{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "57538e97",
   "metadata": {},
   "source": [
    "# Data Imports\n",
    "## Import EIS Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "aa88f841",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sXRD_EISData found: /Users/leppin/Documents/SYNC/People/Carl/Minor_Revisions_ACS_Catalysis/Research_ Scharf_2025_Role of defects in reversible surface restructuring and activity of Co3O4 oxygen evolution electrocatalysts Kopie 2/EIS_sXRD/AllEISData_sXRD.parquet\n",
      "CorrelationMatrices3Ladder found: /Users/leppin/Documents/SYNC/People/Carl/Minor_Revisions_ACS_Catalysis/Research_ Scharf_2025_Role of defects in reversible surface restructuring and activity of Co3O4 oxygen evolution electrocatalysts Kopie 2/EIS_sXRD/CorrelationMatrices3Ladder.parquet\n",
      "CorrelationMatrices2Ladder found: /Users/leppin/Documents/SYNC/People/Carl/Minor_Revisions_ACS_Catalysis/Research_ Scharf_2025_Role of defects in reversible surface restructuring and activity of Co3O4 oxygen evolution electrocatalysts Kopie 2/EIS_sXRD/CorrelationMatrices2Ladder.parquet\n",
      "sXRD_EISParams found: /Users/leppin/Documents/SYNC/People/Carl/Minor_Revisions_ACS_Catalysis/Research_ Scharf_2025_Role of defects in reversible surface restructuring and activity of Co3O4 oxygen evolution electrocatalysts Kopie 2/EIS_sXRD/AllEISParams_sXRD.parquet\n",
      "CorrelationMatrices1Ladder found: /Users/leppin/Documents/SYNC/People/Carl/Minor_Revisions_ACS_Catalysis/Research_ Scharf_2025_Role of defects in reversible surface restructuring and activity of Co3O4 oxygen evolution electrocatalysts Kopie 2/EIS_sXRD/CorrelationMatrices1Ladder.parquet\n",
      "RDE_EISParams found: /Users/leppin/Documents/SYNC/People/Carl/Minor_Revisions_ACS_Catalysis/Research_ Scharf_2025_Role of defects in reversible surface restructuring and activity of Co3O4 oxygen evolution electrocatalysts Kopie 2/EIS_RDE/AllEISParamsRDE.parquet\n",
      "RDE_EISData found: /Users/leppin/Documents/SYNC/People/Carl/Minor_Revisions_ACS_Catalysis/Research_ Scharf_2025_Role of defects in reversible surface restructuring and activity of Co3O4 oxygen evolution electrocatalysts Kopie 2/EIS_RDE/AllEISDataRDE.parquet\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "# get working directory\n",
    "cwd = os.getcwd()\n",
    "\n",
    "# navigate one level up\n",
    "root = os.path.dirname(cwd)\n",
    "\n",
    "for dirpath, dirnames, filenames in os.walk(root):\n",
    "    for filename in filenames:\n",
    "        dir = os.path.join(dirpath, filename)\n",
    "        if filename == 'AllEISData_sXRD.parquet':\n",
    "            sXRD_EISData = pd.read_parquet(dir)\n",
    "            print(f'sXRD_EISData found: {dir}')\n",
    "        elif filename == 'AllEISDataRDE.parquet':\n",
    "            RDE_EISData = pd.read_parquet(dir)\n",
    "            print(f'RDE_EISData found: {dir}')\n",
    "        elif filename == 'AllEISParams_sXRD.parquet':\n",
    "            sXRD_EISParams = pd.read_parquet(dir)\n",
    "            print(f'sXRD_EISParams found: {dir}')\n",
    "        elif filename == 'AllEISParamsRDE.parquet':\n",
    "            RDE_EISParams = pd.read_parquet(dir)\n",
    "            print(f'RDE_EISParams found: {dir}')\n",
    "        elif filename =='CorrelationMatrices3Ladder.parquet':\n",
    "            matrices_3ladder = pd.read_parquet(dir)\n",
    "            print(f'CorrelationMatrices3Ladder found: {dir}')\n",
    "        elif filename =='CorrelationMatrices2Ladder.parquet':\n",
    "            matrices_2ladder = pd.read_parquet(dir)\n",
    "            print(f'CorrelationMatrices2Ladder found: {dir}')\n",
    "        elif filename =='CorrelationMatrices1Ladder.parquet': \n",
    "            matrices_1ladder = pd.read_parquet(dir)\n",
    "            print(f'CorrelationMatrices1Ladder found: {dir}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee77c255",
   "metadata": {},
   "source": [
    "## Import the Skinlayer Thickness\n",
    "Info matrix is needed to link EIS file names to sXRD file names. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "aa019664",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prep11_EIS_1\n",
      "Prep12_EIS_1\n",
      "Prep7_EIS_2\n",
      "Prep8_EIS_2\n",
      "carls_names:  [497. 541. 360. 399.]\n",
      "   scan  potential_RHE  d_skin_avg  d_skin_avg_err\n",
      "0   360           1.00     0.00000         0.13475\n",
      "1   360           1.02     0.00635         0.13476\n",
      "2   360           1.04     0.01521         0.13476\n",
      "3   360           1.06     0.02274         0.13474\n",
      "4   360           1.08     0.02215         0.13469\n",
      "---497.0---\n",
      "---541.0---\n",
      "---360.0---\n",
      "---399.0---\n"
     ]
    },
    {
     "data": {
      "image/png": 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HgFtvvfU4j04IcaIqegH7szYFL5i2yHTc8RhzygtMY6ZOc3L+WnQ3CPFDRUsiSsshjhVCnHikraMQVdB1HU3TmPpwOXimSyklbR2FEFXzgpBdY0WGbJd0xMCcqFsv+QF7x20UYGiwsiaBZcz9gXgQKrKOR10swtr6JJF5jl3K5PlKiLnJDLsQVfjKV76y2EMQQpxEJncyHbFdEuaBsB6Eiu5cqbIhUns6NmdYV0rhTuxkmoqYdGZiJ2xYF0LMTwK7EFX48Ic/vNhDEEKcJModYUoM2S6WoVcWhyql6MmX8MJyXG9KREhas79MB0pRcH00oCERpT0VJSN160KctCSwCyGEEMfRkO3SX3AIFSQtfdr5Ba9cUpeKGNTPsuGRUgonCLH9gKRp0pSM0JKMYerSvlGIk5kEdiGEEOI4yToePbkSTjB9J9O861d2MZ3cwfTgdTJBqMh7PjoajfEo7ekY6Yi8jAtxKpBiNyGq9LOf/Yx3vvOdNDc3Y1kWhmHMOJmmvHgKIWZn+wFdWZu8G5COHAjrbhDSky8B5RfljlQc46AZ85IfkHN84qbBskyctXVJCetCnELk0S5EFb7//e9zww03EAQBy5cvZ/369RLOhRBVC0LF/qzNuOOTihiVQD55/kTZOq2pmRseOUGI44c0JiO0p2KkJKgLccqRR70QVbjrrruwLIvvf//7XHXVVYs9HCHECaa/4DBa8ogaeqXry+QiUzc8sJNpJjr9ZTkIFUXPpz4WYWVNQrrACHGKkke+EFXYtGkT73//+yWsCyEOW9bxGCg4BEoRmzJ7PlCcssjUMmiMR6ZdTylFzvVJRyyWZeIS1oU4hcmjX4gqpFIp6uvrF3sYQogTjBeE7M+VKHjT69bHSh6jpfIi06ih056evsi0HNaD8mVSBiPEKU8CuxBVuOKKK3j++ecXexhCiBOIUorunM14ySMZMdAnAnnRC+grOAAYmkZnOla5bFLJD9GA5mSUhrj0VxfiVCeBXYgqfP7zn2fnzp185jOfQSl16CsIIU55w7bHsO1h6FqlnMUNQrpzduWYjll2MvWCECcIqY9btKdmtncUQpx65DM2Iapw9913s2HDBu68806+/OUvc95551FbWzvjOE3TeOCBB47/AIUQS4rtB/TmSzhBQM3EDqSBUnTnSgSTHWGSURKWMe16gVLk3YDamMmyzMz2jkKIU5OmZLpQiEPS9eo+jNI0jSAIjvFoqrdhwwYANm/evMgjEeLUESrFztECg0WXVMTA1PWJ8pgS+YlFpvUxi+ZkdNr1lFJkHZ+4ZbCqNlEJ+qcKeb4SYm4ywy5EFXbv3r3YQxBCnCCmtnA0J97sD9teJawnLYOmRGTG9QpegGVotKaip1xYF0LMTwK7EFVYsWLFYg9BCHECyDk+/RMtHJMT5S5FL2DIdgGIGNqsdeklPyBUiuZElJZEdMbtCiFObbLoVAghhFgAbhCyP2dT8HxSEy0cg1DRmy8BoAHtqdiMunQvCCn5IXWxCJ2ZuCwyFULMIIFdiFl0d3cf9W309vYuwEiEECeCIFR0ZW3GHI+kZWJMhO7+goM3sZNpUyJCzDRmXC/vBtRETZbXxCslNEIIMZU8Mwgxi7Vr1/KJT3yCnp6ew7qeUopHH32UjRs38qUvfekYjU4IsZQopejJlxgquli6XmnhmHU8sq4PlOvW62LWjOvlXJ9UxGBZJk78oDAvhBCTJLALMYu//Mu/5Itf/CIrVqzgmmuu4Stf+Qo7duyY9dh8Ps+TTz7JX/3VX7Fs2TJuvPFGYrEYN95443EetRBiMQwUXQYKDmgQN8svq14QTtkcCdpS0Vl2MvWJmeVdTjOyyFQIMQ9p6yjEHPbv388999zDgw8+iG2XNzrJZDK0tLRQV1dHqVRieHiY3t5ewjBEKcV5553HX/7lX3LTTTct8ujLpE2aEMfWWMljz3gR2wvIRMt160op9mVtbD8EypsjpSPTezwUPB+loD0do0M2RwLk+UqI+UhgF+IQstksDz74ID/5yU947rnn6O/vr1wWiUQ4++yzectb3sK73/1uLrrookUc6UzyAijEsVP0fHaOFsm6PjVRE30idA8V3UpXmNqoSWsqNu16jh9Q8kNaklFW1iYq1zvVyfOVEHOTkhghDiGTyfDf/tt/4+GHH6a3txfHcejp6WF0dJRSqcSvfvUr/uEf/uGowrrjONx+++0sX76cTCbDRRddxPPPPz/rsbZtc/PNN5NOp1m+fDn/9//+3yO+XyHEkXGDkL3jNjnHJx0xKqHbntrCUddmbI7khSG2H1Ifj7AsE5ewLoSoivRhF+IwWZZFa2vrgt6m7/usXLmSZ599ls7OTr797W9z7bXXsmfPHlKp1LRj77zzToaGhuju7mbLli1cc801nH/++axfv37W2w6CYM4Zq6amJpqbmxf0dxHiZDe1I0x8YidTKO9w2jPRwhHK5S5TA3mgFHknoCZmsjwTxzJOzTmzgYEBBgcHZ5zvOA6mKbFEiNlISYwQS1R7ezuPP/44F1xwwbTz29raeOihh3jTm94EwG233caKFSu4++67Z9zGhg0bGBwcnPXFEcrh/6677lrwsQtxslJKsT9XoidXwtA1EhObIyml6Cs4jDvlrjBNiQgN8ci062Udn7hlsKo2cUrvZHrXXXfN+nwF5UmEgYGB4zwiIZa+U/PtvRBL3I4dOxgZGWHt2rXTzh8dHaWvr49zzjmnct7ZZ58tNZ9CHCeDkx1hONARBmC05FXCesI0qD+ohaPth5i6RmsyekqHdSHEkZHALsQSM1mj/slPfpKampppl+XzeQDS6XTlvEwmUzlfCHHsZB2P3nwJJwhJRYxKZ5e86zNQLNetm5pGW3p6C0cvCHGDct36wTXtQghRDSkWE2IJ8TyP9773vaxdu5ZPf/rTMy6frGfP5XJkMhmg3MXm4Dr3qerr63nqqadmvaypqWkBRi3Eyc/xA7qyJfLugfaN5fPDSt26BnRmYlhTditVSpH3fGqiFh0H1bSfqu644w7e+973zjj/+uuvlxp2IeYgjwwhlogwDLnlllvQNI2vfvWrs/Zlrquro7W1lVdffZVLL70UgE2bNlXaoc3GMIx5LxdCzG9ykem445GMGBi6Vjl/f84mnFgJ1paKETtot9K8FxA3DdpnuexU1dzcPOti92hUPn0QYi5SEiPEEvGxj32M3t5eHnrooXlnmW6++WY+85nPkMvlePHFF3n00Uf54Ac/eBxHKsSpQ010fhm2PaKGTmSis4tSiu5cCW8irTfGI2Si0x+3jl/eUK0xHqEuJnXrQogjJ4FdiCVg79693H///bz44os0NjaSSqVIpVI888wzfOMb35g2Q37PPfdQX19PW1sb7373u/mXf/mXOVs6CiGOzrBdXmSqgJh5IKz3FxyKfgBAOmLSEJ8eyINQUfR9amMR2tOyk6kQ4uhIW0chqjA6Okpvby9r1qyZ9rHtV77yFR555BGSySR/9md/xhvf+MZFHOVMsnOgEEcu7/rsGiuSn9jJdDJ0j9huZZFpzNBZXjN9A6SpLRzX1CVJR6T6tBryfCXE3ORZRIgq/M3f/A1f//rXp/UH/ud//mf+7M/+jMn3vI888gi//vWvOfPMMxdrmEKIBeIGIfuyNjnXIxOx5uwI05GZuZDU9kMMXaMlGZWwLoRYEFISI0QVnnvuOa644gri8XjlvH/8x3+ko6ODn//853z7298G4H//7/+9WEMUQiyQUE1ZZGqZlUWmbjC9I0zHQR1hALxwsoWjRYu0cBRCLBB56y9EFbq7u7niiisqP2/ZsoWuri4+//nPV3Ycfeihh/j5z3++WEMUQiwApRQ9uRLDtoulH1hkGkzscHqgI0yU+EFdX0KlKLg+6YhFZzouLRyFEAtGZtiFqIJt28RiscrPzz33HJqmceWVV1bOW7NmDd3d3YsxPCHEApicWe/Nl4N5fMoi0758CTcIAWiIW2QO2q1UKUXO9YmZBh1paeEohFhYMsMuRBU6OjrYunVr5ecf/ehHZDIZzj333Mp5o6Oj00pmhBAnjiBU7MsWGSy6KCA9ZSfTkZJHzi13hElaBo3xyIzrF7wAU9NoTcakhaMQYsFJYBeiCm9961v56le/yr/8y78Qi8V47LHHePe7340+pX51586dLFu2bBFHKYQ4En4Ysm/cZrDooGsaSetAWC+4PoMTi0wtXaM9NbNFY8kPCJWiNRmlNRWVFo5CiAUnJTFCVOGTn/wkqVSKj3/843z0ox8lFotx1113VS7PZrM8++yzXHLJJYs3SCHEYXODkN1jRfqLDoauk5gS1mcsMk3HKgtQp16/5IfUxyN0ZqRuXQhxbMgMuxBVWLVqFZs3b+bhhx8G4LrrrmP58uWVy19//XU+9rGPyY6jQpxAHD9gz7jNSMklYujTFpGGEzuZBhOLTFtT0Rl16UGoKHg+ddEIyzNxTF3mwIQQx4ZsnCTESUw2IhFidrYfsGesyGjJI2bq08K4UoregkPW8QGoi81s0RgqRdbxSEUsVtUmpN/6ApDnKyHmJs8wQhyF4eFhfv7zn5NIJLjyyisxDOkMIcRSV/B89o7ZjDkeCcuotG6cNOZ4lbAeN3WaE9MXmR7cEUbCuhDiWJPP74Sowr/+679y4YUXMjIyUjnvN7/5Daeffjrvec97eMc73sEll1xCoVBYxFEKIQ4l5/rsHisy6rgkZwnrRS+gvzCxk6mu0ZGeuch0akeYeukII4Q4DiSwC1GFb33rW2iaRn19feW8v/iLv2B0dJSPfOQjvOMd7+BXv/oVX/jCFxZxlEKI+YyVPHaPFRkv+aQjJtZBYT3v+nRlbWBikWkqNqMufbIjTFMiIh1hhBDHjQR2IaqwY8cOzjnnnMrPQ0ND/OxnP+MP//APuf/++3n88cf5vd/7PR588MFFHKUQYi4jtsve8SI51ycTNWcE8azjsT9XYnJRV2sqStyaXuLmhRMdYWLSEUYIcXxJYBeiCsPDwzQ3N1d+fu655wB417veVTnvsssuY+/evcd9bEKIuSmlGCw67MvaFDyfTMSc0ZpxrOTRk3eAAzPrNQftZBoqRd4NyERNltVIRxghxPElK2WEqEJ9fT1DQ0OVn3/2s5+h6/q0vuuaplEqlRZjeEKIWSil6C869OZK2H5IJmrNmBUftt3Kxkga0JmJkbTMGbeTc3ySpkFnOj6t/aMQQhwPMkUgRBXOOOMMHn/8cYaHhxkbG+Ob3/wmv/d7v0cmk6kcs2fPHlpbWxdxlEKISUopevIO3dkSJT8kEzWnhXWlFAMFpxLWdQ2WZ+IzwjqUF6KaukZLKkqtLDIVQiwCCexCVOHjH/84vb29dHZ2smzZMvr7+7njjjumHfPCCy9w7rnnLtIIhRBTDRRdevMl3HD2sN5fcBgpeQCYmsaKTHxGzTqUN1fyQ0VjIkLrQb3YhRDieJGSGCGqcN111/GFL3yBL37xiwB86EMf4uabb65c/vTTT5PP53n729++WEMUQkwot2Z08IJyWNcOCus9eYecW+6zbukayzLxGe0dAfwwpOiHNMbLi0ylI4wQYrHITqdCnMRk50BxqgmVYudogcGiSypiYk5ZYBoqRXeuRMELAIgaOssyM1s3Th477nhkIhZr6pIkZpl9FwtLnq+EmJvMsAshhDhpDBZdxkoeEUOfFtaDULE/Z2P7IQAxU2dZOj6jYwwc2Mk0MbGTqYR1IcRikxp2IQ7D448/zk033cS5557L2rVrK+e/9tpr/P3f/z3d3d2LODohTm2VUphQETcPvLz5Yci+7IGwnrAMlmfmDuvFiZ1MW5Ix6mSRqRBiCZAZdiGqoJTitttu4+tf/zoA8Xgc27Yrl9fV1fE3f/M3KKX4q7/6q8UaphCnrFApenI2edcnFTlQt+4GIV1ZGy8sV3+mIwZtqdismx4ppSh4AUopmhJR2clUCLFkyAy7EFW47777+NrXvsZHPvIRRkZG+B//439Mu7y1tZVLL72U73//+4s0QiFObUNFl9GDSmEcvzyzPhnWa6Im7fOE9fxEbXtLMsqKmoTsZCqEWDIksAtRhQceeIBzzz2XL33pS9TU1Mw667Zu3Tp27969CKMT4tRm+wF9B5XC2H7AvmwRfyKs18csWpOzz5hP1qwbGrSlYiyvScxaLiOEEItFArsQVdi2bRtvfetb5/14vLm5mcHBweM4KiFEuRSmNFEKY5R3HPYDusZtgokeaI3xCE2JyKyP31Apso6Ppeu0pWJ0pmefgRdCiMUkNexCVME0TUql0rzHdHd3k0qljtOIhBBQLoUZsd2JUhgdpRS9eYdw4vKWZHTOhaOTYT1mlsP6XDPwQgix2GSGXYgqnHnmmTz99NPMtW1BqVTiySefZOPGjcd5ZEKcumYrhRm2PZygHNcb4tacYT0IFVnHI27qdKbjEtaFEEuaBHYhqnDLLbewdetWPvGJTxCG4bTLgiDgz//8z+np6eG2225bnAEKcYqZqxRmyHaB8qZIjfHIrNcNQkXW9UlaJstrEjRLWBdCLHFSEiNEFT72sY/x2GOP8U//9E889NBDpNNpAN7znvfwwgsv0NPTw/XXX8+HPvShRR6pEKeGvoLD8CylMJPa5mjJ6IchOScgHTVZnolTK33WhRAnAJlhF6IKhmHwve99j09/+tM4jsP27dtRSvHd736XYrHIpz71KR566KHFHqYQp4Sxkkd/vkSg5i6FiZkzdyf1gpCc65OJmqyskbAuhDhxaGquolwhxKyUUmzfvp3h4WFqamo4/fTTMYyluXX5hg0bANi8efMij0SIhVHyA3aOFhhzfGqiJrqm4fgBu8fLG5lFDJ2VNfEZnV7cIKTgBdRGLVbWxElG5APmpUaer4SYmzxjCXGYNE1j/fr1iz0MIU45oVLsz9qMOz4py0DXtJmlMMnojLDu+AG2H1Ifs1hRkyBhLc032EIIMRcJ7EIIIZa8cjAvMTKxm6lllEthRkoepYlSmPqYRfygMF7yAxw/pCEeYUVNfNZSGSGEWOoksAtRpR07dvD//D//Dy+++CKjo6MEQTDjGE3T2Llz5yKMToiT25jjM1BwCJUiORHKHT9kqFjuChMxNBoT07vCFL0ALwhpTERYUZMgYsiyLSHEiUkCuxBVeP7557nyyiuxbRvTNGlpacE0Zz58ZEmIEAvP9gO6czZFP6QmaqJNlsIUSkw+4tqS03cotb0APwxpTkZZXhPH0iWsCyFOXPIMJkQVPvnJT+I4Dl/4whcoFot0dXWxe/fuWU9H4l//9V85//zzsSyLu+66a87j7rrrLizLIpVKVU5CnMyCsFy3nnXK/dYnQ/lIyaPkz14KE4QKJyiXwaysSUhYF0Kc8GSGXYgq/OpXv+I973kPH/3oR4/J7be1tXHXXXfx4IMPHvLYD3/4w9x///1V33YQBHN2XWhqaqK5ubnq2xLieKrUrdseUVOvBG/bCxicKIWx9OmlMEqpic2UTDrSMQxdNkRaagYGBhgcHJxxvuM4s35yKYSQwC5EVSKRCMuXLz9mt3/DDTcA8IMf/GDBb3tkZISzzjpr1svuvPPOeWf0hVgsSikGii79BQcFxCbqz4NQ0Z0vVY5rT00vhSn5IYau0ZyIkLDkJW4puu+++7j77rtnvaypqek4j0aIE4N8TihEFS655BJ+97vfLfYwAHj44YdpaGhg48aNfPe7313s4QhxTAwWXbpzNm4Qko4Ylbr1nnwJPyxXrjcnIjNKYUpBSF3MojkZXayhCyHEgpPALkQVPvvZz/KLX/yCr33ta4s6jve9731s3bqV/v5+/tf/+l/cdtttvPjii4s6JiEW2lDRoTtnU/JD0hOLTKFct17wyt2Z0hGDuik7lVZKYSyD9nRsRi92IYQ4kcnnhUJU4dFHH+Vtb3sbt912G/fffz8XXHABtbW1M47TNI1PfepTx2wcZ555ZuX7t7/97Xzwgx/k0Ucf5Y1vfOOc16mvr+epp56a9TL5+FksNcNFl/3ZErYfkpnYyRTKLRqn1q23JmOVIA9TSmGSUZJSCrOk3XHHHbz3ve+dcf71118vNexCzEEeGUJUYWqd9zPPPMMzzzwz63HHOrAfTNf1Q7aSNAyjsuW3EEvZiO3SlbMp+sG0sO6HId25ct26BjMWkwahwvFDGpMRWqQUZslrbm6edbF7NCr/dkLMRQK7EFWYa4Z6ofi+j+/7BEGA7/uUSiUsy8Iwpu/K+Nhjj3H55ZeTTqd5+umn+cY3vsETTzxxTMcmxPEwWnLZl7Upej6ZqFUJ60openIOwcQb05ZkdNpupZOlMMmIQUdKSmGEECcnCexCVOHyyy8/prf/mc98ZlrXhL/7u7/jK1/5CmvWrOGaa64hn88D8OCDD/LhD38Y3/dZtWoVX/ziF7nkkkuO6diEONbGSh77xm0KB4V1gCHbpeiX69YzEZOa6PSXrVIwpRQmIi9pQoiTk6Zka0YhDmnfvn1VtXV84oknuOaaa47DiKozWQozVx92IRbbuOOxd9wm5/pkIua0Upe867N/ohQmYuisrIlPC/NBqMg6Hk2JKGvqktJz/QQnz1dCzE26xAhRhWuuuYbx8fF5j/nJT37Cu9/97uM0IiFOfNl5wrrjh/Tmp9StH1TuopQi7/kkIybtskGSEOIkJ4FdiCrs3LmT6667Dtd1Z738mWee4YYbbpi1c4wQYqas47Fn3CbnzAzrbhCyL2sTTHz+25qKEjWnv1zZfoihabQko6SkFEYIcZKTwC5EFb785S/z7LPPcvPNN8+47IUXXuAP/uAPSCQS/OQnP1mE0QlxYsm5fnlm3fFJR+cK6+W03pSIUBO1pl3fDUK8IKQxLl1hhBCnBgnsQlThgx/8IJ/73Od4+OGH+cQnPlE5/ze/+Q3XXHMNlmXxX//1X9P6pAshZsq7PnvGimQnwro5Jax7QUhX1q7sZNoQt2iIR6ZdP1CKghtQE7XoyMSlK4wQ4pQgnyMKUaW//Mu/pKuri3/6p3+is7OTq666iquuugqlFD/84Q8599xzF3uIQixpeddnz3iRrOvNDOtheWbdmwjr9TGLxoPCulKKvFNu4diZiRE1ZM5JCHFqkC4xQhwGpRTvfve7eeyxx8hkMriuyw9/+EPe9KY3LfbQZiVdF8RSUXB9do8XGXc80hETUz8Qtv2JsO5OFK3XxSyaE5FpO5lO3gbAskyc1lTs+A1eHBfyfCXE3GR6QojDoGka//f//l8uuugiHMfh8ccfX7JhXYilouCVZ9bHHY/UQWE9CBVd2VIlrNdGzVnDuhuE+KGiQerWhRCnICmJEWIWq1evnvdy27bRNI0//MM/nHa+pmns3LnzWA5NiBNKyQ/YO2Yz5nikLBProLC+L2vjBCEANVGTlmR0RlgPQkXB86mPRejMxGdcLoQQJzsJ7ELMIgzDeUNBLBYjFotxcEWZVJgJcUB59rwc1pOWiWUcPLN+IKxnIiats4R1pRQ51ydlmXRm4kSkbl0IcQqSwC7ELPbs2bPYQxDihKaUYn/OZth2iZr6tKAdTFxWmgjr6YhBW2pmWAcoeAERQ6MtFSMt/daFEKcomaoQQgix4PqLDoNFB13TiJtG5fxQKfZnbWy/HNZTEYP2VGzWsG77AaFSNMYjNCUiMy4XQohThQR2IYQQC2qs5NGXc/ACRdI6OKyXDoR1y6BjjrBe8gNcP6QhLnXrQgghgV2IKnzmM5/Bsix6enpmvby7u5tIJMLnP//54zwyIZaWohfQlbUp+AHpqFkJ2qFS7M+VKPoBAEnLoD09d1h3/JCGRISVNYlpXWWEEOJUJM+CQlTh8ccf5y1veQvt7e2zXt7R0cFb3/pWHnnkkeM7MCGWkMmdSrOuTzpiVHYhDZWiO1ei6JXDesIy6EjHZt2l1PEDShMz6ytrEtMWqgohxKlKngmFqMLrr7/OmWeeOe8xZ555Jq+//vpxGpEQS0uoyl1fRmyXhKlXZsUnw3phMqybBp1zhfUgxJ4I6ytqE9IRRgghJsizoRBVsG2bRCIx7zGxWIxcLnecRiTE0qGUojdfYsh2MQ2N6MQi08nzJ8N63NTpzMwe1t0gxPYC6uMRVtbEiUpYF0KICnlGFKIKnZ2dvPDCC/Me88ILL9DR0XGcRiTE0jFku/QXHEKlSEzpCDNku+TcqWE9PmdYL3oBdTGrHNan3IYQQggJ7EJU5eqrr+bnP/853/rWt2a9/Jvf/CY/+9nPuOaaa47zyIRYXKMlj+5ciZIfkoocWGSadXyGbQ+AiK7RmY5jzBLWvSCk4AXUxixW1iaISVgXQogZNCVbMwpxSN3d3ZxzzjmMjY1x/fXXc/XVV9PR0UF3dzdPPPEEjz32GHV1dbz00kt0dnYu9nArNmzYAMDmzZsXeSTiZJRzfHaPF8m5PjVRszJ7XvID9o7bKEDXYEVNYtYSFy8IKbgHwnrCkrB+KpPnKyHmJtvGCVGFjo4OfvSjH/He976XRx55hEcffbRymVKKlStX8tBDDy2psC7EsVT0AvZmi+Qcn8yUsO6H5faNkzNB7anYnGE97/nUxiIS1oUQ4hAksAtRpTe84Q1s376dxx9/nBdeeIGxsTFqa2u56KKLuPbaa7Esa7GHKMRxUZ5BL5It+aSiBoZeDutKKbpzNn5YjutNiQipyMyXGS+cCOtRi1W1cQnrQghxCBLYhTgMlmVx4403cuONNy72UIRYFF4QsnfcZrTkkYgYWFM2NeovOJVdTDMRk/rYzDexfhiSd31qohYraxIkLHkZEkKIQ5FFp0IIIaoShIq92SIjtkvM1Kf1SR8teYw5PgAxQ6c1FZ2xi6kfhhP17harahIkZ5l9F0IIMZM8Wwoxi//4j/8A4F3vehfpdLryczVuvfXWYzUsIRZNqBT7skWGii6WoU/r5lL0AvoLDgCGps26i2klrEfKM+sS1oUQonrSJUaIWei6jqZpvPbaa5x22mmVn+ejlELTNIIgOE6jPDTpuiAWgheEdOdsBoouGkwL2+5EiUygFBqwPBMnflBNuh+qyuLUlbUJ0hLWxSzk+UqIucmzphCz+PKXv4ymabS1tQHwla98ZZFHJMTxp5Qi6/h050qMOx6Grk1bIOqHIV3ZclgHaElGZ4T1YGpYr5GwLoQQR0Jm2IU4icmMlThSfqjoK5QYLDgUvZBkxJhWsx4oRde4TSkoLzJtiFs0JaLTbkMpxbjjk7AMVtUmqIlKJyUxN3m+EmJuMtUhhBBimrxbnlUfK7kooCZmTqtJV0rRkytVwnpN1KQxHpl2G0op8m5A1NBpT8UkrJ9klFLYtk8QKAxDmzjp6LqGrs9fPiiEOHwS2IUQQgDlhaUDBYf+gkPe84mbBlFj+voNpRS9eYeCV16rkbQMWpMzO8JMtndsTkZpSkwP8+LEEYaK8XGHoSGbwcHixFebnp48IyM2YVheu6PrGpoGmlYO76apc/rpDdxyy5mL/SsIcVKQwC5ElX72s5/xD//wD7z44ouMjo4ShuGMYzRNw/f9RRidEEfHC8KJlo0eQajIRKzKhkhTDRZdsu5E+0ZTpyMdmxHW3SDEDUKakxHaUzMvF0tDLufy61/30d9fxPMCPC/E90NcN8BxAkqlgGLRI5t1KRY9ikWfYtHD80IsSyeRMNF1DaXKwV4pVfl+fNzBMOTfXYiFIoFdiCp8//vf54YbbiAIApYvX8769esxTXn4iJODG4TsGS+3bIwYOsmoMWvIHrZdRkoeABFdozMdn6V9o6Lg+dTHIizPJGYN/WJx9fUVeP75Hn75y1727s0yPu5w8D/3ZGmLYWgkEiaJhEVLS4JEwiQSOfTOtK+8MniMRi/EqUkShxBVuOuuu7Asi+9///tcddVViz0cIRaMG4TsGSsyPLEZ0tT+6lONOx6DRRcAU9PozMQxDwrjoVLkXY90xGJZJj5tkapYXEoptm8f5fnne/jtb/vZvz9HGEJnZ4rTTquVT0GEWOIksAtRhU2bNnHTTTdJWBcnFccvz6xXE9Z78+WNkXQNOjOxGWFcKUXOLde9d6ZjpKR946Lz/ZCBgSL79mV54YVeXnttmO7uHMmkxapVNaTTsrZAiBOFPKMKUYVUKkV9ff1iD0OIBeP4AXvGbYZtl7ipE50lrAehor/gVGrWNaAjHZs12Be9AFPTaEnGqItJR5jjzbY9+voK9PUV6esr0NubZ+/eLNmsSy7nMjJSorExztlnNxKNyku/ECcaedQKUYUrrriC559/frGHIcSCKE2E9RHbLXeCMWeWrhRcn96Cgx+Wt+owNGhLxUhaM182Sn6ArxStySitqZkdY8Sx47oBP/jBLp55Zj+5nEs+75HPu/i+IpEwSaUsamujrFqVwZASJSFOWPLoFaIKn//859m5cyef+cxnOBZ7jf3rv/4r559/PpZlcdddd815XBiG/Nmf/Rm1tbW0tLRw7733LvhYxMnN9gN2jxXLYd2aGdZDVZ5V78qVKmE9ObHx0WxlLm4QUvJDGuIROjMzF6GKY2dgoMAXvvAy3/72NnbuHCMIFE1N5Vn0N76xlbPOamTlyhoaG+MS1oU4wckMuxCzuP3222ect2HDBu68806+/OUvc95551FbWzvjGE3TeOCBBw77/tra2rjrrrt48MEH5z3uC1/4Ak8//TTbt29nfHyct7zlLZxzzjlcccUVc14nCII5dw5samqiubn5sMcrTkxFz2fPuM1YySNhGTPq0Et+QE++hBuUg7oGtCSj1ETNWWfNvSCk4AXUxyxWZBJYuoTC4+WllwZ46KFtvPrqII2NcdatO3EWjg4MDDA4OLOLjOM40n1LiDnII0OIWfz7v//7nJft2bOHPXv2zHrZkQb2G264AYAf/OAH8x73ta99jf/xP/4Hzc3NNDc380d/9Ef8x3/8x7yBfWRkhLPOOmvWy+688855Z/TFyUEpxbDt0ZMvkXd8EpGZYX3EdhmY6AIDEDd12lIzF5dO8kNF3vOpjVqsqEnMWlYjFp7nBfzgB7v58Y93s2dPltNOq6OmJrrYwzos9913H3ffffeslzU1NR3n0QhxYpDALsQsdu/evdhDmNWWLVs455xzKj+fffbZfO9731vEEYmlzg8VPfkSgwWHUhCSjprT2jEqpRiyXYZtr3JeYzxCQ9yac8Y2UIq865OOlMN6wjp0X25x9IaGinzzm1t5/vkebNvn3HObquqJLoQ48UlgF2IWK1asWOwhzCqfz5PJZCo/ZzIZ8vn8Io5ILGVFL2B/1mak5KJpGrUHlbYopRgsHtgMqdxfffYuMJNCpcg5PknLYHkmLu0bj4MwVLz88gDf/e4OXn55kIaGGGvXHt8SGKUUuZzL0JDN4KDN0JBNoeBVNlcqf9UrP4+M2ORy7qFvWAhRFXmmFaIK99xzD295y1t485vfPOcxzzzzDE899RSf/vSnj9k4UqkU2Wy28nM2myWVSs17nfr6ep566qlZL5OPn09OSilGSh49uRI515+1x7pSioGiy+hkWNc1lh9isyM1Edbjpk5HOk6ttG88psJQsWnTEE89tY9Nm4bo6cmzbl0dtbULUwITBCG9vQVKpQDfD/G8AM8Lp5wCxsYchoZshoZKuG5wWLc/MGDPev4dd9zBe9/73hnnX3/99VLDLsQc5JEhRBXuuusu7rrrrnkD+89//nPuvvvuYxrYzzzzTF599dVKWcymTZvYsGHDvNcxDOOQx4iTRxAqevMlBooOJT8gFTEx9ZmbHPUXHMaccn91S9cOuTPp5MZIEaNc294Ql7B+rISh4tVXB3nqqS42bRqitzdPY2Oc885rwlqA8iPb9vnNb/r51a/6yGaPbBY8EjGoqYmgVDn4B4GaOIWEocL3Q3R99k8AJtfgHCwaPbFq8YU4niSwC7FAPM9DP8IuGb7v4/s+QRDg+z6lUgnLsjCM6S/ON998M//4j//IVVddxfj4OF/60pf46le/uhDDFyeBkh/QlbUZsT0UkIlaM9osKqXoKziMTwnryzNxrHnCeqgUeTfA0DRaU1FaktJr/VgIQ8Urrwzy1FP72Lx5iJ6eAk1NCxfUh4dtXnihl5dfHsTzwqquk0pZNDbGK6empvLXdDoy7/+BV14ZZMOGhqMesxCiTAK7EAvkt7/9LY2NjUd03c985jPTuib83d/9HV/5yldYs2YN11xzTaVO/Y//+I/ZsWMH69atIxKJ8Nd//dfzdogRp46c47MvazPueEQNnZipzwhUSil6Cw7ZibAemZhZny+se0FI3guImzpNiShtqZiE9QUUhoqurizbt4/y6quDbN48TG9vgebmOBs3Hn1QV0qxZ0+WF17oZfv20WmXNTXFufDCNpqbE1iWjmnqWNaBkznL/6HDIf9PhFg4mjoWu8AIcRJ429veVvn+6aefZuXKlaxcuXLGcUEQ0NXVxd69e/nABz7A17/+9eM4yvlNlsLM1YddnPgmu7z05Erk3YBUxJg1gCul6M07ZN2JsG7oLM/EZpTLTD2+6AX4oSITNWlLxaiLzd05RlSvUHB5/fUxduwYZcuWYXp68gwPlygUPJqb43R2pjGPoE2mUoqxMYe+vsLEqUhvb2HG4s+1a2u56KI2Vq+uOWb/nq+8MshZZzVyzz1vqvo68nwlxNxkhl2IOTz99NOV7zVNm7P/uq7rNDQ08P73v192HhXHVTDRsnGg4OAEIZmoiTFL3bAXhHTnS5T8chlE1NBZlolPa+84lR+G5N2AiKHRkozSkYkTlZ0yj9rWrcM888x+tm0bZWjIZnjYRtM06uujLFuWOmSZyWxs2+MXv+ilqytLX18Rx5l9Yahpapx7bjMXXthKU1NiIX4dIcRxJIFdiDmE4YEaT13Xueuuu47pglIhDocbhHRlbYaKLgrm3I007/r05EuEE5+lxkydZen4rMFeKYXth7hBSCpi0paK0hg//BApZvrlL3v55jdfY9u2UZJJk4aGOGef3XhUfdT378/x8MPbGR+ffeFoQ0OM1tYkHR0pzj23iURCFgoLcaKSwC5EFb7yla+wcePGxR6GEEA5hHdlbUZLHhFDJz5HvfrUHusAdTGLpkRkxkJUONBf3dA1mhIROtNx4rIh0lFTSvHTn+7jP/9zB3v3ZjnnnEZisaN76VVK8cILvfzkJ/sIJ96JtbQkaG9P0daWpLU1SXNzgmhU/v2EOFlIYBeiCh/+8IcXewhC4AUhg0WXIdsh5/okLXPWVoxeENKTL2FPlMDoGrQmY2Sisz/lh0qRneiv3pqK0ZKMzhrqxeEJgpDHHtvJE0/soq+vyLnnNh71IlLb9nn00dfZtq28gFTXNa66agVvfGOrfBIixElMArsQQixxoVIM2y4DhXJQV0AmYs1a1lKYKIEJJkpgooZORzo2Z4/1qWG9MxOXEpgF4roBDz20jR//eA/5vMu55zZiHOU6gO7uPA8/vJ2xMQeA2too73nPOjo60gsxZCHEEiaBXQghliilFGOOz0DBIet4uGFI0jKxdG3WEphh22PIPlDPXBM1550tD5Vi3PFJWgad6TiNicgx/X1OFYWCy9e//hpPP92FUoqzzmo8qjdBSilefLGPH/94b6UEZv36Oq6/fi3xuLyMC3EqkEe6EEIsQXnXL+9GWvIo+SExS6fWmr2tYqDKu5vmJ7aO14DWVJSa6NyLDAOlyDkeSctkWSZOQ1zC+kIYHS3x1a9u4tlnu0kmLVauzBxVWN+zZ5yf/Ww/e/ZkgXIJzJVXLueii9rkkxAhTiES2IUQYomY7H0+WHQYLXnYfohlaNTEzDlnyd0gZH+uhBuU69UtXaMzHSNqzl0rHYSKrFsO68szceolrC+IXbvG+OY3t/LLX/bS3JygoyN1RLczudnRz37Wxd69ucr5NTUR3vOe0+jsXNwSGNcNKBZ9bNsjDEHXy61vdV1D08rfV7uTqhCiOhLYhRBikSmlyLvloD7ulIO6oWukI7P3VZ9UcH26p7RsTFoG7anYvNcph3WflGWyvCZOXUzC+tEKgpCnn+7i+9/fxbZtI6xcmTmiXudKKXbtKs+od3UdCOqmqfOGN7Tw5jd3EI8vfGtGpRRKlX+PMFQEgap89f2QYtGnWPQoFn08L8SydBIJk0TCQtc1fF+hVEgYHritmproUXfDEUIcII8mIapw++23c/bZZ/OJT3xisYciTiJKlcPzUNFl3CmXvphVBHWlFKMlj4HigXr1+omWjfOVSfihIuf6pCPlmfXamPTlPlpjYyUefng7zz3XzfBwibPOajzsunLPC3j99TF+8Yse9u/PV863rHJQv+SSdlKphX1jFQQhAwNFenoKBIFC1zUMQ5vx1TR14nGTlpYkDQ0xOjrSNDbGKyfL0gmCqSE/rPzc3CwbNAmxUCSwC1GFBx98UMK6WDBqYrHnYNEh6/iU/ICIoZOOmhiHqEsOlaIv75B1faBcr96WipKZp14dwPEDbC8kHZWwvlBee22Yb397G7/7XT/pdITzzmtCn+eN1lSjoyV27Bhlx44xdu8eJ5hs60M5qL/xja1cfHE7yeTC/ju5bkB3d57BQZvGxjjnn9/CGWc0EInoWJaBZelTTgbRqEFDQ4zGxjjJpHwaI8RikcAuRBVWrlzJwMDAYg9DnARsP6A3V2K05FEKykG9JmZV1fe84PkMFFyciXp1c6JePTZPvbpSioIXECpFQyJCZyZG0pKn/qPheQE//vEefvjDPezaNcbq1TU0NMTnvY5Sin37cmzbNsKOHWMMDdkzjolEdN74xjYuvrhtwXclzeddurpyFAoe7e0prrxyOZdc0sHGjc1E5+jPL4RYOuRRKkQVPvjBD/KFL3yB0dFR6urqFns44gTkh4rBosNg0SXv+pi6Rk20uqDuBCEDBYeCF1TOi5vl/uqmPndv72CiBCZq6DQnonRm5j9eHNroaIkHH3yNF17oIZ/3OOecpkPuKBoEId/73i5eemlwxmXptMW6dXWsW1fH6tU1RCJHvzupUgrb9snnPfJ5l/FxF13XWLYszXnnNXPxxe2sX19f9acBQojFJ4FdiCp88pOf5Ne//jVvfetb+cxnPsPv/d7v0dLSstjDEieAyXrz/kK5/CVUilTEqCo4+6FiqOgy5niV8zSgIR6hIT57i8dJjh9S9H3SEYu2VFQ2RFoAQ0NFHnhgE7/4RTf19THOOefQ/dXLGyht5/XXxwDQNOjsTLNuXS3r1tXR0pI46n8XzwsYHLQrAd33FfG4SSplkU5HWb68hvPPb+aii9ppbz+yzjVCiMUlgV2IKsRiMaAcvq6//vo5j9M0Dd/3j9ewxBJX9AL68pPlLyEJUydiGIcMaOFEyB+23UoHGChvhNSUiMwb9idLYIJQUR+LsCwTJxWRp/qj1d9f4P77X+WXv+yhszNd1YLKQsHjwQdfo6enAEB7e5L3v389mUx0wcY1Pu6wffsoLS0JmpribNjQwLJlGVpbk7S1JWltTdLQEJfZdCFOcPIsLkQVLrvsMpmdFFVTSjFku/TkShS8AEvXqI2aVQX1ccdn2HbxpyT1hGXQnIjMW6sO5c2Q8o6PZWg0paJ0puNYhpTAHK3e3jz33/8KL77Yx4oVGRob569Xh3LpzNe//hojIyUA1qyp4X3vW78gJS9Q/j/W1ZVjaMhm48YWbrhhLaedVkc6LZ+kCHEyksAuRBWefvrpxR6COEEEoaInX2Kg4OAEIamIiXmI2c3JGfVR28NXB4J6xNBpTkRIWoeelfeCkLwbkIwYtKViNB+ixaOozv79Oe6//xV+/es+Vq+uob7+0GG9t7fAN77xGoVCuZTpnHMaue66NRgL9ObJ8wJee22EeNzk939/JTfddPoR9X0XQpw4JLALIcQCcfyAfVmbEbsc1GoOMasehBNBveQypasfpq7RGI8c8vqTSn5AyQ+piZksy8SpOUSLR1GdPXvGeeCBV/ntb/tZu7aWurrYIa+za9cY3/rWdly3vED40kvbueKK5Qv25mmyBGblygxXXbWKa65ZtWCz9kKIpUsCuxCHqVAosH37dvL5PJdddtliD0csETnHpytrM+Z4RA2dmKnPGdL8UDFilxeTTq1Rj+gaDfEImSqD+tSWjY3xCMtq4sQPUTYjqrNz5xhf/vKrvPTSAKedVkdNzaHrzl95ZZBHH91JOPGP+va3r+Sii9oWZDxTS2DOO6+Z9753Peed17wgty2EWPoksAtRpf379/Pxj3+cxx9/nCAIpi0wffbZZ/noRz/Kfffdx1ve8pbFHag4rqbWq+c9n6RlEpmj9EEpxZjjMVicvpg0aug0xCOkI4cufZkUKkVuol693LIxLi0bF0Cp5LNp0xAPP7ydV18dZP36ukMuEvW8gCee2MPvflfeq8EwNN71rrVs2NC4IGPKZh12786SSJhceeVKPvABKYER4lQjgV2IKvT29nLhhRfS39/Pddddx8DAAM8//3zl8gsvvJCBgQG+9a1vSWA/hRxcr56JWBhz1KsXvYD+ieMmxc1yUK+mRn0qPwzJuQEJU6c1FaM1GZV69aNg2x7bto2yZcswr746SF9fgYGBImecUU8qNf/unv39BR5+eEdlI6R43OS97z2NVatqjnpc4+MOe/dmAVi1qoa3vGUZV18tJTBCnIoksAtRhbvvvpuBgQH+67/+i7e+9a3cfffd0wK7ZVlcdtllPPfcc4s4SnG8lGfKfQYKDmOl+evV/TBkoOiSdQ60+7R0jeZklNRhBnWlFEU/wA0UmYhJZyZOXUzq1Y9EseixdetIJaT39xcYGrKxLIOmpjjnndeEZc2/g+yvf93Pj360h2BiAcKKFRluvHHtUbdtHB932LMni6aVg/rFF7fz5jd30tycPKrbFUKcuCSwC1GFH/zgB1x33XW89a1vnfOY5cuX88wzzxzHUYnFUHB9+gtOube6HxK3dKLGzHr1yQ2Thqb0Up/c9Kg+Xt0Op1NvywlCbC8kZuq0JCza03ES8wRKMbdNmwb55je30dWVZXi4RCSi09SU4Nxz5w/pk2zb5/HHd/LaayNAeTOkyy/v5LLLOo+437lSqhLUDUNnzZpaLrmkHNQbG6X8RYhTnQR2IarQ39/PunXr5j3GsiwKhcJxGpE43pwgZKDgMGy7FCd6q9fEzFmDtxuEdOdK08pfUpZBczI6Z337XNwgpOgFmLpGfdyiJRmlLjb/LqdidmGoePLJfTz22Ovs3DnGsmVpzjuvCdOs/t+kqyvHd76znfFxF4BMJsKNN65jxYrMYY/H90NGR0uMjJQYH3dJpSzWravjkks6ePObO2loOHQLSSHEqUECuxBVqK+vp6ura95jtm/fTmtr63EakTheglAxaDsMFlzyro+maaQj5py16nnXpydfqsyqW7pGSzJ62LuN+qGi4PloQCZq0pyI0hCPzHm/Yn6O4/Od7+zgpz/dy+CgzTnnNBGNHt4nFJs2DfGf//l6pQvM+vV1XHfdGhKJ6sqSlFLYts/wcDmke15AbW2M5uYEl1zSwfr19bzhDS1V9XoXQpxaJLALUYVLL72Uxx57jL6+vllD+Y4dO/jhD3/IzTffvAijE8dK3vXZnyuRdTz8UJG0jDl3DlVKMVIqd4CZVB+zaExEDqv8xQ8Vth8QTNxfYyJCU+LwZ+bFAcPDNt/4xms891w3oDjvvKbDLl156aUBHntsJ0qVu8D8/u+v4I1vbK36k44gCHnttRGCQNHQEOfMMxs488wG1q2rY926uqp6vAshTl0S2IWowl/8xV/w6KOPcvnll/N//s//oVgsAuWe7D//+c/5xCc+ga7r/H/+P/+fRR6pWAhKKQaKLn35cqvGuGnM28klVIrefIncxGY5GtCWipGJVv8U64chRS8kVIqEZVCbMGlJxohLnfpR2blzjK9/fQu/+U0/TU1xOjtTh11O9Ktf9fGDH+wGwLJ0PvCB0w+rC4znBWzaNExzc4K3vW05F1zQwqpVNVXVywshBEhgF6IqF154If/2b//GH//xH/POd76zcn4mU65bNU2TL3/5y2zYsGGxhigWiBuE7M/aDNsufqjmbdU4efzUenVL1+hIx4hVuYGRF4QU/QAUJCyDuniExnhEFpQeJaUUv/xlLw89tI2tW0dYs6bmiEpNnn++hx//eC8A0ajBhz50BsuWpau+frmv+zArVqS54YZ1XHnlCll/IIQ4bBLYhajS7bffzmWXXcZ9993HCy+8wPDwMDU1NVx00UX86Z/+KevXr1/sIYqjNO54dGdLjLsepq4dcsfRg+vVk5ZBeypWVZ25G4TYXoCmQdIyqY9ZNCQislPpAhgdLfHEE7v5+c+72L8/z4YNDVXXmU/185/v56mnymtX4nGTm28+g/b2VNXXz+ddXntthNNOq+P97z+diy5qP+wxCCEESGAX4rCsW7eOe++9d7GHIRZYqBR9eYf+QomiF5KMGPPWjPuhYqjoMuZ4lfPqYxZNicghZ0+DUFHwAhSKVMSkPh6hMW4RlaB+1Hw/5LnnuvnRj/awdeswpqkfdhcYKM/OP/lkF88+2w1AImFy661n0tJSfR/0sbESO3aMcfbZjdx885mcdVbTYY1BCCGmksAuRBV++9vfcv755y/2MMQCU0qR9wL68yVGSh6BUnO2aoRysB8teQwf1Fu9LRUlE51/Blcphe2HOEFI0jJoiEdoOYI2j2J2O3aM8r3v7eSllwYYGSmxenXNES3kVErx4x/v5YUXegFIpy1uvXUDjY3Vl9MMDBTZty/HG97Qym23bWDVqtrDHocQQkwlgV2IKrzhDW/g937v9/jYxz7GTTfdRCIhG5mcyEKlGJsI3jnXp+SHRAx9zoWlSilybsBg0cGbTOqUa85bElGih5jBLfdS94kYOs2JCG2p2GG3eRSzGxsr8cMf7uHZZ/eze/c4ra1JNm5sPqwuMEop+voKbNlS3vl0ZKQEQE1NhFtv3UB9fXXBXylFd3eewUGbN72pg9tuO4vWVtmdVAhx9DSllDr0YUKc2q699lp++MMfEoYh6XSaW265hY9+9KOcffbZiz20eU0ugt28efMij2RpcIOQ0ZLHiF3uqe4GIRFTJ2YaGHPMqpf8gP6Cg+0f2AQpYmg0J6Lzdo6ByfIXH4B0pNz15XB3ORWzGxmx+d3vBnjyyX289lq5/GXNmhqiVXbmUUrR01Ngy5ZhXnttmNFRZ9rldXVRPvzhDdTURKu6Pd8P2bZtFF2HSy7p4MMf3kBtrbRqPBzyfCXE3CSwC1Gl/fv3c//99/PlL3+Z/fv3o2kaF110ER/72Md4//vfTzRa3Qv78SQvgGUlP2DIdhmxPWwvwFchcdMgaujztmocKDiMOX7lPF2DpniE2ip2Gi35AbYfVnqptySic/ZwF9VxHJ8tW4Z56aUBXn55kP7+Armcx5o11Ze/KKV4/vleXnyxj/FxZ8blHR0pzjyzgY0bm4nHqwv/2azDtm2jLFuW5q1vXc51160hHj/8Ra6nOnm+EmJuEtiFOExhGPL973+fL37xi5VZ99raWm699VY++tGPcsYZZyz2ECtO9RdApRTDtktv3iHv+qBB3DSwdG3ewF3yA3ryDm5wYFa9LmbRWMVOo0qVF5WGSlEfi9CRjpGU8pcjFoaKPXvGeemlAX772wH2788xOFgkmbRobU1QVxeruk2i4wQ88sjrbN06Mu38ZcvSnHlmA2ecUV/1jDqU/627unIMDtqcfXYTN964jgsuaJG2jUfoVH++EmI+EtiFOAr79+/ngQce4Atf+AIDAwMAXHbZZfzpn/4p73nPexZ5dKf2C2C5P7rNiO1RCkJSEQNLn3+GWynFmOMzUHCYfGKMmzptqVhVi0NDpcg5Ppah0RiP0JmJYx7iPsXctmwZ4gc/2M3rr4/S11dAKWhpSdDcnDjszi+joyW++c2tDAzYQLnk5cIL2zjjjHoymcP/dMx1A157bYREwuTCC9u56ab1NDdLvfrROJWfr4Q4FHklEeIobNmyhVdeeYXh4WGUUjQ0NPDMM8/w/ve/nwsuuIA9e/ZUfVuDg4P8wR/8AclkkvXr1/PTn/501uNuu+02otEoqVSKVColmzUdRE0sKN0xUqA37xAoRW3UPGRYD0JFT75E/5Sw3hiPsDwTryqse0HIuOMTtww6M3FW1CQkrB+hbNbhm9/cyr/8y+946ql9FIs+p51Wx8aNzbS3pw47rO/ePc6XvvRqJayvXVvLRz96Dhde2HZEYX1kpMTLLw/S0ZHippvO4I//+FwJ60KIY0o+pxXiMA0MDPDlL3+ZL33pS5VAfsUVV3DHHXdw3XXXsXfvXv7hH/6Bf/u3f+OOO+7gBz/4QVW3+yd/8ie0trYyODjIT37yE973vvexY8cO6uvrZxz7qU99ir/927+t6naDIJhzxqqpqYnm5uaqbudE4IeKvnyJgaJTqR+vJmzbXkB3voQ/0QHG1DXaU7Gqdxst+QElP6QmatKZjlMbk/rlI6GU4je/6efxx3eyefMQuq5x7rlNRCJH1qNeKcWLL/bxox/tYfKz5EsuaeeKK5YfVheZqbq6cgwN2bzhDa28733r2bCh8Yhu51Q2MDDA4ODgjPMdx8E0JZYIMRt5ZAhRpZ/+9Kf827/9G48++iie51FXV8ef/dmf8cd//MesXbu2ctyqVau47777cByHb3/721Xddj6f55FHHmHXrl0kEgmuu+46zj77bB599FE+8pGPHNW4R0ZGOOuss2a97M477+Suu+46qttfKvKuT3euxGjJBTRqonP3U58UKsWI7TFku5XzkpZBWyqGWUWgm1qv3hiPsKwmLjuVHqGhoSKPPbaT55/voacnz6pVNYfV+/xgvh/ygx/s5ne/K5eqGYbGddet4ZxzjnwDo717s2SzLldcsYJbbjnziPq8C7jvvvu4++67Z72sqUk2mBJiNhLYhajCunXr2LVrF0op3vCGN3DHHXdw0003EYvN/YK9bt06CoVCVbe/Y8cOUqkUnZ2dlfPOPvvsOWfG7733Xu69917Wr1/P5z73OS6//PLD+4VOIm4QMlBwGLJdCp5P3DSIHSI0exPtHcccb9oGSE2JCHVVdICZvN+iF1RaPEq9+pEJgvLupE88sZstW4ZJpSw2bmw+7LKXqUZGbB55ZCddXTmgvPnR+99/Oh0dqSO6PaUUu3dnsW2fK65Yzm23nUUiIZ+iCCGOHwnsQlShu7ub2267jTvuuIMLLrigqut86EMf4uKLL67q2Hw+TyaTmXZeJpNheHh4xrEf//jHuffee0kmkzz00ENcd911vPLKK6xYsaKq+zpZhBMdYPoLDjnXRwMyEWveLi62FzBS8si5/rTzI0a5BOZQQR+m91bPRE2aE1GaEhHpDHKYSiWf3/62n2ef7WbLlmGGh23Wrq09rC4tB/O8gGef7ea553oIgvI7sc7OFO9733rS6cgR3aZSip07x/A8xe//fnlmXVo2CiGONwnsQlShp6eH2traw7rOsmXLWLZsWVXHplIpstnstPOy2Syp1MwZwY0bN1a+/9CHPsTXvvY1fvzjH/NHf/RHs952fX09Tz311KyXnYgfP5d3HfXpyzuMOx5uqOatVZ88fqTkUZqy+RGUO8DUxyKkIvNvgATlNwhFL8CfuL+GRITmRLSqGnlxwPi4wwsv9PCLX/Swa9cYw8MlWlsTh7076VRKKbZvH+WHP9zD2NiB3uoXXNDC1VevPOLZ+snb1TSNt799JR/60BlVb8wk5nbHHXfw3ve+d8b5119/vdSwCzEHeWQIUYXDDeuHa926deTzebq7u+no6ABg06ZN3HrrrYe8rq7rzNed1TCMk6aTTMkP6MuXGCl52H5AzDCojc4dtt0gpDdfmrZLKUAmYlIft6qaUVdKUfJDSkFA3DRojEdoPYwFqaKspyfPL37RzYsv9rF37zjFok9HR4pVq2qOOKhDuWPLD3+4mx07xirntbUlecc7VtHZmT7i21VKsXXrCJZl8Pa3r+Smm07Hkn/zBdHc3DzrYveluPmcEEuFBHYhDtP+/fvp7u7GcWbukgjw5je/+bBvM5VKcf3113PnnXfyz//8z/z0pz/llVde4frrr59x7He+8x2uvvpqotEo3/nOd3jmmWf4l3/5l8O+zxPJ5AZIPRMbIJm6Rk3UmnNRqVKK8Yl+6pNR3dCgNmZRG7MO2eJxkh+G5F0fS9dpjEdpTUbJRE0pf5mH6wYMD9sTpxLDwzb9/UW2bBmmqyuLpmksW5Y6rA2P5rqf557r4bnnuivlL7GYwdvetpwLLmg5qjcBYagqPdavvnoV733v+qOqqRdCiKMlgV2IKv34xz/mE5/4BFu3bp33uCAIjuj277vvPj784Q/T0NBAZ2cn3/rWt6ivr+cb3/gGn/3sZysLUO+9915uv/12AE4//XQeeeQRVq9efUT3eSJQStFbcOjLlyj55Q2Q5lvc6YeKvkKJvHvg36E2atGcjByya8zU+yz5IU4Qko5YtKaiNMSrv/6pZHzcYcuWYXbsGKW3N8/AQJFi0ce2vYmvPkGgSKct1q6tJZU6slpyKP+77NmT5eWXB9myZRjPO/DJyXnnNXHllStIJo+8vty2PQYHbQYGbJqa4rzjHau58cZ1GFL2JIRYZLLTqRBVeOGFF3jzm99MU1MT73nPe/jnf/5nLr/8ctavX88zzzzDa6+9xnXXXcfGjRu58847F3u4FSf6zoFBqOjO2QwUHbxQkY7M36ox7/qVzZIADE2jLRUlFal+biJUirzro2ka9TGLznScuJRCTDMZ0jdtKgfngYEiY2MOuq6RSJjE4yaJhEU8Xv7+aGenyxsVDfDyy0OMj0//ZKu1NcE73rGaZcuOrPylWCyH9KEhG8PQaGpK0NmZ5uKL27nyyhVHNVMvDs+J/nwlxLEkM+xCVOFzn/scsViMX/3qV7S3t/PP//zPvPWtb+XTn/40SinuvPNO/vf//t/83d/93WIP9aThBSH7sjZDRRe0ct35XCUUoVIMFFzGHK9yXipi0Jqsrp/61PvMewEJU6c5GaU1GZu368zJLAwVrhtQKvk4TvlrT09hWkgfHXVIpSyamxOsXl2zoDPRSik2bRrm17/uY9++3LTLDEPjtNPqOPfcJtatqzvsUB2Giv37cwwMFLEsg6amOG98YxvnnNPEmWc2LPjvIoQQR0sCuxBVeP7557nuuutob2+vnBeG5Y/jNU3jnnvu4YknnuDOO+/k4YcfXqxhnjRKfsDecZsR28UydOKmPmtYV0qR9wIGCuUZeABdg+ZElJrDqDVXSmH7IW4QUhs16UjHD+v6JxrPCxgdLTE25jA66lS+Hxmxyec98nkX2/bx/RDfVwRBiO+HFIs+IyOlYxbSJ42MlPje93aye/f0zknt7UnOPbeZs85qOOI+6K4bsHnzMLW1US6+uJ1zzmnmzDMbWLkyIyFdCLFkSWAXogrj4+MsX7688nMkEpmxKdKll17Kgw8+eLyHdtLJuz77xm3GHI+Yqc/ZycWZ2DCp4B2oVY+bOm2p2GG1WvTDkIIbYOoaLckonek40SWwwDAIQnRdO+o3DZ4X0N2dp6srR1dXjn37sgwOFimVfEqlANv2K7PoUJ69Ngwd09QxzcnvNUxTp7Y2yqpVxy7YhqHi+ed7ePrpLny//AYskTA577xmzjuviaamxFHdfi7nsnXrKKtWZXjnO9fw+7+/QkK6EOKEIIFdiCo0NzczOjo67eedO3dOO8bzPGzbPt5DO6mMlTz2ZW1yrkfSMmcN3kFY7hgzUjpQ/qJr0BivfpdSpRRuqChNhP2EZdCSjNKcjC7KwtIwVAwMFNm3L0tXV449e8bp7S2gaaDr5bA8NUhblk46HSGVskilpn9NJi2KRW8inOfYtWuMsTGH8XGHfN5F07RKbXksVi4HicdNovO0xzweenvzPPbYLvr6DrwRvuCCFq68cjmx2NG/VJX/vjnOPruRm246nfPOm9lWUAghlioJ7EJU4bTTTpsW0C+66CKeeOIJtm/fzmmnnUZfXx/f+c53WLdu3SKO8sQ2bLvsz9rkvYB0xJpRez7ZqnGw6FYWlQLURk0aE9GqatWn9lQ3dZ3URD/2+niEeBU92RdST0+ebdtG6OrKsXPnGIODRbJZl/FxB6UgHjcrY1ZKEYaT35cD/mQrQ8PQiEQMIhF94quB74eMjzuUSj7xuEVNTYS2tiSpVO2Sm1H2vICnn97P88/3MPnP2tAQ49pr17BiRWb+K1dBKcXu3VlyOZdLL23nlls2HFV/diGEWAwS2IWowtVXX83f/u3fMjIyQn19PR//+Mf57ne/y8aNGznzzDPZsWMHuVyOv//7v1/soZ5wlFIM2S7duRJFzycTsWYs9HSDkJ6Jto6T4qZOSzJa1eZHgSrPprthSNQo727aMDEjbx3nAOv7IU8+uY8f/nA3+/fnyOc9IhGdTCZKXV2UFSvSh7VBTxCEuG6A605+DYhGDdaurSUeX7p1+L4f8uqrQzzzzH5GR8udX3Rd49JL23nzmzsXpO+574e89toI8Xi5n/oHPnA6mYxsziOEOPFIYBeiCh/72Md485vfjGWVF7pdeumlPPTQQ3zqU59i06ZNrFy5kr//+7+vamdScYBSioGiS0/OxvZDMrNshmT7AfuzpcqsuqlrNCcipOfpGjNVyQ+w/fIupS2xKA2JyLybLh1L/f0FHn54O7/8ZS9jYw5r1tSQTkeOqnWgYejE4zrx+AIO9BgqFj1+/et+Xnyxj0LhQFlTR0eKa69dTUtLckHup1Dw2Lp1hI6OFFddtZJrr10jO5UKIU5Y0oddiJPYUu5rrJSir+DQmy/h+CHp6Mwe6znXpydXYvJJqi5m0ZSobgOjcj/1AFDUxSK0paJVh/yFplR5MeVjj+1ky5Yh6utjrFiRWbKz38fC8LDNCy/08tJLg/hTPilJpy3e9KYO3vCG1gXpee77IXv2ZBkfd1i/vp4bb1zHxRe3n1J/6xPVUn6+EmKxyQy7EOK4U0rRky/Rl3dww5DMLC0UR2yXgaJb+bklGaUuVl0rPzcIKXgBcVOnORGjNXV4/dgX0vi4w3e/u53nnuuhry/PaafVnfRlGUopCgWP4eESw8M227ePsm3b6LRjWloSXHxxO2ed1bAgdfVKqcrC0vb2FJdd1sk737majg6pVz8aSil5syPEEiCBXQhxXIVK0Z0r0V8o4YdqxoZIk2UyoxNdYDSgIx2rardSpRQFL8APFbVRi450bNH6qYeh4tVXB3nkkdd56aUB4nGTjRubl9yiz6Pl+yG7do3T25uvBPTh4VKlTeTB1q6t5eKL21m1auE+YSgUPHbsGCMWM7joojbe8Y7VnH9+iwTNI6CUotDby8i2bQxv2UK+p4fGs86i6ZxzqF+/Ht2U2CDEYpBHnhCzWL169RFdT9O0Ge0exQFBqOjO2fQXHELFjBKVcGLmvVzKAoamsSwTq2phqR+G5NyAmKHTlIrQkY4fVj/2o6WUYnjYZteucXbuHGPbthF6evJ0d+dZvbqGhoYTpMi8Cr4fsnPnGJs3D7Nt2yiuO3s4n2SaGmef3cTFF7cddS/1g8exZ0+WbNZh7do63va25bztbcuPeFOlU1Xo+4zv3s3Itm0Mbd5Mdu9e7OFh3FwOw7IYfOUV9v7kJ2SWL6fl/PNpPOccMsuXyxsiIY4jCexCzCIMwyN6MZIlIXMr+QFdWZsR20PTIBWZ3vfbDxX7c3alE0zE0FmWjh2yi0uoFLYX4E3M1relYjTEq+vHfrQcx2f79lF27Rpn+/ZR9u3LTuwaWkLXNerrY5x7bhORyIm/2LGakJ7JRGhoiFFfH6exMU5DQ4yGhhi1tbEFqU+H8mMsm3Xp6yswPu7S0VEuf7n22jW0t6cW5D5OdkopioODZHfvZnz3boa2bKHQ24s9PEzgOMTq60m1txPJlD8FCRyH4sAAfb/+NQMvv0yypYXadetoOe88YvX1qDCc9ZRqa6N2zZrF/nWFOCnIolMhTmJLZRHXeMljf85m3PGJGjoxU58WqL0gpCtr44YTu1uaBh3p2Iz2jlMppXCCENsLiZk6dTGLtnTsuPRTL5V8fvnLXp5+uos9e8YZHS3h+4qamgh1dTFqa6ML0pbwWHPdYOINhsPoqEMu51Z2PZ08Tf5cLHqV3UcnmabOaafVcuaZjaxdW0s0euz+9qWST19fgcFBm2TSoqUlyZlnNnDRRW2cd16zzPbOQylFaXSU8YmAPrp9O/meHkqjozjj42i6TryxkXhjI1Zi/k9A3HyeYn8/peFhIuk0eiQCE3sFMHGa/L7xrLO4+FOfqnqcS+X5SoilSGbYhRDHTKgU/QWH/oJDwQ1IRYwZM+ZuELIva+NPhPVMxKQ1Nf+Oo97EolJT12hIRGhNRo9LrbrjHAjq27ePMjpaoqMjxemn1y/IbpzHSqnks39/nv37cwwP24yOOoyOligW/cO+rakhfd262mP66YFSiv7+In19BcIQWlsTvOlNnZx/fjPnnde8YC0gT1bFoSEGX36ZgZdeIrt3bzmgj42BUkRra4nV11OzatVh1aVHUikiqRRq9WrcbJbQ90HTyo+9KV/HXn+dwPMOfYNCiKos3VcYIcQJbXLWfNh28UNFTWxm20bHD+jKlvAnPuiri1k0JyJzBu9AKYpeQBgq0hGT5mSUxnhk3pn4hTAZ1H/2sy62bSsH9c7ONKtX1yxYqUcQhPT1FejqytPdncPzQgxDQ9c1DENH08o913Vdw7J0UimLVMoimYxUvp/cHXV0tERXV56urhxdXTkGBopVjcEwNGIxk2jUIBo1iMUMolGz8v2yZZljHtIn5fMu27ePkk5HOOOMBs4/v4Vzz21i7dq6Bfubn4w822Zo0yYGfvc7hjZvptjXh5vPE62pIVpXR3r5cgzr6Gv8NU0jWlMz5+WyOFWIhSWPKCEOw69//WtefPFFRkdHCYKZNbyapvGpw/gI+GSVd326sjZjJQ/T0GZt2zhZ0x5Mbkcft2iMzx3WS36A7QUkLIOGZJSWZJToMSx/CYKQ7u48r78+xnPP7Wf79lFGRhYuqBeLXiVQ79+fo7u7MK0/+ZHQdQ3T1OddBFpTU95RdbJ0Z+r3yeTxqf2fTxCE7N2bY2ysxPr19Vx11Ure9KYO4nFZSDobpRResUh+/34GXn6Zgd/9jnx3N/bwMFYqRbK1lbra2kX/dxVCHB0J7EJUIZvNcuONN/LUU0/Nu7D0VA/sSimGbY+efImc65GwTKKzLBotegH7czYTVTA0JSI0xCNz3qbth3hBSGMiSns6RsoyFjyABEFIb2+B3bvH2bOnvIh0cLDI2Fi5truzM8WqVS1HHdRt2+eJJ3bz6qtDcx6TTFokkxZhqAhDRRAowjAkDMvjdN2QMJz5/zAM1bSwblk6HR0pli1Ls2xZms7OdGUWfikaHS2xc+c4zc0JrrlmNTfcsJa2NllI6uZylZpzN5vFyWbLX0dHsYeH8YpFvEKBQn8/mq6TbG2lecUKdOPEX+wshChbus/cQiwhf/EXf8GTTz7JZZddxkc+8hGWLVuGKR/5TqOUojfv0FcoYfshmYg1a6lKwfXZP2X30vk2RFJKkfcCUOXjltXEsfSFW8wZBCFbt47w0ksDbN8+ysBAkbGxEtmsi2Xp1NZGaW1NctppC1OGsWvXGI88spNc7sCGUJoGzc2JSqhetixNbW103jckSilKJZ983iOf9ygUvInvXUqlgKamOMuWpWltTZ4Q5SOeF7Br1zilUsC55zbxB3+wmosuaj8hxr7QvGKRfE8P+Z4eCj09ZPfuJd/bi5vL4ReL+I5D4LoQhqDrGNEoRiSClUjQcOaZmLHYYv8KQohjQBKHEFV49NFHOf/883nqqafQFzAwniyCUNGVtRksOvhKUROdWa8OkHN8evIHwnpbKkpNdPawHipFzvWxdI3mVIyO1PxdYw5Hsejxm9/08/zzPWzfPkp/fwFd16itjdLcnGDduoWtk/a8gJ/+dB+//GVf5bx162q58MI2OjtTRKOH91SsaRrxuEU8btHUtGDDPO6CIKS/v0h3d55ly9Jcc00H1167hrq6UyN0KqVwxsYY37OH7N69jO/eTa6rCzeXw81mcQsFNMBKp4mk0yRaWsoBPRpFk+chIU4pEtiFqML4+Di33HKLhPVZlLu8FBkuumiaNmPnUjhQKjNkH5hZbk/FyEwJqioMcUdGUL6PlkxRNCMkLIPWVIzW5PwzztXq6yvwy1/28stf9rJ37zijow4tLQnOPrvxmC2k7O0t8J//uYPBQRsol6m8/e0rOf/8U7cVoesG9PTkGRiwaWqKc9FFbVx33VrOOafphP2bKKUo9PUxtnMnoe9jxeOY8ThGLIYZj2NOfA0cpxLQx15/ndz+/Tjj4zhjY+XrpVJE0mmS7e3UplKLWtYSuC6arssCUiGWAHkUClGFdevW0d/fv9jDWHKKXsC+rM1oySWiz+yvDuXZ9558iYJXrq3WgI5UlKidI7erl1JfL6XeXux9e3FHRwh9H9+MEEsmaGxvxWhqIFtTQzSTIVpTQ6qzk1RbW1UzjL4f0t9foLe3wKZNQ/z2t/10d+fx/ZCOjhRr1tQes7KLMFT84hc9PPVUV6XevKMjxY03rqW+/uTZ9fRwTC60zWZd2ttTXHHFci69tIONG5sP+1OGpcB3HMZ37WJ0+3aGt24lu28fpZERVBCgm2b5ZFmV7zXThDDEGR+nNDaGCoJy95baWlLt7RjR6KL+Pkop7KEhRrdtY3T7dnJdXeiRCA1nnEHjOedQs3KlzOwLsUhOvGdIIRbBn/zJn/DXf/3XdHd309HRsdjDWRKyjse+bHkzpIRpED1oo6DQ9ylkc/QFOoFeniXUS0ViL71I37bXcAYH8PN5/HyO0C6hRUz0RIpA0zGCHAz3M7xrO8NhCJqGEY1ixuNEa2qINzZSt3Yt6eXLyaxYQbqjA1/p9PUV6Osr0tubp6cnz969WbJZl3zeZXTUIZk0WbEiTSZzbIJREIR0deXYtWucbdtGK+0UNQ0uv3wZl13WcUrVZSulsG2fYtGntzeP7yuWLUvztrct55JLOjjjjIbj+vdQShG4LqHrErhuOUhHIpjzlJgopQgcB69QqJxKo6OM7tjByNatFAcGsIeHUb5PtK6ORHMzumUR+j7K9wk9r/wmtFQi9DzQ9cobTyMy+0Lr40mFIbmuLka2bWN02zZKIyPTLg9dl8GXX2bw5Zex0mmazj6bxnPOIdnSskgjFuLUJIFdiCpcc801PPnkk1x66aXceeedXHDBBdTW1s567PLly4/v4I4zpRQjtkdXtkh2dJxIMYeTHSc/PoY3NoY3OkppeAi/bRm85Sq0idaL/iu/o/SNBxjP58A0MVMpzFSKRMNK9HicUIEXKtKmTk3UmrbBkgrDcmiybYrDIwy+vpftP30O34jh6nGKxMlazWTTqxiKLCNfDAmCkHjcJJWyqK2Nsnx5Gsta2PICpRQDA0V27Rpn165x9u7N4nnTWzM2NMR417vW0dGxRLqdhAHoC/t3CENFNutSLHqVgF4q+SgF8bhJImGycmUNb3xjG5dc0s6yZZlD3qZSqjwTPTKCb9v4pRJBqVT5fvLn0Pdn7rJZvgHCICgfa9uVkwoCwiBABQGarqMZBrphlEtWkkmsZLK8cHPy/sfG8G27HLw9j8B1CRwHZ3wcIxol1tBA/fr1mPET41MTpRSlkRHGd+8mO7HzqW/bM46LNTRQt24dzvg4o9u3o4IAL5ej5xe/oOcXvyDR0kL9+vVopokKQ1QYwsRXFYYUBwcpDQ8vwm8oxMlJU/P1qBNCAKDr5VIPpdS8NbaapuH7h7975LGy0Ft9+47Dtl/9lt2//BW5vfsw7ALKmQhOpRJByQbDJPaBD2Nd+CYAVBjg/uRHOC++SKBZ+OHEjohQ+RooRaAUcdMgYeqoADw/xPdDfC8sf++FlEo+hYKH54V4rkdoF6GUxwpsLFxUqgHa1qGf/kaMtjXlqe1jwHECfvWrPn75y17y+dl3c2xtTbB+fT2XXtq+4G8UDptSaCNdGF2b0Qd2oSJxVLIWYilUNIWKJSGaQkWTEAZobhGcIppbnPjeRnPyoJuEDcsIG5cTpJoYHfcYHLTJZl0ymQjJpEUiYZJKRWhvT9HYGKehIU5dWmNZW5Ta2lg52E3Zvn7yzZg9PExpeBh7eBh7aIh8T0+5M4ptE3heebZ68jQxaz3X71ox8e+vGUa5JGUinE9+VWFYCe+h71e+Vq5uGBiWVS5riUTK30ciGJEIkUzmhGmbOLmwdTKku7ncrMelly+n7rTTqF+/nnhjY+V837YZ3rKFwVdeIbdvX9X3G62t5f89Olr18Qv9fCXEyURm2IWowq233nrCLoZbCPneXnp/9Wt2PPc8w3v24A4NYkajKCNKYEQINQvPTBB2NtJwy01Y7a0AuGNZtv/T1xl/bVe5l3hQ7iuulEJDA63cDUYBpq4RMXRQVPqPT54mTe7yaVk6sXgEKxPDNBvLuSwM0LKDaL2/RRX2E7SdRrDmjai69gX7O0wG9V/8ogfbnh4Ya2oirF5dy+rVNaxaVUMyuQQ2+gl89P7XMfa9gj64B3KDaIYFKoRgYvyGiTIiYEbAnHKZ76EFHir00ZQCTUOFIf6O31BQCfJ6DbSspnPFala//WzO2thBa2uSTMTFtMewB/op9O0gv6mbXHc3mz2vHNDhwGz4xPcqCPCKxcosOFApgTITCaxEolIDrk85ST313NxcjsFXXmHw5ZexBwdnPcZMJMisXEndunXUrVuHlUzOflw8TssFF9BywQWURkcZeuUVBl99dcYMuqbroOvlr0otek2+ECcTmWEX4iR2pDNWYajY/EoPfb97leGXf8v49tfI9/filkp48Xq8aA2uV17UGQSKIAhJrV7OOX/z/yJalwZg5NXXeeXz/46fy2MYGrquYRg6ujHxxkcpQgUKVQ7rE59iaFo5mJeP1yrnVS3w0Mb70Up5wto2wo4zCVa/AZVuPPR15+C6AS++ODOoNzfHueCCVtasqaG+PrZ03tS5RYzu19C7NqGP7EcrjKISNeW/gTmlblopVOATug6h4xC4DqHSCDWTQDMJMAnQy+eFijAISZg+Sa1IjVUinY7QuKKV2o5WateswR4aojg4iJfP4+bzePk8AFYyWa7Xnvz7aNqBv5WmlUtSJsK5GYtJED9CgecxsnUrgy+/zPiuXdM/baD8JiizciU1K1eSWbWKRPPRdSoKPK9cVqTPXGw++MorNJ51Fm+6556qb09m2IWYm8ywCyFm2PLiDr798c/gDexHL45T0mPkzVpCowbN1tD1ErqhYxjlUN36po2s/5MPoEfKs8r9P3qG/Q89QToSQsMsPbVVuQwGwKp0l1mgwRsWqr4T5TtoY30YW55G791G2HYaKllfDq4TJ6z4vGUztu3zm9/0zxrUL798GWecUb+4Id130OwclHJopTyanUOzs2iDu9HH+tDcIirVUP7dNQPHCXAKLkEQ4nnlN1tKKXRdwzQTmJFU5c2VpWtEJt48GYZOJKKTSFg0NSdoaooTj1vlUpaJeujhzZvRTJNIKoWVSpHq6MBKJk+YspETUej7uLkcpZERhjZtYmTLlvKmSlPEGxtpPPtsatesIVlld6VqGdYS+BRJiFOEBHYhxAz53j7Cvn3oKiBoX4thWTQY+qwdPVquuZzO914DgAoCuv7jPxl66pdgzhHUpoZ1QydmLGBYn8qMohpXgGujj/WiD3eBGUFZUbBiKCuGiqVR6UZUshZV20Yx3sq+fp/du8fZsydLX19h2k02NZWD+plnzh3UlaK8sHOiSwhBCEGACoPy92G5Zho/ANNAi8bQolG0WBRlmJRK/sQCTh9NAw2FEZQwS2NYpXEsZxSzlCXijGJ6RXTlYSgXPfDQfBcCD2VYBMkG7Fgrjhvi9JcIPR9LC4joimg8SrouTjIdJZEwicVM4jGTWMzAihiYhoZp6himjjkR4Gf7RzKiUVJtbaTa2o7BP6AAcLJZCj09FAcGcLNZnGy2srGSXyzOeh0zHqfhrLNoPvdcku3tS+eTHyHEEZPALsQsbr/9djRN47Of/SwtLS3cfvvtVV1P0zQeeOCBYzy6Yy/M5wkdB2VaRBwbzXPANFGGgTKMcpcRQ2P5Le+i8a0XARAUbfb+45fI/W4zhqYRJBKog9vWTQnrEUMneqzC+lSROGHz6nKS9l3wSmi+g1YYhdFedu8YZIfdxs6STY89iGLmgGYL6koBjoNynHIw932U74HroQK/vHV8GKJCVa4LVwrC6QsiA9+nYHsUij5F28XzfeIRRSal0xQJMNwcZmEI3c6ieSU0z0b3SigVEGLiaRa2ZuJrFj4xQiMNuoUKQM9rRCMBUS2kLuFTEwvJWIp0XCceD4llwKhPoGcyICUoS4JXKJDv6aHQ00N+4jRZVnQomq5Tu24dTeeeS926dbLZkRAnGXlECzGLf//3f0fTNP7qr/6KlpYW/v3f/72q650sgd0LQkJVjq66UuD7aJ4HGihdR0/GWfnnHyN97pkAuAPD7PnMP2Hv6wFNRwt8jGKRQNNQEx+bq4mwrlOeWT8uYX0qTQMrClaUkq/zu/4anu+uZ8ieuTBOJ6QjmWdVvcPqFUlWnJaGRgsVKkK7iLKLqGJxIqwH5Rn1yTCu66BraHr5jY1mTvySvoNeymLYY/i5EUZ7B9FLWaJGSEb3ieoBKSsg6YXEPIiY5ZpxPRbHbEthpGoxalZgJJNouoXnBbhuiOsFuG6A54a4bkCp5GOogIwVkNZd0mZAwlRokSjaRB258n0o5AmcEmE2i1FXh5ZKHbOuOmJ2pbExshO7nmb37sU5REcVMx4nksmUT+n0tK+p9nasROI4jVwIcbxJYBdiFrt37waobJI0+fOpYnIWHE1DTczUKRQoiDTWsfp//imx5eXuK8XX97Drf/0r/liu3GWEiVDvexjFAkEyRWiYBKHCmOgEY+kHLSRVIVpxHOxsuZQlmoBoAvSFfYoati1e6K7nN321OMGBkh0NRXu6xOraAqtrCqyIDxNzx8DOQa+HGoyjzDhBrI4g2UQYayBM1KNFYmiWBUa0/EYFBX4JzSmg2zk0J49mj6Pnh9FL4+CVcItF/GKJ5phBbW2cZNIilYyQTCQwIya6YaBrWuWkhSEYBpig+QU0J0CLxcDSQA9QkRDiE7P5QVAuv/E8cJxyAI9GyztsTvmDa5ZV/sTEdVETfcb1VAq9rg4tPn9dv5hJKYU9OMjo9