-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathplot_landda_ioda.py
executable file
·233 lines (191 loc) · 7.52 KB
/
plot_landda_ioda.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
###################################################################### CHJ #####
## Name : plot_landda_ioda.py
## Language : Python 3.7
## Usage : Plot input snow depth ioda data
## Input files : ghcn_snwd_ioda.nc
## NOAA/NWS/NCEP/EMC
## History ===============================
## V000: 2024/04/18: Chan-Hoo Jeon : Preliminary version
###################################################################### CHJ #####
import os, sys
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import numpy as np
import netCDF4 as nc
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy
from mpl_toolkits.axes_grid1 import make_axes_locatable
# HPC machine ('hera','orion')
machine='hera'
print(' You are on', machine)
#### Machine-specific input data ==================================== CHJ =====
# cartopy.config: Natural Earth data for background
# out_fig_dir: directory where the output files are created
# mfdt_kwargs: mfdataset argument
if machine=='hera':
cartopy.config['data_dir']='/scratch2/NCEPDEV/fv3-cam/Chan-hoo.Jeon/tools/NaturalEarth'
out_fig_dir="/scratch2/NCEPDEV/fv3-cam/Chan-hoo.Jeon/tools/fv3sar_pre_plot/Fig/"
mfdt_kwargs={'parallel':False}
elif machine=='orion':
cartopy.config['data_dir']='/home/chjeon/tools/NaturalEarth'
out_fig_dir="/work/noaa/fv3-cam/chjeon/tools/Fig/"
mfdt_kwargs={'parallel':False,'combine':'by_coords'}
else:
sys.exit('ERROR: Required input data are NOT set !!!')
plt.switch_backend('agg')
# Case-dependent input =============================================== CHJ =====
# Path to the directory where the input NetCDF file is located.
dnm_data="/scratch2/NAGAPE/epic/Chan-hoo.Jeon/ghcn_ioda/obs/"
# input file date
#date_input='20230501'
date_input='20000103'
# input file name
fnm_input='fake_ghcn_snwd_ioda_'+date_input+'.nc'
# basic forms of title and file name: base+static field name
out_title_base='LAND-DA::Snow depth (ioda)::'+date_input+'::'
out_fname_base='landda_snwd_ioda_'+date_input+'_'
# Resolution of background natural earth data ('50m' or '110m')
back_res='50m'
# Main part (will be called at the end) =================== CHJ =====
def main():
# ========================================================= CHJ =====
global mdat,lat,lon,extent,c_lon
print(' ===== INPUT: '+fnm_input+' ================================')
# open the data file
fpath=os.path.join(dnm_data,fnm_input)
try: mdat=nc.Dataset(fpath)
except: raise Exception('Could NOT find the file',fpath)
print(mdat)
print(mdat.groups['MetaData'])
print(mdat.groups['ObsError'])
print(mdat.groups['ObsValue'])
print(mdat.groups['PreQC'])
longitude=mdat.groups['MetaData'].variables['longitude'][:]
latitude=mdat.groups['MetaData'].variables['latitude'][:]
datetime=mdat.groups['MetaData'].variables['dateTime'][:]
# stationElevation=mdat.groups['MetaData'].variables['stationElevation'][:]
height=mdat.groups['MetaData'].variables['height'][:]
stationID=mdat.groups['MetaData'].variables['stationIdentification'][:]
# lon_max=np.max(lon)
# Longitude 0:360 => -180:180
# if lon_max>180:
# lon=(lon+180)%360-180
lon=longitude
lat=latitude
# lon,lat=np.meshgrid(longitude,latitude,sparse=False)
# Highest and lowest longitudes and latitudes for plot extent
lon_min=np.min(lon)
lon_max=np.max(lon)
lat_min=np.min(lat)
lat_max=np.max(lat)
extent=[lon_min,lon_max,lat_min,lat_max]
# for CONUS
extent=[-125,-66,23,53]
print(extent)
c_lon=np.mean(extent[:2])
#c_lon=-77.0369 # D.C.
