Package: release.debian.org
Severity: normal
User: release.debian....@packages.debian.org
Usertags: unblock
X-Debbugs-Cc: mailatgo...@gmail.com,
debian-med-packag...@lists.alioth.debian.org
Please unblock package python-multipletau
There are changes fixing broken tests on i386 - bug #860624 and syntax
cleanup (pep8) which shouldn't affect the package.
unblock python-multipletau/0.1.7+ds-1
-- System Information:
Debian Release: 9.0
APT prefers unstable
APT policy: (500, 'unstable')
Architecture: amd64
(x86_64)
Kernel: Linux 4.9.0-2-amd64 (SMP w/4 CPU cores)
Locale: LANG=en_US.UTF-8, LC_CTYPE=en_US.UTF-8 (charmap=UTF-8)
Shell: /bin/sh linked to /bin/dash
Init: sysvinit (via /sbin/init)
diff -Nru python-multipletau-0.1.6+ds/CHANGELOG
python-multipletau-0.1.7+ds/CHANGELOG
--- python-multipletau-0.1.6+ds/CHANGELOG 2016-07-11 20:51:49.000000000
+0200
+++ python-multipletau-0.1.7+ds/CHANGELOG 2017-04-26 14:31:13.000000000
+0200
@@ -1,3 +1,9 @@
+0.1.7
+- code cleanup with pep8 and autopep8
+- always use numpy dtypes
+- fix tests:
+ - take into account floating inaccuracies
+ - support i386 numpy dtypes
0.1.6
- also compute correlation for zero lag time (`G(tau==0)`)
- support NumPy 1.11
diff -Nru python-multipletau-0.1.6+ds/debian/changelog
python-multipletau-0.1.7+ds/debian/changelog
--- python-multipletau-0.1.6+ds/debian/changelog 2016-07-22
15:26:46.000000000 +0200
+++ python-multipletau-0.1.7+ds/debian/changelog 2017-04-26
15:00:54.000000000 +0200
@@ -1,3 +1,10 @@
+python-multipletau (0.1.7+ds-1) unstable; urgency=medium
+
+ * New upstream version 0.1.7+ds (Closes: #860624)
+ * Remove trailing whitespaces from changelog
+
+ -- Alexandre Mestiashvili <a...@biotec.tu-dresden.de> Wed, 26 Apr 2017
15:00:54 +0200
+
python-multipletau (0.1.6+ds-1) unstable; urgency=medium
* Imported Upstream version 0.1.6+ds
@@ -32,16 +39,16 @@
* cme fix dpkg-control; bumped debhelper to compat level 9
[ Alexandre Mestiashvili ]
- * d/control: cme fix dpkg, remove python{,3}-numpy dependency,
+ * d/control: cme fix dpkg, remove python{,3}-numpy dependency,
as it should be resolved by ${python:Depends}
* d/watch: add +ds suffix, as we delete tests/runtests.py
* d/copyright: update copyright year and exclude tests/runtests.py blob
* Imported Upstream version 0.1.5
- * d/control: add python-nose to Build-Depends, add libjs-mathjax to
+ * d/control: add python-nose to Build-Depends, add libjs-mathjax to
-doc package
- * d/patches/packaged-mathjax.path: use packaged copy of MathJax
- * d/rules: remove DH_VERBOSE, add stanza for tests, provide a workaround for
- the lintian's privacy-breach-generic warning, see #739300 for details
+ * d/patches/packaged-mathjax.path: use packaged copy of MathJax
+ * d/rules: remove DH_VERBOSE, add stanza for tests, provide a workaround for
+ the lintian's privacy-breach-generic warning, see #739300 for details
* d/python-multipletau-doc.install: install examples
-- Alexandre Mestiashvili <a...@biotec.tu-dresden.de> Tue, 15 Dec 2015
10:30:01 +0100
diff -Nru python-multipletau-0.1.6+ds/MANIFEST.in
python-multipletau-0.1.7+ds/MANIFEST.in
--- python-multipletau-0.1.6+ds/MANIFEST.in 2016-07-11 20:51:49.000000000
+0200
+++ python-multipletau-0.1.7+ds/MANIFEST.in 2017-04-26 14:31:13.000000000
+0200
@@ -1,11 +1,6 @@
include CHANGELOG
include LICENSE
include README.rst
-include tests/*.py
-include tests/*.md
-include tests/data/*.npy
-include examples/*.py
-include doc/*.py
-include doc/*.rst
-include doc/*.md
-include doc/extensions/*.py
+recursive-include examples *.py
+recursive-include doc *.py *.md *.rst
+recursive-include tests *.py *.md test_*.npy
diff -Nru python-multipletau-0.1.6+ds/multipletau/__init__.py
python-multipletau-0.1.7+ds/multipletau/__init__.py
--- python-multipletau-0.1.6+ds/multipletau/__init__.py 2016-07-11
20:51:49.000000000 +0200
+++ python-multipletau-0.1.7+ds/multipletau/__init__.py 2017-04-26
14:31:13.000000000 +0200
@@ -1,27 +1,27 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
-u"""
-This package provides a multiple-τ algorithm for Python 2.7 and
+u"""
+This package provides a multiple-τ algorithm for Python 2.7 and
Python 3.x and requires the package :py:mod:`numpy`.
Multipe-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`.
-Recommended literature
+Recommended literature
----------------------
- Klaus Schaetzel and Rainer Peters; *Noise on multiple-tau photon
correlation data*. Proc. SPIE 1430, Photon Correlation
- Spectroscopy: Multicomponent Systems, 109 (June 1, 1991);
+ Spectroscopy: Multicomponent Systems, 109 (June 1, 1991);
http://doi.org/10.1117/12.44160
-
-- Thorsten Wohland, Rudolf Rigler, and Horst Vogel; *The Standard
+
+- Thorsten Wohland, Rudolf Rigler, and Horst Vogel; *The Standard
Deviation in Fluorescence Correlation Spectroscopy*. Biophysical
- Journal, 80 (June 1, 2001);
+ Journal, 80 (June 1, 2001);
http://dx.doi.org/10.1016/S0006-3495(01)76264-9
-Obtaining multipletau
+Obtaining multipletau
---------------------
If you have Python and :py:mod:`numpy` installed, simply run
@@ -34,7 +34,7 @@
Citing multipletau
------------------
The multipletau package should be cited like this (replace "x.x.x"
-with the actual version of multipletau that you used and "DD Month YYYY"
+with the actual version of multipletau used and "DD Month YYYY"
with a matching date).
