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import cooler
import numpy as np
from types import SimpleNamespace
import random
import sys
def shuffleIFWithCount(df):
shuf=df[['count','balanced']].sample(frac=1)
df[['count','balanced']]=shuf[['count','balanced']].to_numpy()
return df
def shuffleIF(df):
if len(df)<10:
df = shuffleIFWithCount(df)
return df
min=np.min(df['bin1_id'])
max=np.max(df['bin1_id'])
distance = df['distance'].iloc[0]
bin1_id = np.random.randint(min, high=max, size=int(len(df)*1.5))
bin2_id = bin1_id + distance
pair_id = set(zip(bin1_id,bin2_id))
if len(pair_id)<len(df)-50:
bin1_id = np.random.randint(min, high=max, size=len(df))
bin2_id = bin1_id + distance
extra_pair_id = set(zip(bin1_id,bin2_id))
pair_id.update(extra_pair_id)
if len(pair_id)<len(df):
df = df.sample(len(pair_id))
pair_id = list(pair_id)
random.shuffle(pair_id)
pair_id=np.asarray(pair_id[:len(df)])
df['bin1_id']=pair_id[:,0]
df['bin2_id'] = pair_id[:,1]
return df
class bandmatrix():
def __init__(self, pixels, extent, max_distance_bins=None, bins=None, info=None):
self.extent = extent
self.max_distance_bins = max_distance_bins
self.bmatrix = np.zeros((extent[1] - extent[0], max_distance_bins))
self.offset = extent[0]
self.bmatrix[pixels['bin1_id'] - self.offset, (pixels['bin2_id'] - pixels['bin1_id']).abs()] = pixels[
'balanced']
self.diag_mean = np.nanmean(self.bmatrix, axis=0)
np.nan_to_num(self.bmatrix, copy=False)
self.bins = bins
self.bp2bin = \
bins['start'].reset_index(drop=False).rename(columns={"start": "bp", "index": "bin"}).set_index(
'bp').to_dict()[
'bin']
self.resol = self.bins.iloc[0]['end'] - self.bins.iloc[0]['start']
self.info = info
self.bin2bias = np.zeros(self.extent[1] - self.extent[0] + 1)
if 'full_sum' in self.info:
self.totalRC = self.info['full_sum']
elif 'sum' in info:
self.totalRC = self.info['sum']
else:
self.totalRC = None
self.bin2bias = np.zeros((extent[1] - extent[0]))
for k, v in bins.to_dict()['weight'].items():
self.bin2bias[k - self.offset] = v
self.bin2bias = np.nan_to_num(self.bin2bias)
self.continousRows = {'start_bp': np.inf, 'end_bp': -1, 'O_matrix': None, 'OE_matrix': None, 'bias': None,
'offset_bin': 0}
self.continousRows = SimpleNamespace(**self.continousRows)
def __bandedRows2fullRows(self, x):
"""
coverting rows in bandedMatrix to upper triangle (+ necessary lower triangle) fullMatrix
x???? x???x000
x@@xx ?x@@xx00
x#xxx --> ?@x#xxx0
xxxxx ?@#xxxxx
"""
b, h, w = x.shape
output = np.zeros((b, h, h + w))
output[:b, :h, :w] = x
output = output.reshape(b, -1)[:, :-h].reshape(b, h, -1)[:, :, :h + w]
i_lower = np.tril_indices(h, -1)
for i in range(b):
output[i][i_lower] = output[i].swapaxes(-1, -2)[i_lower]
return output
def __relative_right_shift(self, x):
"""
.........xxxxxx xxxxxx0000000000
........xxxxxx. xxxxxx.000000000
.......xxxxxx.. ---> xxxxxx..00000000
......xxxxxx... xxxxxx...0000000
.....xxxxxx.... xxxxxx....000000
"""
b, h, w = x.shape
output = np.zeros((b, h, 2 * w))
output[:b, :h, :w] = x
return output.reshape(b, -1)[:, :-h].reshape(b, h, -1)[:, :, h - 1:]
def __tril_block(self, top, left, bottom, right, type='o'):
"""
fetch data in lower triangular part without main diagonal
Parameters:
top,left,bottom,right : block coords. left/right < 0
type : o [observe], oe [o/e], b [both]
"""
if left >= 0 or right >= 0:
raise Exception("Trying to access data outside lower triangular part with tril_block")
height = bottom - top
top, bottom = top + left, bottom + right
left, right = -right, -left
if top < 0 or bottom > self.bmatrix.shape[0] - 1:
raise Exception("Accessing values outside the contact map ... valid region:" +
str(10 * self.resol) + '~' + str((self.extent[1] - self.extent[0] - 10) * self.resol))
O = self.bmatrix[top:bottom + 1, left:right + 1]
if type == 'o':
out = self.__relative_right_shift(O[None].swapaxes(-1, 1)).swapaxes(-1, 1)[:, :height + 1, :]
elif type == 'oe':
OE = O / self.diag_mean[left:right + 1]
out = self.__relative_right_shift(OE[None].swapaxes(-1, 1)).swapaxes(-1, 1)[:, :height + 1, :]
