# 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

## 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)

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)

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)
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)
title("measured phase spectrum with associated uncertainties")
xlim(0,50)
xlabel("frequency / kHz",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)
plot(time*1e3, yn,label="measured output signal", color=colors)
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)
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)*dsp.freqz(blow,1,2*np.pi*f/Fs)
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)
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)))
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))),color="r") 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
time = ref_file[:,0]
ref_data = ref_file[:,1]
Ts = 2e-9
N = len(time)

#%% hydrophone calibration data
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
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)
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)
xlim(0.5, 80)
ylim(-0.2, 0.3)
xlabel("frequency / MHz", fontsize=22); tick_params(which= "both", labelsize=18)
ylim(-1,1) 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

1. DFT of $$y$$ and propagation of uncertainties to the frequency domain

2. Propagation of uncertainties associated with amplitude and phase of system to corr. real and imaginary parts

3. Division in the frequency domain and propagation of uncertainties

4. Multiplication with low-pass filter and propagation of uncertainties

5. 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,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,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,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,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)
plot(time * 1e6, ref_data, "--", label= "reference data", linewidth=2, color=colors)
fill_between(time * 1e6, xh + 2 * ux, xh - 2 * ux, alpha=0.2, color=colors)
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)
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

### 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: 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.

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:

### 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:

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

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

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

None()

:numpy.fft.rfftfreq

PyDynamic.uncertainty.propagate_DFT.GUM_iDFT(F, UF, Nx=None, Cc=None, Cs=None, returnC=False)[source]

GUM propagation of the squared uncertainty UF associated with the DFT values F through the inverse DFT

The matrix UF is assumed to be for real and imaginary part with blocks: UF = [[u(R,R), u(R,I)],[u(I,R),u(I,I)]] and real and imaginary part obtained from calling rfft (DFT for real-valued signal)

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 real and imaginary parts of F

• Nx (int, optional) – number of samples of iDFT result

• Cc (np.ndarray, optional) – cosine part of sensitivities (without scaling factor 1/N)

• Cs (np.ndarray, optional) – sine part of sensitivities (without scaling factor 1/N)

• returnC (if true, return sensitivity matrix blocks (without scaling factor 1/N)) –

Returns

• x (np.ndarray) – vector of time domain signal values

• Ux (np.ndarray) – covariance matrix associated with x

References

PyDynamic.uncertainty.propagate_DFT.Time2AmpPhase(x, Ux)[source]

Transformation from time domain to amplitude and phase

Parameters
• x (np.ndarray of shape (N,)) – time domain signal

• Ux (np.ndarray of shape (N,N)) – squared uncertainty associated with x

Returns

• A (np.ndarray) – amplitude values

• P (np.ndarray) – phase values

• UAP (np.ndarray) – covariance matrix associated with [A,P]

PyDynamic.uncertainty.propagate_DFT.Time2AmpPhase_multi(x, Ux, selector=None)[source]

Transformation from time domain to amplitude and phase

Perform transformation for a set of M signals of the same type.

Parameters
• x (np.ndarray of shape (M, nx)) – M time domain signals of length nx

• Ux (np.ndarray of shape (M,)) – squared standard deviations representing noise variances of the signals x

• selector (np.ndarray of shape (L,), optional) – indices of amplitude and phase values that should be returned; default is 0:N-1

Returns

• A (np.ndarray of shape (M,N)) – amplitude values

• P (np.ndarray of shape (M,N)) – phase values

• UAP (np.ndarray of shape (M, 3N)) – diagonals of the covariance matrices: [diag(UAA), diag(UAP), diag(UPP)]

### 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:

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

PyDynamic.deconvolution.fit_filter

PyDynamic.uncertainty.propagate_filter.IIRuncFilter(x, noise, b, a, Uab)[source]

Uncertainty propagation for the signal x and the uncertain IIR filter (b,a)

Parameters
• x (np.ndarray) – filter input signal

• noise (float) – signal noise standard deviation

• b (np.ndarray) – filter numerator coefficients

• a (np.ndarray) – filter denominator coefficients

• Uab (np.ndarray) – covariance matrix for (a[1:],b)

Returns

• y (np.ndarray) – filter output signal

• Uy (np.ndarray) – uncertainty associated with y

References

### 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:

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

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

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 == 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

### 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:

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 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

## 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:

### 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:

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

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

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

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

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

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

### 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:

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:

### 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:

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:

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

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.

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

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:

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:

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

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:

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

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