# %%
# -*- coding: utf-8 -*-
"""
======================================
Optimal Transport for 1D distributions
======================================

This example illustrates the computation of EMD and Sinkhorn transport plans
and their visualization.

"""

# Author: Remi Flamary <remi.flamary@unice.fr>
#
# License: MIT License
# sphinx_gallery_thumbnail_number = 3

import numpy as np
import matplotlib.pylab as pl
import ot
import ot.plot
from ot.datasets import make_1D_gauss as gauss

##############################################################################
# Generate data
# -------------


# %% parameters

n = 100  # nb bins

# bin positions
x = np.arange(n, dtype=np.float64)

# Gaussian distributions
a = gauss(n, m=20, s=5)  # m= mean, s= std
b = gauss(n, m=60, s=10)

# loss matrix
M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))
M /= M.max()


##############################################################################
# Plot distributions and loss matrix
# ----------------------------------

# %% plot the distributions

pl.figure(1, figsize=(6.4, 3))
pl.plot(x, a, "b", label="Source distribution")
pl.plot(x, b, "r", label="Target distribution")
pl.legend()

# %% plot distributions and loss matrix

pl.figure(2, figsize=(5, 5))
ot.plot.plot1D_mat(a, b, M, "Cost matrix M")

##############################################################################
# Solve EMD
# ---------


# %% EMD

# use fast 1D solver
G0 = ot.emd_1d(x, x, a, b)

# Equivalent to
# G0 = ot.emd(a, b, M)

pl.figure(3, figsize=(5, 5))
ot.plot.plot1D_mat(a, b, G0, "OT matrix G0")

##############################################################################
# Solve Sinkhorn
# --------------


# %% Sinkhorn

lambd = 1e-3
Gs = ot.sinkhorn(a, b, M, lambd, verbose=True)

pl.figure(4, figsize=(5, 5))
ot.plot.plot1D_mat(a, b, Gs, "OT matrix Sinkhorn")

pl.show()