def fits_to_png(inpath, outpath, coords=''):
import numpy as np
import img_scale
import pylab
import pyfits
fn = inpath
sig_fract = 5.0
percent_fract = 0.01
hdulist = pyfits.open(fn)
img_header = hdulist[0].header
img_data_raw = hdulist[0].data
hdulist.close()
width=img_data_raw.shape[0]
height=img_data_raw.shape[1]
img_data_raw = np.array(img_data_raw, dtype=float)
sky, num_iter = img_scale.sky_mean_sig_clip(img_data_raw, sig_fract, percent_fract, max_iter=10)
img_data = img_data_raw - sky
min_val = 10.0
new_img = img_scale.log(img_data, scale_min = min_val)
pylab.imshow(new_img, interpolation='nearest', origin='lower',cmap=pylab.cm.gray)
if coords != '':
for coord in coords:
circle=pylab.Circle((coord['x'],coord['y']),radius=10)
circle.set_edgecolor( 'red' )
circle.set_facecolor( 'none' ) # "none" not None
circle.set_alpha( 0 )
pylab.gca().add_artist(circle)
pylab.text(coord['x']+15, coord['y']-20, coord['id'], color='red')
pylab.axis('off')
pylab.savefig(outpath)
pylab.clf()
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)
def plot_image():
rotated = np.flip(np.rot90(stamp, 2), 1)
plt.imshow(img_scale.log(rotated, scale_min=5, scale_max=10000),
cmap='Greys',
interpolation='none')
stamp = data[round(gal.y - dy):round(gal.y + dy),
round(gal.x - dx):round(gal.x + dx)]
ax = fig.add_subplot(1, len(filters) + 1, 1 + f)
#ax = fig.add_subplot(1,4, 1+f)
plt.axis('off')
plot_image()
plt.title(fil)
if f == 0:
image_b = np.flip(np.rot90(stamp, 2), 1)
if f == 1:
image_g = np.flip(np.rot90(stamp, 2), 1)
if f == 2:
image_r = np.flip(np.rot90(stamp, 2), 1)
alldata.append(stamp)
bx = fig.add_subplot(1, len(filters) + 1, 4)
#bx = fig.add_subplot(1, 4, 4)
img = np.zeros([120, 120, 3])
img[:, :, 0] = img_scale.log(image_r, scale_min=5, scale_max=10000)
img[:, :, 1] = img_scale.log(image_g, scale_min=5, scale_max=10000)
img[:, :, 2] = img_scale.log(image_b, scale_min=5, scale_max=10000)
plt.imshow(img)
plt.axis('off')
plt.title('color')
pdf.savefig(dpi=1000)
plt.close()
os.system('open %s &' % 'ad_poster.pdf')
min_val = 0.0
print "... min. and max. value : ", numpy.min(img_data), numpy.max(img_data)
'''
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(image)
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(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)
print "sky = ", sky, "(", num_iter, ")"
img_data = img_data_raw - sky
min_val = 0.0
print "... min. and max. value : ", numpy.min(img_data), numpy.max(img_data)
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)
def create_images_png(filename, outfilename='Default'):
"""Creates the original, clean, and mask images in single a PNG.
Useful for checking how well LACosmic worked.
You may want to adjust the size of the "cut" images so to
capture your source star.
Parameters:
filename : string
Name of the original FITS image, including the path.
outfilename : string, optional
Name of the outfile PNG.
Returns:
nothing
Outputs:
PNG file. ``<file rootname>.png`` by default.
Shows both full frame images and "cut" images of the source.
