#several major imporvements on all fronts
Valentin Heimhuber
7 years ago
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# -*- coding: utf-8 -*-
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"""
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Created on Thu Jun 28 12:15:17 2018
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@author: z5025317
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"""
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# -*- coding: utf-8 -*-
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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 creating future climate variability deviation plots for NARCLIM variables.
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#This code is derived from P1_NARCliM_First_Pass_variab_deviation_plots.py to deal with variables for which we don't have NARCLIM deltas.
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#These variables are Sea Level, Acidity and...
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# it uses CSIRO CC in Australia CMIP-5 Deltas instead
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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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import csv
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matplotlib.style.use('ggplot')
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# Load my own functions
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os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/Analysis/Code')
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import climdata_fcts as fct
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import silo as sil
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import re
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#==========================================================#
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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/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/')
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#==========================================================#
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#==========================================================#
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#set input parameters
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#==========================================================#
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Case_Study_Name = 'CASESTUDY2'
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Casestudy2_csv_path = "Data/NARCLIM_Site_CSVs/CASESTUDY2/CASESTDUY2_NARCLIM_Point_Sites.csv"
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Estuary = 'HUNTER' # 'Belongil'
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Base_period_start = '1970-01-01' #Start of interval for base period of climate variability
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Base_period_DELTA_start = '1990-01-01' #Start of interval used for calculating mean and SD for base period (Delta base period)
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Base_period_end = '2009-01-01' #use last day that's not included in period as < is used for subsetting
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Clim_var_type = 'Acidity' #Name of climate variable in NARCLIM models '*' will create pdf for all variables in folder
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[minplotDelta, maxplotDelta]=[0,1]
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#Source for present day climate data (historic time series) can be either: 'Station' or 'SILO'
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Location = 'Estuary' # pick locaiton for extracting the SILO data can be: Estuary, Catchment, or Ocean
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presentdaybar = False #include a bar for present day variability in the plots?
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present_day_plot = 'yes' #save a time series of present day
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Version = "V1"
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Stats = 'mindaily' #'maxdaily' #maximum takes the annual max Precipitation instead of the sum
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ALPHA_figs = 1 #Set alpha of figure background (0 makes everything around the plot panel transparent)
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#==========================================================#
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#==========================================================#
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#set directory path for output files
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output_directory = 'Output/' + Case_Study_Name + '/' + Estuary + '/' + '/Clim_Deviation_Plots/'
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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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#==========================================================#
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#read ensemble change CSV file
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#==========================================================#
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clim_var_csv_path = './Data/Non_NARCLIM/AUZ_CC_CSIRO_Deltas/Brisbane_Ocean_Variables.csv'
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df = pd.read_csv(clim_var_csv_path, index_col=0, parse_dates = True)
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df = df.filter(regex= 'RCP 8.5|Variable')
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df = df[df.Variable=='Ocean pH']
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Ensemble_Delta_df = pd.DataFrame(index=['Median', '10th', '90th'], columns =['near', 'far'])
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Ensemble_Delta_df['near'] = re.split('\(|\)|\ to |', df.iloc[0,1])[0:3]
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Ensemble_Delta_df['far'] = re.split('\(|\)|\ to |', df.iloc[0,2])[0:3]
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Ensemble_Delta_df = Ensemble_Delta_df.astype(float).fillna(0.0)
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#==========================================================#
