def test_square_matrices_1(self):
op4 = OP4()
# matrices = op4.read_op4(os.path.join(op4Path, fname))
form1 = 1
form2 = 2
form3 = 2
from numpy import matrix, ones, reshape, arange
A1 = matrix(ones((3, 3), dtype="float64"))
A2 = reshape(arange(9, dtype="float64"), (3, 3))
A3 = matrix(ones((1, 1), dtype="float32"))
matrices = {"A1": (form1, A1), "A2": (form2, A2), "A3": (form3, A3)}
for (is_binary, fname) in [(False, "small_ascii.op4"), (True, "small_binary.op4")]:
op4_filename = os.path.join(op4Path, fname)
op4.write_op4(op4_filename, matrices, name_order=None, precision="default", is_binary=False)
matrices2 = op4.read_op4(op4_filename, precision="default")
(form1b, A1b) = matrices2["A1"]
(form2b, A2b) = matrices2["A2"]
self.assertEqual(form1, form1b)
self.assertEqual(form2, form2b)
(form1b, A1b) = matrices2["A1"]
(form2b, A2b) = matrices2["A2"]
(form3b, A3b) = matrices2["A3"]
self.assertEqual(form1, form1b)
self.assertEqual(form2, form2b)
self.assertEqual(form3, form3b)
self.assertTrue(array_equal(A1, A1b))
self.assertTrue(array_equal(A2, A2b))
self.assertTrue(array_equal(A3, A3b))
del A1b, A2b, A3b
del form1b, form2b, form3b
def test_intersect_time():
image = np.random.rand(5, 3600)
spec = LinearTimeSpectrogram(image,
np.linspace(0, 0.25 * (image.shape[1] - 1), image.shape[1]),
np.array([8, 6, 4, 2, 0]),
datetime(2010, 1, 1, 0, 15),
datetime(2010, 1, 1, 0, 30),
900,
0.25
)
image = np.random.rand(5, 3600)
spec2 = LinearTimeSpectrogram(image,
np.linspace(0, 0.25 * (image.shape[1] - 1), image.shape[1]),
np.array([9, 7, 5, 3, 1]),
datetime(2010, 1, 1, 0, 15),
datetime(2010, 1, 1, 0, 30),
901,
0.25
)
one, other = LinearTimeSpectrogram.intersect_time(
[spec, spec2]
)
assert one.shape[1] == other.shape[1]
assert one.shape[1] == 3596
assert np.array_equal(one.data, spec.data[:, 4:])
assert np.array_equal(other.data, spec2.data[:, :-4])
assert np.array_equal(one.time_axis, other.time_axis)
assert one.t_init == other.t_init
assert is_linear(one.time_axis)
assert is_linear(other.time_axis)
def testReadWrite(self):
original = self._encoder(self.n, name=self.name)
originalValue = original.encode([1,0,1,0,1,0,1,0,1])
proto1 = PassThroughEncoderProto.new_message()
original.write(proto1)
# Write the proto to a temp file and read it back into a new proto
with tempfile.TemporaryFile() as f:
proto1.write(f)
f.seek(0)
proto2 = PassThroughEncoderProto.read(f)
encoder = PassThroughEncoder.read(proto2)
self.assertIsInstance(encoder, PassThroughEncoder)
self.assertEqual(encoder.name, original.name)
self.assertEqual(encoder.verbosity, original.verbosity)
self.assertEqual(encoder.w, original.w)
self.assertEqual(encoder.n, original.n)
self.assertEqual(encoder.description, original.description)
self.assertTrue(numpy.array_equal(encoder.encode([1,0,1,0,1,0,1,0,1]),
originalValue))
self.assertEqual(original.decode(encoder.encode([1,0,1,0,1,0,1,0,1])),
encoder.decode(original.encode([1,0,1,0,1,0,1,0,1])))
# Feed in a new value and ensure the encodings match
result1 = original.encode([0,1,0,1,0,1,0,1,0])
result2 = encoder.encode([0,1,0,1,0,1,0,1,0])
self.assertTrue(numpy.array_equal(result1, result2))
def test_rescale(arr, ndv, dst_dtype):
if dst_dtype == np.__dict__['uint16']:
assert np.array_equal(
_rescale(arr, ndv, dst_dtype),
np.concatenate(
[
(arr).astype(dst_dtype),
_simple_mask(
arr.astype(dst_dtype),
(ndv, ndv, ndv)
).reshape(1, arr.shape[1], arr.shape[2])
]
)
)
else:
assert np.array_equal(
_rescale(arr, ndv, dst_dtype),
np.concatenate(
[
(arr / 257.0).astype(dst_dtype),
_simple_mask(
arr.astype(dst_dtype),
(ndv, ndv, ndv)
).reshape(1, arr.shape[1], arr.shape[2])
]
)
)
def testNegativeDelta(self):
self.assertTrue(
np.array_equal(self._Range(5, -1, -1), np.array([5, 4, 3, 2, 1, 0])))
self.assertTrue(
np.allclose(self._Range(2.5, 0, -0.5), np.array([2.5, 2, 1.5, 1, 0.5])))
self.assertTrue(
np.array_equal(self._Range(-5, -10, -3), np.array([-5, -8])))
def test_train(self):
# y = x + 0
x1 = np.array([2, 4])
y1 = np.array([2, 4])
m1 = LinearRegression(1)
m1.train(x1, y1, n_iter=1, lr=0.1)
# expected W and b after 1 iteration with lr 0.1
exp_W1 = np.array([1.0])
exp_b1 = 0.3
self.assertTrue(np.array_equal(m1.W, exp_W1))
self.assertAlmostEqual(m1.b[0], exp_b1)
# y = x1 + x2 + 0
x2 = np.array([[2, 2],
[4, 4]])
y2 = np.array([4, 8])
m2 = LinearRegression(2)
m2.train(x2, y2, n_iter=1, lr=0.1)
# expected W and b after 1 iteration with lr 0.1
exp_W2 = np.array([2.0, 2.0])
exp_b2 = 0.6
self.assertTrue(np.array_equal(m2.W, exp_W2))
self.assertAlmostEqual(m2.b[0], exp_b2)
def _test_column_grouping(m=10, n=5000, num_repeat=5, verbose=False):
print('\nTesting column_grouping ...')
A = np.array([[True, False, False, False, False],
[True, True, False, True, True]])
grps1 = _column_group_loop(A)
grps2 = _column_group_recursive(A)
grps3 = [np.array([0]),
np.array([1, 3, 4]),
np.array([2])]
print('OK' if all([np.array_equal(a, b) for (a, b) in zip(grps1, grps2)]) else 'Fail')
print('OK' if all([np.array_equal(a, b) for (a, b) in zip(grps1, grps3)]) else 'Fail')
for i in range(0, num_repeat):
A = np.random.rand(m, n)
B = A > 0.5
start = time.time()
grps1 = _column_group_loop(B)
elapsed_loop = time.time() - start
start = time.time()
grps2 = _column_group_recursive(B)
elapsed_recursive = time.time() - start
if verbose:
print('Loop :', elapsed_loop)
print('Recursive:', elapsed_recursive)
print('OK' if all([np.array_equal(a, b) for (a, b) in zip(grps1, grps2)]) else 'Fail')
# sorted_idx = np.concatenate(grps)
# print B
# print sorted_idx
# print B[:,sorted_idx]
return
def Color_Features_Extract(img_folder):
print "Color_Features_Extract Start"
starttime = datetime.datetime.now()
back = np.array([255,128,128])
image_num = len(os.listdir(seg_img_folder))
Color_Features = []
for index, image_name in enumerate(os.listdir(img_folder)):
image_path = img_folder + str("/") +image_name
image = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2LAB)
rows, columns, lab = image.shape
# Make densely-sampling color features
pixel_index = 0
for x in range(rows):
for y in range(columns):
if pixel_index % 9 == 0 and np.array_equal(image[x][y],back) == False:
Color_Features.append(image[x][y].tolist())
pixel_index += 1
# Get CodeBook of Color_Features
Color_Features = np.float32(Color_Features)
# Define criteria = ( type, max_iter = 10 , epsilon = 1.0 )
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
# Set flags (Just to avoid line break in the code)
flags = cv2.KMEANS_RANDOM_CENTERS
# Apply KMeans
compactness,labels,centers = cv2.kmeans(Color_Features,800,None,criteria,10,flags)
Image_Color_Features = [[0 for x in range(800)] for y in range(image_num)]
color_index = 0
for image_index, image_name in enumerate(os.listdir(img_folder)):
image_path = img_folder + str("/") +image_name
image = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2LAB)
rows, columns, lab = image.shape
pixel_index = 0
for x in range(rows):
for y in range(columns):
if pixel_index % 9 == 0 and np.array_equal(image[x][y],back) == False:
Image_Color_Features[image_index][labels[color_index]] += 1
color_index += 1
pixel_index += 1
print image_name
endtime = datetime.datetime.now()
print "Time: " + str((endtime - starttime).seconds) + "s"
print "Color_Features_Extract End"
return Image_Color_Features
def test_distance(self):
a = np.array([1, 2])
b = np.array([2, 1])
self.assertEquals(np.sqrt(2), distance(a, b))
self.assertAlmostEquals(0.2117,
distance(a, b, func=sigmod), places=3)
self.assertAlmostEquals(0.26,
distance(a, b, func=gaussian), places=1)
a = np.array([[1, 2], [2,1]])
b = np.array([[2, 1], [1,2]])
self.assertTrue(np.array_equal(np.array([np.sqrt(2), np.sqrt(2)]),
distance(a,b)))
a = np.array([[1, 2], [2,1]])
b = np.array([2, 1])
self.assertTrue(np.array_equal(np.array([np.sqrt(2), np.sqrt(0)]),
distance(a,b)))
a = np.array([[1, 2], [2,1]])
b = np.array([[2, 1], [1,2]])
self.assertTrue(np.array_equal(np.array([2, 2]),
distance(a,b, mode='manhatton')))
a = np.array([[2, 1], [2,1]])
b = np.array([[2, 1], [1,2]])
print distance(a,b, mode='pearson')
def _testFloodFill(SegmentationHelper):
filledMask = SegmentationHelper._floodFill(
testImage, (1, 1), 5, connectivity=8)
assert numpy.array_equal(
filledMask,
numpy.array([
[255, 255, 0, 0, 0, 0],
[255, 255, 255, 255, 0, 0],
[0, 255, 0, 255, 255, 255],
[0, 255, 255, 255, 0, 0],
[0, 0, 255, 0, 0, 0],
[0, 0, 0, 255, 0, 0]
], dtype=numpy.uint8))
assert numpy.array_equal(testImage, originalTestImage)
# Now, with connectivity=4
filledMask = SegmentationHelper._floodFill(
testImage, (1, 1), 5, connectivity=4)
assert numpy.array_equal(
filledMask,
numpy.array([
[255, 255, 0, 0, 0, 0],
[255, 255, 255, 255, 0, 0],
[0, 255, 0, 255, 255, 255],
[0, 255, 255, 255, 0, 0],
[0, 0, 255, 0, 0, 0],
[0, 0, 0, 0, 0, 0]
], dtype=numpy.uint8))
assert numpy.array_equal(testImage, originalTestImage)
def test_rasterize_supported_dtype(basic_geometry):
""" Supported data types should return valid results """
with Env():
supported_types = (
('int16', -32768),
('int32', -2147483648),
('uint8', 255),
('uint16', 65535),
('uint32', 4294967295),
('float32', 1.434532),
('float64', -98332.133422114)
)
for dtype, default_value in supported_types:
truth = np.zeros(DEFAULT_SHAPE, dtype=dtype)
truth[2:4, 2:4] = default_value
result = rasterize(
[basic_geometry],
out_shape=DEFAULT_SHAPE,
default_value=default_value,
dtype=dtype
)
assert np.array_equal(result, truth)
assert np.dtype(result.dtype) == np.dtype(truth.dtype)
result = rasterize(
[(basic_geometry, default_value)],
out_shape=DEFAULT_SHAPE
)
if np.dtype(dtype).kind == 'f':
assert np.allclose(result, truth)
else:
assert np.array_equal(result, truth)
def test_Period():
p = Period(numbers[0:2], units[0])
assert sa_asserts.sa_access(p)
assert i_asserts.interval(p, start=numbers[0], stop=numbers[1])
assert np.array_equal(p.info['start'], p.start)
assert np.array_equal(p.info['stop'], p.stop)
assert np.array_equal(p.info['length'], p.length)
def testReadWrite(self):
"""Test ScalarEncoder Cap'n Proto serialization implementation."""
