From 6e64c163882134b1f302efc03ed522e30cdaaa3b Mon Sep 17 00:00:00 2001
From: Gerard Gorman <gerard@devitocodes.com>
Date: Tue, 2 Apr 2024 10:39:44 +0100
Subject: [PATCH 01/29] arch: Use get_nvidia_cc to get Nvidia gpu architecture
 rather than using 'native'. This fixes compilation errors on the Nvidia
 Jetson.

---
 devito/arch/compiler.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/devito/arch/compiler.py b/devito/arch/compiler.py
index 8151a9f68f..c51bd90daf 100644
--- a/devito/arch/compiler.py
+++ b/devito/arch/compiler.py
@@ -668,7 +668,11 @@ def __init_finalize__(self, **kwargs):
                     proc_link_flags.append(i)
             self.ldflags.extend(proc_link_flags)
 
-        self.cflags.append('-arch=native')
+        cc = get_nvidia_cc()
+        if cc:
+            self.cflags.append(f'-arch=sm_{cc}')
+        else:
+            self.cflags.append('-arch=native')
 
         # Disable `warning #1650-D: result of call is not used`
         # See `https://gist.github.com/gavinb/f2320f9eaa0e0a7efca6877a34047a9d` about

From cf4b65ee84721c41540752a03707b31c44d0a297 Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Thu, 28 Mar 2024 14:23:58 -0400
Subject: [PATCH 02/29] api: support sinc interpolation

---
 devito/operations/interpolators.py            | 65 ++++++++++++++++++-
 devito/types/sparse.py                        | 24 +++++--
 examples/seismic/acoustic/acoustic_example.py |  2 +-
 examples/seismic/source.py                    |  2 +
 tests/test_interpolation.py                   |  6 +-
 5 files changed, 91 insertions(+), 8 deletions(-)

diff --git a/devito/operations/interpolators.py b/devito/operations/interpolators.py
index 98e0105e8a..ced9fda21a 100644
--- a/devito/operations/interpolators.py
+++ b/devito/operations/interpolators.py
@@ -5,11 +5,15 @@
 from cached_property import cached_property
 
 from devito.finite_differences.differentiable import Mul
+<<<<<<< HEAD
 from devito.finite_differences.elementary import floor
+=======
+from devito.finite_differences.elementary import floor, sqrt
+>>>>>>> 5815a494d (api: support sinc interpolation)
 from devito.symbolics import retrieve_function_carriers, retrieve_functions, INT
 from devito.tools import as_tuple, flatten, filter_ordered
 from devito.types import (ConditionalDimension, Eq, Inc, Evaluable, Symbol,
-                          CustomDimension)
+                          CustomDimension, SubFunction)
 from devito.types.utils import DimensionTuple
 
 __all__ = ['LinearInterpolator', 'PrecomputedInterpolator']
@@ -133,6 +137,9 @@ def inject(self, *args, **kwargs):
     def interpolate(self, *args, **kwargs):
         pass
 
+    def _arg_defaults(self, **args):
+        return {}
+
 
 class WeightedInterpolator(GenericInterpolator):
 
@@ -421,3 +428,59 @@ def _weights(self):
                    for (ri, rd) in enumerate(self._rdim)]
         return Mul(*[self.interpolation_coeffs.subs(mapper)
                      for mapper in mappers])
+
+
+class SincInterpolator(PrecomputedInterpolator):
+    """
+    Hicks windowed sinc interpolation scheme.
+
+    Arbitrary source and receiver positioning in finite‐difference schemes
+    using Kaiser windowed sinc functions
+
+    https://library.seg.org/doi/10.1190/1.1451454
+
+    """
+
+    # Table 1
+    _b_table = {1: 0.0, 2: 1.84, 3: 3.04,
+                4: 4.14, 5: 5.26, 6: 6.40,
+                7: 7.51, 8: 8.56, 9: 9.56, 10: 10.64}
+
+    @property
+    def interpolation_coeffs(self):
+        coeffs = {}
+        shape = (self.sfunction.npoint, 2 * self.r)
+        for r in self._rdim:
+            dimensions = (self.sfunction._sparse_dim, r.parent)
+            sf = SubFunction(name="wsinc%s" % r.name, dtype=self.sfunction.dtype,
+                             shape=shape, dimensions=dimensions,
+                             space_order=0, alias=self.sfunction.alias,
+                             distributor=self.sfunction._distributor,
+                             parent=self.sfunction)
+            coeffs[r] = sf
+        return coeffs
+
+    @property
+    def _weights(self):
+        return Mul(*[w._subs(rd, rd-rd.parent.symbolic_min)
+                     for (rd, w) in self.interpolation_coeffs.items()])
+
+    def _arg_defaults(self, coords=None, sfunc=None):
+        args = {}
+        b = self._b_table[self.r]
+        b0 = np.i0(b)
+        if coords is None or sfunc is None:
+            raise ValueError("No coordinates or sparse function provided")
+        # Coords to indices
+        coords = (coords - np.array(sfunc.grid.origin)) / np.array(sfunc.grid.spacing)
+        coords = np.floor(coords) - coords + 1 - self.r
+        # Precompute sinc
+        for j, r in enumerate(self._rdim):
+            data = np.zeros((coords.shape[0], 2*self.r), dtype=sfunc.dtype)
+            for ri in range(2*self.r):
+                rpos = ri + coords[:, j]
+                num = np.i0(b*sqrt(1 - (rpos/self.r)**2))
+                data[:, ri] = num / b0 * np.sinc(rpos)
+            args[self.interpolation_coeffs[r].name] = data
+
+        return args
diff --git a/devito/types/sparse.py b/devito/types/sparse.py
index 6829698582..602c137c7f 100644
--- a/devito/types/sparse.py
+++ b/devito/types/sparse.py
@@ -12,7 +12,8 @@
 
 from devito.finite_differences import generate_fd_shortcuts
 from devito.mpi import MPI, SparseDistributor
-from devito.operations import LinearInterpolator, PrecomputedInterpolator
+from devito.operations import (LinearInterpolator, PrecomputedInterpolator,
+                               SincInterpolator)
 from devito.symbolics import indexify, retrieve_function_carriers
 from devito.tools import (ReducerMap, as_tuple, flatten, prod, filter_ordered,
                           is_integer, dtype_to_mpidtype)
@@ -29,6 +30,10 @@
            'PrecomputedSparseTimeFunction', 'MatrixSparseTimeFunction']
 
 
+_interpolators = {'linear': LinearInterpolator, 'sinc': SincInterpolator}
+_default_radius = {'linear': 1, 'sinc': 2}
+
+
 class AbstractSparseFunction(DiscreteFunction):
 
     """
@@ -799,16 +804,18 @@ class SparseFunction(AbstractSparseFunction):
 
     is_SparseFunction = True
 
-    _radius = 1
     """The radius of the stencil operators provided by the SparseFunction."""
 
     _sub_functions = ('coordinates',)
 
-    __rkwargs__ = AbstractSparseFunction.__rkwargs__ + ('coordinates',)
+    __rkwargs__ = AbstractSparseFunction.__rkwargs__ + ('coordinates', 'interpolator')
 
     def __init_finalize__(self, *args, **kwargs):
         super().__init_finalize__(*args, **kwargs)
-        self.interpolator = LinearInterpolator(self)
+
+        interp = kwargs.get('interpolator', 'linear')
+        self.interpolator = _interpolators[interp](self)
+        self._radius = kwargs.get('r', _default_radius[interp])
 
         # Set up sparse point coordinates
         coordinates = kwargs.get('coordinates', kwargs.get('coordinates_data'))
@@ -827,6 +834,15 @@ def _decomposition(self):
         mapper = {self._sparse_dim: self._distributor.decomposition[self._sparse_dim]}
         return tuple(mapper.get(d) for d in self.dimensions)
 
+    def _arg_defaults(self, alias=None):
+        defaults = super()._arg_defaults(alias=alias)
+
+        key = alias or self
+        coords = 
+        defaults.update(key.interpolator._arg_defaults(coords=coords,
+                                                       sfunc=key))
+        return defaults
+
 
 class SparseTimeFunction(AbstractSparseTimeFunction, SparseFunction):
     """
diff --git a/examples/seismic/acoustic/acoustic_example.py b/examples/seismic/acoustic/acoustic_example.py
index 7e922cbb6e..f2376105e0 100644
--- a/examples/seismic/acoustic/acoustic_example.py
+++ b/examples/seismic/acoustic/acoustic_example.py
@@ -4,7 +4,7 @@
 from devito.logger import info
 from devito import Constant, Function, smooth, norm
 from examples.seismic.acoustic import AcousticWaveSolver
-from examples.seismic import demo_model, setup_geometry, seismic_args
+from examples.seismic import demo_model, setup_geometry, seismic_args, Receiver
 
 
 def acoustic_setup(shape=(50, 50, 50), spacing=(15.0, 15.0, 15.0),
diff --git a/examples/seismic/source.py b/examples/seismic/source.py
index a736c5bb4f..d0cdef2613 100644
--- a/examples/seismic/source.py
+++ b/examples/seismic/source.py
@@ -117,6 +117,8 @@ def __args_setup__(cls, *args, **kwargs):
                 raise TypeError("Need either `npoint` or `coordinates`")
             kwargs['npoint'] = coordinates.shape[0]
 
+        kwargs.setdefault('r', 1)
+        kwargs.setdefault('interpolator', 'linear')
         return args, kwargs
 
     def __init_finalize__(self, *args, **kwargs):
diff --git a/tests/test_interpolation.py b/tests/test_interpolation.py
index 506a3b88de..ae013572c8 100644
--- a/tests/test_interpolation.py
+++ b/tests/test_interpolation.py
@@ -752,12 +752,14 @@ def test_inject_function():
             assert u.data[1, i, j] == 0
 
 
-def test_interpolation_radius():
+@pytest.mark.parametrize('r, interp', [(2, 'linear'), (4, 'cubic')])
+def test_interpolation_radius(rm interp):
     nt = 11
 
     grid = Grid(shape=(5, 5))
     u = TimeFunction(name="u", grid=grid, space_order=0)
-    src = SparseTimeFunction(name="src", grid=grid, nt=nt, npoint=1)
+    src = SparseTimeFunction(name="src", grid=grid, nt=nt, npoint=1,
+                             r=r, interpolator=interp)
     try:
         src.interpolate(u)
         assert False

From 83bbef339ec0cb343bfd4cadabc49e32c38bab45 Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Mon, 1 Apr 2024 12:56:00 -0400
Subject: [PATCH 03/29] api: numpify sinc precompute and add test

---
 devito/operations/interpolators.py            | 33 +++++---
 devito/types/sparse.py                        | 11 ++-
 examples/seismic/acoustic/acoustic_example.py | 21 +++--
 examples/seismic/acoustic/operators.py        | 24 ++----
 examples/seismic/source.py                    |  2 -
 examples/seismic/utils.py                     | 31 ++++++--
 tests/test_interpolation.py                   | 79 ++++++++++++++++++-
 7 files changed, 150 insertions(+), 51 deletions(-)

diff --git a/devito/operations/interpolators.py b/devito/operations/interpolators.py
index ced9fda21a..6871b69da3 100644
--- a/devito/operations/interpolators.py
+++ b/devito/operations/interpolators.py
@@ -2,21 +2,18 @@
 from functools import wraps
 
 import sympy
+import numpy as np
 from cached_property import cached_property
 
 from devito.finite_differences.differentiable import Mul
-<<<<<<< HEAD
 from devito.finite_differences.elementary import floor
-=======
-from devito.finite_differences.elementary import floor, sqrt
->>>>>>> 5815a494d (api: support sinc interpolation)
 from devito.symbolics import retrieve_function_carriers, retrieve_functions, INT
 from devito.tools import as_tuple, flatten, filter_ordered
 from devito.types import (ConditionalDimension, Eq, Inc, Evaluable, Symbol,
                           CustomDimension, SubFunction)
 from devito.types.utils import DimensionTuple
 
-__all__ = ['LinearInterpolator', 'PrecomputedInterpolator']
+__all__ = ['LinearInterpolator', 'PrecomputedInterpolator', 'SincInterpolator']
 
 
 def check_radius(func):
@@ -129,6 +126,8 @@ class GenericInterpolator(ABC):
     Abstract base class defining the interface for an interpolator.
     """
 
+    _name = "generic"
+
     @abstractmethod
     def inject(self, *args, **kwargs):
         pass
@@ -137,6 +136,10 @@ def inject(self, *args, **kwargs):
     def interpolate(self, *args, **kwargs):
         pass
 
+    @property
+    def name(self):
+        return self._name
+
     def _arg_defaults(self, **args):
         return {}
 
@@ -149,6 +152,8 @@ class WeightedInterpolator(GenericInterpolator):
     and multiplied at a given point: `w[x, y] = wx[x] * wy[y]`
     """
 
+    _name = 'weighted'
+
     def __init__(self, sfunction):
         self.sfunction = sfunction
 
@@ -377,6 +382,9 @@ class LinearInterpolator(WeightedInterpolator):
     ----------
     sfunction: The SparseFunction that this Interpolator operates on.
     """
+
+    _name = 'linear'
+
     @property
     def _weights(self):
         c = [(1 - p) * (1 - r) + p * r
@@ -410,6 +418,8 @@ class PrecomputedInterpolator(WeightedInterpolator):
     sfunction: The SparseFunction that this Interpolator operates on.
     """
 
+    _name = 'precomp'
+
     def _positions(self, implicit_dims):
         if self.sfunction.gridpoints is None:
             return super()._positions(implicit_dims)
@@ -441,12 +451,14 @@ class SincInterpolator(PrecomputedInterpolator):
 
     """
 
+    _name = 'sinc'
+
     # Table 1
-    _b_table = {1: 0.0, 2: 1.84, 3: 3.04,
+    _b_table = {2: 2.94, 3: 4.53,
                 4: 4.14, 5: 5.26, 6: 6.40,
                 7: 7.51, 8: 8.56, 9: 9.56, 10: 10.64}
 
-    @property
+    @cached_property
     def interpolation_coeffs(self):
         coeffs = {}
         shape = (self.sfunction.npoint, 2 * self.r)
@@ -473,13 +485,14 @@ def _arg_defaults(self, coords=None, sfunc=None):
             raise ValueError("No coordinates or sparse function provided")
         # Coords to indices
         coords = (coords - np.array(sfunc.grid.origin)) / np.array(sfunc.grid.spacing)
-        coords = np.floor(coords) - coords + 1 - self.r
+        coords = coords - np.floor(coords)
+
         # Precompute sinc
         for j, r in enumerate(self._rdim):
             data = np.zeros((coords.shape[0], 2*self.r), dtype=sfunc.dtype)
             for ri in range(2*self.r):
-                rpos = ri + coords[:, j]
-                num = np.i0(b*sqrt(1 - (rpos/self.r)**2))
+                rpos = ri - self.r + 1 - coords[:, j]
+                num = np.i0(b*np.sqrt(1 - (rpos/self.r)**2))
                 data[:, ri] = num / b0 * np.sinc(rpos)
             args[self.interpolation_coeffs[r].name] = data
 
diff --git a/devito/types/sparse.py b/devito/types/sparse.py
index 602c137c7f..98e0e3a2dd 100644
--- a/devito/types/sparse.py
+++ b/devito/types/sparse.py
@@ -31,7 +31,7 @@
 
 
 _interpolators = {'linear': LinearInterpolator, 'sinc': SincInterpolator}
-_default_radius = {'linear': 1, 'sinc': 2}
+_default_radius = {'linear': 1, 'sinc': 4}
 
 
 class AbstractSparseFunction(DiscreteFunction):
@@ -808,14 +808,17 @@ class SparseFunction(AbstractSparseFunction):
 
     _sub_functions = ('coordinates',)
 
-    __rkwargs__ = AbstractSparseFunction.__rkwargs__ + ('coordinates', 'interpolator')
+    __rkwargs__ = AbstractSparseFunction.__rkwargs__ + ('coordinates', 'interpolation')
 
     def __init_finalize__(self, *args, **kwargs):
         super().__init_finalize__(*args, **kwargs)
 
-        interp = kwargs.get('interpolator', 'linear')
+        interp = kwargs.get('interpolation', 'linear')
+        self.interpolation = interp
         self.interpolator = _interpolators[interp](self)
         self._radius = kwargs.get('r', _default_radius[interp])
+        if interp == 'sinc' and self._radius < 2:
+            raise ValueError("The 'sinc' interpolator requires a radius of at least 2")
 
         # Set up sparse point coordinates
         coordinates = kwargs.get('coordinates', kwargs.get('coordinates_data'))
@@ -838,7 +841,7 @@ def _arg_defaults(self, alias=None):
         defaults = super()._arg_defaults(alias=alias)
 
         key = alias or self
-        coords = 
+        coords = defaults.get(key.coordinates.name, key.coordinates.data)
         defaults.update(key.interpolator._arg_defaults(coords=coords,
                                                        sfunc=key))
         return defaults
diff --git a/examples/seismic/acoustic/acoustic_example.py b/examples/seismic/acoustic/acoustic_example.py
index f2376105e0..8a341f8a5c 100644
--- a/examples/seismic/acoustic/acoustic_example.py
+++ b/examples/seismic/acoustic/acoustic_example.py
@@ -4,7 +4,7 @@
 from devito.logger import info
 from devito import Constant, Function, smooth, norm
 from examples.seismic.acoustic import AcousticWaveSolver
-from examples.seismic import demo_model, setup_geometry, seismic_args, Receiver
+from examples.seismic import demo_model, setup_geometry, seismic_args
 
 
 def acoustic_setup(shape=(50, 50, 50), spacing=(15.0, 15.0, 15.0),
@@ -15,7 +15,7 @@ def acoustic_setup(shape=(50, 50, 50), spacing=(15.0, 15.0, 15.0),
                        fs=fs, **kwargs)
 
     # Source and receiver geometries
-    geometry = setup_geometry(model, tn)
+    geometry = setup_geometry(model, tn, **kwargs)
 
     # Create solver object to provide relevant operators
     solver = AcousticWaveSolver(model, geometry, kernel=kernel,
@@ -65,16 +65,21 @@ def run(shape=(50, 50, 50), spacing=(20.0, 20.0, 20.0), tn=1000.0,
 
 @pytest.mark.parametrize('shape', [(101,), (51, 51), (16, 16, 16)])
 @pytest.mark.parametrize('k', ['OT2', 'OT4'])
-def test_isoacoustic_stability(shape, k):
+@pytest.mark.parametrize('interp', ['linear', 'sinc'])
+def test_isoacoustic_stability(shape, k, interp):
     spacing = tuple([20]*len(shape))
-    _, _, _, [rec, _] = run(shape=shape, spacing=spacing, tn=20000.0, nbl=0, kernel=k)
+    _, _, _, [rec, _] = run(shape=shape, spacing=spacing, tn=20000.0, nbl=0, kernel=k,
+                            interpolation=interp)
     assert np.isfinite(norm(rec))
 
 
-@pytest.mark.parametrize('fs, normrec, dtype', [(True, 369.955, np.float32),
-                                                (False, 459.1678, np.float64)])
-def test_isoacoustic(fs, normrec, dtype):
-    _, _, _, [rec, _] = run(fs=fs, dtype=dtype)
+@pytest.mark.parametrize('fs, normrec, dtype, interp', [
+    (True, 369.955, np.float32, 'linear'),
+    (False, 459.1678, np.float64, 'linear'),
+    (True, 402.216, np.float32, 'sinc'),
+    (False, 509.0681, np.float64, 'sinc')])
+def test_isoacoustic(fs, normrec, dtype, interp):
+    _, _, _, [rec, _] = run(fs=fs, dtype=dtype, interpolation=interp)
     assert np.isclose(norm(rec), normrec, rtol=1e-3, atol=0)
 
 
diff --git a/examples/seismic/acoustic/operators.py b/examples/seismic/acoustic/operators.py
index 3623915a28..64e7f63284 100644
--- a/examples/seismic/acoustic/operators.py
+++ b/examples/seismic/acoustic/operators.py
@@ -1,6 +1,5 @@
 from devito import Eq, Operator, Function, TimeFunction, Inc, solve, sign
 from devito.symbolics import retrieve_functions, INT
-from examples.seismic import PointSource, Receiver
 
 
 def freesurface(model, eq):
@@ -119,11 +118,8 @@ def ForwardOperator(model, geometry, space_order=4,
     u = TimeFunction(name='u', grid=model.grid,
                      save=geometry.nt if save else None,
                      time_order=2, space_order=space_order)
-    src = PointSource(name='src', grid=geometry.grid, time_range=geometry.time_axis,
-                      npoint=geometry.nsrc)
-
-    rec = Receiver(name='rec', grid=geometry.grid, time_range=geometry.time_axis,
-                   npoint=geometry.nrec)
+    src = geometry.src
+    rec = geometry.rec
 
     s = model.grid.stepping_dim.spacing
     eqn = iso_stencil(u, model, kernel)
@@ -160,10 +156,8 @@ def AdjointOperator(model, geometry, space_order=4,
 
     v = TimeFunction(name='v', grid=model.grid, save=None,
                      time_order=2, space_order=space_order)
-    srca = PointSource(name='srca', grid=model.grid, time_range=geometry.time_axis,
-                       npoint=geometry.nsrc)
-    rec = Receiver(name='rec', grid=model.grid, time_range=geometry.time_axis,
-                   npoint=geometry.nrec)
+    srca = geometry.new_src(name='srca', src_type=None)
+    rec = geometry.rec
 
     s = model.grid.stepping_dim.spacing
     eqn = iso_stencil(v, model, kernel, forward=False)
@@ -206,8 +200,7 @@ def GradientOperator(model, geometry, space_order=4, save=True,
                      else None, time_order=2, space_order=space_order)
     v = TimeFunction(name='v', grid=model.grid, save=None,
                      time_order=2, space_order=space_order)
-    rec = Receiver(name='rec', grid=model.grid, time_range=geometry.time_axis,
-                   npoint=geometry.nrec)
+    rec = geometry.rec
 
     s = model.grid.stepping_dim.spacing
     eqn = iso_stencil(v, model, kernel, forward=False)
@@ -244,11 +237,8 @@ def BornOperator(model, geometry, space_order=4,
     m = model.m
 
     # Create source and receiver symbols
-    src = Receiver(name='src', grid=model.grid, time_range=geometry.time_axis,
-                   npoint=geometry.nsrc)
-
-    rec = Receiver(name='rec', grid=model.grid, time_range=geometry.time_axis,
-                   npoint=geometry.nrec)
+    src = geometry.src
+    rec = geometry.rec
 
     # Create wavefields and a dm field
     u = TimeFunction(name="u", grid=model.grid, save=None,
diff --git a/examples/seismic/source.py b/examples/seismic/source.py
index d0cdef2613..a736c5bb4f 100644
--- a/examples/seismic/source.py
+++ b/examples/seismic/source.py
@@ -117,8 +117,6 @@ def __args_setup__(cls, *args, **kwargs):
                 raise TypeError("Need either `npoint` or `coordinates`")
             kwargs['npoint'] = coordinates.shape[0]
 
-        kwargs.setdefault('r', 1)
-        kwargs.setdefault('interpolator', 'linear')
         return args, kwargs
 
     def __init_finalize__(self, *args, **kwargs):
diff --git a/examples/seismic/utils.py b/examples/seismic/utils.py
index e6c0648a83..ef517d7ce7 100644
--- a/examples/seismic/utils.py
+++ b/examples/seismic/utils.py
@@ -3,13 +3,14 @@
 
 from devito import error, configuration, warning
 from devito.tools import Pickable
+from devito.types.sparse import _default_radius
 
 from .source import *
 
 __all__ = ['AcquisitionGeometry', 'setup_geometry', 'seismic_args']
 
 
-def setup_geometry(model, tn, f0=0.010):
+def setup_geometry(model, tn, f0=0.010, interpolation='linear', **kwargs):
     # Source and receiver geometries
     src_coordinates = np.empty((1, model.dim))
     src_coordinates[0, :] = np.array(model.domain_size) * .5
@@ -18,8 +19,10 @@ def setup_geometry(model, tn, f0=0.010):
 
     rec_coordinates = setup_rec_coords(model)
 
+    r = kwargs.get('r', _default_radius[interpolation])
     geometry = AcquisitionGeometry(model, rec_coordinates, src_coordinates,
-                                   t0=0.0, tn=tn, src_type='Ricker', f0=f0)
+                                   t0=0.0, tn=tn, src_type='Ricker', f0=f0,
+                                   interpolation=interpolation, r=r)
 
     return geometry
 
@@ -58,7 +61,7 @@ class AcquisitionGeometry(Pickable):
     """
 
     __rargs__ = ('grid', 'rec_positions', 'src_positions', 't0', 'tn')
-    __rkwargs__ = ('f0', 'src_type')
+    __rkwargs__ = ('f0', 'src_type', 'interpolation', 'r')
 
     def __init__(self, model, rec_positions, src_positions, t0, tn, **kwargs):
         """
@@ -85,6 +88,8 @@ def __init__(self, model, rec_positions, src_positions, t0, tn, **kwargs):
         self._dt = model.critical_dt
         self._t0 = t0
         self._tn = tn
+        self._interpolation = kwargs.get('interpolation', 'linear')
+        self._r = kwargs.get('r', _default_radius[self.interpolation])
 
     def resample(self, dt):
         self._dt = dt
@@ -140,6 +145,14 @@ def nsrc(self):
     def dtype(self):
         return self.grid.dtype
 
+    @property
+    def r(self):
+        return self._r
+
+    @property
+    def interpolation(self):
+        return self._interpolation
+
     @property
     def rec(self):
         return self.new_rec()
@@ -147,7 +160,8 @@ def rec(self):
     def new_rec(self, name='rec'):
         return Receiver(name=name, grid=self.grid,
                         time_range=self.time_axis, npoint=self.nrec,
-                        coordinates=self.rec_positions)
+                        coordinates=self.rec_positions,
+                        interpolation=self.interpolation, r=self._r)
 
     @property
     def adj_src(self):
@@ -157,7 +171,8 @@ def adj_src(self):
         adj_src = sources[self.src_type](name='rec', grid=self.grid, f0=self.f0,
                                          time_range=self.time_axis, npoint=self.nrec,
                                          coordinates=self.rec_positions,
-                                         t0=self._t0w, a=self._a)
+                                         t0=self._t0w, a=self._a,
+                                         interpolation=self.interpolation, r=self._r)
         # Revert time axis to have a proper shot record and not compute on zeros
         for i in range(self.nrec):
             adj_src.data[:, i] = adj_src.wavelet[::-1]
@@ -172,12 +187,14 @@ def new_src(self, name='src', src_type='self'):
             warning("No source type defined, returning uninitiallized (zero) source")
             return PointSource(name=name, grid=self.grid,
                                time_range=self.time_axis, npoint=self.nsrc,
-                               coordinates=self.src_positions)
+                               coordinates=self.src_positions,
+                               interpolation=self.interpolation, r=self._r)
         else:
             return sources[self.src_type](name=name, grid=self.grid, f0=self.f0,
                                           time_range=self.time_axis, npoint=self.nsrc,
                                           coordinates=self.src_positions,
-                                          t0=self._t0w, a=self._a)
+                                          t0=self._t0w, a=self._a,
+                                          interpolation=self.interpolation, r=self._r)
 
 
 sources = {'Wavelet': WaveletSource, 'Ricker': RickerSource, 'Gabor': GaborSource}
diff --git a/tests/test_interpolation.py b/tests/test_interpolation.py
index ae013572c8..664b882f4f 100644
--- a/tests/test_interpolation.py
+++ b/tests/test_interpolation.py
@@ -8,9 +8,10 @@
                     Function, TimeFunction, DefaultDimension, Eq, switchconfig,
                     PrecomputedSparseFunction, PrecomputedSparseTimeFunction,
                     MatrixSparseTimeFunction)
+from devito.operations.interpolators import LinearInterpolator, SincInterpolator
 from examples.seismic import (demo_model, TimeAxis, RickerSource, Receiver,
                               AcquisitionGeometry)
-from examples.seismic.acoustic import AcousticWaveSolver
+from examples.seismic.acoustic import AcousticWaveSolver, acoustic_setup
 import scipy.sparse
 
 
@@ -753,15 +754,87 @@ def test_inject_function():
 
 
 @pytest.mark.parametrize('r, interp', [(2, 'linear'), (4, 'cubic')])
-def test_interpolation_radius(rm interp):
+def test_interpolation_radius(r, interp):
     nt = 11
 
     grid = Grid(shape=(5, 5))
     u = TimeFunction(name="u", grid=grid, space_order=0)
     src = SparseTimeFunction(name="src", grid=grid, nt=nt, npoint=1,
-                             r=r, interpolator=interp)
+                             r=r, interpolation=interp)
     try:
         src.interpolate(u)
         assert False
     except ValueError:
         assert True
+
+
+def test_interp_default():
+    nt = 3
+    grid = Grid(shape=(5, 5))
+
+    src = SparseTimeFunction(name="src", grid=grid, nt=nt, npoint=1)
+    assert isinstance(src.interpolator, LinearInterpolator)
+    assert src.r == 1
+
+    src = SparseTimeFunction(name="src", grid=grid, nt=nt, npoint=1, interpolation='sinc')
+    assert isinstance(src.interpolator, SincInterpolator)
+    assert src.r == 2
+
+    src = SparseTimeFunction(name="src", grid=grid, nt=nt, npoint=1,
+                             interpolation='sinc', r=6)
+    assert isinstance(src.interpolator, SincInterpolator)
+    assert src.r == 6
+
+
+@pytest.mark.parametrize('r, tol', [(2, 0.051), (3, 0.003), (4, 0.008),
+                                    (5, 0.002), (6, 0.0005), (7, 8e-5),
+                                    (8, 6e-5), (9, 5e-5), (10, 4.2e-5)])
+def test_sinc_accuracy(r, tol):
+    so = max(2, r)
+    solver_lin = acoustic_setup(preset='constant-isotropic', shape=(101, 101),
+                                spacing=(10, 10), interpolation='linear', space_order=so)
+    solver_sinc = acoustic_setup(preset='constant-isotropic', shape=(101, 101),
+                                 spacing=(10, 10), interpolation='sinc', r=r,
+                                 space_order=so)
+
+    # On node source
+    s_node = [500, 500]
+    src_n = solver_lin.geometry.src
+    src_n.coordinates.data[:] = s_node
+
+    # Half node src
+    s_mid = [505, 505]
+    src_h = solver_lin.geometry.src
+    src_h.coordinates.data[:] = s_mid
+
+    # On node rec
+    r_node = [750, 750]
+    rec_n = solver_lin.geometry.new_src(name='rec', src_type=None)
+    rec_n.coordinates.data[:] = r_node
+
+    # Half node rec for linear
+    r_mid = [755, 755]
+    rec_hl = solver_lin.geometry.new_src(name='recl', src_type=None)
+    rec_hl.coordinates.data[:] = r_mid
+
+    # Half node rec for sinc
+    r_mid = [755, 755]
+    rec_hs = solver_lin.geometry.new_src(name='recs', src_type=None)
+    rec_hs.coordinates.data[:] = r_mid
+
+    # Reference solution, on node
+    _, un, _ = solver_lin.forward(src=src_n, rec=rec_n)
+    # Linear interp on half node
+    _, ul, _ = solver_lin.forward(src=src_h, rec=rec_hl)
+    # Sinc interp on half node
+    _, us, _ = solver_sinc.forward(src=src_h, rec=rec_hs)
+
+    # Check sinc is more accuracte
+    nref = np.linalg.norm(rec_n.data)
+    err_lin = np.linalg.norm(rec_n.data - rec_hl.data)/nref
+    err_sinc = np.linalg.norm(rec_n.data - rec_hs.data)/nref
+
+    print(f"Error linear: {err_lin}, Error sinc: {err_sinc}")
+    assert np.isclose(err_sinc, 0, rtol=0, atol=tol)
+    assert err_sinc < err_lin
+    assert err_lin > 0.01

From 4aadbf86383c38c182d69ee4a19be83e8bd57168 Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Mon, 1 Apr 2024 15:23:47 -0400
Subject: [PATCH 04/29] mpi: revamp conftest

---
 conftest.py                 | 56 ++++++++++++++++++++++++++-----------
 tests/test_autotuner.py     |  1 -
 tests/test_builtins.py      |  5 ----
 tests/test_dse.py           |  1 -
 tests/test_interpolation.py |  4 +--
 tests/test_mpi.py           |  4 +--
 tests/test_operator.py      |  1 -
 tests/test_pickle.py        | 12 ++++----
 8 files changed, 47 insertions(+), 37 deletions(-)

diff --git a/conftest.py b/conftest.py
index cd74558788..3a4d6d4ef4 100644
--- a/conftest.py
+++ b/conftest.py
@@ -34,16 +34,12 @@ def skipif(items, whole_module=False):
     accepted.update({'device', 'device-C', 'device-openmp', 'device-openacc',
                      'device-aomp', 'cpu64-icc', 'cpu64-icx', 'cpu64-nvc', 'cpu64-arm',
                      'cpu64-icpx', 'chkpnt'})
-    accepted.update({'nompi', 'nodevice'})
+    accepted.update({'nodevice'})
     unknown = sorted(set(items) - accepted)
     if unknown:
         raise ValueError("Illegal skipif argument(s) `%s`" % unknown)
     skipit = False
     for i in items:
-        # Skip if no MPI
-        if i == 'nompi' and MPI is None:
-            skipit = "mpi4py/MPI not installed"
-            break
         # Skip if won't run on GPUs
         if i == 'device' and isinstance(configuration['platform'], Device):
             skipit = "device `%s` unsupported" % configuration['platform'].name
@@ -171,6 +167,9 @@ def parallel(item):
         os.environ['DEVITO_MPI'] = scheme
         try:
             check_call(call)
+            return True
+        except:
+            return False
         finally:
             os.environ['DEVITO_MPI'] = '0'
 
@@ -189,18 +188,21 @@ def pytest_runtest_setup(item):
         partest = int(partest)
     except ValueError:
         pass
-    if item.get_closest_marker("parallel") and not partest:
-        # Blow away function arg in "master" process, to ensure
-        # this test isn't run on only one process
-        dummy_test = lambda *args, **kwargs: True
-        # For pytest <7
-        if item.cls is not None:
-            attr = item.originalname or item.name
-            setattr(item.cls, attr, dummy_test)
+    if item.get_closest_marker("parallel"):
+        if MPI is None:
+            pytest.skip("mpi4py/MPI not installed")
         else:
-            item.obj = dummy_test
-        # For pytest >= 7
-        setattr(item, '_obj', dummy_test)
+            # Blow away function arg in "master" process, to ensure
+            # this test isn't run on only one process
+            dummy_test = lambda *args, **kwargs: True
+            # For pytest <7
+            if item.cls is not None:
+                attr = item.originalname or item.name
+                setattr(item.cls, attr, dummy_test)
+            else:
+                item.obj = dummy_test
+            # For pytest >= 7
+            setattr(item, '_obj', dummy_test)
 
 
 def pytest_runtest_call(item):
@@ -211,7 +213,27 @@ def pytest_runtest_call(item):
         pass
     if item.get_closest_marker("parallel") and not partest:
         # Spawn parallel processes to run test
-        parallel(item)
+        passed = parallel(item)
+        if not passed:
+            pytest.fail(f"{item} failed in parallel execution")
+        else:
+            pytest.skip(f"{item}t passed in parallel execution")
+
+
+@pytest.hookimpl(tryfirst=True, hookwrapper=True)
+def pytest_runtest_makereport(item, call):
+    outcome = yield
+    result = outcome.get_result()
+
+    partest = os.environ.get('DEVITO_MPI', 0)
+    try:
+        partest = int(partest)
+    except ValueError:
+        pass
+
+    if item.get_closest_marker("parallel") and not partest:
+        if call.when == 'call' and result.outcome == 'skipped':
+            result.outcome = 'passed'
 
 
 # A list of optimization options/pipelines to be used in testing
diff --git a/tests/test_autotuner.py b/tests/test_autotuner.py
index 496afa3666..72233d3fa0 100644
--- a/tests/test_autotuner.py
+++ b/tests/test_autotuner.py
@@ -180,7 +180,6 @@ def test_discarding_runs():
     assert op._state['autotuning'][1]['tuned']['nthreads'] == 1
 
 
-@skipif('nompi')
 @pytest.mark.parallel(mode=[(2, 'diag'), (2, 'full')])
 def test_at_w_mpi():
     """Make sure autotuning works in presence of MPI. MPI ranks work
diff --git a/tests/test_builtins.py b/tests/test_builtins.py
index 4ffe02b552..d086415376 100644
--- a/tests/test_builtins.py
+++ b/tests/test_builtins.py
@@ -3,7 +3,6 @@
 from scipy.ndimage import gaussian_filter
 from scipy.misc import ascent
 
-from conftest import skipif
 from devito import ConditionalDimension, Grid, Function, TimeFunction, switchconfig
 from devito.builtins import (assign, norm, gaussian_smooth, initialize_function,
                              inner, mmin, mmax, sum, sumall)
@@ -92,7 +91,6 @@ def test_assign_subsampled_timefunction(self):
 
         assert np.all(f.data == 1)
 
-    @skipif('nompi')
     @pytest.mark.parallel(mode=4)
     def test_assign_parallel(self):
         a = np.arange(64).reshape((8, 8))
@@ -175,7 +173,6 @@ def test_gs_2d_float(self, sigma):
 
         assert np.amax(np.abs(sp_smoothed - np.array(dv_smoothed))) <= 1e-5
 
-    @skipif('nompi')
     @pytest.mark.parallel(mode=[(4, 'full')])
     def test_gs_parallel(self):
         a = np.arange(64).reshape((8, 8))
@@ -238,7 +235,6 @@ def test_nbl_zero(self):
 
         assert np.all(a[:] - np.array(f.data[:]) == 0)
 
-    @skipif('nompi')
     @pytest.mark.parallel(mode=4)
     def test_if_parallel(self):
         a = np.arange(36).reshape((6, 6))
@@ -294,7 +290,6 @@ def test_if_halo(self, ndim, nbl):
             assert np.take(f._data_with_outhalo, 0, axis=-1)[7] == 1
             assert np.take(f._data_with_outhalo, -1, axis=-1)[7] == 3
 
-    @skipif('nompi')
     @pytest.mark.parametrize('nbl', [0, 2])
     @pytest.mark.parallel(mode=4)
     def test_if_halo_mpi(self, nbl):
diff --git a/tests/test_dse.py b/tests/test_dse.py
index acb3a1721b..9435d58c54 100644
--- a/tests/test_dse.py
+++ b/tests/test_dse.py
@@ -2799,7 +2799,6 @@ def test_fullopt(self):
         vexpanded = 2 if configuration['language'] == 'openmp' else 0
         assert len(FindNodes(VExpanded).visit(pbs['x0_blk0'])) == vexpanded
 
-    @skipif(['nompi'])
     @switchconfig(profiling='advanced')
     @pytest.mark.parallel(mode=[(1, 'full')])
     def test_fullopt_w_mpi(self):
diff --git a/tests/test_interpolation.py b/tests/test_interpolation.py
index 664b882f4f..34bd39bbe0 100644
--- a/tests/test_interpolation.py
+++ b/tests/test_interpolation.py
@@ -753,7 +753,7 @@ def test_inject_function():
             assert u.data[1, i, j] == 0
 
 
-@pytest.mark.parametrize('r, interp', [(2, 'linear'), (4, 'cubic')])
+@pytest.mark.parametrize('r, interp', [(2, 'linear'), (4, 'sinc')])
 def test_interpolation_radius(r, interp):
     nt = 11
 
@@ -778,7 +778,7 @@ def test_interp_default():
 
     src = SparseTimeFunction(name="src", grid=grid, nt=nt, npoint=1, interpolation='sinc')
     assert isinstance(src.interpolator, SincInterpolator)
-    assert src.r == 2
+    assert src.r == 4
 
     src = SparseTimeFunction(name="src", grid=grid, nt=nt, npoint=1,
                              interpolation='sinc', r=6)
diff --git a/tests/test_mpi.py b/tests/test_mpi.py
index 12733de0a2..d6cb431a90 100644
--- a/tests/test_mpi.py
+++ b/tests/test_mpi.py
@@ -2,7 +2,7 @@
 import pytest
 from cached_property import cached_property
 
-from conftest import skipif, _R, assert_blocking, assert_structure
+from conftest import _R, assert_blocking, assert_structure
 from devito import (Grid, Constant, Function, TimeFunction, SparseFunction,
                     SparseTimeFunction, Dimension, ConditionalDimension, SubDimension,
                     SubDomain, Eq, Ne, Inc, NODE, Operator, norm, inner, configuration,
@@ -19,8 +19,6 @@
 
 from examples.seismic.acoustic import acoustic_setup
 
-pytestmark = skipif(['nompi'], whole_module=True)
-
 
 class TestDistributor(object):
 
diff --git a/tests/test_operator.py b/tests/test_operator.py
index 73fb633a88..5698f8f913 100644
--- a/tests/test_operator.py
+++ b/tests/test_operator.py
@@ -1207,7 +1207,6 @@ def test_incomplete_override(self):
         except:
             assert False
 
-    @skipif('nompi')
     @pytest.mark.parallel(mode=1)
     def test_new_distributor(self):
         """
diff --git a/tests/test_pickle.py b/tests/test_pickle.py
index 6e2e851ae7..1089ca5bf2 100644
--- a/tests/test_pickle.py
+++ b/tests/test_pickle.py
@@ -5,7 +5,6 @@
 import numpy as np
 from sympy import Symbol
 
-from conftest import skipif
 from devito import (Constant, Eq, Function, TimeFunction, SparseFunction, Grid,
                     Dimension, SubDimension, ConditionalDimension, IncrDimension,
                     TimeDimension, SteppingDimension, Operator, MPI, Min, solve,
@@ -79,10 +78,12 @@ def test_function(self, pickle):
         assert f.dtype == new_f.dtype
         assert f.shape == new_f.shape
 
-    def test_sparse_function(self, pickle):
+    @pytest.mark.parametrize('interp', ['linear', 'sinc'])
+    def test_sparse_function(self, pickle, interp):
         grid = Grid(shape=(3,))
         sf = SparseFunction(name='sf', grid=grid, npoint=3, space_order=2,
-                            coordinates=[(0.,), (1.,), (2.,)])
+                            coordinates=[(0.,), (1.,), (2.,)],
+                            interpolation=interp)
         sf.data[0] = 1.
 
         pkl_sf = pickle.dumps(sf)
@@ -91,6 +92,7 @@ def test_sparse_function(self, pickle):
         # .data is initialized, so it should have been pickled too
         assert np.all(sf.data[0] == 1.)
         assert np.all(new_sf.data[0] == 1.)
+        assert new_sf.interpolation == interp
 
         # coordinates should also have been pickled
         assert np.all(sf.coordinates.data == new_sf.coordinates.data)
@@ -633,7 +635,6 @@ def test_elemental(self, pickle):
 
         assert str(op) == str(new_op)
 
-    @skipif(['nompi'])
     @pytest.mark.parallel(mode=[1])
     def test_mpi_objects(self, pickle):
         grid = Grid(shape=(4, 4, 4))
@@ -682,7 +683,6 @@ def test_threadid(self, pickle):
         assert tid.symbolic_min.name == new_tid.symbolic_min.name
         assert tid.symbolic_max.name == new_tid.symbolic_max.name
 
-    @skipif(['nompi'])
     @pytest.mark.parallel(mode=[2])
     def test_mpi_grid(self, pickle):
         grid = Grid(shape=(4, 4, 4))
@@ -703,7 +703,6 @@ def test_mpi_grid(self, pickle):
             assert new_grid.distributor.comm.size == 1
         MPI.COMM_WORLD.Barrier()
 
-    @skipif(['nompi'])
     @pytest.mark.parallel(mode=[(1, 'full')])
     def test_mpi_fullmode_objects(self, pickle):
         grid = Grid(shape=(4, 4, 4))
@@ -743,7 +742,6 @@ def test_mpi_fullmode_objects(self, pickle):
             assert v[0] is d.symbolic_min
             assert v[1] == Min(d.symbolic_max, d.symbolic_min)
 
-    @skipif(['nompi'])
     @pytest.mark.parallel(mode=[(1, 'basic'), (1, 'full')])
     def test_mpi_operator(self, pickle):
         grid = Grid(shape=(4,))

From c4ac44ee942253cab095374c502ee1ea7d90006e Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Mon, 1 Apr 2024 22:40:05 -0400
Subject: [PATCH 05/29] mpi: fix sinc setup with mpi and add back missing mpi
 example tests

---
 .github/workflows/pytest-core-mpi.yml | 11 ++++++++++-
 devito/operations/interpolators.py    |  8 +++++++-
 2 files changed, 17 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/pytest-core-mpi.yml b/.github/workflows/pytest-core-mpi.yml
index 923971f89b..05100d4d2d 100644
--- a/.github/workflows/pytest-core-mpi.yml
+++ b/.github/workflows/pytest-core-mpi.yml
@@ -48,6 +48,11 @@ jobs:
         python3 scripts/clear_devito_cache.py
         python3 -m pytest --cov --cov-config=.coveragerc --cov-report=xml -m parallel tests/
 
+    - name: Test examples
+      run: |
+        python3 scripts/clear_devito_cache.py
+        python3 -m pytest --cov --cov-config=.coveragerc --cov-report=xml examples/seismic
+
     - name: Upload coverage to Codecov
       uses: codecov/codecov-action@v4
       with:
@@ -78,4 +83,8 @@ jobs:
       
       - name: Test with pytest
         run: |
-          docker run --rm -e CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }} -e OMP_NUM_THREADS=1 --name testrun devito_img pytest tests/test_mpi.py
\ No newline at end of file
+          docker run --rm -e CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }} -e OMP_NUM_THREADS=1 --name testrun devito_img pytest tests/test_mpi.py
+
+      - name: Test examples with MPI
+        run: |
+          docker run --rm --env DEVITO_MPI=1 --name examplerun mpiexec -n 2 pytest examples/seismic
diff --git a/devito/operations/interpolators.py b/devito/operations/interpolators.py
index 6871b69da3..dddb2274e7 100644
--- a/devito/operations/interpolators.py
+++ b/devito/operations/interpolators.py
@@ -484,7 +484,13 @@ def _arg_defaults(self, coords=None, sfunc=None):
         if coords is None or sfunc is None:
             raise ValueError("No coordinates or sparse function provided")
         # Coords to indices
-        coords = (coords - np.array(sfunc.grid.origin)) / np.array(sfunc.grid.spacing)
+        if sfunc.grid._distributor.nprocs == 1:
+            origin = sfunc.grid.origin
+        else:
+            # Already shifted to zero through scatter
+            origin = tuple([0]*sfunc.grid.dim)
+
+        coords = (coords - np.array(origin)) / np.array(sfunc.grid.spacing)
         coords = coords - np.floor(coords)
 
         # Precompute sinc

From 4db1120225d0ebc703f15ced8f18e75a1660718a Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Tue, 2 Apr 2024 10:44:50 -0400
Subject: [PATCH 06/29] api: fix sparse position index for negative location

---
 .github/workflows/pytest-core-mpi.yml         |  6 +++--
 devito/operations/interpolators.py            | 27 ++++++++++---------
 devito/types/sparse.py                        | 10 ++++---
 docker/entrypoint.sh                          |  2 +-
 examples/seismic/acoustic/acoustic_example.py |  2 +-
 examples/seismic/utils.py                     |  4 ++-
 6 files changed, 29 insertions(+), 22 deletions(-)

diff --git a/.github/workflows/pytest-core-mpi.yml b/.github/workflows/pytest-core-mpi.yml
index 05100d4d2d..b0fb73abc2 100644
--- a/.github/workflows/pytest-core-mpi.yml
+++ b/.github/workflows/pytest-core-mpi.yml
@@ -51,7 +51,8 @@ jobs:
     - name: Test examples
       run: |
         python3 scripts/clear_devito_cache.py
-        python3 -m pytest --cov --cov-config=.coveragerc --cov-report=xml examples/seismic
+        DEVITO_MPI=1 mpirun -n 2 python3 -m pytest --cov --cov-config=.coveragerc --cov-report=xml examples/seismic/acoustic
+        DEVITO_MPI=1 mpirun -n 2 python3 -m pytest --cov --cov-config=.coveragerc --cov-report=xml examples/seismic/tti
 
     - name: Upload coverage to Codecov
       uses: codecov/codecov-action@v4
@@ -87,4 +88,5 @@ jobs:
 
       - name: Test examples with MPI
         run: |
-          docker run --rm --env DEVITO_MPI=1 --name examplerun mpiexec -n 2 pytest examples/seismic
+          docker run --rm -e DEVITO_MPI=1 -e OMP_NUM_THREADS=1 --name examplerun devito_img mpiexec -n 2 pytest examples/seismic/acoustic
+          docker run --rm -e DEVITO_MPI=1 -e OMP_NUM_THREADS=1 --name examplerun devito_img mpiexec -n 2 pytest examples/seismic/tti
diff --git a/devito/operations/interpolators.py b/devito/operations/interpolators.py
index dddb2274e7..38a3571b17 100644
--- a/devito/operations/interpolators.py
+++ b/devito/operations/interpolators.py
@@ -23,7 +23,7 @@ def wrapper(interp, *args, **kwargs):
         funcs = set().union(*[retrieve_functions(a) for a in args])
         so = min({f.space_order for f in funcs if not f.is_SparseFunction} or {r})
         if so < r:
-            raise ValueError("Space order %d smaller than interpolation r %d" % (so, r))
+            raise ValueError("Space order %d too small for interpolation r %d" % (so, r))
         return func(interp, *args, **kwargs)
     return wrapper
 
@@ -216,7 +216,7 @@ def _positions(self, implicit_dims):
         return [Eq(v, INT(floor(k)), implicit_dims=implicit_dims)
                 for k, v in self.sfunction._position_map.items()]
 
-    def _interp_idx(self, variables, implicit_dims=None):
+    def _interp_idx(self, variables, implicit_dims=None, pos_only=None):
         """
         Generate interpolation indices for the DiscreteFunctions in ``variables``.
         """
@@ -235,6 +235,14 @@ def _interp_idx(self, variables, implicit_dims=None):
                     for ((k, c), p) in zip(mapper.items(), pos)})
                     for v in variables}
 
+        # Position only replacement, not radius dependent.
+        # E.g src.inject(vp(x)*src) needs to use vp[posx] at all points
+        # not vp[posx + rx]
+        if pos_only is not None:
+            idx_subs.update({v: v.subs({k: p
+                             for (k, p) in zip(mapper, pos)})
+                             for v in pos_only})
+
         return idx_subs, temps
 
     @check_radius
@@ -359,10 +367,9 @@ def _inject(self, field, expr, implicit_dims=None):
         # summing temp that wouldn't allow collapsing
         implicit_dims = implicit_dims + tuple(r.parent for r in self._rdim)
 
-        variables = variables + list(fields)
-
         # List of indirection indices for all adjacent grid points
-        idx_subs, temps = self._interp_idx(variables, implicit_dims=implicit_dims)
+        idx_subs, temps = self._interp_idx(list(fields), implicit_dims=implicit_dims,
+                                           pos_only=variables)
 
         # Substitute coordinate base symbols into the interpolation coefficients
         eqns = [Inc(_field.xreplace(idx_subs),
@@ -424,7 +431,7 @@ def _positions(self, implicit_dims):
         if self.sfunction.gridpoints is None:
             return super()._positions(implicit_dims)
         # No position temp as we have directly the gridpoints
-        return [Eq(p, k, implicit_dims=implicit_dims)
+        return [Eq(p, floor(k), implicit_dims=implicit_dims)
                 for (k, p) in self.sfunction._position_map.items()]
 
     @property
@@ -484,13 +491,7 @@ def _arg_defaults(self, coords=None, sfunc=None):
         if coords is None or sfunc is None:
             raise ValueError("No coordinates or sparse function provided")
         # Coords to indices
-        if sfunc.grid._distributor.nprocs == 1:
-            origin = sfunc.grid.origin
-        else:
-            # Already shifted to zero through scatter
-            origin = tuple([0]*sfunc.grid.dim)
-
-        coords = (coords - np.array(origin)) / np.array(sfunc.grid.spacing)
+        coords = coords / np.array(sfunc.grid.spacing)
         coords = coords - np.floor(coords)
 
         # Precompute sinc
diff --git a/devito/types/sparse.py b/devito/types/sparse.py
index 98e0e3a2dd..921c115a22 100644
--- a/devito/types/sparse.py
+++ b/devito/types/sparse.py
@@ -811,15 +811,17 @@ class SparseFunction(AbstractSparseFunction):
     __rkwargs__ = AbstractSparseFunction.__rkwargs__ + ('coordinates', 'interpolation')
 
     def __init_finalize__(self, *args, **kwargs):
-        super().__init_finalize__(*args, **kwargs)
-
-        interp = kwargs.get('interpolation', 'linear')
+        # Interpolation method
+        interp = kwargs.pop('interpolation', 'linear')
         self.interpolation = interp
         self.interpolator = _interpolators[interp](self)
-        self._radius = kwargs.get('r', _default_radius[interp])
+        self._radius = kwargs.pop('r', _default_radius[interp])
         if interp == 'sinc' and self._radius < 2:
             raise ValueError("The 'sinc' interpolator requires a radius of at least 2")
 
+        # Set space ordert to `r` for safety
+        super().__init_finalize__(*args, **kwargs)
+
         # Set up sparse point coordinates
         coordinates = kwargs.get('coordinates', kwargs.get('coordinates_data'))
         self._coordinates = self.__subfunc_setup__(coordinates, 'coords')
diff --git a/docker/entrypoint.sh b/docker/entrypoint.sh
index 2a3fab2bbd..077a84cd05 100644
--- a/docker/entrypoint.sh
+++ b/docker/entrypoint.sh
@@ -10,7 +10,7 @@ fi
 
 if [[ "$DEVITO_ARCH" = "icx" || "$DEVITO_ARCH" = "icc" ]]; then
     echo "Initializing oneapi environement"
-    source /opt/intel/oneapi/setvars.sh
+    source /opt/intel/oneapi/setvars.sh intel64
 fi
 
 if [[ -z "${DEPLOY_ENV}" ]]; then
diff --git a/examples/seismic/acoustic/acoustic_example.py b/examples/seismic/acoustic/acoustic_example.py
index 8a341f8a5c..aced327cdc 100644
--- a/examples/seismic/acoustic/acoustic_example.py
+++ b/examples/seismic/acoustic/acoustic_example.py
@@ -60,7 +60,7 @@ def run(shape=(50, 50, 50), spacing=(20.0, 20.0, 20.0), tn=1000.0,
     solver.jacobian(dm, autotune=autotune)
     info("Applying Gradient")
     solver.jacobian_adjoint(rec, u, autotune=autotune, checkpointing=checkpointing)
-    return summary.gflopss, summary.oi, summary.timings, [rec, u.data]
+    return summary.gflopss, summary.oi, summary.timings, [rec, u.data._local]
 
 
 @pytest.mark.parametrize('shape', [(101,), (51, 51), (16, 16, 16)])
diff --git a/examples/seismic/utils.py b/examples/seismic/utils.py
index ef517d7ce7..42ca0ced72 100644
--- a/examples/seismic/utils.py
+++ b/examples/seismic/utils.py
@@ -13,9 +13,11 @@
 def setup_geometry(model, tn, f0=0.010, interpolation='linear', **kwargs):
     # Source and receiver geometries
     src_coordinates = np.empty((1, model.dim))
-    src_coordinates[0, :] = np.array(model.domain_size) * .5
     if model.dim > 1:
+        src_coordinates[0, :] = np.array(model.domain_size) * .5
         src_coordinates[0, -1] = model.origin[-1] + model.spacing[-1]
+    else:
+        src_coordinates[0, 0] = 2 * model.spacing[0]
 
     rec_coordinates = setup_rec_coords(model)
 

From 99448f1d6d970ca2a7aff2699bc93a5a5baa23dc Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Tue, 2 Apr 2024 23:15:25 -0400
Subject: [PATCH 07/29] examples: improved fs only check z derivs

---
 .github/workflows/pytest-core-mpi.yml  |  5 -----
 examples/seismic/acoustic/operators.py | 27 ++++++++++++++++++++------
 2 files changed, 21 insertions(+), 11 deletions(-)

diff --git a/.github/workflows/pytest-core-mpi.yml b/.github/workflows/pytest-core-mpi.yml
index b0fb73abc2..99d5f5fda2 100644
--- a/.github/workflows/pytest-core-mpi.yml
+++ b/.github/workflows/pytest-core-mpi.yml
@@ -85,8 +85,3 @@ jobs:
       - name: Test with pytest
         run: |
           docker run --rm -e CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }} -e OMP_NUM_THREADS=1 --name testrun devito_img pytest tests/test_mpi.py
-
-      - name: Test examples with MPI
-        run: |
-          docker run --rm -e DEVITO_MPI=1 -e OMP_NUM_THREADS=1 --name examplerun devito_img mpiexec -n 2 pytest examples/seismic/acoustic
-          docker run --rm -e DEVITO_MPI=1 -e OMP_NUM_THREADS=1 --name examplerun devito_img mpiexec -n 2 pytest examples/seismic/tti
diff --git a/examples/seismic/acoustic/operators.py b/examples/seismic/acoustic/operators.py
index 64e7f63284..d06d572c3f 100644
--- a/examples/seismic/acoustic/operators.py
+++ b/examples/seismic/acoustic/operators.py
@@ -1,5 +1,5 @@
 from devito import Eq, Operator, Function, TimeFunction, Inc, solve, sign
-from devito.symbolics import retrieve_functions, INT
+from devito.symbolics import retrieve_functions, INT, retrieve_derivatives
 
 
 def freesurface(model, eq):
@@ -14,13 +14,21 @@ def freesurface(model, eq):
     eq : Eq
         Time-stepping stencil (time update) to mirror at the freesurface.
     """
-    lhs, rhs = eq.evaluate.args
+    lhs, rhs = eq.args
     # Get vertical dimension and corresponding subdimension
-    zfs = model.grid.subdomains['fsdomain'].dimensions[-1]
+    fsdomain = model.grid.subdomains['fsdomain']
+    zfs = fsdomain.dimensions[-1]
     z = zfs.parent
 
-    # Functions present in the stencil
-    funcs = retrieve_functions(rhs)
+    # Retrieve vertical derivatives
+    Dz = {d for d in retrieve_derivatives(rhs) if z in d.dims}
+    # Remove inner duplicate
+    Dz = Dz - {d for D in Dz for d in retrieve_derivatives(D.expr) if z in d.dims}
+    Dz = {d: d._eval_at(lhs).evaluate for d in Dz}
+
+    # Finally get functions for evaluated derivatives
+    funcs = {f for f in retrieve_functions(Dz.values())}
+
     mapper = {}
     # Antisymmetric mirror at negative indices
     # TODO: Make a proper "mirror_indices" tool function
@@ -29,7 +37,14 @@ def freesurface(model, eq):
         if (zind - z).as_coeff_Mul()[0] < 0:
             s = sign(zind.subs({z: zfs, z.spacing: 1}))
             mapper.update({f: s * f.subs({zind: INT(abs(zind))})})
-    return Eq(lhs, rhs.subs(mapper), subdomain=model.grid.subdomains['fsdomain'])
+
+    # Mapper for vertical derivatives
+    dzmapper = {d: v.subs(mapper) for d, v in Dz.items()}
+
+    fs_eq = [eq.func(lhs, rhs.subs(dzmapper), subdomain=fsdomain)]
+    fs_eq.append(eq.func(lhs._subs(z, 0), 0, subdomain=fsdomain))
+
+    return fs_eq
 
 
 def laplacian(field, model, kernel):

From a83fac76aeb2e0158ad0e1495d90bfd330cf1fe6 Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Wed, 3 Apr 2024 08:48:59 -0400
Subject: [PATCH 08/29] misc: review cleanup

---
 .github/workflows/pytest-core-mpi.yml  |  2 +-
 devito/types/sparse.py                 | 21 ++++++++++++++-------
 examples/seismic/acoustic/operators.py | 10 +++++-----
 3 files changed, 20 insertions(+), 13 deletions(-)

diff --git a/.github/workflows/pytest-core-mpi.yml b/.github/workflows/pytest-core-mpi.yml
index 99d5f5fda2..a3963fe866 100644
--- a/.github/workflows/pytest-core-mpi.yml
+++ b/.github/workflows/pytest-core-mpi.yml
@@ -48,7 +48,7 @@ jobs:
         python3 scripts/clear_devito_cache.py
         python3 -m pytest --cov --cov-config=.coveragerc --cov-report=xml -m parallel tests/
 
-    - name: Test examples
+    - name: Test examples with MPI
       run: |
         python3 scripts/clear_devito_cache.py
         DEVITO_MPI=1 mpirun -n 2 python3 -m pytest --cov --cov-config=.coveragerc --cov-report=xml examples/seismic/acoustic
diff --git a/devito/types/sparse.py b/devito/types/sparse.py
index 921c115a22..a5f711f7cf 100644
--- a/devito/types/sparse.py
+++ b/devito/types/sparse.py
@@ -812,14 +812,9 @@ class SparseFunction(AbstractSparseFunction):
 
     def __init_finalize__(self, *args, **kwargs):
         # Interpolation method
-        interp = kwargs.pop('interpolation', 'linear')
-        self.interpolation = interp
-        self.interpolator = _interpolators[interp](self)
-        self._radius = kwargs.pop('r', _default_radius[interp])
-        if interp == 'sinc' and self._radius < 2:
-            raise ValueError("The 'sinc' interpolator requires a radius of at least 2")
+        self.__interp_setup__(**kwargs)
 
-        # Set space ordert to `r` for safety
+        # Initialization
         super().__init_finalize__(*args, **kwargs)
 
         # Set up sparse point coordinates
@@ -827,6 +822,18 @@ def __init_finalize__(self, *args, **kwargs):
         self._coordinates = self.__subfunc_setup__(coordinates, 'coords')
         self._dist_origin = {self._coordinates: self.grid.origin_offset}
 
+    def __interp_setup__(self, interp='linear', r=None, **kwargs):
+        self.interpolation = interp
+        self.interpolator = _interpolators[interp](self)
+        self._radius = r or _default_radius[interp]
+        if interp == 'sinc':
+            if self._radius < 2:
+                raise ValueError("'sinc' interpolator requires a radius of at least 2")
+            elif self._radius > 10:
+                raise ValueError("'sinc' interpolator requires a radius of at most 10")
+        elif interp == 'linear' and self._radius != 1:
+            self._radius = 1
+
     @cached_property
     def _coordinate_symbols(self):
         """Symbol representing the coordinate values in each Dimension."""
diff --git a/examples/seismic/acoustic/operators.py b/examples/seismic/acoustic/operators.py
index d06d572c3f..c8a2fdec5c 100644
--- a/examples/seismic/acoustic/operators.py
+++ b/examples/seismic/acoustic/operators.py
@@ -21,13 +21,13 @@ def freesurface(model, eq):
     z = zfs.parent
 
     # Retrieve vertical derivatives
-    Dz = {d for d in retrieve_derivatives(rhs) if z in d.dims}
+    dzs = {d for d in retrieve_derivatives(rhs) if z in d.dims}
     # Remove inner duplicate
-    Dz = Dz - {d for D in Dz for d in retrieve_derivatives(D.expr) if z in d.dims}
-    Dz = {d: d._eval_at(lhs).evaluate for d in Dz}
+    dzs = dzs - {d for D in dzs for d in retrieve_derivatives(D.expr) if z in d.dims}
+    dzs = {d: d._eval_at(lhs).evaluate for d in dzs}
 
     # Finally get functions for evaluated derivatives
-    funcs = {f for f in retrieve_functions(Dz.values())}
+    funcs = {f for f in retrieve_functions(dzs.values())}
 
     mapper = {}
     # Antisymmetric mirror at negative indices
@@ -39,7 +39,7 @@ def freesurface(model, eq):
             mapper.update({f: s * f.subs({zind: INT(abs(zind))})})
 
     # Mapper for vertical derivatives
-    dzmapper = {d: v.subs(mapper) for d, v in Dz.items()}
+    dzmapper = {d: v.subs(mapper) for d, v in dzs.items()}
 
     fs_eq = [eq.func(lhs, rhs.subs(dzmapper), subdomain=fsdomain)]
     fs_eq.append(eq.func(lhs._subs(z, 0), 0, subdomain=fsdomain))

From 353af4f7d3a38e855f3c88d91af8317d9eda0e10 Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Wed, 3 Apr 2024 09:15:43 -0400
Subject: [PATCH 09/29] api: switch to recommended scipy i0 when available

---
 .github/workflows/pytest-core-mpi.yml         |  8 ++++++
 devito/operations/interpolators.py            | 28 +++++++++++++------
 devito/types/sparse.py                        | 12 ++++----
 examples/seismic/acoustic/acoustic_example.py |  2 +-
 examples/seismic/utils.py                     |  2 ++
 5 files changed, 37 insertions(+), 15 deletions(-)

diff --git a/.github/workflows/pytest-core-mpi.yml b/.github/workflows/pytest-core-mpi.yml
index a3963fe866..62ae44f511 100644
--- a/.github/workflows/pytest-core-mpi.yml
+++ b/.github/workflows/pytest-core-mpi.yml
@@ -70,9 +70,12 @@ jobs:
             - name: gcc
               arch: gcc
               os: ubuntu-latest
+              mpiflag: ""
             - name: icx
               arch: icx
               os: ubuntu-latest
+              # Need safe math for icx due to inaccuracy with mpi+sinc interpolation
+              mpiflag: "-e DEVITO_SAFE_MATH=1"
       
       steps:
       - name: Checkout devito
@@ -85,3 +88,8 @@ jobs:
       - name: Test with pytest
         run: |
           docker run --rm -e CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }} -e OMP_NUM_THREADS=1 --name testrun devito_img pytest tests/test_mpi.py
+
+      - name: Test examples with MPI
+        run: |
+          docker run --rm ${{ matrix.mpiflag }} -e DEVITO_MPI=1 -e OMP_NUM_THREADS=1 --name examplerun devito_img mpiexec -n 2 pytest examples/seismic/acoustic
+          docker run --rm -e DEVITO_MPI=1 -e OMP_NUM_THREADS=1 --name examplerun devito_img mpiexec -n 2 pytest examples/seismic/tti
\ No newline at end of file
diff --git a/devito/operations/interpolators.py b/devito/operations/interpolators.py
index 38a3571b17..3abf7669f5 100644
--- a/devito/operations/interpolators.py
+++ b/devito/operations/interpolators.py
@@ -5,8 +5,14 @@
 import numpy as np
 from cached_property import cached_property
 
+try:
+    from scipy.special import i0
+except ImportError:
+    from numpy import i0
+
 from devito.finite_differences.differentiable import Mul
 from devito.finite_differences.elementary import floor
+from devito.logger import warning
 from devito.symbolics import retrieve_function_carriers, retrieve_functions, INT
 from devito.tools import as_tuple, flatten, filter_ordered
 from devito.types import (ConditionalDimension, Eq, Inc, Evaluable, Symbol,
@@ -216,7 +222,7 @@ def _positions(self, implicit_dims):
         return [Eq(v, INT(floor(k)), implicit_dims=implicit_dims)
                 for k, v in self.sfunction._position_map.items()]
 
-    def _interp_idx(self, variables, implicit_dims=None, pos_only=None):
+    def _interp_idx(self, variables, implicit_dims=None, pos_only=()):
         """
         Generate interpolation indices for the DiscreteFunctions in ``variables``.
         """
@@ -238,10 +244,8 @@ def _interp_idx(self, variables, implicit_dims=None, pos_only=None):
         # Position only replacement, not radius dependent.
         # E.g src.inject(vp(x)*src) needs to use vp[posx] at all points
         # not vp[posx + rx]
-        if pos_only is not None:
-            idx_subs.update({v: v.subs({k: p
-                             for (k, p) in zip(mapper, pos)})
-                             for v in pos_only})
+        idx_subs.update({v: v.subs({k: p for (k, p) in zip(mapper, pos)})
+                         for v in pos_only})
 
         return idx_subs, temps
 
@@ -368,7 +372,7 @@ def _inject(self, field, expr, implicit_dims=None):
         implicit_dims = implicit_dims + tuple(r.parent for r in self._rdim)
 
         # List of indirection indices for all adjacent grid points
-        idx_subs, temps = self._interp_idx(list(fields), implicit_dims=implicit_dims,
+        idx_subs, temps = self._interp_idx(fields, implicit_dims=implicit_dims,
                                            pos_only=variables)
 
         # Substitute coordinate base symbols into the interpolation coefficients
@@ -465,6 +469,14 @@ class SincInterpolator(PrecomputedInterpolator):
                 4: 4.14, 5: 5.26, 6: 6.40,
                 7: 7.51, 8: 8.56, 9: 9.56, 10: 10.64}
 
+    def __init__(self, sfunction):
+        if i0 is np.i0:
+            warning("""
+Using `numpy.i0`. We (and numpy) recommend to install scipy to improve the performance
+of the SincInterpolator that uses i0 (Bessel function).
+""")
+        super().__init__(sfunction)
+
     @cached_property
     def interpolation_coeffs(self):
         coeffs = {}
@@ -487,7 +499,7 @@ def _weights(self):
     def _arg_defaults(self, coords=None, sfunc=None):
         args = {}
         b = self._b_table[self.r]
-        b0 = np.i0(b)
+        b0 = i0(b)
         if coords is None or sfunc is None:
             raise ValueError("No coordinates or sparse function provided")
         # Coords to indices
@@ -499,7 +511,7 @@ def _arg_defaults(self, coords=None, sfunc=None):
             data = np.zeros((coords.shape[0], 2*self.r), dtype=sfunc.dtype)
             for ri in range(2*self.r):
                 rpos = ri - self.r + 1 - coords[:, j]
-                num = np.i0(b*np.sqrt(1 - (rpos/self.r)**2))
+                num = i0(b*np.sqrt(1 - (rpos/self.r)**2))
                 data[:, ri] = num / b0 * np.sinc(rpos)
             args[self.interpolation_coeffs[r].name] = data
 
diff --git a/devito/types/sparse.py b/devito/types/sparse.py
index a5f711f7cf..85910ff955 100644
--- a/devito/types/sparse.py
+++ b/devito/types/sparse.py
@@ -822,16 +822,16 @@ def __init_finalize__(self, *args, **kwargs):
         self._coordinates = self.__subfunc_setup__(coordinates, 'coords')
         self._dist_origin = {self._coordinates: self.grid.origin_offset}
 
