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value | code stringlengths 51 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
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17,800 | CellProfiler/centrosome | centrosome/neighmovetrack.py | NeighbourMovementTracking.calculate_costs | def calculate_costs(self, detections_1, detections_2, calculate_match_cost, params):
"""
Calculates assignment costs between detections and 'empty' spaces. The smaller cost the better.
@param detections_1: cell list of size n in previous frame
@param detections_2: cell list of size m in current frame
@return: cost matrix (n+m)x(n+m) extended by cost of matching cells with emptiness
"""
global invalid_match
size_sum = len(detections_1) + len(detections_2)
# Cost matrix extended by matching cells with nothing
# (for detection 1 it means losing cells, for detection 2 it means new cells).
cost_matrix = numpy.zeros((size_sum, size_sum))
# lost cells cost
cost_matrix[0:len(detections_1), len(detections_2):size_sum] = params["default_empty_cost"] + (1 - numpy.eye(
len(detections_1), len(detections_1))) * invalid_match
# new cells cost
cost_matrix[len(detections_1):size_sum, 0:len(detections_2)] = params["default_empty_cost"] + (1 - numpy.eye(
len(detections_2), len(detections_2))) * invalid_match
# increase costs for reliable detections
for row in [i for i in range(0, len(detections_1)) if detections_1[i].is_reliable() and (
not params["check_if_big"] or self.is_cell_big(detections_1[i]))]:
cost_matrix[row, len(detections_2):size_sum] *= params["default_empty_reliable_cost_mult"]
for col in [i for i in range(0, len(detections_2)) if detections_2[i].is_reliable() and (
not params["check_if_big"] or self.is_cell_big(detections_2[i]))]:
cost_matrix[len(detections_1):size_sum, col] *= params["default_empty_reliable_cost_mult"]
# calculate cost of matching cells
def cost_if_not_too_far(detection_1, detection_2):
if detection_1.distance(detection_2) <= self.parameters_tracking["max_distance"]:
return calculate_match_cost(detection_1, detection_2)
else:
return invalid_match
cost_matrix[0:len(detections_1), 0:len(detections_2)] = [[cost_if_not_too_far(d1, d2) for d2 in detections_2] for d1 in detections_1]
return cost_matrix | python | def calculate_costs(self, detections_1, detections_2, calculate_match_cost, params):
global invalid_match
size_sum = len(detections_1) + len(detections_2)
# Cost matrix extended by matching cells with nothing
# (for detection 1 it means losing cells, for detection 2 it means new cells).
cost_matrix = numpy.zeros((size_sum, size_sum))
# lost cells cost
cost_matrix[0:len(detections_1), len(detections_2):size_sum] = params["default_empty_cost"] + (1 - numpy.eye(
len(detections_1), len(detections_1))) * invalid_match
# new cells cost
cost_matrix[len(detections_1):size_sum, 0:len(detections_2)] = params["default_empty_cost"] + (1 - numpy.eye(
len(detections_2), len(detections_2))) * invalid_match
# increase costs for reliable detections
for row in [i for i in range(0, len(detections_1)) if detections_1[i].is_reliable() and (
not params["check_if_big"] or self.is_cell_big(detections_1[i]))]:
cost_matrix[row, len(detections_2):size_sum] *= params["default_empty_reliable_cost_mult"]
for col in [i for i in range(0, len(detections_2)) if detections_2[i].is_reliable() and (
not params["check_if_big"] or self.is_cell_big(detections_2[i]))]:
cost_matrix[len(detections_1):size_sum, col] *= params["default_empty_reliable_cost_mult"]
# calculate cost of matching cells
def cost_if_not_too_far(detection_1, detection_2):
if detection_1.distance(detection_2) <= self.parameters_tracking["max_distance"]:
return calculate_match_cost(detection_1, detection_2)
else:
return invalid_match
cost_matrix[0:len(detections_1), 0:len(detections_2)] = [[cost_if_not_too_far(d1, d2) for d2 in detections_2] for d1 in detections_1]
return cost_matrix | [
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@param detections_1: cell list of size n in previous frame
@param detections_2: cell list of size m in current frame
@return: cost matrix (n+m)x(n+m) extended by cost of matching cells with emptiness | [
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17,801 | CellProfiler/centrosome | centrosome/neighmovetrack.py | NeighbourMovementTracking.solve_assignement | def solve_assignement(self, costs):
"""
Solves assignment problem using Hungarian implementation by Brian M. Clapper.
@param costs: square cost matrix
@return: assignment function
@rtype: int->int
"""
if costs is None or len(costs) == 0:
return dict()
n = costs.shape[0]
pairs = [(i, j) for i in range(0, n) for j in range(0, n) if costs[i, j] < invalid_match]
costs_list = [costs[i, j] for (i, j) in pairs]
assignment = lapjv.lapjv(list(zip(*pairs))[0], list(zip(*pairs))[1], costs_list)
indexes = enumerate(list(assignment[0]))
return dict([(row, col) for row, col in indexes]) | python | def solve_assignement(self, costs):
if costs is None or len(costs) == 0:
return dict()
n = costs.shape[0]
pairs = [(i, j) for i in range(0, n) for j in range(0, n) if costs[i, j] < invalid_match]
costs_list = [costs[i, j] for (i, j) in pairs]
assignment = lapjv.lapjv(list(zip(*pairs))[0], list(zip(*pairs))[1], costs_list)
indexes = enumerate(list(assignment[0]))
return dict([(row, col) for row, col in indexes]) | [
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17,802 | CellProfiler/centrosome | centrosome/smooth.py | circular_gaussian_kernel | def circular_gaussian_kernel(sd,radius):
"""Create a 2-d Gaussian convolution kernel
sd - standard deviation of the gaussian in pixels
radius - build a circular kernel that convolves all points in the circle
bounded by this radius
"""
i,j = np.mgrid[-radius:radius+1,-radius:radius+1].astype(float) / radius
mask = i**2 + j**2 <= 1
i = i * radius / sd
j = j * radius / sd
kernel = np.zeros((2*radius+1,2*radius+1))
kernel[mask] = np.e ** (-(i[mask]**2+j[mask]**2) /
(2 * sd **2))
#
# Normalize the kernel so that there is no net effect on a uniform image
#
kernel = kernel / np.sum(kernel)
return kernel | python | def circular_gaussian_kernel(sd,radius):
i,j = np.mgrid[-radius:radius+1,-radius:radius+1].astype(float) / radius
mask = i**2 + j**2 <= 1
i = i * radius / sd
j = j * radius / sd
kernel = np.zeros((2*radius+1,2*radius+1))
kernel[mask] = np.e ** (-(i[mask]**2+j[mask]**2) /
(2 * sd **2))
#
# Normalize the kernel so that there is no net effect on a uniform image
#
kernel = kernel / np.sum(kernel)
return kernel | [
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17,803 | CellProfiler/centrosome | centrosome/threshold.py | get_threshold | def get_threshold(threshold_method, threshold_modifier, image,
mask=None, labels = None,
threshold_range_min = None, threshold_range_max = None,
threshold_correction_factor = 1.0,
adaptive_window_size = 10, **kwargs):
"""Compute a threshold for an image
threshold_method - one of the TM_ methods above
threshold_modifier - TM_GLOBAL to calculate one threshold over entire image
TM_ADAPTIVE to calculate a per-pixel threshold
TM_PER_OBJECT to calculate a different threshold for
each object
image - a NxM numpy array of the image data
Returns a tuple of local_threshold and global_threshold where:
* global_threshold is the single number calculated using the threshold
method over the whole image
* local_threshold is the global_threshold for global methods. For adaptive
and per-object thresholding, local_threshold is a matrix of threshold
values representing the threshold to be applied at each pixel of the
image.
Different methods have optional and required parameters:
Required:
TM_PER_OBJECT:
labels - a labels matrix that defines the extents of the individual objects
to be thresholded separately.
Optional:
All:
mask - a mask of the significant pixels in the image
threshold_range_min, threshold_range_max - constrain the threshold
values to be examined to values between these limits
threshold_correction_factor - the calculated threshold is multiplied
by this number to get the final threshold
TM_MOG (mixture of Gaussians):
object_fraction - fraction of image expected to be occupied by objects
(pixels that are above the threshold)
TM_OTSU - We have algorithms derived from Otsu. There is a three-class
version of Otsu in addition to the two class. There is also
an entropy measure in addition to the weighted variance.
two_class_otsu - assume that the distribution represents
two intensity classes if true, three if false.
use_weighted_variance - use Otsu's weighted variance if true,
an entropy measure if false
assign_middle_to_foreground - assign pixels in the middle class
in a three-class Otsu to the foreground if true
or the background if false.
"""
global_threshold = get_global_threshold(
threshold_method, image, mask, **kwargs)
global_threshold *= threshold_correction_factor
if not threshold_range_min is None:
global_threshold = max(global_threshold, threshold_range_min)
if not threshold_range_max is None:
global_threshold = min(global_threshold, threshold_range_max)
if threshold_modifier == TM_GLOBAL:
local_threshold=global_threshold
elif threshold_modifier == TM_ADAPTIVE:
local_threshold = get_adaptive_threshold(
threshold_method, image, global_threshold,
mask, adaptive_window_size = adaptive_window_size, **kwargs)
local_threshold = local_threshold * threshold_correction_factor
elif threshold_modifier == TM_PER_OBJECT:
local_threshold = get_per_object_threshold(
threshold_method, image, global_threshold, mask, labels,
threshold_range_min, threshold_range_max,**kwargs)
local_threshold = local_threshold * threshold_correction_factor
else:
raise NotImplementedError("%s thresholding is not implemented"%(threshold_modifier))
if isinstance(local_threshold, np.ndarray):
#
# Constrain thresholds to within .7 to 1.5 of the global threshold.
#
threshold_range_min = max(threshold_range_min, global_threshold * .7)
threshold_range_max = min(threshold_range_max, global_threshold * 1.5)
if not threshold_range_min is None:
local_threshold[local_threshold < threshold_range_min] = \
threshold_range_min
if not threshold_range_max is None:
local_threshold[local_threshold > threshold_range_max] = \
threshold_range_max
if (threshold_modifier == TM_PER_OBJECT) and (labels is not None):
local_threshold[labels == 0] = 1.0
else:
if not threshold_range_min is None:
local_threshold = max(local_threshold, threshold_range_min)
if not threshold_range_max is None:
local_threshold = min(local_threshold, threshold_range_max)
return local_threshold, global_threshold | python | def get_threshold(threshold_method, threshold_modifier, image,
mask=None, labels = None,
threshold_range_min = None, threshold_range_max = None,
threshold_correction_factor = 1.0,
adaptive_window_size = 10, **kwargs):
global_threshold = get_global_threshold(
threshold_method, image, mask, **kwargs)
global_threshold *= threshold_correction_factor
if not threshold_range_min is None:
global_threshold = max(global_threshold, threshold_range_min)
if not threshold_range_max is None:
global_threshold = min(global_threshold, threshold_range_max)
if threshold_modifier == TM_GLOBAL:
local_threshold=global_threshold
elif threshold_modifier == TM_ADAPTIVE:
local_threshold = get_adaptive_threshold(
threshold_method, image, global_threshold,
mask, adaptive_window_size = adaptive_window_size, **kwargs)
local_threshold = local_threshold * threshold_correction_factor
elif threshold_modifier == TM_PER_OBJECT:
local_threshold = get_per_object_threshold(
threshold_method, image, global_threshold, mask, labels,
threshold_range_min, threshold_range_max,**kwargs)
local_threshold = local_threshold * threshold_correction_factor
else:
raise NotImplementedError("%s thresholding is not implemented"%(threshold_modifier))
if isinstance(local_threshold, np.ndarray):
#
# Constrain thresholds to within .7 to 1.5 of the global threshold.
#
threshold_range_min = max(threshold_range_min, global_threshold * .7)
threshold_range_max = min(threshold_range_max, global_threshold * 1.5)
if not threshold_range_min is None:
local_threshold[local_threshold < threshold_range_min] = \
threshold_range_min
if not threshold_range_max is None:
local_threshold[local_threshold > threshold_range_max] = \
threshold_range_max
if (threshold_modifier == TM_PER_OBJECT) and (labels is not None):
local_threshold[labels == 0] = 1.0
else:
if not threshold_range_min is None:
local_threshold = max(local_threshold, threshold_range_min)
if not threshold_range_max is None:
local_threshold = min(local_threshold, threshold_range_max)
return local_threshold, global_threshold | [
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threshold_modifier - TM_GLOBAL to calculate one threshold over entire image
TM_ADAPTIVE to calculate a per-pixel threshold
TM_PER_OBJECT to calculate a different threshold for
each object
image - a NxM numpy array of the image data
Returns a tuple of local_threshold and global_threshold where:
* global_threshold is the single number calculated using the threshold
method over the whole image
* local_threshold is the global_threshold for global methods. For adaptive
and per-object thresholding, local_threshold is a matrix of threshold
values representing the threshold to be applied at each pixel of the
image.
Different methods have optional and required parameters:
Required:
TM_PER_OBJECT:
labels - a labels matrix that defines the extents of the individual objects
to be thresholded separately.
Optional:
All:
mask - a mask of the significant pixels in the image
threshold_range_min, threshold_range_max - constrain the threshold
values to be examined to values between these limits
threshold_correction_factor - the calculated threshold is multiplied
by this number to get the final threshold
TM_MOG (mixture of Gaussians):
object_fraction - fraction of image expected to be occupied by objects
(pixels that are above the threshold)
TM_OTSU - We have algorithms derived from Otsu. There is a three-class
version of Otsu in addition to the two class. There is also
an entropy measure in addition to the weighted variance.
two_class_otsu - assume that the distribution represents
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use_weighted_variance - use Otsu's weighted variance if true,
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assign_middle_to_foreground - assign pixels in the middle class
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17,804 | CellProfiler/centrosome | centrosome/threshold.py | get_global_threshold | def get_global_threshold(threshold_method, image, mask = None, **kwargs):
"""Compute a single threshold over the whole image"""
if mask is not None and not np.any(mask):
return 1
if threshold_method == TM_OTSU:
fn = get_otsu_threshold
elif threshold_method == TM_MOG:
fn = get_mog_threshold
elif threshold_method == TM_BACKGROUND:
fn = get_background_threshold
elif threshold_method == TM_ROBUST_BACKGROUND:
fn = get_robust_background_threshold
elif threshold_method == TM_RIDLER_CALVARD:
fn = get_ridler_calvard_threshold
elif threshold_method == TM_KAPUR:
fn = get_kapur_threshold
elif threshold_method == TM_MCT:
fn = get_maximum_correlation_threshold
else:
raise NotImplementedError("%s algorithm not implemented"%(threshold_method))
kwargs = dict([(k, v) for k, v in kwargs.items()
if k in fn.args])
return fn(image, mask, **kwargs) | python | def get_global_threshold(threshold_method, image, mask = None, **kwargs):
if mask is not None and not np.any(mask):
return 1
if threshold_method == TM_OTSU:
fn = get_otsu_threshold
elif threshold_method == TM_MOG:
fn = get_mog_threshold
elif threshold_method == TM_BACKGROUND:
fn = get_background_threshold
elif threshold_method == TM_ROBUST_BACKGROUND:
fn = get_robust_background_threshold
elif threshold_method == TM_RIDLER_CALVARD:
fn = get_ridler_calvard_threshold
elif threshold_method == TM_KAPUR:
fn = get_kapur_threshold
elif threshold_method == TM_MCT:
fn = get_maximum_correlation_threshold
else:
raise NotImplementedError("%s algorithm not implemented"%(threshold_method))
kwargs = dict([(k, v) for k, v in kwargs.items()
if k in fn.args])
return fn(image, mask, **kwargs) | [
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17,805 | CellProfiler/centrosome | centrosome/threshold.py | get_per_object_threshold | def get_per_object_threshold(method, image, threshold, mask=None, labels=None,
threshold_range_min = None,
threshold_range_max = None,
**kwargs):
"""Return a matrix giving threshold per pixel calculated per-object
image - image to be thresholded
mask - mask out "don't care" pixels
labels - a label mask indicating object boundaries
threshold - the global threshold
"""
if labels is None:
labels = np.ones(image.shape,int)
if not mask is None:
labels[np.logical_not(mask)] = 0
label_extents = scipy.ndimage.find_objects(labels,np.max(labels))
local_threshold = np.ones(image.shape,image.dtype)
for i, extent in enumerate(label_extents, start=1):
label_mask = labels[extent]==i
if not mask is None:
label_mask = np.logical_and(mask[extent], label_mask)
values = image[extent]
per_object_threshold = get_global_threshold(
method, values, mask = label_mask, **kwargs)
local_threshold[extent][label_mask] = per_object_threshold
return local_threshold | python | def get_per_object_threshold(method, image, threshold, mask=None, labels=None,
threshold_range_min = None,
threshold_range_max = None,
**kwargs):
if labels is None:
labels = np.ones(image.shape,int)
if not mask is None:
labels[np.logical_not(mask)] = 0
label_extents = scipy.ndimage.find_objects(labels,np.max(labels))
local_threshold = np.ones(image.shape,image.dtype)
for i, extent in enumerate(label_extents, start=1):
label_mask = labels[extent]==i
if not mask is None:
label_mask = np.logical_and(mask[extent], label_mask)
values = image[extent]
per_object_threshold = get_global_threshold(
method, values, mask = label_mask, **kwargs)
local_threshold[extent][label_mask] = per_object_threshold
return local_threshold | [
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image - image to be thresholded
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17,806 | CellProfiler/centrosome | centrosome/threshold.py | mad | def mad(a):
'''Calculate the median absolute deviation of a sample
a - a numpy array-like collection of values
returns the median of the deviation of a from its median.
'''
a = np.asfarray(a).flatten()
return np.median(np.abs(a - np.median(a))) | python | def mad(a):
'''Calculate the median absolute deviation of a sample
a - a numpy array-like collection of values
returns the median of the deviation of a from its median.
