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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PyEIS: Tutorial on importing experimental data, linear Kramers-Kronig analysis, masking data, and equivalent circuit fitting\n",
"This notebook introduces how to import experimental data files and use the build-in functions for linear Kramers-Kronig analysis and equivalent circuit fitting with PyEIS in Jupyter Lab.\n",
"\n",
"The experimental data that is exemplified herein was genereated from a macrodisk electrode that was placed in a three-electrode cell with a outer-sphere redox-active mediator in solution. The E$_{1/2}$ of the redox couple is around -0.9 V, and the data included was measured at -1.05 and -1.10 V, thus the oxidized specie is attrached to the electrode/electrolyte interface where it is reduced and diffuses away. We can apply the Randles circuit to the experimenta data due to the electrode geometry and assume semi-infinite linear mass-transport to the surface of the electrode.\n",
"\n",
"\n",
"The Initial step, is to import the PyEIS into the notebook using the following line:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from PyEIS import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importing and plotting data\n",
"Supported data files are imported into a pandas dataframe [1] through the EIS_exp() function. This function requires a folder \"path\", datafile name \"data\", and have the possiblity of limiting cycle number through \"cycle\", as well as the data can be limited by frequency using \"mask\". In this following example, we'll import data from two different files that are loacted in the same folder and look at the data structure. First, we'll import the two data files in seperate parameters."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "' Freq(Hz)'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/.conda/envs/EIS/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
"File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: ' Freq(Hz)'",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[29], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ex1 \u001b[38;5;241m=\u001b[39m \u001b[43mEIS_exp\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m./\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mEIS_10mV_Timing task2025_04_29_11_50_C001.z60\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/.conda/envs/EIS/lib/python3.10/site-packages/PyEIS/PyEIS.py:2239\u001b[0m, in \u001b[0;36mEIS_exp.__init__\u001b[0;34m(self, path, data, cycle, mask)\u001b[0m\n\u001b[1;32m 2237\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdf_raw0\u001b[38;5;241m.\u001b[39mappend(extract_dta(path\u001b[38;5;241m=\u001b[39mpath, EIS_name\u001b[38;5;241m=\u001b[39mdata[j])) \u001b[38;5;66;03m#reads all datafiles\u001b[39;00m\n\u001b[1;32m 2238\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m data[j]\u001b[38;5;241m.\u001b[39mfind(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.z\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m: \u001b[38;5;66;03m#file is a .z file\u001b[39;00m\n\u001b[0;32m-> 2239\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdf_raw0\u001b[38;5;241m.\u001b[39mappend(\u001b[43mextract_solar\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mEIS_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mj\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;66;03m#reads all datafiles\u001b[39;00m\n\u001b[1;32m 2240\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2241\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mData file(s) could not be identified\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
"File \u001b[0;32m~/.conda/envs/EIS/lib/python3.10/site-packages/PyEIS/PyEIS_Data_extraction.py:134\u001b[0m, in \u001b[0;36mextract_solar\u001b[0;34m(path, EIS_name)\u001b[0m\n\u001b[1;32m 132\u001b[0m init \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(path\u001b[38;5;241m+\u001b[39mEIS_name, encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlatin1\u001b[39m\u001b[38;5;124m'\u001b[39m, sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124m'\u001b[39m, names\u001b[38;5;241m=\u001b[39mdummy_col)\n\u001b[1;32m 133\u001b[0m ZC \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mIndex(init\u001b[38;5;241m.\u001b[39mA)\n\u001b[0;32m--> 134\u001b[0m header_loc \u001b[38;5;241m=\u001b[39m \u001b[43mZC\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m Freq(Hz)\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 136\u001b[0m header_names_raw \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(path\u001b[38;5;241m+\u001b[39mEIS_name, sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124m'\u001b[39m, skiprows\u001b[38;5;241m=\u001b[39mheader_loc, encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlatin1\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;66;03m#locates number of skiplines\u001b[39;00m\n\u001b[1;32m 137\u001b[0m header_names \u001b[38;5;241m=\u001b[39m []\n",
"File \u001b[0;32m~/.conda/envs/EIS/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m 3808\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m 3809\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m 3810\u001b[0m ):\n\u001b[1;32m 3811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3814\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3815\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3816\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3817\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
"\u001b[0;31mKeyError\u001b[0m: ' Freq(Hz)'"
]
}
],
"source": [
"ex1 = EIS_exp(\n",
" path='./', \n",
" data=['EIS_10mV_Timing task2025_04_29_11_50_C001.z60']\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"ex2 = EIS_exp(path='https://raw.githubusercontent.com/kbknudsen/PyEIS/master/Tutorials/data/', data=['ex2.mpt'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data contained within each parameter (ex1 and ex2) can be accesed by calling ex1/ex2.df_raw (here we'll call df_raw.head() to only view the top five rows) and as illustrated below, a number of coloumn names exist where the most significant coloumns are f (frequency), w (angular frequency), re (real), im (imaginary), and cycle_number. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" .dataframe thead th {\n",
" text-align: right;\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>f</th>\n",
" <th>re</th>\n",
" <th>im</th>\n",
" <th>Z_mag</th>\n",
" <th>Z_phase</th>\n",
" <th>times</th>\n",
" <th>E_avg</th>\n",
" <th>I_avg</th>\n",
" <th>Cs/µF</th>\n",
" <th>Cp/µF</th>\n",
" <th>cycle_number</th>\n",
" <th>I Range</th>\n",
" <th>|Ewe|/V</th>\n",
" <th>|I|/A</th>\n",
" <th>Y_re</th>\n",
" <th>Y_im</th>\n",
" <th>Y_mag</th>\n",
" <th>Y_phase</th>\n",
" <th>Unnamed: 18</th>\n",
" <th>w</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>200019.480</td>\n",
" <td>428.90558</td>\n",
" <td>372.31183</td>\n",
" <td>567.95782</td>\n",
" <td>-40.959641</td>\n",
" <td>13125.243035</td>\n",
" <td>-1.050056</td>\n",
" <td>-0.003182</td>\n",
" <td>0.002137</td>\n",
" <td>0.000918</td>\n",
" <td>1.0</td>\n",
" <td>12</td>\n",
" <td>0.009270</td>\n",
" <td>0.000016</td>\n",
" <td>0.001330</td>\n",
" <td>0.001154</td>\n",
" <td>0.001761</td>\n",
" <td>40.959641</td>\n",
" <td>NaN</td>\n",
" <td>1.256759e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>149677.700</td>\n",
" <td>389.09244</td>\n",
" <td>779.30804</td>\n",
" <td>871.04187</td>\n",
" <td>-63.467991</td>\n",
" <td>13125.926037</td>\n",
" <td>-1.050440</td>\n",
" <td>-0.003601</td>\n",
" <td>0.001364</td>\n",
" <td>0.001092</td>\n",
" <td>1.0</td>\n",
" <td>12</td>\n",
" <td>0.010166</td>\n",
" <td>0.000012</td>\n",
" <td>0.000513</td>\n",
" <td>0.001027</td>\n",
" <td>0.001148</td>\n",
" <td>63.467991</td>\n",
" <td>NaN</td>\n",
" <td>9.404527e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>112011.700</td>\n",
" <td>790.36896</td>\n",
" <td>1276.19290</td>\n",
" <td>1501.11670</td>\n",
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" <td>13126.609058</td>\n",
" <td>-1.050325</td>\n",
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" <td>0.000351</td>\n",
" <td>0.000566</td>\n",
" <td>0.000666</td>\n",
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" <td>7.037903e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>83828.102</td>\n",
" <td>1450.48320</td>\n",
" <td>1320.92870</td>\n",
" <td>1961.82420</td>\n",
" <td>-42.323559</td>\n",
" <td>13127.165037</td>\n",
" <td>-1.050293</td>\n",
" <td>-0.003622</td>\n",
" <td>0.001437</td>\n",
" <td>0.000652</td>\n",
" <td>1.0</td>\n",
" <td>12</td>\n",
" <td>0.010902</td>\n",
" <td>0.000006</td>\n",
" <td>0.000377</td>\n",
" <td>0.000343</td>\n",
" <td>0.000510</td>\n",
" <td>42.323559</td>\n",
" <td>NaN</td>\n",
" <td>5.267075e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>62734.359</td>\n",
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" <td>1040.74610</td>\n",
" <td>2134.64650</td>\n",
" <td>-29.179655</td>\n",
" <td>13127.722056</td>\n",
" <td>-1.050266</td>\n",
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" <td>12</td>\n",
" <td>0.010428</td>\n",
" <td>0.000005</td>\n",
" <td>0.000409</td>\n",
" <td>0.000228</td>\n",
" <td>0.000468</td>\n",
" <td>29.179655</td>\n",
" <td>NaN</td>\n",
" <td>3.941716e+05</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" f re im Z_mag Z_phase times \\\n",
"0 200019.480 428.90558 372.31183 567.95782 -40.959641 13125.243035 \n",
"1 149677.700 389.09244 779.30804 871.04187 -63.467991 13125.926037 \n",
"2 112011.700 790.36896 1276.19290 1501.11670 -58.229328 13126.609058 \n",
"3 83828.102 1450.48320 1320.92870 1961.82420 -42.323559 13127.165037 \n",
"4 62734.359 1863.74980 1040.74610 2134.64650 -29.179655 13127.722056 \n",
"\n",
" E_avg I_avg Cs/µF Cp/µF cycle_number I Range |Ewe|/V \\\n",
"0 -1.050056 -0.003182 0.002137 0.000918 1.0 12 0.009270 \n",
"1 -1.050440 -0.003601 0.001364 0.001092 1.0 12 0.010166 \n",
"2 -1.050325 -0.003597 0.001113 0.000805 1.0 12 0.010198 \n",
"3 -1.050293 -0.003622 0.001437 0.000652 1.0 12 0.010902 \n",
"4 -1.050266 -0.003596 0.002438 0.000579 1.0 12 0.010428 \n",
"\n",
" |I|/A Y_re Y_im Y_mag Y_phase Unnamed: 18 \\\n",
"0 0.000016 0.001330 0.001154 0.001761 40.959641 NaN \n",
"1 0.000012 0.000513 0.001027 0.001148 63.467991 NaN \n",
"2 0.000007 0.000351 0.000566 0.000666 58.229328 NaN \n",
"3 0.000006 0.000377 0.000343 0.000510 42.323559 NaN \n",
"4 0.000005 0.000409 0.000228 0.000468 29.179655 NaN \n",
"\n",
" w \n",
"0 1.256759e+06 \n",
"1 9.404527e+05 \n",
"2 7.037903e+05 \n",
"3 5.267075e+05 \n",
"4 3.941716e+05 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ex1.df_raw.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>f</th>\n",
" <th>re</th>\n",
" <th>im</th>\n",
" <th>Z_mag</th>\n",
" <th>Z_phase</th>\n",
" <th>times</th>\n",
" <th>E_avg</th>\n",
" <th>I_avg</th>\n",
" <th>Cs/µF</th>\n",
" <th>Cp/µF</th>\n",
" <th>cycle_number</th>\n",
" <th>I Range</th>\n",
" <th>|Ewe|/V</th>\n",
" <th>|I|/A</th>\n",
" <th>Y_re</th>\n",
" <th>Y_im</th>\n",
" <th>Y_mag</th>\n",
" <th>Y_phase</th>\n",
" <th>Unnamed: 18</th>\n",
" <th>w</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>200019.480</td>\n",
" <td>424.92365</td>\n",
" <td>375.77924</td>\n",
" <td>567.24786</td>\n",
" <td>-41.487789</td>\n",
" <td>17279.765173</td>\n",
" <td>-1.101518</td>\n",
" <td>-0.005266</td>\n",
" <td>0.002117</td>\n",
" <td>0.000929</td>\n",
" <td>1.0</td>\n",
" <td>12</td>\n",
" <td>0.010313</td>\n",
" <td>0.000018</td>\n",
" <td>0.001321</td>\n",
" <td>0.001168</td>\n",
" <td>0.001763</td>\n",
" <td>41.487789</td>\n",
" <td>NaN</td>\n",
" <td>1.256759e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>149677.700</td>\n",
" <td>396.26755</td>\n",
" <td>828.15405</td>\n",
" <td>918.07794</td>\n",
" <td>-64.429123</td>\n",
" <td>17280.448178</td>\n",
" <td>-1.100589</td>\n",
" <td>-0.004103</td>\n",
" <td>0.001284</td>\n",
" <td>0.001045</td>\n",
" <td>1.0</td>\n",
" <td>12</td>\n",
" <td>0.010202</td>\n",
" <td>0.000011</td>\n",
" <td>0.000470</td>\n",
" <td>0.000983</td>\n",
" <td>0.001089</td>\n",
" <td>64.429123</td>\n",
" <td>NaN</td>\n",
" <td>9.404527e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>112011.700</td>\n",
" <td>854.82782</td>\n",
" <td>1316.61760</td>\n",
" <td>1569.78100</td>\n",
" <td>-57.005928</td>\n",
" <td>17281.131160</td>\n",
" <td>-1.100517</td>\n",
" <td>-0.004097</td>\n",
" <td>0.001079</td>\n",
" <td>0.000759</td>\n",
" <td>1.0</td>\n",
" <td>12</td>\n",
" <td>0.010199</td>\n",
" <td>0.000006</td>\n",
" <td>0.000347</td>\n",
" <td>0.000534</td>\n",
" <td>0.000637</td>\n",
" <td>57.005928</td>\n",
" <td>NaN</td>\n",
" <td>7.037903e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>83828.102</td>\n",
" <td>1515.67830</td>\n",
" <td>1308.02000</td>\n",
" <td>2002.04820</td>\n",
" <td>-40.793968</td>\n",
" <td>17281.688179</td>\n",
" <td>-1.100429</td>\n",
" <td>-0.004093</td>\n",
" <td>0.001451</td>\n",
" <td>0.000620</td>\n",
" <td>1.0</td>\n",
" <td>12</td>\n",
" <td>0.010834</td>\n",
" <td>0.000005</td>\n",
" <td>0.000378</td>\n",
" <td>0.000326</td>\n",
" <td>0.000499</td>\n",
" <td>40.793968</td>\n",
" <td>NaN</td>\n",
" <td>5.267075e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>62734.359</td>\n",
" <td>1899.30960</td>\n",
" <td>997.15741</td>\n",
" <td>2145.15720</td>\n",
" <td>-27.699944</td>\n",
" <td>17282.245162</td>\n",
" <td>-1.100481</td>\n",
" <td>-0.004097</td>\n",
" <td>0.002544</td>\n",
" <td>0.000550</td>\n",
" <td>1.0</td>\n",
" <td>12</td>\n",
" <td>0.010455</td>\n",
" <td>0.000005</td>\n",
" <td>0.000413</td>\n",
" <td>0.000217</td>\n",
" <td>0.000466</td>\n",
" <td>27.699944</td>\n",
" <td>NaN</td>\n",
" <td>3.941716e+05</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" f re im Z_mag Z_phase times \\\n",
"0 200019.480 424.92365 375.77924 567.24786 -41.487789 17279.765173 \n",
"1 149677.700 396.26755 828.15405 918.07794 -64.429123 17280.448178 \n",
"2 112011.700 854.82782 1316.61760 1569.78100 -57.005928 17281.131160 \n",
"3 83828.102 1515.67830 1308.02000 2002.04820 -40.793968 17281.688179 \n",
"4 62734.359 1899.30960 997.15741 2145.15720 -27.699944 17282.245162 \n",
"\n",
" E_avg I_avg Cs/µF Cp/µF cycle_number I Range |Ewe|/V \\\n",
"0 -1.101518 -0.005266 0.002117 0.000929 1.0 12 0.010313 \n",
"1 -1.100589 -0.004103 0.001284 0.001045 1.0 12 0.010202 \n",
"2 -1.100517 -0.004097 0.001079 0.000759 1.0 12 0.010199 \n",
