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1 import os
1 import os
2 import datetime
2 import datetime
3 import numpy
3 import numpy
4
4
5 from schainpy.model.graphics.jroplot_base import Plot, plt
5 from schainpy.model.graphics.jroplot_base import Plot, plt
6 from schainpy.model.graphics.jroplot_spectra import SpectraPlot, RTIPlot, CoherencePlot, SpectraCutPlot
6 from schainpy.model.graphics.jroplot_spectra import SpectraPlot, RTIPlot, CoherencePlot, SpectraCutPlot
7 from schainpy.utils import log
7 from schainpy.utils import log
8 # libreria wradlib
8 # libreria wradlib
9 import wradlib as wrl
9 import wradlib as wrl
10
10
11 EARTH_RADIUS = 6.3710e3
11 EARTH_RADIUS = 6.3710e3
12
12
13
13
14 def ll2xy(lat1, lon1, lat2, lon2):
14 def ll2xy(lat1, lon1, lat2, lon2):
15
15
16 p = 0.017453292519943295
16 p = 0.017453292519943295
17 a = 0.5 - numpy.cos((lat2 - lat1) * p)/2 + numpy.cos(lat1 * p) * \
17 a = 0.5 - numpy.cos((lat2 - lat1) * p)/2 + numpy.cos(lat1 * p) * \
18 numpy.cos(lat2 * p) * (1 - numpy.cos((lon2 - lon1) * p)) / 2
18 numpy.cos(lat2 * p) * (1 - numpy.cos((lon2 - lon1) * p)) / 2
19 r = 12742 * numpy.arcsin(numpy.sqrt(a))
19 r = 12742 * numpy.arcsin(numpy.sqrt(a))
20 theta = numpy.arctan2(numpy.sin((lon2-lon1)*p)*numpy.cos(lat2*p), numpy.cos(lat1*p)
20 theta = numpy.arctan2(numpy.sin((lon2-lon1)*p)*numpy.cos(lat2*p), numpy.cos(lat1*p)
21 * numpy.sin(lat2*p)-numpy.sin(lat1*p)*numpy.cos(lat2*p)*numpy.cos((lon2-lon1)*p))
21 * numpy.sin(lat2*p)-numpy.sin(lat1*p)*numpy.cos(lat2*p)*numpy.cos((lon2-lon1)*p))
22 theta = -theta + numpy.pi/2
22 theta = -theta + numpy.pi/2
23 return r*numpy.cos(theta), r*numpy.sin(theta)
23 return r*numpy.cos(theta), r*numpy.sin(theta)
24
24
25
25
26 def km2deg(km):
26 def km2deg(km):
27 '''
27 '''
28 Convert distance in km to degrees
28 Convert distance in km to degrees
29 '''
29 '''
30
30
31 return numpy.rad2deg(km/EARTH_RADIUS)
31 return numpy.rad2deg(km/EARTH_RADIUS)
32
32
33
33
34
34
35 class SpectralMomentsPlot(SpectraPlot):
35 class SpectralMomentsPlot(SpectraPlot):
36 '''
36 '''
37 Plot for Spectral Moments
37 Plot for Spectral Moments
38 '''
38 '''
39 CODE = 'spc_moments'
39 CODE = 'spc_moments'
40 # colormap = 'jet'
40 # colormap = 'jet'
41 # plot_type = 'pcolor'
41 # plot_type = 'pcolor'
42
42
43 class DobleGaussianPlot(SpectraPlot):
43 class DobleGaussianPlot(SpectraPlot):
44 '''
44 '''
45 Plot for Double Gaussian Plot
45 Plot for Double Gaussian Plot
46 '''
46 '''
47 CODE = 'gaussian_fit'
47 CODE = 'gaussian_fit'
48 # colormap = 'jet'
48 # colormap = 'jet'
49 # plot_type = 'pcolor'
49 # plot_type = 'pcolor'
50
50
51 class DoubleGaussianSpectraCutPlot(SpectraCutPlot):
51 class DoubleGaussianSpectraCutPlot(SpectraCutPlot):
52 '''
52 '''
53 Plot SpectraCut with Double Gaussian Fit
53 Plot SpectraCut with Double Gaussian Fit
54 '''
54 '''
55 CODE = 'cut_gaussian_fit'
55 CODE = 'cut_gaussian_fit'
56
56
57 class SnrPlot(RTIPlot):
57 class SnrPlot(RTIPlot):
58 '''
58 '''
59 Plot for SNR Data
59 Plot for SNR Data
60 '''
60 '''
61
61
62 CODE = 'snr'
62 CODE = 'snr'
63 colormap = 'jet'
63 colormap = 'jet'
64
64
65 def update(self, dataOut):
65 def update(self, dataOut):
66
66
67 data = {
67 data = {
68 'snr': 10*numpy.log10(dataOut.data_snr)
68 'snr': 10*numpy.log10(dataOut.data_snr)
69 }
69 }
70
70
71 return data, {}
71 return data, {}
72
72
73 class DopplerPlot(RTIPlot):
73 class DopplerPlot(RTIPlot):
74 '''
74 '''
75 Plot for DOPPLER Data (1st moment)
75 Plot for DOPPLER Data (1st moment)
76 '''
76 '''
77
77
78 CODE = 'dop'
78 CODE = 'dop'
79 colormap = 'jet'
79 colormap = 'jet'
80
80
81 def update(self, dataOut):
81 def update(self, dataOut):
82
82
83 data = {
83 data = {
84 'dop': 10*numpy.log10(dataOut.data_dop)
84 'dop': 10*numpy.log10(dataOut.data_dop)
85 }
85 }
86
86
87 return data, {}
87 return data, {}
88
88
89 class PowerPlot(RTIPlot):
89 class PowerPlot(RTIPlot):
90 '''
90 '''
91 Plot for Power Data (0 moment)
91 Plot for Power Data (0 moment)
92 '''
92 '''
93
93
94 CODE = 'pow'
94 CODE = 'pow'
95 colormap = 'jet'
95 colormap = 'jet'
96
96
97 def update(self, dataOut):
97 def update(self, dataOut):
98 data = {
98 data = {
99 'pow': 10*numpy.log10(dataOut.data_pow/dataOut.normFactor)
99 'pow': 10*numpy.log10(dataOut.data_pow/dataOut.normFactor)
100 }
100 }
101 return data, {}
101 return data, {}
102
102
103 class SpectralWidthPlot(RTIPlot):
103 class SpectralWidthPlot(RTIPlot):
104 '''
104 '''
105 Plot for Spectral Width Data (2nd moment)
105 Plot for Spectral Width Data (2nd moment)
106 '''
106 '''
107
107
108 CODE = 'width'
108 CODE = 'width'
109 colormap = 'jet'
109 colormap = 'jet'
110
110
111 def update(self, dataOut):
111 def update(self, dataOut):
112
112
113 data = {
113 data = {
114 'width': dataOut.data_width
114 'width': dataOut.data_width
115 }
115 }
