Batch Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________\n",
      "Found data for NM9505 30C (266, 269, 270).xlsx\n",
      "Found data for NMC640 30C (243, 246, 259).xlsx\n",
      "Found data for NMC811 30C (239, 272, 276).xlsx\n",
      "\n",
      "C:\\Users\\Mitch\\Lab\\Atlung Modeling\\AMIDR\\Analyzed Data\\Material Comparison (NM9505 30C, NMC640 30C, NMC811 30C).jpg\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x540 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<amidr.MATCOMPARE at 0x24cd3ed70a0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "radius9 = 1.318e-4 #NM9505\n",
    "radius6 = 0.784e-4 #NMC640\n",
    "radius8 = 1.736e-4 #NMC811\n",
    "ionsat_inputs9 = [303.15, 275, 66, 3.7, 149, 4.0] #NM9505\n",
    "ionsat_inputs6 = [303.15, 279, 74, 3.8, 148, 4.1] #NMC640\n",
    "ionsat_inputs8 = [303.15, 275.5, 70, 3.7, 178, 4.1] #NMC811\n",
    "\n",
    "from amidr import BIOCONVERT\n",
    "from amidr import AMIDR\n",
    "from amidr import BINAVERAGE\n",
    "'''\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/215'\n",
    "formfiles = ['215_230224_C01.mpt', '215_230301_D_C01.mpt']\n",
    "dfiles = ['215_230316_D4,2-3,7V_C01.mpt', '215_230320_D3,7-3,45V_C01.mpt']\n",
    "cfiles = ['215_230324_C3,45-3,7V_C01.mpt', '215_230331_C3,7-4,2V_C01.mpt']\n",
    "cellname = '215 NM9505 30C'\n",
    "BIOCONVERT(cellpath, formfiles, dfiles, cfiles, cellname)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/217'\n",
    "formfiles = ['217_230224_C03.mpt', '217_230227_C03.mpt', '217_230301_D_C03.mpt']\n",
    "dfiles = ['217_230316_D4,2-3,7V_C03.mpt', '217_230320_D3,7-3,45V_C03.mpt']\n",
    "cfiles = ['217_230324_C3,45-3,7V_C03.mpt', '217_230331_C3,7-4,2V_C03.mpt']\n",
    "cellname = '217 NM9505 30C'\n",
    "BIOCONVERT(cellpath, formfiles, dfiles, cfiles, cellname)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/243'\n",
    "formfiles = ['243_230502_C06.mpt']\n",
    "dfiles = ['243_230504_D4,2-3,7V_C06.mpt', '243_230507_D3,7-3,2V_C06.mpt']\n",
    "cfiles = ['243_230509_C3,5-3,7V_C06.mpt', '243_230512_C3,7-4,2V_C06.mpt']\n",
    "cellname = '243 NMC640 30C'\n",
    "BIOCONVERT(cellpath, formfiles, dfiles, cfiles, cellname)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/246'\n",
    "formfiles = ['246_230502_C08.mpt']\n",
    "dfiles = ['246_230504_D4,2-3,7V_C08.mpt', '246_230507_D3,7-3,2V_C08.mpt']\n",
    "cfiles = ['246_230509_C3,5-3,7V_C08.mpt', '246_230512_C3,7-4,2V_C08.mpt']\n",
    "cellname = '246 NMC640 30C'\n",
    "BIOCONVERT(cellpath, formfiles, dfiles, cfiles, cellname)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/259'\n",
    "formfiles = ['259_230525_C09.mpt']\n",
    "dfiles = ['259_230529_D4,2-3,7V_C09.mpt', '259_230531_D3,7-3,2V_C09.mpt']\n",
    "cfiles = ['259_230601_C3,5-3,7V_C09.mpt', '259_230604_C3,7-4,2V_C09.mpt']\n",
    "cellname = '259 NMC640 30C'\n",
    "BIOCONVERT(cellpath, formfiles, dfiles, cfiles, cellname)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/239'\n",
    "formfiles = ['239_230502_C05.mpt']\n",
    "dfiles = ['239_230504_D4,2-3,7V_C05.mpt', '239_230507_D3,7-3,2V_C05.mpt']\n",
    "cfiles = ['239_230509_C3,5-3,7V_C05.mpt', '239_230515_C3,7-4,2V_C05.mpt']\n",
    "cellname = '239 NMC811 30C'\n",
    "BIOCONVERT(cellpath, formfiles, dfiles, cfiles, cellname)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/272'\n",
    "formfiles = ['272_230607_C06.mpt']\n",
    "dfiles = ['272_230609_D4,2-3,7V_C06.mpt', '272_230612_D3,7-3,2V_C06.mpt']\n",
    "cfiles = ['272_230615_C3,5-3,7V_C06.mpt', '272_230623_C3,7-4,2V_C06.mpt']\n",
    "cellname = '272 NMC811 30C'\n",
    "BIOCONVERT(cellpath, formfiles, dfiles, cfiles, cellname)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/276'\n",
    "formfiles = ['276_230607_C11.mpt']\n",
    "dfiles = ['276_230609_D4,2-3,7V_C11.mpt', '276_230612_D3,7-3,2V_C11.mpt']\n",
    "cfiles = ['276_230615_C3,5-3,7V_C11.mpt', '276_230623_C3,7-4,2V_C11.mpt']\n",
    "cellname = '276 NMC811 30C'\n",
    "BIOCONVERT(cellpath, formfiles, dfiles, cfiles, cellname)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/266'\n",
    "formfiles = ['266_230530_C08.mpt']\n",
    "dfiles = ['266_230602_D4,2-3,95V_C08.mpt', '266_230604_D3,95-3,7V_C08.mpt', '266_230606_D3,7-3,45V_C08.mpt']\n",
