PACE_Argo_boxplots.ipynb

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    "import math, pylab, csv\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from datetime import date\n",
    "from itertools import groupby\n",
    "from collections import Counter\n",
    "from matplotlib.path import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import earthaccess\n",
    "import xarray as xr\n",
    "from xarray.backends.api import open_datatree\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import numpy as np\n",
    "#%matplotlib widget\n",
    "from scipy.ndimage import generic_filter\n",
    "from scipy.ndimage import gaussian_filter\n",
    "from scipy.interpolate import griddata\n",
    "import seaborn as sns \n",
    "import pandas"
   ]
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    "# %matplotlib widget"
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    "fontsize = 20\n",
    "\n",
    "plt.rc('font', size=fontsize)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=fontsize)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=fontsize)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=fontsize)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=fontsize)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=fontsize)    # legend fontsize\n",
    "plt.rc('figure', titlesize=fontsize)  # fontsize of the figure title"
   ]
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 202kB\n",
       "Dimensions:                        (obs: 223, NbSample: 20)\n",
       "Dimensions without coordinates: obs, NbSample\n",
       "Data variables: (12/27)\n",
       "    amplitude                      (obs) float64 2kB ...\n",
       "    effective_area                 (obs) float32 892B ...\n",
       "    effective_contour_height       (obs) float32 892B ...\n",
       "    effective_contour_latitude     (obs, NbSample) float64 36kB ...\n",
       "    effective_contour_longitude    (obs, NbSample) float64 36kB ...\n",
       "    effective_contour_shape_error  (obs) float64 2kB ...\n",
       "    ...                             ...\n",
       "    speed_contour_longitude        (obs, NbSample) float64 36kB ...\n",
       "    speed_contour_shape_error      (obs) float64 2kB ...\n",
       "    speed_radius                   (obs) float64 2kB ...\n",
       "    time                           (obs) datetime64[ns] 2kB ...\n",
       "    track                          (obs) uint32 892B ...\n",
       "    uavg_profile                   (obs, NbSample) float64 36kB ...\n",
       "Attributes: (12/19)\n",
       "    Metadata_Conventions:      Unidata Dataset Discovery v1.0\n",
       "    comment:                   Surface product; mesoscale eddies\n",
       "    creator_email:             aviso@altimetry.fr\n",
       "    creator_url:               https://www.aviso.altimetry.fr\n",
       "    date_created:              2024-08-06T09:38:06Z\n",
       "    framework_used:            https://github.com/AntSimi/py-eddy-tracker\n",
       "    ...                        ...\n",
       "    standard_name_vocabulary:  NetCDF Climate and Forecast (CF) Metadata Conv...\n",
       "    summary:                   This dataset contains eddy atlas from all-sate...\n",
       "    time_coverage_duration:    P2396D\n",
       "    time_coverage_end:         2024-07-23T00:00:00Z\n",
       "    time_coverage_start:       2018-01-01T00:00:00Z\n",
       "    title:                     Mesoscale Cyclonic Eddies in Altimeter Observa...</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-4fb8c585-0cc4-4b68-945c-94e7734c56a9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4fb8c585-0cc4-4b68-945c-94e7734c56a9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>obs</span>: 223</li><li><span>NbSample</span>: 20</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-207cd93c-9799-4cb9-afd1-6449e7407c68' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-207cd93c-9799-4cb9-afd1-6449e7407c68' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-f4c6e775-d4a2-486e-a070-20955ee14fd9' class='xr-section-summary-in' type='checkbox'  ><label for='section-f4c6e775-d4a2-486e-a070-20955ee14fd9' class='xr-section-summary' >Data variables: <span>(27)</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>amplitude</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dc86cfc7-9d6b-46de-a204-31083832a9af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc86cfc7-9d6b-46de-a204-31083832a9af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ee8fb01-0dbf-4309-8d7b-dc8f64d9ba81' class='xr-var-data-in' type='checkbox'><label for='data-8ee8fb01-0dbf-4309-8d7b-dc8f64d9ba81' 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'><dt><span>comment :</span></dt><dd>Magnitude of the height difference between the extremum of SSH within the eddy and the SSH around the effective contour defining the eddy edge</dd><dt><span>long_name :</span></dt><dd>Amplitude</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>0.004</dd><dt><span>max :</span></dt><dd>1.405</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_area</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-32749edf-588a-47d6-98f2-2ab2031ef6ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32749edf-588a-47d6-98f2-2ab2031ef6ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6b5639e-90ed-41c9-a7b2-097c2abc7f22' class='xr-var-data-in' type='checkbox'><label for='data-b6b5639e-90ed-41c9-a7b2-097c2abc7f22' 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'><dt><span>comment :</span></dt><dd>Area enclosed by the effective contour in m^2</dd><dt><span>long_name :</span></dt><dd>Effective area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>min :</span></dt><dd>128509144.0</dd><dt><span>max :</span></dt><dd>325393650000.0</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_contour_height</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-17e90e4f-48c9-4f83-a73a-e94f883c3b4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17e90e4f-48c9-4f83-a73a-e94f883c3b4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb6cab06-914f-4630-914e-61dfa181c083' class='xr-var-data-in' type='checkbox'><label for='data-eb6cab06-914f-4630-914e-61dfa181c083' 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'><dt><span>comment :</span></dt><dd>SSH filtered height for effective contour</dd><dt><span>long_name :</span></dt><dd>Effective Contour Height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>-0.918</dd><dt><span>max :</span></dt><dd>0.446</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_contour_latitude</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dd86e056-365f-4541-9897-501a22ff8aca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd86e056-365f-4541-9897-501a22ff8aca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18b4813f-582d-413e-8a85-cb28e5cee4e9' class='xr-var-data-in' type='checkbox'><label