[skip ci] Updates

_downloads/05dcb96dde29aaeb57cb05530a922d73/example_schism_native.ipynb

@@ -15,61 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom datetime import timedelta, datetime\nfrom opendrift.readers import reader_schism_native\nfrom opendrift.readers import reader_global_landmask\nfrom opendrift.models.oceandrift import OceanDrift"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## MODEL\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "o = OceanDrift(loglevel=20)  # Set loglevel to 0 for debug information"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## READERS\n Creating and adding reader using a native SCHISM netcdf output file\n SCHISM reader\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "reader_landmask = reader_global_landmask.Reader()\n\n# NZTM proj4 string found at https://spatialreference.org/ref/epsg/nzgd2000-new-zealand-transverse-mercator-2000/\nproj4str_nztm = '+proj=tmerc +lat_0=0 +lon_0=173 +k=0.9996 +x_0=1600000 +y_0=10000000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs'\nschism_native = reader_schism_native.Reader(\n\tfilename = 'https://thredds.met.no/thredds/dodsC/metusers/knutfd/thredds/netcdf_unstructured_samples/schism_marl20080101_00z_3D.nc',\n\tproj4 = proj4str_nztm,\n\tuse_3d = True)\n# schism_native.plot_mesh(variable = ['sea_floor_depth_below_sea_level']) # check reader was correctly loaded\n\no.add_reader([reader_landmask,schism_native])\no.set_config('general:use_auto_landmask', False) # prevent opendrift from making a new dynamical landmask with global_landmask\n\n# Seed elements at defined positions, depth and time\no.seed_elements(lon=174.046669, lat=-40.928116, radius=20, number=100,\n                z=np.linspace(0,-10, 100), time=schism_native.start_time)\n\no.seed_elements(lon= 173.8839, lat=-40.9160, radius=20, number=100,\n                z=np.linspace(0,-10, 100), time=schism_native.start_time)\n\no.seed_elements(lon=174.2940, lat=-41.0888, radius=20, number=100,\n                z=np.linspace(0,-10, 100), time=schism_native.start_time)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Running model\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "o.run(time_step=900,\n\t  end_time = schism_native.start_time+timedelta(days=0.1))\n\t  # outfile='schism_native_output.nc')\n\n# Print and plot results\nprint(o)\no.plot(fast=True)\no.animation()\no.animation_profile()"
+        "import numpy as np\nfrom datetime import timedelta, datetime\nfrom opendrift.readers import reader_schism_native\nfrom opendrift.readers import reader_global_landmask\nfrom opendrift.models.oceandrift import OceanDrift\n\no = OceanDrift(loglevel=20)  # Set loglevel to 0 for debug information\n\n\ntry:\n    # NZTM proj4 string found at https://spatialreference.org/ref/epsg/nzgd2000-new-zealand-transverse-mercator-2000/\n    proj4str_nztm = '+proj=tmerc +lat_0=0 +lon_0=173 +k=0.9996 +x_0=1600000 +y_0=10000000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs'\n    schism_native = reader_schism_native.Reader(\n        filename = 'https://thredds.met.no/thredds/dodsC/metusers/knutfd/thredds/netcdf_unstructured_samples/schism_marl20080101_00z_3D.nc',\n        proj4 = proj4str_nztm,\n        use_3d = True)\nexcept:\n    print('Thredds server not available, cannot run example')\n    schism_native = None\n\n\nif schism_native is not None:\n    o.add_reader([schism_native])\n    o.set_config('general:use_auto_landmask', False) # prevent opendrift from making a new dynamical landmask with global_landmask\n\n    # Seed elements at defined positions, depth and time\n    o.seed_elements(lon=174.046669, lat=-40.928116, radius=20, number=100,\n                    z=np.linspace(0,-10, 100), time=schism_native.start_time)\n\n    o.seed_elements(lon= 173.8839, lat=-40.9160, radius=20, number=100,\n                    z=np.linspace(0,-10, 100), time=schism_native.start_time)\n\n    o.seed_elements(lon=174.2940, lat=-41.0888, radius=20, number=100,\n                    z=np.linspace(0,-10, 100), time=schism_native.start_time)\n\n    #%%\n    # Running model\n    o.run(time_step=900,\n          end_time = schism_native.start_time+timedelta(days=0.1))\n          # outfile='schism_native_output.nc')\n\n    # Print and plot results\n    print(o)\n    o.plot(fast=True)\n    o.animation()\n    o.animation_profile()"
       ]
     }
   ],

