def main() :
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('file', type = str, default = None, help = 'FITS file')
parser.add_argument("--list", default=False, help="List the extensions only?", action="store_true")
parser.add_argument('--raw_lris', action="store_true")
parser.add_argument('--exten', type=int, help="FITS extension")
pargs = parser.parse_args()
# List?
if pargs.list:
from astropy.io import fits
hdu = fits.open(pargs.file)
print(hdu.info())
return
kludge_fil = 'tmp_ginga.fits'
# Setup for PYPIT imports
import subprocess
from astropy.io import fits
from pypit import pyputils
msgs = pyputils.get_dummy_logger()
from pypit import arlris
# Extension
if pargs.exten is not None:
hdu = fits.open(pargs.file)
img = hdu[pargs.exten].data
# Write
msgs.warn('Writing kludge file to {:s}'.format(kludge_fil))
hdunew = fits.PrimaryHDU(img)
hdulist = fits.HDUList([hdunew])
hdulist.writeto(kludge_fil,clobber=True)
#
pargs.file = kludge_fil
# RAW_LRIS??
if pargs.raw_lris:
#
img, head, _ = arlris.read_lris(pargs.file)
# Generate hdu
hdu = fits.PrimaryHDU(img)
hdulist = fits.HDUList([hdu])
# Write
msgs.warn('Writing kludge file to {:s}'.format(kludge_fil))
hdulist.writeto(kludge_fil,clobber=True)
pargs.file = kludge_fil
# Spawn ginga
subprocess.call(["ginga", pargs.file])
if pargs.raw_lris:
msgs.warn('Removing kludge file {:s}'.format(kludge_fil))
subprocess.call(["rm", pargs.file])
def main(args):
# List only?
from astropy.io import fits
if args.list:
hdu = fits.open(args.file)
print(hdu.info())
return
kludge_fil = 'tmp_ginga.fits'
# Setup for PYPIT imports
import subprocess
from pypit import pyputils
msgs = pyputils.get_dummy_logger()
from pypit import arlris
# Extension
if args.exten is not None:
hdu = fits.open(args.file)
img = hdu[args.exten].data
# Write
msgs.warn('Writing kludge file to {:s}'.format(kludge_fil))
hdunew = fits.PrimaryHDU(img)
hdulist = fits.HDUList([hdunew])
hdulist.writeto(kludge_fil,clobber=True)
#
args.file = kludge_fil
# RAW_LRIS??
if args.raw_lris:
#
img, head, _ = arlris.read_lris(args.file)
# Generate hdu
hdu = fits.PrimaryHDU(img)
hdulist = fits.HDUList([hdu])
# Write
msgs.warn('Writing kludge file to {:s}'.format(kludge_fil))
hdulist.writeto(kludge_fil,clobber=True)
args.file = kludge_fil
# Spawn ginga
subprocess.call(["ginga", args.file])
if args.raw_lris:
msgs.warn('Removing kludge file {:s}'.format(kludge_fil))
subprocess.call(["rm", args.file])
def show_image(inp, chname='Image', wcs_img=None, **kwargs):
""" Displays input image in Ginga viewer
Supersedes method in xastropy
Parameters
----------
inp : str or ndarray (2D)
If str, assumes the image is written to disk
wcs_img : str, optional
If included, use this in WCS. Mainly to show wavelength array
Returns
-------
"""
from astropy.io import fits
from pypit import arlris
if isinstance(inp, basestring):
if '.fits' in inp:
if 'raw_lris' in kwargs.keys():
img, head, _ = arlris.read_lris(inp)
else:
hdu = fits.open(inp)
try:
exten = kwargs['exten']
except KeyError:
exten = 0
img = hdu[exten].data
else:
img = inp
viewer = connect_to_ginga()
ch = viewer.channel(chname)
name = 'image'
# Header
header = {}
header['NAXIS1'] = img.shape[1]
header['NAXIS2'] = img.shape[0]
if wcs_img is not None:
header['WCS-XIMG'] = wcs_img
#header['WCS-XIMG'] = '/home/xavier/REDUX/Keck/LRIS/2017mar20/lris_red_setup_C/MF_lris_red/MasterWave_C_02_aa.fits'
# Giddy up
ch.load_np(name, img, 'fits', header)
return viewer, ch
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('file', type=str, default=None, help='FITS file')
parser.add_argument("--list",
default=False,
help="List the extensions only?",
action="store_true")
parser.add_argument('--raw_lris', action="store_true")
parser.add_argument('--exten', type=int, help="FITS extension")
pargs = parser.parse_args()
