#!/usr/bin/python
# -*- coding: utf-8 -*-
"""Compare to numpy data"""
import sys

import numpy as np

import multipletau

from test_correlate import get_sample_arrays_cplx


def test_corresponds_ac():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)

    a = np.concatenate(get_sample_arrays_cplx()).real
    m = 16

    restau = multipletau.autocorrelate(a=1*a,
                                       m=m,
                                       copy=True,
                                       normalize=True,
                                       dtype=np.float_)

    reslin = multipletau.correlate_numpy(a=1*a,
                                         v=1*a,
                                         copy=True,
                                         normalize=True,
                                         dtype=np.float_)

    idx = np.array(restau[:, 0].real, dtype=int)[:m]

    assert np.allclose(reslin[idx, 1], restau[:m, 1])


def test_corresponds_ac_first_loop():
    """
    numpy correlation:
    G_m = sum_i(a_i*a_{i+m})

    multipletau correlation 2nd order:
    b_j = (a_{2i} + a_{2i+1} / 2)
    G_m = sum_j(b_j*b_{j+1})
        = 1/4*sum_i(a_{2i}   * a_{2i+m}   +
                    a_{2i}   * a_{2i+m+1} +
                    a_{2i+1} * a_{2i+m}   +
                    a_{2i+1} * a_{2i+m+1}
                    )

    The values after the first m+1 lag times in the multipletau
    correlation differ from the normal correlation, because the
    traces are averaged over two consecutive items, effectively
    halving the size of the trace. The multiple-tau correlation
    can be compared to the regular correlation by using an even
    sized sequence (here 222) in which the elements 2i and 2i+1
    are equal, as is done in this test.
    """
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)

    a = [arr / np.average(arr) for arr in get_sample_arrays_cplx()]
    a = np.concatenate(a)[:222]
    # two consecutive elements are the same, so the multiple-tau method
    # corresponds to the numpy correlation for the first loop.
    a[::2] = a[1::2]

    for m in [2, 4, 6, 8, 10, 12, 14, 16]:
        restau = multipletau.correlate(a=a,
                                       v=a.imag+1j*a.real,
                                       m=m,
                                       copy=True,
                                       normalize=False,
                                       dtype=np.complex_)

        reslin = multipletau.correlate_numpy(a=a,
                                             v=a.imag+1j*a.real,
                                             copy=True,
                                             normalize=False,
                                             dtype=np.complex_)

        idtau = np.where(restau[:, 0] == m+2)[0][0]
        tau3 = restau[idtau, 1]  # m+1 initial bins

        idref = np.where(reslin[:, 0] == m+2)[0][0]
        tau3ref = reslin[idref, 1]

        assert np.allclose(tau3, tau3ref)


def test_corresponds_ac_nonormalize():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)

    a = np.concatenate(get_sample_arrays_cplx()).real
    m = 16

    restau = multipletau.autocorrelate(a=1*a,
                                       m=m,
                                       copy=True,
                                       normalize=False,
                                       dtype=np.float_)

    reslin = multipletau.correlate_numpy(a=1*a,
                                         v=1*a,
                                         copy=True,
                                         normalize=False,
                                         dtype=np.float_)

    idx = np.array(restau[:, 0].real, dtype=int)[:m+1]

    assert np.allclose(reslin[idx, 1], restau[:m+1, 1])


def test_corresponds_cc():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)

    a = np.concatenate(get_sample_arrays_cplx())
    m = 16

    restau = multipletau.correlate(a=a,
                                   v=a.imag+1j*a.real,
                                   m=m,
                                   copy=True,
                                   normalize=True,
                                   dtype=np.complex_)

    reslin = multipletau.correlate_numpy(a=a,
                                         v=a.imag+1j*a.real,
                                         copy=True,
                                         normalize=True,
                                         dtype=np.complex_)

    idx = np.array(restau[:, 0].real, dtype=int)[:m+1]

    assert np.allclose(reslin[idx, 1], restau[:m+1, 1])


def test_corresponds_cc_nonormalize():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)

    a = np.concatenate(get_sample_arrays_cplx())
    m = 16

    restau = multipletau.correlate(a=a,
                                   v=a.imag+1j*a.real,
                                   m=m,
                                   copy=True,
                                   normalize=False,
                                   dtype=np.complex_)

    reslin = multipletau.correlate_numpy(a=a,
                                         v=a.imag+1j*a.real,
                                         copy=True,
                                         normalize=False,
                                         dtype=np.complex_)

    idx = np.array(restau[:, 0].real, dtype=int)[:m+1]

    assert np.allclose(reslin[idx, 1], restau[:m+1, 1])


if __name__ == "__main__":
    # Run all tests
    loc = locals()
    for key in list(loc.keys()):
        if key.startswith("test_") and hasattr(loc[key], "__call__"):
            loc[key]()