.. topic:: cite
diff -Nru python-multipletau-0.1.6+ds/multipletau/_multipletau.py
python-multipletau-0.1.7+ds/multipletau/_multipletau.py
--- python-multipletau-0.1.6+ds/multipletau/_multipletau.py 2016-07-11
20:51:49.000000000 +0200
+++ python-multipletau-0.1.7+ds/multipletau/_multipletau.py 2017-04-26
14:31:13.000000000 +0200
@@ -1,6 +1,6 @@
#!/usr/bin/python
# -*- coding: utf-8 -*-
-"""
+"""
A multiple-τ algorithm for Python 2.7 and 3.x.
Copyright (c) 2014 Paul Müller
@@ -11,7 +11,7 @@
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
-
+
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in
the documentation and/or other materials provided with the
@@ -43,15 +43,15 @@
def autocorrelate(a, m=16, deltat=1, normalize=False,
copy=True, dtype=None):
- """
+ """
Autocorrelation of a 1-dimensional sequence on a log2-scale.
- This computes the correlation similar to
+ This computes the correlation similar to
:py:func:`numpy.correlate` for positive :math:`k` on a base 2
logarithmic scale.
:func:`numpy.correlate(a, a, mode="full")[len(a)-1:]`
-
+
:math:`z_k = \Sigma_n a_n a_{n+k}`
@@ -88,10 +88,11 @@
curve decaying to zero.
For experiments like e.g. fluorescence correlation spectroscopy,
- the signal can be normalized to :math:`M-k` by invoking ``normalize =
True``.
+ the signal can be normalized to :math:`M-k`
+ by invoking ``normalize = True``.
- For normalizing according to the behavior of :py:func:`numpy.correlate`,
- use ``normalize = False``.
+ For normalizing according to the behavior
+ of :py:func:`numpy.correlate`, use ``normalize = False``.
For complex arrays, this method falls back to the method
:func:`correlate`.
@@ -100,7 +101,7 @@
Examples
--------
>>> from multipletau import autocorrelate
- >>> autocorrelate(range(42), m=2, dtype=np.float)
+ >>> autocorrelate(range(42), m=2, dtype=np.float_)
array([[ 0.00000000e+00, 2.38210000e+04],
[ 1.00000000e+00, 2.29600000e+04],
[ 2.00000000e+00, 2.21000000e+04],
@@ -126,21 +127,21 @@
copy=copy,
dtype=dtype)
elif dtype.kind != "f":
- warnings.warn("Input dtype is not float; casting to np.float!")
- dtype = np.dtype(np.float)
+ warnings.warn("Input dtype is not float; casting to np.float_!")
+ dtype = np.dtype(np.float_)
# If copy is false and dtype is the same as the input array,
# then this line does not have an effect:
trace = np.array(a, dtype=dtype, copy=copy)
# Check parameters
- if m//2 != m/2:
+ if m // 2 != m / 2:
mold = m
- m = np.int((m//2 + 1) * 2)
+ m = np.int_((m // 2 + 1) * 2)
warnings.warn("Invalid value of m={}. Using m={} instead"
.format(mold, m))
else:
- m = np.int(m)
+ m = np.int_(m)
N = N0 = trace.shape[0]
@@ -148,12 +149,12 @@
# The integer k defines how many times we can average over
# two neighboring array elements in order to obtain an array of
# length just larger than m.
- k = np.int(np.floor(np.log2(N/m)))
+ k = np.int_(np.floor(np.log2(N / m)))
# In the base2 multiple-tau scheme, the length of the correlation
# array is (only taking into account values that are computed from
# traces that are just larger than m):
- lenG = m + k*(m//2) + 1
+ lenG = m + k * (m // 2) + 1
G = np.zeros((lenG, 2), dtype=dtype)
@@ -166,13 +167,13 @@
if normalize:
trace -= traceavg
assert traceavg != 0, "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!"
+ assert N >= 2 * m, "len(a) must be larger than 2m!"
# Calculate autocorrelation function for first m+1 bins
# Discrete convolution of m elements
- for n in range(0, m+1):
+ for n in range(0, m + 1):
G[n, 0] = deltat * n
# This is the computationally intensive step
G[n, 1] = np.sum(trace[:N - n] * trace[n:])
@@ -181,7 +182,7 @@
# Now that we calculated the first m elements of G, let us
# go on with the next m/2 elements.
# Check if len(trace) is even:
- if N%2 == 1:
+ if N % 2 == 1:
N -= 1
# Add up every second element
trace = (trace[:N:2] + trace[1:N:2]) / 2
@@ -189,9 +190,9 @@
# Start iteration for each m/2 values
for step in range(1, k + 1):
# Get the next m/2 values via correlation of the trace
- for n in range(1, m//2 + 1):
- npmd2 = n + m//2
- idx = m + n + (step - 1) * m//2
+ for n in range(1, m // 2 + 1):
+ npmd2 = n + m // 2
+ idx = m + n + (step - 1) * m // 2
if len(trace[:N - npmd2]) == 0:
# This is a shortcut that stops the iteration once the
# length of the trace is too small to compute a corre-
@@ -223,7 +224,7 @@
normstat[idx] = N - npmd2
normnump[idx] = N
# Check if len(trace) is even:
- if N%2 == 1:
+ if N % 2 == 1:
N -= 1
# Add up every second element
trace = (trace[:N:2] + trace[1:N:2]) / 2
@@ -239,7 +240,7 @@
def correlate(a, v, m=16, deltat=1, normalize=False,
copy=True, dtype=None):
- """
+ """
Cross-correlation of two 1-dimensional sequences
on a log2-scale.
