-
Notifications
You must be signed in to change notification settings - Fork 2
/
visualisation_3bands.py
408 lines (340 loc) · 17.7 KB
/
visualisation_3bands.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
import matplotlib
matplotlib.use("Agg")
from visualisation_1band import BoxLayout_main
import math
import numpy as np
import sys
import os
import glob
import urllib.request
import pandas as pd
from astropy.wcs import WCS
from astropy.visualization import make_lupton_rgb
import astropy.io.fits as pyfits
from kivy.app import App
from kivy.properties import NumericProperty
from kivy.uix.boxlayout import BoxLayout
from kivy.uix.popup import Popup
from kivy.uix.button import Button
from kivy.garden.matplotlib.backend_kivyagg import FigureCanvasKivyAgg
from kivy.uix.label import Label
from kivy.uix.slider import Slider
from kivy.uix.textinput import TextInput
import matplotlib.pyplot as plt
from functools import partial
from kivy.core.window import Window
import platform
class BoxLayoutColor(BoxLayout_main):
def scale_val(self,image_array):
if len(np.shape(image_array)) == 2:
image_array = [image_array]
vmin = np.min([self.background_rms_image(5, image_array[i]) for i in range(len(image_array))])
xl, yl = np.shape(image_array[0])
box_size = 14 # in pixel
xmin = int((xl) / 2 - (box_size / 2))
xmax = int((xl) / 2 + (box_size / 2))
vmax = np.max([image_array[i][xmin:xmax, xmin:xmax] for i in range(len(image_array))])
return vmin, vmax
def showplot_rgb(self,rimage, gimage, bimage):
vmin, vmax = self.scale_val([rimage, gimage, bimage])
img = np.zeros((rimage.shape[0], rimage.shape[1], 3), dtype=float)
img[:, :, 0] = self.sqrt_sc(rimage, scale_min=vmin, scale_max=vmax)
img[:, :, 1] = self.sqrt_sc(gimage, scale_min=vmin, scale_max=vmax)
img[:, :, 2] = self.sqrt_sc(bimage, scale_min=vmin, scale_max=vmax)
return img
def sqrt_sc(self,inputArray, scale_min=None, scale_max=None):
#
imageData = np.array(inputArray, copy=True)
if scale_min is None:
scale_min = imageData.min()
if scale_max is None:
scale_max = imageData.max()
imageData = imageData.clip(min=scale_min, max=scale_max)
imageData = imageData - scale_min
indices = np.where(imageData < 0)
imageData[indices] = 0.00001
imageData = np.sqrt(imageData)
imageData = imageData / np.sqrt(scale_max - scale_min)
return imageData
def update(self,event):
self.diplaystate = 0
plt.clf()
self.textnumber.text = str(self.counter)
self.draw_plot(self.scale_state)
self.oo.draw_idle()
self.tclass.text = self.classification[self.counter]
self.tsubclass.text = self.subclassification[self.counter]
self.tname.text = self.listimage[self.counter]
self.update_df()
def draw_plot(self,scale_state,defaultvalue=True,max=1,min=0):
try:
image_B, image_G, image_R = [pyfits.open(self.pathtofile + self.listimage[self.counter])[0].data,
pyfits.open(self.pathtofile + self.listimage[self.counter])[1].data,
pyfits.open(self.pathtofile + self.listimage[self.counter])[2].data]
print (np.shape(image_B),np.shape(image_R),np.shape(image_G))
image_color = self.showplot_rgb(image_R, image_G, image_B)
if defaultvalue == True:
self.scale_min_b, self.scale_max_b = self.scale_val(image_B)
self.scale_min_g, self.scale_max_g = self.scale_val(image_G)
self.scale_min_r, self.scale_max_r = self.scale_val(image_R)
else:
self.scale_min_b = min
self.scale_max_b = max
self.scale_min_r = min
self.scale_max_r = max
self.scale_min_g = min
self.scale_max_g = max
if scale_state == 'linear':
image_B = image_B.clip(min=self.scale_min_b, max=self.scale_max_b)
image_B = (image_B - self.scale_min_b) / (self.scale_max_b - self.scale_min_b)
