DeepLearningMRRSpectra.ipynb

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      "2024-03-25 07:50:16.534232: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'cmweather'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mxarray\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mxr\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcmweather\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcluster\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m KMeans\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'cmweather'"
     ]
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   ],
   "source": [
    "import tensorflow as tf\n",
    "import xarray as xr\n",
    "import cmweather\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy.interpolate import interp1d\n",
    "import skimage\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import urllib\n",
    "from PIL import Image\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6457a8e-21ad-4476-9526-e7aaf0f0d6a6",
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   "source": [
    "ds = xr.open_mfdataset('mrrpro_data/crocus*20240109*.nc')"
   ]
  },
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   "execution_count": 20,
   "id": "3d0c5b58-95a6-4d3e-be30-11620add82f7",
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       "    time_coverage_start     (time) |S128 b&#x27;2024-01-09T00:00:00Z&#x27; ... b&#x27;2024-0...\n",
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       "    ...                      ...\n",
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       "    N                       (time, n_spectra, spectrum_n_samples) float64 dask.array&lt;chunksize=(360, 128, 64), meta=np.ndarray&gt;\n",
       "    D                       (time, n_spectra, spectrum_n_samples) float64 dask.array&lt;chunksize=(360, 128, 64), meta=np.ndarray&gt;\n",
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       "    instrument_name:  METEK MRR Pro 1.2.5, ID: MRRPro97, METEK Serial Number:...\n",
       "    site_name:        cms-mrr-002\n",
       "    field_names:      Za,Z,Zea,Ze,RR,LWC,PIA,VEL,WIDTH,SNR,spectrum_reflectiv...</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-624025a1-35da-4b72-a666-84249270fdaf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-624025a1-35da-4b72-a666-84249270fdaf' 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'>time</span>: 8280</li><li><span class='xr-has-index'>range</span>: 128</li><li><span>sweep</span>: 1</li><li><span>n_spectra</span>: 128</li><li><span>spectrum_n_samples</span>: 64</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7ebdb6c8-8df3-46fc-9bc0-5e9b44f775e3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7ebdb6c8-8df3-46fc-9bc0-5e9b44f775e3' class='xr-section-summary' >Coordinates: <span>(4)</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'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 35.0 ... 4.41e+03 4.445e+03</div><input id='attrs-b30eff0d-6205-4cd6-b54e-90ceda26fbbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b30eff0d-6205-4cd6-b54e-90ceda26fbbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5d2e3dd-1e71-48ef-bfed-b0c078cace9b' class='xr-var-data-in' type='checkbox'><label for='data-e5d2e3dd-1e71-48ef-bfed-b0c078cace9b' 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>standard name :</span></dt><dd>projection_range_coordinate</dd><dt><span>long_name :</span></dt><dd>range_to_measurement_volume</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>spacing_is_constant :</span></dt><dd>true</dd><dt><span>meters_to_center_of_first_gate :</span></dt><dd>0.0</dd><dt><span>meters_between_gates :</span></dt><dd>35.0</dd><dt><span>axis :</span></dt><dd>radial_range_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([   0.,   35.,   70.,  105.,  140.,  175.,  210.,  245.,  280.,  315.,\n",
       "        350.,  385.,  420.,  455.,  490.,  525.,  560.,  595.,  630.,  665.,\n",
       "        700.,  735.,  770.,  805.,  840.,  875.,  910.,  945.,  980., 1015.,\n",
       "       1050., 1085., 1120., 1155., 1190., 1225., 1260., 1295., 1330., 1365.,\n",
       "       1400., 1435., 1470., 1505., 1540., 1575., 1610., 1645., 1680., 1715.,\n",
       "       1750., 1785., 1820., 1855., 1890., 1925., 1960., 1995., 2030., 2065.,\n",
       "       2100., 2135., 2170., 2205., 2240., 2275., 2310., 2345., 2380., 2415.,\n",
       "       2450., 2485., 2520., 2555., 2590., 2625., 2660., 2695., 2730., 2765.,\n",
       "       2800., 2835., 2870., 2905., 2940., 2975., 3010., 3045., 3080., 3115.,\n",
       "       3150., 3185., 3220., 3255., 3290., 3325., 3360., 3395., 3430., 3465.,\n",
       "       3500., 3535., 3570., 3605., 3640., 3675., 3710., 3745., 3780., 3815.,\n",
       "       3850., 3885., 3920., 3955., 3990., 4025., 4060., 4095., 4130., 4165.,\n",
       "       4200., 4235., 4270., 4305., 4340., 4375., 4410., 4445.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-09T00:00:00.006162944 .....</div><input id='attrs-901c1370-e920-408e-9fa9-64a1dff8ea48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-901c1370-e920-408e-9fa9-64a1dff8ea48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-511a4ef5-0b88-4cb9-923f-3f35fdb7c000' class='xr-var-data-in' type='checkbox'><label for='data-511a4ef5-0b88-4cb9-923f-3f35fdb7c000' 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>standard name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time_in_seconds_since_volume_start</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-01-09T00:00:00.006162944&#x27;, &#x27;2024-01-09T00:00:10.006153216&#x27;,\n",
       "       &#x27;2024-01-09T00:00:20.006164992&#x27;, ..., &#x27;2024-01-09T22:59:30.006166016&#x27;,\n",
       "       &#x27;2024-01-09T22:59:40.006158080&#x27;, &#x27;2024-01-09T22:59:50.006151936&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360,), meta=np.ndarray&gt;</div><input id='attrs-561d07ff-df89-4225-9055-17858ae080d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-561d07ff-df89-4225-9055-17858ae080d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13c17fde-9790-4e0b-a4b4-09b8f9ccb846' class='xr-var-data-in' type='checkbox'><label for='data-13c17fde-9790-4e0b-a4b4-09b8f9ccb846' 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>standard name :</span></dt><dd>ray_elevation_angle</dd><dt><span>long_name :</span></dt><dd>elevation_angle_from_horizontal_plane</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>axis :</span></dt><dd>radial_elevation_coordinate</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 32.34 kiB </td>\n",
       "                        <td> 1.41 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (8280,) </td>\n",
       "                        <td> (360,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 23 chunks in 47 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "\n",
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       "  <line x1=\"73\" y1=\"0\" x2=\"73\" y2=\"25\" />\n",
       "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
       "  <line x1=\"83\" y1=\"0\" x2=\"83\" y2=\"25\" />\n",
       "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
       "  <line x1=\"99\" y1=\"0\" x2=\"99\" y2=\"25\" />\n",
       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
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       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>azimuth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360,), meta=np.ndarray&gt;</div><input id='attrs-05e978fa-ed36-4790-95c5-5520887077d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05e978fa-ed36-4790-95c5-5520887077d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd69d933-8954-4ccc-8fc8-a7bda5b8b05f' class='xr-var-data-in' type='checkbox'><label for='data-fd69d933-8954-4ccc-8fc8-a7bda5b8b05f' 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>standard name :</span></dt><dd>ray_azimuth_angle</dd><dt><span>long_name :</span></dt><dd>azimuth_angle_from_true_north</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>axis :</span></dt><dd>radial_azimuth_coordinate</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 32.34 kiB </td>\n",
       "                        <td> 1.41 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (8280,) </td>\n",
       "                        <td> (360,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 23 chunks in 47 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
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       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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       "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
