This script coalesces the results from running Fraunhofer FIT ZETA and merges all of the output files from the evaluation task.

coalesce_zeta.py

#! /usr/bin/env python3

"""

This script coalesces the results from running Fraunhofer FIT ZETA and
merges all of the output files from the evaluation task.

From the terminal, run something like:

`python3 coalesce_zeta_results.py -d ~/Desktop/HCA\ 2016\ Data/assay/resultPath -o ~/Desktop/coalesced.csv`

"""

import os
import pandas as pd
import argparse
import ntpath
import itertools as itt

parser = argparse.ArgumentParser()

parser.add_argument('--input-directory', '-d')
parser.add_argument('--output', '-o')

args = parser.parse_args()

base = os.path.expanduser(args.input_directory)
intervals = 'VAC3-1H96H-20x', 'VAC3-24H96H-20X', 'VAC3-96H96H-20X'

compounds = {
    'Cpd 01': list(range(14, 24)),
    'Cpd 05': list(range(26, 36)),
    'Cpd 10': list(range(38, 48)),
    'Cpd 14': list(range(50, 60)),
    'Cpd 21': list(range(62, 72)),
    'Negative': list(range(74, 84))
}

positive_compounds = sorted(compound for compound in compounds if compound != 'Negative')

valid_wells = set(itt.chain.from_iterable(compounds.values()))

concentrations = {}
calcium = {}
ca = {}

for compound, cwells in compounds.items():
    for well in cwells[0:5]:
        calcium[well] = 'low'
    for well in cwells[5:10]:
        calcium[well] = 'high'
        
    for i in range(5):
        concentrations[cwells[i]] = i + 1
        concentrations[cwells[i + 5]] = i + 1
        
    for well in cwells:
        ca[well] = compound
        
results = {}

for interval in intervals:
    interval_path = os.path.join(base, interval)
    
    if os.path.exists(os.path.join(interval_path, '.DS_Store')):
        os.remove(os.path.join(interval_path, '.DS_Store'))
    
    for well_code in os.listdir(interval_path):
        well_path = os.path.join(interval_path, well_code)
        
        if not os.path.isdir(well_path):
            continue
        
        well = int(well_code[1:])
        if well not in valid_wells:
            continue
        
        data_path = min(f for f in os.listdir(well_path) if f.endswith('.csv'))
        df = pd.read_csv(os.path.join(well_path, data_path))

        images = df['Image Path'].map(lambda path: ntpath.basename(os.path.normpath(path)))

        image_well = images.map(lambda v: int(v[1:3]))
        time = images.map(lambda v: 1 if '1H96H' in v else (24 if '24H96H' in v else 96))
        replicate = images.map(lambda v: int(v[-5:-4]))

        df['Image Path'] = images
        df['Well'] = image_well
        df['Interval'] = time
        df['Replicate'] = replicate
        df['Calcium'] = image_well.map(calcium)
        df['Concentration'] = image_well.map(concentrations)
        df['Compound'] = image_well.map(ca)

        del df['Barcode ']
        del df['Site ']
        del df['Class 2 ']
        del df['Well ']
            
        results[interval, well] = df
    
results = pd.concat(list(results.values()))
results.to_csv(args.output)