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plot_calibration_BEC.py
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plot_calibration_BEC.py
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from pathlib import Path
import matplotlib.pylab as plt
import json
#from ngen_cal import plot_objective, plot_stuff, plot_obs, plot_output, plot_parameter_space
import pandas as pd
import os
import sys
sys.path.append("/home/west/Projects/CAMELS/NextGenCode/Model_evaluation")
import objectives as OB
import numpy as np
import shutil
from datetime import datetime
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 29 14:01:06 2022
@author: west
"""
from pathlib import Path
import matplotlib.pylab as plt
import json
#from ngen_cal import plot_objective, plot_stuff, plot_obs, plot_output, plot_parameter_space
import pandas as pd
import os,glob
import subprocess
import time
import yaml
import seaborn as sns
import geopandas as gp
from hydrotools.metrics import metrics
import math as m
# BChoat: Seems like very few of these pptx/docx imports are actually used
import pptx
from pptx.chart.data import CategoryChartData
from pptx.enum.chart import XL_CHART_TYPE
from pptx.enum.text import PP_ALIGN
from pptx.util import Inches
from pptx import Presentation
#import df2img
from docx import Document
from docx.shared import Inches
from docx.enum.text import WD_PARAGRAPH_ALIGNMENT
from docx.enum.section import WD_ORIENT
from docx.enum.section import WD_SECTION
def change_orientation2():
current_section = document.sections[-1]
new_width, new_height = current_section.page_height, current_section.page_width
# new_section = document.add_section(WD_SECTION.NEW_PAGE)
current_section.orientation = WD_ORIENT.LANDSCAPE
current_section.page_width = new_width
current_section.page_height = new_height
return current_section
def plot_calibration_1(Model_name,Q_cms,Runoff_m3s,Next_Total_out,NWM_21,Objective,total_area,output_figure):
plt.rc('axes', titlesize=12)
Q_cms.index.names = ['time']
# Q_cms_stats=pd.concat([Q_cms,NWM_21],axis=1).dropna()
# print('Q_cms')
#print(Q_cms)
# print('NWM_21')
# print(NWM_21)
Q_cms_stats = pd.merge(Q_cms, NWM_21,
left_index=True, right_index=True,
how = 'left').dropna()
KGE=metrics.kling_gupta_efficiency(Q_cms_stats['q_cms_obs'].values, Q_cms_stats['q_cms_sim'].values)
# print(f"q_cms_obs: {Q_cms_stats['q_cms_obs'].values}; q_cms_sim: {Q_cms_stats['q_cms_sim'].values}")
# print(f'KGE: {KGE}')
Nash=metrics.nash_sutcliffe_efficiency(Q_cms_stats['q_cms_obs'].values, Q_cms_stats['q_cms_sim'].values)
NNash=1./(2.-Nash)
# print(f'NWM_21: {NWM_21}')
if(len(NWM_21)>0):
Nash_NWM=metrics.nash_sutcliffe_efficiency(Q_cms_stats['q_cms_obs'].values, Q_cms_stats['flow_cms'].values)
NNash_NWM=1./(2.-Nash_NWM)
KGE_NWM=metrics.kling_gupta_efficiency(Q_cms_stats['q_cms_obs'].values, Q_cms_stats['flow_cms'].values)
else:
Nash_NWM=""
NNash_NWM=""
KGE_NWM=""
#mean_bias=metrics.mean_error(Q_cms['q_cms_obs'], Q_cms[['q_cms_sim']])
#mean_absolute_error=OB.mean_absolute_error(Q_cms['q_cms_obs'], Q_cms[['q_cms_sim']])
#RMSE=metrics.root_mean_squared_error(Q_cms['q_cms_obs'], Q_cms[['q_cms_sim']])
#NRMSE = sqrt(RMSE)/Total_dailyNoNAN['Obs_Q'].mean()
#NRMSE=OB.NRMSE(Total_dailyNoNAN[Var_name_out],Total_dailyNoNAN['Obs_Q'])
NWM_21_Acum=NWM_21.cumsum(axis= 0)*3.600/(total_area*1000)
Q_cms_Acum=Q_cms.cumsum(axis= 0)*3.600/(total_area*1000)
if(len(Runoff_m3s)>0): Runoff_m3s_Acum=Runoff_m3s.cumsum(axis= 0)
if(len(Next_Total_out)>0): Next_Total_out_Acum=Next_Total_out.cumsum(axis= 0)
num_subplots = 3 #If plotting cumulative precipitation/soil moisture, use 3 subplots (flow, cumulative rain, soil moisture)
plt.rcParams["figure.figsize"] = (8,8)
fig, ax_compare = plt.subplots(num_subplots, 1) #Create the plot for this zone
#ax_compare[0].set_title('Cumulative Precipitation')
if(len(NWM_21)>0): ax_compare[0].plot (NWM_21.index,NWM_21_Acum['flow_cms'], label = 'NWM 2.1 runoff', color= 'forestgreen',linewidth=2.5) #Plot obs data on comparison figure
ax_compare[0].plot (Q_cms_Acum.index,Q_cms_Acum['q_cms_obs'],'o',markersize=2, label = 'Obs discharge', color= 'blue') #Plot obs data on comparison figure
if('q_cms_sim' in Q_cms_Acum.columns): ax_compare[0].plot (Q_cms_Acum.index,Q_cms_Acum['q_cms_sim'], label = Model_name, color= 'orange',linewidth=2.5) #Plot obs data on comparison figure
Model_name_NOAH="NOM_"+Model_name.split("_")[1]
if('q_cms_NOAH_sim' in Q_cms_Acum.columns) & ("PET" in Model_name): ax_compare[0].plot (Q_cms_Acum.index,Q_cms_Acum['q_cms_NOAH_sim'], label = Model_name_NOAH, color= 'orange',linewidth=2.5,linestyle="dashed") #Plot obs data on comparison figure
if(len(Runoff_m3s)>0): ax_compare[0].plot (Runoff_m3s_Acum.index,Runoff_m3s_Acum, label = Model_name+'-cat', color= 'mediumpurple',linewidth=2) #Plot obs data on comparison figure
if(len(Next_Total_out)>0): ax_compare[0].plot (Next_Total_out.index,Next_Total_out_Acum, label = Model_name+'-nex', color= 'salmon',linewidth=1.2)
ax_compare[0].set_xlim([Q_cms.index.min(),Q_cms.index.max()])
#ax_compare[0].plot (Total.index,Total_Acum['Rainfall_from_forcing'],label = 'Precipitation', color= 'silver') #Plot obs data on comparison figure
