def test_light_source_planar_hillshading():
"""Ensure that the illumination intensity is correct for planar
surfaces."""
def plane(azimuth, elevation, x, y):
"""Create a plane whose normal vector is at the given azimuth and
elevation."""
theta, phi = _azimuth2math(azimuth, elevation)
a, b, c = _sph2cart(theta, phi)
z = -(a*x + b*y) / c
return z
def angled_plane(azimuth, elevation, angle, x, y):
"""Create a plane whose normal vector is at an angle from the given
azimuth and elevation."""
elevation = elevation + angle
if elevation > 90:
azimuth = (azimuth + 180) % 360
elevation = (90 - elevation) % 90
return plane(azimuth, elevation, x, y)
y, x = np.mgrid[5:0:-1, :5]
for az, elev in itertools.product(range(0, 390, 30), range(0, 105, 15)):
ls = mcolors.LightSource(az, elev)
# Make a plane at a range of angles to the illumination
for angle in range(0, 105, 15):
z = angled_plane(az, elev, angle, x, y)
h = ls.hillshade(z)
assert_array_almost_equal(h, np.cos(np.radians(angle)))
def test_average_pooling_feature_map_fprop_weight_and_bias():
"""
Test that AveragePoolingFeatureMap really does what we think it does
on a forward pass.
"""
apfmap = AveragePoolingFeatureMap((2, 2), (4, 4))
apfmap.biases[:] = -9.0
apfmap.weights[:] = 4.4
inp = np.arange(1, 17).reshape((4, 4))
out = apfmap.fprop(inp)
assert_array_almost_equal(
out,
1.7159
* np.tanh(
2.0
/ 3.0
* (
-9
+ 4.4
* np.array(
[
[(1 + 2 + 5 + 6) / 4.0, (3 + 4 + 7 + 8) / 4.0],
[(9 + 10 + 13 + 14) / 4.0, (11 + 12 + 15 + 16) / 4.0],
]
)
)
),
)
def test_light_source_hillshading():
"""Compare the current hillshading method against one that should be
mathematically equivalent. Illuminates a cone from a range of angles."""
def alternative_hillshade(azimuth, elev, z):
illum = _sph2cart(*_azimuth2math(azimuth, elev))
illum = np.array(illum)
dy, dx = np.gradient(-z)
dy = -dy
dz = np.ones_like(dy)
normals = np.dstack([dx, dy, dz])
dividers = np.zeros_like(z)[..., None]
for i, mat in enumerate(normals):
for j, vec in enumerate(mat):
dividers[i, j, 0] = np.linalg.norm(vec)
normals /= dividers
# once we drop support for numpy 1.7.x the above can be written as
# normals /= np.linalg.norm(normals, axis=2)[..., None]
# aviding the double loop.
intensity = np.tensordot(normals, illum, axes=(2, 0))
intensity -= intensity.min()
intensity /= intensity.ptp()
return intensity
y, x = np.mgrid[5:0:-1, :5]
z = -np.hypot(x - x.mean(), y - y.mean())
for az, elev in itertools.product(range(0, 390, 30), range(0, 105, 15)):
ls = mcolors.LightSource(az, elev)
h1 = ls.hillshade(z)
h2 = alternative_hillshade(az, elev, z)
assert_array_almost_equal(h1, h2)
def test_light_source_hillshading():
"""Compare the current hillshading method against one that should be
mathematically equivalent. Illuminates a cone from a range of angles."""
def alternative_hillshade(azimuth, elev, z):
illum = _sph2cart(*_azimuth2math(azimuth, elev))
illum = np.array(illum)
dy, dx = np.gradient(-z)
dy = -dy
dz = np.ones_like(dy)
normals = np.dstack([dx, dy, dz])
normals /= np.linalg.norm(normals, axis=2)[..., None]
intensity = np.tensordot(normals, illum, axes=(2, 0))
intensity -= intensity.min()
intensity /= intensity.ptp()
return intensity
y, x = np.mgrid[5:0:-1, :5]
z = -np.hypot(x - x.mean(), y - y.mean())
for az, elev in itertools.product(range(0, 390, 30), range(0, 105, 15)):
ls = mcolors.LightSource(az, elev)
h1 = ls.hillshade(z)
h2 = alternative_hillshade(az, elev, z)
assert_array_almost_equal(h1, h2)
def test_light_source_shading_color_range():
# see also
#http://matplotlib.org/examples/pylab_examples/shading_example.html
from matplotlib.colors import LightSource
from matplotlib.colors import Normalize
refinput = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
norm = Normalize(vmin=0, vmax=50)
ls = LightSource(azdeg=0, altdeg=65)
testoutput = ls.shade(refinput, plt.cm.jet, norm=norm)
refoutput = np.array([
[[0., 0., 0.58912656, 1.],
[0., 0., 0.67825312, 1.],
[0., 0., 0.76737968, 1.],
[0., 0., 0.85650624, 1.]],
[[0., 0., 0.9456328, 1.],
[0., 0., 1., 1.],
[0., 0.04901961, 1., 1.],
[0., 0.12745098, 1., 1.]],
[[0., 0.22156863, 1., 1.],
[0., 0.3, 1., 1.],
[0., 0.37843137, 1., 1.],
[0., 0.45686275, 1., 1.]]
])
assert_array_almost_equal(refoutput, testoutput)
def test_basic_gridded_to_gridded_collocation(self):
gd_copy = self.gd.copy()
res = self.gd.collocated_onto(self.gd)
assert self.gd == gd_copy
res_1 = self.gd.sampled_from(self.gd)
# The two data results should be identical, although the metadata is slightly different for some reason
assert_array_almost_equal(res[0].data, res_1[0].data)
def test_as_meg_type_evoked():
"""Test interpolation of data on to virtual channels."""