u3Yw8MYkUh5EW0sVv46cdIti3xPTyWgu+Pjc95mtK6OVHs7qfZ2ku3tpNrapOuKEKcwCexCzGLFihXz/nxq0kisW8Gqv/5jrNryxjdjv3yJff/074SOe9Ch5aCveT5aoUCYSGKaJlFTx9SAiRCrZYfQckNo+WG0cGZfbWVGy73BowlUNIFK1KJqWlCpetCqK+PwQ43dYwme765n+0hqWslLXczl4o4Rzm8ZI26VNzxSCvBNAmpAS6Bcpxy884OY/j5MK1qufY/XENR2oKIZNLeA5uTQ7DF014bARfO9ia8u6DqBEWPMMQmMOppXJTl9ZZL6miqegpUqh3HfR5VKhIXCgYWDkxsGTfm7o2lgGGiJRDnoz0XT0KJRiETKtzs8TFgolIN7Oo2WTEqpzDwC12V8927Gduxg9PXX5w3fh6JHImSWLye9bBmpjg6S7e1YJ8hGTEKI40MCuxBVeOWVVzjnnHMOedwDDzzAH/7hHx6HER1/NReex4r/fht6tFyXPvDYT+j5+n9Or8uG8iY9gYfmlTfdiY7miSkPE6+8EU9xrBxiq6D5DprvQGH6dunKMFGZZsJMC6qmGZVpAsPC8XV6C1F68zF6cnF68jEGilFCNX3GeE1tnos7RljfkEfXQIUK5frlmWnPnShzmag713VUvAaVrJuYQXfR3Dz62H70kX1gmBBMbJ6km+WFrYZV3pzIrAEjgu0qRnMBmYzBmtYo6zqjWFaVYXgygBtGebZ8akif0hrxiGlaeVY9Gi0H96EhwvFxtFisHNzT6XKwX2hhWC4psm2U607/HSY3PJr8Go+X34AcwRsIpVR5syXXrexSGrguKgwxIhGMaLRy0k1z2gLNwHXxCgW8QgG/WMQrFHDz+fIM+Z495U80DmIlk4S+T+A4c47JiEbJrFhROS1095aFoJTCt228QoFoba10hBFikUlgF6IK73jHO3jhhRfo7Oyc85ivfe1r/Lf/9t9OysDedN2VtN98A5quo4KQri9+g7Ef/BeGW0CvnIrlr14BLZwZZGajNB2VrCt3akk3opJ15UDsFMAtojkFNGdi100nXwn6WuCjjfagj/YAEKLRoy/jv8K3sYWVMMvCUUsP2dg8yoWtQ7TEiqgghMKBnS6Z7OICEyUo5kS/8Km3Uq6DV1YUlWyYqF33wYjMGppDBWM5H89XtDVYrF8Rp6XOPOqAfaTX90slgiAgEo/PDIi6Xg7FSpVLZfJ5gkIBRkfRk0lIpTAmZ92P5P6VKn9KYNvlU7FYDuqeV35zNEUYBJQKBZx8Hs9xCIKAIAzLO+IGAYHnlX8XxynvnDp5+5NvZia+Dz2v3Oaw2u1GNK0c3C2rvHuvN/suttOuYhhkVqygbt06atetI97QUB5OGOKXSuXNoCa+Bo5DvL6eREvLkgvoUA7p7vh4edfZ0VGMSAQrmSS7Zw9mPE6ytZVYff2SHLsQJzsJ7EJUIZfLcfXVV/Pss89SW1s74/KHHnqI22+/nXXr1h3/wR1DmmHQ+dEP0HjVZSjXJfvD7zF47/8PeveQovo915SmocwYgZXCT9Thp5oIUk34iTo0VQ7ghAHKC3DCDEWjk1I8ih21sAMT2zewXR23WCBR6KHJ288qdtNMeQdHHUVnuI+P8O/sYhVPcA39xjLaEgXa4uXTuuQwcd0HTxG6k51bJn9RrRxEI5HDy6ITC0un/a4KXE9RckMKpZBUXGdlW5T1y+PEosc/6CilyA8O0r9lC6P79pUHqGlEEgkiySSRVIpoMkkkmcSwLLxSCc+28YpFvGIRt1jEs20CzyNeU0NNZye1K1eSbmtDs6zyzP/kaUpgnizl8YpFnPFxglJpekcdmFigq+MUi5RyOZxsltL4OE6hUH3IXtg/FkGpRFAqzXtYJJOhdu1a6tato2b16vKmUAfRdB1rYpfWpSz0fUqjo9hDQ3i5HJF0mlhjI01nn03Dhg0kmpoYfu01Bl95hUJfH+O7dhGtrSXR0kIkk5GWkUIcJxLYhajCf/7nf/KOd7yDG264gR//+MdEprxAP/roo3zoQx9i5cqV/PSnPz3i+xgcHOS2227j6aefprOzk/vuu48rrrhixnG2bfNHf/RHPProo9TV1fH5z3+eD3zgA0d8v3MxkgnW/X//kkRjmuF//Ay5h79JODYyy9x1eabcNVPYRoacXsu4VsuYVsdwWEd/2MCAX0fejxJ4OhSBoSMdVSNwYD1Bkjyr2M0Z+g7ODX9LFIfV7OZPuA8/1ozfeDoqmp4cJITagXCuG8Bh7qI62++uwA/KAb3kKFxfEbE0YhGd1gaL1e1ROpsjxz3YqDBkdN8++jZvpjB00B9cKdxCAbdQgIGBqm/THh/HHh+nb/NmzEiEmpYWatrbybS2YkQiOPk89tgYxdFRimNjFMfH8Wx7QX4fTdfLizktC8M0K+UsumWhRSLlhbQTnz5M/q11yzpwXCRS/h7KHY9KJYIwJNR1QiBUimBiRj5wXcxYDCuZrJzMye8TiVkD+okiDIJyP/fRUZyxMcIwJFZXR6qtjfq3vIX6M86gfv16kq2tlb9j2xvfiJPNMvTqqwy89BLDW7eS378fr1jEiEQqn2xM/YRjof7dhRBlEtiFqMLb3vY2HnjgAW699VZuvvlmvv3tbwPwxBNP8P73v5/29naefPJJ2o5iA5k/+ZM/obW1lcHBQX7yk5/wvve9jx07dlBfXz/tuDvvvJOhoSG6u7vZsmUL11xzDeeffz7r16+f9XaDIJhzq++mpiaam5unnaeUArtAy9VvYvjzdzL86ovTLne1KDutM+mmg96gma6ghTFVi/J0OHQFwRGztICaiENzpEBjpEhzpEBTtEhD1CZiNhIGb8Mb2oE5vAtNhZiFAYzdA4R1y/Faz4DIws50BiHkCgFFJ8TQNWJRjZq0QX3apC5tUpcxSCeMymZThq5jGgbmxFdd07Bdl1IVZReHw3ddhnbsoP//3959x0dVpf8D/9wyvU+SSYGQ0ALSlbJSFAsISBV1ccU1gLq+VmV1V6zrT5BdQF1d69rARURQFhBdV5CvBaUIq7KigiC9hZCemUym3rnn98edmWTIJJk0MsDzfr3mFXLnljM3J+SZM895zt69SkAexvE8snr0gCktDc6KCvjD+diB6moEPJ6YEW2O56HS6aIPtV4PXqVC1enT0eBfCgRQduIEyk6cAMdx4AQBslR34nCieFGExmSC1mxWHhYLtGYz1AYDRI0mdsVUxpRqOIEAIEnKJyNaLXizGbzJBJyZb80YmNsN2emE7PEoFXTC6T9gDBBF5dOCcK48p9MpefvnwegxYwwBlwu+SIAeDEJjNkNjs8HarRvseXmwdOkCe48e0Fgs9Z5HYzajw/DhyBo2DJ6iIpT8+CNKfvgBQY8HXHiOBc/zyr95Hpwowtq5c9xzFRcXo6SkpM52v98PkerHExIXx1h7fO5IyLnpqaeewiOPPIJ7770XEyZMwMSJE2G327F582Z06dKl2ed1u92w2+04fPhwNE/+iiuuQH5+PmbOnBmzb2ZmJlavXo0RI0YAAGbMmIGcnBw88cQTdc7bu3dvlJSUxP3jCCjB/7x582K2/fPuJ3Hilb/AgNhVFA+jM/6LX+FH9IOEhiegcWDQi0EYxSBMYgBGlfJvoxiAhpfCe3BgQM3X8P9EWiEEnSBBJ0rKV0GClg9CRLjWOc8pFWLq5JeHBbwQi/ZCKD8W/TSAcTyY3gZZYwLTGMG0RjCNCUytT7jaTETtQN2kF5BiEZGVokG6XYsUswY6jQpqUYRGpVIC9HCQXt8Ie0iWUe33w+PzKV/9foTOyOk+E5NlBDwe+N1u+KuqlK9uNwJVVfBUVMQEzqJGg24XX4yLhw+H0aR82uD1+3GqogJV4VFQJssIeL0IBQJQ6XQQNZp62xv0elF58iQqT56E69SpeoN0rdkMvd0Ovd0OrcWiBNzh0W+u9kh4OD1Hpdc371OIcM49Ako1Hk6jAWcygTebwanVyiJgTieYx6Pso1IpwXitSjssGFQCf0lSUnvCwTtvtYI/R+vTBz0eeIqK4C0thcpggNZuh97hgL1HD1g6d4YlN1cZRW+HfPR58+bF/f8KUAYRipvwqQ8hFwp6K0tIEzz00EM4ceIEXnjhBbzyyiuwWq347LPPWhSsA8CBAwdgNBpjJrX27du3zsh4RUUFTp8+HVOxpm/fvti+fXuLrl+b3mKMBuvV0OM7DMI3GIJipAMAjGoZZnUQmSaGTjYg2wpkmRmyzAwdzAyZZgaHgWHXwSJs//l0q7ULEIAGqhRGqXWQsi9BKK0bxNM/Q3AWgmMyuOoy8NVlMbsyjgNTG8F0VoRMDsgmB6DSxj1tJFD3BxkuyjGjbxcrcjNMMGjVEFoQ9Ag8D7NOB3O4jB9jDL5gENU+H9weD0pLSlBVVgZPZSW8FRXwVlbC73KhsbEWg9WK3pdeit6XXALxjBQOnUaDrhkZcHk8OFVRAV8gAI3BABgMjbZXpdMhrXt3pHXvDjkUQtXp03AWFEAOhaIBuu5sVhWpVZ4SkhStPCM7neBEUfk+GFTSZkymusE3xykLSqnVNSP3wSBYRQVC1dWQ9XolcI93bJIJBYPwlpTAU1QEcBwM6enIHjkS6ZdcAluPHjBmZcV+UkEIOWdQwE5IE7300ks4deoUtmzZgs8//xw9e/Zs8TndbjfMZnPMNrPZjLKysjr7AYApPFIa2c+d4PLlibh81g149d1/gRkzYc/IxW2ZBjxiBFL0Hth1DOoG/tdwe4PgeUAUVBjUIx2+QAjfH4w/ut/WmNaMYO6lkKrLIZQfBe9zKZVmQjUpKBxj4PxVgL8KQuUJAICstUAOB++yIQUhCGAyh2yHCX0GW9El0wy1quGgRwqFEJAkBCQJUigESZYRCoWi/w5KEgJeL4RQCIIsgwuXHfR5PMqjuhqVZWWoKC5GKME0E43RCL3ZDJPNhrw+fZDbo0d09JQxhsrqapRVVcGg1cJhsShvFPR6mHQ6VLjdKKyoQDBOmcKG8IIAS4cOsIQXGGtXHKeMjKtU0XQZFggAajV4rTaxYJvjlPQYUQS0WiXYr6xEyOOpCdyNxnavT89kOVqeMuT3I+T3I+ByIej1Qp+WhrS+feG45BI4BgyAJTeXqroQch6ggJ2QOPgGUhhq69+/f8z3HMdBakYer9FohMvlitnmcrlgNBrr7AcoVWsiAX68/Wqz2+3YtGlT3OfS0tLqbMvqloErH38AGx9egOLK06gqTkGqRYtMuwEZNj0y7HqY9SqUV/lR4vSh3OVFmcuHUqcXvkAIZr0aN4zsBoNWhRF9s+ANSNh3vOEl19sSM9ghGcLzAFi4jrq/CrzfrSx25KsCX10OTlYCed7nVFYlLTkAThCR2rETuvfqgezuHWG222rOyxiq/X54/X4EJAl+SUIgGERAkiDXGv1mjMHncsFTXg5PWRk85eWoLitTyg02kdFqhd3hgDU1FWa7HSarFSabDUaLBUKc3N+gJKGsqgqlVVWQwsG42+dDmcuFdJsNqSYTOI6D3WSC1WBAicuFEpcruu85SxCU2vItwXHKirIaDZjfD7myEiwy4m6xKMG9LEcfrNa/Ec7hjgb/tb4CUN5QSBIQDCqlLoNB5d/BYE1uGGOQQ6HYSj2MATodOJUqpn68qNHAlJ2NtP794ejfH/aLLoLYgtr5LJLzX1kJ5vdD7NCh5fezlrvuugs33nhjne2TJ0+mHHZC6kG/GYTEcfnll5/Vqh7du3eH2+1GQUEBOoRHK3fv3o1bb701Zj+bzYaMjAz89NNPGD58eHS/3r1713tuQRAafL4xIZmhqMKLoopwvnM4oOA5DiJfN4/c5Qngw22Hcf1l3aBRC7j64mz4AiEcPe0689RnH1dTRz1kTAWgxEf+gARWVYEUOKHxFsNbXqQ8F5JQcuwwSo4dBjZsgMluhyMnB+asLKjsdrBaI5dyKAR/VRV8LpfytaoK3ooKeMrLmzQZkxdFiBoNNCYTzCkpsKWnw5GZicwOHaBPsESg2+dDqcsFZ3V13OKbkiyjoKwMpS4XMm02WA0G8DyPdKsVDosFTo8HZVVV0Rz3tqQWRfCcMo8BjEXby8L/Dslyo+k/bSocuHMaDVggoOTDRybz1qqOwoXbyBBeBSBajajmwYVr2Ct1/+WYhxwKIRgIKCU1fT4EfT5l8q9WC5VOB6PFAp3ZDF1GBnSdO0PXpQs0NhvUZrNSitFuh7qBN+4NYYyBVVUpk3IrK6Or3jKfDwiFIB07BlW3bhCys1tltN7hcNSZ7A4AmrZYoIuQ8wRNOiUkSdx4442wWCx46aWX8PnnnyM/Pz9ulZgHHngAu3fvxr/+9S/s3bsXY8eOxfbt2+NWiYkE6vVVianPF0s/wsaHF4BTa6C21ly/sWC9tswUA6YM7wJR4CGFZHy47TBOlVXXf8BZptOooFWJMOo0yMs2o1tWTaqLz+NBweHDOHnoEE4cOABvnJQjThBgcjjAGIO/qiqmIkt9OI6D1mqFISUFOpsNar0eYniEVNRoIGq1DeYYa1UqGLVaqEQRIVmOptqEZDn6kEKhmBH+RBg0GmTZ7TBoY/P3A+ER+vKqqiany9SH5ziYdDqYdDqY9XqoGxlRlWUZTo8HFW43XMlQKpAxZXJqZAGr2gtZ1f6FCNehZ2cE5pHVc2WOQ9DnQyA8gi6HQlDp9coEXJ0OpvR0GNPSYEhNhSElBYaUFPCyDLm8HNBoIGRmQtWjB4Qz/n9I+GUEApDLyhAqK1NWt3W7lQA9vOosp9UqnzAAkCsqwOn10WvycdaiaA3N/f+KkAsBjbATkiReeeUV5OfnIyUlBR07dsSqVatgt9uxYsUKLFy4MPpHbP78+bj99tuRmZkJm82Gl19+ud6Sjq0pEqwLPAehvgottRSWVWPDf49i/KWdIQo8JgztjPc3H0Sp64xFaRiDyIXgMImw6gSUlVehxOUF40QwQQXwIiCICVdz4TkOGrUArVqAViVAoxah14iwGNSwGjUwGzSwGTVQifHPJzOGAAB1RgZSzWaY+/aFt6ICzoICOAsK4C4uVkYkQyG4CgvrbYegUkFrsUBvt8OQkgJ9Sgp0VmuLJv35gsFWLwMJANV+Pw4UFkKv0SAlnB4j8DzUoohMmw0ZVitcHg+q/X4Ew7n4QUlCMPxmoT4cx4HnOKhFMRqgGxqoQBMPz/OwGY2wGY0IhkKodLtR7nbD24yUolYRzpWvTZZlBNxu+FwuBCJv7jgOvCiCFwTwkbKHoggWCsHvdoPJcnTxKnNWFiwdOtQE6Skp9aa08BkZYC4XQocPQ66ogCo3F2K3bsrE2UbIHg/k0lKESkshl5aCVVeDeb3RAJ03GIA4K5nyWi2Y0wnp0CHI5eUQO3eG2LWrMl+AEHJW0Ag7IS1QVlaGzZs3Q6/XY9SoURCSrAJDa42wy4yB5wAhHIA1JVuoR7YN1wzqBACo9gWx9st9EEIBZFi1SLcbkJ5mRYrdDL5WkOAPSDhVVIGCwjIUFBajvKRcecPAcWAqHUSdCfY0G1KsRqSadUixaGHSq6FVCY1OCI1HCoXg8njg9HhQ5fU2OEIdCgTgLCyEq6AAVcXFEFSqaP1wjckErckEjdncYGnEZMdzHGxGI1JNJugaSVNgjCEYCilpIeH+wXNczM8zHlmW4fb54PJ6EZSk6L3iOC5ajpPjOBi0Wlj0+jqVeHyBAMrDwXtTcu4FnofdaIRapUK1z4cqr7fRMprxSIGAkv7kciEUCEBtMEBrNsOUkQG1TqektXg8CHi9YKEQ5PCD4zjobDZYO3SAOSMDpowM6O32Jr+RY5IEuawM4HkIDgfEzp2VHPnIKH6trywUUkbTKyqU8pY+nzJBN1JzPsFrs2BQGeHneQjp6VB17w6+1gJLLUUj7ITUjwJ2QhLw6quv4q233sKGDRuiKSo7d+7E2LFjUV5eDgAYNGgQvvjiCxgSKI13trRGwC5abOA5ZVRdaGyQm8lAKKhUYol8lSX075mNEZf2aearAHxBGacq/OBkCSkmFcyG+KUXGyLLDC5PAB5/AIyFwKBMFPWHJ4qS+HRqNVLNZlj0eogtfEPqCwTg8npR5fXC7fMlnJvOcxwsBgPsRiOMWm1MgCgzhsqqKhRXVMDt8cTm7IcnbjJZhkoQkJmWhvTU1Jg31rIsw+V2o8LlgrOqCn5JigbPoWAQsiRBliSEwl9lSUIoEAAvitCaTNBarbDn5MDaoQOs2dnQWiwx7WOyDCkQgBSu6sIJAvRWa6vkgjPGwDweyJWV0fSVyOuOPpQXCRYKKaPoej1wxj1szjVZZaVS795qVR4Wi1L73mxO+A3AmShgJ6R+lBJDSAJWrVqlVNOolS/6wAMPoKKiAjNnzkRRURE+/vhjvPbaa7j//vvbsaWtjNXkq/O1/75LfnBBXzgoD4QD8xAYOEBQAYIKTFCBqXRgggr/O82gOVSJwV2tMacPyByK/BoU+rUo9GtRHlQhQ+NHttaLTjovzKISSGtVPLo44lepCEghlJW7UVbuQrXHh6qgCl6ZR1BSUjfAZIgqQBRlZNhVUKsu3BJ3kt8PX1UVBFGEoFJBUKvBi2K9wRtjDNVeL1wuF+RgELwowmA0QiWKUIkiREGAKvzgOA4yY5BlGXJ4IqYcfoRkGdU+X0JvjORQCJLPB8nvVx6BAGRJQlE48NZoNMjMykJmhw4wmUzgOQ52sxl2sxlVVVU4efIkioqKIMsywHEwm83Izs6GIy2tTqAf+STAajbDGq665PF4UFpailOFhfCHXzMvitBoNMqEYLUaGosF1qwsWDt2hDkrq06d+9qiE0e1TX+T2RiO48AZDEoFF78/sjE2pz7yCP+MWuuaTKcDczoRKihA6MQJQKNRFq3SaMDbbOAtFghpaeBttsZPSghpFI2wE5KADh06YPz48XjjjTcAAKWlpUhPT8ftt9+O119/HQDwq1/9CpIkYefOne3Z1BitMcJusKcof/9lGZzPBc7rVFJT1PpwYK4G0xjAdBYwtUH5t9qgBOtqPZhKr+Sgg2Gg2YkUVQBFASVILwmolSD/DCGZQZJkmPggsnVe5Op9yDH4EZSBE1UqHHGKOO4ScdqnhleSYZKdMAWKYas+BL2/GFoVB2uHTFhtRmjVHJqUw3OeYYwhUF0Nd0kJ5GAQOosFsiwrNbzDI8iAUp1GUKuVGt/BoFLJhDFwggBBpVLyr8MrrKr0emjNZmjN5gZTOWRJUlJDvF7I4fNF2oRaf3rkUAiS3w85FALP87ETccOTcdU6HUSdDqJaDUGthqhSQatSwSDL0IZCMb1IBuBTqyHKMtRnvEmQ9XqwtDRwFguYywU4neCqqsCfsR8DENRqEUpJgWAyQQxXaxFUqnM21amtMMYAv1+pWx9ZqEoQIGRlQXv55Qmfh0bYCakfjbATkoCysrKYMmTbtm0DAFx33XXRbZdddhneeuuts920NqERBPAcwAOA3wPO7wQf9IJpTJBt2QildkbIlA6mMYKpDYCohiwzBPwS/P4QAhJDwC8gWM0QkCQEg0EEJIbvZBWAyEQ1CUAIiFN4kOc4iAIgihz2CDqIgh6iyEEtctBpeDhsKuRmqzDIqkKqVUSqRXkIkgcF33+PUz/9BOepU/D4yiFmZraoJnWTRfKHIxVCaucT1y4DGH7lnCAoo5+iCAhCq725YLIMb2UlqktLwatUMFqtcHTsiJTcXDCeR5DnEfT54K+uRsDthqeyEoHqanA8D43RCK3JBLXBAJVOF31Ifj8qTpxA5fHj8LlcKC0uBi8I0JrNUBuNCAWDSllCrxchv18ZXY4cr9Eokxtrv75wvjoXPofBbofWYom+GYjMC2hs1VQWCCBUWIhQYSHg94MHoD9jUirvcEDo2FFZsTQiPPobTS0pK4NcVgbmcoEDoPb5gIIC8KmpEDp1Ap/AxM4LEcdxSppN+FMExhjkoiLgXK/nT0gSoYCdkATY7XaUlpZGv//qq6/A8zyGDRsW3cZxHHw+X7zDzzmCVg1OEKD2loALaRFUm+Az58BjzoXHmI0Ar0GgisFfKsHv9yAkKZUx1CoOGo0AtcCgUQswGLSwihy0ah42kwCrUYRey4PnY1Nsa74y8DwHvZaHVs1Dp+HCX5Xv9VoeKRYRWnV9aS0mdL38cmT06YOTO3fi9M8/o/zoUagNBhjT0iCo1a03OsqYsviNJNUE6OEXwvG8EnxHanALQs22cNUQCAIgSWA+n7J4jt+vnAdQJg9Ggvgm5DozxpTl6Ssq4K2ogFqvhz0rCxm5ucjo1w/6nBzwZjOkwkIlB1mrBWexRHOOIznWDY2ad7z4YgSqq1F58iQqT55E+dGj8JSXw11cDFGthkqng8Fuh8nhgD4lBXq7HXqbDSqdTrn34aA9ksPNcRx4larBtJLGcGo1xJwcCJ06KVVQTp0Cq6xUJkdmZkLo2DE2x/vM48NpHrzBAHTqBNnjQej4cSXoBJSyh6Wl4FNSlMA9nD7DIiUeJammLzCmpIdotQnlcrNgUOkDXm/N8ZFzK/9Q2sjz4NPSEqoG01qYLDcr157jLuxPtQhpCxSwE5KAiy66CB999BEWLFgAQRDw3nvvYfDgwdHVRgHg6NGjyMjIaMdWth7RYgWzpaPIkwGPORc+cyfIajNEFoIKMlRMgkHDQWtRw25TIyVVD0uqCeZUAyxpZujdZdCWFMBo1cHssECnSWzl2NZisNvRY/RoZPbtixPffYfi/ftReeIEQsEgeEGIrg5Z+5FQlY5aK1RClpXAWhSV8nYqFXiVSgmyVaqa1S1rB+vx7oEs16QS+P1gXq+y4mUkiGes5jqiCJmxaH53KBBAKBBQ8rzD5R55UYRGr0dmly7IyMuDo18/aLKzIXbsCN7hACcIEHNzETx0CKHCQsinT4MzGsGZzRASDAbVBgMcPXrA0aMHQpIEV2EhqktKoDGZoLfbW1y+srk4joOQlgYhLU25hzzfrAmQvF4PvmdPsJwcSMePQz59GgCiI/BQq5XR48ZGkFWqaD3z6OJLwaDyMw4/0JQJz0ePQuzaFXx6epv8PjFJUlY3DS+gxNxuQKtVrpmSQqlAhLQjymEnJAH//ve/MWXKFGg0GoiiCI/Hg2XLluGWW26J7pOdnY0hQ4Zg7dq17djSWM3NCXW5/Pj329sgWlNhsBig14vQ61XQqQAt80Mn+2E0qmBOt0I0m+qM+rFAAP6dOxE6dkypJNGOlXMYY6g8cQLOU6fgczrhKS+Hp6ICkt+PoN+vTHD0+cAJAjQmEzQmE9R6vRKchEdQo0E6zyuBs1pdUxJPp2vySHgjDY6OugYrKxGorETQ6UTQ64UUXjhIpdMpedzhyaNqrRY6iwVag0H5NCEjAym9e0OdnQ2hQwelcscZwRZjDHJxMYKHDkEuLoZcXQ3eYlEmMVJgFoN5vZBOnFAC9yT4k8lZLBDz8pSKLy0QDdArK5UVXOMsEBbB2+0Qu3dv8JOK2kJFRRAcDmivvDLh9lAOOyH1oxF2QhIwadIkvPbaa9FJp9OnT48J1r/88ku43W6MGTOmvZrYqsxmDW6556pmH8+p1dBcfDH8oRBCx4+D8bxSyaIdcBwHW6dOsHXqFN0WCgbhc7ngc7ngDQfxFcePw1NejuqSElS63RBFERqdDhqrFaJOp6RM1ArSmxugM8YQ8vsRdLuViZbBYJ0HC1c4EXU6qEwmaNPSYFKpYLDZoNPpoFWplPxuiwVaux0qs1nJrw6PwnMGA4TMzAbTJziOg5CeDj4tDaGCAkiHDysL6jid4I1GZdQ9ydYVaC+cTgdVXh5Yp05Kuk0goLxJq/XJB0RRuV8cp3xS4vPFPBD5tARQUmYib/a02pp/R35eZ66cynGQy8shHTwIBAJgTieC330HoVMnCJ06NSlthfn9Sk320lIlbaieNyCcyQTOZIJcXAxIEuTycgS+/RZCTo6SYtRab1AJIQmhEXZCzmPtPWIlV1cj8N13CJ08CT41FdzZnPyZoEgecsjthrugAK7CQjgrKuAsKoI/GITf5wPjOGhTU6FNSYGmCTW0GWOQvF4E3W4EqqqiQbqo0UBlNELUasGHU2kElQoqgwEaiwVqkwkqgwF6hyP60KWmgheVMRYmSUowx7deqhELBhE6cQJSQQHkigpltFUUlUmaLajbXe/1QiElLcTnAwKB2HShMwJWLvyG6Vx+A8EYUz6lEcVmB7tMkiAdOQL51KnoNk6vh9i9O3irtd7rsurqmgm1VVV1d+I45ZMwiwW81ap8IhPpa4EApMOHo/n8AMAZDMo1LZZ620oj7IS0LgrYCTmPJcMfQNnphH/nTsiFhY1OmotO2myL/NxQSElvCQaBYLAmzYUxJc9cpwNnNEJwOCCkpCCk18NVUIDKgwdRtm8fqk6cgK+sDH6nE4JOB63dDp3dDkGrhRwIQAqnrEQfPh9YKKSMkhuNUBuN0NpssHTuDH1GBoyZmdDa7RD1eqj0eog6Xbvkfde5T7KsjMCeOqXkuLvdYH4/eIMBnMFQk5Pf1PMypowOR3K3GasZWTaZlOC8djWd8ARcJklg1dXKCp08rwTuev0FPcIru1yQ9u8Hq66u2ajVxt672l/jUavBp6QoD6u10TdDcmUlpAMHlJ9DGB/+hIY3m5V5HLVQwE5I66KAnZDzWLL8AQyVlyOwcydCJSUQHI6a0TtJAgsEojWco5P4ak3WQwN1rxljyjHBoHKe8OI6qPWIlFCM5p+HJ4hyogjOaFQCUb0evN0O3maLG7gwxuApLkbFgQNKAL93L7ylpfCVlyPk94MPV0cRIw+9Hob0dGV0PD0dxsxMGDIzoUtJOacCTeb1IlRYCOnUKcjl5UqwHQqBi/zZiJSkjFTEAZQc/DPKWEbmAkCtBqfTgTeZIKSmKvfcbm/wkxcWCilvIIqKEDp9GnJVFeD1AhqNMjeiGaP/kTcP0Uo85yAmywidPInQsWM11YUawen14FNTlQmkJlPT75ssI3TiBELHj9e5JqfTgTObleDdYoFcVQUxPZ0CdkJaCQXshJzHkukPYKioCIHvv0eovBycSqXkAfN8dIVEXq8Hb7VGR3ijub/BYE0Ar1LVBPnBoBKsC4LyvFodU5GFU6uVoC4SoIeDxZjJos0c0Zb8fjgPH0blwYPwO53Q2GzQ2e3QRh42WzR95XwQ+ZnILpcySu73g0VGy/1+5dOLUCi6qma0rF8kbUcQlCo0KSlKgB5nEmxC7QgEECouVgL3khJl9D8QSDjnPpIewlwuJf88sjhUuE+gNct+niXM64V08qTyhih8vxEpmxn5eajVyn1vpXkkzOuFdPAg5PLy+nfieXBmM0x3353wm6Jk+v+KkGRDATsh57Fk+wMoFRQguHs3wHHgrVbwkRE5szmmOgmTJMjh0nJyRYUyuuvzKSsoiqLycb5OV1PVJFJDW6OpmQB4jgVe5yomy0oQHwwqAWLkTVOtUpZt8bOQq6shFxUpOffl5UrOvUYD3mSqM2LPZBmsqgqy2w1Op4Ngs0Ho2BEIBhEqKYFcVQXm8Sj9S6NRAnit9pz6NKQ9MEkCc7kghx/M5YotdanRwPLwwwmfL9n+vyIkmZw/Q0CEkKQnduigpJ2o1Q2OunGiCCElBUJKCoCaAJ55PMrH+pHgnILydsfxfMwql2cLbzCA79IFQm4u5JISSAUFysh7ZSUgy0rKh0YDVlUF5vWCMxoh5uRA7NQJQlZWtP+JsgzmciFUVqYsulRergTvFRXKm0GzmQL3enCiCC6c1gTUrBjLXC6EioqiC0wRQlqOAnZCyFnVnNrRkQAe4QCekAiO5yGkp0NIT4dcVaWUqCwoUN7gVVWBM5kgdugAsVMnZcGhM4JvjufBWa1KlZWuXSF7PJDLyiCdPKnUpy8sVEbczeZzukrN2RBZMRYGg7LKrMPR3k0i5LxBATshhJDzAm8yge/ZE2LXrggVFoK53UolE7s94U9jeL0evF4PoUMHZWGpo0eVwP306TYP3Flkgm7sxlqNO7srBhNCkgcF7IQQQs4rnEoFsdZCWc06B89DyMgAn54OubgY0tGjCBUVKaudhivTsNrlEyOPCFGsqUJTa3El8HxMWVEmSUq50XDuN3dGDfqaBillL3m7vc3Tj5gsRxd7YpKkzDNJwjUUCLmQUMBOCCGE1CO6IqzDoeTKHz2KUGkpAIAPT67l1OqaSkSCoEzGDFewiQTkss+nVHJhrKa8qCiCD690yhuNSiAeGb0/cyEpWVZSfUpKlOPqKUHaHNEA3edTyqsypswR0WrB63SQy8uVybhNWDSMENK6KGAnhBBCGsFxHASHA3xamjL5OVzXv76KRJGVTSPlSaNlSmW5pqyoThctV5oIIScH0v79kE6cUFJ0IlWSmlOHPhhUJoiGF7GKBOjR+viRvH7GENy/H6HwpF7eYlFSgyg1h5CzigJ2QgghJEHRiZUJ7Ae1Whl9b6VqKbxOB1W/fhAyMpQguqhIydNPSWk06GeMKSkuHg9kn0+pja/XQ8jMVBZTCgfo8d4AqC+5BHJmpnLNkhLlmnZ7wm80CCEtRwE7IYQQco6IpujY7ZAOH4Z05AjkkhIlnYbnoyvLRpdYieTYB4PKKLpOBzEjA0JaGviUFCVIbyTNheM4CFlZ4FNTIR06pEzELSmpmYRLaTKEtDkK2AkhhJBzDKdSQdWjB4T0dAR/+SWaVx/JfedrrzbLceDtdiXdJSVFWRW2GSktnFoN1UUXKSP8v/yi1L0/fVp5MrxiMafRKKlChJBWRb9VhBBCyDmKt1qhHjwYzOOJBucczyv/rvW1NUfBeZsN6iFDEDp1qmY1Yrcb8Pshu91KxZvaK54SQlqMAnZCCCHkHMbxPDij8axfU+zYEejYUVnh1OcDc7kgO53Ko7ISXDMWSSOExEcBOyGEEEKajeM4cDodoNNBSE8HAGWUnSrJENJqKGAnhBBCSKtqq9VgCblQ0dRuQgghhBBCkhgF7IQQQgghhCQxCtgJIYQQQghJYhSwE0IIIYQQksRo0ikhJEZxcTFeeeWV6Pd33XUXHA5HO7YoOdF9ahzdo8TQfSKENIZj0fWLCSHnm969ewMA9uzZk/Axe/bsQZ8+faLf7969O3oeUoPuU+PoHiWG7pOiOf9fEXKhoJQYQgghhBBCkhgF7IQQQgghhCQxCtgJIYQQQghJYhSwE0IIIYQQksRo0ikh5zGTyYRgMIiuXbsmfIzf78ehQ4ei33ft2hUajaYtmndOo/vUOLpHiaH7pDh06BBUKhWqqqrauymEJB0q60jIecxgMKC6urpJx4iiiLS0tJjvSV10nxpH9ygxdJ8UKpUKBoOhvZtBSFKiEXZCCCGEEEKSGOWwE0IIIYQQksQoYCeEEEIIISSJUcBOCCGEEEJIEqOAnRBCCCGEkCRGATshhBBCCCFJjAJ2Qi5Qr776Ki655BKoVCrMmzev3v1kWcZ9990Hq9WK9PR0PPfcc2evkUkg0fv09NNPo1evXjCZTMjLy8PSpUvPXiPbWaL3KMLpdCIjIwOjRo1q+8Ylkabcp/Xr16Nv374wGAzo2rUrvv7667PTSEJIUrowi70SQpCZmYl58+Zh5cqVDe732muv4csvv8T+/fvhdDpxxRVXoF+/frj66qvPUkvbV6L3ieM4rFy5En379sWePXswevRo5OXlYfjw4Weppe0n0XsUMXfuXHTr1q2NW5V8Er1PP/zwA+655x6sXLkSQ4YMwalTp8DzNL5GyIWM/gcg5AI1ZcoUTJo0CVartcH9li9fjjlz5sDhcKB79+6444478Pbbb5+dRiaBRO/TAw88gAEDBkAQhOgbmh07dpydRrazRO8RAOzevRtff/01Zs2a1fYNSzKJ3qeFCxfiz3/+My699FLwPI+OHTsiKyvr7DSSEJKUKGAnhDTo559/Rr9+/aLfR0aQSf2CwSB27NiB3r17t3dTks4f/vAHPPvsszRi3IBvvvkGJSUl6NatG7Kzs3HffffB7/e3d7MIIe2I/sckhDTI7XbDbDZHvzebzXC73e3YouR3//33Izc3F2PGjGnvpiSV9957Dw6HA5dddll7NyWpFRQUYM2aNdiyZQu+//57fPvtt3j66afbu1mEkHZEATshpEFGoxEulyv6vcvlgtFobMcWJbdFixbhiy++wJo1a8BxXHs3J2lUV1dj7ty5+Nvf/tbeTUl6Op0Os2fPRmZmJlJTU/GnP/0J69evb+9mEULaEU06JYQ0qFevXvjpp5+iaTG7d++mVI96/OMf/8CSJUuwZcsW2O329m5OUjlw4ACOHDmCwYMHAwC8Xi98Ph969OiBX375pZ1bl1z69OkT82aP3vgRQihgJ+QCJUkSJElCKBSCJEnw+XxQqVQQBCFmv1tuuQXPPPMMrrnmGjidTixevBjLli1rp1affYnep7fffhsLFy7E5s2bL7gJgoncoz59+uD48ePR71etWoW1a9dizZo17dHkdpFoX5oxYwZeeukljB07FiqVCs899xzGjx/fTq0mhCQFRgi5IM2dO5cBiHksXbqUbd68mRkMhuh+oVCI3XvvvcxisbC0tDT27LPPtmOrz75E71Nubi5TqVTMYDBEHwsWLGjHlp89id6j2pYuXcquvvrqs9zS9pXofZJlmT3yyCPMbrczh8PBZs+ezXw+Xzu2nBDS3jjGGDv7bxMIIYQQQgghiaBJp4QQQgghhCQxCtgJIYQQQghJYhSwE0IIIYQQksQoYCeEEEIIISSJUcBOCCGEEEJIEqOAnRBCCCGEkCRGATshhBBCCCFJjAJ2QgghhBBCkhgF7IQQQgghhCQxCtgJIaSNfPnll+A4DvPmzWvRed566y1wHIe33nqrVdpFksvRo0fBcRxmzJjR5GNHjhyJvn37QpblFrWBMYb+/fvjsssua9F5CCFtgwJ2Qsg5qSVBzvnUhnOJx+OB1WoFx3G4+eab2+w63377LTiOw7PPPhv3+by8PHAch2HDhrVZG/bt24fZs2ejT58+sFgsUKvVyMrKwvjx4/Hmm2/C7/e3+Bpr1qzB5s2b8cQTT4DneciyjE6dOoHjOPz8888NHhv5WajVahQXF4PjOMyfPx9bt27FmjVrWtw2QkjrooCdEELayJAhQ7B3717cc8897d2UpLBq1So4nU5wHIf3338fZWVlbXKddevWAQCmTp1a57lNmzbhwIED4DgO27dvx+7du1v9+vPnz0fv3r3x8ssvw2w2Iz8/H3PmzMG4ceOwb98+3H777Rg+fHiLrsEYw5///Gfk5eXhuuuuAwDwPI9Zs2YBAJYsWdLg8atXr4bT6cTkyZPhcDgAAJMnT8ZFF12EP//5z2CMtah9hJDWRQE7IYS0Eb1ej549eyI1NbW9m5IU3njjDfA8jwceeAB+vx/Lli1rk+usW7cO/fv3R+fOneO2AQAeeuihmO9by8KFCzF37lx06NABO3bswNdff40XX3wRCxcuxJtvvolDhw7ho48+gslkatF1PvvsM+zfvx/5+fngOC66/bbbboMgCFi+fDkCgUC9x0cC+t/97ncx2/Pz87F//358/vnnLWofIaSVMUIIaWVHjhxhAFh+fj7bu3cvmzx5MrPZbEyv17Phw4ezjRs3xj3O5/OxRYsWsT59+jCdTsdMJhMbMWIEW7VqVcx+c+fOZQDiPpYuXRqz7yeffMLGjRvHUlJSmFqtZl26dGFz5sxhFRUVda6fk5PDcnJymNvtZnPmzGHZ2dlMrVazrl27sieffJLJstykNmzatIkBYHPnzo25znfffcf+8Ic/sH79+jGbzcY0Gg3r1q0b+9Of/sTKy8vrtGvp0qVxX1s8d955JwPAPvjgg7jP79ixgwFg119/fXTb6dOn2f3338/y8vKYXq9nFouF5eXlsfz8fHbo0KFGr5mIn376iQFgo0ePZmVlZUytVrOLLrqowWPeeecddvHFFzOtVsvS0tLYLbfcwgoKCtjIkSNZfX++9u7dywCwefPm1XmutLSUaTQa1r17dxYMBllGRgaz2WzM6/XW24ZPPvmEDRs2jOn1emaz2djkyZPZ3r17WX5+PgPAjhw5Et33yJEjTKVSMZVKxX766acGX5vP54s5LvL7kqhp06YxAOzgwYN1nhs/fjwDwN577724x0buUZcuXWL6NGOMHT16lAFgN910U8JtIYS0PfFsvTEghFx4jhw5gqFDh6Jv37648847UVhYiFWrVmHcuHFYuXIlpk2bFt03EAhgzJgx+Oqrr9CzZ0/cfffd8Hg8WLNmDaZNm4Zdu3Zh4cKFAIArrrgClZWVeOGFF9C/f39MmTIlep4BAwZE//3EE09g3rx5sNvtmDBhAhwOB3788Uc888wzWL9+PbZv3w6z2RzT5mAwiDFjxuDUqVMYN24cRFHEBx98gIcffhg+nw9z585tUhviWbx4MdatW4eRI0di1KhRkGUZO3fuxN///nds2LAB//3vf5s9Apufn4/XX38db7/9NiZPnlzn+ciodiTv3uPxYPjw4Th06BBGjx6NiRMngjGGY8eO4cMPP8QNN9yALl26NKsttUVGsmfMmAG73Y6JEydi7dq12LJlS9yJjk8//TQeeugh2Gw25Ofnw2Kx4NNPP8Xw4cNhsVjqvc77778PANE0kdqWLVsGv9+PGTNmQBRFTJ8+Hc8++yxWr16N3/72t3X2f++993DzzTdDq9Xi17/+NTIzM/H1119j6NCh6N+/f539ly5dimAwiJtuugl9+vRp8H5oNJoGn28IYwxffPEFMjIy0LVr1zrP33HHHfj444+xZMmSmN+xiMjo+m233RYzOg8AOTk56NChAz777DMwxuo8TwhpJ+38hoEQch6KjBgCYHPmzIl57ttvv2WiKDKr1cqcTmd0+8KFCxkANm7cOBYMBqPbi4qKWE5ODgPAtm3bVuca9Y1KfvHFFwwAGzp0aJ3R9MiI9X333RezPXKdcePGMY/HE9MGi8XCLBYLCwQCCbehvhH2o0ePMkmS6uy/ZMkSBoA9+eSTcdubyAg7Y4zl5eUxtVrNysrKYrb7fD5ms9mYw+GI3uN///vfce8FY4z5/X7mcrkSumZDvF4vs9lszGKxRO/rRx99xACwW265pc7+hw4dYqIostTUVHb8+PHodlmW2U033RTtW/EMGjSIde3aNe5zPXv2ZDzPsxMnTjDGakb9R4wYUWdfl8vFrFYrU6vVbNeuXTHPPfTQQ9E21B5hv+qqqxgAtnjx4oZvyBmaOsIeGSGfMGFC3OclSWJZWVmM4zh2+PDhmOf8fj9LS0tjoiiywsLCuMdPmTKFAWB79uxp0usghLQdymEnhLQZi8WCxx9/PGbboEGDMH36dFRWVkYnBwLAP//5T3Ach7///e8QxZoP/xwOB/7f//t/ABqfSFfbiy++CEAZzbZarTHPzZgxAwMGDMCKFSvqPVan08W0YfLkyXA6nfjll18SbkN9cnJyIAhCne2zZs2C2WzGxo0bW3T+/Px8BAIBvPvuuzHbP/roI1RUVGD69Okx9xhAzOuNUKvVLc61BpQJjhUVFZg2bVr0OmPHjkVGRgbWrFmDioqKmP1XrlwJSZIwe/ZsZGdnR7dzHIcnn3wy7r0DgBMnTuC7776LO7q+ZcsW7Nu3D6NGjULHjh0BAH369MHAgQOxdetW7N27N2b/Dz/8EJWVlZg+fXqd0fTHHnusTp8CgMLCQgCInr+tHD9+HACQmZkZ93lBEDBr1iwwxvDmm2/GPPfhhx+ipKQEEydOREZGRtzjI9sj1yGEtD8K2AkhbeaSSy6JG/BdccUVAIDvv/8eAFBVVYWDBw8iKysLPXv2rLP/VVddFbN/IrZv3w6VSoXVq1dj3rx5dR6BQAAlJSV1KpVYLBZ069atzvkigeOZwWVzBINBvPzyyxgxYgTsdjsEQQDHceB5Hi6XCwUFBS06/6233gqe5+tM6jwzHQZQ6nh36NABTz75JMaOHYsXX3wRO3fuRCgUalEbaoukw8ycOTO6LZKS4vP5sHz58pj9Iz/nESNG1DlXTk5OTBBf2wcffAAgfjpMvDYANfdi8eLFCbfBaDQ2mvbUliJ91maz1bvP7bffDp7nsXTp0pifZeR13nHHHfUea7fbAQClpaWt0VxCSCugHHZCSJtJT0+Puz0ygud0OmO+1jdiGNleWVmZ8LXLysogSRKeeOKJBvdzu91ISUmJfh9v5BRAdES6NQLZadOmYd26dejSpQsmT56MjIyMaE7z888/3+Ia3R07dsTVV1+NTz/9FHv37sVFF12E4uJifPLJJxgwYAD69esX3ddsNmPHjh2YO3cu/v3vf0dH91NTU3HXXXfhscceg0qlanZb9u7di61bt6Jnz5649NJLY56bMWMGnn32WSxevBh/+MMfotsj/aG+/pOeno6jR4/W2f7+++8jIyOjznUqKiqwZs0aWK3WmLkGAHDzzTfj/vvvx9tvv41FixZFfw6JtOFMmZmZ2Lt3b4vfcDUm8imFz+erd5+cnByMHj0aGzduxPr16zFx4kQcPXoUn332GXJycjBmzJh6j/V6vTHXIYS0PxphJ4S0maKiorjbT58+DQDRyYORr5HtZ4qkGjQ02fBMFosFNpsNjLEGHzk5OQmfszV89913WLduHUaNGoVffvkFS5cuxaJFizBv3jw8/vjjDZbia4r8/HwANaPqK1asgCRJ0e21dezYEW+++SaKi4uxe/duvPjii0hJScH8+fMxf/78FrUjMrK9b98+cBwX8+jbty8AYPfu3fj666+jx0QmAtfXf+JtLy0txZYtWzBlyhTwfOyftrfffhs+nw+VlZXQ6XQxbUhJSUEgEEBZWRnWrl3bojZERuPbuiRipG56Y3XsIyUbI6Pqb775JhhjuO222+rco9oi541chxDS/miEnRDSZv73v/+hqqqqTlrMl19+CQC4+OKLAQAmkwldu3bF4cOHceDAAXTv3j1m/02bNgFQUmwiInnM9Y14X3rppfj444+xZ88e9O7du1Vez5kaa0M8Bw8eBABMmjSpTh75N998Ex3dbKmpU6fCbDbjnXfewcKFC7Fs2TKIotjgCqMcx6F3797o3bs3pkyZgk6dOuGDDz7AX/7yl2a1we/3Y/ny5eB5HjNmzIhbceTkyZPYuHEjFi9eHF159OKLL8a6deuwdevWaDpUxLFjx3DixIk65/noo48QCoXipsNEAtbf/OY30Ov1dZ53Op1Ys2YNFi9eHL0/kb65devW6GJEEW63G7t27apznpkzZ2LRokVYu3Ytfv75Z/Tq1SvebQGg3JvmVorp3bs3BEHAvn37Gtxv0qRJyMjIwPr163HixAksXbo0mt/ekH379oHn+egbKkJIEmiv2a6EkPNXIlViLBZLTJWYBQsWMABs8uTJMRVUSkpKWG5uLgPAtmzZEt1eVVXFOI5jl19+edw2fPbZZ9EqMQUFBXWed7vdbPv27THbInXY44nUXd+0aVPCbYhXJWb79u0MAJs6dWrMvkVFReySSy5hAOq0oalVYiJuv/12BoA988wzDACbNGlSnX12797NTp8+XWf7t99+ywCwIUOGxGzfu3cv27t3b0LXX7FiRbTqTn2cTiczGAxMr9ezyspKxhhjBw8erLdKzG9+85u4VWImTpzIrFZrTBUfxhjbtm0bA8B69epVbxtCoVC0QtD+/fuj7bJYLE2qEsNYTT/Ozc1l3377bdzrbdiwgV155ZXR75tTh33gwIFMpVLFVDOK5+GHH2YA2GWXXcYAsIkTJza4v8/nY2q1mg0cODDhthBC2h6NsBNC2szll1+OJUuW4L///S+GDx8ercMuyzJef/31mBroc+bMwYYNG/Dhhx+if//+uPbaa+HxeLB69WoUFxfjwQcfjJkAaDQa8atf/QpbtmzB9OnTkZeXB0EQMGnSJPTr1w9XX301nnzySTzyyCPo3r07rr32WnTu3BlutxvHjh3DV199hREjRuCTTz5p9utrrA3xDB48GMOHD8f777+PYcOGYcSIESgqKsKGDRvQo0cPZGVlNbs9Z8rPz8eSJUvwyCOPRL8/06effooHHngAQ4cORV5eHhwOB06ePIkPP/wwuippbRdddBEAJLR0fSQd5vbbb693H7PZjBtvvBFvvfUW3nnnHdx9993o2rUr5s+fj0cffRT9+/fHtGnTonXYy8vL0b9/f/z444/Rc7jdbnz66ae4/vrr6+TbR9pw22231dsGnucxc+ZMzJs3D2+88Qb+9re/wWw24x//+Ad++9vfYtiwYTF12H/44QeMHDkSX331VZ3UkkcffTQ6d2Lw4MEYNmwYBg0aBKPRiKKiImzevBkHDhzAoEGDGr1/Dbn++uuxc+dOfPHFFxg/fny9+91xxx146qmnsGXLFgB1VzY905dffolAIIDrr7++Re0jhLSy9n7HQAg5/9QeMfz555/ZpEmTmNVqZTqdjg0bNox98skncY/zer1swYIFrHfv3kyr1TKj0ciGDx/OVq5cGXf/AwcOsAkTJjC73c44jos7Cr1lyxZ24403sszMTKZSqVhqairr378/++Mf/1hnBLSpI+yNtaG+OuxlZWXs97//PcvJyWEajYZ16dKFPfLII6y6ujpuG5o7ws4YY926dWMAmN1uZ36/v87zP//8M/vjH//IBg4cyFJTU5larWY5OTns+uuvj6l7H4EGaqDX9ssvvzAALD09vc6o95kio+D9+/eP2f7222+zAQMGMI1Gw1JTU9n06dNZQUEB6927N7NYLNH9/vWvfzEAbO3atTHHV1ZWMr1ez9RqNSspKWmwDcePH2c8z7O0tLSY+7R+/Xo2dOhQptPpmNVqZZMmTWJ79+6NriYab8VcxpT7es8997DevXszk8nEVCoVy8jIYGPHjmVLlixp8UqnRUVFTK1Ws1//+teN7jtq1CgGgHXs2DFu/f/afvOb3zC1Ws2KiooSbgshpO1xjCUwTEIIIU1w9OhRdO7cGfn5+XjrrbfauznkPOJyuZCeno4BAwZg+/btAJRKLx988AFKS0vj5qi3tlAohC5duiAQCEQnRLeHO++8E8uWLcPRo0frraneFMXFxcjNzcXNN9/cpDUPCCFtj6rEEEIISTolJSUIBoMx2yRJwv333w+fzxedXBoIBPDxxx/jmmuuafVgvbKyEh6PJ2YbYwx//etfcfz48bgTXM+m+fPnQ61WY8GCBa1yvoULF0IQhGZPMiaEtB3KYSeEEJJ01q5di8cffxyjRo1CdnY2ysvLsXnzZuzfvx8DBgzA7NmzASirsUZqpre2HTt2YNq0abjmmmuQm5sLt9uNHTt2YNeuXcjOzsa8efPa5LqJSk9PxzvvvIM9e/ZAluUGSzU2hjGGzMxMLF++vN71EAgh7YdSYgghrY5SYkhLff/99/jLX/6Cb775JloXvHPnzpg6dSoeeuihuCvotrYjR47gsccew7Zt21BSUgJJktCxY0dMmDABjz76aL2LKhFCSGujgJ0QQgghhJAkRjnshBBCCCGEJDEK2AkhhBBCCEliFLATQgghhBCSxChgJ4QQQgghJIlRwE4IIYQQQkgSo4CdEEIIIYSQJEYBOyGEEEIIIUmMAnZCCCGEEEKSGAXshBBCCCGEJDEK2AkhhBBCCEliFLATQgghhBCSxChgJ4QQQgghJIlRwE4IIYQQQkgSo4CdEEIIIYSQJEYBOyGEEEIIIUmMAnZCCCGEEEKSGAXshBBCCCGEJDEK2AkhhBBCCEliFLATQgghhBCSxChgJ4QQQgghJIlRwE4IIYQQQkgSo4CdEEIIIYSQJEYBOyGEEEIIIUmMAnZCCCGEEEKSGAXshBBCCCGEJDEK2AkhhBBCCEliFLATQgghhBCSxChgJ4QQQgghJIlRwE4IIYQQQkgSo4CdEEIIIYSQJEYBOyGEEEIIIUmMAnZCCCGEEEKSGAXstbz11lvgOC760Gq1yMvLwz333IOioqJ2adOCBQswadIkpKeng+M4zJs3L+5+v/zyC/74xz9i2LBh0Gq14DgOR48ebdY1Z8yYEXMfzrwnEV9++SU4jsOaNWtijv/pp59www03ICcnB1qtFh06dMDo0aPx0ksvNakdhYWFePjhh3HllVfCZDKB4zh8+eWXzXpNpGHnct9///33MW3aNHTp0gV6vR49evTA/fffj8rKyiZfk/r+hSnZ+v+8efPq7Yccx2Hbtm0Jn+vM13bmY8eOHdF9OY7DPffcE3N8SUkJ7r33XvTs2RM6nQ4OhwNDhgzBQw89BLfbnXA79u3bhwcffBADBgyAyWRCZmYmxo8fj++++y7hc5DEJVufBpT/1373u9+hc+fO0Ol06Nq1K/70pz+hrKysSee5UPu02CZnPcfNnz8fnTt3hs/nw9atW/Hqq69i/fr12L17N/R6/Vlty2OPPYaMjAxcfPHF2LhxY737bd++HS+++CJ69eqFiy66CLt27WrRdTUaDZYsWVJnuyAIDR739ddf48orr0SnTp1wxx13ICMjAydOnMCOHTvwwgsvYPbs2Qm34ZdffsFTTz2F7t27o2/fvti+fXuTXwdpmnOx7//ud79DVlYWbrnlFnTq1Ak//fQTXn75Zaxfvx7/+9//oNPpmnRd6vsXrmTp/1OnTkW3bt3qbH/00UfhdrsxePDgJp8z8trOFO86EeXl5Rg0aBBcLhdmzZqFnj17oqysDD/++CNeffVV/P73v4fRaEzo+kuWLMGbb76J66+/HnfddRecTidef/11XHrppfjkk08watSoJr8m0rhk6dNutxtDhw5FdXU17rrrLmRnZ+OHH37Ayy+/jE2bNmHnzp3g+aaNIV9wfZqRqKVLlzIA7Ntvv43Z/qc//YkBYCtXrox7nNvtbrM2HTlyhDHGWElJCQPA5s6dG3e/srIy5nK5GGOM/e1vf2MAosc2VX5+PjMYDI3ut2nTJgaArV69Orrt2muvZWlpaayioqLO/kVFRU1qh8vlYmVlZYwxxlavXs0AsE2bNjXpHCQx53Lfj9cnli1bxgCwxYsXN+ma1PcvTMnY/890/PhxxnEcu+OOO5p0XH2vLR4A7O67745+//TTTzMAbNu2bXX2dTqdzOv1JtyO7777jlVVVcVsKy0tZWlpaWz48OEJn4ckJtn69IoVKxgA9p///Cdm++OPP84AsP/9738Jn+tC7dOUEpOAq666CgBw5MgRzJgxA0ajEYcOHcK1114Lk8mE6dOnAwBkWcbzzz+P3r17Q6vVIj09HXfeeScqKipizpebm4sJEybg//7v/zBgwABotVr06tUL77//fp1r5+bmJtRGu90Ok8nUshfaCg4dOoTevXvDarXWec7hcDTpXCaTCXa7vZVaRprjXOj7V1xxRZ1t1113HQBg7969TXi1LUN9//zTnv3/TO+++y4YY9Frng2HDh2CIAi49NJL6zxnNptj0sQaM3DgwDojlykpKbjsssvO6u/pha69+rTL5QIApKenx2zPzMwEgCZ/Etpc53KfpoA9AYcOHQKg/CAAQJIkjBkzBg6HA8888wyuv/56AMCdd96JBx54AMOHD8cLL7yAmTNnYsWKFRgzZgyCwWDMOQ8cOIBp06Zh3LhxWLRoEURRxI033ohPP/307L64BpSWltZ5RH7p6pOTk4OdO3di9+7dZ6mVpC2dq33/9OnTAIDU1NRmHU99nwDJ1f9XrFiB7OxsXH755c16LU6ns06fbix3OCcnB6FQCMuXL2/WNRNx+vTpZv+ekqZrrz59+eWXg+d53HvvvdixYwdOnjyJ9evXY8GCBZgyZQp69uzZ5NdyofXpVsth/6HYCadPaq3TtYhFK6K/w9Ls4yOdwOfzYdu2bZg/fz50Oh0mTJiA7du3w+/348Ybb8SiRYuix2zduhVLlizBihUrcPPNN0e3X3nllRg7dixWr14ds33//v1Yu3Ytpk6dCgC47bbb0LNnTzz00EMYPXp0s9veWqqrq5GWllZn+5gxY/DJJ5/Ue9ycOXMwbtw4DBgwAEOGDMFll12Gq6++GldeeSVUKlVbNrndeD/5BKFwgJgMhIwM6MaObdax50vff+qppyAIAm644YYmH0t9v2nuu+8L7NpV3N7NAAAMGODA889f1ezjk7X/79mzBz/++CMefPBBcBzXrNcWL59Wo9HA5/PVe8ysWbPw3HPPYcaMGXjyySdxxRVX4PLLL8e1114Li6X5f2MjtmzZgu3bt+Oxxx5r8blayxf33YfiFs4Ba02OAQNw1fPPN/v4ZOnTvXr1whtvvIE5c+Zg6NCh0WPz8/PjzhlKxIXWp1stYHf6JJR6A611unZ1ZifIycnBihUr0KFDh+i23//+9zH7rF69GhaLBaNHj0ZpaWl0e+Qjk02bNsV08KysrOjH9oDyUcytt96Kp556CqdPn0ZGRkZrv6wm0Wq1+Oijj+psb+xd4+jRo7F9+3YsWrQIGzduxPbt2/H0008jLS0NS5YswaRJk9qqye0mdPo0QseOtXczWsX50PdXrlyJN998Ew8++CC6d+/e5OOp7zfNrl3F+Oqrk+3djFaRrP1/xYoVANCidJh//OMfyMvLi9nW2ETq9PR0/PDDD5g/fz7WrVuH1157Da+99hrUajUee+wxPPbYY81+A1FcXIybb74ZnTt3xoMPPtisc7SF4l27cPKrr9q7Ga0mmfp0hw4dMGTIEFx77bXIycnBli1b8OKLLyI1NRXPPPNMk1/bhdanWy1gt2iTp+BMS9sS6QSiKCI9PR09evSImb0siiI6duwYc8yBAwfgdDrrzVUtLo4dgerWrVudThHpeEePHm33gF0QhGbPcB48eDDef/99BAIB/PDDD1i3bh2ee+453HDDDdi1axd69erVyq1tX0I7/6zO1JL2nOt9f8uWLbjtttswZswYLFiwoFnnoL7fNAMGNC0/vy21tC3J2P8ZY1i5ciX69OmDfv36Nfu1DRkyBIMGDWrycZmZmXj11Vfxyiuv4MCBA9i4cSOeeuopPP7448jMzMTtt9/e5HNWV1djwoQJqKqqwtatWxOuynE2OAYMaO8mxGhpe5KlT2/btg0TJkzAjh07ov1wypQpMJvNeOKJJzBr1qwm//94ofXpVouyW5KCkmwa6wQajaZO+SFZluFwOKIjIWeK9xH7+U6tVmPw4MEYPHgw8vLyMHPmTKxevRpz585t76a1quamnySjc7nv//DDD5g0aRL69OmDNWvWQBTbbxDhQun7AFqUgpJskrH/b9u2DceOHYtJWWgPHMchLy8PeXl5GD9+PLp3744VK1Y0ObgJBAKYOnUqfvzxR2zcuBF9+vRpoxY3T0vST5JRsvTp119/Henp6XXaMmnSJMybNw9ff/31WR/QONf6dPIMi5/junbtis8++wzDhw9PaLbzwYMHwRiLeVe6f/9+AIlXxziXRH5JCwsL27klpLUlQ98/dOgQxo4dC4fDgfXr1yfViB31/fNbW/f/FStWgOO4mBSE9talSxfYbLYm92lZlnHrrbfi888/x7/+9S+MHDmyjVpIWqIt+nRRURFCoVCdYyMTWCWpfedAngt9mqrEtJJf//rXCIVC+Mtf/lLnOUmS6qy6eOrUKaxbty76vcvlwttvv40BAwa0ezpMS2zatAmMsTrb169fDwDo0aPH2W4SaWPt3fdPnz6Na665BjzPY+PGje32aRb1/QtTW/b/YDCI1atXY8SIEejUqVObtL8h//3vf1FdXV1n+zfffIOysrIm9+nZs2dj1apVeOWVV6ITFEnyaYs+nZeXh6KiojorNr/77rsAgIsvvrh1X0Q9zuU+TSPsrWTkyJG48847sWjRIuzatQvXXHMNVCoVDhw4gNWrV+OFF16IqViRl5eH2267Dd9++y3S09Pxz3/+E0VFRVi6dGnMeZcvX45jx47B4/EAADZv3oy//vWvAIDf/va3yMnJAaDMBI8sfx5Ztvrll1+G1WqF1WqtszRvYyRJwjvvvBP3ueuuuw4GgyHuc7Nnz4bH48F1112Hnj17IhAI4Ouvv8aqVauQm5uLmTNnNqkdkde6Z88eAMr92Lp1KwAkVWWBC1l79/2xY8fi8OHDePDBB7F169Zo/wCUCUZNrTxDfZ80RVv1fwDYuHEjysrKWqX2+oYNG7Bv374624cNG4YuXbrEPWb58uVYsWIFrrvuOgwcOBBqtRp79+7FP//5T2i1Wjz66KMJX//555/HK6+8gqFDh0Kv19f5HWvod4ucXW3Rp++55x4sXboUEydOxOzZs5GTk4OvvvoK7777LkaPHo1f/epXTW7nBdenW30ppnNYIqtnNbYS4htvvMEGDhzIdDodM5lMrG/fvuzBBx9kp06diu6Tk5PDxo8fzzZu3Mj69evHNBoN69mzZ8yqiREjR45kAOI+aq98eOTIkXr3y8nJadJ9yM/Pr/dcqLWCarzVHjds2MBmzZrFevbsyYxGI1Or1axbt25s9uzZTV7tkTHWYDtI6zmX+35DfWTkyJFNug/U9y9Mydj/GWPspptuYiqVKrrqbUteW32PpUuXRvfFGatC/vjjj+yBBx5gl1xyCbPb7UwURZaZmcluvPHGJq1MyVjiv1ukdSRjn963bx+74YYbWHZ2NlOpVCwnJ4fNmTOHVVdXN+u1XWh9mgu/IHIW5ebmok+fPvjPf/7T3k0h5Kyivk8uZNT/yfmG+vTZQznshBBCCCGEJDHKYb+AlJeXIxCof3ErQRDOyoQ9p9MJr9fb4D7n8sRbknyo75PzjdvthtvtbnCftLS0RheSaSmv1wun09ngPna7HWq1uk3bQc591KcbRgH7BWTq1Kn4qoEV3HJycnD06NE2b8e9996LZcuWNbgPZWqR1kR9n5xvnnnmGTzxxBMN7nPkyJE2LxO8atWqRidUb9q0CVdccUWbtoOc+6hPN4xy2C8gO3fuREVFRb3P63Q6DB8+vM3b8fPPP+PUqVMN7tPclSYJiYf6PjnfHD58GIcPH25wnxEjRkCr1bZpOwoLC6OVjOozcOBA2Gy2Nm0HOfdRn24YBeyEEEIIIYQkMZp0SgghhBBCSBKjgJ0QQgghhJAkRgE7IYQQQgghSYwCdkIIIYQQQpIYBeyEEEIIIYQkMQrYCSGEEEIISWIUsBNCCCGEEJLEKGAnhBBCCCEkiVHATgghhBBCSBKjgJ0QQgghhJAkRgE7IYQQQgghSYwCdkIIIYQQQpIYBeyEEEIIIYQkMQrYCSGEEEIISWIUsBNCCCGEEJLEKGAnhBBCCCEkif1/Ewd5JtHuDqQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "colors = ['lightblue', 'lightcoral', 'darkblue', 'darkred']\n",
    "\n",
    "# get working directory\n",
    "cwd = os.getcwd()\n",
    "\n",
    "# navigate one level up\n",
    "root = os.path.dirname(cwd)\n",
    "\n",
    "for dirpath, dirnames, filenames in os.walk(root):\n",
    "    for filename in filenames:\n",
    "        if filename == 'InfoMatrix.xlsx':\n",
    "            info_matrix = pd.read_excel(os.path.join(dirpath, filename))\n",
    "\n",
    "\n",
    "samples_of_interest = sorted(sXRD_EISParams['sample'].unique())\n",
    "carls_names = np.array([])\n",
    "\n",
    "for i_sample_of_intrest, sample_of_interest in enumerate(samples_of_interest):\n",
    "    print(sample_of_interest)\n",
    "    carls_name = np.array(info_matrix.loc[info_matrix['Name Nastia'].str.contains(sample_of_interest, na=False), 'Name Carl'])\n",
    "    if len(carls_name) == 0:\n",
    "        print(f\"No match found for: {sample_of_interest}\")\n",
    "    carls_names = np.append(carls_names, carls_name) \n",
    "print('carls_names: ', carls_names)\n",
    "\n",
    "xrd_results = { 'name carl':   np.array([]),\n",
    "                'name nastia': np.array([]),\n",
    "                'potential (V)':   np.array([]),\n",
    "                'skinlayer thickness (nm)': np.array([]), \n",
    "                'error sl thickness (nm)': np.array([])}\n",
    "\n",
    "\n",
    "\n",
    "XRDData = pd.read_csv('Skin_layer_continuous_with_potential_calibration_at_1p54_VRHE.csv', sep = '\\t')\n",
    "print(XRDData.head())\n",
    "fig, axs = plt.subplots(figsize = (3, 3), constrained_layout = True)\n",
    "axs.tick_params(which = 'both', direction = 'in', bottom = True, top = True, left = True, right = True)\n",
    "ic = 0\n",
    "for carls_name in carls_names: \n",
    "    if carls_name in np.unique(XRDData['scan']): \n",
    "        print(f'---{carls_name}---')\n",
    "        xrd_results['name nastia']              = np.append(xrd_results['name nastia'], np.full(len(XRDData['potential_RHE'][XRDData['scan']==carls_name]), samples_of_interest[ic]))\n",
    "        xrd_results['name carl']                = np.append(xrd_results['name carl'],   np.full(len(XRDData['potential_RHE'][XRDData['scan']==carls_name]), carls_name))\n",
    "        xrd_results['potential (V)']            = np.append(xrd_results['potential (V)'], XRDData['potential_RHE'][XRDData['scan']==carls_name])\n",
    "        xrd_results['skinlayer thickness (nm)'] = np.append(xrd_results['skinlayer thickness (nm)'], XRDData['d_skin_avg'][XRDData['scan']==carls_name])\n",
    "        xrd_results['error sl thickness (nm)']  = np.append(xrd_results['error sl thickness (nm)'], XRDData['d_skin_avg_err'][XRDData['scan']==carls_name])\n",
    "\n",
    "\n",
    "        axs.fill_between(XRDData['potential_RHE'][XRDData['scan']==carls_name], XRDData['d_skin_avg'][XRDData['scan']==carls_name]-XRDData['d_skin_avg_err'][XRDData['scan']==carls_name], \n",
    "                         XRDData['d_skin_avg'][XRDData['scan']==carls_name]+XRDData['d_skin_avg_err'][XRDData['scan']==carls_name], alpha = 0.5, color = colors[ic])\n",
    "        axs.plot(XRDData['potential_RHE'][XRDData['scan']==carls_name], XRDData['d_skin_avg'][XRDData['scan']==carls_name], label = samples_of_interest[ic], color = colors[ic])\n",
    "        axs.set_xlabel('potential vs. Ag/AgCl (V)', fontsize = 12)\n",
    "        axs.set_ylabel('skinlayer thickness (nm)',  fontsize = 12)\n",
    "        ic += 1\n",
    "    else:\n",
    "        print(f'problem with {carls_name}')\n",
    "fig.legend(bbox_to_anchor=(0.5, -0.1), loc = 'center', fontsize = 10, ncols = 4, frameon = False)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "xrd_results = pd.DataFrame(xrd_results)\n",
    "#xrd_results.sort_values(by='potential (V)', inplace = True)\n",
    "xrd_results = xrd_results.pivot(index='potential (V)', columns='name nastia', values=xrd_results.keys().tolist()).swaplevel(0, 1, axis=1)\n",
    "xrd_results.sort_index(axis=1, inplace = True)\n",
    "xrd_results.reset_index(drop=True, inplace=True)\n",
    "xrd_results.head()\n",
    "xrd_results.to_parquet('xrd_results.parquet')\n",
    "#xrd_results.to_clipboard()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0e9d9a7",
   "metadata": {},
   "source": [
    "# Nyquist Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04068ebe",
   "metadata": {},
   "source": [
    "## Nyquist Plot - As-prepared Prep 11 = sample 1 (Figure 7 Main Text top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "a5c3ea3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "available potentials [0.982, 1.132, 1.232, 1.332, 1.382, 1.482, 1.582, 1.602, 1.622, 1.642, 1.662, 1.682, 1.702, 1.722]\n",
      "choosen potentials [1.482, 1.622, 1.682, 1.702]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_24349/3415115614.py:134: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.savefig(fname = f'impedance_spectra_as_prepared.png', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n",
      "/opt/homebrew/anaconda3/lib/python3.12/site-packages/IPython/core/pylabtools.py:170: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import os\n",
    "import numpy as np\n",
    "from numpy.typing import NDArray\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit import Model, Parameter\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "from matplotlib.ticker import FixedLocator, StrMethodFormatter\n",
    "\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (2.5, 2.5)\n",
    "plt.rcParams['figure.dpi'] = 120\n",
    "\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "plt.rcParams['ytick.minor.width'] = 1\n",
    "\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "\n",
    "\n",
    "#select potentials and samples for comparison\n",
    "target = 'Prep11_EIS_1'\n",
    "\n",
    "available_potentials = sorted(sXRD_EISData.loc[sXRD_EISData['fname'].str.contains(target), 'potential RHE'].unique())\n",
    "print('available potentials', available_potentials)\n",
    "target_potentials = [ 1.47,\n",
    "                      1.63,\n",
    "                      1.69,\n",
    "                      1.71]\n",
    "closest_potentials = [available_potentials[np.argmin(np.abs(np.array(available_potentials) - target_potential))] for target_potential in target_potentials]\n",
    "print('choosen potentials', closest_potentials)\n",
    "\n",
    "\n",
    "mask = (\n",
    "    sXRD_EISData['fname'].str.contains(target) &\n",
    "    sXRD_EISData['potential RHE'].round(3).isin(closest_potentials)  # rounding avoids float-equality issues\n",
    ")\n",
    "data_for_plot =  sXRD_EISData.loc[mask]\n",
    "data_for_plot = data_for_plot.sort_values(by=\" Frequency (Hz)\", ascending=True)\n",
    "\n",
    "colors =  ['paleturquoise', 'aquamarine', 'darkcyan', 'darkslategrey', 'orange', 'yellow', 'green']\n",
    "alphas =  [1, 1, 1, 1, 1, 1, 1, 1]\n",
    "markers = ['x', 's', 'o', '^', 'v', 'd', '*', '+', 'D']\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 1, tight_layout = True)\n",
    "ax_inset = inset_axes(ax, width=\"44%\", height=\"44%\", \n",
    "                    bbox_to_anchor=(-0.08, -0.08, 1, 1), bbox_transform=ax.transAxes)\n",
    "\n",
    "#ax.set_title('A) impedance spectrum')\n",
    "ax.tick_params(which='both', direction = 'in', bottom = True, top = True, right = True, left = True)\n",
    "for ipotential, potential in enumerate(closest_potentials):\n",
    "    ax.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' ZHIT (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' ZHIT (Ohm)\"], \n",
    "            marker = markers[ipotential], markersize = 3, fillstyle = 'none', linestyle = 'none', color = colors[ipotential], \n",
    "            label = f'{np.round(data_for_plot.loc[data_for_plot['potential RHE'] == potential, 'potential RHE'].iloc[0], 2):.2f} V')\n",
    "    ax.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' fit 3-ladder (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' fit 3-ladder (Ohm)\"], \n",
    "            marker = 'none', markersize = 3, fillstyle = 'none', linestyle = '-', color = colors[ipotential])\n",
    "ax.set_xlim(-20, 2200)\n",
    "ax.set_ylim(-20, 2200)\n",
    "ax.set_ylabel(r'$-\\mathrm{Imaginary \\ part \\ of \\ impedance} \\ (\\Omega)$')\n",
    "ax.set_xlabel(r'$\\mathrm{Real \\ part \\  of \\ impedance} \\ (\\Omega)$')\n",
    "#ax.axhline(y=0, color='black', linestyle='-')\n",
    "ax.set_aspect('equal')\n",
    "#ax[0].legend(fontsize = 14)\n",
    "ax.xaxis.set_major_locator(FixedLocator([0, 500, 1000, 1500, 2000]))\n",
    "ax.xaxis.set_major_formatter(StrMethodFormatter('{x:.0f}'))\n",
    "ax.yaxis.set_major_locator(FixedLocator([0, 500, 1000, 1500, 2000]))\n",
    "ax.yaxis.set_major_formatter(StrMethodFormatter('{x:.0f}'))\n",
    "ax.yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "ax.xaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "\n",
    "# Create inset plot\n",
    "\n",
    "ax_inset.tick_params(which='both', direction = 'in', bottom = True, top = True, right = True, left = True)\n",
    "for ipotential, potential in enumerate(closest_potentials):\n",
    "    ax_inset.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' ZHIT (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' ZHIT (Ohm)\"], \n",
    "                  marker = markers[ipotential], markersize = 3, fillstyle = 'none', linestyle = 'none', color = colors[ipotential])\n",
    "    ax_inset.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' fit 3-ladder (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' fit 3-ladder (Ohm)\"], \n",
    "                  marker = 'none', markersize = 3, fillstyle = 'none', linestyle = '-', color = colors[ipotential])\n",
    "ax_inset.set_ylim(-10, 260)\n",
    "ax_inset.set_xlim(40, 310)\n",
    "\n",
    "ax_inset.set_aspect('equal', adjustable='box')\n",
    "ax_inset.yaxis.set_major_locator(FixedLocator([0, 100, 200]))\n",
    "ax_inset.xaxis.set_major_locator(FixedLocator([0, 100, 200, 300]))\n",
    "ax_inset.yaxis.set_minor_locator(AutoMinorLocator(n=2))\n",
    "ax_inset.xaxis.set_minor_locator(AutoMinorLocator(n=2))\n",
    "ax.plot([], [], color = 'darkslategrey', label = 'fit')\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "labels  = labels[::2] + labels[1::2]\n",
    "handles = handles[::2]+ handles[1::2]\n",
    "ax.legend(handles, labels, loc = 'lower right', ncols = 2, frameon = False, \n",
    "           labelspacing=0.2,\n",
    "           handletextpad=0.3,\n",
    "           columnspacing=0.7,\n",
    "           borderpad=0.2,\n",
    "           borderaxespad=1.0,\n",
    "           handlelength=1.0,\n",
    "           markerscale=0.9)\n",
    "plt.savefig(fname = f'impedance_spectra_as_prepared.png', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b554285",
