def image_asics(data1=None, data2=None, all1=None):
"""
Return an image of detectors on the focal plane.
Each asic has 124 TES and 4 thermometers.
Parameters
----------
data1 : 1D array of shape 128 or 256
Signal of 1 or 2 asics at a given time sample.
data2 : same as data1
all1 : bool
True if you have 2 asics and False if you have only one.
"""
if all1 is not None:
nn = len(all1)/2
data2 = all1[nn:]
data1 = all1[0:nn]
if data1 is not None:
a1 = qp()
a1.assign_asic(1)
a1.pix_grid[1,16] = 1005
a1.pix_grid[2,16] = 1006
a1.pix_grid[3,16] = 1007
a1.pix_grid[4,16] = 1008
if data2 is not None:
a2 = qp()
a2.assign_asic(2)
a2.pix_grid[0,15] = 1004
a2.pix_grid[0,14] = 1003
a2.pix_grid[0,13] = 1002
a2.pix_grid[0,12] = 1001
nrows = 17
ncols = 17
img = np.zeros((nrows,ncols)) + np.nan
for row in range(nrows):
for col in range(ncols):
if data1 is not None:
physpix = a1.pix_grid[row , col]
if physpix in a1.TES2PIX[0]:
TES = a1.pix2tes(physpix)
img[row,col] = data1[TES - 1]
if data2 is not None:
physpix = a2.pix_grid[row, col]
if physpix in a2.TES2PIX[1]:
TES = a2.pix2tes(physpix)
img[row,col] = data2[TES - 1]
return img
def read_data_from_20170905():
'''
read the data from the measurement campaign of 5/6 Sept 2017
'''
fitslist=['QUBIC_TES_20170905T082847UTC.fits',
'QUBIC_TES_20170905T091628UTC.fits',
'QUBIC_TES_20170905T095550UTC.fits',
'QUBIC_TES_20170905T145150UTC.fits',
'QUBIC_TES_20170905T154040UTC.fits',
'QUBIC_TES_20170905T165531UTC.fits',
'QUBIC_TES_20170905T172101UTC.fits',
'QUBIC_TES_20170906T075624UTC.fits',
'QUBIC_TES_20170906T085757UTC.fits',
'QUBIC_TES_20170906T091909UTC.fits',
'QUBIC_TES_20170906T094433UTC.fits',
'QUBIC_TES_20170906T101411UTC.fits',
'QUBIC_TES_20170906T113411UTC.fits',
'QUBIC_TES_20170906T121954UTC.fits',
'QUBIC_TES_20170906T123554UTC.fits']
datlist=[]
for F in fitslist:
datlist.append(qp())
datlist[-1].read_fits(F)
return datlist
def qs2array(file, FREQ_SAMPLING, timerange=None):
"""
Loads qubic instance to create 'dd' which is TOD for each TES
Also normalises raw data
Also returns 'time' which is linear time array
Can also specify a timerange
"""
a = qp()
a.read_fits(file)
npix = a.NPIXELS
nsamples = len(a.timeline(TES=1))
dd = np.zeros((npix, nsamples))
##### Normalisation en courant
#Rfb=100e3 #changing to read from pystudio dictionary
Rfb = a.Rfeedback
NbSamplesPerSum = 64. #this could also be a.NPIXELS_sampled
gain=1./2.**7*20./2.**16/(NbSamplesPerSum*Rfb)
for i in xrange(npix):
dd[i,:] = a.timeline(TES=i+1)
dd[i,:] = gain * dd[i,:]
time = np.arange(nsamples)/FREQ_SAMPLING
if timerange is not None:
print('Selecting time range: {} to {} sec'.format(timerange[0], timerange[1]))
oktime = (time >= timerange[0]) & (time <= timerange[1])
time = time[oktime]
dd = dd[:,oktime]
return time, dd, a
def make_translation_table():
go = qp()
transdic = []
RTlist = read_txtcolumns('P73_room_temperature.txt')
npts_rt = len(RTlist)
for val in RTlist:
entry = {}
PIX = int(eval(val[0]))
entry['PIX'] = PIX
try:
entry['R300'] = eval(val[1])
except:
if val[1] == 'HS':
entry['R300'] = 'open'
else:
entry['R300'] = val[1]
if PIX in go.TES2PIX[0, :]:
asic = 1
elif PIX in go.TES2PIX[1, :]:
asic = 2
else:
entry['TES'] = -1
entry['ASIC'] = -1
transdic.append(entry)
continue
go.assign_asic(asic)
TES = go.pix2tes(PIX)
entry['TES'] = TES
entry['ASIC'] = asic
transdic.append(entry)
transdic = assign_openloop_results(transdic, 1)
transdic = assign_openloop_results(transdic, 2)
# assign the results of the Carbon Fibre tests
transdic = assign_carbon_fibre_results(transdic)
# make sure all entries have all keys
allkeys = ['TES', 'PIX', 'ASIC', 'R300', 'OpenLoop', 'CarbonFibre']
nkeys = len(allkeys)
for entry in transdic:
for key in allkeys:
if not key in entry:
entry[key] = 'no data'
return transdic
def assign_room_temperature_results(transdic, detector_name):
'''