"3 -1.100429 -0.004093 0.001451 0.000620 1.0 12 0.010834 \n",
"4 -1.100481 -0.004097 0.002544 0.000550 1.0 12 0.010455 \n",
"\n",
" |I|/A Y_re Y_im Y_mag Y_phase Unnamed: 18 \\\n",
"0 0.000018 0.001321 0.001168 0.001763 41.487789 NaN \n",
"1 0.000011 0.000470 0.000983 0.001089 64.429123 NaN \n",
"2 0.000006 0.000347 0.000534 0.000637 57.005928 NaN \n",
"3 0.000005 0.000378 0.000326 0.000499 40.793968 NaN \n",
"4 0.000005 0.000413 0.000217 0.000466 27.699944 NaN \n",
"\n",
" w \n",
"0 1.256759e+06 \n",
"1 9.404527e+05 \n",
"2 7.037903e+05 \n",
"3 5.267075e+05 \n",
"4 3.941716e+05 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ex2.df_raw.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data contained within any of these can be called by calling the parameter.\"name of coloumn\", as illustrated below"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 200019.480\n",
"1 149677.700\n",
"2 112011.700\n",
"3 83828.102\n",
"4 62734.359\n",
"Name: f, dtype: float64\n",
"\n",
"0 424.92365\n",
"1 396.26755\n",
"2 854.82782\n",
"3 1515.67830\n",
"4 1899.30960\n",
"Name: re, dtype: float64\n"
]
}
],
"source": [
"print(ex1.df_raw.f[0:5])\n",
"print()\n",
"print(ex2.df_raw.re[0:5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hereby the impedance can directly be plotted by calling e.g. the real and imaginary coloumns, as shown"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, \"-Z'' [$\\\\Omega$]\")"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = figure(dpi=90, facecolor='w', edgecolor='k')\n",
"fig.subplots_adjust(left=0.1, right=0.95, hspace=0.5, bottom=0.1, top=0.95)\n",
"ax = fig.add_subplot(111)\n",
"\n",
"ax.plot(ex1.df_raw.re, ex1.df_raw.im, 'o--')\n",
"\n",
"ax.set_xlabel(\"Z' [$\\Omega$]\")\n",
"ax.set_ylabel(\"-Z'' [$\\Omega$]\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, as illustrated in the Nyquist above each datafiles contains two impedance spectra, i.e. two cycles, as can also be viewed by the cycle number. To seperate the two cycles, each parameter (ex1 and ex2) contains a dataframe (df) that is sorted based on the cycle number. Each dataframe contained the impedance data from their seperate cycle and can be assesed by calling e.g. ex1.df[0] or ex1.df[1] for the first and second cycle, respectively."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>f</th>\n",
" <th>re</th>\n",
" <th>im</th>\n",
" <th>Z_mag</th>\n",
" <th>Z_phase</th>\n",
" <th>times</th>\n",
" <th>E_avg</th>\n",
" <th>I_avg</th>\n",
" <th>Cs/µF</th>\n",
" <th>Cp/µF</th>\n",
" <th>cycle_number</th>\n",
" <th>I Range</th>\n",
" <th>|Ewe|/V</th>\n",
" <th>|I|/A</th>\n",
" <th>Y_re</th>\n",
" <th>Y_im</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>200019.480</td>\n",
" <td>428.90558</td>\n",
" <td>372.31183</td>\n",
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" <td>0.000016</td>\n",
" <td>0.001330</td>\n",
" <td>0.001154</td>\n",
" <td>0.001761</td>\n",
" <td>40.959641</td>\n",
" <td>NaN</td>\n",
" <td>1.256759e+06</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>149677.700</td>\n",
" <td>389.09244</td>\n",
" <td>779.30804</td>\n",
" <td>871.04187</td>\n",
" <td>-63.467991</td>\n",
" <td>13125.926037</td>\n",
" <td>-1.050440</td>\n",
" <td>-0.003601</td>\n",
" <td>0.001364</td>\n",
" <td>0.001092</td>\n",
" <td>1.0</td>\n",
" <td>12</td>\n",
" <td>0.010166</td>\n",
" <td>0.000012</td>\n",
" <td>0.000513</td>\n",
" <td>0.001027</td>\n",
" <td>0.001148</td>\n",
" <td>63.467991</td>\n",
" <td>NaN</td>\n",
" <td>9.404527e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>112011.700</td>\n",
" <td>790.36896</td>\n",
" <td>1276.19290</td>\n",
" <td>1501.11670</td>\n",
" <td>-58.229328</td>\n",
" <td>13126.609058</td>\n",
" <td>-1.050325</td>\n",
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" <td>0.001113</td>\n",
" <td>0.000805</td>\n",
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" <td>0.010198</td>\n",
" <td>0.000007</td>\n",
" <td>0.000351</td>\n",
" <td>0.000566</td>\n",
" <td>0.000666</td>\n",
" <td>58.229328</td>\n",
" <td>NaN</td>\n",
" <td>7.037903e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>83828.102</td>\n",
" <td>1450.48320</td>\n",
" <td>1320.92870</td>\n",
" <td>1961.82420</td>\n",
" <td>-42.323559</td>\n",
" <td>13127.165037</td>\n",
" <td>-1.050293</td>\n",
" <td>-0.003622</td>\n",
" <td>0.001437</td>\n",
" <td>0.000652</td>\n",
" <td>1.0</td>\n",
" <td>12</td>\n",
" <td>0.010902</td>\n",
" <td>0.000006</td>\n",
" <td>0.000377</td>\n",
" <td>0.000343</td>\n",
" <td>0.000510</td>\n",
" <td>42.323559</td>\n",
" <td>NaN</td>\n",
" <td>5.267075e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>62734.359</td>\n",
" <td>1863.74980</td>\n",
" <td>1040.74610</td>\n",
" <td>2134.64650</td>\n",
" <td>-29.179655</td>\n",
" <td>13127.722056</td>\n",
" <td>-1.050266</td>\n",
" <td>-0.003596</td>\n",
" <td>0.002438</td>\n",
" <td>0.000579</td>\n",
" <td>1.0</td>\n",
" <td>12</td>\n",
" <td>0.010428</td>\n",
" <td>0.000005</td>\n",
" <td>0.000409</td>\n",
" <td>0.000228</td>\n",
" <td>0.000468</td>\n",
" <td>29.179655</td>\n",
" <td>NaN</td>\n",
" <td>3.941716e+05</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" f re im Z_mag Z_phase times \\\n",
"0 200019.480 428.90558 372.31183 567.95782 -40.959641 13125.243035 \n",
"1 149677.700 389.09244 779.30804 871.04187 -63.467991 13125.926037 \n",
"2 112011.700 790.36896 1276.19290 1501.11670 -58.229328 13126.609058 \n",
"3 83828.102 1450.48320 1320.92870 1961.82420 -42.323559 13127.165037 \n",
"4 62734.359 1863.74980 1040.74610 2134.64650 -29.179655 13127.722056 \n",
"\n",
" E_avg I_avg Cs/µF Cp/µF cycle_number I Range |Ewe|/V \\\n",
"0 -1.050056 -0.003182 0.002137 0.000918 1.0 12 0.009270 \n",
"1 -1.050440 -0.003601 0.001364 0.001092 1.0 12 0.010166 \n",
"2 -1.050325 -0.003597 0.001113 0.000805 1.0 12 0.010198 \n",
"3 -1.050293 -0.003622 0.001437 0.000652 1.0 12 0.010902 \n",
"4 -1.050266 -0.003596 0.002438 0.000579 1.0 12 0.010428 \n",
"\n",
" |I|/A Y_re Y_im Y_mag Y_phase Unnamed: 18 \\\n",
"0 0.000016 0.001330 0.001154 0.001761 40.959641 NaN \n",
"1 0.000012 0.000513 0.001027 0.001148 63.467991 NaN \n",
"2 0.000007 0.000351 0.000566 0.000666 58.229328 NaN \n",
"3 0.000006 0.000377 0.000343 0.000510 42.323559 NaN \n",
"4 0.000005 0.000409 0.000228 0.000468 29.179655 NaN \n",
"\n",
" w \n",
"0 1.256759e+06 \n",
"1 9.404527e+05 \n",
"2 7.037903e+05 \n",
"3 5.267075e+05 \n",
"4 3.941716e+05 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ex1.df[0].head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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" <th>times</th>\n",
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" <th>I_avg</th>\n",
" <th>Cs/µF</th>\n",
" <th>Cp/µF</th>\n",
" <th>cycle_number</th>\n",
" <th>I Range</th>\n",
" <th>|Ewe|/V</th>\n",
" <th>|I|/A</th>\n",
" <th>Y_re</th>\n",
" <th>Y_im</th>\n",
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" <tr>\n",
" <th>59</th>\n",
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" <tr>\n",
" <th>60</th>\n",
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" <td>887.19147</td>\n",
" <td>-63.723076</td>\n",
" <td>14603.110115</td>\n",
" <td>-1.050444</td>\n",
" <td>-0.003171</td>\n",
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" <td>0.001075</td>\n",
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" <td>0.001011</td>\n",
" <td>0.001127</td>\n",
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" <td>1527.95950</td>\n",
" <td>-57.935913</td>\n",
" <td>14603.793117</td>\n",
" <td>-1.050284</td>\n",
" <td>-0.003169</td>\n",
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" <td>0.000654</td>\n",
" <td>57.935913</td>\n",
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" <td>7.037903e+05</td>\n",
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" <tr>\n",
" <th>62</th>\n",
" <td>83828.102</td>\n",
" <td>1486.03610</td>\n",
" <td>1327.09240</td>\n",
" <td>1992.35470</td>\n",
" <td>-41.766186</td>\n",
" <td>14604.350107</td>\n",
" <td>-1.050366</td>\n",
" <td>-0.003163</td>\n",
" <td>0.001431</td>\n",
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" <td>0.000334</td>\n",
" <td>0.000502</td>\n",
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" <td>NaN</td>\n",
" <td>5.267075e+05</td>\n",
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" <td>1884.14940</td>\n",
" <td>1044.49850</td>\n",
" <td>2154.29710</td>\n",
" <td>-29.002268</td>\n",
" <td>14604.906123</td>\n",
" <td>-1.050274</td>\n",
" <td>-0.003148</td>\n",
" <td>0.002429</td>\n",
" <td>0.000571</td>\n",
" <td>2.0</td>\n",
" <td>12</td>\n",
" <td>0.010448</td>\n",
" <td>0.000005</td>\n",
" <td>0.000406</td>\n",
" <td>0.000225</td>\n",
" <td>0.000464</td>\n",
" <td>29.002268</td>\n",
" <td>NaN</td>\n",
" <td>3.941716e+05</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" f re im Z_mag Z_phase times \\\n",
"59 200019.480 418.34854 383.73511 567.68665 -42.528969 14602.427109 \n",
"60 149677.700 392.76862 795.51337 887.19147 -63.723076 14603.110115 \n",
"61 112011.700 811.14404 1294.87660 1527.95950 -57.935913 14603.793117 \n",
"62 83828.102 1486.03610 1327.09240 1992.35470 -41.766186 14604.350107 \n",
"63 62734.359 1884.14940 1044.49850 2154.29710 -29.002268 14604.906123 \n",
"\n",
" E_avg I_avg Cs/µF Cp/µF cycle_number I Range |Ewe|/V \\\n",
"59 -1.050143 -0.002997 0.002074 0.000947 2.0 12 0.009578 \n",
"60 -1.050444 -0.003171 0.001337 0.001075 2.0 12 0.010163 \n",
"61 -1.050284 -0.003169 0.001097 0.000788 2.0 12 0.010151 \n",
"62 -1.050366 -0.003163 0.001431 0.000635 2.0 12 0.010887 \n",
"63 -1.050274 -0.003148 0.002429 0.000571 2.0 12 0.010448 \n",
"\n",
" |I|/A Y_re Y_im Y_mag Y_phase Unnamed: 18 \\\n",
"59 0.000017 0.001298 0.001191 0.001762 42.528969 NaN \n",
"60 0.000011 0.000499 0.001011 0.001127 63.723076 NaN \n",
"61 0.000007 0.000347 0.000555 0.000654 57.935913 NaN \n",
"62 0.000005 0.000374 0.000334 0.000502 41.766186 NaN \n",
"63 0.000005 0.000406 0.000225 0.000464 29.002268 NaN \n",
"\n",
" w \n",
"59 1.256759e+06 \n",
"60 9.404527e+05 \n",
"61 7.037903e+05 \n",
"62 5.267075e+05 \n",
"63 3.941716e+05 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ex1.df[1].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note, in the above that the coloum cycle_number is == 1 and 2 in each dataframe. The same data can now shown in the Nyquist plot as shown"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x731d9f3fae00>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = figure(dpi=90, facecolor='w', edgecolor='k')\n",
"fig.subplots_adjust(left=0.1, right=0.95, hspace=0.5, bottom=0.1, top=0.95)\n",
"ax = fig.add_subplot(111)\n",
"\n",
"ax.plot(ex1.df[0].re, ex1.df[0].im, 'o--', label='cycle 1, datafile 1')\n",
"ax.plot(ex1.df[1].re, ex1.df[1].im, 'o--', label='cycle 2, datafile 1')\n",
"\n",
"ax.plot(ex2.df[0].re, ex2.df[0].im, 'D--', label='cycle 1, datafile 2')\n",
"ax.plot(ex2.df[1].re, ex2.df[1].im, 'D--', label='cycle 2, datafile 2')\n",
"\n",
"ax.set_xlabel(\"Z' [$\\Omega$]\")\n",
"ax.set_ylabel(\"-Z'' [$\\Omega$]\")\n",
"\n",
"ax.legend(loc=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The procedure of acceses data and plotting it in Nyquist and/or Bode plot is automatically incorporated in the EIS_plot function as exemplified below. A full description of EIS_plot() function is exemplfied in the PyEIS_simulation_example"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 720x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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XXkliYmKoS5QqyNnwWqXzDpdT9qrwkSvCUUknY49vW+v/b6pNQKR+U0AOgPbt2/Pmm28G7fWMMYwYMSIswoVIVezYsYOuXbt6bfv6668ZPnx4aAo6xsSJE5k+fTpz586lX79+vPXWWxQXFzNp0qRQlyZVVJz6c8UH2KKIaT/Y68pwZIsTG0SbUChtSctlzurd7DyUT+cWcdwwoCPHJTcJdVkitaaAHACxsbEMGzYs1GVIAxbRtDvtr6skAPjgyN7JwY8uO9KDfOwNQxatR/+nVj3IVdWuXTtWr17tta1nz541et260KFDB84//3zmz5/P008/zdy5c+nRowdDhw4NdWlSCWMM2Wufp2DX0gqPa9r3Dl3RDbC5q3Zx04J1OMv8aHnq6xRevvxUJp5es58rIuFCAVmkHrBFxNSo3zeqZS+Szn3J7w1DcV0vCmCVFdQRFUX//v2D8lo1NWnSJD7//HOmTp3K2rVreeyxx/QpTZhzFR0mbclN5Kf8p4KjNHtEoB0uKOHrlHT+sGAdrmM6/VwGblqwjrO6taRHUnxoChQJAAXkWgplH7B6kKUq6sMNQ8uXLyctLQ1wT6+2c+dO3nvvPQCGDRtGcnIyADNmzGDGjBl89dVXXp/afPbZZ+Tl5ZGTkwPAxo0bPeePHDmSuLi4SmsYPXo0SUlJzJw5E7vdzvXXh8cUeOJbUerPpH56zTGzu5T+QVPmZ6Nmj6g2YwwHcorYmpHH1vR8tmbksS0jn5T0PLZm5JGRX+L/XMBp4F+rdvH4yBODV7RIgCkg18K0adMAaNGiRaN6bal/wv2GoWnTprF8+XLP82XLlrFs2TLAu1fZ5XLhdDrL/XE4efJkdu7c6Xm+YMECFixYAMD27dvp0qVLpTVERUVx3XXXMWvWLC644ALat29fu29K6oQxhpxfXuHQ8nswziLPdntca5IveoOIhA5h/cdguChxutiVWUCKVwjOY2tGPtsy8skvqeYNj8fYeSg/QJWKhIZldBlSJGg2bNhA7969Wb9+Pb161f8p0kSCyVWcQ/pXt5K36d9e22M6DCf5onlExLcJUWXhKa/IwdYMd/gt/d/SELwzswDnsf0RAfTnET1CegVZP2ultnQFWUREwl5x+i+kfnq1e5EPD4tmZzxAszMewrLZQ1ZbqBhjSM8rPhqC00vDsDsEH8wpqnyQSkTYLLq0iKN7yzi6tYyne8s4ureMJ8IGo+es5tiIbQE2CybpJj2p5xSQRUQkrOVseJ2Mr2/HOAo822yxSSRf8BpxXc4PYWV1z+ky7M4qOHoVON3dD1z6PKfIUfkglYiPstP9SPj1hOAk9/92ahZLhN33UvCnd2rGD7uyvLbZLHjlitN0g57UewrIIiISllwl+WR8fTu5G+d5bY9uN5hWR/qNG4KCEifby7VCuP93+6F8Spy1b4VIjo/yhN5uLePpkRR3JBTH06pJVI1mbCl2Hl218LikeC47pS2TTu+kcCwNggJykLhcLtLS0khPTyc7O5vY2FiSk5OrdCPQDTfcAEDr1q15/PHH67pUEZGQKz70q7ulImOj1/am/e6h+ZkzsOyRIarMrboLZGTmH22FcM8Gke/pB957uLDW9dgs6NQslu5J8XRrGee5Ity9pft5Ykxg3y+Xy7ApNc/z/I6zunHr4C4BfQ2RUFJArkNZWVm89tprLFmyhG+//Za8vLxyx8TGxjJo0CBGjRrFtddeS8uWLcsd89prr2FZFt27d1dAFpEGL/e3t0n/6lZMydGfmbbo5iRfMIe4bheHsDI3fwtkPHHxSfTr2NT7KvCRMJxZ4H9qtKqKibB5wq9XCE6Kp0vzOKIifLdC1IXdWQVeM12c0Eqr50nDooBcBwoKCnjkkUeYPXu2Z15Wf5OF5Ofns3TpUpYuXcoDDzzA7bffzkMPPUR8vD6iEpHGxeUo5NCyu8lZ/6rX9ujWA0i+eD6RiZ1DVNlRmw7m+Fwgw2ng3k82+j6pGprHRnpCb2kI7tEynu5JcbRNiMFmC4/Fa35Ly/V6fmJrBWRpWBSQA2zLli1cdtllbNy40ROKK+rtKt1njKGwsJCZM2eycOFC5s+fz4ABA4JSs4hIqJVkpZD66dUUp63z2p7Y53ZaDPkrlj0q6DU5XYZNqbn8tPew+7Eni+93ZpYLx9XVvmkMPVrG0y3JuxWie8s4mscF//usid9SjwbkxJgI2iREh7AakcBTQA6gdevWMXz4cLKzswF3+DXGYLfbOe200+jduzfNmzcnNjaW7Oxsdu/ezdq1az0LHJSG5a1btzJs2DDeeOMNxo0bF7LvR0QkGPK2LCRtyU2Y4hzPNisqkeTzXib+uMuCUkOJ08WvB91heM2eLH7ee5i1e7NrtGBGpN2iawsfrRAt4+naMo7YyPo/JV3ZgHxCchMtyy4NjgJygOzbt49Ro0Zx+PBhTzDu2bMnd999N+PHj69wqdvNmzfz9ttvM3v2bNLS0rAsi8LCQq666irmzZvn9zwRkfrMOIo49N/7yV77vNf2qOTTaHXx20Q2614nr1vkcLLhQA5r9rivDP+85zDr9mdT5HBVfnIlJg/qzLOXnow9TFoh6sKWtFwW/XrQ87x9U109loZHATlAbrrpJvbu3esJx1OmTOHRRx8lMrLyO4ePP/54pk2bxn333cfjjz/OE088gcPhwOl0cv311wP+e5hFROqjksM7SF10DcUHf/TannDKzbQYOhNbRExAXqegxMn/9mV7tUmsP5BTranTLAuOT4qnX4dmdGgaw1PLt2IMXotklC6Qcdew7g06HPu6QfGD9QeZu2oXE7U4iDQgCsgB8M0337Bo0SLPR0wzZ87knnvuqfY4sbGxzJgxgzFjxjB69GgOHDiAw+HwhG4RkYYgb+vHpH8xCVdRlmebFdmEpHNfoEnPKys8t6Lp1