def press(button):
if button == "Quit":
app.stop()
else:
in1 = app.getEntry("Objects_blue")
in2 = app.getEntry("Objects_green")
in3 = app.getEntry("Objects_red")
in4 = app.getEntry("Dark")
in5 = app.getEntry("Bias")
in6 = app.getEntry("Flat_blue")
in7 = app.getEntry("Flat_green")
in8 = app.getEntry("Flat_red")
b, g, r = calib4.calibration(in1, in2, in3, in4, in5, in6, in7, in8)
hot_b, fixed_b = remove_deadhot.find_outlier_pixels(b / 65536.0 *
255.0)
hot_g, fixed_g = remove_deadhot.find_outlier_pixels(g / 65536.0 *
255.0)
hot_r, fixed_r = remove_deadhot.find_outlier_pixels(r / 65536.0 *
255.0)
newb, newg, newr = alignment5.alignment(fixed_b, fixed_g, fixed_r)
biasfiles = [line.rstrip('\n') for line in open(in1)]
cat = get_pkg_data_filename(biasfiles[0])
hdu = fits.open(cat)[0]
wcs = WCS(hdu.header)
n1 = int(hdu.header['NAXIS1'])
n2 = int(hdu.header['NAXIS2'])
bmin, bmax = newb.mean() - 2.0 * newb.std(), newb.mean(
) + 5 * newb.std()
gmin, gmax = newg.mean() - 2.0 * newg.std(), newg.mean(
) + 5 * newg.std()
rmin, rmax = newr.mean() - 2.0 * newr.std(), newr.mean(
) + 5 * newr.std()
img = zeros((n1, n2, 3))
img[:, :, 0] = img_scale.linear(newr, scale_min=rmin, scale_max=rmax)
img[:, :, 1] = img_scale.linear(0.9 * newg,
scale_min=gmin,
scale_max=gmax)
img[:, :, 2] = img_scale.linear(newb, scale_min=bmin, scale_max=bmax)
fig = pylab.figure()
pylab.clf()
fig.add_subplot(111, projection=wcs)
plt.imshow(img, cmap=plt.cm.viridis)
plt.xlabel('RA')
plt.ylabel('DEC')
fig.set_size_inches(10, 10, forward=True)
plt.savefig('img')
plt.close()
def doScaling(self, img, rgb):
for j in range(3):
smin, it = img_scale.sky_mean_sig_clip(rgb[j], self.SKY_SIGMA,
self.SKY_CONVERGENCE)
pxmax = max(rgb[j].flatten())
smax = int(self.wb[j] *
(self.SCALE_RANGE / self.opt['scaling'] + smin))
# smax = int((self.SCALE_RANGE + smin) / self.wb[j])
if self.SCALE_METHOD == self.scaleMethods[0]:
img[:, :, j] = img_scale.linear(rgb[j],
scale_min=smin,
scale_max=smax)
elif self.SCALE_METHOD == self.scaleMethods[1]:
img[:, :, j] = img_scale.sqrt(rgb[j],
scale_min=smin,
scale_max=smax)
elif self.SCALE_METHOD == self.scaleMethods[2]:
img[:, :, j] = img_scale.log(rgb[j],
scale_min=smin,
scale_max=smax)
elif self.SCALE_METHOD == self.scaleMethods[3]:
img[:, :, j] = img_scale.asinh(rgb[j],
scale_min=smin,
scale_max=smax)
input7, input8)
hot_b, fixed_b = remove_deadhot.find_outlier_pixels(b / 65536.0 * 255.0)
hot_g, fixed_g = remove_deadhot.find_outlier_pixels(g / 65536.0 * 255.0)
hot_r, fixed_r = remove_deadhot.find_outlier_pixels(r / 65536.0 * 255.0)
newb, newg, newr = alignment5.alignment(fixed_b, fixed_g, fixed_r)
biasfiles = [line.rstrip('\n') for line in open(input1)]
cat = get_pkg_data_filename(biasfiles[0])
hdu = fits.open(cat)[0]
wcs = WCS(hdu.header)
n1 = int(hdu.header['NAXIS1'])
n2 = int(hdu.header['NAXIS2'])
bmin, bmax = newb.mean() - 2.0 * newb.std(), newb.mean() + 10 * newb.std()
gmin, gmax = newg.mean() - 2.0 * newg.std(), newg.mean() + 10 * newg.std()
rmin, rmax = newr.mean() - 2.0 * newr.std(), newr.mean() + 10 * newr.std()
img = zeros((n1, n2, 3))
img[:, :, 2] = img_scale.linear(newb, scale_min=bmin, scale_max=bmax)
img[:, :, 1] = img_scale.linear(0.9 * newg, scale_min=gmin, scale_max=gmax)
img[:, :, 0] = img_scale.linear(newr, scale_min=rmin, scale_max=rmax)
fig = pylab.figure()
pylab.clf()
fig.add_subplot(111, projection=wcs)
pylab.xlabel('RA')
pylab.ylabel('DEC')
pylab.imshow(img, cmap=plt.cm.viridis)
fig.set_size_inches(10, 10, forward=True)
pylab.savefig('final_img')
pylab.close()
pylab.savefig(image)
pylab.clf()
'''
new_img = img_scale.log(img_data, scale_min=min_val)
pylab.imshow(new_img,
