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160 lines
6.9 KiB
Python
160 lines
6.9 KiB
Python
5 years ago
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# -*- coding: utf-8 -*-
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#==========================================================#
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#Last Updated - June 2018
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#@author: z5025317 Valentin Heimhuber
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#code for combining all extracted RMA2 and 11 results into a single data frame and save it to CSV
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#==========================================================#
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#Load packages
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#==========================================================#
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import numpy as np
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import os
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import pandas as pd
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import glob
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import matplotlib
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import matplotlib.pyplot as plt
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from datetime import datetime
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from datetime import timedelta
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#==========================================================#
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#==========================================================#
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#Input parameters and directories
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#==========================================================#
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# Set working direcotry (where postprocessed NARClIM data is located)
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#set beginning and end years and corresponding scenario code
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fs=['Hwq003', 'Hwq005']
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HDvariables = ['depth', 'elev','vel']
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WQvariables = ['sal']
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Subset_years = False
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startyear=1999 #years need to be adjusted based on the time period of the model runs
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endyear=2004
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#year=range(startyear, endyear+1)
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#set directory path for output files
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output_directory = 'H:/WRL_Projects/Hunter_CC_Modeling/Module_6/03_Results/Output/Postprocessed/Compound_data/'
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nodes_csv = 'H:/WRL_Projects/Hunter_CC_Modeling/Module_6/03_Results/Chainages/Hunter_nodes.csv'
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#read csv file with nodes and chainages to extract data from
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node = pd.read_csv(nodes_csv)['Hunter'].values
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chainages = pd.read_csv(nodes_csv)['x_km'].values
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#==========================================================#
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#==========================================================#
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#output_directory = 'J:/Project wrl2016032/NARCLIM_Raw_Data/Extracted'
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if not os.path.exists(output_directory):
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os.makedirs(output_directory)
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print('-------------------------------------------')
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print("output directory folder didn't exist and was generated")
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print('-------------------------------------------')
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#==========================================================#
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#==========================================================#
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#data extraction for RMA11 Variables
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#==========================================================#
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WQ_Summary_df = pd.DataFrame()
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for variable in WQvariables:
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for f in fs:
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f = 'Hwq003'
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input_directory = 'H:/WRL_Projects/Hunter_CC_Modeling/Module_6/03_Results/Output2/'+ f
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# Set working direcotry (where postprocessed NARClIM data is located)
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os.chdir(input_directory)
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Summary_df = pd.DataFrame()
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df = pd.DataFrame()
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for NODE in node:
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NODE = str(NODE)
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#set input and output directories
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#==========================================================#
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#Load data file
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Clim_Var_CSVs = glob.glob('*_'+ NODE + '_*WQ*')
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print Clim_Var_CSVs
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print NODE
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clim_var_csv_path = Clim_Var_CSVs[0]
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df = pd.read_csv(clim_var_csv_path, index_col=False, sep=' ')
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df.index = pd.to_datetime(df.Year, format = '%Y') + pd.to_timedelta(df.Hour, unit='h')
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df= df.drop(columns=['Year', 'Hour'])
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#df.columns = [NODE+'_Sal'] #, NODE+'_Tem']
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df.columns = [NODE + '_'+ variable + '_'+ f]
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#df = df.loc[~df.index.duplicated(keep='first')]
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Summary_df = pd.concat([Summary_df, df], axis=1)
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out_path = input_directory + '/' + f + '_' + variable + '4.csv'
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print('writing ' + out_path)
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Summary_df.to_csv(out_path)
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#Optionally cut down the summary df to common years
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if Subset_years:
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Summary_df = Summary_df[datetime.strptime(str(startyear) + ' 01 01', '%Y %m %d').date():datetime.strptime(str(endyear) + ' 06 31', '%Y %m %d').date()]
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WQ_Summary_df = pd.concat([WQ_Summary_df ,Summary_df], axis=1, join='outer')
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#==========================================================#
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##==========================================================#
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##data extraction for RMA2 variables
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##==========================================================#
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#HD_Summary_df = pd.DataFrame()
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#for variable in HDvariables:
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# Summary_df = pd.DataFrame()
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# df = pd.DataFrame()
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# for f in fs:
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# #set input and output directories
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# input_directory = 'H:/WRL_Projects/Hunter_CC_Modeling/Module_6/03_Results/Output/' + f
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# # Set working direcotry (where postprocessed NARClIM data is located)
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# os.chdir(input_directory)
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# #==========================================================#
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# #Load data file
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# if variable == 'depth' or variable == 'elev':
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# Clim_Var_CSVs = glob.glob('*' + variable + '*')
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# clim_var_csv_path = Clim_Var_CSVs[0]
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# df = pd.read_csv(clim_var_csv_path, index_col=False, sep=' ')
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# df.index = pd.to_datetime(df.Year, format = '%Y') + pd.to_timedelta(df.Hour, unit='h')
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# df= df.drop(columns=['Year', 'Hour'])
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# a=len(df.columns)-1
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# df=df.iloc[:,:a]
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# if variable == 'vel':
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# #x velocity
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# Clim_Var_CSVs = glob.glob('*' +'x'+ variable + '*')
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# clim_var_csv_path = Clim_Var_CSVs[0]
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# df = pd.read_csv(clim_var_csv_path, index_col=False, sep=' ')
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# df.index = pd.to_datetime(df.Year, format = '%Y') + pd.to_timedelta(df.Hour, unit='h')
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# dfx= df.drop(columns=['Year', 'Hour','1'])
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# #y velocity
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# Clim_Var_CSVs = glob.glob('*' +'y'+ variable + '*')
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# clim_var_csv_path = Clim_Var_CSVs[0]
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# df = pd.read_csv(clim_var_csv_path, index_col=False, sep=' ')
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# df.index = pd.to_datetime(df.Year, format = '%Y') + pd.to_timedelta(df.Hour, unit='h')
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# dfy= df.drop(columns=['Year', 'Hour','1'])
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# df = np.sqrt(dfx*dfx + dfy*dfy)
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#
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# df.columns = df.columns + '_'+ variable + '_'+ f
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# Summary_df = pd.concat([Summary_df, df], axis=1, join='outer')
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# #Optionally cut down the summary df to common years
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# if Subset_years:
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# Summary_df = Summary_df[datetime.strptime(str(startyear) + ' 01 01', '%Y %m %d').date():datetime.strptime(str(endyear) + ' 06 31', '%Y %m %d').date()]
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# HD_Summary_df = pd.concat([HD_Summary_df , Summary_df], axis=1, join='outer')
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##==========================================================#
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#
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#
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#
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#
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##==========================================================#
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##generate and safe the final data frame as csv
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##==========================================================#
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#Compound_df = pd.concat([WQ_Summary_df , HD_Summary_df], axis=1, join='outer')
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#var = 'Scn_'
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#for f in fs:
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# var = var+ '_' + f
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#WQvars = 'WQ'
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#for variabs in WQvariables:
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# WQvars = WQvars + '_' + variabs
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#out_path = output_directory + var + '_' + WQvars + '_' + str(startyear) + '_' + str(endyear) + '_compound.csv'
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#Compound_df.to_csv(out_path)
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# #==========================================================#
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#
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#
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