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COSMIT no spaces around power `**`. Fun: https://gist.github.com/1671995

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This commit is contained in:
Andreas Mueller
2012-06-29 11:27:23 +02:00
parent 1251f77d6a
commit f7c56202d0
19 changed files with 47 additions and 47 deletions
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skimage/feature/greycomatrix.py
+6 -6
View File
@@ -181,11 +181,11 @@ def greycoprops(P, prop='contrast'):
# create weights for specified property
I, J = np.ogrid[0:num_level, 0:num_level]
if prop == 'contrast':
weights = (I - J) ** 2
weights = (I - J)**2
elif prop == 'dissimilarity':
weights = np.abs(I - J)
elif prop == 'homogeneity':
weights = 1. / (1. + (I - J) ** 2)
weights = 1. / (1. + (I - J)**2)
elif prop in ['ASM', 'energy', 'correlation']:
pass
else:
@@ -193,10 +193,10 @@ def greycoprops(P, prop='contrast'):
# compute property for each GLCM
if prop == 'energy':
asm = np.apply_over_axes(np.sum, (P ** 2), axes=(0, 1))[0, 0]
asm = np.apply_over_axes(np.sum, (P**2), axes=(0, 1))[0, 0]
results = np.sqrt(asm)
elif prop == 'ASM':
results = np.apply_over_axes(np.sum, (P ** 2), axes=(0, 1))[0, 0]
results = np.apply_over_axes(np.sum, (P**2), axes=(0, 1))[0, 0]
elif prop == 'correlation':
results = np.zeros((num_dist, num_angle), dtype=np.float64)
I = np.array(range(num_level)).reshape((num_level, 1, 1, 1))
@@ -204,9 +204,9 @@ def greycoprops(P, prop='contrast'):
diff_i = I - np.apply_over_axes(np.sum, (I * P), axes=(0, 1))[0, 0]
diff_j = J - np.apply_over_axes(np.sum, (J * P), axes=(0, 1))[0, 0]
std_i = np.sqrt(np.apply_over_axes(np.sum, (P * (diff_i) ** 2),
std_i = np.sqrt(np.apply_over_axes(np.sum, (P * (diff_i)**2),
axes=(0, 1))[0, 0])
std_j = np.sqrt(np.apply_over_axes(np.sum, (P * (diff_j) ** 2),
std_j = np.sqrt(np.apply_over_axes(np.sum, (P * (diff_j)**2),
axes=(0, 1))[0, 0])
cov = np.apply_over_axes(np.sum, (P * (diff_i * diff_j)),
axes=(0, 1))[0, 0]
skimage/feature/harris.py
+1 -1
View File
@@ -42,7 +42,7 @@ def _compute_harris_response(image, eps=1e-6, gaussian_deviation=1):
Wyy = ndimage.gaussian_filter(imy * imy, 1.5, mode='constant')
# determinant and trace
Wdet = Wxx * Wyy - Wxy ** 2
Wdet = Wxx * Wyy - Wxy**2
Wtr = Wxx + Wyy
# Alternate formula for Harris response.
# Alison Noble, "Descriptions of Image Surfaces", PhD thesis (1989)
skimage/feature/hog.py
+2 -2
View File
@@ -95,7 +95,7 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8),
cell are used to vote into the orientation histogram.
"""
magnitude = sqrt(gx ** 2 + gy ** 2)
magnitude = sqrt(gx**2 + gy**2)
orientation = arctan2(gy, (gx + 1e-15)) * (180 / pi) + 90
sy, sx = image.shape
@@ -166,7 +166,7 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8),
for y in range(n_blocksy):
block = orientation_histogram[y:y + by, x:x + bx, :]
eps = 1e-5
normalised_blocks[y, x, :] = block / sqrt(block.sum() ** 2 + eps)
normalised_blocks[y, x, :] = block / sqrt(block.sum()**2 + eps)
"""
The final step collects the HOG descriptors from all blocks of a dense
skimage/filter/edges.py
+2 -2
View File
@@ -37,7 +37,7 @@ def sobel(image, mask=None):
Note that ``scipy.ndimage.sobel`` returns a directional Sobel which
has to be further processed to perform edge detection.
