<gh_stars>10-100
""" Module providing testing for
`~halotools.mock_observables.conditional_cylindrical_isolation` function.
"""
from __future__ import absolute_import, division, print_function, unicode_literals

import numpy as np
from astropy.utils.misc import NumpyRNGContext
import pytest

from ..conditional_cylindrical_isolation import conditional_cylindrical_isolation
from ..cylindrical_isolation import cylindrical_isolation

from ...tests.cf_helpers import generate_locus_of_3d_points

__all__ = ('test_agreement_with_unconditioned_calculation',
    'test_conditional_cylindrical_isolation_cond_func1',
    'test_conditional_cylindrical_isolation_cond_func2',
    'test_conditional_cylindrical_isolation_cond_func3',
    'test_conditional_cylindrical_isolation_cond_func4',
    'test_conditional_cylindrical_isolation_cond_func5')

fixed_seed = 43


@pytest.mark.installation_test
def test_agreement_with_unconditioned_calculation():
    """ Verify that the `~halotools.mock_observables.cylindrical_isolation` function
    agrees exactly with the `~halotools.mock_observables.conditional_cylindrical_isolation` function
    for a random distribution of points when cond_func=0 is selected.

    Test cases with and without PBCs, as well as with and without variable search radii.
    """
    npts1, npts2 = 100, 90
    with NumpyRNGContext(fixed_seed):
        sample1 = np.random.random((npts1, 3))
        sample2 = np.random.random((npts2, 3))

    rp_max, pi_max = 0.1, 0.1

    unconditioned_result = cylindrical_isolation(sample1, sample2, rp_max, pi_max)
    conditioned_result = conditional_cylindrical_isolation(sample1, sample2, rp_max, pi_max)
    assert np.all(unconditioned_result == conditioned_result)

    unconditioned_result = cylindrical_isolation(sample1, sample2, rp_max, pi_max, period=1)
    conditioned_result = conditional_cylindrical_isolation(sample1, sample2, rp_max, pi_max, period=1)
    assert np.all(unconditioned_result == conditioned_result)

    with NumpyRNGContext(fixed_seed):
        rp_max = np.random.uniform(0, 0.1, npts1)
        pi_max = np.random.uniform(0, 0.1, npts1)

    unconditioned_result = cylindrical_isolation(sample1, sample2, rp_max, pi_max)
    conditioned_result = conditional_cylindrical_isolation(sample1, sample2, rp_max, pi_max)
    assert np.all(unconditioned_result == conditioned_result)

    unconditioned_result = cylindrical_isolation(sample1, sample2, rp_max, pi_max, period=1)
    conditioned_result = conditional_cylindrical_isolation(sample1, sample2, rp_max, pi_max, period=1)
    assert np.all(unconditioned_result == conditioned_result)


def test_conditional_cylindrical_isolation_cond_func1():
    """ For cond_func = 1, and for a set of points that are spatially
    nearby each other when properly accounting for PBCs, verify that the
    `~halotools.mock_observables.conditional_cylindrical_isolation` function
    always returns False when every mark1 exceeds every mark2, and conversely.
    """
    sample1 = generate_locus_of_3d_points(10, xc=0.05, yc=0.05, zc=0.05, seed=fixed_seed)
    sample2 = generate_locus_of_3d_points(10, xc=0.95, yc=0.95, zc=0.95, seed=fixed_seed)

    rp_max, pi_max = 0.2, 0.2
    iso = cylindrical_isolation(sample1, sample2, rp_max, pi_max, period=[1, 1, 1])
    assert np.all(iso == False)

    marks1 = np.ones(len(sample1))
    marks2 = np.zeros(len(sample2))

    cond_func = 1
    marked_iso1a = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks1, marks2, cond_func, period=1)
    marked_iso1b = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks2, marks1, cond_func, period=1)
    assert np.all(marked_iso1a == False)
    assert np.all(marked_iso1b == True)


def test_conditional_cylindrical_isolation_cond_func2():
    """ For cond_func = 2, and for a set of points that are spatially
    nearby each other when properly accounting for PBCs, verify that the
    `~halotools.mock_observables.conditional_cylindrical_isolation` function
    always returns True when every mark1 exceeds every mark2, and conversely.
    """
    sample1 = generate_locus_of_3d_points(10, xc=0.05, yc=0.05, zc=0.05, seed=fixed_seed)
    sample2 = generate_locus_of_3d_points(10, xc=0.95, yc=0.95, zc=0.95, seed=fixed_seed)