'''
a = np.asfarray(a).flatten()
return np.median(np.abs(a - np.median(a))) | [
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17,807 | CellProfiler/centrosome | centrosome/threshold.py | get_kapur_threshold | def get_kapur_threshold(image, mask=None):
"""The Kapur, Sahoo, & Wong method of thresholding, adapted to log-space."""
cropped_image = np.array(image.flat) if mask is None else image[mask]
if np.product(cropped_image.shape)<3:
return 0
if np.min(cropped_image) == np.max(cropped_image):
return cropped_image[0]
log_image = np.log2(smooth_with_noise(cropped_image, 8))
min_log_image = np.min(log_image)
max_log_image = np.max(log_image)
histogram = scipy.ndimage.histogram(log_image,
min_log_image,
max_log_image,
256)
histogram_values = (min_log_image + (max_log_image - min_log_image)*
np.arange(256, dtype=float) / 255)
# drop any zero bins
keep = histogram != 0
histogram = histogram[keep]
histogram_values = histogram_values[keep]
# check for corner cases
if np.product(histogram_values)==1:
return 2**histogram_values[0]
# Normalize to probabilities
p = histogram.astype(float) / float(np.sum(histogram))
# Find the probabilities totals up to and above each possible threshold.
lo_sum = np.cumsum(p);
hi_sum = lo_sum[-1] - lo_sum;
lo_e = np.cumsum(p * np.log2(p));
hi_e = lo_e[-1] - lo_e;
# compute the entropies
lo_entropy = lo_e / lo_sum - np.log2(lo_sum);
hi_entropy = hi_e / hi_sum - np.log2(hi_sum);
sum_entropy = lo_entropy[:-1] + hi_entropy[:-1];
sum_entropy[np.logical_not(np.isfinite(sum_entropy))] = np.Inf
entry = np.argmin(sum_entropy);
return 2**((histogram_values[entry] + histogram_values[entry+1]) / 2); | python | def get_kapur_threshold(image, mask=None):
cropped_image = np.array(image.flat) if mask is None else image[mask]
if np.product(cropped_image.shape)<3:
return 0
if np.min(cropped_image) == np.max(cropped_image):
return cropped_image[0]
log_image = np.log2(smooth_with_noise(cropped_image, 8))
min_log_image = np.min(log_image)
max_log_image = np.max(log_image)
histogram = scipy.ndimage.histogram(log_image,
min_log_image,
max_log_image,
256)
histogram_values = (min_log_image + (max_log_image - min_log_image)*
np.arange(256, dtype=float) / 255)
# drop any zero bins
keep = histogram != 0
histogram = histogram[keep]
histogram_values = histogram_values[keep]
# check for corner cases
if np.product(histogram_values)==1:
return 2**histogram_values[0]
# Normalize to probabilities
p = histogram.astype(float) / float(np.sum(histogram))
# Find the probabilities totals up to and above each possible threshold.
lo_sum = np.cumsum(p);
hi_sum = lo_sum[-1] - lo_sum;
lo_e = np.cumsum(p * np.log2(p));
hi_e = lo_e[-1] - lo_e;
# compute the entropies
lo_entropy = lo_e / lo_sum - np.log2(lo_sum);
hi_entropy = hi_e / hi_sum - np.log2(hi_sum);
sum_entropy = lo_entropy[:-1] + hi_entropy[:-1];
sum_entropy[np.logical_not(np.isfinite(sum_entropy))] = np.Inf
entry = np.argmin(sum_entropy);
return 2**((histogram_values[entry] + histogram_values[entry+1]) / 2); | [
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17,808 | CellProfiler/centrosome | centrosome/threshold.py | weighted_variance | def weighted_variance(image, mask, binary_image):
"""Compute the log-transformed variance of foreground and background
image - intensity image used for thresholding
mask - mask of ignored pixels
binary_image - binary image marking foreground and background
"""
if not np.any(mask):
return 0
#
# Clamp the dynamic range of the foreground
#
minval = np.max(image[mask])/256
if minval == 0:
return 0
fg = np.log2(np.maximum(image[binary_image & mask], minval))
bg = np.log2(np.maximum(image[(~ binary_image) & mask], minval))
nfg = np.product(fg.shape)
nbg = np.product(bg.shape)
if nfg == 0:
return np.var(bg)
elif nbg == 0:
return np.var(fg)
else:
return (np.var(fg) * nfg + np.var(bg)*nbg) / (nfg+nbg) | python | def weighted_variance(image, mask, binary_image):
if not np.any(mask):
return 0
#
# Clamp the dynamic range of the foreground
#
minval = np.max(image[mask])/256
if minval == 0:
return 0
fg = np.log2(np.maximum(image[binary_image & mask], minval))
bg = np.log2(np.maximum(image[(~ binary_image) & mask], minval))
nfg = np.product(fg.shape)
nbg = np.product(bg.shape)
if nfg == 0:
return np.var(bg)
elif nbg == 0:
return np.var(fg)
else:
return (np.var(fg) * nfg + np.var(bg)*nbg) / (nfg+nbg) | [
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17,809 | CellProfiler/centrosome | centrosome/threshold.py | sum_of_entropies | def sum_of_entropies(image, mask, binary_image):
"""Bin the foreground and background pixels and compute the entropy
of the distribution of points among the bins
"""
mask=mask.copy()
mask[np.isnan(image)] = False
if not np.any(mask):
return 0
#
# Clamp the dynamic range of the foreground
#
minval = np.max(image[mask])/256
if minval == 0:
return 0
clamped_image = image.copy()
clamped_image[clamped_image < minval] = minval
#
# Smooth image with -8 bits of noise
#
image = smooth_with_noise(clamped_image, 8)
im_min = np.min(image)
im_max = np.max(image)
#
# Figure out the bounds for the histogram
#
upper = np.log2(im_max)
lower = np.log2(im_min)
if upper == lower:
# All values are the same, answer is log2 of # of pixels
return math.log(np.sum(mask),2)
#
# Create log-transformed lists of points in the foreground and background
#
fg = image[binary_image & mask]
bg = image[(~ binary_image) & mask]
if len(fg) == 0 or len(bg) == 0:
return 0
log_fg = np.log2(fg)
log_bg = np.log2(bg)
#
# Make these into histograms
hfg = numpy_histogram(log_fg, 256, range=(lower,upper))[0]
hbg = numpy_histogram(log_bg, 256, range=(lower,upper))[0]
#hfg = scipy.ndimage.histogram(log_fg,lower,upper,256)
#hbg = scipy.ndimage.histogram(log_bg,lower,upper,256)
#
# Drop empty bins
#
hfg = hfg[hfg>0]
hbg = hbg[hbg>0]
if np.product(hfg.shape) == 0:
hfg = np.ones((1,),int)
if np.product(hbg.shape) == 0:
hbg = np.ones((1,),int)
#
# Normalize
#
hfg = hfg.astype(float) / float(np.sum(hfg))
hbg = hbg.astype(float) / float(np.sum(hbg))
#
# Compute sum of entropies
#
return np.sum(hfg * np.log2(hfg)) + np.sum(hbg*np.log2(hbg)) | python | def sum_of_entropies(image, mask, binary_image):
mask=mask.copy()
mask[np.isnan(image)] = False
if not np.any(mask):
return 0
#
# Clamp the dynamic range of the foreground
#
minval = np.max(image[mask])/256
if minval == 0:
return 0
clamped_image = image.copy()
clamped_image[clamped_image < minval] = minval
#
# Smooth image with -8 bits of noise
#
image = smooth_with_noise(clamped_image, 8)
im_min = np.min(image)
im_max = np.max(image)
#
# Figure out the bounds for the histogram
#
upper = np.log2(im_max)
lower = np.log2(im_min)
if upper == lower:
# All values are the same, answer is log2 of # of pixels
return math.log(np.sum(mask),2)
#
# Create log-transformed lists of points in the foreground and background
#
fg = image[binary_image & mask]
bg = image[(~ binary_image) & mask]
if len(fg) == 0 or len(bg) == 0:
return 0
log_fg = np.log2(fg)
log_bg = np.log2(bg)
#
# Make these into histograms
hfg = numpy_histogram(log_fg, 256, range=(lower,upper))[0]
hbg = numpy_histogram(log_bg, 256, range=(lower,upper))[0]
#hfg = scipy.ndimage.histogram(log_fg,lower,upper,256)
#hbg = scipy.ndimage.histogram(log_bg,lower,upper,256)
#
# Drop empty bins
#
hfg = hfg[hfg>0]
hbg = hbg[hbg>0]
if np.product(hfg.shape) == 0:
hfg = np.ones((1,),int)
if np.product(hbg.shape) == 0:
hbg = np.ones((1,),int)
#
# Normalize
#
hfg = hfg.astype(float) / float(np.sum(hfg))
hbg = hbg.astype(float) / float(np.sum(hbg))
#
# Compute sum of entropies
#
return np.sum(hfg * np.log2(hfg)) + np.sum(hbg*np.log2(hbg)) | [
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17,810 | CellProfiler/centrosome | centrosome/threshold.py | log_transform | def log_transform(image):
'''Renormalize image intensities to log space
Returns a tuple of transformed image and a dictionary to be passed into
inverse_log_transform. The minimum and maximum from the dictionary
can be applied to an image by the inverse_log_transform to
convert it back to its former intensity values.
'''
orig_min, orig_max = scipy.ndimage.extrema(image)[:2]
#
# We add 1/2 bit noise to an 8 bit image to give the log a bottom
#
limage = image.copy()
noise_min = orig_min + (orig_max-orig_min)/256.0+np.finfo(image.dtype).eps
limage[limage < noise_min] = noise_min
d = { "noise_min":noise_min}
limage = np.log(limage)
log_min, log_max = scipy.ndimage.extrema(limage)[:2]
d["log_min"] = log_min
d["log_max"] = log_max
return stretch(limage), d | python | def log_transform(image):
'''Renormalize image intensities to log space
Returns a tuple of transformed image and a dictionary to be passed into
inverse_log_transform. The minimum and maximum from the dictionary
can be applied to an image by the inverse_log_transform to
convert it back to its former intensity values.
'''
orig_min, orig_max = scipy.ndimage.extrema(image)[:2]
#
# We add 1/2 bit noise to an 8 bit image to give the log a bottom
#
limage = image.copy()
noise_min = orig_min + (orig_max-orig_min)/256.0+np.finfo(image.dtype).eps
limage[limage < noise_min] = noise_min
d = { "noise_min":noise_min}
limage = np.log(limage)
log_min, log_max = scipy.ndimage.extrema(limage)[:2]
d["log_min"] = log_min
d["log_max"] = log_max
return stretch(limage), d | [
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17,811 | CellProfiler/centrosome | centrosome/threshold.py | numpy_histogram | def numpy_histogram(a, bins=10, range=None, normed=False, weights=None):
'''A version of numpy.histogram that accounts for numpy's version'''
args = inspect.getargs(np.histogram.__code__)[0]
if args[-1] == "new":
return np.histogram(a, bins, range, normed, weights, new=True)
return np.histogram(a, bins, range, normed, weights) | python | def numpy_histogram(a, bins=10, range=None, normed=False, weights=None):
'''A version of numpy.histogram that accounts for numpy's version'''
args = inspect.getargs(np.histogram.__code__)[0]
if args[-1] == "new":
return np.histogram(a, bins, range, normed, weights, new=True)
return np.histogram(a, bins, range, normed, weights) | [
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17,812 | CellProfiler/centrosome | centrosome/rankorder.py | rank_order | def rank_order(image, nbins=None):
"""Return an image of the same shape where each pixel has the
rank-order value of the corresponding pixel in the image.
The returned image's elements are of type np.uint32 which
simplifies processing in C code.
"""
flat_image = image.ravel()
sort_order = flat_image.argsort().astype(np.uint32)
flat_image = flat_image[sort_order]
sort_rank = np.zeros_like(sort_order)
is_different = flat_image[:-1] != flat_image[1:]
np.cumsum(is_different, out=sort_rank[1:])
original_values = np.zeros((sort_rank[-1]+1,),image.dtype)
original_values[0] = flat_image[0]
original_values[1:] = flat_image[1:][is_different]
int_image = np.zeros_like(sort_order)
int_image[sort_order] = sort_rank
if nbins is not None:
max_ranked_data = np.max(int_image)
while max_ranked_data >= nbins:
#
# Decimate the bins until there are fewer than nbins
#
hist = np.bincount(int_image)
#
# Rank the bins from lowest count to highest
order = np.argsort(hist)
#
# find enough to maybe decimate to nbins
#
candidates = order[:max_ranked_data+2-nbins]
to_delete = np.zeros(max_ranked_data+2, bool)
to_delete[candidates] = True
#
# Choose candidates that are either not next to others
# or have an even index so as not to delete adjacent bins
#
td_mask = to_delete[:-1] & (
((np.arange(max_ranked_data+1) & 2) == 0) |
(~ to_delete[1:]))
if td_mask[0]:
td_mask[0] = False
#
# A value to be deleted has the same index as the following
# value and the two end up being merged
#
rd_translation = np.cumsum(~td_mask)-1
#
# Translate the rankings to the new space
#
int_image = rd_translation[int_image]
#
# Eliminate the bins with low counts
#
original_values = original_values[~td_mask]
max_ranked_data = len(original_values)-1
return (int_image.reshape(image.shape), original_values) | python | def rank_order(image, nbins=None):
flat_image = image.ravel()
sort_order = flat_image.argsort().astype(np.uint32)
flat_image = flat_image[sort_order]
sort_rank = np.zeros_like(sort_order)
is_different = flat_image[:-1] != flat_image[1:]
np.cumsum(is_different, out=sort_rank[1:])
original_values = np.zeros((sort_rank[-1]+1,),image.dtype)
original_values[0] = flat_image[0]
original_values[1:] = flat_image[1:][is_different]
int_image = np.zeros_like(sort_order)
int_image[sort_order] = sort_rank
if nbins is not None:
max_ranked_data = np.max(int_image)
while max_ranked_data >= nbins:
#
# Decimate the bins until there are fewer than nbins
#
hist = np.bincount(int_image)
#
# Rank the bins from lowest count to highest
order = np.argsort(hist)
#
# find enough to maybe decimate to nbins
#
candidates = order[:max_ranked_data+2-nbins]
to_delete = np.zeros(max_ranked_data+2, bool)
to_delete[candidates] = True
#
# Choose candidates that are either not next to others
# or have an even index so as not to delete adjacent bins
#
td_mask = to_delete[:-1] & (
((np.arange(max_ranked_data+1) & 2) == 0) |
(~ to_delete[1:]))
if td_mask[0]:
td_mask[0] = False
#
# A value to be deleted has the same index as the following
# value and the two end up being merged
#
rd_translation = np.cumsum(~td_mask)-1
#
# Translate the rankings to the new space
#
int_image = rd_translation[int_image]
#
# Eliminate the bins with low counts
#
original_values = original_values[~td_mask]
max_ranked_data = len(original_values)-1
return (int_image.reshape(image.shape), original_values) | [
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17,813 | CellProfiler/centrosome | centrosome/haralick.py | quantize | def quantize(image, nlevels):
"""Quantize an image into integers 0, 1, ..., nlevels - 1.
image -- a numpy array of type float, range [0, 1]
nlevels -- an integer
"""
tmp = np.array(image // (1.0 / nlevels), dtype='i1')
return tmp.clip(0, nlevels - 1) | python | def quantize(image, nlevels):
tmp = np.array(image // (1.0 / nlevels), dtype='i1')
return tmp.clip(0, nlevels - 1) | [
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17,814 | CellProfiler/centrosome | centrosome/haralick.py | cooccurrence | def cooccurrence(quantized_image, labels, scale_i=3, scale_j=0):
"""Calculates co-occurrence matrices for all the objects in the image.
Return an array P of shape (nobjects, nlevels, nlevels) such that
P[o, :, :] is the cooccurence matrix for object o.
quantized_image -- a numpy array of integer type
labels -- a numpy array of integer type
scale -- an integer
For each object O, the cooccurrence matrix is defined as follows.
Given a row number I in the matrix, let A be the set of pixels in
O with gray level I, excluding pixels in the rightmost S
columns of the image. Let B be the set of pixels in O that are S
pixels to the right of a pixel in A. Row I of the cooccurence
matrix is the gray-level histogram of the pixels in B.
"""
labels = labels.astype(int)
nlevels = quantized_image.max() + 1
nobjects = labels.max()
if scale_i < 0:
scale_i = -scale_i
scale_j = -scale_j
if scale_i == 0 and scale_j > 0:
image_a = quantized_image[:, :-scale_j]
image_b = quantized_image[:, scale_j:]
labels_ab = labels_a = labels[:, :-scale_j]
labels_b = labels[:, scale_j:]
elif scale_i > 0 and scale_j == 0:
image_a = quantized_image[:-scale_i, :]
image_b = quantized_image[scale_i:, :]
labels_ab = labels_a = labels[:-scale_i, :]
labels_b = labels[scale_i:, :]
elif scale_i > 0 and scale_j > 0:
image_a = quantized_image[:-scale_i, :-scale_j]
image_b = quantized_image[scale_i:, scale_j:]
labels_ab = labels_a = labels[:-scale_i, :-scale_j]
labels_b = labels[scale_i:, scale_j:]
else:
# scale_j should be negative
image_a = quantized_image[:-scale_i, -scale_j:]
image_b = quantized_image[scale_i:, :scale_j]
labels_ab = labels_a = labels[:-scale_i, -scale_j:]
labels_b = labels[scale_i:, :scale_j]
equilabel = ((labels_a == labels_b) & (labels_a > 0))
if np.any(equilabel):
Q = (nlevels*nlevels*(labels_ab[equilabel]-1)+
nlevels*image_a[equilabel]+image_b[equilabel])
R = np.bincount(Q)
if R.size != nobjects*nlevels*nlevels:
S = np.zeros(nobjects*nlevels*nlevels-R.size)
R = np.hstack((R, S))
P = R.reshape(nobjects, nlevels, nlevels)
pixel_count = fix(scind.sum(equilabel, labels_ab,
np.arange(nobjects, dtype=np.int32)+1))
pixel_count = np.tile(pixel_count[:,np.newaxis,np.newaxis],
(1,nlevels,nlevels))
return (P.astype(float) / pixel_count.astype(float), nlevels)
else:
return np.zeros((nobjects, nlevels, nlevels)), nlevels | python | def cooccurrence(quantized_image, labels, scale_i=3, scale_j=0):
labels = labels.astype(int)
nlevels = quantized_image.max() + 1
nobjects = labels.max()
if scale_i < 0:
scale_i = -scale_i
scale_j = -scale_j
if scale_i == 0 and scale_j > 0:
image_a = quantized_image[:, :-scale_j]
image_b = quantized_image[:, scale_j:]
labels_ab = labels_a = labels[:, :-scale_j]
labels_b = labels[:, scale_j:]
elif scale_i > 0 and scale_j == 0:
image_a = quantized_image[:-scale_i, :]
image_b = quantized_image[scale_i:, :]
labels_ab = labels_a = labels[:-scale_i, :]
labels_b = labels[scale_i:, :]
elif scale_i > 0 and scale_j > 0:
image_a = quantized_image[:-scale_i, :-scale_j]
image_b = quantized_image[scale_i:, scale_j:]
labels_ab = labels_a = labels[:-scale_i, :-scale_j]
labels_b = labels[scale_i:, scale_j:]
else:
# scale_j should be negative
image_a = quantized_image[:-scale_i, -scale_j:]
image_b = quantized_image[scale_i:, :scale_j]
labels_ab = labels_a = labels[:-scale_i, -scale_j:]
labels_b = labels[scale_i:, :scale_j]
equilabel = ((labels_a == labels_b) & (labels_a > 0))
if np.any(equilabel):
Q = (nlevels*nlevels*(labels_ab[equilabel]-1)+
nlevels*image_a[equilabel]+image_b[equilabel])
R = np.bincount(Q)
if R.size != nobjects*nlevels*nlevels:
S = np.zeros(nobjects*nlevels*nlevels-R.size)
R = np.hstack((R, S))
P = R.reshape(nobjects, nlevels, nlevels)
pixel_count = fix(scind.sum(equilabel, labels_ab,
np.arange(nobjects, dtype=np.int32)+1))
pixel_count = np.tile(pixel_count[:,np.newaxis,np.newaxis],
(1,nlevels,nlevels))
return (P.astype(float) / pixel_count.astype(float), nlevels)
else:
return np.zeros((nobjects, nlevels, nlevels)), nlevels | [
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17,815 | CellProfiler/centrosome | centrosome/haralick.py | Haralick.H5 | def H5(self):
"Inverse difference moment."
t = 1 + toeplitz(self.levels) ** 2
repeated = np.tile(t[np.newaxis], (self.nobjects, 1, 1))
return (1.0 / repeated * self.P).sum(2).sum(1) | python | def H5(self):
"Inverse difference moment."
t = 1 + toeplitz(self.levels) ** 2
repeated = np.tile(t[np.newaxis], (self.nobjects, 1, 1))
return (1.0 / repeated * self.P).sum(2).sum(1) | [
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17,816 | CellProfiler/centrosome | centrosome/haralick.py | Haralick.H6 | def H6(self):
"Sum average."
if not hasattr(self, '_H6'):
self._H6 = ((self.rlevels2 + 2) * self.p_xplusy).sum(1)
return self._H6 | python | def H6(self):
"Sum average."
if not hasattr(self, '_H6'):
self._H6 = ((self.rlevels2 + 2) * self.p_xplusy).sum(1)
return self._H6 | [
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17,817 | CellProfiler/centrosome | centrosome/haralick.py | Haralick.H8 | def H8(self):
"Sum entropy."
return -(self.p_xplusy * np.log(self.p_xplusy + self.eps)).sum(1) | python | def H8(self):
"Sum entropy."
return -(self.p_xplusy * np.log(self.p_xplusy + self.eps)).sum(1) | [
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17,818 | CellProfiler/centrosome | centrosome/haralick.py | Haralick.H10 | def H10(self):
"Difference variance."
c = (self.rlevels * self.p_xminusy).sum(1)
c1 = np.tile(c, (self.nlevels,1)).transpose()
e = self.rlevels - c1
return (self.p_xminusy * e ** 2).sum(1) | python | def H10(self):
"Difference variance."
c = (self.rlevels * self.p_xminusy).sum(1)
c1 = np.tile(c, (self.nlevels,1)).transpose()
e = self.rlevels - c1
return (self.p_xminusy * e ** 2).sum(1) | [
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17,819 | CellProfiler/centrosome | centrosome/haralick.py | Haralick.H11 | def H11(self):
"Difference entropy."
return -(self.p_xminusy * np.log(self.p_xminusy + self.eps)).sum(1) | python | def H11(self):
"Difference entropy."