interpolation='nearest',
origin='lower',
cmap=pylab.cm.hot)
pylab.axis('off')
pylab.savefig(image)
pylab.clf()
sys.exit()
new_img = img_scale.linear(img_data, scale_min=min_val)
pylab.imshow(new_img,
interpolation='nearest',
origin='lower',
cmap=pylab.cm.hot)
pylab.axis('off')
pylab.savefig(image)
pylab.clf()
new_img = img_scale.asinh(img_data, scale_min=min_val, non_linear=0.01)
pylab.imshow(new_img,
interpolation='nearest',
origin='lower',
cmap=pylab.cm.hot)
pylab.axis('off')
pylab.savefig(image)
pylab.clf()
new_img = img_scale.sqrt(img_data, scale_min=min_val)
pylab.imshow(new_img, interpolation="nearest", origin="lower", cmap=pylab.cm.hot)
pylab.axis("off")
pylab.savefig("sqrt.png")
pylab.clf()
new_img = img_scale.power(img_data, power_index=3.0, scale_min=min_val)
pylab.imshow(new_img, interpolation="nearest", origin="lower", cmap=pylab.cm.hot)
pylab.axis("off")
pylab.savefig("power.png")
pylab.clf()
new_img = img_scale.log(img_data, scale_min=min_val)
pylab.imshow(new_img, interpolation="nearest", origin="lower", cmap=pylab.cm.hot)
pylab.axis("off")
pylab.savefig("log.png")
pylab.clf()
new_img = img_scale.linear(img_data, scale_min=min_val)
pylab.imshow(new_img, interpolation="nearest", origin="lower", cmap=pylab.cm.hot)
pylab.axis("off")
pylab.savefig("linear.png")
pylab.clf()
new_img = img_scale.asinh(img_data, scale_min=min_val, non_linear=0.01)
pylab.imshow(new_img, interpolation="nearest", origin="lower", cmap=pylab.cm.hot)
pylab.axis("off")
pylab.savefig("asinh_beta_01.png")
pylab.clf()
new_img = img_scale.asinh(img_data, scale_min=min_val, non_linear=0.5)
pylab.imshow(new_img, interpolation="nearest", origin="lower", cmap=pylab.cm.hot)
pylab.axis("off")
pylab.savefig("asinh_beta_05.png")
pylab.clf()
new_img = img_scale.asinh(img_data, scale_min=min_val, non_linear=2.0)
for wl in ['8.0', '4.5', '3.6']:
data.append(fits.getdata('Downloads/SAGE_LMC_IRAC'+wl+'_2_mosaic.fits'))
data = np.array(data)
data = np.swapaxes(data, 0, 2)
data = np.swapaxes(data, 0, 1)
data = block_reduce(data, block_size=(2, 2, 1))
for i in range(3):
tmp = np.nanmin(data[:, :, i])
data[:, :, i] += np.abs(tmp)
for i in range(3):
tmp = np.nanmax(data[:, :, i])
data[:, :, i] *= 1.0/tmp
data[:, :, 0] = IS.linear(data[:, :, 0], scale_min=0.0027, scale_max=0.006)
data[:, :, 1] = IS.linear(data[:, :, 1], scale_min=0.00025, scale_max=0.0016)
data[:, :, 2] = IS.linear(data[:, :, 2], scale_min=0.0005, scale_max=0.0021)
'''
fig = plt.figure(dpi=100)
ax = fig.add_subplot(111)
col = ['r', 'g', 'b']
for i in range(3):
tmp = data[:, :, i]
ax.hist(tmp[~np.isnan(tmp)], bins='auto', color=col[i], histtype='step')
#ax.set_xscale('log')
#ax.set_yscale('log')
plt.show()
'''
fig = plt.figure(figsize=(4.983, 4.983))
import pyfits
import numpy as np
import pylab as py
import img_scale
Target_name = str(raw_input('Object Name:'))
print ''
R = str(raw_input('R filter filename:'))
print ''
B = str(raw_input('B filter filename:'))
print ''
V = str(raw_input('V filter filename:'))
print ''
r = pyfits.getdata(R)
v = pyfits.getdata(V)
b = pyfits.getdata(B)
img = np.zeros((b.shape[0], b.shape[1], 3), dtype=float)
img[:, :, 0] = img_scale.linear(r, scale_min=0, scale_max=10000)
img[:, :, 1] = img_scale.linear(v, scale_min=0, scale_max=10000)
img[:, :, 2] = img_scale.linear(b, scale_min=0, scale_max=10000)
py.clf()
py.imshow(img, aspect='equal')
py.title('Blue = B, Visible = V, Red = R')
py.savefig(Target_name + '_RGB.png')
def MakeMovieImages(Ntimesteps,
imagesDir,
outputDir,
movie_array,
file_names,
stretchValues,
tracerNames,
rotationPerMyr=rotationPerMyr,
tstretch=tstretch):
print('making images...')
