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Con_P_regional_Recyc_Output_monthly_LAMACLIMA.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Jun 16 13:24:45 2016
@author: Ent00002
"""
"""
Created on Mon Feb 18 15:30:43 2019
@author: bened003
"""
# This script is almost similar as the Con_E_Recyc_Output script from WAM-2layers from Ruud van der Ent
# We have implemented a datelist function so the model can run for multiple years without having problems with leap years
#%% Import libraries
import numpy as np
import scipy.io as sio
import calendar
import datetime
import os
from getconstants_pressure_LAMACLIMA import getconstants_pressure_CESM
#from timeit import default_timer as timer
import datetime as dt
import sys
# to create datelist
def get_times_daily(startdate, enddate):
""" generate a dictionary with date/times"""
numdays = enddate - startdate
dateList = []
for x in range (0, numdays.days + 1):
dateList.append(startdate + dt.timedelta(days = x))
return dateList
def remove_leap_days(datelist):
for jos in datelist:
if ((jos.year % 400 == 0) or (jos.year % 100 != 0) and (jos.year % 4 == 0)):
if ((jos.month==2) and (jos.day==29)):
datelist.remove(jos)
return datelist
model=sys.argv[1]
case=sys.argv[2]
start_year=sys.argv[3]
end_year=sys.argv[4]
#%%BEGIN OF INPUT (FILL THIS IN)
months_length_leap = [31,29,31,30,31,30,31,31,30,31,30,31]
months_length_nonleap = [31,28,31,30,31,30,31,31,30,31,30,31]
years = np.arange(np.int(start_year),np.int(end_year)) #fill in the years # If I fill in more than one year than I need to set the months to 12
# Manage the extent of your dataset (FILL THIS IN)
# Define the latitude and longitude cell numbers to consider and corresponding lakes that should be considered part of the land
if model =='cesm':
latnrs = np.arange(0,192) # minimal domain
lonnrs = np.arange(0,288)
elif model=='ecearth':
latnrs = np.arange(0,292) # minimal domain
lonnrs = np.arange(0,362)
elif model=='mpiesm':
latnrs = np.arange(0,96) # minimal domain
lonnrs = np.arange(0,192)
os.chdir(r'/scratch/leuven/projects/lt1_2020_es_pilot/project_output/bclimate/sdeherto/wam2layer/scripts')
if model=='cesm':
area_mask = 'gridarea.nc'
lsm_data_CESM = 'landmask_cesm.nc' #insert landseamask here
if model=='mpiesm':
area_mask = 'gridarea_mpiesm.nc'
lsm_data_CESM = 'landmask_mpiesm.nc' #insert landseamask here
if model=='ecearth':
area_mask = 'gridarea_ecearth.nc'
lsm_data_CESM = 'landmask_ecearth.nc' #insert landseamask here
latitude,longitude,lsm,g,density_water,timestep,A_gridcell,L_N_gridcell,L_S_gridcell,L_EW_gridcell,gridcell = \
getconstants_pressure_CESM(model,latnrs,lonnrs,lsm_data_CESM,area_mask)
interdata_folder = r'/scratch/leuven/projects/lt1_2020_es_pilot/project_output/bclimate/sdeherto/wam2layer/output/'+model+'/'+case+'/' # insert interdata folder here
output_folder = r'/scratch/leuven/projects/lt1_2020_es_pilot/project_output/bclimate/sdeherto/wam2layer/output/'+model+'/'+case+'/output/' # insert output folder here
sub_interdata_folder = os.path.join(interdata_folder, 'Regional_scale_forward_daily') # Insert sub-interdata folder here
daily=0
timetracking = 0 # 0 for not tracking time and 1 for tracking time
#END OF INPUT
#%% Datapaths (FILL THIS IN)
def data_path(y,a,month,years,timetracking):
load_Sa_track = os.path.join(sub_interdata_folder, str(y).zfill(4) + '-' + str(month).zfill(2) + '-' + str(a).zfill(2) + 'Sa_reg.npz')
load_Sa_time = os.path.join(sub_interdata_folder, str(y).zfill(4) + '-' + str(month).zfill(2) + '-' + str(a).zfill(2) + 'Sa_time.npz')
load_fluxes_and_storages = os.path.join(interdata_folder, str(y).zfill(4) + '-' + str(month).zfill(2) + '-' + str(a).zfill(2) + 'fluxes_storages.mat')
save_path = os.path.join(output_folder, 'P_track_regional_scales_full' + str(years[0]) + '-' + str(years[-1]) + '-timetracking' + str(timetracking))
save_path_daily = os.path.join(output_folder, 'P_track_regional_scales_daily_full' + str(y) + '-timetracking' + str(timetracking))
