Nezar Abdennur nvictus

I hereby claim:

To claim this, I am signing this object:

	import numpy as np


	def ou_approx(t, theta=0.15, sigma=0.2, x0=0, xm=0):
	"""
	Ornstein-Uhlenbeck process sampled using an approximate updating formula, first-order in the time step.

	The approximation gets worse as the time steps get larger.

	Parameters

	#!/usr/bin/env python
	import hashlib
	import os.path as op
	import os
	import re
	import warnings
	from contextlib import closing
	from urllib.parse import urlsplit
	from urllib.request import urlopen

	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