116
116
117 return data, {}
117 return data, {}
118
118
119 class SkyMapPlot(Plot):
119 class SkyMapPlot(Plot):
120 '''
120 '''
121 Plot for meteors detection data
121 Plot for meteors detection data
122 '''
122 '''
123
123
124 CODE = 'param'
124 CODE = 'param'
125
125
126 def setup(self):
126 def setup(self):
127
127
128 self.ncols = 1
128 self.ncols = 1
129 self.nrows = 1
129 self.nrows = 1
130 self.width = 7.2
130 self.width = 7.2
131 self.height = 7.2
131 self.height = 7.2
132 self.nplots = 1
132 self.nplots = 1
133 self.xlabel = 'Zonal Zenith Angle (deg)'
133 self.xlabel = 'Zonal Zenith Angle (deg)'
134 self.ylabel = 'Meridional Zenith Angle (deg)'
134 self.ylabel = 'Meridional Zenith Angle (deg)'
135 self.polar = True
135 self.polar = True
136 self.ymin = -180
136 self.ymin = -180
137 self.ymax = 180
137 self.ymax = 180
138 self.colorbar = False
138 self.colorbar = False
139
139
140 def plot(self):
140 def plot(self):
141
141
142 arrayParameters = numpy.concatenate(self.data['param'])
142 arrayParameters = numpy.concatenate(self.data['param'])
143 error = arrayParameters[:, -1]
143 error = arrayParameters[:, -1]
144 indValid = numpy.where(error == 0)[0]
144 indValid = numpy.where(error == 0)[0]
145 finalMeteor = arrayParameters[indValid, :]
145 finalMeteor = arrayParameters[indValid, :]
146 finalAzimuth = finalMeteor[:, 3]
146 finalAzimuth = finalMeteor[:, 3]
147 finalZenith = finalMeteor[:, 4]
147 finalZenith = finalMeteor[:, 4]
148
148
149 x = finalAzimuth * numpy.pi / 180
149 x = finalAzimuth * numpy.pi / 180
150 y = finalZenith
150 y = finalZenith
151
151
152 ax = self.axes[0]
152 ax = self.axes[0]
153
153
154 if ax.firsttime:
154 if ax.firsttime:
155 ax.plot = ax.plot(x, y, 'bo', markersize=5)[0]
155 ax.plot = ax.plot(x, y, 'bo', markersize=5)[0]
156 else:
156 else:
157 ax.plot.set_data(x, y)
157 ax.plot.set_data(x, y)
158
158
159 dt1 = self.getDateTime(self.data.min_time).strftime('%y/%m/%d %H:%M:%S')
159 dt1 = self.getDateTime(self.data.min_time).strftime('%y/%m/%d %H:%M:%S')
160 dt2 = self.getDateTime(self.data.max_time).strftime('%y/%m/%d %H:%M:%S')
160 dt2 = self.getDateTime(self.data.max_time).strftime('%y/%m/%d %H:%M:%S')
161 title = 'Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n' % (dt1,
161 title = 'Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n' % (dt1,
162 dt2,
162 dt2,
163 len(x))
163 len(x))
164 self.titles[0] = title
164 self.titles[0] = title
165
165
166
166
167 class GenericRTIPlot(Plot):
167 class GenericRTIPlot(Plot):
168 '''
168 '''
169 Plot for data_xxxx object
169 Plot for data_xxxx object
170 '''
170 '''
171
171
172 CODE = 'param'
172 CODE = 'param'
173 colormap = 'viridis'
173 colormap = 'viridis'
174 plot_type = 'pcolorbuffer'
174 plot_type = 'pcolorbuffer'
175
175
176 def setup(self):
176 def setup(self):
177 self.xaxis = 'time'
177 self.xaxis = 'time'
178 self.ncols = 1
178 self.ncols = 1
179 self.nrows = self.data.shape('param')[0]
179 self.nrows = self.data.shape('param')[0]
180 self.nplots = self.nrows
180 self.nplots = self.nrows
181 self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.08, 'right':0.95, 'top': 0.95})
181 self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.08, 'right':0.95, 'top': 0.95})
182
182
183 if not self.xlabel:
183 if not self.xlabel:
184 self.xlabel = 'Time'
184 self.xlabel = 'Time'
185
185
186 self.ylabel = 'Range [km]'
186 self.ylabel = 'Range [km]'
187 if not self.titles:
187 if not self.titles:
188 self.titles = ['Param {}'.format(x) for x in range(self.nrows)]
188 self.titles = ['Param {}'.format(x) for x in range(self.nrows)]
189
189
190 def update(self, dataOut):
190 def update(self, dataOut):
191
191
192 data = {
192 data = {
193 'param' : numpy.concatenate([getattr(dataOut, attr) for attr in self.attr_data], axis=0)
193 'param' : numpy.concatenate([getattr(dataOut, attr) for attr in self.attr_data], axis=0)
194 }
194 }
195
195
196 meta = {}
196 meta = {}
197
197
198 return data, meta
198 return data, meta
199
199
200 def plot(self):
200 def plot(self):
201 # self.data.normalize_heights()
201 # self.data.normalize_heights()
202 self.x = self.data.times
202 self.x = self.data.times
203 self.y = self.data.yrange
203 self.y = self.data.yrange
204 self.z = self.data['param']
204 self.z = self.data['param']
205 self.z = 10*numpy.log10(self.z)
205 self.z = 10*numpy.log10(self.z)
206 self.z = numpy.ma.masked_invalid(self.z)
206 self.z = numpy.ma.masked_invalid(self.z)
207
207
208 if self.decimation is None:
208 if self.decimation is None:
209 x, y, z = self.fill_gaps(self.x, self.y, self.z)
209 x, y, z = self.fill_gaps(self.x, self.y, self.z)
210 else:
210 else:
211 x, y, z = self.fill_gaps(*self.decimate())
211 x, y, z = self.fill_gaps(*self.decimate())
212
212
213 for n, ax in enumerate(self.axes):
213 for n, ax in enumerate(self.axes):
