Code Duplication - IAA-CSIC/MUTIS - Measure and Improve Code Quality continuously with Scrutinizer

Code Duplication Length = 57-57 lines in 2 locations

mutis/lib/correlation.py 2 locations


    return np.mean(udcf[msk])


def welsh_ab(t1, d1, t2, d2, t, dt):
    """Welsh (1999) correlation with adaptative binning.

    This function implements the correlation function proposed
    by Welsh (1999), which allows for the computation of the correlation
    for -discrete- signals non-uniformly sampled in time.

    Parameters
    ----------
    t1 : :class:`~numpy.ndarray`
        Times corresponding to the first signal.
    d1 : :class:`~numpy.ndarray`
        Values of the first signal.
    t2 : :class:`~numpy.ndarray`
        Times corresponding to the second signal.
    d2 : :class:`~numpy.ndarray`
        Values of the second signal.
    t  : :class:`~numpy.ndarray`
        Times on which to compute the correlation (binning).
    dt : :class:`~numpy.ndarray`
        Size of the bins on which to compute the correlation.

    Returns
    -------
    res : :class:`~numpy.ndarray` (size `len(t)`)
        Values of the correlation at the times `t`.

    Examples
    --------
    An example of raw usage would be:

    >>> import numpy as np
    >>> from mutis.lib.correlation import welsh_ab
    >>> t1 = np.linspace(1, 10, 100); s1 = np.sin(t1)
    >>> t2 = np.linspace(1, 10, 100); s2 = np.cos(t2)
    >>> t = np.linspace(1, 10, 100);  dt = np.full(t.shape, 0.1)
    >>> welsh_ab_p(t1, d1, t2, d2, t, dt)

    However, it is recommended to be used as expalined in the
    standard MUTIS' workflow notebook.
    """

    if t.size != dt.size:
        log.error("Error, t and dt not the same size")
        return False
    if t1.size != d1.size:
        log.error("Error, t1 and d1 not the same size")
        return False
    if t2.size != d2.size:
        log.error("Error, t2 and d2 not the same size")
        return False

    # res = np.array([])
    res = np.empty(t.size)
    for i in range(t.size):
        res[i] = welsh_ab_p(t1, d1, t2, d2, t[i], dt[i])
    return res




    return np.mean(udcf[mask])


def kroedel_ab(t1, d1, t2, d2, t, dt):
    """Krolik & Edelson (1988) correlation with adaptative binning.

    This function implements the correlation function proposed by
    Krolik & Edelson (1988), which allows for the computation of
    the correlation for -discrete- signals non-uniformly sampled
    in time.

    Parameters
    ----------
    t1 : :class:`~numpy.ndarray`
        Times corresponding to the first signal.
    d1 : :class:`~numpy.ndarray`
        Values of the first signal.
    t2 : :class:`~numpy.ndarray`
        Times corresponding to the second signal.
    d2 : :class:`~numpy.ndarray`
        Values of the second signal.
    t  : :class:`~numpy.ndarray`
        Times on which to compute the correlation (binning).
    dt : :class:`~numpy.ndarray`
        Size of the bins on which to compute the correlation.

    Returns
    -------
    res : :class:`~numpy.ndarray` (size `len(t)`)
        Values of the correlation at the times `t`.

    Examples
    --------
    An example of raw usage would be:

    >>> import numpy as np
    >>> from mutis.lib.correlation import kroedel_ab
    >>> t1 = np.linspace(1, 10, 100); s1 = np.sin(t1)
    >>> t2 = np.linspace(1, 10, 100); s2 = np.cos(t2)
    >>> t = np.linspace(1, 10, 100);  dt = np.full(t.shape, 0.1)
    >>> kroedel_ab_p(t1, d1, t2, d2, t, dt)

    However, it is recommended to be used as expalined in the
    standard MUTIS' workflow notebook.
    """

    if t.size != dt.size:
        log.error("Error, t and dt not the same size")
        return False
    if t1.size != d1.size:
        log.error("Error, t1 and d1 not the same size")
        return False
    if t2.size != d2.size:
        log.error("Error, t2 and d2 not the same size")
        return False

    res = np.empty(t.size)
    for i in range(t.size):
        res[i] = kroedel_ab_p(t1, d1, t2, d2, t[i], dt[i])
    return res