originalValue = self._l.encode(1)
proto1 = ScalarEncoderProto.new_message()
self._l.write(proto1)
# Write the proto to a temp file and read it back into a new proto
with tempfile.TemporaryFile() as f:
proto1.write(f)
f.seek(0)
proto2 = ScalarEncoderProto.read(f)
encoder = ScalarEncoder.read(proto2)
self.assertIsInstance(encoder, ScalarEncoder)
self.assertEqual(encoder.w, self._l.w)
self.assertEqual(encoder.minval, self._l.minval)
self.assertEqual(encoder.maxval, self._l.maxval)
self.assertEqual(encoder.periodic, self._l.periodic)
self.assertEqual(encoder.n, self._l.n)
self.assertEqual(encoder.radius, self._l.radius)
self.assertEqual(encoder.resolution, self._l.resolution)
self.assertEqual(encoder.name, self._l.name)
self.assertEqual(encoder.verbosity, self._l.verbosity)
self.assertEqual(encoder.clipInput, self._l.clipInput)
self.assertTrue(numpy.array_equal(encoder.encode(1), originalValue))
self.assertEqual(self._l.decode(encoder.encode(1)),
encoder.decode(self._l.encode(1)))
# Feed in a new value and ensure the encodings match
result1 = self._l.encode(7)
result2 = encoder.encode(7)
self.assertTrue(numpy.array_equal(result1, result2))
def test_A(self):
cid0 = CORD2R()
Lx = 2.
Ly = 0.
Lz = 3.
Fy = 1.
origin = array([-Lx, 0., -Lz])
z_axis = origin + array([0., 0., 1.])
xz_plane = origin + array([1., 0., 1.])
rid = 0
data = [1, rid] + list(origin) + list(z_axis) + list(xz_plane)
Fxyz = [0., -Fy, 0.]
Mxyz = [0., 0., 0.]
cid_new = CORD2R(data=data)
model = None
Fxyz_local, Mxyz_local = TransformLoadWRT(Fxyz, Mxyz, cid0, cid_new,
model, is_cid_int=False)
r = array([Lx, Ly, Lz])
F = array([0., -Fy, 0.])
M = cross(r, F)
self.assertTrue(array_equal(Fxyz_local, F)), "expected=%s actual=%s" % (F, Fxyz_local)
self.assertTrue(array_equal(Mxyz_local, cross(r, F))), "expected=%s actual=%s" % (M, Mxyz_local)
def test_convert_r_matrix():
is_na = robj.baseenv.get("is.na")
seriesd = _test.getSeriesData()
frame = pd.DataFrame(seriesd, columns=['D', 'C', 'B', 'A'])
# Null data
frame["E"] = [np.nan for item in frame["A"]]
r_dataframe = convert_to_r_matrix(frame)
assert np.array_equal(convert_robj(r_dataframe.rownames), frame.index)
assert np.array_equal(convert_robj(r_dataframe.colnames), frame.columns)
assert all(is_na(item) for item in r_dataframe.rx(True, "E"))
for column in frame[["A", "B", "C", "D"]]:
coldata = r_dataframe.rx(True, column)
original_data = frame[column]
assert np.array_equal(convert_robj(coldata),
original_data)
# Pandas bug 1282
frame["F"] = ["text" if item % 2 == 0 else np.nan for item in range(30)]
# FIXME: Ugly, this whole module needs to be ported to nose/unittest
try:
wrong_matrix = convert_to_r_matrix(frame)
except TypeError:
pass
except Exception:
raise
def test_n_largest_area_contours_images__with_invert(self):
# given
image = cv2.imread("./images/SnipNLargestAreaContours/"
"test_n_largest_area_contours_image__with_invert__input_image.png",
cv2.IMREAD_GRAYSCALE)
n = 2
invert_flag = True
snip_n_largest_area_contours = SnipNLargestAreaContours(image, n, invert_flag)
expected_largest_contour_image_1 = cv2.imread(
"./images/SnipNLargestAreaContours/test_n_largest_area_contours_images__with_invert__snipped_image_1.png",
flags=cv2.IMREAD_GRAYSCALE)
expected_largest_contour_image_2 = cv2.imread(
"./images/SnipNLargestAreaContours/test_n_largest_area_contours_images__with_invert__snipped_image_2.png",
flags=cv2.IMREAD_GRAYSCALE)
expected_n_largest_area_contours_images = [expected_largest_contour_image_1, expected_largest_contour_image_2]
# when
actual_n_largest_area_contours_images = snip_n_largest_area_contours.n_largest_area_contours_images
# that
self.assertEqual(np.array_equal(actual_n_largest_area_contours_images[0],
expected_n_largest_area_contours_images[0]), True)
self.assertEqual(np.array_equal(actual_n_largest_area_contours_images[1],
expected_n_largest_area_contours_images[1]), True)
def test_convert_matrix():
mat = _test_matrix()
converted = convert_robj(mat)
assert np.array_equal(converted.index, ['a', 'b', 'c'])
assert np.array_equal(converted.columns, ['one', 'two', 'three'])
def test_convert_r_dataframe():
is_na = robj.baseenv.get("is.na")
seriesd = _test.getSeriesData()
frame = pd.DataFrame(seriesd, columns=['D', 'C', 'B', 'A'])
# Null data
frame["E"] = [np.nan for item in frame["A"]]
# Some mixed type data
frame["F"] = ["text" if item % 2 == 0 else np.nan for item in range(30)]
r_dataframe = convert_to_r_dataframe(frame)
assert np.array_equal(convert_robj(r_dataframe.rownames), frame.index)
assert np.array_equal(convert_robj(r_dataframe.colnames), frame.columns)
assert all(is_na(item) for item in r_dataframe.rx2("E"))
for column in frame[["A", "B", "C", "D"]]:
coldata = r_dataframe.rx2(column)
original_data = frame[column]
assert np.array_equal(convert_robj(coldata), original_data)
for column in frame[["D", "E"]]:
for original, converted in zip(frame[column],
r_dataframe.rx2(column)):
if pd.isnull(original):
assert is_na(converted)
else:
assert original == converted
def test_mle(self):
states = ["0", "1", "2", "3"]
alphabet = ["A", "C", "G", "T"]
training_data = [("AACCCGGGTTTTTTT", "001112223333333"),
("ACCGTTTTTTT", "01123333333"),
("ACGGGTTTTTT", "01222333333"),
("ACCGTTTTTTTT", "011233333333"), ]
training_outputs = array([[0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3], [
0, 1, 1, 2, 3, 3, 3, 3, 3, 3, 3], [0, 1, 2, 2, 2, 3, 3, 3, 3, 3, 3], [0, 1, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3]])
training_states = array([[0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3], [
0, 1, 1, 2, 3, 3, 3, 3, 3, 3, 3], [0, 1, 2, 2, 2, 3, 3, 3, 3, 3, 3], [0, 1, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3]])
p_initial = array([1., 0., 0., 0.])
p_transition = array([[0.2, 0.8, 0., 0.],
[0., 0.5, 0.5, 0.],
[0., 0., 0.5, 0.5],
[0., 0., 0., 1.]])
