endrebak

from typing import List
import polars as pl
import numpy as np

import bioframe.core.arrops as arrops

import pyoframe as pf

import polars as pl

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