def convert_frame_to_counts(path, counts_path):
"""
This function ...
:param path:
:param counts_path:
:return:
"""
# Open the HDUList
hdulist = open_fits(path)
# Get calibrated image in nanomaggies/pixel
img = hdulist[0].data
nrowc = img.shape[0] # ysize (x = columns, y = rows)
# Get the sky HDU (header)
sky = hdulist[2].data
allsky = sky.field("allsky")[
0] # for some reason, "allsky" has 3 axes (1, 192, 256), therefore [0]
xinterp = sky.field(
"xinterp"
)[0] # columns (xsize = 2048) # for some reason, "xinterp" has 2 axes (1, 2048)
yinterp = sky.field(
"yinterp"
)[0] # rows (ysize = 1489) # for some reason, "yinterp" has 2 axes (1, 1489)
# Split x, y and z values that are not masked
# x_values, y_values, z_values = split_xyz(allsky, arrays=True)
allsky_xrange = np.arange(allsky.shape[1], dtype=float)
allsky_yrange = np.arange(allsky.shape[0], dtype=float)
# INTERPOLATE SKY
skyf = interp2d(allsky_xrange, allsky_yrange, allsky)
simg = skyf(xinterp, yinterp)
# Get the calibration HDU (header)
calib = hdulist[1].data
replicate = np.ones(nrowc)
cimg = np.outer(replicate, calib)
# DECALIBRATE
dn = img / cimg + simg
# CREATE FRAME AND SAVE
dn_frame = Frame(dn)
dn_frame.unit = "count"
# Set the WCS of THE DN FRAME
dn_frame.wcs = CoordinateSystem(hdulist[0].header)
# Save
name = fs.name(path)
dn_path = fs.join(counts_path, name)
dn_frame.saveto(dn_path)
def get_gain_and_dark_variance_from_photofield(path, band):
"""
This function ...
:param path:
:param band:
:return:
"""
# Get the hdulist
hdulist = open_fits(path)
# HDU0: Empty Header
# HDU1: photoField Table
# Data Model: photoField: https://data.sdss.org/datamodel/files/BOSS_PHOTOOBJ/RERUN/RUN/photoField.html
column_names = hdulist[1].columns
tbdata = hdulist[1].data
gain_2d = tbdata.field("gain")
dark_variance_2d = tbdata.field("dark_variance")
gain_1d = gain_2d[:, sdss_filter_number[band]]
dark_variance_1d = dark_variance_2d[:, sdss_filter_number[band]]
if not is_constant_array(gain_1d):
raise ValueError("Gain 1D not constant: " + str(gain_1d))
if not is_constant_array(dark_variance_1d):
raise ValueError("Dark variance 1D not constant: " +
str(dark_variance_1d))
gain = gain_1d[0]
dark_variance = dark_variance_1d[0]
# Return the values
return gain, dark_variance
def _make_pretty_from_fits(fname=None,
title=None,
figsize=(10, 10 / 1.325),
dpi=150,
alpha=0.2,
number_ticks=7,
clip_percent=99.9,
**kwargs):
with open_fits(fname) as hdu:
header = hdu[0].header
data = hdu[0].data
data = focus_utils.mask_saturated(data)
wcs = WCS(header)
if not title:
field = header.get('FIELD', 'Unknown field')
exptime = header.get('EXPTIME', 'Unknown exptime')
filter_type = header.get('FILTER', 'Unknown filter')
try:
date_time = header['DATE-OBS']
except KeyError:
# If we don't have DATE-OBS, check filename for date
try:
basename = os.path.splitext(os.path.basename(fname))[0]
date_time = date_parser.parse(basename).isoformat()
except Exception:
# Otherwise use now
date_time = current_time(pretty=True)
date_time = date_time.replace('T', ' ', 1)
title = '{} ({}s {}) {}'.format(field, exptime, filter_type, date_time)
norm = ImageNormalize(interval=PercentileInterval(clip_percent), stretch=LogStretch())
fig = Figure()
FigureCanvas(fig)
fig.set_size_inches(*figsize)
fig.dpi = dpi
if wcs.is_celestial:
ax = fig.add_subplot(1, 1, 1, projection=wcs)