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       "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
       "  <line x1=\"83\" y1=\"0\" x2=\"83\" y2=\"25\" />\n",
       "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
       "  <line x1=\"99\" y1=\"0\" x2=\"99\" y2=\"25\" />\n",
       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
       "  <line x1=\"109\" y1=\"0\" x2=\"109\" y2=\"25\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >8280</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8b5f0710-35ea-477c-86df-d369e8a85943' class='xr-section-summary-in' type='checkbox'  ><label for='section-8b5f0710-35ea-477c-86df-d369e8a85943' class='xr-section-summary' >Data variables: <span>(31)</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>volume_number</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-c070a105-579e-40bc-875f-fe08d2ccc728' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c070a105-579e-40bc-875f-fe08d2ccc728' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b8fa46d-2212-49fb-9e5c-0d136ff17e59' class='xr-var-data-in' type='checkbox'><label for='data-8b8fa46d-2212-49fb-9e5c-0d136ff17e59' 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, 0, ..., 0, 0, 0], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_coverage_start</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>|S128</div><div class='xr-var-preview xr-preview'>b&#x27;2024-01-09T00:00:00Z&#x27; ... b&#x27;20...</div><input id='attrs-8298191b-d5be-4679-8ea6-6c2aaecee800' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8298191b-d5be-4679-8ea6-6c2aaecee800' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-644a7515-3753-48b8-9e95-602d23baf7cb' class='xr-var-data-in' type='checkbox'><label for='data-644a7515-3753-48b8-9e95-602d23baf7cb' 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([b&#x27;2024-01-09T00:00:00Z&#x27;, b&#x27;2024-01-09T00:00:00Z&#x27;,\n",
       "       b&#x27;2024-01-09T00:00:00Z&#x27;, ..., b&#x27;2024-01-09T22:00:00Z&#x27;,\n",
       "       b&#x27;2024-01-09T22:00:00Z&#x27;, b&#x27;2024-01-09T22:00:00Z&#x27;], dtype=&#x27;|S128&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_coverage_end</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>|S128</div><div class='xr-var-preview xr-preview'>b&#x27;2024-01-09T00:59:50Z&#x27; ... b&#x27;20...</div><input id='attrs-57409d8e-a28d-44db-b278-2d0eade1f9fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57409d8e-a28d-44db-b278-2d0eade1f9fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83c9a399-34f8-4f12-8e2f-7835cf0e87ff' class='xr-var-data-in' type='checkbox'><label for='data-83c9a399-34f8-4f12-8e2f-7835cf0e87ff' 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([b&#x27;2024-01-09T00:59:50Z&#x27;, b&#x27;2024-01-09T00:59:50Z&#x27;,\n",
       "       b&#x27;2024-01-09T00:59:50Z&#x27;, ..., b&#x27;2024-01-09T22:59:50Z&#x27;,\n",
       "       b&#x27;2024-01-09T22:59:50Z&#x27;, b&#x27;2024-01-09T22:59:50Z&#x27;], dtype=&#x27;|S128&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_reference</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>|S128</div><div class='xr-var-preview xr-preview'>b&#x27;1970-01-01T00:00:00Z&#x27; ... b&#x27;19...</div><input id='attrs-385e1b08-6755-42f0-9368-313b90f0aab6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-385e1b08-6755-42f0-9368-313b90f0aab6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8693598d-bc2f-40b9-b6b5-e65e4ad797f7' class='xr-var-data-in' type='checkbox'><label for='data-8693598d-bc2f-40b9-b6b5-e65e4ad797f7' 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([b&#x27;1970-01-01T00:00:00Z&#x27;, b&#x27;1970-01-01T00:00:00Z&#x27;,\n",
       "       b&#x27;1970-01-01T00:00:00Z&#x27;, ..., b&#x27;1970-01-01T00:00:00Z&#x27;,\n",
       "       b&#x27;1970-01-01T00:00:00Z&#x27;, b&#x27;1970-01-01T00:00:00Z&#x27;], dtype=&#x27;|S128&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>instrument_type</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>|S128</div><div class='xr-var-preview xr-preview'>b&#x27;radar&#x27; b&#x27;radar&#x27; ... b&#x27;radar&#x27;</div><input id='attrs-601e3298-085a-4e5b-90cb-f1ecc4f8797d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-601e3298-085a-4e5b-90cb-f1ecc4f8797d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67ab0a09-4150-4121-b427-a9d9cc053b35' class='xr-var-data-in' type='checkbox'><label for='data-67ab0a09-4150-4121-b427-a9d9cc053b35' 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([b&#x27;radar&#x27;, b&#x27;radar&#x27;, b&#x27;radar&#x27;, ..., b&#x27;radar&#x27;, b&#x27;radar&#x27;, b&#x27;radar&#x27;],\n",
       "      dtype=&#x27;|S128&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>transfer_function</span></div><div class='xr-var-dims'>(time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360, 128), meta=np.ndarray&gt;</div><input id='attrs-9edb669f-0386-49c7-a580-69988576edf8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9edb669f-0386-49c7-a580-69988576edf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b5aca98-6668-47d8-a6d0-e7498c9dd8ca' class='xr-var-data-in' type='checkbox'><label for='data-8b5aca98-6668-47d8-a6d0-e7498c9dd8ca' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 8.09 MiB </td>\n",
       "                        <td> 360.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (8280, 128) </td>\n",
       "                        <td> (360, 128) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 23 chunks in 70 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"77\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"27\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "  <line x1=\"0\" y1=\"10\" x2=\"27\" y2=\"10\" />\n",
       "  <line x1=\"0\" y1=\"15\" x2=\"27\" y2=\"15\" />\n",
       "  <line x1=\"0\" y1=\"20\" x2=\"27\" y2=\"20\" />\n",
       "  <line x1=\"0\" y1=\"31\" x2=\"27\" y2=\"31\" />\n",
       "  <line x1=\"0\" y1=\"36\" x2=\"27\" y2=\"36\" />\n",
       "  <line x1=\"0\" y1=\"41\" x2=\"27\" y2=\"41\" />\n",
       "  <line x1=\"0\" y1=\"46\" x2=\"27\" y2=\"46\" />\n",
       "  <line x1=\"0\" y1=\"52\" x2=\"27\" y2=\"52\" />\n",
       "  <line x1=\"0\" y1=\"62\" x2=\"27\" y2=\"62\" />\n",
       "  <line x1=\"0\" y1=\"67\" x2=\"27\" y2=\"67\" />\n",
       "  <line x1=\"0\" y1=\"73\" x2=\"27\" y2=\"73\" />\n",
       "  <line x1=\"0\" y1=\"78\" x2=\"27\" y2=\"78\" />\n",
       "  <line x1=\"0\" y1=\"83\" x2=\"27\" y2=\"83\" />\n",
       "  <line x1=\"0\" y1=\"93\" x2=\"27\" y2=\"93\" />\n",
       "  <line x1=\"0\" y1=\"99\" x2=\"27\" y2=\"99\" />\n",
       "  <line x1=\"0\" y1=\"104\" x2=\"27\" y2=\"104\" />\n",
       "  <line x1=\"0\" y1=\"109\" x2=\"27\" y2=\"109\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"27\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 27.618325801124225,0.0 27.618325801124225,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
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       "  <text x=\"13.809163\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >128</text>\n",
       "  <text x=\"47.618326\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,47.618326,60.000000)\">8280</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>calibration_constant</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.011e+07 2.011e+07 ... 2.011e+07</div><input id='attrs-8e730be3-3128-44fb-bfa4-a9eb71d39682' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e730be3-3128-44fb-bfa4-a9eb71d39682' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e216bdf3-8c2b-4d69-8d46-e86ce0de2614' class='xr-var-data-in' type='checkbox'><label for='data-e216bdf3-8c2b-4d69-8d46-e86ce0de2614' 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([20107000., 20107000., 20107000., ..., 20107000., 20107000.,\n",
       "       20107000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.969e+36 9.969e+36 ... 9.969e+36</div><input id='attrs-47a86b5d-13ac-4d1c-a4de-ffd3eacd5caa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47a86b5d-13ac-4d1c-a4de-ffd3eacd5caa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3f7cedb-5c26-4486-8adc-2cd6659fa353' class='xr-var-data-in' type='checkbox'><label for='data-e3f7cedb-5c26-4486-8adc-2cd6659fa353' 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>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([9.96920997e+36, 9.96920997e+36, 9.96920997e+36, ...,\n",
       "       9.96920997e+36, 9.96920997e+36, 9.96920997e+36])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.969e+36 9.969e+36 ... 9.969e+36</div><input id='attrs-fe268931-5619-4f6d-b988-11603a7df2f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe268931-5619-4f6d-b988-11603a7df2f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cef69a24-ec4f-4cc4-9a98-f45fbaa860a6' class='xr-var-data-in' type='checkbox'><label for='data-cef69a24-ec4f-4cc4-9a98-f45fbaa860a6' 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>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([9.96920997e+36, 9.96920997e+36, 9.96920997e+36, ...,\n",
