forked from RMiftakhov/EFramework
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathvisualization_helpers.py
441 lines (376 loc) · 18.8 KB
/
visualization_helpers.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
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
from random import randint
import plotly.express as px
import streamlit as st
import numpy as np
from utils import fspectra, signaltonoise_dB, signaltonoise
import plotly.graph_objects as go
import tempfile
from streamlit_image_comparison import image_comparison
class VISUALIZATION:
"""The class for 2D/3D visualization
includes main ui implemented in streamlit
as viz_seismic_3D, viz_seismic_2D and viz_sidebyside_3D
"""
def __init__(self, _seismic_data, seismic_type):
self._seismic_type = seismic_type
self._vm = _seismic_data.get_vm()
self._n_samples = _seismic_data.get_n_zslices()
self._n_il = _seismic_data.get_n_ilines()
self._n_xl = _seismic_data.get_n_xlines()
self._sample_rate = _seismic_data.get_sample_rate()
self._cmap_option = "RdBu"
def plot_slice(self, data, indx_old, indx_new, last_section, cmap, vmin, vmax):
"""The function that calculated which
direction to visualize and viz it
Args:
data (SeismicData): Data to viz
indx_old (int): Previous viz control
indx_new (int): Current viz control
last_section (int): Previous viz section 0,1, or 2
cmap (str): Colormap from px.colors.named_colorscales()
vmin (float): min range
vmax (float): max range
Returns:
_type_: returns seis - current section, seis_plt - viz plot,
indx_old - updated control, last_section - viz section
"""
seis = np.array([[[255, 0, 0], [0, 255, 0], [0, 0, 255]],
[[0, 255, 0], [0, 0, 255], [255, 0, 0]]
], dtype=np.uint8)
seis_plt = px.imshow(seis)
difference = np.abs(indx_old - indx_new)
if np.any(difference):
find_indx = np.nonzero(difference)[0]
else:
find_indx = [last_section]
if find_indx[0] == 0:
seis = data.get_iline(indx_new[0]).T
seis_plt = plot_inl(seis, cmap, vmin, vmax)
last_section = 0
elif find_indx[0] == 1:
seis = data.get_xline(indx_new[1]).T
seis_plt = plot_xln(seis, cmap, vmin, vmax)
last_section = 1
elif find_indx[0] == 2:
seis = data.get_zslice(indx_new[2]).T
seis_plt = plot_top(seis, cmap, vmin, vmax)
last_section = 2
seis_plt.update_layout(
plot_bgcolor='rgba(0,0,0,0)')
# seis_plt.update_layout(yaxis = dict(scaleanchor = 'x'))
indx_old = indx_new
return seis, seis_plt, indx_old, last_section
def plotly_color_select(self, key, index=1):
""" Lists available colormaps
Returns:
str: selected colormap
"""
colorscales = ["gray", "Greys", "RdBu", "RdGy", "Blues", "Reds"] #px.colors.named_colorscales()
option = st.selectbox(
'Color Map',
(colorscales), index=index, key=key)
return option
def viz_data_3d(self, data, is_fspect, key=0, is_show_metrics=True):
"""Viz data in 3D with slider in 3 direction from streamlit
+ show metrics of the data
+ show freq spectrum plot
Args:
data (SeismicData): Data for viz
is_fspect (bool): if to show freq spectrum plot
key (int, optional): Unique identifier. Defaults to 0.
is_show_metrics (bool, optional): if to show data metrics. Defaults to True.