    "cfiles = ['266_230612_C3,5-3,7V_C08.mpt', '266_230619_C3,7-3,95V_C08.mpt', '266_230623_C3,95-4,2V_C08.mpt']\n",
    "cellname = '266 NM9505 30C'\n",
    "BIOCONVERT(cellpath, formfiles, dfiles, cfiles, cellname)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/269'\n",
    "formfiles = ['269_230530_C12.mpt']\n",
    "dfiles = ['269_230602_D4,2-3,95V_C12.mpt', '269_230604_D3,95-3,7V_C12.mpt', '269_230606_D3,7-3,45V_C12.mpt']\n",
    "cfiles = ['269_230612_C3,5-3,7V_C12.mpt', '269_230619_C3,7-3,95V_C12.mpt', '269_230623_C3,95-4,2V_C12.mpt']\n",
    "cellname = '269 NM9505 30C'\n",
    "BIOCONVERT(cellpath, formfiles, dfiles, cfiles, cellname)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/270'\n",
    "formfiles = ['270_230530_C02.mpt']\n",
    "dfiles = ['270_230602_D4,2-3,95V_C02.mpt', '270_230604_D3,95-3,7V_C02.mpt', '270_230606_D3,7-3,45V_C02.mpt']\n",
    "cfiles = ['270_230612_C3,5-3,7V_C02.mpt', '270_230619_C3,7-3,95V_C02.mpt', '270_230623_C3,95-4,2V_C02.mpt']\n",
    "cellname = '270 NM9505 30C'\n",
    "BIOCONVERT(cellpath, formfiles, dfiles, cfiles, cellname)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/215'\n",
    "uhpc_file = '215 NM9505 30C Discharge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius9, R_corr=True, ionsat_inputs = ionsat_inputs9)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "uhpc_file = '215 NM9505 30C Charge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius9, R_corr=True, ionsat_inputs = ionsat_inputs9)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/217'\n",
    "uhpc_file = '217 NM9505 30C Discharge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius9, R_corr=True, ionsat_inputs = ionsat_inputs9)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "uhpc_file = '217 NM9505 30C Charge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius9, R_corr=True, ionsat_inputs = ionsat_inputs9)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/243'\n",
    "uhpc_file = '243 NMC640 30C Discharge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius6, R_corr=True, ionsat_inputs = ionsat_inputs6)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "uhpc_file = '243 NMC640 30C Charge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius6, R_corr=True, ionsat_inputs = ionsat_inputs6)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/246'\n",
    "uhpc_file = '246 NMC640 30C Discharge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius6, R_corr=True, ionsat_inputs = ionsat_inputs6)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "uhpc_file = '246 NMC640 30C Charge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius6, R_corr=True, ionsat_inputs = ionsat_inputs6)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/259'\n",
    "uhpc_file = '259 NMC640 30C Discharge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius6, R_corr=True, ionsat_inputs = ionsat_inputs6)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "uhpc_file = '259 NMC640 30C Charge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius6, R_corr=True, ionsat_inputs = ionsat_inputs6)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/239'\n",
    "uhpc_file = '239 NMC811 30C Discharge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius8, R_corr=True, ionsat_inputs = ionsat_inputs8)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "uhpc_file = '239 NMC811 30C Charge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius8, R_corr=True, ionsat_inputs = ionsat_inputs8)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/272'\n",
    "uhpc_file = '272 NMC811 30C Discharge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius8, R_corr=True, ionsat_inputs = ionsat_inputs8)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "uhpc_file = '272 NMC811 30C Charge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius8, R_corr=True, ionsat_inputs = ionsat_inputs8)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/276'\n",
    "uhpc_file = '276 NMC811 30C Discharge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius8, R_corr=True, ionsat_inputs = ionsat_inputs8)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "uhpc_file = '276 NMC811 30C Charge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius8, R_corr=True, ionsat_inputs = ionsat_inputs8)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/266'\n",
    "uhpc_file = '266 NM9505 30C Discharge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius9, R_corr=True, ionsat_inputs = ionsat_inputs9)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "uhpc_file = '266 NM9505 30C Charge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius9, R_corr=True, ionsat_inputs = ionsat_inputs9)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/269'\n",
    "uhpc_file = '269 NM9505 30C Discharge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius9, R_corr=True, ionsat_inputs = ionsat_inputs9)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "uhpc_file = '269 NM9505 30C Charge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius9, R_corr=True, ionsat_inputs = ionsat_inputs9)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/270'\n",
    "uhpc_file = '270 NM9505 30C Discharge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius9, R_corr=True, ionsat_inputs = ionsat_inputs9)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "uhpc_file = '270 NM9505 30C Charge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True)\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius9, R_corr=True, ionsat_inputs = ionsat_inputs9)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/266'\n",
    "uhpc_file = '266 NM9505 30C Discharge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True, force2e = True, parselabel = '2e')\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius9, R_corr=True, ionsat_inputs = ionsat_inputs9)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "uhpc_file = '266 NM9505 30C Charge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True, force2e = True, parselabel = '2e')\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius9, R_corr=True, ionsat_inputs = ionsat_inputs9)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/269'\n",
    "uhpc_file = '269 NM9505 30C Discharge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True, force2e = True, parselabel = '2e')\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius9, R_corr=True, ionsat_inputs = ionsat_inputs9)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "uhpc_file = '269 NM9505 30C Charge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True, force2e = True, parselabel = '2e')\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius9, R_corr=True, ionsat_inputs = ionsat_inputs9)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "\n",
    "cellpath = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data/270'\n",
    "uhpc_file = '270 NM9505 30C Discharge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True, force2e = True, parselabel = '2e')\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius9, R_corr=True, ionsat_inputs = ionsat_inputs9)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "uhpc_file = '270 NM9505 30C Charge.csv'\n",
    "amidr_data = AMIDR(cellpath, uhpc_file, single_pulse=True, force2e = True, parselabel = '2e')\n",
    "amidr_data.plot_protocol()\n",
    "amidr_data.plot_caps() \n",
    "fit_data = amidr_data.fit_atlung(radius9, R_corr=True, ionsat_inputs = ionsat_inputs9)\n",
    "amidr_data.make_summary_graph(fit_data=fit_data)\n",
    "\n",
    "path = 'C:/Users/Mitch/Lab/Atlung Modeling/AMIDR/Analyzed Data'\n",
    "\n",
    "cells = ['215', '217']\n",
    "matname = 'NM9505 30C Old'\n",
    "BINAVERAGE(path, cells, matname, binsize = 0.025)\n",
    "\n",
    "cells = ['243', '246', '259']\n",
    "matname = 'NMC640 30C'\n",
    "BINAVERAGE(path, cells, matname, binsize = 0.025)\n",
    "\n",
    "cells = ['239', '272', '276']\n",
    "matname = 'NMC811 30C'\n",
    "BINAVERAGE(path, cells, matname, binsize = 0.025)\n",
    "\n",
    "cells = ['266', '269', '270']\n",
    "matname = 'NM9505 30C'\n",
    "BINAVERAGE(path, cells, matname, binsize = 0.0125)\n",
    "\n",
    "cells = ['266', '269', '270']\n",
    "matname = 'NM9505 30C'\n",
    "BINAVERAGE(path, cells, matname, binsize = 0.0125, parselabel = '2e')\n",
    "'''\n",
    "mats = ['NM9505 30C', 'NMC640 30C', 'NMC811 30C']\n",
    "\n",
    "from amidr import MATCOMPARE\n",
    "MATCOMPARE(path, mats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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