_downloads/0bb1828d1f8a83156cdd5b8df3b17496/example_oil_verticalmixing.ipynb

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import os\nfrom datetime import timedelta\nfrom opendrift.readers import reader_netCDF_CF_generic\nfrom opendrift.models.openoil import OpenOil\n\n\no = OpenOil(loglevel=20)  # Set loglevel to 0 for debug information\n\nncfile = 'oilmixing.nc'\nimport_file = False  # Set to True to import previous run\n\nif import_file is True:\n    o.io_import_file(ncfile)\nelse:\n    reader_arome = reader_netCDF_CF_generic.Reader('https://thredds.met.no/thredds/dodsC/mepslatest/meps_lagged_6_h_latest_2_5km_latest.nc')\n    reader_norkyst = reader_netCDF_CF_generic.Reader('https://thredds.met.no/thredds/dodsC/sea/norkyst800m/1h/aggregate_be')\n\n    o.add_reader([reader_norkyst, reader_arome])\n\n    # Adjusting some configuration\n    #o.set_config('vertical_mixing:diffusivitymodel', 'windspeed_Sundby1983')\n\n    # Seed oil elements at defined position and time\n    o.seed_elements(lon=4.9, lat=62.1, z=0, radius=1000, number=2000,\n                    time=reader_arome.start_time)\n\n    # Running model\n    o.run(end_time=reader_arome.start_time + timedelta(hours=12),\n          time_step=900, time_step_output=1800, outfile=ncfile)"
+        "import os\nfrom datetime import timedelta\nfrom opendrift.readers import reader_netCDF_CF_generic\nfrom opendrift.models.openoil import OpenOil\n\n\no = OpenOil(loglevel=20)  # Set loglevel to 0 for debug information\n\nncfile = 'oilmixing.nc'\nimport_file = False  # Set to True to import previous run\n\nif import_file is True:\n    o.io_import_file(ncfile)\nelse:\n    reader_arome = reader_netCDF_CF_generic.Reader('https://thredds.met.no/thredds/dodsC/mepslatest/meps_lagged_6_h_latest_2_5km_latest.nc')\n    reader_norkyst = reader_netCDF_CF_generic.Reader('https://thredds.met.no/thredds/dodsC/sea/norkyst800m/1h/aggregate_be')\n\n    o.add_reader([reader_norkyst, reader_arome])\n\n    # Adjusting some configuration\n    #o.set_config('vertical_mixing:diffusivitymodel', 'windspeed_Sundby1983')\n\n    # Seed oil elements at defined position and time\n    o.seed_elements(lon=4.9, lat=62.1, z=0, radius=1000, number=5000,\n                    time=reader_arome.start_time, oil_type='GENERIC DIESEL')\n\n    # Running model\n    o.run(end_time=reader_arome.start_time + timedelta(hours=12),\n          time_step=900, time_step_output=1800, outfile=ncfile)"
       ]
     },
     {

_downloads/21aec2838f082c0630d5ae182d739dec/example_long_leeway_backtrack.py

@@ -21,7 +21,7 @@
 # 2. forwards simulation from a uniform grid of possible initial locations, selecting the origins of particles actually hitting the observed target
 #
 # We use 24 hours from the NorKyst ocean model (800m pixel size) and Arome atmospheric model (2.5km pixel size)
-o = Leeway(loglevel=50)
+o = Leeway(loglevel=10)
 reader_arome = reader_netCDF_CF_generic.Reader(o.test_data_folder() +
     '16Nov2015_NorKyst_z_surface/arome_subset_16Nov2015.nc')
 reader_norkyst = reader_netCDF_CF_generic.Reader(o.test_data_folder() +
@@ -47,7 +47,7 @@
 
 #%%
 # Simulating first backwards for 24 hours:
-o.seed_elements(lon=ilon, lat=ilat, radius=5000, radius_type='uniform', number=30000,
+o.seed_elements(lon=ilon, lat=ilat, radius=5000, radius_type='uniform', number=10000,
                  time=end_time, object_type=object_type)
 o.run(duration=duration, time_step=-900, time_step_output=3600, outfile=outfile)
 #od = opendrift.open_xarray(outfile)
@@ -62,7 +62,7 @@
 
 #%%
 # Simulating then forwards, starting at a uniform grid 24 hours earlier (440 x 320 = 140800 elements at ~500m separation)
-o = Leeway(loglevel=50)
+o = Leeway(loglevel=10)
 o.add_reader([reader_norkyst, reader_arome])
 o.seed_elements(lon=lons, lat=lats, radius=0,
                 time=start_time, object_type=object_type)
@@ -71,7 +71,8 @@
 
