_id stringlengths 2 7 | title stringlengths 1 88 | partition stringclasses 3
values | text stringlengths 75 19.8k | language stringclasses 1
value | meta_information dict |
|---|---|---|---|---|---|
q17700 | ParallelRunner.launch_simulation | train | def launch_simulation(self, parameter):
"""
Launch a single simulation, using SimulationRunner's facilities.
This function is used by ParallelRunner's run_simulations to map
simulation running over the parameter list.
Args:
parameter (dict): the parameter combinatio... | python | {
"resource": ""
} |
q17701 | JsonHelper.stringify | train | def stringify(self, obj, beautify=False, raise_exception=False):
"""Alias of helper.string.serialization.json.stringify"""
return self.helper.string.serialization.json.stringify(
obj=obj,
beautify=beautify,
raise_exception=raise_exception) | python | {
"resource": ""
} |
q17702 | JsonHelper.parse | train | def parse(self, text, encoding='utf8', raise_exception=False):
"""Alias of helper.string.serialization.json.parse"""
return self.helper.string.serialization.json.parse(
text=text,
encoding=encoding,
raise_exception=raise_exception) | python | {
"resource": ""
} |
q17703 | DatabaseManager.new | train | def new(cls, script, commit, params, campaign_dir, overwrite=False):
"""
Initialize a new class instance with a set configuration and filename.
The created database has the same name of the campaign directory.
Args:
script (str): the ns-3 name of the script that will be use... | python | {
"resource": ""
} |
q17704 | DatabaseManager.load | train | def load(cls, campaign_dir):
"""
Initialize from an existing database.
It is assumed that the database json file has the same name as its
containing folder.
Args:
campaign_dir (str): The path to the campaign directory.
"""
# We only accept absolute ... | python | {
"resource": ""
} |
q17705 | DatabaseManager.get_next_rngruns | train | def get_next_rngruns(self):
"""
Yield the next RngRun values that can be used in this campaign.
"""
available_runs = [result['params']['RngRun'] for result in
self.get_results()]
yield from DatabaseManager.get_next_values(available_runs) | python | {
"resource": ""
} |
q17706 | DatabaseManager.insert_result | train | def insert_result(self, result):
"""
Insert a new result in the database.
This function also verifies that the result dictionaries saved in the
database have the following structure (with {'a': 1} representing a
dictionary, 'a' a key and 1 its value)::
{
... | python | {
"resource": ""
} |
q17707 | DatabaseManager.get_results | train | def get_results(self, params=None, result_id=None):
"""
Return all the results available from the database that fulfill some
parameter combinations.
If params is None (or not specified), return all results.
If params is specified, it must be a dictionary specifying the result
... | python | {
"resource": ""
} |
q17708 | DatabaseManager.wipe_results | train | def wipe_results(self):
"""
Remove all results from the database.
This also removes all output files, and cannot be undone.
"""
# Clean results table
self.db.purge_table('results')
# Get rid of contents of data dir
map(shutil.rmtree, glob.glob(os.path.jo... | python | {
"resource": ""
} |
q17709 | DatabaseManager.get_all_values_of_all_params | train | def get_all_values_of_all_params(self):
"""
Return a dictionary containing all values that are taken by all
available parameters.
Always returns the parameter list in alphabetical order.
"""
values = collections.OrderedDict([[p, []] for p in
... | python | {
"resource": ""
} |
q17710 | SerializationHelper.serialize | train | def serialize(self, obj, method='json', beautify=False, raise_exception=False):
"""Alias of helper.string.serialization.serialize"""
return self.helper.string.serialization.serialize(
obj=obj, method=method, beautify=beautify, raise_exception=raise_exception) | python | {
"resource": ""
} |
q17711 | SerializationHelper.deserialize | train | def deserialize(self, text, method='json', encoding='utf8', raise_exception=False):
"""Alias of helper.string.serialization.deserialize"""
return self.helper.string.serialization.deserialize(
text, method=method, encoding=encoding, raise_exception=raise_exception) | python | {
"resource": ""
} |
q17712 | GridRunner.run_program | train | def run_program(self, command, working_directory=os.getcwd(),
environment=None, cleanup_files=True,
native_spec="-l cputype=intel"):
"""
Run a program through the grid, capturing the standard output.
"""
try:
s = drmaa.Session()
... | python | {
"resource": ""
} |
q17713 | adjacent | train | def adjacent(labels):
'''Return a binary mask of all pixels which are adjacent to a pixel of
a different label.
