Merge pull request #86 from PyLops/feat-stacked

Feature: stacked

docs/source/api/index.rst

@@ -17,6 +17,7 @@ DistributedArray
 
     Partition
     DistributedArray
+    StackedDistributedArray
 
 Linear operators
 ----------------
@@ -31,6 +32,7 @@ Templates
 
     MPILinearOperator
     asmpilinearoperator
+    MPIStackedLinearOperator
 
 Basic Operators
 ~~~~~~~~~~~~~~~
@@ -41,7 +43,9 @@ Basic Operators
    :toctree: generated/
 
     MPIBlockDiag
+    MPIStackedBlockDiag
     MPIVStack
+    MPIStackedVStack
     MPIHStack
 
 Derivatives

examples/plot_distributed_array.py

@@ -71,12 +71,25 @@
 pylops_mpi.plot_local_arrays(arr1, "Distributed Array - 1", vmin=0, vmax=1)
 pylops_mpi.plot_local_arrays(arr2, "Distributed Array - 2", vmin=0, vmax=1)
 
+###############################################################################
+# **Scaling** - Each process operates on its local portion of
+# the array and scales the corresponding elements by a given scalar.
+scale_arr = .5 * arr1
+pylops_mpi.plot_local_arrays(scale_arr, "Scaling", vmin=0, vmax=1)
+
 ###############################################################################
 # **Element-wise Addition** - Each process operates on its local portion of
 # the array and adds the corresponding elements together.
 sum_arr = arr1 + arr2
 pylops_mpi.plot_local_arrays(sum_arr, "Addition", vmin=0, vmax=1)
 
+###############################################################################
+# **Element-wise In-place Addition** - Similar to the previous one but the
+# addition is performed directly on one of the addends without creating a new
+# distributed array.
+sum_arr += arr2
+pylops_mpi.plot_local_arrays(sum_arr, "Addition", vmin=0, vmax=1)
+
 ###############################################################################
 # **Element-wise Subtraction** - Each process operates on its local portion
 # of the array and subtracts the corresponding elements together.

examples/plot_stacked_array.py

@@ -0,0 +1,155 @@
+"""
+Stacked Array
+=========================
+This example shows how to use the :py:class:`pylops_mpi.StackedDistributedArray`.
+This class provides a way to combine and act on multiple :py:class:`pylops_mpi.DistributedArray`
+within the same program. This is very useful in scenarios where an array can be logically
+divided in subarrays and each of them lends naturally to distribution across multiple processes in
+a parallel computing environment.
+"""
+
+from matplotlib import pyplot as plt
+import numpy as np
+from mpi4py import MPI
+
+import pylops
+import pylops_mpi
+
+plt.close("all")
+np.random.seed(42)
+rank = MPI.COMM_WORLD.Get_rank()
+size = MPI.COMM_WORLD.Get_size()
+
+###############################################################################
+# Let's start by defining two distributed array
+subarr1 = pylops_mpi.DistributedArray(global_shape=size * 10,
+                                      partition=pylops_mpi.Partition.SCATTER,
+                                      axis=0)
+subarr2 = pylops_mpi.DistributedArray(global_shape=size * 4,
+                                      partition=pylops_mpi.Partition.SCATTER,
+                                      axis=0)
+# Filling the local arrays
+subarr1[:], subarr2[:] = 1, 2
+
+###############################################################################
+# We combine them into a single
+# :py:class:`pylops_mpi.StackedDistributedArray` object.
+arr1 = pylops_mpi.StackedDistributedArray([subarr1, subarr2])
+if rank == 0:
+    print('Stacked array:', arr1)
+
+# Extract and print full array
+full_arr1 = arr1.asarray()
+if rank == 0:
+    print('Full array:', full_arr1)
+
+# Modify the part of the first array in rank0
+if rank == 0:
+    arr1[0][:] = 10
+full_arr1 = arr1.asarray()
+if rank == 0:
+    print('Modified full array:', full_arr1)
+
+###############################################################################