for='data-18b4813f-582d-413e-8a85-cb28e5cee4e9' 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'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Latitudes of effective contour</dd><dt><span>long_name :</span></dt><dd>Effective Contour Latitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[4460 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_contour_longitude</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a5548e99-945b-4eb8-9bae-944c3bdb5908' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5548e99-945b-4eb8-9bae-944c3bdb5908' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea10b783-6293-414b-9262-9afd283b49bf' class='xr-var-data-in' type='checkbox'><label for='data-ea10b783-6293-414b-9262-9afd283b49bf' 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'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Longitudes of the effective contour</dd><dt><span>long_name :</span></dt><dd>Effective Contour Longitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[4460 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_contour_shape_error</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-61b587e4-9160-4511-9889-1241b0de06cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61b587e4-9160-4511-9889-1241b0de06cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa78d922-7ad4-4402-a73d-9a0c84b107f7' class='xr-var-data-in' type='checkbox'><label for='data-fa78d922-7ad4-4402-a73d-9a0c84b107f7' 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'><dt><span>comment :</span></dt><dd>Error criterion between the effective contour and its best fit circle</dd><dt><span>long_name :</span></dt><dd>Effective Contour Shape Error</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>min :</span></dt><dd>2.5</dd><dt><span>max :</span></dt><dd>75.5</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_radius</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7875e64b-33d6-40b4-92e3-07f41a1f48d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7875e64b-33d6-40b4-92e3-07f41a1f48d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5149130-e8af-4b11-bbbb-61b8b4c29220' class='xr-var-data-in' type='checkbox'><label for='data-b5149130-e8af-4b11-bbbb-61b8b4c29220' 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'><dt><span>comment :</span></dt><dd>Radius of the best fit circle corresponding to the effective contour</dd><dt><span>long_name :</span></dt><dd>Effective Radius</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>7050.0</dd><dt><span>max :</span></dt><dd>378400.0</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inner_contour_height</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-273f5ab2-4098-4c55-8665-18577ba288ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-273f5ab2-4098-4c55-8665-18577ba288ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb0bf987-b9be-45ff-8d1a-b0c723133541' class='xr-var-data-in' type='checkbox'><label for='data-fb0bf987-b9be-45ff-8d1a-b0c723133541' 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'><dt><span>comment :</span></dt><dd>SSH filtered height for the smallest detected contour</dd><dt><span>long_name :</span></dt><dd>Inner Contour Height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>-1.462</dd><dt><span>max :</span></dt><dd>0.406</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b6a5fb45-6924-410e-9640-bec8dc1be253' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6a5fb45-6924-410e-9640-bec8dc1be253' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ef71936-bd72-4388-a333-d7cbde534041' class='xr-var-data-in' type='checkbox'><label for='data-1ef71936-bd72-4388-a333-d7cbde534041' 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'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>Latitude center of the best fit circle</dd><dt><span>long_name :</span></dt><dd>Eddy Center Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>min :</span></dt><dd>-78.05134</dd><dt><span>max :</span></dt><dd>84.03748</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_max</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-94b802b1-e90d-4c9a-a03e-eca9cdaa4599' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94b802b1-e90d-4c9a-a03e-eca9cdaa4599' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2242860-b572-460f-9ef1-74c18a633ff7' class='xr-var-data-in' type='checkbox'><label for='data-b2242860-b572-460f-9ef1-74c18a633ff7' 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'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>Latitude of the inner contour</dd><dt><span>long_name :</span></dt><dd>Latitude of the SSH maximum</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>min :</span></dt><dd>-90.0</dd><dt><span>max :</span></dt><dd>84.12464</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-32371e83-4245-4688-9ac1-148df657dc31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32371e83-4245-4688-9ac1-148df657dc31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d341033f-88fe-40ab-9bae-fb5028a23056' class='xr-var-data-in' type='checkbox'><label for='data-d341033f-88fe-40ab-9bae-fb5028a23056' 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'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Longitude center of the best fit circle</dd><dt><span>long_name :</span></dt><dd>Eddy Center Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>min :</span></dt><dd>-28.260529</dd><dt><span>max :</span></dt><dd>367.2987</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_max</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dbe95302-33b1-40c3-8ef3-7ec022e01a50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbe95302-33b1-40c3-8ef3-7ec022e01a50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e114967d-6d96-413f-bf3d-ec1fd7eb7fe1' class='xr-var-data-in' type='checkbox'><label for='data-e114967d-6d96-413f-bf3d-ec1fd7eb7fe1' 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'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Longitude of the inner contour</dd><dt><span>long_name :</span></dt><dd>Longitude of the SSH maximum</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>min :</span></dt><dd>-28.13443</dd><dt><span>max :</span></dt><dd>367.44983</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>num_contours</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b05144da-0bfe-4929-a12a-0b036ee9b052' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b05144da-0bfe-4929-a12a-0b036ee9b052' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d339a671-33eb-4622-acdb-034a715d0274' class='xr-var-data-in' type='checkbox'><label for='data-d339a671-33eb-4622-acdb-034a715d0274' 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'><dt><span>comment :</span></dt><dd>Number of contours