##// END OF EJS Templates
schain
RSS
Fix weatherplot for two channels
jespinoza -
r1544:24f1bea5f47d
parent child
Show More
Side by Side Unified
Context file:
r1544:24f1bea5f47d Loading diff...:
Show file before | Show file after | Hide comments
@@ -1,691 +1,697
1 import os
1 import os
2 import datetime
2 import datetime
3 import warnings
3 import warnings
4 import numpy
4 import numpy
5 from mpl_toolkits.axisartist.grid_finder import FixedLocator, DictFormatter
5 from mpl_toolkits.axisartist.grid_finder import FixedLocator, DictFormatter
6
6
7 from schainpy.model.graphics.jroplot_base import Plot, plt
7 from schainpy.model.graphics.jroplot_base import Plot, plt
8 from schainpy.model.graphics.jroplot_spectra import SpectraPlot, RTIPlot, CoherencePlot, SpectraCutPlot
8 from schainpy.model.graphics.jroplot_spectra import SpectraPlot, RTIPlot, CoherencePlot, SpectraCutPlot
9 from schainpy.utils import log
9 from schainpy.utils import log
10
10
11
11
12 EARTH_RADIUS = 6.3710e3
12 EARTH_RADIUS = 6.3710e3
13
13
14
14
15 def antenna_to_cartesian(ranges, azimuths, elevations):
15 def antenna_to_cartesian(ranges, azimuths, elevations):
16 """
16 """
17 Return Cartesian coordinates from antenna coordinates.
17 Return Cartesian coordinates from antenna coordinates.
18
18
19 Parameters
19 Parameters
20 ----------
20 ----------
21 ranges : array
21 ranges : array
22 Distances to the center of the radar gates (bins) in kilometers.
22 Distances to the center of the radar gates (bins) in kilometers.
23 azimuths : array
23 azimuths : array
24 Azimuth angle of the radar in degrees.
24 Azimuth angle of the radar in degrees.
25 elevations : array
25 elevations : array
26 Elevation angle of the radar in degrees.
26 Elevation angle of the radar in degrees.
27
27
28 Returns
28 Returns
29 -------
29 -------
30 x, y, z : array
30 x, y, z : array
31 Cartesian coordinates in meters from the radar.
31 Cartesian coordinates in meters from the radar.
32
32
33 Notes
33 Notes
34 -----
34 -----
35 The calculation for Cartesian coordinate is adapted from equations
35 The calculation for Cartesian coordinate is adapted from equations
36 2.28(b) and 2.28(c) of Doviak and Zrnic [1]_ assuming a
36 2.28(b) and 2.28(c) of Doviak and Zrnic [1]_ assuming a
37 standard atmosphere (4/3 Earth's radius model).
37 standard atmosphere (4/3 Earth's radius model).
38
38
39 .. math::
39 .. math::
40
40
41 z = \\sqrt{r^2+R^2+2*r*R*sin(\\theta_e)} - R
41 z = \\sqrt{r^2+R^2+2*r*R*sin(\\theta_e)} - R
42
42
43 s = R * arcsin(\\frac{r*cos(\\theta_e)}{R+z})
43 s = R * arcsin(\\frac{r*cos(\\theta_e)}{R+z})
44
44
45 x = s * sin(\\theta_a)
45 x = s * sin(\\theta_a)
46
46
47 y = s * cos(\\theta_a)
47 y = s * cos(\\theta_a)
48
48
49 Where r is the distance from the radar to the center of the gate,
49 Where r is the distance from the radar to the center of the gate,
50 :math:`\\theta_a` is the azimuth angle, :math:`\\theta_e` is the
50 :math:`\\theta_a` is the azimuth angle, :math:`\\theta_e` is the
51 elevation angle, s is the arc length, and R is the effective radius
51 elevation angle, s is the arc length, and R is the effective radius
52 of the earth, taken to be 4/3 the mean radius of earth (6371 km).
52 of the earth, taken to be 4/3 the mean radius of earth (6371 km).
53
53
54 References
54 References
55 ----------
55 ----------
56 .. [1] Doviak and Zrnic, Doppler Radar and Weather Observations, Second
56 .. [1] Doviak and Zrnic, Doppler Radar and Weather Observations, Second
57 Edition, 1993, p. 21.
57 Edition, 1993, p. 21.
58
58
59 """
59 """
60 theta_e = numpy.deg2rad(elevations) # elevation angle in radians.
60 theta_e = numpy.deg2rad(elevations) # elevation angle in radians.
61 theta_a = numpy.deg2rad(azimuths) # azimuth angle in radians.
61 theta_a = numpy.deg2rad(azimuths) # azimuth angle in radians.
62 R = 6371.0 * 1000.0 * 4.0 / 3.0 # effective radius of earth in meters.
62 R = 6371.0 * 1000.0 * 4.0 / 3.0 # effective radius of earth in meters.
63 r = ranges * 1000.0 # distances to gates in meters.
63 r = ranges * 1000.0 # distances to gates in meters.
64
64
65 z = (r ** 2 + R ** 2 + 2.0 * r * R * numpy.sin(theta_e)) ** 0.5 - R
65 z = (r ** 2 + R ** 2 + 2.0 * r * R * numpy.sin(theta_e)) ** 0.5 - R
66 s = R * numpy.arcsin(r * numpy.cos(theta_e) / (R + z)) # arc length in m.
66 s = R * numpy.arcsin(r * numpy.cos(theta_e) / (R + z)) # arc length in m.
67 x = s * numpy.sin(theta_a)
67 x = s * numpy.sin(theta_a)
68 y = s * numpy.cos(theta_a)
68 y = s * numpy.cos(theta_a)
69 return x, y, z
69 return x, y, z
70
70
71 def cartesian_to_geographic_aeqd(x, y, lon_0, lat_0, R=EARTH_RADIUS):
71 def cartesian_to_geographic_aeqd(x, y, lon_0, lat_0, R=EARTH_RADIUS):
72 """
72 """
73 Azimuthal equidistant Cartesian to geographic coordinate transform.
73 Azimuthal equidistant Cartesian to geographic coordinate transform.
74
74
75 Transform a set of Cartesian/Cartographic coordinates (x, y) to
75 Transform a set of Cartesian/Cartographic coordinates (x, y) to
76 geographic coordinate system (lat, lon) using a azimuthal equidistant
76 geographic coordinate system (lat, lon) using a azimuthal equidistant
77 map projection [1]_.
77 map projection [1]_.