return load_Sa_track,load_Sa_time,load_fluxes_and_storages,save_path,save_path_daily
#%% Runtime & Results
#start1 = timer()
startyear = years[0]
E_per_year_per_month = np.zeros((len(years),12,len(latitude),len(longitude)))
P_track_per_year_per_month = np.zeros((len(years),12,len(latitude),len(longitude)))
P_per_year_per_month = np.zeros((len(years),12,len(latitude),len(longitude)))
Sa_track_down_per_year_per_month = np.zeros((len(years),12,len(latitude),len(longitude)))
Sa_track_top_per_year_per_month = np.zeros((len(years),12,len(latitude),len(longitude)))
W_down_per_year_per_month = np.zeros((len(years),12,len(latitude),len(longitude)))
W_top_per_year_per_month = np.zeros((len(years),12,len(latitude),len(longitude)))
north_loss_per_year_per_month = np.zeros((len(years),12,1,len(longitude)))
south_loss_per_year_per_month = np.zeros((len(years),12,1,len(longitude)))
#east_loss_per_year_per_month = np.zeros((len(years),12,1,len(latitude)))
#west_loss_per_year_per_month = np.zeros((len(years),12,1,len(latitude)))
down_to_top_per_year_per_month = np.zeros((len(years),12,len(latitude),len(longitude)))
top_to_down_per_year_per_month = np.zeros((len(years),12,len(latitude),len(longitude)))
water_lost_per_year_per_month = np.zeros((len(years),12,len(latitude),len(longitude)))
for year in years[:]:
#start = timer()
#CESM does not have leap years, so the datelist and the 2 lines bellow are not necessary
if model !='cesm':
if calendar.isleap(year): # if no leap year # specific for my dataset as 2006 is a leap year
datelist = get_times_daily(dt.date(year,1,1), dt.date(year,12, 31))
datelist=remove_leap_days(datelist)
else:
datelist = get_times_daily(dt.date(year,1,1), dt.date(year,12, 31))
else: # no leap in cesm
datelist = get_times_daily(dt.date(year,1,1), dt.date(year,12, 31))
ly = int(calendar.isleap(year))
final_time = 364+ly
E_per_day = np.zeros((365+ly,len(latitude),len(longitude)))
P_track_per_day = np.zeros((365+ly,len(latitude),len(longitude)))
P_per_day = np.zeros((365+ly,len(latitude),len(longitude)))
Sa_track_down_per_day = np.zeros((365+ly,len(latitude),len(longitude)))
Sa_track_top_per_day = np.zeros((365+ly,len(latitude),len(longitude)))
W_down_per_day = np.zeros((365+ly,len(latitude),len(longitude)))
W_top_per_day = np.zeros((365+ly,len(latitude),len(longitude)))
north_loss_per_day = np.zeros((365+ly,1,len(longitude)))
south_loss_per_day = np.zeros((365+ly,1,len(longitude)))
#east_loss_per_day = np.zeros((365+ly,1,len(latitude)))
#west_loss_per_day = np.zeros((365+ly,1,len(latitude)))
down_to_top_per_day = np.zeros((365+ly,len(latitude),len(longitude)))
top_to_down_per_day = np.zeros((365+ly,len(latitude),len(longitude)))
water_lost_per_day = np.zeros((365+ly,len(latitude),len(longitude)))
#water_lost_top_per_day = np.zeros((365+ly,len(latitude),len(longitude)))
for i,date in enumerate(datelist):
a=date.day
yearnumber = date.year
monthnumber = date.month
print (i, yearnumber, monthnumber, a)
datapath = data_path(yearnumber,a,monthnumber,years,timetracking)
print (datapath[0])
if i > final_time: # a = 365 (366th index) and not a leapyear\
pass
else:
#load tracked data
loading_ST = np.load(datapath[0])#,verify_compressed_data_integrity=False)
# load the total moisture data from fluxes and storages
loading_FS = sio.loadmat(datapath[2],verify_compressed_data_integrity=False)
# save per day
E_per_day[i,:,:] = loading_ST['E_per_day']
P_track_per_day[i,:,:] = loading_ST['P_reg_per_day']
P_per_day[i,:,:] = loading_ST['P_per_day']
Sa_track_down_per_day[i,:,:] = loading_ST['Sa_reg_down_per_day']
Sa_track_top_per_day[i,:,:] = loading_ST['Sa_reg_top_per_day']
W_down_per_day[i,:,:] = loading_ST['W_down_per_day']
W_top_per_day[i,:,:] = loading_ST['W_top_per_day']
north_loss_per_day[i,:,:] = loading_ST['north_loss_per_day']
south_loss_per_day[i,:,:] = loading_ST['south_loss_per_day']
#east_loss_per_day[i,:,:] = loading_ST['east_loss_per_day']
#west_loss_per_day[i,:,:] = loading_ST['west_loss_per_day']
#down_to_top_per_day[i,:,:] = np.sum(down_to_top, axis =0)
