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1 '''
1 '''
2 Created on Oct 24, 2016
2 Created on Oct 24, 2016
3
3
4 @author: roj- LouVD
4 @author: roj- LouVD
5 '''
5 '''
6
6
7 import numpy
7 import numpy
8 import datetime
8 import datetime
9 import time
9 import time
10
10
11 from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation
11 from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation
12 from schainpy.model.data.jrodata import Parameters
12 from schainpy.model.data.jrodata import Parameters
13
13
14
14
15 class BLTRParametersProc(ProcessingUnit):
15 class BLTRParametersProc(ProcessingUnit):
16 '''
16 '''
17 Processing unit for BLTR parameters data (winds)
17 Processing unit for BLTR parameters data (winds)
18
18
19 Inputs:
19 Inputs:
20 self.dataOut.nmodes - Number of operation modes
20 self.dataOut.nmodes - Number of operation modes
21 self.dataOut.nchannels - Number of channels
21 self.dataOut.nchannels - Number of channels
22 self.dataOut.nranges - Number of ranges
22 self.dataOut.nranges - Number of ranges
23
23
24 self.dataOut.data_snr - SNR array
24 self.dataOut.data_snr - SNR array
25 self.dataOut.data_output - Zonal, Vertical and Meridional velocity array
25 self.dataOut.data_output - Zonal, Vertical and Meridional velocity array
26 self.dataOut.height - Height array (km)
26 self.dataOut.height - Height array (km)
27 self.dataOut.time - Time array (seconds)
27 self.dataOut.time - Time array (seconds)
28
28
29 self.dataOut.fileIndex -Index of the file currently read
29 self.dataOut.fileIndex -Index of the file currently read
30 self.dataOut.lat - Latitude coordinate of BLTR location
30 self.dataOut.lat - Latitude coordinate of BLTR location
31
31
32 self.dataOut.doy - Experiment doy (number of the day in the current year)
32 self.dataOut.doy - Experiment doy (number of the day in the current year)
33 self.dataOut.month - Experiment month
33 self.dataOut.month - Experiment month
34 self.dataOut.day - Experiment day
34 self.dataOut.day - Experiment day
35 self.dataOut.year - Experiment year
35 self.dataOut.year - Experiment year
36 '''
36 '''
37
37
38 def __init__(self):
38 def __init__(self):
39 '''
39 '''
40 Inputs: None
40 Inputs: None
41 '''
41 '''
42 ProcessingUnit.__init__(self)
42 ProcessingUnit.__init__(self)
43 self.dataOut = Parameters()
43 self.dataOut = Parameters()
44
44
45 def setup(self, mode):
45 def setup(self, mode):
46 '''
46 '''
47 '''
47 '''
48 self.dataOut.mode = mode
48 self.dataOut.mode = mode
49
49
50 def run(self, mode, snr_threshold=None):
50 def run(self, mode, snr_threshold=None):
51 '''
51 '''
52 Inputs:
52 Inputs:
53 mode = High resolution (0) or Low resolution (1) data
53 mode = High resolution (0) or Low resolution (1) data
54 snr_threshold = snr filter value
54 snr_threshold = snr filter value
55 '''
55 '''
56
56
57 if not self.isConfig:
57 if not self.isConfig:
58 self.setup(mode)
58 self.setup(mode)
59 self.isConfig = True
59 self.isConfig = True
60
60
61 if self.dataIn.type == 'Parameters':
61 if self.dataIn.type == 'Parameters':
62 self.dataOut.copy(self.dataIn)
62 self.dataOut.copy(self.dataIn)
63
63
64 self.dataOut.data_param = self.dataOut.data[mode]
64 self.dataOut.data_param = self.dataOut.data[mode]
65 self.dataOut.heightList = self.dataOut.height[0]
65 self.dataOut.heightList = self.dataOut.height[0]
66 self.dataOut.data_snr = self.dataOut.data_snr[mode]
66 self.dataOut.data_snr = self.dataOut.data_snr[mode]
67 SNRavg = numpy.average(self.dataOut.data_snr, axis=0)
67 SNRavg = numpy.average(self.dataOut.data_snr, axis=0)
68 SNRavgdB = 10*numpy.log10(SNRavg)
