def setup_cube(ngroups, nrows, ncols):
''' Set up fake data to test.'''
nints = 1
data_model = RampModel()
data_model.data = np.zeros(shape=(nints, ngroups, nrows, ncols), dtype=np.float32)
data_model.pixeldq = np.zeros(shape=(nrows, ncols), dtype=np.int32)
data_model.groupdq = np.zeros(shape=(nints, ngroups, nrows, ncols), dtype=np.float32)
data_model.meta.subarray.xstart = 1
data_model.meta.subarray.ystart = 1
data_model.meta.subarray.xsize = ncols
data_model.meta.subarray.ysize = nrows
data_model.meta.exposure.ngroups = ngroups
data_model.meta.instrument.name = 'NIRCAM'
saturation_model = SaturationModel()
saturation_model.data = np.zeros(shape=(2048, 2048), dtype=np.float32)
saturation_model.dq = np.zeros(shape=(2048, 2048), dtype=np.int32)
saturation_model.meta.subarray.xstart = 1
saturation_model.meta.subarray.ystart = 1
saturation_model.meta.subarray.xsize = 2048
saturation_model.meta.subarray.ysize = 2048
saturation_model.meta.instrument.name = 'NIRCAM'
saturation_model.meta.description = 'Fake data.'
saturation_model.meta.telescope = 'JWST'
saturation_model.meta.reftype = 'SaturationModel'
saturation_model.meta.author = 'Alicia'
saturation_model.meta.pedigree = 'Dummy'
saturation_model.meta.useafter = '2015-10-01T00:00:00'
return data_model, saturation_model
def _cube(ngroups, nrows, ncols):
nints = 1
data_model = RampModel((nints, ngroups, nrows, ncols))
data_model.meta.subarray.xstart = 1
data_model.meta.subarray.ystart = 1
data_model.meta.subarray.xsize = ncols
data_model.meta.subarray.ysize = nrows
data_model.meta.exposure.ngroups = ngroups
data_model.meta.instrument.name = 'NIRCAM'
data_model.meta.instrument.detector = 'NRCA1'
data_model.meta.observation.date = '2017-10-01'
data_model.meta.observation.time = '00:00:00'
saturation_model = SaturationModel((2048, 2048))
saturation_model.meta.subarray.xstart = 1
saturation_model.meta.subarray.ystart = 1
saturation_model.meta.subarray.xsize = 2048
saturation_model.meta.subarray.ysize = 2048
saturation_model.meta.instrument.name = 'NIRCAM'
saturation_model.meta.description = 'Fake data.'
saturation_model.meta.telescope = 'JWST'
saturation_model.meta.reftype = 'SaturationModel'
saturation_model.meta.author = 'Alicia'
saturation_model.meta.pedigree = 'Dummy'
saturation_model.meta.useafter = '2015-10-01T00:00:00'
return data_model, saturation_model
def get_sat_vals(self, file):
"""Read in SSB-format saturation reference file
and deal with bad values
"""
sat_model = SaturationModel(file)
svals = sat_model.data
#set pixels with saturation level of 0.
#to 65535
bad = svals == 0
svals[bad] = 65535
return svals
def _cube():
# create a JWST datamodel for NIRSPEC IRS2 data
data_model = RampModel((1, 5, 3200, 2048))
data_model.data = np.ones(((1, 5, 3200, 2048)))
data_model.groupdq = np.zeros(((1, 5, 3200, 2048)))
data_model.pixeldq = np.zeros(((3200, 2048)))
data_model.meta.instrument.name = 'NIRSPEC'
data_model.meta.instrument.detector = 'NRS1'
data_model.meta.instrument.filter = 'F100LP'
data_model.meta.observation.date = '2019-07-19'
data_model.meta.observation.time = '23:23:30.912'
data_model.meta.exposure.type = 'NRS_LAMP'
data_model.meta.subarray.name = 'FULL'
data_model.meta.subarray.xstart = 1
data_model.meta.subarray.xsize = 2048
data_model.meta.subarray.ystart = 1
data_model.meta.subarray.ysize = 2048
data_model.meta.exposure.nrs_normal = 16
data_model.meta.exposure.nrs_reference = 4
data_model.meta.exposure.readpatt = 'NRSIRS2RAPID'
# create a saturation model for the saturation step
saturation_model = SaturationModel((2048, 2048))
saturation_model.data = np.ones(
(2048, 2048)) * 60000 # saturation limit for every pixel is 60000
saturation_model.meta.description = 'Fake data.'
