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SP.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"from plio.io import io_spectral_profiler\n",
"from libpysat.examples import get_path\n",
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"from libpysat.spectral.spectra import *\n",
"from libpysat.utils.utils import *"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"scrolled": false
},
"outputs": [
{
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" <td>0.0545</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows × 312 columns</p>\n",
"</div>"
],
"text/plain": [
" 512.6 518.4 524.7 530.4 536.5 \\\n",
"minor id \n",
"RAW 0 5123.0000 5887.0000 6375.0000 6806.0000 7494.0000 \n",
"REF1 0 0.0402 0.0487 0.0497 0.0520 0.0532 \n",
"REF2 0 0.0397 0.0482 0.0492 0.0515 0.0527 \n",
"QA 0 288.0000 288.0000 288.0000 288.0000 288.0000 \n",
"RAW 1 5164.0000 5955.0000 6461.0000 6905.0000 7614.0000 \n",
"REF1 1 0.0414 0.0502 0.0513 0.0537 0.0549 \n",
"REF2 1 0.0409 0.0497 0.0507 0.0531 0.0544 \n",
"QA 1 288.0000 288.0000 288.0000 288.0000 288.0000 \n",
"RAW 2 5080.0000 5820.0000 6296.0000 6711.0000 7375.0000 \n",
"REF1 2 0.0391 0.0474 0.0484 0.0506 0.0518 \n",
"\n",
" 542.8000000000001 548.7 554.5 560.5 566.7 \\\n",
"minor id \n",
"RAW 0 7585.0000 7525.0000 7860.0000 8582.0000 9196.0000 \n",
"REF1 0 0.0551 0.0559 0.0571 0.0594 0.0612 \n",
"REF2 0 0.0545 0.0553 0.0565 0.0588 0.0605 \n",
"QA 0 288.0000 288.0000 288.0000 288.0000 288.0000 \n",
"RAW 1 7709.0000 7642.0000 7986.0000 8730.0000 9366.0000 \n",
"REF1 1 0.0568 0.0576 0.0588 0.0612 0.0631 \n",
"REF2 1 0.0562 0.0570 0.0582 0.0606 0.0624 \n",
"QA 1 288.0000 288.0000 288.0000 288.0000 288.0000 \n",
"RAW 2 7473.0000 7416.0000 7741.0000 8445.0000 9038.0000 \n",
"REF1 2 0.0537 0.0545 0.0556 0.0579 0.0596 \n",
"\n",
" ... CALIBRATION SP_PELTIER TC_MI_STATUS \\\n",
"minor id ... \n",
"RAW 0 ... 0 1 1 \n",
"REF1 0 ... 0 1 1 \n",
"REF2 0 ... 0 1 1 \n",
"QA 0 ... 0 1 1 \n",
"RAW 1 ... 0 1 1 \n",
"REF1 1 ... 0 1 1 \n",
"REF2 1 ... 0 1 1 \n",
"QA 1 ... 0 1 1 \n",
"RAW 2 ... 0 1 1 \n",
"REF1 2 ... 0 1 1 \n",
"\n",
" CLOCK_COUNT_ERR_FLAG SPATIAL_RESOLUTION_FLAG \\\n",
"minor id \n",
"RAW 0 0 65 \n",
"REF1 0 0 65 \n",
"REF2 0 0 65 \n",
"QA 0 0 65 \n",
"RAW 1 0 65 \n",
"REF1 1 0 65 \n",
"REF2 1 0 65 \n",
"QA 1 0 65 \n",
"RAW 2 0 65 \n",
"REF1 2 0 65 \n",
"\n",
" GEOMETRIC_INFO_RECAL_FLAG SUPPORT_IMAGE_LINE_POSITION \\\n",
"minor id \n",
"RAW 0 67 27 \n",
"REF1 0 67 27 \n",
"REF2 0 67 27 \n",
"QA 0 67 27 \n",
"RAW 1 67 55 \n",
"REF1 1 67 55 \n",
"REF2 1 67 55 \n",
"QA 1 67 55 \n",
"RAW 2 67 83 \n",
"REF1 2 67 83 \n",
"\n",
" SUPPORT_IMAGE_COLUMN_POSITION THUMBNAIL_LINE_POSITION \\\n",
"minor id \n",
"RAW 0 480 13 \n",
"REF1 0 480 13 \n",
"REF2 0 480 13 \n",
"QA 0 480 13 \n",
"RAW 1 480 27 \n",
"REF1 1 480 27 \n",
"REF2 1 480 27 \n",
"QA 1 480 27 \n",
"RAW 2 480 40 \n",
"REF1 2 480 40 \n",
"\n",
" THUMBNAIL_COLUMN_POSITION \n",
"minor id \n",
"RAW 0 228 \n",
"REF1 0 228 \n",
"REF2 0 228 \n",
"QA 0 228 \n",
"RAW 1 228 \n",
"REF1 1 228 \n",
"REF2 1 228 \n",
"QA 1 228 \n",
"RAW 2 228 \n",
"REF1 2 228 \n",
"\n",
"[10 rows x 312 columns]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = Spectra.from_spectral_profiler(get_path('SP_2C_02_02358_S138_E3586.spc'))\n",
"s.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
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" <td>9102.0</td>\n",
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" <td>66</td>\n",