ax_compare[0].legend(loc='upper left')
#ax_compare[0].set_xlabel ('Date');
ax_compare[0].set_ylabel('Cumulative Q (m)')
if(len(NWM_21)>0): ax_compare[1].plot (NWM_21.index,NWM_21['flow_cms'], label = 'NWM 2.1 runoff'+" (KGE={:.2f}".format(KGE_NWM)+")", color= 'forestgreen',linewidth=2.5) #Plot obs data on comparison figure
ax_compare[1].plot(Q_cms.index,Q_cms['q_cms_obs'],'o',markersize=2, label = 'Obs runoff', color= 'blue') #Plot obs data on comparison figure
if('q_cms_sim' in Q_cms_Acum.columns): ax_compare[1].plot(Q_cms.index,Q_cms['q_cms_sim'], label = Model_name+" (KGE={:.2f}".format(KGE)+")", color= 'orange',linewidth=1.2) #Plot obs data on comparison figure
ax_compare[1].set_xlim([Q_cms.index.min(),Q_cms.index.max()])
if(len(Next_Total_out)>0): ax_compare[1].plot(Next_Total_out.index,Next_Total_out, label = Model_name, color= 'salmon',linewidth=1) #Plot obs data on comparison figure
ax_compare[1].legend(loc='upper left')
#ax_compare[1].set_xlabel ('Date');
ax_compare[1].set_ylabel('Q ($m^3$/s)')
Objective.index=Objective.index.astype('int')
ax_compare[2].plot(Objective.index,Objective.iloc[:,0],marker='o', markersize=5, label = Model_name, linestyle='dashed', linewidth=0.5)
ax_compare[2].plot ([Objective.index[0],Objective.index[-1]],[KGE_NWM,KGE_NWM], label = "NWM 2.1", color= 'forestgreen',linewidth=2.5)
ax_compare[2].set_xlabel ('Iteration number')
ax_compare[2].set_ylabel('KGE')
ax_compare[2].legend(loc='upper left')
ax_compare[2].set_xlim([Objective.index[0]-1,Objective.index[-1]+1])
# print(f'KGE_NWM: {KGE_NWM}')
min_lim=min(KGE_NWM,0)
ax_compare[2].set_ylim([min_lim,1])
#ax_compare[2].set_ylim([Objective.iloc[:,0].min(),Objective.iloc[:,0].max()])
plt.figtext(0.05,0.64,"(a)",fontsize=14)
plt.figtext(0.05,0.39,"(b)",fontsize=14)
plt.figtext(0.05,0.08,"(c)",fontsize=14)
fig.savefig(output_figure, bbox_inches='tight',dpi=300)
#plt.close(fig)
def Generate_Scatter_plot2(Model_name,Q_cms,Runoff_m3s,Next_Total_out,NWM_21,Objective,total_area,output_figure):
import sys
from hydrotools.metrics import metrics
import seaborn as sns
import numpy as np
from math import sqrt
sns.set_style("whitegrid")
# Total_daily=Total.resample('D').apply(lambda x: np.sum(x.values))
#if(len(NWM_21)>0):
# NWM_21_daily=NWM_21.resample('D').apply(lambda x: np.sum(x.values))
# else:
# NWM_21_daily=pd.DataFrame()
Q_cmh=Q_cms*3.600/(total_area*1000)
Total_month=Q_cmh.resample('M').sum()
Q_cmsNoNAN=Q_cms.dropna()
#Nash=OB.nash_sutcliffe(Total_dailyNoNAN[Var_name_out],Total_dailyNoNAN['Obs_Q'])
Nash=metrics.nash_sutcliffe_efficiency(Q_cmsNoNAN['q_cms_obs'], Q_cmsNoNAN['q_cms_sim'])
NNash=1./(2.-Nash)
LogNash=metrics.nash_sutcliffe_efficiency(Q_cmsNoNAN['q_cms_obs'], Q_cmsNoNAN['q_cms_sim'],log=True)
#LogNNash=1./(2.-LogNash)
KGE=metrics.kling_gupta_efficiency(Q_cmsNoNAN['q_cms_obs'], Q_cmsNoNAN['q_cms_sim'])
select=Q_cmsNoNAN['q_cms_obs']>=Q_cmsNoNAN['q_cms_obs'].quantile(0.7)
Temp=metrics.nash_sutcliffe_efficiency(Q_cmsNoNAN['q_cms_obs'][select], Q_cmsNoNAN['q_cms_sim'][select])
NNash70=1./(2.-Temp)
select=Q_cmsNoNAN['q_cms_obs']<=Q_cmsNoNAN['q_cms_obs'].quantile(0.3)
Temp=metrics.nash_sutcliffe_efficiency(Q_cmsNoNAN['q_cms_obs'][select], Q_cmsNoNAN['q_cms_sim'][select])
NNash30=1./(2.-Temp)
TotQVolError=100.*(Q_cmsNoNAN['q_cms_sim'].sum()-Q_cmsNoNAN['q_cms_obs'].sum())/Q_cmsNoNAN['q_cms_obs'].sum()
flood_criteria=Q_cmsNoNAN['q_cms_obs'].quantile(0.7)
Q_cmsNoNAN['simulated_flood'] = (Q_cmsNoNAN['q_cms_sim'] >= flood_criteria)
Q_cmsNoNAN['observed_flood'] = (Q_cmsNoNAN['q_cms_obs'] >= flood_criteria)
contingency_table = metrics.compute_contingency_table(Q_cmsNoNAN['observed_flood'],Q_cmsNoNAN['simulated_flood'])
TS = metrics.threat_score(contingency_table)
coefOfpers = metrics.coefficient_of_persistence(Q_cmsNoNAN['q_cms_obs'],Q_cmsNoNAN['q_cms_sim'])
if(len(NWM_21)>0):
NWM_21NaN=NWM_21.loc[Q_cmsNoNAN.index]
Nash_NWM=metrics.nash_sutcliffe_efficiency(Q_cmsNoNAN['q_cms_obs'], NWM_21NaN['flow_cms'])
NNash_NWM=1./(2.-Nash_NWM)
KGE_NWM=metrics.kling_gupta_efficiency(Q_cmsNoNAN['q_cms_obs'], NWM_21NaN['flow_cms'])
else:
Nash_NWM=""
KGE_NWM=metrics.kling_gupta_efficiency(Q_cmsNoNAN['q_cms_obs'], NWM_21NaN['flow_cms'])
mean_bias=metrics.mean_error(Q_cmsNoNAN['q_cms_obs'], Q_cmsNoNAN['q_cms_sim'])
mean_absolute_error=OB.mean_absolute_error(Q_cmsNoNAN['q_cms_obs'], Q_cmsNoNAN['q_cms_sim'])
RMSE=metrics.root_mean_squared_error(Q_cmsNoNAN['q_cms_obs'], Q_cmsNoNAN['q_cms_sim'])
NRMSE = m.sqrt(RMSE)/Q_cmsNoNAN['q_cms_obs'].mean()
NRMSE=OB.NRMSE(Q_cmsNoNAN['q_cms_sim'],Q_cmsNoNAN['q_cms_obs'])
########### Create plot
# if output_figure = False, then do not create plot
if output_figure != False:
num_subplots = 2 #If plotting cumulative precipitation/soil moisture, use 3 subplots (flow, cumulative rain, soil moisture)
plt.rcParams["figure.figsize"] = (4,8)
fig, ax_compare = plt.subplots(num_subplots, 1) #Create the plot for this zone
ax_compare[0].scatter(Q_cmsNoNAN['q_cms_obs'],Q_cmsNoNAN['q_cms_sim'], label = Model_name,s=13,edgecolors='black',color='silver',linewidths=0.3)
