Commit 71088481 authored by Alexandre Mestiashvili's avatar Alexandre Mestiashvili
Browse files

New upstream version 0.3.3+ds

parent 5646c081

CHANGELOG

+4 −0
Original line number Diff line number Diff line 
0.3.3

 - fix: add constant `ZERO_CUTOFF` that defines when a trace average is

   treated as zero

 - enh: replace asserts with raises (#15)

0.3.2

 - fix: multipletau package not available when building the docs

0.3.1

README.rst

+1 −1
Original line number Diff line number Diff line
@@ -3,7 +3,7 @@ multipletau 


|PyPI Version| |Tests Status| |Coverage Status| |Docs Status|



Multipe-tau correlation is computed on a logarithmic scale (less

Multiple-tau correlation is computed on a logarithmic scale (less

data points are computed) and is thus much faster than conventional

correlation on a linear scale such as `numpy.correlate <http://docs.scipy.org/doc/numpy/reference/generated/numpy.correlate.html>`__.

docs/index.rst

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@@ -29,6 +29,9 @@ Cross-correlation (NumPy) 
-------------------------

.. autofunction:: correlate_numpy



Constants

---------

.. autodata:: multipletau.core.ZERO_CUTOFF



Examples

========

multipletau/__init__.py

+1 −1
Original line number Diff line number Diff line
@@ -4,7 +4,7 @@ u""" 
Multipletau provides a multiple-τ algorithm for Python 2.7 and

Python 3.x with :py:mod:`numpy` as its sole dependency.



Multipe-τ correlation is computed on a logarithmic scale (less

Multiple-τ correlation is computed on a logarithmic scale (less

data points are computed) and is thus much faster than conventional

correlation on a linear scale such as  :py:func:`numpy.correlate`.

multipletau/core.py

+45 −25
Original line number Diff line number Diff line
@@ -41,6 +41,11 @@ import warnings 
__all__ = ["autocorrelate", "correlate", "correlate_numpy"]





#: Defines the cutoff when the absolute mean trace divided by the median

#: of the absolute value of the trace is treated as zero.

ZERO_CUTOFF = 1e-15





class DtypeWarning(UserWarning):

    pass
@@ -138,15 +143,18 @@ def autocorrelate(a, m=16, deltat=1, normalize=False, copy=True, dtype=None, 
           [  4.00000000e+00,   2.03775000e+04],

           [  8.00000000e+00,   1.50612000e+04]])

    """

    assert isinstance(copy, bool)

    assert isinstance(normalize, bool)

    msg = "'normalize' and 'ret_sum' must not both be true"

    assert not (normalize and ret_sum), msg



    if not isinstance(normalize, bool):

        raise ValueError("`normalize` must be boolean!")

    if not isinstance(copy, bool):

        raise ValueError("`copy` must be boolean!")

    if not isinstance(ret_sum, bool):

        raise ValueError("`ret_sum` must be boolean!")

    if normalize and ret_sum:

        raise ValueError("'normalize' and 'ret_sum' must not both be True!")

    compress_values = ["average", "first", "second"]

    assert any(compress in s for s in compress_values), \

        "Unvalid string of compress. Possible values are " + \

        ','.join(compress_values)

    if compress not in compress_values:

        raise ValueError("Invalid value for `compress`! Possible values "

                         "are '{}'.".format(','.join(compress_values)))



    if dtype is None:

        dtype = np.dtype(a[0].__class__)
@@ -204,10 +212,12 @@ def autocorrelate(a, m=16, deltat=1, normalize=False, copy=True, dtype=None, 
    # We use the fluctuation of the signal around the mean

    if normalize:

        trace -= traceavg

        assert traceavg != 0, "Cannot normalize: Average of `a` is zero!"

        if np.abs(traceavg) / np.median(np.abs(trace)) < ZERO_CUTOFF:

            raise ValueError("Cannot normalize: Average of `a` is zero!")



    # Otherwise the following for-loop will fail:

    assert N >= 2 * m, "len(a) must be larger than 2m!"

    if N < 2 * m:

        raise ValueError("`len(a)` must be >= `2m`!")



