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heatmap.py
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from __future__ import division
from functools import reduce
##############################
# USER INPUTS
# reference
#https://www.epd.gov.hk/epd/english/environmentinhk/air/guide_ref/guide_aqa_model_g5.html
#https://www.epd.gov.hk/epd/english/environmentinhk/air/guide_ref/guide_aqa_model_g1.html
##############################
path_cmaq = r'C:\Users\CHA82870\OneDrive - Mott MacDonald\Documents\scrapPath\cmaqAll'
#comment out if not use
#pollutants = 1 #TSP
#pollutants = 2 #RSP (PM10) daily
#pollutants = 3 #RSP (PM10) annual
#pollutants = 4 #FSP (PM2.5) daily
#pollutants = 5 #FSP (PM2.5) annual
#pollutants = 6 #NO2 hourly
#pollutants = 7 #NO2 Annual
#pollutants = 8 #Ozone 8-hourly
##############################
#CODES DO NOT MODIFY
##############################
import time
start_time = time.time()
from joblib import Parallel, delayed
import multiprocessing
import pandas as pd
from pandas import ExcelWriter
import math
import numpy as np
import glob
def getFiles (path, type):
filteredFiles = []
allFiles = glob.glob(path + "/*.{}".format(type))
return allFiles
def get_nlargest(df, n, adj):
result = {}
for cols in df.columns[1:]: #skip index
tem = df[cols].nlargest(n).tolist()[-1] + adj
result[cols] = tem
return result
#merge = pd.DataFrame()
def calculate(i):
pollutants = i
print(i)
if pollutants == 1: #TSP
factor_annual = 1
factor_daily = 1
factor_aermod = 1
RSP_an_adj = 0
RSP_10_adj = 0
elif pollutants == 2: #RSP daily
factor_annual = 1
factor_daily = 1
factor_aermod = 1
RSP_an_adj = 15.6
RSP_10_adj = 26.5
elif pollutants == 3: #RSP annual
factor_annual = 1
factor_daily = 1
factor_aermod = 1
RSP_an_adj = 15.6
RSP_10_adj = 26.5
elif pollutants == 4: #FSP daily
factor_annual = 0.71
factor_daily = 0.75
factor_aermod = 1
RSP_an_adj = 15.6*factor_annual
RSP_10_adj = 26.5*factor_daily
elif pollutants == 5: #FSP annaul
factor_annual = 0.71
factor_daily = 0.75
factor_aermod = 1
RSP_an_adj = 15.6*factor_annual
RSP_10_adj = 26.5*factor_daily
elif pollutants == 6: #NO2 hourly
factor_annual = 1
factor_daily = 1
factor_aermod = 1
RSP_an_adj = 0*factor_annual
RSP_10_adj = 0*factor_daily
elif pollutants == 7: #NO2 Annual
factor_annual = 1
factor_daily = 1
factor_aermod = 1
RSP_an_adj = 0*factor_annual
RSP_10_adj = 0*factor_daily
elif pollutants == 8: #Ozone 8-hourly
factor_annual = 1
factor_daily = 1
factor_aermod = 1
RSP_an_adj = 0*factor_annual
RSP_10_adj = 0*factor_daily
output = pd.DataFrame()
files_cmaq = getFiles(path_cmaq, 'txt')
for cmaq in files_cmaq:
xy = cmaq.replace(".txt", "").split('_')
x = xy[-2]
y = xy[-1]
#print("({},{})".format(x,y))
data = pd.read_csv(cmaq, sep='\s+')
data = data.drop([0,1], axis = 0)
data = data.apply(pd.to_numeric)
"""
index = data.index.tolist()
re_index = index[-8:] + index[:-8]
data = data.reindex(re_index) #move the last 7 rows to the top
#data = data.drop(data.index[0:9])
print(data)
for a in list(range(7)): #set YYYY to be the year in the 9th row
data.iloc[a,0] = data.iloc[7,0]
data = data.reset_index(drop=True)
"""
data = data[17:-7]
data_an = data*factor_annual
data_24 = data.groupby(np.arange(len(data))//24).mean()
data_24.iloc[:,1:] = data_24.iloc[:,1:]*factor_daily + RSP_10_adj
#print(data)
