specdal_plus_library.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f01587a5-be73-4158-83e5-fe166e06887a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from specdal import Collection\n",
    "from specdal import Spectrum\n",
    "from specdal import readers\n",
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4e02da2e-6ab5-4932-a954-923abc9b3e89",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Multiple wavelength spacings found in dataset. This may indicate input files \n",
      "from multiple datasets are being processed simultaneously, and can cause \n",
      "unpredictable behavior.\n",
      "\n",
      "WARNING:root:Multiple wavelength spacings found in dataset. This may indicate input files \n",
      "from multiple datasets are being processed simultaneously, and can cause \n",
      "unpredictable behavior.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'santaella'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datadir = 'C:/Users/g512/Downloads/svc_data/'\n",
    "c = Collection(name='santaella', directory=datadir)\n",
    "\n",
    "#Objetos\n",
    "c.data           # Reflectance/Target Radiance/Reference Radiance of all documents in different wavelength \n",
    "c.measure_type   # Type of measure of the data\n",
    "c.radiance       # Radiance of all documents in different wavelength\n",
    "c.spectra        # All spectral objects that are in the collection\n",
    "c.spectra_dict   # Spectral objects related with the file name\n",
    "c.spectra_radiance # All spectral objects measured in radiance that are in the collection\n",
    "c.name           # Name of the collection\n",
    "\n",
    "#Functions\n",
    "#Check because there are other functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6afe572-0615-4269-b5fa-6da0e3c586e4",
   "metadata": {},
   "source": [
    "## Extract metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd7ae00a-e2b8-47cc-8fb9-f82a843ad301",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Multiple wavelength spacings found in dataset. This may indicate input files \n",
      "from multiple datasets are being processed simultaneously, and can cause \n",
      "unpredictable behavior.\n",
      "\n"
     ]
    },
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       "                                                        file_path  \\\n",
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       "gr081722_0027   C:\\Users\\g512\\Downloads\\svc_data\\gr081722_0027...   \n",
       "\n",
       "                     file_name          instrument_type integration_time_ref  \\\n",
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       "HR.051722.0005  HR.051722.0005  HN: 8203022 (XHR-1024i)         [10, 40, 10]   \n",
       "HR.051722.0006  HR.051722.0006  HN: 8203022 (XHR-1024i)         [10, 40, 10]   \n",
       "HR.051722.0007  HR.051722.0007  HN: 8203022 (XHR-1024i)         [10, 40, 10]   \n",
       "gr081722_0027    gr081722_0027  HN: 8203022 (XHR-1024i)         [30, 40, 10]   \n",
       "\n",
       "               integration_time_tgt   338.0   339.4   340.9   342.4   343.8  \\\n",
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       "\n",
       "                2514.5  2516.8  2519.1  \n",
       "HR.051722.0004  1.0714  1.1220  0.9268  \n",
       "HR.051722.0005  0.0000 -0.0244 -0.0976  \n",
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       "\n",
       "[5 rows x 1029 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_fields = ['file_path', 'file_name', 'instrument_type', 'integration_time_ref', 'integration_time_tgt']\n",
    "\n",
    "# Metadata with data\n",
    "c.data_with_meta(fields=meta_fields, data=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a1caac44-14df-4a81-9ffd-e4e11724fe23",
   "metadata": {},
   "outputs": [
    {
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       "      <td>C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...</td>\n",
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       "                                                        file_path  \\\n",
       "HR.051722.0004  C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...   \n",
       "HR.051722.0005  C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...   \n",
       "HR.051722.0006  C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...   \n",
       "HR.051722.0007  C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...   \n",
       "gr081722_0027   C:\\Users\\g512\\Downloads\\svc_data\\gr081722_0027...   \n",
       "\n",
       "                     file_name          instrument_type integration_time_ref  \\\n",
       "HR.051722.0004  HR.051722.0004  HN: 8203022 (XHR-1024i)         [10, 40, 10]   \n",
       "HR.051722.0005  HR.051722.0005  HN: 8203022 (XHR-1024i)         [10, 40, 10]   \n",
       "HR.051722.0006  HR.051722.0006  HN: 8203022 (XHR-1024i)         [10, 40, 10]   \n",
