXC8/z1m1t3MJgH4B008CPzP/xjjnNtmZvH4unMPB+ab2QIzKwh4I8zsNzP7FRgBXFaRH0oCJ6R+fVq89ip1Bp3L1pdeIuWGEeSlZ3hdlkil061FXaYM68UtJ7YtczdsQbfuk18t4aJJs5m36h+zUlUJDSIbcEKLE7ijxx18ce4XvDHwDc489EzmbpzLTT/cxIB3BjD6p9HMWjdrj3vjivf69+/Phx9+yO7du9m5cycff/wxALVr16Zly5ZMmzYN8LXU/vrrrwCceOKJjB8/vvAYRcPfRx99RGZmJlu3buWHH37gyCOP3ON8tWrVYufOnft8XxV40mLn7zI9rpjlcynSCuecexV4da9tUoBiI7Zz7k78rXpS/VhYGE0ffJCINm3Z+OijrLrgAuJffIGweA2KFimqW4u65dK6Vh27dYMsqHAk7ageo/h5/c98tvIzvln1DR8u+5B6EfU4KfEkTml5Cp0adtLgC4917dqVIUOG0KlTJxo1arRHEJsyZQrXXnstDz30EDk5OZx//vl06tSJcePGcd1119GxY0dyc3Pp378/EyZMAKBjx44MGDCALVu2MHr06D2urwOoX78+ffr0oX379gwcOJCHH36Y4OBgOnXqxGWXXUaXLl0q9PMfDE/msatsNI9d1ZP+00+svfkWLCiIuHHPEt2jh9cliVQ7BS12Bd261W0gRlFZeVkkpSTx2crP+DHlR7LysmgW3YyTW57MKS1PoU3dNjUy5FW2eezKItBz4AVKVZnHTqqDNXMgOQkS+0FCxQarmD59SHxnKinDr2P1FVcS9/RT1D7hhAqtQaS6K+jWrUrX2B2s8OBwjm9xPMe3OJ707HS+X/M9n638jMm/TebVxa9ySJ1DGNhyIANbDqR5bU2RJZWXWuxQi91BWTMHJp8BedkQHAZDZ1R4uAPI27mTNcOuIvP330l4+SWijzqqwmsQkeprW+Y2vk7+ms9Wfsb8TfMBaF+/PQNbDuTklifTKKp6z69ZnVrsqqoDbbHTsEI5OMlJvlDn8nzPyUmelBFcqxYJL00gLDGRNdddz+6FCz2pQ0Sqp3oR9RjSbgiTB07m60Ffc2u3W8lzeTw+93GOn3Y8V3x5BdP+msb2zO1elxowagDyzsH87NVih1rsDkolabErkLNpE6suvIj8nTtpMeU/hB96qGe1iEj1tzJtJV+s/ILPVn5G8o5kQiyE3nG9OaXlKQxIGEBUaJTXJZaLlStXUqtWLerXr18jrzH0knOOrVu3snPnTlq2bLnHuv212CnYoWB30Dy8xq442atXk3zRRVhQMC2mTCEsPq7knUREysA5xx/b/uDzlZ/z+crP2bhrIxHBERyTcAwDWw6kX1y/Kn23i5ycHFJSUsjMzPS6lBopIiKC+Pj4f9yaTMGuBAp21Ufmkr9YdcklBNeNJXHKFEIaNPC6JBHZS1Wb9Li08l0+v2z6hc9Xfs5XyV+RmpVKw8iGXHjYhZzX5jzqhNfxukSpJhTsSqBgV73s+uUXVl9xJWEtWtDijckE167tdUki4lfd72VbICc/h/+u+y9T/pjCrHWziAyJ5NzW53Lx4RcTF6PeBCkbDZ6QGiWqSxfix40ja/ly1lw7nPzdu70uSUT8ipv0uDoKDQqlf3x/XjrhJaafPp0TWpzA1D+ncsr7p3Db/93Gb1t+87pEqaYU7KRaiunXl7h/P8bu+fNJufFGXHa21yWJCH/fyzbYKJd72VYFbeu1ZWzfsXx+7ucMPXwoM9fO5PxPz+fyLy7n/9b8H/ku3+sSpRpRVyzqiq3OUt95lw333UftU0+l2eP/xoL0fxkRr1XXa+xKKz07nfeWvsd//vgPGzI20LJOS67qcBWntTpNI0+lVHSNXQkU7Kq3LRMnsvnJp6h74QU0Hj1a/3CKSKWQk5/DV8lf8dri11iSuoQjmxzJ6F6jaVmnZck7S42ma+ykRmtw1VXUu/IKUt96m83jxnldjogI4LsO79RWp/Lu6e8yutdo/tz6J+fOOJcXFrxAVl6W1+VJFaVgJzVCo5EjqTPoXLa+OIGtr7/udTkiIoWCLIjBbQcz4+wZHN/ieF789UUGzRjEnPVzvC5NqiAFO6kRzIymDzxArRNPZNOjj7H9/Q+8LklEZA8NIhvw7/7/ZsLxE8jNz+XKr67k7pl3k5qZ6nVpUoUo2EmNYcHBNHvicaJ7H8X6e+5h5zffeF2SiMg/9InrwwdnfsBVHa7isxWfcfqHp/PB0g90z1YpFQU7qVGCwsKIHz+eiA7tWXvzLWTMnu11SSIi/xAREsGIriOYdvo0WtVpxb2z7uWKL69gRdoKr0uTSk7BTmqcoOhomr/0EmGJLUgZfh27Fy3yuiQRkWIdWvdQXj/5de476j6WpC7h3Bnn8twvz2lwheyTgp3USMGxsSRMeoXgunVZc9XVZK9a5XVJIiLFCrIgBrUZxIyzZnBS4km8tPAlzp1xLrPXq8dB/knBTmqs0MaNaP7qK+Aca64dTt6OHV6XJCKyTw0iG/Bov0d56YSXyHf5XPXVVdyZdCdbd1fP27LJwVGwkxotrEUL4saPI3v1atbefAsuN9frkkRE9qt3s968f8b7XN3xar5I/oIzPjyD95e+r1uTCaBgJ0J0jx40vf8+Mn76iY2PPOp1OSIiJYoIieCGLjcw/fTpHBp7KPfNuo/Lv7ic5duXe12aeEzBTgSIHTSIepddRuqUKWx76y2vyxERKZVDYg/htZNfY0zvMSzbvoxBHw9i3PxxZOZmel2aeETBTsSv0W0jiTn6aDaOfZiMWbO8LkdEpFSCLIizW5/NjLNmMDBxIBMXTeScGecwa53+HauJFOxE/Cw4mGZPPkF4q1ak3HQzWStWel2SiJSjeatSef77ZcxbVT3v5FA/sj4P93uYiSdOJMiC+NfX/+KOH+9gy+4tXpcmFcg0kzV0797dzZ071+sypJLITllL8uDBBNeqReI7UwmOjfW6JBEpo3mrUrlo0myyc/MJCwliyrBedGtR1+uyAiYrL4tJiyYxadEkIkMiuaXbLZzT+hyCTO051YGZzXPOdS9unf6ERfYSFh9H/HPjyVm3jpQbb8Ll5HhdkoiU0ewVW8nOzSffQU5uPrNXVO8pQsKDw7mu83W8d8Z7tKnbhgf++wCXfXEZS1OXel2aBJiCnUgxorp2pcmDY9j1889sePAh3aNRpIrr1ao+YSFBBBuEhgTRq1V9r0uqEK3qtOK1k17jwT4PsjJtJYM/Hsyz859ld+5ur0uTAFFXLOqKlX3b9ORTbJ04kcZ33UW9Sy/xuhwRKYN5q1KZvWIrvVrVr9bdsPuSmpnKk3Of5KPlHxEfE889ve6hT1wfr8uSg7C/rlgFOxTsqrw1cyA5CRL7QUKPcj20y88nZcQI0r/7noQJLxLTv3+5Hl9EpKLNWT+HB2c/SPKOZAYmDuT2HrfTILKB12XJAVCwK4GCXRW2Zg5MPgPysiE4DIbOKPdwl5+RQfLFl5CzejWJU98mvHXrcj2+iEhFy87L5pVFrzBx0UQigiO4qdtNDGozSIMrqggNnpDqKznJF+pcnu85OancTxEUHU3CC89jkZGsuXY4uanVc6oEEak5woLDuLbztbx3xnscVv8wHpz9IJd+finLUpd5XZqUkYKdVG2J/XwtdRbse07sF5DThDZtSsLzz5G7aRMpN9yAy84OyHlERCpSyzotmXTiJMb2HcuqHau46LOLmL1+ttdlSRl4FuzMrJ6ZfW1mS/3PxV7JamZD/dssNbOhRZb/YGZLzGyB/9HIvzzczN4xs2Vm9rOZJVbQRxIvJPTwdb8ee3dAumGLiuzUiaaPPMzuufNYf/8DGikrItWCmXHGIWfw3hnv0SymGcO/Gc5XyV95XZYcJC9b7EYB3zrnWgPf+t/vwczqAfcBPYEewH17BcCLnHOd/Y9N/mVXAqnOuUOBp4HHAvkhpBJI6AH9bg1oqCtQ59RTaTD8WtLef5/tU6cG/HwiIhWlUVQjXj/5ddo3aM/I/xvJu0ve9bokOQheBrszgcn+15OBs4rZ5iTga+fcNudcKvA1cPIBHHc6cJyZWdnLFfFpcP31RB/dnw0PP8LuBQu8LkdEKqmqeAuzOuF1eOmEl+gX348HZz/IhF8nqHeiivEy2DV2zq33v94ANC5mmzhgTZH3Kf5lBV7zd8OOLhLeCvdxzuUCaUDNmIlSKoQFBRH32GOENm5Myo03kbu1es9gLyIHruAWZk9+tYSLJs2uUuEuMiSSZwY8wxmHnMHzC57nkTmPkO/yvS5LSimgwc7MvjGzxcU8ziy6nfP9d+BA/0twkXOuA9DP/zig2WPN7Gozm2tmczdv3nyAp5aaLjg2lvhxz5K3fTtrb7kVl5vrdUkiUolU9VuYhQaF8mCfBxl6+FDe/vNtRv04ipw83V6xKghosHPOHe+ca1/M4yNgo5k1BfA/byrmEGuBhCLv4/3LcM4VPO8E3sJ3Dd4e+5hZCFAH+MffKOfcy8657s657g0bNiyPjys1TMThh9Pk/vvZ9fPPbH7mGa/LEZFKpDrcwizIghh55Ehu6XYLnyd/zvXfXc+unF1elyUl8LIrdgZQMMp1KPBRMdt8CZxoZnX9gyZOBL40sxAzawBgZqHAacDiYo47CPjO6QIBCZDYs88i9vwhbJ30Cju+1CgyEfHp1qIuU4b14pYT2zJlWK8qfQuzy9tfzpjeY5i9fjbDvhpGambV6VauiTy784SZ1QfeBZoDq4DBzrltZtYduMY5N8y/3RXAXf7dxjrnXjOzaOBHIBQIBr4BbnHO5ZlZBPAm0AXYBpzvnFuxv1p05wkpi/zsbFZdfAnZy5eTOO1dwlu18rokEZFy9/3q77ntx9toGt2Ul094maYxTb0uqcbSLcVKoGBXg5XTfWZz1q9n5TnnElyvHi3ffYeg6OhyLFJEpHKYu2EuI74bQVRoFC+f8DKtYvUfWS/olmIixSm4z+x3Y33Pa+Yc9KFCmzYl7qknyV65kvWjR2t6ABGplro36c5rJ79Gnsvj0i8uZeHmhV6XJHtRsJOaq5zvMxt91FE0vPkmdnz2OalvvFFORYpIdVfV5rtrW68tbwx8g9phtRn21TB+WvuT1yVJEQp2UnMF4D6z9YcNo9YJx7Px34+zS937IlKCqjrfXUKtBN4Y+AYtarfg+m+v57MVn3ldkvgp2EnNFYD7zJoZTR9+mLCEBFJuvpmcTcXN4iMi4lOV57trENmAV096lc6NOnNH0h1M+WOK1yUJCnZS0wXgPrPBtWoRP34c+ekZrL3pZlyOJvUUkeJV9fnuaoXVYsIJEzg24VgenfMo438Zr2uMPaZgJxIA4a1b0/ShB9k9fz4bH3/c63JEpJKqDvPdhQeH8+QxT3Ju63N5eeHLjJk9hrz8PK/LqrFCvC5ApLqqc+qpZC5cyLbJbxDZsRN1TjvV65JEpBLq1qJulQx0RYUEhXDfUfdRL6IeExdNZHvmdh7t/yjhweFel1bjqMVOJIAajRxJZLdurB89msy//vK6HBGRgDEzRnQdwe1H3s43q79h+DfDSc9O97qsGkfBTiSALDSUuKefIigmmrU3jCBv506vSxIRCahLDr+ER/o9wvyN87niyyvYsnuL1yXVKAp2IgEW2qgR8c88Q/batawbdScuP9/rkkREAuq0Vqcx7thxrExbydDPh5KyM8XrkmoMBTuRChDVrRuNb7+N9G+/ZeukV7wuR0Qk4PrF92PiiRPZnrWdoZ8PZfOuzV6XVCMo2IkcqDVzIOnJA74FWd1LLqH2Kaew+ZlnyJg1K0DFiYgcnEDcAaNzo868etKrpGWncd+s+zQVSgVQsBM5EGW4v6yZ0fTBMYQf0oq1t44kZ+PGABYqIlJ6gbwDRtt6bbm5280krU1i2l/Tyu24UjwFO5EDUcb7ywZFRxP37LPkZ2Wx9tZbcbm5ASpURKT0An0HjAvaXcBRTY/iiblPsHrH6nI9tuxJwU7kQJTD/WXDW7Wi6f33sXvuPDaPGx+AIkVEDkyg74ARZEE82OdBQoJCuHPmneTm6z+1gWLq74bu3bu7ubphu5TWmjm+lrrEfmW6Fdn60aPZPm06CRNfJqbfgQdEEZHyNG9VKrNXbKVXq/oBmzD585Wfc/uPt3NDlxu4uuPVATlHTWBm85xz3Ytdp2CnYCfeyM/MJHnwEHI3baLlhx8Q2qTJvjcupzApIuK12//vdr5e9TVTTp3C4fUP97qcKml/wU5dsSIeCYqIIO6ZZ8jPzmbtLfu53q4MAzZERCqbu3vdTb3IetyZdCeZuZlel1PtKNiJeCi8VUuaPvAAu+fPZ/Ozzxa/URkHbIiIVCZ1wuvwYJ8HWZG2gmfn7+PfPTloCnYiHqtz+mnEDh7M1omTSP+///vnBuUwYENEpDLp3aw3F7a7kP/88R9mr5/tdTnViq6xQ9fYiffyMzNJPv8Cctev911v17TpnhvoGjsRqWZ25+5m8MeD2Z27m/fPfJ/aYbW9LqnK0DV2IpVcUEQEcU8/hcvJ8V1vl5Oz5wYJPaDfrQp1IlJtRIZE8mi/R9m6eysP//yw1+VUGwp2IpVEeMuWNHlwDLt/+YVNzzzjdTkiIgF3RIMjuLrT1Xy64lO+SP7C63KqBQU7kYq2n3vN1jn1VGLPH8K2V15l5/ffe1CciEjFuqrDVXRo0IEH//sgm3Zt8rqcKk/BTqQilWLqksZ33kn4YYexftSd5Kxb50GRIiIVJyQohIf7Pkx2Xjb3/nQvuva/bBTsRCpSKaYuCQoPJ/6Zp3G5uay9+RZcdrYHhYqIVJzEOomM7D6Sn9b9xDtL3vG6nCpNwU6kIpVy6pKwFi1o+tCD7P71VzY9/UzF1igi4oHBbQfTJ64PT859kuS0ZK/LqbIU7EQqUkIPGDoDjr3b97yfUa61Bw6k7oUXsO2119j53XcVWKSISMUzM8b0HkN4SDh3zbyL3Px93I1H9kvBTqSiHcDUJY3uuIOIww9n3ag7yU5ZWwHFiYiUr3mrUnn++2XMW5Va4raNohoxutdoFm1ZxMRFEyuguupHwU6kEgsKDyfumachP5+1t+h6OxGpWuatSuWiSbN58qslXDRpdqnC3UmJJ3Fqq1N56deXWLxlcQVUWb0o2IlUcmHNm9N07FgyFy5k05NPel2OiEipzV6xlezcfPId5OTmM3vF1lLtd1fPu2gQ2YA7k+5kd+7uAFdZvSjYiVQBtU86kbqXXMK2yW+w46uvvC5HRKRUerWqT1hIEMEGoSFB9GpVv1T71Q6rzUN9HyJ5RzJPz3s6wFVWLwp2IlVE49tGEtGhA+vvvofs1au9LkdEpETdWtRlyrBe3HJiW6YM60W3FnVLvW+vpr24+LCLefvPt5m1dlYAq6xeTBMBQvfu3d3cuXO9LkOkRNkpa1l5zjmExcfT4u23CAoP97okEZGAyczNZMgnQ0jPTuf9M9+nTngdr0uqFMxsnnOue3HrPGmxM7N6Zva1mS31Pxcb4c1sqH+bpWY21L+slpktKPLYYmbP+NddZmabi6wbVoEfSyTgwuLjaPboI2T+/jubHnvM63JERAIqIiSCR/o9wrbMbYydPdbrcqoEr7piRwHfOudaA9/63+/BzOoB9wE9gR7AfWZW1zm30znXueABrALeL7LrO0XWTwr4JxGpYLWOPZZ6l19O6ltvs+Ozz7wuR0QkoA6vfzjXdr6Wz5M/57MV+jevJF4FuzOByf7Xk4GzitnmJOBr59w251wq8DVwctENzKwN0Aj4532ZRKqxRrfcTGTnzqy/ZzRZK1d6XY6ISEBd0f4KOjXsxEM/P8SGjA1el1OpeRXsGjvn1vtfbwAaF7NNHLCmyPsU/7KizsfXQlf0QsFzzWyhmU03s4R9FWBmV5vZXDObu3nz5oP4CCLesdBQ4p5+CgsLY+1NN5Ofmel1SSIiARMSFMLDfR8mNz+X0T+NJt/le11SpRWwYGdm35jZ4mIeZxbdzh/KDnYEx/nA20XefwwkOuc64mvhm1zsXr7zvuyc6+6c696wYcODPL2Id0KbNqXZvx8ja8kSNo592OtyREQCqnnt5tx25G3MXj+bt/98u+QdaqiABTvn3PHOufbFPD4CNppZUwD/86ZiDrEWKNriFu9fhn+/TkCIc25ekXNudc5l+d9OArqV88cSqVRi+ven/tVXs33aNNJmzPC6HBGRgBrUehD94/vz9LynWZG2wutyKiWvumJnAEP9r4cCHxWzzZfAiWZW1z9q9kT/sgIXsGdrXUFILHAG8Ee5VSxSSTUccQNR3buz/r77yVq2zOtyREQCxsx4oPcDRIZEcmfSneTk53hdUqXjVbB7FDjBzJYCx/vfY2bdzWwSgHNuG/Ag8D//Y4x/WYHB7BXsgBFm9puZ/QqMAC4L6KcQqQQsJIRmTz5JUGQkKTfdRP6uXV6XJCISMA0iG3DfUffx+9bfeXnhy16XU+logmI0QbFUDxmzZrH6ymHUOfNMmj36iNfliIgE1N0z7+bTFZ/yxsA36Niwo9flVKhKN0GxiJS/6N69aXDttaR9+CHb33u/5B1ERKqwUT1G0SiqEXfNvItdOeqpKKBgJ1KNNLhuOFG9erFhzBgyl/zldTkiIgFTK6wWY/uOZfWO1Tw17ymvy6k0FOxEqhELDibuiccJql2LtTfdRF56htcliYgEzJFNjuTSwy/lnSXvkJSiexWAgp1ItRPSoAFxTzxJ9qpVbLj/fnQdrYhUZzd0vYFDYw/l3ln3sj1zu9fleE7BTqQaiu7Zg4YjbmDHJ5+w/Z13vS5HRCRgwoPDeaTfI2zP2s6Y2WNq/H9mFexEqqn6V19NdN++bHz4YTJ//93rckREAqZdvXZc1/k6vl71NZ+s+MTrcjylYCdSTVlQEM3+/RjBdeuSctPN5O3c6XVJIiIBc/kRl9OlURce+fkR1qevL3mHakrBTqQaC6lXj7innyJn7VrW3zO6xndRiEj1FRwUzNi+Y8lzedzz0z3ku3yvS/KEgp1INRfVtSuNbr6JnV9+SeqUt7wuR0QkYBJqJXBHjzuYs2EOU/6Y4nU5ngjZ30ozu6UUx8hwzr1UTvWISADUu+IKds2dx8bHHiOyU0ciO3TwuiQRkYA4+9Cz+X719zwz7xmOanoUh9Y91OuSKlRJLXa3ATFArf08bg1kgSJSdhYURLNHHyGkYQPW3nQzeWlpXpckIhIQZsZ9ve8jJiyGu2beRU5ejtclVaj9ttgBbzrnxuxvAzOLLsd6RCRAgmNjiX/6aZIvvoR1d91N/HPjMTOvyxIRKXcNIhtw31H3ceP3N/Liry8yousIr0uqMPttsXPO3V7SAUqzjYhUDpGdOtF45K2kf/st216f7HU5IiIBc2zzYzn70LN5ZfErLNi0wOtyKoyVZpScmcUClwKJFGnlc85ViwjcvXt3N3fuXK/LEKkQzjnWjhjBzu9/IPE/bxLZubPXJYmIBER6djqDPh6EYbx3xntEhUZ5XVK5MLN5zrnuxa0r7ajYz/CFukXAvCIPEalizIymY8cS2qQJKTffQm5qqtcliYgERExYDGP7jmVt+loen/u41+VUiNIGuwjn3C3Oudecc5MLHgGtTEQCJrh2beKeeYa8LVtYN2oULr9mzvckItVft8bduKz9ZUz/azr/t+b/vC4n4Eob7N40s6vMrKmZ1St4BLQyEQmoyPZH0GjUHWT8349sfeUVr8sREQmY6ztfT5u6bbhv1n1sy9zmdTkBVdpglw08DvyXv7thdVGaSBVX98ILqTXwZDY/8yy79nWd6Zo5kPSk71lEpAoKCw7j4b4PsyN7B2P+O6Za34WntMHuVuBQ51yic66l/9EqkIWJSOCZGU0ffJCw+HjW3nIruVu37rnBmjkw+Qz4bqzvWeFORKqotvXackOXG/h29bfMWD7D63ICprTBbhmwK5CFiIg3gmNiiHv2GfK2b2fd7Xfg8vL+XpmcBHnZ4PJ8z8lJ3hUqIlJGlx5+Kd0ad+OROY+wNn2t1+UERGmDXQawwMxeMrNxBY9AFiYiFSeiXTsa33M3GT/9xJaXitwhMLEfBIeBBfueE/t5V6SISBkFBwUztu9YAO6ZeQ/5rvoNHCttsPsQGAvMQtOdiFRLseedR+3TT2fLc8+TMftn38KEHjB0Bhx7t+85oYe3RYqIlFFcTByjeoxi7sa5vPn7m16XU+5KNUFxdacJikV88jMyWHneYPJ27KDVB+8T0rCh1yWJiJQ75xw3fX8TSWuTmHraVNrUbeN1SQfkoCcoNrOXS3HwErcRkaohKDqauGeeJj89nbUjb9vzejsRkWrCzLiv933UCqvFXUl3kZ2X7XVJ5SakhPVnmVnmftYbMKAc6xERj0W0aUOT++5j/Z13suX552k4olrcOVBEZA/1IuoxpvcYrv/uep5f8Dw3d7vZ65LKRUnB7rZSHEPD5ESqmdizz2LX//7HlhcnENm1GzF9+3hdkohIuTs64WjObX0ury1+jaPjj6Zr465el1RmusYOXWMnUpz83btJHjyE3C1baPnhB4Q2bux1SSIi5W5Xzi7OnXEuDsd7Z7xHdGi01yWV6KCvsRORmisoMpK4Z58hPyuLtTffgsuuPtegiIgUiAqN4uF+D7M+Yz3//t+/97ndvFWpPP/9MuatSq3A6g6cgp2I7FN4q1Y0fXAMu+fPZ8PDD3tdjohIQHRp1IUr2l/B+0vf57vV3/1j/bxVqVw0aTZPfrWEiybNrtThTsFORParzqmnUv+qYWyf+g6pU6d6XY6ISEAM7zScdvXa8cB/H2Dr7j1vrzh7xVayc/PJd5CTm8/sFVv3cRTvlSrYmVkbM5toZl+Z2XcFj0AXJyKVQ8ObbiLm6KPZ8NBYMubofrEiUv2EBofySN9HSM9O5/7/3k/RMQi9WtUnLCSIYIPQkCB6tarvYaX7V6rBE2b2KzAB390mCie2cs5Vi7tPaPCESMnydu4kecj55KWmkjhtGmHxcQd2gDVzfPeaTeynO1iISKX1xm9v8PjcxxnTewxntz67cPm8VanMXrGVXq3q061FXQ8r3P/gidIGu3nOuW7lXlkloWAnUjpZK1eSPHgIoc2akfj2WwRFRZVuxzVzYPIZkJftu+esbk8mIpVUvsvnqq+uYvGWxUw/YzoJtRK8LukfymNU7MdmNtzMmppZvYJHGYuqZ2Zfm9lS/3Ox8dfMvjCz7Wb2yV7LW5rZz2a2zMzeMbMw//Jw//tl/vWJZalTRP4W3rIlcU89RdbSpay78y5KPV1ScpIv1Lk833Oypr8UkcopyIJ4qM9DBFkQ98y8h7z8ku/AUzBi9q2fV3s+cra0wW4ovsmKZ+Hrjp0HlLWJaxTwrXOuNfCt/31xHgcuKWb5Y8DTzrlDgVTgSv/yK4FU//Kn/duJSDmJ6deXRrfeys4vv2TLiy+WbqfEfr6WOgv2PSf2C2yRIiJl0DSmKXf1vIv5m+bz+m+v73fbghGzT3y5hLs+WOT5yNlSBTvnXMtiHq3KeO4zgcn+15OBs/Zx7m+BnUWXmZkBxwLTi9m/6HGnA8f5txeRclLvisupfcbpbBk3np3ffFPyDgk9fN2vx96tblgRqRJOa3UaJ7Q4gecWPMeSbUv2uV3BiNmC/guvR86WdlTsTDMba2Ynm1mtcjp3Y+fcev/rDcCBTGtfH9junMv1v08BCq7kjgPWAPjXp/m334OZXW1mc81s7ubNmw+mfpEay8xoOmYMER06sO72O8j866+Sd0roAf1uVagTkSrBzBjdazSx4bGMShpFVl5WsdsVjJgtCFRBHo+cLW1X7CXAEuBcYJY/ED1d0k5m9o2ZLS7mcWbR7ZzvQp0KvbeZc+5l51x351z3hg0bVuSpRaqFoIgI4p8bT1B0NCnXXU9uauWdsFNE5GDUjajLmN5jWLZ9Gc/98lyx23RrUZcpw3px60ltefjsDtx6YlumDOvl2cjZkNJs5JxbaWaZQLb/MQA4rBT7Hb+vdWa20cyaOufWm1lTYFMpawbYCsSaWYi/VS4eWOtftxZIAFLMLASo499eRMpZaOPGxI8fx6pLLmXtLbfQfOJELKRU/6yIiFQJ/eL7MbjNYCb/Npn+8f05ssmR/9imW4u6nk+BUqC0XbHLgQ/xdZe+ArR3zp1cxnPPwDcoA//zR6Xd0d/C9z0wqJj9ix53EPCdK/XQPRE5UJGdO9NkzBh2/Xc2Gx/b930WD8iaOZD0pO9ZRMRjt3a/lfha8YydPbZUo2S9VNqu2HHAauACYAQw1MwOKeO5HwVOMLOlwPH+95hZdzObVLCRmSUB0/ANgkgxs5P8q+4AbjGzZfiuoXvFv/wVoL5/+S3se7StiJST2LPPot7QoaS++Sbb33uvbAcrmPPuu7G+Z4U7EfFYVGgUI7qOYHnacj5b+ZnX5exXqSYoLtzYLAa4HBgJxDvnggNVWEXSBMUiZedyc1lz9b/I+N//aDF5MlFduxzcgZKe9IU6l+ebHuXYu32DLkREPJTv8hnyyRDSs9OZcfYMQoNCPaulzBMUm9mTZvYz8DPQCbgXaF1+JYpIVWchIcQ9/RShzZqSMmIEOevXl7xTcTTnnYhUQkEWxA1dbiAlPYUPln7gdTn7VNpbig0CkpxzGwNfUsVTi51I+clatozkIecTlphIiyn/ISgi4sAPovvKikgl5Jzjks8vYX3Gej49+1MiQg7i37dyUOYWO+fcdKCnmT3hf5xerhWKSLURfuihNHv8cTJ//53194wu/W3HitKcdyJSCZkZN3a9kU27NvHOkne8LqdYpe2KfQS4Efjd/xhhZg8HsjARqbpqHTuAhjfeyI5PPmHbK6+UvIOISBVxZJMj6dW0F68seoWMnAyvy/mH0o6KPRU4wTn3qnPuVeBk4LTAlSUiVV39f11N7VMGsunJp0j/v//zuhwRkXIzossIUrNS+c/v//G6lH8obbADiC3yuk451yEi1YyZ0XTsWMIPa8faW0eStWKF1yWJiJSLDg07MCBhAK//9jppWWlel7OH0ga7R4BfzOx1M5sMzAPGBq4sEakOgiIjSXjuOSw8nJTh15G3Y4fXJYmIlIvru1xPRk4Gry1+zetS9lDawRNvA72A94H3gKOcc5XzqkERqVRCmzUjftyzZK9dy9qbbsZlZ3tdkohImbWp24aBLQcy5Y8pbNm9xetyCu032JlZ14IH0BRI8T+a+ZeJiJQoqls3mj7wABmzZrF+9EGOlBURqWSu63wdOfk5TFw40etSCpV0t+4n/c8RQHfgV8CAjsBc4KjAlSYi1UnsOWeTu3EDm58dR0jjJjS65ebAnEhz4IlIBWleuzlnHXoW0/6axtAjhtIsppnXJe2/xc45N8A5NwBYD3R1znV3znUDugBrK6JAEak+6l9zDbFDhrD15ZfZNmVK+Z9A95kVkQp2TadrAJjw6wSPK/Ep7eCJts65RQVvnHOLgcMCU5KIVFdmRpPR9xBz7LFsfGgsO77+unxPkJwEedm++8zmZfvei4gEUJPoJgxpO4SPln9Eclqy1+WUOtgtNLNJZnaM/zERWBjIwkSkerKQEOKefILIjh1ZN/I2ds2fX34H131mRcQDV3a4krCgMF5e+LLXpZQ62F0O/Ibv7hMFd6C4PFBFiUj1FhQZSfyEFwlt2pQ11w4na/ny8jlwQg8YOgOOvdv3rGvsRKQCNIhswPntzufTlZ+yMm2lp7VYaUanmdlxwCzn3O7Al1Txunfv7ubOnet1GSI1TnZKCsnnX4CFhZL49lRCGzfyuiQRkYOydfdWBr4/kBNanMDYvoGd6tfM5jnnuhe3rrQtdpcCv5rZbDN73MxON7O65VeiiNREYfHxJLw0gfztaaz517/IS0/3uiQRkYNSP7I+j/R7hBu63OBpHaWdoHioc64NcA6wBnge2BzIwkSkZog84gjixo0ja9kyUm64QRMYi0iVdVzz42gS3cTTGkoV7MzsYjN7CZgOHA88B+iqZBEpFzF9+9Bs7EPs+u9s1t19Dy4/3+uSRESqpJImKC7wDLAcmAB875xLDlRBIlIz1TnzTHI2bmLzU08R2rgRjUaOrJgTa0JjEalGShXsnHMNzOwIoD8w1sxaA0ucc5cEtDoRqVHqXzWM3A0b2DrpFYLrN6D+5ZcF9oQFExrnZfumR9FIWhGp4koV7MysNtAcaAEkAnUA9ZWISLkyMxrffRe5W7ey6bHHsJAQ6l1yceBOWNyExgp2IlKFlbYrdmaRx3POuZTAlSQiNZkFBxP3xOOszc9j49ixEGTUu+iiwJysYELjghY7TWgsIlVcabtiOwa6EBGRAhYaStyTT5Jyyy1sfPAhMKPehReW/4kKJjTWNXYiUk2Utiu2IXA7cAQQUbDcOXdsgOoSkRrOwsKIf+opUm66mY1jHsSCgqh7/vnlf6KEHgp0IlJtlHaC4inAn0BL4AEgGfhfgGoSEQH84e6Zp4kZMIAN9z9A6jvvel2SiEilVtpgV9859wqQ45z7P+fcFYBa60Qk4CwsjLhnnyHm6KPZcN99pE6b5nVJIiKVVmmDXY7/eb2ZnWpmXYB6AapJRGQPQWFhxI0fR/TR/dkw+l62v/ee1yWJiFRKpQ12D5lZHeBWYCQwCbg5YFWJiOwlKCyM+HHjiO7Xj/X3jGb7e+97XZKISKVTYrAzs2CgtXMuzTm32Dk3wDnXzTk3owLqExEpFBQeTvxz44nu3Zv199zD9g8+9LokEZFKpcRg55zLAy6ogFpEREoUFB5O/PPPEX3UUay/6y7SPvrI65JERCqN0nbF/mRmz5lZPzPrWvAIaGUiIvsQFBFB/AvPE31UL9aNupO0GepAEBGB0t95orP/eUyRZQ6NjBURjwRFRBD//POsuXY460bdCWbUOf10r8sSEfFUae88MSDQhYiIHKigyEgSXnyBNddcy7o7RoEFUee0U70uS0TEM/sNdmZ2y/7WO+eeOpiTmlk94B0gEd9kx4Odc6nFbPcF0AuY6Zw7rcjyKUB3fNOwzAH+5ZzLMbNjgI+Alf5N33fOjUFEqq3CcPeva1h3++1YkFH7lFO8LktExBMlXWNXy//oDlwLxPkf1wBlucZuFPCtc6418K3/fXEeBy4pZvkUoB3QAYgEhhVZl+Sc6+x/KNSJ1ABBUVEkvDSBqK5dWXvb7aR9+qnXJYmIeGK/wc4594Bz7gEgHujqnLvVOXcr0A1oXobznglM9r+eDJy1j/N/C+wsZvlnzg9fi118GWoRkWqgMNx16cK6kbex9dXX8P0TISJSc5R2VGxjILvI+2z/soPV2Dm33v96w8Eey8xC8bXofVFk8VFm9quZfW5mR5ShRhGpYoKio0mYNJFaJ57Ipn//m40PPoTLy/O6LBGRClPaUbFvAHPM7AP/+7OA1/e3g5l9AzQpZtXdRd8455yZHex/q18AfnTOJfnfzwdaOOfSzewU4EOg9T7quxq4GqB587I0PopIZRIUEUHc00+x6Ykn2fbqq+SsX0/ck08QFBXldWkiIgFnpe2q8M9b18//9kfn3C8HfVKzJcAxzrn1ZtYU+ME513Yf2x4DjCw6eMK//D6gC3COcy5/H/smA92dc1v2V0/37t3d3LlzD/hziEjltu2tt9j40FgiDj+chBdfIKRhQ69LEhEpMzOb55zrXty60nbF4pyb75x71v846FDnNwMY6n89FN9I1lIzs2HAScAFRUOdmTUxM/O/7oHv820tY60iUkXVu/BC4p97jqzly0k+/wKyli/3uiQRkYAqdbArZ48CJ5jZUuB4/3vMrLuZTSrYyMySgGnAcWaWYmYn+VdNwHdd3n/NbIGZ3etfPghYbGa/AuOA852unhap0WodO4AWb7xBflYWyRdcSMacOYE94Zo5kPSk71lEpIKVuiu2OlNXrEj1l52yljX/+hfZq1fT7OGxgblLxZo5MPkMyMuG4DAYOgMSepT/eUSkRiuXrlgRkaosLD6OxLemENW5M+tuu50tEyaU/3QoyUm+UOfyfM/JSSXvIyJSjhTsRKTGCK5Th4RXJlH79NPZ/MyzbLj3XlxOTvmdILGfr6XOgn3Pif1K3kdEpByVdroTEZFqISgsjGb/fozQ+Di2vjiBnPUbiHvmaYJjYsp+8IQevu7X5CRfqFM3rIhUMLXYiUiNY2Y0uvFGmj70IBn//S+rLrqY7OTk8jl4Qg/od6tCnYh4QsFORGqs2EGDSJgwgZwNG1hxzrmkTpum25CJSJWmYCciNVpMv760+uhDIjt2ZMPoe0m5/gZyt23zuiwRkYOiYCciNV5okyY0f/UVGt1xBxk//siKM88kPcmjEa2aB09EykDBTkQEsKAg6l9+GYnT3iUkNpY1V13NhgcfIj8zs+KKKJgH77uxvmeFOxE5QAp2IiJFRLRrR+L06dQbeimpU6aw8txBZP7+e8WcXPPgiUgZKdiJiOwlKDycxnfeScIrk8jfsYOVQ85n66RJuLy8wJ5Y8+CJSBnplmLolmIism+5qalsuPc+dn79NVFHHkmzxx4ltFmzwJ1wzRzNgyci+7W/W4op2KFgJyL755wj7YMP2fjQQxAcTJP77qPOaad6XZaI1FC6V6yISBmYGbHnnE3Ljz4k/JBDWDdyJGtH3kbejh1elyYisgcFOxGRUgpLSKDFf96kwYgb2PH556w46ywy5mjkqohUHgp2IiIHwEJCaDh8OIlvv4WFhrJ66GVsevJJXHa216WJiCjYiYgcjMiOHWn1/vvEDhrE1omTWHn++WQtX+51WSJSwynYiYgcpKDoaJo+OIb4558jd/0GVp5zLtumTNH9ZkXEMwp2IiJlVOu442g14yOievZg44MPsebKYWQu+cvrskSkBlKwExEpByENG5Lw0ks0vnc0uxctYuVZZ7H29tvJXrPG69JEpAZRsBMRKSdmRr0LL+TQr7+i/pVXsPPLr1h+yqlsePAhcrds8bo8EakBFOxERMpZcGwsjUaO5JCvviT2nHNInTqVZSecyKZnniFv587AF7BmDiQ96XsWkRpFd55Ad54QkcDKTk5m87jx7PjsM4Lq1KHB1VdR96KLCIqIKP+TrZkDk8+AvGzf/WaHztCtyUSqGd15QkTEQ2GJicQ99SQt33+PyI4d2fT4Eyw/8SRS33kXl5NTvidLTvKFOpfne05OKt/ji0ilpmAnIlJBIg4/nOYTX6b5G5MJbdaMDffdx4rTTmfHZ5/h8vPL5ySJ/XwtdRbse07sVz7HFZEqQV2xqCtWRCqec470779n89PPkLV0KeGHH0ajm28hum8fzKxsB18zx9dSl9hP3bAi1dD+umIV7FCwExHvuLw8dnzyCZvHjSdn7VqievSg3hWXE9OvHxYc7HV5IlIJKdiVQMFORLzmsrNJfXcaW16aQN7mLYQ0bkzsuedQ55xzCYuP87o8EalEFOxKoGAnIpWFy85m5/c/sH3aNDJ++gmA6N69iT3vPGodOwALC/O4QhHxmoJdCRTsRKQyylm7lu3vf8D2998nd/16guvVo85ZZxE76FzCW7WqmCJ0vZ5IpaNgVwIFOxGpzFxeHhk//cT2adPY+f0PkJtLZPduxA4aRO2TTiIoMjIwJ9aceCKVkuaxExGpwiw4mJj+/YkfP57W339Ho5G3krd5C+tH3cnS/kezYcwYMv/4o/xPrDnxRKocBTsRkSokpGFD6g8bRqsvPqf5G5OJOeYYtk9/j5Vnn8PKcweROvUd8tLTy+dkmhNPpMpRVyzqihWRqi0vLY20GR+zfdo0sv76C4uMJKZ/f2L69SW6b19CmzQ5+IPrGjuRSkfX2JVAwU5EqgPnHJmLF7N9+nuk//ADuRs3AhDeujXR/foR068vkd26EaSRtSJVWqULdmZWD3gHSASSgcHOudRitvsC6AXMdM6dVmT568DRQJp/0WXOuQXmm679WeAUYJd/+fyS6lGwE5HqxjlH1tKlZCTNJH1mErvnzsPl5GCRkUT37El0v77E9OtHWPPmXpcqIgdof8EupKKL8RsFfOuce9TMRvnf31HMdo8DUcC/ill3m3Nu+l7LBgKt/Y+ewIv+ZxGRGsXMiGjThog2bah/5RXkZ2SQMWeOL+glJZH+ww9sBEJbNCembz+i+/UlukcPgqKivC5dRMrAq2B3JnCM//Vk4AeKCXbOuW/N7Ji9l5dw3DecrxlytpnFmllT59z6MlUrIlLFBUVHU2vAAGoNGABA9qpVpCfNJCMpie3vvUfqlClYWBhR3bsXdtuGHXJI2e9bKyIVyquu2O3OuVj/awNSC94Xs+0xwMhiumKPArKAb4FRzrksM/sEeNQ5N9O/3bfAHc65f/SzmtnVwNUAzZs377Zq1ary+ngiIlVKflYWu+fN8wW9mUlkLV0GQEjTpsT07UPUkUcS2bEjoS1aKOiJVAKedMWa2TdAcUOx7i76xjnnzOxA0+WdwAYgDHgZX2vfmAM5gHPuZf++dO/eXSNIRKTGCgoPJ7p3b6J794Y7bidn/XrSZ84kI2kmOz7/gu3TfFe9BNepQ0THjkR27Ehkp45EdOhASN26HlcvIkUFLNg5547f1zoz21jQRWpmTYFNB3jsgq7VLDN7DRjpf78WSCiyabx/mYiIlFJo06bUPe886p53Hi43l6zly9n966/sXriQzF8XsmXmTPD39oS2aE5kx05/h7127XQ/WxEPeXWN3QxgKPCo//mjA9m5SCg04CxgcZHjXm9mU/ENmkjT9XUiIgfPQkKIaNuWiLZtqTt4MAB56RlkLl7M7oUL2b3wV3bNns2Ojz/2bR8aSvjhh+0R9kITEtSFK1JBvLrGrj7wLtAcWIVvupNtZtYduMY5N8y/XRLQDogBtgJXOue+NLPvgIaAAQv8+6T7g95zwMn4pju5vLjr6/am6U5ERA6ec47cDRvY/evCwrCXufg3XGYmAMF16xLRsYMv6HXsRGTHDgTXqeNx1SJVV6Wbx66yUbATESlfLjeXrKVL9wh72ctXFHbhhiUmEtG+PZEd2hPRoQMR7dppqhWRUlKwK4GCnYhI4OXt3Onrwv11IbsXLSJz8eLCu2MQFET4oYcS0aE9ke3bE9G+AxFt2+h6PZFiKNiVQMFORMQbORs3kfnbYl/gW7SYzEWLyNu+HfBfr9e27R5hL/yQVliIV5eHi1QOCnYlULATEakcnHPkrF1H5uJFf4e9334jPz0dAIuMJOKww3xduO07ENH+CMJatMCCgjyuXKTiKNiVQMFORKTycvn5ZCevInPxInYvXkzmosVk/vFH4eCMoFq1iDjiiMKwF9n+CEKaNdNIXKm2FOxKoGAnIlK1FMyvl7moSNj76y/IyQEguF49XxfuEe19zx06ENKggcdVi5QPBbsSKNiJiFR9+dnZZC1Z4h+Y8RuZixaRtXw55OcDENKkCZFdOhPdsxdRPXsQlpioVj2pkjy5pZiIiEhFCgoLI7JDByI7dChclp+RQeaff/rC3qLF7Jo7l52ffwFASKNGRPXqSXTPnkT17ElYfLxXpYuUGwU7ERGptoKio4nq1o2obt0A/+CMVavImP0zu+b8TMZPs9gxw3fXjNC4OKJ69iS6Zw+ievYktElxtzsXqdzUFYu6YkVEairnHNnLlpHx8xx2/fwzu+bMIS8tDYCwFi2I6tmTqJ49iO7ZU9foSaWha+xKoGAnIiLgG4GbtWQJGT//zK7ZP7Nr7tzCqVbCDj2E6B49ierVk6gjjySkbl2Pq5WaSsGuBAp2IiJSHJebS+Yff5Axeza7fp7DrnnzcLt3gxnhbdsWXp8XdWR3gmvV8rpcqSEU7EqgYCciUoHWzIHkJEjsBwk9qtR5XXY2uxcvZtfPP5Px8xx2z5+Py86GoCAijjii8Pq8qK5dCYqOLucPIOKjYFcCBTsRkQqyZg5MPgPysiE4DIbOqJhwF6Dz5mdlsXvBr/6g9zO7Fy70zaUXEkJkhw5E9+1DreOOI7xtW02tIuVG052IiEjlkJzkC1cuz/ecnFQxwS5A5w0KDye6Zw+ie/agITeQv2sXu375hV2zfUFvy3PPs2X8c4Q2a0bMccdR67hjierWDQsNLYcPJfJPCnYiIlJxEvv5WswKWs4S+1Wr8wZFRRHTpw8xffoAkLtlCzu//570b79j+7vvkvrmmwTVrk3M0UdT67hjie7bj+AYddlK+VFXLOqKFRGpUFX4GruyyN+1i/SffiL92+9I/+EH8rZvx0JDierVi1onHE/tE08kODa2wuuSqkfX2JVAwU5ERCqSy81l9y+/sPPb79j53XfkrF4NoaHE9O9PndNOJeaYYwiKjPS6TKmkFOxKoGAnIiJecc6R+dvv7PjkE3Z8+im5mzcTFBVFrRNOoPZppxF9VC8sRFdOyd8U7EqgYCciIpWBy8tj1//+R9onn7Dzy6/I37mT4Pr1qX3yydQ5/TQiOnXS6FpRsCuJgp2IiFQ2+dnZpP/f/7Hjk09J//57XHY2ofHx1D7tVOqcdhrhhx7qdYniEQW7EijYiYhIZZa3cyc7v/6GHZ98Qsbs2ZCfT/hhh1HntFOpfdrphDZu5HWJUoEU7EqgYCciIlVF7ubN7Pj8C9I++YTMhQshOJiYAcdQd8j5RPfpjQUFeV2iBJiCXQkU7EREpCrKTk5m+/TpbH/vffJSUwmNjyd28GBizzmbkAYNvC5PAkTBrgQKdiIiUpXlZ2ez8+uv2T71HXb9738QGkqt44+j7pAhRPXsqQEX1YyCXQkU7EREpLrIWrGC7e+8y/YPPyQ/LY2wxERiBw+mztlnEVK3rtflSTlQsCuBgp2IiFQ3+ZmZ7PzyS1KnvsPuX37BwsKofcbp1L/sMo2oreIU7EqgYCciItVZ5pK/SH3rLdI+/BCXlUV0/37Uv+wyoo46St20VZCCXQkU7EREpCbITU1l+9SpbJvyFnlbthDeti31LruMOqeegoWFeV2elJKCXQkU7EREpCbJz8pixyefsu3118laupSQhg2pe/HF1D1/CMF16nhdnpRgf8FOk92IiIgEwpo5kPSk77mSCQoPJ/bcc2g54yMSJk4kvE0bNj/9NMuOO54tL75IfkaG1yXKQVKLHWqxExGRcrZmDkw+A/KyITgMhs6AhB5eV7VfmUuWsHn8eNK/+Zbg+vVpcM01xA4ZTJC6aCsdtdiJiIhUpOQkX6hzeb7n5KR9b1uWlr1ybBWMaNuWhOeeI3Hq24Qfcggbx45lxcBTSPvoI1xeXpmPLxVDwU5ERKS8JfbztdRZsO85sV/x2xW07H031vd8IAGtLPvuR2TnzjSf/DoJkyYRXKcO6+4YxcqzzmLnt9+iXr7Kz5NgZ2b1zOxrM1vqfy52xkQz+8LMtpvZJ3stTzKzBf7HOjP70L/8GDNLK7Lu3gr4OCIiIntK6OHrfj327v13wx5Iy1557lsCMyOmbx8Sp08j7pmncTm5pFx3PavOv4CMnyvfNYPyN69a7EYB3zrnWgPf+t8X53Hgkr0XOuf6Oec6O+c6A/8F3i+yOqlgnXNuTDnXLSIiUjoJPaDfrfu/tq60LXvlvW8pWVAQtU8+mVaffEyTB8eQs2EDq4cOZfWwq9j922/lfj4pO08GT5jZEuAY59x6M2sK/OCca7uPbY8BRjrnTitmXW1gFdDCObdjf9vujwZPiIiIZ9bM8bW2JfY78AEWZdn3IORnZpL61ttsfekl8tLSqHXyyTQcMYLwVi0Dfm75W6Wbx87MtjvnYv2vDUgteF/Mtsew72B3KXCGc25QkW3fA1KAdf79iv0vhZldDVwN0Lx5826rVq0qy0cSERGpMfJ27mTba6+x9fXJuKwsYs85mwbXXUdokyZel1YjeDIq1sy+MbPFxTzOLLqd8yXLg02XFwBvF3k/H1/rXSdgPPDhvnZ0zr3snOvunOvesGHDgzy9iIhIzRNcqxYNR4zg0K+/ou6FF7L9w49YfuJJbHzs3+SmpnpdXo0WsGDnnDveOde+mMdHwEZ/Fyz+500HenwzawD0AD4tcs4dzrl0/+vPgFD/diIiIlLOQurXp8ndd3HI559T+5RT2DZ5MstPOJHNL7ygSY494tXgiRnAUP/rocBHB3GMQcAnzrnMggVm1sTftYuZ9cD3+baWsVYRERHZj7D4OJo9+gitPvqQqF492TJuPMtOPIltb7xJfna21+XVKF4Fu0eBE8xsKXC8/z1m1t3MJhVsZGZJwDTgODNLMbOTihzjfPbshgVf2FtsZr8C44DznSbdERERqRDhrVv/PcnxoYey8eGHWXHyQN8kx/p1XCF0SzE0KlZERKq5Ch49C+CcI2PWLDY/9TSZv/1GrYEn0/TBhwiOia6Q81dn+xs8EVLRxYiIiEgF8ui+tWZGTJ8+RB91FFtfeYXNTz9D1pK/iB/3LOGHHhrw89dUuqWYiIhIdRbAO1SUhgUF0eCqq2j+6qvkpaWxcvAQ0j79tOQd5aAo2ImIiFRnFXCHitKI7tWTlu+/R0Tbtqy7dSQbxj6M08CKcqdgJyIiUp2V9r61FSC0cWNavDGZekMvJfXNN1l16VByNmzwrJ7qSIMn0OAJERGRgNnHwI0dn3/O+rvvwcLDiXvqSaKPOsrDIqsWT+48ISIiIjVcwcCN78b6ntfMKVxVe+BAEqdPI7h+PVZfOYwtE17C5ed7WGz1oGAnIiIivtCV9OQe4avMShi4Ed6qFS3feYfaAwey+ZlnSBl+HXlpaeV3/hpIwU5ERKSm20/LWpmUYuBGUHQ0zZ54nMb33EP6Tz+x8txB7P7tt/I5fw2kYCciIlLTBWpKlFIO3DAz6l18EYlvvoHLzWXVBReyffr08qmhhlGwExERqekCOSVKQg/od2upRuNGdu5My/ffI6p7N9bfM5p1d99NfmZmifvJ3zQqFo2KFRER8eK2Y/vi8vLY/NxzbH1xAuGHHUb8s88Q1ry5pzVVJhoVKyIiIvt3AC1rgWbBwTS68UbiJ7xIzrp1rDx3EDu/+97rsqoEBTsRERGplGodcwwt35tOWEICKcOHs+mpp3G5uV6XVakp2ImIiEilFRYfT4u33yL2vPPY+vLLrB52Fblbt3pdVqWlYCciIiKVWlB4OE0fHEPThx9m9y+/sPLsc9g1/xevy6qUFOxERESkSog952wSp76NRUSw6tJL2fbGG2gQ6J4U7ERERKTKiDjsMFpOn0ZM//5sfPgR1t5yC3npGV6XVWko2ImIiEiVEly7NvHPjafhrbew88uvSB48mKxly7wuq1JQsBMREZEqx4KCaHDVVTR/9VXy0tJYOXgIaZ9+6nVZnlOwExERkSoruldPWr7/HhHt2rHu1pFseGgsLjvb67I8o2AnIiIiVVpo48a0mPw69YYOJfU//2HVJZeSs2GD12V5QsFOREREqjwLDaXxnaOIe+ZpspYuZeXZ55Axa5bXZVU4BTsRERGpNmqffDKJ06cRXL8eq68cxpYJE3D5+V6XVWEU7ERERKRaCW/VipbvvEPtU05h8zPPknLtcPLS0rwuq0Io2ImIiEi1ExQdTbMnHqfx6HtInzWLlecOYvdvv3ldVsAp2ImIiEi1ZGbUu+giEt98A5eby6oLLmT79OlelxVQCnYiIiJSrUV27kzL998jqnt31t8zmnV33U1+ZqbXZQWEgp2IiIhUeyH16pEw8WUaDL+WtPffJ/mCC8levdrrssqdgp2IiIjUCBYcTMMRI4if8CI569ax8txB7PzuO6/LKlcKdiIiIlKj1DrmGFq+9x5hzZuTMvw6Nj31NC431+uyyoWCnYiIiNQ4YfFxtHhrCrGDB7P15ZdZPewqcrdu9bqsMlOwExERkRopKDycpmMeoOnDD7P7l19YefY57Jo/3+uyykTBTkRERGq02HPOJvGdqVhEBKsuHcq2N97AOed1WQfFs2BnZvXM7GszW+p/rlvMNp3N7L9m9puZLTSzIUXWtTSzn81smZm9Y2Zh/uXh/vfL/OsTK/BjiYiISBUU0a4dLadPI6Z/fzY+/Ahrb7mFvPQMr8s6YF622I0CvnXOtQa+9b/f2y7gUufcEcDJwDNmFutf9xjwtHPuUCAVuNK//Eog1b/8af92IiIiIvsVXLs28c+Np+Gtt7Dzy69IHjyYrGXLvC7rgHgZ7M4EJvtfTwbO2nsD59xfzrml/tfrgE1AQzMz4FhgejH7Fz3udOA4//YiIiIi+2VBQTS46iqav/YaeWlprBw8hLRPPoU1cyDpSd9zJRbi4bkbO+fW+19vABrvb2Mz6wGEAcuB+sB251zB2OQUIM7/Og5YA+CcyzWzNP/2W8q3fBEREamuonv2oOX777P25ptZN3Iku9tm0rhTGhYWBkNnQEIPr0ssVkBb7MzsGzNbXMzjzKLbOd8Vivu8StHMmgJvApc75/LLqbarzWyumc3dvHlzeRxSREREqpHQxo1oMfl16h3fntQlEaz+IZb8rGxITvK6tH0KaIudc+74fa0zs41m1tQ5t94f3DbtY7vawKfA3c652f7FW4FYMwvxt9rFA2v969YCCUCKmYUAdfzb713by8DLAN27d6+aQ19EREQkoCw0lMZ33Eb4rkGs/28Ua5LqkXBpj0o7rYiXdc0AhvpfDwU+2nsD/0jXD4A3nHMF19MVtPB9DwwqZv+ixx0EfOeq6phlERER8V5CD2IfmE6zK45h18Yw1ox9lfzdu72uqlheBrtHgRPMbClwvP89ZtbdzCb5txkM9AcuM7MF/kdn/7o7gFvMbBm+a+he8S9/BajvX34LxY+2FRERESm9hB7UuW0CzR57lF0//8yaa4f/He4q0cAKU2OWryt27ty5XpchIiIiVUDaRx+xbtSdRPXqScJdVxD0znmQlw1BwdDlYuh0QUAHV5jZPOdc9+LWeTkqVkRERKTKqXOmbwzoulF3sub21SS0zyYoOA/y8mDua7Dgbc9GzlbWa/9EREREKq06Z55Js0cfYdef61mTVI/83IIpc52v9c6jkbMKdiIiIiIHoc6ZZ9L0kYd9Ayp+PYJ8Fw4WDMFhkNjPk5rUFSsiIiJykGLPOguA9XfeRUrsABKu6IYdeoxnExgr2ImIiIiUQexZZ+Gystlw332sb9GaZgO8uyuFgp2IiIhIGdUdMhjy84js2tXTOhTsRERERMpB3Qsu8LoEDZ4QERERqS4U7ERERESqCQU7ERERkWpCwU5ERESkmlCwExEREakmFOxEREREqgkFOxEREZFqQsFOREREpJpQsBMRERGpJhTsRERERKoJBTsRERGRakLBTkRERKSaULATERERqSYU7ERERESqCQU7ERERkWrCnHNe1+A5M9sMrCqyqA6QVsrdS7NtWbdpAGwpZT2V1YH8TCvrOct6vIPZX9/F8qfvor6LlYW+i/ouHqwWzrmGxa5xzumx1wN4uTy3Les2wFyvfyYV+TOtrOcs6/EOZn99Fyv/98KLc+q7qO9iZTmnvouV77uortjifVzO25bXNlWZF5+vvM9Z1uMdzP76LpY/fRf1Xaws9F3Ud7HcqSu2CjCzuc657l7XIaLvolQW+i5KZVHZvotqsasaXva6ABE/fRelstB3USqLSvVdVIudiIiISDWhFjsRERGRakLBTkRERKSaULATERERqSYU7ERERESqCQW7Ks7MDjOzCWY23cyu9boeqbnM7Cwzm2hm75jZiV7XIzWXmbUys1fMbLrXtUjNY2bRZjbZ/+/hRRV9fgU7D5nZq2a2ycwW77X8ZDNbYmbLzGzU/o7hnPvDOXcNMBjoE8h6pfoqp+/ih865q4BrgCGBrFeqr3L6Lq5wzl0Z2EqlJjnA7+U5wHT/v4dnVHStCnbeeh04uegCMwsGngcGAocDF5jZ4WbWwcw+2evRyL/PGcCnwGcVW75UI69TDt9Fv3v8+4kcjNcpv++iSHl5nVJ+L4F4YI1/s7wKrBGAkIo+ofzNOfejmSXutbgHsMw5twLAzKYCZzrnHgFO28dxZgAzzOxT4K0AlizVVHl8F83MgEeBz51z8wNcslRT5fXvokh5OpDvJZCCL9wtwIMGNLXYVT5x/J30wfcFidvXxmZ2jJmNM7OXUIudlK8D+i4CNwDHA4PM7JpAFiY1zoH+u1jfzCYAXczszkAXJzXWvr6X7wPnmtmLeHCPWbXYVXHOuR+AHzwuQwTn3DhgnNd1iDjntuK71lOkwjnnMoDLvTq/Wuwqn7VAQpH38f5lIhVN30WpLPRdlMqoUn4vFewqn/8Brc2spZmFAecDMzyuSWomfRelstB3USqjSvm9VLDzkJm9DfwXaGtmKWZ2pXMuF7ge+BL4A3jXOfebl3VK9afvolQW+i5KZVSVvpfmnPO6BhEREREpB2qxExEREakmFOxEREREqgkFOxEREZFqQsFOREREpJpQsBMRERGpJhTsRERERKoJBTsRET8zizWz4UXeNzOz6QE4z/1mttbMxuxjfbKZNTCzSDNbYGbZZtagvOsQkepHwU5E5G+xQGGwc86tc84NCtC5nnbO3bu/DZxzu51znYF1AapBRKqZEK8LEBGpRB4FDjGzBcDXwPPAJ8659mZ2GXAWEA20Bp4AwoBLgCzgFOfcNjM7xL9fQ2AXcJVz7s/9ndTM6gNvA3H4Zre3cv9kIlIjqMVORORvo4DlzrnOzrnbilnfHjgHOBIYC+xyznXBF8Yu9W/zMnCDc64bMBJ4oRTnvQ+Y6Zw7AvgAaF62jyEiNZVa7ERESu9759xOYKeZpQEf+5cvAjqaWQzQG5hmVtjoFl6K4/bHFxhxzn1qZqnlW7aI1BQKdiIipZdV5HV+kff5+P49DQK2+6+LExGpcOqKFRH5206g1sHu7JzbAaw0s/MAzKdTKXb9EbjQv89AoO7B1iAiNZuCnYiIn3NuK/CTmS02s8cP8jAXAVea2a/Ab8CZpdjnAaC/mf2Gr0t29UGeW0RqOHPOeV2DiEiNYmb3A+nOuSdKuX0y0N05tyWQdYlI1acWOxGRipcOXL2vCYoLFExQDITiu45PRGS/1GInIiIiUk2oxU5ERESkmlCwExEREakmFOxEREREqgkFOxEREZFqQsFOREREpJr4f612pCUOmOvNAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hs_3 = ml_2.head(x=r, y=0, t=ts, layers=1)\n", + "hd_3 = ml_2.head(x=r, y=0, t=td, layers=15)\n", + "plt.figure(figsize = (10, 7))\n", + "plt.semilogx(ts, hs, '.', label='shallow obs')\n", + "plt.semilogx(td, hd, '.', label='deep obs')\n", + "plt.semilogx(ts, hs_3[0], label='shallow ttim')\n", + "plt.semilogx(td, hd_3[0], label='deep ttim')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.title('TTim Multi - Layer Unconfined Model Results')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We already see significant improvement from the previous single-layer model. The fit is better, and we have a much better confidence interval on the parameters. The AIC and BIC indicators have also significantly improved.\n", + "\n", + "What if we take into account the described stratification of the aquifer? In that case, we could try to stratify our model into two: The first 6 m and the deeper layers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Calibration of the Stratified Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this final example, we will assume the storage is distributed according to the sediment stratification in the aquifer. We will adjust two different ```Saq```values, one for the first 6 m of the aquifer and another for the deeper layers. We assume the hydraulic conductivity and the anisotropy is constant." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Model Figure - One - layer model\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1,1,1)\n", + "\n", + "\n", + "#Aquifer 1:\n", + "for i in range(21,6,-1):\n", + " ground = plt.Rectangle((-20,-i), width = 150, height = 1, fc = 'w', zorder=0, alpha=0.9, hatch = 'oo', ec = 'k', ls = '--')\n", + " ax.add_patch(ground)\n", + "#Aquifer 2:\n", + "for i in range(6,-2,-1):\n", + " ground = plt.Rectangle((-20,-i), width = 150, height = 1, fc = 'w', zorder=0, alpha=0.9, hatch = '..', ec = 'k', ls = '--')\n", + " ax.add_patch(ground)\n", + " \n", + "\n", + "\n", + "well = plt.Rectangle((-1.5,-21), width = 3, height = 23, fc = 'w', zorder=1, ec = 'k')\n", + "ax.add_patch(well)\n", + "\n", + "#Wellhead\n", + "wellhead = plt.Rectangle((-2,2),width = 4, height = 2.5, fc = 'w', zorder=2, ec='k')\n", + "ax.add_patch(wellhead)\n", + "\n", + "#Screen for the well:\n", + "screen = plt.Rectangle((-1.5,-21), width = 3, height = 11, fc = 'w', alpha=1, zorder = 2, ec = \"k\", ls = '--', hatch = \"-\")\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "\n", + "#Piezometers\n", + "piez1 =plt.Rectangle((89,-21), width = 2, height = 23, fc = 'w', zorder=1, ec = 'k')\n", + "screen_piez_1 = plt.Rectangle((89,-19), width = 2, height = 7, fc = 'w', alpha=1, zorder = 2, ec = \"k\", ls = '--', hatch = \"-\")\n", + "screen_piez_1.set_linewidth(2)\n", + "screen_piez_2 = plt.Rectangle((89,-3), width = 2, height = 0.5, fc = 'w', alpha=1, zorder = 2, ec = \"k\", ls = '--', hatch = \"-\")\n", + "screen_piez_2.set_linewidth(2)\n", + "\n", + "ax.add_patch(piez1)\n", + "ax.add_patch(screen_piez_1)\n", + "ax.add_patch(screen_piez_2)\n", + "\n", + "\n", + "#last line\n", + "line = plt.Line2D(xdata= [-200,1200], ydata = [2,2], color = \"k\")\n", + "ax.add_line(line)\n", + "\n", + "#Water table\n", + "line2 = plt.Line2D(xdata = [-200,1200], ydata = [0,0], color = 'b')\n", + "ax.add_line(line2)\n", + "\n", + "ax.text(-18,0.5, s = 'Water Table', fontsize = 'large', color = 'b',bbox = {'fc' : 'w'})\n", + "ax.text(93, -3, s = 'Shallow piezometer', bbox = {'fc' : 'w'})\n", + "ax.text(93, -16, s = 'Deeper piezometer', bbox = {'fc' : 'w'})\n", + "\n", + "ax.set_xlim([-20,130])\n", + "ax.set_ylim([-21,7])\n", + "ax.set_xlabel('Distance [m]')\n", + "ax.set_ylabel('Relative height [m]')\n", + "ax.set_title('Conceptual Model 3 - Multi-layer Model 3D - Vennebulten Example');" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 21\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml_3 = Model3D(kaq=10, z=zlayers, Saq=Saq, kzoverkh=0.1, phreatictop=True, \\\n", + " tmin=1e-4, tmax=1.1)\n", + "w_3 = Well(ml_3, xw=0, yw=0, rw=0.1, tsandQ=[(0, Q)], layers=range(nlay))\n", + "ml_3.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 119\n", + " # data points = 48\n", + " # variables = 5\n", + " chi-square = 5.6235e-04\n", + " reduced chi-square = 1.3078e-05\n", + " Akaike info crit = -535.020588\n", + " Bayesian info crit = -525.664583\n", + "[[Variables]]\n", + " kaq0_20: 74.7835895 +/- 2.28391453 (3.05%) (init = 50)\n", + " Saq0: 0.02068784 +/- 0.00156494 (7.56%) (init = 0.1)\n", + " Saq1_7: 4.4944e-04 +/- 5.8482e-05 (13.01%) (init = 0.0001)\n", + " Saq7_20: 2.3179e-05 +/- 1.1194e-06 (4.83%) (init = 0.0001)\n", + " kzoverkh: 3.7920e-04 +/- 7.9863e-05 (21.06%) (init = 0.1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_20, kzoverkh) = -0.966\n", + " C(kaq0_20, Saq7_20) = -0.755\n", + " C(Saq1_7, kzoverkh) = -0.738\n", + " C(Saq7_20, kzoverkh) = 0.710\n", + " C(kaq0_20, Saq1_7) = 0.632\n", + " C(kaq0_20, Saq0) = -0.614\n", + " C(Saq0, kzoverkh) = 0.602\n", + " C(Saq0, Saq1_7) = -0.598\n", + " C(Saq1_7, Saq7_20) = -0.559\n", + " C(Saq0, Saq7_20) = 0.489\n" + ] + } + ], + "source": [ + "ca_4 = Calibrate(ml_3)\n", + "ca_4.set_parameter(name='kaq0_20', initial=50)\n", + "ca_4.set_parameter(name='Saq0', initial=0.1)\n", + "ca_4.set_parameter(name='Saq1_7', initial=1e-4, pmin=0)\n", + "ca_4.set_parameter(name='Saq7_20', initial=1e-4, pmin=0)\n", + "ca_4.set_parameter_by_reference(name='kzoverkh', parameter=ml_3.aq.kzoverkh[:], \\\n", + " initial=0.1, pmin=0)\n", + "ca_4.series(name='obs1', x=r, y=0, layer=1,t=ts, h=hs)\n", + "ca_4.series(name='obs2', x=r, y=0, layer=15, t=td, h=hd)\n", + "ca_4.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_2074.7835892.2839153.054032-infinf50[74.7835894785934, 74.7835894785934, 74.783589...