if(len(NWM_21)>0): ax_compare[0].scatter(Q_cmsNoNAN['q_cms_obs'],NWM_21NaN['flow_cms'], label = 'NWM2.1',s=13,edgecolors='forestgreen',facecolors='none',linewidths=0.5,alpha=0.5)
ax_compare[0].set_xlabel ('Obs Q ($m^3$/s)')
ax_compare[0].set_ylabel('Sim Q ($m^3$/s)')
ax_compare[0].set_aspect('equal',adjustable='box')
ax_compare[0].legend(loc='upper left')
max_y=1.05*max(Q_cmsNoNAN['q_cms_obs'].max(),Q_cmsNoNAN['q_cms_sim'].max())
ax_compare[0].set_xlim([0,max_y])
ax_compare[0].set_ylim([0,max_y])
ax_compare[0].plot ([0,max_y],[0,max_y],linestyle='dashed',linewidth=0.5, color= 'r') #Plot obs data on comparison figure
if(len(NWM_21)>0):
ax_compare[0].set_title("NNSE = " + str(round(NNash,2)) +"(NWM 2.1: "+ str(round(NNash_NWM,2))+")")
else:
ax_compare[0].set_title("NNSE = " + str(round(NNash,2)) +" - No NWM 2.1")
Total_month['Month']=Total_month.index.month
#Total_month=Total_month[['Month',Var_name_out,'Obs_Q']]
ax_compare[1]
Obs_Month=Total_month[['Month','q_cms_obs']]
Obs_Month['Variable']='Obs'
Obs_Month=Obs_Month.rename(columns={'q_cms_obs':"Q"})
Sim_Month=Total_month[['Month','q_cms_sim']]
Sim_Month['Variable']='Sim'
Sim_Month=Sim_Month.rename(columns={'q_cms_sim':"Q"})
Month_Q=pd.concat([Obs_Month,Sim_Month])
sns.boxplot(x='Month',y="Q",hue='Variable',data=Month_Q,ax=ax_compare[1])
ax_compare[1].set_xlabel ('Month');
ax_compare[1].set_ylabel('Q (Sum m/month)')
ax_compare[1].yaxis.grid(True)
ax_compare[1].xaxis.grid(True)
plt.figtext(0.005,0.5,"(d)",fontsize=14)
plt.figtext(0.005,0.08,"(e)",fontsize=14)
#ax_compare[1].set_aspect('equal',adjustable='box')
# ax_compare[1].scatter(Total_daily['Obs_Q'],Total_daily[Var_name_out], label = 'Daily Observed versus Simulated Q(m/d)', color= 'b') #Plot obs data on comparison figure
# max_y=1.05*max(Total_daily[Var_name_out].max(),Total_daily['Obs_Q'].max())
# ax_compare[1].set_xlim([0,max_y])
# ax_compare[1].set_ylim([0,max_y])
# ax_compare[1].plot ([0,max_y],[0,max_y],linestyle='dashed',linewidth=0.5,alpha=0.8, color= 'r') #Plot obs data on comparison figure
#ax_compare[1].set_title("Q Seasonality ")
fig.savefig(output_figure, bbox_inches='tight',dpi=300)
#plt.close(fig)
return NNash,LogNash,NNash70,NNash30,TotQVolError,KGE,Nash,mean_bias,mean_absolute_error,RMSE,NRMSE,TS,coefOfpers
######
def plot_map_bymodel(Selected_calibration,All_Stats,stats,title,output_figure,MinModels,MinThreshold):
import math as m
Model_RR=['NWM2.1',"CFE","CFE_X","Topmodel"]
if(stats=='KGE') | (stats=='NNash'):
Selected_calibration['Best']=''
Selected_calibration['Dif']=-9
for i in range (0,len(Selected_calibration)):
hru_id=Selected_calibration.index[i]
Temp=All_Stats[All_Stats.index==hru_id]
if(len(Temp)>=MinModels):
Temp=Temp.sort_values(by=[stats],ascending=False)
Selected_calibration.at[hru_id,'Best']=Temp.iloc[0]['Model_RR']
if(Temp.iloc[0]['Model_RR']=='NWM2.1'):
Selected_calibration.at[hru_id,'Dif']=abs(Temp.iloc[0][stats]-Temp.iloc[1:4][stats].max())
else:
Selected_calibration.at[hru_id,'Dif']=abs(Temp.iloc[0][stats]-Temp[Temp['Model_RR']=='NWM2.1'][stats].values[0])
nmodels=len(Model_RR)
nrow=m.ceil(nmodels/2)
#figsize=(nrow*4, ncols*3)
f, axes = plt.subplots(figsize=(16, 9.5), ncols=2, nrows=nrow)
us_states_file="/home/west/us_states.json"
us_states = gp.read_file(us_states_file)
us_states=us_states[us_states.name != "Alaska"]
us_states_bounds = us_states.geometry.total_bounds
#contiguous_usa = gp.read_file(gplt.datasets.get_path('contiguous_usa'))
# countries[countries["name"] == "United States of America"].plot(color="lightgrey",
# ax=ax)
#norm = mpl.colors.Normalize(vmin=0, vmax=2)
#cmap = mpl.cm.ScalarMappable(norm=norm, cmap='bwr').cmap
for i in range(0,len(Model_RR)):
if(i==0): ax1=0;ax2=0;xtext=0.15;ytext=0.65
if(i==1): ax1=0;ax2=1;xtext=0.55;ytext=0.65
if(i==2): ax1=1;ax2=0;xtext=0.15;ytext=0.4
if(i==3): ax1=1;ax2=1;xtext=0.55;ytext=0.4
if(i==4): ax1=2;ax2=0;xtext=0.15;ytext=0.15
if(i==5): ax1=2;ax2=1;xtext=0.55;ytext=0.15
us_states.plot(ax=axes[ax1][ax2],color='silver', edgecolor='white')
#Hyd_Sig.plot(ax=axes[0][0],column='Min_col',cmap='RdYlBu',categorical=True,legend="True" )
All_Models_temp=All_Stats[All_Stats['Model_RR']==Model_RR[i]]
CAMELS_516_temp=Selected_calibration[(Selected_calibration['Best']==Model_RR[i]) & (Selected_calibration['Dif']>=MinThreshold)]
#if(ax2==1):
# if(ax2==0):
# im=All_Models_temp.plot.scatter(x='Long',y='Lat',s=60,
# c=stats,colormap='bwr',vmin=0,vmax=1,
# title=Model_RR[i],ax=axes[ax1][ax2],colorbar=False);
im=axes[ax1][ax2].scatter(All_Models_temp['Long'],All_Models_temp['Lat'],s=60,
c=All_Models_temp[stats],vmin=0,vmax=1,cmap='Spectral');
if(Model_RR[i]=="Topmodel"): Model_RR_str="TOPMODEL"
else: Model_RR_str=Model_RR[i]
axes[ax1][ax2].set_title(Model_RR_str, fontsize=13,weight='bold')
# else:
# im=All_Models_temp.plot.scatter(x='Long',y='Lat',s=60,
# c=stats,colormap='bwr',vmin=0,vmax=1,
# title=Model_RR[i],ax=axes[ax1][ax2]);
axes[ax1][ax2].scatter(CAMELS_516_temp['Long'],CAMELS_516_temp['Lat'],s=110, facecolors='none', edgecolors='black',linewidth=2)
#All_Models_temp=All_Models_temp.loc[['10244950','05591550','10336660','09306242']]