p_emission = array(
[[0.66666667, 0.11111111, 0.11111111, 0.11111111],
[0.08333333, 0.75, 0.08333333, 0.08333333],
[0.08333333, 0.08333333, 0.75, 0.08333333],
[0.03125, 0.03125, 0.03125, 0.90625]])
p_initial_out, p_transition_out, p_emission_out = MarkovModel._mle(
len(states), len(alphabet), training_outputs, training_states, None, None, None)
self.assertTrue(
array_equal(around(p_initial_out, decimals=3), around(p_initial, decimals=3)))
self.assertTrue(
array_equal(around(p_transition_out, decimals=3), around(p_transition, decimals=3)))
self.assertTrue(
array_equal(around(p_emission_out, decimals=3), around(p_emission, decimals=3)))
def testString(self):
indices = constant_op.constant([[4], [3], [1], [7]],
dtype=dtypes.int32)
updates = constant_op.constant(["four", "three", "one", "seven"],
dtype=dtypes.string)
expected = np.array([b"", b"one", b"", b"three", b"four",
b"", b"", b"seven"])
scatter = self.scatter_nd(indices, updates, shape=(8,))
with self.cached_session() as sess:
result = sess.run(scatter)
self.assertAllEqual(expected, result)
# Same indice is updated twice by same value.
indices = constant_op.constant([[4], [3], [3], [7]],
dtype=dtypes.int32)
updates = constant_op.constant(["a", "b", "b", "c"],
dtype=dtypes.string)
expected = np.array([b"", b"", b"", b"bb", b"a", b"", b"", b"c"])
scatter = self.scatter_nd(indices, updates, shape=(8,))
with self.cached_session() as sess:
result = sess.run(scatter)
self.assertAllEqual(expected, result)
# Same indice is updated twice by different value.
indices = constant_op.constant([[4], [3], [3], [7]],
dtype=dtypes.int32)
updates = constant_op.constant(["a", "b", "c", "d"],
dtype=dtypes.string)
expected = [np.array([b"", b"", b"", b"bc", b"a", b"", b"", b"d"]),
np.array([b"", b"", b"", b"cb", b"a", b"", b"", b"d"])]
scatter = self.scatter_nd(indices, updates, shape=(8,))
with self.cached_session() as sess:
result = sess.run(scatter)
self.assertTrue(np.array_equal(result, expected[0]) or
np.array_equal(result, expected[1]))
def _serialize_volume_info(volume_info):
"""An implementation of nibabel.freesurfer.io._serialize_volume_info, since
old versions of nibabel (<=2.1.0) don't have it."""
keys = ['head', 'valid', 'filename', 'volume', 'voxelsize', 'xras', 'yras',
'zras', 'cras']
diff = set(volume_info.keys()).difference(keys)
if len(diff) > 0:
raise ValueError('Invalid volume info: %s.' % diff.pop())
strings = list()
for key in keys:
if key == 'head':
if not (np.array_equal(volume_info[key], [20]) or np.array_equal(
volume_info[key], [2, 0, 20])):
warnings.warn("Unknown extension code.")
strings.append(np.array(volume_info[key], dtype='>i4').tostring())
elif key in ('valid', 'filename'):
val = volume_info[key]
strings.append('{} = {}\n'.format(key, val).encode('utf-8'))
elif key == 'volume':
val = volume_info[key]
strings.append('{} = {} {} {}\n'.format(
key, val[0], val[1], val[2]).encode('utf-8'))
else:
val = volume_info[key]
strings.append('{} = {:0.10g} {:0.10g} {:0.10g}\n'.format(
key.ljust(6), val[0], val[1], val[2]).encode('utf-8'))
return b''.join(strings)
def test_save_and_load(self):
states = "NR"
alphabet = "AGTC"
p_initial = array([1.0, 0.0])
p_transition = array([[0.75, 0.25], [0.25, 0.75]])
p_emission = array(
[[0.45, 0.36, 0.06, 0.13], [0.24, 0.18, 0.12, 0.46]])
markov_model_save = MarkovModel.MarkovModel(
states,
alphabet,
p_initial,
p_transition,
p_emission)
handle = StringIO()
MarkovModel.save(markov_model_save, handle)
handle.seek(0)
markov_model_load = MarkovModel.load(handle)
self.assertEqual(''.join(markov_model_load.states), states)
self.assertEqual(''.join(markov_model_load.alphabet), alphabet)
self.assertTrue(array_equal(markov_model_load.p_initial, p_initial))
self.assertTrue(array_equal
(markov_model_load.p_transition, p_transition))
self.assertTrue(array_equal(markov_model_load.p_emission, p_emission))
def test_find_optimal_paras_rf(self):
# first, load the file
f = h5py.File('tuning_ref_results/NIS_results.hdf5', 'r+')
# get the filter
grp = f['ICA/tuning']
wica = grp['Wica'][:] # should be
# get the reference results
ica_optx = grp.attrs['ica_optx']
ica_opty = grp.attrs['ica_opty']
ica_optfreq = grp.attrs['ica_optfreq']
ica_optor = grp.attrs['ica_optor']
ica_optphase = grp.attrs['ica_optphase']
# now shuffle this Wica. 1024x256
wica = transpose_c_and_f(wica.T)
# get result
result = tuning.find_optimal_paras_rf(w=wica, legacy=True)
# print result
self.assertTrue(np.allclose(result['optx'], ica_optx))
self.assertTrue(np.allclose(result['opty'], ica_opty))
self.assertTrue(np.array_equal(result['optfreq'], ica_optfreq))
self.assertTrue(np.array_equal(result['optor'], ica_optor))
self.assertTrue(np.array_equal(result['optphase'], ica_optphase))
f.close()
def test_tz_localize_dti(self):
from pandas.tseries.offsets import Hour
dti = DatetimeIndex(start='1/1/2005', end='1/1/2005 0:00:30.256',
freq='L')
dti2 = dti.tz_localize('US/Eastern')
dti_utc = DatetimeIndex(start='1/1/2005 05:00',
end='1/1/2005 5:00:30.256', freq='L',
tz='utc')
self.assert_(np.array_equal(dti2.values, dti_utc.values))
dti3 = dti2.tz_convert('US/Pacific')
self.assert_(np.array_equal(dti3.values, dti_utc.values))
dti = DatetimeIndex(start='11/6/2011 1:59',
end='11/6/2011 2:00', freq='L')
self.assertRaises(pytz.AmbiguousTimeError, dti.tz_localize,
'US/Eastern')
dti = DatetimeIndex(start='3/13/2011 1:59', end='3/13/2011 2:00',
freq='L')
self.assertRaises(pytz.AmbiguousTimeError, dti.tz_localize,
'US/Eastern')
def test_Moster13SmHm_behavior(): """ """ default_model = Moster13SmHm() mstar1 = default_model.mean_stellar_mass(prim_haloprop = 1.e12) ratio1 = mstar1/3.4275e10 np.testing.assert_array_almost_equal(ratio1, 1.0, decimal=3) default_model.param_dict['n10'] *= 1.1 mstar2 = default_model.mean_stellar_mass(prim_haloprop = 1.e12) assert mstar2 > mstar1 default_model.param_dict['n11'] *= 1.1 mstar3 = default_model.mean_stellar_mass(prim_haloprop = 1.e12) assert mstar3 == mstar2 mstar4_z1 = default_model.mean_stellar_mass(prim_haloprop = 1.e12, redshift=1) default_model.param_dict['n11'] *= 1.1 mstar5_z1 = default_model.mean_stellar_mass(prim_haloprop = 1.e12, redshift=1) assert mstar5_z1 != mstar4_z1 mstar_realization1 = default_model.mc_stellar_mass(prim_haloprop = np.ones(1e4)*1e12, seed=43) mstar_realization2 = default_model.mc_stellar_mass(prim_haloprop = np.ones(1e4)*1e12, seed=43) mstar_realization3 = default_model.mc_stellar_mass(prim_haloprop = np.ones(1e4)*1e12, seed=44) assert np.array_equal(mstar_realization1, mstar_realization2) assert not np.array_equal(mstar_realization1, mstar_realization3) measured_scatter1 = np.std(np.log10(mstar_realization1)) model_scatter = default_model.param_dict['scatter_model_param1'] np.testing.assert_allclose(measured_scatter1, model_scatter, rtol=1e-3) default_model.param_dict['scatter_model_param1'] = 0.3 mstar_realization4 = default_model.mc_stellar_mass(prim_haloprop = np.ones(1e4)*1e12, seed=43) measured_scatter4 = np.std(np.log10(mstar_realization4)) np.testing.assert_allclose(measured_scatter4, 0.3, rtol=1e-3)
def test_tabulate():
scalar_no_units = lambda wlen: math.sqrt(wlen)
scalar_with_units = lambda wlen: math.sqrt(wlen.value)
array_no_units = lambda wlen: 1. + np.sqrt(wlen)
array_with_units = lambda wlen: np.sqrt(wlen.value)
add_units = lambda fval: (lambda wlen: fval(wlen) * u.erg)
wlen = np.arange(1, 3) * u.Angstrom
for v in True, False:
# Test each mode without any function return units.
f1 = tabulate_function_of_wavelength(scalar_no_units, wlen, v)
assert f1[1] == None
f2 = tabulate_function_of_wavelength(scalar_with_units, wlen, v)
assert f2[1] == None
f3 = tabulate_function_of_wavelength(array_no_units, wlen, v)
assert f3[1] == None
f4 = tabulate_function_of_wavelength(scalar_with_units, wlen, v)
assert f4[1] == None
# Now test with return units.
g1 = tabulate_function_of_wavelength(
add_units(scalar_no_units), wlen, v)
assert np.array_equal(f1[0], g1[0]) and g1[1] == u.erg
g2 = tabulate_function_of_wavelength(
add_units(scalar_with_units), wlen, v)
assert np.array_equal(f2[0], g2[0]) and g2[1] == u.erg
g3 = tabulate_function_of_wavelength(
add_units(array_no_units), wlen, v)
assert np.array_equal(f3[0], g3[0]) and g3[1] == u.erg
g4 = tabulate_function_of_wavelength(
add_units(scalar_with_units), wlen, v)
assert np.array_equal(f4[0], g4[0]) and g4[1] == u.erg
def test_setPairMask(self):
'''check different setPairMask arguments.
