forked from pyrocko/contrib-snufflings
-
Notifications
You must be signed in to change notification settings - Fork 0
/
beam_stack.py
325 lines (282 loc) · 12.2 KB
/
beam_stack.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
from __future__ import print_function
from pyrocko.gui.snuffling import Snuffling, Param, Switch, Choice
from pyrocko.model import Station, dump_stations
from pyrocko import orthodrome as ortho
from pyrocko import util, io, trace
import numpy as num
from matplotlib.colorbar import ColorbarBase
from matplotlib.colors import Normalize
from collections import defaultdict
from matplotlib import cm
r_earth = 6371000.785
torad = num.pi/180.
onedeg = r_earth*torad
def to_cartesian(items, reflatlon):
res = defaultdict()
for i, item in enumerate(items):
y, x = ortho.latlon_to_ne(reflatlon, item)
depth = item.depth
elevation = item.elevation
dz = elevation - depth
lat = item.lat/180.*num.pi
z = r_earth+dz*num.sin(lat)
res[item] = (x, y, z)
return res
class BeamForming(Snuffling):
'''
<html>
<body>
<h1>Beam Forming</h1>
Mark a time frame using extended markers. If no time window is selected,
all traces currently visible in the viewer will be stacked.<br>
Set a backzimuth and horizontal slowness of the passing wave field.<br>
If no reference point is defined by 'Center lat' and 'Center lon'
sliders the geographical center is calculated by taking the average of
latitudes and longitudes. This can also be done by pressing <b>Set center by
mean lat/lon</b><br>
If sampling rates differ and <b>Tread different dt by</b> is set to <b>downsample</b>,
traces with highest sampling rates (smalles deltat) will be stacked, first.
The current stacked trace will then be downsampled to match the next lower sampling
rate. The stacking will proceed until another lower sampling rate is found, etc.<br>
Else, if <b>Tread different dt by</b> is set to <b>oversample</b> all traces will by
resampled in the frequency domain to match the highest occurring sampling rate.
<p>
<b>Tread different dt by </b> - oversample:|downsample.<br>
<b>pre-normalize by std</b> - normalize traces using their standard deviation.<br>
<b>multiply 1/[no. of traces]</b> - stacked trace's will be normalized by
the number of summed traces.<br>
<b>Add Shifted Trace</b> - Add time shifted traces to the viewer.<br>
<b>Plot</b> - map station distribution together with the applied time shifts. The
arrow indicates the applied back azimuth. Grey dots indicate stations which have not
been considered in the stacking.</br>
<b>Save Traces</b> - write stacked traces to mseed file.
</p>
</body>
</html>
'''
def setup(self):
self.set_name("Beam Forming")
self.add_parameter(Param('Center lat', 'lat_c', 90., -90., 90.,
high_is_none=True))
self.add_parameter(Param('Center lon', 'lon_c', 180., -180., 180.,
high_is_none=True))
self.add_parameter(Param('Back azimuth', 'bazi', 0., 0., 360.))
self.add_parameter(Param('slowness', 'slow', 0.1, 0., 1.))
self.add_parameter(Choice('slowness unit', 'unit', 's/km',['s/km',
's/deg']))
self.add_parameter(Choice('Treat different dt by', 'diff_dt_treat',
'oversample',['oversample', 'downsample']))
self.add_parameter(Switch('pre-normalize by std ', 'normalize_std', False))
self.add_parameter(Switch('multiply 1/[no. of traces]', 'post_normalize', False))
self.add_parameter(Switch('Add Shifted Traces', 'add_shifted', False))
self.add_trigger('plot', self.plot)
self.add_trigger('Save Station', self.save_station)
self.add_trigger('Save Traces', self.save)
self.add_trigger('Set center by mean lat/lon', self.set_center_latlon)
self.station_c = None
self.z_c = None
self.stacked_traces = None
def panel_visibility_changed(self, bool):
if bool:
viewer = self.get_viewer()
self._param_controls['unit'].choosen.connect(
self.set_slowness_ranges)
def set_slowness_ranges(self, ident, state):
if state == 's/km':
self.set_parameter_range('slow', 0, 1)
self.set_parameter('slow', self.slow/onedeg*1000.)