#axes[ax1][ax2].scatter(All_Models_temp['Long'],All_Models_temp['Lat'],s=350, facecolors='none', edgecolors='green',linewidth=2)
axes[ax1][ax2].set_yticklabels([])
axes[ax1][ax2].set_xticklabels([])
axes[ax1][ax2].set_xlim(([-125,-65]))
axes[ax1][ax2].set_ylim(([25,50]))
axes[ax1][ax2].xaxis.label.set_visible(False)
axes[ax1][ax2].yaxis.label.set_visible(False)
#plt.title(models[i])
#plt.figtext(xtext,ytext,models[i],fontsize=10)
plt.figtext(0.1,0.55,"(a)",fontsize=15)
plt.figtext(0.52,0.55,"(b)",fontsize=15)
plt.figtext(0.1,0.15,"(c)",fontsize=15)
plt.figtext(0.52,0.15,"(d)",fontsize=15)
cbar_ax = f.add_axes([0.93, 0.15, 0.02, 0.7])
f.colorbar(im, cax=cbar_ax).set_label(label=stats,size=15,weight='bold')
f.savefig(output_figure, bbox_inches='tight',dpi=300)
#####
def plot_box(Selected_calibration,All_Stats,stats,title,output_figure,MinModels):
import math as m
import seaborn as sns
Model_RR=['NWM2.1',"CFE","CFE_X","Topmodel"]
if(stats=='KGE') | (stats=='NNash'):
All_Stats_Best=All_Stats.copy()
Selected_calibration['Best']=''
for i in range (0,len(Selected_calibration)):
hru_id=Selected_calibration.index[i]
Temp=All_Stats[All_Stats.index==hru_id]
if(len(Temp)>=MinModels):
Temp=Temp.sort_values(by=[stats],ascending=False)
Selected_calibration.at[hru_id,'Best']=Temp.iloc[0]['Model_RR']
Temp_2=pd.DataFrame(data=Temp.iloc[0])
Temp_2.at['Model_RR',hru_id]='Best'
All_Stats_Best=pd.concat([All_Stats_Best,Temp_2.transpose()])
#figsize=(nrow*4, ncols*3)
Model_RR_Best=['NWM2.1',"CFE","CFE_X","TOPMODEL","Best"]
All_Stats_Best['Model_RR'][All_Stats_Best['Model_RR']=="Topmodel"]="TOPMODEL"
fig, ax = plt.subplots(figsize=(7.5, 4.5)) #Create the plot for this zone
#fig.set_size_inches(0.8*len(Daily_Q_all_Model['Source'].unique()),4)
All_Stats_Best[stats][All_Stats_Best[stats]<-1]=-1
Line_75=All_Stats_Best[stats][All_Stats_Best['Model_RR']=="NWM2.1"].quantile(0.75)
Line_50=All_Stats_Best[stats][All_Stats_Best['Model_RR']=="NWM2.1"].quantile(0.5)
Line_25=All_Stats_Best[stats][All_Stats_Best['Model_RR']=="NWM2.1"].quantile(0.25)
#plt.rcParams["figure.figsize"] = (5,10)
sns.boxplot(x='Model_RR',y=stats,data=All_Stats_Best,ax=ax,color='silver',width=0.5,order=Model_RR_Best)
#ax2 = ax.twinx()
ax.hlines(Line_25, -0.5, 4.5,label='NWM 2.1 25th Percentile',linestyle="dashed", color ="orange")
ax.hlines(Line_50, -0.5, 4.5,label='NWM 2.1 50th Percentile',linestyle="dashed", color ="green")
ax.hlines(Line_75, -0.5, 4.5,label='NWM 2.1 75th Percentile',linestyle="dashed", color ="blue")
fig.legend(loc = "lower center",ncol=3 )
ax.set_xlabel ('',fontsize=14)
ax.set_ylabel(stats)
#ax.set_xticklabels(Daily_Q_all_Model['Source'].unique(),rotation=90)
ax.yaxis.grid(True)
#ax.set_yscale("log")
#ax.set_title(Title_str)
fig.savefig(output_figure, bbox_inches='tight',dpi=300)
#plt.close(fig)
def NumberOfModels_plot(Rainfall_Runoff_ModelAr,stats_name,stats_name_axis,stats_criteria,Selected_calibration,All_Stats,stats,title,output_figure,MinModels):
CB_color_cycle = [
'#f781bf', '#a65628', '#984ea3',
'#999999', '#e41a1c', '#dede00','#377eb8','#ff7f00', '#4daf4a']
Temp=Rainfall_Runoff_ModelAr.copy()
Temp.append("NWM2.1")
NumberOfModels=pd.DataFrame(index=Temp)
for j in range(0,len(stats_name)):
stats=stats_name[j]
NumberOfModels[stats]=0
for i in range (0,len(Selected_calibration)):
hru_id=Selected_calibration.index[i]
Temp=All_Stats[All_Stats.index==hru_id]
if(len(Temp)>=MinModels):
if(stats_criteria[j]=="max"):
Temp=Temp.sort_values(by=[stats],ascending=False)
NumberOfModels.at[Temp.iloc[0]['Model_RR'],stats]=NumberOfModels.at[Temp.iloc[0]['Model_RR'],stats]+1
elif(stats_criteria[j]=="min"):
Temp=Temp.sort_values(by=[stats],ascending=True)
NumberOfModels.at[Temp.iloc[0]['Model_RR'],stats]=NumberOfModels.at[Temp.iloc[0]['Model_RR'],stats]+1
else:
Temp[stats]=np.abs(Temp[stats])
Temp=Temp.sort_values(by=[stats],ascending=True)
NumberOfModels.at[Temp.iloc[0]['Model_RR'],stats]=NumberOfModels.at[Temp.iloc[0]['Model_RR'],stats]+1
# print(stats)
# print(hru_id)
# print(Temp.iloc[0]['Model_RR'])
for i in range(0,len(stats_name)):
NumberOfModels=NumberOfModels.rename(columns={stats_name[i]:stats_name_axis[i]})
NumberOfModels.index=NumberOfModels.index.str.upper()
if output_figure != False:
fig, ax = plt.subplots() #Create the plot for this zone
NumberOfModels.transpose().plot(kind="bar", stacked=True,ax=ax, colormap='BrBG')
plt.title(title)
ax.legend(loc='upper center',ncol=4)
NMax=NumberOfModels.sum().max()
ax.set_ylim([0,NMax+4])
ax.set_xlabel ("Statistics")
ax.set_ylabel ("Number of locations where model is best")
fig.savefig(output_figure, bbox_inches='tight',dpi=300)
return NumberOfModels
#plt.close(fig)
#########################################################################
# functions above here
# processes below
#######################################################################
# get date and use to label log file
current_date = datetime.now().strftime("%Y-%m-%d %H:%M")
cwd = os.getcwd()
log_file = f'{cwd}/plot_calibration_{current_date}.log'
# create log file
with open(log_file, 'w') as file:
pass
CB_color_cycle = ['#377eb8', '#ff7f00', '#4daf4a',
'#f781bf', '#a65628', '#984ea3',
'#999999', '#e41a1c', '#dede00']