'''
bdc = self.bdc
dall = bdc(self.nickel)
bdc.maskAllPairs(False)
self.assertEqual(0, len(bdc(self.nickel)))
for i in range(4):
bdc.setPairMask(0, i, True)
dst0a = bdc(self.nickel)
bdc.setPairMask(range(4), 0, True, others=False)
dst0b = bdc(self.nickel)
self.assertTrue(numpy.array_equal(dst0a, dst0b))
bdc.maskAllPairs(False)
bdc.setPairMask(0, -7, True)
dst0c = bdc(self.nickel)
self.assertTrue(numpy.array_equal(dst0a, dst0c))
bdc.maskAllPairs(False)
bdc.setPairMask(0, 'all', True)
dst0d = bdc(self.nickel)
self.assertTrue(numpy.array_equal(dst0a, dst0d))
bdc.setPairMask('all', 'all', False)
self.assertEqual(0, len(bdc(self.nickel)))
bdc.setPairMask('all', range(4), True)
dall2 = bdc(self.nickel)
self.assertTrue(numpy.array_equal(dall, dall2))
self.assertRaises(ValueError, bdc.setPairMask, 'fooo', 2, True)
self.assertRaises(ValueError, bdc.setPairMask, 'aLL', 2, True)
return
def test_two_triangles_without_edges():
grid = two_triangles_with_depths()
grid.edges = None
grid.save_as_netcdf("2_triangles_without_edges.nc")
# read it back in and check it out
ug = UGrid.from_ncfile("2_triangles_without_edges.nc", load_data=True)
assert ug.nodes.shape == (4, 2)
assert ug.nodes.shape == grid.nodes.shape
# not ideal to pull specific values out, but how else to test?
assert np.array_equal(ug.nodes[0, :], (0.1, 0.1))
assert np.array_equal(ug.nodes[-1, :], (3.1, 2.1))
assert np.array_equal(ug.nodes, grid.nodes)
assert ug.faces.shape == grid.faces.shape
assert ug.edges is None
depths = find_depths(ug)
assert depths.data.shape == (4,)
assert depths.data[0] == 1
assert depths.attributes["units"] == "unknown"
def test_StatesMatrixMerger():
val_mat1_text = list(tools.ngram(test_text_df["text"].values[0], [1]))
val_mat2_text = list(tools.ngram(test_text_df["text"].values[1], [1]))
val_mat1 = ValuesMatrix(val_mat1_text, force2d="as_col")
val_mat2 = ValuesMatrix(val_mat2_text, force2d="as_col")
idx_data_mat1 = val_mat1.build_index_data_matrix()
idx_data_mat2 = val_mat2.build_index_data_matrix()
idx_data_mat1_old_ref_data = idx_data_mat1.states_matrix._ref_data
idx_data_mat2_old_ref_data = idx_data_mat2.states_matrix._ref_data
old_idx_data_mat1 = idx_data_mat1.index_matrix.copy()
old_idx_data_mat2 = idx_data_mat2.index_matrix.copy()
assert len(idx_data_mat1_old_ref_data) > 0
assert len(idx_data_mat2_old_ref_data) > 0
states_mats_merger = StatesMatrixMerger(idx_data_mat1.states_matrix,
idx_data_mat2.states_matrix)
assert len(states_mats_merger._unique_states_matrix_ids) > 1
states_mats_merger.update()
assert len(idx_data_mat1_old_ref_data) == 0
assert len(idx_data_mat2_old_ref_data) == 0
assert id(idx_data_mat1.states_matrix) == id(idx_data_mat2.states_matrix)
assert len(idx_data_mat2.states_matrix._ref_data) > 1
assert np.array_equal(idx_data_mat1.index_matrix, old_idx_data_mat1)
assert not np.array_equal(idx_data_mat2.index_matrix, old_idx_data_mat2)
assert np.array_equal(idx_data_mat1._data, val_mat1)
assert np.array_equal(idx_data_mat2._data, val_mat2)
def _read_volume_info(fobj):
"""An implementation of nibabel.freesurfer.io._read_volume_info, since old
versions of nibabel (<=2.1.0) don't have it.
"""
volume_info = dict()
head = np.fromfile(fobj, '>i4', 1)
if not np.array_equal(head, [20]): # Read two bytes more
head = np.concatenate([head, np.fromfile(fobj, '>i4', 2)])
if not np.array_equal(head, [2, 0, 20]):
warnings.warn("Unknown extension code.")
return volume_info
volume_info['head'] = head
for key in ['valid', 'filename', 'volume', 'voxelsize', 'xras', 'yras',
'zras', 'cras']:
pair = fobj.readline().decode('utf-8').split('=')
if pair[0].strip() != key or len(pair) != 2:
raise IOError('Error parsing volume info.')
if key in ('valid', 'filename'):
volume_info[key] = pair[1].strip()
elif key == 'volume':
volume_info[key] = np.array(pair[1].split()).astype(int)
else:
volume_info[key] = np.array(pair[1].split()).astype(float)
# Ignore the rest
return volume_info
def _fit_xdawn(epochs_data, y, n_components, reg=None, signal_cov=None,
events=None, tmin=0., sfreq=1., method_params=None, info=None):
"""Fit filters and coefs using Xdawn Algorithm.
Xdawn is a spatial filtering method designed to improve the signal
to signal + noise ratio (SSNR) of the event related responses. Xdawn was
originally designed for P300 evoked potential by enhancing the target
response with respect to the non-target response. This implementation is a
generalization to any type of event related response.
Parameters
----------
epochs_data : array, shape (n_epochs, n_channels, n_times)
The epochs data.
y : array, shape (n_epochs)
The epochs class.
n_components : int (default 2)
The number of components to decompose the signals signals.
reg : float | str | None (default None)
If not None (same as ``'empirical'``, default), allow
regularization for covariance estimation.
If float, shrinkage is used (0 <= shrinkage <= 1).
For str options, ``reg`` will be passed as ``method`` to
:func:`mne.compute_covariance`.
signal_cov : None | Covariance | array, shape (n_channels, n_channels)
The signal covariance used for whitening of the data.
if None, the covariance is estimated from the epochs signal.
events : array, shape (n_epochs, 3)
The epochs events, used to correct for epochs overlap.
tmin : float
Epochs starting time. Only used if events is passed to correct for
epochs overlap.
sfreq : float
Sampling frequency. Only used if events is passed to correct for
epochs overlap.
Returns
-------
filters : array, shape (n_channels, n_channels)
The Xdawn components used to decompose the data for each event type.
patterns : array, shape (n_channels, n_channels)
The Xdawn patterns used to restore the signals for each event type.
evokeds : array, shape (n_class, n_components, n_times)
The independent evoked responses per condition.
"""
n_epochs, n_channels, n_times = epochs_data.shape
classes = np.unique(y)
# XXX Eventually this could be made to deal with rank deficiency properly
# by exposing this "rank" parameter, but this will require refactoring
# the linalg.eigh call to operate in the lower-dimension
# subspace, then project back out.
# Retrieve or compute whitening covariance
if signal_cov is None:
signal_cov = _regularized_covariance(
np.hstack(epochs_data), reg, method_params, info, rank='full')
elif isinstance(signal_cov, Covariance):
signal_cov = signal_cov.data
if not isinstance(signal_cov, np.ndarray) or (
not np.array_equal(signal_cov.shape,
np.tile(epochs_data.shape[1], 2))):
raise ValueError('signal_cov must be None, a covariance instance, '
'or an array of shape (n_chans, n_chans)')
# Get prototype events
if events is not None:
evokeds, toeplitzs = _least_square_evoked(
epochs_data, events, tmin, sfreq)
else:
evokeds, toeplitzs = list(), list()
for c in classes:
# Prototyped response for each class
evokeds.append(np.mean(epochs_data[y == c, :, :], axis=0))
toeplitzs.append(1.)
filters = list()
patterns = list()
for evo, toeplitz in zip(evokeds, toeplitzs):
# Estimate covariance matrix of the prototype response
evo = np.dot(evo, toeplitz)
evo_cov = _regularized_covariance(evo, reg, method_params, info,
rank='full')
# Fit spatial filters
try:
evals, evecs = linalg.eigh(evo_cov, signal_cov)
except np.linalg.LinAlgError as exp:
raise ValueError('Could not compute eigenvalues, ensure '
'proper regularization (%s)' % (exp,))
evecs = evecs[:, np.argsort(evals)[::-1]] # sort eigenvectors
evecs /= np.apply_along_axis(np.linalg.norm, 0, evecs)
_patterns = np.linalg.pinv(evecs.T)
filters.append(evecs[:, :n_components].T)
patterns.append(_patterns[:, :n_components].T)
filters = np.concatenate(filters, axis=0)
patterns = np.concatenate(patterns, axis=0)
evokeds = np.array(evokeds)
return filters, patterns, evokeds
def check_y_valid_values_for_pairs(y):
"""Checks that y values are in [-1, 1]"""
if not np.array_equal(np.abs(y), np.ones_like(y)):
raise ValueError("When training on pairs, the labels (y) should contain "
"only values in [-1, 1]. Found an incorrect value.")
def test_normalize_distribution_invalid_input(distribution):
_, f = distribution
f_normalized = normalize_distribution(f)
assert np.array_equal(f_normalized, f)
def losscp(list):
newlist = np.sort(list)
if np.array_equal(np.array(list), np.array(newlist)):
return 1
else:
return 0
def test_simple_grid(self):
text_grid = self.text_grid
result_grid = self.result_grid
goal_building = GoalBuilding2D(text_grid)
assert goal_building
assert np.array_equal(goal_building.grid, result_grid)
if (k % 6) % 2 == 0:
if stats.mode(data_int[i], axis=None)[0][0] == 0:
bounds_learned[int(k / 6), k % 6] = 0
else:
bounds_learned[int(k / 6),
k % 6] = np.min(data_int[i])
if (k % 6) % 2 != 0:
if stats.mode(data_int[i], axis=None)[0][0] == 0:
bounds_learned[int(k / 6), k % 6] = 0
else:
bounds_learned[int(k / 6),
k % 6] = np.max(data_int[i])
k += 1
bounds_learned = bounds_learned.astype(np.int64)
if not np.array_equal(bounds_learned, bounds_prev):
totSam1, fp, LeConsReject, constrRej1, constrRej2, objVal2 = gfl.generateSample2(
num_nurses, num_days, num_shifts, orderingNotImp, numSam,
num_constrType, constrList, bounds_learned, bounds)
totSam2, fn, TrConsReject, constrRej3, constrRej4, objVal1 = gfl.generateSample1(
num_nurses, num_days, num_shifts, orderingNotImp, numSam,
num_constrType, constrList, bounds, bounds_learned)
else:
totSam1, fp, LeConsReject, constrRej1, constrRej2, objVal2 = totSam1_prev, fp_prev, LeConsReject_prev, constrRej1_prev, constrRej2_prev, objVal2_prev
totSam2, fn, TrConsReject, constrRej3, constrRej4, objVal1 = totSam2_prev, fn_prev, TrConsReject_prev, constrRej3_prev, constrRej4_prev, objVal1_prev
totSam2_prev, fn_prev, TrConsReject_prev, constrRej3_prev, constrRej4_prev, objVal1_prev = totSam2, fn, TrConsReject, constrRej3, constrRej4, objVal1
totSam1_prev, fp_prev, LeConsReject_prev, constrRej1_prev, constrRej2_prev, objVal2_prev = totSam1, fp, LeConsReject, constrRej1, constrRej2, objVal2
bounds_prev = bounds_learned
row = []
row.extend([num_nurses])
def get_next_state_and_cost(self,s,u):
cost = 0
r_curr = self.r.copy()
pos = s[0:2]
res = s[2]
s_next = s.copy()
#extract current action
a = np.argmax(self.get_action(u))
# print "current state: " + str(s)
# print "action: " + str(a)
# print "current resources: " + str(r_curr)
if a < 4:
#movement action
# print "movement action"
next_pos = self.m[a] + pos
if next_pos[0] < 0 or next_pos[0] > 6 or next_pos[1] < 0 or next_pos[1] > 6:
#wall collision
return s_next,self.collision_with_wall_cost
else:
s_next[0:2] = next_pos
if res:
cost = self.travel_with_resource_cost
else:
cost = self.travel_empty_cost
return s_next,cost
else:
# print "pick/place action"
if a == 4:
#pick action
if res:
#trying to pick when full
return s_next,self.pick_when_full_cost
else:
#check if picked in resource position
if any(np.array_equal(pos, k) for k in self.r):
# print "picked up resource"
#remove resource from r list
for i,k in enumerate(self.r):
if np.array_equal(pos, k):
r_curr = np.delete(r_curr,i,axis=0)
break
s_next[3:] = np.zeros(4)
for i,k in enumerate(r_curr):
if np.array_equal(self.r_perm[0], k):
s_next[3] = 1
elif np.array_equal(self.r_perm[1], k):
s_next[4] = 1
elif np.array_equal(self.r_perm[2], k):
s_next[5] = 1
elif np.array_equal(self.r_perm[3], k):
s_next[6] = 1
else:
pass
cost = self.pick_when_empty_cost
s_next[2] = 1
else:
# print "dud pick"
s_next[2] = 0
cost = self.pick_dud_cost
self.r = r_curr
return s_next,cost
else:
#drop action
if res:
#drop carrying resource
if np.array_equal(pos, self.home):
# print "dropped in home position - congrats"
res = 0
cost = self.drop_success_cost
s_next[2] = 0
else:
# print "dropped at wrong spot"
res = 0
#add resource to new spot
r_curr = np.append(r_curr,[pos],axis=0)
# print r_curr
self.r = r_curr
cost = self.drop_wrong_spot_cost
s_next[2] = 0
return s_next,cost
else:
# print "dropped nothing"
cost = self.drop_nothing_cost
return s_next,cost
def animate(self,i):
#offset
self.ax.clear()
xo,yo = 0.5,0.5
borders_x = [0,0,7,7,0]
borders_y = [0,7,7,0,0]
r = self.r_sim.copy()
s = self.sim_result[:,i]
robot_x_empty = []
robot_y_empty = []
robot_x_full = []
robot_y_full = []
#robot1
if self.s_prev[2] == 0 and s[2] == 1:
#robot 1 picked up resource
for j,k in enumerate(self.r_sim):
if np.array_equal(s[0:2], k):
r = np.delete(r,j,axis=0)
break
robot_x_full.append(s[0]+ xo)
robot_y_full.append(s[1]+ yo)
elif self.s_prev[2] == 1 and s[2] == 0:
#robot1 dropped resource
r = np.append(r,[s[0:2]],axis=0)
robot_x_empty.append(s[0]+ xo)
robot_y_empty.append(s[1]+ yo)
elif self.s_prev[2] == 1 and s[2] == 1:
robot_x_full.append(s[0]+ xo)
robot_y_full.append(s[1]+ yo)
elif self.s_prev[2] == 0 and s[2] == 0:
robot_x_empty.append(s[0]+ xo)
robot_y_empty.append(s[1]+ yo)
self.s_prev = s.copy()
self.r_sim = r.copy()
r = r.T
xr = r[0] + xo
yr = r[1] + yo
xh,yh = 3+xo,3+yo
#text overlays
t1 = "Step: " + str(i+1)
t2 = "Robot Position(x,y): (" + str(s[0]) + "," + str(s[1]) + ")"
if s[2]:
t3 = "Robot carrying resource - TRUE"
else:
t3 = "Robot carrying resource - FALSE"
a = int(self.sim_control[i])
if a == 0:
t4 = "Next action - up"
elif a == 1:
t4 = "Next action - down"
elif a == 1:
t4 = "Next action - left"
elif a == 1:
t4 = "Next action - right"
elif a == 1:
t4 = "Next action - pick"
else:
t4 = "Next action - drop"
self.ax.text(0.2, 6.7, t1, ha='left', wrap=True)
self.ax.text(0.2, 6.5, t2, ha='left', wrap=True)
self.ax.text(0.2, 6.3, t3, ha='left', wrap=True)
self.ax.text(0.2, 6.1, t4, ha='left', wrap=True)
self.ax.scatter(robot_x_full,robot_y_full,color='red',marker ='o',s=10**2.5,alpha = 0.2)
self.ax.scatter(robot_x_empty,robot_y_empty,color='green',marker ='o',s=10**2.5,alpha = 0.2)
self.ax.scatter(xh,yh,color='blue',marker ='s',s=10**3,alpha = 0.2)
self.ax.scatter(xr,yr,color='red',alpha = 0.2)
self.ax.plot(borders_x,borders_y)
def eq(x: Union[np.ndarray, list, tuple], y: Union[np.ndarray, list, tuple]) -> bool:
"""
Determines x and y have the same shape and elements.
If x or y is a list or tuple, we convert to numpy arrays.
"""
return np.array_equal(np.array(x), np.array(y))
def equals(x: Union[np.ndarray, list, tuple], y: Union[np.ndarray, list, tuple]) -> bool:
"""
Functional equivalent to eq without infix.
"""
return np.array_equal(np.array(x), np.array(y))
def set_probes(self,
probe_or_probegroup,
group_mode='by_probe',
in_place=False):
"""
Attach a Probe to a recording.
For this Probe.device_channel_indices is used to link contacts to recording channels.
If some contacts of the Probe are not connected (device_channel_indices=-1)
then the recording is "sliced" and only connected channel are kept.
The probe order is not kept. Channel ids are re-ordered to match the channel_ids of the recording.
Parameters
----------
probe_or_probegroup: Probe, list of Probe, or ProbeGroup
The probe(s) to be attached to the recording
group_mode: str
'by_probe' or 'by_shank'. Adds grouping property to the recording based on the probes ('by_probe')
or shanks ('by_shanks')
in_place: bool
False by default.