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17,820 | CellProfiler/centrosome | centrosome/haralick.py | Haralick.H12 | def H12(self):
"Information measure of correlation 1."
maxima = np.vstack((self.hx, self.hy)).max(0)
return (self.H9() - self.hxy1) / maxima | python | def H12(self):
"Information measure of correlation 1."
maxima = np.vstack((self.hx, self.hy)).max(0)
return (self.H9() - self.hxy1) / maxima | [
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17,821 | CellProfiler/centrosome | centrosome/haralick.py | Haralick.H13 | def H13(self):
"Information measure of correlation 2."
# An imaginary result has been encountered once in the Matlab
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return np.sqrt(1 - np.exp(-2 * (self.hxy2 - self.H9()))) | python | def H13(self):
"Information measure of correlation 2."
# An imaginary result has been encountered once in the Matlab
# version. The reason is unclear.
return np.sqrt(1 - np.exp(-2 * (self.hxy2 - self.H9()))) | [
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17,822 | CellProfiler/centrosome | centrosome/zernike.py | construct_zernike_lookuptable | def construct_zernike_lookuptable(zernike_indexes):
"""Return a lookup table of the sum-of-factorial part of the radial
polynomial of the zernike indexes passed
zernike_indexes - an Nx2 array of the Zernike polynomials to be
computed.
"""
n_max = np.max(zernike_indexes[:,0])
factorial = np.ones((1 + n_max,), dtype=float)
factorial[1:] = np.cumproduct(np.arange(1, 1 + n_max, dtype=float))
width = int(n_max//2 + 1)
lut = np.zeros((zernike_indexes.shape[0],width), dtype=float)
for idx, (n, m) in enumerate(zernike_indexes):
alt = 1
npmh = (n+m)//2
nmmh = (n-m)//2
for k in range(0,nmmh+1):
lut[idx,k] = \
(alt * factorial[n-k] /
(factorial[k]*factorial[npmh-k]*factorial[nmmh-k]))
alt = -alt
return lut | python | def construct_zernike_lookuptable(zernike_indexes):
n_max = np.max(zernike_indexes[:,0])
factorial = np.ones((1 + n_max,), dtype=float)
factorial[1:] = np.cumproduct(np.arange(1, 1 + n_max, dtype=float))
width = int(n_max//2 + 1)
lut = np.zeros((zernike_indexes.shape[0],width), dtype=float)
for idx, (n, m) in enumerate(zernike_indexes):
alt = 1
npmh = (n+m)//2
nmmh = (n-m)//2
for k in range(0,nmmh+1):
lut[idx,k] = \
(alt * factorial[n-k] /
(factorial[k]*factorial[npmh-k]*factorial[nmmh-k]))
alt = -alt
return lut | [
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17,823 | CellProfiler/centrosome | centrosome/zernike.py | score_zernike | def score_zernike(zf, radii, labels, indexes=None):
"""Score the output of construct_zernike_polynomials
zf - the output of construct_zernike_polynomials which is I x J x K
where K is the number of zernike polynomials computed
radii - a vector of the radius of each of N labeled objects
labels - a label matrix
outputs a N x K matrix of the scores of each of the Zernikes for
each labeled object.
"""
if indexes is None:
indexes = np.arange(1,np.max(labels)+1,dtype=np.int32)
else:
indexes = np.array(indexes, dtype=np.int32)
radii = np.asarray(radii, dtype=float)
n = radii.size
k = zf.shape[2]
score = np.zeros((n,k))
if n == 0:
return score
areas = np.square(radii)
areas *= np.pi
for ki in range(k):
zfk=zf[:,:,ki]
real_score = scipy.ndimage.sum(zfk.real,labels,indexes)
real_score = fixup_scipy_ndimage_result(real_score)
imag_score = scipy.ndimage.sum(zfk.imag,labels,indexes)
imag_score = fixup_scipy_ndimage_result(imag_score)
# one_score = np.sqrt(real_score**2+imag_score**2) / areas
np.square(real_score, out=real_score)
np.square(imag_score, out=imag_score)
one_score = real_score + imag_score
np.sqrt(one_score, out=one_score)
one_score /= areas
score[:,ki] = one_score
return score | python | def score_zernike(zf, radii, labels, indexes=None):
if indexes is None:
indexes = np.arange(1,np.max(labels)+1,dtype=np.int32)
else:
indexes = np.array(indexes, dtype=np.int32)
radii = np.asarray(radii, dtype=float)
n = radii.size
k = zf.shape[2]
score = np.zeros((n,k))
if n == 0:
return score
areas = np.square(radii)
areas *= np.pi
for ki in range(k):
zfk=zf[:,:,ki]
real_score = scipy.ndimage.sum(zfk.real,labels,indexes)
real_score = fixup_scipy_ndimage_result(real_score)
imag_score = scipy.ndimage.sum(zfk.imag,labels,indexes)
imag_score = fixup_scipy_ndimage_result(imag_score)
# one_score = np.sqrt(real_score**2+imag_score**2) / areas
np.square(real_score, out=real_score)
np.square(imag_score, out=imag_score)
one_score = real_score + imag_score
np.sqrt(one_score, out=one_score)
one_score /= areas
score[:,ki] = one_score
return score | [
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... | Score the output of construct_zernike_polynomials
zf - the output of construct_zernike_polynomials which is I x J x K
where K is the number of zernike polynomials computed
radii - a vector of the radius of each of N labeled objects
labels - a label matrix
outputs a N x K matrix of the scores of each of the Zernikes for
each labeled object. | [
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17,824 | CellProfiler/centrosome | centrosome/lapjv.py | slow_augmenting_row_reduction | def slow_augmenting_row_reduction(n, ii, jj, idx, count, x, y, u, v, c):
'''Perform the augmenting row reduction step from the Jonker-Volgenaut algorithm
n - the number of i and j in the linear assignment problem
ii - the unassigned i
jj - the j-index of every entry in c
idx - the index of the first entry for each i
count - the number of entries for each i
x - the assignment of j to i
y - the assignment of i to j
u - the dual variable "u" which will be updated. It should be
initialized to zero for the first reduction transfer.
v - the dual variable "v" which will be reduced in-place
c - the cost for each entry.
returns the new unassigned i
'''
#######################################
#
# From Jonker:
#
# procedure AUGMENTING ROW REDUCTION;
# begin
# LIST: = {all unassigned rows};
# for all i in LIST do
# repeat
# ul:=min {c[i,j]-v[j] for j=l ...n};
# select j1 with c [i,j 1] - v[j 1] = u1;
# u2:=min {c[i,j]-v[j] for j=l ...n,j< >jl} ;
# select j2 with c [i,j2] - v [j2] = u2 and j2 < >j 1 ;
# u[i]:=u2;
# if ul <u2 then v[jl]:=v[jl]-(u2-ul)
# else if jl is assigned then jl : =j2;
# k:=y [jl]; if k>0 then x [k]:=0; x[i]:=jl; y [ j l ] : = i ; i:=k
# until ul =u2 (* no reduction transfer *) or k=0 i~* augmentation *)
# end
ii = list(ii)
k = 0
limit = len(ii)
free = []
while k < limit:
i = ii[k]
k += 1
j = jj[idx[i]:(idx[i] + count[i])]
uu = c[idx[i]:(idx[i] + count[i])] - v[j]
order = np.lexsort([uu])
u1, u2 = uu[order[:2]]
j1,j2 = j[order[:2]]
i1 = y[j1]
if u1 < u2:
v[j1] = v[j1] - u2 + u1
elif i1 != n:
j1 = j2
i1 = y[j1]
if i1 != n:
if u1 < u2:
k -= 1
ii[k] = i1
else:
free.append(i1)
x[i] = j1
y[j1] = i
return np.array(free,np.uint32) | python | def slow_augmenting_row_reduction(n, ii, jj, idx, count, x, y, u, v, c):
'''Perform the augmenting row reduction step from the Jonker-Volgenaut algorithm
n - the number of i and j in the linear assignment problem
ii - the unassigned i
jj - the j-index of every entry in c
idx - the index of the first entry for each i
count - the number of entries for each i
x - the assignment of j to i
y - the assignment of i to j
u - the dual variable "u" which will be updated. It should be
initialized to zero for the first reduction transfer.
v - the dual variable "v" which will be reduced in-place
c - the cost for each entry.
returns the new unassigned i
'''
#######################################
#
# From Jonker:
#
# procedure AUGMENTING ROW REDUCTION;
# begin
# LIST: = {all unassigned rows};
# for all i in LIST do
# repeat
# ul:=min {c[i,j]-v[j] for j=l ...n};
# select j1 with c [i,j 1] - v[j 1] = u1;
# u2:=min {c[i,j]-v[j] for j=l ...n,j< >jl} ;
# select j2 with c [i,j2] - v [j2] = u2 and j2 < >j 1 ;
# u[i]:=u2;
# if ul <u2 then v[jl]:=v[jl]-(u2-ul)
# else if jl is assigned then jl : =j2;
# k:=y [jl]; if k>0 then x [k]:=0; x[i]:=jl; y [ j l ] : = i ; i:=k
# until ul =u2 (* no reduction transfer *) or k=0 i~* augmentation *)
# end
ii = list(ii)
k = 0
limit = len(ii)
free = []
while k < limit:
i = ii[k]
k += 1
j = jj[idx[i]:(idx[i] + count[i])]
uu = c[idx[i]:(idx[i] + count[i])] - v[j]
order = np.lexsort([uu])
u1, u2 = uu[order[:2]]
j1,j2 = j[order[:2]]
i1 = y[j1]
if u1 < u2:
v[j1] = v[j1] - u2 + u1
elif i1 != n:
j1 = j2
i1 = y[j1]
if i1 != n:
if u1 < u2:
k -= 1
ii[k] = i1
else:
free.append(i1)
x[i] = j1
y[j1] = i
return np.array(free,np.uint32) | [
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n - the number of i and j in the linear assignment problem
ii - the unassigned i
jj - the j-index of every entry in c
idx - the index of the first entry for each i
count - the number of entries for each i
x - the assignment of j to i
y - the assignment of i to j
u - the dual variable "u" which will be updated. It should be
initialized to zero for the first reduction transfer.
v - the dual variable "v" which will be reduced in-place
c - the cost for each entry.
returns the new unassigned i | [
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17,825 | koszullab/instaGRAAL | instagraal/linkage.py | collapse_degenerate_markers | def collapse_degenerate_markers(linkage_records):
"""Group all markers with no genetic distance as distinct features
to generate a BED file with.
Simple example with sixteen degenerate markers:
>>> marker_features = [
... ['36915_sctg_207_31842', 1, 0, 207, 31842],
... ['36941_sctg_207_61615', 1, 0, 207, 61615],
... ['36956_sctg_207_77757', 1, 0, 207, 77757],
... ['36957_sctg_207_78332', 1, 0, 207, 78332],
... ['36972_sctg_207_94039', 1, 0, 207, 94039],
... ['36788_sctg_207_116303', 1, 0.652, 207, 116303],
... ['36812_sctg_207_158925', 1, 1.25, 207, 158925],
... ['36819_sctg_207_165424', 1, 1.25, 207, 165424],
... ['36828_sctg_207_190813', 1, 1.25, 207, 190813],
... ['36830_sctg_207_191645', 1, 1.25, 207, 191645],
... ['36834_sctg_207_195961', 1, 1.25, 207, 195961],
... ['36855_sctg_207_233632', 1, 1.25, 207, 233632],
... ['36881_sctg_207_258658', 1, 1.25, 207, 258658],
... ['82072_sctg_486_41893', 1, 3.756, 486, 41893],
... ['85634_sctg_516_36614', 1, 3.756, 516, 36614],
... ['85638_sctg_516_50582', 1, 3.756, 516, 50582]]
>>> len(marker_features)
16
>>> collapsed_features = collapse_degenerate_markers(marker_features)
>>> len(collapsed_features)
5
The degenerate features (identical linkage group, genetic distance and
original scaffold) are collapsed into a region:
>>> collapsed_features[0]
[1, 31842, 94039, 207]
The format is [linkage group, start, end, original scaffold].
If a singleton (non-degenerate) feature is found, the region is simply
a single point in the genome:
>>> collapsed_features[1]
[1, 116303, 116303, 207]
so 'start' and 'end' are identical.
Two markers are not considered degenerate if they belong to different
original scaffolds, even if they are in terms of genetic linkage:
>>> collapsed_features[2]
[1, 158925, 258658, 207]
>>> collapsed_features[3:]
[[1, 41893, 41893, 486], [1, 36614, 50582, 516]]
"""
def degeneracy(linkage_record):
linkage_group, genetic_distance, scaffold = (
linkage_record[1],
linkage_record[2],
linkage_record[3],
)
return (linkage_group, genetic_distance, scaffold)
degenerate_records = []
for _, degenerate_group in itertools.groupby(
linkage_records, key=degeneracy
):
group_list = list(degenerate_group)
start_record, end_record = group_list[0], group_list[-1]
assert (start_record[1], start_record[2], start_record[3]) == (
end_record[1],
end_record[2],
end_record[3],
)
start_position = start_record[-1]
end_position = end_record[-1]
scaffold = start_record[3]
linkage_group = start_record[1]
record = [linkage_group, start_position, end_position, scaffold]
degenerate_records.append(record)
return degenerate_records | python | def collapse_degenerate_markers(linkage_records):
def degeneracy(linkage_record):
linkage_group, genetic_distance, scaffold = (
linkage_record[1],
linkage_record[2],
linkage_record[3],
)
return (linkage_group, genetic_distance, scaffold)
degenerate_records = []
for _, degenerate_group in itertools.groupby(
linkage_records, key=degeneracy
):
group_list = list(degenerate_group)
start_record, end_record = group_list[0], group_list[-1]
assert (start_record[1], start_record[2], start_record[3]) == (
end_record[1],
end_record[2],
end_record[3],
)
start_position = start_record[-1]
end_position = end_record[-1]
scaffold = start_record[3]
linkage_group = start_record[1]
record = [linkage_group, start_position, end_position, scaffold]
degenerate_records.append(record)
return degenerate_records | [
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"... | Group all markers with no genetic distance as distinct features
to generate a BED file with.
Simple example with sixteen degenerate markers:
>>> marker_features = [
... ['36915_sctg_207_31842', 1, 0, 207, 31842],
... ['36941_sctg_207_61615', 1, 0, 207, 61615],
... ['36956_sctg_207_77757', 1, 0, 207, 77757],
... ['36957_sctg_207_78332', 1, 0, 207, 78332],
... ['36972_sctg_207_94039', 1, 0, 207, 94039],
... ['36788_sctg_207_116303', 1, 0.652, 207, 116303],
... ['36812_sctg_207_158925', 1, 1.25, 207, 158925],
... ['36819_sctg_207_165424', 1, 1.25, 207, 165424],
... ['36828_sctg_207_190813', 1, 1.25, 207, 190813],
... ['36830_sctg_207_191645', 1, 1.25, 207, 191645],
... ['36834_sctg_207_195961', 1, 1.25, 207, 195961],
... ['36855_sctg_207_233632', 1, 1.25, 207, 233632],
... ['36881_sctg_207_258658', 1, 1.25, 207, 258658],
... ['82072_sctg_486_41893', 1, 3.756, 486, 41893],
... ['85634_sctg_516_36614', 1, 3.756, 516, 36614],
... ['85638_sctg_516_50582', 1, 3.756, 516, 50582]]
>>> len(marker_features)
16
>>> collapsed_features = collapse_degenerate_markers(marker_features)
>>> len(collapsed_features)
5
The degenerate features (identical linkage group, genetic distance and
original scaffold) are collapsed into a region:
>>> collapsed_features[0]
[1, 31842, 94039, 207]
The format is [linkage group, start, end, original scaffold].
If a singleton (non-degenerate) feature is found, the region is simply
a single point in the genome:
>>> collapsed_features[1]
[1, 116303, 116303, 207]
so 'start' and 'end' are identical.
Two markers are not considered degenerate if they belong to different
original scaffolds, even if they are in terms of genetic linkage:
>>> collapsed_features[2]
[1, 158925, 258658, 207]
>>> collapsed_features[3:]
[[1, 41893, 41893, 486], [1, 36614, 50582, 516]] | [
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17,826 | koszullab/instaGRAAL | instagraal/linkage.py | linkage_group_ordering | def linkage_group_ordering(linkage_records):
"""Convert degenerate linkage records into ordered info_frags-like records
for comparison purposes.
Simple example:
>>> linkage_records = [
... ['linkage_group_1', 31842, 94039, 'sctg_207'],
... ['linkage_group_1', 95303, 95303, 'sctg_207'],
... ['linkage_group_2', 15892, 25865, 'sctg_308'],
... ['linkage_group_2', 41893, 41893, 'sctg_486'],
... ['linkage_group_3', 36614, 50582, 'sctg_516'],
... ]
>>> ordering = linkage_group_ordering(linkage_records)
Each key of the record is a newly-formed 'scaffold' (linkage group):
>>> sorted(ordering.keys())
['linkage_group_1', 'linkage_group_2', 'linkage_group_3']
Records are in the form [init_contig, frag_id, start, end, orientation].