assign the Room Temperature Results
'''
RTlist = read_txtcolumns('%s_room_temperature.txt' % detector_name)
if not isinstance(RTlist, list):
print('WARNING! Could not find the Room Temperature test results')
return transdic
go = qp()
npts_rt = len(RTlist)
for val in RTlist:
entry = {}
PIX = int(eval(val[0]))
entry['PIX'] = PIX
try:
entry['R300'] = eval(val[1])
except:
if val[1] == 'HS':
entry['R300'] = 'open'
else:
entry['R300'] = val[1]
if PIX in go.TES2PIX[0, :]:
asic = 1
elif PIX in go.TES2PIX[1, :]:
asic = 2
else:
entry['TES'] = -1
entry['ASIC'] = -1
transdic.append(entry)
continue
go.assign_asic(asic)
TES = go.pix2tes(PIX)
entry['TES'] = TES
entry['ASIC'] = asic
transdic.append(entry)
return transdic
def read_data_from_20170804():
'''
read data from the test campaign of 2017-08-04
I-V curves of P73 at different bath temperatures
'''
files=[]
datlist=[]
files.append('QUBIC_TES_20170804T134238UTC.fits')
files.append('QUBIC_TES_20170804T144916UTC.fits')
files.append('QUBIC_TES_20170804T150120UTC.fits')
files.append('QUBIC_TES_20170804T151319UTC.fits')
files.append('QUBIC_TES_20170804T152431UTC.fits')
files.append('QUBIC_TES_20170804T153704UTC.fits')
files.append('QUBIC_TES_20170804T155105UTC.fits')
files.append('QUBIC_TES_20170804T160111UTC.fits')
idx=0
for F in files:
datlist.append(qp())
datlist[idx].read_fits(F)
idx+=1
return datlist
def plot_V_phi(Asic):
# Mutual inductance
Min=1./10.4E-6
AsicNum=Asic
### V Phi plot ###
# !!!!!!!!!!!!!!!! Attention un peu long , a remanier !!!!!!!!!!!!!!
for z in range (128):
# the first 8 index are too low to induce curves, so we start at 7
for i in range(9):
thedir = dirs[i+7]
# reading the data from qubicpack
b = qp()
b.read_qubicstudio_dataset(thedir, asic=AsicNum)
Rbias=b.Rbias
# Amplitude peak to peak of the sinus
amp=b.max_bias-b.min_bias
# offset of the sinus signal
offset=0.5*(b.max_bias+b.min_bias)
Vbias=(offset+amp*b.bias_phase()/2.)
# Ind : this allow to avoid the noncoherent points in raw data for the flux
ind=np.argsort(Vbias)
# temp is a temporaly variable that will be used after for the filter
if i == 0:
Vsqoffset=np.mean(b.timeline(TES=z+1)*62.5/(70.*100)/(b.nsamples-b.n_masked()))
temp = b.timeline(TES=z+1)[ind]*62.5/(70.*100)/(b.nsamples-b.n_masked())-Vsqoffset
# savitzky golay filter
filt = sp.signal.savgol_filter(temp, 51, 3)
### plot parameters ###
plt.plot(((Vbias[ind]/Rbias*Min)-(Vbias[ind]/Rbias*Min)[0])/2.,filt*-1)
plt.grid()
plt.xlabel('Flux (in quantum of flux)')
plt.ylabel('Tension ($\mu$V)')
plt.title('ASIC%i (input), SQUID number %i'%(AsicNum,(z+1)))
save("./TEST/Analysis"+day+"/ASIC%i_Vphi/ASIC%i_%i"%(AsicNum,AsicNum,(z+1)),ext="png",close=True,verbose=True)
import glob
from qubicpack import qubicpack as qp
import string
basedir = '/Users/hamilton/Qubic/Grounding/'
dirs = glob.glob(basedir+'/*')
motor = []
grounding = []
for i in range(len(dirs)):
bla = string.split(dirs[i],'_')
grounding.append(bla[-2])
motor.append(bla[-1])
a1 = qp()
a1.read_qubicstudio_dataset(dirs[0], asic=1)
a2 = qp()
a2.read_qubicstudio_dataset(dirs[0], asic=2)
nsamples = len(a1.timeline(TES=66))
FREQ_SAMPLING = 1./a1.sample_period()
spectrum, freq = mlab.psd(2
a1.timeline(TES=66), Fs=FREQ_SAMPLING, NFFT=nsamples, window=mlab.window_hanning)
ok1 = np.array(FitsArray('/Users/hamilton/Qubic/ExternalSource/ScanAz2019-01-30_OK_Asic1.fits'), dtype=bool)
ok2 = np.array(FitsArray('/Users/hamilton/Qubic/ExternalSource/ScanAz2019-01-30_OK_Asic2.fits'), dtype=bool)
ok = np.append(ok1,ok2)
spectrum_f[okfit],
np.ones(np.sum(okfit)),
guess,
functname=dl.gauss,
fixpars=[1, 0, 0, 0, 0],
nohesse=True,
force_chi2_ndf=True)
return t_data, data, FREQ_SAMPLING, freq_f, spectrum_f, amp_peak, okfit, res
# =========== Test with one TES ==============
# Read data from a given ASIC
AsicNum = 1
a = qp()
a.read_qubicstudio_dataset(thedir, asic=AsicNum)
freq_mod = 1.