# BChoat; if output already produced for an HRU, should it be overwritten?
# if True, then checks if a sample file name exists and assume it indicates
# that all desired files exist
# overwrite_previous = False
# directory
Hydrofabrics_folder="/home/west/Projects/CAMELS/CAMELS_Files_Ngen/"
# topmodel outputs are placed in different folder since I moved CFE so I wouldn't mess it up
Hydrofabrics_folder_HD_tm="/media/Expansion/Projects/CAMELS/CAMELS_Files_Ngen/"
# Hydrofabrics_folder_HD_tm="/media/Expansion/Projects/CAMELS/CAMELS_Files_Ngen_Original/"
# directory holding usgs discharge data
dir_usgs = '/media/Expansion/Projects/CAMELS/USGS_Q_20071001_20131001'
# directory holding nwm 2.1 results
dir_nwm = '/media/Expansion/Projects/CAMELS/nwm_v2.1_chrt_camels513_csv/csv'
Hydrofabrics_folder_HD_cfe="/media/Expansion/Projects/CAMELS/CAMELS_Files_Ngen_Original/"
Select_calibration_file="/home/west/Projects/CAMELS/CAMELS_Files_Ngen/CAMELS_v3_calib_BestModelsAll.csv"
ALL=pd.read_csv(Select_calibration_file,dtype={'hru_id_CAMELS': str})
ALL=ALL.set_index(['hru_id_CAMELS'])
ALL=ALL.rename(columns={'N_Nexus':'N_nexus'})
ALL=ALL.drop(['Q_Best_Topmodel','Q_Best_CFE_X'],axis=1).dropna()
# BChoat 2024/01/26: Subset to only those catchments in the new topmodel
# output folder on expansion drive
# get hru_ids for hrus already reran with topmodel
files_tm = [f for f in os.listdir(f'{Hydrofabrics_folder_HD_tm}Results')]
files_tm = [f for f in files_tm if 'camels_' in f]
# print(files_tm)
hru_tm = [x.split('_')[1] for x in files_tm]
# print(f'hru_tm: {hru_tm}')
ALL = ALL[ALL.index.isin(hru_tm)]
# print(f'ALL: {ALL}')
Selected_calibration=ALL.copy()
# Selected_calibration['Lat']=-9.0
# Selected_calibration['Long']=-9.0
# for i in range (0,len(Selected_calibration)):
# hru_id=Selected_calibration.index[i]
# Folder=Selected_calibration.iloc[i]['Folder_CAMELS']
# Hydrofabrics=Hydrofabrics_folder+"/"+Folder+"/spatial/catchment_data.geojson"
# basin = gp.read_file(Hydrofabrics)
# basin = basin.to_crs({'init':'epsg:4269'})
# basin_id=basin.sort_values(by="area_sqkm",ascending=True).iloc[0].id
# nexus_id=basin.sort_values(by="area_sqkm",ascending=True).iloc[0].toid
# Selected_calibration.at[hru_id,'Lat']=basin.sort_values(by="area_sqkm",ascending=True).iloc[0].geometry.centroid.y
# Selected_calibration.at[hru_id,'Long']=basin.sort_values(by="area_sqkm",ascending=True).iloc[0].geometry.centroid.x
# directory
# NextGen_folder="/home/west/git_repositories/ngen_11112022/ngen"
# NextGen_folder = '/home/west/git_repositories/ngen_20240111_calib/ngen'
NextGen_folder = '/home/west/git_repositories/ngen_20240327_calib/ngen'
os.environ['PATH'] += os.pathsep+"/home/west/TauDEMDependencies/mpich/mpich-install"
os.environ['PATH']=NextGen_folder+"/venv/bin:/home/west/anaconda3/bin:/home/west/anaconda3/condabin:/sbin:/bin:/usr/bin:/usr/local/bin:/snap/bin"
str_sub="source "+NextGen_folder+"/venv/bin/activate"
out=subprocess.call(str_sub,shell=True)
Results_calibration=Hydrofabrics_folder_HD_tm+"Calib_plots_WithNoah/"
# Results_calibration="/media/Expansion/Projects/CAMELS/TESTINGPLOTS/Calib_plots_WithNoah/"
# directory
if not os.path.exists(Results_calibration):
print('creating calibration output directory')
os.mkdir(Results_calibration)
Rainfall_Runoff_ModelAr=["CFE","CFE_X","Topmodel"]
# Rainfall_Runoff_ModelAr=["Topmodel"]
#Model="NOAH-CFE"
eval_type="Streamflow"
flag_Plot_cat=0
b_doc_name=Results_calibration+"Appendix_B_11222022.docx"
# filename of output file(s) a .csv and .xlsx will be saved as file_out
# name with .csv extension
file_out = f'{Results_calibration}NWM_performance_first.csv'
# try to read in file_out in case it was created in previous execution (so we
# don't reprocess results that have already been processed)
# otherwise, create empty dataframe to add results to
try:
All_Stats = pd.read_csv(f"{Results_calibration}/NWM_performance_first.csv",
dtype={'hru_id_CAMELS': 'string'},
).drop_duplicates(
subset=['hru_id_CAMELS', 'Model']
).reset_index(
drop = True
).set_index('hru_id_CAMELS')
# All_Stats = pd.read_csv(file_out,
# dtype = {'hru_id_CAMELS': 'string'}) #,
# index_col = 0)
# subset Selected_calibration to only HRUs that have not been previously processed
Selected_calibration = Selected_calibration[
~Selected_calibration.index.isin(All_Stats.index)
]
except:
All_Stats=pd.DataFrame()
document = Document()
change_orientation2()
document.add_heading('Appendix B: Plot for calibrated basins ', 0)
p = document.add_paragraph()
for i in range(0,len(Rainfall_Runoff_ModelAr)):
Selected_calibration[Rainfall_Runoff_ModelAr[i]]=0
#Selected_calibration=Selected_calibration.loc[['02430085']]
#Selected_calibration=Selected_calibration[(Selected_calibration['frac_snow']<0.1) & (Best_PET.str.contains('PET')) & (Selected_calibration['N_nexus']>=2)]
#Selected_calibration=Selected_calibration.sort_values(by="NCat")
count=0
#Selected_calibration=Selected_calibration.loc[['02430085','11284400','14362250']]
#Selected_calibration=Selected_calibration.loc[['11284400']]
# Selected_calibration=Selected_calibration.loc[['01052500', '01031500']]
# Selected_calibration=Selected_calibration.loc[['01052500', '01031500', '01055000']]
# Selected_calibration=Selected_calibration.loc[['01052500', '01055000']]
for i in range(0,len(Selected_calibration)):
Folder_CAMELS=Selected_calibration.iloc[i]['Folder_CAMELS']
print (f'Processing: {Folder_CAMELS}')
hru_id_long = Folder_CAMELS.split('_')[1:3]
hru_id_long = f'{hru_id_long[0]}_{hru_id_long[1]}'
