{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np\n", "import datetime\n", "from datetime import datetime\n", "import math\n", "import os" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "font = {'family' : 'normal',\n", " 'weight' : 'bold',\n", " 'size' : 20}\n", "\n", "plt.rc('font', **font)\n", "\n", "# plt.rcParams.update({'font.size': 20})" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysis of Phenology from Crop files\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# AquaCrop Crop stages\n", "- Stage: 0: before/after planting\n", "- Stage: 1: emergence or transplant recovery\n", "- Stage: 2: vegetative stage\n", "- Stage: 3: flowering\n", "- Stage: 4: yield formation and ripening" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "E = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Po\\Output\\05-01-Crop.out', header=None, skiprows=4, skipfooter=30, engine='python', delimiter='\\s+')# delim_whitespace=True) # \n", "g = E.loc[1,0:20].copy()\n", "\n", "E.loc[1,] = ''\n", "\n", "for i in range(len(g)):\n", " E.loc[1,i+5] = g[i]\n", " \n", "for i in range(26):\n", " E.loc[1,i] = str(E.loc[0,i])+' '+str(E.loc[1,i])\n", " \n", "E = E.rename(columns=E.iloc[1])\n", "\n", "E = E[2:]" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DayMonthYearDAPStageGD °C-dayZ mStExp %StSto %StSen %...Trx mmTr mmTrW mmTr/Trx %WP g/m2Biomass ton/haHI %YieldPart ton/haBrelative %WPet kg/m3
2142017-9020.40.00-9-9-9...0.00.00.01000.00.000-9.90.000-90.00
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" ], "text/plain": [ " Day Month Year DAP Stage GD °C-day Z m StExp % StSto % StSen % ... \\\n", "2 1 4 2017 -9 0 20.4 0.00 -9 -9 -9 ... \n", "3 2 4 2017 -9 0 21.1 0.00 -9 -9 -9 ... \n", "4 3 4 2017 -9 0 21.1 0.00 -9 -9 -9 ... \n", "5 4 4 2017 -9 0 21.8 0.00 -9 -9 -9 ... \n", "6 5 4 2017 -9 0 20.4 0.00 -9 -9 -9 ... \n", "\n", " Trx mm Tr mm TrW mm Tr/Trx % WP g/m2 Biomass ton/ha HI % YieldPart ton/ha \\\n", "2 0.0 0.0 0.0 100 0.0 0.000 -9.9 0.000 \n", "3 0.0 0.0 0.0 100 0.0 0.000 -9.9 0.000 \n", "4 0.0 0.0 0.0 100 0.0 0.000 -9.9 0.000 \n", "5 0.0 0.0 0.0 100 0.0 0.000 -9.9 0.000 \n", "6 0.0 0.0 0.0 100 0.0 0.000 -9.9 0.000 \n", "\n", " Brelative % WPet kg/m3 \n", "2 -9 0.00 \n", "3 -9 0.00 \n", "4 -9 0.00 \n", "5 -9 0.00 \n", "6 -9 0.00 \n", "\n", "[5 rows x 26 columns]" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "E.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [], "source": [ "G = E.iloc[0:0, ].copy()\n", "G['Onset'] = 0" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [], "source": [ "# Read AquaCrop output files and extract the main information\n", "\n", "loc = r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Gaoua\\Output'\n", "\n", "j = 30\n", "for file in os.listdir(loc):\n", " #print(file)\n", " fName, fExt = os.path.splitext(file) \n", " if fName[6:] == 'Crop':\n", " #print(fName)\n", " E = pd.read_csv(loc+ '\\\\' +file, header=None, skiprows=4, skipfooter=30, engine='python', delimiter='\\s+')\n", " g = E.loc[1,0:20].copy()\n", "\n", " E.loc[1,] = ''\n", "\n", " for i in range(len(g)):\n", " E.loc[1,i+5] = g[i]\n", "\n", " for i in range(26):\n", " E.loc[1,i] = str(E.loc[0,i])+' '+str(E.loc[1,i])\n", "\n", " E = E.rename(columns=E.iloc[1])\n", "\n", " E = E[2:]\n", " \n", " for i in range(2017,2021):\n", " F = E.loc[E['Year '] == str(i)].copy()\n", " F = F.replace('-9', 0.0)\n", " F.iloc[:,:26] = F.iloc[:,:26].astype(float)\n", " \n", " \n", " F['Onset'] = fName[:5] \n", " \n", " F = F.iloc[j:,:]\n", "\n", " G = G.append(F, sort=False)\n", " \n", " j = j +1" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 130, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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321.05.02017.00.01.018.70.30.00.00.0...0.00.0100.032.00.0-9.90.00.00.005-01
332.05.02017.00.01.019.90.30.00.00.0...0.00.0100.032.00.0-9.90.00.00.005-01
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354.05.02017.00.01.019.60.30.00.00.0...0.00.0100.032.00.0-9.90.00.00.005-01
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" ], "text/plain": [ " Day Month Year DAP Stage GD °C-day Z m StExp % StSto % \\\n", "32 1.0 5.0 2017.0 0.0 1.0 18.7 0.3 0.0 0.0 \n", "33 2.0 5.0 2017.0 0.0 1.0 19.9 0.3 0.0 0.0 \n", "34 3.0 5.0 2017.0 0.0 1.0 19.3 0.3 0.0 0.0 \n", "35 4.0 5.0 2017.0 0.0 1.0 19.6 0.3 0.0 0.0 \n", "36 5.0 5.0 2017.0 0.0 1.0 19.1 0.3 0.0 0.0 \n", "\n", " StSen % ... Tr mm TrW mm Tr/Trx % WP g/m2 Biomass ton/ha HI % \\\n", "32 0.0 ... 0.0 0.0 100.0 32.0 0.0 -9.9 \n", "33 0.0 ... 0.0 0.0 100.0 32.0 0.0 -9.9 \n", "34 0.0 ... 0.0 0.0 100.0 32.0 0.0 -9.9 \n", "35 0.0 ... 0.0 0.0 100.0 32.0 0.0 -9.9 \n", "36 0.0 ... 0.0 0.0 100.0 32.0 0.0 -9.9 \n", "\n", " YieldPart ton/ha Brelative % WPet kg/m3 Onset \n", "32 0.0 0.0 0.0 05-01 \n", "33 0.0 0.0 0.0 05-01 \n", "34 0.0 0.0 0.0 05-01 \n", "35 0.0 0.0 0.0 05-01 \n", "36 0.0 0.0 0.0 05-01 \n", "\n", "[5 rows x 27 columns]" ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "G.