Depth2Pressure.ipynb

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    "from xgcm import Grid\n",
    "\n",
    "import numpy as np  # numerical library\n",
    "import xarray as xr  # netCDF library\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt  # plotting library\n",
    "import cartopy  # Map projections libary\n",
    "import cartopy.crs as ccrs  # Projections list\n",
    "import cftime\n",
    "\n",
    "import seawater as sw"
   ]
  },
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    "sw.pres(np.arange(10,1900,10),24.5)"
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       "Data variables:\n",
       "    phi      (z) float64 80B 2.148 1.901 1.535 1.587 ... 1.502 1.283 0.5689\n",
       "    theta    (z) float64 80B 0.6931 1.099 1.386 1.609 ... 2.197 2.303 2.398</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9e34e536-962e-4ffa-99cf-0895cd5e2d2a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9e34e536-962e-4ffa-99cf-0895cd5e2d2a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>z</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-75b1d4fb-6612-44c3-9a9b-6e28c908519f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-75b1d4fb-6612-44c3-9a9b-6e28c908519f' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 3 4 5 6 7 8 9 10 11</div><input id='attrs-4a9ae93c-b6a3-4176-9427-bd7216eb839d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a9ae93c-b6a3-4176-9427-bd7216eb839d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58093e5e-79c4-441a-9879-9bb6c765c598' class='xr-var-data-in' type='checkbox'><label for='data-58093e5e-79c4-441a-9879-9bb6c765c598' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46d0e66c-301d-4428-86f7-f00e918d6cc6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-46d0e66c-301d-4428-86f7-f00e918d6cc6' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>phi</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.148 1.901 1.535 ... 1.283 0.5689</div><input id='attrs-44db13ba-45cd-4684-9b71-ca07aabe301c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44db13ba-45cd-4684-9b71-ca07aabe301c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8eeb9e09-36a6-4671-b260-f43f5a1b1dc6' class='xr-var-data-in' type='checkbox'><label for='data-8eeb9e09-36a6-4671-b260-f43f5a1b1dc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2.14821375, 1.90059436, 1.53549853, 1.5865849 , 1.68497524,\n",
       "       1.5060338 , 1.08118427, 1.5020866 , 1.28261326, 0.5689208 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>theta</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6931 1.099 1.386 ... 2.303 2.398</div><input id='attrs-f82b18b8-e0e6-497d-be8f-627ad085b9b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f82b18b8-e0e6-497d-be8f-627ad085b9b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0f4ad56-ae01-475d-9960-10434d6661d8' class='xr-var-data-in' type='checkbox'><label for='data-d0f4ad56-ae01-475d-9960-10434d6661d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.69314718, 1.09861229, 1.38629436, 1.60943791, 1.79175947,\n",
       "       1.94591015, 2.07944154, 2.19722458, 2.30258509, 2.39789527])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-041334b3-364e-440b-82a3-6945bc89f6aa' class='xr-section-summary-in' type='checkbox'  ><label for='section-041334b3-364e-440b-82a3-6945bc89f6aa' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>z</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ebd468ea-fbb9-41f0-abbd-8d816420a9f2' class='xr-index-data-in' type='checkbox'/><label for='index-ebd468ea-fbb9-41f0-abbd-8d816420a9f2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2, 3, 4, 5, 6, 7, 8, 9, 10, 11], dtype=&#x27;int64&#x27;, name=&#x27;z&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4b723a5c-ee88-4e8a-be86-07e59acd62c2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4b723a5c-ee88-4e8a-be86-07e59acd62c2' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 240B\n",
       "Dimensions:  (z: 10)\n",
       "Coordinates:\n",
       "  * z        (z) int64 80B 2 3 4 5 6 7 8 9 10 11\n",
       "Data variables:\n",
       "    phi      (z) float64 80B 2.148 1.901 1.535 1.587 ... 1.502 1.283 0.5689\n",
       "    theta    (z) float64 80B 0.6931 1.099 1.386 1.609 ... 2.197 2.303 2.398"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = np.arange(2, 12)\n",
    "theta = xr.DataArray(np.log(z), dims=['z'], coords={'z': z})\n",
    "phi = xr.DataArray(np.flip(np.log(z)*0.5+ np.random.rand(len(z))), dims=['z'], coords={'z':z})\n",
    "ds = xr.Dataset({'phi': phi, 'theta': theta})\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "346b5025-5a34-4166-9dfc-1329e7d7811b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_profile():\n",
    "    fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=[8,5])\n",
    "    ds.theta.plot(ax=ax1, y='z', marker='.', yincrease=False)\n",
    "    ds.phi.plot(ax=ax2, y='z', marker='.', yincrease=False)\n",
