iteraa.plot
===========

.. py:module:: iteraa.plot


Functions
---------

.. autoapisummary::

   iteraa.plot.plotRadarDatapoints
   iteraa.plot.createSimplexAx
   iteraa.plot.mapAlfaToSimplex
   iteraa.plot.plotTSNE


Module Contents
---------------

.. py:function:: plotRadarDatapoints(AA, X, sampIDs=[0], archSpaceIDs=[0, 1], sepSamps=False, showLabel=True, labelAll=False, showLegend=False, figSize=(6, 6), dpi=DPI, title=None, figNamePrefix='')

.. py:function:: createSimplexAx(AA, archIDs=[0, 1, 2], gridOn=True, showLabel=True, labelAll=False, figSize=(3, 3), gridLineWidth=0.5, gridcolor='k', bordercolor='k', fontcolor='k')

   # groupColor = None, color = None, marker = None, size = None
   groupColor:

       Dimension:      nData x 1

       Description:    Contains the category of data point.


.. py:function:: mapAlfaToSimplex(alfa, AA)

   alfa:    2D-array (nArchetypes x nData)


.. py:function:: plotTSNE(X, figNamePrefix='', figSize=(3, 3), numComponents=2, markIdxs=[], markerSize=1, colourInstances=False, perplexity=30.0, earlyExaggeration=12.0, learningRate='auto', nIter=1000, angle=0.5, metric='euclidean', init='pca', method='barnes_hut', minGradNorm=1e-07, nIterWithoutProgress=300, nJobs=NUM_JOBS, randomState=RANDOM_STATE)

   Conduct t-stochastic neighbour embedding and visualise the results.

   :param X: Whole data set.
   :type X: numpy.ndarray

   :rtype: None