-    def __interp_setup__(self, interp='linear', r=None, **kwargs):
-        self.interpolation = interp
-        self.interpolator = _interpolators[interp](self)
-        self._radius = r or _default_radius[interp]
-        if interp == 'sinc':
+    def __interp_setup__(self, interpolation='linear', r=None, **kwargs):
+        self.interpolation = interpolation
+        self.interpolator = _interpolators[interpolation](self)
+        self._radius = r or _default_radius[interpolation]
+        if interpolation == 'sinc':
             if self._radius < 2:
                 raise ValueError("'sinc' interpolator requires a radius of at least 2")
             elif self._radius > 10:
                 raise ValueError("'sinc' interpolator requires a radius of at most 10")
-        elif interp == 'linear' and self._radius != 1:
+        elif interpolation == 'linear' and self._radius != 1:
             self._radius = 1
 
     @cached_property
diff --git a/examples/seismic/acoustic/acoustic_example.py b/examples/seismic/acoustic/acoustic_example.py
index aced327cdc..bbcc4a7c41 100644
--- a/examples/seismic/acoustic/acoustic_example.py
+++ b/examples/seismic/acoustic/acoustic_example.py
@@ -103,4 +103,4 @@ def test_isoacoustic(fs, normrec, dtype, interp):
     run(shape=shape, spacing=spacing, nbl=args.nbl, tn=tn, fs=args.fs,
         space_order=args.space_order, preset=preset, kernel=args.kernel,
         autotune=args.autotune, opt=args.opt, full_run=args.full,
-        checkpointing=args.checkpointing, dtype=args.dtype)
+        checkpointing=args.checkpointing, dtype=args.dtype, interpolation=args.interp)
diff --git a/examples/seismic/utils.py b/examples/seismic/utils.py
index 42ca0ced72..835961fa02 100644
--- a/examples/seismic/utils.py
+++ b/examples/seismic/utils.py
@@ -253,4 +253,6 @@ def __call__(self, parser, args, values, option_string=None):
                         type=float, help="Simulation time in millisecond")
     parser.add_argument("-dtype", action=_dtype_store, dest="dtype", default=np.float32,
                         choices=['float32', 'float64'])
+    parser.add_argument("-interp", dest="interp", default="linear",
+                        choices=['linear', 'sinc'])
     return parser

From 7394bc5393da46b9e339d5cd5fece44e722e986d Mon Sep 17 00:00:00 2001
From: Mathias Louboutin <mathias.louboutin@gmail.com>
Date: Mon, 30 Oct 2023 09:55:32 -0400
Subject: [PATCH 10/29] examples: fix subdomain notebook

---
 examples/userapi/03_subdomains.ipynb | 109 +++++++++++----------------
 1 file changed, 46 insertions(+), 63 deletions(-)

