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"
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" | \n",
" 0.0 | \n",
" 0.05 | \n",
" 0.116666666667 | \n",
"
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" 430.10 | \n",
" 257.315595 | \n",
" 257.462298 | \n",
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"
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"
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"
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"
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"
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"
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"
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"
\n",
"
704 rows \u00d7 3 columns
\n",
"
"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"*AuNPs Glass (good)*\tSpectral unit:nanometers\tPerturbation unit:no unit\n",
"\n",
" 0.000000 0.050000 0.116667\n",
"430.10 257.315595 257.462298 257.216689\n",
"430.47 267.776463 267.823164 267.707553\n",
"430.85 278.697354 278.704053 278.718440\n",
"431.22 290.288222 290.344919 290.219305\n",
"431.59 302.609089 302.635784 302.620169\n",
"431.96 314.499957 314.616650 314.641033\n",
"432.33 327.720825 327.857516 327.931897\n",
"432.70 340.491693 340.758382 340.682762\n",
"433.08 355.492584 355.819271 355.763649\n",
"433.45 369.703452 370.100137 370.154513\n",
"433.82 384.024319 384.261002 384.225378\n",
"434.19 399.695187 400.011868 400.016242\n",
"434.56 415.366055 415.592734 415.587106\n",
"434.93 430.516923 430.543600 430.647970\n",
"435.30 445.457791 445.534465 445.768834\n",
"435.68 462.498682 462.435355 462.779722\n",
"436.05 479.079550 479.136220 479.370586\n",
"436.42 495.810417 495.737086 496.031451\n",
"436.79 511.271285 511.087952 511.582315\n",
"437.16 527.992153 527.788818 528.323179\n",
"437.53 545.853021 545.679684 546.284043\n",
"437.90 563.923889 563.790549 564.294907\n",
"438.27 581.584756 581.481415 581.875772\n",
"438.64 600.155624 600.162281 600.346636\n",
"439.01 617.616492 617.573147 617.797500\n",
"439.38 633.597360 633.494012 633.718364\n",
"439.75 649.098227 648.834878 648.999229\n",
"440.13 666.689119 666.505767 666.630116\n",
"440.50 683.479986 683.346633 683.530980\n",
"440.87 697.580854 697.167499 697.341845\n",
"... ... ... ...\n",
"669.87 187.957941 188.223341 187.406734\n",
"670.21 185.978739 186.254137 185.567528\n",
"670.55 184.249536 184.544933 183.788322\n",
"670.89 182.770334 182.985728 182.319116\n",
"671.23 181.051131 181.156524 180.619911\n",
"671.57 179.541928 179.527319 179.120705\n",
"671.91 177.932726 177.878115 177.441499\n",
"672.25 176.223523 176.228910 175.832293\n",
"672.58 174.564297 174.529683 174.213064\n",
"672.92 172.775095 172.700478 172.503858\n",
"673.26 171.265892 171.101274 170.874652\n",
"673.60 169.806689 169.562069 169.315446\n",
"673.94 168.317487 168.032865 167.856241\n",
"674.28 166.958284 166.683661 166.477035\n",
"674.62 165.489082 165.274456 165.167829\n",
"674.96 163.929879 163.725252 163.538623\n",
"675.30 162.560677 162.246047 162.109417\n",
"675.64 161.131474 160.806843 160.580211\n",
"675.97 159.492248 159.317615 159.080982\n",
"676.31 157.923045 157.708411 157.521776\n",
"676.65 156.393843 156.229206 155.962570\n",
"676.99 155.104640 154.900002 154.743365\n",
"677.33 153.545438 153.340797 153.184159\n",
"677.67 152.126235 151.961593 151.714953\n",
"678.01 150.527033 150.432388 150.135747\n",
"678.34 149.007806 149.053161 148.726518\n",
"678.68 147.548604 147.573956 147.287312\n",
"679.02 146.069401 146.044752 145.828106\n",
"679.36 144.630199 144.625547 144.398900\n",
"679.70 143.220996 143.266343 142.979695\n",
"\n",
"[704 rows x 3 columns] ID: 88136848"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For more on the various ways to select data from dataframes, see the [[pandas indexing and selection](http://pandas.pydata.org/pandas-docs/stable/indexing.html) tutorial. We will focus mainly on slicing below, which is where `skspec` implements its own slicing objects."
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Approximate slicing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use `ts.nearby` to do approximate index slicing. \n",
"\n",
"- By default, this work on the spectral/row axis. \n",
"- **The limits provided must be within the limits of the dataset**. \n",
"- This is inherently label based! \n",
" - Meaning even integers will be interpreted as wavelengths. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t1.nearby[550.0:650.0].plot(cbar=True);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"svg": [
"\n",
"\n",
"\n",
"