78
78
79 .. math::
79 .. math::
80
80
81 lat = \\arcsin(\\cos(c) * \\sin(lat_0) +
81 lat = \\arcsin(\\cos(c) * \\sin(lat_0) +
82 (y * \\sin(c) * \\cos(lat_0) / \\rho))
82 (y * \\sin(c) * \\cos(lat_0) / \\rho))
83
83
84 lon = lon_0 + \\arctan2(
84 lon = lon_0 + \\arctan2(
85 x * \\sin(c),
85 x * \\sin(c),
86 \\rho * \\cos(lat_0) * \\cos(c) - y * \\sin(lat_0) * \\sin(c))
86 \\rho * \\cos(lat_0) * \\cos(c) - y * \\sin(lat_0) * \\sin(c))
87
87
88 \\rho = \\sqrt(x^2 + y^2)
88 \\rho = \\sqrt(x^2 + y^2)
89
89
90 c = \\rho / R
90 c = \\rho / R
91
91
92 Where x, y are the Cartesian position from the center of projection;
92 Where x, y are the Cartesian position from the center of projection;
93 lat, lon the corresponding latitude and longitude; lat_0, lon_0 are the
93 lat, lon the corresponding latitude and longitude; lat_0, lon_0 are the
94 latitude and longitude of the center of the projection; R is the radius of
94 latitude and longitude of the center of the projection; R is the radius of
95 the earth (defaults to ~6371 km). lon is adjusted to be between -180 and
95 the earth (defaults to ~6371 km). lon is adjusted to be between -180 and
96 180.
96 180.
97
97
98 Parameters
98 Parameters
99 ----------
99 ----------
100 x, y : array-like
100 x, y : array-like
101 Cartesian coordinates in the same units as R, typically meters.
101 Cartesian coordinates in the same units as R, typically meters.
102 lon_0, lat_0 : float
102 lon_0, lat_0 : float
103 Longitude and latitude, in degrees, of the center of the projection.
103 Longitude and latitude, in degrees, of the center of the projection.
104 R : float, optional
104 R : float, optional
105 Earth radius in the same units as x and y. The default value is in
105 Earth radius in the same units as x and y. The default value is in
106 units of meters.
106 units of meters.
107
107
108 Returns
108 Returns
109 -------
109 -------
110 lon, lat : array
110 lon, lat : array
111 Longitude and latitude of Cartesian coordinates in degrees.
111 Longitude and latitude of Cartesian coordinates in degrees.
112
112
113 References
113 References
114 ----------
114 ----------
115 .. [1] Snyder, J. P. Map Projections--A Working Manual. U. S. Geological
115 .. [1] Snyder, J. P. Map Projections--A Working Manual. U. S. Geological
116 Survey Professional Paper 1395, 1987, pp. 191-202.
116 Survey Professional Paper 1395, 1987, pp. 191-202.
117
117
118 """
118 """
119 x = numpy.atleast_1d(numpy.asarray(x))
119 x = numpy.atleast_1d(numpy.asarray(x))
120 y = numpy.atleast_1d(numpy.asarray(y))
120 y = numpy.atleast_1d(numpy.asarray(y))
121
121
122 lat_0_rad = numpy.deg2rad(lat_0)
122 lat_0_rad = numpy.deg2rad(lat_0)
123 lon_0_rad = numpy.deg2rad(lon_0)
123 lon_0_rad = numpy.deg2rad(lon_0)
124
124
125 rho = numpy.sqrt(x*x + y*y)
125 rho = numpy.sqrt(x*x + y*y)
126 c = rho / R
126 c = rho / R
127
127
128 with warnings.catch_warnings():
128 with warnings.catch_warnings():
129 # division by zero may occur here but is properly addressed below so
129 # division by zero may occur here but is properly addressed below so
130 # the warnings can be ignored
130 # the warnings can be ignored
131 warnings.simplefilter("ignore", RuntimeWarning)
131 warnings.simplefilter("ignore", RuntimeWarning)
132 lat_rad = numpy.arcsin(numpy.cos(c) * numpy.sin(lat_0_rad) +
132 lat_rad = numpy.arcsin(numpy.cos(c) * numpy.sin(lat_0_rad) +
133 y * numpy.sin(c) * numpy.cos(lat_0_rad) / rho)
133 y * numpy.sin(c) * numpy.cos(lat_0_rad) / rho)
134 lat_deg = numpy.rad2deg(lat_rad)
134 lat_deg = numpy.rad2deg(lat_rad)
135 # fix cases where the distance from the center of the projection is zero
135 # fix cases where the distance from the center of the projection is zero
136 lat_deg[rho == 0] = lat_0
136 lat_deg[rho == 0] = lat_0
137
137
138 x1 = x * numpy.sin(c)
138 x1 = x * numpy.sin(c)
139 x2 = rho*numpy.cos(lat_0_rad)*numpy.cos(c) - y*numpy.sin(lat_0_rad)*numpy.sin(c)
139 x2 = rho*numpy.cos(lat_0_rad)*numpy.cos(c) - y*numpy.sin(lat_0_rad)*numpy.sin(c)
140 lon_rad = lon_0_rad + numpy.arctan2(x1, x2)
140 lon_rad = lon_0_rad + numpy.arctan2(x1, x2)
141 lon_deg = numpy.rad2deg(lon_rad)
141 lon_deg = numpy.rad2deg(lon_rad)
142 # Longitudes should be from -180 to 180 degrees
142 # Longitudes should be from -180 to 180 degrees
143 lon_deg[lon_deg > 180] -= 360.
143 lon_deg[lon_deg > 180] -= 360.
144 lon_deg[lon_deg < -180] += 360.
144 lon_deg[lon_deg < -180] += 360.
145
145
146 return lon_deg, lat_deg
146 return lon_deg, lat_deg
147
147
148 def antenna_to_geographic(ranges, azimuths, elevations, site):
148 def antenna_to_geographic(ranges, azimuths, elevations, site):
149
149
150 x, y, z = antenna_to_cartesian(numpy.array(ranges), numpy.array(azimuths), numpy.array(elevations))
150 x, y, z = antenna_to_cartesian(numpy.array(ranges), numpy.array(azimuths), numpy.array(elevations))
151 lon, lat = cartesian_to_geographic_aeqd(x, y, site[0], site[1], R=6370997.)
151 lon, lat = cartesian_to_geographic_aeqd(x, y, site[0], site[1], R=6370997.)