# List?
if pargs.list:
from astropy.io import fits
hdu = fits.open(pargs.file)
print(hdu.info())
return
kludge_fil = 'tmp_ginga.fits'
# Setup for PYPIT imports
import subprocess
from astropy.io import fits
from pypit import pyputils
msgs = pyputils.get_dummy_logger()
from pypit import arlris
# Extension
if pargs.exten is not None:
hdu = fits.open(pargs.file)
img = hdu[pargs.exten].data
# Write
msgs.warn('Writing kludge file to {:s}'.format(kludge_fil))
hdunew = fits.PrimaryHDU(img)
hdulist = fits.HDUList([hdunew])
hdulist.writeto(kludge_fil, clobber=True)
#
pargs.file = kludge_fil
# RAW_LRIS??
if pargs.raw_lris:
#
img, head, _ = arlris.read_lris(pargs.file)
# Generate hdu
hdu = fits.PrimaryHDU(img)
hdulist = fits.HDUList([hdu])
# Write
msgs.warn('Writing kludge file to {:s}'.format(kludge_fil))
hdulist.writeto(kludge_fil, clobber=True)
pargs.file = kludge_fil
# Spawn ginga
subprocess.call(["ginga", pargs.file])
if pargs.raw_lris:
msgs.warn('Removing kludge file {:s}'.format(kludge_fil))
subprocess.call(["rm", pargs.file])
def load_frames(slf, fitsdict, ind, det, frametype='<None>', msbias=None,
trim=True, transpose=False):
"""
Load data frames, usually raw.
Bias subtract (if not msbias!=None) and trim (if True)
Parameters
----------
fitsdict : dict
Contains relevant information from fits header files
ind : list or array
integers of indices
det : int
Detector number, starts at 1
Returns
-------
frames : ndarray (3 dimensional)
One image per ind
"""
def load_indfr(name,ext):
msgs.work("Trim and overscan has not been applied")
temp = pyfits.getdata(name, ext)
return temp
msgs.info("Loading individual {0:s} frames".format(frametype))
if np.size(ind) == 0:
msgs.warn("No {0:s} frames to load".format(frametype))
return None
msgs.work("Implement multiprocessing here (better -- at the moment it's slower than not) to speed up data reading")
for i in range(np.size(ind)):
# Instrument specific read
if slf._argflag['run']['spectrograph'] in ['lris_blue', 'lris_red']:
temp, head0, _ = arlris.read_lris(fitsdict['directory'][ind[i]]+fitsdict['filename'][ind[i]], det=det)
else:
temp = pyfits.getdata(fitsdict['directory'][ind[i]]+fitsdict['filename'][ind[i]], slf._spect['fits']['dataext'])
temp = temp.astype(float) # Let us avoid uint16
if transpose: temp = temp.T
if msbias is not None:
if type(msbias) is np.ndarray:
temp -= msbias # Subtract the master bias frame
elif type(msbias) is str:
if msbias == "overscan":
arproc.sub_overscan(slf, det, temp)
else:
msgs.error("Could not subtract bias level when loading {0:s} frames".format(frametype))
if trim:
temp = arproc.trim(slf, temp, det)
if i == 0:
frames = np.zeros((temp.shape[0], temp.shape[1], np.size(ind)))
frames[:,:,i] = temp.copy()
else:
frames[:,:,i] = temp.copy()
del temp
# pool = mpPool(processes=np.min([slf._argflag['run']['ncpus'],np.size(ind)]))
# async_results = []
# for i in range(np.size(ind)):
# async_results.append(pool.apply_async(pyfits.getdata, (fitsdict['directory'][ind[i]]+fitsdict['filename'][ind[i]], slf._spect['fits']['dataext'])))
# pool.close()
# pool.join()
# map(ApplyResult.wait, async_results)
# for j in range(np.size(ind)):
# if j == 0:
# temp = async_results[j].get()
# frames = np.zeros((temp.shape[0], temp.shape[1], np.size(ind)))
# if msbias is None:
# frames[:,:,i] = temp
# else:
# frames[:,:,i] = temp - msbias
# del temp
# else:
# if msbias is None:
# frames[:,:,i] = async_results[j].get()
# else:
# frames[:,:,i] = async_results[j].get() - msbias
if np.size(ind) == 1:
msgs.info("Loaded {0:d} {1:s} frame successfully".format(np.size(ind), frametype))
else:
msgs.info("Loaded {0:d} {1:s} frames successfully".format(np.size(ind), frametype))
return frames
def load_frames(slf,
fitsdict,
ind,
det,
frametype='<None>',
msbias=None,
trim=True,
transpose=False):
"""
Load data frames, usually raw.