       "       9.96920997e+36, 9.96920997e+36, 9.96920997e+36])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>altitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.969e+36 9.969e+36 ... 9.969e+36</div><input id='attrs-b10729c4-b86a-48b0-9383-e2e5ca4ea689' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b10729c4-b86a-48b0-9383-e2e5ca4ea689' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c31b237d-7856-41b5-8e3d-5280a4e12bab' class='xr-var-data-in' type='checkbox'><label for='data-c31b237d-7856-41b5-8e3d-5280a4e12bab' 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>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([9.96920997e+36, 9.96920997e+36, 9.96920997e+36, ...,\n",
       "       9.96920997e+36, 9.96920997e+36, 9.96920997e+36])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>doppler_shift_spectrum</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-4731710c-a5d5-49fb-88b4-9760fe9e8ff5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4731710c-a5d5-49fb-88b4-9760fe9e8ff5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d6baa66-aef0-49ce-8123-a076c88e7ffe' class='xr-var-data-in' type='checkbox'><label for='data-3d6baa66-aef0-49ce-8123-a076c88e7ffe' 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., 0., ..., 0., 0., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sweep_number</span></div><div class='xr-var-dims'>(time, sweep)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360, 1), meta=np.ndarray&gt;</div><input id='attrs-4ddef92c-a090-48bd-a7bb-665252b75c94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ddef92c-a090-48bd-a7bb-665252b75c94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cf83df6-4a65-486e-b5d6-a67269a50ff2' class='xr-var-data-in' type='checkbox'><label for='data-4cf83df6-4a65-486e-b5d6-a67269a50ff2' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 32.34 kiB </td>\n",
       "                        <td> 1.41 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (8280, 1) </td>\n",
       "                        <td> (360, 1) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 23 chunks in 70 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "  <line x1=\"0\" y1=\"52\" x2=\"25\" y2=\"52\" />\n",
       "  <line x1=\"0\" y1=\"62\" x2=\"25\" y2=\"62\" />\n",
       "  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n",
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       "  <line x1=\"0\" y1=\"99\" x2=\"25\" y2=\"99\" />\n",
       "  <line x1=\"0\" y1=\"104\" x2=\"25\" y2=\"104\" />\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sweep_mode</span></div><div class='xr-var-dims'>(time, sweep)</div><div class='xr-var-dtype'>|S128</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360, 1), meta=np.ndarray&gt;</div><input id='attrs-fbdbdeaf-efe5-4bbe-bd62-c56d3e255737' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fbdbdeaf-efe5-4bbe-bd62-c56d3e255737' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea50783d-6bfc-4ccd-b32b-babe1c808bbf' class='xr-var-data-in' type='checkbox'><label for='data-ea50783d-6bfc-4ccd-b32b-babe1c808bbf' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
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       "                <tbody>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Z</span></div><div class='xr-var-dims'>(time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360, 128), meta=np.ndarray&gt;</div><input id='attrs-6431df5f-b3ca-4126-aee7-0a291160ec34' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6431df5f-b3ca-4126-aee7-0a291160ec34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b947359-e669-41aa-82ea-563d4ebe01f2' class='xr-var-data-in' type='checkbox'><label for='data-3b947359-e669-41aa-82ea-563d4ebe01f2' 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>standard_name :</span></dt><dd>log_reflectivity</dd><dt><span>long_name :</span></dt><dd></dd><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>field_folds :</span></dt><dd>false</dd><dt><span>fold_limit_lower :</span></dt><dd>0.0</dd><dt><span>fold_limit_upper :</span></dt><dd>0.0</dd><dt><span>thresholding_xml :</span></dt><dd></dd><dt><span>legend_xml :</span></dt><dd></dd><dt><span>is_discreet :</span></dt><dd>false</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                        <td> (360, 128) </td>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
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       "    </tr>\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 8.09 MiB </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Ze</span></div><div class='xr-var-dims'>(time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360, 128), meta=np.ndarray&gt;</div><input id='attrs-39b7c49b-e93f-4414-b1b8-26d14deefa42' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39b7c49b-e93f-4414-b1b8-26d14deefa42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-575de4f4-3d5c-44ea-a95f-59b90a779ee8' class='xr-var-data-in' type='checkbox'><label for='data-575de4f4-3d5c-44ea-a95f-59b90a779ee8' 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>standard_name :</span></dt><dd>equivalent_reflectivity_factor</dd><dt><span>long_name :</span></dt><dd></dd><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>field_folds :</span></dt><dd>false</dd><dt><span>fold_limit_lower :</span></dt><dd>0.0</dd><dt><span>fold_limit_upper :</span></dt><dd>0.0</dd><dt><span>thresholding_xml :</span></dt><dd></dd><dt><span>legend_xml :</span></dt><dd></dd><dt><span>is_discreet :</span></dt><dd>false</dd></dl></div><div class='xr-var-data'><table>\n",
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       "            <table style=\"border-collapse: collapse;\">\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                    <tr>\n",
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       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RR</span></div><div class='xr-var-dims'>(time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360, 128), meta=np.ndarray&gt;</div><input id='attrs-1d2abd3d-edd2-4d98-b0d4-4acfb365dbc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d2abd3d-edd2-4d98-b0d4-4acfb365dbc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1abadf8a-5fa8-4f2e-ac7c-eee59c2df808' class='xr-var-data-in' type='checkbox'><label for='data-1abadf8a-5fa8-4f2e-ac7c-eee59c2df808' 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>standard_name :</span></dt><dd>rainfall_rate</dd><dt><span>long_name :</span></dt><dd></dd><dt><span>units :</span></dt><dd>mm h-1</dd><dt><span>field_folds :</span></dt><dd>false</dd><dt><span>fold_limit_lower :</span></dt><dd>0.0</dd><dt><span>fold_limit_upper :</span></dt><dd>0.0</dd><dt><span>thresholding_xml :</span></dt><dd></dd><dt><span>legend_xml :</span></dt><dd></dd><dt><span>is_discreet :</span></dt><dd>false</dd></dl></div><div class='xr-var-data'><table>\n",
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       "            <table style=\"border-collapse: collapse;\">\n",
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       "                        <td> </td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 23 chunks in 47 graph layers </td>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWC</span></div><div class='xr-var-dims'>(time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360, 128), meta=np.ndarray&gt;</div><input id='attrs-feef9f9c-425b-47c9-acb9-6efcaa8d583e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-feef9f9c-425b-47c9-acb9-6efcaa8d583e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f43f861-23d0-4661-87a4-13b9c16b34c5' class='xr-var-data-in' type='checkbox'><label for='data-2f43f861-23d0-4661-87a4-13b9c16b34c5' 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>standard_name :</span></dt><dd>mass_concentration_of_liquid_water_in_air</dd><dt><span>long_name :</span></dt><dd></dd><dt><span>units :</span></dt><dd>g m-3</dd><dt><span>field_folds :</span></dt><dd>false</dd><dt><span>fold_limit_lower :</span></dt><dd>0.0</dd><dt><span>fold_limit_upper :</span></dt><dd>0.0</dd><dt><span>thresholding_xml :</span></dt><dd></dd><dt><span>legend_xml :</span></dt><dd></dd><dt><span>is_discreet :</span></dt><dd>false</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 8.09 MiB </td>\n",
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       "                        <td> (8280, 128) </td>\n",
