Metadata-Version: 2.1
Name: asreview-visualization
Version: 0.2.2
Summary: Visualization tools for the ASReview project
Home-page: https://github.com/asreview/asreview-visualization
Author: Utrecht University
Author-email: asreview@uu.nl
License: UNKNOWN
Project-URL: Bug Reports, https://github.com/asreview/asreview-visualization/issues
Project-URL: Source, https://github.com/asreview/asreview-visualization
Description: # ASReview-visualization
        
        ![Deploy and release](https://github.com/asreview/asreview-visualization/workflows/Deploy%20and%20release/badge.svg)![Build status](https://github.com/asreview/asreview-visualization/workflows/test-suite/badge.svg)
        
        This is a plotting/visualization supplemental package for the
        [ASReview](https://github.com/asreview/asreview) software. It is a fast way to
        create a visual impression of the ASReview with different datasets, models and
        model parameters.
        
        ## Installation
        
        The easiest way to install the visualization package is to install from PyPI:
        
        ``` bash
        pip install asreview-visualization
        ```
        
        After installation of the visualization package, `asreview` should automatically
        detect it. Test this by:
        
        ```bash
        asreview --help
        ```
        
        It should list the 'plot' modus.
        
        ## Basic usage
        
        State files that were created with the same ASReview settings can be put
        together/averaged by putting them in the same directory. State files with
        different settings/datasets should be put in different directories to compare
        them.
        
        As an example consider the following directory structure, where we have two
        datasets, called `ace` and `ptsd`, each of which have 8 runs:
        
        ```
        ├── ace
        │   ├── results_0.h5
        │   ├── results_1.h5
        │   ├── results_2.h5
        │   ├── results_3.h5
        │   ├── results_4.h5
        │   ├── results_5.h5
        │   ├── results_6.h5
        │   └── results_7.h5
        └── ptsd
            ├── results_0.h5
            ├── results_1.h5
            ├── results_2.h5
            ├── results_3.h5
            ├── results_4.h5
            ├── results_5.h5
            ├── results_6.h5
            └── results_7.h5
        ```
        
        Then we can plot the results by:
        
        ```bash
        asreview plot ace ptsd
        ```
        
        By default, the values shown are expressed as percentages of the total number of papers. Use the
        `-a` or `--absolute-values` flags to have them expressed in absolute numbers:
        
        ```bash
        asreview plot ace ptsd --absolute-values
        ```
        
        
        ## Plot types
        
        There are currently four plot types implemented: _inclusion_, _discovery_,
        _limit_, _progression_. They can be individually selected with the `-t` or
        `--type` switch. Multiple plots can be made by using `,` as a separator:
        
        ```bash
        asreview plot ace ptsd --type 'inclusion,discovery'
        ```
        
        ### Inclusion
        
        This figure shows the number/percentage of included papers found as a function
        of the number/percentage of papers reviewed. Initial included/excluded papers
        are subtracted so that the line always starts at (0,0).
        
        The quicker the line goes to a 100%, the better the performance.
        
        ![alt text](https://github.com/msdslab/asreview-visualization/blob/master/docs/inclusions.png?raw=true "Inclusions")
        
        ### Discovery
        
        This figure shows the distribution of the number of papers that have to be
        read before discovering each inclusion. Not every paper is equally hard to
        find.
        
        The closer to the left, the better.
        
        ![alt text](https://github.com/msdslab/asreview-visualization/blob/master/docs/discovery.png?raw=true "Discovery")
        
        
        ### Limit
        
        This figure shows how many papers need to be read with a given criterion. A
        criterion is expressed as "after reading _y_ % of the papers, at most an
        average of _z_ included papers have been not been seen by the reviewer, if he
        is using max sampling.". Here, _y_ is shown on the y-axis, while three values
        of _z_ are plotted as three different lines with the same color. The three
        values for _z_ are 0.1, 0.5 and 2.0.
        
        The quicker the lines touch the black (`y=x`) line, the better.
        
        ![alt text](https://github.com/msdslab/asreview-visualization/blob/master/docs/limits.png?raw=true "Limits")
        
        ### Progression
        
        This figure shows the average inclusion rate as a function of time, number of
        papers read. The more concentrated on the left, the better. The thick line is
        the average of individual runs (thin lines). The visualization package will
        automatically detect which are directories and which are files. The curve is
        smoothed out by using a Gaussian smoothing algorithm.
        
        ![alt text](https://github.com/msdslab/asreview-visualization/blob/master/docs/progression.png?raw=true "Progression")
        
        
        ## API
        
        To make use of the more advanced features, you can also use the visualization
        package as a library. The advantage is that you can make more reproducible
        plots where text, etc. is in the place *you* want it. Examples can be found in
        module `asreviewcontrib.visualization.quick`. Those are the scripts that are
        used for the command line interface.
        
        ```python
        from asreviewcontrib.visualization.plot import Plot
        
        with Plot.from_paths(["PATH_1", "PATH_2"]) as plot:
        	inc_plot = plot.new("inclusion")
        	inc_plot.set_grid()
        	inc_plot.set_xlim(0, 30)
        	inc_plot.set_ylim(0, 101)
        	inc_plot.set_legend()
        	inc_plot.show()
        	inc_plot.save("SOME_FILE.png")
        ```
        
        Of course fill in `PATH_1` and `PATH_2` as the files you would like to plot.
        
        If the customization is not sufficient, you can also directly manipulate the
        `self.ax` and `self.fig` attributes of the plotting class.
        
Keywords: asreview plot visualization
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