214
214
215 self.zmax = self.zmax if self.zmax is not None else numpy.max(
215 self.zmax = self.zmax if self.zmax is not None else numpy.max(
216 self.z[n])
216 self.z[n])
217 self.zmin = self.zmin if self.zmin is not None else numpy.min(
217 self.zmin = self.zmin if self.zmin is not None else numpy.min(
218 self.z[n])
218 self.z[n])
219
219
220 if ax.firsttime:
220 if ax.firsttime:
221 if self.zlimits is not None:
221 if self.zlimits is not None:
222 self.zmin, self.zmax = self.zlimits[n]
222 self.zmin, self.zmax = self.zlimits[n]
223
223
224 ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
224 ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
225 vmin=self.zmin,
225 vmin=self.zmin,
226 vmax=self.zmax,
226 vmax=self.zmax,
227 cmap=self.cmaps[n]
227 cmap=self.cmaps[n]
228 )
228 )
229 else:
229 else:
230 if self.zlimits is not None:
230 if self.zlimits is not None:
231 self.zmin, self.zmax = self.zlimits[n]
231 self.zmin, self.zmax = self.zlimits[n]
232 ax.collections.remove(ax.collections[0])
232 ax.collections.remove(ax.collections[0])
233 ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
233 ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
234 vmin=self.zmin,
234 vmin=self.zmin,
235 vmax=self.zmax,
235 vmax=self.zmax,
236 cmap=self.cmaps[n]
236 cmap=self.cmaps[n]
237 )
237 )
238
238
239
239
240 class PolarMapPlot(Plot):
240 class PolarMapPlot(Plot):
241 '''
241 '''
242 Plot for weather radar
242 Plot for weather radar
243 '''
243 '''
244
244
245 CODE = 'param'
245 CODE = 'param'
246 colormap = 'seismic'
246 colormap = 'seismic'
247
247
248 def setup(self):
248 def setup(self):
249 self.ncols = 1
249 self.ncols = 1
250 self.nrows = 1
250 self.nrows = 1
251 self.width = 9
251 self.width = 9
252 self.height = 8
252 self.height = 8
253 self.mode = self.data.meta['mode']
253 self.mode = self.data.meta['mode']
254 if self.channels is not None:
254 if self.channels is not None:
255 self.nplots = len(self.channels)
255 self.nplots = len(self.channels)
256 self.nrows = len(self.channels)
256 self.nrows = len(self.channels)
257 else:
257 else:
258 self.nplots = self.data.shape(self.CODE)[0]
258 self.nplots = self.data.shape(self.CODE)[0]
259 self.nrows = self.nplots
259 self.nrows = self.nplots
260 self.channels = list(range(self.nplots))
260 self.channels = list(range(self.nplots))
261 if self.mode == 'E':
261 if self.mode == 'E':
262 self.xlabel = 'Longitude'
262 self.xlabel = 'Longitude'
263 self.ylabel = 'Latitude'
263 self.ylabel = 'Latitude'
264 else:
264 else:
265 self.xlabel = 'Range (km)'
265 self.xlabel = 'Range (km)'
266 self.ylabel = 'Height (km)'
266 self.ylabel = 'Height (km)'
267 self.bgcolor = 'white'
267 self.bgcolor = 'white'
268 self.cb_labels = self.data.meta['units']
268 self.cb_labels = self.data.meta['units']
269 self.lat = self.data.meta['latitude']
269 self.lat = self.data.meta['latitude']
270 self.lon = self.data.meta['longitude']
270 self.lon = self.data.meta['longitude']
271 self.xmin, self.xmax = float(
271 self.xmin, self.xmax = float(
272 km2deg(self.xmin) + self.lon), float(km2deg(self.xmax) + self.lon)
272 km2deg(self.xmin) + self.lon), float(km2deg(self.xmax) + self.lon)
273 self.ymin, self.ymax = float(
273 self.ymin, self.ymax = float(
274 km2deg(self.ymin) + self.lat), float(km2deg(self.ymax) + self.lat)
274 km2deg(self.ymin) + self.lat), float(km2deg(self.ymax) + self.lat)
275 # self.polar = True
275 # self.polar = True
276
276
277 def plot(self):
277 def plot(self):
278
278
279 for n, ax in enumerate(self.axes):
279 for n, ax in enumerate(self.axes):
280 data = self.data['param'][self.channels[n]]
280 data = self.data['param'][self.channels[n]]
281
281
282 zeniths = numpy.linspace(
282 zeniths = numpy.linspace(
283 0, self.data.meta['max_range'], data.shape[1])
283 0, self.data.meta['max_range'], data.shape[1])
284 if self.mode == 'E':
284 if self.mode == 'E':
285 azimuths = -numpy.radians(self.data.yrange)+numpy.pi/2
285 azimuths = -numpy.radians(self.data.yrange)+numpy.pi/2
286 r, theta = numpy.meshgrid(zeniths, azimuths)
286 r, theta = numpy.meshgrid(zeniths, azimuths)
287 x, y = r*numpy.cos(theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])), r*numpy.sin(
287 x, y = r*numpy.cos(theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])), r*numpy.sin(
288 theta)*numpy.cos(numpy.radians(self.data.meta['elevation']))
288 theta)*numpy.cos(numpy.radians(self.data.meta['elevation']))
289 x = km2deg(x) + self.lon
289 x = km2deg(x) + self.lon
290 y = km2deg(y) + self.lat
290 y = km2deg(y) + self.lat
291 else:
291 else:
292 azimuths = numpy.radians(self.data.yrange)
292 azimuths = numpy.radians(self.data.yrange)
293 r, theta = numpy.meshgrid(zeniths, azimuths)
293 r, theta = numpy.meshgrid(zeniths, azimuths)
294 x, y = r*numpy.cos(theta), r*numpy.sin(theta)