diff --git a/examples/userapi/03_subdomains.ipynb b/examples/userapi/03_subdomains.ipynb
index 696cef59fd..7355e36868 100644
--- a/examples/userapi/03_subdomains.ipynb
+++ b/examples/userapi/03_subdomains.ipynb
@@ -432,15 +432,7 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Operator `Kernel` ran in 0.01 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#NBVAL_IGNORE_OUTPUT\n",
     "grid = Grid(shape = (10, 10), subdomains = (mid, ))\n",
@@ -563,15 +555,7 @@
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Operator `Kernel` ran in 0.01 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#NBVAL_IGNORE_OUTPUT\n",
     "op2 = Operator([eq1, eq2, eq3])()"
@@ -630,7 +614,22 @@
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mloubout/.local/lib/python3.8/site-packages/numpy/core/getlimits.py:518: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
+      "  setattr(self, word, getattr(machar, word).flat[0])\n",
+      "/home/mloubout/.local/lib/python3.8/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
+      "  return self._float_to_str(self.smallest_subnormal)\n",
+      "/home/mloubout/.local/lib/python3.8/site-packages/numpy/core/getlimits.py:518: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
+      "  setattr(self, word, getattr(machar, word).flat[0])\n",
+      "/home/mloubout/.local/lib/python3.8/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
+      "  return self._float_to_str(self.smallest_subnormal)\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "from devito import (TimeFunction, VectorTimeFunction, TensorTimeFunction,\n",
@@ -742,18 +741,7 @@
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Operator `initdamp` ran in 0.02 s\n",
-      "Operator `pad_lam` ran in 0.02 s\n",
-      "Operator `pad_mu` ran in 0.02 s\n",
-      "Operator `pad_b` ran in 0.01 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#NBVAL_IGNORE_OUTPUT\n",
     "so = 4 # FD space order (Note that the time order is by default 1).\n",
@@ -822,7 +810,7 @@
     "\n",
     "u_v_l = Eq(v.forward, model.damp * (v + s*ro*div(tau)), subdomain = model.grid.subdomains['lower'])\n",
     "u_t_l = Eq(tau.forward, model.damp * (tau + s * l * diag(div(v.forward))\n",
-    "                                      + s * mu * (grad(v.forward) + grad(v.forward).T)),\n",
+    "                                      + s * mu * (grad(v.forward) + grad(v.forward).transpose(inner=False))),\n",
     "           subdomain = model.grid.subdomains['lower'])"
    ]
   },
@@ -838,20 +826,13 @@
    "execution_count": 33,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Operator `Kernel` ran in 13.59 s\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "PerformanceSummary([(PerfKey(name='section0', rank=None),\n",
-       "                     PerfEntry(time=13.584479000000002, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
+       "                     PerfEntry(time=12.63165, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
        "                    (PerfKey(name='section1', rank=None),\n",
-       "                     PerfEntry(time=0.0003199999999999996, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
+       "                     PerfEntry(time=0.0011449999999999989, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
       ]
      },
      "execution_count": 33,
@@ -879,50 +860,42 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 800x600 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 800x600 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 800x600 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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IIYQQl+EMJ7ECB5yEEEIIcRkOOIkVOOAk5DbHmQReVFS+z/V9VstVFt5OvtHkPqvlrLZNiZ4QQpzDASchhBBCXMbj2l9Ft0luLzhrTQghhBBC3ApnOAm5hbAqj1uxAeDSJcO+csX8OLKerOOKpF4RUrsrsrfcV726eR0pj1er5riOXs+KrUMZntwueF37q+g2ye0FZzgJIYQQQohb4QwnIYQQQlzGAxU/e0UfztsPDjgJqWI4k7PNpG1924pdnnLulMcrC6syvJTOzWyr5ZzVcSa9U24nhNxucMBJCCGEEJdhHE5iBQ44CSGEEOIyHHASK/CZEkIIIYQQt8IZTkIqEbOwRlZ9My9ccGw722fVN1OGRdIxCw/kio+j7q/oLHSQo/PUt2/UR9XqdVv19QwMdGw722fV11NCP09SVeAMJ7ECnykhhBBCCHErnOEkhBBCiMtwhpNYgQNOQtyI1Yw9VqXy7Gxr5XRp2YYuw/r5GXZ4uGMbAO64w7Br1zbsWrUM2+dSrlrp7FnDlifuTPeWN0hqyYFONOfQUMPWTrywerDdPnnS+PzXXw373Dlrp33xolpOv/+OTs2ZpC5P21XpXUKJnRBSlam0HxFXrlxBSkoK/v73v6NVq1YIDQ1FtWrVUKtWLfTq1QurV692Wj85ORndu3dHeHg4/Pz80KRJE7z00ku4YPatTwghhBC34+mmP3J7UWnPdPPmzYiPj8ebb76JY8eOoV27dujbty/uvPNOrFq1Cj169MCoUaNQWlpapu7bb7+NLl26YN26dWjatCl69uyJnJwcTJ06FXFxcTgrpyQIIYQQQkiVotIkdU9PT/Tr1w/PPfcc2rdvr+xbtGgRHn/8ccydOxdt27bFsGHD7PtSU1MxYcIEeHl5YdWqVejWrRsAoKCgAL169UJKSgpGjx6NpUuXVtalEFIGKZ2bZQYCzKVzKd1KW9/OyzPsy5fVclKBltKrlL0jI9U6MTGGHREoJPGDB9WCcvsLYR89atjHjql1pIZ95oxh5+crxUqcLRO/hqdcLg4AAQGGfeedhi0vFoCPuOCo+vUNu0kTo5C0AaCdsX3igiHJHz6sFpOXKy/VzFUCAHx9DVs+Vymv69tm0rsuw5tlLqLUTtyNByo+FSVTW95+VNoMZ6dOnbB06dIyg00AeOyxxzB8+HAAwCeffKLsS0xMRGlpKUaMGGEfbAKAv78/5s2bB09PTyQlJeGg/g+SEEIIIW7Hy01/5PaiyrhJtGjRAgCQlZVl/6ywsNDu2zl48OAyderVq4e2bdsCAJYvX14JZ0kIIYQQQspLlRlwpqenAwBqiyWwaWlpKCgoAADExcU5rGf7PDU11c1nSAghhBAdLhoiVqgSYZFOnjyJ+fPnAwD69etn/zwjIwMAEBoaiqCgIId169atq5R1xOXLl3FZOLzl5uaaliXECs7CHUm/PelzCQA5OYZtFn5H9+E0ixSkhy4SLoqKW2J06HljY8sWtdKsHYa9Q9jaD7hccVKiNUgXxQK1ZRQKu8jE1ikRtvIPR/Pz9BbnI22faz9cbfgLW7o8hgk7WHegvKa2AEBEmzYObQDAw+3s5pFso8WDJi6ugPpszZ4/YO7DKZ95SIhaR35F6v6dEvp0EkJuBjf9R0RRURGGDBmCnJwcxMbGYtSoUfZ9edf+WwfIBQIagde+WZ0NIhMTExESEmL/sw1SCSGEEHJjeKDiZze5aOj246YPOEePHo2UlBTccccdWLp0KXx8fCr8GAkJCcjJybH/ST9RQgghhBDiXm6qpP7cc89h3rx5qFGjBjZs2IBGjRop+20yer4WRkViC/weHBxsWsbX1xe+Mh4JIS4gZXQ9WY7VEEdSRpWRgvQQRxIpo8owRrpbc9jRvcbG52sN+6uv7GbJN98odWQgIxnNVtcL5OWayd76l4n86VjdSTm5LduTx9FleLktpXv9vKX8b3be1bWHFPx//2e3w4WtRZSCp4i4Ef3QQ4Ytommcb9NSqbNnj2HLMEt6KGG5Ld0yZL/T+6DsQ9INQ5fXZcgsyuukImBqS2KFm/ZMJ0yYgHfffRehoaFYv369fZW6pP41p7Ts7Gy7vK5jm62sLx3YCCGEEEJIleGmDDj/8Y9/4K233kJISAjWr19vugK9cePG8Pe/6va/R04NCGyft2zZ0uF+QgghhLgPrlInVqh0SX3ixIn497//jZCQEGzYsAGtWrUyLevj44NHHnkES5YswYIFC9CxY0dl/y+//IJt27YBAPr06ePW8ya/H8yyBjnLICNVWZl1RpdKdYndhpQ9pWwOAO2MxdAIO7jN2JiiZdcSsWhPi+XRUjaXEjOgSuXyy8BfKyfl5LtM7DIZe/7wB8OWCoSWDUjJGiQzCsmV6bpbjbzJcin4Tz+p5cSS8dPiY2nr90TuOyHsNK1cmHBPiBT2Xf/5j1FG+17q+uijdjtu4B/tth48QMrtsq/J7EZ6H5SuF86yXcm+JuV1ZicirkJJnVihUp/pyy+/jGnTpiE0NPS6g00bEydOhIeHBz766COsW7fO/nlBQQGefPJJFBcXo1+/fmii/7MjhBBCCCFVgkobcK5cuRKvv/46ACAmJgbvvfcehg8fXubvhRdeUOq1bNkS06dPR3FxMbp3746OHTviscceQ0xMDFJSUtC4cWPMmTOnsi6DEEIIIYKqKKkvWbIEHTp0QI0aNRAQEIDmzZvjX//6F65oMX2t8u2336J///6oWbMmqlevjgYNGuDZZ5/F6dOnndY7deoUxo4diwYNGsDX1xc1a9ZE//79sXfvXqf1CgsLMW3aNDRv3hwBAQGoUaMGOnTogKVLlzqtZ6v77rvvol27dggLC0P16tURGRmJbt26YdGiReW67orEo7S0tLQyDjR//nyMGDHiuuXq1auHo3q0ZADJycmYPn06du3ahfz8fERFReHRRx9FQkKCaVB4M3JzcxESEoL09BwEBZmvbie/D5wFcZcy+m+/Gfa5c2odKZ3/+qth66vPpWwZKXRqGVO8UdGPaqXPPjPsjz6ym8ekrAzgqLCzhS2/uPXeHiHsaCln9+ihFuzd2+G+vUeNgOe6LHzggGFLiVjKwoAqDcv7LeVefaW1vHfSBaFZM7WcdEdoWV+I519+adgrVqiVxL4j4p/TCbWUsiJeroAPFXZ9rU6kdCeQ34dDhijl0rzvsdsyHr+8d7pULgNxiIRtZRIE3HGHYdeoYdhm8jpAiZ0AeXm5aNgwBDk5OfaoMLb/pdugJlaoCC4A+COgHM8q48aNw4wZM+Dt7Y1OnTohMDAQGzduRHZ2Ntq1a4f169fDz8/PcntLly7FoEGDUFRUhFatWqFBgwbYs2cPjhw5gpo1a2LLli2I0X2hcDVbYvv27XH69GlER0cjLi4OGRkZ2L17N7y9vbF48WKH7oAFBQXo0qULtm3bhtDQUHTq1AkXLlzAxo0bUVRUhAkTJuDNN990eK7Hjh3DQw89hB9//BHh4eFo06YNAgICkJWVhe+++w7dunWzNGh1B5Xmw2mbwXSV+Ph4xMfHV9wJEUIIIeSG8UDFB2p3tb0VK1ZgxowZCAwMxObNm+0Lis+ePYtOnTphy5YtmDx5sumATefEiRN44oknUFRUhA8++AAjR44EABQXF2P48OH47LPPMHjwYOzcuRMeHsZZl5aWYuDAgTh9+jSGDh2Kjz76CF7XfrnNnTsXo0aNwrBhw5Ceno5aml/7pEmTsG3bNsTGxmLjxo0Iv/ar8dtvv0WHDh0wffp0dOjQAT20iYGLFy+iS5cuOHjwIF599VVMmjQJ1cREQkFBAdLSdG/0yoN+uYQQQgi5LZg6dSqAq+s/ZPSa8PBwvP/++wCAWbNmIUfmGXbCO++8g4KCAsTHx9sHmwDg5eWF2bNnIyQkBLt378b69euVemvXrkVqaipCQ0Px/vvv2webADBy5Eh07twZFy5cwIwZM5R6v/32G2bPng0AmD17tn2wCQD3338/XnzxRQCwuyhKEhMTcfDgQYwcORKvvPKKMtgEAH9/f9x3332WrtsdcMBJCCGEEJfxctNfeTl+/Dh2794NABg8eHCZ/e3atUPdunVx+fJlrFmzxlKby69F/3DUXmBgIHr16gUAWLZsmcN6vXr1sqfgltja0+utWbMGhYWFiIqKQtu2bU3r7dixAydOGI4+V65csQ9U//73v1u6tsrmpmYaIuRmYRb6CDDPGiT9NDX3SWWf9EMMDVXLSR/DrvHC82/+fMOeOVOpc/K77+y2cIVU/DQB9WWWYYxk/Ibq/furlcaOtZt7A/9ktz/8UC2WJNbynXlCnsV2YWvOmaa5i/TMYcUmtpeJDaSnG6GU/u//pH+X5rCo3InGduvOO4fZ7X79hkHytPCZbHnha7sdPWuWUu7SkiV2+6D4XHaN77SzOSo6Tkxiot2utXatUq7Rs88atnBFWp9szBFIH1lA7avS11PPSCT7u3wPZF918P/RDv05SVUlNTUVABAWFoYGDRo4LBMXF4esrCykpqZi0KBBTtvLy8vD4WsO6GbxwuPi4vDpp5/aj62fi7N6AJCeno78/HwEXAsPd7160dHRCAsLw/nz5/Hdd98hIuKqJ/7evXtx9uxZREREICYmBvv378eyZctw4sQJ1KhRA+3bt0e3bt3g6Xnz5hk54CSEEEKIy1SVOJwZGRkAgKioKNMydevWVco6Qy5gNmvTrL3rnYutXmlpKY4ePYqmTZtaqgcAkZGROH/+vHLMffv22fdNnDgR//rXvyDXhE+bNg0tWrTAihUrnLbtTiipE0IIIcRl3BkWKTc3V/m7rIf+ENhSYAfIZBIaNnk7NzfXtIzenrM2zdq73rlImV3WdfUazl0LnZKamopp06ZhzJgxOHToEHJycrBhwwY0atQIqampeOSRR1wODXWjcIaT/G4wk9GdZQ2SUrmUKfUMQhK54FCG5QGAe71FyKPR79jNQqFha0qpEo5H/kKM1MrdJx3EX37Zbha88E+7Pf5lKLz9ZylhpwhbjxH3i7DlxUu9Vosvpcjg1Uw+BwAfXJ9CbfuUsOWXp7NzMOL+nDljSO9z5tRTasyZI9PkdrBbzz//J6XcFOEZcN+b/5/YMcVufqd9sctnKZP1Rgi3CQBo9uSTdttHxEXqOm6c3a5V6x5ZRQlL5cz9Q3chsYKZxE55nbgb20ygjVdeeQWvvvrqzTmZKoxtNvPKlSsYNGgQZgkXoPj4eGzYsAGNGzfGgQMHsHDhQgwdOrTSz5EznIQQQghxGQ9U/OymLcBQVlYWcnJy7H8JCQmm52GLyZ2vp8IVXLg2w2AltqeM8W3Wpll71zuXC2KmQ9Z19RrkuY4aNapMnaioKDzyyCMArsY1vxlwwEkIIYSQKklwcLDy5yszHGjUr18fwNVBqhm2fbayzqhXz1A/MjMzy9Webft69Tw8PJTjXK8ecDW4u37M6Ohoh7bE9vmvMjtJJUJJndy2OMsgZLYSHVAlSGk7k9Hld43MTxCxZ6VaUKxMPiakUrnKuUBrW667lmJv9cmTlXKZTxmyrvyBuy7gkCilxooDZFYjmaJNl6alP1FNYd9lYuvlREqbMvmORIobRQKX56AttVZWvYsUUIrUDqjXJG35MPXV9WKZOoyMHG+/rUrYb7/d1W4//LDhtvDBYcO+7z//VOo0ee01uy2dFvQzEDmR0ES4W0Tu32+379VmesJ797LbcgJDT9xm1o+tSu2U14lOVVk01KJFCwBX/RkzMjIcrlTfs+eqM4uM0WlGcHAwYmJicPjwYezZswexsbGW22vZsiX27t1r329Wr2HDhoo/p60ds3pHjhzB+fNXvyFs12ur5+HhgdLSUpw9e7aMKwJwNfg9AIdhmioDznASQggh5JYnMjISrVq1AgAsWLCgzP4tW7YgKysLvr6+6N69u6U2baknHbV34cIFrFq1CgDQt29fh/VWrlzpUB63tafX6969O3x8fJCZmYmtW7ea1mvTpo09JBIA1KpVC+2uLRpwJJlfuXIFmzdvBgA88MADji7V7XDASQghhBCXcecq9fIyadIkAMAbb7yBvXsNHeHcuXMYM2YMAGDs2LEICQmx71u+fDmaNGmCzp07l2lv3Lhx8Pf3R3JyMj4UakNxcTHGjBmD7OxstGrVCl27dlXqdevWDS1atEB2djbGjBmDYiG5zZ07FykpKQgMDMRzzz2n1KtRowb++te/AgDGjBljX30OXI21OW3aNADASy+9VOZcX3nlFQBXMw7tEAqaLf/6kSNHEBQUhBEjRji8d+7Go1QGavqdkJubi5CQEKSn5yAo6PqOw+TWRI+eIQNgS1lRlxitrEaPiVG3e/c2bP/P5hob174AbBwQGv1R8bn0bZGB2gGgfs+exsZSQ+IdPFxd3f355/vF1mphS9lcDwUi+7/jIOlXuceh3bChIbXLoPaAeo8iRdPhWmx2qe54ixvhLJKAWfSAw4fVcjI4enq6nGX40cQGAOmCIMVuZ/dO3p9H7NagQaoEt2C+WG3/6KN28+i1WRIb0sVCKt31hd1My7+M//kfu1kwxEjBt2KFWky/Rzbkc5HPS98n7erCG8KJax25DcjLy0XDhiHIycmxL1ax/S/dDyDIefXyHw9ALKAczyrPPfcc3n33XVSrVg2dO3dGQEAAUlJSkJ2djbZt22LDhg3w8/Ozl58/fz5GjBiBevXqKbE3bSxZsgSDBg1CcXExWrdujfr162P37t04cuQIatasiS1btiBG/6cA4NChQ2jfvj3OnDmD6OhotGrVChkZGdi1axe8vb2xePFi+0yoxJZKc/v27ahRowY6deqE/Px8pKSk4MqVKxg/fjymT5/u8NqnTJmCyZMnw9vbGw888ABq1aqFvXv34ujRo/Dz88OSJUvsi4cqG85wEkIIIcRlqtIMJwDMmDEDixYtwoMPPoht27ZhzZo1iIyMxBtvvIGNGzcqg00r9O/fHzt37kTfvn1x5MgRLF++HMXFxXjmmWfw/fffOxxsAkDjxo2xb98+PPPMMyguLsby5cuRkZGBvn37YufOnQ4Hm8DVnOebNm1CYmIi6tSpgzVr1mD79u148MEHsXjxYtPBJgC8/PLL+Oqrr9ClSxccPHgQq1atQnFxMYYPH469e/fetMEmwEVDhBBCCLnNGDBgAAYMGGCp7PDhwzFcpJB1xP3334+kpKRyn0etWrUwa9YsJS6mFXx8fDBx4kRMnDix3Mfs2rVrGYm/KsABJyGEEEJcxgNG3MyKbJPcXtCHkz6ctxUyFJLu9/ebiJ5zSkTPOabFpDHz22ws3Br791f3eb75L2PjxRft5i6tDZlpJkzYbYTt8/HHSp25l4bZ7VGjZAigz7TWdwtb+iveIWw1tI88soeHYffooZaSoZ5atzZsGSXE/4IMOwTzmFIXL6rlnKSqs6M7CEpJTDoVan6NBYFGqCYRUQg7dxq2vqDzyy8Nu7RUhkjaoRZUfD/PCVuGkGql1Rlitz74wAgVNbL6J0qpwieecHhUGS4pAirKutNrCwsAoOSFfyjlliwx7EOH4BDdz1b6dNYU0a5qiGhXeqQVhkm6vXDmw3kI7vHhbAzXfDhJ1YQ+nIQQQgghxK1QUieEEEKIy1SVwO+kakNJnZL6LY+U0WXoI2cZhKSMLj/XkYsPBw40bM/E15VyhS+/bLeljK6r8zLaTFwTEQBJBPjtOlCK7cCGDWvFlgx3pKcnCxX2fcI2Yss1aaJK6kOHGraU0e+tr4UAkvGFMjIMW8SIQ16eWkc+GBnjyGpKG2fI+EnS1nVckV8YdwjXApmBRIvntO+o8Z0g5fVPP1WbPnhQSuopwv5O2NlqJdQWtrFatEuXbkqp9QuFeN62rd3cc9AImKRnJ5IquJTXfaZMUcqVJBjx+xYuND43C5cEqJ4KUl6Xn4eGqnVkyCTK67c+ziT1dLhHUm8ISuq3E5zhJIQQQojLcIaTWIHPlBBCCCGEuBVK6pTUb0nM1FopozvLICRtXeGtX9+wRTIY+M8yVqIXipXogPlKYj0ccLNuhnR6ZNYau920qVHm0qWZWi3ZurjwMivOjbYbNjRWR48da5R47DG1Rs1zQhZOTTVsuYwfUJf8m0ni3ppgInXUatXMy+nbjtCPKbevXDFs2TEc1XN0TH15tVyG3aKF3Tx1h3q/Fy0ybBliLz1dRguQ7hCAurJd6sxtlFLVqz9rt3/4wfg8eqyR//nAWrVtqYibRUAAAB+xgr1grLGCXSSxgp5sRd4uKak7yyAlJXZnXg/k1sCZpJ4B90jqDUBJ/XaCM5yEEEIIIcSt0IeTEEIIIS7jgYqfvWLg99sPDjgJIYQQ4jJcNESswAEnuSWRrnnSvVD6cOrhjn4VUYRk+CQtOY2SVcf/s7nGhpMMQtJvs5Gw75FxhwBsHG5klOl8t8zM856wRQgiAGqmoI7CflQp9fe/G36So0cbn0df2GdsfPWd2nRWlmE7y/gj/RxlvBv5ue4LKctJu6J9OOXDlDagdg5pO4ufJbeFM2PNunuUYn/rcJ/d7tHjXrs9Z47hP/vvf98HFeEoif8T9ial1KVLhgPy3Xc/Y7dTUgy/307hw5Q6niJuU5r4XO+r7UQ/9heOlvE9RtrtFSvUOvJdku+RWXQqfVt2DfpwEvL7hANOQgghhLgMZziJFfhMCSGEEEKIW+EMJ7klcBbtRia4cRYWSSrGMmRLu3ZquYg9K42NV16xm84yCMnwR1JGX/noJ0q5v3QWWXoUGV1+LrLgXK1lt1q0aG+3X31VLdWrjZDoN20ybBnjRsrKgKp7yrg2etoYuW0mozuT1J1pr2bn4ywjkVnmIquSupkfhr4tbT0Vj9CZo+sbGYD+9UIHu92u3V1KlVdfHWS3U1Nl3qkv1LYVtwojHFfnzoa8/sUXat/qJewSIa/rCYRkP35A9O8I4VvSrp1sDUhONmx56+Q7pj9+X1/DduZRQYn91ocznMQKfKaEEEIIIcStcIaTEEIIIS7DGU5iBQ44SZXFLJsQoMp6OTmGLSU+XSmVUl6zZoZ9r/ePasHERLt5QMimJ0QRKYYCagYhuRJdldAB4F1hZ8oWhD1YqTFoUGO7PWWK8Xn0yW1q0wvFKmp5I8xkcwC4Q6yAv/NOw7YqqZutRNeP60RGL7Hwr8UTJeY7zeR1wHwFu1VJ/cwZwz53zrzcASGBiz7TKy5OqdJs6R/t9ssvG+4Rn3+uSu/AAmFLed3oP3/5y9+UGikpRr/rJJ7/JS0jkUiypfTvZqLf3ztPzZF1spmRZWmP6GbOXFhkd/DzM2xnHhWU1wm5feGAkxBCCCEuwxlOYgUOOAkhhBDiMhxwEitwwEluCZwtPpZSnlRAdSKFDt41Xki0o99Ryh3bscNuHxWfhwk7rkkTpc6RWUZAbvOA7oAqozcX9v+zW88/rwr2Ukb3TxYr6A8eVMopN0XK3jKyvZTQAaB2bcOWsrkuqZtI51IOdxabHRYXnJvh7e3s34+PKOej7PEO9bfbiiwvO5TuZiB1YrlPRjwHVIldRkaXHXLLFqVKdBNj33/+Y6wEr1WrsVLu7befFlv/Ffb3wlb7VufO/7DbP/9s9Me4R/6glCsQ/eao+DxU9PvId95R6nSdM8dunzxpPAsZAEF/98zyA+ieF/rqdkLI7QkHnIQQQghxGc5wEivwmRJCCCGEELfCGU5CCCGEuAxnOIkVOOAkVQoZCsksig1gngxGZhPSfcPatBEb8+fbzcIPP1TKSc9I+YLI6ti6VanTtI7cMssgBKjhjwy/zYQEw29z6hQtBNDSpeLkNL9NifQ3lA6r0odT2oBpuKMSzRdSiTx0yfHnOlZ8M61itS3nSYw8hW34dko/TwDwlB3HmV+r9NuUdY6JwEN6yKXvvrOb/qKDv/Xmo0qx6tWN55eY+P/EHtlXZbgkQPa7pk3/x25fPK721TbCj3ej+Fz2rLu0d8JHvDxt2hnnI91Vrb6jzqJnyX0MkUTI7QUHnIQQQghxGQ8AHh4eFdtmaWmFtkduPpy1JoQQQgghboUznKRKIaVTZ5K6s4xCNrTIRWhUJDIKzZxpN3VhskDY9wrb5+OP7XbXgTJIEnDp0kyxJVtsoLVuZBGS4Y8UGf3zz9UqP/9s2FJnlJmBAKCO0PXr1jVsKbVrsnBJdUNOlve7SAtDJalIqbyisSrxO5feDXeC6qHGc/bUtWCzuD++voZ9/LhaR8YOku4R2jOfOmWQ3b50yegnb78ts1Cpsrfsd7I/dh34rFJqvejHTZ54wm7vc9jSVVqK96WRkNebNHGcgQgwz0Kku7rI2yifCyX1Wwhvb6CCZzhRWlq1v2xIueGAkxBCCCGuwwEnsQAldUIIIYQQ4lY4w0luKnJVOmAuqeuyudyWdaR6rKxKB4A3P7ObJ8Vq4RNaMZl3pn7PnnZ77qVhdnvDhrVarR3Cltl8/qKUGjTIyCgjMwgpK9GlhA6o2qLMDFS/vlpOrkw3kdELi9TfmGYrzitiYuHKFcOuVs3x58723WgdZ1i9PtkH5cp2APCp5Tj7kmL7+akNSs1YZi7Sn7noD1OmDLDbJ08a/efzz9W+BXwqbKM/btgQo5Sa+6jRj0f2NI5zYtUqw9ZajhDvS63PjPeozQtT7bbMOgSYu73o77JZFiLK67cQ7prhJLcVnOEkhBBCCCFuhTOchBBCCHEdznASC3CGkxBCCCGEuJWbOsP5j3/8A//+978BAK+99hpefvllh+WSk5Px1ltvYdeuXcjPz0e9evXQr18/JCQkIFCPsUFuaaRvnQyFpIdFktvS1ytGuKuFHdymVvroI7t5WHys/+pqKTeEL90o39/EjtVaLemM2tFutWjRXikl/Tb9k1caGzJEju6wZua3KX02ATWLkHgvCi4ZV6j7Lt6or6buW+lKObN9FV3HFf9OZ6GUikTmIn8ZbspZzCUzpD8noPQH/+pGP5kypZcoovat1FSR4QhfCFvtq6NGGc7NIy8b/bulCOe0ESryfakl3qOwHj3sdkzMH5U60lfT6rvMr/NbFC8vwLOC569KSq5fhtxS3LQZzm3btmH69OnXzU7w9ttvo0uXLli3bh2aNm2Knj17IicnB1OnTkVcXBzOSs90QgghhBBS5bgpA86CggIMHz4ctWvXxl/+oq+0NEhNTcWECRPg5eWF1atXY/PmzVi8eDF+/vlndO7cGYcOHcLo0aMr8cwJIYQQouDt7Z6/G2DJkiXo0KEDatSogYCAADRv3hz/+te/cMWqLKPx7bffon///qhZsyaqV6+OBg0a4Nlnn8Xp06ed1jt16hTGjh2LBg0awNfXFzVr1kT//v2xd+9ep/UKCwsxbdo0NG/eHAEBAahRowY6dOiApTKiiQX+8Y9/wMPDAx4eHpiihEapfG6KpJ6QkID09HSsXr0aixcvNi2XmJiI0tJSjBgxAt26dbN/7u/vj3nz5iE6OhpJSUk4ePAgmuhpZUiVRYZC0iVds+xCeigVS6GQXlVfzGMnTxrtic81YRrVJ0+224OH+4g9Hwhbk0DRXNiPGqfwqloq+qSQ+aWMLtEzCJnJ6FJCB1ASGGy3L1VwuCMXv6OrFGbXUBGhlKTbQnXxHDxrOSptoXHRV2U/iRad/dVXVQn7L395VGylC/t77WBGWKPBw40sRAtEv4947TWlhhTr5XsUKf75tdHO57DQ4Z1lBpPeCLLf6omdJAyTVMXw9q5Skvq4ceMwY8YMeHt7o1OnTggMDMTGjRvx4osvYtWqVVi/fj389JBlTli6dCkGDRqEoqIitGrVCg0aNMCePXswa9YsLFmyBFu2bEGM9Oe6RlpaGtq3b4/Tp08jOjoavXv3RkZGBpYuXYoVK1Zg8eLF6NOnT5l6BQUF6NKlC7Zt24bQ0FA8/PDDuHDhAjZu3IjNmzdjwoQJePPNN6973lJJLq0Ci7AqfYZz06ZNmDlzJoYNG4bu3bublissLMTq1Vd9jwYPHlxmf7169dC2bVsAwPLly91zsoQQQgi5ZVixYgVmzJiBwMBA7Ny5E1999RWSkpKQnp6O2NhYbNmyBZPFj6vrceLECTzxxBMoKirCBx98gF27dmHRokVIS0vDkCFDcOrUKQwePLjMgK60tBQDBw7E6dOnMXToUKSlpWHRokXYtWsXPvjgAxQVFWHYsGE4KX9cXmPSpEnYtm0bYmNjkZ6ejqSkJHz11VfYsWMHAgMDMX36dHz55ZdOz9uqklyZVOqA88KFC/h//+//oWbNmnjnnXeclk1LS0NBwdWs1nFxcQ7L2D5PTU2t0PMkhBBCiEWqkKQ+derVZAQTJ05Ey5bGEtDw8HC8//77AIBZs2YhJyfHUnvvvPMOCgoKEB8fj5EjR9o/9/LywuzZsxESEoLdu3dj/fr1Sr21a9ciNTUVoaGheP/99+ElpuVHjhyJzp0748KFC5gxY4ZS77fffsPs2bMBALNnz0a4UDXuv/9+vPjiiwCA119/3el525TkuXPnIiQkxNK1uptKldRfeOEFZGRkYPny5ahRo4bTshkZGQCA0NBQBAUFOSxTt25dpSy59ZASGqDK6Hl5jm1A/S6SinPwYeEXo818H5X1hX2fpqlmPvX/2e3P6+0Xe3YLO1Q9IRguH3//u9Ferzaaf8/CPYYtL1b6BdSpo9YxkdGlhA5UrIx+O0joVnGWxcgZZvdVkYW1Z2QqsV+8aN6I1KP3GP2n10BVwvv73++y2//+dzex5xftYEY//vxzo3+/8YvR7+974w2lxklxk46KzyPFOxY8ZIhSp35945+9lNH1d1luy1dCSupcvU6scPz4cezefbV/O1JG27Vrh7p16yIrKwtr1qzBoEGDrtumTUF11F5gYCB69eqFTz/9FMuWLcNDDz1Upl6vXr0cRtMZPHgwUlJSsGzZMiQmJto/X7NmDQoLCxEVFWVXcfV6kydPxo4dO3DixAlERESUKaMryc5cFyuTSpvhXL9+PT744AMMHDgQvXv3vm75vGvfQgEBAaZlbA8xNzfXaVuXL19Gbm6u8kcIIYSQCsDLq+JnN11w1LWpnWFhYWjQoIHDMuVRRvPy8nD4mjNyeZVW2/b16qWnpyM/P99yvejoaISFhQEAvhMpZ22UR0mubCplwJmTk4Mnn3wSd955J2bOnFkZh1RITExESEiI/c82M0oIIYSQ2wOb2hkVFWVapjzK6NGjR+22WZtm7V3vXGz1SktLleNYuYbIa6qXo2uwKcmzZ8++rpJc2VSKpD5u3DgcO3YMixYtUvwRnGGT0eXIX+fCNf0lODjYtAxw1Zdh/Pjx9u3c3FwOOm8izuReqSTKR3/5slpOrmxt1kzs+HSt3TwtXmLAycp0LeHAqFFySwbNln2xg1KnYcNWdluJ1LVpk1JOkUelzCJlc71vyndG1NHdEazI6L8nqdxVrN4jKb1bkdcBwF8+c/lc9YKyw5vJ61rfGj16gN1escLoj+npO7SzkvWM/j1qVKzdXqu9E7VeecVuyxXr8h27a+1aSJoNNSR1uWJdX6Uu33OrbiFcpV7FcHFG0inXYnTriqSvry98RZICSUUqo7I9Z22atXe9c5Eyu6x7I9dQXiW5sqmUAefy5cvh7e2N999/3+60a+PgtZAf8+bNQ3JyMmrVqoWFCxei/jXHvOzsbOTl5Tn048zKygIAe1kznHVQQgghhFRN9MmhV155Ba/q8ebITVeSrVBpi4aKioqwefNm0/1Hjx7F0aNHUa9ePQBA48aN4e/vj4KCAuzZswcdO3YsU2fPNQd6uRKNEEIIIZWIG2c4s7KyFBXT2eRRRSqjsj1bm45We5u1FxQUhPPnz5ueywWxQk7WdfUaXFGSK5tK8eHMzs5GaWmpw78nnngCwNVc6tKXwcfHB4888ggAYMGCBWXa/OWXX7Bt29Ug2o4CpxJCCCGkEnBjWKTg4GDlz9mA06Z22tRPR1hVRgHYJ8AAIDMzs1zt2bavV8/Dw0M5zvXqAcCxY8fKHFMqyR06dFD+1q1bB+CqktyhQwcMHDjQtG13clMyDVll4sSJWLp0KT766CP069cPDz/8MICrAU2ffPJJFBcXo1+/fswydAtgll3IWVgkaevIH3BR1UXooa++spvS10xH9piCF/6p7FsXcEhs/SjsO4TdWakzdqxhR1/YZ2xofqRKPCeZKUja+q9T4bAqM9rofm2uZhEiFYvVjET+Zil2APMXQfYnrW9FNzH63dix99rt555T+yogQ30Z/XvdOqPfFySp70QTEx9Oad8l3j0AiHr6absdHm6EbNJ9OM0u1VnWIfka0Z+T2GjRogUA4Ny5c8jIyHC4Ur08ymhwcDBiYmJw+PBh7NmzB7GxsWXKmLXXsmVL7N27177frF7Dhg0Vf05bO2b1jhw5gvPnzwMwrtdGeZXkyuam5FK3SsuWLTF9+nQUFxeje/fu6NixIx577DHExMQgJSUFjRs3xpw5c272aRJCCCG/X6pI4PfIyEi0anV1wZwjZXTLli3IysqCr6+v00yHEpuC6qi9CxcuYNWqVQCAvn37Oqy3cuVKh/K4rT29Xvfu3eHj44PMzExs3brVtF6bNm2UGJyuKMmVTZUecALA888/jw0bNuChhx7Cvn378MUXXyAwMBAJCQnYvXt3lfVVIIQQQkjlMmnSJADAG2+8gb17jUQg586dw5gxYwAAY8eOVfwxly9fjiZNmqBzZ10NuOob6e/vj+TkZHz44Yf2z4uLizFmzBhkZ2ejVatW6Nq1q1KvW7duaNGiBbKzszFmzBgUC5lv7ty5SElJQWBgIJ577jmlXo0aNfDXv/4VADBmzBicO3fOvm/v3r2YNm0aAOCll14q342pAtx0SX3+/PmYP3++0zLx8fGIj4+vnBMibseqpC6Tr+g/dmUUIewwwr6UfPON3T6vHTdU2NX797fb41/WCkKmKJNhJwzJpEmTe5Qajz0mNr76zrB1vwD5A+kOIdFLSV1KrQAKixzL6M6kW4Y/cj9m99hKuCRAfa4+2jNX+sNvvxm21KNliCQAEEGgH3vMkNRnz1b76sGDcnuTsI1+//LLjZU6b4n3JXTJErst3zH57gGAp3gvIyN72W19ckW+52aSOt1Fqji2wO9VgN69e+Nvf/sb3n33XbRp0wadO3dGQEAAUlJSkJ2djbZt2+K1115T6uTk5ODQoUO4pP9DAhAREYH58+dj0KBBGDlyJObNm4f69etj9+7dOHLkCGrWrIkFCxbA49oiJxseHh74/PPP0b59e3zyySfYsmULWrVqhYyMDOzatQve3t745JNPUKtW2fRjU6dOxa5du7B9+3Y0bNgQnTp1Qn5+PlJSUnDlyhWMHz8ePXr0qNgbVwlU+RlOQgghhBCrzJgxA4sWLcKDDz6Ibdu2Yc2aNYiMjMQbb7yBjRs3ws/Pr1zt9e/fHzt37kTfvn1x5MgRLF++HMXFxXjmmWfw/fffIyYmxmG9xo0bY9++fXjmmWdQXFyM5cuXIyMjA3379sXOnTtNFzz7+/tj06ZNSExMRJ06dbBmzRps374dDz74IBYvXozp06eX+55UBarGTxJCCCGE3Jq46HPpTgYMGIABAwZcvyCA4cOHY/jw4U7L3H///UhKSir3edSqVQuzZs3CrFmzylXPx8cHEydOxMSJE8t9TB0rSnJlULV6CLltMZOCdQVDbsuV7TJBCwAoPyhnGdKdXDWriyPRckMsK3/7z8VaSbkyXcZWa2O3hg5Va9Q8J+rIkBz6l7CU0WvXNmwhqZZU91eqFFmUFquajF6s39ZKoCqsWJbPQcrrgPl74K09c08psct+IiV1fbm36HeyPw4dqkrqL73URmztFbZR5+231Yf31mbjfYkQkrp8U/SoEFFCUo8Za0jqevpnMxndmaQut6vCMyeEXB8OOAkhhBDiOlVwhpNUPdhDCCGEEOI6HHASC7CHkErHqqQuy+mLeCOqi/WxQroTIeDLdO5ooW/uDfyT2JOilZStGKt1PTwMKbLMAsHUVMO+fNmw9bBdd95p2PKihM+Afk/MZHR3SujulMMrYsWx2f+2ijjvipRo9WdktoJdf+b+0odE9hPZf0S4FADqqnXRH3v0UCX1l182+nFp6UqxRyY82KTU2RtohItpKS4iTVzgaahIST3iBeN9DQ0NU8pJzwCrkjoh5NaDA05CCCGEuI47wiKVllZse+Smw7BIhBBCCCHErXCGkxBCCCGu4w4fTs5w3nZwwEnchvSnkz5YMrOIMx9O+f1VJoPpwYOGvWeP3ZR5gdRAM1AcL0WGMqihYQBAOgIaPpzSb/Pe+vJIAJJP6Ue7iu58KrdlKCRvH7td5MSH0xW/TVf8GivCZ86dfndmbd+sdQtW/T6tPr+S6kZ/8DTpM2X6lvThPGX0x3t7qn21Rw8j1NeqVTKjkAxypL4TH35o+HDOFi+C//Lldlt7I5T3Ur6v4eF/VIodE/GUzHw45XcGoIZJk8+cIZIIqbpwwEkIIYQQ1+EMJ7EAfTgJIYQQQohb4QwnqRSkBColXl1Sl5KjlMdq1dIa/M6Q6HLz8oz2RJEo/SR697abSS/IHb9oBQOEbYSUiY8XHx84oFaR6VLMQtro29Wr202zDDSAdRnWinRuVea+VcPQVOZ5ywkds3vvTOJ19lzldfiIfuJUUpf9TvZHra/GxxuS9qpVMmTSemGr74TM6Df7zd52O0xI6pnq2SjvZbCQ1Gvdp0rq8h7Je2KWdQy4dfvnbQtnOIkFOOAkhBBCiOtwwEksQEmdEEIIIYS4Fc5wkkrHmXwspTOpEJaR1IVEJ3IOoUTY+sJ2ucz8zBOHxY6zWsGawjYkx9atxceHM9Qq8kKkBCovQt8W5S5dgCWcyeZWZEZ3SpEVMcEh27hZK+UrS651JrdLOdkn0KQ/6X1L9jspqWeofbV1aylpS0ldvjFq1IUzZ8T7It4jWeOoejbKe6lI6g+r5fz8DFuetrPvCVLFcEfg95KS65chtxSc4SSEEEIIIW6FM5yEEEIIcR13+HByhvO2gzOchBBCCCHErXCGk7gNMx8saethkSTSJa2MD+fRo3ZTZjiRv6Du0qrsPRomtrYLWz8Jo2bDhkaIpNhYUWTnOf10DSz62RUWGWfrLJuQWcYmnRv1c3M2QWElm0u1atbaq4iJELNrtRpSqiLuaWX5Fcp+4mPVP1hmHTqn9tXYPoYt+3d6unxj9FBhh+zW3qOP2O37RAl99kLJPCTeV/1dNnM9ld8N+r02+z5hpqGbBGc4iQU4w0kIIYQQQtwKZzgJIYQQ4jqc4SQW4ICTVApW5HV9W0ptnmdPqwWFRCcFcR9hezZpolTZsUNuHRO2HmvIkBabNTM+9b8gzkFkUQGgftlalNTNZHSrWVWsSrry1PT/CVKClJK4Xk5uK89FBqKqCP1ZlnOq8ZucXJlyxr4SIehYlWutPherz8iVsE+mWYecSeryQFpflf24WTOjr6uSuv5OGO+LfI9ainfMR4Q+AjRHFfG+6u9y9erGcV35niCE3BpwwEkIIYQQ1+EMJ7EAB5yEEEIIcR13BH53luWC3JJwwEkqHWdSmZkyjexsteCZM3bTTFLHH/6gVPnuO7klswvpS1uNTEMxMeLjkycNW/8ylNq0M9lTZhfKdtycVfnQmextJpXLU9Pr+HiLGQU9fIA8iWyLKWBuhu7p5J+ep9jn78zPIPD6kQQA9RaZSe8VIQXL4/iHWutbSgfQ+6roxzExUkaXGbb0d8J4X5T3SLxjTiV18b7q73JgoHEOZi4HlNAJufXhgJMQQgghruMOSZ0znLcdDItECCGEEELcCmc4SaXgijymyL+6pC6CWcvm/GWZyEilyuEDckuGpdYilqOG4ybkOegXYbYyXdOwpUQrZVhn98RM/dWDXPv5OT6sU6n8ksUo/FZ1fYmu35thZWbElSXdOmbXpx9fRB+Xq8J9tHJS3pbPVR7m4kXzQ1n1RpD9xDQIPKD2O9kZ9HdHbKuvSA1h6++E8b4cPiw+bmY0oLizACiQGzL4vHY+VroJV6lXcTjDSSzAGU5CCCGEEOJWOMNJCCGEENfhDCexAGc4CSGEEEKIW+EMJ6kwXMmQY9V3UfrVAVCyp8jwwEqHrinDvAC/bpBb0p9PDwETbLfCwx0fswy+voYtndK0X/2uuEJKX00zP00A8EGhsWGWSsclB1rtBMU+s+w9+razjD0Sq4mGTMM+OYkUpGRFcpZqSO5z5tcq9kn/TulbWb26j1kVxb/T6mSOknVIv0HyYmV/1BH9WOnfot+XfSeME//1V/FxZ+Md0x+XErZbvjvau2z2nK12W2d9RvdzJm7CHXE46ah728EBJyGEEEJcxx2SekW3R246lNQJIYQQQohb4U8IUmVRfuBq8WWk2GIqqYeGKnVycuSWkJ/LBHSRkqj4WOqe+q9vqes6y2Jjgplsrp+DqWwOmGuQJnI4ACWUjgy5o3swXMiGw31SKb18Wa1jpkw7U8qk9F5Nj8wjMLsk/fKkshwUZFxfYKC/sNU6gaGGtKyElNJvSpFJSCnxuS57m8nt+qPUwyk5xFmqKbP+CCj9WL1fckN/J4x+p7xH4h1zJqnLR+6tXZy31t/JLQhnOIkFOMNJCCGEEELcCn9CEEIIIcR1OMNJLMAnSqosyveNtoy3BBbQNFlVyrW2LNjyd55FGV3uCgpy/Lki4wLWV03LRqRULuTR335Tq+SchMN9qvuBqiZL25lUbnWVsZTRzbAqrztTmaV8bJYYCgBCQgy7Rg1P8XmwUq6GSMxjKUIAYJ7FKFD1Bahe3Tiu6Sps/Z664Mph/X+68b4ol+TswQiUHq29yxxXEHewZMkSvPfee/j+++9RWFiImJgYPP7443j++edRzWK/lXz77bd444038PXXXyMnJwe1a9dGjx49MHnyZNx1112m9U6dOoXXXnsNq1evxokTJxAaGoo//elPSEhIQMuWLU3rFRYW4u2338aCBQtw+PBh+Pj4oHnz5hg7diweffTRMuWvXLmCr7/+GuvWrcOmTZuQnp6O/Px83HHHHXjggQcwatQoPPLII+W+7oqEkjohhBBCXMc2w1nRfy4ybtw4DBgwAFu3bsUDDzyAhx9+GJmZmXjxxRfRqVMnXLTkJG2wdOlStGnTBkuXLkW9evXwl7/8BZ6enpg1axbuvfdeHFbyvRqkpaXh3nvvxXvvvQdPT0/07t0b9erVw9KlS9G6dWssX77cYb2CggJ07NgREydORGZmJh5++GE88MAD2Lp1K/r3748XXnihTJ3NmzcjPj4eb775Jo4dO4Z27dqhb9++uPPOO7Fq1Sr06NEDo0aNQmlpabmuvSLhgJMQQgghtwUrVqzAjBkzEBgYiJ07d+Krr75CUlIS0tPTERsbiy1btmDy5MmW2ztx4gSeeOIJFBUV4YMPPsCuXbuwaNEipKWlYciQITh16hQGDx5cZiBXWlqKgQMH4vTp0xg6dCjS0tKwaNEi7Nq1Cx988AGKioowbNgwnDx5sswxJ02ahG3btiE2Nhbp6elISkrCV199hR07diAwMBDTp0/Hl19+qdTx9PREv3798PXXX+PXX3/Fl19+iUWLFmH//v1YuHAhvLy8MHfuXHz66aeu3dgKgGIGqbIo0p0WwdnSLyVNq1V/MMv2dHnd2FZ+CAeKOs4iSputXoYmoyryqKzjRJs2kc0BoOCScVfOiu+ws2cd24Aqo+fnm562pRjMzuKQW1V75T6rwb6dSfdm3gjZ2ebnI887IMCwpYQOqEHTw8N9HNr+gZp7hEV/BCWQvNkNs/qQ9L4qti8qC++LTWxAvi/K6Vjxh4D2vmrnw/jetwHuCPzuYtT+qVOnAgAmTpyoSNbh4eF4//330b59e8yaNQuTJ09GiPSfMeGdd95BQUEB4uPjMXLkSHF6Xpg9ezZWrVqF3bt3Y/369XjooYfs+9euXYvU1FSEhobi/fffh5e4npEjR2Lx4sVISUnBjBkzkJiYaN/322+/Yfbs2QCA2bNnI1x80dx///148cUXMXnyZLz++uvo0aOHfV+nTp3QqVMnh9fw2GOPYcOGDZg3bx4++eQTDBs27LrX7Q44w0kIIYQQ16kikvrx48exe/duAMDgwYPL7G/Xrh3q1q2Ly5cvY82aNZbatMnejtoLDAxEr169AADLli1zWK9Xr14I1B3FRXt6vTVr1qCwsBBRUVFo27atab0dO3bgxIkTlq4BAFq0aAEAyMrKslynouGAkxBCCCG3PKmpqQCAsLAwNGjQwGGZuLg4pawz8vLy7P6ZtnpW27NtX6+ebXGP1XrR0dEICwsDAHz33XfXvQYb6enpAIDatWtbrlPRcMBJCCGEENepIjOcGRkZAICoqCjTMnXr1lXKOuPo0aN226xNs/audy62eqWlpcpxrFxDZGSkw2OacfLkScyfPx8A0K9fP0t13AF9OEmVRfHt0tLvyI4rfzUp7mDSUQ+6y6MzH07j16b0a8SdTlKiSGdP6aennYOC2Req/rk48YIiwz/w7DG12K+/Gva5c45Pwarbn64AyYw90pfRWXghmehJ1tczKZn5dzrzzZS3W2Y40m+3WTgn6bvqLEOSDA+l9AWo/rDyWu+4w7Br11Z/04eHG6GV/Ks7yRplFv5K3ghnF+tsFa54APmn5A55geY+nMpzFuegdyV55UqP1joAfTiJM3Jzc5VtX19f+MovFEHetdRnAdL5WsMmb+vtOmvPWZtm7V3vXKTMLutW9DUUFRVhyJAhyMnJQWxsLEaNGnXdOu6CM5yEEEIIcR03znDWrVsXISEh9j+5wIZcn9GjRyMlJQV33HEHli5dCh8fPW1t5cEZTkIIIYRUSbKyshAcbCgDZrObABB0LZtGvi5HCC5cUwJkm9drz9amo1XtZu0FBQXh/PnzpudyQSgSsm5FXsNzzz2HefPmoUaNGtiwYQMaNWrktLy74YCTVBh6FAsrirEzNx1FagvV9FrxReApZA9FnZO6MoBatQw7PV1mdtHlS0OmOHNGfNxMnIOWLUXRW6XWquvMZmGNZGYgb3+limzupEm4I8BcJpb3sbqa0MaJFKyWk/fO58J5xyehS7wHxbYzuVfeS3GySjggrXMFS1lW3LsoeUEAEC62mxjhRQoDw+y2HgbPimsCoKre8jaI7lgmY5MMpVSrluNQSgDgU1RgbJj5BegnJE9C9ke9r4r7pfRvSHlOfyeMjiP7grxBziR1Ja2W9k4Uaf3YhiuhtFyMpENuFDeGRQoODrY0OASA+vXrA3C+Etu2z1bWGfXq1bPbmZmZiI2Ntdxe/fr1cf78eWRmZjo9Dw8PD+U4tnbM6gHAsWPHrnsNEyZMwLvvvovQ0FCsX7/evkr9ZnJTJPXCwkK8++67aNeuHcLCwlC9enVERkaiW7duWLRokcM6ycnJ6N69O8LDw+Hn54cmTZrgpZdeUn4lEEIIIeT3iW1Qde7cOdMFNXv27AEAp2klbQQHByMmJkapZ7U92/b16jVs2FDx57xevSNHjuD8+as/+s0Gkf/4xz/w1ltvISQkBOvXrzdd8V7ZVPqA89ixY2jRogWee+45HDp0CG3btrWne/r666+xZMmSMnXefvttdOnSBevWrUPTpk3Rs2dP5OTkYOrUqYiLi8NZfaqHEEIIIZVDFVmlHhkZiVatWgEAFixYUGb/li1bkJWVBV9fX3Tv3t1Sm3369DFt78KFC1i1ahUAoG/fvg7rrVy50qE8bmtPr9e9e3f4+PggMzMTW7duNa3Xpk0bRERElNk/ceJE/Pvf/0ZISAg2bNhgvx9VgUqV1C9evIguXbrg4MGDePXVVzFp0iRUq1bNvr+goABpaWlKndTUVEyYMAFeXl5YtWoVunXrZi/bq1cvpKSkYPTo0Vi6dGllXgopJ1blMYmyUFeXSoX+6y00zEJZRoSaAIBrP1QBAN98I1cA6ityjR8wx4+Lj6UUqC+tlfKmtPUZeHkdQt8ugCGjHzuqVpGSr7OmzQ4jZdxr0TQcbnseExKOdu+wQ5yE2Qk505ydLTl3JY2RWYdy5jMgbB+hC0cpGjEQJWSqklZGaJJjWlQAuS1/88rnov8WlrfETCkHgMhIoz/4VzdZma5XMnsWTqIeKP0b8mT1d8J4X+R7JPuJ8u5B++ci/TW0d/mSdl8d4ezxkyrADeY+N23TBSZNmoQ+ffrgjTfeQLdu3ewzhufOncOYMWMAAGPHjlX8MZcvX46EhATUqVMHKSkpSnvjxo3De++9h+TkZHz44Yd4+umnAQDFxcUYM2YMsrOz0apVK3Tt2lWp161bN7Ro0QKpqakYM2YM/vvf/9qzDc2dOxcpKSkIDAzEc889p9SrUaMG/vrXv2LGjBkYM2YMNm7ciDuuvT979+7FtGnTAAAvvfRSmWt/+eWXMW3aNLuMXpUGm0AlDzgTExNx8OBBjBw5Eq+88kqZ/f7+/rjvvvvK1CktLcWIESPsg01b2Xnz5iE6OhpJSUk4ePAgmjRp4u5LIIQQQkgVpXfv3vjb3/6Gd999F23atEHnzp0REBCAlJQUZGdno23btnjttdeUOjk5OTh06BAuOQhHFhERgfnz52PQoEEYOXIk5s2bh/r162P37t04cuQIatasiQULFsDDw0Op5+Hhgc8//xzt27fHJ598gi1btqBVq1bIyMjArl274O3tjU8++QS1tB+8wNX0nLt27cL27dvRsGFDdOrUCfn5+UhJScGVK1cwfvx4Ja0lcHUm9fXXXwcAxMTE4L333nN4f8LDw/Hmm2+W655WFJU24Lxy5Yo9P+jf//53S3UKCwuxevVqAI7TStWrVw9t27bFN998Y/+FQgghhJBKpArNcALAjBkz0LZtW7z33nvYtm0brly5grvvvhsTJ07E888/X+7QQP3790d0dDSmTp2Kb775BqmpqahduzaeeeYZTJ48GTVr1nRYr3Hjxti3bx+mTJmCL7/8EsuXL0dISAj69u2Ll156ydSP1N/fH5s2bcJbb72F//3f/8WaNWvg4+ODBx98EGPHjkX//v3L1LH5dQJX/T/NfEDr1at3+w849+7di7NnzyIiIgIxMTHYv38/li1bhhMnTqBGjRpo3749unXrBk9Pw600LS0NBQVXV2s6Sw9l6wCEEEIIIQMGDMCAAQMslR0+fDiGDx/utMz999+PpKSkcp9HrVq1MGvWLMyaNatc9Xx8fDBx4kRMnDjRUnkr13CzqbQB5759+wBcdeqdOHEi/vWvf6G0tNS+f9q0aWjRogVWrFhhT+lkW2UWGhqqxMOSlCdNFakaOPPnlO58ioua7sMpZIjqwo9MybtwLQeujWbD5VY4zDFCvSiujPIcrPpw6iGApN+mtxHqQx5HD9Mjm7tyxfHpAGq4mjp1DLumn7gr2j3BHnFgM6dE/STMsuDomPlWuuKQ58zv07TTaNvy+uR9KBNKyegbnsLJNUoLQRLVzHBmPHXReJbSL1J/ljJkklW31vr1ZXaibGOH3rfM+qB+fWJbddVVw4ipGPekWTPx8XzjPuo+nEogG9k5tfORj0hetyt+3+QmUcVmOEnVpNJWqZ+7Fq8tNTUV06ZNw5gxY3Do0CHk5OTYA5KmpqbikUcewZVr/1UrKsXT5cuXkZubq/wRQgghhJDKodIGnLbZzCtXrmDQoEGYNWsWGjVqhODgYMTHx2PDhg2oXr06Dhw4gIULF1bosRMTE5XUWLZZUUIIIYTcILbA7xX5xyj+tx2VNmctJXFHyeOjoqLwyCOPICkpCcnJyRg6dGiFpXhKSEjA+PHj7du5ubkcdFYyZvKYM3VVKrcl4Xcp5TzF8/PfscNun5eFDh5U6rRpI7e0+EAKhpycni4+lvGFdKQuKE9cZsTR2jhpEmlIV4WlGi2lcj3JhCKdy2uXuqke28dMKtcfjLx2GR5Kfu5EujXLqlTmWNJ2pjlbzb4jt81iF+kuAmauBXqoKCHL1xQPo6aIlnGqjvq9JJuQWYz0Zy77g3z+0bXE/db7llnMJScuA0r/huZGoWC8L8p7NNHoZ3pwKyVflnhf9XfZrNtZ/Z4ghNwaVNprGx0d7dB2VObXa7nlbGmbsrOzkZeX59CP00qaKl9fX6f5VwkhhBDiIvThJBaoNEm9ZcuW9jhVZpmBbJ/b/DIbN24Mf/+rv5PLm1aKEEIIIZVAFck0RKo2lfZEa9WqhXbt2uGbb75BcnJymRygV65cwebNmwEADzzwAICrYQEeeeQRLFmyBAsWLEDHjh2VOr/88gu2bdsGwEgjRaoOVuQxPTGMREpt+mrfCDGjLQXaEmGflUu6AfzxvgKx1VjY+kkYWufBg0bGlRNnjdhtEfpCNnlR0vdIk49lRiFd/bWhK/dygW90fXGFmsuAsvL62sy/0wPpB5PZYGrXVstJBUGs3D71m3FPTp1Sq8jfldlHDVuXj60setf7ibytSlal+mo5GR6vZg2xjlrK5rpUfk1hAaDq3nKJOaDq0WfOGLborDWVtDxAzVaG3H7kqKejKgDUeyIfX0EtkYFId02Q/U72R62vyn4s+7e6Sl1/J4z3Rb5H8h0rgYpydqL/OLtWidXABhyXEHJrUKm51G3ZhRITE7FD+N0VFRVhwoQJOHLkCIKCgjBixAj7vokTJ8LDwwMfffQR1q1bZ/+8oKAATz75JIqLi9GvXz9mGSKEEEJuBpzhJBao1AFn586d8dprr+G3335D+/bt0bZtW/Tr1w8NGzbEzJkz4efnh88//1yJ2t+yZUtMnz4dxcXF6N69Ozp27IjHHnsMMTExSElJQePGjTFnzpzKvAxCCCGEEFIOKv0nxMsvv4wHHngA77zzDnbu3Indu3ejVq1aGD58OF588UWHM5XPP/88YmNjMX36dOzatQv5+fmIiopCQkICEhISTIPCk6qJMzlMqoIyrnUZSV30kzDZtrB1T+HwL7+020FBRgaKvDw9LZnUfI2EAnv2GPJor4YN1SpSepX90YnPgNwlZXM9tW5Y0WljY4eQzfVkB2bSuZTDdalcSL4F4VF2W48Pf/g7wz5m3Eblufz2m1onJ8ewpWyqLzjXPB8cUq2aum3mlhESoparUcOwa9UypOTISGPhYkyMuogxRqzC9j+baWzoN0VK7/LeS3cGXYYX5aLFvQ9toq7c1vu7Q/S+Jfud9DPQ+qrqDi/7kOz36jsRFHSPsfHlYrsp3zH9n4l8LyHeV/3a5Htu5hXAya4qji0sUkW3SW4rbspr3LVrV3Tt2rVcdeLj4xEfH++mMyKEEEIIIe6CvxsJIYQQ4joMi0QsUKk+nIQQQggh5PcHf0KQSsEsapDuhiZ99ZyFRUKzZnYzWPirVRc+ckrWIQBYscJuPv644cM5Z05jreBeYRuhb5KThQ/nC0raIjUsjhOfYn9vIzRPTIzhU+gDk5A9gBq2R/oNFher5aR/pghdJP3nTlxQM98cOGDY0kVRjxSkhDjKNmzpf2cV3R9T37aCPK60dT9SeR0yMY8SSkkLQyVdXmNiDL/WZs2ilHIRcSaZneTz02NASb9b0cHDtMQVYTHG8yuEST/RkRd1992GrabYQvKbckumGpKBjdR34vHHxYZ4j+Q7pnsry/dSvq/6uyy7sfw+kLbuzsfJryoGZziJBfhECSGEEOI6HHASC1BSJ4QQQgghboU/IYjbMAtxIqVNXVKX21KNLJMNtZ0In9Wqld0M3bDBbp+GxtKldvPZ7xbY7TlzNHkcPwr7kN1auLCb3R4+XJVXW8rMWVJS1U9cSK8+8qZInVrXhfPzDVvK9Xr8JBFm58QlIyjNASPHQpnkRFJylqfgLPuPTHAjM/nongSynK+vYcvnD1ibyNBDKUkZ/fJlw9YVbBmVSN5GeX26B4N8ZNLNQL93TZoY7gnNmj1gtyNkdiE9lJLUk+WBdN8EUc5HStPyRuh9S7484hz2nlX76sKFcuuQsOXLqL4Tzz4rNu4z3iOZu0sN7ATlvZRuHWdXqMXkJZlJ6s76DKPnVAE4w0kswBlOQgghhBDiVvgTghBCCCGuw8DvxAIccJJKxyxLjKNtG7p6mJltyJlR7drZ7XAhqWtKKQ6LlDb3HF5pt+vW7aWUy8oSqXREJpYzZwxtev58VXJs+Y7YXrvWsPXl3nLb7Ata/6K9807DlpmBaqkZcvbvN+wffnB8SLmYHlBXCEvZW1+5fccdjk9HlpPKr75tJpUC5vKoPDddUpeSuLT1ZEtyW/YheR9kkihAleGlRP/TT2o52Z6U5Zs2NdwZYmMfgMQ//IixIeV2/cHoJ2VDvxES6d8gVqbPH6cWk/1YzTRkiOJ16wo5HOr7It8juWZe6zKAeC/l+1rGPUZg1k+orhJy68PXmBBCCCGuQx9OYgH6cBJCCCGEELfCnxCEEEIIcR3OcBIL8ImSSkF+d1j14ZTl9HA3MkRNlPBXixBpa34UvmYAcFTYMYmJdvvll1UfzlGjYsXWUmGn2K1Zs1Qfzh49DLGgq4wP9O236olLBzZ5sTLEUWMt85EIKXPK1whx8/0WtZi8JzL6jvRx1EMXST9LeQp6xCWzfbK+f3WZqQbqQ5O+h7ofotzWkifZqa59VQWadCgZiwlAwSXjuUh/Tnl/9Mw3Zvt0/1B5edIdU36ek6PWad7c8Lut2USct9ZXcUiEKzJ7mLqjrfDvXZ9sXPesWdBIEfYpYbe3Wy+/rFUR78tR8bGPsCP0lFHivZR9U3+Xzb4PnPlwcixSxeCAk1iAkjohhBBCCHEr/AlBCCGEENdhWCRiAQ44SaVjVVKXth5KRYk2NMQIvyJDsQT+3/+pdYR9bIcRGmbkokyl3Cj8SWwtF7ZRp7R0JSQLFxqyfNdX6xs7dB1WnriZni3jDgEoCDdk9B9ERBs9iY08lLx3Unl1JpVHRjq2AcDfWwTAUWINCYnXWcgeq8jO4Up7WkfxF+35i5sS0SzUbhc0kcKwGuJI2s6kdykTy9ujPyN5eUFtjOfqf6fWuJmkLhvX/8HXr283F75qfKz3VdmPgWBhG/1+5MPqO3FslFFH3uH6spB49/Tto58ZH+tZrGT/ZFgkQm5f+BoTQgghxHXow0ksQB9OQgghhBDiVvgTglQK0h3HbMU6oC4yNss6BKjKadoxf7vdqHNnu11Lk9TThL1P2JETJyrl+vdfYLeXLJHa8o/CXq/UmT/fkNRfftmQSqNlih4AOHDAsKW2KG+EtvrY2UppiVTope1MKpfb/kW5xsZZ7UC6DuoIZymEbnTJsbOV7ZecyPoWUhL5a+fdqFao3Y6MNCRnKa/r29J29ozMVspH6yvO5T0xuwatbx0pMvrd/Plyj9pXVYwO0L+/kWkIEwcrpeT7IrMLKR4a4t0D1PfSanYh+f47+56ge18VgzOcxAKc4SSEEEIIIW6FPyEIIYQQ4jqc4SQW4BMllY7VVepm8hqgrgqWQaUbtTeCV2vqMeShTgg79/PPlXJTDklJXQZ4l5L6L0qd0tK1dnvhwm52e1KrVupJyNXHEjOfAwAQKrG8J/qKc7N9UjYPq16gVpJapx6RW2IlOrcWdL2kuiGpOlO9L+aZH9aGn592OvIUQg3b85J2ffKazKRp/brFtr+4pkaRquwdHu4vbONzs9XrQJlbZOBMMzZLEKD1rYULDVv2R72vqhj9e8oU49Pcxuo7Id8Xua5decfEuweYB3u36kbDVeq3EBxwEgtQUieEEEIIIW6FPyEIIYQQ4jIl8ERJBc9fVXR75ObDJ0oIIYQQQtwKZzjJTUV30/H1dWwHBKjlzp0zbCUDTHyc0XbHjkodGSbpvPj8AFT+uOW/djso6P/Z7by8BaKUHiZok9165x3Dh3PInq5KqagOIn6OTENTrZpha3F1IpsY4W6cJeKRfoTB3sKXUfppHlPbVhpx5lxrEnOpoMjI0pOn+WLmi+fizIfTSkIhZ5GU5KkGBPgr5YKCjG3/UJNsSXocI3my8t5p5cLEfQiLMW5+/frm4YDkeSs+uAe1c5D9QTrhxsTYzcwmat96Z4jc2gRzjBsWFNTHbjcS/X6bVkM+InnayjsWFwfJSZFdSD7jkBC1bbN3ni58tw5FRRWTaExv80ZYsmQJ3nvvPXz//fcoLCxETEwMHn/8cTz//POoJt8vi3z77bd444038PXXXyMnJwe1a9dGjx49MHnyZNx1112m9U6dOoXXXnsNq1evxokTJxAaGoo//elPSEhIQMuWLU3rFRYW4u2338aCBQtw+PBh+Pj4oHnz5hg7diweffTRSr32ioIznIQQQgi5bRg3bhwGDBiArVu34oEHHsDDDz+MzMxMvPjii+jUqRMuXrxYrvaWLl2KNm3aYOnSpahXrx7+8pe/wNPTE7NmzcK9996Lw3oO22ukpaXh3nvvxXvvvQdPT0/07t0b9erVw9KlS9G6dWssX77cYb2CggJ07NgREydORGZmJh5++GE88MAD2Lp1K/r3748XXnih0q69IuGAkxBCCCEuY5vhrOg/V1ixYgVmzJiBwMBA7Ny5E1999RWSkpKQnp6O2NhYbNmyBZMnT7bc3okTJ/DEE0+gqKgIH3zwAXbt2oVFixYhLS0NQ4YMwalTpzB48GCUlpYq9UpLSzFw4ECcPn0aQ4cORVpaGhYtWoRdu3bhgw8+QFFREYYNG4aTikR3lUmTJmHbtm2IjY1Feno6kpKS8NVXX2HHjh0IDAzE9OnT8eWXX7r92isaihakSiFn+4OCDFsPiyNlVKmAnsg25MyINjKkERAlJPVM8Xm2fhKfGVrgCy8Ykvorr/xJFNKztxyxW2fO7LXbn3+uSiYv9u9gbMhfmvIitF/LPsKOkrp5de31zTYJ+yO/uZ3FoRIScUlgsFJMhrW5YBJJyRWpHHBNOpVtm0U+AoCcHMMODPQRtiGBBcaocpjnBZFxScroeuMmBw4W9zQ4ULvf8sQPihupz5DIY9WpY9gdOthNLZqX0u9kfyyL0Y+ViRLR77O1GjKaU5TcId4x+e4B6iXIbqa/y/I9v4lqH7lNmDp1KgBg4sSJimQdHh6O999/H+3bt8esWbMwefJkhOj+HQ545513UFBQgPj4eIwcOdL+uZeXF2bPno1Vq1Zh9+7dWL9+PR566CH7/rVr1yI1NRWhoaF4//334SVCnY0cORKLFy9GSkoKZsyYgcTERPu+3377DbNnzwYAzJ49G+HiO//+++/Hiy++iMmTJ+P1119Hjx493HrtFQ1nOAkhhBDiMlVlhvP48ePYvXs3AGDw4MFl9rdr1w5169bF5cuXsWbNGktt2mRvR+0FBgaiV6+raY2XLVvmsF6vXr0Q6CAAr609vd6aNWtQWFiIqKgotG3b1rTejh07cOKEESHXHdde0XDASQghhBCXqSoDztTUVABAWFgYGjRo4LBM3LXFbbayzsjLy7P7Z8Zpi+Ku155t+3r10tPTkZ+fb7ledHQ0wsLCAADfffddmXoVde3ugJI6ualYXX0sF0kDqhot60gFNOK++5Q69UWDx4TeVwiN7dvt5gvCTeaVV+SvRl2yFAdGut2aP1+V1Nu3j7bbf+zSxdgh07Lo37TSx0deoFUt2mpqJ1HOmXpsJmFX9CpVV9DPwcot0sv4m1XSb4orFy/L6e1J6tc37CZN7Oa2k0b/mT9fr5QubDmjoufcMvqxIqknGv1e6yWoL23ZZ8Q7pi/2l7dRRpnQ32WzjEJcpU4AIDc3V9n29fWFrwxnIMjIyAAAREVFOdwPAHXr1lXKOuPo0aN226xNs/audy62eqWlpTh69CiaNm1qqR4AREZG4vz588oxK/ra3QFnOAkhhBDiMsXFFT+7WVx8te26desiJCTE/if9HXXyrsVmC9Dj6Als8rY+kHXWnrM2zdq73rlImV3WdfUaKvra3QF/QxJCCCGkSpKVlYXgYGMRo9nsJqn6cMBJbirOJFBnkrpZHSX4uLZKHSNG2M0Hrq0CBNQV6wCQLaTO0Il/s9tbt75rt9u2fVOrJaJcw5ArDh5cqpRq29YI1j1iRCO7PXasYbcM1eT6TZsM+4AIU69HFZc3U65ml4HDhTxbZlvc8LPH1GJm8c9dWYnuzI3CDGcr4F05B9mf9PpRkSYhEIS8BkB1gzgmbpi8WfrFyefSrJlhi9XnALA325DOZ71hfP7RR3JmQo+UIGUy2d4QpdTWrcZqWX/Rv2W/10U5ZVu8R/Idy/tVrSPvsVwzYVVSN8tJQKoe7gz8HhwcrAw4nRF0LeSB9InUuXDNJ8hKm0EihEJ+fr7Dld1m7QUFBeH8+fOm53JB+CbJuq5eQ0VfuzugpE4IIYSQW57613yfs7KyTMvY9tWXftIm1KtXz25nZupTE87bs21fr56Hh4dynOvVA4Bj137gymNW9LW7Aw44CSGEEOIyVWWVeosWLQAA586dM10Ys2fPHgBwmlbSRnBwMGKupZO11bPanm37evUaNmyo+HNer96RI0dw/vzV5My265V2RV27O+CAkxBCCCG3PJGRkWjVqhUAYMGCBWX2b9myBVlZWfD19UX37t0ttdmnTx/T9i5cuIBVq1YBAPr27euw3sqVKx3K3Lb29Hrdu3eHj48PMjMzsXXrVtN6bdq0QUREhP1zd1x7RUPPGHJTceabJRIzlInm4yCOLgBjZSMA1XcRAERcMx8RziVcxDIrg9j3x5dP2+3+/dXsNEuWyF+M3wvbByqGD96BA4YfjXTTrP5wNCT39A41NqQUIv05AdXHUDpaSl9D3e9Tyi/CDzFK8/UMbxLhqJgSsUmGSwJU98crV+A2ZHYaZ/2kVi3Dll3DP9sIngwA2CTu108/GfYvv6jl9Hvp6EC6dCX9NkUf/PFkmHoKmwxbfczSh1PvW7K/N7db/ft7KaX+GGP0Y5j0/XBtW74v8j2SN7JY8/vVn4XZ515ejsvRb/PWwZ0+nOVl0qRJ6NOnD9544w1069bNPpt37tw5jBkzBgAwduxYxR9z+fLlSEhIQJ06dZCSkqK0N27cOLz33ntITk7Ghx9+iKeffhoAUFxcjDFjxiA7OxutWrVC165dlXrdunVDixYtkJqaijFjxuC///2vPdvQ3LlzkZKSgsDAQDz33HNKvRo1auCvf/0rZsyYgTFjxmDjxo244447AAB79+7FtGnTAAAvvfRShVx7ZcJXmhBCCCG3Bb1798bf/vY3vPvuu2jTpg06d+6MgIAApKSkIDs7G23btsVrr72m1MnJycGhQ4dwyUFs3IiICMyfPx+DBg3CyJEjMW/ePNSvXx+7d+/GkSNHULNmTSxYsAAeHh5KPQ8PD3z++edo3749PvnkE2zZsgWtWrVCRkYGdu3aBW9vb3zyySeoJX+gXmPq1KnYtWsXtm/fjoYNG6JTp07Iz89HSkoKrly5gvHjx5dJa+nqtVcmlNQJIYQQ4jLujMPpCjNmzMCiRYvw4IMPYtu2bVizZg0iIyPxxhtvYOPGjfDz8ytXe/3798fOnTvRt29fHDlyBMuXL0dxcTGeeeYZfP/993Y/T53GjRtj3759eOaZZ1BcXIzly5cjIyMDffv2xc6dO+2yu46/vz82bdqExMRE1KlTB2vWrMH27dvx4IMPYvHixZg+fXqlXXtF4lFaWlp6045+k8jNzUVISAjS03MQFHRzwgP8npFfJM6St0hbl2vN5BazqDMA4Lnla2PjS5FCaMMGtaDUif/wB8MePdpufl1rgFJl4kTD3r5dxof5TT9Du1W3riHLy6g4ekYzqW5LOypUC94r5dH9+w37558NW08Ho5yauHli1SQAoGFDhydRWMsImHNMk1TNEiQ5e+ZmOEuQJMPs6JMFUjr3OSlWfUo3g3SZoQeqdG4mm+sHvvtuw46NNWwt21VmtvF9I09B2gAg1wtIeT0rS8jh0M+tht168MHadvuNN9RSfzq52NiYM8ewpfuAfiNlViwxs1LS7k92W/fwMLt1ulQu3R7kczWzAXMZnriPvLxcNGwYgpycHHtYHdv/0p07cxAYWLH/Sy9cyEXr1urxyK0NZzgJIYQQQohboQ8nIYQQQlymKi0aIlUXDjjJLYGz7DRm8qpnUaF5JSkZt2+vlpP6r5SZxYF02XvgQFnMkDMPHKgNM6RqKb9cdSlSyq1Spq5VS5WZYmIMeTNanqDUZ/VVyXJluzywfhJSZhYn5CNWYUdrK7KjmxnbBd7GuepNS0ndLLuMLqnKx+JfJFwL9GxA64463meWGUhHdih9xbnJyu0jJ/3t9uEdahUzNwNnSaNkP7l0SUZHUCMlSBeS3r0dntpVksXNlNcg/Q/0dEDyfREPRr5joaHqqnl5DVbcJgghtzcccBJCCCHEZTjDSaxAH05CCCGEEOJWOMNJCCGEEJfhDCexQqUPODMzM/Gvf/0LGzZsQGZmJkpLS1G7dm386U9/wvjx49G8eXOH9ZKTk/HWW29h165dyM/PR7169dCvXz8kJCQoeUjJ7w/p6+frK3Y4S30j0f3VpIOg9NsT4YD06EKyinSLk58D6peo3Cf99PQ6snvL+rq7ovQPPBhu+BE2aSJ8O7UwPYp/p4xrozdu5t95+LBh6/exZk276X/nnXY7Si8nL1A+THmx+rOUD+DMGcM+dcq8nN6GDf185DOXjpGaM+SRsyLE0Sbjc3l79C4nL8/q15YsJ90sdb9muU/2Ib2v+svYWnKnfObO/tubxCvz9VWzJTFTECFEUqmS+s6dO9GsWTO89957yM/PR9euXdG9e3d4eHjgk08+QVxcHJYsWVKm3ttvv40uXbpg3bp1aNq0KXr27ImcnBxMnToVcXFxOOvM6Z8QQgghbqOqBX4nVZNKHXCOHDkSeXl5GDlyJDIyMvDFF19g2bJlOHz4MF5++WUUFRVh5MiRSnqp1NRUTJgwAV5eXli9ejU2b96MxYsX4+eff0bnzp1x6NAhjBYBuQkhhBBSeVT0YNMdEj25+VSa6HHu3Dns27cPADBlyhRUq1bNvs/T0xOvvvoqpk+fjuzsbPz0009o0aIFACAxMRGlpaUYMWIEunXrZq/j7++PefPmITo6GklJSTh48CCaSKmI3NbILyPLX0yyYH6+YetSq9QwpS1076NaVhWpRkqVUpcV77jDsOvWNWyp4upJXgICDFv+6telUrltFn7nqBZKqUmTTnY7QsrHevgkGZvJaiglWUfGNXIW48pMUtcfslnMHWdtW3CVAKD4RJwoMkIPHdyjFpP3WJ6CvFQpcwOqei+z5cjuqLct25N1cnLUOmbquO4dEdFMdDAzfw1n74TFF87s8VFqJ+T3SaXNcPoqznXOCb/2j6GwsBCrV68GAAwePLhMuXr16qFt27YAgOXLl1fAWRJCCCGkPHCGk1ih0gacgYGBaH8twPbLL7+MK1eu2PeVlJTg1VdfxcWLF9GtWzfUvTb1k5aWhoKCAgBAXJnoxVA+T01NdefpE0IIIYQQF6lUcePDDz9E9+7dMXfuXKxevRpxcXHw8vJCamoqjh8/jqFDh2LWrFn28hkZGQCA0NBQBAUFOWzTNji1lSW3Nq7IbeK3Cy5fFjuCtPQ0snFZyZk2LTVQIRkXFanStJRA5WLvvDy16doi8ZDwKjFdYazvk8qmvgJaKtryfOTn+qVK5fxspCEfx7TrqpRTVjZLqTw93bBlNiJnB9ZP3MpUht4xzNJL6TdPZshp2NCwxfUUhEcpVeTCe5mQSD9t+SzMXCL005GnLVVreRx9Wz7LQ4cM+9df1TryK9KpAi6fi1n2JR3pC2KSAuqypsLLV8wqlNtvTRgWiVihUl/vxo0bY/v27Rg6dCjWr1+P48eP2/fdc8896NChA4KDjX/kedf+WwdIJzYNW0ik3Nxc0zKXL1/GZTEScVaWEEIIIYRULJW6Sn3r1q2IjY3FgQMHsGDBApw8eRLnz5/HqlWrcOXKFTz55JN48sknK/y4iYmJCAkJsf/Vlas1CCGEEOIyDItErFBpM5zZ2dno06cPzp49i+3bt6N169b2fT169MA999yD2NhY/Pe//8WQIUPQsWNHu4yery/hFFy4pkvJmVGdhIQEjB8/3r6dm5vLQWcVQZfQrMooZuWkTFlYy1/Z5yOlVz8/w3amTZtEao97NFqpsmmTYUv1OD1dbfvgwWJhGzP3utQtkfJoWPUCu+1zQa0ULOTa6DjjXM9f8LHbUp4FVMlf3jupmgNAuJCdI+MN21NK7fpy6Kwswz53zrD1i5UHNlvOrEdJl8/SbOk/oGjdJZHGeUv1+Kx2rbI7yKZ1rx4pnYcFFooGRf/RY817h9rN7Gyjf+pBAZKTHdu//iq/C8WSdQANGxodQEr5Zdzfl24xbHlg2Tl0XwD5voh7X+htXIO+sF26t7iySp3yOiG3F5U2w7l69WqcOXMG0dHRymDThvw8+do3bP1r/yyys7Pt8rpO1rV/avWlE5WGr68vgoODlT9CCCGE3DhcpU6sUGkDzszMTADOZyJDQkIAAOfPnwdw1efT3//qL+g9MgWfwPZ5y5YtK+xcCSGEEGINDjiJFSptwFmnTh0AwMGDB5GjRywGcOXKFezduxcA0KBBAwCAj48PHnnkEQDAggULytT55ZdfsG3bNgBAnz593HLehBBCCCHkxqg0L5lu3bohICAA+fn5ePrpp/Hf//7XvsK8sLAQ48ePR2ZmJqpVq4ZHH33UXm/ixIlYunQpPvroI/Tr1w8PP/wwAKCgoABPPvkkiouL0a9fP2YZug2x6sMlfwlLz4vfflPL1ZROdzVrGrbuHyjj4pw54/CY/lo6oB49jDBCMixSerru+X7EbsmwNp9+aoTvOXhQVQGkv+HDDxs+cy2baTfIJANQmAhdE1ZL9c0raRJmt6Vrpe6PJ++xPJ/AQMMvMrSdGl7I84KIBuGscek0aebsV10LcSWfmfApLAlU751yWHHeJq65zpqGZ/Z5taD01TxmkmpIc/XZe8Dwp123zvh8xQq16d27ZSQNLdyUHdWPWCRIQo8ehu2/Zb1aTTqF7t9v2DJOl+6iJN8X0fflO6Z7PVmdoaKv5q0PwyIRK1Taq37nnXdizpw5GDFiBJYsWYJNmzahVatWqFatGvbs2YPjx4/D09MT7777LqKjjS/Sli1bYvr06Rg/fjy6d++OP//5z7jrrrvwzTff4Ndff0Xjxo0xZ86cyroMQgghhBBSTir1t+WQIUMQGxuLd955B19//TVSUlJQWlqK2rVr4/HHH8ff/vY3PPDAA2XqPf/884iNjcX06dOxa9cu5OfnIyoqCgkJCUhISDANCk8IIYQQ98IZTmKFShczmjdvjo8++qjc9eLj4xEfH++GMyJVCSmvSdvLq2xZG/KLSUbQkpF4AKBmEyGDy/A5mjyuaKpSXt+xw/HJAWg5OtRuDx9u/Gg6elRNWrB7t5TvZTyeTFFGpBYCsGdPc2Ebn/fu7aOU69Chkd2OqiUkWRnuRssm4ymld3HdYeGhSjkZ/kYq4BcvGnZZ7wND3q4mpG4RGejqtoVvIf2fj9y+Is4BF2GKlMel6u1TVKAWlDr8YSH/6+GzTKTzzGzjWjctVKtI6VzapaXfa2crn5N0tTdch1q1UvvW8OGG3bJol7GxUDsJ2Y/lNcXEGLb+Tpi8L+dEF9aj15l5R+jvstk7Twi5veDrTQghhBCXsQV+r+g2ye1FpWYaIoQQQgghvz84w0kqHavyuEQuoAVU6U3Wkbae0ObESeP3VYSUD/UMOVJGz8gwbJk5R6701eg+1ji5SxPV+LCvvtrAbu/fL1dri6XtEHIogNLSdLu9fPk94hTuUcr17m3Y8fGGrNumjWHHNFPP1fPsaWNDyqtyBTYAHyEfSzv4TkOGLyxSf7+aLT6/6ET2torsDzIJjr6Y3ce7xNiQq+OzxcnpUrlEroAPv0vZpXhbrDBs2TX01ed5eT+KLWlrIRUgXS+MhxYba/SfiRPVGt1r7TU2ZomFlHpflb4PUjqXtnw/tG35Hsl3zNkMl3xf9XfZSh1n3xnk5kMfTmIFDjgJIYQQ4jIccBIrUFInhBBCCCFuhTOchBBCCHEZznASK3DASaoUZiFS9HApZuFT5JeUntBGRgcKbxZht330LFXSp1OGERINFEl/TgDeX37p8CT6jh2rlptihEx69dVYu52aKp3UhC8eADXTjJGeSPUHBD791PDpXLLEsLt0Mcq0bau23KKF4ZfoLCqOf3XhC2niNOujPRTvQE9Hxcpg5R+Ls3A5cp8nStSdsnHp4ClCQBVcUoUe2U8Of2fYqalq01u3GvaGDYZ96ZKZnyYgnx9QKOyaWjkjFFaLFsazfPVVo0SvWqqvL96ZZdgijVGRFq/KW94HGe5IvgfaO1EYbrwvJ4W7sXzH9OfoyrvMsEiE3L7w9SaEEEKIyzAsErECfTgJIYQQQohb4QwnqVJYleFkmBSzEEl6tBsZwkWq5o2aabGCpAQp9VVppxuhigCgQNTxX7LE2KHp+r2ExB4+q5Pdfu01QzZdty4YKkK7FRmJgP1aOUN6v3TJ2LdqVUNhq6GUYmMNefW++4zPmzZVW65f3/htWru2keFIZu+RCZqAsiGKbFREiBuz2Y9Lmjx+4YJxrvL5/yqUbT0q1g8/GPZ33xn2/v16+CQpl8v+ILMEiYxPANRwR1HCVn0dHn7YyDY1ebLx+R8vbTQ23hASOgCsXWs3C0TnV/NRQZXRZd+//37Hn0O9R/I+ynfMmVRu9r7q25TUb03ow0mswBlOQgghhBDiVvh7khBCCCEuwxlOYgUOOMlNRZdXpVTqTGqT2UqsrFgHVHVbquOBgf5KuYgWLYyNc+cMW2Tf8dbSGF0Skvp5oTMGL1+ulPMWbfzxKUN6nT9/mN1+/fVIpc7MmY+KrW+ErUvqImsQxHlDpMTBt0qN/fvrObSBekq5Bg0Mfbx+fTi0w8PVs7njDsMOCjJsXWo3e85mGaQAVcrNyzNs+bgANWGSlIWlnZGhS+W/WLAB9X7nC1t2ajU7ERAr7PZ269ln1RfhpZcMu+ZXnxgb//mP3Sz6RvYFVbyXMrr3nXeqpyB9J9q0MWzR709kq++EfF/MVqY7k8qdva9m7y+zC9063K4Dzry8PCQmJiIpKQmZmZkICAhA69atMWHCBHTq1On6DTigpKQEH374If773//ixx+vuuXcc889ePLJJ/H000/Dw8PDtG5ycjLeeust7Nq1C/n5+ahXrx769euHhIQEBOo+TYLDhw9jypQpSE5OxpkzZ3DnnXciPj4e//znPxEdHV2m/OnTp7Fu3TqsW7cOu3fvRlZWFjw9PREVFYWuXbti/PjxqC+//C1CSZ0QQgghRHD69GnExcUhMTEReXl56NmzJ5o2bYq1a9ciPj4eM2fOLHebxcXF6N+/P0aPHo0DBw6gY8eO6NixI/bv349Ro0bhscceQ0lJicO6b7/9Nrp06YJ169ahadOm6NmzJ3JycjB16lTExcXhrJaO2MbWrVvRvHlzfPzxxwgNDUWfPn0QGhqKjz/+GPfeey927NhRps748ePxxBNPYNGiRfD390evXr3QsWNHnD9/HjNnzkSzZs2wQcaCswgHnIQQQghxGdsMZ0X/3UxGjhyJtLQ0dO7cGYcPH8bixYuxefNmfPnll/D09MS4ceOwb9++crU5c+ZMLFu2DHXq1MGBAwewcuVKrFy5Ej/88AMiIiKwZMkSvP/++2XqpaamYsKECfDy8sLq1auxefNmLF68GD///DM6d+6MQ4cOYfTo0WXqFRQUYMCAASgoKEBCQgIOHDiAhQsX4sCBA0hISEB+fj4GDBiAixcvKvXCwsLwP//zP8jMzMT333+PxYsXY/Xq1Thy5AgGDhyI/Px8DBw4EL/99lu5rt+jtLS0tFw1bgNyc3MREhKC9PQcBAXpK4LJzURK6q5IqvlC2dQDv8t3ys/PsHUpWMa8DjubZmyIYNqKDQD/9392U0rqulgrRY9gKUkMHWrYWrD4T9YZsuwssTB5925d4t0t7Axhy1++Mtg4AIgbAfku3KGVk4HJpUwsb55ex2gvKMjQR52tZrcacUA+27w8uWRdXxUuNXZ5H6QcfspJHdneRa2cFK7lfWgg7FZKjVatDFcF+ZiHPXxaKac86E8/Nc5G+AJo3RvSUyFM3tSOHdWCDz/s0D4f3shuHzyoVpGTJ2bvkf5cAwIM2xWXCkrqVYu8vFw0bBiCnJwcBAdffbdt/0sTEnJQvXrF/i+9dCkXiYnq8SqLH3/8EU2bNoWXlxd+/vln1Kunuhg99dRTmDdvHgYOHIjPP//cUpslJSWoU6cOTp48ic8++wyPP/64sv+zzz7D0KFDERERYZewbQwYMABLlizBU089hQ8//FCp98svvyA6OholJSX46aef0ET8A3v//ffxzDPPoFGjRvjpp5+UNktKSvCHP/wBaWlpmDNnDkaNGmXpOgoKClCrVi3k5eXh008/xZAhQyzVAzjDSQghhJAbwBb4vSL/bmbg9+XXfO/btm1bZrAJAIMHDwYArFq1CleuXLHU5vbt23Hy5En4+vqiX79+Zfb369cPPj4+OHHiBHbu3Gn/vLCwEKtXr1aOK6lXrx7aXksht1xbM2DbHjhwoDLYBABPT0889thjAIBly5ZZugYA8Pf3R+PGjQEAWVrGvevBASchhBBCyDVSr+WxjYuLc7jf9nl+fj7StZjM12uzadOmqO4gSLGfnx+aXguAnCry6KalpaGgoMDS+aRq+XetXodezxlXrlzB0WtKS+3atS3XAzjgJIQQQsgNcLv5cGZkXHVJioqKcrg/ODjYLvPbyt5omwBQ91pSBtmmzQ4NDUWQ9E25Tr28vDycuxa2w+yYtnpnzpxBvvRHc8K8efNw9uxZ+Pn5oVu3bpbq2GBYJFKlsOq3JX29pB/Z5cuOywBqaBbph6Yv7pM/WP/wB8OvLbiDcCTUnQrFdpjw5zyplsJ5YRcIf7xar71m7JDpbQAMGz7cbsev6Gu358xRpZ7//MfY/vXXQ2KPbE9mKgJUv0aRfkfJlgMA4uYpnqgBJra6nZfnJ2z9172XiV1sYgOqd6z0rdS/NPNNbOkBqUti8hzkuUZo5eSX+H12q3btxnb7qafUGtKvP2KHkLGemq8WXLXKbso+JK9av4thcuPBBw27Qwe1oNjOrWX07/SfjCL6OyHfF7MQR/r7Jt9LZ+Xot0mckZur+mX7+vrC19fXrcfMu7YwICBA/04zCAwMRG5ubpnzu9E2AfWab7Ses7oylFJubq7TYwDA/v378fe//x0AMHnyZNSsWdNpeR0OOAkhhBDiMkVFFf9jwTbDWVemYgXwyiuv4NVXXzWt949//AMrV64s9/H+85//oF27duWu93vh2LFj6NmzJy5cuIBevXph4sSJ5W6DA05CCCGEuIw7B5xZWVnKKvXrzW6eOHEChw4dclrGERdE6AubdO1MZraVt7qC3tU2b7Ses7rymp1dx8mTJ9G5c2f88ssveOihh7B48WKnAerN4ICT3BI4k+GkLb+LdB8guS0XFurhk8yIjb3XbvvHW3MwukvI64Aqj0ohRp5CmJBTASBstxHuKKJPst3+/7RwFAMH/tFuz59vyLpLlxp2RoYeSulHYUu5XQ8VJOOtnXVSzgwz2dzRtiN0Sd2Z3G7lHKQgrWcDkjKRlM3vUUo1aGC4MDwqkkEJDwjck71NbXrKZ4YtVpSeP6k6X0jXCxkGWjoz6GethD+SoY/i45ViBTFGP/5JJKs6JR6l/k7IwYR83+TaB30cYPaOmmUWIsQR0l/SCp999hk+++yz6xd0Qv369bF3715kZuouSFeRUrrVjDu2cmZtAsaqb9mmzc7OzkZeXp5DP05H9YKCghAWFobz588jMzMTzZs3N60XHh5uKqefPn0anTp1QlpaGuLj47FixQqXXRq4aIgQQgghLnO7hUVq2bIlAGDPnj0O99s+DwgIQKNGjRyWMWvzhx9+wCV9DQCAixcv4ocfflDKAkDjxo3h7+9v6XxkvfJch17PxpkzZ9CpUyf89NNP6Ny5M1auXOlwhb1VOOAkhBBCCLlG7969AVxNC+loRnLBggUAgJ49e6KaXEXnhAcffBC1atXC5cuXkZSUVGZ/UlISCgsLERERgdatW9s/9/HxwSOPPKIcV/LLL79g27arSkqfPn2UfbbthQsXlkmZWVJSgkWLFgEA+vbtC52zZ8+iU6dO+OGHH9C5c2esWrUKfnIloAsw0xAzDd0S6L92zbIQSSlQlwXN9uk/NmV7MntKnTqG/Yc/qHWCj4oUZ8mG7F0mI9GmTXbzhND1s+Xx1RrwF7bMZxOqSzk9exq20HhPxPzJbq9YoVaR6n1KimFfuXJYO4sjwpar2aXUrq/WlH5DMsORs0DJ8kE7k9rll7zM+KPLQvL9riFsGT8uWm25Wozd7tzZ+FzeXgC49j8JABBx+GtjY+lSw9bcI7JFZALpmFCgNq34OoUKO0L+c9NXn5vI6Ln171WK/SRWox8/btjyndBlbzmpId8JM1vfdiapc2X6rYGzTENPPpkDH5+K/V9aWJiLefNuTqYh4Oqg84svvkB8fDxWrlxpH2ytXbsWPXv2RGlpKVJTU3Hvver7NWzYMOzatQtjx47FWC1r3DvvvIPnn38ederUwTfffIMGDa5mJcvIyED79u1x/PhxzJw5s0y9vXv3Ii4uDp6envjyyy/x8LV3vaCgAL169UJKSgr69euHpfK759r+hg0b4sSJE5g0aRJef/11+76XXnoJU6dORWRkJNLS0pTB5Pnz59GxY0fs27evzPXfCPSmIYQQQggRzJ07Fz/++COSk5Nx9913o3379jh9+jQ2b96M0tJSzJgxo8xgE7jqo3no0CGc1WOLAXj22Wfx9ddfY/ny5WjWrBnir/0wTE5ORkFBAR599FGMGTOmTL2WLVti+vTpGD9+PLp3744///nPuOuuu/DNN9/g119/RePGjTFnzpwy9fz9/bF48WJ07doVU6dOxcqVK9GsWTMcOHAABw4cQEBAAJYsWVJmMPnUU09h37598PDwQFhYGP761786vEe9e/e2zwZbgQNOQgghhLhMURHgWcEOejcz8DsA3HXXXdizZw8SExORlJSEL774AgEBAXjooYfwwgsvoLOUPyzi5eWFpUuX4sMPP8R//vMfpFyTlZo2bYonn3wSI0eONF39/fzzzyM2NhbTp0/Hrl27kJ+fj6ioKCQkJCAhIcE0KHzbtm3x/fff47XXXkNycjKSkpJw5513YtiwYfjnP/+Ju+++u0yd8+evLlssLS3F4sWLTa+nfv365RpwUlKnpH5LIiV2+cV0yUlsdikZZmcbtoiPC0ANHi/blov49IxejY2F4KiZL+RnIaEDUCX2DRvs5llxQvrvYrNF9JqCqcjt4aGhxkaXLoYtZVdt+8dsI7C5ftpbthi2zIJ28KCUwE9rZySvRMrtusO8lNuln5H8DyZlc0BdZS7f4XCtnLGWu0kTQ7tt0cIooYfek0r1PaEnjA3dPaKqPUtx4qcCDDcBPULMr8IjQkZLMYv2AADyf5k8HSmb62sJ5DaDu9/6OJPUn3jCPZL6xx/fPEmdVDyc4SSEEEKIy9yOM5yk4uGAkxBCCCEuwwEnsQLDIhFCCCGEELfCGU5yy2M1o4lZaBf9l7SZf6j0dzt2TK0j/UWbNDH856J6h6oFw4V3Xq1axsfCadJ/v0j/AjU7kcxAo/sHyn3HhB9h6JIldvsuYQOAvwitdM+DDxp2XJxSbszYNsbGfffZzbRjRtCmw4dVx9ajR41tmUhH+s8Cqm+tvN/y+ekhd6QfobiN0CNFxRgRjtAoUgQf+u47w96xQ600RQRJ3r7dbhaIkEaA6rGaLWwZ4kiNfKd6noYJu5ZWzj821tiQTqUya5DmfJp5wWjx4PfG5/piWQcxpwGo91uPgGLmq+nsfWNGod8PtsDvFd0mub3gDCchhBBCCHEr/A1KCCGEEJcpKgJMovncUJvk9oIDTnJLIkOrSOlFynjOUr6KJD9l5FoznGUxMgvHlF0/TCnXrEcvu+0ZGWnsEFqwvxaTKPqbb+x2sNCj9SBE2cI+b2LrSdr8hUwcLOywzz9Xysmr8BTn2qhhQ8PW9Wx5fTXvMOzGoWo5GXPH7MHqsaukLn/2nGF/p/k6SBk8Pd1ulojP5f3Rt2UwJz0bkNn/QzPZHJBBmrRwR+3bqwWljC7skvuMnMcHDqhV5KVKGd3ZP26rWYNkmCT5XpnJ6zoMhUQI4YCTEEIIIS7DGU5iBfpwEkIIIYQQt8IZTnLLY1Wucyaxlxdnkrpcka2Xk1JnkyaGPBoxvL6xQ6YtAoBmzexmuEj5E66trj4t/ASk3O5MFpanJ+voXwwyz091od1K2x8q8nb7mNiOjuUIfbKj0MTWF2AXmOyTtqzv6Fg29POW1yvzoEjZ/K5q1dRKbcRqf7nKvG1btZyIGHDiklh9vskoIvsZYG21P2BNRpdZtQDV68HqKnXK6L8fOMNJrMABJyGEEEJchgNOYgVK6oQQQgghxK1whpMQQgghLlNcXPEznAz8fvvBASe5rbgZ/pyA6j9nFiJJLycj+9SpY/jp/aH9I0qdYJl1RmYA0nw479qzx6F9XoQU0kMAiVNQ/Dl1X095GXobZnhasJ3V0bP0wGSfme0MbxMbUH0zZXSgUK2cDHkUJp0c5TPSMjYpPpxiX25olFLsp58M+/hxw5Y+wGYZgwDzrFr6tpnfprwcvT0zv036bBJCnMEBJyGEEEJcxh3+lvThvP2gDychhBBCCHErnOEkty0VLa87y6RihjNJXclIlG3Yp06pdSIjDbk1Jt6wg6U8CwCpqYb93Xd2M+z77w17/361zsGDdlOotYrUDpjL7c7CC8ltKXXrExdWZHD9l7GZRK8/Sh+TfWayOaBK5+FyR5MmakHp6tC8uWHfd59ht2ihVMkNjLDbhw8bnx/7Tm1aSudWwx2Zyei6pC4THMkMQmahj/RjUUYnOpzhJFbgDCchhBBCCHErnOEkhBBCiMtwhpNYgQNO8rvhRiV2PWmMxExm1GVPM3n0zBnDlvI6oGaUEYl9UKtWhFKufgtjO6JjR2OHkM2V5c8A8PPPdjNclAuXeq924CJxslJed7ayXcrrziR1aTtb2S5vq5lsDqjSubS977zT2KhfX60UE2PYUka/+2613B/+4LDciWzjSPJ5AeqzNJPNAUAkjVL6nexPulRutsrc2Sp12dfNVqLr25TRCSGuUG5J/dChQ5g5cyaGDx+O2NhYeHt7w8PDA1OmTLlu3eTkZHTv3h3h4eHw8/NDkyZN8NJLL+GC/m2rcfjwYQwfPhyRkZHw9fVFZGQkhg8fjiNHjpT39AkhhBBSgRQXX/0BXZF/jMN5+1HuGc7Zs2djxowZ5T7Q22+/jfHjx8PDwwPt27dHzZo18c0332Dq1KlISkrCli1bEB4eXqbe1q1b0bVrVxQUFKBp06Zo164dDhw4gI8//hhLly5FcnIy2uiLJwghhBBSKRQVAaWlFdsmB5y3H+UecDZr1gwvvPACWrRogZYtW2Lq1Kn49NNPndZJTU3FhAkT4OXlhVWrVqFbt24AgIKCAvTq1QspKSkYPXo0li5dqtQrKCjAgAEDUFBQgISEBEydOtW+b9KkSUhMTMSAAQNw6NAh+Pn5lfdSyO8YV+R1s5W6zvbpMrzcZzaxL+VUQJVef/vNsKU8C6jybXi4IevWqtXSbkd2awlJWHUhhB875rgxbZ+3sIPFyQXrJyR9A0Tw+TJL951FMLeh+znIbakfyyXYAFCrlmHLH7SRkY5tQJXYxb7zl/yVYvJ2ndxi2PJ56e4R8jbIf6h6f5T9xurqc7N9ejkrfVrv35TRCSE3SrkHnE899ZSy7el5fVU+MTERpaWlGDFihH2wCQD+/v6YN28eoqOjkZSUhIMHD6KJ8IWaP38+Tpw4gUaNGpWR7KdMmYKkpCSkpaXhk08+wahRo8p7KYQQQgi5QTjDSazg9rBIhYWFWL16NQBg8ODBZfbXq1cPbdu2BQAsX75c2WfbHjhwYJmBraenJx577DEAwLJlyyr8vAkhhBBCSMXg9gFnWloaCgquynZxel7ha9g+T5WBq8V2eesRQgghpHKo6AVDtj9ye+H2sEgZGRkAgNDQUARJXytB3bp1lbIAkJeXh3PnzgEAoqKinNY7c+YM8vPzESDjghBiEWf+adKXzeoXoLOwSNJ/TmZ5yc83bN238+JFw5Yyk/QVBFR/QelOKSMc6S6OoaH+wm5kt8PrN1LKhYvffGGhIniRPKh+QvJCzFIsAWWdVh2hO8NadWyUfpvi4s9nG7+19dNWfDDFvdP9MeW2vCRp633GLLyQ7oIuL8Ms3JHu1irLyfacub+aZc+izyYhpKJx+4Az75qXvLPBYOC1b9fc3Nwy9ZzVDRTfyrm5uablLl++jMuXLytlCSGEEHLj0IeTWOF3kdoyMTERISEh9j/bzCghhBBCCHE/bp/htMno+VIz1LAFfg8ODi5Tz1ldGTBe1tVJSEjA+PHj7du5ubkcdBJTpJwof2VL+VGXKa2GRZJyqyznTAI1U6Ol1A4AYhLfVM3W5WOza9LPQVWtPUW5MPF5mKyiuAz4hTo+pqNtR+jStNyW9+GyFpnpgpDE5b0zuz96OWfyuBXkPQDMn7PVbEBmn+vbVrMGSSijE1cpLq74Gc6SkuuXIbcWbh9w1r8W0y47Oxt5eXkO/TizsrKUssDVAWdYWBjOnz+PzMxMNG/e3LReeHi4U8ne19cXvvo3PyGEEEJumKIiwEKExHLBAefth9sl9caNG8Pf/+rChD179jgsY/u8ZUs1KLVtu7z1CCGEEEJI1cHtM5w+Pj545JFHsGTJEixYsAAdO3ZU9v/yyy/Ytm0bAKBPnz7Kvj59+iA5ORkLFy7EK6+8osTiLCkpwaJFiwAAffv2dfNVkN8rrsiMzuRMM9lTyrrO5Gxni73lPrOV7bosbCYfO8PMfcBZthyz+s72OZOwzfbpC97Nrt0VedyZNG224txZlh+r8rhZOavZrpg1iLgbznASK1TKoqGJEyfCw8MDH330EdatW2f/vKCgAE8++SSKi4vRr18/JcsQAAwfPhwRERFIS0vD5MmTlX2TJ09GWloaIiMjMWzYsMq4DEIIIYQQ4gLlnuHcu3cvxowZY9/++eefAQAffPABvvzyS/vny5cvR+3atQFclbynT5+O8ePHo3v37vjzn/+Mu+66C9988w1+/fVXNG7cGHPmzClzLH9/fyxevBhdu3bF1KlTsXLlSjRr1gwHDhzAgQMHEBAQgCVLljCPOiGEEHKT4AwnsUK5B5y5ubnYuXNnmc+PHTuGY8eO2bdl3EsAeP755xEbG4vp06dj165dyM/PR1RUFBISEpCQkGAaFL5t27b4/vvv8dprryE5ORlJSUm48847MWzYMPzzn//E3XffXd5LIOSGcTVYvNw2kz311edmMrwugct1c/L1cyabW5Xe5bZV6b0q40xythqc3ey5KKvzXaij1zOT0bn6nBByK1Hu3yQdOnRAaWnpdf/kinMb8fHxWLt2Lc6dO4dLly4hLS0NU6dONR1s2oiJicHHH3+M48ePo7CwEMePH8fHH3/MwSYhhBBykykurvi0llUh8HteXh4mTZqExo0bw8/PD+Hh4XjkkUewceNGl9ssKSnBBx98gNatWyMoKAhBQUFo3bo15s6di9LrxJZKTk5G9+7dER4eDj8/PzRp0gQvvfSSEiLSEYcPH8bw4cMRGRkJX19fREZGYvjw4Thy5Ijl875w4QKio6Ph4eEBDw8PZYLRKr+LwO+EEEIIIVY5ffo04uLikJiYiLy8PPTs2RNNmzbF2rVrER8fj5kzZ5a7zeLiYvTv3x+jR4/GgQMH0LFjR3Ts2BH79+/HqFGj8Nhjj6HExJfg7bffRpcuXbBu3To0bdoUPXv2RE5ODqZOnYq4uDic1YMsX2Pr1q1o3rw5Pv74Y4SGhqJPnz4IDQ3Fxx9/jHvvvRc7duywdO5///vfcfTo0XJfs4QDTkIIIYS4TEXPbtr+biYjR45EWloaOnfujMOHD2Px4sXYvHkzvvzyS3h6emLcuHHYt29fudqcOXMmli1bhjp16uDAgQNYuXIlVq5ciR9++AERERFYsmQJ3n///TL1UlNTMWHCBHh5eWH16tXYvHkzFi9ejJ9//hmdO3fGoUOHMHr06DL1CgoKMGDAABQUFCAhIQEHDhzAwoULceDAASQkJCA/Px8DBgzARd2PS2PDhg2YM2cOnnnmmXJdr45H6fXmcG9DcnNzERISgvT0HAQFmWcoIuRGcSYLmYXpcRa6yJkvpZmvprT1sEFmvp7OzkG2YfX6rHxeHsz8FZ2FXJK+jDJkk7OsUc78LM3acJaxycwf02rmKqvXR0hFk5eXi4YNQ5CTk2PP7Gf7X1q9eg48PCr2f2lpaS4uXVKPV1n8+OOPaNq0Kby8vPDzzz+jXr16yv6nnnoK8+bNw8CBA/H5559barOkpAR16tTByZMn8dlnn+Hxxx9X9n/22WcYOnQoIiIikJWVpYSBHDBgAJYsWYKnnnoKH374oVLvl19+QXR0NEpKSvDTTz8p0X7ef/99PPPMM2jUqBF++umnMqEl//CHPyAtLQ1z5szBqFGjHJ53bm4umjVrBm9vb+zfvx+B1+K0ZWVlITIy0tK12+AMJyGEEELINZYvXw7g6qJlfbAJAIMHDwYArFq1Clf0X/EmbN++HSdPnoSvry/69etXZn+/fv3g4+ODEydOKAuzCwsLsXr1auW4knr16qFt27bKeevXMXDgQGWwCQCenp547LHHAADLli0zPe9x48bh2LFj+M9//uM0o6MVOOAkhBBCiMvcbpJ6amoqACAuLs7hftvn+fn5SE9PL1ebTZs2RXVd0gDg5+eHpk2bKmUBIC0tDQUFBZbOR9Yrz3Xo9WysXr0aH330EZ5++ml06tTJ8YWVA7dnGiLk94xVmdNZKCWzffp3ltxnFu5Il+HNJHr9HOSPeCnDW80GZPWfh1nYKGdYlZzlPimP6xmRzKRuZ1mjzMInuZoNiCGOCLl5ZGRkAACioqIc7g8ODkZwcDByc3ORkZGBe+6554bbBIC6desiNTXVXlbWCw0NNY3oU7duXaUscHWF/blz55we01bvzJkzyM/PV2Ywf/vtNzz99NOoW7cu/v3vf1/3+qzAASchhBBCXKaoCPDwqNg2batLcnNzlc99fX3hqztUVzB5eXkA4FRCDgwMRG5ubpnzu9E2AfWab7Ses7qBIm9ubm6uUm7s2LH49ddfsXbt2grzoeWAkxBCCCFVEtssnI1XXnkFr776qmn5f/zjH1i5cmW5j/Of//wH7dq1K3e925Fly5ZhwYIFGDFiBB5++OEKa5cDTkJuEhUht0vM5HZn0rbcdrb63MqKemdtW/m8PFhdpW5FwtbrmK1mr4i2nZ2r2TkQUvUpQWlpReeivNpeVlaWMsN2vdnNEydO4NChQ+U+mgyebpOu8/Pzr1ve6uyfq23eaD1ndeU12+qePXsWf/3rXxEREYG33nrL9JiuwAEnIYQQQqokNn9Jq3z22Wf47LPPbuiY9evXx969e5GZmelwv5TSHWVVNGsTgGmbwNXBtd6mzc7OzkZeXp5DP05H9YKCghAWFobz588jMzMTzZs3N60XHh5ul9O3bNmC06dPIzIyEr179zY91/79+8PX1xfDhw/H8OHDTctJOOAkhBBCyA1QfO2votu8ObRs2RLLli3Dnj17HO63fR4QEIBGjRpZbhMAfvjhB1y6dKnMSvWLFy/ihx9+UMoCQOPGjeHv74+CggLs2bMHHTt2ND0fWc+2nZycjD179qBnz56W6wHAsWPHnKavtGUo6tChg2kZHYZFIoQQQsgNUOymv5uDbWZv69atDmckFyxYAADo2bMnqulhLkx48MEHUatWLVy+fBlJSUll9iclJaGwsBARERFo3bq1/XMfHx888sgjynElv/zyC7Zt2wYA6NOnj7LPtr1w4cIyKTNLSkqwaNEiAEDfvn3tn/fu3RulpaWmfzaysrJQWlrq1J9Wh5mGmGmI3EI4y+wjseo/6Uo5szrO4h9bPW9XcObvaPa/wJWQRK6WM4N+muRWwlmmIeAUgIr+X5oLoOZNyTQEXB14ffHFF4iPj8fKlSvhdy3e2dq1a9GzZ0+UlpYiNTUV9957r1Jv2LBh2LVrF8aOHYuxY8cq+9555x08//zzqFOnDr755hs0aNAAwNVwRu3bt8fx48cxc+bMMvX27t2LuLg4eHp64ssvv7Qv5CkoKECvXr2QkpKCfv36YenSpUq9goICNGzYECdOnMCkSZPw+uuv2/e99NJLmDp1KiIjI5GWlma/vuvhcS0cgSuZhiipE0IIIeQGuL0kdQCYO3cufvzxRyQnJ+Puu+9G+/btcfr0aWzevBmlpaWYMWNGmcEmcNVH89ChQzh79myZfc8++yy+/vprLF++HM2aNUN8fDwAIDk5GQUFBXj00UcxZsyYMvVatmyJ6dOnY/z48ejevTv+/Oc/46677sI333yDX3/9FY0bN8acOXPK1PP398fixYvRtWtXTJ06FStXrkSzZs1w4MABHDhwAAEBAViyZInlweaNQkmdEEIIIURw1113Yc+ePZg4cSICAwPxxRdfYN++fXjooYeQnJyMv/3tb+Vu08vLC0uXLsWcOXNwzz33ICUlBSkpKWjatCnmzJmDxYsXl0lBaeP555/Hhg0b8NBDD2Hfvn344osvEBgYiISEBOzevRvh4eEO67Vt2xbff/89hg0bhvPnzyMpKQnnz5/HsGHD8P3336NNmzblvg5XoaROSZ3chtyo9G613I3WrwhcyUhUkfV1KJWT2xHnkvoxuEdSj7xpkjqpeDjDSQghhBBC3Ap9OAkhhBByA9x+Ppyk4uGAk5DbkIqWdc3kZHdK5RWNVUncDErlhBDiOhxwEkIIIeQGKEHFz0hWdKpMcrPhgJMQQgghNwAldXJ9OOAk5HfMjcrEsr47g7u7CmVwQgipGnDASQghhJAbgDOc5PowLBIhhBBCCHErnOEkhBBCyA1Qgopf5MNFQ7cbHHASQioE+ksSQggxgwNOQgghhNwA9OEk14c+nIQQQgghxK1whpMQQgghNwBnOMn14YCTEEIIITcAB5zk+lBSJ4QQQgghboUznIQQQgi5AZhLnVwfznASQgghhBC3whlOQgghhNwADPxOrg9nOAkhhBBCiFvhDCchhBBCbgCuUifXhzOchBBCCCHErXCGkxBCCCE3AGc4yfXhgJMQQgghNwAHnOT6UFInhBBCCCFuhTOchBBCCLkBOMNJrg9nOAkhhBBCiFvhDCchhBBCboBSVHyg9tIKbo/cbDjDSQghhBBC3ApnOAkhhBByA9CHk1wfznASQgghhBC3whlOQgghhNwAnOEk14cDTkIIIYTcABxwkuvjkqR+6NAhzJw5E8OHD0dsbCy8vb3h4eGBKVOmOCxfUlKCbdu24Z///CfatWuHO+64A9WqVUN4eDi6dOmC//3f/0VpqfMVad9++y369++PmjVronr16mjQoAGeffZZnD592pVLIIQQQgghlYRLM5yzZ8/GjBkzLJc/cuQI2rZtCwAICwtDXFwcatSogSNHjiA5ORnJyclYuHAhkpKS4OPjU6b+0qVLMWjQIBQVFaFVq1Zo0KAB9uzZg1mzZmHJkiXYsmULYmJiXLkUQgghhNwQnOEk18elGc5mzZrhhRdewP/+7//ip59+wtChQ52W9/DwQKdOnbB27VqcPn0aX331FRYuXIhdu3Zh06ZNCAgIwJdffok33nijTN0TJ07giSeeQFFRET744APs2rULixYtQlpaGoYMGYJTp05h8ODB150hJYQQQgghNweXBpxPPfUU/v3vf2Pw4MFo0qQJPD2dN3P33XcjJSUFDz/8MLy8vJR9f/7znzFx4kQAwCeffFKm7jvvvIOCggLEx8dj5MiR9s+9vLwwe/ZshISEYPfu3Vi/fr0rl0IIIYSQG6LETX/kdqJKhEVq0aIFACArK6vMvuXLlwMABg8eXGZfYGAgevXqBQBYtmyZG8+QEEIIIb8n8vLyMGnSJDRu3Bh+fn4IDw/HI488go0bN7rcZklJCT744AO0bt0aQUFBCAoKQuvWrTF37tzrKrXJycno3r07wsPD4efnhyZNmuCll17ChQsXnNY7fPgwhg8fjsjISPj6+iIyMhLDhw/HkSNHrnu+GRkZePbZZ9GoUSP4+/sjODgYTZo0wYgRIyzVl1SJAWd6ejoAoHbt2srneXl5OHz4MAAgLi7OYV3b56mpqW48Q0IIIYQ4pthNfzeP06dPIy4uDomJicjLy0PPnj3RtGlTrF27FvHx8Zg5c2a52ywuLkb//v0xevRoHDhwAB07dkTHjh2xf/9+jBo1Co899hhKShzP7L799tvo0qUL1q1bh6ZNm6Jnz57IycnB1KlTERcXh7Nnzzqst3XrVjRv3hwff/wxQkND0adPH4SGhuLjjz/Gvffeix07dpie7+eff4577rkHs2bNQvXq1dGzZ0906NABXl5emD9/Pn788cdyXf9ND4tUUFCAd999FwDQr18/Zd/Ro0ftdlRUlMP6devWBXB1FE4IIYQQcqOMHDkSaWlp6Ny5M1auXAl/f38AwJo1a9CrVy+MGzcOf/7zn3HvvfdabnPmzJlYtmwZ6tSpg2+++QYNGjQAcHX80q5dOyxZsgR/+tOfMHbsWKVeamoqJkyYAC8vL6xatQrdunUDcHX81KtXL6SkpGD06NFYunSpUq+goAADBgxAQUEBEhISMHXqVPu+SZMmITExEQMGDMChQ4fg5+en1E1JScGQIUNw1113YfHixWjfvr2y/+jRo/D19bV87UAVmOEcM2YMMjIyEBERgUmTJin78vLy7HZAQIDD+oGBgQCA3Nxc02NcvnwZubm5yh8hhBBCKoISVPzs5s3z4fzxxx/xxRdfwMvLC/PmzbMPNgGge/fuGD58OEpKSpCYmGi5zZKSEkybNg0AMG3aNPtgEwAaNGhg35eYmFhmljMxMRGlpaUYMWKEfbAJAP7+/pg3bx48PT2RlJSEgwcPKvXmz5+PEydOoFGjRmXCVk6ZMgWNGjVCVlZWmfUzxcXFeOqpp1BSUoKkpKQyg00AqF+/fhlV+nrc1AHna6+9ho8//hjVq1fH4sWLcccdd7jlOImJiQgJCbH/2WZFCSGEEEIktrUjbdu2xf/f3n1HRXnsfQD/Lr2jBkUQBStcwXjFGiU3do2iRrEfRXJMNDGxRBMBlajXFmOi15Zgi5ioFwsSJZbgajSgxoBYXiwUsWAhohyKrIIs8/7B3Q3L7rIssKDy/Zyz57DPlGeeJxP4Oc8zM66urmrpijklUVFRePHiRYXqPHfuHDIyMmBubq72NBcoecJrZmaGBw8e4Pz588rjhYWFOHz4sMp5S3N1dVUuO6lod9nrGDt2rNrkbiMjI4wZMwaA+hyYqKgo3L59Gz4+PujevXuFrq8iai3gXL16Nb788kuYm5sjMjJSecNKs7W1Vf6cn5+vsR7Fy7J2dnZazxUcHIycnBzlR9PkJCIiIqqM1+sdTsWcEF1zR/Lz85VzUCpap6enJywsLNTSLS0t4enpqZIXAJKTkyGTySrUnrJzWSp6HWXL/frrrwCAf/3rXygqKkJERARmz56Njz/+GCtXrlQbSa2oWnmHc/369ZgzZw7MzMwQERGBgQMHasxX+l8Wd+/eRbt27dTyKIJHNzc3reczNzfX+10DIiIiqghDLGNUe4/UFXNCtM0dsbOzg52dHXJzc3Hr1i20bdu2ynUCJXNSLl68qDInRfFzvXr1VAbhypYrnRcoeSXxyZMn5Z5TUS4zMxP5+fnKVxevXLkCADAxMUGXLl3UAtJ58+Zh1qxZ+OabbyCRSLReT1k1PsK5ceNGzJgxQxlsDh48WGteOzs75Q5C8fHxGvMojnt7e1d/Y4mIiKjWlJ1/UVBQYPBzKuaPaJs7AlRs/kh11FnVcuWVVZQrW1YRqK5YsQJ3797Fzp07kZmZifT0dKxatQomJiZYvXq1xs16ylOjI5yhoaH49NNPlcGmr6+vzjLDhw/HqlWrsHv3brz//vsqaU+fPkVUVBQAYMSIEQZpMxEREZXHcFtblp1zsXDhQixatEhrqblz5+LQoUN6n23r1q3w8fHRu9zrSLEe6IsXL7B79270799fmfb555+juLgYgYGBWLFiBWbMmFFuMFxajQWcW7ZswbRp0/QKNgFg1qxZ2LhxI6RSKbZs2YIPP/wQQMksqmnTpiE7OxudO3dWuSFERET06ktPT1eZo6Hr9bgHDx4gKSlJ7/OUXjxd8eha29yR0vnLmz9SWmXrrGq58sqWvmZNZd3c3DTGVh9//DECAwORl5eHP//8E7169dLattIqFXAmJCRg2rRpyu83b94EAGzatAm//PKL8nhkZCScnJxw6dIlTJ06FUIItGjRAvv371dbL0ohLCxM5buzszPCwsIwbtw4TJkyBdu2bYObmxvi4uKQlpYGR0dH7N69W6/3CIiIiKi6GG6EU/G+ZEXt3LkTO3furNKZ3dzckJCQgLt372pML728YnnzR8rWCUBrnYDmOSmKn7Ozs5GXl6fxPU5N5WxtbdGgQQNkZWXh7t27aN++vdZyDg4OKqOULVq0wIULF9CiRQuN7bS1tUXDhg2RmZmJhw8far2esioVcObm5qpM21e4d+8e7t27p/yueNciOztbOUR748aNcmc4lQ04AWDUqFFo0aIFli9fjpiYGFy8eBFOTk745JNPEBISAkdHx8pcBhEREZEKb29vHDhwQOfcEWtra7Rp06bCdQLA1atX8fz5c7WZ6s+ePcPVq1dV8gKAu7s7rKysIJPJEB8fr3E0UdtcFm9vb0ilUsTHx2PIkCEVLtexY0fs27dP6+5Fcrkc2dnZAFTfA9WlUpOGevbsCSGEzo8i2q5o/vL2Ee3YsSMiIiLw6NEjFBQU4Pbt29iwYQODTSIiolr1ei2L9N577wEo2RZS04jk7t27AQBDhgyBqalphep866230LhxYxQUFCAiIkItPSIiAoWFhXB2dkbXrl2Vx83MzJSTqxXnLe3OnTs4e/YsgJI5L6UpvoeHh6stJl9cXIw9e/YAUJ8D4+fnB4lEghs3bqgMIiqcOnUKL168gEQi0brkkia1vtMQERER0cvC09MTw4YNg1wux+TJk/Hs2TNl2tGjRxEWFgYjIyMEBwerlfX394eHhwc2bNigctzIyAiBgYEAgMDAQLWlj4KCggCUrBtedpH2oKAgSCQSbN++HceOHVMel8lkmDx5MuRyOfz8/ODh4aFSLiAgAM7OzkhOTkZISIhKWkhICJKTk+Hi4gJ/f3+VtFatWmHChAkoLCzEhx9+iJycHGXanTt3MH36dADAyJEj4ezsrOUuqpOI8oYVX1O5ubmwt7dHSkoObG0r/m4IERFRXZSXl4vWre2Rk5OjfKdS8bcU2AzAstzy+nsGYIrK+WrSo0eP4OPjg5SUFDg5OeHtt9/Go0ePcPr0aQghsHbtWsyYMUOtXM+ePXH69GmNs+nlcjlGjRqFyMhIWFlZoW/fvgAAqVQKmUyGkSNHYs+ePWoBJwCsWbMGs2fPhkQiwTvvvINGjRohJiYGDx8+hLu7O2JjY+Hg4KBW7syZM+jfvz9kMhm8vLzg5eWFxMREJCYmwtraGlKpFN26dVMrl5OTg969eyMhIQEODg7o1q0bnj9/jj/++ANPnz5F+/btcfLkSTRo0KDC95QjnERERFQFxQb61J5GjRohPj4eQUFBsLGxwcGDB3HlyhUMGDAAUqlUY7Cpi7GxMfbv34/Q0FC0bdsWJ06cwIkTJ+Dp6YnQ0FDs3btXY7AJAJ999hmOHz+OAQMG4MqVKzh48CBsbGwQHByMuLg4jcEmULI95+XLl+Hv74+srCxEREQgKysL/v7+uHz5ssZgEwDs7e1x5swZLF++HM7Ozjh58iTOnj2L1q1b46uvvsK5c+f0CjYBjnByhJOIiEiH8kc4Q2GYEc6Pam2Ek6pfrWxtSURERK8Lwy2LRK8PPlInIiIiIoPiCCcRERFVAUc4STeOcBIRERGRQXGEk4iIiKqAI5ykG0c4iYiIiMigOMJJREREVcARTtKNAScRERFVgSEWaq/dhd+p+vGROhEREREZFEc4iYiIqAqKUf2PwDnC+brhCCcRERERGRRHOImIiKgKOGmIdOMIJxEREREZFEc4iYiIqAo4wkm6cYSTiIiIiAyKI5xERERUBRzhJN0YcBIREVEVMOAk3fhInYiIiIgMiiOcREREVAXc2pJ04wgnERERERkURziJiIioCri1JenGEU4iIiIiMiiOcBIREVEVcJY66cYRTiIiIiIyKI5wEhERURVwhJN0Y8BJREREVcBlkUg3PlInIiIiIoPiCCcRERFVAR+pk24c4SQiIiIig+IIJxEREVUBRzhJN45wEhEREZFBcYSTiIiIqoAjnKQbRziJiIiIyKA4wklERERVUIzqH5HkOpyvGwacREREVAVc+J104yN1IiIiIjIoBpxERERUBXIDfWpXXl4e5s2bB3d3d1haWsLBwQGDBw/GyZMnK11ncXExNm3ahK5du8LW1ha2trbo2rUrNm/eDCFEuWWlUikGDRoEBwcHWFpawsPDA/Pnz8fTp0/LLZeamoqAgAC4uLjA3NwcLi4uCAgIQFpaWrnljhw5giFDhqBx48YwNTWFra0tOnbsiKVLlyIvL0/va5cIXVf4GsrNzYW9vT1SUnJga2tX280hIiJ6qeXl5aJ1a3vk5OTAzq7k76bibykQAMCsms9YCCBM5Xw16dGjR3j77beRnJwMJycn+Pj44K+//kJMTAwAYO3atZg+fbpedcrlcowePRoHDhyAlZUV+vTpA6AkkHz27BlGjRqF8PBwGBmpjwWuWbMGs2fPhkQiwdtvvw1HR0fExMQgIyMD7u7uiI2NhYODg1q5M2fOoH///pDJZPD09ISXlxcSExNx9epVWFtbQyqVolu3bmrlAgMD8fXXXwMAOnXqhJYtWyIrKwtnzpyBTCZD69atERMTA0dHxwpfP0c4iYiIqApevxHOKVOmIDk5GX369EFqair27t2L06dP45dffoGRkRFmzZqFK1eu6FXn+vXrceDAATRp0gSJiYk4dOgQDh06hKtXr8LZ2Rn79u3Dd999p1bu4sWLmDNnDoyNjXH48GGcPn0ae/fuxc2bN9GnTx8kJSXho48+Uisnk8kwevRoyGQyBAcHIzExEeHh4UhMTERwcDDy8/MxevRoPHv2TO18X3/9NUxNTREdHY24uDiEh4cjOjoat2/fRvv27ZGSkoKFCxfqdf0MOImIiIj+59q1azh48CCMjY2xbds2WFlZKdMGDRqEgIAAFBcXY8WKFRWus7i4GCtXrgQArFy5Es2bN1emNW/eXJm2YsUKFBerTphasWIFhBB4//338e677yqPW1lZYdu2bTAyMkJERARu3LihUi4sLAwPHjxAmzZtsHTpUpW0pUuXok2bNkhPT8ePP/6okqZ4ZaBfv37o16+fSlrDhg0xd+5cAMC5c+cqfP0AA04iIiKqktdrhDMyMhIA0KNHD7i6uqqljx8/HgAQFRWFFy9eVKjOc+fOISMjA+bm5vDz81NL9/Pzg5mZGR48eIDz588rjxcWFuLw4cMq5y3N1dUVPXr0UGl32esYO3as2mN6IyMjjBkzBgBw4MABlTQLC4sKXZOmR/jlYcBJRERE9D8XL14EUPLuoiaK4/n5+UhJSdGrTk9PT40BnaWlJTw9PVXyAkBycjJkMlmF2lO6nD7XUbZc3759YWJiguPHj0MqlaqkZWZmKt/tnDp1qsZ6tWHASURERFXweo1w3rp1CwDQrFkzjel2dnbKiUyKvFWtEwCaNm2qVqfi53r16sHW1rbC5fLy8vDkyZNyz6kol5mZifz8fOVxd3d3rF+/HkII9OvXD126dMHYsWMxYMAAuLm54dGjR9iyZQtGjx5d/kWXwYXfiYiIqAoMt9NQbm6uylFzc3OYm5tX87lUKZb8sba21prHxsYGubm5au2rap2A6jVXtVx5ZRXlFGVL5/voo4/QokULTJw4EXFxcYiLi1Om+fr6ah01LQ9HOImIiOil1LRpU9jb2ys/uibqzJ07Fx4eHnp/YmNja+iKXg0LFizAgAED4O3tjbi4ODx9+hRpaWlYsmQJfv75Z3Tv3h3R0dF61VmpEc6kpCRER0fjwoULuHDhAq5fvw65XI4lS5ZgwYIFFa7nu+++wyeffAIAmDx5MrZu3ao174ULF/DVV1/h999/R05ODpycnODr64uQkBA0atSoMpdBREREVWa4rS3T09NV1uHUNbr54MEDJCUl6X220ounKx5dl37MrC1/RdcIrWydVS1XXtnS11y67K5du7Bs2TK8+eabiIqKgolJSajYvHlzLFiwACYmJggODsZHH32ElJQUGBsba21baZUa4fz+++8xY8YM7NixA4mJiZDL9R9KT0tLw9y5cyGRSHTm3b9/P7p164b9+/fD1dUVw4YNg5GRETZs2IA333wTqamplbkMIiIieokp3pdUfHQFnDt37oQQQu/PwIEDlXW4ubkBAO7evavxHKUfpSvy6qKrTqAkuC5bp+Ln7Oxsrbv7aCpna2uLBg0alHtORTkHBweVx+lhYWEAgFGjRimDzdIUs+Vv3bqlc7ei0ioVcHp5eeHzzz/Hrl27cP36dUycOFGv8sXFxQgICIBEIoG/v3+5eR88eIBJkyahqKgImzZtwp9//ok9e/YgOTkZEyZMwF9//YXx48fr3BKKiIiIDOH1mjTk7e0NAIiPj9eYrjhubW2NNm3a6FXn1atX8fz5c7X0Z8+e4erVqyp5gZIJPIp1QHW1p3Q5fa6jbDlFgKpt9LZkd6kSWVlZGvNoUqmA84MPPsCqVaswfvx4eHh4aNyGqTxr165FTEwMVq5cqfNfB//5z38gk8nQt29fTJkyRXnc2NgY33//Pezt7REXF6f3uwREREREZb333nsASraF1DQ6uHv3bgDAkCFDYGpqWqE633rrLTRu3BgFBQWIiIhQS4+IiEBhYSGcnZ3RtWtX5XEzMzMMHjxY5byl3blzB2fPngUADB8+XCVN8T08PFxtMfni4mLs2bMHADBixAiVtCZNmgCAynqgpf3xxx/Knys6wgvUwqShpKQkzJ8/H++88w4+/vhjnfkVC5dqWvDUxsYGQ4cOBaC+cCkRERHVhNdrhNPT0xPDhg2DXC7H5MmTVbZ+PHr0KMLCwmBkZITg4GC1sv7+/vDw8MCGDRtUjhsZGSEwMBBAyT7lZZc+CgoKAgAEBwerDeIFBQVBIpFg+/btOHbsmPK4TCbD5MmTIZfL4efnBw8PD5VyAQEBcHZ2RnJyMkJCQlTSQkJCkJycDBcXF7UnzSNHjgQA/Pe//0V4eLhKWlpaGmbOnAkA6NOnz8u7l7pcLsekSZMgkUiwbds2ne9v5uXlKd/P1HfhUiIiIqLK2Lx5M1q3bg2pVIqWLVtizJgx6NWrFwYPHgy5XI41a9bgzTffVCt39+5dJCUl4fHjx2pp06dPx/Dhw3H//n14eXlh2LBhGDZsGLy8vHD//n2MHDkS06ZNUyvn7e2Nb7/9FnK5HIMGDUKvXr0wZswYtGrVCidOnIC7uztCQ0PVyllZWWHv3r2wsrLC8uXL0a5dO4wbNw7t2rXD8uXLYW1tjX379sHS0lKl3JQpU+Dr6wshhDL/6NGj0atXL3h6eiIpKQkuLi7YvHmzXve0RgPOVatW4fz581i2bBlatmypM//t27eVP+tauLSii68SERFRdXq9RjgBoFGjRoiPj0dQUBBsbGxw8OBBXLlyBQMGDIBUKsWMGTP0rtPY2Bj79+9HaGgo2rZtixMnTuDEiRPw9PREaGgo9u7dq/UVxc8++wzHjx/HgAEDcOXKFRw8eBA2NjYIDg5GXFyc1m0me/TogcuXL8Pf3x9ZWVmIiIhAVlYW/P39cfnyZXTr1k2tjImJCQ4dOoQdO3agb9++yMjIQGRkJOLj4+Hh4YGQkBBcuXIFLVq00Ov6a2zh98TERCxcuBDdu3ev8H8ofRYuLW/x1YKCAhQUFCi/V3ShViIiItLFcMsi1SY7OzusWLFC59qfpZ06darcdCMjI0ydOlXvbSGBki0n+/btq3e5Vq1aYceOHXqVUUzq1jWxWx81EnAWFRVh0qRJMDIywg8//KD3JKOqWrFiBRYvXqx2PC+PgScREZEuir+XmleEKTLAGQ1RJ9WmGgk4ly1bhoSEBKxcuRLu7u4VLld24dLSU/EVKrL4anBwMGbPnq38fv/+fbRt2xbe3k0r3BYiIqK6Li8vT/m32MzMDI0bN0ZGhtQg52rcuDHMzMwMUjfVvBoJOBUzzaOionDkyBGVNMV7mocPH0bPnj0B/D0k7erqqsx39+5dtGvXTq1uTQuellV271UbGxukp6dDCIFmzZqp7WRA+snNzUXTpk15H6uI97F68D5WD97H6vG63EchBPLy8uDs7Kw8ZmFhgVu3bqGwsNAg5zQzM4OFhYVB6qaaV2PvcAIod6/SjIwMZGRkqByzs7NDq1atkJqaivj4eI0Bp7aFS8tjZGQEFxcX5bucih0MqGp4H6sH72P14H2sHryP1eN1uI+anjJaWFgwKKQKqZGXKS9duqR1O6mFCxcCKNlLXXGsNMXCpZoWPH369CmioqIAqC9cSkREREQvhxpf+F1fs2bNgpWVFaRSKbZs2aI8LpfLMW3aNGRnZ6Nz587o379/LbaSiIiIiLSp1CP1hIQElcVJb968CQDYtGkTfvnlF+XxyMhIODk5VamBzs7OCAsLw7hx4zBlyhRs27YNbm5uiIuLQ1paGhwdHbF7926di8hrYm5ujoULF6q830n6432sHryP1YP3sXrwPlYP3keiEhKheY2Dcp06dQq9evXSme/WrVs699lctGgRFi9ejMmTJ2Pr1q1a8124cAHLly9HTEwMcnJy4OTkBF9fX4SEhOi1tRIRERER1axKBZxERERERBX10r/DSURERESvNgacRERERGRQdTLg3LdvH3r27In69evD2toa7du3x9dff40XL17UdtNeCi9evMCJEyfwxRdfoHPnzqhXrx5MTU3RuHFjDB06FIcPH9ZYbtGiRZBIJOV+bty4UcNXU7sCAgJ03pPnz59rLHvhwgWMGjUKjo6OsLCwQPPmzTF9+nQ8evSohq+idt2+fVvnPVR8fv/9d2W5utofk5KSsH79egQEBKBdu3YwMTGBRCLB0qVLdZaVSqUYNGgQHBwcYGlpCQ8PD8yfP1+5o5s2qampCAgIgIuLC8zNzeHi4oKAgACkpaVV12XVOH3vY3FxMc6ePYsvv/wSPj4+eOONN2BqagoHBwf069cPu3bt0rItJBAWFqazrx47dsyQl0tkcDW68PvLYNasWVi7di1MTEzQu3dv2NjY4OTJkwgMDERUVBSio6NhaWlZ282sVadPn0a/fv0AlGwt5uPjA2tra1y7dg1RUVGIiorClClTEBoaqnF1gPbt2+Of//ynxro1LRxcF/To0QOtWrXSmGZsbKx2bP/+/Rg3bhyKiorQuXNnNG/eHPHx8diwYQP27duH2NhYrfW9bmxsbDBp0iSt6deuXUNcXBxsbW3RsWNHtfS61h+///57rF27Vu9ya9aswezZsyGRSPD222/D0dERMTExWL58OSIiIhAbGwsHBwe1cmfOnEH//v0hk8ng6ekJHx8fJCYmYseOHdi/fz+kUim6detWHZdWo/S9j2lpaejRowcAoEGDBujUqRPq16+PtLQ0SKVSSKVShIeHIyIiQut2jS1btoSPj4/GtCZNmuh/EUQvE1GHREZGCgDCxsZGXLhwQXk8MzNTtGvXTgAQc+bMqcUWvhxOnDgh/Pz8xO+//66WFh4eLoyNjQUAsWPHDpW0hQsXCgBi4cKFNdTSl9+kSZMEALF9+/YKl7l//76wsrISAMSmTZuUx4uKisSECRMEANG5c2dRXFxsgBa/et59910BQHz44Ycqx+tqf9yyZYv4/PPPxa5du8T169fFxIkTBQCxZMkSrWUSEhKERCIRxsbG4siRI8rj+fn5ok+fPgKA8PPzUyuXn58vnJ2dBQARHByskhYcHCwAiKZNmwqZTFZ9F1hD9L2Pqamponfv3uLo0aOiqKhIJe3UqVPC2tpaABCLFy9WK7t9+3YBQEyaNMkQl0L0UqhTAWfnzp0FALF06VK1tJiYGAFAmJubi+zs7Fpo3atj8uTJAoDo06ePyvG6+ge+PJUJOL/44gsBQPTt21ctLS8vT9jb2wsA4tixY9XY0lfTvXv3hJGRkQAg/vjjD5U09scSij5YXsA5atQoAUB88MEHamm3b99W3uPr16+rpG3cuFEAEG3atBFyuVwlTS6XizZt2ggAIjQ0tHouphZV5D6WZ8mSJQKAaNmypVoaA06qC+rMO5z3799HXFwcAGD8+PFq6T4+PmjatCkKCgpw5MiRmm7eK6VDhw4AgPT09FpuyespMjISgOZ+amNjg6FDhwIADhw4UKPtehmFhYWhuLgYnp6e6Nq1a20355VUWFiofC9bU59zdXVVPipW9E0FxfexY8fCyEj1z4mRkRHGjBkDgH0V4O9NojrzDufFixcBlLxb07x5c415OnXqhPT0dFy8eBHjxo2ryea9UlJSUgBA6y5SCQkJCAoKQlZWFuzt7dGhQwcMGTIEtra2NdnMl8pvv/2G//u//0NeXh7eeOMNdOnSBYMGDVLbfSQvLw+pqakASvqjJp06dcJPP/2k7NN1WVhYGABg8uTJWvOwP5YvOTkZMpkMQPl9LiYmRq3PKb6XV650vrpM1+9NoGTy1YIFC/Do0SPY2NjAy8sLQ4cO1fjuLNGrps4EnLdu3QIANGvWTGuepk2bquQldRkZGco/8n5+fhrzKCYWlWZvb49169bB39/f0E18Kf34449qx5ycnPDDDz9g4MCBymO3b99W/qytr7Kfljh9+jRSU1NhZmaGiRMnas3H/lg+RT+qV6+e1iBcU5/Ly8vDkydPAOjuq5mZmcjPz4e1tXW1tftVIpPJsG7dOgDaf28CJROwzpw5o3LMwsICixYtQmBgoEHbSGRodeaRel5eHgCU+wvPxsYGAJCbm1sjbXrVFBUVYcKECcjJyUG7du0wdepUlfSWLVti+fLluHjxIrKyspCVlYXY2Fj4+voiJycHkyZNwq5du2qp9bWjffv2WLt2LRITE5Gbm4u//voL0dHR6N69Ox4+fIihQ4fi1KlTyvyKfgpo76vspyV++OEHANA6AsT+WDGV/d2oT18tW7aumTZtGm7dugVnZ2fMmzdPLb1x48aYP38+zp8/j8zMTOTm5iIuLg7+/v4oKChAUFAQli9fXgstJ6pGtf0SaU1ZtmyZACB69OihNc+8efMEANG/f/8abNmrQzFZ6I033hBJSUl6lZ0+fboAIBo2bCgKCgoM1MJXR3FxsRg2bJgAINq3b688fubMGQFAABAvXrzQWDY6OloAEGZmZjXU2pdPTk6OciZ/6VnVFVWX+qOuyS67du0SAESTJk201rF582bl5CCF+/fvK/tqSkqKxnLJycnKPA8ePKjahdSyyk4a+ve//y0ACAsLCxEbG6v3eb/99lvlhNaMjAy9yxO9LOrMCKfiUVF+fr7WPIrFje3s7GqkTa+SmTNnYtu2bahfvz6OHz+ONm3a6FV+0aJFMDY2RmZmJs6fP2+gVr46JBIJFi9eDAC4fPmyciJB6Uea2voq+ykQHh4OmUwGFxcXDBgwQO/y7I9/q+zvRn36atmydcXq1avx5ZdfwtzcHJGRkcrJV/qYOXMmHBwcUFBQgOjoaAO0kqhm1JmA083NDUD5MwQVaYq8VGLOnDlYt24d6tWrh+joaOVsS300aNAAjRo1AgDcu3evupv4SvrHP/6h/FlxT1xdXZXH7t69q7Ec++nfj9MDAgLUZkdXBPvj3xT9KDs7W+UxeWma+pytrS0aNGgAQHdfdXBwqHPvb65fvx5z5syBmZkZIiIiVN7V1oexsTFat24NgH2VXm11JuBUBElPnjzROtkiPj4eAODt7V1j7XrZzZ07F6tXr4a9vT2io6O1zkbVRS6XIycnBwA4O/h/FBMugL/viZ2dnXIHIUV/LKuu99Nr167h/PnzkEgkeP/99ytVB/vj39zd3WFlZQVA/z6n+M6+qmrjxo2YMWOGMtgcPHhwlepT/K6o632VXm11JuB0cXFB586dAQC7d+9WS4+NjUV6ejrMzc0xaNCgmm7eSykoKAirVq2Cvb09jh8/rrx/lXHo0CHIZDJIJJJKB62vm/DwcAAlQaa7u7vy+PDhwwFo7qdPnz5VzrgeMWJEDbTy5bNt2zYAQK9evdCiRYtK1cH++DczMzNlQKSpz925cwdnz54F8HffVFB8Dw8PR3FxsUpacXEx9uzZA6Bu9dXQ0FB8+umnymDT19e3SvUlJCQgOTkZANClS5fqaCJR7ajtl0hrkratLR8/fsytLcuYP3++ACDq1asn/vzzT53579y5I3766Sfx7NkztbTIyEjRoEEDAUBMmDDBEM19KV28eFEcPHhQbfKPXC4XW7duFRYWFgKAWLBggUp66a0tN2/erDxeVFSk3F6vrm5tWVhYKBo1aiQAiF27dmnNx/74t4pMdrlw4YJya8ujR48qj+uzteW8efNU0hSTMF1cXF7JrS3Lqsh93Lx5s5BIJMLMzExERUVVqN78/HyxYcMGkZubq5Z2+vRp4ebmJgAIHx+fSred6GUgEUKIWohza83MmTOxbt06mJqaok+fPrC2tsaJEyeQnZ2NHj164Pjx47C0tKztZtaqQ4cOYdiwYQBKFm729PTUmM/BwQHffPMNAODSpUvo0KEDbGxs0KFDBzRp0gTPnj3DtWvXlAse9+rVC4cOHVJZKuV19vPPP2P48OGoX78+vL294ejoiOzsbCQmJirfeRs3bhx+/PFHmJioLom7b98+jBs3DnK5HF27doWbmxvi4uKQlpYGR0dHxMbGKh+91yWRkZEYMWIE6tWrh4cPH8LCwkJjvrrcHxMSEjBt2jTl95s3b+Lx48dwcXFBkyZNlMcjIyNVFiFfs2YNZs+eDYlEgnfeeQeNGjVCTEwMHj58CHd3d8TGxmpcfurMmTPo378/ZDIZvLy84OXlhcTERCQmJsLa2hpSqRTdunUz7EUbgL738dKlS/D29oYQAh4eHuXufKVYyxgoeXe2fv36MDc3R4cOHdCsWTMUFRUhOTkZiYmJAIB27drh119/LXfReKKXXi0HvLViz5494l//+pews7MTlpaWwsvLS3z11Vev/fIoFaXY11fXx9XVVVnm8ePHIjAwUPTu3Vs0a9ZMWFtbC1NTU+Hk5CR8fX3F7t271fZaft2lpaWJWbNmCR8fH9GkSRNhYWEhzM3NRbNmzcTIkSPF4cOHyy0fHx8vRowYIRo2bCjMzMyEq6ur+OSTT+r00ii+vr4CgJg2bVq5+epyf/ztt98q9P/vrVu31MoeP35cDBw4UDRo0ECYm5uL1q1bi+DgYI2jb6WlpKQIf39/4ezsLExNTYWzs7Pw9/cXqampBrpKw9P3PlY0f9k/uwUFBSIkJES8++67onnz5sLW1laYmJiIhg0bir59+4pNmzbxbxO9FurcCCcRERER1aw6M2mIiIiIiGoHA04iIiIiMigGnERERERkUAw4iYiIiMigGHASERERkUEx4CQiIiIig2LASUREREQGxYCTiIiIiAyKAScRERERGRQDTiIiIiIyKAacRERERGRQDDiJiIiIyKAYcBIRERGRQf0/LIFNbXNqFjQAAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 800x600 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -945,6 +918,16 @@
     "plot_image(tau[2, 2].data[1, mid_x, :, :], cmap=\"seismic\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from devito import norm\n",
+    "assert np.isclose(norm(v[0]), 0.10312, rtol=1e-4)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -957,7 +940,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -971,7 +954,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,