@@ -251,9 +252,9 @@
:math:`z_k = \Sigma_n a_n v_{n+k}`
- Note that only the correlation in the positive direction is computed.
- To obtain the correlation for negative lag times swap the input variables
- ``a`` and ``v``.
+ Note that only the correlation in the positive direction is
+ computed. To obtain the correlation for negative lag times
+ swap the input variables ``a`` and ``v``.
Parameters
----------
@@ -277,29 +278,30 @@
Returns
-------
cross_correlation : ndarray of shape (N,2)
- the lag time (1st column) and the cross-correlation (2nd column).
+ the lag time (column 1) and the cross-correlation (column2).
Notes
-----
.. versionchanged :: 0.1.6
- Compute the correlation for zero lag time and correctly normalize
- the correlation for a complex input sequence `v`.
+ Compute the correlation for zero lag time and correctly
+ normalize the correlation for a complex input sequence `v`.
The algorithm computes the correlation with the convention of the
curve decaying to zero.
For experiments like e.g. fluorescence correlation spectroscopy,
- the signal can be normalized to :math:`M-k` by invoking ``normalize =
True``.
+ the signal can be normalized to :math:`M-k`
+ by invoking ``normalize = True``.
- For normalizing according to the behavior of :py:func:`numpy.correlate`,
- use ``normalize = False``.
+ For normalizing according to the behavior of
+ :py:func:`numpy.correlate`, use ``normalize = False``.
Examples
--------
>>> from multipletau import correlate
- >>> correlate(range(42), range(1,43), m=2, dtype=np.float)
+ >>> correlate(range(42), range(1,43), m=2, dtype=np.float_)
array([[ 0.00000000e+00, 2.46820000e+04],
[ 1.00000000e+00, 2.38210000e+04],
[ 2.00000000e+00, 2.29600000e+04],
@@ -322,17 +324,18 @@
if dtype != dtype2:
if dtype.kind == "c" or dtype2.kind == "c":
# The user might try to combine complex64 and float128.
- warnings.warn("Input dtypes not equal; casting to np.complex!")
- dtype = np.dtype(np.complex)
+ warnings.warn(
+ "Input dtypes not equal; casting to np.complex_!")
+ dtype = np.dtype(np.complex_)
else:
- warnings.warn("Input dtypes not equal; casting to np.float!")
- dtype = np.dtype(np.float)
+ warnings.warn("Input dtypes not equal; casting to np.float_!")
+ dtype = np.dtype(np.float_)
else:
dtype = np.dtype(dtype)
- if not dtype.kind in ["c", "f"]:
- warnings.warn("Input dtype is not float; casting to np.float!")
- dtype = np.dtype(np.float)
+ if dtype.kind not in ["c", "f"]:
+ warnings.warn("Input dtype is not float; casting to np.float_!")
+ dtype = np.dtype(np.float_)
trace1 = np.array(v, dtype=dtype, copy=copy)
@@ -350,26 +353,25 @@
np.conjugate(trace1, out=trace1)
# Check parameters
- if m//2 != m/2:
+ if m // 2 != m / 2:
mold = m
- m = np.int(m//2 + 1) * 2
+ m = np.int_(m // 2 + 1) * 2
warnings.warn("Invalid value of m={}. Using m={} instead"
.format(mold, m))
else:
- m = np.int(m)
-
+ m = np.int_(m)
N = N0 = trace1.shape[0]
# Find out the length of the correlation function.
# The integer k defines how many times we can average over
# two neighboring array elements in order to obtain an array of
# length just larger than m.
- k = np.int(np.floor(np.log2(N/m)))
+ k = np.int_(np.floor(np.log2(N / m)))
# In the base2 multiple-tau scheme, the length of the correlation
# array is (only taking into account values that are computed from
# traces that are just larger than m):
- lenG = m + k * m//2 + 1
+ lenG = m + k * m // 2 + 1
G = np.zeros((lenG, 2), dtype=dtype)
normstat = np.zeros(lenG, dtype=dtype)
@@ -381,7 +383,7 @@
trace2 -= traceavg2
# Otherwise the following for-loop will fail:
- assert N >= 2*m, "len(a) must be larger than 2m!"
+ assert N >= 2 * m, "len(a) must be larger than 2m!"
# Calculate autocorrelation function for first m+1 bins
for n in range(0, m + 1):
@@ -390,7 +392,7 @@
normstat[n] = N - n
normnump[n] = N
# Check if len(trace) is even:
- if N%2 == 1:
+ if N % 2 == 1:
N -= 1
# Add up every second element
trace1 = (trace1[:N:2] + trace1[1:N:2]) / 2
@@ -399,9 +401,9 @@
for step in range(1, k + 1):
# Get the next m/2 values of the trace
- for n in range(1, m//2 + 1):
- npmd2 = (n + m//2)
- idx = m + n + (step - 1) * m//2
+ for n in range(1, m // 2 + 1):
+ npmd2 = (n + m // 2)
+ idx = m + n + (step - 1) * m // 2
if len(trace1[:N - npmd2]) == 0:
# Abort
G = G[:idx - 1]
@@ -416,7 +418,7 @@
normnump[idx] = N
# Check if len(trace) is even:
- if N%2 == 1:
+ if N % 2 == 1:
N -= 1
# Add up every second element
trace1 = (trace1[:N:2] + trace1[1:N:2]) / 2
@@ -457,7 +459,7 @@
Returns
-------
cross_correlation : ndarray of shape (N,2)
- the lag time (1st column) and the cross-correlation (2nd column).
+ the lag time (column 1) and the cross-correlation (column 2).
