"""
Operators concatenation
=======================

This example shows how to use 'stacking' operators such as
:py:class:`pylops.VStack`, :py:class:`pylops.HStack`,
:py:class:`pylops.Block`, :py:class:`pylops.BlockDiag`,
and :py:class:`pylops.Kronecker`.

These operators allow for different combinations of multiple linear operators
in a single operator. Such functionalities are used within PyLops as the basis
for the creation of complex operators as well as in the definition of various
types of optimization problems with regularization or preconditioning.

Some of this operators naturally lend to embarassingly parallel computations.
Within PyLops we leverage the multiprocessing module to run multiple processes
at the same time evaluating a subset of the operators involved in one of the
stacking operations.
"""
import matplotlib.pyplot as plt
import numpy as np

import pylops

plt.close("all")

###############################################################################
# Let's start by defining two second derivatives :py:class:`pylops.SecondDerivative`
# that we will be using in this example.
D2hop = pylops.SecondDerivative(dims=(11, 21), axis=1, dtype="float32")
D2vop = pylops.SecondDerivative(dims=(11, 21), axis=0, dtype="float32")

###############################################################################
# Chaining of operators represents the simplest concatenation that
# can be performed between two or more linear operators.
# This can be easily achieved using the ``*`` symbol
#
#    .. math::
#       \mathbf{D_{cat}}=  \mathbf{D_v} \mathbf{D_h}
Nv, Nh = 11, 21
X = np.zeros((Nv, Nh))
X[int(Nv / 2), int(Nh / 2)] = 1

D2op = D2vop * D2hop
Y = D2op * X

fig, axs = plt.subplots(1, 2, figsize=(10, 3))
fig.suptitle("Chain", fontsize=14, fontweight="bold", y=0.95)
im = axs[0].imshow(X, interpolation="nearest")
axs[0].axis("tight")
axs[0].set_title(r"$x$")
plt.colorbar(im, ax=axs[0])
im = axs[1].imshow(Y, interpolation="nearest")
axs[1].axis("tight")
axs[1].set_title(r"$y=(D_x+D_y) x$")
plt.colorbar(im, ax=axs[1])
plt.tight_layout()
plt.subplots_adjust(top=0.8)

###############################################################################
# We now want to *vertically stack* three operators
#
#    .. math::
#       \mathbf{D_{Vstack}} =
#        \begin{bmatrix}
#          \mathbf{D_v}    \\
#          \mathbf{D_h}
#        \end{bmatrix}, \qquad
#       \mathbf{y} =
#        \begin{bmatrix}
#          \mathbf{D_v}\mathbf{x}    \\
#          \mathbf{D_h}\mathbf{x}
#        \end{bmatrix}
Nv, Nh = 11, 21
X = np.zeros((Nv, Nh))
X[int(Nv / 2), int(Nh / 2)] = 1
Dstack = pylops.VStack([D2vop, D2hop])

Y = np.reshape(Dstack * X.ravel(), (Nv * 2, Nh))

fig, axs = plt.subplots(1, 2, figsize=(10, 3))
fig.suptitle("Vertical stacking", fontsize=14, fontweight="bold", y=0.95)
im = axs[0].imshow(X, interpolation="nearest")
axs[0].axis("tight")
axs[0].set_title(r"$x$")
plt.colorbar(im, ax=axs[0])
im = axs[1].imshow(Y, interpolation="nearest")
axs[1].axis("tight")
axs[1].set_title(r"$y$")
plt.colorbar(im, ax=axs[1])
plt.tight_layout()
plt.subplots_adjust(top=0.8)

###############################################################################
# Similarly we can now *horizontally stack* three operators
#
#    .. math::
#       \mathbf{D_{Hstack}} =
#        \begin{bmatrix}
#           \mathbf{D_v}  & 0.5*\mathbf{D_v} & -1*\mathbf{D_h}
#        \end{bmatrix}, \qquad
#       \mathbf{y} =
#        \mathbf{D_v}\mathbf{x}_1 + 0.5*\mathbf{D_v}\mathbf{x}_2 -
#        \mathbf{D_h}\mathbf{x}_3
Nv, Nh = 11, 21
X = np.zeros((Nv * 3, Nh))
X[int(Nv / 2), int(Nh / 2)] = 1
X[int(Nv / 2) + Nv, int(Nh / 2)] = 1
X[int(Nv / 2) + 2 * Nv, int(Nh / 2)] = 1

