https://github.com/bjoernludwigptb/pydynamic

This fork is actually a proof-of-concept for Push-Mirroring of the PyDynamic repo in the internal GitLab instance

https://github.com/bjoernludwigptb/pydynamic

Repository

This fork is actually a proof-of-concept for Push-Mirroring of the PyDynamic repo in the internal GitLab instance

https://github.com/BjoernLudwigPTB/PyDynamic/blob/main/

# PyDynamic
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Python package for the analysis of dynamic measurements

The goal of this package is to provide a starting point for users in 
metrology and related areas who deal with time-dependent, i.e. *dynamic*, 
measurements. The software is part of a joint research project of the 
national metrology institutes from Germany and the UK, i.e. 
[Physikalisch-Technische Bundesanstalt](http://www.ptb.de/cms/en.html) and 
the [National Physical Laboratory](http://www.npl.co.uk).

PyDynamic offers propagation of *uncertainties* for
- application of the discrete Fourier transform and its inverse
- filtering with an FIR or IIR filter with uncertain coefficients
- design of a FIR filter as the inverse of a frequency response with 
  uncertain coefficients
- design on an IIR filter as the inverse of a frequency response with 
  uncertain coefficients
- deconvolution in the frequency domain by division
- multiplication in the frequency domain
- transformation from amplitude and phase to a representation by real and 
  imaginary parts

For the validation of the propagation of uncertainties, the Monte-Carlo 
method can be applied using a memory-efficient implementation of Monte-Carlo 
for digital filtering

The documentation for PyDynamic can be found on [ReadTheDocs](http://pydynamic
.readthedocs.io)

Get in contact via [@PyDynamic](https://twitter.com/PyDynamic)

![PyDynamic packages](http://mathmet.org/projects/14SIP08/PyDynamic_scheme.png)


### Installation
If you just want to use the software, the easiest way is to run from your 
system's command line
```
pip install 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 
uses **Python version 3.x**. Usage in any Python environment on your computer
 is then possible by
```python
import PyDynamic
```
or, for example, for the module containing the Fourier domain uncertainty 
methods:
```python
from PyDynamic.uncertainty import propagate_DFT
```
Updates can then be installed via
```
pip install --upgrade PyDynamic
```

For collaboration we recommend forking the repository as described [here
](https://help.github.com/en/articles/fork-a-repo), apply the changes and 
open a Pull Request on GitHub as described [here
](https://help.github.com/en/articles/creating-a-pull-request). In this way 
any changes to PyDynamic can be applied very easily.

If you have downloaded this software, we would be very thankful for letting 
us know. You may, for instance, drop an email to one of the [authors
](https://github.com/PTB-PSt1/PyDynamic/people) (e.g. 
[Sascha Eichstdt](mailto:sascha.eichstaedt@ptb.de), [Bjrn Ludwig
](mailto:bjoern.ludwig@ptb.de) or [Maximilian Gruber
](mailto:maximilian.gruber@ptb.de))


### Examples
Uncertainty propagation for the application of a 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*.
```python
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*.
```python
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.
```python
from PyDynamic.uncertainty.propagate_MonteCarlo import SMC
y, uy, Py = SMC(x, sigma, b, a, Uab, runs=1000, Perc=[0.025,0.975])
```

![PyDynamic Workflow Deconvolution
](http://mathmet.org/projects/14SIP08/Deconvolution.png) 

### Roadmap

#### Next steps

1. Interpolation of timeseries with associated uncertainties
2. Extension of the testsignals module
3. Extension of data transformation functions to data streams applications  

#### Longterm plans

1. Implementation of other measurement (sensor) models
2. Extension to more complex noise and uncertainty models
3. Support for derivation of noise models

### Citation

If you publish results obtained with the help of PyDynamic, please cite

Sascha Eichstdt, Clemens Elster, Ian M. Smith, and Trevor J. Esward
*Evaluation of dynamic measurement uncertainty  an open-source software 
package to bridge theory and practice*
**J. Sens. Sens. Syst.**, 6, 97-105, 2017, DOI: [10.5194/jsss-6-97-2017
](https://doi.org/10.5194/jsss-6-97-2017)

##### Acknowledgement
This work is part of the Joint Research Project [17IND12 Met4FoF
](http://met4fof.eu) of the European Metrology Programme for Innovation and 
Research (EMPIR).

This work was part of the Joint Support for Impact project [14SIP08
](http://mathmet.org/projects/14SIP08) of the European Metrology Programme for
Innovation and Research (EMPIR). The [EMPIR](http://msu.euramet.org) is 
jointly funded by the EMPIR participating countries within EURAMET and the 
European Union.

##### Disclaimer
This software was developed at Physikalisch-Technische Bundesanstalt (PTB) 
and National Physical Laboratory (NPL). The software is made available "as 
is" free of cost. PTB and NPL assume no responsibility whatsoever for its use
by other parties, and makes no guarantees, expressed or implied, about its 
quality, reliability, safety, suitability or any other characteristic. In no
event will PTB and NPL be liable for any direct, indirect or consequential 
damage arising in connection with the use of this software.

##### License
PyDynamic is distributed under the LGPLv3 license with the exception of the 
module `impinvar.py` in the package `misc`, which is distributed under the 
GPLv3 license. 

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