    "    ds.swap_dims({'z': 'theta'}).phi.plot(ax=ax3, y='theta', marker='.', yincrease=False)\n",
    "    fig.subplots_adjust(wspace=0.5)\n",
    "    return ax3\n",
    "\n",
    "plot_profile();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b9da5461-e611-429d-bda9-9a58739368e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First create an xgcm grid object\n",
    "grid = Grid(ds, coords={'Z': {'center':'z'}}, periodic=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "288148bc-0f75-48ba-9c23-d97c88acd405",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the target values in density, linearly spaced\n",
    "theta_target = np.linspace(0, 3, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8e85d07b-6d51-4892-b6a6-e4e7716d004a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pvb/miniconda3/envs/ocean/lib/python3.11/site-packages/xgcm/grid.py:989: FutureWarning: From version 0.8.0 the Axis computation methods will be removed, in favour of using the Grid computation methods instead. i.e. use `Grid.transform` instead of `Axis.transform`\n",
      "  warnings.warn(\n"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;phi&#x27; (theta: 20)&gt; Size: 160B\n",
       "array([       nan,        nan,        nan,        nan,        nan,\n",
       "       2.08938672, 1.99295968, 1.89215378, 1.69177038, 1.54345607,\n",
       "       1.57960441, 1.65533893, 1.56543688, 1.16648406, 1.47437815,\n",
       "       0.7896268 ,        nan,        nan,        nan,        nan])\n",
       "Coordinates:\n",
       "  * theta    (theta) float64 160B 0.0 0.1579 0.3158 0.4737 ... 2.684 2.842 3.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'phi'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>theta</span>: 20</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-406a32c3-1c9d-4fd4-a920-1812dd347bf4' class='xr-array-in' type='checkbox' checked><label for='section-406a32c3-1c9d-4fd4-a920-1812dd347bf4' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan 2.089 1.993 ... 1.166 1.474 0.7896 nan nan nan nan</span></div><div class='xr-array-data'><pre>array([       nan,        nan,        nan,        nan,        nan,\n",
       "       2.08938672, 1.99295968, 1.89215378, 1.69177038, 1.54345607,\n",
       "       1.57960441, 1.65533893, 1.56543688, 1.16648406, 1.47437815,\n",
       "       0.7896268 ,        nan,        nan,        nan,        nan])</pre></div></div></li><li class='xr-section-item'><input id='section-cfef4a26-fc8b-46f8-ab22-97984369700b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cfef4a26-fc8b-46f8-ab22-97984369700b' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>theta</span></div><div class='xr-var-dims'>(theta)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.1579 0.3158 ... 2.842 3.0</div><input id='attrs-1c616622-cc14-4502-b4aa-5cd363c34a7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c616622-cc14-4502-b4aa-5cd363c34a7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cd39c39-6473-47f0-8454-97c064f4c3b4' class='xr-var-data-in' type='checkbox'><label for='data-0cd39c39-6473-47f0-8454-97c064f4c3b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.      , 0.157895, 0.315789, 0.473684, 0.631579, 0.789474, 0.947368,\n",
       "       1.105263, 1.263158, 1.421053, 1.578947, 1.736842, 1.894737, 2.052632,\n",
       "       2.210526, 2.368421, 2.526316, 2.684211, 2.842105, 3.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-780923c1-e401-492a-87ad-c4472b8a9383' class='xr-section-summary-in' type='checkbox'  ><label for='section-780923c1-e401-492a-87ad-c4472b8a9383' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>theta</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-95a4d445-2758-4b98-ad77-189500e79b37' class='xr-index-data-in' type='checkbox'/><label for='index-95a4d445-2758-4b98-ad77-189500e79b37' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                0.0, 0.15789473684210525,  0.3157894736842105,\n",
       "       0.47368421052631576,   0.631578947368421,  0.7894736842105263,\n",
       "        0.9473684210526315,  1.1052631578947367,   1.263157894736842,\n",
       "        1.4210526315789473,  1.5789473684210527,  1.7368421052631577,\n",
       "         1.894736842105263,   2.052631578947368,  2.2105263157894735,\n",
       "        2.3684210526315788,   2.526315789473684,  2.6842105263157894,\n",
       "        2.8421052631578947,                 3.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;theta&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4104df18-78ab-4b4b-b0c9-17d6d839246b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4104df18-78ab-4b4b-b0c9-17d6d839246b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'phi' (theta: 20)> Size: 160B\n",
       "array([       nan,        nan,        nan,        nan,        nan,\n",
       "       2.08938672, 1.99295968, 1.89215378, 1.69177038, 1.54345607,\n",
       "       1.57960441, 1.65533893, 1.56543688, 1.16648406, 1.47437815,\n",