    # Calculate autocorrelation function for first m+1 bins

    # Discrete convolution of m elements
@@ -382,22 +392,27 @@ def correlate(a, v, m=16, deltat=1, normalize=False, copy=True, dtype=None, 
           [  8.00000000e+00,   1.58508000e+04]])



    """

    assert isinstance(copy, bool)

    assert isinstance(normalize, bool)

    msg = "'normalize' and 'ret_sum' must not both be true"

    assert not (normalize and ret_sum), msg



    if not isinstance(normalize, bool):

        raise ValueError("`normalize` must be boolean!")

    if not isinstance(copy, bool):

        raise ValueError("`copy` must be boolean!")

    if not isinstance(ret_sum, bool):

        raise ValueError("`ret_sum` must be boolean!")

    if normalize and ret_sum:

        raise ValueError("'normalize' and 'ret_sum' must not both be True!")

    compress_values = ["average", "first", "second"]

    assert any(compress in s for s in compress_values), \

        "Unvalid string of compress. Possible values are " + \

        ','.join(compress_values)

    if compress not in compress_values:

        raise ValueError("Invalid value for `compress`! Possible values "

                         "are '{}'.".format(','.join(compress_values)))



    # See `autocorrelation` for better documented code.

    traceavg1 = np.average(v)

    traceavg2 = np.average(a)

    if normalize:

        assert traceavg1 != 0, "Cannot normalize: Average of `v` is zero!"

        assert traceavg2 != 0, "Cannot normalize: Average of `a` is zero!"

        if np.abs(traceavg1) / np.median(np.abs(v)) < ZERO_CUTOFF:

            raise ValueError("Cannot normalize: Average of `v` is zero!")

        if np.abs(traceavg2) / np.median(np.abs(a)) < ZERO_CUTOFF:

            raise ValueError("Cannot normalize: Average of `a` is zero!")



    if dtype is None:

        dtype = np.dtype(v[0].__class__)
@@ -430,7 +445,8 @@ def correlate(a, v, m=16, deltat=1, normalize=False, copy=True, dtype=None, 


    trace2 = np.array(a, dtype=dtype, copy=copy)



    assert trace1.shape[0] == trace2.shape[0], "`a`,`v` must have same length!"

    if trace1.size != trace2.size:

        raise ValueError("`a` and `v` must have same length!")



    # Complex data

    if dtype.kind == "c":
@@ -467,7 +483,8 @@ def correlate(a, v, m=16, deltat=1, normalize=False, copy=True, dtype=None, 
        trace2 -= traceavg2



    # Otherwise the following for-loop will fail:

    assert N >= 2 * m, "len(a) must be larger than 2m!"

    if N < 2 * m:

        raise ValueError("`len(a)` must be >= `2m`!")



    # Calculate autocorrelation function for first m+1 bins

    for n in range(0, m + 1):
@@ -571,7 +588,8 @@ def correlate_numpy(a, v, deltat=1, normalize=False, 
    ab = np.array(a, dtype=dtype, copy=copy)

    vb = np.array(v, dtype=dtype, copy=copy)



    assert ab.shape[0] == vb.shape[0], "`a`,`v` must have same length!"

    if ab.size != vb.size:

        raise ValueError("`a` and `v` must have same length!")



    avg = np.average(ab)

    vvg = np.average(vb)
@@ -579,8 +597,10 @@ def correlate_numpy(a, v, deltat=1, normalize=False, 
    if normalize:

        ab -= avg

        vb -= vvg

        assert avg != 0, "Cannot normalize: Average of `a` is zero!"

        assert vvg != 0, "Cannot normalize: Average of `v` is zero!"

        if np.abs(avg) / np.median(np.abs(ab)) < ZERO_CUTOFF:

            raise ValueError("Cannot normalize: Average of `a` is zero!")

        if np.abs(vvg) / np.median(np.abs(vb)) < ZERO_CUTOFF:

            raise ValueError("Cannot normalize: Average of `v` is zero!")



    Gd = np.correlate(ab, vb, mode="full")[len(ab) - 1:]