else:
OE = O / self.diag_mean[left:right + 1]
out = np.concatenate((O[None], OE[None]))
out = self.__relative_right_shift(out.swapaxes(-1, 1)).swapaxes(-1, 1)[:, :height + 1, :]
return out[..., ::-1]
def rows(self, firstRow, lastRow, type='o', returnBias=False):
"""
fetch rows [firstRow,lastRow] of contacts
Parameters
----------
firstRow : inclusive first row in bp
lastRow : inclusive last row in bp
type : o [observe], oe [o/e], b [both]
returnBias : If true, return bias in an array for bins [first row,last row + max_distance_bins)
"""
firstRow = firstRow // self.resol * self.resol
lastRow = lastRow // self.resol * self.resol
ORows = None
OERows = None
if firstRow < 0 or lastRow < 0 or firstRow > (self.extent[1] - self.extent[0]) * self.resol or lastRow > (
self.extent[1] - self.extent[0]) * self.resol:
raise Exception("Accessing values outside the contact map ... valid region: 0 ~ "
+ str((self.extent[1] - self.extent[0]) * self.resol))
firstRowRelativeBin = self.bp2bin[firstRow] - self.offset
lastRowRelativeBin = self.bp2bin[lastRow] - self.offset
ORows = self.bmatrix[firstRowRelativeBin:lastRowRelativeBin + 1, :][None]
if type == 'o':
outRows = ORows
elif type == 'oe':
OERows = (ORows / self.diag_mean)
outRows = OERows
elif type == 'b':
OERows = (ORows / self.diag_mean)
outRows = np.concatenate((ORows, OERows), axis=0)
outRows = self.__bandedRows2fullRows(outRows)
if returnBias:
bias = self.bin2bias[firstRowRelativeBin:lastRowRelativeBin + self.max_distance_bins]
# print('bias.shape',bias.shape)
# p2ll = self.p2ll(output[-1,:,:],cw=3) # prefer to use obs to compuate p2ll
return outRows, bias
return outRows
def __squareFromContinousRows(self, xCenter, yCenter, w, type='o', meta=True):
"""
fetch a (2w+1)*(2w+1) square of contacts centered at (xCenter,yCenter) from continousrows efficiently
Parameters
----------
xCenter : xCenter in bp
yCenter : yCenter in bp
w : block width = 2w+1, in bins
type : o [observe], oe [o/e], b [both]
"""
if xCenter < self.continousRows.start_bp or xCenter > self.continousRows.end_bp:
print('miss')
rowStep = 1000
startRow_bp = np.max([0, xCenter // (rowStep * self.resol) * (rowStep - 2 * w) * self.resol])
endRow_bp = np.min(
[startRow_bp + (rowStep + 2 * w) * self.resol, (self.extent[1] - self.offset - 1) * self.resol])
mat, bias = self.rows(startRow_bp, endRow_bp, type='b', returnBias=True)
self.continousRows.start_bp = startRow_bp
self.continousRows.end_bp = endRow_bp
self.continousRows.O_matrix = mat[0, :, :]
self.continousRows.OE_matrix = mat[1, :, :]
self.continousRows.bias = bias
else:
print('hit')
xCenterRelativeBin = (xCenter - self.continousRows.start_bp) // self.resol
yCenterRelativeBin = (yCenter - self.continousRows.start_bp) // self.resol
# = {'start_bp': v, 'end_bp': v, 'O_matrix': None, 'OE_matrix': None, 'bias':None, 'offset_bin': 0}
if type == 'o':
output = self.continousRows.O_matrix[xCenterRelativeBin - w:xCenterRelativeBin + w + 1,
yCenterRelativeBin - w:yCenterRelativeBin + w + 1][None]
elif type == 'oe':
output = self.continousRows.OE_matrix[xCenterRelativeBin - w:xCenterRelativeBin + w + 1,
yCenterRelativeBin - w:yCenterRelativeBin + w + 1][None]
else:
OEsquare = self.continousRows.OE_matrix[xCenterRelativeBin - w:xCenterRelativeBin + w + 1,
yCenterRelativeBin - w:yCenterRelativeBin + w + 1][None]
Osquare = self.continousRows.O_matrix[xCenterRelativeBin - w:xCenterRelativeBin + w + 1,
yCenterRelativeBin - w:yCenterRelativeBin + w + 1][None]
output = np.concatenate((Osquare, OEsquare))
if meta:
xBias = self.continousRows.bias[xCenterRelativeBin - w:xCenterRelativeBin + w + 1]
yBias = self.continousRows.bias[yCenterRelativeBin - w:yCenterRelativeBin + w + 1]
bias = np.concatenate((xBias, yBias))
p2ll,crk = self.p2ll(output[-1, :, :], cw=3) # prefer to use obs to compuate p2ll
return output, np.concatenate((bias, [self.totalRC, p2ll,yCenterRelativeBin,crk]))
return output
def p2ll(self, x, cw=3):
"""
P2LL for a peak.