       "       0.7896268 ,        nan,        nan,        nan,        nan])\n",
       "Coordinates:\n",
       "  * theta    (theta) float64 160B 0.0 0.1579 0.3158 0.4737 ... 2.684 2.842 3.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and transform\n",
    "phi_transformed = grid.transform(ds.phi, 'Z', theta_target, target_data=ds.theta)\n",
    "phi_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "35b7753b-2ed4-45ba-b6fa-83965811cc3c",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;phi&#x27; (theta: 20)&gt; Size: 160B\n",
       "array([       nan,        nan,        nan,        nan,        nan,\n",
       "       2.08938672, 1.99295968, 1.89215378, 1.69177038, 1.54345607,\n",
       "       1.57960441, 1.65533893, 1.56543688, 1.16648406, 1.47437815,\n",
       "       0.7896268 ,        nan,        nan,        nan,        nan])\n",
       "Coordinates:\n",
       "  * theta    (theta) float64 160B 0.0 0.1579 0.3158 0.4737 ... 2.684 2.842 3.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'phi'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>theta</span>: 20</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ecd5bde2-40f7-4694-8d5c-e7c12bab1bce' class='xr-array-in' type='checkbox' checked><label for='section-ecd5bde2-40f7-4694-8d5c-e7c12bab1bce' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan 2.089 1.993 ... 1.166 1.474 0.7896 nan nan nan nan</span></div><div class='xr-array-data'><pre>array([       nan,        nan,        nan,        nan,        nan,\n",
       "       2.08938672, 1.99295968, 1.89215378, 1.69177038, 1.54345607,\n",
       "       1.57960441, 1.65533893, 1.56543688, 1.16648406, 1.47437815,\n",
       "       0.7896268 ,        nan,        nan,        nan,        nan])</pre></div></div></li><li class='xr-section-item'><input id='section-13c67e97-b994-4ac6-b955-1e3c81d8b5bd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-13c67e97-b994-4ac6-b955-1e3c81d8b5bd' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>theta</span></div><div class='xr-var-dims'>(theta)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.1579 0.3158 ... 2.842 3.0</div><input id='attrs-0541913c-e14f-4ed8-b720-8dc84002f5d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0541913c-e14f-4ed8-b720-8dc84002f5d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1a9dbc1-a5db-4302-b47f-c34d2201fab8' class='xr-var-data-in' type='checkbox'><label for='data-e1a9dbc1-a5db-4302-b47f-c34d2201fab8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.      , 0.157895, 0.315789, 0.473684, 0.631579, 0.789474, 0.947368,\n",
       "       1.105263, 1.263158, 1.421053, 1.578947, 1.736842, 1.894737, 2.052632,\n",
       "       2.210526, 2.368421, 2.526316, 2.684211, 2.842105, 3.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6d7dcdde-ca2c-4586-b8c4-026e5f5b03ed' class='xr-section-summary-in' type='checkbox'  ><label for='section-6d7dcdde-ca2c-4586-b8c4-026e5f5b03ed' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>theta</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2d9024a8-f47d-47c9-a50f-73164a88eb3c' class='xr-index-data-in' type='checkbox'/><label for='index-2d9024a8-f47d-47c9-a50f-73164a88eb3c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                0.0, 0.15789473684210525,  0.3157894736842105,\n",
       "       0.47368421052631576,   0.631578947368421,  0.7894736842105263,\n",
       "        0.9473684210526315,  1.1052631578947367,   1.263157894736842,\n",
       "        1.4210526315789473,  1.5789473684210527,  1.7368421052631577,\n",
       "         1.894736842105263,   2.052631578947368,  2.2105263157894735,\n",
       "        2.3684210526315788,   2.526315789473684,  2.6842105263157894,\n",
       "        2.8421052631578947,                 3.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;theta&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-de618e0f-c431-4464-89ee-9fedff5f0f3c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-de618e0f-c431-4464-89ee-9fedff5f0f3c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'phi' (theta: 20)> Size: 160B\n",
       "array([       nan,        nan,        nan,        nan,        nan,\n",
       "       2.08938672, 1.99295968, 1.89215378, 1.69177038, 1.54345607,\n",
       "       1.57960441, 1.65533893, 1.56543688, 1.16648406, 1.47437815,\n",
       "       0.7896268 ,        nan,        nan,        nan,        nan])\n",
       "Coordinates:\n",
       "  * theta    (theta) float64 160B 0.0 0.1579 0.3158 0.4737 ... 2.684 2.842 3.0"
      ]
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     "execution_count": 9,
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",
      "text/plain": [
       "<Figure size 800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plot_profile()\n",
    "phi_transformed.plot(ax=ax, y='theta', marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e5c5bca-71e1-4d60-8287-3a2b80e2cd38",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}