    rp_max, pi_max = 0.2, 0.2
    iso = cylindrical_isolation(sample1, sample2, rp_max, pi_max, period=[1, 1, 1])
    assert np.all(iso == False)

    marks1 = np.ones(len(sample1))
    marks2 = np.zeros(len(sample2))

    cond_func = 2
    marked_iso2a = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks1, marks2, cond_func, period=1)
    marked_iso2b = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks2, marks1, cond_func, period=1)
    assert np.all(marked_iso2a == True)
    assert np.all(marked_iso2b == False)


def test_conditional_cylindrical_isolation_cond_func3():
    """ For cond_func = 3, and for a set of points that are spatially
    nearby each other when properly accounting for PBCs, verify that the
    `~halotools.mock_observables.conditional_cylindrical_isolation` function
    always returns True when every mark1 is distinct from every mark2, and conversely.
    """
    sample1 = generate_locus_of_3d_points(10, xc=0.05, yc=0.05, zc=0.05, seed=fixed_seed)
    sample2 = generate_locus_of_3d_points(10, xc=0.95, yc=0.95, zc=0.95, seed=fixed_seed)

    rp_max, pi_max = 0.2, 0.2
    iso = cylindrical_isolation(sample1, sample2, rp_max, pi_max, period=[1, 1, 1])
    assert np.all(iso == False)

    marks1 = np.ones(len(sample1))
    marks2 = np.zeros(len(sample2))

    cond_func = 3

    marked_iso3 = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks1, marks2, cond_func, period=1)
    assert np.all(marked_iso3 == True)

    marked_iso3 = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks1, marks1, cond_func, period=1)
    assert np.all(marked_iso3 == False)


def test_conditional_cylindrical_isolation_cond_func4():
    """ For cond_func = 4, and for a set of points that are spatially
    nearby each other when properly accounting for PBCs, verify that the
    `~halotools.mock_observables.conditional_cylindrical_isolation` function
    always returns False when every mark1 is distinct from every mark2, and conversely.
    """
    sample1 = generate_locus_of_3d_points(10, xc=0.05, yc=0.05, zc=0.05, seed=fixed_seed)
    sample2 = generate_locus_of_3d_points(10, xc=0.95, yc=0.95, zc=0.95, seed=fixed_seed)

    rp_max, pi_max = 0.2, 0.2
    iso = cylindrical_isolation(sample1, sample2, rp_max, pi_max, period=[1, 1, 1])
    assert np.all(iso == False)

    marks1 = np.ones(len(sample1))
    marks2 = np.zeros(len(sample2))

    cond_func = 4

    marked_iso4 = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks1, marks2, cond_func, period=1)
    assert np.all(marked_iso4 == False)

    marked_iso4 = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks1, marks1, cond_func, period=1)
    assert np.all(marked_iso4 == True)


def test_conditional_cylindrical_isolation_cond_func5():
    """ For cond_func = 5, consider a set of points that are spatially
    nearby each other when properly accounting for PBCs.
    Verify that the `~halotools.mock_observables.conditional_cylindrical_isolation` function
    always returns the appropriate answer in both appropriate limits of
    w_1[0] to (w_2[0]+w_1[1]).
    """
    sample1 = generate_locus_of_3d_points(10, xc=0.05, yc=0.05, zc=0.05, seed=fixed_seed)
    sample2 = generate_locus_of_3d_points(10, xc=0.95, yc=0.95, zc=0.95, seed=fixed_seed)

    # First verify that the two points are not isolated when ignoring the marks
    rp_max, pi_max = 0.2, 0.2
    iso = cylindrical_isolation(sample1, sample2, rp_max, pi_max, period=[1, 1, 1])
    assert np.all(iso == False)

