dvha-stats 0.1.2

Simple DICOM tag editor built with wxPython and pydicom

  

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DVHA Stats

A library of prediction and statistical process control tools. Although based on work in DVH Analytics, all tools in this library are generic and not specifically applicable to any one field.

What does it do?

• Read data from CSV or supply as numpy array
• Plotting
  • Simple one-variable plots from data
  • Control Charts
  • Multivariate Control Charts
• Perform Box-Cox transformations
• Calculate Pearson-R correlation matrices
• Perform Multi-Variable Linear Regressions
• Perform Principal Component Analysis (PCA)

Coming Soon:

• Multi-Variable Regression 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 >3.5
• SciPy
• NumPy
• Scikit-learn
• regressors
• matplotlib

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

Pearson-R Correlation Matrix

>>> corr_mat = s.pearson_r_matrix()
>>> corr_mat.show()

Univariate Control Chart

>>> ucc = s.univariate_control_charts()
>>> ucc["V1"].show()  # or ucc[0].show(), can provide index or var_name

Hotelling T^2

Example to calculate a Multivariate Control Chart with Hotelling T^2 values

>>> ht2 = s.hotelling_t2()
>>> ht2.show()

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()

Principal Component Analysis (PCA)

>>> pca = s.pca()
>>> pca.show()