def _get_contour(fits_fn, sigma_level):
with fits.open(fits_fn) as f:
imgdata = f[0].data
med = np.nanmedian(imgdata)
mad = np.nanmedian(np.abs(imgdata - med))
sigma = mad / mad2sigma
signal.signal(signal.SIGALRM, _contour_timeout_handler)
signal.alarm(10)
try:
cs = make_contours.contour(fits_fn, sigma * sigma_level, 2.0)
except TimeoutException as te:
print("contour timeout on %s" % osp.basename(fits_fn))
cs = None
finally:
signal.alarm(0)
return cs
# calculation mad2sigma = np.sqrt(2) * erfinv(2 * 0.75 - 1) # the following is needed to derive some statistics when the input radio image is not a SNR image med = np.median(imgdata) mad = np.median(np.abs(imgdata - med)) sigma = mad / mad2sigma import make_contours from PIL import Image from matplotlib import pyplot as plt from matplotlib.pyplot import cm from matplotlib.path import Path import matplotlib.patches as patches cs = make_contours.contour(radio_fits, sigma) cs['contours'] = map(points_to_dict, cs['contours']) #print json.dumps(cs) # Plot the infrared results fig = plt.figure(1, (12, 4)) # Download contour data contours = cs sf_x = 500. / contours['width'] sf_y = 500. / contours['height'] verts_all = []
# calculation mad2sigma=np.sqrt(2)*erfinv(2*0.75-1) # the following is needed to derive some statistics when the input radio image is not a SNR image med=np.median(imgdata) mad=np.median(np.abs(imgdata-med)) sigma=mad/mad2sigma import make_contours from PIL import Image from matplotlib import pyplot as plt from matplotlib.pyplot import cm from matplotlib.path import Path import matplotlib.patches as patches cs = make_contours.contour(radio_fits, sigma) cs['contours'] = map(points_to_dict, cs['contours']) #print json.dumps(cs) # Plot the infrared results fig = plt.figure(1,(12,4)) # Download contour data contours = cs sf_x = 500./contours['width'] sf_y = 500./contours['height'] verts_all = []
# Load the radio data
radio_fits = '%s/%s_radio.fits' % (imgdir,galname)
with fits.open(radio_fits) as f:
imgdata = f[0].data
# convert median absolute difference into sigma in radio image noise calculation
mad2sigma=np.sqrt(2)*erfinv(2*0.75-1)
# compute sigma for the contour calculation
med=np.median(imgdata)
mad=np.median(np.abs(imgdata-med))
sigma=mad/mad2sigma
# Create contours
cs = make_contours.contour(radio_fits, sigma * 3, interval)
cs['contours'] = map(make_contours.points_to_dict, cs['contours'])
# Write contours to JSON file
with open('%s/%s_contours.json' % (imgdir,galname),'w') as fobj:
json.dump(cs,fobj)
# Load the heatmap data
im_standard = Image.open('%s/%s_heatmap.png' % (imgdir,galname))
sf_x = float(xsize_pix)/cs['width']
sf_y = float(ysize_pix)/cs['height']
verts_all = []
codes_all = []