Notes
@@ -485,7 +487,7 @@
N = len(Gd)
m = N - np.arange(N)
Gd /= m * avg * vvg
-
+
G = np.zeros((len(Gd), 2), dtype=dtype)
G[:, 1] = Gd
G[:, 0] = np.arange(len(Gd)) * deltat
diff -Nru python-multipletau-0.1.6+ds/multipletau/_version.py
python-multipletau-0.1.7+ds/multipletau/_version.py
--- python-multipletau-0.1.6+ds/multipletau/_version.py 2016-07-11
20:51:49.000000000 +0200
+++ python-multipletau-0.1.7+ds/multipletau/_version.py 2017-04-26
14:31:13.000000000 +0200
@@ -12,7 +12,7 @@
# Put the entire script into a `True` statement and add the hint
# `pragma: no cover` to ignore code coverage here.
-if True: # pragma: no cover
+if True: # pragma: no cover
import imp
import os
from os.path import join, abspath, dirname
@@ -31,8 +31,8 @@
"""
# make sure we are in a directory that belongs to the correct
# repository.
- ourdir = dirname(abspath(__file__))
-
+ ourdir = dirname(abspath(__file__))
+
def _minimal_ext_cmd(cmd):
# construct minimal environment
env = {}
@@ -44,25 +44,25 @@
env['LANGUAGE'] = 'C'
env['LANG'] = 'C'
env['LC_ALL'] = 'C'
- out = subprocess.Popen(cmd, stdout = subprocess.PIPE,
env=env).communicate()[0]
+ cmd = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env)
+ out = cmd.communicate()[0]
return out
-
+
# change directory
olddir = abspath(os.curdir)
os.chdir(ourdir)
-
+
try:
out = _minimal_ext_cmd(['git', 'describe', '--tags', 'HEAD'])
GIT_REVISION = out.strip().decode('ascii')
except OSError:
GIT_REVISION = ""
-
+
# go back to original directory
os.chdir(olddir)
-
+
return GIT_REVISION
-
-
+
def load_version(versionfile):
""" load version from version_save.py
"""
@@ -70,63 +70,64 @@
try:
_version_save = imp.load_source("_version_save", versionfile)
longversion = _version_save.longversion
- except:
+ except BaseException:
try:
from ._version_save import longversion
- except:
+ except BaseException:
try:
from _version_save import longversion
- except:
+ except BaseException:
pass
-
+
return longversion
-
-
+
def save_version(version, versionfile):
""" save version to version_save.py
"""
- data="#!/usr/bin/env python\n"+\
- "# This file was created automatically\n"+\
- "longversion='{VERSION}'"
+ data = "#!/usr/bin/env python\n" \
+ + "# This file was created automatically\n" \
+ + "longversion='{VERSION}'"
try:
with open(versionfile, "w") as fd:
fd.write(data.format(VERSION=version))
- except:
- warnings.warn("Could not write package version to
{}.".format(versionfile))
-
+ except BaseException:
+ msg = "Could not write package version to {}.".format(versionfile)
+ warnings.warn(msg)
+
versionfile = join(dirname(abspath(__file__)), "_version_save.py")
-
- ## Determine the accurate version
+
+ # Determine the accurate version
longversion = ""
-
+
# 1. git describe
try:
# Get the version using `git describe`
longversion = git_describe()
- except:
+ except BaseException:
pass
-
+
# 2. previously created version file
if longversion == "":
# Either this is this is not a git repository or we are in the
# wrong git repository.
# Get the version from the previously generated `_version_save.py`
longversion = load_version(versionfile)
-
+
# 3. last resort: date
if longversion == "":
print("Could not determine version. Reason:")
print(traceback.format_exc())
ctime = os.stat(__file__)[8]
longversion = time.strftime("%Y.%m.%d-%H-%M-%S", time.gmtime(ctime))
- print("Using creation time to determine version:
{}".format(longversion))
-
+ print("Using creation time as version: {}".format(longversion))
+
if not hasattr(sys, 'frozen'):
# Save the version to `_version_save.py` to allow distribution using
# `python setup.py sdist`.
- # This is only done if the program is not frozen (with e.g.
pyinstaller),
+ # This is only done if the program is not frozen (with e.g.
+ # pyinstaller),
if longversion != load_version(versionfile):
save_version(longversion, versionfile)
-
+
# PEP 440-conform development version:
version = ".dev".join(longversion.split("-")[:2])
Binary files
/tmp/KSEqptcII_/python-multipletau-0.1.6+ds/tests/data/test_correlate.py_test_cc_m.npy
and
/tmp/jL4wHAUHTI/python-multipletau-0.1.7+ds/tests/data/test_correlate.py_test_cc_m.npy
differ
Binary files
/tmp/KSEqptcII_/python-multipletau-0.1.6+ds/tests/data/test_correlate.py_test_cc_normalize.npy
and
/tmp/jL4wHAUHTI/python-multipletau-0.1.7+ds/tests/data/test_correlate.py_test_cc_normalize.npy
differ
Binary files
/tmp/KSEqptcII_/python-multipletau-0.1.6+ds/tests/data/test_correlate.py_test_cc_simple.npy
and
/tmp/jL4wHAUHTI/python-multipletau-0.1.7+ds/tests/data/test_correlate.py_test_cc_simple.npy