indices = np.where(image_B < 0)
image_B[indices] = 0.0
indices = np.where(image_B > 1)
image_B[indices] = 1.0
image_G = image_G.clip(min=self.scale_min_g, max=self.scale_max_g)
image_G = (image_G - self.scale_min_g) / (self.scale_max_g - self.scale_min_g)
indices = np.where(image_G < 0)
image_G[indices] = 0.0
indices = np.where(image_G > 1)
image_G[indices] = 1.0
image_R = image_R.clip(min=self.scale_min_r, max=self.scale_max_r)
image_R = (image_R - self.scale_min_r) / (self.scale_max_r - self.scale_min_r)
indices = np.where(image_R < 0)
image_R[indices] = 0.0
indices = np.where(image_R > 1)
image_R[indices] = 1.0
elif scale_state == 'log':
factor = math.log10(self.scale_max_b - self.scale_min_b)
indices0 = np.where(image_B < self.scale_min_b)
indices1 = np.where((image_B >= self.scale_min_b) & (image_B <= self.scale_max_b))
indices2 = np.where(image_B > self.scale_max_b)
image_B[indices0] = 0.0
image_B[indices2] = 1.0
try:
image_B[indices1] = np.log10(image_B[indices1]) / (factor * 1.0)
except:
print ("Error on math.log10 ")
factor = math.log10(self.scale_max_r - self.scale_min_r)
indices0 = np.where(image_R < self.scale_min_r)
indices1 = np.where((image_R >= self.scale_min_r) & (image_R <= self.scale_max_r))
indices2 = np.where(image_R > self.scale_max_r)
image_R[indices0] = 0.0
image_R[indices2] = 1.0
try:
image_R[indices1] = np.log10(image_R[indices1]) / (factor * 1.0)
except:
print ("Error on math.log10 ")
factor = math.log10(self.scale_max_g - self.scale_min_g)
indices0 = np.where(image_G < self.scale_min_g)
indices1 = np.where((image_G >= self.scale_min_g) & (image_G <= self.scale_max_g))
indices2 = np.where(image_G > self.scale_max_g)
image_G[indices0] = 0.0
image_G[indices2] = 1.0
try:
image_G[indices1] = np.log10(image_G[indices1]) / (factor * 1.0)
except:
print ("Error on math.log10 ")
elif scale_state == 'sqrt':
image_B = image_B.clip(min=self.scale_min_b, max=self.scale_max_b)
image_B = image_B - self.scale_min_b
indices = np.where(image_B < 0)
image_B[indices] = 0.0
image_B = np.sqrt(image_B)
image_B = image_B / math.sqrt(self.scale_max_b - self.scale_min_b)
image_R = image_R.clip(min=self.scale_min_r, max=self.scale_max_r)
image_R = image_R - self.scale_min_r
indices = np.where(image_R < 0)
image_R[indices] = 0.0
image_R = np.sqrt(image_R)
image_R = image_R / math.sqrt(self.scale_max_r - self.scale_min_r)
image_G = image_G.clip(min=self.scale_min_g, max=self.scale_max_g)
image_G = image_G - self.scale_min_g
indices = np.where(image_G < 0)
image_G[indices] = 0.0
image_G = np.sqrt(image_G)
image_G = image_G / math.sqrt(self.scale_max_g - self.scale_min_g)
elif scale_state == 'asinh':
factor = np.arcsinh((self.scale_max_b - self.scale_min_b) / 2.0)
indices0 = np.where(image_B < self.scale_min_b)
indices1 = np.where((image_B >= self.scale_min_b) & (image_B <= self.scale_max_b))
indices2 = np.where(image_B > self.scale_max_b)
image_B[indices0] = 0.0
image_B[indices2] = 1.0
image_B[indices1] = np.arcsinh((image_B[indices1] - self.scale_min_b) / 2.0) / factor
factor = np.arcsinh((self.scale_max_r - self.scale_min_r) / 2.0)
indices0 = np.where(image_R < self.scale_min_r)
indices1 = np.where((image_R >= self.scale_min_r) & (image_R <= self.scale_max_r))
indices2 = np.where(image_R > self.scale_max_r)
image_R[indices0] = 0.0
image_R[indices2] = 1.0
image_R[indices1] = np.arcsinh((image_R[indices1] - self.scale_min_r) / 2.0) / factor
factor = np.arcsinh((self.scale_max_g - self.scale_min_g) / 2.0)
indices0 = np.where(image_G < self.scale_min_g)
indices1 = np.where((image_G >= self.scale_min_g) & (image_G <= self.scale_max_g))
