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 specific to radiation oncology. See our documentation for advanced uses.

• Read data from CSV, supply as numpy array or dict

• Basic plotting

  • Simple one-variable plots from data
  • Control Charts (Univariate, Multivariate, & Risk-Adjusted)
  • Heat Maps (correlations, PCA, etc.)

• Perform Box-Cox transformations
• Calculate Correlation matrices
• Perform Multi-Variable Linear Regressions
• Perform Principal Component Analysis (PCA)

• Free software: MIT license
• Documentation: Read the docs
• Tested on Python 3.6, 3.7, 3.8

• scipy
• numpy
• scikit-learn
• regressors
• matplotlib
• PTable

>>> from dvhastats.ui import DVHAStats
>>> s = DVHAStats("your_data.csv")  # use s = DVHAStats() for test data

>>> s.var_names
['V1', 'V2', 'V3', 'V4', 'V5', 'V6']

>>> s.show('V1')  # or s.show(0), can provide index or var_name

>>> ht2_bc = s.hotelling_t2(box_cox=True)
>>> ht2_bc.show()

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