differ
diff -Nru python-multipletau-0.1.6+ds/tests/test_ac_cc.py
python-multipletau-0.1.7+ds/tests/test_ac_cc.py
--- python-multipletau-0.1.6+ds/tests/test_ac_cc.py 2016-07-11
20:51:49.000000000 +0200
+++ python-multipletau-0.1.7+ds/tests/test_ac_cc.py 2017-04-26
14:31:13.000000000 +0200
@@ -21,38 +21,46 @@
from test_autocorrelate import get_sample_arrays
-def test_ac_cc_simple():
+def test_ac_cc_m():
myframe = sys._getframe()
myname = myframe.f_code.co_name
print("running ", myname)
arrs = get_sample_arrays()
-
- rescc = []
- for a in arrs:
+
+ ms = [8, 16, 32, 64, 128]
+ a = np.concatenate(arrs)
+
+ res = []
+ for m in ms:
+ r = multipletau.autocorrelate(a=a,
+ m=m,
+ deltat=1,
+ normalize=False,
+ copy=True,
+ dtype=np.float_)
+ res.append(r)
+ res = np.concatenate(res)
+
+ rescc = []
+ for m in ms:
r = multipletau.correlate(a=a, v=a,
- m=16,
+ m=m,
deltat=1,
normalize=False,
copy=True,
- dtype=np.float)
+ dtype=np.float_)
rescc.append(r)
+ # test minimal length of array
+ _r2 = multipletau.correlate(a=a[:2*m], v=a[:2*m],
+ m=m,
+ deltat=1,
+ normalize=False,
+ copy=True,
+ dtype=np.float_)
rescc = np.concatenate(rescc)
-
- resac = []
- for a in arrs:
- r = multipletau.autocorrelate(a=a,
- m=16,
- deltat=1,
- normalize=False,
- copy=True,
- dtype=np.float)
- resac.append(r)
-
- resac = np.concatenate(resac)
-
- assert np.all(resac==rescc)
+ assert np.all(res==rescc)
def test_ac_cc_normalize():
@@ -69,7 +77,7 @@
deltat=1,
normalize=True,
copy=True,
- dtype=np.float)
+ dtype=np.float_)
res.append(r)
res = np.concatenate(res)
@@ -81,7 +89,7 @@
deltat=1,
normalize=True,
copy=True,
- dtype=np.float)
+ dtype=np.float_)
rescc.append(r)
rescc = np.concatenate(rescc)
@@ -89,46 +97,38 @@
assert np.all(res==rescc)
-def test_ac_cc_m():
+def test_ac_cc_simple():
myframe = sys._getframe()
myname = myframe.f_code.co_name
print("running ", myname)
arrs = get_sample_arrays()
-
- ms = [8, 16, 32, 64, 128]
- a = np.concatenate(arrs)
-
- res = []
- for m in ms:
- r = multipletau.autocorrelate(a=a,
- m=m,
- deltat=1,
- normalize=False,
- copy=True,
- dtype=np.float)
- res.append(r)
- res = np.concatenate(res)
-
- rescc = []
- for m in ms:
+
+ rescc = []
+ for a in arrs:
r = multipletau.correlate(a=a, v=a,
- m=m,
+ m=16,
deltat=1,
normalize=False,
copy=True,
- dtype=np.float)
+ dtype=np.float_)
rescc.append(r)
- # test minimal length of array
- _r2 = multipletau.correlate(a=a[:2*m], v=a[:2*m],
- m=m,
- deltat=1,
- normalize=False,
- copy=True,
- dtype=np.float)
rescc = np.concatenate(rescc)
- assert np.all(res==rescc)
+
+ resac = []
+ for a in arrs:
+ r = multipletau.autocorrelate(a=a,
+ m=16,
+ deltat=1,
+ normalize=False,
+ copy=True,
+ dtype=np.float_)
+ resac.append(r)
+
+ resac = np.concatenate(resac)
+
+ assert np.all(resac==rescc)
if __name__ == "__main__":
diff -Nru python-multipletau-0.1.6+ds/tests/test_autocorrelate.py
python-multipletau-0.1.7+ds/tests/test_autocorrelate.py
--- python-multipletau-0.1.6+ds/tests/test_autocorrelate.py 2016-07-11
20:51:49.000000000 +0200
+++ python-multipletau-0.1.7+ds/tests/test_autocorrelate.py 2017-04-26
14:31:13.000000000 +0200
@@ -41,89 +41,6 @@
return arrs
-def test_ac_simple():
- myframe = sys._getframe()
- myname = myframe.f_code.co_name
- print("running ", myname)
-
- arrs = get_sample_arrays()
-
- res = []
- for a in arrs:
- r = multipletau.autocorrelate(a=a,
- m=16,
- deltat=1,
- normalize=False,
- copy=True,
- dtype=np.float)
- res.append(r)
-
- res = np.concatenate(res)
-
#np.save(os.path.dirname(__file__)+"/data/"+os.path.basename(__file__)+"_"+myname+".npy",
res)
- ref = get_reference_data(myname, __file__)
-
- assert np.all(res==ref)
-
-
-def test_ac_normalize():
- myframe = sys._getframe()
- myname = myframe.f_code.co_name
- print("running ", myname)
-
- arrs = get_sample_arrays()
-
- res = []
- for a in arrs:
- r = multipletau.autocorrelate(a=a,
- m=16,
- deltat=1,
- normalize=True,
- copy=True,
- dtype=np.float)
- res.append(r)
-
- res = np.concatenate(res)
-
#np.save(os.path.dirname(__file__)+"/data/"+os.path.basename(__file__)+"_"+myname+".npy",
res)
- ref = get_reference_data(myname, __file__)
-
- assert np.all(res==ref)
-
-
-def test_ac_m():
- myframe = sys._getframe()
- myname = myframe.f_code.co_name
- print("running ", myname)
-
- arrs = get_sample_arrays()
-
- ms = [8, 16, 32, 64, 128]
- a = np.concatenate(arrs)
-
- res = []
- for m in ms:
- r = multipletau.autocorrelate(a=a,
- m=m,
- deltat=1,
- normalize=False,
- copy=True,
- dtype=np.float)
- res.append(r)
-
- # test minimal length of array