"""
return np.sqrt(hsobel(image, mask) ** 2 + vsobel(image, mask) ** 2)
return np.sqrt(hsobel(image, mask)**2 + vsobel(image, mask)**2)
def hsobel(image, mask=None):
@@ -137,7 +137,7 @@ def prewitt(image, mask=None):
Return the square root of the sum of squares of the horizontal
and vertical Prewitt transforms.
"""
return np.sqrt(hprewitt(image, mask) ** 2 + vprewitt(image, mask) ** 2)
return np.sqrt(hprewitt(image, mask)**2 + vprewitt(image, mask)**2)
def hprewitt(image, mask=None):
skimage/filter/lpi_filter.py
+1 -1
View File
@@ -232,7 +232,7 @@ def wiener(data, impulse_response=None, filter_params={}, K=0.25,
F, G = filt._prepare(data)
_min_limit(F)
H_mag_sqr = np.abs(F) ** 2
H_mag_sqr = np.abs(F)**2
F = 1 / F * H_mag_sqr / (H_mag_sqr + K)
return _centre(np.abs(ifftshift(np.dual.ifftn(G * F))), data.shape)
skimage/filter/tests/test_tv_denoise.py
+3 -3
View File
@@ -27,8 +27,8 @@ class TestTvDenoise():
grad_denoised = ndimage.morphological_gradient(
denoised_lena, size=((3, 3)))
# test if the total variation has decreased
assert (np.sqrt((grad_denoised ** 2).sum())
< np.sqrt((grad ** 2).sum()) / 2)
assert (np.sqrt((grad_denoised**2).sum())
< np.sqrt((grad**2).sum()) / 2)
denoised_lena_int = filter.tv_denoise(img_as_uint(lena),
weight=60.0, keep_type=True)
assert denoised_lena_int.dtype is np.dtype('uint16')
@@ -39,7 +39,7 @@ class TestTvDenoise():
a sphere.
"""
x, y, z = np.ogrid[0:40, 0:40, 0:40]
mask = (x - 22) ** 2 + (y - 20) ** 2 + (z - 17) ** 2 < 8 ** 2
mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2
mask = 100 * mask.astype(np.float)
mask += 60
mask += 20 * np.random.randn(*mask.shape)
skimage/filter/thresholding.py
+1 -1
View File
@@ -127,7 +127,7 @@ def threshold_otsu(image, nbins=256):
# Clip ends to align class 1 and class 2 variables:
# The last value of `weight1`/`mean1` should pair with zero values in
# `weight2`/`mean2`, which do not exist.
variance12 = weight1[:-1] * weight2[1:] * (mean1[:-1] - mean2[1:]) ** 2
variance12 = weight1[:-1] * weight2[1:] * (mean1[:-1] - mean2[1:])**2
idx = np.argmax(variance12)
threshold = bin_centers[:-1][idx]
skimage/filter/tv_denoise.py
+4 -4
View File
@@ -56,12 +56,12 @@ def _tv_denoise_3d(im, weight=100, eps=2.e-4, n_iter_max=200):
d[:, :, 1:] += pz[:, :, :-1]
out = im + d
E = (d ** 2).sum()
E = (d**2).sum()
gx[:-1] = np.diff(out, axis=0)
gy[:, :-1] = np.diff(out, axis=1)
gz[:, :, :-1] = np.diff(out, axis=2)
norm = np.sqrt(gx ** 2 + gy ** 2 + gz ** 2)
norm = np.sqrt(gx**2 + gy**2 + gz**2)
E += weight * norm.sum()
norm *= 0.5 / weight
norm += 1.