'''
high = labels.max()+1
if high > np.iinfo(labels.dtype).max:
labels = labels.astype(np.int)
image_with_high_background = labels.copy()
image_with_high_backgr... | python | {
"resource": ""
} |
q17714 | binary_thin | train | def binary_thin(image, strel1, strel2):
"""Morphologically thin an image
strel1 - the required values of the pixels in order to survive
strel2 - at each pixel, the complement of strel1 if we care about the value
"""
hit_or_miss = scind.binary_hit_or_miss(image, strel1, strel2)
return np.logical_... | python | {
"resource": ""
} |
q17715 | strel_disk | train | def strel_disk(radius):
"""Create a disk structuring element for morphological operations
radius - radius of the disk
"""
iradius = int(radius)
x,y = np.mgrid[-iradius:iradius+1,-iradius:iradius+1]
radius2 = radius * radius
strel = np.zeros(x.shape)
strel[x*x+y*y <= radius2] =... | python | {
"resource": ""
} |
q17716 | strel_octagon | train | def strel_octagon(radius):
"""Create an octagonal structuring element for morphological operations
radius - the distance from the origin to each edge of the octagon
"""
#
# Inscribe a diamond in a square to get an octagon.
#
iradius = int(radius)
i, j = np.mgrid[-iradius:(iradius + ... | python | {
"resource": ""
} |
q17717 | strel_pair | train | def strel_pair(x, y):
"""Create a structing element composed of the origin and another pixel
x, y - x and y offsets of the other pixel
returns a structuring element
"""
x_center = int(np.abs(x))
y_center = int(np.abs(y))
result = np.zeros((y_center * 2 + 1, x_center * 2 + 1), bool... | python | {
"resource": ""
} |
q17718 | cpmaximum | train | def cpmaximum(image, structure=np.ones((3,3),dtype=bool),offset=None):
"""Find the local maximum at each point in the image, using the given structuring element
image - a 2-d array of doubles
structure - a boolean structuring element indicating which
local elements should be sampled
... | python | {
"resource": ""
} |
q17719 | convex_hull_image | train | def convex_hull_image(image):
'''Given a binary image, return an image of the convex hull'''
labels = image.astype(int)
points, counts = convex_hull(labels, np.array([1]))
output = np.zeros(image.shape, int)
for i in range(counts[0]):
inext = (i+1) % counts[0]
draw_line(output, point... | python | {
"resource": ""
} |
q17720 | triangle_areas | train | def triangle_areas(p1,p2,p3):
"""Compute an array of triangle areas given three arrays of triangle pts
p1,p2,p3 - three Nx2 arrays of points
"""
v1 = (p2 - p1).astype(np.float)
v2 = (p3 - p1).astype(np.float)
# Original:
# cross1 = v1[:,1] * v2[:,0]
# cross2 = v2[:,1] * v1[:,0]
... | python | {
"resource": ""
} |
q17721 | minimum_distance2 | train | def minimum_distance2(hull_a, center_a, hull_b, center_b):
'''Return the minimum distance or 0 if overlap between 2 convex hulls
hull_a - list of points in clockwise direction
center_a - a point within the hull
hull_b - list of points in clockwise direction
center_b - a point within the hull
... | python | {
"resource": ""
} |
q17722 | slow_minimum_distance2 | train | def slow_minimum_distance2(hull_a, hull_b):
'''Do the minimum distance by exhaustive examination of all points'''
d2_min = np.iinfo(int).max
for a in hull_a:
if within_hull(a, hull_b):
return 0
for b in hull_b:
if within_hull(b, hull_a):
return 0
for pt_a in h... | python | {
"resource": ""
} |
q17723 | lines_intersect | train | def lines_intersect(pt1_p, pt2_p, pt1_q, pt2_q):
'''Return true if two line segments intersect
pt1_p, pt2_p - endpoints of first line segment
pt1_q, pt2_q - endpoints of second line segment
'''
#
# The idea here is to do the cross-product of the vector from
# point 1 to point 2 of one segmen... | python | {
"resource": ""
} |
q17724 | find_farthest | train | def find_farthest(point, hull):
'''Find the vertex in hull farthest away from a point'''
d_start = np.sum((point-hull[0,:])**2)
d_end = np.sum((point-hull[-1,:])**2)
if d_start > d_end:
# Go in the forward direction
i = 1
inc = 1
term = hull.shape[0]
d2_max = d_st... | python | {
