-
Notifications
You must be signed in to change notification settings - Fork 0
/
plot_surface_heights_thermistor.py
322 lines (272 loc) · 12.2 KB
/
plot_surface_heights_thermistor.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
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
# -*- coding: utf-8 -*-
"""
@author: bav@geus.dk
tip list:
%matplotlib inline
%matplotlib qt
import pdb; pdb.set_trace()
"""
import matplotlib.pyplot as plt
import pandas as pd
import os
import matplotlib
matplotlib.use('Agg')
import tocgen
# def main(
data_type = 'stations'
path_new = '../aws-l3-dev/'+data_type+'/'
filename = 'plot_compilations/surface_height_'+data_type+'.md'
df_meta = pd.read_csv(path_new+'../AWS_'+data_type+'_metadata.csv')
df_meta = df_meta.set_index(data_type[:-1]+'_id')
f = open(filename, "w")
def Msg(txt):
f = open(filename, "a")
print(txt)
f.write(txt + "\n")
plt.close('all')
for station in df_meta.index:
# for station in ['KAN_M']:
Msg('## '+station)
if not os.path.isfile(path_new+station+'/'+station+'_day.csv'):
continue
df_new = pd.read_csv(path_new+station+'/'+station+'_day.csv')
df_new.time = pd.to_datetime(df_new.time, utc=True)
df_new = df_new.set_index('time')
fig, ax_list = plt.subplots(4,1,sharex=True, figsize=(10,15))
plt.subplots_adjust(right=0.75,left=0.08,hspace=0.02)
var_list = [v for v in ['z_boom_u','z_boom_l','z_stake','z_pt_cor'] \
if v in df_new.columns]
for var in var_list:
if var in df_new.columns:
if not df_new[var].isnull().all():
ax_list[0].plot(df_new.index, df_new[var].values,
marker='.',markeredgecolor='None', linestyle='None',
label=var)
var_list = [v for v in ['z_surf_combined','z_ice_surf','snow_height'] \
if v in df_new.columns]
for var in var_list:
if var in df_new.columns:
if not df_new[var].isnull().all():
ax_list[1].plot(df_new.index, df_new[var].values,
marker='.',markeredgecolor='None', linestyle='None',
label=var)
else:
print(var,'all nan')
var_list = ['d_t_i_'+str(i) for i in range(1,12) \
if 'd_t_i_'+str(i) in df_new.columns]
for var in var_list:
if var in df_new.columns:
if not df_new[var].isnull().all():
ax_list[2].plot(df_new.index, df_new[var].values,
marker='.',markeredgecolor='None', linestyle='None',
label=var)
var_list = ['t_i_'+str(i) for i in range(1,12) \
if 't_i_'+str(i) in df_new.columns]
for var in var_list:
if var in df_new.columns:
if not df_new[var].isnull().all():
ax_list[3].plot(df_new.index, df_new[var].values,
marker='.',markeredgecolor='None', linestyle='None',
label=var)
if 't_i_10m' in df_new.columns:
ax_list[3].plot(df_new.index, df_new['t_i_10m'].values,
marker='o',color='k',markeredgecolor='None', linestyle='None',
label='t_i_10m')
for i in range(4):
ax_list[i].set_ylabel('Height (m)')
ax_list[i].grid()
ax_list[i].legend(loc='center left', bbox_to_anchor=(1, 0.5))
if i > 1:
ax_list[i].legend(loc='center left', bbox_to_anchor=(1, 0.5),ncols=2)
ax_list[2].set_ylabel('Depth (m)')
ax_list[2].invert_yaxis()
ax_list[3].set_ylabel('Temperature (°C)')
ax_list[0].set_title(station)
fig.savefig('figures/surface_height/%s/%s.png' % (data_type, station), dpi=300, bbox_inches="tight")
Msg(''%(station,data_type, station))
Msg(' ')
tocgen.processFile(filename, filename[:-3]+"_toc.md")
f.close()
# %%
import matplotlib.pyplot as plt
import pandas as pd
import os
import tocgen
# def main(
data_type = 'sites'
path_new = '../aws-l3-dev/'+data_type+'/'
filename = 'plot_compilations/surface_height_'+data_type+'.md'
df_meta = pd.read_csv(path_new+'../AWS_'+data_type+'_metadata.csv')
