{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "791d589f", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline\n", "import seaborn as sns\n", "from scipy.stats import sem\n", "from functools import reduce" ] }, { "cell_type": "code", "execution_count": 2, "id": "159c4ad6", "metadata": {}, "outputs": [], "source": [ "Ls_JulA1 = pd.read_csv(\"ImageData/2021-07-06_A/data_06-07-2021_A1thresh80.csv\")\n", "Ls_JulA2 = pd.read_csv(\"ImageData/2021-07-06_A/data_06-07-2021_A2thresh80.csv\")\n", "Ls_JulB1 = pd.read_csv(\"ImageData/2021-07-06_B/data_06-07-2021_B1thresh80.csv\")\n", "Ls_JulB2 = pd.read_csv(\"ImageData/2021-07-06_B/data_06-07-2021_B2thresh80.csv\")\n", "Ls_JulC = pd.read_csv(\"ImageData/2021-07-06_C/data_06-07-2021_Cthresh80.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "77f1cea4", "metadata": {}, "outputs": [], "source": [ "Ll_MarA = pd.read_csv(\"ImageData/2021-03-09_A/data_09-03-2021_A.csv\")\n", "Ll_MarB = pd.read_csv(\"ImageData/2021-03-09_B/data_09-03-2021_B.csv\")\n", "Ll_MarC = pd.read_csv(\"ImageData/2021-03-09_C/data_09-03-2021_C.csv\")\n", "Ll_MarD = pd.read_csv(\"ImageData/2021-03-30_A/data_30-03-2021_A.csv\")\n", "Ll_MarE = pd.read_csv(\"ImageData/2021-03-30_B/data_30-03-2021_B.csv\")\n", "Ll_MarF = pd.read_csv(\"ImageData/2021-03-30_C/data_30-03-2021_C.csv\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "fa3ae6d7", "metadata": {}, "outputs": [], "source": [ "Ps_A = pd.read_csv(\"ImageData/2021-08-11/data_11-08-2021_A.csv\")\n", "Ps_B = pd.read_csv(\"ImageData/2021-08-11/data_11-08-2021_B.csv\")\n", "Ps_C = pd.read_csv(\"ImageData/2021-08-11/data_11-08-2021_C.csv\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "001ad93b", "metadata": {}, "outputs": [], "source": [ "Pl = pd.read_csv(\"ImageData/2021-08-26/data_26-08-2021.csv\")" ] }, { "cell_type": "code", "execution_count": 6, "id": "4c514ba4", "metadata": {}, "outputs": [], "source": [ "Ls_JulA1['RH'] = np.repeat([70,75,80,85,90,95,96,95,90,85,80,75,70],5)\n", "Ls_JulA2['RH'] = np.repeat([70,75,80,85,90,95,96,95,90,85,80,75,70],5)\n", "Ls_JulB1['RH'] = np.repeat([70,75,83,85,90,95,96,95,90,85,80,75,65],5)\n", "Ls_JulB2['RH'] = np.repeat([70,75,83,85,90,95,96,95,90,85,80,75,65],5)\n", "Ls_JulC['RH'] = np.repeat([60,70,75,80,85,90,95,95,90,85,80,75,60],5)\n", "Ll_MarA['RH'] = np.repeat([45,50,60,70,80,85,90,95,99,95,90,85,80,70,60,50],5)\n", "Ll_MarB['RH'] = np.repeat([45,50,60,70,80,85,90,95,99,95,90,85,80,70,60,50],5)\n", "Ll_MarC['RH'] = np.repeat([42,50,60,70,80,85,90,95,99,95,90,85,80,70,52,50],5)\n", "Ll_MarD['RH'] = np.repeat([50,60,70,80,85,90,95,99,93,90,85,80,70,56,50],5)\n", "Ll_MarE['RH'] = np.repeat