"""
pylab.ioff()
# create page for plots
page_width = 21.59 / 2
page_height = 27.94 / 2
fig = pylab.figure(figsize=(page_width, page_height))
file_clean = (filename.split('.fits')[0] + '.clean.fits')
file_mask = (filename.split('.fits')[0] + '.mask.fits')
scmax = 7000 # scale_max for raw and clean
scmin = 3 # scale_min for raw and clean
# Plot the original image
pylab.subplot(3, 2, 1, aspect='equal') # 311
image_orig = fits.open(filename)
image_orig_ext = image_orig[1].data
image_orig_scaled = img_scale.log(image_orig_ext, \
scale_min=scmin, \
scale_max=scmax)
plt_orig = pylab.imshow(image_orig_scaled, aspect='equal')
pylab.title('Original (SCI)')
# Plot cut of original image
pylab.subplot(3, 2, 2, aspect='equal')
image_orig_cut = img_scale.log(image_orig_ext[175:275,175:275], \
scale_min=scmin, \
scale_max=scmax)
plt_orig_cut = pylab.imshow(image_orig_cut, aspect='equal')
pylab.title('Original (SCI)')
image_orig.close()
# Plot the mask image
pylab.subplot(3, 2, 3) #312
image_mask = fits.open(file_mask)
image_mask_ext = image_mask[0].data
plt_orig = pylab.imshow(image_mask_ext, aspect='equal', vmin=-2,
vmax=1) #-2, -5
pylab.title('Mask')
# Plot cut of the mask image
pylab.subplot(3, 2, 4)
plt_mask_cut = pylab.imshow(image_mask_ext[175:275,175:275], \
aspect='equal', vmin=-2, vmax=1)
pylab.title('Mask')
image_mask.close()
# Plot the LACosmic-cleaned image
pylab.subplot(3, 2, 5) #313
image_clean = fits.open(file_clean)
image_clean_ext = image_clean[0].data
image_clean_scaled = img_scale.log(image_clean_ext, \
scale_min=scmin, \
scale_max=scmax)
plt_clean = pylab.imshow(image_clean_scaled, aspect='equal')
pylab.title('Clean')
#Plot cut of the LACosmic-cleaned image
pylab.subplot(3, 2, 6)
image_clean_cut = img_scale.log(image_clean_ext[175:275,175:275], \
scale_min=scmin, scale_max=scmax)
plt_clean_cut = pylab.imshow(image_clean_cut, aspect='equal')
pylab.title('Clean')
image_clean.close()
if outfilename == 'Default':
pylab.savefig(filename.split('.fits')[0] + '.png')
else:
pylab.savefig(outfilename)
pylab.close()
pylab.ion()
def create_diagnostic_png(filename, outfilename='Default'):
pylab.ioff()
# create page for plots
page_width = 21.59 / 2
page_height = 27.94 / 2
fig = pylab.figure(figsize=(page_width, page_height))
file_clean = (filename.split('.fits')[0] + '.clean.fits')
file_mask = (filename.split('.fits')[0] + '.mask.fits')
scmax = 7000 # scale_max for raw and clean
scmin = 3 # scale_min for raw and clean
# Plot the original image
pylab.subplot(3, 2, 1, aspect='equal') # 311
image_orig = pyfits.open(filename)
image_orig_ext = image_orig[1].data
image_orig_scaled = img_scale.log(image_orig_ext, \
scale_min=scmin, \
scale_max=scmax)
plt_orig = pylab.imshow(image_orig_scaled, aspect='equal')
pylab.title('Original (SCI)')
# Plot cut of original image
pylab.subplot(3, 2, 2, aspect='equal')
image_orig_cut = img_scale.log(image_orig_ext[175:275,175:275], \
scale_min=scmin, \
scale_max=scmax)
plt_orig_cut = pylab.imshow(image_orig_cut, aspect='equal')
pylab.title('Original (SCI)')
image_orig.close()
# Plot the mask image
pylab.subplot(3, 2, 3) #312
image_mask = pyfits.open(file_mask)
image_mask_ext = image_mask[0].data
plt_orig = pylab.imshow(image_mask_ext, aspect='equal', vmin=-2,
vmax=1) #-2, -5
pylab.title('Mask')
# Plot cut of the mask image
pylab.subplot(3, 2, 4)
plt_mask_cut = pylab.imshow(image_mask_ext[175:275,175:275], \
aspect='equal', vmin=-2, vmax=1)
pylab.title('Mask')
image_mask.close()
# Plot the LACosmic-cleaned image
pylab.subplot(3, 2, 5) #313
image_clean = pyfits.open(file_clean)
image_clean_ext = image_clean[0].data
image_clean_scaled = img_scale.log(image_clean_ext, \
scale_min=scmin, \
scale_max=scmax)
plt_clean = pylab.imshow(image_clean_scaled, aspect='equal')
pylab.title('Clean')
#Plot cut of the LACosmic-cleaned image
pylab.subplot(3, 2, 6)
image_clean_cut = img_scale.log(image_clean_ext[175:275,175:275], \
scale_min=scmin, scale_max=scmax)
plt_clean_cut = pylab.imshow(image_clean_cut, aspect='equal')
pylab.title('Clean')
image_clean.close()
if outfilename == 'Default':
pylab.savefig(filename.split('.fits')[0] + '.png')
else:
pylab.savefig(outfilename)
pylab.close()
pylab.ion()