68 SNRavgdB = 10*numpy.log10(SNRavg)
69 self.dataOut.data_snr_avg_db = SNRavgdB.reshape(1, *SNRavgdB.shape)
69 self.dataOut.data_snr_avg_db = SNRavgdB.reshape(1, *SNRavgdB.shape)
70
70
71 # Censoring Data
71 if snr_threshold is not None:
72 if snr_threshold is not None:
72 for i in range(3):
73 for i in range(3):
73 self.dataOut.data_param[i][SNRavgdB <= snr_threshold] = numpy.nan
74 self.dataOut.data_param[i][SNRavgdB <= snr_threshold] = numpy.nan
74
75
75 # TODO
76 # TODO
76
77
77 class OutliersFilter(Operation):
78 class OutliersFilter(Operation):
78
79
79 def __init__(self):
80 def __init__(self):
80 '''
81 '''
81 '''
82 '''
82 Operation.__init__(self)
83 Operation.__init__(self)
83
84
84 def run(self, svalue2, method, factor, filter, npoints=9):
85 def run(self, svalue2, method, factor, filter, npoints=9):
85 '''
86 '''
86 Inputs:
87 Inputs:
87 svalue - string to select array velocity
88 svalue - string to select array velocity
88 svalue2 - string to choose axis filtering
89 svalue2 - string to choose axis filtering
89 method - 0 for SMOOTH or 1 for MEDIAN
90 method - 0 for SMOOTH or 1 for MEDIAN
90 factor - number used to set threshold
91 factor - number used to set threshold
91 filter - 1 for data filtering using the standard deviation criteria else 0
92 filter - 1 for data filtering using the standard deviation criteria else 0
92 npoints - number of points for mask filter
93 npoints - number of points for mask filter
93 '''
94 '''
94
95
95 print(' Outliers Filter {} {} / threshold = {}'.format(svalue, svalue, factor))
96 print(' Outliers Filter {} {} / threshold = {}'.format(svalue, svalue, factor))
96
97
97
98
98 yaxis = self.dataOut.heightList
99 yaxis = self.dataOut.heightList
99 xaxis = numpy.array([[self.dataOut.utctime]])
100 xaxis = numpy.array([[self.dataOut.utctime]])
100
101
101 # Zonal
102 # Zonal
102 value_temp = self.dataOut.data_output[0]
103 value_temp = self.dataOut.data_output[0]
103
104
104 # Zonal
105 # Zonal
105 value_temp = self.dataOut.data_output[1]
106 value_temp = self.dataOut.data_output[1]
106
107
107 # Vertical
108 # Vertical
108 value_temp = numpy.transpose(self.dataOut.data_output[2])
109 value_temp = numpy.transpose(self.dataOut.data_output[2])
109
110
110 htemp = yaxis
111 htemp = yaxis
111 std = value_temp
112 std = value_temp
112 for h in range(len(htemp)):
113 for h in range(len(htemp)):
113 nvalues_valid = len(numpy.where(numpy.isfinite(value_temp[h]))[0])
114 nvalues_valid = len(numpy.where(numpy.isfinite(value_temp[h]))[0])
114 minvalid = npoints
115 minvalid = npoints
115
116
116 #only if valid values greater than the minimum required (10%)
117 #only if valid values greater than the minimum required (10%)
117 if nvalues_valid > minvalid:
118 if nvalues_valid > minvalid:
118
119
119 if method == 0:
120 if method == 0:
120 #SMOOTH
121 #SMOOTH
121 w = value_temp[h] - self.Smooth(input=value_temp[h], width=npoints, edge_truncate=1)
122 w = value_temp[h] - self.Smooth(input=value_temp[h], width=npoints, edge_truncate=1)
122
123
123
124
124 if method == 1:
125 if method == 1:
125 #MEDIAN
126 #MEDIAN
126 w = value_temp[h] - self.Median(input=value_temp[h], width = npoints)
127 w = value_temp[h] - self.Median(input=value_temp[h], width = npoints)
127
128
128 dw = numpy.std(w[numpy.where(numpy.isfinite(w))],ddof = 1)
129 dw = numpy.std(w[numpy.where(numpy.isfinite(w))],ddof = 1)
129
130
130 threshold = dw*factor
131 threshold = dw*factor
131 value_temp[numpy.where(w > threshold),h] = numpy.nan
132 value_temp[numpy.where(w > threshold),h] = numpy.nan
