File: //usr/lib/python2.7/dist-packages/matplotlib/tests/test_mlab.py
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from matplotlib.externals import six
import tempfile
from numpy.testing import assert_allclose, assert_array_equal
import numpy.ma.testutils as matest
import numpy as np
from nose.tools import (assert_equal, assert_almost_equal, assert_not_equal,
assert_true, assert_raises)
import matplotlib.mlab as mlab
import matplotlib.cbook as cbook
from matplotlib.testing.decorators import knownfailureif, CleanupTestCase
try:
from mpl_toolkits.natgrid import _natgrid
HAS_NATGRID = True
except ImportError:
HAS_NATGRID = False
class general_testcase(CleanupTestCase):
def test_colinear_pca(self):
a = mlab.PCA._get_colinear()
pca = mlab.PCA(a)
assert_allclose(pca.fracs[2:], 0., atol=1e-8)
assert_allclose(pca.Y[:, 2:], 0., atol=1e-8)
def test_prctile(self):
# test odd lengths
x = [1, 2, 3]
assert_equal(mlab.prctile(x, 50), np.median(x))
# test even lengths
x = [1, 2, 3, 4]
assert_equal(mlab.prctile(x, 50), np.median(x))
# derived from email sent by jason-sage to MPL-user on 20090914
ob1 = [1, 1, 2, 2, 1, 2, 4, 3, 2, 2, 2, 3,
4, 5, 6, 7, 8, 9, 7, 6, 4, 5, 5]
p = [0, 75, 100]
expected = [1, 5.5, 9]
# test vectorized
actual = mlab.prctile(ob1, p)
assert_allclose(expected, actual)
# test scalar
for pi, expectedi in zip(p, expected):
actuali = mlab.prctile(ob1, pi)
assert_allclose(expectedi, actuali)
def test_norm(self):
np.random.seed(0)
N = 1000
x = np.random.standard_normal(N)
targ = np.linalg.norm(x)
res = mlab._norm(x)
assert_almost_equal(targ, res)
class spacing_testcase(CleanupTestCase):
def test_logspace_tens(self):
xmin = .01
xmax = 1000.
N = 6
res = mlab.logspace(xmin, xmax, N)
targ = np.logspace(np.log10(xmin), np.log10(xmax), N)
assert_allclose(targ, res)
def test_logspace_primes(self):
xmin = .03
xmax = 1313.
N = 7
res = mlab.logspace(xmin, xmax, N)
targ = np.logspace(np.log10(xmin), np.log10(xmax), N)
assert_allclose(targ, res)
def test_logspace_none(self):
xmin = .03
xmax = 1313.
N = 0
res = mlab.logspace(xmin, xmax, N)
targ = np.logspace(np.log10(xmin), np.log10(xmax), N)
assert_array_equal(targ, res)
assert_equal(res.size, 0)
def test_logspace_single(self):
xmin = .03
xmax = 1313.
N = 1
res = mlab.logspace(xmin, xmax, N)
targ = np.logspace(np.log10(xmin), np.log10(xmax), N)
assert_array_equal(targ, res)
assert_equal(res.size, 1)
class stride_testcase(CleanupTestCase):
def get_base(self, x):
y = x
while y.base is not None:
y = y.base
return y
def calc_window_target(self, x, NFFT, noverlap=0):
'''This is an adaptation of the original window extraction
algorithm. This is here to test to make sure the new implementation
has the same result'''
step = NFFT - noverlap
ind = np.arange(0, len(x) - NFFT + 1, step)
n = len(ind)
result = np.zeros((NFFT, n))
# do the ffts of the slices
for i in range(n):
result[:, i] = x[ind[i]:ind[i]+NFFT]
return result
def test_stride_windows_2D_ValueError(self):
x = np.arange(10)[np.newaxis]
assert_raises(ValueError, mlab.stride_windows, x, 5)
def test_stride_windows_0D_ValueError(self):
x = np.array(0)
assert_raises(ValueError, mlab.stride_windows, x, 5)
def test_stride_windows_noverlap_gt_n_ValueError(self):
x = np.arange(10)
assert_raises(ValueError, mlab.stride_windows, x, 2, 3)
def test_stride_windows_noverlap_eq_n_ValueError(self):
x = np.arange(10)
assert_raises(ValueError, mlab.stride_windows, x, 2, 2)
def test_stride_windows_n_gt_lenx_ValueError(self):
x = np.arange(10)
assert_raises(ValueError, mlab.stride_windows, x, 11)
def test_stride_windows_n_lt_1_ValueError(self):
x = np.arange(10)
assert_raises(ValueError, mlab.stride_windows, x, 0)
def test_stride_repeat_2D_ValueError(self):
x = np.arange(10)[np.newaxis]
assert_raises(ValueError, mlab.stride_repeat, x, 5)
def test_stride_repeat_axis_lt_0_ValueError(self):
x = np.array(0)
assert_raises(ValueError, mlab.stride_repeat, x, 5, axis=-1)
def test_stride_repeat_axis_gt_1_ValueError(self):
x = np.array(0)
assert_raises(ValueError, mlab.stride_repeat, x, 5, axis=2)
def test_stride_repeat_n_lt_1_ValueError(self):
x = np.arange(10)
assert_raises(ValueError, mlab.stride_repeat, x, 0)
def test_stride_repeat_n1_axis0(self):
x = np.arange(10)
y = mlab.stride_repeat(x, 1)
assert_equal((1, ) + x.shape, y.shape)
assert_array_equal(x, y.flat)
assert_true(self.get_base(y) is x)
def test_stride_repeat_n1_axis1(self):
x = np.arange(10)
y = mlab.stride_repeat(x, 1, axis=1)
assert_equal(x.shape + (1, ), y.shape)
assert_array_equal(x, y.flat)
assert_true(self.get_base(y) is x)
def test_stride_repeat_n5_axis0(self):
x = np.arange(10)
y = mlab.stride_repeat(x, 5)
yr = np.repeat(x[np.newaxis], 5, axis=0)
assert_equal(yr.shape, y.shape)
assert_array_equal(yr, y)
assert_equal((5, ) + x.shape, y.shape)
assert_true(self.get_base(y) is x)
def test_stride_repeat_n5_axis1(self):
x = np.arange(10)
y = mlab.stride_repeat(x, 5, axis=1)
yr = np.repeat(x[np.newaxis], 5, axis=0).T
assert_equal(yr.shape, y.shape)
assert_array_equal(yr, y)
assert_equal(x.shape + (5, ), y.shape)
assert_true(self.get_base(y) is x)
def test_stride_windows_n1_noverlap0_axis0(self):
x = np.arange(10)
y = mlab.stride_windows(x, 1)
yt = self.calc_window_target(x, 1)
assert_equal(yt.shape, y.shape)
assert_array_equal(yt, y)
assert_equal((1, ) + x.shape, y.shape)
assert_true(self.get_base(y) is x)
def test_stride_windows_n1_noverlap0_axis1(self):
x = np.arange(10)
y = mlab.stride_windows(x, 1, axis=1)
yt = self.calc_window_target(x, 1).T
assert_equal(yt.shape, y.shape)
assert_array_equal(yt, y)
assert_equal(x.shape + (1, ), y.shape)
assert_true(self.get_base(y) is x)
def test_stride_windows_n5_noverlap0_axis0(self):
x = np.arange(100)
y = mlab.stride_windows(x, 5)
yt = self.calc_window_target(x, 5)
assert_equal(yt.shape, y.shape)
assert_array_equal(yt, y)
assert_equal((5, 20), y.shape)
assert_true(self.get_base(y) is x)
def test_stride_windows_n5_noverlap0_axis1(self):
x = np.arange(100)
y = mlab.stride_windows(x, 5, axis=1)
yt = self.calc_window_target(x, 5).T
assert_equal(yt.shape, y.shape)
assert_array_equal(yt, y)
assert_equal((20, 5), y.shape)
assert_true(self.get_base(y) is x)
def test_stride_windows_n15_noverlap2_axis0(self):
x = np.arange(100)
y = mlab.stride_windows(x, 15, 2)
yt = self.calc_window_target(x, 15, 2)
assert_equal(yt.shape, y.shape)
assert_array_equal(yt, y)
assert_equal((15, 7), y.shape)
assert_true(self.get_base(y) is x)
def test_stride_windows_n15_noverlap2_axis1(self):
x = np.arange(100)
y = mlab.stride_windows(x, 15, 2, axis=1)
yt = self.calc_window_target(x, 15, 2).T
assert_equal(yt.shape, y.shape)
assert_array_equal(yt, y)
assert_equal((7, 15), y.shape)
assert_true(self.get_base(y) is x)
def test_stride_windows_n13_noverlapn3_axis0(self):
x = np.arange(100)
y = mlab.stride_windows(x, 13, -3)
yt = self.calc_window_target(x, 13, -3)
assert_equal(yt.shape, y.shape)
assert_array_equal(yt, y)
assert_equal((13, 6), y.shape)
assert_true(self.get_base(y) is x)
def test_stride_windows_n13_noverlapn3_axis1(self):
x = np.arange(100)
y = mlab.stride_windows(x, 13, -3, axis=1)
yt = self.calc_window_target(x, 13, -3).T
assert_equal(yt.shape, y.shape)
assert_array_equal(yt, y)
assert_equal((6, 13), y.shape)
assert_true(self.get_base(y) is x)
def test_stride_windows_n32_noverlap0_axis0_unflatten(self):
n = 32
x = np.arange(n)[np.newaxis]
x1 = np.tile(x, (21, 1))
x2 = x1.flatten()
y = mlab.stride_windows(x2, n)
assert_equal(y.shape, x1.T.shape)
assert_array_equal(y, x1.T)
def test_stride_windows_n32_noverlap0_axis1_unflatten(self):
n = 32
x = np.arange(n)[np.newaxis]
x1 = np.tile(x, (21, 1))
x2 = x1.flatten()
y = mlab.stride_windows(x2, n, axis=1)
assert_equal(y.shape, x1.shape)
assert_array_equal(y, x1)
def test_stride_ensure_integer_type(self):
N = 100
x = np.empty(N + 20, dtype='>f4')
x.fill(np.NaN)
y = x[10:-10]
y.fill(0.3)
# previous to #3845 lead to corrupt access
y_strided = mlab.stride_windows(y, n=33, noverlap=0.6)
assert_array_equal(y_strided, 0.3)
# previous to #3845 lead to corrupt access
y_strided = mlab.stride_windows(y, n=33.3, noverlap=0)
assert_array_equal(y_strided, 0.3)
# even previous to #3845 could not find any problematic
# configuration however, let's be sure it's not accidentally
# introduced
y_strided = mlab.stride_repeat(y, n=33.815)
assert_array_equal(y_strided, 0.3)
class csv_testcase(CleanupTestCase):
def setUp(self):
if six.PY3:
self.fd = tempfile.TemporaryFile(suffix='csv', mode="w+",
newline='')
else:
self.fd = tempfile.TemporaryFile(suffix='csv', mode="wb+")
def tearDown(self):
self.fd.close()
def test_recarray_csv_roundtrip(self):
expected = np.recarray((99,),
[(str('x'), np.float),
(str('y'), np.float),
(str('t'), np.float)])
# initialising all values: uninitialised memory sometimes produces
# floats that do not round-trip to string and back.
expected['x'][:] = np.linspace(-1e9, -1, 99)
expected['y'][:] = np.linspace(1, 1e9, 99)
expected['t'][:] = np.linspace(0, 0.01, 99)
mlab.rec2csv(expected, self.fd)
self.fd.seek(0)
actual = mlab.csv2rec(self.fd)
assert_allclose(expected['x'], actual['x'])
assert_allclose(expected['y'], actual['y'])
assert_allclose(expected['t'], actual['t'])
def test_rec2csv_bad_shape_ValueError(self):
bad = np.recarray((99, 4), [(str('x'), np.float),
(str('y'), np.float)])
# the bad recarray should trigger a ValueError for having ndim > 1.
assert_raises(ValueError, mlab.rec2csv, bad, self.fd)
def test_csv2rec_names_with_comments(self):
self.fd.write('# comment\n1,2,3\n4,5,6\n')
self.fd.seek(0)
array = mlab.csv2rec(self.fd, names='a,b,c')
assert len(array) == 2
assert len(array.dtype) == 3
class window_testcase(CleanupTestCase):
def setUp(self):
np.random.seed(0)
self.n = 1000
self.x = np.arange(0., self.n)
self.sig_rand = np.random.standard_normal(self.n) + 100.
