Load dataset
##############################

Let's load the **wine** dataset to demonstrate the plots.

.. code:: python

	# Load library
	from sklearn.datasets import load_wine
	# Load dataset
	data = load_wine()
	X = data.data
	y = data.target
	labels = data.feature_names

	from pca import pca
	# Initialize
	model = pca(normalize=True)

	# Fit transform and include the column labels and row labels
	results = model.fit_transform(X, col_labels=labels, row_labels=y)

	# [pca] >Normalizing input data per feature (zero mean and unit variance)..
	# [pca] >The PCA reduction is performed to capture [95.0%] explained variance using the [13] columns of the input data.
	# [pca] >Fitting using PCA..
	# [pca] >Computing loadings and PCs..
	# [pca] >Computing explained variance..
	# [pca] >Number of components is [10] that covers the [95.00%] explained variance.
	# [pca] >The PCA reduction is performed on the [13] columns of the input dataframe.
	# [pca] >Fitting using PCA..
	# [pca] >Computing loadings and PCs..
	# [pca] >Outlier detection using Hotelling T2 test with alpha=[0.05] and n_components=[10]
	# [pca] >Outlier detection using SPE/DmodX with n_std=[2]


Scatter plot
###############


.. code:: python

	# Make scatterplot
	model.scatter()

	# Make scatterplot
	model.scatter(density=True)

	# Gradient over the samples. High dense areas will be more colourful.
	model.scatter(gradient='#FFFFFF', edgecolor=None)

	# Include the outlier detection
	model.scatter(SPE=True, density=True)

	# Include the outlier detection
	model.scatter(HT2=True, density=True)

	# Look at different PCs: 1st PC=1  vs PC=3
	model.scatter(PC=[0, 2])


.. |figP1| image:: ../figs/wine_scatter.png
.. |figP1d| image:: ../figs/wine_scatter_density.png
.. |figP1g| image:: ../figs/wine_scatter_gradient.png
.. |figP2| image:: ../figs/wine_scatter_spe.png
.. |figP3| image:: ../figs/wine_scatter_hotel.png
.. |figP4| image:: ../figs/wine_scatter_PC13.png
.. |figP7| image:: ../figs/wine_scatter_density.png

.. table:: Scatterplots
   :align: center

   +----------+----------+
   |       |figP1|       |
   +----------+----------+
   | |figP1d| | |figP1g| |
   +----------+----------+
   | |figP2|  | |figP3|  |
   +----------+----------+
   | |figP4|  | |figP7|  |
   +----------+----------+


Biplot
###############

.. code:: python

	# Make biplot
	model.biplot()

	# Here again, many other options can be turned on and off
	model.biplot(SPE=True, HT2=True, legend=1)


.. |figP5| image:: ../figs/wine_biplot.png
.. |figP6| image:: ../figs/wine_biplot_with_outliers.png

.. table:: Biplots
   :align: center

   +----------+
   | |figP5|  |
   +----------+
   | |figP6|  |
   +----------+


Biplot (only arrows)
########################

.. code:: python

	# Make plot with parameters: set cmap to None and label and legend to False. Only directions will be plotted.
	model.biplot(cmap=None, legend=False)


.. image:: ../figs/biplot_only_directions.png
   :width: 600
   :align: center


Explained variance plot
##############################


.. code:: python

	model.plot()

.. image:: ../figs/wine_explained_variance.png
   :width: 600
   :align: center



Alpha Transparency
##############################

.. code:: python

	fig, ax = model.scatter(alpha=1)


Markers
##############################

.. code:: python

	import numpy as np
	from sklearn.datasets import make_friedman1
	from pca import pca

	# Make data set
	X, _ = make_friedman1(n_samples=200, n_features=30, random_state=0)

	# All available markers
	markers = np.array(['.', 'o', 'v', '^', '<', '>', '8', 's', 'p', '*', 'h', 'H', 'D', 'd', 'P', 'X'])
	# Generate random integers
	random_integers = np.random.randint(0, len(markers), size=X.shape[0])
	# Draw markers
	marker = markers[random_integers]

	# Init
	model = pca(verbose=3)
	# Fit
	model.fit_transform(X)
	# Make plot with markers
	fig, ax = model.biplot(c=[0, 0, 0], 
			       marker=marker,
			       title='Demonstration of specifying markers per sample.',
			       n_feat=5,
			       legend=False)

.. |figP8| image:: ../figs/marker_example_1.png

.. table:: Biplots with markers
   :align: center

   +----------+
   | |figP8|  |
   +----------+



Control color/marker/size per sample
####################################

.. code:: python

	import numpy as np
	import matplotlib.pyplot as plt
	import matplotlib.colors as mcolors
	from sklearn.datasets import make_friedman1
	from pca import pca

	# Make data set
	X, _ = make_friedman1(n_samples=200, n_features=30, random_state=0)

	# All available markers
	markers = np.array(['.', 'o', 'v', '^', '<', '>', '8', 's', 'p', '*', 'h', 'H', 'D', 'd', 'P', 'X'])
	# Create colors
	cmap = plt.cm.get_cmap('tab20c', len(markers))
	# Generate random integers
	random_integers = np.random.randint(0, len(markers), size=X.shape[0])
	# Draw markers
	marker = markers[random_integers]
	# Set colors
	color = cmap.colors[random_integers, 0:3]
	# Set Size
	size = np.random.randint(50, 1000, size=X.shape[0])
	# Set alpha
	alpha = np.random.rand(1, X.shape[0])[0][random_integers]

	# Init
	model = pca(verbose=3)
	# Fit
	model.fit_transform(X)
	# Make plot with blue arrows and text
	fig, ax = model.biplot(
				SPE=True,
				HT2=True,
				c=color,
				s=size,
				marker=marker,
				alpha=alpha,
				color_arrow='k',
				title='Demonstration of specifying colors, markers, alpha, and size per sample.',
				n_feat=5,
				fontsize=20,
				fontweight='normal',
				arrowdict={'fontsize': 18},
				density=True,
				density_on_top=False,
				)



.. |figP9| image:: ../figs/custom_example_2.png

.. table:: Biplot with customized properties per sample.
   :align: center

   +----------+
   | |figP9|  |
   +----------+


3D plots
###############

All plots can also be created in 3D by setting the ``d3=True`` parameter.

.. code:: python

	model.biplot3d()


.. image:: ../figs/wine_biplot3d.png
   :width: 600
   :align: center



Toggle visible status
##############################

The visible status for can be turned on and off.

.. code:: python

	# Make plot but not visible.
	fig, ax = model.biplot(visible=False)

	# Set the figure again to True and show the figure.
	fig.set_visible(True)
	fig



.. include:: add_bottom.add