- _r2 = multipletau.autocorrelate(a=a[:2*m],
- m=m,
- deltat=1,
- normalize=False,
- copy=True,
- dtype=np.float)
-
- res = np.concatenate(res)
-
#np.save(os.path.dirname(__file__)+"/data/"+os.path.basename(__file__)+"_"+myname+".npy",
res)
- ref = get_reference_data(myname, __file__)
-
- assert np.all(res==ref)
-
-
def test_ac_copy():
myframe = sys._getframe()
myname = myframe.f_code.co_name
@@ -138,7 +55,7 @@
deltat=1,
normalize=True,
copy=True,
- dtype=np.float)
+ dtype=np.float_)
res1.append(r)
res2 = []
@@ -148,7 +65,7 @@
deltat=1,
normalize=True,
copy=False,
- dtype=np.float)
+ dtype=np.float_)
res2.append(r)
# simple test if result is the same
@@ -160,7 +77,7 @@
# make sure the copy function really changes something
assert not np.all(arrs == refarrs)
-
+
def test_ac_dtype():
myframe = sys._getframe()
myname = myframe.f_code.co_name
@@ -175,7 +92,7 @@
deltat=1,
normalize=True,
copy=True,
- dtype=np.float)
+ dtype=np.float_)
ri = multipletau.autocorrelate(a=a,
m=16,
@@ -191,12 +108,47 @@
copy=True,
dtype=None)
- assert ri.dtype == np.dtype(np.float), "if wrong dtype, dtype should
default to np.float"
- assert ri2.dtype == np.dtype(np.float), "if wrong dtype, dtype should
default to np.float"
+ assert ri.dtype == np.dtype(np.float_), "if wrong dtype, dtype should
default to np.float_"
+ assert ri2.dtype == np.dtype(np.float_), "if wrong dtype, dtype should
default to np.float_"
assert np.all(rf == ri), "result should be the same, because input us the
same"
assert np.all(rf == ri2), "result should be the same, because input us the
same"
+def test_ac_m():
+ myframe = sys._getframe()
+ myname = myframe.f_code.co_name
+ print("running ", myname)
+
+ arrs = get_sample_arrays()
+
+ ms = [8, 16, 32, 64, 128]
+ a = np.concatenate(arrs)
+
+ res = []
+ for m in ms:
+ r = multipletau.autocorrelate(a=a,
+ m=m,
+ deltat=1,
+ normalize=False,
+ copy=True,
+ dtype=np.float_)
+ res.append(r)
+
+ # test minimal length of array
+ _r2 = multipletau.autocorrelate(a=a[:2*m],
+ m=m,
+ deltat=1,
+ normalize=False,
+ copy=True,
+ dtype=np.float_)
+
+ res = np.concatenate(res)
+
#np.save(os.path.dirname(__file__)+"/data/"+os.path.basename(__file__)+"_"+myname+".npy",
res)
+ ref = get_reference_data(myname, __file__)
+
+ assert np.allclose(res, ref, atol=0, rtol=1e-15)
+
+
def test_ac_m_wrong():
myframe = sys._getframe()
myname = myframe.f_code.co_name
@@ -210,41 +162,89 @@
deltat=1,
normalize=True,
copy=True,
- dtype=np.float)
+ dtype=np.float_)
r2 = multipletau.autocorrelate(a=a,
m=15,
deltat=1,
normalize=True,
copy=True,
- dtype=np.float)
+ dtype=np.float_)
r3 = multipletau.autocorrelate(a=a,
m=15.5,
deltat=1,
normalize=True,
copy=True,
- dtype=np.float)
+ dtype=np.float_)
r4 = multipletau.autocorrelate(a=a,
m=14.5,
deltat=1,
normalize=True,
copy=True,
- dtype=np.float)
+ dtype=np.float_)
r5 = multipletau.autocorrelate(a=a,
m=16.,
deltat=1,
normalize=True,
copy=True,
- dtype=np.float)
+ dtype=np.float_)
assert np.all(r1==r2)
assert np.all(r1==r3)
assert np.all(r1==r4)
assert np.all(r1==r5)
+def test_ac_normalize():
+ myframe = sys._getframe()
+ myname = myframe.f_code.co_name
+ print("running ", myname)
+
+ arrs = get_sample_arrays()
+
+ res = []
+ for a in arrs:
+ r = multipletau.autocorrelate(a=a,
+ m=16,
+ deltat=1,
+ normalize=True,
+ copy=True,
+ dtype=np.float_)
+ res.append(r)
+
+ res = np.concatenate(res)
+
#np.save(os.path.dirname(__file__)+"/data/"+os.path.basename(__file__)+"_"+myname+".npy",
res)
+ ref = get_reference_data(myname, __file__)
+
+ assert np.allclose(res, ref, atol=0, rtol=1e-14)
+
+
+def test_ac_simple():
+ myframe = sys._getframe()
+ myname = myframe.f_code.co_name
+ print("running ", myname)
+
+ arrs = get_sample_arrays()
+
+ res = []
+ for a in arrs:
+ r = multipletau.autocorrelate(a=a,
+ m=16,
+ deltat=1,
+ normalize=False,
+ copy=True,
+ dtype=np.float_)
+ res.append(r)
+
+ res = np.concatenate(res)
+
#np.save(os.path.dirname(__file__)+"/data/"+os.path.basename(__file__)+"_"+myname+".npy",
res)
+ ref = get_reference_data(myname, __file__)
+
+ assert np.allclose(res, ref, atol=0, rtol=1e-15)
+
+
if __name__ == "__main__":
# Run all tests
loc = locals()
diff -Nru python-multipletau-0.1.6+ds/tests/test_basic.py
python-multipletau-0.1.7+ds/tests/test_basic.py
--- python-multipletau-0.1.6+ds/tests/test_basic.py 2016-07-11
20:51:49.000000000 +0200
+++ python-multipletau-0.1.7+ds/tests/test_basic.py 2017-04-26
14:31:13.000000000 +0200
@@ -13,7 +13,7 @@
def test_ac():
- ist = autocorrelate(range(42), m=2, dtype=np.dtype(float))
+ ist = autocorrelate(range(42), m=2, dtype=np.float_)