# validation tests
evoked = read_evokeds(evoked_fname, condition="Left Auditory")
assert_raises(ValueError, evoked.as_type, "meg")
assert_raises(ValueError, evoked.copy().pick_types(meg="grad").as_type, "meg")
# channel names
ch_names = evoked.info["ch_names"]
virt_evoked = evoked.pick_channels(ch_names=ch_names[:10:1], copy=True).as_type("mag")
assert_true(all("_virtual" in ch for ch in virt_evoked.info["ch_names"]))
# pick from and to channels
evoked_from = evoked.pick_channels(ch_names=ch_names[2:10:3], copy=True)
evoked_to = evoked.pick_channels(ch_names=ch_names[0:10:3], copy=True)
info_from, info_to = evoked_from.info, evoked_to.info
# set up things
args1, args2 = _setup_args(info_from), _setup_args(info_to)
args1.update(coils2=args2["coils1"]), args2.update(coils2=args1["coils1"])
# test cross dots
cross_dots1 = _do_cross_dots(**args1)
cross_dots2 = _do_cross_dots(**args2)
assert_array_almost_equal(cross_dots1, cross_dots2.T)
# correlation test
evoked = evoked.pick_channels(ch_names=ch_names[:10:]).copy()
data1 = evoked.pick_types(meg="grad").data.ravel()
data2 = evoked.as_type("grad").data.ravel()
assert_true(np.corrcoef(data1, data2)[0, 1] > 0.95)
def test_volsurf_projections(self):
white = surf.generate_plane((0, 0, 0), (0, 1, 0), (0, 0, 1), 10, 10)
pial = white + np.asarray([[1, 0, 0]])
above = pial + np.asarray([[3, 0, 0]])
vg = volgeom.VolGeom((10, 10, 10), np.eye(4))
vs = volsurf.VolSurfMaximalMapping(vg, white, pial)
dx = pial.vertices - white.vertices
for s, w in ((white, 0), (pial, 1), (above, 4)):
xyz = s.vertices
ws = vs.surf_project_weights(True, xyz)
delta = vs.surf_unproject_weights_nodewise(ws) - xyz
assert_array_equal(delta, np.zeros((100, 3)))
assert_true(np.all(w == ws))
vs = volsurf.VolSurfMaximalMapping(vg, white, pial, nsteps=2)
n2vs = vs.get_node2voxels_mapping()
assert_equal(n2vs, dict((i, {i: 0.0, i + 100: 1.0}) for i in xrange(100)))
nd = 17
ds_mm_expected = np.sum((above.vertices - pial.vertices[nd, :]) ** 2, 1) ** 0.5
ds_mm = vs.coordinates_to_grey_distance_mm(nd, above.vertices)
assert_array_almost_equal(ds_mm_expected, ds_mm)
ds_mm_nodewise = vs.coordinates_to_grey_distance_mm(True, above.vertices)
assert_array_equal(ds_mm_nodewise, np.ones((100,)) * 3)
def test_filter_proces_with_plain_array_h05(self):
st = self.test_array
target = self.targ_h05
res = mft._filter_process(0.5 * pq.s, 0.5 * pq.s, st * pq.s,
2.01 * pq.s, np.array([[0.5], [1.7], [0.4]]))
self.assertNotIsInstance(res, pq.Quantity)
assert_array_almost_equal(res, target, decimal=3)
def test_average_node_edge_length(self):
for side in xrange(1, 5):
s_flat = surf.generate_plane((0, 0, 0), (0, 0, 1), (0, 1, 0), 6, 6)
rnd_xyz = 0 * np.random.normal(size=s_flat.vertices.shape)
s = surf.Surface(s_flat.vertices + rnd_xyz, s_flat.faces)
nvertices = s.nvertices
sd = np.zeros((nvertices,))
c = np.zeros((nvertices,))
def d(src, trg, vertices=s.vertices):
s = vertices[src, :]
t = vertices[trg, :]
delta = s - t
print s, t, delta
return np.sum(delta ** 2) ** .5
for i_face in s.faces:
for i in xrange(3):
src = i_face[i]
trg = i_face[(i + 1) % 3]
sd[src] += d(src, trg)
sd[trg] += d(src, trg)
c[src] += 1
c[trg] += 1
print i, src, trg, d(src, trg)
assert_array_almost_equal(sd / c, s.average_node_edge_length)
def _scalar_tester(norm_instance, vals):
"""
Checks if scalars and arrays are handled the same way.
Tests only for float.
"""
scalar_result = [norm_instance(float(v)) for v in vals]
assert_array_almost_equal(scalar_result, norm_instance(vals))
def test_MultipleFilterAlgorithm_with_plain_array_h05(self):
st = self.test_array
target = [self.targ_h05_dt05]
res = mft.multiple_filter_test([0.5] * pq.s, st * pq.s, 2.1 * pq.s, 5,
100, dt=0.5 * pq.s)
self.assertNotIsInstance(res, pq.Quantity)
assert_array_almost_equal(res, target, decimal=9)
def test_surf_normalized(self):
def assert_is_unit_norm(v):
assert_almost_equal(1., np.sum(v*v))
assert_equal(v.shape, (len(v),))
def assert_same_direction(v,w):
assert_almost_equal(v.dot(w),(v.dot(v)*w.dot(w))**.5)
def helper_test_vec_normalized(v):
v_norm=surf.normalized(v)
assert_is_unit_norm(v_norm)
assert_same_direction(v,v_norm)
return v_norm
sizes=[(8,),(7,4)]
for size in sizes:
v=np.random.normal(size=size)
if len(size)==1:
helper_test_vec_normalized(v)
else:
# test for vectors and for matrix
v_n = surf.normalized(v)
n_vecs=v.shape[1]
for i in xrange(n_vecs):
v_n_i=helper_test_vec_normalized(v[i,:])
assert_array_almost_equal(v_n_i, v_n[i,:])
def test_surf_fs_asc(self, temp_fn):
s = surf.generate_sphere(5) * 100
surf_fs_asc.write(temp_fn, s, overwrite=True)
t = surf_fs_asc.read(temp_fn)
assert_array_almost_equal(s.vertices, t.vertices)
assert_array_almost_equal(s.vertices, t.vertices)
theta = np.asarray([0, 0., 180.])
r = s.rotate(theta, unit='deg')
l2r = surf.get_sphere_left_right_mapping(s, r)
l2r_expected = [0, 1, 2, 6, 5, 4, 3, 11, 10, 9, 8, 7, 15, 14, 13, 12,
16, 19, 18, 17, 21, 20, 23, 22, 26, 25, 24]
assert_array_equal(l2r, np.asarray(l2r_expected))
sides_facing = 'apism'
for side_facing in sides_facing:
l, r = surf.reposition_hemisphere_pairs(s + 10., t + (-10.),
side_facing)
m = surf.merge(l, r)
# not sure at the moment why medial rotation
# messes up - but leave for now
eps = 666 if side_facing == 'm' else .001
assert_true((abs(m.center_of_mass) < eps).all())
def test_from_nu(self):
""" Check conversions from nu to omega and nu to lambda """
domain = Domain()
self.assertEqual(nu_to_omega(domain.centre_nu), domain.centre_omega)
self.assertEqual(nu_to_lambda(domain.centre_nu), domain.centre_lambda)
assert_array_almost_equal(nu_to_omega(domain.nu), domain.omega)
assert_array_almost_equal(nu_to_lambda(domain.nu), domain.Lambda)
def test_threshold_detection(self):
# Test whether spikes are extracted at the correct times from
# an analog signal.
# Load membrane potential simulated using Brian2
# according to make_spike_extraction_test_data.py.
curr_dir = os.path.dirname(os.path.realpath(__file__))
npz_file_loc = os.path.join(curr_dir,'spike_extraction_test_data.npz')
iom2 = neo.io.PyNNNumpyIO(npz_file_loc)
data = iom2.read()
vm = data[0].segments[0].analogsignals[0]
spike_train = stgen.threshold_detection(vm)
try:
len(spike_train)
except TypeError: # Handles an error in Neo related to some zero length
# spike trains being treated as unsized objects.
warnings.warn(("The spike train may be an unsized object. This may be related "
"to an issue in Neo with some zero-length SpikeTrain objects. "
"Bypassing this by creating an empty SpikeTrain object."))
