examples: Update reduced tti

devito/ir/xdsl_iet/cluster_to_ssa.py

@@ -189,7 +189,6 @@ def _visit_math_nodes(self, dim: SteppingDimension, node: Expr,
         if isinstance(node, Indexed):
             # If we have a time function, we compute its time offset
             if isinstance(node.function, TimeFunction):
-                import pdb;pdb.set_trace()
                 time_offset = (node.indices[dim] - dim) % node.function.time_size
             elif isinstance(node.function, (Function, PointSource)):
                 # import pdb;pdb.set_trace()

tests/test_xdsl_passes.py

@@ -211,69 +211,3 @@ def test_unary(shape, steps):
     orig_data = u.data_with_halo.copy()
 
     assert np.isclose(xdsl_data, orig_data, rtol=1e-06).all()
-
-
-def test_xdsl_sine():
-    import numpy as np
-    from devito import (Function, TimeFunction, cos, sin, solve,
-                        Eq, Operator)
-    from examples.seismic import TimeAxis, RickerSource, Receiver, demo_model
-    from matplotlib import pyplot as plt
-
-    # We will start with the definitions of the grid and the physical parameters $v_{p}, \theta, \epsilon, \delta$. For simplicity, we won't use any absorbing boundary conditions to avoid reflections of outgoing waves at the boundaries of the computational domain, but we will have boundary conditions (zero Dirichlet) at $x=0,nx$ and $z=0,nz$ for the solution of the Poisson equation. We use a homogeneous model. The model is discretized with a grid of $101 \times 101$ and spacing of 10 m. The $v_{p}, \epsilon, \delta$ and $\theta$ parameters of this model are 3600 m∕s, 0.23, 0.17, and 45°, respectively. 
-
-    # NBVAL_IGNORE_OUTPUT   
-
-    shape = (101, 101)  # 101x101 grid
-    spacing = (10., 10.)  # spacing of 10 meters
-    nbl = 0  # number of pad points
-
-    model = demo_model('layers-tti', spacing=spacing, space_order=8,
-                       shape=shape, nbl=nbl, nlayers=1)
-
-    # initialize Thomsem parameters to those used in Mu et al., (2020)
-    model.update('vp', np.ones(shape)*3.6) # km/s
-    model.update('epsilon', np.ones(shape)*0.23)
-    model.update('delta', np.ones(shape)*0.17)
-    model.update('theta', np.ones(shape)*(45.*(np.pi/180.)))  # radians
-
-    # In cell below, symbols used in the PDE definition are obtained from the `model` object. Note that trigonometric functions proper of Devito are exploited.
-
-    # %%
-    # Get symbols from model
-    theta = model.theta
-    delta = model.delta
-    epsilon = model.epsilon
-    m = model.m
-
-    # Use trigonometric functions from Devito
-    costheta = cos(theta)
-
-    # Values used to compute the time sampling
-    epsilonmax = np.max(np.abs(epsilon.data[:]))
-    deltamax = np.max(np.abs(delta.data[:]))
-    etamax = max(epsilonmax, deltamax)
-    vmax = model._max_vp
-    max_cos_sin = np.amax(np.abs(np.cos(theta.data[:]) - np.sin(theta.data[:])))
-    dvalue = min(spacing)
-
-    # Compute the dt and set time range
-    t0 = 0.  # Simulation time start
-    tn = 150.  # Simulation time end (0.15 second = 150 msec)
-    dt = (dvalue/(np.pi*vmax))*np.sqrt(1/(1+etamax*(max_cos_sin)**2))  # eq. above (cell 3)
-    time_range = TimeAxis(start=t0, stop=tn, step=dt)
-    print("time_range; ", time_range)
-
-    # time stepping 
-    p = TimeFunction(name="p", grid=model.grid, time_order=2, space_order=2)
-    q = Function(name="q", grid=model.grid, space_order=8)
-
-    # Main equations
-    term1_p = (1 + 2*delta*(costheta**2) + 2*epsilon*costheta**4)*q.dx4
-
-    # Create stencil and boundary condition expressions
-    x, z = model.grid.dimensions
-
-    optime = Operator([term1_p], opt='xdsl')
-    optime.apply(time_M=100, dt=dt)
-    import pdb;pdb.set_trace()