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Day ', 'Month ', 'Year ', 'DAP ', 'Stage ', 'GD °C-day', 'Z m',\n", " 'StExp %', 'StSto %', 'StSen %', 'StSalt %', 'StWeed %', 'CC %',\n", " 'CCw %', 'StTr %', 'Kc(Tr) -', 'Trx mm', 'Tr mm', 'TrW mm', 'Tr/Trx %',\n", " 'WP g/m2', 'Biomass ton/ha', 'HI %', 'YieldPart ton/ha', 'Brelative %',\n", " 'WPet kg/m3', 'Onset'],\n", " dtype='object')" ] }, "execution_count": 131, "metadata": {}, "output_type": "execute_result" } ], "source": [ "G.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 308, "metadata": {}, "outputs": [], "source": [ "G = G.rename(columns={\"Year \": \"Year\", \"Month \": \"Month\", \"Day \": \"Day\"})\n", "G['Time'] = pd.to_datetime(G[['Year', 'Month', 'Day']]) \n", "\n", "G['Mon-Day'] = G['Time'].dt.strftime('%b-%d')\n", "\n", "G = G.reset_index()\n", "\n", "G = G.iloc[:,1:]" ] }, { "cell_type": "code", "execution_count": 309, "metadata": {}, "outputs": [], "source": [ "for i in range(len(G)):\n", " G.at[i,'Onset'] = datetime.strptime(G.at[i,'Onset'], '%m-%d')#.strftime('%b-%d')\n", " G.at[i,'Onset'] = G.at[i,'Onset'].strftime('%b-%d')" ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DayMonthYearDAPStageGD °C-dayZ mStExp %StSto %StSen %...Tr/Trx %WP g/m2Biomass ton/haHI %YieldPart ton/haBrelative %WPet kg/m3OnsetTimeMon-Day
01.05.02017.00.01.018.70.30.00.00.0...100.032.00.0-9.90.00.00.0May-012017-05-01May-01
12.05.02017.00.01.019.90.30.00.00.0...100.032.00.0-9.90.00.00.0May-012017-05-02May-02
23.05.02017.00.01.019.30.30.00.00.0...100.032.00.0-9.90.00.00.0May-012017-05-03May-03
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DayMonthYearDAPStageGD °C-dayZ mStExp %StSto %StSen %...Tr/Trx %WP g/m2Biomass ton/haHI %YieldPart ton/haBrelative %WPet kg/m3OnsetTimeMon-Day
2906120.07.02020.01.01.018.40.300.00.00.0...100.032.20.000-9.90.0000.00.00Jul-202020-07-20Jul-20
2906221.07.02020.02.01.017.10.300.00.00.0...100.032.20.000-9.90.0000.00.00Jul-202020-07-21Jul-21
2906322.07.02020.03.01.019.10.300.00.00.0...100.032.20.000-9.90.0000.00.00Jul-202020-07-22Jul-22
2906423.07.02020.04.01.017.40.300.00.00.0...100.032.20.000-9.90.0000.00.00Jul-202020-07-23Jul-23
2906524.07.02020.05.01.017.90.300.00.00.0...100.032.20.000-9.90.0000.00.00Jul-202020-07-24Jul-24
2906625.07.02020.06.01.018.80.300.00.00.0...100.032.20.000-9.90.0000.00.00Jul-202020-07-25Jul-25
2906726.07.02020.07.02.017.40.300.00.00.0...100.032.20.002-9.90.00091.00.00Jul-202020-07-26Jul-26
2906827.07.02020.08.02.017.90.300.00.00.0...100.032.20.003-9.90.00088.00.00Jul-202020-07-27Jul-27
2906928.07.02020.09.02.017.90.300.00.00.0...100.032.20.005-9.90.00086.00.00Jul-202020-07-28Jul-28
2907029.07.02020.010.02.018.50.300.00.00.0...100.032.20.008-9.90.00084.00.00Jul-202020-07-29Jul-29
2907130.07.02020.011.02.018.80.300.00.00.0...100.032.20.010-9.90.00082.00.00Jul-202020-07-30Jul-30
2907231.07.02020.012.02.015.90.300.00.00.0...100.032.20.013-9.90.00081.00.00Jul-202020-07-31Jul-31
290731.08.02020.013.02.017.40.300.00.00.0...100.032.20.017-9.90.00079.00.00Jul-202020-08-01Aug-01
290742.08.02020.014.02.017.90.300.00.00.0...100.032.20.020-9.90.00077.00.00Jul-202020-08-02Aug-02
290753.08.02020.015.02.017.60.310.00.00.0...100.032.20.025-9.90.00075.00.00Jul-202020-08-03Aug-03
290764.08.02020.016.02.018.40.320.00.00.0...100.032.20.030-9.90.00074.00.00Jul-202020-08-04Aug-04
290775.08.02020.017.02.017.80.340.00.00.0...100.032.20.035-9.90.00072.00.00Jul-202020-08-05Aug-05
290786.08.02020.018.02.018.70.351.00.00.0...100.032.20.042-9.90.00070.00.00Jul-202020-08-06Aug-06
290797.08.02020.019.02.016.20.360.00.00.0...100.032.20.049-9.90.00068.00.00Jul-202020-08-07Aug-07
290808.08.02020.020.02.016.70.370.00.00.0...100.032.20.057-9.90.00067.00.00Jul-202020-08-08Aug-08
290819.08.02020.021.02.017.80.390.00.00.0...100.032.20.067-9.90.00065.00.00Jul-202020-08-09Aug-09
2908210.08.02020.022.02.017.60.400.00.00.0...100.032.20.078-9.90.00063.00.00Jul-202020-08-10Aug-10
2908311.08.02020.023.02.016.60.410.00.00.0...100.032.20.090-9.90.00062.00.00Jul-202020-08-11Aug-11
2908412.08.02020.024.02.017.70.420.00.00.0...100.032.20.104-9.90.00060.00.00Jul-202020-08-12Aug-12
2908513.08.02020.025.02.018.30.440.00.00.0...100.032.20.120-9.90.00059.00.00Jul-202020-08-13Aug-13
2908614.08.02020.026.02.016.10.450.00.00.0...100.032.20.137-9.90.00057.00.00Jul-202020-08-14Aug-14
2908715.08.02020.027.02.016.00.460.00.00.0...100.032.10.158-9.90.00055.00.00Jul-202020-08-15Aug-15
2908816.08.02020.028.02.017.60.470.00.00.0...100.032.10.181-9.90.00054.00.00Jul-202020-08-16Aug-16
2908917.08.02020.029.02.017.90.480.00.00.0...100.032.10.207-9.90.00053.00.00Jul-202020-08-17Aug-17
2909018.08.02020.030.02.018.80.500.00.00.0...100.032.10.236-9.90.00051.00.00Jul-202020-08-18Aug-18
..................................................................