spike_train = neo.core.SpikeTrain([],t_start=spike_train.t_start,
t_stop=spike_train.t_stop,
units=spike_train.units)
# Correct values determined previously.
true_spike_train = [0.0123, 0.0354, 0.0712, 0.1191,
0.1694, 0.22, 0.2711]
# Does threshold_detection gives the correct number of spikes?
self.assertEqual(len(spike_train),len(true_spike_train))
# Does threshold_detection gives the correct times for the spikes?
try:
assert_array_almost_equal(spike_train,spike_train)
except AttributeError: # If numpy version too old to have allclose
self.assertTrue(np.array_equal(spike_train,spike_train))
def testGeodeticConversionsArray(self):
lat, lon = np.mgrid[-89:89:5,-179:179:5]
x, y, z = geodetic2EcefZero(np.deg2rad(lat), np.deg2rad(lon))
r = ecef2Geodetic(x, y, z)
#print np.rad2deg(r)
assert_array_almost_equal(np.rad2deg(r),[lat,lon], 11)
def test_PowerNorm():
a = np.array([0, 0.5, 1, 1.5], dtype=np.float)
pnorm = mcolors.PowerNorm(1)
norm = mcolors.Normalize()
assert_array_almost_equal(norm(a), pnorm(a))
a = np.array([-0.5, 0, 2, 4, 8], dtype=np.float)
expected = [0, 0, 1/16, 1/4, 1]
pnorm = mcolors.PowerNorm(2, vmin=0, vmax=8)
assert_array_almost_equal(pnorm(a), expected)
assert_equal(pnorm(a[0]), expected[0])
assert_equal(pnorm(a[2]), expected[2])
assert_array_almost_equal(a[1:], pnorm.inverse(pnorm(a))[1:])
# Clip = True
a = np.array([-0.5, 0, 1, 8, 16], dtype=np.float)
expected = [0, 0, 0, 1, 1]
pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=True)
assert_array_almost_equal(pnorm(a), expected)
assert_equal(pnorm(a[0]), expected[0])
assert_equal(pnorm(a[-1]), expected[-1])
# Clip = True at call time
a = np.array([-0.5, 0, 1, 8, 16], dtype=np.float)
expected = [0, 0, 0, 1, 1]
pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=False)
assert_array_almost_equal(pnorm(a, clip=True), expected)
assert_equal(pnorm(a[0], clip=True), expected[0])
assert_equal(pnorm(a[-1], clip=True), expected[-1])
def testBBMerge(self):
bb1 = BoundingBox(latSouth=-55, lonWest=95, latNorth=-45, lonEast=109)
bb2 = BoundingBox(latSouth=44, lonWest=-164, latNorth=74, lonEast=-35)
bb = BoundingBox.mergedBoundingBoxes([bb1,bb2])
assert_array_equal([bb.latSouth,bb.latNorth,bb.lonWest,bb.lonEast],
[bb1.latSouth,bb2.latNorth,bb1.lonWest,bb2.lonEast])
assert_array_almost_equal(bb.center, [21.136113246, -150])
def test_light_source_masked_shading():
"""Array comparison test for a surface with a masked portion. Ensures that
we don't wind up with "fringes" of odd colors around masked regions."""
y, x = np.mgrid[-1.2:1.2:8j, -1.2:1.2:8j]
z = 10 * np.cos(x ** 2 + y ** 2)
z = np.ma.masked_greater(z, 9.9)
cmap = plt.cm.copper
ls = mcolors.LightSource(315, 45)
rgb = ls.shade(z, cmap)
# Result stored transposed and rounded for for more compact display...
expect = np.array(
[
[
[0.00, 0.46, 0.91, 0.91, 0.84, 0.64, 0.29, 0.00],
[0.46, 0.96, 1.00, 1.00, 1.00, 0.97, 0.67, 0.18],
[0.91, 1.00, 1.00, 1.00, 1.00, 1.00, 0.96, 0.36],
[0.91, 1.00, 1.00, 0.00, 0.00, 1.00, 1.00, 0.51],
[0.84, 1.00, 1.00, 0.00, 0.00, 1.00, 1.00, 0.44],
[0.64, 0.97, 1.00, 1.00, 1.00, 1.00, 0.94, 0.09],
[0.29, 0.67, 0.96, 1.00, 1.00, 0.94, 0.38, 0.01],
[0.00, 0.18, 0.36, 0.51, 0.44, 0.09, 0.01, 0.00],
],
[
[0.00, 0.29, 0.61, 0.75, 0.64, 0.41, 0.18, 0.00],
[0.29, 0.81, 0.95, 0.93, 0.85, 0.68, 0.40, 0.11],
[0.61, 0.95, 1.00, 0.78, 0.78, 0.77, 0.52, 0.22],
[0.75, 0.93, 0.78, 0.00, 0.00, 0.78, 0.54, 0.19],
[0.64, 0.85, 0.78, 0.00, 0.00, 0.78, 0.45, 0.08],
[0.41, 0.68, 0.77, 0.78, 0.78, 0.55, 0.25, 0.02],
[0.18, 0.40, 0.52, 0.54, 0.45, 0.25, 0.00, 0.00],
[0.00, 0.11, 0.22, 0.19, 0.08, 0.02, 0.00, 0.00],
],
[
[0.00, 0.19, 0.39, 0.48, 0.41, 0.26, 0.12, 0.00],
[0.19, 0.52, 0.73, 0.78, 0.66, 0.46, 0.26, 0.07],
[0.39, 0.73, 0.95, 0.50, 0.50, 0.53, 0.30, 0.14],
[0.48, 0.78, 0.50, 0.00, 0.00, 0.50, 0.23, 0.12],
[0.41, 0.66, 0.50, 0.00, 0.00, 0.50, 0.11, 0.05],
[0.26, 0.46, 0.53, 0.50, 0.50, 0.11, 0.03, 0.01],
[0.12, 0.26, 0.30, 0.23, 0.11, 0.03, 0.00, 0.00],
[0.00, 0.07, 0.14, 0.12, 0.05, 0.01, 0.00, 0.00],
],
[
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 0.00, 0.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 0.00, 0.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
],
]
).T
assert_array_almost_equal(rgb, expect, decimal=2)
def testEllipsoidLineIntersection(self):
for ellipsoidLineIntersection in ellipsoidLineIntersectionFns:
p1 = np.array(geodetic2Ecef(np.deg2rad(30),np.deg2rad(60),0))
p2 = np.array(geodetic2Ecef(np.deg2rad(-30),np.deg2rad(-60),0))
i1 = ellipsoidLineIntersection(wgs84A, wgs84B, p1, [p1-p2], directed=False)
assert_array_almost_equal(i1, [p1])
def test_shift2(self):
# C yields a value based on shifted contexts
res = []
for t in self.daterange:
self.ctx.set_date(t)
res.append(self.ctx[D])
assert_array_almost_equal(res, [(1,2,3), (3,5,7), (6,9,12)])
def test_from_lambda(self):
""" Check conversions from lambda to nu and lambda to omega """
domain = Domain()
self.assertEqual(lambda_to_nu(domain.centre_lambda), domain.centre_nu)
self.assertEqual(lambda_to_omega(domain.centre_lambda),
domain.centre_omega)
assert_array_almost_equal(lambda_to_nu(domain.Lambda), domain.nu)
assert_array_almost_equal(lambda_to_omega(domain.Lambda), domain.omega)
def test_rgb_hsv_round_trip():
for a_shape in [(500, 500, 3), (500, 3), (1, 3), (3,)]:
np.random.seed(0)
tt = np.random.random(a_shape)
assert_array_almost_equal(tt,
mcolors.hsv_to_rgb(mcolors.rgb_to_hsv(tt)))
assert_array_almost_equal(tt,
mcolors.rgb_to_hsv(mcolors.hsv_to_rgb(tt)))
def test_surface_flatten(self, dim):
def unit_vec3(dim, scale):
v = [0, 0, 0]
v[dim] = float(scale)
return tuple(v)
origin = (0, 0, 0)
plane_size = 10
scale = 1.
