-
Notifications
You must be signed in to change notification settings - Fork 0
/
all_data_catchment_level.py
144 lines (120 loc) · 5.41 KB
/
all_data_catchment_level.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
"""
Script to create table of catchment-mean ET, P, RDN and LAI estimates
@author: Jess Baker, j.c.baker@leeds.ac.uk
"""
import pandas as pd
import numpy as np
from datetime import datetime
fpath = '/nfs/a68/gyjcab/datasets/et_analysis/'
fnames = [# catchment data
'all_basin_catchment_et_2002_2019_jpl_mascon_chirps_hires.npy',
# satellite data
'satellite_all_basins_et_2003_2013_interannual.npy',
'satellite_all_basins_rdn_2003_2013_interannual.npy',
'satellite_all_basins_modis_mod15a2h_lai_2003_2013_interannual.npy',
# reanalysis data
'reanalysis_all_basins_et_2003_2013_interannual.npy',
'reanalysis_all_basins_pre_2003_2013_interannual.npy',
'reanalysis_all_basins_rdn_2003_2013_interannual.npy',
'reanalysis_all_basins_lai_high_2003_2013_interannual.npy',
# cmip5 data
'cmip5_all_basins_et_1994_2004_interannual_trim.npy',
'cmip5_all_basins_pr_1994_2004_interannual_trim.npy',
'cmip5_all_basins_rdn_1994_2004_interannual_trim.npy',
'cmip5_all_basins_lai_1994_2004_interannual_trim.npy',
# cmip6 data
'cmip6_all_basins_et_2003_2013_interannual.npy',
'cmip6_all_basins_pr_2003_2013_interannual.npy',
'cmip6_all_basins_rdn_2003_2013_interannual.npy',
'cmip6_all_basins_lai_2003_2013_interannual.npy']
columns = ['date', 'catchment', 'product', 'variable', 'data']
all_df = pd.DataFrame(columns=columns)
#fnames = ['satellite_all_basins_et_2003_2013_interannual.npy']
#fnames = [#'cmip6_all_basins_et_2003_2013_interannual.npy',
# 'cmip6_all_basins_pr_2003_2013_interannual.npy']
# #'cmip6_all_basins_rdn_2003_2013_interannual.npy',
# #'cmip6_all_basins_lai_2003_2013_interannual.npy']
counter = 0
for fname in fnames:
print(fname)
if 'all_basin_catchment_et' in fname:
pass
else:
variable = fname.split('_')[3].upper()
data = np.load(fpath+fname, allow_pickle=True).item()
catchments = data.keys()
#for catchment in catchments:
for catchment in ['amazon']:
catchment_data = data[catchment]
#print(catchment_data)
#print(catchment_data.keys())
for variable_key in catchment_data.keys():
#print(variable_key)
temp_df = pd.DataFrame(columns=columns)
df = catchment_data[variable_key]
#print(df)
if 'all_basin_catchment_et' in fname:
if variable_key in ['ET', 'R', 'P', 'dS_dt']:
datemin = datetime(2003, 1, 1, 0, 0)
datemax = datetime(2013, 12, 1, 0, 0)
idx = pd.date_range(datemin, datemax, freq='MS')
df = df.reindex(idx, fill_value=np.nan)
temp_df['date'] = df.index
temp_df['catchment'] = catchment
temp_df['variable'] = variable_key
if variable_key == 'dS_dt':
temp_df['product'] = 'grace'
if variable_key == 'R':
temp_df['product'] = 'ana river records'
if variable_key == 'P':
temp_df['product'] = 'chirps'
if variable_key == 'ET':
temp_df['product'] = 'catchment_balance:P-R-dS_dt'
temp_df['data'] = df.values
#print(temp_df)
#assert False
elif 'cmip6_all_' in fname:
if variable_key == 'dates':
continue
else:
if variable_key == 'dates':
assert False
#assert False
datemin = datetime(2003, 1, 1, 0, 0)
datemax = datetime(2013, 12, 1, 0, 0)
idx = pd.date_range(datemin, datemax, freq='MS')
temp_df['date'] = idx
temp_df['catchment'] = catchment
temp_df['product'] = 'cmip6_' + variable_key
temp_df['variable'] = variable
temp_df['data'] = df
#print(temp_df)
#assert False
elif 'cmip5_all_' in fname:
if variable_key == 'dates':
continue
else:
datemin = datetime(1994, 1, 1, 0, 0)
datemax = datetime(2004, 12, 1, 0, 0)
idx = pd.date_range(datemin, datemax, freq='MS')
temp_df['date'] = idx
temp_df['catchment'] = catchment
temp_df['product'] = 'cmip5_' + variable_key
temp_df['variable'] = variable
temp_df['data'] = df
#print(temp_df)
#assert False
else:
temp_df['date'] = df.index
temp_df['catchment'] = catchment
temp_df['product'] = variable_key
temp_df['variable'] = variable
col = df.columns[0]
temp_df['data'] = df[col].values
#print(temp_df)
# append temp_df to all_df
all_df = all_df.append(temp_df, ignore_index=True)
#print(all_df.tail(3))
print(all_df.head(5))
print(all_df.tail(5))
all_df.to_csv('~/for_hess/amazon_et_estimates_2003_2013.csv')