    # The first sample1 mark is 1, the second sample1 mark is also 1
    # All sample2 marks are 0
    # Thus w_1[0] > (w_2[0]+w_1[1]) NEVER holds,
    # and so the marked isolation should always be True
    marks1a, marks1b = np.ones(len(sample1)), np.ones(len(sample1))
    marks1 = np.vstack([marks1a, marks1b]).T
    marks2a, marks2b = np.zeros(len(sample2)), np.zeros(len(sample2))
    marks2 = np.vstack([marks2a, marks2b]).T

    cond_func = 5
    marked_iso5 = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks1, marks2, cond_func, period=1)
    assert np.all(marked_iso5 == True)

    # The first sample1 mark is 1, the second sample1 mark is 0
    # All sample2 marks are 0
    # Thus w_1[0] > (w_2[0]+w_1[1]) ALWAYS holds,
    # and so the marked isolation should be equivalent to the unmarked isolation
    marks1a, marks1b = np.ones(len(sample1)), np.zeros(len(sample1))
    marks1 = np.vstack([marks1a, marks1b]).T
    marks2a, marks2b = np.zeros(len(sample2)), np.zeros(len(sample2))
    marks2 = np.vstack([marks2a, marks2b]).T

    cond_func = 5
    marked_iso5 = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks1, marks2, cond_func, period=1)
    assert np.all(marked_iso5 == False)

""" Module providing testing for
`~halotools.mock_observables.conditional_cylindrical_isolation` function.
"""
from __future__ import absolute_import, division, print_function, unicode_literals

import numpy as np
from astropy.utils.misc import NumpyRNGContext
import pytest

from ..conditional_cylindrical_isolation import conditional_cylindrical_isolation
from ..cylindrical_isolation import cylindrical_isolation

from ...tests.cf_helpers import generate_locus_of_3d_points

__all__ = ('test_agreement_with_unconditioned_calculation',
    'test_conditional_cylindrical_isolation_cond_func1',
    'test_conditional_cylindrical_isolation_cond_func2',
    'test_conditional_cylindrical_isolation_cond_func3',
    'test_conditional_cylindrical_isolation_cond_func4',
    'test_conditional_cylindrical_isolation_cond_func5')

fixed_seed = 43


@pytest.mark.installation_test
def test_agreement_with_unconditioned_calculation():
    """ Verify that the `~halotools.mock_observables.cylindrical_isolation` function
    agrees exactly with the `~halotools.mock_observables.conditional_cylindrical_isolation` function
    for a random distribution of points when cond_func=0 is selected.

    Test cases with and without PBCs, as well as with and without variable search radii.
    """
    npts1, npts2 = 100, 90
    with NumpyRNGContext(fixed_seed):
        sample1 = np.random.random((npts1, 3))
        sample2 = np.random.random((npts2, 3))

    rp_max, pi_max = 0.1, 0.1

    unconditioned_result = cylindrical_isolation(sample1, sample2, rp_max, pi_max)
    conditioned_result = conditional_cylindrical_isolation(sample1, sample2, rp_max, pi_max)
    assert np.all(unconditioned_result == conditioned_result)

    unconditioned_result = cylindrical_isolation(sample1, sample2, rp_max, pi_max, period=1)
    conditioned_result = conditional_cylindrical_isolation(sample1, sample2, rp_max, pi_max, period=1)
    assert np.all(unconditioned_result == conditioned_result)

    with NumpyRNGContext(fixed_seed):
        rp_max = np.random.uniform(0, 0.1, npts1)
        pi_max = np.random.uniform(0, 0.1, npts1)

    unconditioned_result = cylindrical_isolation(sample1, sample2, rp_max, pi_max)
    conditioned_result = conditional_cylindrical_isolation(sample1, sample2, rp_max, pi_max)
    assert np.all(unconditioned_result == conditioned_result)

    unconditioned_result = cylindrical_isolation(sample1, sample2, rp_max, pi_max, period=1)
    conditioned_result = conditional_cylindrical_isolation(sample1, sample2, rp_max, pi_max, period=1)
    assert np.all(unconditioned_result == conditioned_result)


def test_conditional_cylindrical_isolation_cond_func1():
    """ For cond_func = 1, and for a set of points that are spatially
    nearby each other when properly accounting for PBCs, verify that the
    `~halotools.mock_observables.conditional_cylindrical_isolation` function
    always returns False when every mark1 exceeds every mark2, and conversely.
    """
    sample1 = generate_locus_of_3d_points(10, xc=0.05, yc=0.05, zc=0.05, seed=fixed_seed)
    sample2 = generate_locus_of_3d_points(10, xc=0.95, yc=0.95, zc=0.95, seed=fixed_seed)