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#==========================================================#
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#load present day climate variable time series
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#==========================================================#
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#Present_day_df,minplotDelta, maxplotDelta = fct.import_present_day_climdata_csv(Estuary, Clim_var_type)
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Present_day_Var_CSV = glob.glob('./Data/Non_NARCLIM/Hunter_Lower_Acidity2.csv')
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Present_day_df = pd.read_csv(Present_day_Var_CSV[0],parse_dates=True, index_col=0)
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Present_day_df.columns = ['PH']
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df = Present_day_df
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df.index = df.index.to_period('D')
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Present_day_df = df
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Present_day_df = df.groupby(df.index).mean().reset_index()
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Present_day_df.columns = ['Date','PH']
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Present_day_df.index = Present_day_df['Date']
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Present_day_df= Present_day_df['PH']
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#Present_day_df.index = pd.to_datetime(Present_day_df.index,format='%Y%m%d')
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if Stats == 'mindaily':
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Present_day_df_annual = Present_day_df.resample('A').min()
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Present_day_df_annual = Present_day_df_annual.replace(0, np.nan)
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if Stats == 'mean':
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Present_day_df_annual = Present_day_df.resample('A').mean()
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Present_day_df_annual = Present_day_df_annual.replace(0, np.nan)
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Present_Day_ref_df = Present_day_df_annual
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#Subset to present day variability period
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Present_Day_ref_df = pd.DataFrame(Present_Day_ref_df.loc[(Present_Day_ref_df.index >= Base_period_start) & (Present_Day_ref_df.index <= Base_period_end)])
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#Subset to present day variability delta base period (this is the statistical baseline used for present day conditions)
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Present_Day_Delta_ref_df = pd.DataFrame(Present_Day_ref_df.loc[(Present_Day_ref_df.index >= Base_period_DELTA_start) & (Present_Day_ref_df.index <= Base_period_end)])
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Present_Day_Mean = np.percentile(Present_Day_Delta_ref_df, 50)
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Present_Day_SD = np.std(Present_Day_Delta_ref_df)
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#create data frame for floating stacked barplots
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index=['-2std', '-1std', 'Med', '1std', '2std']
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columns = ['present','near future', 'far future']
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Plot_in_df = pd.DataFrame(index=index, columns =columns)
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#
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Plot_in_df['present'] = [float(Present_Day_Mean-2*Present_Day_SD),float(Present_Day_Mean-Present_Day_SD), float(Present_Day_Mean),
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float(Present_Day_Mean+Present_Day_SD), float(Present_Day_Mean+2*Present_Day_SD)]
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Plot_in_df['near future'] = [float(Present_Day_Mean + Ensemble_Delta_df.near['10th']),np.NaN, float(Present_Day_Mean + Ensemble_Delta_df.near['Median']),
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np.NaN, float(Present_Day_Mean + Ensemble_Delta_df.near['90th'])]
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Plot_in_df['far future'] = [float(Present_Day_Mean + Ensemble_Delta_df.far['10th']),np.NaN, float(Present_Day_Mean + Ensemble_Delta_df.far['Median']),
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np.NaN, float(Present_Day_Mean + Ensemble_Delta_df.far['90th'])]
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#Create a second data frame that has the values in a way that they can be stacked up in bars with the correct absolute values
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Plot_in_df2 = pd.DataFrame(index=index, columns =columns )
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#
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index=['-2std', '-1std', 'Med', '1std', '2std']
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columns = ['near future', 'far future']
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Plot_in_df2 = pd.DataFrame(index=index, columns =columns )
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Plot_in_df2['near future'] = [float(Present_Day_Mean + Ensemble_Delta_df.near['10th']),np.NaN, float(Ensemble_Delta_df.near['Median']-Ensemble_Delta_df.near['10th']),
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np.NaN, float(Ensemble_Delta_df.near['90th']-Ensemble_Delta_df.near['Median'])]
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Plot_in_df2['far future'] = [float(Present_Day_Mean + Ensemble_Delta_df.far['10th']),np.NaN, float(Ensemble_Delta_df.far['Median']-Ensemble_Delta_df.far['10th']),
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np.NaN, float(Ensemble_Delta_df.far['90th']-Ensemble_Delta_df.far['Median'])]
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#transpose the data frame
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Plot_in_df_tp = Plot_in_df2.transpose()
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#do the individual plots
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xmin = int(min(Plot_in_df.min(axis=1))-minplotDelta)