"""
vm, n_samples, n_il, n_xl = self._vm, self._n_samples, self._n_il, self._n_xl
if 'viz_'+str(key) not in st.session_state:
st.session_state['viz_'+str(key)] = {"iline_old": 0, "xline_old" : 0,
"t_old" : 0, "last_section" : 0}
if is_show_metrics:
col1, col2, col3, col4, col5 = st.columns(5)
col2.metric("Number of Samples", n_samples)
col3.metric("Number of Inline", n_il)
col4.metric("Number of Xline", n_xl)
col5.metric("Sample Rate", self._sample_rate)
with col1:
self._cmap_option = self.plotly_color_select(key+1)
states = st.session_state['viz_'+str(key)]
col1, col2, col3 = st.columns(3)
with col1:
inline_indx = st.slider('Inline', 0, n_il-1, n_il//2, key=key+2)
with col2:
xline_indx = st.slider('Xline', 0, n_xl-1, n_xl//2, key=key+3)
with col3:
time_indx = st.slider('Time', 0, n_samples-1, n_samples//2, key=key+4)
index_old = np.array([states['iline_old'], states['xline_old'], states['t_old']])
index_new = np.array([inline_indx, xline_indx, time_indx])
seis, seis_3d_plot, index_old, last_section = \
self.plot_slice(data, index_old, index_new, states['last_section'], cmap=self._cmap_option, vmin=-vm, vmax=vm)
states.update({"iline_old": index_old[0], "xline_old" : index_old[1],
"t_old" : index_old[2], "last_section" : last_section})
st.write(seis_3d_plot)
if is_fspect:
fspect_plt = self.plot_fspectra(seis, 'Original')
st.write(fspect_plt)
def viz_sidebyside_3d(self, data, data2, minmax=False, key=0):
"""Viz 2 sets of data in 3D in side-by-side fashion
with shared sliders
Args:
data (_type_): Data1 to viz
data2 (_type_): Data2 to viz
key (int, optional): Unique identifier. Defaults to 0.
"""
vm, n_samples, n_il, n_xl = self._vm, self._n_samples, self._n_il, self._n_xl
vmin, vmax = -vm, vm
vmin2, vmax2 = vmin, vmax
if not minmax:
vmin2 = 0
vmax2 = 100
if 'viz_'+str(key) not in st.session_state:
st.session_state['viz_'+str(key)] = {"iline_old": 0, "xline_old" : 0,
"t_old" : 0, "last_section" : 0}
states = st.session_state['viz_'+str(key)]
col1, col2, col3 = st.columns(3)
with col1:
inline_indx = st.slider('Inline', 0, n_il-1, n_il//2, key=key+2)
with col2:
xline_indx = st.slider('Xline', 0, n_xl-1, n_xl//2, key=key+3)
with col3:
time_indx = st.slider('Time', 0, n_samples-1, n_samples//2, key=key+4)
index_old = np.array([states['iline_old'], states['xline_old'], states['t_old']])
index_new = np.array([inline_indx, xline_indx, time_indx])
col1, col2 = st.columns(2)
with col1:
self._cmap_option = self.plotly_color_select(key+1)
_ , seis_3d_plot, _ , _ = \
self.plot_slice(data, index_old, index_new, states['last_section'], cmap=self._cmap_option, vmin=vmin, vmax=vmax)
seis_3d_plot.update(layout_coloraxis_showscale=False)
st.write(seis_3d_plot)
with col2:
self._cmap_option = self.plotly_color_select(key+5)
_ , attr_3d_plot, index_old, last_section = \
self.plot_slice(data2, index_old, index_new, states['last_section'], cmap=self._cmap_option, vmin=vmin2, vmax=vmax2)
st.write(attr_3d_plot)
states.update({"iline_old": index_old[0], "xline_old" : index_old[1],
"t_old" : index_old[2], "last_section" : last_section})
def viz_data_2d(self, data, is_fspect, key=0, is_show_metrics=True):
"""Viz data in 2D
+ show metrics of the data
+ show freq spectrum plot
Args:
data (SeismicData): Data for viz
is_fspect (bool): if to show freq spectrum plot
key (int, optional): Unique identifier. Defaults to 0.
is_show_metrics (bool, optional): if to show data metrics. Defaults to True.
