Metadata-Version: 2.1
Name: dvha-stats
Version: 0.1.6
Summary: Simple DICOM tag editor built with wxPython and pydicom
Home-page: https://github.com/cutright/DVHA-Stats
Author: Dan Cutright
Author-email: dan.cutright@gmail.com
Maintainer: Dan Cutright
Maintainer-email: dan.cutright@gmail.com
License: MIT License
Download-URL: https://github.com/cutright/DVHA-Stats/archive/master.zip
Description: =========
        dvhastats
        =========
        
        .. image:: https://user-images.githubusercontent.com/4778878/92505112-351c7780-f1c9-11ea-9b5c-0de1ad2d131d.png
           :width: 400
           :align: center
           :alt: DVH Analytics logo
        
        |build| |pypi| |Docs| |lgtm| |Codecov|
        
        A library of prediction and statistical process control tools. Although based
        on work in `DVH Analytics <http://www.dvhanalytics.com>`__, all statistical tools in
        this library are generic and not radiation oncology.
        
        What does it do?
        ----------------
        * Read data from CSV or supply as numpy array
        * Basic plotting
            * Simple one-variable plots from data
            * Control Charts (Univariate and Multivariate)
            * Heat Maps (correlations, PCA, etc.)
        * Perform Box-Cox transformations
        * Calculate Correlation matrices
        * Perform Multi-Variable Linear Regressions
        * Perform Principal Component Analysis (PCA)
        
        Other information
        -----------------
        
        -  Free software: `MIT license <https://github.com/cutright/DVHA-Stats/blob/master/LICENSE>`__
        -  Documentation: `Read the docs <https://dvha-stats.readthedocs.io>`__
        -  Tested on Python 3.6, 3.7, 3.8
        
        Dependencies
        ------------
        
        -  `scipy <https://scipy.org>`__
        -  `numpy <http://www.numpy.org>`__
        -  `scikit-learn <http://scikit-learn.org>`__
        -  `regressors <https://pypi.org/project/regressors/>`__
        -  `matplotlib <http://matplotlib.org>`__
        
        
        Basic Usage
        ------------
        
        .. code-block:: python
        
            from dvhastats.ui import DVHAStats
            s = DVHAStats("tests/testdata/multivariate_data.csv")
        
            >>> s.var_names
            ['V1', 'V2', 'V3', 'V4', 'V5', 'V6']
        
            >>> s.show('V1')  # or s.show(0), can provide index or var_name
        
        |plot|
        
        .. code-block:: python
        
            >>> s.show(0, plot_type="hist")
        
        |hist|
        
        Pearson-R Correlation Matrix
        ############################
        .. code-block:: python
        
            pearson_mat = s.correlation_matrix()
            >>> pearson_mat.show()
        
        |pearson|
        
        Spearman Correlation Matrix
        ###########################
        .. code-block:: python
        
            spearman_mat = s.correlation_matrix("Spearman")
            >>> spearman_mat.show()
        
        |spearman|
        
        Univariate Control Chart
        ########################
        .. code-block:: python
        
            ucc = s.univariate_control_charts()
            >>> ucc["V1"].show()  # or ucc[0].show(), can provide index or var_name
        
        |control-chart|
        
        Multivariate Control Chart
        ##########################
        .. code-block:: python
        
            ht2 = s.hotelling_t2()
            >>> ht2.show()
        
        |hotelling-t2|
        
        Multivariate Control Chart (w/ non-normal data)
        ###############################################
        .. code-block:: python
        
            ht2_bc = s.hotelling_t2(box_cox=True)
            >>> ht2_bc.show()
        
        |hotelling-t2-bc|
        
        Principal Component Analysis (PCA)
        ##################################
        .. code-block:: python
        
            pca = s.pca()
            >>> pca.show()
        
        |pca|
        
        .. |build| image:: https://github.com/cutright/DVHA-Stats/workflows/build/badge.svg
           :target: https://github.com/cutright/DVHA-Stats/actions
           :alt: build
        .. |pypi| image:: https://img.shields.io/pypi/v/dvha-stats.svg
           :target: https://pypi.org/project/dvha-stats
           :alt: PyPI
        .. |lgtm| image:: https://img.shields.io/lgtm/grade/python/g/cutright/DVHA-Stats.svg?logo=lgtm&label=code%20quality
           :target: https://lgtm.com/projects/g/cutright/DVHA-Stats/context:python
           :alt: lgtm
        .. |Codecov| image:: https://codecov.io/gh/cutright/DVHA-Stats/branch/master/graph/badge.svg
           :target: https://codecov.io/gh/cutright/DVHA-Stats
           :alt: Codecov
        .. |Docs| image:: https://readthedocs.org/projects/dvha-stats/badge/?version=latest
           :target: https://dvha-stats.readthedocs.io/en/latest/?badge=latest
           :alt: Documentation Status
        
        .. |plot| image:: https://user-images.githubusercontent.com/4778878/91908372-0c4c2d80-ec71-11ea-9dfc-7c4f6c209542.png
           :width: 350
        .. |hist| image:: https://user-images.githubusercontent.com/4778878/92502706-e4efe600-f1c5-11ea-9f63-4218899e885b.png
           :width: 350
        .. |pearson| image:: https://user-images.githubusercontent.com/4778878/92064453-1ea69400-ed63-11ea-8f72-5034c577c1e3.png
           :width: 350
        .. |spearman| image:: https://user-images.githubusercontent.com/4778878/92177010-4a7a5600-ee05-11ea-91b9-2a0128eafe5b.png
           :width: 310
        .. |control-chart| image:: https://user-images.githubusercontent.com/4778878/91908380-0fdfb480-ec71-11ea-9394-d029a8a6727e.png
           :width: 350
        .. |hotelling-t2| image:: https://user-images.githubusercontent.com/4778878/91908391-166e2c00-ec71-11ea-941b-321e01f56542.png
           :width: 350
        .. |hotelling-t2-bc| image:: https://user-images.githubusercontent.com/4778878/91908394-179f5900-ec71-11ea-88a0-9c95d714fb4c.png
           :width: 350
        .. |pca| image:: https://user-images.githubusercontent.com/4778878/92050205-16922880-ed52-11ea-9967-d390577380b6.png
           :width: 350
Keywords: stats,statistical process control,control charts
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >3.5
Description-Content-Type: text/markdown