294 x, y = r*numpy.cos(theta), r*numpy.sin(theta)
295 self.y = zeniths
295 self.y = zeniths
296
296
297 if ax.firsttime:
297 if ax.firsttime:
298 if self.zlimits is not None:
298 if self.zlimits is not None:
299 self.zmin, self.zmax = self.zlimits[n]
299 self.zmin, self.zmax = self.zlimits[n]
300 ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)),
300 ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)),
301 x, y, numpy.ma.array(data, mask=numpy.isnan(data)),
301 x, y, numpy.ma.array(data, mask=numpy.isnan(data)),
302 vmin=self.zmin,
302 vmin=self.zmin,
303 vmax=self.zmax,
303 vmax=self.zmax,
304 cmap=self.cmaps[n])
304 cmap=self.cmaps[n])
305 else:
305 else:
306 if self.zlimits is not None:
306 if self.zlimits is not None:
307 self.zmin, self.zmax = self.zlimits[n]
307 self.zmin, self.zmax = self.zlimits[n]
308 ax.collections.remove(ax.collections[0])
308 ax.collections.remove(ax.collections[0])
309 ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)),
309 ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)),
310 x, y, numpy.ma.array(data, mask=numpy.isnan(data)),
310 x, y, numpy.ma.array(data, mask=numpy.isnan(data)),
311 vmin=self.zmin,
311 vmin=self.zmin,
312 vmax=self.zmax,
312 vmax=self.zmax,
313 cmap=self.cmaps[n])
313 cmap=self.cmaps[n])
314
314
315 if self.mode == 'A':
315 if self.mode == 'A':
316 continue
316 continue
317
317
318 # plot district names
318 # plot district names
319 f = open('/data/workspace/schain_scripts/distrito.csv')
319 f = open('/data/workspace/schain_scripts/distrito.csv')
320 for line in f:
320 for line in f:
321 label, lon, lat = [s.strip() for s in line.split(',') if s]
321 label, lon, lat = [s.strip() for s in line.split(',') if s]
322 lat = float(lat)
322 lat = float(lat)
323 lon = float(lon)
323 lon = float(lon)
324 # ax.plot(lon, lat, '.b', ms=2)
324 # ax.plot(lon, lat, '.b', ms=2)
325 ax.text(lon, lat, label.decode('utf8'), ha='center',
325 ax.text(lon, lat, label.decode('utf8'), ha='center',
326 va='bottom', size='8', color='black')
326 va='bottom', size='8', color='black')
327
327
328 # plot limites
328 # plot limites
329 limites = []
329 limites = []
330 tmp = []
330 tmp = []
331 for line in open('/data/workspace/schain_scripts/lima.csv'):
331 for line in open('/data/workspace/schain_scripts/lima.csv'):
332 if '#' in line:
332 if '#' in line:
333 if tmp:
333 if tmp:
334 limites.append(tmp)
334 limites.append(tmp)
335 tmp = []
335 tmp = []
336 continue
336 continue
337 values = line.strip().split(',')
337 values = line.strip().split(',')
338 tmp.append((float(values[0]), float(values[1])))
338 tmp.append((float(values[0]), float(values[1])))
339 for points in limites:
339 for points in limites:
340 ax.add_patch(
340 ax.add_patch(
341 Polygon(points, ec='k', fc='none', ls='--', lw=0.5))
341 Polygon(points, ec='k', fc='none', ls='--', lw=0.5))
342
342
343 # plot Cuencas
343 # plot Cuencas
344 for cuenca in ('rimac', 'lurin', 'mala', 'chillon', 'chilca', 'chancay-huaral'):
344 for cuenca in ('rimac', 'lurin', 'mala', 'chillon', 'chilca', 'chancay-huaral'):
345 f = open('/data/workspace/schain_scripts/{}.csv'.format(cuenca))
345 f = open('/data/workspace/schain_scripts/{}.csv'.format(cuenca))
346 values = [line.strip().split(',') for line in f]
346 values = [line.strip().split(',') for line in f]
347 points = [(float(s[0]), float(s[1])) for s in values]
347 points = [(float(s[0]), float(s[1])) for s in values]
348 ax.add_patch(Polygon(points, ec='b', fc='none'))
348 ax.add_patch(Polygon(points, ec='b', fc='none'))
349
349
350 # plot grid
350 # plot grid
351 for r in (15, 30, 45, 60):
351 for r in (15, 30, 45, 60):
352 ax.add_artist(plt.Circle((self.lon, self.lat),
352 ax.add_artist(plt.Circle((self.lon, self.lat),
353 km2deg(r), color='0.6', fill=False, lw=0.2))
353 km2deg(r), color='0.6', fill=False, lw=0.2))
354 ax.text(
354 ax.text(
355 self.lon + (km2deg(r))*numpy.cos(60*numpy.pi/180),
355 self.lon + (km2deg(r))*numpy.cos(60*numpy.pi/180),
356 self.lat + (km2deg(r))*numpy.sin(60*numpy.pi/180),
356 self.lat + (km2deg(r))*numpy.sin(60*numpy.pi/180),
357 '{}km'.format(r),
357 '{}km'.format(r),
358 ha='center', va='bottom', size='8', color='0.6', weight='heavy')
358 ha='center', va='bottom', size='8', color='0.6', weight='heavy')
359
359
360 if self.mode == 'E':
360 if self.mode == 'E':
361 title = 'El={}$^\circ$'.format(self.data.meta['elevation'])
361 title = 'El={}$^\circ$'.format(self.data.meta['elevation'])
362 label = 'E{:02d}'.format(int(self.data.meta['elevation']))
362 label = 'E{:02d}'.format(int(self.data.meta['elevation']))
363 else:
363 else:
364 title = 'Az={}$^\circ$'.format(self.data.meta['azimuth'])
364 title = 'Az={}$^\circ$'.format(self.data.meta['azimuth'])
365 label = 'A{:02d}'.format(int(self.data.meta['azimuth']))
365 label = 'A{:02d}'.format(int(self.data.meta['azimuth']))