selected for this eddy</dd><dt><span>long_name :</span></dt><dd>Number of contours</dd><dt><span>min :</span></dt><dd>1</dd><dt><span>max :</span></dt><dd>703</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=uint16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>num_point_e</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-190739ec-68a6-4781-a562-c2b9cf585a97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-190739ec-68a6-4781-a562-c2b9cf585a97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f974f82c-db33-4c74-a478-92de53a219c2' class='xr-var-data-in' type='checkbox'><label for='data-f974f82c-db33-4c74-a478-92de53a219c2' 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'><dt><span>description :</span></dt><dd>Number of points for effective contour before resampling</dd><dt><span>long_name :</span></dt><dd>number of points for effective contour</dd><dt><span>units :</span></dt><dd>ordinal</dd><dt><span>min :</span></dt><dd>10</dd><dt><span>max :</span></dt><dd>145</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=uint16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>num_point_s</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ea95d11b-e3ff-4f34-ae22-1f93b051bf5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea95d11b-e3ff-4f34-ae22-1f93b051bf5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97d81b79-7ebe-468c-8f1e-680779c4ccd9' class='xr-var-data-in' type='checkbox'><label for='data-97d81b79-7ebe-468c-8f1e-680779c4ccd9' 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'><dt><span>description :</span></dt><dd>Number of points for speed contour before resampling</dd><dt><span>long_name :</span></dt><dd>number of points for speed contour</dd><dt><span>units :</span></dt><dd>ordinal</dd><dt><span>min :</span></dt><dd>10</dd><dt><span>max :</span></dt><dd>135</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=uint16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>observation_flag</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-694fc352-ff2f-44c9-811c-a14a13cf1d30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-694fc352-ff2f-44c9-811c-a14a13cf1d30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f9e1810-bcb1-46df-8bdf-3dd9e97c72a9' class='xr-var-data-in' type='checkbox'><label for='data-7f9e1810-bcb1-46df-8bdf-3dd9e97c72a9' 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'><dt><span>comment :</span></dt><dd>Flag indicating if the value is interpolated between two observations or not (0: observed eddy, 1: interpolated eddy)</dd><dt><span>long_name :</span></dt><dd>Virtual Eddy Position</dd><dt><span>min :</span></dt><dd>0</dd><dt><span>max :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>observation_number</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-40dd1bb4-c3b2-484f-a2f4-e84ad601d312' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40dd1bb4-c3b2-484f-a2f4-e84ad601d312' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6f3a4f7-7e77-412c-9637-f0e8b87f3cfc' class='xr-var-data-in' type='checkbox'><label for='data-a6f3a4f7-7e77-412c-9637-f0e8b87f3cfc' 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'><dt><span>comment :</span></dt><dd>Observation sequence number, days starting at the eddy first detection</dd><dt><span>long_name :</span></dt><dd>Eddy temporal index in a trajectory</dd><dt><span>min :</span></dt><dd>0</dd><dt><span>max :</span></dt><dd>1961</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=uint16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_area</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0b0863dd-ea45-45ae-a5eb-df6d8f205089' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b0863dd-ea45-45ae-a5eb-df6d8f205089' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1afcf950-470e-482f-bb70-40f81dea8417' class='xr-var-data-in' type='checkbox'><label for='data-1afcf950-470e-482f-bb70-40f81dea8417' 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'><dt><span>comment :</span></dt><dd>Area enclosed by the speed contour in m^2</dd><dt><span>long_name :</span></dt><dd>Speed area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>min :</span></dt><dd>128509144.0</dd><dt><span>max :</span></dt><dd>258590110000.0</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_average</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-acaa2a46-1ee3-4c23-bc41-8164bf8b410d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-acaa2a46-1ee3-4c23-bc41-8164bf8b410d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34ad163f-ec27-4112-90fa-ad99a7b318bb' class='xr-var-data-in' type='checkbox'><label for='data-34ad163f-ec27-4112-90fa-ad99a7b318bb' 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'><dt><span>comment :</span></dt><dd>Average speed of the contour defining the radius scale “speed_radius”</dd><dt><span>long_name :</span></dt><dd>Maximum circum-averaged Speed</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>min :</span></dt><dd>0.0</dd><dt><span>max :</span></dt><dd>5.6524</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_contour_height</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6f036dc7-053b-42d8-bde3-1e55de0395ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f036dc7-053b-42d8-bde3-1e55de0395ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a599500-be84-4289-9ee1-1800adb14bec' class='xr-var-data-in' type='checkbox'><label for='data-1a599500-be84-4289-9ee1-1800adb14bec' 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'><dt><span>comment :</span></dt><dd>SSH filtered height for speed contour</dd><dt><span>long_name :</span></dt><dd>Speed Contour Height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>-0.918</dd><dt><span>max :</span></dt><dd>0.446</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_contour_latitude</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-965d9a12-50f9-4d40-b067-68749dba5f1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-965d9a12-50f9-4d40-b067-68749dba5f1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb947507-3693-455b-8826-561bf02cfc97' class='xr-var-data-in' type='checkbox'><label for='data-bb947507-3693-455b-8826-561bf02cfc97' 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'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Latitudes of speed contour</dd><dt><span>long_name :</span></dt><dd>Speed Contour Latitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[4460 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_contour_longitude</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c8a5bb9b-5f64-4712-922c-35e9bc04ddf2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8a5bb9b-5f64-4712-922c-35e9bc04ddf2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02eeee9d-94a8-4c44-a5fd-b8d21bf3b5cd' class='xr-var-data-in' type='checkbox'><label for='data-02eeee9d-94a8-4c44-a5fd-b8d21bf3b5cd' 