XKLHKzbl+1pkfhp72E2Hsyt1pLKNgtOap1A3/ZN6dOhKf06NOXUtk1JiDn667JnqyblQmJjWCBjS1ouN/m4QRHgpgXrOKtbywb9/UvjooAcAE8//bTn6yuuuKJG4bisfv36sXLlSs466yz27NlT2/JERMKCcZaQueIhDq95xmt7ZFJvWo18m6gWPSs839fVyyeXpnBG52ZkFTjYlJZLda4lRNgserdJcAfh9s3o26Epp7RNIC6q4l+NE0/vxFndWvKvVbs8Qb0xLJAxZ/VufF14N7hn8fjXql08PvLEoNclUhcUkGupuLiYr776CgC73c7MmTMDMm6nTp1YtGgRgwYNIi8vTzdAiEi95sjZTeqiaynav9Jre5NeE2k5fBa2SP/3acDRq5fHBjQDfL8zq9LXj46wcUrbRPq0b0rfDk3p274pJ7dNIDqiZjfM9UiKb3RhcOeh/FrtF6lPFJBraeXKleTn52NZFoMHD6ZDh8AtfdqrVy+eeOIJ/vjHPwZsTBGRYMvfsZi0zyfgKszwbLMiYmk54jkSTvp9pefnFjm49f1ffF699CU20sZp7ZrSp727RaJvh6ac1DqBSHvwFtJoiDq3qPiPmMr2i9QnCsi1VLYFYtiwYQEff/LkyYwcORKgSjf8iYiEC+NykPn9DA6v+pvX9sjmPWl18dtEJfWu8Pw9WQX887sdvPz9TrIqWYnu+OR4/u/c4+jbvhk9k+OJUBgOuBsGdOSpr1Nw+blBcZJu0pMGRAG5lg4ePDrVTZs2berkNTp3Dv3qUSIi1eHI20/aouso3PuN1/b4E64macTz2KL8r7z2897DzFq+lXfW7sNRxRvsLj25Ldf161irmqVixyU34eXLT22UNyhK46OAXEtO59FJ5O32+j/5u4hITZRkbiFnw2s4sneCZZG/cwmm8JBnv2WPpsXwv5PQ+waf91S4XIZFv6XyzPKtLNuaUW6/P7p6GVyN9QZFaXwUkGupdevWnq8PHDhQJ6+xa9cuwN1i0bZt2zp5DRGRmsrZ8DrpX94CxveqcxHNetBq5HyiW51Wbl9BiZN5P+7m799sY1NaXrn9lgWX9m7D3cO6sym1/I16unoZfI3xBkVpfBSQa6lsYF2xYkXAx3/55ZeZPHkyAD169GDTpk0Bfw0RkZoqydxyJBz7XoUutvMFtBr5FrboRK/tB3OKeP677by4cifpecXlzouPsjPx9E7cMaQr3Y+E3zO7tNDVSxEJCgXkWho0aBCRkZGUlJSwbNkyMjIyaNmyZUDG3rJlC/fdd59nkRAtFiIi4SZnw2t+rxwDRCWf4hWONxzI4ZlvtvLWmr0UO8uH6naJMfxpSFduGtiJ5nFR5fbr6qWIBIMCci01adKEs846i6VLl1JcXMyDDz7ICy+8UOtxU1NTufjii8nOztZKeiISlowxFOz8osJjHNk7Mcbw5ZZ0Zi3fyueb0nwed1q7RO4e1p0rTm1HVIRmoBCR0FJADoDJkyezdOlSAF555RWGDBnC+PHjazze1q1bueiii0hJSdECISISllxFh0n74kaK09ZVeNyveS0475nl/LI/x+f+USe25q5h3RjevaV+3olI2FBADoBx48Zx+umns3r1alwuFxMnTiQzM5Pbbrut2mO9+OKL/PnPfyYnJ0e/LEQkLBWlriV10TU4slL8HmOwcBmLCT/2ZqfTOxzHRNj4ff+O3Dm0Kye0SqjrckVEqk2fYwXIq6++SkJCApZl4XA4uOOOOxgxYgRLliyp9Nzs7Gzmzp3LSSedxB//+Edyco7+MvnHP/6BMUZhWUTCQs6G19j/76Fe4diKagqW968Tl7F4IHMyO51Hb2Ru1SSK6ef3ZOeD5/Li705ROBaRsKUryAHSu3dvFixYwMUXX4zT6cQYw/Lly1m+fDlt27ZlyJAh9O7dmxYtWhATE0N2dja7du3i559/5vvvv6e4uNirz9hut/PPf/6Tm2++mdtvvz2E35mICLhK8sn4+g5yN77utT2mw3C2nvgPXl/5Ky32/5v29lT2OFqxIO8cTzg+qXUT7hranfF92xMTqfniRST8KSAH0HnnnceXX37J1Vdfzf79+wH3TSz79u1jwYIFLFiwwOd5pcG49Ga85ORk5s2bxwUXXBC02kVE/CnJ3ELqp1dRnP6L1/a9nW7l3l1jWPV9ChAJXOu1/7zjk7hraHcu6JmsT8FEpF5RQA6woUOH8tNPP3H33Xfz7rvv4nQ6y/1iOLZlouwsFePHj+epp57yWoBERCRU8ra8T9qSP2CKj7Z+FdubMjX3ThasOAXI9To+0m5xTZ/23DW0O6e0S0REpD5SQK4DrVu35q233mLGjBnMnj2bJUuWsGHDBq8WirJfd+vWjUsuuYSbbrqJE0/0Pb+npnkTkWDZkpbL3FXbOHH3UwwpfMdr3/qSHty6/x72Olt5bW8RF8ktg7rwx8FdaJsYE8xyRUQCTgG5DnXv3p2nn34agPT0dLZt20Z6ejpZWVlERUWRnJxMz549adOmTYXjXH/99QC6qiwidW7uql08tHApf2/xNP2ivVfufCP3Qv6aNYFiIj3bjkuK586h3fh9vw7ER+tXiog0DPppFiRJSUkkJSXV6Ny5c+cGuBoRkfK2pOXy2kfz+LDV32lhz/Zsz3PF8H+Zt/BxwVmebUO7teCuod255KTW2GzqLxaRhkUBWUREMMbF2s//jzktX8RmHW3p2lLSgdsy7iXF0RELuKpPe+4a2o3+HZuFrFYRkbqmgCwi0sg5C9JJ+3wC/TO+gDIXgz/MP4sHM28m38QCMOqk1rw1vm+IqhQRCR4tFNJIORwOlixZwr333suQIUNITk4mMjKSZs2a0a9fP+6//3527txZ5fEWL17MmDFjaNeuHTExMXTq1Inx48fzww8/NMoxRMJdSeYWDv33/9j//kXsmnM8BTu/8OwrMhE8lPkH7j50hyccA5zURgt7iEjjYBlNj1Bjl112GQBt27bl+eefr1evfcopp/DLL79UeExcXByzZ8/23CTozx133MGzzz7rc5/dbudvf/sb9957b6MZoyIbNmygd+/erF+/nl69etV4HJHayNnwOulf3gLGWW7fHkcyt2Xcyy8lPTzbLMBmwa9/HkGPpPggVipSM/pZK7WlK8i18MEHH/Dhhx9WaTnpcHvt7OxsbDYb5513Hi+//DLr168nIyODbdu28fzzz9OyZUvy8/OZOHEiixYt8jvOM8884wmUF198MatWrSItLY1ly5YxcOBAnE4nU6ZM4f33328UY4iEu5LMLX7DsTFwV9Z9bHD08Npus+CVK05TOBaRRkNXkGvBZrNhWRbdu3dn8+bN9eq177vvPm688UaOP/54n/t/++03+vfvT15eHieddBIbNmwod0x6ejrdunUjJyeHs88+my+//BKb7ejfXAUFBfTp04dNmzbRpUsXNm3aRFRUVIMdoyp0VUNC7dC3f+Hwmqf87rd634XjtKn8a9Uudh7Kp3OLOCad3knhWOoV/ayV2tIV5EbqySef9BuOAU444QQmTpwIwMaNG332I8+bN4+cHPfqWk888YRXoASIjY1lxowZAOzYscPnleiGNIZIuCt2ONjzv7crPCaueC89kuJ5fOSJzL+2H4+PPFHhWEQaHc1iEQA5OTnMmzcv1GUEXO/evT1f79u3j86dO3vt/+ijjwDo2rUrAwYM8DnGmDFjiImJobCwkA8//JCxY8c22DFEwtmvBw7z3Tu/5xz2VnhcRGLnCveLiDQGCsgBkJqa6rnaGiyWZdX58tMHDx70fN20adNy+3/66ScABg0a5HeM6Oho+vTpw8qVK1mzZk2DHkMkHLlchn98m0Lxd3dxRdzRmSqMActrfQ8LLBsJvYP7s0xEJBypxSJAjDFBfQRD6c1oLVq0oGfPnl779u7d62lJ6NatW4XjlO7fvHmzV+0NaQyRcLTjUD7nvriC3G/v9grHBSYGrGN+/Fs2ks59ichmPRARaex0BbkWOnXqhGWFdonVDh061Mm4c+fOZd26dQDcfPPN2O12r/3p6emer1u3bl3hWK1atQKgqKiI3NxcEhISGtwYIuHEGMPc1bu568P13B37Itc2WezZV2hrSsffLSYmLpGc9XNxZO8kIrEzCb0nKhyLiByhgFwLO3bsCHUJdWLjxo3cfvvtAHTu3Jk///nP5Y7Jy8vzfB0TE1PheLGxRxcaKBsqG9IYvqSmppKWlua1LSUlpcLxRWpiS1ouc1bvZuehfJLio9hwIIevt6bzUNM5/L7J557jXJFN6Xb5F0S36gNAiyGPhapkEZGwpoAsXlJTUxk9ejS5ublERUUxf/58n/3HZdsLanoVvSGN4cvs2bN5+OGHAzaeiC9zV+3ipgXrcHp1/Bj+0vQ1JiQcnW3FimpGh3Gfe8KxiIj4p4AsHocPH+aCCy5g69at2O125s+fz5lnnunz2CZNmni+LigoqHDcsvvLnteQxvDl1ltv5fLLL/falpKSotkvJGC2pOVy04J1uI4Jx/c3ncekhE88W2xRTWlz2SKiW/cNeo0iIvWRArIA7jaDkSNHsnbtWizL4pVXXmHcuHF+j09KSvJ8XXa2C19SU1MB9ywQZUNlQxrDl1atWnl6lkXqwpzVu8tdOZ6S+CZ/SPjIs8WKSqT1pZ8S3aZ/0OsTEamvNIuFUFhYyOjRo1mxYgUAzz77bKXT1rVv397Tf7tt27YKj92+fTsAxx9/vFcLQ0MaQyQUVu44VOaZ4Z7E+dyS+IFnS5EVR5uxnxDT9vSg1yYiUp8pIDdyJSUljBs3jqVLlwLw+OOPc9ttt1Xp3L593R/Xfv/9936PKSoq8swx3K9fvwY9hkiwGGP429ItfLOtNCAb7kp8h1sT3/cck+eK4fMOs4lpNzA0RYqI1GMKyI2Y0+nkmmuu8Syb/OCDD3L//fdX+fzRo0cD7quuP/74o89jPvroIwoLCwH3SnQNeQyRYMgtcnDVG2v4y6LfPNtuT3yX2xLf8zzPc8Xwh4z/Y9Qw/f9URKQmFJAbKWMMkyZN4r333L9U77zzTh555JFqjfH73//e05pw//3343K5vPYXFhYybdo0ALp06cLIkSMb9BgidW1bRh6D//lfFvxvv2fbnxIXcEfiu57n+a5obsr4CzePvYYeSfGhKFNEpN5TQG6kbr/9dl5//XUAxo8fzyOPPEJubq7fh8PhKDdGUlIS06dPB+Crr75izJgx/Pjjj6Snp/PNN98wYsQIfv31VwCefvppoqKiGvQYInVpyeY0Bvz9W3LSNnFv4pv8vcUzvNdmGncmvuM5pphovur0T+bd9ScmDOgYwmpFROo5I40SUK3H3Llz/Y71pz/9ye95NpvNzJw5s9J6GtIYFVm/fr0BzPr162s1jjQeLpfLzPw6xdjv/cj87qE7zZZnos22WZHlHtufTTD5O78KdbkiYUE/a6W2dAVZau3ZZ5/l888/Z/To0bRp04aoqCg6dOjA1VdfzYoVK7j33nsb1RgigZJf7ODa+T9z3ycb6WTfx+PNX8DC+Dy25Yh/EttpRJArFBFpmCxjjO+ftiIScBs2bKB3796sX7+eXr16hbocCWM7DuVz2WurWbsvG4B7E99kcuJ//B7ftP8ULR0tcoR+1kptaaEQEZEws3RLOle+8SMZ+SWebd1jMio8x5G9s67LEhFpNBSQRUTChDGGf3y7nSmfbMRZZv3o/h2acn77GNjr/9yIxM5BqFBEpHFQD7KISBgoKHEy4Z213P3RBq9wfH3/Diw+bw/s/cLPmRZYdhJ6V7z6pYiIVJ2uIIuIhNiuzHzGvf4ja/Yc9myz2yyevuQkbmizjrRFk/yfbNlIOvclIpv1CEKlIiKNgwKyiEgILd+azhXz1pCWV+zZlhQfxbvX9eN02w8c/ORaME7PvuaDH8NVlIUjeycRiZ1J6D1R4VhEJMAUkEVEgmhLWi5zVu9mx6F8MvKK+TolHWeZuYT6tE/k/esHkJzzHQc/uhJcR2/UazninySeclMIqhYRaVwUkANgxowZVTpu6tSpdVyJiISzuat2cdOCdV6BuKzxfdvz0u9OwUpdwcGPfwfOo1eVWwx9SuFYRCRIFJADYPr06ViWVelxCsgijdeWtFxuWrAOl59wfP+IHjx20QkU7f+eAx+OwTgKPPuaD36Mpn1vD1KlIiKigBwgla23UpUALSIN15zVu/1eOe4SsY/+aZ9y8D+7KdizHFxHrxw3O+NBmg2YEqQqRUQEFJADYtq0aaEuQUTC3Lb0PJ/bx8Ut5fHmL2DPdFGQ6b2vaf97aTbwoSBUJyIiZSkgB4ACsohUpMTpYu2+w+W2d4nYx+PNX8DC5eMsiya9JurTJxGRENBCISIidcjpMkx8Zy1b0vPL7ftd3FLslgubzwxsyN3wWl2XJyIiPiggi4jUEWMMt77/P+b/7HuN6J6ROys835Fd8X4REakbarEQEakDxhimfLKRV77f5dnWOiGaN64+ja9SMshN+40hWb9CBff3RiR2DkKlIiJyLAVkEZE68OiXW3hm+TbP8xZxkSy5aSC92yYyLPkw+9+7C6cp8HO2BZaNhN4Tg1OsiIh4UYuFiEiA/f2bbUxbvMnzvEm0nc9udIfjkqwU9i88H2fevjJnHNOEbNlIOvclLSEtIhIiuoIcAFpJT0RK/euHXdz90QbP85gIGx/fcDoDOjWjJGsr+987H2fu0Z7k+OPG0WzQdHI3zsORvZOIxM4k9J6ocCwiEkIKyAGglfREGq8tabnMWb2bnYfyyS9x8tGGg559kXaLhdf3Z1j3JEoOb3NfOc7d49kf1+NSki+ch2WPpMWQx0JRvoiI+KCAHCBaSU+k8Zm7ahc3LVjnc4U8mwVvXtOXi05sTcnh7e4rxzm7Pfvjuo+h1UVvYtkjg1ixiIhUhQJyAGihEJHGZ0taLjctWIfLz9/Gj154Apef2o6Swzs48N75OHOOzmYR1300rUa+pXAsIhKmFJADQAFZpPGZs3q3zyvH4F4h7/g9n3Lgw4MU7v0vpvjoKnpx3S6h1cj5WPaoIFUqIiLVpYAsIlIDOw+VXxkPYFzcUh5v/gL2TBcFmd774rpdTKuL31Y4FhEJc5rmTUSkBlrElQ+5XSL28XjzF7Bw+Tyn+ZmPKRyLiNQDCsgiItWUnlfE57+lltv+u7il2C0XNj/35Ob+9lYdVyYiIoGgFot64J///CeHDh0CNFWcSKjlFjkY9eoqtvlosTguco+PM45yZO+sq7JERCSAFJADwG63A9C8eXPefPNNLrzwwoCO/49//INt29xL1iogi4ROkcPJuNdXs2p3lmfb6Z2aMbRrC7IP7WLI4d/w010BQERi57ovUkREak0tFgFgjMEYw6FDhxg1ahSPPPJInbyGiISO02W4/u21LNmc7tnWp30iX9w0kL8Ob8r/ue4mxpXj52wLLDsJvScGp1gREakVBeQAsSwLy7JwuVxMnz6dSy65hMOHD1d+ooiEPWMMf/rPL7y7bp9nW4+keBbdOJA4Ryr7F56P4/BW/wNYNpLOfUnLR4uI1BNqsQggYwyWZWGMYdGiRQwYMID333+f3r17h7o0EamB0mWkP9lwkA0Hj14dHtAsnXf6/xe+fp49e7/FFGV59sV2Oo/mQ58k77f5OLJ3EpHYmYTeExWORUTqEQXkAGratCnR0dGkprrvbk9JSWHgwIG8/PLLXHPNNSGuTkSqw98y0lcnLOORhNmw3smxt+nFdDqXVqPfwxYRS/SQx4JWq4iIBJZaLAIoOTmZn376iYEDB3quJufn53Pddddxxx134HQ6Q12iiFRB6TLSx4bjLhH7eDjxeTC+78RrMfRJbBGxQahQRETqkgJygLVr145vvvmGyZMne7Vc/POf/2T48OEcOHAg1CWKSCX8LSNdOs+xhe+bZvN+e7uOKxMRkWBQQK4DERERPP/887z++uvExMR4QvJ3331H3759+e6770JdoohUYMX2Qz6394yseB5jzXMsItIwKCDXoeuuu44VK1bQtWtXwD3TxYEDBxgxYgTPPfdciKsTEV8++/Ug3+0oH5CPj9jFGdEbKjxX8xyLiDQMCsh17NRTT2XNmjWMHDnS03JRUlLCnXfeyfjx4ykoKAh1iSJyxJeb07js9R9xHdNBcXJkCvOTHyLeVuTnTM1zLCLSkCggB0HTpk355JNPmDZtmme+ZGMM77zzDgMHDvSskiciobMsJZ0xc1dR5Dh6A54F9I/ayBvJ02luzy1ztOV9suY5FhFpUDTNWxBNmzaNM844g2uvvZbMzEwAfvnlF/r378+8efMYNWpUiCsUaZz+uz2DS+asoqDERZeIffwubikjOxbSsVkMrp2fEkGx59iE3pNI7HsXuRtf1zzHIiINlAJykF144YWsXr2acePGsXbtWizLIisri7Fjx/J///d/PPzww6EuUaRRKF0EZM3uLJZvy6DEaRgXt5THm7+A3XLBYeCw98dsiX3uoMXQJ7Esixaa51hEpMFSi0UIdO3alZUrV3L99dd7+pJdLhePPvooI0eO9FxdFpG6MXfVLk568mueWJrCl1vSKXEaukTs4/HmL2CzfE/hlnDqZE84FhGRhk0BOUSio6OZO3cus2fPJjIy0tOXvHjxYgYMGMC6detCXaJIg1S6CMixN+JVNsexLbKJwrGISCOhgBxit9xyC8uXL6ddu3aebdu2bePMM89k3rx5IaxMpGEqXQTk2BjcISK1wvM0x7GISOOhgBwGzjjjDH7++WfOPvtsT8tFQUEBEydO5NZbb6WkpCTUJYo0GL8dzPGx1dDWnlHheZrjWESk8dBNemEiKSmJJUuWcP/99/PUU095Wi5eeumlUJcm0mBk5hfz3Q53j3/pbBUdIlLpZD/AqdFb/ZxlgWXTHMciIo2IAnIYsdlsPPnkkwwcOJCJEyeSm5vruaIsIrWTV+TgkjmrSM8r9p6t4hiGY2Y51hzHIiKNjgJyGLrsssvo1asXl112Gb/++muoyxGp17ak5fLKD7t4c80eDuQUeWarsPAdjlsOfQpn/kHNcSwi0ogpIAeIMb7vfK+pnj17smrVKiZOnMh7770X0LFFGou5q3Zx04J1OMv851k6W4UvFuDMP6g5jkVEGjkF5ABwuXz/sq2t+Ph43n33XT766CPNjSxSTaXTuTmP+dv15Ch/vcZumq1CREQUkOuB0aNHh7oEkXpnzqpdOI33zXgJVj6Don+p8DzNViEiIgrIItLgGGN4/5cDFd6MV55mqxARETcF5BD74IMPyM7OBuD3v/99iKsRqb+2pOUyZ/VudhzKZ0t6HiVZW3i8tZ+b8QxgWd6r5mm2ChEROUIBOcSmTJnCtm3bAAVkkZrydTPevYkV3IxngavH9TRrlqzZKkREpBwF5DCguY5Faq70ZjzXMf3GlfUaJ9jyNVuFiIj4pIAsIvXanNW7cRqq2W+sm/FERMQ/W6gLEBGpjW3peZUu/uHNAsuum/FERMQvXUEWkXpnS1ou7333X1odeJdhWTu5tsWeChf/8N6gm/FERKRiCsgiUq/MXbWLzz+dxWPNjrRTxFR+TmTzE4hKPkU344mISJUoIItI2CvJ3ELOhtfITNvK/g05PNbsS2w+mif8iet+iW7IExGRKlNAFpGwUxqIHdk7cRVnk7/jCyxc2ICrm1RnJC3+ISIi1aeALCIhVTYMRyR2xhbVlMyV08A4jx5k8NFMXAXqNxYRkRpQQA6xt956i4KCglCXIVJjxwbchF4TiGx+nM/tQOVh2IfqTBMe3eYMIhI7q99YRERqTAE5xE4//fRQlyBSYzkbXif9y1u8Au7hNc/Q5MTryP31De/tPz515Kuq9w5Xh8tYWDYbyRfOVSgWEZFaUUAWkSore1XYikokd8McMMcEXuMkd+NrPs6um2BcyrIsktVOISIiAaCALCJV4utqcai4jIUBFrsu4vhmLrp1OoFOA25SOBYRkYBQQBaRco7tH47pMJT0JTeDj5XqgsEY7z5kg8Vfsibz1z89So+k+JDUJCIiDZcCsoh48dlX/OPMoL2+y7gnrCgbiF1YLC/oQ56JZY+jFQvzz+GhcRcpHIuISJ1QQBYRj5LMLUfCcc2vFB/tNLawyjwrbVX2Cr4+wrDBxszDV5Noy6dDRCp7HK1YkHcOO51tObFVE0af1obFp3dSOBYRkTqjgCzSyJVtpyjJ2lLrHmOXsbEwbzjj4pdht8oE5CMTGXuF5grCcFkWYLfgwxtOVzAWEZE6p4As0ohV98a7Y3uBS2+W+3feuSTY8r0C7os5l3F5/FdewRcot+3YMOyLzYJXrjhN4VhERIJCAVmkkSptpzDGWeVF6syxbRNYPJA5mYX5I8odu9PZlqeyry233dc2Xyxg5Imt6N02kUlqqRARkSBSQBZpZEpbKvJSPoAqhmP3lWKL69MeYnDM/6p9Bbi67EeuGE8Y0DHgY4uIiFRGAVmkEanpXMalV4pXFp/CyuJT6qQ2CxjQqRln90jSFWMREQkpBWSRRqK6LRU/FR3PXmdynV0ptgEXntiKxOgIOreIUygWEZGwoYAs0giUZG4hbfENVWqpKG2nuPfQ7bUKxXYLHr3oRLIKS9h5KJ/EmAjAIruwRIFYRETCmgKySANX7ZkqjrRTlA3Hx4bdzi3iOPe4JL7cku73uQKwiIjUVwrIIg3Y0bYKV6VXjreUdODLggG8l3cOu5xtuWlg50qv9p5zXHKFz0VEROojBWSRBmzXd09iq6StorSl4ub0+9npbIvdglev1AwSIiLSeCkgizRQn338DMenvE5ll45LWypKrxrfO7y7WiNERKRRU0AWaYC2bF3HcSkPVNpW8VPR8dx76Hb2uNryL101FhERARSQRRqk3xb9id6W8bvfGHBh495Dt9O2w4ksubqvrhqLiIgcoYAs0sBs2bqOkxzfV9pa8UDmZPa42ioci4iIHEMBWaSe2bJ1Hb/89wVsebtxRTYBAzZHLq74jpw8ZPKRq8cVjzE/9zw+KBjBK1ecpnAsIiJyDAVkER8WL17M7NmzWb16NYcOHaJVq1acddZZ3H777Zxxxhkhq+uzj5/huJS/0MdyuTcUl9lZDM6P5tILU+HVY2Mg97hb+fXc4QrHIiIiPthCXYBIuLnjjju48MIL+eijj9i/fz9FRUXs3r2b+fPnM3jwYJ566qmg11SSuYWtH/+Bnin3Y8Pl9zgbBquSq8cbIgbyt6suVjgWERHxQwFZpIxnnnmGZ599FoCLL76YVatWkZaWxrJlyxg4cCBOp5MpU6bw/vvvB62mnA2vs3veKdi2vo5lUWEAriwcGwMnjno+sAWKiIg0MArIIkekp6czffp0AM4++2w++ugjBgwYQFJSEsOGDWPp0qX07NkTgHvuuYfi4uIKRguMkswtpC25GePyf9W4OrZEnEaPricHZCwREZGGSgFZ5Ih58+aRk5MDwBNPPIHN5v2fR2xsLDNmzABgx44dLFq0qM5r2rX6FSxc2CqYsq06WrfvHZBxREREGjIFZJEjPvroIwC6du3KgAEDfB4zZswYYmJiAPjwww/rvKZtu38L6HhpmekBHU9ERKQhUkAWOeKnn34CYNCgQX6PiY6Opk+fPgCsWbOmzms6VBIT0PH2OpIDOp6IiEhDpIAsAuzdu9fTXtGtW7cKjy3dv3nzZowJTOuDP02i7BXu31HcmqqUYAw4jY39ra8IUGUiIiINlwKyCO4b9Eq1bt26wmNbtWoFQFFREbm5uXVa1wnNK06/vzh6VHmsv2RN5vIhQ2pbkoiISIOnhUJEgLy8PM/XpT3G/sTGxnq+zs3NJSEhwedxqamppKWleW1LSUmpVl3Nk7tzeKf//Xscrao81shRd2vuYxERkSpQQBYBr1YJq7LJhKto9uzZPPzww7UaI6HXBLLWPINxec9k4TIWBouF+edwY8KHRFaweEipCQM61qoWERGRxkItFiJAkyZNPF8XFBRUeGzZ/WXPO9att97K+vXrvR4ffPBBteqKbH4cyee+WC60Gyz+kjWZh8ZdxJak6zAGn73IpdvXW5r7WEREpKp0BVkESEpK8nx98ODBCo9NTU0F3DNaVBSQW7Vq5elXro2EXtcT034wu1a/zLZdv7HXkcz+1lfw1yFD3C0TA15hzTMf0tzK8nm+01iMvqvuZ9wQERFpKBSQRYD27duTkJBATk4O27Ztq/DY7du3A3D88ccHrB2jMpHNetD9vCfp7md/v7tT+fTNW+iRNofIMtvXWycrHIuIiFSTArLIEX379mX58uV8//33fo8pKiryzJfcr1+/YJVWJRdf+yLwote2iiesExEREV/UgyxyxOjRowHYtm0bP/74o89jPvroIwoLCwH3qnoiIiLS8Cggixzx+9//3jNl2/3334/L5T0zRGFhIdOmTQOgS5cujBw5Mug1ioiISN1TQBY5IikpienTpwPw1VdfMWbMGH788UfS09P55ptvGDFiBL/++isATz/9NFFRUSGsVkREROqKepBFyrj77rvZsWMHzz33HJ988gmffPKJ136bzcYTTzzBZZddVqPxi4qKgOovGCIiIlVX+jO29GeuSHUpIIsc49lnn+Xiiy9m9uzZrFq1ikOHDtGqVSvOOuss7rjjDs4444waj717924Axo4dG6BqRUTEn927d9O3b99QlyH1kGWMr+UFRKQuZGVlsXz5cjp27Eh0dHSVzklJSWHs2LF88MEH9OjRo44rlKrSv0t40r9LeAr2v0tRURG7d+9m2LBhNGvWrM5fTxoeXUEWCaJmzZrVePaLHj160KtXrwBXJLWlf5fwpH+X8BTMfxddOZba0E16IiIiIiJlKCCLiIiIiJShgCwiIiIiUoYCskiYS05OZtq0aSQnJ4e6FClD/y7hSf8u4Un/LlLfaBYLEREREZEydAVZRERERKQMBWQRERERkTIUkEVEREREylBAFhEREREpQwFZJIwtXryYMWPG0K5dO2JiYujUqRPjx4/nhx9+CHVpQeFwOFiyZAn33nsvQ4YMITk5mcjISJo1a0a/fv24//772blzZ5XHC8T72ZDGCLS0tDSSkpKwLAvLspgwYUKl54TLexEuYwTSd999x4033shxxx1HfHw8TZs25YQTTuCqq67ipZdeqvDccHk/wmUMaYSMiISl22+/3QA+H3a73cycOTPUJda5k08+2e97UPqIi4szr732WqVjBeL9bEhj1IVrrrnGq5brr7++wuPD5b0IlzECpbCw0Fx//fWV/rdTl99LQxpDGicFZJEw9PTTT3t+iF988cVm1apVJi0tzSxbtswMHDjQs2/hwoWhLrVOde7c2dhsNnPeeeeZl19+2axfv95kZGSYbdu2meeff960bNnSAMayLPPpp5/6HScQ72dDGqMufPbZZwYw3bp1q1JADpf3IlzGCBSHw2FGjhzpec1rrrnGLF261Ozbt8+kpaWZH374wUyfPt107969zr6XhjSGNF4KyCJhJi0tzSQkJBjAnH322cbpdHrtz8/PNz179jSA6dKliykqKgpRpXVvypQpZtOmTX73//rrryY+Pt4A5qSTTvJ5TCDez4Y0Rl3Izc01Xbp0MYD5/PPPKw3I4fJehMsYgfTkk0963v9XX321WueGy/sRLmNI46aALBJmyl71WLVqlc9j/v3vf3uO+c9//hPcAsPMbbfd5nkvduzYUW5/IN7PhjRGXbjzzjsNYK688kpjjKk0IIfLexEuYwRKZmamiYuLM4C5+uqrq31+uLwf4TKGNG4KyCJhZtiwYQYwXbt29XtMYWGhiYmJMYCZMGFCEKsLPy+++KLnl9yKFSvK7Q/E+9mQxgi0VatWGbvdbpo2bWr27dtnjKk8IIfLexEuYwTKc88953nvf/rpp2qfHy7vR7iMIY2bZrEQCTM//fQTAIMGDfJ7THR0NH369AFgzZo1QakrXB08eNDzddOmTcvtD8T72ZDGCCSHw8Ef/vAHnE4nf/3rX2nbtm2VzguX9yJcxgiURYsWAdCxY0fP6wE4nU5cLlel54fL+xEuY0jjpoAsEkb27t1LTk4OAN26davw2NL9mzdvxhhT57WFq/fffx+AFi1a0LNnT699gXg/G9IYgTZz5kzWrVvHGWecwS233FKlc8LlvQiXMQJp9erVAAwYMICSkhJmzpxJr169iImJITIyks6dO3PTTTexZcuWcueGy/sRLmOIKCCLhJH09HTP161bt67w2FatWgFQVFREbm5undYVrubOncu6desAuPnmm7Hb7V77A/F+NqQxAiklJYUZM2Zgt9t58cUXsdmq9uskXN6LcBkjUAoKCjz1tGjRgrPOOov77ruPjRs34nA4cLlc7Nq1i1deeYVTTjmF9957z+v8cHk/wmUMEQVkkTCSl5fn+TomJqbCY2NjYz1fN8Yf7Bs3buT2228HoHPnzvz5z38ud0wg3s+GNEYg3XTTTRQWFnLnnXdy2mmnVfm8cHkvwmWMQDl8+LDn63nz5vHDDz8wfPhwVq5cSUFBAWlpabzyyis0a9aMwsJCrr32WtauXes5J1zej3AZQ0QBWSSMlP2Iz7KsEFYS3lJTUxk9ejS5ublERUUxf/58n/3HgXg/G9IYgfKvf/2Lr7/+mk6dOvHwww9X69xweS/CZYxAKdtjXFxczBlnnMEXX3zBwIEDiYmJISkpiRtvvJFFixZhs9koKipi2rRpnnPC5f0IlzFEFJBFwkiTJk08XxcUFFR4bNn9Zc9r6A4fPswFF1zA1q1bsdvtzJ8/nzPPPNPnsYF4PxvSGIFw8OBBpkyZAsBzzz1HfHx8tc4Pl/ciXMYIlGPHnDZtGpGRkeWOGzRoEBdffDHgXoK5sLCw3Pn1/T0Np38Xqb8UkEXCSFJSkufrsrMz+JKamgq478RuLD/Y8/LyGDlyJGvXrsWyLF555RXGjRvn9/hAvJ8NaYxAeOCBB8jMzGTs2LGMHj262ueHy3sRLmMESkJCAtHR0Z7nZ511lt9jS/cVFRWRkpIChM/7ES5jiCggi4SR9u3bk5CQAMC2bdsqPHb79u0AHH/88Y3iY8TCwkJGjx7NihUrAHj22WeZOHFihecE4v1sSGMEQulrf/DBB1iW5fNR6vXXX/dse+211wL2fTSkMQLFsixOOOEEACIiIioMe82bN/d8nZ2dDYTP+xEuY4goIIuEmb59+wLw/fff+z2mqKjIM89nv379glJXKJWUlDBu3DiWLl0KwOOPP85tt91WpXMD8X42pDHCQbi8F+EyRqAMGDAAcM9PXRp8fcnIyPB83axZM8/X4fJ+hMsY0rgpIIuEmdKPrbdt28aPP/7o85iPPvrI0zs4ZsyYoNUWCk6nk2uuucazCMKDDz7I/fffX+XzA/F+NqQxauvVV1/l559/rvBR6pJLLvFsK9uOES7vRbiMESiXXnqp5+tly5b5Pa50X3x8PMcdd5xne7i8H+EyhjRywVqyT0SqJi0tzSQkJBjAnHPOOcbpdHrtLygoMCeeeKIBTJcuXUxRUVGIKq17LpfLXH/99Z7lc++8885qjxGI97MhjREMpf9e/paaDpf3IlzGCJSSkhJzwgknGMD06dPHFBQUlDtm6dKlxrIsn/8+4fJ+hMsY0rgpIIuEoaefftoTMkaNGmVWr15t0tLSzPLly82gQYM8+xYuXBjqUuvUbbfd5vlex48fb3Jycip8lJSU+BwnEO9nQxqjrlUWkI0Jn/ciXMYIlC+++MLY7XYDmIEDB5olS5aY9PR0s2PHDvP3v//dNGnSxACmRYsWZseOHXXyvTSkMaTxUkAWCVN/+tOfPD/Aj33YbDYzc+bMUJdY5/x9//4ec+fO9TtWIN7PhjRGXapKQDYmfN6LcBkjUObMmWOio6P91tO6dWuzcuXKOv1eGtIY0jgpIIuEsc8//9yMHj3atGnTxkRFRZkOHTqYq6++2nz//fehLi0oAhmQjQnM+9mQxqgrVQ3IxoTPexEuYwTKxo0bzS233GK6detmYmJiTEJCgunbt6+ZPn26ycjIqPT8cHk/wmUMaXwsY8osOSMiIiIi0shpFgsRERERkTIUkEVEREREylBAFhEREREpQwFZRERERKQMBWQRERERkTIUkEVEREREylBAFhEREREpQwFZRERERKQMBWQRERERkTIUkEVEREREylBAFhEREREpQwFZRERERKQMBWQREfGrS5cuWJbl8zF27NhQl+dl+vTpfmu1LIsdO3aEukQRqSciQl2AiIg0Pjk5OaxcuZL9+/dz8OBB7HY7rVu3pkOHDgwaNIjo6OhQlygijZgCsohIHbMsK+BjGmPKbZs+fToPP/wwANOmTWP69OkBfc1WrVqRkJDged62bdtqj7Fw4UJmz57Nt99+S0lJic9j4uLiOOecc7jnnnsYNmxYlcdu0aIF3bt39zwvKChg37591a5RREQBWUREquSJJ55gwoQJNTp3/fr13Hjjjfzwww+VHpufn8/HH3/Mxx9/zMiRI3n55Zdp3759pefdfvvt3H777Z7ny5Yt4+yzz65RvSLSuCkgi4jUsbJXNWsiPT2dw4cPe543a9aslhUF15IlS/jd735Hdna2Z1tUVBTDhw/nzDPPpE2bNjidTg4cOMDy5cv57rvvcDqdACxatIgzzjiDTz75hNNOOy1E34GINDYKyCIidSwlJaXG5+7du5e+fft6nluWxZtvvhmIsoLim2++YeTIkTgcDsBd/4033shjjz1GcnKyz3N27drFlClTePfddwH3ezBs2DBWrVpFz549g1a7iDRemsVCRCRMlZSUcPnll5OamurZNnXqVC6++OIQVlV1Bw4c4KqrrvKEY7vdzmuvvcbLL7/sNxwDdOrUiX//+9/MnDnTsy07O5vLLruMvLy8Oq9bREQBWUQkTN15552sXLnS8/yiiy5i6tSpIayoeu655x7279/vef7000/z+9//vsrn33vvvUyZMsXzfOPGjTzyyCMBrVFExBcFZBGRMPTGG28we/Zsz/Nu3brx1ltvYbPVjx/bKSkp/Pvf//Y8Hzp0qNcNdFX16KOPerVVzJ49m0OHDgWkRhERf+rHT1oRkUZk3bp13HzzzZ7nsbGxvP/++zRv3jyEVVXP7NmzPTfaATzwwAM1mu4uKiqKu+++2/M8JyeH1157LRAlioj4pYAsIhJGsrKyGDduHAUFBZ5tL730EqeeemoIq6q+JUuWeL5u27YtF1xwQY3HGj9+PBERR+8pLzu2iEhdUEAWEQkTxhiuvfZatm7d6tn2xz/+keuuuy6EVVVfWloaGzZs8DwfMmRIrRZLiY+P95ri7dtvv/Xc+CciUhcUkEVEwsSMGTP49NNPPc8HDRrErFmzqnz+9OnTMcZgjAn4KnrVsW3bNq+V/nr37l3rMcuOkZeXR1paWq3HFBHxRwFZRCQMfP7558yYMcPzvHXr1rz33ntERkaGsKqaSU9P93reokWLWo95bP/1sa8hIhJICsgiIiG2fft2xo8fj8vlAiAiIoJ3332Xdu3ahbiymsnKyvJ6HhMTU+sxY2NjvZ6XXZVPRCTQFJBFREKosLCQcePGeU1dNnPmTIYOHRrCqmonKirK63kgwuyxYxwbmEVEAkkBWUQkhCZPnszPP//seX7llVdy5513hq6gADh2lbxdu3bVeszdu3dX+BoiIoGkgCwiEiIvvvii15y+vXr14l//+lfoCgqQsgt7AF5/ANTU2rVrPV/HxsbSoUOHWo8pIuKPArKISAj88MMP3HHHHZ7nTZs25T//+Q/x8fEhrCow2rZtS7du3TzPv//++3J9ydWxadMmdu7c6Xk+aNCgWk0bJyJSGQVkEZEgS0tL43e/+x3FxcUAWJbF66+/znHHHRfiygLnkksu8XxdXFzMwoULazzWO++84/V81KhRNR5LRKQqFJBFRILI6XRy1VVXsWfPHs+2v/zlL4wZMyaEVQXeTTfd5HWVd+bMmZ5ZOqojPz+f559/3vM8Ojqaa6+9NiA1ioj4o4AsIhJEDzzwAEuXLvU8P//8873mP24oTjrpJK655hrP802bNvHiiy9We5y//e1vXouC/OlPf9INeiJS5xSQRUSC5P3332fmzJme5126dGH+/PnYbA3zR/FTTz3lFWb//Oc/s2XLliqfv2bNGv72t795nnft2pWpU6cGtEYREV8a5k9lEZEws2nTJiZMmOB5HhMTw8KFC2nZsmXoiqpjbdq0Yd68edjtdgByc3O56KKLOHjwYKXn7tq1i0suuYSSkhLA3Voxf/58EhIS6rRmERFQQBYRqXO5ublcdtll5OTkeLbNnj2bvn37hrCq4Ljwwgt57rnnPM+3bt3KkCFD2Lx5s99zNmzYwFlnncX+/fsBsNlszJs3j4EDB9Z5vSIioIAsIlLnbrjhBjZu3Oh5fvPNNzNx4sQQVhRckydP5tlnn/XctJeSksKAAQN46aWXMMZ4jjPG8MILLzBw4EDP4iJ2u5158+ZxxRVXhKR2EWmcLFP2p5OIiATUhx9+yNixY722derUicjIyFqNe/vtt3P77bfXaoyq6NKli2cO4rlz53q1ifgzYcIEXn/99SqNv337drp06QLAjh076Nq1a5XOu/76670WWfFl2bJlnH322T5fS0SkIhGhLkBEpCHLzMwsty0QSy8fOnSo1mOIiIhvCsgiIhJQrVu3pnv37lU6tuyV9MjIyCqf17p16xrVJiJSFWqxEBERv2rSYhEu1GIhIjWlm/RERERERMpQQBYRkSqZOHEilmV5HsfefBhq06dP96qv7NVjEZHqUEAWERERESlDN+mJiIhfXbp0ISLC96+Ktm3bBrmairVo0aLCm/xqO7WeiDQeuklPRERERKQMtViIiIiIiJShgCwiIiIiUoYCsoiIiIhIGQrIIiIiIiJlKCCLiIiIiJShgCwiIiIiUoYCsoiIiIhIGQrIIiIiIiJlKCCLiIiIiJShgCwiIiIiUoYCsoiIiIhIGQrIIiIiIiJlKCCLiIiIiJShgCwiIiIiUoYCsoiIiIhIGQrIIiIiIiJlKCCLiIiIiJShgCwiIiIiUoYCsoiIiIhIGQrIIiIiIiJlKCCLiIiIiJQRUZOTdu3aFeg6qi0yMpK2bduGuowKZWVlsXz5cjp27Eh0dHSoyxERERFpdIqKiti9ezfDhg2jWbNmVTrHMsaY6r6QzWbDsqzqnhZQPXr0YNOmTSGtoTIffvghY8eODXUZIiIiIo3eBx98wJgxY6p0bI2uIAPUIFcHVKhfvyo6duwIuP9BevToEeJqRERERBqflJQUxo4d68llVVHjgGxZVkhCaqhetyZK2yp69OhBr169QlyNiIiISONVnXbXGgdkgPbt2/Pmm2/WZohqMcYwYsSIkLd3iIiIiEjDVauAHBsby7BhwwJVi4iIiIhIyGmaNxERERGRMurlTXr1pQc53Li+eRZy08rvaJKMbejtwS9IREREJAzVKCBPmzYNgBYtWgS0mHB/7Xov5yDkHAh1FSIiIiJhrVYBORRC+dr1lSk4jPntc3dA9sVVEtyCRERERMJYrW7Sk/Bm8jMxv30GW78BZwUhODcN1w9zsE4eixWnK/MiIiLSuCkgN0AmLwPz6yLY9l9wOap20vbvMLtWYY4/D+vEi7Ci4uq2SBEREZEwpYDcAHhuvnM5oCgXSvJrNpCzBH5dhPntM0x0AkTFA0fmnNaNfCIiItJI1HlAdrlcpKWlkZ6eTnZ2NrGxsSQnJ9O+fftKz73hhhsAaN26NY8//nhdl1p/5Rzw318MYNmg80DIPwSF2Ue3GxcU5UBxnvfxxriPK3tsST4mdRPEtYS45pj/Pq8ZMURERKRBqpOAnJWVxWuvvcaSJUv49ttvycvLK3dMbGwsgwYNYtSoUVx77bW0bNmy3DGvvfYalmXRvXt3BeQKVbCyYNchWCeNxEpojWvvat9Xlx0lkJoCu1b7H6cgC7P0ySMvZwE2MM5aVS0iIiISjgK6UEhBQQF/+ctf6NKlC/fccw+ff/45ubm5GGPKPfLz81m6dCl33303HTt25P777/cZpKUKLD//jE1aYTtjIlZCa/fz4lzfD3sEtjNvwTrvQbBHVf56xigci4iISIMVsIC8ZcsWTj/9dJ544gmys7MrXczDstxXPY0xFBYWMnPmTE477TRWr67gKqZUjy0CU3gYV/omXDu+8d+bXJKHa/syTHEWRMXW7jXz0jGZu2s3hoiIiATVgw8+yKhRo2jfvj2WZTFhwoQqn5uTk8N9993H+eefT3JyMpZlMX369Cqd26dPH9q3b4/T6f/C2+DBg0lKSqK4uLjKNdVWQALyunXrOP3009m4cSNwNPza7Xb69+/PhAkTuOuuu/jLX/7CbbfdxpgxY+jUqZMnRJcev3XrVoYNG8bChQsDUVbj4Sjwvb04F7Prv3AoBYpzKh6jJA9y9rn7kn2JjIEew6FDP2jZDb9tHc5izBcP41rzFqYot6rfgYiIiITQrFmzyMjIYPTo0URFVeHT5DIyMjJ4+eWXKSoqYuzYsdU6d9KkSezbt4/Fixf73L9582ZWrFjBddddV+26aqPWPcj79u1j1KhRHD58GMuyMMbQs2dP7r77bsaPH09cnP/pwjZv3szbb7/N7NmzSUtLw7IsCgsLueqqq5g3b15tS2s8ouN9T+cWXYOp2qLj/W9PbOF+2LpDXrr3TXxlGQNblmJ2rYJTLoOuZ2HZAtrNIyIiIgGUk5OD7cjv6jfeeKNa53bu3JnMzEwsyyI9PZ1XX321yueOHz+eKVOmMGfOHEaOHFlu/5w5c4CjEzcES60D8k033cTevXs94XjKlCk8+uijREZGVnru8ccfz7Rp07jvvvt4/PHHeeKJJ3A4HDidTq6//nqASls1BOh5dvmZKHyxbL6vEJf2MBsXdD+z8nFcjiPhu8y/jaMYHEXexxXlYlbPg5/exsQ09e5v1mwXIiLSSPi9ST4yDlv7AcEvyAdbLS5klXYC1ETz5s259NJLWbhwIRkZGV6TNjidTt544w0GDBjAySefXOPXqIlaBeRvvvmGRYsWed6YmTNncs8991R7nNjYWGbMmMGYMWMYPXo0Bw4cwOFweEK3VKaC/2PGJWHFt4YmrTGp6/3+B2q16wfFeZi9P5QPur74CtKOIti3AdJ3eG93lrivOIuIiNRDrl0rwFFY8wEchXhdVCpVnIdr29KajRkRg61TFS5q1QOTJk3i7bff5s033+SOO+7wbF+8eDH79u1j6tSpQa+pVgH56aef9nx9xRVX1Cgcl9WvXz9WrlzJWWedxZ49e2o1lgCR8dg6nOF5alX2V2p0AsYWCfgIyPZoSGznvlJdnOfuWT5WRDR06gstu8KedZB3yP9rFedhSgqwImt5U6CIiEhdcxT6v9+nVkwdjVu/jBgxgq5duzJnzhyvgDxnzhzi4uK4+uqrg15Tja+nFxcX89VXXwHum/FmzpwZkII6derEokWLiI9398LW5rJ9o1eT9y4yDqKalH/ENMWWfBK29gOwdR3u3uZPfHM4fhh07offq9uFhzEf3oNr9TxM5q7q1ykiIiLV4nA4vB7h8im9ZVlMnDiR//3vf6xZswZw3/j38ccfM27cOBITE4NeU42vIK9cuZL8/Hwsy2Lw4MF06NAhYEX16tWLJ554gj/+8Y8BG7NBi/RzM56/7RWoci/UsWO7nOAqOXqzoGVBy85wYJN7+WtfHEWwdTlm63KMPdK9tHVkLFreWkREwkpETO3O99digVXzsat53o4dO+jatavXtq+//prhw4fX7PUDbOLEiUyfPp25c+fSr18/3nrrLYqLi5k0aVJI6qlxQC7bAjFs2LCAFFPW5MmTPXczVuWGv8YsFA3+/l7TFOdjsrbD4d3uxUSqehXbWQIFWe6HiIhIGKltr69rx3L3wlzHiorH1iXwGcqXdu3alVtromfPnkF57aro0KED559/PvPnz+fpp59m7ty59OjRg6FDh4aknhoH5IMHD3q+btOmTUCKOVbnzp3rZFypO1ZUHFarXpiWx8PhXZjoleUPcjndM2Y4iv3PuwxQko8xLix/KwWKiIjUBwH8pLemoqKi6N+/f9BeryYmTZrE559/ztSpU1m7di2PPfZYyFptaxyQy654YrfbA1KMNByWPRJadMeceJ7vv5rB3WKRsdM984WvPqiCLMziGXDaFVhtTqrbgkVEROpIuEzlVpHly5eTlpYGuDPezp07ee+99wB3p0BycjIAM2bMYMaMGXz11VdeHQSfffYZeXl55OS4FybbuHGj5/yRI0dWuC5GqdGjR5OUlMTMmTOx2+2eKX9DocYBuXXr1p6vDxw4EJBijrVrl/vmrcjISNq2bVsnryEhFBENrY93h+RCPyv9Ze3GLHsa0/ZkrFN/h9UscL3uIiIi4jZt2jSWL1/ueb5s2TKWLVsGePcqu1wunE5nuRv8Jk+ezM6dOz3PFyxYwIIFCwDYvn07Xbp0qbSGqKgorrvuOmbNmsUFF1xA+/bta/dN1YJlangL45IlS7jggguwLIvzzz+fzz77LKCFvfzyy0yePBmAHj16sGnTpoCOHwwbNmygd+/erF+/nl69eoW6nJDwOzk6NrDZoDALNi7xH5CPFRkHMQlglfnUQjfziYiIiB81yWM1voI8aNAgIiMjKSkpYdmyZeVWP6mNLVu2cN9993n+OgmXaUik+ir7WMkUZWO2fl9+h7PEd49ySb6fwC0iIiISGDUOyE2aNOGss85i6dKlFBcX8+CDD/LCCy/UuqDU1FQuvvhisrOztZJeI2BFJ2JOPNd3n7LLCakp7qniSqeP88VZUncFioiISKNTq+kBSlsgAF555RXeeuutWhWzdetWhgwZQkpKihYIEbDZoU1P6HUBJHfzf1xeGq6lT2L2/IRxVTArhoiIiEgV1Gqp6XHjxnH66aezevVqXC4XEydOJDMzk9tuu63aY7344ov8+c9/JicnR+G4sfE1zY1xua8aO4shMho6ngbZB6HIxxLXAKmbMKmbwB6FiYxxj1l2ejj1KYuIiEgV1XqC2VdffZWEhAQsy8LhcHDHHXcwYsQIlixZUum52dnZzJ07l5NOOok//vGPnqlBAP7xj39gjFFYbgRs7Qdg6zLM+9H1bGzdz8NqfwZEJbgPrMp8yM5iKMyGnAOQve/oIzetbr8JERERaTBqdQUZoHfv3ixYsICLL77YM+3H8uXLWb58OW3btmXIkCH07t2bFi1aEBMTQ3Z2Nrt27eLnn3/m+++/p7i42KvP2G63889//pObb76Z22/XFb/GzopPgrghcHg3Zusxi44Y4+4/dpZUvOAIVNzDLCIiIlJGrQMywHnnnceXX37J1Vdfzf79+wH3zBP79u3zmgfvWKXBuPRmvOTkZObNm8cFF1wQiLKkgbAsGzTrjDnhXCjx0WJhXJC5F9K2Qt4h34PkpuL6/lWsk0ZhJdbNyo8iIiLSMARsDd+hQ4fy008/cfXVV2OzuYc9tj3i2Bkpyu4fP348//vf/xSOxT9/7TaWDVp0hJ7DIaqClXp2rMQsehDXipcxh/fWSYkiIiJS/wXkCnKp1q1b89ZbbzFjxgxmz57NkiVL2LBhg1cwLvt1t27duOSSS7jppps48cQTfY6pad7E49ib+YzL3XNctn3CVtmy5wZ2/YDZ9QMmIgaiE8AeeXS3buYTERFp9AIakEt1796dp59+GoD09HS2bdtGeno6WVlZREVFkZycTM+ePWnTpuKPukvX4C67rLU0Xv4WHTFF2Zj0TZCXCtHxx+w04CjyPVeyo9D9EBERESmjTgJyWUlJSSQlJdXo3Llz5wa4msqlpaVx4oknkpGRAbhD+muvvRb0OqTqrOhErPYDMAWZGMsOxln+oJJCSNsOaSkVLyxSnIcpKcSKjKm7gkVERMLMgw8+yNq1a/n555/Zt29ftfJPTk4OjzzyiOf89PR0pk2bxvTp0ys9t0+fPqSmprJr1y7sdt+fAg8ePJhNmzaxb98+oqKiqvFd1VzAepAbijvvvNMTjqV+sWKbQ2Ss752RMdDuRDjpPGhbwTrshYcxH92L66f5mOwDdVOoiIhImJk1axYZGRmMHj262iE0IyODl19+maKiIsaOHVutcydNmsS+fftYvHixz/2bN29mxYoVXHfddUELxxCEK8j1yeeff878+fPp1q0b27ZtC3U5UhciY6BtTzi0E4p8LG8NUFIAm7/CbP4KY492t21ERANHbhJUn7KIiDQwOTk5nkkW3njjjWqd27lzZzIzM7Esi/T0dF599dUqnzt+/HimTJnCnDlzGDlyZLn9c+bMAeCGG26oVk21pYB8RF5enmfp7NmzZ3PhhReGuCKpEV+r8rkcRx+lqroAjbMI8osCU5uIiDRKrm+e9b1gVRhdcCkNxzVRm0XdmjdvzqWXXsrChQvJyMigZcuWnn1Op5M33niDAQMGcPLJJ9f4NWqiRgH5sssuA6Bt27Y8//zzAS0oVK/94IMPsmPHDq688kpNNVeP+b2RzxjIO4jJ2AJF2eVv5gNwOY/e1FfRwiMFWZjD+7CatgtQ1SIiEs5cXz4O+Zk1H6Agy/f9MTkHcX10X83GjGuO7dwHal5TGJk0aRJvv/02b775JnfccYdn++LFi9m3bx9Tp04Nek01CsgffPABlmXRvXv3QNcTktdevXo1zz33HE2bNmXWrFkBG1fCh2VZ0KQNxLeG3AMYy+Y/BLsM5KTDtu98H1OSj/nsIUybXljHnwtte7sXMxERkYYpPxPy6+D+JOOsm3HrmREjRtC1a1fmzJnjFZDnzJlDXFwcV199ddBravQtFg6Hgz/84Q84nU7++te/0rZt21CXJHXIsixIaIvJ2AzFfnqQbRY0TYboJlCY7X+wAxswBzaAPQoTGeu+QbBsUA6jj85EREQcDofXc7vdXqv2iECxLIuJEycydepU1qxZQ79+/cjIyODjjz/myiuvJDExMeg1NfqAPHPmTNatW8cZZ5zBLbfcEupyJKxUcZEaZ7H7UXi4bssREZHQiGteu/P9tVhYdohtVrMxq1nTjh076Nq1q9e2r7/+muHDh9fs9QNs4sSJTJ8+nblz59KvXz/eeustiouLmTRpUkjqqVVAzsnJYd68eYGqJehSUlKYMWMGdrudF198sVYN6qmpqaSleTfgp6Sk1LZEqStVuZnPV5+yMe4b/Izl7mWuSEk+xlGMFRG8aWlERCTwatvr61r0EGTvK78joTW2kY/UauyqateuHatXr/ba1rNnz6C8dlV06NCB888/n/nz5/P0008zd+5cevTowdChQ0NST60CcmpqKhMnTgxULVViWVbAlp++6aabKCws5J577uG0006r1VizZ8/m4YcfDkhdUvcqvJkvP919M1/3M/0P0LQj5GTC1uWQudP3MQVZmA/vxnQZhNXtLKzmnQJQuYiI1DtNkqu3vQ5ERUXRv3//oL1eTUyaNInPP/+cqVOnsnbtWh577LGQtYDUusUiUGG1qgL1Rv3rX//i66+/plOnTgEJtrfeeiuXX36517aUlJRqT5gtoWVZFsQnQ1wSZvvX4CjwfeDh3YAFp4yB1W9C/iHfx5UUwJalmC1L3X3K9ij1KouINDL14Wf88uXLPZ+EO51Odu7cyXvvvQfAsGHDSE52h/kZM2YwY8YMvvrqK4YNG+Y5/7PPPiMvL4+cnBwANm7c6Dl/5MiRxMX5+OT2GKNHjyYpKYmZM2dit9u5/vrrA/o9VkeNAnKnTp1C3tTdoUOHGp978OBBpkyZAsBzzz1HfLyPj9KrqVWrVrRq1arW40h4sCwLY/O95OVRBnL2gKuCpavLKilwP9SrLCIiYWbatGksX77c83zZsmUsW7YM8O5VdrlcOJ3OchdIJ0+ezM6dRz9RXbBgAQsWLABg+/btdOnSpdIaoqKiuO6665g1axYXXHAB7du3r903VQs1Csg7duwIcBnB9cADD5CZmcnYsWMZPXp0qMuR+s7nnMoO9z1+zmL3/MoVKczWvMoiIhJSpWG4MtOnT2f69OnltgcqGz7zzDM888wzARmrNhrlLBaly0iXzqlckddff53XX38dgLlz5zJhwoS6Lk/Cha8b+QDs0RAVD9l73PMk++tVtkdD275Yu1Zjtn4DOQd8H1ec655XuXlnrC6DoPMZWDHBn9JGRERE3BplQBapCn838pUyLY/DZG6HzG2+D3AWwb7VmBYdoOv98MWjkJfuf8DMnZjMnfDzO5iIaHdAj4h29yurT1lERCRoGmVAfvXVV8nN9bNIxBF9+vQB4JJLLmHGjBmAu/dapJQVEYOVfCKu3INQkuf7IJcDsna4H87iqg/uKHI/ShXnYfIPYcW1qE3JIiIiUgWNMiD36NGjyse2aNGi1lPASQNX1RtWo+Mot/iIMe6J4qPifc+RWarwMOajKZjmnaDdaZD6GxT5+CNPV5pFRERqrVEGZJGA8rnoiNPdn+wsxhOK/fUqR8RCl+FYWbsxy56GYj9XowEyd7kfIiIiUmcUkEVqqaJeZVNSiDm8Ew7v8t9i4SiAXf91Lz4Sk1hxQK5MUTbm4K/QsrtW8BMREakhBWSROmRFxmAl9cS06IHZ8bV3X3FZxTmYtI1Q7Kc3PiIGmiRB1p6KX7AoF/P1U2CLwETEuNs/IqLBHgkcaQVRG4aIiEiFFJD9CPYKgdKwWTY7xhYJ+AnIpXzNqQwQ2xTbBQ9j8tJh7zrMuoXuWTL8cTmOhu2iHO99JQXuceJahnzBHxERkXCkgCwSLMf2KhvjDrIuB5gji4n461O2bJjMHZDQBuv4czApyyq+qa8iBZmYj/8M0QmYll0h5yA4S9zLYGsJbBEREQVkkWDx16tsjIHCLMzhXe7FR3we5MKkbYC0DZjYlmC3QWzTYwdyh9xWJ8DBXyE/o+KCinJg3/8q3G+2r4AmyRCfhPnxDchNK3+cgrSIiDQwCsgiIWZZFsQ2x4ptjqsg0/+cyqUKMqDr6b73RTXB1mUYAK5PHoDc1JoXVpSD+eFflR/nKMRk7oLYZphVrylEi4hIvaeALBJOatsT7CzGFGRCTDOw+fnPO7oJtOgKh7b7nku5uvIPYRY/XPExhdmYDZ9CZAxERGO2LHVfwbZsgOX+vi0L4pOxzrrNfZPht89VKWy7vnlWoVxERAJKAVkknPiaUxncs1DENIecfeAo9H++sxizewXYIsFmQWwintkrSsU2wzbsTndrR1465qsnoCAzYN+CT8W5mF/er/y4nIOYBbdwtGYfN8vmpeP6+mn3e2KPgLTNUFLgYzDdaCsiIjWjgCwSRiqaUxnAJJ0AhZmYvT+Cq8T/ga4S6HaG732RcRhj3K0dTZIxkbG+A3JcC6wTL8LkpkFeOuz7peLXDKgKwq2zGA5urHyI7P24PpoCiW0hsQ0c/A1KCt2h2rIfPU5XmkVE5BgKyCL1iLtfuQUmIhqKaxhWS/IxW7/ARCdCdFOw28vf8AcQk4B13AjPtVzXood8z5wRnQAd+7tD9oEN7hkxwkX+IffjwIYKDtKVZhER8aaALFIf+WrFMMbd0xsV577iWzp1nC8uBxQccj+6+rlqHXnMnMxNkn0f1yQZW/9r3cP6C9HxSVj9r3VfwXUUYv73PhRmlz8uIgaSuoOjGA7tCM4V6+wDuL57Eav9qdD2ZKzoJnX/miIiEtaCHpALCwvJz88nMjKS+Ph4bDZb5SeJiJdKWzGMC7N9mXsZ65oqycO1bSnENMOKbQZd+vsOrGXDegUh2mp78tH6fvvCd0COa4Ft+N1ABWE7sR3WBVPdV6pdDsyXf4Pcg+WPs0dBRFQVbkQ0sHs1Zvdq9zN7lDuoR8YcvdFRbRgiIo1KnQXknJwcli5dyg8//MBPP/3E1q1b2bNnD8XFxV7HtWzZks6dO9OrVy/69+/P0KFDOeWUU+qqLJFGwbJsGJvd905bhPsmvqqEZ0cB5BZgcvf7P8blcM+cERED3QdBSX75Y4694h0R6butIyLy6NcVhW175JHls8EktgFf3+uRUGuKciD7AOa7F6Ewy//3UcpZ7H4UlQnwFV2NFxGRBiegATkrK4t3332Xt99+mxUrVuBwODz7/C3dnJ6eTkZGBj/99BNvvPEGAK1bt2bs2LFce+21nHmmn5XFRKRi/mbEiIzD1n4AxlkMhYcxB9a6A2FNOQrdM2dUcowrdb375jjL7g7Svl4zItYdtm1291zPpTN2WBxtFY6MxZQUuFtKcLlbRByFRw6w8Ewbd+T7t6ITIDkBExXnJyCXHdyPnIO4ls7E6jIQOvTDivLz3oqISINgGX/JtRp++eUX/vGPfzB//nyKiooAPHfJ13R468h8sD179uSOO+7g97//PbGxsbUtNag2bNhA7969Wb9+Pb169Qp1OSI+uXYsh2IfbQilMz3U26unFsQ0BXs0RETDz++52zqOnWs6pinWaVdg9q6DlKXgquT7tUVAVLw7xEdE4zWNnloxRETCTk3yWK2uIK9bt45p06bx8ccfA+WvEluWxXHHHUevXr3o2LEj7dq1o0mTJsTGxlJSUkJBQQHp6ens3buXlJQU1q9fT1ZWltdYmzZt4tZbb2XatGncf//9TJ48mejo6NqULSJlVXCl2WrXH4pzoCALk/6r++a+esN4XzHu5mf1QVsExm7H6jHU3YdckOX7uFIuBxQeDlSRIiIShmoUkA8cOMD999/Pm2++iTHGKxj36tWLMWPGMHz4cM4880zi4qr3UWRKSgrLly9nyZIlLFq0iNxc95WttLQ07rnnHmbNmsVTTz3F5ZdfXpPSReQYld3wR3QiRCdisrb7vtJsj4LE9uAoct8sV9+uOLsccGiLu8nCX+2WdaSloxLhNMWdiIjUWI0C8vHHH09eXp4nGLdr144JEyZw/fXXc9xxx9WqoB49etCjRw8mTZpEcXExn376Kf/6179YvHgxTqeT3bt3