   "metadata": {},
   "source": [
    "## Nyquist Plot - annealed Prep 12 = sample 1 (Figure 7 bottom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "b6f7f96d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_24349/529703297.py:135: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.savefig(fname = f'impedance_spectra_annealed.png', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "available potentials [1.012, 1.162, 1.262, 1.362, 1.412, 1.512, 1.612, 1.632, 1.652, 1.672, 1.692, 1.712, 1.732, 1.752]\n",
      "closest_potentials [1.512, 1.612, 1.692, 1.712]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import os\n",
    "import numpy as np\n",
    "from numpy.typing import NDArray\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit import Model, Parameter\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "from matplotlib.ticker import FixedLocator, StrMethodFormatter\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (2.5, 2.5)\n",
    "\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "\n",
    "\n",
    "#select potentials and samples for comparison\n",
    "target = 'Prep12_EIS_1'\n",
    "available_potentials = sorted(sXRD_EISData.loc[sXRD_EISData['fname'].str.contains(target), 'potential RHE'].unique())\n",
    "print('available potentials', available_potentials)\n",
    "\n",
    "target_potentials = [ 1.47,\n",
    "                      1.61,\n",
    "                      1.69,\n",
    "                      1.71]\n",
    "closest_potentials = [available_potentials[np.argmin(np.abs(np.array(available_potentials) - target_potential))] for target_potential in target_potentials]\n",
    "\n",
    "print('closest_potentials', closest_potentials)\n",
    "\n",
    "mask = (\n",
    "    sXRD_EISData['fname'].str.contains(target) &\n",
    "    sXRD_EISData['potential RHE'].round(3).isin(closest_potentials)  # rounding avoids float-equality issues\n",
    ")\n",
    "data_for_plot =  sXRD_EISData.loc[mask]\n",
    "data_for_plot = data_for_plot.sort_values(by=\" Frequency (Hz)\", ascending=True)\n",
    "\n",
    "colors =  ['paleturquoise', 'aquamarine', 'darkcyan', 'darkslategrey', 'orange', 'yellow', 'green']\n",
    "alphas =  [1, 1, 1, 1, 1, 1, 1, 1]\n",
    "markers = ['x', 's', 'o', '^', 'v', 'd', '*', '+', 'D']\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 1, tight_layout = True)\n",
    "\n",
    "\n",
    "ax.tick_params(which='both', direction = 'in', bottom = True, top = True, right = True, left = True)\n",
    "for ipotential, potential in enumerate(closest_potentials):\n",
    "    ax.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' ZHIT (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' ZHIT (Ohm)\"], \n",
    "            marker = markers[ipotential], markersize = 3, fillstyle = 'none', linestyle = 'none', color = colors[ipotential], \n",
    "            label = f'{data_for_plot.loc[data_for_plot['potential RHE'] == potential, 'potential RHE'].iloc[0]:.2f} V')\n",
    "    ax.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' fit 3-ladder (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' fit 3-ladder (Ohm)\"], \n",
    "            marker = 'none', markersize = 3, fillstyle = 'none', linestyle = '-', color = colors[ipotential])\n",
    "ax.set_xlim(-20, 2200)\n",
    "ax.set_ylim(-20, 2200)\n",
    "\n",
    "ax.set_ylabel(r'$-\\mathrm{Imaginary \\ part \\ of \\ impedance} \\ (\\Omega)$')\n",
    "ax.set_xlabel(r'$\\mathrm{Real \\ part \\  of \\ impedance} \\ (\\Omega)$')\n",
    "ax.set_aspect('equal')\n",
    "\n",
    "\n",
    "ax.xaxis.set_major_locator(FixedLocator([0, 500, 1000, 1500, 2000]))\n",
    "ax.xaxis.set_major_formatter(StrMethodFormatter('{x:.0f}'))\n",
    "ax.yaxis.set_major_locator(FixedLocator([0, 500, 1000, 1500, 2000]))\n",
    "ax.yaxis.set_major_formatter(StrMethodFormatter('{x:.0f}'))\n",
    "ax.yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "ax.xaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "\n",
    "\n",
    "# Create inset plot\n",
    "ax_inset = inset_axes(ax, width=\"44%\", height=\"44%\", \n",
    "                    bbox_to_anchor=(-0.08, -0.08, 1, 1), bbox_transform=ax.transAxes)\n",
    "ax_inset.tick_params(which='both', direction = 'in', bottom = True, top = True, right = True, left = True)\n",
    "for ipotential, potential in enumerate(closest_potentials):\n",
    "    ax_inset.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' ZHIT (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' ZHIT (Ohm)\"], \n",
    "                  marker = markers[ipotential], markersize = 3, fillstyle = 'none', linestyle = 'none', color = colors[ipotential])\n",
    "    ax_inset.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' fit 3-ladder (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' fit 3-ladder (Ohm)\"], \n",
    "                  marker = 'none', markersize = 3, fillstyle = 'none', linestyle = '-', color = colors[ipotential])\n",
    "ax_inset.set_ylim(-10,260)\n",
    "ax_inset.set_xlim(40, 310)\n",
    "\n",
    "ax_inset.set_aspect('equal', adjustable='box')\n",
    "ax_inset.yaxis.set_major_locator(FixedLocator([0, 100, 200]))\n",
    "ax_inset.xaxis.set_major_locator(FixedLocator([0, 100, 200, 300]))\n",
    "ax_inset.yaxis.set_minor_locator(AutoMinorLocator(n=2))\n",
    "ax_inset.xaxis.set_minor_locator(AutoMinorLocator(n=2))\n",
    "\n",
    "ax.plot([], [], color = 'darkslategrey', label = 'fit')\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "labels  = labels[::2] + labels[1::2]\n",
    "handles = handles[::2]+ handles[1::2]\n",
    "ax.legend(handles, labels, loc = 'lower right', ncols = 2, frameon = False, \n",
    "           labelspacing=0.2,\n",
    "           handletextpad=0.3,\n",
    "           columnspacing=0.7,\n",
    "           borderpad=0.2,\n",
    "           borderaxespad=1.0,\n",
    "           handlelength=1.0,\n",
    "           markerscale=0.9)\n",
    "\n",
    "plt.savefig(fname = f'impedance_spectra_annealed.png', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4377496",
   "metadata": {},
   "source": [
    "## Nyquist Plot - annealed Prep 12 = sample 1 (Figure S9 - right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "5a8c6122",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.012, 1.162, 1.262, 1.362, 1.412, 1.512, 1.612, 1.632, 1.652, 1.672, 1.692, 1.712, 1.732, 1.752]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_24349/3193623986.py:120: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.savefig(fname = f'impedance_spectra_annealed_SI.png', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n"
     ]
    },
    {
     "data": {
      "image/png": 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B+gEINzdAhwLAHYSpiET1ZNooraN7Ya6uJU4RwHwVFLX/+dhmngUCPWbyS02yBKOhrKJLXWedZa4NpzlGNzc31qxZw7vvvsv58+eJiooiMFBelgLW7rS7tz/63EwCo6reTSFTCwxWx6hQWb/2qsKSFqOyeK2gUaFFXaDDxz2RwlNPQ9+7QamGvI0l5XiNg6CX6mSCbebZFSWFlCwP8saFmbVcpC1TfzhdjzEgIICAgABnm9GoEBYLhTrrpICnHNLAcRQv8HZxta47FLpkFIDwQ1rYrc7Ske3RDZ0+m3D30xTsG4dH/3WQZd2hhdIfQj+p8drFsggE6lJO0QsXVCga/azz9YZTHeO5c+c4fPhwOeGIqVOnOsmixoE+NxNhsXax5H3SjkMEtsdwfh9qhUClUiN0l8AXFG4l55zqMo/TXeYReOB73D03E3DpG/QHhuIeULy21HdanZ2iBUEeJgzFTjEIDTFEsoYL11gzGUfjNMf44Ycf8thjj+Hv74+np6eUrlAornvHmF861ovcYnQYCjdvsgt0BHp4WI/1qeBRkm/GhTOd54JCSUa/oRQe+w4AN49zthLAt24TYWYE35EsOcVgNEwmCk/nd9pkKsBpT2Xx4sWsX7+e0aNHO8uEeqPC7Wi1ID+zxDHKsV4cSH46/n6hKIpywVyEsigbikB4uKEQhZiKW4KlF3ZbIoajdN9qvd5zCLi2rvVtzQg2kswJcuzSPiMRsJ95lrHH9jtKSEho0AXkTnOMOTk5zdIpOgK5xVgPFK9hdLWYEUoX0F0slVe808SSR++jj4BCQVjKJlLDBtEicHPJeflboCi+VoIRZgTfcomTWCd+NCjR4oIShXSOPPPc+HCaYxw2bBi7d+9mwIBrXyDb2LiWvdIA+Zklu388/Vo4wiSZ4jWMRWhQUwgF1n3PqEBh0zk0Q1iq1RFaWo3B0LYH5O6wL0e3BfyfqPZ2a0kiE4PdmKILCsLxYCKRjqpVs8dZe6Wd5hiDgoIYOXIk999/Py1btrTLs8WAuV6xtRhdNO64emidbE0zoXgNY2ZuAS20CsgvVpku/QtQASbI1HuS7TqVKL8QyH2uJN+lZY0XdWdiIIMiaV2iCwosCHJLi83KNFqctkbg2LFj9OjRg4SEBPbu3Su99u3b5yyTGg0FxWOMnn7B0uL35oDBYGDq1KlERETg7e1Nv379OHDggJS/ZMkSgoKC8Pf3Z968eZQOYHno0CG6deuGh4cHAwYM4MKFWs7kFq9hFIY8FEolFBQ7Rq+SU9J8bwMXsBQZiE9JQpm3xr4M0+UaL+rWSxv8oAVuzKUDAcgL9ZsKTmsx7ty501m3bvTYWozNbXzRZDIRFRXFvn37CA8PZ/369dx3330kJiayZ88eli1bxsGDB/H09GTw4MF06NCBadOmYTAYGD16NPPnzycmJoaFCxcSExPD3r17K72X2Wy2G86IdLmKVgmhQb7WBP0V8AWsQjsUaMLQiWiC9XsIDDDRxesHzNkHUQGo24Lf46AdWqN6HiNb2tHig5oJROCOqpqrmg5paWmkp6eXSzcYDLi4NI9Z9kZRCyGEXetAqby+F7tKjrGZrWH09PS02/L50EMP8dRTT3H69GlWr17No48+Ks08zp07l1WrVjFt2jR27dqFRqNh+vTpADz33HO8/fbbnD9/ntatK54lzszMtJPe79I+ihdnTaB9iCvdu/cGU6x165/FGgLGIyeZNomfkh3QjSJ/f8L03yNtTAl8EXwm1aiOCejYRDJg3fc8gQi8bd63mbB8+XJeeqninT/NRUzCaY4xIyOD2bNn8/PPP9sF1QHrX/vrFUNuCh17dcXcvSNmdfP4klVGfHw8mZmZREdHExcXx7hx46S8rl27Si2+uLg4unXrJuV5eHhIE1yVOcaynDiTyNgnFrH7g2JhWbMBXKxOEaBQFURc/xe42HYMvY//q9T6RlfQDq+ybNv+ZxMWsjFKXWgBfM0l6Tx5WU7TwWlNsyeffJJz586xYsUKPDw8+Oabb+jduzfvvvuus0xyKkIILu7/nMsHPiOkdRRh7doREeVL6tGNdq3p5oJerycmJoZnn30WHx8fdDod3t7eUr63tzc6nVXnq2xe2fyacs9tvQkNibBGC8w0QmFJ3uWwoVxs8wC9j/+LsNxN1m42AEUQ72ddplMJtv3POaWcIoAK+/FheVlO08FpLcbt27fz+++/06pVK1QqFSNHjqRz5848/PDDPPbYY84yy2koFAoyL8Xj7Wudhc5OS8M3OJjzf/xCfpFXswprYDQaGTNmDNHR0VLXWqvVkpubK52Tm5uLVqutMK9sfkX4+/uXGccWdFRfQaUQJeFSC4Hi7YBtkj4n/Mp3uKpyMNzQEg2X7QusYpmOQKBEIUmH3U0LjpAN1D56X1Ng5syZjBkzplz6iBEj5DHGayU/P59WrVoB1sBYRqORdu3aERsb6yyTnI6rbwRgHVawDSdcPBWHrhk5RovFwsSJE1EoFHz++efSrHunTp2IjY1l+HBrt9Uaz6SzlLd8+XKpDL1eb7e7qCJUKpV9flE+XLjMpbN/E+5d3NIs/vYLrPulXcnBpI7G1a8FFJZyjNUs09FhkpxiL/z4B4GSY2yOBAcHVygordE0n1l3p3WlIyIiOHv2LADR0dFs3LiRHTt22O2bvt6I6H2f1G32DQrCYrGQdjG5WYU1ePTRR7ly5Qpff/21XesiJiaGjz76iHPnzpGamsrSpUuZNMk64TFw4ED0ej2ffvopBoOBRYsW0atXrxqPLwLSOkZT1kUoKN5ZVHx7qcNrguOHL4F+v/21VSzTOUa2pJQThjtDCKnwPJmmhdNajDNmzCA2Npbo6Gjmzp3L2LFjEUKwePFiZ5nkdNx9AqQWlNFgIOX8OXrfP6vZhDW4cOECK1euxM3NzU57c+vWrQwbNowZM2bQt29fzGYzjzzyiCQmotFo2LhxI9OmTWPWrFn06dOHNWvWVHabiilex+gVGGwNSQDYhvv0ri0wC3e0JNLpTjcUiuLBR7+nQB1R6TKdFAr5vlSXW4+ZjypQ3pZpeihEIxnZT05ORqfT0aFDB2ebck3YgoPblp3UZmugMT+LS/s+AuDQ1q3k5uiZ8tFeWcG7llQo4HHpT9AXr3448gbotoNNPSwP0EFRsA+unXLAE8zKSFTtzpcINZZiLUlkYCCLImlFjxJwQYlPqaU5frgynusnVvq1CKdUV6btN+XoeNiV0WhGSsPCwpxtgtMxF5XMshbm67hz1puyU3QQIrA9ubE/YjEX4Gf8s8QpAvmacE73mEdWZA8GZd8GgErnDsIEivJrEG3b/Wx4oqIQCz6om+Vky/VIgzrGPXv2VH8ScNttt9WzJfVLXf+ymQ350nuVqydRvW+v4myZ2qBw8+bC+TiCggPBXGQ3up7UZhwZUX0ZnNWvJNHjb+sSHbfyoSUMpUISdMCLB2nFByTUp/nXLdeFiMTAgQPtjhUKhd0aPdv42vW6wNtkKGkxeviFOtGSZkh+Or4+LVC6uECW2RoqVQ0IuCH+DSLzV0PZuZyCX8o5xmyK0GECQIsLw2lpJyEm0zxo0Flpo9EovdasWcPQoUM5fvw4eXl5HD9+nHvvvbf2g+rNCHORtcVosVjQePk72ZpmRLEWY0Sb9gT5BVl3vZiK8xSAO3jkpNhfU8ESHQuC77gsLeIeTktZgbuZ0qBPVaUq2Uj/4osv8ueff+Lr6wtAly5d+Oyzz+jdu7fd1rDrCXOhtcVoyM/H01/WYXQYxVqMiX+fICxIY20NFA/dmpUaVBYDlP07VMESnd/JJBHrHy8F8Aup/IJVO1OehW5eOG0dY0ZGRjlJLYVCQUZGhpMscj5FBdkAFBbIjtGhFK9hPPHbTjLjfrJu9yv+5icH34fOJQp8rMcWs4rTZ26HNmeky9eSxLuc4SdKWpUKIKeUtqK83a954bR+wO233864ceN48803iYqKIjExkXnz5nHHHXc4yySnY9Jbf8CF+QX4t5Ido8MoXsMYGBpKoPbXkm40EHF6AxhBdLU6O2WmmTYenuASJZ2TiYGsUk7QFzV5mORZ6GaM0xzjRx99xIQJE+jSpYvUcrzjjjv49NNPnWWS07EYCwDrUh2PZqbF6FSK40n3u3sk4vh2+ywvbzS9c0umT4LAJdNkf06pWeib8GcIoSzjbH1aXGuOJh3lu6PfSccje4ykR0QPp9nT1HFqaIOff/6Zy5cvc+nSJcLDw8uFOLieEMKCsBhRAIX5+WjlrrTjCOpA/pk9GAuycNe3QGM5L+160QTkljs9J/p2fJXW9Yt6zHaz0Lfj3D9YFTnAruFdmfr5VP5K+ktK//7Y9xx67hDHLx2XHWYdcOqUWm5uLrt27eLSpUvMmzeP1NRUhBCEhNR9v+nrr7/Oe++9R3Z2NtHR0ezZswcvLy+WLFnCW2+9hdlsZvr06bz22mtSS/XQoUNMmzaNs2fP0qdPH/73v/8RGWkNWKTX63nkkUfYtGkTfn5+vPbaa/UyOWQp0kutFkN+AZ7+8p5bh+GqxU2txNM/GFGYYlWNsA0HZgFRSBum8/UeZCZ2x7fY/+0gVZqFvocQ3BpIibsyBzh51WSOXzoupa85uIbbO9zOX0l/cUfHO7i3673sPLOT7499z8IfF/L1n19z8nLJmtrvj33Pikkr2Hxss13ZsrO0x2mO8ejRo9x99934+Phw+fJl5s2bx19//cXKlSvZsGFDncpctmwZ27ZtY//+/bRq1YrY2FhcXV3ZsmVLnWXz58+fz9WrV0lOTiYuLo4hQ4bQs2fPKrculpXVL01QUFCFyiT6zJIYJi4aNzkI1jViF9v7rz9QqVRcOH2CCFMBFH+0FlQo3c2UXobo6V7Auf2/crDvDaRRSHapscVvucRmLuOD2qGz0GWd4H3d72PKqimcSD4hpb3585uoFCpyC+1buAnpCSSkWxeX//r3r/z6969S3oLNCwBQKpREBUQBcCTpCIPfHkxmfok49LW2Lh0Z+7mxhE1w2l7pgQMHMnbsWGbOnImfnx9ZWVnk5eVxww03cOnSpeoLKIPZbKZVq1bs3bu33MMZN24cnTt35vnnnwfgs88+Y9WqVezevZuffvqJWbNmSUo/BQUFBAYGSurQoaGhfP311/Tv3x+AKVOmEBkZWam0e+fOnUlPT6/w4YLV0S5YsEA6FkJw6bf/YdJdsTvPo0UHgruPbFbBsBqKcvvVD2yH1FgOfvcZNwV8jcLN2jXO9LoRT+15NKHZAOSkh/Hd5zDq1YP8L1jP1VLb/pRYIx2oUEgO0RF7oc0WM30W9bHrBrsoXTBZTFVc5Rg8XT0J8goiMSOR+ffNZ9PRTRy9eFTK7xnRk0PPHao21Igj9zMvWLCgyrAJaWlpFeY5Gqe1GGNjY/n1V+tfN9uP38vLi7y8vDqVd+nSJQoKCtiwYQNLly7F19eXp59+mkceeaTOsvm+vr6kpKTY5Xft2tUust21Ulag1kZzFKhtSOx+oOmnwWKiU+hhFEpTsQAj+Kf+BQHWUyxmF3wuJnPvHd3xDmxBUbFKDsAtBDKYFtKEy7XMRJduHWboMjhx+YSdUwTKOcUAzwAy8jPwcPWgoKiAO2+4k5E9RvLLqV/YdHQTLw1/ifV/rrfrMkcHR3N7h9tZsXcFPVr1wMfdh6MXj5Kjz5HOyS/KJz/Dui7zpe+tzuiG0Bt45u5n+CPxD5bvWs6o5aO4MeJGoPIWpLO27dUnTnOMfn5+pKamEhpasvUtKSmpzuOLycnJ5OTkcObMGRITE4mPj2fQoEF07NixzrL5tnO8vLwqvNZRlBaotdHcBGqdSvGstHdAMGSdkbrOZl8lKn/rjLNSZYKe4Hu1BWaEFOXPHRX9Cayw2NpitpiZ+MlETlw+Uek57mp39EY9YHVEz9zzDGt+X8Oynct496F3+WDXB2w/tZ3tp6yz670ie/H8sOcZ3n14hWOShy8c5kjSESk9wj+CpMwkBt0wCK2rlh1/70BXaivqqSunmPLZFFr5W0WkNx/bLI1H2rrc10OwOqc5xrFjxzJ58mSWLVsGWFt8jz/+OBMmTKhTee7uVrmUF198EXd3d7p168ZDDz3Eli1b6iybbzsnLy9Pcp7VSepDRbL6JVQURS2i932k/rEKhVJpjZhoEaRdTObuZ5uPQK1TCWyPRZeO0nsoXNknhTNQtbCUOzWv/SASlDopKvStBNY59Gnp1uGVnCscTDhYqVO8qfVNPDHoCfbE7+HD3R8S4R/Bd0e/k67vFdmLqbdMpXdk73IOUKlU0iOiR4WtuU8mf1Ju/PKf//snO07tkNI6t+xMj1Y9+OL3L1AqlFiEhYuZF6X8fm36EeIdwndHv+PlH162K9/REzeNJWyC0xzj/PnzefTRR6VJjMjISEaOHMkzzzxTp/Lat2+Pq6ur3Zjctcrm+/n5ERISQmxsLLfccku5ayujnKx+NXgGhGIwFOHm7oawWEg6FdesBGqdjdkiUKlUFJ7bilupIVuLRYWSEsGS1aHLSVf/Ax0l473bSWUX6bWecDFbzDz82cN2Y3al6RDSgaFdhvL29rdxV7vz+/nfGb9yPGB1gh9P/LjczHFVDrAyKjq/rLO0tS5PXTll17q0cfDcQen9q1texWgumZBydCuysYRNcJpj1Gg0fPbZZyxdupSzZ88SEhJCRETdB7I9PT154IEHWLRoEf/3f//HuXPn+Oqrr9iwYQO5ubnMmDGDcePG4enpydKlS5k9ezZgL5s/YcKEcrL5MTExvPLKK6xfv55Tp06xadOmaxpjFBYTid8voeDKaVAo0YZ1ImLoXBQuHoCFrNRUTv1xhH+uvn4XujsaY/ZlVBYTKvNRaY80gFJrr+KU69aSXHVJ61CBdcLF1nqszba//37730qd4v0972fBfQv4cM+HAMy7Z55dvq0V1jOyZ43uVVtq0ro8cuEI3x//nm7h3TiTcoZCk1XV3Gg2EuwVzJLRSzicdJhlO5fx2W+fMbX/1Hqx1Vk4dR3jgQMH+PTTT7l48SLh4eFMnTqVm2++uc7lLVu2jGnTphEYGEhgYCALFy7k1ltvBaizbP7LL7/M9OnTCQ0Nxc/Pj/fff/+aVMaTtrxF1smS3ReGDOsyHVeNBiyFGAry6Tnin7JArQOx6NLh3BLUrnlgBNQg3EFRJrxQUYESha8SAQTgyiyiJZ3Fmky4HE06yif7PmH7qe38nfK3lO7l5sVDfR5ixd4VuKvd+ebIN3xz5BvA2jp88d4XG8W4XWmHabaY6buob4UtyLS8NKZ+PpWRPUYC8N+N/yUpM4n0vHS83LzKnd8UcZpjXL16NdOnT2fUqFHceOONJCYmMmjQID766CMpCFJt8fX15Ztvvqkw79lnn+XZZ5+tMK9Pnz4cP368wjx3d3e++OKLOtlTEfmXT6FUu2Mx6lG5eaFy9SD/8incIq1d78L8fLoNvdth95MBlbnUZFlxV1pRwXyKyd0bUZx/K0G10lnMK8zjrnfuIj3PfplWr8hevD/ufdb8bv1jW1HrsDE4xbKolKpyXe4CQwFv/PyGtJzIlpeam2qd1c6zfg7NAac5xoULF/Ldd98xZMgQKW3btm08/vjjdXaMTQdr10yl8bRqBQII60LjwoICWVnHkQiBWqmCNv9BHIlH4VKskFOqF7027D0y1BHka6zeUgnsI504ym8XrIh98fu4/4P7JaeoQEGHkA78nfI3hy8c5h9L/gE0rtZhTSjb5TZbzOz4e0eFrchuYd244HGBnIKccnlNEac5xpSUFO6+275ldNddd5GamuokixoGz5Y3UHg1EQBj3lWExYR/13uklopWo0YZ+zXm9negCm7vPEObC8YClEoFKUnnCTLmo7LFkvZUoCj+A5Xl2opMdaTUmhTAVYokRZ3KJlwOJhzkyfVP2k1O9IroxcrJKwnUBtLqmVa08G7Bvwb8C2i8rcOaUrYV+c72d6R1kceTj6PQN5/NCE5zjIMHD2bbtm0MHVoSmvKnn35i8ODBzjKpQYgYOpecswcxFWShULng32UwQV3uIPW0dflEl+iWmC/8jiX7Ih73LnKytc2AwlywmBDxi1F550mLuxWexS11A4giBSjM4OqCAuv4YmmnWNGEy5nUM9z+1u0UGgvt0vu07oNapWbJtiUAvDrq1WY1MVG6FdnKrxXT/zedbuHdOH7pOI0k4KhDcJpjbNmyJWPGjGHo0KG0bt2axMREtmzZwtSpU3nxxRel815++eUqSml6KJQuuHj6YSrIwqt1L8JuHELu/o/Ax7qgNiOrgPCgACdb2YzQZ8C5JYR6JyOKXFG4FllFaW0NNw2YNK6I4smu2wjidoIr3OViW5cYnxrP14e/lpatdA/vTqfQTqw7tI4Pd3/Ih7uts829Insx5eYpDVXTBmfKLVNYvmu5XddapWwYkY36xmmO8cSJE/Tt25erV69y9epVwDoJEhsbK53TXPcJm4t3Gqg0WjDqsShLHkN6voLwlvKMtMMwlN6lVNyiKQ5jsDbsPbJcW5GjbillxypyuIJ9KxAqX5f4r9v+xXvj3yMlJ4V1h9Y1q65zdZTuWn+w6wPSSCPMr3mEQXaaY6xsZ0hzwE7ZpYIN9bYwqSqNFpeom1BcOgr5WQB0CFQgCnNRuHmXu06mDgR3wlIwD6WwoMjeb00r3vCS5dqKDHUEorj5qFBYY7fkVDC2+OneT+2cokqhwizMnE47zemU03yw+wOg+XWdq8PWtW7l14rpq6Y7fFbakco9taH5/jlrpAhhwWKwKnWr3KxbC822mRdhwVVpKtPKaT588MEH9OzZE7VabacwBFbFo/DwcLy9vXn44YcpKipRtklISOCWW27Bw8ODnj17cuzYsZrfVKlCqVBSZNCXpHmUvHW35Fo9IuBRaEJVPANjG1s8mnSU5zc+z+NflmzPbBPUhi1PbAFg59876bKgC8t2Lmv2XeeqmHLLFNzUbmQV/4Fv6jitxRgfH8/s2bP5448/yinqlP5RNEWqkl6ythZLluuknztJTsp5XLyt0ZjOqTpwQ9twXNr2byhzG4zQ0FAWLFjA2rVr7dJjY2N58skn+fnnn2nfvj33338/CxcuZOHChYBVNm7IkCHs2LGDVatWMWrUKM6cOVPl3lmbJqaPIpfwC0tw1R9BFIHCB7Buq0cAhUrrgmTvq+fp9uUuTj9m7QbPIhqzxUyvVcM5dqnEEauUKlZOXMk3f1nXyw7vPtxOfaY5d51tVKaZGOgRiN6kr+CKuuMs5R6nOcaJEycSHh7OihUr8PT0rP6CZoKtGw2gdPXg0sEv8A0oDlGnUOLjB3/+sZtQSwva3BTlHCPriZEjRwKwZcsWu/S1a9dy//3306dPHwCef/55Jk+ezMKFCzl9+jRxcXHs3bsXjUbDjBkzeO2119i7dy+33357pffKzMykS5cuLP3PdJ68zZqmcMFufDFTHYFFae0uF7l5c+rRqeRhxAdrWIOVe1faOUXbwuY7lloDtvWK7MXGmRuvC2dYmuXLl1epmdgccJpjjIuLY9++fQ2qmNEYsMWOBnBx90IbEAzFg/2iIBeFhzdKpYJzh7bT5qbmvXTJRlxcHIMGDZKOu3btSlJSEjqdjri4ONq3b28nImDT06zKMUrndoyG1vdhyf4TpVIPBmt6lmsrLAqrA0QICl29KHSxYFvMeOTCEZ748gmpnL5Rffnv0P8ycvlIaYLlemkhXo847al26dKFK1euVH9iM6N0i1Gl8cS/7U0leWbrrMCV84n0HXv9SI5VpJdpS69KL7MmfLTmGzj/Jkqht066lOpGSxQWorycjMJsQYUCH4uKe965B4PJIJ2So8/h57ifAesEy4LhC+Q4Kc0YpzXXPv74Y2bOnMnEiRPLidPedtttTrKq/rF3jFo8AiLIPbsLFApUHl5YzGbCew65riTHKtLLtKVXpZdZFf7+/uz89Wcizz0BWXswWfxxcc2EoJJutG3Sxd2UR5FSiUqpwhdXfvlsAek66xiaq8qVInMRp1NPczr19HU9wWKjsWgm1idOq8XJkyfZuXMnP/74o126QqHAbDZXclXTRlhMpB38EgDvkDaYLhzEVesr/UDNJiNpyancNO0d5xnpBGx6mTZOnDhBREQEWq2WTp06ER8fj8FgkLrTJ06c4KmnnqqyTJVKRedOnTFfLF6Ko9KDnzWvbDfaxZCPISyS5Et/88OvX3LwgDUYm5+HH2unr+XnuJ95e/vbtPBuwcpJK6/77nNj0UysT5z2hP/973/z6quvkp2djdFolF5NfUa6KpK2vIXuolXFp3W/UfgEtkR15S98fFsAClJPHiWsz9hmKzlmMpkoLCzEbDbbvR8/fjzffPMNhw8fJicnh0WLFklCIh06dOCGG25gyZIlGAwGPv74YxQKhSQnVyUKF5RRT4I6EJXQW0OlYt+NVpv05OeDRZjZ8OmzHNxfEqFSCEGYbxhFZut38tVRr8rd5+sEpznGnJwcZs+ejbe3NyqVyu7VXMm/fAqVxhON1h+VWoMu8woWkxF//zDCw2/AL6w9Ub2rn1Boqrzyyiu4u7uzcuVKFi1ahLu7O6tXr6Zr164sXbqU4cOHEx4eTsuWLaWIjmCdtf7555/x9fXlgw8+4Ntvv61Zl82QgyLxTTBeRbgC3cp3o00qNYSFcXj/JtKT46VLtRot2fpsur3c7bpfo3g94rSu9JAhQ9i/f78UMuB6QQiBh591TFWfk47W3xoMTO3qRud/3EnRodWoIm9qlso6CxYsKLew28aUKVOYMmVKhXnR0dHs37+/9jcszAVhndBSFK+IsutGA355l8j2DGPnd/8npXVu2ZkZA2bw2LrHajwDvVm3mRyzVWmmUBTipnCr8r2Pyofh2uG1r5NMg+A0xxgcHMx9993H/fffbxcpEJqfcIQNm+SYh1+xM9S42+ULiwXzlROYUv+WlXUcgaFkVwvpQEtbhlViR2U2oBAqMtMvk68r2bFx8vJJKZxoRVv8yjpBozBixGh3ToEoqPJ9piWT97PeR4mScHW47CQbGU6NK929e3fOnj0rBbuH5iEcUdle6Yihc8lLOoq7r3Xg2rdlOykvPe0ChempBAgzKJvHzJ5zEXBsDmTtw+gShZpE1ka8R4a6Fba1ihaFilPZOax+/3HKLOAhXZdOZEBkue7zZt1mzhvPO8RCc/G/88bzvJ/1PhqFhkd8H3FI2c0FZ+2VlkUkGhCF0gW3wEguHd1OeM8haIMjUQozFouZ/JxURGhfSN/tbDObD67FO6rMaRBo60a7Sq1Irf4y//fRv9HlXJUuCfUJ5cHeD/LOjndwVblK3efNus1km7LJFtn1YqoZMwWiQHaQjQS5aVIPVLVX2mIooDA3nZRzx2nfpg/kXcFsNqEUZvwubUMIo6ys4xAU0PUtRPpvqI1XIc2WakbgAkKw/YdVZF69JF0R5R9FYmYix5Kt2wD/M+Q/bNZt5qLxIiZMNbijAlHc8lSixFIs41P6fXXIDtKe62Kv9KJFi3juuecA7MRoy9JcxxihZIG3yWwm7cxhgkNbYjabsJhNKI06MBtBdowOQMBfs1AYr2LRKPnyrnfJVLdCYF31YLGY+HnnRrsrEjMTAWjbry0DRg5A76+vUbdZhQozZskpgtVJqlDZnWPDXBxwprQjLYvsIJ1LgzrG0jOLe/furfCc5jDGWBXmIuvgu3B1xcffFwCL2YTQeBJ7WUGbVh0I+MdDTrSwGVG8pU/pbSHLtRVmRYm+4onda7GYra1AFxdXevt34GBaLNOnTKdLpy7Wy8u0EityZGWdn4/Sp0az0nmWvHITNhUhO0jn0KCOsbSqyvU4xgglLUa1uxZVcWQms8X6AxFCcPx8FncOi3KWec0LlXVZztrO75GpbgUIUChJSfqbLRuXS6eZTEUcTLPuvGnXtl1FJQHWLrGttadChRIlaoW6TktwbDPbOZYcqcyqsDnI1Tmr5aU+DYA8xtiACCEkkVpXN58Sx1jccrlyPpE7n658iEGmFhQkQ/opLLiR5W1rLQosFjPfffY8hsKSPevurp78a9I0WkS0rHJbm82BqVEz02/mNZlnc2y1dZCZlsxruq9Mzbi+N302MMJchLBYnaCbdxAKhfXjN5tNCIvluhOPqF+KJ0E0heS4hwICBYLd339A6sW/7c6cMH4cke3b4ObmVmWJKlT4K/0JVzvuGQ3XDmeiz0Qi1BH4K/3tuuaVkWnJZLNus8NskCmP3GJsIITFxIXvXwOsAhLuomRPuNlsIj/jKj3HLnaWec0Ona64RegNZoULoMBssfDbT6vszvvn1H/Rrpr1cbaxw/rswta2BZlkTGKzbnOj6FI7a61hfdKgjjEvLw8vL6+GvGWjIWnLW2Sd+hWwCkio1CVdNovZhKHA0mzFI5xKus0xwtHfNmEylmgsLnxhYbXfRzXqBt2ZUtZBVtZ1NmOWdt/IOJ4GdYxhYWGStt6wYcPKSY41Z/Ivn8LF0x+VAlRqDfqcdNx9rDLwAYGtMLa/fsI7NARarSeQBxqw7XQ5uH21lD99yvQaOcVbjA+wZP8fPJ2yEkOxHF6RxYKuqAiVQkG/li157bbb6F6BDNe1YHOQK7JX2G0pLI2tS+3sVqOz1hrWJw3qGNVqNfn5+Xh6ela6XKdZIwQexaIRpR2jWq2hVVhosxaQcBZrB7yHzTH6BYaRftm6/TS6TbT9iUJIO2KSMwR7Y1WcTS3iqu5/VZb/U2IiPyUmonVx4ZbwcIc7yUd8H2F51vJKl/acN55vFM6xudGgjvGee+6hQ4cOtGnTBr1eX6lS9549exrSrAbBs+UNZBzfikd0TwDUbvYtRFlAon7IcmsFgMVi5uxJ6zral194udxES3KGYHusmfMpguwCoAY7XUqjM5kkJ/mPli354M47HeYgw9XhVS40b0zjjc2FBnWMn3/+ORs2bODs2bP8/vvvdgGQmhMViUhEDJ2LIStZEpDwCo4ErEt4MlLP4eHuD2ZZQMLR5GisLfRdm5djMZt4+YWX8fay31l0JMHMmt0WLBVvQqk1By5fpuf//se6e+9lbMeO11zecO3wKrvUZsxcMl6qMK+pc12ISLi4uPDQQ9ZdHQkJCcyfP78hb+9UFEoX/DoN4tKBNYR26o9P+A0oFdbFxflZV/D/xzQKU44728xmR5GLGxaLmf0/fcr0KdMrdIr/21X1PmYF1sU/KoUCs6iZ97QAD/3wA4BDnGN1Xeqa7sWWqRlOa56sWrWKgoICfvjhB5KSkoiMjGTo0KHNIsZ0ZSIS5iKrgMT5gxu5ceI/oDAbs9mEi8JC4ZYXEUX5soCEozDriydelBz97VssZhMB/gF2p1TlFG3OkFL/19Qp2hA41jlW1aU2Y26W3enrQkSiNGfOnOHOO+9Er9cTGRlJUlISGo2G7du306FDB2eZVa9YpAiBCnKvnMPHzx+L2YTRaEYUZYG5SBaQcCT+1v9ss9HBQdZhjOQMwde/mUlMK+/ovDIz8cwpWQZTqNWSXRxEXgGEeHggFApyDQYKTNWPQwrgQQc5x+q61PJEjONwmmN88sknuf/++3njjTdwcXHBbDYzb948nnzySbs91c0Jc5EeAI/W3fH0sob/NFtMCE8fYtMyZQEJR2IxIYr9VmjEDYy4ayAqlYrkDMHbm02YyjQUvTIz8c7MJCAlhbIyJiE5ObQMDmZs3748eu+9UvqxtDRmbt/Ob5cvV2vOuB9/pIO//zVPyDzi+wjvZr1baX5zHWtsaJy2JfDQoUMsXrxYCmqkUql45ZVXOHTokLNMqncsxco6ShfXcvukbQISKv8oZ5nX7FBYQ0Nz7tRBaXnO+t/snaJXZiZh8fG0josjsAKnCKBJSSHj+HE+WLmS/1tdshaye3Aw+8eP5+ikSdzcsmUFV5ZgEYKZ27dfa5UAq8pPZdREsUemepzmGDUaDXl5eXZpeXl5zSo2bVlskmOYiqR90haz9Yt85Xwifcc+7izTmiElzqPIUIirqyvJGYILaSVn+KWmWh1iamoVrsaeT7/5hrc/+8wuzeYg/1GNczx45UoN71I1UeqoKvPlfdTXjtMc45AhQ3jooYc4ceIEBQUFxMbGEhMTw9ChQ51lUr0hLCYu/PAauQm/4x3ShsCQkmUHZrMJiywg4XgUpcUYBAqFgvW/lYwJqg0GWsXH19ghlubz775jdwU9mw/uvBPXKsL/WoRg/d9/V5pfU6obQ5S709eO0xzjG2+8gYeHB926dcPLy4sePXqg0Wh4/fXXnWVSvZG05S0yjm9FWMy07jcK//CSySWzxUxOSgo9R1+bjJVM5Yji2eSkUq3FqBMn6uQUbTyxaBFFRvtua/fgYP6YMKHKcqf/9NM13LUEuTtdvzjNMfr4+PD9999z+fJlDh48SHJyMt9//z2+vr7OMqneyL98ClfvFniFdUSl1lCQlSrlBQaEExDZSxaQqEdsUm+2OWj33Fzc9fprLnfMnDnl0roHB9Ovii51ntHIsbS0SvNrSnXd6RXZK675HtczTtdjDAkJoU+fPoSEhDiszAMHDqBUKnnllVektCVLlhAUFIS/vz/z5s2TWhFgnQjq1q0bHh4eDBgwgAsXLkh5er2emJgYvLy8iIiIYN26dXW2S+NunYkuyEqR0lzUGiJbWfdJm9PO1Lns5k56ejrDhg3D09OTDh06sGPHjqovcCmJ2W0LYWAj4vTpaluLKqUSlbLqn8eF5GROnTtXLv2DO++s8roRGzdWmV8ZaWlpLFiwgAULFnDkzSNlI77aYRCGyjMdQGlbFixYQJoDnH1jotntP7NYLDz55JP06dNHStuyZQvLli3j4MGDeHp6MnjwYDp06MC0adMwGAyMHj2a+fPnExMTw8KFC4mJiZFELubPn8/Vq1dJTk4mLi6OIUOG0LNnzyrXWprNZrsF3kaPECxXD+IaZY0l4uppv1ZR3iddPbNmzSIkJIT09HS2b9/O2LFjiY+Px9/fv+IL3FtiIR4Fgi59h2IwGAAVWCxoDPZOQ1AyVePp7s7KV17hhuLtZ1/++COvrVhRsthbCLu4RJOfeYY/vv7arrzqluRcKDPpWFPS09N56aWXpON3Z79baTCt+qasLWPGjHFIuWlpaaSnp5dLNxgM0gqWhsDpLUZH8/HHH3PTTTdxww03SGmrV6/m0UcfpW3btoSEhDB37lz+9z+rasquXbvQaDRMnz4dNzc3nnvuOQ4fPsz58+ela59//nm8vb3p168fI0aMYO3atVXakJmZSZcuXaRX78mvsenwFWmftHeL1kDxPumUsxgNhQhz9bL21ys6nY7vvvuOl156CQ8PD4YPH07Xrl3ZtGlTpdeYzRaKFH54FGZy36QFnD1nVdVpceFCudai7VihUNg5RYCHhg3j+UcekXoYZYO1FRmN/HrwYLn7+7i6lktzNIOuVKw1oEKFRlF/QzNpaWmcPXvWLu3s2bMYDAbM1/g9Xr58ud1vx/ZKSEggM7Phwjo0K8eYkZHBO++8Y/eXDCAuLo5u3bpJx127dpVadGXzPDw8pC19WVlZpKSkVHptTTFb4NVN8Vw6up2si6co0uus6WYjuuxU1J2HgVoeY6yM+Ph4tFot4eEls/bVPYfMrGwmbO7F0xsHonZR8/O+IwAEJCdXeL4AbosQdk7RRseoKNITEuyGX0rz36VLy6V9fNddVVXJYXgoPOyOVah4zO+xeo0muHz5ckaOHGmXNnLkyAZ3XvVJs3KMzz33HHPmzCk3gaPT6fD2Lum+ent7o9PpKswrnW87p7Sgaelra4OHq4rC3HTSUs9hLJ4MMJtNWFzdufLTYiyFubUu83qhqmdUFds3bucIvfAozGTiU9bJiNJf+NJuTgHsvkClXKnCCRcWFZVLc8Te6JrwiO8jeCg8UBX/q2lLsXPnzs1KWNbROG2M0dvbW1LzLo2/v3+d/ur89ddfHDp0iGXLlpXL02q1dvfKzc1Fq9VWmFc633ZOXl6e9MMsfW1l+Pv7lwsPKwoyKPrlRRQqF2nXi8U2W2oxoTDo5H3SlVDVM6qKPIOFed+G87rPO3w/dCFQviVQenzR0TzRsyfvHjlS5oaCJ3r1cuh9mlOs6ZkzZ1Y4XjlixIgGHWN0mmOsrFtSV3bv3s3p06cJCwsDICcnBxcXFxISEujUqROxsbEMH25dGHvixAnpr2WnTp1YvrwkxrBer5f0FP38/AgJCSE2NpZbbrml3LWVoVKpyp2jTzvHKaAoKwWV0n474LHfD9On3634y/ukK6Rdu3bodDqSk5Ol53vixAkmTZpU4flJSUkUFRXRpm1b9m7ewrhjYWTPGwkZOk4XVCzAAKCu4LkBxRM3cPrXXyWV77JU9p3wz88ns7DQeiAESoOBX8LCqEtbzVBm0mjEiBF13imWkJAA1E21xmw206pVKy5evCiltWrVipSUFAqq+HxrQnBwMMEVTF419I64BneML75ojZtsNBql9zbOnDlDZGRkncr95z//KWk9AjzxxBO0bt2a//znP+zfv58ZM2Ywbtw4PD09Wbp0KbNnzwZg4MCB6PV6Pv30UyZMmMCiRYvo1asXrVtbJ0hiYmJ45ZVXWL9+PadOnWLTpk0cOHCg1vZZjNZ1c8bsNJS2FmNx2NT4Y8dwbdGFO4dF1anuzR2tVsuIESOYP38+7733Hjt27OD48eOMGDGiwvNt0nUajYZ27doVpxoICtCSW8UPN6KS9YcuLi4EBQVRaDRSVMHkgr+PT6Vlhnh6EuIgKT2bHaWP68q1iL6qVCo8PDzsbPHw8ECj0TQL2UBwgmO0LYMxmUx2cV+USiUhISF88skndSrXw8MDD4+SgWh3d3e0Wi2+vr4MGzaMGTNm0LdvX8xmM4888ghTp04FrD+ejRs3Mm3aNGbNmkWfPn1Ys2aNVM7LL7/M9OnTCQ0Nxc/Pj/fff7/WsmjCYuLyns/wDmmDd2h0iYCExUxBZiZCKOV90tWwfPlyJk+eTEBAAOHh4Xz11VeVLtVJSUmpMF1GpqYohKP7tDXAaDQye/Zsli5diru7e/UXNCHKhjQAuPDDa2Qc30r3kXPtwqZevXqR375eRYs+D3DTuDkNbaqMTJOhot9VfeKUWWm1Ws369eubnVOsjPzLp/AIiECl1qC7WjIu4+vbgjadetAtzFXe9SIj04hw2nKdu+66i58ctKG+KeDuax2PyUtNlNJcXFzpMmA4ivwUDH987iTLZGRkyuK0WenAwEAeeOABhg8fTuvWrVGW2pf68ssvO8usesGz5Q24YV3r5unXwi5PCAEqDZivbTZPRkbGcTjNMZ44cYLevXtz+fJlLpeShi+75ao5EDF0LgV/WrcRerdsL6VnpiXi1ycGy5nmF0dbRqYp4zTHWHYBdHNGoXQhr6CQIt0pUKnxa2mV2dflXMFt28tydEAZmUZGo9gSKITAYrFIr6aObYF46cWzhQXZpKWeQ+lp3V5osZgxqzRkWhQIQ+23GMo4jg8++ICePXuiVqtZsGCBXd5nn31GeHg43t7ePPzwwxSV2v6XkJDALbfcgoeHBz179uTYsWPXZIfBYGDq1KlERERIoiWl18zWVTqvrvzzn/8kNDQUb29vunbtyvfff9/gtth+R7YF6Q2F0xxjRkYGEyZMICgoCBcXF9RqtfRqjoiiQut2wDK7Xi6nZ5NS6I7m5uazraupERoayoIFC7j//vvt0mNjY3nyySfZuHEjFy9e5OLFiyxcuFDKHzduHHfeeSeZmZk88sgjjBo1ClMNQqpWhslkIioqin379pGdnc2cOXO477770Ol0dtJ5cXFxbN26lU8//RRAks6bPXs2mZmZ9O/fn5iYmDrbYeOpp54iMTGR3NxcPv30U2JiYsjIyHCKLQ2OcBITJ04U/fr1Exs3bhRarVZs3LhR9O3bV7z33nvOMskhdOrUSXTq1KlcevLW18XVX98VBcc2CHHmJ1F4/Ftxbtti8c2/B4uc1ItOsFSmLI8++qiYP3++dPyf//xHTJs2TTreuXOniIiIEEII8ffffwtPT09RWFgo5UdGRopff/3VoTaFhoaKP//8Uzz00ENi4cKFUvqqVavEbbfdJoQQYtu2baJt27ZSXn5+vnB3dxfnzp1zmB2HDh0Sbm5u4tixY06xpbLfVX3htBbj9u3bWb9+PSNHjkSlUjFy5EjWrFnDl19+6SyT6gVbIKwWER0JCO+Im5stnrQZIQfBatRUJFeXlJSETqcjLi6O9u3b2+3hrYskXVXEx8eTmZlJdHR0naXzrpWZM2fi7u5Onz59uOOOO+jatavTbGlInOYY8/PzadWqFWDdlmc0GmnXrh2xsbHOMqleSNryFrpzv6NSa8hNOQfFYzFuGk/UqOjaUiMv7m6kVCRXZ0uvqxRaTbGF1Hj22Wfx8fGps3TetbJ8+XJ0Oh3bt2/nrrvuQqFQOM2WhsRpjjEiIkJSAY6Ojmbjxo3s2LGj2WxCt5F/+RTeYda91RmJxxEWqwiBUqWiVfSN8uLuRkxFcnW29LpKodUEo9HImDFjiI6OloRW6iqd5whUKhWDBg1i+/btbNmyxam2NBROc4wzZsyQWodz585l/Pjx3HXXXcypIPJaU8fdOxAAr4BQlC4lk0vCtrhbplFik6uzceLECSIiItBqtXTq1In4+Hg7KbCaSNJVh8ViYeLEiSgUCj7//HNpXW9FtpSWziudV1o6z5GYTCbOnj3bKGypb5zmGGfOnMmoUaMAGD16NBcuXCAuLo558+Y5y6R6wbPlDbgWRwcMbHeTlJ6XexVT4A1YiuQdL87GZDJRWFiI2Wy2ez9+/Hi++eYbDh8+TE5ODosWLZI0IDt06MANN9zAkiVLMBgMfPzxxygUCm699dZrsuXRRx/lypUrfP3113ayYjExMXz00UecO3eO1NRUli5dKtlSWjrPYDCUk86rCzk5OaxduxadTofJZOLrr79m586d3HbbbQ1ui1NosGme64Sys2cWs1Fc+ultkbnjHXH5+wVCnPlJiDM/iZQ9y0TuhieFbv1jIv/7/zrRYpn58+cLrGLe0mvVqlVCCOuMa8uWLYVWqxWTJ0+2m4WOj48XN998s3BzcxM9evQQf/311zXZkZiYKADh5uYmPD09pdeePXuEEEK8+uqrIiAgQPj6+op///vfwmKxSNf+8ccfomvXrsLNzU3ceuutIjEx8ZpsycnJEQMHDhQ+Pj7C29tb9OzZU3zzzTdSfkPaIkTDz0o7RXbMxoEDB/j000+5ePEi4eHhTJ06lZtvvtlZ5jiEiuSRzEWFnP3mGbzD2hMaahVOvXL5DOasZHzzUzC7+uAz5h1nmCsj0yS4LmTHwBqWdODAgeTl5XHjjTeSn5/PoEGDpLCmzQmL0X5xN1gXeGfqITbuHHFXG8UGJBkZmWKctld64cKFfPfddwwZMkRK27ZtG48//nilsTyaLLo0AoIiUZQSqTWbTVyKP8uZ4+eZ+sQXTjRORkamLE5zjCkpKdx99912aXfddRepqalOsshxlJ6FO3H8KKqsswSEd0QI6z5wIQRms5H4Pw/wj/FPywu8ZWQqwfY7SkhIuKY4NbXFaX24wYMHs23bNru0n376icGDBzvJovrh8vZlKFUu5Kacw1IqkJKbUs3gAX258bY7nWidjIxMRTitxdiyZUvGjBnD0KFDad26NYmJiWzZsoWpU6faRQ9siqK1pbdAXfpuPlC8uFtY8AmNRqFQ0LJ1N/zNuZj+Wodr2CJnmisj02ix/Y4aeh2kU4Vq+/bty9WrV7l69SoAffr0sVsc2hxEa929rJHsvPxD8ApqJaULYZGVu2VkGimyUG094xFgHT8MbN9P0qwzGPLJzMzDx1AATd/3y8g0O+R1IvWMpsMd6HLSSU9LxGKxavUZDHoMV+MoSE/EqM93soUyMjJlkR1jPaN09yMjOx2dLhOlUgWAxWxC5eKCu8aVIn3TUh2RkbkekB1jPWLTYlTorhIY2AqFwvpxm81GTp84y4nTF9Hc/E8nWykjI1MW2THWI0lb3iLj+FbC2vfDyztISjebzQSRg0fHgXhH93WihTIyMhXhNMe4efNmnLhNu0HIv3wKbUh7VGpX9LosKd3fP5QO3XrQ1kuekZaRaYw4zTFOmzaNyMhIFi5caBdXurnh7hMAgCovRUpzUWtw63A7Ku9gZ5klIyNTBU5zjMnJySxevJjt27cTFRXFqFGjyu2Eaep4trwBV42H9cDDzy5PCAvCZHSCVTIyMtXhNMfo6urKhAkT2L17N8eOHSMqKoqYmBjatGnDkiVLyMzMdJZpDiNi6Fy8w28AwNUnREpPT0sk+dQ+CjOTnWWajIxMFTSKyZfAwEBCQkLw8fHBYDDw3XffERkZydq1a51tWp2wiUh06dod96i+FBkK0OvzALBYzOh0mSiUYJSX6sjIVEnnzp3p3LkzCQkJDXpfpzrGHTt2MHbsWCIiIvjll194/fXXSUpK4uDBg3z11VdNPv6LQqGAjHhcNR4lYVPN1u7z1QsX5KU6MjKNFKdtCYyOjiYzM5NJkyZx9OhROnToYJc/dOhQ3NzcnGTdtWETkRCFOXDxd3JTE9EGtUKhUKFSueLmpkUX0EFeqiMjUw3XnYjEf//7X8aNG4e7u3ul5yQlJTWgRY7n6u9fEdQyiuwLx3HzDcJV44lSqSS0ZXv8RdMKQC4jcz3hlK600Wjkzz//bBbqOVWhNOkBCGrVDrVryR8AYbEgzCZnmSUjI1MNTnGMarWar776qsl2lWuKRmtdouMeegM2GZ38/Gwuxu0h/fQhzv2x3YnWycjIVIbTJl/uuusufvrpJ2fdvkHIzcmiIC8TfUGe1Dou1OehUClBWDj3xy9OtlBGRqYinDbGGBgYyAMPPMDw4cNp3bo1SmWJj26Kqt0VETpoJiL+F5Qq++iA7kV5FCoU9B3zuBOtk5GRqQynKnj37t2by5cv220JbFbjjsYClCoXCgt10nIdP/8wsi+fId+nI+FyECwZmUaJrOBdj9hmpfU5VyXHqFa7EnTj3Xgny7PSMjKNlUax80UIgcVikV51wWAwMHXqVCIiIvD29qZfv34cOHBAyl+yZAlBQUH4+/szb948O2WfQ4cO0a1bNzw8PBgwYAAXLlyQ8vR6PTExMXh5eREREcG6detqbJNtVtrDw7NMfeVZaRmZxozTHGNGRgYTJkwgKCgIFxcX1Gq19KoLJpOJqKgo9u3bR3Z2NnPmzOG+++5Dp9OxZcsWli1bxsGDB4mLi2Pr1q18+umngNWhjh49mtmzZ5OZmUn//v2JiYmRyp0/fz5Xr14lOTmZ9evXM3PmTE6fPl2lLWazmZMnT+Lq4QOAxsuqxSiEID0tkYtxe+V90jIyFZCWlsbJkyfLvQwGA+ZS4YfrHeEkJk6cKPr16yc2btwotFqt2Lhxo+jbt6947733HHaP0NBQ8eeff4qHHnpILFy4UEpftWqVuO2224QQQmzbtk20bdtWysvPzxfu7u7i3LlzQgghQkJCxN69e6X8yZMnixdffLHSe3bq1EkEBQUJQGx+7WFhOP6t0B//VogzPwlj3Pfi3LbF4sLG/4q0z6c5rJ4yMs2F+fPnC6DCV1BQUIPZ4bQW4/bt21m/fj0jR45EpVIxcuRI1qxZw5dffumQ8uPj48nMzCQ6Opq4uDi6desm5XXt2lXaalQ2z8PDQ9rSl5WVRUpKSqXXVoVCoWDA4PtwdfNEUyw9Ztsn7WHS4e7t75B6ysjIOB6nOcb8/HxatbLGWdZoNBiNRtq1a2cXV7qu2MYFn332WXx8fNDpdHh7e0v53t7e6HRWZZuyeaXzbed4eXlVeG1VdGwThrfWk6yU89J4plrthrubF6oiPe63zbzmesrIyNQPTpuVjoiI4OzZs0RHRxMdHc3GjRsJCAjA09Oz+ourwGg0MmbMGKKjo3nxxRcB0Gq15ObmSufk5uai1WorzCudbzsnLy9Pcp6lr60Mf39/trz9KADuustYAsNQKl1RKlWEtGyHwVKEyj/qmuopI9McmTlzJmPGjCmXPmLECFxcGs5dOa3FOGPGDKl1OHfuXMaPH89dd911TVJjFouFiRMnolAo+Pzzz6U1kZ06dbJriZ44cUJS6yibp9frJT1FPz8/QkJCKr22MlQqFV6uxR+tZwAqVcmEkrBYyE1JlLcDyshUQHBwsKTBWPql0WhQqVQNZofTWowzZ5Z0JUePHs2FCxfQ6XTl5Mdqw6OPPsqVK1f46aef7P66xMTEMGPGDMaNG4enpydLly5l9uzZAAwcOBC9Xs+nn37KhAkTWLRoEb169aJ169bSta+88grr16/n1KlTbNq0yW4ZUGXY9km7hXaU0nR5GeRcOoU5M5lzGb/Qpu+dda6rjIxM/eE0xwhw7tw5Dh8+TF5enpS2f/9+pk6dWuuyLly4wMqVK3FzcyMwMFBK37p1K8OGDWPGjBn07dsXs9nMI488It1Do9GwceNGpk2bxqxZs+jTpw9r1qyRrn/55ZeZPn06oaGh+Pn58f7779fIeefkZODpFyzFkgYoKMgFhQVXFxV9R8nbAWVkGisKIZwTw/TDDz/ksccew9/f325cUaFQcO7cOWeY5BBs3eyflk4jvE0XDIX5aNys9TMZDeRfPIr56iX8J3zkTDNlZJoUtt9VTVaEOAKntRgXL17M+vXrGT16tLNMqF/0OQAYDCWO0UWtwafNTRg0VU/eyMjIOBenTb7k5OQ0X6cIeAdZlyKVFqgF63ZAoz6vokusWMzw8z9hVWeI/cR6LCMj06A4zTEOGzaM3bt3O+v29Y5XsWN0d7eugRTF2wEv/X0AfXolIRvSjsKqThC7AjLj4Ofp8EVfa7qMjEyD4bSudFBQECNHjuT++++nZcuWdnlNXo9RWFAoFJiKClG6uBZrTQrMZiPuRdm4eVbSlT7zDWSfsb5XaaD9GDi1Bs5+B8E9Gsh4GRkZpznGY8eO0aNHDxISEuxixjYLPUZhVQi6HLeXoA79cHf3QqFQEhLaDn1+OqKooPw1FjOc2VBy3O8FiB5pdYzlzrVuLURZN8ENGRmZqpH1GOuB7KxMAII73oyra0lcGyEs6PNyUCnKLARIOwo/xkDW3yVpf6+D9OPlC7cY4bcJ1vc3fyE7R5lmjW02OiEhgbZt2zbYfZ26jrG5olFbP1Y3N09pn7S+IJekM3/gkX4RNy9f+wvOfgeZZZYhZJy0voJ7WluOFqP1dXAKJH1dcp7sHGVkHE6DOsZFixbx3HPPAUj7mCuiqY8xumutOoxFRXpci2elXV3daaHVYtG54VpaWcdihvPbSo47TQHfNvDbi9BmOAxfD8IM+8dBxu9QcAkiiveS2hyk7Bxlmim2dYvVbcN1NA3qGPfv3y+937t3b4XnNIsxRqytxJycdIKCIgBQuajJ7zQEX4VAYSrCIgRXL/9J8I5HIf2vkktTDoKpeAwyuJu123z1IOiLhW09wqHTf+DkItC2kZ2jjEw94LSdL82Vzp07g9nAye+XYzDo0WisLcZkVz8OeUejMhlwMZtw1XjQ6ZcJhF7cVnFBwT0gNADSdpSkaVqAIbXkWKGCkMFwZZu1FSk7R5lmSkPvfGkUMV+aHcVxa2xOEcAtJQ7fq/GYXTS46c7Ra9Md9k5R6Qpdplvfh4WCNr3EKSqLJ3BKO0WAiLFW5ygjI+NQnDb5smfPngrT3dzciIyMpEWLFg1skQNRuQJgMhXh4mJ97631w1d/lWzaEXrhR3wzjmHybIlLfnHoWEsRXP0GggDLFdAXlxV4i7UbnZ9YUr57S9BfhvS9JWOOcmtRRsZhOM0xDhw4EIVCYRetz3asUCi44447WLNmTRN1kNY6lY54mKptSaJ38XKD4nWOqvwrJZf4Aoqskiei0IAwwNWScVmUauvMdFGW9bjgErS6X3aKMjIOxmld6TVr1nDfffcRGxtLXl4esbGxjBgxglWrVnH48GEsFss1idY6leL9zbY1jEIIRLGz9MmIJerMagAUxWkEA7Zet8r2pnQYWQX0eA1C77Uemoubk63uh1vWyU5RRsbBOG3yJTo6msOHD+Pj4yOlZWdn06tXLxISErh06RJ9+vThypUrVZTS+OjcuTOiMJe4bZ/YpSepvDmtCaDV3yvpePR1BKAAa9fZ1krUBEOf5bBvDLZWJwo1CKPVYdoconsYBPaTnaLMdcN1IzuWkZFRbmmOQqEgIyMDgLCwsBoFnWqMWIpbjGaTEZWLmiSVliN+HUAIqRutAGiBtc1uKf7fkGbvFME6uSKM9q3Ef/zP6hBlpygjUy84rSs9YMAAJk6cyOnTp9Hr9fz9999MmTKFgQMHAnD06NFy4hJNBZWL1WEpVda/O2EmHR4mPT6ZJ4g6/bn1pCBKPn0LoLKpjgtrhl/v4rxC6//uYSVdZxcP2SnKyNQjTnOMH3/8MTqdjhtuuAGtVkvnzp3Jzc3lo4+sytaurq6sWrXKWeY5BFuL+IqrLwVKVzoeWYxbYXpJ99kCmIrfm6+WutICWX9a39oc4n1n5K6zjEwD4bSudHBwMDt27ODSpUtcvnyZsLAwwsLCpPyG3gLkaEov1VHlxDNw1yP4Zhy3zj7bPnUlYKdDqwSFC4gi62H4KLh5jdxtlrluuW5FJMLDwwkLC0MIIS1vseoXNm1sTjHZ1Y+8pB2EZhzH7O+CSmOynmBrKUr+rniwURRZF3S3HAL9v5IdooyME3Dq5Mvs2bP5+eefyczMtMszm5uPnH9oUTYK3SUAVGpTSYYZUFE8C0PxGkWDdfZ5RBK4+shOUea6x1kiEk5rmj355JOcO3eOFStW4OHhwTfffEPv3r159913nWVSvXDF1RfXvLMlky22CWcNxU6x2DOWdopugbJTlJFxIk5rMW7fvp3ff/+dVq1aoVKpGDlyJJ07d+bhhx/msccec5ZZDsNisaBUKjFbzLi7XC5Zr62AkkWMgNoHjNnW96FDrC1FGRkZp+I0x5ifn0+rVtaAURqNBqPRSLt27YiNjXWWSQ5FqVSS7OKF26l5eFqsXWlpvWLp5Zs2pxg2Evp/ac1Lfw4K9oL7zYDK3pkKrGsbA1+wLv6WkZFxOE5zjBEREZw9e5bo6Giio6PZuHEjAQEBeHp6OsskhyKEhdD0Q5CxzzqWCCVdackxFns69zC4+TNImwPZy0sK0VesWQlAxkLw/Rco/WVHKSPjYJzmGGfMmEFsbCzR0dHMnTuXsWPHIoRg8eLFzjLJoSiEBVPi26hVZXZclm79hQqrU4u+GxJ8a3+T7A9L3mcshDZnwLVd3Y2WkZEBnOgYZ86cKb0fPXo0Fy5cQKfT0aFDB2eZ5FiECRdTqX3etm40gBsgTbKZQfepY+6p2wL+TzimLBmZ6xinr2O0UXpxd7Pg3OsoMJQc25yihlJO0YEofcFvVj0ULCNz/eE0xxgfH8/s2bP5448/yMvLs8srKipyklUOQgjMuScpp61dG6eoHQUuIcVjiNhPvmQuKn++Jdu6a0ZGRuaacdovaeLEiYSHh7NixYpmM+FSgkBpybFPqqlTDH4HtEOrHiv0nWztNpvTrY5S5Q9e99XdXBkZGTuc5hjj4uLYt28fLi7NsJVjuFwiQgvVO0X/58AlqHqHaMO1nTyWKHNdcN3tle7SpQtXrlyR1jI2K0Qp9e3qnKI8kywj0+hwmmP8+OOPmTlzJhMnTiQkJMQu77bbbnOSVQ6mAqe4Nuw9Ej36Yla4WOO6KIxAHCoUROHJeCKcYamMTKPEWXulneYYT548yc6dO/nxxx/t0hUKRfMRkfC3/rc27D3itbciyk7HKMC2edqM4Ax5LMD6RfBExb/p2HC2ysjISDhNROLf//43r776KtnZ2RiNRunV5GekbXjB2t7v8Wq73zmjHVDeKaKo8DIb+Zh5lVOsJan+bJSRkakQpwXD8vHxIScnp/oTmxidO3eG3DimnN9Fviqg/AmKqh1iudOBdnjJXWyZ65qGDobltBbjkCFD2L9/f/UnNkFyPEPKO0WFosQp1uJPkQDiyZNbjjIyDYhTQxvcd9993H///YSGhtrlvfzyy06y6tpIS0sjPT0dgrwBOHFoK7u//4Csq8mYTaWHCJT4BoQyaPQcuvQdUm25Akgkv36MroS0tDSWLy8RtJg5cybBwcENaoMjkOvRuGgq9XBaV/r222+vMF2hUPDrr782sDWO4eTJk3Tp0oWgTm2J+G9vDu/6qtprbh85m9uG/bPa81QoeIFOjjCzRtjqYuPEiRNNMg6PXI/GRV3rcd3Eld65c6ezbl3vKEx5xAzpR8yQfuXynnzmSbvjnd/9H2079SOsdbeGMk9GRqYaGtwxvvjii1XmKxQKXnrppQaypmakp6czZcoUdu3aRXh4OMuXL2fQoEGVnp+Wo+CTqbukeefY/v2lvLdfexuwd5CrlkziuQ+OoGgGQcBkZJoDDe4Y9+6tQnyVkljMjYlZs2YREhJCeno627dvZ+zYscTHx+Pv71/h+a56vZ1Qd5d9+zhR7Byf/NTE21NdePu1tyXnaLaY2PnJUgY98nSN52Vq27VoiK5IXe4h16Np16Ou19SWhISEeiu7IhrcMTa1LrROp+O7777j3LlzeHh4MHz4cLp27cqmTZt4+OGHK7xGIYTdKkUl4BkfT367yrf+7d//P/71yEvEk1ehc1RVs+5RRqY5IE1glsFisTRoo8lpky9Nhb/++otBgwbZhXh9/PHH0Wg0vPnmm3bn2gaWFUolrhUoBhk8PABo4Ws9Tk1Ntcvv1LITORgxV+AaVSjwKQlCLf0FrenG+tqebzAY7P5Kt23bFo1G49B71OUauR5I1zWGetT2murqkZ6eXqFjtNFQ7srp0jZ33HEHmzZtwsvLy9mmVIhOp8Pb29suzdvbm4yMjHLnBgUF4enpid5gKJcH4FpQQFGxcyyLj4c1OmBp51cVtVUaqe35Li4uBAUF2R07+h51uUauR8mxo+/RENfUpR42GlKe0OmOcdeuXRiNRmebUSlarZbc3Fy7tNzcXLRabblzg4OD0el0LFiwoNIJpPlPPcWCBQvqw1QZmSZPVb+dp59+usHscHpXWqlUcvXq1UonMpyNTqfD39+f8+fPS+EXbr/9diZNmlTpGGNl4yRgbVU2xgWtMjKNgcby25EdYw0YM2YMPj4+vPfee+zYsYPJkydXOSstIyPTtHF6V7opsHz5ciZPnkxAQADh4eF89dVXslOUkWnGyC1GGRkZmTI4fatFY1zQLSMjc33jdMcoL6OUkZFpbDjdMU6ePBk3Nzdnm+EQ0tPTGTZsGJ6ennTo0IEdO3Y42yQJg8HA1KlTiYiIwNvbm379+nHgwAEpf8mSJQQFBeHv78+8efPs/mAdOnSIbt264eHhwYABA7hw4YKUp9friYmJwcvLi4iICNatW9dgdTpw4ABKpZJXXnmlydbj9ddfp1WrVnh5eXHjjTdKMdabUj2OHj3KLbfcgre3N23atGHlypWAdbfKnDlz8PX1pUWLFrz99tt2123dupXo6Gg8PT0ZMWIEWVlZUp7Tf0tCxmGMGTNGTJ06VeTn54tNmzYJf39/kZGR4WyzhBBC6HQ68dJLL4kLFy4Is9ks1q1bJwICAkReXp748ccfRXh4uDh79qy4cuWK6NKli1i5cqUQQojCwkIRHh4uVqxYIfR6vfjvf/8r+vfvL5X773//W9x9990iJydHHDhwQPj6+oq///673utjNpvFTTfdJPr27SsWLlwohBBNrh7vv/++uP3228WFCxeExWIRx44dE4WFhU2uHl26dBEvvfSSMJvN4vDhw0Kr1Yq4uDixbNky0b17d5GamirOnDkjWrZsKbZv3y6EECI1NVX4+PiIH3/8UeTn54tJkyaJmJgYqUxn/5Zkx+gg8vLyhFqtFhcvXpTSBgwYID799FMnWlU1oaGh4s8//xQPPfSQ5FyEEGLVqlXitttuE0IIsW3bNtG2bVspLz8/X7i7u4tz584JIYQICQkRe/fulfInT54sXnzxxXq3/YMPPhCzZ88WkydPlmxvSvUwmUwiNDRUnD17tlxeU6qHEEJotVpx5swZ6bhPnz7i22+/Ff369ROrV6+W0ufPny8mTZokhBDiww8/FIMGDZLyzp07JzQajSgoKGgUvyWnd6WbC/Hx8Wi1WsLDw6W0rl27NpiwZm2Jj48nMzOT6Oho4uLi6NatRA+ytN1l8zw8PGjbti0nT54kKyuLlJSUSq+tLzIyMnjnnXfK7ZBoSvW4dOkSBQUFbNiwgRYtWtChQwdWrFjR5OoBVu2ANWvWYDKZ+OOPP0hKSqJfv361qkfr1q1Rq9UkJCQ0it+SvI7RQdRmT7WzsY1DPfvss/j4+JSz3dvbG51OB1ReL51OJ51Tep976Wvri+eee04auypNU6pHcnIyOTk5nDlzhsTEROLj4xk0aBAdO3ZsUvUAa/ymSZMmsWjRIgA++eQTQkNDq61Hq1atKqyH0Wh0+m9JbjE6iNrsqXYmRqORMWPGEB0dLYkGl7W9tN1V1ct2jm3CoOy19cFff/3FoUOHeOSRR8rlNaV6uLu7A1bhZnd3d7p168ZDDz3Eli1bmlQ9MjMzGTZsGG+88QYGg4EjR47w7LPPcuTIkWuqh7N/S7JjdBDt2rVDp9ORnJwspTW2uBwWi4WJEyeiUCj4/PPPpTWknTp1IjY2VjqvtN1l8/R6PQkJCXTu3Bk/Pz9CQkIqvbY+2L17N6dPnyYsLIyQkBC++uorXnvtNR5++OEmVY/27dvj6upqt463KT6PhIQEPD09eeCBB1CpVHTr1o2bb76Z3bt316oeiYmJGI1G2rZt2zh+Sw02mnkd8MADD4hp06aJgoIC8f333zeqWWkhhJg+fbq47bbbhF6vt0v/4YcfRKtWrURCQoJISUkR3bp1s5sFDQsLE5988okoLCwUzz33nN0s6NNPPy3uuecekZubK37//Xfh5+dXr7Og+fn54sqVK9Jr7Nix4plnnhFZWVlNqh5CCDF+/Hjxz3/+UxQWFoq4uDgRHBws9uzZ06TqkZ2dLby9vcV3330nLBaLOHnypAgODha//PKLeP/990WPHj1EWlqaiI+PF2FhYeVmpbdu3Sry8/PFlClT7Galnf1bkh2jA0lLSxNDhgwR7u7uol27duKXX35xtkkSiYmJAhBubm7C09NTeu3Zs0cIIcSrr74qAgIChK+vr/j3v/8tLBaLdO0ff/whunbtKtzc3MStt94qEhMTpbyCggIxfvx44enpKcLDw8UXX3zRoPUqPSvd1OqRlZUlRo8eLbRarYiKihIfffRRk6zHtm3bRPfu3YVWqxURERFiyZIlQgjrkqonnnhC+Pj4iKCgIPHWW2/ZXffjjz+KNm3aCHd3d3HfffeJzMxMKc/ZvyWn75WWkZGRaWzIY4wyMjIyZZAdo4yMjEwZZMcoIyMjUwbZMcrIyMiUQXaMMjIyMmWQHaOMjIxMGWTHKCMjI1MG2THKyMjIlEF2jDIyMjJlkB2jjIyMTBlkx3gdM3DgQJ5//nlnm1FnjEYjEyZMwN/fH61WS05OTrlzhgwZwsKFC51gXdWYTCYUCgW7du1ytilVsn//frp3747FYqnVdffddx+fffZZ/RjVAMiOsZEycOBAXF1d0Wq1eHt707lzZz766CNnm+UQHOWQN2zYwM6dO0lMTESn0+Hj41PunK1bt/LCCy9c872uVx5//HFeeukllMoSV/HLL78waNAgvL298fT0pEePHnzwwQd2AbteeeUVnnnmGfR6vTPMvmZkx9iImTdvHjqdjuzsbObPn8+MGTPYvXu3s826JoqKihxWVkJCAm3atCmn9izjGHbu3ElKSgr33XeflLZq1SpGjhzJ6NGjSUpKIiMjg8WLF7NkyRKmTJkinde9e3ciIyNZs2aNEyx3AA2q5SNTYwYMGCCee+45u7SAgADxxhtvSMd6vV48++yzok2bNsLX11fceuut4siRI1L++++/Lzp37iy8vLxEixYtRExMjEhPT6/yHmVtmDVrlhg1apTQarWibdu24vPPP7c7pyb3mDVrlnjwwQeFr6+vePTRR4VSqRRqtVqSPquMrKws8cgjj4iwsDAREBAg7rnnHklbcPLkyUKtVgulUik8PT3FPffcU6PPccCAAeKxxx4TY8eOFV5eXiIsLEysW7dOHD9+XPTr109otVrRp08fOw3DmnwO1T2L1NRUMWrUKOHj4yNat24t1qxZIwCxc+fOGn+Os2fPFuPGjRPe3t4iPDxcLF++3M6GgoIC8dxzz4l27doJrVYrWrduLT777LMa2VcRs2bNstNIzMnJEV5eXuLNN98sd+4ff/whFAqFpLcohBAvvPBCpc+lsSM7xkZK6R+00WgUX3zxhQDEDz/8IJ0zefJkMWjQIHHx4kVhNBrFe++9J4KCgkRWVpYQQogNGzaI06dPC7PZLBITE0Xfvn3FQw89VOE9KrPBzc1NbN68WRiNRvHjjz8KtVot9u3bJ51Tk3t4eHiIH3/8UZjNZpGfn1/tfW3ce++9YuDAgeLKlSsiPz9fPPHEEyI8PFzk5eUJIaxR52655ZYaf462Yx8fH7F7925hNpvFO++8Izw8PMTQoUPF+fPnhcFgEKNHjxZ33XVXrT6H6p7F4MGDxd133y0yMjJERkaGGDZsmJ1jrMnn6O3tLXbs2CHMZrPYsGGDUCqVIj4+XjpnwoQJok+fPiIuLk5YLBaRnJwsDh8+XCP7KuKmm26StBWFsOonAnbR+0rTpk0bMXfuXOn466+/FgEBAVU8ncaL7BgbKQMGDBAajUb4+PgIlUolVCqVeO2116T8q1evCqCcOnN0dLRdyMrSfPvtt8Lf39/uHtU5xtGjR9uljR07VkydOrXSayq6R+kfeE3uK4QQly9fFoA4evSolFZUVCQCAgLEunXrhBB1d4yl7c/OzhaAWLt2rZS2YcMG4evra3dNVZ9Ddc/i0qVLAhDHjx+X8o4fP27nGMtS0ef48MMP250TGBgovvzySyGEEOnp6QIQhw4dKldWXb4rQgjRrl078cEHH0jH69atE4DQ6XQVnt+rVy/xr3/9Szr++eefhVKprLT8xow8xtiIefrpp8nOziYrK4spU6bw888/YzKZADh79iwAN910E76+vtIrOTmZS5cuAfDtt99y8803ExwcjLe3NxMnTiQzMxOz2VxjG1q3bl3u+OLFi9JxTe5RtoyaYLtH27ZtpTS1Wk1kZCRJSUm1Lq80oaGh0ntPT88K00oHlIKqP4fqnoXteZQuo2x5NfkcW7ZsaXdNaTvPnz8PQIcOHcrVtybflYrw9/e3m+nv2LEjAMeOHSt3bn5+PqdPn5bOAcjJycHPz6/S8hszsmNsAnh5ebFs2TLOnTvHsmXLAAgJCQHg+PHjZGdnS6+CggL+85//cOnSJcaMGcPjjz9OUlISubm5rF69GsBu9rA6EhMTyx3b4v3W9B6lZzQrOq4IW2jNhIQEKc1kMpGUlERERESN7XcUVX0O1T0L23mlyyj93hHPKioqCoAzZ86Uy6vOvsro1auXXSznHj16cOutt7J48eJy5y5duhSNRsP48eOltNjYWHr37l0j+xsbsmNsImg0Gl588UUWLlxITk4OkZGRjBw5klmzZnHhwgXAGjZz69atXLlyBZ1Oh8ViITAwEDc3N+Lj4yv8QlfHli1b+PHHHzGbzWzbto2NGzfy8MMPA9T5HiEhIRX+gEsTGhrK0KFDmTt3Lqmpqej1ep555hlcXV0ZNmxYretxrVT1OVT3LMLCwhg0aBDz5s0jKyuLrKws/vvf/0plO+JZBQUFMW7cOGbNmsXp06cBuHLlCkeOHKnWvsoYPXo0O3bssGu1rl27ltOnTzNr1iwKCwuxWCy89dZbvPnmm2zYsIGgoCDp3G3btjFq1Kha1aOxIDvGJsTEiRMJCAjgtddeA6xf0l69ejF48GC8vLzo0KEDK1asQAhBx44dWbx4MZMmTcLLy4vJkycTExNT63tOnTqVTz75BF9fX2bNmsWHH37IrbfeClDne8ydO5fTp0/j5+eHr69vpeetXr2aqKgoevbsSXh4OCdPnmT79u12AeUbiqo+B6j6WQCsWbMGV1dXqT4PPvigdK2jntWKFSsYMGAAQ4YMQavVcsstt0gtvursq4hBgwYRHBzM999/D8Crr75Kx44dSUpKYvny5Xz11Vfs3buXp59+moKCAu69917+9a9/AdbW6fnz55k4cWKt69EYkINhyVTKwIED6d+/P6+88oqzTXEq1/PnsG/fPmbNmsVff/1VoyEQG8OHD2fUqFFSq7qp4eJsA2RkZBov/fv3r3CypTo2b95cD9Y0HHJXWkZGRqYMcldaRkZGpgxyi1FGRkamDLJjlJGRkSmD7BhlZGRkyiA7RhkZGZkyyI5RRkZGpgyyY5SRkZEpg+wYZWRkZMogO0YZGRmZMsiOUUZGRqYMsmOUkZGRKYPsGGVkZGTKIDtGGRkZmTLIjlFGRkamDLJjlJGRkSmDUx3jBx98QM+ePVGr1SxYsKDKcy0WC3PmzMHX15cWLVrw9ttvS3kmk4kHHniAVq1aoVAoygUuKk2HDh1Yt25dufRJkybx1FNP1bUqNaY2df7www9p06YN3t7eRERElIsDkpKSwgMPPICPjw8BAQE8++yz5cooLCzEx8eHAwcOlMu77bbb+L//+79rqk9NqE2dwRpfpWvXrnh6etK2bVt+++03oPk+Z6i8zl988QVarVZ6ubu7o1QquXr1arkymlKdbeEXbC+1Ws19990HWGPg9O/fn4CAAHx9fRk0aBB///13heXUW50dEYPVbDKJXSsWiF/+79/Sa9eKBcJsMlV53caNG8WmTZvEgw8+KObPn1/lucuWLRPdu3cXqamp4syZM6Jly5Zi+/btQghrQPp33nlH/Pbbb0Kj0Yjz589XWs5LL70k7r33Xru0/Px8odVqpeDktaGoIEukn9giigqya3R+bep8/vx5KSD65cuXRadOncQPP/wg5ffp00e89dZbIj8/XxQUFNjFLS7Nww8/LB577DG7tAsXLgi1Wi1SU1NrZHc5rsYJseNxIa6eqvbU2tT56NGjonXr1uLAgQPCbDaLixcviuTkZCGE857zF+KCeF/E272+EBeqvMZRdS7LkiVLxIABAyrMc/R3W280ivePHBFP/vqreP/IEaE3Gqs8vzZ1LkvHjh3FqlWrhBDW+OFxcXHCbDYLs9ks3nvvPdG7d+8Kr3N0nW04xDGe+vUb8cbggHKvUzu/rdH1jz76aLUfZL9+/eyCg8+fP19MmjSp3HnV/WASEhKEq6uryMjIkNLWrVsnbrjhhhrZWhrdlVMicftScW7bYpG4fanQXaneSdioSZ1Lc/