-
Notifications
You must be signed in to change notification settings - Fork 1
/
plot_ppt_aet_plc_timeseries.py
executable file
·186 lines (155 loc) · 5.9 KB
/
plot_ppt_aet_plc_timeseries.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
#!/usr/bin/env python
"""
Plot DJF for each year of the Millennium drought
"""
__author__ = "Martin De Kauwe"
__version__ = "1.0 (25.07.2019)"
__email__ = "mdekauwe@gmail.com"
import os
import xarray as xr
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import colors
import cartopy.crs as ccrs
import cartopy
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import sys
import matplotlib.ticker as mticker
from cartopy.mpl.geoaxes import GeoAxes
from mpl_toolkits.axes_grid1 import AxesGrid
from calendar import monthrange
def main(plot_dir):
plc_rf_all = np.zeros(0)
plc_wsf_all = np.zeros(0)
plc_dsf_all = np.zeros(0)
plc_grw_all = np.zeros(0)
plc_saw_all = np.zeros(0)
start_yr = 2000
end_yr = 2004
nyears = (end_yr - start_yr) + 1
nmonths = 12
fdir = "outputs"
fname = os.path.join(fdir, "cable_out_2000.nc")
ds = xr.open_dataset(fname)
iveg = ds["iveg"][:,:].values
idx_rf = np.argwhere(iveg == 18.0)
idx_wsf = np.argwhere(iveg == 19.0)
idx_dsf = np.argwhere(iveg == 20.0)
idx_grw = np.argwhere(iveg == 21.0)
idx_saw = np.argwhere(iveg == 22.0)
plc_rf_all = np.zeros((nyears * nmonths, len(idx_rf)))
plc_wsf_all = np.zeros((nyears * nmonths, len(idx_wsf)))
plc_dsf_all = np.zeros((nyears * nmonths, len(idx_dsf)))
plc_grw_all = np.zeros((nyears * nmonths, len(idx_grw)))
plc_saw_all = np.zeros((nyears * nmonths, len(idx_saw)))
et_rf_all = np.zeros((nyears * nmonths, len(idx_rf)))
et_wsf_all = np.zeros((nyears * nmonths, len(idx_wsf)))
et_dsf_all = np.zeros((nyears * nmonths, len(idx_dsf)))
et_grw_all = np.zeros((nyears * nmonths, len(idx_grw)))
et_saw_all = np.zeros((nyears * nmonths, len(idx_saw)))
ppt_rf_all = np.zeros((nyears * nmonths, len(idx_rf)))
ppt_wsf_all = np.zeros((nyears * nmonths, len(idx_wsf)))
ppt_dsf_all = np.zeros((nyears * nmonths, len(idx_dsf)))
ppt_grw_all = np.zeros((nyears * nmonths, len(idx_grw)))
ppt_saw_all = np.zeros((nyears * nmonths, len(idx_saw)))
nyear = 0
cnt = 0
for year in np.arange(start_yr, end_yr):
print(year)
fdir = "outputs"
fname = os.path.join(fdir, "cable_out_%d.nc" % (year))
ds = xr.open_dataset(fname)
plc_vals = ds["plc"][:,0,:,:].values
et_vals = ds["Evap"][:,:,:].values
ppt_vals = ds["Rainf"][:,:,:].values
idx = nyear + cnt
plc_rf = np.zeros((12,len(idx_rf)))
et_rf = np.zeros((12,len(idx_rf)))
ppt_rf = np.zeros((12,len(idx_rf)))
for i in range(len(idx_rf)):
(row, col) = idx_rf[i]
plc_rf[:,i] = plc_vals[:,row,col]