       "HR.051722.0007  HR.051722.0007  HN: 8203022 (XHR-1024i)         [10, 40, 10]   \n",
       "gr081722_0027    gr081722_0027  HN: 8203022 (XHR-1024i)         [30, 40, 10]   \n",
       "\n",
       "               integration_time_tgt measurement_type  gps_time_ref  \\\n",
       "HR.051722.0004         [10, 40, 10]         Radiance      110313.0   \n",
       "HR.051722.0005        [100, 40, 10]         Radiance      110313.0   \n",
       "HR.051722.0006        [200, 40, 10]         Radiance      110313.0   \n",
       "HR.051722.0007        [150, 40, 10]         Radiance      110313.0   \n",
       "gr081722_0027          [50, 40, 10]         Radiance       75959.0   \n",
       "\n",
       "                gps_time_tgt  longitude_ref  longitude_tgt  ...  \\\n",
       "HR.051722.0004      110417.0      -4.820705      -4.820700  ...   \n",
       "HR.051722.0005      110457.0      -4.820705      -4.820707  ...   \n",
       "HR.051722.0006      110531.0      -4.820705      -4.820692  ...   \n",
       "HR.051722.0007      110601.0      -4.820705      -4.820693  ...   \n",
       "gr081722_0027        80021.0      -4.796922      -4.796898  ...   \n",
       "\n",
       "                                                          factors  \\\n",
       "HR.051722.0004  1.000, 1.000, 1.000 [Overlap: Preserve, Matchi...   \n",
       "HR.051722.0005  1.000, 1.000, 1.000 [Overlap: Preserve, Matchi...   \n",
       "HR.051722.0006  1.000, 1.000, 1.000 [Overlap: Preserve, Matchi...   \n",
       "HR.051722.0007  1.000, 1.000, 1.000 [Overlap: Preserve, Matchi...   \n",
       "gr081722_0027   1.029, 1.024, 1.000 [Overlap: Remove @ 990,190...   \n",
       "\n",
       "                inclinometer_x_ref inclinometer_x_tgt inclinometer_y_ref  \\\n",
       "HR.051722.0004                   1                  2                  2   \n",
       "HR.051722.0005                   1                  0                  2   \n",
       "HR.051722.0006                   1                  0                  2   \n",
       "HR.051722.0007                   1                  2                  2   \n",
       "gr081722_0027                    1                  1                  0   \n",
       "\n",
       "                inclinometer_y_tgt  sun_zenith_ref sun_zenith_tgt  \\\n",
       "HR.051722.0004                   3          NoData         NoData   \n",
       "HR.051722.0005                   5          NoData         NoData   \n",
       "HR.051722.0006                   6          NoData         NoData   \n",
       "HR.051722.0007                  -2          NoData         NoData   \n",
       "gr081722_0027                    0            None           None   \n",
       "\n",
       "               sun_azimuth_ref sun_azimuth_tgt  \\\n",
       "HR.051722.0004          NoData          NoData   \n",
       "HR.051722.0005          NoData          NoData   \n",
       "HR.051722.0006          NoData          NoData   \n",
       "HR.051722.0007          NoData          NoData   \n",
       "gr081722_0027             None            None   \n",
       "\n",
       "                                                          weather  \n",
       "HR.051722.0004  {\"temperature\":\"17,3\",\"condition\":\"Sunny\",\"hum...  \n",
       "HR.051722.0005  {\"temperature\":\"17,3\",\"condition\":\"Sunny\",\"hum...  \n",
       "HR.051722.0006  {\"temperature\":\"17,3\",\"condition\":\"Sunny\",\"hum...  \n",
       "HR.051722.0007  {\"temperature\":\"17,3\",\"condition\":\"Sunny\",\"hum...  \n",
       "gr081722_0027                                                None  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fields is equal to None\n",
    "c.data_with_meta(fields=None, data=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "13bf1991-76b8-4da7-bcfd-cac8b219392e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>file_path</th>\n",
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       "      <th>integration_time_ref</th>\n",
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       "      <th>HR.051722.0004</th>\n",
       "      <td>C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...</td>\n",
       "      <td>HR.051722.0004</td>\n",
       "      <td>HN: 8203022 (XHR-1024i)</td>\n",
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       "      <th>HR.051722.0005</th>\n",
       "      <td>C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...</td>\n",
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       "      <td>HR.051722.0006</td>\n",
       "      <td>HN: 8203022 (XHR-1024i)</td>\n",
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       "      <td>[200, 40, 10]</td>\n",
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       "      <th>HR.051722.0007</th>\n",
       "      <td>C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...</td>\n",
       "      <td>HR.051722.0007</td>\n",
       "      <td>HN: 8203022 (XHR-1024i)</td>\n",