self.sig_ones = np.ones_like(self.x)
self.sig_slope = np.linspace(-10., 90., self.n)
def check_window_apply_repeat(self, x, window, NFFT, noverlap):
'''This is an adaptation of the original window application
algorithm. This is here to test to make sure the new implementation
has the same result'''
step = NFFT - noverlap
ind = np.arange(0, len(x) - NFFT + 1, step)
n = len(ind)
result = np.zeros((NFFT, n))
if cbook.iterable(window):
windowVals = window
else:
windowVals = window(np.ones((NFFT,), x.dtype))
# do the ffts of the slices
for i in range(n):
result[:, i] = windowVals * x[ind[i]:ind[i]+NFFT]
return result
def test_window_none_rand(self):
res = mlab.window_none(self.sig_ones)
assert_array_equal(res, self.sig_ones)
def test_window_none_ones(self):
res = mlab.window_none(self.sig_rand)
assert_array_equal(res, self.sig_rand)
def test_window_hanning_rand(self):
targ = np.hanning(len(self.sig_rand)) * self.sig_rand
res = mlab.window_hanning(self.sig_rand)
assert_allclose(targ, res, atol=1e-06)
def test_window_hanning_ones(self):
targ = np.hanning(len(self.sig_ones))
res = mlab.window_hanning(self.sig_ones)
assert_allclose(targ, res, atol=1e-06)
def test_apply_window_1D_axis1_ValueError(self):
x = self.sig_rand
window = mlab.window_hanning
assert_raises(ValueError, mlab.apply_window, x, window, axis=1,
return_window=False)
def test_apply_window_1D_els_wrongsize_ValueError(self):
x = self.sig_rand
window = mlab.window_hanning(np.ones(x.shape[0]-1))
assert_raises(ValueError, mlab.apply_window, x, window)
def test_apply_window_0D_ValueError(self):
x = np.array(0)
window = mlab.window_hanning
assert_raises(ValueError, mlab.apply_window, x, window, axis=1,
return_window=False)
def test_apply_window_3D_ValueError(self):
x = self.sig_rand[np.newaxis][np.newaxis]
window = mlab.window_hanning
assert_raises(ValueError, mlab.apply_window, x, window, axis=1,
return_window=False)
def test_apply_window_hanning_1D(self):
x = self.sig_rand
window = mlab.window_hanning
window1 = mlab.window_hanning(np.ones(x.shape[0]))
y, window2 = mlab.apply_window(x, window, return_window=True)
yt = window(x)
assert_equal(yt.shape, y.shape)
assert_equal(x.shape, y.shape)
assert_allclose(yt, y, atol=1e-06)
assert_array_equal(window1, window2)
def test_apply_window_hanning_1D_axis0(self):
x = self.sig_rand
window = mlab.window_hanning
y = mlab.apply_window(x, window, axis=0, return_window=False)
yt = window(x)
assert_equal(yt.shape, y.shape)
assert_equal(x.shape, y.shape)
assert_allclose(yt, y, atol=1e-06)
def test_apply_window_hanning_els_1D_axis0(self):
x = self.sig_rand
window = mlab.window_hanning(np.ones(x.shape[0]))
window1 = mlab.window_hanning
y = mlab.apply_window(x, window, axis=0, return_window=False)
yt = window1(x)
assert_equal(yt.shape, y.shape)
assert_equal(x.shape, y.shape)
assert_allclose(yt, y, atol=1e-06)
def test_apply_window_hanning_2D_axis0(self):
x = np.random.standard_normal([1000, 10]) + 100.
window = mlab.window_hanning
y = mlab.apply_window(x, window, axis=0, return_window=False)
yt = np.zeros_like(x)
for i in range(x.shape[1]):
yt[:, i] = window(x[:, i])
assert_equal(yt.shape, y.shape)
assert_equal(x.shape, y.shape)
assert_allclose(yt, y, atol=1e-06)
def test_apply_window_hanning_els1_2D_axis0(self):
x = np.random.standard_normal([1000, 10]) + 100.
window = mlab.window_hanning(np.ones(x.shape[0]))
window1 = mlab.window_hanning
y = mlab.apply_window(x, window, axis=0, return_window=False)
yt = np.zeros_like(x)
for i in range(x.shape[1]):
yt[:, i] = window1(x[:, i])
assert_equal(yt.shape, y.shape)
assert_equal(x.shape, y.shape)
assert_allclose(yt, y, atol=1e-06)
def test_apply_window_hanning_els2_2D_axis0(self):
x = np.random.standard_normal([1000, 10]) + 100.
window = mlab.window_hanning
window1 = mlab.window_hanning(np.ones(x.shape[0]))
y, window2 = mlab.apply_window(x, window, axis=0, return_window=True)
yt = np.zeros_like(x)
for i in range(x.shape[1]):
yt[:, i] = window1*x[:, i]
assert_equal(yt.shape, y.shape)
assert_equal(x.shape, y.shape)
assert_allclose(yt, y, atol=1e-06)
assert_array_equal(window1, window2)
def test_apply_window_hanning_els3_2D_axis0(self):
x = np.random.standard_normal([1000, 10]) + 100.
window = mlab.window_hanning
window1 = mlab.window_hanning(np.ones(x.shape[0]))
y, window2 = mlab.apply_window(x, window, axis=0, return_window=True)
yt = mlab.apply_window(x, window1, axis=0, return_window=False)
assert_equal(yt.shape, y.shape)
assert_equal(x.shape, y.shape)
assert_allclose(yt, y, atol=1e-06)
assert_array_equal(window1, window2)
def test_apply_window_hanning_2D_axis1(self):
x = np.random.standard_normal([10, 1000]) + 100.
window = mlab.window_hanning
y = mlab.apply_window(x, window, axis=1, return_window=False)
yt = np.zeros_like(x)
for i in range(x.shape[0]):
yt[i, :] = window(x[i, :])
assert_equal(yt.shape, y.shape)
assert_equal(x.shape, y.shape)
assert_allclose(yt, y, atol=1e-06)
def test_apply_window_hanning_2D__els1_axis1(self):
x = np.random.standard_normal([10, 1000]) + 100.
window = mlab.window_hanning(np.ones(x.shape[1]))
window1 = mlab.window_hanning
y = mlab.apply_window(x, window, axis=1, return_window=False)
yt = np.zeros_like(x)
for i in range(x.shape[0]):
yt[i, :] = window1(x[i, :])
assert_equal(yt.shape, y.shape)
assert_equal(x.shape, y.shape)
assert_allclose(yt, y, atol=1e-06)
def test_apply_window_hanning_2D_els2_axis1(self):
x = np.random.standard_normal([10, 1000]) + 100.
window = mlab.window_hanning
window1 = mlab.window_hanning(np.ones(x.shape[1]))
y, window2 = mlab.apply_window(x, window, axis=1, return_window=True)
yt = np.zeros_like(x)
for i in range(x.shape[0]):
yt[i, :] = window1 * x[i, :]
assert_equal(yt.shape, y.shape)
assert_equal(x.shape, y.shape)
assert_allclose(yt, y, atol=1e-06)
assert_array_equal(window1, window2)
def test_apply_window_hanning_2D_els3_axis1(self):
x = np.random.standard_normal([10, 1000]) + 100.
window = mlab.window_hanning
window1 = mlab.window_hanning(np.ones(x.shape[1]))
y = mlab.apply_window(x, window, axis=1, return_window=False)
yt = mlab.apply_window(x, window1, axis=1, return_window=False)
assert_equal(yt.shape, y.shape)
assert_equal(x.shape, y.shape)
assert_allclose(yt, y, atol=1e-06)
def test_apply_window_stride_windows_hanning_2D_n13_noverlapn3_axis0(self):
x = self.sig_rand
window = mlab.window_hanning
yi = mlab.stride_windows(x, n=13, noverlap=2, axis=0)
y = mlab.apply_window(yi, window, axis=0, return_window=False)
yt = self.check_window_apply_repeat(x, window, 13, 2)
assert_equal(yt.shape, y.shape)
assert_not_equal(x.shape, y.shape)
assert_allclose(yt, y, atol=1e-06)
def test_apply_window_hanning_2D_stack_axis1(self):
ydata = np.arange(32)
ydata1 = ydata+5
ydata2 = ydata+3.3
ycontrol1 = mlab.apply_window(ydata1, mlab.window_hanning)
ycontrol2 = mlab.window_hanning(ydata2)
ydata = np.vstack([ydata1, ydata2])
ycontrol = np.vstack([ycontrol1, ycontrol2])
ydata = np.tile(ydata, (20, 1))
ycontrol = np.tile(ycontrol, (20, 1))
result = mlab.apply_window(ydata, mlab.window_hanning, axis=1,
return_window=False)
assert_allclose(ycontrol, result, atol=1e-08)
def test_apply_window_hanning_2D_stack_windows_axis1(self):
ydata = np.arange(32)
ydata1 = ydata+5
ydata2 = ydata+3.3
ycontrol1 = mlab.apply_window(ydata1, mlab.window_hanning)
ycontrol2 = mlab.window_hanning(ydata2)
ydata = np.vstack([ydata1, ydata2])
ycontrol = np.vstack([ycontrol1, ycontrol2])
ydata = np.tile(ydata, (20, 1))
ycontrol = np.tile(ycontrol, (20, 1))
result = mlab.apply_window(ydata, mlab.window_hanning, axis=1,
return_window=False)
assert_allclose(ycontrol, result, atol=1e-08)
def test_apply_window_hanning_2D_stack_windows_axis1_unflatten(self):
n = 32
ydata = np.arange(n)
ydata1 = ydata+5
ydata2 = ydata+3.3
ycontrol1 = mlab.apply_window(ydata1, mlab.window_hanning)
ycontrol2 = mlab.window_hanning(ydata2)
ydata = np.vstack([ydata1, ydata2])
ycontrol = np.vstack([ycontrol1, ycontrol2])
ydata = np.tile(ydata, (20, 1))
ycontrol = np.tile(ycontrol, (20, 1))
ydata = ydata.flatten()
ydata1 = mlab.stride_windows(ydata, 32, noverlap=0, axis=0)
result = mlab.apply_window(ydata1, mlab.window_hanning, axis=0,
return_window=False)
assert_allclose(ycontrol.T, result, atol=1e-08)
class detrend_testcase(CleanupTestCase):
def setUp(self):
np.random.seed(0)
n = 1000
x = np.linspace(0., 100, n)
self.sig_zeros = np.zeros(n)
self.sig_off = self.sig_zeros + 100.
self.sig_slope = np.linspace(-10., 90., n)
self.sig_slope_mean = x - x.mean()
sig_rand = np.random.standard_normal(n)
sig_sin = np.sin(x*2*np.pi/(n/100))
sig_rand -= sig_rand.mean()
sig_sin -= sig_sin.mean()
self.sig_base = sig_rand + sig_sin
self.atol = 1e-08
def test_detrend_none_0D_zeros(self):
input = 0.
targ = input
res = mlab.detrend_none(input)
assert_equal(input, targ)
def test_detrend_none_0D_zeros_axis1(self):
input = 0.
targ = input
res = mlab.detrend_none(input, axis=1)
assert_equal(input, targ)
def test_detrend_str_none_0D_zeros(self):
input = 0.
targ = input
res = mlab.detrend(input, key='none')
assert_equal(input, targ)
def test_detrend_detrend_none_0D_zeros(self):
input = 0.