nyZdGlSxfx7rvvCiGE2LJli+jfv3+Nrt2xY4cIDg4WplJ/qBYvXiyGDBlS4/tLFOYI8escIZa6CPEm1v9/nWNNr4aa1Hns2LFi5cqV1ZbVkM/5fREvXhYnJadoe18THFlnIYTo3Llzpec6ss56o1H844svBG+8Ib3+8cUX1TpHIWr/3T506JDw8PAQubm55fJMJpNYvny58PPzq/BaR9a5NA7pSod3uxkXVzf8W7Vjxldx+Ldqh4urG6263eyI4gGIi4ujW7du0nHXrl3tYt7WlDZt2tCrVy++/vprKW3NmjW1jsqWm3SYtGPfgVKFX/uBoFSRduw7cpMO19qmqli7di1eXl60bNmSgoICxowZA8Aff/xBVFQUQ4YMITAwkEGDBnHq1KkKyxg4cCCurq5s375dSvviiy/qFImOy7/BkXcgsCsMXWP9/8g71nQH8Mcff5Cenk50dDStWrVizpw5GAyGWpfjqOdswx9XZhHNLKLxx7VOZVRGTev8119/kZCQIH0HyuLIOn8SG8uBy5eZ3bMn5x95hNk9e3Lg8mU+iY2tdVnVsXr1akaOHFku+mO3bt1wc3Nj1qxZzJs3r8JrHf2cbTjEMWoDQug+bDKZF+PZt2oRmRfj6X7vFDz9WziieMA67uDt7S0de3t7o9Pp6lRWTEwMX3zxBQBXr17ll19+YcKECbUqw8XNBwCfyN74tu6HT6Q1sLiLu0+dbKqM8ePHk5eXR2xsLFOmTJG+PMnJyXz55ZfMnj2by5cvc8899zBy5Ei7GMA2lEol48aNk+p87NgxEhMTGTlyZN0N6zELbphg/d+BJCcns2HDBvbu3ctff/3FoUOHeP311+tUliOec0NQ0zqvXr2aESNG2P0OyuKoOidkZwPwZK9eRPn48GSvXnbpjsJkMvHll19WGGb1+PHj5ObmsmLFCrp3715pGfXxnB02+dJn7OO4uLoRu+0LXFzd6Dv2cUcVDYBWqyU3N1c6zs3NRavV1qmsBx98kD/++IOLFy+yfv16brrpJiIjI2tVhntgFAqVK3nJsWTF7yEvORaFiwb3gKg62VQdXbp0wcPDg5dfftl6f3d3+vfvz5AhQ3B1deXpp58mJSWFs2fPVnh9TEwM3333HXq9ni+++IJRo0bh4eFRd4OOLoO4Ndb/HYi7uzuPP/44oaGhBAYG8tRTT7Fly5Y6leWI52wjkyKWcZZlnCWTojqVURk1qbPZbGbdunVMmjSpyrIcVee2xfG+3z58mMScHJb++adduqP46aefUCqVDB48uMJ8d3d3pk6dysMPP0xGRkaF5zjyOdtwmGO0tRoBh7cWATp16kRsqWb8iRMn6Ny5c53KCggI4K677mLdunV1bnYrlC54BEdjKsgi+9xvmAqy8AiKRqGsv4i0JpNJcnxdunRBoVDU+Npu3boRFRXFpk2bWLduXd27Gi1vhl5PwtVY2DrR+n+vJ63pDqBsvWpTx7I44jkD+OFq1332xxU/B3ana1LnX375BSEEd911V5VlOarO07p25R8tW/J/R47QesUK3vvrL/7RsiXTunatU3mVsXr1asaPH49Kpar0HCEEeXl5JCcnV5jvqDqXvanDKCosENkpSaKosKBG5xuNRqHX68X06dPFc889J/R6vd0EQWnef/990aNHD5GWlibi4+NFWFiYNCsthBCFhYVCr9cLjUYj/v77b6HX66u891dffSVatWolNBqNyMzMrHklS2EyFAiD7qr0Mhmqr3dt6vzZZ5+J1NRUYbFYxOHDh0V4eLh47733hBBCXL16Vfj7+4tffvlFmEwmsXTpUtG+fftKyxJCiNdee01ERESI0NDQKs+rEVdPCbFjdo1mpWtT548//lj07t1bpKamiszMTNG/f3+xcOFCKd8Zz7kuOLLOQggxfvx4MWfOnBrd21F1ru2sdG3qLIQQOTk5wt3dXRw9etQu/fDhw2L37t3CYDAInU4nnn76adGyZUthMBgqLcvRz9mhjrG2zJ8/XwB2L9uU/Z49e4Snp6d0rtlsFk888YTw8fERQUFB4q233rIrKzIyslxZVaHX64W3t7cYNWqUw+tVFbWp84wZM0RwcLDw9PQUbdu2Fa+88oqwWCxS/q+//io6duwotFqtuPXWW8XJkyervPfFixeFUqkUTz75ZL3UrTJqU2eLxSKeffZZ4e/vL4KDg8Xjjz8uCgsLpfzm+Jyrq3NeXp7w8PCo8fKTplBnIYT45JNPRNeuXcuVc/DgQdGjRw+h1WqFv7+/uPvuu8WxY8eqvLej6yzHlZaRkZEpg7wlUEZGRqYMsmOUkZGRKYPsGGVkZGTKIDtGGRkZmTLIjlFGRkamDE1GdmzPnj0MGDAArVbLwIEDy+Xn5eUxffp0/P398fX1rXSHQFOSZrKRk5NDSEgId955p5TmdAmuoiz4fbr1/2pwlATXuXPn6NOnD35+fvj7+zNy5EiuXLlS7vrmJLUGVvm51q1b4+XlxQMPPEB2JdvymtJ3uyrZMbAucvf09JTyX3311QrLqa86O8wxCmEh4/g20g9vIuP4NoSwVHtNaGgoCxYs4P7776/2XA8PD/75z3/y4osvVpg/depUfHx8SExMJD09nSeffLLC8yZMmMDatWvt0goKCti4cWPtVswLAXlXwFQ7gYPa1NnG/PnziY6OLpd+6623sn79ejQaTZXXO6zONoqy4NfBkPCJ9f9qnGNt6nzs2DEee+wxVqxYQV5eHrt37yYqKgqAoKAg1q9fT2ZmJikpKXTs2JHZs2eXK8PNzY3777+/XJ2TkpI4ePAgDz30UM3rWkyuPpdtJ7ZJr1x9bpXnO6rOO3fuZMGCBWzZsoWrV68SEBDAY489VmE5Dn/OQKHBwOW0NAprIORRmzpv3boVnU4nvaKjo8tdd/r0aSn/v//9b4Xl1EedAcfsfLFYzCLxh9fE4UUDpFfiD68Ji8Vco+trI1O0bt26cnp0J0+eFK1bt67Rbg6HyRSlnxbizE9CxP8iRNopIYyF1V9TiprWOTY2VvTp00d88sknYtCgQRWe05ASXMKQKcTWXkJ8gRC/DLT+v7WXNb0aHCnBZTAYxLPPPituvPHGCvMdKrUmhJjz5RzBdKTXk1/WbJH8tdZ57ty5dgvyL126JFxdXUV+fn65c+tDguv1lStF9xEjxBuffFLjaxwhOwaIixcvVntto5Ydy4z9mYxjW/BuexPtxr+Nd9ubyDi2hczYnx1RfLUcOnSIdu3aMXnyZAICAujXr59dV6Q0DpMpshSr2Li4Q3YSJO6F9L9L0h3E7Nmzeeutt1Aq6/6oHFZnW0sx8zB0mQ937rT+n3m4Ri3HmlATCS5fX1/c3d158803K+0uOVJq7dSVU7y/831ujLiRlZNWcmPEjby38z3+vlKxqnRtqa7OotQeDCEERUVFxMfHlyvH0RJc6ZmZbPjpJwC+3raNq1nX/nwrojLZsb59+xIWFsaUKVMqFZBo1LJjFqP1Iba46SG8om6kxU0P2aXXN8nJyfz888/cfvvtpKSkMGfOHIYPH17pWIxDZYrCe4N3GAiL1UGaHae88uWXXxIcHMytt956zWU5pM5//dvqBIMHQrcF1rRuC6zHmYet+ddITSS4srOzycrK4rXXXqN9+/YVluNIqbUPdn2AyWxi7uC5TLt1Gk8NfgqT2cQHuz+odVkVUVWd77nnHtauXcuJEyfQ6/UsWLAAhUJBfn5+hWU58rv92caNGIqKGHXnnRiKivhs48a6VbAKKpMd27NnDxcuXODo0aMUFBQwZcqUSstotLJjSrV1jCv19y/JS/yL1N+/tEuvb9zd3YmKimLatGmo1WoeeughQkND+f333ys836EyRZf+hNxkUCjBNxJUjlFdyc/PZ/78+bzxxhsOKc8hdb7xDfDvBWm74PgCa9rxBdZj/17W/GukprJj3t7eTJo0iREjRmCxVDye7SiptRkDZ+CicuGtX97ik72fsPSXpbioXJgxYEaty6qIqup855138uKLLzJq1CiioqJo27YtXl5ehIeHV1iWo77bttZi6/BwHouJoXV4eL20GiuTHbv11ltRq9UEBQXxf//3f2zZsoXCwsIKy6gP2TGHaGT5d70L3cXjZBzbQm6C1RkFdB+Kf9eqJZIcRUUSXFXJVZWWKfruu++q/GtUKcpimSST3uoQ/aLAxXF/COLj4zl//jx9+vQBQK/XU1hYSIcOHTh9+nSty3NInV394I5frN3mEy9B2u4Sp3jHL9b8a6Q2smMmk4mUlJRyIsY2ykqtffLJJ3Wy6YbQG3js9sd4Z/s7TP/fdACevPNJOoZ2rFN5ZamuzrNmzWLWLKsg8JkzZ3j//fcrdYwOec7A4ZMnMRQVcf7SJQaVKuPwyZPc3b9/ncqsiJrIjtmGkUQlsg6OqnNpHOIYFQolEUOfRtuqGxajAaVag3/Xu1Aoqm6QmkwmTCYTZrMZk8lEYWEharW6wg/JYrFQVFSE0WjEYrFQWFiISqVCrVYzcOBAhBB8/vnnxMTEsHHjRq5cucJNN91U6b1jYmJ4+umnSUtL48cff6x9pQPagcYb3P1q5RBrWucuXbqQlJQkHX/11Vd88803bNiwQUozGAzSl8VgMFBYWIibm1ul977mOoO9c6yhU6zNc54yZQrvvfce99xzD2q1mrfffpthw4YBsGvXLry9venevTu5ubnMnTuXXr16Vato/cwzz2A2mxk0aFDd6gy8NPwl7u58t3R8c9uq9ScdVWe9Xs+5c+fo1KkTSUlJPPLIIzz//PNVjjk74jkPvvlm/j53joJSrTRPd3fu/Mc/HFJnsIpNb968udyyqpMnT2IymejSpQu5ubnMmTOHwYMH4+7uXum9HfLdLs01Td1cI7WRKdq5c2e5cydPnizlHzt2TPTu3Vt4enqKnj17in379lV576YizWRj1apV5WalnSrBZcgU4uC0Gs1GO0qCa/PmzaJjx47C09NTBAUFiQceeEAkJiZWee+mLrWWkZEhOnfuLDw8PER4eLhYsmRJtfduKt/tymTHduzYIdq1ayc8PDxEixYtRExMTLXRLGXZMRkZGZl6Rt4SKCMjI1MG2THKyMjIlEF2jDIyMjJlkB2jjIyMTBlkxygjI+MwTp06RdeuXfHy8sLd3Z2vvvrK2SbViSYjO/b666/TqVMnvLy8aN++PatWrbLLT0lJ4YEHHsDHx4eAgACeffbZcmU0NTmqBQsWoFar7eSZbDhddqwWOEqCKzExEYVCYfd52LaClaapPefqJLj+9a9/ER0djUKhYNeuXZWWc9ddd7F48eJy6S+++CKjR4+ua1VqxRtvvMF9991HXl4eer2eBx98EIDPPvvMTjavseNUx1gbmSKFQsHatWvJzs5mw4YN/Oc//2H//v1S/vDhw7n55pu5cuUKly5dYvz48eXKqA85qtpSW9mxyZMn28kzlcZpsmO1xFESXAAqlcru86hoT2xTe87VSXD16NGDlStX0qZNmyrLiYmJKVdngLVr1zbIcwbrZ9ypU6cGuVe94pDVkGUwF2QJ4/mDwmJ2vOyYjXHjxok333xTCCHEli1bRP/+/Wt0nSPlqC4ZDMJssQizxSIuVREMvCJqUuf58+eLadOmVVtWg8qO2bBYhLCYrf/XkGuV4Dp//rxQqVQ1upejZccMFoP4Nf9X6WWw1Ox5O0KCy0aHDh3Ezp07K73WFn/6+PHjUtqBAweEr6+vXZzq+uKee+4RSqVSaDQa4enpKbp06SJWr14tEhIShEajESqVSnh6eopOnTrVuy3XikNbjBZ9NobD69Bvmodh/4cYj33jyOIljEYjBw8epHPnzoBVtikqKoohQ4YQGBjIoEGDOHXqVIXXOkqOKrmoiEP5+Xav5CLHKevY2LBhAwEBAdx44418++23dSrD4dJMwgips+G0yvq/MNatnAqoToLLbDbTsmVLIiMjmTNnDgUFBRWW40jZsSJRhM6i47jhuPTSWXQUCcc/78okuGqCVqtlxIgRdq3GNWvWMGbMmGp7FY5g69at3HrrraxcuRKdTkdAQABg/f59+OGHDBw4EJ1Ox8mTJ+vdlmvFIXulhamIomPfYIrfCWYjCu9QRO4VhLFiNYxrZe7cuURFRXH33da9q8nJyXz55Zds3ryZQYMG8e677zJy5Eji4uLK7dMsLUd1991311mOKlStJlSt5orRaHfsSMaOHcu//vUvAgMD2bFjB2PGjCE8PJy+ffvWuiybNNOjjz4qSTMtW7asboadLqUglP2+9dXRMRuoSktwqdVqRowYweuvv84LL7xAYGAghw8fpnv37ly6dInJkyfzzDPP8N5775Urx1HPGWC/fj/HDcft0lbnrqabphu3e9xe16qWwybB9fnnn9e5jJiYGGbOnMmrr76K2Wxm/fr1dvvrK+P1lSs5ff58re/XoXVr5k2fXhdTGzWOcYyFuZj+torSurS9Fdfu91Pw7RxHFF2OxYsX8+uvv7Jnzx5JhcTd3Z3+/fszZMgQAJ5++mleeeUVzp49S4cOHcqVERMTQ//+/a9Zjqq+KT1Wc/fddzN+/Hg2bdpUJ8f44IMP8tRTT3Hx4kW+//77ukszWfSVpysr3+RfU0pLcAE89dRTvPnmm7zwwgtotVp69uwJQGRkJEuWLOH++++v0DFC03nONiqT4KoNd911F3q9nv3795Obm4uHh0eN9DxPnz/P4SbQkmsoHKOu4+aNS8e7MMXvxJSwF3P6WUcUW45ly5axcuVK9u7di7+/v5TepUsXYmNja1yOI+SorhiNXDEapVai7TjM1TF6jBWhVCorlV6qDodJMyndIeB5yHilVOEvOMQpQu1kx6r7PBwlO9ZQ1ESCqzpcXFx48MEHpYnK8ePHV/kZ2ujQunWd7lfT62piQ2PCMY7RxRVNr3GoOw3FGLfF2qUGFOrKJbCgdjJF//vf/3j11VfZs2cPLVu2tMsbPXo0//nPf9i+fTu33347//d//0dISEiFAaRsXKsclc0BlnaMNXGKtanz5s2bGTBgAF5eXuzatYsvvviCrVu3SvlOkR0D8BprdYze4yB3HXiNqfJ0R0lw/fHHH/j5+REdHc2VK1d49tlnGT58eJX3doTs2C3ut9Bd053VuaultIneE9EqtZVe4ygJLoCioiIsFosU1qCwsBCNRlOps4mJiWHo0KHo9XoOHTpUozrWd3c4ODiYS5cuYTKZcHFxiNupX+pjRsdckF2jWenayBRFRUUJtVotPD09pdeiRYuk/F9//VV07NhRaLVaceutt4qTJ09Wee+mIEf14IMPCl9fX6HVakXXrl3Fl19+aVeWU2XHaoGjJLi++OILERkZKTw8PERYWJh47LHHRF5eXpX3dtRzru2stKMkuIQQYsCAAeXKqmoVghBCtG/fvtJAYfXJgAEDxOrVq8u9LywsFHfffbfw9fWttJ6NCVl2TEZGRqYM8pZAGRkZmTLIjlFGRkamDLJjlJGRkSmD7BhlZGRkyiA7RhkZGZkyyI5RRkZGpgyyY5SRkZEpg+wYZWRkZMogO0YZGRmZMsiOUUZGRqYMsmOUkZGRKYPsGGVkZGTKIDtGGRkZmTLIjlFGRkamDLJjlJGRkSmD7BhlZGRkyvD/AYMooJW1bIgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import os\n",
    "import numpy as np\n",
    "from numpy.typing import NDArray\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit import Model, Parameter\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (2.5, 2.5)\n",
    "\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#select potentials and samples for comparison\n",
    "target = 'Prep12_EIS_1'\n",
    "\n",
    "available_potentials = sorted(sXRD_EISData.loc[sXRD_EISData['fname'].str.contains(target), 'potential RHE'].unique())\n",
    "\n",
    "target_potentials = available_potentials.copy()\n",
    "\n",
    "closest_potentials = [available_potentials[np.argmin(np.abs(np.array(available_potentials) - target_potential))] for target_potential in target_potentials]\n",
    "print(closest_potentials)\n",
    "\n",
    "\n",
    "mask = (\n",
    "    sXRD_EISData['fname'].str.contains(target) &\n",
    "    sXRD_EISData['potential RHE'].round(3).isin(closest_potentials)  # rounding avoids float-equality issues\n",
    ")\n",
    "data_for_plot =  sXRD_EISData.loc[mask]\n",
    "data_for_plot = data_for_plot.sort_values(by=\" Frequency (Hz)\", ascending=True)\n",
    "\n",
    "colors =  ['saddlebrown', 'peru', 'sandybrown', 'burlywood', 'peachpuff', 'paleturquoise', 'darkorange', 'orange', 'gold', 'aquamarine', 'darkgreen', 'lightgreen', 'darkcyan', 'darkslategrey']\n",
    "alphas =  [1, 1, 1, 1, 1, 1, 1, 1]\n",
    "markers = ['v', '8', '<', 'p', '>', 'x', 'h', 'D', '*',  's', 'H', 'P', 'o', '^']\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 1, tight_layout = True)\n",
    "ax_inset = inset_axes(ax, width=\"44%\", height=\"44%\", \n",
    "                    bbox_to_anchor=(-0.08, -0.08, 1, 1), bbox_transform=ax.transAxes)\n",
    "\n",
    "#ax.set_title('A) impedance spectrum')\n",
    "ax.tick_params(which='both', direction = 'in', bottom = True, top = True, right = True, left = True)\n",
    "for ipotential, potential in enumerate(closest_potentials):\n",
    "    ax.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' ZHIT (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' ZHIT (Ohm)\"], marker = markers[ipotential], markersize = 3, fillstyle = 'none', linestyle = 'none', color = colors[ipotential], label = f'{data_for_plot.loc[data_for_plot['potential RHE'] == potential, 'potential RHE'].iloc[0]:.2f} V')\n",
    "    ax.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' fit 3-ladder (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' fit 3-ladder (Ohm)\"], marker = 'none', markersize = 3, fillstyle = 'none', linestyle = '-', color = colors[ipotential])\n",
    "ax.set_xlim(-20, 8500)\n",
    "ax.set_ylim(-20, 8500)\n",
    "ax.set_ylabel(r'$-\\mathrm{Imaginary \\ part \\ of \\ impedance} \\ (\\Omega)$')\n",
    "ax.set_xlabel(r'$\\mathrm{Real \\ part \\  of \\ impedance} \\ (\\Omega)$')\n",
    "#ax.axhline(y=0, color='black', linestyle='-')\n",
    "ax.set_aspect('equal')\n",
    "#ax[0].legend(fontsize = 14)\n",
    "ax.yaxis.set_major_locator(MaxNLocator(nbins=5))\n",
    "ax.xaxis.set_major_locator(MaxNLocator(nbins=5))\n",
    "ax.yaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "ax.xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "\n",
    "# Create inset plot\n",
    "\n",
    "ax_inset.tick_params(which='both', direction = 'in', bottom = True, top = True, right = True, left = True)\n",
    "for ipotential, potential in enumerate(closest_potentials):\n",
    "    ax_inset.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' ZHIT (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' ZHIT (Ohm)\"], marker = markers[ipotential], markersize = 3, fillstyle = 'none', linestyle = 'none', color = colors[ipotential])\n",
    "    ax_inset.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' fit 3-ladder (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' fit 3-ladder (Ohm)\"], marker = 'none', markersize = 3, fillstyle = 'none', linestyle = '-', color = colors[ipotential])\n",
    "ax_inset.set_ylim(-10, 260)\n",
    "ax_inset.set_xlim(40, 310)\n",
    "\n",
    "ax_inset.set_aspect('equal', adjustable='box')\n",
    "ax_inset.yaxis.set_major_locator(FixedLocator([0, 100, 200]))\n",
    "ax_inset.xaxis.set_major_locator(FixedLocator([0, 100, 200, 300]))\n",
    "ax_inset.yaxis.set_minor_locator(AutoMinorLocator(n=2))\n",
    "ax_inset.xaxis.set_minor_locator(AutoMinorLocator(n=2))\n",
    "ax.plot([], [], color = 'darkslategrey', label = 'fit')\n",
    "\n",
    "fig.legend(bbox_to_anchor = (0.5, -0.18), loc = 'lower center', ncols = 5, frameon = False, \n",
    "           labelspacing=0.2, \n",
    "           handletextpad=0.3,\n",
    "           columnspacing=0.7,\n",
    "           borderpad=0.2,\n",
    "           borderaxespad=1.0,\n",
    "           handlelength=1.0,\n",
    "           markerscale=0.9)\n",
    "\n",
    "plt.savefig(fname = f'impedance_spectra_annealed_SI.png', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73e7da11",
   "metadata": {},
   "source": [
    "## Nyquist Plot - As-prepared Prep 11 = sample 1 for the SI (Figure S9-left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "d2a69fd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.982, 1.132, 1.232, 1.332, 1.382, 1.482, 1.582, 1.602, 1.622, 1.642, 1.662, 1.682, 1.702, 1.722]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_24349/4125067685.py:123: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.savefig(fname = f'impedance_spectra_as_prepared_SI.png', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n"
     ]
    },
    {
     "data": {
      "image/png": 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5+dIpShoczTw8SC0qIA03EIIu2QnkaZvSzJQHTWMwnv8Xah9AD2iDwJIL3i3gzn1wsilgAXMARLwMfsPs19Vj4Zdys9DnKOYjbLO8rpyF1hv1DP9oOGdzzjrN79myJ6+NeI21h9byR9IfzBo8i67RXSvNSqvVjXNS5XJRzDHecccd/P777/Tr108pEyQSp7hZjaD2xstqxNfDA1+jbU2iEAJVSb6t26zC5hQBWk+GorWgKm1pFneEoMcclun8RqZ9L7Qf7qjtAhGVZ6Frg8FkYOmfS3lhzQtkF2Xb093d3DFbzDw95Gk2JWxi9+ndDP7XYOBiy1CtVl8T44Y1QTHHGBoaysiRI7n77ruJjIx0yCuLASOR1DepRiNFpYOAvlYjqR5BNDPmkpuThj7jIOGZObZtgOXHFz2bQ+pjNodp9YRuv4BaYxeIMGElD5vqjwYV4XhxPy2u2NaDKQdZvW81B84eYEfiDgpKLk4YRQZE8sIdLzCw3UDavdyOn4/+zLIpy2TL8DJRzDEeOnSILl26kJSUZJdDgothDiSS+qZsDaPazeb1inBjj38s1rzjWLL/IvjCHtQ4jtXhAYgHsc+hqA1gSQW3NnaBiPKtQwvC7iSvBIPJwLAPh3Eu13FfdYhvCNlF2QzvMpx+1/VjwZYFAPxz4D/pEt1FtgwvE8V+LrZs2eL0VRYH5mqmbOeLkuuwGhIGg4GpU6cSHR2Nv78/N910Ezt37rTnz58/n9DQUIKDg5k9ezblQ53v2bOHTp064ePjQ79+/Thz5kyd2Rmh0RChstr3LpdofIg05BCYf5bgvDN4lWSWay2WesIAJxfSbbD/6YM75tIJl96EEMKVS3TtTNpJmxfaODhFfy9/AF4b+Rpdo7vyye+f0HFuRz7a8tFVN5lS9t0p32CqbxRf4A22sZvyXwbZvG9cmM1mYmJi2LFjB1FRUaxatYphw4aRnJzMtm3b+Oijj9i1axe+vr4MGjSItm3b8sADD2AwGLjrrruYM2cO48ePZ968eYwfP57t27fXiZ1qoG3uMdIDLwoldCs8RYEuGzdzCSrPFJtTNACepeOJ54EosDcKyy3RsSIoKm1h+uFOX0I5ga7GdpUtzNYb9Wz6exP7U/bb86KDo5k7fC79r+tPq+db8fHvH8suswtQzDFmZ2czc+ZMfvnlFwdFYLCpBF/NyJ0vjvj6+jrsgb/vvvt48sknOX78OMuXL+fhhx+274mdNWsWS5Ys4YEHHmDr1q14enry4IMPAvDCCy/wwQcfcPr0aVq2bOn0XhaLpcr3PjQ0tFr1JquxiMO+zR3S9vm1ovW5o3izDY2P0TYbbQZ7w8+3JajKLdAut0SnuNxy7tsIx7MWaxYtVgtTlk7h4NmDDullC7GHdhxKz5ievPfre0DddZkzMzPJysqqlG4wGHB3d60baQg7XxT7GXniiSc4deoUixYtwsfHh2+//Zbu3bvz73//WymTJPVEYmIiOTk5xMbGkpCQQKdOnex5cXFx9i9GxTwfH59L/ujk5OTQsWNHp6+FCxdWa1d6SRE5motrC5uWrmEsibkRlTb4YkGtj+1/ryi4aYjtbwEUdYAYm205GNGXzkJrULGFTD7iJDnULBTA/A3zKzlFgLnD59I1uiufbvu0XrrMCxcudPqeJiUlVWrYNAYUazFu2rSJv/76i+bNm+Pm5sbIkSPp0KEDU6ZM4dFHH1XKLEkdo9frGT9+PM899xwBAQHodDr8/f3t+f7+/uh0tu5mxbyK+a6mmTGPFIsH5z0DcRMWehYmkWEIJNJUwHn1LYTrD4E3CFFs6zm3iYS8RbZutKkpK7uuJ1dlBk5SUG6CRQCq0r725S7NEUKw7M9lvLTuJXtaREAEL93xEtNXTmfV3lWyy1yHKOYYi4qKaN7c1m3x9PTEZDLRpk0b4uPjlTJJUseYTCZGjx5NbGysvWut1WopKLcvuaCgAK1W6zSvYr6rSTVb7WOFPhYjGR6B+Gf+TXr6AZqcWmTzcB6g8gE6AOy+eHLEY+SqzORgxA93e7ACFRBULkxBdZSNJRrMBjYc3sDh1MP2vLhmcSyasIjlfy0H5CxzXaOYY4yOjubkyZPExsYSGxvLmjVrCAkJcdg3LWk8WK1WJkyYgEqlYtmyZfZlWe3btyc+Pp7hw4cDcOTIEfvYUvv27R26v3q9/pIxXYKDg9myZYvTvOrkulKNRvZ4heMubA7NqLIt1YnJTKZF3mE8RJFtRtoN8HdyAZVt/DAIDT64k4cJN1R2KbFLUZXIQ1P/pvh5+RGfGs9N828ClNmyN336dEaPHl0pfcSIES4fY2wIKFajadOmER8fT2xsLLNmzWLMmDEIIXjzzTeVMumaIut0Ar8vmkuLrv3ocOu9+ATWrRbmww8/THp6Oj///LPDF2n8+PFMmzaNsWPH4uvry/vvv8/MmTMB6N+/P3q9nsWLF3P//ffz+uuv061btyonXgDc3NxqNWhftlQnvXTY3eDmQXhxJoFZx7EWF9iX6QirClWWgKZcDGeg8oKQp4BkjFi5gC1yYE+COXmZs9BLdizhQMoB1Co1VnExhswztz3DgLYDFO8yh4WFOZ24aowRAkFBxzh9+nT733fddRdnzpxBp9PRtm1bpUy6pji9ZzPJe38jee9vtOl9R506xjNnzvDZZ5/h5eXlIEb8008/cccddzBt2jR69uyJxWLhoYcesqsreXp6smbNGh544AFmzJhBjx49WLFiRZ3YqAY65hwlPeiiPPV16XtQ5Z1B63HMpshdAiqv0nnm8sOEogRU7ggERaUTLl640ZfQy3KMRrORp1c/DYBVWHFTu/HU4Kd4a+NbLP1zKU8MekJ2meuZBtMGbtasmdImNHqyTh1ly6cvEdqqA2f2bQUgIKIFgRExdXrfFi1aOKxTrchzzz3Hc8895zSvR48eHD582GmeK7Eai9jv67hN72R4V+LOvo8GnW2ZjgHssaryfCCwmJXNFpDrdj2IRLJVFwVoTVhZzOkqBSLKr01ctXcVefo8ALSeWhbev5C/Tv8F2MYSJfVPvTrGbdu2XVa5vn371rEldUtDjfny54p3STmwjZQDF59DYVYqp3ZvolXPWxW0THnSS4rI9rg4eBhqyCXduwkdfYJsTlHDRYmxSDcItM0657o1J8erCUEIyrv+siNns9BVjSf6eviiM+iYuHgicO3Kf11zMV/69+/vcFxeTr3sGK7+Bd4NlVY3DiJxxw+0G3A3x7Z8C4DVbObU7l+vecfYzJhHqsWdNM9gEIIeBUnk6TPxPpts6za7Y1vU3QFsAaNLP6MqL4JVPvRWhfI9tjgwvrjhg3uVM9Fl0fe8Nd7oTReDSf3z1n+icbs4WSOX3yhHvTpGk+ni2q6vv/6aL7/8kjfffJOWLVty+vRpXnjhBe677776NKlOaKg7X2L/MZRf3dxJS9hjT3Pz8KbnmMcUtKphkGq22uO4eFuNpLv5EprwBSrVKdu3xAw4m9TWBCBU8HtpSAM/3PG8xL6JeevnAdid4vT+01m4dSHrD6/n0JxDrqrSVUtD2PlSr47Rze3ilqiXX36ZvXv3EhgYCEDHjh1ZunQp3bt3Z+zYsfVp1jWDt38QLbr25/SeTfa0XvfPwj8sSkGrlKdsqY6mVOXaolJzMDCWwRy27Y3WA0Ygi8qz0W4hlGBFV7onug+h7KHyTpCyMcVj6cdIybUpfatUKubcOYcsnW2rnRxPbDgoule6osSYSqUiOzu7ijMkrqBF1352x+jlF0S3u6cpbJHyRLi7E2nIJc3TFvDeBPxj9zi8RarNKeZhW64D4IFtwsXDtjkhGyPWctfaTTa5mBwmXKoaUxRCMPeHucC1O57YUFHMMQ4YMICxY8fy7rvvEhMTQ3JyMrNnz+aWW25RyqRrAjfNxXVnN42bhbtH41yHVhPUZj1xumS7YwQIsdpCiFpVatRYbeOLEYBeRa5Hc3I00QSX5FD+p12FbetfxQmXsjHFsjF1N7UbFquF4Z2Hc0P0DYAcT2xoKPYkPv30U8xmMx07dsTPz4+4uDgMBgOffPKJUiZdE6T/bRtfdPf0puvIBxW2pmFg1Rewz6+V/VioNey/4TNEYG/UXlZoDnTH5hi9S2ebjWlM8+pDkMr2w6IGQkq3/s0glnFE26/3yg+v2K4rBF4aL5ZNWQZAcnYyc4fPZe7wuXKdYgNDMccYGhrKL7/8wrlz59i5cydnz57l119/rVYW6lJ8/PHHdO3aFY1G4xCgfOvWrajVarRarf1VXtMvKSmJ3r174+PjQ9euXTl06OIAuNVq5fHHHycwMJCmTZvywQcf1No+pRFWK8n7fwegZfdbULs1mGWsinLUZOGCx0XFWX9TEUW6NKwXSj8HQU5OcvPlmLqECxgA8MbNLhQBtjHFuevmMuHzCZzLswnKuqvdWXDfAnaeson0yjHFhoui34yCggK2bt3KuXPnmD17NufPn0cIQXh4eK2uFxERwdy5c1m5cmWlvFatWnHy5Emn540dO5ahQ4eyefNmlixZwqhRozhx4gTu7u588sknbN26lRMnTpCfn0///v3p1KkTAwcOrJWNSpJ1OoHiXNvsaYuuMggZ2CZektx88bIYKHHzBCEodPNg0MEHcHMvXdidieOkCyDcw9mObdLEGzeH+NBVjSmarWYeWv4QIMcUGzqKOcaDBw8yZMgQAgICSEtLY/bs2Rw4cIDPPvuM1atX1+qaI0eOBGDDhg3VFyzH8ePHSUhIYPv27Xh6ejJt2jTeeusttm/fzoABA1i+fDlPPfWUfa/oQw89xP/+979qHeOViKXWJcn7LoorxHQboIgNDQ3bxEuObf0ioBJm/rF7HD7m5IsTL2E4TLrkaJpjUV1cy2jEihGrfcKlqnWKckzROfUpgnu5KPZkHn/8cebMmcOJEyfQaGyLWnv37s2uXbvq5H5nz54lLCyMNm3a8Oqrr9oXkSckJHDdddc5bIavTiy1fF5VXIlYal1ycRtgDIGRVQsxXM3UNN6O2qynW+EpAFRWE90P/R9huZsxmkr7z8HYQhfkYp90cRNWym9zEQiHCZd3f3kXuLhOcd4I27rFxjSm6Mq4LA1RBFexFmN8fLw98FXZsh0/Pz8KCwtdfq927dpx6NAhrrvuOo4dO8aYMWPw9fVl1qxZlxRDrU5I9WrCZNBz7ojtRyemW39ljWlAWPUF7C038VKGxsMAWkp3ulwk2JTCyGM/s6izLa6LN25ocad3io7vD37PixYz5wvO28v/X5//I6MgA5BjilcTijnGoKAgzp8/T0REhD0tJSWl1uOL1REeHm6/bvv27XnxxRdZsGABs2bNuqQYanVCqlcTqfG7sJhsEwUtuvZX1pg6pKa7jtJNBtJLu9FCrSEjfDLhaRtwUxfbHGNFhJodnZ4BdKhR4Y0b1irGFAH+u/2/QOMbU2wIu1PqEsUc45gxY5g0aRIfffQRAOfOneOxxx7j/vvvr/N7q9Vq+x7t9u3bk5iYiMFgsHenjxw5wpNPPmnPj4+Pt3enywupVkVtxVLrkuT9NntUajXRXfooYkODpKSAWFURJ30iUVlNdDjyJG5qE1arO+psM0TAypYXxxaFyp0sla3HoMUNN1T8+cd3HEg5wM2xN7Pj5A77pe+Iu4PuMd0BOaZYHQ1RBFcxxzhnzhwefvhhu/5iixYtGDlyJM8880ytr2k2mzGbzVgsFsxmMyUlJWg0GrZv307r1q1p3rw5iYmJvPbaa4wbNw6Atm3bcv311zN//nyeffZZu7p0nz425zF+/HjeffddBg8eTH5+PosWLWLZsmXV2lFbsdS6oExqLCvpCAChLdvj6eN3ibOuDVINBvb4NsffXGwfX/QynwY9FLjfQGDTPeBxcWwRBNZyw/I6zBRhYetm2+fhz6Q/AVtslvT8dM7mnmX9zPVKVO2qoiGK4Cr2E+bp6cnSpUu5cOECu3bt4vTp03z77bdX9Ga89tpreHt789lnn/H666/j7e3N8uXL2bdvHzfddBO+vr4MHjyYkSNH8tRTT9nPW7lyJb/88guBgYF8/PHHfPfdd/ZfqmnTptGvXz/atGlDr169ePLJJ6+qpTplUmP6AtsgdmbSERbc1YpTuzdd4szGTwQmIg05FLj7OGb4QeCNe2wLu0sJNqXQonCvQ7GMlGPsXPcJwX62XoBVWPF09+TmNjcDckzxakYlqlMQrWN27tzJ4sWLOXv2LFFRUUydOpVevXopZY5LaGg6jPE/r+Tn92baj8PadCYz8RBdhj/ArY++paBlrqfG731BOpbzR/ihia27G3R+D3333o6qqcXBKX7U8nsA2uUcYXvESAD8rWo+eP0ezqUkOL10txbd2P38bpd1n9fp1pFvyQcgwM22GL388XDt8EplhmuHu+Te1VGXn3clv0uKdaWXL1/Ogw8+yKhRo7jhhhtITk5m4MCBfPrpp0ycOFEpsxodsf8Yys8qFQiB2l2DUZePu6ePlBoDzhUXcK50RlplNXHD0Zmo1BZElgZVU5Pj2CKwI9ymr+iFmn1/fM+5lAS6NO/iEPe5bK3ilYwplndwfmo/zJhJNafa83OsjktYcqw5fJj7IRYsDmkLcxfar1GVM5U4RzHHOG/ePL7//nuGDh1qT9u4cSOPPfaYdIwuxNs/CA8vX4x6HT4BTchLT6bPlBel1JjBwF5P27hW2fiiv+E4mEHvEY6Px1nHsUWVB6hs3efjBzezZ+vXAHan2CGyA0fTjpKcnczaR9fWyJYyR2gVVopFMUaM9ryKTrAqyjvFMkylsa1zrDlOnWlFxykd5UUUc4wZGRkMGTLEIW3w4MGcP3++ijOuHhpSaAN9QS5GfREAuux0gqJaS6kxLo4vpmv86H7o/2iWsRazWou7rw6fDmft5YJNKQQYz3FK2wezFdYue4mMlL8druXr4UuX5l04mnb0ssYVyxyhEIJiUYyhdL91faBGTVO3pmRZsio5zuX5yxuEg2wIoQ0Um3wZNGgQGzdudEj7+eefGTRokEIWNU5Sj+yk/DaNgdPnS6kxQG0otO94oTRcqbtVZxOmrUB73WasKjcO/vE9GSl/06f//fyj9T/s+UXGIr7464vLWqu4TreO06bT5FhzyBW5VTpFFSq0Ki3eqosGBaoCCVJdVLTQoCFIFYQaNe6lbRytSouPyqfS9cqwYiXdko65VFjXHXdaaloSrA4mx5pDiimF5fnLWadbV209GjuKtRgjIyMZPXo0t99+Oy1btiQ5OZkNGzYwdepUXn75ZXu5V199VSkTa01DCm1w9rBtCYlKrWbIrP8Q013ukQY4V1xIqn3Hy0VVHJEXypcRL5PrdXFs8cemL5ORcozf1vwbgPTUE5xMuhgeoql/Ux7p90iV44rrdOvIM+dhwkSRKHJqjwoVAeoACq2FWLCgRo2HyoN8az7B6mAKrYUUCtuusGC1bUF6vjWfAlFAoDqQCQETWJ6/nHyrbQxRgwYTJoJUQViwUCAKnN7XjJkMcwZGYeu+W7Ao3oJsCIvHFXOMR44coWfPnly4cIELFy4AtlCZ8fHx9jIVFb4lNedcqWMMv+4GOg66+uPpuALb+GKofWyx2fl1FHi0wb84EZV7VqlTLBtb1GAVgrXLXqKo0DZOdzLR5hT9vfwpKCngjVFvMPXmqU7vtbZwLcnmZKd5nipPTMKEFSvq0n8AbrhhwUK+NZ8AdQATAiY4nZUuo+y4fHqAWwD5lny7oyxzpnnWPHsEw7L/9eKi0IUnnkRqIh3OvRZRzDFWtTNE4joMRQVknrIt7G7eubfC1jQcyo8vluFtTgdf7Hujg00pBBrPcsq3F/v/WE9Gyt9063sPh/5ch9lsa10VlBRU2X1eW7iWC5YL6ITzffVq1PiqfMkX+ZUcYZlTg4vO7nJabRXLlO8OO3Om2eZsCkWhQ+BXAwbSzel4OImFfS0hlUobMeeO7EJYbeNnUXFX9/pQV1I2vvhDk+7si/uIkJw/8TJmsbLPAnIDbF1oAIPal9Rzp9lc2oXOSjlud4pg60J/NvEz1Gq1Q4uu2FpMCSWV7qtBg6/KlzyRhxVrlY7QVV3XS11nnW4d7hZ3rMJKkSiyT8aUiBJKKMEDD7Tqq08XwBUoNvmSmJjI0KFDCQkJwcPDw+ElcQ3nyo0vNutwo2J2VKWsDrB06VKioqLw9/dnypQpGI0XHU91yupXwjmjgb1+rVBZTXSLn4GXMQsE5PqWdaFt01WFqgDWLnuJ4tIudEqybZinibYJAG+MeoMu0V0cJlRyrDlOnSLYJj4KRSGa0n9lTnG4djgTAiYwIWBCvY7nld13UuAkpgdNJ1AV6KBC7qHyYJh2WL3Z05BQrMU4YcIEoqKiWLRoEb6+vkqZ0agpm3gJi+2Ep69y+6OrUlaPj4/niSee4JdffuG6667j7rvvZt68ecybZ9MvrE5ZvbakGo3sdQtApTLR4+BDRJ5fh8nbB831xeBu60IDZGtasG+nrQvd9ea7ObhzLVaLbSb3gu4C3Vp0o0nnJk4nPMpQoybKPYpUcyqW0n/B6mAmBEyotf11SZB7EGqLGouwYMJEL+9euKuuzU6lYrVOSEhgx44diqlnNGayTh1l88LnyDhhk8EKjbkeYbWiUkjdpSpl9ZUrV3L33XfTo0cPAF588UUmTZrEvHnzLqmsXhWXUk+PCA0lEjPpXJx80GiLWdlsgb0LrRZm0s4l2rvQqclH7E4RbF3oxx9+nNPm0w7XL+8UwTbTrLPqsGDBDTfUqCuN9TUkKrZWy+8Wbogq23WJYjXq2LEj6enpNG/e/NKFJTXizxXv2rvRAEd++ZITO37gzuc/o1XPWxW0zJGEhAQHQY64uDhSUlLQ6XTVKqtX5xjL1NOdMWfOHObOnUu4IZd0TSD2ZTpZkKtpjkXlgZswojHns3bZHHsX+vy548BF1ZwXZ75InnseYGsVWstFllaV+1c2odKQW4nVUX5VyMKFC3nllVecllNKSq8uUWyM8b///S/Tp09n1apVbNu2zeF1tVNTeX1X0+rG8ovkVTTv1BtjsY5Tu39VxJ6qcKaOXpZ+KWX12nLOYOCAJtC+TMfk5Q1dARW4CSMCFX/s3kFGyt906T0KtfpikKv0/HRmTZuFxd9i34JX3imCrRUaqA4kWhNNsDrYPo4ouXxcGTahtijWYjx69Chbtmzhxx9/dEhXqVT2eCyS2hH7j6H8XPp3k5h26LLTG6RwhDN19LL0Symr1xZhLt1pUrrbReOnt3ejrSoN6SkJbFrzIQDnkg5gtdo+iw9OfpCmoU0JaxLmsLylPOW7y0pvq5NcGYo5xqeffpo33niDyZMnN7rJF6V3vnj6+qFSq+3jirmppxqkcESZOnoZR44cITo6Gq1We0ll9aq4lHp6aHE6Vl0elI2fletGY9Gzdtlcexf6QkYyAI899Bit29j27FbnFB8NevRyq37VUZ8q29f0zpf8/Hxmzpx56YKSGpN16qh9/WLWqaOKC0dUpaw+btw4+vXrxyOPPEJsbCyvv/66XVnpUsrqVVGteroQnEs+hc/JVwnN+gk8YeXgBeR4NAcEW35cTMbZY4Q1u47M1ERA8ODkB4mOsS3hccfdvsfY4Z644alq3PvPG6LKdl2i2Bjj0KFD+eOPP5S6faMmNWGPw7HSwhFVKavHxcXx/vvvM3z4cKKiooiMjOTFF1+0n1edsnptSNXr2K9tjm+hbT2k1c8WtsCi8sBqtfDnz0sAyEw9QZlT7Ni+oz28rzOnGKwO5tGgR3ko8KFa2yVpeCjWYgwLC2PYsGHcfffdDpEC4eoUjlASky4HldoNdx/bIH96qWP09PXn1n++p7hwxNy5cyst7C5j8uTJTJ482WlebGysS388Iwy53HRoOj4laZi8fNBEFwOgFkZ+/+FjzKaLSjcPTn6Qtm1s8YgQlNeZALgqlt9Iao+icaU7d+7MyZMnOXnypD1dCkfUnMw933B+55d4Bjen/f8tIe1vW2ySZh1v4vr+oxS2ruGQbiwBte0jr/Yz2SddzELNjl+W28uVtRTtOHGKjXk8USJFJBoFRWnHAFC7e1Ccl01+xhkAmrXvqaRZDY6w4kw2tf8XQbm78clKJ1fTnNRzp9iy9iN7a7F8S9EqrKhVjqNNNRlPPJSZyfzduzl64QIdmjTh2Z496exknE7S8Gh8S9avEYTVTMqG99ClJmDIsW1j84loS1rCxUh2Ee27K2Vew0MIjmn86XR0Jj6GdFb2X8AF90jWLp1ExlmbIverL72Kv9/FtZPOnGLFlmKZ89uXkUGJ2YybWk1THx/yjUaO5dhmt/00GuIvXOCrY8f4R0QE/p6epOl00lk2YOrVMb7++uu88MILAA5itBWRY4yXJmXDe2Qf/gmNb4h96UnJhRQy0mzjZiq1G+HX3aCkiQ2KVL2O054htM6z/XDk+jfntx8Xk3H2b3y0QYy7524Hp1iRspZieUdYYDRyvri4UtnkCusvC00XtwruTE8HwMvNze4sw3180Hp40D08XDrKBkK9OsbyA+nbt293WqYxjDHWR8yXorS/8fBvSvjNE0nZ8A4AuSkJ/J2QCUBwVGs0nk50+q9RIgy59Dw0/WJr0SvSPgs97p67L060OCE9W8WZYzHsy8ggKe9/Ffa61I6ScpsYMoqLobiYk3l5fH3sGINjYnirb99r1kE2hJgv9eoYy4sIyDFG11CcYdvHKwQYS4rRZWcAkJ1yggV3tWpw+6OVIr2kkKACm6hGWWvRbDJU6j7bKZ2JPnDKyvKtFqziGCqoYnm3jdjAQE7l56MC3NVqDBYLUb6+pBUV0dTXl/SiItxVKqyAtYpw7gL4OTmZX5KTr3kHqSSKrWNszJTtfKnL3S++kddjLDhP9kHbj41KBQUFFyXqQ2Kub5D7oxXBaiIw/kl8StJY2X8BWaWtxQcnP1hl9zk1R/DpRgvLtliwCpw6xWg/m5SbT+nayiKTCasQWITAYLHgplJxrqgIK5BeVISbSoVZCKxC4O3mRlPvqlv0ZQ7yhv/9j9tWr+ZQZuaVvw9XCWXfHaVaiyAd41VL9O2zCO44GGG1LTr2CG7OiRPp9nyTXtcg90crQarBQJ5aw8q+C0gK78WW0tZibKvYSmXPXbDyyUYz735v5u/Ui53m8k6xbLBndFtb97vYbHsG6UVFDit7girsCil/rLdYOK/XO6Q7G0Qqc5Ddli9n1bFjl1VfyZUjHeNVikrtTmi3kfbjyL5T8A6ydbncPb0pOH+Wf4x7ssHtj1aCCEsRIXl7ydU2xyTc2PHTZ7z60qt4eXk5lNufZOH9dRaOpTrfEd2piU25uyzvvb177c5MBYT7+BAbFMRtMTEMiYkh0s+P+9q14+s77+S+du2I9PPjtpgYekVGOjjBXIPB4boVUQMWIbh3/fprrvWoFPU6xlhYWIifn3JK0o2NsvWLYOtaC2Eb0Dcb9Irvj24wWE2U7JqMj/E8+b4RHNjxHVMnTHboQqdmC37YY3OIzijrRidkZ9uPo/38EICnuzvdmja95GzymHbtHI7LZrf3nz9PgdFIRpHzsKqAw2TPz8nJbDpzhpV33FHpmhLXUa+OsVmzZnYpqTvuuKOS5JikZhSn29bfRYRFYj76IyHeUJxry1N6f7RSVFoRYDXhkbeflf0XYHT3ZvOa/zDnmWft5S9OrlR9zbIssxC4qVQucUqdw8L48s477ceHMjN5dts2fk5OrnaCx710nHLcjz/SNjhYsYmZhjBzXJfUa1dao9FQVPrLWNVyHcnlU5Rmmyn18/bEeG4/1/fqRZOoKHqMman4/ugGgdUEOyfibswkOawnaWcSeO7JJ+1d6AOnrPbJlYqoAb+cHMJPnyYwKwv/nBx65eQw28cH/6wsduzbx0YXfoY7h4Xx0z33cGDiRG6Liamy3C3R0Xi5uWERguFr1shudR1Rry3G2267jbZt29KqVSv0ej19+/Z1Wq4xqHjXNeaSQgw5Z/Hy9EQFGDy1+LWIpk/z1jTtMUlp8xTDQQvTXIw5ayfugEmoiHY7iZ/W1oUuc4oV8cvJwTc/H4uXFxFJSQ5jgUXAhoQENvz8sz1t886dDPzHP9D6+KArLua2S8iiXYoyB7nq2DHG/fgjlgrLejadOYMVW3c+pbCQXitX8ue4cfXecmwImol1Sb06xmXLlrF69WpOnjzJX3/95RDvQ3L5CKuZ02texd3NjSYBgZjcPCnxtKm8qE0GtE2aKWxhA0IIVvZfQHHyz0Q2sY1vO3OKfjk5+OfkEJKR4XR2uCp+/fNPfv3zYnydE8nJzJxw5fFdxrRrR9vgYJ7dto2Nycn2dCsXu9MDmjdny9mzzN+926FbLrly6tUxuru7c9999wG2sYk5c+bU5+3rjbre+ZKy4T2s6Udo2aw5hf6RFHmWW4vn6U3mwe8J7TyiUewiqjVl3WhDOqeadKOpx06gslOsrUOsisXffsuZ1FQG9e7t0tbj2PXr7ZMw5tJ1kB8MGMCI77+3Two1FhrC+KViIhJLliyhuLiY9evXk5KSQosWLbj99tsbXZiDuqAo7W88NB6oVSqsqsrDxFaz8dp2imBzjJk7Wdl3AYVnNuPXxK+SUww6f57miYnVOsTgwEBy8vLsx54eHhiMxmpvvXnXLjbv2uXy1uOtq1ZxoaQEtUrF2/36kVNSwpmCAu6Ts9MuRzHHeOLECW699Vb0ej0tWrQgJSUFT09PNm3aRNu2Ve9bvRqoj5gvBUU6sFoI9PTH5FEaIEoIUKnY+c3ndLYGX9tbAa0mMGVzKrQbTQt3OjhFv5wc/LOzCTl//pKtxPJOEajkFNUqVZXb+xZ/+y0Wi4UnqhDirQmdw8LYNGYMPVaswGS18u7evZwpKMDH3Z1nezYuebmGMH6p2ALvJ554grvvvpv09HT27t1Leno6Y8aM4YknnlDKpKsGn6ZtoDR6nadRdzGwE4AQpPx99NreCmg1wa4pYDVQkrGDjDw1/yt1ikHnz9MyIYEml+EUL+tWVTjFMpZ9/z0fLF3qgjvZnOOe8eO5r107Ajw9ua9dO0UmXq4FFGsx7tmzh2+//dYew8PNzY3XXnuN6OhopUy6amjSdRg5R3/F29OL8vslrGYzuRkZCKG+trcCWk2QtYt3Rm3FP/8vFv5uRnB5XefyqFUqIsLCeHf2bK4vN9a1/+hRXvz3v8nKyUEIgfkS4X6Xff89XTt0oF+PHrWvUykV1z9K6gbFHKOnpyeFhYUO27IKCwsbbdQxV6LPPAWAt6cnJndvm4IEcGTXPhJ372iQoVLrFXMxGM5T6OGPvlDDmUwLGoPhsp1iVQ6xjK4dOrDhv/+1H+8/epSZr7+Ozok2YxlPvvkmrzz2GHcOkOtLrwYUjRJ43333ceTIEYqLi4mPj2f8+PHcfvvtSpl01WAtPE/zrkPwCY8loOMQQsNa4uamIfnQHrkVEAEbOgNWtNlb+WKbrTUXnZBQySkKHPcnq1UqmjVtyhfvvsuPn37q1Ck6o2uHDvznhRfwruZH3WK18uK//82fBw7UqDYSZVDMMb7zzjv4+PjQqVMn/Pz86NKlC56enrz99tu1vubHH39M165d0Wg0laLSLV26lKioKPz9/ZkyZQrGcoPoSUlJ9O7dGx8fH7p27cqhQ4fseVarlccff5zAwECaNm3KBx98UGv7XIIQNGkaRWjrbni2ugk3jRdabRB5mZmYDIZrdiugHSGg5AIr+y4gKx/Sc8G7oADfKvYilzlLf622xg6xPF07dOCjl18mMiys2hUBL/7rXxjLKXpLGiaKOcaAgAB++OEH0tLS2LVrF6mpqfzwww8EBgbW+poRERHMnTuXu+++2yE9Pj6eJ554gjVr1nD27FnOnj3LvHnz7Pljx47l1ltvJScnh4ceeohRo0ZhLpWS+uSTT9i6dSsnTpxgx44dvPvuu2zevLnWNl4poqQAN3cN+uyz9kkXIay4WQWDn/yX3AqIAEykBQTyzU5ba7FlfLzdAYpy/6sAhMDfzcKnr7xSK4dYnrIudo+4uCrL5OTns3TNmiu6j6TuUVx2LDw8nB49ehAeHn7F1xo5ciTDhw+v5FxXrlzJ3XffTY8ePQgICODFF1/kf//7HwDHjx8nISGB559/Hi8vL6ZNm4bVarXv5V6+fDlPPfUUYWFhtGnThoceesh+blVYLBa72GbFV+YV7G0VVjOZfyy13ePCafuXXKVS0+HGW+nU+xpenlNGcRogcDPncSYTvPPzcS83c1ymlFPmKNu5FzC8cCenVr/jMhMeuffearvVn371FelZWS6739VOZmam0++KwWDAcomJrbrimogSmJCQ4LD9MC4ujpSUFHQ6HQkJCVx33XUOkz5xcXEcPXqUAQMGkJCQQKdOnRzy1q9fX+39cnJy6Nixo9O8OXPmVBl8/lKkbHgPP3fb/gdVWCxqt4uPTwhB3undBMYNa3SLu7Oyspg8eTJbt24lKiqKhQsXVr2d1KwDQCVsLf6YcmOL5R0igLe7haf7NaOlbySLv1rNHc984nCpzMxMFi5caD+ePn06YZexNKasW13VhIzFauWTr77ilccub+VAbe2oCyraYrFYcHNzu6JrLly4kFdeecVpXmho6BVdu7ZcE45Rp9Ph739x21zZ3zqdrlJeWb5Op6vy3LK8+qYo7W9CO9mEN3yDIu3p2ZnJ6Irz8Qxq0eicIsCMGTMIDw8nKyuLTZs2MWbMGBITEwkODnZSutx0itWKplyLo3xr0Qoc6nADk2PasjXf+ZrPrKwshy/s6NGjL9shlU3IzHj1VfSlQrTlKbnE7hlX2eFqKtrSunXrK3aMDRHFu9L1gVartetAAva/tVptpbyyfK1WW+W5ZXlKcObAJorPJ2EqzgPAbDZRlH0Wq9XCzm8+59TuTYrZVhfodDq+//57XnnlFXx8fBg+fDhxcXGsXbu22vOEEDRNTq5yeY4aaH3oMEeOp/NIrjutet3hctvLWo6tmjev9Jo8cqTL71eXlHV3T5486ZBuNBoV6+7WJddEi7F9+/bEx8fbj48cOUJ0dDRarZb27duTmJiIwWCwd6ePHDnCk08+6XBuWXf6yJEjl9yqFBwcXGUUxCvpGvhGXk/24Z/Iid9EZD+btJihRIdF423f8eK3+9dGtRUwMTERrVZLVNTFdZllQx3OKGsvmizQpDSGc/kudHlHqQZ8MzJYHd6Wb4acc7XpgM05frdgQZ1cuz6pqrt79uzZK+7uTp8+ndGjR1dKHzFihH0DSH2jmGP09/ev1FIDm1PJycmp1TXNZjNmsxmLxYLZbKakpASNRsO4cePo168fjzzyCLGxsbz++utMnDgRgLZt23L99dczf/58nn32WZYtW4ZKpaJPqTLK+PHjeffddxk8eDD5+fksWrSIZcuWVWuHm5tbnezzjBzwENmHf8InIAS1xrYwvqREh9lQQl5WdqPc8VLVUEd2FYoyRqsHYEQXcgsq1XYQospWowpodfIk8eFNwZDrUrvLU9cxxq82OyoSFhbmdGhAyc0einWlxSX2mNaG1157DW9vbz777DNef/11vL29Wb58OXFxcbz//vsMHz6cqKgoIiMjefHFF+3nrVy5kl9++YXAwEA+/vhjvvvuO/sv1bRp0+jXrx9t2rShV69ePPnkk4rpSBqyba0a7+CLeou6wlx+W72O37/5ulEGv7rUUEdFCs1+FBvATQXC/2J40qo+bSogZv8B6No4JfAktUMl6sJDVcPLL78MwNtvv83s2bMd8k6cOMHx48c5cBXvDqjLX+X8Qz9gzkggICAY95AWWK0Wdv++jh2rvyEoqjWTPtnW6BZ363Q6goODOX36NM2a2X4QBgwYwMSJE5kyZYpDWT8/P4xGI82jmlHirqfQ4IYhp3KvxBmtmzevlGYwGEhKSrpYpnXrWrViyq5RW23BhmBHWS/MaDRy9uxZe7q7uzuenp51MiGpZAu33rvSZesDzWazQ9wXtVpNeHg4n3/+eX2bdHUgBH5e7qhb3WBvbZeUFJGdmgo03uBXWq2WESNGMGfOHBYsWMDmzZs5fPgwI0aMqFS2TMvT08sbT+GJ0deIWe+ORW+u1b3d3d0dxs9qO951pWKrDcEONzc33NzcKtlSXFys6GRkXVHvjnHLli2YTCZmzpzJ+++/j7e396VPkoCpGLXajZLcNLxKl+p4enjToWMHWrT/R6Pe8bJw4UImTZpESEgIUVFRfP31106X6mRkZChgnaQxUu9d6TJCQkKqHEC/mqmr5r81/xzqzAQKss7gH9rCIS8nPZGgPtMb5RpGybWLkl1pxSZfBg8ezM/loq01JspivrhyZrowwbY+0cMnwCFdCIHFapZOUdJoKPvulB9XrW8UW67TpEkT7rnnHoYPH07Lli1Rqy/66FdffVUpsxosbipbw97LNxAAq9XC+bN/YxQWVBlJnNq9qVGtX5RIlEQxx3jkyBG6d+9OWloaaWlp9vTG0PKpi5gvaQl/EBl7A75hMajcNBQX5VFi0oNKjZsul1ONbGG35NqlIcR8UcwxVrUzROIck9FA4ak9aCPaAFBiKEZtNeNhLsEirI1uYbdEoiQNYq+0EAKr1Wp/SSoT0KwtIdf3sx8b9IX8snQJybv/JDvwhka3sLs+qAth49pgMBiYOnUq0dHR+Pv7c9NNN7Fz5057/vz58wkNDSU4OJjZs2c7bI7Ys2cPnTp1wsfHh379+nHmzJkrsuX//u//iIiIwN/fn7i4OH744QdF7FAaxRxjdnY2999/P6Ghobi7u6PRaOwvSQWEIKJ1FzxKd7wIIchKO0d+vo6E9BI63DNLYQOvTupC2Lg2mM1mYmJi2LFjB3l5eTz++OMMGzYMnU7Hhg0b+Oijj9i1axcJCQn89NNPLF68GLA51LvuuouZM2eSk5PDzTffzPjx42ttB8CTTz5JcnIyBQUFLF68mPHjx5OdnV3vdiiOUIgJEyaIm266SaxZs0ZotVqxZs0a0bNnT7FgwQKlTHIJ7du3F+3bt3fpNU3554U48bOwJPwgxImfhTjxszAc/k5sfnm4OL3nN5fe61rk4YcfFnPmzLEfP/vss+KBBx6wH2/ZskVER0cLIYQ4duyY8PX1FSUlJfb8Fi1aiN9+c+1ziIiIEHv37hX33XefmDdvnj19yZIlom/fvkIIITZu3Chat25tzysqKhLe3t7i1KlTLrFhz549wsvLSxw6dEgRO+riu3S5KNZi3LRpE6tWrWLkyJG4ubkxcuRIVqxYwVdffaWUSQ0SYTVzYdcXAKhKhWkNhmI8vHy5ZfwMWlzXTknzGiXOxIkvR9jYVSQmJpKTk0NsbKxTW8ruVTHPx8fHJRN/06dPx9vbmx49enDLLbcQFxeniB1KophjLCoqonnp/lRPT09MJhNt2rRxkAeT2FS7PawlgC2EAYCHh223kBBWVO6Nbxug0lyJsPGVotfrGT9+PM899xwBAQHVCiXXlS0LFy5Ep9OxadMmBg8ejEqlUsQOJVHMMUZHR9tFL2NjY1mzZg2bN2+273eV2ChK+xuvwKYOaSV6HVmZyVw4/if4OFOxllwJVyJsfCWYTCZGjx5NbGysXWylOqHkurTFzc2NgQMHsmnTJjZs2KCYHUqhmGOcNm2avXU4a9Ysxo0bx+DBg3n88ceVMslluHrny5mDm7DoCwFbONeM9BPoCrMpST3R6BS7GwKXK2xcPv9Kn7XVamXChAmoVCq7JmhVtpTdq2KeXq+3f/Zchdls5uTJk/VqR0PY+aKYY5w+fTqjRo0C4K677uLMmTMkJCRUkiK71vGNvJ6S3HQw27rTRmMxKmHBy1AAFjOndjuPVyK5NGVixuWFjS0WC+PGjePbb79l37595OfnVylsbDAY+O9//+sgbFxbHn74YdLT0/nmm28c1HPGjx/Pp59+yqlTpzh//jzvv/++3Zb+/fuj1+tZvHgxBoOB119/nW7dutGyZcta2ZCfn8/KlSvR6XSYzWa++eYbtmzZQt++fevVjgaBIlM+jRhXz6RZ8lNF9sb5wnrsRyFO/Czy96wQ618YIna9PFD8b0J7kX/+rMvuda0xZ84cgU3D1v5asmSJEMI26xoZGSm0Wq2YNGmSwyx0YmKi6NWrl/Dy8hJdunQRBw4cuCI7kpOTBSC8vLyEr6+v/bVt2zYhhBBvvPGGCAkJEYGBgeLpp58WVqvVfu7u3btFXFyc8PLyEn369BHJycm1tiM/P1/0799fBAQECH9/f9G1a1fx7bff2vPry44ylJyVVkxdB2Dnzp0sXryYs2fPEhUVxdSpU+nVq5dS5rgElyqCCIFI2oxKXFz0npV1hh8+/Q856en0mfIiN459/MrvI5E0QK5JdZ3ly5fTv39/CgsLueGGGygqKmLgwIGXDGZ/TWEqRiWsGAsvyrMFBobT/9ZbaBnbmm53T1PQOImk8aLYXul58+bx/fffM3ToUHvaxo0beeyxx+xjF9c6Qp+LCrCURiwRQqDReNLqpjsIiGqNm8ZDWQMlkkaKYi3GjIwMhgwZ4pA2ePBgzp8/r5BFDY/CY1sB0Hg6LmESQmBViUahRCSRNEQUc4yDBg1i48aNDmk///wzgwYNUsiihoca29iie+mCbovFRGrSAVLOHEbkSRl/iaSuUKwrHRkZyejRo7n99ttp2bIlycnJbNiwgalTp9oXt8K1LVp7/uQ+RHQ7/MJtQYzy8zMxCjNY1FDY+MJCSCQNBUWFanv27MmFCxe4cOECAD169HBYKHqtdxW14dehLvceGA161FYTHmYDPn6ByhkmkTRypFBtHVB+1X+tlxoIQVh0W1RcXKqjL8rj188/p/+Q2wgb/YIrTJVIGhxl352kpKQrDj1bWxqEUK3ECaZiVFixGPX2pKjoDrSJ60qT25/BLThGOdskkkaOogu8GyOuWpRaFi7VbDLirvHAZDKg0diUdKxN41D7R1yxrRJJQ+aaXOAtqZ7ihF8AcHO3KZq7u9vWLAphRS2lxiSSOkWxMUZJ9biVitKWTUAZjXoK8jMpST6MKc9dRgSUSOoQxVqM69atQ/biq6AoC6OxBENBlj3pQmYyusJsREGWVNSRSOoYxRzjAw88QIsWLZg3b55DXOlrHiEg4zABTWPw0AaXJglMJj1ehgJUVhkqVSKpaxRzjKmpqbz55pts2rSJmJgYRo0aVWknzDWJqRisFqwaX4TVUpoo8EJF1pH9ZPq2l6FSJZI6RjHH6OHhwf3338/vv//OoUOHiImJYfz48bRq1Yr58+eTk5OjlGnKUmKTiDcYjQizLZaxSqUmvFVXItt2pvO455W0TiK5JmgQs9JNmjQhPDycgIAADAYD33//PS1atGDlypVKm1bviJI8AMwXTqIuJx4hhMDsFyIVdSSSekBRx7h582bGjBlDdHQ0v/76K2+//TYpKSns2rWLr7/++qqN/3IlMV9K0hIA8AttYZ+RLsjPIuXMYUqK8ji9Z7NLbZVIGhoNIeaLYst1YmNjycnJYeLEiRw8eJC2bds65N9+++14eXkpZJ1y5J37G3VUO4z6Avya2MYSCwoysVotqHU5nNr9q1yqI5HUMYo5xueff56xY8fi7e1dZZmUlJR6tMh11DrYuBCEtb4BN3cNHt7a0iSByWgABKqifHqOecm1xkokDYyy744rox3WFEW60iaTib17917z6jmVMBXj5q6hIOOUw4y0t5cvxuwMcvzj5Iy0RFIPKOIYNRoNX3/99TXZVa6W0hnpYqMec6ljVKnUhDdrS1DT5nS4Z5aS1kkk1wyKTb4MHjyYn3/+WanbN0wMNsfo46O1C0ZAaSgDIdB4yh8SiaQ+UGyMsUmTJtxzzz0MHz6cli1bolZf9NHXrGq3oRAA/yYt7EkFBVnk5qRBRhKndm+SEy8SST2gWIvxyJEjdO/enbS0NP744w+2b9/O9u3b2bFjR53ds3///nh5eaHVatFqtQ4RCufPn09oaCjBwcHMnj3bYR/3nj176NSpEz4+PvTr148zZ87UjYHaMIzFhRSkHbcnFeZfwGoxo9Llyj3SEkk9cc0peH/22WeMHz/eIW3Dhg189NFH7Nq1C19fXwYNGkTbtm154IEHMBgM3HXXXcyZM4fx48czb948xo8fz/bt211rmBCQnYiHjx8ar1h7ctkeabOQe6QlkvqiQciOCSEcWmjlu9X1wfLly3n44YftMuqzZs1iyZIlPPDAA2zduhVPT08efPBBAF544QU++OADTp8+TcuWLZ1ez2KxVLlcJzQ0lLCwsMoZpXukLRpfKMnHDdv74iUEmUf2I1r2o7mckZY0QjIzM8nKyqqUbjAYcHdXxkUp1pXOzs7m/vvvJzQ0FHd3dzQajf1VlzzxxBOEhoYyaNAgDh8+DEBCQgKdOnWyl4mLi7M7top5Pj4+l1ynmJOTQ8eOHZ2+Fi5c6PQcUZIPQPa5I1jMJsCmxRjeuhuR7brIGWlJo2XhwoVOvytJSUmKaSYo5hifeOIJTp06xaJFi/Dx8eHbb7+le/fu/Pvf/66ze7799tucPn2alJQUBg0axNChQyksLESn0+Hv728v5+/vj06nA6iUVzHfVaQV6zjrEUxCy1s4EdSGFI8yyTErVkMxKQdd3HWXSCRVophj3LRpE6tWrWLkyJG4ubkxcuRIVqxYwVdffVVn9+zZsydarRZvb29mz56Nn58fu3btQqvVUlBQYC9XUFCAVmvbeVIxr2K+K0g1GNjjGcrfvs3I82lCknc4+/1bc9LiTsbhXzHkpsuJF4mkHlFsjLGoqIjmzZsD4Onpiclkok2bNg5xpesatVqNEIL27dsTHx/P8OHDAduMedl2pPbt2zt0f/V6vUN4VGcEBwdXObkUGhpaKS0CE5GGHNI8S1uJKhWRhhwCcpNxK87GrFLRc7SceJE0TqZPn87o0aMrpY8YMUKxMUbFHGN0dDQnT54kNjaW2NhY1qxZQ0hICL6+vpc+uRbk5eWxZ88e+vbti0ql4qOPPiInJ4cbb7wRk8nEtGnTGDt2LL6+vrz//vvMnDkTsC3x0ev1LF68mPvvv5/XX3+dbt26VTnxAuDm5lajfZ5qQyGdC5PtjhGgW+Ep3JpEkVPSGe+043IroKTREhYW5nRC0tNTuaBvinWlp02bZm8dzpo1i3HjxjF48OA6kxozmUw899xzdu3HH374gQ0bNhAQEMAdd9zBtGnT6NmzJ+3atWPIkCFMnToVsD2cNWvW8K9//YvAwEC2bdvGihUrXGrbOaOB3f6xDmn7/FphEWA2m9FF9nXp/SQSSfU0mLjSqamp6HS6SvJjVxs1jYWbajSyp6jIaV5M0maCEn4lsSCQWx99y2U2SiRXA0rGlVZ0HeOpU6fYt28fhYWF9rQ//vjD3lq7FohwdyfSkEuaZ5A9LdKQQ2DuaTQXElEVS6kxiaS+UcwxfvLJJzz66KMEBwc7jCuqVKpryjGqzXq6FSaR5tndntat8BQ63XmKDAV4+gXK8UWJpJ5RzDG++eabrFq1irvuukspExoEVn0B2wIchw92BLSlD2AqyKTAryMhypgmkVyzKDb5kp+f32idYk1ivhw1WcjX+NmPNRYTuRo/jniGYTaVkJRwuC5NlUgaHA0h5otijvGOO+7g999/V+r2DYYO+nS8LSX2Y5ObhuCSbFrvmYjfhc/pec8jClonkVybKNaVDg0NZeTIkdx9991ERkY65F3teow1ivkS0IzgoiJS3WwitCqriZsPTkVtOGz72Tr5DIR9ASoBpjPg0abuDJdIGgANIeaLYo7x0KFDdOnShaSkJIcm87UWB+ZkSgLnIzoDNqfY/dD/oc7djgi8GVFwFHXKN+C1H0KbQckuCHkRQp4BlYwvLZHUFdecHmNDItVgIKFZN8DmFG88MJXwzB+xWt0wFyaj0eRBB4Ak0Jf+eOSvgJBnlTJZIrkmaBB6jNcqEZgINeZzwd2Hf+y5l7CcrWAGtbsFD8s5CK58zspmH5CruqggbsCCB2qC8WQc0fVnvETSiKlXx/j666/zwgsvAPDyyy9XWe5qH2O8XFSGArRmPRfcffDSpdkS3QE94AVkAc24OEVmdSPXM5YcjAShoRgLxVi4tgYfJJK6p14d4x9//GH/u6rQANfSGGNqsY7TPuGorCYcvJsHNsdYcexZbQGVmiA0ROBNPPn2LGs92CuRXCvUq2PcsGGD/e9rfoxRCIQhH5UmkO6H/g9/wwkwY3siboCjNi4rmy0g1/sGsjFgBS5gBECDCi3uuMl2o0TiMuQYo1KYilEDXuYSEKXtPXdsztEXsElV2hyiR3NyNNFYnMxE+6NBLZ2iROJSFHOM27Ztc5ru5eVFixYtaNq0aT1b5DrKC9lWtZ4xVa9jj38sKosRh360Ow6txTKnKBBU9H9qIA9bfJhg5PIdSeOg7LuTlJRkD1BX3yjmGPv3749KpXKIDlh2rFKpuOWWW1ixYsVV7SCrI8KQS4DVg+sSnqTZ+XVYLSrUbqXvRQEgYGXUAnI0zRGAtUJrUQ2oSj1lMB4ESccokbgMxbYErlixgmHDhhEfH09hYSHx8fGMGDGCJUuWsG/fPqxWa52J1tY1ZTtfqtv9oiq6QJixwN6NtjtFT2yTLipba9Gi8kCo3KDcpJQaCMGTYDwIxoMZxMqlOpJGQ9l3R6nWIijYYnz55ZfZt28fAQEBgK35vHjxYrp160ZSUhLLli2jR48eSplXtwhBqpsXJ30j+Ych72K6FQgqP67YHBA2x1iKurRYTunki+xCSySuRzHHmJ2dXWlpjkqlIjs7G4BmzZq5PERpg8FUbBsysJoIKjhoSxOANxBZflwR7AOLwvZn+WU55bvQB1MO8v3B7+15I7uMpEt0l7qth0TSSFHMMfbr148JEybw9ttvEx0dzZkzZ3juuefo378/AAcPHqwkLtFYECX5qK1mbjr0CBproc3bqYEAW2vRNq4obOOKKpXdKZZfkuOWcoasg38Rn5/Bp+l/czTtKNlF2fb8pX8s5dSbp1CrFRstkUiuWhRzjP/9738ZO3Ys119/vb3l2L9/f1auXAmAh4cHS5YsUcq8OiXNYGCvf2u6l7WY1dgWdUddHFdUYXUYVywjPeVvTh/YwsFt35JRkFHlPc7knGHpn0uZevO1o4YukbgKxRxjWFgYmzdv5ty5c6SlpdGsWTOaNWtmz1dScqiuCdel4uPTHIf1N+Vaiw7jiqWtxYyUYxw7sIn921ZTWHDhsu7zwa8fSMcokdQCxRd4R0VF0axZM4QQWK2lM7SNufsnBBkqd9of/SfNzq9DWEHlDTS/2FosXxZUpJ05ysr/zKCoCofopnLDIix4abwoMZUQ4B1Avj6fJwY9US9VkkgaG4p5oOzsbO6//35CQ0Nxd3dHo9HYX40aUzFNDXmoLLaF2So14F++tYi9C52RksCWtR+y9J0pTp2ir4ctiJi72vb7VmKyKYHn6/Pp1qIbk3tNrtu6SCSNFMUc4xNPPMGpU6dYtGgRPj4+fPvtt3Tv3p1///vfSpnkMqqN+aLP52+vJgTmH7Ade8LKmxaQ5NvLobWYlnyEFf+ZwbYfP8FkKHZ6nyKjLR61wWKwp0UERPDynS/z2cTPGnfLW9JoaQgxXxTrSm/atIm//vqL5s2b4+bmxsiRI+nQoQNTpkzh0UcfVcqsOkfo0mn199P4GNPti7nLd6Ezzh7n7wOb2PnrckyGokrnh/iEkF18cfa5ibYJ7SPac33E9YQHhMtlOhKJC1DMMRYVFdG8ua3r6Onpiclkok2bNsTHxytlksuoLuZLmsFAUPEJ24G/44SLVQjWLnuJjJS/q7x2mVMM8A7gnwP/yagbRklHKGlUXNMxX6Kjozl58iSxsbHExsayZs0aQkJC8PX1VcqkusdiJOL4c6hL0hDAlx0cu9C//7CQjJS/UanUCHFxKbevTyBFxXn244iACNY/tp6uLbrWcwUkkmsDxRzjtGnTiI+PJzY2llmzZjFmzBiEELz55ptKmVT3GIsoQYUPoPKE5KY97U4xLSWBbT/+F8DBKQJ2pyhbiRJJ/aAS5eVtFCQ1NRWdTkfbtm2VNuWKqFZuLDMB4+8D8DBlsnLQAk406UfZcpxl7z2IsaTyFsgHJz9IbKtYPDw8UKvUoLqoqqMu/ReliWK4dnhdVksiqXcuJd1Xlyi+jrGM8ou7GyVWE8UH/4mPKZOVfReQFNwLZ06xvCNUqVROQz2I0l3UltJ/p02n+Xfuv/FR+fBQ4EP1WSuJpFGimGNMTExk5syZ7N69m8LCQoc8o9GokFV1iNWEd95+oLQLrfYg7cxRlr4zBZNRz4OTH+T6ttfj5uZ2iQtVTbEoZmHuQtmClEiuEMUc44QJE4iKimLRokWNe8KlDIsRYdGhAozuXqUtxQcwGfW8+tKr+Pv5X/ISl4MJE/mW/EsXlEgkVaKYY0xISGDHjh24uzeY3nzdYTUh/pyIWhhZ2X8BVqtg5YJHMZYU8c4b76Bxr2K3T+k+aYlEUr8otjWiY8eOpKenK3X7OqXSzherCbJ3A5AY3oet6z6mqOAC789/v2qnCLVyiho0BLgF1MJqiaRhcE3vfPnvf//L9OnTmTBhAuHh4Q55ffv2VciqOsRiYmXfBVgEbN+wiPfnv49arSY1W/DNnxZSsgRWJ+sDVEB0GIzp5U6zEFVpmu3/skmYMgJUAUwOnFzHFZFIGj+KOcajR4+yZcsWfvzxR4d0lUqFxWJRyCrXUGnni6kILDkkRvbhh/+9yvvz3yU9V8U3f5pJzqx+tZQAzmTCO9+b6RQayv+GDqVzWBgA63TrOGc6hxUratQEuwfXYa0kkvqhIex8UWwdY3R0NE899RSTJ0+uNPlyJTOzSlNp7ZW5GNa0YuU/XiShaS+aFG7jt3grP+yxVnOV6nmrb19m9+zpCnMlkgbLNbmOMT8/n5kzZyp1+/rDmI8wnScxsg9N8raw/Hcz+5Og+bFjaPPzsarVpeELBKhUGL28ON2xY7WXfGbbNmL8/RnTrl09VUIiubZQbPJl6NCh/PHHH0rdvn6wmuCvB1EBfvl/8OkvZhL35NDur78IunABD5MJL4MBr5IS+//+eXm037nzkpf+55YtdW+/RHKNomhog2HDhnH33XcTERHhkPfqq68qZJVrsFgszJ07Fy90PN1mA2/ct4/d27eQlGTg+oSES042aywWOuzYwdGbb66yTGaxc41GV5KZmcnChQvtx9OnTyesdHzzaqEx1AFkPeobxcYYBwwY4DRdpVLx22+/1bM11ZOVlcXkyZPZunUrUVFRLFy4kIEDBzot26FDBwwGA0lJSXSMhMNvwwe3f8Ssz4uJOXyYgIKCy75vga8vp2+4wWmeRq3G+OSTtarP5XL06FE6luvWHzly5KqLxdMY6gDXZj2uyTHGLVdRV3DGjBmEh4eTlZXFpk2bGDNmDImJiQQHVz0LHKiFA/Ntw4eLNhaDEPhfwilWXM/tX1SEe0kJZi+vSmW7V1jiJJFIXEe9O8aXX3652nyVSsUrr7xST9ZcGp1Ox/fff8+pU6fw8fFh+PDhxMXFsXbtWqZMmeL0HIvFQrgW3EpHcI+lgv/585fsQjvL73zoEAduvJHyc9geajUf33prbaojkTQ4MjMzycrKqpRuMBgU2xlX73fdvn17tfnO1GSUJDExEa1WS1RUlD0tLi6u2uZ9fn4+Wm/HtOYnT1ZZvrqdf2aTifdjYnj5t98wBAYyqlcvnu3Z076WsSKX2/1wZTfF1fdUog51cV9Zj8tj4cKFVTaGlFq6V++O8WrqQoOtxejv7yjw4O/vT3Z2dhVn2MjQ2VfgAFDV4xVAYVAgQXm5WIRz9/jF118TlZAAwJdvvFET8yUSSS24BhQcrgytVktBhbHBgoICtFqt0/IpKSkUFxdjtYLHJGgbAR68y/GCqmeRhUqFKjSU9KwsrE7mwgK0WvJzcoBL7wYo21/qinIGg8HheMSIEXh6etbpPV1d7nLroJR9l1uuMdej4verPErtglNcwfuWW25h7dq1+Pn5KWlGleh0OoKDgzl9+rRdTHfAgAFMnDjR6RhjeHg4hYWF9g+tWmVF1TSYwnOV40KXp3VpYLCGhMViIafUIQMEBwdfdbuSGkMdoHHXA8BsNlcqm56ejq+vL5mZmfVmXxmKO0a1Ws2FCxeqneFVmtGjRxMQEMCCBQvYvHkzkyZNuuSstEQiuXqRXenLYOHChUyaNImQkBCioqL4+uuvpVOUSBoxssUokUgkFVBsr7REIpE0VKRjlEgkkgoo7hgb2oJuiUQiUdwxKjzEWSdkZWVxxx134OvrS9u2bdm8ebPSJjmlf//+eHl5odVq0Wq1DB061J43f/58QkNDCQ4OZvbs2Q7Pac+ePXTq1AkfHx/69evHmTNn6tXujz/+mK5du6LRaJg7d65D3tKlS4mKisLf358pU6Y4hOJNSkqid+/e+Pj40LVrVw4dOmTPs1qtPP744wQGBtK0aVM++OADxeqxdetW1Gq1/blotVqHHWMNqR4Gg4GpU6cSHR2Nv78/N910EzvLyebV9nOk1+sZP348fn5+REdH8+WXX9ZpPSohFGby5MmiqKhIaTNcyujRo8XUqVNFUVGRWLt2rQgODhbZ2dlKm1WJfv36ieXLl1dK//HHH0VUVJQ4efKkSE9PFx07dhSfffaZEEKIkpISERUVJRYtWiT0er14/vnnxc0331yvdq9Zs0asXbtW3HvvvWLOnDn29MOHD4vAwECxe/dukZeXJwYOHChefPFFe36PHj3Eyy+/LPR6vVi4cKFo2bKlMJlMQgghPvroI9G5c2dx/vx5ceLECREZGSk2bdqkSD22bNkiWrduXeV5DakeOp1OvPLKK+LMmTPCYrGIL7/8UoSEhIjCwsIr+hw9/fTTYsiQISI/P1/s3LlTBAYGimPHjtVZPSqiuGNsbBQWFgqNRiPOnj1rT+vXr59YvHixglY5pyrHeN9994l58+bZj5csWSL69u0rhBBi48aNDl/aoqIi4e3tLU6dOlX3Blfg4YcfdnAozz77rHjggQfsx1u2bBHR0dFCCCGOHTsmfH19RUlJiT2/RYsW4rfffhNCCHHTTTc5vBdz5swREydOrOMa2KhYj+ocY0OuRxkRERFi7969V/Q5Cg8PF9u3b7fnT5o0Sbz88sv1VAMhFO9KNzZqIzqhJE888QShoaEMGjSIw4cPA7aY3506dbKXKW9/xTwfH5/Kwb8UwpndKSkp6HQ6EhISuO666xy20VVXL6Wf2dmzZwkLC6NNmza8+uqr9q1xDb0eiYmJ5OTkEBsbW+vPUW5uLhkZGYrWQzpGF1OV6IROp1PIoqp5++23OX36NCkpKQwaNIihQ4dSWFhYqQ7l7W/I9XNmd1n6peyurs71Tbt27Th06BAZGRmsXbuWVatW8a9//cupndBw6lE2Lvjcc88REBBQ689RWZny24Tr+3lIx+hiaio6oSQ9e/ZEq9Xi7e3N7Nmz8fPzY9euXZXqUN7+hlw/Z3aXpV/K7urqXN+Eh4fTrl071Go17du358UXX+S7775zaic0jHqYTCZGjx5NbGysXXO1tp+jsjKFhYVOz60PpGN0MW3atEGn05GammpPu1pk6NVqNUII2rdvT3x8vD29vP0V8/R6PUlJSQ2ifs7sjo6ORqvV0r59exITEx3UXaqrV0N6ZmXPBWiQ9bBarUyYMAGVSsWyZcvsS/Bq+zkKCgoiPDxc2edRb6OZ1xD33HOPeOCBB0RxcbH44YcfGuSsdG5urvjll19ESUmJMBgM4v333xdNmzYVeXl5Yv369aJ58+YiKSlJZGRkiE6dOjnMJjZr1kx8/vnnoqSkRLzwwgv1PittMpmEXq8XDz74oHjhhReEXq8XZrNZHD58WAQFBYm9e/eKvLw8MWjQoEqz0nPnzhUlJSXi008/dZjN/fDDD0WXLl1EZmamSExMFM2aNavzWemq6rFlyxaRkpIihBDixIkTIi4uTrz55psNth4PPvig6Nu3r9Dr9Q7pV/I5euqpp8Rtt90mCgoKxF9//SWCgoLkrPTVTmZmphg6dKjw9vYWbdq0Eb/++qvSJlUiMzNTdOvWTWi1WhEUFCQGDBgg9u3bZ89/4403REhIiAgMDBRPP/20sFqt9rzdu3eLuLg44eXlJfr06SOSk5Pr1fY5c+YIbBq/9teSJUuEELaZz8jISKHVasWkSZMcZm8TExNFr169hJeXl+jSpYs4cOCAPc9isYh//vOfIiAgQISGhor33ntPsXq8++67IjIyUvj4+IiYmBjx0ksv2R1fQ6tHcnKyAISXl5fw9fW1v7Zt2yaEqP3nqLi4WIwbN074+vqKqKgo8cUXX9RpPSqiuIiERCKRNDTkGKNEIpFUQDpGiUQiqYB0jBKJRFIB6RglEomkAtIxSiQSSQWkY5RIJJIKSMcokUgkFZCOUSKRSCogHaNEIpFUQDpGiUQiqYB0jNco/fv358UXX1TajFpjMpm4//77CQ4ORqvVkp+fX6nM0KFDmTdvngLWVY/ZbEalUrF161alTamWP/74g86dO2O1Wmt03rBhw1i6dGndGFVPSMfYAOnfvz8eHh5otVr8/f3p0KEDn376qdJmuQRXOeTVq1ezZcsWkpOT0el0BAQEVCrz008/8dJLL13xva5VHnvsMV555RXU6otu4tdff2XgwIH4+/vj6+tLly5d+Pjjjx2CXL322ms888wz6PV6Jcx2CdIxNlBmz56NTqcjLy+POXPmMG3aNH7//Xelzboiykfsu1KSkpJo1apVJRVoiWvYsmULGRkZDBs2zJ62ZMkSRo4cyV133UVKSgrZ2dm8+eabzJ8/n8mTJ9vLde7cmRYtWrBixQoFLHcR9arlI7ks+vXrJ1544QWHtJCQEPHOO+/Yj/V6vXjuuedEq1atRGBgoOjTp4/Yv3+/Pf/DDz8UHTp0EH5+fqJp06Zi/PjxIisrq9p7VLRhxowZYtSoUUKr1YrWrVuLZcuWOZS5nHvMmDFD3HvvvSIwMFA8/PDDQq1WC41GY5enqorc3Fzx0EMPiWbNmomQkBBx22232fX4Jk2aJDQajVCr1cLX11fcdtttl/U+9uvXTzz66KNizJgxws/PTzRr1kx8+eWX4vDhw+Kmm24SWq1W9OjRw0H373Leh0s9i/Pnz4tRo0aJgIAA0bJlS7FixQoBiC1btlz2+zhz5kwxduxY4e/vL6KiosTChQsdbCguLhYvvPCCaNOmjdBqtaJly5Zi6dKll2WfM2bMmCHGjx9vP87Pzxd+fn7i3XffrVR29+7dQqVSOeg+vvTSS1U+l6sB6RgbIOW/0CaTSXzxxRcCEOvXr7eXmTRpkhg4cKA4e/asMJlMYsGCBSI0NFTk5uYKIYRYvXq1OH78uLBYLCI5OVn07NlT3HfffU7vUZUNXl5eYt26dcJkMokff/xRaDQasWPHDnuZy7mHj4+P+PHHH4XFYhFFRUWXvG8Zd955p+jfv79IT08XRUVF4p///KeIiooShYWFQgiblmHv3r0v+30sOw4ICBC///67sFgs4l//+pfw8fERt99+uzh9+rQwGAzirrvuEoMHD67R+3CpZzFo0CAxZMgQkZ2dLbKzs8Udd9zh4Bgv53309/cXmzdvFhaLRaxevVqo1WqRmJhoL3P//feLHj16iISEBGG1WkVqaqpdX/NS9jnjxhtvFPPnz7cf//jjjwJwiH5ZnlatWolZs2bZj7/55hsREhJSzdNp2EjH2ADp16+f8PT0FAEBAcLNzU24ubmJt956y55/4cIFAVRSNI6NjXUaDlUIIb777jsRHBzscI9LOca77rrLIW3MmDFi6tSpVZ7j7B7lv+CXc18hhEhLSxOAOHjwoD3NaDSKkJAQ8eWXXwohau8Yy9ufl5cnALFy5Up72urVq0VgYKDDOdW9D5d6FufOnROAOHz4sD3v8OHDDo6xIs7exylTpjiUadKkifjqq6+EEEJkZWUJQOzZs6fStWrzWRFCiDZt2oiPP/7Yfvzll18KQOh0Oqflu3XrJh555BH78S+//CLUanWV12/oyDHGBspTTz1FXl4eubm5TJ48mV9++QWz2QzAyZMnAbjxxhsJDAy0v1JTUzl37hwA3333Hb169SIsLAx/f38mTJhATk6OPQzn5dCyZctKx2fPnrUfX849Kl7jcii7R+vWre1pGo2GFi1akJKSUuPrlSciIsL+t6+vr9O08kGYoPr34VLPoux5lL9GxetdzvsYGRnpcE55O0+fPg1A27ZtK9X3cj4rzggODnaY6W/Xrh0Ahw4dqlS2qKiI48eP28sA5OfnExQUVOX1GzrSMTZw/Pz8+Oijjzh16hQfffQRYIsiB3D48GHy8vLsr+LiYp599lnOnTvH6NGjeeyxx0hJSaGgoIDly5cDOMweXork5ORKx2Xxsi/3HuVnNJ0dO6N58+aAbYKlDLPZTEpKCtHR0Zdtv6uo7n241LMoK1f+GuX/dsWziomJAeDEiROV8i5lX1V069bNIY5zly5d6NOnD2+++Walsu+//z6enp6MGzfOnhYfH0/37t0vy/6GiHSMVwGenp68/PLLzJs3j/z8fFq0aMHIkSOZMWMGZ86cAWyhJn/66SfS09PR6XRYrVaaNGmCl5cXiYmJTj/Ql2LDhg38+OOPWCwWNm7cyJo1a5gyZQpAre8RHh7u9AtcnoiICG6//XZmzZrF+fPn0ev1PPPMM3h4eHDHHXfUuB5XSnXvw6WeRbNmzRg4cCCzZ88mNzeX3Nxcnn/+efu1XfGsQkNDGTt2LDNmzOD48eMApKens3///kvaVxV33XUXmzdvdmi1rly5kuPHjzNjxgxKSkqwWq289957vPvuu6xevZrQ0FB72Y0bNzJq1Kga1aMhIR3jVcKECRMICQnhrbfeAmwf0m7dujFo0CD8/Pxo27YtixYtQghBu3btePPNN5k4cSJ+fn5MmjSJ8ePH1/ieU6dO5fPPPycwMJAZM2bwySef0KdPH4Ba32PWrFkcP36coKAgAgMDqyy3fPlyYmJi6Nq1K1FRURw9epRNmzY5BGGvL6p7H6D6ZwGwYsUKPDw87PW599577ee66lktWrSIfv36MXToULRaLb1797a3+C5lnzMGDhxIWFgYP/zwAwBvvPEG7dq1IyUlhYULF/L111+zfft2nnrqKYqLi7nzzjt55JFHAFvr9PTp00yYMKHG9WgoyGBYEqf079+fm2++mddee01pUxTlWn4fduzYwYwZMzhw4MBlDYGUMXz4cEaNGmVvVV+NuCttgEQiaZjcfPPNTidbLsW6devqwJr6RXalJRKJpAKyKy2RSCQVkC1GiUQiqYB0jBKJRFIB6RglEomkAtIxSiQSSQWkY5RIJJIKSMcokUgkFZCOUSKRSCogHaNEIpFUQDpGiUQiqYB0jBKJRFIB6RglEomkAtIxSiQSSQWkY5RIJJIKSMcokUgkFVDUMWZlZXHHHXfg6+tL27Zt2bx5c5Vlz549y5133klgYCAtW7Zk1apVDvn/+c9/aNGiBQEBAfTr14+///670jXOnTuHRqMhNTXVIV0IQUxMTL0IbH788cd07doVjUbD3Llzqy37ySef0KpVK/z9/YmOjnaIBXLq1Cl69OhBUFAQwcHBjBw50mkMj5KSEgICAti5c2elvL59+/Kf//zniut0KWpS57lz56LRaNBqtfZXeRrjcwZbXJm4uDh8fX1p3bo1f/75Z6UyjzzyCCqVyml0v6vtOZeFYCh7aTQahg0bZs/fs2cPnTp1wsfHh379+tnj1VSkbdu2fPnll5XSJ06cyJNPPln7yigTtdXG6NGjxdSpU0VRUZFYu3atCA4OFtnZ2U7L9u3bV8yaNUsYjUaxf/9+0aRJE/H3338LIYTYs2eP8PPzE4cPHxZms1m88MIL4qabbnJ6nQEDBoh3333XIW3btm0iJCREGI1G11bQCWvWrBFr164V9957r5gzZ061ZU+fPm0Pip6Wlibat28v1q9fL4QQoqCgQJw6dUpYrVZhMBjEM888I+655x6n15kyZYp49NFHHdLOnDkjNBqNOH/+/BXX6VLUpM5z5swRDzzwgNO8xvqcDx48KFq2bCl27twpLBaLOHv2rEhNTXUos2/fPtGnT59qg95fTc+5Iu3atRNLliwRQghRUlIioqKixKJFi4RerxfPP/+8uPnmm52e98orr4g777zTIa2oqEhotVqxb9++2lRDCCGESxyjxWwWWxfNFb/+52n7a+uiucJiNld5TmFhodBoNA4PuV+/fmLx4sVOy6pUKpGfn29Pe/DBB+3B1FevXi169eplzzt69Kjw9/d3et/PP/9cdO3a1SHt4YcfFtOmTbu8ylbAWJwrso5sEMbivBqd9/DDD9fow5OWliY6duwo/v3vf1fKMxgM4rnnnhM33HCD03M3b94swsLChLnc83jzzTfF0KFDa2SznQsJQmx+TIgLf9fotMupc3WOUann/IU4Iz4UiQ6vL8SZyzr3cuo8ZswY8dlnn1WZb7VaRe/evcXevXurdYyufs56k0l8uH+/eOK338SH+/cLvcl0WefV9LO9Z88e4ePjIwoKCoQQQmzcuFG0bt3anl9UVCS8vb3FqVOnKp2blJQkPDw8HBpUX375pbj++usv+/7OcElX+sS2texZtYCDPyy2v/asWsCJ7VV3WRITE9Fqtfa4uwBxcXEOsWzLtWrtr/JpZWUHDRqEwWDgwIEDmEwmli9fzuDBg53e95577iEhIcEeZtJoNPLNN9/UKjJbUcYx0v5cQuG5Q6T9uZiijGM1vsalWLlyJX5+fkRGRlJcXMzo0aMd8gMDA/H29ubdd9+tsuvQv39/PDw82LRpkz3tiy++qHmdDQWw5Qn4Xyc4sAD+F2c7NhTUuF7VsXr1akJCQrjhhhv47rvv7OlKPedcjORgtB/nYCS33PGVsnv3brKysoiNjaV58+Y8/vjjGAwGe/6yZcto164d3bp1q/Y6LnvOQInZzC2rVvHo5s18sG8fj27ezC2rVlFiNtf4Wpdi+fLljBw50h4BMiEhgU6dOtnzfXx8aN26tVPf0KpVK7p168Y333xjT1uxYkWt6lwelzjGqE69cPfwIrh5G6Z9nUBw8za4e3jRvFOvKs/R6XT4+/s7pPn7+6PT6SqV9fPzo3fv3sydOxeDwcDevXtZvXo1RUVFAGi1WoYPH06PHj3w9vbmq6++4oMPPnB6X39/f4YNG8YXX3wBwE8//URgYCC9elVtqzMKUvaReeh7ULsRdF1/ULuReeh7ClL21eg6l2LcuHEUFhYSHx/P5MmTK4UPzcvLIzc3l7feeovrrrvO6TXUajVjx4611/nQoUMkJyczcuTImhmT9ifs/xc0iYPbV9j+3/8vW7qLGDNmDMeOHeP8+fPMnz+fyZMns3v3bkCZ51xGMB7MIJYZxBKMR+0qVwWpqamsXr2a7du3c+DAAfbs2cPbb78NQH5+Pm+88QZvvPHGJa/jsucMfB4fz860NGZ27crphx5iZteu7ExL4/P4+BpfqzrMZjNfffWVQ6jVmvgGgPHjx9vrfOHCBX799Vfuv//+K7LLJY5RGxJO5zsmkXM2kR1LXifnbCKd75yMb3DTqs/RaikocGxpFBQUVBpsL+OLL74gMTGRqKgoZsyYwcSJE+2tzc8++4yvv/6akydPotfrmTFjBiNGjKjy3uPHj7cP2H7xxRe1ehPdvQIACGjRncCWNxHQorst3Tugxte6HDp27IiPjw+vvvpqpTx/f38mTpzIiBEjsFqtTs8fP34833//PXq9ni+++IJRo0bh4+NTO2O6zIDr77f972Lat29PeHg47u7uDBkyhHHjxrF27VpAmedcH3h7e/PYY48RERFBkyZNePLJJ9mwYQNgm4x6+OGHCQsLu6xrueo5J+XlAfBEt27EBATwRGlrtSzdVfz888+o1WoGDRpkT6upb7j33nvZvXs3Z8+eZdWqVdx44420aNHiiuxy2ax0jzGP4e7hRfzGL3D38KLnmMeqLd+mTRt0Op3DzOGRI0fo0KGD0/ItWrRg/fr1ZGVl8ddff3HhwgV69uwJ2H4Zhw8fTkxMDBqNhkceeYT9+/eTm5vr9FpDhw4lNzeXTZs2sX79+lo1u72bxKBy86AwNZ7cxG0UpsajcvfEOySmxte6XMxmMydPnqwyLyMjo8pf1U6dOhETE8PatWv58ssvr6yrcfAjSFhh+7+OUavV9iEUJZ5zGTkY+YiTfMRJh261K+jYsSMqlcp+XP7vLVu2MH/+fMLDwwkPDwega9eubNy40em1XPWcWwcGAvDBvn0k5+fz/t69DumuYvny5YwbNw43Nzd7Wvv27Ykv1zLV6/UkJSVV6RtCQkIYPHgwX375pUu60eBCx1jWagQu2VoE26/CiBEjmDNnDnq9nvXr13P48OEqWwAJCQnodDoMBgPLly/nr7/+sgf07t69O+vXryc1NRWLxcKiRYto1qwZQUFBTq+l0WgYM2YMDzzwAB07dqyyC1odKrU7PmGxmItzyTv1J+biXHxCY1Gpqw/VbTabKSkpwWKxOPztjGXLlpGZmYkQgv379/Phhx9yyy23ALB161b279+PxWIhNzeXWbNm0a1bt0pdkPKMHz+eZ555BovFwsCBA2tcZyJ7Qbcn4EI8/DTB9n+3J2zpLqrzunXryM/Px2q18ttvv/HFF19w5513Aso8Z4AgPBy6z8F4EHSJ7nRN6jx58mQWLFhAZmYmubm5fPDBB9xxxx0AbN68mfj4eA4ePMjBgwcBWytrwIABVd77ip8z8EBcHP+IjOQ/+/fTctEiFhw4wD8iI3kgLs4ldQZbK3DdunVMnDjRIb1///7o9XoWL16MwWDg9ddfp1u3brRs2bLKa40fP54PP/yQ/fv3VxqHrxVXNHVTAWNJscjLSBHGkuLLKp+ZmSmGDh0qvL29RZs2bcSvv/5qz1uxYoVo3769/fidd94RwcHBwtfXV9x6663i2LFj9jyLxSKefvppERERIfz9/cWNN94odu7cWe29//jjDwE4neW9XMyGYmHQXbC/zIZL13vOnDkCcHiVLVPYtm2b8PX1tZedNm2aCAsLE76+vqJ169bitddeE1arVQghxLp160S7du2Er6+vCA0NFffcc49ITk6u9t5nz54VarVaPPHEE7WusxDCNhu9eeZlz0rXpM733nuvCAwMFFqtVsTFxYmvvvrKnqfUc64NNamz1WoVzz33nAgODhZhYWHiscceEyUlJU6vSzWz0mW46jnXdFa6JnUWwrZyIC4uzum1du/eLeLi4oSXl5fo06fPJT/ber1e+Pv7i1GjRl1+BatBxpWWSCSSCsgtgRKJRFIB6RglEomkAtIxSiQSSQWkY5RIJJIKSMcokUgkFVDUMdZEpmjbtm3069cPrVZL//79HfJ0Oh0333wzISEhBAYGMnDgQI4dc75vuc5kii4TV8mOwbUpwfXTTz8RGxuLr68vI0aMcLq4+2qT4ILq65yRkcE999xDQEAAISEhPPfcc5XOv9rqXJ3s2PHjxxk2bBihoaE0adKEu+66i7S0NKfXaZSyYzWRKdqzZ49YsWKFeOutt0S/fv0c8oxGo0hISBAWi0VYLBaxYMEC0b17d6fXqSuZosvFVbJjikpwGXKE2PWA7f/LwFUSXOfPnxcBAQHixx9/FEVFRWLixIli/PjxTq9zNUlwXUp2rEePHuK9994TRUVFori4WBw+fNjpda6mOlekvOzYX3/9JRYvXixycnJESUmJeOyxx8SgQYOcntegZceEEMJqtYgLh34SmXu/FxcO/SSsVstln1sTmaIvv/yykmMsj9lsFgsXLhRBQUFO810mU2S1ClGQJoTJ+ULcS3GlsmOKSa0ZcoT4qZsQX2D7/zKdY9k9r0SC65NPPhEDBw60H586dUp4enqK4uLKC+tdLcGVX5wvfor/yf7KL86/9Eniyuu8YcOGKrUIK+JyeTkhhL6kRKSePy/0VSw4d8aVyo5V5Pjx40Kr1TrNa9CyY0JYSdnwLmfWz+fszx9wZv18Uja8ixDOBQ3qik6dOuHl5cWMGTOYPXu20zIukynKToSMeEjeDlnHwGy49Dm1oCrZMUUkuIy58NsgyNkHYf1t//82yJbuIqqT4KooR9WyZUs0Gg1JSUmVruNKCS6AOevmMPTfQ+2vuevm1uo6zqiuzrt37yYmJoahQ4fSpEkTBg4c6HTIBFxfZ4AFK1Zw+//9Hx+WqtfUBRVlxyqybdu2KvdJN2jZsZz4X8g+tAH/1jfSZtwH+Le+kexDG8iJ/8UVl79sDh8+TEFBAYsWLaJz585VlnOJTJG1dA+ouzfkpVx0kNaq94bWhqpkx+pdgqu8U+w4B27dYvvfxc6xOgmumshRuVKC6+/0v/lwy4fcEH0Dn038jBuib2DBlgUcS3eN/mZ1dU5NTeWrr75i5syZpKWlcdtttzFy5Eine5BdWWeArJwcVv/8MwDfbNzIhSrEOq4EZ7Jj5Tl58iTPP/98tbJrDVZ2zGqy/bo1vfE+/GJuoOmN9zmk1yfe3t5MnTqVKVOmkJ2d7bSMS2WKorqDfzMQVpuDtLhWeaWMirJj9S7BdeDpiy3FTnNtaZ3mXmw5Hni6xnVyRnUSXDWVo3KVBNfHWz/GbDEza9AsHujzAE8OehKzxczHv39c8wo6obo6e3t7c/PNNzN06FA8PDx46qmnyMjIqFJlyZXyckvXrMFgNDLq1lsxGI0sXbOm1nWsCmeyY2WkpaUxePBg5s2bZxdPcUaDlR1TazwBOP/XVxQmH+D8X185pNc3QggKCwsrzcSW4VKZonN7oSAVVGoIbAFurhUxLU952bF6l+C64R0I7gaZW+HwXFva4bm24+ButnwXUJ0EV0U5quTkZEwmE61bt3Z6LVdJcE3rPw13N3fe+/U9Pt/+Oe//+j7ubu5M6zetVterSHV1rph3KVxV57LWYsuoKB4dP56WUVF10mp0JjsGtpbfrbfeyv/93//x8MMPV3uNBis7Fhw3mJDOt1OQ9BeJK5+gIOkvQjrfTnCc8zGvMmoiU2S1WikpKcFkMjn8DbB//362bduG0WikqKiIZ555hsDAQNq1a1flva9Ypkhd+iDNeptDjOkDoW0vprugztXJjtW7BJdHENzyq80JHnkFNg2w/R/czZbu4fy+Na1zdRJco0aNYs+ePWzcuJHi4mJeeeUVRo8ejbe3d5X3doUE1/UR1/PogEc5kHKAB//3IAdSDvDYgMdoF1H158tVdb7rrrs4dOgQmzZtwmKx8K9//Yvw8HBiY2PrtM77jh7FYDRy+tw5Bk6ezOlz5zAYjexzEl6gNnWGqmXHCgoKGDJkCHfeeSfPPvvsZdnbYGXHajMrXROZoi1btlQqO2nSJCGEELt27RJdunQRWq1WBAcHiyFDhohDhw5Ve+8rlimyWoUoSK/xrLSrZMcUk+Cqxay0KyW4fvzxR9GqVSvh7e0thg0bJnJyqr+/qyS4ajor7co6//bbb6Jdu3ZCq9WKPn36iKNHj1Z7b1fU2Ww2iw+WLhWvf/KJ/fWvZcscZryvpM5CVC07tnTpUgEIX19fh1d1SNkxifIYc21jije8U21LUSK5WpGOUSKRSCog90pLJBJJBaRjlEgkkgpIxyiRSCQVkI5RIpFIKiAdo0QicQl///03cXFx+Pn54e3tzddff620SbXmqtFjfPvtt2nfvj1+fn5cd911LFmyxJ536tQpevToQVBQEMHBwYwcOZL09PRK17jaNOvKyM/PJzw8nFtvvdUh/VrTY9y1axcDBw4kKCiIpk2bMmXKlEpbBOHqfM7V6TFeLc/5nXfeYdiwYRQWFqLX67n33nsBWLp0aaXPbkNHUccYERHB3Llzufvuuy9ZVqVSsXLlSvLy8li9ejXPPvssf/zxBwChoaGsWrWKnJwcMjIyaNeuHTNnzqx0DS8vL+6++25WrlzpkJ6SksKuXbu47777XFOxaqhJncuYM2dOpZ0Oe/fu5cUXX2T9+vXk5OTQp08fpk6dWuncqKgo+vTpw1dffeWQvmPHDnQ6HUOHDq1dRWpATep86NAhHn30URYtWkRhYSG///47MTExAOTl5TFjxgzOnj3LyZMnKSws5Kmnnqp0javtOVdX56vpOaekpNC+ffs6v0+94JJl4hWwFOcK0+ldwmq5PE3Gmuq3CSHE2LFjK4mvCiGEwWAQzz33nLjhhhucnudKzbpzBoOwWK3CYrWKcwZDjc693DrHx8eLHj16iM8//9xBi1AxPUYhbLt+rBbb/zXgSrUJK/Lzzz+Ljh07Os1ztTahwWoQvxX9Zn8ZrJf3vK+0zoo+5xpw2223CbVaLTw9PYWvr6/o2LGjWL58uUhKShKenp7Czc1N+Pr6ivbt29e5La7ApS1Gqz4Pw74v0a+djeGPTzAd+taVl7djMpnYtWtXJY22wMBAvL29effdd6uUNXeVZl2q0cieoiKHV6rR9co6M2fO5L333kOtdnxUiugxAggTnJ8Jx91s/wtTza9RDdVpE1akOp0+V2oTGoURnVXHYcNh+0tn1WEUrnne1dVZsedcQ3766Sf69OnDZ599hk6nIyQkBLDpJX7yySf0798fnU7H0Wr2Wjck3F1xEWE2Yjz0LebELWAxofKPQBSkI0wlrrh8JWbNmkVMTAxDhgxxSM/Ly6OgoIDPP/+8SpGE8pp1Q4YMqbVmXYRGQ4RGQ3qpkEXZsSv56quvCAsLo0+fPpXEWMvrMQI0b96c7du3O71OeT3GV199tXZ6jGUcL6celPeh7dXOdZunymsTajQaRowYwdtvv81LL73kUG7nzp0sXLiQHTt2OL2Oq54zwB/6PzhsOOyQtrxgOZ08OzHAZ0CNr1eR6upcX8/57c8+4/jp0zWyu23Llsx+8MEanXO14BoF75ICzMd+AYsJ99Z98L71GVdc1ilvvvkmv/32G6tXr3Yqx+Tv78/EiRMZMWIEVqtzBXFXatbVFUVFRcyZM4d33nEu51XveowAVn3N0mtBddqEZRw9epRRo0axfPnyase0robnDNXXub6e8/HTp9l39GiNXjV1pFcTLmkxqrz8cW83GHPiFsxJ27FkORfRvFI++ugjPvvsM7Zv305wcHCV5cxmMxkZGU4Vn6GyZt3nn39eY1vSTSbSTSZ7K7HsuJmHa/QYExMTOX36tL2loNfrKSkpoW3bthw/ftxBjxHgkUce4emnnyY3N9ep9NjQoUOZOnWqXY/xtddeq7lRam8IeRGyy50b8pIt3UVUp00IkJSUxJAhQ3j//fft0lxV4YrnXB9UV+f6es5tW7assd2Xe05N9CQbCq5xjO4eeHYbi6b97ZgSNti61IBK41XteWazGbPZ7KDfptFoKolWAvzvf//jjTfeYNu2bURGRjrkbd26FX9/fzp37kxBQQGzZs2iW7duTp1iGVeqWVfmAMs7xstxipdb544dO5KSkmI//vrrr/n2229ZvXo1YNNjfP/993n00UcJDw+vez3GMvzG2Byj/1go+BL8Lq19V5PnXKZNeNttt6HRaBy0Cc+dO8ett97K888/z7hx4y7LXFdoE/b27k1nz84sL1huT5vgPwGt2rlyOLiuzvX1nOuySxwWFsa5c+cwm824u7vE5dQ9dTGjYynOu6xZ6Zrot8XExAiNRuOgz/b6668LIYRYt26daNeunfD19RWhoaHinnvuEcnJydXe21U6fTWlppp1ZSxZssRhVloxPcZa4Cptwrlz51bS6bvULKernnNNZ6VdVeer6Tn369dPLF++vNLfJSUlYsiQISIwMNCp/mJDRMqOSSQSSQXklkCJRCKpgHSMEolEUgHpGCUSiaQC0jFKJBJJBaRjlEgkkgpIxyiRSCQVkI5RIpFIKiAdo0QikVRAOkaJRCKpgHSMEolEUgHpGCUSiaQC0jFKJBJJBaRjlEgkkgpIxyiRSCQVkI5RIpFIKiAdo0QikVTg/wHzTihGMU7mWwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import os\n",
    "import numpy as np\n",
    "from numpy.typing import NDArray\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit import Model, Parameter\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "from matplotlib.ticker import FixedLocator, StrMethodFormatter\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (2.5, 2.5)\n",
    "plt.rcParams['figure.dpi'] = 120\n",
    "\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "plt.rcParams['ytick.minor.width'] = 1\n",
    "\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "\n",
    "\n",
    "#select potentials and samples for comparison\n",
    "target = 'Prep11_EIS_1'\n",
    "\n",
    "available_potentials = sorted(sXRD_EISData.loc[sXRD_EISData['fname'].str.contains(target), 'potential RHE'].unique())\n",
    "\n",
    "\n",
    "closest_potentials = available_potentials\n",
    "print(closest_potentials)\n",
    "\n",
    "\n",
    "mask = (\n",
    "    sXRD_EISData['fname'].str.contains(target) &\n",
    "    sXRD_EISData['potential RHE'].round(3).isin(closest_potentials)  # rounding avoids float-equality issues\n",
    ")\n",
    "data_for_plot =  sXRD_EISData.loc[mask]\n",
    "data_for_plot = data_for_plot.sort_values(by=\" Frequency (Hz)\", ascending=True)\n",
    "\n",
    "colors =  ['saddlebrown', 'peru', 'sandybrown', 'burlywood', 'peachpuff', 'paleturquoise', 'darkorange', 'orange', 'gold', 'aquamarine', 'darkgreen', 'lightgreen', 'darkcyan', 'darkslategrey']\n",
    "alphas =  [1, 1, 1, 1, 1, 1, 1, 1]\n",
    "markers = ['v', '8', '<', 'p', '>', 'x', 'h', 'D', '*',  's', 'H', 'P', 'o', '^']\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 1, tight_layout = True)\n",
    "ax_inset = inset_axes(ax, width=\"44%\", height=\"44%\", \n",
    "                    bbox_to_anchor=(-0.08, -0.08, 1, 1), bbox_transform=ax.transAxes)\n",
    "\n",
    "#ax.set_title('A) impedance spectrum')\n",
    "ax.tick_params(which='both', direction = 'in', bottom = True, top = True, right = True, left = True)\n",
    "for ipotential, potential in enumerate(closest_potentials):\n",
    "    ax.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' ZHIT (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' ZHIT (Ohm)\"], marker = markers[ipotential], markersize = 3, fillstyle = 'none', linestyle = 'none', color = colors[ipotential], label = f'{data_for_plot.loc[data_for_plot['potential RHE'] == potential, 'potential RHE'].iloc[0]:.2f} V')\n",
    "    ax.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' fit 3-ladder (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' fit 3-ladder (Ohm)\"], marker = 'none', markersize = 3, fillstyle = 'none', linestyle = '-', color = colors[ipotential])\n",
    "ax.set_xlim(-20, 2100)\n",
    "ax.set_ylim(-20, 2100)\n",
    "ax.set_ylabel(r'$-\\mathrm{Imaginary \\ part \\ of \\ impedance} \\ (\\Omega)$')\n",
    "ax.set_xlabel(r'$\\mathrm{Real \\ part \\  of \\ impedance} \\ (\\Omega)$')\n",
    "#ax.axhline(y=0, color='black', linestyle='-')\n",
    "ax.set_aspect('equal')\n",
    "#ax[0].legend(fontsize = 14)\n",
    "ax.xaxis.set_major_locator(FixedLocator([0, 500, 1000, 1500, 2000]))\n",
    "ax.xaxis.set_major_formatter(StrMethodFormatter('{x:.0f}'))\n",
    "ax.yaxis.set_major_locator(FixedLocator([0, 500, 1000, 1500, 2000]))\n",
    "ax.yaxis.set_major_formatter(StrMethodFormatter('{x:.0f}'))\n",
    "ax.yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "ax.xaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "\n",
    "# Create inset plot\n",
    "\n",
    "ax_inset.tick_params(which='both', direction = 'in', bottom = True, top = True, right = True, left = True)\n",
    "for ipotential, potential in enumerate(closest_potentials):\n",
    "    ax_inset.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' ZHIT (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' ZHIT (Ohm)\"], marker = markers[ipotential], markersize = 3, fillstyle = 'none', linestyle = 'none', color = colors[ipotential])\n",
    "    ax_inset.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' fit 3-ladder (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' fit 3-ladder (Ohm)\"], marker = 'none', markersize = 3, fillstyle = 'none', linestyle = '-', color = colors[ipotential])\n",
    "ax_inset.set_ylim(-10, 260)\n",
    "ax_inset.set_xlim(40, 310)\n",
    "ax_inset.set_aspect('equal', adjustable='box')\n",
    "\n",
    "ax_inset.yaxis.set_major_locator(FixedLocator([0, 100, 200]))\n",
    "ax_inset.xaxis.set_major_locator(FixedLocator([0, 100, 200, 300]))\n",
    "ax_inset.yaxis.set_minor_locator(AutoMinorLocator(n=2))\n",
    "ax_inset.xaxis.set_minor_locator(AutoMinorLocator(n=2))\n",
    "ax.plot([], [], color = 'darkslategrey', label = 'fit')\n",
    "\n",
    "fig.legend(bbox_to_anchor = (0.5, -0.18), loc = 'lower center', ncols = 5, frameon = False, \n",
    "           labelspacing=0.2,\n",
    "           handletextpad=0.3,\n",
    "           columnspacing=0.7,\n",
    "           borderpad=0.2,\n",
    "           borderaxespad=1.0,\n",
    "           handlelength=1.0,\n",
    "           markerscale=0.9)\n",
    "\n",
    "plt.savefig(fname = f'impedance_spectra_as_prepared_SI.png', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc886642",
   "metadata": {},
   "source": [
    "## Inductive Loop (Figure S10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "cc114282",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "selected potential [1.702] V\n",
      "selected potential [1.692] V\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import os\n",
    "import numpy as np\n",
    "from numpy.typing import NDArray\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit import Model, Parameter\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "from matplotlib.ticker import FixedLocator, StrMethodFormatter\n",
    "\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (2.5, 2.5)\n",
    "plt.rcParams['figure.dpi'] = 120\n",
    "\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "plt.rcParams['ytick.minor.width'] = 1\n",
    "\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "\n",
    "#select potentials and samples for comparison\n",
    "target = 'Prep11_EIS_1'\n",
    "\n",
    "available_potentials = sorted(sXRD_EISData.loc[sXRD_EISData['fname'].str.contains(target), 'potential RHE'].unique())\n",
    "\n",
    "target_potentials = [1.7]\n",
    "closest_potentials = [available_potentials[np.argmin(np.abs(np.array(available_potentials) - target_potential))] for target_potential in target_potentials]\n",
    "\n",
    "print(f'selected potential {closest_potentials} V')\n",
    "\n",
    "\n",
    "mask = (\n",
    "    sXRD_EISData['fname'].str.contains(target) &\n",
    "    sXRD_EISData['potential RHE'].round(3).isin(closest_potentials)  # rounding avoids float-equality issues\n",
    ")\n",
    "data_for_plot =  sXRD_EISData.loc[mask]\n",
    "data_for_plot = data_for_plot.sort_values(by=\" Frequency (Hz)\", ascending=True)\n",
    "\n",
    "colors =  ['darkcyan', 'darkslategrey', 'orange', 'yellow', 'green']\n",
    "alphas =  [1, 1, 1, 1, 1, 1, 1, 1]\n",
    "markers = ['o', '^', 'v', 'd', '*', '+', 'D']\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 1, tight_layout = True)\n",
    "\n",
    "#ax.set_title('A) impedance spectrum')\n",
    "ax.tick_params(which='both', direction = 'in', bottom = True, top = True, right = True, left = True)\n",
    "for ipotential, potential in enumerate(closest_potentials):\n",
    "    ax.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' ZHIT (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' ZHIT (Ohm)\"], marker = markers[ipotential], markersize = 3, fillstyle = 'none', linestyle = 'none', color = 'blue', label = f'as-prepared {data_for_plot.loc[data_for_plot['potential RHE'] == potential, 'potential RHE'].iloc[0]:.2f} V')\n",
    "\n",
    "\n",
    "ax.set_xlim(45, 85)\n",
    "ax.set_ylim(-12.5, 27.5)\n",
    "ax.set_ylabel(r'$-\\mathrm{Imaginary \\ part \\ of \\ impedance} \\ (\\Omega)$')\n",
    "ax.set_xlabel(r'$\\mathrm{Real \\ part \\  of \\ impedance} \\ (\\Omega)$')\n",
    "#ax.axhline(y=0, color='black', linestyle='-')\n",
    "ax.set_aspect('equal')\n",
    "#ax[0].legend(fontsize = 14)\n",
    "ax.yaxis.set_major_locator(FixedLocator([-10, 0, 10, 20, 30]))\n",
    "ax.xaxis.set_major_formatter(StrMethodFormatter('{x:.0f}'))\n",
    "ax.xaxis.set_major_locator(FixedLocator([50, 60, 70, 80]))\n",
    "ax.xaxis.set_major_formatter(StrMethodFormatter('{x:.0f}'))\n",
    "ax.yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "ax.xaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "\n",
    "\n",
    "\n",
    "target = 'Prep12_EIS_1'\n",
    "\n",
    "available_potentials = sorted(sXRD_EISData.loc[sXRD_EISData['fname'].str.contains(target), 'potential RHE'].unique())\n",
    "\n",
    "target_potentials = [1.7]\n",
    "closest_potentials = [available_potentials[np.argmin(np.abs(np.array(available_potentials) - target_potential))] for target_potential in target_potentials]\n",
    "print(f'selected potential {closest_potentials} V')\n",
    "mask = (\n",
    "    sXRD_EISData['fname'].str.contains(target) &\n",
    "    sXRD_EISData['potential RHE'].round(3).isin(closest_potentials)  # rounding avoids float-equality issues\n",
    ")\n",
    "data_for_plot = sXRD_EISData.loc[mask]\n",
    "data_for_plot = data_for_plot.sort_values(by=\" Frequency (Hz)\", ascending=True)\n",
    "\n",
    "\n",
    "for ipotential, potential in enumerate(closest_potentials):\n",
    "    ax.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' ZHIT (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' ZHIT (Ohm)\"], marker = markers[ipotential], markersize = 3, fillstyle = 'none', linestyle = 'none', color = 'red', label = f'annealed {data_for_plot.loc[data_for_plot['potential RHE'] == potential, 'potential RHE'].iloc[0]:.2f} V')\n",
    "\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "labels  = labels[::2] + labels[1::2]\n",
    "handles = handles[::2]+ handles[1::2]\n",
    "ax.legend(handles, labels, loc = 'lower right', ncols = 1, frameon = False, \n",
    "           labelspacing=0.2,  \n",
    "           handletextpad=0.3, \n",
    "           columnspacing=0.7,\n",
    "           borderpad=0.2,\n",
    "           borderaxespad=0.8,\n",
    "           handlelength=1.0,\n",
    "           markerscale=0.9)\n",
    "#plt.savefig(fname = f'impedance_spectrum_ind_loop_as_prepared_annealed.png', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n",
    "plt.savefig(fname = f'impedance_spectrum_ind_loop_as_prepared_annealed.pdf', dpi = 600, bbox_inches='tight')\n",
    "#plt.savefig(fname = f'impedance_spectrum_ind_loop_as_prepared_annealed.tiff', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40019289",
   "metadata": {},
   "source": [
    "# Potential Dependane of Fit Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ddf4084",
   "metadata": {},
   "source": [
    "## Fit Pars - As-prepared Prep 11 = sample 1 (Figure 8 - left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "50606cb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:83: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:83: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:83: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:169: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:169: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:169: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:83: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:83: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:83: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:169: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:169: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:169: SyntaxWarning: invalid escape sequence '\\m'\n",
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_24349/3882669910.py:83: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  subgroups = np.array(['$R_{\\mathrm{CT,1}}$', '$R_{\\mathrm{CT,2}}$', '$R_{\\mathrm{CT,O_2}}$'])\n",
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_24349/3882669910.py:83: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  subgroups = np.array(['$R_{\\mathrm{CT,1}}$', '$R_{\\mathrm{CT,2}}$', '$R_{\\mathrm{CT,O_2}}$'])\n",
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_24349/3882669910.py:83: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  subgroups = np.array(['$R_{\\mathrm{CT,1}}$', '$R_{\\mathrm{CT,2}}$', '$R_{\\mathrm{CT,O_2}}$'])\n",
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_24349/3882669910.py:169: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  subgroups = np.array(['$C_{\\mathrm{DL}}$', '$C_{\\mathrm{Pseudo,1}}$', '$C_{\\mathrm{Pseudo,2}}$'])\n",
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_24349/3882669910.py:169: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  subgroups = np.array(['$C_{\\mathrm{DL}}$', '$C_{\\mathrm{Pseudo,1}}$', '$C_{\\mathrm{Pseudo,2}}$'])\n",
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_24349/3882669910.py:169: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  subgroups = np.array(['$C_{\\mathrm{DL}}$', '$C_{\\mathrm{Pseudo,1}}$', '$C_{\\mathrm{Pseudo,2}}$'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 462x562.5 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.ticker import ScalarFormatter\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import os\n",
    "from numpy.typing import NDArray\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "###set the global parameters for plotting###\n",
    "plt.rcParams['figure.figsize'] = (3.5*1.1, 2*3.75/1.6)          # standard figsize\n",
    "\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "clist = ['grey', 'magenta', 'orange', 'mediumseagreen']\n",
    "colors = LinearSegmentedColormap.from_list(\"custom_cmap\", clist, N=4)\n",
    "\n",
    "#choose the data set and fetch the electrochemical data \n",
    "target = 'Prep11_EIS_1'\n",
    "available_potentials = sorted(sXRD_EISParams.loc[sXRD_EISParams['sample'].str.contains(target), 'potential RHE'].unique())[:-1]\n",
    "mask = (\n",
    "    sXRD_EISParams['sample'].str.contains(target) &\n",
    "    sXRD_EISParams['potential RHE'].round(3).isin(available_potentials) &  # rounding avoids float-equality issues\n",
    "    sXRD_EISParams['model'].str.contains('3-ladder'))\n",
    "\n",
    "data_for_plot =  sXRD_EISParams.loc[mask].sort_values('potential RHE')\n",
    "\n",
    "# extract and coerce to numeric data in numpy arrays\n",
    "potential_skin_layer  = pd.to_numeric(xrd_results[('Prep11_EIS_1', 'potential (V)')],            errors='coerce').to_numpy()\n",
    "thickness_skin_layer  = pd.to_numeric(xrd_results[('Prep11_EIS_1', 'skinlayer thickness (nm)')], errors='coerce').to_numpy()\n",
    "error_skin_layer      = pd.to_numeric(xrd_results[('Prep11_EIS_1', 'error sl thickness (nm)')],  errors='coerce').to_numpy()\n",
    "# build bounds for error bars\n",
    "lower_error_skin_layer  = thickness_skin_layer - error_skin_layer\n",
    "upper_error_skin_layer = thickness_skin_layer + error_skin_layer\n",
    "\n",
    "\n",
    "nrows = 2\n",
    "ncols = 2\n",
    "fig = plt.figure()\n",
    "gs = gridspec.GridSpec(nrows, ncols, width_ratios=[2,1], height_ratios=[1,1])\n",
    "plt.subplots_adjust(wspace=1, hspace=0.4) \n",
    "\n",
    "axs = [fig.add_subplot(gs[i, j]) for i in range(nrows) for j in range(ncols)]\n",
    "\n",
    "bar_width = 0.015 # Width of bars\n",
    "subgroups = np.array(['$R_{\\mathrm{CT,1}}$', '$R_{\\mathrm{CT,2}}$', '$R_{\\mathrm{CT,O_2}}$'])\n",
    "# Accumulate bottom values for stacking\n",
    "bottom_values = np.zeros(len(data_for_plot.loc[data_for_plot['parameter'] == 'Rseries (Ohm)', 'potential RHE']))\n",
    "# Plot each subgroup\n",
    "for i_subgroup, subgroup in enumerate(['Rct1 (Ohm)', 'Rct2 (Ohm)', 'RctO2 (Ohm)']):\n",
    "    axs[0].bar(\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'potential RHE'],\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'],\n",
    "        bar_width,\n",
    "        label=subgroups[i_subgroup],\n",
    "        bottom=bottom_values, \n",
    "        color = colors(i_subgroup+1), \n",
    "        linewidth = 1,\n",
    "        yerr=data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'error'], \n",
    "        capsize = 1.5, \n",
    "        zorder = 1,\n",
    "        error_kw={'elinewidth': 1, 'capthick': 1}\n",
    "    )\n",
    "    axs[1].bar(\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'potential RHE'],\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'],\n",
    "        bar_width,\n",
    "        label=subgroups[i_subgroup],\n",
    "        bottom=bottom_values, \n",
    "        color = colors(i_subgroup+1), \n",
    "        linewidth = 1,\n",
    "        yerr=data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'error'], \n",
    "        capsize = 1.5, \n",
    "        zorder = 1,\n",
    "        error_kw={'elinewidth': 1, 'capthick': 1}\n",
    "    )\n",
    "    bottom_values += data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'].to_numpy()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "sec_ax = axs[0].twinx()\n",
    "sec_ax.tick_params(bottom = False, top = False, right = True, left = False)\n",
    "sec_ax.fill_between(potential_skin_layer, lower_error_skin_layer, upper_error_skin_layer, color = 'blue', alpha = 0.2, zorder = 0)\n",
    "sec_ax.plot(potential_skin_layer, thickness_skin_layer, color = 'blue', alpha = 0.4, zorder = 0)#, label = 'skin layer annealed')\n",
    "sec_ax.set_ylabel('Skin layer thickness (nm)', color = 'blue')\n",
    "sec_ax.tick_params(axis=\"y\", which = 'both', colors=\"blue\")\n",
    "sec_ax.yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "sec_ax.set_ylim(bottom = -0.4, top = 2.9)\n",
    "sec_ax.set_yticks([0, 1, 2])\n",
    "sec_ax.set_xlim(left = axs[0].get_xlim()[0], right = axs[0].get_xlim()[1])\n",
    "\n",
    "h1, l1 = axs[0].get_legend_handles_labels()\n",
    "h2, l2 = sec_ax.get_legend_handles_labels()\n",
    "master_legend = axs[0].legend(h1 + h2, l1 + l2, loc='upper left', ncol=1, frameon=False,                              \n",
    "                              labelspacing=0.2,\n",
    "                              handletextpad=0.3,\n",
    "                              columnspacing=0.7,\n",
    "                              borderpad=0.2,\n",
    "                              borderaxespad=0.8,\n",
    "                              handlelength=1.0,\n",
    "                              markerscale=0.9)\n",
    "\n",
    "axs[0].set_yticks([0, 5000, 10000, 15000, 20000])\n",
    "axs[0].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "positions = np.linspace(start = 1, stop = 1.8, num = 5)\n",
    "axs[0].set_xticks(positions)\n",
    "axs[0].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[0].set_xlabel('Potential vs RHE (V)')\n",
    "axs[0].set_ylabel(r'${\\mathrm{Resistance}} \\ (\\mathrm{\\Omega})$')\n",
    "axs[0].patch.set_visible(False)\n",
    "sec_ax.set_zorder(0)\n",
    "axs[0].set_zorder(1)\n",
    "axs[0].set_ylim(bottom = 0, top = 25000)\n",
    "axs[0].spines[\"right\"].set_color(\"blue\")\n",
    "\n",
    "axs[1].yaxis.set_major_locator(MaxNLocator(nbins=4))\n",
    "axs[1].yaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[1].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[1].set_ylabel(r'${\\mathrm{Resistance}} \\ (\\mathrm{\\Omega})$')\n",
    "axs[1].patch.set_visible(False)\n",
    "axs[1].set_ylim(bottom = 0, top = 7500)\n",
    "axs[1].set_xlim(left = 1.55, right = 1.77)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#axs[0].set_xlim(left = 0.95, right = 1.77)\n",
    "subgroups = np.array(['$C_{\\mathrm{DL}}$', '$C_{\\mathrm{Pseudo,1}}$', '$C_{\\mathrm{Pseudo,2}}$'])\n",
    "# Accumulate bottom values for stacking\n",
    "bottom_values = np.zeros(len(data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'potential RHE']))\n",
    "# Plot each subgroup\n",
    "for i_subgroup, subgroup in enumerate(['Cdl (F)', 'Cpseudo1 (F)', 'Cpseudo2 (F)']):\n",
    "    axs[2].bar(\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'potential RHE'],\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'],\n",
    "        bar_width,\n",
    "        label=subgroups[i_subgroup],\n",
    "        bottom=bottom_values, \n",
    "        color = colors(i_subgroup), \n",
    "        linewidth = 1,\n",
    "        yerr=data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'error'], \n",
    "        capsize = 1.5, \n",
    "        zorder = 1,\n",
    "        error_kw={'elinewidth': 1, 'capthick': 1}\n",
    "    )\n",
    "    axs[3].bar(\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'potential RHE'],\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'],\n",
    "        bar_width,\n",
    "        label=subgroups[i_subgroup],\n",
    "        bottom=bottom_values, \n",
    "        color = colors(i_subgroup), \n",
    "        linewidth = 1,\n",
    "        yerr=data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'error'], \n",
    "        capsize = 1.5, \n",
    "        zorder = 1,\n",
    "        error_kw={'elinewidth': 1, 'capthick': 1}\n",
    "    )\n",
    "    bottom_values += data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'].to_numpy()\n",
    "\n",
    "\n",
    "#axs[1].semilogy()\n",
    "\n",
    "axs[2].yaxis.set_major_locator(MaxNLocator(nbins=4))\n",
    "\n",
    "axs[2].set_xlabel('Potential vs RHE (V)')\n",
    "\n",
    "\n",
    "sec_ax = axs[2].twinx()\n",
    "sec_ax.tick_params(bottom = False, top = False, right = True, left = False)\n",
    "sec_ax.fill_between(potential_skin_layer, lower_error_skin_layer, upper_error_skin_layer, color = 'blue', alpha = 0.2, zorder = 0)\n",
    "sec_ax.plot(potential_skin_layer, thickness_skin_layer, color = 'blue', alpha = 0.4, zorder = 0)#, label = 'skin layer annealed')\n",
    "sec_ax.set_ylabel('Skin layer thickness (nm)', color = 'blue')\n",
    "sec_ax.tick_params(axis=\"y\", which = 'both', colors=\"blue\")\n",
    "sec_ax.yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "sec_ax.set_yticks([0, 1, 2])\n",
    "sec_ax.set_ylim(bottom = -0.4, top = 2.9)\n",
    "sec_ax.set_xlim(left = axs[2].get_xlim()[0], right = axs[2].get_xlim()[1])\n",
    "sec_ax.set_zorder(0)\n",
    "h1, l1 = axs[2].get_legend_handles_labels()\n",
    "h2, l2 = sec_ax.get_legend_handles_labels()\n",
    "master_legend = axs[2].legend(h1 + h2, l1 + l2, loc='upper left', ncol=1, frameon=False,\n",
    "                              labelspacing=0.2,\n",
    "                              handletextpad=0.3,\n",
    "                              columnspacing=0.7,\n",
    "                              borderpad=0.2,\n",
    "                              borderaxespad=0.8,\n",
    "                              handlelength=1.0,\n",
    "                              markerscale=0.9)\n",
    "axs[2].set_zorder(1)\n",
    "axs[2].patch.set_visible(False)\n",
    "\n",
    "positions = np.linspace(start = 1, stop = 1.8, num = 5)\n",
    "axs[2].set_xticks(positions)\n",
    "axs[2].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[2].set_ylabel(r'${\\mathrm{Capacitance}} \\ (\\mathrm{\\mu F})$')\n",
    "axs[2].set_yticks([0, 50e-6, 100e-6, 150e-6, 200e-6, 250e-6])\n",
    "axs[2].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "axs[2].spines[\"right\"].set_color(\"blue\")\n",
    "\n",
    "axs[2].set_ylim(bottom = 0, top = 300e-6) \n",
    "\n",
    "axs[2].yaxis.set_major_formatter(FuncFormatter(lambda v, pos: f\"{v*1e6:.0f}\"))  # µF labels: 1,10,100,1000,10000\n",
    "axs[0].set_xlim(left = 0.9, right = 1.77)\n",
    "axs[2].set_xlim(left = axs[0].get_xlim()[0], right = axs[0].get_xlim()[1])\n",
    "\n",
    "axs[3].yaxis.set_major_formatter(FuncFormatter(lambda v, pos: f\"{v*1e6:.0f}\"))  # µF labels: 1,10,100,1000,10000\n",
    "axs[3].set_xlim(left = axs[1].get_xlim()[0], right = axs[1].get_xlim()[1])\n",
    "axs[3].set_ylim(bottom = 0, top = 1590e-6)\n",
    "axs[3].set_ylabel(r'${\\mathrm{Capacitance}} \\ (\\mathrm{\\mu F})$')\n",
    "\n",
    "axs[3].set_yticks([0, 500e-6, 1000e-6, 1500e-6])\n",
    "axs[3].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "axs[3].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "for iax, ax in enumerate(axs):\n",
    "    if iax % 2 == 0: \n",
    "        ax.yaxis.set_label_coords(-0.35, 0.5)\n",
    "    else:\n",
    "        ax.yaxis.set_label_coords(-0.55, 0.5)\n",
    "\n",
    "plt.savefig(fname = f'potential_dependence_as-prepared.tif', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51af3e7e",
   "metadata": {},
   "source": [
    "## Fit Pars - annealed Prep 12 = sample 1 (Figure 8 - right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "f48beade",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:85: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:85: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:85: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:174: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:174: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:174: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:85: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:85: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:85: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:174: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:174: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:174: SyntaxWarning: invalid escape sequence '\\m'\n",
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_24349/399210477.py:85: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  subgroups = np.array(['$R_{\\mathrm{CT,1}}$', '$R_{\\mathrm{CT,2}}$', '$R_{\\mathrm{CT,O_2}}$'])\n",
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_24349/399210477.py:85: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  subgroups = np.array(['$R_{\\mathrm{CT,1}}$', '$R_{\\mathrm{CT,2}}$', '$R_{\\mathrm{CT,O_2}}$'])\n",
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_24349/399210477.py:85: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  subgroups = np.array(['$R_{\\mathrm{CT,1}}$', '$R_{\\mathrm{CT,2}}$', '$R_{\\mathrm{CT,O_2}}$'])\n",
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_24349/399210477.py:174: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  subgroups = np.array(['$C_{\\mathrm{DL}}$', '$C_{\\mathrm{Pseudo,1}}$', '$C_{\\mathrm{Pseudo,2}}$'])\n",
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_24349/399210477.py:174: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  subgroups = np.array(['$C_{\\mathrm{DL}}$', '$C_{\\mathrm{Pseudo,1}}$', '$C_{\\mathrm{Pseudo,2}}$'])\n",