Since fragment ids are meaningless in non-HiC contexts a negative
identifier is set so it is understood that region was added due to
linkage data (-1 is for recovering data after first-pass polishing and -2
is for sequence insertions after long read based polishing).
>>> ordering['linkage_group_1']
[['sctg_207', -3, 31842, 94039, 1], ['sctg_207', -3, 95303, 95303, 1]]
>>> ordering['linkage_group_2']
[['sctg_308', -3, 15892, 25865, 1], ['sctg_486', -3, 41893, 41893, 1]]
Orientations are always set to 1 by default.
>>> ordering['linkage_group_3']
[['sctg_516', -3, 36614, 50582, 1]]
"""
new_records = dict()
for lg_name, linkage_group in itertools.groupby(
linkage_records, operator.itemgetter(0)
):
new_records[lg_name] = []
for record in linkage_group:
init_contig = record[-1]
start = record[1]
end = record[2]
new_record = [init_contig, -3, start, end, 1]
new_records[lg_name].append(new_record)
return new_records | python | def linkage_group_ordering(linkage_records):
new_records = dict()
for lg_name, linkage_group in itertools.groupby(
linkage_records, operator.itemgetter(0)
):
new_records[lg_name] = []
for record in linkage_group:
init_contig = record[-1]
start = record[1]
end = record[2]
new_record = [init_contig, -3, start, end, 1]
new_records[lg_name].append(new_record)
return new_records | [
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... | Convert degenerate linkage records into ordered info_frags-like records
for comparison purposes.
Simple example:
>>> linkage_records = [
... ['linkage_group_1', 31842, 94039, 'sctg_207'],
... ['linkage_group_1', 95303, 95303, 'sctg_207'],
... ['linkage_group_2', 15892, 25865, 'sctg_308'],
... ['linkage_group_2', 41893, 41893, 'sctg_486'],
... ['linkage_group_3', 36614, 50582, 'sctg_516'],
... ]
>>> ordering = linkage_group_ordering(linkage_records)
Each key of the record is a newly-formed 'scaffold' (linkage group):
>>> sorted(ordering.keys())
['linkage_group_1', 'linkage_group_2', 'linkage_group_3']
Records are in the form [init_contig, frag_id, start, end, orientation].
Since fragment ids are meaningless in non-HiC contexts a negative
identifier is set so it is understood that region was added due to
linkage data (-1 is for recovering data after first-pass polishing and -2
is for sequence insertions after long read based polishing).
>>> ordering['linkage_group_1']
[['sctg_207', -3, 31842, 94039, 1], ['sctg_207', -3, 95303, 95303, 1]]
>>> ordering['linkage_group_2']
[['sctg_308', -3, 15892, 25865, 1], ['sctg_486', -3, 41893, 41893, 1]]
Orientations are always set to 1 by default.
>>> ordering['linkage_group_3']
[['sctg_516', -3, 36614, 50582, 1]] | [
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17,827 | koszullab/instaGRAAL | instagraal/linkage.py | compare_orderings | def compare_orderings(info_frags_records, linkage_orderings):
"""Given linkage groups and info_frags records, link pseudo-chromosomes to
scaffolds based on the initial contig composition of each group. Because
info_frags records are usually richer and may contain contigs not found
in linkage groups, those extra sequences are discarded.
Example with two linkage groups and two chromosomes:
>>> linkage_orderings = {
... 'linkage_group_1': [
... ['sctg_516', -3, 36614, 50582, 1],
... ['sctg_486', -3, 41893, 41893, 1],
... ['sctg_486', -3, 50054, 62841, 1],
... ['sctg_207', -3, 31842, 94039, 1],
... ['sctg_558', -3, 51212, 54212, 1],
... ],
... 'linkage_group_2': [
... ['sctg_308', -3, 15892, 25865, 1],
... ['sctg_842', -3, 0, 8974, 1],
... ['sctg_994', -3, 0, 81213, 1],
... ],
... }
>>> info_frags = {
... 'scaffold_A': [
... ['sctg_308', 996, 15892, 25865, 1],
... ['sctg_778', 1210, 45040, 78112, -1],
... ['sctg_842', 124, 0, 8974, 1],
... ],
... 'scaffold_B': [
... ['sctg_516', 5, 0, 38000, 1],
... ['sctg_486', 47, 42050, 49000, 1],
... ['sctg_1755', 878, 95001, 10844, -1],
... ['sctg_842', 126, 19000, 26084, 1],
... ['sctg_207', 705, 45500, 87056, 1],
... ],
... 'scaffold_C': [
... ['sctg_558', 745, 50045, 67851, 1],
... ['sctg_994', 12, 74201, 86010, -1],
... ],
... }
>>> matching_pairs = compare_orderings(info_frags, linkage_orderings)
>>> matching_pairs['scaffold_B']
(3, 'linkage_group_1', {'sctg_558': 'sctg_207'})
>>> matching_pairs['scaffold_A']
(2, 'linkage_group_2', {'sctg_994': 'sctg_842'})
"""
scaffolds = info_frags_records.keys()
linkage_groups = linkage_orderings.keys()
best_matching_table = dict()
for scaffold, linkage_group in itertools.product(
scaffolds, linkage_groups
):
lg_ordering = [
init_contig
for init_contig, _ in itertools.groupby(
linkage_orderings[linkage_group], operator.itemgetter(0)
)
]
scaffold_ordering = [
init_contig
for init_contig, bin_group in itertools.groupby(
info_frags_records[scaffold], operator.itemgetter(0)
)
if init_contig in lg_ordering
]
overlap = set(lg_ordering).intersection(set(scaffold_ordering))
missing_locations = dict()
for missing_block in sorted(set(lg_ordering) - set(overlap)):
for i, init_contig in enumerate(lg_ordering):
if init_contig == missing_block:
try:
block_before = lg_ordering[i - 1]
except IndexError:
block_before = "beginning"
missing_locations[missing_block] = block_before
try:
if len(overlap) > best_matching_table[scaffold][0]:
best_matching_table[scaffold] = (
len(overlap),
linkage_group,
missing_locations,
)
except KeyError:
best_matching_table[scaffold] = (
len(overlap),
linkage_group,
missing_locations,
)
return best_matching_table | python | def compare_orderings(info_frags_records, linkage_orderings):
scaffolds = info_frags_records.keys()
linkage_groups = linkage_orderings.keys()
best_matching_table = dict()
for scaffold, linkage_group in itertools.product(
scaffolds, linkage_groups
):
lg_ordering = [
init_contig
for init_contig, _ in itertools.groupby(
linkage_orderings[linkage_group], operator.itemgetter(0)
)
]
scaffold_ordering = [
init_contig
for init_contig, bin_group in itertools.groupby(
info_frags_records[scaffold], operator.itemgetter(0)
)
if init_contig in lg_ordering
]
overlap = set(lg_ordering).intersection(set(scaffold_ordering))
missing_locations = dict()
for missing_block in sorted(set(lg_ordering) - set(overlap)):
for i, init_contig in enumerate(lg_ordering):
if init_contig == missing_block:
try:
block_before = lg_ordering[i - 1]
except IndexError:
block_before = "beginning"
missing_locations[missing_block] = block_before
try:
if len(overlap) > best_matching_table[scaffold][0]:
best_matching_table[scaffold] = (
len(overlap),
linkage_group,
missing_locations,
)
except KeyError:
best_matching_table[scaffold] = (
len(overlap),
linkage_group,
missing_locations,
)
return best_matching_table | [
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")... | Given linkage groups and info_frags records, link pseudo-chromosomes to
scaffolds based on the initial contig composition of each group. Because
info_frags records are usually richer and may contain contigs not found
in linkage groups, those extra sequences are discarded.
Example with two linkage groups and two chromosomes:
>>> linkage_orderings = {
... 'linkage_group_1': [
... ['sctg_516', -3, 36614, 50582, 1],
... ['sctg_486', -3, 41893, 41893, 1],
... ['sctg_486', -3, 50054, 62841, 1],
... ['sctg_207', -3, 31842, 94039, 1],
... ['sctg_558', -3, 51212, 54212, 1],
... ],
... 'linkage_group_2': [
... ['sctg_308', -3, 15892, 25865, 1],
... ['sctg_842', -3, 0, 8974, 1],
... ['sctg_994', -3, 0, 81213, 1],
... ],
... }
>>> info_frags = {
... 'scaffold_A': [
... ['sctg_308', 996, 15892, 25865, 1],
... ['sctg_778', 1210, 45040, 78112, -1],
... ['sctg_842', 124, 0, 8974, 1],
... ],
... 'scaffold_B': [
... ['sctg_516', 5, 0, 38000, 1],
... ['sctg_486', 47, 42050, 49000, 1],
... ['sctg_1755', 878, 95001, 10844, -1],
... ['sctg_842', 126, 19000, 26084, 1],
... ['sctg_207', 705, 45500, 87056, 1],
... ],
... 'scaffold_C': [
... ['sctg_558', 745, 50045, 67851, 1],
... ['sctg_994', 12, 74201, 86010, -1],
... ],
... }
>>> matching_pairs = compare_orderings(info_frags, linkage_orderings)
>>> matching_pairs['scaffold_B']
(3, 'linkage_group_1', {'sctg_558': 'sctg_207'})
>>> matching_pairs['scaffold_A']
(2, 'linkage_group_2', {'sctg_994': 'sctg_842'}) | [
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17,828 | koszullab/instaGRAAL | instagraal/linkage.py | get_missing_blocks | def get_missing_blocks(info_frags_records, matching_pairs, linkage_orderings):
"""Get missing blocks in a scaffold based on the genetic map order.
Given matching scaffold blocks/genetic map blocks (based on restriction
sites and SNP markers, respectively), move around the scaffold blocks
such that they map the genetic map order.
Parameters
----------
info_frags_records : dict
A dictionary representing the scaffolds and their block order as
described in an info_frags.txt file
matching_pairs : dict
A list of best matching pairs in the form
(scaffold_block, linkage group)
linkage_orderings : dict
A dictionary representing the genetic map and the linkage groups
as described in csv file.
Example
-------
>>> linkage_orderings = {
... 'linkage_group_1': [
... ['sctg_516', -3, 36614, 50582, 1],
... ['sctg_486', -3, 41893, 41893, 1],
... ['sctg_486', -3, 50054, 62841, 1],
... ['sctg_207', -3, 31842, 94039, 1],
... ['sctg_558', -3, 51212, 54212, 1],
... ],
... 'linkage_group_2': [
... ['sctg_308', -3, 15892, 25865, 1],
... ['sctg_842', -3, 0, 8974, 1],
... ['sctg_994', -3, 0, 81213, 1],
... ],
... }
>>> info_frags = {
... 'scaffold_A': [
... ['sctg_308', 996, 15892, 25865, 1],
... ['sctg_778', 1210, 45040, 78112, -1],
... ['sctg_842', 124, 0, 8974, 1],
... ],
... 'scaffold_B': [
... ['sctg_516', 5, 0, 38000, 1],
... ['sctg_486', 47, 42050, 49000, 1],
... ['sctg_1755', 878, 95001, 10844, -1],
... ['sctg_842', 126, 19000, 26084, 1],
... ['sctg_207', 705, 45500, 87056, 1],
... ],
... 'scaffold_C': [
... ['sctg_558', 745, 50045, 67851, 1],
... ['sctg_994', 12, 74201, 86010, -1],
... ],
... }
>>> matching_pairs = compare_orderings(info_frags, linkage_orderings)
>>> new_records = get_missing_blocks(info_frags, matching_pairs,
... linkage_orderings)
>>> for my_bin in new_records['scaffold_A']:
... print(list(my_bin))
...
['sctg_308', 996, 15892, 25865, 1]
['sctg_778', 1210, 45040, 78112, -1]
['sctg_842', 124, 0, 8974, 1]
['sctg_842', 126, 19000, 26084, 1]
['sctg_994', 12, 74201, 86010, -1]
>>> for my_bin in new_records['scaffold_B']:
... print(list(my_bin))
...
['sctg_516', 5, 0, 38000, 1]
['sctg_486', 47, 42050, 49000, 1]
['sctg_1755', 878, 95001, 10844, -1]
['sctg_207', 705, 45500, 87056, 1]
['sctg_558', 745, 50045, 67851, 1]
"""
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
touched_lgs = set()
def record_length(key_record_tuple):
return len(key_record_tuple[1])
new_scaffolds = copy.deepcopy(info_frags_records)
for scaffold_name in collections.OrderedDict(
sorted(new_scaffolds.items(), key=record_length, reverse=True)
):
scaffold = new_scaffolds[scaffold_name]
new_scaffold = []
corresponding_lg = matching_pairs[scaffold_name][1]
if corresponding_lg in touched_lgs:
continue
else:
touched_lgs.add(corresponding_lg)
scaffold_block_names = {my_bin[0] for my_bin in scaffold}
lg_block_names = [
my_block[0] for my_block in linkage_orderings[corresponding_lg]
]
touched_bins = set()
for lg_block_name in lg_block_names:
if lg_block_name in scaffold_block_names:
for my_bin in scaffold:
if tuple(my_bin) in new_scaffold:
continue
elif (
my_bin[0] == lg_block_name
or my_bin[0] not in lg_block_names
):
new_scaffold.append(tuple(my_bin))
touched_bins.add(tuple(my_bin))
else:
break
for other_name, other_scaffold in new_scaffolds.items():
if other_name == scaffold_name:
continue
i = 0
for my_bin in other_scaffold:
if tuple(my_bin) in new_scaffold:
i += 1
continue
elif my_bin[0] == lg_block_name:
moved_bin = tuple(other_scaffold.pop(i))
new_scaffold.append(tuple(moved_bin))
touched_bins.add(tuple(moved_bin))
i -= 1
i += 1
for remaining_bin in scaffold:
if tuple(remaining_bin) not in touched_bins:
new_scaffold.append(tuple(remaining_bin))
touched_bins.add(tuple(remaining_bin))
if len(new_scaffold) > 0:
new_scaffolds[scaffold_name] = new_scaffold
return new_scaffolds | python | def get_missing_blocks(info_frags_records, matching_pairs, linkage_orderings):
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
touched_lgs = set()
def record_length(key_record_tuple):
return len(key_record_tuple[1])
new_scaffolds = copy.deepcopy(info_frags_records)
for scaffold_name in collections.OrderedDict(
sorted(new_scaffolds.items(), key=record_length, reverse=True)
):
scaffold = new_scaffolds[scaffold_name]
new_scaffold = []
corresponding_lg = matching_pairs[scaffold_name][1]
if corresponding_lg in touched_lgs:
continue
else:
touched_lgs.add(corresponding_lg)
scaffold_block_names = {my_bin[0] for my_bin in scaffold}
lg_block_names = [
my_block[0] for my_block in linkage_orderings[corresponding_lg]
]
touched_bins = set()
for lg_block_name in lg_block_names:
if lg_block_name in scaffold_block_names:
for my_bin in scaffold:
if tuple(my_bin) in new_scaffold:
continue
elif (
my_bin[0] == lg_block_name
or my_bin[0] not in lg_block_names
):
new_scaffold.append(tuple(my_bin))
touched_bins.add(tuple(my_bin))
else:
break
for other_name, other_scaffold in new_scaffolds.items():
if other_name == scaffold_name:
continue
i = 0
for my_bin in other_scaffold:
if tuple(my_bin) in new_scaffold:
i += 1
continue
elif my_bin[0] == lg_block_name:
moved_bin = tuple(other_scaffold.pop(i))
new_scaffold.append(tuple(moved_bin))
touched_bins.add(tuple(moved_bin))
i -= 1
i += 1
for remaining_bin in scaffold:
if tuple(remaining_bin) not in touched_bins:
new_scaffold.append(tuple(remaining_bin))
touched_bins.add(tuple(remaining_bin))
if len(new_scaffold) > 0:
new_scaffolds[scaffold_name] = new_scaffold
return new_scaffolds | [
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Given matching scaffold blocks/genetic map blocks (based on restriction
sites and SNP markers, respectively), move around the scaffold blocks
such that they map the genetic map order.
Parameters
----------
info_frags_records : dict
A dictionary representing the scaffolds and their block order as
described in an info_frags.txt file
matching_pairs : dict
A list of best matching pairs in the form
(scaffold_block, linkage group)
linkage_orderings : dict
A dictionary representing the genetic map and the linkage groups
as described in csv file.
Example
-------
>>> linkage_orderings = {
... 'linkage_group_1': [
... ['sctg_516', -3, 36614, 50582, 1],
... ['sctg_486', -3, 41893, 41893, 1],
... ['sctg_486', -3, 50054, 62841, 1],
... ['sctg_207', -3, 31842, 94039, 1],
... ['sctg_558', -3, 51212, 54212, 1],
... ],
... 'linkage_group_2': [
... ['sctg_308', -3, 15892, 25865, 1],
... ['sctg_842', -3, 0, 8974, 1],
... ['sctg_994', -3, 0, 81213, 1],
... ],
... }
>>> info_frags = {
... 'scaffold_A': [
... ['sctg_308', 996, 15892, 25865, 1],
... ['sctg_778', 1210, 45040, 78112, -1],
... ['sctg_842', 124, 0, 8974, 1],
... ],
... 'scaffold_B': [
... ['sctg_516', 5, 0, 38000, 1],
... ['sctg_486', 47, 42050, 49000, 1],
... ['sctg_1755', 878, 95001, 10844, -1],
... ['sctg_842', 126, 19000, 26084, 1],
... ['sctg_207', 705, 45500, 87056, 1],
... ],
... 'scaffold_C': [
... ['sctg_558', 745, 50045, 67851, 1],
... ['sctg_994', 12, 74201, 86010, -1],
... ],
... }
>>> matching_pairs = compare_orderings(info_frags, linkage_orderings)
>>> new_records = get_missing_blocks(info_frags, matching_pairs,
... linkage_orderings)
>>> for my_bin in new_records['scaffold_A']:
... print(list(my_bin))
...
['sctg_308', 996, 15892, 25865, 1]
['sctg_778', 1210, 45040, 78112, -1]
['sctg_842', 124, 0, 8974, 1]
['sctg_842', 126, 19000, 26084, 1]
['sctg_994', 12, 74201, 86010, -1]
>>> for my_bin in new_records['scaffold_B']:
... print(list(my_bin))
...
['sctg_516', 5, 0, 38000, 1]
['sctg_486', 47, 42050, 49000, 1]
['sctg_1755', 878, 95001, 10844, -1]
['sctg_207', 705, 45500, 87056, 1]
['sctg_558', 745, 50045, 67851, 1] | [
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17,829 | koszullab/instaGRAAL | instagraal/parse_info_frags.py | parse_info_frags | def parse_info_frags(info_frags):
"""Import an info_frags.txt file and return a dictionary where each key
is a newly formed scaffold and each value is the list of bins and their
origin on the initial scaffolding.