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xmax = int(max(Plot_in_df.max(axis=1))+maxplotDelta)
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#define colour scheme
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#likert_colors = ['none', 'firebrick','firebrick','lightcoral','lightcoral']
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likert_colors = ['none', 'darkblue', 'darkblue','cornflowerblue','cornflowerblue']
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uni_colors = ['none', 'cornflowerblue', 'cornflowerblue','cornflowerblue','cornflowerblue']
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#plot the stacked barplot
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fig = plt.figure(figsize=(14,8))
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ax=plt.subplot(2,4,3)
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Plot_in_df_tp.plot.bar(stacked=True, color=uni_colors, edgecolor='none', legend=False, ax=ax, width=0.5)
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df = pd.DataFrame(Plot_in_df.iloc[2,[1,2]])
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index2=['Med']
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columns2 = ['near future', 'far future']
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Plot_in_df3 = pd.DataFrame(index=index2, columns =columns2)
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Plot_in_df3['near future'] = df.iloc[1,0]
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Plot_in_df3['far future'] = df.iloc[0,0]
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Plot_in_df3 = Plot_in_df3.transpose()
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plt.plot(Plot_in_df3['Med'], linestyle="", markersize=52,
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marker="_", color='darkblue', label="Median")
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z = plt.axhline(float(Present_Day_Mean-2*Present_Day_SD), linestyle='-', color='black', alpha=.5)
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z.set_zorder(-1)
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z = plt.axhline(float(Present_Day_Mean+2*Present_Day_SD), linestyle='-', color='black', alpha=.5)
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z.set_zorder(-1)
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z = plt.axhline(float(Present_Day_Mean-Present_Day_SD), linestyle='--', color='black', alpha=.5)
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z.set_zorder(-1)
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z = plt.axhline(float(Present_Day_Mean+Present_Day_SD), linestyle='--', color='black', alpha=.5)
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z.set_zorder(-1)
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z = plt.axhline(float(Present_Day_Mean), linestyle='--', color='red', alpha=.5)
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z.set_zorder(-1)
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plt.ylim(xmin, xmax)
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plt.title(Clim_var_type)
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ax.grid(False)
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for tick in ax.get_xticklabels():
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tick.set_rotation(0)
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fig.tight_layout()
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fig.patch.set_alpha(ALPHA_figs)
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#plot the present day time series
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ax=plt.subplot(2,2,1)
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Present_Day_ref_df.plot(legend=False, ax=ax)
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z = plt.axhline(float(Present_Day_Mean-2*Present_Day_SD), linestyle='-', color='black', alpha=.5)
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z.set_zorder(-1)
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z = plt.axhline(float(Present_Day_Mean+2*Present_Day_SD), linestyle='-', color='black', alpha=.5)
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z.set_zorder(-1)
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z = plt.axhline(float(Present_Day_Mean-Present_Day_SD), linestyle='--', color='black', alpha=.5)
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z.set_zorder(-1)
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z = plt.axhline(float(Present_Day_Mean+Present_Day_SD), linestyle='--', color='black', alpha=.5)
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z.set_zorder(-1)
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z = plt.axhline(float(Present_Day_Mean), linestyle='--', color='red', alpha=.5)
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z.set_zorder(-1)
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#fig.patch.set_facecolor('deepskyblue')
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fig.tight_layout()
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fig.patch.set_alpha(ALPHA_figs)
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plt.title(Clim_var_type + ' ' + Stats + ' annual present day')
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plt.ylim(xmin, xmax)
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ax.grid(False)
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if presentdaybar == False:
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out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_' + Base_period_start + '_' + Base_period_end + Version + '_' + '_NPDB.png'
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out_path = output_directory + '/' + out_file_name
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fig.savefig(out_path)
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@ -1,90 +0,0 @@
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#R code for creating ggplots of time series with smooth (GAM) and linear term
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######################
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#Import Libraries and set working directory
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######################
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library(zoo)
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library(hydroTSM) #you need to install these packages first before you can load them here
