Metadata-Version: 2.1
Name: DoEgen
Version: 0.4.3
Summary: DoEgen: A Python Library for Optimised Design of Experiment Generation and Evaluation
Home-page: https://github.com/sebhaan/DoEgen
Author: Sebastian Haan
Author-email: sebastian.haan@sydney.edu.au
License: UNKNOWN
Description: DoEgen: A Python Library for Optimised Design of Experiment Generation and Evaluation
        =====================================================================================
        
        DoEgen is a Python library aiming to assist in generating optimised
        Design of Experiments (DoE), evaluating design efficiencies, and
        analysing experiment results.
        
        In a first step, optimised designs can be automatically generated and
        efficiencies evaluated for any mixture of factor-levels for numeric and
        categorical factors. Designs are automatically evaluated as function of
        number of experiment runs and the most efficient designs are suggested.
        In particular DoEgen provides computation of a wide range of design
        efficiencies and allows to import and evaluate externally generated
        designs as well.
        
        The second part of DoEgen assists in analysing any derived experiment
        results in terms of factor importance, correlations, and response
        analysis for best parameter space selection.
        
        Author: Sebastian Haan
        
        Table of Contents
        -----------------
        
        -   [Definitions](#definitions)
        -   [Functionality](#functionality)
        -   [Installation And Requirements](#installation-and-requirements)
            -   [Requirements](#requirements)
            -   [User Templates](#user-templates)
            -   [Running tests](#running-tests)
            -   [Documentation](#documentation)
        -   [Main Modules and Usage](#main-modules-and-usage)
            -   [Design Generation](#design-generation)
            -   [Design Efficiencies](#design-efficiencies)
            -   [Design Selection](#design-selection)
            -   [Experiment Result Analysis](#experiment-result-analysis)
        -   [Use Case Study](#use-case-study)
        -   [Comparison to Other DoE Tools](#comparison-to-other-doe-tools)
        -   [Literature](#literature)
        -   [Attribution and Acknowledgments](#attribution-and-acknowledgements)
        -   [License](#license)
        
        Definitions
        -----------
        
        An Experiment Design is typically defined by:
        
        -   Number of Factors: the parameters or variates of the experiment
        -   Number of Runs: the number of experiments
        -   Levels: The number of value options for each factor, which can be
            either numeric values (discrete or continuous) or categorical.
            Discrete levels for continuous factors can be obtained by providing
            the minimum and maximum of the factor range and the number of
            levels. The more levels, the more “fine-grained” the experiment will
            evaluate this factor, but also more experimental runs are required.
        
        The goal of optimising an experimental design is to provide an efficient
        design that is near-optimal in terms of, e.g., orthogonality, level
        balance, and two-way interaction coverage, yet can be performed with a
        minimum number of experimental runs, which are often costly or
        time-consuming.
        
        Functionality
        -------------
        
        If you would like to jumpstart a new experiment and to skip the
        technical details, you can find a summary of the main usage of DoEgen in
        [Use Case Study](#use-case-study).
        
        Currently, the (preliminary) release contains several functions for
        generating and evaluating designs. Importing and evaluating external
        designs is supported (e.g. for comparison to other DoE generator tools).
        DoE also implements several functions for experiment result analysis and
        visualisation of parameter space.
        
        The main functionalities are (sorted in order of typical experiment
        process):
        
        -   Reading Experiment Setup Table and Settings (Parameter Name, Levels
            for each factor, Maximum number of runs, Min/Max etc)
        -   Generating optimised design arrays for a range of runs (given
            maximum number of runs, and optional computation-time constraints,
            see `settings_design.yaml`).
        -   Evaluation and visualisation of more than ten design efficiencies
            such as level balance, orthogonality, D-efficiencies etc (see
            [Design Efficiencies](#design-efficiencies) for the complete list).
        -   Automatic suggestion of minimum, optimal, and best designs within a
            given range of experiment runs.
        -   Import and evaluation of externally generated design arrays.
        -   Experiment result analysis: Template table for experiment results,
            multi-variant RMSE computation, best model/parameter selection,
            Factor Importance computation, pairwise response surface and
            correlation computation, factor correlation analysis and Two-way
            interaction response plots.
        -   Visualisation of experiment results.
        
        Installation And Requirements
        -----------------------------
        
        ### Requirements
        
        -   Python \>= 3.6
        -   SWIG \>=3.0.12
        -   OApackage
        -   xlrd
        -   XlsxWriter
        -   Numpy
        -   Pandas
        -   PyYAML
        -   scikit-learn
        -   matplotlib
        -   seaborn
        
        The DoEgen package is currently considered experimental and has been
        tested with the libraries specified in `requirements.txt`.
        
        The OApackage requires an installation of SWIG (tested with SWIG
        3.0.12), which can be found at
        https://www.dev2qa.com/how-to-install-swig-on-macos-linux-and-windows/or
        can be installed via conda
        
        ``` sh
        conda install swig
        ```
        
        After installing `swig` and `numpy`, DoEgen can be installed either with
        
        ``` sh
        python setup.py build 
        python setup.py install
        ```
        
        or using pip
        
        ``` sh
        pip install DoEgen
        ```
        
        Note that OAPackage can be also installed manually by following
        installation instructions and documentation for OApackage (tested with
        OApackage 2.6.6), which can be found at
        https://pypi.org/project/OApackage/.
        
        ### User Templates
        
        1.  The factor (parameter) settings of experiment are defined in an
            experiment setup table (see `Experiment_results_template.xlsx`). A
            new excel setup template table can be also created with
            `create_setupfile.py`. Each factor is on a new row and specified by
            `Parameter Name`, `Parameter Type` , `Level Number`, `Minimum`,
            `Maximum`, `Include (Y/N)` (optional, by default all will be included), `Levels` (optional)
            If `Levels` are provided pleae seperate each level by a comma; 
            Levels can be a mix of numerical and string entries (NUmbre of entries should match `Level Number`)
        
        2.  After the experiment is run, the results have to be filled in an
            experiment result table (see `Experiment_results_template.xlsx`). A
            new excel result template table can be also created with
            `create_resultfile.py` The result table allows to fill in multiple
            output properties (Y\_label: output target to be predicted) and
            experiment positions. The results have to be provided in the table
            with the following columns:
        
        -   `Nexp`: Run\# of experiment, need to match Run\# in Experiment setup
            and design.
        -   `PID`: Identifier\# of label of location (point) in experiment
            (e.g. if experiment is run at different locations simultaneously).
        -   `Y Label`: Identifier\# or label of Y-Variate (target property that
            has to be predicted or evaluated, e.g. Rain and Temperature). This
            allows to include multi-output models with distinct target
            properties. Note that currently each Y variate is evaluated
            separately.
        -   `Y Exp` The experiment result for Y
        -   `Y Truth` (optional) if the true value available is available for Y.
            This is required to calculate the RMSE and to select best parameter
            space.
        -   Not currently considered (yet) in result stats computation:
            `Std Y Exp`, `Std Y Truth`, `Weight PID`
        
        <figure>
        <img src="https://github.com/sebhaan/DoEgen/blob/main/figures/Setup_header.png" width="600" alt="" /><figcaption>Experiment Setup Table Header.</figcaption>
        </figure>
        
        <figure>
        <img src="https://github.com/sebhaan/DoEgen/blob/main/figures/Result_header.png" width="600" alt="" /><figcaption>Experiment Result Table Header.</figcaption>
        </figure>
        
        ### Running Tests
        
        To verify that DoEgen works, you can run the example experiment
        
        ``` bash
        $ python -m doegen.init_tests
        $ python -m doegen.doegen test/settings_design_test.yaml
        $ python -m doegen.doeval test/settings_expresults_test.yaml
        ```
        
        ### Documentation
        
        Please do not modify `README.md`. Instead make any changes in the master
        documentation file `MANUAL.md` (uses pandoc markdown syntax) and then
        convert to the inferior Github markdown flavor (note that the new
        github-flavored markdown format gfm option does not correctly solve
        figure caption and resize options):
        
        ``` bash
        pandoc -f markdown -t markdown_github MANUAL.md -o README.md
        ```
        
        and to pdf:
        
        ``` bash
        pandoc -V geometry:margin=1.2in MANUAL.md -o docs/MANUAL.pdf
        ```
        
        or as standalone html:
        
        ``` bash
        pandoc MANUAL.md -o MANUAL.html
        ```
        
        Main Modules and Usage
        ----------------------
        
        ### Design Generation
        
        Design generation with `doegen.py`: Main model for generating optimised
        designs and computation of efficiencies. Settings are specified in
        settings yaml file `settings_design.yaml`. If the yaml and .xlsx
        template files are not yet in your working directory (e.g. after first
        DoEgen installation), you can create in the the yaml and excel template
        files with
        
        ``` bash
        $ python -m doegen.init_config
        ```
        
        Before running `doegen.py`,two things have to be the done:
        
        1.  fill in experiment setup table (see template provided
            `Experiment_setup_template.xlsx` or example in `test/` folder)
        2.  provide settings in settings file (see `settings_design.yaml`)
        
        Now you are ready to run the design generation
        
        ``` bash
        $ python -m doegen.doegen settings_design.yaml
        ```
        
        This will produce a number of files for different experiment run length
        (see folder `test/results/DesignArray_Nrun...`):
        
        -   The optimised design array `EDarray_[factor_levelels]_Nrun.csv`.
        -   A table of design efficiencies
            `Efficiencies_[factor_levelels]_Nrun.csv`
        -   Table of two-way Interaction balance `Table_Interaction_Balance.txt`
        -   Table of Pearson correlation coefficients between all factor pairs
            `Table_Pearson_Correlation.csv`
        -   Plot of pairwise correlation including regression fit
            `pairwise_correlation.png` (see example plot below)
        
        Besides the default optimisation (based on function
        `doegen.deogen.optimize_design`), DoEgen also allows the to construct
        full orthogonal designs using the function `doegen.doegen.gen_highD`,
        which is based on OApackage orthogonal arrays and extensions. However,
        this works only for special cases with limited number of factors and
        design levels. Thus, it is currently not fully automated but might
        assist advanced users to construct optimal designs.
        
        ### Design Selection
        
        DoEgen will select by default three designs based on the following
        citeria:
        
        1.  minimum Design with the criteria:
        
        -   number of runs \>= number of factors + 1
        -   center balance \> 95%
        -   level balance \> 95%
        -   Orthogonal Balance \> 90%
        -   Two Level interaction Balance \> 90%
        -   Two Level Interaction Minimum One = 100%
        
        1.  optimal Design with the criteria:
        
        -   center balance \> 98%
        -   level balance \> 98%
        -   Orthogonal Balance \> 95%
        -   Two Level interaction Balance \> 95%
        -   Two Level Interaction Minimum One = 100%
        
        1.  best design which is based on best score that is sum of efficiencies
            above and includes a small penalty for runsize relative to maximum
            runsize
        
        This will deliver (see folder `test/results/`):
        
        -   Overview summary of the three designs and their main efficiencies:
            `Experiment_Design_selection_summary.txt`
        -   Three tables (`Designtable_minimum/optimal/best...csv`) for the
            there suggested designs that are converted in the actual level
            values
        -   An overview of the efficiencies is plotted as function of exp run
            and saved in `Efficiencies_[factor_levels].png`
        
        In case the user wants to select another design for a different run
        size, one can covert the design array into a design table with the
        function `doegen.deogen.array2valuetable()`.
        
        <figure>
        <img src="https://github.com/sebhaan/DoEgen/blob/main/figures/Efficiencies.png" width="400" alt="" /><figcaption>Example overview plot of the main efficiencies (from 0=worst to 100=best) as function of number of experiments.</figcaption>
        </figure>
        
        ### Design Efficiencies
        
        DoEgen computes more than ten efficiencies and saves them as .csv file
        for each generated design array. All indicators, except for the
        canonical correlations, have a range from 0 (worst possible) to 1
        (optimal):
        
        -   Center Balance: 100% \[1 - Sum(Center-Deviation)/Array Size\],
            i.e. the average center balance over all factors.
        -   Level Balance: Defined as 100% \[1 - Sum(Imbalance)/Array Size\],
            the average level balance over all factors.
        -   Orthogonality: Defined as 100% \[1 - Orthogonality\], i.e. the
            average orthogonality over all factor pairs.
        -   Two-way Interaction Balance: Similar to level balance but for
            pairwise factor balance.
        -   Two-way Interaction with at least one occurrence: 100% \[1 - Sum(Not
            at least one pairwise factor occurrence)/number of pairwise
            combinations\]; 100% if all factor-level pair combinations occur at
            least once.
        -   D-Eff: D-Efficiency (model includes main term and quadratic).
        -   D1 Eff: only main terms
        -   D2 Eff: main, quadratic, and interaction terms
        -   A-Eff: A-efficiency (main term and quadratic)
        -   A1-Eff: only main terms
        -   A2-Eff: main, quadratic, and interaction terms
        
        For further inspection, `doegen.deogen.evaluate_design2` creates also
        the following tables and plots:
        
        -   Table of Pearson Correlation (same as above if normalised discrete
            variables)
        -   Table of Two-way Interaction Balance
        -   Cornerplot of pairwise factor relation with Y
        
        <figure>
        <img src="https://github.com/sebhaan/DoEgen/blob/main/figures/pairwise_correlation.png" width="600" alt="" /><figcaption>Pairwise factor correlation plot of an example 8 factor design array with a mix of 3- and 2-level factors. The lines and blue shadows correspond to the linear regression fit and its uncertainty. Two pairs are 100% orthogonal if the linear regression line is horizontal. The diagonal bar charts show the histogram of level values for each factor (perfect level balance if histogram is flat).</figcaption>
        </figure>
        
        ### Experiment Result Analysis
        
        Experiment Result Analysis with `doeval.py`: The experiment results have
        to be provided in a result table with the format as specified in
        \#user-templates, and specifications in the `settings_expresults.yaml`
        file. Then run
        
        ``` bash
        $ python -m doegen.doeval settings_expresults.yaml
        ```
        
        This will create the following stats tables and plots (see folder
        `test/expresults/` as example):
        
        -   A valuation of the factors in term of “importance”, which is defined
            by the maximum change (range) in the average Y between any factor
            levels. Results are visualized in bar plot and saved as csv,
            including, min, max, std deviation across all levels
        -   Computes RMSE between experiment result and ground truth; results
            saved as csv.
        -   Ranks list of top experiments and their parameters based on RMSE
        -   Computes average and variance of best parameters weighted with RMSE;
            saved to csv file
        -   An overview plot of all the correlation plots between Y and each
            factor (see function `plot_regression`)
        -   Moreover it will plot Y value for each pairwise combination of
            factors (see function `plot_3dmap`), which allows the user to
            visualise categorical factors
        
        <figure>
        <img src="https://github.com/sebhaan/DoEgen/blob/main/figures/Expresult_correlation_X_1.png" width="600" alt="" /><figcaption>Overview plot of X-Y Correlation for each factor as function of their level values. On top the linear regression coefficient <code>r</code> is shown along the linear regression fit and its uncertainty (line and shadow).</figcaption>
        </figure>
        
        <figure>
        <img src="https://github.com/sebhaan/DoEgen/blob/main/figures/Expresult_pairwise-correlation_1.png" width="600" alt="" /><figcaption>Cornerplot of pairwise factor relation with Y. The color(bar) indicates the value of Y.</figcaption>
        </figure>
        
        Use Case Study
        --------------
        
        Here we demonstrate a typical use case where we would like to first
        generate and select an optimal experiment design. Then subsequently
        after running the experiment we would like to answer the question which
        is the best parameter space and what parameters are important. Our case
        study is given by the test example, which consists of 8 factors
        (parameters) that are specified in the experiment setup table
        `Experiment_setup_test.xlsx`.
        
        <figure>
        <img src="https://github.com/sebhaan/DoEgen/blob/main/figures/Setup_header_test.png" width="600" alt="" /><figcaption>Test Experiment Setup Table with 6 discrete and 2 categorical factors. Each factor can have a certain number of levels (values), which are here either 3 or 2</figcaption>
        </figure>
        
        The first goal is to generate an efficient design with only a fraction
        of the entire parameter combination (in our case the full factorial
        would be 3<sup>6</sup> × 2<sup>2</sup> = 2916). The maximum number of
        experiments (in this case we choose 150) is set in the file
        `settings_design_test.yaml`, which also specifies input and output
        directory names, as well as the maximum time for optimising one run (in
        this case 100 seconds per design optimisation). This configuration will
        generate and optimize a range of experiments with different design run
        sizes from 12 to 150, in steps of 6 runsizes (since the lowest common
        multiple of our mix of 2 and 3 factor levels is 6). Note that the user
        can also choose a different stepsize, which can done by setting the
        value in the setting parameter `delta_nrun`. Now we are all setup to
        start the experiment design generation and optimisation script, which we
        do by running the script doegen.py with the settings file as argument:
        
        ``` bash
        $ cd DoEgen
        $ python -m doegen.doegen test/settings_design_test.yaml
        ```
        
        This will generate for each runsize an optimised design array and a list
        of efficiencies and diagnostic tables and plots (see [Design
        Generation](#design-generation) for more details). To simplify the
        selection of the generated experiment designs, DoEgen suggests
        automatically three designs: 1) one minimum design (lowest number of
        runs at given efficiency threshold), 2) one optimal design, and 3) one
        best design (either equal or has larger experiment run number than
        optimal design). In our case the three design are selected for run
        numbers 30 (minimum), 72 (optimal), 90 (best). Since the optimal design
        has basically almost the same efficiencies as the best design (see
        figure below) but at a lower cost of experiment runs, we choose for our
        experiment the optimal design, which is given in the table
        `Designtable_optimal_Nrun72.csv`.
        
        <figure>
        <img src="https://github.com/sebhaan/DoEgen/blob/main/figures/Results_overview.png" width="600" alt="" /><figcaption>Result Overview of Experiment Design Generation and the three suggested choices. The most important criteria for a good design are orthogonality (100% means that all factor pairs are 100% orthogonal to each other), level/center balance (100% is best) and two-way interaction balance (100% is best). We also want to make sure that at each pairwise interaction occurs at least one (100% Two-Level Min Efficiency). D-efficiency maximises the determinant of the information matrix <span class="math inline">|<em>X</em><sup><em>T</em></sup><em>X</em>|</span>, which corresponds to minimizing the generalized variance of the parameter estimates for a pre-specified model <span class="math inline"><em>X</em></span>. Here, D1-efficiency defines the model with only the main effects, while D-efficiency includes also all quadratic terms in the model <span class="math inline"><em>X</em></span>. Typically D1-efficiency should be larger than 60%, while D-efficiency only increases if number of experiments is much larger than the number of model terms. In this case study we consider only D1-efficiency given that we want to minimize the number of experiments.</figcaption>
        </figure>
        
        <figure>
        <img src="https://github.com/sebhaan/DoEgen/blob/main/figures/Designtable_optimal_Nrun72.png" width="600" alt="" /><figcaption>Header with first 5 rows of the optimal design with 72 experiments</figcaption>
        </figure>
        
        Now it is time to run the experiment. In our example, we produce just
        some random data for the 72 experiments with 10 sensor locations (PID 1
        to 10) and one output variable Y (e.g. temperature). To analyse the
        experiment, the results have to written in a structured table with the
        format as given in `experiment_results_Nrun72.xlsx` (see description in
        figure below).
        
        <figure>
        <img src="https://github.com/sebhaan/DoEgen/blob/main/figures/Experiment_result_Nrun72_header.png" width="600" alt="" /><figcaption>Header with first rows of the experiment result table for 72 experiments. Note that the <code>Nexp</code> number has to match the experiment design table <code>Nexp</code>. Each experiment (label <code>Nexp</code>) can have multiple locations or points (identifier# <code>PID</code>), e.g., if experiment is run at different locations simultaneously. In addition, it is possible that one has multiple output Y-variates, labeled with identifier <code>Y :abel</code> (target property that has to be predicted or evaluated, e.g. Rain and Temperature). The column <code>Y Exp</code> holds the experiment result for Y while the column <code>Y Truth</code>holds the ground truth value, which is required to calculate the RMSE and to select best parameter space.</figcaption>
        </figure>
        
        To run the experiment analysis script, settings such as for input output
        directory names are given in the settings file
        `settings_expresults_test.yaml`, and we can now run the analysis script
        with
        
        ``` bash
        $ python -m doegen.doeval test/settings_expresults_test.yaml
        ```
        
        This analysis produces a range of diagnostic tables and result plots for
        each output variable Y (in our case we have only one Y). One of the
        question of this example use case is to identify what factors are
        important, which is given in the figure `Ybarplot.png`. The “importance”
        basically indicates how much a factor changes Y (defined by the maximum
        average change in Y between any levels). This has the advantage to
        identify also important factors that have either a low linear regression
        coefficients with Y (see r values in plot `Expresult_correlation_X.png`)
        or are categorical. Such insight can be valuable to determine, e.g.,
        which factors should be investigated in more detail in a subsequent
        experiment or to estiamate which factors have no effect on Y.
        
        <figure>
        <img src="https://github.com/sebhaan/DoEgen/blob/main/figures/Ybarplot_1.png" width="600" alt="" /><figcaption>Factor Importance ranked from maximum to lowest change (range) in Y</figcaption>
        </figure>
        
        Another important question is what are the best parameter values based
        on the obtained experiment results so far? This question can be answered
        by computing the Root-Mean-Square-Error between experiment results and
        ground truth (or alternatively the likelihood if the model predictions
        include also uncertainties). Table `Experiment_1_RMSE_Top10_sorted.csv`
        provides an overview of the top 10 experiments sorted as function of
        their RMSE. Moroever we can calculate the (RMSE-weighted) average of
        each factor for the top experiments as shown in bar plot below.
        
        <figure>
        <img src="figures/Top10.png" width="600" alt="" /><figcaption>Picture of Table <code>Experiment_1_RMSE_Top10_sorted.csv</code> which shows the factor values of the top 10 experiments based on their RSME values.</figcaption>
        </figure>
        
        <figure>
        <img src="https://github.com/sebhaan/DoEgen/blob/main/figures/BestFactor_Avg1.png" width="600" alt="" /><figcaption>Factor values of the top 10 experiments based on their RSME values. The bar heights indicate the top factor’s average value and the dark lines their standard deviation. Note that the average and their standard deviation are computed with the weights <span class="math inline"><em>R</em><em>M</em><em>S</em><em>E</em><sup> − 2</sup></span>.</figcaption>
        </figure>
        
        Furthermore, multiple other diagnostics plots such as factor-Y
        correlation and pairwise correlation maps are generated (see [Experiment
        Result Analysis](#experiment-result-analysis) for more details).
        
        Comparison to Other DoE Tools
        -----------------------------
        
        The aim of DoEgen is to provide an open-source tool for researchers to
        create optimised designs and a framework for transparent evaluation of
        experiment designs. Moreover, DoEgen aims to assist the result analysis
        that may allow the researcher a subsequent factor selection, parameter
        fine-tuning, or model building. The design generation function of DoEgen
        is build upon the excellent package `OApackage` and extends it further
        in terms of design efficiency evaluation, filtering, automation, and
        experiment analysis. There are multiple other tools available for DoE;
        the table below provides a brief (preliminary, subjective, and
        oversimplified) summary of the main advantages and disadvantages for
        each tool that has been tested. Users are encouraged to test these tools
        themselves.
        
        | Feature                   |  SAS JMP  |  pyDOE2 | OApackage |   DoEgen   |
        |---------------------------|:---------:|:-------:|:---------:|:----------:|
        | Open-Source               | no (paid) |   yes   |    yes    |     yes    |
        | Design Optimisation Score | very good | limited |    good   |    good    |
        | Optimal Runsize Finder    |     no    |    no   |     no    |     yes    |
        | Design Efficiency Eval    |    yes    |    no   |  limited  |     yes    |
        | Exp Result Analysis       |    yes    |    no   |     no    |     yes    |
        | Development Stage         |  advanced |  early  |  moderate | very early |
        
        Literature
        ----------
        
        [OApackage: A Python package for generation and analysis of orthogonal
        arrays, optimal designs and conference
        designs](https://doi.org/10.21105/joss.01097), P.T. Eendebak, A.R.
        Vazquez, Journal of Open Source Software, 2019
        
        [pyDOE2: An experimental design package for
        python](https://github.com/clicumu/pyDOE2)
        
        Dean, A., Morris, M., Stufken, J. and Bingham, D. eds., 2015. Handbook
        of design and analysis of experiments (Vol. 7). CRC Press.
        
        Goos, P. and Jones, B., 2011. Optimal design of experiments: a case
        study approach. John Wiley & Sons.
        
        Kuhfeld, W.F., 2010. Discrete choice. SAS Technical Papers, 2010,
        pp.285-663.
        
        Zwerina, K., Huber, J. and Kuhfeld, W.F., 1996. A general method for
        constructing efficient choice designs. Durham, NC: Fuqua School of
        Business, Duke University.
        
        Cheong, Y.P. and Gupta, R., 2005. Experimental design and analysis
        methods for assessing volumetric uncertainties. SPE Journal, 10(03),
        pp.324-335.
        
        JMP, A. and Proust, M., 2010. Design of experiments guide. Cary, NC: SAS
        Institute Inc.
        
        Attribution and Acknowledgments
        -------------------------------
        
        Acknowledgments are an important way for us to demonstrate the value we
        bring to your research. Your research outcomes are vital for ongoing
        funding of the Sydney Informatics Hub.
        
        If you make use of this code for your research project, please include
        the following acknowledgment:
        
        “This research was supported by the Sydney Informatics Hub, a Core
        Research Facility of the University of Sydney.”
        
        Project Contributors
        --------------------
        
        Key project contributors to the DoEgen project are:
        
        -   Sebastian Haan (Sydney Informatics Hub, University of Sydney): Main
            contributor and software development of DoEgen.
        -   Christopher Howden (Sydney Informatics Hub, University of Sydney):
            Statistical consultancy, literature suggestions, and documentation.
        -   Danial Azam (School of Geophysics, University of Sydney): Testing
            DoEgen on applications for computational geosciences.
        -   Joel Nothman (Sydney Informatics Hub, University of Sydney): Code
            review and improvements with focus on doegen.py.
        -   Dietmar Muller (School of Geophysics, University of Sydney):
            Suggesting the need for this project and developing real-world use
            cases for geoscience research.
        
        DoEgen has benefited from the OApackage library
        [OApackage](https://github.com/eendebakpt/oapackage) for the design
        optimisation code and we would like to thank the researchers who have
        made their code available as open-source.
        
        License
        -------
        
        Copyright 2021 Sebastian Haan, The University of Sydney
        
        DoEgen is free software: you can redistribute it and/or modify it under
        the terms of the GNU Affero General Public License (AGPL version 3) as
        published by the Free Software Foundation.
        
        This program is distributed in the hope that it will be useful, but
        WITHOUT ANY WARRANTY; without even the implied warranty of
        MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero
        General Public License for more details.
        
        You should have received a copy of the GNU Affero General Public License
        along with this program (see LICENSE.md). If not, see
        <https://www.gnu.org/licenses/>.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.6
Description-Content-Type: text/markdown