"""
new_scaffolds = {}
with open(info_frags, "r") as info_frags_handle:
current_new_contig = None
for line in info_frags_handle:
if line.startswith(">"):
current_new_contig = str(line[1:-1])
new_scaffolds[current_new_contig] = []
elif line.startswith("init_contig"):
pass
else:
(init_contig, id_frag, orientation, pos_start, pos_end) = str(
line[:-1]
).split("\t")
start = int(pos_start)
end = int(pos_end)
ori = int(orientation)
fragid = int(id_frag)
assert start < end
assert ori in {-1, 1}
new_scaffolds[current_new_contig].append(
[init_contig, fragid, start, end, ori]
)
return new_scaffolds | python | def parse_info_frags(info_frags):
new_scaffolds = {}
with open(info_frags, "r") as info_frags_handle:
current_new_contig = None
for line in info_frags_handle:
if line.startswith(">"):
current_new_contig = str(line[1:-1])
new_scaffolds[current_new_contig] = []
elif line.startswith("init_contig"):
pass
else:
(init_contig, id_frag, orientation, pos_start, pos_end) = str(
line[:-1]
).split("\t")
start = int(pos_start)
end = int(pos_end)
ori = int(orientation)
fragid = int(id_frag)
assert start < end
assert ori in {-1, 1}
new_scaffolds[current_new_contig].append(
[init_contig, fragid, start, end, ori]
)
return new_scaffolds | [
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17,830 | koszullab/instaGRAAL | instagraal/parse_info_frags.py | format_info_frags | def format_info_frags(info_frags):
"""A function to seamlessly run on either scaffold dictionaries or
info_frags.txt files without having to check the input first.
"""
if isinstance(info_frags, dict):
return info_frags
else:
try:
scaffolds = parse_info_frags(info_frags)
return scaffolds
except OSError:
print("Error when opening info_frags.txt")
raise | python | def format_info_frags(info_frags):
if isinstance(info_frags, dict):
return info_frags
else:
try:
scaffolds = parse_info_frags(info_frags)
return scaffolds
except OSError:
print("Error when opening info_frags.txt")
raise | [
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17,831 | koszullab/instaGRAAL | instagraal/parse_info_frags.py | plot_info_frags | def plot_info_frags(scaffolds):
"""A crude way to visualize new scaffolds according to their origin on the
initial scaffolding. Each scaffold spawns a new plot. Orientations are
represented by different colors.
"""
scaffolds = format_info_frags(scaffolds)
for name, scaffold in scaffolds.items():
plt.figure()
xs = range(len(scaffold))
color = []
names = {}
ys = []
for my_bin in scaffold:
current_color = "r" if my_bin[4] > 0 else "g"
color += [current_color]
name = my_bin[0]
if name in names:
ys.append(names[name])
else:
names[name] = len(names)
ys.append(names[name])
plt.scatter(xs, ys, c=color)
plt.show() | python | def plot_info_frags(scaffolds):
scaffolds = format_info_frags(scaffolds)
for name, scaffold in scaffolds.items():
plt.figure()
xs = range(len(scaffold))
color = []
names = {}
ys = []
for my_bin in scaffold:
current_color = "r" if my_bin[4] > 0 else "g"
color += [current_color]
name = my_bin[0]
if name in names:
ys.append(names[name])
else:
names[name] = len(names)
ys.append(names[name])
plt.scatter(xs, ys, c=color)
plt.show() | [
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17,832 | koszullab/instaGRAAL | instagraal/parse_info_frags.py | remove_spurious_insertions | def remove_spurious_insertions(scaffolds):
"""Remove all bins whose left and right neighbors belong to the same,
different scaffold.
Example with three such insertions in two different scaffolds:
>>> scaffolds = {
... "scaffold1": [
... ["contig1", 0, 0, 100, 1],
... ["contig1", 1, 100, 200, 1],
... ["contig23", 53, 1845, 2058, -1], # <-- insertion
... ["contig1", 4, 254, 408, 1],
... ["contig1", 7, 805, 1253, 1],
... ["contig5", 23, 1500, 1605, -1],
... ["contig65", 405, 32145, 45548, -1], # <-- insertion
... ["contig5", 22, 1385, 1499, -1],
... ],
... "scaffold2": [
... ["contig8", 0, 0, 250, 1],
... ["contig17", 2454, 8754, -1], # <-- insertion
... ["contig8", 2, 320, 480, 1],
... ],
... }
>>> new_scaffolds = remove_spurious_insertions(scaffolds)
>>> for my_bin in new_scaffolds['scaffold1']:
... print(my_bin)
...
['contig1', 0, 0, 100, 1]
['contig1', 1, 100, 200, 1]
['contig1', 4, 254, 408, 1]
['contig1', 7, 805, 1253, 1]
['contig5', 23, 1500, 1605, -1]
['contig5', 22, 1385, 1499, -1]
>>> for my_bin in new_scaffolds['scaffold2']:
... print(my_bin)
...
['contig8', 0, 0, 250, 1]
['contig8', 2, 320, 480, 1]
"""
scaffolds = format_info_frags(scaffolds)
new_scaffolds = {}
for name, scaffold in scaffolds.items():
new_scaffold = []
if len(scaffold) > 2:
for i in range(len(scaffold)):
# First take care of edge cases: *-- or --*
if i == 0:
if not (
scaffold[i][0] != scaffold[i + 1][0]
and scaffold[i + 1][0] == scaffold[i + 2][0]
):
new_scaffold.append(scaffold[i])
elif i == len(scaffold) - 1:
if not (
scaffold[i][0] != scaffold[i - 1][0]
and scaffold[i - 1][0] == scaffold[i - 2][0]
):
new_scaffold.append(scaffold[i])
# Otherwise, looking for -*-
else:
if not (
scaffold[i - 1][0] == scaffold[i + 1][0]
and scaffold[i - 1][0] != scaffold[i][0]
):
new_scaffold.append(scaffold[i])
else:
# Can't remove insertions if 2 bins or less
new_scaffold = copy.deepcopy(scaffold)
new_scaffolds[name] = new_scaffold
return new_scaffolds | python | def remove_spurious_insertions(scaffolds):
scaffolds = format_info_frags(scaffolds)
new_scaffolds = {}
for name, scaffold in scaffolds.items():
new_scaffold = []
if len(scaffold) > 2:
for i in range(len(scaffold)):
# First take care of edge cases: *-- or --*
if i == 0:
if not (
scaffold[i][0] != scaffold[i + 1][0]
and scaffold[i + 1][0] == scaffold[i + 2][0]
):
new_scaffold.append(scaffold[i])
elif i == len(scaffold) - 1:
if not (
scaffold[i][0] != scaffold[i - 1][0]
and scaffold[i - 1][0] == scaffold[i - 2][0]
):
new_scaffold.append(scaffold[i])
# Otherwise, looking for -*-
else:
if not (
scaffold[i - 1][0] == scaffold[i + 1][0]
and scaffold[i - 1][0] != scaffold[i][0]
):
new_scaffold.append(scaffold[i])
else:
# Can't remove insertions if 2 bins or less
new_scaffold = copy.deepcopy(scaffold)
new_scaffolds[name] = new_scaffold
return new_scaffolds | [
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different scaffold.
Example with three such insertions in two different scaffolds:
>>> scaffolds = {
... "scaffold1": [
... ["contig1", 0, 0, 100, 1],
... ["contig1", 1, 100, 200, 1],
... ["contig23", 53, 1845, 2058, -1], # <-- insertion
... ["contig1", 4, 254, 408, 1],
... ["contig1", 7, 805, 1253, 1],
... ["contig5", 23, 1500, 1605, -1],
... ["contig65", 405, 32145, 45548, -1], # <-- insertion
... ["contig5", 22, 1385, 1499, -1],
... ],
... "scaffold2": [
... ["contig8", 0, 0, 250, 1],
... ["contig17", 2454, 8754, -1], # <-- insertion
... ["contig8", 2, 320, 480, 1],
... ],
... }
>>> new_scaffolds = remove_spurious_insertions(scaffolds)
>>> for my_bin in new_scaffolds['scaffold1']:
... print(my_bin)
...
['contig1', 0, 0, 100, 1]
['contig1', 1, 100, 200, 1]
['contig1', 4, 254, 408, 1]
['contig1', 7, 805, 1253, 1]
['contig5', 23, 1500, 1605, -1]
['contig5', 22, 1385, 1499, -1]
>>> for my_bin in new_scaffolds['scaffold2']:
... print(my_bin)
...
['contig8', 0, 0, 250, 1]
['contig8', 2, 320, 480, 1] | [
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17,833 | koszullab/instaGRAAL | instagraal/parse_info_frags.py | rearrange_intra_scaffolds | def rearrange_intra_scaffolds(scaffolds):
"""Rearranges all bins within each scaffold such that all bins belonging
to the same initial contig are grouped together in the same order. When
two such groups are found, the smaller one is moved to the larger one.
"""
scaffolds = format_info_frags(scaffolds)
new_scaffolds = {}
ordering = dict()
for name, scaffold in scaffolds.items():
new_scaffold = []
ordering = dict()
order = 0
my_blocks = []
for _, my_block in itertools.groupby(scaffold, operator.itemgetter(0)):
my_bins = list(my_block)
my_blocks.append(my_bins)
block_length = len(my_bins)
block_name = my_bins[0][0]
if block_name in ordering.keys():
if block_length > ordering[block_name][1]:
ordering[block_name] = (order, block_length)
else:
ordering[block_name] = (order, block_length)
order += 1
def block_order(block):
return ordering[block[0][0]][0]
for my_block in sorted(my_blocks, key=block_order):
for my_bin in my_block:
new_scaffold.append(my_bin)
new_scaffolds[name] = copy.deepcopy(new_scaffold)
return new_scaffolds | python | def rearrange_intra_scaffolds(scaffolds):
scaffolds = format_info_frags(scaffolds)
new_scaffolds = {}
ordering = dict()
for name, scaffold in scaffolds.items():
new_scaffold = []
ordering = dict()
order = 0
my_blocks = []
for _, my_block in itertools.groupby(scaffold, operator.itemgetter(0)):
my_bins = list(my_block)
my_blocks.append(my_bins)
block_length = len(my_bins)
block_name = my_bins[0][0]
if block_name in ordering.keys():
if block_length > ordering[block_name][1]:
ordering[block_name] = (order, block_length)
else:
ordering[block_name] = (order, block_length)
order += 1
def block_order(block):
return ordering[block[0][0]][0]
for my_block in sorted(my_blocks, key=block_order):
for my_bin in my_block:
new_scaffold.append(my_bin)
new_scaffolds[name] = copy.deepcopy(new_scaffold)
return new_scaffolds | [
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17,834 | koszullab/instaGRAAL | instagraal/parse_info_frags.py | write_fasta | def write_fasta(
init_fasta, info_frags, output=DEFAULT_NEW_GENOME_NAME, junction=False
):
"""Convert an info_frags.txt file into a fasta file given a reference.
Optionally adds junction sequences to reflect the possibly missing base
pairs between two newly joined scaffolds.
"""
init_genome = {
record.id: record.seq for record in SeqIO.parse(init_fasta, "fasta")
}
my_new_records = []
with open(info_frags, "r") as info_frags_handle:
current_seq = ""
current_id = None
previous_contig = None
for line in info_frags_handle:
if line.startswith(">"):
previous_contig = None
if current_id is not None:
new_record = SeqRecord(
current_seq, id=current_id, description=""
)
my_new_records.append(new_record)
current_seq = ""
current_id = str(line[1:])
elif line.startswith("init_contig"):
previous_contig = None
else:
(init_contig, _, orientation, pos_start, pos_end) = str(
line[:-1]
).split("\t")
start = int(pos_start)
end = int(pos_end)
ori = int(orientation)
assert start < end
assert ori in {-1, 1}
seq_to_add = init_genome[init_contig][start:end]
if ori == 1:
current_seq += seq_to_add
elif ori == -1:
current_seq += seq_to_add.reverse_complement()
if junction and previous_contig not in {init_contig, None}:
error_was_raised = False
try:
extra_seq = Seq(junction, IUPAC.ambiguous_dna)
current_seq = extra_seq + current_seq
except TypeError:
if not error_was_raised:
print("Invalid junction sequence")
error_was_raised = True
previous_contig = init_contig
new_record = SeqRecord(current_seq, id=current_id, description="")
my_new_records.append(new_record)
SeqIO.write(my_new_records, output, "fasta") | python | def write_fasta(
init_fasta, info_frags, output=DEFAULT_NEW_GENOME_NAME, junction=False
):
init_genome = {
record.id: record.seq for record in SeqIO.parse(init_fasta, "fasta")
}
my_new_records = []
with open(info_frags, "r") as info_frags_handle:
current_seq = ""
current_id = None
previous_contig = None
for line in info_frags_handle:
if line.startswith(">"):
previous_contig = None
if current_id is not None:
new_record = SeqRecord(
current_seq, id=current_id, description=""
)
my_new_records.append(new_record)
current_seq = ""
current_id = str(line[1:])
elif line.startswith("init_contig"):
previous_contig = None
else:
(init_contig, _, orientation, pos_start, pos_end) = str(
line[:-1]
).split("\t")
start = int(pos_start)
end = int(pos_end)
ori = int(orientation)
assert start < end
assert ori in {-1, 1}
seq_to_add = init_genome[init_contig][start:end]
if ori == 1:
current_seq += seq_to_add
elif ori == -1:
current_seq += seq_to_add.reverse_complement()
if junction and previous_contig not in {init_contig, None}:
error_was_raised = False
try:
extra_seq = Seq(junction, IUPAC.ambiguous_dna)
current_seq = extra_seq + current_seq
except TypeError:
if not error_was_raised:
print("Invalid junction sequence")
error_was_raised = True
previous_contig = init_contig
new_record = SeqRecord(current_seq, id=current_id, description="")
my_new_records.append(new_record)
SeqIO.write(my_new_records, output, "fasta") | [
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Optionally adds junction sequences to reflect the possibly missing base
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17,835 | koszullab/instaGRAAL | instagraal/parse_info_frags.py | is_block | def is_block(bin_list):
"""Check if a bin list has exclusively consecutive bin ids.
"""
id_set = set((my_bin[1] for my_bin in bin_list))
start_id, end_id = min(id_set), max(id_set)
return id_set == set(range(start_id, end_id + 1)) | python | def is_block(bin_list):
id_set = set((my_bin[1] for my_bin in bin_list))
start_id, end_id = min(id_set), max(id_set)
return id_set == set(range(start_id, end_id + 1)) | [
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17,836 | koszullab/instaGRAAL | instagraal/leastsqbound.py | internal2external_grad | def internal2external_grad(xi, bounds):
"""
Calculate the internal to external gradiant
Calculates the partial of external over internal
"""
ge = np.empty_like(xi)
for i, (v, bound) in enumerate(zip(xi, bounds)):
a = bound[0] # minimum
b = bound[1] # maximum
if a == None and b == None: # No constraints
ge[i] = 1.0
elif b == None: # only min
ge[i] = v / np.sqrt(v ** 2 + 1)
elif a == None: # only max
ge[i] = -v / np.sqrt(v ** 2 + 1)
else: # both min and max
ge[i] = (b - a) * np.cos(v) / 2.
return ge | python | def internal2external_grad(xi, bounds):
ge = np.empty_like(xi)
for i, (v, bound) in enumerate(zip(xi, bounds)):
a = bound[0] # minimum
b = bound[1] # maximum
if a == None and b == None: # No constraints
ge[i] = 1.0
elif b == None: # only min
ge[i] = v / np.sqrt(v ** 2 + 1)
elif a == None: # only max
ge[i] = -v / np.sqrt(v ** 2 + 1)
else: # both min and max
ge[i] = (b - a) * np.cos(v) / 2.
return ge | [
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17,837 | koszullab/instaGRAAL | instagraal/leastsqbound.py | internal2external | def internal2external(xi, bounds):
""" Convert a series of internal variables to external variables"""
xe = np.empty_like(xi)
for i, (v, bound) in enumerate(zip(xi, bounds)):
a = bound[0] # minimum
b = bound[1] # maximum
if a == None and b == None: # No constraints
xe[i] = v
elif b == None: # only min
xe[i] = a - 1. + np.sqrt(v ** 2. + 1.)
elif a == None: # only max
xe[i] = b + 1. - np.sqrt(v ** 2. + 1.)
else: # both min and max
xe[i] = a + ((b - a) / 2.) * (np.sin(v) + 1.)
return xe | python | def internal2external(xi, bounds):
xe = np.empty_like(xi)
for i, (v, bound) in enumerate(zip(xi, bounds)):
a = bound[0] # minimum
b = bound[1] # maximum
if a == None and b == None: # No constraints
xe[i] = v
elif b == None: # only min
xe[i] = a - 1. + np.sqrt(v ** 2. + 1.)
elif a == None: # only max
xe[i] = b + 1. - np.sqrt(v ** 2. + 1.)
else: # both min and max
xe[i] = a + ((b - a) / 2.) * (np.sin(v) + 1.)
return xe | [
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17,838 | koszullab/instaGRAAL | instagraal/leastsqbound.py | external2internal | def external2internal(xe, bounds):
""" Convert a series of external variables to internal variables"""
xi = np.empty_like(xe)
for i, (v, bound) in enumerate(zip(xe, bounds)):
a = bound[0] # minimum
b = bound[1] # maximum
if a == None and b == None: # No constraints
xi[i] = v
elif b == None: # only min
xi[i] = np.sqrt((v - a + 1.) ** 2. - 1)
elif a == None: # only max
xi[i] = np.sqrt((b - v + 1.) ** 2. - 1)
else: # both min and max
xi[i] = np.arcsin((2. * (v - a) / (b - a)) - 1.)
return xi | python | def external2internal(xe, bounds):
xi = np.empty_like(xe)
for i, (v, bound) in enumerate(zip(xe, bounds)):
a = bound[0] # minimum
b = bound[1] # maximum
if a == None and b == None: # No constraints
xi[i] = v
elif b == None: # only min
xi[i] = np.sqrt((v - a + 1.) ** 2. - 1)
elif a == None: # only max
xi[i] = np.sqrt((b - v + 1.) ** 2. - 1)
else: # both min and max
xi[i] = np.arcsin((2. * (v - a) / (b - a)) - 1.)
return xi | [
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17,839 | koszullab/instaGRAAL | instagraal/leastsqbound.py | calc_cov_x | def calc_cov_x(infodic, p):
"""
Calculate cov_x from fjac, ipvt and p as is done in leastsq
"""
fjac = infodic["fjac"]
ipvt = infodic["ipvt"]
n = len(p)
# adapted from leastsq function in scipy/optimize/minpack.py
perm = np.take(np.eye(n), ipvt - 1, 0)
r = np.triu(np.transpose(fjac)[:n, :])
R = np.dot(r, perm)
try:
cov_x = np.linalg.inv(np.dot(np.transpose(R), R))
except LinAlgError:
cov_x = None
return cov_x | python | def calc_cov_x(infodic, p):
fjac = infodic["fjac"]
ipvt = infodic["ipvt"]
n = len(p)
# adapted from leastsq function in scipy/optimize/minpack.py
perm = np.take(np.eye(n), ipvt - 1, 0)
r = np.triu(np.transpose(fjac)[:n, :])
R = np.dot(r, perm)
try:
cov_x = np.linalg.inv(np.dot(np.transpose(R), R))
except LinAlgError:
cov_x = None
return cov_x | [
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17,840 | koszullab/instaGRAAL | instagraal/leastsqbound.py | leastsqbound | def leastsqbound(func, x0, bounds, args=(), **kw):
"""
Constrained multivariant Levenberg-Marquard optimization
Minimize the sum of squares of a given function using the
Levenberg-Marquard algorithm. Contraints on parameters are inforced using
variable transformations as described in the MINUIT User's Guide by
Fred James and Matthias Winkler.
