Metadata-Version: 2.1
Name: plitlib
Version: 0.1.8
Summary: A wrapper for automating common matplotlib tasks
Home-page: https://github.com/awslabs/plit
Author: Josiah Davis
License: Apache 2.0
Description: # plit
        
        `plit` is a [Matplotlib](https://matplotlib.org/) wrapper that automates the
        undifferentiated heavy-lifting of writing boilerplate code while maintaining
        the power and feel of Matplotlib. 
        
        ![](https://img.shields.io/badge/License-Apache%202.0-blue.svg) 
        ![](https://readthedocs.org/projects/plit/badge/?version=latest)
        ![](https://img.shields.io/badge/code_style-black-000000.svg)
        <!-- ![](https://img.shields.io/github/v/release/awslabs/plit.svg) -->
        
        There are two components to `plit`:
        * **Wrappers** around core chart types for standard line, scatter, histograms, and
          bar charts.
        * **Templates** that are built from these primatives for specific analytic tasks.
        
        Here is an example chart created with `plit`:
        
        ![](https://github.com/awslabs/plit/raw/main/figures/calibration.png)
        
        See the [PRFAQ](PRFAQ.md) for more information.
        
        # Install
        
        ```Python
        git clone https://github.com/awslabs/plit.git
        cd plit
        pip install -r requirements.txt
        pip install .
        ```
        
        # Quick Start 
        
        The best place to get started is the wrappers. There are three main wrappers
        included in `plit`. The naming is consistent with matplotlib. They work with
        multi-series by default.
        
        * `plot`: for line and scatter charts.
        * `hist`: for histograms.
        * `bar`: for bar charts.
        
        ## Create a line chart 
        
        Create a line and scatter chart using the `plot` function.
        
        ```Python
        import numpy as np
        x = [np.arange(10)]
        y = [np.random.random(size=(10,1)) for _ in range(4)]
        
        from plit import plot
        
        plot(x, y, list("ABCD"), 'X', 'Y');
        ```
        
        ## Create a scatter chart
        
        By simply changing the `marker_type='o'` you switch from line to scatter chart.
        
        ```Python
        from plit import plot
        
        x = [np.random.random(size=(10,1)) for _ in range(4)]
        plot(x, y, list("ABCD"), 'X', 'Y', marker_type='o')
        ```
        
        ## Create a histogram
        
        Create a histogram using the `hist` function.
        
        ```Python
        from plit import hist
        
        x = [np.random.normal(size=(100,1)), np.random.gamma(shape=1, size=(100,1)) - 2]
        hist(x, list("AB"), 'X', title='Histogram', bins=20)
        ```
        
        ## Create a bar chart
        
        Create a grouped bar chart with the `bar` function.
        
        ```Python
        from plit import bar
        
        x = [f"Group {i+1}"for i in range(6)]
        y = [np.random.random(size=(6)) for _ in range(2)]
        bar(x, y, list("AB"),'X', 'Y', colors=list("kb"), title='Bar Chart')
        ```
        
        ## Example notebooks 
        
        The best way to go deeper is to look at the examples notebooks:
        
        - [quick-start notebook](https://github.com/awslabs/plit/blob/main/notebooks/quick-start.ipynb) gives an overview of core
          functionality including creating core chart types.
        - [plit-vs-matplotlib](https://github.com/awslabs/plit/blob/main/notebooks/plit-vs-matplotlib.ipynb) shows the difference
          between matplotlib and plit with a simple example.
        - [creating-templates-file](https://github.com/awslabs/plit/blob/main/notebooks/creating-templates.ipynb) demonstrates
          how to use partial functions to simplify and streamline your visualization
        workflow.
        - [accuracy-vs-coverage](https://github.com/awslabs/plit/blob/main/notebooks/accuracy-vs-coverage.ipynb) shows an illustrative
          example using a template created for visualizing accuracy and coverage.
        - [precision-vs-recall](https://github.com/awslabs/plit/blob/main/notebooks/precision-recall-curve.ipynb) shows an illustrative
          example using a template created for choosing a threshold using precision and
        recall. 
        - [softmax-calibration](https://github.com/awslabs/plit/blob/main/notebooks/softmax-calibration.ipynb) shows an illustrative
          example using a template created for evaluating the calibration for softmax
        output. 
        
Keywords: plit visualization data science analytics analysis matplotlib
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
