def make_id_focalplane():
'''
make the matrix which has all the translation info for pixel identities
'''
global FPidentity
tes_grid = assign_tes_grid()
# initialize the matrix
names = 'index,row,col,quadrant,matrix,TES,PIX,ASIC'
fmts = 'int,int,int,int,a4,int,int,int'
FPidentity = np.recarray(names=names,formats=fmts,shape=(34*34))
fp_idx = 0
for j in range(34):
row = 33 - j
for i in range(34):
col = i
if row < 17:
if col < 17:
quadrant = 3
matrix = 'P87'
tes_y = 16 - col
tes_x = row
else:
quadrant = 4
matrix = 'PXX'
tes_y = col - 17
tes_x = row
else:
if col < 17:
quadrant = 2
matrix = 'PXX'
tes_x = col
tes_y = row - 17
else:
quadrant = 1
matrix = 'PXX'
tes_x = 33 - col
tes_y = row - 17
asic_no = tes_grid[tes_x,tes_y].ASIC
TES_no = tes_grid[tes_x,tes_y].TES
PIX = tes2pix(TES_no,asic_no)
rotated_asic = 2*(quadrant-3) + asic_no
if rotated_asic < 1:
rotated_asic += 8
if asic_no==0: rotated_asic = 0
FPidentity[fp_idx].index = fp_idx
FPidentity[fp_idx].quadrant = quadrant
FPidentity[fp_idx].matrix = matrix
FPidentity[fp_idx].TES = TES_no
FPidentity[fp_idx].PIX = PIX
FPidentity[fp_idx].ASIC = rotated_asic
FPidentity[fp_idx].row = row
FPidentity[fp_idx].col = col
fp_idx += 1
return FPidentity
def plot_rt_analysis(reslist,xwin=True):
'''
plot the results of the R-T analysis
we need to run fit_timeline() for this!
'''
global FIGSIZE
TES=reslist[0]['TES']
detector_name=reslist[0]['DET_NAME']
asic=reslist[0]['ASIC']
PIX=tes2pix(TES,asic)
Tbath=[]
R=[]
dates=[]
for res in reslist:
Tbath.append(1000*res['Tbath'])
R.append(res['R amplitude'])
dates.append(res['date'])
ntemps=len(Tbath)
sorted_index=sorted(range(ntemps), key=lambda i: Tbath[i])
Tsorted=np.array(Tbath)[sorted_index]
Rsorted=np.array(R)[sorted_index]
# show the dates of the measurements (just the day, not the time)
date_txt='Measurements taken:'
dates.sort()
d_prev=''
for d in dates:
d_str=d.strftime('%Y-%m-%d')
if d_str!=d_prev: date_txt+='\n %s' % d_str
d_prev=d_str
ttl='Array %s: Critical Temperature for TES %i (PIX %i) on ASIC %i' % (detector_name,TES,PIX,asic)
pngname='QUBIC_Array-%s_TES%03i_ASIC%i_Tcritical.png' % (detector_name,TES,asic)
if xwin: plt.ion()
else: plt.ioff()
fig=plt.figure(figsize=FIGSIZE)
fig.canvas.set_window_title('plt: '+ttl)
ax=plt.gca()
plt.title(ttl)
ax.plot(Tsorted,1e6*Rsorted,marker='D',color='blue')
ax.set_xlabel('T$_\mathrm{bath}$ / mK')
ax.set_ylabel('R$_\mathrm{TES}$ / $\mu\Omega$')
text_x=0.98
text_y=0.02
boxprops = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
ax.text(text_x,text_y,date_txt,va='bottom',ha='right',fontsize=10,transform = ax.transAxes,bbox=boxprops)
plt.savefig(pngname,format='png',dpi=100,bbox_inches='tight')
if xwin:plt.show()
else: plt.close('all')
return
def plot_timeline(self,
TES,
timeline_index=None,
fit=False,
ipeak0=None,
ipeak1=None,
plot_bias=True,
xwin=True,
timeaxis='pps',
ax=None,
fontsize=12):
'''
plot the timeline
'''
if not self.exist_timeline_data():
self.printmsg('ERROR! No timeline data.')