    rp_max, pi_max = 0.2, 0.2
    iso = cylindrical_isolation(sample1, sample2, rp_max, pi_max, period=[1, 1, 1])
    assert np.all(iso == False)

    marks1 = np.ones(len(sample1))
    marks2 = np.zeros(len(sample2))

    cond_func = 1
    marked_iso1a = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks1, marks2, cond_func, period=1)
    marked_iso1b = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks2, marks1, cond_func, period=1)
    assert np.all(marked_iso1a == False)
    assert np.all(marked_iso1b == True)


def test_conditional_cylindrical_isolation_cond_func2():
    """ For cond_func = 2, and for a set of points that are spatially
    nearby each other when properly accounting for PBCs, verify that the
    `~halotools.mock_observables.conditional_cylindrical_isolation` function
    always returns True when every mark1 exceeds every mark2, and conversely.
    """
    sample1 = generate_locus_of_3d_points(10, xc=0.05, yc=0.05, zc=0.05, seed=fixed_seed)
    sample2 = generate_locus_of_3d_points(10, xc=0.95, yc=0.95, zc=0.95, seed=fixed_seed)

    rp_max, pi_max = 0.2, 0.2
    iso = cylindrical_isolation(sample1, sample2, rp_max, pi_max, period=[1, 1, 1])
    assert np.all(iso == False)

    marks1 = np.ones(len(sample1))
    marks2 = np.zeros(len(sample2))

    cond_func = 2
    marked_iso2a = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks1, marks2, cond_func, period=1)
    marked_iso2b = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks2, marks1, cond_func, period=1)
    assert np.all(marked_iso2a == True)
    assert np.all(marked_iso2b == False)


def test_conditional_cylindrical_isolation_cond_func3():
    """ For cond_func = 3, and for a set of points that are spatially
    nearby each other when properly accounting for PBCs, verify that the
    `~halotools.mock_observables.conditional_cylindrical_isolation` function
    always returns True when every mark1 is distinct from every mark2, and conversely.
    """
    sample1 = generate_locus_of_3d_points(10, xc=0.05, yc=0.05, zc=0.05, seed=fixed_seed)
    sample2 = generate_locus_of_3d_points(10, xc=0.95, yc=0.95, zc=0.95, seed=fixed_seed)

    rp_max, pi_max = 0.2, 0.2
    iso = cylindrical_isolation(sample1, sample2, rp_max, pi_max, period=[1, 1, 1])
    assert np.all(iso == False)

    marks1 = np.ones(len(sample1))
    marks2 = np.zeros(len(sample2))

    cond_func = 3

    marked_iso3 = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks1, marks2, cond_func, period=1)
    assert np.all(marked_iso3 == True)

    marked_iso3 = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks1, marks1, cond_func, period=1)
    assert np.all(marked_iso3 == False)


def test_conditional_cylindrical_isolation_cond_func4():
    """ For cond_func = 4, and for a set of points that are spatially
    nearby each other when properly accounting for PBCs, verify that the
    `~halotools.mock_observables.conditional_cylindrical_isolation` function
    always returns False when every mark1 is distinct from every mark2, and conversely.
    """
    sample1 = generate_locus_of_3d_points(10, xc=0.05, yc=0.05, zc=0.05, seed=fixed_seed)
    sample2 = generate_locus_of_3d_points(10, xc=0.95, yc=0.95, zc=0.95, seed=fixed_seed)

    rp_max, pi_max = 0.2, 0.2
    iso = cylindrical_isolation(sample1, sample2, rp_max, pi_max, period=[1, 1, 1])
    assert np.all(iso == False)

    marks1 = np.ones(len(sample1))
    marks2 = np.zeros(len(sample2))

    cond_func = 4

    marked_iso4 = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks1, marks2, cond_func, period=1)
    assert np.all(marked_iso4 == False)

    marked_iso4 = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks1, marks1, cond_func, period=1)
    assert np.all(marked_iso4 == True)


def test_conditional_cylindrical_isolation_cond_func5():
    """ For cond_func = 5, consider a set of points that are spatially
    nearby each other when properly accounting for PBCs.
    Verify that the `~halotools.mock_observables.conditional_cylindrical_isolation` function
    always returns the appropriate answer in both appropriate limits of
    w_1[0] to (w_2[0]+w_1[1]).
    """
    sample1 = generate_locus_of_3d_points(10, xc=0.05, yc=0.05, zc=0.05, seed=fixed_seed)
    sample2 = generate_locus_of_3d_points(10, xc=0.95, yc=0.95, zc=0.95, seed=fixed_seed)

    # First verify that the two points are not isolated when ignoring the marks
    rp_max, pi_max = 0.2, 0.2
    iso = cylindrical_isolation(sample1, sample2, rp_max, pi_max, period=[1, 1, 1])
    assert np.all(iso == False)

    # The first sample1 mark is 1, the second sample1 mark is also 1
    # All sample2 marks are 0
    # Thus w_1[0] > (w_2[0]+w_1[1]) NEVER holds,
    # and so the marked isolation should always be True
    marks1a, marks1b = np.ones(len(sample1)), np.ones(len(sample1))
    marks1 = np.vstack([marks1a, marks1b]).T
    marks2a, marks2b = np.zeros(len(sample2)), np.zeros(len(sample2))
    marks2 = np.vstack([marks2a, marks2b]).T

    cond_func = 5
    marked_iso5 = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks1, marks2, cond_func, period=1)
    assert np.all(marked_iso5 == True)

    # The first sample1 mark is 1, the second sample1 mark is 0
    # All sample2 marks are 0
    # Thus w_1[0] > (w_2[0]+w_1[1]) ALWAYS holds,
    # and so the marked isolation should be equivalent to the unmarked isolation
    marks1a, marks1b = np.ones(len(sample1)), np.zeros(len(sample1))
    marks1 = np.vstack([marks1a, marks1b]).T
    marks2a, marks2b = np.zeros(len(sample2)), np.zeros(len(sample2))
    marks2 = np.vstack([marks2a, marks2b]).T

    cond_func = 5
    marked_iso5 = conditional_cylindrical_isolation(sample1, sample2,
        rp_max, pi_max, marks1, marks2, cond_func, period=1)
    assert np.all(marked_iso5 == False)
Module providing testing for `~halotools.mock_observables.conditional_cylindrical_isolation` function. Verify that the `~halotools.mock_observables.cylindrical_isolation` function     agrees exactly with the `~halotools.mock_observables.conditional_cylindrical_isolation` function     for a random distribution of points when cond_func=0 is selected.      Test cases with and without PBCs, as well as with and without variable search radii. For cond_func = 1, and for a set of points that are spatially     nearby each other when properly accounting for PBCs, verify that the     `~halotools.mock_observables.conditional_cylindrical_isolation` function     always returns False when every mark1 exceeds every mark2, and conversely. For cond_func = 2, and for a set of points that are spatially     nearby each other when properly accounting for PBCs, verify that the     `~halotools.mock_observables.conditional_cylindrical_isolation` function     always returns True when every mark1 exceeds every mark2, and conversely. For cond_func = 3, and for a set of points that are spatially     nearby each other when properly accounting for PBCs, verify that the     `~halotools.mock_observables.conditional_cylindrical_isolation` function     always returns True when every mark1 is distinct from every mark2, and conversely. For cond_func = 4, and for a set of points that are spatially     nearby each other when properly accounting for PBCs, verify that the     `~halotools.mock_observables.conditional_cylindrical_isolation` function     always returns False when every mark1 is distinct from every mark2, and conversely. For cond_func = 5, consider a set of points that are spatially     nearby each other when properly accounting for PBCs.     Verify that the `~halotools.mock_observables.conditional_cylindrical_isolation` function     always returns the appropriate answer in both appropriate limits of     w_1[0] to (w_2[0]+w_1[1]). # First verify that the two points are not isolated when ignoring the marks # The first sample1 mark is 1, the second sample1 mark is also 1 # All sample2 marks are 0 # Thus w_1[0] > (w_2[0]+w_1[1]) NEVER holds, # and so the marked isolation should always be True # The first sample1 mark is 1, the second sample1 mark is 0 # All sample2 marks are 0 # Thus w_1[0] > (w_2[0]+w_1[1]) ALWAYS holds, # and so the marked isolation should be equivalent to the unmarked isolation