"resource": ""
} |
q17725 | find_visible | train | def find_visible(hull, observer, background):
'''Given an observer location, find the first and last visible
points in the hull
The observer at "observer" is looking at the hull whose most distant
vertex from the observer is "background. Find the vertices that are
the furthest di... | python | {
"resource": ""
} |
q17726 | distance2_to_line | train | def distance2_to_line(pt, l0, l1):
'''The perpendicular distance squared from a point to a line
pt - point in question
l0 - one point on the line
l1 - another point on the line
'''
pt = np.atleast_1d(pt)
l0 = np.atleast_1d(l0)
l1 = np.atleast_1d(l1)
reshape = pt.ndim == 1
if... | python | {
"resource": ""
} |
q17727 | within_hull | train | def within_hull(point, hull):
'''Return true if the point is within the convex hull'''
h_prev_pt = hull[-1,:]
for h_pt in hull:
if np.cross(h_pt-h_prev_pt, point - h_pt) >= 0:
return False
h_prev_pt = h_pt
return True | python | {
"resource": ""
} |
q17728 | calculate_extents | train | def calculate_extents(labels, indexes):
"""Return the area of each object divided by the area of its bounding box"""
fix = fixup_scipy_ndimage_result
areas = fix(scind.sum(np.ones(labels.shape),labels,np.array(indexes, dtype=np.int32)))
y,x = np.mgrid[0:labels.shape[0],0:labels.shape[1]]
xmin = fix(... | python | {
"resource": ""
} |
q17729 | calculate_perimeters | train | def calculate_perimeters(labels, indexes):
"""Count the distances between adjacent pixels in the perimeters of the labels"""
#
# Create arrays that tell whether a pixel is like its neighbors.
# index = 0 is the pixel -1,-1 from the pixel of interest, 1 is -1,0, etc.
#
m = table_idx_from_labels(l... | python | {
"resource": ""
} |
q17730 | calculate_solidity | train | def calculate_solidity(labels,indexes=None):
"""Calculate the area of each label divided by the area of its convex hull
labels - a label matrix
indexes - the indexes of the labels to measure
"""
if indexes is not None:
""" Convert to compat 32bit integer """
indexes = np.array(i... | python | {
"resource": ""
} |
q17731 | white_tophat | train | def white_tophat(image, radius=None, mask=None, footprint=None):
'''White tophat filter an image using a circular structuring element
image - image in question
radius - radius of the circular structuring element. If no radius, use
an 8-connected structuring element.
mask - mask of sig... | python | {
"resource": ""
} |
q17732 | black_tophat | train | def black_tophat(image, radius=None, mask=None, footprint=None):
'''Black tophat filter an image using a circular structuring element
image - image in question
radius - radius of the circular structuring element. If no radius, use
an 8-connected structuring element.
mask - mask of sig... | python | {
"resource": ""
} |
q17733 | grey_erosion | train | def grey_erosion(image, radius=None, mask=None, footprint=None):
'''Perform a grey erosion with masking'''
if footprint is None:
if radius is None:
footprint = np.ones((3,3),bool)
radius = 1
else:
footprint = strel_disk(radius)==1
else:
radius = ma... | python | {
"resource": ""
} |
q17734 | opening | train | def opening(image, radius=None, mask=None, footprint=None):
'''Do a morphological opening
image - pixel image to operate on
radius - use a structuring element with the given radius. If no radius,
use an 8-connected structuring element.
mask - if present, only use unmasked pixels for op... | python | {
"resource": ""
} |
q17735 | closing | train | def closing(image, radius=None, mask=None, footprint = None):
'''Do a morphological closing
image - pixel image to operate on
radius - use a structuring element with the given radius. If no structuring
element, use an 8-connected structuring element.
mask - if present, only use unmaske... | python | {
"resource": ""
} |
q17736 | openlines | train | def openlines(image, linelength=10, dAngle=10, mask=None):
"""
Do a morphological opening along lines of different angles.
Return difference between max and min response to different angles for each pixel.