ax.coords.grid(True, color='white', ls='-', alpha=alpha)
ra_axis = ax.coords['ra']
ra_axis.set_axislabel('Right Ascension')
ra_axis.set_major_formatter('hh:mm')
ra_axis.set_ticks(
number=number_ticks,
color='white',
exclude_overlapping=True
)
dec_axis = ax.coords['dec']
dec_axis.set_axislabel('Declination')
dec_axis.set_major_formatter('dd:mm')
dec_axis.set_ticks(
number=number_ticks,
color='white',
exclude_overlapping=True
)
else:
ax = fig.add_subplot(111)
ax.grid(True, color='white', ls='-', alpha=alpha)
ax.set_xlabel('X / pixels')
ax.set_ylabel('Y / pixels')
im = ax.imshow(data, norm=norm, cmap=palette, origin='lower')
fig.colorbar(im)
fig.suptitle(title)
new_filename = fname.replace('.fits', '.jpg')
fig.savefig(new_filename, bbox_inches='tight')
# explicitly close and delete figure
fig.clf()
del fig
return new_filename
def test_fitsblob_pickle(self):
fits = open_fits(AARTFAAC_FITS)
accessor = FitsImageBlob(fits)
pickled = cPickle.dumps(accessor)
unpickled = cPickle.loads(pickled)
self.assertEqual(type(unpickled), type(accessor))
def convert_frame_to_nanomaggies(path, name, fields_path, photofields_path,
poisson_path):
"""
This function ...
:param path:
:param name:
:param fields_path:
:param photofields_path:
:param poisson_path:
:return:
"""
# Inform the user
log.info("Converting the ")
# EXAMPLE FILE NAME: frame-u-004294-4-0231.fits
# FIELD URL = field_url_start / RERUN / RUN + / photoField-6digits-CAMCOL.fits
# example: http://data.sdss3.org/sas/dr12/env/BOSS_PHOTOOBJ/301/4294/photoField-004294-5.fits
# FRAME URL = $BOSS_PHOTOOBJ / frames / RERUN / RUN / CAMCOL + /frame-[ugriz]-6digits-CAMCOL-FRAMESEQ.fits.bz2
# example: http://data.sdss3.org/sas/dr10/boss/photoObj/frames/301/4294/5/frame-i-004294-5-0229.fits.bz2
splitted = name.split("-")
band = splitted[1]
digits = splitted[2]
camcol = splitted[3]
frameseq = splitted[4]
# Determine the path to the corresponding field file
field_file_path = fs.join(photofields_path,
"photoField-" + digits + "-" + camcol + ".fits")
# Get the gain and dark variance
gain, dark_variance = get_gain_and_dark_variance_from_photofield(
field_file_path, band)
# Calculate the error
# dn_err = sqrt(dn/gain+darkVariance) # NOISE IN DN
# img_err = dn_err*cimg # NOISE IN NANOMAGGIES / PIXEL
# Load the image in DN
dn_frame = Frame.from_file(path)
# COMPUTE THE DN ERROR
dn_error = np.sqrt(dn_frame.data / gain + dark_variance)
# CONVERT ERROR MAP IN DN TO ERROR MAP IN NANOMAGGIES PER PIXEL
# COMPUTE THE CALIBRATION FRAME AGAIN
raw_frame_path = fs.join(fields_path, name + ".fits")
hdulist = open_fits(raw_frame_path)
img = hdulist[0].data
nrowc = img.shape[0] # ysize (x = columns, y = rows)
# Get the calibration HDU (header)
calib = hdulist[1].data
replicate = np.ones(nrowc)
cimg = np.outer(replicate, calib)
# IMAGE ERROR MAP IN NANOMAGGIES PER PIXEL
img_error = dn_error * cimg
error_map = Frame(img_error)
# SET THE WCS OF THE IMAGE ERROR MAP
error_map.wcs = dn_frame.wcs
# SAVE the error map
poisson_error_map_path = fs.join(poisson_path, name + ".fits")
error_map.saveto(poisson_error_map_path)
def test_fitsblob_pickle(self):
fits = open_fits(AARTFAAC_FITS)
accessor = FitsImageBlob(fits)
pickled = cPickle.dumps(accessor)
unpickled = cPickle.loads(pickled)
self.assertEqual(type(unpickled), type(accessor))