et_rf[:,i] = et_vals[:,row,col]
ppt_rf[:,i] = ppt_vals[:,row,col]
plc_rf_all[idx:(idx+12),:] = plc_rf
et_rf_all[idx:(idx+12),:] = et_rf
ppt_rf_all[idx:(idx+12),:] = ppt_rf
plc_wsf = np.zeros((12,len(idx_wsf)))
et_wsf = np.zeros((12,len(idx_wsf)))
ppt_wsf = np.zeros((12,len(idx_wsf)))
for i in range(len(idx_wsf)):
(row, col) = idx_wsf[i]
plc_wsf[:,i] = plc_vals[:,row,col]
et_wsf[:,i] = et_vals[:,row,col]
ppt_wsf[:,i] = ppt_vals[:,row,col]
plc_wsf_all[idx:(idx+12),:] = plc_wsf
et_wsf_all[idx:(idx+12),:] = et_wsf
ppt_wsf_all[idx:(idx+12),:] = ppt_wsf
plc_dsf = np.zeros((12,len(idx_dsf)))
et_dsf = np.zeros((12,len(idx_dsf)))
ppt_dsf = np.zeros((12,len(idx_dsf)))
for i in range(len(idx_dsf)):
(row, col) = idx_dsf[i]
plc_dsf[:,i] = plc_vals[:,row,col]
et_dsf[:,i] = et_vals[:,row,col]
ppt_dsf[:,i] = ppt_vals[:,row,col]
plc_dsf_all[idx:(idx+12),:] = plc_dsf
et_dsf_all[idx:(idx+12),:] = et_dsf
ppt_dsf_all[idx:(idx+12),:] = ppt_dsf
plc_grw = np.zeros((12,len(idx_grw)))
et_grw = np.zeros((12,len(idx_grw)))
ppt_grw = np.zeros((12,len(idx_grw)))
for i in range(len(idx_grw)):
(row, col) = idx_grw[i]
plc_grw[:,i] = plc_vals[:,row,col]
et_grw[:,i] = et_vals[:,row,col]
ppt_grw[:,i] = ppt_vals[:,row,col]
plc_grw_all[idx:(idx+12),:] = plc_grw
et_grw_all[idx:(idx+12),:] = et_grw
ppt_grw_all[idx:(idx+12),:] = ppt_grw
plc_saw = np.zeros((12,len(idx_saw)))
et_saw = np.zeros((12,len(idx_saw)))
ppt_saw = np.zeros((12,len(idx_saw)))
for i in range(len(idx_saw)):
(row, col) = idx_saw[i]
plc_saw[:,i] = plc_vals[:,row,col]
et_saw[:,i] = et_vals[:,row,col]
ppt_saw[:,i] = ppt_vals[:,row,col]
plc_saw_all[idx:(idx+12),:] = plc_saw
et_saw_all[idx:(idx+12),:] = et_saw
ppt_saw_all[idx:(idx+12),:] = ppt_saw
nyear += 1
cnt += 12
sec_2_day = 86400.0
cnt = 0
for year in np.arange(start_yr, end_yr):
for month in range(1,12+1):
days_in_month = monthrange(year, month)[1]
conv = sec_2_day * days_in_month
ppt_rf_all[cnt,:] *= conv
et_rf_all[cnt,:] *= conv
ppt_wsf_all[cnt,:] *= conv
et_wsf_all[cnt,:] *= conv
ppt_dsf_all[cnt,:] *= conv
et_dsf_all[cnt,:] *= conv
ppt_saw_all[cnt,:] *= conv
et_saw_all[cnt,:] *= conv
cnt += 1
from scipy import stats
a = ppt_wsf_all[:,0] - et_wsf_all[:,0]
b = plc_wsf_all[:,0]
r, pvalue = stats.pearsonr(a, b)
print("r: %f; p-value: %f" % (r, pvalue))
fig, ax1 = plt.subplots()
sec_2_day = 86400.0
ax1.plot(ppt_wsf_all[:,0] - et_wsf_all[:,0], color="red")
ax1.set_ylabel("PPT-AET")
ax2 = ax1.twinx()
ax2.plot(plc_wsf_all[:,0], color="green")
ax2.set_ylabel("PLC")
plt.show()
if __name__ == "__main__":
plot_dir = "plots"
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
main(plot_dir)