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'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Longitudes of speed contour</dd><dt><span>long_name :</span></dt><dd>Speed Contour Longitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[4460 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_contour_shape_error</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d7035da2-f54f-49eb-a603-42db765f1ffa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7035da2-f54f-49eb-a603-42db765f1ffa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04bfbaba-5109-4268-afe4-0847f3896f1b' class='xr-var-data-in' type='checkbox'><label for='data-04bfbaba-5109-4268-afe4-0847f3896f1b' 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'><dt><span>comment :</span></dt><dd>Error criterion between the speed contour and its best fit circle</dd><dt><span>long_name :</span></dt><dd>Speed Contour Shape Error</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>min :</span></dt><dd>1.5</dd><dt><span>max :</span></dt><dd>93.0</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_radius</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ad0474d0-fdd4-4dca-9f3f-d9e64958b4ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad0474d0-fdd4-4dca-9f3f-d9e64958b4ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c541b9b9-b28a-4d23-93da-5aeaf0d018cb' class='xr-var-data-in' type='checkbox'><label for='data-c541b9b9-b28a-4d23-93da-5aeaf0d018cb' 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'><dt><span>comment :</span></dt><dd>Radius of the best fit circle corresponding to the contour of maximum circum-average speed</dd><dt><span>long_name :</span></dt><dd>Speed Radius</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>7050.0</dd><dt><span>max :</span></dt><dd>342200.0</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b1b3b832-6421-44c0-8c3c-05d494134b2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1b3b832-6421-44c0-8c3c-05d494134b2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f5e774b-7a9b-4ac9-8792-17e816d7a202' class='xr-var-data-in' type='checkbox'><label for='data-0f5e774b-7a9b-4ac9-8792-17e816d7a202' 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'><dt><span>axis :</span></dt><dd>T</dd><dt><span>comment :</span></dt><dd>Date of this observation</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>min :</span></dt><dd>24837.0</dd><dt><span>max :</span></dt><dd>27232.0</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>track</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1b83ffc3-ddff-4808-a441-a9485755320b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b83ffc3-ddff-4808-a441-a9485755320b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a89aa921-da38-42ce-be22-c94e50a7a42e' class='xr-var-data-in' type='checkbox'><label for='data-a89aa921-da38-42ce-be22-c94e50a7a42e' 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'><dt><span>comment :</span></dt><dd>Trajectory identification number</dd><dt><span>long_name :</span></dt><dd>Trajectory number</dd><dt><span>min :</span></dt><dd>0</dd><dt><span>max :</span></dt><dd>191385</dd></dl></div><div class='xr-var-data'><pre>[223 values with dtype=uint32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uavg_profile</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f88d658a-5d2d-4876-86cd-68b86a10c2e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f88d658a-5d2d-4876-86cd-68b86a10c2e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-927b131c-e2f0-43e3-a6a8-2ae513dd1409' class='xr-var-data-in' type='checkbox'><label for='data-927b131c-e2f0-43e3-a6a8-2ae513dd1409' 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'><dt><span>comment :</span></dt><dd>Speed averaged values from the effective contour inwards to the smallest contour, evenly spaced points</dd><dt><span>long_name :</span></dt><dd>Radial Speed Profile</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>[4460 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8dcfd508-b0bb-439c-9fc6-e8c3ac662480' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8dcfd508-b0bb-439c-9fc6-e8c3ac662480' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-d56285b8-82dd-453f-afd8-29dccdf42576' class='xr-section-summary-in' type='checkbox'  ><label for='section-d56285b8-82dd-453f-afd8-29dccdf42576' class='xr-section-summary' >Attributes: <span>(19)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>comment :</span></dt><dd>Surface product; mesoscale eddies</dd><dt><span>creator_email :</span></dt><dd>aviso@altimetry.fr</dd><dt><span>creator_url :</span></dt><dd>https://www.aviso.altimetry.fr</dd><dt><span>date_created :</span></dt><dd>2024-08-06T09:38:06Z</dd><dt><span>framework_used :</span></dt><dd>https://github.com/AntSimi/py-eddy-tracker</dd><dt><span>framework_version :</span></dt><dd>v3.6.1</dd><dt><span>input_maps_processing :</span></dt><dd>DUACS DT2018 until 2021-12-07, DUACS DT2021 since 2021-12-08</dd><dt><span>input_product_reference :</span></dt><dd>https://www.aviso.altimetry.fr/en/data/products/value-added-products/global-mesoscale-eddy-trajectory-product.html</dd><dt><span>institution :</span></dt><dd>CLS/CNES in collaboration with IMEDEA</dd><dt><span>license :</span></dt><dd>https://www.aviso.altimetry.fr/fileadmin/documents/data/License_Aviso.pdf</dd><dt><span>product_version :</span></dt><dd>3.2exp</dd><dt><span>project :</span></dt><dd>SSALTO/DUACS</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table</dd><dt><span>summary :</span></dt><dd>This dataset contains eddy atlas from all-satellite NRT CMEMS maps over global ocean (SEALEVEL_GLO_PHY_L4_NRT_OBSERVATIONS*)</dd><dt><span>time_coverage_duration :</span></dt><dd>P2396D</dd><dt><span>time_coverage_end :</span></dt><dd>2024-07-23T00:00:00Z</dd><dt><span>time_coverage_start :</span></dt><dd>2018-01-01T00:00:00Z</dd><dt><span>title :</span></dt><dd>Mesoscale Cyclonic Eddies in Altimeter Observations of ADT</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 202kB\n",
       "Dimensions:                        (obs: 223, NbSample: 20)\n",
       "Dimensions without coordinates: obs, NbSample\n",
       "Data variables: (12/27)\n",
       "    amplitude                      (obs) float64 2kB ...\n",
       "    effective_area                 (obs) float32 892B ...\n",
       "    effective_contour_height       (obs) float32 892B ...\n",
       "    effective_contour_latitude     (obs, NbSample) float64 36kB ...\n",
       "    effective_contour_longitude    (obs, NbSample) float64 36kB ...\n",
       "    effective_contour_shape_error  (obs) float64 2kB ...\n",
       "    ...                             ...\n",
       "    speed_contour_longitude        (obs, NbSample) float64 36kB ...\n",
       "    speed_contour_shape_error      (obs) float64 2kB ...\n",
