forked from ruudvdent/WAM2layersPython
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Con_E_Recyc_Output.py
240 lines (194 loc) · 13 KB
/
Con_E_Recyc_Output.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 16 13:24:45 2016
@author: Ent00002
"""
# delayed runs, comment the 2 lines below when unused
# import time
# time.sleep(100)
#%% Import libraries
import numpy as np
import scipy.io as sio
import calendar
import datetime
from getconstants import getconstants
from timeit import default_timer as timer
import os
#%%BEGIN OF INPUT1 (FILL THIS IN)
years = np.arange(2002,2009) # Note that this can be forward as tracking was already finished, whereas the masterscript must be in backward order
yearpart = np.arange(0,366) # for a full (leap)year fill in np.arange(0,366)
daily = 1 # 1 for writing out daily data, 0 for only monthly data
timetracking = 1 # 0 for not tracking time and 1 for tracking time
# 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
latnrs = np.arange(7,114)
lonnrs = np.arange(0,240)
# the lake numbers below belong to the ERA-Interim data on 1.5 degree starting at Northern latitude 79.5 and longitude -180
lake_mask_1 = np.array([9,9,9,12,12,21,21,22,22,23,24,25,23,23,25,25,53,54,61,23,24,23,24,25,27,22,23,24,25,26,27,28,22,25,26,27,28,23,23,12,18])
lake_mask_2 = np.array([120+19,120+40,120+41,120+43,120+44,120+61,120+62,120+62,120+63,120+62,120+62,120+62,120+65,120+66,120+65,120+66,142-120,142-120,143-120,152-120,152-120,153-120,153-120,153-120,153-120,154-120,154-120,154-120,154-120,154-120,154-120,154-120,155-120,155-120,155-120,155-120,155-120,159-120,160-120,144-120,120+55])
lake_mask = np.transpose(np.vstack((lake_mask_1,lake_mask_2))) #recreate the arrays of the matlab model
# obtain the constants
invariant_data = 'Interim_data/full/invariants.nc'#invariants
latitude,longitude,lsm,g,density_water,timestep,A_gridcell,L_N_gridcell,L_S_gridcell,L_EW_gridcell,gridcell = getconstants(latnrs,lonnrs,lake_mask,invariant_data)
interdata_folder = r'C:\Users\bec\Desktop\WAM2\interdata'
output_folder = r'C:\Users\bec\Desktop\WAM2\output'
sub_interdata_folder = os.path.join(interdata_folder, 'continental_backward')
#END OF INPUT
#%% Datapaths (FILL THIS IN)
def data_path(y,a,years,timetracking):
load_Sa_track = os.path.join(sub_interdata_folder, str(y) + '-' + str(a) + 'Sa_track.mat')
load_Sa_time = os.path.join(sub_interdata_folder, str(y) + '-' + str(a) + 'Sa_time.mat')
load_fluxes_and_storages = os.path.join(interdata_folder, str(y) + '-' + str(a) + 'fluxes_storages.mat')
save_path = os.path.join(output_folder, 'E_track_continental_full' + str(years[0]) + '-' + str(years[-1]) + '-timetracking' + str(timetracking) + '.mat')
save_path_daily = os.path.join(output_folder, 'E_track_continental_daily_full' + str(y) + '-timetracking' + str(timetracking) + '.mat')
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)))
E_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)))
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)))
if timetracking == 1:
Sa_time_down_per_year_per_month = np.zeros((len(years),12,len(latitude),len(longitude)))
Sa_time_top_per_year_per_month = np.zeros((len(years),12,len(latitude),len(longitude)))
E_time_per_year_per_month = np.zeros((len(years),12,len(latitude),len(longitude)))
for i in range(len(years)):
y = years[i]
ly = int(calendar.isleap(y))
final_time = 364+ly
E_per_day = np.zeros((365+ly,len(latitude),len(longitude)))
E_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)))
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)))
if timetracking == 1:
Sa_time_down_per_day = np.zeros((365+ly,len(latitude),len(longitude)))
Sa_time_top_per_day = np.zeros((365+ly,len(latitude),len(longitude)))
E_time_per_day = np.zeros((365+ly,len(latitude),len(longitude)))
for j in range(len(yearpart)):
start = timer()
a = yearpart[j]
datapath = data_path(y,a,years,timetracking)
if a > final_time: # a = 365 (366th index) and not a leapyear\
pass
else:
# load tracked data
loading_ST = sio.loadmat(datapath[0],verify_compressed_data_integrity=False)
Sa_track_top = loading_ST['Sa_track_top']
Sa_track_down = loading_ST['Sa_track_down']
north_loss = loading_ST['north_loss']
south_loss = loading_ST['south_loss']
down_to_top = loading_ST['down_to_top']
top_to_down = loading_ST['top_to_down']
water_lost = loading_ST['water_lost']
Sa_track = Sa_track_top + Sa_track_down
if timetracking == 1:
loading_STT = sio.loadmat(datapath[1],verify_compressed_data_integrity=False)
Sa_time_top = loading_STT['Sa_time_top']
Sa_time_down = loading_STT['Sa_time_down']
# load the total moisture data
loading_FS = sio.loadmat(datapath[2],verify_compressed_data_integrity=False)
Fa_E_top = loading_FS['Fa_E_top']
Fa_N_top = loading_FS['Fa_N_top']
Fa_E_down = loading_FS['Fa_E_down']
Fa_N_down = loading_FS['Fa_N_down']
Fa_Vert = loading_FS['Fa_Vert']
E = loading_FS['E']
P = loading_FS['P']
W_top = loading_FS['W_top']
W_down = loading_FS['W_down']
W = W_top + W_down
# compute tracked evaporation
E_track = E[:,:,:] * (Sa_track_down[1:,:,:] / W_down[1:,:,:])
# save per day
E_per_day[a,:,:] = np.sum(E, axis =0)
E_track_per_day[a,:,:] = np.sum(E_track, axis =0)
P_per_day[a,:,:] = np.sum(P, axis =0)
Sa_track_down_per_day[a,:,:] = np.mean(Sa_track_down[1:,:,:], axis =0)
Sa_track_top_per_day[a,:,:] = np.mean(Sa_track_top[1:,:,:], axis =0)
W_down_per_day[a,:,:] = np.mean(W_down[1:,:,:], axis =0)
W_top_per_day[a,:,:] = np.mean(W_top[1:,:,:], axis =0)
north_loss_per_day[a,:,:] = np.sum(north_loss, axis =0)
south_loss_per_day[a,:,:] = np.sum(south_loss, axis =0)
down_to_top_per_day[a,:,:] = np.sum(down_to_top, axis =0)
top_to_down_per_day[a,:,:] = np.sum(top_to_down, axis =0)
water_lost_per_day[a,:,:] = np.sum(water_lost, axis =0)
if timetracking == 1:
# compute tracked evaporation time
E_time = 0.5 * ( Sa_time_down[:-1,:,:] + Sa_time_down[1:,:,:] ) # seconds
# save per day
Sa_time_down_per_day[a,:,:] = np.mean(Sa_time_down[:-1,:,:], axis=0) # seconds
Sa_time_top_per_day[a,:,:] = np.mean(Sa_time_top[:-1,:,:], axis=0) # seconds
E_time_per_day[a,:,:] = np.sum((E_time * E_track), axis = 0) / E_track_per_day[a,:,:] # seconds
# remove nans
where_are_NaNs = np.isnan(E_time_per_day)
E_time_per_day[where_are_NaNs] = 0
end = timer()
print 'Runtime output for day ' + str(a+1) + ' in year ' + str(y) + ' 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
sio.savemat(datapath[4],
{'E_per_day':E_per_day,'E_track_per_day':E_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},do_compression=True)
# values per month
for m in range(12):
first_day = int(datetime.date(y,m+1,1).strftime("%j"))
last_day = int(datetime.date(y,m+1,calendar.monthrange(y,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[y-startyear,m,:,:] = (np.squeeze(np.sum(E_per_day[days,:,:], axis = 0)))
E_track_per_year_per_month[y-startyear,m,:,:] = (np.squeeze(np.sum(E_track_per_day[days,:,:], axis = 0)))
P_per_year_per_month[y-startyear,m,:,:] = (np.squeeze(np.sum(P_per_day[days,:,:], axis = 0)))
Sa_track_down_per_year_per_month[y-startyear,m,:,:] = (np.squeeze(np.mean(Sa_track_down_per_day[days,:,:], axis = 0)))
Sa_track_top_per_year_per_month[y-startyear,m,:,:] = (np.squeeze(np.mean(Sa_track_top_per_day[days,:,:], axis = 0)))
W_down_per_year_per_month[y-startyear,m,:,:] = (np.squeeze(np.mean(W_down_per_day[days,:,:], axis = 0)))
W_top_per_year_per_month[y-startyear,m,:,:] = (np.squeeze(np.mean(W_top_per_day[days,:,:], axis = 0)))
north_loss_per_year_per_month[y-startyear,m,:,:] = (np.squeeze(np.sum(north_loss_per_day[days,:,:], axis = 0)))
south_loss_per_year_per_month[y-startyear,m,:,:] = (np.squeeze(np.sum(south_loss_per_day[days,:,:], axis = 0)))
down_to_top_per_year_per_month[y-startyear,m,:,:] = (np.squeeze(np.sum(down_to_top_per_day[days,:,:], axis = 0)))
top_to_down_per_year_per_month[y-startyear,m,:,:] = (np.squeeze(np.sum(top_to_down_per_day[days,:,:], axis = 0)))
water_lost_per_year_per_month[y-startyear,m,:,:] = (np.squeeze(np.sum(water_lost_per_day[days,:,:], axis = 0)))
if timetracking == 1:
Sa_time_down_per_year_per_month[y-startyear,m,:,:] = (np.squeeze(np.mean(Sa_time_down_per_day[days,:,:], axis = 0)))
Sa_time_top_per_year_per_month[y-startyear,m,:,:] = (np.squeeze(np.mean(Sa_time_top_per_day[days,:,:], axis =0)))
E_time_per_year_per_month[y-startyear,m,:,:] = (np.squeeze(np.sum( E_time_per_day[days,:,:]
* E_track_per_day[days,:,:], axis=0)) / np.squeeze(E_track_per_year_per_month[y-startyear,m,:,:]))
# remove nans
where_are_NaNs = np.isnan(E_time_per_year_per_month)
E_time_per_year_per_month[where_are_NaNs] = 0
elif 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
sio.savemat(datapath[3],
{'E_per_year_per_month':E_per_year_per_month,'E_track_per_year_per_month':E_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})
end1 = timer()
print 'The total runtime of Con_E_Recyc_Output is',(end1-start1),' seconds.'