{
"cells": [
{
"cell_type": "code",
"execution_count": 36,
"id": "4ffedbf9",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import glob\n",
"import os\n",
"from pathlib import Path"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "2ae91bea",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sampleid :c210 sc\n",
" cm-1 %T\n",
"0 4000.0 99.24\n",
"1 3999.0 99.24\n",
"2 3998.0 99.25\n",
"3 3997.0 99.25\n",
"4 3996.0 99.25\n",
"... ... ...\n",
"3346 654.0 42.35\n",
"3347 653.0 42.24\n",
"3348 652.0 42.20\n",
"3349 651.0 42.12\n",
"3350 650.0 42.01\n",
"\n",
"[3351 rows x 2 columns]\n",
" cm-1 %T\n",
"0 4000.0 99.24\n",
"1 3999.0 99.24\n",
"2 3998.0 99.25\n",
"3 3997.0 99.25\n",
"4 3996.0 99.25\n",
"... ... ...\n",
"3346 654.0 42.35\n",
"3347 653.0 42.24\n",
"3348 652.0 42.20\n",
"3349 651.0 42.12\n",
"3350 650.0 42.01\n",
"\n",
"[3351 rows x 2 columns]\n"
]
}
],
"source": [
"\"\"\"\"\"\"\n",
"#alternative way\n",
"\"\"\"\"\"\"\n",
"\n",
"\n",
"#list_of_files = glob.glob(r'*.csv') # * means all if need specific format then *.csv\n",
"#latest_file = max(list_of_files, key=os.path.getctime)\n",
"#print(latest_file)\n",
"#a=(Path(latest_file).stem[15:])\n",
"\n",
"\n",
"a=input('sampleid :')\n",
"\n",
"prefix=\"ATR IR of \"\n",
"\n",
"#data=pd.read_csv(prefix+str(a)+'.csv')\n",
"data=pd.read_csv(prefix+str(a)+'.csv', sep=';', decimal=',', skiprows=1)\n",
"print (data)\n",
"df = pd.DataFrame(data, columns= [\"cm-1\",'%T'])\n",
"print (df)\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "e71d646c",
"metadata": {},
"outputs": [],
"source": [
"#df1=df.drop(df.index[3101:3176])\n",
"#df2=df.drop(df.index[0:1])\n",
"#print(df2)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "8e39e67f",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" cm-1 %T\n",
"0 4000.0 99.24\n",
"1 3999.0 99.24\n",
"2 3998.0 99.25\n",
"3 3997.0 99.25\n",
"4 3996.0 99.25\n",
"... ... ...\n",
"3346 654.0 42.35\n",
"3347 653.0 42.24\n",
"3348 652.0 42.20\n",
"3349 651.0 42.12\n",
"3350 650.0 42.01\n",
"\n",
"[3351 rows x 2 columns]\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df3=df.astype(float)\n",
"\n",
"print(df3)\n",
"df3.plot(x ='cm-1', y='%T')\n",
"plt.gca().invert_xaxis()\n",
"plt.xlabel('Wavenumber (cm-1)')\n",
"plt.ylabel('Transmission (%)')\n",
"plt.legend( [ 'ATR IR measurement of '+a ] ) # This is f(x)\n",
" \n",
"\n",
"plt.savefig('IR Spectrum of sample '+a+'.pdf') # produces a PDF file containing the figure\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2db748e1",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "b7c7d373",
"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": "3.7.10"
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 5
}