 #%%
 # Finding the elements actually hitting the target (within 5 km) after 24 hours:
-lon, lat = o.get_lonlats()
+lon = o.result.lon
+lat = o.result.lat
 lonend = lon[:, -1]
 latend = lat[:, -1]
 geod = pyproj.Geod(ellps='WGS84')

_downloads/25515e713591ae533f37ccd0977d382a/example_oil_verticalmixing.py

@@ -27,8 +27,8 @@
     #o.set_config('vertical_mixing:diffusivitymodel', 'windspeed_Sundby1983')
 
     # Seed oil elements at defined position and time
-    o.seed_elements(lon=4.9, lat=62.1, z=0, radius=1000, number=2000,
-                    time=reader_arome.start_time)
+    o.seed_elements(lon=4.9, lat=62.1, z=0, radius=1000, number=5000,
+                    time=reader_arome.start_time, oil_type='GENERIC DIESEL')
 
     # Running model
     o.run(end_time=reader_arome.start_time + timedelta(hours=12),

_downloads/328bb9a2268c424591c715d1fd64b02c/example_wind_measurements.ipynb

@@ -148,7 +148,7 @@
       },
       "outputs": [],
       "source": [
-        "radius = 100\nnumber = 100\n  \no = OceanDrift(loglevel=20)\no.add_reader([reader_norkyst])\no.seed_elements(lon=lon, lat=lat, radius=radius, number=number, time=time_start, wind_drift_factor=wind_drift_factor)\no.run(end_time=time_end, time_step=time_step, time_step_output=time_step_output)\n\no2 = OceanDrift(loglevel=20)\no2.set_config('drift:max_speed', 10) #Necessary for the moment because of a bug to be solved\no2.add_reader([c, reader_norkyst])\no2.seed_elements(lon=lon, lat=lat, radius=radius, number=number, time=time_start, wind_drift_factor=wind_drift_factor)\no2.run(end_time=time_end, time_step=time_step, time_step_output=time_step_output)\n\no.plot(fast = False, compare=o2, background=['x_sea_water_velocity', 'y_sea_water_velocity'], legend=['Norkyst only', 'Gaussian measurement'], buffer = .023, markersize = 70, linewidth = 1, title = \"\", xlocs = mpl.ticker.MaxNLocator(5), ylocs = mpl.ticker.MaxNLocator(5), clabel = r\"Wind speed $\\mathrm{(m.s^{-1})}$\", cpad = 0.08, text=text)\n\nlon_drifters_1, lat_drifters_1 = o.get_lonlats()\nlon_drifters_2, lat_drifters_2 = o2.get_lonlats()\n\nskillscores = phy.skillscore_liu_weissberg(lon_drifters_1, lat_drifters_1, lon_drifters_2, lat_drifters_2)\n\nplt.figure()\nplt.hist(skillscores, bins = 100, range = (0, 1), facecolor = 'none', edgecolor = 'C0')\nplt.xlabel(\"Skillscore\")\nplt.ylabel(\"Histogram (percentages)\")\nplt.show()\n\nplt.figure()\nplt.hist(skillscores, bins = 100, range = (0.9, 0.95), facecolor = 'none', edgecolor = 'C0')\nplt.xlabel(\"Skillscore\")\nplt.ylabel(\"Histogram (percentages)\")\nplt.show()"
+        "radius = 100\nnumber = 100\n  \no = OceanDrift(loglevel=20)\no.add_reader([reader_norkyst])\no.seed_elements(lon=lon, lat=lat, radius=radius, number=number, time=time_start, wind_drift_factor=wind_drift_factor)\no.run(end_time=time_end, time_step=time_step, time_step_output=time_step_output)\n\no2 = OceanDrift(loglevel=20)\no2.set_config('drift:max_speed', 10) #Necessary for the moment because of a bug to be solved\no2.add_reader([c, reader_norkyst])\no2.seed_elements(lon=lon, lat=lat, radius=radius, number=number, time=time_start, wind_drift_factor=wind_drift_factor)\no2.run(end_time=time_end, time_step=time_step, time_step_output=time_step_output)\n\no.plot(fast = False, compare=o2, background=['x_sea_water_velocity', 'y_sea_water_velocity'], legend=['Norkyst only', 'Gaussian measurement'], buffer = .023, markersize = 70, linewidth = 1, title = \"\", xlocs = mpl.ticker.MaxNLocator(5), ylocs = mpl.ticker.MaxNLocator(5), clabel = r\"Wind speed $\\mathrm{(m.s^{-1})}$\", cpad = 0.08, text=text)\n\nskillscores = phy.skillscore_liu_weissberg(o.result.lon, o.result.lat, o2.result.lon, o2.result.lat)\n\nplt.figure()\nplt.hist(skillscores, bins = 100, range = (0, 1), facecolor = 'none', edgecolor = 'C0')\nplt.xlabel(\"Skillscore\")\nplt.ylabel(\"Histogram (percentages)\")\nplt.show()\n\nplt.figure()\nplt.hist(skillscores, bins = 100, range = (0.9, 0.95), facecolor = 'none', edgecolor = 'C0')\nplt.xlabel(\"Skillscore\")\nplt.ylabel(\"Histogram (percentages)\")\nplt.show()"
       ]
     }
   ],