Useful internally when extractor do self.set_probegroup(probe)
Returns
-------
sub_recording: BaseRecording
A view of the recording (ChannelSliceRecording or clone or itself)
"""
from spikeinterface import ChannelSliceRecording
assert group_mode in (
'by_probe',
'by_shank'), "'group_mode' can be 'by_probe' or 'by_shank'"
# handle several input possibilities
if isinstance(probe_or_probegroup, Probe):
probegroup = ProbeGroup()
probegroup.add_probe(probe_or_probegroup)
elif isinstance(probe_or_probegroup, ProbeGroup):
probegroup = probe_or_probegroup
elif isinstance(probe_or_probegroup, list):
assert all([isinstance(e, Probe) for e in probe_or_probegroup])
probegroup = ProbeGroup()
for probe in probe_or_probegroup:
probegroup.add_probe(probe)
else:
raise ValueError('must give Probe or ProbeGroup or list of Probe')
# handle not connected channels
assert all(probe.device_channel_indices is not None for probe in probegroup.probes), \
'Probe must have device_channel_indices'
# this is a vector with complex fileds (dataframe like) that handle all contact attr
arr = probegroup.to_numpy(complete=True)
# keep only connected contact ( != -1)
keep = arr['device_channel_indices'] >= 0
if np.any(~keep):
warn(
'The given probes have unconnected contacts: they are removed')
arr = arr[keep]
inds = arr['device_channel_indices']
order = np.argsort(inds)
inds = inds[order]
# check
if np.max(inds) >= self.get_num_channels():
raise ValueError(
'The given Probe have "device_channel_indices" that do not match channel count'
)
new_channel_ids = self.get_channel_ids()[inds]
arr = arr[order]
arr['device_channel_indices'] = np.arange(arr.size, dtype='int64')
# create recording : channel slice or clone or self
if in_place:
if not np.array_equal(new_channel_ids, self.get_channel_ids()):
raise Exception(
'set_proce(inplace=True) must have all channel indices')
sub_recording = self
else:
if np.array_equal(new_channel_ids, self.get_channel_ids()):
sub_recording = self.clone()
else:
sub_recording = ChannelSliceRecording(self, new_channel_ids)
# create a vector that handle all conatcts in property
sub_recording.set_property('contact_vector', arr, ids=None)
# planar_contour is saved in annotations
for probe_index, probe in enumerate(probegroup.probes):
contour = probe.probe_planar_contour
if contour is not None:
sub_recording.set_annotation(
f'probe_{probe_index}_planar_contour',
contour,
overwrite=True)
# duplicate positions to "locations" property
ndim = probegroup.ndim
locations = np.zeros((arr.size, ndim), dtype='float64')
for i, dim in enumerate(['x', 'y', 'z'][:ndim]):
locations[:, i] = arr[dim]
sub_recording.set_property('location', locations, ids=None)
# handle groups
groups = np.zeros(arr.size, dtype='int64')
if group_mode == 'by_probe':
for group, probe_index in enumerate(np.unique(arr['probe_index'])):
mask = arr['probe_index'] == probe_index
groups[mask] = group
elif group_mode == 'by_shank':
assert all(probe.shank_ids is not None for probe in probegroup.probes), \
'shank_ids is None in probe, you cannot group by shank'
for group, a in enumerate(
np.unique(arr[['probe_index', 'shank_ids']])):
mask = (arr['probe_index'] == a['probe_index']) & (
arr['shank_ids'] == a['shank_ids'])
groups[mask] = group
sub_recording.set_property('group', groups, ids=None)
return sub_recording
def evaluate(sess, enqueue_op, image_paths_placeholder, labels_placeholder,
phase_train_placeholder, batch_size_placeholder,
control_placeholder, embeddings, labels, image_paths,
actual_issame, batch_size, nrof_folds, log_dir, step,
summary_writer, stat, epoch, distance_metric, subtract_mean,
use_flipped_images, use_fixed_image_standardization):
start_time = time.time()
# Run forward pass to calculate embeddings
print('Runnning forward pass on LFW images')
# Enqueue one epoch of image paths and labels
nrof_embeddings = len(
actual_issame) * 2 # nrof_pairs * nrof_images_per_pair
nrof_flips = 2 if use_flipped_images else 1
nrof_images = nrof_embeddings * nrof_flips
labels_array = np.expand_dims(np.arange(0, nrof_images), 1)
image_paths_array = np.expand_dims(
np.repeat(np.array(image_paths), nrof_flips), 1)
control_array = np.zeros_like(labels_array, np.int32)
if use_fixed_image_standardization:
control_array += np.ones_like(
labels_array) * facenet.FIXED_STANDARDIZATION
if use_flipped_images:
# Flip every second image
control_array += (labels_array % 2) * facenet.FLIP
sess.run(
enqueue_op, {
image_paths_placeholder: image_paths_array,
labels_placeholder: labels_array,
control_placeholder: control_array
})
embedding_size = int(embeddings.get_shape()[1])
assert nrof_images % batch_size == 0, 'The number of LFW images must be an integer multiple of the LFW batch size'
nrof_batches = nrof_images // batch_size
emb_array = np.zeros((nrof_images, embedding_size))
lab_array = np.zeros((nrof_images, ))
for i in range(nrof_batches):
feed_dict = {
phase_train_placeholder: False,
batch_size_placeholder: batch_size
}
emb, lab = sess.run([embeddings, labels], feed_dict=feed_dict)
lab_array[lab] = lab
emb_array[lab, :] = emb
if i % 10 == 9:
print('.', end='')
sys.stdout.flush()
print('')
embeddings = np.zeros((nrof_embeddings, embedding_size * nrof_flips))
if use_flipped_images:
# Concatenate embeddings for flipped and non flipped version of the images
embeddings[:, :embedding_size] = emb_array[0::2, :]
embeddings[:, embedding_size:] = emb_array[1::2, :]
else:
embeddings = emb_array
assert np.array_equal(
lab_array, np.arange(nrof_images)
) == True, 'Wrong labels used for evaluation, possibly caused by training examples left in the input pipeline'
_, _, accuracy, val, val_std, far = lfw.evaluate(
embeddings,
actual_issame,
nrof_folds=nrof_folds,
distance_metric=distance_metric,
subtract_mean=subtract_mean)
print('Accuracy: %2.5f+-%2.5f' % (np.mean(accuracy), np.std(accuracy)))
print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))
lfw_time = time.time() - start_time
# Add validation loss and accuracy to summary
summary = tf.Summary()
#pylint: disable=maybe-no-member
summary.value.add(tag='lfw/accuracy', simple_value=np.mean(accuracy))
summary.value.add(tag='lfw/val_rate', simple_value=val)
summary.value.add(tag='time/lfw', simple_value=lfw_time)
summary_writer.add_summary(summary, step)
with open(os.path.join(log_dir, 'lfw_result.txt'), 'at') as f:
f.write('%d\t%.5f\t%.5f\n' % (step, np.mean(accuracy), val))
stat['lfw_accuracy'][epoch - 1] = np.mean(accuracy)
stat['lfw_valrate'][epoch - 1] = val
def test_augmentations_wont_change_float_input(augmentation_cls, params,
float_image):
float_image_copy = float_image.copy()
aug = augmentation_cls(p=1, **params)
aug(image=float_image)
assert np.array_equal(float_image, float_image_copy)
def find_storm_objects(
all_storm_ids, all_times_unix_sec, storm_ids_to_keep,
times_to_keep_unix_sec, allow_missing=False):
"""Finds storm objects.
N = total number of storm objects
n = number of storm objects to keep
:param all_storm_ids: length-N list of storm IDs (strings).
:param all_times_unix_sec: length-N numpy array of valid times.
:param storm_ids_to_keep: length-n list of storm IDs (strings).
:param times_to_keep_unix_sec: length-n numpy array of valid times.
:param allow_missing: Boolean flag. If True, this method will allow storm
objects to be missing (i.e., some objects defined by `storm_ids_to_keep`
and `storm_ids_to_keep` may not be present in `all_storm_ids` and
`all_times_unix_sec`). If False, this method will error out if it finds
missing objects.
:return: relevant_indices: length-n numpy array of indices.
[all_storm_ids[k] for k in relevant_indices] = storm_ids_to_keep
all_times_unix_sec[relevant_indices] = times_to_keep_unix_sec
:raises: ValueError: if `all_storm_ids` and `all_times_unix_sec` contain any
duplicate pairs.
:raises: ValueError: if any desired storm object is not found.
"""
error_checking.assert_is_boolean(allow_missing)
error_checking.assert_is_numpy_array(
numpy.array(all_storm_ids), num_dimensions=1)
num_storm_objects_total = len(all_storm_ids)
error_checking.assert_is_numpy_array(
all_times_unix_sec,
exact_dimensions=numpy.array([num_storm_objects_total]))
error_checking.assert_is_numpy_array(
numpy.array(storm_ids_to_keep), num_dimensions=1)
num_storm_objects_to_keep = len(storm_ids_to_keep)
error_checking.assert_is_numpy_array(
times_to_keep_unix_sec,
exact_dimensions=numpy.array([num_storm_objects_to_keep]))
all_object_ids = [
'{0:s}_{1:d}'.format(all_storm_ids[i], all_times_unix_sec[i])
for i in range(num_storm_objects_total)]
object_ids_to_keep = [
'{0:s}_{1:d}'.format(storm_ids_to_keep[i],
times_to_keep_unix_sec[i])
for i in range(num_storm_objects_to_keep)]
this_num_unique = len(set(all_object_ids))
if this_num_unique != len(all_object_ids):
error_string = (
'Only {0:d} of {1:d} original storm objects are unique.'
).format(this_num_unique, len(all_object_ids))
raise ValueError(error_string)
all_object_ids_numpy = numpy.array(all_object_ids, dtype='object')
object_ids_to_keep_numpy = numpy.array(object_ids_to_keep, dtype='object')
sort_indices = numpy.argsort(all_object_ids_numpy)
relevant_indices = numpy.searchsorted(
all_object_ids_numpy[sort_indices], object_ids_to_keep_numpy,
side='left'
).astype(int)
relevant_indices[relevant_indices < 0] = 0
relevant_indices[
relevant_indices >= len(all_object_ids_numpy)
] = len(all_object_ids_numpy) - 1
relevant_indices = sort_indices[relevant_indices]
if allow_missing:
bad_indices = numpy.where(
all_object_ids_numpy[relevant_indices] != object_ids_to_keep_numpy
)[0]
relevant_indices[bad_indices] = -1
return relevant_indices
if not numpy.array_equal(all_object_ids_numpy[relevant_indices],
object_ids_to_keep_numpy):
missing_object_flags = (
all_object_ids_numpy[relevant_indices] != object_ids_to_keep_numpy)
error_string = (
'{0:d} of {1:d} desired storm objects are missing. Their ID-time '
'pairs are listed below.\n{2:s}'
).format(numpy.sum(missing_object_flags), num_storm_objects_to_keep,
str(object_ids_to_keep_numpy[missing_object_flags]))
raise ValueError(error_string)
return relevant_indices
def fit_decomposition_method(self, training_set_inputs, training_set_outputs, test_set_inputs, test_set_outputs, max_number_of_training_iterations = 1000, verbose = False):
print()
print("Optimizing the neural network...")