c9VVV7F7927uvvvuWr2WiFRDRT3NyScBFbRrRMZhtevnbl8wTsyBdb5XBLRHQWIHd1DN3uv7irUtAuJbH12eOueAn+ngKliNz5/oeN/bo+Kh65lwaCcc2Ohn5T4gLw3XsllYJ14ErXp6WsVERKR+qVFALr2q26dPH6ZMmcIVV1xRJ9O1RUVFcemll3LppZeya9cunnrqKebOnUteXp6nBhEJjkqvNEPF7RrRiZ6nJjrx6BRqxxxnSz4RAFd+hu+wHRGDre1pnqeugkzfi6ZExWN1HnpkVooizN7VFS/TDdC9kpuCkztDyw6w4Qv/IfnAesyB9dCyG5x4EbQ/DcvSdJYiIvVJjQLyySefzCOPPMLo0aMDXY9fnTp14tlnn2Xq1Kk8+uij6kMWCUNVCtFVPa6CsF3V4yzLct9IFxGN8RXIS49LPgmKczGHUirvs7bZ3ctVV9ZNkbEN89/n3T3O0U0gMhawdCOfiEg9UKOAvHbt2pB9dJiUlMTf//73Gs+OISL1Q0DDNlQcpJu0Blpjsvf4vmp9rGNbMYxx9x87i8v3Krsc7hv/Krv5T0REwkaNAnI49NWFQw0iUn/U6qp1RCxWi+6Y/DT3Mt7+WjFcDkjfCalboNjHgikABZmYwhysmISqFS4iIkEX9KWmRUTCVWUh2oprCUng2r4MSvJ8DBABrbpDcldY/zmU+Oh5LinALPoLnHo5dBui/mQRkTCkn8wiItVV2SdYls2zFLZPxfmY1a9jvnoCk7UnsLWJiEit6QqyiEh1+WvFsEW69+Xur9o46SmYxTMwPc/D6j0aK0I3H4uIhAMFZBGRaqqsFcM4T8JsXVl+h8t1ZNq5krIHw2+fYzYtwcQ2hYgY93bNdiEiEjK1CsgzZsyo0nFTp06tzcuIiNQrlj0Kc8I5vmfEMAbSt8O+DeWDcv6h4BUpIiJ+1SogT58+vUqzSSggi4gcYVmQ3A2atYM9v0Dmbt/HFWZhjEs38YmIhECtf/IaYyp8iIg0SpFxENWk/CM60b0vMga6DoAoP/3MxfmYr57EHN4X3LpFRKR2V5CnTZsWqDpERBqUivqUjXHB4V2YjBT3ynz+pG/BLH4YThoJJ47EqmhmDBERCRgFZBGRILMsGzTrAokdML98WvHBLgdm/UewazX0/z1Wq+ODUqOISGOmWSxERELEskVgYhOBY9rRHCXgKPLenr0fs/QJTGQcxCS651oGzXYhIlIHFJBFREKp5wjfs10UF8CedZB1TA9ySb77UcpRULf1iYg0QgrIIiKh5GvREZcDLDt0G+gOyLvX+l62GsC46rQ8EZHGSAFZRCSE/N3MZ4wLsnZgbBGQkAzrP/eeN7lUUR6mKBcrukkdVyoi0nhogk0RkTBkWTas5t2wugyHFt3d08L54nJgPp+OSU8Jan0iIg2ZVtITEQljVkQ0VuuTcVW0YEhBJuarJ+DUcdDzgiot4CQiIv5pJT0RkfogJsG739hRfGSmiyOMC7N2AaRuhjNuUMuFiEgt1LoHubLV8nQlQ0QkAHqPOjp7hTHgKITsA7BjtfcNfPvWYT64CxPXAuxR7m2aCk5EpFq0UIiISD1w7M18xhjI3oOJaw7bvoec1DI7XZCXfvS5poITEakWBWQRkXrIsixo2hHikjEJbWDrt7D/V98Hu5zBLU5EpJ7TLBYiIvWYFRmD1f50rD5XQZSPOZUBCnNwpW4KbmEiIvWYArKISD1nWRZWYgewR/o5wsDSmbh++RDj0sIiIiKVUUAWEWkUDGz4CLP0CUzB4VAXIyIS1sJ6Jb1//vOfHDp0CNBUcSIilTp2KjiXE4oLgDKzDaWnYD57EDPoZmxtewe9RBGR+qBWAdlutwPQvHlz3nzzTS688MKAFFXqH//4B9u2bQMUkEVEKlV2KrhShdnuWS6yDxzdVpwPy2fhioj2XqEvthm28/WzVkSkVgG5dA7kQ4cOMWrUKKZNm8ZDDz0UkMLKvobmUhYRqdyxU8GVcnUcCGv/DXvWuudQLuUo8l5sREREgAD0IFuWhWVZuFwupk+fziWXXMLhw+pvExEJF7b4ZKyBN8Gpl/qf6UJERDwC0oNcepXXGMOiRYsYMGAA77//Pr17q79NRCQcWPYo6DkS0/I4+PppcDnKH1RSiHE5sGxhfXuKiASI64sZUJBVfkeZdquqHNMQBWQWi6ZNm5KcnOx5npKSwsCBA5k/f34ghhcRkQCwLAtb8vEQHe/7AEcRZsljmLyM4BYmIqFRkAUFh8s/Du/D9cXDuD79C2Tu9n2Mr9DcgAQkICcnJ/PTTz8xcOBAz9Xk/Px8rrvuOu644w6czvBbxWnPnj288MILTJo0iQEDBtCpUydiY2OJi4uje/fuXH311Xz22WehLlNEJLgyd2E+m4pr79pQVyIidc3fKpvOEji0C3IOes+M04gEbB7kdu3a8c033zB58mSvlot//vOfDB8+nAMHDlQ+SBB98skn3HrrrcyZM4cff/yR3bt3U1hYSEFBAdu2beOdd95h5MiRjBkzhvz8/MoHFBGpL2KbQWxT9yMmEY5tqXAUwrfP4frpbYyvVgwRqdeMMbhSlkJRbs0HaeCLDgW00SwiIoLnn3+egQMHcsstt1BYWIgxhu+++46+ffuyYMECBg8eHMiXrLGYmBguuOACzjnnHPr06UO7du1o3bo1hw4dYv369cyaNYtvv/2Wjz76iBtvvFHtIiLSYBzbN+gqPAzr/g07VntfLdr8JSZlOSau+dEQ3SQZ29Dbg1itiASSMS7M2ndg09IqHF06i5gpv6s4D+MswfK7gmf9ZhljfHzXVWOz2bAsi+7du7N582avfevWrWPcuHFs27bNczU5MjKSp556ij/96U9VGv+4445j69atWJYV9DYNYwyjRo1i0aJFAGzfvp0uXbpUa4wNGzbQu3dv1q9fT69eveqgShGRwDDOEszWr2DDZxVfVYprjm30U8ErTEQCxuUohBUvwr5fKj4wJgEumA6RsfDpA+6eY1+6DMI28MaA1xloNcljdbbU9KmnnsqaNWsYOXKkp+WipKSEO++8k/Hjx1NQUFBXLx0QlmUxadIkz/M1a9aEsBoRkbpl2SOxjrsABt8CzTv6P9CplguR+siVlw5f/tVHOLYgusnRtqvYphDXAltsM2wR0d4tWZGx3qfuWIlr9+pgfQtBVadz+TRt2pRPPvmEhx9+mEceeQRwX5l95513WL9+Pf/5z3/o1q1bXZZQK5GRRz82iImJqeBIEZH6z7IsrFYn4hryJ/h8KpQUlj+oKAfX/97HOulirIjo4BcpItXmStsCK14ofyU4oRUMvxdbfEu/55ZtyTIuB+brJyFt69EDfpiLq0W3Cseoj+rsCnJZ06ZN45NPPqF58+aeVfF++eUX+vfvzyeffBKMEmrknXfeAdxBuU+fPiGuRkQkOGzxLaGi8LvxU8yiBzG711CLLj0RqSOuvatx7Vjufqx7B5bPKh+OW58A50+rVrC1bBFw+g3uK86lHEXw3fOYBnbTXlACMsCFF17I6tWrOfXUUz0tF1lZWYwdO5Zp06YFq4xKpaWl8e2333L55Zd7bsy78847adeuXYgrExEJI/mHMN/NxiyfhckOr1mKRBq99Z/A2v/Amnfh1yXll5Q/7mys4fdgi6z+p+O2hDZw2u/AKhMhD+3ErP+gViWHm6Aul9S1a1dWrlzJLbfcwuuvv+5ZovrRRx9l9erVvPXWWzRv3jyYJQEwYcIEXn/99XLbmzVrxpQpU3jggQcqHSM1NZW0tDSvbSkpKQGrUUQkqGISvGe0MMbdcnHstG8HNmAW/R8mqon7nNK73jXbhUjoFOa4H770vxZbj7NrNbzVZTAm9VfY/sPRjRsX4WrTG1ur42s1drgI+nqi0dHRzJ07lzPOOIM777yTkpISjDEsXryYAQMGsHDhQk499dRgl1WOzWbjhhtu4Morr/S0hVRk9uzZPPzww0GoTEQkCHqPgpLSOeANlBS4A3NOKuxeV/6Xb3Gu+yEiIWWcxb7vHwCIjq91OAawLBvmtGvci4kc3l/6yrDiBczIR7Gi/KzWWY8ErcXiWLfccgvLly/3al3Ytm0bZ555JvPmzQtqLS+99BI5OTlkZ2eze/duPvzwQ0aMGMEzzzzDKaecwrvvvlvpGLfeeivr16/3enzwwQd1X7yISB2wtR+ArcuwI4/hWN3Ogch49009J5wD7XuDzR7qMkWkDFOSj1n1r/ItFaWOXRSoFmzRTaDfeCjbplGYjfn+1QZxb0LQryCXdcYZZ/Dzzz9z5ZVX8vXXX2NZFgUFBUycOJHvv/+ekpKSoNQRHR1NdLT7hpSEhAQ6dOjA6NGjmTx5Mi+++CLXXHMN3bp1o3///n7HaNWqFa1atQpKvSIiwWbZo6DDGZjdK9wr7bU+3j0d3K9fupelPZa/X9AiUidM4WHMT2/BruBNS2sln4A54Tz45eOjG/f9D/PhPZjSq8j1tN0qZFeQSyUlJbFkyRLuvfderyWqX3rpJXbv3h3S2mbOnElcXBxOp5N//OMfIa1FRCTUrMhYrPang+3IFJhRsd5Xj8rKz8DsXx+84kQaMZOXhlnzRuXh2Aps7LMsC6vnRdD2JO8dhYche5/7kbUroK8ZLCEPyODu933yySd57733aNKkiSckh1qTJk08K678/PPPIa5GRCT0rOgErPYDqvSL1nz7HGbvuiBUJdJ4mey97qWjdx+TU2wRkNAGEtsdfTTrFPDXtyKioc/VYPlpuTL1c/q3kLZYHOuyyy6jV69eXHbZZfz666+hLgcAh0OrRomIlGXFNoe2/TD7foToMjfjuFxQnA8cucDhcmC+ex7OvAWrQ9+Q1CrSUBljIHMb5tdFsHut987Etljn/BkrOiEotdgS2+GKiq14mfp6ptYBOdBXenv27MmqVauYOHEi7733XkDHrq6MjAzWr3d/RNi9e/eQ1iIiEk6sJq2gzSkYjvkdUJANKf89ehe9y4n57gUY9AesTqcHv1CRBsa1d7V7hhlHEaRuDnk49mhgN+3WKiC76mjVlPj4eN59910++ugjMjMz6+Q1fv31V0488US/+51OJ3/84x89NwpefvnldVKHiEh9ZSV2wKT9Cs7ioxtjE+G4obDlW/fUcADGhVn5MricWF0GhaZYkYZi/SfuVfGcJeWnc0tsizUiBOG4AQqrFotjjR49us7G7t27NyNHjuTSSy+lX79+tG3blqioKFJTU1m5ciXPPfcca9a4m92HDRvGNddcU2e1iIjUW/Yo74AMENMEjj8SkouPzKVsjHv6pzVvQUSUe1tsM2znTw1uvSL1mDEu96c0vhYBsWxYI+7DiglROC5dXKg4H1zOMnXVzyvLYR2Q65LL5eKTTz7hk08+qfC43/3ud8ydOxebLSzuZxQRqR+i449eSS7OO7q9pODolWURqTJjXJh9P/mfQjG6CVZMYnCLKqt0caG0rbD1u6PbI6NDV1MthCwgf/DBB2RnZwPw+9//Puiv/+233/L111/z7bffsmPHDlJTU8nLyyMxMZGuXbsycOBArr32WgYOHBj02kRE6o3IOO/nxoCrxL0kdXSc+0ryhsV+7mQP/WxFIvWBMS7M3jWw9Rv/AbkKq/7WJVv7AQC4Wp3snm6utP3j8H5cOQexJbQOYXXVF7KAPGXKFLZt2waEJiAPGTKEIUOGBP11RUQaktJfiscyxkDeQUzmDoiK8313e2EurgO/YGtzct0WKVKPGZcTs28NbF4KqVtCXU6lbHEtcLXuCXvKTPG4aTH0D37Wq42Q9g2Ew1zHIiISeJZlYTVpg63jQP93txsXfPMcrp/nY9R2IVKOOxz/CL9+Xnk4DvAiILVy3Ajv5ztXYZz1a9rcRtuDLCIiYcDlhE1fYTK2YU67AqvlcVgh/qhYJBwYlxOzdxVs+Ayy9nrvtEdBXAvvUNwkObgFVsBKPhGT2Bay97s3lBRgdv2A1XVwaAurBgVkERGpW7HNvJ87Stw385SVvh3++wKm+0BMk1bev/gj4/y2cog0RMblxOz+HjZ8CtkHvXe2PRlr8K1YpbPBhCHLZsd0OR3+9+HRjVu+AgVkERERN19TubkO74f/Pgs5qUc3FmbDhi8gMgbskUe3xzYFBWRp4DwLgID7v4Ut30JehvdBnU7HOmMSlr0exLduw+HXxUdv1ju0E1f2AWyJbUJaVlXVg3dYREQaGlvTtpgLZ2DWvAnb/uu9s6TQewGEcOqtFKkrpQuAGOOeS/jYmV+6D8Pqdy1WPZl21haTiKvNSbD7p6Mbf/scTp8Qspqqo368yyIi0uBY9khsp0+EQTdDRP2cK1UkYApz3I+i3PLh+IQLsfpfV2/Cscfx53g/37263tysV8/eaRERaWhsnU/HumCa/xW3HEWY7L2+94k0AMblcC8d7UtEDLbTLq+XN69aScdD03ZHN5QUYnasCF1B1aCALCIiIWcltHYvUe1LSSHm+1dxHfxF04NKg+NyFGN+ePXosuzHqqcr0QFYlg26DPLemLI0NMVUU8h6kN966y0KCjTvpYiIHFFRr/GhXbDyX5heF0P3EVj+5lYWqUdchdnwzSz3/78bqu7DYOOio0vMZ+7GdXgvtqbtQ1tXJUIWkE8//fRQvbSIiISjZp0gItb9tcsB+Yfc/1uqMAd+eheTuRvT52pskbGhqVMkAFyHdsA3/3DPWFGRen6Tqi0qHlfbXrDrx6Mbf/sczpgUuqKqQLNYiIhIWLANvd3ruXGWYH7+N6R8XWajC7Z9B5m7cHU/0/vmPs2XLPWEa/t3sHqe9x+A4J7eMK5l2C4AUmM9z/MOyLvX4Op3HbYwnstZAVlERMKSZY/E6n8trtYnwQ+vgqPo6M7M3bBmAUTGQumd/ZovWcKccTkwP73j/UdfqW6DsfpPqH8zVVSB1aI7pln7oysCOopg+3dw3NmhLawCCsgiIhLWbB37YprPwHz7LBwuM5uFcUFx3tHn9fyjaGmYXN88C7lpYJzutqFjZ6uwRcBpl2E7/oLQFBgElmVhug6Gn989unHrsrAOyDX6aXLw4MHKD6pjqamplR8kIiINgtUkCev8qdD9LP8HaYYLCUdZuyB7H+QcLB+OYxJh8C0NOhx7dBvm/sSnVNYeXFm7Q1dPJWoUkHv06MFDDz3E4cOHA11Ppb7++msGDRrEiy++GPTXFhGR0LHsEdgGTICoON8HFOXg2v9LUGsSqZS/+Y1tETD0T9ja9wluPSFii4yB9qd4b/zyr7g+vNv9+GJGaArzo0YBOS8vj7/+9a906tSJKVOmsGtX3U5P4nK5WLhwIQMHDuTcc89l1apVdfp6IiISxuyRvrcbA8v/gWvtu5hjVyITCQGTtRuK8nzvjIrD1qJbcAsKtZ7nez93FLuX1y44DAVZISnJnxoF5F69emGMIScnh2eeeYbu3btz0UUX8e9//5vc3NyAFbdu3Tr+8pe/0LFjR6644gpWr16NMYaIiAhOOOGEgL2OiIjUIxX2Ghv4bTHmy7/iKgj+p5wipUzmbsxXTwB+Wn/q4cp4tWVr3sV95bweqFGV69at4/nnn+fhhx/m0KFDOJ1OvvjiC7744guioqIYPnw4Z599NoMHD+aUU04hISGh0jGNMaSkpPDjjz+ybNkylixZws6dO732A1x00UXMmjWL448/viali4hIfVd2vmSAknz3FaiyQSRjO3z2IK5BN2Fre3LQS5TGzXVoJyx9EhyFoS4l/EREQbGj8uNCrEYB2Waz8ac//YkJEyYwa9Ys/v73v5OVlQVAUVGRJyyXat++PR07dqRt27Y0adKEmJgYHA4HhYWFpKens2fPHrZv305hoff/kcouKXr22WczY8YMBg8eXJOSRUSkgTh2vmQAV/Z++O55OLz/6MbifFj+d1wR0RAZ494W2wzb+VODVKk0Rq6MbfD1U97TEoK7d77sCpAxlV88bJD8tUiFmVpd505ISGDq1Knce++9zJkzh9mzZ/Pbb795BVuAPXv2sHfvXj+juB17DkBMTAyXX345d9xxB3379q1NqSIi0oDZEtviOn8qrH4ddvyA19VkR1H5sCJSB1ypm2H5LHAWH91os0OPIdC8k3dbRaSfm00lLASkESQuLo7bbruN2267jR9//JF33nmHRYsW8dtvv3mO8RWAfWnSpAlnn302l156KePGjatSe4aIiIjNHoU540ZM8nGw+k189n66nEGvSxoH14H18O0/vWetsNmh92isEy7CKnv1uDGLbVa97SES8E7p/v37079/f5566in27t3LqlWr+Omnn9i6dSu7d+8mKyuLgoICIiIiiI+Pp23btnTu3JlevXrRr18/+vbtS2Rk/bj8LiIi4cWyLKzuw3H98iEUZpc/oCgX147vsXUZGPzipMFxfTHDPfuCy1F+tgpbBPS9Aqv7CKxGeEOeP/WlxalObyVs3749l156KZdeemldvoyIiIi3igLJ96/gytyBddoVWFp9T2qjIOvIDaI+nH49ti5nBrUcCRz9ZBARkcZn0xLM10/hKi4IdSVSTxmX0/8iIFHxCsf1nAKyiIg0PLHNILbp0Ud0k/LzJ6dugsXTcGUfCEmJUn+ZknzMurfdM6X4Yq8fc/2Kf/oXFBGRBsdXn6Mrex989wIc3nd0Y14GLHoQV1xziIg5ur1Jss/p5ERMfjrmfwthh1b1bcgUkEVEpFGwJbbDde4D8MOrsGddmT0G8g95H+xQ64V4M8ZA1nbM+o9g7y+hLkfqmFosRESk0bBFxmENvg16XQxWBdNuGVfwipKwZ1xOzP6fMeveKx+OLcu96EfZlp4wm7JMqq9OriB369YtIOPY7XYSExNp2rQp3bt3p2/fvpx33nn06NEjIOOLiEjjY1k2rJMvw9WsE6x4EXzN019SiCkpxIqMKb9PGhVTko/ZuxpSvoX07d47E9tgnX0vVmzz0BQndaZOAvKOHTuwLKvSxUEqO6bsvIHLly9nzpw5AAwbNownn3yS/v37B6ZgERFpdGwd++OKTvA9X7KjCPPpA9BvPHTop3lsGxnX3tVQku9eWKY4H3auhsw93ge16Io17E6s6CahKVLqVJ31IJcG32N/qJQNxMeG47LHGmP8hudly5Zx5pln8sQTT3DXXXcFqmQREWlsKgq+hdmY716A1idBv/FYiW2CV5eE1vpP3PMbG44G5bLanIQ15DasiOiQlCd1r04C8ty5cwFYv349s2bN8oTdjh07MnToUE488USaNWtGdHQ0OTk57Nmzh7Vr1/Ltt99SUlKCZVmcdNJJ3HPPPZSUlJCZmcn69etZunQp+/fvx7IsHA4H9957L61bt+aaa66pi29DREQaupiEo/3GxkBJoXtVtLIObsQs+j9MdBOITgCOhGrNdNEgGWOgIBsKc3wf0HEA1sAbsTSVW4NWJ/+6119/PW+++SazZ8/G5XJx2mmnMXPmTM4555wKz8vMzOSpp57imWee4ddff+W1117jk08+ISEhAQCn08lrr73GPffcQ05ODsYY7r77bsaOHUtcXFxdfCsiItKQ9R7lvkJYyrggcxfs+rl8QCrKdT+kwTLGYNI2lv8jqZQ9CmvQTVg2zXHQ0NXJv/CKFSuYNGkShYWFjB49mu+//77ScAzQvHlzHnvsMT777DOio6P573//y3XXXefZb7fbmTRpEosXLyY6OhrLskhLS+PNN9+si29DREQaOFv7Adi6DDv66Ho2Vp/rYNCN0L432CqY6aIkL3iFSp0zxoXZ/zP8ttj/AiBRsQrHjUSd/CvfddddlJSUkJyczLx584iKiqrW+cOHD2fq1KkYY/j444/57LPPvPafccYZ/PGPf/T0KC9ZsiRgtYuISONmWTZsrXtj9b0WTh4FNj8fthZk49r1Y3CLkzphXA7Mjm9h7Xuwb0Ooy5EwEPCAvGHDBlavXo1lWVx55ZUkJibWaJw//OEP2I78lVba01zWxIkTPV+vXbu2Rq8hIiLijxWfjHXcBRDlr4XPwIoXcP1/e3ceF1XZ/g/8c2bYBkHZJHFFccvQzPSnaGZqbmRA8rRSCWmSZrR8M63sMe0pM0tzScsyNdN6ylxzwwUi81Er0xQVBYUQk0UdkB2G8/tj4ngGhmGAWZiZz/v1mtfrnJn7nHNhpBc3133dCR+iquiaRWMj0xEryyCe2QYc/x4oyDY8uOZ25WS3TF6DfPLkrd2J7rjjjkbfx9fXF/7+/sjOztabAPfq1Qvu7u4oLi5GXl5eo59DRERUF8HJFWJ9Ld6yzwK7Z6Oq0wDAt1PtGWdndyjaDTBfkNRoVeVFwG9rtTXnqNE5y8kVUHnrJsUerS0ZHlmRyRPkrKws6Vij0RgYWT+NRgNRFHHlyhW9n/v6+qK4uBhFRawDIyIiM5F3ugC0Lb8qSnXfqywH0n4Brl0COvQFuMFIs1UVPw8oUWu7lpQX61mQJwC9wyH0egACZ4wdlskTZHk3CflsckNdvXpVmhlWqVR6x1RUVBj8nIiIqMl6jADKa3SvqNIAV1O0L/nMo/oKoP4bcHYFlM7a91StgMBhFguX6lGi1vY41sfVA8LgqRBu62nRkKj5MfmPRp07dwagbZXy/fffIzc3t1H3Wb58OURRhCAI0j3lysvLkZeXB0EQ4Ofn16SYiYiI6uTsDrh46L7cWgHd7gOGvQi08K1xwT/9lEtv3npRs1BVWar9b6OPQglh3DwmxwTADDPII0eOhKenJwoLC6FWq/HII49g+/btUi9jY2zZsgUffPCBtLNeeHh4rTEnTpxAZWUlBEFA165dTRY/ERGRXH31w2LoexCTtwNnduofUFaIqqvJULRp/Locahptf+NzwG8bgMoy/YNcPSC4tbJsYNRsmXwG2dXVFXFxcVILtqSkJPTt2xfffvstysvLDV6blpaG2NhYPPLII1L9sbe3N6ZMmVJr7ObNm6XjgQMHmvaLICIiMpKgdIKizwTA1UP/gCoNkLgIVQfeh5iTYtngCGJpPsTTPwA/LwcK/rZ2OGQjzLKT3r///W/Ex8dL7d4uXbqEqKgoeHp6YuDAgbj99tvh7e0NFxcXFBYW4vLly/jjjz+QnKztPVidXCuVSnz22Wdo3Vp31WhJSQnWrVsnnY8dO9YcXwYREZHxqmuO65J7AeLBDyC6ePxTo+yq+zm3rjYpUVMBMe8scHYvcPWctcMhG2OWBNnZ2Rn79+9HREQEEhISIAgCRFFEQUEB9u/fj/379+u9rrrmGNAuvPvyyy8RGRlZa1xaWhqio6OlZw0ePNgcXwYREZHxnFQAbtQ/rryw9qI/arKqrF9vbRteVQEU5wPpx4BCPa1gndy0P6TIqbzMHiPZDrMkyADg6emJAwcOYNWqVZg3b57Uqq16dliQ9ZWsfq/6/bCwMCxatEjv4jwACA4Oxvz5880VOhERUcPp65GrKQM05do2cHXVvpJpnP7xVncKTeU/i/Fq9DZ2awkMngqFf3eLh0e2xWwJcrUpU6Zg8uTJ2LNnD+Lj4/H777/j0qVLUKvVKC8vR8uWLeHn54fevXtj0KBBePTRR9G+fXtzh0VERGRSdZVHiNfTIGafBvIuAtkX6k6U5X2VyWhiZRmQ/5c2OTbUMaTtnRAGPgOhrlpxIhmzJ8gAoFAoEBoaitDQUEs8joiIqNkQfIIAQQFR6QT4dQGS9+pPkgtzId7MgeDpb/kgbYwoikCpGqI6Hbh2EbiRCZTVvWmY0O8JoNsInd9eExlikQS5OaqsrERCQgL27t2LI0eOICUlBWq1Gi1atEBQUBBGjRqFqVOnolOnTtYOlYiIbJzg3VmbJOecBpxc9CfIogbivneAkFgIAcGWD7IZ06kv1lRoj69nahPjm/Xst+DqAaH7SPMHSXbFYRPkfv364dSpU7Xez8/Px/Hjx3H8+HEsW7YMK1aswMSJE60QIRER2RPBqxMgCBDTDt96s+KfGuVq5cUQkz4G+vwL6DmGM57VquuLNZXaBLnW9tAGKJTmi4vslsUT5OzsbFy4cAE3btxAYWEhPDw84O3tjW7duuG2226zWBwFBQVQKBQYOXIkHn74YQwePBgBAQHIz8/H7t278e9//xvXrl1DTEwMWrduzfIQIiJqMqFVR4g9huvOIF+/DGT8Doga7bkoQjz5PXAjA/h/0RCcXPXfzEGIoggUq4GyRnb+EEy+5QM5AIskyH/++SdWrlyJXbt24fLly3WO69ChA8aPH4/Y2Fj07t3brDE98sgjmDx5Mrp3113J6uPjg2nTpmHEiBHo378/ioqKMGPGDCbIRERkGgpnALIE2ac94OYBXDwClBffev+vYxAvH4fo7gMo/vnn2sF6JYtiFcT0Q0B53fXFUHkDnQZqa5H1JdH6uosQ1UMQ5T3WTEytViMuLg4bNmwAoNvODYDUH7nmewDw1FNP4eOPP4aXl5e5wqvXCy+8gOXLlwMA0tPTG1yPnJycjODgYJw+fRp33MEtRomICKhK/0l/H+TKMuDSMcM1te7eUIR9aL7gmhFRrIJ48Sfg5GbdHxzkXFtAiPgYAmeJyYDG5GNm+47KyspC//79sWHDhlpJcDVD769fvx4DBgzA339bb1vI4OBbiySq+zgTERE1ibM74OIhe7UAlC7azSu6DgH8u9Z9rYO0ghOrNBBTDwAnfqg7OQYAhROTYzILs5RYlJWVYfTo0bh48aI0I6xUKjF69GiEhoaiT58+8Pf3h4eHB4qKipCTk4M///wTu3fvxt69e1FRUQFAu2PeqFGjcPz4cbi4uJgjVIOys7Ol41atWln8+UREZH8U7QbofV/UlEO8flFbTnHj8j8bXdRQUQbxehrQsr3d1iaLVZUQU/cDp3bo/zOQY3JMZmKWBPnDDz/E2bNnpRKKsWPHYvny5ejSpYve8d26dcOQIUMwdepUXLp0CS+88AJ27doFADh79iw+/PBDvPHGG+YI1aDNmzcD0NYl9+jRw+LPJyIixyEoXSC07gnROxBicrz+5LCyFOKfm4GAXhA9boPQqgPQorXdzKKKmgqIF+KB5F21v36VN+Cs0n2P9cVkJiZPkEVRxNKlS6WZ4+joaKxevdroVjWdO3fGjh07MHnyZKxZswaiKGLJkiUWT5DXrFmDkydPAgBiY2OhVBpuE5OTk4PcXN26sdTUVLPFR0RE9klwcoNo6N/Mq+e09cod+kIsygYgQFQ4AUrnWzOqzu51zlQ3V2Jl2T/J8W7dLh+CAhgyFYr2/awXHDkckyfIR48eRW5uLgRBQPv27bFy5coG93EUBAErVqzA/v37kZmZiby8PBw5cgSDBg0ydbh6nTlzBnFx2lXCnTp1wsyZM+u9ZsWKFZg7d665QyMiIkfg5nmr3lgUtXW48vrjvEtAZTkQ2F/b57eqQvuyMdIGIKII3LwKnE+qkRwrgXumQdGur9ViJMdk8gT53Llz0vGjjz4KV9fG1Ui5urrisccew8KFC6X7WiJBzsnJQVhYGAoLC+Hi4oKNGzcaVX88bdo0PPzwwzrvpaamIiIiwkyREhGR3Qoef2vnOEC7Ocb5RCBftnBdnQWklQNdBmlnj5uRqqSlQGEuAPGfxF4ABAHw8NdtU1e9AUhV1T+L8WSL9xVKCENfgBBg3ravRPqYPEHOycmRjmv2GG6obt266b2vueTn52PMmDFIS0uDUqnExo0bMXjwYKOu9ff3h7+/v5kjJCIiR6CvPEIMHAbx6Grgr19vvXkzFzj/M9B1MODsdut92U5zt5LVGszZU7kwFyjQ0/2p4Cqqtryk3W5bUAJF125tkFKDcO+LENqwRSpZh8kTZDe3W/+DFhcbaM1ihJKSEum4sTPRxioqKkJoaChOnDgBQRDw+eefIzIy0qzPJCIiMpagdAYGTYHo6glcOHjrgxI1cHo34OIO4J+SRmc3VDl7QnD2ANSZQPF1i8UpilVA2c26PtV+VlbHx9VcWjA5JqsyeYIs3y76yJEjUi1vYxw5ckQ6btOmTZPiMqS0tBRhYWE4fPgwAGDp0qWIiYkx2/OIiIgaQ1AogH5PAK6eEE9vu/WBKAJlst3mygqBXz6DwZ3AGtlTWaobrsnZHYJ/MMQjXxhIkI2ktMhGv0R1MnlfmOqSBFEUsXXrVqSkpDTqPufPn8eWLVuk85CQEJPEV1NFRQUiIyNx8KD2p/H58+dj+vTpZnkWERFRUwmCACE4DEL/p5p2o8IciGk/Q6xqYKJcXqTdCbDm6/oliLveBLJO1H2toABcPbXt2ppZ3TSRnMkT5A4dOqBfv34QBAGlpaV46KGH8NdffzXoHpmZmYiIiEBpaSkEQUC/fv3QsWNHU4cKjUaDJ554Quq5PHv2bMyaNcvkzyEiIjI1oet92t7AjSVWQfx1LcR9/4GYe8Hw0IpiiDcuoeryEaCiqPYA9RXg7AHtgjtDXFsAd4wC7gwDhk0HVF76x9lJX2eyXWb5Hca7776LcePGQRAEnDt3DnfeeSfefPNNPPPMM/Dx