"""
vm, n_samples, n_il, n_xl = self._vm, self._n_samples, self._n_il, self._n_xl
if is_show_metrics:
col1, col2, col3, col4 = st.columns(4)
col2.metric("Number of Samples", n_samples)
col3.metric("Number of Inline", n_il)
col4.metric("Number of Xline", n_xl)
with col1:
self._cmap_option = self.plotly_color_select(key+1)
seis_plt = plot_seis(data,cmap=self._cmap_option, vmin=-vm, vmax=vm)
st.write(seis_plt)
if is_fspect:
with st.expander("Amplitude spectra"):
fspect_plt = self.plot_fspectra(data, 'Original')
st.write(fspect_plt)
def viz_sidebyside_2d(self, data1, data2, is_fspect, minmax = False, key=0, is_show_metrics=True):
"""Viz data in 2D
+ show metrics of the data
+ show freq spectrum plot
Args:
data1 (SeismicData): Data for viz
data2 (SeismicData): Data for viz
is_fspect (bool): if to show freq spectrum plot
minmax (bool): min-max normalization
key (int, optional): Unique identifier. Defaults to 0.
is_show_metrics (bool, optional): if to show data metrics. Defaults to True.
"""
vm, n_samples, n_il, n_xl = self._vm, self._n_samples, self._n_il, self._n_xl
vmin, vmax = -vm, vm
if minmax:
vmin = np.min(data1) if np.min(data1) < np.min(data2) else np.min(data2)
vmax = np.max(data1) if np.max(data1) > np.max(data2) else np.max(data2)
if is_show_metrics:
col1, col2, col3, col4 = st.columns(4)
col2.metric("Number of Samples", n_samples)
col3.metric("Number of Inline", n_il)
col4.metric("Number of Xline", n_xl)
with col1:
self._cmap_option = self.plotly_color_select(key+1)
col1, col2 = st.columns(2)
data1_plt = plot_seis(data1,cmap=self._cmap_option, vmin=vmin, vmax=vmax)
col1.write(data1_plt)
data2_plt = plot_seis(data2,cmap=self._cmap_option, vmin=vmin, vmax=vmax)
col2.write(data2_plt)
if is_fspect:
with st.expander("Amplitude spectra"):
smooth = 2.0
fspect_plt = plot_fspectra(data1, "data1", self._sample_rate, smooth, data2=data2, data2_name="data2")
st.write(fspect_plt)
def compare_two_fig_2D(self, data1, data1_name, data2, data2_name, is_fspect, sample_rate):
"""Viz 2 sets of data with comparison slider
Args:
data1 (SeismicData): 2D Data1 to viz
data1_name (str): Name Data1
data2 (SeismicData): 2D Data2 to viz
data2_name (str): Name Data2
is_fspect (bool): if to show freq spectrum plot
sample_rate (float): sampling rate
"""
vm, cmap_option = self._vm, self._cmap_option
compare_two_fig_2D_helper(data1, data2, cmap_option, vm)
if is_fspect:
with st.expander("Amplitude spectra"):
smooth = 2.0
fspect_plt = plot_fspectra(data1, data1_name, sample_rate, smooth, data2=data2, data2_name=data2_name)
st.write(fspect_plt)
def plot_fspectra(self, data1, data1_name):
col1, col2, col3, col4, col5 = st.columns(5)
smooth = col2.slider('Smoothing', 0.0, 4.0, 2.0, key=100)
sample_rate = col1.number_input("Sample Rate", min_value=1, value=int(self._sample_rate/1000), format='%i', key=101)
#col3.text((signaltonoise(data1)))
fspect_plt = plot_fspectra(data1, data1_name, sample_rate, smooth)
return fspect_plt
def plot_fspectra_2(self, data1, data1_name, data2, data2_name):
col1, col2, col3, col4, col5 = st.columns(5)
smooth = col2.slider('Smoothing', 0.0, 4.0, 2.0, key=102)