vec1 = unit_vec3(dim, scale=scale)
vec2 = unit_vec3((dim + 1) % 3, scale=scale)
plane = generate_plane(origin, vec1, vec2, plane_size, plane_size)
noise_level = .05
nan_vertices_ratio = .05
# add some noise to spatial coordinates
vertices = plane.vertices
noise = np.random.uniform(size=vertices.shape,
low=-.5,
high=.5) * noise_level * scale
vertices_noisy = vertices + noise
# make some vertices NaN (as might be the case for flat surfaces)
nan_count_float = plane.nvertices * nan_vertices_ratio
nan_count = np.ceil(nan_count_float).astype(np.int)
nan_vertices = np.random.random_integers(plane.nvertices,
size=(nan_count,)) - 1
vertices_noisy[nan_vertices, dim] = np.nan
plane_noisy = Surface(vertices_noisy, plane.faces)
# compute normals
f_normal = plane_noisy.face_normals
# find average normal
non_nan_f_normal = np.logical_not(np.any(np.isnan(f_normal), axis=1))
f_normal_avg = np.mean(f_normal[non_nan_f_normal], axis=0)
# test average normal
assert_array_almost_equal(plane.nanmean_face_normal, f_normal_avg,
decimal=2)
# the output has only x and y coordinates; with z-coordinates set
# to zero, the coordinates must be at similar pairwise distances
max_deformation = .1
x, y = flat_surface2xy(plane_noisy, max_deformation)
n_vertices = plane.nvertices
z = np.zeros((n_vertices,))
flat_xyz = np.asarray((x, y, z))
# nodes are rotated must have same pairwise distance as
# the original surface
max_difference = 3 * noise_level
SurfingSurfaceTests.assert_coordinates_almost_equal_modulo_rotation(
flat_xyz.T, plane.vertices, max_difference)
def testArray(self):
f = ufunc_mod.UnaryCallable()
a = numpy.arange(5, dtype=float)
b = f(a)
assert_array_almost_equal(b, a*2.0)
c = numpy.zeros(5, dtype=float)
d = f(a,output=c)
self.assertTrue(c is d)
assert_array_almost_equal(d, a*2.0)
def test_colormap_reversing(name):
"""Check the generated _lut data of a colormap and corresponding
reversed colormap if they are almost the same."""
cmap = plt.get_cmap(name)
cmap_r = cmap.reversed()
if not cmap_r._isinit:
cmap._init()
cmap_r._init()
assert_array_almost_equal(cmap._lut[:-3], cmap_r._lut[-4::-1])
def assert_array_almost_equal(self, a, b):
"""Assertion that two arrays compare almost equal.
The arrays are converted to :class:`numpy.ma.MaskedArray` using
the :meth:`_tomasked` method of the test class before the
comparison is made.
"""
assert_array_almost_equal(self._tomasked(a), self._tomasked(b))
def test_training(self):
inputs = np.array([[0, 0], [1, 1], [2, 2]])
targets = np.array([[0], [1], [2]])
data_set = NumericalDataSet(inputs, targets)
lin_reg = SciPyLinReg(SciPyLinReg.ORDINARY)
lin_reg.train(data_set)
assert_array_almost_equal([0.5, 0.5], lin_reg.get_params())
def test_basic_ungridded_to_gridded_collocation(self):
gd_copy = self.gd.copy()
res = self.ug.collocated_onto(self.gd)
assert self.gd == gd_copy
res_1 = self.gd.sampled_from(self.ug)
# The two data results should be identical, although the metadata is slightly different for some reason
assert_array_almost_equal(res[0].data, res_1[0].data)
# This dataset should still be the same as the alternative one (this checks data and metadata)
assert self.ug == self.ug_1
def test_spike_triggered_average_with_n_spikes_on_constant_function(self):
'''Signal should average to the input'''
const = 13.8
x = const * np.ones(201)
asiga = AnalogSignal(
np.array([x]).T, units='mV', sampling_rate=10 / ms)
st = SpikeTrain([3, 5.6, 7, 7.1, 16, 16.3], units='ms', t_stop=20)
window_starttime = -2 * ms
window_endtime = 2 * ms
STA = sta.spike_triggered_average(
asiga, st, (window_starttime, window_endtime))
a = int(((window_endtime - window_starttime) *
asiga.sampling_rate).simplified)
cutout = asiga[0: a]
cutout.t_start = window_starttime
assert_array_almost_equal(STA, cutout, 12)
def test_average_pooling_feature_map_fprop():
"""
Test that AveragePoolingFeatureMap really does what we think it does
on a forward pass.