       "    speed_radius                   (obs) float64 2kB ...\n",
       "    time                           (obs) datetime64[ns] 2kB ...\n",
       "    track                          (obs) uint32 892B ...\n",
       "    uavg_profile                   (obs, NbSample) float64 36kB ...\n",
       "Attributes: (12/19)\n",
       "    Metadata_Conventions:      Unidata Dataset Discovery v1.0\n",
       "    comment:                   Surface product; mesoscale eddies\n",
       "    creator_email:             aviso@altimetry.fr\n",
       "    creator_url:               https://www.aviso.altimetry.fr\n",
       "    date_created:              2024-08-06T09:38:06Z\n",
       "    framework_used:            https://github.com/AntSimi/py-eddy-tracker\n",
       "    ...                        ...\n",
       "    standard_name_vocabulary:  NetCDF Climate and Forecast (CF) Metadata Conv...\n",
       "    summary:                   This dataset contains eddy atlas from all-sate...\n",
       "    time_coverage_duration:    P2396D\n",
       "    time_coverage_end:         2024-07-23T00:00:00Z\n",
       "    time_coverage_start:       2018-01-01T00:00:00Z\n",
       "    title:                     Mesoscale Cyclonic Eddies in Altimeter Observa..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_dataset('Edward_Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20240723.nc')\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d5ca866e-0650-4eaf-889d-5f583306aaef",
   "metadata": {},
   "outputs": [],
   "source": [
    "def in_eddy(ds,float_lat,float_lon,float_time):\n",
    "    \"\"\"\n",
    "    float_lat: degrees north\n",
    "    float_lon: degrees east\n",
    "    float_time: should be in format 'YYYY-MM-DD'\n",
    "    \"\"\"\n",
    "\n",
    "    def all_equal(iterable):\n",
    "        g = groupby(iterable)\n",
    "        return next(g, True) and not next(g, False)\n",
    "\n",
    "    in_eddy_flag = False \n",
    "\n",
    "    float_time = np.datetime64(float_time)\n",
    "    if float_time in ds.time: # some dates not in dateset        \n",
    "        for i in np.where(ds.time == float_time)[0]: # usually will only be 1 eddy, but sometimes there are 2 after a split\n",
    "            contour_lons = np.array(ds.effective_contour_longitude[i]) # eddy lons\n",
    "            contour_lats = np.array(ds.effective_contour_latitude[i]) # eddy lats\n",
    "            \n",
    "            if all_equal(contour_lons): # eddy break\n",
    "                pass\n",
    "            else:\n",
    "                poly = Path([(contour_lats[j],contour_lons[j]) for j in np.arange(0,len(contour_lats))]) # set up the polygon\n",
    "                if poly.contains_points([(float_lat,float_lon)]): #find if point is inside the polygon\n",
    "                    in_eddy_flag = True \n",
    "\n",
    "    return in_eddy_flag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc64a12c-e504-4cdd-a475-12e8acec6c16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in_eddy(ds,35,289.5,'2024-05-08')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ddcd141d-a1b6-4c92-8d82-eb49e37ac174",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4286be1ce17849d9b23e8ed16bfe958b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "98aff6b65cb0474480fced27123b7be7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b39ae96e7c54032afcd93277fe96058",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PACE CHL\n",
    "results = earthaccess.search_datasets(instrument=\"oci\")\n",
    "\n",
    "tspan = (\"2024-05-08\", \"2024-05-08\")\n",
    "#bbox = (-58, 37.5, -52, 41) #howie\n",
    "bbox = (-73, 35, -70, 37) #edward\n",
    "# bbox=(-61, 34, -59, 39) #patrick\n",
    "clouds = (0, 100)\n",
    "results = earthaccess.search_data(\n",
    "    short_name=\"PACE_OCI_L2_BGC_NRT\",\n",
    "    temporal=tspan,\n",
    "    bounding_box=bbox,\n",
    "    cloud_cover=clouds,\n",
    ")\n",
    "\n",
    "paths = earthaccess.open(results) #'streaming' data\n",
    "\n",
    "datatree = open_datatree(paths[0])\n",
    "datatree\n",
    "dataset = xr.merge(datatree.to_dict().values())\n",
    "dataset\n",
    "dataset = dataset.set_coords((\"longitude\", \"latitude\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d9ac1282-a59b-4ba0-a9e7-ca271c3057f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-73.0, -68.0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()#projection=ccrs.PlateCarree())\n",
    "#ax.gridlines(draw_labels={\"left\": \"y\", \"bottom\": \"x\"})\n",
    "plot=dataset[\"chlor_a\"].plot(x=\"longitude\", y=\"latitude\", cmap=\"Spectral_r\", vmax=0.5, ax=ax) #tried adding ,transform=ccrs.PlateCarree()\n",
    "#plot=plt.pcolor(dataset[\"longitude\"].values,dataset[\"latitude\"].values,dataset[\"chlor_a\"].values, cmap=\"Spectral_r\", vmax=0.5,transform=ccrs.PlateCarree())\n",
    "ax.grid(True)\n",
    "ax.set_ylim([34,37.5])\n",
    "ax.set_xlim([-73,-68])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e1537977-e443-4d6b-bddf-e2df6f3664e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray ()> Size: 8B\n",
      "array(71564)\n"
     ]
    }
   ],
   "source": [
    "# crop swot xarray\n",
    "# Create a mask for the desired latitude range\n",
    "bbox = (-73, 34, -68, 37.5) #edward\n",
    "mask = (dataset.longitude >= bbox[0]) & (dataset.longitude <= bbox[2]) & (dataset.latitude >= bbox[1]) & (dataset.latitude <= bbox[3])\n",
    "print(mask.sum())\n",
    "# Check if the mask is valid (non-empty)\n",
    "if mask.sum() == 0:\n",
    "    mask = (dataset.longitude >= bbox[0]) & (dataset.longitude <= bbox[2])\n",
    "    if mask.sum() == 0:\n",
    "        print('Mask via longitude only')\n",
    "        mask = (dataset.latitude >= bbox[1]) & (dataset.latitude <= bbox[3])\n",
    "        print(mask.sum())\n",
    "    else:\n",
    "        print('Mask via latitude only')\n",
    "        print(mask.sum())\n",
    "# Apply the mask to slice the dataset\n",
    "ds_masked = dataset.where(mask, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "630f327d-4240-4999-9624-f1f39ebca86c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lat=ds_masked.latitude.values.flatten()\n",
    "lon=ds_masked.longitude.values.flatten()+360\n",
    "chl=ds_masked.chlor_a.values.flatten()\n",
    "\n",
    "chl_inside,chl_outside=[],[]\n",
    "\n",
    "for index in np.arange(0,len(lat)):\n",
    "    val=in_eddy(ds,lat[index],lon[index],'2024-05-08')\n",
    "    if val:\n",
    "        chl_inside.append(chl[index])\n",
    "        # chl_outside.append(np.nan)\n",
    "    else:\n",
    "        chl_outside.append(chl[index])\n",