+# Let's now create a second :py:class:`pylops_mpi.StackedDistributedArray` object
+# and perform different mathematical operations on those two objects.
+subarr1_ = pylops_mpi.DistributedArray(global_shape=size * 10,
+                                       partition=pylops_mpi.Partition.SCATTER,
+                                       axis=0)
+subarr2_ = pylops_mpi.DistributedArray(global_shape=size * 4,
+                                       partition=pylops_mpi.Partition.SCATTER,
+                                       axis=0)
+# Filling the local arrays
+subarr1_[:], subarr2_[:] = 5, 6
+arr2 = pylops_mpi.StackedDistributedArray([subarr1_, subarr2_])
+if rank == 0:
+    print('Stacked array 2:', arr2)
+
+full_arr2 = arr2.asarray()
+if rank == 0:
+    print('Full array2:', full_arr2)
+
+###############################################################################
+# **Negation**
+neg_arr = -arr1
+full_neg_arr = neg_arr.asarray()
+if rank == 0:
+    print('Negated full array:', full_neg_arr)
+
+###############################################################################
+# **Element-wise Addition**
+sum_arr = arr1 + arr2
+full_sum_arr = sum_arr.asarray()
+if rank == 0:
+    print('Summed full array:', full_sum_arr)
+
+###############################################################################
+# **Element-wise Subtraction**
+sub_arr = arr1 - arr2
+full_sub_arr = sub_arr.asarray()
+if rank == 0:
+    print('Subtracted full array:', full_sub_arr)
+
+###############################################################################
+# **Multiplication**
+mult_arr = arr1 * arr2
+full_mult_arr = mult_arr.asarray()
+if rank == 0:
+    print('Multipled full array:', full_mult_arr)
+
+###############################################################################
+# **Dot-product**
+dot_arr = arr1.dot(arr2)
+if rank == 0:
+    print('Dot-product:', dot_arr)
+    print('Dot-product (np):', np.dot(full_arr1, full_arr2))
+
+###############################################################################
+# **Norms**
+l0norm = arr1.norm(0)
+l1norm = arr1.norm(1)
+l2norm = arr1.norm(2)
+linfnorm = arr1.norm(np.inf)
+
+if rank == 0:
+    print('L0 norm', l0norm, np.linalg.norm(full_arr1, 0))
+    print('L1 norm', l1norm, np.linalg.norm(full_arr1, 1))
+    print('L2 norm', l2norm, np.linalg.norm(full_arr1, 2))
+    print('Linf norm', linfnorm, np.linalg.norm(full_arr1, np.inf))
+
+###############################################################################
+# Now that we have a way to stack multiple :py:class:`pylops_mpi.StackedDistributedArray` objects,
+# let's see how we can apply operators to them. More specifically this can be
+# done using the :py:class:`pylops_mpi.MPIStackedVStack` operator that takes multiple
+# :py:class:`pylops_mpi.MPILinearOperator` objects, each acting on one specific
+# distributed array
+x = pylops_mpi.DistributedArray(global_shape=size * 10,
+                                partition=pylops_mpi.Partition.SCATTER,
+                                axis=0)
+# Filling the local arrays
+x[:] = 1.
+
+# Make stacked operator
+mop1 = pylops_mpi.MPIBlockDiag([pylops.MatrixMult(np.ones((5, 10))), ])
+mop2 = pylops_mpi.MPIBlockDiag([pylops.MatrixMult(2 * np.ones((8, 10))), ])
+mop = pylops_mpi.MPIStackedVStack([mop1, mop2])
+
+y = mop.matvec(x)
+y_arr = y.asarray()
+xadj = mop.rmatvec(y)
+xadj_arr = xadj.asarray()
+
+if rank == 0:
+    print('StackedVStack y', y, y_arr, y_arr.shape)
+    print('StackedVStack xadj', xadj, xadj_arr, xadj_arr.shape)
+
+###############################################################################
+# Finally, let's solve now an inverse problem using stacked arrays instead
+# of distributed arrays
+x0 = x.copy()
+x0[:] = 0.
+xinv = pylops_mpi.cgls(mop, y, x0=x0, niter=15, tol=1e-10, show=False)[0]
+xinv_array = xinv.asarray()
+
+if rank == 0:
+    print('xinv_array', xinv_array)