saturation_model.meta.telescope = 'JWST'
saturation_model.meta.reftype = 'SaturationModel'
saturation_model.meta.useafter = '2015-10-01T00:00:00'
saturation_model.meta.instrument.name = 'NIRSPEC'
saturation_model.meta.instrument.detector = 'NRS1'
saturation_model.meta.author = 'Clare'
saturation_model.meta.pedigree = 'Dummy'
saturation_model.meta.subarray.xstart = 1
saturation_model.meta.subarray.xsize = 2048
saturation_model.meta.subarray.ystart = 1
saturation_model.meta.subarray.ysize = 2048
return data_model, saturation_model
def _cube(xstart, ystart, ngroups, nrows, ncols):
nints = 1
# create a JWST datamodel for MIRI data
data_model = RampModel((nints, ngroups, nrows, ncols))
data_model.data += 1
data_model.meta.instrument.name = 'MIRI'
data_model.meta.instrument.detector = 'MIRIMAGE'
data_model.meta.instrument.filter = 'F1500W'
data_model.meta.instrument.band = 'N/A'
data_model.meta.observation.date = '2016-06-01'
data_model.meta.observation.time = '00:00:00'
data_model.meta.exposure.type = 'MIR_IMAGE'
data_model.meta.subarray.name = 'MASK1550'
data_model.meta.subarray.xstart = xstart
data_model.meta.subarray.xsize = ncols
data_model.meta.subarray.ystart = ystart
data_model.meta.subarray.ysize = nrows
# create a saturation model for the saturation step
saturation_model = SaturationModel((1032, 1024))
saturation_model.meta.description = 'Fake data.'
saturation_model.meta.telescope = 'JWST'
saturation_model.meta.reftype = 'SaturationModel'
saturation_model.meta.author = 'Alicia'
saturation_model.meta.pedigree = 'Dummy'
saturation_model.meta.useafter = '2015-10-01T00:00:00'
saturation_model.meta.instrument.name = 'MIRI'
saturation_model.meta.instrument.detector = 'MIRIMAGE'
saturation_model.meta.subarray.xstart = 1
saturation_model.meta.subarray.xsize = 1024
saturation_model.meta.subarray.ystart = 1
saturation_model.meta.subarray.ysize = 1032
return data_model, saturation_model
def make_files(self):
#loop over detector
for det in self.detectors:
satfile = [s for s in self.satfiles if det in s][0]
sat_model = SaturationModel(satfile)
sat = sat_model.data
#pixels with saturation level of 0.
#set sat level to 65535
bad = sat == 0
sat[bad] = 65535
linfile = [s for s in self.linfiles if det in s][0]
lin_model = LinearityModel(linfile)
lin = lin_model.coeffs
#pixels with bad linearity coeffs
#set so no correction is applied
nans = np.isnan(lin[1, :, :])
tmp = lin[1, :, :]
tmp[nans] = 1.
lin[1, :, :] = tmp
for i in range(2, 7):
tmp = lin[i, :, :]
nans = np.isnan(tmp)
tmp[nans] = 0.
lin[i, :, :] = tmp
#superbias file
sbfile = [s for s in self.sbfiles if det in s][0]
sb_model = SuperBiasModel(sbfile)
superbias = sb_model.data
#linearize the saturation values
sat_lin = self.linearize(sat - superbias, lin)
#loop over readout patterns
for readpat in self.readpatts:
nframe, nskip = self.readpatts[readpat]
#optional output plot
if self.plot:
xx = 400
yy = 400
fsize = 12
f = plt.figure()
a = f.add_subplot(111)
a2 = a.twiny()
f.subplots_adjust(bottom=0.2)
xs = np.arange(self.maxgroups * (nframe + nskip))
a.plot(xs,
np.repeat(sat[yy, xx], len(xs)),
linestyle=':',
color='blue',
linewidth=2,
label='Original Saturation')
a.plot(xs,
np.repeat(sat_lin[yy, xx] + superbias[yy, xx],
len(xs)),
linestyle=':',
color='red',
linewidth=2,
label='Linearized Saturation')
a.plot(xs,
np.repeat(superbias[yy, xx], len(xs)),
linestyle=':',
color='black',
linewidth=2,
label='Superbias Level')
#loop over groups