From e177cd9936f16a2ce917e8b0178244208ac6193d Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Mon, 30 Oct 2023 16:08:12 -0400
Subject: [PATCH 11/29] docs: add sparse function tuto draft

---
 examples/userapi/07_sparse_operations.ipynb | 760 ++++++++++++++++++++
 1 file changed, 760 insertions(+)
 create mode 100644 examples/userapi/07_sparse_operations.ipynb

diff --git a/examples/userapi/07_sparse_operations.ipynb b/examples/userapi/07_sparse_operations.ipynb
new file mode 100644
index 0000000000..b3281b3c42
--- /dev/null
+++ b/examples/userapi/07_sparse_operations.ipynb
@@ -0,0 +1,760 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sparse Operations in Devito\n",
+    "\n",
+    "Devito provides a robust API for sparse operations, encompassing two crucial functionalities: `injection` and `interpolation`. These operations are fundamental for handling sparse data, such as point sources and measurements, within numerical simulations.\n",
+    "\n",
+    "The `injection` operation involves injecting values at specified sparse positions, simulating scenarios like point sources in physical systems. Mathematically, if we denote a `SparseTimeFunction` as $ S(t, x_s) $, where $ t $ represents time and $ x_s $ denotes sparse spatial coordinates, the injection operation can be expressed as:\n",
+    "\n",
+    "$$\n",
+    "u(t, x) = u(t, x) + S(t, x_s) \\cdot \\delta(x - x_s)\n",
+    "$$\n",
+    "\n",
+    "Here, $ \\delta(x - x_s) $ is the Dirac delta functions, and $ t $ and $ x_s $ represent the time and sparse spatial coordinates of the injection point, respectively.\n",
+    "\n",
+    "On the other hand, the `interpolation` operation reads the values of a field at sparse positions, mimicking point measurements. If $ F(t, x) $ is a field ('Function') defined on the full grid, the interpolation operation can be expressed as:\n",
+    "\n",
+    "$$\n",
+    "I(t, r) = F(t, x_r)\n",
+    "$$\n",
+    "\n",
+    "Here, $ I(t, r) $ represents the interpolated values at time $ t $ and sparse spatial coordinates $ x_r $.\n",
+    "\n",
+    "In Devito, sparse operations are defined as methods for various sparse function types, including `SparseFunction`, `SparseTimeFunction`, `PrecomputedSparseFunction`, and `PrecomputedSparseTimeFunction`. For practicality, this tutorial focuses on time-dependent functions, but it's crucial to note that all operations discussed here apply to spatial-only functions as well.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from devito import *\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import AutoMinorLocator, FixedLocator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid = Grid((51, 51))\n",
+    "nt = 11\n",
+    "f = TimeFunction(name=\"f\", grid=grid, space_order=4)\n",
+    "u = TimeFunction(name=\"u\", grid=grid, space_order=4, save=nt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We first define five sparse position for the purpose of this tutorial. We consider four points in a 2D grid intentionally not aligned with the grid points to highlight the interpolation and injection operations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x128bd3f90>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "npoint = 5\n",
+    "coords = np.random.rand(npoint, 2)/2 + .25\n",
+    "base = np.floor(coords / grid.spacing)*grid.spacing\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "ax.set_xlim([0, 1])\n",
+    "ax.set_ylim([0, 1])\n",
+    "ax.scatter(coords[:, 0], coords[:, 1], s=10, label=\"Sparse positions\")\n",
+    "ax.grid(which = \"major\")\n",
+    "ax.grid(which = \"minor\", alpha = 0.2)\n",
+    "ax.xaxis.set_minor_locator(FixedLocator(np.linspace(0, 1, 51)))\n",
+    "ax.yaxis.set_minor_locator(FixedLocator(np.linspace(0, 1, 51)))\n",
+    "ax.set_title(\"Off the grid sparse positions\")\n",
+    "for i in range(npoint):\n",
+    "    ax.annotate(\"(%.3f, %.3f)\" % (coords[i, 0], coords[i, 1]), coords[i, :])\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SparseFunction\n",
+    "\n",
+    "A `SparseFunction` is a devito object that represent a `Function` defined at a set of sparse positions. It contains the coordinates of the sparse positions and the data at thos positions. The coordinates are stored in a `SubFunction` object as a `Function` of shape `(npoints, ndim)` where `npoints` is the number of sparse positions and `ndim` is the dimension of the grid.\n",
+    "\n",
+    "A `SparseFunction` comes with the two main methods `inject(field, expr)` and `interpolate(field)` that respectively inject `expr` into `field` at the sparse positons and interpolate `field` at the sparse positions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "    Tensor symbol representing a space- and time-varying sparse array in symbolic\n",
+      "    equations.\n",
+      "\n",
+      "    Like SparseFunction, SparseTimeFunction carries multi-dimensional data that\n",
+      "    are not aligned with the computational grid. As such, each data value is\n",
+      "    associated some coordinates.\n",
+      "    A SparseTimeFunction provides symbolic interpolation routines to convert\n",
+      "    between TimeFunctions and sparse data points. These are based upon standard\n",
+      "    [bi,tri]linear interpolation.\n",
+      "\n",
+      "    Parameters\n",
+      "    ----------\n",
+      "    name : str\n",
+      "        Name of the symbol.\n",
+      "    npoint : int\n",
+      "        Number of sparse points.\n",
+      "    nt : int\n",
+      "        Number of timesteps along the time Dimension.\n",
+      "    grid : Grid\n",
+      "        The computational domain from which the sparse points are sampled.\n",
+      "    coordinates : np.ndarray, optional\n",
+      "        The coordinates of each sparse point.\n",
+      "    space_order : int, optional\n",
+      "        Discretisation order for space derivatives. Defaults to 0.\n",
+      "    time_order : int, optional\n",
+      "        Discretisation order for time derivatives. Defaults to 1.\n",
+      "    shape : tuple of ints, optional\n",
+      "        Shape of the object. Defaults to ``(nt, npoint)``.\n",
+      "    dimensions : tuple of Dimension, optional\n",
+      "        Dimensions associated with the object. Only necessary if the SparseFunction\n",
+      "        defines a multi-dimensional tensor.\n",
+      "    dtype : data-type, optional\n",
+      "        Any object that can be interpreted as a numpy data type. Defaults\n",
+      "        to ``np.float32``.\n",
+      "    initializer : callable or any object exposing the buffer interface, optional\n",
+      "        Data initializer. If a callable is provided, data is allocated lazily.\n",
+      "    allocator : MemoryAllocator, optional\n",
+      "        Controller for memory allocation. To be used, for example, when one wants\n",
+      "        to take advantage of the memory hierarchy in a NUMA architecture. Refer to\n",
+      "        `default_allocator.__doc__` for more information.\n",
+      "\n",
+      "    Examples\n",
+      "    --------\n",
+      "\n",
+      "    Creation\n",
+      "\n",
+      "    >>> from devito import Grid, SparseTimeFunction\n",
+      "    >>> grid = Grid(shape=(4, 4))\n",
+      "    >>> sf = SparseTimeFunction(name='sf', grid=grid, npoint=2, nt=3)\n",
+      "    >>> sf\n",
+      "    sf(time, p_sf)\n",
+      "\n",
+      "    Inspection\n",
+      "\n",
+      "    >>> sf.data\n",
+      "    Data([[0., 0.],\n",
+      "          [0., 0.],\n",
+      "          [0., 0.]], dtype=float32)\n",
+      "    >>> sf.coordinates\n",
+      "    sf_coords(p_sf, d)\n",
+      "    >>> sf.coordinates_data\n",
+      "    array([[0., 0.],\n",
+      "           [0., 0.]], dtype=float32)\n",
+      "\n",
+      "    Symbolic interpolation routines\n",
+      "\n",
+      "    >>> from devito import TimeFunction\n",
+      "    >>> f = TimeFunction(name='f', grid=grid)\n",
+      "    >>> exprs0 = sf.interpolate(f)\n",
+      "    >>> exprs1 = sf.inject(f, sf)\n",
+      "\n",
+      "    Notes\n",
+      "    -----\n",
+      "    The parameters must always be given as keyword arguments, since SymPy\n",
+      "    uses ``*args`` to (re-)create the Dimension arguments of the symbolic object.\n",
+      "    \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(SparseTimeFunction.__doc__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from itertools import product\n",
+    "pos = tuple(product((0, grid.spacing[1]), (0, grid.spacing[1])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = SparseTimeFunction(name=\"s\", grid=grid, npoint=npoint, nt=nt,\n",
+    "                                  coordinates=coords)\n",
+    "\n",
+    "interp_points = np.concatenate([base+p for p in pos])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x128d62dd0>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "ax.set_xlim([0, 1])\n",
+    "ax.set_ylim([0, 1])\n",
+    "ax.scatter(coords[:, 0], coords[:, 1], s=10, label=\"Sparse positions\")\n",
+    "ax.scatter(interp_points[:, 0], interp_points[:, 1], s=10, label=\"Interpolation support\")\n",
+    "ax.grid(which = \"major\")\n",
+    "ax.grid(which = \"minor\", alpha = 0.2)\n",
+    "ax.xaxis.set_minor_locator(FixedLocator(np.linspace(0, 1, 51)))\n",
+    "ax.yaxis.set_minor_locator(FixedLocator(np.linspace(0, 1, 51)))\n",
+    "ax.set_title(\"Off the grid sparse positions\")\n",
+    "for i in range(npoint):\n",
+    "    ax.annotate(\"(%.3f, %.3f)\" % (coords[i, 0], coords[i, 1]), coords[i, :])\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We first look at the interpolation section and how it is internally lowered into a linear interpolation method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Eq(posx, (int)(floor((-o_x + s_coords(p_s, 0))/h_x)))\n",
+      "Eq(posy, (int)(floor((-o_y + s_coords(p_s, 1))/h_y)))\n",
+      "Eq(px, -floor((-o_x + s_coords(p_s, 0))/h_x) + (-o_x + s_coords(p_s, 0))/h_x)\n",
+      "Eq(py, -floor((-o_y + s_coords(p_s, 1))/h_y) + (-o_y + s_coords(p_s, 1))/h_y)\n",
+      "Eq(sum, 0.0)\n",
+      "Inc(sum, (rsx*px + (1 - rsx)*(1 - px))*(rsy*py + (1 - rsy)*(1 - py))*f(t, rsx + posx, rsy + posy))\n",
+      "Eq(s(time, p_s), sum)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\n\".join([str(s) for s in s.interpolate(f).evaluate]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "which in term of generated code will lead to the following C code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Eq(posx, (int)(floor((-o_x + s_coords(p_s, 0))/h_x)))\n",
+      "Eq(posy, (int)(floor((-o_y + s_coords(p_s, 1))/h_y)))\n",
+      "Eq(px, -floor((-o_x + s_coords(p_s, 0))/h_x) + (-o_x + s_coords(p_s, 0))/h_x)\n",
+      "Eq(py, -floor((-o_y + s_coords(p_s, 1))/h_y) + (-o_y + s_coords(p_s, 1))/h_y)\n",
+      "Eq(sum, 0.0)\n",
+      "Inc(sum, (rsx*px + (1 - rsx)*(1 - px))*(rsy*py + (1 - rsy)*(1 - py))*f(t, rsx + posx, rsy + posy))\n",
+      "Eq(s(time, p_s), sum)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\n\".join([str(s) for s in s.interpolate(f).evaluate]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f.data.fill(0)\n",
+    "op = Operator([Eq(f.forward, f+1)] + s.interpolate(f))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Operator `Kernel` ran in 0.01 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "PerformanceSummary([(PerfKey(name='section0', rank=None),\n",
+       "                     PerfEntry(time=0.000147, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
+       "                    (PerfKey(name='section1', rank=None),\n",
+       "                     PerfEntry(time=0.0001, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "op()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Operator `Kernel` ran in 0.01 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Data([[10.      , 10.      , 10.      , 10.      , 10.      ],\n",
+       "      [11.      , 11.      , 11.      , 11.      , 11.      ],\n",
+       "      [12.      , 12.      , 12.      , 12.      , 11.999999],\n",
+       "      [13.      , 13.      , 13.      , 13.      , 12.999999],\n",
+       "      [14.      , 14.      , 14.      , 14.      , 14.      ],\n",
+       "      [15.      , 15.      , 15.      , 15.      , 15.      ],\n",
+       "      [16.      , 16.      , 16.      , 16.      , 16.      ],\n",
+       "      [17.      , 17.      , 17.      , 17.      , 17.      ],\n",
+       "      [18.      , 18.      , 18.      , 18.      , 18.      ],\n",
+       "      [19.      , 19.      , 19.      , 19.      , 18.999998],\n",
+       "      [ 0.      ,  0.      ,  0.      ,  0.      ,  0.      ]],\n",
+       "     dtype=float32)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "op()\n",
+    "s.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Operator `Kernel` ran in 0.01 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "PerformanceSummary([(PerfKey(name='section0', rank=None),\n",
+       "                     PerfEntry(time=2.9e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "op = Operator(s.inject(u, expr=s))\n",
+    "op()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x12982d050>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "ax.imshow(u.data[1], vmin=0, vmax=1, cmap=\"jet\", extent=[0,1,0,1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## PrecomputedSparseFunction\n",
+    "\n",
+    "In some cases, simple linear interpolation may not be sufficient for two main reasons:\n",
+    "- The polynomial approximation isn't accurate enough\n",
+    "- The interpolation coefficients could be precomputed as they are not time-dependent\n",
+    "\n",
+    "`PrecomputedSparseFunction` offer the interface to answer these two points by allowing the user to provide arbitrary precomputed interpolation weights over an arbitrary large support. \n",
+    "\n",
+    "To illustrate this capability, we show in the following how to use `PrecomputedSparseFunction` to compute a simple local average over a `4x4` window centered on a sparse point ( average over `[x-1, x, x+1, x+2]` in 1D)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "    Tensor symbol representing a sparse array in symbolic equations; unlike\n",
+      "    SparseFunction, PrecomputedSparseFunction uses externally-defined data\n",
+      "    for interpolation.\n",
+      "\n",
+      "    Parameters\n",
+      "    ----------\n",
+      "    name : str\n",
+      "        Name of the symbol.\n",
+      "    npoint : int\n",
+      "        Number of sparse points.\n",
+      "    grid : Grid\n",
+      "        The computational domain from which the sparse points are sampled.\n",
+      "    r : int\n",
+      "        Number of gridpoints in each Dimension to interpolate a single sparse\n",
+      "        point to. E.g. `r=2` for linear interpolation.\n",
+      "    coordinates : np.ndarray, optional\n",
+      "        The coordinates of each sparse point.\n",
+      "    gridpoints : np.ndarray, optional\n",
+      "        An array carrying the *reference* grid point corresponding to each\n",
+      "        sparse point.  Of all the gridpoints that one sparse point would be\n",
+      "        interpolated to, this is the grid point closest to the origin, i.e. the\n",
+      "        one with the lowest value of each coordinate Dimension. Must be a\n",
+      "        two-dimensional array of shape `(npoint, grid.ndim)`.\n",
+      "    interpolation_coeffs : np.ndarray, optional\n",
+      "        An array containing the coefficient for each of the r^2 (2D) or r^3\n",
+      "        (3D) gridpoints that each sparse point will be interpolated to. The\n",
+      "        coefficient is split across the n Dimensions such that the contribution\n",
+      "        of the point (i, j, k) will be multiplied by\n",
+      "        `interp_coeffs[..., i]*interp_coeffs[...,j]*interp_coeffs[...,k]`.\n",
+      "        So for `r=6`, we will store 18 coefficients per sparse point (instead of\n",
+      "        potentially 216).  Must be a three-dimensional array of shape\n",
+      "        `(npoint, grid.ndim, r)`.\n",
+      "    space_order : int, optional\n",
+      "        Discretisation order for space derivatives. Defaults to 0.\n",
+      "    shape : tuple of ints, optional\n",
+      "        Shape of the object. Defaults to `(npoint,)`.\n",
+      "    dimensions : tuple of Dimension, optional\n",
+      "        Dimensions associated with the object. Only necessary if the SparseFunction\n",
+      "        defines a multi-dimensional tensor.\n",
+      "    dtype : data-type, optional\n",
+      "        Any object that can be interpreted as a numpy data type. Defaults\n",
+      "        to `np.float32`.\n",
+      "    initializer : callable or any object exposing the buffer interface, optional\n",
+      "        Data initializer. If a callable is provided, data is allocated lazily.\n",
+      "    allocator : MemoryAllocator, optional\n",
+      "        Controller for memory allocation. To be used, for example, when one wants\n",
+      "        to take advantage of the memory hierarchy in a NUMA architecture. Refer to\n",
+      "        `default_allocator.__doc__` for more information.\n",
+      "\n",
+      "    Notes\n",
+      "    -----\n",
+      "    The parameters must always be given as keyword arguments, since SymPy\n",
+      "    uses `*args` to (re-)create the Dimension arguments of the symbolic object.\n",
+      "    \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(PrecomputedSparseFunction.__doc__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeffs = np.ones((5, 2, 5))\n",
+    "s = PrecomputedSparseTimeFunction(name=\"s\", grid=grid, npoint=npoint, nt=nt, \n",
+    "                                  interpolation_coeffs=coeffs,\n",
+    "                                  coordinates=coords, r=2)\n",
+    "\n",
+    "\n",
+    "pos = tuple(product((-grid.spacing[1], 0, grid.spacing[1],2*grid.spacing[1]),\n",
+    "                    (-grid.spacing[1], 0, grid.spacing[1],2*grid.spacing[1])))\n",
+    "interp_points = np.concatenate([base+p for p in pos])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x129aa0ed0>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "ax.set_xlim([0, 1])\n",
+    "ax.set_ylim([0, 1])\n",
+    "ax.scatter(coords[:, 0], coords[:, 1], s=10, label=\"Sparse positions\")\n",
+    "ax.scatter(interp_points[:, 0], interp_points[:, 1], s=10, label=\"Interpolation support\")\n",
+    "ax.grid(which = \"major\")\n",
+    "ax.grid(which = \"minor\", alpha = 0.2)\n",
+    "ax.xaxis.set_minor_locator(FixedLocator(np.linspace(0, 1, 51)))\n",
+    "ax.yaxis.set_minor_locator(FixedLocator(np.linspace(0, 1, 51)))\n",
+    "ax.set_title(\"Off the grid sparse positions\")\n",
+    "for i in range(npoint):\n",
+    "    ax.annotate(\"(%.3f, %.3f)\" % (coords[i, 0], coords[i, 1]), coords[i, :])\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f.data.fill(0)\n",
+    "op = Operator([Eq(f.forward, f+1)] + s.interpolate(f/16))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Operator `Kernel` ran in 0.01 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Data([[0., 0., 0., 0., 0.],\n",
+       "      [1., 1., 1., 1., 1.],\n",
+       "      [2., 2., 2., 2., 2.],\n",
+       "      [3., 3., 3., 3., 3.],\n",
+       "      [4., 4., 4., 4., 4.],\n",
+       "      [5., 5., 5., 5., 5.],\n",
+       "      [6., 6., 6., 6., 6.],\n",
+       "      [7., 7., 7., 7., 7.],\n",
+       "      [8., 8., 8., 8., 8.],\n",
+       "      [9., 9., 9., 9., 9.],\n",
+       "      [0., 0., 0., 0., 0.]], dtype=float32)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "op()\n",
+    "s.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Operator `Kernel` ran in 0.01 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "PerformanceSummary([(PerfKey(name='section0', rank=None),\n",
+       "                     PerfEntry(time=3.5e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "u.data.fill(0)\n",
+    "op = Operator(s.inject(u, expr=s))\n",
+    "op()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x129c6e850>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "ax.imshow(u.data[1], vmin=0, vmax=2, cmap=\"jet\", extent=[0,1,0,1])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From bc7bb4a18a091348ccbfa8d676965aa7f7d0baa8 Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Thu, 4 Apr 2024 09:34:36 -0400
Subject: [PATCH 12/29] examples: add sinc interpoaltion to interpolation
 notebook

---
 devito/types/sparse.py                      |   5 +
 examples/userapi/03_subdomains.ipynb        |  68 ++--
 examples/userapi/07_sparse_operations.ipynb | 409 +++++++++++++-------
 3 files changed, 316 insertions(+), 166 deletions(-)

diff --git a/devito/types/sparse.py b/devito/types/sparse.py
index 85910ff955..4741b97bd1 100644
--- a/devito/types/sparse.py
+++ b/devito/types/sparse.py
@@ -754,6 +754,11 @@ class SparseFunction(AbstractSparseFunction):
         Controller for memory allocation. To be used, for example, when one wants
         to take advantage of the memory hierarchy in a NUMA architecture. Refer to
         `default_allocator.__doc__` for more information.
+    interpolation: String, optional, default='linear'
+        The interpolation type to be used by the SparseFunction. Supported types
+        are 'linear' and 'sinc'.
+    r: int, optional, default=1 for 'linear', 4 for 'sinc'
+        The radius of the interpolation operators provided by the SparseFunction.
 
     Examples
     --------
diff --git a/examples/userapi/03_subdomains.ipynb b/examples/userapi/03_subdomains.ipynb
index 7355e36868..686459507c 100644
--- a/examples/userapi/03_subdomains.ipynb
+++ b/examples/userapi/03_subdomains.ipynb
@@ -432,7 +432,15 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Operator `Kernel` ran in 0.01 s\n"
+     ]
+    }
+   ],
    "source": [
     "#NBVAL_IGNORE_OUTPUT\n",
     "grid = Grid(shape = (10, 10), subdomains = (mid, ))\n",
@@ -555,7 +563,15 @@
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Operator `Kernel` ran in 0.01 s\n"
+     ]
+    }
+   ],
    "source": [
     "#NBVAL_IGNORE_OUTPUT\n",
     "op2 = Operator([eq1, eq2, eq3])()"
@@ -614,22 +630,7 @@
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/mloubout/.local/lib/python3.8/site-packages/numpy/core/getlimits.py:518: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
-      "  setattr(self, word, getattr(machar, word).flat[0])\n",
-      "/home/mloubout/.local/lib/python3.8/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
-      "  return self._float_to_str(self.smallest_subnormal)\n",
-      "/home/mloubout/.local/lib/python3.8/site-packages/numpy/core/getlimits.py:518: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
-      "  setattr(self, word, getattr(machar, word).flat[0])\n",
-      "/home/mloubout/.local/lib/python3.8/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
-      "  return self._float_to_str(self.smallest_subnormal)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "from devito import (TimeFunction, VectorTimeFunction, TensorTimeFunction,\n",
@@ -741,7 +742,15 @@
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Operator `initdamp` ran in 0.01 s\n"
+     ]
+    }
+   ],
    "source": [
     "#NBVAL_IGNORE_OUTPUT\n",
     "so = 4 # FD space order (Note that the time order is by default 1).\n",
@@ -826,13 +835,20 @@
    "execution_count": 33,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Operator `Kernel` ran in 4.04 s\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "PerformanceSummary([(PerfKey(name='section0', rank=None),\n",
-       "                     PerfEntry(time=12.63165, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
+       "                     PerfEntry(time=4.029181999999999, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
        "                    (PerfKey(name='section1', rank=None),\n",
-       "                     PerfEntry(time=0.0011449999999999989, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
+       "                     PerfEntry(time=0.003197, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
       ]
      },
      "execution_count": 33,
@@ -860,7 +876,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 2 Axes>"
       ]
@@ -870,7 +886,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 2 Axes>"
       ]
@@ -880,7 +896,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 2 Axes>"
       ]
@@ -890,7 +906,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 2 Axes>"
       ]
@@ -954,7 +970,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,
diff --git a/examples/userapi/07_sparse_operations.ipynb b/examples/userapi/07_sparse_operations.ipynb
index b3281b3c42..362fb12063 100644
--- a/examples/userapi/07_sparse_operations.ipynb
+++ b/examples/userapi/07_sparse_operations.ipynb
@@ -55,7 +55,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We first define five sparse position for the purpose of this tutorial. We consider four points in a 2D grid intentionally not aligned with the grid points to highlight the interpolation and injection operations."
+    "We first define five sparse positions for the purpose of this tutorial. We consider four points in a 2D grid intentionally not aligned with the grid points to highlight the interpolation and injection operations."
    ]
   },
   {
@@ -65,17 +65,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x128bd3f90>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x1000 with 1 Axes>"
       ]
@@ -102,7 +92,8 @@
     "ax.set_title(\"Off the grid sparse positions\")\n",
     "for i in range(npoint):\n",
     "    ax.annotate(\"(%.3f, %.3f)\" % (coords[i, 0], coords[i, 1]), coords[i, :])\n",
-    "ax.legend()"
+    "ax.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -111,7 +102,7 @@
    "source": [
     "## SparseFunction\n",
     "\n",
-    "A `SparseFunction` is a devito object that represent a `Function` defined at a set of sparse positions. It contains the coordinates of the sparse positions and the data at thos positions. The coordinates are stored in a `SubFunction` object as a `Function` of shape `(npoints, ndim)` where `npoints` is the number of sparse positions and `ndim` is the dimension of the grid.\n",
+    "A `SparseFunction` is a devito object representing a `Function` defined at sparse positions. It contains the coordinates of the sparse positions and the data at those positions. The coordinates are stored in a `SubFunction` object as a `Function` of shape `(npoints, ndim)` where `npoints` is the number of sparse positions and `ndim` is the number of dimension of the grid.\n",
     "\n",
     "A `SparseFunction` comes with the two main methods `inject(field, expr)` and `interpolate(field)` that respectively inject `expr` into `field` at the sparse positons and interpolate `field` at the sparse positions."
    ]
@@ -148,19 +139,18 @@
       "        The computational domain from which the sparse points are sampled.\n",
       "    coordinates : np.ndarray, optional\n",
       "        The coordinates of each sparse point.\n",
-      "    space_order : int, optional\n",
-      "        Discretisation order for space derivatives. Defaults to 0.\n",
-      "    time_order : int, optional\n",
-      "        Discretisation order for time derivatives. Defaults to 1.\n",
-      "    shape : tuple of ints, optional\n",
-      "        Shape of the object. Defaults to ``(nt, npoint)``.\n",
+      "    space_order : int, optional, default=0\n",
+      "        Discretisation order for space derivatives.\n",
+      "    time_order : int, optional, default=1\n",
+      "        Discretisation order for time derivatives.\n",
+      "    shape : tuple of ints, optional, default=(nt, npoint)\n",
+      "        Shape of the object.\n",
       "    dimensions : tuple of Dimension, optional\n",
       "        Dimensions associated with the object. Only necessary if the SparseFunction\n",
       "        defines a multi-dimensional tensor.\n",
-      "    dtype : data-type, optional\n",
-      "        Any object that can be interpreted as a numpy data type. Defaults\n",
-      "        to ``np.float32``.\n",
-      "    initializer : callable or any object exposing the buffer interface, optional\n",
+      "    dtype : data-type, optional, default=np.float32\n",
+      "        Any object that can be interpreted as a numpy data type.\n",
+      "    initializer : callable or any object exposing the buffer interface, default=None\n",
       "        Data initializer. If a callable is provided, data is allocated lazily.\n",
       "    allocator : MemoryAllocator, optional\n",
       "        Controller for memory allocation. To be used, for example, when one wants\n",
@@ -209,14 +199,20 @@
     "print(SparseTimeFunction.__doc__)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multilinear interpolation"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from itertools import product\n",
-    "pos = tuple(product((0, grid.spacing[1]), (0, grid.spacing[1])))"
+    "from itertools import product"
    ]
   },
   {
@@ -225,10 +221,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "s = SparseTimeFunction(name=\"s\", grid=grid, npoint=npoint, nt=nt,\n",
-    "                                  coordinates=coords)\n",
+    "s = SparseTimeFunction(name=\"s\", grid=grid, npoint=npoint, nt=nt, coordinates=coords)\n",
     "\n",
-    "interp_points = np.concatenate([base+p for p in pos])"
+    "d1, d2 = grid.spacing\n",
+    "interp_points = np.concatenate([base+(s1*d1, s2*d2) for (s1, s2) in s._point_support])"
    ]
   },
   {
@@ -238,17 +234,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x128d62dd0>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x1000 with 1 Axes>"
       ]
@@ -271,7 +257,8 @@
     "ax.set_title(\"Off the grid sparse positions\")\n",
     "for i in range(npoint):\n",
     "    ax.annotate(\"(%.3f, %.3f)\" % (coords[i, 0], coords[i, 1]), coords[i, :])\n",
-    "ax.legend()"
+    "ax.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -305,43 +292,53 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "which in term of generated code will lead to the following C code:"
+    "f.data.fill(0)\n",
+    "op = Operator([Eq(f.forward, f+1)] + s.interpolate(f))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Eq(posx, (int)(floor((-o_x + s_coords(p_s, 0))/h_x)))\n",
-      "Eq(posy, (int)(floor((-o_y + s_coords(p_s, 1))/h_y)))\n",
-      "Eq(px, -floor((-o_x + s_coords(p_s, 0))/h_x) + (-o_x + s_coords(p_s, 0))/h_x)\n",
-      "Eq(py, -floor((-o_y + s_coords(p_s, 1))/h_y) + (-o_y + s_coords(p_s, 1))/h_y)\n",
-      "Eq(sum, 0.0)\n",
-      "Inc(sum, (rsx*px + (1 - rsx)*(1 - px))*(rsy*py + (1 - rsy)*(1 - py))*f(t, rsx + posx, rsy + posy))\n",
-      "Eq(s(time, p_s), sum)\n"
+      "Operator `Kernel` ran in 0.01 s\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Data([[0.       , 0.       , 0.       , 0.       , 0.       ],\n",
+       "      [1.       , 1.       , 1.       , 1.       , 1.       ],\n",
+       "      [2.       , 2.       , 2.       , 2.       , 2.       ],\n",
+       "      [3.       , 3.0000002, 3.       , 3.       , 3.       ],\n",
+       "      [4.       , 4.       , 4.       , 4.       , 4.       ],\n",
+       "      [5.       , 5.       , 5.       , 5.       , 4.9999995],\n",
+       "      [6.       , 6.0000005, 6.       , 6.       , 6.       ],\n",
+       "      [7.       , 7.       , 7.       , 7.       , 7.       ],\n",
+       "      [8.       , 8.       , 8.       , 8.       , 8.       ],\n",
+       "      [9.       , 9.       , 9.       , 9.       , 9.       ],\n",
+       "      [0.       , 0.       , 0.       , 0.       , 0.       ]],\n",
+       "     dtype=float32)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(\"\\n\".join([str(s) for s in s.interpolate(f).evaluate]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "f.data.fill(0)\n",
-    "op = Operator([Eq(f.forward, f+1)] + s.interpolate(f))"
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "op()\n",
+    "s.data"
    ]
   },
   {
@@ -360,9 +357,7 @@
      "data": {
       "text/plain": [
        "PerformanceSummary([(PerfKey(name='section0', rank=None),\n",
-       "                     PerfEntry(time=0.000147, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
-       "                    (PerfKey(name='section1', rank=None),\n",
-       "                     PerfEntry(time=0.0001, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
+       "                     PerfEntry(time=3.4e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
       ]
      },
      "execution_count": 11,
@@ -372,6 +367,7 @@
    ],
    "source": [
     "#NBVAL_IGNORE_OUTPUT\n",
+    "op = Operator(s.inject(u, expr=s))\n",
     "op()"
    ]
   },
@@ -379,6 +375,163 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(u.data[1], vmin=0, vmax=1, cmap=\"jet\", extent=[0,1,0,1])\n",
+    "plt.colorbar(fraction=0.046, pad=0.04)\n",
+    "plt.title(\"Linear weights\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kaiser sinc interpolation\n",
+    "\n",
+    "Graham J. Hicks pioneered an approach to address one of the significant challenges in seismic simulation: the accurate and flexible positioning of sources and receivers in finite-difference computational grids. By integrating Kaiser windowed sinc functions into the interpolation process, Hicks' method facilitates arbitrary placement of these elements, overcoming the limitations imposed by the inherent discreteness of computational grids. This technique is especially beneficial in seismic wave propagation models, where precise source and receiver locations are crucial for accurate subsurface imaging.\n",
+    "\n",
+    "## Mathematical Basis of the Interpolation\n",
+    "\n",
+    "The interpolation of a field value, $f$, at an arbitrary point using the Kaiser windowed sinc function can be mathematically expressed as follows:\n",
+    "\n",
+    "$$\n",
+    "f(x) = \\sum_{n=-r}^{r} f_n \\cdot \\text{sinc}\\left( \\frac{x - x_n}{\\Delta x} \\right) \\cdot w\\left( \\frac{x - x_n}{\\alpha} \\right)\n",
+    "$$\n",
+    "\n",
+    "where:\n",
+    "- $f_n$ represents the field values at grid points.\n",
+    "- $x$ denotes the arbitrary (non-grid-aligned) position where the field value is interpolated.\n",
+    "- $x_n$ refers to the position of the $n$-th grid point.\n",
+    "- $\\Delta x$ is the grid spacing.\n",
+    "- $w(\\cdot)$ is the Kaiser window function applied to modulate the sinc function, defined as:\n",
+    "  $$\n",
+    "  w(x) = \\frac{I_0\\left( \\beta \\sqrt{1 - \\left( \\frac{x}{\\alpha} \\right)^2} \\right)}{I_0(\\beta)}\n",
+    "  $$\n",
+    "- $I_0$ is the modified Bessel function of the first kind and zero order.\n",
+    "- $\\beta$ is the Kaiser window's shape parameter, controlling sidelobe attenuation.\n",
+    "- $\\alpha$ is a scaling factor that adjusts the effective width of the window, enhancing the function's ability to interpolate over a wider area without significant loss of accuracy.\n",
+    "\n",
+    "## Practical Implications for Seismic Modeling\n",
+    "\n",
+    "This interpolation formula is pivotal for improving the fidelity of finite-difference simulations in seismic applications. By allowing for the precise positioning of sources and receivers, it enables more accurate modeling of wave propagation through complex geological structures. The flexibility offered by this method significantly enhances the quality of seismic images, providing clearer insights into the Earth's subsurface.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = SparseTimeFunction(name=\"s\", grid=grid, npoint=npoint, nt=nt, coordinates=coords, interpolation='sinc')\n",
+    "\n",
+    "interp_points = np.concatenate([base+(s1*d1, s2*d2) for (s1, s2) in s._point_support])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#NBVAL_IGNORE_OUTPUT\n",
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "ax.set_xlim([0, 1])\n",
+    "ax.set_ylim([0, 1])\n",
+    "ax.scatter(coords[:, 0], coords[:, 1], s=10, label=\"Sparse positions\")\n",
+    "ax.scatter(interp_points[:, 0], interp_points[:, 1], s=10, label=\"Interpolation support\")\n",
+    "ax.grid(which = \"major\")\n",
+    "ax.grid(which = \"minor\", alpha = 0.2)\n",
+    "ax.xaxis.set_minor_locator(FixedLocator(np.linspace(0, 1, 51)))\n",
+    "ax.yaxis.set_minor_locator(FixedLocator(np.linspace(0, 1, 51)))\n",
+    "ax.set_title(\"Off the grid sparse positions\")\n",
+    "for i in range(npoint):\n",
+    "    ax.annotate(\"(%.3f, %.3f)\" % (coords[i, 0], coords[i, 1]), coords[i, :])\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Eq(posx, floor((-o_x + s_coords(p_s, 0))/h_x))\n",
+      "Eq(posy, floor((-o_y + s_coords(p_s, 1))/h_y))\n",
+      "Eq(sum, 0.0)\n",
+      "Inc(sum, wsincrsx(p_s, rsx + 3)*wsincrsy(p_s, rsy + 3)*f(t, rsx + posx, rsy + posy))\n",
+      "Eq(s(time, p_s), sum)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\n\".join([str(s) for s in s.interpolate(f).evaluate]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Eq(posx, floor((-o_x + s_coords(p_s, 0))/h_x))\n",
+      "Eq(posy, floor((-o_y + s_coords(p_s, 1))/h_y))\n",
+      "Eq(sum, 0.0)\n",
+      "Inc(sum, wsincrsx(p_s, rsx + 3)*wsincrsy(p_s, rsy + 3)*f(t, rsx + posx, rsy + posy))\n",
+      "Eq(s(time, p_s), sum)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\n\".join([str(s) for s in s.interpolate(f).evaluate]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f.data.fill(0)\n",
+    "op = Operator([Eq(f.forward, f+1)] + s.interpolate(f))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -390,21 +543,21 @@
     {
      "data": {
       "text/plain": [
-       "Data([[10.      , 10.      , 10.      , 10.      , 10.      ],\n",
-       "      [11.      , 11.      , 11.      , 11.      , 11.      ],\n",
-       "      [12.      , 12.      , 12.      , 12.      , 11.999999],\n",
-       "      [13.      , 13.      , 13.      , 13.      , 12.999999],\n",
-       "      [14.      , 14.      , 14.      , 14.      , 14.      ],\n",
-       "      [15.      , 15.      , 15.      , 15.      , 15.      ],\n",
-       "      [16.      , 16.      , 16.      , 16.      , 16.      ],\n",
-       "      [17.      , 17.      , 17.      , 17.      , 17.      ],\n",
-       "      [18.      , 18.      , 18.      , 18.      , 18.      ],\n",
-       "      [19.      , 19.      , 19.      , 19.      , 18.999998],\n",
-       "      [ 0.      ,  0.      ,  0.      ,  0.      ,  0.      ]],\n",
+       "Data([[0.        , 0.        , 0.        , 0.        , 0.        ],\n",
+       "      [0.9925686 , 0.9952191 , 0.99392927, 0.9933772 , 0.9967097 ],\n",
+       "      [1.9851372 , 1.9904382 , 1.9878585 , 1.9867544 , 1.9934194 ],\n",
+       "      [2.9777079 , 2.9856577 , 2.9817874 , 2.9801316 , 2.9901292 ],\n",
+       "      [3.9702744 , 3.9808764 , 3.975717  , 3.9735088 , 3.9868388 ],\n",
+       "      [4.9628468 , 4.976095  , 4.9696455 , 4.966884  , 4.9835467 ],\n",
+       "      [5.9554157 , 5.9713154 , 5.963575  , 5.9602633 , 5.9802585 ],\n",
+       "      [6.9479847 , 6.9665327 , 6.957505  , 6.95364   , 6.976965  ],\n",
+       "      [7.940549  , 7.961753  , 7.951434  , 7.9470177 , 7.9736776 ],\n",
+       "      [8.933123  , 8.956971  , 8.945365  , 8.940394  , 8.9703865 ],\n",
+       "      [0.        , 0.        , 0.        , 0.        , 0.        ]],\n",
        "     dtype=float32)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -417,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -431,10 +584,10 @@
      "data": {
       "text/plain": [
        "PerformanceSummary([(PerfKey(name='section0', rank=None),\n",
-       "                     PerfEntry(time=2.9e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
+       "                     PerfEntry(time=5.3e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -447,24 +600,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x12982d050>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
+       "<Figure size 1000x1000 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -473,8 +616,11 @@
    ],
    "source": [
     "#NBVAL_IGNORE_OUTPUT\n",
-    "fig, ax = plt.subplots(figsize=(10, 10))\n",
-    "ax.imshow(u.data[1], vmin=0, vmax=1, cmap=\"jet\", extent=[0,1,0,1])"
+    "plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(u.data[1], vmin=0, vmax=1, cmap=\"jet\", extent=[0,1,0,1])\n",
+    "plt.colorbar(fraction=0.046, pad=0.04)\n",
+    "plt.title(\"Sinc weights\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -494,7 +640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -534,16 +680,15 @@
       "        So for `r=6`, we will store 18 coefficients per sparse point (instead of\n",
       "        potentially 216).  Must be a three-dimensional array of shape\n",
       "        `(npoint, grid.ndim, r)`.\n",
-      "    space_order : int, optional\n",
-      "        Discretisation order for space derivatives. Defaults to 0.\n",
-      "    shape : tuple of ints, optional\n",
-      "        Shape of the object. Defaults to `(npoint,)`.\n",
+      "    space_order : int, optional, default=0\n",
+      "        Discretisation order for space derivatives.\n",
+      "    shape : tuple of ints, optional, default=(npoint,)\n",
+      "        Shape of the object.\n",
       "    dimensions : tuple of Dimension, optional\n",
       "        Dimensions associated with the object. Only necessary if the SparseFunction\n",
       "        defines a multi-dimensional tensor.\n",
-      "    dtype : data-type, optional\n",
-      "        Any object that can be interpreted as a numpy data type. Defaults\n",
-      "        to `np.float32`.\n",
+      "    dtype : data-type, optional, default=np.float32\n",
+      "        Any object that can be interpreted as a numpy data type.\n",
       "    initializer : callable or any object exposing the buffer interface, optional\n",
       "        Data initializer. If a callable is provided, data is allocated lazily.\n",
       "    allocator : MemoryAllocator, optional\n",
@@ -565,7 +710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -582,22 +727,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x129aa0ed0>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x1000 with 1 Axes>"
       ]
@@ -620,12 +755,13 @@
     "ax.set_title(\"Off the grid sparse positions\")\n",
     "for i in range(npoint):\n",
     "    ax.annotate(\"(%.3f, %.3f)\" % (coords[i, 0], coords[i, 1]), coords[i, :])\n",
-    "ax.legend()"
+    "ax.legend()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -635,7 +771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -661,7 +797,7 @@
        "      [0., 0., 0., 0., 0.]], dtype=float32)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -674,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -688,10 +824,10 @@
      "data": {
       "text/plain": [
        "PerformanceSummary([(PerfKey(name='section0', rank=None),\n",
-       "                     PerfEntry(time=3.5e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
+       "                     PerfEntry(time=3.6e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -705,24 +841,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x129c6e850>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
+       "<Figure size 1000x1000 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -731,8 +857,11 @@
    ],
    "source": [
     "#NBVAL_IGNORE_OUTPUT\n",
-    "fig, ax = plt.subplots(figsize=(10, 10))\n",
-    "ax.imshow(u.data[1], vmin=0, vmax=2, cmap=\"jet\", extent=[0,1,0,1])"
+    "plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(u.data[1], vmin=0, vmax=2, cmap=\"jet\", extent=[0,1,0,1])\n",
+    "plt.colorbar(fraction=0.046, pad=0.04)\n",
+    "plt.title(\"Precomputed weights\")\n",
+    "plt.show()"
    ]
   }
  ],
@@ -752,7 +881,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,

From 969bdc1c7dd564f60b18fd78e0bc81d6933cecd2 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Fri, 5 Apr 2024 12:53:50 +0000
Subject: [PATCH 13/29] pip prod(deps): update ipyparallel requirement from
 <8.8 to <8.9

Updates the requirements on [ipyparallel](https://ipython.org) to permit the latest version.

---
updated-dependencies:
- dependency-name: ipyparallel
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 requirements-mpi.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements-mpi.txt b/requirements-mpi.txt
index b4862d295a..4323182cff 100644
--- a/requirements-mpi.txt
+++ b/requirements-mpi.txt
@@ -1,2 +1,2 @@
 mpi4py<3.1.6
-ipyparallel<8.8
\ No newline at end of file
+ipyparallel<8.9
\ No newline at end of file

From 65ee2e1e8d7218ba23554ae92831cff988db3157 Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Fri, 5 Apr 2024 09:39:35 -0400
Subject: [PATCH 14/29] CI: revamp parallel marker

---
 conftest.py               | 121 ++++++++++++---------------
 tests/test_autotuner.py   |   2 +-
 tests/test_benchmark.py   |   2 +-
 tests/test_builtins.py    |   8 +-
 tests/test_data.py        |  44 +++++-----
 tests/test_dle.py         |   2 +-
 tests/test_dse.py         |   2 +-
 tests/test_gpu_common.py  |   6 +-
 tests/test_gpu_openacc.py |   4 +-
 tests/test_gpu_openmp.py  |   6 +-
 tests/test_linearize.py   |   7 +-
 tests/test_mpi.py         | 170 +++++++++++++++++++-------------------
 tests/test_operator.py    |   2 +-
 tests/test_pickle.py      |   8 +-
 tests/test_sparse.py      |   4 +-
 tests/test_subdomains.py  |   4 +-
 16 files changed, 189 insertions(+), 203 deletions(-)

diff --git a/conftest.py b/conftest.py
index 3a4d6d4ef4..bc2526c3d7 100644
--- a/conftest.py
+++ b/conftest.py
@@ -122,7 +122,7 @@ def EVAL(exprs, *args):
     return processed[0] if isinstance(exprs, str) else processed
 
 
-def parallel(item):
+def parallel(item, m):
     """
     Run a test in parallel. Readapted from:
 
@@ -131,47 +131,44 @@ def parallel(item):
     mpi_exec = 'mpiexec'
     mpi_distro = sniff_mpi_distro(mpi_exec)
 
-    marker = item.get_closest_marker("parallel")
-    mode = as_tuple(marker.kwargs.get("mode", 2))
-    for m in mode:
-        # Parse the `mode`
-        if isinstance(m, int):
-            nprocs = m
-            scheme = 'basic'
-        else:
-            if len(m) == 2:
-                nprocs, scheme = m
-            else:
-                raise ValueError("Can't run test: unexpected mode `%s`" % m)
-
-        pyversion = sys.executable
-        # Only spew tracebacks on rank 0.
-        # Run xfailing tests to ensure that errors are reported to calling process
-        if item.cls is not None:
-            testname = "%s::%s::%s" % (item.fspath, item.cls.__name__, item.name)
-        else:
-            testname = "%s::%s" % (item.fspath, item.name)
-        args = ["-n", "1", pyversion, "-m", "pytest", "--runxfail", "-s",
-                "-q", testname]
-        if nprocs > 1:
-            args.extend([":", "-n", "%d" % (nprocs - 1), pyversion, "-m", "pytest",
-                         "--runxfail", "--tb=no", "-q", testname])
-        # OpenMPI requires an explicit flag for oversubscription. We need it as some
-        # of the MPI tests will spawn lots of processes
-        if mpi_distro == 'OpenMPI':
-            call = [mpi_exec, '--oversubscribe', '--timeout', '300'] + args
+    # Parse the `mode`
+    if isinstance(m, int):
+        nprocs = m
+        scheme = 'basic'
+    else:
+        if len(m) == 2:
+            nprocs, scheme = m
         else:
-            call = [mpi_exec] + args
+            raise ValueError("Can't run test: unexpected mode `%s`" % m)
 
-        # Tell the MPI ranks that they are running a parallel test
-        os.environ['DEVITO_MPI'] = scheme
-        try:
-            check_call(call)
-            return True
-        except:
-            return False
-        finally:
-            os.environ['DEVITO_MPI'] = '0'
+    pyversion = sys.executable
+    # Only spew tracebacks on rank 0.
+    # Run xfailing tests to ensure that errors are reported to calling process
+    if item.cls is not None:
+        testname = "%s::%s::%s" % (item.fspath, item.cls.__name__, item.name)
+    else:
+        testname = "%s::%s" % (item.fspath, item.name)
+    args = ["-n", "1", pyversion, "-m", "pytest", "--runxfail", "-q", testname]
+    if nprocs > 1:
+        args.extend([":", "-n", "%d" % (nprocs - 1), pyversion, "-m", "pytest",
+                     "--runxfail", "--tb=no", "-q", testname])
+    # OpenMPI requires an explicit flag for oversubscription. We need it as some
+    # of the MPI tests will spawn lots of processes
+    if mpi_distro == 'OpenMPI':
+        call = [mpi_exec, '--oversubscribe', '--timeout', '300'] + args
+    else:
+        call = [mpi_exec] + args
+
+    # Tell the MPI ranks that they are running a parallel test
+    os.environ['DEVITO_MPI'] = scheme
+    try:
+        check_call(call)
+        res = True
+    except:
+        res = False
+    finally:
+        os.environ['DEVITO_MPI'] = '0'
+    return res
 
 
 def pytest_configure(config):
@@ -182,55 +179,45 @@ def pytest_configure(config):
     )
 
 
-def pytest_runtest_setup(item):
-    partest = os.environ.get('DEVITO_MPI', 0)
-    try:
-        partest = int(partest)
-    except ValueError:
-        pass
-    if item.get_closest_marker("parallel"):
-        if MPI is None:
-            pytest.skip("mpi4py/MPI not installed")
-        else:
-            # Blow away function arg in "master" process, to ensure
-            # this test isn't run on only one process
-            dummy_test = lambda *args, **kwargs: True
-            # For pytest <7
-            if item.cls is not None:
-                attr = item.originalname or item.name
-                setattr(item.cls, attr, dummy_test)
-            else:
-                item.obj = dummy_test
-            # For pytest >= 7
-            setattr(item, '_obj', dummy_test)
+def pytest_generate_tests(metafunc):
+    # Process custom parallel marker as a parametrize to avoid
+    # running a single test for all modes
+    if 'mode' in metafunc.fixturenames:
+        markers = metafunc.definition.iter_markers()
+        for marker in markers:
+            if marker.name == 'parallel':
+                mode = list(as_tuple(marker.kwargs.get('mode', 2)))
+                metafunc.parametrize("mode", mode)
 
 
+@pytest.hookimpl(tryfirst=True, hookwrapper=True)
 def pytest_runtest_call(item):
     partest = os.environ.get('DEVITO_MPI', 0)
     try:
         partest = int(partest)
     except ValueError:
         pass
+
     if item.get_closest_marker("parallel") and not partest:
         # Spawn parallel processes to run test
-        passed = parallel(item)
-        if not passed:
-            pytest.fail(f"{item} failed in parallel execution")
+        outcome = parallel(item, item.funcargs['mode'])
+        if outcome:
+            pytest.skip(f"{item} success in parallel")
         else:
-            pytest.skip(f"{item}t passed in parallel execution")
+            pytest.fail(f"{item} failed in parallel")
+    else:
+        outcome = yield
 
 
 @pytest.hookimpl(tryfirst=True, hookwrapper=True)
 def pytest_runtest_makereport(item, call):
     outcome = yield
     result = outcome.get_result()
-
     partest = os.environ.get('DEVITO_MPI', 0)
     try:
         partest = int(partest)
     except ValueError:
         pass
-
     if item.get_closest_marker("parallel") and not partest:
         if call.when == 'call' and result.outcome == 'skipped':
             result.outcome = 'passed'
diff --git a/tests/test_autotuner.py b/tests/test_autotuner.py
index 72233d3fa0..ca1644316c 100644
--- a/tests/test_autotuner.py
+++ b/tests/test_autotuner.py
@@ -181,7 +181,7 @@ def test_discarding_runs():
 
 
 @pytest.mark.parallel(mode=[(2, 'diag'), (2, 'full')])
-def test_at_w_mpi():
+def test_at_w_mpi(mode):
     """Make sure autotuning works in presence of MPI. MPI ranks work
     in isolation to determine the best block size, locally."""
     grid = Grid(shape=(8, 8))
diff --git a/tests/test_benchmark.py b/tests/test_benchmark.py
index 2b0988fc33..92ae2b36ed 100644
--- a/tests/test_benchmark.py
+++ b/tests/test_benchmark.py
@@ -71,7 +71,7 @@ def test_bench(mode, problem, op):
 
 @pytest.mark.parallel(mode=2)
 @switchconfig(profiling='advanced')
-def test_run_mpi():
+def test_run_mpi(mode):
     """
     Test the `run` mode over MPI, with all key arguments used.
     """
diff --git a/tests/test_builtins.py b/tests/test_builtins.py
index d086415376..8bb68d976c 100644
--- a/tests/test_builtins.py
+++ b/tests/test_builtins.py
@@ -92,7 +92,7 @@ def test_assign_subsampled_timefunction(self):
         assert np.all(f.data == 1)
 
     @pytest.mark.parallel(mode=4)
-    def test_assign_parallel(self):
+    def test_assign_parallel(self, mode):
         a = np.arange(64).reshape((8, 8))
         grid = Grid(shape=a.shape)
 
@@ -174,7 +174,7 @@ def test_gs_2d_float(self, sigma):
         assert np.amax(np.abs(sp_smoothed - np.array(dv_smoothed))) <= 1e-5
 
     @pytest.mark.parallel(mode=[(4, 'full')])
-    def test_gs_parallel(self):
+    def test_gs_parallel(self, mode):
         a = np.arange(64).reshape((8, 8))
         grid = Grid(shape=a.shape)
 
@@ -236,7 +236,7 @@ def test_nbl_zero(self):
         assert np.all(a[:] - np.array(f.data[:]) == 0)
 
     @pytest.mark.parallel(mode=4)
-    def test_if_parallel(self):
+    def test_if_parallel(self, mode):
         a = np.arange(36).reshape((6, 6))
         grid = Grid(shape=(18, 18))
         x, y = grid.dimensions
@@ -292,7 +292,7 @@ def test_if_halo(self, ndim, nbl):
 
     @pytest.mark.parametrize('nbl', [0, 2])
     @pytest.mark.parallel(mode=4)
-    def test_if_halo_mpi(self, nbl):
+    def test_if_halo_mpi(self, nbl, mode):
         """
         Test that FD halo is padded as well.
         """
diff --git a/tests/test_data.py b/tests/test_data.py
index 232aff9c97..a0833722fe 100644
--- a/tests/test_data.py
+++ b/tests/test_data.py
@@ -492,7 +492,7 @@ class TestDataDistributed(object):
     """
 
     @pytest.mark.parallel(mode=4)
-    def test_localviews(self):
+    def test_localviews(self, mode):
         grid = Grid(shape=(4, 4))
         x, y = grid.dimensions
         glb_pos_map = grid.distributor.glb_pos_map
@@ -520,7 +520,7 @@ def test_localviews(self):
             assert np.all(u.data_ro_with_halo._local[2] == 0.)
 
     @pytest.mark.parallel(mode=4)
-    def test_trivial_insertion(self):
+    def test_trivial_insertion(self, mode):
         grid = Grid(shape=(4, 4))
         u = Function(name='u', grid=grid, space_order=0)
         v = Function(name='v', grid=grid, space_order=1)
@@ -536,7 +536,7 @@ def test_trivial_insertion(self):
         assert np.all(v.data_with_halo._local == 1.)
 
     @pytest.mark.parallel(mode=4)
-    def test_indexing(self):
+    def test_indexing(self, mode):
         grid = Grid(shape=(4, 4))
         x, y = grid.dimensions
         glb_pos_map = grid.distributor.glb_pos_map
@@ -567,7 +567,7 @@ def test_indexing(self):
             assert np.all(u.data[:, 2] == [myrank, myrank])
 
     @pytest.mark.parallel(mode=4)
-    def test_slicing(self):
+    def test_slicing(self, mode):
         grid = Grid(shape=(4, 4))
         x, y = grid.dimensions
         glb_pos_map = grid.distributor.glb_pos_map
@@ -594,7 +594,7 @@ def test_slicing(self):
             assert u.data[:2, 2:].size == u.data[2:, :2].size == u.data[:2, :2].size == 0
 
     @pytest.mark.parallel(mode=4)
-    def test_slicing_ns(self):
+    def test_slicing_ns(self, mode):
         # Test slicing with a negative step
         grid = Grid(shape=(4, 4))
         x, y = grid.dimensions
@@ -619,7 +619,7 @@ def test_slicing_ns(self):
             assert np.all(u.data == [[5, 4], [1, 0]])
 
     @pytest.mark.parallel(mode=4)
-    def test_getitem(self):
+    def test_getitem(self, mode):
         # __getitem__ mpi slicing tests:
         grid = Grid(shape=(8, 8))
         x, y = grid.dimensions
@@ -697,7 +697,7 @@ def test_getitem(self):
             assert np.all(result4 == [[28, 27, 26]])
 
     @pytest.mark.parallel(mode=4)
-    def test_big_steps(self):
+    def test_big_steps(self, mode):
         # Test slicing with a step size > 1
         grid = Grid(shape=(8, 8))
         x, y = grid.dimensions
@@ -749,7 +749,7 @@ def test_big_steps(self):
             assert np.all(r3 == [[0]])
 
     @pytest.mark.parallel(mode=4)
-    def test_setitem(self):
+    def test_setitem(self, mode):
         # __setitem__ mpi slicing tests
         grid = Grid(shape=(12, 12))
         x, y = grid.dimensions
@@ -810,7 +810,7 @@ def test_setitem(self):
                                                [0, 0, 0, 0, 0, 0]])
 
     @pytest.mark.parallel(mode=4)
-    def test_hd_slicing(self):
+    def test_hd_slicing(self, mode):
         # Test higher dimension slices
         grid = Grid(shape=(4, 4, 4))
         x, y, z = grid.dimensions
@@ -889,7 +889,7 @@ def test_hd_slicing(self):
                                          [63]])
 
     @pytest.mark.parallel(mode=4)
-    def test_niche_slicing(self):
+    def test_niche_slicing(self, mode):
         grid0 = Grid(shape=(8, 8))
         x0, y0 = grid0.dimensions
         glb_pos_map0 = grid0.distributor.glb_pos_map
@@ -1029,7 +1029,7 @@ def test_niche_slicing(self):
         ((8, 8, 8), (slice(None, None, 1), 5, slice(None, None, 1)),
          (slice(None, None, 1), 1, slice(None, None, 1)),
          (slice(None, None, 1), 7, slice(None, None, 1)))])
-    def test_niche_slicing2(self, shape, slice0, slice1, slice2):
+    def test_niche_slicing2(self, shape, slice0, slice1, slice2, mode):
         grid = Grid(shape=shape)
         f = Function(name='f', grid=grid)
         f.data[:] = 1
@@ -1063,7 +1063,7 @@ def test_empty_slicing(self):
         assert(g.data[1:1, 0:0, 1:1].shape == (0, 0, 0))
 
     @pytest.mark.parallel(mode=4)
-    def test_neg_start_stop(self):
+    def test_neg_start_stop(self, mode):
         grid0 = Grid(shape=(8, 8))
         f = Function(name='f', grid=grid0, space_order=0, dtype=np.int32)
         dat = np.arange(64, dtype=np.int32)
@@ -1094,7 +1094,7 @@ def test_neg_start_stop(self):
             assert np.count_nonzero(h.data[:]) == 0
 
     @pytest.mark.parallel(mode=4)
-    def test_indexing_in_views(self):
+    def test_indexing_in_views(self, mode):
         grid = Grid(shape=(4, 4))
         x, y = grid.dimensions
         glb_pos_map = grid.distributor.glb_pos_map
@@ -1158,7 +1158,7 @@ def test_indexing_in_views(self):
             assert view2.size == 0
 
     @pytest.mark.parallel(mode=4)
-    def test_from_replicated_to_distributed(self):
+    def test_from_replicated_to_distributed(self, mode):
         shape = (4, 4)
         grid = Grid(shape=shape)
         x, y = grid.dimensions
@@ -1207,7 +1207,7 @@ def test_from_replicated_to_distributed(self):
             assert False
 
     @pytest.mark.parallel(mode=4)
-    def test_misc_setup(self):
+    def test_misc_setup(self, mode):
         """Test setup of Functions with mixed distributed/replicated Dimensions."""
         grid = Grid(shape=(4, 4))
         _, y = grid.dimensions
@@ -1248,7 +1248,7 @@ def test_misc_setup(self):
             assert True
 
     @pytest.mark.parallel(mode=4)
-    def test_misc_data(self):
+    def test_misc_data(self, mode):
         """
         Test data insertion/indexing for Functions with mixed
         distributed/replicated Dimensions.
@@ -1294,7 +1294,7 @@ def test_misc_data(self):
         (slice(None, None, -1), slice(0, 1, 1), slice(None, None, -1)),
         (0, slice(None, None, -1), slice(None, None, -1)),
         (slice(0, 1, 1), slice(None, None, -1), slice(None, None, -1))])
-    def test_inversions(self, gslice):
+    def test_inversions(self, gslice, mode):
         """ Test index flipping along different axes."""
         nx = 8
         ny = 8
@@ -1337,7 +1337,7 @@ def test_inversions(self, gslice):
             assert res.shape == vdat[tuple(sl)].shape
 
     @pytest.mark.parallel(mode=4)
-    def test_setitem_shorthands(self):
+    def test_setitem_shorthands(self, mode):
         # Test setitem with various slicing shorthands
         nx = 8
         ny = 8
@@ -1387,7 +1387,7 @@ class TestDataGather(object):
 
     @pytest.mark.parallel(mode=4)
     @pytest.mark.parametrize('rank', [0, 1, 2, 3])
-    def test_simple_gather(self, rank):
+    def test_simple_gather(self, rank, mode):
         """ Test a simple gather on various ranks."""
         grid = Grid(shape=(10, 10), extent=(9, 9))
         f = Function(name='f', grid=grid, dtype=np.int32)
@@ -1408,7 +1408,7 @@ def test_simple_gather(self, rank):
         (None, None, -2),
         (1, 8, 3),
         ((0, 4), None, (2, 1))])
-    def test_sliced_gather_2D(self, start, stop, step):
+    def test_sliced_gather_2D(self, start, stop, step, mode):
         """ Test gather for various 2D slices."""
         grid = Grid(shape=(10, 10), extent=(9, 9))
         f = Function(name='f', grid=grid, dtype=np.int32)
@@ -1442,7 +1442,7 @@ def test_sliced_gather_2D(self, start, stop, step):
         (None, None, -2),
         (1, 8, 3),
         ((0, 4, 4), None, (2, 1, 1))])
-    def test_sliced_gather_3D(self, start, stop, step):
+    def test_sliced_gather_3D(self, start, stop, step, mode):
         """ Test gather for various 3D slices."""
         grid = Grid(shape=(10, 10, 10), extent=(9, 9, 9))
         f = Function(name='f', grid=grid, dtype=np.int32)
@@ -1469,7 +1469,7 @@ def test_sliced_gather_3D(self, start, stop, step):
             assert ans == np.array(None)
 
     @pytest.mark.parallel(mode=[4, 6])
-    def test_gather_time_function(self):
+    def test_gather_time_function(self, mode):
         """ Test gathering of TimeFunction objects. """
         grid = Grid(shape=(11, 11))
         f = TimeFunction(name='f', grid=grid, save=11)
diff --git a/tests/test_dle.py b/tests/test_dle.py
index b2896dac4f..42e52297c7 100644
--- a/tests/test_dle.py
+++ b/tests/test_dle.py
@@ -148,7 +148,7 @@ def test_cache_blocking_structure_subdims():
 
 
 @pytest.mark.parallel(mode=[(1, 'full')])  # Shortcut to put loops in nested efuncs
-def test_cache_blocking_structure_distributed():
+def test_cache_blocking_structure_distributed(mode):
     """
     Test cache blocking in multiple nested elemental functions.
     """
diff --git a/tests/test_dse.py b/tests/test_dse.py
index 9435d58c54..871a575fce 100644
--- a/tests/test_dse.py
+++ b/tests/test_dse.py
@@ -2801,7 +2801,7 @@ def test_fullopt(self):
 
     @switchconfig(profiling='advanced')
     @pytest.mark.parallel(mode=[(1, 'full')])
-    def test_fullopt_w_mpi(self):
+    def test_fullopt_w_mpi(self, mode):
         tti_noopt = self.tti_operator(opt=None)
         rec0, u0, v0, _ = tti_noopt.forward()
         tti_agg = self.tti_operator(opt='advanced')
diff --git a/tests/test_gpu_common.py b/tests/test_gpu_common.py
index 7c06ce9bf1..1204eb2c13 100644
--- a/tests/test_gpu_common.py
+++ b/tests/test_gpu_common.py
@@ -1184,7 +1184,7 @@ def test_streaming_split_noleak(self):
     @pytest.mark.skip(reason="Unsupported MPI + .dx when streaming backwards")
     @pytest.mark.parallel(mode=4)
     @switchconfig(safe_math=True)  # Or NVC will crash
-    def test_streaming_w_mpi(self):
+    def test_streaming_w_mpi(self, mode):
         nt = 5
         grid = Grid(shape=(16, 16))
 
@@ -1382,7 +1382,7 @@ def test_deviceid(self):
 
     @skipif('device-openmp')
     @pytest.mark.parallel(mode=1)
-    def test_deviceid_w_mpi(self):
+    def test_deviceid_w_mpi(self, mode):
         self.check_deviceid()
 
     def test_devicerm(self):
@@ -1503,7 +1503,7 @@ def test_empty_arrays(self):
 
     @skipif('device-openmp')
     @pytest.mark.parallel(mode=4)
-    def test_degenerate_subdomainset(self):
+    def test_degenerate_subdomainset(self, mode):
         """
         MFE for issue #1766
         """
diff --git a/tests/test_gpu_openacc.py b/tests/test_gpu_openacc.py
index 5bb9424b86..c3056cb5cf 100644
--- a/tests/test_gpu_openacc.py
+++ b/tests/test_gpu_openacc.py
@@ -248,7 +248,7 @@ def test_iso_acoustic(self, opt):
 class TestMPI(object):
 
     @pytest.mark.parallel(mode=2)
-    def test_basic(self):
+    def test_basic(self, mode):
         grid = Grid(shape=(6, 6))
         x, y = grid.dimensions
         t = grid.stepping_dim
@@ -276,5 +276,5 @@ def test_basic(self):
                                         [11., 16., 17., 17., 16., 11.]])
 