132 value_temp[numpy.where(w < -1*threshold),h] = numpy.nan
133 value_temp[numpy.where(w < -1*threshold),h] = numpy.nan
133
134
134
135
135 #At the end
136 #At the end
136 if svalue2 == 'inHeight':
137 if svalue2 == 'inHeight':
137 value_temp = numpy.transpose(value_temp)
138 value_temp = numpy.transpose(value_temp)
138 output_array[:,m] = value_temp
139 output_array[:,m] = value_temp
139
140
140 if svalue == 'zonal':
141 if svalue == 'zonal':
141 self.dataOut.data_output[0] = output_array
142 self.dataOut.data_output[0] = output_array
142
143
143 elif svalue == 'meridional':
144 elif svalue == 'meridional':
144 self.dataOut.data_output[1] = output_array
145 self.dataOut.data_output[1] = output_array
145
146
146 elif svalue == 'vertical':
147 elif svalue == 'vertical':
147 self.dataOut.data_output[2] = output_array
148 self.dataOut.data_output[2] = output_array
148
149
149 return self.dataOut.data_output
150 return self.dataOut.data_output
150
151
151
152
152 def Median(self,input,width):
153 def Median(self,input,width):
153 '''
154 '''
154 Inputs:
155 Inputs:
155 input - Velocity array
156 input - Velocity array
156 width - Number of points for mask filter
157 width - Number of points for mask filter
157
158
158 '''
159 '''
159
160
160 if numpy.mod(width,2) == 1:
161 if numpy.mod(width,2) == 1:
161 pc = int((width - 1) / 2)
162 pc = int((width - 1) / 2)
162 cont = 0
163 cont = 0
163 output = []
164 output = []
164
165
165 for i in range(len(input)):
166 for i in range(len(input)):
166 if i >= pc and i < len(input) - pc:
167 if i >= pc and i < len(input) - pc:
167 new2 = input[i-pc:i+pc+1]
168 new2 = input[i-pc:i+pc+1]
168 temp = numpy.where(numpy.isfinite(new2))
169 temp = numpy.where(numpy.isfinite(new2))
169 new = new2[temp]
170 new = new2[temp]
170 value = numpy.median(new)
171 value = numpy.median(new)
171 output.append(value)
172 output.append(value)
172
173
173 output = numpy.array(output)
174 output = numpy.array(output)
174 output = numpy.hstack((input[0:pc],output))
175 output = numpy.hstack((input[0:pc],output))
175 output = numpy.hstack((output,input[-pc:len(input)]))
176 output = numpy.hstack((output,input[-pc:len(input)]))
176
177
177 return output
178 return output
178
179
179 def Smooth(self,input,width,edge_truncate = None):
180 def Smooth(self,input,width,edge_truncate = None):
180 '''
181 '''
181 Inputs:
182 Inputs:
182 input - Velocity array
183 input - Velocity array
183 width - Number of points for mask filter
184 width - Number of points for mask filter
184 edge_truncate - 1 for truncate the convolution product else
185 edge_truncate - 1 for truncate the convolution product else
185
186
186 '''
187 '''
187
188
188 if numpy.mod(width,2) == 0:
189 if numpy.mod(width,2) == 0:
189 real_width = width + 1
190 real_width = width + 1
190 nzeros = width / 2
191 nzeros = width / 2
191 else:
192 else:
192 real_width = width
193 real_width = width
193 nzeros = (width - 1) / 2
194 nzeros = (width - 1) / 2
194
195
195 half_width = int(real_width)/2
196 half_width = int(real_width)/2
196 length = len(input)
197 length = len(input)
197
198
198 gate = numpy.ones(real_width,dtype='float')
199 gate = numpy.ones(real_width,dtype='float')
199 norm_of_gate = numpy.sum(gate)
200 norm_of_gate = numpy.sum(gate)
200
201
201 nan_process = 0
202 nan_process = 0
202 nan_id = numpy.where(numpy.isnan(input))
203 nan_id = numpy.where(numpy.isnan(input))
203 if len(nan_id[0]) > 0:
204 if len(nan_id[0]) > 0:
204 nan_process = 1
205 nan_process = 1
205 pb = numpy.zeros(len(input))
206 pb = numpy.zeros(len(input))
206 pb[nan_id] = 1.
