-
Notifications
You must be signed in to change notification settings - Fork 2
/
step03_portObsToNCDF.py
248 lines (197 loc) · 7.12 KB
/
step03_portObsToNCDF.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
#put observed sea ice into netcdf!
#! /usr/bin/python
#from grass import script as gs
import os
import glob
import numpy as np
from netCDF4 import Dataset
import nio
from datetime import datetime
from datetime import timedelta
import matplotlib.pylab as plt
plt.close('all')
import cProfile, pstats, StringIO
pr = cProfile.Profile()
pr.enable()
# need to fix
# 1. time stamping
folderpath=u'/Volumes/Pitcairn/seaicePPF/northernHemisphere/nsidcObservations/'
pathIn=u'/Volumes/Pitcairn/seaicePPF/p/cesm0005/CESM-CAM5-BGC-LE/ice/proc/tseries/daily/aice_d/'
fn_sat='/Volumes/Pitcairn/seaicePPF/northernHemisphere/analysisOutput/satelliteObs.timeseries.nc'
path=folderpath+'/supportingFiles'
os.chdir(path)
f = open('coast_25n.msk', 'r')
coastgrid = np.fromfile(f,dtype=np.uint8)
f.close()
coastgrid=coastgrid.reshape((448,304))
f = open('gsfc_25n.msk', 'r')
landmask = np.fromfile(f, dtype=np.uint8)
f.close()
landmask=landmask.reshape((448,304))
f = open('psn25lats_v2.dat', 'r')
latgrid = np.fromfile(f, dtype=np.int32)/100000.
f.close()
latgrid=latgrid.reshape((448,304))
f = open('psn25lons_v2.dat', 'r')
longrid = np.fromfile(f, dtype=np.int32)/100000.
f.close()
longrid=longrid.reshape((448,304))
#plt.figure()
#plt.imshow(longrid, vmin=-180, vmax=180)
#plt.show()
#
#plt.figure()
#plt.imshow(latgrid, vmin=0, vmax=90)
#plt.show()
yrs=range(1979, 2016)
# time is in "days since 1979-01-01 00:00:00"
reftime=datetime(min(yrs), 1, 1, 0, 0, 0)
endtime=datetime(max(yrs), 12, 31, 0, 0, 0)
totalTime=endtime-reftime
numFiles=12784+365+192
#totalTime.days
timestep=np.zeros(numFiles)
sicAll=np.zeros((numFiles, 448,304))
timeBounds=np.zeros((numFiles,2))
itters=[]
mD=[]
i=0
startTime = datetime.now()
dates=[]
#path=u'/Users/katherinebarnhart/Desktop/RESEARCH/seaIcePPF/b*timeseries.nc'
dirList=glob.glob(pathIn+'b*nh*.nc')
f=nio.open_file(dirList[0], 'r')
fillVal=f.variables['aice_d'].__dict__['_FillValue']
f.close()
del f
for y in yrs:
path=folderpath+str(y)
os.chdir(path)
filenames = sorted(glob.glob('nt*.bin'))
if (len(filenames)>367) or (len(filenames)==263):
datesSort=[fn[3:11] for fn in filenames]
udates=sorted(set(datesSort))
newFiles=[]
for ud in udates:
tempFiles = sorted(glob.glob('nt*'+ud+'*.bin'))
if len(tempFiles)==1:
newFiles.append(tempFiles[0])
else:
nums=[int(fn[13:15]) for fn in tempFiles]
moreFiles=sorted(glob.glob('nt*'+ud+'*'+str(max(nums))+'*.bin'))
newFiles.append(moreFiles[0])
filenames=newFiles
print len(filenames)
# multiple versions
for filename in filenames:
f = open(filename, 'r')
data = np.fromfile(f, dtype=np.uint8)
f.close()
year=str(y)
month_num = int(filename[7:9])
day=int(filename[9:11])
if (i==0):
print 'firstday'
date=datetime(y, month_num, day-1, 12)
dates.append(date)
sic=data[300:]/250.*100.
sic=sic.reshape((448,304))
landMask=sic>100
sic[landMask]=fillVal
sicAll[i,:,:]=sic
timestep[i]=0.5
timeBounds[i, 0]=timestep[i]-0.5
timeBounds[i, 1]=timestep[i]+0.5
itters.append(i)
i+=1
if (i>0):
date=datetime(y, month_num, day, 12)
dates.append(date)
localdt=dates[-1]-dates[-2]
missingDays=localdt.total_seconds()/(3600*24.)
