qetpy.utils package¶

qetpy.utils._utils.calc_offset(x, fs=1.0, sgfreq=100.0, is_didv=False)¶

Calculates the DC offset of time series trace.

Parameters:

x : ndarray: Array to calculate offsets of.
fs : float, optional: Sample rate of the data being taken, assumed to be in units of Hz.
sgfreq : float, optional: The frequency of signal generator (if is_didv is True. If False, then this is ignored).
is_didv : bool, optional: If False, average of full trace is returned. If True, then the average of n Periods is returned (where n is the max number of full periods present in a trace).

Returns:

offset : ndarray: Array of offsets with same shape as input x minus the last dimension
std : ndarray: Array of std with same shape as offset

qetpy.utils._utils.lowpassfilter(traces, cut_off_freq=100000, fs=625000.0, order=1)¶

Applies a low pass filter to the inputted time series traces

Parameters:	traces : ndarray An array of shape (# traces, # bins per trace). cut_off_freq : float, int, optional The cut off 3dB frequency for the low pass filter, defaults to 100kHz. fs : float, int, optional Digitization rate of data, defaults to 625e3 Hz. order : int, optional The order of the low pass filter, defaults to 1.
Returns:	filt_traces : ndarray Array of low pass filtered traces with the same shape as inputted traces.

qetpy.utils._utils.align_traces(traces, lgcjustshifts=False, n_cut=5000, cut_off_freq=5000.0, fs=625000.0)¶

Function to align dIdV traces if each trace does not trigger at the same point. Uses a convolution of the traces to find the time offset.

Parameters:

traces : ndarray: Array of shape (# traces, # bins per trace).
lgcjustshifts : boolean, optional: If False, the aligned traces and the phase shifts are returned. If True, just the phase shifts are returned. Default is False.
n_cut : int, optional: The number of bins to use to do the convolution. Just need enough information to see the periodic signal. Default is 5000.
cut_off_freq : float or int, optional: 3dB cut off frequency for filter. Default is 5000 Hz.
fs : float or int, optional: Sample rate of data in Hz. Default is 625e3 Hz.

Returns:

shifts : ndarray: Array of phase shifts for each trace in units of bins.
masked_aligned : masked ndarray, optional: Array of time shift corrected traces, same shape as input traces. The masked array masks the np.NaN values in the time shifted traces so that normal numpy functions will ignore the nan’s in computations.

qetpy.utils._utils.get_offset_from_muon(avemuon, qetbias, rn, rload, rsh=0.005, nbaseline=6000, lgcfullrtn=True)¶

Function to calculate the offset in the measured TES current using an average muon.

Parameters:

avemuon : array: An average of ‘good’ muons in time domain, referenced to TES current
qetbias : float: Applied QET bias current
rn : float: Normal resistance of the TES
rload : float: Load resistance of TES circuit (rp + rsh)
rsh : float, optional: Value of the shunt resistor for the TES circuit
nbaseline : int, optional: The number of bins to use to calculate the baseline, i.e. [0:nbaseline]
lgcfullrtn : bool, optional: If True, the offset, r0, i0, and bias power is returned, If False, just the offset is returned

Returns:

qetpy.utils._utils.powertrace_simple(trace, ioffset, qetbias, rload, rsh)¶

Function to convert time series trace from units of TES current to units of power.

The function takes into account the second order depenace on current, but assumes the infinite irwin loop gain approximation.

Parameters:	trace : array Time series trace, referenced to TES current ioffset : float The offset in the measured TES current qetbias : float Applied QET bias current rload : float Load resistance of TES circuit (rp + rsh) rsh : float, optional Value of the shunt resistor for the TES circuit
Returns:	trace_p : array Time series trace, in units of power referenced to the TES

qetpy.utils._utils.integrate_powertrace_simple(trace, time, nbasepre, nbasepost, ioffset, qetbias, rload, rsh)¶

Function to calculate the energy collected by the TESs by integrating the power in the TES as a function of time.

Parameters:

trace : array: Time series trace, referenced to TES current
time : array: Array of time values corresponding to the trace array
nbasepre : int: The bin number corresponding to the pre-pulse baseline, i.e. [0:nbasepre]
nbasepost : int: The bin number corresponding to the post-pulse baseline, i.e. [nbasepost:-1]
ioffset : float: The offset in the measured TES current
qetbias : float: Applied QET bias current
rload : float: Load resistance of TES circuit (rp + rsh)
rsh : float, optional: Value of the shunt resistor for the TES circuit

Returns:

qetpy.utils._utils.stdcomplex(x, axis=0)¶

Function to return complex standard deviation (individually computed for real and imaginary components) for an array of complex values.

Parameters:	x : ndarray An array of complex values from which we want the complex standard deviation. axis : int, optional Which axis to take the standard deviation of (should be used if the dimension of the array is greater than 1)
Returns:	std_complex : ndarray The complex standard deviation of the inputted array, along the specified axis.

qetpy.utils._utils.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.utils._utils.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