def test_norm_sparse(self):
"""
Test that norm produces the correct result against numpy
"""
grid = Grid((101, 101), extent=(1000., 1000.))
nrec = 101
rec0 = SparseTimeFunction(name='rec0', grid=grid, nt=1001, npoint=nrec)
rec0.data[:, :] = 1 + np.random.rand(*rec0.shape).astype(grid.dtype)
term1 = np.linalg.norm(rec0.data)
term2 = norm(rec0)
assert np.isclose(term1 / term2 - 1, 0.0, rtol=0.0, atol=1e-5)
def __new__(cls, **kwargs):
name = kwargs.pop('name')
grid = kwargs.pop('grid')
time_range = kwargs.pop('time_range')
time_order = kwargs.pop('time_order', 2)
p_dim = kwargs.pop('dimension', Dimension(name='p_%s' % name))
coordinates = kwargs.pop('coordinates',
kwargs.pop('coordinates_data', None))
# Either `npoint` or `coordinates` must be provided
npoint = kwargs.pop('npoint', None)
if npoint is None:
if coordinates is None:
raise TypeError("Need either `npoint` or `coordinates`")
npoint = coordinates.shape[0]
# Create the underlying SparseTimeFunction object
obj = SparseTimeFunction.__new__(cls,
name=name,
grid=grid,
dimensions=(grid.time_dim, p_dim),
npoint=npoint,
nt=time_range.num,
time_order=time_order,
coordinates=coordinates,
**kwargs)
obj._time_range = time_range._rebuild()
# If provided, copy initial data into the allocated buffer
data = kwargs.get('data')
if data is not None:
obj.data[:] = data
return obj
def __new__(cls, *args, **kwargs):
options = kwargs.get('options', {})
if cls in _SymbolCache:
obj = sympy.Function.__new__(cls, *args, **options)
obj._cached_init()
else:
name = kwargs.pop('name')
grid = kwargs.pop('grid')
time_range = kwargs.pop('time_range')
time_order = kwargs.pop('time_order', 2)
p_dim = kwargs.pop('dimension', Dimension(name='p_%s' % name))
coordinates = kwargs.pop('coordinates', kwargs.pop('coordinates_data', None))
# Either `npoint` or `coordinates` must be provided
npoint = kwargs.pop('npoint', None)
if npoint is None:
if coordinates is None:
raise TypeError("Need either `npoint` or `coordinates`")
npoint = coordinates.shape[0]
# Create the underlying SparseTimeFunction object
obj = SparseTimeFunction.__new__(cls, name=name, grid=grid,
dimensions=(grid.time_dim, p_dim),
npoint=npoint, nt=time_range.num,
time_order=time_order,
coordinates=coordinates, **kwargs)
obj._time_range = time_range._rebuild()
# If provided, copy initial data into the allocated buffer
data = kwargs.get('data')
if data is not None:
obj.data[:] = data
return obj
def test_min_max_sparse(self):
"""
Test that mmin/mmax work on SparseFunction
"""
grid = Grid((101, 101), extent=(1000., 1000.))
nrec = 101
rec0 = SparseTimeFunction(name='rec0', grid=grid, nt=1001, npoint=nrec)
rec0.data[:, :] = 1 + np.random.randn(*rec0.shape).astype(grid.dtype)
term1 = np.min(rec0.data)
term2 = mmin(rec0)
assert np.isclose(term1 / term2 - 1, 0.0, rtol=0.0, atol=1e-5)
term1 = np.max(rec0.data)
term2 = mmax(rec0)
assert np.isclose(term1 / term2 - 1, 0.0, rtol=0.0, atol=1e-5)
def __new__(cls, *args, **kwargs):
options = kwargs.get('options', {})
key = cls._cache_key(*args, **kwargs)
obj = cls._cache_get(key)
if obj is not None:
newobj = sympy.Function.__new__(cls, *args, **options)
newobj.__init_cached__(key)
return newobj
# Not in cache. Create a new PointSouce via devito.SparseTimeFunction
name = kwargs.pop('name')
grid = kwargs.pop('grid')
time_range = kwargs.pop('time_range')
time_order = kwargs.pop('time_order', 2)
p_dim = kwargs.pop('dimension', Dimension(name='p_%s' % name))
coordinates = kwargs.pop('coordinates', kwargs.pop('coordinates_data', None))
# Either `npoint` or `coordinates` must be provided
npoint = kwargs.pop('npoint', None)
if npoint is None:
if coordinates is None:
raise TypeError("Need either `npoint` or `coordinates`")
npoint = coordinates.shape[0]
# Create the underlying SparseTimeFunction object
obj = SparseTimeFunction.__new__(cls, name=name, grid=grid,
dimensions=(grid.time_dim, p_dim),
npoint=npoint, nt=time_range.num,
time_order=time_order,
coordinates=coordinates, **kwargs)
obj._time_range = time_range._rebuild()
# If provided, copy initial data into the allocated buffer
data = kwargs.get('data')
if data is not None:
obj.data[:] = data
return obj
def __new__(cls, *args, **kwargs):
options = kwargs.get('options', {})
key = cls
obj = cls._cache_get(key)
if obj is not None:
newobj = sympy.Function.__new__(cls, *args, **options)
newobj.__init_cached__(key)
return newobj
p_dim = kwargs.get('dimension', Dimension('p_%s' % kwargs.get("name")))
npoint = kwargs.get("npoint")
coords = kwargs.get("coordinates")
if npoint is None:
if coords is None:
raise TypeError("Need either `npoint` or `coordinates`")
else:
npoint = coords.shape[0]
grid = kwargs.get("grid")
ntime = kwargs.get("ntime")
if kwargs.get("data") is None:
if ntime is None:
error('Either data or ntime are required to'
'initialise source/receiver objects')
else:
ntime = kwargs.get("ntime") or kwargs.get("data").shape[0]
# Create the underlying SparseTimeFunction object
kwargs["nt"] = ntime
kwargs['npoint'] = npoint
obj = SparseTimeFunction.__new__(cls,
dimensions=[grid.time_dim, p_dim],
**kwargs)
# If provided, copy initial data into the allocated buffer
if kwargs.get("data") is not None:
obj.data[:] = kwargs.get("data")
return obj
def test_inner_sparse(self):
"""
Test that inner produces the correct result against numpy
"""
grid = Grid((101, 101), extent=(1000., 1000.))
nrec = 101
rec0 = SparseTimeFunction(name='rec0', grid=grid, nt=1001, npoint=nrec)
rec1 = SparseTimeFunction(name='rec1', grid=grid, nt=1001, npoint=nrec)
rec0.data[:, :] = 1 + np.random.randn(*rec0.shape).astype(grid.dtype)
rec1.data[:, :] = 1 + np.random.randn(*rec1.shape).astype(grid.dtype)
term1 = inner(rec0, rec1)
term2 = np.inner(rec0.data.reshape(-1), rec1.data.reshape(-1))
assert np.isclose(term1 / term2 - 1, 0.0, rtol=0.0, atol=1e-5)