This effectively removes dots and only keeps lines.
image - pixel image to operate on
le... | python | {
"resource": ""
} |
q17737 | pattern_of | train | def pattern_of(index):
'''Return the pattern represented by an index value'''
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) | python | {
"resource": ""
} |
q17738 | index_of | train | def index_of(pattern):
'''Return the index of a given pattern'''
return (pattern[0,0] * 2**0 + pattern[0,1] * 2**1 + pattern[0,2] * 2**2 +
pattern[1,0] * 2**3 + pattern[1,1] * 2**4 + pattern[1,2] * 2**5 +
pattern[2,0] * 2**6 + pattern[2,1] * 2**7 + pattern[2,2] * 2**8) | python | {
"resource": ""
} |
q17739 | make_table | train | def make_table(value, pattern, care=np.ones((3,3),bool)):
'''Return a table suitable for table_lookup
value - set all table entries matching "pattern" to "value", all others
to not "value"
pattern - a 3x3 boolean array with the pattern to match
care - a 3x3 boolean array where each v... | python | {
"resource": ""
} |
q17740 | branchpoints | train | def branchpoints(image, mask=None):
'''Remove all pixels from an image except for branchpoints
image - a skeletonized image
mask - a mask of pixels excluded from consideration
1 0 1 ? 0 ?
0 1 0 -> 0 1 0
0 1 0 0 ? 0
'''
global branchpoints_table
if mask is None:
... | python | {
"resource": ""
} |
q17741 | branchings | train | def branchings(image, mask=None):
'''Count the number of branches eminating from each pixel
image - a binary image
mask - optional mask of pixels not to consider
This is the count of the number of branches that
eminate from a pixel. A pixel with neighbors fore
and aft has branches fore and... | python | {
"resource": ""
} |
q17742 | bridge | train | def bridge(image, mask=None, iterations = 1):
'''Fill in pixels that bridge gaps.
1 0 0 1 0 0
0 0 0 -> 0 1 0
0 0 1 0 0 1
'''
global bridge_table
if mask is None:
masked_image = image
else:
masked_image = image.astype(bool).copy()
masked_image[~mask] = F... | python | {
"resource": ""
} |
q17743 | clean | train | def clean(image, mask=None, iterations = 1):
'''Remove isolated pixels
0 0 0 0 0 0
0 1 0 -> 0 0 0
0 0 0 0 0 0
Border pixels and pixels adjoining masks are removed unless one valid
neighbor is true.
'''
global clean_table
if mask is None:
masked_image = imag... | python | {
"resource": ""
} |
q17744 | diag | train | def diag(image, mask=None, iterations=1):
'''4-connect pixels that are 8-connected
0 0 0 0 0 ?
0 0 1 -> 0 1 1
0 1 0 ? 1 ?
'''
global diag_table
if mask is None:
masked_image = image
else:
masked_image = image.astype(bool).copy()
masked_image[~ma... | python | {
"resource": ""
} |
q17745 | endpoints | train | def endpoints(image, mask=None):
'''Remove all pixels from an image except for endpoints
image - a skeletonized image
mask - a mask of pixels excluded from consideration
1 0 0 ? 0 0
0 1 0 -> 0 1 0
0 0 0 0 0 0
'''
global endpoints_table
if mask is None:
masked... | python | {
"resource": ""
} |
q17746 | fill | train | def fill(image, mask=None, iterations=1):
'''Fill isolated black pixels
1 1 1 1 1 1
1 0 1 -> 1 1 1
1 1 1 1 1 1
'''
global fill_table
if mask is None:
masked_image = image
else:
masked_image = image.astype(bool).copy()
masked_image[~mask] = True
r... | python | {
"resource": ""
} |
q17747 | fill4 | train | def fill4(image, mask=None, iterations=1):
'''Fill 4-connected black pixels
x 1 x x 1 x
1 0 1 -> 1 1 1
x 1 x x 1 x
'''
global fill4_table
if mask is None:
masked_image = image
else:
masked_image = image.astype(bool).copy()
masked_image[~mask] = True
... | python | {
"resource": ""
} |
q17748 | majority | train | def majority(image, mask=None, iterations=1):
'''A pixel takes the value of the majority of its neighbors
'''
global majority_table
if mask is None:
masked_image = image
else:
masked_image = image.astype(bool).copy()
masked_image[~mask] = False
result = table_lookup(... | python | {
"resource": ""
} |
q17749 | remove | train | def remove(image, mask=None, iterations=1):
'''Turn 1 pixels to 0 if their 4-connected neighbors are all 0
? 1 ? ? 1 ?
1 1 1 -> 1 0 1
? 1 ? ? 1 ?
'''
global remove_table
if mask is None:
masked_image = image
else:
masked_image = image.astype(bool).copy()
... | python | {
"resource": ""
} |
q17750 | spur | train | def spur(image, mask=None, iterations=1):
'''Remove spur pixels from an image
0 0 0 0 0 0
0 1 0 -> 0 0 0
0 0 1 0 0 ?