elif state == 's/deg':
self.set_parameter_range('slow', 0, 100)
self.set_parameter('slow', self.slow/1000.*onedeg)
def call(self):
self.cleanup()
c_station_id = ('_', 'STK')
if self.unit == 's/deg':
slow_factor = 1./onedeg
elif self.unit == 's/km':
slow_factor = 1./1000.
slow = self.slow*slow_factor
if self.stacked_traces is not None:
self.add_traces(self.stacked_traces)
viewer = self.get_viewer()
if self.station_c:
viewer.stations.pop(c_station_id)
stations = self.get_stations()
if len(stations) == 0:
self.fail('No station meta information found')
traces = list(self.chopper_selected_traces(fallback=True))
traces = [tr for trs in traces for tr in trs ]
visible_nslcs = [tr.nslc_id for tr in traces]
stations = [x for x in stations if util.match_nslcs(
"%s.%s.%s.*" % x.nsl(), visible_nslcs)]
if not self.lat_c or not self.lon_c or not self.z_c:
self.lat_c, self.lon_c, self.z_c = self.center_lat_lon(stations)
self.set_parameter('lat_c', self.lat_c)
self.set_parameter('lon_c', self.lon_c)
self.station_c = Station(lat=float(self.lat_c),
lon=float(self.lon_c),
elevation=float(self.z_c),
depth=0.,
name='Array Center',
network=c_station_id[0],
station=c_station_id[1])
viewer.add_stations([self.station_c])
lat0 = num.array([self.lat_c]*len(stations))
lon0 = num.array([self.lon_c]*len(stations))
lats = num.array([s.lat for s in stations])
lons = num.array([s.lon for s in stations])
ns, es = ortho.latlon_to_ne_numpy(lat0, lon0, lats, lons)
theta = num.float(self.bazi*num.pi/180.)
R = num.array([[num.cos(theta), -num.sin(theta)],
[num.sin(theta), num.cos(theta)]])
distances = R.dot(num.vstack((es, ns)))[1]
channels = set()
self.stacked = {}
num_stacked = {}
self.t_shifts = {}
shifted_traces = []
taperer = trace.CosFader(xfrac=0.05)
if self.diff_dt_treat=='downsample':
traces.sort(key=lambda x: x.deltat)
elif self.diff_dt_treat=='oversample':
dts = [t.deltat for t in traces]
for tr in traces:
tr.resample(min(dts))
for tr in traces:
if tr.nslc_id[:2] == c_station_id:
continue
tr = tr.copy(data=True)
tr.ydata = tr.ydata.astype(num.float64)
tr.ydata -= tr.ydata.mean(dtype=num.float64)
tr.taper(taperer)
try:
stack_trace = self.stacked[tr.channel]
num_stacked[tr.channel] += 1
except KeyError:
stack_trace = tr.copy(data=True)
stack_trace.set_ydata(num.zeros(
len(stack_trace.get_ydata())))
stack_trace.set_codes(network=c_station_id[0],
station=c_station_id[1],
location='',
channel=tr.channel)
self.stacked[tr.channel] = stack_trace
channels.add(tr.channel)
num_stacked[tr.channel] = 1
nslc_id = tr.nslc_id
try:
stats = [x for x in stations if util.match_nslc(
'%s.%s.%s.*' % x.nsl(), nslc_id)]
stat = stats[0]
except IndexError:
break
i = stations.index(stat)
d = distances[i]
t_shift = d*slow
tr.shift(t_shift)
stat = viewer.get_station(tr.nslc_id[:2])
self.t_shifts[stat] = t_shift
if self.normalize_std:
tr.ydata = tr.ydata/tr.ydata.std()
if num.abs(tr.deltat-stack_trace.deltat)>0.000001:
if self.diff_dt_treat=='downsample':
