Metadata-Version: 1.2
Name: permittivitycalc
Version: 0.6.1
Summary: Scripts to calculate and plot the complex permittivity from S-parameter data
Home-page: https://github.com/boivinalex/permittivitycalc
Author: Alexandre Boivin
Author-email: alex.boivin@utoronto.ca
License: UNKNOWN
Description: permittivitycalc 
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        Scripts to calculate and plot the complex permittivity (and permeability) from S-parameter data acquired from transmission-line measurements
        
        Overview
        --------
        permittivitycalc is a Python package made to take S-parameter data output from METAS VNA Tools II (https://www.metas.ch/vnatools) and process it to determine and plot the complex electric permittivity (and magnetic permeability) of a material measured in a coaxial transmission line.
        
        Two analytical calculation methiods are currently implemented:
        
        - The New Non-iterative Method for permittivity calculation from S-parameters from [Boughriet1997]_ which assumes that the material is non-magnetic (i.e. \mu = 1).
        
        - The Nicholson-Ross-Weir method to calculate the complex permittivity and permeability of a sample. This method, however, is unstable at multiples of one-half wavelength in the sample [NicolsonRoss1970]_ [Weir1974]_.
        
        permittivitycalc can also use MCMC-based Bayesian model fitting/parameter estimation to fit Cole-Cole type models directly to the measured S-parameters to determine the frequency-dependant complex permittivity (and permeability, if desired). Cole-Cole models support multiple relaxation poles as well as a conductivity term.
        
        You can use permittivitycalc to:
        
        - Input and plot raw S-parameter data in tabular form with or without uncertainties.
        - Calculate and plot the complex permittivity with full propagation of uncertainties.
        - Perform connector de-embedding on the raw S-parameters to extract the sample S-parameters, if necessary. Example: de-embedding washers used to cap the transmission line when measuring powdered samples.
        - Correct for the boundary effect in the transmission line when measuring powdered samples after [Hickson2017]_.
        - Correct for the air gap when measuring solid samples after [Baker-Jarvis1993]_.
        - Plot data from multiple measurements together for comparison.
        - Model measured S-parameters directly with a Cole-Cole model using MCMC-based Bayesian model fitting.
        
        Usage
        -----
        For usage examples and a walkthrough on how to use permittivitycalc, see the `Docs <https://permittivitycalc.readthedocs.io>`_
        
        Installation
        ------------
        
        Requirements
        ^^^^^^^^^^^^
        
        permittivitycalc was written for Python 3 and tested on the following versions of Python:
        
        - 3.6
        - 3.7
        
        permittivitycalc uses the following packages:
        
        - tkinter
        - numpy 
        - uncertainties
        - scipy
        - matplotlib
        - seaborn
        - cycler
        - lmfit
        - emcee
        - corner
        
        Installing Anaconda
        ^^^^^^^^^^^^^^^^^^^
        
        We recommend using `Anaconda`_ to manage your Python environments.
        
        .. _`Anaconda`: https://www.anaconda.com/distribution/
        
        1. `Install Anaconda <https://www.anaconda.com/download/>`_.
        
        2. Open a terminal window and create a `conda virtual environment`_ (name it anything you like, and set the python version to a compatible version in `Requirements`_)::
        
            conda create --name your_env_name anaconda python=3.7
        
        3. Activate the environment::
        
            conda activate your_env_name
        
        .. _`conda virtual environment`: https://conda.io/docs/using/envs
        
        Quick Install
        ^^^^^^^^^^^^^
        
        Install permittivitycalc with pip::
        
        	pip install permittivitycalc
        
        Manual Install
        ^^^^^^^^^^^^^^
        
        1. (Optional) Fork `permittivitycalc on Github <https://github.com/boivinalex/permittivitycalc>`_
        
        2. Clone or download the repository.
        
        3. Navigate to the permittivitycalc root directory and install with::
        
        	python setup.py install
        
        Contributors
        ------------
        permittivitycalc was developed with the aid of these `contributors <https://github.com/boivinalex/permittivitycalc/graphs/contributors>`_.
        
        References
        ----------
        .. [Baker-Jarvis1993] Baker-Jarvis, J., Janezic, M. D., Grosvenor Jr, J. H., & Geyer, R. G. (1993). Transmission/reflection and short-circuit line methods for measuring permittivity and permeability. NIST Technical Note 1355-R. Boulder, CO. http://doi.org/10.6028/NIST.TN.1355r
        .. [Boughriet1997] Boughriet, A. H., Legrand, C., & Chapoton, A. (1997). Noniterative stable transmission/reflection method for low-loss material complex permittivity determination. IEEE Transactions on Microwave Theory and Techniques, 45(1), 52–57. http://doi.org/10.1109/22.552032
        .. [Hickson2017] Hickson, D., Sotodeh, S., Daly, M. G., Ghent, R., & Nolan, M. C. (2017). Improvements on effective permittivity measurements of powdered alumina: Implications for bulk permittivity properties of asteroid regoliths. Advances in Space Research, 59(1), 472–482. http://doi.org/10.1016/j.asr.2016.08.011
        .. [NicolsonRoss1970] Nicolson, A. M., & Ross, G. F. (1970). Measurement of the Intrinsic Properties of Materials by Time-Domain Techniques. IEEE Transactions on Instrumentation and Measurement, 19(4), 377–382. http://doi.org/10.1109/TIM.1970.4313932
        .. [Weir1974] Weir, W. B. (1974). Automatic Measurement of Complex Dielectric Constant and Permeability at Microwave Frequencies. Proceedings of the IEEE, 62(1), 33–36. http://doi.org/10.1109/PROC.1974.9382
Keywords: python dielectric permittivity s-parameter data analysis
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.6, <3.8
