def check_divergence(self):
"""
Used to terminate the program if a blowup occurs in any segment
of the solver, resulting in the values becoming infinity or
undefined.
"""
if (af.any_true(af.isinf(self.f)) or af.any_true(af.isnan(self.f))):
raise SystemExit('Solver Diverging!')
def _get_mask(X, value_to_mask):
"""Compute the boolean mask X == value_to_mask."""
# BUG: doesnt work properly
npdtype = typemap(X.dtype())
if is_scalar_nan(value_to_mask):
if npdtype.kind == "f":
return af.isnan(X)
elif X.dtype.kind in ("i", "u"):
# can't have NaNs in integer array.
return af.constant(0, X.shape[0], X.shape[1], dtype=af.Dtype.b8)
else:
# np.isnan does not work on object dtypes.
return _object_dtype_isnan(X) #todo:fix
else:
return X == value_to_mask
def __init__(self, phase_obj_3d, wavelength, **kwargs):
super().__init__(phase_obj_3d, wavelength, **kwargs)
self.distance_end_to_center = np.sum(
self.slice_separation) / 2. + phase_obj_3d.slice_separation[-1]
self.distance_beginning_to_center = np.sum(self.slice_separation) / 2.
self.slice_separation = np.append(phase_obj_3d.slice_separation,
[phase_obj_3d.slice_separation[-1]])
self.slice_separation = np.asarray([sum(self.slice_separation[x:x+self.slice_binning_factor]) \
for x in range(0,len(self.slice_separation),self.slice_binning_factor)])
if self.slice_separation.shape[0] != self.shape[2]:
print(
"Number of slices does not match with number of separations!")
# generate green's function convolution kernel
fxlin, fylin = self._genFrequencyGrid()
kernel_mask = (self.RI / self.wavelength)**2 > 1.01 * (
fxlin * af.conjg(fxlin) + fylin * af.conjg(fylin))
self.green_kernel_2d = -0.25j * af.exp(
2.0j * np.pi * self.fzlin * self.pixel_size_z) / np.pi / self.fzlin
self.green_kernel_2d *= kernel_mask
self.green_kernel_2d[af.isnan(self.green_kernel_2d) == 1] = 0.0
def simple_arith(verbose = False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(3,3,dtype=af.Dtype.u32)
b = af.constant(4, 3, 3, dtype=af.Dtype.u32)
display_func(a)
display_func(b)
c = a + b
d = a
d += b
display_func(c)
display_func(d)
display_func(a + 2)
display_func(3 + a)
c = a - b
d = a
d -= b
display_func(c)
display_func(d)
display_func(a - 2)
display_func(3 - a)
c = a * b
d = a
d *= b
display_func(c * 2)
display_func(3 * d)
display_func(a * 2)
display_func(3 * a)
c = a / b
d = a
d /= b
display_func(c / 2.0)
display_func(3.0 / d)
display_func(a / 2)
display_func(3 / a)
c = a % b
d = a
d %= b
display_func(c % 2.0)
display_func(3.0 % d)
display_func(a % 2)
display_func(3 % a)
c = a ** b
d = a
d **= b
display_func(c ** 2.0)
display_func(3.0 ** d)
display_func(a ** 2)
display_func(3 ** a)
display_func(a < b)
display_func(a < 0.5)
display_func(0.5 < a)
display_func(a <= b)
display_func(a <= 0.5)
display_func(0.5 <= a)
display_func(a > b)
display_func(a > 0.5)
display_func(0.5 > a)
display_func(a >= b)
display_func(a >= 0.5)
display_func(0.5 >= a)
display_func(a != b)
display_func(a != 0.5)
display_func(0.5 != a)
display_func(a == b)
display_func(a == 0.5)
display_func(0.5 == a)
display_func(a & b)
display_func(a & 2)
c = a
c &= 2
display_func(c)
display_func(a | b)
display_func(a | 2)
c = a
c |= 2
display_func(c)
display_func(a >> b)
display_func(a >> 2)
c = a
c >>= 2
display_func(c)
display_func(a << b)
display_func(a << 2)
c = a
c <<= 2
display_func(c)
display_func(-a)
display_func(+a)
display_func(~a)
display_func(a)
display_func(af.cast(a, af.Dtype.c32))
display_func(af.maxof(a,b))
display_func(af.minof(a,b))
display_func(af.rem(a,b))
a = af.randu(3,3) - 0.5
b = af.randu(3,3) - 0.5
display_func(af.abs(a))
display_func(af.arg(a))
display_func(af.sign(a))
display_func(af.round(a))
display_func(af.trunc(a))
display_func(af.floor(a))
display_func(af.ceil(a))
display_func(af.hypot(a, b))
display_func(af.sin(a))
display_func(af.cos(a))
display_func(af.tan(a))
display_func(af.asin(a))
display_func(af.acos(a))
display_func(af.atan(a))
display_func(af.atan2(a, b))
c = af.cplx(a)
d = af.cplx(a,b)
display_func(c)
display_func(d)
display_func(af.real(d))
display_func(af.imag(d))
display_func(af.conjg(d))
display_func(af.sinh(a))
display_func(af.cosh(a))
display_func(af.tanh(a))
display_func(af.asinh(a))
display_func(af.acosh(a))
display_func(af.atanh(a))
a = af.abs(a)
b = af.abs(b)
display_func(af.root(a, b))
display_func(af.pow(a, b))
display_func(af.pow2(a))
display_func(af.exp(a))
display_func(af.expm1(a))
display_func(af.erf(a))
display_func(af.erfc(a))
display_func(af.log(a))
display_func(af.log1p(a))
display_func(af.log10(a))
display_func(af.log2(a))
display_func(af.sqrt(a))
display_func(af.cbrt(a))
a = af.round(5 * af.randu(3,3) - 1)
b = af.round(5 * af.randu(3,3) - 1)
display_func(af.factorial(a))
display_func(af.tgamma(a))
display_func(af.lgamma(a))
display_func(af.iszero(a))
display_func(af.isinf(a/b))
display_func(af.isnan(a/a))
a = af.randu(5, 1)
b = af.randu(1, 5)
c = af.broadcast(lambda x,y: x+y, a, b)
display_func(a)
display_func(b)
display_func(c)
@af.broadcast
def test_add(aa, bb):
return aa + bb
display_func(test_add(a, b))
def transform(self, X):
"""Impute all missing values in X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data to complete.