([50,60,70,80,85,90,95,99,95,90,83,80,68,60,50],5)\n", "Ll_MarF['RH'] = np.repeat([48,60,70,81,86,90,95,99,95,90,83,80,70,60,50],5)\n", "Ps_A['RH'] = np.repeat([60,65,70,75,80,85,90,94,94],5)\n", "Ps_B['RH'] = np.repeat([60,65,70,75,80,85,90,94,94],5)\n", "Ps_C['RH'] = np.repeat([60,65,70,75,80,85,90,94,94],5)\n", "Pl['RH'] = [48,48,48,48,48,48,48,48,48,48,48,95,95,95,95,95,95,95,95,95,95,95,90,90,90,90,90,50,50,50,50,50]" ] }, { "cell_type": "code", "execution_count": 7, "id": "be7bd32f", "metadata": {}, "outputs": [], "source": [ "Ls_JulA1['Direction'] = np.repeat([\"1Increasing\",\"2Decreasing\"],[35,30])\n", "Ls_JulA2['Direction'] = np.repeat([\"1Increasing\",\"2Decreasing\"],[35,30])\n", "Ls_JulB1['Direction'] = np.repeat([\"1Increasing\",\"2Decreasing\"],[35,30])\n", "Ls_JulB2['Direction'] = np.repeat([\"1Increasing\",\"2Decreasing\"],[35,30])\n", "Ls_JulC['Direction'] = np.repeat([\"1Increasing\",\"2Decreasing\"],[35,30])\n", "Ll_MarA['Direction'] = np.repeat([\"1Increasing\",\"2Decreasing\"],[45,35])\n", "Ll_MarB['Direction'] = np.repeat([\"1Increasing\",\"2Decreasing\"],[45,35])\n", "Ll_MarC['Direction'] = np.repeat([\"1Increasing\",\"2Decreasing\"],[45,35])\n", "Ll_MarD['Direction'] = np.repeat([\"1Increasing\",\"2Decreasing\"],[40,35])\n", "Ll_MarE['Direction'] = np.repeat([\"1Increasing\",\"2Decreasing\"],[40,35])\n", "Ll_MarF['Direction'] = np.repeat([\"1Increasing\",\"2Decreasing\"],[40,35])\n", "Ps_A['Direction'] = np.repeat(\"1Increasing\",45)\n", "Ps_B['Direction'] = np.repeat(\"1Increasing\",45)\n", "Ps_C['Direction'] = np.repeat(\"1Increasing\",45)\n", "Pl['Direction'] = np.repeat([\"1Increasing\",\"2Decreasing\"],[22,10])" ] }, { "cell_type": "code", "execution_count": 8, "id": "c0a7e592", "metadata": {}, "outputs": [], "source": [ "#Ll_MarB = Ll_MarB[Ll_MarB['RH']>50]" ] }, { "cell_type": "code", "execution_count": 9, "id": "84d8910e", "metadata": {}, "outputs": [], "source": [ "dfs = [Ls_JulA1,Ls_JulA2,Ls_JulB1,Ls_JulB2,Ls_JulC,Ll_MarA,Ll_MarB,Ll_MarC,Ll_MarD,Ll_MarE,Ll_MarF,Ps_A,Ps_B,Ps_C,Pl]\n", "for x in dfs:\n", " x.drop('Name',axis=1, inplace=True)\n", " x.set_index(pd.MultiIndex.from_tuples(list(zip(x['Direction'].tolist(),x['RH'].tolist()))), inplace=True)\n", " x.rename(columns = {'DiameterB':'DiameterA','DiameterB.1':'DiameterB'}, inplace=True)" ] }, { "cell_type": "code", "execution_count": 10, "id": "231318a2", "metadata": {}, "outputs": [], "source": [ "Ls_A1av = Ls_JulA1.groupby(['Direction','RH']).mean()\n", "Ls_A2av = Ls_JulA2.groupby(['Direction','RH']).mean()\n", "Ls_B1av = Ls_JulB1.groupby(['Direction','RH']).mean()\n", "Ls_B2av = Ls_JulB2.groupby(['Direction','RH']).mean()\n", "Ls_Cav = Ls_JulC.groupby(['Direction','RH']).mean()\n", "Ll_Aav = Ll_MarA.groupby(['Direction','RH']).mean()\n", "Ll_Bav = Ll_MarB.groupby(['Direction','RH']).mean()\n", "Ll_Cav = Ll_MarC.groupby(['Direction','RH']).mean()\n", "Ll_Dav = Ll_MarD.groupby(['Direction','RH']).mean()\n", "Ll_Eav = Ll_MarE.groupby(['Direction','RH']).mean()\n", "Ll_Fav = Ll_MarF.groupby(['Direction','RH']).mean()\n", "Ps_Aav = Ps_A.groupby(['Direction','RH']).mean()\n", "Ps_Bav = Ps_B.groupby(['Direction','RH']).mean()\n", "Ps_Cav = Ps_C.groupby(['Direction','RH']).mean()\n", "Pl_av = Pl.groupby(['Direction','RH']).mean()" ] }, { "cell_type": "code", "execution_count": 11, "id": "3d188cdb", "metadata": {}, "outputs": [], "source": [ "Ls_A1av['Expt'] = \"Lily_stat_A1\"\n", "Ls_A2av['Expt'] = \"Lily_stat_A2\"\n", "Ls_B1av['Expt'] = \"Lily_stat_B1\"\n", "Ls_B2av['Expt'] = \"Lily_stat_B2\"\n", "Ls_Cav['Expt'] = \"Lily_stat_C\"\n", "Ll_Aav['Expt'] = \"Lily_lev_A\"\n", "Ll_Bav['Expt'] = \"Lily_lev_B\"\n", "Ll_Cav['Expt'] = \"Lily_lev_C\"\n", "Ll_Dav['Expt'] = \"Lily_lev_D\"\n", "Ll_Eav['Expt'] = \"Lily_lev_E\"\n", "Ll_Fav['Expt'] = \"Lily_lev_F\"\n", "Ps_Aav['Expt'] = \"EC_stat_A\"\n", "Ps_Bav['Expt'] = \"EC_stat_B\"\n", "Ps_Cav['Expt'] = \"EC_stat_C\"\n", "Pl_av['Expt'] = \"EC_lev\"" ] }, { "cell_type": "code", "execution_count": 12, "id": "38106883", "metadata": {}, "outputs": [], "source": [ "Ls_A1av['ID'] = \"Lily_stat\"\n", "Ls_A2av['ID'] = \"Lily_stat\"\n", "Ls_B1av['ID'] = \"Lily_stat\"\n", "Ls_B2av['ID'] = \"Lily_stat\"\n", "Ls_Cav['ID'] = \"Lily_stat\"\n", "Ll_Aav['ID'] = \"Lily_lev\"\n", "Ll_Bav['ID'] = \"Lily_lev\"\n", "Ll_Cav['ID'] = \"Lily_lev\"\n", "Ll_Dav['ID'] = \"Lily_lev\"\n", "Ll_Eav['ID'] = \"Lily_lev\"\n", "Ll_Fav['ID'] = \"Lily_lev\"\n", "Ps_Aav['ID'] = \"EC_stat\"\n", "Ps_Bav['ID'] = \"EC_stat\"\n", "Ps_Cav['ID'] = \"EC_stat\"\n", "Pl_av['ID'] = \"EC_lev\"" ] }, { "cell_type": "code", "execution_count": 13, "id": "2de92503", "metadata": {}, "outputs": [], "source": [ "dfs_av = [Ls_A1av,Ls_A2av,Ls_B1av,Ls_B2av,Ls_Cav,Ll_Aav,Ll_Bav,Ll_Cav,Ll_Dav,Ll_Eav,Ll_Fav,Ps_Aav,Ps_Bav,Ps_Cav,Pl_av]\n", "\n", "for x in dfs_av:\n", " x.reset_index(inplace=True)" ] }, { "cell_type": "code", "execution_count": 14, "id": "dc2a20db", "metadata": {}, "outputs": [], "source": [ "for x in dfs_av:\n", " x['AreaRatio'] = x['Area']/x['Area'][0]\n", " x['DiameterRatio'] = x['DiameterA']/x['DiameterB']" ] }, { "cell_type": "code", "execution_count": 15, "id": "916673fd", "metadata": {}, "outputs": [], "source": [ "All = reduce(lambda left,right: pd.merge(left,right, how='outer'),dfs_av)" ] }, { "cell_type": "code", "execution_count": 16, "id": "9f907e40", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DirectionRHAreaDiameterADiameterBExptIDAreaRatioDiameterRatio