     @pytest.mark.parallel(mode=2)
-    def test_iso_ac(self):
+    def test_iso_ac(self, mode):
         TestOperator().iso_acoustic(opt='advanced')
diff --git a/tests/test_gpu_openmp.py b/tests/test_gpu_openmp.py
index ebda431a37..5f500c02c3 100644
--- a/tests/test_gpu_openmp.py
+++ b/tests/test_gpu_openmp.py
@@ -24,7 +24,7 @@ def test_init_omp_env(self):
             'if (deviceid != -1)\n{\n  omp_set_default_device(deviceid);\n}'
 
     @pytest.mark.parallel(mode=1)
-    def test_init_omp_env_w_mpi(self):
+    def test_init_omp_env_w_mpi(self, mode):
         grid = Grid(shape=(3, 3, 3))
 
         u = TimeFunction(name='u', grid=grid)
@@ -321,7 +321,7 @@ def test_iso_acoustic(self, opt):
 class TestMPI(object):
 
     @pytest.mark.parallel(mode=[2, 4])
-    def test_mpi_nocomms(self):
+    def test_mpi_nocomms(self, mode):
         grid = Grid(shape=(3, 3, 3))
 
         u = TimeFunction(name='u', grid=grid, dtype=np.int32)
@@ -337,5 +337,5 @@ def test_mpi_nocomms(self):
         assert np.all(np.array(u.data[0, :, :, :]) == time_steps)
 
     @pytest.mark.parallel(mode=[2, 4])
-    def test_iso_ac(self):
+    def test_iso_ac(self, mode):
         TestOperator().iso_acoustic(opt='advanced')
diff --git a/tests/test_linearize.py b/tests/test_linearize.py
index 7f87eecedd..b236170e9d 100644
--- a/tests/test_linearize.py
+++ b/tests/test_linearize.py
@@ -31,7 +31,7 @@ def test_basic():
 
 
 @pytest.mark.parallel(mode=[(1, 'basic'), (1, 'diag2'), (1, 'full')])
-def test_mpi():
+def test_mpi(mode):
     grid = Grid(shape=(4, 4))
 
     u = TimeFunction(name='u', grid=grid, space_order=2)
@@ -153,15 +153,13 @@ def test_interpolation_msf():
 
 
 @pytest.mark.parallel(mode=[(1, 'diag2')])
-def test_codegen_quality0():
+def test_codegen_quality0(mode):
     grid = Grid(shape=(4, 4))
-
     u = TimeFunction(name='u', grid=grid, space_order=2)
 
     eqn = Eq(u.forward, u.dx2 + 1.)
 
     op = Operator(eqn, opt=('advanced', {'linearize': True}))
-
     assert 'uL0' in str(op)
 
     exprs = FindNodes(Expression).visit(op)
@@ -172,6 +170,7 @@ def test_codegen_quality0():
     # for the efunc args
     # (the other three obviously are _POSIX_C_SOURCE, START, STOP)
     assert len(op._headers) == 6
+    return "bonjour"
 
 
 def test_codegen_quality1():
diff --git a/tests/test_mpi.py b/tests/test_mpi.py
index d6cb431a90..dabd82dda7 100644
--- a/tests/test_mpi.py
+++ b/tests/test_mpi.py
@@ -23,7 +23,7 @@
 class TestDistributor(object):
 
     @pytest.mark.parallel(mode=[2, 4])
-    def test_partitioning(self):
+    def test_partitioning(self, mode):
         grid = Grid(shape=(15, 15))
         f = Function(name='f', grid=grid)
 
@@ -37,7 +37,7 @@ def test_partitioning(self):
         assert distributor.nprocs_local == distributor.nprocs
 
     @pytest.mark.parallel(mode=[2, 4])
-    def test_partitioning_fewer_dims(self):
+    def test_partitioning_fewer_dims(self, mode):
         """Test domain decomposition for Functions defined over a strict subset
         of grid-decomposed dimensions."""
         size_x, size_y = 16, 16
@@ -55,7 +55,7 @@ def test_partitioning_fewer_dims(self):
         assert f.shape == expected[distributor.nprocs][distributor.myrank]
 
     @pytest.mark.parallel(mode=[2, 4])
-    def test_partitioning_fewer_dims_timefunc(self):
+    def test_partitioning_fewer_dims_timefunc(self, mode):
         """Test domain decomposition for Functions defined over a strict subset
         of grid-decomposed dimensions."""
         size_x, size_y = 16, 16
@@ -80,7 +80,7 @@ def test_partitioning_fewer_dims_timefunc(self):
         assert f.shape[1:] == expected[distributor.nprocs][distributor.myrank]
 
     @pytest.mark.parallel(mode=9)
-    def test_neighborhood_horizontal_2d(self):
+    def test_neighborhood_horizontal_2d(self, mode):
         grid = Grid(shape=(3, 3))
         x, y = grid.dimensions
 
@@ -111,7 +111,7 @@ def test_neighborhood_horizontal_2d(self):
         assert expected[distributor.myrank][y] == distributor.neighborhood[y]
 
     @pytest.mark.parallel(mode=9)
-    def test_neighborhood_diagonal_2d(self):
+    def test_neighborhood_diagonal_2d(self, mode):
         grid = Grid(shape=(3, 3))
         x, y = grid.dimensions
 
@@ -142,7 +142,7 @@ def test_neighborhood_diagonal_2d(self):
                    for i in [(LEFT, LEFT), (LEFT, RIGHT), (RIGHT, LEFT), (RIGHT, RIGHT)])
 
     @pytest.mark.parallel(mode=[2, 4])
-    def test_ctypes_neighborhood(self):
+    def test_ctypes_neighborhood(self, mode):
         grid = Grid(shape=(4, 4))
         distributor = grid.distributor
 
@@ -163,7 +163,7 @@ def test_ctypes_neighborhood(self):
         assert all(getattr(value._obj, k) == v for k, v in mapper.items())
 
     @pytest.mark.parallel(mode=[4])
-    def test_custom_topology(self):
+    def test_custom_topology(self, mode):
         grid = Grid(shape=(15, 15))
         f = Function(name='f', grid=grid)
 
@@ -222,7 +222,7 @@ def test_custom_topology(self):
         (256, ('*', 32, 2), (4, 32, 2)),
     ])
     @pytest.mark.parallel(mode=[2])
-    def test_custom_topology_v2(self, comm_size, topology, dist_topology):
+    def test_custom_topology_v2(self, comm_size, topology, dist_topology, mode):
         dummy_comm = Bunch(size=comm_size)
         custom_topology = CustomTopology(topology, dummy_comm)
         assert custom_topology == dist_topology
@@ -231,7 +231,7 @@ def test_custom_topology_v2(self, comm_size, topology, dist_topology):
 class TestFunction(object):
 
     @pytest.mark.parallel(mode=2)
-    def test_halo_exchange_bilateral(self):
+    def test_halo_exchange_bilateral(self, mode):
         """
         Test halo exchange between two processes organised in a 2x1 cartesian grid.
 
@@ -282,7 +282,7 @@ def test_halo_exchange_bilateral(self):
         ((1, 0), (0, 1)),
     ])
     @pytest.mark.parallel(mode=2)
-    def test_halo_exchange_bilateral_asymmetric(self, paddings):
+    def test_halo_exchange_bilateral_asymmetric(self, paddings, mode):
         """
         Test halo exchange between two processes organised in a 2x1 cartesian grid.
 
@@ -332,7 +332,7 @@ def test_halo_exchange_bilateral_asymmetric(self, paddings):
         assert np.all(f._data_ro_with_inhalo[:, -2:] == 0.)
 
     @pytest.mark.parallel(mode=4)
-    def test_halo_exchange_quadrilateral(self):
+    def test_halo_exchange_quadrilateral(self, mode):
         """
         Test halo exchange between four processes organised in a 2x2 cartesian grid.
 
@@ -412,7 +412,7 @@ def test_halo_exchange_quadrilateral(self):
         ((15, 15), [((0, 8), (0, 8)), ((0, 8), (8, 15)),
                     ((8, 15), (0, 8)), ((8, 15), (8, 15))]),
     ])
-    def test_local_indices(self, shape, expected):
+    def test_local_indices(self, shape, expected, mode):
         grid = Grid(shape=shape)
         f = Function(name='f', grid=grid)
 
@@ -421,7 +421,7 @@ def test_local_indices(self, shape, expected):
 
     @pytest.mark.parallel(mode=4)
     @pytest.mark.parametrize('shape', [(1,), (2, 3), (4, 5, 6)])
-    def test_mpi4py_nodevmpi(self, shape):
+    def test_mpi4py_nodevmpi(self, shape, mode):
 
         with switchconfig(mpi=False):
             # Mimic external mpi init
@@ -443,7 +443,7 @@ class TestSparseFunction(object):
         ((8, ), ((1.,), (3.,), (5.,), (7.,)), 1),
         ((8, ), ((1.,), (2.,), (3.,), (4.,), (5.,), (6.,), (7.,), (8.,)), 2)
     ])
-    def test_ownership(self, shape, coords, points):
+    def test_ownership(self, shape, coords, points, mode):
         """Given a sparse point ``p`` with known coordinates, this test checks
         that the MPI rank owning ``p`` is retrieved correctly."""
         grid = Grid(shape=shape, extent=shape)
@@ -463,7 +463,7 @@ def test_ownership(self, shape, coords, points):
         ([(1.5, 1.5), ], [[], [], [], [0.]],
          [(slice(0, -1), ), (slice(0, -1), ), (slice(0, -1), ), (slice(0, 1), )])
     ])
-    def test_local_indices(self, coords, expected, expectedinds):
+    def test_local_indices(self, coords, expected, expectedinds, mode):
         grid = Grid(shape=(4, 4), extent=(3.0, 3.0))
 
         data = np.array([0., 1., 2., 3.])
@@ -483,7 +483,7 @@ def test_local_indices(self, coords, expected, expectedinds):
         assert sf.local_indices == expectedinds
 
     @pytest.mark.parallel(mode=4)
-    def test_scatter_gather(self):
+    def test_scatter_gather(self, mode):
         """
         Test scattering and gathering of sparse data from and to a single MPI rank.
 
@@ -533,7 +533,7 @@ def test_scatter_gather(self):
     @pytest.mark.parallel(mode=4)
     @switchconfig(condition=isinstance(configuration['compiler'],
                   (OneapiCompiler)), safe_math=True)
-    def test_sparse_coords(self):
+    def test_sparse_coords(self, mode):
         grid = Grid(shape=(21, 31, 21), extent=(20, 30, 20))
         x, y, z = grid.dimensions
 
@@ -556,7 +556,7 @@ def test_sparse_coords(self):
             assert sf.data[i] == coords_loc
 
     @pytest.mark.parallel(mode=4)
-    def test_sparse_coords_issue1823(self):
+    def test_sparse_coords_issue1823(self, mode):
         grid = Grid((101, 101, 101), extent=(1000, 1000, 1000))
         coords = np.array([[1000., 0., 900.], [1000., 300., 700.],
                            [1000., 500., 500.], [1000., 700., 300.],
@@ -573,7 +573,7 @@ def test_sparse_coords_issue1823(self):
 
     @pytest.mark.parallel(mode=4)
     @pytest.mark.parametrize('r', [2])
-    def test_precomputed_sparse(self, r):
+    def test_precomputed_sparse(self, r, mode):
         grid = Grid(shape=(4, 4), extent=(3.0, 3.0))
 
         coords = np.array([(1.0, 1.0), (2.0, 2.0), (1.0, 2.0), (2.0, 1.0)])
@@ -601,7 +601,7 @@ def test_precomputed_sparse(self, r):
         assert np.all(sf1.data == 4)
 
     @pytest.mark.parallel(mode=4)
-    def test_sparse_first(self):
+    def test_sparse_first(self, mode):
         """
         Tests custom sprase function with sparse dimension as first index.
         """
@@ -639,7 +639,7 @@ class SparseFirst(SparseFunction):
         assert np.allclose(s.data, expected)
 
     @pytest.mark.parallel(mode=[(4, 'diag2')])
-    def test_no_grid_dim_slow(self):
+    def test_no_grid_dim_slow(self, mode):
         shape = (12, 13, 14)
         nfreq = 5
         nrec = 2
@@ -669,7 +669,7 @@ class CoordSlowSparseFunction(SparseFunction):
         assert np.all(s.data == 1)
 
     @pytest.mark.parallel(mode=4)
-    def test_no_grid_dim_slow_time(self):
+    def test_no_grid_dim_slow_time(self, mode):
         shape = (12, 13, 14)
         nfreq = 5
         nrec = 2
@@ -702,7 +702,7 @@ class CoordSlowSparseFunction(SparseTimeFunction):
 class TestOperatorSimple(object):
 
     @pytest.mark.parallel(mode=[2, 4, 8])
-    def test_trivial_eq_1d(self):
+    def test_trivial_eq_1d(self, mode):
         grid = Grid(shape=(32,))
         x = grid.dimensions[0]
         t = grid.stepping_dim
@@ -724,7 +724,7 @@ def test_trivial_eq_1d(self):
             assert np.all(f.data_ro_domain[0] == 7.)
 
     @pytest.mark.parallel(mode=[2])
-    def test_trivial_eq_1d_asymmetric(self):
+    def test_trivial_eq_1d_asymmetric(self, mode):
         grid = Grid(shape=(32,))
         x = grid.dimensions[0]
         t = grid.stepping_dim
@@ -743,7 +743,7 @@ def test_trivial_eq_1d_asymmetric(self):
             assert f.data_ro_domain[0, -1] == 2.
 
     @pytest.mark.parallel(mode=2)
-    def test_trivial_eq_1d_save(self):
+    def test_trivial_eq_1d_save(self, mode):
         grid = Grid(shape=(32,))
         x = grid.dimensions[0]
         time = grid.time_dim
@@ -765,7 +765,7 @@ def test_trivial_eq_1d_save(self):
 
     @pytest.mark.parallel(mode=[(4, 'basic'), (4, 'diag'), (4, 'overlap'),
                                 (4, 'overlap2'), (4, 'diag2'), (4, 'full')])
-    def test_trivial_eq_2d(self):
+    def test_trivial_eq_2d(self, mode):
         grid = Grid(shape=(8, 8,))
         x, y = grid.dimensions
         t = grid.stepping_dim
@@ -801,7 +801,7 @@ def test_trivial_eq_2d(self):
 
     @pytest.mark.parallel(mode=[(8, 'basic'), (8, 'diag'), (8, 'overlap'),
                                 (8, 'overlap2'), (8, 'diag2'), (8, 'full')])
-    def test_trivial_eq_3d(self):
+    def test_trivial_eq_3d(self, mode):
         grid = Grid(shape=(8, 8, 8))
         x, y, z = grid.dimensions
         t = grid.stepping_dim
@@ -842,7 +842,7 @@ def test_trivial_eq_3d(self):
         assert np.all(f.data_ro_domain[0, 1:-1, 1:-1, 1:-1] == interior)
 
     @pytest.mark.parallel(mode=[(4, 'basic'), (4, 'diag')])
-    def test_multiple_eqs_funcs(self):
+    def test_multiple_eqs_funcs(self, mode):
         grid = Grid(shape=(12,))
         x = grid.dimensions[0]
         t = grid.stepping_dim
@@ -874,7 +874,7 @@ def test_multiple_eqs_funcs(self):
         assert calls[0].ncomps == 2
 
     @pytest.mark.parallel(mode=2)
-    def test_reapply_with_different_functions(self):
+    def test_reapply_with_different_functions(self, mode):
         grid1 = Grid(shape=(30, 30, 30))
         f1 = Function(name='f', grid=grid1, space_order=4)
 
@@ -894,7 +894,7 @@ def test_reapply_with_different_functions(self):
 class TestCodeGeneration(object):
 
     @pytest.mark.parallel(mode=1)
-    def test_avoid_haloupdate_as_nostencil_basic(self):
+    def test_avoid_haloupdate_as_nostencil_basic(self, mode):
         grid = Grid(shape=(12,))
 
         f = TimeFunction(name='f', grid=grid)
@@ -907,7 +907,7 @@ def test_avoid_haloupdate_as_nostencil_basic(self):
         assert len(calls) == 0
 
     @pytest.mark.parallel(mode=1)
-    def test_avoid_haloupdate_as_nostencil_advanced(self):
+    def test_avoid_haloupdate_as_nostencil_advanced(self, mode):
         grid = Grid(shape=(4, 4))
         u = TimeFunction(name='u', grid=grid, space_order=4, time_order=2, save=None)
         v = TimeFunction(name='v', grid=grid, space_order=0, time_order=0, save=5)
@@ -927,7 +927,7 @@ def test_avoid_haloupdate_as_nostencil_advanced(self):
         assert len(calls) == 0
 
     @pytest.mark.parallel(mode=1)
-    def test_avoid_redundant_haloupdate(self):
+    def test_avoid_redundant_haloupdate(self, mode):
         grid = Grid(shape=(12,))
         x = grid.dimensions[0]
         t = grid.stepping_dim
@@ -946,7 +946,7 @@ def test_avoid_redundant_haloupdate(self):
         assert len(calls) == 1
 
     @pytest.mark.parallel(mode=1)
-    def test_avoid_haloupdate_if_distr_but_sequential(self):
+    def test_avoid_haloupdate_if_distr_but_sequential(self, mode):
         grid = Grid(shape=(12,))
         x = grid.dimensions[0]
         t = grid.stepping_dim
@@ -970,7 +970,7 @@ def test_avoid_haloupdate_if_distr_but_sequential(self):
         assert len(calls) == 0
 
     @pytest.mark.parallel(mode=1)
-    def test_avoid_haloupdate_with_subdims(self):
+    def test_avoid_haloupdate_with_subdims(self, mode):
         grid = Grid(shape=(4,))
         x = grid.dimensions[0]
         t = grid.stepping_dim
@@ -996,7 +996,7 @@ def test_avoid_haloupdate_with_subdims(self):
         assert len(calls) == 1
 
     @pytest.mark.parallel(mode=1)
-    def test_avoid_haloupdate_with_constant_index(self):
+    def test_avoid_haloupdate_with_constant_index(self, mode):
         grid = Grid(shape=(4,))
         x = grid.dimensions[0]
         t = grid.stepping_dim
@@ -1010,7 +1010,7 @@ def test_avoid_haloupdate_with_constant_index(self):
         assert len(calls) == 0
 
     @pytest.mark.parallel(mode=1)
-    def test_do_haloupdate_with_constant_locindex(self):
+    def test_do_haloupdate_with_constant_locindex(self, mode):
         """
         Like `test_avoid_haloupdate_with_constant_index`, there is again
         a constant index, but this time along a loc-index (`t` Dimension),
@@ -1029,7 +1029,7 @@ def test_do_haloupdate_with_constant_locindex(self):
         assert len(calls) == 1
 
     @pytest.mark.parallel(mode=1)
-    def test_hoist_haloupdate_if_no_flowdep(self):
+    def test_hoist_haloupdate_if_no_flowdep(self, mode):
         grid = Grid(shape=(12,))
         x = grid.dimensions[0]
         t = grid.stepping_dim
@@ -1055,7 +1055,7 @@ def test_hoist_haloupdate_if_no_flowdep(self):
         assert len(calls) == 2
 
     @pytest.mark.parallel(mode=1)
-    def test_hoist_haloupdate_with_subdims(self):
+    def test_hoist_haloupdate_with_subdims(self, mode):
         """
         This test stems from https://github.com/devitocodes/devito/issues/1119
 
@@ -1088,7 +1088,7 @@ def test_hoist_haloupdate_with_subdims(self):
         assert len(calls) == 0
 
     @pytest.mark.parallel(mode=1)
-    def test_hoist_haloupdate_from_innerloop(self):
+    def test_hoist_haloupdate_from_innerloop(self, mode):
         grid = Grid(shape=(4, 4, 4))
         x, y, z = grid.dimensions
 
@@ -1108,7 +1108,7 @@ def test_hoist_haloupdate_from_innerloop(self):
         assert op.body.body[-1].body[1].body[0].body[0].body[1].is_Iteration
 
     @pytest.mark.parallel(mode=2)
-    def test_unhoist_haloupdate_if_invariant(self):
+    def test_unhoist_haloupdate_if_invariant(self, mode):
         """
         Test an Operator that computes coupled equations in which the first
         one *does require* a halo update on a Dimension-invariant Function.
@@ -1140,7 +1140,7 @@ def test_unhoist_haloupdate_if_invariant(self):
             assert np.allclose(f.data_ro_domain[5:], [67., 67., 62., 56., 30.], rtol=R)
 
     @pytest.mark.parallel(mode=[(2, 'basic'), (2, 'diag')])
-    def test_redo_haloupdate_due_to_antidep(self):
+    def test_redo_haloupdate_due_to_antidep(self, mode):
         grid = Grid(shape=(12,))
         x = grid.dimensions[0]
         t = grid.stepping_dim
@@ -1163,7 +1163,7 @@ def test_redo_haloupdate_due_to_antidep(self):
             assert np.all(g.data_ro_domain[1, :-1] == 2.)
 
     @pytest.mark.parallel(mode=[(1, 'full')])
-    def test_avoid_fullmode_if_crossloop_dep(self):
+    def test_avoid_fullmode_if_crossloop_dep(self, mode):
         grid = Grid(shape=(4, 4))
         x, y = grid.dimensions
 
@@ -1184,7 +1184,7 @@ def test_avoid_fullmode_if_crossloop_dep(self):
         assert np.all(f.data[:] == 2.)
 
     @pytest.mark.parallel(mode=2)
-    def test_avoid_haloudate_if_flowdep_along_other_dim(self):
+    def test_avoid_haloudate_if_flowdep_along_other_dim(self, mode):
         grid = Grid(shape=(10,))
         x = grid.dimensions[0]
         t = grid.stepping_dim
@@ -1219,7 +1219,7 @@ def test_avoid_haloudate_if_flowdep_along_other_dim(self):
             assert np.allclose(g.data_ro_domain[0, 5:], [4.8, 4.8, 4.8, 4.8, 2.], rtol=R)
 
     @pytest.mark.parallel(mode=2)
-    def test_unmerge_haloupdate_if_no_locindices(self):
+    def test_unmerge_haloupdate_if_no_locindices(self, mode):
         grid = Grid(shape=(10,))
         x = grid.dimensions[0]
         t = grid.stepping_dim
@@ -1260,7 +1260,7 @@ def test_unmerge_haloupdate_if_no_locindices(self):
             assert np.allclose(g.data_ro_domain[0, 5:], [16., 16., 14., 13., 6.], rtol=R)
 
     @pytest.mark.parallel(mode=1)
-    def test_merge_haloupdate_if_diff_locindices_v0(self):
+    def test_merge_haloupdate_if_diff_locindices_v0(self, mode):
         grid = Grid(shape=(101, 101))
         x, y = grid.dimensions
         t = grid.stepping_dim
@@ -1281,7 +1281,7 @@ def test_merge_haloupdate_if_diff_locindices_v0(self):
         op.cfunction
 
     @pytest.mark.parallel(mode=2)
-    def test_merge_haloupdate_if_diff_locindices_v1(self):
+    def test_merge_haloupdate_if_diff_locindices_v1(self, mode):
         """
         This test is a revisited, more complex version of
         `test_merge_haloupdate_if_diff_locindices_v0`. And in addition to
@@ -1335,7 +1335,7 @@ def test_merge_haloupdate_if_diff_locindices_v1(self):
 
     @pytest.mark.parallel(mode=1)
     @switchconfig(autopadding=True)
-    def test_process_but_avoid_haloupdate_along_replicated(self):
+    def test_process_but_avoid_haloupdate_along_replicated(self, mode):
         dx = Dimension(name='dx')
         grid = Grid(shape=(10, 10))
         x, y = grid.dimensions
@@ -1357,7 +1357,7 @@ def test_process_but_avoid_haloupdate_along_replicated(self):
             assert calls[0].arguments[0] is u
 
     @pytest.mark.parallel(mode=1)
-    def test_conditional_dimension(self):
+    def test_conditional_dimension(self, mode):
         """
         Test the case of Functions in the condition of a ConditionalDimension.
         """
@@ -1381,7 +1381,7 @@ def test_conditional_dimension(self):
         assert len(calls) == 0
 
     @pytest.mark.parallel(mode=1)
-    def test_conditional_dimension_v2(self):
+    def test_conditional_dimension_v2(self, mode):
         """
         Make sure optimizations don't move around halo exchanges if embedded
         within conditionals.
@@ -1414,7 +1414,7 @@ def test_conditional_dimension_v2(self):
         ('f[t,x-1,y-1] + f[t,x+1,y+1]', {'cr', 'rr', 'rc', 'cl', 'll', 'lc'}),
     ])
     @pytest.mark.parallel(mode=[(1, 'diag')])
-    def test_diag_comm_scheme(self, expr, expected):
+    def test_diag_comm_scheme(self, expr, expected, mode):
         """
         Check that the 'diag' mode does not generate more communications
         than strictly necessary.
@@ -1432,7 +1432,7 @@ def test_diag_comm_scheme(self, expr, expected):
         assert destinations == expected
 
     @pytest.mark.parallel(mode=[(1, 'full')])
-    def test_poke_progress(self):
+    def test_poke_progress(self, mode):
         grid = Grid(shape=(4, 4))
         x, y = grid.dimensions
         t = grid.stepping_dim
@@ -1485,7 +1485,7 @@ def test_poke_progress(self):
             assert call._single_thread
 
     @pytest.mark.parallel(mode=[(1, 'diag2')])
-    def test_diag2_quality(self):
+    def test_diag2_quality(self, mode):
         grid = Grid(shape=(10, 10, 10))
 
         f = TimeFunction(name='f', grid=grid, space_order=2)
@@ -1509,7 +1509,7 @@ def test_diag2_quality(self):
         (1, 'diag2'),
         (1, 'full'),
     ])
-    def test_min_code_size(self):
+    def test_min_code_size(self, mode):
         grid = Grid(shape=(10, 10, 10))
 
         f = TimeFunction(name='f', grid=grid, space_order=2)
@@ -1550,7 +1550,7 @@ def test_min_code_size(self):
             assert len(FindNodes(ComputeCall).visit(op)) == 1
 
     @pytest.mark.parallel(mode=[(1, 'diag2')])
-    def test_many_functions(self):
+    def test_many_functions(self, mode):
         grid = Grid(shape=(10, 10, 10))
 
         eqns = []
@@ -1570,7 +1570,7 @@ def test_many_functions(self):
     @pytest.mark.parallel(mode=[
         (1, 'full'),
     ])
-    def test_profiled_regions(self):
+    def test_profiled_regions(self, mode):
         grid = Grid(shape=(10, 10, 10))
 
         f = TimeFunction(name='f', grid=grid, space_order=2)
@@ -1584,7 +1584,7 @@ def test_profiled_regions(self):
                                              'remainder0', 'compute0']
 
     @pytest.mark.parallel(mode=1)
-    def test_enforce_haloupdate_if_unwritten_function(self):
+    def test_enforce_haloupdate_if_unwritten_function(self, mode):
         grid = Grid(shape=(16, 16))
 
         u = TimeFunction(name='u', grid=grid)
@@ -1607,7 +1607,7 @@ def test_enforce_haloupdate_if_unwritten_function(self):
 class TestOperatorAdvanced(object):
 
     @pytest.mark.parallel(mode=4)
-    def test_injection_wodup(self):
+    def test_injection_wodup(self, mode):
         """
         Test injection operator when the sparse points don't need to be replicated
         ("wodup" -> w/o duplication) over multiple MPI ranks.
@@ -1640,7 +1640,7 @@ def test_injection_wodup(self):
     @pytest.mark.parallel(mode=4)
     @switchconfig(condition=isinstance(configuration['compiler'],
                   (OneapiCompiler)), safe_math=True)
-    def test_injection_wodup_wtime(self):
+    def test_injection_wodup_wtime(self, mode):
         """
         Just like ``test_injection_wodup``, but using a SparseTimeFunction
         instead of a SparseFunction. Hence, the data scattering/gathering now
@@ -1666,7 +1666,7 @@ def test_injection_wodup_wtime(self):
         assert np.all(f.data[2] == 3.25)
 
     @pytest.mark.parallel(mode=4)
-    def test_injection_dup(self):
+    def test_injection_dup(self, mode):
         """
         Test injection operator when the sparse points are replicated over
         multiple MPI ranks.
@@ -1721,7 +1721,7 @@ def test_injection_dup(self):
             assert np.all(f.data_ro_domain == [[3.75, 1.25], [1.25, 0.]])
 
     @pytest.mark.parallel(mode=4)
-    def test_interpolation_wodup(self):
+    def test_interpolation_wodup(self, mode):
         grid = Grid(shape=(4, 4), extent=(3.0, 3.0))
 
         f = Function(name='f', grid=grid, space_order=1)
@@ -1748,7 +1748,7 @@ def test_interpolation_wodup(self):
         assert np.all(sf.data == 4.)
 
     @pytest.mark.parallel(mode=4)
-    def test_interpolation_dup(self):
+    def test_interpolation_dup(self, mode):
         """
         Test interpolation operator when the sparse points are replicated over
         multiple MPI ranks.
@@ -1800,7 +1800,7 @@ def test_interpolation_dup(self):
         assert np.all(sf.data == [1.5, 2.5, 2.5, 3.5][grid.distributor.myrank])
 
     @pytest.mark.parallel(mode=2)
-    def test_subsampling(self):
+    def test_subsampling(self, mode):
         grid = Grid(shape=(40,))
         x = grid.dimensions[0]
         t = grid.stepping_dim
@@ -1837,7 +1837,7 @@ def test_subsampling(self):
         assert len(FindNodes(Call).visit(conditional[0])) == 0
 
     @pytest.mark.parallel(mode=2)
-    def test_arguments_subrange(self):
+    def test_arguments_subrange(self, mode):
         """
         Test op.apply when a subrange is specified for a distributed dimension.
         """
@@ -1858,7 +1858,7 @@ def test_arguments_subrange(self):
             assert np.all(f.data_ro_domain[1, -4:] == 0.)
 
     @pytest.mark.parallel(mode=2)
-    def test_bcs_basic(self):
+    def test_bcs_basic(self, mode):
         """
         Test MPI in presence of boundary condition loops. Here, no halo exchange
         is expected (as there is no stencil in the computed expression) but we
@@ -1897,7 +1897,7 @@ def test_bcs_basic(self):
             assert np.all(u.data_ro_domain[0, -thickness:] == range(2, thickness+2))
 
     @pytest.mark.parallel(mode=2)
-    def test_interior_w_stencil(self):
+    def test_interior_w_stencil(self, mode):
         grid = Grid(shape=(10,))
         x = grid.dimensions[0]
         t = grid.stepping_dim
@@ -1916,7 +1916,7 @@ def test_interior_w_stencil(self):
             assert np.all(u.data_ro_domain[0, :-2] == 3.)
 
     @pytest.mark.parallel(mode=4)
-    def test_misc_dims(self):
+    def test_misc_dims(self, mode):
         """
         Test MPI in presence of Functions with mixed distributed/replicated
         Dimensions, with only a strict subset of the Grid dimensions used.
@@ -1959,7 +1959,7 @@ def test_misc_dims(self):
             assert(np.all(u.data[1, 3, :] == 8.0))
 
     @pytest.mark.parallel(mode=9)
-    def test_nontrivial_operator(self):
+    def test_nontrivial_operator(self, mode):
         """
         Test MPI in a non-trivial scenario: ::
 
@@ -2040,7 +2040,7 @@ def test_nontrivial_operator(self):
             assert np.all(u.data_ro_domain[1] == 3)
 
     @pytest.mark.parallel(mode=[(4, 'basic'), (4, 'overlap'), (4, 'full')])
-    def test_coupled_eqs_mixed_dims(self):
+    def test_coupled_eqs_mixed_dims(self, mode):
         """
         Test an Operator that computes coupled equations over partly disjoint sets
         of Dimensions (e.g., one Eq over [x, y, z], the other Eq over [x, yi, zi]).
@@ -2089,7 +2089,7 @@ def test_coupled_eqs_mixed_dims(self):
         assert np.all(v.data_ro_domain[1, :, 3] == 0.)
 
     @pytest.mark.parallel(mode=2)
-    def test_haloupdate_same_timestep(self):
+    def test_haloupdate_same_timestep(self, mode):
         """
         Test an Operator that computes coupled equations in which the second
         one requires a halo update right after the computation of the first one.
@@ -2112,7 +2112,7 @@ def test_haloupdate_same_timestep(self):
         assert np.all(v.data_ro_domain[-1, :, 1:-1] == 6.)
 
     @pytest.mark.parallel(mode=2)
-    def test_haloupdate_same_timestep_v2(self):
+    def test_haloupdate_same_timestep_v2(self, mode):
         """
         Similar to test_haloupdate_same_timestep, but switching the expression that
         writes to subsequent time step. Also checks halo update call placement.
@@ -2143,7 +2143,7 @@ def test_haloupdate_same_timestep_v2(self):
         assert np.all(v.data_ro_domain[-1, :, 1:-1] == 6.)
 
     @pytest.mark.parallel(mode=4)
-    def test_haloupdate_multi_op(self):
+    def test_haloupdate_multi_op(self, mode):
         """
         Test that halo updates are carried out correctly when multiple operators
         are applied consecutively.
@@ -2168,7 +2168,7 @@ def test_haloupdate_multi_op(self):
         assert (np.isclose(norm(f), 17.24904, atol=1e-4, rtol=0))
 
     @pytest.mark.parallel(mode=1)
-    def test_haloupdate_issue_1613(self):
+    def test_haloupdate_issue_1613(self, mode):
         """
         Test the HaloScheme construction and generation when using u.dt2.
 
@@ -2194,7 +2194,7 @@ def test_haloupdate_issue_1613(self):
         assert dims[0].origin is t
 
     @pytest.mark.parallel(mode=[(4, 'basic'), (4, 'diag2'), (4, 'overlap2')])
-    def test_cire(self):
+    def test_cire(self, mode):
         """
         Check correctness when the DSE extracts aliases and places them
         into offset-ed loop (nest). For example, the compiler may generate:
@@ -2236,7 +2236,7 @@ def test_cire(self):
         assert u0_norm == u1_norm
 
     @pytest.mark.parallel(mode=[(4, 'overlap2'), (4, 'diag2')])
-    def test_cire_with_shifted_diagonal_halo_touch(self):
+    def test_cire_with_shifted_diagonal_halo_touch(self, mode):
         """
         Like ``test_cire`` but now the diagonal halos required to compute
         the aliases are shifted due to the iteration space. Basically, this
@@ -2274,7 +2274,7 @@ def test_cire_with_shifted_diagonal_halo_touch(self):
         {'cire-rotate': True},  # Issue #1490
         {'min-storage': True},  # Issue #1491
     ])
-    def test_cire_options(self, opt_options):
+    def test_cire_options(self, opt_options, mode):
         """
         MFEs for issues #1490 and #1491.
         """
@@ -2309,7 +2309,7 @@ def test_cire_options(self, opt_options):
         assert np.allclose(p.data, p1.data, rtol=10e-11)
 
     @pytest.mark.parallel(mode=[(4, 'full')])
-    def test_staggering(self):
+    def test_staggering(self, mode):
         """
         Test MPI in presence of staggered grids.
 
@@ -2339,7 +2339,7 @@ def test_staggering(self):
         assert np.isclose(norm(uxy), 61427.853, rtol=1.e-4)
 
     @pytest.mark.parallel(mode=2)
-    def test_op_new_dist(self):
+    def test_op_new_dist(self, mode):
         """
         Test that an operator made with one distributor produces correct results
         when executed with a different distributor.
@@ -2367,7 +2367,7 @@ def test_op_new_dist(self):
         assert abs(norm(u) - norm(u2)) < 1.e-3
 
     @pytest.mark.parallel(mode=[(4, 'full')])
-    def test_misc_subdims(self):
+    def test_misc_subdims(self, mode):
         """
         Test MPI full mode with an Operator having:
 
@@ -2410,7 +2410,7 @@ def test_misc_subdims(self):
         assert np.all(u.data[1, :, 1:] == 1.)
 
     @pytest.mark.parallel(mode=[(4, 'basic'), (4, 'full')])
-    def test_misc_subdims_3D(self):
+    def test_misc_subdims_3D(self, mode):
         """
         Test `SubDims` in 3D (so that spatial blocking is introduced).
 
@@ -2447,7 +2447,7 @@ def test_misc_subdims_3D(self):
         assert np.all(u.data[1, :, :, -2:] == 1.)
 
     @pytest.mark.parallel(mode=[(4, 'full')])
-    def test_custom_subdomain(self):
+    def test_custom_subdomain(self, mode):
         """
         This test uses a custom SubDomain such that we end up with two loop
         nests with a data dependence across them inducing two halo exchanges,
@@ -2497,7 +2497,7 @@ def define(self, dimensions):
         assert np.isclose(norm(v), 21.14994, atol=1e-5, rtol=0)
 
     @pytest.mark.parallel(mode=2)
-    def test_overriding_from_different_grid(self):
+    def test_overriding_from_different_grid(self, mode):
         """
         MFE for issue #1629.
         """
@@ -2523,7 +2523,7 @@ def test_overriding_from_different_grid(self):
         assert np.all(u3.data[0, 3:-3, 3:-3] == 1.)
 
     @pytest.mark.parallel(mode=4)
-    def test_fission_due_to_antidep(self):
+    def test_fission_due_to_antidep(self, mode):
         grid = Grid(shape=(16, 16, 64), dtype=np.float64)
 
         u = TimeFunction(name='u', grid=grid, space_order=4)
@@ -2604,7 +2604,7 @@ def norms(self):
         ((60, 70), 'OT2', 8, False),
     ])
     @pytest.mark.parallel(mode=1)
-    def test_adjoint_codegen(self, shape, kernel, space_order, save):
+    def test_adjoint_codegen(self, shape, kernel, space_order, save, mode):
         solver = acoustic_setup(shape=shape, spacing=[15. for _ in shape], kernel=kernel,
                                 tn=500, space_order=space_order, nrec=130,
                                 preset='layers-isotropic', dtype=np.float64)
@@ -2653,12 +2653,12 @@ def run_adjoint_F(self, nd):
     @pytest.mark.parametrize('nd', [1, 2, 3])
     @pytest.mark.parallel(mode=[(4, 'basic'), (4, 'diag'), (4, 'overlap'),
                                 (4, 'overlap2'), (4, 'full')])
-    def test_adjoint_F(self, nd):
+    def test_adjoint_F(self, nd, mode):
         self.run_adjoint_F(nd)
 
     @pytest.mark.parallel(mode=[(8, 'diag2'), (8, 'full')])
     @switchconfig(openmp=False)
-    def test_adjoint_F_no_omp(self):
+    def test_adjoint_F_no_omp(self, mode):
         """
         ``run_adjoint_F`` with OpenMP disabled. By disabling OpenMP, we can
         practically scale up to higher process counts.
diff --git a/tests/test_operator.py b/tests/test_operator.py
index 5698f8f913..82f39bfea4 100644
--- a/tests/test_operator.py
+++ b/tests/test_operator.py
@@ -1208,7 +1208,7 @@ def test_incomplete_override(self):
             assert False
 
     @pytest.mark.parallel(mode=1)
-    def test_new_distributor(self):
+    def test_new_distributor(self, mode):
         """
         Test that `comm` and `nb` are correctly updated when a different distributor
         from that it was originally built with is required by an operator.
diff --git a/tests/test_pickle.py b/tests/test_pickle.py
index 1089ca5bf2..cad0e03a63 100644
--- a/tests/test_pickle.py
+++ b/tests/test_pickle.py
@@ -636,7 +636,7 @@ def test_elemental(self, pickle):
         assert str(op) == str(new_op)
 
     @pytest.mark.parallel(mode=[1])
-    def test_mpi_objects(self, pickle):
+    def test_mpi_objects(self, pickle, mode):
         grid = Grid(shape=(4, 4, 4))
 
         # Neighbours
@@ -684,7 +684,7 @@ def test_threadid(self, pickle):
         assert tid.symbolic_max.name == new_tid.symbolic_max.name
 
     @pytest.mark.parallel(mode=[2])
-    def test_mpi_grid(self, pickle):
+    def test_mpi_grid(self, pickle, mode):
         grid = Grid(shape=(4, 4, 4))
 
         pkl_grid = pickle.dumps(grid)
@@ -704,7 +704,7 @@ def test_mpi_grid(self, pickle):
         MPI.COMM_WORLD.Barrier()
 
     @pytest.mark.parallel(mode=[(1, 'full')])
-    def test_mpi_fullmode_objects(self, pickle):
+    def test_mpi_fullmode_objects(self, pickle, mode):
         grid = Grid(shape=(4, 4, 4))
         x, y, _ = grid.dimensions
 
@@ -743,7 +743,7 @@ def test_mpi_fullmode_objects(self, pickle):
             assert v[1] == Min(d.symbolic_max, d.symbolic_min)
 
     @pytest.mark.parallel(mode=[(1, 'basic'), (1, 'full')])
-    def test_mpi_operator(self, pickle):
+    def test_mpi_operator(self, pickle, mode):
         grid = Grid(shape=(4,))
         f = TimeFunction(name='f', grid=grid)
 
diff --git a/tests/test_sparse.py b/tests/test_sparse.py
index 31b6af0fdd..136d1ef9e3 100644
--- a/tests/test_sparse.py
+++ b/tests/test_sparse.py
@@ -343,7 +343,7 @@ def test_precomputed_subpoints_inject_dt2(self):
         assert m.data[0, 40, 39] == pytest.approx(2.0)
 
     @pytest.mark.parallel(mode=4)
-    def test_mpi(self):
+    def test_mpi(self, mode):
         # Shape chosen to get a source in multiple ranks
         shape = (91, 91)
         grid = Grid(shape=shape)
@@ -458,7 +458,7 @@ def test_subs(self, sptype):
 
     @switchconfig(safe_math=True)
     @pytest.mark.parallel(mode=[1, 4])
-    def test_mpi_no_data(self):
+    def test_mpi_no_data(self, mode):
         grid = Grid((11, 11), extent=(10, 10))
         time = grid.time_dim
         # Base object
diff --git a/tests/test_subdomains.py b/tests/test_subdomains.py
index 61441dea65..839cb2299d 100644
--- a/tests/test_subdomains.py
+++ b/tests/test_subdomains.py
@@ -168,7 +168,7 @@ def define(self, dimensions):
 
     @pytest.mark.parametrize('spec', sd_specs)
     @pytest.mark.parallel(mode=[2, 3])
-    def test_subdomains_mpi(self, spec):
+    def test_subdomains_mpi(self, spec, mode):
 
         class sd0(SubDomain):
             name = 'd0'
@@ -362,7 +362,7 @@ class MySubdomains2(SubDomainSet):
         assert((np.array(f.data[:]+g.data[:]) == expected).all())
 
     @pytest.mark.parallel(mode=[(4, 'basic'), (4, 'overlap')])
-    def test_subdomainset_mpi(self):
+    def test_subdomainset_mpi(self, mode):
 
         n_domains = 5
 

From e3d5511cd5b6d8155f0374ff961e569ee438ad9b Mon Sep 17 00:00:00 2001
From: Fabio Luporini <fabio@devitocodes.com>
Date: Mon, 8 Apr 2024 10:42:49 +0000
Subject: [PATCH 15/29] compiler: Tweak device-aware blocking

---
 devito/passes/clusters/blocking.py | 14 +++++++++++---
 tests/test_dle.py                  | 25 +++++++++++++++++++++++--
 2 files changed, 34 insertions(+), 5 deletions(-)

diff --git a/devito/passes/clusters/blocking.py b/devito/passes/clusters/blocking.py
index 5ac3539c43..a63fc1622e 100644
--- a/devito/passes/clusters/blocking.py
+++ b/devito/passes/clusters/blocking.py
@@ -160,13 +160,16 @@ def __init__(self, options):
     def _make_key_hook(self, cluster, level):
         return (is_on_device(cluster.functions, self.gpu_fit),)
 
+    def _has_other_blockable_dim(self, cluster, d):
+        return any(cluster.properties.is_parallel_relaxed(i) and
+                   not self._has_short_trip_count(i)
+                   for i in set(cluster.ispace.itdims) - {d})
+
     def callback(self, clusters, prefix):
         if not prefix:
             return clusters
 
         d = prefix[-1].dim
-        if self._has_short_trip_count(d):
-            return clusters
 
         processed = []
         for c in clusters:
@@ -174,7 +177,12 @@ def callback(self, clusters, prefix):
                 return clusters
 
             if is_on_device(c.functions, self.gpu_fit):
-                if self._has_data_reuse(c):
+                if self._has_short_trip_count(d):
+                    if self._has_other_blockable_dim(c, d):
+                        return clusters
+                    else:
+                        properties = c.properties.block(d, 'small')
+                elif self._has_data_reuse(c):
                     properties = c.properties.block(d)
                 else:
                     properties = c.properties.block(d, 'small')
diff --git a/tests/test_dle.py b/tests/test_dle.py
index 42e52297c7..d9150116bd 100644
--- a/tests/test_dle.py
+++ b/tests/test_dle.py
@@ -6,7 +6,7 @@
 
 from conftest import assert_structure, assert_blocking, _R, skipif
 from devito import (Grid, Function, TimeFunction, SparseTimeFunction, SpaceDimension,
-                    CustomDimension, Dimension, SubDimension,
+                    CustomDimension, Dimension, DefaultDimension, SubDimension,
                     PrecomputedSparseTimeFunction, Eq, Inc, ReduceMin, ReduceMax,
                     Operator, configuration, dimensions, info, cos)
 from devito.exceptions import InvalidArgument
@@ -205,7 +205,8 @@ def test_cache_blocking_structure_optrelax_customdim():
     x, y, z = grid.dimensions
 
     u = TimeFunction(name="u", grid=grid)
-    f = Function(name="f", grid=grid, dimensions=(d, x, y, z), shape=(2,) + grid.shape)
+    f = Function(name="f", grid=grid, dimensions=(d, x, y, z),
+                 shape=(2,) + grid.shape)
 
     eqn = Eq(f, u[d, x, y, z] + u[d, x + 1, y, z])
 
@@ -216,6 +217,26 @@ def test_cache_blocking_structure_optrelax_customdim():
                      'd,x0_blk0,y0_blk0,z0_blk0,x,y,z')
 
 
+def test_cache_blocking_structure_optrelax_defaultdim_alone():
+    grid = Grid(shape=(8, 8, 8))
+    d = DefaultDimension(name='d', default_value=2)
+    time = grid.time_dim
+    x, y, z = grid.dimensions
+
+    u = TimeFunction(name="u", grid=grid)
+    f = Function(name="f", grid=grid, dimensions=(d, x, y, z),
+                 shape=(2,) + grid.shape)
+
+    eqn = Inc(f, u*cos(time*d))
+
+    op = Operator(eqn, opt=('advanced', {'blockrelax': 'device-aware'}))
+
+    _, _ = assert_blocking(op, {'d0_blk0', 'x0_blk0'})
+    assert_structure(op,
+                     ['t,d0_blk0,d', 't,d,x0_blk0,y0_blk0,z0_blk0,x,y,z'],
+                     't,d0_blk0,d,d,x0_blk0,y0_blk0,z0_blk0,x,y,z')
+
+
 def test_cache_blocking_structure_leftright_subdims():
     grid = Grid(shape=(12, 12))
     nbl = 3

From a26db7c43e1f48c3079671faebcc71c2b6e40661 Mon Sep 17 00:00:00 2001
From: Fabio Luporini <fabio@devitocodes.com>
Date: Mon, 8 Apr 2024 12:16:27 +0000
Subject: [PATCH 16/29] tests: Refactor device-aware blocking tests

---
 tests/test_dle.py | 231 +++++++++++++++++++++++-----------------------
 1 file changed, 114 insertions(+), 117 deletions(-)

diff --git a/tests/test_dle.py b/tests/test_dle.py
index d9150116bd..0a44da9fbe 100644
--- a/tests/test_dle.py
+++ b/tests/test_dle.py
@@ -179,141 +179,138 @@ def test_cache_blocking_structure_distributed(mode):
         assert iters[4].dim is z
 
 
-def test_cache_blocking_structure_optrelax():
-    grid = Grid(shape=(8, 8, 8))
+class TestBlockingOptRelax:
 
-    u = TimeFunction(name="u", grid=grid, space_order=2)
-    src = SparseTimeFunction(name="src", grid=grid, nt=3, npoint=1,
-                             coordinates=np.array([(0.5, 0.5, 0.5)]))
-
-    eqns = [Eq(u.forward, u.dx)]
-    eqns += src.inject(field=u.forward, expr=src)
-
-    op = Operator(eqns, opt=('advanced', {'blockrelax': True}))
-
-    bns, _ = assert_blocking(op, {'x0_blk0', 'p_src0_blk0'})
-
-    iters = FindNodes(Iteration).visit(bns['p_src0_blk0'])
-    assert len(iters) == 5
-    assert iters[0].dim.is_Block
-    assert iters[1].dim.is_Block
-
-
-def test_cache_blocking_structure_optrelax_customdim():
-    grid = Grid(shape=(8, 8, 8))
-    d = CustomDimension(name='d', symbolic_size=2)
-    x, y, z = grid.dimensions
-
-    u = TimeFunction(name="u", grid=grid)
-    f = Function(name="f", grid=grid, dimensions=(d, x, y, z),
-                 shape=(2,) + grid.shape)
-
-    eqn = Eq(f, u[d, x, y, z] + u[d, x + 1, y, z])
-
-    op = Operator(eqn, opt=('advanced', {'blockrelax': True}))
-
-    _, _ = assert_blocking(op, {'x0_blk0'})
-    assert_structure(op, ['d,x0_blk0,y0_blk0,z0_blk0,x,y,z'],
-                     'd,x0_blk0,y0_blk0,z0_blk0,x,y,z')
-
-
-def test_cache_blocking_structure_optrelax_defaultdim_alone():
-    grid = Grid(shape=(8, 8, 8))
-    d = DefaultDimension(name='d', default_value=2)
-    time = grid.time_dim
-    x, y, z = grid.dimensions
-
-    u = TimeFunction(name="u", grid=grid)
-    f = Function(name="f", grid=grid, dimensions=(d, x, y, z),
-                 shape=(2,) + grid.shape)
-
-    eqn = Inc(f, u*cos(time*d))
-
-    op = Operator(eqn, opt=('advanced', {'blockrelax': 'device-aware'}))
-
-    _, _ = assert_blocking(op, {'d0_blk0', 'x0_blk0'})
-    assert_structure(op,
-                     ['t,d0_blk0,d', 't,d,x0_blk0,y0_blk0,z0_blk0,x,y,z'],
-                     't,d0_blk0,d,d,x0_blk0,y0_blk0,z0_blk0,x,y,z')
-
-
-def test_cache_blocking_structure_leftright_subdims():
-    grid = Grid(shape=(12, 12))
-    nbl = 3
+    def test_basic(self):
+        grid = Grid(shape=(8, 8, 8))
 
-    damp = Function(name='damp', grid=grid)
+        u = TimeFunction(name="u", grid=grid, space_order=2)
+        src = SparseTimeFunction(name="src", grid=grid, nt=3, npoint=1,
+                                 coordinates=np.array([(0.5, 0.5, 0.5)]))
 
-    eqns = [Eq(damp, 0.)]
-    for d in damp.dimensions:
-        # Left
-        dl = SubDimension.left(name='%sl' % d.name, parent=d, thickness=nbl)
-        eqns.extend([Inc(damp.subs({d: dl}), 1.)])
-        # right
-        dr = SubDimension.right(name='%sr' % d.name, parent=d, thickness=nbl)
-        eqns.extend([Inc(damp.subs({d: dr}), 1.)])
+        eqns = [Eq(u.forward, u.dx)]
+        eqns += src.inject(field=u.forward, expr=src)
 
-    op = Operator(eqns, opt=('fission', 'blocking', {'blockrelax': 'device-aware'}))
+        op = Operator(eqns, opt=('advanced', {'blockrelax': True}))
 
-    bns, _ = assert_blocking(op,
-                             {'x0_blk0', 'xl0_blk0', 'xr0_blk0', 'x1_blk0', 'x2_blk0'})
-    assert all(IsPerfectIteration().visit(i) for i in bns.values())
-    assert all(len(FindNodes(Iteration).visit(i)) == 4 for i in bns.values())
+        bns, _ = assert_blocking(op, {'x0_blk0', 'p_src0_blk0'})
 
+        iters = FindNodes(Iteration).visit(bns['p_src0_blk0'])
+        assert len(iters) == 5
+        assert iters[0].dim.is_Block
+        assert iters[1].dim.is_Block
 
-@pytest.mark.parametrize('opt, expected', [('noop', ('ijk', 'ikl')),
-                         (('advanced', {'blockinner': True, 'blockrelax': True}),
-                         ('i0_blk0ijk', 'i0_blk0ikl'))])
-def test_cache_blocking_structure_optrelax_linalg(opt, expected):
-    mat_shape = (4, 4)
+    def test_customdim(self):
+        grid = Grid(shape=(8, 8, 8))
+        d = CustomDimension(name='d', symbolic_size=2)
+        x, y, z = grid.dimensions
 
-    i, j, k, l = dimensions('i j k l')
-    A = Function(name='A', shape=mat_shape, dimensions=(i, j))
-    B = Function(name='B', shape=mat_shape, dimensions=(j, k))
-    C = Function(name='C', shape=mat_shape, dimensions=(j, k))
-    D = Function(name='D', shape=mat_shape, dimensions=(i, k))
-    E = Function(name='E', shape=mat_shape, dimensions=(k, l))
-    F = Function(name='F', shape=mat_shape, dimensions=(i, l))
+        u = TimeFunction(name="u", grid=grid)
+        f = Function(name="f", grid=grid, dimensions=(d, x, y, z),
+                     shape=(2,) + grid.shape)
 
-    eqs = [Inc(D, A*B + A*C), Inc(F, D*E)]
+        eqn = Eq(f, u[d, x, y, z] + u[d, x + 1, y, z])
 
-    A.data[:] = 1
-    B.data[:] = 1
-    C.data[:] = 1
-    E.data[:] = 1
+        op = Operator(eqn, opt=('advanced', {'blockrelax': True}))
 
-    op0 = Operator(eqs, opt=opt)
-    op0.apply()
-    assert_structure(op0, expected)
-    assert np.linalg.norm(D.data) == 32.0
-    assert np.linalg.norm(F.data) == 128.0
+        assert_blocking(op, {'x0_blk0'})
+        assert_structure(op, ['d,x0_blk0,y0_blk0,z0_blk0,x,y,z'],
+                         'd,x0_blk0,y0_blk0,z0_blk0,x,y,z')
 
+    def test_defaultdim_alone(self):
+        grid = Grid(shape=(8, 8, 8))
+        d = DefaultDimension(name='d', default_value=2)
+        time = grid.time_dim
+        x, y, z = grid.dimensions
 
-def test_cache_blocking_structure_optrelax_prec_inject():
-    grid = Grid(shape=(10, 10))
-    dt = grid.stepping_dim.spacing
+        u = TimeFunction(name="u", grid=grid)
+        f = Function(name="f", grid=grid, dimensions=(d, x, y, z),
+                     shape=(2,) + grid.shape)
+
+        eqn = Inc(f, u*cos(time*d))
+
+        op = Operator(eqn, opt=('advanced', {'blockrelax': 'device-aware'}))
+
+        assert_blocking(op, {'d0_blk0', 'x0_blk0'})
+        assert_structure(op,
+                         ['t,d0_blk0,d', 't,d,x0_blk0,y0_blk0,z0_blk0,x,y,z'],
+                         't,d0_blk0,d,d,x0_blk0,y0_blk0,z0_blk0,x,y,z')
+
+    def test_leftright_subdims(self):
+        grid = Grid(shape=(12, 12))
+        nbl = 3
+
+        damp = Function(name='damp', grid=grid)
+
+        eqns = [Eq(damp, 0.)]
+        for d in damp.dimensions:
+            # Left
+            dl = SubDimension.left(name='%sl' % d.name, parent=d, thickness=nbl)
+            eqns.extend([Inc(damp.subs({d: dl}), 1.)])
+            # right
+            dr = SubDimension.right(name='%sr' % d.name, parent=d, thickness=nbl)
+            eqns.extend([Inc(damp.subs({d: dr}), 1.)])
+
+        op = Operator(eqns, opt=('fission', 'blocking', {'blockrelax': 'device-aware'}))
+
+        bns, _ = assert_blocking(op, {'x0_blk0', 'xl0_blk0', 'xr0_blk0',
+                                      'x1_blk0', 'x2_blk0'})
+        assert all(IsPerfectIteration().visit(i) for i in bns.values())
+        assert all(len(FindNodes(Iteration).visit(i)) == 4 for i in bns.values())
+
+    @pytest.mark.parametrize('opt, expected', [('noop', ('ijk', 'ikl')),
+                             (('advanced', {'blockinner': True, 'blockrelax': True}),
+                             ('i0_blk0ijk', 'i0_blk0ikl'))])
+    def test_linalg(self, opt, expected):
+        mat_shape = (4, 4)
+
+        i, j, k, l = dimensions('i j k l')
+        A = Function(name='A', shape=mat_shape, dimensions=(i, j))
+        B = Function(name='B', shape=mat_shape, dimensions=(j, k))
+        C = Function(name='C', shape=mat_shape, dimensions=(j, k))
+        D = Function(name='D', shape=mat_shape, dimensions=(i, k))
+        E = Function(name='E', shape=mat_shape, dimensions=(k, l))
+        F = Function(name='F', shape=mat_shape, dimensions=(i, l))
+
+        eqs = [Inc(D, A*B + A*C), Inc(F, D*E)]
+
+        A.data[:] = 1
+        B.data[:] = 1
+        C.data[:] = 1
+        E.data[:] = 1
+
+        op0 = Operator(eqs, opt=opt)
+        op0.apply()
+        assert_structure(op0, expected)
+        assert np.linalg.norm(D.data) == 32.0
+        assert np.linalg.norm(F.data) == 128.0
+
+    def test_prec_inject(self):
+        grid = Grid(shape=(10, 10))
+        dt = grid.stepping_dim.spacing
 
-    u = TimeFunction(name="u", grid=grid, time_order=2, space_order=4)
+        u = TimeFunction(name="u", grid=grid, time_order=2, space_order=4)
 
-    # The values we put it don't matter, we won't run an Operator
-    points = [(0.05, 0.9), (0.01, 0.8), (0.07, 0.84)]
-    gridpoints = [(5, 90), (1, 80), (7, 84)]
-    interpolation_coeffs = np.ndarray(shape=(3, 2, 2))
-    sf = PrecomputedSparseTimeFunction(
-        name='s', grid=grid, r=2, npoint=len(points), nt=5,
-        gridpoints=gridpoints, interpolation_coeffs=interpolation_coeffs
-    )
+        # The values we put it don't matter, we won't run an Operator
+        points = [(0.05, 0.9), (0.01, 0.8), (0.07, 0.84)]
+        gridpoints = [(5, 90), (1, 80), (7, 84)]
+        interpolation_coeffs = np.ndarray(shape=(3, 2, 2))
+        sf = PrecomputedSparseTimeFunction(
+            name='s', grid=grid, r=2, npoint=len(points), nt=5,
+            gridpoints=gridpoints, interpolation_coeffs=interpolation_coeffs
+        )
 
-    eqns = sf.inject(field=u.forward, expr=sf * dt**2)
+        eqns = sf.inject(field=u.forward, expr=sf * dt**2)
 
-    op = Operator(eqns, opt=('advanced', {'blockrelax': 'device-aware',
-                                          'openmp': True,
-                                          'par-collapse-ncores': 1}))
+        op = Operator(eqns, opt=('advanced', {'blockrelax': 'device-aware',
+                                              'openmp': True,
+                                              'par-collapse-ncores': 1}))
 
-    assert_structure(op, ['t', 't,p_s0_blk0,p_s,rsx,rsy'],
-                     't,p_s0_blk0,p_s,rsx,rsy')
+        assert_structure(op, ['t', 't,p_s0_blk0,p_s,rsx,rsy'],
+                         't,p_s0_blk0,p_s,rsx,rsy')
 
 
-class TestBlockingParTile(object):
+class TestBlockingParTile:
 
     @pytest.mark.parametrize('par_tile,expected', [
         ((16, 16, 16), ((16, 16, 16), (16, 16, 16))),
@@ -603,7 +600,7 @@ def test_cache_blocking_imperfect_nest_v2(blockinner):
     assert np.allclose(u.data, u2.data, rtol=1e-07)
 
 
-class TestNodeParallelism(object):
+class TestNodeParallelism:
 
     def test_nthreads_generation(self):
         grid = Grid(shape=(10, 10))
@@ -1166,7 +1163,7 @@ def test_parallel_prec_inject(self):
         assert 'omp for collapse' in iterations[1].pragmas[0].value
 
 
-class TestNestedParallelism(object):
+class TestNestedParallelism:
 
     def test_basic(self):
         grid = Grid(shape=(3, 3, 3))

From 3c8edeaa1bca045c3cb795eb612fc19aaef49767 Mon Sep 17 00:00:00 2001
From: Fabio Luporini <fabio@devitocodes.com>
Date: Thu, 11 Apr 2024 15:19:25 +0000
Subject: [PATCH 17/29] compiler: Make code gen of elementary funcs dtype-aware

---
 devito/passes/clusters/aliases.py      |  2 +-
 devito/symbolics/inspection.py         | 22 ++++--------
 devito/symbolics/printer.py            | 26 +++++++++++++-
 examples/performance/00_overview.ipynb | 48 +++++++++++++-------------
 tests/test_symbolics.py                | 15 +++++++-
 5 files changed, 71 insertions(+), 42 deletions(-)

diff --git a/devito/passes/clusters/aliases.py b/devito/passes/clusters/aliases.py
index f5f481244e..7814ecd67f 100644
--- a/devito/passes/clusters/aliases.py
+++ b/devito/passes/clusters/aliases.py
@@ -845,7 +845,7 @@ def lower_schedule(schedule, meta, sregistry, ftemps):
         # This prevents cases such as `floor(a*b)` with `a` and `b` floats
         # that would creat a temporary `int r = b` leading to erronous
         # numerical results
-        dtype = sympy_dtype(pivot, meta.dtype)
+        dtype = sympy_dtype(pivot, base=meta.dtype)
 
         if writeto:
             # The Dimensions defining the shape of Array
diff --git a/devito/symbolics/inspection.py b/devito/symbolics/inspection.py
index 99e752abce..437d48fff0 100644
--- a/devito/symbolics/inspection.py
+++ b/devito/symbolics/inspection.py
@@ -3,7 +3,6 @@
 import numpy as np
 from sympy import (Function, Indexed, Integer, Mul, Number,
                    Pow, S, Symbol, Tuple)
-from sympy.core.operations import AssocOp
 
 from devito.finite_differences import Derivative
 from devito.finite_differences.differentiable import IndexDerivative
@@ -291,21 +290,14 @@ def has_integer_args(*args):
     return res
 
 
-def sympy_dtype(expr, default):
+def sympy_dtype(expr, base=None):
     """
-    Infer the dtype of the expression
-    or default if could not be determined.
+    Infer the dtype of the expression.
     """
-    # Symbol/... without argument, check its dtype
-    if len(expr.args) == 0:
+    dtypes = {base} - {None}
+    for i in expr.free_symbols:
         try:
-            return expr.dtype
+            dtypes.add(i.dtype)
         except AttributeError:
-            return default
-    else:
-        if not (isinstance(expr.func, AssocOp) or expr.is_Pow):
-            return default
-        else:
-            # Infer expression dtype from its arguments
-            dtype = infer_dtype([sympy_dtype(a, default) for a in expr.args])
-            return dtype or default
+            pass
+    return infer_dtype(dtypes)
diff --git a/devito/symbolics/printer.py b/devito/symbolics/printer.py
index 7e720ae689..b3fecd47ec 100644
--- a/devito/symbolics/printer.py
+++ b/devito/symbolics/printer.py
@@ -12,7 +12,7 @@
 from sympy.printing.c import C99CodePrinter
 
 from devito.arch.compiler import AOMPCompiler
-from devito.symbolics.inspection import has_integer_args
+from devito.symbolics.inspection import has_integer_args, sympy_dtype
 from devito.types.basic import AbstractFunction
 
 __all__ = ['ccode']
@@ -89,6 +89,25 @@ def _print_Rational(self, expr):
         else:
             return '%d.0F/%d.0F' % (p, q)
 
+    def _print_math_func(self, expr, nest=False, known=None):
+        cls = type(expr)
+        name = cls.__name__
+        if name not in self._prec_funcs:
+            return super()._print_math_func(expr, nest=nest, known=known)
+
+        try:
+            cname = self.known_functions[name]
+        except KeyError:
+            return super()._print_math_func(expr, nest=nest, known=known)
+
+        dtype = sympy_dtype(expr)
+        if dtype is np.float32:
+            cname += 'f'
+
+        args = ', '.join((self._print(arg) for arg in expr.args))
+
+        return '%s(%s)' % (cname, args)
+
     def _print_Pow(self, expr):
         # Need to override because of issue #1627
         # E.g., (Pow(h_x, -1) AND h_x.dtype == np.float32) => 1.0F/h_x
@@ -255,6 +274,11 @@ def _print_Fallback(self, expr):
     _print_Basic = _print_Fallback
 