    "        # chl_inside.append(np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eaa2f902-3d48-4a56-9703-596603c027df",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = xr.open_dataset('moana_dataset_merged.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a34f7a1d-0cd5-41b4-b0b1-e7c3b3c23003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24593332"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nanmean(chl_inside)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2aff6815-550e-498d-817d-e9d697477629",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray ()> Size: 8B\n",
      "array(71564)\n"
     ]
    }
   ],
   "source": [
    "# Create a mask for the desired latitude range\n",
    "bbox = (-73, 34, -68, 37.5) #edward\n",
    "mask = (dataset.longitude >= bbox[0]) & (dataset.longitude <= bbox[2]) & (dataset.latitude >= bbox[1]) & (dataset.latitude <= bbox[3])\n",
    "print(mask.sum())\n",
    "# Check if the mask is valid (non-empty)\n",
    "if mask.sum() == 0:\n",
    "    mask = (dataset.longitude >= bbox[0]) & (dataset.longitude <= bbox[2])\n",
    "    if mask.sum() == 0:\n",
    "        print('Mask via longitude only')\n",
    "        mask = (dataset.latitude >= bbox[1]) & (dataset.latitude <= bbox[3])\n",
    "        print(mask.sum())\n",
    "    else:\n",
    "        print('Mask via latitude only')\n",
    "        print(mask.sum())\n",
    "# Apply the mask to slice the dataset\n",
    "ds_masked_moana = dataset.where(mask, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "750680e3-0f1c-4b18-9e35-33251d3f02d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pico=ds_masked_moana[\"picoeuk_moana\"].values.flatten()\n",
    "lon = ds_masked_moana['longitude'].values.flatten()+360\n",
    "lat = ds_masked_moana['latitude'].values.flatten()\n",
    "pro = ds_masked_moana['prococcus_moana'].values.flatten()\n",
    "pico = ds_masked_moana['picoeuk_moana'].values.flatten()\n",
    "syn = ds_masked_moana['syncoccus_moana'].values.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cd8407eb-306b-4284-9c45-cd08b5c9eb4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pico_out,pico_in,pro_in,pro_out,syn_in,syn_out=[],[],[],[],[],[]\n",
    "\n",
    "for index in np.arange(0,len(lat)):\n",
    "    val=in_eddy(ds,lat[index],lon[index],'2024-05-08')\n",
    "    if val:\n",
    "        pico_in.append(pico[index])\n",
    "        pro_in.append(pro[index])\n",
    "        syn_in.append(syn[index])\n",
    "    else:\n",
    "        pico_out.append(pico[index])\n",
    "        pro_out.append(pro[index])\n",
    "        syn_out.append(syn[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "037de18a-baa8-4ed2-a9de-bb30b604738d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3910.8284"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nanmean(pico_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a625cadb-dff6-46f7-805d-08698c52e33b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIME</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>ID</th>\n",
       "      <th>MLD</th>\n",
       "      <th>SURF_TEMP</th>\n",
       "      <th>SURF_PSAL</th>\n",
       "      <th>SURF_CHLA</th>\n",
       "      <th>SURF_BBP</th>\n",
       "      <th>SURF_NPP</th>\n",
       "      <th>SURF_SPICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4/4/24</td>\n",
       "      <td>40.753500</td>\n",
       "      <td>-63.066400</td>\n",
       "      <td>F4903273_102</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9.687000</td>\n",
       "      <td>34.224998</td>\n",
       "      <td>1.038409</td>\n",
       "      <td>0.002721</td>\n",
       "      <td>32.318576</td>\n",
       "      <td>0.472714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/4/24</td>\n",
       "      <td>40.753500</td>\n",
       "      <td>-63.066400</td>\n",
       "      <td>F4903273_102</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9.687000</td>\n",
       "      <td>34.224998</td>\n",
       "      <td>2.480414</td>\n",
       "      <td>0.003883</td>\n",
       "      <td>141.143959</td>\n",
       "      <td>0.472714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4/15/24</td>\n",
       "      <td>40.036700</td>\n",
       "      <td>-64.519400</td>\n",
       "      <td>F4903273_103</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.135000</td>\n",
       "      <td>32.966999</td>\n",
       "      <td>2.480414</td>\n",
       "      <td>0.003883</td>\n",
       "      <td>141.143959</td>\n",
       "      <td>-0.776015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/15/24</td>\n",
       "      <td>40.036700</td>\n",
       "      <td>-64.519400</td>\n",
       "      <td>F4903273_103</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.135000</td>\n",
       "      <td>32.966999</td>\n",
       "      <td>1.342333</td>\n",
       "      <td>0.003276</td>\n",
       "      <td>113.786147</td>\n",
       "      <td>-0.776015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/25/24</td>\n",
       "      <td>39.539300</td>\n",
       "      <td>-66.138300</td>\n",
       "      <td>F4903273_104</td>\n",
       "      <td>11.9</td>\n",
       "      <td>11.368000</td>\n",
       "      <td>34.050999</td>\n",
       "      <td>1.342333</td>\n",
       "      <td>0.003276</td>\n",
       "      <td>113.786147</td>\n",
       "      <td>0.632496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>6/19/24</td>\n",
       "      <td>33.401985</td>\n",
       "      <td>-70.987251</td>\n",
       "      <td>F4902590_75</td>\n",
       "      <td>20.9</td>\n",
       "      <td>24.373000</td>\n",
       "      <td>36.501999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.700197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>6/29/24</td>\n",
       "      <td>33.648960</td>\n",
       "      <td>-71.725128</td>\n",
       "      <td>F4902590_76</td>\n",
       "      <td>10.1</td>\n",
       "      <td>25.901999</td>\n",
       "      <td>36.586000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.231510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>7/10/24</td>\n",
       "      <td>33.678604</td>\n",
       "      <td>-72.388229</td>\n",
       "      <td>F4902590_77</td>\n",
       "      <td>11.2</td>\n",
       "      <td>27.011999</td>\n",
       "      <td>36.549999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.555775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>7/20/24</td>\n",
       "      <td>33.112515</td>\n",
       "      <td>-73.547974</td>\n",
       "      <td>F4902590_78</td>\n",
       "      <td>21.4</td>\n",
       "      <td>27.721001</td>\n",
       "      <td>36.612000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.832016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>7/30/24</td>\n",
       "      <td>32.435528</td>\n",
       "      <td>-73.987350</td>\n",
       "      <td>F4902590_79</td>\n",
       "      <td>22.9</td>\n",