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_24349/399210477.py:174: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  subgroups = np.array(['$C_{\\mathrm{DL}}$', '$C_{\\mathrm{Pseudo,1}}$', '$C_{\\mathrm{Pseudo,2}}$'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 462x562.5 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.ticker import ScalarFormatter\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import os\n",
    "from numpy.typing import NDArray\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "###set the global parameters for plotting###\n",
    "plt.rcParams['figure.figsize'] = (3.5*1.1, 2*3.75/1.6)          # standard figsize\n",
    "\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "clist = ['grey', 'magenta', 'orange', 'mediumseagreen']\n",
    "colors = LinearSegmentedColormap.from_list(\"custom_cmap\", clist, N=4)\n",
    "\n",
    "\n",
    "#choose the data set and fetch the electrochemical data \n",
    "target = 'Prep12_EIS_1'\n",
    "available_potentials = sorted(sXRD_EISParams.loc[sXRD_EISParams['sample'].str.contains(target), 'potential RHE'].unique())[:-1]\n",
    "mask = (\n",
    "    sXRD_EISParams['sample'].str.contains(target) &\n",
    "    sXRD_EISParams['potential RHE'].round(3).isin(available_potentials) & # rounding avoids float-equality issues\n",
    "    sXRD_EISParams['model'].str.contains('3-ladder')\n",
    ")\n",
    "data_for_plot =  sXRD_EISParams.loc[mask].sort_values('potential RHE')\n",
    "\n",
    "# extract and coerce to numeric data in numpy arrays\n",
    "potential_skin_layer  = pd.to_numeric(xrd_results[('Prep12_EIS_1', 'potential (V)')],            errors='coerce').to_numpy()\n",
    "thickness_skin_layer  = pd.to_numeric(xrd_results[('Prep12_EIS_1', 'skinlayer thickness (nm)')], errors='coerce').to_numpy()\n",
    "error_skin_layer      = pd.to_numeric(xrd_results[('Prep12_EIS_1', 'error sl thickness (nm)')],  errors='coerce').to_numpy()\n",
    "# build bounds for error bars\n",
    "lower_error_skin_layer  = thickness_skin_layer - error_skin_layer\n",
    "upper_error_skin_layer = thickness_skin_layer + error_skin_layer\n",
    "\n",
    "\n",
    "nrows = 2\n",
    "ncols = 2\n",
    "fig = plt.figure()\n",
    "gs = gridspec.GridSpec(nrows, ncols, width_ratios=[2,1], height_ratios=[1,1])\n",
    "plt.subplots_adjust(wspace=1, hspace=0.4) \n",
    "\n",
    "axs = [fig.add_subplot(gs[i, j]) for i in range(nrows) for j in range(ncols)]\n",
    "\n",
    "bar_width = 0.015 # Width of bars\n",
    "subgroups = np.array(['$R_{\\mathrm{CT,1}}$', '$R_{\\mathrm{CT,2}}$', '$R_{\\mathrm{CT,O_2}}$'])\n",
    "# Accumulate bottom values for stacking\n",
    "bottom_values = np.zeros(len(data_for_plot.loc[data_for_plot['parameter'] == 'Rseries (Ohm)', 'potential RHE']))\n",
    "# Plot each subgroup\n",
    "for i_subgroup, subgroup in enumerate(['Rct1 (Ohm)', 'Rct2 (Ohm)', 'RctO2 (Ohm)']):\n",
    "    axs[0].bar(\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'potential RHE'],\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'],\n",
    "        bar_width,\n",
    "        label=subgroups[i_subgroup],\n",
    "        bottom=bottom_values, \n",
    "        color = colors(i_subgroup+1), \n",
    "        linewidth = 1,\n",
    "        yerr=data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'error'], \n",
    "        capsize = 1.5, \n",
    "        zorder = 1,\n",
    "        error_kw={'elinewidth': 1, 'capthick': 1}\n",
    "    )\n",
    "    axs[1].bar(\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'potential RHE'],\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'],\n",
    "        bar_width,\n",
    "        label=subgroups[i_subgroup],\n",
    "        bottom=bottom_values, \n",
    "        color = colors(i_subgroup+1), \n",
    "        linewidth = 1,\n",
    "        yerr=data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'error'], \n",
    "        capsize = 1.5, \n",
    "        zorder = 1,\n",
    "        error_kw={'elinewidth': 1, 'capthick': 1}\n",
    "    )\n",
    "    bottom_values += data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'].to_numpy()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "sec_ax = axs[0].twinx()\n",
    "sec_ax.tick_params(bottom = False, top = False, right = True, left = False)\n",
    "sec_ax.fill_between(potential_skin_layer, lower_error_skin_layer, upper_error_skin_layer, color = 'red', alpha = 0.2, zorder = 0)\n",
    "sec_ax.plot(potential_skin_layer, thickness_skin_layer, color = 'red', alpha = 0.4, zorder = 0)#, label = 'skin layer annealed')\n",
    "sec_ax.set_ylabel('Skin layer thickness (nm)', color = 'red')\n",
    "sec_ax.tick_params(axis=\"y\", which = 'both', colors=\"red\")\n",
    "sec_ax.yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "sec_ax.set_ylim(bottom = -0.4, top = 2.9)\n",
    "sec_ax.set_yticks([0, 1, 2])\n",
    "sec_ax.set_xlim(left = axs[0].get_xlim()[0], right = axs[0].get_xlim()[1])\n",
    "\n",
    "h1, l1 = axs[0].get_legend_handles_labels()\n",
    "h2, l2 = sec_ax.get_legend_handles_labels()\n",
    "master_legend = axs[0].legend(h1 + h2, l1 + l2, loc='upper right', ncol=1, frameon=False,                              \n",
    "                              labelspacing=0.2,\n",
    "                              handletextpad=0.3,\n",
    "                              columnspacing=0.7,\n",
    "                              borderpad=0.2,\n",
    "                              borderaxespad=0.8,\n",
    "                              handlelength=1.0,\n",
    "                              markerscale=0.9)\n",
    "\n",
    "axs[0].yaxis.set_major_locator(FixedLocator([100000, 200000, 300000, 400000]))\n",
    "axs[0].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "positions = np.linspace(start = 1, stop = 1.8, num = 5)\n",
    "axs[0].set_xticks(positions)\n",
    "axs[0].spines[\"right\"].set_color(\"red\")\n",
    "axs[0].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[0].set_xlabel('Potential vs RHE (V)')\n",
    "axs[0].set_ylabel(r'${\\mathrm{Resistance}} \\ (\\mathrm{\\Omega})$')\n",
    "axs[0].patch.set_visible(False)\n",
    "sec_ax.set_zorder(0)\n",
    "axs[0].set_zorder(1)\n",
    "axs[0].set_ylim(bottom = 0, top = 420000)\n",
    "\n",
    "\n",
    "axs[1].yaxis.set_major_locator(MaxNLocator(nbins=4))\n",
    "axs[1].yaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "#positions = np.linspace(start = 1, stop = 1.8, num = 5)\n",
    "#axs[0].set_xticks(positions)\n",
    "axs[1].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "#axs[1].set_xlabel('Potential vs RHE (V)')\n",
    "axs[1].set_ylabel(r'${\\mathrm{Resistance}} \\ (\\mathrm{\\Omega})$')\n",
    "axs[1].patch.set_visible(False)\n",
    "axs[1].set_ylim(bottom = 0, top = 7500)\n",
    "axs[1].set_xlim(left = 1.55, right = 1.77)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#axs[0].set_xlim(left = 0.95, right = 1.77)\n",
    "subgroups = np.array(['$C_{\\mathrm{DL}}$', '$C_{\\mathrm{Pseudo,1}}$', '$C_{\\mathrm{Pseudo,2}}$'])\n",
    "# Accumulate bottom values for stacking\n",
    "bottom_values = np.zeros(len(data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'potential RHE']))\n",
    "# Plot each subgroup\n",
    "for i_subgroup, subgroup in enumerate(['Cdl (F)', 'Cpseudo1 (F)', 'Cpseudo2 (F)']):\n",
    "    axs[2].bar(\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'potential RHE'],\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'],\n",
    "        bar_width,\n",
    "        label=subgroups[i_subgroup],\n",
    "        bottom=bottom_values, \n",
    "        color = colors(i_subgroup), \n",
    "        linewidth = 1,\n",
    "        yerr=data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'error'], \n",
    "        capsize = 1.5, \n",
    "        zorder = 1,\n",
    "        error_kw={'elinewidth': 1, 'capthick': 1}\n",
    "    )\n",
    "    axs[3].bar(\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'potential RHE'],\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'],\n",
    "        bar_width,\n",
    "        label=subgroups[i_subgroup],\n",
    "        bottom=bottom_values, \n",
    "        color = colors(i_subgroup), \n",
    "        linewidth = 1,\n",
    "        yerr=data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'error'], \n",
    "        capsize = 1.5, \n",
    "        zorder = 1,\n",
    "        error_kw={'elinewidth': 1, 'capthick': 1}\n",
    "    )\n",
    "    bottom_values += data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'].to_numpy()\n",
    "\n",
    "\n",
    "#axs[1].semilogy()\n",
    "\n",
    "axs[2].yaxis.set_major_locator(MaxNLocator(nbins=4))\n",
    "\n",
    "axs[2].set_xlabel('Potential vs RHE (V)')\n",
    "\n",
    "\n",
    "sec_ax = axs[2].twinx()\n",
    "sec_ax.tick_params(bottom = False, top = False, right = True, left = False)\n",
    "sec_ax.fill_between(potential_skin_layer, lower_error_skin_layer, upper_error_skin_layer, color = 'red', alpha = 0.2, zorder = 0)\n",
    "sec_ax.plot(potential_skin_layer, thickness_skin_layer, color = 'red', alpha = 0.4, zorder = 0)#, label = 'skin layer annealed')\n",
    "\n",
    "sec_ax.set_ylabel('Skin layer thickness (nm)', color = 'red')\n",
    "sec_ax.tick_params(axis=\"y\", which = 'both', colors=\"red\")\n",
    "sec_ax.yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "sec_ax.set_yticks([0, 1, 2])\n",
    "sec_ax.set_ylim(bottom = -0.4, top = 2.9)\n",
    "sec_ax.set_xlim(left = axs[2].get_xlim()[0], right = axs[2].get_xlim()[1])\n",
    "sec_ax.set_zorder(0)\n",
    "h1, l1 = axs[2].get_legend_handles_labels()\n",
    "h2, l2 = sec_ax.get_legend_handles_labels()\n",
    "master_legend = axs[2].legend(h1 + h2, l1 + l2, loc='upper left', ncol=1, frameon=False,\n",
    "                             labelspacing=0.2,\n",
    "                              handletextpad=0.3,\n",
    "                              columnspacing=0.7,\n",
    "                              borderpad=0.2,\n",
    "                              borderaxespad=0.8,\n",
    "                              handlelength=1.0,\n",
    "                              markerscale=0.9)\n",
    "axs[2].set_zorder(1)\n",
    "axs[2].patch.set_visible(False)\n",
    "\n",
    "positions = np.linspace(start = 1, stop = 1.8, num = 5)\n",
    "axs[2].set_xticks(positions)\n",
    "axs[2].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[2].set_ylabel(r'${\\mathrm{Capacitance}} \\ (\\mathrm{\\mu F})$')\n",
    "axs[2].set_yticks([0, 50e-6, 100e-6, 150e-6, 200e-6, 250e-6])\n",
    "axs[2].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "axs[2].spines[\"right\"].set_color(\"red\")\n",
    "\n",
    "\n",
    "axs[2].set_ylim(bottom = 0, top = 300e-6) \n",
    "axs[2].yaxis.set_major_formatter(FuncFormatter(lambda v, pos: f\"{v*1e6:.0f}\"))  # µF labels\n",
    "\n",
    "axs[0].set_xlim(left = 0.93, right = 1.77)\n",
    "axs[2].set_xlim(left = axs[0].get_xlim()[0], right = axs[0].get_xlim()[1])\n",
    "\n",
    "axs[3].yaxis.set_major_formatter(FuncFormatter(lambda v, pos: f\"{v*1e6:.0f}\"))  # µF labels\n",
    "axs[3].set_xlim(left = axs[1].get_xlim()[0], right = axs[1].get_xlim()[1])\n",
    "axs[3].set_ylim(bottom = 0, top = 1590e-6)\n",
    "axs[3].set_ylabel(r'${\\mathrm{Capacitance}} \\ (\\mathrm{\\mu F})$')\n",
    "\n",
    "axs[3].set_yticks([0, 500e-6, 1000e-6, 1500e-6])\n",
    "axs[3].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "axs[3].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "for iax, ax in enumerate(axs):\n",
    "    if iax % 2 == 0: \n",
    "        ax.yaxis.set_label_coords(-0.40, 0.5)\n",
    "\n",
    "    else:\n",
    "        ax.yaxis.set_label_coords(-0.55, 0.5)\n",
    "\n",
    "plt.savefig(fname = f'potential_dependence_annealed.tif', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "510b8700",
   "metadata": {},
   "source": [
    "\n",
    "$\\mathrm{\\textcolor{red}{Analog \\ of \\ Figure \\ 8 \\ - \\ left \\ with \\ a \\ second \\ data \\ set}}$\n",
    "## Fit Pars - annealed Prep 7 = sample 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "ac418022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 420x562.5 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.ticker import ScalarFormatter\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import os\n",
    "from numpy.typing import NDArray\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "###set the global parameters for plotting###\n",
    "plt.rcParams['figure.figsize'] = (3.5, 2*3.75/1.6)          # standard figsize\n",
    "\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "clist = ['grey', 'magenta', 'orange', 'mediumseagreen']\n",
    "colors = LinearSegmentedColormap.from_list(\"custom_cmap\", clist, N=4)\n",
    "\n",
    "\n",
    "#choose the data set and fetch the electrochemical data \n",
    "target = 'Prep7_EIS_2'\n",
    "available_potentials = sorted(sXRD_EISParams.loc[sXRD_EISParams['sample'].str.contains(target), 'potential RHE'].unique())[:-1]\n",
    "mask = (\n",
    "    sXRD_EISParams['sample'].str.contains(target) &\n",
    "    sXRD_EISParams['potential RHE'].round(3).isin(available_potentials) & # rounding avoids float-equality issues\n",
    "    sXRD_EISParams['model'].str.contains('3-ladder')\n",
    ")\n",
    "data_for_plot =  sXRD_EISParams.loc[mask].sort_values('potential RHE')\n",
    "\n",
    "# extract and coerce to numeric data in numpy arrays\n",
    "potential_skin_layer  = pd.to_numeric(xrd_results[('Prep7_EIS_2', 'potential (V)')],            errors='coerce').to_numpy()\n",
    "thickness_skin_layer  = pd.to_numeric(xrd_results[('Prep7_EIS_2', 'skinlayer thickness (nm)')], errors='coerce').to_numpy()\n",
    "error_skin_layer      = pd.to_numeric(xrd_results[('Prep7_EIS_2', 'error sl thickness (nm)')],  errors='coerce').to_numpy()\n",
    "# build bounds for error bars\n",
    "lower_error_skin_layer  = thickness_skin_layer - error_skin_layer\n",
    "upper_error_skin_layer = thickness_skin_layer + error_skin_layer\n",
    "\n",
    "\n",
    "nrows = 2\n",
    "ncols = 2\n",
    "fig = plt.figure()\n",
    "gs = gridspec.GridSpec(nrows, ncols, width_ratios=[2,1], height_ratios=[1,1])\n",
    "plt.subplots_adjust(wspace=1, hspace=0.4) \n",
    "\n",
    "axs = [fig.add_subplot(gs[i, j]) for i in range(nrows) for j in range(ncols)]\n",
    "\n",
    "bar_width = 0.015 # Width of bars\n",
    "subgroups = np.array([r'$R_{\\mathrm{CT,1}}$', r'$R_{\\mathrm{CT,2}}$', r'$R_{\\mathrm{CT,O_2}}$'])\n",
    "# Accumulate bottom values for stacking\n",
    "bottom_values = np.zeros(len(data_for_plot.loc[data_for_plot['parameter'] == 'Rseries (Ohm)', 'potential RHE']))\n",
    "# Plot each subgroup\n",
    "for i_subgroup, subgroup in enumerate(['Rct1 (Ohm)', 'Rct2 (Ohm)', 'RctO2 (Ohm)']):\n",
    "    axs[0].bar(\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'potential RHE'],\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'],\n",
    "        bar_width,\n",
    "        label=subgroups[i_subgroup],\n",
    "        bottom=bottom_values, \n",
    "        color = colors(i_subgroup+1), \n",
    "        linewidth = 1,\n",
    "        yerr=data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'error'], \n",
    "        capsize = 1.5, \n",
    "        zorder = 1,\n",
    "        error_kw={'elinewidth': 1, 'capthick': 1}\n",
    "    )\n",
    "    axs[1].bar(\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'potential RHE'],\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'],\n",
    "        bar_width,\n",
    "        label=subgroups[i_subgroup],\n",
    "        bottom=bottom_values, \n",
    "        color = colors(i_subgroup+1), \n",
    "        linewidth = 1,\n",
    "        yerr=data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'error'], \n",
    "        capsize = 1.5, \n",
    "        zorder = 1,\n",
    "        error_kw={'elinewidth': 1, 'capthick': 1}\n",
    "    )\n",
    "    bottom_values += data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'].to_numpy()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "sec_ax = axs[0].twinx()\n",
    "sec_ax.tick_params(bottom = False, top = False, right = True, left = False)\n",
    "sec_ax.fill_between(potential_skin_layer, lower_error_skin_layer, upper_error_skin_layer, color = 'blue', alpha = 0.2, zorder = 0)\n",
    "sec_ax.plot(potential_skin_layer, thickness_skin_layer, color = 'blue', alpha = 0.4, zorder = 0)#, label = 'skin layer annealed')\n",
    "sec_ax.set_ylabel('Skin layer thickness (nm)', color = 'blue')\n",
    "sec_ax.tick_params(axis=\"y\", which = 'both', colors=\"blue\")\n",
    "sec_ax.yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "sec_ax.set_ylim(bottom = -0.4, top = 2.9)\n",
    "sec_ax.set_yticks([0, 1, 2])\n",
    "sec_ax.set_xlim(left = axs[0].get_xlim()[0], right = axs[0].get_xlim()[1])\n",
    "\n",
    "h1, l1 = axs[0].get_legend_handles_labels()\n",
    "h2, l2 = sec_ax.get_legend_handles_labels()\n",
    "master_legend = axs[0].legend(h1 + h2, l1 + l2, loc='upper left', ncol=1, frameon=False,                              \n",
    "                              labelspacing=0.2,\n",
    "                              handletextpad=0.3,\n",
    "                              columnspacing=0.7,\n",
    "                              borderpad=0.2,\n",
    "                              borderaxespad=0.8,\n",
    "                              handlelength=1.0,\n",
    "                              markerscale=0.9)\n",
    "\n",
    "axs[0].set_yticks([0, 5000, 10000, 15000, 20000, 25000])\n",
    "axs[0].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "positions = np.linspace(start = 1, stop = 1.8, num = 5)\n",
    "axs[0].set_xticks(positions)\n",
    "axs[0].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[0].set_xlabel('Potential vs RHE (V)')\n",
    "axs[0].set_ylabel(r'${\\mathrm{Resistance}} \\ (\\mathrm{\\Omega})$')\n",
    "axs[0].patch.set_visible(False)\n",
    "sec_ax.set_zorder(0)\n",
    "axs[0].set_zorder(1)\n",
    "axs[0].set_ylim(bottom = 0, top = 24000)\n",
    "axs[0].spines[\"right\"].set_color(\"blue\")\n",
    "\n",
    "axs[1].yaxis.set_major_locator(MaxNLocator(nbins=4))\n",
    "axs[1].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "axs[1].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[1].set_ylabel(r'${\\mathrm{Resistance}} \\ (\\mathrm{\\Omega})$')\n",
    "axs[1].patch.set_visible(False)\n",
    "axs[1].set_ylim(bottom = 0, top = 9000)\n",
    "axs[1].set_xlim(left = 1.5, right = 1.77)\n",
    "\n",
    "subgroups = np.array([r'$C_{\\mathrm{DL}}$', r'$C_{\\mathrm{Pseudo,1}}$', r'$C_{\\mathrm{Pseudo,2}}$'])\n",
    "# Accumulate bottom values for stacking\n",
    "bottom_values = np.zeros(len(data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'potential RHE']))\n",
    "# Plot each subgroup\n",
    "for i_subgroup, subgroup in enumerate(['Cdl (F)', 'Cpseudo1 (F)', 'Cpseudo2 (F)']):\n",
    "    axs[2].bar(\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'potential RHE'],\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'],\n",
    "        bar_width,\n",
    "        label=subgroups[i_subgroup],\n",
    "        bottom=bottom_values, \n",
    "        color = colors(i_subgroup), \n",
    "        linewidth = 1,\n",
    "        yerr=data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'error'], \n",
    "        capsize = 1.5, \n",
    "        zorder = 1,\n",
    "        error_kw={'elinewidth': 1, 'capthick': 1}\n",
    "    )\n",
    "    axs[3].bar(\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'potential RHE'],\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'],\n",
    "        bar_width,\n",
    "        label=subgroups[i_subgroup],\n",
    "        bottom=bottom_values, \n",
    "        color = colors(i_subgroup), \n",
    "        linewidth = 1,\n",
    "        yerr=data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'error'], \n",
    "        capsize = 1.5, \n",
    "        zorder = 1,\n",
    "        error_kw={'elinewidth': 1, 'capthick': 1}\n",
    "    )\n",
    "    bottom_values += data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'].to_numpy()\n",
    "\n",
    "\n",
    "axs[2].yaxis.set_major_locator(MaxNLocator(nbins=4))\n",
    "\n",
    "axs[2].set_xlabel('Potential vs RHE (V)')\n",
    "\n",
    "\n",
    "sec_ax = axs[2].twinx()\n",
    "sec_ax.tick_params(bottom = False, top = False, right = True, left = False)\n",
    "sec_ax.fill_between(potential_skin_layer, lower_error_skin_layer, upper_error_skin_layer, color = 'blue', alpha = 0.2, zorder = 0)\n",
    "sec_ax.plot(potential_skin_layer, thickness_skin_layer, color = 'blue', alpha = 0.4, zorder = 0)#, label = 'skin layer annealed')\n",
    "sec_ax.set_ylabel('Skin layer thickness (nm)', color = 'blue')\n",
    "sec_ax.tick_params(axis=\"y\", which = 'both', colors=\"blue\")\n",
    "sec_ax.yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "sec_ax.set_yticks([0, 1, 2])\n",
    "sec_ax.set_ylim(bottom = -0.4, top = 2.9)\n",
    "sec_ax.set_xlim(left = axs[2].get_xlim()[0], right = axs[2].get_xlim()[1])\n",
    "sec_ax.set_zorder(0)\n",
    "h1, l1 = axs[2].get_legend_handles_labels()\n",
    "h2, l2 = sec_ax.get_legend_handles_labels()\n",
    "master_legend = axs[2].legend(h1 + h2, l1 + l2, loc='upper left', ncol=1, frameon=False,\n",
    "                             labelspacing=0.2,\n",
    "                              handletextpad=0.3,\n",
    "                              columnspacing=0.7,\n",
    "                              borderpad=0.2,\n",
    "                              borderaxespad=0.8,\n",
    "                              handlelength=1.0,\n",
    "                              markerscale=0.9)\n",
    "axs[2].set_zorder(1)\n",
    "axs[2].patch.set_visible(False)\n",
    "#axs[1].set_ylim(top = 6000e-6)\n",
    "\n",
    "positions = np.linspace(start = 1, stop = 1.8, num = 5)\n",
    "axs[2].set_xticks(positions)\n",
    "axs[2].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[2].set_ylabel(r'${\\mathrm{Capacitance}} \\ (\\mathrm{\\mu F})$')\n",
    "axs[2].set_yticks([0, 50e-6, 100e-6, 150e-6, 200e-6, 250e-6])\n",
    "axs[2].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "axs[2].spines[\"right\"].set_color(\"blue\")\n",
    "#axs[1].semilogy()\n",
    "\n",
    "axs[2].set_ylim(bottom = 0, top = 300e-6) \n",
    "\n",
    "axs[2].yaxis.set_major_formatter(FuncFormatter(lambda v, pos: f\"{v*1e6:.0f}\"))  # µF labels\n",
    "axs[0].set_xlim(left = 0.93, right = 1.77)\n",
    "axs[2].set_xlim(left = axs[0].get_xlim()[0], right = axs[0].get_xlim()[1])\n",
    "\n",
    "axs[3].yaxis.set_major_formatter(FuncFormatter(lambda v, pos: f\"{v*1e6:.0f}\"))  # µF labels\n",
    "axs[3].set_xlim(left = axs[1].get_xlim()[0], right = axs[1].get_xlim()[1])\n",
    "axs[3].set_ylim(bottom = 0, top = 790e-6)\n",
    "axs[3].set_ylabel(r'${\\mathrm{Capacitance}} \\ (\\mathrm{\\mu F})$')\n",
    "\n",
    "axs[3].yaxis.set_major_locator(MaxNLocator(nbins=4))\n",
    "axs[3].yaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "#positions = np.linspace(start = 1, stop = 1.8, num = 5)\n",
    "#axs[0].set_xticks(positions)\n",
    "axs[3].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "for iax, ax in enumerate(axs):\n",
    "    if iax % 2 == 0: \n",
    "        ax.yaxis.set_label_coords(-0.35, 0.5)\n",
    "        #ax.xaxis.set_major_formatter(formatter_x) # apply formatting\n",
    "        #ax.set_xlim(left = 0.97, right = 1.75)\n",
    "    else:\n",
    "        ax.yaxis.set_label_coords(-0.55, 0.5)\n",
    "\n",
    "plt.savefig(fname = f'potential_dependence_as-prepared_7.tif', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2eddb1b",
   "metadata": {},
   "source": [
    "\n",
    "$\\mathrm{\\textcolor{red}{Analog \\ of \\ Figure \\ 8 \\ - \\ right \\ with \\ a \\ second \\ data \\ set}}$\n",
    "## Fit Pars - annealed Prep 8 = sample 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "43bb034b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 420x562.5 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.ticker import ScalarFormatter\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import os\n",
    "from numpy.typing import NDArray\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "###set the global parameters for plotting###\n",
    "plt.rcParams['figure.figsize'] = (3.5, 2*3.75/1.6)          # standard figsize\n",
    "\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "clist = ['grey', 'magenta', 'orange', 'mediumseagreen']\n",
    "colors = LinearSegmentedColormap.from_list(\"custom_cmap\", clist, N=4)\n",
    "\n",
    "#choose the data set and fetch the electrochemical data \n",
    "target = 'Prep8_EIS_2'\n",
    "available_potentials = sorted(sXRD_EISParams.loc[sXRD_EISParams['sample'].str.contains(target), 'potential RHE'].unique())[:-1]\n",
    "mask = (\n",
    "    sXRD_EISParams['sample'].str.contains(target) &\n",
    "    sXRD_EISParams['potential RHE'].round(3).isin(available_potentials) & # rounding avoids float-equality issues\n",
    "    sXRD_EISParams['model'].str.contains('3-ladder')\n",
    ")\n",
    "data_for_plot =  sXRD_EISParams.loc[mask].sort_values('potential RHE')\n",
    "\n",
    "# extract and coerce to numeric data in numpy arrays\n",
    "potential_skin_layer  = pd.to_numeric(xrd_results[('Prep8_EIS_2', 'potential (V)')],            errors='coerce').to_numpy()\n",
    "thickness_skin_layer  = pd.to_numeric(xrd_results[('Prep8_EIS_2', 'skinlayer thickness (nm)')], errors='coerce').to_numpy()\n",
    "error_skin_layer      = pd.to_numeric(xrd_results[('Prep8_EIS_2', 'error sl thickness (nm)')],  errors='coerce').to_numpy()\n",
    "# build bounds for error bars\n",
    "lower_error_skin_layer  = thickness_skin_layer - error_skin_layer\n",
    "upper_error_skin_layer = thickness_skin_layer + error_skin_layer\n",
    "\n",
    "\n",
    "nrows = 2\n",
    "ncols = 2\n",
    "fig = plt.figure()\n",
    "gs = gridspec.GridSpec(nrows, ncols, width_ratios=[2,1], height_ratios=[1,1])\n",
    "plt.subplots_adjust(wspace=1, hspace=0.4) \n",
    "\n",
    "axs = [fig.add_subplot(gs[i, j]) for i in range(nrows) for j in range(ncols)]\n",
    "\n",
    "bar_width = 0.015 # Width of bars\n",
    "subgroups = np.array([r'$R_{\\mathrm{CT,1}}$', r'$R_{\\mathrm{CT,2}}$', r'$R_{\\mathrm{CT,O_2}}$'])\n",
    "# Accumulate bottom values for stacking\n",
    "bottom_values = np.zeros(len(data_for_plot.loc[data_for_plot['parameter'] == 'Rseries (Ohm)', 'potential RHE']))\n",
    "# Plot each subgroup\n",
    "for i_subgroup, subgroup in enumerate(['Rct1 (Ohm)', 'Rct2 (Ohm)', 'RctO2 (Ohm)']):\n",
    "    axs[0].bar(\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'potential RHE'],\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'],\n",
    "        bar_width,\n",
    "        label=subgroups[i_subgroup],\n",
    "        bottom=bottom_values, \n",
    "        color = colors(i_subgroup+1), \n",
    "        linewidth = 1,\n",
    "        yerr=data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'error'], \n",
    "        capsize = 1.5, \n",
    "        zorder = 1,\n",
    "        error_kw={'elinewidth': 1, 'capthick': 1}\n",
    "    )\n",
    "    axs[1].bar(\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'potential RHE'],\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'],\n",
    "        bar_width,\n",
    "        label=subgroups[i_subgroup],\n",
    "        bottom=bottom_values, \n",
    "        color = colors(i_subgroup+1), \n",
    "        linewidth = 1,\n",
    "        yerr=data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'error'], \n",
    "        capsize = 1.5, \n",
    "        zorder = 1,\n",
    "        error_kw={'elinewidth': 1, 'capthick': 1}\n",
    "    )\n",
    "    bottom_values += data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'].to_numpy()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "sec_ax = axs[0].twinx()\n",
    "sec_ax.tick_params(bottom = False, top = False, right = True, left = False)\n",
    "sec_ax.fill_between(potential_skin_layer, lower_error_skin_layer, upper_error_skin_layer, color = 'red', alpha = 0.2, zorder = 0)\n",
    "sec_ax.plot(potential_skin_layer, thickness_skin_layer, color = 'red', alpha = 0.4, zorder = 0)#, label = 'skin layer annealed')\n",
    "sec_ax.set_ylabel('Skin layer thickness (nm)', color = 'red')\n",
    "sec_ax.tick_params(axis=\"y\", which = 'both', colors=\"red\")\n",
    "sec_ax.yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "sec_ax.set_ylim(bottom = -0.4, top = 2.9)\n",
    "sec_ax.set_yticks([0, 1, 2])\n",
    "sec_ax.set_xlim(left = axs[0].get_xlim()[0], right = axs[0].get_xlim()[1])\n",
    "\n",
    "h1, l1 = axs[0].get_legend_handles_labels()\n",
    "h2, l2 = sec_ax.get_legend_handles_labels()\n",
    "master_legend = axs[0].legend(h1 + h2, l1 + l2, loc='upper right', ncol=1, frameon=False,                              \n",
    "                              labelspacing=0.2,\n",
    "                              handletextpad=0.3,\n",
    "                              columnspacing=0.7,\n",
    "                              borderaxespad=0.8,\n",
    "                              handlelength=1.0,\n",
    "                              markerscale=0.9)\n",
    "\n",
    "axs[0].yaxis.set_major_locator(MaxNLocator(nbins=4))\n",
    "axs[0].yaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "positions = np.linspace(start = 1, stop = 1.8, num = 5)\n",
    "axs[0].set_xticks(positions)\n",
    "axs[0].spines[\"right\"].set_color(\"red\")\n",
    "axs[0].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[0].set_xlabel('Potential vs RHE (V)')\n",
    "axs[0].set_ylabel(r'${\\mathrm{Resistance}} \\ (\\mathrm{\\Omega})$')\n",
    "axs[0].patch.set_visible(False)\n",
    "sec_ax.set_zorder(0)\n",
    "axs[0].set_zorder(1)\n",
    "axs[0].set_ylim(bottom = 0, top = 190000)\n",
    "\n",
    "\n",
    "axs[1].yaxis.set_major_locator(MaxNLocator(nbins=4))\n",
    "axs[1].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "#positions = np.linspace(start = 1, stop = 1.8, num = 5)\n",
    "#axs[0].set_xticks(positions)\n",
    "axs[1].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "#axs[1].set_xlabel('Potential vs RHE (V)')\n",
    "axs[1].set_ylabel(r'${\\mathrm{Resistance}} \\ (\\mathrm{\\Omega})$')\n",
    "axs[1].patch.set_visible(False)\n",
    "axs[1].set_ylim(bottom = 0, top = 18000)\n",
    "axs[1].set_xlim(left = 1.5, right = 1.77)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#axs[0].set_xlim(left = 0.95, right = 1.77)\n",
    "subgroups = np.array([r'$C_{\\mathrm{DL}}$', r'$C_{\\mathrm{Pseudo,1}}$', r'$C_{\\mathrm{Pseudo,2}}$'])\n",
    "# Accumulate bottom values for stacking\n",
    "bottom_values = np.zeros(len(data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'potential RHE']))\n",
    "# Plot each subgroup\n",
    "for i_subgroup, subgroup in enumerate(['Cdl (F)', 'Cpseudo1 (F)', 'Cpseudo2 (F)']):\n",
    "    axs[2].bar(\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'potential RHE'],\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'],\n",
    "        bar_width,\n",
    "        label=subgroups[i_subgroup],\n",
    "        bottom=bottom_values, \n",
    "        color = colors(i_subgroup), \n",
    "        linewidth = 1,\n",
    "        yerr=data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'error'], \n",
    "        capsize = 1.5, \n",
    "        zorder = 1,\n",
    "        error_kw={'elinewidth': 1, 'capthick': 1}\n",
    "    )\n",
    "    axs[3].bar(\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'potential RHE'],\n",
    "        data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'],\n",
    "        bar_width,\n",
    "        label=subgroups[i_subgroup],\n",
    "        bottom=bottom_values, \n",
    "        color = colors(i_subgroup), \n",
    "        linewidth = 1,\n",
    "        yerr=data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'error'], \n",
    "        capsize = 1.5, \n",
    "        zorder = 1,\n",
    "        error_kw={'elinewidth': 1, 'capthick': 1}\n",
    "    )\n",
    "    bottom_values += data_for_plot.loc[data_for_plot['parameter'] == subgroup, 'value'].to_numpy()\n",
    "\n",
    "\n",
    "#axs[1].semilogy()\n",
    "\n",
    "axs[2].yaxis.set_major_locator(MaxNLocator(nbins=4))\n",
    "\n",
    "axs[2].set_xlabel('Potential vs RHE (V)')\n",
    "\n",
    "\n",
    "sec_ax = axs[2].twinx()\n",
    "sec_ax.tick_params(bottom = False, top = False, right = True, left = False)\n",
    "sec_ax.fill_between(potential_skin_layer, lower_error_skin_layer, upper_error_skin_layer, color = 'red', alpha = 0.2, zorder = 0)\n",
    "sec_ax.plot(potential_skin_layer, thickness_skin_layer, color = 'red', alpha = 0.4, zorder = 0)#, label = 'skin layer annealed')\n",
    "\n",
    "sec_ax.set_ylabel('Skin layer thickness (nm)', color = 'red')\n",
    "sec_ax.tick_params(axis=\"y\", which = 'both', colors=\"red\")\n",
    "sec_ax.yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "sec_ax.set_yticks([0, 1, 2])\n",
    "sec_ax.set_ylim(bottom = -0.4, top = 2.9)\n",
    "sec_ax.set_xlim(left = axs[2].get_xlim()[0], right = axs[2].get_xlim()[1])\n",
    "sec_ax.set_zorder(0)\n",
    "h1, l1 = axs[2].get_legend_handles_labels()\n",
    "h2, l2 = sec_ax.get_legend_handles_labels()\n",
    "master_legend = axs[2].legend(h1 + h2, l1 + l2, loc='upper left', ncol=1, frameon=False,\n",
    "                             labelspacing=0.2,\n",
    "                              handletextpad=0.3,\n",
    "                              columnspacing=0.7,\n",
    "                              borderpad=0.2,\n",
    "                              borderaxespad=0.8,\n",
    "                              handlelength=1.0,\n",
    "                              markerscale=0.9)\n",
    "axs[2].set_zorder(1)\n",
    "axs[2].patch.set_visible(False)\n",
    "#axs[1].set_ylim(top = 6000e-6)\n",
    "\n",
    "positions = np.linspace(start = 1, stop = 1.8, num = 5)\n",
    "axs[2].set_xticks(positions)\n",
    "axs[2].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[2].set_ylabel(r'${\\mathrm{Capacitance}} \\ (\\mathrm{\\mu F})$')\n",
    "axs[2].set_yticks([0, 50e-6, 100e-6, 150e-6, 200e-6, 250e-6])\n",
    "axs[2].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "axs[2].spines[\"right\"].set_color(\"red\")\n",
    "#axs[1].semilogy()\n",
    "\n",
    "axs[2].set_ylim(bottom = 0, top = 300e-6) \n",
    "\n",
    "axs[2].yaxis.set_major_formatter(FuncFormatter(lambda v, pos: f\"{v*1e6:.0f}\"))  # µF labels\n",
    "\n",
    "axs[0].set_xlim(left = 0.93, right = 1.77)\n",
    "axs[2].set_xlim(left = axs[0].get_xlim()[0], right = axs[0].get_xlim()[1])\n",
    "\n",
    "axs[3].yaxis.set_major_formatter(FuncFormatter(lambda v, pos: f\"{v*1e6:.0f}\"))  # µF labels\n",
    "axs[3].set_xlim(left = axs[1].get_xlim()[0], right = axs[1].get_xlim()[1])\n",
    "axs[3].set_ylim(bottom = 0, top = 790e-6)\n",
    "axs[3].set_ylabel(r'${\\mathrm{Capacitance}} \\ (\\mathrm{\\mu F})$')\n",
    "\n",
    "axs[3].yaxis.set_major_locator(MaxNLocator(nbins=4))\n",
    "axs[3].yaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[3].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "for iax, ax in enumerate(axs):\n",
    "    if iax % 2 == 0: \n",
    "        ax.yaxis.set_label_coords(-0.40, 0.5)\n",
    "    else:\n",
    "        ax.yaxis.set_label_coords(-0.6, 0.5)\n",
    "\n",
    "plt.savefig(fname = f'potential_dependence_annealed8.tif', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "973f9561",
   "metadata": {},
   "source": [
    "# Making an Overview Plot (All Results) on Log-scale for Resistances (Figure S17) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "a5469ed1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import ScalarFormatter\n",
    "from matplotlib.ticker import LogLocator\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.ticker import ScalarFormatter\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "from matplotlib.ticker import FixedLocator, StrMethodFormatter\n",
    "\n",
    "###set the global parameters for plotting###\n",
    "plt.rcParams['figure.figsize'] = (3.5, 2*3.75/1.6)          # standard figsize\n",
    "\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "\n",
    "colors = ['lightblue', 'lightcoral', 'darkblue', 'darkred']\n",
    "markers = ['s', 'o', '^','v', 'p', 'h']\n",
    "linestyles = ['-', '-', '-.', '-.', ':', ':', '-.', ':', '-', '--', '-.', ':', '-', '--', '-.', ':']\n",
    "\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(nrows = 2, ncols = 3, constrained_layout = True, sharex = True, figsize=(8,4))\n",
    "for i in range(2): \n",
    "    for j in range(3): \n",
    "        axs[i,j].tick_params(which = 'both', direction = 'in', bottom = True, top = True, left = True, right = True)\n",
    "        axs[i,j].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "    \n",
    "\n",
    "\n",
    "samples = sorted(sXRD_EISParams['sample'].unique())\n",
    "for i_sample, sample in enumerate(samples):\n",
    "    data_for_plot = sXRD_EISParams.loc[sXRD_EISParams['sample'].str.contains(sample) & sXRD_EISParams['model'].str.contains('3-ladder')]\n",
    "\n",
    "    if '11'   in sample: color = 'lightblue';  label = 'as-prepared 1'\n",
    "    elif '12' in sample: color = 'lightcoral'; label = 'annealed 1'\n",
    "    elif '7'  in sample: color = 'darkblue';   label = 'as-prepared 2'\n",
    "    elif '8'  in sample: color = 'darkred';    label = 'annealed 2'\n",
    "    data_for_plot = data_for_plot.sort_values(by=\"potential RHE\")                \n",
    "    axs[0,0].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Rct1 (Ohm)', 'potential RHE'],   data_for_plot.loc[data_for_plot['parameter'] == 'Rct1 (Ohm)', 'value'],   data_for_plot.loc[data_for_plot['parameter'] == 'Rct1 (Ohm)', 'error'],    color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "    axs[0,1].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Rct2 (Ohm)', 'potential RHE'],   data_for_plot.loc[data_for_plot['parameter'] == 'Rct2 (Ohm)', 'value'],   data_for_plot.loc[data_for_plot['parameter'] == 'Rct2 (Ohm)', 'error'],    color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "    axs[0,2].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'RctO2 (Ohm)', 'potential RHE'],  data_for_plot.loc[data_for_plot['parameter'] == 'RctO2 (Ohm)', 'value'],  data_for_plot.loc[data_for_plot['parameter'] == 'RctO2 (Ohm)', 'error'],   color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "    axs[1,0].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'potential RHE'],      data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'value'],      data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'error'],       color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "    axs[1,1].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo1 (F)', 'potential RHE'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo1 (F)', 'value'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo1 (F)', 'error'],  color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "    axs[1,2].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'potential RHE'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'value'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'error'],  color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none', label = label)\n",
    "samples = sorted(RDE_EISParams['annealing'].unique(), reverse = True)\n",
    "for i_sample, sample in enumerate(samples, start = 4):\n",
    "    data_for_plot = RDE_EISParams.loc[RDE_EISParams['annealing'].str.contains(sample) & RDE_EISParams['sample'].str.contains('Sample6')]\n",
    "    if data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'annealing'].iloc[0] == 'before':\n",
    "        label = 'as-prepared RDE'; color = 'blue'\n",
    "    else:\n",
    "        label = 'annealed RDE'; color = 'red'\n",