2912118.09.02020.061.04.018.30.820.00.00.0...100.011.32.90116.20.47039.00.31Jul-202020-09-18Sep-18
2912219.09.02020.062.04.018.80.830.00.00.0...100.011.32.95718.00.53338.00.34Jul-202020-09-19Sep-19
2912320.09.02020.063.04.015.90.840.00.00.0...100.011.33.01419.90.59938.00.38Jul-202020-09-20Sep-20
2912421.09.02020.064.04.017.10.850.00.00.0...100.011.33.07121.70.66637.00.41Jul-202020-09-21Sep-21
2912522.09.02020.065.04.017.80.860.00.00.0...100.011.33.12823.50.73637.00.45Jul-202020-09-22Sep-22
2912623.09.02020.066.04.018.20.870.00.00.0...100.010.03.17825.40.80636.00.48Jul-202020-09-23Sep-23
2912724.09.02020.067.04.018.30.880.00.00.0...100.011.63.23727.20.88036.00.51Jul-202020-09-24Sep-24
2912825.09.02020.068.04.015.30.890.00.00.0...100.011.63.29629.00.95635.00.55Jul-202020-09-25Sep-25
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2913128.09.02020.071.04.018.10.920.00.00.0...100.011.63.46834.51.19734.00.66Jul-202020-09-28Sep-28
2913229.09.02020.072.04.016.50.930.00.00.0...100.011.63.52236.31.28034.00.70Jul-202020-09-29Sep-29
2913330.09.02020.073.04.018.50.940.00.00.0...100.011.63.57438.21.36434.00.73Jul-202020-09-30Sep-30
291341.010.02020.074.04.016.90.940.00.00.0...100.011.33.62240.01.44933.00.77Jul-202020-10-01Oct-01
291352.010.02020.075.04.017.70.950.00.00.0...100.011.93.67140.01.46833.00.77Jul-202020-10-02Oct-02
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90 rows × 29 columns

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" ], "text/plain": [ " Day Month Year DAP Stage GD °C-day Z m StExp % StSto % \\\n", "29061 20.0 7.0 2020.0 1.0 1.0 18.4 0.30 0.0 0.0 \n", "29062 21.0 7.0 2020.0 2.0 1.0 17.1 0.30 0.0 0.0 \n", "29063 22.0 7.0 2020.0 3.0 1.0 19.1 0.30 0.0 0.0 \n", "29064 23.0 7.0 2020.0 4.0 1.0 17.4 0.30 0.0 0.0 \n", "29065 24.0 7.0 2020.0 5.0 1.0 17.9 0.30 0.0 0.0 \n", "29066 25.0 7.0 2020.0 6.0 1.0 18.8 0.30 0.0 0.0 \n", "29067 26.0 7.0 2020.0 7.0 2.0 17.4 0.30 0.0 0.0 \n", "29068 27.0 7.0 2020.0 8.0 2.0 17.9 0.30 0.0 0.0 \n", "29069 28.0 7.0 2020.0 9.0 2.0 17.9 0.30 0.0 0.0 \n", "29070 29.0 7.0 2020.0 10.0 2.0 18.5 0.30 0.0 0.0 \n", "29071 30.0 7.0 2020.0 11.0 2.0 18.8 0.30 0.0 0.0 \n", "29072 31.0 7.0 2020.0 12.0 2.0 15.9 0.30 0.0 0.0 \n", "29073 1.0 8.0 2020.0 13.0 2.0 17.4 0.30 0.0 0.0 \n", "29074 2.0 8.0 2020.0 14.0 2.0 17.9 0.30 0.0 0.0 \n", "29075 3.0 8.0 2020.0 15.0 2.0 17.6 0.31 0.0 0.0 \n", "29076 4.0 8.0 2020.0 16.0 2.0 18.4 0.32 0.0 0.0 \n", "29077 5.0 8.0 2020.0 17.0 2.0 17.8 0.34 0.0 0.0 \n", "29078 6.0 8.0 2020.0 18.0 2.0 18.7 0.35 1.0 0.0 \n", "29079 7.0 8.0 2020.0 19.0 2.0 16.2 0.36 0.0 0.0 \n", "29080 8.0 8.0 2020.0 20.0 2.0 16.7 0.37 0.0 0.0 \n", "29081 9.0 8.0 2020.0 21.0 2.0 17.8 0.39 0.0 0.0 \n", "29082 10.0 8.0 2020.0 22.0 2.0 17.6 0.40 0.0 0.0 \n", "29083 11.0 8.0 2020.0 23.0 2.0 16.6 0.41 0.0 0.0 \n", "29084 12.0 8.0 2020.0 24.0 2.0 17.7 0.42 0.0 0.0 \n", "29085 13.0 8.0 2020.0 25.0 2.0 18.3 0.44 0.0 0.0 \n", "29086 14.0 8.0 2020.0 26.0 2.0 16.1 0.45 0.0 0.0 \n", "29087 15.0 8.0 2020.0 27.0 2.0 16.0 0.46 0.0 0.0 \n", "29088 16.0 8.0 2020.0 28.0 2.0 17.6 0.47 0.0 0.0 \n", "29089 17.0 8.0 2020.0 29.0 2.0 17.9 0.48 0.0 0.0 \n", "29090 18.0 8.0 2020.0 30.0 2.0 18.8 0.50 0.0 0.0 \n", "... ... ... ... ... ... ... ... ... ... \n", "29121 18.0 9.0 2020.0 61.0 4.0 18.3 0.82 0.0 0.0 \n", "29122 19.0 9.0 2020.0 62.0 4.0 18.8 0.83 0.0 0.0 \n", "29123 20.0 9.0 2020.0 63.0 4.0 15.9 0.84 0.0 0.0 \n", "29124 21.0 9.0 2020.0 64.0 4.0 17.1 0.85 0.0 0.0 \n", "29125 22.0 9.0 2020.0 65.0 4.0 17.8 0.86 0.0 0.0 \n", "29126 23.0 9.0 2020.0 66.0 4.0 18.2 0.87 0.0 0.0 \n", "29127 24.0 9.0 2020.0 67.0 4.0 18.3 0.88 0.0 0.0 \n", "29128 25.0 9.0 2020.0 68.0 4.0 15.3 0.89 0.0 0.0 \n", "29129 26.0 9.0 2020.0 69.0 4.0 17.8 0.90 0.0 0.0 \n", "29130 27.0 9.0 2020.0 70.0 4.0 18.4 0.91 0.0 0.0 \n", "29131 28.0 9.0 2020.0 71.0 4.0 18.1 0.92 0.0 0.0 \n", "29132 29.0 9.0 2020.0 72.0 4.0 16.5 0.93 0.0 0.0 \n", "29133 30.0 9.0 2020.0 73.0 4.0 18.5 0.94 0.0 0.0 \n", "29134 1.0 10.0 2020.0 74.0 4.0 16.9 0.94 0.0 0.0 \n", "29135 2.0 10.0 2020.0 75.0 4.0 17.7 0.95 0.0 0.0 \n", "29136 3.0 10.0 2020.0 76.0 4.0 17.9 0.96 0.0 0.0 \n", "29137 4.0 10.0 2020.0 77.0 4.0 17.4 0.97 0.0 0.0 \n", "29138 5.0 10.0 2020.0 78.0 4.0 17.2 0.98 0.0 0.0 \n", "29139 6.0 10.0 2020.0 79.0 4.0 17.7 0.99 0.0 0.0 \n", "29140 7.0 10.0 2020.0 80.0 4.0 18.1 1.00 0.0 0.0 \n", "29141 8.0 10.0 2020.0 81.0 4.0 17.7 1.00 0.0 0.0 \n", "29142 9.0 10.0 2020.0 82.0 4.0 13.9 1.00 0.0 0.0 \n", "29143 10.0 10.0 2020.0 83.0 4.0 17.6 1.00 0.0 0.0 \n", "29144 11.0 10.0 2020.0 84.0 4.0 17.9 1.00 0.0 0.0 \n", "29145 12.0 10.0 2020.0 85.0 4.0 17.7 1.00 0.0 0.0 \n", "29146 13.0 10.0 2020.0 0.0 0.0 18.2 1.00 0.0 0.0 \n", "29147 14.0 10.0 2020.0 0.0 0.0 18.9 1.00 0.0 0.0 \n", "29148 15.0 10.0 2020.0 0.0 0.0 17.9 1.00 0.0 0.0 \n", "29149 16.0 10.0 2020.0 0.0 0.0 18.0 1.00 0.0 0.0 \n", "29150 17.0 10.0 2020.0 0.0 0.0 18.9 1.00 0.0 0.0 \n", "\n", " StSen % ... Tr/Trx % WP g/m2 Biomass ton/ha HI % \\\n", "29061 0.0 ... 