"""
check_is_fitted(self)
X = self._validate_input(X, in_fit=False)
#X = af.Array.to_ndarray(X)
X_indicator = super()._transform_indicator(X)
statistics = self.statistics_
if X.shape[1] != statistics.shape[0]:
raise ValueError(
f"X has {X.shape[1]} features per sample, expected {self.statistics_.shape[0]}"
)
# Delete the invalid columns if strategy is not constant
if self.strategy == "constant":
valid_statistics = statistics
else:
# same as af.isnan but also works for object dtypes
# invalid_mask = _get_mask(statistics, np.nan) # BUG: af runtime error
invalid_mask = af.isnan(statistics) # FIXME
valid_mask = invalid_mask.logical_not()
valid_statistics = statistics[valid_mask]
valid_statistics_indexes = np.flatnonzero(valid_mask)
if af.any_true(invalid_mask):
missing = af.arange(X.shape[1])[invalid_mask]
if self.verbose:
warnings.warn(
f"Deleting features without observed values: {missing}"
)
X = X[:, valid_statistics_indexes]
# Do actual imputation
if sp.issparse(X):
if self.missing_values == 0:
raise ValueError(
"Imputation not possible when missing_values == 0 and input is sparse."
"Provide a dense array instead.")
else:
mask = _get_mask(X.data, self.missing_values)
indexes = af.repeat(af.arange(len(X.indptr) - 1, dtype=af.int),
af.diff(X.indptr))[mask]
X.data[mask] = valid_statistics[indexes].astype(X.dtype,
copy=False)
else:
# mask = _get_mask(X, self.missing_values) # BUG
mask = af.isnan(X) # FIXME
# n_missing = af.sum(mask, axis=0) # BUG af
n_missing = af.sum(mask, dim=0)
coordinates = af.where(mask.T)[::-1] # BUG
valid_statistics = valid_statistics.to_ndarray().ravel()
n_missing = n_missing.to_ndarray().ravel()
values = np.repeat(valid_statistics, n_missing) # BUG
values = af.interop.from_ndarray(values)
odims = X.dims()
X = af.flat(X)
X[coordinates] = values
X = af.moddims(X, *odims)
return super()._concatenate_indicator(X, X_indicator)
def isnan(x):
if not isinstance(x, afnumpy.ndarray):
return numpy.isnan(x)
s = arrayfire.isnan(x.d_array)
return afnumpy.ndarray(x.shape, dtype=pu.typemap(s.dtype()), af_array=s)
def check_divergence(self):
if (af.any_true(af.isinf(self.f)) or af.any_true(af.isnan(self.f))):
raise SystemExit('Solver Diverging!')