print(' c_lon=',c_lon)
# Variables
# vars_out=["ObsValue","ObsError","PreQC"]
vars_out=["ObsValue"]
for svar in vars_out:
svar_plot(svar)
# MERRA2 plot ================================================= CHJ =====
def svar_plot(svar):
# ============================================================= CHJ =====
print(' ===== '+svar+' ===== IODA total snow depth =============')
# Extract data array
sfld=mdat.groups[svar].variables['totalSnowDepth'][:]
out_title_fld=out_title_base+svar
out_fname=out_fname_base+svar
cs_cmap='gist_ncar_r'
lb_ext='neither'
tick_ln=1.5
tick_wd=0.45
tlb_sz=3
scat_sz=1.5
n_rnd=2
cmap_range='fixed'
print(' svar name=',svar)
# Max and Min of the field
fmax=np.max(sfld)
fmin=np.min(sfld)
print(' fld_max=',fmax)
print(' flx_min=',fmin)
# Make the colormap range symmetry
print(' cmap range=',cmap_range)
if cmap_range=='symmetry':
tmp_cmp=max(abs(fmax),abs(fmin))
cs_min=round(-tmp_cmp,n_rnd)
cs_max=round(tmp_cmp,n_rnd)
elif cmap_range=='round':
cs_min=round(fmin,n_rnd)
cs_max=round(fmax,n_rnd)
elif cmap_range=='real':
cs_min=fmin
cs_max=fmax
elif cmap_range=='fixed':
cs_min=0
cs_max=2000.0
else:
sys.exit('ERROR: wrong colormap-range flag !!!')
print(' cs_max=',cs_max)
print(' cs_min=',cs_min)
print(' extent=',extent)
# Plot field
fig,ax=plt.subplots(1,1,subplot_kw=dict(projection=ccrs.Robinson(c_lon)))
ax.set_extent(extent, ccrs.PlateCarree())
# Call background plot
back_plot(ax)
ax.set_title(out_title_fld,fontsize=9)
# cs=ax.pcolormesh(lon,lat,sfld,cmap=cs_cmap,rasterized=True,
# vmin=cs_min,vmax=cs_max,transform=ccrs.PlateCarree())
cs=ax.scatter(lon,lat,transform=ccrs.PlateCarree(),c=sfld,cmap=cs_cmap,
vmin=cs_min,vmax=cs_max,s=scat_sz)
divider=make_axes_locatable(ax)
ax_cb=divider.new_horizontal(size="3%",pad=0.1,axes_class=plt.Axes)
fig.add_axes(ax_cb)
cbar=plt.colorbar(cs,cax=ax_cb,extend=lb_ext)
cbar.ax.tick_params(labelsize=8)
cbar.set_label(svar,fontsize=8)
# Output figure
ndpi=300
out_file(out_fname,ndpi)
# Background plot ========================================== CHJ =====
def back_plot(ax):
# ========================================================== CHJ =====
fline_wd=0.5 # line width
falpha=0.3 # transparency
# natural_earth
# land=cfeature.NaturalEarthFeature('physical','land',back_res,
# edgecolor='face',facecolor=cfeature.COLORS['land'],
# alpha=falpha)
lakes=cfeature.NaturalEarthFeature('physical','lakes',back_res,
edgecolor='blue',facecolor='none',
linewidth=fline_wd,alpha=falpha)
coastline=cfeature.NaturalEarthFeature('physical','coastline',
back_res,edgecolor='blue',facecolor='none',
linewidth=fline_wd,alpha=falpha)
states=cfeature.NaturalEarthFeature('cultural','admin_1_states_provinces',
back_res,edgecolor='black',facecolor='none',
linewidth=fline_wd,linestyle=':',alpha=falpha)
borders=cfeature.NaturalEarthFeature('cultural','admin_0_countries',
back_res,edgecolor='red',facecolor='none',
linewidth=fline_wd,alpha=falpha)
# ax.add_feature(land)
ax.add_feature(lakes)
ax.add_feature(states)
ax.add_feature(borders)
ax.add_feature(coastline)
# Output file ============================================= CHJ =====
def out_file(out_file,ndpi):
# ========================================================= CHJ =====
# Output figure
plt.savefig(out_fig_dir+out_file+'.png',dpi=ndpi,bbox_inches='tight')
plt.close('all')
# Main call ================================================ CHJ =====
if __name__=='__main__':
main()