indices2 = np.where(image_G > self.scale_max_g)
image_G[indices0] = 0.0
image_G[indices2] = 1.0
image_G[indices1] = np.arcsinh((image_G[indices1] - self.scale_min_g) / 2.0) / factor
plt.style.use('dark_background')
plt.subplot(2, 2, 1)
plt.imshow(image_B,cmap=self.colormap,origin='lower')
plt.subplot(2, 2, 1).text(5, 5, 'G', fontsize=18, ha='center', va='center')
plt.style.use('dark_background')
plt.axis('off')
plt.subplot(2, 2, 2)
plt.imshow(image_G,cmap=self.colormap,origin='lower')
plt.subplot(2, 2, 2).text(5, 5, 'R', fontsize=18, ha='center', va='center')
plt.gcf().set_facecolor('black')
plt.axis('off')
plt.subplot(2, 2, 3)
plt.imshow(image_R,cmap=self.colormap,origin='lower')
plt.subplot(2, 2, 3).text(5, 5, 'I', fontsize=18, ha='center', va='center')
plt.gcf().set_facecolor('black')
plt.axis('off')
plt.subplot(2, 2, 4)
plt.imshow(image_color,origin='lower')
plt.gcf().set_facecolor('black')
plt.axis('off')
plt.subplots_adjust(top=0.99, bottom=0.01, left=0.01, right=0.99, hspace=0.0,
wspace=0.0)
except IndexError:
popup = Popup(title=' ', content=Label(text='Incorrect format for color image'), size_hint=(None, None),
size=(400, 100))
popup.open()
def build(self):
# Please enter the path of ds9 executable here:
os_def = platform.system()
print(os_def)
if os_def == 'Linux':
self.pathds9 = '/usr/local/bin/ds9'
print('If your DS9 is not in /usr/local/bin/ds9 please edit the right path')
if os_def == 'Windows':
self.pathds9 = 'C:\\SAOImageDS9\\ds9.exe'
print('If your DS9 is not in C:\\SAOImageDS9\\ds9.exe please edit the right path')
#this is for mac but don't know the path
if os_def == 'Darwin':
self.pathds9 = 'ds9'
print('Edit the right path to your ds9 executable depending on your OS')
else :
print('Edit the right path to your ds9 executable depending on your OS')
self.pathtofile = './files_to_visualize/'
self.listimage = sorted([os.path.basename(x) for x in glob.glob(self.pathtofile+ '*.fits')])
self.counter = 0
self.number_graded = 0
self.COUNTER_MIN = 0
self.COUNTER_MAX = len(self.listimage)
self.classification = ['None'] * len(self.listimage)
self.subclassification = ['None'] * len(self.listimage)
self.comment = [' '] * len(self.listimage)
self.scale_min = 0
self.scale_max = 1
self.scale_min_r=0
self.scale_min_g = 0
self.scale_min_b = 0
self.scale_max_r = 1
self.scale_max_g = 1
self.scale_max_b = 1
self.limit_max = 1
self.limit_min = 0
self.step = (self.scale_max - self.scale_min) / 10.
self.scale_state = 'asinh'
self.diplaystate = 0
self.colormap = 'gray' # 'gist_yarg'
self.df = self.obtain_df()
self.oo = FigureCanvasKivyAgg(plt.gcf())
superBox = BoxLayout(orientation='vertical')
horizontalBoxup = BoxLayout(orientation='horizontal', size_hint_y=0.1)
horizontalBox = BoxLayout(orientation='horizontal')
self.tname = Label(text=self.listimage[self.counter], font_size=20, size_hint_y=0.1)
# button1 = Button(text="One",size_hint_x=0.1)
self.draw_plot(self.scale_state)
horizontalBox.add_widget(self.oo)
verticalBox1 = BoxLayout(orientation='horizontal', size_hint_y=0.1)
verticalBox = BoxLayout(orientation='horizontal', size_hint_y=0.15)
button3 = Button(text="Sure Lens", background_color=(0.4, 1, 0, 1))
button32 = Button(text="Maybe Lens", background_color=(0.4, 1, 0, 1))
button4 = Button(text="Flexion", background_color=(0.4, 1, 0, 1))
button5 = Button(text="Non Lens", background_color=(0.4, 1, 0, 1))
button6 = Button(text="Merger")
button7 = Button(text="Spiral")
button8 = Button(text="Ring")
button9 = Button(text="Elliptical")
button10 = Button(text="Disk")
button3.bind(on_press=partial(self.classify, 'L', 1))