100.0 32.2 0.000 -9.9 \n", "29062 0.0 ... 100.0 32.2 0.000 -9.9 \n", "29063 0.0 ... 100.0 32.2 0.000 -9.9 \n", "29064 0.0 ... 100.0 32.2 0.000 -9.9 \n", "29065 0.0 ... 100.0 32.2 0.000 -9.9 \n", "29066 0.0 ... 100.0 32.2 0.000 -9.9 \n", "29067 0.0 ... 100.0 32.2 0.002 -9.9 \n", "29068 0.0 ... 100.0 32.2 0.003 -9.9 \n", "29069 0.0 ... 100.0 32.2 0.005 -9.9 \n", "29070 0.0 ... 100.0 32.2 0.008 -9.9 \n", "29071 0.0 ... 100.0 32.2 0.010 -9.9 \n", "29072 0.0 ... 100.0 32.2 0.013 -9.9 \n", "29073 0.0 ... 100.0 32.2 0.017 -9.9 \n", "29074 0.0 ... 100.0 32.2 0.020 -9.9 \n", "29075 0.0 ... 100.0 32.2 0.025 -9.9 \n", "29076 0.0 ... 100.0 32.2 0.030 -9.9 \n", "29077 0.0 ... 100.0 32.2 0.035 -9.9 \n", "29078 0.0 ... 100.0 32.2 0.042 -9.9 \n", "29079 0.0 ... 100.0 32.2 0.049 -9.9 \n", "29080 0.0 ... 100.0 32.2 0.057 -9.9 \n", "29081 0.0 ... 100.0 32.2 0.067 -9.9 \n", "29082 0.0 ... 100.0 32.2 0.078 -9.9 \n", "29083 0.0 ... 100.0 32.2 0.090 -9.9 \n", "29084 0.0 ... 100.0 32.2 0.104 -9.9 \n", "29085 0.0 ... 100.0 32.2 0.120 -9.9 \n", "29086 0.0 ... 100.0 32.2 0.137 -9.9 \n", "29087 0.0 ... 100.0 32.1 0.158 -9.9 \n", "29088 0.0 ... 100.0 32.1 0.181 -9.9 \n", "29089 0.0 ... 100.0 32.1 0.207 -9.9 \n", "29090 0.0 ... 100.0 32.1 0.236 -9.9 \n", "... ... ... ... ... ... ... \n", "29121 0.0 ... 100.0 11.3 2.901 16.2 \n", "29122 0.0 ... 100.0 11.3 2.957 18.0 \n", "29123 0.0 ... 100.0 11.3 3.014 19.9 \n", "29124 0.0 ... 100.0 11.3 3.071 21.7 \n", "29125 0.0 ... 100.0 11.3 3.128 23.5 \n", "29126 0.0 ... 100.0 10.0 3.178 25.4 \n", "29127 0.0 ... 100.0 11.6 3.237 27.2 \n", "29128 0.0 ... 100.0 11.6 3.296 29.0 \n", "29129 0.0 ... 100.0 11.6 3.355 30.8 \n", "29130 0.0 ... 100.0 11.6 3.414 32.7 \n", "29131 0.0 ... 100.0 11.6 3.468 34.5 \n", "29132 0.0 ... 100.0 11.6 3.522 36.3 \n", "29133 0.0 ... 100.0 11.6 3.574 38.2 \n", "29134 0.0 ... 100.0 11.3 3.622 40.0 \n", "29135 0.0 ... 100.0 11.9 3.671 40.0 \n", "29136 0.0 ... 100.0 11.9 3.717 40.0 \n", "29137 0.0 ... 100.0 11.9 3.759 40.0 \n", "29138 0.0 ... 100.0 11.9 3.798 40.0 \n", "29139 0.0 ... 100.0 11.9 3.832 40.0 \n", "29140 0.0 ... 100.0 11.9 3.860 40.0 \n", "29141 0.0 ... 100.0 11.9 3.883 40.0 \n", "29142 0.0 ... 100.0 11.9 3.899 40.0 \n", "29143 0.0 ... 100.0 11.9 3.909 40.0 \n", "29144 0.0 ... 100.0 11.9 3.912 40.0 \n", "29145 0.0 ... 100.0 11.9 3.912 40.0 \n", "29146 0.0 ... 100.0 11.9 3.912 40.0 \n", "29147 0.0 ... 100.0 11.9 3.912 40.0 \n", "29148 0.0 ... 100.0 11.9 3.912 40.0 \n", "29149 0.0 ... 100.0 11.9 3.912 40.0 \n", "29150 0.0 ... 100.0 11.9 3.912 40.0 \n", "\n", " YieldPart ton/ha Brelative % WPet kg/m3 Onset Time Mon-Day \n", "29061 0.000 0.0 0.00 Jul-20 2020-07-20 Jul-20 \n", "29062 0.000 0.0 0.00 Jul-20 2020-07-21 Jul-21 \n", "29063 0.000 0.0 0.00 Jul-20 2020-07-22 Jul-22 \n", "29064 0.000 0.0 0.00 Jul-20 2020-07-23 Jul-23 \n", "29065 0.000 0.0 0.00 Jul-20 2020-07-24 Jul-24 \n", "29066 0.000 0.0 0.00 Jul-20 2020-07-25 Jul-25 \n", "29067 0.000 91.0 0.00 Jul-20 2020-07-26 Jul-26 \n", "29068 0.000 88.0 0.00 Jul-20 2020-07-27 Jul-27 \n", "29069 0.000 86.0 0.00 Jul-20 2020-07-28 Jul-28 \n", "29070 0.000 84.0 0.00 Jul-20 2020-07-29 Jul-29 \n", "29071 0.000 82.0 0.00 Jul-20 2020-07-30 Jul-30 \n", "29072 0.000 81.0 0.00 Jul-20 2020-07-31 Jul-31 \n", "29073 0.000 79.0 0.00 Jul-20 2020-08-01 Aug-01 \n", "29074 0.000 77.0 0.00 Jul-20 2020-08-02 Aug-02 \n", "29075 0.000 75.0 0.00 Jul-20 2020-08-03 Aug-03 \n", "29076 0.000 74.0 0.00 Jul-20 2020-08-04 Aug-04 \n", "29077 0.000 72.0 0.00 Jul-20 2020-08-05 Aug-05 \n", "29078 0.000 70.0 0.00 Jul-20 2020-08-06 Aug-06 \n", "29079 0.000 68.0 0.00 Jul-20 2020-08-07 Aug-07 \n", "29080 0.000 67.0 0.00 Jul-20 2020-08-08 Aug-08 \n", "29081 0.000 65.0 0.00 Jul-20 2020-08-09 Aug-09 \n", "29082 0.000 63.0 0.00 Jul-20 2020-08-10 Aug-10 \n", "29083 0.000 62.0 0.00 Jul-20 2020-08-11 Aug-11 \n", "29084 0.000 60.0 0.00 Jul-20 2020-08-12 Aug-12 \n", "29085 0.000 59.0 0.00 Jul-20 2020-08-13 Aug-13 \n", "29086 0.000 57.0 0.00 Jul-20 2020-08-14 Aug-14 \n", "29087 0.000 55.0 0.00 Jul-20 2020-08-15 Aug-15 \n", "29088 0.000 54.0 0.00 Jul-20 2020-08-16 Aug-16 \n", "29089 0.000 53.0 0.00 Jul-20 2020-08-17 Aug-17 \n", "29090 0.000 51.0 0.00 Jul-20 2020-08-18 Aug-18 \n", "... ... ... ... ... ... ... \n", "29121 0.470 39.0 0.31 Jul-20 2020-09-18 Sep-18 \n", "29122 0.533 38.0 0.34 Jul-20 2020-09-19 Sep-19 \n", "29123 0.599 38.0 0.38 Jul-20 2020-09-20 Sep-20 \n", "29124 0.666 37.0 0.41 Jul-20 2020-09-21 Sep-21 \n", "29125 0.736 37.0 0.45 Jul-20 2020-09-22 Sep-22 \n", "29126 0.806 36.0 0.48 Jul-20 2020-09-23 Sep-23 \n", "29127 0.880 36.0 0.51 Jul-20 2020-09-24 Sep-24 \n", "29128 0.956 35.0 0.55 Jul-20 2020-09-25 Sep-25 \n", "29129 1.035 35.0 0.59 Jul-20 2020-09-26 Sep-26 \n", "29130 1.115 35.0 0.62 Jul-20 2020-09-27 Sep-27 \n", "29131 1.197 34.0 0.66 Jul-20 2020-09-28 Sep-28 \n", "29132 1.280 34.0 0.70 Jul-20 2020-09-29 Sep-29 \n", "29133 1.364 34.0 0.73 Jul-20 2020-09-30 Sep-30 \n", "29134 1.449 33.0 0.77 Jul-20 2020-10-01 Oct-01 \n", "29135 1.468 33.0 0.77 Jul-20 2020-10-02 Oct-02 \n", "29136 1.487 33.0 0.76 Jul-20 2020-10-03 Oct-03 \n", "29137 1.504 33.0 0.77 Jul-20 2020-10-04 Oct-04 \n", "29138 1.519 33.0 0.76 Jul-20 2020-10-05 Oct-05 \n", "29139 1.533 33.0 0.76 Jul-20 2020-10-06 Oct-06 \n", "29140 1.544 32.0 0.76 Jul-20 2020-10-07 Oct-07 \n", "29141 1.553 32.0 0.75 Jul-20 2020-10-08 Oct-08 \n", "29142 1.560 32.0 0.75 Jul-20 2020-10-09 Oct-09 \n", "29143 1.563 32.0 0.74 Jul-20 2020-10-10 Oct-10 \n", "29144 1.565 33.0 0.74 Jul-20 2020-10-11 Oct-11 \n", "29145 1.565 33.0 0.73 Jul-20 2020-10-12 Oct-12 \n", "29146 1.565 33.0 0.73 Jul-20 2020-10-13 Oct-13 \n", "29147 1.565 33.0 0.73 Jul-20 2020-10-14 Oct-14 \n", "29148 1.565 33.0 0.73 Jul-20 2020-10-15 Oct-15 \n", "29149 1.565 33.0 0.73 Jul-20 2020-10-16 Oct-16 \n", "29150 1.565 33.0 0.73 Jul-20 2020-10-17 Oct-17 \n", "\n", "[90 rows x 29 columns]" ] }, "execution_count": 313, "metadata": {}, "output_type": "execute_result" } ], "source": [ "G_d" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 314, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Day', 'Month', 'Year', 'DAP ', 'Stage ', 'GD °C-day', 'Z m', 'StExp %',\n", " 'StSto %', 'StSen %', 'StSalt %', 'StWeed %', 'CC %', 'CCw %', 'StTr %',\n", " 'Kc(Tr) -', 'Trx mm', 'Tr mm', 'TrW mm', 'Tr/Trx %', 'WP g/m2',\n", " 'Biomass ton/ha', 'HI %', 'YieldPart ton/ha', 'Brelative %',\n", " 'WPet kg/m3', 'Onset', 'Time', 'Mon-Day'],\n", " dtype='object')" ] }, "execution_count": 314, "metadata": {}, "output_type": "execute_result" } ], "source": [ "G.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 318, "metadata": {}, "outputs": [], "source": [ "Pr = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Po\\Weather_Po.csv')" ] }, { "cell_type": "code", "execution_count": 319, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayDatesPrecipitationRsiTemp_maxTemp_minAvgEref
02017414/1/20170.0213.21990739.86666726.93333333.4000003.982072
12017424/2/20170.0206.42592640.90000028.06666734.4833333.891717
22017434/3/20170.0220.46759341.36666728.13333334.7500004.165795
32017444/4/20170.0222.84838040.66666729.50000035.0833334.222461
42017454/5/20170.0202.63425939.13333326.70000032.9166673.767886
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" ], "text/plain": [ " Year Month Day Dates Precipitation Rsi Temp_max \\\n", "0 2017 4 1 4/1/2017 0.0 213.219907 39.866667 \n", "1 2017 4 2 4/2/2017 0.0 206.425926 40.900000 \n", "2 2017 4 3 4/3/2017 0.0 220.467593 41.366667 \n", "3 2017 4 4 4/4/2017 0.0 222.848380 40.666667 \n", "4 2017 4 5 4/5/2017 0.0 202.634259 39.133333 \n", "\n", " Temp_min Avg Eref \n", "0 26.933333 33.400000 3.982072 \n", "1 28.066667 34.483333 3.891717 \n", "2 28.133333 34.750000 4.165795 \n", "3 29.500000 35.083333 4.222461 \n", "4 26.700000 32.916667 3.767886 " ] }, "execution_count": 319, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Pr.head()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "# Pr.loc[Pr['Year']==2019]" ] }, { "cell_type": "code", "execution_count": 320, "metadata": {}, "outputs": [], "source": [ "for i in range(len(Pr)):\n", " Pr.at[i,'Dates'] = datetime.strptime(Pr.at[i,'Dates'], '%m/%d/%Y').strftime('%b-%d')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 321, "metadata": {}, "outputs": [], "source": [ "Pr_d = Pr.loc[(Pr['Year']==year)]\n", "\n", "Pr_d = Pr_d.reset_index()\n", "\n", "Pr_d = Pr_d.iloc[:, 1:]\n", "\n", "a = Pr_d.index[Pr_d['Dates']==day]\n", "\n", "Pr_d = Pr_d[a[0]: a[0]+91]" ] }, { "cell_type": "code", "execution_count": 322, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayDatesPrecipitationRsiTemp_maxTemp_minAvgEref