df_meta = df_meta.set_index(data_type[:-1]+'_id')
plt.close('all')
region_list = ['KAN', 'UPE','THU','KPC','SCO','TAS','QAS','NUK']
station_list_list = [[v for v in df_meta.index if st in v] for st in region_list]
station_list_list = station_list_list+[['SWC','JAR']]
fig, ax_list = plt.subplots(5, 3, figsize=(10, 15))
ax_list = ax_list.flatten()
plt.subplots_adjust(right=0.7, left=0.08, hspace=0.02, wspace=0.3)
# Make the last two panels (11th and 12th) invisible
for ax in ax_list[10:]:
ax.set_visible(False)
# Create the upper 9 panels and share x-axis and ticks
for i, station_list in enumerate(station_list_list[:9]): # Limit to the first 9 panels
for station in station_list:
if station in ['KAN_B', 'NUK_K']:
continue
print('## ' + station)
if not os.path.isfile(path_new + station + '/' + station + '_day.csv'):
continue
df_new = pd.read_csv(path_new + station + '/' + station + '_day.csv')
df_new.time = pd.to_datetime(df_new.time, utc=True)
df_new = df_new.set_index('time')
var_list = [v for v in ['z_surf_combined'] if v in df_new.columns]
for var in var_list:
if var in df_new.columns:
if not df_new[var].isnull().all():
ax_list[i].plot(df_new.index, df_new[var].values,
marker='.', markeredgecolor='None', linestyle='None',
alpha=0.5, label=station)
else:
print(var, 'all nan')
ax_list[i].legend(loc='lower left')
ax_list[i].grid(True)
ax_list[i].tick_params(axis='x', rotation=45)
if i <6:
ax_list[i].set_xticklabels([])
ax_list[i].set_ylim(-100,20)
ax_list[i].set_xlim(pd.to_datetime('1995'),
pd.to_datetime('2025'))
# Combine the 10th panel across the 11th and 12th positions
gs = ax_list[9].get_gridspec()
ax_list[9].remove() # Remove the 10th panel from its current spot
ax_large = fig.add_subplot(gs[4, :]) # Create a large subplot spanning 11th and 12th positions
# Plot data for the 10th panel (adjust this depending on the correct station_list)
for station in ['DY2', 'NAU','CEN','TUN',
'NAE','NSE','SDL','SDM']:
if station in ['KAN_B', 'NUK_K']:
continue
if not os.path.isfile(path_new + station + '/' + station + '_day.csv'):
continue
df_new = pd.read_csv(path_new + station + '/' + station + '_day.csv')
df_new.time = pd.to_datetime(df_new.time, utc=True)
df_new = df_new.set_index('time')
var_list = [v for v in ['z_surf_combined'] if v in df_new.columns]
for var in var_list:
if var in df_new.columns and not df_new[var].isnull().all():
ax_large.plot(df_new.index, df_new[var].values,
marker='.', markeredgecolor='None', linestyle='None',
alpha=0.5, label=station)
ax_large.legend(loc='lower center',ncols=3, bbox_to_anchor=(0.5, 1.05))
ax_large.grid(True)
ax_large.tick_params(axis='x', rotation=45)
# Common ylabel for all panels
for ax in ax_list[:10]:
ax.set_ylabel('')
fig.supylabel('Surface height relative to installation (m)')
# Save the figure
fig.savefig('figures/surface_height/overview.png', dpi=300)
# %%
import matplotlib.pyplot as plt
import pandas as pd
import os
import matplotlib
import matplotlib.dates as mdates
from datetime import datetime
matplotlib.use('Agg')
import tocgen
import numpy as np
# Initialize
data_type = 'sites'
path_new = '../aws-l3-dev/' + data_type + '/'
filename = 'plot_compilations/snow_height_' + data_type + '.md'
df_meta = pd.read_csv(path_new + '../AWS_' + data_type + '_metadata.csv')
df_meta = df_meta.set_index(data_type[:-1] + '_id')
f = open(filename, "w")
def Msg(txt):
with open(filename, "a") as f:
print(txt)
f.write(txt + "\n")
plt.close('all')
# Loop over each station
for station in df_meta.index:
# for station in ['QAS_U']:
Msg('## ' + station)