satramp = np.zeros((self.maxgroups, 2048, 2048))
linsatramp = np.zeros((self.maxgroups, 2048, 2048))
xfmeans = []
for i in range(self.maxgroups):
#exposure time to the final frame in the group
exptime = self.frametime * (nframe + nskip) * (i + 1)
satslope = sat_lin / exptime
#now calculate signals for each frame within the
#group, by reducing exposure time by one frametime
#for each
fsigs = np.zeros((nframe, 2048, 2048))
fsigs[0, :, :] = sat_lin
for frame in range(1, nframe):
fsigs[frame, :, :] = satslope * (
exptime - (self.frametime * frame))
linsatramp[i, :, :] = np.mean(fsigs,
axis=0) + superbias
#non-linearize fsigs
fsigs_nl = self.unlinearize(fsigs, lin, sat - superbias)
#add superbias back in
fsigs_nl += superbias
satramp[i, :, :] = np.mean(fsigs_nl, axis=0)
#print("Group: {}".format(i))
#print("Exptime: {}".format(exptime))
#print("Sat: {}, Lin Sat: {}".format(sat[yy,xx],sat_lin[yy,xx]))
#print("Satslope: {}".format(satslope[yy,xx]))
#print("Frame exps: {}".format(exptime-(self.frametime*np.arange(1,nframe))))
#print("signals: {}".format(fsigs[:,yy,xx]))
#print("mean signal: {}".format(satramp[i,yy,xx]))
#if i == 1:
# stop
if self.plot:
xf = np.array([-1])
xf = np.append(
xf,
np.arange((i + 1) * (nframe + nskip) - nframe,
(i + 1) * (nframe + nskip)))
yf = np.array([superbias[yy, xx]])
yf = np.append(
yf, fsigs[:, yy, xx][::-1] + superbias[yy, xx])
yfnl = np.array([superbias[yy, xx]])
yfnl = np.append(yfnl, fsigs_nl[:, yy, xx][::-1])
if i == 9:
a.plot(xf,
yf,
mfc='red',
mec='red',
color='black',
marker='o',
linestyle='-',
label='Linearized Frames',
alpha=0.5)
#a.plot(np.mean(xf[1:]),linsatramp[i,yy,xx],color='red',marker='8',mfc='none',mec='red',markersize=10,label='Linearized Frames Mean')
a.plot(xf,
yfnl,
mfc='black',
mec='black',
color='black',
marker='o',
linestyle='-',
label='Non-Linearized Frames',
alpha=0.5)
#a.plot(np.mean(xf[1:]),linsatramp[i,yy,xx],color='red',marker='8',mfc='none',mec='red',markersize=10,label='Linearized Frames Mean')
else:
a.plot(xf,
yf,
mfc='red',
mec='red',
color='black',
marker='o',
linestyle='-',
alpha=0.5)
#a.plot(np.mean(xf[1:]),linsatramp[i,yy,xx],color='red',mfc='none',mec='red',marker='8',markersize=10)
a.plot(xf,
yfnl,
mfc='black',
mec='black',
color='black',
marker='o',
linestyle='-',
alpha=0.5)
xfmeans.append(np.mean(xf[1:]))
#optional plot
if self.plot:
new_tick_locations = np.array(xfmeans)
a.plot(xfmeans,
satramp[:, yy, xx],
color='blue',
marker='8',
markersize=10,
label='Non-Linear Frames Mean')
a.legend(loc='lower right', numpoints=1, fontsize=fsize)
a.set_xlabel('Frame Number')
a.set_ylabel('Signal (DN)')
a.set_title('NRC{}, Readpattern: {}, Pixel ({},{})'.format(
det, readpat, xx, yy))
a.set_xlim(-1, np.max(xf) + 1)
a.set_ylim(0, 60000)
# Move twinned axis ticks and label from top to bottom
a2.xaxis.set_ticks_position("bottom")
a2.xaxis.set_label_position("bottom")
# Offset the twin axis below the host
a2.spines["bottom"].set_position(("axes", -0.15))
# Turn on the frame for the twin axis, but then hide all
# but the bottom spine
a2.set_frame_on(True)
a2.patch.set_visible(False)
for sp in a2.spines.itervalues():
sp.set_visible(False)
a2.spines["bottom"].set_visible(True)
a2.set_xticks(new_tick_locations)
a2.set_xticklabels(np.arange(len(new_tick_locations)))
a2.set_xlabel("Group Number")
f.savefig(
'GroupSatPlot_forTR_{}_{}_C_filledcircs.png'.format(
det, readpat))
#save output file
self.savefile(satramp, det, readpat)