targ = input
res = mlab.detrend(input, key=mlab.detrend_none)
assert_equal(input, targ)
def test_detrend_none_0D_off(self):
input = 5.5
targ = input
res = mlab.detrend_none(input)
assert_equal(input, targ)
def test_detrend_none_1D_off(self):
input = self.sig_off
targ = input
res = mlab.detrend_none(input)
assert_array_equal(res, targ)
def test_detrend_none_1D_slope(self):
input = self.sig_slope
targ = input
res = mlab.detrend_none(input)
assert_array_equal(res, targ)
def test_detrend_none_1D_base(self):
input = self.sig_base
targ = input
res = mlab.detrend_none(input)
assert_array_equal(res, targ)
def test_detrend_none_1D_base_slope_off_list(self):
input = self.sig_base + self.sig_slope + self.sig_off
targ = input.tolist()
res = mlab.detrend_none(input.tolist())
assert_equal(res, targ)
def test_detrend_none_2D(self):
arri = [self.sig_base,
self.sig_base + self.sig_off,
self.sig_base + self.sig_slope,
self.sig_base + self.sig_off + self.sig_slope]
input = np.vstack(arri)
targ = input
res = mlab.detrend_none(input)
assert_array_equal(res, targ)
def test_detrend_none_2D_T(self):
arri = [self.sig_base,
self.sig_base + self.sig_off,
self.sig_base + self.sig_slope,
self.sig_base + self.sig_off + self.sig_slope]
input = np.vstack(arri)
targ = input
res = mlab.detrend_none(input.T)
assert_array_equal(res.T, targ)
def test_detrend_mean_0D_zeros(self):
input = 0.
targ = 0.
res = mlab.detrend_mean(input)
assert_almost_equal(res, targ)
def test_detrend_str_mean_0D_zeros(self):
input = 0.
targ = 0.
res = mlab.detrend(input, key='mean')
assert_almost_equal(res, targ)
def test_detrend_detrend_mean_0D_zeros(self):
input = 0.
targ = 0.
res = mlab.detrend(input, key=mlab.detrend_mean)
assert_almost_equal(res, targ)
def test_detrend_mean_0D_off(self):
input = 5.5
targ = 0.
res = mlab.detrend_mean(input)
assert_almost_equal(res, targ)
def test_detrend_str_mean_0D_off(self):
input = 5.5
targ = 0.
res = mlab.detrend(input, key='mean')
assert_almost_equal(res, targ)
def test_detrend_detrend_mean_0D_off(self):
input = 5.5
targ = 0.
res = mlab.detrend(input, key=mlab.detrend_mean)
assert_almost_equal(res, targ)
def test_detrend_mean_1D_zeros(self):
input = self.sig_zeros
targ = self.sig_zeros
res = mlab.detrend_mean(input)
assert_allclose(res, targ, atol=self.atol)
def test_detrend_mean_1D_base(self):
input = self.sig_base
targ = self.sig_base
res = mlab.detrend_mean(input)
assert_allclose(res, targ, atol=self.atol)
def test_detrend_mean_1D_base_off(self):
input = self.sig_base + self.sig_off
targ = self.sig_base
res = mlab.detrend_mean(input)
assert_allclose(res, targ, atol=self.atol)
def test_detrend_mean_1D_base_slope(self):
input = self.sig_base + self.sig_slope
targ = self.sig_base + self.sig_slope_mean
res = mlab.detrend_mean(input)
assert_allclose(res, targ, atol=self.atol)
def test_detrend_mean_1D_base_slope_off(self):
input = self.sig_base + self.sig_slope + self.sig_off
targ = self.sig_base + self.sig_slope_mean
res = mlab.detrend_mean(input)
assert_allclose(res, targ, atol=1e-08)
def test_detrend_mean_1D_base_slope_off_axis0(self):
input = self.sig_base + self.sig_slope + self.sig_off
targ = self.sig_base + self.sig_slope_mean
res = mlab.detrend_mean(input, axis=0)
assert_allclose(res, targ, atol=1e-08)
def test_detrend_mean_1D_base_slope_off_list(self):
input = self.sig_base + self.sig_slope + self.sig_off
targ = self.sig_base + self.sig_slope_mean
res = mlab.detrend_mean(input.tolist())
assert_allclose(res, targ, atol=1e-08)
def test_detrend_mean_1D_base_slope_off_list_axis0(self):
input = self.sig_base + self.sig_slope + self.sig_off
targ = self.sig_base + self.sig_slope_mean
res = mlab.detrend_mean(input.tolist(), axis=0)
assert_allclose(res, targ, atol=1e-08)
def test_demean_0D_off(self):
input = 5.5
targ = 0.
res = mlab.demean(input, axis=None)
assert_almost_equal(res, targ)
def test_demean_1D_base_slope_off(self):
input = self.sig_base + self.sig_slope + self.sig_off
targ = self.sig_base + self.sig_slope_mean
res = mlab.demean(input)
assert_allclose(res, targ, atol=1e-08)
def test_demean_1D_base_slope_off_axis0(self):
input = self.sig_base + self.sig_slope + self.sig_off
targ = self.sig_base + self.sig_slope_mean
res = mlab.demean(input, axis=0)
assert_allclose(res, targ, atol=1e-08)
def test_demean_1D_base_slope_off_list(self):
input = self.sig_base + self.sig_slope + self.sig_off
targ = self.sig_base + self.sig_slope_mean
res = mlab.demean(input.tolist())
assert_allclose(res, targ, atol=1e-08)
def test_detrend_mean_2D_default(self):
arri = [self.sig_off,
self.sig_base + self.sig_off]
arrt = [self.sig_zeros,
self.sig_base]
input = np.vstack(arri)
targ = np.vstack(arrt)
res = mlab.detrend_mean(input)
assert_allclose(res, targ, atol=1e-08)
def test_detrend_mean_2D_none(self):
arri = [self.sig_off,
self.sig_base + self.sig_off]
arrt = [self.sig_zeros,
self.sig_base]
input = np.vstack(arri)
targ = np.vstack(arrt)
res = mlab.detrend_mean(input, axis=None)
assert_allclose(res, targ,
atol=1e-08)
def test_detrend_mean_2D_none_T(self):
arri = [self.sig_off,
self.sig_base + self.sig_off]
arrt = [self.sig_zeros,
self.sig_base]
input = np.vstack(arri).T
targ = np.vstack(arrt)
res = mlab.detrend_mean(input, axis=None)
assert_allclose(res.T, targ,
atol=1e-08)
def test_detrend_mean_2D_axis0(self):
arri = [self.sig_base,
self.sig_base + self.sig_off,
self.sig_base + self.sig_slope,
self.sig_base + self.sig_off + self.sig_slope]
arrt = [self.sig_base,
self.sig_base,
self.sig_base + self.sig_slope_mean,
self.sig_base + self.sig_slope_mean]
input = np.vstack(arri).T
targ = np.vstack(arrt).T
res = mlab.detrend_mean(input, axis=0)
assert_allclose(res, targ,
atol=1e-08)
def test_detrend_mean_2D_axis1(self):
arri = [self.sig_base,
self.sig_base + self.sig_off,
self.sig_base + self.sig_slope,
self.sig_base + self.sig_off + self.sig_slope]
arrt = [self.sig_base,
self.sig_base,
self.sig_base + self.sig_slope_mean,
self.sig_base + self.sig_slope_mean]
input = np.vstack(arri)
targ = np.vstack(arrt)
res = mlab.detrend_mean(input, axis=1)
assert_allclose(res, targ,
atol=1e-08)
def test_detrend_mean_2D_axism1(self):
arri = [self.sig_base,
self.sig_base + self.sig_off,
self.sig_base + self.sig_slope,
self.sig_base + self.sig_off + self.sig_slope]
arrt = [self.sig_base,
self.sig_base,
self.sig_base + self.sig_slope_mean,
self.sig_base + self.sig_slope_mean]
input = np.vstack(arri)
targ = np.vstack(arrt)
res = mlab.detrend_mean(input, axis=-1)
assert_allclose(res, targ,
atol=1e-08)
def test_detrend_mean_2D_none(self):
arri = [self.sig_off,
self.sig_base + self.sig_off]
arrt = [self.sig_zeros,
self.sig_base]
input = np.vstack(arri)
targ = np.vstack(arrt)
res = mlab.detrend_mean(input, axis=None)
assert_allclose(res, targ,
atol=1e-08)
def test_detrend_mean_2D_none_T(self):
arri = [self.sig_off,
self.sig_base + self.sig_off]
arrt = [self.sig_zeros,
self.sig_base]
input = np.vstack(arri).T
targ = np.vstack(arrt)
res = mlab.detrend_mean(input, axis=None)
assert_allclose(res.T, targ,
atol=1e-08)
def test_detrend_mean_2D_axis0(self):
arri = [self.sig_base,
self.sig_base + self.sig_off,
self.sig_base + self.sig_slope,
self.sig_base + self.sig_off + self.sig_slope]
arrt = [self.sig_base,
self.sig_base,
self.sig_base + self.sig_slope_mean,
self.sig_base + self.sig_slope_mean]
input = np.vstack(arri).T
targ = np.vstack(arrt).T
res = mlab.detrend_mean(input, axis=0)
assert_allclose(res, targ,
atol=1e-08)
def test_detrend_mean_2D_axis1(self):
arri = [self.sig_base,
self.sig_base + self.sig_off,
self.sig_base + self.sig_slope,
self.sig_base + self.sig_off + self.sig_slope]
arrt = [self.sig_base,
self.sig_base,
self.sig_base + self.sig_slope_mean,
self.sig_base + self.sig_slope_mean]
input = np.vstack(arri)
targ = np.vstack(arrt)
res = mlab.detrend_mean(input, axis=1)
assert_allclose(res, targ,
atol=1e-08)
def test_detrend_mean_2D_axism1(self):
arri = [self.sig_base,
self.sig_base + self.sig_off,
self.sig_base + self.sig_slope,
self.sig_base + self.sig_off + self.sig_slope]
arrt = [self.sig_base,
self.sig_base,
self.sig_base + self.sig_slope_mean,
self.sig_base + self.sig_slope_mean]
input = np.vstack(arri)
targ = np.vstack(arrt)
res = mlab.detrend_mean(input, axis=-1)
assert_allclose(res, targ,
atol=1e-08)
def test_detrend_2D_default(self):
arri = [self.sig_off,
self.sig_base + self.sig_off]
arrt = [self.sig_zeros,
self.sig_base]
input = np.vstack(arri)