# Reinitializing the number of function and gradient evaluations
self.NbrFuncEval = 0
self.NbrGradEval = 0
self.NormGradAtOptimalPoint = 0
# Setting the number of inputs per neuron in the hidden layer
self.number_of_inputs_per_neuron = training_set_inputs.shape[1]
# Getting the training set size
P = len(training_set_inputs)
# Getting the number of neurons in the hidden layer
N = self.hidden_layer_sizes
# Getting the value of the regularization parameter rho to use on the global error computation
rho = self.rho
#getting the value of the spread in the activation function sigma
sigma = self.sigma
#getting the value of the solver
solver = self.solver
# Input data
X = training_set_inputs
# Output data
Y = training_set_outputs
# Preparing the initial guesses for the parameters to be minimized
self.__random_start()
# Computing the initial output on training data
self.result_initial_output_train = self.predict(training_set_inputs)
start_time = time.time()
# Starting the two-block decomposition method optimization of the MLP
# Layer1 to layer2 weights
v = np.asfarray(self.synaptic_weights_output_layer).flatten()
# Inputs to layer1 weights
w = np.asfarray(self.synaptic_weights_hidden_layer)
# Layer1 noises
b = list(np.asfarray(self.noises).flatten())
result_outputs_test = self.predict(test_set_inputs)
test_error = self.get_error(result_outputs_test, test_set_outputs)
if (verbose):
print ()
print("Initial v: ", v)
print()
print("Initial w: ", w)
print()
print("Initial b: ", b)
print()
print("Initial test error: ", test_error)
i = 1
flag = 1
nfe = 0
nje = 0
err_count = 0
best_error_and_iteration = [float("inf"), 0]
while (flag and (i <= 100)):
if (verbose):
print()
print("Iteration ", i)
# Step1: minimization with respect to v
optimized1 = minimize(self.__error_extreme, v, args=(w, b, X, Y, N, P, sigma, rho), method = solver, options=dict({'maxiter':max_number_of_training_iterations}))
nfe += optimized1.nfev
nje += optimized1.njev
new_v = optimized1.x
omega = np.asarray(b + list(np.asfarray(w).flatten()))
# Step2: minimization with respect to c
optimized2 = minimize(self.__error_extreme2, omega, args=(new_v, X, Y, N, P, sigma, rho), method = solver, options=dict({'maxiter':max_number_of_training_iterations}))
nfe += optimized2.nfev
nje += optimized2.njev
result = optimized2.x
# Update the model's weights but befor doing so we save them
#in order to set them back in case we are dealing with an increase in weights
new_w = result[self.hidden_layer_sizes:].reshape(self.number_of_inputs_per_neuron, self.hidden_layer_sizes)
new_b = result[:self.hidden_layer_sizes]
old_w = self.synaptic_weights_hidden_layer
old_v = self.synaptic_weights_output_layer
old_b = self.noises
self.synaptic_weights_hidden_layer = new_w
self.synaptic_weights_output_layer = new_v
self.noises = new_b
# Computing the new error and comparing with the old one. we update the weights if and only if we get an improvment in error
result_outputs_test = self.predict(test_set_inputs)
new_test_error = self.get_error(result_outputs_test, test_set_outputs)
if (verbose):
print ()
print(" new v: ", new_v)
print()
print(" new w: ", new_w)
print()
print(" new w: ", new_b)
print()
print(" new test error: ", new_test_error)
# If the error increses or stayed constant for more than 4 times stop training: Early stopping method.
if ((np.array_equal(new_v, v) and np.array_equal(new_w, w) and np.array_equal(new_b, b)) or err_count == 3):
flag = 0
# Increment this counter if the error increases or stays constant
if (new_test_error >= test_error):
err_count += 1
# Reset this counter to 0 if the error decreases
else:
err_count = 0
# Decide if the actual situation is better than the previous or not
if (new_test_error < best_error_and_iteration[0]):
best_error_and_iteration[0] = new_test_error
best_error_and_iteration[1] = i
# If it is not, put back the old weights
else:
self.synaptic_weights_hidden_layer = old_w
self.synaptic_weights_output_layer = old_v
self.noises = old_b
v = new_v
w = new_w
b = list(new_b.flatten())
test_error = new_test_error
i += 1
if (verbose):
print ()
print ()
print ("Best computed error: ", best_error_and_iteration[0])
print ()
print ("Best computed error iteration: ", best_error_and_iteration[1])
print ()
self.training_time = time.time() - start_time
self.NbrFuncEval = nfe
self.NbrGradEval = nje
self.NormGradAtOptimalPoint = linalg.norm(optimized2.jac)
self.NbrOuterIter = i-1
def test_imgaug_image_only_augmentations(augmentation_cls, image, mask):
aug = augmentation_cls(p=1)
data = aug(image=image, mask=mask)
assert data["image"].dtype == np.uint8
assert data["mask"].dtype == np.uint8
assert np.array_equal(data["mask"], mask)
def compare(array1, array2, name="output"):
diffs = np.where(array1 - array2 != 0)[0]
print(f"indexes of {name}: {len(diffs)} differences: {diffs}")
assert(np.array_equal(array1, array2))
def test_empty_input(self):
audio = np.array([])
aug = naa.MaskAug(sampling_rate=44100)
augmented_audio = aug.augment(audio)
self.assertTrue(np.array_equal(audio, augmented_audio))
def om_validate3A(data_3A, predK, mask=None):
"""Constructs a matrix that describes the relationship between the clusters
:param data_3A: inputted data for subchallenge 3A
:param predK: number of clusters
:param mask: mask used
:return:
"""
# read in the data
data_3A = data_3A.split('\n')
data_3A = filter(None, data_3A)
if len(data_3A) != predK:
raise ValidationError("Input file contains a different number of lines (%d) than expected (%d)")
data_3A = [x.split('\t') for x in data_3A]
for i in range(len(data_3A)):
if len(data_3A[i]) != 2:
raise ValidationError("Number of tab separated columns in line %d is not 2" % (i+1))
try:
data_3A[i][0] = int(data_3A[i][0])
data_3A[i][1] = int(data_3A[i][1])
except ValueError:
raise ValidationError("Entry in line %d could not be cast as integer" % (i+1))
if [x[0] for x in data_3A] != range(1, predK+1):
raise ValidationError("First column must have %d entries in acending order starting with 1" % predK)
for i in range(len(data_3A)):
if data_3A[i][1] not in set(range(predK+1)):
raise ValidationError("Parent node label in line %d is not valid." % (i+1))
# Since cluster zero is not included in file
ad_cluster = np.zeros((len(data_3A)+1, len(data_3A)+1), dtype=int)
# file starts at one
for i in range(len(data_3A)):
ad_cluster[data_3A[i][1]][data_3A[i][0]] = 1
# fill in a matrix which tells you whether or not one cluster is a descendant of another
for i in range(len(data_3A)+1):
for j in range(len(data_3A)+1):
if (ad_cluster[j][i] == 1):
for k in range(len(data_3A)+1):
if(ad_cluster[k][j] == 1):
ad_cluster[k][i] = 1
if(ad_cluster[i][k] == 1):
ad_cluster[j][k] = 1
# check if all nodes are connected. If there are not, we could possibly run the above code again
if (not np.array_equal(np.nonzero(ad_cluster[0])[0], map(lambda x: x+1, range(len(data_3A))))):
for i in range(len(data_3A)+1):
for j in range(len(data_3A)+1):
if (ad_cluster[j][i] == 1):
for k in range(len(data_3A)+1):
if(ad_cluster[k][j] == 1):
ad_cluster[k][i] = 1
if(ad_cluster[i][k] == 1):
ad_cluster[j][k] = 1
if (not np.array_equal(np.nonzero(ad_cluster[0])[0], map(lambda x: x+1, range(len(data_3A))))):
raise ValidationError("Root of phylogeny not ancestor of all clusters / Tree is not connected.")
# print ad_cluster
ad_cluster = np.delete(ad_cluster, 0, 0)
ad_cluster = np.delete(ad_cluster, 0, 1)
return ad_cluster
def run_pipe_with_loader_ts(cache_loc=None):
steps = []
# Loader - transform (5, 2) to (5, 8)
# as each DataFile contains np.zeros((2, 2))
file_mapping = get_fake_mapping(100)
loader = BPtLoader(estimator=Identity(),
inds=[0, 1],
file_mapping=file_mapping,
n_jobs=1,
fix_n_jobs=False,
cache_loc=None)
steps.append(('loader', loader))
# Add transformer to ones
# input here should be (5, 8) of real val, original
# inds of 0 should work on half
to_ones = ToFixedTransformer(to=1)
st = ScopeTransformer(estimator=to_ones, inds=[0])
steps.append(('to_ones', st))
# Add basic linear regression model
# Original inds should work on all
model = BPtModel(estimator=LinearRegression(), inds=[0, 1])
param_dists = {'estimator__fit_intercept': Choice([True, False])}
search_model = NevergradSearchCV(estimator=model,
ps=get_param_search(),
param_distributions=param_dists)
steps.append(('model', search_model))
# Create pipe
pipe = BPtPipeline(steps=steps,
cache_loc=cache_loc)
X = np.arange(100).reshape((50, 2))
y = np.ones(50)
pipe.fit(X, y, fit_index=np.arange(50))
# Make sure fit worked correctly
assert pipe[0].n_features_in_ == 2
assert pipe[1].n_features_in_ == 8
assert pipe[1].estimator_.n_features_in_ == 4
assert len(pipe.mapping_[0]) == 4
assert len(pipe.mapping_[1]) == 4
assert 7 in pipe.mapping_[1]
# Make sure reverse transform works
X_df = pd.DataFrame(X)
X_trans = pipe.transform_df(X_df)
assert X_trans.shape == (50, 8)
assert X_trans.loc[4, '1_3'] == 9
assert X_trans.loc[1, '1_2'] == 3
assert X_trans.loc[4, '0_0'] == 1
assert X_trans.loc[0, '0_0'] == 1
# Make sure predict works,
# seems safe to assume model
# can learn to predict 1's
# as all targets are 1's.
# but may need to change?
preds = pipe.predict(X)
assert np.all(preds > .99)
# Check bpt pipeline coef attribute
assert np.array_equal(pipe[-1].best_estimator_.coef_,
pipe.coef_)
# Clean fake file mapping
clean_fake_mapping(100)
return pipe
def compare_images(expected, actual, tol, in_decorator=False):
"""
Compare two "image" files checking differences within a tolerance.