152
152
153 return lon, lat
153 return lon, lat
154
154
155 def ll2xy(lat1, lon1, lat2, lon2):
155 def ll2xy(lat1, lon1, lat2, lon2):
156
156
157 p = 0.017453292519943295
157 p = 0.017453292519943295
158 a = 0.5 - numpy.cos((lat2 - lat1) * p)/2 + numpy.cos(lat1 * p) * \
158 a = 0.5 - numpy.cos((lat2 - lat1) * p)/2 + numpy.cos(lat1 * p) * \
159 numpy.cos(lat2 * p) * (1 - numpy.cos((lon2 - lon1) * p)) / 2
159 numpy.cos(lat2 * p) * (1 - numpy.cos((lon2 - lon1) * p)) / 2
160 r = 12742 * numpy.arcsin(numpy.sqrt(a))
160 r = 12742 * numpy.arcsin(numpy.sqrt(a))
161 theta = numpy.arctan2(numpy.sin((lon2-lon1)*p)*numpy.cos(lat2*p), numpy.cos(lat1*p)
161 theta = numpy.arctan2(numpy.sin((lon2-lon1)*p)*numpy.cos(lat2*p), numpy.cos(lat1*p)
162 * numpy.sin(lat2*p)-numpy.sin(lat1*p)*numpy.cos(lat2*p)*numpy.cos((lon2-lon1)*p))
162 * numpy.sin(lat2*p)-numpy.sin(lat1*p)*numpy.cos(lat2*p)*numpy.cos((lon2-lon1)*p))
163 theta = -theta + numpy.pi/2
163 theta = -theta + numpy.pi/2
164 return r*numpy.cos(theta), r*numpy.sin(theta)
164 return r*numpy.cos(theta), r*numpy.sin(theta)
165
165
166
166
167 def km2deg(km):
167 def km2deg(km):
168 '''
168 '''
169 Convert distance in km to degrees
169 Convert distance in km to degrees
170 '''
170 '''
171
171
172 return numpy.rad2deg(km/EARTH_RADIUS)
172 return numpy.rad2deg(km/EARTH_RADIUS)
173
173
174
174
175
175
176 class SpectralMomentsPlot(SpectraPlot):
176 class SpectralMomentsPlot(SpectraPlot):
177 '''
177 '''
178 Plot for Spectral Moments
178 Plot for Spectral Moments
179 '''
179 '''
180 CODE = 'spc_moments'
180 CODE = 'spc_moments'
181 # colormap = 'jet'
181 # colormap = 'jet'
182 # plot_type = 'pcolor'
182 # plot_type = 'pcolor'
183
183
184 class DobleGaussianPlot(SpectraPlot):
184 class DobleGaussianPlot(SpectraPlot):
185 '''
185 '''
186 Plot for Double Gaussian Plot
186 Plot for Double Gaussian Plot
187 '''
187 '''
188 CODE = 'gaussian_fit'
188 CODE = 'gaussian_fit'
189 # colormap = 'jet'
189 # colormap = 'jet'
190 # plot_type = 'pcolor'
190 # plot_type = 'pcolor'
191
191
192 class DoubleGaussianSpectraCutPlot(SpectraCutPlot):
192 class DoubleGaussianSpectraCutPlot(SpectraCutPlot):
193 '''
193 '''
194 Plot SpectraCut with Double Gaussian Fit
194 Plot SpectraCut with Double Gaussian Fit
195 '''
195 '''
196 CODE = 'cut_gaussian_fit'
196 CODE = 'cut_gaussian_fit'
197
197
198 class SnrPlot(RTIPlot):
198 class SnrPlot(RTIPlot):
199 '''
199 '''
200 Plot for SNR Data
200 Plot for SNR Data
201 '''
201 '''
202
202
203 CODE = 'snr'
203 CODE = 'snr'
204 colormap = 'jet'
204 colormap = 'jet'
205
205
206 def update(self, dataOut):
206 def update(self, dataOut):
207
207
208 data = {
208 data = {
209 'snr': 10*numpy.log10(dataOut.data_snr)
209 'snr': 10*numpy.log10(dataOut.data_snr)
210 }
210 }
211
211
212 return data, {}
212 return data, {}
213
213
214 class DopplerPlot(RTIPlot):
214 class DopplerPlot(RTIPlot):
215 '''
215 '''
216 Plot for DOPPLER Data (1st moment)
216 Plot for DOPPLER Data (1st moment)
217 '''
217 '''
218
218
219 CODE = 'dop'
219 CODE = 'dop'
220 colormap = 'jet'
220 colormap = 'jet'
221
221
222 def update(self, dataOut):
222 def update(self, dataOut):
223
223
224 data = {
224 data = {
225 'dop': 10*numpy.log10(dataOut.data_dop)
225 'dop': 10*numpy.log10(dataOut.data_dop)
226 }
226 }
227
227
228 return data, {}
228 return data, {}
229
229
230 class PowerPlot(RTIPlot):
230 class PowerPlot(RTIPlot):
231 '''
231 '''
232 Plot for Power Data (0 moment)
232 Plot for Power Data (0 moment)
233 '''
233 '''
234
234
235 CODE = 'pow'
235 CODE = 'pow'
236 colormap = 'jet'
236 colormap = 'jet'
237
237
238 def update(self, dataOut):
238 def update(self, dataOut):
239 data = {
239 data = {
240 'pow': 10*numpy.log10(dataOut.data_pow/dataOut.normFactor)
240 'pow': 10*numpy.log10(dataOut.data_pow/dataOut.normFactor)
241 }
241 }
242 return data, {}
242 return data, {}
243
243
244 class SpectralWidthPlot(RTIPlot):
244 class SpectralWidthPlot(RTIPlot):
245 '''
245 '''
246 Plot for Spectral Width Data (2nd moment)
246 Plot for Spectral Width Data (2nd moment)
247 '''
247 '''
248
248
249 CODE = 'width'
249 CODE = 'width'
250 colormap = 'jet'
250 colormap = 'jet'
251
251
252 def update(self, dataOut):
252 def update(self, dataOut):
253
253
254 data = {
254 data = {
255 'width': dataOut.data_width
255 'width': dataOut.data_width
256 }
256 }
257
257
258 return data, {}
258 return data, {}
259
259
260 class SkyMapPlot(Plot):
260 class SkyMapPlot(Plot):
261 '''
261 '''
262 Plot for meteors detection data
262 Plot for meteors detection data
263 '''
263 '''
264
264
265 CODE = 'param'
265 CODE = 'param'
266
266
267 def setup(self):
267 def setup(self):
268
268
269 self.ncols = 1
269 self.ncols = 1
270 self.nrows = 1
270 self.nrows = 1
271 self.width = 7.2
271 self.width = 7.2
272 self.height = 7.2
272 self.height = 7.2
273 self.nplots = 1
273 self.nplots = 1
274 self.xlabel = 'Zonal Zenith Angle (deg)'
274 self.xlabel = 'Zonal Zenith Angle (deg)'
275 self.ylabel = 'Meridional Zenith Angle (deg)'
275 self.ylabel = 'Meridional Zenith Angle (deg)'