@@ -147,10 +147,10 @@ def _tv_denoise_2d(im, weight=50, eps=2.e-4, n_iter_max=200):
d[:, 1:] += py[:, :-1]
out = im + d
E = (d ** 2).sum()
E = (d**2).sum()
gx[:-1] = np.diff(out, axis=0)
gy[:, :-1] = np.diff(out, axis=1)
norm = np.sqrt(gx ** 2 + gy ** 2)
norm = np.sqrt(gx**2 + gy**2)
E += weight * norm.sum()
norm *= 0.5 / weight
norm += 1
skimage/measure/_regionprops.py
+2 -2
View File
@@ -210,8 +210,8 @@ def regionprops(label_image, properties=['Area', 'Centroid'],
b = mu[1, 1] / mu[0, 0]
c = mu[0, 2] / mu[0, 0]
#: eigen values of inertia tensor
l1 = (a + c) / 2 + sqrt(4 * b ** 2 + (a - c) ** 2) / 2
l2 = (a + c) / 2 - sqrt(4 * b ** 2 + (a - c) ** 2) / 2
l1 = (a + c) / 2 + sqrt(4 * b**2 + (a - c)**2) / 2
l2 = (a + c) / 2 - sqrt(4 * b**2 + (a - c)**2) / 2
# cached results which are used by several properties
_filled_image = None
skimage/measure/tests/test_find_contours.py
+1 -1
View File
@@ -17,7 +17,7 @@ a[1, 1:-1] = 0
## [ 1., 1., 1., 1., 1., 1., 1., 1.]], dtype=float32)
x, y = np.mgrid[-1:1:5j, -1:1:5j]
r = np.sqrt(x ** 2 + y ** 2)
r = np.sqrt(x**2 + y**2)
def test_binary():
skimage/morphology/selem.py
+2 -2
View File
@@ -111,6 +111,6 @@ def disk(radius, dtype=np.uint8):
"""
L = np.linspace(-radius, radius, 2 * radius + 1)
(X, Y) = np.meshgrid(L, L)
s = X ** 2
s += Y ** 2
s = X**2
s += Y**2
return np.array(s <= radius * radius, dtype=dtype)
skimage/morphology/skeletonize.py
+5 -5
View File
@@ -244,11 +244,11 @@ def medial_axis(image, mask=None, return_distance=False):
# OR
# 3. Keep if # pixels in neighbourhood is 2 or less
# Note that table is independent of image
center_is_foreground = (np.arange(512) & 2 ** 4).astype(bool)
center_is_foreground = (np.arange(512) & 2**4).astype(bool)
table = (center_is_foreground # condition 1.
&
(np.array([ndimage.label(_pattern_of(index), _eight_connect)[1] !=
ndimage.label(_pattern_of(index & ~ 2 ** 4),
ndimage.label(_pattern_of(index & ~ 2**4),
_eight_connect)[1]
for index in range(512)]) # condition 2
|
@@ -311,9 +311,9 @@ def _pattern_of(index):
Return the pattern represented by an index value
Byte decomposition of index
"""
return np.array([[index & 2 ** 0, index & 2 ** 1, index & 2 ** 2],
[index & 2 ** 3, index & 2 ** 4, index & 2 ** 5],
[index & 2 ** 6, index & 2 ** 7, index & 2 ** 8]], bool)
return np.array([[index & 2**0, index & 2**1, index & 2**2],
[index & 2**3, index & 2**4, index & 2**5],
[index & 2**6, index & 2**7, index & 2**8]], bool)
def _table_lookup(image, table):
skimage/morphology/tests/test_skeletonize.py
+2 -2
View File
@@ -80,8 +80,8 @@ class TestSkeletonize():
# foreground object 3
ir, ic = np.indices(image.shape)
circle1 = (ic - 135) ** 2 + (ir - 150) ** 2 < 30 ** 2
circle2 = (ic - 135) ** 2 + (ir - 150) ** 2 < 20 ** 2
circle1 = (ic - 135)**2 + (ir - 150)**2 < 30**2
circle2 = (ic - 135)**2 + (ir - 150)**2 < 20**2
image[circle1] = 1
image[circle2] = 0
result = skeletonize(image)
skimage/morphology/tests/test_watershed.py
+1 -1
View File
@@ -72,7 +72,7 @@ def diff(a, b):
a = a.astype(np.float64)
b = np.asarray(b)
b = b.astype(np.float64)
t = ((a - b) ** 2).sum()
t = ((a - b)**2).sum()
return math.sqrt(t)
skimage/segmentation/random_walker_segmentation.py
+1 -1
View File
@@ -63,7 +63,7 @@ def _make_graph_edges_3d(n_x, n_y, n_z):
def _compute_weights_3d(data, beta=130, eps=1.e-6):
gradients = _compute_gradients_3d(data) ** 2
gradients = _compute_gradients_3d(data)**2
beta /= 10 * data.std()
gradients *= beta
weights = np.exp(- gradients)
skimage/transform/_warp_zoo.py
+1 -1
View File
@@ -7,7 +7,7 @@ from ._warp import warp
def _swirl_mapping(xy, center, rotation, strength, radius):
x, y = xy.T
x0, y0 = center
rho = np.sqrt((x - x0) ** 2 + (y - y0) ** 2)
rho = np.sqrt((x - x0)**2 + (y - y0)**2)