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       "      <td>[150, 40, 10]</td>\n",
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       "    <tr>\n",
       "      <th>gr081722_0027</th>\n",
       "      <td>C:\\Users\\g512\\Downloads\\svc_data\\gr081722_0027...</td>\n",
       "      <td>gr081722_0027</td>\n",
       "      <td>HN: 8203022 (XHR-1024i)</td>\n",
       "      <td>[30, 40, 10]</td>\n",
       "      <td>[50, 40, 10]</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        file_path  \\\n",
       "HR.051722.0004  C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...   \n",
       "HR.051722.0005  C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...   \n",
       "HR.051722.0006  C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...   \n",
       "HR.051722.0007  C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...   \n",
       "gr081722_0027   C:\\Users\\g512\\Downloads\\svc_data\\gr081722_0027...   \n",
       "\n",
       "                     file_name          instrument_type integration_time_ref  \\\n",
       "HR.051722.0004  HR.051722.0004  HN: 8203022 (XHR-1024i)         [10, 40, 10]   \n",
       "HR.051722.0005  HR.051722.0005  HN: 8203022 (XHR-1024i)         [10, 40, 10]   \n",
       "HR.051722.0006  HR.051722.0006  HN: 8203022 (XHR-1024i)         [10, 40, 10]   \n",
       "HR.051722.0007  HR.051722.0007  HN: 8203022 (XHR-1024i)         [10, 40, 10]   \n",
       "gr081722_0027    gr081722_0027  HN: 8203022 (XHR-1024i)         [30, 40, 10]   \n",
       "\n",
       "               integration_time_tgt  \n",
       "HR.051722.0004         [10, 40, 10]  \n",
       "HR.051722.0005        [100, 40, 10]  \n",
       "HR.051722.0006        [200, 40, 10]  \n",
       "HR.051722.0007        [150, 40, 10]  \n",
       "gr081722_0027          [50, 40, 10]  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Metadata without data\n",
    "c.data_with_meta(fields=meta_fields, data=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8680510b-57e8-4cb8-91b6-b7d52fa71d5d",
   "metadata": {},
   "source": [
    "## Extract data (Reference Radiance, Target Radiance, Reflectance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b149efde-30e3-4320-a19f-158614e77636",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Multiple wavelength spacings found in dataset. This may indicate input files \n",
      "from multiple datasets are being processed simultaneously, and can cause \n",
      "unpredictable behavior.\n",
      "\n",
      "WARNING:root:Multiple wavelength spacings found in dataset. This may indicate input files \n",
      "from multiple datasets are being processed simultaneously, and can cause \n",
      "unpredictable behavior.\n",
      "\n",
      "WARNING:root:Multiple wavelength spacings found in dataset. This may indicate input files \n",
      "from multiple datasets are being processed simultaneously, and can cause \n",
      "unpredictable behavior.\n",
      "\n",
      "WARNING:root:Multiple wavelength spacings found in dataset. This may indicate input files \n",
      "from multiple datasets are being processed simultaneously, and can cause \n",
      "unpredictable behavior.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "datadir = 'C:/Users/g512/Downloads/svc_data/'\n",
    "a = Collection(name='pruebas', directory=datadir, measure_type='ref_radiance')\n",
    "b = Collection(name='pruebas', directory=datadir, measure_type='tgt_radiance')\n",
    "c = Collection(name='pruebas', directory=datadir, measure_type='pct_reflect')\n",
    "\n",
    "ref_radiance = a.data\n",
    "tgt_radiance = b.data\n",
    "reflectance = c.data\n",
    "radiance = c.radiance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0af94a71-1f5f-4d5f-aef2-348fc8bcc5f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = Spectrum(measure_type='pct_reflect',filepath='C:/Users/g512/Downloads/svc_data/gr081722_0027.sig')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "80969abc-6883-47fb-a6e0-dcc61a3ecfcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Multiple wavelength spacings found in dataset. This may indicate input files \n",
      "from multiple datasets are being processed simultaneously, and can cause \n",
      "unpredictable behavior.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>file_path</th>\n",
       "      <th>file_name</th>\n",
       "      <th>instrument_type</th>\n",
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       "      <th>integration_time_tgt</th>\n",
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       "      <th>gps_time_ref</th>\n",
       "      <th>gps_time_tgt</th>\n",
       "      <th>longitude_ref</th>\n",
       "      <th>longitude_tgt</th>\n",
       "      <th>...</th>\n",
       "      <th>2498.5</th>\n",
       "      <th>2500.8</th>\n",
       "      <th>2503.1</th>\n",
       "      <th>2505.4</th>\n",
       "      <th>2507.7</th>\n",