def simple_arith(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(3, 3)
b = af.constant(4, 3, 3)
display_func(a)
display_func(b)
c = a + b
d = a
d += b
display_func(c)
display_func(d)
display_func(a + 2)
display_func(3 + a)
c = a - b
d = a
d -= b
display_func(c)
display_func(d)
display_func(a - 2)
display_func(3 - a)
c = a * b
d = a
d *= b
display_func(c * 2)
display_func(3 * d)
display_func(a * 2)
display_func(3 * a)
c = a / b
d = a
d /= b
display_func(c / 2.0)
display_func(3.0 / d)
display_func(a / 2)
display_func(3 / a)
c = a % b
d = a
d %= b
display_func(c % 2.0)
display_func(3.0 % d)
display_func(a % 2)
display_func(3 % a)
c = a**b
d = a
d **= b
display_func(c**2.0)
display_func(3.0**d)
display_func(a**2)
display_func(3**a)
display_func(a < b)
display_func(a < 0.5)
display_func(0.5 < a)
display_func(a <= b)
display_func(a <= 0.5)
display_func(0.5 <= a)
display_func(a > b)
display_func(a > 0.5)
display_func(0.5 > a)
display_func(a >= b)
display_func(a >= 0.5)
display_func(0.5 >= a)
display_func(a != b)
display_func(a != 0.5)
display_func(0.5 != a)
display_func(a == b)
display_func(a == 0.5)
display_func(0.5 == a)
a = af.randu(3, 3, dtype=af.Dtype.u32)
b = af.constant(4, 3, 3, dtype=af.Dtype.u32)
display_func(a & b)
display_func(a & 2)
c = a
c &= 2
display_func(c)
display_func(a | b)
display_func(a | 2)
c = a
c |= 2
display_func(c)
display_func(a >> b)
display_func(a >> 2)
c = a
c >>= 2
display_func(c)
display_func(a << b)
display_func(a << 2)
c = a
c <<= 2
display_func(c)
display_func(-a)
display_func(+a)
display_func(~a)
display_func(a)
display_func(af.cast(a, af.Dtype.c32))
display_func(af.maxof(a, b))
display_func(af.minof(a, b))
display_func(af.rem(a, b))
a = af.randu(3, 3) - 0.5
b = af.randu(3, 3) - 0.5
display_func(af.abs(a))
display_func(af.arg(a))
display_func(af.sign(a))
display_func(af.round(a))
display_func(af.trunc(a))
display_func(af.floor(a))
display_func(af.ceil(a))
display_func(af.hypot(a, b))
display_func(af.sin(a))
display_func(af.cos(a))
display_func(af.tan(a))
display_func(af.asin(a))
display_func(af.acos(a))
display_func(af.atan(a))
display_func(af.atan2(a, b))
c = af.cplx(a)
d = af.cplx(a, b)
display_func(c)
display_func(d)
display_func(af.real(d))
display_func(af.imag(d))
display_func(af.conjg(d))
display_func(af.sinh(a))
display_func(af.cosh(a))
display_func(af.tanh(a))
display_func(af.asinh(a))
display_func(af.acosh(a))
display_func(af.atanh(a))
a = af.abs(a)
b = af.abs(b)
display_func(af.root(a, b))
display_func(af.pow(a, b))
display_func(af.pow2(a))
display_func(af.sigmoid(a))
display_func(af.exp(a))
display_func(af.expm1(a))
display_func(af.erf(a))
display_func(af.erfc(a))
display_func(af.log(a))
display_func(af.log1p(a))
display_func(af.log10(a))
display_func(af.log2(a))
display_func(af.sqrt(a))
display_func(af.cbrt(a))
a = af.round(5 * af.randu(3, 3) - 1)
b = af.round(5 * af.randu(3, 3) - 1)
display_func(af.factorial(a))
display_func(af.tgamma(a))
display_func(af.lgamma(a))
display_func(af.iszero(a))
display_func(af.isinf(a / b))
display_func(af.isnan(a / a))
a = af.randu(5, 1)
b = af.randu(1, 5)
c = af.broadcast(lambda x, y: x + y, a, b)
display_func(a)
display_func(b)
display_func(c)
@af.broadcast
def test_add(aa, bb):
return aa + bb
display_func(test_add(a, b))
af.display(af.log(a))
af.display(af.log1p(a))
af.display(af.log10(a))
af.display(af.log2(a))
af.display(af.sqrt(a))
af.display(af.cbrt(a))
a = af.round(5 * af.randu(3, 3) - 1)
b = af.round(5 * af.randu(3, 3) - 1)
af.display(af.factorial(a))
af.display(af.tgamma(a))
af.display(af.lgamma(a))
af.display(af.iszero(a))
af.display(af.isinf(a / b))
af.display(af.isnan(a / a))
a = af.randu(5, 1)
b = af.randu(1, 5)
c = af.broadcast(lambda x, y: x + y, a, b)
af.display(a)
af.display(b)
af.display(c)
@af.broadcast
def test_add(aa, bb):
return aa + bb
af.display(test_add(a, b))
def isnan(x):
if not isinstance(x, afnumpy.ndarray):
return numpy.isnan(x)
s = arrayfire.isnan(x.d_array)
return afnumpy.ndarray(x.shape, dtype=pu.typemap(s.dtype()), af_array=s)
def hasnan(arr):
return af.any_true(af.isnan(arr))
af.display(af.erfc(a))
af.display(af.log(a))
af.display(af.log1p(a))
af.display(af.log10(a))
af.display(af.log2(a))
af.display(af.sqrt(a))
af.display(af.cbrt(a))
a = af.round(5 * af.randu(3,3) - 1)
b = af.round(5 * af.randu(3,3) - 1)
af.display(af.factorial(a))
af.display(af.tgamma(a))
af.display(af.lgamma(a))
af.display(af.iszero(a))
af.display(af.isinf(a/b))
af.display(af.isnan(a/a))
a = af.randu(5, 1)
b = af.randu(1, 5)
c = af.broadcast(lambda x,y: x+y, a, b)
af.display(a)
af.display(b)
af.display(c)
@af.broadcast
def test_add(aa, bb):
return aa + bb
af.display(test_add(a, b))