 
+# Lifted from SymPy so that we go through our own `_print_math_func`
+for k in ('exp log sin cos tan ceiling floor').split():
+    setattr(CodePrinter, '_print_%s' % k, CodePrinter._print_math_func)
+
+
 # Always parenthesize IntDiv and InlineIf within expressions
 PRECEDENCE_VALUES['IntDiv'] = 1
 PRECEDENCE_VALUES['InlineIf'] = 1
diff --git a/examples/performance/00_overview.ipynb b/examples/performance/00_overview.ipynb
index 7684d756ca..abe32cd8f1 100644
--- a/examples/performance/00_overview.ipynb
+++ b/examples/performance/00_overview.ipynb
@@ -208,7 +208,7 @@
       "      {\n",
       "        for (int z = z_m; z <= z_M; z += 1)\n",
       "        {\n",
-      "          u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 1][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 2][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 5][z + 4]/h_y) + (-8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 5][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 7][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 8][z + 4]/h_y) + (8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 1][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 3][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y) + (6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 3][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 6][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 7][z + 4]/h_y))*sin(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
+      "          u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 1][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 2][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 5][z + 4]/h_y) + (-8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 5][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 7][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 8][z + 4]/h_y) + (8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 1][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 3][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y) + (6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 3][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 6][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 7][z + 4]/h_y))*sinf(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
       "        }\n",
       "      }\n",
       "    }\n",
@@ -283,7 +283,7 @@
       "      {\n",
       "        for (int z = z_m; z <= z_M; z += 1)\n",
       "        {\n",
-      "          u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 1][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 2][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 5][z + 4]/h_y) + (-8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 5][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 7][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 8][z + 4]/h_y) + (8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 1][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 3][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y) + (6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 3][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 6][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 7][z + 4]/h_y))*sin(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
+      "          u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 1][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 2][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 5][z + 4]/h_y) + (-8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 5][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 7][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 8][z + 4]/h_y) + (8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 1][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 3][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y) + (6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 3][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 6][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 7][z + 4]/h_y))*sinf(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
       "        }\n",
       "      }\n",
       "    }\n",
@@ -341,7 +341,7 @@
       "          {\n",
       "            for (int z = z_m; z <= z_M; z += 1)\n",
       "            {\n",
-      "              u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 1][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 2][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 5][z + 4]/h_y) + (-8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 5][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 7][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 8][z + 4]/h_y) + (8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 1][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 3][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y) + (6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 3][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 6][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 7][z + 4]/h_y))*sin(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
+      "              u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 1][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 2][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 5][z + 4]/h_y) + (-8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 5][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 7][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 8][z + 4]/h_y) + (8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 1][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 3][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y) + (6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 3][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 6][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 7][z + 4]/h_y))*sinf(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
       "            }\n",
       "          }\n",
       "        }\n",
@@ -431,7 +431,7 @@
       "                  {\n",
       "                    for (int z = z0_blk1; z <= MIN(z_M, z0_blk1 + z0_blk1_size - 1); z += 1)\n",
       "                    {\n",
-      "                      u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 1][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 2][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 5][z + 4]/h_y) + (-8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 5][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 7][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 8][z + 4]/h_y) + (8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 1][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 3][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y) + (6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 3][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 6][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 7][z + 4]/h_y))*sin(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
+      "                      u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 1][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 2][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 5][z + 4]/h_y) + (-8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 5][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 7][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 8][z + 4]/h_y) + (8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 1][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 3][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y) + (6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 3][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 6][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 7][z + 4]/h_y))*sinf(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
       "                    }\n",
       "                  }\n",
       "                }\n",
@@ -479,7 +479,7 @@
    "source": [
     "### Code motion\n",
     "\n",
-    "The `advanced` mode has a code motion pass. In explicit PDE solvers, this is most commonly used to lift expensive time-invariant sub-expressions out of the inner loops. The pass is however quite general in that it is not restricted to the concept of time-invariance -- any sub-expression invariant with respect to a subset of `Dimension`s is a code motion candidate. In our running example, `sin(f)` gets hoisted out of the inner loops since it is determined to be an expensive invariant sub-expression. In other words, the compiler trades the redundant computation of `sin(f)` for additional storage (the `r0[...]` array)."
+    "The `advanced` mode has a code motion pass. In explicit PDE solvers, this is most commonly used to lift expensive time-invariant sub-expressions out of the inner loops. The pass is however quite general in that it is not restricted to the concept of time-invariance -- any sub-expression invariant with respect to a subset of `Dimension`s is a code motion candidate. In our running example, `sinf(f)` gets hoisted out of the inner loops since it is determined to be an expensive invariant sub-expression. In other words, the compiler trades the redundant computation of `sinf(f)` for additional storage (the `r0[...]` array)."
    ]
   },
   {
@@ -503,7 +503,7 @@
       "    {\n",
       "      for (int z = z_m; z <= z_M; z += 1)\n",
       "      {\n",
-      "        r0[x][y][z] = sin(f[x + 1][y + 1][z + 1]);\n",
+      "        r0[x][y][z] = sinf(f[x + 1][y + 1][z + 1]);\n",
       "      }\n",
       "    }\n",
       "  }\n",
@@ -567,9 +567,9 @@
       "         {\n",
       "           for (int z = z_m; z <= z_M; z += 1)\n",
       "           {\n",
-      "-            u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 1][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 2][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 5][z + 4]/h_y) + (-8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 5][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 7][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 8][z + 4]/h_y) + (8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 1][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 3][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y) + (6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 3][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 6][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 7][z + 4]/h_y))*sin(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
+      "-            u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 1][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 2][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 5][z + 4]/h_y) + (-8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 5][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 7][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 8][z + 4]/h_y) + (8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 1][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 3][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y) + (6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 3][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 6][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 7][z + 4]/h_y))*sinf(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
       "+            float r0 = 1.0F/h_y;\n",
-      "+            u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F*r0)*(8.33333333e-2F*r0*u[t0][x + 4][y + 1][z + 4] - 6.66666667e-1F*r0*u[t0][x + 4][y + 2][z + 4] + 6.66666667e-1F*r0*u[t0][x + 4][y + 4][z + 4] - 8.33333333e-2F*r0*u[t0][x + 4][y + 5][z + 4]) + (-8.33333333e-2F*r0)*(8.33333333e-2F*r0*u[t0][x + 4][y + 4][z + 4] - 6.66666667e-1F*r0*u[t0][x + 4][y + 5][z + 4] + 6.66666667e-1F*r0*u[t0][x + 4][y + 7][z + 4] - 8.33333333e-2F*r0*u[t0][x + 4][y + 8][z + 4]) + (8.33333333e-2F*r0)*(8.33333333e-2F*r0*u[t0][x + 4][y][z + 4] - 6.66666667e-1F*r0*u[t0][x + 4][y + 1][z + 4] + 6.66666667e-1F*r0*u[t0][x + 4][y + 3][z + 4] - 8.33333333e-2F*r0*u[t0][x + 4][y + 4][z + 4]) + (6.66666667e-1F*r0)*(8.33333333e-2F*r0*u[t0][x + 4][y + 3][z + 4] - 6.66666667e-1F*r0*u[t0][x + 4][y + 4][z + 4] + 6.66666667e-1F*r0*u[t0][x + 4][y + 6][z + 4] - 8.33333333e-2F*r0*u[t0][x + 4][y + 7][z + 4]))*sin(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
+      "+            u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F*r0)*(8.33333333e-2F*r0*u[t0][x + 4][y + 1][z + 4] - 6.66666667e-1F*r0*u[t0][x + 4][y + 2][z + 4] + 6.66666667e-1F*r0*u[t0][x + 4][y + 4][z + 4] - 8.33333333e-2F*r0*u[t0][x + 4][y + 5][z + 4]) + (-8.33333333e-2F*r0)*(8.33333333e-2F*r0*u[t0][x + 4][y + 4][z + 4] - 6.66666667e-1F*r0*u[t0][x + 4][y + 5][z + 4] + 6.66666667e-1F*r0*u[t0][x + 4][y + 7][z + 4] - 8.33333333e-2F*r0*u[t0][x + 4][y + 8][z + 4]) + (8.33333333e-2F*r0)*(8.33333333e-2F*r0*u[t0][x + 4][y][z + 4] - 6.66666667e-1F*r0*u[t0][x + 4][y + 1][z + 4] + 6.66666667e-1F*r0*u[t0][x + 4][y + 3][z + 4] - 8.33333333e-2F*r0*u[t0][x + 4][y + 4][z + 4]) + (6.66666667e-1F*r0)*(8.33333333e-2F*r0*u[t0][x + 4][y + 3][z + 4] - 6.66666667e-1F*r0*u[t0][x + 4][y + 4][z + 4] + 6.66666667e-1F*r0*u[t0][x + 4][y + 6][z + 4] - 8.33333333e-2F*r0*u[t0][x + 4][y + 7][z + 4]))*sinf(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
       "           }\n",
       "         }\n",
       "       }\n",
@@ -604,8 +604,8 @@
       "           for (int z = z_m; z <= z_M; z += 1)\n",
       "           {\n",
       "             float r0 = 1.0F/h_y;\n",
-      "-            u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F*r0)*(8.33333333e-2F*r0*u[t0][x + 4][y + 1][z + 4] - 6.66666667e-1F*r0*u[t0][x + 4][y + 2][z + 4] + 6.66666667e-1F*r0*u[t0][x + 4][y + 4][z + 4] - 8.33333333e-2F*r0*u[t0][x + 4][y + 5][z + 4]) + (-8.33333333e-2F*r0)*(8.33333333e-2F*r0*u[t0][x + 4][y + 4][z + 4] - 6.66666667e-1F*r0*u[t0][x + 4][y + 5][z + 4] + 6.66666667e-1F*r0*u[t0][x + 4][y + 7][z + 4] - 8.33333333e-2F*r0*u[t0][x + 4][y + 8][z + 4]) + (8.33333333e-2F*r0)*(8.33333333e-2F*r0*u[t0][x + 4][y][z + 4] - 6.66666667e-1F*r0*u[t0][x + 4][y + 1][z + 4] + 6.66666667e-1F*r0*u[t0][x + 4][y + 3][z + 4] - 8.33333333e-2F*r0*u[t0][x + 4][y + 4][z + 4]) + (6.66666667e-1F*r0)*(8.33333333e-2F*r0*u[t0][x + 4][y + 3][z + 4] - 6.66666667e-1F*r0*u[t0][x + 4][y + 4][z + 4] + 6.66666667e-1F*r0*u[t0][x + 4][y + 6][z + 4] - 8.33333333e-2F*r0*u[t0][x + 4][y + 7][z + 4]))*sin(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
-      "+            u[t1][x + 4][y + 4][z + 4] = pow(r0, 2)*(6.94444444e-3F*(u[t0][x + 4][y][z + 4] + u[t0][x + 4][y + 8][z + 4]) + 4.44444444e-1F*(u[t0][x + 4][y + 2][z + 4] + u[t0][x + 4][y + 6][z + 4]) + 1.11111111e-1F*(-u[t0][x + 4][y + 1][z + 4] + u[t0][x + 4][y + 3][z + 4] + u[t0][x + 4][y + 5][z + 4] - u[t0][x + 4][y + 7][z + 4]) - 9.02777778e-1F*u[t0][x + 4][y + 4][z + 4])*sin(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
+      "-            u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F*r0)*(8.33333333e-2F*r0*u[t0][x + 4][y + 1][z + 4] - 6.66666667e-1F*r0*u[t0][x + 4][y + 2][z + 4] + 6.66666667e-1F*r0*u[t0][x + 4][y + 4][z + 4] - 8.33333333e-2F*r0*u[t0][x + 4][y + 5][z + 4]) + (-8.33333333e-2F*r0)*(8.33333333e-2F*r0*u[t0][x + 4][y + 4][z + 4] - 6.66666667e-1F*r0*u[t0][x + 4][y + 5][z + 4] + 6.66666667e-1F*r0*u[t0][x + 4][y + 7][z + 4] - 8.33333333e-2F*r0*u[t0][x + 4][y + 8][z + 4]) + (8.33333333e-2F*r0)*(8.33333333e-2F*r0*u[t0][x + 4][y][z + 4] - 6.66666667e-1F*r0*u[t0][x + 4][y + 1][z + 4] + 6.66666667e-1F*r0*u[t0][x + 4][y + 3][z + 4] - 8.33333333e-2F*r0*u[t0][x + 4][y + 4][z + 4]) + (6.66666667e-1F*r0)*(8.33333333e-2F*r0*u[t0][x + 4][y + 3][z + 4] - 6.66666667e-1F*r0*u[t0][x + 4][y + 4][z + 4] + 6.66666667e-1F*r0*u[t0][x + 4][y + 6][z + 4] - 8.33333333e-2F*r0*u[t0][x + 4][y + 7][z + 4]))*sinf(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
+      "+            u[t1][x + 4][y + 4][z + 4] = pow(r0, 2)*(6.94444444e-3F*(u[t0][x + 4][y][z + 4] + u[t0][x + 4][y + 8][z + 4]) + 4.44444444e-1F*(u[t0][x + 4][y + 2][z + 4] + u[t0][x + 4][y + 6][z + 4]) + 1.11111111e-1F*(-u[t0][x + 4][y + 1][z + 4] + u[t0][x + 4][y + 3][z + 4] + u[t0][x + 4][y + 5][z + 4] - u[t0][x + 4][y + 7][z + 4]) - 9.02777778e-1F*u[t0][x + 4][y + 4][z + 4])*sinf(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
       "           }\n",
       "         }\n",
       "       }\n",
@@ -640,8 +640,8 @@
       "           for (int z = z_m; z <= z_M; z += 1)\n",
       "           {\n",
       "             float r0 = 1.0F/h_y;\n",
-      "-            u[t1][x + 4][y + 4][z + 4] = pow(r0, 2)*(6.94444444e-3F*(u[t0][x + 4][y][z + 4] + u[t0][x + 4][y + 8][z + 4]) + 4.44444444e-1F*(u[t0][x + 4][y + 2][z + 4] + u[t0][x + 4][y + 6][z + 4]) + 1.11111111e-1F*(-u[t0][x + 4][y + 1][z + 4] + u[t0][x + 4][y + 3][z + 4] + u[t0][x + 4][y + 5][z + 4] - u[t0][x + 4][y + 7][z + 4]) - 9.02777778e-1F*u[t0][x + 4][y + 4][z + 4])*sin(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
-      "+            u[t1][x + 4][y + 4][z + 4] = (r0*r0)*(f[x + 1][y + 1][z + 1]*f[x + 1][y + 1][z + 1])*(6.94444444e-3F*(u[t0][x + 4][y][z + 4] + u[t0][x + 4][y + 8][z + 4]) + 4.44444444e-1F*(u[t0][x + 4][y + 2][z + 4] + u[t0][x + 4][y + 6][z + 4]) + 1.11111111e-1F*(-u[t0][x + 4][y + 1][z + 4] + u[t0][x + 4][y + 3][z + 4] + u[t0][x + 4][y + 5][z + 4] - u[t0][x + 4][y + 7][z + 4]) - 9.02777778e-1F*u[t0][x + 4][y + 4][z + 4])*sin(f[x + 1][y + 1][z + 1]);\n",
+      "-            u[t1][x + 4][y + 4][z + 4] = pow(r0, 2)*(6.94444444e-3F*(u[t0][x + 4][y][z + 4] + u[t0][x + 4][y + 8][z + 4]) + 4.44444444e-1F*(u[t0][x + 4][y + 2][z + 4] + u[t0][x + 4][y + 6][z + 4]) + 1.11111111e-1F*(-u[t0][x + 4][y + 1][z + 4] + u[t0][x + 4][y + 3][z + 4] + u[t0][x + 4][y + 5][z + 4] - u[t0][x + 4][y + 7][z + 4]) - 9.02777778e-1F*u[t0][x + 4][y + 4][z + 4])*sinf(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
+      "+            u[t1][x + 4][y + 4][z + 4] = (r0*r0)*(f[x + 1][y + 1][z + 1]*f[x + 1][y + 1][z + 1])*(6.94444444e-3F*(u[t0][x + 4][y][z + 4] + u[t0][x + 4][y + 8][z + 4]) + 4.44444444e-1F*(u[t0][x + 4][y + 2][z + 4] + u[t0][x + 4][y + 6][z + 4]) + 1.11111111e-1F*(-u[t0][x + 4][y + 1][z + 4] + u[t0][x + 4][y + 3][z + 4] + u[t0][x + 4][y + 5][z + 4] - u[t0][x + 4][y + 7][z + 4]) - 9.02777778e-1F*u[t0][x + 4][y + 4][z + 4])*sinf(f[x + 1][y + 1][z + 1]);\n",
       "           }\n",
       "         }\n",
       "       }\n",
@@ -719,7 +719,7 @@
       "        #pragma omp simd aligned(f,u:32)\n",
       "        for (int z = z_m; z <= z_M; z += 1)\n",
       "        {\n",
-      "          u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*r0[y + 1][z] + (-8.33333333e-2F/h_y)*r0[y + 4][z] + (8.33333333e-2F/h_y)*r0[y][z] + (6.66666667e-1F/h_y)*r0[y + 3][z])*sin(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
+      "          u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*r0[y + 1][z] + (-8.33333333e-2F/h_y)*r0[y + 4][z] + (8.33333333e-2F/h_y)*r0[y][z] + (6.66666667e-1F/h_y)*r0[y + 3][z])*sinf(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
       "        }\n",
       "      }\n",
       "    }\n",
@@ -783,7 +783,7 @@
       "      {\n",
       "        for (int z = z_m; z <= z_M; z += 1)\n",
       "        {\n",
-      "          u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_x)*r0[x + 1][y + 2][z] + (-8.33333333e-2F/h_x)*r0[x + 4][y + 2][z] + (8.33333333e-2F/h_x)*r0[x][y + 2][z] + (6.66666667e-1F/h_x)*r0[x + 3][y + 2][z] + (-6.66666667e-1F/h_y)*r1[x + 2][y + 1][z] + (-8.33333333e-2F/h_y)*r1[x + 2][y + 4][z] + (8.33333333e-2F/h_y)*r1[x + 2][y][z] + (6.66666667e-1F/h_y)*r1[x + 2][y + 3][z])*sin(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
+      "          u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_x)*r0[x + 1][y + 2][z] + (-8.33333333e-2F/h_x)*r0[x + 4][y + 2][z] + (8.33333333e-2F/h_x)*r0[x][y + 2][z] + (6.66666667e-1F/h_x)*r0[x + 3][y + 2][z] + (-6.66666667e-1F/h_y)*r1[x + 2][y + 1][z] + (-8.33333333e-2F/h_y)*r1[x + 2][y + 4][z] + (8.33333333e-2F/h_y)*r1[x + 2][y][z] + (6.66666667e-1F/h_y)*r1[x + 2][y + 3][z])*sinf(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
       "        }\n",
       "      }\n",
       "    }\n",
@@ -854,7 +854,7 @@
       "        #pragma omp simd aligned(f,u:32)\n",
       "        for (int z = z_m; z <= z_M; z += 1)\n",
       "        {\n",
-      "          u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_x)*r0[x + 1][y][z] + (-8.33333333e-2F/h_x)*r0[x + 4][y][z] + (8.33333333e-2F/h_x)*r0[x][y][z] + (6.66666667e-1F/h_x)*r0[x + 3][y][z] + (-6.66666667e-1F/h_y)*r1[y + 1][z] + (-8.33333333e-2F/h_y)*r1[y + 4][z] + (8.33333333e-2F/h_y)*r1[y][z] + (6.66666667e-1F/h_y)*r1[y + 3][z])*sin(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
+      "          u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_x)*r0[x + 1][y][z] + (-8.33333333e-2F/h_x)*r0[x + 4][y][z] + (8.33333333e-2F/h_x)*r0[x][y][z] + (6.66666667e-1F/h_x)*r0[x + 3][y][z] + (-6.66666667e-1F/h_y)*r1[y + 1][z] + (-8.33333333e-2F/h_y)*r1[y + 4][z] + (8.33333333e-2F/h_y)*r1[y][z] + (6.66666667e-1F/h_y)*r1[y + 3][z])*sinf(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
       "        }\n",
       "      }\n",
       "    }\n",
@@ -918,7 +918,7 @@
       "      {\n",
       "        for (int z = z_m; z <= z_M; z += 1)\n",
       "        {\n",
-      "          u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 1][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 2][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 5][z + 4]/h_y) + (-8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 5][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 7][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 8][z + 4]/h_y) + (8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 1][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 3][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y) + (6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 3][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 6][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 7][z + 4]/h_y))*sin(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
+      "          u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 1][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 2][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 5][z + 4]/h_y) + (-8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 5][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 7][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 8][z + 4]/h_y) + (8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 1][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 3][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y) + (6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 3][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 6][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 7][z + 4]/h_y))*sinf(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
       "        }\n",
       "      }\n",
       "    }\n",
@@ -986,7 +986,7 @@
       "        #pragma omp simd aligned(f,u:32)\n",
       "        for (int z = z_m; z <= z_M; z += 1)\n",
       "        {\n",
-      "          u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*r0[y + 1][z] + (-8.33333333e-2F/h_y)*r0[y + 4][z] + (8.33333333e-2F/h_y)*r0[y][z] + (6.66666667e-1F/h_y)*r0[y + 3][z])*sin(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
+      "          u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*r0[y + 1][z] + (-8.33333333e-2F/h_y)*r0[y + 4][z] + (8.33333333e-2F/h_y)*r0[y][z] + (6.66666667e-1F/h_y)*r0[y + 3][z])*sinf(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
       "        }\n",
       "      }\n",
       "    }\n",
@@ -1025,7 +1025,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `lift` pass triggers CIRE for dimension-invariant sub-expressions. As seen before, this leads to producing one tensor temporary. By setting `cire-mingain` to a larger value, we can avoid a grid-size temporary to be allocated, in exchange for a trascendental function (`sin(...)`) to be computed at each iteration."
+    "The `lift` pass triggers CIRE for dimension-invariant sub-expressions. As seen before, this leads to producing one tensor temporary. By setting `cire-mingain` to a larger value, we can avoid a grid-size temporary to be allocated, in exchange for a trascendental function (`sinf(...)`) to be computed at each iteration."
    ]
   },
   {
@@ -1049,7 +1049,7 @@
       "      {\n",
       "        for (int z = z_m; z <= z_M; z += 1)\n",
       "        {\n",
-      "          u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 1][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 2][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 5][z + 4]/h_y) + (-8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 5][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 7][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 8][z + 4]/h_y) + (8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 1][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 3][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y) + (6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 3][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 6][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 7][z + 4]/h_y))*sin(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
+      "          u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 1][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 2][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 5][z + 4]/h_y) + (-8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 5][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 7][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 8][z + 4]/h_y) + (8.33333333e-2F/h_y)*(8.33333333e-2F*u[t0][x + 4][y][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 1][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 3][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 4][z + 4]/h_y) + (6.66666667e-1F/h_y)*(8.33333333e-2F*u[t0][x + 4][y + 3][z + 4]/h_y - 6.66666667e-1F*u[t0][x + 4][y + 4][z + 4]/h_y + 6.66666667e-1F*u[t0][x + 4][y + 6][z + 4]/h_y - 8.33333333e-2F*u[t0][x + 4][y + 7][z + 4]/h_y))*sinf(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
       "        }\n",
       "      }\n",
       "    }\n",
@@ -1113,7 +1113,7 @@
       "      {\n",
       "        for (int z = z_m; z <= z_M; z += 1)\n",
       "        {\n",
-      "          u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*r0[x][y + 1][z] + (-8.33333333e-2F/h_y)*r0[x][y + 4][z] + (8.33333333e-2F/h_y)*r0[x][y][z] + (6.66666667e-1F/h_y)*r0[x][y + 3][z])*sin(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
+      "          u[t1][x + 4][y + 4][z + 4] = ((-6.66666667e-1F/h_y)*r0[x][y + 1][z] + (-8.33333333e-2F/h_y)*r0[x][y + 4][z] + (8.33333333e-2F/h_y)*r0[x][y][z] + (6.66666667e-1F/h_y)*r0[x][y + 3][z])*sinf(f[x + 1][y + 1][z + 1])*pow(f[x + 1][y + 1][z + 1], 2);\n",
       "        }\n",
       "      }\n",
       "    }\n",
@@ -1222,7 +1222,7 @@
       "        #pragma omp simd aligned(f:32)\n",
       "        for (int z = z_m; z <= z_M; z += 1)\n",
       "        {\n",
-      "          r0[x][y][z] = sin(f[x + 1][y + 1][z + 1]);\n",
+      "          r0[x][y][z] = sinf(f[x + 1][y + 1][z + 1]);\n",
       "        }\n",
       "      }\n",
       "    }\n",
@@ -1336,7 +1336,7 @@
       "      #pragma omp simd aligned(f:32)\n",
       "      for (int z = z_m; z <= z_M; z += 1)\n",
       "      {\n",
-      "        r0[x][y][z] = sin(f[x + 1][y + 1][z + 1]);\n",
+      "        r0[x][y][z] = sinf(f[x + 1][y + 1][z + 1]);\n",
       "      }\n",
       "    }\n",
       "  }\n",
@@ -1505,7 +1505,7 @@
       "        #pragma omp simd aligned(f:32)\n",
       "        for (int z = z_m; z <= z_M; z += 1)\n",
       "        {\n",
-      "          r0[x][y][z] = sin(f[x + 1][y + 1][z + 1]);\n",
+      "          r0[x][y][z] = sinf(f[x + 1][y + 1][z + 1]);\n",
       "        }\n",
       "      }\n",
       "    }\n",
@@ -1646,7 +1646,7 @@
       "        #pragma omp simd aligned(f:32)\n",
       "        for (int z = z_m; z <= z_M; z += 1)\n",
       "        {\n",
-      "          r0[x][y][z] = sin(f[x + 1][y + 1][z + 1]);\n",
+      "          r0[x][y][z] = sinf(f[x + 1][y + 1][z + 1]);\n",
       "        }\n",
       "      }\n",
       "    }\n",
diff --git a/tests/test_symbolics.py b/tests/test_symbolics.py
index 3d3500c98e..79ecdd50f9 100644
--- a/tests/test_symbolics.py
+++ b/tests/test_symbolics.py
@@ -6,7 +6,8 @@
 
 from sympy import Expr, Symbol
 from devito import (Constant, Dimension, Grid, Function, solve, TimeFunction, Eq,  # noqa
-                    Operator, SubDimension, norm, Le, Ge, Gt, Lt, Abs, sin, cos, Min, Max)
+                    Operator, SubDimension, norm, Le, Ge, Gt, Lt, Abs, sin, cos,
+                    Min, Max)
 from devito.ir import Expression, FindNodes
 from devito.symbolics import (retrieve_functions, retrieve_indexed, evalrel,  # noqa
                               CallFromPointer, Cast, DefFunction, FieldFromPointer,
@@ -259,6 +260,18 @@ def test_integer_abs():
     assert ccode(Abs(i1 - .5)) == "fabs(i1 - 5.0e-1F)"
 
 
+def test_cos_vs_cosf():
+    a = dSymbol('a', dtype=np.float32)
+    assert ccode(cos(a)) == "cosf(a)"
+
+    b = dSymbol('b', dtype=np.float64)
+    assert ccode(cos(b)) == "cos(b)"
+
+    # Doesn't make much sense, but it's legal
+    c = dSymbol('c', dtype=np.int32)
+    assert ccode(cos(c)) == "cos(c)"
+
+
 def test_intdiv():
     a = Symbol('a')
     b = Symbol('b')

From 0f35ff95922360d37762043039672d8e0e4d29ae Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Sun, 14 Apr 2024 11:53:36 -0400
Subject: [PATCH 18/29] docker: fix oneapi setup

---
 docker/Dockerfile.devito |  2 +-
 docker/Dockerfile.intel  | 10 ++++++++--
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/docker/Dockerfile.devito b/docker/Dockerfile.devito
index aeda36d615..635e5d7899 100644
--- a/docker/Dockerfile.devito
+++ b/docker/Dockerfile.devito
@@ -26,7 +26,7 @@ RUN python3 -m venv /venv && \
     /venv/bin/pip install --no-cache-dir --upgrade pip && \
     /venv/bin/pip install --no-cache-dir jupyter && \
     /venv/bin/pip install --no-cache-dir wheel && \
-    eval "$MPI4PY_FLAGS /venv/bin/pip install --no-cache-dir mpi4py" && \
+    eval $MPI4PY_FLAGS /venv/bin/pip install --no-cache-dir mpi4py && \
     /venv/bin/pip install --no-cache-dir -e /app/devito[extras,mpi,tests] && \
     rm -rf ~/.cache/pip
 
diff --git a/docker/Dockerfile.intel b/docker/Dockerfile.intel
index 48757d9776..5b75f203b5 100644
--- a/docker/Dockerfile.intel
+++ b/docker/Dockerfile.intel
@@ -59,6 +59,8 @@ RUN apt-get update -y && apt-get dist-upgrade -y && \
             # Development packages
             libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev level-zero-dev
 
+ENV MPI4PY_FLAGS='. /opt/intel/oneapi/setvars.sh intel64'
+
 ##############################################################
 # ICC image
 # This is a legacy setup that is not built anymore but kept for reference
@@ -70,12 +72,13 @@ RUN apt-get update -y && apt-get install -y intel-oneapi-compiler-dpcpp-cpp-and-
     rm -rf /var/lib/apt/lists/*
 
 # Devito config
+ENV I_MPI_CC="icc"
+ENV I_MPI_CXX="icpc"
 ENV DEVITO_ARCH="icc"
 ENV DEVITO_LANGUAGE="openmp"
 ENV DEVITO_PLATFORM="intel64"
 # MPICC compiler for mpi4py
 ENV MPICC=mpiicc
-ENV MPI4PY_FLAGS='. /opt/intel/oneapi/setvars.sh && CFLAGS="-cc=icc"'
 
 ##############################################################
 # ICX OpenMP image
@@ -87,11 +90,12 @@ RUN apt-get update -y && apt-get install -y intel-oneapi-compiler-dpcpp-cpp inte
     rm -rf /var/lib/apt/lists/*
 
 # Devito config
+ENV I_MPI_CC="icx"
+ENV I_MPI_CXX="icpx"
 ENV DEVITO_ARCH="icx"
 ENV DEVITO_LANGUAGE="openmp"
 # MPICC compiler for mpi4py
 ENV MPICC=mpiicc
-ENV MPI4PY_FLAGS='. /opt/intel/oneapi/setvars.sh && CFLAGS="-cc=icx"'
 
 ##############################################################
 # ICX SYCL CPU image
@@ -102,6 +106,7 @@ FROM icx as cpu-sycl
 ENV DEVITO_ARCH="sycl"
 ENV DEVITO_LANGUAGE="sycl"
 ENV DEVITO_PLATFORM="intel64"
+ENV MPICC=sycl
 
 ##############################################################
 # ICX SYCL GPU image
@@ -112,3 +117,4 @@ FROM icx as gpu-sycl
 ENV DEVITO_ARCH="sycl"
 ENV DEVITO_LANGUAGE="sycl"
 ENV DEVITO_PLATFORM="intelgpuX"
+ENV MPICC=sycl

From 74577c2f4836d8242d26daecf743d341c72f4ac2 Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Sun, 14 Apr 2024 17:43:24 -0400
Subject: [PATCH 19/29] docker: make sure mpi4py version requirement is met
 using the requirement file

---
 docker/Dockerfile.devito | 4 ++--
 docker/Dockerfile.intel  | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docker/Dockerfile.devito b/docker/Dockerfile.devito
index 635e5d7899..7908497ca5 100644
--- a/docker/Dockerfile.devito
+++ b/docker/Dockerfile.devito
@@ -26,8 +26,8 @@ RUN python3 -m venv /venv && \
     /venv/bin/pip install --no-cache-dir --upgrade pip && \
     /venv/bin/pip install --no-cache-dir jupyter && \
     /venv/bin/pip install --no-cache-dir wheel && \
-    eval $MPI4PY_FLAGS /venv/bin/pip install --no-cache-dir mpi4py && \
-    /venv/bin/pip install --no-cache-dir -e /app/devito[extras,mpi,tests] && \
+    eval "$MPI4PY_FLAGS /venv/bin/pip install --no-cache-dir -r /app/devito/requirements-mpi.txt" && \
+    /venv/bin/pip install --no-cache-dir -e /app/devito[extras,tests] && \
     rm -rf ~/.cache/pip
 
 # Usefull utilities
diff --git a/docker/Dockerfile.intel b/docker/Dockerfile.intel
index 5b75f203b5..24e57ea147 100644
--- a/docker/Dockerfile.intel
+++ b/docker/Dockerfile.intel
@@ -59,7 +59,7 @@ RUN apt-get update -y && apt-get dist-upgrade -y && \
             # Development packages
             libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev level-zero-dev
 
-ENV MPI4PY_FLAGS='. /opt/intel/oneapi/setvars.sh intel64'
+ENV MPI4PY_FLAGS='. /opt/intel/oneapi/setvars.sh intel64 && '
 
 ##############################################################
 # ICC image

From f21f8ae79026d9b38abedd7c11572f78f612ba67 Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Mon, 15 Apr 2024 09:33:32 -0400
Subject: [PATCH 20/29] api: remove un-necessary extra eqs and temp for
 gridpoint precomputed

---
 devito/operations/interpolators.py          |  13 ++-
 devito/types/sparse.py                      |   2 +-
 docker/Dockerfile.intel                     |   2 -
 examples/userapi/07_sparse_operations.ipynb | 116 ++++++++------------
 4 files changed, 55 insertions(+), 78 deletions(-)

diff --git a/devito/operations/interpolators.py b/devito/operations/interpolators.py
index 3abf7669f5..8983b3acef 100644
--- a/devito/operations/interpolators.py
+++ b/devito/operations/interpolators.py
@@ -319,7 +319,7 @@ def _interpolate(self, expr, increment=False, self_subs={}, implicit_dims=None):
         rhs = Symbol(name='sum', dtype=self.sfunction.dtype)
         summands = [Eq(rhs, 0., implicit_dims=implicit_dims)]
         # Substitute coordinate base symbols into the interpolation coefficients
-        summands.extend([Inc(rhs, (_expr * self._weights).xreplace(idx_subs),
+        summands.extend([Inc(rhs, (self._weights * _expr).xreplace(idx_subs),
                              implicit_dims=implicit_dims)])
 
         # Write/Incr `self`
@@ -377,7 +377,7 @@ def _inject(self, field, expr, implicit_dims=None):
 
         # Substitute coordinate base symbols into the interpolation coefficients
         eqns = [Inc(_field.xreplace(idx_subs),
-                    (_expr * self._weights).xreplace(idx_subs),
+                    (self._weights * _expr).xreplace(idx_subs),
                     implicit_dims=implicit_dims)
                 for (_field, _expr) in zip(fields, _exprs)]
 
@@ -432,11 +432,12 @@ class PrecomputedInterpolator(WeightedInterpolator):
     _name = 'precomp'
 
     def _positions(self, implicit_dims):
-        if self.sfunction.gridpoints is None:
+        if self.sfunction.gridpoints_data is None:
             return super()._positions(implicit_dims)
-        # No position temp as we have directly the gridpoints
-        return [Eq(p, floor(k), implicit_dims=implicit_dims)
-                for (k, p) in self.sfunction._position_map.items()]
+        else:
+            # No position temp as we have directly the gridpoints
+            return[Eq(p, k, implicit_dims=implicit_dims)
+                   for (k, p) in self.sfunction._position_map.items()]
 
     @property
     def interpolation_coeffs(self):
diff --git a/devito/types/sparse.py b/devito/types/sparse.py
index 4741b97bd1..dfc193e72c 100644
--- a/devito/types/sparse.py
+++ b/devito/types/sparse.py
@@ -1154,7 +1154,7 @@ def _position_map(self):
         the position. We mitigate this problem by computing the positions
         individually (hence the need for a position map).
         """
-        if self.gridpoints is not None:
+        if self.gridpoints_data is not None:
             ddim = self.gridpoints.dimensions[-1]
             return OrderedDict((self.gridpoints._subs(ddim, di), p)
                                for (di, p) in zip(range(self.grid.dim),
diff --git a/docker/Dockerfile.intel b/docker/Dockerfile.intel
index 24e57ea147..73db50f7ea 100644
--- a/docker/Dockerfile.intel
+++ b/docker/Dockerfile.intel
@@ -106,7 +106,6 @@ FROM icx as cpu-sycl
 ENV DEVITO_ARCH="sycl"
 ENV DEVITO_LANGUAGE="sycl"
 ENV DEVITO_PLATFORM="intel64"
-ENV MPICC=sycl
 