tes_num = 44
t_data, data, FREQ_SAMPLING, freq_f, spectrum_f, amp_peak, okfit, res = get_amp_first_harmonic(
a, tes_num, freq_mod)
print('FREQ_SAMPLING = {}'.format(FREQ_SAMPLING))
# Signal as a function of time
plot(t_data, (data - np.mean(data)) / np.std(data), label='Data')
# Look at the amplitude of the peak
print('Amplitude = {}'.format(res[1][2]))
figure('TES spectrum')
$auth: Steve Torchinsky <*****@*****.**>
$created: Fri 15 Dec 2017 17:58:35 CET
$license: GPLv3 or later, see https://www.gnu.org/licenses/gpl-3.0.txt
This is free software: you are free to change and
redistribute it. There is NO WARRANTY, to the extent
permitted by law.
this is a translation of the Dscript: Comp_offset_ASIC1b.dscript
once tested, it will be moved into qubicpack acquisition.py
'''
from qubicpack import qubicpack as qp
import numpy as np
go = qp()
go.assign_asic(1)
go.assign_integration_time(0.5)
client = go.connect_QubicStudio()
if client is None: quit()
count = 10
consigne = 0
# first switch off the loop
client.sendActivatePID(go.QS_asic_index, 0)
# make sure relay=10kOhm val=1 -> 10kOhm, val=0 -> 100kOhm
client.sendSetFeedbackRelay(go.QS_asic_index, 1)
def plot_physical_layout(a1=None,
a2=None,
figsize=(16, 16),
xwin=True,
lutmin=None,
lutmax=None,
timeline_index=None,
tmin=None,
tmax=None):
'''
plot an image of the TES array labeling each pixel
plot the I-V curves in the appropriate boxes if a1 and/or a2 given
'''
obj_list = [a1, a2]
asic1_obj = None
asic2_obj = None
temperature = None
temperature_str = ''
detector_name = 'undefined'
for obj in obj_list:
if isinstance(obj, qp):
temperature = obj.temperature
detector_name = obj.detector_name
if obj.asic == 1:
asic1_obj = obj
elif obj.asic == 2:
asic2_obj = obj
else:
print(
'PROBLEM! QubicPack object does not have a valid ASIC definition'
)
return None
if asic1_obj is None:
asic1_obj = qp()
asic1_obj.assign_asic(1)
asic1_fontsize = figsize[0]
if asic2_obj is None:
asic2_obj = qp()
asic2_obj.assign_asic(2)
asic2_fontsize = figsize[0]
# Option: plot timeline instead of I-V
if timeline_index is not None:
print('plotting timelines in focal plane')
tdata = asic1_obj.tdata[timeline_index]
timeline_npts = tdata['TIMELINE'].shape[1]
ilim = [None, None]
tlim = [0, timeline_npts]
if tmin is not None:
tlim[0] = tmin
if tmax is not None:
tlim[1] = tmax
if lutmax is None:
lutmax = tdata['TIMELINE'].max() - tdata['TIMELINE'].min()
if lutmin is None:
lutmin = 0.0
# Option: a1 and a2 can be simply arrays with numbers to use as the colours for each pixel
asic1_mapdata = False
asic2_mapdata = False
if (isinstance(a1, np.ndarray) or isinstance(a1, list)) and len(a1) == 128:
print('using mapping data for asic 1')
asic1_mapdata = True
if (isinstance(a2, np.ndarray) or isinstance(a2, list)) and len(a2) == 128:
print('using mapping data for asic 2')
asic2_mapdata = True
if lutmin is None:
if asic1_mapdata and asic2_mapdata:
lutmin = min(min(a1), min(a2))
elif asic1_mapdata:
lutmin = min(a1)
elif asic2_mapdata:
lutmin = min(a2)
if lutmax is None:
if asic1_mapdata and asic2_mapdata:
lutmax = max(max(a1), max(a2))
elif asic1_mapdata:
lutmax = max(a1)
elif asic2_mapdata:
lutmax = max(a2)
asic1_data = True
asic1_iv_data = True
asic1_timeline_data = True
asic1_fontsize = 8
asic2_data = True
asic2_iv_data = True
asic2_timeline_data = True
asic2_fontsize = 8
if not asic1_obj.exist_iv_data():
asic1_iv_data = False
if not asic1_obj.exist_timeline_data() or timeline_index is None:
asic1_timeline_data = False
if not asic1_iv_data and not asic1_timeline_data:
asic1_data = False
asic1_fontsize = figsize[0]
asic1_subttl = 'ASIC 1 blue background'
else:
asic1_subttl = 'Array %s ASIC1 black curves' % asic1_obj.detector_name
if not asic2_obj.exist_iv_data():
asic2_iv_data = False
if not asic2_obj.exist_timeline_data() or timeline_index is None:
asic2_timeline_data = False
if not asic2_iv_data and not asic2_timeline_data:
asic2_data = False
asic2_fontsize = figsize[0]
asic2_subttl = 'ASIC 2 green background'
else:
asic2_subttl = 'Array %s ASIC2 blue curves' % asic2_obj.detector_name
asic1_obj.figsize = figsize
fontsize = figsize[0]
ttlfontsize = figsize[0] * 1.2
nrows = asic1_obj.pix_grid.shape[0]
ncols = asic1_obj.pix_grid.shape[1]
if temperature is not None:
temperature_str = '%.0fmK' % (1000 * temperature)
if xwin: plt.ion()
else: plt.ioff()
fig, ax = plt.subplots(nrows, ncols, figsize=asic1_obj.figsize)
fig.text(0.5, 0.985, 'QUBIC TES array', ha='center', fontsize=ttlfontsize)
pngname = 'TES_Array-%s_%s.png' % (detector_name, temperature_str)
if xwin: fig.canvas.set_window_title('plt: QUBIC TES array')
ngood = 0
npix = 0
if asic1_data:
asic1_subttl+=', data from %s, T$_\mathrm{bath}$=%.3fmK'\
% (asic1_obj.obsdate.strftime('%Y-%m-%d %H:%M'),asic1_obj.temperature*1000)
ngood += asic1_obj.ngood()
npix += asic1_obj.NPIXELS
if asic2_data:
asic2_subttl+=', data from %s, T$_\mathrm{bath}$=%.3fmK'\
% (asic2_obj.obsdate.strftime('%Y-%m-%d %H:%M'),asic2_obj.temperature*1000)
ngood += asic2_obj.ngood()
npix += asic2_obj.NPIXELS
subttl = asic1_subttl + '\n' + asic2_subttl
if asic1_iv_data or asic2_iv_data:
subttl += '\nbad pixels in black background. %i good pixels out of %i = %.1f%%' % (
ngood, npix, 100.0 * ngood / npix)
subttl += '\nV$_\mathrm{turnover}$ from red to blue (%.1fV to %.1fV)' % (
lutmin, lutmax)
fig.suptitle(subttl, fontsize=fontsize)
for row in range(nrows):
for col in range(ncols):
TES = 0
ax[row, col].get_xaxis().set_visible(False)
ax[row, col].get_yaxis().set_visible(False)
# the pixel identity associated with its physical location in the array
physpix = asic1_obj.pix_grid[row, col]
pix_index = physpix - 1
face_colour = 'black'
label_colour = 'white'
text_y = 0.5
text_x = 0.5
if physpix == 0:
pix_label = ''
label_colour = 'black'
face_colour = 'black'
elif physpix in asic1_obj.TES2PIX[0]:
TES = asic1_obj.pix2tes(physpix)
TES_index = TES - 1
pix_label = str('%i' % TES)
label_colour = 'yellow'
face_colour = 'blue'
curve_colour = 'black'
fontsize = asic1_fontsize
if asic1_iv_data:
Iadjusted = asic1_obj.adjusted_iv(TES)
turnover = asic1_obj.turnover(TES)
text_x = max(asic1_obj.vbias)
text_y = min(Iadjusted)
if (asic1_obj.is_good_iv(TES)
is not None) and (not asic1_obj.is_good_iv(TES)):
face_colour = 'black'
label_colour = 'white'
curve_colour = 'white'
else:
face_colour = mylut(turnover, lutmin, lutmax)
asic1_obj.draw_iv(Iadjusted,
colour=curve_colour,
axis=ax[row, col])
if asic1_timeline_data:
face_colour = 'white'
label_colour = 'black'
tline = asic1_obj.timeline(timeline_index=timeline_index,
TES=TES)
ax[row, col].plot(tline, color=curve_colour)
ax[row, col].set_xlim(tlim)
# get min/max from timeline window
negpeak = min(tline[tlim[0]:tlim[1]])
pospeak = max(tline[tlim[0]:tlim[1]])
ilim[0] = negpeak
ilim[1] = pospeak
text_x = tlim[1]
text_y = ilim[0]
ax[row, col].set_xlim(tlim)
ax[row, col].set_ylim(ilim)
elif asic1_mapdata:
face_colour = mylut(a1[TES_index], lutmin, lutmax)
elif physpix in asic2_obj.TES2PIX[1]:
TES = asic2_obj.pix2tes(physpix)
TES_index = TES - 1
pix_label = str('%i' % TES)
label_colour = 'black'
face_colour = 'green'
curve_colour = 'blue'
fontsize = asic2_fontsize
if asic2_iv_data:
Iadjusted = asic2_obj.adjusted_iv(TES)
turnover = asic2_obj.turnover(TES)
text_x = max(asic2_obj.vbias)
text_y = min(Iadjusted)
curve_colour = 'blue'
if (asic2_obj.is_good_iv(TES)
is not None) and (not asic2_obj.is_good_iv(TES)):
face_colour = 'black'
label_colour = 'white'
curve_colour = 'white'
else:
face_colour = mylut(turnover, lutmin, lutmax)
asic2_obj.draw_iv(Iadjusted,
colour=curve_colour,
axis=ax[row, col])
if asic2_timeline_data:
face_colour = 'white'
tline = asic2_obj.timeline(timeline_index=timeline_index,
TES=TES)
ax[row, col].plot(tline, color=curve_colour)
ax[row, col].set_xlim(tlim)
# get min/max from timeline window
negpeak = min(tline[tlim[0]:tlim[1]])
pospeak = max(tline[tlim[0]:tlim[1]])
ilim[0] = negpeak
ilim[1] = pospeak
text_x = tlim[1]
text_y = ilim[0]
ax[row, col].set_xlim(tlim)
ax[row, col].set_ylim(ilim)
elif asic2_mapdata:
face_colour = mylut(a2[TES_index], lutmin, lutmax)
else:
pix_label = '???'
label_colour = 'blue'
face_colour = 'yellow'
ax[row, col].set_facecolor(face_colour)
ax[row, col].text(text_x,
text_y,
pix_label,
va='center',
ha='center',
color=label_colour,
fontsize=fontsize)
plt.savefig(pngname, format='png', dpi=100, bbox_inches='tight')
if xwin: plt.show()
else: plt.close('all')
return
import os,sys
jupyter=False
if sys.argv[0].find('ipykernel')>=0:jupyter=True
# make plots within the notebook if using jupyter notebook
if jupyter:
%matplotlib notebook
import matplotlib.pyplot as plt
from qubicpack import qubicpack as qp
from qubicpack.temperature_analysis import *
from qubicpack.plot_physical_layout import *
# create the qubicpack object and read the data
d0=qp()
if jupyter: d0.figsize=9,5
# the first data file contains measurements at high temperature
# these are used to correct the Normal Resistance
d0.read_fits('QUBIC_timeline_20180301T184953UTC.fits')
# The data contains multiple timelines at different temperatures
# you can print out a summary table
print_datlist(d0)
# we will work with the first timeline which is at 601mK
# (we set timeline_index=0)
# first of all, have a look at all timelines for all the TES
d0.plot_timeline_physical_layout(timeline_index=0)
def plot_histo_sig(Asic):
AsicNum=Asic
histo=np.empty(16) # create tab for peak to peak val
data=np.empty((128,16)) # create a tab for each squid to keep all ptp value for each
invdata=np.empty((16,128))
for i in range (16) :
thedir = dirs[i]
h = qp()
h.read_qubicstudio_dataset(thedir, asic=AsicNum)
for z in range (128):
# Amplitude peak to peak of the sinus
amp=h.max_bias-h.min_bias
# offset of the sinus signal
offset=0.5*(h.max_bias+h.min_bias)
Vbias=(offset+amp*h.bias_phase()/2.)
# Ind : this allow to avoid the noncoherent points in raw data for the flux
ind=np.argsort(Vbias)
# temp is a temporaly variable that will be used after for the filter
if i == 0 :
Vsqoffset=np.mean(h.timeline(TES=z+1)*62.5/(70.*100)/(h.nsamples-h.n_masked()))
temp = h.timeline(TES=z+1)[ind]*62.5/(70.*100)/(h.nsamples-h.n_masked())-Vsqoffset
# savitzky golay filter
filt = sp.signal.savgol_filter(temp, 51, 3)
histo[i] = np.max(filt) - np.min(filt)
data[z,i]=histo[i]
invdata[i,z]=histo[i]
plt.plot(data)
plt.grid()
plt.ylabel("PtP value")
plt.xlabel("Number of SQUID")
save("./TEST/Analysis"+day+"/Results/ASIC%i_data_plot1"%AsicNum,ext="png",close=True,verbose=True)
plt.plot(invdata[:,:])
plt.grid()
plt.xlabel("Intensity (index of I)")
plt.ylabel("SQUIDs")
save("./TEST/Analysis"+day+"/Results/ASIC%i_data_plot2"%AsicNum,ext="png",close=True,verbose=True)
# argmax take the position of the maxvalue for each squid
plt.hist(np.argmax(data, axis=1), range=[0,16], bins=16)
plt.grid()
plt.ylabel("Number of SQUIDs")
plt.xlabel("Index of current")
plt.title("Histogram of the optimum current for the SQUID response for ASIC 1")
save("./TEST/Analysis"+day+"/Results/ASIC%i_Histogram"%AsicNum,ext="png",close=True,verbose=True)
plt.hist(data[:,9],range=[0,30], bins=30, alpha = 0.5, color= 'r' ,label="Isq = 25.5 µA")
plt.hist(data[:,10],range=[0,30], bins=30, alpha = 0.5, color= 'b',label="Isq = 28 µA ")
plt.hist(data[:,11],range=[0,30], bins=30, alpha = 0.5, color= 'g', label="Isq = 30.6 µA")
plt.legend()
plt.grid()
plt.xlabel("Voltage ($\mu$V)")
plt.xlim(0,25)
plt.ylabel('Number of SQUID')
plt.title("ASIC 1 histogram")
save("./TEST/Analysis"+day+"/Results/ASIC%i_Histogram_multiindex"%AsicNum,ext="png",close=True,verbose=True)
dat=np.empty(16)
ind=np.empty(16)
file = open("mean.txt", "w")
for z in range (16):
file.write("for index %i"%z)
a=np.shape((np.where(data[:,z]>= 10)))
prct=a[1]/128. *100
file.write("%f working squid" %prct)
file.write("median = %f" %np.median(data[:,z]))
file.write("\n")
dat[z]= prct
ind[z]= z
file.close()
plt.plot(ind,dat)
plt.grid()
plt.ylabel("Distribution of working SQUID >10µV ($\mu$V)")
plt.xlabel('Index')
plt.title("Working SQUID by index")
save("./TEST/Analysis"+day+"/Results/ASIC%i_percentage"%AsicNum,ext="png",close=True,verbose=True)
def plot_physical_layout(xwin=True, figsize=(16, 16), a1=None, a2=None):
'''
plot an image of the TES array labeling each pixel
plot the I-V curves in the appropriate boxes if a1 and/or a2 given
'''
obj_list = [a1, a2]
asic1_obj = None
asic2_obj = None
for obj in obj_list:
if isinstance(obj, qp):
if obj.asic == 1:
asic1_obj = obj
elif obj.asic == 2:
asic2_obj = obj
else:
print(
'PROBLEM! QubicPack object does not have a valid ASIC definition'
)
return None
if asic1_obj == None:
asic1_obj = qp()
asic1_obj.assign_asic(1)
asic1_fontsize = figsize[0]
if asic2_obj == None:
asic2_obj = qp()
asic2_obj.assign_asic(2)
asic2_fontsize = figsize[0]
asic1_data = True
asic1_fontsize = 8
asic2_data = True
asic2_fontsize = 8
if not isinstance(asic1_obj.adu, np.ndarray):
asic1_data = False
asic1_fontsize = figsize[0]
if not isinstance(asic2_obj.adu, np.ndarray):
asic2_data = False
asic2_fontsize = figsize[0]
asic1_obj.figsize = figsize
fontsize = figsize[0]
ttlfontsize = figsize[0] * 1.2
nrows = asic1_obj.pix_grid.shape[0]
ncols = asic1_obj.pix_grid.shape[1]
if xwin: plt.ion()
else: plt.ioff()
fig, ax = plt.subplots(nrows, ncols, figsize=asic1_obj.figsize)
fig.text(0.5, 0.985, 'QUBIC TES array', ha='center', fontsize=ttlfontsize)
pngname = 'TES_ARRAY.png'
if xwin: fig.canvas.set_window_title('plt: QUBIC TES array')
asic1_subttl = 'ASIC1 blue background'
asic2_subttl = 'ASIC2 green background'
ngood = 0
if asic1_data:
asic1_subttl += asic1_obj.obsdate.strftime(
', data from %Y-%m-%d %H:%M')
ngood += asic1_obj.ngood()
if asic2_data:
asic2_subttl += asic2_obj.obsdate.strftime(
', data from %Y-%m-%d %H:%M')
ngood += asic2_obj.ngood()
subttl = asic1_subttl + '\n' + asic2_subttl
if asic1_data or asic2_data:
subttl += '\nbad pixels in red background. %i good pixels.' % ngood
fig.suptitle(subttl, fontsize=fontsize)
for row in range(nrows):
for col in range(ncols):
TES = 0
ax[row, col].get_xaxis().set_visible(False)
ax[row, col].get_yaxis().set_visible(False)
#ax[row,col].set_xlim([0,1])
#ax[row,col].set_ylim([0,1])
# the pixel identity associated with its physical location in the array
physpix = asic1_obj.pix_grid[row, col]
pix_index = physpix - 1
text_y = 0.5
text_x = 0.5
if physpix == 0:
pix_label = ''
label_colour = 'black'
face_colour = 'black'
elif physpix in asic1_obj.TES2PIX[0]:
TES = asic1_obj.pix2tes(physpix)
pix_label = str('%i' % TES)
label_colour = 'white'
face_colour = 'blue'
if asic1_data:
fontsize = asic1_fontsize
Iadjusted = asic1_obj.adjusted_iv(TES)
text_x = max(asic1_obj.vbias)
text_y = min(Iadjusted)
asic1_obj.draw_iv(Iadjusted,
colour='yellow',
axis=ax[row, col])
if (not asic1_obj.is_good_iv(TES)
== None) and (not asic1_obj.is_good_iv(TES)):
face_colour = 'red'
label_colour = 'white'
elif physpix in asic2_obj.TES2PIX[1]:
TES = asic2_obj.pix2tes(physpix)
pix_label = str('%i' % TES)
label_colour = 'white'
face_colour = 'green'
if asic2_data:
fontsize = asic2_fontsize
Iadjusted = asic2_obj.adjusted_iv(TES)
text_x = max(asic1_obj.vbias)
text_y = min(Iadjusted)
asic2_obj.draw_iv(Iadjusted,
colour='blue',
axis=ax[row, col])
if (not asic2_obj.is_good_iv(TES)
== None) and (not asic2_obj.is_good_iv(TES)):
face_colour = 'red'
label_colour = 'white'
else:
pix_label = '???'
label_colour = 'blue'
face_colour = 'yellow'
ax[row, col].set_facecolor(face_colour)
ax[row, col].text(text_x,
text_y,
pix_label,
va='center',
ha='center',
color=label_colour,
fontsize=fontsize)
plt.savefig(pngname, format='png', dpi=100, bbox_inches='tight')
if xwin: plt.show()
else: plt.close('all')
return
def plot_noise() :
####################################################
### variable declaration ###
fmi=input("Enter frequence min value: ")
fma=input("Enter frequence max value: ")
fmin=int(fmi)
fmax=int(fma)
file = open("Noise.txt", "w")
current_noise_test=np.zeros((2,16,128))#ASIC/index/TES
Ibias=[]
Noise_plot=[]
#########################################################
### loop for filling noise ###
#selection courant et ASIC
for i in range (2):
AsicNum = i+1
for j in range (16):
thedir = dirs[j]
print(thedir)
b = qp()
b.read_qubicstudio_dataset(thedir, asic=AsicNum)
#passe a travers tous les TES
Rbias=b.Rbias
Min=1./10.4E-6
for z in range (128):
amp=b.max_bias-b.min_bias
offset=0.5*(b.max_bias+b.min_bias)
Vbias=(offset+amp*b.bias_phase()/2.)
ind=np.argsort(Vbias)
Vsqoffset=np.mean(b.timeline(TES=z+1)*62.5/(70.*100)/(b.nsamples-b.n_masked()))
temp = b.timeline(TES=z+1)[ind]*62.5/(70.*100)/(b.nsamples-b.n_masked())-Vsqoffset
filt = sp.signal.savgol_filter(temp, 51, 3)
phi0=((Vbias[ind]/Rbias*Min)-(Vbias[ind]/Rbias*Min)[0])/2.
#Bruit de lecture
timeline_brut=b.timeline(TES=z+1)
Timeline_volt=timeline_brut*62.5/(70.*100)/(b.nsamples-b.n_masked())
fs = 156.25
f, t, Sxx = scsig.spectrogram(Timeline_volt, fs,nperseg=2**10,window='hann')
indf=(f>fmin) & (f<fmax)
a=np.median(Sxx[indf,0])
val=np.sqrt(a)
#Bruit du SQUID
DeltaX=max(np.gradient(phi0))
slope_plot=(np.gradient(filt)/DeltaX)
#slope=(max(slope_plot))
slope_filt = sp.signal.savgol_filter(slope_plot, 51, 3)
slope=(max(slope_filt))
#plt.plot(slope_plot)
phi0_Hz=(val*1e-6)/(slope*1e-6)
A_Hz=phi0_Hz*0.2e-6
noise=A_Hz*1e12
Ibias.append(j)
Noise_plot.append(noise)
current_noise_test[i,j,z]= noise
file.write("ASIC n° %i ... Index n° %i ... TES n° %i ... SQUID Noise = %d pA(Hz)^-1/2\n" %(i+1,j,z+1,noise))
file.close()
#TES=127
for TES in range (128):
plt.plot(current_noise_test[0,:,TES],label="ASIC 1",color="blue")
plt.plot(current_noise_test[1,:,TES],label='ASIC 2',color="red")
plt.legend()
plt.title("Noise regarding of the Index number, TES : %i" %(TES+1))
plt.ylabel("Noise pA/(Hz)^1/2")
plt.grid()
save("./TEST/Analysis"+day+"/Noise"+day+"/Graph/Noise_Index_TES%i"%(TES+1),ext="png",close=True,verbose=True)
#ind=4
for ind in range (16):
plt.hist(current_noise_test[0,ind,:],bins=25,alpha=0.5,label="ASIC 1",color="blue")
plt.hist(current_noise_test[1,ind,:],bins=25,alpha=0.5,label="ASIC 2",color="red")
plt.grid()
plt.legend()
plt.title("Number of TES regarding the noise, index : %i" %(ind))
plt.xlabel("Noise pA/(Hz)^1/2")
plt.ylabel("Number of TESs")
save("./TEST/Analysis"+day+"/Noise"+day+"/Hist/TES_Noise_ind%i"%(ind),ext="png",close=True,verbose=True)
###########################################
###3D plot ###
current_noise=np.zeros((2,16,128))#ASIC/TES/Index
for i in range (2):
current_noise[i,:,:]=i+1
for k in range (16):
current_noise[:,k,:]=k
for z in range (128):
current_noise[:,:,z]=z+1
from mpl_toolkits import mplot3d
fig = plt.figure()
ax = plt.axes(projection='3d')
# Data for a three-dimensional line
zline = Noise_plot
xline = current_noise
yline = Ibias
ax.scatter3D(yline, xline, zline,c=zline, cmap='jet')
#ax.set_ylim(0,6)
#ax.set_zlim(0,300)
#ax.set_xlim(74.5,75.5)
ax.set_ylabel('TES')
ax.set_xlabel('Index')
plt.xticks([0,2,4,6,8,10,12,14])
ax.set_zlabel('Noise')
ax.set_title('Noise distribution for all TESs')
save("./TEST/Analysis"+day+"Noise"+day+"/3D_data_plot",ext="png",close=True,verbose=True)
###########################################################################
### Noise Histogram ###
z=0
#for ind in range (9):
plt.hist(current_noise_test[z,9,:],bins=50,alpha=0.5,label="25.5 µA",color="red")
plt.hist(current_noise_test[z,10,:],bins=50,alpha=0.5,label="28 µA",color="blue")
plt.hist(current_noise_test[z,11,:],bins=50,alpha=0.5,label="30.6 µA",color="green")
plt.grid()
plt.legend()
plt.title("ASIC %i" %(z+1))
plt.xlabel("Noise pA/(Hz)^1/2")
plt.xlim(0,500)
plt.ylabel("Number of TESs")
save("./TEST/Analysis"+day+"Noise"+day+"/Noise_ASIC1",ext="png",close=True,verbose=True)
z=1
#for ind in range (9):
plt.hist(current_noise_test[z,10,:],bins=50,alpha=0.5,label="28 µA",color="red")
plt.hist(current_noise_test[z,11,:],bins=50,alpha=0.5,label="30.6 µA",color="blue")
plt.hist(current_noise_test[z,12,:],bins=50,alpha=0.5,label="33.2 µA",color="green")
plt.grid()
plt.legend()
plt.title("ASIC %i" %(z+1))
plt.xlabel("Noise pA/(Hz)^1/2")
plt.xlim(0,500)
plt.ylabel("Number of TESs")
save("./TEST/Analysis"+day+"Noise"+day+"/Noise_ASIC2",ext="png",close=True,verbose=True)
from copy import copy
reload(sys)
sys.setdefaultencoding('utf8')
from satorchipy.datefunctions import *
def read_bath_temperature(qpobject):
qpobject.writelog('reading temperature')
Tbath=qpobject.oxford_read_bath_temperature()
if Tbath is None:
qpobject.writelog('ERROR! Could not read bath temperature.')