       "                        <td> (360, 128) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PIA</span></div><div class='xr-var-dims'>(time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360, 128), meta=np.ndarray&gt;</div><input id='attrs-b47beced-f44f-4bac-bee9-27ec720e01c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b47beced-f44f-4bac-bee9-27ec720e01c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc4f2185-e592-454b-8f68-4f1fdd93672d' class='xr-var-data-in' type='checkbox'><label for='data-dc4f2185-e592-454b-8f68-4f1fdd93672d' 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>standard_name :</span></dt><dd>path_integrated_rain_attenuation</dd><dt><span>long_name :</span></dt><dd></dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>field_folds :</span></dt><dd>false</dd><dt><span>fold_limit_lower :</span></dt><dd>0.0</dd><dt><span>fold_limit_upper :</span></dt><dd>0.0</dd><dt><span>thresholding_xml :</span></dt><dd></dd><dt><span>legend_xml :</span></dt><dd></dd><dt><span>is_discreet :</span></dt><dd>false</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 8.09 MiB </td>\n",
       "                        <td> 360.00 kiB </td>\n",
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       "                        <td> (360, 128) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 23 chunks in 47 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VEL</span></div><div class='xr-var-dims'>(time, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360, 128), meta=np.ndarray&gt;</div><input id='attrs-646bf0b9-431b-4581-8194-812a81290b8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-646bf0b9-431b-4581-8194-812a81290b8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2489f83e-b6c1-4367-88fe-4cc2d1bc2cb8' class='xr-var-data-in' type='checkbox'><label for='data-2489f83e-b6c1-4367-88fe-4cc2d1bc2cb8' 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>standard_name :</span></dt><dd>radial_velocity_of_scatterers_towards_instrument</dd><dt><span>long_name :</span></dt><dd></dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>field_folds :</span></dt><dd>true</dd><dt><span>fold_limit_lower :</span></dt><dd>-0.0</dd><dt><span>fold_limit_upper :</span></dt><dd>11.890331</dd><dt><span>thresholding_xml :</span></dt><dd></dd><dt><span>legend_xml :</span></dt><dd></dd><dt><span>is_discreet :</span></dt><dd>false</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.04 MiB </td>\n",
       "                        <td> 180.00 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (360, 128) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 23 chunks in 47 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"77\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 8.09 MiB </td>\n",
       "                        <td> 360.00 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (360, 128) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 23 chunks in 47 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ML</span></div><div class='xr-var-dims'>(time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360, 128), meta=np.ndarray&gt;</div><input id='attrs-3a12d7c9-1d76-4d3e-b3c5-7602bde791dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a12d7c9-1d76-4d3e-b3c5-7602bde791dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2134c935-7cb6-4ba8-a5a8-e5402d705f19' class='xr-var-data-in' type='checkbox'><label for='data-2134c935-7cb6-4ba8-a5a8-e5402d705f19' 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>standard_name :</span></dt><dd>melting_layer</dd><dt><span>long_name :</span></dt><dd></dd><dt><span>units :</span></dt><dd></dd><dt><span>field_folds :</span></dt><dd>false</dd><dt><span>fold_limit_lower :</span></dt><dd>0.0</dd><dt><span>fold_limit_upper :</span></dt><dd>0.0</dd><dt><span>thresholding_xml :</span></dt><dd></dd><dt><span>legend_xml :</span></dt><dd></dd><dt><span>is_discreet :</span></dt><dd>false</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 8.09 MiB </td>\n",
       "                        <td> 360.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (8280, 128) </td>\n",
       "                        <td> (360, 128) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 23 chunks in 47 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SNR</span></div><div class='xr-var-dims'>(time, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360, 128), meta=np.ndarray&gt;</div><input id='attrs-1a87f9b2-9486-41c6-aca6-ecd59316aee3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a87f9b2-9486-41c6-aca6-ecd59316aee3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcd4b3d8-9843-4ebc-bf35-d3b981adf2c6' class='xr-var-data-in' type='checkbox'><label for='data-bcd4b3d8-9843-4ebc-bf35-d3b981adf2c6' 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>standard_name :</span></dt><dd>signal_to_noise_ratio</dd><dt><span>long_name :</span></dt><dd></dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>field_folds :</span></dt><dd>false</dd><dt><span>fold_limit_lower :</span></dt><dd>0.0</dd><dt><span>fold_limit_upper :</span></dt><dd>0.0</dd><dt><span>thresholding_xml :</span></dt><dd></dd><dt><span>legend_xml :</span></dt><dd></dd><dt><span>is_discreet :</span></dt><dd>false</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.04 MiB </td>\n",
       "                        <td> 180.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (8280, 128) </td>\n",
       "                        <td> (360, 128) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 23 chunks in 47 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>index_spectra</span></div><div class='xr-var-dims'>(time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360, 128), meta=np.ndarray&gt;</div><input id='attrs-721ce805-3ac3-4d8d-884d-8fb8e01689db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-721ce805-3ac3-4d8d-884d-8fb8e01689db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce1568e9-7fbe-4da1-b114-81488da96061' class='xr-var-data-in' type='checkbox'><label for='data-ce1568e9-7fbe-4da1-b114-81488da96061' 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>standard_name :</span></dt><dd>index variable spectra</dd><dt><span>long_name :</span></dt><dd></dd><dt><span>units :</span></dt><dd></dd><dt><span>field_folds :</span></dt><dd>false</dd><dt><span>fold_limit_lower :</span></dt><dd>0.0</dd><dt><span>fold_limit_upper :</span></dt><dd>0.0</dd><dt><span>thresholding_xml :</span></dt><dd></dd><dt><span>legend_xml :</span></dt><dd></dd><dt><span>is_discreet :</span></dt><dd>true</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 8.09 MiB </td>\n",
       "                        <td> 360.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (8280, 128) </td>\n",
       "                        <td> (360, 128) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 23 chunks in 47 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"77\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "  <!-- Horizontal lines -->\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spectrum_raw</span></div><div class='xr-var-dims'>(time, n_spectra, spectrum_n_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360, 128, 64), meta=np.ndarray&gt;</div><input id='attrs-e5ecec55-ab8e-4ec7-ba2c-451fd61fafad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5ecec55-ab8e-4ec7-ba2c-451fd61fafad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5105ad5e-5e1b-4673-8b65-0609d11f14dd' class='xr-var-data-in' type='checkbox'><label for='data-5105ad5e-5e1b-4673-8b65-0609d11f14dd' 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>standard name :</span></dt><dd>log_attenuated_power</dd><dt><span>long_name :</span></dt><dd></dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>index_var_name :</span></dt><dd></dd><dt><span>block_avg_length :</span></dt><dd>0</dd><dt><span>is_spectrum :</span></dt><dd>true</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 517.50 MiB </td>\n",
       "                        <td> 22.50 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (8280, 128, 64) </td>\n",
       "                        <td> (360, 128, 64) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 23 chunks in 47 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N</span></div><div class='xr-var-dims'>(time, n_spectra, spectrum_n_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360, 128, 64), meta=np.ndarray&gt;</div><input id='attrs-f2fee437-e9cc-4f28-8a49-468b032140b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2fee437-e9cc-4f28-8a49-468b032140b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68eea5f7-ae17-4cab-bd60-53f6bd4f0f2d' class='xr-var-data-in' type='checkbox'><label for='data-68eea5f7-ae17-4cab-bd60-53f6bd4f0f2d' 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>standard name :</span></dt><dd>drop_size_distribution</dd><dt><span>long_name :</span></dt><dd></dd><dt><span>units :</span></dt><dd>1</dd><dt><span>index_var_name :</span></dt><dd></dd><dt><span>block_avg_length :</span></dt><dd>0</dd><dt><span>is_spectrum :</span></dt><dd>true</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 517.50 MiB </td>\n",