# print(f'hru_id_long: {hru_id_long}')
hru_id=Selected_calibration.index[i]
# directory
# work_dir=Hydrofabrics_folder_HD+Folder_CAMELS+"/"
# Obs_Q_file=work_dir+"Validation/usgs_hourly_flow_2007-2019_"+hru_id+".csv"
# Obs_Q_file = f'{work_dir}Validation/usgs_hourly_flow_calibration.csv'
Obs_Q_file = f'{dir_usgs}/usgs_hourly_flow_calibration_{hru_id}.csv'
# NWM_21_file=work_dir+"Validation/nwm_v2.1_chrt."+hru_id+".csv"
NWM_21_file= f'{dir_nwm}/nwm_v2.1_chrt.{hru_id}.csv'
# BChoat 2024/01/26:
# check if Obs_Q_file and NWM_21_file exists,
# if not just continue for now. Fix later. Looks like usgs data produced with
# /home/west/Projects/CAMELS/params_code/generate_validation_data.sh
# 2024/05/05 Updated to saved observed data during calibration
# if not os.path.exists(f'{work_dir}/Validation'):
if not os.path.exists(Obs_Q_file):
with open(log_file, 'a') as file:
file.write(f'{Obs_Q_file} does not exist\n\n')
continue
# os.makedirs(f'{work_dir}/Validation')
# if not os.path.exists(Obs_Q_file):
# shutil.copy(f'{Hydrofabrics_folder_HD_cfe}/')
# for j in range(0,3):
for j in range(0, len(Rainfall_Runoff_ModelAr)):
Rainfall_Runoff_Model=Rainfall_Runoff_ModelAr[j]
# since I stored topmodel results in different location
# define Hydrofabrics_folder_HD based on if CFE ot Topmodel
if Rainfall_Runoff_Model == 'Topmodel':
Hydrofabrics_folder_HD = Hydrofabrics_folder_HD_tm
obj_log_name = "objective_log.txt"
h5_name = "flowveldepth_Ngen.h5_last"
else:
Hydrofabrics_folder_HD = Hydrofabrics_folder_HD_cfe
obj_log_name = "ngen-calibration_objective.txt"
h5_name = "flowveldepth_Ngen.h5"
# bchoat adding after editing to work of expansion
work_dir = Hydrofabrics_folder_HD
PET=Selected_calibration.iloc[i]['A_Best1']
#PET="PET_4"
if(hru_id=='02430085') & (hru_id=='02465493'):
PET="PET_4"
#PET="NOAH"
os.chdir(Hydrofabrics_folder_HD)
# os.chdir(work_dir)
Model=PET+"_"+Rainfall_Runoff_Model
Model_2="NOAH"+"_"+Rainfall_Runoff_Model
calib_config_dir=work_dir+"/"+Model+"/"
#Final_Folder=Hydrofabrics_folder_HD+"/Results/"+Folder_CAMELS+"/Calibration/"+Model
# directory
calib_config_dir=Hydrofabrics_folder_HD+"/Results/"+Folder_CAMELS+"/Calibration/"+Model+"/" +Model+"/"
calib_config_dir_NOAH=Hydrofabrics_folder_HD+"/Results/"+Folder_CAMELS+"/Calibration/"+Model_2+"/" +Model_2+"/"
# print(f'calib_config_dir: {calib_config_dir}\n..._NOAH: {calib_config_dir_NOAH}')
# BChoat; edit so if defined plot already exists, then assume no need to produce further analysis,
# so continue
# if not overwrite_previous:
# check get if plot already exists (assumes if the defined file exists, then no need for further analysis
# test_file = Results_calibration+hru_id+"_"+Model+"_Scatter.png"
# print(f'line 598; test_file; {test_file}')
# if os.path.exists(test_file):
# continue
# print("didn't pass due to test_file existing")
Model_name=Model.replace("PET_","PET")
Model_name=Model_name.replace("NOAH","NOM").upper()
if(Selected_calibration['frac_snow'].iloc[i]>=0.1) & ("PET" in PET): Model_name="NOM_"+Model_name
############################################################################################################
# BChoat 2024/01/26
# I'm not sure any of the code between here and the next "#############" line far below is needed at this point
# between here and ~line 803
#############################################################################################################
# if(not os.path.exists(Final_Folder)): os.makedirs(Final_Folder)
# if(os.path.exists(calib_config_dir)):
# Run_mv="mv "+calib_config_dir+" " +Final_Folder
# out=subprocess.call(Run_mv,shell=True)
if(Rainfall_Runoff_Model=="CFE"):
calib_config_base=calib_config_dir+"/calib_config_CAMELS_CFE_Calib_Sep_2.yaml"
iterations=300
elif(Rainfall_Runoff_Model=="CFE_X"):
calib_config_base=calib_config_dir+"/calib_config_CAMELS_CFE_X2_Calib_Sep_2.yaml"
if(not os.path.exists(calib_config_base)):
calib_config_base=calib_config_dir+"/calib_config_CAMELS_CFE_X_Calib_Sep_2.yaml"
iterations=300
elif(Rainfall_Runoff_Model=="Topmodel"):
calib_config_base=calib_config_dir+"/calib_config_CAMELS_Topmodel_Calib_Sep_2.yaml"
iterations=120
if('PET' in PET) & (Selected_calibration['frac_snow'].iloc[i]<0.1):
if(Rainfall_Runoff_Model=="CFE"): calib_realiz_base=calib_config_dir+"Realization_pet_cfe_calibNC.json"
elif(Rainfall_Runoff_Model=="CFE_X"): calib_realiz_base=calib_config_dir+"Realization_pet_cfe_X_calibNC.json"
elif(Rainfall_Runoff_Model=="Topmodel"): calib_realiz_base=calib_config_dir+"Realization_pet_Topmodel_calibNC.json"
else: print("error: model does not exist")
elif('PET' in PET) & (Selected_calibration['frac_snow'].iloc[i]>=0.1):
if(Rainfall_Runoff_Model=="CFE"): calib_realiz_base=calib_config_dir+"Realization_noahowp_pet_cfe_calibNC.json"
elif(Rainfall_Runoff_Model=="CFE_X"): calib_realiz_base=calib_config_dir+"Realization_noahowp_pet_cfe_X_calibNC.json"
elif(Rainfall_Runoff_Model=="Topmodel"): calib_realiz_base=calib_config_dir+"Realization_noahowp_pet_Topmodel_calibNC.json"
else: print("error: model does not exist")
else:
if(Rainfall_Runoff_Model=="CFE"): calib_realiz_base=calib_config_dir+"Realization_noahowp_cfe_calibNC.json"
elif(Rainfall_Runoff_Model=="CFE_X"): calib_realiz_base=calib_config_dir+"Realization_noahowp_cfe_X_calibNC.json"
elif(Rainfall_Runoff_Model=="Topmodel"): calib_realiz_base=calib_config_dir+"Realization_noahowp_Topmodel_calibNC.json"
else: print("error: model does not exist")
PET="NOAH"