276 self.polar = True
276 self.polar = True
277 self.ymin = -180
277 self.ymin = -180
278 self.ymax = 180
278 self.ymax = 180
279 self.colorbar = False
279 self.colorbar = False
280
280
281 def plot(self):
281 def plot(self):
282
282
283 arrayParameters = numpy.concatenate(self.data['param'])
283 arrayParameters = numpy.concatenate(self.data['param'])
284 error = arrayParameters[:, -1]
284 error = arrayParameters[:, -1]
285 indValid = numpy.where(error == 0)[0]
285 indValid = numpy.where(error == 0)[0]
286 finalMeteor = arrayParameters[indValid, :]
286 finalMeteor = arrayParameters[indValid, :]
287 finalAzimuth = finalMeteor[:, 3]
287 finalAzimuth = finalMeteor[:, 3]
288 finalZenith = finalMeteor[:, 4]
288 finalZenith = finalMeteor[:, 4]
289
289
290 x = finalAzimuth * numpy.pi / 180
290 x = finalAzimuth * numpy.pi / 180
291 y = finalZenith
291 y = finalZenith
292
292
293 ax = self.axes[0]
293 ax = self.axes[0]
294
294
295 if ax.firsttime:
295 if ax.firsttime:
296 ax.plot = ax.plot(x, y, 'bo', markersize=5)[0]
296 ax.plot = ax.plot(x, y, 'bo', markersize=5)[0]
297 else:
297 else:
298 ax.plot.set_data(x, y)
298 ax.plot.set_data(x, y)
299
299
300 dt1 = self.getDateTime(self.data.min_time).strftime('%y/%m/%d %H:%M:%S')
300 dt1 = self.getDateTime(self.data.min_time).strftime('%y/%m/%d %H:%M:%S')
301 dt2 = self.getDateTime(self.data.max_time).strftime('%y/%m/%d %H:%M:%S')
301 dt2 = self.getDateTime(self.data.max_time).strftime('%y/%m/%d %H:%M:%S')
302 title = 'Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n' % (dt1,
302 title = 'Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n' % (dt1,
303 dt2,
303 dt2,
304 len(x))
304 len(x))
305 self.titles[0] = title
305 self.titles[0] = title
306
306
307
307
308 class GenericRTIPlot(Plot):
308 class GenericRTIPlot(Plot):
309 '''
309 '''
310 Plot for data_xxxx object
310 Plot for data_xxxx object
311 '''
311 '''
312
312
313 CODE = 'param'
313 CODE = 'param'
314 colormap = 'viridis'
314 colormap = 'viridis'
315 plot_type = 'pcolorbuffer'
315 plot_type = 'pcolorbuffer'
316
316
317 def setup(self):
317 def setup(self):
318 self.xaxis = 'time'
318 self.xaxis = 'time'
319 self.ncols = 1
319 self.ncols = 1
320 self.nrows = self.data.shape('param')[0]
320 self.nrows = self.data.shape('param')[0]
321 self.nplots = self.nrows
321 self.nplots = self.nrows
322 self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.08, 'right':0.95, 'top': 0.95})
322 self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.08, 'right':0.95, 'top': 0.95})
323
323
324 if not self.xlabel:
324 if not self.xlabel:
325 self.xlabel = 'Time'
325 self.xlabel = 'Time'
326
326
327 self.ylabel = 'Range [km]'
327 self.ylabel = 'Range [km]'
328 if not self.titles:
328 if not self.titles:
329 self.titles = ['Param {}'.format(x) for x in range(self.nrows)]
329 self.titles = ['Param {}'.format(x) for x in range(self.nrows)]
330
330
331 def update(self, dataOut):
331 def update(self, dataOut):
332
332
333 data = {
333 data = {
334 'param' : numpy.concatenate([getattr(dataOut, attr) for attr in self.attr_data], axis=0)
334 'param' : numpy.concatenate([getattr(dataOut, attr) for attr in self.attr_data], axis=0)
335 }
335 }
336
336
337 meta = {}
337 meta = {}
338
338
339 return data, meta
339 return data, meta
340
340
341 def plot(self):
341 def plot(self):
342 # self.data.normalize_heights()
342 # self.data.normalize_heights()
343 self.x = self.data.times
343 self.x = self.data.times
344 self.y = self.data.yrange
344 self.y = self.data.yrange
345 self.z = self.data['param']
345 self.z = self.data['param']
346 self.z = 10*numpy.log10(self.z)
346 self.z = 10*numpy.log10(self.z)
347 self.z = numpy.ma.masked_invalid(self.z)
347 self.z = numpy.ma.masked_invalid(self.z)
348
348
349 if self.decimation is None:
349 if self.decimation is None:
350 x, y, z = self.fill_gaps(self.x, self.y, self.z)
350 x, y, z = self.fill_gaps(self.x, self.y, self.z)
351 else:
351 else:
352 x, y, z = self.fill_gaps(*self.decimate())
352 x, y, z = self.fill_gaps(*self.decimate())
353
353
354 for n, ax in enumerate(self.axes):
354 for n, ax in enumerate(self.axes):
355
355
356 self.zmax = self.zmax if self.zmax is not None else numpy.max(
356 self.zmax = self.zmax if self.zmax is not None else numpy.max(
357 self.z[n])
357 self.z[n])
358 self.zmin = self.zmin if self.zmin is not None else numpy.min(
358 self.zmin = self.zmin if self.zmin is not None else numpy.min(
359 self.z[n])
359 self.z[n])
360
360
361 if ax.firsttime:
361 if ax.firsttime:
362 if self.zlimits is not None:
362 if self.zlimits is not None:
363 self.zmin, self.zmax = self.zlimits[n]
363 self.zmin, self.zmax = self.zlimits[n]
364
364
365 ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
365 ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
366 vmin=self.zmin,
366 vmin=self.zmin,
367 vmax=self.zmax,
367 vmax=self.zmax,
368 cmap=self.cmaps[n]
368 cmap=self.cmaps[n]
369 )
369 )
370 else:
370 else:
371 if self.zlimits is not None:
371 if self.zlimits is not None:
372 self.zmin, self.zmax = self.zlimits[n]