       "                        <td> 22.50 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (8280, 128, 64) </td>\n",
       "                        <td> (360, 128, 64) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 23 chunks in 47 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>D</span></div><div class='xr-var-dims'>(time, n_spectra, spectrum_n_samples)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360, 128, 64), meta=np.ndarray&gt;</div><input id='attrs-b1f10baf-00f2-4714-8b6a-4ff4ee0fb277' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1f10baf-00f2-4714-8b6a-4ff4ee0fb277' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2217a287-c699-4874-861b-662d25bd4d3e' class='xr-var-data-in' type='checkbox'><label for='data-2217a287-c699-4874-861b-662d25bd4d3e' 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>standard name :</span></dt><dd>drop_sizes</dd><dt><span>long_name :</span></dt><dd></dd><dt><span>units :</span></dt><dd>1</dd><dt><span>index_var_name :</span></dt><dd></dd><dt><span>block_avg_length :</span></dt><dd>0</dd><dt><span>is_spectrum :</span></dt><dd>true</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 517.50 MiB </td>\n",
       "                        <td> 22.50 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (8280, 128, 64) </td>\n",
       "                        <td> (360, 128, 64) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 23 chunks in 70 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "\n",
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       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"98\" x2=\"106\" y2=\"98\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"98\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"98\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,98.20656109524188 80.58823529411765,98.20656109524188\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"93.294544\" y=\"118.206561\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >64</text>\n",
       "  <text x=\"126.000852\" y=\"84.397398\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,84.397398)\">128</text>\n",
       "  <text x=\"35.294118\" y=\"82.912443\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,82.912443)\">8280</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8ddb1f76-564c-4c05-b281-6028594d32f4' class='xr-section-summary-in' type='checkbox'  ><label for='section-8ddb1f76-564c-4c05-b281-6028594d32f4' class='xr-section-summary' >Indexes: <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-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-901ac1a6-238f-4e6b-9d08-50e753847e8b' class='xr-index-data-in' type='checkbox'/><label for='index-901ac1a6-238f-4e6b-9d08-50e753847e8b' 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(Float64Index([   0.0,   35.0,   70.0,  105.0,  140.0,  175.0,  210.0,  245.0,\n",
       "               280.0,  315.0,\n",
       "              ...\n",
       "              4130.0, 4165.0, 4200.0, 4235.0, 4270.0, 4305.0, 4340.0, 4375.0,\n",
       "              4410.0, 4445.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;range&#x27;, length=128))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-08bc4b5d-a7d7-4aa5-a537-18b247a74190' class='xr-index-data-in' type='checkbox'/><label for='index-08bc4b5d-a7d7-4aa5-a537-18b247a74190' 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(DatetimeIndex([&#x27;2024-01-09 00:00:00.006162944&#x27;,\n",
       "               &#x27;2024-01-09 00:00:10.006153216&#x27;,\n",
       "               &#x27;2024-01-09 00:00:20.006164992&#x27;,\n",
       "               &#x27;2024-01-09 00:00:30.006149888&#x27;,\n",
       "               &#x27;2024-01-09 00:00:40.006150912&#x27;,\n",
       "               &#x27;2024-01-09 00:00:50.006139136&#x27;,\n",
       "               &#x27;2024-01-09 00:01:00.006153984&#x27;,\n",
       "               &#x27;2024-01-09 00:01:10.006181120&#x27;,\n",
       "               &#x27;2024-01-09 00:01:20.006139904&#x27;,\n",
       "               &#x27;2024-01-09 00:01:30.006156800&#x27;,\n",
       "               ...\n",
       "               &#x27;2024-01-09 22:58:20.006149120&#x27;,\n",
       "               &#x27;2024-01-09 22:58:30.006162944&#x27;,\n",
       "               &#x27;2024-01-09 22:58:40.006142976&#x27;,\n",
       "               &#x27;2024-01-09 22:58:50.006139904&#x27;,\n",
       "               &#x27;2024-01-09 22:59:00.006153216&#x27;,\n",
       "               &#x27;2024-01-09 22:59:10.006164992&#x27;,\n",
       "               &#x27;2024-01-09 22:59:20.006145024&#x27;,\n",
       "               &#x27;2024-01-09 22:59:30.006166016&#x27;,\n",
       "               &#x27;2024-01-09 22:59:40.006158080&#x27;,\n",
       "               &#x27;2024-01-09 22:59:50.006151936&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=8280, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-20f8ad73-ce72-48c8-8a20-fd37726d975c' class='xr-section-summary-in' type='checkbox'  ><label for='section-20f8ad73-ce72-48c8-8a20-fd37726d975c' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF/Radial</dd><dt><span>version :</span></dt><dd>1.3</dd><dt><span>title :</span></dt><dd>METEK MRR Pro 1.2.5 Data</dd><dt><span>institution :</span></dt><dd></dd><dt><span>references :</span></dt><dd></dd><dt><span>source :</span></dt><dd></dd><dt><span>history :</span></dt><dd></dd><dt><span>comment :</span></dt><dd></dd><dt><span>instrument_name :</span></dt><dd>METEK MRR Pro 1.2.5, ID: MRRPro97, METEK Serial Number:  0515119169, Software:  MRR Pro 1.2.5</dd><dt><span>site_name :</span></dt><dd>cms-mrr-002</dd><dt><span>field_names :</span></dt><dd>Za,Z,Zea,Ze,RR,LWC,PIA,VEL,WIDTH,SNR,spectrum_reflectivity,N</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                 (time: 8280, range: 128, sweep: 1, n_spectra: 128,\n",
       "                             spectrum_n_samples: 64)\n",
       "Coordinates:\n",
       "  * range                   (range) float32 0.0 35.0 70.0 ... 4.41e+03 4.445e+03\n",
       "  * time                    (time) datetime64[ns] 2024-01-09T00:00:00.0061629...\n",
       "    elevation               (time) float32 dask.array<chunksize=(360,), meta=np.ndarray>\n",
       "    azimuth                 (time) float32 dask.array<chunksize=(360,), meta=np.ndarray>\n",
       "Dimensions without coordinates: sweep, n_spectra, spectrum_n_samples\n",
       "Data variables: (12/31)\n",
       "    volume_number           (time) int32 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0\n",
       "    time_coverage_start     (time) |S128 b'2024-01-09T00:00:00Z' ... b'2024-0...\n",
       "    time_coverage_end       (time) |S128 b'2024-01-09T00:59:50Z' ... b'2024-0...\n",
       "    time_reference          (time) |S128 b'1970-01-01T00:00:00Z' ... b'1970-0...\n",
       "    instrument_type         (time) |S128 b'radar' b'radar' ... b'radar' b'radar'\n",
       "    transfer_function       (time, range) float64 dask.array<chunksize=(360, 128), meta=np.ndarray>\n",
       "    ...                      ...\n",
       "    ML                      (time, range) float64 dask.array<chunksize=(360, 128), meta=np.ndarray>\n",
       "    SNR                     (time, range) float32 dask.array<chunksize=(360, 128), meta=np.ndarray>\n",
       "    index_spectra           (time, range) float64 dask.array<chunksize=(360, 128), meta=np.ndarray>\n",
       "    spectrum_raw            (time, n_spectra, spectrum_n_samples) float64 dask.array<chunksize=(360, 128, 64), meta=np.ndarray>\n",
       "    N                       (time, n_spectra, spectrum_n_samples) float64 dask.array<chunksize=(360, 128, 64), meta=np.ndarray>\n",
       "    D                       (time, n_spectra, spectrum_n_samples) float64 dask.array<chunksize=(360, 128, 64), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:      CF/Radial\n",
       "    version:          1.3\n",
       "    title:            METEK MRR Pro 1.2.5 Data\n",
       "    institution:      \n",
       "    references:       \n",
       "    source:           \n",
       "    history:          \n",
       "    comment:          \n",
       "    instrument_name:  METEK MRR Pro 1.2.5, ID: MRRPro97, METEK Serial Number:...\n",
       "    site_name:        cms-mrr-002\n",
       "    field_names:      Za,Z,Zea,Ze,RR,LWC,PIA,VEL,WIDTH,SNR,spectrum_reflectiv..."