#!/usr/bin/python
# -*- coding: utf-8 -*-
"""Compare to numpy data"""
import sys

import numpy as np

import multipletau

from test_correlate import get_sample_arrays_cplx


def test_corresponds_ac():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)

    a = np.concatenate(get_sample_arrays_cplx()).real
    m = 16

    restau = multipletau.autocorrelate(a=1*a,
                                       m=m,
                                       copy=True,
                                       normalize=True,
                                       dtype=np.float_)

    reslin = multipletau.correlate_numpy(a=1*a,
                                         v=1*a,
                                         copy=True,
                                         normalize=True,
                                         dtype=np.float_)

    idx = np.array(restau[:, 0].real, dtype=int)[:m]

    assert np.allclose(reslin[idx, 1], restau[:m, 1])


def test_corresponds_ac_first_loop():
    """
    numpy correlation:
    G_m = sum_i(a_i*a_{i+m})

    multipletau correlation 2nd order:
    b_j = (a_{2i} + a_{2i+1} / 2)
    G_m = sum_j(b_j*b_{j+1})
        = 1/4*sum_i(a_{2i}   * a_{2i+m}   +
                    a_{2i}   * a_{2i+m+1} +
                    a_{2i+1} * a_{2i+m}   +
                    a_{2i+1} * a_{2i+m+1}
                    )

    The values after the first m+1 lag times in the multipletau
    correlation differ from the normal correlation, because the
    traces are averaged over two consecutive items, effectively
    halving the size of the trace. The multiple-tau correlation
    can be compared to the regular correlation by using an even
    sized sequence (here 222) in which the elements 2i and 2i+1
    are equal, as is done in this test.
    """
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)

    a = [arr / np.average(arr) for arr in get_sample_arrays_cplx()]
    a = np.concatenate(a)[:222]
    # two consecutive elements are the same, so the multiple-tau method
    # corresponds to the numpy correlation for the first loop.
    a[::2] = a[1::2]

    for m in [2, 4, 6, 8, 10, 12, 14, 16]:
        restau = multipletau.correlate(a=a,
                                       v=a.imag+1j*a.real,
                                       m=m,
                                       copy=True,
                                       normalize=False,
                                       dtype=np.complex_)

        reslin = multipletau.correlate_numpy(a=a,
                                             v=a.imag+1j*a.real,
                                             copy=True,
                                             normalize=False,
                                             dtype=np.complex_)

        idtau = np.where(restau[:, 0] == m+2)[0][0]
        tau3 = restau[idtau, 1]  # m+1 initial bins

        idref = np.where(reslin[:, 0] == m+2)[0][0]
        tau3ref = reslin[idref, 1]

        assert np.allclose(tau3, tau3ref)


def test_corresponds_ac_nonormalize():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)

    a = np.concatenate(get_sample_arrays_cplx()).real
    m = 16

    restau = multipletau.autocorrelate(a=1*a,
                                       m=m,
                                       copy=True,
                                       normalize=False,
                                       dtype=np.float_)

    reslin = multipletau.correlate_numpy(a=1*a,
                                         v=1*a,
                                         copy=True,
                                         normalize=False,
                                         dtype=np.float_)

    idx = np.array(restau[:, 0].real, dtype=int)[:m+1]

    assert np.allclose(reslin[idx, 1], restau[:m+1, 1])


def test_corresponds_cc():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)

    a = np.concatenate(get_sample_arrays_cplx())
    m = 16

    restau = multipletau.correlate(a=a,
                                   v=a.imag+1j*a.real,
                                   m=m,
                                   copy=True,
                                   normalize=True,
                                   dtype=np.complex_)

    reslin = multipletau.correlate_numpy(a=a,
                                         v=a.imag+1j*a.real,
                                         copy=True,
                                         normalize=True,
                                         dtype=np.complex_)

    idx = np.array(restau[:, 0].real, dtype=int)[:m+1]

    assert np.allclose(reslin[idx, 1], restau[:m+1, 1])


def test_corresponds_cc_nonormalize():
    myframe = sys._getframe()
    myname = myframe.f_code.co_name
    print("running ", myname)

    a = np.concatenate(get_sample_arrays_cplx())
    m = 16

    restau = multipletau.correlate(a=a,
                                   v=a.imag+1j*a.real,
                                   m=m,
                                   copy=True,
                                   normalize=False,
                                   dtype=np.complex_)

    reslin = multipletau.correlate_numpy(a=a,
                                         v=a.imag+1j*a.real,
                                         copy=True,
                                         normalize=False,
                                         dtype=np.complex_)

    idx = np.array(restau[:, 0].real, dtype=int)[:m+1]

    assert np.allclose(reslin[idx, 1], restau[:m+1, 1])


if __name__ == "__main__":
    # Run all tests
    loc = locals()
    for key in list(loc.keys()):
        if key.startswith("test_") and hasattr(loc[key], "__call__"):
            loc[key]()

#!/usr/bin/python # -*- coding: utf-8 -*- Compare to numpy data numpy correlation:     G_m = sum_i(a_i*a_{i+m})      multipletau correlation 2nd order:     b_j = (a_{2i} + a_{2i+1} / 2)     G_m = sum_j(b_j*b_{j+1})         = 1/4*sum_i(a_{2i}   * a_{2i+m}   +                     a_{2i}   * a_{2i+m+1} +                     a_{2i+1} * a_{2i+m}   +                     a_{2i+1} * a_{2i+m+1}                     )      The values after the first m+1 lag times in the multipletau     correlation differ from the normal correlation, because the     traces are averaged over two consecutive items, effectively     halving the size of the trace. The multiple-tau correlation     can be compared to the regular correlation by using an even     sized sequence (here 222) in which the elements 2i and 2i+1     are equal, as is done in this test. # two consecutive elements are the same, so the multiple-tau method # corresponds to the numpy correlation for the first loop. # m+1 initial bins # Run all tests