def bright_pix(self, fitsfile, hdu):
""" Does the work of finding the bright pixels and columns. """
im = image_utils.unbias_and_trim(afwImage.ImageF(fitsfile, hdu))
imarr = im.getArray()
mean = np.mean(imarr)
sigma = np.std(imarr)
threshold = self.nsig * sigma + mean
# Find bright pixels.
pixels = np.where(imarr > threshold)
# Find bright columns.
col_means = [np.mean(imarr[:, i]) for i in range(im.getWidth())]
columns = np.where(col_means > threshold)
# Weed out bright pixels that are already in bright columns or rows.
indx = [
i for i in range(len(pixels[1])) if pixels[1][i] not in columns[0]
]
pixels = (pixels[0][indx], pixels[1][indx])
tup = list(zip(pixels[1], pixels[0]))
sorted_tup = sorted(tup)
return len(sorted_tup), sorted_tup, columns
def dark_pct(files, percentile=90., hdu=2, gain=1):
if percentile < 0 or percentile > 100:
raise RuntimeError("percentile must be between 0 and 100")
# Read exposure time from the primary HDU of the first file.
exptime = afwImage.readMetadata(files[0], 1).get('EXPTIME')
im = unbias_and_trim(fits_median(files))
im *= gain
im /= exptime
npix = im.getHeight() * im.getWidth()
imarr = np.sort(im.getArray().reshape(npix))
return imarr[int(npix * float(percentile) / 100.)]
def dark_curr(files, hdu=2, gain=1, count=1000, dx=100, dy=100, seed=None):
random.seed(seed)
exptime = afwImage.readMetadata(files[0], 1).get('EXPTIME')
im = unbias_and_trim(fits_median(files, hdu=hdu))
# Generate dx by dy boxes at random locations to perform
# estimates, then take the median. This avoids bright defects.
xarr = random.randint(im.getWidth() - dx - 1, size=count)
yarr = random.randint(im.getHeight() - dy - 1, size=count)
signal = []
for x, y in zip(xarr, yarr):
bbox = afwGeom.Box2I(afwGeom.Point2I(int(x), int(y)),
afwGeom.Extent2I(dx, dy))
subim = im.Factory(im, bbox)
signal.append(np.mean(subim.getArray()))
dark_current = np.median(signal) * gain / exptime
return dark_current
o = open(outfilepath, "w+")
o.write('\t'.join(
['filename', 'exptime', 'amp', 'wavelength', 'median', 'photodiode',
'\n']))
for i, fname in enumerate(files):
#get wavelength, pd reading, and exptime
md = afwImage.readMetadata(fname, 1)
wl = md.get("MONO_WAVELENG")
photodiode = md.get("K_PHOT_CURRENT")
exptime = md.get("EXPTIME")
for amp in amps:
#open image, trim and debias, and get median
im = afwImage.ImageF(fname, amp + 1)
im2 = iu.unbias_and_trim(im)
ampmedian = afwMath.makeStatistics(im2, afwMath.MEDIAN).getValue()
o.write('\t'.join([
fname,
str(exptime),
str(amp),
str(wl),
str(ampmedian),
str(photodiode), '\n'
]))
print "%i of %i done " % (i, len(files))
#write column names to output file
f = open(outfile, "w+")
f.write('\t'.join([
"agg_amp", "vic_01", "vic_02", "vic_03", "vic_04", "vic_05", "vic_06",
"vic_07", "vic_08", "vic_09", "vic_10", "vic_11", "vic_12", "vic_13",
"vic_14", "vic_15", "vic_16", "\n"
]))
#find appropriate image
files = glob.glob("/Users/amali/Desktop/900nm*.fits")
fname = files[0]
#bias correct aggressor image
im_a = afwImage.ImageF(fname, agg + 1)
im_a2 = iu.unbias_and_trim(im_a)
#######The DM stack stuff I couldn't get to work
#im_a = afwImage.ExposureF(fname, agg+1)
#im_a_i = im_a.getMaskedImage().getImage()
#im_a2 = iu.unbias_and_trim(im_a_i)
#ds9.mtv(im_a2)
#threshold = afwDetect.Threshold(30000)
#fs = afwDetect.FootprintSet(im_a.getMaskedImage(), threshold)
#fs.setMask(im_a.getMaskedImage().getMask(), "DETECTED")
#ds9.mtv(im_a)
##########using numpy instead