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library(lubridate)
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library(mgcv)
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library(ggplot2)
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library(gridExtra)
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library(scales)
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options(scipen=999)
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setwd("C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/")
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######################
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######################
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#Set inputs
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######################
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Case.Study <- "CASESTUDY2"
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Estuary <- "HUNTER"
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Climvar <- 'tasmean'
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ggplotGAM.k <- 7
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######################
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######################
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#Set input file paths
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######################
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AirT_CSV_Path <- "./Data/Ocean_Data/BOM_monthly_SL_Hunter_Newcastle.txt"
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dat = readLines(AirT_CSV_Path)
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dat = as.data.frame(do.call(rbind, strsplit(dat, split=" {2,10}")), stringsAsFactors=FALSE)
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colnames(dat) <-dat[3,]
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dat2 = dat[-c(1:3), ]
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dat2 = dat2[-(720:726),]
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dat2 = dat2[-(1:108),]
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dat2 = dat2[,-1]
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dat2$Date <- as.yearmon(dat2[,1], "%m %Y")
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SeaLev.df <- dat2
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head(SeaLev.df)
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SeaLev.df$MSL <- as.numeric(SeaLev.df$Mean)
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SeaLev.df$Julday1 <- seq(1,length(SeaLev.df[,1]),1)
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linear.trend.model_EC_all <- lm(MSL ~ Julday1, SeaLev.df)
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SeaLev.pvalNCV_ECall <- summary(linear.trend.model_EC_all )$coefficients[2,4]
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SeaLev.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 12
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######################
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#Plot
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######################
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##################################### Full Time Period
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p1air <- ggplot(SeaLev.df, aes(y=MSL, x=Date)) + geom_line(alpha=0.5) +
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ggtitle(paste(Estuary, " - Linear and smooth trend in monthly mean sea level (BOM Gauge) | lin trend was ",
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round(100*SeaLev.lintrend,3), ' cm/year with p=', round(SeaLev.pvalNCV_ECall,10), sep=" ")) +
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theme(plot.title=element_text(face="bold", size=9)) +
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geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
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stat_smooth(method=gam, formula=y~s(x, k=4), se=T, size=0.5, col="red") +
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ylab("Monthly Mean Sea Level [m]") + xlab("Time")
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#export to png
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png.name <- paste('./Output/', Case.Study, '/', Estuary, '/Trends_MonthlyMeanSeaLevel_full_period_', Sys.Date(),".png", sep="")
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png(file = png.name, width = 10.5, height = 7, units='in', res=500)
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grid.arrange(p1air,ncol=1)
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dev.off()
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#multiple smooths
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p1air <- ggplot(SeaLev.df, aes(y=MSL, x=Date)) + geom_line(alpha=0.5) +
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ggtitle(paste(Estuary, " - Linear and smooth trend in monthly mean sea level (BOM Gauge) | lin trend was ",
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round(100*SeaLev.lintrend,3), ' cm/year with p=', round(SeaLev.pvalNCV_ECall,10), sep=" ")) +
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theme(plot.title=element_text(face="bold", size=9)) +
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stat_smooth(method=gam, formula=y~s(x, k=13), se=T, size=0.5, col="red") +
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#stat_smooth(method=gam, formula=y~s(x, k=8), se=T, size=0.5, cor="blue") +
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stat_smooth(method=gam, formula=y~s(x, k=5), se=T, size=0.5, col="green") +
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ylab("Monthly Mean Sea Level [m]") + xlab("Time")
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#export to png
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png.name <- paste('./Output/', Case.Study, '/', Estuary, '/Trends_MonthlyMeanSeaLevel_MultiGAM_full_period_', Sys.Date(),".png", sep="")
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png(file = png.name, width = 10.5, height = 7, units='in', res=500)
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grid.arrange(p1air,ncol=1)
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dev.off()
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