Saq00.0206880.0015657.564524-infinf0.1[0.02068783521410092]
Saq1_70.0004490.00005813.0121630.0inf0.0001[0.00044944008673608593, 0.0004494400867360859...
Saq7_200.0000230.0000014.8290720.0inf0.0001[2.3179487157021228e-05, 2.3179487157021228e-0...
kzoverkh0.0003790.00008021.0612110.0inf0.1[0.0003791957224930087, 0.0003791957224930087,...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_20 74.783589 2.283915 3.054032 -inf inf 50 \n", + "Saq0 0.020688 0.001565 7.564524 -inf inf 0.1 \n", + "Saq1_7 0.000449 0.000058 13.012163 0.0 inf 0.0001 \n", + "Saq7_20 0.000023 0.000001 4.829072 0.0 inf 0.0001 \n", + "kzoverkh 0.000379 0.000080 21.061211 0.0 inf 0.1 \n", + "\n", + " parray \n", + "kaq0_20 [74.7835894785934, 74.7835894785934, 74.783589... \n", + "Saq0 [0.02068783521410092] \n", + "Saq1_7 [0.00044944008673608593, 0.0004494400867360859... \n", + "Saq7_20 [2.3179487157021228e-05, 2.3179487157021228e-0... \n", + "kzoverkh [0.0003791957224930087, 0.0003791957224930087,... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.003422795065626066\n" + ] + } + ], + "source": [ + "display(ca_4.parameters)\n", + "print('RMSE:', ca_4.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hs_4 = ml_3.head(x=r, y=0, t=ts, layers=1)\n", + "hd_4 = ml_3.head(x=r, y=0, t=td, layers=15)\n", + "plt.figure(figsize = (10, 7))\n", + "plt.semilogx(ts, hs, '.', label='shallow obs')\n", + "plt.semilogx(td, hd, '.', label='deep obs')\n", + "plt.semilogx(ts, hs_4[0], label='shallow ttim')\n", + "plt.semilogx(td, hd_4[0], label='deep ttim')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('drawdown [m]')\n", + "plt.title('TTim Stratified Unconfined Model Results')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we see that the model fit has significantly improved. AIC and BIC indicators are lower than the previous multi-layer model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 9. Analysis and comparison of model results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9.1. Analysis of models with single layer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Kruseman and de Ridder (K&dR) applied graphical analysis using the Neuman method considering a single layer unconfined aquifer and the drawdown of the deeper well. The same conceptualization and data have been used by Xinzhu (2020) to model the aquifer parameters in AQTESOLV (Duffield, 2007) and MLU (Carlson and Randall, 2012). However, for the latter, since MLU is a similar model method as in TTim, Xinzhu used the same approach in this notebook by representing the one-layer unconfined aquifer in a two-layer configuration: a shallow 0.1 m thick with phreatic storage and the aquifer thickness with elastic storage.\n", + "\n", + "The table below summarises the results obtained by each approach." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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k [m/d]Ss [1/m]Sy [-]kz/khRMSE
K&dR730.0000250.0050.000548-
AQTESOLV63.8050.0000270.0110.000690.003041
MLU74.6570.0000280.0050.0007370.003216
ttim116.8056990.000010.0001329.8795120.010072
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" + ], + "text/plain": [ + " k [m/d] Ss [1/m] Sy [-] kz/kh RMSE\n", + "K&dR 73 0.000025 0.005 0.000548 -\n", + "AQTESOLV 63.805 0.000027 0.011 0.00069 0.003041\n", + "MLU 74.657 0.000028 0.005 0.000737 0.003216\n", + "ttim 116.805699 0.00001 0.000132 9.879512 0.010072" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t1 = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'Sy [-]', 'kz/kh'], \\\n", + " index=['K&dR', 'AQTESOLV', 'MLU', 'ttim'])\n", + "t1.loc['K&dR'] = [73, 2.476e-05, 0.005, 0.000548]\n", + "t1.loc['AQTESOLV'] = [63.805, 2.663e-05, 0.011, 0.000690]\n", + "t1.loc['MLU'] = [74.657, 2.767e-05, 0.005, 0.000737]\n", + "t1.loc['ttim'] = ca_2.parameters['optimal'].values \n", + "t1['RMSE'] = ['-', 0.003041, 0.003216, ca_2.rmse()]\n", + "t1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TTim has overall found a different solution than the other presented results. It also could not reach the RMSE of the other solvers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9.2. Analysis of multi-layer models\n", + "\n", + "Xinzhu (2020) also applied the multi-layer approach to MLU model and the different results are presented in the table below:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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k [m/d]Sy [-]Ss [1/m]kzoverkhRMSE
MLU62.6570.00120.0000280.0025950.013540
ttim-multilayer31.634660.055280.0000350.0100030.009938
ttim-stratified Ss74.7835890.0206880.0000230.0003790.003423
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" + ], + "text/plain": [ + " k [m/d] Sy [-] Ss [1/m] kzoverkh RMSE\n", + "MLU 62.657 0.0012 0.000028 0.002595 0.013540\n", + "ttim-multilayer 31.63466 0.05528 0.000035 0.010003 0.009938\n", + "ttim-stratified Ss 74.783589 0.020688 0.000023 0.000379 0.003423" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t2 = pd.DataFrame(columns=['k [m/d]', 'Sy [-]', 'Ss [1/m]','kzoverkh'], \\\n", + " index=['MLU', 'ttim-multilayer', 'ttim-stratified Ss'])\n", + "t2.loc['MLU'] = [62.657, 0.0012, 2.790e-05, 0.002595]\n", + "t2.loc['ttim-multilayer'] = ca_3.parameters['optimal'].values\n", + "t2.iloc[2, 0:2] = ca_4.parameters['optimal'].values[0:2]\n", + "t2.iloc[2, 2:4] = ca_4.parameters['optimal'].values[3:5]\n", + "t2.loc[:,'RMSE'] = pd.Series([0.013540, ca_3.rmse(), ca_4.rmse()], index = t2.index)\n", + "t2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The multi-layer approach allowed us to fit both piezometers and better represent the vertical component of flow. However, the parameters were sensitive to the conceptualization applied. The stratified model had much larger hydraulic conductivity in comparison to the multi-layer model. In the stratified approach, the fit has significantly improved." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Kruseman, G.P., De Ridder, N.A., Verweij, J.M., 1970. Analysis and evaluationof pumping test data. volume 11. International institute for land reclamation and improvement The Netherlands.\n", + "* Neuman, S.P., Witherspoon, P.A., 1969. Applicability of current theories of flow in leaky aquifers. Water Resources Research 5, 817–829.\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Next Notebook: [Unconfined 2 - Moench](unconfined2_moench.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pumpingtests/unconfined2_moench.ipynb b/pumpingtests/unconfined2_moench.ipynb new file mode 100644 index 0000000..e912cbe --- /dev/null +++ b/pumpingtests/unconfined2_moench.ipynb @@ -0,0 +1,1080 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2.Test for an anisotropic water-table aquifer - Moench Example\n", + "**This test is taken from examples presented in MLU tutorial.**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Introduction and Conceptual Model\n", + "\n", + "This test is based on a synthetic example reported by Barlow and Moench (1999), utilizing an analytical solution developed by Moench and Allen (1997) for the transient flow of partially-penetrating wells in unconfined aquifers. The data reported has been used in MLU (Carlson and Randall, 2012) to check the model performance.\n", + "\n", + "We will reproduce the work of Yang (2020) to explore the performance of anisotropic water table aquifer modelling with TTim and compare the results with the published values and the MLU solution.\n", + "\n", + "The conceptual model of the test is of an aquifer, partially saturated with water (10 m water table). A pumping well is screened from 5 to 10 m depth. The well and the well-casing radius is 0.1 m. Drawdown is recorded at the pumping well and four piezometers located at two different distances and two different depths. Two piezometers, PS1 and PS2, are located at one-meter depth below the water table and 3.16 and 31.6 m distance, respectively. Another two (PD1 and PD2) piezometers are at 7.5 m depth below the water table and the same distances, directly below the previous piezometers. The figure below shows the location of the well and the piezometers\n", + "\n", + "Pumping starts at time t = 0 at a constant rate of 172.8 m3/d. Drawdown is recorded until t = 3 days." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "##Now printing the conceptual model figure:\n", + "\n", + "fig = plt.figure(figsize=(14, 9))\n", + "ax = fig.add_subplot(1,1,1)\n", + "#sky\n", + "sky = plt.Rectangle((-20,2), width = 70, height = 5, fc = 'b', zorder=0, alpha=0.1)\n", + "ax.add_patch(sky)\n", + "\n", + "#Aquifer:\n", + "ground = plt.Rectangle((-20,-10), width = 70, height = 12, fc = np.array([209,179,127])/255, zorder=0, alpha=0.9)\n", + "ax.add_patch(ground)\n", + "\n", + "\n", + "\n", + "well = plt.Rectangle((-0.5,-10), width = 1, height = 12, fc = np.array([200,200,200])/255, zorder=1)\n", + "ax.add_patch(well)\n", + "\n", + "#Wellhead\n", + "wellhead = plt.Rectangle((-0.75,2),width = 1.5, height = 1.5, fc = np.array([200,200,200])/255, zorder=2, ec='k')\n", + "ax.add_patch(wellhead)\n", + "\n", + "#Screen for the well:\n", + "screen = plt.Rectangle((-0.5,-10), width = 1, height = 5, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen.set_linewidth(2)\n", + "ax.add_patch(screen)\n", + "pumping_arrow = plt.Arrow(x = 1.1,y = 3, dx = 3, dy = 0, color = \"#00035b\")\n", + "ax.add_patch(pumping_arrow)\n", + "ax.text(x = 4.2, y = 3, s = r'$ Q = 172.8 \\frac{m^3}{d}$', fontsize = 'large' )\n", + "\n", + "#Piezometers\n", + "piez1 =plt.Rectangle((31.4,-10), width = 0.5, height = 12, fc = np.array([200,200,200])/255, zorder=1)\n", + "screen_piez_1 = plt.Rectangle((31.4,-8), width = 0.5, height = 1, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_1.set_linewidth(2)\n", + "screen_piez_2 = plt.Rectangle((31.4,-1.5), width = 0.5, height = 1, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_2.set_linewidth(2)\n", + "\n", + "ax.add_patch(piez1)\n", + "ax.add_patch(screen_piez_1)\n", + "ax.add_patch(screen_piez_2)\n", + "\n", + "piez2 =plt.Rectangle((2.75,-10), width = 0.5, height = 12, fc = np.array([200,200,200])/255, zorder=1)\n", + "screen_piez_3 = plt.Rectangle((2.75,-8), width = 0.5, height = 1, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_3.set_linewidth(2)\n", + "screen_piez_4 = plt.Rectangle((2.75,-1.5), width = 0.5, height = 1, fc = np.array([200,200,200])/255, alpha=1, zorder = 2, ec = \"k\", ls = '--')\n", + "screen_piez_4.set_linewidth(2)\n", + "\n", + "ax.add_patch(piez2)\n", + "ax.add_patch(screen_piez_3)\n", + "ax.add_patch(screen_piez_4)\n", + "\n", + "#last line\n", + "line = plt.Line2D(xdata= [-200,1200], ydata = [2,2], color = \"k\")\n", + "ax.add_line(line)\n", + "\n", + "#Water table\n", + "line2 = plt.Line2D(xdata = [-200,1200], ydata = [0,0], color = 'b')\n", + "ax.add_line(line2)\n", + "\n", + "ax.text(-9,0.5, s = 'Water Table', fontsize = 'large', color = 'b',bbox = {'fc' : 'w'})\n", + "ax.text(4.5, -1, s = 'PS1', bbox = {'fc' : 'w'})\n", + "ax.text(34, -1, s = 'PS2', bbox = {'fc' : 'w'})\n", + "ax.text(4.5, -7.5, s = 'PD1', bbox = {'fc' : 'w'})\n", + "ax.text(34, -7.5, s = 'PD2', bbox = {'fc' : 'w'})\n", + "\n", + "ax.set_xlim([-10,50])\n", + "ax.set_ylim([-10,4])\n", + "ax.set_xlabel('Distance [m]')\n", + "ax.set_ylabel('Relative height [m]')\n", + "ax.set_title('Conceptual Model - Vennebulten Example');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1. Import required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from ttim import *\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Set basic parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "b = 10 #aquifer thickness in m\n", + "Q = 172.8 #constant discharge rate in m^3/d\n", + "rw = 0.1 #well radius in m\n", + "rc = 0.1 #casing radius in m\n", + "r1 = 3.16 # distance of closer wells in m \n", + "r2 = 31.6 # distance of wells more far away in m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Load datasets of observation wells\n", + "\n", + "The dataset for each well consists of a column with the time data in seconds and drawdown in meters. We are loading it and converting it to days and meters." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "data0 = np.loadtxt('data/moench_pumped.txt', skiprows=1)\n", + "t0 = data0[:, 0] / 60 / 60 / 24 #convert time from seconds to days\n", + "h0 = -data0[:, 1] #converting drawdown to heads\n", + "data1 = np.loadtxt('data/moench_ps1.txt', skiprows=1)\n", + "t1 = data1[:, 0] / 60 / 60 / 24 #convert time from seconds to days\n", + "h1 = -data1[:, 1]\n", + "data2 = np.loadtxt('data/moench_pd1.txt', skiprows=1)\n", + "t2 = data2[:, 0] / 60 / 60 / 24 #convert time from seconds to days\n", + "h2 = -data2[:, 1]\n", + "data3 = np.loadtxt('data/moench_ps2.txt', skiprows=1)\n", + "t3 = data3[:, 0] / 60 / 60 / 24 #convert time from seconds to days\n", + "h3 = -data3[:, 1]\n", + "data4 = np.loadtxt('data/moench_pd2.txt', skiprows=1)\n", + "t4 = data4[:, 0] / 60 / 60 / 24 #convert time from seconds to days\n", + "h4 = -data4[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4. Creating a TTim model\n", + "\n", + "We will create an initial model divided in the same way as in the MLU documentation. A first layer with 0.1 m thick and phreatic storage, followed by a 2 m thick layer where the shallow piezometers are located, a 3 m layer and finally, a 5 m layer where the pump is placed and the last piezometers are screened. Additionally, we will set the model parameters with the given ones in Barlow and Moench (1999) and compare the results with the heads given in the paper." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#Set kaq, Saq, Sy and kzoverkh as given in Moench (1997)\n", + "kaq = 1e-4 * 60 * 60 * 24 #convert from m/s to m/d\n", + "Sy = 0.2\n", + "Saq = 2e-5\n", + "zh = 0.5 #kzoverkh" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml1 = Model3D(kaq=kaq, z=[0, -0.1, -2.1, -5.1, -10.1], Saq=[Sy, Saq, Saq, Saq], \\\n", + " kzoverkh=zh, tmin=1e-5, tmax=3)\n", + "w1 = Well(ml1, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=3)\n", + "ml1.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5. Check the TTim model with values obtained by Moench" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm1 = ml1.head(r1, 0, t1, layers=1)[0]\n", + "hm2 = ml1.head(r1, 0, t2, layers=3)[0]\n", + "hm3 = ml1.head(r2, 0, t3, layers=1)[0]\n", + "hm4 = ml1.head(r2, 0, t4, layers=3)[0]\n", + "hm0 = ml1.head(0, 0, t0, layers=3)[0]\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t0, h0, '.', label='pumped')\n", + "plt.semilogx(t0, hm0, label='ttim pumped')\n", + "plt.semilogx(t1, h1, '.', label='PS1')\n", + "plt.semilogx(t1, hm1, label='ttim PS1')\n", + "plt.semilogx(t2, h2, '.', label='PD1')\n", + "plt.semilogx(t2, hm2, label='ttim PD1')\n", + "plt.semilogx(t3, h3, '.', label='PS2')\n", + "plt.semilogx(t3, hm3, label='ttim PS2')\n", + "plt.semilogx(t4, h4, '.', label='PD2')\n", + "plt.semilogx(t4, hm4, label='ttim PD2')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('head [m]')\n", + "plt.title('Model Results - Simulation 1')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visually, TTim's solution is in good agreement with the model, but we can see that the simulated values in the pumping well and PD1 are a little below the values obtained in the analytical solution." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5.1. Calculate RMSE of TTim model" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.06131792435676353\n" + ] + } + ], + "source": [ + "rmse = np.sqrt(np.sum(np.sum((h1-hm1)**2)+np.sum((h2-hm2)**2)+np.sum((h3-hm3)**2)+np.sum((h4-hm4)**2)+np.sum((h0-hm0)**2))/\\\n", + " (len(h1)+len(h2)+len(h3)+len(h4)+len(h0)))\n", + "\n", + "print('RMSE:', rmse)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model has obtained a very close result with a good approximation to the analytical solution." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6. Model Calibration\n", + "\n", + "Now, instead of using the given values, we will try to find them through the TTim optimization framework. One can learn more about the calibration procedure and how to set the parameters in the following notebook: [Unconfined Test - Vennebulten](unconfined1_vennebulten.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml2 = Model3D(kaq=1, z=[0, -0.1, -2.1, -5.1, -10.1], Saq=[0.1, 1e-4, 1e-4, 1e-4], \\\n", + " kzoverkh=1, tmin=1e-5, tmax=3)\n", + "w2 = Well(ml2, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=3)\n", + "ml2.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "............................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 57\n", + " # data points = 70\n", + " # variables = 4\n", + " chi-square = 0.00754821\n", + " reduced chi-square = 1.1437e-04\n", + " Akaike info crit = -631.445846\n", + " Bayesian info crit = -622.451865\n", + "[[Variables]]\n", + " kaq0_3: 9.06766484 +/- 0.02235216 (0.25%) (init = 1)\n", + " Saq0: 0.17287565 +/- 0.00437375 (2.53%) (init = 0.2)\n", + " Saq1_3: 3.8699e-05 +/- 3.5515e-06 (9.18%) (init = 0.0001)\n", + " kzoverkh: 0.53504852 +/- 0.01014208 (1.90%) (init = 0.1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_3, kzoverkh) = -0.832\n", + " C(kaq0_3, Saq0) = -0.533\n", + " C(Saq0, kzoverkh) = 0.339\n", + " C(Saq1_3, kzoverkh) = -0.127\n" + ] + } + ], + "source": [ + "ca2 = Calibrate(ml2)\n", + "ca2.set_parameter(name='kaq0_3', initial=1)\n", + "ca2.set_parameter(name='Saq0', initial=0.2)\n", + "ca2.set_parameter(name='Saq1_3', initial=1e-4, pmin=0)\n", + "ca2.set_parameter_by_reference(name='kzoverkh', parameter=ml2.aq.kzoverkh, \\\n", + " initial=0.1, pmin=0)\n", + "ca2.series(name='pumped', x=0, y=0, t=t0, h=h0, layer=3)\n", + "ca2.series(name='PS1', x=r1, y=0, t=t1, h=h1, layer=1)\n", + "ca2.series(name='PD1', x=r1, y=0, t=t2, h=h2, layer=3)\n", + "ca2.series(name='PS2', x=r2, y=0, t=t3, h=h3, layer=1)\n", + "ca2.series(name='PD2', x=r2, y=0, t=t4, h=h4, layer=3)\n", + "ca2.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_39.0676650.0223520.246504-infinf1[9.067664842599923, 9.067664842599923, 9.06766...
Saq00.1728760.0043742.529999-infinf0.2[0.17287564535992025]
Saq1_30.0000390.0000049.1772340.0inf0.0001[3.869900362540868e-05, 3.869900362540868e-05,...
kzoverkh0.5350490.0101421.8955440.0inf0.1[0.5350485185874931, 0.5350485185874931, 0.535...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_3 9.067665 0.022352 0.246504 -inf inf 1 \n", + "Saq0 0.172876 0.004374 2.529999 -inf inf 0.2 \n", + "Saq1_3 0.000039 0.000004 9.177234 0.0 inf 0.0001 \n", + "kzoverkh 0.535049 0.010142 1.895544 0.0 inf 0.1 \n", + "\n", + " parray \n", + "kaq0_3 [9.067664842599923, 9.067664842599923, 9.06766... \n", + "Saq0 [0.17287564535992025] \n", + "Saq1_3 [3.869900362540868e-05, 3.869900362540868e-05,... \n", + "kzoverkh [0.5350485185874931, 0.5350485185874931, 0.535... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.010384195093738518\n" + ] + } + ], + "source": [ + "display(ca2.parameters)\n", + "print('RMSE:', ca2.rmse())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The values are close to the values in Moench." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm0_2 = ml2.head(0, 0, t0, layers=3)[0]\n", + "hm1_2 = ml2.head(r1, 0, t1, layers=1)[0]\n", + "hm2_2 = ml2.head(r1, 0, t2, layers=3)[0]\n", + "hm3_2 = ml2.head(r2, 0, t3, layers=1)[0]\n", + "hm4_2 = ml2.head(r2, 0, t4, layers=3)[0]\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t0, h0, '.', label='pumped')\n", + "plt.semilogx(t0, hm0_2, label='ttim pumped')\n", + "plt.semilogx(t1, h1, '.', label='PS1')\n", + "plt.semilogx(t1, hm1_2, label='ttim PS1')\n", + "plt.semilogx(t2, h2, '.', label='PD1')\n", + "plt.semilogx(t2, hm2_2, label='ttim PD1')\n", + "plt.semilogx(t3, h3, ',', label='PS2')\n", + "plt.semilogx(t3, hm3_2, label='ttim PS2')\n", + "plt.semilogx(t4, h4, '.', label='PD2')\n", + "plt.semilogx(t4, hm4_2, label='ttim PD2')\n", + "plt.xlabel('time [d]')\n", + "plt.ylabel('head [m]')\n", + "plt.title('Model Results - Calibration 1')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7. Creating and calibrating a model with more layers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The calibration resulted in a lower error than the simulated model, which could mean that we could improve the conceptualization of the model in TTim. In this next step, we will increase the number of layers to better account for the vertical flow component. We will create an 18-layers model with 0.5 m thick layers, except at the layers in the piezometers where a 1 m thickness has been established to make sure the piezometers lie at the centre of the layer:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. -0.5 -1.5 -2. -2.5 -3. -3.5 -4. -4.5 -5. -5.5 -6.\n", + " -6.5 -7. -8. -8.5 -9. -9.5 -10. ]\n", + "2e-05\n" + ] + } + ], + "source": [ + "## Model Configuration:\n", + "\n", + "nlay = 18\n", + "zlayers = (np.concatenate((np.array([0,-0.5,-1.5]),np.linspace(-2,-7,11),np.array([-8,-8.5,-9,-9.5,-10])))) #elevation of each layer\n", + "Saq_n = 1e-4 * np.ones(nlay)\n", + "Saq_n[0] = 0.1 # Setting the first storage as specific yield\n", + "print(zlayers)\n", + "print(Saq)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 9\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml3 = Model3D(kaq=1, z=zlayers, Saq=Saq_n, \\\n", + " kzoverkh=1, tmin=1e-5, tmax=3)\n", + "w3 = Well(ml3, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=range(9,18))\n", + "ml3.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..............................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 59\n", + " # data points = 70\n", + " # variables = 4\n", + " chi-square = 0.00667651\n", + " reduced chi-square = 1.0116e-04\n", + " Akaike info crit = -640.035843\n", + " Bayesian info crit = -631.041862\n", + "[[Variables]]\n", + " kaq0_19: 8.69563509 +/- 0.02074132 (0.24%) (init = 1)\n", + " Saq0: 0.19607178 +/- 0.00461632 (2.35%) (init = 0.2)\n", + " Saq1_19: 3.8968e-05 +/- 3.3211e-06 (8.52%) (init = 0.0001)\n", + " kzoverkh: 0.48796230 +/- 0.00856631 (1.76%) (init = 0.1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_19, kzoverkh) = -0.842\n", + " C(kaq0_19, Saq0) = -0.549\n", + " C(Saq0, kzoverkh) = 0.364\n", + " C(Saq1_19, kzoverkh) = -0.135\n" + ] + } + ], + "source": [ + "ca3 = Calibrate(ml3)\n", + "ca3.set_parameter(name='kaq0_19', initial=1, pmin=0)\n", + "ca3.set_parameter(name='Saq0', initial=0.2, pmin=0)\n", + "ca3.set_parameter(name='Saq1_19', initial=1e-4, pmin=0)\n", + "ca3.set_parameter_by_reference(name='kzoverkh', parameter=ml3.aq.kzoverkh, \\\n", + " initial=0.1, pmin=0)\n", + "ca3.series(name='pumped', x=0, y=0, t=t0, h=h0, layer=15)\n", + "ca3.series(name='PS1', x=r1, y=0, t=t1, h=h1, layer=1)\n", + "ca3.series(name='PD1', x=r1, y=0, t=t2, h=h2, layer=13)\n", + "ca3.series(name='PS2', x=r2, y=0, t=t3, h=h3, layer=1)\n", + "ca3.series(name='PD2', x=r2, y=0, t=t4, h=h4, layer=13)\n", + "ca3.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq0_198.6956350.0207410.2385260inf1[8.695635092973196, 8.695635092973196, 8.69563...