'''
global spur_table_1,spur_table_2
if mask is None:
masked_image = image
else:
masked_image = image.astype(bool).copy()
masked_image[~... | python | {
"resource": ""
} |
q17751 | thicken | train | def thicken(image, mask=None, iterations=1):
'''Thicken the objects in an image where doing so does not connect them
0 0 0 ? ? ?
0 0 0 -> ? 1 ?
0 0 1 ? ? ?
1 0 0 ? ? ?
0 0 0 -> ? 0 ?
0 0 1 ? ? ?
'''
global thicken_table
if mask is None:
masked_image ... | python | {
"resource": ""
} |
q17752 | distance_color_labels | train | def distance_color_labels(labels):
'''Recolor a labels matrix so that adjacent labels have distant numbers
'''
#
# Color labels so adjacent ones are most distant
#
colors = color_labels(labels, True)
#
# Order pixels by color, then label #
#
rlabels = labels.ravel()
orde... | python | {
"resource": ""
} |
q17753 | skeletonize | train | def skeletonize(image, mask=None, ordering = None):
'''Skeletonize the image
Take the distance transform.
Order the 1 points by the distance transform.
Remove a point if it has more than 1 neighbor and if removing it
does not change the Euler number.
image - the binary image to be skel... | python | {
"resource": ""
} |
q17754 | skeletonize_labels | train | def skeletonize_labels(labels):
'''Skeletonize a labels matrix'''
#
# The trick here is to separate touching labels by coloring the
# labels matrix and then processing each color separately
#
colors = color_labels(labels)
max_color = np.max(colors)
if max_color == 0:
return label... | python | {
"resource": ""
} |
q17755 | skeleton_length | train | def skeleton_length(labels, indices=None):
'''Compute the length of all skeleton branches for labeled skeletons
labels - a labels matrix
indices - the indexes of the labels to be measured. Default is all
returns an array of one skeleton length per label.
'''
global __skel_length_table
... | python | {
"resource": ""
} |
q17756 | distance_to_edge | train | def distance_to_edge(labels):
'''Compute the distance of a pixel to the edge of its object
labels - a labels matrix
returns a matrix of distances
'''
colors = color_labels(labels)
max_color = np.max(colors)
result = np.zeros(labels.shape)
if max_color == 0:
return resul... | python | {
"resource": ""
} |
q17757 | is_local_maximum | train | def is_local_maximum(image, labels, footprint):
'''Return a boolean array of points that are local maxima
image - intensity image
labels - find maxima only within labels. Zero is reserved for background.
footprint - binary mask indicating the neighborhood to be examined
must be a ma... | python | {
"resource": ""
} |
q17758 | is_obtuse | train | def is_obtuse(p1, v, p2):
'''Determine whether the angle, p1 - v - p2 is obtuse
p1 - N x 2 array of coordinates of first point on edge
v - N x 2 array of vertex coordinates
p2 - N x 2 array of coordinates of second point on edge
returns vector of booleans
'''
p1x = p1[:,1]
p1y ... | python | {
"resource": ""
} |
q17759 | stretch | train | def stretch(image, mask=None):
'''Normalize an image to make the minimum zero and maximum one
image - pixel data to be normalized
mask - optional mask of relevant pixels. None = don't mask
returns the stretched image
'''
image = np.array(image, float)
if np.product(image.shape) == 0:
... | python | {
"resource": ""
} |
q17760 | median_filter | train | def median_filter(data, mask, radius, percent=50):
'''Masked median filter with octagonal shape
data - array of data to be median filtered.
mask - mask of significant pixels in data
radius - the radius of a circle inscribed into the filtering octagon
percent - conceptually, order the significant pi... | python | {
"resource": ""
} |
q17761 | bilateral_filter | train | def bilateral_filter(image, mask, sigma_spatial, sigma_range,
sampling_spatial = None, sampling_range = None):
"""Bilateral filter of an image
image - image to be bilaterally filtered
mask - mask of significant points in image
sigma_spatial - standard deviation of the spatial Gaus... | python | {
"resource": ""
} |
q17762 | laplacian_of_gaussian | train | def laplacian_of_gaussian(image, mask, size, sigma):
'''Perform the Laplacian of Gaussian transform on the image
image - 2-d image array
mask - binary mask of significant pixels
size - length of side of square kernel to use
sigma - standard deviation of the Gaussian
'''
half_size = size//... | python | {
"resource": ""
} |
q17763 | roberts | train | def roberts(image, mask=None):
'''Find edges using the Roberts algorithm
image - the image to process
mask - mask of relevant points
The algorithm returns the magnitude of the output of the two Roberts
convolution kernels.