372 self.zmin, self.zmax = self.zlimits[n]
373 ax.collections.remove(ax.collections[0])
373 ax.collections.remove(ax.collections[0])
374 ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
374 ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
375 vmin=self.zmin,
375 vmin=self.zmin,
376 vmax=self.zmax,
376 vmax=self.zmax,
377 cmap=self.cmaps[n]
377 cmap=self.cmaps[n]
378 )
378 )
379
379
380
380
381 class PolarMapPlot(Plot):
381 class PolarMapPlot(Plot):
382 '''
382 '''
383 Plot for weather radar
383 Plot for weather radar
384 '''
384 '''
385
385
386 CODE = 'param'
386 CODE = 'param'
387 colormap = 'seismic'
387 colormap = 'seismic'
388
388
389 def setup(self):
389 def setup(self):
390 self.ncols = 1
390 self.ncols = 1
391 self.nrows = 1
391 self.nrows = 1
392 self.width = 9
392 self.width = 9
393 self.height = 8
393 self.height = 8
394 self.mode = self.data.meta['mode']
394 self.mode = self.data.meta['mode']
395 if self.channels is not None:
395 if self.channels is not None:
396 self.nplots = len(self.channels)
396 self.nplots = len(self.channels)
397 self.nrows = len(self.channels)
397 self.nrows = len(self.channels)
398 else:
398 else:
399 self.nplots = self.data.shape(self.CODE)[0]
399 self.nplots = self.data.shape(self.CODE)[0]
400 self.nrows = self.nplots
400 self.nrows = self.nplots
401 self.channels = list(range(self.nplots))
401 self.channels = list(range(self.nplots))
402 if self.mode == 'E':
402 if self.mode == 'E':
403 self.xlabel = 'Longitude'
403 self.xlabel = 'Longitude'
404 self.ylabel = 'Latitude'
404 self.ylabel = 'Latitude'
405 else:
405 else:
406 self.xlabel = 'Range (km)'
406 self.xlabel = 'Range (km)'
407 self.ylabel = 'Height (km)'
407 self.ylabel = 'Height (km)'
408 self.bgcolor = 'white'
408 self.bgcolor = 'white'
409 self.cb_labels = self.data.meta['units']
409 self.cb_labels = self.data.meta['units']
410 self.lat = self.data.meta['latitude']
410 self.lat = self.data.meta['latitude']
411 self.lon = self.data.meta['longitude']
411 self.lon = self.data.meta['longitude']
412 self.xmin, self.xmax = float(
412 self.xmin, self.xmax = float(
413 km2deg(self.xmin) + self.lon), float(km2deg(self.xmax) + self.lon)
413 km2deg(self.xmin) + self.lon), float(km2deg(self.xmax) + self.lon)
414 self.ymin, self.ymax = float(
414 self.ymin, self.ymax = float(
415 km2deg(self.ymin) + self.lat), float(km2deg(self.ymax) + self.lat)
415 km2deg(self.ymin) + self.lat), float(km2deg(self.ymax) + self.lat)
416 # self.polar = True
416 # self.polar = True
417
417
418 def plot(self):
418 def plot(self):
419
419
420 for n, ax in enumerate(self.axes):
420 for n, ax in enumerate(self.axes):
421 data = self.data['param'][self.channels[n]]
421 data = self.data['param'][self.channels[n]]
422
422
423 zeniths = numpy.linspace(
423 zeniths = numpy.linspace(
424 0, self.data.meta['max_range'], data.shape[1])
424 0, self.data.meta['max_range'], data.shape[1])
425 if self.mode == 'E':
425 if self.mode == 'E':
426 azimuths = -numpy.radians(self.data.yrange)+numpy.pi/2
426 azimuths = -numpy.radians(self.data.yrange)+numpy.pi/2
427 r, theta = numpy.meshgrid(zeniths, azimuths)
427 r, theta = numpy.meshgrid(zeniths, azimuths)
428 x, y = r*numpy.cos(theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])), r*numpy.sin(
428 x, y = r*numpy.cos(theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])), r*numpy.sin(
429 theta)*numpy.cos(numpy.radians(self.data.meta['elevation']))
429 theta)*numpy.cos(numpy.radians(self.data.meta['elevation']))
430 x = km2deg(x) + self.lon
430 x = km2deg(x) + self.lon
431 y = km2deg(y) + self.lat
431 y = km2deg(y) + self.lat
432 else:
432 else:
433 azimuths = numpy.radians(self.data.yrange)
433 azimuths = numpy.radians(self.data.yrange)
434 r, theta = numpy.meshgrid(zeniths, azimuths)
434 r, theta = numpy.meshgrid(zeniths, azimuths)
435 x, y = r*numpy.cos(theta), r*numpy.sin(theta)
435 x, y = r*numpy.cos(theta), r*numpy.sin(theta)
436 self.y = zeniths
436 self.y = zeniths
437
437
438 if ax.firsttime:
438 if ax.firsttime:
439 if self.zlimits is not None:
439 if self.zlimits is not None:
440 self.zmin, self.zmax = self.zlimits[n]
440 self.zmin, self.zmax = self.zlimits[n]
441 ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)),
441 ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)),
442 x, y, numpy.ma.array(data, mask=numpy.isnan(data)),
442 x, y, numpy.ma.array(data, mask=numpy.isnan(data)),
443 vmin=self.zmin,
443 vmin=self.zmin,
444 vmax=self.zmax,
444 vmax=self.zmax,
445 cmap=self.cmaps[n])
445 cmap=self.cmaps[n])
446 else:
446 else:
447 if self.zlimits is not None:
447 if self.zlimits is not None:
448 self.zmin, self.zmax = self.zlimits[n]
448 self.zmin, self.zmax = self.zlimits[n]
449 ax.collections.remove(ax.collections[0])
449 ax.collections.remove(ax.collections[0])
450 ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)),
450 ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)),
451 x, y, numpy.ma.array(data, mask=numpy.isnan(data)),
451 x, y, numpy.ma.array(data, mask=numpy.isnan(data)),
