# -*- coding: utf-8 -*-
"""
Some convenient visalisation routines.
"""
from __future__ import absolute_import
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # noqa F401
[docs]def embedding_plot(dmap_instance, dim=2, scatter_kwargs=None, show=True):
"""
Creates diffusion map embedding scatterplot. By default, the first two diffusion
coordinates are plotted against each other.
Parameters
----------
dmap_instance : DiffusionMap Instance
An instance of the DiffusionMap class.
dim: int, optional, 2 or 3.
Optional argument that controls if a two- or three dimensional plot is produced.
scatter_kwargs : dict, optional
Optional arguments to be passed to the scatter plot, e.g. point color,
point size, colormap, etc.
show : boolean, optional
If true, calls plt.show()
Returns
-------
fig : pyplot figure object
Figure object where everything is plotted on.
Examples
--------
# Plots the top two diffusion coords, colored by the first coord.
>>> scatter_kwargs = {'s': 2, 'c': mydmap.dmap[:,0], 'cmap': 'viridis'}
>>> embedding_plot(mydmap, scatter_kwargs)
"""
if scatter_kwargs is None:
scatter_kwargs = {}
fig = plt.figure(figsize=(6, 6))
if (dim == 2):
plt.scatter(dmap_instance.dmap[:, 0], dmap_instance.dmap[:, 1], **scatter_kwargs)
plt.title('Embedding given by first two DCs.')
plt.xlabel(r'$\psi_1$')
plt.ylabel(r'$\psi_2$')
elif (dim == 3):
ax = fig.add_subplot(111, projection='3d')
ax.scatter(dmap_instance.dmap[:, 0], dmap_instance.dmap[:, 1], dmap_instance.dmap[:, 2], **scatter_kwargs)
ax.set_title('Embedding given by first three DCs.')
ax.set_xlabel(r'$\psi_1$')
ax.set_ylabel(r'$\psi_2$')
ax.set_zlabel(r'$\psi_3$')
plt.axis('tight')
if show:
plt.show()
return fig
[docs]def data_plot(dmap_instance, n_evec=1, dim=2, scatter_kwargs=None, show=True):
"""
Creates diffusion map embedding scatterplot. By default, the first two diffusion
coordinates are plotted against each other. This only plots against the first two or three
(as controlled by 'dim' parameter) dimensions of the data, however:
effectively this assumes the data is two resp. three dimensional.
Parameters
----------
dmap_instance : DiffusionMap Instance
An instance of the DiffusionMap class.
n_evec: int, optional
The eigenfunction that should be used to color the plot.
dim: int, optional, 2 or 3.
Optional argument that controls if a two- or three dimensional plot is produced.
scatter_kwargs : dict, optional
Optional arguments to be passed to the scatter plot, e.g. point color,
point size, colormap, etc.
show : boolean, optional
If true, calls plt.show()
Returns
-------
fig : pyplot figure object
Figure object where everything is plotted on.
"""
if scatter_kwargs is None:
scatter_kwargs = {}
fig = plt.figure(figsize=(6, 6))
if (dim == 2):
plt.scatter(dmap_instance.data[:, 0], dmap_instance.data[:, 1], c=dmap_instance.dmap[:, n_evec-1], **scatter_kwargs)
plt.title('Data coloured with first DC.')
plt.xlabel('x')
plt.ylabel('y')
elif (dim == 3):
ax = fig.add_subplot(111, projection='3d')
ax.scatter(dmap_instance.data[:, 0], dmap_instance.data[:, 1], dmap_instance.data[:, 2], c=dmap_instance.dmap[:, n_evec-1], **scatter_kwargs)
ax.set_title('Data coloured with first DC.')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
plt.axis('tight')
if show:
plt.show()
return fig