28720201014Oct-140.034200.78125033.323.928.603.573489
28820201015Oct-150.000122.78819430.221.826.002.118607
28920201016Oct-160.102211.99652833.522.027.753.736570
29020201017Oct-170.051198.78472234.023.928.953.551757
29120201018Oct-180.000144.79861129.922.926.402.510963
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" ], "text/plain": [ " Year Month Day Dates Precipitation Rsi Temp_max Temp_min \\\n", "287 2020 10 14 Oct-14 0.034 200.781250 33.3 23.9 \n", "288 2020 10 15 Oct-15 0.000 122.788194 30.2 21.8 \n", "289 2020 10 16 Oct-16 0.102 211.996528 33.5 22.0 \n", "290 2020 10 17 Oct-17 0.051 198.784722 34.0 23.9 \n", "291 2020 10 18 Oct-18 0.000 144.798611 29.9 22.9 \n", "\n", " Avg Eref \n", "287 28.60 3.573489 \n", "288 26.00 2.118607 \n", "289 27.75 3.736570 \n", "290 28.95 3.551757 \n", "291 26.40 2.510963 " ] }, "execution_count": 322, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Pr_d.tail()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# H = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Po\\H_new.csv')" ] }, { "cell_type": "code", "execution_count": 323, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DayMonthYearDAPStageWCTot mmWr(Zx) mmZ mWr mmWr(SAT) mmWr(FC) mmWr(exp) mmWr(sto) mmWr(sen) mmWr(PWP) mmOnsetTimeMon-Day
01.05.02017.00.01.0171.9149.70.322.1148.359.254.635.836.426.1May-012017-05-01May-01
12.05.02017.00.01.0171.8149.60.322.0148.359.254.635.936.426.1May-012017-05-02May-02
23.05.02017.00.01.0171.8149.50.322.0148.359.254.635.236.426.1May-012017-05-03May-03
34.05.02017.00.01.0171.7149.40.321.9148.359.254.635.436.426.1May-012017-05-04May-04
45.05.02017.00.01.0171.6149.30.321.8148.359.254.635.836.426.1May-012017-05-05May-05
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" ], "text/plain": [ " Day Month Year DAP Stage WCTot mm Wr(Zx) mm Z m Wr mm \\\n", "0 1.0 5.0 2017.0 0.0 1.0 171.9 149.7 0.3 22.1 \n", "1 2.0 5.0 2017.0 0.0 1.0 171.8 149.6 0.3 22.0 \n", "2 3.0 5.0 2017.0 0.0 1.0 171.8 149.5 0.3 22.0 \n", "3 4.0 5.0 2017.0 0.0 1.0 171.7 149.4 0.3 21.9 \n", "4 5.0 5.0 2017.0 0.0 1.0 171.6 149.3 0.3 21.8 \n", "\n", " Wr(SAT) mm Wr(FC) mm Wr(exp) mm Wr(sto) mm Wr(sen) mm Wr(PWP) mm \\\n", "0 148.3 59.2 54.6 35.8 36.4 26.1 \n", "1 148.3 59.2 54.6 35.9 36.4 26.1 \n", "2 148.3 59.2 54.6 35.2 36.4 26.1 \n", "3 148.3 59.2 54.6 35.4 36.4 26.1 \n", "4 148.3 59.2 54.6 35.8 36.4 26.1 \n", "\n", " Onset Time Mon-Day \n", "0 May-01 2017-05-01 May-01 \n", "1 May-01 2017-05-02 May-02 \n", "2 May-01 2017-05-03 May-03 \n", "3 May-01 2017-05-04 May-04 \n", "4 May-01 2017-05-05 May-05 " ] }, "execution_count": 323, "metadata": {}, "output_type": "execute_result" } ], "source": [ "H.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 324, "metadata": {}, "outputs": [], "source": [ "H_0 = H.loc[H['Year']==year] \n", "\n", "H_0 = H_0.reset_index()\n", "\n", "H_0 = H_0.iloc[:, 1:]\n", "\n", "b = H_0.index[H_0['Mon-Day']==day]\n", "\n", "H_0 = H_0[b[0]:]\n", "\n" ] }, { "cell_type": "code", "execution_count": 325, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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DayMonthYearDAPStageWCTot mmWr(Zx) mmZ mWr mmWr(SAT) mmWr(FC) mmWr(exp) mmWr(sto) mmWr(sen) mmWr(PWP) mmOnsetTimeMon-Day
8020.07.02020.081.04.0303.0267.21.0267.2470.6268.8247.2186.6186.6154.6May-012020-07-20Jul-20
8121.07.02020.082.04.0314.6279.11.0279.1470.6268.8248.9187.6187.6154.6May-012020-07-21Jul-21
8222.07.02020.083.04.0308.9273.31.0273.3470.6268.8248.1187.1187.1154.6May-012020-07-22Jul-22
8323.07.02020.084.04.0315.5279.81.0279.8470.6268.8241.6183.1183.1154.6May-012020-07-23Jul-23
8424.07.02020.085.04.0309.6273.81.0273.8470.6268.8245.1185.2185.2154.6May-012020-07-24Jul-24
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" ], "text/plain": [ " Day Month Year DAP Stage WCTot mm Wr(Zx) mm Z m Wr mm \\\n", "80 20.0 7.0 2020.0 81.0 4.0 303.0 267.2 1.0 267.2 \n", "81 21.0 7.0 2020.0 82.0 4.0 314.6 279.1 1.0 279.1 \n", "82 22.0 7.0 2020.0 83.0 4.0 308.9 273.3 1.0 273.3 \n", "83 23.0 7.0 2020.0 84.0 4.0 315.5 279.8 1.0 279.8 \n", "84 24.0 7.0 2020.0 85.0 4.0 309.6 273.8 1.0 273.8 \n", "\n", " Wr(SAT) mm Wr(FC) mm Wr(exp) mm Wr(sto) mm Wr(sen) mm Wr(PWP) mm \\\n", "80 470.6 268.8 247.2 186.6 186.6 154.6 \n", "81 470.6 268.8 248.9 187.6 187.6 154.6 \n", "82 470.6 268.8 248.1 187.1 187.1 154.6 \n", "83 470.6 268.8 241.6 183.1 183.1 154.6 \n", "84 470.6 268.8 245.1 185.2 185.2 154.6 \n", "\n", " Onset Time Mon-Day \n", "80 May-01 2020-07-20 Jul-20 \n", "81 May-01 2020-07-21 Jul-21 \n", "82 May-01 2020-07-22 