def write_master_bias(outname=None):
par = common.ci_misc_params()
if outname is None:
outname = par['master_bias_filename']
outname = os.path.join(os.environ[par['etc_env_var']], outname)
assert (not os.path.exists(outname))
ci_extnames = common.valid_image_extname_list()
hdus = []
for ci_extname in ci_extnames:
print('Working on master bias for: ' + ci_extname)
bias_image = master_bias_1camera(ci_extname)
bias_image = bias_image.astype('float32')
if len(hdus) == 0:
hdu = fits.PrimaryHDU(bias_image)
else:
hdu = fits.ImageHDU(bias_image)
hdu.header = master_bias_header_cards(hdu, ci_extname)
hdus.append(hdu)
hdul = fits.HDUList(hdus)
hdul.writeto(outname)
def write_master_dark(outname=None):
par = common.ci_misc_params()
if outname is None:
outname = os.path.join(
'/project/projectdirs/desi/users/ameisner/CI/post_install_calibs/CI_master_dark-20190330.fits'
)
assert (not os.path.exists(outname))
ci_extnames = common.valid_image_extname_list()
hdus = []
for ci_extname in ci_extnames:
print('Working on master dark for: ' + ci_extname)
dark_image = master_dark_1camera(ci_extname)
dark_image = dark_image.astype('float32')
# convert to counts per second !!!
dark_image = dark_image / dark_exptime
dark_image = dark_image.astype('float32')
if len(hdus) == 0:
hdu = fits.PrimaryHDU(dark_image)
else:
hdu = fits.ImageHDU(dark_image)
hdu.header = master_dark_header_cards(hdu, ci_extname)
hdus.append(hdu)
hdul = fits.HDUList(hdus)
hdul.writeto(outname)
def write_master_flat():
par = common.ci_misc_params()
outname = par['master_flat_filename']
outname = os.path.join(os.environ[par['etc_env_var']], outname)
assert (not os.path.exists(outname))
flat, ivar = create_master_flat()
flat = flat.astype('float32')
ci_extnames = common.valid_image_extname_list()
hdus = []
for ci_extnum in range(len(ci_extnames)):
ci_extname = common.ci_extnum_to_extname(ci_extnum, fz=False)
print('Assembling HDU for: ' + ci_extname)
if len(hdus) == 0:
hdu = fits.PrimaryHDU(flat)
else:
hdu = fits.ImageHDU(flat)
hdu.header = master_flat_header_cards(hdu, ci_extname, ivar)
hdus.append(hdu)
hdul = fits.HDUList(hdus)
hdul.writeto(outname)
def __init__(self, image_list, dummy_fz_header=None):
# images is a dictionary of CI_image objects
par = common.ci_misc_params()
self.images = dict(zip(common.valid_image_extname_list(),
par['n_cameras']*[None]))
self.assign_image_list(image_list)
self.dummy_fz_header = dummy_fz_header
self.pixels_calibrated = None
def create_catalog_1exp(telra, teldec, outname=None):
tabs = []
for extname in common.valid_image_extname_list():
tab = corner_catalog_1ext(telra, teldec, extname)
tabs.append(tab)
tab = vstack(tabs)
if outname is not None:
assert (not os.path.exists(outname))
tab.write(outname, format='fits')
return tab
def plot_master_biases():
# compare biases based on forDK samples to those post-installation
ci_extnames = common.valid_image_extname_list()
for ci_extname in ci_extnames:
if ci_extname == 'CIC':
continue
bias_new = fits.getdata(
'/global/cscratch1/sd/ameisner/CI_master_bias.fits',
extname=ci_extname)
bias_old = fits.getdata(
'/project/projectdirs/desi/users/ameisner/CI/ci_reduce_etc/CI_master_bias.fits.gz',
extname=ci_extname)
print(ci_extname)
print(np.median(bias_new), 'new')
print(np.median(bias_old), 'old')
bias_old -= np.median(bias_old)
bias_new -= np.median(bias_new)
vmax = max(scoreatpercentile(bias_old, 97.5),
scoreatpercentile(bias_new, 97.5))
vmin = max(scoreatpercentile(bias_old, 2.5),
scoreatpercentile(bias_old, 2.5))
print(vmin, vmax)
print('~' * 30)
plt.imshow(bias_old, vmin=vmin, vmax=vmax, cmap='gray')
outname = '/global/cscratch1/sd/ameisner/old_bias_' + ci_extname + '.png'
plt.gca().get_xaxis().set_visible(False)
plt.gca().get_yaxis().set_visible(False)
dpi = 300
plt.savefig(outname, dpi=dpi, bbox_inches='tight')
plt.cla()
plt.imshow(bias_new, vmin=vmin, vmax=vmax, cmap='gray', origin='lower')
outname = outname.replace('old', 'new')
plt.gca().get_xaxis().set_visible(False)
plt.gca().get_yaxis().set_visible(False)
plt.savefig(outname, dpi=dpi, bbox_inches='tight')
import astropy.io.fits as fits
import ci_reduce.common as common
import numpy as np
fname = '/project/projectdirs/desi/users/ameisner/CI/post_install_calibs/CI_master_dark.fits'
extnames = common.valid_image_extname_list()
for extname in extnames:
im = fits.getdata(fname, extname=extname)
print(extname, np.median(im))