       "      <th>2509.9</th>\n",
       "      <th>2512.2</th>\n",
       "      <th>2514.5</th>\n",
       "      <th>2516.8</th>\n",
       "      <th>2519.1</th>\n",
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       "      <th>HR.051722.0004</th>\n",
       "      <td>C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...</td>\n",
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       "      <td>[10, 40, 10]</td>\n",
       "      <td>[10, 40, 10]</td>\n",
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       "      <td>110313.0</td>\n",
       "      <td>110417.0</td>\n",
       "      <td>-4.820705</td>\n",
       "      <td>-4.820700</td>\n",
       "      <td>...</td>\n",
       "      <td>1438.71</td>\n",
       "      <td>1661.70</td>\n",
       "      <td>1540.77</td>\n",
       "      <td>1197.90</td>\n",
       "      <td>993.50</td>\n",
       "      <td>907.68</td>\n",
       "      <td>796.22</td>\n",
       "      <td>600.30</td>\n",
       "      <td>477.94</td>\n",
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       "      <th>HR.051722.0005</th>\n",
       "      <td>C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...</td>\n",
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       "      <td>110457.0</td>\n",
       "      <td>-4.820705</td>\n",
       "      <td>-4.820707</td>\n",
       "      <td>...</td>\n",
       "      <td>34.46</td>\n",
       "      <td>17.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.15</td>\n",
       "      <td>37.14</td>\n",
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       "      <td>-10.39</td>\n",
       "      <td>-43.72</td>\n",
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       "    <tr>\n",
       "      <th>HR.051722.0006</th>\n",
       "      <td>C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...</td>\n",
       "      <td>HR.051722.0006</td>\n",
       "      <td>HN: 8203022 (XHR-1024i)</td>\n",
       "      <td>[10, 40, 10]</td>\n",
       "      <td>[200, 40, 10]</td>\n",
       "      <td>Radiance</td>\n",
       "      <td>110313.0</td>\n",
       "      <td>110531.0</td>\n",
       "      <td>-4.820705</td>\n",
       "      <td>-4.820692</td>\n",
       "      <td>...</td>\n",
       "      <td>68.92</td>\n",
       "      <td>104.40</td>\n",
       "      <td>70.84</td>\n",
       "      <td>36.30</td>\n",
       "      <td>46.43</td>\n",
       "      <td>37.82</td>\n",
       "      <td>19.42</td>\n",
       "      <td>30.02</td>\n",
       "      <td>41.56</td>\n",
       "      <td>10.93</td>\n",
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       "    <tr>\n",
       "      <th>HR.051722.0007</th>\n",
       "      <td>C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...</td>\n",
       "      <td>HR.051722.0007</td>\n",
       "      <td>HN: 8203022 (XHR-1024i)</td>\n",
       "      <td>[10, 40, 10]</td>\n",
       "      <td>[150, 40, 10]</td>\n",
       "      <td>Radiance</td>\n",
       "      <td>110313.0</td>\n",
       "      <td>110601.0</td>\n",
       "      <td>-4.820705</td>\n",
       "      <td>-4.820693</td>\n",
       "      <td>...</td>\n",
       "      <td>103.38</td>\n",
       "      <td>95.70</td>\n",
       "      <td>70.84</td>\n",
       "      <td>54.45</td>\n",
       "      <td>55.71</td>\n",
       "      <td>18.91</td>\n",
       "      <td>29.13</td>\n",
       "      <td>50.03</td>\n",
       "      <td>31.17</td>\n",
       "      <td>-54.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gr081722_0027</th>\n",
       "      <td>C:\\Users\\g512\\Downloads\\svc_data\\gr081722_0027...</td>\n",
       "      <td>gr081722_0027</td>\n",
       "      <td>HN: 8203022 (XHR-1024i)</td>\n",
       "      <td>[30, 40, 10]</td>\n",
       "      <td>[50, 40, 10]</td>\n",
       "      <td>Radiance</td>\n",
       "      <td>75959.0</td>\n",
       "      <td>80021.0</td>\n",
       "      <td>-4.796922</td>\n",
       "      <td>-4.796898</td>\n",
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       "      <td>151.20</td>\n",
       "      <td>186.12</td>\n",
       "      <td>180.50</td>\n",
       "      <td>123.27</td>\n",
       "      <td>99.17</td>\n",
       "      <td>110.22</td>\n",
       "      <td>65.91</td>\n",
       "      <td>38.84</td>\n",
       "      <td>10.06</td>\n",
       "      <td>31.65</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1077 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        file_path  \\\n",
       "HR.051722.0004  C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...   \n",
       "HR.051722.0005  C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...   \n",
       "HR.051722.0006  C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...   \n",
       "HR.051722.0007  C:\\Users\\g512\\Downloads\\svc_data\\HR.051722.000...   \n",
       "gr081722_0027   C:\\Users\\g512\\Downloads\\svc_data\\gr081722_0027...   \n",
       "\n",
       "                     file_name          instrument_type integration_time_ref  \\\n",
       "HR.051722.0004  HR.051722.0004  HN: 8203022 (XHR-1024i)         [10, 40, 10]   \n",
       "HR.051722.0005  HR.051722.0005  HN: 8203022 (XHR-1024i)         [10, 40, 10]   \n",
       "HR.051722.0006  HR.051722.0006  HN: 8203022 (XHR-1024i)         [10, 40, 10]   \n",
       "HR.051722.0007  HR.051722.0007  HN: 8203022 (XHR-1024i)         [10, 40, 10]   \n",
       "gr081722_0027    gr081722_0027  HN: 8203022 (XHR-1024i)         [30, 40, 10]   \n",
       "\n",
       "               integration_time_tgt measurement_type  gps_time_ref  \\\n",
       "HR.051722.0004         [10, 40, 10]         Radiance      110313.0   \n",
       "HR.051722.0005        [100, 40, 10]         Radiance      110313.0   \n",
       "HR.051722.0006        [200, 40, 10]         Radiance      110313.0   \n",
       "HR.051722.0007        [150, 40, 10]         Radiance      110313.0   \n",
       "gr081722_0027          [50, 40, 10]         Radiance       75959.0   \n",
       "\n",
       "                gps_time_tgt  longitude_ref  longitude_tgt  ...   2498.5  \\\n",
       "HR.051722.0004      110417.0      -4.820705      -4.820700  ...  1438.71   \n",
       "HR.051722.0005      110457.0      -4.820705      -4.820707  ...    34.46   \n",
       "HR.051722.0006      110531.0      -4.820705      -4.820692  ...    68.92   \n",
       "HR.051722.0007      110601.0      -4.820705      -4.820693  ...   103.38   \n",
       "gr081722_0027        80021.0      -4.796922      -4.796898  ...   151.20   \n",
       "\n",
       "                 2500.8   2503.1   2505.4  2507.7  2509.9  2512.2  2514.5  \\\n",
       "HR.051722.0004  1661.70  1540.77  1197.90  993.50  907.68  796.22  600.30   \n",
       "HR.051722.0005    17.40     0.00    18.15   37.14    9.46    0.00    0.00   \n",
       "HR.051722.0006   104.40    70.84    36.30   46.43   37.82   19.42   30.02   \n",
       "HR.051722.0007    95.70    70.84    54.45   55.71   18.91   29.13   50.03   \n",
       "gr081722_0027    186.12   180.50   123.27   99.17  110.22   65.91   38.84   \n",
       "\n",
       "                2516.8  2519.1  \n",
       "HR.051722.0004  477.94  415.34  \n",
       "HR.051722.0005  -10.39  -43.72  \n",
       "HR.051722.0006   41.56   10.93  \n",
       "HR.051722.0007   31.17  -54.65  \n",
       "gr081722_0027    10.06   31.65  \n",
       "\n",
       "[5 rows x 1077 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.data_with_meta(fields=None)"
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  },
  {
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   "execution_count": 9,
   "id": "1d76fc5a-6c78-4d6b-bee0-48f75e2f109a",
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       "      <th>wavelength</th>\n",
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       "      <th>338.0</th>\n",
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       "      <td>122072.50</td>\n",
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       "      <td>36534.80</td>\n",
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       "    <tr>\n",
       "      <th>342.4</th>\n",
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       "      <td>135550.35</td>\n",
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       "      <td>135550.35</td>\n",
       "      <td>37673.78</td>\n",
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       "      <th>343.8</th>\n",
       "      <td>131883.20</td>\n",
       "      <td>131883.20</td>\n",
       "      <td>131883.20</td>\n",
       "      <td>131883.20</td>\n",
       "      <td>36379.27</td>\n",
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       "      <th>2509.9</th>\n",
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       "      <th>2514.5</th>\n",
       "      <td>560.28</td>\n",
       "      <td>560.28</td>\n",
       "      <td>560.28</td>\n",
       "      <td>560.28</td>\n",
       "      <td>106.81</td>\n",
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       "    <tr>\n",
       "      <th>2516.8</th>\n",
       "      <td>425.99</td>\n",
       "      <td>425.99</td>\n",
       "      <td>425.99</td>\n",
       "      <td>425.99</td>\n",
       "      <td>90.54</td>\n",
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       "    <tr>\n",
       "      <th>2519.1</th>\n",
       "      <td>448.13</td>\n",
       "      <td>448.13</td>\n",
       "      <td>448.13</td>\n",
       "      <td>448.13</td>\n",
       "      <td>137.15</td>\n",
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       "            HR.051722.0004  HR.051722.0005  HR.051722.0006  HR.051722.0007  \\\n",
       "wavelength                                                                   \n",
       "338.0            122072.50       122072.50       122072.50       122072.50   \n",
       "339.4            130698.00       130698.00       130698.00       130698.00   \n",
       "340.9            134982.60       134982.60       134982.60       134982.60   \n",
       "342.4            135550.35       135550.35       135550.35       135550.35   \n",
       "343.8            131883.20       131883.20       131883.20       131883.20   \n",
       "...                    ...             ...             ...             ...   \n",
       "2509.9              907.68          907.68          907.68          907.68   \n",
       "2512.2              776.80          776.80          776.80          776.80   \n",
       "2514.5              560.28          560.28          560.28          560.28   \n",
       "2516.8              425.99          425.99          425.99          425.99   \n",
       "2519.1              448.13          448.13          448.13          448.13   \n",
       "\n",
       "            gr081722_0027  \n",
       "wavelength                 \n",
       "338.0            32964.40  \n",
       "339.4            36037.60  \n",
       "340.9            36534.80  \n",
       "342.4            37673.78  \n",
       "343.8            36379.27  \n",
       "...                   ...  \n",
       "2509.9             174.52  \n",
       "2512.2             141.23  \n",
       "2514.5             106.81  \n",
       "2516.8              90.54  \n",
       "2519.1             137.15  \n",
       "\n",
       "[1024 rows x 5 columns]"
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       "      <td>2566.43</td>\n",
       "      <td>8042.56</td>\n",
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       "      <th>342.4</th>\n",
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       "      <td>2580.77</td>\n",
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       "      <th>343.8</th>\n",
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       "      <td>38.84</td>\n",
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       "      <td>41.56</td>\n",
       "      <td>31.17</td>\n",
       "      <td>10.06</td>\n",
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       "      <th>2519.1</th>\n",
       "      <td>415.34</td>\n",
       "      <td>-43.72</td>\n",
       "      <td>10.93</td>\n",
       "      <td>-54.65</td>\n",
       "      <td>31.65</td>\n",
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       "            HR.051722.0004  HR.051722.0005  HR.051722.0006  HR.051722.0007  \\\n",
       "wavelength                                                                   \n",
       "338.0            123337.50         3744.40         1574.93         2336.03   \n",
       "339.4            132177.60         3982.59         1670.72         2474.22   \n",
       "340.9            136576.40         4119.36         1728.66         2566.43   \n",
       "342.4            136988.55         4146.81         1749.81         2580.77   \n",
       "343.8            133425.00         4091.70         1707.84         2522.23   \n",
       "...                    ...             ...             ...             ...   \n",
       "2509.9              907.68            9.46           37.82           18.91   \n",
       "2512.2              796.22            0.00           19.42           29.13   \n",
       "2514.5              600.30            0.00           30.02           50.03   \n",
       "2516.8              477.94          -10.39           41.56           31.17   \n",
       "2519.1              415.34          -43.72           10.93          -54.65   \n",
       "\n",
       "            gr081722_0027  \n",
       "wavelength                 \n",
       "338.0             7173.60  \n",
       "339.4             7780.08  \n",
       "340.9             8042.56  \n",
       "342.4             8498.03  \n",
       "343.8             8505.80  \n",
       "...                   ...  \n",
       "2509.9             110.22  \n",
       "2512.2              65.91  \n",
       "2514.5              38.84  \n",
       "2516.8              10.06  \n",
       "2519.1              31.65  \n",
       "\n",
       "[1024 rows x 5 columns]"
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       "      <td>8042.56</td>\n",
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       "      <td>2580.77</td>\n",
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       "      <td>18.91</td>\n",
       "      <td>110.22</td>\n",
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       "      <th>2512.2</th>\n",
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       "      <td>30.02</td>\n",
       "      <td>50.03</td>\n",
       "      <td>38.84</td>\n",
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       "      <th>2516.8</th>\n",
       "      <td>477.94</td>\n",
       "      <td>-10.39</td>\n",
       "      <td>41.56</td>\n",
       "      <td>31.17</td>\n",
       "      <td>10.06</td>\n",
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       "    <tr>\n",