Jul-22 \n", "83 May-01 2020-07-23 Jul-23 \n", "84 May-01 2020-07-24 Jul-24 " ] }, "execution_count": 325, "metadata": {}, "output_type": "execute_result" } ], "source": [ "H_0.head()" ] }, { "cell_type": "code", "execution_count": 326, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "19267" ] }, "execution_count": 326, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(H_0)" ] }, { "cell_type": "code", "execution_count": 327, "metadata": {}, "outputs": [], "source": [ "H_FC = []\n", "H_PWP = []\n", "H_Wr = []\n", "H_exp = []\n", "H_sen = []\n", "H_sto = [] \n", "\n", "for j in q:\n", "# print(j)\n", " H_FC.append(H_0.loc[H_0['Mon-Day']== j]['Wr(FC) mm'])\n", " H_PWP.append(H_0.loc[H_0['Mon-Day']== j]['Wr(PWP) mm'])\n", " H_Wr.append(H_0.loc[H_0['Mon-Day']== j]['Wr mm'])\n", " H_exp.append(H_0.loc[H_0['Mon-Day']== j]['Wr(exp) mm'])\n", " H_sen.append(H_0.loc[H_0['Mon-Day']== j]['Wr(sen) mm'])\n", " H_sto.append(H_0.loc[H_0['Mon-Day']== j]['Wr(sto) mm'])" ] }, { "cell_type": "code", "execution_count": 328, "metadata": {}, "outputs": [], "source": [ "# s = ['May-01', '' , '', '', '', '', '', '','',\n", "# 'May-10', '', '', '', '', '' , '','' , '', '', 'May-20', '', '', '', '', '', '', '', '', '', 'May-30',\n", "# '', '', '', '', '','' ,'' , '','','' , 'Jun-10', '', '', '', '', '', '', '', '', '', 'Jun-20', '', '', '', '','' , '','' ,'' , '', 'Jun-30',\n", "# '', '','', '','' ,'' ,'' ,'' , '', 'Jul-10','' , '', '', '','' , '', '','' ,'' , \n", "s = [ 'Jul-20','' , '','' ,'' , '','' ,'' , '', '', '','Jul-31', \n", " '','' , '', '', '', '', '', '', '','Aug-10', '', '', '', '','' , '', '', '', '', 'Aug-20', '', '', '', '', '', '', '', '','' ,'' ,'Aug-30', '', \n", " '', '' , '', '', '', '', '', '', '','Sep-10', '', '', '', '','' , '','' , '', '', 'Sep-20', '', '', '', '', '', '', '', '', '', 'Sep-30',\n", " '', '', '', '','' ,'' ,'' ,'' , '','Oct-10','' , '', '', '','' , '', '','' ,'' , 'Oct-20','' , '','' ,'' , '','' ,'' , '', '', '','Oct-31',\n", " '', '' , '', '', '', '', '', '', '','Nov-10', '', '', '', '','' , '','' , '', '', 'Nov-20', '', '', '', '', '', '', '', '', '', 'Nov-30',]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 329, "metadata": {}, "outputs": [], "source": [ "\n", "flierprops = dict(marker='o', markersize=12, markeredgecolor='none', ) #markerfacecolor='green', markerfacecolor='firebrick'\n", "medianprops = dict(linestyle='--', linewidth=2.5, color='blue')\n", "\n", "\n", "colors = ['black', 'red', 'tab:green', 'black', 'red']\n", "colors_FC = dict(color=colors[0])\n", "colors_PWP = dict(color=colors[1])\n", "colors_Exp = dict(color=colors[2]) \n", "colors_Sen = dict(color=colors[3]) \n", "colors_Sto = dict(color=colors[4]) \n" ] }, { "cell_type": "code", "execution_count": 331, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "fig8, (ax2, ax, ax3, ax1,) = plt.subplots(4, 1, figsize=(18, 25)) # sharex = True,\n", "\n", "\n", "ax3.bar(Pr_d['Dates'], Pr_d['Precipitation'], label='Precipitation')# c=C['Month'],cmap='RdYlGn')\n", "ax3.set_ylabel('Rain (mm)', fontweight='bold')\n", "ax3.set_xticks(p)\n", "\n", "ax.scatter(G_d['Mon-Day'], G_d['StExp %'], label='Leaf exp (%)', ) #c=G_d['StExp %'], cmap='jet')#label='May')# c=C['Month'],cmap='RdYlGn')\n", "ax.scatter(G_d['Mon-Day'], G_d['StSto %'], marker = '+' , label='Stomatal clo (%)' , color = 'darkslategray',) #c=G_d['StExp %'], cmap='RdYlGn_r')#label='May')# c=C['Month'],cmap='RdYlGn')\n", "ax.set_ylabel('Stress (%)', fontweight='bold')\n", "\n", "ax2.scatter(G_d['Mon-Day'], G_d['Biomass ton/ha'], label='Biomass (ton/ha)',) # c=G_d['Biomass ton/ha'], cmap='jet_r')#label='May')# c=C['Month'],cmap='RdYlGn')\n", "ax2.scatter(G_d['Mon-Day'], G_d['YieldPart ton/ha'], marker = 'x' , label='Yield (ton/ha)' , color = 'darkslategray',) # c=G_d['YieldPart ton/ha'], cmap='RdYlGn')#label='May')# c=C['Month'],cmap='RdYlGn')\n", "ax2.set_ylabel('Yield/Biomass ', fontweight='bold')\n", "\n", "ax2.set_xticks(p)\n", "ax.set_xticks(p)\n", "\n", "#Soil water content \n", "ax1.boxplot(H_Wr, flierprops=flierprops, medianprops=medianprops)\n", "ax1.boxplot(H_FC, showbox=False, showcaps=False, sym='', whis=0, medianprops=colors_FC) \n", "ax1.boxplot(H_PWP, showbox=False, showcaps=False, sym='', whis=0, medianprops=colors_PWP) \n", "\n", "# ax1.set_title('Water content in the effective root zone', fontsize=25, fontweight='bold')\n", "ax1.set_ylabel('Water Content (mm)', fontweight='bold')\n", "ax1.set_xlabel('Dates ', fontweight='bold')\n", "\n", "ax1.set_xticklabels(s, rotation=0, ) #fontsize=16\n", "\n", "# ax2.set_ylim([-1,2])\n", "\n", "\n", "ax3.legend(fontsize = 20)\n", "ax2.legend(fontsize = 20)\n", "# ax1.legend()\n", "ax.legend(loc='best', fontsize = 20)\n", "#plt.colorbar(sctr, ax=ax, )\n", "\n", "# Add legend\n", "\n", "lines = []\n", "styles = ['--', '--', '--', '--', '--']\n", "colors = ['black', 'red', 'blue', 'green', 'red']\n", "x = np.linspace(0, 0, 10)\n", "\n", "for i in range(5):\n", " lines += ax.plot(x, np.sin(x - i * np.pi / 2),\n", " styles[i], color=colors[i])\n", "\n", "# Create the second legend and add the artist manually.