Nezar Abdennur nvictus

## gillespie_nrm.py
from pqdict import pqdict
from numpy import array, zeros, log, seterr
from numpy.random import rand
from collections import Counter
from matplotlib import pyplot as plt

seterr(divide="ignore")


class Reaction(object):

## read_motifs.py
import numpy as np
import pandas as pd


def _probability_mat_to_information_mat(prob_df, bg_df):
    """
    Converts a probability matrix to an information matrix.
    Taken from logomaker (https://github.com/jbkinney/logomaker).

    """

## preferred.py
def geomprog(start, mul):
    """
    Generate a geometric progression.

    Beginning with integer `start`, generate an unbounded geometric
    progression with ratio `mul`.

    """
    start = int(start)
    yield start

## mutual_info.py
'''
Non-parametric computation of entropy and mutual-information

Adapted by G Varoquaux for code created by R Brette, itself
from several papers (see in the code).

These computations rely on nearest-neighbor statistics
'''
import numpy as np

## pandas_jupyter_paginator.py
"""
Forked from: https://gist.github.com/nokados/e8f0a64b55099f2f07a50f2b090c91c7

Changes
* Added slider control to scroll through pages of really large dataframes.
* Reduce flicker by making events trigger widget element updates instead of
  clearing output and re-rendering.
* Add support for dataframe CSS styling.
* Register custom pandas accessor

## godsnot.json
[
    "#000000",
    "#FFFF00",
    "#1CE6FF",
    "#FF34FF",
    "#FF4A46",
    "#008941",
    "#006FA6",
    "#A30059",
    "#FFDBE5",

## post_mortem_hook.py

def set_postmortem_hook():
    import sys, traceback, ipdb
    def _excepthook(exc_type, value, tb):
        traceback.print_exception(exc_type, value, tb)
        print()
        ipdb.pm()
    sys.excepthook = _excepthook

## .inputrc
"\e[A": history-search-backward
"\e[B": history-search-forward
set show-all-if-ambiguous on
set completion-ignore-case on

## load-dense-contact-matrix.ipynb

      
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                nvictus
                / load-dense-contact-matrix.ipynb
            
            
              Last active
              July 16, 2017 19:16
            
              
                load dense contact matrix
              
          
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## tbwarn.py
import traceback
import warnings
import sys

def warn_with_traceback(message, category, filename, lineno, file=None, line=None):

    log = file if hasattr(file,'write') else sys.stderr
    traceback.print_stack(file=log)
    log.write(warnings.formatwarning(message, category, filename, lineno, line))
	from pqdict import pqdict
	from numpy import array, zeros, log, seterr
	from numpy.random import rand
	from collections import Counter
	from matplotlib import pyplot as plt

	seterr(divide="ignore")


	class Reaction(object):
	import numpy as np
	import pandas as pd


	def _probability_mat_to_information_mat(prob_df, bg_df):
	"""
	Converts a probability matrix to an information matrix.
	Taken from logomaker (https://github.com/jbkinney/logomaker).

	"""
	def geomprog(start, mul):
	"""
	Generate a geometric progression.

	Beginning with integer `start`, generate an unbounded geometric
	progression with ratio `mul`.

	"""
	start = int(start)
	yield start
	'''
	Non-parametric computation of entropy and mutual-information

	Adapted by G Varoquaux for code created by R Brette, itself
	from several papers (see in the code).

	These computations rely on nearest-neighbor statistics
	'''
	import numpy as np
	"""
	Forked from: https://gist.github.com/nokados/e8f0a64b55099f2f07a50f2b090c91c7

	Changes
	* Added slider control to scroll through pages of really large dataframes.
	* Reduce flicker by making events trigger widget element updates instead of
	clearing output and re-rendering.
	* Add support for dataframe CSS styling.
	* Register custom pandas accessor
	[
	"#000000",
	"#FFFF00",
	"#1CE6FF",
	"#FF34FF",
	"#FF4A46",
	"#008941",
	"#006FA6",
	"#A30059",
	"#FFDBE5",

	def set_postmortem_hook():
	import sys, traceback, ipdb
	def _excepthook(exc_type, value, tb):
	traceback.print_exception(exc_type, value, tb)
	print()
	ipdb.pm()
	sys.excepthook = _excepthook
	"\e[A": history-search-backward
	"\e[B": history-search-forward
	set show-all-if-ambiguous on
	set completion-ignore-case on
	import traceback
	import warnings
	import sys

	def warn_with_traceback(message, category, filename, lineno, file=None, line=None):

	log = file if hasattr(file,'write') else sys.stderr
	traceback.print_stack(file=log)
	log.write(warnings.formatwarning(message, category, filename, lineno, line))