# since saved topmodel in new dir and diff name of objective file,
# conditionally set file name here
# objective_log = calib_config_dir+'/ngen-calibration_objective.txt'
# objective_log_2 = calib_config_dir_NOAH+'/ngen-calibration_objective.txt'
objective_log = f"{calib_config_dir}/{obj_log_name}"
objective_log_2 = f"{calib_config_dir_NOAH}/{obj_log_name}"
# if(os.path.exists(objective_log)):
# os.makedirs(Hydrofabrics_folder_HD+"/Results/"+Folder_CAMELS+"/Calibration2/"+Model+"/")
# str_sub="mv "+calib_config_dir+" "+Hydrofabrics_folder_HD+"/Results/"+Folder_CAMELS+"/Calibration2/"+Model+"/"
# out=subprocess.call(str_sub,shell=True)
if( os.path.exists(objective_log)):
if((os.path.exists(objective_log_2))):
print ("BOTH AVALILABLE "+Folder_CAMELS)
if(not os.path.exists(objective_log)):
ori_calib_config_dir=work_dir+"/"+Model+"/"+"/" + obj_log_name # 'ngen-calibration_objective.txt'
if((os.path.exists(objective_log))):
print ("File Exists in original " + ori_calib_config_dir)
str_sub="mv "+work_dir+"/"+Model+" "+Hydrofabrics_folder_HD+"/Results/"+Folder_CAMELS+"/Calibration/"+Model+"/"
out=subprocess.call(str_sub,shell=True)
size=0
# print(f"line 668; objective_log:{objective_log}")
if(os.path.exists(objective_log)):
size=os.path.getsize(objective_log)
# print(f"line 670; size: {size}\nSelected_cali[N_nexus]: {Selected_calibration.iloc[i]['N_nexus']}")
if(Selected_calibration.iloc[i]['N_nexus']>=2) & (size>0):
# print(f'line 672; in if(....>=2) & size>0 ')
flag_noFlow=0
flag_NOAH_noFlow=0
# print(Folder_CAMELS + " " + Model)
#catchment_data_file = Base_directory/'spatial/catchment_data.geojson'
#nexus_data_file = Base_directory/'spatial/nexus_data.geojson'
# cross_walk_file = work_dir+'parameters/cross-walk.json'
cross_walk_file = f'{Hydrofabrics_folder_HD_cfe}/camels_{hru_id_long}/parameters/cross-walk.json'
# catchment_file=work_dir+'spatial/catchment_data.geojson'
catchment_file = f'{Hydrofabrics_folder_HD_cfe}/camels_{hru_id_long}/spatial/catchment_data.geojson'
zones = gp.GeoDataFrame.from_file(catchment_file)
# nexus_file=work_dir+'spatial/nexus_data.geojson'
nexus_file=f'{Hydrofabrics_folder_HD_cfe}/camels_{hru_id_long}/spatial/nexus_data.geojson'
nexus = gp.GeoDataFrame.from_file(nexus_file)
params_file = calib_config_dir+'*.parquet'
params_file = glob.glob(params_file)[0]
# str_sub="mv ./Best_Results "+calib_config_dir
# out=subprocess.call(str_sub,shell=True)
output_file = calib_config_dir+"/Best_Results/"+h5_name # 'flowveldepth_Ngen.h5'
output_file_NOAH = calib_config_dir_NOAH+"/Best_Results/"+h5_name # 'flowveldepth_Ngen.h5'
ngen_objective = calib_config_dir+obj_log_name # 'ngen-calibration_objective.txt'
# Read objective_log
Objective = pd.read_csv(ngen_objective,index_col=0,header=None)
Indi_zero=Objective[Objective.iloc[:,0] == 0].index.values
if(len(Indi_zero)==0):
ind=0
else:
ind=Indi_zero[len(Indi_zero)-1]
Objective=Objective.iloc[ind:,:]
#Objective=Objective.reset_index()
Objective=Objective[1].astype(float).to_frame()
Diff_objective=Objective.diff(periods=1,axis=0)
Diff_objective['Param']="-9"
# Read calib_config_file file
try: # BChoat; adding try since topmodel saved in different location w/different
# ngen-cal version/different params
with open(calib_config_base, 'r') as stream:
data_loaded = yaml.safe_load(stream)
# print(f"data_loaded: {data_loaded}")
start_t_calib=data_loaded['general']['evaluation_start']
end_t_calib = data_loaded['general']['evaluation_stop']
n_iterations = data_loaded['general']['iterations']
except:
with open(calib_config_base, 'r') as stream:
data_loaded = yaml.safe_load(stream)
start_t_calib = data_loaded['model']['eval_params']['evaluation_start']
end_t_calib = data_loaded['model']['eval_params']['evaluation_stop']
n_iterations = data_loaded['general']['iterations']
if(Objective.index[len(Objective)-1]>=n_iterations):
Selected_calibration.at[hru_id,Rainfall_Runoff_ModelAr[j]]=1
# Read realization file
# if(hru_id=='02430085') & (PET=="NOAH"):
# calib_realiz_base="/media/west/Expansion/Projects/CAMELS/CAMELS_Files_Ngen//Results/camels_02430085_18693151/Calibration/NOAH_CFE/NOAH_CFE/Realization_noahowp_cfe_calib_NCCsv.json"
with open(calib_realiz_base) as fp:
data = json.load(fp)
start_t_real = data['time']['start_time']
end_t_real = data['time']['end_time']
print(f'start_t_real: {start_t_real}')
print(f'end_t_real: {end_t_real}')
# Read calib_config_file file
# with open(calib_config_file, 'r') as stream:
# data_loaded = yaml.safe_load(stream)
# start_t_calib=data_loaded['general']['evaluation_start']
# end_t_calib = data['time']['end_time']
# Read observed
# Obs_Q_cms=pd.read_csv(Obs_Q_file,parse_dates=True,index_col=1)
# Obs_Q_cms=Obs_Q_cms.loc[:,['q_cms']]
# Obs_Q_cms.rename(columns = {'q_cms':'q_cms_obs'}, inplace = True)
# BChoat; updating to reflect use of usgs Q data downloaded during calibration
Obs_Q_cms=pd.read_csv(Obs_Q_file,parse_dates=True,index_col=0)
# print(f'~line851; Obs_Q_cms.index: {Obs_Q_cms.index}')
# print(Obs_Q_cms.columns)
Obs_Q_cms=Obs_Q_cms.loc[:,['obs_flow']]
Obs_Q_cms.rename(columns = {'obs_flow':'q_cms_obs'}, inplace = True)
Obs_Q_cms = Obs_Q_cms[(Obs_Q_cms.index >= start_t_real) & (Obs_Q_cms.index <= end_t_real)]
# print(f'~line857; Obs_Q_cms.index: {Obs_Q_cms.index}')
# Read crosswalk
data = json.load(open(cross_walk_file))
Nexus=list(data.keys())
for ii in range(0,len(Nexus)):
# print(f'cross-walk: {ii}')
data[Nexus[ii]].keys()
# print(f'{data[Nexus[ii]].keys()}')
if('Gage_no' in data[Nexus[ii]].keys()):
Nex=Nexus[ii]
