View pyranges.py
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import pyranges as pr | |
gr = pr.PyRanges(chromosomes=["chr1"] * 2 + ["chr2"] * 2, starts=[0, 100, 200, 300], ends=[50, 150, 250, 350], strands=["+", "+", "-", "-"]) | |
gr.spliced_subsequence(0, -75, by="gene") | |
+--------------+-----------+-----------+--------------+------------+ | |
| Chromosome | Start | End | Strand | gene | | |
| (category) | (int64) | (int32) | (category) | (object) | | |
|--------------+-----------+-----------+--------------+------------| | |
| chr1 | 0 | 25 | + | 1 | |
View chart.js
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function rest(dag) { | |
layering = d3.layeringSimplex() | |
decrossing = d3.decrossOpt() | |
coords = d3.coordQuad() |
View karabiner
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{ | |
"global": { | |
"check_for_updates_on_startup": true, | |
"show_in_menu_bar": true, | |
"show_profile_name_in_menu_bar": false | |
}, | |
"profiles": [ | |
{ | |
"complex_modifications": { | |
"parameters": { |
View ucorrelate.py
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@numba.jit | |
def ucorrelate(t, u, maxlag=None): | |
"""Compute correlation of two signals defined at uniformly-spaced points. | |
The correlation is defined only for positive lags (including zero). | |
The input arrays represent signals defined at uniformily-spaced | |
points. This function is equivalent to :func:`numpy.correlate`, but can | |
efficiently compute correlations on a limited number of lags. | |
Note that binning point-processes with uniform bins, provides | |
signals that can be passed as argument to this function. | |
Arguments: |
View test_pyramid.py
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half_window_size = 3 | |
len_haps = 10 | |
# half_window_size += 1 | |
for i in range(0, half_window_size): | |
for j in range(i): | |
j1 = i - j |
View calc_autocovar.pyx
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#!/usr/bin/env python3 | |
#cython: language_level=3, boundscheck=False, wraparound=False, initializedcheck=False, cdivision=True | |
import sys, math, gzip | |
import numpy as np | |
import pandas as pd | |
from time import time | |
from libc.math cimport exp, fabs |
View calc_covar.pyx
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#!/usr/bin/env python3 | |
#cython: language_level=3, boundscheck=False, wraparound=False, initializedcheck=False, cdivision=True | |
import sys, math, gzip | |
import numpy as np | |
import pandas as pd | |
from time import time | |
from libc.math cimport exp, fabs |
View calc_covar.pyx
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#!/usr/bin/env python3 | |
#cython: language_level=3, boundscheck=False, wraparound=False, initializedcheck=False, cdivision=True | |
import sys, math, gzip | |
import numpy as np | |
# cimport numpy as cnp | |
import pandas as pd | |
from time import time |
View get_lncrna_overlaps_and_nearest.py
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import pyranges_db as db | |
import pyranges as pr | |
gr = db.gencode.genes("human") # takes a while to download from ftp | |
# Wall time: 2min 1s | |
gr.to_gtf("gencode_human.gtf.gz") # takes a while to gzip and write to disk | |
# Wall time: 4min 3s | |
# subset for faster operations |
View fisher_exact_gene_biotypes.py
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rule gene_biotypes: | |
input: | |
regions = gene_biotype_infiles, | |
annotation = "{prefix}/data/{genome}/annotation.tsv" | |
output: | |
"{prefix}/data/{genome}/{hmm_or_anatomy}_regions/{cutoff}/gene_biotype_counts.tsv" | |
run: | |
df = pd.read_table(input.regions, header=0) | |
df2 = pd.read_csv(input.annotation, header=0, sep=",") |
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