def plot_timeline_focalplane(self):
'''
plot all the timelines in the focal plane
'''
args= {}
args['title'] = 'QUBIC Focal Plane: %s' % self.dataset_name
subttl_list = []
obsdates = []
for idx,asic_obj in enumerate(self.asic_list):
if asic_obj is None: continue
if not asic_obj.exist_timeline_data(): continue
key = 'ASIC%i' % (idx+1)
subttl_list.append(asic_obj.infotext())
args[key] = asic_obj.timeline_array()
obsdates.append(asic_obj.obsdate)
args['subtitle'] = '\n'.join(subttl_list)
args['obsdate'] = min(obsdates)
plot_fp(args)
return args
def plot_iv_focalplane(self,labels=True):
'''
plot all the I-V curves in the focal plane
'''
args= {}
args['title'] = 'QUBIC Focal Plane I-V curves: %s' % self.dataset_name
if not labels: args['nolabels'] = True
subttl_list = []
obsdates = []
ngood = []
tot_ngood = 0
tot_npixels = 0
for idx,asic_obj in enumerate(self.asic_list):
if asic_obj is None: continue
if not asic_obj.exist_iv_data(): continue
obsdates.append(asic_obj.obsdate)
key = 'ASIC%i' % (idx+1)
subttl_list.append(asic_obj.infotext())
bias,adu = asic_obj.best_iv_curve()
args[key] = adu
keyx = '%s x-axis' % key
args[keyx] = bias
keygood = '%s good' % key
args[keygood] = asic_obj.is_good_iv()
keybg = '%s bg' % key
args[keybg] = asic_obj.turnover()
filtersummary = asic_obj.filterinfo()
for idx,f in enumerate(filtersummary):
if f['ignore_turnover']:
args[keybg][idx] = None
ngood = asic_obj.ngood()
if ngood is not None:
tot_ngood += ngood
subttl_list.append('%i flagged as bad pixels : yield = %.1f%%' %
(asic_obj.NPIXELS-ngood,100.0*ngood/asic_obj.NPIXELS))
tot_npixels += asic_obj.NPIXELS
if tot_npixels>0:
subttl_list.append('overall yield %i/%i = %.1f%%' % (tot_ngood,tot_npixels,100.0*tot_ngood/tot_npixels))
args['subtitle'] = '\n'.join(subttl_list)
if len(obsdates)>0:
args['obsdate'] = min(obsdates)
plot_fp(args)
return args
def plot_timeline_physical_layout(self,
timeline_index=None,
xwin=True,
imin=None,
imax=None,
tmin=None,
tmax=None,
lutmin=None,
lutmax=None):
'''
plot the timeline curves in thumbnails mapped to the physical location of each detector
'''
TES2PIX = assign_pix2tes(self.obsdate)
if not self.exist_timeline_data(): return None
ntimelines = self.ntimelines()
if timeline_index is None:
# by default, plot the first one.
timeline_index = 0
if timeline_index >= ntimelines:
self.printmsg('Please enter a timeline between 0 and %i' %
(ntimelines - 1))
return None
tdata = self.tdata[timeline_index]
keys = tdata.keys()
timeline_npts = tdata['TIMELINE'].shape[1]
if lutmax is None:
lutmax = tdata['TIMELINE'].max() - tdata['TIMELINE'].min()
if lutmin is None:
lutmin = 0.0
if 'DATE-OBS' in keys:
timeline_date = tdata['DATE-OBS']
else:
timeline_date = self.obsdate
if 'BEG-OBS' in keys:
timeline_start = tdata['BEG-OBS']
else:
timeline_start = timeline_date
ttl = str('QUBIC Timeline curves (%s)' %
(timeline_start.strftime('%Y-%b-%d %H:%M UTC')))
if 'TES_TEMP' in keys:
tempstr = '%.0f mK' % (1000 * tdata['TES_TEMP'])
else:
if self.temperature is None:
tempstr = 'unknown'
else:
tempstr = str('%.0f mK' % (1000 * self.temperature))
subttl = str('Array %s, ASIC #%i, T$_\mathrm{bath}$=%s' %
(self.detector_name, self.asic, tempstr))
# use the plot_fp algorithm to plot the focal plane
asic_key = 'ASIC%i' % self.asic
args = {}
args['title'] = ttl
args['subtitle'] = subttl
pngname = str('QUBIC_Array-%s_ASIC%i_timeline_%s.png' %
(self.detector_name, self.asic,
timeline_start.strftime('%Y%m%dT%H%M%SUTC')))
pngname_fullpath = self.output_filename(pngname)
args['pngname'] = pngname_fullpath
# plot subsection of timeline
tlim = [0, timeline_npts]
if tmin is None:
tlim[0] = 0
else:
tlim[0] = tmin
if tmax is None:
tlim[1] = timeline_npts
else:
tlim[1] = tmax
args[asic_key] = self.timeline_array(
timeline_index=timeline_index)[:, tlim[0]:tlim[1]]
plot_fp(args)
return args
def plot_ASD_physical_layout(self,
timeline_index=0,
xwin=True,
amin=None,
amax=None,
nbins=None):
'''
plot the ASD for each TES in it's location in the focal plane
'''
if not self.exist_timeline_data():
print('ERROR! No timeline data!')
return None
ntimelines = self.ntimelines()
if timeline_index >= ntimelines:
print(
'ERROR! timeline index out of range. Enter an index between 0 and %i'
% (ntimelines - 1))
return None
if nbins is None: nbins = 1
Tbath = self.tdata[timeline_index]['TES_TEMP']
obsdate = self.tdata[timeline_index]['BEG-OBS']
fs = 1.0 / self.sample_period()
pngname = str(
'QUBIC_Array-%s_ASIC%i_ASD_%s.png' %
(self.detector_name, self.asic, obsdate.strftime('%Y%m%dT%H%M%SUTC')))
pngname_fullpath = self.output_filename(pngname)
ttl = 'Amplitude Spectral Density (%s)' % obsdate.strftime(
'%Y-%m-%d %H:%M')
subttl = '\nQUBIC Array %s, ASIC %i, T$_\mathrm{bath}$=%.1f mK' % (
self.detector_name, self.asic, 1000 * Tbath)
timeline_npts = self.timeline_array(timeline_index=timeline_index).shape[1]
psd_npts = timeline_npts // 2
if np.modf(0.5 * timeline_npts)[0] == 0.5:
psd_npts += 1
freq_array = np.zeros((self.NPIXELS, psd_npts))
psd_array = np.zeros((self.NPIXELS, psd_npts))
for TES_idx in range(self.NPIXELS):
TES = TES_idx + 1
timeline = self.timeline(TES, timeline_index)
current = self.ADU2I(timeline)
PSD, freqs = mlab.psd(current,
Fs=fs,
NFFT=timeline_npts // nbins,
window=mlab.window_hanning,
detrend='mean')
ASD = np.sqrt(PSD)
logASD = np.log10(ASD)
logFreq = np.log10(freqs)
psd_array[TES_idx, :] = logASD
freq_array[TES_idx, :] = logFreq
args = {}
args['title'] = ttl
args['subtitle'] = subttl
args['obsdate'] = obsdate
args['pngname'] = pngname_fullpath
key = 'ASIC%i' % self.asic
args[key] = psd_array
args['%s x-axis'] = freq_array
plot_fp(args)
return args