button32.bind(on_press=partial(self.classify, 'ML', 1))
button4.bind(on_press=partial(self.classify, 'F', 1))
button5.bind(on_press=partial(self.classify, 'NL', 1))
button6.bind(on_press=partial(self.classify, 'Merger', 2))
button7.bind(on_press=partial(self.classify, 'Spiral', 2))
button8.bind(on_press=partial(self.classify, 'Ring', 2))
button9.bind(on_press=partial(self.classify, 'Elliptical', 2))
button10.bind(on_press=partial(self.classify, 'Disk', 2))
buttonscale1= Button(text="Linear")
buttonscale2 = Button(text="Sqrt")
buttonscale3 = Button(text="Log")
buttonscale4 = Button(text="Asinh")
buttoncolormap1= Button(text="Inverted")
buttoncolormap2 = Button(text="Bb8")
buttoncolormap3 = Button(text="Gray")
buttonscale1.bind(on_press=partial(self.change_scale, 'linear'))
buttonscale2.bind(on_press=partial(self.change_scale, 'sqrt'))
buttonscale3.bind(on_press=partial(self.change_scale, 'log'))
buttonscale4.bind(on_press=partial(self.change_scale, 'asinh'))
buttoncolormap1.bind(on_press=partial(self.change_colormap, 'gist_yarg'))
buttoncolormap2.bind(on_press=partial(self.change_colormap, 'hot'))
buttoncolormap3.bind(on_press=partial(self.change_colormap, 'gray'))
LSbutton = Button(text="LS", font_size=25, size_hint_x=0.1)
LSbutton.bind(on_press=self.get_legacy_survey)
savebutton = Button(text="Save csv", background_color=(0, 1, 0.4, 1), font_size=25, size_hint_x=0.4 )
savebutton.bind(on_press=self.save_csv)
commentbutton = Button(text="Comment", background_color=(0, 1, 0.4, 1), font_size=25, size_hint_x=0.3)
commentbutton.bind(on_press=self.add_comment)
self.textnumber = TextInput(text=str(self.counter), multiline=False, font_size=25, size_hint_x=0.1)
self.textnumber.bind(on_text_validate=self.change_number)
tnumber = Label(text=str(' ' + str(self.COUNTER_MAX-1)), font_size=25, size_hint_x=0.1)
buttonds9 = Button(text="ds9", font_size=25, size_hint_x=0.1)
buttonds9.bind(on_press=self.open_ds9)
self.tclass = Label(text=self.classification[self.counter], font_size=25, size_hint_x=0.1)
self.tsubclass = Label(text=self.subclassification[self.counter], font_size=25, size_hint_x=0.1)
horizontalBoxup.add_widget(LSbutton)
horizontalBoxup.add_widget(buttonds9)
horizontalBoxup.add_widget(savebutton)
horizontalBoxup.add_widget(commentbutton)
horizontalBoxup.add_widget(self.tclass)
horizontalBoxup.add_widget(self.tsubclass)
horizontalBoxup.add_widget(self.textnumber)
horizontalBoxup.add_widget(tnumber)
verticalBox.add_widget(button3)
verticalBox.add_widget(button32)
verticalBox.add_widget(button4)
verticalBox.add_widget(button5)
verticalBox.add_widget(button6)
verticalBox.add_widget(button7)
verticalBox.add_widget(button8)
verticalBox.add_widget(button9)
verticalBox.add_widget(button10)
bforward = Button(text=" --> ")
bbackward = Button(text=" <-- ")
bforward.bind(on_press=self.forward)
bbackward.bind(on_press=self.backward)
verticalBox1.add_widget(bbackward)
verticalBox1.add_widget(bforward)
verticalBox1.add_widget(buttonscale1)
verticalBox1.add_widget(buttonscale2)
verticalBox1.add_widget(buttonscale3)
verticalBox1.add_widget(buttonscale4)
verticalBox1.add_widget(buttoncolormap1)
verticalBox1.add_widget(buttoncolormap2)
verticalBox1.add_widget(buttoncolormap3)
superBox.add_widget(horizontalBoxup)
superBox.add_widget(horizontalBox)
superBox.add_widget(verticalBox1)
superBox.add_widget(verticalBox)
superBox.add_widget(self.tname)
'''
self._keyboard = Window.request_keyboard(self, self._keyboard_closed)
self._keyboard.bind(on_key_down=self._on_keyboard_down)
'''
return superBox
if __name__ == '__main__':
BoxLayoutColor().run()