def makeBinDist(self, transformedCP, averageCoverages, kmerNormPC1, kmerPCs, contigGCs, contigLengths):
"""Determine the distribution of the points in this bin
The distribution is largely normal, except at the boundaries.
"""
#print "MBD", self.id, self.binSize
self.binSize = self.rowIndices.shape[0]
if(0 == np.size(self.rowIndices)):
return
# get the centroids
(self.covMedians, self.covStdevs) = self.getCentroidStats(transformedCP)
(self.lengthMean, self.lengthStd) = self.getCentroidStats(contigLengths)
self.kValMeanNormPC1 = np_median(kmerPCs[self.rowIndices])
self.kValStdevNormPC1 = np_std(kmerPCs[self.rowIndices])
self.kMedian = np_median(kmerPCs[self.rowIndices], axis=0)
self.kStdevs = np_std(kmerPCs[self.rowIndices], axis=0)
cvals = self.getAverageCoverageDist(averageCoverages)
self.cValMedian = np_around(np_median(cvals), decimals=3)
self.cValStdev = np_around(np_std(cvals), decimals=3)
self.gcMedian = np_median(contigGCs[self.rowIndices])
self.gcStdev = np_std(contigGCs[self.rowIndices])
# work out the total size
self.totalBP = sum([contigLengths[i] for i in self.rowIndices])
# set the acceptance ranges
self.makeLimits()
def makeBinDist(self, transformedCP, averageCoverages, kmerNormPC1,
kmerPCs, contigGCs, contigLengths):
"""Determine the distribution of the points in this bin
The distribution is largely normal, except at the boundaries.
"""
#print("MBD", self.id, self.binSize)
self.binSize = self.rowIndices.shape[0]
if (0 == np.size(self.rowIndices)):
return
# get the centroids
(self.covMedians,
self.covStdevs) = self.getCentroidStats(transformedCP)
(self.lengthMean,
self.lengthStd) = self.getCentroidStats(contigLengths)
self.kValMeanNormPC1 = np_median(kmerPCs[self.rowIndices])
self.kValStdevNormPC1 = np_std(kmerPCs[self.rowIndices])
self.kMedian = np_median(kmerPCs[self.rowIndices], axis=0)
self.kStdevs = np_std(kmerPCs[self.rowIndices], axis=0)
cvals = self.getAverageCoverageDist(averageCoverages)
self.cValMedian = np_around(np_median(cvals), decimals=3)
self.cValStdev = np_around(np_std(cvals), decimals=3)
self.gcMedian = np_median(contigGCs[self.rowIndices])
self.gcStdev = np_std(contigGCs[self.rowIndices])
# work out the total size
self.totalBP = sum([contigLengths[i] for i in self.rowIndices])
# set the acceptance ranges
self.makeLimits()
def populateImageMaps(self):
"""Load the transformed data into the main image maps"""
# reset these guys... JIC
self.imageMaps = np_zeros((self.numImgMaps, self.PM.scaleFactor, self.PM.scaleFactor))
self.im2RowIndicies = {}
# add to the grid wherever we find a contig
row_index = -1
for point in np_around(self.PM.transformedCP):
row_index += 1
# can only bin things once!
if row_index not in self.PM.binnedRowIndicies and row_index not in self.PM.restrictedRowIndicies:
# add to the row_index dict so we can relate the
# map back to individual points later
p = tuple(point)
if p in self.im2RowIndicies:
self.im2RowIndicies[p].append(row_index)
else:
self.im2RowIndicies[p] = [row_index]
# now increment in the grid
# for each point we encounter we incrmement
# it's position + the positions to each side
# and touching each corner
self.incrementViaRowIndex(row_index, p)
def decrementViaRowIndex(self, rowIndex, point=None):
"""Wrapper to decrement about point"""
if point is None:
point = tuple(np_around(self.PM.transformedCP[rowIndex]))
# px = point[0]
# py = point[1]
# pz = point[2]
multiplier = np_log10(self.PM.contigLengths[rowIndex])
self.decrementAboutPoint(0, point[0], point[1], multiplier=multiplier)
if self.numImgMaps > 1:
self.decrementAboutPoint(1, self.PM.scaleFactor - point[2] - 1, point[1], multiplier=multiplier)
self.decrementAboutPoint(
2, self.PM.scaleFactor - point[2] - 1, self.PM.scaleFactor - point[0] - 1, multiplier=multiplier
)
def findNewClusterCenters(self, ss=0):
"""Find a putative cluster"""
inRange = lambda x, l, u: x >= l and x < u
# we work from the top view as this has the base clustering
max_index = np_argmax(self.blurredMaps[0])
max_value = self.blurredMaps[0].ravel()[max_index]
max_x = int(max_index / self.PM.scaleFactor)
max_y = max_index - self.PM.scaleFactor * max_x
max_z = -1
ret_values = [max_value, max_x, max_y]
start_span = int(1.5 * self.span)
span_len = 2 * start_span + 1
if self.debugPlots:
self.plotRegion(max_x, max_y, max_z, fileName="Image_" + str(self.imageCounter), tag="column", column=True)
self.imageCounter += 1
# make a 3d grid to hold the values
working_block = np_zeros((span_len, span_len, self.PM.scaleFactor))
# go through the entire column
(x_lower, x_upper) = self.makeCoordRanges(max_x, start_span)
(y_lower, y_upper) = self.makeCoordRanges(max_y, start_span)
super_putative_row_indices = []
for p in self.im2RowIndicies:
if inRange(p[0], x_lower, x_upper) and inRange(p[1], y_lower, y_upper):
for row_index in self.im2RowIndicies[p]:
# check that the point is real and that it has not yet been binned
if row_index not in self.PM.binnedRowIndicies and row_index not in self.PM.restrictedRowIndicies:
# this is an unassigned point.
multiplier = np_log10(self.PM.contigLengths[row_index])
self.incrementAboutPoint3D(
working_block, p[0] - x_lower, p[1] - y_lower, p[2], multiplier=multiplier
)
super_putative_row_indices.append(row_index)
# blur and find the highest value
bwb = ndi.gaussian_filter(working_block, 8) # self.blurRadius)
densest_index = np_unravel_index(np_argmax(bwb), (np_shape(bwb)))
max_x = densest_index[0] + x_lower
max_y = densest_index[1] + y_lower
max_z = densest_index[2]
# now get the basic color of this dense point
putative_center_row_indices = []
(x_lower, x_upper) = self.makeCoordRanges(max_x, self.span)
(y_lower, y_upper) = self.makeCoordRanges(max_y, self.span)
(z_lower, z_upper) = self.makeCoordRanges(max_z, 2 * self.span)
for row_index in super_putative_row_indices:
p = np_around(self.PM.transformedCP[row_index])
if inRange(p[0], x_lower, x_upper) and inRange(p[1], y_lower, y_upper) and inRange(p[2], z_lower, z_upper):
# we are within the range!
putative_center_row_indices.append(row_index)
# make sure we have something to go on here
if np_size(putative_center_row_indices) == 0:
# it's all over!
return None
if np_size(putative_center_row_indices) == 1:
# get out of here but keep trying
# the calling function may restrict these indices
return [[np_array(putative_center_row_indices)], ret_values]
else:
total_BP = sum([self.PM.contigLengths[i] for i in putative_center_row_indices])
if not self.isGoodBin(total_BP, len(putative_center_row_indices), ms=5): # Can we trust very small bins?.
# get out of here but keep trying
# the calling function should restrict these indices
return [[np_array(putative_center_row_indices)], ret_values]
else:
# we've got a few good guys here, partition them up!
# shift these guys around a bit
center_k_vals = np_array([self.PM.kmerVals[i] for i in putative_center_row_indices])
k_partitions = self.partitionVals(center_k_vals)
if len(k_partitions) == 0:
return None
else:
center_c_vals = np_array([self.PM.transformedCP[i][-1] for i in putative_center_row_indices])
# center_c_vals = np_array([self.PM.averageCoverages[i] for i in putative_center_row_indices])
center_c_vals -= np_min(center_c_vals)
c_max = np_max(center_c_vals)
if c_max != 0:
center_c_vals /= c_max
c_partitions = self.partitionVals(center_c_vals)
# take the intersection of the two partitions
tmp_partition_hash_1 = {}
id = 1
for p in k_partitions:
for i in p:
tmp_partition_hash_1[i] = id
id += 1
tmp_partition_hash_2 = {}
id = 1
for p in c_partitions:
for i in p:
try:
tmp_partition_hash_2[(tmp_partition_hash_1[i], id)].append(i)
except KeyError:
tmp_partition_hash_2[(tmp_partition_hash_1[i], id)] = [i]
id += 1
partitions = [
np_array([putative_center_row_indices[i] for i in tmp_partition_hash_2[key]])
for key in tmp_partition_hash_2.keys()
]
# pcs = [[self.PM.averageCoverages[i] for i in p] for p in partitions]
# print pcs
return [partitions, ret_values]