for i in range(Ntimesteps):
output_name = str(outputDir + 'rgb_' + str(i) + '.png')
currentTime = (float(i) / float(tstretch))
if EndAtCorrectRot:
rot_angle = (-1. * (Ntimesteps - 1) *
(rotationPerMyr / tstretch)) + (i * rotationPerMyr /
tstretch)
else:
rot_angle = (i * rotationPerMyr / tstretch)
if '.fits' in (movie_array[i, 0]):
stindex = np.where(file_names == movie_array[i, 0])
tracerR = (tracerNames[stindex])[0]
imgR = img_scale.linear(pyfits.getdata(
str(imagesDir + movie_array[i, 0])),
scale_min=stretchValues[stindex, 0],
scale_max=stretchValues[stindex, 1])
if FadeTracersStart:
try:
if movie_array[i - framerate, 0] != movie_array[i, 0]:
stretchValues[stindex,
1] = stretchValues[stindex, 1] / 1.1
except:
print("Fade start has failed")
if FadeTracersEnd:
try:
if movie_array[i + framerate, 0] != movie_array[i, 0]:
stretchValues[stindex,
1] = stretchValues[stindex, 1] * 1.1
except:
print("Nearing end of the movie")
else:
imgR = None
tracerR = 'N/A'
if '.fits' in (movie_array[i, 1]):
stindex = np.where(file_names == movie_array[i, 1])
imgG = img_scale.linear(pyfits.getdata(
str(imagesDir + movie_array[i, 1])),
scale_min=stretchValues[stindex, 0],
scale_max=stretchValues[stindex, 1])
tracerG = (tracerNames[stindex])[0]
if FadeTracersStart:
try:
if movie_array[i - framerate, 1] != movie_array[i, 1]:
stretchValues[stindex,
1] = stretchValues[stindex, 1] / 1.1
except:
print("Fade start has failed")
if FadeTracersEnd:
try:
if movie_array[i + framerate, 1] != movie_array[i, 1]:
stretchValues[stindex,
1] = stretchValues[stindex, 1] * 1.1
except:
print("Nearing end of the movie")
else:
imgG = None
tracerG = 'N/A'
if '.fits' in (movie_array[i, 2]):
stindex = np.where(file_names == movie_array[i, 2])
tracerB = (tracerNames[stindex])[0]
imgB = img_scale.linear(pyfits.getdata(
str(imagesDir + movie_array[i, 2])),
scale_min=stretchValues[stindex, 0],
scale_max=stretchValues[stindex, 1])
if FadeTracersStart:
try:
if movie_array[i - framerate, 2] != movie_array[i, 2]:
stretchValues[stindex,
1] = stretchValues[stindex, 1] / 1.1
except:
print("Fade start has failed")
if FadeTracersEnd:
try:
if movie_array[i + framerate, 2] != movie_array[i, 2]:
stretchValues[stindex,
1] = stretchValues[stindex, 1] * 1.1
except:
print("Nearing end of the movie")
else:
imgB = None
tracerB = 'N/A'
assemble3colImage(imgR, imgG, imgB, rot_angle, output_name, tracerR,
tracerG, tracerB, currentTime)