# BChoat: Gauge_id is never actually used anywhere ... should it be?
Gauge_id=data[Nexus[ii]]['Gage_no'][0]
# print(f'Nex: {Nex}')
# print(f'Gauge_id: {Gauge_id}')
# Read parameters
param=pd.read_parquet(params_file, engine='pyarrow')
param=param.set_index(['param'])
param=param.transpose()
param=param.drop('model')
param=param.drop('sigma')
param=param.drop('0')
param=param.astype(float)
param_norm=param.copy()
for j in range(0,len(param_norm.columns)):
param_norm[param_norm.columns[j]]=(param_norm[param_norm.columns[j]]-param_norm[param_norm.columns[j]].loc['min'])/(param_norm[param_norm.columns[j]].loc['max']-param_norm[param_norm.columns[j]].loc['min'])
param_norm=param_norm.drop('max')
param_norm=param_norm.drop('min')
param=param.drop('max')
param=param.drop('min')
Objective=Objective.rename(columns={Objective.columns[0]:"Objective"})
Objective.index=Objective.index.astype('str')
param=pd.concat([param,Objective],axis=1)
if(len(param.Objective.unique())<2):
# print('line 768; in if(lenParam.Objective.unique())<2)')
str_sub="rm -rf "+calib_config_dir
out=subprocess.call(str_sub,shell=True)
else:
# print('line 772; in else()')
Final_param=param.sort_values(by='Objective',ascending=False).iloc[0]
# print(f'output_file: {output_file}')
if(not os.path.exists(output_file)):
# print("Did not find output, Need to re-run")
realiz_file=work_dir+"Realization_"+Model+"Best.json"
with open(calib_realiz_base) as fp:
data = json.load(fp)
# print(f"data: {data.keys()}")
for i_p in range(0,len(Final_param)):
param_name=Final_param.index[i_p]
param_value=Final_param.iloc[i_p]
# BChoat; adding try since topmodel in diff location with different config file
# due to different version of ngen-cal being used
try:
if(param_name in data['global']['formulations'][0]['params']['modules'][1]['params']['model_params']):
data['global']['formulations'][0]['params']['modules'][1]['params']['model_params'][param_name]=param_value
else:
print("Key does not exist " + param_name)
except Exception as e:
print(e)
# if(param_name in data['global']['formulations'][0]['params']['modules'][1]['params']['model_params']):
# data['global']['formulations'][0]['params']['modules'][1]['params']['model_params'][param_name]=param_value
# else:
# print("Key does not exist " + param_name)
json_object=json.dumps(data,indent=4)
with open(realiz_file,"w") as outfile:
outfile.write(json_object)
#define the ngen
os.chdir(work_dir)
str_sub="rm -rf ngen"
out=subprocess.call(str_sub,shell=True)
str_sub="ln -s "+NextGen_folder
out=subprocess.call(str_sub,shell=True)
print("Will run nextgen")
Run_nextgen="./ngen/cmake_build/ngen "+ "./spatial/catchment_data.geojson '' ./spatial/nexus_data.geojson '' "+realiz_file
# if(len(zones)>8):
# Run_nextgen="./ngen/cmake_build_paral/ngen "+ "./spatial/catchment_data.geojson '' ./spatial/nexus_data.geojson '' "+Realiz_out+ " " +hru_id+"_8.json"
if("PET" in Model):
ID=Model.split("_")[1]
for idd in range(1,6):
Run_sed="sed -i 's/pet_method="+str(idd)+"/pet_method='"+str(ID)+"'/g' ./PET/*"
out=subprocess.call(Run_sed,shell=True)
out=subprocess.call(Run_nextgen,shell=True)
os.makedirs(calib_config_dir+"/Best_Results/", exist_ok=True)
output_file = calib_config_dir+"/Best_Results/"+h5_name # 'flowveldepth_Ngen.h5'
output_file_NOAH = calib_config_dir_NOAH+"/Best_Results/"+h5_name # 'flowveldepth_Ngen.h5'
#mv_files="mv flowveldepth_Ngen.h5 "+output_file
#out=subprocess.call(mv_files,shell=True)
#mv_files="mv *.csv "+calib_config_dir+"/Best_Results/"
#out=subprocess.call(mv_files,shell=True)
# BChoat - Not sure if code above here and below prevous "#####" line is needed
####################################################################################
# Read simulated
if(os.path.exists(output_file)):
Sim_Q_cms = pd.read_hdf(output_file)
Sim_Q_cms.index = Sim_Q_cms.index.map(lambda x: 'wb-'+str(x))
Sim_Q_cms = Sim_Q_cms[Sim_Q_cms.columns.drop(list(Sim_Q_cms.filter(regex='v')))]
Sim_Q_cms = Sim_Q_cms[Sim_Q_cms.columns.drop(list(Sim_Q_cms.filter(regex='d')))]
Sim_Q_cms=Sim_Q_cms.transpose().loc[:,[Nex]]
# print(f'Nex: {Nex}')
# print(f'Sim_Q_cms: \n{Sim_Q_cms.head(20)}')
Sim_Q_cms.rename(columns = {Nex:'q_cms_sim'}, inplace = True)
dt_range = pd.date_range(start_t_real,end_t_real, len(Sim_Q_cms)).round('min')
# print(f'dt_range: \n{dt_range}')
Sim_Q_cms.index=dt_range
# print(f'Sim_Q_cms: \n{Sim_Q_cms.head(20)}')
Sim_Q_cms_H = Sim_Q_cms.resample('1H').first()
# print(f'Sim_Q_cms_H:\n {Sim_Q_cms_H.head(20)}')
# BCHOAT ADDING FOR TROUBLESHOOTING
# print(os.getcwd())
# Sim_Q_cms.to_csv(f'{hru_id}_{Rainfall_Runoff_Model}.csv')
# Sim_Q_cms_H.to_csv(f'{hru_id}_{Rainfall_Runoff_Model}_H.csv')
tnx_file = glob.glob(calib_config_dir+"/Best_Results/"+"tnx-*.csv")[0]
tnx_df = pd.read_csv(tnx_file, index_col=0, parse_dates=[1], names=['ts', 'time', 'q_cms_sim']).set_index('time')
# print(f'~line985')
# print(f'Sim_Q_cms_H.index: {Sim_Q_cms_H.index}')
# print(f'tnx_df.index: {tnx_df.index}')
Sim_Q_cms_H=Sim_Q_cms_H['q_cms_sim']+tnx_df['q_cms_sim']
# Sim_Q_cms_H = Sim_Q_cms_H['q_cms_sim']
if(os.path.exists(output_file_NOAH)):