targ = np.vstack(arrt)
res = mlab.detrend(input)
assert_allclose(res, targ, atol=1e-08)
def test_detrend_2D_none(self):
arri = [self.sig_off,
self.sig_base + self.sig_off]
arrt = [self.sig_zeros,
self.sig_base]
input = np.vstack(arri)
targ = np.vstack(arrt)
res = mlab.detrend(input, axis=None)
assert_allclose(res, targ, atol=1e-08)
def test_detrend_str_mean_2D_axis0(self):
arri = [self.sig_base,
self.sig_base + self.sig_off,
self.sig_base + self.sig_slope,
self.sig_base + self.sig_off + self.sig_slope]
arrt = [self.sig_base,
self.sig_base,
self.sig_base + self.sig_slope_mean,
self.sig_base + self.sig_slope_mean]
input = np.vstack(arri).T
targ = np.vstack(arrt).T
res = mlab.detrend(input, key='mean', axis=0)
assert_allclose(res, targ,
atol=1e-08)
def test_detrend_str_constant_2D_none_T(self):
arri = [self.sig_off,
self.sig_base + self.sig_off]
arrt = [self.sig_zeros,
self.sig_base]
input = np.vstack(arri).T
targ = np.vstack(arrt)
res = mlab.detrend(input, key='constant', axis=None)
assert_allclose(res.T, targ,
atol=1e-08)
def test_detrend_str_default_2D_axis1(self):
arri = [self.sig_base,
self.sig_base + self.sig_off,
self.sig_base + self.sig_slope,
self.sig_base + self.sig_off + self.sig_slope]
arrt = [self.sig_base,
self.sig_base,
self.sig_base + self.sig_slope_mean,
self.sig_base + self.sig_slope_mean]
input = np.vstack(arri)
targ = np.vstack(arrt)
res = mlab.detrend(input, key='default', axis=1)
assert_allclose(res, targ,
atol=1e-08)
def test_detrend_detrend_mean_2D_axis0(self):
arri = [self.sig_base,
self.sig_base + self.sig_off,
self.sig_base + self.sig_slope,
self.sig_base + self.sig_off + self.sig_slope]
arrt = [self.sig_base,
self.sig_base,
self.sig_base + self.sig_slope_mean,
self.sig_base + self.sig_slope_mean]
input = np.vstack(arri).T
targ = np.vstack(arrt).T
res = mlab.detrend(input, key=mlab.detrend_mean, axis=0)
assert_allclose(res, targ,
atol=1e-08)
def test_demean_2D_default(self):
arri = [self.sig_base,
self.sig_base + self.sig_off,
self.sig_base + self.sig_slope,
self.sig_base + self.sig_off + self.sig_slope]
arrt = [self.sig_base,
self.sig_base,
self.sig_base + self.sig_slope_mean,
self.sig_base + self.sig_slope_mean]
input = np.vstack(arri).T
targ = np.vstack(arrt).T
res = mlab.demean(input)
assert_allclose(res, targ,
atol=1e-08)
def test_demean_2D_none(self):
arri = [self.sig_off,
self.sig_base + self.sig_off]
arrt = [self.sig_zeros,
self.sig_base]
input = np.vstack(arri)
targ = np.vstack(arrt)
res = mlab.demean(input, axis=None)
assert_allclose(res, targ,
atol=1e-08)
def test_demean_2D_axis0(self):
arri = [self.sig_base,
self.sig_base + self.sig_off,
self.sig_base + self.sig_slope,
self.sig_base + self.sig_off + self.sig_slope]
arrt = [self.sig_base,
self.sig_base,
self.sig_base + self.sig_slope_mean,
self.sig_base + self.sig_slope_mean]
input = np.vstack(arri).T
targ = np.vstack(arrt).T
res = mlab.demean(input, axis=0)
assert_allclose(res, targ,
atol=1e-08)
def test_demean_2D_axis1(self):
arri = [self.sig_base,
self.sig_base + self.sig_off,
self.sig_base + self.sig_slope,
self.sig_base + self.sig_off + self.sig_slope]
arrt = [self.sig_base,
self.sig_base,
self.sig_base + self.sig_slope_mean,
self.sig_base + self.sig_slope_mean]
input = np.vstack(arri)
targ = np.vstack(arrt)
res = mlab.demean(input, axis=1)
assert_allclose(res, targ,
atol=1e-08)
def test_demean_2D_axism1(self):
arri = [self.sig_base,
self.sig_base + self.sig_off,
self.sig_base + self.sig_slope,
self.sig_base + self.sig_off + self.sig_slope]
arrt = [self.sig_base,
self.sig_base,
self.sig_base + self.sig_slope_mean,
self.sig_base + self.sig_slope_mean]
input = np.vstack(arri)
targ = np.vstack(arrt)
res = mlab.demean(input, axis=-1)
assert_allclose(res, targ,
atol=1e-08)
def test_detrend_bad_key_str_ValueError(self):
input = self.sig_slope[np.newaxis]
assert_raises(ValueError, mlab.detrend, input, key='spam')
def test_detrend_bad_key_var_ValueError(self):
input = self.sig_slope[np.newaxis]
assert_raises(ValueError, mlab.detrend, input, key=5)
def test_detrend_mean_0D_d0_ValueError(self):
input = 5.5
assert_raises(ValueError, mlab.detrend_mean, input, axis=0)
def test_detrend_0D_d0_ValueError(self):
input = 5.5
assert_raises(ValueError, mlab.detrend, input, axis=0)
def test_detrend_mean_1D_d1_ValueError(self):
input = self.sig_slope
assert_raises(ValueError, mlab.detrend_mean, input, axis=1)
def test_detrend_1D_d1_ValueError(self):
input = self.sig_slope
assert_raises(ValueError, mlab.detrend, input, axis=1)
def test_demean_1D_d1_ValueError(self):
input = self.sig_slope
assert_raises(ValueError, mlab.demean, input, axis=1)
def test_detrend_mean_2D_d2_ValueError(self):
input = self.sig_slope[np.newaxis]
assert_raises(ValueError, mlab.detrend_mean, input, axis=2)
def test_detrend_2D_d2_ValueError(self):
input = self.sig_slope[np.newaxis]
assert_raises(ValueError, mlab.detrend, input, axis=2)
def test_demean_2D_d2_ValueError(self):
input = self.sig_slope[np.newaxis]
assert_raises(ValueError, mlab.demean, input, axis=2)
def test_detrend_linear_0D_zeros(self):
input = 0.
targ = 0.
res = mlab.detrend_linear(input)
assert_almost_equal(res, targ)
def test_detrend_linear_0D_off(self):
input = 5.5
targ = 0.
res = mlab.detrend_linear(input)
assert_almost_equal(res, targ)
def test_detrend_str_linear_0D_off(self):
input = 5.5
targ = 0.
res = mlab.detrend(input, key='linear')
assert_almost_equal(res, targ)
def test_detrend_detrend_linear_0D_off(self):
input = 5.5
targ = 0.
res = mlab.detrend(input, key=mlab.detrend_linear)
assert_almost_equal(res, targ)
def test_detrend_linear_1d_off(self):
input = self.sig_off
targ = self.sig_zeros
res = mlab.detrend_linear(input)
assert_allclose(res, targ, atol=self.atol)
def test_detrend_linear_1d_slope(self):
input = self.sig_slope
targ = self.sig_zeros
res = mlab.detrend_linear(input)
assert_allclose(res, targ, atol=self.atol)
def test_detrend_linear_1d_slope_off(self):
input = self.sig_slope + self.sig_off
targ = self.sig_zeros
res = mlab.detrend_linear(input)
assert_allclose(res, targ, atol=self.atol)
def test_detrend_str_linear_1d_slope_off(self):
input = self.sig_slope + self.sig_off
targ = self.sig_zeros
res = mlab.detrend(input, key='linear')
assert_allclose(res, targ, atol=self.atol)
def test_detrend_detrend_linear_1d_slope_off(self):
input = self.sig_slope + self.sig_off
targ = self.sig_zeros
res = mlab.detrend(input, key=mlab.detrend_linear)
assert_allclose(res, targ, atol=self.atol)
def test_detrend_linear_1d_slope_off_list(self):
input = self.sig_slope + self.sig_off
targ = self.sig_zeros
res = mlab.detrend_linear(input.tolist())
assert_allclose(res, targ, atol=self.atol)
def test_detrend_linear_2D_ValueError(self):
input = self.sig_slope[np.newaxis]
assert_raises(ValueError, mlab.detrend_linear, input)
def test_detrend_str_linear_2d_slope_off_axis0(self):
arri = [self.sig_off,
self.sig_slope,
self.sig_slope + self.sig_off]
arrt = [self.sig_zeros,
self.sig_zeros,
self.sig_zeros]
input = np.vstack(arri).T
targ = np.vstack(arrt).T
res = mlab.detrend(input, key='linear', axis=0)
assert_allclose(res, targ, atol=self.atol)
def test_detrend_detrend_linear_1d_slope_off_axis1(self):
arri = [self.sig_off,
self.sig_slope,
self.sig_slope + self.sig_off]
arrt = [self.sig_zeros,
self.sig_zeros,
self.sig_zeros]
input = np.vstack(arri).T
targ = np.vstack(arrt).T
res = mlab.detrend(input, key=mlab.detrend_linear, axis=0)
assert_allclose(res, targ, atol=self.atol)
def test_detrend_str_linear_2d_slope_off_axis0(self):
arri = [self.sig_off,
self.sig_slope,
self.sig_slope + self.sig_off]
arrt = [self.sig_zeros,
self.sig_zeros,
self.sig_zeros]
input = np.vstack(arri)
targ = np.vstack(arrt)
res = mlab.detrend(input, key='linear', axis=1)
assert_allclose(res, targ, atol=self.atol)
def test_detrend_detrend_linear_1d_slope_off_axis1(self):
arri = [self.sig_off,
self.sig_slope,
self.sig_slope + self.sig_off]
arrt = [self.sig_zeros,
self.sig_zeros,
self.sig_zeros]
input = np.vstack(arri)
targ = np.vstack(arrt)
res = mlab.detrend(input, key=mlab.detrend_linear, axis=1)
assert_allclose(res, targ, atol=self.atol)
class spectral_testcase_nosig_real_onesided(CleanupTestCase):
def setUp(self):
self.createStim(fstims=[],
iscomplex=False, sides='onesided', nsides=1)
def createStim(self, fstims, iscomplex, sides, nsides, len_x=None,
NFFT_density=-1, nover_density=-1, pad_to_density=-1,
pad_to_spectrum=-1):
Fs = 100.