       "      <th>2519.1</th>\n",
       "      <td>415.34</td>\n",
       "      <td>-43.72</td>\n",
       "      <td>10.93</td>\n",
       "      <td>-54.65</td>\n",
       "      <td>31.65</td>\n",
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       "            HR.051722.0004  HR.051722.0005  HR.051722.0006  HR.051722.0007  \\\n",
       "wavelength                                                                   \n",
       "338.0            123337.50         3744.40         1574.93         2336.03   \n",
       "339.4            132177.60         3982.59         1670.72         2474.22   \n",
       "340.9            136576.40         4119.36         1728.66         2566.43   \n",
       "342.4            136988.55         4146.81         1749.81         2580.77   \n",
       "343.8            133425.00         4091.70         1707.84         2522.23   \n",
       "...                    ...             ...             ...             ...   \n",
       "2509.9              907.68            9.46           37.82           18.91   \n",
       "2512.2              796.22            0.00           19.42           29.13   \n",
       "2514.5              600.30            0.00           30.02           50.03   \n",
       "2516.8              477.94          -10.39           41.56           31.17   \n",
       "2519.1              415.34          -43.72           10.93          -54.65   \n",
       "\n",
       "            gr081722_0027  \n",
       "wavelength                 \n",
       "338.0             7173.60  \n",
       "339.4             7780.08  \n",
       "340.9             8042.56  \n",
       "342.4             8498.03  \n",
       "343.8             8505.80  \n",
       "...                   ...  \n",
       "2509.9             110.22  \n",
       "2512.2              65.91  \n",
       "2514.5              38.84  \n",
       "2516.8              10.06  \n",
       "2519.1              31.65  \n",
       "\n",
       "[1024 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tgt_radiance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a069c884-fa27-40b5-b5cd-0891c8e5c831",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HR.051722.0004</th>\n",
       "      <th>HR.051722.0005</th>\n",
       "      <th>HR.051722.0006</th>\n",
       "      <th>HR.051722.0007</th>\n",
       "      <th>gr081722_0027</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wavelength</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>338.0</th>\n",
       "      <td>1.0104</td>\n",
       "      <td>0.0307</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.0191</td>\n",
       "      <td>0.2176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339.4</th>\n",
       "      <td>1.0113</td>\n",
       "      <td>0.0305</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>0.0189</td>\n",
       "      <td>0.2159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340.9</th>\n",
       "      <td>1.0118</td>\n",
       "      <td>0.0305</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>0.0190</td>\n",
       "      <td>0.2201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342.4</th>\n",
       "      <td>1.0106</td>\n",
       "      <td>0.0306</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.0190</td>\n",
       "      <td>0.2256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343.8</th>\n",
       "      <td>1.0117</td>\n",
       "      <td>0.0310</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.0191</td>\n",
       "      <td>0.2338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2509.9</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0104</td>\n",
       "      <td>0.0417</td>\n",
       "      <td>0.0208</td>\n",
       "      <td>0.6316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2512.2</th>\n",
       "      <td>1.0250</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0250</td>\n",
       "      <td>0.0375</td>\n",
       "      <td>0.4667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2514.5</th>\n",
       "      <td>1.0714</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0536</td>\n",
       "      <td>0.0893</td>\n",
       "      <td>0.3636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2516.8</th>\n",
       "      <td>1.1220</td>\n",
       "      <td>-0.0244</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.0732</td>\n",
       "      <td>0.1111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2519.1</th>\n",
       "      <td>0.9268</td>\n",
       "      <td>-0.0976</td>\n",
       "      <td>0.0244</td>\n",
       "      <td>-0.1220</td>\n",
       "      <td>0.2308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1024 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            HR.051722.0004  HR.051722.0005  HR.051722.0006  HR.051722.0007  \\\n",
       "wavelength                                                                   \n",
