Merge pull request #111 from PyLops/doc-tutorialmdd

tutorials/mdd.py

@@ -0,0 +1,234 @@
+"""
+Multi-Dimensional Deconvolution
+===============================
+This example shows how to set-up and run a Multi-Dimensional Deconvolution
+problem in a distributed fashion, leveraging the :py:class:`pylops_mpi.waveeqprocessing.MDC`
+class.
+
+More precisely, compared to its counterpart in the PyLops documentation, this example distributes
+the frequency slices of the kernel of the MDC operator across multiple processes. Whilst both the
+entire model and data sit on all processes, within the MDC operator, and more precisely when the
+:py:class:`pylops_mpi.signalprocessing.Fredholm1` is called, different groups of frequencies are
+processed by the different ranks.
+
+"""
+
+import numpy as np
+from scipy.signal import filtfilt
+from matplotlib import pyplot as plt
+from mpi4py import MPI
+
+from pylops.utils.seismicevents import hyperbolic2d, makeaxis
+from pylops.utils.tapers import taper3d
+from pylops.utils.wavelets import ricker
+
+import pylops_mpi
+from pylops_mpi.DistributedArray import local_split, Partition
+
+plt.close("all")
+rank = MPI.COMM_WORLD.Get_rank()
+size = MPI.COMM_WORLD.Get_size()
+dtype = np.float32
+cdtype = np.complex64
+
+###############################################################################
+# Let's start by creating a set of hyperbolic events to be used as
+# our MDC kernel as well as the model
+
+# Input parameters
+par = {
+    "ox": -300,
+    "dx": 10,
+    "nx": 61,
+    "oy": -500,
+    "dy": 10,
+    "ny": 101,
+    "ot": 0,
+    "dt": 0.004,
+    "nt": 400,
+    "f0": 20,
+    "nfmax": 200,
+}
+
+t0_m = 0.2
+vrms_m = 1100.0
+amp_m = 1.0
+
+t0_G = (0.2, 0.5, 0.7)
+vrms_G = (1200.0, 1500.0, 2000.0)
+amp_G = (1.0, 0.6, 0.5)
+
+# Taper
+tap = taper3d(par["nt"], (par["ny"], par["nx"]), (5, 5), tapertype="hanning")
+
+# Create axis
+t, t2, x, y = makeaxis(par)
+
+# Create wavelet
+wav = ricker(t[:41], f0=par["f0"])[0]
+
+# Generate model
+mrefl, mwav = hyperbolic2d(x, t, t0_m, vrms_m, amp_m, wav)
+
+# Generate operator
+G, Gwav = np.zeros((par["ny"], par["nx"], par["nt"])), np.zeros(
+    (par["ny"], par["nx"], par["nt"])
+)
+for iy, y0 in enumerate(y):
+    G[iy], Gwav[iy] = hyperbolic2d(x - y0, t, t0_G, vrms_G, amp_G, wav)
+G, Gwav = G * tap, Gwav * tap
+
+# Add negative part to data and model
+mrefl = np.concatenate((np.zeros((par["nx"], par["nt"] - 1)), mrefl), axis=-1)
+mwav = np.concatenate((np.zeros((par["nx"], par["nt"] - 1)), mwav), axis=-1)
+Gwav2 = np.concatenate((np.zeros((par["ny"], par["nx"], par["nt"] - 1)), Gwav), axis=-1)
+
+# Move to frequency
+Gwav_fft = np.fft.rfft(Gwav2, 2 * par["nt"] - 1, axis=-1)
+Gwav_fft = (Gwav_fft[..., : par["nfmax"]])
+
+# Move frequency/time to first axis
+mrefl, mwav = mrefl.T, mwav.T
+Gwav_fft = Gwav_fft.transpose(2, 0, 1)
+
+# Choose how to split frequencies to ranks
+nf = par["nfmax"]
+nf_rank = local_split((nf,), MPI.COMM_WORLD, Partition.SCATTER, 0)
+nf_ranks = np.concatenate(MPI.COMM_WORLD.allgather(nf_rank))
+ifin_rank = np.insert(np.cumsum(nf_ranks)[:-1], 0, 0)[rank]
+ifend_rank = np.cumsum(nf_ranks)[rank]
+
+# Extract batch of frequency slices (in practice, this will be directly read from input file)
+G = Gwav_fft[ifin_rank:ifend_rank].astype(cdtype)
+
+###############################################################################
+# Let's now define the distributed operator and model as well as compute the
+# data
+
+# Define operator
+MDCop = pylops_mpi.waveeqprocessing.MPIMDC((1.0 * par["dt"] * np.sqrt(par["nt"])) * G,
+                                           nt=2 * par["nt"] - 1, nv=1, nfreq=nf,
+                                           dt=par["dt"], dr=1.0, twosided=True,
+                                           fftengine="scipy", prescaled=True)
+
+# Create model
+m = pylops_mpi.DistributedArray(global_shape=(2 * par["nt"] - 1) * par["nx"] * 1,
