qetpy.detcal package¶

Submodules¶

qetpy.detcal.cut module¶

qetpy.detcal.cut.symmetrizedist(vals)¶

Function to symmetrize a distribution about zero. Useful for if the distribution of some value centers around a nonzero value, but should center around zero. An example of this would be when most of the measured slopes are nonzero, but we want the slopes with zero values (e.g. lots of muon tails, which we want to cut out). To do this, the algorithm randomly chooses points in a histogram to cut out until the histogram is symmetric about zero.

Parameters:	vals : ndarray A 1-d array of the values that will be symmetrized.
Returns:	czeromeanslope : ndarray A boolean mask of the values that should be kept.

qetpy.detcal.cut.pileupcut(traces, fs=625000.0, outlieralgo='removeoutliers', nsig=2, removemeans=False)¶

Function to automatically cut out outliers of the optimum filter amplitudes of the inputted traces.

Parameters:

traces : ndarray: 2-dimensional array of traces to do cuts on
fs : float, optional: Digitization rate that the data was taken at
outlieralgo : string, optional: Which outlier algorithm to use. If set to “removeoutliers”, uses the removeoutliers algorithm that removes data based on the skewness of the dataset. If set to “iterstat”, uses the iterstat algorithm to remove data based on being outside a certain number of standard deviations from the mean
nsig : float, optional: If outlieralgo is “iterstat”, this can be used to tune the number of standard deviations from the mean to cut outliers from the data when using iterstat on the optimum filter amplitudes. Default is 2.
removemeans : boolean, optional: Boolean flag on if the mean of each trace should be removed before doing the optimal filter (True) or if the means should not be removed (False). This is useful for dIdV traces, when we want to cut out pulses that have smaller amplitude than the dIdV overshoot. Default is False.

Returns:

cpileup : ndarray: Boolean array giving which indices to keep or throw out based on the outlier algorithm

qetpy.detcal.cut.slopecut(traces, fs=625000.0, outlieralgo='removeoutliers', nsig=2, is_didv=False, symmetrizeflag=False, sgfreq=100.0)¶

Function to automatically cut out outliers of the slopes of the inputted traces. Includes a routine that attempts to symmetrize the distribution of slopes around zero, which is useful when the majority of traces have a slope.

Parameters:

traces : ndarray: 2-dimensional array of traces to do cuts on
fs : float, optional: Digitization rate that the data was taken at
outlieralgo : string, optional: Which outlier algorithm to use. If set to “removeoutliers”, uses the removeoutliers algorithm that removes data based on the skewness of the dataset. If set to “iterstat”, uses the iterstat algorithm to remove data based on being outside a certain number of standard deviations from the mean
nsig : float, optional: If outlieralgo is “iterstat”, this can be used to tune the number of standard deviations from the mean to cut outliers from the data when using iterstat on the slopes. Default is 2.
is_didv : bool, optional: Boolean flag on whether or not the trace is a dIdV curve
symmetrizeflag : bool, optional: Flag for whether or not the slopes should be forced to have an average value of zero. Should be used if most of the traces have a slope
sgfreq : float, optional: If is_didv is True, then the sgfreq is used to know where the flat parts of the traces should be

Returns:

cslope : ndarray: Boolean array giving which indices to keep or throw out based on the outlier algorithm

qetpy.detcal.cut.baselinecut(traces, fs=625000.0, outlieralgo='removeoutliers', nsig=2, is_didv=False, sgfreq=100.0)¶

Function to automatically cut out outliers of the baselines of the inputted traces.

Parameters:

traces : ndarray: 2-dimensional array of traces to do cuts on
fs : float, optional: Digitization rate that the data was taken at
outlieralgo : string, optional: Which outlier algorithm to use. If set to “removeoutliers”, uses the removeoutliers algorithm that removes data based on the skewness of the dataset. If set to “iterstat”, uses the iterstat algorithm to remove data based on being outside a certain number of standard deviations from the mean
nsig : float, optional: If outlieralgo is “iterstat”, this can be used to tune the number of standard deviations from the mean to cut outliers from the data when using iterstat on the baselines. Default is 2.
is_didv : bool, optional: Boolean flag on whether or not the trace is a dIdV curve
sgfreq : float, optional: If is_didv is True, then the sgfreq is used to know where the flat parts of the traces should be

Returns:

cbaseline : ndarray: Boolean array giving which indices to keep or throw out based on the outlier algorithm

qetpy.detcal.cut.chi2cut(traces, fs=625000.0, outlieralgo='iterstat', nsig=2)¶

Function to automatically cut out outliers of the baselines of the inputted traces.

Parameters:

traces : ndarray: 2-dimensional array of traces to do cuts on
fs : float, optional: Digitization rate that the data was taken at
outlieralgo : string, optional: Which outlier algorithm to use. If set to “removeoutliers”, uses the removeoutliers algorithm that removes data based on the skewness of the dataset. If set to “iterstat”, uses the iterstat algorithm to remove data based on being outside a certain number of standard deviations from the mean
nsig : float, optional: If outlieralgo is “iterstat”, this can be used to tune the number of standard deviations from the mean to cut outliers from the data when using iterstat on the Chi2s. Default is 2.

Returns:

cchi2 : ndarray: Boolean array giving which indices to keep or throw out based on the outlier algorithm

qetpy.detcal.cut.autocuts(traces, fs=625000.0, is_didv=False, sgfreq=200.0, symmetrizeflag=False, outlieralgo='removeoutliers', lgcpileup1=True, lgcslope=True, lgcbaseline=True, lgcpileup2=True, lgcchi2=True, nsigpileup1=2, nsigslope=2, nsigbaseline=2, nsigpileup2=2, nsigchi2=3)¶

Function to automatically cut out bad traces based on the optimum filter amplitude, slope, baseline, and chi^2 of the traces.

Parameters:

traces : ndarray: 2-dimensional array of traces to do cuts on
fs : float, optional: Sample rate that the data was taken at
is_didv : bool, optional: Boolean flag on whether or not the trace is a dIdV curve
sgfreq : float, optional: If is_didv is True, then the sgfreq is used to know where the flat parts of the traces should be
symmetrizeflag : bool, optional: Flag for whether or not the slopes should be forced to have an average value of zero. Should be used if most of the traces have a slope
outlieralgo : string, optional: Which outlier algorithm to use. If set to “removeoutliers”, uses the removeoutliers algorithm that removes data based on the skewness of the dataset. If set to “iterstat”, uses the iterstat algorithm to remove data based on being outside a certain number of standard deviations from the mean
lgcpileup1 : boolean, optional: Boolean value on whether or not do the pileup1 cut (this is the initial pileup cut that is always done whether or not we have dIdV data). Default is True.
lgcslope : boolean, optional: Boolean value on whether or not do the slope cut. Default is True.
lgcbaseline : boolean, optional: Boolean value on whether or not do the baseline cut. Default is True.
lgcpileup2 : boolean, optional: Boolean value on whether or not do the pileup2 cut (this cut is only done when is_didv is also True). Default is True.
lgcchi2 : boolean, optional: Boolean value on whether or not do the chi2 cut. Default is True.
nsigpileup1 : float, optional: If outlieralgo is “iterstat”, this can be used to tune the number of standard deviations from the mean to cut outliers from the data when using iterstat on the optimum filter amplitudes. Default is 2.
nsigslope : float, optional: If outlieralgo is “iterstat”, this can be used to tune the number of standard deviations from the mean to cut outliers from the data when using iterstat on the slopes. Default is 2.
nsigbaseline : float, optional: If outlieralgo is “iterstat”, this can be used to tune the number of standard deviations from the mean to cut outliers from the data when using iterstat on the baselines. Default is 2.
nsigpileup2 : float, optional: If outlieralgo is “iterstat”, this can be used to tune the number of standard deviations from the mean to cut outliers from the data when using iterstat on the optimum filter amplitudes after the mean has been subtracted. (only used if is_didv is True). Default is 2.
nsigchi2 : float, optional: This can be used to tune the number of standard deviations from the mean to cut outliers from the data when using iterstat on the chi^2 values. Default is 3. This is always used, as iterstat is always used for the chi^2 cut.

Returns:

ctot : ndarray: Boolean array giving which indices to keep or throw out based on the autocuts algorithm

qetpy.detcal.didv module¶

qetpy.detcal.didv.didvinitfromdata(tmean, didvmean, didvstd, offset, offset_err, fs, sgfreq, sgamp, rshunt, r0=0.3, r0_err=0.001, rload=0.01, rload_err=0.001, priors=None, invpriorscov=None, add180phase=False, dt0=1e-05)¶

Function to initialize and process a dIdV dataset without having all of the traces, but just the parameters that are required for fitting. After running, this returns a DIDV class object that is ready for fitting.

Parameters:

tmean : ndarray: The average trace in time domain, units of Amps
didvstd : ndarray: The complex standard deviation of the didv in frequency space for each frequency
didvmean : ndarray: The average trace converted to didv
offset : float: The offset (i.e. baseline value) of the didv trace, in Amps
offset_err : float: The error in the offset of the didv trace, in Amps
fs : float: Sample rate of the data taken, in Hz
sgfreq : float: Frequency of the signal generator, in Hz
sgamp : float: Amplitude of the signal generator, in Amps (equivalent to jitter in the QET bias)
rshunt : float: Shunt resistance in the circuit, Ohms
r0 : float, optional: Resistance of the TES in Ohms
r0_err : float, optional: Error in the resistance of the TES (Ohms)
rload : float, optional: Load resistance of the circuit (rload = rshunt + rparasitic), Ohms
rload_err : float, optional: Error in the load resistance, Ohms
priors : ndarray, optional: Prior known values of Irwin’s TES parameters for the trace. Should be in the order of (rload,r0,beta,l,L,tau0,dt)
invpriorscov : ndarray, optional: Inverse of the covariance matrix of the prior known values of Irwin’s TES parameters for the trace (any values that are set to zero mean that we have no knowledge of that parameter)
add180phase : boolean, optional: If the signal generator is out of phase (i.e. if it looks like –__ instead of __–), then this should be set to True. Adds half a period of the signal generator to the dt0 attribute
dt0 : float, optional: The value of the starting guess for the time offset of the didv when fitting. The best way to use this value if it isn’t converging well is to run the fit multiple times, setting dt0 equal to the fit’s next value, and seeing where the dt0 value converges. The fit can have a difficult time finding the value on the first run if it the initial value is far from the actual value, so a solution is to do this iteratively.

Returns:

didvobj : Object: A DIDV class object that can be used to fit the dIdV and return the fit parameters.

qetpy.detcal.didv.onepoleimpedance(freq, A, tau2)¶

Function to calculate the impedance (dvdi) of a TES with the 1-pole fit

Parameters:	freq : array_like, float The frequencies for which to calculate the admittance (in Hz) A : float The fit parameter A in the complex impedance (in Ohms), superconducting: A=rload, normal: A = rload+Rn tau2 : float The fit parameter tau2 in the complex impedance (in s), superconducting: tau2=L/rload, normal: tau2=L/(rload+Rn)
Returns:	dvdi : array_like, float The complex impedance of the TES with the 1-pole fit

qetpy.detcal.didv.onepoleadmittance(freq, A, tau2)¶

Function to calculate the admittance (didv) of a TES with the 1-pole fit

Parameters:	freq : array_like, float The frequencies for which to calculate the admittance (in Hz) A : float The fit parameter A in the complex impedance (in Ohms), superconducting: A=rload, normal: A = rload+Rn tau2 : float The fit parameter tau2 in the complex impedance (in s), superconducting: tau2=L/rload, normal: tau2=L/(rload+Rn)
Returns:	1.0/dvdi : array_like, float The complex admittance of the TES with the 1-pole fit

qetpy.detcal.didv.twopoleimpedance(freq, A, B, tau1, tau2)¶

Function to calculate the impedance (dvdi) of a TES with the 2-pole fit

Parameters:

freq : array_like, float: The frequencies for which to calculate the admittance (in Hz)
A : float: The fit parameter A in the complex impedance (in Ohms), A = rload + r0*(1+beta)
B : float: The fit parameter B in the complex impedance (in Ohms), B = r0*l*(2+beta)/(1-l) (where l is Irwin’s loop gain)
tau1 : float: The fit parameter tau1 in the complex impedance (in s), tau1=tau0/(1-l)
tau2 : float: The fit parameter tau2 in the complex impedance (in s), tau2=L/(rload+r0*(1+beta))

Returns:

dvdi : array_like, float: The complex impedance of the TES with the 2-pole fit

qetpy.detcal.didv.twopoleadmittance(freq, A, B, tau1, tau2)¶

Function to calculate the admittance (didv) of a TES with the 2-pole fit

Parameters:

freq : array_like, float: The frequencies for which to calculate the admittance (in Hz)
A : float: The fit parameter A in the complex impedance (in Ohms), A = rload + r0*(1+beta)
B : float: The fit parameter B in the complex impedance (in Ohms), B = r0*l*(2+beta)/(1-l) (where l is Irwin’s loop gain)
tau1 : float: The fit parameter tau1 in the complex impedance (in s), tau1=tau0/(1-l)
tau2 : float: The fit parameter tau2 in the complex impedance (in s), tau2=L/(rload+r0*(1+beta))

Returns:

1.0/dvdi : array_like, float: The complex admittance of the TES with the 2-pole fit

qetpy.detcal.didv.threepoleimpedance(freq, A, B, C, tau1, tau2, tau3)¶

Function to calculate the impedance (dvdi) of a TES with the 3-pole fit

Parameters:

freq : array_like, float: The frequencies for which to calculate the admittance (in Hz)
A : float: The fit parameter A in the complex impedance (in Ohms)
B : float: The fit parameter B in the complex impedance (in Ohms)
C : float: The fit parameter C in the complex impedance
tau1 : float: The fit parameter tau1 in the complex impedance (in s)
tau2 : float: The fit parameter tau2 in the complex impedance (in s)
tau3 : float: The fit parameter tau3 in the complex impedance (in s)

Returns:

dvdi : array_like, float: The complex impedance of the TES with the 3-pole fit

qetpy.detcal.didv.threepoleadmittance(freq, A, B, C, tau1, tau2, tau3)¶

Function to calculate the admittance (didv) of a TES with the 3-pole fit

Parameters:

freq : array_like, float: The frequencies for which to calculate the admittance (in Hz)
A : float: The fit parameter A in the complex impedance (in Ohms)
B : float: The fit parameter B in the complex impedance (in Ohms)
C : float: The fit parameter C in the complex impedance
tau1 : float: The fit parameter tau1 in the complex impedance (in s)
tau2 : float: The fit parameter tau2 in the complex impedance (in s)
tau3 : float: The fit parameter tau3 in the complex impedance (in s)

Returns:

1.0/dvdi : array_like, float: The complex admittance of the TES with the 3-pole fit

qetpy.detcal.didv.twopoleimpedancepriors(freq, rload, r0, beta, l, L, tau0)¶

Function to calculate the impedance (dvdi) of a TES with the 2-pole fit from Irwin’s TES parameters

Parameters:

freq : array_like, float: The frequencies for which to calculate the admittance (in Hz)
rload : float: The load resistance of the TES (in Ohms)
r0 : float: The resistance of the TES (in Ohms)
beta : float: The current sensitivity of the TES
l : float: Irwin’s loop gain
L : float: The inductance in the TES circuit (in Henrys)
tau0 : float: The thermal time constant of the TES (in s)

Returns:

dvdi : array_like, float: The complex impedance of the TES with the 2-pole fit from Irwin’s TES parameters

qetpy.detcal.didv.twopoleadmittancepriors(freq, rload, r0, beta, l, L, tau0)¶

Function to calculate the admittance (didv) of a TES with the 2-pole fit from Irwin’s TES parameters

Parameters:

freq : array_like, float: The frequencies for which to calculate the admittance (in Hz)
rload : float: The load resistance of the TES (in Ohms)
r0 : float: The resistance of the TES (in Ohms)
beta : float: The current sensitivity of the TES, beta=d(log R)/d(log I)
l : float: Irwin’s loop gain, l = P0*alpha/(G*Tc)
L : float: The inductance in the TES circuit (in Henrys)
tau0 : float: The thermal time constant of the TES (in s), tau0=C/G

Returns:

1.0/dvdi : array_like, float: The complex admittance of the TES with the 2-pole fit from Irwin’s TES parameters

qetpy.detcal.didv.convolvedidv(x, A, B, C, tau1, tau2, tau3, sgamp, rshunt, sgfreq, dutycycle)¶

Function to convert the fitted TES parameters for the complex impedance to a TES response to a square wave jitter in time domain.

Parameters:

x : array_like: Time values for the trace (in s)
A : float: The fit parameter A in the complex impedance (in Ohms)
B : float: The fit parameter B in the complex impedance (in Ohms)
C : float: The fit parameter C in the complex impedance
tau1 : float: The fit parameter tau1 in the complex impedance (in s)
tau2 : float: The fit parameter tau2 in the complex impedance (in s)
tau3 : float: The fit parameter tau3 in the complex impedance (in s)
sgamp : float: The peak-to-peak size of the square wave jitter (in Amps)
rshunt : float: The shunt resistance of the TES electronics (in Ohms)
sgfreq : float: The frequency of the square wave jitter (in Hz)
dutycycle : float: The duty cycle of the square wave jitter (between 0 and 1)

Returns:

np.real(st) : ndarray: The response of a TES to a square wave jitter in time domain with the given fit parameters. The real part is taken in order to ensure that the trace is real

qetpy.detcal.didv.squarewaveguessparams(trace, sgamp, rshunt)¶

Function to guess the fit parameters for the 1-pole fit.

Parameters:	trace : array_like The trace in time domain (in Amps). sgamp : float The peak-to-peak size of the square wave jitter (in Amps) rshunt : float Shunt resistance of the TES electronics (in Ohms)
Returns:	A0 : float Guess of the fit parameter A (in Ohms) tau20 : float Guess of the fit parameter tau2 (in s)

qetpy.detcal.didv.guessdidvparams(trace, flatpts, sgamp, rshunt, L0=1e-07)¶

Function to find the fit parameters for either the 1-pole (A, tau2, dt), 2-pole (A, B, tau1, tau2, dt), or 3-pole (A, B, C, tau1, tau2, tau3, dt) fit.

Parameters:	trace : array_like The trace in time domain (in Amps) flatpts : array_like The flat parts of the trace (in Amps) sgamp : float The peak-to-peak size of the square wave jitter (in Amps) rshunt : float Shunt resistance of the TES electronics (in Ohms) L0 : float, optional The guess of the inductance (in Henries)
Returns:	A0 : float Guess of the fit parameter A (in Ohms) B0 : float Guess of the fit parameter B (in Ohms) tau10 : float Guess of the fit parameter tau1 (in s) tau20 : float Guess of the fit parameter tau2 (in s) isloopgainsub1 : boolean Boolean flag that gives whether the loop gain is greater than one (False) or less than one (True)

qetpy.detcal.didv.fitdidv(freq, didv, yerr=None, A0=0.25, B0=-0.6, C0=-0.6, tau10=-0.0003183098861837907, tau20=1.5915494309189535e-06, tau30=0.0, dt=-1e-05, poles=2, isloopgainsub1=None)¶

Function to find the fit parameters for either the 1-pole (A, tau2, dt), 2-pole (A, B, tau1, tau2, dt), or 3-pole (A, B, C, tau1, tau2, tau3, dt) fit.

Parameters:

freq : ndarray: Frequencies corresponding to the didv
didv : ndarray: Complex impedance extracted from the trace in frequency space
yerr : ndarray, NoneType, optional: Error at each frequency of the didv. Should be a complex number, e.g. yerr = yerr_real + 1.0j * yerr_imag, where yerr_real is the standard deviation of the real part of the didv, and yerr_imag is the standard deviation of the imaginary part of the didv. If left as None, then each frequency will be assumed to be equally weighted.
A0 : float, optional: Guess of the fit parameter A (in Ohms). Default is 0.25.
B0 : float, optional: Guess of the fit parameter B (in Ohms). Default is -0.6.
C0 : float, optional: Guess of the fit parameter C (unitless). Default is -0.6.
tau10 : float, optional: Guess of the fit parameter tau1 (in s). Default is -1.0/(2*pi*5e2).
tau20 : float, optional: Guess of the fit parameter tau2 (in s). Default is 1.0/(2*pi*1e5).
tau30 : float, optional: Guess of the fit parameter tau3 (in s). Default is 0.0.
dt : float, optional: Guess of the time shift (in s). Default is -10.0e-6.
poles : int, optional: The number of poles to use in the fit (should be 1, 2, or 3). Default is 2.
isloopgainsub1 : boolean, NoneType, optional: If set, should be used to specify if the fit should be done assuming that the Irwin loop gain is less than 1 (True) or greater than 1 (False). Default is None, in which case loop gain less than 1 and greater than 1 fits will be done, returning the one with a lower Chi^2.

Returns:

popt : ndarray: The fitted parameters for the specificed number of poles
pcov : ndarray: The corresponding covariance matrix for the fitted parameters
cost : float: The cost of the the fit

qetpy.detcal.didv.converttotesvalues(popt, pcov, r0, rload, r0_err=0.001, rload_err=0.001)¶

Function to convert the fit parameters for either 1-pole (A, tau2, dt), 2-pole (A, B, tau1, tau2, dt), or 3-pole (A, B, C, tau1, tau2, tau3, dt) fit to the corresponding TES parameters: 1-pole (rtot, L, r0, rload, dt), 2-pole (rload, r0, beta, l, L, tau0, dt), and 3-pole (no conversion done).

Parameters:

popt : ndarray: The fit parameters for either the 1-pole, 2-pole, or 3-pole fit
pcov : ndarray: The corresponding covariance matrix for the fit parameters
r0 : float: The resistance of the TES (in Ohms)
rload : float: The load resistance of the TES circuit (in Ohms)
r0_err : float, optional: The error in the r0 value (in Ohms). Default is 0.001.
rload_err : float, optional: The error in the rload value (in Ohms). Default is 0.001.

Returns:

popt_out : ndarray: The TES parameters for the specified fit
pcov_out : ndarray: The corresponding covariance matrix for the TES parameters

qetpy.detcal.didv.fitdidvpriors(freq, didv, priors, invpriorscov, yerr=None, rload=0.35, r0=0.13, beta=0.5, l=10.0, L=5e-07, tau0=0.0005, dt=-1e-05)¶

Function to directly fit Irwin’s TES parameters (rload, r0, beta, l, L, tau0, dt) with the knowledge of prior known values any number of the parameters. In order for the degeneracy of the parameters to be broken, at least 2 fit parameters should have priors knowledge. This is usually rload and r0, as these can be known from IV data.

Parameters:

freq : ndarray: Frequencies corresponding to the didv
didv : ndarray: Complex impedance extracted from the trace in frequency space
priors : ndarray: Prior known values of Irwin’s TES parameters for the trace. Should be in the order of (rload,r0,beta,l,L,tau0,dt)
invpriorscov : ndarray: Inverse of the covariance matrix of the prior known values of Irwin’s TES parameters for the trace (any values that are set to zero mean that we have no knowledge of that parameter)
yerr : ndarray, optional: Error at each frequency of the didv. Should be a complex number, e.g. yerr = yerr_real + 1.0j * yerr_imag, where yerr_real is the standard deviation of the real part of the didv, and yerr_imag is the standard deviation of the imaginary part of the didv. If left as None, then each frequency will be assumed to be equally weighted.
rload : float, optional: Guess of the load resistance of the TES circuit (in Ohms). Default is 0.35.
r0 : float, optional: Guess of the resistance of the TES (in Ohms). Default is 0.130.
beta : float, optional: Guess of the current sensitivity beta (unitless). Default is 0.5.
l : float, optional: Guess of Irwin’s loop gain (unitless). Default is 10.0.
L : float, optional: Guess of the inductance (in Henrys). Default is 500.0e-9.
tau0 : float, optional: Guess of the thermal time constant (in s). Default is 500.0e-6.
dt : float, optional: Guess of the time shift (in s). Default is -10.0e-6.

Returns:

popt : ndarray: The fitted parameters in the order of (rload, r0, beta, l, L, tau0, dt)
pcov : ndarray: The corresponding covariance matrix for the fitted parameters
cost : float: The cost of the the fit

qetpy.detcal.didv.convertfromtesvalues(popt, pcov)¶

Function to convert from Irwin’s TES parameters (rload, r0, beta, l, L, tau0, dt) to the fit parameters (A, B, tau1, tau2, dt)

Parameters:	popt : ndarray Irwin’s TES parameters in the order of (rload, r0, beta, l, L, tau0, dt), should be a 1-dimensional np.array of length 7 pcov : ndarray The corresponding covariance matrix for Irwin’s TES parameters. Should be a 2-dimensional, 7-by-7 np.array
Returns:	popt_out : ndarray The fit parameters in the order of (A, B, tau1, tau2, dt) pcov_out : ndarray The corresponding covariance matrix for the fit parameters

qetpy.detcal.didv.findpolefalltimes(params)¶

Function for taking TES params from a 1-pole, 2-pole, or 3-pole didv and calculating the falltimes (i.e. the values of the poles in the complex plane)

Parameters:

params : ndarray: TES parameters for either 1-pole, 2-pole, or 3-pole didv. This will be a 1-dimensional np.array of varying length, depending on the fit. 1-pole fit has 3 parameters (A,tau2,dt), 2-pole fit has 5 parameters (A,B,tau1,tau2,dt), and 3-pole fit has 7 parameters (A,B,C,tau1,tau2,tau3,dt). The parameters should be in that order, and any other number of parameters will print a warning and return zero.

Returns:

np.sort(falltimes) : ndarray: The falltimes for the didv fit, sorted from fastest to slowest.

qetpy.detcal.didv.deconvolvedidv(x, trace, rshunt, sgamp, sgfreq, dutycycle)¶

Function for taking a trace with a known square wave jitter and extracting the complex impedance via deconvolution of the square wave and the TES response in frequency space.

Parameters:	x : ndarray Time values for the trace trace : ndarray The trace in time domain (in Amps) rshunt : float Shunt resistance for electronics (in Ohms) sgamp : float Peak to peak value of square wave jitter (in Amps, jitter in QET bias) sgfreq : float Frequency of square wave jitter dutycycle : float duty cycle of square wave jitter
Returns:	freq : ndarray The frequencies that each point of the trace corresponds to didv : ndarray Complex impedance of the trace in frequency space zeroinds : ndarray Indices of the frequencies where the trace’s Fourier Transform is zero. Since we divide by the FT of the trace, we need to know which values should be zero, so that we can ignore these points in the complex impedance.

class qetpy.detcal.didv.DIDV(rawtraces, fs, sgfreq, sgamp, rshunt, tracegain=1.0, r0=0.3, r0_err=0.001, rload=0.01, rload_err=0.001, dutycycle=0.5, add180phase=False, priors=None, invpriorscov=None, dt0=1e-05)¶

Bases: object

Class for fitting a didv curve for different types of models of the didv. Also gives various other useful values pertaining to the didv. This class supports doing 1, 2, and 3 pole fits, as well as a 2 pole priors fit. This is supported in a way that does one dataset at a time.

Attributes:

rawtraces : ndarray: The array of rawtraces to use when fitting the didv. Should be of shape (number of traces, length of trace in bins). This can be any units, as long as tracegain will convert this to Amps.
fs : float: Sample rate of the data taken, in Hz
sgfreq : float: Frequency of the signal generator, in Hz
sgamp : float: Amplitude of the signal generator, in Amps (equivalent to jitter in the QET bias)
r0 : float: Resistance of the TES in Ohms
r0_err : float: Error in the resistance of the TES (Ohms)
rload : float: Load resistance of the circuit (rload = rshunt + rparasitic), Ohms
rload_err : float: Error in the load resistance, Ohms
rshunt : float: Shunt resistance in the circuit, Ohms
tracegain : float: The factor that the rawtraces should be divided by to convert the units to Amps. If rawtraces already has units of Amps, then this should be set to 1.0
dutycycle : float: The duty cycle of the signal generator, should be a float between 0 and 1. Set to 0.5 by default
add180phase : boolean: If the signal generator is out of phase (i.e. if it looks like –__ instead of __–), then this should be set to True. Adds half a period of the signal generator to the dt0 attribute
priors : ndarray: Prior known values of Irwin’s TES parameters for the trace. Should be in the order of (rload,r0,beta,l,L,tau0,dt)
invpriorscov : ndarray: Inverse of the covariance matrix of the prior known values of Irwin’s TES parameters for the trace (any values that are set to zero mean that we have no knowledge of that parameter)
dt0 : float: The value of the starting guess for the time offset of the didv when fitting. The best way to use this value if it isn’t converging well is to run the fit multiple times, setting dt0 equal to the fit’s next value, and seeing where the dt0 value converges. The fit can have a difficult time finding the value on the first run if it the initial value is far from the actual value, so a solution is to do this iteratively.
freq : ndarray: The frequencies of the didv fit
time : ndarray: The times the didv trace
ntraces : float: The number of traces in the data
traces : ndarray: The traces being used in units of Amps and also truncated so as to include only an integer number of signal generator periods
flatinds : ndarray: The indices where the traces are flat
tmean : ndarray: The average trace in time domain, units of Amps
zeroinds : ndarray: The indices of the didv fit in frequency space where the values should be zero
didvstd : ndarray: The complex standard deviation of the didv in frequency space for each frequency
didvmean : ndarray: The average trace converted to didv
offset : float: The offset (i.e. baseline value) of the didv trace, in Amps
offset_err : float: The error in the offset of the didv trace, in Amps
fitparams1 : ndarray: The fit parameters of the 1-pole fit, in order of (A, tau2, dt)
fitcov1 : ndarray: The corresponding covariance for the 1-pole fit parameters
fitcost1 : float: The cost of the 1-pole fit
irwinparams1 : ndarray: The Irwin parameters of the 1-pole fit, in order of (rtot, L , r0, rload, dt)
irwincov1 : ndarray: The corresponding covariance for the Irwin parameters for the 1-pole fit
falltimes1 : ndarray: The fall times of the 1-pole fit, same as tau2, in s
didvfit1_timedomain : ndarray: The 1-pole fit in time domain
didvfit1_freqdomain : ndarray: The 1-pole fit in frequency domain
fitparams2 : ndarray: The fit parameters of the 2-pole fit, in order of (A, B, tau1, tau2, dt)
fitcov2 : ndarray: The corresponding covariance for the 2-pole fit parameters
fitcost2 : float: The cost of the 2-pole fit
irwinparams2 : ndarray: The Irwin parameters of the 2-pole fit, in order of (rload, r0, beta, l, L, tau0, dt)
irwincov2 : ndarray: The corresponding covariance for the Irwin parameters for the 2-pole fit
falltimes2 : ndarray: The fall times of the 2-pole fit, tau_plus and tau_minus, in s
didvfit2_timedomain : ndarray: The 2-pole fit in time domain
didvfit2_freqdomain : ndarray: The 2-pole fit in frequency domain
fitparams3 : ndarray: The fit parameters of the 3-pole fit, in order of (A, B, C, tau1, tau2, tau3, dt)
fitcov3 : ndarray: The corresponding covariance for the 3-pole fit parameters
fitcost3 : float: The cost of the 3-pole fit
irwinparams3 : NoneType: The Irwin parameters of the 3-pole fit, this returns None now, as there is no model that we convert to
irwincov3 : NoneType: The corresponding covariance for the Irwin parameters for the 3-pole fit, also returns None
falltimes3 : ndarray: The fall times of the 3-pole fit in s
didvfit3_timedomain : ndarray: The 3-pole fit in time domain
didvfit3_freqdomain : ndarray: The 3-pole fit in frequency domain
fitparams2priors : ndarray: The fit parameters of the 2-pole priors fit, in order of (A, B, tau1, tau2, dt), converted from the Irwin parameters
fitcov2priors : ndarray: The corresponding covariance for the 2-pole priors fit parameters
fitcost2priors : float: The cost of the 2-pole priors fit
irwinparams2priors : ndarray: The Irwin parameters of the 2-pole priors fit, in order of (rload, r0, beta, l, L, tau0, dt)
irwincov2priors : ndarray: The corresponding covariance for the Irwin parameters for the 2-pole priors fit
falltimes2priors : ndarray: The fall times of the 2-pole priors fit, tau_plus and tau_minus, in s
didvfit2priors_timedomain : ndarray: The 2-pole priors fit in time domain
didvfit2priors_freqdomain : ndarray: The 2-pole priors fit in frequency domain

Methods

`doallfits`()	This module does all of the fits consecutively.
`dofit`(poles)	This method does the fit that is specified by the variable poles.
`dopriorsfit`()	This module runs the priorsfit, assuming that the priors and invpriorscov attributes have been set to the proper values.
`get_irwinparams_dict`(poles[, lgcpriors])	Returns a dictionary with the irwin fit parameters for a given number of poles
`plot_didv_flipped`([poles, plotpriors, …])	Module to plot the flipped trace in time domain.
`plot_full_trace`([poles, plotpriors, …])	Module to plot the entire trace in time domain
`plot_re_im_didv`([poles, plotpriors, …])	Module to plot the real and imaginary parts of the didv in frequency space.
`plot_single_period_of_trace`([poles, …])	Module to plot a single period of the trace in time domain
`plot_zoomed_in_trace`([poles, zoomfactor, …])	Module to plot a zoomed in portion of the trace in time domain.
`processtraces`()	This method processes the traces loaded to the DIDV class object.

doallfits()¶: This module does all of the fits consecutively. The priors fit is not done if the attributes priors and invpriorscov have not yet been set.

dofit(poles)¶

This method does the fit that is specified by the variable poles. If the processtraces module has not been run yet, then this module will run that first. This module does not do the priors fit.

Parameters:	poles : int The fit that should be run. Should be 1, 2, or 3.

dopriorsfit()¶: This module runs the priorsfit, assuming that the priors and invpriorscov attributes have been set to the proper values.

get_irwinparams_dict(poles, lgcpriors=False)¶

Returns a dictionary with the irwin fit parameters for a given number of poles

Parameters:	poles: int The number of poles used for the fit lgcpriors: bool, optional If true, the values from the priors fit are returned
Returns:	return_dict: dictionary The irwim parameters stored in a dictionary

plot_didv_flipped(poles='all', plotpriors=True, lgcsave=False, savepath='', savename='')¶

Module to plot the flipped trace in time domain. This function should be used to test if there are nonlinearities in the didv

Parameters:

poles : int, string, array_like, optional: The pole fits that we want to plot. If set to “all”, then plots all of the fits. Can also be set to just one of the fits. Can be set as an array of different fits, e.g. [1, 2]
plotpriors : boolean, optional: Boolean value on whether or not the priors fit should be plotted.
lgcsave : boolean, optional: Boolean value on whether or not the figure should be saved
savepath : string, optional: Where the figure should be saved. Saved in the current directory by default.
savename : string, optional: A string to append to the end of the file name if saving. Empty string by default.

plot_full_trace(poles='all', plotpriors=True, lgcsave=False, savepath='', savename='')¶

Module to plot the entire trace in time domain

Parameters:

poles : int, string, array_like, optional: The pole fits that we want to plot. If set to “all”, then plots all of the fits. Can also be set to just one of the fits. Can be set as an array of different fits, e.g. [1, 2]
plotpriors : boolean, optional: Boolean value on whether or not the priors fit should be plotted.
lgcsave : boolean, optional: Boolean value on whether or not the figure should be saved
savepath : string, optional: Where the figure should be saved. Saved in the current directory by default.
savename : string, optional: A string to append to the end of the file name if saving. Empty string by default.

plot_re_im_didv(poles='all', plotpriors=True, lgcsave=False, savepath='', savename='')¶

Module to plot the real and imaginary parts of the didv in frequency space. Currently creates two different plots.

Parameters:

poles : int, string, array_like, optional: The pole fits that we want to plot. If set to “all”, then plots all of the fits. Can also be set to just one of the fits. Can be set as an array of different fits, e.g. [1, 2]
plotpriors : boolean, optional: Boolean value on whether or not the priors fit should be plotted.
lgcsave : boolean, optional: Boolean value on whether or not the figure should be saved
savepath : string, optional: Where the figure should be saved. Saved in the current directory by default.
savename : string, optional: A string to append to the end of the file name if saving. Empty string by default.

plot_single_period_of_trace(poles='all', plotpriors=True, lgcsave=False, savepath='', savename='')¶

Module to plot a single period of the trace in time domain

Parameters:

poles : int, string, array_like, optional: The pole fits that we want to plot. If set to “all”, then plots all of the fits. Can also be set to just one of the fits. Can be set as an array of different fits, e.g. [1, 2]
plotpriors : boolean, optional: Boolean value on whether or not the priors fit should be plotted.
lgcsave : boolean, optional: Boolean value on whether or not the figure should be saved
savepath : string, optional: Where the figure should be saved. Saved in the current directory by default.
savename : string, optional: A string to append to the end of the file name if saving. Empty string by default.

plot_zoomed_in_trace(poles='all', zoomfactor=0.1, plotpriors=True, lgcsave=False, savepath='', savename='')¶

Module to plot a zoomed in portion of the trace in time domain. This plot zooms in on the overshoot of the didv.

Parameters:

poles : int, string, array_like, optional: The pole fits that we want to plot. If set to “all”, then plots all of the fits. Can also be set to just one of the fits. Can be set as an array of different fits, e.g. [1, 2]
zoomfactor : float, optional, optional: Number between zero and 1 to show different amounts of the zoomed in trace.
plotpriors : boolean, optional: Boolean value on whether or not the priors fit should be plotted.
lgcsave : boolean, optional: Boolean value on whether or not the figure should be saved
savepath : string, optional: Where the figure should be saved. Saved in the current directory by default.
savename : string, optional: A string to append to the end of the file name if saving. Empty string by default.

processtraces()¶: This method processes the traces loaded to the DIDV class object. This sets up the object for fitting.

qetpy.detcal.iv module¶

qetpy.detcal.iv.fitfunc(x, b, m)¶

Function to use for fitting to a straight line

Parameters:	x : array_like x-values of the data b : float y-intercept of line m : float slope of line
Returns:	linfunc : array_like Outputted line for x with slope m and intercept b

qetpy.detcal.iv.findnormalinds(vb, dites, dites_err, tol=10)¶

Function to determine the indices of the normal data in an IV curve

Parameters:	vb : array_like Bias voltage, should be a 1d array or list dites : array_like The current read out by the electronics with some offset from the true current dites_err : array_like The error in the current tol : float, optional The tolerance in the reduced chi-squared for the cutoff on which points to use
Returns:	normalinds : iterable The iterable which stores the range of the normal indices

class qetpy.detcal.iv.IV(dites, dites_err, vb, vb_err, rload, rload_err, chan_names, normalinds=None)¶

Bases: object

Class for creating the IV curve and calculating various values, such as the normal resistance, the resistance of the TES, the power, etc., as well as the corresponding errors. This class supports data for multple bath temperatures, multiple channels, and multiple bias points.

Note: If different bath temperatures have different numbers of bias points (iters), then the user should pad the end of the arrays with NaN so that the data can be put into an ndarray and loaded into this class.

Attributes:

dites : ndarray: The current read out by the electronics
dites_err : ndarray: The error in the current read out by the electronics
vb : ndarray: The bias voltage (vb = qet bias * rshunt)
vb_err : ndarray: The corresponding error in the bias voltage
rload : scalar, ndarray: The load resistance, this can be scalar if using the same rload for all values. If 1-dimensional, then this should be the load resistance for each channel. If 2-dimensional, this should be the load resistance for each bath temperature and each bias point, where the shape is (ntemps, nch). If 3-dimensional, then this should be with shape (ntemps, nch, niters)
rload_err : scalar, ndarray: The corresponding error in the load resistance, should be the same type as rload
chan_names : array_like: Array of strings corresponding to the names of each channel in the data. Should have the same length as the nch axis in dites
ioff : ndarray: The current offset calculated from the fit, shape (ntemps, nch)
ioff_err : ndarray: The corresponding error in the current offset
rfit : ndarray: The total resistance (rnorm + rload) from the fit, shape (ntemps, nch)
rfit_err : ndarray: The corresponding error in the fit resistance
rnorm : ndarray: The normal resistance of the TES, using the fit, shape (ntemps, nch)
rnorm_err : ndarray: The corresponding error in the normal resistance
ites : ndarray: The calculated current through the TES, shape (ntemps, nch, niters)
ites_err : ndarray: The corresponding error in the current through the TES
r0 : ndarray: The calculated resistance of the TES, shape (ntemps, nch, niters)
r0_err : ndarray: The corresponding error in the resistance of the TES
ptes : ndarray: The calculated power of the TES, shape (ntemps, nch, niters)
ptes_err : ndarray: The corresponding error in the power of the TES

Methods

`calc_iv`()	Method to calculate the IV curve for the intialized object.
`plot_all_curves`([temps, chans, showfit, …])	Function to plot the IV, resistance, and power curves for the data in an IV object.
`plot_iv`([temps, chans, showfit, lgcsave, …])	Function to plot the IV curves for the data in an IV object.
`plot_pv`([temps, chans, lgcsave, savepath, …])	Function to plot the power curves for the data in an IV object.
`plot_rv`([temps, chans, lgcsave, savepath, …])	Function to plot the resistance curves for the data in an IV object.

calc_iv()¶: Method to calculate the IV curve for the intialized object. Calculates the power and resistance of each bias point, as well as saving the fit parameters from the fit to the normal points and the calculated normal reistance from these points.

plot_all_curves(temps='all', chans='all', showfit=True, lgcsave=False, savepath='', savename='')¶

Function to plot the IV, resistance, and power curves for the data in an IV object.

Parameters:

temps : string, array_like, int, optional: Which bath temperatures to plot. Setting to “all” plots all of them. Can also set to a subset of bath temperatures, or just one
chans : string, array_like, int, optional: Which bath temperatures to plot. Setting to “all” plots all of them. Can also set to a subset of bath temperatures, or just one
showfit : boolean, optional: Boolean flag to also plot the linear fit to the normal data
lgcsave : boolean, optional: Boolean flag to save the plot
savepath : string, optional: Path to save the plot to, saves it to the current directory by default
savename : string, optional: Name to append to the plot file name, if saving

plot_iv(temps='all', chans='all', showfit=True, lgcsave=False, savepath='', savename='')¶

Function to plot the IV curves for the data in an IV object.

Parameters:

temps : string, array_like, int, optional: Which bath temperatures to plot. Setting to “all” plots all of them. Can also set to a subset of bath temperatures, or just one
chans : string, array_like, int, optional: Which bath temperatures to plot. Setting to “all” plots all of them. Can also set to a subset of bath temperatures, or just one
showfit : boolean, optional: Boolean flag to also plot the linear fit to the normal data
lgcsave : boolean, optional: Boolean flag to save the plot
savepath : string, optional: Path to save the plot to, saves it to the current directory by default
savename : string, optional: Name to append to the plot file name, if saving

plot_pv(temps='all', chans='all', lgcsave=False, savepath='', savename='')¶

Function to plot the power curves for the data in an IV object.

Parameters:

temps : string, array_like, int, optional: Which bath temperatures to plot. Setting to “all” plots all of them. Can also set to a subset of bath temperatures, or just one
chans : string, array_like, int, optional: Which bath temperatures to plot. Setting to “all” plots all of them. Can also set to a subset of bath temperatures, or just one
lgcsave : boolean, optional: Boolean flag to save the plot
savepath : string, optional: Path to save the plot to, saves it to the current directory by default
savename : string, optional: Name to append to the plot file name, if saving

plot_rv(temps='all', chans='all', lgcsave=False, savepath='', savename='')¶

Function to plot the resistance curves for the data in an IV object.

Parameters:

temps : string, array_like, int, optional: Which bath temperatures to plot. Setting to “all” plots all of them. Can also set to a subset of bath temperatures, or just one
chans : string, array_like, int, optional: Which bath temperatures to plot. Setting to “all” plots all of them. Can also set to a subset of bath temperatures, or just one
lgcsave : boolean, optional: Boolean flag to save the plot
savepath : string, optional: Path to save the plot to, saves it to the current directory by default
savename : string, optional: Name to append to the plot file name, if saving

qetpy.detcal.noise module¶

qetpy.detcal.noise.slope(x, y, removemeans=True)¶

Computes the maximum likelihood slope of a set of x and y points.

Parameters:	x : array_like Array of real-valued independent variables. y : array_like Array of real-valued dependent variables. removemeans : boolean Boolean flag for if the mean of x should be subtracted. This should be set to True if x has not already had its mean subtracted. Set to False if the mean has been subtracted. Default is True.
Returns:	slope : float Maximum likelihood slope estimate, calculated as sum((x-<x>)(y-<y>))/sum((x-<x>)**2)

qetpy.detcal.noise.fill_negatives(arr)¶

Simple helper function to remove negative and zero values from PSD’s and replace them with interpolated values.

Parameters:	arr: ndarray Array of values to replace neagive values on
Returns:	arr: ndarray Modified input array with the negative and zero values replace by interpelate values

class qetpy.detcal.noise.Noise(traces, fs, channames, tracegain=1.0, fname=None, time=None)¶

Bases: object

This class allows the user to calculate the power spectral densities of signals from detectors, study correlations, and decouple the intrinsic noise from cross channel correlated noise.

Attributes:

traces : ndarray: Array of the traces to use in the noise analysis. Should be shape (# of traces, # of channels, # of bins)
fs : float: The digitization rate of the data in Hz.
channames : list: A list of strings that name each of the channels.
time : ndarray: The time values for each bin in each trace.
fname : str: The file name of the data, this will be used when saving the file.
tracegain : float: The factor that traces should be divided by to convert the units to Amps. If rawtraces already has units of Amps, then this should be set to 1.0
freqs : ndarray: The frequencies that correspond to each value in the spectral densities
psd : ndarray: The power spectral density of the data in A^2/Hz
realpsd : ndarray: The real power spectral density of the data in A^2/Hz
imagpsd : ndarray: The imaginary power spectral density of the data in A^2/Hz
corrcoeff : ndarray: The array of the correlation coefficients between each of the channels
uncorrnoise : ndarray: The uncorrelated noise psd in A^2/Hz
corrnoise : ndarray: The correlated noise psd in A^2/Hz
realcsd : ndarray: The real part of the cross spectral density in A^2/Hz
imagcsd : ndarray: The imaginary part of the cross spectral density in A^2/Hz
realcsdstd : ndarray: The standard deviation of the real part of the cross spectral density at each frequency
imagcsdstd : ndarray: The standard deviation of the imaginary part of the cross spectral density at each frequency
csd : ndarray: The cross spectral density of the traces
chandict : dict: A dictionary that stores the channel number for each channel name.

Methods

`calculate_corrcoeff`()	Calculates the correlations between channels as a function of frequency.
`calculate_csd`()	Calculates the csd for each channel in traces.
`calculate_psd`()	Calculates the psd for each channel in traces.
`calculate_uncorr_noise`()	Calculates the uncorrelated and correlated total noise.
`plot_corrcoeff`([lgcsmooth, nwindow, …])	Function to plot the cross channel correlation coefficients.
`plot_csd`([whichcsd, lgcreal, lgcsave, savepath])	Function to plot the cross channel noise spectrum referenced to the TES line in units of Amperes^2/Hz
`plot_decorrelatednoise`([lgcoverlay, …])	Function to plot the de-correlated noise spectrum referenced to the TES line in units of Amperes/sqrt(Hz) from fitted parameters calculated calculate_deCorrelated_noise
`plot_psd`([lgcoverlay, lgcsave, savepath])	Function to plot the noise spectrum referenced to the TES line in units of Amperes/sqrt(Hz).
`plot_reim_psd`([lgcsave, savepath])	Function to plot the real vs imaginary noise spectrum referenced to the TES line in units of Amperes/sqrt(Hz).
`remove_trace_slope`()	Function to remove the slope from each trace.
`save`(path)	Saves the noise object as a pickle file

calculate_corrcoeff()¶: Calculates the correlations between channels as a function of frequency. Stores results in self.corrcoeff

calculate_csd()¶: Calculates the csd for each channel in traces. Stores csd in self.csd

calculate_psd()¶: Calculates the psd for each channel in traces. Stores psd in self.psd

calculate_uncorr_noise()¶: Calculates the uncorrelated and correlated total noise.

plot_corrcoeff(lgcsmooth=True, nwindow=7, lgcsave=False, savepath=None)¶

Function to plot the cross channel correlation coefficients. Since there are typically few traces, the correlations are often noisy. a savgol_filter is used to smooth out some of the noise

Parameters:	lgcsmooth : boolean, optional If True, a savgol_filter will be used when plotting. nwindow : int, optional the number of bins used for the window in the savgol_filter lgcsave : boolean, optional If True, the figure is saved in the user provided directory savepath : str, optional Absolute path for the figure to be saved

plot_csd(whichcsd=['01'], lgcreal=True, lgcsave=False, savepath=None)¶

Function to plot the cross channel noise spectrum referenced to the TES line in units of Amperes^2/Hz

Parameters:

whichcsd : list, optional: a list of strings, where each element of the list refers to the pair of indices of the desired csd plot
lgcreal : boolean, optional: If True, the Re(csd) is plotted. If False, the Im(csd) is plotted
lgcsave : boolean, optional: If True, the figure is saved in the user provided directory
savepath : str, optional: Absolute path for the figure to be saved

plot_decorrelatednoise(lgcoverlay=False, lgcdata=True, lgcuncorrnoise=True, lgccorrelated=False, lgcsum=False, lgcsave=False, savepath=None)¶

Function to plot the de-correlated noise spectrum referenced to the TES line in units of Amperes/sqrt(Hz) from fitted parameters calculated calculate_deCorrelated_noise

Parameters:

lgcoverlay : boolean, optional: If True, de-correlated for all channels are overlayed in a single plot, If False, the noise for each channel is plotted in a seperate subplot
lgcdata : boolean, optional: Only applies when lgcoverlay = False. If True, the csd data is plotted
lgcuncorrnoise : boolean, optional: Only applies when lgcoverlay = False. If True, the de-correlated noise is plotted
lgccorrelated : boolean, optional: Only applies when lgcoverlay = False. If True, the correlated component of the fitted noise is plotted
lgcsum : boolean, optional: Only applies when lgcoverlay = False. If True, the sum of the fitted de-correlated noise and and correlated noise is plotted
lgcsave : boolean, optional: If True, the figure is saved in the user provided directory
savepath : str, optional: Absolute path for the figure to be saved

plot_psd(lgcoverlay=True, lgcsave=False, savepath=None)¶

Function to plot the noise spectrum referenced to the TES line in units of Amperes/sqrt(Hz).

Parameters:	lgcoverlay : boolean, optional If True, psd’s for all channels are overlayed in a single plot, If False, each psd for each channel is plotted in a seperate subplot lgcsave : boolean, optional If True, the figure is saved in the user provided directory savepath : str, optional Absolute path for the figure to be saved

plot_reim_psd(lgcsave=False, savepath=None)¶

Function to plot the real vs imaginary noise spectrum referenced to the TES line in units of Amperes/sqrt(Hz). This is done to check for thermal muon tails making it passed the quality cuts

Parameters:	lgcsave : boolean, optional If True, the figure is saved in the user provided directory savepath : str, optional Absolute path for the figure to be saved

remove_trace_slope()¶: Function to remove the slope from each trace. self.traces is changed to be the slope subtracted traces.

save(path)¶

Saves the noise object as a pickle file

Parameters:	path : str Path where the noise object should be saved.

qetpy.detcal.sim module¶

qetpy.detcal.sim.loadfromdidv(DIDVobj, G=5e-10, qetbias=0.00016, tc=0.04, tload=0.9, tbath=0.02, squiddc=2.5e-12, squidpole=0.0, squidn=1.0, noisetype='transition', lgcpriors=False)¶

Function for loading the parameters from a DIDV class object.

Parameters:

DIDVobj : Object: A DIDV class object after a fit has been run, such that there are Irwin parameters that can be used to model the noise.
G : float, optional: The thermal conductance of the TES in W/K
qetbias : float, optional: The QET bias in Amps
tc : float: The critical temperature of the TES in K
tload : float: The effective temperature of the load resistor in K
tbath : float: The bath temperature in K
squiddc : float, optional: The DC value of the SQUID and downstream electronics noise, in Amps/rtHz. The SQUID/electronics noise should have been fit beforehand, using the following model:

(squiddc*(1.0+(squidpole/f)**squidn))**2.0
squidpole : float, optional: The frequency pole for the SQUID and downstream electronics noise, in Hz. The SQUID/electronics noise should have been fit beforehand, using the following model:

(squiddc*(1.0+(squidpole/f)**squidn))**2.0
squidn : float, optional: The power of the SQUID and downstream electronics noise, in Hz. The SQUID/electronics noise should have been fit beforehand, using the following model:

(squiddc*(1.0+(squidpole/f)**squidn))**2.0
noisetype : str, optional: The type of the noise that is to be loaded. The options are transition : Use the Irwin parameters from the two pole fit as the transition noise model superconducting : Use the Irwin parameters from the one pole fit as the superconducting noise model normal : Use the Irwin parameters from the one pole fit as the normal noise model
lgcpriors : bool, optional: If True, the priors fit values are loaded from the didv object, if False, the regular fit values are loaded

Returns:

TESobj : Object: A TESnoise class object with all of the fit parameters loaded.

class qetpy.detcal.sim.TESnoise(freqs=None, rload=0.012, r0=0.15, rshunt=0.005, beta=1.0, loopgain=10.0, inductance=4e-07, tau0=0.0005, G=5e-10, qetbias=0.00016, tc=0.04, tload=0.9, tbath=0.02, n=5.0, lgcb=True, squiddc=2.5e-12, squidpole=0.0, squidn=1.0)¶

Bases: object

Class for the simulation of the TES noise using the simple Irwin theory. Supports noise simulation for in transition, superconducting, and normal.

Attributes:

freqs : float, array_like: The frequencies for which we will calculate the noise simulation
rload : float: The load resistance of the TES (sum of shunt and parasitic resistances) in Ohms
r0 : float: The bias resistance of the TES in Ohms
rshunt : float: The shunt resistance of the TES circuit in Ohms
beta : float: The current sensitivity of the TES (dlogR/dlogI), unitless
loopgain : float: The Irwin loop gain of the TES, unitless
inductance : float: The inductance of the TES circuit in Henries
tau0 : float: The thermal time constant (equals C/G) in s
G : float: The thermal conductance of the TES in W/K
qetbias : float: The QET bias in Amps
tc : float: The critical temperature of the TES in K
tload : float: The effective temperature of the load resistor in K
tbath : float: The bath temperature in K
n : float: The power-law dependence of the power flow to the heat bath
lgcb : boolean: Boolean flag that determines whether we use the ballistic (True) or diffusive limit when calculating TFN power noise
squiddc : float: The frequency pole for the SQUID and downstream electronics noise, in Hz. The SQUID/electronics noise should have been fit beforehand, using the following model:

(squiddc*(1.0+(squidpole/f)**squidn))**2.0
squidpole : float: The frequency pole for the SQUID and downstream electronics noise, in Hz. The SQUID/electronics noise should have been fit beforehand, using the following model:

(squiddc*(1.0+(squidpole/f)**squidn))**2.0
squidn : float: The power of the SQUID and downstream electronics noise, in Hz. The SQUID/electronics noise should have been fit beforehand, using the following model:

(squiddc*(1.0+(squidpole/f)**squidn))**2.0
f_tfn : float: Function that estimates the noise suppression of the thermal fluctuation noise due to the difference in temperature between the bath and the TES. Supports the ballistic and diffusive limits, which is chosen via lgcb

Methods

`dIdP`([freqs])	The two-pole dIdP function determined from the TES parameters.
`dIdV`([freqs])	The two-pole dIdV function determined from the TES parameters.
`dIdVnormal`([freqs])	The one-pole dIdV function determined from the TES parameters for when the TES is normal.
`dIdVsc`([freqs])	The one-pole dIdV function determined from the TES parameters for when the TES is superconducting.
`s_iload`([freqs])	The Johnson load current noise determined from the TES parameters for in transition.
`s_iloadnormal`([freqs])	The Johnson load current noise determined from the TES parameters for normal.
`s_iloadsc`([freqs])	The Johnson load current noise determined from the TES parameters for superconducting.
`s_isquid`([freqs])	The SQUID and downstream electronics current noise, currently is using a 1/f model that must be specified when initializing the class.
`s_ites`([freqs])	The Johnson TES current noise determined from the TES parameters for in transition.
`s_itesnormal`([freqs])	The Johnson TES current noise determined from the TES parameters for normal.
`s_itfn`([freqs])	The thermal fluctuation noise in current determined from the TES parameters for in transition.
`s_itot`([freqs])	The total current noise for the TES in transition.
`s_itotnormal`([freqs])	The total current noise for the TES when normal.
`s_itotsc`([freqs])	The total current noise for the TES when superconducting.
`s_pload`([freqs])	The Johnson load power noise determined from the TES parameters for in transition.
`s_psquid`([freqs])	The SQUID and downstream electronics power noise, currently is using a 1/f model that must be specified when initializing the class.
`s_ptes`([freqs])	The Johnson TES power noise determined from the TES parameters for in transition.
`s_ptfn`([freqs])	The thermal fluctuation noise in power determined from the TES parameters for in transition.
`s_ptot`([freqs])	The total power noise for the TES in transition.
`s_vload`([freqs])	The Johnson load voltage noise determined from the TES parameters.
`s_vtes`([freqs])	The Johnson TES voltage noise determined from the TES parameters for in transition.
`s_vtesnormal`([freqs])	The Johnson TES voltage noise determined from the TES parameters for normal.

dIdP(freqs=None)¶

The two-pole dIdP function determined from the TES parameters.

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	dIdP : float, ndarray The two-pole dIdP function

dIdV(freqs=None)¶

The two-pole dIdV function determined from the TES parameters.

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	dIdV : float, ndarray The two-pole dIdV function

dIdVnormal(freqs=None)¶

The one-pole dIdV function determined from the TES parameters for when the TES is normal.

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	dIdVnormal : float, ndarray The one-pole dIdV function for when the TES is normal.

dIdVsc(freqs=None)¶

The one-pole dIdV function determined from the TES parameters for when the TES is superconducting.

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	dIdVsc : float, ndarray The one-pole dIdV function for when the TES is superconducting.

s_iload(freqs=None)¶

The Johnson load current noise determined from the TES parameters for in transition.

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	s_iload : float, ndarray The Johnson load current noise at the specified frequencies

s_iloadnormal(freqs=None)¶

The Johnson load current noise determined from the TES parameters for normal.

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	s_iloadnormal : float, ndarray The Johnson load current noise at the specified frequencies

s_iloadsc(freqs=None)¶

The Johnson load current noise determined from the TES parameters for superconducting.

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	s_iloadsc : float, ndarray The Johnson load current noise at the specified frequencies

s_isquid(freqs=None)¶

The SQUID and downstream electronics current noise, currently is using a 1/f model that must be specified when initializing the class.

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	s_isquid : float, ndarray The SQUID and downstream electronics current noise at the specified frequencies

s_ites(freqs=None)¶

The Johnson TES current noise determined from the TES parameters for in transition. This noise has both an electronic and thermal component.

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	s_ites : float, ndarray The Johnson TES current noise at the specified frequencies

s_itesnormal(freqs=None)¶

The Johnson TES current noise determined from the TES parameters for normal.

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	s_itesnormal : float, ndarray The Johnson TES current noise at the specified frequencies

s_itfn(freqs=None)¶

The thermal fluctuation noise in current determined from the TES parameters for in transition.

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	s_itfn : float, ndarray The thermal fluctuation noise in current at the specified frequencies

s_itot(freqs=None)¶

The total current noise for the TES in transition. This is calculated by summing each of the current noise sources together. Units are [A^2/Hz].

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	s_itot : float, ndarray The total current noise at the specified frequencies

s_itotnormal(freqs=None)¶

The total current noise for the TES when normal. This is calculated by summing each of the current noise sources together. Units are [A^2/Hz].

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	s_itotnormal : float, ndarray The total current noise at the specified frequencies

s_itotsc(freqs=None)¶

The total current noise for the TES when superconducting. This is calculated by summing each of the current noise sources together. Units are [A^2/Hz].

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	s_itotsc : float, ndarray The total current noise at the specified frequencies

s_pload(freqs=None)¶

The Johnson load power noise determined from the TES parameters for in transition.

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	s_pload : float, ndarray The Johnson load power noise at the specified frequencies

s_psquid(freqs=None)¶

The SQUID and downstream electronics power noise, currently is using a 1/f model that must be specified when initializing the class. This is only used for when the TES is in transition.

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	s_psquid : float, ndarray The SQUID and downstream electronics power noise at the specified frequencies

s_ptes(freqs=None)¶

The Johnson TES power noise determined from the TES parameters for in transition.

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	s_ptes : float, ndarray The Johnson TES power noise at the specified frequencies

s_ptfn(freqs=None)¶

The thermal fluctuation noise in power determined from the TES parameters for in transition.

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	s_ptfn : float, ndarray The thermal fluctuation noise in power at the specified frequencies

s_ptot(freqs=None)¶

The total power noise for the TES in transition. This is calculated by summing each of the current noise sources together and using dIdP to convert to power noise. Units are [W^2/Hz].

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	s_ptot : float, ndarray The total power noise at the specified frequencies

s_vload(freqs=None)¶

The Johnson load voltage noise determined from the TES parameters. This formula holds no matter where we are in transition.

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	s_vload : float, ndarray The Johnson load voltage noise at the specified frequencies

s_vtes(freqs=None)¶

The Johnson TES voltage noise determined from the TES parameters for in transition.

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	s_vtes : float, ndarray The Johnson TES voltage noise at the specified frequencies

s_vtesnormal(freqs=None)¶

The Johnson TES voltage noise determined from the TES parameters for normal.

Parameters:	freqs : float, ndarray, optional The frequencies for which we will calculate the noise simulation. If left as None, the function will use the values from the initialization.
Returns:	s_vtesnormal : float, ndarray The Johnson TES voltage noise at the specified frequencies

qetpy.detcal package¶

Submodules¶

qetpy.detcal.cut module¶

qetpy.detcal.didv module¶

qetpy.detcal.iv module¶

qetpy.detcal.noise module¶

qetpy.detcal.sim module¶

Module contents¶