from .baseconfig import F2003Class, fortran_class, numpy_1d, numpy_2d, np, numpy_1d_or_null
from ctypes import POINTER, c_int, c_double, byref
[docs]
class SourceWindow(F2003Class):
"""
Abstract base class for a number count/lensing/21cm source window function.
A list of instances of these classes can be assigned to the SourceWindows field of :class:`.model.CAMBparams`.
Note that source windows can currently only be used in flat models.
"""
_fields_ = [("source_type", c_int, {"names": ["21cm", "counts", "lensing"], "start": 1}),
("bias", c_double),
("dlog10Ndm", c_double)]
_fortran_class_module_ = "SourceWindows"
_fortran_class_name_ = "TSourceWindow"
[docs]
@fortran_class
class GaussianSourceWindow(SourceWindow):
"""
A Gaussian W(z) source window function.
"""
_fields_ = [("redshift", c_double),
("sigma", c_double)]
_fortran_class_name_ = "TGaussianSourceWindow"
[docs]
@fortran_class
class SplinedSourceWindow(SourceWindow):
"""
A numerical W(z) source window function constructed by interpolation from a numerical table.
"""
_fortran_class_name_ = "TSplinedSourceWindow"
_methods_ = [("SetTable", [POINTER(c_int), numpy_1d, numpy_1d, numpy_1d_or_null]),
("SetTable2DBias", [POINTER(c_int), POINTER(c_int), numpy_1d, numpy_1d, numpy_1d, numpy_2d])
]
def __init__(self, **kwargs):
z = kwargs.pop('z', None)
if z is not None:
self.set_table(z, kwargs.pop('W'), kwargs.pop('bias_z', None),
kwargs.pop('k_bias', None), kwargs.pop('bias_kz', None))
super().__init__(**kwargs)
def __getstate__(self):
raise TypeError("Cannot save class with splines")
[docs]
def set_table(self, z, W, bias_z=None, k_bias=None, bias_kz=None):
"""
Set arrays of z and W(z) for cublic spline interpolation. Note that W(z) is the total count distribution
observed, not a fractional selection function on an underlying distribution.
:param z: array of redshift values (monotonically increasing)
:param W: array of window function values. It must be well enough sampled to smoothly cubic-spline interpolate
:param bias_z: optional array of bias values at each z for scale-independent bias
:param k_bias: optional array of k values for bias
:param bias_kz: optional 2D contiguous array for space-dependent bias(k, z).
Must ensure range of k is large enough to cover required vaules.
"""
if len(W) != len(z) or z[-1] < z[1] or len(z) < 5:
raise ValueError(
"Redshifts must be well sampled and in ascending order, with window function the same length as z")
z = np.ascontiguousarray(z, dtype=np.float64)
W = np.ascontiguousarray(W, dtype=np.float64)
if bias_z is not None:
if bias_kz is not None:
raise ValueError("set bias_k or bias_zk")
bias_z = np.ascontiguousarray(bias_z, dtype=np.float64)
if len(bias_z) != len(z):
raise ValueError("bias array must be same size as the redshift array")
if bias_kz is not None:
k = np.ascontiguousarray(k_bias, dtype=np.float64)
if bias_kz.shape[0] != len(k) or bias_kz.shape[1] != len(z):
raise ValueError('Bias array does not match shape of k,z arrays')
self.f_SetTable2DBias(byref(c_int(len(z))), byref(c_int(len(k))), z, k, W, bias_kz)
else:
self.f_SetTable(byref(c_int(len(z))), z, W, bias_z)