01Increasing701040.50000032.49789441.316362Lily_stat_A1Lily_stat1.0000000.786562
11Increasing751078.90000033.28592842.040414Lily_stat_A1Lily_stat1.0369050.791760
21Increasing801195.40000034.23964645.625205Lily_stat_A1Lily_stat1.1488710.750455
31Increasing851182.40000034.06168744.928438Lily_stat_A1Lily_stat1.1363770.758132
41Increasing901207.70000034.82821544.957304Lily_stat_A1Lily_stat1.1606920.774695
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1811Increasing943037.50000061.05980164.521723EC_stat_CEC_stat1.1304010.946345
1821Increasing487180.63636455.852560166.929872EC_levEC_lev1.0000000.334587
1831Increasing955595.81818259.403974121.190310EC_levEC_lev0.7792930.490171
1842Decreasing507825.50000057.134049181.164304EC_levEC_lev1.0898060.315371
1852Decreasing907652.40000057.960229172.290173EC_levEC_lev1.0656990.336411
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186 rows × 9 columns

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" ], "text/plain": [ " Direction RH Area DiameterA DiameterB Expt \\\n", "0 1Increasing 70 1040.500000 32.497894 41.316362 Lily_stat_A1 \n", "1 1Increasing 75 1078.900000 33.285928 42.040414 Lily_stat_A1 \n", "2 1Increasing 80 1195.400000 34.239646 45.625205 Lily_stat_A1 \n", "3 1Increasing 85 1182.400000 34.061687 44.928438 Lily_stat_A1 \n", "4 1Increasing 90 1207.700000 34.828215 44.957304 Lily_stat_A1 \n", ".. ... .. ... ... ... ... \n", "181 1Increasing 94 3037.500000 61.059801 64.521723 EC_stat_C \n", "182 1Increasing 48 7180.636364 55.852560 166.929872 EC_lev \n", "183 1Increasing 95 5595.818182 59.403974 121.190310 EC_lev \n", "184 2Decreasing 50 7825.500000 57.134049 181.164304 EC_lev \n", "185 2Decreasing 90 7652.400000 57.960229 172.290173 EC_lev \n", "\n", " ID AreaRatio DiameterRatio \n", "0 Lily_stat 1.000000 0.786562 \n", "1 Lily_stat 1.036905 0.791760 \n", "2 Lily_stat 1.148871 0.750455 \n", "3 Lily_stat 1.136377 0.758132 \n", "4 Lily_stat 1.160692 0.774695 \n", ".. ... ... ... \n", "181 EC_stat 1.130401 0.946345 \n", "182 EC_lev 1.000000 0.334587 \n", "183 EC_lev 0.779293 0.490171 \n", "184 EC_lev 1.089806 0.315371 \n", "185 EC_lev 1.065699 0.336411 \n", "\n", "[186 rows x 9 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "All" ] }, { "cell_type": "code", "execution_count": 17, "id": "80691c4b", "metadata": {}, "outputs": [], "source": [ "All.to_csv('AllDataProcessed.csv')" ] }, { "cell_type": "code", "execution_count": null, "id": "1e914115", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }