# Source code for ot.plot

"""
Functions for plotting OT matrices

.. warning::
Note that by default the module is not import in :mod:ot. In order to
use it you need to explicitly import :mod:ot.plot

"""

# Author: Remi Flamary <remi.flamary@unice.fr>
#

import numpy as np
import matplotlib.pylab as pl
from matplotlib import gridspec

[docs]
def plot1D_mat(a, b, M, title=''):
r""" Plot matrix :math:\mathbf{M}  with the source and target 1D distribution

Creates a subplot with the source distribution :math:\mathbf{a} on the left and
target distribution :math:\mathbf{b} on the top. The matrix :math:\mathbf{M} is shown in between.

Parameters
----------
a : ndarray, shape (na,)
Source distribution
b : ndarray, shape (nb,)
Target distribution
M : ndarray, shape (na, nb)
Matrix to plot
"""
na, nb = M.shape

gs = gridspec.GridSpec(3, 3)

xa = np.arange(na)
xb = np.arange(nb)

ax1 = pl.subplot(gs[0, 1:])
pl.plot(xb, b, 'r', label='Target distribution')
pl.yticks(())
pl.title(title)

ax2 = pl.subplot(gs[1:, 0])
pl.plot(a, xa, 'b', label='Source distribution')
pl.gca().invert_xaxis()
pl.gca().invert_yaxis()
pl.xticks(())

pl.subplot(gs[1:, 1:], sharex=ax1, sharey=ax2)
pl.imshow(M, interpolation='nearest')
pl.axis('off')

pl.xlim((0, nb))
pl.tight_layout()

[docs]
def plot2D_samples_mat(xs, xt, G, thr=1e-8, **kwargs):
r""" Plot matrix :math:\mathbf{G} in 2D with lines using alpha values

Plot lines between source and target 2D samples with a color
proportional to the value of the matrix :math:\mathbf{G} between samples.

Parameters
----------
xs : ndarray, shape (ns,2)
Source samples positions
b : ndarray, shape (nt,2)
Target samples positions
G : ndarray, shape (na,nb)
OT matrix
thr : float, optional
threshold above which the line is drawn
**kwargs : dict
parameters given to the plot functions (default color is black if
nothing given)
"""

if ('color' not in kwargs) and ('c' not in kwargs):
kwargs['color'] = 'k'
mx = G.max()
if 'alpha' in kwargs:
scale = kwargs['alpha']
del kwargs['alpha']
else:
scale = 1
for i in range(xs.shape[0]):
for j in range(xt.shape[0]):
if G[i, j] / mx > thr:
pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]],
alpha=G[i, j] / mx * scale, **kwargs)