x = np.arange(0, 10, 1/Fs)
if len_x is not None:
x = x[:len_x]
# get the stimulus frequencies, defaulting to None
fstims = [Fs/fstim for fstim in fstims]
# get the constants, default to calculated values
if NFFT_density is None:
NFFT_density_real = 256
elif NFFT_density < 0:
NFFT_density_real = NFFT_density = 100
else:
NFFT_density_real = NFFT_density
if nover_density is None:
nover_density_real = 0
elif nover_density < 0:
nover_density_real = nover_density = NFFT_density_real//2
else:
nover_density_real = nover_density
if pad_to_density is None:
pad_to_density_real = NFFT_density_real
elif pad_to_density < 0:
pad_to_density = int(2**np.ceil(np.log2(NFFT_density_real)))
pad_to_density_real = pad_to_density
else:
pad_to_density_real = pad_to_density
if pad_to_spectrum is None:
pad_to_spectrum_real = len(x)
elif pad_to_spectrum < 0:
pad_to_spectrum_real = pad_to_spectrum = len(x)
else:
pad_to_spectrum_real = pad_to_spectrum
if pad_to_spectrum is None:
NFFT_spectrum_real = NFFT_spectrum = pad_to_spectrum_real
else:
NFFT_spectrum_real = NFFT_spectrum = len(x)
nover_spectrum_real = nover_spectrum = 0
NFFT_specgram = NFFT_density
nover_specgram = nover_density
pad_to_specgram = pad_to_density
NFFT_specgram_real = NFFT_density_real
nover_specgram_real = nover_density_real
if nsides == 1:
# frequencies for specgram, psd, and csd
# need to handle even and odd differently
if pad_to_density_real % 2:
freqs_density = np.linspace(0, Fs/2,
num=pad_to_density_real,
endpoint=False)[::2]
else:
freqs_density = np.linspace(0, Fs/2,
num=pad_to_density_real//2+1)
# frequencies for complex, magnitude, angle, and phase spectrums
# need to handle even and odd differently
if pad_to_spectrum_real % 2:
freqs_spectrum = np.linspace(0, Fs/2,
num=pad_to_spectrum_real,
endpoint=False)[::2]
else:
freqs_spectrum = np.linspace(0, Fs/2,
num=pad_to_spectrum_real//2+1)
else:
# frequencies for specgram, psd, and csd
# need to handle even and odd differentl
if pad_to_density_real % 2:
freqs_density = np.linspace(-Fs/2, Fs/2,
num=2*pad_to_density_real,
endpoint=False)[1::2]
else:
freqs_density = np.linspace(-Fs/2, Fs/2,
num=pad_to_density_real,
endpoint=False)
# frequencies for complex, magnitude, angle, and phase spectrums
# need to handle even and odd differently
if pad_to_spectrum_real % 2:
freqs_spectrum = np.linspace(-Fs/2, Fs/2,
num=2*pad_to_spectrum_real,
endpoint=False)[1::2]
else:
freqs_spectrum = np.linspace(-Fs/2, Fs/2,
num=pad_to_spectrum_real,
endpoint=False)
freqs_specgram = freqs_density
# time points for specgram
t_start = NFFT_specgram_real//2
t_stop = len(x) - NFFT_specgram_real//2+1
t_step = NFFT_specgram_real - nover_specgram_real
t_specgram = x[t_start:t_stop:t_step]
if NFFT_specgram_real % 2:
t_specgram += 1/Fs/2
if len(t_specgram) == 0:
t_specgram = np.array([NFFT_specgram_real/(2*Fs)])
t_spectrum = np.array([NFFT_spectrum_real/(2*Fs)])
t_density = t_specgram
y = np.zeros_like(x)
for i, fstim in enumerate(fstims):
y += np.sin(fstim * x * np.pi * 2) * 10**i
if iscomplex:
y = y.astype('complex')
self.Fs = Fs
self.sides = sides
self.fstims = fstims
self.NFFT_density = NFFT_density
self.nover_density = nover_density
self.pad_to_density = pad_to_density
self.NFFT_spectrum = NFFT_spectrum
self.nover_spectrum = nover_spectrum
self.pad_to_spectrum = pad_to_spectrum
self.NFFT_specgram = NFFT_specgram
self.nover_specgram = nover_specgram
self.pad_to_specgram = pad_to_specgram
self.t_specgram = t_specgram
self.t_density = t_density
self.t_spectrum = t_spectrum
self.y = y
self.freqs_density = freqs_density
self.freqs_spectrum = freqs_spectrum
self.freqs_specgram = freqs_specgram
self.NFFT_density_real = NFFT_density_real
def check_freqs(self, vals, targfreqs, resfreqs, fstims):
assert_true(resfreqs.argmin() == 0)
assert_true(resfreqs.argmax() == len(resfreqs)-1)
assert_allclose(resfreqs, targfreqs, atol=1e-06)
for fstim in fstims:
i = np.abs(resfreqs - fstim).argmin()
assert_true(vals[i] > vals[i+2])
assert_true(vals[i] > vals[i-2])
def check_maxfreq(self, spec, fsp, fstims):
# skip the test if there are no frequencies
if len(fstims) == 0:
return
# if twosided, do the test for each side
if fsp.min() < 0:
fspa = np.abs(fsp)
zeroind = fspa.argmin()
self.check_maxfreq(spec[:zeroind], fspa[:zeroind], fstims)
self.check_maxfreq(spec[zeroind:], fspa[zeroind:], fstims)
return
fstimst = fstims[:]
spect = spec.copy()
# go through each peak and make sure it is correctly the maximum peak
while fstimst:
maxind = spect.argmax()
maxfreq = fsp[maxind]
assert_almost_equal(maxfreq, fstimst[-1])
del fstimst[-1]
spect[maxind-5:maxind+5] = 0
def test_spectral_helper_raises_complex_same_data(self):
# test that mode 'complex' cannot be used if x is not y
assert_raises(ValueError, mlab._spectral_helper,
x=self.y, y=self.y+1, mode='complex')
def test_spectral_helper_raises_magnitude_same_data(self):
# test that mode 'magnitude' cannot be used if x is not y
assert_raises(ValueError, mlab._spectral_helper,
x=self.y, y=self.y+1, mode='magnitude')
def test_spectral_helper_raises_angle_same_data(self):
# test that mode 'angle' cannot be used if x is not y
assert_raises(ValueError, mlab._spectral_helper,
x=self.y, y=self.y+1, mode='angle')
def test_spectral_helper_raises_phase_same_data(self):
# test that mode 'phase' cannot be used if x is not y
assert_raises(ValueError, mlab._spectral_helper,
x=self.y, y=self.y+1, mode='phase')
def test_spectral_helper_raises_unknown_mode(self):
# test that unknown value for mode cannot be used
assert_raises(ValueError, mlab._spectral_helper,
x=self.y, mode='spam')
def test_spectral_helper_raises_unknown_sides(self):
# test that unknown value for sides cannot be used
assert_raises(ValueError, mlab._spectral_helper,
x=self.y, y=self.y, sides='eggs')
def test_spectral_helper_raises_noverlap_gt_NFFT(self):
# test that noverlap cannot be larger than NFFT
assert_raises(ValueError, mlab._spectral_helper,
x=self.y, y=self.y, NFFT=10, noverlap=20)
def test_spectral_helper_raises_noverlap_eq_NFFT(self):
# test that noverlap cannot be equal to NFFT
assert_raises(ValueError, mlab._spectral_helper,
x=self.y, NFFT=10, noverlap=10)
def test_spectral_helper_raises_winlen_ne_NFFT(self):
# test that the window length cannot be different from NFFT
assert_raises(ValueError, mlab._spectral_helper,
x=self.y, y=self.y, NFFT=10, window=np.ones(9))
def test_single_spectrum_helper_raises_mode_default(self):
# test that mode 'default' cannot be used with _single_spectrum_helper
assert_raises(ValueError, mlab._single_spectrum_helper,
x=self.y, mode='default')
def test_single_spectrum_helper_raises_mode_psd(self):
# test that mode 'psd' cannot be used with _single_spectrum_helper
assert_raises(ValueError, mlab._single_spectrum_helper,
x=self.y, mode='psd')
def test_spectral_helper_psd(self):
freqs = self.freqs_density
spec, fsp, t = mlab._spectral_helper(x=self.y, y=self.y,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=self.nover_density,
pad_to=self.pad_to_density,
sides=self.sides,
mode='psd')
assert_allclose(fsp, freqs, atol=1e-06)
assert_allclose(t, self.t_density, atol=1e-06)
assert_equal(spec.shape[0], freqs.shape[0])
assert_equal(spec.shape[1], self.t_specgram.shape[0])
def test_spectral_helper_magnitude_specgram(self):
freqs = self.freqs_specgram
spec, fsp, t = mlab._spectral_helper(x=self.y, y=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides,
mode='magnitude')
assert_allclose(fsp, freqs, atol=1e-06)
assert_allclose(t, self.t_specgram, atol=1e-06)
assert_equal(spec.shape[0], freqs.shape[0])
assert_equal(spec.shape[1], self.t_specgram.shape[0])
def test_spectral_helper_magnitude_magnitude_spectrum(self):
freqs = self.freqs_spectrum
spec, fsp, t = mlab._spectral_helper(x=self.y, y=self.y,
NFFT=self.NFFT_spectrum,
Fs=self.Fs,
noverlap=self.nover_spectrum,
pad_to=self.pad_to_spectrum,
sides=self.sides,
mode='magnitude')
assert_allclose(fsp, freqs, atol=1e-06)
assert_allclose(t, self.t_spectrum, atol=1e-06)
assert_equal(spec.shape[0], freqs.shape[0])
assert_equal(spec.shape[1], 1)
def test_csd(self):
freqs = self.freqs_density
spec, fsp = mlab.csd(x=self.y, y=self.y+1,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=self.nover_density,
pad_to=self.pad_to_density,
sides=self.sides)
assert_allclose(fsp, freqs, atol=1e-06)
assert_equal(spec.shape, freqs.shape)
def test_psd(self):
freqs = self.freqs_density
spec, fsp = mlab.psd(x=self.y,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=self.nover_density,
pad_to=self.pad_to_density,
sides=self.sides)
assert_equal(spec.shape, freqs.shape)
self.check_freqs(spec, freqs, fsp, self.fstims)
def test_psd_detrend_mean_func_offset(self):
if self.NFFT_density is None:
return
freqs = self.freqs_density
ydata = np.zeros(self.NFFT_density)
ydata1 = ydata+5
ydata2 = ydata+3.3
ydata = np.vstack([ydata1, ydata2])
ydata = np.tile(ydata, (20, 1))
ydatab = ydata.T.flatten()
ydata = ydata.flatten()
ycontrol = np.zeros_like(ydata)
spec_g, fsp_g = mlab.psd(x=ydata,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=0,
sides=self.sides,
detrend=mlab.detrend_mean)
spec_b, fsp_b = mlab.psd(x=ydatab,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=0,
sides=self.sides,
detrend=mlab.detrend_mean)
spec_c, fsp_c = mlab.psd(x=ycontrol,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=0,
sides=self.sides)
assert_array_equal(fsp_g, fsp_c)
assert_array_equal(fsp_b, fsp_c)
assert_allclose(spec_g, spec_c, atol=1e-08)
# these should not be almost equal
assert_raises(AssertionError,
assert_allclose, spec_b, spec_c, atol=1e-08)
def test_psd_detrend_mean_str_offset(self):
if self.NFFT_density is None:
return
freqs = self.freqs_density
ydata = np.zeros(self.NFFT_density)
ydata1 = ydata+5
ydata2 = ydata+3.3
ydata = np.vstack([ydata1, ydata2])
ydata = np.tile(ydata, (20, 1))
ydatab = ydata.T.flatten()
ydata = ydata.flatten()
ycontrol = np.zeros_like(ydata)
spec_g, fsp_g = mlab.psd(x=ydata,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=0,
sides=self.sides,
detrend='mean')
spec_b, fsp_b = mlab.psd(x=ydatab,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=0,
sides=self.sides,
detrend='mean')
spec_c, fsp_c = mlab.psd(x=ycontrol,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=0,
sides=self.sides)
assert_array_equal(fsp_g, fsp_c)
assert_array_equal(fsp_b, fsp_c)
assert_allclose(spec_g, spec_c, atol=1e-08)
# these should not be almost equal
assert_raises(AssertionError,
assert_allclose, spec_b, spec_c, atol=1e-08)
def test_psd_detrend_linear_func_trend(self):
if self.NFFT_density is None:
return
freqs = self.freqs_density
ydata = np.arange(self.NFFT_density)
ydata1 = ydata+5
ydata2 = ydata+3.3
ydata = np.vstack([ydata1, ydata2])
ydata = np.tile(ydata, (20, 1))
ydatab = ydata.T.flatten()
ydata = ydata.flatten()
ycontrol = np.zeros_like(ydata)
spec_g, fsp_g = mlab.psd(x=ydata,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=0,
sides=self.sides,
detrend=mlab.detrend_linear)
spec_b, fsp_b = mlab.psd(x=ydatab,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=0,
sides=self.sides,
detrend=mlab.detrend_linear)
spec_c, fsp_c = mlab.psd(x=ycontrol,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=0,
sides=self.sides)
assert_array_equal(fsp_g, fsp_c)
assert_array_equal(fsp_b, fsp_c)
assert_allclose(spec_g, spec_c, atol=1e-08)
# these should not be almost equal
assert_raises(AssertionError,
assert_allclose, spec_b, spec_c, atol=1e-08)
def test_psd_detrend_linear_str_trend(self):
if self.NFFT_density is None:
return
freqs = self.freqs_density
ydata = np.arange(self.NFFT_density)
ydata1 = ydata+5
ydata2 = ydata+3.3
ydata = np.vstack([ydata1, ydata2])
ydata = np.tile(ydata, (20, 1))
ydatab = ydata.T.flatten()
ydata = ydata.flatten()
ycontrol = np.zeros_like(ydata)
spec_g, fsp_g = mlab.psd(x=ydata,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=0,
sides=self.sides,
detrend='linear')
spec_b, fsp_b = mlab.psd(x=ydatab,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=0,
sides=self.sides,
detrend='linear')
spec_c, fsp_c = mlab.psd(x=ycontrol,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=0,
sides=self.sides)
assert_array_equal(fsp_g, fsp_c)
