def subtract(self, other, out=None):
'''
Overloads - for data containers.
Returns the difference of the container data with another container
data viewed as vectors.
other: DataContainer
'''
if not isinstance(other, (DataContainer, Number)):
return NotImplemented
if isinstance(other, Number):
tmp = other + numpy.zeros(self.shape, self.dtype)
other = self.copy()
other.fill(tmp)
assert_validities(self, other)
pl_one = numpy.asarray([1.0, 0.0], dtype=numpy.float32)
mn_one = numpy.asarray([-1.0, 0.0], dtype=numpy.float32)
if out is None:
z = self.same_object()
z.handle = pysirf.cSIRF_axpby \
(pl_one.ctypes.data, self.handle, mn_one.ctypes.data, other.handle)
check_status(z.handle)
else:
assert_validities(self, out)
z = out
try_calling(pysirf.cSIRF_axpbyAlt \
(pl_one.ctypes.data, self.handle, mn_one.ctypes.data, other.handle, z.handle))
return z
def axpby(self, a, b, y, out=None, **kwargs):
'''
Addition for data containers.
Returns the sum of the container data with another container
data viewed as vectors.
y: DataContainer
out: DataContainer to store the result to.
'''
# if isinstance(other , ( Number, int, float, numpy.float32 )):
# tmp = other + numpy.zeros(self.as_array().shape)
# other = self.copy()
# other.fill(tmp)
assert_validities(self, y)
alpha = numpy.asarray([a.real, a.imag], dtype=numpy.float32)
beta = numpy.asarray([b.real, b.imag], dtype=numpy.float32)
if out is None:
z = self.same_object()
z.handle = pysirf.cSIRF_axpby \
(alpha.ctypes.data, self.handle, beta.ctypes.data, y.handle)
else:
assert_validities(self, out)
z = out
try_calling(pysirf.cSIRF_axpbyAlt \
(alpha.ctypes.data, self.handle, beta.ctypes.data, y.handle, z.handle))
check_status(z.handle)
return z
def divide(self, other):
'''
Returns the elementwise ratio of this and another container
data viewed as vectors.
other: DataContainer
'''
assert_validities(self, other)
z = self.same_object()
z.handle = pysirf.cSIRF_divide(self.handle, other.handle)
check_status(z.handle)
return z
def dot(self, other):
'''
Returns the dot product of the container data with another container
data viewed as vectors.
other: DataContainer
'''
assert_validities(self, other)
handle = pysirf.cSIRF_dot(self.handle, other.handle)
check_status(handle)
r = pyiutil.floatDataFromHandle(handle)
pyiutil.deleteDataHandle(handle)
return r
def log(self, out=None):
'''Returns the element-wise log of the DataContainer data
uses NumPy
'''
if out is None:
z = self.clone()
else:
assert_validities(self, out)
z = out
z.fill(numpy.log(self.as_array()))
return z
def __add__(self, other):
'''
Overloads + for data containers.
Returns the sum of the container data with another container
data viewed as vectors.
other: DataContainer
'''
assert_validities(self, other)
one = numpy.asarray([1.0, 0.0], dtype = numpy.float32)
z = self.same_object()
z.handle = pysirf.cSIRF_axpby \
(one.ctypes.data, self.handle, one.ctypes.data, other.handle)
check_status(z.handle)
return z;
def maximum(self, other, out=None):
'''Element-wise maximum of DataContainer elements.
Compare two DataContainers and returns a new array containing the element-wise maxima. Output can be pre-allocated in variable out.
uses NumPy
SIRF/CIL compatibility
'''
if out is None:
z = self.clone()
else:
assert_validities(self, out)
z = out
if isinstance(other, Number):
z.fill(numpy.maximum(self.as_array(), other))
else:
assert_validities(self, other)
z.fill(numpy.maximum(self.as_array(), other.as_array()))
return z
def power(self, other, out=None):
'''Power function for DataContainers
uses NumPy
SIRF/CIL compatibility
'''
if out is None:
z = self.clone()
else:
assert_validities(self, out)
z = out
if isinstance(other, Number):
tmp = numpy.power(self.as_array(), other)
z.fill(tmp)
else:
assert_validities(self, other)
z.fill(numpy.power(self.as_array(), other.as_array()))
return z
def dot(self, other):
'''
Returns the dot product of the container data with another container
data viewed as vectors.
other: DataContainer
'''
assert_validities(self,other)
# Check if input are the same size
if numpy.prod(self.dimensions()) != numpy.prod(other.dimensions()):
raise ValueError("Input sizes are expected to be equal, got " + numpy.prod(self.dimensions()) + " and " + numpy.prod(other.dimensions()) + " instead.")
handle = pysirf.cSIRF_dot(self.handle, other.handle)
check_status(handle)
re = pyiutil.floatReDataFromHandle(handle)
im = pyiutil.floatImDataFromHandle(handle)
pyiutil.deleteDataHandle(handle)
if im == 0:
return re
else:
return re + 1j*im
def divide(self, other, out=None):
'''
Returns the elementwise ratio of this and another container
data viewed as vectors.
other: DataContainer
out: DataContainer to store the result to.
'''
if isinstance(other, (Number, int, float, numpy.float32)):
tmp = other + numpy.zeros_like(self.as_array().shape)
other = self.copy()
other.fill(tmp)
assert_validities(self, other)
if out is None:
z = self.same_object()
else:
assert_validities(self, out)
z = out
z.handle = pysirf.cSIRF_divide(self.handle, other.handle)
check_status(z.handle)
return z
def divide(self, other, out=None):
'''
Returns the elementwise ratio of this and another container
data viewed as vectors.
other: DataContainer
out: DataContainer to store the result to.