366
366
367 self.save_labels = ['{}-{}'.format(lbl, label) for lbl in self.labels]
367 self.save_labels = ['{}-{}'.format(lbl, label) for lbl in self.labels]
368 self.titles = ['{} {}'.format(
368 self.titles = ['{} {}'.format(
369 self.data.parameters[x], title) for x in self.channels]
369 self.data.parameters[x], title) for x in self.channels]
370
370
371 class WeatherPlot(Plot):
371 class WeatherPlot(Plot):
372 CODE = 'weather'
372 CODE = 'weather'
373 plot_name = 'weather'
373 plot_name = 'weather'
374 plot_type = 'ppistyle'
374 plot_type = 'ppistyle'
375 buffering = False
375 buffering = False
376
376
377 def setup(self):
377 def setup(self):
378 self.ncols = 1
378 self.ncols = 1
379 self.nrows = 1
379 self.nrows = 1
380 self.nplots= 1
380 self.nplots= 1
381 self.ylabel= 'Range [Km]'
381 self.ylabel= 'Range [Km]'
382 self.titles= ['Weather']
382 self.titles= ['Weather']
383 self.colorbar=False
383 self.colorbar=False
384 self.width =8
384 self.width =8
385 self.height =8
385 self.height =8
386 self.ini =0
386 self.ini =0
387 self.len_azi =0
387 self.len_azi =0
388 self.buffer_ini = None
388 self.buffer_ini = None
389 self.buffer_azi = None
389 self.buffer_azi = None
390 self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08})
390 self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08})
391 self.flag =0
391 self.flag =0
392 self.indicador= 0
392 self.indicador= 0
393 self.last_data_azi = None
393 self.last_data_azi = None
394 self.val_mean = None
394 self.val_mean = None
395
395
396 def update(self, dataOut):
396 def update(self, dataOut):
397
397
398 data = {}
398 data = {}
399 meta = {}
399 meta = {}
400 if hasattr(dataOut, 'dataPP_POWER'):
400 if hasattr(dataOut, 'dataPP_POWER'):
401 factor = 1
401 factor = 1
402
402
403 if hasattr(dataOut, 'nFFTPoints'):
403 if hasattr(dataOut, 'nFFTPoints'):
404 factor = dataOut.normFactor
404 factor = dataOut.normFactor
405
405
406 ####print("factor",factor)
406 ####print("factor",factor)
407 data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor))
407 data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor))
408 ####print("weather",data['weather'])
408 ####print("weather",data['weather'])
409 data['azi'] = dataOut.data_azi
409 data['azi'] = dataOut.data_azi
410 return data, meta
410 return data, meta
411
411
412 def get2List(self,angulos):
412 def get2List(self,angulos):
413 list1=[]
413 list1=[]
414 list2=[]
414 list2=[]
415 for i in reversed(range(len(angulos))):
415 for i in reversed(range(len(angulos))):
416 diff_ = angulos[i]-angulos[i-1]
416 diff_ = angulos[i]-angulos[i-1]
417 if diff_ >1.5:
417 if diff_ >1.5:
418 list1.append(i-1)
418 list1.append(i-1)
419 list2.append(diff_)
419 list2.append(diff_)
420 return list(reversed(list1)),list(reversed(list2))
420 return list(reversed(list1)),list(reversed(list2))
421
421
422 def fixData360(self,list_,ang_):
422 def fixData360(self,list_,ang_):
423 if list_[0]==-1:
423 if list_[0]==-1:
424 vec = numpy.where(ang_<ang_[0])
424 vec = numpy.where(ang_<ang_[0])
425 ang_[vec] = ang_[vec]+360
425 ang_[vec] = ang_[vec]+360
426 return ang_
426 return ang_
427 return ang_
427 return ang_
428
428
429
429
430 def fixData360HL(self,angulos):
430 def fixData360HL(self,angulos):
431 vec = numpy.where(angulos>=360)
431 vec = numpy.where(angulos>=360)
432 angulos[vec]=angulos[vec]-360
432 angulos[vec]=angulos[vec]-360
433 return angulos
433 return angulos
434
434
435 def search_pos(self,pos,list_):
435 def search_pos(self,pos,list_):
436 for i in range(len(list_)):
436 for i in range(len(list_)):
437 if pos == list_[i]:
437 if pos == list_[i]:
438 return True,i
438 return True,i
439 i=None
439 i=None
440 return False,i
440 return False,i
441
441
442 def fixDataComp(self,ang_,list1_,list2_):
442 def fixDataComp(self,ang_,list1_,list2_):
443 size = len(ang_)
443 size = len(ang_)
444 size2 = 0
444 size2 = 0
445 for i in range(len(list2_)):
445 for i in range(len(list2_)):
446 size2=size2+list2_[i]-1
446 size2=size2+list2_[i]-1
447 new_size= size+size2
447 new_size= size+size2
448 ang_new = numpy.zeros(new_size)
448 ang_new = numpy.zeros(new_size)
449 ang_new2 = numpy.zeros(new_size)
449 ang_new2 = numpy.zeros(new_size)
450
450
451 tmp = 0
451 tmp = 0
452 c = 0
452 c = 0
453 for i in range(len(ang_)):
453 for i in range(len(ang_)):
454 ang_new[tmp +c] = ang_[i]
454 ang_new[tmp +c] = ang_[i]
455 ang_new2[tmp+c] = ang_[i]
455 ang_new2[tmp+c] = ang_[i]
456 condition , value = self.search_pos(i,list1_)
456 condition , value = self.search_pos(i,list1_)
457 if condition:
457 if condition:
458 pos = tmp + c + 1
458 pos = tmp + c + 1
459 for k in range(list2_[value]-1):
459 for k in range(list2_[value]-1):
460 ang_new[pos+k] = ang_new[pos+k-1]+1
460 ang_new[pos+k] = ang_new[pos+k-1]+1
461 ang_new2[pos+k] = numpy.nan