207 pb[nan_id] = 1.
207 input[nan_id] = 0.
208 input[nan_id] = 0.
208
209
209 if edge_truncate == True:
210 if edge_truncate == True:
210 output = numpy.convolve(input/norm_of_gate,gate,mode='same')
211 output = numpy.convolve(input/norm_of_gate,gate,mode='same')
211 elif edge_truncate == False or edge_truncate == None:
212 elif edge_truncate == False or edge_truncate == None:
212 output = numpy.convolve(input/norm_of_gate,gate,mode='valid')
213 output = numpy.convolve(input/norm_of_gate,gate,mode='valid')
213 output = numpy.hstack((input[0:half_width],output))
214 output = numpy.hstack((input[0:half_width],output))
214 output = numpy.hstack((output,input[len(input)-half_width:len(input)]))
215 output = numpy.hstack((output,input[len(input)-half_width:len(input)]))
215
216
216 if nan_process:
217 if nan_process:
217 pb = numpy.convolve(pb/norm_of_gate,gate,mode='valid')
218 pb = numpy.convolve(pb/norm_of_gate,gate,mode='valid')
218 pb = numpy.hstack((numpy.zeros(half_width),pb))
219 pb = numpy.hstack((numpy.zeros(half_width),pb))
219 pb = numpy.hstack((pb,numpy.zeros(half_width)))
220 pb = numpy.hstack((pb,numpy.zeros(half_width)))
220 output[numpy.where(pb > 0.9999)] = numpy.nan
221 output[numpy.where(pb > 0.9999)] = numpy.nan
221 input[nan_id] = numpy.nan
222 input[nan_id] = numpy.nan
222 return output
223 return output
223
224
224 def Average(self,aver=0,nhaver=1):
225 def Average(self,aver=0,nhaver=1):
225 '''
226 '''
226 Inputs:
227 Inputs:
227 aver - Indicates the time period over which is averaged or consensus data
228 aver - Indicates the time period over which is averaged or consensus data
228 nhaver - Indicates the decimation factor in heights
229 nhaver - Indicates the decimation factor in heights
229
230
230 '''
231 '''
231 nhpoints = 48
232 nhpoints = 48
232
233
233 lat_piura = -5.17
234 lat_piura = -5.17
234 lat_huancayo = -12.04
235 lat_huancayo = -12.04
235 lat_porcuya = -5.8
236 lat_porcuya = -5.8
236
237
237 if '%2.2f'%self.dataOut.lat == '%2.2f'%lat_piura:
238 if '%2.2f'%self.dataOut.lat == '%2.2f'%lat_piura:
238 hcm = 3.
239 hcm = 3.
239 if self.dataOut.year == 2003 :
240 if self.dataOut.year == 2003 :
240 if self.dataOut.doy >= 25 and self.dataOut.doy < 64:
241 if self.dataOut.doy >= 25 and self.dataOut.doy < 64:
241 nhpoints = 12
242 nhpoints = 12
242
243
243 elif '%2.2f'%self.dataOut.lat == '%2.2f'%lat_huancayo:
244 elif '%2.2f'%self.dataOut.lat == '%2.2f'%lat_huancayo:
244 hcm = 3.
