Metadata-Version: 2.1
Name: dvha-stats
Version: 0.1.4
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: <a>
          <img src="https://user-images.githubusercontent.com/4778878/30754005-b7a7e808-9f86-11e7-8b0f-79d1006babdf.jpg" width='480' alt="DVHA logo"/>
        </a>
        
        ---------  
        # DVHA Stats
        A library of prediction and statistical process control tools. Although based 
        on work in [DVH Analytics](http://dvhanalytics.com), all statistical tools in 
        this library are generic and not radiation oncology.
        
        [![build](https://github.com/cutright/DVHA-Stats/workflows/build/badge.svg)](https://github.com/cutright/DVHA-Stats/actions)
        <a href="https://pypi.org/project/dvha-stats/">
          <img src="https://img.shields.io/pypi/v/dvha-stats.svg" alt="PyPi Version" /></a>
        <a href="https://lgtm.com/projects/g/cutright/DVHA-Stats/context:python">
          <img src="https://img.shields.io/lgtm/grade/python/g/cutright/DVHA-Stats.svg?logo=lgtm&label=code%20quality" alt="LGTM Code Quality" /></a>
        <a href="https://codecov.io/gh/cutright/DVHA-Stats">
          <img src="https://codecov.io/gh/cutright/DVHA-Stats/branch/master/graph/badge.svg" />
        </a>
        
        ### What does it do?
        * Read data from CSV or supply as numpy array 
        * 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)
        
        ### Coming Soon:
        - [ ] Multi-Variable Regression residual and quantile plots
        - [ ] Backward-elimination for Multi-Variable Linear Regressions
        - [ ] Risk-Adjusted Control Charts using Multi-Variable Linear Regressions
        - [ ] Machine learning regressions based on scikit-learn
        
        
        **NOTE**: This project is brand new and very much under construction.
        
        Source-Code Installation
        ---------
        ~~~
        pip install dvha-stats
        ~~~
        or
        ~~~
        pip install git+https://github.com/cutright/DVHA-Stats.git
        ~~~
        Or clone the project and run:
        ~~~
        python setup.py install
        ~~~
        
        Dependencies
        ---------
        * [Python](https://www.python.org) >3.5
        * [SciPy](https://scipy.org)
        * [NumPy](http://numpy.org)
        * [Scikit-learn](http://scikit-learn.org)
        * [regressors](https://pypi.org/project/regressors/)
        * [matplotlib](http://matplotlib.org/)
        
        ### Initialize and Plot Data
        ~~~
        >>> from dvhastats.stats 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
        ~~~
        <img src='https://user-images.githubusercontent.com/4778878/91908372-0c4c2d80-ec71-11ea-9dfc-7c4f6c209542.png' align='center' width='350' alt="Data Plot">
        
        ### Correlation Matrix
        ~~~
        >>> pearson_mat = s.correlation_matrix()
        >>> pearson_mat.show()
        ~~~
        <img src='https://user-images.githubusercontent.com/4778878/92064453-1ea69400-ed63-11ea-8f72-5034c577c1e3.png' align='center' width='350' alt="Pearson-R Correlation Matrix">
        
        Like-wise, a Spearman correlation matrix:
        ~~~
        >>> spearman_mat = s.correlation_matrix("Spearman")
        >>> spearman_mat.show()
        ~~~
        <img src='https://user-images.githubusercontent.com/4778878/92177010-4a7a5600-ee05-11ea-91b9-2a0128eafe5b.png' align='center' width='350' alt="Spearman Correlation Matrix">
        
        
        ### Univariate Control Chart
        ~~~
        >>> ucc = s.univariate_control_charts()
        >>> ucc["V1"].show()  # or ucc[0].show(), can provide index or var_name
        ~~~
        <img src='https://user-images.githubusercontent.com/4778878/91908380-0fdfb480-ec71-11ea-9394-d029a8a6727e.png' align='center' width='350' alt="Univariate Control Chart">
        
        ### Hotelling T^2
        Example to calculate a Multivariate Control Chart with Hotelling T^2 values
        ~~~
        >>> ht2 = s.hotelling_t2()
        >>> ht2.show()
        ~~~
        
        <img src='https://user-images.githubusercontent.com/4778878/91908391-166e2c00-ec71-11ea-941b-321e01f56542.png' align='center' width='350' alt="Multivariate Control Chart">
        
        ### Hotelling T^2 with Box-Cox Transformation
        Example to calculate the Hotelling T^2 values and apply a Box-Cox transformation
        ~~~
        >>> ht2_bc = s.hotelling_t2(box_cox=True)
        >>> ht2_bc.show()
        ~~~
        
        <img src='https://user-images.githubusercontent.com/4778878/91908394-179f5900-ec71-11ea-88a0-9c95d714fb4c.png' align='center' width='350' alt="Multivariate Control Chart with Box-Cox Transformation">
        
        ### Principal Component Analysis (PCA)
        ~~~
        >>> pca = s.pca()
        >>> pca.show()
        ~~~
        <img src='https://user-images.githubusercontent.com/4778878/92050205-16922880-ed52-11ea-9967-d390577380b6.png' align='center' width='350' alt="PCA Feature Heat Map">
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