assert_array_equal(fsp_b, fsp_c)
assert_allclose(spec_g, spec_c, atol=1e-08)
# these should not be almost equal
assert_raises(AssertionError,
assert_allclose, spec_b, spec_c, atol=1e-08)
def test_psd_window_hanning(self):
if self.NFFT_density is None:
return
freqs = self.freqs_density
ydata = np.arange(self.NFFT_density)
ydata1 = ydata+5
ydata2 = ydata+3.3
ycontrol1, windowVals = mlab.apply_window(ydata1,
mlab.window_hanning,
return_window=True)
ycontrol2 = mlab.window_hanning(ydata2)
ydata = np.vstack([ydata1, ydata2])
ycontrol = np.vstack([ycontrol1, ycontrol2])
ydata = np.tile(ydata, (20, 1))
ycontrol = np.tile(ycontrol, (20, 1))
ydatab = ydata.T.flatten()
ydataf = ydata.flatten()
ycontrol = ycontrol.flatten()
spec_g, fsp_g = mlab.psd(x=ydataf,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=0,
sides=self.sides,
window=mlab.window_hanning)
spec_b, fsp_b = mlab.psd(x=ydatab,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=0,
sides=self.sides,
window=mlab.window_hanning)
spec_c, fsp_c = mlab.psd(x=ycontrol,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=0,
sides=self.sides,
window=mlab.window_none)
spec_c *= len(ycontrol1)/(np.abs(windowVals)**2).sum()
assert_array_equal(fsp_g, fsp_c)
assert_array_equal(fsp_b, fsp_c)
assert_allclose(spec_g, spec_c, atol=1e-08)
# these should not be almost equal
assert_raises(AssertionError,
assert_allclose, spec_b, spec_c, atol=1e-08)
def test_psd_window_hanning_detrend_linear(self):
if self.NFFT_density is None:
return
freqs = self.freqs_density
ydata = np.arange(self.NFFT_density)
ycontrol = np.zeros(self.NFFT_density)
ydata1 = ydata+5
ydata2 = ydata+3.3
ycontrol1 = ycontrol
ycontrol2 = ycontrol
ycontrol1, windowVals = mlab.apply_window(ycontrol1,
mlab.window_hanning,
return_window=True)
ycontrol2 = mlab.window_hanning(ycontrol2)
ydata = np.vstack([ydata1, ydata2])
ycontrol = np.vstack([ycontrol1, ycontrol2])
ydata = np.tile(ydata, (20, 1))
ycontrol = np.tile(ycontrol, (20, 1))
ydatab = ydata.T.flatten()
ydataf = ydata.flatten()
ycontrol = ycontrol.flatten()
spec_g, fsp_g = mlab.psd(x=ydataf,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=0,
sides=self.sides,
detrend=mlab.detrend_linear,
window=mlab.window_hanning)
spec_b, fsp_b = mlab.psd(x=ydatab,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=0,
sides=self.sides,
detrend=mlab.detrend_linear,
window=mlab.window_hanning)
spec_c, fsp_c = mlab.psd(x=ycontrol,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=0,
sides=self.sides,
window=mlab.window_none)
spec_c *= len(ycontrol1)/(np.abs(windowVals)**2).sum()
assert_array_equal(fsp_g, fsp_c)
assert_array_equal(fsp_b, fsp_c)
assert_allclose(spec_g, spec_c, atol=1e-08)
# these should not be almost equal
assert_raises(AssertionError,
assert_allclose, spec_b, spec_c, atol=1e-08)
def test_psd_windowarray(self):
freqs = self.freqs_density
spec, fsp = mlab.psd(x=self.y,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=self.nover_density,
pad_to=self.pad_to_density,
sides=self.sides,
window=np.ones(self.NFFT_density_real))
assert_allclose(fsp, freqs, atol=1e-06)
assert_equal(spec.shape, freqs.shape)
def test_psd_windowarray_scale_by_freq(self):
freqs = self.freqs_density
win = mlab.window_hanning(np.ones(self.NFFT_density_real))
spec, fsp = mlab.psd(x=self.y,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=self.nover_density,
pad_to=self.pad_to_density,
sides=self.sides,
window=mlab.window_hanning)
spec_s, fsp_s = mlab.psd(x=self.y,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=self.nover_density,
pad_to=self.pad_to_density,
sides=self.sides,
window=mlab.window_hanning,
scale_by_freq=True)
spec_n, fsp_n = mlab.psd(x=self.y,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=self.nover_density,
pad_to=self.pad_to_density,
sides=self.sides,
window=mlab.window_hanning,
scale_by_freq=False)
assert_array_equal(fsp, fsp_s)
assert_array_equal(fsp, fsp_n)
assert_array_equal(spec, spec_s)
assert_allclose(spec_s*(win**2).sum(),
spec_n/self.Fs*win.sum()**2,
atol=1e-08)
def test_complex_spectrum(self):
freqs = self.freqs_spectrum
spec, fsp = mlab.complex_spectrum(x=self.y,
Fs=self.Fs,
sides=self.sides,
pad_to=self.pad_to_spectrum)
assert_allclose(fsp, freqs, atol=1e-06)
assert_equal(spec.shape, freqs.shape)
def test_magnitude_spectrum(self):
freqs = self.freqs_spectrum
spec, fsp = mlab.magnitude_spectrum(x=self.y,
Fs=self.Fs,
sides=self.sides,
pad_to=self.pad_to_spectrum)
assert_equal(spec.shape, freqs.shape)
self.check_maxfreq(spec, fsp, self.fstims)
self.check_freqs(spec, freqs, fsp, self.fstims)
def test_angle_spectrum(self):
freqs = self.freqs_spectrum
spec, fsp = mlab.angle_spectrum(x=self.y,
Fs=self.Fs,
sides=self.sides,
pad_to=self.pad_to_spectrum)
assert_allclose(fsp, freqs, atol=1e-06)
assert_equal(spec.shape, freqs.shape)
def test_phase_spectrum(self):
freqs = self.freqs_spectrum
spec, fsp = mlab.phase_spectrum(x=self.y,
Fs=self.Fs,
sides=self.sides,
pad_to=self.pad_to_spectrum)
assert_allclose(fsp, freqs, atol=1e-06)
assert_equal(spec.shape, freqs.shape)
def test_specgram_auto(self):
freqs = self.freqs_specgram
spec, fsp, t = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides)
specm = np.mean(spec, axis=1)
assert_allclose(fsp, freqs, atol=1e-06)
assert_allclose(t, self.t_specgram, atol=1e-06)
assert_equal(spec.shape[0], freqs.shape[0])
assert_equal(spec.shape[1], self.t_specgram.shape[0])
# since we are using a single freq, all time slices
# should be about the same
if np.abs(spec.max()) != 0:
assert_allclose(np.diff(spec, axis=1).max()/np.abs(spec.max()), 0,
atol=1e-02)
self.check_freqs(specm, freqs, fsp, self.fstims)
def test_specgram_default(self):
freqs = self.freqs_specgram
spec, fsp, t = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides,
mode='default')
specm = np.mean(spec, axis=1)
assert_allclose(fsp, freqs, atol=1e-06)
assert_allclose(t, self.t_specgram, atol=1e-06)
assert_equal(spec.shape[0], freqs.shape[0])
assert_equal(spec.shape[1], self.t_specgram.shape[0])
# since we are using a single freq, all time slices
# should be about the same
if np.abs(spec.max()) != 0:
assert_allclose(np.diff(spec, axis=1).max()/np.abs(spec.max()), 0,
atol=1e-02)
self.check_freqs(specm, freqs, fsp, self.fstims)
def test_specgram_psd(self):
freqs = self.freqs_specgram
spec, fsp, t = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides,
mode='psd')
specm = np.mean(spec, axis=1)
assert_allclose(fsp, freqs, atol=1e-06)
assert_allclose(t, self.t_specgram, atol=1e-06)
assert_equal(spec.shape[0], freqs.shape[0])
assert_equal(spec.shape[1], self.t_specgram.shape[0])
# since we are using a single freq, all time slices
# should be about the same
if np.abs(spec.max()) != 0:
assert_allclose(np.diff(spec, axis=1).max()/np.abs(spec.max()), 0,
atol=1e-02)
self.check_freqs(specm, freqs, fsp, self.fstims)
def test_specgram_complex(self):
freqs = self.freqs_specgram
spec, fsp, t = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides,
mode='complex')
specm = np.mean(np.abs(spec), axis=1)
assert_allclose(fsp, freqs, atol=1e-06)
assert_allclose(t, self.t_specgram, atol=1e-06)
assert_equal(spec.shape[0], freqs.shape[0])
assert_equal(spec.shape[1], self.t_specgram.shape[0])
self.check_freqs(specm, freqs, fsp, self.fstims)
def test_specgram_magnitude(self):
freqs = self.freqs_specgram
spec, fsp, t = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides,
mode='magnitude')
specm = np.mean(spec, axis=1)
assert_allclose(fsp, freqs, atol=1e-06)
assert_allclose(t, self.t_specgram, atol=1e-06)
assert_equal(spec.shape[0], freqs.shape[0])
assert_equal(spec.shape[1], self.t_specgram.shape[0])
# since we are using a single freq, all time slices
# should be about the same
if np.abs(spec.max()) != 0:
assert_allclose(np.diff(spec, axis=1).max()/np.abs(spec.max()), 0,
atol=1e-02)
self.check_freqs(specm, freqs, fsp, self.fstims)
def test_specgram_angle(self):
freqs = self.freqs_specgram
spec, fsp, t = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides,
mode='angle')
specm = np.mean(spec, axis=1)
assert_allclose(fsp, freqs, atol=1e-06)
assert_allclose(t, self.t_specgram, atol=1e-06)
assert_equal(spec.shape[0], freqs.shape[0])
assert_equal(spec.shape[1], self.t_specgram.shape[0])
def test_specgram_phase(self):
freqs = self.freqs_specgram
spec, fsp, t = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides,
mode='phase')
specm = np.mean(spec, axis=1)
assert_allclose(fsp, freqs, atol=1e-06)
assert_allclose(t, self.t_specgram, atol=1e-06)
assert_equal(spec.shape[0], freqs.shape[0])
assert_equal(spec.shape[1], self.t_specgram.shape[0])
def test_psd_csd_equal(self):
freqs = self.freqs_density
Pxx, freqsxx = mlab.psd(x=self.y,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=self.nover_density,
pad_to=self.pad_to_density,
sides=self.sides)
Pxy, freqsxy = mlab.csd(x=self.y, y=self.y,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=self.nover_density,
pad_to=self.pad_to_density,
sides=self.sides)
assert_array_equal(Pxx, Pxy)
assert_array_equal(freqsxx, freqsxy)
def test_specgram_auto_default_equal(self):
'''test that mlab.specgram without mode and with mode 'default' and
'psd' are all the same'''
freqs = self.freqs_specgram
speca, freqspeca, ta = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides)
specb, freqspecb, tb = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides,
mode='default')
assert_array_equal(speca, specb)
assert_array_equal(freqspeca, freqspecb)
assert_array_equal(ta, tb)
def test_specgram_auto_psd_equal(self):
'''test that mlab.specgram without mode and with mode 'default' and
'psd' are all the same'''
freqs = self.freqs_specgram
speca, freqspeca, ta = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides)
specc, freqspecc, tc = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides,
mode='psd')
assert_array_equal(speca, specc)
assert_array_equal(freqspeca, freqspecc)
assert_array_equal(ta, tc)
def test_specgram_complex_mag_equivalent(self):
freqs = self.freqs_specgram
specc, freqspecc, tc = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides,
mode='complex')
specm, freqspecm, tm = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides,
mode='magnitude')
assert_array_equal(freqspecc, freqspecm)
assert_array_equal(tc, tm)
assert_allclose(np.abs(specc), specm, atol=1e-06)
def test_specgram_complex_angle_equivalent(self):
freqs = self.freqs_specgram
specc, freqspecc, tc = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides,
mode='complex')
speca, freqspeca, ta = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides,
mode='angle')
assert_array_equal(freqspecc, freqspeca)
assert_array_equal(tc, ta)
assert_allclose(np.angle(specc), speca, atol=1e-06)
def test_specgram_complex_phase_equivalent(self):
freqs = self.freqs_specgram
specc, freqspecc, tc = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides,
mode='complex')
specp, freqspecp, tp = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides,
mode='phase')
assert_array_equal(freqspecc, freqspecp)
assert_array_equal(tc, tp)
assert_allclose(np.unwrap(np.angle(specc), axis=0), specp,
atol=1e-06)
def test_specgram_angle_phase_equivalent(self):
freqs = self.freqs_specgram
speca, freqspeca, ta = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides,
mode='angle')
specp, freqspecp, tp = mlab.specgram(x=self.y,
NFFT=self.NFFT_specgram,
Fs=self.Fs,
noverlap=self.nover_specgram,
pad_to=self.pad_to_specgram,
sides=self.sides,
mode='phase')
assert_array_equal(freqspeca, freqspecp)
assert_array_equal(ta, tp)
assert_allclose(np.unwrap(speca, axis=0), specp,
atol=1e-06)
def test_psd_windowarray_equal(self):
freqs = self.freqs_density
win = mlab.window_hanning(np.ones(self.NFFT_density_real))
speca, fspa = mlab.psd(x=self.y,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=self.nover_density,
pad_to=self.pad_to_density,
sides=self.sides,
window=win)
specb, fspb = mlab.psd(x=self.y,
NFFT=self.NFFT_density,
Fs=self.Fs,
noverlap=self.nover_density,
pad_to=self.pad_to_density,
sides=self.sides)
assert_array_equal(fspa, fspb)
assert_allclose(speca, specb, atol=1e-08)
class spectral_testcase_nosig_real_twosided(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