    "    data_for_plot = data_for_plot.sort_values(by=\"potential RHE\")                \n",
    "\n",
    "\n",
    "    axs[0,0].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Rct1 (Ohm)', 'potential RHE'],   data_for_plot.loc[data_for_plot['parameter'] == 'Rct1 (Ohm)', 'value'],   data_for_plot.loc[data_for_plot['parameter'] == 'Rct1 (Ohm)', 'error'],    color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "    axs[0,1].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Rct2 (Ohm)', 'potential RHE'],   data_for_plot.loc[data_for_plot['parameter'] == 'Rct2 (Ohm)', 'value'],   data_for_plot.loc[data_for_plot['parameter'] == 'Rct2 (Ohm)', 'error'],    color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "    axs[0,2].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'RctO2 (Ohm)', 'potential RHE'],  data_for_plot.loc[data_for_plot['parameter'] == 'RctO2 (Ohm)', 'value'],  data_for_plot.loc[data_for_plot['parameter'] == 'RctO2 (Ohm)', 'error'],   color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "    axs[1,0].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'potential RHE'],      data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'value'],      data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'error'],       color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "    axs[1,1].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo1 (F)', 'potential RHE'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo1 (F)', 'value'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo1 (F)', 'error'],  color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "    axs[1,2].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'potential RHE'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'value'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'error'],  color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none', label = label)\n",
    "    i_sample += 1\n",
    "\n",
    "axs[0,0].set_ylabel(r'$R_{\\mathrm{CT,1}} \\ (\\mathrm{\\Omega})$')\n",
    "axs[0,0].set_yscale('log')\n",
    "axs[0,0].set_ylim(bottom = 1, top = 8e5)\n",
    "\n",
    "axs[0,0].yaxis.set_major_locator(LogLocator(base = 10))\n",
    "\n",
    "axs[0,1].set_ylabel(r'$R_{\\mathrm{CT,2}} \\ (\\mathrm{\\Omega})$')\n",
    "axs[0,1].set_yscale('log')\n",
    "axs[0,1].set_ylim(bottom = 1, top = 8e5)\n",
    "axs[0,1].yaxis.set_major_locator(LogLocator(base = 10))\n",
    "\n",
    "axs[0,2].set_ylabel(r'$R_{\\mathrm{CT,O_2}} \\ (\\mathrm{\\Omega})$')\n",
    "axs[0,2].set_yscale('log')\n",
    "axs[0,2].set_ylim(bottom = 1, top = 8e5)\n",
    "axs[0,2].yaxis.set_major_locator(LogLocator(base = 10))\n",
    "\n",
    "axs[1,0].set_ylabel(r'$C_{\\mathrm{DL}} \\ (\\mathrm{\\mu F})$')\n",
    "axs[1,0].set_ylim(bottom = 0, top = 160e-6)\n",
    "axs[1,0].yaxis.set_major_locator(FixedLocator([0, 50e-6, 100e-6, 150e-6, 200e-6]))\n",
    "axs[1,0].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "axs[1,0].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[1,0].yaxis.set_major_formatter(FuncFormatter(lambda val, pos: f\"{val*1e6:.0f}\"))\n",
    "\n",
    "axs[1,1].set_ylabel(r'$C_{\\mathrm{Pseudo,1}} \\ (\\mathrm{\\mu F})$')\n",
    "axs[1,1].yaxis.set_major_locator((FixedLocator([0, 50e-6, 100e-6, 150e-6, 200e-6, 250e-6])))\n",
    "axs[1,1].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "axs[1,1].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[1,1].yaxis.set_major_formatter(FuncFormatter(lambda val, pos: f\"{val*1e6:.0f}\"))\n",
    "\n",
    "axs[1,2].set_ylabel(r'$C_{\\mathrm{Pseudo,2}} \\ (\\mathrm{\\mu F})$')\n",
    "axs[1,2].set_ylim(top = 5100e-6)\n",
    "axs[1,2].yaxis.set_major_locator((FixedLocator([0, 1000e-6, 2000e-6, 3000e-6, 4000e-6, 5000e-6])))\n",
    "axs[1,2].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "axs[1,2].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[1,2].yaxis.set_major_formatter(FuncFormatter(lambda val, pos: f\"{val*1e6:.0f}\"))\n",
    "\n",
    "axs[1,1].set_xlabel(r'Potential vs RHE (V)')\n",
    "axs[1,1].set_xlim(left = 0.85, right = 1.82)\n",
    "fig.legend(bbox_to_anchor = (0.5, -0.03), loc = 'center', ncol = 6, frameon = False)\n",
    "plt.savefig(fname = f'EIS_FitResults_SI.tif', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52e65c48",
   "metadata": {},
   "source": [
    "# Create the Inset for $C_{Pseudo,2}$ (Figure S17 Inset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "28537328",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 180x180 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import ScalarFormatter\n",
    "from matplotlib.ticker import LogLocator\n",
    "\n",
    "###set the global parameters for plotting###\n",
    "plt.rcParams['figure.figsize'] = (3.5, 2*3.75/1.6)          # standard figsize\n",
    "\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "colors = ['lightblue', 'lightcoral', 'darkblue', 'darkred']\n",
    "markers = ['s', 'o', '^','v', 'p', 'h']\n",
    "linestyles = ['-', '-', '-.', '-.', ':', ':', '-.', ':', '-', '--', '-.', ':', '-', '--', '-.', ':']\n",
    "\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(nrows = 1, ncols = 1, constrained_layout = True, sharex = True, figsize=(1.5,1.5))\n",
    "axs.tick_params(which = 'both', direction = 'in', bottom = True, top = True, left = True, right = True)\n",
    "axs.xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "    \n",
    "\n",
    "samples = sorted(sXRD_EISParams['sample'].unique())\n",
    "for i_sample, sample in enumerate(samples):\n",
    "    data_for_plot = sXRD_EISParams.loc[sXRD_EISParams['sample'].str.contains(sample) & sXRD_EISParams['model'].str.contains('3-ladder')]\n",
    "\n",
    "    if '11'   in sample: color = 'lightblue';  label = 'as-prepared sXRD 1'\n",
    "    elif '12' in sample: color = 'lightcoral'; label = 'annealed sXRD 1'\n",
    "    elif '7'  in sample: color = 'darkblue';   label = 'as-prepared sXRD 2'\n",
    "    elif '8'  in sample: color = 'darkred';    label = 'annealed sXRD 2'\n",
    "    data_for_plot = data_for_plot.sort_values(by=\"potential RHE\")                \n",
    "    axs.errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'potential RHE'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'value'], \n",
    "                 data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'error'],  color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none', label = label)\n",
    "samples = sorted(RDE_EISParams['annealing'].unique(), reverse = True)\n",
    "for i_sample, sample in enumerate(samples, start = 4):\n",
    "\n",
    "    data_for_plot = RDE_EISParams.loc[RDE_EISParams['annealing'].str.contains(sample) & RDE_EISParams['sample'].str.contains('Sample6')]\n",
    "    if data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'annealing'].iloc[0] == 'before':\n",
    "        label = 'as-prepared RDE'; color = 'blue'\n",
    "    else:\n",
    "        label = 'annealed RDE'; color = 'red'\n",
    "    data_for_plot = data_for_plot.sort_values(by=\"potential RHE\")                \n",
    "\n",
    "    axs.errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'potential RHE'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'value'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'error'],  color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none', label = label)\n",
    "    \n",
    "\n",
    "\n",
    "axs.set_xlim(left = 1.4, right = 1.79)\n",
    "axs.set_ylim(bottom = 0, top = 900e-6)\n",
    "axs.yaxis.set_major_locator((FixedLocator([0, 200e-6, 400e-6, 600e-6, 800e-6])))\n",
    "axs.yaxis.set_minor_locator(AutoMinorLocator(n=8))\n",
    "axs.xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs.yaxis.set_major_formatter(FuncFormatter(lambda val, pos: f\"{val*1e6:.0f}\"))\n",
    "plt.savefig(fname = f'EIS_FitResults_SI_inset.tif', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight', transparent = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "7c4a4f83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import ScalarFormatter\n",
    "from matplotlib.ticker import LogLocator\n",
    "\n",
    "###set the global parameters for plotting###\n",
    "plt.rcParams['figure.figsize'] = (3.5/2, 2*3.75/1.6/2)          # standard figsize\n",
    "\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "colors = ['lightblue', 'lightcoral', 'darkblue', 'darkred']\n",
    "markers = ['s', 'o', '^','v', 'p', 'h']\n",
    "linestyles = ['-', '-', '-.', '-.', ':', ':', '-.', ':', '-', '--', '-.', ':', '-', '--', '-.', ':']\n",
    "\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(nrows = 1, ncols = 1, constrained_layout = True, sharex = True, figsize=(1.2,1.2))\n",
    "axs.tick_params(which = 'both', direction = 'in', bottom = True, top = True, left = True, right = True)\n",
    "axs.xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "    \n",
    "\n",
    "samples = sorted(sXRD_EISParams['sample'].unique())\n",
    "for i_sample, sample in enumerate(samples):\n",
    "    data_for_plot = sXRD_EISParams.loc[sXRD_EISParams['sample'].str.contains(sample) & sXRD_EISParams['model'].str.contains('3-ladder')]\n",
    "\n",
    "    if '11'   in sample: color = 'lightblue';  label = 'as-prepared sXRD 1'\n",
    "    elif '12' in sample: color = 'lightcoral'; label = 'annealed sXRD 1'\n",
    "    elif '7'  in sample: color = 'darkblue';   label = 'as-prepared sXRD 2'\n",
    "    elif '8'  in sample: color = 'darkred';    label = 'annealed sXRD 2'\n",
    "    data_for_plot = data_for_plot.sort_values(by=\"potential RHE\")                \n",
    "    axs.errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'potential RHE'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'value'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'error'],  color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none', label = label)\n",
    "samples = sorted(RDE_EISParams['annealing'].unique(), reverse = True)\n",
    "\n",
    "axs.set_xlim(left = 1.4, right = 1.79)\n",
    "axs.set_ylim(bottom = 0, top = 900e-6)\n",
    "axs.yaxis.set_major_locator((FixedLocator([0, 200e-6, 400e-6, 600e-6, 800e-6])))\n",
    "axs.xaxis.set_major_locator((FixedLocator([1.4, 1.5, 1.6, 1.7])))\n",
    "axs.yaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs.xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs.yaxis.set_major_formatter(FuncFormatter(lambda val, pos: f\"{val*1e6:.0f}\"))\n",
    "plt.savefig(fname = f'EIS_FitResults_SI_inset2.tif', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight', transparent = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dbb5bde",
   "metadata": {},
   "source": [
    "# RDE Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "ebcaffbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##sXRD as-prepared## \n",
      "potential: 1.702 V\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "###sXRD annealed## \n",
      "potential: 1.712 V\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "###RDE as-prepared## \n",
      "potential: 1.7304999828338623 V\n",
      "['Sample6_20250325_0p1M_NaOH_before_annealing_EIS_sequence_60Ohm_correction_3000rpm_with_bypass_2nd_run_10_PEIS_C01.mpr']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##RDE annealed## \n",
      "potential: 1.7304999828338623 V\n",
      "['Sample6_20250325_0p1M_NaOH_after_annealing_EIS_sequence_60Ohm_correction_3000rpm_with_bypass_auto_range_10_PEIS_C01.mpr']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "def NyquistPlot(experiment, fit, ax): \n",
    "    ax.tick_params(which='both', direction = 'in', bottom = True, top = True, right = True, left = True)\n",
    "    ax.plot(experiment.real, -experiment.imag, marker = 'o', fillstyle = 'none', linestyle = 'none', color = 'black', zorder =10, label = 'experiment')\n",
    "    ax.plot(fit.real, -fit.imag, linestyle = '-',  marker = 'x',   color = 'grey', zorder = 5, label = 'fit')\n",
    "    ax.set_xlim(0, 150)\n",
    "    ax.set_ylim(0, 150)\n",
    "    #ax.set_ylabel(r'$-$Imaginary part of impedance ($\\Omega$)')\n",
    "    #ax.set_xlabel(r'Real part of impedance ($\\Omega$)')\n",
    "    ax.set_ylabel(r'$-\\mathrm{Imaginary \\ part \\ of \\ impedance} \\ (\\Omega)$')\n",
    "    ax.set_xlabel(r'$\\mathrm{Real \\ part \\  of \\ impedance} \\ (\\Omega)$')\n",
    "    #ax.axhline(y=0, color='black', linestyle='-')\n",
    "    ax.set_aspect('equal')\n",
    "    ax.xaxis.set_major_locator(FixedLocator([0, 50, 100, 150]))\n",
    "    ax.xaxis.set_major_formatter(StrMethodFormatter('{x:.0f}'))\n",
    "    ax.yaxis.set_major_locator(FixedLocator([0, 50, 100, 150]))\n",
    "    ax.xaxis.set_major_formatter(StrMethodFormatter('{x:.0f}'))\n",
    "    ax.yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "    ax.xaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "info = '##sXRD as-prepared##'\n",
    "target_potential = 1.71\n",
    "selected_data = sXRD_EISData.loc[(sXRD_EISData['label']=='pristine') & (sXRD_EISData['fname'].str.contains('Prep11'))]\n",
    "available_potentials = selected_data['potential RHE'].unique()\n",
    "closest_potential = available_potentials[np.argmin(np.abs(available_potentials - target_potential))]\n",
    "print(f'{info} \\npotential: {closest_potential} V')\n",
    "selected_data = selected_data.loc[selected_data['potential RHE'] == closest_potential]\n",
    "\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (2.5,2.5), tight_layout = True)\n",
    "NyquistPlot(experiment =selected_data[\"Z' ZHIT (Ohm)\"].to_numpy() + 1j*selected_data[\"Z'' ZHIT (Ohm)\"].to_numpy(), fit = selected_data[\"Z' fit 3-ladder (Ohm)\"].to_numpy() + 1j*selected_data[\"Z'' fit 3-ladder (Ohm)\"].to_numpy(), ax = ax)\n",
    "ax.legend(frameon = False)\n",
    "#plt.savefig(f\"as_prepared+fit_sXRD.tiff\", dpi=600, format=\"tiff\", pil_kwargs={\"compression\": \"tiff_lzw\"})\n",
    "plt.show()\n",
    "\n",
    "\n",
    "info = '###sXRD annealed##'\n",
    "target_potential = 1.71\n",
    "selected_data = sXRD_EISData.loc[(sXRD_EISData['label']=='annealed') & (sXRD_EISData['fname'].str.contains('Prep12'))]\n",
    "available_potentials = selected_data['potential RHE'].unique()\n",
    "closest_potential = available_potentials[np.argmin(np.abs(available_potentials - target_potential))]\n",
    "print(f'{info} \\npotential: {closest_potential} V')\n",
    "selected_data = selected_data.loc[selected_data['potential RHE'] == closest_potential]\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (2.5,2.5), tight_layout = True)\n",
    "NyquistPlot(experiment =selected_data[\"Z' ZHIT (Ohm)\"].to_numpy() + 1j*selected_data[\"Z'' ZHIT (Ohm)\"].to_numpy(), fit = selected_data[\"Z' fit 3-ladder (Ohm)\"].to_numpy() + 1j*selected_data[\"Z'' fit 3-ladder (Ohm)\"].to_numpy(), ax = ax)\n",
    "ax.legend(frameon = False)\n",
    "#plt.savefig(f\"annealed+fit_sXRD.tiff\", dpi=600, format=\"tiff\", pil_kwargs={\"compression\": \"tiff_lzw\"})\n",
    "plt.show()\n",
    "\n",
    "info = '###RDE as-prepared##'\n",
    "target_potential = 1.71\n",
    "selected_data = RDE_EISData.loc[(RDE_EISData['Annealing']=='before') & (RDE_EISData['Sample'].str.contains('Sample6'))]\n",
    "available_potentials = selected_data['potential vs RHE (V)'].unique()\n",
    "closest_potential = available_potentials[np.argmin(np.abs(available_potentials - target_potential))]\n",
    "print(f'{info} \\npotential: {closest_potential} V')\n",
    "selected_data = selected_data.loc[selected_data['potential vs RHE (V)'] == closest_potential]\n",
    "print(selected_data['fname'].unique())\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (2.5,2.5), tight_layout = True)\n",
    "NyquistPlot(experiment =selected_data[\"Z' ZHIT /Ohm\"].to_numpy() + 1j*selected_data[\"Z'' ZHIT /Ohm\"].to_numpy(), fit = selected_data[\"Z' fit /Ohm\"].to_numpy() + 1j*selected_data[\"Z'' fit /Ohm\"].to_numpy(), ax = ax)\n",
    "ax.legend(frameon = False)\n",
    "plt.savefig(f\"as_prepared+fit_RDE.tiff\", dpi=600, format=\"tiff\", pil_kwargs={\"compression\": \"tiff_lzw\"})\n",
    "plt.show()\n",
    "\n",
    "\n",
    "info = '##RDE annealed##'\n",
    "target_potential = 1.71\n",
    "selected_data = RDE_EISData.loc[(RDE_EISData['Annealing']=='after') & (RDE_EISData['Sample'].str.contains('Sample6'))]\n",
    "available_potentials = selected_data['potential vs RHE (V)'].unique()\n",
    "closest_potential = available_potentials[np.argmin(np.abs(available_potentials - target_potential))]\n",
    "print(f'{info} \\npotential: {closest_potential} V')\n",
    "selected_data = selected_data.loc[selected_data['potential vs RHE (V)'] == closest_potential]\n",
    "print(selected_data['fname'].unique())\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (2.5,2.5), tight_layout = True)\n",
    "NyquistPlot(experiment =selected_data[\"Z' ZHIT /Ohm\"].to_numpy() + 1j*selected_data[\"Z'' ZHIT /Ohm\"].to_numpy(), fit = selected_data[\"Z' fit /Ohm\"].to_numpy() + 1j*selected_data[\"Z'' fit /Ohm\"].to_numpy(), ax = ax)\n",
    "ax.legend(frameon = False)\n",
    "plt.savefig(f\"annealed+fit_RDE.tiff\", dpi=600, format=\"tiff\", pil_kwargs={\"compression\": \"tiff_lzw\"})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86ce99b5",
   "metadata": {},
   "source": [
    "# Comparison of Models with One, Two, and Three Time Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "162c8f36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "available potentials [0.982, 1.132, 1.232, 1.332, 1.382, 1.482, 1.582, 1.602, 1.622, 1.642, 1.662, 1.682, 1.702, 1.722]\n",
      "choosen potentials [1.702]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import os\n",
    "import numpy as np\n",
    "from numpy.typing import NDArray\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit import Model, Parameter\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "from matplotlib.ticker import FixedLocator, StrMethodFormatter\n",
    "\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (2.5, 2.5)\n",
    "plt.rcParams['figure.dpi'] = 120\n",
    "\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "plt.rcParams['ytick.minor.width'] = 1\n",
    "\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "\n",
    "\n",
    "AllEISData = sXRD_EISData\n",
    "#select potentials and samples for comparison\n",
    "target = 'Prep11_EIS_1'\n",
    "\n",
    "available_potentials = sorted(AllEISData.loc[AllEISData['fname'].str.contains(target), 'potential RHE'].unique())\n",
    "print('available potentials', available_potentials)\n",
    "target_potentials = [1.15, 1.5, 1.6, 1.7, 1.75, 1.8]\n",
    "target_potentials = [ 1.71]\n",
    "closest_potentials = [available_potentials[np.argmin(np.abs(np.array(available_potentials) - target_potential))] for target_potential in target_potentials]\n",
    "print('choosen potentials', closest_potentials)\n",
    "\n",
    "\n",
    "mask = (\n",
    "    AllEISData['fname'].str.contains(target) &\n",
    "    AllEISData['potential RHE'].round(3).isin(closest_potentials)  # rounding avoids float-equality issues\n",
    ")\n",
    "data_for_plot =  AllEISData.loc[mask]\n",
    "data_for_plot = data_for_plot.sort_values(by=\" Frequency (Hz)\", ascending=True)\n",
    "\n",
    "colors =  ['darkslategrey']#['paleturquoise', 'aquamarine', 'darkcyan', 'darkslategrey', 'orange', 'yellow', 'green']\n",
    "alphas =  [1, 1, 1, 1, 1, 1, 1, 1]\n",
    "markers = ['o']#['x', 's', 'o', '^', 'v', 'd', '*', '+', 'D']\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 1, tight_layout = True)\n",
    "\n",
    "#ax.set_title('A) impedance spectrum')\n",
    "ax.tick_params(which='both', direction = 'in', bottom = True, top = True, right = True, left = True)\n",
    "for ipotential, potential in enumerate(closest_potentials):\n",
    "    ax.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' ZHIT (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' ZHIT (Ohm)\"], marker = markers[ipotential], markersize = 3, fillstyle = 'none', linestyle = 'none', color = 'black', label = 'experiment', zorder = 10)#f'{np.round(data_for_plot.loc[data_for_plot['potential RHE'] == potential, 'potential RHE'].iloc[0], 2):.2f} V', zorder = 8)\n",
    "    ax.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' fit 1-ladder (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' fit 1-ladder (Ohm)\"], marker = 'x', markersize = 3, fillstyle = 'none', linestyle = '-', color = 'cyan', zorder = 7,        label = 'fit with model 1', alpha = 1)\n",
    "    ax.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' fit 2-ladder (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' fit 2-ladder (Ohm)\"], marker = 'x', markersize = 3, fillstyle = 'none', linestyle = '-', color = 'blue', zorder = 7,        label = 'fit with model 2', alpha = 1)\n",
    "    ax.plot(data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z' fit 3-ladder (Ohm)\"], -data_for_plot.loc[data_for_plot['potential RHE'] == potential, \"Z'' fit 3-ladder (Ohm)\"], marker = 'x', markersize = 3, fillstyle = 'none', linestyle = '-', color = 'darkorchid', zorder = 9,  label = 'fit with model 3')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlim(-20, 250)\n",
    "ax.set_ylim(-20, 250)\n",
    "ax.set_ylabel(r'$-\\mathrm{Imaginary \\ part \\ of \\ impedance} \\ (\\Omega)$')\n",
    "ax.set_xlabel(r'$\\mathrm{Real \\ part \\  of \\ impedance} \\ (\\Omega)$')\n",
    "#ax.axhline(y=0, color='black', linestyle='-')\n",
    "ax.set_aspect('equal')\n",
    "#ax[0].legend(fontsize = 14)\n",
    "ax.xaxis.set_major_locator(FixedLocator([0, 100, 200, 300, 400]))\n",
    "ax.xaxis.set_major_formatter(StrMethodFormatter('{x:.0f}'))\n",
    "ax.yaxis.set_major_locator(FixedLocator([0, 100, 200, 300, 400]))\n",
    "ax.yaxis.set_major_formatter(StrMethodFormatter('{x:.0f}'))\n",
    "ax.yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "ax.xaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels, loc = 'upper right', ncols = 1, frameon = False, \n",
    "           labelspacing=0.2,\n",
    "           handletextpad=0.3,\n",
    "           columnspacing=0.7,\n",
    "           borderpad=0.2,\n",
    "           borderaxespad=1.0,\n",
    "           handlelength=1.0,\n",
    "           markerscale=0.9)\n",
    "plt.savefig(fname = f'impedance_spectra_as_prepared_fit_comparison.png', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31d9c514",
   "metadata": {},
   "source": [
    "# Three Time Constants: Overview All EIS Parameters in Portrait Orientation (Figure S15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "ca7fc6af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import ScalarFormatter\n",
    "from matplotlib.ticker import LogLocator\n",
    "\n",
    "###set the global parameters for plotting###\n",
    "plt.rcParams['figure.figsize'] = (3.5, 2*3.75/1.6)          # standard figsize\n",
    "\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "\n",
    "colors = ['lightblue', 'lightcoral', 'darkblue', 'darkred']\n",
    "markers = ['s', 'o', '^','v', '<', '>']\n",
    "linestyles = ['-', '-', '-.', '-.', ':', ':', '-.', ':', '-', '--', '-.', ':', '-', '--', '-.', ':']\n",
    "\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(nrows = 3, ncols = 2, constrained_layout = True, sharex = True, figsize=(4,5))\n",
    "for i in range(3): \n",
    "    for j in range(2): \n",
    "        axs[i,j].tick_params(which = 'both', direction = 'in', bottom = True, top = True, left = True, right = True)\n",
    "        axs[i,j].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "    \n",
    "samples = sorted(sXRD_EISParams['sample'].unique())\n",
    "\n",
    "\n",
    "for i_sample, sample in enumerate(samples):\n",
    "    data_for_plot = sXRD_EISParams.loc[sXRD_EISParams['sample'].str.contains(sample) & sXRD_EISParams['model'].str.contains('3-ladder')]\n",
    "    if '11'   in sample: color = 'lightblue'; label = 'as-prepared 1'\n",
    "    elif '12' in sample: color = 'lightcoral'; label = 'annealed 1'\n",
    "    elif '7'  in sample: color = 'darkblue'; label = 'as-prepared 2'\n",
    "    elif '8'  in sample: color = 'darkred'; label = 'annealed 2'\n",
    "    data_for_plot = data_for_plot.sort_values(by=\"potential RHE\")                \n",
    "    axs[0,0].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Rct1 (Ohm)', 'potential RHE'],   data_for_plot.loc[data_for_plot['parameter'] == 'Rct1 (Ohm)', 'value'],   data_for_plot.loc[data_for_plot['parameter'] == 'Rct1 (Ohm)', 'error'],    color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "    axs[1,0].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Rct2 (Ohm)', 'potential RHE'],   data_for_plot.loc[data_for_plot['parameter'] == 'Rct2 (Ohm)', 'value'],   data_for_plot.loc[data_for_plot['parameter'] == 'Rct2 (Ohm)', 'error'],    color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "    axs[2,0].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'RctO2 (Ohm)', 'potential RHE'],  data_for_plot.loc[data_for_plot['parameter'] == 'RctO2 (Ohm)', 'value'],  data_for_plot.loc[data_for_plot['parameter'] == 'RctO2 (Ohm)', 'error'],   color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "    axs[0,1].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'potential RHE'],      data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'value'],      data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'error'],       color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "    axs[1,1].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo1 (F)', 'potential RHE'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo1 (F)', 'value'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo1 (F)', 'error'],  color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "    axs[2,1].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'potential RHE'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'value'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo2 (F)', 'error'],  color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none', label = label)\n",
    "\n",
    "\n",
    "axs[0,0].set_ylabel(r'$R_{\\mathrm{CT,1}} \\ (\\mathrm{\\Omega})$')\n",
    "axs[0,0].set_yscale('log')\n",
    "axs[0,0].set_ylim(bottom = 1, top = 8e5)\n",
    "\n",
    "axs[0,0].yaxis.set_major_locator(LogLocator(base = 10))\n",
    "\n",
    "axs[1,0].set_ylabel(r'$R_{\\mathrm{CT,2}} \\ (\\mathrm{\\Omega})$')\n",
    "axs[1,0].set_yscale('log')\n",
    "axs[1,0].set_ylim(bottom = 1, top = 8e5)\n",
    "axs[1,0].yaxis.set_major_locator(LogLocator(base = 10))\n",
    "\n",
    "axs[2,0].set_ylabel(r'$R_{\\mathrm{CT,O_2}} \\ (\\mathrm{\\Omega})$')\n",
    "axs[2,0].set_yscale('log')\n",
    "axs[2,0].set_ylim(bottom = 1, top = 8e5)\n",
    "axs[2,0].yaxis.set_major_locator(LogLocator(base = 10))\n",
    "\n",
    "axs[0,1].set_ylabel(r'$C_{\\mathrm{DL}} \\ (\\mathrm{\\mu F})$')\n",
    "axs[0,1].yaxis.set_major_locator(FixedLocator([0, 5e-6, 10e-6, 15e-6, 20e-6, 25e-6]))\n",
    "axs[0,1].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "axs[0,1].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[0,1].yaxis.set_major_formatter(FuncFormatter(lambda val, pos: f\"{val*1e6:.0f}\"))\n",
    "axs[0,1].set_ylim(top = 28e-6)\n",
    "\n",
    "axs[1,1].set_xlim(left = 0.85, right = 1.82)\n",
    "axs[1,1].set_ylabel(r'$C_{\\mathrm{Pseudo,1}} \\ (\\mathrm{\\mu F})$')\n",
    "axs[1,1].yaxis.set_major_locator(FixedLocator([0, 50e-6, 100e-6, 150e-6, 200e-6, 250e-6]))\n",
    "axs[1,1].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "axs[1,1].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[1,1].yaxis.set_major_formatter(FuncFormatter(lambda val, pos: f\"{val*1e6:.0f}\"))\n",
    "axs[1,1].set_ylim(top = 270e-6)\n",
    "\n",
    "axs[2,1].set_ylabel(r'$C_{\\mathrm{Pseudo,2}} \\ (\\mathrm{\\mu F})$')\n",
    "axs[2,1].yaxis.set_major_locator(FixedLocator([0, 1000e-6, 2000e-6, 3000e-6, 4000e-6, 5000e-6]))\n",
    "axs[2,1].xaxis.set_major_locator(FixedLocator([1.0, 1.2, 1.4, 1.6, 1.8]))\n",
    "axs[2,1].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "axs[2,1].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[2,1].yaxis.set_major_formatter(FuncFormatter(lambda val, pos: f\"{val*1e6:.0f}\"))\n",
    "axs[2,1].set_ylim(top = 5200e-6)\n",
    "\n",
    "axs[2,0].set_xlabel(r'Potential vs RHE (V)')\n",
    "axs[2,1].set_xlabel(r'Potential vs RHE (V)')\n",
    "\n",
    "handles, labels = fig.axes[5].get_legend_handles_labels()\n",
    "\n",
    "order = [0, 2, 1, 3]   # new order of legend entries\n",
    "fig.legend([handles[i] for i in order],\n",
    "          [labels[i] for i in order], \n",
    "          bbox_to_anchor = (0.5, -0.05), loc = 'center', ncol = 2, frameon = False)\n",
    "plt.savefig(fname = f'EIS_FitResults_portrait_SI.tif', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34bd6ab5",
   "metadata": {},
   "source": [
    "# Two Time Constants: Overview All EIS Parameters in Portrait Orientation (Figure S14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "787de259",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import ScalarFormatter\n",
    "from matplotlib.ticker import LogLocator\n",
    "\n",
    "###set the global parameters for plotting###\n",
    "plt.rcParams['figure.figsize'] = (3.5, 2*3.75/1.6)          # standard figsize\n",
    "\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "\n",
    "colors = ['lightblue', 'lightcoral', 'darkblue', 'darkred']\n",
    "markers = ['s', 'o', '^','v', '<', '>']\n",
    "linestyles = ['-', '-', '-.', '-.', ':', ':', '-.', ':', '-', '--', '-.', ':', '-', '--', '-.', ':']\n",
    "\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(nrows = 2, ncols = 2, constrained_layout = True, sharex = True, figsize=(4,5*2/3))\n",
    "for i in range(2): \n",
    "    for j in range(2): \n",
    "        axs[i,j].tick_params(which = 'both', direction = 'in', bottom = True, top = True, left = True, right = True)\n",
    "        axs[i,j].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "    \n",
    "samples = sorted(sXRD_EISParams['sample'].unique())\n",
    "\n",
    "\n",
    "for i_sample, sample in enumerate(samples):\n",
    "    data_for_plot = sXRD_EISParams.loc[sXRD_EISParams['sample'].str.contains(sample) & sXRD_EISParams['model'].str.contains('2-ladder')]\n",
    "    if '11'   in sample: color = 'lightblue'; label = 'as-prepared 1'\n",
    "    elif '12' in sample: color = 'lightcoral'; label = 'annealed 1'\n",
    "    elif '7'  in sample: color = 'darkblue'; label = 'as-prepared 2'\n",
    "    elif '8'  in sample: color = 'darkred'; label = 'annealed 2'\n",
    "    data_for_plot = data_for_plot.sort_values(by=\"potential RHE\")                \n",
    "    try:\n",
    "        mask = data_for_plot['error'] > data_for_plot['value']\n",
    "        data_for_plot.loc[mask, 'error'] = data_for_plot.loc[mask, 'value']\n",
    "        axs[0,0].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Rct1 (Ohm)', 'potential RHE'],   data_for_plot.loc[data_for_plot['parameter'] == 'Rct1 (Ohm)', 'value'],   data_for_plot.loc[data_for_plot['parameter'] == 'Rct1 (Ohm)', 'error'],    color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "        axs[1,0].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'RctO2 (Ohm)', 'potential RHE'],  data_for_plot.loc[data_for_plot['parameter'] == 'RctO2 (Ohm)', 'value'],  data_for_plot.loc[data_for_plot['parameter'] == 'RctO2 (Ohm)', 'error'],   color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "        axs[0,1].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'potential RHE'],      data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'value'],      data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'error'],       color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "        axs[1,1].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo1 (F)', 'potential RHE'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo1 (F)', 'value'], data_for_plot.loc[data_for_plot['parameter'] == 'Cpseudo1 (F)', 'error'],  color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none', label = label)\n",
    "    except KeyError:\n",
    "        pass\n",
    "    except Exception:\n",
    "        raise\n",
    "\n",
    "axs[0,0].set_ylabel(r'$R_{\\mathrm{CT,OER1}} \\ \\mathrm{(M2)} \\ (\\mathrm{\\Omega})$')\n",
    "axs[0,0].set_yscale('log')\n",
    "axs[0,0].set_ylim(bottom = 1, top = 5e5)\n",
    "\n",
    "axs[0,0].yaxis.set_major_locator(LogLocator(base = 10))\n",
    "\n",
    "\n",
    "\n",
    "axs[1,0].set_ylabel(r'$R_{\\mathrm{CT,OER2}} \\ \\mathrm{(M2)} \\ (\\mathrm{\\Omega})$')\n",
    "axs[1,0].set_yscale('log')\n",
    "axs[1,0].set_ylim(bottom = 1, top = 5e5)\n",
    "axs[1,0].yaxis.set_major_locator(LogLocator(base = 10))\n",
    "\n",
    "axs[0,1].set_ylabel(r'$C_{\\mathrm{DL}} \\ \\mathrm{(M2)} \\ (\\mathrm{\\mu F})$')\n",
    "axs[0,1].yaxis.set_major_locator(FixedLocator([0, 50e-6, 100e-6, 150e-6, 200e-6]))\n",
    "axs[0,1].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "axs[0,1].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[0,1].yaxis.set_major_formatter(FuncFormatter(lambda val, pos: f\"{val*1e6:.0f}\"))\n",
    "\n",
    "axs[1,1].set_ylabel(r'$C_{\\mathrm{Pseudo}}  \\ \\mathrm{(M2)} \\ (\\mathrm{\\mu F})$')\n",
    "axs[1,1].set_xlim(left = 0.85, right = 1.82)\n",
    "axs[1,1].yaxis.set_major_locator(FixedLocator([0, 200e-6, 400e-6, 600e-6]))\n",
    "axs[1,1].xaxis.set_major_locator(FixedLocator([1.0, 1.2, 1.4, 1.6, 1.8]))\n",
    "axs[1,1].yaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[1,1].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[1,1].yaxis.set_major_formatter(FuncFormatter(lambda val, pos: f\"{val*1e6:.0f}\"))\n",
    "\n",
    "\n",
    "axs[1,0].set_xlabel(r'Potential vs RHE (V)')\n",
    "axs[1,1].set_xlabel(r'Potential vs RHE (V)')\n",
    "\n",
    "handles, labels = fig.axes[3].get_legend_handles_labels()\n",
    "\n",
    "order = [0, 2, 1, 3]   # new order of legend entries\n",
    "fig.legend([handles[i] for i in order],\n",
    "          [labels[i] for i in order], \n",
    "          bbox_to_anchor = (0.5, -0.07), loc = 'center', ncol = 2, frameon = False)\n",
    "\n",
    "plt.savefig(fname = f'EIS_FitResults_SI_2-ladder.tif', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight', transparent= True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c954a60",
   "metadata": {},
   "source": [
    "# One Time Constants: Overview All EIS Parameters (Figure S13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "5436182d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import ScalarFormatter\n",
    "from matplotlib.ticker import LogLocator\n",
    "\n",
    "###set the global parameters for plotting###\n",
    "plt.rcParams['figure.figsize'] = (3.5, 2*3.75/1.6)          # standard figsize\n",
    "\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "\n",
    "colors = ['lightblue', 'lightcoral', 'darkblue', 'darkred']\n",
    "markers = ['s', 'o', '^','v', '<', '>']\n",
    "linestyles = ['-', '-', '-.', '-.', ':', ':', '-.', ':', '-', '--', '-.', ':', '-', '--', '-.', ':']\n",
    "\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(nrows = 1, ncols = 2, constrained_layout = True, sharex = True, figsize=(4,5*1/3))\n",
    "for i in range(2): \n",
    "    axs[i].tick_params(which = 'both', direction = 'in', bottom = True, top = True, left = True, right = True)\n",
    "    axs[i].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "    \n",
    "samples = sorted(sXRD_EISParams['sample'].unique())\n",
    "\n",
    "\n",
    "for i_sample, sample in enumerate(samples):\n",
    "    data_for_plot = sXRD_EISParams.loc[sXRD_EISParams['sample'].str.contains(sample) & sXRD_EISParams['model'].str.contains('1-ladder')]\n",
    "    if data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'label'].iloc[0] == 'pristine':\n",
    "        label = 'as-prepared'\n",
    "    else:\n",
    "        label = 'annealed'\n",
    "    if '11'   in sample: color = 'lightblue'; label = 'as-prepared 1'\n",
    "    elif '12' in sample: color = 'lightcoral'; label = 'annealed 1'\n",
    "    elif '7'  in sample: color = 'darkblue'; label = 'as-prepared 2'\n",
    "    elif '8'  in sample: color = 'darkred'; label = 'annealed 2'\n",
    "    data_for_plot = data_for_plot.sort_values(by=\"potential RHE\")                \n",
    "    try:\n",
    "        mask = data_for_plot['error'] > data_for_plot['value']\n",
    "        data_for_plot.loc[mask, 'error'] = data_for_plot.loc[mask, 'value']\n",
    "        axs[0].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'RctO2 (Ohm)', 'potential RHE'],  data_for_plot.loc[data_for_plot['parameter'] == 'RctO2 (Ohm)', 'value'],  data_for_plot.loc[data_for_plot['parameter'] == 'RctO2 (Ohm)', 'error'],   color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none')\n",
    "        axs[1].errorbar(data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'potential RHE'],      data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'value'],      data_for_plot.loc[data_for_plot['parameter'] == 'Cdl (F)', 'error'],       color = color, marker = markers[i_sample], linestyle = linestyles[i_sample], fillstyle = 'none', label = label)\n",
    "    except KeyError:\n",
    "        pass\n",
    "    except Exception:\n",
    "        raise\n",
    "\n",
    "axs[1].set_ylabel(r'$C_{\\mathrm{DL}} \\ \\mathrm{(M1)} \\ (\\mathrm{\\mu F})$')\n",
    "axs[1].yaxis.set_major_locator(FixedLocator([0, 100e-6, 200e-6, 300e-6, 400e-6]))\n",
    "axs[1].xaxis.set_major_locator(FixedLocator([1.0, 1.2, 1.4, 1.6, 1.8]))\n",
    "axs[1].yaxis.set_minor_locator(AutoMinorLocator(n=5))\n",
    "axs[1].xaxis.set_minor_locator(AutoMinorLocator(n=4))\n",
    "axs[1].yaxis.set_major_formatter(FuncFormatter(lambda val, pos: f\"{val*1e6:.0f}\"))\n",
    "\n",
    "\n",
    "axs[0].set_ylabel(r'$R_{\\mathrm{CT,OER}} \\ \\mathrm{(M1)} \\ (\\mathrm{\\Omega})$')\n",
    "axs[0].set_yscale('log')\n",
    "axs[0].set_ylim(bottom = 1, top = 8e5)\n",
    "axs[0].set_xlim(left = 0.85, right = 1.82)\n",
    "axs[0].yaxis.set_major_locator(LogLocator(base = 10))\n",
    "\n",
    "\n",
    "\n",
    "axs[0].set_xlabel(r'Potential vs RHE (V)')\n",
    "axs[1].set_xlabel(r'Potential vs RHE (V)')\n",
    "handles, labels = fig.axes[1].get_legend_handles_labels()\n",
    "\n",
    "order = [0, 2, 1, 3]   # new order of legend entries\n",
    "fig.legend([handles[i] for i in order],\n",
    "          [labels[i] for i in order], \n",
    "          bbox_to_anchor = (0.5, -0.1), loc = 'center', ncol = 2, frameon = False)\n",
    "\n",
    "plt.savefig(fname = f'EIS_FitResults_SI_1-ladder.tif', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight', transparent = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "173282c6",
   "metadata": {},
   "source": [
    "# Correlation Matrices\n",
    "## Correlation Matrices - Model with Three Time Constants\n",
    "### Correlation Matrices - Model with Three Time Constants - Capacitve Regime (upper left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "f3e75bd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:14: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:14: SyntaxWarning: invalid escape sequence '\\m'\n",
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_24349/3008347410.py:14: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  parameters = np.array([r'$R_{\\mathrm{series}}$', r'$R_L$', '$R_{\\mathrm{CT,1}}$', r'$R_{\\mathrm{CT,2}}$', r'$R_{\\mathrm{CT,O_2}}$', r'$C_{\\mathrm{DL}}$', r'$C_{\\mathrm{Pseudo,1}}$', r'$C_{\\mathrm{Pseudo,2}}$', r'$L$'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import os\n",
    "import numpy as np\n",
    "from numpy.typing import NDArray\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit import Model, Parameter\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "\n",