if int(missingDays)==2:
print i, 'filling', str(missingDays)
sicAll[i,:,:]=sicAll[i-1,:,:]
timestep[i]=timestep[i-1]+1.
timeBounds[i, 0]=timestep[i]-0.5
timeBounds[i, 1]=timestep[i]+0.5
itters.append(i)
i+=1
mD.append(missingDays)
if missingDays>20:
print i, 'filling', str(missingDays)
missing=missingDays
while missing>1:
sicAll[i,:,:]=sicAll[i-1,:,:]
timestep[i]=timestep[i-1]+1.
timeBounds[i, 0]=timestep[i]-0.5
timeBounds[i, 1]=timestep[i]+0.5
itters.append(i)
i+=1
mD.append(missingDays)
missing=missing-1
date=datetime(y, month_num, day, 12)
dates.append(date)
date=datetime(y, month_num, day, 12)
dates.append(date)
dt=date-reftime
sic=data[300:]/250.*100.
sic=sic.reshape((448,304))
landMask=sic>100
sic[landMask]=fillVal
sicAll[i,:,:]=sic
timestep[i]=dt.total_seconds()/(3600*24.)
timeBounds[i, 0]=timestep[i]-0.5
timeBounds[i, 1]=timestep[i]+0.5
print i, timestep[i] ,filename, (datetime.now()-startTime)
itters.append(i)
i+=1
# remove extra
# 2. get this into an netcdf:
dirList=glob.glob(pathIn+'*nc')
f=nio.open_file(dirList[0], 'r')
#fn_sat='/Users/katherinebarnhart/Desktop/RESEARCH/seaIcePPF/satelliteObs.timeseries.nc'
fsat=Dataset(fn_sat, 'w',format='NETCDF4')
# create all the dimentions, set time to unlimited
fsat.createDimension('time', None)
fsat.createDimension('ni', 304)
fsat.createDimension('nj', 448)
fsat.createDimension('d2', 2)
fsatVars={}
for key in {'TLAT', 'TLON','time', 'time_bounds'}:
#print 'creating ', key
# the netCDF4 module requires that if a fill value exists, it must be declared when the variable is created.
try:
fsatVars[key]=fsat.createVariable(key, f.variables[key].typecode(), f.variables[key].dimensions, fill_value=f.variables[key].__dict__['_FillValue'])
except:
fsatVars[key]=fsat.createVariable(key, f.variables[key].typecode(), f.variables[key].dimensions)
# sett all the attribute keys.
atts = f.variables[key].__dict__
for attKey in atts.keys():
if attKey != '_FillValue':
setattr(fsat.variables[key],attKey,atts[attKey])
setattr(fsat.variables['time'],'calendar','gregorian')
setattr(fsat.variables['time'], 'units', 'days since '+str(yrs[0])+'-01-01 00:00:00')
setattr(fsat.variables['time_bounds'],'calendar','gregorian')
setattr(fsat.variables['time_bounds'], 'units', 'days since '+str(yrs[0])+'-01-01 00:00:00')
monthAvgKey='satelliteSIC'
fsatVars[monthAvgKey]=fsat.createVariable(monthAvgKey, f.variables['aice_d'].typecode(), f.variables['aice_d'].dimensions,fill_value=f.variables['aice_d'].__dict__['_FillValue'])
#print 'creating aice_d_monthAvg'
atts = f.variables['aice_d'].__dict__
for attKey in atts.keys():
if attKey is not '_FillValue':
setattr(fsat.variables[monthAvgKey],attKey,atts[attKey])
setattr(fsat.variables[monthAvgKey],'long_name','Sea Ice Concentration (satellite)')
fsatVars['time'][:]=timestep
fsatVars['time_bounds'][:,:]=timeBounds
fsatVars['TLAT'][:,:]=latgrid
fsatVars['TLON'][:,:]=longrid
fsatVars[monthAvgKey][:,:,:]=sicAll
fsat.close()
(datetime.now()-startTime)
#pr.disable()
#s = StringIO.StringIO()
#sortby = 'cumulative'
#ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
#ps.print_stats()
#print s.getvalue()