       "338.0               1.0104          0.0307          0.0129          0.0191   \n",
       "339.4               1.0113          0.0305          0.0128          0.0189   \n",
       "340.9               1.0118          0.0305          0.0128          0.0190   \n",
       "342.4               1.0106          0.0306          0.0129          0.0190   \n",
       "343.8               1.0117          0.0310          0.0129          0.0191   \n",
       "...                    ...             ...             ...             ...   \n",
       "2509.9              1.0000          0.0104          0.0417          0.0208   \n",
       "2512.2              1.0250          0.0000          0.0250          0.0375   \n",
       "2514.5              1.0714          0.0000          0.0536          0.0893   \n",
       "2516.8              1.1220         -0.0244          0.0976          0.0732   \n",
       "2519.1              0.9268         -0.0976          0.0244         -0.1220   \n",
       "\n",
       "            gr081722_0027  \n",
       "wavelength                 \n",
       "338.0              0.2176  \n",
       "339.4              0.2159  \n",
       "340.9              0.2201  \n",
       "342.4              0.2256  \n",
       "343.8              0.2338  \n",
       "...                   ...  \n",
       "2509.9             0.6316  \n",
       "2512.2             0.4667  \n",
       "2514.5             0.3636  \n",
       "2516.8             0.1111  \n",
       "2519.1             0.2308  \n",
       "\n",
       "[1024 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reflectance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c0568223-ec73-4317-a115-9c9d603d4969",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\g512\\Documents\\git_repos\\00-CSIC\\SpecDAL\\specdal\\operators\\interpolate.py:12: FutureWarning: is_monotonic is deprecated and will be removed in a future version. Use is_monotonic_increasing instead.\n",
      "  if series.index.is_monotonic:\n",
      "C:\\Users\\g512\\Documents\\git_repos\\00-CSIC\\SpecDAL\\specdal\\operators\\interpolate.py:12: FutureWarning: is_monotonic is deprecated and will be removed in a future version. Use is_monotonic_increasing instead.\n",
      "  if series.index.is_monotonic:\n",
      "C:\\Users\\g512\\Documents\\git_repos\\00-CSIC\\SpecDAL\\specdal\\operators\\interpolate.py:12: FutureWarning: is_monotonic is deprecated and will be removed in a future version. Use is_monotonic_increasing instead.\n",
      "  if series.index.is_monotonic:\n",
      "C:\\Users\\g512\\Documents\\git_repos\\00-CSIC\\SpecDAL\\specdal\\operators\\interpolate.py:12: FutureWarning: is_monotonic is deprecated and will be removed in a future version. Use is_monotonic_increasing instead.\n",
      "  if series.index.is_monotonic:\n",
      "C:\\Users\\g512\\Documents\\git_repos\\00-CSIC\\SpecDAL\\specdal\\operators\\interpolate.py:12: FutureWarning: is_monotonic is deprecated and will be removed in a future version. Use is_monotonic_increasing instead.\n",
      "  if series.index.is_monotonic:\n",
      "C:\\Users\\g512\\Documents\\git_repos\\00-CSIC\\SpecDAL\\specdal\\operators\\interpolate.py:12: FutureWarning: is_monotonic is deprecated and will be removed in a future version. Use is_monotonic_increasing instead.\n",
      "  if series.index.is_monotonic:\n",
      "C:\\Users\\g512\\Documents\\git_repos\\00-CSIC\\SpecDAL\\specdal\\operators\\interpolate.py:12: FutureWarning: is_monotonic is deprecated and will be removed in a future version. Use is_monotonic_increasing instead.\n",
      "  if series.index.is_monotonic:\n",
      "C:\\Users\\g512\\Documents\\git_repos\\00-CSIC\\SpecDAL\\specdal\\operators\\interpolate.py:12: FutureWarning: is_monotonic is deprecated and will be removed in a future version. Use is_monotonic_increasing instead.\n",
      "  if series.index.is_monotonic:\n",
      "C:\\Users\\g512\\Documents\\git_repos\\00-CSIC\\SpecDAL\\specdal\\operators\\interpolate.py:12: FutureWarning: is_monotonic is deprecated and will be removed in a future version. Use is_monotonic_increasing instead.\n",
      "  if series.index.is_monotonic:\n",
      "C:\\Users\\g512\\Documents\\git_repos\\00-CSIC\\SpecDAL\\specdal\\operators\\interpolate.py:12: FutureWarning: is_monotonic is deprecated and will be removed in a future version. Use is_monotonic_increasing instead.\n",
      "  if series.index.is_monotonic:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='wavelength'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "c.stitch()\n",
    "c.interpolate(spacing=1)\n",
    "c.data.plot(figsize=(10,10))\n",
    "#plt.xlim(950,1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5522813d-68f0-4119-b6a9-09f80038e970",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:specdal_test]",
   "language": "python",
   "name": "conda-env-specdal_test-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}