jblitzar/github-python-corpus · Datasets at Hugging Face

text stringlengths 1 93.6k
elif values[0] == 'vt':
#texture coordinate (u, v)
self.texcoords.append([float(a) for a in values[1:3] ])

elif values[0] == 'f':
#face description
face_vertices = []
face_texcoords = []
for v in values[1:]:
w = v.split('/')
face_vertices.append(int(w[0]))
if len(w) >= 2 and len(w[1]) > 0:
face_texcoords.append(int(w[1]))
else:
color_fixed = True
face_texcoords.append(0)
self.faces.append([face_vertices, face_texcoords])


for f in self.faces:
if not color_fixed:
f.append(three_d_object.decide_face_color(f[-1], self.texture, self.texcoords))
else:
f.append((50, 50, 50)) #default color

# cv2.imwrite('texture_marked.png', self.texture)

def decide_face_color(hex_color, texture, textures):
#doesnt use proper texture
#takes the color at the mean of the texture coords

h, w, _ = texture.shape
col = np.zeros(3)
coord = np.zeros(2)
all_us = []
all_vs = []

for i in hex_color:
t = textures[i - 1]
coord = np.array([t[0], t[1]])
u , v = int(w(t[0]) - 0.0001), int(h(1-t[1])- 0.0001)
all_us.append(u)
all_vs.append(v)

u = int(sum(all_us)/len(all_us))
v = int(sum(all_vs)/len(all_vs))

# all_us.append(all_us[0])
# all_vs.append(all_vs[0])
# for i in range(len(all_us) - 1):
# texture = cv2.line(texture, (all_us[i], all_vs[i]), (all_us[i + 1], all_vs[i + 1]), (0,0,255), 2)
# pass

col = np.uint8(texture[v, u])
col = [int(a) for a in col]
col = tuple(col)
return (col)
# <FILESEP>
#! /usr/bin/env python

import argparse
import os
import pysam
import pandas as pd
import numpy as np
from scipy.optimize import nnls
import scipy.sparse
import multiprocessing as mp
from hashed_read_genome_array import HashedReadBAMGenomeArray, ReadKeyMapFactory, read_length_nmis, get_hashed_counts
from plastid.genomics.roitools import SegmentChain, positionlist_to_segments
import sys
from time import strftime

parser = argparse.ArgumentParser(description='Use linear regression to identify likely sites of translation. Regression will be performed for ORFs '
'defined by find_orfs_and_types.py using a metagene profile constructed from annotated CDSs. If '
'multiple ribosome profiling datasets are to be analyzed separately (e.g. if they were collected under '
'different drug treatments), then this program should be run separately for each, ideally in separate '
'subfolders indicated by SUBDIR.')

parser.add_argument('bamfiles', nargs='+', help='Path to transcriptome-aligned BAM file(s) for read data')
parser.add_argument('--subdir', default=os.path.curdir,
help='Convenience argument when dealing with multiple datasets. In such a case, set SUBDIR to an appropriate name (e.g. HARR, '
'CHX) to avoid file conflicts. (Default: current directory)')
parser.add_argument('--restrictbystarts', nargs='+',
help='Subdirectory/subdirectories or filename(s) containing regression output to use to restrict ORFs for regression. If a '
'directory or list of directories, file(s) of name REGRESSFILE (regression.h5 by default) will be searched for within them. '
'For use to restrict regression on e.g. CHX or no-drug data based only on positive hits from e.g. HARR or LTM data. '
'Value(s) of MINWSTART indicate the minimum W statistic to require. If multiple directories/files are provided, start '
'sites will be taken from their union.')
parser.add_argument('--minwstart', type=float, nargs='+', default=[0],
help='Minimum W_start statistic to require for regression output in RESTRICTBYSTARTS. If only one value is given, it will be '
'assumed to apply to all; if multiple values are given, the number of values must match the number of values provided for '
'RESTRICTBYSTARTS. Ignored if RESTRICTBYSTARTS not included. (Default: 0)')
parser.add_argument('--orfstore', default='orf.h5',
help='Path to pandas HDF store containing ORFs to regress; generated by find_orfs_and_types.py (Default: orf.h5)')
parser.add_argument('--inbed', default='transcripts.bed', help='Transcriptome BED-file (Default: transcripts.bed)')
parser.add_argument('--offsetfile', default='offsets.txt',
help='Path to 2-column tab-delimited file with 5\' offsets for variable P-site mappings. First column indicates read length, '
'second column indicates offset to apply. Read lengths are calculated after trimming up to MAX5MIS 5\' mismatches. Accepted '
'read lengths are defined by those present in the first column of this file. If SUBDIR is set, this file is assumed to be '

elif values[0] == 'vt':