The following is the canonical citation for the algorithm:
L. Rob... | python | {
"resource": ""
} |
q17764 | sobel | train | def sobel(image, mask=None):
'''Calculate the absolute magnitude Sobel to find the edges
image - image to process
mask - mask of relevant points
Take the square root of the sum of the squares of the horizontal and
vertical Sobels to get a magnitude that's somewhat insensitive to
direction.
... | python | {
"resource": ""
} |
q17765 | prewitt | train | def prewitt(image, mask=None):
'''Find the edge magnitude using the Prewitt transform
image - image to process
mask - mask of relevant points
Return the square root of the sum of squares of the horizontal
and vertical Prewitt transforms.
'''
return np.sqrt(hprewitt(image,mask)**2 + vprewi... | python | {
"resource": ""
} |
q17766 | hprewitt | train | def hprewitt(image, mask=None):
'''Find the horizontal edges of an image using the Prewitt transform
image - image to process
mask - mask of relevant points
We use the following kernel and return the absolute value of the
result at each point:
1 1 1
0 0 0
-1 -1 -1
'''
... | python | {
"resource": ""
} |
q17767 | gabor | train | def gabor(image, labels, frequency, theta):
'''Gabor-filter the objects in an image
image - 2-d grayscale image to filter
labels - a similarly shaped labels matrix
frequency - cycles per trip around the circle
theta - angle of the filter. 0 to 2 pi
Calculate the Gabor filter centered on the ce... | python | {
"resource": ""
} |
q17768 | enhance_dark_holes | train | def enhance_dark_holes(image, min_radius, max_radius, mask=None):
'''Enhance dark holes using a rolling ball filter
image - grayscale 2-d image
radii - a vector of radii: we enhance holes at each given radius
'''
#
# Do 4-connected erosion
#
se = np.array([[False, True, False],
... | python | {
"resource": ""
} |
q17769 | granulometry_filter | train | def granulometry_filter(image, min_radius, max_radius, mask=None):
'''Enhances bright structures within a min and max radius using a rolling ball filter
image - grayscale 2-d image
radii - a vector of radii: we enhance holes at each given radius
'''
#
# Do 4-connected erosion
#
se = np.... | python | {
"resource": ""
} |
q17770 | velocity_kalman_model | train | def velocity_kalman_model():
'''Return a KalmanState set up to model objects with constant velocity
The observation and measurement vectors are i,j.
The state vector is i,j,vi,vj
'''
om = np.array([[1,0,0,0], [0, 1, 0, 0]])
tm = np.array([[1,0,1,0],
[0,1,0,1],
... | python | {
"resource": ""
} |
q17771 | reverse_velocity_kalman_model | train | def reverse_velocity_kalman_model():
'''Return a KalmanState set up to model going backwards in time'''
om = np.array([[1,0,0,0], [0, 1, 0, 0]])
tm = np.array([[1,0,-1,0],
[0,1,0,-1],
[0,0,1,0],
[0,0,0,1]])
return KalmanState(om, tm) | python | {
"resource": ""
} |
q17772 | line_integration | train | def line_integration(image, angle, decay, sigma):
'''Integrate the image along the given angle
DIC images are the directional derivative of the underlying
image. This filter reconstructs the original image by integrating
along that direction.
image - a 2-dimensional array
angle - shear angle ... | python | {
"resource": ""
} |
q17773 | variance_transform | train | def variance_transform(img, sigma, mask=None):
'''Calculate a weighted variance of the image
This function caluclates the variance of an image, weighting the
local contributions by a Gaussian.
img - image to be transformed
sigma - standard deviation of the Gaussian
mask - mask of relevant pixe... | python | {
"resource": ""
} |
q17774 | inv_n | train | def inv_n(x):
'''given N matrices, return N inverses'''
#
# The inverse of a small matrix (e.g. 3x3) is
#
# 1
# ----- C(j,i)
# det(A)
#
# where C(j,i) is the cofactor of matrix A at position j,i
#
assert x.ndim == 3
assert x.shape[1] == x.shape[2]
c = np.array([ [... | python | {
"resource": ""
} |
q17775 | det_n | train | def det_n(x):
'''given N matrices, return N determinants'''
assert x.ndim == 3
assert x.shape[1] == x.shape[2]
if x.shape[1] == 1:
return x[:,0,0]
result = np.zeros(x.shape[0])
for permutation in permutations(np.arange(x.shape[1])):
sign = parity(permutation)
result += np... | python | {
"resource": ""
} |
q17776 | parity | train | def parity(x):
'''The parity of a permutation
The parity of a permutation is even if the permutation can be
formed by an even number of transpositions and is odd otherwise.