'''
if not isinstance (other, ( DataContainer , Number )):
return NotImplemented
if isinstance(other , Number ):
tmp = other + numpy.zeros(self.shape, self.dtype)
other = self.copy()
other.fill(tmp)
assert_validities(self, other)
if out is None:
out = self.same_object()
out.handle = pysirf.cSIRF_ratio(self.handle, other.handle)
check_status(out.handle)
#out = self.copy()
else:
assert_validities(self, out)
try_calling(pysirf.cSIRF_divide(self.handle, other.handle, out.handle))
return out
def axpby(self, a, b, y, out=None, **kwargs):
'''
Addition for data containers.
Returns the sum of the container data with another container
data viewed as vectors.
a: multiplier to self, can be a number or a DataContainer
b: multiplier to y, can be a number or a DataContainer
y: DataContainer
out: DataContainer to store the result to.
'''
# splits axpby in 3 steps if a and b are not numbers as
# pysirf.cSIRF_axpby requires them as numbers
if not (isinstance(a, Number) and isinstance(b, Number)):
if out is None:
out = y.multiply(b)
else:
y.multiply(b, out=out)
tmp = self.multiply(a)
out.add(tmp, out=out)
return out
assert_validities(self, y)
alpha = numpy.asarray([a.real, a.imag], dtype=numpy.float32)
beta = numpy.asarray([b.real, b.imag], dtype=numpy.float32)
if out is None:
z = self.same_object()
z.handle = pysirf.cSIRF_axpby \
(alpha.ctypes.data, self.handle, beta.ctypes.data, y.handle)
else:
assert_validities(self, out)
z = out
try_calling(pysirf.cSIRF_axpbyAlt \
(alpha.ctypes.data, self.handle, beta.ctypes.data, y.handle, z.handle))
check_status(z.handle)
return z
def add(self, other, out=None):
'''
Addition for data containers.
Returns the sum of the container data with another container
data viewed as vectors.
other: DataContainer
out: DataContainer to store the result to.
'''
if isinstance(other, (Number, int, float, numpy.float32)):
tmp = other + numpy.zeros(self.as_array().shape)
other = self.copy()
other.fill(tmp)
assert_validities(self, other)
one = numpy.asarray([1.0, 0.0], dtype=numpy.float32)
if out is None:
z = self.same_object()
else:
assert_validities(self, out)
z = out
z.handle = pysirf.cSIRF_axpby \
(one.ctypes.data, self.handle, one.ctypes.data, other.handle)
check_status(z.handle)
return z
def sapyb(self, a, y, b, out=None, **kwargs):
'''
Addition for data containers. Can be in place.
Returns the sum of the container data with another container
data viewed as vectors.
a: multiplier to self, can be a number or a DataContainer
b: multiplier to y, can be a number or a DataContainer
y: DataContainer
out: DataContainer to store the result to, can be self or y.
'''
assert_validities(self, y)
if out is not None:
assert_validities(self, out)
z = out
else:
z = self.same_object()
if isinstance(a, Number):
alpha = numpy.asarray([a.real, a.imag], dtype=numpy.float32)
if isinstance(b, Number):
#a is scalar, b is scalar
beta = numpy.asarray([b.real, b.imag], dtype=numpy.float32)
if out is None:
z.handle = pysirf.cSIRF_axpby(alpha.ctypes.data,
self.handle,
beta.ctypes.data, y.handle)
else:
try_calling(
pysirf.cSIRF_axpbyAlt(alpha.ctypes.data, self.handle,
beta.ctypes.data, y.handle,
z.handle))
else:
#a is scalar, b is array
one = numpy.asarray([1.0, 0.0], dtype=numpy.float32)
tmp = y.multiply(b)
if out is None:
z.handle = pysirf.cSIRF_axpby(alpha.ctypes.data,
self.handle, one.ctypes.data,
tmp.handle)
else:
try_calling(
pysirf.cSIRF_axpbyAlt(alpha.ctypes.data, self.handle,
one.ctypes.data, tmp.handle,
z.handle))
else:
assert_validities(self, a)
if isinstance(b, Number):
#a is array, b is scalar
one = numpy.asarray([1.0, 0.0], dtype=numpy.float32)
beta = numpy.asarray([b.real, b.imag], dtype=numpy.float32)
tmp = self.multiply(a)
if out is None:
z.handle = pysirf.cSIRF_axpby(one.ctypes.data, tmp.handle,
beta.ctypes.data, y.handle)
else:
try_calling(
pysirf.cSIRF_axpbyAlt(one.ctypes.data, tmp.handle,
beta.ctypes.data, y.handle,
z.handle))
else:
#a is array, b is array
assert_validities(self, b)
if out is None:
try:
z.handle = pysirf.cSIRF_xapyb(self.handle, a.handle,
y.handle, b.handle)
check_status(z.handle)
except error as e:
if 'NotImplemented' in str(e):
tmp = self.multiply(a)
z = y.multiply(b)
z.add(tmp, out=z)
else:
raise RuntimeError(str(e))
else:
try:
try_calling(
pysirf.cSIRF_xapybAlt(self.handle, a.handle,
y.handle, b.handle,
z.handle))
except error as e:
if 'NotImplemented' in str(e):
tmp = self.multiply(a)
y.multiply(b, out=z)
z.add(tmp, out=z)
else:
raise RuntimeError(str(e))
check_status(z.handle)
return z
