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            "Topic :: Scientific/Engineering",
            "Topic :: Software Development :: Libraries :: Python Modules",
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        "description": "<img src=\"https://raw.githubusercontent.com/PTB-M4D/PyDynamic/main/docs/PyDynamic_logo.svg\" alt=\"the logo of PyDynamic\" title=\"PyDynamic logo\">\n<p align=\"center\">\n  <!-- CircleCI Tests -->\n  <a href=\"https://circleci.com/gh/PTB-M4D/PyDynamic\"><img alt=\"CircleCI pipeline status badge\" src=\"https://circleci.com/gh/PTB-M4D/PyDynamic.svg?style=shield\"></a>\n  <!-- ReadTheDocs Documentation -->\n  <a href=\"https://pydynamic.readthedocs.io/en/latest/index.html\">\n    <img src=\"https://readthedocs.org/projects/pydynamic/badge/?version=latest\" alt=\"PyDynamic's ReadTheDocs status\">\n  </a>\n  <!-- CodeCov(erage) -->\n  <a href=\"https://codecov.io/gh/PTB-M4D/PyDynamic\">\n    <img src=\"https://codecov.io/gh/PTB-M4D/PyDynamic/branch/main/graph/badge.svg\" alt=\" PyDynamic's CodeCov badge\">\n  </a>\n  <!-- Codacy -->\n  <a href=\"https://www.codacy.com/gh/PTB-M4D/PyDynamic/dashboard?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=PTB-M4D/PyDynamic&amp;utm_campaign=Badge_Grade\">\n    <img src=\"https://app.codacy.com/project/badge/Grade/db86b58d6fa5446e8408644c8196f5e2\" alt=\" PyDynamic's Codacy badge\">\n  </a>\n  <!-- PyPI Version -->\n  <a href=\"https://pypi.org/project/pydynamic\">\n    <img src=\"https://img.shields.io/pypi/v/pydynamic.svg?label=release&color=blue&style=flat-square\" alt=\" PyDynamic's PyPI version number\">\n  </a>\n  <!-- PyPI License -->\n  <a href=\"https://www.gnu.org/licenses/lgpl-3.0.en.html\">\n    <img alt=\"PyPI - license badge\" src=\"https://img.shields.io/pypi/l/pydynamic?color=bright\">\n  </a>\n  <!-- Zenodo DOI -->\n  <a href=\"https://doi.org/10.5281/zenodo.1489877\">\n    <img src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.1489877.svg\" alt=\"DOI\"></a>\n</p>\n\n# Python library for the analysis of dynamic measurements\n\n<p align=\"justify\">\nThe goal of this library is to provide a starting point for users in metrology and\nrelated areas who deal with time-dependent i.e., <i>dynamic</i>, measurements. The\ninitial version of this software was developed as part of a joint research project of\nthe national metrology institutes from Germany and the UK, i.e.\n<a href=\"https://www.ptb.de/cms/en.html\">Physikalisch-Technische Bundesanstalt</a> \nand the <a href=\"https://www.npl.co.uk\">National Physical Laboratory</a>.\n</p>\n\n<p align=\"justify\">\nFurther development and explicit use of PyDynamic is part of the European research\nproject <a href=\"https://www.ptb.de/empir2018/met4fof/home/\">EMPIR 17IND12 \nMet4FoF</a> and the German research project <a href=\"https://famous-project.\neu\">FAMOUS</a>.\n</p>\n\n## Table of content\n\n- [Quickstart](#quickstart)\n- [Features](#features)\n- [Module diagram](#module-diagram)\n- [Documentation](#documentation)\n- [Installation](#installation)\n- [Contributing](#contributing)\n- [Examples](#examples)\n- [Roadmap](#roadmap)\n- [Citation](#citation)\n- [Acknowledgement](#acknowledgement)\n- [Disclaimer](#disclaimer)\n- [License](#license)\n\n## Quickstart\n\nTo dive right into it, install PyDynamic and execute one of the examples:\n\n```shell\n(my_PyDynamice_venv) $ pip install PyDynamic\nCollecting PyDynamic\n[...]\nSuccessfully installed PyDynamic-[...]\n(my_PyDynamice_venv) $ python\nPython 3.9.7 (default, Aug 31 2021, 13:28:12) \n[GCC 11.1.0] on linux\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n```\n```python\n>>> from PyDynamic.examples.uncertainty_for_dft.deconv_DFT import DftDeconvolutionExample\n>>> DftDeconvolutionExample()\nPropagating uncertainty associated with measurement through DFT\nPropagating uncertainty associated with calibration data to real and imag part\nPropagating uncertainty through the inverse system\nPropagating uncertainty through the low-pass filter\nPropagating uncertainty associated with the estimate back to time domain\n```\n\nYou will see a couple of plots opening up to observe the results. For \nfurther information just read on and visit our \n[tutorial section](#examples). \n\n## Features\n\nPyDynamic offers propagation of *uncertainties* for\n\n- application of the discrete Fourier transform and its inverse\n- filtering with an FIR or IIR filter with uncertain coefficients\n- design of a FIR filter as the inverse of a frequency response with\n  uncertain coefficients\n- design on an IIR filter as the inverse of a frequency response with\n  uncertain coefficients\n- deconvolution in the frequency domain by division\n- multiplication in the frequency domain\n- transformation from amplitude and phase to a representation by real and\n  imaginary parts\n- 1-dimensional interpolation\n\nFor the validation of the propagation of uncertainties, the Monte-Carlo\nmethod can be applied using a memory-efficient implementation of Monte-Carlo\nfor digital filtering.\n\n## Module diagram\n\nThe fundamental structure of PyDynamic is shown in the following figure.\n\n![PyDynamic module diagram](https://raw.githubusercontent.com/PTB-M4D/PyDynamic/main/docs/PyDynamic_module_diagram.png)\n\nHowever, imports should generally be possible without explicitly naming all packages\nand modules in the path, so that for example the following import statements are all\nequivalent.\n\n```python\nfrom PyDynamic.uncertainty.propagate_filter import FIRuncFilter\nfrom PyDynamic.uncertainty import FIRuncFilter\nfrom PyDynamic import FIRuncFilter\n```\n\n## Documentation\n\nThe documentation for PyDynamic can be found on\n[ReadTheDocs](http://pydynamic.readthedocs.io)\n\n## Installation\n\nThe installation of PyDynamic is as straightforward as the Python ecosystem suggests.\nDetailed instructions on different options to install PyDynamic you can find in the \n[installation section of the docs](https://pydynamic.readthedocs.io/en/latest/INSTALL.html).\n\n## Contributing\n\nWhenever you are involved with PyDynamic, please respect our [Code of Conduct\n](https://github.com/PTB-M4D/PyDynamic/blob/main/CODE_OF_CONDUCT.md).\nIf you want to contribute back to the project, after reading our Code of Conduct,\ntake a look at our open developments in the [project board\n](https://github.com/PTB-M4D/PyDynamic/projects/1), [pull requests\n](https://github.com/PTB-M4D/PyDynamic/pulls) and search [the issues\n](https://github.com/PTB-M4D/PyDynamic/issues). If you find something similar to\nyour ideas or troubles, let us know by leaving a comment or remark. If you have\nsomething new to tell us, feel free to open a feature request or bug report in the\nissues. If you want to contribute code or improve our documentation, please check our\n[contribution advices and tips](https://pydynamic.readthedocs.io/en/latest/CONTRIBUTING.html).\n\nIf you have downloaded this software, we would be very thankful for letting\nus know. You may, for instance, drop an email to one of the [authors\n](https://github.com/PTB-M4D/PyDynamic/graphs/contributors) (e.g.\n[Sascha Eichst\u00e4dt](mailto:sascha.eichstaedt@ptb.de), [Bj\u00f6rn Ludwig\n](mailto:bjoern.ludwig@ptb.de) or [Maximilian Gruber\n](mailto:maximilian.gruber@ptb.de))\n\n## Examples\n\nWe have collected extended material for an easier introduction to PyDynamic in the\npackage _examples_. Detailed assistance on getting started you can find in the\ncorresponding sections of the docs:\n\n* [examples](https://pydynamic.readthedocs.io/en/latest/Examples.html)\n* [tutorials](https://pydynamic.readthedocs.io/en/latest/Tutorials.html)\n\nIn various Jupyter Notebooks and scripts we demonstrate the use of\nthe provided methods to aid the first steps in PyDynamic. New features are introduced\nwith an example from the beginning if feasible. We are currently moving this supporting\ncollection to an external repository on GitHub. They will be available at\n[github.com/PTB-M4D/PyDynamic_tutorials](https://github.com/PTB-M4D/PyDynamic_tutorials) \nin the near future.\n\n## Roadmap\n\n1. Implementation of robust measurement (sensor) models\n1. Extension to more complex noise and uncertainty models\n1. Introducing uncertainty propagation for Kalman filters\n\nFor a comprehensive overview of current development activities and upcoming tasks,\ntake a look at the [project board](https://github.com/PTB-M4D/PyDynamic/projects/1),\n[issues](https://github.com/PTB-M4D/PyDynamic/issues) and\n[pull requests](https://github.com/PTB-M4D/PyDynamic/pulls).\n\n## Citation\n\nIf you publish results obtained with the help of PyDynamic, please use the above linked\n[Zenodo DOI](https://doi.org/10.5281/zenodo.1489877) for the code itself or cite\n\nSascha Eichst\u00e4dt, Clemens Elster, Ian M. Smith, and Trevor J. Esward\n*Evaluation of dynamic measurement uncertainty \u2013 an open-source software\npackage to bridge theory and practice*\n**J. Sens. Sens. Syst.**, 6, 97-105, 2017, DOI: [10.5194/jsss-6-97-2017\n](https://doi.org/10.5194/jsss-6-97-2017)\n\n## Acknowledgement\n\nPart of this work is developed as part of the Joint Research Project [17IND12 Met4FoF\n](http://met4fof.eu) of the European Metrology Programme for Innovation and\nResearch (EMPIR).\n\nThis work was part of the Joint Support for Impact project\n[14SIP08](https://www.euramet.org/research-innovation/search-research-projects/details/project/standards-and-software-to-maximise-end-user-uptake-of-nmi-calibrations-of-dynamic-force-torque-and/)\nof the European Metrology Programme for Innovation and Research (EMPIR). The\n[EMPIR](http://msu.euramet.org) is jointly funded by the EMPIR participating \ncountries within EURAMET and the European Union.\n\n## Disclaimer\n\nThis software is developed at Physikalisch-Technische Bundesanstalt (PTB). The\nsoftware is made available \"as is\" free of cost. PTB assumes no responsibility\nwhatsoever for its use by other parties, and makes no guarantees, expressed or\nimplied, about its quality, reliability, safety, suitability or any other\ncharacteristic. In no event will PTB be liable for any direct, indirect or\nconsequential damage arising in connection with the use of this software.\n\n## License\n\nPyDynamic is distributed under the [LGPLv3 license\n](https://github.com/PTB-M4D/PyDynamic/blob/main/licence.txt)\nexcept for the module [`impinvar.py`\n](https://github.com/PTB-M4D/PyDynamic/blob/main/src/PyDynamic/misc/impinvar.py) \nin the package [`misc`\n](https://pydynamic.readthedocs.io/en/main/PyDynamic.misc.html), \nwhich is distributed under the [GPLv3 license\n](https://github.com/PTB-M4D/PyDynamic/blob/main/src/PyDynamic/misc/impinvar_license.txt).\n\n\n",
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