Quickstart¶

Requirements¶

Make sure that Python 3.5 or higher is installed. A convenient way to install Python and many useful packages for scientific computing is to use the Anaconda distribution.

Required Python packages

numpy
hoggorm >= 0.11.0
matplotlib >= 2.1.1

Installation and updates¶

Installation¶

Install hoggormplot easily from the command line from the PyPI - the Python Packaging Index.

pip install hoggormplot

Upgrading¶

To upgrade hoggormplot from a previously installed older version execute the following from the command line:

pip install --upgrade hoggormplot

If you need more information on how to install Python packages using pip, please see the pip documentation.

Documentation¶

Documentation at Read the Docs
Jupyter notebooks with examples of how to use hoggormplot

Example¶

import hoggormplot as hopl

# Compute PCA model with
# - 5 components
# - standardised/scaled variables
# - KFold cross validation with 4 folds
>>> model = ho.nipalsPCA(arrX=myData, numComp=5, Xstand=True, cvType=["Kfold", 4])

# Extract results from PCA model
>>> scores = model.X_scores()
>>> loadings = model.X_loadings()
>>> cumulativeCalibratedExplainedVariance_allVariables = model.X_cumCalExplVar_indVar()
>>> cumulativeValidatedExplainedVariance_total = model.X_cumValExplVar()

# Plot results with HoggormPlot
# Get multiple plots with the main hoggormplot function
>>> hopl.plot(model, plots=[1, 2, 3, 6], cumulative=True, line=True)
>>> hopl.plot(model)
>>> hopl.plot(model, plots=['scores', 'loadings', 'explainedVariance'], cumulative=True)