"""
apfmap = AveragePoolingFeatureMap((2, 2), (4, 4))
apfmap.biases[:] = 0.
apfmap.weights[:] = 1.
inp = np.arange(1, 17).reshape((4, 4))
out = apfmap.fprop(inp)
assert_array_almost_equal(
out,
1.7159 * np.tanh(2. / 3. * np.array([[(1 + 2 + 5 + 6) / 4.,
(3 + 4 + 7 + 8) / 4.],
[(9 + 10 + 13 + 14) / 4.,
(11 + 12 + 15 + 16) / 4.]])))
def test_silhouette_metric(self):
"""
Test the silhouette metric of the k-elbow visualizer
"""
visualizer = KElbowVisualizer(KMeans(random_state=0),
k=5,
metric="silhouette",
timings=False)
visualizer.fit(X)
expected = np.array([0.691636, 0.456646, 0.255174, 0.239842])
assert len(visualizer.k_scores_) == 4
visualizer.poof()
self.assert_images_similar(visualizer)
assert_array_almost_equal(visualizer.k_scores_, expected)
def test_multi_convolutional_feature_map_singleplane_bprop():
size = (20, 20)
elems = np.prod(size)
fsize = (5, 5)
osize = (16, 16)
mfmap = MultiConvolutionalFeatureMap(fsize, size, 1)
mfmap.initialize()
in1 = random.normal(size=size)
dout = np.ones(osize)
bprop = lambda inp: mfmap.bprop(dout, inp)
grad1 = lambda var: bprop((var.reshape(size), ))[0].reshape(elems)
func1 = lambda var: mfmap.fprop((var.reshape(size), )).sum()
varied_input = random.normal(size=size)
fd_grad1 = fd_grad(func1, varied_input.reshape(elems), 1e-4)
real_grad1 = grad1(varied_input)
assert_array_almost_equal(fd_grad1, real_grad1)
def test_distortion_metric(self):
"""
Test the distortion metric of the k-elbow visualizer
"""
visualizer = KElbowVisualizer(KMeans(random_state=0),
k=5,
metric="distortion",
timings=False)
visualizer.fit(X)
expected = np.array([7.677785, 8.364319, 8.893634, 8.013021])
assert len(visualizer.k_scores_) == 4
visualizer.poof()
self.assert_images_similar(visualizer)
assert_array_almost_equal(visualizer.k_scores_, expected)
def test_colormap_reversing(name):
"""Check the generated _lut data of a colormap and corresponding
reversed colormap if they are almost the same."""
should_have_warning = {
'spectral', 'spectral_r', 'Vega10', 'Vega10_r', 'Vega20', 'Vega20_r',
'Vega20b', 'Vega20b_r', 'Vega20c', 'Vega20c_r'
}
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
cmap = plt.get_cmap(name)
assert len(w) == (1 if name in should_have_warning else 0)
cmap_r = cmap.reversed()
if not cmap_r._isinit:
cmap._init()
cmap_r._init()
assert_array_almost_equal(cmap._lut[:-3], cmap_r._lut[-4::-1])
def test_corrcoef_binned_short_input(self):
'''
Test if input list of one binned spike train yields 1.0.
'''
# Calculate correlation
binned_st = conv.BinnedSpikeTrain(self.st_0,
t_start=0 * pq.ms,
t_stop=50. * pq.ms,
binsize=1 * pq.ms)
result = sc.corrcoef(binned_st, fast=False)
target = np.array(1.)
# Check result and dimensionality of result
self.assertEqual(result.ndim, 0)
assert_array_almost_equal(result, target)
assert_array_almost_equal(result, sc.corrcoef(binned_st, fast=True))
def test_empty_spike_train(self):
"""
Test whether a warning is yielded in case of empty spike train.
Also check correctness of the output array.
"""
# st_2 is empty
binned_12 = conv.BinnedSpikeTrain([self.st_1, self.st_2],
bin_size=1 * pq.ms)
with self.assertWarns(UserWarning):
result = sc.correlation_coefficient(binned_12, fast=False)
# test for NaNs in the output array
target = np.zeros((2, 2)) * np.NaN
target[0, 0] = 1.0
assert_array_almost_equal(result, target)
def test_light_source_masked_shading():
"""Array comparison test for a surface with a masked portion. Ensures that
we don't wind up with "fringes" of odd colors around masked regions."""
y, x = np.mgrid[-1.2:1.2:8j, -1.2:1.2:8j]
z = 10 * np.cos(x**2 + y**2)
z = np.ma.masked_greater(z, 9.9)
cmap = plt.cm.copper
ls = mcolors.LightSource(315, 45)
rgb = ls.shade(z, cmap)
# Result stored transposed and rounded for for more compact display...
expect = np.array([[[0.90, 0.88, 0.91, 0.91, 0.84, 0.64, 0.36, 0.00],
[0.88, 0.96, 1.00, 1.00, 1.00, 0.97, 0.64, 0.18],
[0.91, 1.00, 1.00, 1.00, 1.00, 1.00, 0.74, 0.34],
[0.91, 1.00, 1.00, 0.00, 0.00, 1.00, 0.52, 0.30],
[0.84, 1.00, 1.00, 0.00, 0.00, 1.00, 0.25, 0.13],
[0.64, 0.97, 1.00, 1.00, 1.00, 0.23, 0.07, 0.03],
[0.36, 0.64, 0.74, 0.52, 0.25, 0.07, 0.00, 0.01],
[0.00, 0.18, 0.34, 0.30, 0.13, 0.03, 0.01, 0.00]],
[[0.90, 0.82, 0.85, 0.82, 0.68, 0.46, 0.24, 0.00],
[0.82, 0.91, 0.95, 0.93, 0.85, 0.68, 0.39, 0.10],
[0.85, 0.95, 1.00, 0.78, 0.78, 0.77, 0.42, 0.18],
[0.82, 0.93, 0.78, 0.00, 0.00, 0.78, 0.30, 0.15],
[0.68, 0.85, 0.78, 0.00, 0.00, 0.78, 0.13, 0.06],
[0.46, 0.68, 0.77, 0.78, 0.78, 0.13, 0.03, 0.01],
[0.24, 0.39, 0.42, 0.30, 0.13, 0.03, 0.00, 0.00],
[0.00, 0.10, 0.18, 0.15, 0.06, 0.01, 0.00, 0.00]],
[[0.90, 0.79, 0.81, 0.76, 0.58, 0.35, 0.17, 0.00],
[0.79, 0.88, 0.92, 0.88, 0.73, 0.50, 0.24, 0.05],
[0.81, 0.92, 1.00, 0.50, 0.50, 0.53, 0.22, 0.09],
[0.76, 0.88, 0.50, 0.00, 0.00, 0.50, 0.12, 0.05],
[0.58, 0.73, 0.50, 0.00, 0.00, 0.50, 0.03, 0.01],
[0.35, 0.50, 0.53, 0.50, 0.50, 0.02, 0.00, 0.00],
[0.17, 0.24, 0.22, 0.12, 0.03, 0.00, 0.00, 0.00],
[0.00, 0.05, 0.09, 0.05, 0.01, 0.00, 0.00, 0.00]],
[[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 0.00, 0.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 0.00, 0.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00]]]).T
assert_array_almost_equal(rgb, expect, decimal=2)
def test_light_source_shading_default():
"""Array comparison test for the default "hsv" blend mode. Ensure the
default result doesn't change without warning."""
y, x = np.mgrid[-1.2:1.2:8j, -1.2:1.2:8j]
z = 10 * np.cos(x**2 + y**2)
cmap = plt.cm.copper
ls = mcolors.LightSource(315, 45)
rgb = ls.shade(z, cmap)
# Result stored transposed and rounded for for more compact display...
expect = np.array([[[0.87, 0.85, 0.90, 0.90, 0.82, 0.62, 0.34, 0.00],
[0.85, 0.94, 0.99, 1.00, 1.00, 0.96, 0.62, 0.17],
[0.90, 0.99, 1.00, 1.00, 1.00, 1.00, 0.71, 0.33],
[0.90, 1.00, 1.00, 1.00, 1.00, 0.98, 0.51, 0.29],
[0.82, 1.00, 1.00, 1.00, 1.00, 0.64, 0.25, 0.13],
[0.62, 0.96, 1.00, 0.98, 0.64, 0.22, 0.06, 0.03],
[0.34, 0.62, 0.71, 0.51, 0.25, 0.06, 0.00, 0.01],
[0.00, 0.17, 0.33, 0.29, 0.13, 0.03, 0.01, 0.00]],
[[0.87, 0.79, 0.83, 0.80, 0.66, 0.44, 0.23, 0.00],
[0.79, 0.88, 0.93, 0.92, 0.83, 0.66, 0.38, 0.10],
[0.83, 0.93, 0.99, 1.00, 0.92, 0.75, 0.40, 0.18],
[0.80, 0.92, 1.00, 0.99, 0.93, 0.75, 0.28, 0.14],
[0.66, 0.83, 0.92, 0.93, 0.87, 0.44, 0.12, 0.06],
[0.44, 0.66, 0.75, 0.75, 0.44, 0.12, 0.03, 0.01],
[0.23, 0.38, 0.40, 0.28, 0.12, 0.03, 0.00, 0.00],
[0.00, 0.10, 0.18, 0.14, 0.06, 0.01, 0.00, 0.00]],
[[0.87, 0.75, 0.78, 0.73, 0.55, 0.33, 0.16, 0.00],
[0.75, 0.85, 0.90, 0.86, 0.71, 0.48, 0.23, 0.05],
[0.78, 0.90, 0.98, 1.00, 0.82, 0.51, 0.21, 0.08],
[0.73, 0.86, 1.00, 0.97, 0.84, 0.47, 0.11, 0.05],
[0.55, 0.71, 0.82, 0.84, 0.71, 0.20, 0.03, 0.01],
[0.33, 0.48, 0.51, 0.47, 0.20, 0.02, 0.00, 0.00],
[0.16, 0.23, 0.21, 0.11, 0.03, 0.00, 0.00, 0.00],
[0.00, 0.05, 0.08, 0.05, 0.01, 0.00, 0.00, 0.00]],
[[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00]]]).T
assert_array_almost_equal(rgb, expect, decimal=2)
def test_min_step_generator_with_base_step01():
desired = 0.1
step_gen = nd.MinStepGenerator(base_step=desired, num_steps=1, offset=0)
methods = ['forward', 'backward', 'central', 'complex']
for n in range(1, 5):
for order in [1, 2, 4, 6, 8]:
min_length = n + order - 1
lengths = [
min_length, min_length,
max(min_length // 2, 1),
max(min_length // 4, 1)
]
for m, method in zip(lengths, methods):
h = [h for h in step_gen(0, method=method, n=n, order=order)]
# print(len(h), n, order, method)
assert_array_almost_equal((h[-1] - desired) / desired, 0)
assert_equal(m, len(h))
def test_corrcoef_binned_same_spiketrains(self):
'''
Test if the correlation coefficient between two identical binned spike
trains evaluates to a 2x2 matrix of ones.
'''
# Calculate correlation
binned_st = conv.BinnedSpikeTrain(
[self.st_0, self.st_0], t_start=0 * pq.ms, t_stop=50. * pq.ms,
binsize=1 * pq.ms)
result = sc.corrcoef(binned_st, fast=False)
target = np.ones((2, 2))
# Check dimensions
self.assertEqual(len(result), 2)
# Check result
assert_array_almost_equal(result, target)
assert_array_almost_equal(result, sc.corrcoef(binned_st, fast=True))
def test_SymLogNorm():
"""
Test SymLogNorm behavior
"""
norm = mcolors.SymLogNorm(3, vmax=5, linscale=1.2)
vals = np.array([-30, -1, 2, 6], dtype=float)
normed_vals = norm(vals)
expected = [0., 0.53980074, 0.826991, 1.02758204]
assert_array_almost_equal(normed_vals, expected)
_inverse_tester(norm, vals)
_scalar_tester(norm, vals)
_mask_tester(norm, vals)
# Ensure that specifying vmin returns the same result as above
norm = mcolors.SymLogNorm(3, vmin=-30, vmax=5, linscale=1.2)
normed_vals = norm(vals)
assert_array_almost_equal(normed_vals, expected)
def testBBPole(self):
# north pole
bb = BoundingBox(latSouth=60, lonWest=-180, latNorth=90, lonEast=180)
assert_array_almost_equal(bb.center, [90, 0])
assert_array_almost_equal(bb.size, [6695.78581964, 6695.78581964])
# south pole
bb = BoundingBox(latSouth=-90, lonWest=-180, latNorth=-60, lonEast=180)
assert_array_almost_equal(bb.center, [-90, 0])
assert_array_almost_equal(bb.size, [6695.78581964, 6695.78581964])
def test_Function():
p = pd.read_csv('../../orcl_2000.csv', index_col=[0], parse_dates=True)
p.columns = [str.lower(col) for col in p.columns]
# func = Function('ACD')
# ret = func(p)
ret = ta.ACD(p)
print('length of ret is {0}, NA number is {1}'.format(
len(ret),
ret.isnull().sum()))
benchmark_ret = benchmark.ACD(p)
# benchmark_ret = talib.abstract.AROON(p)
print('length of ret is {0}, NA number is {1}'.format(
len(benchmark_ret),
benchmark_ret.isnull().sum()))
assert_array_almost_equal(ret.dropna(), benchmark_ret.dropna())
def test_geometric_models(self):
np.random.seed(10)
periods = 1000
price = 70.0
mu = 0.05
sigma = 0.3
period_duration = 1.0
np.random.seed(10)
iterative_ret = bm.generate_gbm_prices(periods, price, mu, sigma,
period_duration)
np.random.seed(10)
vectorised_ret = bm.generate_gbm_prices_vec(periods, price, mu, sigma,
period_duration)
# Equal to 6 decimal places
np_utils.assert_array_almost_equal(iterative_ret, vectorised_ret)
def testArray(self):
f = ufunc_ext.BinaryCallable()
a = numpy.random.randn(5)
b = numpy.random.randn(5)
assert_array_almost_equal(f(a,b), (a*2+b*3))
c = numpy.zeros(5, dtype=float)
d = f(a,b,output=c)
self.assert_(c is d)
assert_array_almost_equal(d, a*2 + b*3)
assert_array_almost_equal(f(a, 2.0), a*2 + 6.0)
assert_array_almost_equal(f(1.0, b), 2.0 + b*3)
def test_zscore_list_dup(self):
'''
Test zscore on a list of AnalogSignalArray objects, asking to return a
duplicate.
'''
signal1 = neo.AnalogSignalArray(np.transpose(
np.vstack([self.test_seq1, self.test_seq1])),
units='mV',
t_start=0. * pq.ms,
sampling_rate=1000. * pq.Hz,
dtype=float)
signal2 = neo.AnalogSignalArray(np.transpose(
np.vstack([self.test_seq1, self.test_seq2])),
units='mV',
t_start=0. * pq.ms,
sampling_rate=1000. * pq.Hz,
dtype=float)
signal_list = [signal1, signal2]
m = np.mean(np.hstack([self.test_seq1, self.test_seq1]))
s = np.std(np.hstack([self.test_seq1, self.test_seq1]))
target11 = (self.test_seq1 - m) / s
target21 = (self.test_seq1 - m) / s
m = np.mean(np.hstack([self.test_seq1, self.test_seq2]))
s = np.std(np.hstack([self.test_seq1, self.test_seq2]))
target12 = (self.test_seq1 - m) / s
target22 = (self.test_seq2 - m) / s
# Call elephant function
result = elephant.signal_processing.zscore(signal_list, inplace=False)
assert_array_almost_equal(result[0].magnitude,
np.transpose(np.vstack([target11,
target12])),
decimal=9)
assert_array_almost_equal(result[1].magnitude,
np.transpose(np.vstack([target21,
target22])),
decimal=9)
# Assert original signal is untouched
self.assertEqual(signal1.magnitude[0, 0], self.test_seq1[0])
self.assertEqual(signal2.magnitude[0, 1], self.test_seq2[0])
def test_calinski_harabaz_metric(self):
"""
Test the calinski-harabaz metric of the k-elbow visualizer
"""
visualizer = KElbowVisualizer(KMeans(random_state=0),
k=5,
metric="calinski_harabaz",
timings=False)
visualizer.fit(X)
expected = np.array([
81.662726256035683, 50.992378259195554, 40.952179227847012,
35.939494
])
self.assertEqual(len(visualizer.k_scores_), 4)
visualizer.poof()
self.assert_images_similar(visualizer)
assert_array_almost_equal(visualizer.k_scores_, expected)
def test_zscore_single_multidim_inplace(self):
"""
Test z-score on a single AnalogSignal with multiple dimensions, asking
for an inplace operation.
"""
signal = neo.AnalogSignal(
np.vstack([self.test_seq1, self.test_seq2]), units='mV',
t_start=0. * pq.ms, sampling_rate=1000. * pq.Hz, dtype=float)
m = np.mean(signal.magnitude, axis=0, keepdims=True)
s = np.std(signal.magnitude, axis=0, keepdims=True)
target = (signal.magnitude - m) / s
assert_array_almost_equal(
elephant.signal_processing.zscore(
signal, inplace=True).magnitude, target, decimal=9)
# Assert original signal is overwritten
self.assertEqual(signal[0, 0].magnitude, target[0, 0])
def test_cross_correlation_env(self):
'''
Envelope of sine vs cosine
'''
# Sine with phase shift phi vs cosine for different frequencies
nlags = 800 # nlags need to be smaller than N/2 b/c border effects
signal = np.zeros((self.n_samples, 2))
signal[:, 0] = 0.2 * np.sin(2. * np.pi * self.freq * self.time)
signal[:, 1] = 5.3 * np.cos(2. * np.pi * self.freq * self.time)
# Convert signal to neo.AnalogSignal
signal = neo.AnalogSignal(signal,
units='mV',
t_start=0. * pq.ms,
sampling_rate=self.sampling_rate,
dtype=float)
env = elephant.signal_processing.cross_correlation_function(
signal, [0, 1], nlags=nlags, env=True)
# Envelope should be one for sinusoidal function
assert_array_almost_equal(env, np.ones_like(env), decimal=2)
def test_zscore_single_inplace_int(self):
"""
Test if the z-score is correctly calculated even if the input is an
AnalogSignal of type int, asking for an inplace operation.
"""
signal = neo.AnalogSignal(
self.test_seq1, units='mV',
t_start=0. * pq.ms, sampling_rate=1000. * pq.Hz, dtype=int)
m = np.mean(self.test_seq1)
s = np.std(self.test_seq1)
target = (self.test_seq1 - m) / s
assert_array_almost_equal(
elephant.signal_processing.zscore(signal, inplace=True).magnitude,
target.reshape(-1, 1).astype(int), decimal=9)
# Assert original signal is overwritten
self.assertEqual(signal[0].magnitude, target.astype(int)[0])
def test_wavelet_phase(self):
"""
Tests phase properties of the obtained wavelet transform
"""
# check that the phase of WT is (almost) same as that of the original
# sinusoid
wt = elephant.signal_processing.wavelet_transform(self.test_data,
self.test_freq1)
phase = np.angle(wt[int(len(wt)/3):int(len(wt)//3*2), 0])
true_phase = self.true_phase1[int(len(wt)/3):int(len(wt)//3*2)]
assert_array_almost_equal(np.exp(1j*phase), np.exp(1j*true_phase),
decimal=6)
# check that zero padding hardly affect the result
wt_padded = elephant.signal_processing.wavelet_transform(
self.test_data, self.test_freq1, zero_padding=False)
phase_padded = np.angle(wt_padded[int(len(wt)/3):int(len(wt)//3*2), 0])
assert_array_almost_equal(np.exp(1j*phase_padded), np.exp(1j*phase),
decimal=9)
def assert_output_data_almost_equal(self, component_names):
for component in component_names:
self.assertIn(component, self.found_components)
expected_data = self.expected_sim.output.continuous_data[component]
data = self.output_sim.output.continuous_data[component]
assert_array_almost_equal(expected_data.x_data,
data.x_data,
decimal=5)
if np.issubdtype(data.y_data.dtype, np.number):
assert_array_almost_equal(expected_data.y_data,
data.y_data,
decimal=5)
else:
# These are arrays of strings. Compare them as lists:
self.assertEqual(expected_data.y_data.tolist(),
data.y_data.tolist())
def test_light_source_shading_color_range():
# see also
#http://matplotlib.org/examples/pylab_examples/shading_example.html
from matplotlib.colors import LightSource
from matplotlib.colors import Normalize
refinput = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
norm = Normalize(vmin=0, vmax=50)
ls = LightSource(azdeg=0, altdeg=65)
testoutput = ls.shade(refinput, plt.cm.jet, norm=norm)
refoutput = np.array([[[0., 0., 0.58912656, 1.], [0., 0., 0.67825312, 1.],
[0., 0., 0.76737968, 1.], [0., 0., 0.85650624, 1.]],
[[0., 0., 0.9456328, 1.], [0., 0., 1., 1.],
[0., 0.04901961, 1., 1.], [0., 0.12745098, 1., 1.]],
[[0., 0.22156863, 1., 1.], [0., 0.3, 1., 1.],
[0., 0.37843137, 1., 1.], [0., 0.45686275, 1.,
1.]]])
assert_array_almost_equal(refoutput, testoutput)
def test_zscore_list_inplace(self):
"""
Test zscore on a list of AnalogSignal objects, asking for an
inplace operation.
"""
signal1 = neo.AnalogSignal(np.transpose(
np.vstack([self.test_seq1, self.test_seq1])),
units='mV',
t_start=0. * pq.ms,
sampling_rate=1000. * pq.Hz,
dtype=float)
signal2 = neo.AnalogSignal(np.transpose(
np.vstack([self.test_seq1, self.test_seq2])),
units='mV',
t_start=0. * pq.ms,
sampling_rate=1000. * pq.Hz,
dtype=float)
signal_list = [signal1, signal2]
m = np.mean(np.hstack([self.test_seq1, self.test_seq1]))
s = np.std(np.hstack([self.test_seq1, self.test_seq1]))
target11 = (self.test_seq1 - m) / s
target21 = (self.test_seq1 - m) / s
m = np.mean(np.hstack([self.test_seq1, self.test_seq2]))
s = np.std(np.hstack([self.test_seq1, self.test_seq2]))
target12 = (self.test_seq1 - m) / s
target22 = (self.test_seq2 - m) / s
# Call elephant function
result = elephant.signal_processing.zscore(signal_list, inplace=True)
assert_array_almost_equal(result[0].magnitude,
np.transpose(np.vstack([target11,
target12])),
decimal=9)
assert_array_almost_equal(result[1].magnitude,
np.transpose(np.vstack([target21,
target22])),
decimal=9)
# Assert original signal is overwritten
self.assertEqual(signal1[0, 0].magnitude, target11[0])
self.assertEqual(signal2[0, 0].magnitude, target21[0])
def test_distortion_metric(self):
"""
Test the distortion metric of the k-elbow visualizer
"""
visualizer = KElbowVisualizer(
KMeans(random_state=0),
k=5,
metric="distortion",
timings=False,
locate_elbow=False,
)
visualizer.fit(self.clusters.X)
expected = np.array([69.100065, 54.081571, 43.146921, 34.978487])
assert len(visualizer.k_scores_) == 4
visualizer.finalize()
self.assert_images_similar(visualizer, tol=0.03)
assert_array_almost_equal(visualizer.k_scores_, expected)
def test_buffering_read_write():
assert not os.path.exists('testfile.tar')
try:
df = pandas.DataFrame(numpy.random.normal(size=(100000, 10)),
columns=['x%d' % i for i in range(10)])
writer = PandasBufferingStreamWriter('testfile.tar',
max_chunk_cells=1000000)
for _, row in df.iterrows():
writer.write_row(row)
writer.close()
reader = PandasBufferingStreamReader('testfile.tar')
new_values = []
for row in reader:
new_values.append(row)
new_df = pandas.DataFrame(new_values)
assert_array_almost_equal(numpy.asarray(df), numpy.asarray(new_df))
assert_list_equal(list(df.columns), list(new_df.columns))
finally:
os.remove('testfile.tar')
def test_grid_search_iid():
# test the iid parameter
# noise-free simple 2d-data
X, y = make_blobs(centers=[[0, 0], [1, 0], [0, 1], [1, 1]],
random_state=0,
cluster_std=0.1,
shuffle=False,
n_samples=80)
# split dataset into two folds that are not iid
# first one contains data of all 4 blobs, second only from two.
mask = np.ones(X.shape[0], dtype=np.bool)
mask[np.where(y == 1)[0][::2]] = 0
mask[np.where(y == 2)[0][::2]] = 0
# this leads to perfect classification on one fold and a score of 1/3 on
# the other
svm = SVC(kernel='linear')
# create "cv" for splits
cv = [[mask, ~mask], [~mask, mask]]
# once with iid=True (default)
grid_search = GridSearchCV(svm, param_grid={'C': [1, 10]}, cv=cv)
grid_search.fit(X, y)
first = grid_search.grid_scores_[0]
tm.assert_equal(first.parameters['C'], 1)
tm.assert_array_almost_equal(first.cv_validation_scores, [1, 1. / 3.])
# for first split, 1/4 of dataset is in test, for second 3/4.
# take weighted average
tm.assert_almost_equal(first.mean_validation_score,
1 * 1. / 4. + 1. / 3. * 3. / 4.)
# once with iid=False
grid_search = GridSearchCV(svm,
param_grid={'C': [1, 10]},
cv=cv,
iid=False)
grid_search.fit(X, y)
first = grid_search.grid_scores_[0]
assert first.parameters['C'] == 1
# scores are the same as above
tm.assert_array_almost_equal(first.cv_validation_scores, [1, 1. / 3.])
# averaged score is just mean of scores
tm.assert_almost_equal(first.mean_validation_score,
np.mean(first.cv_validation_scores))
def test_rbf_kernel_forward(self):
'''Make sure we know the RBF kernel is working'''
# Test basic functionality
x1 = np.array([0.0, 0.0])
x2 = np.array([1.0, 1.0])
log_l = np.log(2.0)
eps = 1e-5
sq_dist = ((x2 - x1)**2).sum()
expected = np.exp(-1.0 / np.exp(log_l) * sq_dist)
x1_var = make_torch_variable([x1], requires_grad=False)
x2_var = make_torch_variable([x2], requires_grad=False)
log_l_var = make_torch_variable([log_l], requires_grad=True)
test = rbf_kernel_forward(x1_var, x2_var, log_l_var, eps=eps)
assert_array_almost_equal(expected, test.data.numpy()[0, 0], decimal=5)
# Make sure the gradient gets through
test.sum().backward()
self.assertIsNotNone(log_l_var.grad)
# Test safety valve
bad_log_l = -1e6
expected_bad = np.exp(-1.0 / eps * sq_dist)
bad_log_l_var = make_torch_variable([bad_log_l], requires_grad=True)
test_bad = rbf_kernel_forward(x1_var, x2_var, bad_log_l_var, eps=eps)
assert_array_almost_equal(expected_bad,
test_bad.data.numpy()[0, 0],
decimal=5)
# Make sure the gradient gets through
test_bad.sum().backward()
self.assertIsNotNone(bad_log_l_var.grad)