245 hcm = 3.
245 if self.dataOut.year == 2003 :
246 if self.dataOut.year == 2003 :
246 if self.dataOut.doy >= 25 and self.dataOut.doy < 64:
247 if self.dataOut.doy >= 25 and self.dataOut.doy < 64:
247 nhpoints = 12
248 nhpoints = 12
248
249
249
250
250 elif '%2.2f'%self.dataOut.lat == '%2.2f'%lat_porcuya:
251 elif '%2.2f'%self.dataOut.lat == '%2.2f'%lat_porcuya:
251 hcm = 5.#2
252 hcm = 5.#2
252
253
253 pdata = 0.2
254 pdata = 0.2
254 taver = [1,2,3,4,6,8,12,24]
255 taver = [1,2,3,4,6,8,12,24]
255 t0 = 0
256 t0 = 0
256 tf = 24
257 tf = 24
257 ntime =(tf-t0)/taver[aver]
258 ntime =(tf-t0)/taver[aver]
258 ti = numpy.arange(ntime)
259 ti = numpy.arange(ntime)
259 tf = numpy.arange(ntime) + taver[aver]
260 tf = numpy.arange(ntime) + taver[aver]
260
261
261
262
262 old_height = self.dataOut.heightList
263 old_height = self.dataOut.heightList
263
264
264 if nhaver > 1:
265 if nhaver > 1:
265 num_hei = len(self.dataOut.heightList)/nhaver/self.dataOut.nmodes
266 num_hei = len(self.dataOut.heightList)/nhaver/self.dataOut.nmodes
266 deltha = 0.05*nhaver
267 deltha = 0.05*nhaver
267 minhvalid = pdata*nhaver
268 minhvalid = pdata*nhaver
268 for im in range(self.dataOut.nmodes):
269 for im in range(self.dataOut.nmodes):
269 new_height = numpy.arange(num_hei)*deltha + self.dataOut.height[im,0] + deltha/2.
270 new_height = numpy.arange(num_hei)*deltha + self.dataOut.height[im,0] + deltha/2.
270
271
271
272
272 data_fHeigths_List = []
273 data_fHeigths_List = []
273 data_fZonal_List = []
274 data_fZonal_List = []
274 data_fMeridional_List = []
275 data_fMeridional_List = []
275 data_fVertical_List = []
276 data_fVertical_List = []
276 startDTList = []
277 startDTList = []
277
278
278
279
279 for i in range(ntime):
280 for i in range(ntime):
280 height = old_height
281 height = old_height
281
282
282 start = datetime.datetime(self.dataOut.year,self.dataOut.month,self.dataOut.day) + datetime.timedelta(hours = int(ti[i])) - datetime.timedelta(hours = 5)
283 start = datetime.datetime(self.dataOut.year,self.dataOut.month,self.dataOut.day) + datetime.timedelta(hours = int(ti[i])) - datetime.timedelta(hours = 5)
283 stop = datetime.datetime(self.dataOut.year,self.dataOut.month,self.dataOut.day) + datetime.timedelta(hours = int(tf[i])) - datetime.timedelta(hours = 5)
284 stop = datetime.datetime(self.dataOut.year,self.dataOut.month,self.dataOut.day) + datetime.timedelta(hours = int(tf[i])) - datetime.timedelta(hours = 5)
284
285
285
286
286 limit_sec1 = time.mktime(start.timetuple())
287 limit_sec1 = time.mktime(start.timetuple())
287 limit_sec2 = time.mktime(stop.timetuple())
288 limit_sec2 = time.mktime(stop.timetuple())
288
289
289 t1 = numpy.where(self.f_timesec >= limit_sec1)
290 t1 = numpy.where(self.f_timesec >= limit_sec1)
290 t2 = numpy.where(self.f_timesec < limit_sec2)
291 t2 = numpy.where(self.f_timesec < limit_sec2)