Parameters:
x : sqaure matrix, peak and its surrandings
cw : lower-left corner width
"""
c = x.shape[0] // 2
llcorner = x[-cw:, :cw].flatten()
if sum(llcorner) == 0:
return 0,np.sum(x[c,c]>x[c-1:c+2,c-1:c+2])
return x[c, c] / (sum(llcorner) / len(llcorner)),np.sum(x[c,c]>x[c-1:c+2,c-1:c+2])
def square(self, xCenter, yCenter, w, type='o', meta=True, cache=False):
"""
fetch a (2w+1)*(2w+1) square of contacts centered at (xCenter,yCenter)
Parameters
----------
xCenter : xCenter in bp
yCenter : yCenter in bp
w : block width = 2w+1, in bins
type : o [observe], oe [o/e], b [both]
"""
# print(xCenter,yCenter)
tril = None
xCenter = xCenter // self.resol * self.resol
yCenter = yCenter // self.resol * self.resol
# if xCenter > yCenter:
# tmp = xCenter
# xCenter = yCenter
# yCenter = tmp
# if xCenter - w * self.resol < 0 or yCenter - w * self.resol < 0 or \
# xCenter + w * self.resol > (
# self.extent[1] - self.extent[0] - 1) * self.resol or yCenter + w * self.resol > (
# self.extent[1] - self.extent[0] - 1) * self.resol:
# raise Exception("Accessing values outside the contact map ... valid region: 0 ~ "
# + str((self.extent[1] - self.extent[0]) * self.resol))
# if cache:
# # print("cache")
# return self.__squareFromContinousRows(xCenter, yCenter, w, type, meta)
xCenterRelativeBin = self.bp2bin[xCenter] - self.offset
yCenterRelativeBin = self.bp2bin[yCenter] - self.offset - xCenterRelativeBin
# if yCenterRelativeBin + 2 * w >= self.max_distance_bins:
# raise Exception("max distance in this bcool file is ", self.max_distance_bins * self.resol)
topleft = [xCenterRelativeBin - w, yCenterRelativeBin - 2 * w]
bottomright = [xCenterRelativeBin + w, yCenterRelativeBin + 2 * w]
if topleft[1] < 0:
tril = (topleft[0], topleft[1], bottomright[0], -1)
topleft[1] = 0
tril_part = self.__tril_block(tril[0], tril[1], tril[2], tril[3], type)
Osquare = self.bmatrix[topleft[0]:bottomright[0] + 1, topleft[1]:bottomright[1] + 1]
if type == 'o':
Osquare = Osquare[None]
if tril is not None:
Osquare = np.concatenate((tril_part, Osquare), axis=-1)
output = self.__relative_right_shift(Osquare)[:, :, :2 * w + 1]
elif type == 'oe':
OEsquare = (Osquare / self.diag_mean[topleft[1]:bottomright[1] + 1])[None]
if tril is not None:
OEsquare = np.concatenate((tril_part, OEsquare), axis=-1)
output = self.__relative_right_shift(OEsquare)[:, :, :2 * w + 1]
else:
OEsquare = Osquare / self.diag_mean[topleft[1]:bottomright[1] + 1]
output = np.concatenate((Osquare[None], OEsquare[None]))
if tril is not None:
output = np.concatenate((tril_part, output), axis=-1)
output = self.__relative_right_shift(output)[:, :, :2 * w + 1]
if meta:
xBias = self.bin2bias[self.bp2bin[xCenter] - self.offset - w:self.bp2bin[xCenter] - self.offset + w + 1]
yBias = self.bin2bias[self.bp2bin[yCenter] - self.offset - w:self.bp2bin[yCenter] - self.offset + w + 1]
bias = np.concatenate((xBias, yBias))
p2ll,crk = self.p2ll(output[-1, :, :], cw=3) # prefer to use obs to compuate p2ll
return output, np.concatenate((bias, [self.totalRC, p2ll,yCenterRelativeBin,crk]))
return output
class bcool(cooler.Cooler):
def __init__(self, store):
super().__init__(store)
def bchr(self, chrom, max_distance=None, annotate=True,decoy=False,restrictDecoy=False):
'''
get banded matrix for a given chrom
'''
balance = True
resol = self.info['bin-size']
if max_distance is not None and 'max_distance' in self.info and max_distance > self.info['max_distance']:
raise Exception("max distance in this bcool file is ", self.info['max_distance'])
else:
if 'max_distance' in self.info:
max_distance = self.info['max_distance']
else:
max_distance = 3000000
pixels = self.matrix(balance=balance, as_pixels=True).fetch(chrom)
pixels=pixels[(pixels['bin2_id']-pixels['bin1_id']).abs()<max_distance//resol].reset_index(drop=True)
if decoy:
np.random.seed(0)
pixels['distance']=(pixels['bin2_id']-pixels['bin1_id']).abs()
if restrictDecoy:
pixels = pixels.groupby('distance').apply(shuffleIFWithCount)
else:
pixels=pixels.groupby('distance').apply(shuffleIF)
if annotate:
bins = self.bins().fetch(chrom)
info = self.info
else:
bins = None
info = None
extent = self.extent(chrom)
bmatrix = bandmatrix(pixels, extent, max_distance // resol, bins, info)
return bmatrix
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