soll = np.array([[ 0.00000000e+00, 2.38210000e+04],
[ 1.00000000e+00, 2.29600000e+04],
[ 2.00000000e+00, 2.21000000e+04],
@@ -23,7 +23,7 @@
def test_cc():
- ist = correlate(range(42), range(1,43), m=2, dtype=np.dtype(float))
+ ist = correlate(range(42), range(1,43), m=2, dtype=np.float_)
soll = np.array([[ 0.00000000e+00, 2.46820000e+04],
[ 1.00000000e+00, 2.38210000e+04],
[ 2.00000000e+00, 2.29600000e+04],
diff -Nru python-multipletau-0.1.6+ds/tests/test_correlate.py
python-multipletau-0.1.7+ds/tests/test_correlate.py
--- python-multipletau-0.1.6+ds/tests/test_correlate.py 2016-07-11
20:51:49.000000000 +0200
+++ python-multipletau-0.1.7+ds/tests/test_correlate.py 2017-04-26
14:31:13.000000000 +0200
@@ -12,6 +12,7 @@
import warnings
import zipfile
+
# Add parent directory to beginning of path variable
DIR = dirname(abspath(__file__))
sys.path = [split(DIR)[0]] + sys.path
@@ -20,10 +21,11 @@
from test_autocorrelate import get_reference_data
+
def get_sample_arrays_cplx():
a = [-4.3, 1, 9, -99.2, 13]
b = [9921, 281, 23.5, 5.3, 77]
- c = [ 12, 0, 2, 1.3, 33]
+ c = [ 12, 0, 2.1, 1.3, 33]
d = [ 32, .1, -2, 6.3, 88]
l = [ 33, 92, 47, 54, 99]
r = [ 0, 1, 12, 4, 0]
@@ -39,107 +41,6 @@
return arrs
-def test_cc_simple():
- myframe = sys._getframe()
- myname = myframe.f_code.co_name
- print("running ", myname)
-
- arrs = get_sample_arrays_cplx()
-
- res = []
- for a in arrs:
- r = multipletau.correlate(a=a,
- v=a,
- m=16,
- deltat=1,
- normalize=False,
- copy=True,
- dtype=np.complex)
- res.append(r)
- res = np.concatenate(res)
-
-
#np.save(os.path.dirname(__file__)+"/data/"+os.path.basename(__file__)+"_"+myname+".npy",
res)
- ref = get_reference_data(myname, __file__)
-
- assert np.all(res==ref)
-
- # also check result of autocorrelate
- res2 = []
- for a in arrs:
- r = multipletau.autocorrelate(a=a,
- m=16,
- deltat=1,
- normalize=False,
- copy=True,
- dtype=np.complex)
- res2.append(r)
- res2 = np.concatenate(res2)
-
- assert np.all(res==res2)
-
-
-def test_cc_normalize():
- myframe = sys._getframe()
- myname = myframe.f_code.co_name
- print("running ", myname)
-
- arrs = get_sample_arrays_cplx()
-
- res = []
- for a in arrs:
- r = multipletau.correlate(a=a.real,
- v=a.imag,
- m=16,
- deltat=1,
- normalize=True,
- copy=True,
- dtype=np.float)
- res.append(r)
-
- res = np.concatenate(res)
-
#np.save(os.path.dirname(__file__)+"/data/"+os.path.basename(__file__)+"_"+myname+".npy",
res)
- ref = get_reference_data(myname, __file__)
-
- assert np.all(res==ref)
-
-
-def test_cc_m():
- myframe = sys._getframe()
- myname = myframe.f_code.co_name
- print("running ", myname)
-
- arrs = get_sample_arrays_cplx()
-
- ms = [4, 8, 10, 16, 20, 64, 128]
- a = np.concatenate(arrs)
-
- res = []
- for m in ms:
- r = multipletau.correlate(a=a,
- v=a,
- m=m,
- deltat=1,
- normalize=False,
- copy=True,
- dtype=np.complex)
- res.append(r)
-
- # test minimal length of array
- _r2 = multipletau.correlate(a=a[:2*m],
- v=a[:2*m],
- m=m,
- deltat=1,
- normalize=False,
- copy=True,
- dtype=np.complex)
-
- res = np.concatenate(res)
-
#np.save(os.path.dirname(__file__)+"/data/"+os.path.basename(__file__)+"_"+myname+".npy",
res)
- ref = get_reference_data(myname, __file__)
-
- assert np.all(res==ref)
-
-
def test_cc_copy():
myframe = sys._getframe()
myname = myframe.f_code.co_name
@@ -176,7 +77,7 @@
# make sure the copy function really changes something
assert not np.all(arrs == refarrs)
-
+
def test_cc_dtype():
myframe = sys._getframe()
myname = myframe.f_code.co_name
@@ -192,7 +93,7 @@
deltat=1,
normalize=True,
copy=True,
- dtype=np.float)
+ dtype=np.float_)
ri = multipletau.correlate(a=a,
v=a,
@@ -200,18 +101,18 @@
deltat=1,
normalize=True,
copy=True,
- dtype=np.uint)
+ dtype=np.int_)
- ri2 = multipletau.correlate(a=np.array(a, dtype=np.uint),
- v=np.array(a, dtype=np.uint),
+ ri2 = multipletau.correlate(a=np.array(a, dtype=np.int_),
+ v=np.array(a, dtype=np.int_),
m=16,
deltat=1,
normalize=True,
copy=True,
dtype=None)
- assert ri.dtype == np.dtype(np.float), "if wrong dtype, dtype should
default to np.float"
- assert ri2.dtype == np.dtype(np.float), "if wrong dtype, dtype should
default to np.float"
+ assert ri.dtype == np.dtype(np.float_), "if wrong dtype, dtype should
default to np.float_"
+ assert ri2.dtype == np.dtype(np.float_), "if wrong dtype, dtype should
default to np.float_"
assert np.all(rf == ri), "result should be the same, because input us the
same"
assert np.all(rf == ri2), "result should be the same, because input us the
same"
@@ -230,16 +131,53 @@