291 time_select = []
292 time_select = []
292 for val_sec in t1[0]:
293 for val_sec in t1[0]:
293 if val_sec in t2[0]:
294 if val_sec in t2[0]:
294 time_select.append(val_sec)
295 time_select.append(val_sec)
295
296
296
297
297 time_select = numpy.array(time_select,dtype = 'int')
298 time_select = numpy.array(time_select,dtype = 'int')
298 minvalid = numpy.ceil(pdata*nhpoints)
299 minvalid = numpy.ceil(pdata*nhpoints)
299
300
300 zon_aver = numpy.zeros([self.dataOut.nranges,self.dataOut.nmodes],dtype='f4') + numpy.nan
301 zon_aver = numpy.zeros([self.dataOut.nranges,self.dataOut.nmodes],dtype='f4') + numpy.nan
301 mer_aver = numpy.zeros([self.dataOut.nranges,self.dataOut.nmodes],dtype='f4') + numpy.nan
302 mer_aver = numpy.zeros([self.dataOut.nranges,self.dataOut.nmodes],dtype='f4') + numpy.nan
302 ver_aver = numpy.zeros([self.dataOut.nranges,self.dataOut.nmodes],dtype='f4') + numpy.nan
303 ver_aver = numpy.zeros([self.dataOut.nranges,self.dataOut.nmodes],dtype='f4') + numpy.nan
303
304
304 if nhaver > 1:
305 if nhaver > 1:
305 new_zon_aver = numpy.zeros([num_hei,self.dataOut.nmodes],dtype='f4') + numpy.nan
306 new_zon_aver = numpy.zeros([num_hei,self.dataOut.nmodes],dtype='f4') + numpy.nan
306 new_mer_aver = numpy.zeros([num_hei,self.dataOut.nmodes],dtype='f4') + numpy.nan
307 new_mer_aver = numpy.zeros([num_hei,self.dataOut.nmodes],dtype='f4') + numpy.nan
307 new_ver_aver = numpy.zeros([num_hei,self.dataOut.nmodes],dtype='f4') + numpy.nan
308 new_ver_aver = numpy.zeros([num_hei,self.dataOut.nmodes],dtype='f4') + numpy.nan
308
309
309 if len(time_select) > minvalid:
310 if len(time_select) > minvalid:
310 time_average = self.f_timesec[time_select]
311 time_average = self.f_timesec[time_select]
311
312
312 for im in range(self.dataOut.nmodes):
313 for im in range(self.dataOut.nmodes):
313
314
314 for ih in range(self.dataOut.nranges):
315 for ih in range(self.dataOut.nranges):
315 if numpy.sum(numpy.isfinite(self.f_zon[time_select,ih,im])) >= minvalid:
316 if numpy.sum(numpy.isfinite(self.f_zon[time_select,ih,im])) >= minvalid:
316 zon_aver[ih,im] = numpy.nansum(self.f_zon[time_select,ih,im]) / numpy.sum(numpy.isfinite(self.f_zon[time_select,ih,im]))
317 zon_aver[ih,im] = numpy.nansum(self.f_zon[time_select,ih,im]) / numpy.sum(numpy.isfinite(self.f_zon[time_select,ih,im]))
317
318
318 if numpy.sum(numpy.isfinite(self.f_mer[time_select,ih,im])) >= minvalid:
319 if numpy.sum(numpy.isfinite(self.f_mer[time_select,ih,im])) >= minvalid:
319 mer_aver[ih,im] = numpy.nansum(self.f_mer[time_select,ih,im]) / numpy.sum(numpy.isfinite(self.f_mer[time_select,ih,im]))
320 mer_aver[ih,im] = numpy.nansum(self.f_mer[time_select,ih,im]) / numpy.sum(numpy.isfinite(self.f_mer[time_select,ih,im]))
320
321
321 if numpy.sum(numpy.isfinite(self.f_ver[time_select,ih,im])) >= minvalid:
322 if numpy.sum(numpy.isfinite(self.f_ver[time_select,ih,im])) >= minvalid:
322 ver_aver[ih,im] = numpy.nansum(self.f_ver[time_select,ih,im]) / numpy.sum(numpy.isfinite(self.f_ver[time_select,ih,im]))
323 ver_aver[ih,im] = numpy.nansum(self.f_ver[time_select,ih,im]) / numpy.sum(numpy.isfinite(self.f_ver[time_select,ih,im]))
323
324
324 if nhaver > 1:
325 if nhaver > 1:
325 for ih in range(num_hei):
326 for ih in range(num_hei):
326 hvalid = numpy.arange(nhaver) + nhaver*ih
327 hvalid = numpy.arange(nhaver) + nhaver*ih
327
328
328 if numpy.sum(numpy.isfinite(zon_aver[hvalid,im])) >= minvalid:
329 if numpy.sum(numpy.isfinite(zon_aver[hvalid,im])) >= minvalid:
329 new_zon_aver[ih,im] = numpy.nansum(zon_aver[hvalid,im]) / numpy.sum(numpy.isfinite(zon_aver[hvalid,im]))
330 new_zon_aver[ih,im] = numpy.nansum(zon_aver[hvalid,im]) / numpy.sum(numpy.isfinite(zon_aver[hvalid,im]))
330
331
331 if numpy.sum(numpy.isfinite(mer_aver[hvalid,im])) >= minvalid:
332 if numpy.sum(numpy.isfinite(mer_aver[hvalid,im])) >= minvalid:
332 new_mer_aver[ih,im] = numpy.nansum(mer_aver[hvalid,im]) / numpy.sum(numpy.isfinite(mer_aver[hvalid,im]))
333 new_mer_aver[ih,im] = numpy.nansum(mer_aver[hvalid,im]) / numpy.sum(numpy.isfinite(mer_aver[hvalid,im]))
333
334
334 if numpy.sum(numpy.isfinite(ver_aver[hvalid,im])) >= minvalid:
335 if numpy.sum(numpy.isfinite(ver_aver[hvalid,im])) >= minvalid:
335 new_ver_aver[ih,im] = numpy.nansum(ver_aver[hvalid,im]) / numpy.sum(numpy.isfinite(ver_aver[hvalid,im]))
336 new_ver_aver[ih,im] = numpy.nansum(ver_aver[hvalid,im]) / numpy.sum(numpy.isfinite(ver_aver[hvalid,im]))
336 if nhaver > 1:
337 if nhaver > 1:
337 zon_aver = new_zon_aver
338 zon_aver = new_zon_aver
338 mer_aver = new_mer_aver
339 mer_aver = new_mer_aver
339 ver_aver = new_ver_aver
340 ver_aver = new_ver_aver
340 height = new_height
341 height = new_height
341
342
342
343
343 tstart = time_average[0]
344 tstart = time_average[0]
344 tend = time_average[-1]
345 tend = time_average[-1]
345 startTime = time.gmtime(tstart)
346 startTime = time.gmtime(tstart)
346
347
347 year = startTime.tm_year
348 year = startTime.tm_year
348 month = startTime.tm_mon
349 month = startTime.tm_mon
349 day = startTime.tm_mday
350 day = startTime.tm_mday
350 hour = startTime.tm_hour
351 hour = startTime.tm_hour
351 minute = startTime.tm_min
352 minute = startTime.tm_min
352 second = startTime.tm_sec
353 second = startTime.tm_sec
353
354
354 startDTList.append(datetime.datetime(year,month,day,hour,minute,second))
355 startDTList.append(datetime.datetime(year,month,day,hour,minute,second))
355
356
356
357
357 o_height = numpy.array([])
358 o_height = numpy.array([])
358 o_zon_aver = numpy.array([])
359 o_zon_aver = numpy.array([])
359 o_mer_aver = numpy.array([])
360 o_mer_aver = numpy.array([])
360 o_ver_aver = numpy.array([])
361 o_ver_aver = numpy.array([])
361 if self.dataOut.nmodes > 1:
362 if self.dataOut.nmodes > 1:
362 for im in range(self.dataOut.nmodes):
363 for im in range(self.dataOut.nmodes):
363
364
364 if im == 0:
365 if im == 0:
365 h_select = numpy.where(numpy.bitwise_and(height[0,:] >=0,height[0,:] <= hcm,numpy.isfinite(height[0,:])))
366 h_select = numpy.where(numpy.bitwise_and(height[0,:] >=0,height[0,:] <= hcm,numpy.isfinite(height[0,:])))
366 else:
367 else:
367 h_select = numpy.where(numpy.bitwise_and(height[1,:] > hcm,height[1,:] < 20,numpy.isfinite(height[1,:])))
368 h_select = numpy.where(numpy.bitwise_and(height[1,:] > hcm,height[1,:] < 20,numpy.isfinite(height[1,:])))
368
369
369
370
370 ht = h_select[0]
371 ht = h_select[0]
371
372
372 o_height = numpy.hstack((o_height,height[im,ht]))
373 o_height = numpy.hstack((o_height,height[im,ht]))
373 o_zon_aver = numpy.hstack((o_zon_aver,zon_aver[ht,im]))
374 o_zon_aver = numpy.hstack((o_zon_aver,zon_aver[ht,im]))
374 o_mer_aver = numpy.hstack((o_mer_aver,mer_aver[ht,im]))
375 o_mer_aver = numpy.hstack((o_mer_aver,mer_aver[ht,im]))
375 o_ver_aver = numpy.hstack((o_ver_aver,ver_aver[ht,im]))
376 o_ver_aver = numpy.hstack((o_ver_aver,ver_aver[ht,im]))
376
377
377 data_fHeigths_List.append(o_height)
378 data_fHeigths_List.append(o_height)
378 data_fZonal_List.append(o_zon_aver)
379 data_fZonal_List.append(o_zon_aver)
379 data_fMeridional_List.append(o_mer_aver)
380 data_fMeridional_List.append(o_mer_aver)
380 data_fVertical_List.append(o_ver_aver)
381 data_fVertical_List.append(o_ver_aver)
381
382
382
383
383 else:
384 else:
384 h_select = numpy.where(numpy.bitwise_and(height[0,:] <= hcm,numpy.isfinite(height[0,:])))
385 h_select = numpy.where(numpy.bitwise_and(height[0,:] <= hcm,numpy.isfinite(height[0,:])))
385 ht = h_select[0]
386 ht = h_select[0]
386 o_height = numpy.hstack((o_height,height[im,ht]))
387 o_height = numpy.hstack((o_height,height[im,ht]))
387 o_zon_aver = numpy.hstack((o_zon_aver,zon_aver[ht,im]))
388 o_zon_aver = numpy.hstack((o_zon_aver,zon_aver[ht,im]))
388 o_mer_aver = numpy.hstack((o_mer_aver,mer_aver[ht,im]))
389 o_mer_aver = numpy.hstack((o_mer_aver,mer_aver[ht,im]))
389 o_ver_aver = numpy.hstack((o_ver_aver,ver_aver[ht,im]))
390 o_ver_aver = numpy.hstack((o_ver_aver,ver_aver[ht,im]))
390
391
391 data_fHeigths_List.append(o_height)
392 data_fHeigths_List.append(o_height)
392 data_fZonal_List.append(o_zon_aver)
393 data_fZonal_List.append(o_zon_aver)
393 data_fMeridional_List.append(o_mer_aver)
394 data_fMeridional_List.append(o_mer_aver)
394 data_fVertical_List.append(o_ver_aver)
395 data_fVertical_List.append(o_ver_aver)
395
396
396
397
397 return startDTList, data_fHeigths_List, data_fZonal_List, data_fMeridional_List, data_fVertical_List
398 return startDTList, data_fHeigths_List, data_fZonal_List, data_fMeridional_List, data_fVertical_List
398
399
399
400
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