Parameters:
* func functions to call for optimization.
* x0 Starting estimate for the minimization.
* bounds (min,max) pair for each element of x, defining the bounds on
that parameter. Use None for one of min or max when there is
no bound in that direction.
* args Any extra arguments to func are places in this tuple.
Returns: (x,{cov_x,infodict,mesg},ier)
Return is described in the scipy.optimize.leastsq function. x and con_v
are corrected to take into account the parameter transformation, infodic
is not corrected.
Additional keyword arguments are passed directly to the
scipy.optimize.leastsq algorithm.
"""
# check for full output
if "full_output" in kw and kw["full_output"]:
full = True
else:
full = False
# convert x0 to internal variables
i0 = external2internal(x0, bounds)
# perfrom unconstrained optimization using internal variables
r = leastsq(err, i0, args=(bounds, func, args), **kw)
# unpack return convert to external variables and return
if full:
xi, cov_xi, infodic, mesg, ier = r
xe = internal2external(xi, bounds)
cov_xe = i2e_cov_x(xi, bounds, cov_xi)
# XXX correct infodic 'fjac','ipvt', and 'qtf'
return xe, cov_xe, infodic, mesg, ier
else:
xi, ier = r
xe = internal2external(xi, bounds)
return xe, ier | python | def leastsqbound(func, x0, bounds, args=(), **kw):
# check for full output
if "full_output" in kw and kw["full_output"]:
full = True
else:
full = False
# convert x0 to internal variables
i0 = external2internal(x0, bounds)
# perfrom unconstrained optimization using internal variables
r = leastsq(err, i0, args=(bounds, func, args), **kw)
# unpack return convert to external variables and return
if full:
xi, cov_xi, infodic, mesg, ier = r
xe = internal2external(xi, bounds)
cov_xe = i2e_cov_x(xi, bounds, cov_xi)
# XXX correct infodic 'fjac','ipvt', and 'qtf'
return xe, cov_xe, infodic, mesg, ier
else:
xi, ier = r
xe = internal2external(xi, bounds)
return xe, ier | [
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Minimize the sum of squares of a given function using the
Levenberg-Marquard algorithm. Contraints on parameters are inforced using
variable transformations as described in the MINUIT User's Guide by
Fred James and Matthias Winkler.
Parameters:
* func functions to call for optimization.
* x0 Starting estimate for the minimization.
* bounds (min,max) pair for each element of x, defining the bounds on
that parameter. Use None for one of min or max when there is
no bound in that direction.
* args Any extra arguments to func are places in this tuple.
Returns: (x,{cov_x,infodict,mesg},ier)
Return is described in the scipy.optimize.leastsq function. x and con_v
are corrected to take into account the parameter transformation, infodic
is not corrected.
Additional keyword arguments are passed directly to the
scipy.optimize.leastsq algorithm. | [
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"-",
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] | 1c02ca838e57d8178eec79f223644b2acd0153dd | https://github.com/koszullab/instaGRAAL/blob/1c02ca838e57d8178eec79f223644b2acd0153dd/instagraal/leastsqbound.py#L132-L183 |
17,841 | guykisel/inline-plz | inlineplz/interfaces/github.py | GitHubInterface.start_review | def start_review(self):
"""Mark our review as started."""
if self.set_status:
self.github_repo.create_status(
state="pending",
description="Static analysis in progress.",
context="inline-plz",
sha=self.last_sha,
) | python | def start_review(self):
if self.set_status:
self.github_repo.create_status(
state="pending",
description="Static analysis in progress.",
context="inline-plz",
sha=self.last_sha,
) | [
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17,842 | guykisel/inline-plz | inlineplz/interfaces/github.py | GitHubInterface.finish_review | def finish_review(self, success=True, error=False):
"""Mark our review as finished."""
if self.set_status:
if error:
self.github_repo.create_status(
state="error",
description="Static analysis error! inline-plz failed to run.",
context="inline-plz",
sha=self.last_sha,
)
elif success:
self.github_repo.create_status(
state="success",
description="Static analysis complete! No errors found in your PR.",
context="inline-plz",
sha=self.last_sha,
)
else:
self.github_repo.create_status(
state="failure",
description="Static analysis complete! Found errors in your PR.",
context="inline-plz",
sha=self.last_sha,
) | python | def finish_review(self, success=True, error=False):
if self.set_status:
if error:
self.github_repo.create_status(
state="error",
description="Static analysis error! inline-plz failed to run.",
context="inline-plz",
sha=self.last_sha,
)
elif success:
self.github_repo.create_status(
state="success",
description="Static analysis complete! No errors found in your PR.",
context="inline-plz",
sha=self.last_sha,
)
else:
self.github_repo.create_status(
state="failure",
description="Static analysis complete! Found errors in your PR.",
context="inline-plz",
sha=self.last_sha,
) | [
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17,843 | guykisel/inline-plz | inlineplz/interfaces/github.py | GitHubInterface.out_of_date | def out_of_date(self):
"""Check if our local latest sha matches the remote latest sha"""
try:
latest_remote_sha = self.pr_commits(self.pull_request.refresh(True))[-1].sha
print("Latest remote sha: {}".format(latest_remote_sha))
try:
print("Ratelimit remaining: {}".format(self.github.ratelimit_remaining))
except Exception:
print("Failed to look up ratelimit remaining")
return self.last_sha != latest_remote_sha
except IndexError:
return False | python | def out_of_date(self):
try:
latest_remote_sha = self.pr_commits(self.pull_request.refresh(True))[-1].sha
print("Latest remote sha: {}".format(latest_remote_sha))
try:
print("Ratelimit remaining: {}".format(self.github.ratelimit_remaining))
except Exception:
print("Failed to look up ratelimit remaining")
return self.last_sha != latest_remote_sha
except IndexError:
return False | [
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17,844 | guykisel/inline-plz | inlineplz/interfaces/github.py | GitHubInterface.position | def position(self, message):
"""Calculate position within the PR, which is not the line number"""
if not message.line_number:
message.line_number = 1
for patched_file in self.patch:
target = patched_file.target_file.lstrip("b/")
if target == message.path:
offset = 1
for hunk in patched_file:
for position, hunk_line in enumerate(hunk):
if hunk_line.target_line_no == message.line_number:
if not hunk_line.is_added:
# if the line isn't an added line, we don't want to comment on it
return
return position + offset
offset += len(hunk) + 1 | python | def position(self, message):
if not message.line_number:
message.line_number = 1
for patched_file in self.patch:
target = patched_file.target_file.lstrip("b/")
if target == message.path:
offset = 1
for hunk in patched_file:
for position, hunk_line in enumerate(hunk):
if hunk_line.target_line_no == message.line_number:
if not hunk_line.is_added:
# if the line isn't an added line, we don't want to comment on it
return
return position + offset
offset += len(hunk) + 1 | [
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17,845 | koszullab/instaGRAAL | instagraal/pyramid_sparse.py | abs_contact_2_coo_file | def abs_contact_2_coo_file(abs_contact_file, coo_file):
"""Convert contact maps between old-style and new-style formats.
A legacy function that converts contact maps from the older GRAAL format to
the simpler instaGRAAL format. This is useful with datasets generated by
Hi-C box.
Parameters
----------
abs_contact_file : str, file or pathlib.Path
The input old-style contact map.
coo_file : str, file, or pathlib.Path
The output path to the generated contact map; must be writable.
"""
sparse_dict = dict()
h = open(abs_contact_file, "r")
all_lines = h.readlines()
n_lines = len(all_lines)
for i in range(1, n_lines):
line = all_lines[i]
dat = line.split()
mates = [int(dat[0]), int(dat[1])]
mates.sort()
f1 = mates[0] - 1
f2 = mates[1] - 1
if f1 in sparse_dict:
if f2 in sparse_dict[f1]:
sparse_dict[f1][f2] += 1
else:
sparse_dict[f1][f2] = 1
else:
sparse_dict[f1] = dict()
sparse_dict[f1][f2] = 1
keys = list(sparse_dict.keys())
keys.sort()
h.close()
h_coo = open(coo_file, "w")
h_coo.write("%s\t%s\t%s\n" % ("id_frag_a", "id_frag_b", "n_contact"))
for fa in keys:
d_fb = sparse_dict[fa]
keys_b = list(d_fb.keys())
keys_b.sort()
for fb in keys_b:
nc = d_fb[fb]
h_coo.write("%s\t%s\t%s\n" % (str(fa), str(fb), str(nc)))
h_coo.close()
h.close() | python | def abs_contact_2_coo_file(abs_contact_file, coo_file):
sparse_dict = dict()
h = open(abs_contact_file, "r")
all_lines = h.readlines()
n_lines = len(all_lines)
for i in range(1, n_lines):
line = all_lines[i]
dat = line.split()
mates = [int(dat[0]), int(dat[1])]
mates.sort()
f1 = mates[0] - 1
f2 = mates[1] - 1
if f1 in sparse_dict:
if f2 in sparse_dict[f1]:
sparse_dict[f1][f2] += 1
else:
sparse_dict[f1][f2] = 1
else:
sparse_dict[f1] = dict()
sparse_dict[f1][f2] = 1
keys = list(sparse_dict.keys())
keys.sort()
h.close()
h_coo = open(coo_file, "w")
h_coo.write("%s\t%s\t%s\n" % ("id_frag_a", "id_frag_b", "n_contact"))
for fa in keys:
d_fb = sparse_dict[fa]
keys_b = list(d_fb.keys())
keys_b.sort()
for fb in keys_b:
nc = d_fb[fb]
h_coo.write("%s\t%s\t%s\n" % (str(fa), str(fb), str(nc)))
h_coo.close()
h.close() | [
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A legacy function that converts contact maps from the older GRAAL format to
the simpler instaGRAAL format. This is useful with datasets generated by
Hi-C box.
Parameters
----------
abs_contact_file : str, file or pathlib.Path
The input old-style contact map.
coo_file : str, file, or pathlib.Path
The output path to the generated contact map; must be writable. | [
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17,846 | koszullab/instaGRAAL | instagraal/pyramid_sparse.py | fill_sparse_pyramid_level | def fill_sparse_pyramid_level(pyramid_handle, level, contact_file, nfrags):
"""Fill a level with sparse contact map data
Fill values from the simple text matrix file to the hdf5-based pyramid
level with contact data.
Parameters
----------
pyramid_handle : h5py.File
The hdf5 file handle containing the whole dataset.
level : int
The level (resolution) to be filled with contact data.
contact_file : str, file or pathlib.Path
The binned contact map file to be converted to hdf5 data.
nfrags : int
The number of fragments/bins in that specific level.
"""
sparse_dict = dict()
h = open(contact_file, "r")
all_lines = h.readlines()
n_lines = len(all_lines)
for i in range(1, n_lines):
line = all_lines[i]
dat = line.split()
mates = [int(dat[0]), int(dat[1])]
nc = int(dat[2])
mates.sort()
f1 = mates[0]
f2 = mates[1]
if f1 in sparse_dict:
if f2 in sparse_dict[f1]:
sparse_dict[f1][f2] += nc
else:
sparse_dict[f1][f2] = nc
else:
sparse_dict[f1] = dict()
sparse_dict[f1][f2] = nc
keys = list(sparse_dict.keys())
keys.sort()
out_r = []
out_c = []
out_d = []
for r in keys:
data = sparse_dict[r]
for c in list(data.keys()):
out_r.append(r)
out_c.append(c)
out_d.append(data[c])
n_on_pxls = len(out_d)
level_hdf5 = pyramid_handle.create_group(str(level))
data_2_sparse = level_hdf5.create_dataset("data", (3, n_on_pxls), "i")
data_nfrags = level_hdf5.create_dataset("nfrags", (1, 1), "i")
np_csr = np.zeros((3, n_on_pxls), dtype=np.int32)
np_csr[0, :] = out_r
np_csr[1, :] = out_c
np_csr[2, :] = out_d
data_2_sparse[0, :] = out_r
data_2_sparse[1, :] = out_c
data_2_sparse[2, :] = out_d
data_nfrags[:] = nfrags | python | def fill_sparse_pyramid_level(pyramid_handle, level, contact_file, nfrags):
sparse_dict = dict()
h = open(contact_file, "r")
all_lines = h.readlines()
n_lines = len(all_lines)
for i in range(1, n_lines):
line = all_lines[i]
dat = line.split()
mates = [int(dat[0]), int(dat[1])]
nc = int(dat[2])
mates.sort()
f1 = mates[0]
f2 = mates[1]
if f1 in sparse_dict:
if f2 in sparse_dict[f1]:
sparse_dict[f1][f2] += nc
else:
sparse_dict[f1][f2] = nc
else:
sparse_dict[f1] = dict()
sparse_dict[f1][f2] = nc
keys = list(sparse_dict.keys())
keys.sort()
out_r = []
out_c = []
out_d = []
for r in keys:
data = sparse_dict[r]
for c in list(data.keys()):
out_r.append(r)
out_c.append(c)
out_d.append(data[c])
n_on_pxls = len(out_d)
level_hdf5 = pyramid_handle.create_group(str(level))
data_2_sparse = level_hdf5.create_dataset("data", (3, n_on_pxls), "i")
data_nfrags = level_hdf5.create_dataset("nfrags", (1, 1), "i")
np_csr = np.zeros((3, n_on_pxls), dtype=np.int32)
np_csr[0, :] = out_r
np_csr[1, :] = out_c
np_csr[2, :] = out_d
data_2_sparse[0, :] = out_r
data_2_sparse[1, :] = out_c
data_2_sparse[2, :] = out_d
data_nfrags[:] = nfrags | [
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Fill values from the simple text matrix file to the hdf5-based pyramid
level with contact data.
Parameters
----------
pyramid_handle : h5py.File
The hdf5 file handle containing the whole dataset.
level : int
The level (resolution) to be filled with contact data.
contact_file : str, file or pathlib.Path
The binned contact map file to be converted to hdf5 data.
nfrags : int
The number of fragments/bins in that specific level. | [
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17,847 | koszullab/instaGRAAL | instagraal/pyramid_sparse.py | init_frag_list | def init_frag_list(fragment_list, new_frag_list):
"""Adapt the original fragment list to fit the build function requirements
Parameters
----------
fragment_list : str, file or pathlib.Path
The input fragment list.
new_frag_list : str, file or pathlib.Path
The output fragment list to be written.
Returns
-------
i : int
The number of records processed this way.
"""
handle_frag_list = open(fragment_list, "r")
handle_new_frag_list = open(new_frag_list, "w")
handle_new_frag_list.write(
"%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n"
% (
"id",
"chrom",
"start_pos",
"end_pos",
"size",
"gc_content",
"accu_frag",
"frag_start",
"frag_end",
)
)
handle_frag_list.readline()
i = 0
while 1:
line_frag = handle_frag_list.readline()
if not line_frag:
handle_frag_list.close()
handle_new_frag_list.close()
break
i += 1
data = line_frag.split("\t")
id_init = data[0]
contig_name = data[1]
start_pos = data[2]
end_pos = data[3]
length_kb = data[4]
gc_content = str(float(data[5]))
accu_frag = str(1)
frag_start = id_init
frag_end = id_init
handle_new_frag_list.write(
"%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n"
% (
id_init,
contig_name,
start_pos,
end_pos,
length_kb,
gc_content,
accu_frag,
frag_start,
frag_end,
)
)
return i | python | def init_frag_list(fragment_list, new_frag_list):
handle_frag_list = open(fragment_list, "r")
handle_new_frag_list = open(new_frag_list, "w")
handle_new_frag_list.write(
"%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n"
% (
"id",
"chrom",
"start_pos",
"end_pos",
"size",
"gc_content",
"accu_frag",
"frag_start",
"frag_end",
)
)
handle_frag_list.readline()
i = 0
while 1:
line_frag = handle_frag_list.readline()
if not line_frag:
handle_frag_list.close()
handle_new_frag_list.close()
break
i += 1
data = line_frag.split("\t")
id_init = data[0]
contig_name = data[1]
start_pos = data[2]
end_pos = data[3]
length_kb = data[4]
gc_content = str(float(data[5]))
accu_frag = str(1)
frag_start = id_init
frag_end = id_init
handle_new_frag_list.write(
"%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n"
% (
id_init,
contig_name,
start_pos,
end_pos,
length_kb,
gc_content,
accu_frag,
frag_start,
frag_end,
)
)
return i | [
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Parameters
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fragment_list : str, file or pathlib.Path
The input fragment list.
new_frag_list : str, file or pathlib.Path
The output fragment list to be written.