\n", "from matplotlib.legend import Legend\n", "leg = Legend(ax1, lines[:3], ['Wr at FC', 'Wr at PWP', 'actual Wr'], loc='upper left', fontsize = 20, frameon=False)\n", "ax1.add_artist(leg);\n", "\n", "# leg1 = Legend(ax1, lines[2:], ['actual Wr', 'Wr at Leaf exp', 'Wr at Sto clo'], loc='upper left', frameon=False)\n", "# ax1.add_artist(leg1);\n", "\n", "# plt.subplots_adjust(wspace=0.003)\n", "plt.subplots_adjust(hspace=0.15)\n", "\n", "# plt.savefig(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Gaoua\\Stress_variation_10-May-20.png')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 332, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Pitch - Employee meeting \n", "fig9, ax3 = plt.subplots(1, 1, figsize=(20, 8)) # sharex = True,\n", "\n", "\n", "ax3.bar(Pr_d['Dates'], Pr_d['Precipitation'], label='Precipitation')# c=C['Month'],cmap='RdYlGn')\n", "ax3.set_title('Rainfall distribution in 2019 - Dori', fontsize=20, fontweight='bold')\n", "ax3.set_ylabel('Rain (mm)', fontweight='bold')\n", "ax3.set_xticks(p,)\n", "# ax3.set_xticklabels(p, rotation=45,)\n", "\n", "# ax.scatter(G_d['Mon-Day'], G_d['StExp %'], label='Leaf exp (%)', color = 'tab:red') #c=G_d['StExp %'], cmap='jet')#label='May')# c=C['Month'],cmap='RdYlGn')\n", "# ax.scatter(G_d['Mon-Day'], G_d['StSto %'], marker = 'x', s=50, label='Stomatal clo (%)' , color = 'darkslategray',) #c=G_d['StExp %'], cmap='RdYlGn_r')#label='May')# c=C['Month'],cmap='RdYlGn')\n", "# ax.set_title('Induced Water Stress', fontsize=20, fontweight='bold')\n", "# ax.set_ylabel('Stress (%)', fontweight='bold')\n", "\n", "# ax2.scatter(G_d['Mon-Day'], G_d['Biomass ton/ha'], label='Biomass (ton/ha)',) # c=G_d['Biomass ton/ha'], cmap='jet_r')#label='May')# c=C['Month'],cmap='RdYlGn')\n", "# ax2.scatter(G_d['Mon-Day'], G_d['YieldPart ton/ha'], marker = 'x' , label='Yield (ton/ha)' , color = 'darkslategray',) # c=G_d['YieldPart ton/ha'], cmap='RdYlGn')#label='May')# c=C['Month'],cmap='RdYlGn')\n", "# ax2.set_ylabel('Yield/Biomass ', fontweight='bold')\n", "\n", "ax2.set_xticks(p)\n", "\n", "# ax.set_xticks(p)\n", "\n", "#Soil water content \n", "# ax1.boxplot(H_Wr, flierprops=flierprops, medianprops=medianprops)\n", "# ax1.boxplot(H_FC, showbox=False, showcaps=False, sym='', whis=0, medianprops=colors_FC) \n", "# ax1.boxplot(H_PWP, showbox=False, showcaps=False, sym='', whis=0, medianprops=colors_PWP) \n", "\n", "# ax1.set_title('Water content in the effective root zone', fontsize=25, fontweight='bold')\n", "ax1.set_ylabel('Water Content (mm)', fontweight='bold')\n", "ax1.set_xlabel('Dates ', fontweight='bold')\n", "\n", "ax1.set_xticklabels(s, rotation=0, ) #fontsize=16\n", "\n", "# ax1.set_ylim([40,200])\n", "\n", "\n", "ax3.legend(fontsize = 20)\n", "ax2.legend(fontsize = 20)\n", "# ax1.legend()\n", "ax.legend(loc='center left', fontsize = 18)\n", "#plt.colorbar(sctr, ax=ax, )\n", "\n", "# Add legend\n", "\n", "lines = []\n", "styles = ['--', '--', '--', '--', '--']\n", "colors = ['black', 'red', 'blue', 'green', 'red']\n", "x = np.linspace(0, 0, 10)\n", "\n", "for i in range(5):\n", " lines += ax.plot(x, np.sin(x - i * np.pi / 2),\n", " styles[i], color=colors[i])\n", "\n", "# Create the second legend and add the artist manually.\n", "from matplotlib.legend import Legend\n", "leg = Legend(ax1, lines[:3], ['Wr at FC', 'Wr at PWP', 'actual Wr'], loc='upper left', fontsize = 20, frameon=False)\n", "ax1.add_artist(leg);\n", "\n", "# leg1 = Legend(ax1, lines[2:], ['actual Wr', 'Wr at Leaf exp', 'Wr at Sto clo'], loc='upper left', frameon=False)\n", "# ax1.add_artist(leg1);\n", "\n", "# plt.subplots_adjust(wspace=0.003)\n", "plt.subplots_adjust(hspace=0.2)\n", "\n", "# plt.savefig(r'C:\\Users\\sagoungbome\\Pictures\\Delft GI Fellow\\Rain_Dori-2019.png')\n", "\n", "# plt.savefig(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\AquaCrop Sims\\Gaoua\\Stress_variation_10-May-20.png')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }