PyDynamic - Analysis of dynamic measurements¶

PyDynamic is a Python software package developed jointly by mathematicians from Physikalisch-Technische Bundesanstalt (Germany) and National Physical Laboratory (UK) as part of the joint European Research Project EMPIR 14SIP08 Dynamic.

For the PyDynamic homepage go to GitHub.

PyDynamic is written in Python 3 and strives to run with all Python versions with upstream support. Currently it is tested to work with Python 3.5 to 3.8.

Installation¶

There is a quick way to get started but we advise to setup a virtual environment and guide through the process in the section Proper Python setup with virtual environment

Quick setup (not recommended)¶

If you just want to use the software, the easiest way is to run from your system’s command line

pip install --user PyDynamic

This will download the latest version from the Python package repository and copy it into your local folder of third-party libraries. Note that PyDynamic runs with Python versions 3.5 to 3.8. Usage in any Python environment on your computer is then possible by

import PyDynamic

or, for example, for the module containing the Fourier domain uncertainty methods:

from PyDynamic.uncertainty import propagate_DFT

Updating to the newest version¶

Updates can then be installed via

pip install --user --upgrade PyDynamic

Proper Python setup with virtual environment (recommended)¶

The setup described above allows the quick and easy use of PyDynamic, but it also has its downsides. When working with Python we should rather always work in so-called virtual environments, in which our project specific dependencies are satisfied without polluting or breaking other projects’ dependencies and to avoid breaking all our dependencies in case of an update of our Python distribution.

If you are not familiar with Python virtual environments you can get the motivation and an insight into the mechanism in the official docs.

Create a virtual environment and install requirements¶

Creating a virtual environment with Python built-in tools is easy and explained in more detail in the official docs of Python itself.

It boils down to creating an environment anywhere on your computer, then activate it and finally install PyDynamic and its dependencies.

venv creation and installation in Windows¶

In your Windows command prompt execute the following:

> py -3 -m venv LOCAL\PATH\TO\ENVS\PyDynamic_venv
> LOCAL\PATH\TO\ENVS\PyDynamic_venv\Scripts\activate.bat
(PyDynamic_venv) > pip install PyDynamic

venv creation and installation on Mac and Linux¶

In your terminal execute the following:

$ python3 -m venv /LOCAL/PATH/TO/ENVS/PyDynamic_venv
$  /LOCAL/PATH/TO/ENVS/PyDynamic_venv/bin/activate
(PyDynamic_venv) $ pip install PyDynamic

Updating to the newest version¶

Updates can then be installed on all platforms after activating the virtual environment via:

(PyDynamic_venv) $ pip install --upgrade PyDynamic

Examples¶

On the project website in the examples and tutorials subfolders and the separate PyDynamic_tutorials repository you can find various examples illustrating the application of PyDynamic. Here is just a short list to get you started.

Quick Examples¶

Uncertainty propagation for the application of an FIR filter with coefficients b with which an uncertainty ub is associated. The filter input signal is x with known noise standard deviation sigma. The filter output signal is y with associated uncertainty uy.

from PyDynamic.uncertainty.propagate_filter import FIRuncFilter
y, uy = FIRuncFilter(x, sigma, b, ub)

Uncertainty propagation through the application of the discrete Fourier transform (DFT). The time domain signal is x with associated squared uncertainty ux. The result of the DFT is the vector X of real and imaginary parts of the DFT applied to x and the associated uncertainty UX.

from PyDynamic.uncertainty.propagate_DFT import GUM_DFT
X, UX = GUM_DFT(x, ux)

Sequential application of the Monte Carlo method for uncertainty propagation for the case of filtering a time domain signal x with an IIR filter b,a with uncertainty associated with the filter coefficients Uab and signal noise standard deviation sigma. The filter output is the signal y and the Monte Carlo method calculates point-wise uncertainties uy and coverage intervals Py corresponding to the specified percentiles.

from PyDynamic.uncertainty.propagate_MonteCarlo import SMC
y, uy, Py = SMC(x, sigma, b, a, Uab, runs=1000, Perc=[0.025,0.975])

Detailed examples¶

More comprehensive examples you can find in provided Jupyter notebooks, which require additional dependencies to be installed. This can be achieved by appending [examples] to PyDynamic in all of the above, e.g.

pip install PyDynamic[examples]

Afterwards you can browser through the following list:

%pylab inline
import numpy as np
import scipy.signal as dsp
from palettable.colorbrewer.qualitative import Dark2_8

colors = Dark2_8.mpl_colors
rst = np.random.RandomState(1)

Populating the interactive namespace from numpy and matplotlib

Design of a digital deconvolution filter (FIR type)¶

from PyDynamic.deconvolution.fit_filter import LSFIR_unc
from PyDynamic.misc.SecondOrderSystem import *
from PyDynamic.misc.testsignals import shocklikeGaussian
from PyDynamic.misc.filterstuff import kaiser_lowpass, db
from PyDynamic.uncertainty.propagate_filter import FIRuncFilter
from PyDynamic.misc.tools import make_semiposdef

# parameters of simulated measurement
Fs = 500e3
Ts = 1 / Fs

# sensor/measurement system
f0 = 36e3; uf0 = 0.01*f0
S0 = 0.4; uS0= 0.001*S0
delta = 0.01; udelta = 0.1*delta

# transform continuous system to digital filter
bc, ac = sos_phys2filter(S0,delta,f0)
b, a = dsp.bilinear(bc, ac, Fs)

# Monte Carlo for calculation of unc. assoc. with [real(H),imag(H)]
f = np.linspace(0, 120e3, 200)
Hfc = sos_FreqResp(S0, delta, f0, f)
Hf = dsp.freqz(b,a,2*np.pi*f/Fs)[1]

runs = 10000
MCS0 = S0 + rst.randn(runs)*uS0
MCd  = delta+ rst.randn(runs)*udelta
MCf0 = f0 + rst.randn(runs)*uf0
HMC = np.zeros((runs, len(f)),dtype=complex)
for k in range(runs):
    bc_,ac_ = sos_phys2filter(MCS0[k], MCd[k], MCf0[k])
    b_,a_ = dsp.bilinear(bc_,ac_,Fs)
    HMC[k,:] = dsp.freqz(b_,a_,2*np.pi*f/Fs)[1]

H = np.r_[np.real(Hf), np.imag(Hf)]
uAbs = np.std(np.abs(HMC),axis=0)
uPhas= np.std(np.angle(HMC),axis=0)
UH= np.cov(np.hstack((np.real(HMC),np.imag(HMC))),rowvar=0)
UH= make_semiposdef(UH)

Problem description¶

Assume information about a linear time-invariant (LTI) measurement system to be available in terms of its frequency response values \(H(j\omega)\) at a set of frequencies together with associated uncertainties:

\[u(\mathbf{H}) = (u(\vert H(j\omega_1)\vert),\ldots,u(\vert H(j\omega_N)\vert),u(\angle H(j\omega_1)),\ldots,u(\angle H(j\omega_N)))\]

figure(figsize=(16,8))
errorbar(f*1e-3, np.abs(Hf), uAbs, fmt=".", color=colors[0])
title("measured amplitude spectrum with associated uncertainties")
xlim(0,50)
xlabel("frequency / kHz",fontsize=20)
ylabel("magnitude / au",fontsize=20);

figure(figsize=(16,8))
errorbar(f*1e-3, np.angle(Hf), uPhas, fmt=".", color=colors[1])
title("measured phase spectrum with associated uncertainties")
xlim(0,50)
xlabel("frequency / kHz",fontsize=20)
ylabel("phase / rad",fontsize=20);

Simulated measurement¶

Measurements with this system are then modeled as a convolution of the system’s impulse response

\[h(t) = \mathcal{F}^{-1}(H(j\omega))\]

with the input signal \(x(t)\), after an analogue-to-digital conversion producing the measured signal

\[y[n] = (h\ast x)(t_n) \qquad n=1,\ldots,M\]

# simulate input and output signals
time = np.arange(0, 4e-3 - Ts, Ts)
#x = shocklikeGaussian(time, t0 = 2e-3, sigma = 1e-5, m0=0.8)
m0 = 0.8; sigma = 1e-5; t0 = 2e-3
x = -m0*(time-t0)/sigma * np.exp(0.5)*np.exp(-(time-t0) ** 2 / (2 * sigma ** 2))
y = dsp.lfilter(b, a, x)
noise = 1e-3
yn = y + rst.randn(np.size(y)) * noise

figure(figsize=(16,8))
plot(time*1e3, x, label="system input signal", color=colors[0])
plot(time*1e3, yn,label="measured output signal", color=colors[1])
legend(fontsize=20)
xlim(1.8,4); ylim(-1,1)
xlabel("time / ms",fontsize=20)
ylabel(r"signal amplitude / $m/s^2$",fontsize=20);

Design of the deconvolution filter¶

The aim is to derive a digital filter with finite impulse response (FIR)

\[g(z) = \sum_{k=0}^K b_k z^{-k}\]

such that the filtered signal

\[\hat{x}[n] = (g\ast y)[n] \qquad n=1,\ldots,M\]

<<<<<<< HEAD is an estimate of the system’s input signal at the discrete time points ======= is an estimate of the system’s input signal at the discrete time points. >>>>>>> devel1

Publication

Elster and Link “Uncertainty evaluation for dynamic measurements modelled by a linear time-invariant system” Metrologia, 2008
Vuerinckx R, Rolain Y, Schoukens J and Pintelon R “Design of stable IIR filters in the complex domain by automatic delay selection” IEEE Trans. Signal Process. 44 2339–44, 1996

Determine FIR filter coefficients such that

\[H(j\omega) g(e^{j\omega/F_s}) \approx e^{-j\omega n_0 / F_s} \qquad \text{for} \qquad \vert\omega\vert\leq\omega_1\]

with a pre-defined time delay \(n_0\) to improve the fit quality (typically half the filter order).

Consider as least-squares problem

\[(y-Xb)^TW^{-1}(y-Xb)\]

with - \(y\) real and imaginary parts of the reciprocal and phase shifted measured frequency response values - \(X\) the model matrix with entries \(e^{-j k \omega/Fs}\) - \(b\) the sought FIR filter coefficients - \(W\) a weighting matrix (usually derived from the uncertainties associated with the frequency response measurements

Filter coefficients and associated uncertainties are thus obtained as

\[b = \left( X^TW^{-1}X \right)^{-1}X^TW^{-1}y\]

\[u_b= \left( X^TW^{-1}X \right)^{-1} X^TW^{-1}U_yW^{-1}X\left( X^TW^{-1}X \right)^{-1}\]

# Calculation of FIR deconvolution filter and its assoc. unc.
N = 12; tau = N//2
bF, UbF = deconv.LSFIR_unc(H,UH,N,tau,f,Fs)

Least-squares fit of an order 12 digital FIR filter to the
reciprocal of a frequency response given by 400 values
and propagation of associated uncertainties.
Final rms error = 1.545423e+01

figure(figsize=(16,8))
errorbar(range(N+1), bF, np.sqrt(np.diag(UbF)), fmt="o", color=colors[3])
xlabel("FIR coefficient index", fontsize=20)
ylabel("FIR coefficient value", fontsize=20);

In order to render the ill-posed estimation problem stable, the FIR inverse filter is accompanied with an FIR low-pass filter.

Application of the deconvolution filter for input estimation is then carried out as

\[\hat{x}[n-n_0] = (g\ast(g_{low}\ast y)[n]\]

with point-wise associated uncertainties calculated as

\[u^2(\hat{x}[n-n_0] = b^TU_{x_{low}[n]}b + x_{low}^T[n]U_bx_{low}[n] + trace(U_{x_{low}[n]}U_b)\]

fcut = f0+10e3; low_order = 100
blow, lshift = kaiser_lowpass(low_order, fcut, Fs)
shift = -tau - lshift

figure(figsize=(16,10))
HbF = dsp.freqz(bF,1,2*np.pi*f/Fs)[1]*dsp.freqz(blow,1,2*np.pi*f/Fs)[1]
semilogy(f*1e-3, np.abs(Hf), label="measured frequency response")
semilogy(f*1e-3, np.abs(HbF),label="inverse filter")
semilogy(f*1e-3, np.abs(Hf*HbF), label="compensation result")
legend();

xhat,Uxhat = FIRuncFilter(yn,noise,bF,UbF,shift,blow)

figure(figsize=(16,8))
plot(time*1e3,x, label='input signal')
plot(time*1e3,yn,label='output signal')
plot(time*1e3,xhat,label='estimate of input')
legend(fontsize=20)
xlabel('time / ms',fontsize=22)
ylabel('signal amplitude / au',fontsize=22)
tick_params(which="both",labelsize=16)
xlim(1.9,2.4); ylim(-1,1);

figure(figsize=(16,10))
plot(time*1e3,Uxhat)
xlabel('time / ms',fontsize=22)
ylabel('signal uncertainty / au',fontsize=22)
subplots_adjust(left=0.15,right=0.95)
tick_params(which='both', labelsize=16)
xlim(1.9,2.4);

Basic workflow in PyDynamic¶

Fit an FIR filter to the reciprocal of the measured frequency response

from PyDynamic.deconvolution.fit_filter import LSFIR_unc
bF, UbF = LSFIR_unc(H,UH,N,tau,f,Fs, verbose=False)

with

H the measured frequency response values
UH the covariance (i.e. uncertainty) associated with real and imaginary parts of H
N the filter order
tau the filter delay in samples
f the vector of frequencies at which H is given
Fs the sampling frequency for the digital FIR filter

Propagate the uncertainty associated with the measurement noise and the FIR filter through the deconvolution process

xhat,Uxhat = FIRuncFilter(yn,noise,bF,UbF,shift,blow)

with

yn the noisy measurement
noise the std of the noise
shift the total delay of the FIR filter and the low-pass filter
blow the coefficients of the FIR low-pass filter

%pylab inline
import scipy.signal as dsp

Populating the interactive namespace from numpy and matplotlib

Uncertainty propagation for IIR filters¶

from PyDynamic.misc.testsignals import rect
from PyDynamic.uncertainty.propagate_filter import IIRuncFilter
from PyDynamic.uncertainty.propagate_MonteCarlo import SMC
from PyDynamic.misc.tools import make_semiposdef

Digital filters with infinite impulse response (IIR) are a common tool in signal processing. Consider the measurand to be the output signal of an IIR filter with z-domain transfer function

\[G(z) = \frac{\sum_{n=0}^{N_b} b_n z^{-n}}{1 + \sum_{m=1}^{N_a} a_m z^{-m}} .\]

The measurement model is thus given by

\[y[k] = \sum_{n=0}^{N_b} b_n x[k-n] - \sum_{m=1}^{N_a} a_m y[k-m]\]

As input quantities to the model the input signal values \(x[k]\) and the IIR filter coefficients \((b_0,\ldots,a_{N_a})\) are considered.

Linearisation-based uncertainty propagation¶

Scientific publication

A. Link and C. Elster,
“Uncertainty evaluation for IIR filtering using a
state-space approach,”
Meas. Sci. Technol., vol. 20, no. 5, 2009.

The linearisation method for the propagation of uncertainties through the IIR model is based on a state-space model representation of the IIR filter equation

where

The linearization-based uncertainty propagation method for IIR filters provides

propagation schemes for white noise and colored noise in the filter input signal
incorporation of uncertainties in the IIR filter coefficients
online evaluation of the point-wise uncertainties associated with the IIR filter output

Implementation in PyDynamic¶

y,Uy = IIRuncFilter(x,noise,b,a,Uab)

with

x the filter input signal sequency
noise the standard deviation of the measurement noise in x
b,a the IIR filter coefficient
Uab the covariance matrix associated with \((a_1,\ldots,b_{N_b})\)

Remark

Implementation for more general noise processes than white noise is considered for one of the next revisions.

Example¶

# parameters of simulated measurement
Fs = 100e3
Ts = 1.0/Fs

# nominal system parameter
fcut = 20e3
L = 6
b,a = dsp.butter(L,2*fcut/Fs,btype='lowpass')

f = linspace(0,Fs/2,1000)
figure(figsize=(16,8))
semilogy(f*1e-3, abs(dsp.freqz(b,a,2*np.pi*f/Fs)[1]))
ylim(0,10);
xlabel("frequency / kHz",fontsize=18); ylabel("frequency response amplitude / au",fontsize=18)
ax2 = gca().twinx()
ax2.plot(f*1e-3, unwrap(angle(dsp.freqz(b,a,2*np.pi*f/Fs)[1])),color="r")
ax2.set_ylabel("frequency response phase / rad",fontsize=18);

time = np.arange(0,499*Ts,Ts)
t0 = 100*Ts; t1 = 300*Ts
height = 0.9
noise = 1e-3
x = rect(time,t0,t1,height,noise=noise)

figure(figsize=(16,8))
plot(time*1e3, x, label="input signal")
legend(fontsize=20)
xlabel('time / ms',fontsize=18)
ylabel('signal amplitude / au',fontsize=18);

# uncertain knowledge: fcut between 19.8kHz and 20.2kHz
runs = 10000
FC = fcut + (2*np.random.rand(runs)-1)*0.2e3
AB = np.zeros((runs,len(b)+len(a)-1))

for k in range(runs):
    bb,aa = dsp.butter(L,2*FC[k]/Fs,btype='lowpass')
    AB[k,:] = np.hstack((aa[1:],bb))

Uab = make_semiposdef(np.cov(AB,rowvar=0))

Uncertain knowledge: low-pass cut-off frequency is between \(19.8\) and \(20.2\) kHz

figure(figsize=(16,8))
subplot(121)
errorbar(range(len(b)), b, sqrt(diag(Uab[L:,L:])),fmt=".")
title(r"coefficients $b_0,\ldots,b_n$",fontsize=20)
subplot(122)
errorbar(range(len(a)-1), a[1:], sqrt(diag(Uab[:L, :L])),fmt=".");
title(r"coefficients $a_1,\ldots,a_n$",fontsize=20);

Estimate of the filter output signal and its associated uncertainty

y,Uy = IIRuncFilter(x,noise,b,a,Uab)

figure(figsize=(16,8))
plot(time*1e3, x, label="input signal")
plot(time*1e3, y, label="output signal")
legend(fontsize=20)
xlabel('time / ms',fontsize=18)
ylabel('signal amplitude / au',fontsize=18);

figure(figsize=(16,8))
plot(time*1e3, Uy, "r", label="uncertainty")
legend(fontsize=20)
xlabel('time / ms',fontsize=18)
ylabel('signal amplitude / au',fontsize=18);

Monte-Carlo method for uncertainty propagation¶

The linearisation-based uncertainty propagation can become unreliable due to the linearisation errors. Therefore, a Monte-Carlo method for digital filters with uncertain coefficients has been proposed in

S. Eichstädt, A. Link, P. Harris, and C. Elster,
“Efficient implementation of a Monte Carlo method
for uncertainty evaluation in dynamic measurements,”
Metrologia, vol. 49, no. 3, 2012.

The proposed Monte-Carlo method provides - a memory-efficient implementation of the GUM Monte-Carlo method - online calculation of point-wise uncertainties, estimates and coverage intervals by taking advantage of the sequential character of the filter equation

\[y[k] = \sum_{n=0}^{N_b} b_n x[k-n] - \sum_{m=1}^{N_a} a_m y[k-m]\]

yMC,UyMC = SMC(x,noise,b,a,Uab,runs=10000)

figure(figsize=(16,8))
plot(time*1e3, Uy, "r", label="uncertainty (linearisation)")
plot(time*1e3, UyMC, "g", label="uncertainty (Monte Carlo)")
legend(fontsize=20)
xlabel('time / ms',fontsize=18)
ylabel('signal amplitude / au',fontsize=18);

SMC progress:  0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Basic workflow in PyDynamic¶

Using GUM linearization¶

y,Uy = IIRuncFilter(x,noise,b,a,Uab)

Using sequential GUM Monte Carlo method¶

yMC,UyMC = SMC(x,noise,b,a,Uab,runs=10000)

SMC progress:  0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

%pylab inline
colors = [[0.1,0.6,0.5], [0.9,0.2,0.5], [0.9,0.5,0.1]]

Populating the interactive namespace from numpy and matplotlib

Deconvolution in the frequency domain (DFT)¶

from PyDynamic.uncertainty.propagate_DFT import GUM_DFT,GUM_iDFT
from PyDynamic.uncertainty.propagate_DFT import DFT_deconv, AmpPhase2DFT
from PyDynamic.uncertainty.propagate_DFT import DFT_multiply

#%% reference data
ref_file = np.loadtxt("DFTdeconv reference_signal.dat")
time = ref_file[:,0]
ref_data = ref_file[:,1]
Ts = 2e-9
N = len(time)

#%% hydrophone calibration data
calib = np.loadtxt("DFTdeconv calibration.dat")
f = calib[:,0]
FR = calib[:,1]*np.exp(1j*calib[:,3])
Nf = 2*(len(f)-1)

uAmp = calib[:,2]
uPhas= calib[:,4]
UAP = np.r_[uAmp,uPhas*np.pi/180]**2

#%% measured hydrophone output signal
meas = np.loadtxt("DFTdeconv measured_signal.dat")
y = meas[:,1]
# assumed noise std
noise_std = 4e-4
Uy = noise_std**2

Consider knowledge about the measurement system is available in terms of its frequency response with uncertainties associated with amplitude and phase values.

\[\mathbf{H} = (\vert H(f_1) \vert, \ldots, \angle H(f_N))\]

\[u_H = (u_{\vert H(f_1) \vert}, \ldots, u_{\angle H(f_N)})\]

figure(figsize=(16,8))
errorbar(f * 1e-6, abs(FR), 2 * sqrt(UAP[:len(UAP) // 2]), fmt= ".-", alpha=0.2, color=colors[0])
xlim(0.5, 80)
ylim(0.04, 0.24)
xlabel("frequency / MHz", fontsize=22); tick_params(which= "both", labelsize=18)
ylabel("amplitude / V/MPa", fontsize=22);

figure(figsize=(16,8))
errorbar(f * 1e-6, unwrap(angle(FR)) * pi / 180, 2 * UAP[len(UAP) // 2:], fmt= ".-", alpha=0.2, color=colors[0])
xlim(0.5, 80)
ylim(-0.2, 0.3)
xlabel("frequency / MHz", fontsize=22); tick_params(which= "both", labelsize=18)
ylim(-1,1)
ylabel("phase / rad", fontsize=22);

The measurand is the input signal \(\mathbf{x}=(x_1,\ldots,x_M)\) to the measurement system with corresponding measurement model given by

\[y[n] = (h\ast x)[n] + \varepsilon[n]\]

Input estimation is here to be considered in the Fourier domain.

The estimation model equation is thus given by

\[\hat{x} = \mathcal{F}^{-1}\left( \frac{Y(f)}{H(f)}H_L(f) \right)\]

with - \(Y(f)\) the DFT of the measured system output signal - \(H_L(f)\) the frequency response of a low-pass filter

Estimation steps

DFT of \(y\) and propagation of uncertainties to the frequency domain
Propagation of uncertainties associated with amplitude and phase of system to corr. real and imaginary parts
Division in the frequency domain and propagation of uncertainties
Multiplication with low-pass filter and propagation of uncertainties
Inverse DFT and propagation of uncertainties to the time domain

Propagation from time to frequency domain¶

With the DFT defined as

\[Y_k = \sum_{n=0}^{N-1} y_n \exp(-\mathrm{j} k\beta_n)\]

with \(\beta_n = 2\pi n /N\), the uncertainty associated with the DFT outcome represented in terms of real and imaginary parts, is given by

\[\begin{split}U_{Y} = \left( \begin{array}{cc} C_{\cos} U_y C_{\cos}^{\sf T} & C_{\cos} U_y C_{\sin}^{\sf T} \\ (C_{\cos} U_y C_{\sin}^{\sf T})^{\sf T} & C_{\sin} U_y C_{\sin}^{\sf T} \end{array}\right)\end{split}\]

Y,UY = GUM_DFT(y,Uy,N=Nf)

figure(figsize=(18,6))
subplot(121)
errorbar(time*1e6, y, sqrt(Uy)*ones_like(y),fmt=".-")
xlabel("time / µs",fontsize=20); ylabel("pressure / Bar",fontsize=20)
subplot(122)
errorbar(f*1e-6, Y[:len(f)],sqrt(UY[:len(f)]),label="real part")
errorbar(f*1e-6, Y[len(f):],sqrt(UY[len(f):]),label="imaginary part")
legend()
xlabel("frequency / MHz",fontsize=20); ylabel("amplitude / au",fontsize=20);

Uncertainties for measurement system w.r.t. real and imaginary parts¶

In practice, the frequency response of the measurement system is characterised in terms of its amplitude and phase values at a certain set of frequencies. GUM uncertainty evaluation, however, requires a representation by real and imaginary parts.

\[H_k = A_k \cos(P_k) + \mathrm{j} A_k\sin(P_k)\]

GUM uncertainty propagation

\[\begin{split}C_{RI} = \left( \begin{array}{cc} R_{A} & R_{P} \\ I_{A} & I_{P} \end{array}\right) .\end{split}\]

\[\begin{split}U_H = C_{RI} \left( \begin{array}{cc} U_{AA} & U_{AP} \\ U_{AP}^{\sf T} & U_{PP} \end{array} \right) C_{RI}^{\sf T} = \left( \begin{array}{cc} U_{11} & U_{12} \\ U_{21}^{\sf T} & U_{22} \end{array} \right) .\end{split}\]

H, UH = AmpPhase2DFT(np.abs(FR),np.angle(FR),UAP)

Nf = len(f)
figure(figsize=(18,6))
subplot(121)
errorbar(f*1e-6, H[:Nf], sqrt(diag(UH[:Nf,:Nf])),fmt=".-",color=colors[2],alpha=0.2)
xlabel("frequency / MHz",fontsize=20); ylabel("real part / au",fontsize=20)
subplot(122)
errorbar(f*1e-6, H[Nf:],sqrt(diag(UH[Nf:,Nf:])),fmt=".-",color=colors[2],alpha=0.2)
xlabel("frequency / MHz",fontsize=20); ylabel("imaginary part / au",fontsize=20);

Deconvolution in the frequency domain¶

The deconvolution problem can be decomposed into a division by the system’s frequency response followed by a multiplication by a low-pass filter frequency response.

\[X(f) = \frac{Y(f)}{H(f)}H_L(f)\]

which in real and imaginary part becomes

\[X = \frac{(\Re_Y\Re_H + \Im_Y\Im_H)+\mathrm{j}(-\Re_Y\Im_H+\Im_Y\Re_H)}{\Re_H^2+\Im_H^2}(\Re_{H_L} + \mathrm{j}\Im_{H_L})\]

Sensitivities for division part

Uncertainty blocks for multiplication part

# low-pass filter for deconvolution
def lowpass(f,fcut=80e6):
    return 1/(1+1j*f/fcut)**2

HLc = lowpass(f)
HL = np.r_[np.real(HLc), np.imag(HLc)]

XH,UXH = DFT_deconv(H,Y,UH,UY)

XH, UXH = DFT_multiply(XH, UXH, HL)

figure(figsize=(18,6))
subplot(121)
errorbar(f*1e-6, XH[:Nf], sqrt(diag(UXH[:Nf,:Nf])),fmt=".-",color=colors[2],alpha=0.2)
xlabel("frequency / MHz",fontsize=20); ylabel("real part / au",fontsize=20)
subplot(122)
errorbar(f*1e-6, XH[Nf:],sqrt(diag(UXH[Nf:,Nf:])),fmt=".-",color=colors[2],alpha=0.2)
xlabel("frequency / MHz",fontsize=20); ylabel("imaginary part / au",fontsize=20);

Propagation from frequency to time domain¶

The inverse DFT equation is given by

\[X_n = \frac{1}{N} \sum_{k=0}^{N-1}(\Re_k\cos(k\beta_n)-\Im_k\sin(k\beta_n))\]

The sensitivities for the GUM propagation of uncertainties are then

GUM uncertainty propagation for the inverse DFT

xh,Uxh = GUM_iDFT(XH,UXH,Nx=N)

ux = np.sqrt(np.diag(Uxh))

figure(figsize=(16,8))
plot(time*1e6,xh,label="estimated pressure signal",linewidth=2,color=colors[0])
plot(time * 1e6, ref_data, "--", label= "reference data", linewidth=2, color=colors[1])
fill_between(time * 1e6, xh + 2 * ux, xh - 2 * ux, alpha=0.2, color=colors[0])
xlabel("time / µs", fontsize=22)
ylabel("signal amplitude / MPa", fontsize=22)
tick_params(which= "major", labelsize=18)
legend(loc= "upper left", fontsize=18, fancybox=True)
xlim(0, 2);

figure(figsize=(16,8))
plot(time * 1e6, ux, label= "uncertainty", linewidth=2, color=colors[0])
xlabel("time / µs", fontsize=22)
ylabel("uncertainty / MPa", fontsize=22)
tick_params(which= "major", labelsize=18)
xlim(0,2);

Summary of PyDynamic workflow for deconvolution in DFT domain¶

Y,UY = GUM_DFT(y,Uy,N=Nf)

H, UH = AmpPhase2DFT(A, P, UAP)

XH,UXH = DFT_deconv(H,Y,UH,UY)

XH, UXH = DFT_multiply(XH, UXH, HL)

Advices and tips for contributors¶

If you want to become active as developer, we provide all important information here to make the start as easy as possible. The code you produce should be seamlessly integrable into PyDynamic by aligning your work with the established workflows. This guide should work on all platforms and provide everything needed to start developing for PyDynamic. Please open an issue or ideally contribute to this guide as a start, if problems or questions arise.

Guiding principles¶

The PyDynamic development process is based on the following guiding principles:

support all major Python versions supported upstream .
actively maintain, ensuring security vulnerabilities or other issues are resolved in a timely manner
employ state-of-the-art development practices and tools, specifically
- follow semantic versioning
- use conventional commit messages
- consider the PEP8 style guide, wherever feasible

Get started developing¶

Get the code on GitHub and locally¶

For collaboration we recommend forking the repository as described here. Simply apply the changes to your fork and open a Pull Request on GitHub as described here. For small changes it will be sufficient to just apply your changes on GitHub and send the PR right away. For more comprehensive work, you should clone your fork and read on carefully.

Initial development setup¶

This guide assumes you already have a valid runtime environment for PyDynamic as described in the README. To start developing, install the required dependencies for your specific Python version. To find it, activate the desired virtual environment and execute:

(PyDynamic_venv) $ python --version
Python 3.8.3

Then upgrade/install pip and pip-tools which we use to pin our dependencies to specific versions:

(PyDynamic_venv) $ pip install --upgrade pip pip-tools

You can then initially install or at any later time update all dependencies to the versions we use. From the repository root run pip-tools’ command pip-sync e.g. for Python 3.8:

(PyDynamic_venv) $ pip-sync requirements/dev-requirements-py38.txt requirements/requirements-py38.txt

Advised toolset¶

We use black to implement our coding style, Sphinx for automated generation of our documentation on ReadTheDocs. We use pytest managed by tox as testing framework backed by hypothesis and coverage. For automated releases we use python-semantic-release in our pipeline on CircleCI . All requirements for contributions are derived from this. If you followed the steps for the initial development setup you have everything at your hands.

Coding style¶

As long as the readability of mathematical formulations is not impaired, our code should follow PEP8. For automating this uniform formatting task we use the Python package black. It is easy to handle and integrable into most common IDEs, such that it is automatically applied.

Commit style¶

Conventional commit messages are required for the following:

Releasing automatically according to semantic versioning
Generating a changelog automatically

Parts of the commit messages and links appear in the changelogs of subsequent releases as a result. We use the following types:

feat: for commits that introduce new features
docs: for commits that contribute significantly to documentation
fix: commits in which bugs are fixed
build: Commits that affect packaging
ci: Commits that affect the CI pipeline
test: Commits that apply significant changes to tests
chore: Commits that affect other non-PyDynamic components (e.g. ReadTheDocs, Git , … )
revert: commits, which undo previous commits using git revert
refactor: commits that merely reformulate, rename or similar
style: commits, which essentially make changes to line breaks and whitespace
wip: Commits which are not recognizable as one of the above-mentioned types until later, usually during a PR merge. The merge commit is then marked as the corresponding type.

Testing¶

We strive to increase our code coverage with every change introduced. This requires that every new feature and every change to existing features is accompanied by appropriate pytest testing. We test the basic components for correctness and, if necessary, the integration into the big picture. It is usually sufficient to create appropriately named methods in one of the existing modules in the subfolder test. If necessary add a new module that is appropriately named.

Workflow for adding completely new functionality¶

In case you add a new feature you generally follow the pattern:

read through and follow this contribution advices and tips, especially regarding the advised tool set and commit style
open an according issue to submit a feature request and get in touch with other PyDynamic developers and users
fork the repository or update the master branch of your fork and create an arbitrary named feature branch from master
decide which package and module your feature should be integrated into
if there is no suitable package or module, create a new one and a corresponding module in the test subdirectory with the same name prefixed by test_
after adding your functionality add it to all higher-level __all__ variables in the module itself and in the higher-level __init__.pys
if new dependencies are introduced, add them to setup.py or dev-requirements.in
during development write tests in alignment with existing test modules, for example test_interpolate or test_propagate_filter
write docstrings in the NumPy docstring format
as early as possible create a draft pull request onto the upstream’s master branch
once you think your changes are ready to merge, request a review from the PTB-PSt1/pydynamic-devs (you will find them in the according drop-down) and mark your PR as ready for review
at the latest now you will have the opportunity to review the documentation automatically generated from the docstrings on ReadTheDocs after your reviewers will set up everything
resolve the conversations and have your pull request merged

Documentation¶

The documentation of PyDynamic consists of three parts. Every adaptation of an existing feature and every new feature requires adjustments on all three levels:

user documentation on ReadTheDocs
examples in the form of Jupyter notebooks for extensive features and Python scripts for features which can be comprehensively described with few lines of commented code
developer documentation in the form of comments in the code

User documentation¶

To locally generate a preview of what ReadTheDocs will generate from your docstrings, you can simply execute after activating your virtual environment:

(PyDynamic_venv) $ sphinx-build docs/ docs/_build
Sphinx v3.1.1 in Verwendung
making output directory...
[...]
build abgeschlossen.

The HTML pages are in docs/_build.

After that you can open the file ./docs/_build/index.html relative to the project’s root with your favourite browser. Simply re-execute the above command after each change to the docstrings to update your local version of the documentation.

Examples¶

We want to provide extensive sample material for all PyDynamic features in order to simplify the use or even make it possible in the first place. We collect the examples in a separate repository PyDynamic_tutorials separate from PyDynamic to better distinguish the core functionality from additional material. Please refer to the corresponding README for more information about the setup and create a pull request accompanying the pull request in PyDynamic according to the same procedure we describe here.

Comments in the code¶

Regarding comments in the code we recommend to invest 45 minutes for the PyCon DE 2019 Talk of Stefan Schwarzer, a 20+-years Python developer: Commenting code - beyond common wisdom.

Manage dependencies¶

As stated in the README and above we use pip-tools for dependency management. The requirements subdirectory contains a requirements.txt and a dev-requirements.txt for all supported Python versions, with a suffix naming the version, for example requirements-py35.txt To keep them up to date semi-automatically we use the bash script requirements/upgrade_dependencies.sh. It contains extensive comments on its use. pip-tools’ command pip-compile finds the right versions from the dependencies liste in setup.py and the dev-requirements.in.

Licensing¶

All contributions are released under PyDynamic’s GNU Lesser General Public License v3.0.

Evaluation of uncertainties¶

The evaluation of uncertainties is a fundamental part of the measurement analysis in metrology. The analysis of dynamic measurements typically involves methods from signal processing, such as digital filtering, the discrete Fourier transform (DFT), or simple tasks like interpolation. For most of these tasks, methods are readily available, for instance, as part of scipy.signal. This module of PyDynamic provides the corresponding methods for the evaluation of uncertainties.

The package consists of the following modules:

PyDynamic.uncertainty.propagate_DFT: Uncertainty evaluation for the DFT
PyDynamic.uncertainty.propagate_filter: Uncertainty evaluation for digital filtering
PyDynamic.uncertainty.propagate_MonteCarlo: Monte Carlo methods for digital filtering
PyDynamic.uncertainty.interpolation: Uncertainty evaluation for interpolation

Uncertainty evaluation for the DFT¶

The PyDynamic.uncertainty.propagate_DFT module implements methods for the propagation of uncertainties in the application of the DFT, inverse DFT, deconvolution and multiplication in the frequency domain, transformation from amplitude and phase to real and imaginary parts and vice versa.

The corresponding scientific publications is: S. Eichstädt und V. Wilkens GUM2DFT — a software tool for uncertainty evaluation of transient signals in the frequency domain. Measurement Science and Technology, 27(5), 055001, 2016. [DOI: 10.1088/0957-0233/27/5/055001]

This module contains the following functions:

GUM_DFT(): Calculation of the DFT of the time domain signal x and propagation of the squared uncertainty Ux associated with the time domain sequence x to the real and imaginary parts of the DFT of x
GUM_iDFT(): GUM propagation of the squared uncertainty UF associated with the DFT values F through the inverse DFT
GUM_DFTfreq(): Return the Discrete Fourier Transform sample frequencies
DFT_transferfunction(): Calculation of the transfer function H = Y/X in the frequency domain with X being the Fourier transform of the system’s input signal and Y that of the output signal
DFT_deconv(): Deconvolution in the frequency domain
DFT_multiply(): Multiplication in the frequency domain
AmpPhase2DFT(): Transformation from magnitude and phase to real and imaginary parts
DFT2AmpPhase(): Transformation from real and imaginary parts to magnitude and phase
AmpPhase2Time(): Transformation from amplitude and phase to time domain
Time2AmpPhase(): Transformation from time domain to amplitude and phase

PyDynamic.uncertainty.propagate_DFT.AmpPhase2DFT(A, P, UAP, keep_sparse=False)[source]¶

Transformation from magnitude and phase to real and imaginary parts

Calculate the vector F=[real,imag] and propagate the covariance matrix UAP associated with [A, P]

Parameters

A (np.ndarray of shape (N,)) – vector of magnitude values
P (np.ndarray of shape (N,)) – vector of phase values (in radians)
UAP (np.ndarray of shape (2N,2N)) – covariance matrix associated with (A,P) or vector of squared standard uncertainties [u^2(A),u^2(P)]
keep_sparse (bool, optional) – whether to transform sparse matrix to numpy array or not

Returns

F (np.ndarray) – vector of real and imaginary parts of DFT result
UF (np.ndarray) – covariance matrix associated with F

PyDynamic.uncertainty.propagate_DFT.AmpPhase2Time(A, P, UAP)[source]¶

Transformation from amplitude and phase to time domain

GUM propagation of covariance matrix UAP associated with DFT amplitude A and phase P to the result of the inverse DFT. Uncertainty UAP is assumed to be given for amplitude and phase with blocks: UAP = [[u(A,A), u(A,P)],[u(P,A),u(P,P)]]

Parameters

A (np.ndarray of shape (N,)) – vector of amplitude values
P (np.ndarray of shape (N,)) – vector of phase values (in rad)
UAP (np.ndarray of shape (2N,2N)) – covariance matrix associated with [A,P]

Returns

x (np.ndarray) – vector of time domain values
Ux (np.ndarray) – covariance matrix associated with x

PyDynamic.uncertainty.propagate_DFT.DFT2AmpPhase(F, UF, keep_sparse=False, tol=1.0, return_type='separate')[source]¶

Transformation from real and imaginary parts to magnitude and phase

Calculate the matrix U_AP = [[U1,U2],[U2^T,U3]] associated with magnitude and phase of the vector F=[real,imag] with associated covariance matrix U_F=[[URR,URI],[URI^T,UII]]

Parameters

F (np.ndarray of shape (2M,)) – vector of real and imaginary parts of a DFT result
UF (np.ndarray of shape (2M,2M)) – covariance matrix associated with F
keep_sparse (bool, optional) – if true then UAP will be sparse if UF is one-dimensional
tol (float, optional) – lower bound for A/uF below which a warning will be issued concerning unreliable results
return_type (str, optional) – If “separate” then magnitude and phase are returned as separate arrays. Otherwise the array [A, P] is returned

If return_type is separate:

Returns

A (np.ndarray) – vector of magnitude values
P (np.ndarray) – vector of phase values in radians, in the range [-pi, pi]
UAP (np.ndarray) – covariance matrix associated with (A,P)

Otherwise:

Returns

AP (np.ndarray) – vector of magnitude and phase values
UAP (np.ndarray) – covariance matrix associated with AP

PyDynamic.uncertainty.propagate_DFT.DFT_deconv(H, Y, UH, UY)[source]¶

Deconvolution in the frequency domain

GUM propagation of uncertainties for the deconvolution X = Y/H with Y and H being the Fourier transform of the measured signal and of the system’s impulse response, respectively. This function returns the covariance matrix as a tuple of blocks if too large for complete storage in memory.

Parameters

H (np.ndarray of shape (2M,)) – real and imaginary parts of frequency response values (N an even integer)
Y (np.ndarray of shape (2M,)) – real and imaginary parts of DFT values
UH (np.ndarray of shape (2M,2M)) – covariance matrix associated with H
UY (np.ndarray of shape (2M,2M)) – covariance matrix associated with Y

Returns

X (np.ndarray of shape (2M,)) – real and imaginary parts of DFT values of deconv result
UX (np.ndarray of shape (2M,2M)) – covariance matrix associated with real and imaginary part of X

References

Eichstädt and Wilkens [Eichst2016]

PyDynamic.uncertainty.propagate_DFT.DFT_multiply(Y, F, UY, UF=None)[source]¶

Multiplication in the frequency domain

GUM uncertainty propagation for multiplication in the frequency domain, where the second factor F may have an associated uncertainty. This method can be used, for instance, for the application of a low-pass filter in the frequency domain or the application of deconvolution as a multiplication with an inverse of known uncertainty.

Parameters

Y (np.ndarray of shape (2M,)) – real and imaginary parts of the first factor
F (np.ndarray of shape (2M,)) – real and imaginary parts of the second factor
UY (np.ndarray either shape (2M,) or shape (2M,2M)) – covariance matrix or squared uncertainty associated with Y
UF (np.ndarray of shape (2M,2M)) – covariance matrix associated with F (optional), default is None

Returns

YF (np.ndarray of shape (2M,)) – the product of Y and F
UYF (np.ndarray of shape (2M,2M)) – the uncertainty associated with YF

PyDynamic.uncertainty.propagate_DFT.DFT_transferfunction(X, Y, UX, UY)[source]¶

Calculation of the transfer function H = Y/X in the frequency domain

Calculate the transfer function with X being the Fourier transform of the system’s input signal and Y that of the output signal.

Parameters

X (np.ndarray) – real and imaginary parts of the system’s input signal
Y (np.ndarray) – real and imaginary parts of the system’s output signal
UX (np.ndarray) – covariance matrix associated with X
UY (np.ndarray) – covariance matrix associated with Y

Returns

H (np.ndarray) – real and imaginary parts of the system’s frequency response
UH (np.ndarray) – covariance matrix associated with H

This function only calls DFT_deconv.

PyDynamic.uncertainty.propagate_DFT.GUM_DFT(x, Ux, N=None, window=None, CxCos=None, CxSin=None, returnC=False, mask=None)[source]¶

Calculation of the DFT with propagation of uncertainty

Calculation of the DFT of the time domain signal x and propagation of the squared uncertainty Ux associated with the time domain sequence x to the real and imaginary parts of the DFT of x.

Parameters

x (numpy.ndarray of shape (M,)) – vector of time domain signal values
Ux (numpy.ndarray) – covariance matrix associated with x, shape (M,M) or vector of squared standard uncertainties, shape (M,) or noise variance as float
N (int, optional) – length of time domain signal for DFT; N>=len(x)
window (numpy.ndarray, optional of shape (M,)) – vector of the time domain window values
CxCos (numpy.ndarray, optional) – cosine part of sensitivity matrix
CxSin (numpy.ndarray, optional) – sine part of sensitivity matrix
returnC (bool, optional) – if true, return sensitivity matrix blocks for later use
mask (ndarray of dtype bool) – calculate DFT values and uncertainties only at those frequencies where mask is True

Returns

F (numpy.ndarray) – vector of complex valued DFT values or of its real and imaginary parts
UF (numpy.ndarray) – covariance matrix associated with real and imaginary part of F

References

Eichstädt and Wilkens [Eichst2016]

PyDynamic.uncertainty.propagate_DFT.GUM_DFTfreq(N, dt=1)[source]¶

Return the Discrete Fourier Transform sample frequencies

Parameters

N (int) – window length
dt (float) – sample spacing (inverse of sampling rate)

Returns

f – Array of length n//2 + 1 containing the sample frequencies

Return type

ndarray

Uncertainty evaluation for digital filtering¶

This module contains functions for the propagation of uncertainties through the application of a digital filter using the GUM approach.

This modules contains the following functions:

FIRuncFilter(): Uncertainty propagation for signal y and uncertain FIR filter theta
IIRuncFilter(): Uncertainty propagation for the signal x and the uncertain IIR filter (b,a)

Note

The Elster-Link paper for FIR filters assumes that the autocovariance is known and that noise is stationary!

PyDynamic.uncertainty.propagate_filter.FIRuncFilter(y, sigma_noise, theta, Utheta=None, shift=0, blow=None, kind='corr')[source]¶

Uncertainty propagation for signal y and uncertain FIR filter theta

Parameters

y (np.ndarray) – filter input signal
sigma_noise (float or np.ndarray) – float: standard deviation of white noise in y 1D-array: interpretation depends on kind
theta (np.ndarray) – FIR filter coefficients
Utheta (np.ndarray) – covariance matrix associated with theta
shift (int) – time delay of filter output signal (in samples)
blow (np.ndarray) – optional FIR low-pass filter
kind (string) – only meaningfull in combination with isinstance(sigma_noise, numpy.ndarray) “diag”: point-wise standard uncertainties of non-stationary white noise “corr”: single sided autocovariance of stationary (colored/corrlated) noise (default)

Returns

x (np.ndarray) – FIR filter output signal
ux (np.ndarray) – point-wise uncertainties associated with x

References

Elster and Link 2008 [Elster2008]

Monte Carlo methods for digital filtering¶

The propagation of uncertainties via the FIR and IIR formulae alone does not enable the derivation of credible intervals, because the underlying distribution remains unknown. The GUM-S2 Monte Carlo method provides a reference method for the calculation of uncertainties for such cases.

This module implements Monte Carlo methods for the propagation of uncertainties for digital filtering.

This module contains the following functions:

MC(): Standard Monte Carlo method for application of digital filter
SMC(): Sequential Monte Carlo method with reduced computer memory requirements
UMC(): Update Monte Carlo method for application of digital filters with reduced computer memory requirements
UMC_generic(): Update Monte Carlo method with reduced computer memory requirements

PyDynamic.uncertainty.propagate_MonteCarlo.MC(x, Ux, b, a, Uab, runs=1000, blow=None, alow=None, return_samples=False, shift=0, verbose=True)[source]¶

Standard Monte Carlo method

Monte Carlo based propagation of uncertainties for a digital filter (b,a) with uncertainty matrix \(U_{\theta}\) for \(\theta=(a_1,\ldots,a_{N_a},b_0,\ldots,b_{N_b})^T\)

Parameters

x (np.ndarray) – filter input signal
Ux (float or np.ndarray) – standard deviation of signal noise (float), point-wise standard uncertainties or covariance matrix associated with x
b (np.ndarray) – filter numerator coefficients
a (np.ndarray) – filter denominator coefficients
Uab (np.ndarray) – uncertainty matrix \(U_\theta\)
runs (int,optional) – number of Monte Carlo runs
return_samples (bool, optional) – whether samples or mean and std are returned

If return_samples is False, the method returns:

Returns

y (np.ndarray) – filter output signal
Uy (np.ndarray) – uncertainty associated with

Otherwise the method returns:

Returns: Y – array of Monte Carlo results
Return type: np.ndarray

References

Eichstädt, Link, Harris and Elster [Eichst2012]

PyDynamic.uncertainty.propagate_MonteCarlo.SMC(x, noise_std, b, a, Uab=None, runs=1000, Perc=None, blow=None, alow=None, shift=0, return_samples=False, phi=None, theta=None, Delta=0.0)[source]¶

Sequential Monte Carlo method

Sequential Monte Carlo propagation for a digital filter (b,a) with uncertainty matrix \(U_{\theta}\) for \(\theta=(a_1,\ldots,a_{N_a},b_0,\ldots,b_{N_b})^T\)

Parameters

x (np.ndarray) – filter input signal
noise_std (float) – standard deviation of signal noise
b (np.ndarray) – filter numerator coefficients
a (np.ndarray) – filter denominator coefficients
Uab (np.ndarray) – uncertainty matrix \(U_\theta\)
runs (int, optional) – number of Monte Carlo runs
Perc (list, optional) – list of percentiles for quantile calculation
blow (np.ndarray) – optional low-pass filter numerator coefficients
alow (np.ndarray) – optional low-pass filter denominator coefficients
shift (int) – integer for time delay of output signals
return_samples (bool, otpional) – whether to return y and Uy or the matrix Y of MC results
theta (phi,) – parameters for AR(MA) noise model \(\epsilon(n) = \sum_k \phi_k\epsilon(n-k) + \sum_k \theta_k w(n-k) + w(n)\) with \(w(n)\sim N(0,noise_std^2)\)
Delta (float,optional) – upper bound on systematic error of the filter

If return_samples is False, the method returns:

Returns

y (np.ndarray) – filter output signal (Monte Carlo mean)
Uy (np.ndarray) – uncertainties associated with y (Monte Carlo point-wise std)
Quant (np.ndarray) – quantiles corresponding to percentiles Perc (if not None)

Otherwise the method returns:

Returns: Y – array of all Monte Carlo results
Return type: np.ndarray

References

Eichstädt, Link, Harris, Elster [Eichst2012]

PyDynamic.uncertainty.propagate_MonteCarlo.UMC(x, b, a, Uab, runs=1000, blocksize=8, blow=1.0, alow=1.0, phi=0.0, theta=0.0, sigma=1, Delta=0.0, runs_init=100, nbins=1000, credible_interval=0.95)[source]¶

Batch Monte Carlo for filtering using update formulae for mean, variance and (approximated) histogram. This is a wrapper for the UMC_generic function, specialised on filters

Parameters

x (np.ndarray, shape (nx, )) – filter input signal
b (np.ndarray, shape (nbb, )) – filter numerator coefficients
a (np.ndarray, shape (naa, )) – filter denominator coefficients, normalization (a[0] == 1.0) is assumed
Uab (np.ndarray, shape (naa + nbb - 1, )) – uncertainty matrix \(U_\theta\)
runs (int, optional) – number of Monte Carlo runs
blocksize (int, optional) – how many samples should be evaluated for at a time
blow (float or np.ndarray, optional) – filter coefficients of optional low pass filter
alow (float or np.ndarray, optional) – filter coefficients of optional low pass filter
phi (np.ndarray, optional,) – see misc.noise.ARMA noise model
theta (np.ndarray, optional) – see misc.noise.ARMA noise model
sigma (float, optional) – see misc.noise.ARMA noise model
Delta (float, optional) – upper bound of systematic correction due to regularisation (assume uniform distribution)
runs_init (int, optional) – how many samples to evaluate to form initial guess about limits
nbins (int, list of int, optional) – number of bins for histogram
credible_interval (float, optional) – must be in [0,1] central credible interval size

By default, phi, theta, sigma are chosen such, that N(0,1)-noise is added to the input signal.

Returns

y (np.ndarray) – filter output signal
Uy (np.ndarray) – uncertainty associated with
y_cred_low (np.ndarray) – lower boundary of credible interval
y_cred_high (np.ndarray) – upper boundary of credible interval
happr (dict) – dictionary keys: given nbin dictionary values: bin-edges val[“bin-edges”], bin-counts val[“bin-counts”]

References

Eichstädt, Link, Harris, Elster [Eichst2012]
ported to python in 2019-08 from matlab-version of Sascha Eichstaedt (PTB) from 2011-10-12
copyright on updating formulae parts is by Peter Harris (NPL)

PyDynamic.uncertainty.propagate_MonteCarlo.UMC_generic(draw_samples, evaluate, runs=100, blocksize=8, runs_init=10, nbins=100, return_samples=False, n_cpu=2)[source]¶

Generic Batch Monte Carlo using update formulae for mean, variance and (approximated) histogram. Assumes that the input and output of evaluate are numeric vectors (but not necessarily of same dimension). If the output of evaluate is multi-dimensional, it will be flattened into 1D.

Parameters

draw_samples (function(int nDraws)) – function that draws nDraws from a given distribution / population needs to return a list of (multi dimensional) numpy.ndarrays
evaluate (function(sample)) – function that evaluates a sample and returns the result needs to return a (multi dimensional) numpy.ndarray
runs (int, optional) – number of Monte Carlo runs
blocksize (int, optional) – how many samples should be evaluated for at a time
runs_init (int, optional) – how many samples to evaluate to form initial guess about limits
nbins (int, list of int, optional) – number of bins for histogram
return_samples (bool, optional) – see return-value of documentation
n_cpu (int, optional) – number of CPUs to use for multiprocessing, defaults to all available CPUs

Example

draw samples from multivariate normal distribution: draw_samples = lambda size: np.random.multivariate_normal(x, Ux, size)

build a function, that only accepts one argument by masking additional kwargs: evaluate = functools.partial(_UMCevaluate, nbb=b.size, x=x, Delta=Delta, phi=phi, theta=theta, sigma=sigma, blow=blow, alow=alow) evaluate = functools.partial(bigFunction, **dict_of_kwargs)

By default the method

Returns

y (np.ndarray) – mean of flattened/raveled simulation output i.e.: y = np.ravel(evaluate(sample))
Uy (np.ndarray) – covariance associated with y
happr (dict) – dictionary of bin-edges and bin-counts
output_shape (tuple) – shape of the unraveled simulation output can be used to reshape y and np.diag(Uy) into original shape

If return_samples is True, the method additionally returns all evaluated samples. This should only be done for testing and debugging reasons, as this removes all memory-improvements of the UMC-method.

Returns: sims – dict of samples and corresponding results of every evaluated simulation samples and results are saved in their original shape
Return type: dict

References

Eichstädt, Link, Harris, Elster [Eichst2012]

Uncertainty evaluation for interpolation¶

Deprecated since version 2.0.0: The module PyDynamic.uncertainty.interpolation will be renamed to PyDynamic.uncertainty.interpolate in the next major release 2.0.0. From version 1.4.3 on you should only use the new module instead.

The PyDynamic.uncertainty.interpolation module implements methods for the propagation of uncertainties in the application of standard interpolation methods as provided by scipy.interpolate.interp1d.

This module for now still contains the following function:

interp1d_unc(): Interpolate arbitrary time series considering the associated uncertainties

PyDynamic.uncertainty.interpolation.interp1d_unc(t_new: numpy.ndarray, t: numpy.ndarray, y: numpy.ndarray, uy: numpy.ndarray, kind: Optional[str] = 'linear', copy=True, bounds_error: Optional[bool] = None, fill_value: Optional[Union[float, Tuple[float, float], str]] = nan, fill_unc: Optional[Union[float, Tuple[float, float], str]] = nan, assume_sorted: Optional[bool] = True, returnC: Optional[bool] = False) → Union[Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray]][source]¶

Interpolate a 1-D function considering the associated uncertainties

t and y are arrays of values used to approximate some function \(f \colon y = f(t)\).

Note that calling interp1d_unc() with NaNs present in input values results in undefined behaviour.

An equal number of each of the original timestamps (or frequencies), values and associated uncertainties is required.

Parameters

t_new ((M,) array_like) – A 1-D array of real values representing the timestamps (or frequencies) at which to evaluate the interpolated values. t_new can be sorted in any order.
t ((N,) array_like) – A 1-D array of real values representing timestamps (or frequencies) in ascending order.
y ((N,) array_like) – A 1-D array of real values. The length of y must be equal to the length of t.
uy ((N,) array_like) – A 1-D array of real values representing the standard uncertainties associated with y.
kind (str, optional) – Specifies the kind of interpolation for y as a string (‘previous’, ‘next’, ‘nearest’ or ‘linear’). Default is ‘linear’.
copy (bool, optional) – If True, the method makes internal copies of t and y. If False, references to t and y are used. The default is to copy.
bounds_error (bool, optional) – If True, a ValueError is raised any time interpolation is attempted on a value outside of the range of x (where extrapolation is necessary). If False, out of bounds values are assigned fill_value. By default, an error is raised unless fill_value=”extrapolate”.
fill_value (array-like or (array-like, array_like) or “extrapolate”, optional) –
- if a ndarray (or float), this value will be used to fill in for requested points outside of the data range. If not provided, then the default is NaN.
- If a two-element tuple, then the first element is used as a fill value for t_new < t[0] and the second element is used for t_new > t[-1]. Anything that is not a 2-element tuple (e.g., list or ndarray, regardless of shape) is taken to be a single array-like argument meant to be used for both bounds as below, above = fill_value, fill_value.
- If “extrapolate”, then points outside the data range will be set to the first or last element of the values.
Both parameters fill_value and fill_unc should be provided to ensure desired behaviour in the extrapolation range.
fill_unc (array-like or (array-like, array_like) or “extrapolate”, optional) – Usage and behaviour as described in fill_value but for the uncertainties. Both parameters fill_value and fill_unc should be provided to ensure desired behaviour in the extrapolation range.
assume_sorted (bool, optional) – If False, values of t can be in any order and they are sorted first. If True, t has to be an array of monotonically increasing values.
returnC (bool, optional) – If True, return sensitivity coefficients for later use. This is only available for interpolation kind ‘linear’ and for fill_unc=”extrapolate” at the moment. If False sensitivity coefficients are not returned and internal computation is slightly more efficient.

If returnC is False, which is the default behaviour, the method returns:

Returns

t_new ((M,) array_like) – interpolation timestamps (or frequencies)
y_new ((M,) array_like) – interpolated values
uy_new ((M,) array_like) – interpolated associated standard uncertainties

Otherwise the method returns:

Returns

t_new ((M,) array_like) – interpolation timestamps (or frequencies)
y_new ((M,) array_like) – interpolated values
uy_new ((M,) array_like) – interpolated associated standard uncertainties
C ((M,N) array_like) – sensitivity matrix \(C\), which is used to compute the uncertainties \(U_{y_{new}} = C \cdot \operatorname{diag}(u_y^2) \cdot C^T\)

References

White [White2017]

Model estimation¶

The estimation of the measurand in the analysis of dynamic measurements typically corresponds to a deconvolution problem. Therefore, a digital filter can be designed whose input is the measured system output signal and whose output is an estimate of the measurand. The package Model estimation implements methods for the design of such filters given an array of frequency response values or the reciprocal of frequency response values with associated uncertainties for the measurement system.

The package Model estimation also contains a function for the identification of transfer function models.

The package consists of the following modules:

PyDynamic.model_estimation.fit_filter: least-squares fit to a given complex frequency response or its reciprocal
PyDynamic.model_estimation.fit_transfer: identification of transfer function models

Fitting filters to frequency response or reciprocal¶

The module PyDynamic.model_estimation.fit_filter contains several functions to carry out a least-squares fit to a given complex frequency response and the design of digital deconvolution filters by least-squares fitting to the reciprocal of a given frequency response each with associated uncertainties.

This module contains the following functions:

LSIIR(): Least-squares IIR filter fit to a given frequency response
LSFIR(): Least-squares fit of a digital FIR filter to a given frequency response
invLSFIR(): Least-squares fit of a digital FIR filter to the reciprocal of a given frequency response.
invLSFIR_unc(): Design of FIR filter as fit to reciprocal of frequency response values with uncertainty
invLSFIR_uncMC(): Design of FIR filter as fit to reciprocal of frequency response values with uncertainty via Monte Carlo
invLSIIR(): Design of a stable IIR filter as fit to reciprocal of frequency response values
invLSIIR_unc(): Design of a stable IIR filter as fit to reciprocal of frequency response values with uncertainty

PyDynamic.model_estimation.fit_filter.LSFIR(H, N, tau, f, Fs, Wt=None)[source]¶

Least-squares fit of a digital FIR filter to a given frequency response.

Parameters

H ((complex) frequency response values of shape (M,)) –
N (FIR filter order) –
tau (delay of filter) –
f (frequencies of shape (M,)) –
Fs (sampling frequency of digital filter) –
Wt ((optional) vector of weights of shape (M,) or shape (M,M)) –

Returns

Return type

filter coefficients bFIR (ndarray) of shape (N+1,)

PyDynamic.model_estimation.fit_filter.LSIIR(Hvals, Nb, Na, f, Fs, tau=0, justFit=False)[source]¶

Least-squares IIR filter fit to a given frequency response.

This method uses Gauss-Newton non-linear optimization and pole mapping for filter stabilization

Parameters

Hvals (numpy array of (complex) frequency response values of shape (M,)) –
Nb (integer numerator polynomial order) –
Na (integer denominator polynomial order) –
f (numpy array of frequencies at which Hvals is given of shape) –
(M,) –
Fs (sampling frequency) –
tau (integer initial estimate of time delay) –
justFit (boolean, when true then no stabilization is carried out) –

Returns

b,a (IIR filter coefficients as numpy arrays)
tau (filter time delay in samples)

References

Eichstädt et al. 2010 [Eichst2010]
Vuerinckx et al. 1996 [Vuer1996]

PyDynamic.model_estimation.fit_filter.invLSFIR(H, N, tau, f, Fs, Wt=None)[source]¶

Least-squares fit of a digital FIR filter to the reciprocal of a given frequency response.

Parameters

H (np.ndarray of shape (M,) and dtype complex) – frequency response values
N (int) – FIR filter order
tau (float) – delay of filter
f (np.ndarray of shape (M,)) – frequencies
Fs (float) – sampling frequency of digital filter
Wt (np.ndarray of shape (M,) - optional) – vector of weights

Returns

bFIR – filter coefficients

Return type

np.ndarray of shape (N,)

References

Elster and Link [Elster2008]

PyDynamic.model_estimation.fit_filter.invLSFIR_unc(H, UH, N, tau, f, Fs, wt=None, verbose=True, trunc_svd_tol=None)[source]¶

Design of FIR filter as fit to reciprocal of frequency response values with uncertainty

Least-squares fit of a digital FIR filter to the reciprocal of a frequency response for which associated uncertainties are given for its real and imaginary part. Uncertainties are propagated using a truncated svd and linear matrix propagation.

Parameters

H (np.ndarray of shape (M,)) – frequency response values
UH (np.ndarray of shape (2M,2M)) – uncertainties associated with the real and imaginary part
N (int) – FIR filter order
tau (float) – delay of filter
f (np.ndarray of shape (M,)) – frequencies
Fs (float) – sampling frequency of digital filter
wt (np.ndarray of shape (2M,) - optional) – array of weights for a weighted least-squares method
verbose (bool, optional) – whether to print statements to the command line
trunc_svd_tol (float) – lower bound for singular values to be considered for pseudo-inverse

Returns

b (np.ndarray of shape (N+1,)) – filter coefficients of shape
Ub (np.ndarray of shape (N+1,N+1)) – uncertainties associated with b

References

Elster and Link [Elster2008]

PyDynamic.model_estimation.fit_filter.invLSFIR_uncMC(H, UH, N, tau, f, Fs, verbose=True)[source]¶

Design of FIR filter as fit to reciprocal of frequency response values with uncertainty

Least-squares fit of a FIR filter to the reciprocal of a frequency response for which associated uncertainties are given for its real and imaginary parts. Uncertainties are propagated using a Monte Carlo method. This method may help in cases where the weighting matrix or the Jacobian are ill-conditioned, resulting in false uncertainties associated with the filter coefficients.

Parameters

H (np.ndarray of shape (M,) and dtype complex) – frequency response values
UH (np.ndarray of shape (2M,2M)) – uncertainties associated with the real and imaginary part of H
N (int) – FIR filter order
tau (int) – time delay of filter in samples
f (np.ndarray of shape (M,)) – frequencies corresponding to H
Fs (float) – sampling frequency of digital filter
verbose (bool, optional) – whether to print statements to the command line

Returns

b (np.ndarray of shape (N+1,)) – filter coefficients of shape
Ub (np.ndarray of shape (N+1, N+1)) – uncertainties associated with b

References

Elster and Link [Elster2008]

PyDynamic.model_estimation.fit_filter.invLSIIR(Hvals, Nb, Na, f, Fs, tau, justFit=False, verbose=True)[source]¶

Design of a stable IIR filter as fit to reciprocal of frequency response values

Least-squares fit of a digital IIR filter to the reciprocal of a given set of frequency response values using the equation-error method and stabilization by pole mapping and introduction of a time delay.

Parameters

Hvals (np.ndarray of shape (M,) and dtype complex) – frequency response values.
Nb (int) – order of IIR numerator polynomial.
Na (int) – order of IIR denominator polynomial.
f (np.ndarray of shape (M,)) – frequencies corresponding to Hvals
Fs (float) – sampling frequency for digital IIR filter.
tau (float) – initial estimate of time delay for filter stabilization.
justFit (bool) – if True then no stabilization is carried out.
verbose (bool) – If True print some more detail about input parameters.

Returns

b (np.ndarray) – The IIR filter numerator coefficient vector in a 1-D sequence
a (np.ndarray) – The IIR filter denominator coefficient vector in a 1-D sequence
tau (int) – time delay (in samples)

References

Eichstädt, Elster, Esward, Hessling [Eichst2010]

PyDynamic.model_estimation.fit_filter.invLSIIR_unc(H, UH, Nb, Na, f, Fs, tau=0)[source]¶

Design of stabel IIR filter as fit to reciprocal of given frequency response with uncertainty

Least-squares fit of a digital IIR filter to the reciprocal of a given set of frequency response values with given associated uncertainty. Propagation of uncertainties is carried out using the Monte Carlo method.

Parameters

H (np.ndarray of shape (M,) and dtype complex) – frequency response values.
UH (np.ndarray of shape (2M,2M)) – uncertainties associated with real and imaginary part of H
Nb (int) – order of IIR numerator polynomial.
Na (int) – order of IIR denominator polynomial.
f (np.ndarray of shape (M,)) – frequencies corresponding to H
Fs (float) – sampling frequency for digital IIR filter.
tau (float) – initial estimate of time delay for filter stabilization.

Returns

b,a (np.ndarray) – IIR filter coefficients
tau (int) – time delay (in samples)
Uba (np.ndarray of shape (Nb+Na+1, Nb+Na+1)) – uncertainties associated with [a[1:],b]

References

Eichstädt, Elster, Esward and Hessling [Eichst2010]

Identification of transfer function models¶

The module PyDynamic.model_estimation.fit_transfer contains a function for the identification of transfer function models.

This module contains the following function:

fit_som(): Fit second-order model to complex-valued frequency response

PyDynamic.model_estimation.fit_transfer.fit_som(f, H, UH=None, weighting=None, MCruns=None, scaling=0.001)[source]¶

Fit second-order model to complex-valued frequency response

Fit second-order model (spring-damper model) with parameters \(S_0, delta\) and \(f_0\) to complex-valued frequency response with uncertainty associated with real and imaginary parts.

For a transformation of an uncertainty associated with amplitude and phase to one associated with real and imaginary parts, see PyDynamic.uncertainty.propagate_DFT.AmpPhase2DFT.

Parameters

f (np.ndarray of shape (M,)) – vector of frequencies
H (np.ndarray of shape (2M,)) – real and imaginary parts of measured frequency response values at frequencies f
UH (np.ndarray of shape (2M,) or (2M,2M)) – uncertainties associated with real and imaginary parts When UH is one-dimensional, it is assumed to contain standard uncertainties; otherwise it is taken as covariance matrix. When UH is not specified no uncertainties assoc. with the fit are calculated.
weighting (str or array) – Type of weighting (None, ‘diag’, ‘cov’) or array of weights ( length two times of f)
MCruns (int, optional) – Number of Monte Carlo trials for propagation of uncertainties. When MCruns is ‘None’, matrix multiplication is used for the propagation of uncertainties. However, in some cases this can cause trouble.
scaling (float) – scaling of least-squares design matrix for improved fit quality

Returns

p (np.ndarray) – vector of estimated model parameters [S0, delta, f0]
Up (np.ndarray) – covariance associated with parameter estimate

Design of deconvolution filters¶

Deprecated since version 1.2.71: The module deconvolution will be combined with the module identification and renamed to model_estimation in the next major release 2.0.0. From then on you should only use the new module Model estimation instead. The functions LSFIR(), LSFIR_unc(), LSIIR(), LSIIR_unc(), LSFIR_uncMC() are then prefixed with an “inv” for “inverse”, indicating the treatment of the reciprocal of frequency response values. Please use the new function names (e.g. PyDynamic.model_estimation.fit_filter.invLSIIR_unc()) starting from version 1.4.1. The old function names without preceding “inv” will only be preserved until the release prior to version 2.0.0.

The PyDynamic.deconvolution.fit_filter module implements methods for the design of digital deconvolution filters by least-squares fitting to the reciprocal of a given frequency response with associated uncertainties.

This module for now still contains the following functions:

LSFIR(): Least-squares fit of a digital FIR filter to the reciprocal of a given frequency response.
LSFIR_unc(): Design of FIR filter as fit to reciprocal of frequency response values with uncertainty
LSFIR_uncMC(): Design of FIR filter as fit to reciprocal of frequency response values with uncertainty via Monte Carlo
LSIIR(): Design of a stable IIR filter as fit to reciprocal of frequency response values
LSIIR_unc(): Design of a stable IIR filter as fit to reciprocal of frequency response values with uncertainty

Fitting filters and transfer functions models¶

Deprecated since version 1.2.71: The package identification will be combined with the package deconvolution and renamed to model_estimation in the next major release 2.0.0. From version 1.4.1 on you should only use the new package Model estimation instead.

The package for now still contains the following modules:

PyDynamic.identification.fit_filter: least-squares fit to a given complex frequency response
PyDynamic.identification.fit_transfer: identification of transfer function models

Fitting filters to frequency response¶

Deprecated since version 1.2.71: The package identification will be combined with the package deconvolution and renamed to model_estimation in the next major release 2.0.0. From version 1.4.1 on you should only use the new package Model estimation instead.

This module contains several functions to carry out a least-squares fit to a given complex frequency response.

This module for now still contains the following functions:

LSIIR(): Least-squares IIR filter fit to a given frequency response
LSFIR(): Least-squares fit of a digital FIR filter to a given frequency response

Identification of transfer function models¶

Deprecated since version 1.2.71: The package identification will be combined with the package deconvolution and renamed to model_estimation in the next major release 2.0.0. From version 1.4.1 on you should only use the new package Model estimation instead.

The module PyDynamic.identification.fit_transfer contains several functions for the identification of transfer function models.

This module for now still contains the following function:

fit_sos(): Fit second-order model to complex-valued frequency response

Miscellaneous¶

The Miscellaneous package provides various functions and methods which are used in the examples and in some of the other implemented routines.

The package contains the following modules:

PyDynamic.misc.SecondOrderSystem: tools for 2nd order systems
PyDynamic.misc.filterstuff: tools for digital filters
PyDynamic.misc.testsignals: test signals
PyDynamic.misc.noise: noise related functions
PyDynamic.misc.tools: miscellaneous useful helper functions

Tools for 2nd order systems¶

The PyDynamic.misc.SecondOrderSystem module is a collection of methods that are used throughout the whole package, specialized for second order dynamic systems, such as the ones used for high-class accelerometers.

This module contains the following functions:

sos_FreqResp(): Calculation of the system frequency response
sos_phys2filter(): Calculation of continuous filter coefficients from physical parameters
sos_absphase(): Propagation of uncertainty from physical parameters to real and imaginary part of system’s transfer function using GUM S2 Monte Carlo
sos_realimag(): Propagation of uncertainty from physical parameters to real and imaginary part of system’s transfer function using GUM S2 Monte Carlo

PyDynamic.misc.SecondOrderSystem.sos_FreqResp(S, d, f0, freqs)[source]¶

Calculation of the system frequency response

The frequency response is calculated from the continuous physical model of a second order system given by

\(H(f) = \frac{4S\pi^2f_0^2}{(2\pi f_0)^2 + 2jd(2\pi f_0)f - f^2}\)

If the provided system parameters are vectors then \(H(f)\) is calculated for each set of parameters. This is helpful for Monte Carlo simulations by using draws from the model parameters

Parameters

S (float or ndarray shape (K,)) – static gain
d (float or ndarray shape (K,)) – damping parameter
f0 (float or ndarray shape (K,)) – resonance frequency
freqs (ndarray shape (N,)) – frequencies at which to calculate the freq response

Returns

H – complex frequency response values

Return type

ndarray shape (N,) or ndarray shape (N,K)

PyDynamic.misc.SecondOrderSystem.sos_absphase(S, d, f0, uS, ud, uf0, f, runs=10000)[source]¶

Propagation of uncertainty from physical parameters to real and imaginary part of system’s transfer function using GUM S2 Monte Carlo.

Parameters

S (float) – static gain
d (float) – damping
f0 (float) – resonance frequency
uS (float) – uncertainty associated with static gain
ud (float) – uncertainty associated with damping
uf0 (float) – uncertainty associated with resonance frequency
f (ndarray, shape (N,)) – frequency values at which to calculate amplitue and phase

Returns

Hmean (ndarray, shape (N,)) – best estimate of complex frequency response values
Hcov (ndarray, shape (2N,2N)) – covariance matrix [ [u(abs,abs), u(abs,phase)], [u(phase,abs), u(phase,phase)] ]

PyDynamic.misc.SecondOrderSystem.sos_phys2filter(S, d, f0)[source]¶

Calculation of continuous filter coefficients from physical parameters.

If the provided system parameters are vectors then the filter coefficients are calculated for each set of parameters. This is helpful for Monte Carlo simulations by using draws from the model parameters

Parameters

S (float) – static gain
d (float) – damping parameter
f0 (float) – resonance frequency

Returns

b,a – analogue filter coefficients

Return type

ndarray

PyDynamic.misc.SecondOrderSystem.sos_realimag(S, d, f0, uS, ud, uf0, f, runs=10000)[source]¶

Propagation of uncertainty from physical parameters to real and imaginary part of system’s transfer function using GUM S2 Monte Carlo.

Parameters

S (float) – static gain
d (float) – damping
f0 (float) – resonance frequency
uS (float) – uncertainty associated with static gain
ud (float) – uncertainty associated with damping
uf0 (float) – uncertainty associated with resonance frequency
f (ndarray, shape (N,)) – frequency values at which to calculate real and imaginary part

Returns

Hmean (ndarray, shape (N,)) – best estimate of complex frequency response values
Hcov (ndarray, shape (2N,2N)) – covariance matrix [ [u(real,real), u(real,imag)], [u(imag,real), u(imag,imag)] ]

Tools for digital filters¶

The PyDynamic.misc.filterstuff module is a collection of methods which are related to filter design.

This module contains the following functions:

db(): Calculation of decibel values \(20\log_{10}(x)\) for a vector of values
ua(): Shortcut for calculation of unwrapped angle of complex values
grpdelay(): Calculation of the group delay of a digital filter
mapinside(): Maps the roots of polynomial with coefficients \(a\) to the unit circle
kaiser_lowpass(): Design of a FIR lowpass filter using the window technique with a Kaiser window.
isstable(): Determine whether a given IIR filter is stable
savitzky_golay(): Smooth (and optionally differentiate) data with a Savitzky-Golay filter

PyDynamic.misc.filterstuff.db(vals)[source]¶: Calculation of decibel values \(20\log_{10}(x)\) for a vector of values

PyDynamic.misc.filterstuff.grpdelay(b, a, Fs, nfft=512)[source]¶

Calculation of the group delay of a digital filter

Parameters

b (ndarray) – IIR filter numerator coefficients
a (ndarray) – IIR filter denominator coefficients
Fs (float) – sampling frequency of the filter
nfft (int) – number of FFT bins

Returns

group_delay (np.ndarray) – group delay values
frequencies (ndarray) – frequencies at which the group delay is calculated

References

Smith, online book [Smith]

PyDynamic.misc.filterstuff.isstable(b, a, ftype='digital')[source]¶

Determine whether IIR filter (b,a) is stable

Determine whether IIR filter (b,a) is stable by checking roots of the polynomial ´a´.

Parameters

b (ndarray) – filter numerator coefficients
a (ndarray) – filter denominator coefficients
ftype (string) – type of filter (digital or analog)

Returns

stable – whether filter is stable or not

Return type

bool

PyDynamic.misc.filterstuff.kaiser_lowpass(L, fcut, Fs, beta=8.0)[source]¶

Design of a FIR lowpass filter using the window technique with a Kaiser window.

This method uses a Kaiser window. Filters of that type are often used as FIR low-pass filters due to their linear phase.

Parameters

L (int) – filter order (window length)
fcut (float) – desired cut-off frequency
Fs (float) – sampling frequency
beta (float) – scaling parameter for the Kaiser window

Returns

blow (ndarray) – FIR filter coefficients
shift (int) – delay of the filter (in samples)

PyDynamic.misc.filterstuff.mapinside(a)[source]¶

Maps the roots of polynomial to the unit circle.

Maps the roots of polynomial with coefficients \(a\) to the unit circle.

Parameters: a (ndarray) – polynomial coefficients
Returns: a – polynomial coefficients with all roots inside or on the unit circle
Return type: ndarray

PyDynamic.misc.filterstuff.savitzky_golay(y, window_size, order, deriv=0, delta=1.0)[source]¶

Smooth (and optionally differentiate) data with a Savitzky-Golay filter

The Savitzky-Golay filter removes high frequency noise from data. It has the advantage of preserving the original shape and features of the signal better than other types of filtering approaches, such as moving averages techniques.

Source obtained from scipy cookbook (online), downloaded 2013-09-13

Parameters

y (ndarray, shape (N,)) – the values of the time history of the signal
window_size (int) – the length of the window. Must be an odd integer number
order (int) – the order of the polynomial used in the filtering. Must be less then window_size - 1.
deriv (int, optional) – The order of the derivative to compute. This must be a nonnegative integer. The default is 0, which means to filter the data without differentiating.
delta (float, optional) – The spacing of the samples to which the filter will be applied. This is only used if deriv > 0. This includes a factor \(n! / h^n\), where \(n\) is represented by deriv and \(1/h\) by delta.

Returns

ys – the smoothed signal (or it’s n-th derivative).

Return type

ndarray, shape (N,)

Notes

The Savitzky-Golay is a type of low-pass filter, particularly suited for smoothing noisy data. The main idea behind this approach is to make for each point a least-square fit with a polynomial of high order over a odd-sized window centered at the point.

References

Savitzky et al. [Savitzky]
Numerical Recipes [NumRec]

PyDynamic.misc.filterstuff.ua(vals)[source]¶: Shortcut for calculation of unwrapped angle of complex values

Test signals¶

The PyDynamic.misc.testsignals module is a collection of test signals which can be used to simulate dynamic measurements and test methods.

This module contains the following functions:

shocklikeGaussian(): signal that resembles a shock excitation as a Gaussian followed by a smaller Gaussian of opposite sign
GaussianPulse(): Generates a Gaussian pulse at \(t_0\) with height \(m_0\) and std \(sigma\)
rect(): Rectangular signal of given height and width \(t_1 - t_0\)
squarepulse(): Generates a series of rect functions to represent a square pulse signal
sine(): Generate a sine signal

PyDynamic.misc.testsignals.GaussianPulse(time, t0, m0, sigma, noise=0.0)[source]¶

Generates a Gaussian pulse at t0 with height m0 and std sigma

Parameters

time (np.ndarray of shape (N,)) – time instants (equidistant)
t0 (float) – time instant of signal maximum
m0 (float) – signal maximum
sigma (float) – std of pulse
noise (float, optional) – std of simulated signal noise

Returns

x – signal amplitudes at time instants

Return type

np.ndarray of shape (N,)

class PyDynamic.misc.testsignals.corr_noise(w, sigma, seed=None)[source]¶: Base class for generation of a correlated noise process.

PyDynamic.misc.testsignals.rect(time, t0, t1, height=1, noise=0.0)[source]¶

Rectangular signal of given height and width t1-t0

Parameters

time (np.ndarray of shape (N,)) – time instants (equidistant)
t0 (float) – time instant of rect lhs
t1 (float) – time instant of rect rhs
height (float) – signal maximum
noise (float or numpy.ndarray of shape (N,), optional) – float: standard deviation of additive white gaussian noise ndarray: user-defined additive noise

Returns

x – signal amplitudes at time instants

Return type

np.ndarray of shape (N,)

PyDynamic.misc.testsignals.shocklikeGaussian(time, t0, m0, sigma, noise=0.0)[source]¶

Generates a signal that resembles a shock excitation as a Gaussian followed by a smaller Gaussian of opposite sign.

Parameters

time (np.ndarray of shape (N,)) – time instants (equidistant)
t0 (float) – time instant of signal maximum
m0 (float) – signal maximum
sigma (float) – std of main pulse
noise (float, optional) – std of simulated signal noise

Returns

x – signal amplitudes at time instants

Return type

np.ndarray of shape (N,)

PyDynamic.misc.testsignals.sine(time, amp=1.0, freq=6.283185307179586, noise=0.0)[source]¶

Generate a sine signal

Parameters

time (np.ndarray of shape (N,)) – time instants
amp (float, optional) – amplitude of the sine (default = 1.0)
freq (float, optional) – frequency of the sine in Hz (default = \(2 * \pi\))
noise (float, optional) – std of simulated signal noise (default = 0.0)

Returns

x – signal amplitude at time instants

Return type

np.ndarray of shape (N,)

PyDynamic.misc.testsignals.squarepulse(time, height, numpulse=4, noise=0.0)[source]¶

Generates a series of rect functions to represent a square pulse signal

Parameters

time (np.ndarray of shape (N,)) – time instants
height (float) – height of the rectangular pulses
numpulse (int) – number of pulses
noise (float, optional) – std of simulated signal noise

Returns

x – signal amplitude at time instants

Return type

np.ndarray of shape (N,)

Noise related functions¶

Collection of noise-signals

This module contains the following functions:

get_alpha(): normal distributed signal amplitudes with equal power spectral density
power_law_noise(): normal distributed signal amplitudes with power spectrum :math:f^lpha
power_law_acf(): (theoretical) autocorrelation function of power law noise
ARMA(): autoregressive moving average noise process

PyDynamic.misc.noise.ARMA(length, phi=0.0, theta=0.0, std=1.0)[source]¶

Generate time-series of a predefined ARMA-process based on this equation: \(\sum_{j=1}^{\min(p,n-1)} \phi_j \epsilon[n-j] + \sum_{j=1}^{\min(q,n-1)} \theta_j w[n-j]\) where w is white gaussian noise. Equation and algorithm taken from [Eichst2012] .

Parameters

length (int) – how long the drawn sample will be
phi (float, list or numpy.ndarray, shape (p, )) – AR-coefficients
theta (float, list or numpy.ndarray) – MA-coefficients
std (float) – std of the gaussian white noise that is feeded into the ARMA-model

Returns

e – time-series of the predefined ARMA-process

Return type

numpy.ndarray, shape (length, )

References

Eichstädt, Link, Harris and Elster [Eichst2012]

PyDynamic.misc.noise.get_alpha(color_value=0)[source]¶

Translate a color (given as string) into an exponent alpha or directly hand through a given numeric value of alpha.

Parameters: color_value (str, int or float) – if string -> check against known colornames -> return alpha if numeric -> directly return value
Returns: alpha
Return type: float

PyDynamic.misc.noise.power_law_acf(N, color_value='white', std=1.0)[source]¶

Return the theoretic right-sided autocorrelation (Rww) of different colors of noise.

Colors of noise are defined to have a power spectral density (Sww) proportional to f^lpha. Sww and Rww form a Fourier-pair. Therefore Rww = ifft(Sww).

PyDynamic.misc.noise.power_law_noise(N=None, w=None, color_value='white', mean=0.0, std=1.0)[source]¶

Generate colored noise by * generate white gaussian noise * multiplying its Fourier-transform with f^(alpha/2) * inverse Fourier-transform to yield the colored/correlated noise * further adjustments to fit to specified mean/std

based on [Zhivomirov2018](A Method for Colored Noise Generation)

Parameters

N (int) – length of noise to be generated
w (numpy.ndarray) – user-defined white noise if provided, N is ignored!
color_value (str, int or float) – if string -> check against known colornames if numeric -> used as alpha to shape PSD
mean (float) – mean of the output signal
std (float) – standard deviation of the output signal

Returns

w_filt

Return type

filtered noise signal

Miscellaneous useful helper functions¶

The PyDynamic.misc.tools module is a collection of miscellaneous helper functions.

This module contains the following functions:

print_vec(): Print vector (1D array) to the console or return as formatted string
print_mat(): Print matrix (2D array) to the console or return as formatted string
make_semiposdef(): Make quadratic matrix positive semi-definite
FreqResp2RealImag(): Calculate real and imaginary parts from frequency response
make_equidistant(): Interpolate non-equidistant time series to equidistant
trimOrPad(): trim or pad (with zeros) a vector to desired length
progress_bar(): A simple and reusable progress-bar

PyDynamic.misc.tools.FreqResp2RealImag(Abs, Phase, Unc, MCruns=10000.0)[source]¶

Calculate real and imaginary parts from frequency response

Calculate real and imaginary parts from amplitude and phase with associated uncertainties.

Parameters

Abs ((N,) array_like) – amplitude values
Phase ((N,) array_like) – phase values in rad
Unc ((2N, 2N) or (2N,) array_like) – uncertainties
MCruns (bool) – Iterations for Monte Carlo simulation

Returns

Re, Im ((N,) array_like) – real and imaginary parts (best estimate)
URI ((2N, 2N) array_like) – uncertainties assoc. with Re and Im

PyDynamic.misc.tools.make_semiposdef(matrix, maxiter=10, tol=1e-12, verbose=False)[source]¶

Make quadratic matrix positive semi-definite by increasing its eigenvalues

Parameters

matrix ((N,N) array_like) – the matrix to process
maxiter (int) – the maximum number of iterations for increasing the eigenvalues
tol (float) – tolerance for deciding if pos. semi-def.
verbose (bool) – If True print smallest eigenvalue of the resulting matrix

Returns

quadratic positive semi-definite matrix

Return type

(N,N) array_like

PyDynamic.misc.tools.print_mat(matrix, prec=5, vertical=False, retS=False)[source]¶

Print matrix (2D array) to the console or return as formatted string

Parameters

matrix ((M,N) array_like) –
prec (int) – the precision of the output
vertical (bool) – print out vertical or not
retS (bool) – print or return string

Returns

s – if retS is True

Return type

str

PyDynamic.misc.tools.print_vec(vector, prec=5, retS=False, vertical=False)[source]¶

Print vector (1D array) to the console or return as formatted string

Parameters

vector ((M,) array_like) –
prec (int) – the precision of the output
vertical (bool) – print out vertical or not
retS (bool) – print or return string

Returns

s – if retS is True

Return type

str

PyDynamic.misc.tools.progress_bar(count, count_max, width=30, prefix='', done_indicator='#', todo_indicator='.', fout=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>)[source]¶

A simple and reusable progress-bar

Parameters

count (int) – current status of iterations, assumed to be zero-based
count_max (int) – total number of iterations
width (int, optional) – width of the actual progressbar (actual printed line will be wider)
prefix (str, optional) – some text that will be printed in front of the bar (i.e. “Progress of ABC:”)
done_indicator (str, optional) – what character is used as “already-done”-indicator
todo_indicator (str, optional) – what character is used as “not-done-yet”-indicator
fout (file-object, optional) – where the progress-bar should be written/printed to

PyDynamic.misc.tools.trimOrPad(array, length, mode='constant')[source]¶: Trim or pad (with zeros) a vector to desired length

Indices and tables¶

References¶

Eichst2016: S. Eichstädt und V. Wilkens GUM2DFT — a software tool for uncertainty evaluation of transient signals in the frequency domain. Meas. Sci. Technol., 27(5), 055001, 2016. https://dx.doi.org/10.1088/0957-0233/27/5/055001
Eichst2012: S. Eichstädt, A. Link, P. M. Harris and C. Elster Efficient implementation of a Monte Carlo method for uncertainty evaluation in dynamic measurements Metrologia, vol 49(3), 401 https://dx.doi.org/10.1088/0026-1394/49/3/401
Eichst2010: S. Eichstädt, C. Elster, T. J. Esward and J. P. Hessling Deconvolution filters for the analysis of dynamic measurement processes: a tutorial Metrologia, vol. 47, nr. 5 https://stacks.iop.org/0026-1394/47/i=5/a=003?key=crossref.310be1c501bb6b6c2056bc9d22ec93d4
Elster2008: C. Elster and A. Link Uncertainty evaluation for dynamic measurements modelled by a linear time-invariant system Metrologia, vol 45 464-473, 2008 https://dx.doi.org/10.1088/0026-1394/45/4/013
Link2009: A. Link and C. Elster Uncertainty evaluation for IIR filtering using a state-space approach Meas. Sci. Technol. vol. 20, 2009 https://dx.doi.org/10.1088/0957-0233/20/5/055104
Vuer1996: R. Vuerinckx, Y. Rolain, J. Schoukens and R. Pintelon Design of stable IIR filters in the complex domain by automatic delay selection IEEE Trans. Signal Proc., 44, 2339-44, 1996 https://dx.doi.org/10.1109/78.536690
Smith: Smith, J.O. Introduction to Digital Filters with Audio Applications, https://ccrma.stanford.edu/~jos/filters/, online book
Savitzky: A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 1964, 36 (8), pp 1627-1639.
NumRec: Numerical Recipes 3rd Edition: The Art of Scientific Computing W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery Cambridge University Press ISBN-13: 9780521880688
White2017: White, D.R. Int J Thermophys (2017) 38: 39. https://doi.org/10.1007/s10765-016-2174-6