_downloads/394bf5bf20f6403b6bab22cfd062a14e/example_chemicaldrift.ipynb

@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "print('Final speciation:')\nfor isp,sp in enumerate(o.name_species):\n    print ('{:32}: {:>6}'.format(sp,sum(o.elements.specie==isp)))\n\nprint('Number of transformations:')\nfor isp in range(o.nspecies):\n    print('{}'.format(['{:>9}'.format(np.int32(item)) for item in o.ntransformations[isp,:]]) )\n\nmass = o.get_property('mass')\nmass_d = o.get_property('mass_degraded')\nmass_v = o.get_property('mass_volatilized')\n\nm_pre = sum(mass[0][-1])\nm_deg = sum(mass_d[0][-1])\nm_vol = sum(mass_v[0][-1])\nm_tot = m_pre + m_deg + m_vol\n\nprint('Mass budget for target chemical:')\nprint('mass preserved       : {:.3f}'.format(m_pre * 1e-6),' g   {:.3f}'.format(m_pre/m_tot*100),'%')\nprint('mass degraded        : {:.3f}'.format(m_deg * 1e-6),' g   {:.3f}'.format(m_deg/m_tot*100),'%')\nprint('mass volatilized     : {:.3f}'.format(m_vol * 1e-6),' g   {:.3f}'.format(m_vol/m_tot*100),'%')\n\nlegend=['dissolved', '', 'SPM', 'sediment', '']\n\no.animation_profile(color='specie',\n            markersize='mass',\n            markersize_scaling=30,\n            alpha=.5,\n            vmin=0,vmax=o.nspecies-1,\n            legend = legend,\n            legend_loc = 3,\n            fps = 10\n            )"
+        "print('Final speciation:')\nfor isp,sp in enumerate(o.name_species):\n    print ('{:32}: {:>6}'.format(sp,sum(o.elements.specie==isp)))\n\nprint('Number of transformations:')\nfor isp in range(o.nspecies):\n    print('{}'.format(['{:>9}'.format(np.int32(item)) for item in o.ntransformations[isp,:]]) )\n\nm_pre = o.result.mass.isel(time=-1).sum()\nm_deg = o.result.mass_degraded.isel(time=-1).sum()\nm_vol = o.result.mass_volatilized.isel(time=-1).sum()\nm_tot = m_pre + m_deg + m_vol\n\nprint('Mass budget for target chemical:')\nprint('mass preserved       : {:.3f}'.format(m_pre * 1e-6),' g   {:.3f}'.format(m_pre/m_tot*100),'%')\nprint('mass degraded        : {:.3f}'.format(m_deg * 1e-6),' g   {:.3f}'.format(m_deg/m_tot*100),'%')\nprint('mass volatilized     : {:.3f}'.format(m_vol * 1e-6),' g   {:.3f}'.format(m_vol/m_tot*100),'%')\n\nlegend=['dissolved', '', 'SPM', 'sediment', '']\n\no.animation_profile(color='specie',\n            markersize='mass',\n            markersize_scaling=30,\n            alpha=.5,\n            vmin=0,vmax=o.nspecies-1,\n            legend = legend,\n            legend_loc = 3,\n            fps = 10\n            )"
       ]
     },
     {

_downloads/3ed36683e33b65bb2398736d832ea232/example_oilspill_seafloor_biodegradation.ipynb