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "af6112f3-216e-4976-82b5-e66b001ece33",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ds['spectrum_raw'].sel(time='2023-12-16T15:40:00').isel(n_spectra=15).values\n",
    "ds = ds.resample(time='60s').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fb08fbd8-c918-439e-8222-e3f0e4b6ccac",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(ds['time'])):\n",
    "    array = ds['spectrum_raw'].isel(time=i).values[-1::-1, :]\n",
    "    time_str = ds['time'][i].dt.strftime('%Y%m%d.%H%M%S').values\n",
    "    fname = '/Users/rjackson/various_notebooks/labelbox_plots/spectra%s.png' % (time_str)\n",
    "    plt.imsave(fname, array, vmin=-10, vmax=50, cmap='ChaseSpectral')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "be81963a-9178-46af-83a5-917177037ba1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1aad5a230>"
      ]
     },
     "execution_count": 21,
     "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": [
    "ds['Ze'].T.plot(cmap='ChaseSpectral')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8e8d6d99-c8ae-47d0-93c8-28a8b7fcb837",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1b02e58d0>"
      ]
     },
     "execution_count": 20,
     "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": [
    "ds['spectrum_raw'].sel(time='2024-01-09T015:00:00').plot(cmap='ChaseSpectral', vmin=-20, vmax=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f911c51a-90a4-4c59-ad50-74585cc47321",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['snr_spectrum'] = 10**(ds['spectrum_raw']/10) / 10**(ds['spectrum_raw'].median(dim='spectrum_n_samples')/10)\n",
    "ds['snr_spectrum'] = 10 * np.log10(ds['snr_spectrum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "6227e60c-3d3a-4219-a575-7ebf1bc725f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1caafe7d0>"
      ]
     },
     "execution_count": 232,
     "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": [
    "ds['snr_spectrum'].sel(time='2024-01-09T04:00:00').plot(cmap='ChaseSpectral', vmin=-10, vmax=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "4215e0c6-86dc-40a2-919f-902603c07921",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1c12ce440>"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_image = ds['snr_spectrum'].sel(time='2024-01-09T04:01:00').values.squeeze()\n",
    "input_mask = input_image > 1\n",
    "label_image = skimage.measure.label(input_mask)\n",
    "props = skimage.measure.regionprops(label_image)\n",
    "plt.pcolormesh(label_image, cmap='Spectral_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "8f7a2c14-8310-4571-a346-c01db5f6b93e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.656854249492381"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props[0].axis_major_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "0cdd101d-3664-4ed5-a36c-32513c0d88fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([11., 46.,  2.,  3., 11.,  2.,  1.,  5.,  2., 19.]),\n",
       " array([-76.73377941, -60.06040147, -43.38702353, -26.71364559,\n",
       "        -10.04026765,   6.63311029,  23.30648824,  39.97986618,\n",
       "         56.65324412,  73.32662206,  90.        ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "orientations = [x.orientation for x in props]\n",
    "plt.hist(np.rad2deg(orientations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "5f97319f-b1bf-41b7-9862-1ac36cd9e315",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(label_image, cmap='Spectral_r')\n",
    "for p in props:\n",
    "    plt.text(p.centroid[1], p.centroid[0], '%02.1f' % np.rad2deg(p.orientation))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "1a5fd731-1189-40a9-87ea-781e155baaf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0/8280\n",
      "100/8280\n",
      "200/8280\n",
      "300/8280\n",
      "400/8280\n",
      "500/8280\n",
      "600/8280\n",
      "700/8280\n",
      "800/8280\n",
      "900/8280\n",
      "1000/8280\n",
      "1100/8280\n",
      "1200/8280\n",
      "1300/8280\n",
      "1400/8280\n",
      "1500/8280\n",
      "1600/8280\n",
      "1700/8280\n",
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     ]
    }
   ],
   "source": [
    "props_list = []\n",
    "ds['snr_spectrum'].load()\n",
    "for i in range(len(ds['time'])):\n",
    "    input_image = ds['snr_spectrum'].isel(time=i).values.squeeze()\n",
    "    input_mask = input_image > 1\n",
    "    label_image = skimage.measure.label(input_mask)\n",
    "    props = skimage.measure.regionprops(label_image)\n",
    "    props_list = props_list + props\n",
    "    if i % 100 == 0:\n",
    "        print('%d/%d' % (i, len(ds['time'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "ec630662-2d5f-4510-b33f-7af3450bf1bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "orientations = np.rad2deg(np.array([p.orientation for p in props_list]))\n",
    "areas = np.array([p.area for p in props_list])\n",
    "orientations = orientations[areas > 100]\n",
    "areas = areas[areas > 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "9414dcd0-59ac-483e-8e14-5f3ab17b7e42",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins_x = np.arange(-90, 90, 5.)\n",
    "bins_y = np.arange(1, 200, 5.)\n",
    "hist_2d, bins_x, bins_y = np.histogram2d(orientations, np.log10(areas), bins=[bins_x, bins_y])\n",
    "bins_x, bins_y = np.meshgrid(bins_x, bins_y, indexing='ij')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "45625fb0-232a-48c7-986b-89663451701e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([5526., 4152., 1476.,  490.,  138.,  188.,  193.,  188.,  175.,\n",
       "          33.]),\n",
       " array([101., 180., 259., 338., 417., 496., 575., 654., 733., 812., 891.]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(areas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "6a867470-44c6-4bb6-80e1-76804b140280",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rjackson/mambaforge/envs/pydda_env/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-19 {color: black;}#sk-container-id-19 pre{padding: 0;}#sk-container-id-19 div.sk-toggleable {background-color: white;}#sk-container-id-19 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-19 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-19 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-19 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-19 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-19 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-19 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-19 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-19 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-19 div.sk-item {position: relative;z-index: 1;}#sk-container-id-19 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-19 div.sk-item::before, #sk-container-id-19 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-19 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-19 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-19 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-19 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-19 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-19 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-19 div.sk-label-container {text-align: center;}#sk-container-id-19 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-19 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=2)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=2)"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster = KMeans(n_clusters=2)\n",