		@@ 102-158 (lines=57) @@
99		return np.mean(udcf[msk])
100
101
102		def welsh_ab(t1, d1, t2, d2, t, dt):
103		"""Welsh (1999) correlation with adaptative binning.
104
105		This function implements the correlation function proposed
106		by Welsh (1999), which allows for the computation of the correlation
107		for -discrete- signals non-uniformly sampled in time.
108
109		Parameters
110		----------
111		t1 : :class:`~numpy.ndarray`
112		Times corresponding to the first signal.
113		d1 : :class:`~numpy.ndarray`
114		Values of the first signal.
115		t2 : :class:`~numpy.ndarray`
116		Times corresponding to the second signal.
117		d2 : :class:`~numpy.ndarray`
118		Values of the second signal.
119		t : :class:`~numpy.ndarray`
120		Times on which to compute the correlation (binning).
121		dt : :class:`~numpy.ndarray`
122		Size of the bins on which to compute the correlation.
123
124		Returns
125		-------
126		res : :class:`~numpy.ndarray` (size `len(t)`)
127		Values of the correlation at the times `t`.
128
129		Examples
130		--------
131		An example of raw usage would be:
132
133		>>> import numpy as np
134		>>> from mutis.lib.correlation import welsh_ab
135		>>> t1 = np.linspace(1, 10, 100); s1 = np.sin(t1)
136		>>> t2 = np.linspace(1, 10, 100); s2 = np.cos(t2)
137		>>> t = np.linspace(1, 10, 100); dt = np.full(t.shape, 0.1)
138		>>> welsh_ab_p(t1, d1, t2, d2, t, dt)
139
140		However, it is recommended to be used as expalined in the
141		standard MUTIS' workflow notebook.
142		"""
143
144		if t.size != dt.size:
145		log.error("Error, t and dt not the same size")
146		return False
147		if t1.size != d1.size:
148		log.error("Error, t1 and d1 not the same size")
149		return False
150		if t2.size != d2.size:
151		log.error("Error, t2 and d2 not the same size")
152		return False
153
154		# res = np.array([])
155		res = np.empty(t.size)
156		for i in range(t.size):
157		res[i] = welsh_ab_p(t1, d1, t2, d2, t[i], dt[i])
158		return res
159
160
161
		@@ 29-85 (lines=57) @@
26		return np.mean(udcf[mask])
27
28
29		def kroedel_ab(t1, d1, t2, d2, t, dt):
30		"""Krolik & Edelson (1988) correlation with adaptative binning.
31
32		This function implements the correlation function proposed by
33		Krolik & Edelson (1988), which allows for the computation of
34		the correlation for -discrete- signals non-uniformly sampled
35		in time.
36
37		Parameters
38		----------
39		t1 : :class:`~numpy.ndarray`
40		Times corresponding to the first signal.
41		d1 : :class:`~numpy.ndarray`
42		Values of the first signal.
43		t2 : :class:`~numpy.ndarray`
44		Times corresponding to the second signal.
45		d2 : :class:`~numpy.ndarray`
46		Values of the second signal.
47		t : :class:`~numpy.ndarray`
48		Times on which to compute the correlation (binning).
49		dt : :class:`~numpy.ndarray`
50		Size of the bins on which to compute the correlation.
51
52		Returns
53		-------
54		res : :class:`~numpy.ndarray` (size `len(t)`)
55		Values of the correlation at the times `t`.
56
57		Examples
58		--------
59		An example of raw usage would be:
60
61		>>> import numpy as np
62		>>> from mutis.lib.correlation import kroedel_ab
63		>>> t1 = np.linspace(1, 10, 100); s1 = np.sin(t1)
64		>>> t2 = np.linspace(1, 10, 100); s2 = np.cos(t2)
65		>>> t = np.linspace(1, 10, 100); dt = np.full(t.shape, 0.1)
66		>>> kroedel_ab_p(t1, d1, t2, d2, t, dt)
67
68		However, it is recommended to be used as expalined in the
69		standard MUTIS' workflow notebook.
70		"""
71
72		if t.size != dt.size:
73		log.error("Error, t and dt not the same size")
74		return False
75		if t1.size != d1.size:
76		log.error("Error, t1 and d1 not the same size")
77		return False
78		if t2.size != d2.size:
79		log.error("Error, t2 and d2 not the same size")
80		return False
81
82		res = np.empty(t.size)
83		for i in range(t.size):
84		res[i] = kroedel_ab_p(t1, d1, t2, d2, t[i], dt[i])
85		return res
86
87
88

IAA-CSIC / MUTIS

Code Duplication Length = 57-57 lines in 2 locations

mutis/lib/correlation.py 2 locations