 ##############################################################
 # ICX SYCL GPU image
@@ -117,4 +116,3 @@ FROM icx as gpu-sycl
 ENV DEVITO_ARCH="sycl"
 ENV DEVITO_LANGUAGE="sycl"
 ENV DEVITO_PLATFORM="intelgpuX"
-ENV MPICC=sycl
diff --git a/examples/userapi/07_sparse_operations.ipynb b/examples/userapi/07_sparse_operations.ipynb
index 362fb12063..20b55e455b 100644
--- a/examples/userapi/07_sparse_operations.ipynb
+++ b/examples/userapi/07_sparse_operations.ipynb
@@ -65,7 +65,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x1000 with 1 Axes>"
       ]
@@ -234,7 +234,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x1000 with 1 Axes>"
       ]
@@ -316,18 +316,17 @@
     {
      "data": {
       "text/plain": [
-       "Data([[0.       , 0.       , 0.       , 0.       , 0.       ],\n",
-       "      [1.       , 1.       , 1.       , 1.       , 1.       ],\n",
-       "      [2.       , 2.       , 2.       , 2.       , 2.       ],\n",
-       "      [3.       , 3.0000002, 3.       , 3.       , 3.       ],\n",
-       "      [4.       , 4.       , 4.       , 4.       , 4.       ],\n",
-       "      [5.       , 5.       , 5.       , 5.       , 4.9999995],\n",
-       "      [6.       , 6.0000005, 6.       , 6.       , 6.       ],\n",
-       "      [7.       , 7.       , 7.       , 7.       , 7.       ],\n",
-       "      [8.       , 8.       , 8.       , 8.       , 8.       ],\n",
-       "      [9.       , 9.       , 9.       , 9.       , 9.       ],\n",
-       "      [0.       , 0.       , 0.       , 0.       , 0.       ]],\n",
-       "     dtype=float32)"
+       "Data([[0., 0., 0., 0., 0.],\n",
+       "      [1., 1., 1., 1., 1.],\n",
+       "      [2., 2., 2., 2., 2.],\n",
+       "      [3., 3., 3., 3., 3.],\n",
+       "      [4., 4., 4., 4., 4.],\n",
+       "      [5., 5., 5., 5., 5.],\n",
+       "      [6., 6., 6., 6., 6.],\n",
+       "      [7., 7., 7., 7., 7.],\n",
+       "      [8., 8., 8., 8., 8.],\n",
+       "      [9., 9., 9., 9., 9.],\n",
+       "      [0., 0., 0., 0., 0.]], dtype=float32)"
       ]
      },
      "execution_count": 10,
@@ -357,7 +356,7 @@
      "data": {
       "text/plain": [
        "PerformanceSummary([(PerfKey(name='section0', rank=None),\n",
-       "                     PerfEntry(time=3.4e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
+       "                     PerfEntry(time=4.6e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
       ]
      },
      "execution_count": 11,
@@ -378,7 +377,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x1000 with 2 Axes>"
       ]
@@ -449,7 +448,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x1000 with 1 Axes>"
       ]
@@ -485,8 +484,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Eq(posx, floor((-o_x + s_coords(p_s, 0))/h_x))\n",
-      "Eq(posy, floor((-o_y + s_coords(p_s, 1))/h_y))\n",
+      "Eq(posx, (int)(floor((-o_x + s_coords(p_s, 0))/h_x)))\n",
+      "Eq(posy, (int)(floor((-o_y + s_coords(p_s, 1))/h_y)))\n",
       "Eq(sum, 0.0)\n",
       "Inc(sum, wsincrsx(p_s, rsx + 3)*wsincrsy(p_s, rsy + 3)*f(t, rsx + posx, rsy + posy))\n",
       "Eq(s(time, p_s), sum)\n"
@@ -501,27 +500,6 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Eq(posx, floor((-o_x + s_coords(p_s, 0))/h_x))\n",
-      "Eq(posy, floor((-o_y + s_coords(p_s, 1))/h_y))\n",
-      "Eq(sum, 0.0)\n",
-      "Inc(sum, wsincrsx(p_s, rsx + 3)*wsincrsy(p_s, rsy + 3)*f(t, rsx + posx, rsy + posy))\n",
-      "Eq(s(time, p_s), sum)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"\\n\".join([str(s) for s in s.interpolate(f).evaluate]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
    "outputs": [],
    "source": [
     "f.data.fill(0)\n",
@@ -530,7 +508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -544,20 +522,20 @@
      "data": {
       "text/plain": [
        "Data([[0.        , 0.        , 0.        , 0.        , 0.        ],\n",
-       "      [0.9925686 , 0.9952191 , 0.99392927, 0.9933772 , 0.9967097 ],\n",
-       "      [1.9851372 , 1.9904382 , 1.9878585 , 1.9867544 , 1.9934194 ],\n",
-       "      [2.9777079 , 2.9856577 , 2.9817874 , 2.9801316 , 2.9901292 ],\n",
-       "      [3.9702744 , 3.9808764 , 3.975717  , 3.9735088 , 3.9868388 ],\n",
-       "      [4.9628468 , 4.976095  , 4.9696455 , 4.966884  , 4.9835467 ],\n",
-       "      [5.9554157 , 5.9713154 , 5.963575  , 5.9602633 , 5.9802585 ],\n",
-       "      [6.9479847 , 6.9665327 , 6.957505  , 6.95364   , 6.976965  ],\n",
-       "      [7.940549  , 7.961753  , 7.951434  , 7.9470177 , 7.9736776 ],\n",
-       "      [8.933123  , 8.956971  , 8.945365  , 8.940394  , 8.9703865 ],\n",
+       "      [0.9967038 , 0.9920868 , 0.99406236, 0.9936394 , 0.99242455],\n",
+       "      [1.9934076 , 1.9841737 , 1.9881247 , 1.9872788 , 1.9848491 ],\n",
+       "      [2.9901118 , 2.9762604 , 2.9821863 , 2.9809184 , 2.9772737 ],\n",
+       "      [3.9868152 , 3.9683473 , 3.9762495 , 3.9745576 , 3.9696982 ],\n",
+       "      [4.9835196 , 4.9604344 , 4.9703097 , 4.968197  , 4.962124  ],\n",
+       "      [5.9802237 , 5.952521  , 5.9643726 , 5.961837  , 5.9545474 ],\n",
+       "      [6.976928  , 6.9446087 , 6.958436  , 6.9554768 , 6.9469705 ],\n",
+       "      [7.9736304 , 7.9366946 , 7.952499  , 7.9491153 , 7.9393964 ],\n",
+       "      [8.970336  , 8.9287815 , 8.946562  , 8.942754  , 8.931824  ],\n",
        "      [0.        , 0.        , 0.        , 0.        , 0.        ]],\n",
        "     dtype=float32)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -570,7 +548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -584,10 +562,10 @@
      "data": {
       "text/plain": [
        "PerformanceSummary([(PerfKey(name='section0', rank=None),\n",
-       "                     PerfEntry(time=5.3e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
+       "                     PerfEntry(time=3.9999999999999996e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -600,12 +578,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x1000 with 2 Axes>"
       ]
@@ -640,7 +618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -710,7 +688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -727,12 +705,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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dk27JbXp2wtpnPBo3boz+/ftj+fLlSEpKQuvWrREZGVnoZ6Cee+45+Pn5oU2bNvD19cXFixfx6aefonv37jle03X16lX06tULXbt2xeHDh6XbgDdq1Egay/SMyahRo5CcnIyVK1fCx8cH8fHxhZrP7Nmz8fvvv6N79+4IDAzE7du3sXz5clStWlV6ZnHgwIHYtGkTRo8ejX379qFNmzbIyMjAX3/9hU2bNknvfZSfypUrY/78+bh27Rpq1aqFjRs3IioqCl988QVUKhUAYOTIkfj8888xePBgnDhxAkFBQfjhhx9w8OBBLFmyRDo/w4cPR2JiIp599llUrVoVsbGxWLZsGRo3biy9/ig3TZs2xcaNGzFp0iQ0b94cnp6e6NmzZ677LliwAN26dUNoaCiGDRsm3ZJbo9GYvcdVYRXmPBMRlaiSvPUdEZFczpw5I/bv31/09/cXVSqV6OfnJ/bv3188e/Zsjn2L4pbcoiiKc+bMEatUqSI6ODiY3Z4bgKjVanPsHxgYKL7++utm227duiVqtVoxICBAmnenTp3EL774osD8hw8filqtVqxQoYLo6ekp9unTR4yOjhYBiB9++KG0n+m2yrndLvnJW3KLoiimpKSI48aNEytWrCh6eHiIPXv2FP/5559C3ZL7888/F9u1aydWrFhRdHFxEatXry5OnjxZTEpKypF54cIF8cUXXxS9vLzE8uXLi2PHjhVTUlLMxvvll1/Ehg0biq6urmJQUJA4f/588auvvspxO/TAwECxe/fuOeYTGRkp9u7dW6xcubLo7OwsVq5cWezfv7946dIls/3S0tLE+fPni/Xr1xddXFzE8uXLi02bNhXDw8PN5p6b9u3bi/Xr1xePHz8uhoaGiq6urmJgYKD46aef5tj31q1b4pAhQ0Rvb2/R2dlZbNCggdmtskVRFH/44QfxueeeE318fERnZ2exWrVq4qhRo8T4+Hhpn9xuyZ2cnCwOGDBALFeunAhAuj13brfkFkVR3LNnj9imTRvRzc1NVKvVYs+ePcULFy6Y7ZPX2jE9Vkzfg8KeZyKikiKIYj7v0EdERGVKVFQUmjRpgm+//RavvvpqSU8nV7NmzUJ4eDju3LkjvfGtPevQoQPu3r2b62WQRERUOvA1RUREZVRKSkqObUuWLIGDgwPatWtXAjMiIiIqnfiaIiKiMuqjjz7CiRMn0LFjRzg5OUm3bx45cqTst5EmIiIqzdgUERGVUa1bt0ZERATmzJmD5ORkVKtWDbNmzcJ7771X0lMjIiIqVSx+TdHvv/+OBQsW4MSJE4iPj8dPP/2EPn365Fuzf/9+TJo0CefPn0dAQACmTZuGwYMH2zBtIiIiIiKiomHxa4oePnyIRo0a5Xj/iLxcvXoV3bt3R8eOHREVFYUJEyZg+PDh2LVrl8WTJSIiIiIiKmo23X1OEIQCnyl65513sG3bNrO77rzyyit48OABdu7caW00ERERERFRkSj21xQdPnwYnTt3NtsWFhaGCRMm5FmTmppq9g7XmZmZSExMRMWKFa1+w0EiIiIiIrJ/oijCYDCgcuXKcHAomptpF3tTlJCQAF9fX7Ntvr6+0Ov1SElJgZubW46aefPmITw8vLinRkREREREduqff/5B1apVi2SsUnn3ualTp2LSpEnS50lJSahWrRouXbqEChUqWDSWqZP08vKy6lkmW+qZbX/ZRqMR+/btQ8eOHaFSqWTNVuo5V2o21xqz5crmWmO2XNlca8yWKzsxMRG1atWCl5eXxbV5KfamyM/PD7du3TLbduvWLajV6lyfJQIAFxcXuLi45NheoUIFVKxY0aJ8URTh5OQEjUZj9Tfc2npm21+20WiEu7s7KlasaNUPdHs9bmZzrTG77GZzrTFbrmyuNWbLlW1SlC+rKZqL8PIRGhqKyMhIs20REREIDQ0t7mgiIiIiIqICWdwUJScnIyoqClFRUQCybrkdFRWFuLg4AFmXvg0aNEjaf/To0YiJicHbb7+Nv/76C8uXL8emTZswceLEojkCIiIiIiIiG1jcFB0/fhxNmjRBkyZNAACTJk1CkyZNMGPGDABAfHy81CABQHBwMLZt24aIiAg0atQICxcuxJdffomwsLAiOgQiIiIiIiLrWfyaog4dOiC/tzZas2ZNrjWnTp2yNIqIiIhIcTIzM/H48WOrX+eRlpZmVb0ttUDWa4qcnJzw+PFjZGRkyJpdksfN7KLPVqlUcHR0tHhOtiiVd58jIiIiUhpRFBEfH4/ExESbfiHMzMzEvXv3ZK8VRRF+fn74559/rPol25ZsW+uZXfqyy5UrBz8/P9neo5RNEREREVEpkJCQgKSkJPj4+MDT09OqN6UURREZGRlwdHS06q/31tYCWb/kJicnWzV3W7NL8riZXbTZoiji0aNHuH37NgDA39/f4rlZg00RERERUQnLyMjAgwcPUKlSJZQrV67U/aJaGJmZmUhLS4OrqyubImbbVGt6257bt2/Dx8dHlkvpiv2W3ERERESUP6PRCABwd3cv4ZkQlQ6mx4LpsVHc2BQRERERlRJyvX6CqLST+7HApoiIiIiIiBSNTRERERERkQz2798PQRDw4MGDfPcLCgrCkiVLZJkTZWFTRERERERWuXPnDt544w1Uq1YNbm5uqF27Nrp27YqDBw+W9NRKpdatWyM+Ph4ajQZA1vt7ent759jvzz//xMiRI+WenqLZ1d3nRFHM941j86uxtK4o6pltn9nZx5E7W6nnXKnZ2ceRO1up51yp2dnHkTtbqefc0vrc9rU2vyjqC1vbt29fpKWlYc2aNQgODkZMTAyOHj2Ku3fv2rxm85OWlgZnZ2er623JtqVepVLB19c3132zf25qlGxdQ3LUFld2fj+7bJ1vbkp1U6TT6aDT6aR3RTYYDHBysmzKoigiOTkZgHUv2LKlntn2l52eng4A0Ov1XGvMLtZsrjVmy5XNtWYf2WlpacjMzJRuU2yLzMxMWWofPHiAAwcOIDIyEu3atYMoiihfvjzatWsHQRCk399UKhWWLVuGX3/9Fb/99hv8/f0xb9489O3bVxpr6tSp+Pnnn3Hjxg34+fmhf//+mDZtGlQqFQBg9uzZ2LJlC8aMGYMPP/wQsbGxSEtLw+bNmzFnzhxcuXIF7u7uaNy4MX788Ud4eHgAAFatWoUlS5bg6tWrCAoKglarxRtvvJHncXfq1An169cHAHz33XdQqVQYNWoUZs2aJX0v79+/j4kTJ2Lbtm1ITU1Fu3btsHDhQtSuXRsAEBsbi/Hjx+PgwYNIS0tDUFAQPvzwQ3Tr1g2//fYbOnfujDt37uD06dMYOnQoAEi3MJ8+fTpmzJiBGjVq4M0338T48eMBAHFxcZgwYQL27t0LBwcHhIWFYcmSJfD19UVmZqZ0fiZOnIhZs2bh/v376Nq1K1asWAEvLy8AyPVc/fDDD9LXrVGcay0jIwOZmZkwGAxITU01+5rBYLA6Ny+luinSarXQarXQ6/XQaDTw8vKSnm4sLFMnqdForP7BZm09s+0v23TbR7VaLf0glitbqedcqdlca8yWK5trzT6yHz9+jHv37sHR0REODg42NUZR/zxAbOJjBHt7oEm1chbXFzZbo9HA09MTv/zyC1q3bi09c5Pbe8/MmjUL8+bNw9KlS/HNN9/g1VdfRYMGDVC3bl0AWetz1apVCAgIwNmzZzFy5Eio1Wq8/fbbALKayytXruDnn3/G5s2b4ejoiNu3b+O1117D/Pnz8cILL+DBgwc4dOiQdP6+++47hIeHY9myZWjSpAlOnTqFkSNHwsvLC6+//nquxy0IAr755hsMHToUR48exfHjxzFq1CgEBgZixIgRAIDhw4fj77//xpYtW6BWqzFlyhT06dMH58+fh0qlwvjx45GWlobffvsNHh4euHDhAtRqtfS9NWW1bdsWixcvxsyZM/HXX38BADw9PaXzbzqOzMxM9O3bF56enti/fz/S09MxduxYvPrqq9i3b590fmJiYrB161Zs3boV9+/fx8svv4wFCxZg7ty5iI+PNztXBoMBBw4cgCAINjfhttTnV2s6X15eXnB1dTX7mumPPUWpVDdFTxIEwaofTqY6a2/tZ0s9s+0r21SjtONmNtcas8tuNteafWTntp812fO2X8Tnv8dIn49uH4Ip3eoWqjb7JUmFyVapVFizZg1GjBiBzz//HE8//TRatmyJQYMGoXHjxmb7vvTSS1JT8f7772PPnj349NNPsXz5cgDAtGnTpGfJgoODcenSJWzYsAHvvPOONJ+0tDR8/fXXqFSpEgDg5MmTSE9PR9++fVGtWjVkZGSgcePG0txnzZqFhQsXSs9IhYSE4OLFi/jiiy8wePDgXI8bAAICArBkyRIIgoA6derg3LlzWLJkCUaOHIm///4bv/zyCw4ePIjWrVsDAL799ltUq1YNP//8M/r164e4uDj07dsXDRs2BABUr149x3kVBAEuLi5S4+zn55fjnJvWz969e3H27FlcvXoVAQEBAICvv/4a9evXx59//omnn34aQNYzL2vWrJGe+Rk4cCD27t0LQRCQkJAgnavAwEAAQIMGDaRn86xZa5auF0tr8/vZZe3jMj+80QIRERFRGXAq7r5ZQwQAK36Lwam4+8WW2bdvX9y8eRO//PILwsLC8Mcff6BZs2ZYs2aN2X6hoaE5Pr948aL0+caNG9GuXTv4+/vD09MT06ZNQ1xcnFlNYGCg1BABQKNGjdCpUyc0aNAA/fr1w5dffon797OO9eHDh7hy5QqGDRsGT09P6eP999/HlStX8j2mVq1amf3SHRoair///hsZGRm4ePEinJyc0LJlS+nrFStWRK1ataTjGTduHN5//320adMGM2fOxJkzZwpxJvN28eJFBAQESA0RANSrVw/lypUzO4dBQUFml8L5+/vj9u3bAMzP1UsvvYSVK1dK54qysCkiIiIiKgOu3n1o0fai4urqii5dumDatGnYvXs3Xn/9dcycObPQ9YcPH8Zrr72Gbt26YevWrTh16hTee+89pKWlme1nep2QiaOjIyIiIrBjxw7UrVsXOp0OderUwdWrV6XXdK1cuRJRUVHSx7lz53DkyBHbDzofw4cPR0xMDAYOHIizZ8+iWbNmWLZsWbFmAshxeawgCNLrdrKfq3r16mHZsmXSuaIsbIqIiIiIyoBgbw+LtheXevXq4eFD80bsyUbkyJEj0uuJDh06hMDAQEydOhXNmjVDzZo1ERsbW6gsQRDQpk0bhIeH4/jx43B2dsZPP/0EX19fVK5cGTExMahRo4bZR3BwcL5jHj16NMdca9asCUdHR9StWxfp6elm+9y7dw+XLl1CvXr1pG0BAQEYPXo0fvzxR/zvf//DypUrc81ydnaWLmHLS926dfHPP//gn3/+kbZduHABDx48MMssSPZzderUKTg7O+Pnn38udH1ZZ1evKSIiIiKi3DWpVh6j2oWYXUL3RvsQNKlWvljy7t27h5deeglDhw5Fw4YN4eHhgQMHDmDBggXo3bu32b7ff/89mjVrhrZt2+K7777DsWPHsGrVKgBAzZo1ERcXh40bN6Jly5bYvn07fvrppwLzjx49isjISDz33HOoVKkSDh8+jDt37kjNVnh4OMaNGweNRoOuXbsiNTUVx48fx/379zFp0qQ8x42Li8OkSZMwatQonDx5EsuWLcPChQulufbu3Vt6HZWXlxemTJmCKlWqSMc8YcIEdOvWDbVq1cL9+/exb98+aU5PCgoKQnJyMiIjI9G4cWO4u7vD3d3dbJ/OnTujQYMGePXVV7FkyRKkp6djzJgxaN++PZo1a1ZgU/XkufLx8cHRo0fNzhWxKSIiIiIqM6Z0q4MudSshNvExQip5FFtDBGTdKa1ly5ZYvHgxrly5AqPRiCpVqmD48OF47733zPYNDw/Hhg0bMGbMGPj7+2P9+vXSsxy9evXChAkTMH78eKSmpqJ79+6YPn06Zs2alW++Wq3G77//jiVLlkCv1yMwMBAff/wxunXrBiDrMjZ3d3csWLAAkydPhoeHBxo0aIAJEybkO+6gQYOQkpKCFi1awNHREePHjzd7I9XVq1dj/Pjx6NGjB9LS0tCuXTv88ssv0uVrGRkZ0Gq1uH79OtRqNbp27YrFixfnmtW6dWuMHDkSr7zyCu7du4eZM2fmOG5BELBlyxa8+eabaNeuHRwcHNC1a1eLLsnL61x17dq10GOUdYJYHO9+VMRMt+S+e/cuKlasaFGtKIpISkqy6baa1tYz2/6yjUYjtm/fjueff96qW9fa63Ezm2uN2WU3m2vNPrIfP34svZeOSqXK9bbWhc023cXNmuO2thbIuvuZXq+HWq2Wbj0NZP1S/9NPP6FPnz7Fll1Ux92xY0c0btwYS5YskT3b3r7fxZ1tekwEBwfnuCX3vXv34O3tjaSkJKjVaovnnhu+poiIiIiIiBSNTRERERERESkaX1NERERERMXGDl6pIdm/f39JT4FKCJ8pIiIiIiIiRWNTREREREREimZXl8+JomjxU7CmGmufurWlntn2mZ19HLmzlXrOlZqdfRy5s5V6zpWanX0cubOVes4trc9tX1svO7OlntnMLuns/H52FcclmaW6KdLpdNDpdNKbUhkMBjg5WTZlURSRnJwMAFbfbtDaembbX3Z6ejqArNvAc60xuzizudaYLVc215p9ZKelpSEzM1O6TbEtMjMzS6TW9ItqRkaG1bf0tkVJHTezi6c2IyMDmZmZMBgMSE1NNfuawWCwOjcvpbop0mq10Gq10vsUeXl5QaPRWDSG6QFqy3sNWFvPbPvLNhqNALLe5Mya9/OwJVup51yp2VxrzJYrm2vNPrIfP36Me/fuwdHREQ4ODjY3RrbUW1trOm5r3/fGluyiqGd26co2PRa8vLxyvE+R6Y89RalUN0VPEgTBqgeZqc7aB6gt9cy2r2xTjdKOm9lca8wuu9lca/aRndt+tjRk1tTbUstsZhd1bX4/u6x9XOaHN1ogIiIiIkWrUaMGlixZYvM4QUFBRTIOyY9NERERERFZZfDgwejTp49FNYIg4Oeffy6W+chlzZo1KFeuXI7tf/75J0aOHCn/hOzIrFmz0KRJk5KeRg52dfkcERERERGQ9Xo5S18nV9wqVapU0lMotURRlG6eVhrxmSIiIiIiKhI9evTA+PHj8fbbb6NChQrw8/PDrFmzpK8HBQUBAF544QUIgiB9DgC//PILmjZtCldXV4SEhCA8PNzsBfWCIOCzzz5Dr1694OHhgblz52L//v0QBAHbtm1DkyZN4ObmhlatWuHcuXNm89q8eTPq168PFxcXBAUFYeHChfkex6JFi9CgQQN4eHggICAAY8aMke4ouH//fgwZMgRJSUkQBAEODg6YPXu2dHzZL5+Li4tD79694enpCbVajX79+uHWrVvS12fNmoWmTZvim2++QVBQEDQaDV555ZV8764WGxuLnj17onz58vD09ESjRo2wfft2ALk/g/Xzzz+bvQZn1qxZaNy4MT7//HMEBwfDw8MD/fr1Q1JSkrSP6RnA8PBwVKpUCWq1GqNHj0ZaWpq0T2pqKiZMmABfX1+4urqibdu2+PPPP6Wvm743O3bsQNOmTeHi4oJvv/0W4eHhOH36NFQqFRwcHLBmzZp8vxdyYVNEREREVJbcOA6c3gBcP14i8V9//TU8PDxw9OhRfPTRR5g9ezYiIiIAQPqlefXq1YiPj5c+P3DgAIYMGYJx48bhwoUL+Pzzz7FmzRrMnTvXbOxZs2bhhRdewNmzZzF06FBp+9tvv40FCxbg2LFjqFSpEnr27CndefHEiRPo168fXnnlFZw9exazZs3C9OnT8/1l3MHBAZ988gnOnz+PtWvXYu/evXj77bcBAK1bt8aSJUugVqsRHx+PmzdvYtKkSTnGyMzMRO/evZGYmIjffvsNERERiImJwcsvv2y2X0xMDLZs2YJff/0Vv/76K3777Td8+OGHec5Nq9UiNTUVv//+O86cOYMPPvgAnp6eee6fm8uXL+P777/HTz/9hB07duDUqVMYM2aM2T6RkZG4ePEi9u/fj/Xr1+PHH39EeHi49PW3334bP/30E9asWYOTJ0+iRo0aCAsLQ2Jiotk4U6ZMwYcffoiLFy+iS5cu+N///of69evjn3/+wc2bN3Ocj5LCy+eIiIiIyoqImXA6tPS/z9tMALqE57l7cWjYsCFmzpwJAKhZsyY+/fRTREZGokuXLtLlZeXKlYOfn59UM3v2bLz99tt4/fXXIQgCQkJCMGfOHLz99tvSWAAwYMAADBkyRPo8JiYGADBjxgx07twZjo6OWLt2LapWrYqffvoJ/fr1w6JFi9CpUydMnz4dAFCrVi1cuHABCxYswODBg3M9hgkTJkj/HRQUhPfffx+jR4/G8uXL4ezsLN1u3c/PL8/LwiIjI3H27FlcvXoVAQEBALIaxvr16+PPP/9E8+bNAWQ1T6tXr4ZarQYADBw4EJGRkTkaQpO4uDj07dsXDRo0gCiKCAwMtPi22I8fP8batWvh5+cHR0dHLFu2DN27d8fChQul74uzszO++uoruLu7o379+pg9ezYmT56MOXPmICUlBStWrMCqVavQrVs3CIKAlStXIiIiAqtWrcLkyZOlrNmzZ6NLly7S556ennBycpKyi+NOctbgM0VEREREZcH14xCyN0QAcHCJ7M8YNWjQwOxzf39/3L59O9+a06dP4/3334eXlxc8PT3h6emJESNGID4+Ho8ePZL2a9asWa71oaGh0n9XqFABtWvXxsWLFwEAFy9eRJs2bcz2b9OmDf7+++88X+OyZ88edOrUCVWqVIGXlxcGDhyIe/fumc2lIBcvXkRAQIDUEAFAvXr1UK5cOWluQFbT5eXlJX1e0PkaN24c3n//fbRp0wYzZ87EmTNnCj0nk2rVqqFKlSrS56GhocjMzER0dLS0rVGjRnB3dzfbJzk5Gf/88w+uXLkCo9GI1q1bS19XqVRo0aKF2bEBeX/PShs2RURERERlwb3Llm0vJk/e/EAQBGRmZuZbk5ycjJkzZ+LUqVOIiopCVFQUzp49i7///tvsjTs9PDyKZc7ZXbt2DT169EDDhg2xefNmnDhxAjqdDgDMXlNTVJyczC/cKuh8DR8+HDExMRg4cCDOnTuHVq1aYdmyZQCyLvvL/h5AwH9v4FxS5PieFQU2RURERERlQcUalm0vISqVKsczNE8//TSio6NRo0aNHB8ODgX/unrkyBHpv+/fv49Lly6hbt26AIC6devi4MGDZvsfPHgQtWrVyvWysxMnTiAzMxMLFy5Eq1atUKtWLdy8edNsH2dn5wLvpFa3bl38888/+Oeff6RtFy5cwIMHD1CvXr0Cjyk/AQEBGD16NDZv3oyJEyfiyy+/BJB19zuDwYCHDx9K+0ZFReWoj4uLMzumI0eOwMHBAbVr15a2nT59GikpKWb7eHp6IiAgANWrV4ezszMOHTokfd1oNOLPP/8s8NgKc+5KApsiIiIiorKgajOIrcebb2szEahaui5fCgoKQmRkJBISEnD//n0AwPTp06U7k50/fx4XL17Ehg0bMG3atEKNOWfOHOzduxfnzp3D4MGD4e3tLb1/0v/+9z9ERkZizpw5uHTpEtauXYtPP/0Ub731Vq5j1ahRA0ajEcuWLUNMTAy++eYbrFixIscxJCcnIzIyEnfv3s31srrOnTujQYMGePXVV3Hy5EkcO3YMgwYNQvv27W26pGzChAnYtWsXrl69ipMnT2L//v1SA9iyZUu4u7vj3XffxZUrV7Bu3bpcbyjh6uqKwYMH4/Tp0zhw4ADGjRuHfv36mb3OKy0tDcOGDcOFCxewfft2zJw5E2PHjoWDgwM8PDwwevRoTJkyBTt37sSFCxcwYsQIPHr0CMOGDct3/kFBQbh69SqioqJw9+5dpKamWn0uihKbIiIiIqKyoks40ofsgthnBTA8Eugyq6RnlMPChQsRERGBgIAA6U08w8LCsGXLFkRERKB58+Zo1aoVFi9ejMDAwEKNOW/ePEyaNAnNmjVDQkICtm7dCmdnZwBZz0Jt2rQJGzZswFNPPYUZM2Zg9uzZed5koVGjRli0aBHmz5+Pp556Ct999x3mzZtntk/r1q0xevRovPzyy/Dx8cHHH3+cYxxBELBlyxaUL18e7dq1Q+fOnRESEoKNGzdacLZyysjIgFarRd26ddGtWzfUrFlTuryvQoUK+Pbbb7F9+3Y0aNAA69evN7slukmNGjXwwgsvoFevXggLC0PDhg2xfPlys306deqEmjVrol27dnj55ZfRq1cvs7E+/PBDvPDCCxg0aBCefvppXL58Gbt27UL58uXznX/fvn3RtWtXdOnSBT4+Pli/fr1N56OoCOKTFx6WQnq9HhqNBnfu3EHFihUtqhVFEUlJSdJdQixlSz2z7S/baDRix44d6Natm8VvCGfPx81srjVml91srjX7yH78+DGuXbuGoKAgqFQqi+8mll1GRobV9bbUiqIIvV4PtVpt1XmzJnv//v149tlnkZiYCC8vrxI5blvr5c6eNWsWtmzZglOnTuVZO2TIEDx48AA//fRTkWZbUpv9MZH9dWUAcO/ePVSqVAlJSUnSXftsVapvya3T6aDT6aTrDg0GQ44XoxVEFEXpzbas/cFmbT2z7S/b9CZxer2ea43ZxZrNtcZsubK51uwjOy0tDZmZmTb/ggygwJsaFFet6e/sGRkZVp03a7JNNRkZGSV23LbWy50tiqJ0G/G8ajMzM/O81bgt2ZbUmuZnMBhyXGKX35vbWqtUN0VarRZarVZ6psjLywsajcaiMUwPUFv+2mNtPbPtL9t0hxa1Wm3VX1RtyVbqOVdqNtcas+XK5lqzj+zHjx/j3r17cHR0hIODg82NkS31tjxTZKq39r1nLM023YShKM6bPZ5za+oFQYAgCFJNbrUODg5m+xRVtiW1pu+pl5dXjmeKTH/sKUqluil6kumbaG2dtQ9QW+qZbV/ZphqlHTezudaYXXazudbsIzu3/WxpyKypt6W2pLI7duxo9syHnNlFUV8S2eHh4QgPD8+3NrebMxRFtiW1+f3ssvZxmR/eaIGIiIiIiBSNTRERERERESkamyIiIiKiUsLWF90TlRVyPxbs6jVFRERERGWRs7MzHBwcEB8fjwoVKsDV1VW6iYAlTK+tseZmB7bUAlm/xKalpeHx48cWz93W7JI8bmYXbbYoikhLS8OdO3fg4OAgvd9UcWNTRERERFTCHBwcEBwcjJs3byI+Pt6qhsgkMzPT6npbakVRREpKCtzc3KxuquzxuJldPNnu7u6oVq2aTfOzBJsiIiIiolLA2dkZ1apVw/379+Hu7m71X+8NBgO8vLys+uu9tbVA1u3ff//9d7Rr186q27/bkl2Sx83sos92dHSEk5NTsdxlLi9sioiIiIhKCUHIem8YV1dXq39RTU1Ntarellog6xfZ9PR0uLq6WtUU2ZJdksfNbPmziwNvtEBERERERIrGpoiIiIiIiBSNTRERERERESkamyIiIiIiIlI0NkVERERERKRodnX3OVEUIYqiVTWW1hVFPbPtMzv7OHJnK/WcKzU7+zhyZyv1nCs1O/s4cmcr9ZwrNTv7OHJnK/WcKzW7qJXqpkin00Gn0yEjIwMAYDAY4ORk2ZRFUURycjIAWH27QWvrmW1/2enp6QAAvV7PtcbsYs3mWmO2XNlca8yWK5trjdlyZRsMBotrClKqmyKtVgutVgu9Xg+NRgMvLy9oNBqLxjB1khqNxupvuLX1zLa/bKPRCABQq9VWvceCLdlKPedKzeZaY7Zc2VxrzJYrm2uN2XJlmxrwolSqm6InCYJg1Ykz1Vn75lC21DPbvrJNNUo7bmZzrTG77GZzrTFbztrsY8iZbWs9s+0r29r55oc3WiAiIiIiIkVjU0RERERERIrGpoiIiIiIiBSNTRERERERESkamyIiIiIiIlI0NkVERERERKRobIqIiIiIiEjR2BQREREREZGisSkiIiIiIiJFY1NERERERESKxqaIiIiIiIgUzamkJ2AJURQhiqJVNZbWFUU9s+0zO/s4cmcr9ZwrNTv7OHJnK/WcKzU7+zhyZyv1nCs1O/s4cmcr9ZwrNbuoleqmSKfTQafTISMjAwBgMBjg5GTZlEVRRHJyMgBAEASL52BLPbPtLzs9PR0AoNfrudaYXazZXGvMliuba43ZcmVzrTFbrmyDwWBxTUFKdVOk1Wqh1Wqh1+uh0Wjg5eUFjUZj0RimTlKj0Vj9Dbe2ntn2l200GgEAarUaKpVK1mylnnOlZnOtMVuubK41ZsuVzbXGbLmyTQ14USrVTdGTBEGw6sSZ6qyptbWe2faVbapR2nEzm2uN2WU3m2uN2XLWZh9Dzmxb65ltX9nWzjc/vNECEREREREpGpsiIiIiIiJSNDZFRERERESkaGyKiIiIiIhI0dgUERERERGRorEpIiIiIiIiRWNTREREREREisamiIiIiIiIFI1NERERERERKRqbIiIiIiIiUjQ2RUREREREpGhsioiIiIiISNGcSnoClhBFEaIoWlVjaV1R1DPbPrOzjyN3tlLPuVKzs48jd7ZSz7lSs7OPI3e2Us+5UrOzjyN3tlLPuVKzi1qpbop0Oh10Oh0yMjIAAAaDAU5Olk1ZFEUkJycDAARBsHgOttQz2/6y09PTAQB6vZ5rjdnFms21xmy5srnWmC1XNtcas+XKNhgMFtcUpFQ3RVqtFlqtFnq9HhqNBl5eXtBoNBaNYeokNRqN1d9wa+uZbX/ZRqMRAKBWq6FSqWTNVuo5V2o21xqz5crmWmO2XNlca8yWK9vUgBelUt0UPUkQBKtOnKnOmlpb65ltX9mmGqUdN7O51phddrO51pgtZ232MeTMtrWe2faVbe1888MbLRARERERkaKxKSIiIiIiIkVjU0RERERERIrGpoiIiIiIiBSNTRERERERESkamyIiIiIiIlI0NkVERERERKRobIqIiIiIiEjR2BQREREREZGisSkiIiIiIiJFY1NERERERESK5lTSE7CEKIoQRdGqGkvriqKe2faZnX0cubOVes6Vmp19HLmzlXrOlZqdfRy5s5V6zpWanX0cubOVes6Vml3USnVTpNPpoNPpkJGRAQAwGAxwcrJsyqIoIjk5GQAgCILFc7Clntn2l52eng4A0Ov1XGvMLtZsrjVmy5XNtcZsubK51pgtV7bBYLC4piCluinSarXQarXQ6/XQaDTw8vKCRqOxaAxTJ6nRaKz+hltbz2z7yzYajQAAtVoNlUola7ZSz7lSs7nWmC1XNtcas+XK5lpjtlzZpga8KJXqpuhJgiBYdeJMddbU2lrPbPvKNtUo7biZzbXG7LKbzbXGbDlrs48hZ7at9cy2r2xr55sf3miBiIiIiIgUjU0REREREREpGpsiIiIiIiJSNDZFRERERESkaGyKiIiIiIhI0dgUERERERGRorEpIiIiIiIiRWNTREREREREisamiIiIiIiIFI1NERERERERKRqbIiIiIiIiUjSnkp6AJURRhCiKVtVYWlcU9cy2z+zs48idrdRzrtTs7OPIna3Uc67U7OzjyJ2t1HOu1Ozs48idrdRzrtTsolaqmyKdTgedToeMjAwAgMFggJOTZVMWRRHJyckAAEEQLJ6DLfXMtr/s9PR0AIBer+daY3axZnOtMVuubK41ZsuVzbXGbLmyDQaDxTUFKdVNkVarhVarhV6vh0ajgZeXFzQajUVjmDpJjUZj9Tfc2npm21+20WgEAKjVaqhUKlmzlXrOlZrNtcZsubK51pgtVzbXGrPlyjY14EWpVDdFTxIEwaoTZ6qzptbWembbV7apRmnHzWyuNWaX3WyuNWbLWZt9DDmzba1ntn1lWzvf/PBGC0REREREpGhsioiIiIiISNHYFBERERERkaKxKSIiIiIiIkVjU0RERERERIrGpoiIiIiIiBSNTRERERERESkamyIiIiIiIlI0NkVERERERKRobIqIiIiIiEjR2BQREREREZGisSkiIiIiIiJFcyrpCVhCFEWIomhVjaV1RVHPbPvMzj6O3NlKPedKzc4+jtzZSj3nSs3OPo7c2Uo950rNzj6O3NlKPedKzS5qpbop0ul00Ol0yMjIAAAYDAY4OVk2ZVEUkZycDAAQBMHiOdhSz2z7y05PTwcA6PV6rjVmF2s21xqz5crmWmO2XNlca8yWK9tgMFhcU5BS3RRptVpotVro9XpoNBp4eXlBo9FYNIapk9RoNFZ/w62tZ7b9ZRuNRgCAWq2GSqWSNVup51yp2VxrzJYrm2uN2XJlc60xW65sUwNelEp1U/QkQRCsOnGmOmtqba1ntn1lm2qUdtzM5lpjdtnN5lpjtpy12ceQM9vWembbV7a1880Pb7RARERERESKxqaIiIiIiIgUjU0REREREREpGpsiIiIiIiJSNDZFRERERESkaGyKiIiIiIhI0dgUERERERGRorEpIiIiIiIiRbOqKdLpdAgKCoKrqytatmyJY8eO5bv/kiVLULt2bbi5uSEgIAATJ07E48ePrZowERERERFRUbK4Kdq4cSMmTZqEmTNn4uTJk2jUqBHCwsJw+/btXPdft24dpkyZgpkzZ+LixYtYtWoVNm7ciHfffdfmyRMREREREdnK4qZo0aJFGDFiBIYMGYJ69ephxYoVcHd3x1dffZXr/ocOHUKbNm0wYMAABAUF4bnnnkP//v0LfHaJiIiIiIhIDk6W7JyWloYTJ05g6tSp0jYHBwd07twZhw8fzrWmdevW+Pbbb3Hs2DG0aNECMTEx2L59OwYOHJhnTmpqKlJTU6XP9Xo9AMBoNMJoNFoyZYiiiPT0dBiNRgiCYFGtrfXMtr9s0/qydJ0VRbZSz7lSs7nWmC1XNtcas+XK5lpjtlzZ1qyxgljUFN29excZGRnw9fU12+7r64u//vor15oBAwbg7t27aNu2rXQCRo8ene/lc/PmzUN4eHiO7fv27YO7u7slUyaySkRERElPgRSCa43kwrVGcuFao+L26NGjIh/ToqbIGvv378cHH3yA5cuXo2XLlrh8+TLGjx+POXPmYPr06bnWTJ06FZMmTZI+1+v1CAgIQMeOHVGxYkWL8kVRhF6vh1qttroLtrae2faXbTQaERERgS5dukClUsmardRzrtRsrjVmy5XNtcZsubK51pgtV/a9e/csrimIRU2Rt7c3HB0dcevWLbPtt27dgp+fX64106dPx8CBAzF8+HAAQIMGDfDw4UOMHDkS7733Hhwccr6sycXFBS4uLjm2q1Qqqx5kTk5OUKlUVn/Dra1ntv1lm3CtMbu4s0241phd3NkmXGvMLu5sE641Zhd3tqXrqzAsutGCs7MzmjZtisjISGlbZmYmIiMjERoammvNo0ePcjQ+jo6OALJOCBERERERUUmy+PK5SZMm4fXXX0ezZs3QokULLFmyBA8fPsSQIUMAAIMGDUKVKlUwb948AEDPnj2xaNEiNGnSRLp8bvr06ejZs6fUHBEREREREZUUi5uil19+GXfu3MGMGTOQkJCAxo0bY+fOndLNF+Li4syeGZo2bRoEQcC0adNw48YNVKpUCT179sTcuXOL7iiIiIiIiIisZNWNFsaOHYuxY8fm+rX9+/ebBzg5YebMmZg5c6Y1UURERERERMXK4jdvJSIiIiIiKkvYFBERERERkaKxKSIiIiIiIkVjU0RERERERIrGpoiIiIiIiBSNTRERERERESkamyIiIiIiIlI0NkVERERERKRoVr15a0kRRRGiKFpVY2ldUdQz2z6zs48jd7ZSz7lSs7OPI3e2Us+5UrOzjyN3tlLPuVKzs48jd7ZSz7lSs4taqW6KdDoddDodMjIyAAAGgwFOTpZNWRRFJCcnAwAEQbB4DrbUM9v+stPT0wEAer2ea43ZxZrNtcZsubK51pgtVzbXGrPlyjYYDBbXFKRUN0VarRZarRZ6vR4ajQZeXl7QaDQWjWHqJDUajdXfcGvrmW1/2UajEQCgVquhUqlkzVbqOVdqNtcas+XK5lpjtlzZXGvMlivb1IAXpVLdFD1JEASrTpypzppaW+uZbV/ZphqlHTezudaYXXazudaYLWdt9jHkzLa1ntn2lW3tfPPDGy0QEREREZGisSkiIiIiIiJFY1NERERERESKxqaIiIiIiIgUjU0REREREREpGpsiIiIiIiJSNDZFRERERESkaGyKiIiIiIhI0dgUERERERGRorEpIiIiIiIiRWNTREREREREiuZU0hOwhCiKEEXRqhpL64qintn2mZ19HLmzlXrOlZqdfRy5s5V6zpWanX0cubOVes6Vmp19HLmzlXrOlZpd1Ep1U6TT6aDT6ZCRkQEAMBgMcHKybMqiKCI5ORkAIAiCxXOwpZ7Z9pednp4OANDr9VxrzC7WbK41ZsuVzbXGbLmyudaYLVe2wWCwuKYgpbop0mq10Gq10Ov10Gg08PLygkajsWgMUyep0Wis/oZbW89s+8s2Go0AALVaDZVKJWu2Us+5UrO51pgtVzbXGrPlyuZaY7Zc2aYGvCiV6qboSYIgWHXiTHXW1Npaz2z7yjbVKO24mc21xuyym821xmw5a7OPIWe2rfXMtq9sa+ebH95ogYiIiIiIFI1NERERERERKRqbIiIiIiIiUjQ2RUREREREpGhsioiIiIiISNHYFBERERERkaKxKSIiIiIiIkVjU0RERERERIrGpoiIiIiIiBSNTRERERERESkamyIiIiIiIlI0p5KegCVEUYQoilbVWFpXFPXMts/s7OPIna3Uc67U7OzjyJ2t1HOu1Ozs48idrdRzrtTs7OPIna3Uc67U7KJWqpsinU4HnU6HjIwMAIDBYICTk2VTFkURycnJAABBECyegy31zLa/7PT0dACAXq/nWmN2sWZzrTFbrmyuNWbLlc21xmy5sg0Gg8U1BSnVTZFWq4VWq4Ver4dGo4GXlxc0Go1FY5g6SY1GY/U33Np6ZttfttFoBACo1WqoVCpZs5V6zpWazbXGbLmyudaYLVc21xqz5co2NeBFqVQ3RU8SBMGqE2eqs6bW1npm21e2qUZpx81srjVml91srjVmy1mbfQw5s22tZ7Z9ZVs73/zwRgtERERERKRobIqIiIiIiEjR2BQREREREZGisSkiIiIiIiJFY1NERERERESKxqaIiIiIiIgUjU0REREREREpGpsiIiIiIiJSNDZFRERERESkaGyKiIiIiIhI0dgUERERERGRorEpIiIiIiIiRXMq6QlYQhRFiKJoVY2ldUVRz2z7zM4+jtzZSj3nSs3OPo7c2Uo950rNzj6O3NlKPedKzc4+jtzZSj3nSs0uaqW6KdLpdNDpdMjIyAAAGAwGODlZNmVRFJGcnAwAEATB4jnYUs9s+8tOT08HAOj1eq41ZhdrNtcas+XK5lpjtlzZXGvMlivbYDBYXFOQUt0UabVaaLVa6PV6aDQaeHl5QaPRWDSGqZPUaDRWf8OtrWe2/WUbjUYAgFqthkqlkjVbqedcqdlca8yWK5trjdlyZXOtMVuubFMDXpRKdVP0JEEQrDpxpjpram2tZ7Z9ZZtqlHbczOZaY3bZzeZaY7actdnHkDPb1npm21e2tfPND2+0QEREREREisamiIiIiIiIFI1NERERERERKRqbIiIiIiIiUjQ2RUREREREpGhsioiIiIiISNHYFBERERERkaKxKSIiIiIiIkVjU0RERERERIrGpoiIgOvHgUu7sv6Vs7aks4mIiIgAOJX0BIiohEXMBA4uBdwCgZRYoM14oEt48deWdDYRERHRv+yqKRJFEaIoWlVjaV1R1DPbPrOzjyN3tuzHff04cHApRAjSBw4uBer0AKo2K77aks7+F9ea8h7fSs3OPo7c2Uo950rNzj6O3NlKPedKzS5qpbop0ul00Ol0yMjIAAAYDAY4OVk2ZVEUkZycDAAQBMHiOdhSz2z7y05PTwcA6PV6Zay1+BjALRAiBCS7+GXVQ8za7lWz+GpLOvtfXGvKenwrNZtrjdlyZXOtMVuubIPBYHFNQUp1U6TVaqHVaqHX66HRaODl5QWNRmPRGKZOUqPRWP0Nt7ae2faXbTQaAQBqtRoqlUrW7BI5bv8QICU265kWAJqU2Kzmwj8EKOixZkttSWf/i2tNWY9vpWZzrTFbrmyuNWbLlW1qwItSqW6KniQIglUnzlRnTa2t9cy2r2xTjWKOO6B51mtxDi7970K0NhOythdnbUlnZ8O1xuyyns21xmw5a7OPIWe2rfXMtq9sa+ebH7tqioioGHQJz3otTnxM1jMtljQWttSWdDYRERHRv9gUEVHWzQm8alp06VmR1JZ0NhERERH4PkVERERERKRwbIqIiIiIiEjR2BQREREREZGisSkiIiIiIiJFY1NERERERESKxqaIiIiIiIgUjU0REREREREpGpsiIiIiIiJSNDZFRARcPw5c2pX1r5y1JZ1NREREBMCppCdARCUsYiZwcCngFgikxAJtxgNdwou/tqSziYiIiP5lV02RKIoQRdGqGkvriqKe2faZnX0cubNlP+7rx4GDSyFCkD5wcClQpwdQtVnx1ZZ09r+41pT3+FZqdvZx5M5W6jlXanb2ceTOVuo5V2p2USvVTZFOp4NOp0NGRgYAwGAwwMnJsimLoojk5GQAgCAIFs/Blnpm2192eno6AECv1ytjrcXHAG6BECEg2cUvqx5i1navmjbXJiYmomXLloiMjES1atVkzS6MsrbWvvrqK+zevRsbNmywqt6WbDnqmV161poc9cy2v2yuNWbLlW0wGCyuKUipboq0Wi20Wi30ej00Gg28vLyg0WgsGsPUSWo0Gqu/4dbWM9v+so1GIwBArVZDpVLJml0ix+0fAqTEZj3TAkCTEpvVXPiHAAU91gpRGx4ejt69e6NBgwZSWVxcHMaMGYN9eyPh6ZCGQY2c8U4PEZq0uDyze/fujaioKNy+fRvly5dH59Am+DD4Kvy9HKXss7fSMXbsPPx55nVUqlQJY8eOxdtvvy2NsWbNGgwdOtRsXBcXF8THx1t03hITEzFu3Dhs3boVgiCgb9++WLp0KTw9PfOtO3z4MKZNm4ajR4/C0dERjRo1wptvvmm21rZt24Y5c+bgzJkzcHV1Rfv27fHTTz9JYzg45HwZ6Lp16/DKK68AyPqZuXDhQpw5cwbPPPNMnnNR6uNbqdmK+7nG7BLL5lpjtlzZpga8KJXqpuhJgiBYdeJMddbU2lrPbPvKNtUo5rgDmme9Fufg0v8uRGszIWu7jbWPHj3CV199hV27dklzysjIQI8ePeDn54dDh48gfttHGPTBBmS6xGNh+7yzO3bsiHfffRf+/v64ceMG3nrrLbwUrcHBlwwQIMKQmomwjSI6P/8UVqz+DmfPnsXQoUNRvnx5jBw5Ujo/arUa0dHRNp231157DfHx8di9ezfu37+PcePGYdSoUVi3bl2eNYcPH0a3bt0wdepULFu2DE5OTjhx4gQcHByk7M2bN2PEiBH44IMP8OyzzyI9PR3nzp3LMa/Vq1cjLCwMer0earUa5cuXl/ZxcXHBgAEDsGzZMrRr1y7f41Di41up2Yr7ucZsrjVml/lsa+ebH7tqioioGHQJz3otTnxM1rM0hWmIClG7fft2uLi4oFWrVtK23bt348KFC9izZw98fX3RuPE6zBaq4Z0PPsG8L/fApXrrXGMmTpwo/XdgYCCmTJmCPn36wDjoIHA3Dt/tOYk0rMRXX30FZ2dn1K9fH1FRUVi0aJHUFAFZP0T9/Pykz0VRRFJSUqEP9+LFi9i5cyf+/PNPNG3aFElJSfjkk0/QvXt3fPzxx6hcuXKe8x83bhymTJkibQsJCcH27dsBZP3Fa/z48ViwYAGGDRsm7VOvXr0cY5UrVw5+fn5wc3PL9S9sPXv2RJcuXZCSkgI3N7dCHxsREZGS8ZbcRJR1c4JaYRbdpKCg2gMHDqBp06Zm2w4fPowGDRrA19dX2hb2ykgYHqbgfJJroeISExPx3XffoXXr1lAFtwJqheHIX/Fo164dnJ2d/xs3LAzR0dG4f/++tC05ORmBgYEICAhA7969cf78eYsO9fDhwyhXrhyaNfvvWDt37gwHBwccPXo015rbt2/j6NGj8PHxQevWreHr64v27dvj4MGD0j4nT57EjRs34ODggCZNmsDf3x/dunXDuXPncoyn1WpRqVIldOrUCV999VWOF5s2a9YM6enpec6HiIiIcmJTRETFIjY2NsczJwkJCWYNEQDp84SEhHzHe+edd+Dh4YGKFSsiLi4OW7ZssWjc2rVr46uvvsKWLVvw7bffIjMzE23atMGNGzcKfUwJCQnw8fEx2+bk5IQKFSrkOf+YmBgAwKxZszBixAjs3LkTTz/9NMLCwnDz5s0c+0ybNg2//vorypcvjw4dOiAxMVEaa/bs2di0aRN2796Nnj17QqvVYtmyZWZ57u7u0Gg0iI2NLfRxERERKR2bIiIqFikpKXB1LdyzP4UxefJknDp1Crt374ajoyMGDRpk0S05Q0NDMWjQIDRu3Bjt27fHjz/+iEqVKmHNmjVFNsfcZGZmAgBGjRqFIUOGoEmTJli8eDFq1aqFyMhIs33ee+899O3bF02bNsXq1ashCAK+//57aazp06ejTZs2aNKkCSZMmIDJkydjwYIFOTLd3Nzw6NGjYj0uIiKisoRNEREVC29vb7NL1wDAz88Pt27dMttm+jz7a33yGq9WrVro0qULNmzYgO3bt+PIkSNWj6tSqdCkSRPpWZrC8PPzw+3bt822paenIzExMc8cf39/ADlfH1SnTh3cuXMnz31cXFwQEhKCuLi4POfTsmVLXL9+HampqWbbExMTUalSpUIeFREREbEpIqIidSruPn48eR0+QbVx4cIFs6+Fhobi7NmzZo1FREQEvLy8cr2pQF5Mz6yYmoFWrVrh999/l24Haxq3du3aKF++fK5jZGRk4OzZswU2Y0/O/8GDBzhx4oS0be/evcjMzETLli1zrQkKCkLlypVz3PXu77//lhqXpk2bwsXFxWwfo9GIa9euITAwMM/5REVFoXz58nBxcZG2XblyBY8fP0aTJk0KfVxERERKx6aIiIrMhzsu4oXlhzBp02lsTKiIs+fOmz1b9Nxzz6FevXoYOHAgTp8+jV27dmH69OkYPny49Iv9sWPHUKdOHem1PkePHsWnn36KqKgoxMbGYu/evejfvz+qV6+O0NBQAMCAAQPg7OyMYcOG4fz589i4cSOWLl2KSZMmSdmzZ8/G7t27ERMTg5MnT+K1115DbGwsBg4cWOjjq1u3Lrp27YoRI0bg2LFjOHLkCN5880288sor0uunbty4gTp16uDYsWMAsu54N3nyZHzyySf44YcfcPnyZUyfPh3R0dHo3LkzgKz39Bg9ejRmzpyJ3bt3Izo6Gm+88QYA4KWXXgIAbN26FV9++SXOnTuHy5cvY9WqVZg3bx7efPNNszkeOHAAISEhqF69euG/cURERArHW3ITUZE4FXcfK37771I050pBcPIJwccr1mDu1Kxbajs6OuLXX3/FG2+8gdDQUHh4eGDQoEGYOnWqVPfo0SNER0dLz/q4u7vjxx9/xMyZM/Hw4UP4+/uja9eumDZtGlxcXPD48WNoNBrs3r0bWq0WTZs2hbe3N2bMmGF2O+779+9jxIgRSEhIQPny5dG0aVMcPHgQISEh0j6zZs3CmjVrcO3atTyP87vvvsPYsWPRuXNnCELWm7dmv9mB0WhEdHS02Wt6JkyYgMePH2PixIlITExEo0aNsGPHDrPbgS9YsABOTk4YOHAgUlJS0LJlS+zdu1d6pkulUkGn02HixIkQRRHBwcFYuHCh2TECwPr16zFixIhCfc+IiIgoC5siIioS1+4+zLFN06Y/vl75Gea8Mx4ODllPTAcGBkrvzwPkfK+gDh06mN1AoUGDBti7d2+umdn3a9iwIQ4cOJDn/BYvXozFixfnqM+effXqVXTo0CHPMQCgQoUKWLdunVT75HsFBQUF5XoDiClTppi9T5HRaDQ7DyqVCh9//DE+/vjjXHO7du2Krl27ms37yezz588jKioKmzZtyvcYiIiIyBybIiICrh+37s1bs9XWE7xzfMm9enO8WM8ZN27cQEBAQLFlW1X7BFEUsX//fvzxxx82jVOS4uPj8fXXX0Oj0ZT0VIiIiOwKmyIipYuYCRxcCrgFAimxQJvxQJdwi2trp8RiffBr6H+1m/TlN9qH4J1u3Ys92+LaXAiCYPfv7WN6jRIRERFZxq6aIlEULXpfkuw1ltYVRT2z7TM7+zhyZ8t+3NePAweXQoQgfeDgUqBOD6BqM4trW8V/i519X8AFh1oI8vZAk2rl855TEWcXujYbrjXlPb6Vmp19HLmzlXrOlZqdfRy5s5V6zpWaXdRKdVOk0+mg0+mQkZEBADAYDHBysmzKoigiOTkZAMyuvZejntn2l52eng4A0Ov1ylhr8TGAWyBECEh2ybo1tQAxa7tXTatq/cS78KveFADMXq8jR3aharPhWlPW41up2VxrzJYrm2uN2XJlGwwGi2sKUqqbIq1WC61WC71eD41GAy8vL4uvlTd1kk++IFmOembbX7bpjmdqtRoqlUrW7BI5bv8QICU265kWAJqU2Kzmwj8EKOixZkttSWf/i2tNWY9vpWZzrTFbrmyuNWbLlW1qwItSqW6KniQIglUnzlRnTa2t9cy2r2xTjWKOO6B51mtxDi7970K0NhMKd9MCW2pLOjsbrjVml/VsrjVmy1mbfQw5s22tZ7Z9ZVs73/zYVVNERMWgS3jWa3GsuYubLbUlnU1ERET0LzZFRJR1cwKvmhZdelYktSWdTURERATAoaQnQEREREREVJLYFBERERERkaKxKSIiIiIiIkVjU0RERERERIrGpoiIiIiIiBSNTRERERERESkamyIiIiIiIlI0NkVEBFw/DlzalfWvnLUlnU1EREQEvnkrEUXMBA4uBdwCgZRYoM14oEt48deWdDYRERHRv+yqKRJFEaIoWlVjaV1R1DPbPrOzjyN3tuzHff04cHApRAjSBw4uBer0AKo2K77aks7+F9ea8h7fSs3OPo7c2Uo950rNzj6O3NlKPedKzS5qpbop0ul00Ol0yMjIAAAYDAY4OVk2ZVEUkZycDAAQBMHiOdhSz2z7y05PTwcA6PV6Zay1+BjALRAiBCS7+GXVQ8za7lWz+GpLOvtfXGvKenwrNZtrjdlyZXOtMVuubIPBYHFNQUp1U6TVaqHVaqHX66HRaODl5QWNRmPRGKZOUqPRWP0Nt7ae2faXbTQaAQBqtRoqlUrW7BI5bv8QICU265kWAJqU2Kzmwj8EKOixZkttSWf/i2tNWY9vpWZzrTFbrmyuNWbLlW1qwItSqW6KniQIglUnzlRnTa2t9cy2r2xTjWKOO6B51mtxDi7970K0NhOythdnbUlnZ8O1xuyyns21xmw5a7OPIWe2rfXMtq9sa+ebH7tqioioGHQJz3otTnxM1jMtljQWttSWdDYRERHRv9gUEVHWzQm8alp06VmR1JZ0NhERERH4PkVERERERKRwbIqIiIiIiEjR2BQREREREZGisSkiIiIiIiJFY1NERERERESKxqaIiIiIiIgUjU0REREREREpGpsiIiIiIiJSNDZFRERERESkaGyKiIiIiIhI0ZxKegKWEEURoihaVWNpXVHUM9s+s7OPI3e2Us+5UrOzjyN3tlLPuVKzs48jd7ZSz7lSs7OPI3e2Us+5UrOLWqluinQ6HXQ6HTIyMgAABoMBTk6WTVkURSQnJwMABEGweA621DPb/rLT09MBAHq9nmuN2cWazbXGbLmyudaYLVc21xqz5co2GAwW1xSkVDdFWq0WWq0Wer0eGo0GXl5e0Gg0Fo1h6iQ1Go3V33Br65ltf9lGoxEAoFaroVKpZM1W6jlXajbXGrPlyuZaY7Zc2VxrzJYr29SAF6VS3RQ9SRAEq06cqc6aWlvrmW1f2aYapR03s7nWmF12s7nWmC1nbfYx5My2tZ7Z9pVt7XzzwxstEBERERGRorEpIiIiIiIiRWNTREREREREisamiIiIiIiIFI1NERERERERKRqbIiIiIiIiUjQ2RUREREREpGhsioiIiIiISNGsaop0Oh2CgoLg6uqKli1b4tixY/nu/+DBA2i1Wvj7+8PFxQW1atXC9u3brZowERERERFRUXKytGDjxo2YNGkSVqxYgZYtW2LJkiUICwtDdHQ0fHx8cuyflpaGLl26wMfHBz/88AOqVKmC2NhYlCtXrijmT0REREREZBOLm6JFixZhxIgRGDJkCABgxYoV2LZtG7766itMmTIlx/5fffUVEhMTcejQIahUKgBAUFCQbbMmIiIiIiIqIhY1RWlpaThx4gSmTp0qbXNwcEDnzp1x+PDhXGt++eUXhIaGQqvVYsuWLahUqRIGDBiAd955B46OjrnWpKamIjU1Vfpcr9cDAIxGI4xGoyVThiiKSE9Ph9FohCAIFtXaWs9s+8s2rS9L11lRZCv1nCs1m2uN2XJlc60xW65srjVmy5VtzRoriEVN0d27d5GRkQFfX1+z7b6+vvjrr79yrYmJicHevXvx6quvYvv27bh8+TLGjBkDo9GImTNn5lozb948hIeH59i+b98+uLu7WzJlIqtERESU9BRIIbjWSC5cayQXrjUqbo8ePSryMS2+fM5SmZmZ8PHxwRdffAFHR0c0bdoUN27cwIIFC/JsiqZOnYpJkyZJn+v1egQEBKBjx46oWLGiRfmiKEKv10OtVlvdBVtbz2z7yzYajYiIiECXLl2kyz3lylbqOVdqNtcas+XK5lpjtlzZXGvMliv73r17FtcUxKKmyNvbG46Ojrh165bZ9lu3bsHPzy/XGn9/f6hUKrNL5erWrYuEhASkpaXB2dk5R42LiwtcXFxybFepVFY9yJycnKBSqaz+hltbz2z7yzbhWmN2cWebcK0xu7izTbjWmF3c2SZca8wu7mxL11dhWHRLbmdnZzRt2hSRkZHStszMTERGRiI0NDTXmjZt2uDy5cvIzMyUtl26dAn+/v65NkRERERERERysvh9iiZNmoSVK1di7dq1uHjxIt544w08fPhQuhvdoEGDzG7E8MYbbyAxMRHjx4/HpUuXsG3bNnzwwQfQarVFdxRERERERERWsvg1RS+//DLu3LmDGTNmICEhAY0bN8bOnTulmy/ExcXBweG/XisgIAC7du3CxIkT0bBhQ1SpUgXjx4/HO++8U3RHQUREREREZCWrbrQwduxYjB07Ntev7d+/P8e20NBQHDlyxJooIiIiIiKiYmXx5XNERERERERlCZsiIiIiIiJSNDZFRERERESkaGyKiIiIiIhI0dgUERERERGRorEpIiIiIiIiRWNTREREREREisamiIiIiIiIFM2qN28tKaIoQhRFq2osrSuKembbZ3b2ceTOVuo5V2p29nHkzlbqOVdqdvZx5M5W6jlXanb2ceTOVuo5V2p2USvVTZFOp4NOp0NGRgYAwGAwwMnJsimLoojk5GQAgCAIFs/Blnpm2192eno6AECv13OtMbtYs7nWmC1XNtcas+XK5lpjtlzZBoPB4pqClOqmSKvVQqvVQq/XQ6PRwMvLCxqNxqIxTJ2kRqOx+htubT2z7S/baDQCANRqNVQqlazZSj3nSs3mWmO2XNlca8yWK5trjdlyZZsa8KJUqpuiJwmCYNWJM9VZU2trPbPtK9tUo7TjZjbXGrPLbjbXGrPlrM0+hpzZttYz276yrZ1vfnijBSIiIiIiUjQ2RUREREREpGhsioiIiIiISNHYFBERERERkaKxKSIiIiIiIkVjU0RERERERIrGpoiIiIiIiBSNTRERERERESkamyIiIiIiIlI0NkVERERERKRobIqIiIiIiEjRnEp6ApYQRRGiKFpVY2ldUdQz2z6zs48jd7ZSz7lSs7OPI3e2Us+5UrOzjyN3tlLPuVKzs48jd7ZSz7lSs4taqW6KdDoddDodMjIyAAAGgwFOTpZNWRRFJCcnAwAEQbB4DrbUM9v+stPT0wEAer2ea43ZxZrNtcZsubK51pgtVzbXGrPlyjYYDBbXFKRUN0VarRZarRZ6vR4ajQZeXl7QaDQWjWHqJDUajdXfcGvrmW1/2UajEQCgVquhUqlkzVbqOVdqNtcas+XK5lpjtlzZXGvMlivb1IAXpVLdFD1JEASrTpypzppaW+uZbV/ZphqlHTezudaYXXazudaYLWdt9jHkzLa1ntn2lW3tfPPDGy0QEREREZGisSkiIiIiIiJFY1NERERERESKxqaIiIiIiIgUjU0REREREREpGpsiIiIiIiJSNDZFRERERESkaGyKiIiIiIhI0dgUERERERGRorEpIiIiIiIiRWNTREREREREiuZU0hOwhCiKEEXRqhpL64qintn2mZ19HLmzlXrOlZqdfRy5s5V6zpWanX0cubOVes6Vmp19HLmzlXrOlZpd1Ep1U6TT6aDT6ZCRkQEAMBgMcHKybMqiKCI5ORkAIAiCxXOwpZ7Z9pednp4OANDr9VxrzC7WbK41ZsuVzbXGbLmyudaYLVe2wWCwuKYgpbop0mq10Gq10Ov10Gg08PLygkajsWgMUyep0Wis/oZbW89s+8s2Go0AALVaDZVKJWu2Us+5UrO51pgtVzbXGrPlyuZaY7Zc2aYGvCiV6qboSYIgWHXiTHXW1Npaz2z7yjbVKO24mc21xuyym821xmw5a7OPIWe2rfXMtq9sa+ebH95ogYiIiIiIFI1NERERERERKRqbIiIiIiIiUjQ2RUREREREpGhsioiIiIiISNHYFBERERERkaKxKSIiIiIiIkVjU0RERERERIrGpoiIiIiIiBSNTRERERERESkamyIiIiIiIlI0NkVERERERKRoTiU9AUuIoghRFK2qsbSuKOqZbZ/Z2ceRO1up51yp2dnHkTtbqedcqdnZx5E7W6nnXKnZ2ceRO1up51yp2UWtVDdFOp0OOp0OGRkZAACDwQAnJ8umLIoikpOTAQCCIFg8B1vqmW1/2enp6QAAvV7PtcbsYs3mWmO2XNlca8yWK5trjdlyZRsMBotrClKqmyKtVgutVgu9Xg+NRgMvLy9oNBqLxjB1khqNxupvuLX1zLa/bKPRCABQq9VQqVSyZiv1nCs1m2uN2XJlc60xW65srjVmy5VtasCLUqluip4kCIJVJ85UZ02trfXMtq9sU43SjpvZXGvMLrvZXGvMlrM2+xhyZttaz2z7yrZ2vvnhjRaIiIiIiEjR2BQREREREZGisSkiIiIiIiJFY1NERERERESKxqaIiIiIiIgUjU0REREREREpGpsiIiIiIiJSNDZFRERERESkaGyKiIiIiIhI0dgUERERERGRorEpIiIiIiIiRXMq6QlYQhRFiKJoVY2ldUVRz2z7zM4+jtzZSj3nSs3OPo7c2Uo950rNzj6O3NlKPedKzc4+jtzZSj3nSs0uaqW6KdLpdNDpdMjIyAAAGAwGODlZNmVRFJGcnAwAEATB4jnYUs9s+8tOT08HAOj1eq41ZhdrNtcas+XK5lpjtlzZXGvMlivbYDBYXFOQUt0UabVaaLVa6PV6aDQaeHl5QaPRWDSGqZPUaDRWf8OtrWe2/WUbjUYAgFqthkqlkjVbqedcqdlca8yWK5trjdlyZXOtMVuubFMDXpRKdVP0JEEQrDpxpjpram2tZ7Z9ZZtqlHbczOZaY3bZzeZaY7actdnHkDPb1npm21e2tfPND2+0QEREREREisamiIiIiIiIFI1NERERERERKRqbIiIiIiIiUjQ2RUREREREpGhsioiIiIiISNHYFBERERERkaKxKSIiIiIiIkVjU0RERERERIrGpoiIiIiIiBSNTRERERERESkamyIiIiIiIlI0p5KegCVEUYQoilbVWFpXFPXMts/s7OPIna3Uc67U7OzjyJ2t1HOu1Ozs48idrdRzrtTs7OPIna3Uc67U7KJWqpsinU4HnU6HjIwMAIDBYICTk2VTFkURycnJAABBECyegy31zLa/7PT0dACAXq/nWmN2sWZzrTFbrmyuNWbLlc21xmy5sg0Gg8U1BSnVTZFWq4VWq4Ver4dGo4GXlxc0Go1FY5g6SY1GY/U33Np6ZttfttFoBACo1WqoVCpZs5V6zpWazbXGbLmyudaYLVc21xqz5co2NeBFqVQ3RU8SBMGqE2eqs6bW1npm21e2qUZpx81srjVml91srjVmy1mbfQw5s22tZ7Z9ZVs73/zwRgtERERERKRobIqIiIiIiEjR2BQREREREZGisSkiIiIiIiJFY1NERERERESKxqaIiIiIiIgUjU0REREREREpGpsiIiIiIiJSNDZFRERERESkaGyKiKhkXT8OXNqV9a+ctSWdTaQEfHwTkZ1wKukJEJGCRcwEDi4F3AKBlFigzXigS3jx15Z0NpES8PFNRHbErpoiURQhiqJVNZbWFUU9s+0zO/s4cmcr6pxfPw4cXAoRgvSBg0uBOj2Aqs2Kr7aks//FtcZsObOzjyNLNh/fXGv8ucbsYs4uaqW6KdLpdNDpdMjIyAAAGAwGODlZNmVRFJGcnAwAEATB4jnYUs9s+8tOT08HAOj1eq614q6NjwHcAiFCQLKLX1Y9xKztXjWLr7aks//FtcZsubJLZK0p/PHNtcafa8wu3myDwWBxTUFKdVOk1Wqh1Wqh1+uh0Wjg5eUFjUZj0RimTlKj0Vj9Dbe2ntn2l200GgEAarUaKpVK1mzFnXP/ECAlNusvsQA0KbFZv3z4hwAFPc5tqS3p7H9xrTFbruwSWWsKf3xzrfHnGrOLN9vUgBelUt0UPUkQBKtOnKnOmlpb65ltX9mmGqUdd4lkBzTPulb/4NL/LlRpMyFre3HWlnT2v7jWmC1nbfYxZMlW+OMb4FpT0nEzu+TWWlGyq6aIiMqYLuFZ1+rHx2T9JdaSXzxsqS3pbCIl4OObiOwImyIiKllVm2Vdq2/hpbE215Z0NpES8PFNRHaC71NERERERESKxqaIiIiIiIgUjU0REREREREpGpsiIiIA9+7dg4+PD65du1bSU1GEKVOm4M033yzpaRAREQFgU0REBACYO3cuevfujaCgIGlbXFwcunfvDnd3d/j4+GDy5MkFvjdCr169UK1aNbi6usLf3x8DBw7EzZs3zfYRRRGLFi3CmDFj4OnpiSpVqmDu3Llm++zfvx9PP/00XFxcUKNGDaxZs8biY3r8+DG0Wi0qVqwIT09P9O3bF7du3Sp0/ejRoyEIApYsWWK2vX///ggMDMzzGK9du2Z2q1XTx5EjR6R93nrrLaxduxYxMTEWHxcREVFRY1NERIr36NEjrFq1CsOGDZO2ZWRkoHv37khLS8OhQ4ewdu1arFmzBjNmzMh3rI4dO2LTpk2Ijo7G5s2bceXKFbz44otm+4wfPx5fffUVBg8ejLNnz+KXX35BixYtpK9fvXoV3bt3R8eOHREVFYUJEyZg+PDh2LVrl0XHNXHiRGzduhXff/89fvvtN9y8eRP/93//V6jan376CUeOHEHlypVzfO2ZZ57Bxo0b8z1GANizZw/i4+Olj6ZNm0pf8/b2RlhYGD777DOLjomIiKg48JbcRKR427dvh4uLC1q1aiVt2717Ny5cuIA9e/bA19cXjRs3xpw5c/DOO+9g1qxZeb5b+8SJE6X/DgwMxJQpU9CnTx8YjUaoVCpcvHgRn332GU6dOoUrV64gODg4x1grVqxAcHAwFi5cCACoW7cu/vjjDyxevBhhYWGFOqakpCSsWrUK69atw7PPPgsAWL16NerWrYsjR46gbt26edbeuHEDb775Jnbt2oXu3bvn+PqYMWOkdyHP7RhNKlasCD8/P7Na07uYA0DPnj3x3nvvYcGCBYU6JqLc3Lt3D3Xr1sWxY8fMnuml4nHhwgU899xziI6OhoeHR0lPh6jI8JkiorLg+nHg0q6sf+WutzX75LfA7x9n/St39r/1B3ZsNnsWAwAOHz6MBg0awNfXV9oWFhYGvV6P8+fPFyo7MTER3333HVq3bi01C1u3bkVISAi2b9+OkSNHombNmhg+fDgSExPNsjt37mw2VlhYGA4fPlzoQztx4gSMRqPZOHXq1EG1atXyHSczMxMDBw7E5MmTUb9+/QJzcjtGk169esHHxwdt27bFL7/8kqO2RYsWuH79Ol/HVZbJ8LMlr0tfe3R+BpX9fOFbqWKhLn0FgG3btqFly5Zwd3NDULWqeKFrhxz7rFmzBg0bNoSrqyt8fHyg1WrNvr5p0yY0adIElStXRlBQkNVNv06nQ1BQEFxdXdGyZUscO3as0LWbN2+Gg4MD+vTpY7Z91qxZqFOnDjw8PFC+fHl07twZR48eNdvn448/Rps2beDu7o5y5crlGLtevXpo1aoVFi1aZM1hEZVabIqI7F3ETGBVF2DfB1n/RsyUr97W7JXPAlvfBC5uzfp35bPyZWerjzu0GZXFBLMvJyQkmDVEAKTPE7YvyDf7nXfegYeHBypWrIi4uDhs2bJF+lpMTAxiY2OxefNmjB8/Hl9++SVOnDhhdvlZXtl6vR4pKSmFOryEhAQ4Ozvn+KXG19cXCQkJuRcBmD9/PpycnDBu3Lh8x8/vGD09PbFw4UJ8//332LZtG9q2bYs+ffrkaIxMl+bFxsYW6pjIzsjwsyXPS1/bN0fataPYNSoQa8JSsWbl8gIvfd28eTMGDhyIIa28ETVChZ3Dq6C/+k+z7EWLFuG9997DlClTcP78eezZs8fs2dsdO3bg1VdfxahRo3Do0CHodDosXrwYn376aeGPHcCPP/6I//3vf5g5cyZOnjyJRo0aISwsDLdv3y6w9tq1a5gxYwaeeeaZHF+rVasWPv30U5w9exZ//PEHgoKC8Nxzz+HOnTvSPkajES+++CLeeOONPDOGDBmCzz77rFCNJpG9sKumSBRFfvCj2D/saq398yfEg0shQvjv4+DSrO3FXW9r9olvIN44aV5/42TWdpmP+1E64HLvQo763NYCAIgXtuSb/dZbb+HkyZPYtWsXHB0dMWjQIGRmZkIURWRkZCA1NRWrVq1C/fr10a5dO3z55ZfYt28f/vrrr3zXoKVrM6/55zXO8ePHsXTpUqxevVraJ6998zvGihUrYuLEiWjRogWaNWuGefPm4bXXXsOCBQvMxnB1dQUAPHz4sOQfS2X8Q/afazL9bNm2bRtcXFzQsmVLaduu7z7Fhdjb+PoFdzxV2R1dazpjdttM6D79BKmpqblmGo1GjB8/Hh9NHYNR5Q+iZkUn1PZ1w0v1XaTsxMRETJs2DWvXrkX//v0REhKCBg0aoGfPntI433zzDfr06YNRo0YhMDAQzz//PKZMmYL58+dLj4/CfOh0OgwfPhyDBw9G3bp18dlnn8Hd3R2rVq3Kty49PR2vvfYa3nnnHQQHB+f4vvfv3x+dOnVCcHAw6tWrh4ULF0Kv1+P06dPSPlOmTMGECRPw1FNP5bluOnfujMTEROzfv7/k1xo/FPtR1Er1a4p0Oh10Oh0yMjIAAAaDAU5Olk1ZFEUkJycDAARBsHgOttQz2/6yTX/10uv19rHW4mMAt0CIEJDskvXaDQFi1navmsVbb2v2jejc629EA9WTijf7iXqNVyxuG4GkbPXly5fHkSNHkJT031xMz2h4VqyCJDf3PLNVKhV8fX3h6+uLzz//HE899RT27NmDFi1aoEKFCnBycoKfnx+uXLkCvV4vPWNy8eJF+Pn5wdvbG//8849Z9rVr1+Dl5YW0tDSkpqYWuFZM+8bFxUGj0fx36PHx0Gg0udZHRETg9u3bCAwMlLZlZGTgrbfewuLFi3HmzBlpnXp6euZ5jLlp0KABdu/ejaSkJCnb9NdpNzc3s2PNiz3/bFHczzWZfrZERkaiYcOGZutn/2+/o56fK1wrBUv1reunQr/tLxw9ehQNGzbMEXnixAncuHEDqfp7aLQqFbeT01G3yg2839UH9f1cgfgYbDl/DpmZmbh8+TK0Wi2Sk5PRokULzJkzB1WrVgUAJCcnS+vZdN4A4Pr16zh37hyqVatW4KlLTU3F6dOnMXHiRLPjateuHQ4cOJDvMzjz5s1D+fLl8cILL+DIkSMwGo15PrbS0tLw+eefQ61WIzg4GElJSWbf75SUFIiimGd9gwYNsGfPHjRr1kzaZnf/D2W23WYbDAaLawpSqpsirVYLrVYLvV4PjUYDLy8vs/+5F4apkzS9KNhSttQz2/6yjUYjAECtVuf5Qvriyraq3j8ESImFiKz9NSmxWb88+IcAhXms2FJva3aV2sDJT3PWV6ld/NlP1Leo9BjfnUmDJlt9hw4dsHDhQqSmpsLHxwcAcOTIEai9PNBCfQvOKQ6Fyjb9QqFSqaDRaPDss8/io48+khoCtVqNCxcuAMi6Vl+j0aBt27bYsWOH2c+7gwcPonXr1tBoNIVaK+3atYNKpcKff/6Jvn37AgCio6Nx/fp1dOzYEZ6enjnqR4wYgR49epiN07VrV7z22msYMmRIntlPHmNuoqOjUblyZenrGo0GJ06cgEqlQsuWLeHm5pZrXXb2/LNFcT/XZPrZkpCQgGrVqpmtu/spmajsng5NSqxUX8MpE0BW05LbGjVdlvbR6p+xsK0DAsu5Yv6xdPT+/G9Ev+mJCv4hSIjcg8zMTCxZsgRLly6FRqPB9OnT8eKLL+L06dNwdnZG9+7dMWnSJBw/fhxPP/00bt26Jd1hMa/sJ924cQMZGRkIDg42279q1aqIiYnJc4w//vgD3333HU6ePAmVSiV9PLn/r7/+iv79++PRo0fw9/dHREQEQkJCAJh/v93c3CAIQp55AQEBuHXrltnX7e7/ocy22+ziuHTTri6fy+19L/jBj6L+sKu1FtAcQpvx2S8ygdBmQtb24q63NbvpQAhVnjavr9I0a7sMx33E/zWpNqy6E87dAR541pD2CQsLQ7169TBo0CCcOXMGu3fvxvTp06EdOw6u7SdAgIiT/zxEvU/1uFl7KISA5jh27Bh0Oh1Onz6NuLg47Nu3DwMGDED16tXRunVrCIKALl264Omnn8aoUaMQExODU6dOYfTo0ejSpQtq164NQRDwxhtvICYmBu+88w6io6Px2WefYdOmTZg4cWKh10a5cuUwbNgw/O9//8P+/ftx8uRJDB06FKGhoQgNDZX2q1u3Ln7++WcIggBvb280aNDA7EOlUsHf3x916tSBIAg4duwYVq5cme8xfv3119iwYQOio6MRHR2NefPmYfXq1XjzzTfN5vjHH3/gmWeegbu7e8k/lsr4h+w/12T62ZKSkiL98i59eFYCylUzr281Nt9zYPoF7b0Z4XhxxFtoVtkRupeqQhCAHx63gxDQHKKYdZndJ598gq5duyI0NBTr16/H33//jf3790MQBIwcORJjx45Fr1694Ovri9atW+OVV14BADg6Otr8/cpre3JyMgYNGoSVK1eiUqVK+e7/7LPPIioqCocOHULXrl3x8ssv486dOxavGzc3Nzx69Kjk1xo/FPtR1Er1M0VEVAhdwoE6PbIuLfEPAQKay1dva/aIvcCJb7IumatSG2g6UJbsU3H30f9qNzQRauIpJz3OlVPD0Xc5Pl6xBnOnZt1S29HREb/++iveeOMNhIaGwsPDA6+//jpmz54NODkBdXog5detiL43B8bQrJsSuLu748cff8TMmTPx8OFD+Pv7o2vXrpg2bRpcXFwAAA4ODti6dSu0Wi3effddaDQadOvWTbr9NgAEBwdj27ZtmDhxIpYuXYqqVaviyy+/NHtB97p166DVavO9rnrx4sVwcHBA3759kZqairCwMCxfvtxsn+jo6EJdumbi7u6OX3/9FfPnz8/zGAFgzpw5iI2NhZOTE+rUqYONGzfixRdfNJvvhg0bMGvWrEJnk52R4WeLt7c37t+/b7bNz88Px9K9gGEbpPpb6d4APshxi3gTf39/AFnP1qLtSKBOD7jExyDkhzmI82yUc59/VapUCd7e3oiLiwOQ1QzMnz8fc+fOxaVLl1C9enXs3bsXAKRnYwri7e0NR0fHHG+0fOvWrTznf+XKFVy7dg09e/aUtmVmZj075uTkhOjoaFSvXh0A4OHhgRo1aqBGjRpo1aoVatasiVWrVmHq1KmFmp9JYmKiNCZRWcCmiKgsqNos61p7Cy8vLZJ6W7Offi3rNUQyZl+9+xAAECVWxx0RuCECmjb98fXKzzDnnfFwcMh6Ej0wMBDbt2/PM7tt/5rIHB0u/cWqQYMG0i9A+alcuTI2bdqE7du34/nnn8/1MpMOHTrg1KlTeY4RGxuL9u3b55vj6uoqvTYzu+yNSUEvVn3ydtkNGjTAL7/8ku8lD6+//jpef/31fMfdsWMHHBwccn3TVypDiulny6m4+7h69yF8gmpj37Yfzb4WGhqKuXPn4rZzNbjUyqqPWLkSarXarKHJrmnTpnBxcUF0dDTatm0LVG0Go2sQrt0cJ73Grk2bNgCy/pBgeg1RYmIi7t69a/Y6PCDrjyqVK1eGs7Mz1q9fj9DQUFSqVKlQh+3s7IzGjRsjMjISL7zwAoCsBicyMhJjx47NtaZOnTo4e/YsgKzHtMFgwEcffQSDwYClS5ciICAgz7zMzEykpqYWam7ZnTt3jo9fKlPYFBGR4gR753zDQffqzfFiPWfcuHEj318gSos9e/bkeNbHnjx8+BCrV6+2+MXYRB/uuIgVv8UAANLuVMStc+dx//59lC9fHgDw3HPPSZe+Tps2DQ8fPsS0adOg1WqlZzOPHTuGQYMGITIyElWqVIFarcbo0aMxc+ZMBAQEoFq1avjggw8AAC+99BKArNtZ9+7dG+PHj8cXX3wBtVqNqVOnok6dOujYsSMA4O7du/jhhx/Qvn173L17F99//z2+//57/PbbbxYd45gxYzBmzBg0b94cLVq0wJIlS/Dw4UMMGTJE2mfQoEGoUqUK5s2bB1dXV7O7xSUlJUm34zdtf/jwIebOnYtevXrB398fd+/ehU6nw40bN6RjBIB//vkHV69eRVxcHDIyMhAVFQUAqFGjBjw9PQFk/bHkxo0bOd5Pjcie8f9GRKQ4TaqVx+j2Ifj8tyvStjfah+Cdbt1LcFaWiYyMtPjGM6XJiy++WCzXhFPZdiruvtQQAYBzpSA4+YTkeelrWFiY+aWv/3r06BGio6OlGwMAwIIFC+Dk5ISBAwciJSUFTZs2RWRkpNRsAcDXX3+NiRMnonv37nBwcED79u2xc+dOs2d7165di7feeguiKCI0NBT79+83uyvjtWvXEBwcjH379qFDhw65Huf//d//4eHDh5gxYwYSEhLQuHFj7Ny50+z9y+Li4qRntQvD0dERf/31F9auXYu7d++iYsWKaN68OQ4cOGD2Rs3z5s3D+vXrpc+bNGkCAGbzXb9+PZ577rkcz5AR2TM2RUSkSFO61cVz9XxxLf4Ogvwr4enACiU9JSIqgOnS1+zyuvR127ZtSEpKyvVSzw4dOuS4dFSlUuHjjz/Gxx9/LD3b8uQfHtRqNVatWoVVq1blOj9vb28cPnzYrP7J7KtXr6JcuXJo1KhRvsc6duxYvPnmm3l+ff/+/fnWr1692izb1dUVP/74Yz4VWZYvX47vvvsuzz9apKWlYcWKFVi3bl2BYxHZEzZFRKRYTaqVR4jGwa6fcSFSkrJw6ev27dvx7rvvmj0DZU/i4uLw7rvvSq+xIior2BQRERGRXTBd+pr9Ejp7u/R1wYIFJT0Fm5juXEdU1rApIiIiIrsxpVtdhNX3w9W7DxHs7YEm1ezzGRciKl3YFBEREZFdaVKtPJshIipShb9tCRERERERURnEpoioLLh+HLi0K+tfuettzT75LfD7x1n/yp1dksdNpAQl+Rjj45uILMDL54jsXcRM4OBSwC0QSIkF2owHuoTbXH/v3j3UrVsXx44dQ1BQUPFkr3wWuHEyq/7kp8DJ1cCIvTbNu9Bsqbc1OxcrVqzAtm3bsHXrVpvGISo1SvIxVsoe30RU+tnVM0WiKPKDH8X+YVdr7Z8/IR5cChHCfx8Hl2Ztt7H+/fffR69evRAYGCjtHxsbi+7du8Pd3R0+3hXw1swPYcxEntlXr17F0KFDERwcDDc3N1SvXh0zZsxAamoqxBPfQLxxEiIE7LlkQOiXyfAauw+VyqvRt29fXL16VRpn8+bN6NKlCypVqgS1Wo3Qpg2wc+3HFh93SkoKxowZg4rlNfDsPht9Nz3CLUN6oetHjRoFQRCweOFHZtn39izGqy90hVqtRrly5TB06FAYDAaz2o0bN6Jx48Zwd3dHYGAgPv74Y7O1NmTIEJw8eRK///57ya8rfpS5D9l/rtnys6kYf64VezY/7Ov/ofyw64+iVqqfKdLpdNDpdMjIyAAAGAwGODlZNmVRFJGcnAwAVr17ui31zLa/7PT0dACAXq+3j7UWHwO4BUKEgGQXv6xaiFnbvWpaXf/o6kWsWrUKmzdvRlJSEgAgIyMD3bp1g6+vL3bt2oWEE9vwxrSPkenigom9c88+ceIEUlNTsXDhQoSEhODChQuYMGEC7t+/jzldNIBbIK4lpuG1ry9hTFtvfDagPPT+bfDuD3+hT58++O233wAAe/bsQdu2bTF16lRoNBp8t2wOen17HhFjqiAkpPDHPWnSJOzevRur546D+uIGTP7lJl797iYiRgcWWP/rr7/i0KFD8K9UHo9Vrkhy85HO2cvrY3BL/As//vgjjEYjxo4diyFDhuDLL78EAEREROC1117D/Pnz8eyzzyI6Ohrjx49Hr1690KZNG2mt/d///R8WLVqEhg0b5vtts+fHGLMV8nPNlp9NxfRzTZbsf3Gt2cn/Q5ltt9kGg8HimoKU6qZIq9VCq9VCr9dDo9HAy8vL4jdZNHWSub2rdHHXM9v+so1GI4Csdy1XqVSyZltV7x8CpMRCRNb+mpTYrP+B+4cAhXms5FEfceEmXF1d0blzZ2nXHTt2IDo6Gnv37oWvry8Q5Iq7e5Ziyp7beKfzTWiM/+TI7tu3L/r27SuN0ahRI1y/fh0rVqzAJ4PDgZOf4tJVIzIyM/FRu1Q4CglAuw54p3pv9OnTB+7u7lCpVFi+fLnZtJ+e8x52bd+K/Wf/QcMq7oU67qSkJHz77bf47rvv0LNVEHB7Gdb2dER93QNc/DsdoVUd86y/ceMGpkyZgp07d6JHt+fgarwPTUoKAOBmXAwiLxlwbOsyNOvUCQDw6aefonv37li6dCkqV66Mn376CX369MHEiROl83Dz5k3MnTsXy5Ytg7OzMwDgxRdfxHPPPQdnZ2e4ubnl+W2z58cYsxXyc82Wn03F9HNNlux/ca3Zyf9DmW232aYGvCjZ1eVzgiDwgx/F/mFXay2gOYQ247Nf6AGhzYSs7TbU/3ExHk2bNjXb98iRI2jQoAH8/Pyk2q79RkCfCkTfelzobL1ejwoVKkBoOhBClafRzN8RDoKANafSkOn/NPQ1euHbb79F586d4ezsnOsYYpWmMMATFdxQ6OM+efIkjEYjunTpIh13XW8HVC2nwpF/0vOsF0URgwYNwuTJk/HUU08Bjs4QgttJuUeup6Ocpyua93hdqunSpQscHBxw7NgxCIKA1NRUuLq6mo3r7u6Oe/fuIS4uTtrWvHlzpKenS3X84EdRfcj+c82Wn03F9HNNlmx+2Nf/Q/lh1x9FrVQ/U0REhdAlHKjTI+vyDv8QIKC5zfWxuj6oXLmy2W4JCQlZzxBl4/vih8Dry3Ar5P+Avn0LzL58+TKWLVsmvZ4GI/Yi+MQ3+NF3N4Yu/AWjt/+OjNHlEBoaiu3bt+c5zscff4zkdEf0m/szYHxQqONOSEiAs7MzypUrZ3bcPt/3R0JIa6DLrFzr5s+fDycnJ4wbN+6/jbW7AX3fB+JjkJD4G3z+ijSrcXJyQoUKFZCQkAAACAsLw8SJEzF48GB07NgRly9fxuLFi6V51ayZdVmOu7s7NBoNYmNj8z0WIrtgy8+mYvi5Jls2EdklNkVEZUHVZlnXu1t4eWle9SkpKXB1dS18fZWmWWPk48aNG+jatSteeukljBgxQtqeULkzxn8VjkFDR2LAgAEwGAyYMWMGXnzxRUREROT4a9C6desQHh6OLVu2wKdxp6zXPNly3K4awMsv1y+fOHECS5cuxcmTJ3P+Vcp0ztSXC4wZMWIErly5gh49esBoNEKtVmPs2LGYM2cOHBzMn7B3c3PDo0ePrDseotLGlp9NRfxzTdZsIrI7dnX5HBHJw9vbG/fv3zfb5ufnh1u3bpltM33+5DNIT7p58yY6duyI1q1b44svvjD7mk6ng1qtxkcffYQmTZqgXbt2+PbbbxEZGYmjR4+a7bthwwYMHz4cmzZtMnu9U2H4+fkhLS0NDx48MNt++/Zt+Pnl3hQdOHAAt2/fRrVq1eDk5AQnJyfExsbif//7H4KDg6Vxb9++bVaXnp6OxMREaVxBEDB//nwkJycjNjYWCQkJaN4866/PpnFMEhMTUalSJYuOjYiIiGzDpoiIJKfi7uPHk9fhE1QbFy5cMPtaaGgozp49a9YAREREQK1Wo3bt2nmOeePGDXTo0AFNmzbF6tWrczwz8ujRoxzbHB0dAQCZmZnStvXr12PIkCFYv349unfvbvGxNW3aFCqVCpGR/13qFh0djevXryM0NDTXmoEDB+LMmTOIioqSPipXrozJkydj586dALLOy4MHD3DixAmpbu/evcjMzETLli1zHFeVKlXg7OyMjRs3onbt2mYN0JUrV/D48WM0adLE4uMjIiIi6/HyOSICAKw5eBVLD9yACAFpdyri1rnzuH//PsqXLw8AeO6551CvXj0MHDgQH330ERISEjBt2jSMGTMGLi4uAIBjx45h0KBBiIyMRJUqVaSGyPS+PHfu3JHyTM+idO/eHUuWLMHs2bOly+feffddBAYGSs3BunXr8Prrr2Pp0qVo2bKl9FodSy7x02g0GDZsGCZNmoQKFSpArVbjzTffRPPmzdGqVStpvzp16mDevHl44YUXULFiRVSsWNFsHJVKBT8/P9SuXRtJSUmoW7cuunbtihEjRmDFihXSLblfeeUV6XVZd+/exQ8//IAOHTrg8ePHWL16NTZv3ozZs2ebjX3gwAGEhISgevXqhT4uIiIish2fKSIinIq7jx9O3pA+d64UBCefEHy8Yo20zdHREb/++iscHR0RGhqK1157DYMGDTL7xf7Ro0eIjo6WbssaERGBy5cvIzIyElWrVoW/v7/0YfLss89i5cqV2LJlC5o0aYKuXbvCxcUFO3fulG5L/cUXXyA9PR1ardZsjAkTJkjjrFmzpsC70SxevBg9evRA37590a5dO/j5+eGbb74x2yc6Olp6b6bC+u6771CnTh106tQJzz//PNq2bZvjMsG1a9eiWbNmaNOmDc6fP489e/agVq1aZvusX7/e7PVWREREJA8+U0REuHb3YY5tmjb98fXKzzDnnfHS5W2BgYE57gqX/V2lO3ToYPb54MGDMXjw4ALz+/bti6FDh+bZ1Ozfvz/X7aIoSg3M1atX0b59+3xzXF1dpTeFfrI++5j5uXbtWo79KlSogHXr1uVZ4+3tjcOHD5ttMxqNZufy/PnziIqKwqZNm/LNJyIioqLHpoiIEOTtkWObe/XmeLGeM27cuIGAgIASmJVlduzYgU8//bSkp2G1+Ph4fP311xa/QTURERHZzqrL53Q6HYKCguDq6oqWLVvi2LFjharbsGEDBEFAnz59rIklomLSpFp5vPh0FbNtb7QPwcfh79pFQwRkvZ6pRYsWJT0Nq3Xu3BlhYWElPQ0iIiJFsviZoo0bN2LSpElYsWIFWrZsiSVLliAsLAzR0dHw8fHJs+7atWt466238Mwzz9g0YSLKxfXjtr3R4PXjGFwpBl37euOCY20Ee3ugSbXy8mSf/Ba4EQ1UqQ00HWjxvG09bqvrbc0mZSnJtXbz1H//Blr4hwN7fozx8U1EFrC4KVq0aBFGjBiBIUOGAABWrFiBbdu24auvvsKUKVNyrcnIyMCrr76K8PBwHDhwIMf7hBCRDSJmAgeXAm6BQEos0GZ81juyW1FfOyUWddqMB54uZL2t2SufBW6czKo/+SlwcjUwYq882bbU25pNylKSay1iJnB4BdDoC2BtTyB0tLzZJXncfHwTkQUsaorS0tJw4sQJTJ06Vdrm4OCAzp0753gRcXazZ8+Gj48Phg0bhgMHDhSYk5qaitTUVOlzvV4PIOuFyaa7WhWWKIpIT0+H0Wgs8M5URV3PbPvLNq0vS9dZUWRbVX/zFHB4BUQHV6Q7uMDo4Arh8AqgVnegciHe68aWeluzT28A4s+b18efB45/AzR6pewe97/sbq0x27raklxr/9YbHbJuXW90cM1qkGTMLsnjLsnHt+LW+b/4c43ZcmVbs8YKIogF3Wopm5s3b6JKlSo4dOiQ2Zsdvv322/jtt99yvPs8APzxxx945ZVXEBUVBW9vbwwePBgPHjzAzz//nGfOrFmzEB6e868y69atg7u7e2GnS0REREREZcyjR48wYMAAJCUlQa1WF8mYxXr3OYPBgIEDB2LlypXw9vYudN3UqVMxadIk6XO9Xo+AgAB07NgxxxspFkQURej1eqjVaqu7YGvrmW1/2UajEREREejSpQtUKpWs2VbV3zwFrO0JEQL0btWgTomDABF4fWvh/6Jqbb2t2ac3ANvfyln//MeFe6bIXo/7X3a31phtXW1JrrV/640Oroho8Am6nB0HVeZjWbNL8rhL8vGtuHX+L/5cY7Zc2ffu3bO4piAWNUXe3t5wdHTErVu3zLbfunVLenf67K5cuYJr166hZ8+e0rbMzMysYCcnREdH5/rO7S4uLnBxccmxXaVSWfUgc3Jygkqlsvobbm09s+0v28Ru1lpgCyB0NMSDS+GUmQpV5mMIbSYU/sXUttTbmt1sIHBqNcQbJ/+rr9I0a3txZ5fkcT/BbtYas62rLcm19m89Dq8AAKgyH0MV+oas2SV53CX5+FbcOn8Cf64xu7izLV1fhWFRU+Ts7IymTZsiMjJSuq12ZmYmIiMjMXbs2Bz716lTB2fPnjXbNm3aNBgMBixdutRubvVLVKp1CQfq9LD+Tkm21NuaPWIvcOIb6+4+Z8/HTcpSkmutS3jW62Gi4rOe7bDkl3t7fozx8U1EFrL48rlJkybh9ddfR7NmzdCiRQssWbIEDx8+lO5GN2jQIFSpUgXz5s2Dq6srnnrqKbP6cuXKAUCO7URkg6rNAK+agLVv/GlLva3ZT78GVE8qmeySPG5SlpJca5WbZDVFFlz+VWTZJXncfHwTkQUsbopefvll3LlzBzNmzEBCQgIaN26MnTt3wtfXFwAQFxcHBwer3hOWiIiIiIhIdlbdaGHs2LG5Xi4HAPv378+3ds2aNdZEEhERERERFQs+pUNERERERIrGpoiIiIiIiBSNTRERERERESkamyIiIqIy5N69e/Dx8cG1a9dKeiqKsGLFCrP3YyQi+8SmiIiIqAyZO3cuevfujaCgIGnbnTt30Lt3b7i7u8PHxweTJ09Genp6vuMEBQXBwcEB5cuXh4ODAwRBwIcffmi2z6ZNm9C4cWO4u7sjMDAQCxYsMPt6fHw8hg8fjtq1a8PBwQETJkyw6phEUcSMGTPg7+8PNzc3dO7cGX///Xe+NZ999hnatGkDjUYDtVqN0NBQ7NixQ/r6tWvXIAhCrh/ff/99jvHu3buHqlWrQhAEPHjwQNo+dOhQnDx5EgcOHLDq2IiodGBTREREVEY8evQIq1atwrBhw6RtGRkZmDNnDtLS0nDo0CGsXbsWa9aswYwZMwocLzw8HH/99Rdu3ryJ+Ph4vPnmm9LXduzYgVdffRWjR4/GuXPnsHz5cixevBiffvqptE9qaiq8vb3x3nvvoVGjRlYf10cffYRPPvkEK1aswNGjR+Hh4YGwsDA8fvw4z5qqVati5syZOH78OI4fP45nn30WvXv3xvnz5wEAAQEBiI+PN/sIDw+Hp6cnunXrlmO8YcOGoWHDhjm2Ozs7Y8CAAfjkk0+sPj4iKnlsiojKguvHgUu7sv6Vu74ks4nshUyPse3bt8PFxQWtWrWStkVsWIHr169jzYf/Q+PGjdGtWzfMmTMHOp0OaWlp+Y7nlfEAvklR8Eu/Dj8/P3h4eEhf++abb9CnTx+MHj0aISEh6N69O6ZOnYr58+dDFEUAWc82ffjhhxg0aBA0Vr4RqiiKWLJkCaZNm4bevXujYcOG+Prrr3Hz5k38/PPPedb17NkTzz33HGrWrIlatWph7ty58PT0xJEjRwAAjo6O8PPzM/v46aef0K9fP3h6epqN9dlnn+HBgwd466238sz65ZdfkJKSYtUxElHJY1NEZO8iZgKrugD7Psj6N2KmfPUlmU1kL2R8jB04cABNmzY1qz+y5j1Uq1YNvtsHS/VhYWHQ6/XSsya5evwA8xcuQUjHV/F0i1ZYMKKL2SV3qampcHV1NStxc3PD9evXERsbW/hjLMDVq1eRkJCAzp07S9s0Gg1atmyJw4cPF2qMjIwMbNiwAQ8fPkRoaGiu+5w4cQJRUVFmz7IBwIULFzB79mx8/fXXeb45fbNmzZCeno6jR48W8qiIqLSx6s1bS4ooitJfnyytsbSuKOqZbZ/Z2ceRO9vi+uvHgYNLIUKQPnBwKVCnB1C1WfHWl2T2v7jW7PO4FZUt82MsNjYW/v7+WfP8tz4hWUS5cuXw/+3df5BU5Z3v8c+B7hkgdp9gCD8dUVADGsVfwTsaV82CZNc1S6pSetmsUq7RNR5vRakYRY0TfwRZr/FqZc/G+CsxdUPwRxmTTbi4SiRxlcRE4V41akoRTQyDYpBzGhC6m+f+MfRsDww/zumeZ/rwvF9VlNKe7/N5ztPfGfwy0z1GW2R21I8efaSkntf8HHvssf1m/49pW3XcuBFq++gE/b/X/6hr//eT+rPO1+13/1CSdOaZZ2revHmaO3euzjjjDL3++uv61re+JUn685//rIkTJ+5ybknOsHbt2rVrJUmjR4/uUztmzBh1d3fvdj1jjF5++eXeb7M74IAD9Oijj2rq1Kn91tx7772aOnWqOjs7e7M//PBDzZkzR7feeqs6Ojr0xhtv9Hsfw4cPl+/7WrNmTZ97dqbP6+rr17Gd7eqZu5rdbC09FIVhqDAMVa1WJUlxHCuXS7ZlY4xKpZIkyfO8xHtopJ7s7GXX/hY0iqJs9Nra1dLwiTLyVGof21Mr0/N44fCBrR/M7B3otezdt3PZlj/G4jjWqFGjtHHjxt768tA/S1UpGt6h3PZt0trV2jx0giRp06ZNPdf2k33hZ47ozT5q3HBtH/a+rvjeQ7r65v+l9vZ2nXPOOfr973+vs88+W+VyWYVCQZdccokWLlyozZs3a+PGjX3OrVKpaNu2bf3n9aNWW6uPokgjRozo/e/lclme5+12PWOMxo4dq1/+8peK41g/+clPNHfuXP3sZz/TlClT+ly7ZcsWLVq0SFdeeWWffV977bWaPHmyzj77bG3cuFGbNm3q3cvOfTBs2DC9//77u9y3E32+A5/XyLaVHcdx4pq9aemhKAgCBUGgKIrk+74KhULi70muTZK+76d+wtPWk5297HK5LEkqFovK5/NWs1PVj5skbXmr52+QJflb3ur5n6Zxk6R9+VhppH4ws3eg17J3385lW/4YGzt2rDZv3tzzZ+WO+o7hFT397gcqbtmi/PYPpXGT9Jcdb1AwefLk/v9c7Sf79NEVVapVbdiwQZ/4xCckSXfccYe+9a1vqbu7Wx//+Me1bNkyLVy4UMccc4x83+9zbrlcTm1tbfv853it9rDDDpPUM7jU1/7lL3/RtGnTdrterf7QQw+V53k67bTT9OKLL+r+++/Xd7/73T7X1l4PdPHFF/fZ97PPPqsXX3xRo0aN6rPm5MmTdc011+iGG27oXWPDhg06+OCDd7lvJ/p8Bz6vkW0re2/vnplGSw9FO6u9VWbaujS1jdaTna3sWk1m7rvjU9IpX5GeufO/vsHmlMt7Hh/o+sHMrkOvZeu+ncu29DG28u0NenP9Jo0+5BN66ueP9uxxR/1/e+NOLXz6bb1XOlATZvbUP3n33SoWizrqqKP6v59+sv9vcaaGDPmJxowZ06cml8vpoIMOkiQtXrxYnZ2dGj169C7ntvO/7wvP8zRp0iSNHTtWv/jFL3TcccdJ6vlKxG9+8xt9+ctf3uN6Oz9n27dv17Zt23apuf/++/W5z31ul30/8sgjfd7h7re//a3+6Z/+SU8//bQmT57cu84bb7yhDz/8UMcff/wu9+pEn9fV1q9hM7vRerKzlZ12v3uSqaEIQD9m3tDzGoO1q3v+djfhUNFQ/WBmA1kxwB9jC//PK7rrl6slSdve+5jWvfSyNmzYoJEjR0ozb9DMyZ/VQb/677rg1+P0P7/4eXU//riuu+46BUGg9vZ2SdJzzz2n888/X8uWLdOECRO0YsUK/eblkTq9M5Q2v6+X/rhR827+tv7xH/+xZ11J69ev1yOPPKLTTz9dH374ob73ve/p4Ycf1i9/+cs++3vxxRd1wAEHqFQq6b333tOqVavU1tamI488cp+OwPM8XX755br55pt1+OGH69BDD9XXv/51jR8/XrNnz+697q//+q/1+c9/Xpdddpkkaf78+Tr11FN15JFHqlQqadGiRVq+fLkef/zxPuu//vrr+tWvfqUlS5bskl0/+NTuWZKmTp2qj370o72PP/3005o0aZImT568T/cEoPUwFAH7g4NO7HmNQcq3vG2ofjCzgawYoI+xlW9v6B2IJKnt44coN3qSbrvr+/rm/CskSUM7TtR1112nRx99VJ2dnfrIRz6iuXPn6sYbb+yt27x5s1577bXeb39qb2/X4sWL9Y1XX9XWrVt16KGH6oorrtC8efP65D/wwAP66le/KmOMOjs7tXz5ck2fPr3PNX/1V3/V++/PP/+8Fi1apIkTJ2rNmjWSpOXLl+uMM87Qm2++2ecHztb72te+pk2bNuniiy/WBx98oE9/+tNaunRpn3e/e+ONN3qHFkl699139eUvf1nr1q2T7/s65phj9Pjjj2vmzJl91r7//vt10EEH6cwzz+w3e1/86Ec/0kUXXZS6HsDgYygCACCj3ly/aZfH/FPm6Af3fEc3XfWV3reQHj16tH7605/u9nUep59+ep93czr++OP161//WsYYbdy4sd/v+x81atQ+vSX2hg0b9vi6gTfffFOHHXaYJkyYsNs1PM/TjTfe2GeQ21ltyKq57777drv3egsWLNCCBQv2fBM77HxOkvTyyy9r1apVeuihh/ZpDQCtiaEIAICMOnTUR3Z5bMTkT+kLR7bpnXfeUUdHxyDsKpklS5ZowYIFiV+Y3yrWrl2rH/zgB6l/OC2A1sBQBABARh138EhdctqkPt9C9+XTJumqvzlrEHeVzMMPPzzYW2hI/Q+VBZBdDEUAAGTY1X8zVbOOGqs312/SoaM+ouMOHjnYWwKAzGEoAgAg4447eCTDEAA0YMhgbwAAAAAABhNDEQAAAACnMRQBADDQ/vQ76Q+P9/zTZq0k/Xll33/azAaAjOA1RQAADKQnuqRn7pSGT5S2vCWd8hVp5g0DX1urX3GXNO1u6YGzpc5L7GUDQIZkaigyxuzyQ9P2tSZpXTPqyc5mdv06trNdPXNXs+vXsZ3t6plbz/7T76Rn7pSR1/tLz9wpTfk76aATB662vn7IsJ57kCdjK3sH555vsvusYzvb1TN3NbvZWnooCsNQYRiqWq1KkuI4Vi6XbMvGGJVKJUna40+0Hoh6srOXXalUJElRFNFrZA9oNr3mSPba1dLwiTLyVGof21Mv0/N44fCBq62rrwxpkyRFwzuU277NTvYOzj3fjmfzeY1sW9lxHCeu2ZuWHoqCIFAQBIqiSL7vq1AoJP6J0bVJ0vf91E942nqys5ddLpclScViMfFPV8/yfZNNr5E9QLXjJklb3ur5Soskf8tbPcPFuEnS3v48a6S2rr684ytFxS1/VH77h3ayd3Du+XY8m89rZNvKrg3gzdTSQ9HOPM9LdXC1ujS1jdaTna3sWo1r9002vUb2ANV2fKrntTjP3Plf34h2yuU9jw9kbV29t+Kunv3bzK7j1PPteDaf18i2WdtsmRqKAADInJk39LwWZ+3qnq+0JBksGqmt1R9xlrRqrTT336WJ0+1lA0CGMBQBADDQDjqx57U4Cb8FvOFaSRp/XM9QNP44+9kAkBH8nCIAAAAATmMoAgAAAOA0hiIAAAAATmMoAgAAAOA0hiIAAAAATmMoAgAAAOA0hiIAAAAATmMoAgBgoP3pd9IfHu/5p81aSfrzyr7/tJkNABnBD28FAGAgPdElPXOnNHyitOUt6ZSvSDNvGPjaWv2Ku6Rpd0sPnC11XmIvGwAyJFNDkTFGxphUNUnrmlFPdjaz69exne3qmbuaXb+O7WxXz9x69p9+Jz1zp4y83l965k5pyt9JB504cLX19UOG9dyDPBlb2Ts493yT3Wcd29munrmr2c3W0kNRGIYKw1DValWSFMexcrlkWzbGqFQqSZI8z0u8h0bqyc5edqVSkSRFUUSvkT2g2fSaI9lrV0vDJ8rIU6l9bE+9TM/jhcMHrrauvjKkTZIUDe9Qbvs2O9k7OPd8O57N5zWybWXHcZy4Zm9aeigKgkBBECiKIvm+r0KhIN/3E61RmyR930/9hKetJzt72eVyWZJULBaVz+etZrt65q5m02uOZI+bJG15q+crLZL8LW/1DBfjJkl7+/Oskdq6+vKOrxQVt/xR+e0f2snewbnn2/FsPq+RbSu7NoA3U0sPRTvzPC/VwdXq0tQ2Wk92trJrNa7dN9n0GtkDVNvxqZ7X4jxz5399I9opl/c8PpC1dfXeirt69m8zu45Tz7fj2XxeI9tmbbNlaigCACBzZt7Q81qctat7vtKSZLBopLZWf8RZ0qq10tx/lyZOt5cNABnCUAQAwEA76MSe1+Ik/BbwhmslafxxPUPR+OPsZwNARvBzigAAAAA4jaEIAAAAgNMYigAAAAA4jaEIAAAAgNMYigAAAAA4jaEIAAAAgNMYigAAAAA4jaEIAAAAgNMYigAAAAA4jaEIAAAAgNNyg72BJIwxMsakqkla14x6srOZXb+O7WxXz9zV7Pp1bGe7euauZtevYzvb1TN3Nbt+HdvZrp65q9nN1tJDURiGCsNQ1WpVkhTHsXK5ZFs2xqhUKkmSPM9LvIdG6snOXnalUpEkRVFEr5E9oNn0Gtm2suk1sm1l02tk28qO4zhxzd609FAUBIGCIFAURfJ9X4VCQb7vJ1qjNkn6vp/6CU9bT3b2ssvlsiSpWCwqn89bzXb1zF3NptfItpVNr5FtK5teI9tWdm0Ab6aWHop25nleqoOr1aWpbbSe7Gxl12pcu2+y6TWy999seo1sm7X1a9jMbrSe7Gxlp93vnvBGCwAAAACcxlAEAAAAwGkMRQAAAACcxlAEAAAAwGkMRQAAAACcxlAEAAAAwGkMRQAAAACcxlAEAAAAwGkMRQAAAACcxlAEAAAAwGkMRQAAAACclhvsDSRhjJExJlVN0rpm1JOdzez6dWxnu3rmrmbXr2M729UzdzW7fh3b2a6euavZ9evYznb1zF3NbraWHorCMFQYhqpWq5KkOI6VyyXbsjFGpVJJkuR5XuI9NFJPdvayK5WKJCmKInqN7AHNptfItpVNr5FtK5teI9tWdhzHiWv2pqWHoiAIFASBoiiS7/sqFAryfT/RGrVJ0vf91E942nqys5ddLpclScViUfl83mq2q2fuaja9RratbHqNbFvZ9BrZtrJrA3gztfRQtDPP81IdXK0uTW2j9WRnK7tW49p9k02vkb3/ZtNrZNusrV/DZnaj9WRnKzvtfveEN1oAAAAA4DSGIgAAAABOYygCAAAA4DSGIgAAAABOYygCAAAA4DSGIgAAAABOYygCAAAA4DSGIgAAAABOYygCAAAA4DSGIgAAAABOYygCAAAA4DSGIgAAAABOyw32BpIwxsgYk6omaV0z6snOZnb9OrazXT1zV7Pr17Gd7eqZu5pdv47tbFfP3NXs+nVsZ7t65q5mN1tLD0VhGCoMQ1WrVUlSHMfK5ZJt2RijUqkkSfI8L/EeGqknO3vZlUpFkhRFEb1G9oBm02tk28qm18i2lU2vkW0rO47jxDV709JDURAECoJAURTJ930VCgX5vp9ojdok6ft+6ic8bT3Z2csul8uSpGKxqHw+bzXb1TN3NZteI9tWNr1Gtq1seo1sW9m1AbyZWnoo2pnneakOrlaXprbRerKzlV2rce2+yabXyN5/s+k1sm3W1q9hM7vRerKzlZ12v3vCGy0AAAAAcBpDEQAAAACnMRQBAAAAcBpDEQAAAACnMRQBAAAAcBpDEQAAAACnMRQBAAAAcBpDEQAAAACnMRQBAAAAcBpDEQAAAACnMRQBAAAAcFpusDeQhDFGxphUNUnrmlFPdjaz69exne3qmbuaXb+O7WxXz9zV7Pp1bGe7euauZtevYzvb1TN3NbvZWnooCsNQYRiqWq1KkuI4Vi6XbMvGGJVKJUmS53mJ99BIPdnZy65UKpKkKIroNbIHNJteI9tWNr1Gtq1seo1sW9lxHCeu2ZuWHoqCIFAQBIqiSL7vq1AoyPf9RGvUJknf91M/4Wnryc5edrlcliQVi0Xl83mr2a6euavZ9BrZtrLpNbJtZdNrZNvKrg3gzdTSQ9HOPM9LdXC1ujS1jdaTna3sWo1r9002vUb2/ptNr5Fts7Z+DZvZjdaTna3stPvdE95oAQAAAIDTGIoAAAAAOI2hCAAAAIDTGIoAAAAAOI2hCAAAAIDTGIoAAAAAOI2hCAAAAIDTGIoAAAAAOI2hCAAAAIDTGIoAAAAAOI2hCAAAAIDTcoO9gSSMMTLGpKpJWteMerKzmV2/ju1sV8/c1ez6dWxnu3rmrmbXr2M729UzdzW7fh3b2a6euavZzdbSQ1EYhgrDUNVqVZIUx7FyuWRbNsaoVCpJkjzPS7yHRurJzl52pVKRJEVRRK+RPaDZ9BrZtrLpNbJtZdNrZNvKjuM4cc3etPRQFASBgiBQFEXyfV+FQkG+7ydaozZJ+r6f+glPW0929rLL5bIkqVgsKp/PW8129cxdzabXyLaVTa+RbSubXiPbVnZtAG+mlh6KduZ5XqqDq9WlqW20nuxsZddqXLtvsuk1svffbHqNbJu19WvYzG60nuxsZafd757wRgsAAAAAnMZQBAAAAMBpDEUAAAAAnMZQBAAAAMBpDEUAAAAAnMZQBAAAAMBpDEUAAAAAnMZQBAAAAMBpDEUAAAAAnMZQBAAAAMBpDEUAAAAAnMZQBAAAAMBpucHeQBLGGBljUtUkrWtGPdnZzK5fx3a2q2fuanb9OrazXT1zV7Pr17Gd7eqZu5pdv47tbFfP3NXsZmvpoSgMQ4VhqGq1KkmK41i5XLItG2NUKpUkSZ7nJd5DI/VkZy+7UqlIkqIootfIHtBseo1sW9n0Gtm2suk1sm1lx3GcuGZvWnooCoJAQRAoiiL5vq9CoSDf9xOtUZskfd9P/YSnrSc7e9nlclmSVCwWlc/nrWa7euauZtNrZNvKptfItpVNr5FtK7s2gDdTSw9FO/M8L9XB1erS1DZaT3a2sms1rt032fQa2ftvNr1Gts3a+jVsZjdaT3a2stPud094owUAAAAATmMoAgAAAOA0hiIAAAAATmMoAgAAAOA0hiIAAAAATmMoAgAAAOA0hiIAAAAATmMoAgAAAOC0VENRGIY65JBDNGzYMJ100kl67rnndnvtPffco1NPPVUjR47UyJEjNWPGjD1eDwAAAAA2JR6KHnzwQc2bN09dXV164YUXNG3aNM2aNUvvvvtuv9cvX75cc+bM0VNPPaUVK1aoo6NDZ555pt55552GNw8AAAAAjUo8FN1+++266KKLdMEFF+jII4/UXXfdpREjRuj+++/v9/of/vCHuvTSS3XsscdqypQpuvfee7V9+3YtW7as4c0DAAAAQKNySS7etm2bnn/+ec2fP7/3sSFDhmjGjBlasWLFPq2xefNmlctlHXjggbu9ZuvWrdq6dWvv76MokiSVy2WVy+UkW5YxRpVKReVyWZ7nJapttJ7s7GXX+itpnzUj29UzdzWbXiPbVja9RratbHqNbFvZaXpsbxINRevXr1e1WtWYMWP6PD5mzBi9+uqr+7TGVVddpfHjx2vGjBm7veaWW27RDTfcsMvjTz31lEaMGJFky0AqTzzxxGBvAY6g12ALvQZb6DUMtM2bNzd9zURDUaMWLlyoxYsXa/ny5Ro2bNhur5s/f77mzZvX+/soitTR0aEzzjhDH/vYxxJlGmMURZGKxWLqKThtPdnZyy6Xy3riiSc0c+ZM5fN5q9munrmr2fQa2bay6TWybWXTa2Tbyn7//fcT1+xNoqFo1KhRGjp0qNatW9fn8XXr1mns2LF7rL3tttu0cOFCPfnkkzrmmGP2eG17e7va29t3eTyfz6f6IMvlcsrn86mf8LT1ZGcvu4ZeI3ugs2voNbIHOruGXiN7oLNr6DWyBzo7aX/ti0RvtNDW1qYTTjihz5sk1N40obOzc7d1t956q2666SYtXbpUJ554YvrdAgAAAECTJf72uXnz5mnu3Lk68cQTNX36dN1xxx3atGmTLrjgAknS+eefrwkTJuiWW26RJP3Lv/yLrr/+ei1atEiHHHKIuru7JUkHHHCADjjggCbeCgAAAAAkl3goOvfcc/Xee+/p+uuvV3d3t4499lgtXbq0980X3n77bQ0Z8l9fgPrOd76jbdu26Qtf+EKfdbq6uvSNb3yjsd0DAAAAQINSvdHCZZddpssuu6zf/7Z8+fI+v1+zZk2aCAAAAACwIvEPbwUAAACA/QlDEQAAAACnMRQBAAAAcBpDEQAAAACnMRQBAAAAcBpDEQAAAACnMRQBAAAAcBpDEQAAAACnpfrhrYPFGCNjTKqapHXNqCc7m9n169jOdvXMXc2uX8d2tqtn7mp2/Tq2s109c1ez69exne3qmbua3WwtPRSFYagwDFWtViVJcRwrl0u2ZWOMSqWSJMnzvMR7aKSe7OxlVyoVSVIURfQa2QOaTa+RbSubXiPbVja9Rrat7DiOE9fsTUsPRUEQKAgCRVEk3/dVKBTk+36iNWqTpO/7qZ/wtPVkZy+7XC5LkorFovL5vNVsV8/c1Wx6jWxb2fQa2bay6TWybWXXBvBmaumhaGee56U6uFpdmtpG68nOVnatxrX7JpteI3v/zabXyLZZW7+GzexG68nOVnba/e4Jb7QAAAAAwGkMRQAAAACcxlAEAAAAwGkMRQAAAACcxlAEAAAAwGkMRQAAAACcxlAEAAAAwGkMRQAAAACcxlAEAAAAwGkMRQAAAACcxlAEAAAAwGm5wd5AEsYYGWNS1SSta0Y92dnMrl/HdrarZ+5qdv06trNdPXNXs+vXsZ3t6pm7ml2/ju1sV8/c1exma+mhKAxDhWGoarUqSYrjWLlcsi0bY1QqlSRJnucl3kMj9WRnL7tSqUiSoiii18ge0Gx6jWxb2fQa2bay6TWybWXHcZy4Zm9aeigKgkBBECiKIvm+r0KhIN/3E61RmyR930/9hKetJzt72eVyWZJULBaVz+etZrt65q5m02tk28qm18i2lU2vkW0ruzaAN1NLD0U78zwv1cHV6tLUNlpPdrayazWu3TfZ9BrZ+282vUa2zdr6NWxmN1pPdray0+53T3ijBQAAAABOYygCAAAA4DSGIgAAAABOYygCAAAA4DSGIgAAAABOYygCAAAA4DSGIgAAAABOYygCAAAA4DSGIgAAAABOYygCAAAA4DSGIgAAAABOyw32BpIwxsgYk6omaV0z6snOZnb9OrazXT1zV7Pr17Gd7eqZu5pdv47tbFfP3NXs+nVsZ7t65q5mN1tLD0VhGCoMQ1WrVUlSHMfK5ZJt2RijUqkkSfI8L/EeGqknO3vZlUpFkhRFEb1G9oBm02tk28qm18i2lU2vkW0rO47jxDV709JDURAECoJAURTJ930VCgX5vp9ojdok6ft+6ic8bT3Z2csul8uSpGKxqHw+bzXb1TN3NZteI9tWNr1Gtq1seo1sW9m1AbyZWnoo2pnneakOrlaXprbRerKzlV2rce2+yabXyN5/s+k1sm3W1q9hM7vRerKzlZ12v3vCGy0AAAAAcBpDEQAAAACnMRQBAAAAcBpDEQAAAACnMRQBAAAAcBpDEQAAAACnMRQBAAAAcBpDEQAAAACnMRQBAAAAcBpDEQAAAACnMRQBAAAAcBpDEQAAAACn5QZ7A0kYY2SMSVWTtK4Z9WRnM7t+HdvZrp65q9n169jOdvXMXc2uX8d2tqtn7mp2/Tq2s109c1ezm62lh6IwDBWGoarVqiQpjmPlcsm2bIxRqVSSJHmel3gPjdSTnb3sSqUiSYqiiF4je0Cz6TWybWXTa2TbyqbXyLaVHcdx4pq9aemhKAgCBUGgKIrk+74KhYJ830+0Rm2S9H0/9ROetp7s7GWXy2VJUrFYVD6ft5rt6pm7mk2vkW0rm14j21Y2vUa2rezaAN5MLT0U7czzvFQHV6tLU9toPdnZyq7VuHbfZNNrZO+/2fQa2TZr69ewmd1oPdnZyk673z3hjRYAAAAAOI2hCAAAAIDTGIoAAAAAOI2hCAAAAIDTGIoAAAAAOI2hCAAAAIDTGIoAAAAAOI2hCAAAAIDTGIoAAAAAOI2hCAAAAIDTGIoAAAAAOC032BtIwhgjY0yqmqR1zagnO5vZ9evYznb1zF3Nrl/HdrarZ+5qdv06trNdPXNXs+vXsZ3t6pm7mt1sLT0UhWGoMAxVrVYlSXEcK5dLtmVjjEqlkiTJ87zEe2iknuzsZVcqFUlSFEX0GtkDmk2vkW0rm14j21Y2vUa2rew4jhPX7E1LD0VBECgIAkVRJN/3VSgU5Pt+ojVqk6Tv+6mf8LT1ZGcvu1wuS5KKxaLy+bzVbFfP3NVseo1sW9n0Gtm2suk1sm1l1wbwZmrpoWhnnuelOrhaXZraRuvJzlZ2rca1+yabXiN7/82m18i2WVu/hs3sRuvJzlZ22v3uCW+0AAAAAMBpDEUAAAAAnMZQBAAAAMBpDEUAAAAAnMZQBAAAAMBpDEUAAAAAnMZQBAAAAMBpDEUAAAAAnMZQBAAAAMBpDEUAAAAAnMZQBAAAAMBpDEUAAAAAnJYb7A0kYYyRMSZVTdK6ZtSTnc3s+nVsZ7t65q5m169jO9vVM3c1u34d29munrmr2fXr2M529cxdzW62lh6KwjBUGIaqVquSpDiOlcsl27IxRqVSSZLkeV7iPTRST3b2siuViiQpiiJ6jewBzabXyLaVTa+RbSubXiPbVnYcx4lr9qalh6IgCBQEgaIoku/7KhQK8n0/0Rq1SdL3/dRPeNp6srOXXS6XJUnFYlH5fN5qtqtn7mo2vUa2rWx6jWxb2fQa2bayawN4M7X0ULQzz/NSHVytLk1to/VkZyu7VuPafZNNr5G9/2bTa2TbrK1fw2Z2o/VkZys77X73hDdaAAAAAOA0hiIAAAAATmMoAgAAAOA0hiIAAAAATmMoAgAAAOA0hiIAAAAATmMoAgAAAOA0hiIAAAAATmMoAgAAAOA0hiIAAAAATmMoAgAAAOC03GBvIAljjIwxqWqS1jWjnuxsZtevYzvb1TN3Nbt+HdvZrp65q9n169jOdvXMXc2uX8d2tqtn7mp2s7X0UBSGocIwVLValSTFcaxcLtmWjTEqlUqSJM/zEu+hkXqys5ddqVQkSVEU0WtkD2g2vUa2rWx6jWxb2fQa2bay4zhOXLM3LT0UBUGgIAgURZF831ehUJDv+4nWqE2Svu+nfsLT1pOdvexyuSxJKhaLyufzVrNdPXNXs+k1sm1l02tk28qm18i2lV0bwJuppYeinXmel+rganVpahutJztb2bUa1+6bbHqN7P03m14j22Zt/Ro2sxutJztb2Wn3uye80QIAAAAApzEUAQAAAHAaQxEAAAAApzEUAQAAAHAaQxEAAAAApzEUAQAAAHAaQxEAAAAApzEUAQAAAHAaQxEAAAAApzEUAQAAAHAaQxEAAAAAp+UGewNJGGNkjElVk7SuGfVkZzO7fh3b2a6euavZ9evYznb1zF3Nrl/HdrarZ+5qdv06trNdPXNXs5utpYeiMAwVhqGq1aokKY5j5XLJtmyMUalUkiR5npd4D43Uk5297EqlIkmKooheI3tAs+k1sm1l02tk28qm18i2lR3HceKavWnpoSgIAgVBoCiK5Pu+CoWCfN9PtEZtkvR9P/UTnrae7Oxll8tlSVKxWFQ+n7ea7eqZu5pNr5FtK5teI9tWNr1Gtq3s2gDeTC09FO3M87xUB1erS1PbaD3Z2cqu1bh232TTa2Tvv9n0Gtk2a+vXsJndaD3Z2cpOu9894Y0WAAAAADiNoQgAAACA0xiKAAAAADiNoQgAAACA0xiKAAAAADiNoQgAAACA0xiKAAAAADiNoQgAAACA0xiKAAAAADiNoQgAAACA0xiKAAAAADiNoQgAAACA03KDvYEkjDEyxqSqSVrXjHqys5ldv47tbFfP3NXs+nVsZ7t65q5m169jO9vVM3c1u34d29munrmr2c3W0kNRGIYKw1DValWSFMexcrlkWzbGqFQqSZI8z0u8h0bqyc5edqVSkSRFUUSvkT2g2fQa2bay6TWybWXTa2Tbyo7jOHHN3rT0UBQEgYIgUBRF8n1fhUJBvu8nWqM2Sfq+n/oJT1tPdvayy+WyJKlYLCqfz1vNdvXMXc2m18i2lU2vkW0rm14j21Z2bQBvppYeinbmeV6qg6vVpalttJ7sbGXXaly7b7LpNbL332x6jWybtfVr2MxutJ7sbGWn3e+e8EYLAAAAAJzGUAQAAADAaQxFAAAAAJzGUAQAAADAaQxFAAAAAJzGUAQAAADAaQxFAAAAAJzGUAQAAADAaamGojAMdcghh2jYsGE66aST9Nxzz+3x+ocfflhTpkzRsGHDdPTRR2vJkiWpNgsAAAAAzZZ4KHrwwQc1b948dXV16YUXXtC0adM0a9Ysvfvuu/1e/+yzz2rOnDm68MILtXLlSs2ePVuzZ8/WSy+91PDmAQAAAKBRiYei22+/XRdddJEuuOACHXnkkbrrrrs0YsQI3X///f1ef+edd+qzn/2srrzySk2dOlU33XSTjj/+eP3rv/5rw5sHAAAAgEblkly8bds2Pf/885o/f37vY0OGDNGMGTO0YsWKfmtWrFihefPm9Xls1qxZeuyxx3abs3XrVm3durX39xs3bpQk/eUvf0myXUmSMUZxHKtSqcjzPKv1ZGcvu1wua/PmzXr//feVz+etZrt65q5m02tk28qm18i2lU2vkW0ruzYTGGMS1+5OoqFo/fr1qlarGjNmTJ/Hx4wZo1dffbXfmu7u7n6v7+7u3m3OLbfcohtuuGGXx4844ogk2wUAAACwn3r//ffl+35T1ko0FNkyf/78Pl9d+uCDDzRx4kS9/fbbqW78U5/6lH7729+m3k8j9WRnKzuKInV0dOiPf/yjisWi1exG68nOVja9RratWnqNbFu19BrZtmo3btyogw8+WAceeGCq+v4kGopGjRqloUOHat26dX0eX7duncaOHdtvzdixYxNdL0nt7e1qb2/f5XHf91N9kA0dOjRVXTPqyc5etiQVi0V6jewBz5boNbLtZEv0Gtl2siV6jWw72VLPy3iaJdFKbW1tOuGEE7Rs2bLex7Zv365ly5aps7Oz35rOzs4+10vSE088sdvrB0IQBINWT3b2shuR5fsm2352I7J832Tbz25Elu+bbPvZjcjyfZNtP7vZPJPwFUoPPvig5s6dq+9+97uaPn267rjjDj300EN69dVXNWbMGJ1//vmaMGGCbrnlFkk9b8l92mmnaeHChTrrrLO0ePFiLViwQC+88II++clP7lNmFEXyfV8bN25seKIE9oRegy30Gmyh12ALvQZbBqLXEr+m6Nxzz9V7772n66+/Xt3d3Tr22GO1dOnS3jdTePvtt/t8Kevkk0/WokWLdN111+maa67R4Ycfrscee2yfByKp59vpurq6+v2WOqCZ6DXYQq/BFnoNttBrsGUgei3xV4oAAAAAYH/SvFcnAQAAAEAGMRQBAAAAcBpDEQAAAACnMRQBAAAAcFrLDEVhGOqQQw7RsGHDdNJJJ+m5557b4/UPP/ywpkyZomHDhunoo4/WkiVLLO0UWZek1+655x6deuqpGjlypEaOHKkZM2bstTeBmqSf12oWL14sz/M0e/bsgd0g9htJe+2DDz5QEAQaN26c2tvbdcQRR/DnKPZJ0l6744479IlPfELDhw9XR0eHrrjiCn344YeWdoss+tWvfqWzzz5b48ePl+d5euyxx/Zas3z5ch1//PFqb2/XYYcdpu9///uJc1tiKHrwwQc1b948dXV16YUXXtC0adM0a9Ysvfvuu/1e/+yzz2rOnDm68MILtXLlSs2ePVuzZ8/WSy+9ZHnnyJqkvbZ8+XLNmTNHTz31lFasWKGOjg6deeaZeueddyzvHFmTtNdq1qxZo69+9as69dRTLe0UWZe017Zt26aZM2dqzZo1euSRR/Taa6/pnnvu0YQJEyzvHFmTtNcWLVqkq6++Wl1dXXrllVd033336cEHH9Q111xjeefIkk2bNmnatGkKw3Cfrn/zzTd11lln6YwzztCqVat0+eWX60tf+pIef/zxZMGmBUyfPt0EQdD7+2q1asaPH29uueWWfq8/55xzzFlnndXnsZNOOsn88z//84DuE9mXtNd2VqlUTKFQMA888MBAbRH7iTS9VqlUzMknn2zuvfdeM3fuXPP3f//3FnaKrEvaa9/5znfMpEmTzLZt22xtEfuJpL0WBIH5zGc+0+exefPmmVNOOWVA94n9hyTz4x//eI/XfO1rXzNHHXVUn8fOPfdcM2vWrERZg/6Vom3btun555/XjBkzeh8bMmSIZsyYoRUrVvRbs2LFij7XS9KsWbN2ez0gpeu1nW3evFnlclkHHnjgQG0T+4G0vXbjjTdq9OjRuvDCC21sE/uBNL3205/+VJ2dnQqCQGPGjNEnP/lJLViwQNVq1da2kUFpeu3kk0/W888/3/stdqtXr9aSJUv0t3/7t1b2DDc0ay7INXNTaaxfv17ValVjxozp8/iYMWP06quv9lvT3d3d7/Xd3d0Dtk9kX5pe29lVV12l8ePH7/LBB9RL02v/+Z//qfvuu0+rVq2ysEPsL9L02urVq/WLX/xCX/ziF7VkyRK9/vrruvTSS1Uul9XV1WVj28igNL32D//wD1q/fr0+/elPyxijSqWiSy65hG+fQ1Ptbi6IokhbtmzR8OHD92mdQf9KEZAVCxcu1OLFi/XjH/9Yw4YNG+ztYD8Sx7HOO+883XPPPRo1atRgbwf7ue3bt2v06NG6++67dcIJJ+jcc8/Vtddeq7vuumuwt4b9zPLly7VgwQL927/9m1544QU9+uij+vnPf66bbrppsLcG7GLQv1I0atQoDR06VOvWrevz+Lp16zR27Nh+a8aOHZvoekBK12s1t912mxYuXKgnn3xSxxxzzEBuE/uBpL32xhtvaM2aNTr77LN7H9u+fbskKZfL6bXXXtPkyZMHdtPIpDSf18aNG6d8Pq+hQ4f2PjZ16lR1d3dr27ZtamtrG9A9I5vS9NrXv/51nXfeefrSl74kSTr66KO1adMmXXzxxbr22ms1ZAh/N4/G7W4uKBaL+/xVIqkFvlLU1tamE044QcuWLet9bPv27Vq2bJk6Ozv7rens7OxzvSQ98cQTu70ekNL1miTdeuutuummm7R06VKdeOKJNraKjEvaa1OmTNGLL76oVatW9f763Oc+1/tOOh0dHTa3jwxJ83ntlFNO0euvv947eEvSH/7wB40bN46BCLuVptc2b968y+BTG8Z7XkMPNK5pc0Gy94AYGIsXLzbt7e3m+9//vvn9739vLr74YvPRj37UdHd3G2OMOe+888zVV1/de/0zzzxjcrmcue2228wrr7xiurq6TD6fNy+++OJg3QIyImmvLVy40LS1tZlHHnnErF27tvdXHMeDdQvIiKS9tjPefQ77Kmmvvf3226ZQKJjLLrvMvPbaa+ZnP/uZGT16tLn55psH6xaQEUl7rauryxQKBfOjH/3IrF692vzHf/yHmTx5sjnnnHMG6xaQAXEcm5UrV5qVK1caSeb22283K1euNG+99ZYxxpirr77anHfeeb3Xr1692owYMcJceeWV5pVXXjFhGJqhQ4eapUuXJsptiaHIGGO+/e1vm4MPPti0tbWZ6dOnm1//+te9/+20004zc+fO7XP9Qw89ZI444gjT1tZmjjrqKPPzn//c8o6RVUl6beLEiUbSLr+6urrsbxyZk/TzWj2GIiSRtNeeffZZc9JJJ5n29nYzadIk881vftNUKhXLu0YWJem1crlsvvGNb5jJkyebYcOGmY6ODnPppZeaDRs22N84MuOpp57q9/+9ar01d+5cc9ppp+1Sc+yxx5q2tjYzadIk873vfS9xrmcMX78EAAAA4K5Bf00RAAAAAAwmhiIAAAAATmMoAgAAAOA0hiIAAAAATmMoAgAAAOA0hiIAAAAATmMoAgAAAOA0hiIAAAAATmMoAgAAAOA0hiIAAAAATmMoAgAAAOA0hiIAAAAATvv/jz1rpMqbqA0AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1000x1000 with 1 Axes>"
       ]
@@ -761,7 +739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -771,7 +749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -797,7 +775,7 @@
        "      [0., 0., 0., 0., 0.]], dtype=float32)"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -810,7 +788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -824,10 +802,10 @@
      "data": {
       "text/plain": [
        "PerformanceSummary([(PerfKey(name='section0', rank=None),\n",
-       "                     PerfEntry(time=3.6e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
+       "                     PerfEntry(time=4.1e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -841,12 +819,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x1000 with 2 Axes>"
       ]
@@ -881,7 +859,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