8anzuuvXr+PLL7/E/PnzoVarpfffe+89k8coiiImTZqETZs2AQBeeuklvPPOOyZ/DhERkdk4q4CSG7XfV7oAvp20pQq5F3Vbp9V0IwPigfchtu4KtO8DuHpo36/SAGKltsNEHQvpIIrA32eAq3p+W+zkAtTc7c/VA0JAP6CFPwSFElXenbRbb9fEDUDIysySII8ZMwZTp06VeiDn5+dj5syZePPNN3H33Xejd+/e8Pf3h7u7O0pKSpCTk4NTp07ht99+Q2VlJURRlHonT506FaNGjTJ5jHFxcVi3bh0AICoqCu+88w4KCwvrHO/m5gYnJ9ZEERGRDXD3AQIHAJWlQPGNW/XJgkKb+JaX1E56c1O1r5qJrWsLIEjW0SntsPZ+oghUlGjvJ6dw0vZn9mpbOy4XDwieAbeGmquLBlETmS3j++STT6QNP6qT3YqKChw9ehRHjx7Ve408MRZFEdOnT8fSpUvNEt/y5cul4w0bNmDDhg0Gx69ZswbR0dFmiYWIiKhR6ppp9WgNof0giJn/001uq4kicD0TuHK69pbOleXaV7XyYiD1F+2GJAonoPCati+zPp5tINzzPMQbF7R1yUQ2yqxTosuXL8f48ePx2muv4fTp0wC0iS8And31qt+rPu7Tpw8WLFiAMWPGmDM8IiIim1bvDGyHQRDTk4Ca/SwEAfDtCHgFaMsjclLr7mpRpQEKsusPRukMYfRsCM4qiIVZ+sfoK6cgaobMXjMwduxYjB07Fv/73/+wc+dOHDt2DBcuXMCNGzdQVFQEDw8PeHt7o2vXrhg4cCAeeOABi+yYJ0/KiYiI7JHg4gHRWaW/LRug7STRLhjw66xdZCfbYKTBWvhBcFYB0L/RCZEtsVhRbUhIiNlatREREVEd6uwIIWi7YDipAB8VkPY/oLSgKQ9qwrVEzQtXnRERETkilxZQdLg1cVWlUOof5+4NIfRdbSeMyjKIiR/p37qayI4wQSYiIrJnddX91nzfq6N2Nrkmj9YQnFylzhZiy7aAQs8mH2zNRnaECTIREZEdM7Ye2NiWa2zNRo6AW9UQEREREck0agZ5xIgRpo7DIEEQcODAAYs+k4iIiIgcU6MS5MTERJ0+xuYk3zyEiIiIiMjcGl2DzD7CRERERGSPGpUgz5kzx9Rx2KWysjIAQGpqqpUjISIiInJM1XlYdV5mDCbIZpSZmQkAiIiIsG4gRERERA4uMzMT/fr1M2qsILJWwmzUajV++ukndOjQAa6urmZ/XmpqKiIiIrB161Z07drV7M8jx8PvMTInfn+ROfH7y3GVlZUhMzMTw4YNg5eXl1HXsA+yGXl5eSE8PNziz+3atSvuuOMOiz+XHAe/x8ic+P1F5sTvL8dk7MxxNfZBJiIiIiKSYYJMRERERCTDBJmIiIiISIYJsh1p3bo15syZg9atW1s7FLJT/B4jc+L3F5kTv7+oIdjFgoiIiIhIhjPIREREREQyTJCJiIiIiGSYIBMRERERyTBBJiIiIiKSYYLsAM6fP4+PPvoIYWFh6Ny5M1QqFVQqFTp37ozHH38c8fHx1g6Rmrm9e/ciPDwcbdu2hZubGzp27IioqCgcPXrU2qGRDaqsrMS+ffvw6quv4p577kHr1q3h7OwMLy8v3H333Zg1axYyMjKsHSbZodzcXPj5+UEQBAiCgOjoaGuHRM0Uu1jYuUWLFuH//u//6h0XGRmJr7/+Gm5ubhaIimzJiy++iKVLl+r9TKlU4v3338err75q4ajIlvXp0wenTp0yOMbd3R0rVqzAxIkTLRQVOYKoqChs3LhROp84cSLWrl1rvYCo2eIMsp0rKCgAAHTp0gVz5sxBUlISrly5gtzcXOzduxdDhw4FAPzwww/8SZpqWbRokZQcP/DAAzh27Bhyc3ORmJiIQYMGQaPRYMaMGdi8ebOVIyVbUlBQAIVCgVGjRmHVqlU4ffo0rl27hosXL+KTTz6Br68viouLERMTg127dlk7XLITe/bswcaNG9GlSxdrh0I2gDPIdm7Dhg1wdXVFZGQkBEGo9blGo0FoaKhUZnHs2DEMGDDA0mFSM5SXl4cuXbrg5s2bGD58OPbv3w+F4tbP1CUlJbjrrruQkpKCwMBApKSkwMXFxYoRk6147bXXMHnyZHTv3l3v5+fOnUP//v1RVFSEXr16ITk52cIRkr0pKipCcHAw0tPTsWfPHowdOxYAZ5CpbpxBtnNRUVH417/+pTc5BrS/In/vvfekc87WULWvvvoKN2/eBAAsWLBAJzkGAJVKhXnz5gEA0tPT+b1DRvvggw/qTI4BoGfPnoiJiQEAnDlzhvXI1GSzZ89Geno6Hn30UYwZM8ba4ZANYIJMCA4Olo6vXLlixUioOdm+fTsAoHPnznX+ViE8PFyqW9+2bZvFYiP7x7+XyFR+/fVXLFu2DK1atcLixYutHQ7ZCCbIhOzsbOm4VatWVoyEmpPjx48DAEJCQuoc4+rqirvuugsA8Pvvv1skLnIM/HuJTKGyshLPPvssNBoN3nvvPQQEBFg7JLIRTJAJP/zwg3Q8ePBgK0ZCzUVWVpZUXlHfgpbqz8+fPw8uaSBTqV746ePjgx49elg5GrJVCxcuxMmTJzFw4EA899xz1g6HbAgTZAeXl5eHd999FwDQqVMnhIaGWjkiag7y8vKk49tuu83gWH9/fwBAWVkZCgsLzRoXOYY1a9bg5MmTAIDY2FgolUorR0S2KDU1FfPmzYNSqcSnn35aax0FkSH8bnFgGo0GTzzxBK5duwYAWLJkCbsQEADtiu9q9fXGVqlU0jETZGqqM2fOIC4uDoD2h/aZM2daOSKyVVOmTEFpaSleeukl9O3b19rhkI1xsnYAdEtZWRkuXbrUpHv4+fnBz8/PqLEvvfQS9u3bBwCYPn06wsPDm/Rssh/yUom6OqAQmVpOTg7CwsJQWFgIFxcXbNy4kfXH1CirV69GQkICOnbsiLlz51o7HLJBTJCbkbNnz0oLnhprzpw5ePvtt+sdN3fuXCxfvhwAMGHCBHz88cdNei7ZFw8PD+m4pKTE4Fj55/LriBoiPz8fY8aMQVpaGpRKJTZu3Mg1EdQo2dnZmDFjBgBg2bJlaNGihZUjIlvEEgsHtHjxYimJHj16NL755hvW+JEO+W8h5N0E9MnJyQGg7WjBBJkao6ioCKGhoThx4gQEQcDnn3+OyMhIa4dFNur111/HjRs3EBERgbCwMGuHQzaKCXIz0rdvX4ii2KRXfbPHq1atwiuvvAIAGDp0KLZs2cK6Y6qlXbt28PT0BABcvHjR4NjqsqDu3buzHIMarLS0FGFhYTh8+DAAYOnSpdImIUSNUf131tatWyEIgt5XtXXr1knvcUc9kmOC7EC+/vprTJ06FQAwYMAA7Ny5E+7u7laOipqrfv36AQCOHDlS55iysjKpX/Ldd99tkbjIflRUVCAyMhIHDx4EAMyfPx/Tp0+3clRERKxBdhibN29GdHQ0qqqq0KdPH+zZs0eaISTSJywsDD/99BMuXryI3377Df379681Zvv27SgtLQUALvKkBqnuolO9Rfns2bMxa9YsK0dF9uCLL76ot6NO9XqfBx98EPPmzQMAdOzY0eyxke0QRHb2t3t79uxBeHg4ysvL0aNHDyQlJUm9a4nqkpeXhy5duuDmzZsYOXIk4uPjdfqIlpaWol+/fjh79iwCAwORkpLCch0yiiiKiImJwbp16wBoO+pwC2CypOoyi4kTJ7K0gvRiiYWdO3ToECZMmIDy8nK0a9cOW7duhbu7OwoLC/W+6utYQI7Dz89Pqmk/cOAAwsPD8dtvvyEvLw9JSUkYMWIEzp49CwD46KOPmByT0eLi4qTkOCoqCu+8806dfycVFhaisrLSyhETkaPhDLKdi46Olv4hMsawYcOQmJhovoDI5sTFxWHZsmV6P1MoFFiwYAFeffVVC0dFtqyhiznXrFmD6Oho8wRDDokzyFQfziATkUFLly7Fnj17EBYWhjZt2sDFxQXt27fH448/jsOHDzM5JiIiu8MZZCIiIiIiGc4gExERERHJMEEmIiIiIpJhgkxEREREJMMEmYiIiIhIhgkyEREREZEME2QiIiIiIhkmyEREREREMkyQiYiIiIhkmCATEREREckwQSYiIiIikmGCTEREREQkwwSZiGza22+/DUEQIAgC7rvvPmuHY3YFBQVo06YNBEFAu3btUFJSUu812dnZmDNnDkJCQuDr6wsnJye9f2YPPPAABEGAUqnEiRMnzPY1BAYGSs+v+YqIiDDbc60pMTGxzq9ZEASsXbvW2iESkYyTtQMgIiLjzZ07F9nZ2QCAOXPmQKVSGRx/6NAhRERE4Nq1a/Xe+91338Xu3btRVVWFF198ET/99JNJYiYisjWcQSYishHp6elYtmwZAKBTp0545plnDI4vKChAZGSkTnLs7u6OwMBABAUFISgoCO3atZM+69u3rzSDm5SUhG3btpn+i6jB399fiiUoKAgBAQF1jk1PT9eZdX377bcb/Ly1a9fq3CMxMbHxwTeASqXS+TqDgoIs8lwiahzOIBMR2Yh58+ahoqICAPDKK6/AycnwX+Hr169HTk4OAEAQBKxatQoxMTFQKpV1XjNjxgxs2bIFgHaGOiwsDIIgmOgrqG3BggWIjo422/2bi4EDByI1NVXnPXP+uRJR03AGmYjIBmRkZGD9+vUAAE9PT0yaNKneaw4ePCgdjxo1CpMnTzaYHANASEgIBg4cCAA4efIktm/f3oSoiYhsExNkIiIbsGzZMlRWVgIAHn/8cbRo0aLeay5evCgd33nnnUY/S558L1mypAFREhHZBybIRETNXGlpKVavXi2dG1uSUFBQIB27u7sb/bxHH30Urq6uAICEhAScOXPG6GuJiOwBa5CJyGGp1WokJSUhKysLarUavr6+6NixI4YNG1ZvdwhD/vrrL/z888/IysqCSqVChw4dMGzYMHh7ezfqfjt27IBarQYAtG3bFoMGDTLqOo1G06jntWzZEvfffz927twJAPjqq6/w/vvvN+peRES2iDPIRORwUlJSEBERgdatWyM8PBzTpk3DG2+8gdjYWIwbNw6+vr6IiYnB33//3aD7pqamYsyYMQgMDMSTTz6JmTNnIi4uDg899BDatm2LmJgY5OfnAwDuu+8+o7sxfPvtt9Lx+PHjDS7ukndoyMjIkN6fO3durd67gYGBdd4nPDxc7/MdhaGexfW9HGHRIZG9Y4JMRA5l/fr16N27N7Zt2ybV9NZUUlKCtWvXomfPnkhISDDqvgkJCbjzzjsRHx8PURRrfV5aWoq1a9diwIAByMzMNDreiooK7Nu3TzofPny40dc2hXwDkYyMDCQnJ1vkuUREzQFLLIjIYXz33XeYOHGiTgI7bNgwjBs3Dn5+frhy5Qq2bt2K48ePA9DW8IaGhuLgwYMICQmp877JyckICwtDcXGx9F5wcDAmTJiAjh07Ij8/HwkJCdi9ezcuXLiAJ598Um8Src/Ro0dx8+ZN6XzIkCEGx8v762ZkZEg/BHh7e8PHx0dnbPv27eu8T7du3XDbbbdJm5LEx8fjjjvuMCpme9CQPsWVlZU6s/VEZPuYIBORQ/j777/x3HPPSYmpu7s7vvnmG4SFhemMe+utt/DJJ58gLi4OVVVVKC0txcSJE3Hy5Em9dcmiKCI2NhaFhYXSe/Pnz8fMmTN1SiFeeeUVHDlyBGFhYUhKSoJCYdwv8I4dOyYde3t7o0OHDgbHy3vtBgYGSolbXFxcgzfWqJ4RB7SJuiOp2bPYkEmTJuHLL7+Uzu+55x5zhEREFsQSCyJyCPPnz8eNGzek8/Xr19dKjqs9//zz+M9//iOdX7hwAZ999pnesXv27MEvv/winb/88suYNWuW3jrhQYMGYdu2bVAoFKiqqjIq7urZbADo1auXUdeYinzGWB4H3fLRRx/pJMfTpk3D5MmTrRgREZkCE2QisnvFxcX46quvpPOxY8diwoQJBq+ZMWMGunfvLp1/+umnesfJkyMfHx/MnTvX4H1DQkLw+OOPGxM2ACAtLU06rm/22NTkz0tPTzc6qbcUfQsP63vFxMSY7Pm7du3Ca6+9Jp2PHDmSfaOJ7AQTZCKye7/88ovUPQIAYmNj673GyclJZyYwJSVFJ1kFtOUV+/fvl84jIyPh6elZ770bkqTJF/QFBAQYfZ0ptG3bVjquqKjA1atXLfr85iw5ORmPPfaY9ENDt27d8P3339e7/TcR2Qb+n0xEdk9ex6tQKDBq1CijrgsNDdWZITx27JjO4q20tDSpPzGgXfBnjCFDhkCpVBrVp1h+fw8PD6Pubyo1n6dWq3WSZmvTt/CwPjdv3kROTk6TnpuXl4cHH3xQWjzp5eWFHTt2NLrPNRE1P0yQicjuXbhwQTru0qWLUds0A0CPHj3g4uKC8vLyWvcBgEuXLumc33777Ubd183NDZ07dzZqIVhJSYnOdZZUc1GivEtHc9CYhYdr165tUplFeXk5JkyYIP23VyqV+O9//4sePXo0+p5E1PywxIKI7J58cZ6/v7/R1zk5OenMUMrvA+jO7gJo0Ayil5eX0TFUq6tvs7lUVFTonDs7O1v0+c3R1KlT8fPPP0vnixcvxujRo60YERGZAxNkIrJ7RUVF0rG7u3uDrpWXGchbuQGQZparubi4GH1fV1dXo8bJZ7tLS0uNvr8pyGeva8biiD788EOdRZlTpkzBCy+8YMWIiMhcmCATkd2TJ7k1k776yJPrmjW5LVu21DmvmUAbIt/8wxD5wry8vDyj728KNZ/Xpk0biz6/Ofnxxx8xc+ZM6fy+++7D8uXLrRgREZkTE2Qisnvy0oeGLNDSaDS4fv263vsAgJ+fn8755cuXjb53VlaWUeM6derUqPubgjxGHx8fiy8SbC5Onz6NJ554QupYERQUhE2bNrHkhMiOMUEmIrvXtWtX6TgtLc3oxWYpKSkoKyuTzrt166bzeXBwsM6GIH/88YdR983MzMS1a9eMGivfrKMhu7uZgvx5jrTNtFxubq5Ox4qWLVtix44d8PX1tXJkRGROTJCJyO4NHDhQOq6qqsK+ffuMum737t113gcAPD09dXa327p1q1H3NXYcANx9993ScVpaWoNLRJri1KlT0nH//v0t9tzmorpjRXp6OgBtx4pvv/3W6G4lRGS7mCATkd0bMmSITteIuraNlqusrMQXX3whnffs2RNdunSpNU6+K94vv/yCQ4cOGbxveXk5Pv744/qD/se9994rHVdVVVlsy+fi4mKcOXNGbxyOIjY2Vue/58KFCzFu3DgrRkRElsIEmYjsnkqlwlNPPSWd7969G9u2bTN4zaJFi3Du3Dnp/LnnntM7LiYmRqdfcExMDHJzc+u8b1xcHC5evGhs6Gjbti169+4tnScmJhp9bVMcPnxYavPm5OSEESNGWOS5zcXChQuxdu1a6XzSpEl4+eWXrRcQEVkUE2Qicgivv/66ziK7qKgo7Ny5U+/YTz/9FK+//rp03q1bN0yZMkXv2LZt2+Ktt96SzlNTUzFgwABs2rRJpw3cH3/8gQcffBCfffYZVCqVzuK7+jz00EPScXx8vNHXNYW8DGXkyJG1OnbYs/j4eMyaNUs6v/fee7Fy5UorRkRElsad9IjIIQQEBODTTz/FY489BlEUUVRUhPHjx2P48OEYO3Ys/Pz8cOXKFWzduhW///67dJ2bmxvWrVtXa1c5uRkzZuDIkSPYvn07ACAjIwMPP/wwXFxc4O/vj4KCAhQUFEjjFy9ejG+++QYZGRkAtLWthkRFRWHevHkAtGUceXl5tTpomNrmzZul4yeffNKsz2puDh8+LHWsAIDz5883qO44MjISCxYsMEdoRGQhTJCJyGE88sgjKC0txeTJk6XygYSEBCQkJOgd7+npiW3btiEkJMTgfZ2cnPDdd9/h+eefx+rVq6X3y8vLdVqzOTs7Y8mSJYiNjdX59X19s7Pdu3fH0KFD8fPPP0Oj0WDTpk11lnyYwvHjx6UOFt7e3pgwYYLZnmULrl692qDx2dnZZoqEiCyFJRZE5FCefvpp/PnnnwgLC9PZxlnOzc0NEydOxLlz5zB8+HCj7uvq6oovvvgChw4dQkxMDIKCgqBSqeDt7Y0+ffpgxowZSE5OxtSpUwHoblvdqlWreu//4osvSsfyxYPmIE/yJ0+e3ODdB4mIbJ0giqJo7SCIiKxBrVYjMTERWVlZyM/Ph4+PDzp16oRhw4aZNSksLS2Fp6cnKisrAQBJSUkYOnSowWs0Gg1uv/12XLhwAQBw7NgxDBgwwOSxFRYWokOHDlCr1XB1dUVqairat29v0mcEBgZK5SVr1qxBdHS0Se9vK+Q9tB35z4GoOWKJBRE5LC8vL0RERFj8ufv375eSY0EQ0Ldv33qvUSqVeOutt/D0008D0HZZ+O6770we26pVq6BWqwEAzz77rMmTYyIiW8ASCyIiCxJFEQsXLpTOBw8eDE9PT6OujYqKQnBwMADtIrrz58+bNLaysjIsXrwYAODh4YE33njDpPfXJyYmBoIgSC9r/MBiCYmJiTpfp3z2mIiaHybIREQmcPToUaPGvf7660hKSpLOJ02aZPQzFAoFlixZAkBbcjF79uyGBVmPFStWSIsK33zzTQQEBJj0/kREtoIlFkREJhAeHo7OnTsjOjoao0aN0tl1r6SkBElJSfjggw9w8OBB6f277rqrwS3URowYgZUrV+Lq1atQKBQoKSkx2IKuIVxcXDBnzhwolUqzbooRGBhY5wJJe03KVSoVgoKC6vzckfpME9kCLtIjIjKBNm3a6LT3atGiBXx9fVFWVoZr165JNcfV/P39kZCQgF69elk6VCIiqgdnkImITMDZ2VnnvKioCEVFRXrHDh48GF999ZXBGUUiIrIeziATEZlAQUEBfvzxRyQkJODkyZPIyMhAfn4+NBoNvL29ERAQgKFDhyIiIgL333+/tcMlIiIDmCATEREREcmwiwURERERkQwTZCIiIiIiGSbIREREREQyTJCJiIiIiGSYIBMRERERyTBBJiIiIiKSYYJMRERERCTDBJmIiIiISIYJMhERERGRDBNkIiIiIiIZJshERERERDL/H1WKJN+gOGiiAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 720x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ex1.EIS_plot(legend='potential', bode='log_im')\n",
"\n",
"ex2.EIS_plot(legend='potential', bode='log_im')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This procedure makes data structure easy to acces and visualize. However, as the two datafiles are a part of the same dataset, a more productive method is to import the two datafiles in the same parameter (ex3) as illustrated below"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 720x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ex3 = EIS_exp(path='https://raw.githubusercontent.com/kbknudsen/PyEIS/master/Tutorials/data/', data=['ex1.mpt','ex2.mpt'])\n",
"\n",
"ex3.EIS_plot(legend='potential', bode='log_im')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data structure in ex3 is similar to the previous examples where ex3.df_raw contains all the imported while ex3.df[cycle_no] contains the data for each cycle number. However, as the cycle number in each datafile is 1 and 2, these have to be corrected so that the cycle number in the second file are reformatted and append on the last cycle number from file 1. The cycle number of the second file therefore becomes 3 and 4, as illustrated below"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1.0\n",
"1 1.0\n",
"2 1.0\n",
"3 1.0\n",
"4 1.0\n",
"Name: cycle_number, dtype: float64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ex3.df[0].cycle_number[0:5] # cycle 1 from first file"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"59 2.0\n",
"60 2.0\n",
"61 2.0\n",
"62 2.0\n",
"63 2.0\n",
"Name: cycle_number, dtype: float64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ex3.df[1].cycle_number[0:5] # cycle 2 from first file"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 3.0\n",
"1 3.0\n",
"2 3.0\n",
"3 3.0\n",
"4 3.0\n",
"Name: cycle_number, dtype: float64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ex3.df[2].cycle_number[0:5] # cycle 1 from second file"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"59 4.0\n",
"60 4.0\n",
"61 4.0\n",
"62 4.0\n",
"63 4.0\n",
"Name: cycle_number, dtype: float64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ex3.df[3].cycle_number[0:5] # cycle 2 from second file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Extracting specific cycles\n",
"Specific cycles can also be extracted from the the dataframe. In the above example, each file contains two impedance spectra from the same applied potential. If we're interested in only using the second cycle from each data file, we can extract these using the build-in cycle parameter in the EIS_exp() function. cycle, which is set equal to [2,4] representing the desired cycle numbers."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 720x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ex4 = EIS_exp(path='https://raw.githubusercontent.com/kbknudsen/PyEIS/master/Tutorials/data/', data=['ex1.mpt','ex2.mpt'], cycle=[2,4])\n",
"\n",
"ex4.EIS_plot(legend='potential', bode='log_im')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Linear Kramers-Kronig test and limiting dataset based on frequency \n",
"The linear Kramers-Kronig analysis (Lin-KK) is an investigation of the requirement of casuality, which dictate that the real and imaginary parts of any impedance function to be interdependent provided that the following conditions are meet: a) causality, the response must be related to the excitation signal, b) linearity, only the first-order term is present in the response signal (requires small excitation signals), c) stability, the system may not drift during the measurment, and d) finite. The Lin-KK analysis is a mathematical analysis of the quality of the collected data and the relative residuals should be free of harmonics. The Lin-KK analysis incorporated in PyEIS is based on Boukamp orginal work [2] with the later optimization for number of RC-elements by Schönleber et al. [3]. The Lin-KK analysis is performed by fitting a number of RC-elements to the experimental data through a weighed complex non-linear least squares fitting procedure (CNLS). The fit is performed utilizing the impedance terms for an RC-element given by eq. 1-3.\n",
"\n",
"### $Z_{fit}(\\omega) = R_s + \\sum\\limits_{k=1}^M \\frac{R_k}{1+j\\omega \\tau_k}$ (1)\n",
"\n",
"where k is specific RC-element and M the total number of RC-element, $\\tau$ the time contant of the RC-element given by eq. 2-4.\n",
"\n",
"### $\\tau_{min} = \\frac{1}{\\omega_{max}}$ (2)\n",
"\n",
"### $\\tau_{max} = \\frac{1}{\\omega_{min}}$ (3)\n",
"\n",
"### $\\tau_k = 10^{\\Big[log(t_{min} + \\frac{k-1}{M-1} \\cdot \\big(\\frac{\\tau_{max}}{\\tau_{min}}\\big) \\Big]}$ (4)\n",
"\n",
"$\\tau$ is thus equially distributed between the mininum and maximum frequencies of the experimental dataset, leaving $R_k$ to be the only unknown in the fit. The CNLS fitting procedure is described in detail in the next section, here the fit is performed for R$_k$ and the residuals is determined using eq. 5-6.\n",
"\n",
"### $\\Delta Z'(\\omega) = \\frac{Z'(\\omega) - Z'_{fit}}{Z(\\omega)}$ (5)\n",
"\n",
"### $\\Delta Z''(\\omega) = \\frac{Z''(\\omega) - Z''_{fit}}{Z(\\omega)}$ (6)\n",
"\n",