stack_trace.downsample_to(tr.deltat)
elif self.diff_dt_treat=='upsample':
print('something went wrong with the upsampling, previously')
stack_trace.add(tr)
if self.add_shifted:
tr.set_station('%s_s' % tr.station)
shifted_traces.append(tr)
if self.post_normalize:
for ch, tr in self.stacked.items():
tr.set_ydata(tr.get_ydata()/num_stacked[ch])
self.cleanup()
for ch, tr in self.stacked.items():
if num_stacked[ch]>1:
self.add_trace(tr)
if self.add_shifted:
self.add_traces(shifted_traces)
def center_lat_lon(self, stations):
'''Calculate a mean geographical centre of the array
using spherical earth'''
lats = num.zeros(len(stations))
lons = num.zeros(len(stations))
elevations = num.zeros(len(stations))
depths = num.zeros(len(stations))
for i, s in enumerate(stations):
lats[i] = s.lat*torad
lons[i] = s.lon*torad
depths[i] = s.depth
elevations[i] = s.elevation
z = num.mean(elevations-depths)
return (lats.mean()*180/num.pi, lons.mean()*180/num.pi, z)
def plot(self):
stations = self.get_stations()
res = to_cartesian(stations, self.station_c)
center_xyz = res[self.station_c]
x = num.zeros(len(res))
y = num.zeros(len(res))
z = num.zeros(len(res))
sizes = num.zeros(len(res))
stat_labels = []
i = 0
for s, xyz in res.items():
x[i] = xyz[0]
y[i] = xyz[1]
z[i] = xyz[2]
try:
sizes[i] = self.t_shifts[s]
stat_labels.append('%s' % (s.nsl_string()))
except AttributeError:
self.fail('Run the snuffling first')
except KeyError:
stat_labels.append('%s' % (s.nsl_string()))
continue
finally:
i += 1
x /= 1000.
y /= 1000.
z /= 1000.
xmax = x.max()
xmin = x.min()
ymax = y.max()
ymin = y.min()
x_range = num.abs(xmax-xmin)
y_range = num.abs(ymax-ymin)
max_range = num.max([x_range, y_range])
fig = self.pylab(get='figure')
cax = fig.add_axes([0.85, 0.2, 0.05, 0.5])
ax = fig.add_axes([0.10, 0.1, 0.70, 0.7])
ax.set_aspect('equal')
cmap = cm.get_cmap('bwr')
ax.scatter(x, y, c=sizes, s=200, cmap=cmap,
vmax=num.max(sizes), vmin=-num.max(sizes))
for i, lab in enumerate(stat_labels):
ax.text(x[i], y[i], lab, size=14)
x = x[num.where(sizes==0.)]
y = y[num.where(sizes==0.)]
ax.scatter(x, y, c='black', alpha=0.4, s=200)
ax.arrow(center_xyz[0]/1000.,
center_xyz[1]/1000.,
-num.sin(self.bazi/180.*num.pi),
-num.cos(self.bazi/180.*num.pi),
head_width=0.2,
head_length=0.2)
ax.set_ylabel("N-S [km]")
ax.set_xlabel("E-W [km]")
ColorbarBase(cax, cmap=cmap,
norm=Normalize(vmin=sizes.min(), vmax=sizes.max()))
fig.canvas.draw()
def save(self):
default_fn = 'BeamTraces_baz%s_slow%s.mseed' % (self.bazi, self.slow)
fn = self.output_filename('Template for output files', default_fn)
io.save((self.stacked.values()), fn)
def set_center_latlon(self):
self.lat_c, self.lon_c, self.z_c = self.center_lat_lon(self.get_stations())
self.set_parameter('lat_c', self.lat_c)
self.set_parameter('lon_c', self.lon_c)
def save_station(self):
fn = self.output_filename('Save Station')
dump_stations([self.station_c], fn)
def __snufflings__():
return [BeamForming()]