From 7e3ff8d89d2ca4b87f400b1f0f03dcc4db5a02aa Mon Sep 17 00:00:00 2001
From: Edward Caunt <ed@devitocodes.com>
Date: Wed, 17 Apr 2024 10:23:46 +0100
Subject: [PATCH 21/29] examples: Fix typo in tutorial numbering

---
 .../{07_sparse_operations.ipynb => 06_sparse_operations.ipynb}    | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename examples/userapi/{07_sparse_operations.ipynb => 06_sparse_operations.ipynb} (100%)

diff --git a/examples/userapi/07_sparse_operations.ipynb b/examples/userapi/06_sparse_operations.ipynb
similarity index 100%
rename from examples/userapi/07_sparse_operations.ipynb
rename to examples/userapi/06_sparse_operations.ipynb

From e859b2104206525bef4a3dec3d5288932d2f22cf Mon Sep 17 00:00:00 2001
From: George Bisbas <g.bisbas18@imperial.ac.uk>
Date: Tue, 20 Jun 2023 11:52:14 +0100
Subject: [PATCH 22/29] misc: Restrict MPI perf logging to rank 0

---
 devito/operator/operator.py | 16 +++++++++++++---
 1 file changed, 13 insertions(+), 3 deletions(-)

diff --git a/devito/operator/operator.py b/devito/operator/operator.py
index 45860303e8..c312b910fa 100644
--- a/devito/operator/operator.py
+++ b/devito/operator/operator.py
@@ -8,8 +8,8 @@
 
 from devito.arch import compiler_registry, platform_registry
 from devito.data import default_allocator
-from devito.exceptions import InvalidOperator, ExecutionError
-from devito.logger import debug, info, perf, warning, is_log_enabled_for
+from devito.exceptions import InvalidOperator
+from devito.logger import debug, info, perf, warning, is_log_enabled_for, set_log_level
 from devito.ir.equations import LoweredEq, lower_exprs
 from devito.ir.clusters import ClusterGroup, clusterize
 from devito.ir.iet import (Callable, CInterface, EntryFunction, FindSymbols, MetaCall,
@@ -909,6 +909,11 @@ def _emit_timings(timings, indent=''):
 
     def _emit_apply_profiling(self, args):
         """Produce a performance summary of the profiled sections."""
+
+        # In case 'MPI0' is selected for logging, restrict result printing to one rank
+        if configuration['mpi']:
+            set_log_level(configuration['log-level'], comm=args.comm)
+
         # Rounder to 2 decimal places
         fround = lambda i: ceil(i * 100) / 100
 
@@ -1002,7 +1007,12 @@ def lower_perfentry(v):
                     if a in args:
                         perf_args[a] = args[a]
                         break
-        perf("Performance[mode=%s] arguments: %s" % (self._mode, perf_args))
+
+        if configuration['mpi']:
+            perf("Performance[mode=%s, mpi=%s] arguments: %s, " %
+                 (self._mode, configuration['mpi'], perf_args))
+        else:
+            perf("Performance[mode=%s] arguments: %s" % (self._mode, perf_args))
 
         return summary
 

From 6ddce4cdcfc994182977c9fa2b6819583f257d90 Mon Sep 17 00:00:00 2001
From: George BIsbas <g.bisbas18@imperial.ac.uk>
Date: Wed, 17 Jan 2024 15:57:13 +0200
Subject: [PATCH 23/29] docs: Update for Custom topology and restore logging

---
 FAQ.md                       |  4 ++--
 devito/logger.py             | 16 ++++++++--------
 devito/operator/operator.py  | 20 +++++++++-----------
 devito/operator/profiling.py |  2 +-
 4 files changed, 20 insertions(+), 22 deletions(-)

diff --git a/FAQ.md b/FAQ.md
index 4b919483d5..ecd011faea 100644
--- a/FAQ.md
+++ b/FAQ.md
@@ -596,9 +596,9 @@ By default, Devito compiles the generated code using flags that maximize the run
 
 [top](#Frequently-Asked-Questions)
 
-## Can I control the MPI domain decomposition
+## Can I control the MPI domain decomposition?
 
-Until Devito v3.5 included, domain decomposition occurs along the fastest axis. As of later versions, domain decomposition occurs along the slowest axis, for performance reasons.  And yes, it is possible to control the domain decomposition in user code, but this is not neatly documented. Take a look at `test_custom_topology` in [this file](https://github.com/devitocodes/devito/blob/master/tests/test_mpi.py). In essence, `Grid` accepts the optional argument `topology`, which allows the user to pass a custom topology as an n-tuple, where `n` is the number of distributed dimensions. For example, for a two-dimensional grid, the topology `(4, 1)` will decompose the slowest axis into four partitions, one partition per MPI rank, while the fastest axis will be replicated over all MPI ranks.
+Until Devito v3.5 included, domain decomposition occurs along the fastest axis. As of later versions, domain decomposition occurs along the slowest axis, for performance reasons.  And yes, it is possible to control the domain decomposition in user code, but this is not neatly documented. Take a look at `class CustomTopology` in [distributed.py](https://github.com/devitocodes/devito/blob/master/devito/mpi/distributed.py) and `test_custom_topology` in [this file](https://github.com/devitocodes/devito/blob/master/tests/test_mpi.py). In essence, `Grid` accepts the optional argument `topology`, which allows the user to pass a custom topology as an n-tuple, where `n` is the number of distributed dimensions. For example, for a two-dimensional grid, the topology `(4, 1)` will decompose the slowest axis into four partitions, one partition per MPI rank, while the fastest axis will be replicated over all MPI ranks.
 
 
 [top](#Frequently-Asked-Questions)
diff --git a/devito/logger.py b/devito/logger.py
index 92ede2e8dc..fe75132162 100644
--- a/devito/logger.py
+++ b/devito/logger.py
@@ -13,13 +13,13 @@
 stream_handler = logging.StreamHandler()
 logger.addHandler(stream_handler)
 
-# Add extra logging levels (note: INFO has value=20, WARNING has value=30)
-DEBUG = logging.DEBUG
+# Add extra logging levels
+DEBUG = logging.DEBUG  # value=10
 PERF = 19
-INFO = logging.INFO
-WARNING = logging.WARNING
-ERROR = logging.ERROR
-CRITICAL = logging.CRITICAL
+INFO = logging.INFO  # value=20
+WARNING = logging.WARNING  # value=30
+ERROR = logging.ERROR  # value=40
+CRITICAL = logging.CRITICAL  # value=50
 
 logging.addLevelName(PERF, "PERF")
 
@@ -71,8 +71,8 @@ def set_log_level(level, comm=None):
     comm : MPI communicator, optional
         An MPI communicator the logger should be collective over. If provided, only
         rank-0 on that communicator will write to the registered handlers, other
-        ranks will use a `logging.NullHandler`.  By default, ``comm`` is set
-        to ``None``, so all ranks will use the default handlers.  This could be
+        ranks will use a `logging.NullHandler`. By default, ``comm`` is set
+        to ``None``, so all ranks will use the default handlers. This could be
         used, for example, if one wants to log to one file per rank.
     """
     from devito import configuration
diff --git a/devito/operator/operator.py b/devito/operator/operator.py
index c312b910fa..6c9a0c70bc 100644
--- a/devito/operator/operator.py
+++ b/devito/operator/operator.py
@@ -6,6 +6,7 @@
 from cached_property import cached_property
 from sympy import sympify
 
+from devito import switchconfig
 from devito.arch import compiler_registry, platform_registry
 from devito.data import default_allocator
 from devito.exceptions import InvalidOperator
@@ -871,7 +872,13 @@ def apply(self, **kwargs):
         # Post-process runtime arguments
         self._postprocess_arguments(args, **kwargs)
 
-        # Output summary of performance achieved
+        # In case MPI is used restrict result logging to one rank only
+        if configuration['mpi']:
+            # Only temporarily change configuration
+            with switchconfig(mpi=True):
+                set_log_level('DEBUG', comm=args.comm)
+                return self._emit_apply_profiling(args)
+
         return self._emit_apply_profiling(args)
 
     # Performance profiling
@@ -910,10 +917,6 @@ def _emit_timings(timings, indent=''):
     def _emit_apply_profiling(self, args):
         """Produce a performance summary of the profiled sections."""
 
-        # In case 'MPI0' is selected for logging, restrict result printing to one rank
-        if configuration['mpi']:
-            set_log_level(configuration['log-level'], comm=args.comm)
-
         # Rounder to 2 decimal places
         fround = lambda i: ceil(i * 100) / 100
 
@@ -1007,12 +1010,7 @@ def lower_perfentry(v):
                     if a in args:
                         perf_args[a] = args[a]
                         break
-
-        if configuration['mpi']:
-            perf("Performance[mode=%s, mpi=%s] arguments: %s, " %
-                 (self._mode, configuration['mpi'], perf_args))
-        else:
-            perf("Performance[mode=%s] arguments: %s" % (self._mode, perf_args))
+        perf("Performance[mode=%s] arguments: %s" % (self._mode, perf_args))
 
         return summary
 
diff --git a/devito/operator/profiling.py b/devito/operator/profiling.py
index fd0defd089..62d842398a 100644
--- a/devito/operator/profiling.py
+++ b/devito/operator/profiling.py
@@ -473,7 +473,7 @@ def add_glb_vanilla(self, key, time):
         ops = sum(v.ops for v in self.input.values())
         traffic = sum(v.traffic for v in self.input.values())
 
-        if np.isnan(traffic):
+        if np.isnan(traffic) or traffic == 0:
             return
 
         gflops = float(ops)/10**9

From e459d5241682094e3eb08ee359203dd25ce81acf Mon Sep 17 00:00:00 2001
From: George Bisbas <g.bisbas18@imperial.ac.uk>
Date: Thu, 18 Apr 2024 11:01:42 +0300
Subject: [PATCH 24/29] misc: Use mpi_switch_log for mpi rank 0 logging

---
 devito/logger.py            | 37 +++++++++++++++++++++++++++++--------
 devito/operator/operator.py | 15 ++++-----------
 2 files changed, 33 insertions(+), 19 deletions(-)

diff --git a/devito/logger.py b/devito/logger.py
index fe75132162..b8fce1b68e 100644
--- a/devito/logger.py
+++ b/devito/logger.py
@@ -4,7 +4,7 @@
 import sys
 from contextlib import contextmanager
 
-__all__ = ('set_log_level', 'set_log_noperf', 'is_log_enabled_for',
+__all__ = ('set_log_level', 'set_log_noperf', 'is_log_enabled_for', 'switch_log_level',
            'log', 'warning', 'error', 'perf', 'hint',
            'RED', 'GREEN', 'BLUE')
 
@@ -13,13 +13,13 @@
 stream_handler = logging.StreamHandler()
 logger.addHandler(stream_handler)
 
-# Add extra logging levels
-DEBUG = logging.DEBUG  # value=10
+# Add extra logging levels (note: INFO has value=20, WARNING has value=30)
+DEBUG = logging.DEBUG
 PERF = 19
-INFO = logging.INFO  # value=20
-WARNING = logging.WARNING  # value=30
-ERROR = logging.ERROR  # value=40
-CRITICAL = logging.CRITICAL  # value=50
+INFO = logging.INFO
+WARNING = logging.WARNING
+ERROR = logging.ERROR
+CRITICAL = logging.CRITICAL
 
 logging.addLevelName(PERF, "PERF")
 
@@ -77,15 +77,36 @@ def set_log_level(level, comm=None):
     """
     from devito import configuration
 
-    if comm is not None:
+    if comm is not None and configuration['mpi']:
         if comm.rank != 0:
             logger.removeHandler(stream_handler)
             logger.addHandler(logging.NullHandler())
+    else:
+        logger.addHandler(stream_handler)
 
     # Triggers a callback to `_set_log_level`
     configuration['log-level'] = level
 
 
+class switch_log_level(object):
+    """
+    A context manager to temporarily change MPI logging.
+    """
+
+    def __init__(self, comm):
+
+        from devito import configuration
+        self.level = configuration['log-level']
+        self.comm = comm
+
+    def __enter__(self):
+        # Limit logging to rank 0
+        set_log_level(self.level, self.comm)
+
+    def __exit__(self, *args):
+        set_log_level(self.level)
+
+
 def set_log_noperf():
     """Do not print performance-related messages."""
     logger.setLevel(WARNING)
diff --git a/devito/operator/operator.py b/devito/operator/operator.py
index 6c9a0c70bc..462e5b335b 100644
--- a/devito/operator/operator.py
+++ b/devito/operator/operator.py
@@ -6,11 +6,10 @@
 from cached_property import cached_property
 from sympy import sympify
 
-from devito import switchconfig
 from devito.arch import compiler_registry, platform_registry
 from devito.data import default_allocator
-from devito.exceptions import InvalidOperator
-from devito.logger import debug, info, perf, warning, is_log_enabled_for, set_log_level
+from devito.exceptions import InvalidOperator, ExecutionError
+from devito.logger import debug, info, perf, warning, is_log_enabled_for, switch_log_level
 from devito.ir.equations import LoweredEq, lower_exprs
 from devito.ir.clusters import ClusterGroup, clusterize
 from devito.ir.iet import (Callable, CInterface, EntryFunction, FindSymbols, MetaCall,
@@ -873,13 +872,8 @@ def apply(self, **kwargs):
         self._postprocess_arguments(args, **kwargs)
 
         # In case MPI is used restrict result logging to one rank only
-        if configuration['mpi']:
-            # Only temporarily change configuration
-            with switchconfig(mpi=True):
-                set_log_level('DEBUG', comm=args.comm)
-                return self._emit_apply_profiling(args)
-
-        return self._emit_apply_profiling(args)
+        with switch_log_level(comm=args.comm):
+            return self._emit_apply_profiling(args)
 
     # Performance profiling
 
@@ -916,7 +910,6 @@ def _emit_timings(timings, indent=''):
 
     def _emit_apply_profiling(self, args):
         """Produce a performance summary of the profiled sections."""
-
         # Rounder to 2 decimal places
         fround = lambda i: ceil(i * 100) / 100
 

From 1646687bb4d3d00f58ac74da8b5eff67444ef066 Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Tue, 23 Apr 2024 17:00:19 -0400
Subject: [PATCH 25/29] fix missing pypi files

---
 MANIFEST.in | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/MANIFEST.in b/MANIFEST.in
index 153224b212..30465f96bb 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,3 +1,7 @@
 include versioneer.py
 include devito/_version.py
-include requirements.txt requirements-optional.txt
+include requirements.txt
+include requirements-optional.txt
+include requirements-testing.txt
+include requirements-mpi.txt
+include requirements-nvidia.txt

From 7aa56df41f15e6a3de3950cb4c3b614946c80711 Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Wed, 24 Apr 2024 11:34:57 -0400
Subject: [PATCH 26/29] docker: build arm base

---
 .github/workflows/docker-bases.yml      | 19 ++++++++++---------
 .github/workflows/pytest-core-nompi.yml |  8 ++++----
 .github/workflows/tutorials.yml         | 12 +++++++-----
 docker/Dockerfile.nvidia                |  2 +-
 4 files changed, 22 insertions(+), 19 deletions(-)

diff --git a/.github/workflows/docker-bases.yml b/.github/workflows/docker-bases.yml
index 9704b64498..31627c0fe1 100644
--- a/.github/workflows/docker-bases.yml
+++ b/.github/workflows/docker-bases.yml
@@ -52,7 +52,7 @@ jobs:
         run: docker system prune -a -f
 
       - name: GCC image
-        uses: docker/build-push-action@v3
+        uses: docker/build-push-action@v5
         with:
           context: .
           file: './docker/Dockerfile.cpu'
@@ -61,6 +61,7 @@ jobs:
           build-args: 'arch=gcc'
           tags: 'devitocodes/bases:cpu-gcc'
 
+
 #######################################################
 ############## Intel OneApi CPU #######################
 #######################################################
@@ -93,7 +94,7 @@ jobs:
         run: docker system prune -a -f
 
       - name: ICX image
-        uses: docker/build-push-action@v3
+        uses: docker/build-push-action@v5
         with:
           context: .
           file: './docker/Dockerfile.intel'
@@ -103,7 +104,7 @@ jobs:
           tags: 'devitocodes/bases:cpu-icx'
 
       - name: SYCL CPU image
-        uses: docker/build-push-action@v3
+        uses: docker/build-push-action@v5
         with:
           context: .
           file: './docker/Dockerfile.intel'
@@ -113,7 +114,7 @@ jobs:
           tags: 'devitocodes/bases:cpu-sycl'
 
       - name: SYCL GPU image
-        uses: docker/build-push-action@v3
+        uses: docker/build-push-action@v5
         with:
           context: .
           file: './docker/Dockerfile.intel'
@@ -154,7 +155,7 @@ jobs:
         run: docker system prune -a -f
 
       - name: NVC image
-        uses: docker/build-push-action@v3
+        uses: docker/build-push-action@v5
         with:
           context: .
           file: './docker/Dockerfile.nvidia'
@@ -164,7 +165,7 @@ jobs:
           tags: 'devitocodes/bases:nvidia-nvc'
 
       - name: NVCC image
-        uses: docker/build-push-action@v3
+        uses: docker/build-push-action@v5
         with:
           context: .
           file: './docker/Dockerfile.nvidia'
@@ -174,7 +175,7 @@ jobs:
           tags: 'devitocodes/bases:nvidia-nvcc'
 
       - name: NVC host image
-        uses: docker/build-push-action@v3
+        uses: docker/build-push-action@v5
         with:
           context: .
           file: './docker/Dockerfile.nvidia'
@@ -215,7 +216,7 @@ jobs:
         run: docker system prune -a -f
 
       - name: AMD image
-        uses: docker/build-push-action@v3
+        uses: docker/build-push-action@v5
         with:
           context: .
           file: './docker/Dockerfile.amd'
@@ -224,7 +225,7 @@ jobs:
           tags: devitocodes/bases:amd
 
       - name: AMD HIP image
-        uses: docker/build-push-action@v3
+        uses: docker/build-push-action@v5
         with:
           context: .
           file: './docker/Dockerfile.amd'
diff --git a/.github/workflows/pytest-core-nompi.yml b/.github/workflows/pytest-core-nompi.yml
index 0d8f5febf6..8dcb22e092 100644
--- a/.github/workflows/pytest-core-nompi.yml
+++ b/.github/workflows/pytest-core-nompi.yml
@@ -36,7 +36,7 @@ jobs:
            pytest-ubuntu-py310-gcc10-noomp,
            pytest-ubuntu-py312-gcc13-omp,
            pytest-ubuntu-py39-gcc9-omp,
-           pytest-osx-py37-clang-omp,
+           pytest-osx-py312-clang-omp,
            pytest-docker-py39-gcc-omp,
            pytest-docker-py39-icx-omp
         ]
@@ -84,8 +84,8 @@ jobs:
           language: "openmp"
           sympy: "1.9"
 
-        - name: pytest-osx-py37-clang-omp
-          python-version: '3.7'
+        - name: pytest-osx-py312-clang-omp
+          python-version: '3.12'
           os: macos-latest
           arch: "clang"
           language: "C"
@@ -112,7 +112,7 @@ jobs:
           test-set: 'adjoint'
 
         exclude:
-        - name: pytest-osx-py37-clang-omp
+        - name: pytest-osx-py312-clang-omp
           set: adjoint
 
     steps:
diff --git a/.github/workflows/tutorials.yml b/.github/workflows/tutorials.yml
index c5b9088d56..434beb7ae7 100644
--- a/.github/workflows/tutorials.yml
+++ b/.github/workflows/tutorials.yml
@@ -22,7 +22,6 @@ jobs:
     env:
       DEVITO_ARCH: "${{ matrix.compiler }}"
       DEVITO_LANGUAGE: ${{ matrix.language }}
-      PYTHON_VERSION: "3.9"
 
     strategy:
       # Prevent all build to stop if a single one fails
@@ -30,7 +29,7 @@ jobs:
       matrix:
         name: [
           tutos-ubuntu-gcc-py39,
-          tutos-osx-clang-py39,
+          tutos-osx-clang-py311,
           tutos-docker-gcc-py39
           ]
 
@@ -39,26 +38,29 @@ jobs:
             os: ubuntu-latest
             compiler: gcc
             language: "openmp"
+            pyver: "3.9"
 
-          - name: tutos-osx-clang-py39
+          - name: tutos-osx-clang-py311
             os: macos-latest
             compiler: clang
             language: "C"
+            pyver: "3.11"
 
           - name: tutos-docker-gcc-py39
             os: ubuntu-latest
             compiler: gcc
             language: "openmp"
+            pyver: "3.9"
 
     steps:
     - name: Checkout devito
       uses: actions/checkout@v4
 
-    - name: Set up Python 3.9
+    - name: Set up Python ${{ matrix.pyver }}
       if: "!contains(matrix.name, 'docker')"
       uses: actions/setup-python@v5
       with:
-        python-version: 3.9
+        python-version: ${{ matrix.pyver }}
 
     - uses: maxim-lobanov/setup-xcode@v1
       if: runner.os == 'macOS'
diff --git a/docker/Dockerfile.nvidia b/docker/Dockerfile.nvidia
index 0df21b4fe5..d7e0a64d49 100644
--- a/docker/Dockerfile.nvidia
+++ b/docker/Dockerfile.nvidia
@@ -130,7 +130,7 @@ RUN python3 -m venv /venv && \
     /venv/bin/pip install --no-cache-dir -r https://raw.githubusercontent.com/devitocodes/devito/master/requirements-nvidia.txt && \
     # Install jupyter and setup nvidia configs.
     /venv/bin/pip install --no-cache-dir jupyter && \
-    /venv/bin/jupyter serverextension enable dask_labextension && \
+    /venv/bin/jupyter server extension enable dask_labextension && \
     rm -rf ~/.cache/pip
 
 RUN apt-get clean && apt-get autoclean && apt-get autoremove -y && \

From a918ec3a641894e356dd47ba528a8e61b1533ef9 Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Thu, 25 Apr 2024 08:56:53 -0400
Subject: [PATCH 27/29] docker: add -t flag for nicer logs

---
 .github/workflows/docker-devito.yml     | 2 +-
 .github/workflows/pytest-core-mpi.yml   | 6 +++---
 .github/workflows/pytest-core-nompi.yml | 2 +-
 .github/workflows/pytest-gpu.yml        | 4 ++--
 .github/workflows/tutorials.yml         | 2 +-
 5 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/.github/workflows/docker-devito.yml b/.github/workflows/docker-devito.yml
index e918753e61..a90c362f85 100644
--- a/.github/workflows/docker-devito.yml
+++ b/.github/workflows/docker-devito.yml
@@ -106,4 +106,4 @@ jobs:
 
       - name: Run tests
         run: |
-         docker run ${{ matrix.flag }} --rm --name testrun 'devitocodes/devito:${{ matrix.tag }}-dev' pytest ${{ matrix.test }}
+         docker run ${{ matrix.flag }} --rm -t --name testrun 'devitocodes/devito:${{ matrix.tag }}-dev' pytest ${{ matrix.test }}
diff --git a/.github/workflows/pytest-core-mpi.yml b/.github/workflows/pytest-core-mpi.yml
index 62ae44f511..dfda8c25e9 100644
--- a/.github/workflows/pytest-core-mpi.yml
+++ b/.github/workflows/pytest-core-mpi.yml
@@ -87,9 +87,9 @@ jobs:
       
       - name: Test with pytest
         run: |
-          docker run --rm -e CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }} -e OMP_NUM_THREADS=1 --name testrun devito_img pytest tests/test_mpi.py
+          docker run --rm -t -e CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }} -e OMP_NUM_THREADS=1 --name testrun devito_img pytest tests/test_mpi.py
 
       - name: Test examples with MPI
         run: |
-          docker run --rm ${{ matrix.mpiflag }} -e DEVITO_MPI=1 -e OMP_NUM_THREADS=1 --name examplerun devito_img mpiexec -n 2 pytest examples/seismic/acoustic
-          docker run --rm -e DEVITO_MPI=1 -e OMP_NUM_THREADS=1 --name examplerun devito_img mpiexec -n 2 pytest examples/seismic/tti
\ No newline at end of file
+          docker run --rm -t ${{ matrix.mpiflag }} -e DEVITO_MPI=1 -e OMP_NUM_THREADS=1 --name examplerun devito_img mpiexec -n 2 pytest examples/seismic/acoustic
+          docker run --rm -t -e DEVITO_MPI=1 -e OMP_NUM_THREADS=1 --name examplerun devito_img mpiexec -n 2 pytest examples/seismic/tti
\ No newline at end of file
diff --git a/.github/workflows/pytest-core-nompi.yml b/.github/workflows/pytest-core-nompi.yml
index 8dcb22e092..ab8221c100 100644
--- a/.github/workflows/pytest-core-nompi.yml
+++ b/.github/workflows/pytest-core-nompi.yml
@@ -133,7 +133,7 @@ jobs:
     - name: Set run prefix
       run: |
           if [[ "${{ matrix.name }}" =~ "docker" ]]; then
-              echo "RUN_CMD=docker run --rm -e CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }} --name testrun devito_img"  >> $GITHUB_ENV
+              echo "RUN_CMD=docker run --rm -t -e CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }} --name testrun devito_img"  >> $GITHUB_ENV
           else
               echo "RUN_CMD=" >> $GITHUB_ENV
           fi
diff --git a/.github/workflows/pytest-gpu.yml b/.github/workflows/pytest-gpu.yml
index cc0533466d..496841d688 100644
--- a/.github/workflows/pytest-gpu.yml
+++ b/.github/workflows/pytest-gpu.yml
@@ -57,7 +57,7 @@ jobs:
           base: "devitocodes/bases:nvidia-nvc"
           tags: ["self-hosted", "nvidiagpu"]
           test_drive_cmd: "nvidia-smi"
-          flags: '--gpus all --rm --name testrun-nvc'
+          flags: '--gpus all --rm -t --name testrun-nvc'
 
         - name: pytest-gpu-omp-amd
           test_files: "tests/test_adjoint.py tests/test_gpu_common.py tests/test_gpu_openmp.py"
@@ -66,7 +66,7 @@ jobs:
           test_drive_cmd: "rocm-smi"
           # Attach the AMD GPU devices `/dev` and add user to video and render (109 on wampa) group
           # Options from https://rocmdocs.amd.com/en/latest/ROCm_Virtualization_Containers/ROCm-Virtualization-&-Containers.html
-          flags: "--network=host --device=/dev/kfd --device=/dev/dri --ipc=host --group-add video --group-add $(getent group render | cut -d: -f3) --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --rm --name testrun-amd"
+          flags: "--network=host --device=/dev/kfd --device=/dev/dri --ipc=host --group-add video --group-add $(getent group render | cut -d: -f3) --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --rm -t --name testrun-amd"
 
     steps:
     - name: Checkout devito
diff --git a/.github/workflows/tutorials.yml b/.github/workflows/tutorials.yml
index 434beb7ae7..292ae9bec8 100644
--- a/.github/workflows/tutorials.yml
+++ b/.github/workflows/tutorials.yml
@@ -75,7 +75,7 @@ jobs:
     - name: Set run prefix
       run: |
           if [ "${{ matrix.name }}" == 'tutos-docker-gcc-py39' ]; then
-              echo "RUN_CMD=docker run --rm --name testrun devito_img" >> $GITHUB_ENV
+              echo "RUN_CMD=docker run --rm -t --name testrun devito_img" >> $GITHUB_ENV
           else
               echo "RUN_CMD=" >> $GITHUB_ENV
           fi

From 00b53adb4b07d6aee8e971c4893c21d31d7707f7 Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Thu, 25 Apr 2024 18:05:46 -0400
Subject: [PATCH 28/29] examples: switch tti fd to Fornberg for better
 stability

---
 examples/seismic/tti/operators.py | 30 +++++++++++++++++++-----------
 1 file changed, 19 insertions(+), 11 deletions(-)

diff --git a/examples/seismic/tti/operators.py b/examples/seismic/tti/operators.py
index 227ebabdd5..f9c20cdda7 100644
--- a/examples/seismic/tti/operators.py
+++ b/examples/seismic/tti/operators.py
@@ -74,18 +74,22 @@ def Gzz_centered(model, field):
     """
     b = getattr(model, 'b', 1)
     costheta, sintheta, cosphi, sinphi = trig_func(model)
+
     order1 = field.space_order // 2
-    Gz = -(sintheta * cosphi * field.dx(fd_order=order1) +
-           sintheta * sinphi * field.dy(fd_order=order1) +
-           costheta * field.dz(fd_order=order1))
+    x, y ,z = field.grid.dimensions
+    dx, dy, dz = x.spacing/2, y.spacing/2, z.spacing/2
+
+    Gz = (sintheta * cosphi * field.dx(fd_order=order1, x0=x+dx) +
+          sintheta * sinphi * field.dy(fd_order=order1, x0=y+dy) +
+          costheta * field.dz(fd_order=order1, x0=z+dz))
 
-    Gzz = (b * Gz * costheta).dz(fd_order=order1).T
+    Gzz = (b * Gz * costheta).dz(fd_order=order1,  x0=z-dz)
     # Add rotated derivative if angles are not zero. If angles are
     # zeros then `0*Gz = 0` and doesn't have any `.dy` ....
     if sintheta != 0:
-        Gzz += (b * Gz * sintheta * cosphi).dx(fd_order=order1).T
+        Gzz += (b * Gz * sintheta * cosphi).dx(fd_order=order1, x0=x-dx)
     if sinphi != 0:
-        Gzz += (b * Gz * sintheta * sinphi).dy(fd_order=order1).T
+        Gzz += (b * Gz * sintheta * sinphi).dy(fd_order=order1, x0=y-dy)
 
     return Gzz
 
@@ -105,17 +109,21 @@ def Gzz_centered_2d(model, field):
     -------
     Rotated second order derivative w.r.t. z.
     """
+    b = getattr(model, 'b', 1)
     costheta, sintheta = trig_func(model)
+
     order1 = field.space_order // 2
-    b = getattr(model, 'b', 1)
-    Gz = -(sintheta * field.dx(fd_order=order1) +
-           costheta * field.dy(fd_order=order1))
-    Gzz = (b * Gz * costheta).dy(fd_order=order1).T
+    x, y = field.grid.dimensions
+    dx, dy = x.spacing/2, y.spacing/2
+
+    Gz = (sintheta * field.dx(fd_order=order1, x0=x+dx) +
+          costheta * field.dy(fd_order=order1, x0=y+dy))
+    Gzz = (b * Gz * costheta).dy(fd_order=order1, x0=y-dy)
 
     # Add rotated derivative if angles are not zero. If angles are
     # zeros then `0*Gz = 0` and doesn't have any `.dy` ....
     if sintheta != 0:
-        Gzz += (b * Gz * sintheta).dx(fd_order=order1).T
+        Gzz += (b * Gz * sintheta).dx(fd_order=order1, xo=x-dx)
     return Gzz
 
 

From d67b9cdeea999f06aa7f2e2d988a3b17b87ff3c4 Mon Sep 17 00:00:00 2001
From: mloubout <mathias.louboutin@gmail.com>
Date: Thu, 25 Apr 2024 18:30:29 -0400
Subject: [PATCH 29/29] CI: update tti test with new fd

---
 .github/workflows/pytest-core-mpi.yml | 4 ++--
 examples/seismic/tti/operators.py     | 6 +++---
 tests/test_dse.py                     | 2 +-
 tests/test_gradient.py                | 2 +-
 4 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/pytest-core-mpi.yml b/.github/workflows/pytest-core-mpi.yml
index dfda8c25e9..1df105361c 100644
--- a/.github/workflows/pytest-core-mpi.yml
+++ b/.github/workflows/pytest-core-mpi.yml
@@ -51,8 +51,8 @@ jobs:
     - name: Test examples with MPI
       run: |
         python3 scripts/clear_devito_cache.py
-        DEVITO_MPI=1 mpirun -n 2 python3 -m pytest --cov --cov-config=.coveragerc --cov-report=xml examples/seismic/acoustic
-        DEVITO_MPI=1 mpirun -n 2 python3 -m pytest --cov --cov-config=.coveragerc --cov-report=xml examples/seismic/tti
+        DEVITO_MPI=1 mpirun -n 2 python3 -m pytest examples/seismic/acoustic
+        DEVITO_MPI=1 mpirun -n 2 python3 -m pytest examples/seismic/tti
 
     - name: Upload coverage to Codecov
       uses: codecov/codecov-action@v4
diff --git a/examples/seismic/tti/operators.py b/examples/seismic/tti/operators.py
index f9c20cdda7..1fa7480bdb 100644
--- a/examples/seismic/tti/operators.py
+++ b/examples/seismic/tti/operators.py
@@ -76,14 +76,14 @@ def Gzz_centered(model, field):
     costheta, sintheta, cosphi, sinphi = trig_func(model)
 
     order1 = field.space_order // 2
-    x, y ,z = field.grid.dimensions
+    x, y, z = field.grid.dimensions
     dx, dy, dz = x.spacing/2, y.spacing/2, z.spacing/2
 
     Gz = (sintheta * cosphi * field.dx(fd_order=order1, x0=x+dx) +
           sintheta * sinphi * field.dy(fd_order=order1, x0=y+dy) +
           costheta * field.dz(fd_order=order1, x0=z+dz))
 
-    Gzz = (b * Gz * costheta).dz(fd_order=order1,  x0=z-dz)
+    Gzz = (b * Gz * costheta).dz(fd_order=order1, x0=z-dz)
     # Add rotated derivative if angles are not zero. If angles are
     # zeros then `0*Gz = 0` and doesn't have any `.dy` ....
     if sintheta != 0:
@@ -123,7 +123,7 @@ def Gzz_centered_2d(model, field):
     # Add rotated derivative if angles are not zero. If angles are
     # zeros then `0*Gz = 0` and doesn't have any `.dy` ....
     if sintheta != 0:
-        Gzz += (b * Gz * sintheta).dx(fd_order=order1, xo=x-dx)
+        Gzz += (b * Gz * sintheta).dx(fd_order=order1, x0=x-dx)
     return Gzz
 
 
diff --git a/tests/test_dse.py b/tests/test_dse.py
index 871a575fce..9db4517cf5 100644
--- a/tests/test_dse.py
+++ b/tests/test_dse.py
@@ -2826,7 +2826,7 @@ def test_opcounts(self, space_order, expected):
 
     @switchconfig(profiling='advanced')
     @pytest.mark.parametrize('space_order,exp_ops,exp_arrays', [
-        (4, 122, 6), (8, 235, 7)
+        (4, 122, 6), (8, 225, 7)
     ])
     def test_opcounts_adjoint(self, space_order, exp_ops, exp_arrays):
         wavesolver = self.tti_operator(space_order=space_order,
diff --git a/tests/test_gradient.py b/tests/test_gradient.py
index 5624c5d461..cfd3a57318 100644
--- a/tests/test_gradient.py
+++ b/tests/test_gradient.py
@@ -244,7 +244,7 @@ def initializer(data):
         ('OT2', (70, 80), (15., 15.), iso_setup, 2),
         ('sls', (70, 80), (20., 20.), vsc_setup, 2),
         ('sls', (70, 80), (20., 20.), vsc_setup, 1),
-        ('centered', (70, 80), (15., 15.), tti_setup, 2),
+        ('centered', (70, 80), (20., 20.), tti_setup, 2),
     ])
     @pytest.mark.parametrize('space_order', [4])
     @pytest.mark.parametrize('dtype', [np.float32, np.float64])