The two given filenames may point to files which are convertible to
PNG via the `.converter` dictionary. The underlying RMS is calculated
with the `.calculate_rms` function.
Parameters
----------
expected : str
The filename of the expected image.
actual : str
The filename of the actual image.
tol : float
The tolerance (a color value difference, where 255 is the
maximal difference). The test fails if the average pixel
difference is greater than this value.
in_decorator : bool
Determines the output format. If called from image_comparison
decorator, this should be True. (default=False)
Returns
-------
comparison_result : None or dict or str
Return *None* if the images are equal within the given tolerance.
If the images differ, the return value depends on *in_decorator*.
If *in_decorator* is true, a dict with the following entries is
returned:
- *rms*: The RMS of the image difference.
- *expected*: The filename of the expected image.
- *actual*: The filename of the actual image.
- *diff_image*: The filename of the difference image.
- *tol*: The comparison tolerance.
Otherwise, a human-readable multi-line string representation of this
information is returned.
Examples
--------
::
img1 = "./baseline/plot.png"
img2 = "./output/plot.png"
compare_images(img1, img2, 0.001)
"""
from matplotlib import _png
if not os.path.exists(actual):
raise Exception("Output image %s does not exist." % actual)
if os.stat(actual).st_size == 0:
raise Exception("Output image file %s is empty." % actual)
# Convert the image to png
extension = expected.split('.')[-1]
if not os.path.exists(expected):
raise IOError('Baseline image %r does not exist.' % expected)
if extension != 'png':
actual = convert(actual, False)
expected = convert(expected, True)
# open the image files and remove the alpha channel (if it exists)
expected_image = _png.read_png_int(expected)
actual_image = _png.read_png_int(actual)
expected_image = expected_image[:, :, :3]
actual_image = actual_image[:, :, :3]
actual_image, expected_image = crop_to_same(actual, actual_image, expected,
expected_image)
diff_image = make_test_filename(actual, 'failed-diff')
if tol <= 0:
if np.array_equal(expected_image, actual_image):
return None
# convert to signed integers, so that the images can be subtracted without
# overflow
expected_image = expected_image.astype(np.int16)
actual_image = actual_image.astype(np.int16)
rms = calculate_rms(expected_image, actual_image)
if rms <= tol:
return None
save_diff_image(expected, actual, diff_image)
results = dict(rms=rms,
expected=str(expected),
actual=str(actual),
diff=str(diff_image),
tol=tol)
if not in_decorator:
# Then the results should be a string suitable for stdout.
template = [
'Error: Image files did not match.',
'RMS Value: {rms}',
'Expected: \n {expected}',
'Actual: \n {actual}',
'Difference:\n {diff}',
'Tolerance: \n {tol}',
]
results = '\n '.join([line.format(**results) for line in template])
return results
"""Validate that the output produced by integration testing on 'label-maker package' matches our expectations"""
import numpy as np
data = np.load('integration/data.npz')
# validate our image data with sums and shapes
assert np.sum(data['x_train']) == 144752757
assert np.sum(data['x_test']) == 52758414
assert data['x_train'].shape == (6, 256, 256, 3)
assert data['x_test'].shape == (2, 256, 256, 3)
# validate our label data with exact matches
expected_y_train = np.array(
[[0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 1],
[0, 1, 1, 0, 0, 0, 1],
[0, 0, 0, 0, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 1]]
)
assert np.array_equal(data['y_train'], expected_y_train)
expected_y_test = np.array(
[[0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 1]]
)
assert np.array_equal(data['y_test'], expected_y_test)
def testCalculateFirstIntentStepRatio(self, goalList, groundTruthRatio):
# pass
firstIntentStepRatio = calculateFirstIntentStepRatio(goalList)
truthValue = np.array_equal(firstIntentStepRatio, groundTruthRatio)
self.assertTrue(truthValue)
def arreq_in_list(myarr, list_arrays):
# funtion to check if the myarr exists in the liust_arraus
return next((True for elem in list_arrays if np.array_equal(elem, myarr)), False)
def testCalculateFinalGoal(self, bean1GridX, bean1GridY, trajectory,
groundTruthAnswer):
# pass
finalGoal = calculateFinalGoal(bean1GridX, bean1GridY, trajectory)
truthValue = np.array_equal(finalGoal, groundTruthAnswer)
self.assertTrue(truthValue)
def maybe_cast_to_integer_array(arr, dtype, copy=False):
"""
Takes any dtype and returns the casted version, raising for when data is
incompatible with integer/unsigned integer dtypes.
.. versionadded:: 0.24.0
Parameters
----------
arr : array-like
The array to cast.
dtype : str, np.dtype
The integer dtype to cast the array to.
copy: boolean, default False
Whether to make a copy of the array before returning.
Returns
-------
int_arr : ndarray
An array of integer or unsigned integer dtype
Raises
------
OverflowError : the dtype is incompatible with the data
ValueError : loss of precision has occurred during casting
Examples
--------
If you try to coerce negative values to unsigned integers, it raises:
>>> Series([-1], dtype="uint64")
Traceback (most recent call last):
...
OverflowError: Trying to coerce negative values to unsigned integers
Also, if you try to coerce float values to integers, it raises:
>>> Series([1, 2, 3.5], dtype="int64")
Traceback (most recent call last):
...
ValueError: Trying to coerce float values to integers
"""
try:
if not hasattr(arr, "astype"):
casted = np.array(arr, dtype=dtype, copy=copy)
else:
casted = arr.astype(dtype, copy=copy)
except OverflowError:
raise OverflowError("The elements provided in the data cannot all be "
"casted to the dtype {dtype}".format(dtype=dtype))
if np.array_equal(arr, casted):
return casted
# We do this casting to allow for proper
# data and dtype checking.
#
# We didn't do this earlier because NumPy
# doesn't handle `uint64` correctly.
arr = np.asarray(arr)
if is_unsigned_integer_dtype(dtype) and (arr < 0).any():
raise OverflowError("Trying to coerce negative values "
"to unsigned integers")
if is_integer_dtype(dtype) and (is_float_dtype(arr) or
is_object_dtype(arr)):
raise ValueError("Trying to coerce float values to integers")
# %timeit triads_classification_tree()
# Compatibility with several conventions
triad_order_bct = 3 + np.array([1, 0, 2, 5, 3, 4, 6, 10, 7, 8, 9, 11, 12
]) # j.neuroimage.2009.10.003
triad_order_egger2014 = 3 + np.array(
[12, 6, 11, 8, 9, 10, 3, 7, 0, 4, 5, 1, 2]) # fnana.2014.00129
triad_order_nn4576 = 3 + np.arange(13) # nn.4576
def index_all(elements, array):
return np.array([np.where(array == x)[0][0] for x in elements])
conv_triad_order_nn4576_to_bct = index_all(triad_order_bct, triad_order_nn4576)
assert np.array_equal(triad_order_nn4576[conv_triad_order_nn4576_to_bct],
triad_order_bct)
conv_triad_order_nn4576_to_egger2014 = index_all(triad_order_egger2014,
triad_order_nn4576)
assert np.array_equal(triad_order_nn4576[conv_triad_order_nn4576_to_egger2014],
triad_order_egger2014)
def identify_triad_node_roles():
triads = triad_patterns()
node_roles = []
for i in range(len(triads)):
triad = triads[i]
triad_node_roles = [0, 1, 2]
if np.array_equal(triad, triad[np.ix_([1, 0, 2], [1, 0, 2])]):
def testCalculateFirstIntentGoalAccord(self, goalList, groundTruthAnswer):
# pass
isFirstIntentGoalAccord = calculateFirstIntentGoalAccord(goalList)
truthValue = np.array_equal(isFirstIntentGoalAccord, groundTruthAnswer)
self.assertTrue(truthValue)
obpStream = ioWrite.Open('HeatMap2D_py.bp', adios2.Mode.Write)
obpStream.Put(varTemperature, temperatures)
obpStream.Close()
if rank == 0:
# ADIOS2 read
ioRead = adios.DeclareIO("ioReader")
ibpStream = ioRead.Open('HeatMap2D_py.bp', adios2.Mode.Read, MPI.COMM_SELF)
var_inTemperature = ioRead.InquireVariable("temperature2D")
if(var_inTemperature is False):
raise ValueError('var_inTemperature is False')
assert var_inTemperature is not None
readOffset = [2, 2]
readSize = [4, 4]
var_inTemperature.SetSelection([readOffset, readSize])
inTemperatures = np.zeros(readSize, dtype=np.int)
ibpStream.Get(var_inTemperature, inTemperatures, adios2.Mode.Sync)
ibpStream.Close()
# print('Incoming temperature map\n', inTemperatures)
expected = np.array([[22, 23, 24, 25],
[32, 33, 34, 35],
[42, 43, 44, 45],
[52, 53, 54, 55]], np.int)
assert np.array_equal(inTemperatures, expected)
def testCalculateGoalCommit(self, goalList, groundTruthAnswer):
# pass
isGoalCommit = calculateGoalCommit(goalList)
truthValue = np.array_equal(isGoalCommit, groundTruthAnswer)
self.assertTrue(truthValue)