+                                partition=Partition.BROADCAST,
+                                dtype=dtype)
+m[:] = mrefl.astype(dtype).ravel()
+
+# Create data
+d = MDCop @ m
+dloc = d.asarray().real.reshape(2 * par["nt"] - 1, par["ny"])
+
+###############################################################################
+# Let's display what we have so far: operator, input model, and data
+
+if rank == 0:
+    fig, axs = plt.subplots(1, 2, figsize=(8, 6))
+    axs[0].imshow(
+        Gwav2[int(par["ny"] / 2)].T,
+        aspect="auto",
+        interpolation="nearest",
+        cmap="gray",
+        vmin=-np.abs(Gwav2.max()),
+        vmax=np.abs(Gwav2.max()),
+        extent=(x.min(), x.max(), t2.max(), t2.min()),
+    )
+    axs[0].set_title("G - inline view", fontsize=15)
+    axs[0].set_xlabel(r"$x_R$")
+    axs[1].set_ylabel(r"$t$")
+    axs[1].imshow(
+        Gwav2[:, int(par["nx"] / 2)].T,
+        aspect="auto",
+        interpolation="nearest",
+        cmap="gray",
+        vmin=-np.abs(Gwav2.max()),
+        vmax=np.abs(Gwav2.max()),
+        extent=(y.min(), y.max(), t2.max(), t2.min()),
+    )
+    axs[1].set_title("G - inline view", fontsize=15)
+    axs[1].set_xlabel(r"$x_S$")
+    axs[1].set_ylabel(r"$t$")
+    fig.tight_layout()
+
+    fig, axs = plt.subplots(1, 2, figsize=(8, 6))
+    axs[0].imshow(
+        mwav,
+        aspect="auto",
+        interpolation="nearest",
+        cmap="gray",
+        vmin=-np.abs(mwav.max()),
+        vmax=np.abs(mwav.max()),
+        extent=(x.min(), x.max(), t2.max(), t2.min()),
+    )
+    axs[0].set_title(r"$m$", fontsize=15)
+    axs[0].set_xlabel(r"$x_R$")
+    axs[0].set_ylabel(r"$t$")
+    axs[1].imshow(
+        dloc,
+        aspect="auto",
+        interpolation="nearest",
+        cmap="gray",
+        vmin=-np.abs(dloc.max()),
+        vmax=np.abs(dloc.max()),
+        extent=(x.min(), x.max(), t2.max(), t2.min()),
+    )
+    axs[1].set_title(r"$d$", fontsize=15)
+    axs[1].set_xlabel(r"$x_S$")
+    axs[1].set_ylabel(r"$t$")
+    fig.tight_layout()
+
+###############################################################################
+# We are now ready to compute the adjoint (i.e., cross-correlation) and invert
+# back for our input model
+
+# Adjoint
+madj = MDCop.H @ d
+madjloc = madj.asarray().real.reshape(2 * par["nt"] - 1, par["nx"])
+
+# Inverse
+m0 = pylops_mpi.DistributedArray(global_shape=(2 * par["nt"] - 1) * par["nx"] * 1,
+                                 partition=Partition.BROADCAST,
+                                 dtype=cdtype)
+m0[:] = 0
+minv = pylops_mpi.cgls(MDCop, d, x0=m0, niter=50, show=True if rank == 0 else False)[0]
+minvloc = minv.asarray().real.reshape(2 * par["nt"] - 1, par["nx"])
+
+if rank == 0:
+    fig = plt.figure(figsize=(8, 6))
+    ax1 = plt.subplot2grid((1, 5), (0, 0), colspan=2)
+    ax2 = plt.subplot2grid((1, 5), (0, 2), colspan=2)
+    ax3 = plt.subplot2grid((1, 5), (0, 4))
+    ax1.imshow(
+        madjloc,
+        aspect="auto",
+        interpolation="nearest",
+        cmap="gray",
+        vmin=-np.abs(madjloc.max()),
+        vmax=np.abs(madjloc.max()),
+        extent=(x.min(), x.max(), t2.max(), t2.min()),
+    )
+    ax1.set_title("Adjoint m", fontsize=15)
+    ax1.set_xlabel(r"$x_V$")
+    ax1.set_ylabel(r"$t$")
+    ax2.imshow(
+        minvloc,
+        aspect="auto",
+        interpolation="nearest",
+        cmap="gray",
+        vmin=-np.abs(minvloc.max()),
+        vmax=np.abs(minvloc.max()),
+        extent=(x.min(), x.max(), t2.max(), t2.min()),
+    )
+    ax2.set_title("Inverted m", fontsize=15)
+    ax2.set_xlabel(r"$x_V$")
+    ax2.set_ylabel(r"$t$")
+    ax3.plot(
+        madjloc[:, int(par["nx"] / 2)] / np.abs(madjloc[:, int(par["nx"] / 2)]).max(), t2, "r", lw=5
+    )
+    ax3.plot(
+        minvloc[:, int(par["nx"] / 2)] / np.abs(minvloc[:, int(par["nx"] / 2)]).max(), t2, "k", lw=3
+    )
+    ax3.set_ylim([t2[-1], t2[0]])
+    fig.tight_layout()