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hpos-web/scripts/tr-smiappdataanalysis.ipynb

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notebook for csv analysis
2023-07-27 10:30:27 +05:30
{
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{
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"text": [
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did49_592.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did17-2_465.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did38_556.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did28-2_517.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did31_532.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did21_492.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_DID006_422.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_DID004_417.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did36_548.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did50_593.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did15-3_468.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did31_531.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did15_451.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did27_512.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did22_497.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did21_494.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_DID006_421.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did26_507.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did22_495.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did20-3_487.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did24_501.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did34_544.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did28-4_524.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did28-3_519.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did40_561.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did24_502.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did45_575.csv\n",
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"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did54_605.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did007_426.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did17-2_463.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did19_479.csv\n",
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"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did23_500.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_DID006_423.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did32_535.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did008_427.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did15_453.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_Did009_430.csv\n",
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"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_DID004_415.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did47_2_586.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did50_594.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_DID004_416.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did49_591.csv\n",
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"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did50_595.csv\n",
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"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did20_483.csv\n",
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"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did47_2_584.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did37_551.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did44_573.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did53_602.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did15_2_456.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did24_503.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did45_577.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did007_424.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did28-4_523.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did23_498.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did28-3_520.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did28-2_518.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did13_2_445.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did16_457.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did42_568.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did28_513.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did010_435.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did33_540.csv\n",
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"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did29_526.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did27_510.csv\n",
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"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did45_576.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did20-4_491.csv\n",
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"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did44_572.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did42_567.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did008_429.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did011_437.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did44_574.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did35_547.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_DID005_418.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did13_442.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_DID003_413.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did36_549.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did52_599.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did47_582.csv\n",
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"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did48_589.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_DID005_419.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did51_597.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did25_504.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did43_571.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did007_425.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did46_579.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did13_444.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did16_458.csv\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:58: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did18-3_477.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did13_2_447.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did36_550.csv\n",
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"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did17-2_464.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did20-3_488.csv\n",
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"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_Did009_432.csv\n",