       "      <td>28.051499</td>\n",
       "      <td>36.594999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.927573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>544 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TIME   LATITUDE  LONGITUDE            ID   MLD  SURF_TEMP  SURF_PSAL  \\\n",
       "0     4/4/24  40.753500 -63.066400  F4903273_102  68.0   9.687000  34.224998   \n",
       "1     4/4/24  40.753500 -63.066400  F4903273_102  68.0   9.687000  34.224998   \n",
       "2    4/15/24  40.036700 -64.519400  F4903273_103  12.0   8.135000  32.966999   \n",
       "3    4/15/24  40.036700 -64.519400  F4903273_103  12.0   8.135000  32.966999   \n",
       "4    4/25/24  39.539300 -66.138300  F4903273_104  11.9  11.368000  34.050999   \n",
       "..       ...        ...        ...           ...   ...        ...        ...   \n",
       "539  6/19/24  33.401985 -70.987251   F4902590_75  20.9  24.373000  36.501999   \n",
       "540  6/29/24  33.648960 -71.725128   F4902590_76  10.1  25.901999  36.586000   \n",
       "541  7/10/24  33.678604 -72.388229   F4902590_77  11.2  27.011999  36.549999   \n",
       "542  7/20/24  33.112515 -73.547974   F4902590_78  21.4  27.721001  36.612000   \n",
       "543  7/30/24  32.435528 -73.987350   F4902590_79  22.9  28.051499  36.594999   \n",
       "\n",
       "     SURF_CHLA  SURF_BBP    SURF_NPP  SURF_SPICE  \n",
       "0     1.038409  0.002721   32.318576    0.472714  \n",
       "1     2.480414  0.003883  141.143959    0.472714  \n",
       "2     2.480414  0.003883  141.143959   -0.776015  \n",
       "3     1.342333  0.003276  113.786147   -0.776015  \n",
       "4     1.342333  0.003276  113.786147    0.632496  \n",
       "..         ...       ...         ...         ...  \n",
       "539        NaN       NaN         NaN    5.700197  \n",
       "540        NaN       NaN         NaN    6.231510  \n",
       "541        NaN       NaN         NaN    6.555775  \n",
       "542        NaN       NaN         NaN    6.832016  \n",
       "543        NaN       NaN         NaN    6.927573  \n",
       "\n",
       "[544 rows x 11 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argo=pandas.read_csv('float_surf.csv')\n",
    "argo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "26e9cb02-add7-4e3a-b79f-493d1bce6f55",
   "metadata": {},
   "outputs": [],
   "source": [
    "lon=np.array(argo['LONGITUDE'])+360\n",
    "lat=np.array(argo['LATITUDE'])\n",
    "spice=np.array(argo['SURF_SPICE'])\n",
    "mld=np.array(argo['MLD'])\n",
    "chl_float=np.array(argo['SURF_CHLA'])\n",
    "bbp_float=np.array(argo['SURF_BBP'])\n",
    "npp_float=np.array(argo['SURF_NPP'])\n",
    "argotime=np.array(argo['TIME'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12df997d-bb97-4854-9d58-b0d7b34bd80d",
   "metadata": {},
   "outputs": [],
   "source": [
    "month=[]\n",
    "day=[]\n",
    "for idx in np.arange(0,len(argotime)):\n",
    "    date_object = datetime.strptime(argotime[idx], '%m/%d/%y')\n",
    "    month.append(date_object.month)\n",
    "    day.append(date_object.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "510eea20-d94a-4bc5-83b3-912565a43739",
   "metadata": {},
   "outputs": [],
   "source": [
    "spice_out,spice_in,mld_out,mld_in,chl_float_in,chl_float_out,bbp_float_in,bbp_float_out,npp_float_in,npp_float_out=[],[],[],[],[],[],[],[],[],[]\n",
    "\n",
    "for index in np.arange(0,len(lat)):\n",
    "    if len(str(day[idx]))==2:\n",
    "        datey='2024-' + '0' + str(month[idx]) + '-' + str(day[idx])\n",
    "    elif len(str(day[idx]))==1:\n",
    "        datey='2024-' + '0' + str(month[idx]) + '-' + '0' + str(day[idx])\n",
    "    val=in_eddy(ds,lat[index],lon[index],datey)\n",
    "    if val:\n",
    "        spice_in.append(spice[index])\n",
    "        mld_in.append(mld[index])\n",
    "        chl_float_in.append(chl_float[index])\n",
    "        bbp_float_in.append(bbp_float[index])\n",
    "        npp_float_in.append(npp_float[index])\n",
    "    else:\n",
    "        spice_out.append(spice[index])\n",
    "        mld_out.append(mld[index])\n",
    "        chl_float_out.append(chl_float[index])\n",
    "        bbp_float_out.append(bbp_float[index])\n",
    "        npp_float_out.append(npp_float[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "57b5b36d-e776-4775-ac52-fe98071b86bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "chl_out = [i for i in chl_outside if not(np.isnan(i))]\n",
    "chl_in = [i for i in chl_inside if not(np.isnan(i))]\n",
    "\n",
    "pico_in = [i for i in pico_in if not(np.isnan(i))]\n",
    "pico_out = [i for i in pico_out if not(np.isnan(i))]\n",
    "\n",
    "pro_in = [i for i in pro_in if not(np.isnan(i))]\n",
    "pro_out = [i for i in pro_out if not(np.isnan(i))]\n",
    "\n",
    "spice_out = [i for i in spice_out if not(np.isnan(i))]\n",
    "spice_in = [i for i in spice_in if not(np.isnan(i))]\n",
    "\n",
    "mld_in = [i for i in mld_in if not(np.isnan(i))]\n",
    "mld_out = [i for i in mld_out if not(np.isnan(i))]\n",
    "\n",
    "chl_float_in = [i for i in chl_float_in if not(np.isnan(i))]\n",
    "chl_float_out = [i for i in chl_float_out if not(np.isnan(i))]\n",
    "\n",
    "bbp_float_in = [i for i in bbp_float_in if not(np.isnan(i))]\n",
    "bbp_float_out = [i for i in bbp_float_out if not(np.isnan(i))]\n",
    "\n",
    "npp_float_in = [i for i in npp_float_in if not(np.isnan(i))]\n",
    "npp_float_out = [i for i in npp_float_out if not(np.isnan(i))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7e173d3e-3374-4f5b-96f5-67c5578047e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22955416076969695"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nanmean(chl_float_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "08f86c78-691a-4c11-8f33-250fddb84ed3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# value=np.concatenate([chl_out,chl_in,pico_out,pico_in,pro_out,pro_in,syn_out,syn_in,spice_out,spice_in,mld_in,mld_out])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1b417c57-0361-46ad-a17d-5f3dda547c68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var=np.concatenate([np.repeat('chl-a',len(chl_out)),np.repeat('chl-a',len(chl_in))])\n",
    "outin=np.concatenate([np.repeat('Outside',len(chl_out)),np.repeat('Inside',len(chl_in))]) \n",
    "value=np.concatenate([chl_out,chl_in])\n",
    "d = {'var': var, 'outin': outin, 'value': value}\n",
    "data = pandas.DataFrame(data=d)\n",
    "\n",
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize=(20,5))\n",
    "\n",
    "ax1=sns.boxplot(x = data['var'], \n",
    "            y = data['value'], \n",
    "            hue = data['outin'],showfliers=False, ax=ax1,palette=\"Paired\")\n",
    "ax1.set(ylim=[0,0.5])\n",
    "ax1.set(xlabel=' ', ylabel='chl-a (mg/$m^{3}$)')\n",