Hstackop = pylops.HStack([D2vop, 0.5 * D2vop, -1 * D2hop])
Y = np.reshape(Hstackop * X.ravel(), (Nv, Nh))

fig, axs = plt.subplots(1, 2, figsize=(10, 3))
fig.suptitle("Horizontal stacking", fontsize=14, fontweight="bold", y=0.95)
im = axs[0].imshow(X, interpolation="nearest")
axs[0].axis("tight")
axs[0].set_title(r"$x$")
plt.colorbar(im, ax=axs[0])
im = axs[1].imshow(Y, interpolation="nearest")
axs[1].axis("tight")
axs[1].set_title(r"$y$")
plt.colorbar(im, ax=axs[1])
plt.tight_layout()
plt.subplots_adjust(top=0.8)

###############################################################################
# We can even stack them both *horizontally* and *vertically* such that we
# create a *block* operator
#
#    .. math::
#       \mathbf{D_{Block}} =
#        \begin{bmatrix}
#           \mathbf{D_v} & 0.5*\mathbf{D_v} & -1*\mathbf{D_h} \\
#           \mathbf{D_h} & 2*\mathbf{D_h}   & \mathbf{D_v} \\
#        \end{bmatrix}, \qquad
#       \mathbf{y} =
#        \begin{bmatrix}
#           \mathbf{D_v} \mathbf{x_1} + 0.5*\mathbf{D_v} \mathbf{x_2} -
#           \mathbf{D_h} \mathbf{x_3} \\
#           \mathbf{D_h} \mathbf{x_1} + 2*\mathbf{D_h} \mathbf{x_2} +
#           \mathbf{D_v} \mathbf{x_3}
#        \end{bmatrix}
Bop = pylops.Block([[D2vop, 0.5 * D2vop, -1 * D2hop], [D2hop, 2 * D2hop, D2vop]])
Y = np.reshape(Bop * X.ravel(), (2 * Nv, Nh))

fig, axs = plt.subplots(1, 2, figsize=(10, 3))
fig.suptitle("Block", fontsize=14, fontweight="bold", y=0.95)
im = axs[0].imshow(X, interpolation="nearest")
axs[0].axis("tight")
axs[0].set_title(r"$x$")
plt.colorbar(im, ax=axs[0])
im = axs[1].imshow(Y, interpolation="nearest")
axs[1].axis("tight")
axs[1].set_title(r"$y$")
plt.colorbar(im, ax=axs[1])
plt.tight_layout()
plt.subplots_adjust(top=0.8)

###############################################################################
# Finally we can use the *block-diagonal operator* to apply three operators
# to three different subset of the model and data
#
#    .. math::
#       \mathbf{D_{BDiag}} =
#        \begin{bmatrix}
#           \mathbf{D_v}  & \mathbf{0}       &  \mathbf{0}  \\
#           \mathbf{0}    & 0.5*\mathbf{D_v} &  \mathbf{0}  \\
#           \mathbf{0}    & \mathbf{0}       &  -\mathbf{D_h}
#        \end{bmatrix}, \qquad
#       \mathbf{y} =
#        \begin{bmatrix}
#           \mathbf{D_v}     \mathbf{x_1}  \\
#           0.5*\mathbf{D_v} \mathbf{x_2}  \\
#           -\mathbf{D_h}  \mathbf{x_3}
#        \end{bmatrix}
BD = pylops.BlockDiag([D2vop, 0.5 * D2vop, -1 * D2hop])
Y = np.reshape(BD * X.ravel(), (3 * Nv, Nh))

fig, axs = plt.subplots(1, 2, figsize=(10, 3))
fig.suptitle("Block-diagonal", fontsize=14, fontweight="bold", y=0.95)
im = axs[0].imshow(X, interpolation="nearest")
axs[0].axis("tight")
axs[0].set_title(r"$x$")
plt.colorbar(im, ax=axs[0])
im = axs[1].imshow(Y, interpolation="nearest")
axs[1].axis("tight")
axs[1].set_title(r"$y$")
plt.colorbar(im, ax=axs[1])
plt.tight_layout()
plt.subplots_adjust(top=0.8)