Tbath=qpobject.temperature
qpobject.writelog('Tbath=%.2f mK' % (1000*Tbath))
return Tbath
# create the qubicpack object
go=qp()
# set verbosity to 2 if you want lots of messages on the screen
go.verbosity=2
# set TESTMODE to False for a real measurement (default)
TESTMODE=False
# default is to ask for parameters
# command line arguments will suppress questions for the corresponding parameters
asic=None
detname=None
meastype=None
# Mon 22 Jan 2018 08:46:16 CET: we have removed the 5x bias factor
go.max_permitted_bias=10.0
min_bias=None
def plot_Fiber_on_focalPlane(dataToPlot,
tagd,
xwin=True,
figsize=(16, 16),
color="black",
pngname='TES_ARRAY.png'):
'''
plot an image of the TES array labeling each pixel
dataToPlot is an array[asic,pixel]
'''
go = qp()
go.figsize = figsize
fontsize = figsize[0]
ttlfontsize = fontsize * 1.2
ttl = 'QUBIC TES array\nASIC1 in blue. ASIC2 in green.'
nrows = go.pix_grid.shape[0]
ncols = go.pix_grid.shape[1]
if xwin: plt.ion()
else: plt.ioff()
count_bad = 0
plt.rc('xtick', labelsize=9) # fontsize of the tick labels
plt.rc('ytick', labelsize=9) # fontsize of the tick labels
fig, ax = plt.subplots(nrows, ncols, figsize=go.figsize)
if xwin: fig.canvas.set_window_title('plt: ' + ttl)
fig.suptitle(ttl, fontsize=ttlfontsize)
TES_translation_table_ASIC1 = go.TES2PIX[0]
TES_translation_table_ASIC2 = go.TES2PIX[1]
#WhichTranslat=TES_translation_table_ASIC1
#if self.asic == "2":
# WhichTranslat=TES_translation_table_ASIC2
for row in range(nrows):
for col in range(ncols):
TES = 0
ax[row, col].get_xaxis().set_visible(False)
ax[row, col].get_yaxis().set_visible(False)
physpix = go.pix_grid[row, col]
pix_index = physpix - 1
text_y = 0.5
text_x = 0.5
asic_of_pixel = -10
if physpix == 0:
pix_label = ''
label_colour = 'black'
face_colour = 'black'
elif physpix in TES_translation_table_ASIC1:
go.assign_asic(1)
asic_of_pixel = 1
TES = go.pix2tes(physpix)
pix_label = str('%i' % TES)
label_colour = 'white'
face_colour = 'blue'
elif physpix in TES_translation_table_ASIC2:
asic_of_pixel = 2
go.assign_asic(2)
TES = go.pix2tes(physpix)
pix_label = str('%i' % TES)
label_colour = 'white'
face_colour = 'green'
else:
pix_label = '???'
label_colour = 'blue'
face_colour = 'yellow'
current = ax[row, col]
current.set_axis_bgcolor(face_colour)
current.text(0.5,
0.5,
pix_label,
color="white",
fontsize=9,
ha='center',
va='center',
transform=current.transAxes,
weight="bold")
if pix_label != '???' and asic_of_pixel > 0:
if tagd[asic_of_pixel - 1][int(
pix_label
) - 1] == 1: # and (int(pix_label)-1 not in self.tes_blacklist):
current.set_axis_bgcolor("red")
current.text(0.5,
0.5,
pix_label,
color="black",
fontsize=9,
ha='center',
va='center',
transform=current.transAxes,
weight="bold")
count_bad += 1
alld = dataToPlot[asic_of_pixel - 1]
d = alld[int(pix_label) - 1]
d = d[2:]
if numpy.size(d) != 0:
mini = d.min()
maxi = d.max()
deltai = maxi - mini
maxi = maxi + 15. * deltai / 100.
mini = mini - 15. * deltai / 100.
current.set_ylim([mini, maxi])
current.plot(d, color=color, linewidth=2.0)
print 'count_bad', count_bad
plt.savefig(pngname, format='png', dpi=100, bbox_inches='tight')
if xwin:
fig.show()
plt.show()
else:
plt.close('all')
return
# import necessary stuff
import os,sys
if sys.argv[0].find('ipykernel')>=0:jupyter=True
if jupyter:
%matplotlib notebook
import matplotlib.pyplot as plt
from qubicpack import qubicpack as qp
from qubicpack.temperature_analysis import *
from qubicpack.plot_physical_layout import *
import datetime as dt
# create the qubicpack object and read the data
d0=qp()
if jupyter:d0.figsize=10,5
result=d0.read_fits('QUBIC_timeline_20180301T142554UTC.fits')
# The data contains multiple timelines at different temperatures
# you can print out a summary table
result=print_datlist(d0)
# we will work with the third timeline.
# (we set timeline_index=2)
# first of all, have a look at all timelines for all the TES
result=d0.plot_timeline_physical_layout(timeline_index=2)
# let's look at one TES and also look at the power spectrum
result=d0.plot_ASD(85,timeline_index=2)