" <td>228</td>\n",
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" 512.6 518.4 524.7 530.4 536.5 542.8000000000001 548.7 554.5 \\\n",
"id \n",
"0 5123.0 5887.0 6375.0 6806.0 7494.0 7585.0 7525.0 7860.0 \n",
"1 5164.0 5955.0 6461.0 6905.0 7614.0 7709.0 7642.0 7986.0 \n",
"2 5080.0 5820.0 6296.0 6711.0 7375.0 7473.0 7416.0 7741.0 \n",
"3 5078.0 5810.0 6287.0 6702.0 7368.0 7458.0 7402.0 7728.0 \n",
"4 5098.0 5845.0 6326.0 6747.0 7420.0 7516.0 7454.0 7785.0 \n",
"\n",
" 560.5 566.7 ... CALIBRATION SP_PELTIER \\\n",
"id ... \n",
"0 8582.0 9196.0 ... 0 1 \n",
"1 8730.0 9366.0 ... 0 1 \n",
"2 8445.0 9038.0 ... 0 1 \n",
"3 8436.0 9025.0 ... 0 1 \n",
"4 8501.0 9102.0 ... 0 1 \n",
"\n",
" TC_MI_STATUS CLOCK_COUNT_ERR_FLAG SPATIAL_RESOLUTION_FLAG \\\n",
"id \n",
"0 1 0 65 \n",
"1 1 0 65 \n",
"2 1 0 65 \n",
"3 1 0 65 \n",
"4 1 0 65 \n",
"\n",
" GEOMETRIC_INFO_RECAL_FLAG SUPPORT_IMAGE_LINE_POSITION \\\n",
"id \n",
"0 67 27 \n",
"1 67 55 \n",
"2 67 83 \n",
"3 67 112 \n",
"4 67 140 \n",
"\n",
" SUPPORT_IMAGE_COLUMN_POSITION THUMBNAIL_LINE_POSITION \\\n",
"id \n",
"0 480 13 \n",
"1 480 27 \n",
"2 480 40 \n",
"3 480 53 \n",
"4 480 66 \n",
"\n",
" THUMBNAIL_COLUMN_POSITION \n",
"id \n",
"0 228 \n",
"1 228 \n",
"2 228 \n",
"3 228 \n",
"4 228 \n",
"\n",
"[5 rows x 312 columns]"
]
},
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],
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"s.loc['RAW'].head()"
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{
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" <th>5</th>\n",
" <td>0.0392</td>\n",
" <td>0.0475</td>\n",
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" <td>67</td>\n",
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" <td>480</td>\n",
" <td>80</td>\n",
" <td>228</td>\n",
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" <th>6</th>\n",
" <td>0.0392</td>\n",
" <td>0.0476</td>\n",
" <td>0.0485</td>\n",
" <td>0.0507</td>\n",
" <td>0.0519</td>\n",
" <td>0.0538</td>\n",
" <td>0.0546</td>\n",
" <td>0.0558</td>\n",
" <td>0.0581</td>\n",
" <td>0.0598</td>\n",
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" <td>480</td>\n",
" <td>93</td>\n",
" <td>228</td>\n",
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" <th>7</th>\n",
" <td>0.0393</td>\n",
" <td>0.0476</td>\n",
" <td>0.0485</td>\n",
" <td>0.0508</td>\n",
" <td>0.0520</td>\n",
" <td>0.0539</td>\n",
" <td>0.0547</td>\n",
" <td>0.0558</td>\n",
" <td>0.0582</td>\n",
" <td>0.0600</td>\n",
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" <td>67</td>\n",
" <td>224</td>\n",
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" <td>106</td>\n",
" <td>228</td>\n",
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" <th>8</th>\n",
" <td>0.0406</td>\n",
" <td>0.0492</td>\n",
" <td>0.0501</td>\n",
" <td>0.0525</td>\n",
" <td>0.0537</td>\n",
" <td>0.0557</td>\n",
" <td>0.0565</td>\n",
" <td>0.0576</td>\n",
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" <td>0.0511</td>\n",
" <td>0.0523</td>\n",
" <td>0.0541</td>\n",
" <td>0.0550</td>\n",
" <td>0.0561</td>\n",
" <td>0.0584</td>\n",
" <td>0.0602</td>\n",
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" <td>0.0528</td>\n",
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" <td>0.0509</td>\n",
" <td>0.0533</td>\n",
" <td>0.0546</td>\n",
" <td>0.0565</td>\n",
" <td>0.0574</td>\n",
" <td>0.0586</td>\n",
" <td>0.0610</td>\n",
" <td>0.0628</td>\n",
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" <td>173</td>\n",