452 vmin=self.zmin,
452 vmin=self.zmin,
453 vmax=self.zmax,
453 vmax=self.zmax,
454 cmap=self.cmaps[n])
454 cmap=self.cmaps[n])
455
455
456 if self.mode == 'A':
456 if self.mode == 'A':
457 continue
457 continue
458
458
459 # plot district names
459 # plot district names
460 f = open('/data/workspace/schain_scripts/distrito.csv')
460 f = open('/data/workspace/schain_scripts/distrito.csv')
461 for line in f:
461 for line in f:
462 label, lon, lat = [s.strip() for s in line.split(',') if s]
462 label, lon, lat = [s.strip() for s in line.split(',') if s]
463 lat = float(lat)
463 lat = float(lat)
464 lon = float(lon)
464 lon = float(lon)
465 # ax.plot(lon, lat, '.b', ms=2)
465 # ax.plot(lon, lat, '.b', ms=2)
466 ax.text(lon, lat, label.decode('utf8'), ha='center',
466 ax.text(lon, lat, label.decode('utf8'), ha='center',
467 va='bottom', size='8', color='black')
467 va='bottom', size='8', color='black')
468
468
469 # plot limites
469 # plot limites
470 limites = []
470 limites = []
471 tmp = []
471 tmp = []
472 for line in open('/data/workspace/schain_scripts/lima.csv'):
472 for line in open('/data/workspace/schain_scripts/lima.csv'):
473 if '#' in line:
473 if '#' in line:
474 if tmp:
474 if tmp:
475 limites.append(tmp)
475 limites.append(tmp)
476 tmp = []
476 tmp = []
477 continue
477 continue
478 values = line.strip().split(',')
478 values = line.strip().split(',')
479 tmp.append((float(values[0]), float(values[1])))
479 tmp.append((float(values[0]), float(values[1])))
480 for points in limites:
480 for points in limites:
481 ax.add_patch(
481 ax.add_patch(
482 Polygon(points, ec='k', fc='none', ls='--', lw=0.5))
482 Polygon(points, ec='k', fc='none', ls='--', lw=0.5))
483
483
484 # plot Cuencas
484 # plot Cuencas
485 for cuenca in ('rimac', 'lurin', 'mala', 'chillon', 'chilca', 'chancay-huaral'):
485 for cuenca in ('rimac', 'lurin', 'mala', 'chillon', 'chilca', 'chancay-huaral'):
486 f = open('/data/workspace/schain_scripts/{}.csv'.format(cuenca))
486 f = open('/data/workspace/schain_scripts/{}.csv'.format(cuenca))
487 values = [line.strip().split(',') for line in f]
487 values = [line.strip().split(',') for line in f]
488 points = [(float(s[0]), float(s[1])) for s in values]
488 points = [(float(s[0]), float(s[1])) for s in values]
489 ax.add_patch(Polygon(points, ec='b', fc='none'))
489 ax.add_patch(Polygon(points, ec='b', fc='none'))
490
490
491 # plot grid
491 # plot grid
492 for r in (15, 30, 45, 60):
492 for r in (15, 30, 45, 60):
493 ax.add_artist(plt.Circle((self.lon, self.lat),
493 ax.add_artist(plt.Circle((self.lon, self.lat),
494 km2deg(r), color='0.6', fill=False, lw=0.2))
494 km2deg(r), color='0.6', fill=False, lw=0.2))
495 ax.text(
495 ax.text(
496 self.lon + (km2deg(r))*numpy.cos(60*numpy.pi/180),
496 self.lon + (km2deg(r))*numpy.cos(60*numpy.pi/180),
497 self.lat + (km2deg(r))*numpy.sin(60*numpy.pi/180),
497 self.lat + (km2deg(r))*numpy.sin(60*numpy.pi/180),
498 '{}km'.format(r),
498 '{}km'.format(r),
499 ha='center', va='bottom', size='8', color='0.6', weight='heavy')
499 ha='center', va='bottom', size='8', color='0.6', weight='heavy')
500
500
501 if self.mode == 'E':
501 if self.mode == 'E':
502 title = 'El={}$^\circ$'.format(self.data.meta['elevation'])
502 title = 'El={}$^\circ$'.format(self.data.meta['elevation'])
503 label = 'E{:02d}'.format(int(self.data.meta['elevation']))
503 label = 'E{:02d}'.format(int(self.data.meta['elevation']))
504 else:
504 else:
505 title = 'Az={}$^\circ$'.format(self.data.meta['azimuth'])
505 title = 'Az={}$^\circ$'.format(self.data.meta['azimuth'])
506 label = 'A{:02d}'.format(int(self.data.meta['azimuth']))
506 label = 'A{:02d}'.format(int(self.data.meta['azimuth']))
507
507
508 self.save_labels = ['{}-{}'.format(lbl, label) for lbl in self.labels]
508 self.save_labels = ['{}-{}'.format(lbl, label) for lbl in self.labels]
509 self.titles = ['{} {}'.format(
509 self.titles = ['{} {}'.format(
510 self.data.parameters[x], title) for x in self.channels]
510 self.data.parameters[x], title) for x in self.channels]
511
511
512 class WeatherParamsPlot(Plot):
512 class WeatherParamsPlot(Plot):
513 #CODE = 'RHI'
513 #CODE = 'RHI'
514 #plot_name = 'RHI'
514 #plot_name = 'RHI'
515 plot_type = 'scattermap'
515 plot_type = 'scattermap'
516 buffering = False
516 buffering = False
517
517
518 def setup(self):
518 def setup(self):
519
519
520 self.ncols = 1
520 self.ncols = 1
521 self.nrows = 1
521 self.nrows = 1
522 self.nplots= 1
522 self.nplots= 1
523 self.ylabel= 'Range [km]'
523 self.ylabel= 'Range [km]'
524 self.xlabel= 'Range [km]'
524 self.xlabel= 'Range [km]'
525 self.polar = True
525 self.polar = True
526 self.grid = True
526 self.grid = True
527 if self.channels is not None:
527 if self.channels is not None:
528 self.nplots = len(self.channels)
528 self.nplots = len(self.channels)
529 self.nrows = len(self.channels)
529 self.ncols = len(self.channels)
530 else:
530 else:
531 self.nplots = self.data.shape(self.CODE)[0]
531 self.nplots = self.data.shape(self.CODE)[0]
532 self.nrows = self.nplots