    "scaler = StandardScaler()\n",
    "scaled_input = scaler.fit_transform(np.stack([orientations, areas], axis=-1))\n",
    "cluster.fit(scaled_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "932d0af5-46b1-4a41-bd40-c3550169cd25",
   "metadata": {},
   "outputs": [],
   "source": [
    "label = cluster.predict(scaled_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "5b8d395e-32fb-46ea-af48-bca68d13b5e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    plt.scatter(orientations[label == i], areas[label == i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "89377587-61ae-4c8c-a913-99357b397966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(626349, 2)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.stack([orientations/90., np.log10(areas)/3], axis=-1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "20bfcf78-d707-403f-aeb7-de16c448a488",
   "metadata": {},
   "outputs": [],
   "source": [
    "import labelbox as lb\n",
    "API_KEY = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VySWQiOiJjbDF3azFhdmkwMHM3MHo5YTlmN29nMm5uIiwib3JnYW5pemF0aW9uSWQiOiJjazk0amF5bjFzYW9lMDcxNnU4ZmlwZTRnIiwiYXBpS2V5SWQiOiJjbHQ4cjZ6MXYwMXFhMDcwcGUxazE4NGt5Iiwic2VjcmV0IjoiMTZiNDliMGZjNTkzNjljODYwMjA4ZWM5YWFiNjNlNTIiLCJpYXQiOjE3MDkzMDM1MzgsImV4cCI6MjM0MDQ1NTUzOH0.VDxRJNh0DpD78N1d7R4aLRZQZmjfXNoKAUGUeR-ldG0\"\n",
    "client = lb.Client(api_key=API_KEY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "9132dfce-0fe2-4f59-8227-5017ec81c64a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "PROJECT_ID = \"clslyo3q507j707505py9669b\"\n",
    "project = client.get_project(PROJECT_ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "a2a5b742-f666-4e6c-a8b7-23b104768b10",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "list indices must be integers or slices, not str",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[270], line 27\u001b[0m\n\u001b[1;32m     24\u001b[0m   \u001b[38;5;28mprint\u001b[39m(export_task\u001b[38;5;241m.\u001b[39merrors)\n\u001b[1;32m     26\u001b[0m export_json \u001b[38;5;241m=\u001b[39m export_task\u001b[38;5;241m.\u001b[39mresult\n\u001b[0;32m---> 27\u001b[0m \u001b[43mexport_json\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdata_row\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not str"
     ]
    }
   ],
   "source": [
    "# Set the export params to include/exclude certain fields. Make sure each of these fields are correctly grabbed \n",
    "export_params= {\n",
    "  \"attachments\": True,\n",
    "  \"metadata_fields\": True,\n",
    "  \"data_row_details\": True,\n",
    "  \"project_details\": True,\n",
    "  \"label_details\": True,\n",
    "  \"performance_details\": True\n",
    "}\n",
    "\n",
    "# You can set the range for last_activity_at and label_created_at. \n",
    "# For context, last_activity_at captures the creation and modification of labels, metadata, status, comments and reviews.\n",
    "# Note: This is an AND logic between the filters, so usually using one filter is sufficient.\n",
    "\n",
    "filters= {\n",
    "  \"last_activity_at\": [\"2024-02-01 00:00:00\", \"2050-01-01 00:00:00\"],\n",
    "  \"workflow_status\": \"Done\"\n",
    "}\n",
    "\n",
    "export_task = project.export_v2(params=export_params, filters=filters)\n",
    "export_task.wait_till_done()\n",
    "\n",
    "if export_task.errors:\n",
    "  print(export_task.errors)\n",
    "\n",
    "export_json = export_task.result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "fa1fbbb4-d0ec-4177-8853-606f16f44395",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'feature_id': 'clsyzj7re00043b6nt7imjw29',\n",
       "  'feature_schema_id': 'clslz14ze0br3073pgh1ghbql',\n",
       "  'name': 'Intereference',\n",
       "  'value': 'intereference',\n",
       "  'annotation_kind': 'ImageSegmentationMask',\n",
       "  'classifications': [],\n",
       "  'mask': {'url': 'https://api.labelbox.com/api/v1/projects/clslyo3q507j707505py9669b/annotations/clsyzj7re00043b6nt7imjw29/index/1/mask'},\n",
       "  'composite_mask': {'url': 'https://api.labelbox.com/api/v1/tasks/clt8rp8ia02gh07xsh8mf4h92/masks/clsyzhunv00023b6n88gq4oxn/index/1',\n",
       "   'color_rgb': [145, 154, 181]}},\n",
       " {'feature_id': 'clsz0g4nx00ck3b6ngs7u0rc8',\n",
       "  'feature_schema_id': 'clslz14ze0br3073pgh1ghbql',\n",
       "  'name': 'Intereference',\n",
       "  'value': 'intereference',\n",
       "  'annotation_kind': 'ImageSegmentationMask',\n",
       "  'classifications': [],\n",
       "  'mask': {'url': 'https://api.labelbox.com/api/v1/projects/clslyo3q507j707505py9669b/annotations/clsz0g4nx00ck3b6ngs7u0rc8/index/1/mask'},\n",
       "  'composite_mask': {'url': 'https://api.labelbox.com/api/v1/tasks/clt8rp8ia02gh07xsh8mf4h92/masks/clsyzhunv00023b6n88gq4oxn/index/1',\n",
       "   'color_rgb': [7, 214, 83]}},\n",
       " {'feature_id': 'clt3d8p5x00033b6ntb36bpvp',\n",
       "  'feature_schema_id': 'clslz14zf0br5073pf2cefh26',\n",
       "  'name': 'Precipitaiton',\n",
       "  'value': 'precipitaiton',\n",
       "  'annotation_kind': 'ImageSegmentationMask',\n",
       "  'classifications': [],\n",
       "  'mask': {'url': 'https://api.labelbox.com/api/v1/projects/clslyo3q507j707505py9669b/annotations/clt3d8p5x00033b6ntb36bpvp/index/1/mask'},\n",
       "  'composite_mask': {'url': 'https://api.labelbox.com/api/v1/tasks/clt8rp8ia02gh07xsh8mf4h92/masks/clsyzhunv00023b6n88gq4oxn/index/1',\n",
       "   'color_rgb': [186, 11, 86]}},\n",
       " {'feature_id': 'clt3d8x2s00073b6nuin1oodn',\n",
       "  'feature_schema_id': 'clslz14ze0br3073pgh1ghbql',\n",
       "  'name': 'Intereference',\n",
       "  'value': 'intereference',\n",
       "  'annotation_kind': 'ImageSegmentationMask',\n",
       "  'classifications': [],\n",
       "  'mask': {'url': 'https://api.labelbox.com/api/v1/projects/clslyo3q507j707505py9669b/annotations/clt3d8x2s00073b6nuin1oodn/index/1/mask'},\n",
       "  'composite_mask': {'url': 'https://api.labelbox.com/api/v1/tasks/clt8rp8ia02gh07xsh8mf4h92/masks/clsyzhunv00023b6n88gq4oxn/index/1',\n",
       "   'color_rgb': [12, 93, 72]}},\n",
       " {'feature_id': 'clt3d9atq000d3b6nhjlio057',\n",
       "  'feature_schema_id': 'clslz14zf0br5073pf2cefh26',\n",