" <td>228</td>\n",
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" <td>0.0499</td>\n",
" <td>0.0509</td>\n",
" <td>0.0534</td>\n",
" <td>0.0546</td>\n",
" <td>0.0565</td>\n",
" <td>0.0574</td>\n",
" <td>0.0586</td>\n",
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" <td>0.0541</td>\n",
" <td>0.0554</td>\n",
" <td>0.0573</td>\n",
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" <td>67</td>\n",
" <td>420</td>\n",
" <td>480</td>\n",
" <td>199</td>\n",
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" 512.6 518.4 524.7 530.4 536.5 542.8000000000001 548.7 \\\n",
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"\n",
" SP_PELTIER TC_MI_STATUS CLOCK_COUNT_ERR_FLAG \\\n",
"minor id \n",
"REF1 5 1 1 0 \n",
" 6 1 1 0 \n",
" 7 1 1 0 \n",
" 8 1 1 0 \n",
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"\n",
" SPATIAL_RESOLUTION_FLAG GEOMETRIC_INFO_RECAL_FLAG \\\n",
"minor id \n",
"REF1 5 65 67 \n",
" 6 65 67 \n",
" 7 65 67 \n",
" 8 65 67 \n",
" 9 65 67 \n",
" 10 65 67 \n",
" 11 65 67 \n",
" 12 65 67 \n",
" 13 65 67 \n",
" 14 65 67 \n",
"\n",
" SUPPORT_IMAGE_LINE_POSITION SUPPORT_IMAGE_COLUMN_POSITION \\\n",
"minor id \n",
"REF1 5 168 480 \n",
" 6 196 480 \n",
" 7 224 480 \n",
" 8 252 480 \n",
" 9 280 480 \n",
" 10 308 480 \n",
" 11 336 480 \n",
" 12 364 480 \n",
" 13 392 480 \n",
" 14 420 480 \n",
"\n",
" THUMBNAIL_LINE_POSITION THUMBNAIL_COLUMN_POSITION \n",
"minor id \n",
"REF1 5 80 228 \n",
" 6 93 228 \n",
" 7 106 228 \n",
" 8 120 228 \n",
" 9 133 228 \n",
" 10 146 228 \n",
" 11 159 228 \n",
" 12 173 228 \n",
" 13 186 228 \n",
" 14 199 228 \n",
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"[10 rows x 312 columns]"
]
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],
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"metadata": {},
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"source": [
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},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
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{
"name": "stderr",
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"text": [
"/Users/krodriguez/repos/PySAT/libpysat/utils/utils.py:276: RuntimeWarning: invalid value encountered in true_divide\n",
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"source": [
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{
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"execution_count": 28,
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{
"data": {
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"data": {
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r+3j9q266mj8qqWEv3rWT/Tf+OK9/5U384EfLe9QOcNMNL7rmf8cscd8A9k283wtcuMKYjSS7gJ8Avj2XGU55+Ut28/KX7L4WP1pSA376J39s0VNYCrPslnkMOJjk1iS7gbuAE1NjTgC/O3j9ZuDT12K9XZI0m22P3Adr6PcCDwM7gQ9X1ekkDwCnquoE8FfAR5Oco3/Efte1nLQkaWuzLMtQVSeBk1Of3T/x+ofAW+Y7NUnS1fIKVUlqkHGXpAYZd0lqkHGXpAZlUTsWk1wEvn6Vf3wPU1e/riB/h+Xg77Ac/B1md0tVrW03aGFxfz6SnKqq9UXP4/nwd1gO/g7Lwd9h/lyWkaQGGXdJatCqxv34oicwB/4Oy8HfYTn4O8zZSq65S5K2tqpH7pKkLRh3SWqQcZekBhl3SWqQcZekBv0/oM5uZvF3KnIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a277f65f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ref1.iloc[4].plot()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a277f64a8>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a278a94a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ref1corr.iloc[4].plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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