532 self.ncols = self.nplots
533 self.channels = list(range(self.nplots))
533 self.channels = list(range(self.nplots))
534
534
535 self.colorbar=True
535 self.colorbar=True
536 if len(self.channels)>1:
537 self.width = 12
538 else:
536 self.width =8
539 self.width =8
537 self.height =8
540 self.height =8
538 self.ini =0
541 self.ini =0
539 self.len_azi =0
542 self.len_azi =0
540 self.buffer_ini = None
543 self.buffer_ini = None
541 self.buffer_ele = None
544 self.buffer_ele = None
542 self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08})
545 self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08})
543 self.flag =0
546 self.flag =0
544 self.indicador= 0
547 self.indicador= 0
545 self.last_data_ele = None
548 self.last_data_ele = None
546 self.val_mean = None
549 self.val_mean = None
547
550
548 def update(self, dataOut):
551 def update(self, dataOut):
549
552
550 vars = {
553 vars = {
551 'S' : 0,
554 'S' : 0,
552 'V' : 1,
555 'V' : 1,
553 'W' : 2,
556 'W' : 2,
554 'SNR' : 3,
557 'SNR' : 3,
555 'Z' : 4,
558 'Z' : 4,
556 'D' : 5,
559 'D' : 5,
557 'P' : 6,
560 'P' : 6,
558 'R' : 7,
561 'R' : 7,
559 }
562 }
560
563
561 data = {}
564 data = {}
562 meta = {}
565 meta = {}
563
566
564 if hasattr(dataOut, 'nFFTPoints'):
567 if hasattr(dataOut, 'nFFTPoints'):
565 factor = dataOut.normFactor
568 factor = dataOut.normFactor
566 else:
569 else:
567 factor = 1
570 factor = 1
568
571
569 if hasattr(dataOut, 'dparam'):
572 if hasattr(dataOut, 'dparam'):
570 tmp = getattr(dataOut, 'data_param')
573 tmp = getattr(dataOut, 'data_param')
571 else:
574 else:
572
575
573 if 'S' in self.attr_data[0]:
576 if 'S' in self.attr_data[0]:
574 tmp = 10*numpy.log10(10.0*getattr(dataOut, 'data_param')[:,0,:]/(factor))
577 tmp = 10*numpy.log10(10.0*getattr(dataOut, 'data_param')[:,0,:]/(factor))
575 else:
578 else:
576 tmp = getattr(dataOut, 'data_param')[:,vars[self.attr_data[0]],:]
579 tmp = getattr(dataOut, 'data_param')[:,vars[self.attr_data[0]],:]
577
580
578 if self.mask:
581 if self.mask:
579 mask = dataOut.data_param[:,3,:] < self.mask
582 mask = dataOut.data_param[:,3,:] < self.mask
580 tmp = numpy.ma.masked_array(tmp, mask=mask)
583 tmp = numpy.ma.masked_array(tmp, mask=mask)
581
584
582 r = dataOut.heightList
585 r = dataOut.heightList
583 delta_height = r[1]-r[0]
586 delta_height = r[1]-r[0]
584 valid = numpy.where(r>=0)[0]
587 valid = numpy.where(r>=0)[0]
585 data['r'] = numpy.arange(len(valid))*delta_height
588 data['r'] = numpy.arange(len(valid))*delta_height
586
589
587 try:
590 data['data'] = [0, 0]
588 data['data'] = tmp[self.channels[0]][:,valid]
591
589 except:
592 #try:
590 data['data'] = tmp[0][:,valid]
593 data['data'][0] = tmp[0][:,valid]
594 data['data'][1] = tmp[1][:,valid]
595 #except:
596 # data['data'] = tmp[0][:,valid]
591
597
592 if dataOut.mode_op == 'PPI':
598 if dataOut.mode_op == 'PPI':
593 self.CODE = 'PPI'
599 self.CODE = 'PPI'
594 self.title = self.CODE
600 self.title = self.CODE
595 elif dataOut.mode_op == 'RHI':
601 elif dataOut.mode_op == 'RHI':
596 self.CODE = 'RHI'
602 self.CODE = 'RHI'
597 self.title = self.CODE
603 self.title = self.CODE
598
604
599 data['azi'] = dataOut.data_azi
605 data['azi'] = dataOut.data_azi
600 data['ele'] = dataOut.data_ele
606 data['ele'] = dataOut.data_ele
601 data['mode_op'] = dataOut.mode_op
607 data['mode_op'] = dataOut.mode_op
602 self.mode = dataOut.mode_op
608 self.mode = dataOut.mode_op
603 var = data['data'].flatten()
609 var = data['data'][0].flatten()
604 r = numpy.tile(data['r'], data['data'].shape[0])
610 r = numpy.tile(data['r'], data['data'][0].shape[0])
605 az = numpy.repeat(data['azi'], data['data'].shape[1])
611 az = numpy.repeat(data['azi'], data['data'][0].shape[1])
606 el = numpy.repeat(data['ele'], data['data'].shape[1])
612 el = numpy.repeat(data['ele'], data['data'][0].shape[1])
607
613
608 # lla = georef.spherical_to_proj(r, data['azi'], data['ele'], (-75.295893, -12.040436, 3379.2147))
614 # lla = georef.spherical_to_proj(r, data['azi'], data['ele'], (-75.295893, -12.040436, 3379.2147))
609
615
610 latlon = antenna_to_geographic(r, az, el, (-75.295893, -12.040436))
616 latlon = antenna_to_geographic(r, az, el, (-75.295893, -12.040436))
611
617
612 if self.mask:
618 if self.mask:
613 meta['lat'] = latlon[1][var.mask==False]
619 meta['lat'] = latlon[1][var.mask==False]
614 meta['lon'] = latlon[0][var.mask==False]
620 meta['lon'] = latlon[0][var.mask==False]
615 data['var'] = numpy.array([var[var.mask==False]])
621 data['var'] = numpy.array([var[var.mask==False]])
616 else:
622 else:
617 meta['lat'] = latlon[1]
623 meta['lat'] = latlon[1]
618 meta['lon'] = latlon[0]
624 meta['lon'] = latlon[0]
619 data['var'] = numpy.array([var])
625 data['var'] = numpy.array([var])
620
626
621 return data, meta
627 return data, meta
622
628
623 def plot(self):
629 def plot(self):
624 data = self.data[-1]
630 data = self.data[-1]
625 z = data['data']
631 z = data['data']
626 r = data['r']
632 r = data['r']
627 self.titles = []
633 self.titles = []
628
634
629 self.ymax = self.ymax if self.ymax else numpy.nanmax(r)