Returns
-------
i : int
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] | 1c02ca838e57d8178eec79f223644b2acd0153dd | https://github.com/koszullab/instaGRAAL/blob/1c02ca838e57d8178eec79f223644b2acd0153dd/instagraal/pyramid_sparse.py#L453-L519 |
17,848 | koszullab/instaGRAAL | instagraal/pyramid_sparse.py | pyramid.zoom_in_pixel | def zoom_in_pixel(self, curr_pixel):
""" return the curr_frag at a higher resolution"""
low_frag = curr_pixel[0]
high_frag = curr_pixel[1]
level = curr_pixel[2]
if level > 0:
str_level = str(level)
low_sub_low = self.spec_level[str_level]["fragments_dict"][
low_frag
]["sub_low_index"]
low_sub_high = self.spec_level[str_level]["fragments_dict"][
low_frag
]["sub_high_index"]
high_sub_low = self.spec_level[str_level]["fragments_dict"][
high_frag
]["sub_low_index"]
high_sub_high = self.spec_level[str_level]["fragments_dict"][
high_frag
]["sub_high_index"]
vect = [low_sub_low, low_sub_high, high_sub_low, high_sub_high]
new_pix_low = min(vect)
new_pix_high = max(vect)
new_level = level - 1
new_pixel = [new_pix_low, new_pix_high, new_level]
else:
new_pixel = curr_pixel
return new_pixel | python | def zoom_in_pixel(self, curr_pixel):
low_frag = curr_pixel[0]
high_frag = curr_pixel[1]
level = curr_pixel[2]
if level > 0:
str_level = str(level)
low_sub_low = self.spec_level[str_level]["fragments_dict"][
low_frag
]["sub_low_index"]
low_sub_high = self.spec_level[str_level]["fragments_dict"][
low_frag
]["sub_high_index"]
high_sub_low = self.spec_level[str_level]["fragments_dict"][
high_frag
]["sub_low_index"]
high_sub_high = self.spec_level[str_level]["fragments_dict"][
high_frag
]["sub_high_index"]
vect = [low_sub_low, low_sub_high, high_sub_low, high_sub_high]
new_pix_low = min(vect)
new_pix_high = max(vect)
new_level = level - 1
new_pixel = [new_pix_low, new_pix_high, new_level]
else:
new_pixel = curr_pixel
return new_pixel | [
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17,849 | koszullab/instaGRAAL | instagraal/pyramid_sparse.py | pyramid.zoom_out_pixel | def zoom_out_pixel(self, curr_pixel):
""" return the curr_frag at a lower resolution"""
low_frag = curr_pixel[0]
high_frag = curr_pixel[1]
level = curr_pixel[2]
str_level = str(level)
if level < self.n_level - 1:
low_super = self.spec_level[str_level]["fragments_dict"][low_frag][
"super_index"
]
high_super = self.spec_level[str_level]["fragments_dict"][
high_frag
]["sub_index"]
new_pix_low = min([low_super, high_super])
new_pix_high = max([low_super, high_super])
new_level = level + 1
new_pixel = [new_pix_low, new_pix_high, new_level]
else:
new_pixel = curr_pixel
return new_pixel | python | def zoom_out_pixel(self, curr_pixel):
low_frag = curr_pixel[0]
high_frag = curr_pixel[1]
level = curr_pixel[2]
str_level = str(level)
if level < self.n_level - 1:
low_super = self.spec_level[str_level]["fragments_dict"][low_frag][
"super_index"
]
high_super = self.spec_level[str_level]["fragments_dict"][
high_frag
]["sub_index"]
new_pix_low = min([low_super, high_super])
new_pix_high = max([low_super, high_super])
new_level = level + 1
new_pixel = [new_pix_low, new_pix_high, new_level]
else:
new_pixel = curr_pixel
return new_pixel | [
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17,850 | koszullab/instaGRAAL | instagraal/pyramid_sparse.py | pyramid.zoom_in_area | def zoom_in_area(self, area):
""" zoom in area"""
x = area[0]
y = area[1]
level = x[2]
logger.debug("x = {}".format(x))
logger.debug("y = {}".format(y))
logger.debug("level = {}".format(level))
if level == y[2] and level > 0:
new_level = level - 1
high_x = self.zoom_in_pixel(x)
high_y = self.zoom_in_pixel(y)
new_x = [
min([high_x[0], high_y[0]]),
min([high_x[1], high_y[1]]),
new_level,
]
new_y = [
max([high_x[0], high_y[0]]),
max([high_x[1], high_y[1]]),
new_level,
]
new_area = [new_x, new_y]
else:
new_area = area
return new_area | python | def zoom_in_area(self, area):
x = area[0]
y = area[1]
level = x[2]
logger.debug("x = {}".format(x))
logger.debug("y = {}".format(y))
logger.debug("level = {}".format(level))
if level == y[2] and level > 0:
new_level = level - 1
high_x = self.zoom_in_pixel(x)
high_y = self.zoom_in_pixel(y)
new_x = [
min([high_x[0], high_y[0]]),
min([high_x[1], high_y[1]]),
new_level,
]
new_y = [
max([high_x[0], high_y[0]]),
max([high_x[1], high_y[1]]),
new_level,
]
new_area = [new_x, new_y]
else:
new_area = area
return new_area | [
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17,851 | guykisel/inline-plz | inlineplz/main.py | load_config | def load_config(args, config_path=".inlineplz.yml"):
"""Load inline-plz config from yaml config file with reasonable defaults."""
config = {}
try:
with open(config_path) as configfile:
config = yaml.safe_load(configfile) or {}
if config:
print("Loaded config from {}".format(config_path))
pprint.pprint(config)
except (IOError, OSError, yaml.parser.ParserError):
traceback.print_exc()
args = update_from_config(args, config)
args.ignore_paths = args.__dict__.get("ignore_paths") or [
"node_modules",
".git",
".tox",
"godeps",
"vendor",
"site-packages",
"venv",
".env",
"spec",
"migrate",
"bin",
"fixtures",
"cassettes",
".cache",
".idea",
".pytest_cache",
"__pycache__",
"dist",
]
if config_path != ".inlineplz.yml":
return args
# fall back to config_dir inlineplz yaml if we didn't find one locally
if args.config_dir and not config:
new_config_path = os.path.join(args.config_dir, config_path)
if os.path.exists(new_config_path):
return load_config(args, new_config_path)
return args | python | def load_config(args, config_path=".inlineplz.yml"):
config = {}
try:
with open(config_path) as configfile:
config = yaml.safe_load(configfile) or {}
if config:
print("Loaded config from {}".format(config_path))
pprint.pprint(config)
except (IOError, OSError, yaml.parser.ParserError):
traceback.print_exc()
args = update_from_config(args, config)
args.ignore_paths = args.__dict__.get("ignore_paths") or [
"node_modules",
".git",
".tox",
"godeps",
"vendor",
"site-packages",
"venv",
".env",
"spec",
"migrate",
"bin",
"fixtures",
"cassettes",
".cache",
".idea",
".pytest_cache",
"__pycache__",
"dist",
]
if config_path != ".inlineplz.yml":
return args
# fall back to config_dir inlineplz yaml if we didn't find one locally
if args.config_dir and not config:
new_config_path = os.path.join(args.config_dir, config_path)
if os.path.exists(new_config_path):
return load_config(args, new_config_path)
return args | [
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17,852 | guykisel/inline-plz | inlineplz/linter_runner.py | LinterRunner.cleanup | def cleanup():
"""Delete standard installation directories."""
for install_dir in linters.INSTALL_DIRS:
try:
shutil.rmtree(install_dir, ignore_errors=True)
except Exception:
print(
"{0}\nFailed to delete {1}".format(
traceback.format_exc(), install_dir
)
)
sys.stdout.flush() | python | def cleanup():
for install_dir in linters.INSTALL_DIRS:
try:
shutil.rmtree(install_dir, ignore_errors=True)
except Exception:
print(
"{0}\nFailed to delete {1}".format(
traceback.format_exc(), install_dir
)
)
sys.stdout.flush() | [
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17,853 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseManager.get | def get(self, item):
"""
This additional code is necessary to properly return the 'volume'
attribute of the instance as a CloudDatabaseVolume object instead of
a raw dict.
"""
resource = super(CloudDatabaseManager, self).get(item)
resource.volume = CloudDatabaseVolume(resource, resource.volume)
return resource | python | def get(self, item):
resource = super(CloudDatabaseManager, self).get(item)
resource.volume = CloudDatabaseVolume(resource, resource.volume)
return resource | [
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17,854 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseManager._create_body | def _create_body(self, name, flavor=None, volume=None, databases=None,
users=None, version=None, type=None):
"""
Used to create the dict required to create a Cloud Database instance.
"""
if flavor is None:
flavor = 1
flavor_ref = self.api._get_flavor_ref(flavor)
if volume is None:
volume = 1
if databases is None:
databases = []
if users is None:
users = []
body = {"instance": {
"name": name,
"flavorRef": flavor_ref,
"volume": {"size": volume},
"databases": databases,
"users": users,
}}
if type is not None or version is not None:
required = (type, version)
if all(required):
body['instance']['datastore'] = {"type": type, "version": version}
else:
raise exc.MissingCloudDatabaseParameter("Specifying a datastore"
" requires both the datastore type as well as the version.")
return body | python | def _create_body(self, name, flavor=None, volume=None, databases=None,
users=None, version=None, type=None):
if flavor is None:
flavor = 1
flavor_ref = self.api._get_flavor_ref(flavor)
if volume is None:
volume = 1
if databases is None:
databases = []
if users is None:
users = []
body = {"instance": {
"name": name,
"flavorRef": flavor_ref,
"volume": {"size": volume},
"databases": databases,
"users": users,
}}
if type is not None or version is not None:
required = (type, version)
if all(required):
body['instance']['datastore'] = {"type": type, "version": version}
else:
raise exc.MissingCloudDatabaseParameter("Specifying a datastore"
" requires both the datastore type as well as the version.")
return body | [
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17,855 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseManager.list_backups | def list_backups(self, instance=None, marker=0, limit=20):
"""
Returns a paginated list of backups, or just for a particular
instance.
"""
return self.api._backup_manager.list(instance=instance, limit=limit,
marker=marker) | python | def list_backups(self, instance=None, marker=0, limit=20):
return self.api._backup_manager.list(instance=instance, limit=limit,
marker=marker) | [
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17,856 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseManager._list_backups_for_instance | def _list_backups_for_instance(self, instance, marker=0, limit=20):
"""
Instance-specific backups are handled through the instance manager,
not the backup manager.
"""
uri = "/%s/%s/backups?limit=%d&marker=%d" % (self.uri_base,
utils.get_id(instance),
int(limit),
int(marker))
resp, resp_body = self.api.method_get(uri)
mgr = self.api._backup_manager
return [CloudDatabaseBackup(mgr, backup)
for backup in resp_body.get("backups")] | python | def _list_backups_for_instance(self, instance, marker=0, limit=20):
uri = "/%s/%s/backups?limit=%d&marker=%d" % (self.uri_base,
utils.get_id(instance),
int(limit),
int(marker))
resp, resp_body = self.api.method_get(uri)
mgr = self.api._backup_manager
return [CloudDatabaseBackup(mgr, backup)
for backup in resp_body.get("backups")] | [
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17,857 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseUserManager.list_user_access | def list_user_access(self, user):
"""
Returns a list of all database names for which the specified user
has access rights.
"""
user = utils.get_name(user)
uri = "/%s/%s/databases" % (self.uri_base, user)
try:
resp, resp_body = self.api.method_get(uri)
except exc.NotFound as e:
raise exc.NoSuchDatabaseUser("User '%s' does not exist." % user)
dbs = resp_body.get("databases", {})
return [CloudDatabaseDatabase(self, db) for db in dbs] | python | def list_user_access(self, user):
user = utils.get_name(user)
uri = "/%s/%s/databases" % (self.uri_base, user)
try:
resp, resp_body = self.api.method_get(uri)
except exc.NotFound as e:
raise exc.NoSuchDatabaseUser("User '%s' does not exist." % user)
dbs = resp_body.get("databases", {})
return [CloudDatabaseDatabase(self, db) for db in dbs] | [
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17,858 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseUserManager.grant_user_access | def grant_user_access(self, user, db_names, strict=True):
"""
Gives access to the databases listed in `db_names` to the user. You may
pass in either a single db or a list of dbs.
If any of the databases do not exist, a NoSuchDatabase exception will
be raised, unless you specify `strict=False` in the call.
"""
user = utils.get_name(user)
uri = "/%s/%s/databases" % (self.uri_base, user)
db_names = self._get_db_names(db_names, strict=strict)
dbs = [{"name": db_name} for db_name in db_names]
body = {"databases": dbs}
try:
resp, resp_body = self.api.method_put(uri, body=body)
except exc.NotFound as e:
raise exc.NoSuchDatabaseUser("User '%s' does not exist." % user) | python | def grant_user_access(self, user, db_names, strict=True):
user = utils.get_name(user)
uri = "/%s/%s/databases" % (self.uri_base, user)
db_names = self._get_db_names(db_names, strict=strict)
dbs = [{"name": db_name} for db_name in db_names]
body = {"databases": dbs}
try:
resp, resp_body = self.api.method_put(uri, body=body)
except exc.NotFound as e:
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17,859 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseBackupManager.list | def list(self, instance=None, limit=20, marker=0):
"""
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"""
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return super(CloudDatabaseBackupManager, self).list()
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if instance is None:
return super(CloudDatabaseBackupManager, self).list()
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17,860 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseInstance.list_databases | def list_databases(self, limit=None, marker=None):
"""Returns a list of the names of all databases for this instance."""
return self._database_manager.list(limit=limit, marker=marker) | python | def list_databases(self, limit=None, marker=None):
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17,861 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseInstance.list_users | def list_users(self, limit=None, marker=None):
"""Returns a list of the names of all users for this instance."""
return self._user_manager.list(limit=limit, marker=marker) | python | def list_users(self, limit=None, marker=None):
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17,862 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseInstance.get_user | def get_user(self, name):
"""
Finds the user in this instance with the specified name, and
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NoSuchDatabaseUser exception is raised.
"""
try:
return self._user_manager.get(name)
except exc.NotFound:
raise exc.NoSuchDatabaseUser("No user by the name '%s' exists." %
name) | python | def get_user(self, name):
try:
return self._user_manager.get(name)
except exc.NotFound:
raise exc.NoSuchDatabaseUser("No user by the name '%s' exists." %
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17,863 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseInstance.get_database | def get_database(self, name):
"""
Finds the database in this instance with the specified name, and
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NoSuchDatabase exception is raised.
"""
try:
return [db for db in self.list_databases()
if db.name == name][0]
except IndexError:
raise exc.NoSuchDatabase("No database by the name '%s' exists." %
name) | python | def get_database(self, name):
try:
return [db for db in self.list_databases()
if db.name == name][0]
except IndexError:
raise exc.NoSuchDatabase("No database by the name '%s' exists." %
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17,864 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseInstance.delete_database | def delete_database(self, name_or_obj):
"""
Deletes the specified database. If no database by that name
exists, no exception will be raised; instead, nothing at all
is done.
"""
name = utils.get_name(name_or_obj)
self._database_manager.delete(name) | python | def delete_database(self, name_or_obj):
name = utils.get_name(name_or_obj)
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17,865 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseInstance.delete_user | def delete_user(self, user):
"""
Deletes the specified user. If no user by that name
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"""
name = utils.get_name(user)
self._user_manager.delete(name) | python | def delete_user(self, user):
name = utils.get_name(user)
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17,866 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseInstance.enable_root_user | def enable_root_user(self):
"""
Enables login from any host for the root user and provides
the user with a generated root password.
"""
uri = "/instances/%s/root" % self.id
resp, body = self.manager.api.method_post(uri)
return body["user"]["password"] | python | def enable_root_user(self):
uri = "/instances/%s/root" % self.id
resp, body = self.manager.api.method_post(uri)
return body["user"]["password"] | [
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17,867 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseInstance.root_user_status | def root_user_status(self):
"""
Returns True or False, depending on whether the root user
for this instance has been enabled.
"""
uri = "/instances/%s/root" % self.id
resp, body = self.manager.api.method_get(uri)
return body["rootEnabled"] | python | def root_user_status(self):
uri = "/instances/%s/root" % self.id
resp, body = self.manager.api.method_get(uri)
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17,868 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseInstance.resize | def resize(self, flavor):
"""Set the size of this instance to a different flavor."""
# We need the flavorRef, not the flavor or size.
flavorRef = self.manager.api._get_flavor_ref(flavor)
body = {"flavorRef": flavorRef}
self.manager.action(self, "resize", body=body) | python | def resize(self, flavor):
# We need the flavorRef, not the flavor or size.
flavorRef = self.manager.api._get_flavor_ref(flavor)
body = {"flavorRef": flavorRef}
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17,869 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseInstance.resize_volume | def resize_volume(self, size):
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curr_size = self.volume.size
if size <= curr_size:
raise exc.InvalidVolumeResize("The new volume size must be larger "
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body = {"volume": {"size": size}}
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curr_size = self.volume.size
if size <= curr_size:
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body = {"volume": {"size": size}}
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17,870 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseInstance.list_backups | def list_backups(self, limit=20, marker=0):
"""
Returns a paginated list of backups for this instance.
"""
return self.manager._list_backups_for_instance(self, limit=limit,
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17,871 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseInstance.create_backup | def create_backup(self, name, description=None):
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"""
return self.manager.create_backup(self, name, description=description) | python | def create_backup(self, name, description=None):
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17,872 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseClient.list_databases | def list_databases(self, instance, limit=None, marker=None):
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17,873 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseClient.create_database | def create_database(self, instance, name, character_set=None,
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17,874 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseClient.list_users | def list_users(self, instance, limit=None, marker=None):
"""Returns all users for the specified instance."""
return instance.list_users(limit=limit, marker=marker) | python | def list_users(self, instance, limit=None, marker=None):
return instance.list_users(limit=limit, marker=marker) | [
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17,875 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseClient.grant_user_access | def grant_user_access(self, instance, user, db_names, strict=True):
"""
Gives access to the databases listed in `db_names` to the user
on the specified instance.
"""
return instance.grant_user_access(user, db_names, strict=strict) | python | def grant_user_access(self, instance, user, db_names, strict=True):
return instance.grant_user_access(user, db_names, strict=strict) | [
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17,876 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseClient.revoke_user_access | def revoke_user_access(self, instance, user, db_names, strict=True):
"""
Revokes access to the databases listed in `db_names` for the user
on the specified instance.
"""
return instance.revoke_user_access(user, db_names, strict=strict) | python | def revoke_user_access(self, instance, user, db_names, strict=True):
return instance.revoke_user_access(user, db_names, strict=strict) | [
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17,877 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseClient.list_flavors | def list_flavors(self, limit=None, marker=None):
"""Returns a list of all available Flavors."""
return self._flavor_manager.list(limit=limit, marker=marker) | python | def list_flavors(self, limit=None, marker=None):
return self._flavor_manager.list(limit=limit, marker=marker) | [
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17,878 | pycontribs/pyrax | pyrax/clouddatabases.py | CloudDatabaseClient._get_flavor_ref | def _get_flavor_ref(self, flavor):
"""
Flavors are odd in that the API expects an href link, not an ID, as with
nearly every other resource. This method takes either a
CloudDatabaseFlavor object, a flavor ID, a RAM size, or a flavor name,
and uses that to determine the appropriate href.
"""
flavor_obj = None
if isinstance(flavor, CloudDatabaseFlavor):
flavor_obj = flavor
elif isinstance(flavor, int):
# They passed an ID or a size
try:
flavor_obj = self.get_flavor(flavor)
except exc.NotFound:
# Must be either a size or bad ID, which will
# be handled below
pass
if flavor_obj is None:
# Try flavor name
flavors = self.list_flavors()
try:
flavor_obj = [flav for flav in flavors
if flav.name == flavor][0]
except IndexError:
# No such name; try matching RAM
try:
flavor_obj = [flav for flav in flavors
if flav.ram == flavor][0]
except IndexError:
raise exc.FlavorNotFound("Could not determine flavor from "
"'%s'." % flavor)
# OK, we have a Flavor object. Get the href
href = [link["href"] for link in flavor_obj.links
if link["rel"] == "self"][0]
return href | python | def _get_flavor_ref(self, flavor):
flavor_obj = None
if isinstance(flavor, CloudDatabaseFlavor):
flavor_obj = flavor
elif isinstance(flavor, int):
# They passed an ID or a size
try:
flavor_obj = self.get_flavor(flavor)
except exc.NotFound:
# Must be either a size or bad ID, which will
# be handled below
pass
if flavor_obj is None:
# Try flavor name
flavors = self.list_flavors()
try:
flavor_obj = [flav for flav in flavors
if flav.name == flavor][0]
except IndexError:
# No such name; try matching RAM
try:
flavor_obj = [flav for flav in flavors
if flav.ram == flavor][0]
except IndexError:
raise exc.FlavorNotFound("Could not determine flavor from "
"'%s'." % flavor)
# OK, we have a Flavor object. Get the href
href = [link["href"] for link in flavor_obj.links
if link["rel"] == "self"][0]
return href | [
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17,879 | pycontribs/pyrax | pyrax/utils.py | runproc | def runproc(cmd):
"""
Convenience method for executing operating system commands.