       "  'name': 'Precipitaiton',\n",
       "  'value': 'precipitaiton',\n",
       "  'annotation_kind': 'ImageSegmentationMask',\n",
       "  'classifications': [],\n",
       "  'mask': {'url': 'https://api.labelbox.com/api/v1/projects/clslyo3q507j707505py9669b/annotations/clt3d9atq000d3b6nhjlio057/index/1/mask'},\n",
       "  'composite_mask': {'url': 'https://api.labelbox.com/api/v1/tasks/clt8rp8ia02gh07xsh8mf4h92/masks/clsyzhunv00023b6n88gq4oxn/index/1',\n",
       "   'color_rgb': [62, 148, 13]}},\n",
       " {'feature_id': 'clt3d9jcf000h3b6n7vsd3417',\n",
       "  'feature_schema_id': 'clslz14ze0br3073pgh1ghbql',\n",
       "  'name': 'Intereference',\n",
       "  'value': 'intereference',\n",
       "  'annotation_kind': 'ImageSegmentationMask',\n",
       "  'classifications': [],\n",
       "  'mask': {'url': 'https://api.labelbox.com/api/v1/projects/clslyo3q507j707505py9669b/annotations/clt3d9jcf000h3b6n7vsd3417/index/1/mask'},\n",
       "  'composite_mask': {'url': 'https://api.labelbox.com/api/v1/tasks/clt8rp8ia02gh07xsh8mf4h92/masks/clsyzhunv00023b6n88gq4oxn/index/1',\n",
       "   'color_rgb': [102, 255, 91]}},\n",
       " {'feature_id': 'clt3d9th4000l3b6nb3o91m7i',\n",
       "  'feature_schema_id': 'clslz14zf0br5073pf2cefh26',\n",
       "  'name': 'Precipitaiton',\n",
       "  'value': 'precipitaiton',\n",
       "  'annotation_kind': 'ImageSegmentationMask',\n",
       "  'classifications': [],\n",
       "  'mask': {'url': 'https://api.labelbox.com/api/v1/projects/clslyo3q507j707505py9669b/annotations/clt3d9th4000l3b6nb3o91m7i/index/1/mask'},\n",
       "  'composite_mask': {'url': 'https://api.labelbox.com/api/v1/tasks/clt8rp8ia02gh07xsh8mf4h92/masks/clsyzhunv00023b6n88gq4oxn/index/1',\n",
       "   'color_rgb': [207, 153, 27]}},\n",
       " {'feature_id': 'clt3da38d000p3b6n9bsy99i6',\n",
       "  'feature_schema_id': 'clslz14zf0br5073pf2cefh26',\n",
       "  'name': 'Precipitaiton',\n",
       "  'value': 'precipitaiton',\n",
       "  'annotation_kind': 'ImageSegmentationMask',\n",
       "  'classifications': [],\n",
       "  'mask': {'url': 'https://api.labelbox.com/api/v1/projects/clslyo3q507j707505py9669b/annotations/clt3da38d000p3b6n9bsy99i6/index/1/mask'},\n",
       "  'composite_mask': {'url': 'https://api.labelbox.com/api/v1/tasks/clt8rp8ia02gh07xsh8mf4h92/masks/clsyzhunv00023b6n88gq4oxn/index/1',\n",
       "   'color_rgb': [157, 116, 62]}}]"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export_json[80]['projects'][PROJECT_ID]['labels'][0]['annotations']['objects']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "b5d4899d-10da-4054-aa28-e76bf6bf7599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEAAAACACAAAAACe6jC2AAAAPklEQVR4nO3RMQ4AIAwCQOr//6yLk6M1Tnc7hIQEAAAAAAAAPpszqUY8STKa+bov2NvvC9IuqHqwoPEBwGkBCL4EDU2V4uUAAAAASUVORK5CYII=",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=L size=64x128>"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Provide a project ID and feature ID. Alternatively, replace the entire mask_url with a URL grabbed from your export.\n",
    "project_id = \"clslyo3q507j707505py9669b\"\n",
    "feature_id = \"clt3d9atq000d3b6nhjlio057\"\n",
    "\n",
    "mask_url = f\"https://api.labelbox.com/api/v1/projects/{project_id}/annotations/{feature_id}/index/1/mask\"\n",
    "\n",
    "# Make the API request\n",
    "req = urllib.request.Request(mask_url, headers=client.headers)\n",
    "\n",
    "# Optionally, print the image of the mask\n",
    "image = Image.open(urllib.request.urlopen(req))\n",
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "7f639ecc-7dde-432c-94ca-e3959f739c8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1cf936c20>"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imdata = np.array((image.getdata())).reshape(128, 64)\n",
    "plt.pcolormesh(imdata> 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "6857e71e-0e64-4223-954e-7270c05e0db9",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_props_interference = []\n",
    "label_props_precip = []\n",
    "for i in range(len(export_json)):\n",
    "    objects = export_json[i]['projects'][PROJECT_ID]['labels'][0]['annotations']['objects']\n",
    "    for obj in objects:\n",
    "        feature_id = obj['feature_id']\n",
    "        # Make the API request\n",
    "        mask_url = f\"https://api.labelbox.com/api/v1/projects/{project_id}/annotations/{feature_id}/index/1/mask\"\n",
    "        req = urllib.request.Request(mask_url, headers=client.headers)\n",
    "\n",
    "        # Optionally, print the image of the mask\n",
    "        image = Image.open(urllib.request.urlopen(req))\n",
    "        imdata = np.array((image.getdata())).reshape(128, 64)\n",
    "        image_mask = imdata > 0\n",
    "        label_image = skimage.measure.label(image_mask)\n",
    "        props = skimage.measure.regionprops(label_image)\n",
    "        label_str = obj['name'].split()[0]\n",
    "        if label_str[0] == \"I\":\n",
    "            label_props_interference = label_props_interference + props\n",
    "        else:\n",
    "            label_props_precip = label_props_precip + props"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "2c1e4773-b56c-4dfd-8da4-a7bd22dbefe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "orientation_interference = np.array([np.rad2deg(p.orientation) for p in label_props_interference])\n",
    "orientation_precip = np.array([np.rad2deg(p.orientation) for p in label_props_precip])\n",
    "area_interference = np.array([p.area for p in label_props_interference])\n",
    "area_precip = np.array([p.area for p in label_props_precip])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "0e078c6b-b247-4f56-9e43-c80723ebced7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d0b40910>]"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "angle_bins = np.linspace(-90, 90, 30)\n",
    "hist_inter, bins = np.histogram(orientation_interference, bins=angle_bins)\n",
    "hist_precip, bins = np.histogram(orientation_precip, bins=angle_bins)\n",
    "plt.step(angle_bins[:-1], hist_inter, label='interference')\n",
    "plt.step(angle_bins[:-1], hist_precip, label='precipitation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "d1705006-57ae-426d-bace-18fb98c47403",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(orientation_interference[area_interference > 36], area_interference[area_interference > 36], color='b', label='Interference')\n",
    "plt.scatter(orientation_precip[area_precip > 36], area_precip[area_precip > 36], color='r', label='Precipitation')\n",
    "plt.xlabel('Orientation of major axis [degrees]')\n",
    "plt.ylabel('Area [pixels]')\n",
    "plt.legend()\n",
    "plt.savefig('hand_label_properties.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dee075f8-3ed7-4567-b256-217a402d115a",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}