"""
data_8 = data.groupby(np.arange(len(data))//8).mean()
data_8.iloc[:,1:] = data_8.iloc[:,1:]
"""
data_8 = data.rolling(8).mean()
#print(data_8)
lst = []
lst.append(get_nlargest(data, 1, 0)) #Max Hourly
lst.append(get_nlargest(data_24, 10,0)) #10th Max Daily
lst.append(get_nlargest(data_8, 10,0)) #10th Max 8-hour
lst.append(get_nlargest(data, 19, 0)) #19th Max Hourly
lst.append((data_an.mean() + RSP_an_adj).to_dict()) #annual average
summary = pd.DataFrame(lst)
summary['Index'] = ['Max hourly','10th Max Daily','10th Max 8 hour average','19th Max hourly','Annual average']
summary = summary.drop(columns = ['Year', 'dd', 'mm', 'hh'])
summary['i'] = x
summary['j'] = y
if pollutants == 1:
lst_rows = ['Max hourly']
summary = summary[summary['Index'].isin(lst_rows)]
summary = summary[['i','j','RSP','Index']]
summary = summary.rename(columns = {'RSP':'TSP Max hourly'})
summary = summary.drop(['Index'], axis = 1)
elif pollutants == 2:
lst_rows = ['10th Max Daily']
summary = summary[summary['Index'].isin(lst_rows)]
summary = summary[['i','j','RSP','Index']]
summary = summary.rename(columns = {'RSP':'RSP 10th Max daily'})
summary = summary.drop(['Index'], axis = 1)
elif pollutants == 3:
lst_rows = ['Annual average']
summary = summary[summary['Index'].isin(lst_rows)]
summary = summary[['i','j','RSP','Index']]
summary = summary.rename(columns = {'RSP':'RSP Annual average'})
summary = summary.drop(['Index'], axis = 1)
elif pollutants == 4:
lst_rows = ['10th Max Daily']
summary = summary[summary['Index'].isin(lst_rows)]
summary = summary[['i','j','RSP','Index']]
summary = summary.rename(columns = {'RSP':'FSP 10th Max Daily'})
summary = summary.drop(['Index'], axis = 1)
elif pollutants == 5:
lst_rows = ['Annual average']
summary = summary[summary['Index'].isin(lst_rows)]
summary = summary[['i','j','RSP','Index']]
summary = summary.rename(columns = {'RSP':'FSP Annual average'})
summary = summary.drop(['Index'], axis = 1)
elif pollutants == 6: #NO2 hourly
lst_rows = ['19th Max hourly']
summary = summary[summary['Index'].isin(lst_rows)]
summary = summary[['i','j','NO2','Index']]
summary = summary.rename(columns = {'NO2':'NO2 19th Max hourly'})
summary = summary.drop(['Index'], axis = 1)
elif pollutants == 7: #NO2 Annual
lst_rows = ['Annual average']
summary = summary[summary['Index'].isin(lst_rows)]
summary = summary[['i','j','NO2','Index']]
summary = summary.rename(columns = {'NO2':'NO2 Annual average'})
summary = summary.drop(['Index'], axis = 1)
elif pollutants == 8: #Ozone 8-hourly
lst_rows = ['10th Max 8 hour average']
summary = summary[summary['Index'].isin(lst_rows)]
summary = summary[['i','j','O3','Index']]
summary = summary.rename(columns = {'O3':'O3 10th Max 8 hour average'})
summary = summary.drop(['Index'], axis = 1)
try:
output = pd.concat([output, summary])
except:
output = summary
output = output.sort_values(['i','j'])
return output
#merge.to_csv('PATH_AQO.csv')
num_cores = multiprocessing.cpu_count()
results = Parallel(n_jobs=num_cores)(delayed(calculate)(i) for i in range(2,9))
merge = reduce(lambda x, y: pd.merge(x, y, on = ['i','j']), results)
merge = merge.apply(pd.to_numeric)
merge = merge.sort_values(['i','j'])
merge.to_csv('PATH_AQO_concurrent_fixed.csv')
#print(merge)
print("--- %s seconds ---" % (time.time() - start_time))