The parity of a permutation is even if there are an even number of
compositions of even size and odd otherwise. A composition... | python | {
"resource": ""
} |
q17777 | dot_n | train | def dot_n(x, y):
'''given two tensors N x I x K and N x K x J return N dot products
If either x or y is 2-dimensional, broadcast it over all N.
Dot products are size N x I x J.
Example:
x = np.array([[[1,2], [3,4], [5,6]],[[7,8], [9,10],[11,12]]])
y = np.array([[[1,2,3], [4,5,6]],[[7,8,9],[10,... | python | {
"resource": ""
} |
q17778 | permutations | train | def permutations(x):
'''Given a listlike, x, return all permutations of x
Returns the permutations of x in the lexical order of their indices:
e.g.
>>> x = [ 1, 2, 3, 4 ]
>>> for p in permutations(x):
>>> print p
[ 1, 2, 3, 4 ]
[ 1, 2, 4, 3 ]
[ 1, 3, 2, 4 ]
[ 1, 3, 4, 2 ]
... | python | {
"resource": ""
} |
q17779 | circular_hough | train | def circular_hough(img, radius, nangles = None, mask=None):
'''Circular Hough transform of an image
img - image to be transformed.
radius - radius of circle
nangles - # of angles to measure, e.g. nangles = 4 means accumulate at
0, 90, 180 and 270 degrees.
Return the Hough transform... | python | {
"resource": ""
} |
q17780 | poisson_equation | train | def poisson_equation(image, gradient=1, max_iter=100, convergence=.01, percentile = 90.0):
'''Estimate the solution to the Poisson Equation
The Poisson Equation is the solution to gradient(x) = h^2/4 and, in this
context, we use a boundary condition where x is zero for background
pixels. Also, we set h... | python | {
"resource": ""
} |
q17781 | KalmanState.predicted_state_vec | train | def predicted_state_vec(self):
'''The predicted state vector for the next time point
From Welch eqn 1.9
'''
if not self.has_cached_predicted_state_vec:
self.p_state_vec = dot_n(
self.translation_matrix,
self.state_vec[:, :, np.newaxis])[:,:,0]... | python | {
"resource": ""
} |
q17782 | KalmanState.predicted_obs_vec | train | def predicted_obs_vec(self):
'''The predicted observation vector
The observation vector for the next step in the filter.
'''
if not self.has_cached_obs_vec:
self.obs_vec = dot_n(
self.observation_matrix,
self.predicted_state_vec[:,:,np.newaxis... | python | {
"resource": ""
} |
q17783 | KalmanState.map_frames | train | def map_frames(self, old_indices):
'''Rewrite the feature indexes based on the next frame's identities
old_indices - for each feature in the new frame, the index of the
old feature
'''
nfeatures = len(old_indices)
noldfeatures = len(self.state_vec)
... | python | {
"resource": ""
} |
q17784 | KalmanState.add_features | train | def add_features(self, kept_indices, new_indices,
new_state_vec, new_state_cov, new_noise_var):
'''Add new features to the state
kept_indices - the mapping from all indices in the state to new
indices in the new version
new_indices - the indices of t... | python | {
"resource": ""
} |
q17785 | KalmanState.deep_copy | train | def deep_copy(self):
'''Return a deep copy of the state'''
c = KalmanState(self.observation_matrix, self.translation_matrix)
c.state_vec = self.state_vec.copy()
c.state_cov = self.state_cov.copy()
c.noise_var = self.noise_var.copy()
c.state_noise = self.state_noise.copy()... | python | {
"resource": ""
} |
q17786 | spline_factors | train | def spline_factors(u):
'''u is np.array'''
X = np.array([(1.-u)**3 , 4-(6.*(u**2))+(3.*(u**3)) , 1.+(3.*u)+(3.*(u**2))-(3.*(u**3)) , u**3]) * (1./6)
return X | python | {
"resource": ""
} |
q17787 | gauss | train | def gauss(x,m_y,sigma):
'''returns the gaussian with mean m_y and std. dev. sigma,
calculated at the points of x.'''
