nvictus

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):

nvictus

from pqdict import pqdict


def dijkstra(graph, source, target=None):
    dist = {}  # lengths of the shortest paths to each node
    pred = {}  # predecessor node in each shortest path

    # Store distance scores in a priority queue dictionary
    pq = pqdict.minpq()
    for node in graph:

nvictus

nvictus

nvictus

CONDA_DIR="$HOME/miniconda"

alias workon='source $CONDA_DIR/bin/activate'

# function workon () {
# 	source "$CONDA_DIR/bin/activate" "$1"
# }

function __conda_user_setup () {
	_workon_tab_completion_bash () {

nvictus

nvictus

def lexbisect(arrays, values, side='left', lo=0, hi=None):
    """
    Bisection search on lexically sorted arrays.
    
    Parameters
    ----------
    arrays : sequence of k 1-D array-like
        Each "array" can be any sequence that supports scalar integer indexing,
        as long as the arrays have the same length and their values are
        lexsorted from left to right.

nvictus

nvictus

"""Run Length Encoding utilities for NumPy arrays.

Authors
-------
- Nezar Abdennur
- Anton Goloborodko

"""
from __future__ import division, print_function
import numpy as np

nvictus

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