def _assertFlattenedEquals(self, expected, actual):
if expected is None:
self.assertEquals(None, actual)
elif isinstance(expected, (list, tuple)):
self.assertEquals(len(expected), len(actual))
for exp, act in zip(expected, actual):
self._assertFlattenedEquals(exp, act)
elif isinstance(expected, dict):
self.assertEquals(len(expected), len(actual))
for k, v in expected.iteritems():
self.assertTrue(k in actual)
self._assertFlattenedEquals(v, actual[k])
elif isinstance(expected, float) and dnp.isnan(expected):
self.assertTrue(dnp.isnan(actual))
elif isinstance(expected, Exception):
self.assertTrue(isinstance(actual, Exception))
self.assertEquals(str(expected), str(actual))
elif isinstance(expected, (dnp.ndarray)):
self.assertTrue(dnp.maths.equaldataset(expected, actual))
else:
self.assertEquals(expected, actual)
def _assertFlattenedEquals(self, expected, actual):
if expected is None:
self.assertEqual(None, actual)
elif isinstance(expected, (list, tuple)):
self.assertEqual(len(expected), len(actual))
for exp, act in zip(expected, actual):
self._assertFlattenedEquals(exp, act)
elif isinstance(expected, dict):
self.assertEqual(len(expected), len(actual))
for k, v in six.iteritems(expected):
self.assertTrue(k in actual)
self._assertFlattenedEquals(v, actual[k])
elif isinstance(expected, float) and dnp.isnan(expected):
self.assertTrue(dnp.isnan(actual))
elif isinstance(expected, Exception):
self.assertTrue(isinstance(actual, Exception))
# We are a little lenient on Exception comparisons because
# Exception helpers are not a perfect flatten/unflatten
# 1) we add the original type of the Exception to the beginning
# of the Exception string
# 2) we add the stack trace to the end of the exception
actualstr = str(actual)
i = actualstr.find("\n\n")
if i >= 0:
actualstr = actualstr[:i]
expstr = str(expected)
i = expstr.find("\n\n")
if i >= 0:
expstr = expstr[:i]
if expstr != actualstr and "Exception: " + expstr != actualstr:
# on mismatch, display error on original string
self.assertEqual(str(expected), str(actual))
elif isinstance(expected, (dnp.ndarray)):
self.assertTrue(dnp.equaldataset(expected, actual))
else:
self.assertEqual(expected, actual)
def _assertFlattenedEquals(self, expected, actual):
if expected is None:
self.assertEquals(None, actual)
elif isinstance(expected, (list, tuple)):
self.assertEquals(len(expected), len(actual))
for exp, act in zip(expected, actual):
self._assertFlattenedEquals(exp, act)
elif isinstance(expected, dict):
self.assertEquals(len(expected), len(actual))
for k, v in expected.iteritems():
self.assertTrue(k in actual)
self._assertFlattenedEquals(v, actual[k])
elif isinstance(expected, float) and dnp.isnan(expected):
self.assertTrue(dnp.isnan(actual))
elif isinstance(expected, Exception):
self.assertTrue(isinstance(actual, Exception))
# We are a little lenient on Exception comparisons because
# Exception helpers are not a perfect flatten/unflatten
# 1) we add the original type of the Exception to the beginning
# of the Exception string
# 2) we add the stack trace to the end of the exception
actualstr = str(actual)
i = actualstr.find("\n\n")
if i >= 0:
actualstr = actualstr[:i]
expstr = str(expected)
i = expstr.find("\n\n")
if i >= 0:
expstr = expstr[:i]
if expstr != actualstr and "Exception: " + expstr != actualstr:
# on mismatch, display error on original string
self.assertEquals(str(expected), str(actual))
elif isinstance(expected, (dnp.ndarray)):
self.assertTrue(dnp.equaldataset(expected, actual))
else:
self.assertEquals(expected, actual)
def peakdet(v, delta, x = None):
"""
Converted from MATLAB script at http://billauer.co.il/peakdet.html
Currently returns two lists of tuples, but maybe arrays would be better
function [maxtab, mintab]=peakdet(v, delta, x)
%PEAKDET Detect peaks in a vector
% [MAXTAB, MINTAB] = PEAKDET(V, DELTA) finds the local
% maxima and minima ("peaks") in the vector V.
% MAXTAB and MINTAB consists of two columns. Column 1
% contains indices in V, and column 2 the found values.
%
% With [MAXTAB, MINTAB] = PEAKDET(V, DELTA, X) the indices
% in MAXTAB and MINTAB are replaced with the corresponding
% X-values.
%
% A point is considered a maximum peak if it has the maximal
% value, and was preceded (to the left) by a value lower by
% DELTA.
% Eli Billauer, 3.4.05 (Explicitly not copyrighted).
% This function is released to the public domain; Any use is allowed.
"""
maxtab = []
mintab = []
if x is None:
x = dnp.arange(len(v))
v = dnp.asarray(v)
if len(v) != len(x):
sys.exit('Input vectors v and x must have same length')
if dnp.isnan(delta):
sys.exit('Input argument delta must be a scalar')
if delta <= 0:
sys.exit('Input argument delta must be positive')
mn, mx = Inf, -Inf
mnpos, mxpos = NaN, NaN
lookformax = True
for i in dnp.arange(len(v)):
this = v[i]
if this > mx:
mx = this
mxpos = x[i]
if this < mn:
mn = this
mnpos = x[i]
if lookformax:
if this < mx-delta:
maxtab.append((mxpos, mx))
mn = this
mnpos = x[i]
lookformax = False
else:
if this > mn+delta:
mintab.append((mnpos, mn))
mx = this
mxpos = x[i]
lookformax = True
return maxtab, mintab