Accepts a single string that would be the command as executed on the
command line.
Returns a 2-tuple consisting of the output of (STDOUT, STDERR). In your
code you should check for an empty STDERR output to determine if your
command completed successfully.
"""
proc = Popen([cmd], shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE,
close_fds=True)
stdoutdata, stderrdata = proc.communicate()
return (stdoutdata, stderrdata) | python | def runproc(cmd):
proc = Popen([cmd], shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE,
close_fds=True)
stdoutdata, stderrdata = proc.communicate()
return (stdoutdata, stderrdata) | [
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17,880 | pycontribs/pyrax | pyrax/utils.py | _join_chars | def _join_chars(chars, length):
"""
Used by the random character functions.
"""
mult = int(length / len(chars)) + 1
mult_chars = chars * mult
return "".join(random.sample(mult_chars, length)) | python | def _join_chars(chars, length):
mult = int(length / len(chars)) + 1
mult_chars = chars * mult
return "".join(random.sample(mult_chars, length)) | [
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17,881 | pycontribs/pyrax | pyrax/utils.py | random_unicode | def random_unicode(length=20):
"""
Generates a random name; useful for testing.
Returns an encoded string of the specified length containing unicode values
up to code point 1000.
"""
def get_char():
return six.unichr(random.randint(32, 1000))
chars = u"".join([get_char() for ii in six.moves.range(length)])
return _join_chars(chars, length) | python | def random_unicode(length=20):
def get_char():
return six.unichr(random.randint(32, 1000))
chars = u"".join([get_char() for ii in six.moves.range(length)])
return _join_chars(chars, length) | [
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17,882 | pycontribs/pyrax | pyrax/utils.py | coerce_to_list | def coerce_to_list(val):
"""
For parameters that can take either a single string or a list of strings,
this function will ensure that the result is a list containing the passed
values.
"""
if val:
if not isinstance(val, (list, tuple)):
val = [val]
else:
val = []
return val | python | def coerce_to_list(val):
if val:
if not isinstance(val, (list, tuple)):
val = [val]
else:
val = []
return val | [
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17,883 | pycontribs/pyrax | pyrax/utils.py | folder_size | def folder_size(pth, ignore=None):
"""
Returns the total bytes for the specified path, optionally ignoring
any files which match the 'ignore' parameter. 'ignore' can either be
a single string pattern, or a list of such patterns.
"""
if not os.path.isdir(pth):
raise exc.FolderNotFound
ignore = coerce_to_list(ignore)
total = 0
for root, _, names in os.walk(pth):
paths = [os.path.realpath(os.path.join(root, nm)) for nm in names]
for pth in paths[::-1]:
if not os.path.exists(pth):
paths.remove(pth)
elif match_pattern(pth, ignore):
paths.remove(pth)
total += sum(os.stat(pth).st_size for pth in paths)
return total | python | def folder_size(pth, ignore=None):
if not os.path.isdir(pth):
raise exc.FolderNotFound
ignore = coerce_to_list(ignore)
total = 0
for root, _, names in os.walk(pth):
paths = [os.path.realpath(os.path.join(root, nm)) for nm in names]
for pth in paths[::-1]:
if not os.path.exists(pth):
paths.remove(pth)
elif match_pattern(pth, ignore):
paths.remove(pth)
total += sum(os.stat(pth).st_size for pth in paths)
return total | [
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17,884 | pycontribs/pyrax | pyrax/utils.py | add_method | def add_method(obj, func, name=None):
"""Adds an instance method to an object."""
if name is None:
name = func.__name__
if sys.version_info < (3,):
method = types.MethodType(func, obj, obj.__class__)
else:
method = types.MethodType(func, obj)
setattr(obj, name, method) | python | def add_method(obj, func, name=None):
if name is None:
name = func.__name__
if sys.version_info < (3,):
method = types.MethodType(func, obj, obj.__class__)
else:
method = types.MethodType(func, obj)
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17,885 | pycontribs/pyrax | pyrax/utils.py | wait_until | def wait_until(obj, att, desired, callback=None, interval=5, attempts=0,
verbose=False, verbose_atts=None):
"""
When changing the state of an object, it will commonly be in a transitional
state until the change is complete. This will reload the object every
`interval` seconds, and check its `att` attribute until the `desired` value
is reached, or until the maximum number of attempts is reached. The updated
object is returned. It is up to the calling program to check the returned
object to make sure that it successfully reached the desired state.
Once the desired value of the attribute is reached, the method returns. If
not, it will re-try until the attribute's value matches one of the
`desired` values. By default (attempts=0) it will loop infinitely until the
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`attempts`. If the attribute has not reached the desired value by then, the
method will exit.
If `verbose` is True, each attempt will print out the current value of the
watched attribute and the time that has elapsed since the original request.
Also, if `verbose_atts` is specified, the values of those attributes will
also be output. If `verbose` is False, then `verbose_atts` has no effect.
Note that `desired` can be a list of values; if the attribute becomes equal
to any of those values, this will succeed. For example, when creating a new
cloud server, it will initially have a status of 'BUILD', and you can't
work with it until its status is 'ACTIVE'. However, there might be a
problem with the build process, and the server will change to a status of
'ERROR'. So for this case you need to set the `desired` parameter to
`['ACTIVE', 'ERROR']`. If you simply pass 'ACTIVE' as the desired state,
this will loop indefinitely if a build fails, as the server will never
reach a status of 'ACTIVE'.
Since this process of waiting can take a potentially long time, and will
block your program's execution until the desired state of the object is
reached, you may specify a callback function. The callback can be any
callable that accepts a single parameter; the parameter it receives will be
either the updated object (success), or None (failure). If a callback is
specified, the program will return immediately after spawning the wait
process in a separate thread.
"""
if callback:
waiter = _WaitThread(obj=obj, att=att, desired=desired, callback=callback,
interval=interval, attempts=attempts, verbose=verbose,
verbose_atts=verbose_atts)
waiter.start()
return waiter
else:
return _wait_until(obj=obj, att=att, desired=desired, callback=None,
interval=interval, attempts=attempts, verbose=verbose,
verbose_atts=verbose_atts) | python | def wait_until(obj, att, desired, callback=None, interval=5, attempts=0,
verbose=False, verbose_atts=None):
if callback:
waiter = _WaitThread(obj=obj, att=att, desired=desired, callback=callback,
interval=interval, attempts=attempts, verbose=verbose,
verbose_atts=verbose_atts)
waiter.start()
return waiter
else:
return _wait_until(obj=obj, att=att, desired=desired, callback=None,
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verbose_atts=verbose_atts) | [
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`['ACTIVE', 'ERROR']`. If you simply pass 'ACTIVE' as the desired state,
this will loop indefinitely if a build fails, as the server will never
reach a status of 'ACTIVE'.
Since this process of waiting can take a potentially long time, and will
block your program's execution until the desired state of the object is
reached, you may specify a callback function. The callback can be any
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17,886 | pycontribs/pyrax | pyrax/utils.py | _wait_until | def _wait_until(obj, att, desired, callback, interval, attempts, verbose,
verbose_atts):
"""
Loops until either the desired value of the attribute is reached, or the
number of attempts is exceeded.
"""
if not isinstance(desired, (list, tuple)):
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start = time.time()
while infinite or (attempt < attempts):
try:
# For servers:
obj.get()
except AttributeError:
try:
# For other objects that don't support .get()
obj = obj.manager.get(obj.id)
except AttributeError:
# punt
raise exc.NoReloadError("The 'wait_until' method is not "
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attval = getattr(obj, att)
if verbose:
elapsed = time.time() - start
msgs = ["Current value of %s: %s (elapsed: %4.1f seconds)" % (
att, attval, elapsed)]
for vatt in verbose_atts:
vattval = getattr(obj, vatt, None)
msgs.append("%s=%s" % (vatt, vattval))
print(" ".join(msgs))
if attval in desired:
return obj
time.sleep(interval)
attempt += 1
return obj | python | def _wait_until(obj, att, desired, callback, interval, attempts, verbose,
verbose_atts):
if not isinstance(desired, (list, tuple)):
desired = [desired]
if verbose_atts is None:
verbose_atts = []
if not isinstance(verbose_atts, (list, tuple)):
verbose_atts = [verbose_atts]
infinite = (attempts == 0)
attempt = 0
start = time.time()
while infinite or (attempt < attempts):
try:
# For servers:
obj.get()
except AttributeError:
try:
# For other objects that don't support .get()
obj = obj.manager.get(obj.id)
except AttributeError:
# punt
raise exc.NoReloadError("The 'wait_until' method is not "
"supported for '%s' objects." % obj.__class__)
attval = getattr(obj, att)
if verbose:
elapsed = time.time() - start
msgs = ["Current value of %s: %s (elapsed: %4.1f seconds)" % (
att, attval, elapsed)]
for vatt in verbose_atts:
vattval = getattr(obj, vatt, None)
msgs.append("%s=%s" % (vatt, vattval))
print(" ".join(msgs))
if attval in desired:
return obj
time.sleep(interval)
attempt += 1
return obj | [
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17,887 | pycontribs/pyrax | pyrax/utils.py | _parse_datetime_string | def _parse_datetime_string(val):
"""
Attempts to parse a string representation of a date or datetime value, and
returns a datetime if successful. If not, a InvalidDateTimeString exception
will be raised.
"""
dt = None
lenval = len(val)
fmt = {19: "%Y-%m-%d %H:%M:%S", 10: "%Y-%m-%d"}.get(lenval)
if fmt is None:
# Invalid date
raise exc.InvalidDateTimeString("The supplied value '%s' does not "
"match either of the formats 'YYYY-MM-DD HH:MM:SS' or "
"'YYYY-MM-DD'." % val)
return datetime.datetime.strptime(val, fmt) | python | def _parse_datetime_string(val):
dt = None
lenval = len(val)
fmt = {19: "%Y-%m-%d %H:%M:%S", 10: "%Y-%m-%d"}.get(lenval)
if fmt is None:
# Invalid date
raise exc.InvalidDateTimeString("The supplied value '%s' does not "
"match either of the formats 'YYYY-MM-DD HH:MM:SS' or "
"'YYYY-MM-DD'." % val)
return datetime.datetime.strptime(val, fmt) | [
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17,888 | pycontribs/pyrax | pyrax/utils.py | rfc2822_format | def rfc2822_format(val):
"""
Takes either a date, a datetime, or a string, and returns a string that
represents the value in RFC 2822 format. If a string is passed it is
returned unchanged.
"""
if isinstance(val, six.string_types):
return val
elif isinstance(val, (datetime.datetime, datetime.date)):
# Convert to a timestamp
val = time.mktime(val.timetuple())
if isinstance(val, numbers.Number):
return email.utils.formatdate(val)
else:
# Bail
return val | python | def rfc2822_format(val):
if isinstance(val, six.string_types):
return val
elif isinstance(val, (datetime.datetime, datetime.date)):
# Convert to a timestamp
val = time.mktime(val.timetuple())
if isinstance(val, numbers.Number):
return email.utils.formatdate(val)
else:
# Bail
return val | [
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17,889 | pycontribs/pyrax | pyrax/utils.py | get_id | def get_id(id_or_obj):
"""
Returns the 'id' attribute of 'id_or_obj' if present; if not,
returns 'id_or_obj'.
"""
if isinstance(id_or_obj, six.string_types + (int,)):
# It's an ID
return id_or_obj
try:
return id_or_obj.id
except AttributeError:
return id_or_obj | python | def get_id(id_or_obj):
if isinstance(id_or_obj, six.string_types + (int,)):
# It's an ID
return id_or_obj
try:
return id_or_obj.id
except AttributeError:
return id_or_obj | [
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17,890 | pycontribs/pyrax | pyrax/utils.py | get_name | def get_name(name_or_obj):
"""
Returns the 'name' attribute of 'name_or_obj' if present; if not,
returns 'name_or_obj'.
"""
if isinstance(name_or_obj, six.string_types):
# It's a name
return name_or_obj
try:
return name_or_obj.name
except AttributeError:
raise exc.MissingName(name_or_obj) | python | def get_name(name_or_obj):
if isinstance(name_or_obj, six.string_types):
# It's a name
return name_or_obj
try:
return name_or_obj.name
except AttributeError:
raise exc.MissingName(name_or_obj) | [
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17,891 | pycontribs/pyrax | pyrax/utils.py | params_to_dict | def params_to_dict(params, dct):
"""
Updates the 'dct' dictionary with the 'params' dictionary, filtering out
all those whose param value is None.
"""
for param, val in params.items():
if val is None:
continue
dct[param] = val
return dct | python | def params_to_dict(params, dct):
for param, val in params.items():
if val is None:
continue
dct[param] = val
return dct | [
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17,892 | pycontribs/pyrax | pyrax/utils.py | dict_to_qs | def dict_to_qs(dct):
"""
Takes a dictionary and uses it to create a query string.
"""
itms = ["%s=%s" % (key, val) for key, val in list(dct.items())
if val is not None]
return "&".join(itms) | python | def dict_to_qs(dct):
itms = ["%s=%s" % (key, val) for key, val in list(dct.items())
if val is not None]
return "&".join(itms) | [
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17,893 | pycontribs/pyrax | pyrax/utils.py | match_pattern | def match_pattern(nm, patterns):
"""
Compares `nm` with the supplied patterns, and returns True if it matches
at least one.
Patterns are standard file-name wildcard strings, as defined in the
`fnmatch` module. For example, the pattern "*.py" will match the names
of all Python scripts.
"""
patterns = coerce_to_list(patterns)
for pat in patterns:
if fnmatch.fnmatch(nm, pat):
return True
return False | python | def match_pattern(nm, patterns):
patterns = coerce_to_list(patterns)
for pat in patterns:
if fnmatch.fnmatch(nm, pat):
return True
return False | [
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17,894 | pycontribs/pyrax | pyrax/utils.py | update_exc | def update_exc(exc, msg, before=True, separator="\n"):
"""
Adds additional text to an exception's error message.
The new text will be added before the existing text by default; to append
it after the original text, pass False to the `before` parameter.
By default the old and new text will be separated by a newline. If you wish
to use a different separator, pass that as the `separator` parameter.
"""
emsg = exc.message
if before:
parts = (msg, separator, emsg)
else:
parts = (emsg, separator, msg)
new_msg = "%s%s%s" % parts
new_args = (new_msg, ) + exc.args[1:]
exc.message = new_msg
exc.args = new_args
return exc | python | def update_exc(exc, msg, before=True, separator="\n"):
emsg = exc.message
if before:
parts = (msg, separator, emsg)
else:
parts = (emsg, separator, msg)
new_msg = "%s%s%s" % parts
new_args = (new_msg, ) + exc.args[1:]
exc.message = new_msg
exc.args = new_args
return exc | [
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17,895 | pycontribs/pyrax | pyrax/utils.py | case_insensitive_update | def case_insensitive_update(dct1, dct2):
"""
Given two dicts, updates the first one with the second, but considers keys
that are identical except for case to be the same.
No return value; this function modified dct1 similar to the update() method.
"""
lowkeys = dict([(key.lower(), key) for key in dct1])
for key, val in dct2.items():
d1_key = lowkeys.get(key.lower(), key)
dct1[d1_key] = val | python | def case_insensitive_update(dct1, dct2):
lowkeys = dict([(key.lower(), key) for key in dct1])
for key, val in dct2.items():
d1_key = lowkeys.get(key.lower(), key)
dct1[d1_key] = val | [
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17,896 | pycontribs/pyrax | pyrax/utils.py | env | def env(*args, **kwargs):
"""
Returns the first environment variable set
if none are non-empty, defaults to "" or keyword arg default
"""
for arg in args:
value = os.environ.get(arg, None)
if value:
return value
return kwargs.get("default", "") | python | def env(*args, **kwargs):
for arg in args:
value = os.environ.get(arg, None)
if value:
return value
return kwargs.get("default", "") | [
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17,897 | pycontribs/pyrax | pyrax/utils.py | to_slug | def to_slug(value, incoming=None, errors="strict"):
"""Normalize string.
Convert to lowercase, remove non-word characters, and convert spaces
to hyphens.
This function was copied from novaclient.openstack.strutils
Inspired by Django's `slugify` filter.
:param value: Text to slugify
:param incoming: Text's current encoding
:param errors: Errors handling policy. See here for valid
values http://docs.python.org/2/library/codecs.html
:returns: slugified unicode representation of `value`
:raises TypeError: If text is not an instance of str
"""
value = safe_decode(value, incoming, errors)
# NOTE(aababilov): no need to use safe_(encode|decode) here:
# encodings are always "ascii", error handling is always "ignore"
# and types are always known (first: unicode; second: str)
value = unicodedata.normalize("NFKD", value).encode(
"ascii", "ignore").decode("ascii")
value = SLUGIFY_STRIP_RE.sub("", value).strip().lower()
return SLUGIFY_HYPHENATE_RE.sub("-", value) | python | def to_slug(value, incoming=None, errors="strict"):
value = safe_decode(value, incoming, errors)
# NOTE(aababilov): no need to use safe_(encode|decode) here:
# encodings are always "ascii", error handling is always "ignore"
# and types are always known (first: unicode; second: str)
value = unicodedata.normalize("NFKD", value).encode(
"ascii", "ignore").decode("ascii")
value = SLUGIFY_STRIP_RE.sub("", value).strip().lower()
return SLUGIFY_HYPHENATE_RE.sub("-", value) | [
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Convert to lowercase, remove non-word characters, and convert spaces
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This function was copied from novaclient.openstack.strutils
Inspired by Django's `slugify` filter.
:param value: Text to slugify
:param incoming: Text's current encoding
:param errors: Errors handling policy. See here for valid
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:returns: slugified unicode representation of `value`
:raises TypeError: If text is not an instance of str | [
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17,898 | pycontribs/pyrax | pyrax/utils.py | _WaitThread.run | def run(self):
"""Starts the thread."""
resp = _wait_until(obj=self.obj, att=self.att,
desired=self.desired, callback=None,
interval=self.interval, attempts=self.attempts,
verbose=False, verbose_atts=None)
self.callback(resp) | python | def run(self):
resp = _wait_until(obj=self.obj, att=self.att,
desired=self.desired, callback=None,
interval=self.interval, attempts=self.attempts,
verbose=False, verbose_atts=None)
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17,899 | pycontribs/pyrax | pyrax/cloudblockstorage.py | assure_volume | def assure_volume(fnc):
"""
Converts a volumeID passed as the volume to a CloudBlockStorageVolume object.
"""
@wraps(fnc)
def _wrapped(self, volume, *args, **kwargs):
if not isinstance(volume, CloudBlockStorageVolume):
# Must be the ID
volume = self._manager.get(volume)
return fnc(self, volume, *args, **kwargs)
return _wrapped | python | def assure_volume(fnc):
@wraps(fnc)
def _wrapped(self, volume, *args, **kwargs):
if not isinstance(volume, CloudBlockStorageVolume):
# Must be the ID
volume = self._manager.get(volume)
return fnc(self, volume, *args, **kwargs)
return _wrapped | [
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