sample_rate = col1.number_input("Sample Rate", min_value=1, value=int(self._sample_rate/1000), format='%i',key=103)
fspect_plt = plot_fspectra(data1, data1_name, sample_rate, smooth, data2=data2, data2_name=data2_name)
st.write(fspect_plt)
return fspect_plt
def plot_fspectra_3(self, data1, data1_name, data2, data2_name, data3, data3_name):
col1, col2, col3, col4, col5 = st.columns(5)
smooth = col2.slider('Smoothing', 0.0, 4.0, 2.0)
sample_rate = col1.number_input("Sample Rate", min_value=1, value=int(self._sample_rate/1000), format='%i')
fspect_plt = plot_fspectra(data1, data1_name, sample_rate, smooth, data2=data2, data2_name=data2_name, data3=data3, data3_name=data3_name)
st.write(fspect_plt)
return fspect_plt
# OVERLAY
"""
def get_flt_mask(self, attr, eps):
flt_mask = np.ones([attr.shape[0], attr.shape[1], 4])
flt_mask[:, :, 0][attr > eps] = 255
flt_mask[:, :, 1][attr > eps] = 0
flt_mask[:, :, 2][attr > eps] = 0
flt_mask[:, :, 3][attr > eps] = 255
return flt_mask
def plot_slice_overlay(self, seis_data, attr_data, indx_old, indx_new, last_section, cmap, vm):
seis = np.array([[[255, 0, 0], [0, 255, 0], [0, 0, 255]],
[[0, 255, 0], [0, 0, 255], [255, 0, 0]]
], dtype=np.uint8)
# seis_plt = px.imshow(seis)
difference = np.abs(indx_old - indx_new)
if np.any(difference):
find_indx = np.nonzero(difference)[0]
else:
find_indx = [last_section]
seis_plt = go.Figure()
if find_indx[0] == 0:
seis = seis_data.get_iline(indx_new[0]).T
attr = attr_data.get_iline(indx_new[0])
attr2 = self.get_flt_mask(attr, 0.9)
attr = self.get_flt_mask(attr.T, 0.9)
# seis_plt.add_trace(px.imshow(seis).data[0])
# seis_plt = plot_inl(seis, cmap, -vm, vm)
seis_plt = plot_inl(seis, cmap, -vm, vm)
seis_plt.add_image(z=attr, colormodel='rgba256')
# seis_plt.add_trace(px.imshow(attr).data[0])
# seis_plt.add_trace(px.imshow(attr).data[0])
# seis_plt.add_trace(px.imshow(attr).data[0], )
last_section = 0
elif find_indx[0] == 1:
seis = seis_data.get_xline(indx_new[1]).T
attr = attr_data.get_xline(indx_new[1]).T
seis_plt = plot_xln(seis, cmap, -vm, vm)
attr = self.get_flt_mask(attr, 0.1)
seis_plt.add_trace(px.imshow(attr).data[0])
last_section = 1
elif find_indx[0] == 2:
seis = seis_data.get_zslice(indx_new[2]).T
attr = attr_data.get_zslice(indx_new[2]).T
seis_plt = plot_top(seis, cmap, -vm, vm)
attr = self.get_flt_mask(attr, 0.1)
seis_plt.add_trace(px.imshow(attr).data[0])
last_section = 2
seis_plt.update_layout(
plot_bgcolor='rgba(0,0,0,0)')
indx_old = indx_new
return seis, seis_plt, indx_old, last_section
def visualize_seis_attr_3D(self, seismic_data, attr_data):
vm, n_samples, n_il, n_xl = self._vm, self._n_samples, self._n_il, self._n_xl
if 'keys' not in st.session_state:
st.session_state.keys = 1
st.session_state.iline_old = 0
st.session_state.xline_old = 0
st.session_state.t_old = 0
# 1 - inline, 2 - xline, 3 - time
st.session_state.last_section = 0
col1, col2, col3 = st.columns(3)
with col1:
inline_indx = st.slider('Inline', 0, n_il-1, n_il//2)
with col2:
xline_indx = st.slider('Xline', 0, n_xl-1, n_xl//2)
with col3:
time_indx = st.slider('Time', 0, n_samples-1, n_samples//2)