#texture coordinate (u, v)

self.texcoords.append([float(a) for a in values[1:3] ])

elif values[0] == 'f':

#face description

face_vertices = []

face_texcoords = []

for v in values[1:]:

w = v.split('/')

face_vertices.append(int(w[0]))

if len(w) >= 2 and len(w[1]) > 0:

face_texcoords.append(int(w[1]))

else:

color_fixed = True

face_texcoords.append(0)

self.faces.append([face_vertices, face_texcoords])

for f in self.faces:

if not color_fixed:

f.append(three_d_object.decide_face_color(f[-1], self.texture, self.texcoords))

else:

f.append((50, 50, 50)) #default color

# cv2.imwrite('texture_marked.png', self.texture)

def decide_face_color(hex_color, texture, textures):

#doesnt use proper texture

#takes the color at the mean of the texture coords

h, w, _ = texture.shape

col = np.zeros(3)

coord = np.zeros(2)

all_us = []

all_vs = []

for i in hex_color:

t = textures[i - 1]

coord = np.array([t[0], t[1]])

u , v = int(w*(t[0]) - 0.0001), int(h*(1-t[1])- 0.0001)

all_us.append(u)

all_vs.append(v)

u = int(sum(all_us)/len(all_us))

v = int(sum(all_vs)/len(all_vs))

# all_us.append(all_us[0])

# all_vs.append(all_vs[0])

# for i in range(len(all_us) - 1):

# texture = cv2.line(texture, (all_us[i], all_vs[i]), (all_us[i + 1], all_vs[i + 1]), (0,0,255), 2)

# pass

col = np.uint8(texture[v, u])

col = [int(a) for a in col]

col = tuple(col)

return (col)

# <FILESEP>

#! /usr/bin/env python

import argparse

import os

import pysam

import pandas as pd

import numpy as np

from scipy.optimize import nnls

import scipy.sparse

import multiprocessing as mp

from hashed_read_genome_array import HashedReadBAMGenomeArray, ReadKeyMapFactory, read_length_nmis, get_hashed_counts

from plastid.genomics.roitools import SegmentChain, positionlist_to_segments

import sys

from time import strftime

parser = argparse.ArgumentParser(description='Use linear regression to identify likely sites of translation. Regression will be performed for ORFs '

'defined by find_orfs_and_types.py using a metagene profile constructed from annotated CDSs. If '

'multiple ribosome profiling datasets are to be analyzed separately (e.g. if they were collected under '

'different drug treatments), then this program should be run separately for each, ideally in separate '

'subfolders indicated by SUBDIR.')

parser.add_argument('bamfiles', nargs='+', help='Path to transcriptome-aligned BAM file(s) for read data')

parser.add_argument('--subdir', default=os.path.curdir,

help='Convenience argument when dealing with multiple datasets. In such a case, set SUBDIR to an appropriate name (e.g. HARR, '

'CHX) to avoid file conflicts. (Default: current directory)')

parser.add_argument('--restrictbystarts', nargs='+',

help='Subdirectory/subdirectories or filename(s) containing regression output to use to restrict ORFs for regression. If a '

'directory or list of directories, file(s) of name REGRESSFILE (regression.h5 by default) will be searched for within them. '

'For use to restrict regression on e.g. CHX or no-drug data based only on positive hits from e.g. HARR or LTM data. '

'Value(s) of MINWSTART indicate the minimum W statistic to require. If multiple directories/files are provided, start '

'sites will be taken from their union.')

parser.add_argument('--minwstart', type=float, nargs='+', default=[0],

help='Minimum W_start statistic to require for regression output in RESTRICTBYSTARTS. If only one value is given, it will be '

'assumed to apply to all; if multiple values are given, the number of values must match the number of values provided for '

'RESTRICTBYSTARTS. Ignored if RESTRICTBYSTARTS not included. (Default: 0)')

parser.add_argument('--orfstore', default='orf.h5',

help='Path to pandas HDF store containing ORFs to regress; generated by find_orfs_and_types.py (Default: orf.h5)')

parser.add_argument('--inbed', default='transcripts.bed', help='Transcriptome BED-file (Default: transcripts.bed)')

parser.add_argument('--offsetfile', default='offsets.txt',

help='Path to 2-column tab-delimited file with 5\' offsets for variable P-site mappings. First column indicates read length, '

'second column indicates offset to apply. Read lengths are calculated after trimming up to MAX5MIS 5\' mismatches. Accepted '

'read lengths are defined by those present in the first column of this file. If SUBDIR is set, this file is assumed to be '