"Traditionally, 7 RC-elements are applied per decade following Boukamps [2] orginal suggestion, however under- and over-fitting can lead to incorrect analysis and recently Schönleber et al. [3] utilized the oscallating R$_k$'s as a descriptor for goodness of fit through the parameter $\\mu$. The $\\mu$-value is normalized between 1 and 0 and a $\\mu$ value equal to 1 suggest underfitting as the mass of negatively signed R$_k$'s is small compared to the mass of positively signed elements. Overfitting starts, when the mass of the negatively signed R$_k$'s starts to increase and $\\mu$ goes to zero following eq. 7.\n",
"\n",
"### $\\mu = \\frac{\\sum\\limits_{R_k<0} |R_k|}{\\sum\\limits_{R_k \\geq 0} |R_k|}$ (7)\n",
"\n",
"This Lin_KK procedure is incorported in PyEIS and set to default. The linear Kramers-Kroning analysis is performed in PyEIS by using the build-in function Lin_KK(). The analysis prints out the cycle number, the number of RC-circuits fitted to the experimental data based on the above-mentioned algorithm, and u - the $\\mu$-value of the fit."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cycle || No. RC-elements || u\n",
"[1] 9 0.82\n",
"[2] 9 0.78\n"
]
},
{
"ename": "AttributeError",
"evalue": "'Series' object has no attribute 'real'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_343009/1823215089.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mex4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLin_KK\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'potential'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/.conda/envs/EIS/lib/python3.10/site-packages/PyEIS/PyEIS.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, num_RC, legend, plot, bode, nyq_xlim, nyq_ylim, weight_func, savefig)\u001b[0m\n\u001b[1;32m 2635\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_circuit_fit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKK_RC80\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLin_KK_Fit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Rs'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mR_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_R\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt_const\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2636\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2637\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'RC simulation circuit not defined'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2638\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' Number of RC = '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber_RC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2639\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_rr_re\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresidual_real\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mre\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mre\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_re\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_circuit_fit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_im\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_circuit_fit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#relative residuals for the real part\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2640\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_rr_im\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresidual_imag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_re\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_circuit_fit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_im\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_circuit_fit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#relative residuals for the imag part\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2641\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2642\u001b[0m \u001b[0;31m### Plotting Linear_kk results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.conda/envs/EIS/lib/python3.10/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 6295\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6296\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6297\u001b[0m ):\n\u001b[1;32m 6298\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6299\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'real'"
]
}
],
"source": [
"ex4.Lin_KK(legend='potential')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Lin_KK() function can also be hardwired by using the build-in num_RC parameter in the Lin_KK() function. In the example below, the number of RC circuits is set to 4.3 RC-elements/decade"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cycle || u\n",
"[1] 0.17\n",
"[2] 0.15\n"
]
},
{
"ename": "AttributeError",
"evalue": "'Series' object has no attribute 'real'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_343009/3639649189.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mex4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLin_KK\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'potential'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_RC\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4.3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'number of RC-elements: [1] = '\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4.3\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mex4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecade\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m' || [2] ='\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4.3\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mex4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecade\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.conda/envs/EIS/lib/python3.10/site-packages/PyEIS/PyEIS.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, num_RC, legend, plot, bode, nyq_xlim, nyq_ylim, weight_func, savefig)\u001b[0m\n\u001b[1;32m 2635\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_circuit_fit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKK_RC80\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLin_KK_Fit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Rs'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mR_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_R\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt_const\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2636\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2637\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'RC simulation circuit not defined'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2638\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' Number of RC = '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber_RC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2639\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_rr_re\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresidual_real\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mre\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mre\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_re\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_circuit_fit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_im\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_circuit_fit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#relative residuals for the real part\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2640\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_rr_im\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresidual_imag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_re\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_circuit_fit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_im\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_circuit_fit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#relative residuals for the imag part\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2641\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2642\u001b[0m \u001b[0;31m### Plotting Linear_kk results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.conda/envs/EIS/lib/python3.10/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 6295\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6296\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6297\u001b[0m ):\n\u001b[1;32m 6298\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6299\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'real'"
]
}
],
"source": [
"ex4.Lin_KK(legend='potential', num_RC=4.3)\n",
"print()\n",
"print('number of RC-elements: [1] = ',int(4.3*ex4.decade[0]),' || [2] =', int(4.3*ex4.decade[1]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the above example, the data is fitted with 31 RC-elements and gives $\\mu$-values of 0.17 and 0.15 for cycle 1 and 2, respectively, clearly illustrating that the data has been overfitted.\n",
"\n",
"The relative residuals of the linear Kramers-Kronig analysis can also be illustrated with the experimental data using the build-in parameter plot, which can be set to 'w_data'"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cycle || No. RC-elements || u\n",
"[1] 9 0.82\n",
"[2] 9 0.78\n"
]
},
{
"ename": "AttributeError",
"evalue": "'Series' object has no attribute 'real'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_343009/3197524054.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mex4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLin_KK\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'potential'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'log_im'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'w_data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/.conda/envs/EIS/lib/python3.10/site-packages/PyEIS/PyEIS.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, num_RC, legend, plot, bode, nyq_xlim, nyq_ylim, weight_func, savefig)\u001b[0m\n\u001b[1;32m 2635\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_circuit_fit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKK_RC80\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLin_KK_Fit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Rs'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mR_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_R\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt_const\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2636\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2637\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'RC simulation circuit not defined'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2638\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' Number of RC = '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber_RC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2639\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_rr_re\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresidual_real\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mre\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mre\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_re\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_circuit_fit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_im\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_circuit_fit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#relative residuals for the real part\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2640\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_rr_im\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresidual_imag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_re\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_circuit_fit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_im\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKK_circuit_fit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#relative residuals for the imag part\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2641\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2642\u001b[0m \u001b[0;31m### Plotting Linear_kk results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.conda/envs/EIS/lib/python3.10/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 6295\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6296\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6297\u001b[0m ):\n\u001b[1;32m 6298\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6299\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'real'"
]
}
],
"source": [
"ex4.Lin_KK(legend='potential', bode='log_im', plot='w_data')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The linear Kramers-Kroning analysis of this specfic set of data (analyzed with a correct number of RC-elements) illustrates that the data contains harmonics suggesting that the ac amplitude was too high. For the purpose of demonstrating the functions of PyEIS, we continue with the dataset none the less.\n",
"\n",
"At high frequencies, the residuals dramaticly increase suggesting a poor data quality and based on this the data can be limited, using the mask parameter in the EIS_exp() function, as shown below where the data is masked between 10$^4$ and 10$^{-1}$"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 720x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ex5 = EIS_exp(path='https://raw.githubusercontent.com/kbknudsen/PyEIS/master/Tutorials/data/', data=['ex1.mpt','ex2.mpt'], cycle=[2,4], mask=[10**4, 10**-1])\n",
"\n",
"ex5.EIS_plot(legend='potential', bode='log_im')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Equivalent Circuit Fitting\n",
"The fitting algorithm in PyEIS relies on minimizing the weighed complex non-linear least squares between the experimental data and the real and imaginary parts of the equivalent circuit, as:\n",
"\n",
"## $S = \\sum{[ \\omega'_i \\cdot (Z'_i - Z'_{i,fit})^{2} + \\omega''_i \\cdot (Z''_i - Z''_{i,fit})^2 ]}$\n",
"\n",
"where $\\omega'_i$ and $\\omega''_i$ are the statistical weights of the data points and fits over all frequencies [4]. The minimization is carried out using Marquardt-Levenberg Algorith implemented through the lmfit python package [5]. Three weight functions are currently avalialbe: unity-, proportional-, and modulus weighing. As a rule of thumb the latter often, but not always, yields in the lowest relative residuals, i.e. the best fits.\n",
"\n",
"## Time constants of -(RQ)- and -(RC)- circuits\n",
"\n",
"For the purpose of fitting, introducing time contants ($\\tau$) into the circuit definations becomes very convinient as fitting bounds can be set to frequencies instead of capacitance or constant phase element values, this makes intially guess of capacitive values equally easy as reading resistances in a Nyquist plot. The time constant or relaxation time is characterized by the response of a first order linear time-invariant system. For -(RC)- systems this is given by\n",
"\n",
"#### $\\tau = R \\cdot C$\n",
"\n",
"The time constant is also given by the angular frequency ($\\omega$), which can be converted into the time domain as follows:\n",
"\n",
"#### $\\tau = \\frac{1}{\\omega} = 2 \\pi f_s$\n",
"\n",
"Where f$_s$ is the summit frequency for an -(RC)- curcuit. For distributed elements such as -(Q)-, the exponent n should be included\n",
"\n",
"#### $\\tau = \\frac{1}{\\omega} = (2 \\pi f_s)^n$\n",
"\n",
"Now R and Q, or C, can be isolated seperately as:\n",
"\n",
"#### $ R \\cdot Q = 1/(2 \\pi f_s)^n$\n",
"\n",
"Where\n",
"\n",
"### $R = \\frac{1}{(2 \\pi f_s)^n \\cdot Q}$\n",
"\n",
"and \n",
"\n",
"### $Q = \\frac{1}{(2 \\pi f_s)^n \\cdot R}$\n",
"\n",
"The impedance of an -(RQ)- circuit can therefore also be given by the following two equations and for a situation where Q is unknown, f$_s$ can instead be used as a fitting parameter:\n",
"\n",
"### $Z_{(RQ)} = \\frac{R}{1 + R \\cdot Q \\cdot (j\\cdot \\omega)^n} = [\\Omega]$\n",
"\n",
"### $Z_{(RQ)} = \\frac{R \\cdot (2 \\pi f_s)^n \\cdot R}{1 + R \\cdot (2 \\pi f_s)^n} = [\\Omega]$ \n",
"\n",
"In a simular fashion, this can be done for R and n. This allows PyEIS to fit or simulate any of the RQ containing circuit using three of the given parameters: R, Q, n, fs. Most applicable is the combinatin of R, n, and fs, as both R and fs can be guessed from the Nyquist and Bode plot, respectivley."
]
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"cell_type": "markdown",
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"source": [
"## The equivalent circuit overview\n",
"\n",
"In order to fit any experimental data to an equivalent circuit included in PyEIS, a \"fit string\" is needed for the EIS_fit() function. All avalaible circuit are illustrated in the equivalent circuit overview with a fit string as noted below\n",
"\n",
"### The Randles Circuit\n",
"The experimental data used in the above examples are as previously mentioned from a macrodisk electrode with a reduction occuring at the electrode/electrolyte interface. This system is well-defined and mass-transport to and from the electrode can be simplfied to a 1D linear situation as mass-transport from the side of the disk electrode can be neglected. The impedance of semi-infinite linear diffusion was solved by Warburg [6] with accurate initial and boundary condtions and the combination of a capacitor (or a constant-phase element) and a restance gives the Randles circuit, which represents the experiment well. The Randles circuit requires the following parameters, which can also be viewed in the description of the simulation function cir_Randles_simplified().\n",
"\n",
"<img src='https://raw.githubusercontent.com/kbknudsen/PyEIS/master/pyEIS_images/Randles_circuit.png' width=\"500\" />\n",
"\n",
"#### Parameters:\n",
"- Rs = Series resistance [ohm]\n",
"- Rct = Charge-transfer resistance [ohm]\n",
"- Q = Constant phase element used to model the double-layer capacitance [F]\n",
"- fs = Summit frequency of the RQ part [Hz]\n",
"- n = Expononent of the CPE [-]\n",
"- sigma = Warburg Constant [ohm/s^1/2]\n",
"\n",
"In order to fit the experimental data, a parameter space is needed for the EIS_fit() function with initial guesses for each parameter including lower and upper bounds. In the example below, we'll take use of the summit frequency instead of using the capacitance as this is more easily read of any Bode plot."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[Fit Statistics]]\n",
" # fitting method = leastsq\n",
" # function evals = 91\n",
" # data points = 80\n",
" # variables = 5\n",
" chi-square = 240.000451\n",
" reduced chi-square = 3.20000601\n",
" Akaike info crit = 97.8891333\n",
" Bayesian info crit = 109.799266\n",
"[[Variables]]\n",
" Rs: 2262.02939 +/- 10.5711484 (0.47%) (init = 2200)\n",
" R: 10915.6245 +/- 105.045803 (0.96%) (init = 17800)\n",
" fs: 4.34797097 +/- 0.07017951 (1.61%) (init = 3.162278)\n",
" n: 0.96665211 +/- 0.00465791 (0.48%) (init = 0.8)\n",
" sigma: 10551.9399 +/- 54.3850066 (0.52%) (init = 1000)\n",
"[[Correlations]] (unreported correlations are < 0.100)\n",
" C(R, fs) = -0.9257\n",
" C(R, n) = -0.8305\n",
" C(fs, n) = +0.7318\n",
" C(R, sigma) = -0.7022\n",
" C(fs, sigma) = +0.6779\n",
" C(n, sigma) = +0.4608\n",
" C(Rs, n) = +0.4510\n",
" C(Rs, R) = -0.3679\n",
" C(Rs, fs) = +0.2092\n",
" C(Rs, sigma) = +0.1443\n",
"None\n",
"[[Fit Statistics]]\n",
" # fitting method = leastsq\n",
" # function evals = 88\n",
" # data points = 80\n",
" # variables = 5\n",
" chi-square = 451.792414\n",
" reduced chi-square = 6.02389886\n",
" Akaike info crit = 148.495694\n",
" Bayesian info crit = 160.405828\n",
"[[Variables]]\n",
" Rs: 2258.76659 +/- 11.2560132 (0.50%) (init = 2200)\n",
" R: 8688.09060 +/- 130.663260 (1.50%) (init = 17800)\n",
" fs: 5.60880616 +/- 0.14948347 (2.67%) (init = 3.162278)\n",
" n: 0.94231695 +/- 0.00734633 (0.78%) (init = 0.8)\n",
" sigma: 9803.73019 +/- 61.5719468 (0.63%) (init = 1000)\n",
"[[Correlations]] (unreported correlations are < 0.100)\n",
" C(R, fs) = -0.9388\n",
" C(R, n) = -0.8818\n",
" C(fs, n) = +0.8185\n",
" C(R, sigma) = -0.6998\n",
" C(fs, sigma) = +0.6653\n",
" C(Rs, n) = +0.4945\n",
" C(n, sigma) = +0.4944\n",
" C(Rs, R) = -0.4058\n",
" C(Rs, fs) = +0.2450\n",
" C(Rs, sigma) = +0.1615\n",
"None\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/grokkingstuff/.conda/envs/EIS/lib/python3.10/site-packages/lmfit/minimizer.py:447: RuntimeWarning: ignoring `maxfev` argument to `Minimizer()`. Use `max_nfev` instead.\n",
" warnings.warn(maxeval_warning.format(maxnfev_alias, 'Minimizer'),\n"
]
}
],
"source": [
"params = Parameters() #creates the parameter space\n",
"\n",
"Rs_guess = 2200 #read of the Nyquist plot\n",
"params.add('Rs', value=Rs_guess, min=2000, max=2500)\n",
"\n",
"Rct_guess = 20000-Rs_guess #read of the Nyquist plot\n",
"params.add('R', value=Rct_guess, min=Rct_guess*.01, max=Rct_guess*10)\n",
"\n",
"fs_guess = 10**0.5 #read of the Bode plot\n",
"params.add('fs', value=fs_guess, min=1, max=10**4)\n",
"\n",
"n_guess = 0.8 #guess\n",
"params.add('n', value=n_guess, min=.7, max=1) #restricted to be within reasonable values for an interface.\n",
"\n",
"sigma_guess = 1000\n",
"params.add('sigma', value=sigma_guess, min=sigma_guess*.0001, max=sigma_guess*1000)\n",
"\n",
"ex5.EIS_fit(params=params, circuit='R-(Q(RW))', weight_func='modulus') #the fit string for a Randles circuit = 'R-(Q(RW))'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The fitting procedure outputs among other the $\\chi^2$, number of evaluations, and the value of each fitted parameter. Note that batch fitting automatically occured as the ex5 contained two EIS spectra. To plot the experimental data with the fitted Randles circuit, the EIS_plot() function is utilized where the parameter fitting is set to 'on'"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'Series' object has no attribute 'real'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_343009/2407746953.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mex5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEIS_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'potential'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'log'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfitting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'on'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/.conda/envs/EIS/lib/python3.10/site-packages/PyEIS/PyEIS.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, bode, fitting, rr, nyq_xlim, nyq_ylim, legend, savefig)\u001b[0m\n\u001b[1;32m 4697\u001b[0m \u001b[0;31m### Nyquist Plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4698\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4699\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mre\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'o'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'-'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel_cycleno\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4700\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfitting\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'on'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4701\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcircuit_fit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcircuit_fit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'o'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmew\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmfc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'none'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4702\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4703\u001b[0m \u001b[0;31m### Bode Plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4704\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbode\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'on'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.conda/envs/EIS/lib/python3.10/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 6295\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6296\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6297\u001b[0m ):\n\u001b[1;32m 6298\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6299\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'real'"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 720x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ex5.EIS_plot(legend='potential', bode='log', fitting='on')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This illustrates that a reasonable fit has been achived for both the kinetic and mass-transport regions. A quantitative analysis of the goodness of fit can be performed by turning on the parameter rr, which depicts the relative residuals between the fit and experimental data (not to be confused with the relative residuals of the Lin_KK() analysis)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'Series' object has no attribute 'real'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_343009/1709235814.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mex5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEIS_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'potential'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'im'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfitting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'on'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'on'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;32m~/.conda/envs/EIS/lib/python3.10/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 6295\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6296\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6297\u001b[0m ):\n\u001b[1;32m 6298\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6299\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'real'"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 720x960 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ex5.EIS_plot(legend='potential', bode='im', fitting='on', rr='on')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This gives the ability to quantitaively investigate the fit as a function of frequency.\n",
"\n",
"The fitted parameters that were given in the text output from the EIS_fit() function above are also saved in the function that was called (e.g. ex5), and can be found within this parameter as ex5.fit_\"name_of_parameter\". As shown below"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[np.float64(-1.0502528475), np.float64(-1.1004496875)]\n",
"[np.float64(2262.029390060915), np.float64(2258.7665903157013)]\n",
"[np.float64(10915.624546919738), np.float64(8688.090604500361)]\n",
"[np.float64(4.3479709652554215), np.float64(5.608806155510205)]\n",
"[np.float64(0.9666521071868985), np.float64(0.9423169544663608)]\n",
"[np.float64(10551.939918859394), np.float64(9803.730194054899)]\n"
]
}
],
"source": [
"print(ex5.fit_E)\n",
"print(ex5.fit_Rs)\n",
"print(ex5.fit_R)\n",
"print(ex5.fit_fs)\n",
"print(ex5.fit_n)\n",
"print(ex5.fit_sigma)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This gives the possiblity of directly analyzing the output parameters in the same notebook following an impedance analysis. For this purpose, the potential is also appended as ex5.fit_E as this makes it easy to for instance investigate the kinetics, as exemplified below"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'ln(R$_{CT}$) [$\\\\Omega$]')"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = figure(dpi=90, facecolor='w', edgecolor='k')\n",
"fig.subplots_adjust(left=0.1, right=0.95, hspace=0.5, bottom=0.1, top=0.95)\n",
"ax = fig.add_subplot(111)\n",
"\n",
"ax.plot(ex5.fit_E, np.log(ex5.fit_R), 'o--')\n",
"\n",
"ax.set_xlabel('E [V]')\n",
"ax.set_ylabel('ln(R$_{CT}$) [$\\Omega$]')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Refereneces\n",
"- [1] McKinney W., \"Data Structures for Statistical Computing in Python\" Proceedings of the 9th Python in Science Conference, (2010), 51-56 (2010)\n",
"- [2] Boukamp B.A., J. Electrochem. Soc., 142, (1995), 1885-1894, \"A Linear Kronig-Kramers Transform Test for Immitance Data Validation\"\n",
"- [3] Schönleber, M. et al., Electochimica Acta, 131, (2014), 20-27 \"A Method for Improving the Robustness of linear Kramers-Kronig Validity Tests\"\n",
"- [4] Lasia A., \"Electrochemical Impedance Spectroscopy and its Applications\". New York: Springer (2014)\n",
"- [5] Newville M., et al. \"LMFIT: Non-Linear Least-Square Minimization and Curve-Fitting for Python\" (2014) https://doi.org/10.5281/zenodo.11813\n",
"- [6] Warburg, E., Annalen der Physik und Chemie, 3, (1899), 493-499, \"Ueber das Verhalten sogenannter unpolarisirbarer Elektroden gegen Wechselstrom\""
]
},
{
"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": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:.conda-EIS]",
"language": "python",
"name": "conda-env-.conda-EIS-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.16"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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