return None
if timeline_index is None:
# by default, plot the first one. For QubicStudio files, there is only one timeline
timeline_index = 0
ntimelines = self.ntimelines()
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()
warning_str = ''
if 'WARNING' in keys and tdata['WARNING']:
warning_str = '\n'.join(tdata['WARNING'])
if 'R_FEEDBK' in keys:
self.Rfeedback = tdata['R_FEEDBK']
if 'NSAMPLES' in keys:
self.nsamples = tdata['NSAMPLES']
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
if 'BIAS_MIN' in keys:
self.min_bias = tdata['BIAS_MIN']
if 'BIAS_MAX' in keys:
self.max_bias = tdata['BIAS_MAX']
biasphase = self.bias_phase()
ttl = str('QUBIC Timeline curve for TES#%3i (%s)' %
(TES, 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))
fbstr = ''
if 'R_FEEDBK' in keys:
if tdata['R_HEATER'] == 1:
onoff = 'ON'
else:
onoff = 'OFF'
fbstr = ', Feedback Relay: %.0fk$\Omega$, Heater %s' % (
tdata['R_FEEDBK'] * 1e-3, onoff)
subttl = str('Array %s, ASIC #%i, Pixel #%i, Temperature %s%s' %
(self.detector_name, self.asic, tes2pix(
TES, self.asic), tempstr, fbstr))
if xwin: plt.ion()
else: plt.ioff()
if ax is None:
newplot = True
fig = plt.figure(figsize=self.figsize)
figure_window_title(fig, ttl)
ax = plt.gca()
else:
newplot = False
ax.set_xlabel('time / seconds', fontsize=fontsize)
ax.set_ylabel('Current / $\mu$A', fontsize=fontsize)
ax.tick_params(axis='both', labelsize=fontsize)
if warning_str:
boxprops = {}
boxprops['alpha'] = 0.4
boxprops['color'] = 'red'
boxprops['boxstyle'] = 'round'
ax.text(0.5,
0.5,
warning_str,
ha='center',
va='center',
fontsize=2 * fontsize,
transform=ax.transAxes,
bbox=boxprops)
TES_idx = TES_index(TES)
timeline = self.timeline(TES, timeline_index)
current = self.ADU2I(timeline) # uAmps
timeline_npts = len(timeline)
self.printmsg(
'DEBUG: calling timeline_timeaxis from plot_timeline() with axistype=%s'
% timeaxis,
verbosity=4)
time_axis = self.timeline_timeaxis(timeline_index, axistype=timeaxis)
fitparms = None
if fit:
fitparms = self.fit_timeline(TES, timeline_index, ipeak0, ipeak1)
ipeak0 = 0
ipeak1 = timeline_npts - 1
peak0 = time_axis[ipeak0]
peak1 = time_axis[ipeak1]
if plot_bias:
if self.timeline_conversion is None:
self.timeline2adu(TES=TES,
timeline_index=timeline_index,
timeaxis=timeaxis)
if self.timeline_conversion is None or self.min_bias is None or self.max_bias is None:
plot_bias = False
else:
ipeak0 = self.timeline_conversion['ipeak0']
ipeak1 = self.timeline_conversion['ipeak1']
peak0 = self.timeline_conversion['peak0']
peak1 = self.timeline_conversion['peak1']
shift = self.timeline_conversion['shift']
if plot_bias:
ysine = None
if biasphase is not None:
self.printmsg('DEBUG: taking ysine from QubicStudio FITS file',
verbosity=4)
ysine = self.timeline_vbias
sinelabel = 'V$_\mathrm{bias}$ from QubicStudio FITS file'
elif fitparms is None:
self.printmsg('DEBUG: taking ysine from peak to peak', verbosity=4)
bias_period = peak1 - peak0
amplitude = 0.5 * (self.max_bias - self.min_bias)
offset = self.min_bias + amplitude
sinelabel = 'sine curve period=%.2f seconds\npeaks determined from TES %i' % (
bias_period, self.timeline_conversion['TES'])
ysine = offset + amplitude * np.sin(
(time_axis - peak0) * 2 * np.pi / bias_period + 0.5 * np.pi +
shift * 2 * np.pi)
else:
self.printmsg(
'DEBUG: taking ysine from timeline fit to sine curve',
verbosity=4)
bias_period = fitparms['period']
amplitude = fitparms['amplitude']
offset = fitparms['offset']
shift = fitparms['phaseshift']
if bias_period is not None and amplitude is not None:
sinelabel = 'best fit sine curve: period=%.2f seconds, amplitude=%.2f $\mu$A' % (
bias_period, amplitude)
ysine = self.model_timeline(time_axis, bias_period, shift,
offset, amplitude)
if ysine is None: plot_bias = False
if newplot:
fig.suptitle(ttl + '\n' + subttl, fontsize=fontsize)
else:
ax.text(0.5,
1.0,
ttl + '\n' + subttl,
va='bottom',
ha='center',
fontsize=fontsize,
transform=ax.transAxes)
curve1 = ax.plot(time_axis, current, label='I-V timeline', color='blue')
#ymax=max([current[ipeak0],current[ipeak1]])
ymax = np.nanmax(current)
if np.isnan(ymax):
ymax = 1.0
ymin = np.nanmin(current)
if np.isnan(ymin):
ymin = 1.0
yrange = ymax - ymin
if np.isnan(yrange) or yrange == 0:
yrange = 0.1
yminmax = (ymin - 0.02 * yrange, ymax + 0.02 * yrange)
ax.plot([peak0, peak0], yminmax, color='red')
ax.plot([peak1, peak1], yminmax, color='red')
ax.set_ylim(yminmax)
if plot_bias:
if fitparms is None:
ax_bias = ax.twinx()
ax_bias.set_ylabel('Bias / V',
rotation=270,
va='bottom',
fontsize=fontsize)
if self.min_bias == self.max_bias:
ax_bias.set_ylim([self.min_bias - 1, self.max_bias + 1])
else:
ax_bias.set_ylim([self.min_bias, self.max_bias])
ax_bias.tick_params(axis='both', labelsize=fontsize)
curve2_ax = ax_bias
else:
curve2_ax = ax
self.printmsg(
'DEBUG: plotting sine curve for bias: len(time_axis)=%i, len(ysine)=%i'
% (len(time_axis), len(ysine)),
verbosity=4)
curve2 = curve2_ax.plot(time_axis,
ysine,
label=sinelabel,
color='green')
curves = curve1 + curve2
else:
curves = curve1
labs = [l.get_label() for l in curves]
ax.legend(curves, labs, loc=0, fontsize=fontsize)
pngname = str('TES%03i_array-%s_ASIC%i_timeline_%s.png' %
(TES, self.detector_name, self.asic,
timeline_start.strftime('%Y%m%dT%H%M%SUTC')))
pngname_fullpath = self.output_filename(pngname)
if newplot and isinstance(pngname_fullpath, str):
plt.savefig(pngname_fullpath,
format='png',
dpi=100,
bbox_inches='tight')
if xwin: plt.show()
else: plt.close('all')
if fitparms: return fitparms
return True