461 ang_new2[pos+k] = numpy.nan
462 tmp = pos +k
462 tmp = pos +k
463 c = 0
463 c = 0
464 c=c+1
464 c=c+1
465 return ang_new,ang_new2
465 return ang_new,ang_new2
466
466
467
467
468 def globalCheckPED(self,angulos):
468 def globalCheckPED(self,angulos):
469 l1,l2 = self.get2List(angulos)
469 l1,l2 = self.get2List(angulos)
470 if len(l1)>0:
470 if len(l1)>0:
471 angulos2 = self.fixData360(list_=l1,ang_=angulos)
471 angulos2 = self.fixData360(list_=l1,ang_=angulos)
472 l1,l2 = self.get2List(angulos2)
472 l1,l2 = self.get2List(angulos2)
473
473
474 ang1_,ang2_ = self.fixDataComp(ang_=angulos2,list1_=l1,list2_=l2)
474 ang1_,ang2_ = self.fixDataComp(ang_=angulos2,list1_=l1,list2_=l2)
475 ang1_ = self.fixData360HL(ang1_)
475 ang1_ = self.fixData360HL(ang1_)
476 ang2_ = self.fixData360HL(ang2_)
476 ang2_ = self.fixData360HL(ang2_)
477
477
478 else:
478 else:
479 ang1_= angulos
479 ang1_= angulos
480 ang2_= angulos
480 ang2_= angulos
481 return ang1_,ang2_
481 return ang1_,ang2_
482
482
483 def analizeDATA(self,data_azi):
483 def analizeDATA(self,data_azi):
484 list1 = []
484 list1 = []
485 list2 = []
485 list2 = []
486 dat = data_azi
486 dat = data_azi
487 for i in reversed(range(1,len(dat))):
487 for i in reversed(range(1,len(dat))):
488 if dat[i]>dat[i-1]:
488 if dat[i]>dat[i-1]:
489 diff = int(dat[i])-int(dat[i-1])
489 diff = int(dat[i])-int(dat[i-1])
490 else:
490 else:
491 diff = 360+int(dat[i])-int(dat[i-1])
491 diff = 360+int(dat[i])-int(dat[i-1])
492 if diff > 1:
492 if diff > 1:
493 list1.append(i-1)
493 list1.append(i-1)
494 list2.append(diff-1)
494 list2.append(diff-1)
495 return list1,list2
495 return list1,list2
496
496
497 def fixDATANEW(self,data_azi,data_weather):
497 def fixDATANEW(self,data_azi,data_weather):
498 list1,list2 = self.analizeDATA(data_azi)
498 list1,list2 = self.analizeDATA(data_azi)
499 if len(list1)== 0:
499 if len(list1)== 0:
500 return data_azi,data_weather
500 return data_azi,data_weather
501 else:
501 else:
502 resize = 0
502 resize = 0
503 for i in range(len(list2)):
503 for i in range(len(list2)):
504 resize= resize + list2[i]
504 resize= resize + list2[i]
505 new_data_azi = numpy.resize(data_azi,resize)
505 new_data_azi = numpy.resize(data_azi,resize)
506 new_data_weather= numpy.resize(date_weather,resize)
506 new_data_weather= numpy.resize(date_weather,resize)
507
507
508 for i in range(len(list2)):
508 for i in range(len(list2)):
509 j=0
509 j=0
510 position=list1[i]+1
510 position=list1[i]+1
511 for j in range(list2[i]):
511 for j in range(list2[i]):
512 new_data_azi[position+j]=new_data_azi[position+j-1]+1
512 new_data_azi[position+j]=new_data_azi[position+j-1]+1
513
513
514 return new_data_azi
514 return new_data_azi
515
515
516 def fixDATA(self,data_azi):
516 def fixDATA(self,data_azi):
517 data=data_azi
517 data=data_azi
518 for i in range(len(data)):
518 for i in range(len(data)):
519 if numpy.isnan(data[i]):
519 if numpy.isnan(data[i]):
520 data[i]=data[i-1]+1
520 data[i]=data[i-1]+1
521 return data
521 return data
522
522
523 def replaceNAN(self,data_weather,data_azi,val):
523 def replaceNAN(self,data_weather,data_azi,val):
524 ####print("----------------activeNEWFUNCTION")
524 ####print("----------------activeNEWFUNCTION")
525 data= data_azi
525 data= data_azi
526 data_T= data_weather
526 data_T= data_weather
527 ####print("data_azi",data_azi)
527 ####print("data_azi",data_azi)
528 ####print("VAL:",val)
528 ####print("VAL:",val)
529 ####print("SHAPE",data_T.shape)
529 ####print("SHAPE",data_T.shape)
530 for i in range(len(data)):
530 for i in range(len(data)):
531 if numpy.isnan(data[i]):
531 if numpy.isnan(data[i]):
532 ####print("NAN")
532 ####print("NAN")
533 data_T[i,:]=numpy.ones(data_T.shape[1])*val
533 #data_T[i,:]=numpy.ones(data_T.shape[1])*val
534 #data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
534 data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
535 return data_T
535 return data_T
536
536
537 def const_ploteo(self,data_weather,data_azi,step,res):
537 def const_ploteo(self,data_weather,data_azi,step,res):
538 if self.ini==0:
538 if self.ini==0:
539 #------- AZIMUTH
539 #------- AZIMUTH
540 n = (360/res)-len(data_azi)
540 n = (360/res)-len(data_azi)
541 #--------------------- new -------------------------
541 #--------------------- new -------------------------
542 ####data_azi_old = data_azi
542 ####data_azi_old = data_azi
543 data_azi_new ,data_azi_old= self.globalCheckPED(data_azi)
543 data_azi_new ,data_azi_old= self.globalCheckPED(data_azi)
544 #------------------------
544 #------------------------
545 ####data_azi_new = self.fixDATA(data_azi)
545 ####data_azi_new = self.fixDATA(data_azi)
546 #ata_azi_new = self.fixDATANEW(data_azi)
546 #ata_azi_new = self.fixDATANEW(data_azi)
547
547
548 start = data_azi_new[-1] + res