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"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did16_459.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did40_562.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did54_607.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did30_530.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did54_606.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did18_469.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did012_441.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did41_563.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did18-2_472.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did34_542.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did33_539.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did43_570.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did28-4_522.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did15-3_466.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did32-2_536.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did14_450.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did31_533.csv\n",
"Processing /kaggle/input/bhopal20230207/HPOSSC_D30A3L0I_did20-2_484.csv\n"
]
},
{
"data": {
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" vertical-align: top;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample ID</th>\n",
" <th>max_427</th>\n",
" <th>wvmax_427</th>\n",
" <th>avg_427</th>\n",
" <th>max_555</th>\n",
" <th>wvmax_555</th>\n",
" <th>avg_555</th>\n",
" <th>ratio_max</th>\n",
" <th>ratio_avg</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0.896</td>\n",
" <td>423.162</td>\n",
" <td>0.769</td>\n",
" <td>0.505</td>\n",
" <td>551.096</td>\n",
" <td>0.491</td>\n",
" <td>0.563</td>\n",
" <td>0.638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>0.715</td>\n",
" <td>423.818</td>\n",
" <td>0.618</td>\n",
" <td>0.407</td>\n",
" <td>555.769</td>\n",
" <td>0.396</td>\n",
" <td>0.569</td>\n",
" <td>0.642</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>0.971</td>\n",
" <td>418.568</td>\n",
" <td>0.788</td>\n",
" <td>0.474</td>\n",
" <td>551.408</td>\n",
" <td>0.462</td>\n",
" <td>0.488</td>\n",
" <td>0.586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>0.978</td>\n",
" <td>422.506</td>\n",
" <td>0.813</td>\n",
" <td>0.513</td>\n",
" <td>554.835</td>\n",
" <td>0.504</td>\n",
" <td>0.525</td>\n",
" <td>0.620</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0.733</td>\n",
" <td>423.818</td>\n",
" <td>0.637</td>\n",
" <td>0.420</td>\n",
" <td>547.664</td>\n",
" <td>0.411</td>\n",
" <td>0.573</td>\n",
" <td>0.645</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>197</th>\n",
" <td>198</td>\n",
" <td>0.793</td>\n",
" <td>421.850</td>\n",
" <td>0.678</td>\n",
" <td>0.436</td>\n",
" <td>550.473</td>\n",
" <td>0.424</td>\n",
" <td>0.550</td>\n",
" <td>0.626</td>\n",
" </tr>\n",
" <tr>\n",
" <th>198</th>\n",
" <td>199</td>\n",
" <td>0.731</td>\n",
" <td>422.178</td>\n",
" <td>0.613</td>\n",
" <td>0.390</td>\n",
" <td>551.720</td>\n",
" <td>0.379</td>\n",
" <td>0.534</td>\n",
" <td>0.619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>199</th>\n",
" <td>200</td>\n",
" <td>0.801</td>\n",
" <td>413.967</td>\n",
" <td>0.728</td>\n",
" <td>0.541</td>\n",
" <td>550.473</td>\n",
" <td>0.531</td>\n",
" <td>0.675</td>\n",
" <td>0.730</td>\n",
" </tr>\n",
" <tr>\n",
" <th>200</th>\n",
" <td>201</td>\n",
" <td>0.739</td>\n",
" <td>423.818</td>\n",
" <td>0.633</td>\n",
" <td>0.421</td>\n",
" <td>551.096</td>\n",
" <td>0.412</td>\n",
" <td>0.570</td>\n",
" <td>0.650</td>\n",
" </tr>\n",
" <tr>\n",
" <th>201</th>\n",
" <td>202</td>\n",
" <td>0.708</td>\n",
" <td>424.145</td>\n",
" <td>0.587</td>\n",
" <td>0.372</td>\n",
" <td>549.225</td>\n",
" <td>0.365</td>\n",
" <td>0.526</td>\n",
" <td>0.622</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>202 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" Sample ID max_427 wvmax_427 avg_427 max_555 wvmax_555 avg_555 \\\n",
"0 1 0.896 423.162 0.769 0.505 551.096 0.491 \n",
"1 2 0.715 423.818 0.618 0.407 555.769 0.396 \n",
"2 3 0.971 418.568 0.788 0.474 551.408 0.462 \n",
"3 4 0.978 422.506 0.813 0.513 554.835 0.504 \n",
"4 5 0.733 423.818 0.637 0.420 547.664 0.411 \n",
".. ... ... ... ... ... ... ... \n",
"197 198 0.793 421.850 0.678 0.436 550.473 0.424 \n",
"198 199 0.731 422.178 0.613 0.390 551.720 0.379 \n",
"199 200 0.801 413.967 0.728 0.541 550.473 0.531 \n",
"200 201 0.739 423.818 0.633 0.421 551.096 0.412 \n",
"201 202 0.708 424.145 0.587 0.372 549.225 0.365 \n",
"\n",
" ratio_max ratio_avg \n",
"0 0.563 0.638 \n",
"1 0.569 0.642 \n",
"2 0.488 0.586 \n",
"3 0.525 0.620 \n",
"4 0.573 0.645 \n",
".. ... ... \n",
"197 0.550 0.626 \n",
"198 0.534 0.619 \n",
"199 0.675 0.730 \n",
"200 0.570 0.650 \n",
"201 0.526 0.622 \n",
"\n",
"[202 rows x 9 columns]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
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},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1800x1800 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# This script processes one or more data files in CSV format TestRight devices,\n",
"# processes the data and plots some key information\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"\n",
"\n",
"midpoint1 = 427\n",
"midpoint2 = 555\n",
"bandwidth1 = 25\n",
"bandwidth2 = 10\n",
"\n",
"%matplotlib inline\n",
"\n",
"def processSingleSampleData(df, sampleID, prdf):\n",
" \n",
" # rename columns to be meaningful\n",
" df.rename(columns = {\"NM\":\"Wavelength\",\"CA\":\"Absorbance\"}, inplace = True)\n",
" \n",
" # 427 nm range\n",
" df1 = df[ (df['Wavelength'] > (midpoint1-bandwidth1)) & (df['Wavelength'] < (midpoint1+bandwidth1)) ]\n",
"\n",
" # 555 nm range\n",
" df2 = df[ (df['Wavelength'] > (midpoint2-bandwidth2)) & (df['Wavelength'] < (midpoint2+bandwidth2)) ]\n",
" \n",
" procData.append({\"Sample ID\": sampleID, \n",
" \"max_427\": round(df1[\"Absorbance\"].max() , 3),\n",
" \"wvmax_427\": round(df1.at[df1[\"Absorbance\"].idxmax(),\"Wavelength\"], 3),\n",
" \"avg_427\": round(df1[\"Absorbance\"].mean(), 3),\n",
" \"max_555\": round(df2[\"Absorbance\"].max(), 3),\n",
" \"wvmax_555\": round(df2.at[df2[\"Absorbance\"].idxmax(),\"Wavelength\"], 3),\n",
" \"avg_555\": round(df2[\"Absorbance\"].mean(), 3),\n",
" \"ratio_max\": round(df2[\"Absorbance\"].max()/df1[\"Absorbance\"].max(), 3),\n",
" \"ratio_avg\": round(df2[\"Absorbance\"].mean()/df1[\"Absorbance\"].mean(), 3)\n",
" })\n",
"\n",
"sampleID = 1\n",
"allDF = pd.DataFrame()\n",
"procData = []\n",
"\n",
"dataFolders = ['/kaggle/input/bhopal20230207/']\n",
"\n",
"for folder in dataFolders:\n",
" for file in os.listdir(folder):\n",
" if file.endswith(\".csv\") and file.startswith('HPOS'):\n",
" fname = folder + file\n",
" print(\"Processing %s\" %(fname) )\n",
" df = pd.read_csv(fname)\n",
" processSingleSampleData(df,sampleID, procData)\n",
" if sampleID == 1:\n",
" allDF[\"Wavelength\"] = df[\"Wavelength\"]\n",
" allDF[\"Sample \" + str(sampleID)] = df[\"Absorbance\"]\n",
" sampleID = sampleID + 1\n",
" \n",
" \n",
"# full plot\n",
"allDF.plot(x=\"Wavelength\", legend=False)\n",
"# 400 - 600 nm range plot\n",
"allDF[0:600].plot(x=\"Wavelength\", legend=False, xticks=np.arange(400, 600, step=5), figsize=(25,25))\n",
"\n",
"procDF = pd.DataFrame(procData)\n",
"\n",
"plt = procDF.plot.scatter(x=\"Sample ID\",y=\"ratio_max\", alpha = 0.6, s=10)\n",
"plt.grid(axis='y')\n",
"procDF"
]
}
],
"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": "3.7.12"
},
"papermill": {
"default_parameters": {},
"duration": 18.481137,
"end_time": "2023-07-24T08:12:26.686195",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2023-07-24T08:12:08.205058",
"version": "2.3.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Reference in New Issue Copy Permalink