iscomplex=False, sides='twosided', nsides=2)
class spectral_testcase_nosig_real_defaultsided(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
iscomplex=False, sides='default', nsides=1)
class spectral_testcase_nosig_complex_onesided(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
iscomplex=True, sides='onesided', nsides=1)
class spectral_testcase_nosig_complex_twosided(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
iscomplex=True, sides='twosided', nsides=2)
class spectral_testcase_nosig_complex_defaultsided(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
iscomplex=True, sides='default', nsides=2)
class spectral_testcase_Fs4_real_onesided(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[4],
iscomplex=False, sides='onesided', nsides=1)
class spectral_testcase_Fs4_real_twosided(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[4],
iscomplex=False, sides='twosided', nsides=2)
class spectral_testcase_Fs4_real_defaultsided(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[4],
iscomplex=False, sides='default', nsides=1)
class spectral_testcase_Fs4_complex_onesided(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[4],
iscomplex=True, sides='onesided', nsides=1)
class spectral_testcase_Fs4_complex_twosided(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[4],
iscomplex=True, sides='twosided', nsides=2)
class spectral_testcase_Fs4_complex_defaultsided(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[4],
iscomplex=True, sides='default', nsides=2)
class spectral_testcase_FsAll_real_onesided(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[4, 5, 10],
iscomplex=False, sides='onesided', nsides=1)
class spectral_testcase_FsAll_real_twosided(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[4, 5, 10],
iscomplex=False, sides='twosided', nsides=2)
class spectral_testcase_FsAll_real_defaultsided(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[4, 5, 10],
iscomplex=False, sides='default', nsides=1)
class spectral_testcase_FsAll_complex_onesided(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[4, 5, 10],
iscomplex=True, sides='onesided', nsides=1)
class spectral_testcase_FsAll_complex_twosided(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[4, 5, 10],
iscomplex=True, sides='twosided', nsides=2)
class spectral_testcase_FsAll_complex_defaultsided(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[4, 5, 10],
iscomplex=True, sides='default', nsides=2)
class spectral_testcase_nosig_real_onesided_noNFFT(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
NFFT_density=None, pad_to_spectrum=None,
iscomplex=False, sides='onesided', nsides=1)
class spectral_testcase_nosig_real_twosided_noNFFT(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
NFFT_density=None, pad_to_spectrum=None,
iscomplex=False, sides='twosided', nsides=2)
class spectral_testcase_nosig_real_defaultsided_noNFFT(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
NFFT_density=None, pad_to_spectrum=None,
iscomplex=False, sides='default', nsides=1)
class spectral_testcase_nosig_complex_onesided_noNFFT(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
NFFT_density=None, pad_to_spectrum=None,
iscomplex=True, sides='onesided', nsides=1)
class spectral_testcase_nosig_complex_twosided_noNFFT(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
NFFT_density=None, pad_to_spectrum=None,
iscomplex=True, sides='twosided', nsides=2)
class spectral_testcase_nosig_complex_defaultsided_noNFFT(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
NFFT_density=None, pad_to_spectrum=None,
iscomplex=True, sides='default', nsides=2)
class spectral_testcase_nosig_real_onesided_nopad_to(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
pad_to_density=None, pad_to_spectrum=None,
iscomplex=False, sides='onesided', nsides=1)
class spectral_testcase_nosig_real_twosided_nopad_to(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
pad_to_density=None, pad_to_spectrum=None,
iscomplex=False, sides='twosided', nsides=2)
class spectral_testcase_nosig_real_defaultsided_nopad_to(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
pad_to_density=None, pad_to_spectrum=None,
iscomplex=False, sides='default', nsides=1)
class spectral_testcase_nosig_complex_onesided_nopad_to(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
pad_to_density=None, pad_to_spectrum=None,
iscomplex=True, sides='onesided', nsides=1)
class spectral_testcase_nosig_complex_twosided_nopad_to(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
NFFT_density=None,
pad_to_density=None, pad_to_spectrum=None,
iscomplex=True, sides='twosided', nsides=2)
class spectral_testcase_nosig_complex_defaultsided_nopad_to(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
NFFT_density=None,
pad_to_density=None, pad_to_spectrum=None,
iscomplex=True, sides='default', nsides=2)
class spectral_testcase_nosig_real_onesided_noNFFT_no_pad_to(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
NFFT_density=None,
pad_to_density=None, pad_to_spectrum=None,
iscomplex=False, sides='onesided', nsides=1)
class spectral_testcase_nosig_real_twosided_noNFFT_no_pad_to(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
NFFT_density=None,
pad_to_density=None, pad_to_spectrum=None,
iscomplex=False, sides='twosided', nsides=2)
class spectral_testcase_nosig_real_defaultsided_noNFFT_no_pad_to(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
NFFT_density=None,
pad_to_density=None, pad_to_spectrum=None,
iscomplex=False, sides='default', nsides=1)
class spectral_testcase_nosig_complex_onesided_noNFFT_no_pad_to(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
NFFT_density=None,
pad_to_density=None, pad_to_spectrum=None,
iscomplex=True, sides='onesided', nsides=1)
class spectral_testcase_nosig_complex_twosided_noNFFT_no_pad_to(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
NFFT_density=None,
pad_to_density=None, pad_to_spectrum=None,
iscomplex=True, sides='twosided', nsides=2)
class spectral_testcase_nosig_complex_defaultsided_noNFFT_no_pad_to(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
NFFT_density=None,
pad_to_density=None, pad_to_spectrum=None,
iscomplex=True, sides='default', nsides=2)
class spectral_testcase_nosig_real_onesided_trim(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=256,
NFFT_density=512, pad_to_spectrum=128,
iscomplex=False, sides='onesided', nsides=1)
class spectral_testcase_nosig_real_twosided_trim(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=256,
NFFT_density=512, pad_to_spectrum=128,
iscomplex=False, sides='twosided', nsides=2)
class spectral_testcase_nosig_real_defaultsided_trim(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=256,
NFFT_density=512, pad_to_spectrum=128,
iscomplex=False, sides='default', nsides=1)
class spectral_testcase_nosig_complex_onesided_trim(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=256,
NFFT_density=512, pad_to_spectrum=128,
iscomplex=True, sides='onesided', nsides=1)
class spectral_testcase_nosig_complex_twosided_trim(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=256,
NFFT_density=512, pad_to_spectrum=128,
iscomplex=True, sides='twosided', nsides=2)
class spectral_testcase_nosig_complex_defaultsided_trim(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=256,
NFFT_density=128, pad_to_spectrum=128,
iscomplex=True, sides='default', nsides=2)
class spectral_testcase_nosig_real_onesided_odd(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=256,
pad_to_density=33, pad_to_spectrum=257,
iscomplex=False, sides='onesided', nsides=1)
class spectral_testcase_nosig_real_twosided_odd(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=256,
pad_to_density=33, pad_to_spectrum=257,
iscomplex=False, sides='twosided', nsides=2)
class spectral_testcase_nosig_real_defaultsided_odd(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=256,
pad_to_density=33, pad_to_spectrum=257,
iscomplex=False, sides='default', nsides=1)
class spectral_testcase_nosig_complex_onesided_odd(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=256,
pad_to_density=33, pad_to_spectrum=257,
iscomplex=True, sides='onesided', nsides=1)
class spectral_testcase_nosig_complex_twosided_odd(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=256,
pad_to_density=33, pad_to_spectrum=257,
iscomplex=True, sides='twosided', nsides=2)
class spectral_testcase_nosig_complex_defaultsided_odd(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=256,
pad_to_density=33, pad_to_spectrum=257,
iscomplex=True, sides='default', nsides=2)
class spectral_testcase_nosig_real_onesided_oddlen(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=255,
NFFT_density=33, pad_to_spectrum=None,
iscomplex=False, sides='onesided', nsides=1)
class spectral_testcase_nosig_real_twosided_oddlen(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=255,
NFFT_density=33, pad_to_spectrum=None,
iscomplex=False, sides='twosided', nsides=2)
class spectral_testcase_nosig_real_defaultsided_oddlen(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=255,
NFFT_density=33, pad_to_spectrum=None,
iscomplex=False, sides='default', nsides=1)
class spectral_testcase_nosig_complex_onesided_oddlen(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=255,
NFFT_density=33, pad_to_spectrum=None,
iscomplex=True, sides='onesided', nsides=1)
class spectral_testcase_nosig_complex_twosided_oddlen(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=255,
NFFT_density=33, pad_to_spectrum=None,
iscomplex=True, sides='twosided', nsides=2)
class spectral_testcase_nosig_complex_defaultsided_oddlen(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=255,
NFFT_density=128, pad_to_spectrum=None,
iscomplex=True, sides='default', nsides=2)
class spectral_testcase_nosig_real_onesided_stretch(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=128,
NFFT_density=128,
pad_to_density=256, pad_to_spectrum=256,
iscomplex=False, sides='onesided', nsides=1)
class spectral_testcase_nosig_real_twosided_stretch(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=128,
NFFT_density=128,
pad_to_density=256, pad_to_spectrum=256,
iscomplex=False, sides='twosided', nsides=2)
class spectral_testcase_nosig_real_defaultsided_stretch(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=128,
NFFT_density=128,
pad_to_density=256, pad_to_spectrum=256,
iscomplex=False, sides='default', nsides=1)
class spectral_testcase_nosig_complex_onesided_stretch(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=128,
NFFT_density=128,
pad_to_density=256, pad_to_spectrum=256,
iscomplex=True, sides='onesided', nsides=1)
class spectral_testcase_nosig_complex_twosided_stretch(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=128,
NFFT_density=128,
pad_to_density=256, pad_to_spectrum=256,
iscomplex=True, sides='twosided', nsides=2)
class spectral_testcase_nosig_complex_defaultsided_stretch(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
len_x=128,
NFFT_density=128,
pad_to_density=256, pad_to_spectrum=256,
iscomplex=True, sides='default', nsides=2)
class spectral_testcase_nosig_real_onesided_overlap(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
nover_density=32,
iscomplex=False, sides='onesided', nsides=1)
class spectral_testcase_nosig_real_twosided_overlap(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
nover_density=32,
iscomplex=False, sides='twosided', nsides=2)
class spectral_testcase_nosig_real_defaultsided_overlap(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
nover_density=32,
iscomplex=False, sides='default', nsides=1)
class spectral_testcase_nosig_complex_onesided_overlap(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
nover_density=32,
iscomplex=True, sides='onesided', nsides=1)
class spectral_testcase_nosig_complex_twosided_overlap(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
nover_density=32,
iscomplex=True, sides='twosided', nsides=2)
class spectral_testcase_nosig_complex_defaultsided_overlap(
spectral_testcase_nosig_real_onesided):
def setUp(self):
self.createStim(fstims=[],
nover_density=32,
iscomplex=True, sides='default', nsides=2)
def test_griddata_linear():