    "ax1.legend([],[], frameon=False)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "\n",
    "var=np.concatenate([np.repeat('pikoeukaryotes',len(pico_out)),np.repeat('pikoeukaryotes',len(pico_in))])\n",
    "outin=np.concatenate([np.repeat('Outside',len(pico_out)),np.repeat('Inside',len(pico_in))]) \n",
    "value=np.concatenate([pico_out,pico_in])\n",
    "d = {'var': var, 'outin': outin, 'value': value}\n",
    "data = pandas.DataFrame(data=d)\n",
    "\n",
    "ax2=sns.boxplot(x = data['var'], \n",
    "            y = data['value'], \n",
    "            hue = data['outin'],showfliers=False, ax=ax2,palette=\"Paired\")\n",
    "ax2.set(xlabel=' ', ylabel='pikoeukaryotes (cells/ml)')\n",
    "ax2.legend([],[], frameon=False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax2.ticklabel_format(axis='y', style='sci', scilimits=(0, 0))\n",
    "\n",
    "\n",
    "var=np.concatenate([np.repeat('prochlorococcus',len(pro_out)),np.repeat('prochlorococcus',len(pro_in))])\n",
    "outin=np.concatenate([np.repeat('Outside',len(pro_out)),np.repeat('Inside',len(pro_in))]) \n",
    "value=np.concatenate([pro_out,pro_in])\n",
    "d = {'var': var, 'outin': outin, 'value': value}\n",
    "data = pandas.DataFrame(data=d)\n",
    "\n",
    "ax3=sns.boxplot(x = data['var'], \n",
    "            y = data['value'], \n",
    "            hue = data['outin'],showfliers=False, ax=ax3,palette=\"Paired\")\n",
    "ax3.set(xlabel=' ', ylabel='prochlorococcus (cells/ml)')\n",
    "ax3.legend([],[], frameon=False)\n",
    "ax3.spines['top'].set_visible(False)\n",
    "ax3.spines['right'].set_visible(False)\n",
    "ax3.ticklabel_format(axis='y', style='sci', scilimits=(0, 0))\n",
    "\n",
    "\n",
    "var=np.concatenate([np.repeat('synechococcus',len(syn_out)),np.repeat('synechococcus',len(syn_in))])\n",
    "outin=np.concatenate([np.repeat('Outside',len(syn_out)),np.repeat('Inside',len(syn_in))]) \n",
    "value=np.concatenate([syn_out,syn_in])\n",
    "d = {'var': var, 'outin': outin, 'value': value}\n",
    "data = pandas.DataFrame(data=d)\n",
    "\n",
    "ax4=sns.boxplot(x = data['var'], \n",
    "            y = data['value'], \n",
    "            hue = data['outin'],showfliers=False, ax=ax4,palette=\"Paired\")\n",
    "ax4.set(xlabel=' ', ylabel='synechococcus (cells/ml)')\n",
    "ax4.legend([],[], frameon=False)\n",
    "ax4.spines['top'].set_visible(False)\n",
    "ax4.spines['right'].set_visible(False)\n",
    "ax4.ticklabel_format(axis='y', style='sci', scilimits=(0, 0))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# sns.color_palette()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a9db2566-45f5-4753-bfa1-2ffd1268fdcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2500x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#argo floats - surface\n",
    "fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(1, 5, figsize=(25,5))\n",
    "\n",
    "var=np.concatenate([np.repeat('chl-a',len(chl_float_out)),np.repeat('chl-a',len(chl_float_in))])\n",
    "outin=np.concatenate([np.repeat('Outside',len(chl_float_out)),np.repeat('Inside',len(chl_float_in))]) \n",
    "value=np.concatenate([chl_float_out,chl_float_in])\n",
    "d = {'var': var, 'outin': outin, 'value': value}\n",
    "data = pandas.DataFrame(data=d)\n",
    "\n",
    "ax1=sns.boxplot(x = data['var'], \n",
    "            y = data['value'], \n",
    "            hue = data['outin'],showfliers=False, ax=ax1,palette=\"Paired\")\n",
    "ax1.set(xlabel=' ', ylabel='chl-a (mg/$m^{3}$)')\n",
    "ax1.legend([],[], frameon=False)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax1.set(ylim=[0,0.5])\n",
    "plt.tight_layout()\n",
    "\n",
    "var=np.concatenate([np.repeat('bb(700)',len(bbp_float_out)),np.repeat('bb(700)',len(bbp_float_in))])\n",
    "outin=np.concatenate([np.repeat('Outside',len(bbp_float_out)),np.repeat('Inside',len(bbp_float_in))]) \n",
    "value=np.concatenate([bbp_float_out,bbp_float_in])\n",
    "d = {'var': var, 'outin': outin, 'value': value}\n",
    "data = pandas.DataFrame(data=d)\n",
    "\n",
    "ax2=sns.boxplot(x = data['var'], \n",
    "            y = data['value'], \n",
    "            hue = data['outin'],showfliers=False, ax=ax2,palette=\"Paired\")\n",
    "ax2.set(xlabel=' ', ylabel='bb(700) ($m^{-1}$)')\n",
    "ax2.legend([],[], frameon=False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "plt.tight_layout()\n",
    "\n",
    "var=np.concatenate([np.repeat('NPP',len(npp_float_out)),np.repeat('NPP',len(npp_float_in))])\n",
    "outin=np.concatenate([np.repeat('Outside',len(npp_float_out)),np.repeat('Inside',len(npp_float_in))]) \n",
    "value=np.concatenate([npp_float_out,npp_float_in])\n",
    "d = {'var': var, 'outin': outin, 'value': value}\n",
    "data = pandas.DataFrame(data=d)\n",
    "\n",
    "ax3=sns.boxplot(x = data['var'], \n",
    "            y = data['value'], \n",
    "            hue = data['outin'],showfliers=False, ax=ax3,palette=\"Paired\")\n",
    "ax3.set(xlabel=' ', ylabel='NPP ()')\n",
    "ax3.legend([],[], frameon=False)\n",
    "ax3.spines['top'].set_visible(False)\n",
    "ax3.spines['right'].set_visible(False)\n",
    "plt.tight_layout()\n",
    "\n",
    "var=np.concatenate([np.repeat('spice',len(spice_out)),np.repeat('spice',len(spice_in))])\n",
    "outin=np.concatenate([np.repeat('Outside',len(spice_out)),np.repeat('Inside',len(spice_in))]) \n",
    "value=np.concatenate([spice_out,spice_in])\n",
    "d = {'var': var, 'outin': outin, 'value': value}\n",
    "data = pandas.DataFrame(data=d)\n",
    "\n",
    "ax4=sns.boxplot(x = data['var'], \n",
    "            y = data['value'], \n",
    "            hue = data['outin'],showfliers=False, ax=ax4,palette=\"Paired\")\n",
    "ax4.set(xlabel=' ', ylabel='spice')\n",
    "ax4.legend([],[], frameon=False)\n",
    "ax4.spines['top'].set_visible(False)\n",
    "ax4.spines['right'].set_visible(False)\n",
    "plt.tight_layout()\n",
    "\n",
    "var=np.concatenate([np.repeat('MLD',len(mld_out)),np.repeat('MLD',len(mld_in))])\n",
    "outin=np.concatenate([np.repeat('Outside',len(mld_out)),np.repeat('Inside',len(mld_in))]) \n",
    "value=np.concatenate([mld_out,mld_in])\n",
    "d = {'var': var, 'outin': outin, 'value': value}\n",
    "data = pandas.DataFrame(data=d)\n",
    "\n",
    "ax5=sns.boxplot(x = data['var'], \n",
    "            y = data['value'], \n",
    "            hue = data['outin'],showfliers=False, ax=ax5,palette=\"Paired\")\n",
    "ax5.set(xlabel=' ', ylabel='mixed layer depth (m)')\n",
    "ax5.legend([],[], frameon=False)\n",
    "ax5.spines['top'].set_visible(False)\n",
    "ax5.spines['right'].set_visible(False)\n",
    "plt.tight_layout()"
   ]
  }
 ],
 "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": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}