endrebak

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 |

endrebak

function rest(dag) {


    layering = d3.layeringSimplex()

    decrossing = d3.decrossOpt()

    coords = d3.coordQuad()

endrebak

{
    "global": {
        "check_for_updates_on_startup": true,
        "show_in_menu_bar": true,
        "show_profile_name_in_menu_bar": false
    },
    "profiles": [
        {
            "complex_modifications": {
                "parameters": {

endrebak

@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:

endrebak

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

endrebak

#!/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

endrebak

#!/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

endrebak

#!/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

endrebak

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

endrebak

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=",")