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Ia correlations #1

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Feb 8, 2023
Merged

Ia correlations #1

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fa418ee
Placed correlation_functions from halotools_ia into main halotools di…
nvanalfen Feb 8, 2023
faea4a3
Changed halotools imports to relative imports
nvanalfen Feb 8, 2023
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36 changes: 36 additions & 0 deletions halotools/correlation_functions/__init__.py
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"""
Package containing functions that calculate many variations on galaxy/halo alignments.
"""
from __future__ import absolute_import

# ellipticity-ellipticity tpcf
from .ee_3d import ee_3d
from .ee_projected import ee_projected

# ellipticity-direction tpcf
from .ed_3d import ed_3d
from .ed_projected import ed_projected

# gravitational shear-intrinsic ellipticity tpcf
from .gi_plus_3d import gi_plus_3d
from .gi_plus_projected import gi_plus_projected
from .gi_minus_3d import gi_minus_3d
from .gi_minus_projected import gi_minus_projected

# intrinsic ellipticity-intrinsic ellipticity tpcf
from .ii_plus_3d import ii_plus_3d
from .ii_plus_projected import ii_plus_projected
from .ii_minus_3d import ii_minus_3d
from .ii_minus_projected import ii_minus_projected

# decomposed functions
from .ee_3d_one_two_halo_decomp import ee_3d_one_two_halo_decomp
from .ed_3d_one_two_halo_decomp import ed_3d_one_two_halo_decomp

__all__ = ('ee_3d', 'ee_projected',
'ed_3d', 'ed_projected',
'gi_plus_3d', 'gi_plus_projected',
'gi_minus_3d', 'gi_minus_projected',
'ii_plus_3d', 'ii_plus_projected',
'ii_minus_3d', 'ii_minus_projected',
'ee_3d_one_two_halo_decomp', 'ed_3d_one_two_halo_decomp')
242 changes: 242 additions & 0 deletions halotools/correlation_functions/alignment_helpers.py
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"""
helper functions used to process arguments passed to the functions in this package

"""
from __future__ import absolute_import, division, print_function, unicode_literals

import numpy as np
from warnings import warn

from ..mock_observables.mock_observables_helpers import enforce_sample_has_correct_shape

__all__ = ('process_projected_alignment_args', 'process_3d_alignment_args')

__author__ = ['Duncan Campbell']


def process_projected_alignment_args(sample1, orientations1, ellipticities1, weights1,
sample2, orientations2, ellipticities2, weights2,
randoms1, ran_weights1, randoms2, ran_weights2):
r"""
process arguments for projected alignment correlation functions
"""

sample1 = enforce_sample_has_correct_shape(sample1)
sample2 = enforce_sample_has_correct_shape(sample2)
N1 = len(sample1)
N2 = len(sample2)

# determine if real randoms are passed
if (randoms1 is not None) and (randoms2 is not None):
using_randoms = True
randoms1 = enforce_sample_has_correct_shape(randoms1)
randoms2 = enforce_sample_has_correct_shape(randoms2)
NR1 = len(randoms1)
NR2 = len(randoms2)
else:
using_randoms = False
ran_weights1 = None
ran_weights2 = None

# check to see if orientations and ellipticities were provided for sample1
if orientations1 is not None:
orientations1 = np.atleast_1d(orientations1).astype(float)
else:
orientations1 = np.ones((N1, 2))
if ellipticities1 is not None:
ellipticities1 = np.atleast_1d(ellipticities1).astype(float)
else:
ellipticities1 = np.ones(N1)

# check to see if orientations and ellipticities were provided for sample2
if orientations2 is not None:
orientations2 = np.atleast_1d(orientations2).astype(float)
else:
orientations2 = np.ones((N2, 2))
if ellipticities2 is not None:
ellipticities2 = np.atleast_1d(ellipticities2).astype(float)
else:
ellipticities2 = np.ones(N2)

# process weights argument
if weights1 is not None:
weights1 = np.atleast_1d(weights1).astype(float)
else:
weights1 = np.ones(len(sample1)).astype(float)

if weights2 is not None:
weights2 = np.atleast_1d(weights2).astype(float)
else:
weights2 = np.ones(len(sample2)).astype(float)

if using_randoms:
if ran_weights1 is not None:
ran_weights1 = np.atleast_1d(ran_weights1).astype(float)
else:
ran_weights1 = np.ones(len(randoms1)).astype(float)
if ran_weights2 is not None:
ran_weights2 = np.atleast_1d(ran_weights2).astype(float)
else:
ran_weights2 = np.ones(len(randoms2)).astype(float)

# process orientations
if np.shape(orientations1) != (N1, 2):
msg = ("`orientations1` is not the correct shape.")
raise ValueError(msg)
if np.shape(orientations2) != (N2, 2):
msg = ("`orientations2` is not the correct shape.")
raise ValueError(msg)

# process ellipticities
if np.shape(ellipticities1) != (N1,):
msg = ("`ellipticities1` is not the correct shape.")
raise ValueError(msg)
if np.shape(ellipticities2) != (N2,):
msg = ("`ellipticities2` is not the correct shape.")
raise ValueError(msg)