index_old = np.array([st.session_state.iline_old, st.session_state.xline_old, st.session_state.t_old])
index_new = np.array([inline_indx, xline_indx, time_indx])
seis, seis_3d_plot, index_old, st.session_state.last_section = self.plot_slice(seismic_data, index_old, index_new, st.session_state.last_section, cmap=self._cmap_option, vm=vm)
# seis_3d_plot, index_old, st.session_state.last_section = self.plot_slice(seismic_data, index_old, index_new, st.session_state.last_section, cmap=self._cmap_option, vm=vm)
seis_3d_plot.add_trace(px.imshow(attr_data[inline_indx, :, :]).data[0])
st.session_state.iline_old, st.session_state.xline_old, st.session_state.t_old = index_old
st.write(seis_3d_plot)
"""
def plot_seis(seis, cmap, vmin, vmax):
seis_plot = px.imshow(seis, zmin=vmin, zmax=vmax, aspect='auto', labels=dict(x="Xline_idx", y="Time_idx", color="Amplitude"), color_continuous_scale=cmap)
return seis_plot
def plot_inl(seis, cmap, vmin, vmax):
seis_plot = px.imshow(seis, zmin=vmin, zmax=vmax, aspect='auto', labels=dict(x="Xline_idx", y="Time_idx", color="Amplitude"), color_continuous_scale=cmap)
return seis_plot
def plot_xln(seis, cmap, vmin, vmax):
seis_plot = px.imshow(seis, zmin=vmin, zmax=vmax, aspect='auto', labels=dict(x="Iline_idx", y="Time_idx", color="Amplitude"), color_continuous_scale=cmap)
return seis_plot
def plot_top(seis, cmap, vmin, vmax):
seis_plot = px.imshow(seis, zmin=vmin, zmax=vmax, aspect='auto', labels=dict(x="Xline_idx", y="Iline_idx", color="Amplitude"), color_continuous_scale=cmap)
return seis_plot
st.experimental_memo
def save_figure_in_temp(fig1):
fig1_path = tempfile.NamedTemporaryFile()
fig1.write_image(fig1_path.name+".jpg")
return fig1_path.name+".jpg"
# @st.experimental_memo(suppress_st_warning=True)
def compare_two_fig_2D_helper(data1, data2, cmap_option, vm):
data1_plt = plot_seis(data1,cmap=cmap_option, vmin=-vm, vmax=vm)
data2_plt = plot_seis(data2,cmap=cmap_option, vmin=-vm, vmax=vm)
fig1_path = save_figure_in_temp(data1_plt)
fig2_path = save_figure_in_temp(data2_plt)
image_comparison(
img1=fig1_path,
img2=fig2_path,
)
def add_trace_to_fspectra_fig(fig, freq, data, data_name, sample_rate, smooth):
_, ampb = fspectra(data, dt=sample_rate, sigma=smooth)
fig.add_trace(go.Scatter(x=freq, y=ampb,
mode='lines',
name=data_name))
return fig
def plot_fspectra(data1, data1_name, sample_rate, smooth, *args, **kwargs):
data2, data3 = kwargs.get('data2', None), kwargs.get('data3', None)
data2_name, data3_name = kwargs.get('data2_name', None), kwargs.get('data3_name', None)
freq, ampa = fspectra(data1, dt=sample_rate, sigma=smooth)
fig = go.Figure()
fig.add_trace(go.Scatter(x=freq, y=ampa,
mode='lines',
name=data1_name))
if data2 is not None:
fig = add_trace_to_fspectra_fig(fig, freq, data2, data2_name, sample_rate, smooth)
if data3 is not None:
fig = add_trace_to_fspectra_fig(fig, freq, data3, data3_name, sample_rate, smooth)
fig.update_layout(title='Amplitude spectra',
xaxis_title='Frequency (Hz)',
yaxis_title='Amplitude',
xaxis_range=[0,110],
)
# print("sample_ratesample_ratesample_ratesample_rate, ", sample_rate)
return fig