# Check if the file exists
if not os.path.isfile(path_new + station + '/' + station + '_day.csv'):
continue
# Read the station data
df_new = pd.read_csv(path_new + station + '/' + station + '_day.csv')
df_new.time = pd.to_datetime(df_new.time)
df_new = df_new.set_index('time')
# Create a figure with two panels
fig, ax_list = plt.subplots(2, 1, sharex=False, figsize=(10, 10))
plt.subplots_adjust(right=0.75, left=0.08, hspace=0.15)
# Top panel: z_surf_combined
if 'z_surf_combined' in df_new.columns and not df_new['z_surf_combined'].isnull().all():
ax_list[0].plot(df_new.index, df_new['z_surf_combined'],
marker='.', markeredgecolor='None', linestyle='None', label='z_surf_combined')
ax_list[0].set_ylabel('Surface Height (m)')
ax_list[0].set_title(station)
ax_list[0].grid()
ax_list[0].legend(loc='center left', bbox_to_anchor=(1, 0.5))
# Bottom panel: snow_height as function of day of year
if 'snow_height' in df_new.columns and not df_new['snow_height'].isnull().all():
for year in df_new.index.year.unique().tolist() + [df_new.index.year.max() + 1]:
# Filter data for the year range September to August Y
start_date = pd.Timestamp(f'{year-1}-09-01')
end_date = pd.Timestamp(f'{year}-08-31')
df_year = df_new.loc[(df_new.index >= start_date) & (df_new.index < end_date), :].copy()
# Calculate the day of the year
df_year['day_of_year'] = df_year.index.dayofyear.values - 244
df_year.loc[df_year['day_of_year'] < 0, 'day_of_year'] = 365 + df_year.loc[df_year['day_of_year'] < 0, 'day_of_year']
# Plot snow_height adjusted by first valid value
if df_year['snow_height'].notnull().any():
first_valid_value = df_year['snow_height'].loc[
slice(df_year['snow_height'].first_valid_index(),
df_year['snow_height'].first_valid_index()+pd.to_timedelta('30 days'))].min()
if year == 2025:
ax_list[1].plot(df_year['day_of_year'],
df_year['snow_height'] - first_valid_value,
label='_no_legend_', linestyle='-', color='w',lw=6, alpha=0.7)
ax_list[1].plot(df_year['day_of_year'],
df_year['snow_height'] - first_valid_value,
label=str(year), linestyle='-', color='k',lw=2)
else:
ax_list[1].plot(df_year['day_of_year'],
df_year['snow_height'] - first_valid_value,
label=str(year), linestyle='-')
# Set x-axis limits
ax_list[1].set_xlim(1, 365) # Adjust for leap years if necessary
# Major ticks and labels every 30 days
labels = []
tick_positions = []
for i in np.cumsum(np.array([30, 31, 30, 31, 31, 29, 31, 30, 31, 30, 31, 31])):
tick_positions.append(i)
# Determine the corresponding month and year for the label
month_label = ''
if i < 30: month_label = 'Sept.'
elif i < 60: month_label = 'Oct.'
elif i < 90: month_label = 'Nov.'
elif i < 120: month_label = 'Dec.'
elif i < 150: month_label = 'Jan.'
elif i < 180: month_label = 'Feb.'
elif i < 210: month_label = 'Mar.'
elif i < 240: month_label = 'Apr.'
elif i < 270: month_label = 'May'
elif i < 300: month_label = 'Jun.'
elif i < 330: month_label = 'Jul.'
elif i < 360: month_label = 'Aug.'
labels.append(month_label)
ax_list[1].set_xticks(tick_positions)
ax_list[1].set_xticklabels(labels, rotation=45, ha='right')
# Add vertical line at day 121
# ax_list[1].axvline(x=121, color='red', linestyle='--', label='Day 121')
ax_list[1].set_ylabel('Snow Height (m)')
ax_list[1].grid()
ax_list[1].legend(title='Year', loc='center left', bbox_to_anchor=(1, 0.5), ncol=1)
# Save the figure
fig.savefig('figures/snow_height/%s/%s.png' % (data_type, station), dpi=300)
Msg(f'')
Msg(' ')
tocgen.processFile(filename, filename[:-3] + "_toc.md")
f.close()