# check to make sure weights are correct shape
if np.shape(weights1) != (N1,):
msg = ("`weights1` is not the correct shape.")
raise ValueError(msg)
if np.shape(weights2) != (N2,):
msg = ("`weights2` is not the correct shape.")
raise ValueError(msg)

if using_randoms:
if np.shape(ran_weights1) != (NR1,):
msg = ("`ran_weights1` is not the correct shape.")
raise ValueError(msg)
if np.shape(ran_weights2) != (NR2,):
msg = ("`ran_weights1` is not the correct shape.")
raise ValueError(msg)

# make sure neither orientations contain a vector of length 0
mag1 = np.sqrt(np.sum(orientations1**2, axis=1)).reshape((len(orientations1), -1))
mag2 = np.sqrt(np.sum(orientations2**2, axis=1)).reshape((len(orientations2), -1))

if np.any(mag1 == 0.0) | np.any(mag2 == 0.0):
msg = ("`orientations1` or `orientations2` contains a vector of length 0.")
raise ValueError(msg)

# normalize the orientation vectors
orientations1 = orientations1/mag1
orientations2 = orientations2/mag2

return sample1, orientations1, ellipticities1, weights1,\
sample2, orientations2, ellipticities2, weights2,\
randoms1, ran_weights1, randoms2, ran_weights2


def process_3d_alignment_args(sample1, orientations1, ellipticities1, weights1,
sample2, orientations2, ellipticities2, weights2,
randoms1, ran_weights1, randoms2, ran_weights2):
r"""
process arguments for 3D alignment correlation functions
"""

sample1 = enforce_sample_has_correct_shape(sample1)
sample2 = enforce_sample_has_correct_shape(sample2)
N1 = len(sample1)
N2 = len(sample2)

# determine if real randoms are passed
if (randoms1 is not None) and (randoms2 is not None):
using_randoms = True
randoms1 = enforce_sample_has_correct_shape(randoms1)
randoms2 = enforce_sample_has_correct_shape(randoms2)
NR1 = len(randoms1)
NR2 = len(randoms2)
else:
using_randoms = False
ran_weights1 = None
ran_weights2 = None

# check to see if orientations and ellipticities were provided for sample1
if orientations1 is not None:
orientations1 = np.atleast_1d(orientations1).astype(float)
else:
orientations1 = np.ones((N1, 3))
if ellipticities1 is not None:
ellipticities1 = np.atleast_1d(ellipticities1).astype(float)
else:
ellipticities1 = np.ones(N1)

# check to see if orientations and ellipticities were provided for sample2
if orientations2 is not None:
orientations2 = np.atleast_1d(orientations2).astype(float)
else:
orientations2 = np.ones((N2, 3))
if ellipticities2 is not None:
ellipticities2 = np.atleast_1d(ellipticities2).astype(float)
else:
ellipticities2 = np.ones(N2)

# process weights argument
if weights1 is not None:
weights1 = np.atleast_1d(weights1).astype(float)
else:
weights1 = np.ones(len(sample1)).astype(float)

if weights2 is not None:
weights2 = np.atleast_1d(weights2).astype(float)
else:
weights2 = np.ones(len(sample2)).astype(float)

if using_randoms:
if ran_weights1 is not None:
ran_weights1 = np.atleast_1d(ran_weights1).astype(float)
else:
ran_weights1 = np.ones(len(randoms1)).astype(float)
if ran_weights2 is not None:
ran_weights2 = np.atleast_1d(ran_weights2).astype(float)
else:
ran_weights2 = np.ones(len(randoms2)).astype(float)

# process orientations
if np.shape(orientations1) != (N1, 3):
msg = ("`orientations1` is not the correct shape.")
raise ValueError(msg)
if np.shape(orientations2) != (N2, 3):
msg = ("`orientations2` is not the correct shape.")
raise ValueError(msg)

# process ellipticities
if np.shape(ellipticities1) != (N1,):
msg = ("`ellipticities1` is not the correct shape.")
raise ValueError(msg)
if np.shape(ellipticities2) != (N2,):
msg = ("`ellipticities2` is not the correct shape.")
raise ValueError(msg)

# check to make sure weights are correct shape
if np.shape(weights1) != (N1,):
msg = ("`weights1` is not the correct shape.")
raise ValueError(msg)
if np.shape(weights2) != (N2,):
msg = ("`weights1` is not the correct shape.")
raise ValueError(msg)

if using_randoms:
if np.shape(ran_weights1) != (NR1,):
msg = ("`ran_weights1` is not the correct shape.")
raise ValueError(msg)
if np.shape(ran_weights2) != (NR2,):
msg = ("`ran_weights2` is not the correct shape.")
raise ValueError(msg)

# make sure neither orientations contain a vector of length 0
mag1 = np.sqrt(np.sum(orientations1**2, axis=1)).reshape((len(orientations1), -1))
mag2 = np.sqrt(np.sum(orientations2**2, axis=1)).reshape((len(orientations2), -1))

if np.any(mag1 == 0.0) | np.any(mag2 == 0.0):
msg = ("`orientations1` or `orientations2` contains a vector of length 0.")
raise ValueError(msg)

# normalize the orientation vectors
orientations1 = orientations1/mag1
orientations2 = orientations2/mag2

return sample1, orientations1, ellipticities1, weights1,\
sample2, orientations2, ellipticities2, weights2,\
randoms1, ran_weights1, randoms2, ran_weights2