#top_to_down_per_day[i,:,:] = np.sum(top_to_down, axis =0)
water_lost_per_day[i,:,:] = loading_ST['water_lost_per_day']
#end = timer()
#print ('Runtime output for day ' + str(a) + 'in month ' + str(monthnumber) + ' in year ' + str(yearnumber) + ' is',(end - start),' seconds')
if daily == 1:
if timetracking == 0: # create dummy values
Sa_time_down_per_day = 0
Sa_time_top_per_day = 0
E_time_per_day = 0
#save per day
np.savez_compressed(datapath[4],E_per_day=E_per_day,P_track_per_day=P_track_per_day,P_per_day=P_per_day,
Sa_track_down_per_day=Sa_track_down_per_day,Sa_track_top_per_day=Sa_track_top_per_day,
Sa_time_down_per_day=Sa_time_down_per_day,Sa_time_top_per_day=Sa_time_top_per_day,
W_down_per_day=W_down_per_day,W_top_per_day=W_top_per_day,
E_time_per_day=E_time_per_day, water_lost_per_day=water_lost_per_day)#, water_lost_top_per_day=water_lost_top_per_day)#},do_compression=True)
# values per month
for m in range(12):
if m == 0:
first_day = int(datetime.date(year,m+1,datelist[0].day).strftime("%j"))
else:
first_day = int(datetime.date(year,m+1,1).strftime("%j"))
last_day = int(datetime.date(year,m+1,calendar.monthrange(year,m+1)[1]).strftime("%j"))
days = np.arange(first_day,last_day+1)-1 # -1 because Python is zero-based
E_per_year_per_month[year-startyear,m,:,:] = (np.squeeze(np.sum(E_per_day[days,:,:], axis = 0)))
P_track_per_year_per_month[year-startyear,m,:,:] = (np.squeeze(np.sum(P_track_per_day[days,:,:], axis = 0)))
P_per_year_per_month[year-startyear,m,:,:] = (np.squeeze(np.sum(P_per_day[days,:,:], axis = 0)))
Sa_track_down_per_year_per_month[year-startyear,m,:,:] = (np.squeeze(np.mean(Sa_track_down_per_day[days,:,:], axis = 0)))
Sa_track_top_per_year_per_month[year-startyear,m,:,:] = (np.squeeze(np.mean(Sa_track_top_per_day[days,:,:], axis = 0)))
W_down_per_year_per_month[year-startyear,m,:,:] = (np.squeeze(np.mean(W_down_per_day[days,:,:], axis = 0)))
W_top_per_year_per_month[year-startyear,m,:,:] = (np.squeeze(np.mean(W_top_per_day[days,:,:], axis = 0)))
north_loss_per_year_per_month[year-startyear,m,:,:] = (np.squeeze(np.sum(north_loss_per_day[days,:,:], axis = 0)))
south_loss_per_year_per_month[year-startyear,m,:,:] = (np.squeeze(np.sum(south_loss_per_day[days,:,:], axis = 0)))
#east_loss_per_year_per_month[year-startyear,m,:,:] = (np.squeeze(np.sum(east_loss_per_day[days,:,:], axis = 0)))
#west_loss_per_year_per_month[year-startyear,m,:,:] = (np.squeeze(np.sum(west_loss_per_day[days,:,:], axis = 0)))
#down_to_top_per_year_per_month[year-startyear,m,:,:] = (np.squeeze(np.sum(down_to_top_per_day[days,:,:], axis = 0)))
#top_to_down_per_year_per_month[year-startyear,m,:,:] = (np.squeeze(np.sum(top_to_down_per_day[days,:,:], axis = 0)))
water_lost_per_year_per_month[year-startyear,m,:,:] = (np.squeeze(np.sum(water_lost_per_day[days,:,:], axis = 0)))
#hallo
if timetracking == 0:
Sa_time_down_per_year_per_month = 0
Sa_time_top_per_year_per_month = 0
E_time_per_year_per_month = 0
# save monthly data
np.savez_compressed(datapath[3],
E_per_year_per_month=E_per_year_per_month,P_track_per_year_per_month=P_track_per_year_per_month,P_per_year_per_month=P_per_year_per_month,
Sa_track_down_per_year_per_month=Sa_track_down_per_year_per_month,Sa_track_top_per_year_per_month=Sa_track_top_per_year_per_month,
Sa_time_down_per_year_per_month=Sa_time_down_per_year_per_month,Sa_time_top_per_year_per_month=Sa_time_top_per_year_per_month,
E_time_per_year_per_month=E_time_per_year_per_month, W_down_per_year_per_month=W_down_per_year_per_month,W_top_per_year_per_month=W_top_per_year_per_month,
north_loss_per_year_per_month=north_loss_per_year_per_month, south_loss_per_year_per_month=south_loss_per_year_per_month,
down_to_top_per_year_per_month=down_to_top_per_year_per_month, top_to_down_per_year_per_month=top_to_down_per_year_per_month,
water_lost_per_year_per_month=water_lost_per_year_per_month)#, water_lost_per_year_per_month=water_lost_top_per_year_per_month)
#end1 = timer()
#print ('The total runtime of Con_E_Recyc_Output is',(end1-start1),' seconds.')