548 start = data_azi_new[-1] + res
549 end = data_azi_new[0] - res
549 end = data_azi_new[0] - res
550 ##### new
550 ##### new
551 self.last_data_azi = end
551 self.last_data_azi = end
552 if start>end:
552 if start>end:
553 end = end + 360
553 end = end + 360
554 azi_vacia = numpy.linspace(start,end,int(n))
554 azi_vacia = numpy.linspace(start,end,int(n))
555 azi_vacia = numpy.where(azi_vacia>360,azi_vacia-360,azi_vacia)
555 azi_vacia = numpy.where(azi_vacia>360,azi_vacia-360,azi_vacia)
556 data_azi = numpy.hstack((data_azi_new,azi_vacia))
556 data_azi = numpy.hstack((data_azi_new,azi_vacia))
557 # RADAR
557 # RADAR
558 val_mean = numpy.mean(data_weather[:,-1])
558 val_mean = numpy.mean(data_weather[:,-1])
559 self.val_mean = val_mean
559 self.val_mean = val_mean
560 data_weather_cmp = numpy.ones([(360-data_weather.shape[0]),data_weather.shape[1]])*val_mean
560 data_weather_cmp = numpy.ones([(360-data_weather.shape[0]),data_weather.shape[1]])*val_mean
561 data_weather = self.replaceNAN(data_weather=data_weather,data_azi=data_azi_old,val=self.val_mean)
561 data_weather = self.replaceNAN(data_weather=data_weather,data_azi=data_azi_old,val=self.val_mean)
562 data_weather = numpy.vstack((data_weather,data_weather_cmp))
562 data_weather = numpy.vstack((data_weather,data_weather_cmp))
563 else:
563 else:
564 # azimuth
564 # azimuth
565 flag=0
565 flag=0
566 start_azi = self.res_azi[0]
566 start_azi = self.res_azi[0]
567 #-----------new------------
567 #-----------new------------
568 data_azi ,data_azi_old= self.globalCheckPED(data_azi)
568 data_azi ,data_azi_old= self.globalCheckPED(data_azi)
569 data_weather = self.replaceNAN(data_weather=data_weather,data_azi=data_azi_old,val=self.val_mean)
569 data_weather = self.replaceNAN(data_weather=data_weather,data_azi=data_azi_old,val=self.val_mean)
570 #--------------------------
570 #--------------------------
571 ####data_azi_old = data_azi
571 ####data_azi_old = data_azi
572 ### weather ###
572 ### weather ###
573 ####data_weather = self.replaceNAN(data_weather=data_weather,data_azi=data_azi_old,val=self.val_mean)
573 ####data_weather = self.replaceNAN(data_weather=data_weather,data_azi=data_azi_old,val=self.val_mean)
574
574
575 ####if numpy.isnan(data_azi[0]):
575 ####if numpy.isnan(data_azi[0]):
576 #### data_azi[0]=self.last_data_azi+1
576 #### data_azi[0]=self.last_data_azi+1
577 ####data_azi = self.fixDATA(data_azi)
577 ####data_azi = self.fixDATA(data_azi)
578 start = data_azi[0]
578 start = data_azi[0]
579 end = data_azi[-1]
579 end = data_azi[-1]
580 self.last_data_azi= end
580 self.last_data_azi= end
581 ####print("start",start)
581 ####print("start",start)
582 ####print("end",end)
582 ####print("end",end)
583 if start< start_azi:
583 if start< start_azi:
584 start = start +360
584 start = start +360
585 if end <start_azi:
585 if end <start_azi:
586 end = end +360
586 end = end +360
587 ####print("start",start)
587 ####print("start",start)
588 ####print("end",end)
588 ####print("end",end)
589 #### AQUI SERA LA MAGIA
589 #### AQUI SERA LA MAGIA
590 pos_ini = int((start-start_azi)/res)
590 pos_ini = int((start-start_azi)/res)
591 len_azi = len(data_azi)
591 len_azi = len(data_azi)
592 if (360-pos_ini)<len_azi:
592 if (360-pos_ini)<len_azi:
593 if pos_ini+1==360:
593 if pos_ini+1==360:
594 pos_ini=0
594 pos_ini=0
595 else:
595 else:
596 flag=1
596 flag=1
597 dif= 360-pos_ini
597 dif= 360-pos_ini
598 comp= len_azi-dif
598 comp= len_azi-dif
599
599
600 #-----------------
600 #-----------------
601 ####print(pos_ini)
601 ####print(pos_ini)
602 ####print(len_azi)
602 ####print(len_azi)
603 ####print("shape",self.res_azi.shape)
603 ####print("shape",self.res_azi.shape)
604 if flag==0:
604 if flag==0:
605 # AZIMUTH
605 # AZIMUTH
606 self.res_azi[pos_ini:pos_ini+len_azi] = data_azi
606 self.res_azi[pos_ini:pos_ini+len_azi] = data_azi
607 # RADAR
607 # RADAR
608 self.res_weather[pos_ini:pos_ini+len_azi,:] = data_weather
608 self.res_weather[pos_ini:pos_ini+len_azi,:] = data_weather
609 else:
609 else:
610 # AZIMUTH
610 # AZIMUTH
611 self.res_azi[pos_ini:pos_ini+dif] = data_azi[0:dif]
611 self.res_azi[pos_ini:pos_ini+dif] = data_azi[0:dif]
612 self.res_azi[0:comp] = data_azi[dif:]
612 self.res_azi[0:comp] = data_azi[dif:]
613 # RADAR
613 # RADAR
614 self.res_weather[pos_ini:pos_ini+dif,:] = data_weather[0:dif,:]
614 self.res_weather[pos_ini:pos_ini+dif,:] = data_weather[0:dif,:]
615 self.res_weather[0:comp,:] = data_weather[dif:,:]
615 self.res_weather[0:comp,:] = data_weather[dif:,:]
616 flag=0
616 flag=0
617 data_azi = self.res_azi
617 data_azi = self.res_azi
618 data_weather = self.res_weather
618 data_weather = self.res_weather
619
619
620 return data_weather,data_azi
620 return data_weather,data_azi
621
621
622 def plot(self):
622 def plot(self):