# z is a linear function of x and y.
def get_z(x, y):
return 3.0*x - y
# Passing 1D xi and yi arrays to griddata.
x = np.asarray([0.0, 1.0, 0.0, 1.0, 0.5])
y = np.asarray([0.0, 0.0, 1.0, 1.0, 0.5])
z = get_z(x, y)
xi = [0.2, 0.4, 0.6, 0.8]
yi = [0.1, 0.3, 0.7, 0.9]
zi = mlab.griddata(x, y, z, xi, yi, interp='linear')
xi, yi = np.meshgrid(xi, yi)
np.testing.assert_array_almost_equal(zi, get_z(xi, yi))
# Passing 2D xi and yi arrays to griddata.
zi = mlab.griddata(x, y, z, xi, yi, interp='linear')
np.testing.assert_array_almost_equal(zi, get_z(xi, yi))
# Masking z array.
z_masked = np.ma.array(z, mask=[False, False, False, True, False])
correct_zi_masked = np.ma.masked_where(xi + yi > 1.0, get_z(xi, yi))
zi = mlab.griddata(x, y, z_masked, xi, yi, interp='linear')
matest.assert_array_almost_equal(zi, correct_zi_masked)
np.testing.assert_array_equal(np.ma.getmask(zi),
np.ma.getmask(correct_zi_masked))
@knownfailureif(not HAS_NATGRID)
def test_griddata_nn():
# z is a linear function of x and y.
def get_z(x, y):
return 3.0*x - y
# Passing 1D xi and yi arrays to griddata.
x = np.asarray([0.0, 1.0, 0.0, 1.0, 0.5])
y = np.asarray([0.0, 0.0, 1.0, 1.0, 0.5])
z = get_z(x, y)
xi = [0.2, 0.4, 0.6, 0.8]
yi = [0.1, 0.3, 0.7, 0.9]
correct_zi = [[0.49999252, 1.0999978, 1.7000030, 2.3000080],
[0.29999208, 0.8999978, 1.5000029, 2.1000059],
[-0.1000099, 0.4999943, 1.0999964, 1.6999979],
[-0.3000128, 0.2999894, 0.8999913, 1.4999933]]
zi = mlab.griddata(x, y, z, xi, yi, interp='nn')
np.testing.assert_array_almost_equal(zi, correct_zi, 5)
# Decreasing xi or yi should raise ValueError.
assert_raises(ValueError, mlab.griddata, x, y, z, xi[::-1], yi,
interp='nn')
assert_raises(ValueError, mlab.griddata, x, y, z, xi, yi[::-1],
interp='nn')
# Passing 2D xi and yi arrays to griddata.
xi, yi = np.meshgrid(xi, yi)
zi = mlab.griddata(x, y, z, xi, yi, interp='nn')
np.testing.assert_array_almost_equal(zi, correct_zi, 5)
# Masking z array.
z_masked = np.ma.array(z, mask=[False, False, False, True, False])
correct_zi_masked = np.ma.masked_where(xi + yi > 1.0, correct_zi)
zi = mlab.griddata(x, y, z_masked, xi, yi, interp='nn')
np.testing.assert_array_almost_equal(zi, correct_zi_masked, 5)
np.testing.assert_array_equal(np.ma.getmask(zi),
np.ma.getmask(correct_zi_masked))
#*****************************************************************
# These Tests where taken from SCIPY with some minor modifications
# this can be retreived from:
# https://github.com/scipy/scipy/blob/master/scipy/stats/tests/test_kdeoth.py
#*****************************************************************
class gaussian_kde_tests():
def test_kde_integer_input(self):
"""Regression test for #1181."""
x1 = np.arange(5)
kde = mlab.GaussianKDE(x1)
y_expected = [0.13480721, 0.18222869, 0.19514935, 0.18222869,
0.13480721]
np.testing.assert_array_almost_equal(kde(x1), y_expected, decimal=6)
def test_gaussian_kde_covariance_caching(self):
x1 = np.array([-7, -5, 1, 4, 5], dtype=np.float)
xs = np.linspace(-10, 10, num=5)
# These expected values are from scipy 0.10, before some changes to
# gaussian_kde. They were not compared with any external reference.
y_expected = [0.02463386, 0.04689208, 0.05395444, 0.05337754,
0.01664475]
# set it to the default bandwidth.
kde2 = mlab.GaussianKDE(x1, 'scott')
y2 = kde2(xs)
np.testing.assert_array_almost_equal(y_expected, y2, decimal=7)
def test_kde_bandwidth_method(self):
np.random.seed(8765678)
n_basesample = 50
xn = np.random.randn(n_basesample)
# Default
gkde = mlab.GaussianKDE(xn)
# Supply a callable
gkde2 = mlab.GaussianKDE(xn, 'scott')
# Supply a scalar
gkde3 = mlab.GaussianKDE(xn, bw_method=gkde.factor)
xs = np.linspace(-7, 7, 51)
kdepdf = gkde.evaluate(xs)
kdepdf2 = gkde2.evaluate(xs)
assert_almost_equal(kdepdf.all(), kdepdf2.all())
kdepdf3 = gkde3.evaluate(xs)
assert_almost_equal(kdepdf.all(), kdepdf3.all())
class gaussian_kde_custom_tests(object):
def test_no_data(self):
"""Pass no data into the GaussianKDE class."""
assert_raises(ValueError, mlab.GaussianKDE, [])
def test_single_dataset_element(self):
"""Pass a single dataset element into the GaussianKDE class."""
assert_raises(ValueError, mlab.GaussianKDE, [42])
def test_silverman_multidim_dataset(self):
"""Use a multi-dimensional array as the dataset and test silverman's
output"""
x1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
assert_raises(np.linalg.LinAlgError, mlab.GaussianKDE, x1, "silverman")
def test_silverman_singledim_dataset(self):
"""Use a single dimension list as the dataset and test silverman's
output."""
x1 = np.array([-7, -5, 1, 4, 5])
mygauss = mlab.GaussianKDE(x1, "silverman")
y_expected = 0.76770389927475502
assert_almost_equal(mygauss.covariance_factor(), y_expected, 7)
def test_scott_multidim_dataset(self):
"""Use a multi-dimensional array as the dataset and test scott's output
"""
x1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
assert_raises(np.linalg.LinAlgError, mlab.GaussianKDE, x1, "scott")
def test_scott_singledim_dataset(self):
"""Use a single-dimensional array as the dataset and test scott's
output"""
x1 = np.array([-7, -5, 1, 4, 5])
mygauss = mlab.GaussianKDE(x1, "scott")
y_expected = 0.72477966367769553
assert_almost_equal(mygauss.covariance_factor(), y_expected, 7)
def test_scalar_empty_dataset(self):
"""Use an empty array as the dataset and test the scalar's cov factor
"""
assert_raises(ValueError, mlab.GaussianKDE, [], bw_method=5)
def test_scalar_covariance_dataset(self):
"""Use a dataset and test a scalar's cov factor
"""
np.random.seed(8765678)
n_basesample = 50
multidim_data = [np.random.randn(n_basesample) for i in range(5)]
kde = mlab.GaussianKDE(multidim_data, bw_method=0.5)
assert_equal(kde.covariance_factor(), 0.5)
def test_callable_covariance_dataset(self):
"""Use a multi-dimensional array as the dataset and test the callable's
cov factor"""
np.random.seed(8765678)
n_basesample = 50
multidim_data = [np.random.randn(n_basesample) for i in range(5)]
def callable_fun(x):
return 0.55
kde = mlab.GaussianKDE(multidim_data, bw_method=callable_fun)
assert_equal(kde.covariance_factor(), 0.55)
def test_callable_singledim_dataset(self):
"""Use a single-dimensional array as the dataset and test the
callable's cov factor"""
np.random.seed(8765678)
n_basesample = 50
multidim_data = np.random.randn(n_basesample)
kde = mlab.GaussianKDE(multidim_data, bw_method='silverman')
y_expected = 0.48438841363348911
assert_almost_equal(kde.covariance_factor(), y_expected, 7)
def test_wrong_bw_method(self):
"""Test the error message that should be called when bw is invalid."""
np.random.seed(8765678)
n_basesample = 50
data = np.random.randn(n_basesample)
assert_raises(ValueError, mlab.GaussianKDE, data, bw_method="invalid")
class gaussian_kde_evaluate_tests(object):
def test_evaluate_diff_dim(self):
"""Test the evaluate method when the dim's of dataset and points are
different dimensions"""
x1 = np.arange(3, 10, 2)
kde = mlab.GaussianKDE(x1)
x2 = np.arange(3, 12, 2)
y_expected = [
0.08797252, 0.11774109, 0.11774109, 0.08797252, 0.0370153
]
y = kde.evaluate(x2)
np.testing.assert_array_almost_equal(y, y_expected, 7)
def test_evaluate_inv_dim(self):
""" Invert the dimensions. i.e., Give the dataset a dimension of
1 [3,2,4], and the points will have a dimension of 3 [[3],[2],[4]].
ValueError should be raised"""
np.random.seed(8765678)
n_basesample = 50
multidim_data = np.random.randn(n_basesample)
kde = mlab.GaussianKDE(multidim_data)
x2 = [[1], [2], [3]]
assert_raises(ValueError, kde.evaluate, x2)
def test_evaluate_dim_and_num(self):
""" Tests if evaluated against a one by one array"""
x1 = np.arange(3, 10, 2)
x2 = np.array([3])
kde = mlab.GaussianKDE(x1)
y_expected = [0.08797252]
y = kde.evaluate(x2)
np.testing.assert_array_almost_equal(y, y_expected, 7)
def test_evaluate_point_dim_not_one(self):
"""Test"""
x1 = np.arange(3, 10, 2)
x2 = [np.arange(3, 10, 2), np.arange(3, 10, 2)]
kde = mlab.GaussianKDE(x1)
assert_raises(ValueError, kde.evaluate, x2)
def test_evaluate_equal_dim_and_num_lt(self):
"""Test when line 3810 fails"""
x1 = np.arange(3, 10, 2)
x2 = np.arange(3, 8, 2)
kde = mlab.GaussianKDE(x1)
y_expected = [0.08797252, 0.11774109, 0.11774109]
y = kde.evaluate(x2)
np.testing.assert_array_almost_equal(y, y_expected, 7)
def test_contiguous_regions():
a, b, c = 3, 4, 5
# Starts and ends with True
mask = [True]*a + [False]*b + [True]*c
expected = [(0, a), (a+b, a+b+c)]
assert_equal(mlab.contiguous_regions(mask), expected)
d, e = 6, 7
# Starts with True ends with False
mask = mask + [False]*e
assert_equal(mlab.contiguous_regions(mask), expected)
# Starts with False ends with True
mask = [False]*d + mask[:-e]
expected = [(d, d+a), (d+a+b, d+a+b+c)]
assert_equal(mlab.contiguous_regions(mask), expected)
# Starts and ends with False
mask = mask + [False]*e
assert_equal(mlab.contiguous_regions(mask), expected)
# No True in mask
assert_equal(mlab.contiguous_regions([False]*5), [])
# Empty mask
assert_equal(mlab.contiguous_regions([]), [])
def test_psd_onesided_norm():
u = np.array([0, 1, 2, 3, 1, 2, 1])
dt = 1.0
Su = np.abs(np.fft.fft(u) * dt)**2 / float(dt * u.size)
P, f = mlab.psd(u, NFFT=u.size, Fs=1/dt, window=mlab.window_none,
detrend=mlab.detrend_none, noverlap=0, pad_to=None,
scale_by_freq=None,
sides='onesided')
Su_1side = np.append([Su[0]], Su[1:4] + Su[4:][::-1])
assert_allclose(P, Su_1side, atol=1e-06)
if __name__ == '__main__':
import nose
import sys
args = ['-s', '--with-doctest']
argv = sys.argv
argv = argv[:1] + args + argv[1:]
nose.runmodule(argv=argv, exit=False)