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import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

def z_factor(p, n):
    numerator = 3 * (np.std(p) + np.std(n))
    denominator = np.abs(np.mean(p) - np.mean(n))
    return 1 - (numerator / denominator)


def multivariate_z_factor(data, labels):

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#!/bin/bash

MY_ARG="world"

python /dev/stdin "$MY_ARG" << EOF
import sys
print(f"Hello {sys.argv[1]}")
EOF

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codon_table = {
    'AUA':'I', 'AUC':'I', 'AUU':'I', 'AUG':'M',
    'ACA':'U', 'ACC':'U', 'ACG':'U', 'ACU':'U',
    'AAC':'N', 'AAU':'N', 'AAA':'K', 'AAG':'K',
    'AGC':'S', 'AGU':'S', 'AGA':'R', 'AGG':'R',
    'CUA':'L', 'CUC':'L', 'CUG':'L', 'CUU':'L',
    'CCA':'P', 'CCC':'P', 'CCG':'P', 'CCU':'P',
    'CAC':'H', 'CAU':'H', 'CAA':'Q', 'CAG':'Q',
    'CGA':'R', 'CGC':'R', 'CGG':'R', 'CGU':'R',
    'GUA':'V', 'GUC':'V', 'GUG':'V', 'GUU':'V',

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#!/bin/bash

# script to install cellprofiler into a conda environment
# named 'cellprofiler'
#
# to activate this environment you will need to load anaconda/4.3.1
# then run
#>>>      source activate cellprofiler

# load anaconda

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#!/usr/bin/env python3

import train_funcs

cell_lines = [
    "MDA-157",
    "MDA-231",
    "MCF7",
    "KPL4",
    "T47D",

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def run_from_ipython():
    try:
        __IPYTHON__
        return True
    except NameError:
        return False