635 self.ymax = self.ymax if self.ymax else numpy.nanmax(r)
630 self.ymin = self.ymin if self.ymin else numpy.nanmin(r)
636 self.ymin = self.ymin if self.ymin else numpy.nanmin(r)
631 self.zmax = self.zmax if self.zmax else numpy.nanmax(z)
637 self.zmax = self.zmax if self.zmax else numpy.nanmax(z)
632 self.zmin = self.zmin if self.zmin is not None else numpy.nanmin(z)
638 self.zmin = self.zmin if self.zmin is not None else numpy.nanmin(z)
633
639
634 if isinstance(data['mode_op'], bytes):
640 if isinstance(data['mode_op'], bytes):
635 data['mode_op'] = data['mode_op'].decode()
641 data['mode_op'] = data['mode_op'].decode()
636
642
637 if data['mode_op'] == 'RHI':
643 if data['mode_op'] == 'RHI':
638 try:
644 try:
639 if self.data['mode_op'][-2] == 'PPI':
645 if self.data['mode_op'][-2] == 'PPI':
640 self.ang_min = None
646 self.ang_min = None
641 self.ang_max = None
647 self.ang_max = None
642 except:
648 except:
643 pass
649 pass
644 self.ang_min = self.ang_min if self.ang_min else 0
650 self.ang_min = self.ang_min if self.ang_min else 0
645 self.ang_max = self.ang_max if self.ang_max else 90
651 self.ang_max = self.ang_max if self.ang_max else 90
646 r, theta = numpy.meshgrid(r, numpy.radians(data['ele']) )
652 r, theta = numpy.meshgrid(r, numpy.radians(data['ele']) )
647 elif data['mode_op'] == 'PPI':
653 elif data['mode_op'] == 'PPI':
648 try:
654 try:
649 if self.data['mode_op'][-2] == 'RHI':
655 if self.data['mode_op'][-2] == 'RHI':
650 self.ang_min = None
656 self.ang_min = None
651 self.ang_max = None
657 self.ang_max = None
652 except:
658 except:
653 pass
659 pass
654 self.ang_min = self.ang_min if self.ang_min else 0
660 self.ang_min = self.ang_min if self.ang_min else 0
655 self.ang_max = self.ang_max if self.ang_max else 360
661 self.ang_max = self.ang_max if self.ang_max else 360
656 r, theta = numpy.meshgrid(r, numpy.radians(data['azi']) )
662 r, theta = numpy.meshgrid(r, numpy.radians(data['azi']) )
657
663
658 self.clear_figures()
664 self.clear_figures()
659
665
660 for i,ax in enumerate(self.axes):
666 for i,ax in enumerate(self.axes):
661
667
662 if ax.firsttime:
668 if ax.firsttime:
663 ax.set_xlim(numpy.radians(self.ang_min),numpy.radians(self.ang_max))
669 ax.set_xlim(numpy.radians(self.ang_min),numpy.radians(self.ang_max))
664 ax.plt = ax.pcolormesh(theta, r, z, cmap=self.colormap, vmin=self.zmin, vmax=self.zmax)
670 ax.plt = ax.pcolormesh(theta, r, z[i], cmap=self.colormap, vmin=self.zmin, vmax=self.zmax)
665 if data['mode_op'] == 'PPI':
671 if data['mode_op'] == 'PPI':
666 ax.set_theta_direction(-1)
672 ax.set_theta_direction(-1)
667 ax.set_theta_offset(numpy.pi/2)
673 ax.set_theta_offset(numpy.pi/2)
668
674
669 else:
675 else:
670 ax.set_xlim(numpy.radians(self.ang_min),numpy.radians(self.ang_max))
676 ax.set_xlim(numpy.radians(self.ang_min),numpy.radians(self.ang_max))
671 ax.plt = ax.pcolormesh(theta, r, z, cmap=self.colormap, vmin=self.zmin, vmax=self.zmax)
677 ax.plt = ax.pcolormesh(theta, r, z[i], cmap=self.colormap, vmin=self.zmin, vmax=self.zmax)
672 if data['mode_op'] == 'PPI':
678 if data['mode_op'] == 'PPI':
673 ax.set_theta_direction(-1)
679 ax.set_theta_direction(-1)
674 ax.set_theta_offset(numpy.pi/2)
680 ax.set_theta_offset(numpy.pi/2)
675
681
676 ax.grid(True)
682 ax.grid(True)
677 if data['mode_op'] == 'RHI':
683 if data['mode_op'] == 'RHI':
678 len_aux = int(data['azi'].shape[0]/4)
684 len_aux = int(data['azi'].shape[0]/4)
679 mean = numpy.mean(data['azi'][len_aux:-len_aux])
685 mean = numpy.mean(data['azi'][len_aux:-len_aux])
680 if len(self.channels) !=1:
686 if len(self.channels) !=1:
681 self.titles = ['RHI {} at AZ: {} CH {}'.format(self.labels[x], str(round(mean,1)), x) for x in range(self.nrows)]
687 self.titles = ['RHI {} at AZ: {} CH {}'.format(self.labels[x], str(round(mean,1)), x) for x in self.channels]
682 else:
688 else:
683 self.titles = ['RHI {} at AZ: {} CH {}'.format(self.labels[0], str(round(mean,1)), self.channels[0])]
689 self.titles = ['RHI {} at AZ: {} CH {}'.format(self.labels[0], str(round(mean,1)), self.channels[0])]
684 elif data['mode_op'] == 'PPI':
690 elif data['mode_op'] == 'PPI':
685 len_aux = int(data['ele'].shape[0]/4)
691 len_aux = int(data['ele'].shape[0]/4)
686 mean = numpy.mean(data['ele'][len_aux:-len_aux])
692 mean = numpy.mean(data['ele'][len_aux:-len_aux])
687 if len(self.channels) !=1:
693 if len(self.channels) !=1:
688 self.titles = ['PPI {} at EL: {} CH {}'.format(self.labels[x], str(round(mean,1)), x) for x in range(self.nrows)]
694 self.titles = ['PPI {} at EL: {} CH {}'.format(self.labels[x], str(round(mean,1)), x) for x in self.channels]
689 else:
695 else:
690 self.titles = ['PPI {} at EL: {} CH {}'.format(self.labels[0], str(round(mean,1)), self.channels[0])]
696 self.titles = ['PPI {} at EL: {} CH {}'.format(self.labels[0], str(round(mean,1)), self.channels[0])]
691 self.mode_value = round(mean,1) No newline at end of file
697 self.mode_value = round(mean,1)
General Comments 0
You need to be logged in to leave comments. Login now