Bias subtract (if not msbias!=None) and trim (if True)
Parameters
----------
fitsdict : dict
Contains relevant information from fits header files
ind : list or array
integers of indices
det : int
Detector number, starts at 1
Returns
-------
frames : ndarray (3 dimensional)
One image per ind
"""
def load_indfr(name, ext):
msgs.work("Trim and overscan has not been applied")
temp = pyfits.getdata(name, ext)
return temp
msgs.info("Loading individual {0:s} frames".format(frametype))
if np.size(ind) == 0:
msgs.warn("No {0:s} frames to load".format(frametype))
return None
msgs.work(
"Implement multiprocessing here (better -- at the moment it's slower than not) to speed up data reading"
)
for i in range(np.size(ind)):
# Instrument specific read
if slf._argflag['run']['spectrograph'] in ['lris_blue', 'lris_red']:
temp, head0, _ = arlris.read_lris(fitsdict['directory'][ind[i]] +
fitsdict['filename'][ind[i]],
det=det)
else:
temp = pyfits.getdata(
fitsdict['directory'][ind[i]] + fitsdict['filename'][ind[i]],
slf._spect['fits']['dataext'])
temp = temp.astype(float) # Let us avoid uint16
if transpose: temp = temp.T
if msbias is not None:
if type(msbias) is np.ndarray:
temp -= msbias # Subtract the master bias frame
elif type(msbias) is str:
if msbias == "overscan":
arproc.sub_overscan(slf, det, temp)
else:
msgs.error(
"Could not subtract bias level when loading {0:s} frames"
.format(frametype))
if trim:
temp = arproc.trim(slf, temp, det)
if i == 0:
frames = np.zeros((temp.shape[0], temp.shape[1], np.size(ind)))
frames[:, :, i] = temp.copy()
else:
frames[:, :, i] = temp.copy()
del temp
# pool = mpPool(processes=np.min([slf._argflag['run']['ncpus'],np.size(ind)]))
# async_results = []
# for i in range(np.size(ind)):
# async_results.append(pool.apply_async(pyfits.getdata, (fitsdict['directory'][ind[i]]+fitsdict['filename'][ind[i]], slf._spect['fits']['dataext'])))
# pool.close()
# pool.join()
# map(ApplyResult.wait, async_results)
# for j in range(np.size(ind)):
# if j == 0:
# temp = async_results[j].get()
# frames = np.zeros((temp.shape[0], temp.shape[1], np.size(ind)))
# if msbias is None:
# frames[:,:,i] = temp
# else:
# frames[:,:,i] = temp - msbias
# del temp
# else:
# if msbias is None:
# frames[:,:,i] = async_results[j].get()
# else:
# frames[:,:,i] = async_results[j].get() - msbias
if np.size(ind) == 1:
msgs.info("Loaded {0:d} {1:s} frame successfully".format(
np.size(ind), frametype))
else:
msgs.info("Loaded {0:d} {1:s} frames successfully".format(
np.size(ind), frametype))
return frames