###############################################################################
# If we consider now the case of having a large number of operators inside a
# blockdiagonal structure, it may be convenient to span multiple processes
# handling subset of operators at the same time. This can be easily achieved
# by simply defining the number of processes we want to use via ``nproc``.
X = np.zeros((Nv * 10, Nh))
for iv in range(10):
    X[int(Nv / 2) + iv * Nv, int(Nh / 2)] = 1

BD = pylops.BlockDiag([D2vop] * 10, nproc=2)
print("BD Operator multiprocessing pool", BD.pool)
Y = np.reshape(BD * X.ravel(), (10 * Nv, Nh))
BD.pool.close()

fig, axs = plt.subplots(1, 2, figsize=(10, 3))
fig.suptitle("Block-diagonal", fontsize=14, fontweight="bold", y=0.95)
im = axs[0].imshow(X, interpolation="nearest")
axs[0].axis("tight")
axs[0].set_title(r"$x$")
plt.colorbar(im, ax=axs[0])
im = axs[1].imshow(Y, interpolation="nearest")
axs[1].axis("tight")
axs[1].set_title(r"$y$")
plt.colorbar(im, ax=axs[1])
plt.tight_layout()
plt.subplots_adjust(top=0.8)

###############################################################################
# Finally we use the *Kronecker operator* and replicate this example on
# `wiki <https://en.wikipedia.org/wiki/Kronecker_product>`_.
#
#    .. math::
#       \begin{bmatrix}
#           1  & 2  \\
#           3  & 4 \\
#       \end{bmatrix} \otimes
#       \begin{bmatrix}
#           0  & 5  \\
#           6  & 7 \\
#       \end{bmatrix} =
#       \begin{bmatrix}
#            0 &  5 &  0 & 10 \\
#            6 &  7 & 12 & 14 \\
#            0 & 15 &  0 & 20 \\
#           18 & 21 & 24 & 28 \\
#       \end{bmatrix}
A = np.array([[1, 2], [3, 4]])
B = np.array([[0, 5], [6, 7]])
AB = np.kron(A, B)

n1, m1 = A.shape
n2, m2 = B.shape

Aop = pylops.MatrixMult(A)
Bop = pylops.MatrixMult(B)

ABop = pylops.Kronecker(Aop, Bop)
x = np.ones(m1 * m2)

y = AB.dot(x)
yop = ABop * x
xinv = ABop / yop

print(f"AB = \n {AB}")

print(f"x = {x}")
print(f"y = {y}")
print(f"yop = {yop}")
print(f"xinv = {xinv}")

###############################################################################
# We can also use :py:class:`pylops.Kronecker` to do something more
# interesting. Any operator can in fact be applied on a single direction of a
# multi-dimensional input array if combined with an :py:class:`pylops.Identity`
# operator via Kronecker product. We apply here the
# :py:class:`pylops.FirstDerivative` to the second dimension of the model.
#
# Note that for those operators whose implementation allows their application
# to a single axis via the ``axis`` parameter, using the Kronecker product
# would lead to slower performance. Nevertheless, the Kronecker product allows
# any other operator to be applied to a single dimension.
Nv, Nh = 11, 21

Iop = pylops.Identity(Nv, dtype="float32")
D2hop = pylops.FirstDerivative(Nh, dtype="float32")

X = np.zeros((Nv, Nh))
X[Nv // 2, Nh // 2] = 1
D2hop = pylops.Kronecker(Iop, D2hop)

Y = D2hop * X.ravel()
Y = Y.reshape(Nv, Nh)

fig, axs = plt.subplots(1, 2, figsize=(10, 3))
fig.suptitle("Kronecker", fontsize=14, fontweight="bold", y=0.95)
im = axs[0].imshow(X, interpolation="nearest")
axs[0].axis("tight")
axs[0].set_title(r"$x$")
plt.colorbar(im, ax=axs[0])
im = axs[1].imshow(Y, interpolation="nearest")
axs[1].axis("tight")
axs[1].set_title(r"$y$")
plt.colorbar(im, ax=axs[1])
plt.tight_layout()
plt.subplots_adjust(top=0.8)