# Ensure that the transformation decays to approximately 1/1000-th
# within the specified radius.
skimage/transform/radon_transform.py
+3 -3
View File
@@ -43,7 +43,7 @@ def radon(image, theta=None):
if theta == None:
theta = np.arange(180)
height, width = image.shape
diagonal = np.sqrt(height ** 2 + width ** 2)
diagonal = np.sqrt(height**2 + width**2)
heightpad = np.ceil(diagonal - height)
widthpad = np.ceil(diagonal - width)
padded_image = np.zeros((int(height + heightpad),
@@ -130,13 +130,13 @@ def iradon(radon_image, theta=None, output_size=None,
th = (np.pi / 180.0) * theta
# if output size not specified, estimate from input radon image
if not output_size:
output_size = int(np.floor(np.sqrt((radon_image.shape[0]) ** 2 / 2.0)))
output_size = int(np.floor(np.sqrt((radon_image.shape[0])**2 / 2.0)))
n = radon_image.shape[0]
img = radon_image.copy()
# resize image to next power of two for fourier analysis
# speeds up fourier and lessens artifacts
order = max(64., 2 ** np.ceil(np.log(2 * n) / np.log(2)))
order = max(64., 2**np.ceil(np.log(2 * n) / np.log(2)))
# zero pad input image
img.resize((order, img.shape[1]))
# construct the fourier filter
skimage/util/dtype.py
+8 -8
View File
@@ -109,21 +109,21 @@ def convert(image, dtype, force_copy=False, uniform=False):
prec_loss()
if copy:
b = np.empty(a.shape, _dtype2(kind, m))
np.floor_divide(a, 2 ** (n - m), out=b, dtype=a.dtype,
np.floor_divide(a, 2**(n - m), out=b, dtype=a.dtype,
casting='unsafe')
return b
else:
a //= 2 ** (n - m)
a //= 2**(n - m)
return a
elif m % n == 0:
# exact upscale to a multiple of n bits
if copy:
b = np.empty(a.shape, _dtype2(kind, m))
np.multiply(a, (2 ** m - 1) // (2 ** n - 1), out=b, dtype=b.dtype)
np.multiply(a, (2**m - 1) // (2**n - 1), out=b, dtype=b.dtype)
return b
else:
a = np.array(a, _dtype2(kind, m, a.dtype.itemsize), copy=False)
a *= (2 ** m - 1) // (2 ** n - 1)
a *= (2**m - 1) // (2**n - 1)
return a
else:
# upscale to a multiple of n bits,
@@ -132,13 +132,13 @@ def convert(image, dtype, force_copy=False, uniform=False):
o = (m // n + 1) * n
if copy:
b = np.empty(a.shape, _dtype2(kind, o))
np.multiply(a, (2 ** o - 1) // (2 ** n - 1), out=b, dtype=b.dtype)
b //= 2 ** (o - m)
np.multiply(a, (2**o - 1) // (2**n - 1), out=b, dtype=b.dtype)
b //= 2**(o - m)
return b
else:
a = np.array(a, _dtype2(kind, o, a.dtype.itemsize), copy=False)
a *= (2 ** o - 1) // (2 ** n - 1)
a //= 2 ** (o - m)
a *= (2**o - 1) // (2**n - 1)
a //= 2**(o - m)
return a
kind = dtypeobj.kind
skimage/util/montage.py
+1 -1
View File
@@ -84,7 +84,7 @@ def montage2d(arr_in, fill='mean', rescale_intensity=False):
if fill == 'mean':
fill = arr_in.mean()
n_missing = int((alpha ** 2.) - n_images)
n_missing = int((alpha**2.) - n_images)
missing = np.ones((n_missing, height, width), dtype=arr_in.dtype) * fill
arr_out = np.vstack((arr_in, missing))