lib.Calibration.Qfiber module

class lib.Calibration.Qfiber.Chi2Implement(functname, x, y, covarin, extra_args=None)

Bases: Chi2Minimizer

Methods

__call__(*pars[, extra_args])

Call self as a function.

class lib.Calibration.Qfiber.Chi2Minimizer(functname, xin, yin, covarin, extra_args=None, invcovar=None)

Bases: object

Parent classes with the model to minimize

Methods

__call__(*pars[, extra_args])

Call self as a function.

class lib.Calibration.Qfiber.MyChi2(xin, yin, covarin, functname, extra_args=None, invcovar=None, diag=False)

Bases: object

Class defining the minimizer and the data

Methods

__call__(*pars[, extra_args])

Call self as a function.

class lib.Calibration.Qfiber.MyChi2_nocov(xin, yin, functname)

Bases: object

Class defining the minimizer and the data

Methods

__call__(*pars)

Call self as a function.

lib.Calibration.Qfiber.butter_bandpass(lowcut, highcut, fs, order=5)

Parameters:

• lowcut (scalar)

• highcut (scalar)

• fs

• order (int) – order of the filter

Returns:

b, a – Numerator (b) and denominator (a) polynomials of the IIR filter.

Return type:

(ndarray, ndarray)

lib.Calibration.Qfiber.butter_bandpass_filter(data, lowcut, highcut, fs, order=5)

Parameters:

• data (array like)

• lowcut

• highcut

• fs

• order

Returns:

y – The output of the digital filter.

Return type:

array

lib.Calibration.Qfiber.calibrate(fib, pow_maynooth, allparams, allerr, allok, cutparam=None, cuterr=None, bootstrap=None)

Parameters:

• fib

• pow_maynooth

• allparams

• allerr

• allok

• cutparam

• cuterr

• bootstrap

lib.Calibration.Qfiber.do_minuit(x, y, covarin, guess, functname=<function thepolynomial>, fixpars=None, chi2=None, rangepars=None, nohesse=False, force_chi2_ndf=False, verbose=True, minos=True, extra_args=None, print_level=0, force_diag=False, nsplit=1, ncallmax=10000, precision=None)

lib.Calibration.Qfiber.exponential_filter1d(input, sigma, axis=-1, output=None, mode='reflect', cval=0.0, truncate=10.0, power=1)

One-dimensional Exponential filter.

Parameters:

• input

• sigma (scalar) – Tau of exponential kernel

• axis (int, optional) – The axis of input along which to calculate. Default is -1.

• output (array or dtype, optional) – The array in which to place the output, or the dtype of the returned array. By default an array of the same dtype as input will be created.

• mode ({‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional) – The mode parameter determines how the input array is extended beyond its boundaries. Default is ‘reflect’.

• cval (scalar, optional) – Value to fill past edges of input if mode is ‘constant’. Default is 0.0.

• truncate (float) – Truncate the filter at this many standard deviations. Default is 4.0.

lib.Calibration.Qfiber.filter_data(time_in, data_in, lowcut=None, highcut=None, rebin=True, verbose=False, notch=None, order=5)

lib.Calibration.Qfiber.fit_all(t, folded, av, initpars=None, fixpars=[0, 0, 0, 0], stop_each=False, functname=<function simsig>, rangepars=None)

Parameters:

• t

• folded

• av

• initpars

• fixpars

• stop_each

• functname

• rangepars

lib.Calibration.Qfiber.fit_average(t, folded, fff, dc, fib, Vtes, initpars=None, fixpars=[0, 0, 0, 0], doplot=True, functname=<function simsig>, clear=True, name='fib')

Parameters:

• t

• folded

• fff (float) – Modulation frequency of the external source.

• dc (float) – Duty cycle of the modulation.

• fib

• Vtes

• initpars

• fixpars

• doplot (bool) – If true make the plot.

• functname

• clear (bool) – If true, clear the window before plotting.

• name

lib.Calibration.Qfiber.fold_data(time, dd, period, nbins, lowcut=None, highcut=None, notch=None, rebin=None, median=False, mode=False, clip=None, return_error=False, return_noise_harmonics=None, silent=False, verbose=None)

Parameters:

• time (array)

• dd (array) – Data signal.

• period (float) – Data will be folded on this period.

• lowcut (If highcut is given but no) – Low cut for the filter.

• highcut (If lowcut is given but no) – High cut for the filter.

• given (If none of them is)

• applied. (no cut frequency filter is)

• highcut

• applied.

• lowcut

• applied.

• given

• applied.

• used. (In any case notch filter can still be)

• nbins

• notch

• return_error

lib.Calibration.Qfiber.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

• data2

• all1 (array) – signal from 2 asics (256 detectors)

lib.Calibration.Qfiber.meancut(data, nsig, med=False, disp=True)

Parameters:

• data (array like)

• nsig (float) – Lower and upper bound factor of sigma clipping.

• med (bool) – If True, perform the median and not the mean.

• disp (bool) – If True, return the dispersion (STD), if False, return the error on the mean (STD/sqrt(N))

Return type:

The mean/median and the dispersion/error.

lib.Calibration.Qfiber.notch_array(freqs, bw)

Returns array to be used with notch_filter

Parameters:

• freqs (list) – Frequencies to filter

• bw (scalar) – The filter bandwidth

lib.Calibration.Qfiber.notch_filter(data, f0, bw, fs)

Parameters:

• data

• f0

• bw

• fs

Returns:

y – Output of the notch filter

Return type:

array

lib.Calibration.Qfiber.power_spectrum(time_in, data_in, rebin=True)

lib.Calibration.Qfiber.printnow(truc)

lib.Calibration.Qfiber.profile(xin, yin, rng=None, nbins=10, fmt=None, plot=True, dispersion=True, log=False, median=False, cutbad=True, rebin_as_well=None, clip=None, mode=False)

lib.Calibration.Qfiber.qgrid()

lib.Calibration.Qfiber.qs2array(dataset, FREQ_SAMPLING=None, timerange=None, asic=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

Parameters:

• dataset (directory containing the QubicStudio dataset)

• FREQ_SAMPLING (this argument is kept for backwards compatibility) – frequency sampling is given by the qubicpack qubicfp object

• timerange (array) – Time range, low and high boundaries

• Returns (time array, data array, qubicfp object)

• -------

lib.Calibration.Qfiber.read_hkintern(basedir, thefieldname=None)

Parameters:

• basedir (str) – directory of the file

• thefieldname (str)

Returns:

• newdate (array) – New time array of te measurement

• hk (array) – Angle position given by the encoder (number of encoder steps).

lib.Calibration.Qfiber.run_asic(fpobj, idnum, Vtes, fff, dc, asic, reselect_ok=False, lowcut=0.5, highcut=15.0, nbins=50, nointeractive=False, doplot=True, notch=None, lastpassallfree=False, name='fib', okfile=None, initpars=None, timerange=None, removesat=False, stop_each=False, rangepars=None)

Parameters:

• fpobj (a qubicpack.qubicfp object)

• idnum (fibre number)

• Vtes (bias voltage (this is normally accessible in the qubicfp object, but maybe not for old data))

• fff (float) – Modulation frequency of the external source

• dc (float) – Duty cycle of the modulation

• asic (the asic number (1 or 2))

• reselect_ok (bool) – If true, you will select the good TES one by one, if False, you will use the file created before.

• lowcut

• highcut

• nbins

• nointeractive

• doplot

• notch

• lastpassallfree

• name

• okfile

• initpars

• timerange

• removesat

• stop_each

• rangepars

lib.Calibration.Qfiber.simsig(x, pars, extra_args=None)

Parameters:

• x (list)

• pars (list) – List with 4 parameters: cycle, ctime, initial time, amplitude

Return type:

A simulated signal.

lib.Calibration.Qfiber.simsig_asym(x, pars, extra_args=None)

lib.Calibration.Qfiber.simsig_fringes(time, stable_time, params)

Simulate a TOD signal obtained during the fringe measurement. This function was done to make a fit. :type time: :param time: Time sampling. :type time: array :type stable_time: :param stable_time: Stable time [s] on each step. :type stable_time: float :type params: :param params: ctime (constant time response of the detector), starting time, the 6 amplitudes. :type params: list

Return type:

The simulated signal.

lib.Calibration.Qfiber.simsig_nonorm(x, pars, extra_args=None)

Same as simsig but without normalisation.

lib.Calibration.Qfiber.statstr(x, divide=False, median=False, cut=None)

lib.Calibration.Qfiber.thepolynomial(x, pars, extra_args=None)

Generic polynomial function

lib.Calibration.Qfiber.vec_interp(x, xin, yin)

lib.Calibration.Qfiber.weighted_mean(x, dx, dispersion=True, renorm=False)

Calculated the weighted mean of data, errors If dispersion is True (default) the error on the mean comes from the RMS of the data, otherwise the error on the weighted mean is analytically calculated from input errors