#new code for preparing the NARCLIM data for use in the rma_preprocessing.py
code= it's fullly working!Development
Valentin Heimhuber
7 years ago
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# -*- coding: utf-8 -*-
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"""
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Created on Fri Jun 08 14:34:51 2018
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@author: z5025317
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"""
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#####################################----------------------------------
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#Last Updated - March 2018
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#@author: z5025317 Valentin Heimhuber
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#code for generating P and ET time series as input to the HUNTER AWBM and RMA models
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#Inputs: Uses CSV files that contain all 12 NARCLIM model runs time series for 1 grid cell created with: P1_NARCliM_NC_to_CSV_CCRC_SS.py
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#####################################----------------------------------
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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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from matplotlib.backends.backend_pdf import PdfPages
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from ggplot import *
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matplotlib.style.use('ggplot')
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#
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# Set working direcotry (where postprocessed NARClIM data is located)
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os.chdir('C:/Users/z5025317/OneDrive - UNSW/Hunter_CC_Modeling/07_Modelling/01_Input/BC_Generation/ClimateData/')
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#set directory path for output files
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output_directory = 'NARCLIM_Processed/Rainfall/'
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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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print('-------------------')
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#####################################----------------------------------
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#set input parameters
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Model_site = 'HUNTER' # 'Belongil'
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Clim_var_type = "pracc" # '*' will create pdf for all variables in folder "pracc*|tasmax*"
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Bias_Corrected = True
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#####################################----------------------------------
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Scenarios = ["NNRP_R1_1950", "NNRP_R2_1950", "NNRP_R3_1950", 'CCCMA3.1_R1_2020', 'CCCMA3.1_R2_2020', 'CCCMA3.1_R3_2020',
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'CSIRO-MK3.0_R1_2020', 'CSIRO-MK3.0_R2_2020', 'CSIRO-MK3.0_R3_2020', 'ECHAM5_R1_2020','ECHAM5_R2_2020',
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'ECHAM5_R3_2020','MIROC3.2_R1_2020', 'MIROC3.2_R2_2020', 'MIROC3.2_R3_2020']
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#write the key plots to a single pdf document
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pdf_out_file_name = Clim_var_type + '_summary_time_series_plots_NARCliM_summary_.pdf'
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pdf_out_path = output_directory +'/' + pdf_out_file_name
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#open pdf and add the plots
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with PdfPages(pdf_out_path) as pdf:
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for scenario in Scenarios:
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#scenario = 'CCCMA3.1_R1_2020'
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print scenario[:-5]
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Catchment_directories = glob.glob('./NARCLIM/*')
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Output_df = pd.DataFrame()
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for Catchment_dir in Catchment_directories:
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Filename = os.path.basename(os.path.normpath(Catchment_dir))
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Catchment = 'Catchment' + '_' + Filename.split('_', 2)[1]
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if Bias_Corrected:
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Catchment_dir = Catchment_dir + '/Bias_corrected'
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if scenario[:4] == 'NNRP':
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clim_var_csv_path = glob.glob(Catchment_dir + '/' + Clim_var_type + '_NNRP*')
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Full_df = pd.read_csv(clim_var_csv_path[0], index_col=0, parse_dates = True)
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Full_df.index = Full_df.index.to_period('D')
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IVM_df = Full_df.filter(regex= scenario[:-5])
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IVM_df = IVM_df.iloc[:,:].loc[(IVM_df.index < '2010-01-01')]
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#calculate daily ETo from the NARCLIM ET flux
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if Clim_var_type == 'evspsblmean' or Clim_var_type == 'potevpmean':
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IVM_df = IVM_df.multiply(86400)
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IVM_df.columns = [Catchment]
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Output_df = pd.concat([Output_df, IVM_df], axis=1)
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else:
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clim_var_csv_path = glob.glob(Catchment_dir + '/' + Clim_var_type + '_GCMS*')
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Full_df = pd.read_csv(clim_var_csv_path[0], index_col=0, parse_dates = True)
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Full_df.index = Full_df.index.to_period('D')
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IVM_df = Full_df.filter(regex= scenario[:-5])
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IVM_df = IVM_df.iloc[:,:].loc[(IVM_df.index > '1989-12-31')]
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if Clim_var_type == 'evspsblmean' or Clim_var_type == 'potevpmean':
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IVM_df = IVM_df.multiply(86400)
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IVM_df.columns = [scenario[:-5],scenario[:-5], scenario[:-5]]
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dfa1 = pd.DataFrame(IVM_df.iloc[:,0]).dropna(axis=0)
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dfa2 = pd.DataFrame(IVM_df.iloc[:,1]).dropna(axis=0)
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dfa3 = pd.DataFrame(IVM_df.iloc[:,2]).dropna(axis=0)
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IVM_df = pd.concat([dfa1, dfa2], axis=0)
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IVM_df = pd.concat([IVM_df, dfa3], axis=0)
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IVM_df.columns = [Catchment]
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Output_df = pd.concat([Output_df, IVM_df], axis=1)
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Output_df.index.name = 'datetime'
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Output_df_annual = Output_df.resample('A').sum()
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Output_df_annual = Output_df_annual.replace(0, np.nan)
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# time series plot annual ALL models
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plt.title(Clim_var_type + ' - Time series - for all catchments')
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Output_df_annual.plot(legend=False)
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pdf.savefig(bbox_inches='tight', pad_inches=0.4)
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plt.close()
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#export the pandas data frame as a CSV file within the output directory
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out_file_name = scenario[:-5] + '_' + Clim_var_type + '_NARCliM.csv'
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out_path = output_directory +'/' + out_file_name
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Output_df.to_csv(out_path)
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