e_y = [np.exp((1.0/(2*float(sigma)**2)*-(n-m_y)**2)) for n in np.array(x)]
y = [1.0/(float(sigma) * np.sqrt(2 * np.pi)) * e for e in e_y]
return np.array(y) | python | {
"resource": ""
} |
q17788 | d2gauss | train | def d2gauss(x,m_y,sigma):
'''returns the second derivative of the gaussian with mean m_y,
and standard deviation sigma, calculated at the points of x.'''
return gauss(x,m_y,sigma)*[-1/sigma**2 + (n-m_y)**2/sigma**4 for n in x] | python | {
"resource": ""
} |
q17789 | spline_matrix2d | train | def spline_matrix2d(x,y,px,py,mask=None):
'''For boundary constraints, the first two and last two spline pieces are constrained
to be part of the same cubic curve.'''
V = np.kron(spline_matrix(x,px),spline_matrix(y,py))
lenV = len(V)
if mask is not None:
indices = np.nonzero(mask.T.fla... | python | {
"resource": ""
} |
q17790 | otsu | train | def otsu(data, min_threshold=None, max_threshold=None,bins=256):
"""Compute a threshold using Otsu's method
data - an array of intensity values between zero and one
min_threshold - only consider thresholds above this minimum value
max_threshold - only consider thresholds below this maxi... | python | {
"resource": ""
} |
q17791 | entropy | train | def entropy(data, bins=256):
"""Compute a threshold using Ray's entropy measurement
data - an array of intensity values between zero and one
bins - we bin the data into this many equally-spaced bins, then pick
the bin index that optimizes the metric
"""
... | python | {
"resource": ""
} |
q17792 | otsu3 | train | def otsu3(data, min_threshold=None, max_threshold=None,bins=128):
"""Compute a threshold using a 3-category Otsu-like method
data - an array of intensity values between zero and one
min_threshold - only consider thresholds above this minimum value
max_threshold - only consider thres... | python | {
"resource": ""
} |
q17793 | outline | train | def outline(labels):
"""Given a label matrix, return a matrix of the outlines of the labeled objects
If a pixel is not zero and has at least one neighbor with a different
value, then it is part of the outline.
"""
output = numpy.zeros(labels.shape, labels.dtype)
lr_different = labels[1... | python | {
"resource": ""
} |
q17794 | euclidean_dist | train | def euclidean_dist(point1, point2):
"""Compute the Euclidean distance between two points.
Parameters
----------
point1, point2 : 2-tuples of float
The input points.
Returns
-------
d : float
The distance between the input points.
Examples
--------
>>> point1 = ... | python | {
"resource": ""
} |
q17795 | Trace.from_detections_assignment | train | def from_detections_assignment(detections_1, detections_2, assignments):
"""
Creates traces out of given assignment and cell data.
"""
traces = []
for d1n, d2n in six.iteritems(assignments):
# check if the match is between existing cells
if d1n < len(dete... | python | {
"resource": ""
} |
q17796 | NeighbourMovementTracking.run_tracking | train | def run_tracking(self, label_image_1, label_image_2):
"""
Tracks cells between input label images.
@returns: injective function from old objects to new objects (pairs of [old, new]). Number are compatible with labels.
"""
self.scale = self.parameters_tracking["avgCellDiameter"] ... | python | {
"resource": ""
} |
q17797 | NeighbourMovementTracking.is_cell_big | train | def is_cell_big(self, cell_detection):
"""
Check if the cell is considered big.
@param CellFeature cell_detection:
@return:
"""
return cell_detection.area > self.parameters_tracking["big_size"] * self.scale * self.scale | python | {
"resource": ""
} |
q17798 | NeighbourMovementTracking.calculate_basic_cost | train | def calculate_basic_cost(self, d1, d2):
"""
Calculates assignment cost between two cells.
"""
distance = euclidean_dist(d1.center, d2.center) / self.scale
area_change = 1 - min(d1.area, d2.area) / max(d1.area, d2.area)
return distance + self.parameters_cost_initial["are... | python | {
"resource": ""
} |
q17799 | NeighbourMovementTracking.calculate_localised_cost | train | def calculate_localised_cost(self, d1, d2, neighbours, motions):
"""
Calculates assignment cost between two cells taking into account the movement of cells neighbours.
:param CellFeatures d1: detection in first frame
:param CellFeatures d2: detection in second frame
"""
... | python | {
"resource": ""
} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.