    "parameters = np.array([r'$R_{\\mathrm{series}}$', r'$R_L$', '$R_{\\mathrm{CT,1}}$', r'$R_{\\mathrm{CT,2}}$', r'$R_{\\mathrm{CT,O_2}}$', r'$C_{\\mathrm{DL}}$', r'$C_{\\mathrm{Pseudo,1}}$', r'$C_{\\mathrm{Pseudo,2}}$', r'$L$'])\n",
    "threshold = 0.98\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (2.5, 2.5)\n",
    "plt.rcParams['figure.dpi'] = 120\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "plt.rcParams['ytick.minor.width'] = 1\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "correlation = matrices_3ladder['Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv'].to_numpy()\n",
    "\n",
    "fig, ax = plt.subplots(constrained_layout = True)\n",
    "ax.tick_params(which = 'both', direction = 'in', bottom = True, top = True, left = True, right = True)\n",
    "c = ax.pcolormesh(parameters[1:], parameters[1:], correlation, cmap='RdBu', vmin = -1, vmax = 1)\n",
    "\n",
    "for i in range(correlation.shape[0]):\n",
    "    for j in range(correlation.shape[1]):\n",
    "        if correlation[i, j] > threshold:\n",
    "            ax.scatter(parameters[1:][j], parameters[1:][i], marker='x', color='black')\n",
    "        if correlation[i, j] < -threshold: \n",
    "            ax.scatter(parameters[1:][j], parameters[1:][i], marker='x', color='black')\n",
    "for label in ax.get_xticklabels():\n",
    "    label.set_rotation(45)\n",
    "    label.set_ha(\"right\")          # align text to the right\n",
    "    label.set_rotation_mode(\"anchor\")  # rotate around the text corner\n",
    "ax.set_aspect('equal')\n",
    "cbar = fig.colorbar(c, ax=ax, shrink=0.57)\n",
    "cbar.set_label(\"Correlation\", fontsize=8)\n",
    "plt.savefig(fname = f'coellation_map_capacitive_3-ladder.tif', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "286af001",
   "metadata": {},
   "source": [
    "### Correlation Matrices - Model with Three Time Constants - OER (Figure S15 lower left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "c5da64a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import os\n",
    "import numpy as np\n",
    "from numpy.typing import NDArray\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit import Model, Parameter\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "\n",
    "threshold = 0.98\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (2.5, 2.5)\n",
    "plt.rcParams['figure.dpi'] = 120\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "plt.rcParams['ytick.minor.width'] = 1\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "correlation = matrices_3ladder['Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv'].to_numpy()\n",
    "\n",
    "fig, ax = plt.subplots(constrained_layout = True)\n",
    "ax.tick_params(which = 'both', direction = 'in', bottom = True, top = True, left = True, right = True)\n",
    "c = ax.pcolormesh(parameters[1:], parameters[1:], correlation, cmap='RdBu', vmin = -1, vmax = 1)\n",
    "\n",
    "for i in range(correlation.shape[0]):\n",
    "    for j in range(correlation.shape[1]):\n",
    "        if correlation[i, j] > threshold:\n",
    "            ax.scatter(parameters[1:][j], parameters[1:][i], marker='x', color='black')\n",
    "        if correlation[i, j] < -threshold: \n",
    "            ax.scatter(parameters[1:][j], parameters[1:][i], marker='x', color='black')\n",
    "for label in ax.get_xticklabels():\n",
    "    label.set_rotation(45)\n",
    "    label.set_ha(\"right\")          # align text to the right\n",
    "    label.set_rotation_mode(\"anchor\")  # rotate around the text corner\n",
    "ax.set_aspect('equal')\n",
    "cbar = fig.colorbar(c, ax=ax, shrink=0.57)\n",
    "cbar.set_label(\"Correlation\", fontsize=8)\n",
    "plt.savefig(fname = f'coellation_map_oer_3-ladder.tif', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "c40b18c7",
   "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>fname</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>error</th>\n",
       "      <th>potential</th>\n",
       "      <th>sample</th>\n",
       "      <th>model</th>\n",
       "      <th>cell</th>\n",
       "      <th>label</th>\n",
       "      <th>edgecolor</th>\n",
       "      <th>potential shift</th>\n",
       "      <th>potential RHE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>Rseries (Ohm)</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>3-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>RL (Ohm)</td>\n",
       "      <td>14.185606</td>\n",
       "      <td>2.233549e-02</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>3-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>Rct1 (Ohm)</td>\n",
       "      <td>4.309411</td>\n",
       "      <td>1.982799e-01</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>3-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>Rct2 (Ohm)</td>\n",
       "      <td>128.740316</td>\n",
       "      <td>2.523552e+00</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>3-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>RctO2 (Ohm)</td>\n",
       "      <td>157.964847</td>\n",
       "      <td>2.521201e+00</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>3-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>Cdl (F)</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>4.887178e-07</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>3-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>Cpseudo1 (F)</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>1.766424e-06</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>3-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>Cpseudo2 (F)</td>\n",
       "      <td>0.000351</td>\n",
       "      <td>9.870725e-06</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>3-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>L (H)</td>\n",
       "      <td>0.008530</td>\n",
       "      <td>2.810317e-04</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>3-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>Rseries (Ohm)</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>2-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>RL (Ohm)</td>\n",
       "      <td>14.447235</td>\n",
       "      <td>6.007652e-02</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>2-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>Rct1 (Ohm)</td>\n",
       "      <td>106.820901</td>\n",
       "      <td>5.401038e+00</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>2-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>RctO2 (Ohm)</td>\n",
       "      <td>180.243626</td>\n",
       "      <td>5.586520e+00</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>2-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>Cdl (F)</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>3.412894e-06</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>2-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>Cpseudo1 (F)</td>\n",
       "      <td>0.000298</td>\n",
       "      <td>1.638976e-05</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>2-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>L (H)</td>\n",
       "      <td>0.015520</td>\n",
       "      <td>7.844972e-04</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>2-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>Rseries (Ohm)</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>1-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>RL (Ohm)</td>\n",
       "      <td>14.412245</td>\n",
       "      <td>1.188133e-01</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>1-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>RctO2 (Ohm)</td>\n",
       "      <td>248.097108</td>\n",
       "      <td>4.858335e+00</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>1-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>Cdl (F)</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>4.341466e-06</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>1-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv</td>\n",
       "      <td>L (H)</td>\n",
       "      <td>0.016109</td>\n",
       "      <td>1.610872e-03</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>1-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        fname      parameter       value  \\\n",
       "0  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv  Rseries (Ohm)   50.000000   \n",
       "1  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv       RL (Ohm)   14.185606   \n",
       "2  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv     Rct1 (Ohm)    4.309411   \n",
       "3  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv     Rct2 (Ohm)  128.740316   \n",
       "4  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv    RctO2 (Ohm)  157.964847   \n",
       "5  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv        Cdl (F)    0.000016   \n",
       "6  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv   Cpseudo1 (F)    0.000156   \n",
       "7  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv   Cpseudo2 (F)    0.000351   \n",
       "8  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv          L (H)    0.008530   \n",
       "0  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv  Rseries (Ohm)   50.000000   \n",
       "1  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv       RL (Ohm)   14.447235   \n",
       "2  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv     Rct1 (Ohm)  106.820901   \n",
       "3  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv    RctO2 (Ohm)  180.243626   \n",
       "4  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv        Cdl (F)    0.000149   \n",
       "5  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv   Cpseudo1 (F)    0.000298   \n",
       "6  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv          L (H)    0.015520   \n",
       "0  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv  Rseries (Ohm)   50.000000   \n",
       "1  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv       RL (Ohm)   14.412245   \n",
       "2  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv    RctO2 (Ohm)  248.097108   \n",
       "3  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv        Cdl (F)    0.000227   \n",
       "4  Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv          L (H)    0.016109   \n",
       "\n",
       "          error  potential        sample     model  cell     label edgecolor  \\\n",
       "0  0.000000e+00        0.7  Prep11_EIS_1  3-ladder  sXRD  pristine      blue   \n",
       "1  2.233549e-02        0.7  Prep11_EIS_1  3-ladder  sXRD  pristine      blue   \n",
       "2  1.982799e-01        0.7  Prep11_EIS_1  3-ladder  sXRD  pristine      blue   \n",
       "3  2.523552e+00        0.7  Prep11_EIS_1  3-ladder  sXRD  pristine      blue   \n",
       "4  2.521201e+00        0.7  Prep11_EIS_1  3-ladder  sXRD  pristine      blue   \n",
       "5  4.887178e-07        0.7  Prep11_EIS_1  3-ladder  sXRD  pristine      blue   \n",
       "6  1.766424e-06        0.7  Prep11_EIS_1  3-ladder  sXRD  pristine      blue   \n",
       "7  9.870725e-06        0.7  Prep11_EIS_1  3-ladder  sXRD  pristine      blue   \n",
       "8  2.810317e-04        0.7  Prep11_EIS_1  3-ladder  sXRD  pristine      blue   \n",
       "0  0.000000e+00        0.7  Prep11_EIS_1  2-ladder  sXRD  pristine      blue   \n",
       "1  6.007652e-02        0.7  Prep11_EIS_1  2-ladder  sXRD  pristine      blue   \n",
       "2  5.401038e+00        0.7  Prep11_EIS_1  2-ladder  sXRD  pristine      blue   \n",
       "3  5.586520e+00        0.7  Prep11_EIS_1  2-ladder  sXRD  pristine      blue   \n",
       "4  3.412894e-06        0.7  Prep11_EIS_1  2-ladder  sXRD  pristine      blue   \n",
       "5  1.638976e-05        0.7  Prep11_EIS_1  2-ladder  sXRD  pristine      blue   \n",
       "6  7.844972e-04        0.7  Prep11_EIS_1  2-ladder  sXRD  pristine      blue   \n",
       "0  0.000000e+00        0.7  Prep11_EIS_1  1-ladder  sXRD  pristine      blue   \n",
       "1  1.188133e-01        0.7  Prep11_EIS_1  1-ladder  sXRD  pristine      blue   \n",
       "2  4.858335e+00        0.7  Prep11_EIS_1  1-ladder  sXRD  pristine      blue   \n",
       "3  4.341466e-06        0.7  Prep11_EIS_1  1-ladder  sXRD  pristine      blue   \n",
       "4  1.610872e-03        0.7  Prep11_EIS_1  1-ladder  sXRD  pristine      blue   \n",
       "\n",
       "   potential shift  potential RHE  \n",
       "0             0.01          1.682  \n",
       "1             0.01          1.682  \n",
       "2             0.01          1.682  \n",
       "3             0.01          1.682  \n",
       "4             0.01          1.682  \n",
       "5             0.01          1.682  \n",
       "6             0.01          1.682  \n",
       "7             0.01          1.682  \n",
       "8             0.01          1.682  \n",
       "0             0.01          1.682  \n",
       "1             0.01          1.682  \n",
       "2             0.01          1.682  \n",
       "3             0.01          1.682  \n",
       "4             0.01          1.682  \n",
       "5             0.01          1.682  \n",
       "6             0.01          1.682  \n",
       "0             0.01          1.682  \n",
       "1             0.01          1.682  \n",
       "2             0.01          1.682  \n",
       "3             0.01          1.682  \n",
       "4             0.01          1.682  "
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sXRD_EISParams.loc[sXRD_EISParams['fname'] == 'Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "b27413a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fname</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>error</th>\n",
       "      <th>potential</th>\n",
       "      <th>sample</th>\n",
       "      <th>model</th>\n",
       "      <th>cell</th>\n",
       "      <th>label</th>\n",
       "      <th>edgecolor</th>\n",
       "      <th>potential shift</th>\n",
       "      <th>potential RHE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>Rseries (Ohm)</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>3-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>RL (Ohm)</td>\n",
       "      <td>14.150845</td>\n",
       "      <td>6.642986e-02</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>3-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>Rct1 (Ohm)</td>\n",
       "      <td>17.696762</td>\n",
       "      <td>3.393983e-01</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>3-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>Rct2 (Ohm)</td>\n",
       "      <td>930.474101</td>\n",
       "      <td>4.148916e+01</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>3-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>RctO2 (Ohm)</td>\n",
       "      <td>10811.464465</td>\n",
       "      <td>5.268848e+02</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>3-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>Cdl (F)</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>3.431776e-07</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>3-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>Cpseudo1 (F)</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>8.672629e-07</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>3-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>Cpseudo2 (F)</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>6.030908e-07</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>3-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>L (H)</td>\n",
       "      <td>0.002826</td>\n",
       "      <td>2.270705e-04</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>3-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>Rseries (Ohm)</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>2-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>RL (Ohm)</td>\n",
       "      <td>14.655441</td>\n",
       "      <td>1.219316e-01</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>2-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>Rct1 (Ohm)</td>\n",
       "      <td>919.596775</td>\n",
       "      <td>1.420905e+05</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>2-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>RctO2 (Ohm)</td>\n",
       "      <td>10680.707971</td>\n",
       "      <td>1.228808e+05</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>2-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>Cdl (F)</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>7.348077e-03</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>2-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>Cpseudo1 (F)</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>7.537123e-03</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>2-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>L (H)</td>\n",
       "      <td>21.752574</td>\n",
       "      <td>2.007388e+06</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>2-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>Rseries (Ohm)</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>1-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>RL (Ohm)</td>\n",
       "      <td>14.778585</td>\n",
       "      <td>2.418067e-01</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>1-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>RctO2 (Ohm)</td>\n",
       "      <td>5483.596463</td>\n",
       "      <td>1.143404e+03</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>1-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>Cdl (F)</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>2.820362e-01</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>1-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv</td>\n",
       "      <td>L (H)</td>\n",
       "      <td>9.565729</td>\n",
       "      <td>8.480057e+06</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Prep11_EIS_1</td>\n",
       "      <td>1-ladder</td>\n",
       "      <td>sXRD</td>\n",
       "      <td>pristine</td>\n",
       "      <td>blue</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        fname      parameter         value  \\\n",
       "0  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv  Rseries (Ohm)     50.000000   \n",
       "1  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv       RL (Ohm)     14.150845   \n",
       "2  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv     Rct1 (Ohm)     17.696762   \n",
       "3  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv     Rct2 (Ohm)    930.474101   \n",
       "4  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv    RctO2 (Ohm)  10811.464465   \n",
       "5  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv        Cdl (F)      0.000006   \n",
       "6  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv   Cpseudo1 (F)      0.000084   \n",
       "7  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv   Cpseudo2 (F)      0.000043   \n",
       "8  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv          L (H)      0.002826   \n",
       "0  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv  Rseries (Ohm)     50.000000   \n",
       "1  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv       RL (Ohm)     14.655441   \n",
       "2  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv     Rct1 (Ohm)    919.596775   \n",
       "3  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv    RctO2 (Ohm)  10680.707971   \n",
       "4  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv        Cdl (F)      0.000090   \n",
       "5  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv   Cpseudo1 (F)      0.000043   \n",
       "6  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv          L (H)     21.752574   \n",
       "0  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv  Rseries (Ohm)     50.000000   \n",
       "1  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv       RL (Ohm)     14.778585   \n",
       "2  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv    RctO2 (Ohm)   5483.596463   \n",
       "3  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv        Cdl (F)      0.000118   \n",
       "4  Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv          L (H)      9.565729   \n",
       "\n",
       "          error  potential        sample     model  cell     label edgecolor  \\\n",
       "0  0.000000e+00       0.15  Prep11_EIS_1  3-ladder  sXRD  pristine      blue   \n",
       "1  6.642986e-02       0.15  Prep11_EIS_1  3-ladder  sXRD  pristine      blue   \n",
       "2  3.393983e-01       0.15  Prep11_EIS_1  3-ladder  sXRD  pristine      blue   \n",
       "3  4.148916e+01       0.15  Prep11_EIS_1  3-ladder  sXRD  pristine      blue   \n",
       "4  5.268848e+02       0.15  Prep11_EIS_1  3-ladder  sXRD  pristine      blue   \n",
       "5  3.431776e-07       0.15  Prep11_EIS_1  3-ladder  sXRD  pristine      blue   \n",
       "6  8.672629e-07       0.15  Prep11_EIS_1  3-ladder  sXRD  pristine      blue   \n",
       "7  6.030908e-07       0.15  Prep11_EIS_1  3-ladder  sXRD  pristine      blue   \n",
       "8  2.270705e-04       0.15  Prep11_EIS_1  3-ladder  sXRD  pristine      blue   \n",
       "0  0.000000e+00       0.15  Prep11_EIS_1  2-ladder  sXRD  pristine      blue   \n",
       "1  1.219316e-01       0.15  Prep11_EIS_1  2-ladder  sXRD  pristine      blue   \n",
       "2  1.420905e+05       0.15  Prep11_EIS_1  2-ladder  sXRD  pristine      blue   \n",
       "3  1.228808e+05       0.15  Prep11_EIS_1  2-ladder  sXRD  pristine      blue   \n",
       "4  7.348077e-03       0.15  Prep11_EIS_1  2-ladder  sXRD  pristine      blue   \n",
       "5  7.537123e-03       0.15  Prep11_EIS_1  2-ladder  sXRD  pristine      blue   \n",
       "6  2.007388e+06       0.15  Prep11_EIS_1  2-ladder  sXRD  pristine      blue   \n",
       "0  0.000000e+00       0.15  Prep11_EIS_1  1-ladder  sXRD  pristine      blue   \n",
       "1  2.418067e-01       0.15  Prep11_EIS_1  1-ladder  sXRD  pristine      blue   \n",
       "2  1.143404e+03       0.15  Prep11_EIS_1  1-ladder  sXRD  pristine      blue   \n",
       "3  2.820362e-01       0.15  Prep11_EIS_1  1-ladder  sXRD  pristine      blue   \n",
       "4  8.480057e+06       0.15  Prep11_EIS_1  1-ladder  sXRD  pristine      blue   \n",
       "\n",
       "   potential shift  potential RHE  \n",
       "0             0.01          1.132  \n",
       "1             0.01          1.132  \n",
       "2             0.01          1.132  \n",
       "3             0.01          1.132  \n",
       "4             0.01          1.132  \n",
       "5             0.01          1.132  \n",
       "6             0.01          1.132  \n",
       "7             0.01          1.132  \n",
       "8             0.01          1.132  \n",
       "0             0.01          1.132  \n",
       "1             0.01          1.132  \n",
       "2             0.01          1.132  \n",
       "3             0.01          1.132  \n",
       "4             0.01          1.132  \n",
       "5             0.01          1.132  \n",
       "6             0.01          1.132  \n",
       "0             0.01          1.132  \n",
       "1             0.01          1.132  \n",
       "2             0.01          1.132  \n",
       "3             0.01          1.132  \n",
       "4             0.01          1.132  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sXRD_EISParams.loc[sXRD_EISParams['fname'] == 'Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c85330d6",
   "metadata": {},
   "source": [
    "## Correlation Matrices - Model with Two Time Constants\n",
    "### Correlation Matrices - Model with Two Time Constants - Capacitive Regime (Figure S14 upper left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "cbc67c7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:15: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:15: SyntaxWarning: invalid escape sequence '\\m'\n",
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_24349/1860899647.py:15: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  parameters = np.array([r'$R_{\\mathrm{series}}$', r'$R_L$', '$R_{\\mathrm{CT,OER1}} \\ \\mathrm{(M2)}$', r'$R_{\\mathrm{CT,OER2}} \\ \\mathrm{(M2)}$', r'$C_{\\mathrm{DL}} \\ \\mathrm{(M2)}$', r'$C_{\\mathrm{Pseudo}} \\ \\mathrm{(M2)}$', r'$L$'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import os\n",
    "import numpy as np\n",
    "from numpy.typing import NDArray\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit import Model, Parameter\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "\n",
    "parameters = np.array([r'$R_{\\mathrm{series}}$', r'$R_L$', '$R_{\\mathrm{CT,OER1}} \\ \\mathrm{(M2)}$', r'$R_{\\mathrm{CT,OER2}} \\ \\mathrm{(M2)}$', r'$C_{\\mathrm{DL}} \\ \\mathrm{(M2)}$', r'$C_{\\mathrm{Pseudo}} \\ \\mathrm{(M2)}$', r'$L$'])\n",
    "threshold = 0.98\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (2.5, 2.5)\n",
    "plt.rcParams['figure.dpi'] = 120\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "plt.rcParams['ytick.minor.width'] = 1\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "correlation = matrices_2ladder['Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv'].to_numpy()\n",
    "\n",
    "fig, ax = plt.subplots(constrained_layout = True)\n",
    "ax.tick_params(which = 'both', direction = 'in', bottom = True, top = True, left = True, right = True)\n",
    "c = ax.pcolormesh(parameters[1:], parameters[1:], correlation, cmap='RdBu', vmin = -1, vmax = 1)\n",
    "\n",
    "for i in range(correlation.shape[0]):\n",
    "    for j in range(correlation.shape[1]):\n",
    "        if correlation[i, j] > threshold:\n",
    "            ax.scatter(parameters[1:][j], parameters[1:][i], marker='x', color='black')\n",
    "        if correlation[i, j] < -threshold: \n",
    "            ax.scatter(parameters[1:][j], parameters[1:][i], marker='x', color='black')\n",
    "for label in ax.get_xticklabels():\n",
    "    label.set_rotation(45)\n",
    "    label.set_ha(\"right\")          # align text to the right\n",
    "    label.set_rotation_mode(\"anchor\")  # rotate around the text corner\n",
    "ax.set_aspect('equal')\n",
    "divider = make_axes_locatable(ax)\n",
    "cax = divider.append_axes(\"right\", size=\"4%\", pad=0.1)\n",
    "cbar = fig.colorbar(c, cax=cax)\n",
    "cbar.set_ticks(np.linspace(-1, 1, 5))\n",
    "cbar.set_label(\"Correlation\", fontsize=8)\n",
    "plt.savefig(fname = f'coellation_map_capacitive_2-ladder.tif', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08bfcc60",
   "metadata": {},
   "source": [
    "### Correlation Matrices - Model with Two Time Constants - OER Regime (Figure S14 lower left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "76f6e02d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import os\n",
    "import numpy as np\n",
    "from numpy.typing import NDArray\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit import Model, Parameter\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "\n",
    "threshold = 0.98\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (2.5, 2.5)\n",
    "plt.rcParams['figure.dpi'] = 120\n",
    "\n",
    "plt.rcParams['font.size'] = 7 \n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "plt.rcParams['ytick.minor.width'] = 1\n",
    "\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "correlation = matrices_2ladder['Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv'].to_numpy()\n",
    "\n",
    "fig, ax = plt.subplots(constrained_layout = True)\n",
    "ax.tick_params(which = 'both', direction = 'in', bottom = True, top = True, left = True, right = True)\n",
    "c = ax.pcolormesh(parameters[1:], parameters[1:], correlation, cmap='RdBu', vmin = -1, vmax = 1)\n",
    "\n",
    "for i in range(correlation.shape[0]):\n",
    "    for j in range(correlation.shape[1]):\n",
    "        if correlation[i, j] > threshold:\n",
    "            ax.scatter(parameters[1:][j], parameters[1:][i], marker='x', color='black')\n",
    "        if correlation[i, j] < -threshold: \n",
    "            ax.scatter(parameters[1:][j], parameters[1:][i], marker='x', color='black')\n",
    "for label in ax.get_xticklabels():\n",
    "    label.set_rotation(45)\n",
    "    label.set_ha(\"right\")          # align text to the right\n",
    "    label.set_rotation_mode(\"anchor\")  # rotate around the text corner\n",
    "ax.set_aspect('equal')\n",
    "divider = make_axes_locatable(ax)\n",
    "cax = divider.append_axes(\"right\", size=\"4%\", pad=0.1)\n",
    "cbar = fig.colorbar(c, cax=cax)\n",
    "cbar.set_ticks(np.linspace(-1, 1, 5))\n",
    "cbar.set_label(\"Correlation\", fontsize=8)\n",
    "plt.savefig(fname = f'coellation_map_oer_2-ladder.tif', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2ad8855",
   "metadata": {},
   "source": [
    "## Correlation Matrices - Model with One Time Constants\n",
    "### Correlation Matrices - Model with One Time Constants - Capacitive Regime (Figure S13 upper left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "a4efcb3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import os\n",
    "import numpy as np\n",
    "from numpy.typing import NDArray\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit import Model, Parameter\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "parameters = np.array([r'$R_{\\mathrm{series}}$', r'$R_L$', r'$R_{\\mathrm{CT,OER}} \\ \\mathrm{(M1)}$', r'$C_{\\mathrm{DL}} \\ \\mathrm{(M1)}$', r'$L$'])\n",
    "threshold = 0.98\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (2.5, 2.5)\n",
    "plt.rcParams['figure.dpi'] = 120\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "plt.rcParams['ytick.minor.width'] = 1\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "plt.rcParams['axes.linewidth'] = 1.0 \n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "correlation = matrices_1ladder['Prep11_EIS_1_01M-NaOH_beam_04_EIS150mV.csv'].to_numpy()\n",
    "\n",
    "fig, ax = plt.subplots(constrained_layout = True)\n",
    "ax.tick_params(which = 'both', direction = 'in', bottom = True, top = True, left = True, right = True)\n",
    "c = ax.pcolormesh(parameters[1:], parameters[1:], correlation, cmap='RdBu', vmin = -1, vmax = 1)\n",
    "\n",
    "for i in range(correlation.shape[0]):\n",
    "    for j in range(correlation.shape[1]):\n",
    "        if correlation[i, j] > threshold:\n",
    "            ax.scatter(parameters[1:][j], parameters[1:][i], marker='x', color='black')\n",
    "        if correlation[i, j] < -threshold: \n",
    "            ax.scatter(parameters[1:][j], parameters[1:][i], marker='x', color='black')\n",
    "for label in ax.get_xticklabels():\n",
    "    label.set_rotation(45)\n",
    "    label.set_ha(\"right\")          # align text to the right\n",
    "    label.set_rotation_mode(\"anchor\")  # rotate around the text corner\n",
    "ax.set_aspect('equal')\n",
    "divider = make_axes_locatable(ax)\n",
    "cax = divider.append_axes(\"right\", size=\"4%\", pad=0.1)\n",
    "cbar = fig.colorbar(c, cax=cax)\n",
    "cbar.set_ticks(np.linspace(-1, 1, 5))\n",
    "cbar.set_label(\"Correlation\", fontsize=8)\n",
    "plt.savefig(fname = f'coellation_map_capacitive_1-ladder.tif', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "885ccae8",
   "metadata": {},
   "source": [
    "### Correlation Matrices - Model with One Time Constants - OER Regime (Figure S13 lower left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "db3cb4e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import os\n",
    "import numpy as np\n",
    "from numpy.typing import NDArray\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit import Model, Parameter\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "\n",
    "threshold = 0.98\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (2.5, 2.5)\n",
    "plt.rcParams['figure.dpi'] = 120\n",
    "plt.rcParams['font.size'] = 7\n",
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['axes.titlesize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['xtick.labelsize'] = 7\n",
    "plt.rcParams['ytick.labelsize'] = 7\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "# Major ticks\n",
    "plt.rcParams['xtick.major.size'] = 4\n",
    "plt.rcParams['ytick.major.size'] = 4\n",
    "plt.rcParams['xtick.major.width'] = 2\n",
    "plt.rcParams['ytick.major.width'] = 2\n",
    "# Minor ticks\n",
    "plt.rcParams['xtick.minor.size'] = 2\n",
    "plt.rcParams['ytick.minor.size'] = 2\n",
    "plt.rcParams['xtick.minor.width'] = 1\n",
    "plt.rcParams['ytick.minor.width'] = 1\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['ytick.right'] = True\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['lines.markersize'] = 6\n",
    "plt.rcParams['legend.loc'] = 'best'\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "correlation = matrices_1ladder['Prep11_EIS_1_01M-NaOH_beam_19_EIS700mV.csv'].to_numpy()\n",
    "\n",
    "fig, ax = plt.subplots(constrained_layout = True)\n",
    "ax.tick_params(which = 'both', direction = 'in', bottom = True, top = True, left = True, right = True)\n",
    "c = ax.pcolormesh(parameters[1:], parameters[1:], correlation, cmap='RdBu', vmin = -1, vmax = 1)\n",
    "\n",
    "for i in range(correlation.shape[0]):\n",
    "    for j in range(correlation.shape[1]):\n",
    "        if correlation[i, j] > threshold:\n",
    "            ax.scatter(parameters[1:][j], parameters[1:][i], marker='x', color='black')\n",
    "        if correlation[i, j] < -threshold: \n",
    "            ax.scatter(parameters[1:][j], parameters[1:][i], marker='x', color='black')\n",
    "for label in ax.get_xticklabels():\n",
    "    label.set_rotation(45)\n",
    "    label.set_ha(\"right\")          # align text to the right\n",
    "    label.set_rotation_mode(\"anchor\")  # rotate around the text corner\n",
    "ax.set_aspect('equal')\n",
    "divider = make_axes_locatable(ax)\n",
    "cax = divider.append_axes(\"right\", size=\"4%\", pad=0.1)\n",
    "cbar = fig.colorbar(c, cax=cax)\n",
    "cbar.set_label(\"Correlation\", fontsize=8)\n",
    "cbar.set_ticks(np.linspace(-1, 1, 5))\n",
    "plt.savefig(fname = f'coellation_map_oer_1-ladder.tif', dpi = 600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "785671e2",
   "metadata": {},
   "source": [
    "# Comparison CV and EIS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adc605a3",
   "metadata": {},
   "source": [
    "## Sum of the Interfacial Resistances at 1.7 V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "fd1bf587",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "available potentials [0.982, 1.132, 1.232, 1.332, 1.382, 1.482, 1.582, 1.602, 1.622, 1.642, 1.662, 1.682, 1.702, 1.722]\n",
      "closest_potentials [1.702]\n",
      "sum_of_interfacial_resistances: 166.0 Ohm, i_R_sum: 0.00015 A\n"
     ]
    }
   ],
   "source": [
    "\n",
    "target = 'Prep11_EIS_1'\n",
    "available_potentials = sorted(sXRD_EISParams.loc[sXRD_EISParams['fname'].str.contains(target), 'potential RHE'].unique())\n",
    "print('available potentials', available_potentials)\n",
    "\n",
    "target_potential = 1.7\n",
    "closest_potential = [available_potentials[np.argmin(np.abs(np.array(available_potentials) - target_potential))]]\n",
    "\n",
    "print('closest_potentials', closest_potential)\n",
    "\n",
    "mask = (\n",
    "    sXRD_EISParams['fname'].str.contains(target) &\n",
    "    sXRD_EISParams['potential RHE'].round(3).isin(closest_potential) &\n",
    "    sXRD_EISParams['model'].str.contains('3-ladder')\n",
    ")\n",
    "\n",
    "data_for_eval =  sXRD_EISParams.loc[mask]\n",
    "sum_of_interfacial_resistances = data_for_eval.loc[data_for_eval[\"parameter\"].str.contains(\"Rct\", case=False, na=False), \"value\"].sum()\n",
    "tafel_slope = 0.057 #Vdec-1\n",
    "i_R_sum = tafel_slope/2.303 *1/sum_of_interfacial_resistances\n",
    "print(f'sum_of_interfacial_resistances: {np.round(sum_of_interfacial_resistances, 0)} Ohm, i_R_sum: {np.round(i_R_sum, 5)} A')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1646e76",
   "metadata": {},
   "source": [
    "## Average Current to Maintain the DC-operating Potential during EIS at 1.7 V "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "7ad388d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "available potentials [0.982, 1.132, 1.232, 1.332, 1.382, 1.482, 1.582, 1.602, 1.622, 1.642, 1.662, 1.682, 1.702, 1.722]\n",
      "closest_potentials [1.702]\n",
      "current to maintain the DC offset: 0.00016 A\n"
     ]
    }
   ],
   "source": [
    "target = 'Prep11_EIS_1'\n",
    "available_potentials = sorted(sXRD_EISData.loc[sXRD_EISData['fname'].str.contains(target), 'potential RHE'].unique())\n",
    "print('available potentials', available_potentials)\n",
    "\n",
    "target_potential = 1.7\n",
    "closest_potential = [available_potentials[np.argmin(np.abs(np.array(available_potentials) - target_potential))]]\n",
    "\n",
    "print('closest_potentials', closest_potential)\n",
    "\n",
    "mask = (\n",
    "    sXRD_EISData['fname'].str.contains(target) &\n",
    "    sXRD_EISData['potential RHE'].round(3).isin(closest_potential)\n",
    ")\n",
    "\n",
    "data_for_eval =  sXRD_EISData.loc[mask]\n",
    "\n",
    "print(f'current to maintain the DC offset: {np.round(data_for_eval[' Current (A)'].mean(), 5)} A')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81bbdc23",
   "metadata": {},
   "source": [
    "## Sum of the Capacitances at 1.38 V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "29dae1d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "available potentials [0.982, 1.132, 1.232, 1.332, 1.382, 1.482, 1.582, 1.602, 1.622, 1.642, 1.662, 1.682, 1.702, 1.722]\n",
      "closest_potentials [1.382]\n",
      "sum_of_capacitances: 0.00016 F\n"
     ]
    }
   ],
   "source": [
    "\n",
    "target = 'Prep11_EIS_1'\n",
    "available_potentials = sorted(sXRD_EISParams.loc[sXRD_EISParams['fname'].str.contains(target), 'potential RHE'].unique())\n",
    "print('available potentials', available_potentials)\n",
    "\n",
    "target_potential = 1.38\n",
    "closest_potential = [available_potentials[np.argmin(np.abs(np.array(available_potentials) - target_potential))]]\n",
    "\n",
    "print('closest_potentials', closest_potential)\n",
    "\n",
    "mask = (\n",
    "    sXRD_EISParams['fname'].str.contains(target) &\n",
    "    sXRD_EISParams['potential RHE'].round(3).isin(closest_potential) &\n",
    "    sXRD_EISParams['model'].str.contains('3-ladder')\n",
    ")\n",
    "\n",
    "data_for_eval =  sXRD_EISParams.loc[mask]\n",
    "sum_of_capacitances = data_for_eval.loc[data_for_eval[\"parameter\"].str.contains(\"C\", case=True, na=False), \"value\"].sum()\n",
    "print(f'sum_of_capacitances: {np.round(sum_of_capacitances, 5)} F')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e1a0df",
   "metadata": {},
   "source": [
    "## Estimation of the Capacitance from the CV at 1.38 V for the SI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "4c5c6eeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential capacitance: 0.0006525 F\n"
     ]
    }
   ],
   "source": [
    "A = 0.125 #cm2\n",
    "current_density_anodic = 4.33/150 * 1e-3 #A cm-2, 150 is the scale factor of the zoom in figure 5 in the main article\n",
    "current_density_cathodic = -3.5/150 * 1e-3#A cm-2\n",
    "\n",
    "current_anodic = current_density_anodic * A\n",
    "current_cathodic = current_density_cathodic * A\n",
    "\n",
    "current_hysteresis = (current_anodic-current_cathodic)\n",
    "\n",
    "sweep_rate = 0.005 #Vs-1\n",
    "differential_capacitance = current_hysteresis/(2*sweep_rate)\n",
    "print(f'differential capacitance: {differential_capacitance} F')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "4c3bf4d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.22e-05"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(current_density_anodic-current_density_cathodic)"
   ]
  }
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