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#! /usr/bin/python

# Forward-recursion with pruning
# An algorithm for doing exact alignment of two sequences
# Forward recursion is used, with pruning of cells

# two sequences to align
S1 = 'APPLR'
S2 = 'APPLS'

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#!/usr/bin/env python

from argparse import ArgumentParser
from BeautifulSoup import BeautifulSoup
import csv

# argparser    
parser = ArgumentParser(description = 'Parses MEME-MAST xml output.',
    usage = 'python mast2tsv.py mast.output.xml mast.output.tsv')
# positional arguments

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#!/usr/bin/env python

# Mining "COSMIC: Catalogue Of Somatic Mutations In Cancer" database

import os
from subprocess import call
import pandas as pd

# Cosmic DB has requires login to download data, so neither BioMart or wget actually work.
call(["wget", "http://cancer.sanger.ac.uk/files/cosmic/current_release/CosmicCompleteExport.tsv.gz"])

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#!/usr/env/python

import os
import h5py
import pandas as pd

projectDir = '/home/afr/workspace/cellprofiler/'

# open hdf5
hdf5 = h5py.File(projectDir + '1315001__2014-01-25T18_26_59-Measurement1.h5', 'r')

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#!/usr/env python

from argparse import ArgumentParser
import os, re
from pybedtools import BedTool
import HTSeq
import numpy as np
import pandas as pd
import string
import itertools

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import pandas as pd
from biomart import BiomartServer, BiomartDataset
from Bio import Entrez


def get_tax_id(specie):
    """Get taxon ID for specie."""
    specie = specie.replace(" ", "+").strip()
    search = Entrez.esearch(term=specie, db="taxonomy", retmode="xml")
    record = Entrez.read(search)

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from scipy.optimize import curve_fit
from scipy import stats
import matplotlib.pyplot as plt


def fit_exponential_neg(x, a, b, c):
    return a * np.exp(-b * x) + c

X = np.array(rpkm_log['mean'])
Y = np.array(rpkm_log['qv2'])

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import networkx as nx
import matplotlib.pyplot as plt
import seaborn as sns

# Getting a specific node
G['PAX5']
# Geting a specific edge
G['PAX5']['NFKB1']

# Getting all edge weights

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import sys
from argparse import ArgumentParser
import pyBigWig
import numpy as np
import multiprocessing
import parmap

"""
Produce bigWig files with the quantiles/mean of signal across a number of bigWig files.
"""

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import sys
import json
import urllib2
import re
from collections import Counter


def get_ids(term, ids=list(), retstart=0, retmax=1000):
    """
    Return all Pubmed Ids of articles containing a term, in a recursive fashion.