623 #print("--------------------------------------",self.ini,"-----------------------------------")
623 #print("--------------------------------------",self.ini,"-----------------------------------")
624 #numpy.set_printoptions(suppress=True)
624 #numpy.set_printoptions(suppress=True)
625 ####print("times: ",self.data.times)
625 ####print("times: ",self.data.times)
626 thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]).strftime('%Y-%m-%d %H:%M:%S')
626 thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]).strftime('%Y-%m-%d %H:%M:%S')
627 #print("times: ",thisDatetime)
627 #print("times: ",thisDatetime)
628 data = self.data[-1]
628 data = self.data[-1]
629 ####ALTURA altura_tmp_h
629 ####ALTURA altura_tmp_h
630 ###print("Y RANGES",self.data.yrange,len(self.data.yrange))
630 ###print("Y RANGES",self.data.yrange,len(self.data.yrange))
631 ###altura_h = (data['weather'].shape[1])/10.0
631 ###altura_h = (data['weather'].shape[1])/10.0
632 ###stoprange = float(altura_h*0.3)#stoprange = float(33*1.5) por ahora 400
632 ###stoprange = float(altura_h*0.3)#stoprange = float(33*1.5) por ahora 400
633 ###rangestep = float(0.03)
633 ###rangestep = float(0.03)
634 ###r = numpy.arange(0, stoprange, rangestep)
634 ###r = numpy.arange(0, stoprange, rangestep)
635 ###print("r",r,len(r))
635 ###print("r",r,len(r))
636 #-----------------------------update----------------------
636 #-----------------------------update----------------------
637 r= self.data.yrange
637 r= self.data.yrange
638 delta_height = r[1]-r[0]
638 delta_height = r[1]-r[0]
639 #print("1",r)
639 #print("1",r)
640 r_mask= numpy.where(r>=0)[0]
640 r_mask= numpy.where(r>=0)[0]
641 r = numpy.arange(len(r_mask))*delta_height
641 r = numpy.arange(len(r_mask))*delta_height
642 #print("2",r)
642 #print("2",r)
643 self.y = 2*r
643 self.y = 2*r
644 ######self.y = self.data.yrange
644 ######self.y = self.data.yrange
645 # RADAR
645 # RADAR
646 #data_weather = data['weather']
646 #data_weather = data['weather']
647 # PEDESTAL
647 # PEDESTAL
648 #data_azi = data['azi']
648 #data_azi = data['azi']
649 res = 1
649 res = 1
650 # STEP
650 # STEP
651 step = (360/(res*data['weather'].shape[0]))
651 step = (360/(res*data['weather'].shape[0]))
652 #print("shape wr_data", wr_data.shape)
652 #print("shape wr_data", wr_data.shape)
653 #print("shape wr_azi",wr_azi.shape)
653 #print("shape wr_azi",wr_azi.shape)
654 #print("step",step)
654 #print("step",step)
655 ####print("Time---->",self.data.times[-1],thisDatetime)
655 ####print("Time---->",self.data.times[-1],thisDatetime)
656 #print("alturas", len(self.y))numpy.where(r>=0)
656 #print("alturas", len(self.y))numpy.where(r>=0)
657 self.res_weather, self.res_azi = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_azi=data['azi'],step=step,res=res)
657 self.res_weather, self.res_azi = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_azi=data['azi'],step=step,res=res)
658 #numpy.set_printoptions(suppress=True)
658 #numpy.set_printoptions(suppress=True)
659 #print("resultado",self.res_azi)
659 #print("resultado",self.res_azi)
660 ###########################/DATA_RM/10_tmp/ch0###############################
660 ###########################/DATA_RM/10_tmp/ch0###############################
661 ################# PLOTEO ###################
661 ################# PLOTEO ###################
662 ##########################################################
662 ##########################################################
663
663
664 for i,ax in enumerate(self.axes):
664 for i,ax in enumerate(self.axes):
665 if ax.firsttime:
665 if ax.firsttime:
666 plt.clf()
666 plt.clf()
667 cgax, pm = wrl.vis.plot_ppi(self.res_weather,r=r,az=self.res_azi,fig=self.figures[0], proj='cg', vmin=8, vmax=35)
667 cgax, pm = wrl.vis.plot_ppi(self.res_weather,r=r,az=self.res_azi,fig=self.figures[0], proj='cg', vmin=8, vmax=35)
668 else:
668 else:
669 plt.clf()
669 plt.clf()
670 cgax, pm = wrl.vis.plot_ppi(self.res_weather,r=r,az=self.res_azi,fig=self.figures[0], proj='cg', vmin=8, vmax=35)
670 cgax, pm = wrl.vis.plot_ppi(self.res_weather,r=r,az=self.res_azi,fig=self.figures[0], proj='cg', vmin=8, vmax=35)
671 caax = cgax.parasites[0]
671 caax = cgax.parasites[0]
672 paax = cgax.parasites[1]
672 paax = cgax.parasites[1]
673 cbar = plt.gcf().colorbar(pm, pad=0.075)
673 cbar = plt.gcf().colorbar(pm, pad=0.075)
674 caax.set_xlabel('x_range [km]')
674 caax.set_xlabel('x_range [km]')
675 caax.set_ylabel('y_range [km]')
675 caax.set_ylabel('y_range [km]')
676 plt.text(1.0, 1.05, 'azimuth '+str(thisDatetime)+" step "+str(self.ini), transform=caax.transAxes, va='bottom',ha='right')
676 plt.text(1.0, 1.05, 'azimuth '+str(thisDatetime)+" step "+str(self.ini), transform=caax.transAxes, va='bottom',ha='right')
677
677
678 self.ini= self.ini+1
678 self.ini= self.ini+1
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