deltat=1,
normalize=True,
copy=True)
- assert np.dtype(rf.dtype) == np.dtype(np.complex)
+ assert np.dtype(rf.dtype) == np.dtype(np.complex_)
print("this should issue a warning of unequal input dtypes, casting to
float")
rf2 = multipletau.correlate(a=a.real,
- v=np.array(a.imag, dtype=np.int),
+ v=np.array(a.imag, dtype=np.int_),
m=16,
deltat=1,
normalize=True,
copy=True)
- assert np.dtype(rf2.dtype) == np.dtype(np.float)
+ assert np.dtype(rf2.dtype) == np.dtype(np.float_)
+
+
+def test_cc_m():
+ myframe = sys._getframe()
+ myname = myframe.f_code.co_name
+ print("running ", myname)
+
+ arrs = get_sample_arrays_cplx()
+
+ ms = [4, 8, 10, 16, 20, 64, 128]
+ a = np.concatenate(arrs)
+
+ res = []
+ for m in ms:
+ r = multipletau.correlate(a=a,
+ v=a,
+ m=m,
+ deltat=1,
+ normalize=False,
+ copy=True,
+ dtype=np.complex_)
+ res.append(r)
+
+ # test minimal length of array
+ _r2 = multipletau.correlate(a=a[:2*m],
+ v=a[:2*m],
+ m=m,
+ deltat=1,
+ normalize=False,
+ copy=True,
+ dtype=np.complex_)
+
+ res = np.concatenate(res)
+
#np.save(os.path.dirname(__file__)+"/data/"+os.path.basename(__file__)+"_"+myname+".npy",
res)
+ ref = get_reference_data(myname, __file__)
+
+ assert np.allclose(res, ref, atol=0, rtol=1e-15)
def test_cc_m_wrong():
@@ -291,6 +229,69 @@
assert np.all(r1==r5)
+def test_cc_normalize():
+ myframe = sys._getframe()
+ myname = myframe.f_code.co_name
+ print("running ", myname)
+
+ arrs = get_sample_arrays_cplx()
+
+ res = []
+ for a in arrs:
+ r = multipletau.correlate(a=a.real,
+ v=a.imag,
+ m=16,
+ deltat=1,
+ normalize=True,
+ copy=True,
+ dtype=np.float_)
+ res.append(r)
+ res = np.concatenate(res)
+
#np.save(os.path.dirname(__file__)+"/data/"+os.path.basename(__file__)+"_"+myname+".npy",
res)
+ ref = get_reference_data(myname, __file__)
+
+ assert np.allclose(res, ref, atol=0, rtol=1e-14)
+
+
+def test_cc_simple():
+ myframe = sys._getframe()
+ myname = myframe.f_code.co_name
+ print("running ", myname)
+
+ arrs = get_sample_arrays_cplx()
+
+ res = []
+ for a in arrs:
+ r = multipletau.correlate(a=a,
+ v=a,
+ m=16,
+ deltat=1,
+ normalize=False,
+ copy=True,
+ dtype=np.complex_)
+ res.append(r)
+ res = np.concatenate(res)
+
+
#np.save(os.path.dirname(__file__)+"/data/"+os.path.basename(__file__)+"_"+myname+".npy",
res)
+ ref = get_reference_data(myname, __file__)
+
+ assert np.allclose(res, ref, atol=0, rtol=1e-15)
+
+ # also check result of autocorrelate
+ res2 = []
+ for a in arrs:
+ r = multipletau.autocorrelate(a=a,
+ m=16,
+ deltat=1,
+ normalize=False,
+ copy=True,
+ dtype=np.complex_)
+ res2.append(r)
+ res2 = np.concatenate(res2)
+
+ assert np.allclose(res, res2, atol=0, rtol=1e-15)
+
+
if __name__ == "__main__":
# Run all tests
loc = locals()
diff -Nru python-multipletau-0.1.6+ds/tests/test_ref_numpy.py
python-multipletau-0.1.7+ds/tests/test_ref_numpy.py
--- python-multipletau-0.1.6+ds/tests/test_ref_numpy.py 2016-07-11
20:51:49.000000000 +0200
+++ python-multipletau-0.1.7+ds/tests/test_ref_numpy.py 2017-04-26
14:31:13.000000000 +0200
@@ -14,7 +14,6 @@
from test_correlate import get_sample_arrays_cplx
-
def test_corresponds_ac():
myframe = sys._getframe()
myname = myframe.f_code.co_name
@@ -27,19 +26,74 @@
m=m,
copy=True,
normalize=True,
- dtype=np.float128)
+ dtype=np.float_)
reslin = multipletau.correlate_numpy(a=1*a,
v=1*a,
copy=True,
normalize=True,
- dtype=np.float128)
+ 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
@@ -52,13 +106,13 @@
m=m,
copy=True,
normalize=False,
- dtype=np.float128)
+ dtype=np.float_)
reslin = multipletau.correlate_numpy(a=1*a,
v=1*a,
copy=True,
normalize=False,
- dtype=np.float128)
+ dtype=np.float_)
idx = np.array(restau[:,0].real, dtype=int)[:m+1]
@@ -78,13 +132,13 @@
m=m,
copy=True,
normalize=True,
- dtype=np.complex256)
+ dtype=np.complex_)
reslin = multipletau.correlate_numpy(a=a,
v=a.imag+1j*a.real,
copy=True,
normalize=True,
- dtype=np.complex256)
+ dtype=np.complex_)
idx = np.array(restau[:,0].real, dtype=int)[:m+1]
@@ -104,74 +158,19 @@
m=m,
copy=True,
normalize=False,
- dtype=np.complex256)
+ dtype=np.complex_)
reslin = multipletau.correlate_numpy(a=a,
v=a.imag+1j*a.real,
copy=True,
normalize=False,
- dtype=np.complex256)
+ 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_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.complex256)
-
- reslin = multipletau.correlate_numpy(a=a,
- v=a.imag+1j*a.real,
- copy=True,
- normalize=False,
- dtype=np.complex256)
-
- 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)
-
-
if __name__ == "__main__":
# Run all tests
loc = locals()
