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99 lines
3.9 KiB
Python
99 lines
3.9 KiB
Python
5 years ago
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# -*- coding: utf-8 -*-
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"""
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Created on Tue Mar 24 19:02:20 2020
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@author: z5025317
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"""
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#this code simply opens up all the MHL tide gauge csv files and writes the key information provided in there into a cvs file for GIS import
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#==========================================================#
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#Load packages
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#==========================================================#
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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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import numpy as np
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import seaborn
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os.chdir('C:/Users/z5025317/UNSW/Danial Khojasteh - HEMIP/RMA_result_explorer_code')
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year = '2015'
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Ocean_gauges_folder = 'J:/Project/wrl2018064 Fisheries RAP/04_Working/05_Modelling/RMA/HEMIP/Global_HEMIP_Data/MHL_Gauge_Data/20200409_Recieved/'
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filenames = glob.glob(Ocean_gauges_folder +'*/' + '*.csv')
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WL_df = pd.DataFrame()
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for tgr_csv_name in filenames:
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print (os.path.basename(tgr_csv_name)[:-11])
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#tgr_csv_name = filenames[3]
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Full_df = pd.read_csv(tgr_csv_name ,nrows=7, skiprows= 17, parse_dates = True)
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Full_df = Full_df.transpose()
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Full_df['River'] = os.path.basename(os.path.dirname(tgr_csv_name))
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Full_df2 = pd.read_csv(tgr_csv_name ,header= 0, skiprows= 30, parse_dates = True)
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Full_df['Start_Date'] =Full_df2.Date[0]
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Full_df['End_Date'] =Full_df2.Date[len(Full_df2.Date)-1]
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Full_df2.index = pd.to_datetime(Full_df2.Date, format = '%d/%m/%Y') + pd.to_timedelta(Full_df2.Time, unit='h')
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Full_df2 = Full_df2.iloc[:,[-2]]
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Full_df2.columns = ['WL[AHD]']
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WL_df = pd.concat([WL_df, Full_df], axis=0)
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#WL_df.to_csv(Ocean_gauges_folder + 'summary_dataframe_of_all_gauges4.csv')
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#create a histogram for each gauge if desired
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#this isn't working yet due to gaps and weird stuff in the data
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ylims = [-1.5,2.2]
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#set plot parameters
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ALPHA_figs = 1
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font = {'family' : 'sans-serif',
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'weight' : 'normal',
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'size' : 6}
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matplotlib.rc('font', **font)
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pdf_out_name = os.path.dirname(tgr_csv_name) + os.path.basename(tgr_csv_name)[:-11]+ '_histogram.pdf'
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fig = plt.figure(figsize=(4,8))
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np.array(np.float16(Full_df2['WL[AHD]'])
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#full period density comparison for individual gauges overlaid by tidal planes
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Wollow_13ExPr = np.nanpercentile(np.array(Full_df2['WL[AHD]']),87)
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np.percentile(Full_df2['WL[AHD]'].astype(float), 50)
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Wollow_6p4ExPr = np.nanpercentile(Full_df2['WL[AHD]'].astype(float),93.6)
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Wollow_1_3ExPr = np.nanpercentile(Full_df2,98.7)
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Wollow_0p01ExPr = np.nanpercentile(Full_df2,99.99)
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Wollow_50ExPr = np.nanpercentile(Full_df2,50)
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ax=plt.subplot(4,1,i)
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plt.title( Gauge + ' water level density comparison - Jan 2016- Feb 2020')
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#WL_df[Gauge].plot.kde(xlim=(-0.5,1),vertical=True, lw=3, ax=ax)
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seaborn.kdeplot(Full_df2['WL[AHD]'], vertical=True, color='royalblue', lw=3)
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plt.axhline(y=Wollow_13ExPr, color='firebrick', linestyle='--', lw=3, alpha=1)
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plt.text( xlocation , (Wollow_13ExPr), '13% exceedance', ha='left', va='bottom')
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plt.axhline(y=Wollow_6p4ExPr, color='orange', linestyle='--', lw=3, alpha=1)
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plt.text(xlocation , (Wollow_6p4ExPr), '6.4% exceedance', ha='left', va='bottom')
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plt.axhline(y=Wollow_1_3ExPr, color='yellow', linestyle='--', lw=3, alpha=1)
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plt.text( xlocation ,Wollow_1_3ExPr , '1.3% exceedance', ha='left', va='bottom')
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plt.axhline(y=Wollow_0p01ExPr, color='limegreen', linestyle='--', lw=3, alpha=1)
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plt.text( xlocation , Wollow_0p01ExPr , '0.01% exceedance', ha='left', va='bottom')
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plt.axhline(y=Wollow_50ExPr, color='cyan', linestyle='--', lw=3, alpha=1)
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plt.text( xlocation , Wollow_50ExPr , '50% exceedance', ha='left', va='bottom')
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plt.ylim(-1,1.7)
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plt.xlabel("Probability/density")
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plt.ylabel("Water level [m AHD]")
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fig.tight_layout()
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fig.savefig(png_out_name)
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plt.close()
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fig.tight_layout()
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fig.savefig(png_out_name)
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