@@ -159,7 +159,7 @@
       },
       "outputs": [],
       "source": [
-        "b = o.get_oil_budget()\nimport matplotlib.pyplot as plt\ntime, time_relative = o.get_time_array()\ntime = np.array([t.total_seconds() / 3600. for t in time_relative])\nfig, ax = plt.subplots()\nax.plot(time, b['mass_submerged'], label='Submerged oil mass')\nax.plot(time, b['mass_surface'], label='Surface oil mass')\nax.plot(time, b['mass_biodegraded'], label='Biodegraded oil mass')\nax.set_title(f'{o.get_oil_name()},  {b[\"oil_density\"].max():.2f} kg/m3')\nplt.legend()\nplt.xlabel('Time [hours]')\nplt.ylabel('Mass oil [kg]')\nplt.show()"
+        "b = o.get_oil_budget()\nimport matplotlib.pyplot as plt\ntime = (o.result.time-o.result.time[0]).dt.total_seconds()/3600  # Hours since start\nfig, ax = plt.subplots()\nax.plot(time, b['mass_submerged'], label='Submerged oil mass')\nax.plot(time, b['mass_surface'], label='Surface oil mass')\nax.plot(time, b['mass_biodegraded'], label='Biodegraded oil mass')\nax.set_title(f'{o.get_oil_name()},  {b[\"oil_density\"].max():.2f} kg/m3')\nplt.legend()\nplt.xlabel('Time [hours]')\nplt.ylabel('Mass oil [kg]')\nplt.show()"
       ]
     },
     {

_downloads/5b1fee5ef5de914da20853ebfde0ca4b/example_fvcom.ipynb

@@ -15,61 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import timedelta\nimport urllib.request as urllib_request\nimport numpy as np\nfrom opendrift.readers import reader_netCDF_CF_unstructured\nfrom opendrift.readers import reader_global_landmask\nfrom opendrift.models.oceandrift import OceanDrift\n\no = OceanDrift(loglevel=20)  # Set loglevel to 0 for debug information\n\nproj = \"+proj=utm +zone=33 +north +ellps=WGS84 +datum=WGS84 +units=m +no_defs\"\nfvcom = reader_netCDF_CF_unstructured.Reader(filename = 'https://thredds.met.no/thredds/dodsC/metusers/knutfd/thredds/netcdf_unstructured_samples/AkvaplanNiva_sample_lonlat_fixed.nc', proj4 = proj)\no.add_reader(fvcom)\nprint(fvcom)\n\n# Seed elements at defined positions, depth and time\nN = 1000\nz = -10*np.random.uniform(0, 1, N)\no.seed_elements(lon=18.0, lat=69.8, radius=2000, number=N,\n                z=z, time=fvcom.start_time)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Running model\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "o.run(time_step=1800, duration=timedelta(hours=12))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Print and plot results\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "print(o)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Animation (current as background not yet working).\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "o.animation(color='z')"
+        "from datetime import timedelta\nimport urllib.request as urllib_request\nimport numpy as np\nfrom opendrift.readers import reader_netCDF_CF_unstructured\nfrom opendrift.readers import reader_global_landmask\nfrom opendrift.models.oceandrift import OceanDrift\n\no = OceanDrift(loglevel=20)  # Set loglevel to 0 for debug information\n\ntry:\n    proj = \"+proj=utm +zone=33 +north +ellps=WGS84 +datum=WGS84 +units=m +no_defs\"\n    fvcom = reader_netCDF_CF_unstructured.Reader(filename = 'https://thredds.met.no/thredds/dodsC/metusers/knutfd/thredds/netcdf_unstructured_samples/AkvaplanNiva_sample_lonlat_fixed.nc', proj4 = proj)\n    o.add_reader(fvcom)\n    print(fvcom)\nexcept:\n    print('Thredds server not available, cannot run example')\n    fvcom = None\n\nif fvcom is not None:\n    # Seed elements at defined positions, depth and time\n    N = 1000\n    z = -10*np.random.uniform(0, 1, N)\n    o.seed_elements(lon=18.0, lat=69.8, radius=2000, number=N,\n                    z=z, time=fvcom.start_time)\n\n    #%%\n    # Running model\n    o.run(time_step=1800, duration=timedelta(hours=12))\n\n    #%%\n    # Print and plot results\n    print(o)\n\n    #%%\n    # Animation (current as background not yet working).\n    o.animation(color='z')\n\n    o.plot(fast=True, buffer = 1.)"
       ]
     },
     {
@@ -78,17 +24,6 @@
       "source": [
         "<img src=\"file://gallery/animations/example_fvcom_0.gif\">\n\n"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "o.plot(fast=True, buffer = 1.)"
-      ]
     }
   ],
   "metadata": {