Saq00.1960720.0046162.3544020inf0.2[0.19607177947520582]
Saq1_190.0000390.0000038.5224390inf0.0001[3.896832991934218e-05, 3.896832991934218e-05,...
kzoverkh0.4879620.0085661.7555260inf0.1[0.4879623030841507, 0.4879623030841507, 0.487...
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0_19 8.695635 0.020741 0.238526 0 inf 1 \n", + "Saq0 0.196072 0.004616 2.354402 0 inf 0.2 \n", + "Saq1_19 0.000039 0.000003 8.522439 0 inf 0.0001 \n", + "kzoverkh 0.487962 0.008566 1.755526 0 inf 0.1 \n", + "\n", + " parray \n", + "kaq0_19 [8.695635092973196, 8.695635092973196, 8.69563... \n", + "Saq0 [0.19607177947520582] \n", + "Saq1_19 [3.896832991934218e-05, 3.896832991934218e-05,... \n", + "kzoverkh [0.4879623030841507, 0.4879623030841507, 0.487... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.009766203739979508\n" + ] + } + ], + "source": [ + "display(ca3.parameters)\n", + "print('RMSE:', ca3.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm0_3 = ml3.head(0, 0, t0, layers=15)[0]\n", + "hm1_3 = ml3.head(r1, 0, t1, layers=1)[0]\n", + "hm2_3 = ml3.head(r1, 0, t2, layers=13)[0]\n", + "hm3_3 = ml3.head(r2, 0, t3, layers=1)[0]\n", + "hm4_3 = ml3.head(r2, 0, t4, layers=13)[0]\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t0, h0, '.', label='pumped')\n", + "plt.semilogx(t0, hm0_3, label='ttim pumped')\n", + "plt.semilogx(t1, h1, '.', label='PS1')\n", + "plt.semilogx(t1, hm1_3, label='ttim PS1')\n", + "plt.semilogx(t2, h2, '.', label='PD1')\n", + "plt.semilogx(t2, hm2_3, label='ttim PD1')\n", + "plt.semilogx(t3, h3, ',', label='PS2')\n", + "plt.semilogx(t3, hm3_3, label='ttim PS2')\n", + "plt.semilogx(t4, h4, '.', label='PD2')\n", + "plt.semilogx(t4, hm4_3, label='ttim PD2');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Increasing the number of layers has not improved the model calibration performance. Let's see how the model behaves with the same parameters from the analytical solution:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7.1. Simulating multi-layered model with Moench's parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 9\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml4 = Model3D(kaq=kaq, z=zlayers, Saq=[Sy]+list(np.repeat(Saq, nlay-1)), \\\n", + " kzoverkh=zh, tmin=1e-5, tmax=3)\n", + "w4 = Well(ml4, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=range(9,18))\n", + "ml4.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hm0_4 = ml4.head(0, 0, t0, layers=16)[0]\n", + "hm1_4 = ml4.head(r1, 0, t1, layers=1)[0]\n", + "hm2_4 = ml4.head(r1, 0, t2, layers=13)[0]\n", + "hm3_4 = ml4.head(r2, 0, t3, layers=1)[0]\n", + "hm4_4 = ml4.head(r2, 0, t4, layers=13)[0]\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(t0, h0, '.', label='pumped')\n", + "plt.semilogx(t0, hm0_4, label='ttim pumped')\n", + "plt.semilogx(t1, h1, '.', label='PS1')\n", + "plt.semilogx(t1, hm1_4, label='ttim PS1')\n", + "plt.semilogx(t2, h2, '.', label='PD1')\n", + "plt.semilogx(t2, hm2_4, label='ttim PD1')\n", + "plt.semilogx(t3, h3, ',', label='PS2')\n", + "plt.semilogx(t3, hm3_4, label='ttim PS2')\n", + "plt.semilogx(t4, h4, '.', label='PD2')\n", + "plt.semilogx(t4, hm4_4, label='ttim PD2');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 7.1.1. Checking RMSE performance improvement" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.013118477167630852\n" + ] + } + ], + "source": [ + "rmse_n = np.sqrt(np.sum(np.sum((h1-hm1_4)**2)+np.sum((h2-hm2_4)**2)+np.sum((h3-hm3_4)**2)+np.sum((h4-hm4_4)**2)+np.sum((h0-hm0_4)**2))/\\\n", + " (len(h1)+len(h2)+len(h3)+len(h4)+len(h0)))\n", + "\n", + "print('RMSE:', rmse_n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8. Summary of and analysis of calibrated values and model errors" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MoenchMoench - more layersTTimTTim - more layers
k[m/d]8.6400008.6400009.0676658.695635
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RMSE0.0613180.0131180.0103840.009766
\n", + "
" + ], + "text/plain": [ + " Moench Moench - more layers TTim TTim - more layers\n", + "k[m/d] 8.640000 8.640000 9.067665 8.695635\n", + "Sy[-] 0.200000 0.200000 0.172876 0.196072\n", + "Ss[1/m] 0.000020 0.000020 0.000039 0.000039\n", + "kz/kh 0.500000 0.500000 0.535049 0.487962\n", + "RMSE 0.061318 0.013118 0.010384 0.009766" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ta = pd.DataFrame(columns=['Moench','Moench - more layers' ,'TTim', 'TTim - more layers'],\\\n", + " index=['k[m/d]', 'Sy[-]', 'Ss[1/m]', 'kz/kh'])\n", + "ta.loc[:, 'TTim - more layers'] = ca3.parameters['optimal'].values\n", + "ta.loc[:, 'TTim'] = ca2.parameters['optimal'].values\n", + "ta.loc[:, 'Moench'] = [kaq, Sy, Saq, zh]\n", + "ta.loc[:, 'Moench - more layers'] = [kaq,Sy, Saq, zh]\n", + "ta.loc['RMSE'] = [rmse,rmse_n, ca2.rmse(), ca3.rmse()]\n", + "ta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The table above shows the TTim model results, with the first two columns showing the model simulated using Moench's parameters (Barlow and Moench, 1999). The first column is the original Model 1, and the second is the last model (Model 4) with more layers. Columns 3 and 4 show the results for the calibration with Conceptual Models 1 and 4.\n", + "\n", + "Overall, the model accuracy improved when we added more layers, so the added complexity resulted in added accuracy. However, when we look at the calibrated data, we see that it did not improve calibration performance, and both the simple model and the more complex one had similar performance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Barlow, P.M., Moench, A.F., 1999. WTAQ, a computer program for calculating drawdowns and estimating hydraulic properties for confined and water-table aquifers. 99-4225, US Dept. of the Interior, US Geological Survey\n", + "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n", + "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Kruseman, G.P., De Ridder, N.A., Verweij, J.M., 1970. Analysis and evaluationof pumping test data. volume 11. International institute for land reclamation and improvement The Netherlands.\n", + "* Moench, Allen, F., 1997. Flow to a well of finite diameter in a homogeneous, anisotropic water table aquifer. Water Resources Research 34, 2431–2432.\n", + "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Next Notebook: [Slug 1 - Pratt County](slug1_pratt_county.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ttim/besselnumba.py b/ttim/besselnumba.py index 1b61582..5202fa9 100644 --- a/ttim/besselnumba.py +++ b/ttim/besselnumba.py @@ -1656,9 +1656,7 @@ def besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): qx = -2.0 / L * (rvz + rvzbar) / biglab qy = -2.0 / L * complex(0, 1) * (rvz - rvzbar) / biglab - qx = qx - 2.0 / L * bigy / biglabcomplex**2 * azero * ( - omegalap + np.conj(omegalap) - ) + qx = qx - 2.0 / L * bigy / biglabcomplex**2 * azero * (omegalap + np.conj(omegalap)) qy = qy - 2.0 / L * bigy / biglabcomplex**2 * azero * complex(0, 1) * ( omegalap - np.conj(omegalap) ) diff --git a/ttim/besselnumba_total.py b/ttim/besselnumba_total.py index 302cef1..7b94a92 100644 --- a/ttim/besselnumba_total.py +++ b/ttim/besselnumba_total.py @@ -2088,9 +2088,7 @@ def besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): qx = -2.0 / L * (rvz + rvzbar) / biglab qy = -2.0 / L * complex(0, 1) * (rvz - rvzbar) / biglab - qx = qx - 2.0 / L * bigy / biglabcomplex**2 * azero * ( - omegalap + np.conj(omegalap) - ) + qx = qx - 2.0 / L * bigy / biglabcomplex**2 * azero * (omegalap + np.conj(omegalap)) qy = qy - 2.0 / L * bigy / biglabcomplex**2 * azero * complex(0, 1) * ( omegalap - np.conj(omegalap) ) diff --git a/ttim/equation.py b/ttim/equation.py index b2eb2d3..80d288e 100644 --- a/ttim/equation.py +++ b/ttim/equation.py @@ -201,7 +201,7 @@ def equation(self): ) mat[ istart : istart + self.nlayers - 1, ieq : ieq + e.nunknowns, : - ] = (head[:-1, :] - head[1:, :]) + ] = head[:-1, :] - head[1:, :] if e == self: for i in range(self.nlayers - 1): mat[istart + i, ieq + istart + i, :] -= ( @@ -271,9 +271,7 @@ def equation(self): istart : istart + self.nlayers - 1, ieq : ieq + e.nunknowns, :, - ] = ( - head[:-1, :] - head[1:, :] - ) + ] = head[:-1, :] - head[1:, :] # Store head in top layer in 2nd to last equation # of this control point mat[istart + self.nlayers - 1, ieq : ieq + e.nunknowns, :] = head[ @@ -400,9 +398,7 @@ def equation(self): :, ] = (qxin - qxout) * np.cos(self.thetacp[icp]) + ( qyin - qyout - ) * np.sin( - self.thetacp[icp] - ) + ) * np.sin(self.thetacp[icp]) ieq += e.nunknowns for i in range(self.model.ngbc): rhs[istart : istart + self.nlayers, i, :] -= ( diff --git a/ttim/model.py b/ttim/model.py index a5de79c..9215d3a 100644 --- a/ttim/model.py +++ b/ttim/model.py @@ -67,7 +67,6 @@ def __init__( self.timmlmodel = timmlmodel if self.timmlmodel is not None: self.timmlmodel.solve() - def __repr__(self): return "Model" diff --git a/ttim/well.py b/ttim/well.py index 7959e10..e3169bd 100644 --- a/ttim/well.py +++ b/ttim/well.py @@ -2,6 +2,7 @@ import matplotlib.pyplot as plt import numpy as np + # from scipy.special import iv # Needed for K1 in Well class, and in CircInhom from scipy.special import kv