#!/usr/bin/env python """ This is free and unencumbered software released into the public domain. Anyone is free to copy, modify, publish, use, compile, sell, or distribute this software, either in source code form or as a compiled binary, for any purpose, commercial or non-commercial, and by any means. In jurisdictions that recognize copyright laws, the author or authors of this software dedicate any and all copyright interest in the software to the public domain. We make this dedication for the benefit of the public at large and to the detriment of our heirs and successors. We intend this dedication to be an overt act of relinquishment in perpetuity of all present and future rights to this software under copyright law. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. For more information, please refer to [http://unlicense.org] """ import numpy as np import pandas as pd import matplotlib.pyplot as plt import os import sys import argparse class Options: def __init__(self): self.mcell3_dir = None self.mcell4_dir = None self.bng_dir = None self.single_bng_run = False self.labels = ['MCell4', 'MCell3R', 'NFSim'] def create_argparse(): parser = argparse.ArgumentParser(description='MCell4 Runner') parser.add_argument('-m4', '--mcell4', type=str, help='mcell4 react_data directory') parser.add_argument('-m3', '--mcell3', type=str, help='mcell3 react_data directory') parser.add_argument('-b', '--bng', type=str, help='bionetgen directory') parser.add_argument('-s', '--single_bng_run', action='store_true', help='the bionetgen directory contains only a single .gdat file') parser.add_argument('-l', '--labels', type=str, help='comma-separated list of labels (used in order -m4,-m3,-b') return parser def process_opts(): parser = create_argparse() args = parser.parse_args() opts = Options() if args.mcell4: opts.mcell4_dir = args.mcell4 if args.mcell3: opts.mcell3_dir = args.mcell3 if args.bng: opts.bng_dir = args.bng if args.single_bng_run: opts.single_bng_run = args.single_bng_run if args.labels: opts.labels = args.labels.split(',') return opts def get_mcell_observables_counts(dir): counts = {} seed_dirs = os.listdir(dir) for seed_dir in seed_dirs: if not seed_dir.startswith('seed_'): continue file_list = os.listdir(os.path.join(dir, seed_dir)) for file in file_list: file_path = os.path.join(dir, seed_dir, file) if os.path.isfile(file_path) and file.endswith('.dat'): observable = os.path.splitext(file)[0] if observable.endswith('_MDLString'): observable = observable[:-len('_MDLString')] if observable not in counts: index = 0 else: index = counts[observable].shape[1] - 1 col_name = 'count' + str(index) df = pd.read_csv(file_path, sep=' ', names=['time', col_name]) if observable not in counts: counts[observable] = df else: # add new column counts[observable][col_name] = df[col_name] return counts def get_bng_observables_counts(file, counts): if not os.path.exists(file): print("Expected file " + file + " not found, skipping it") return with open(file, 'r') as f: first_line = f.readline() header = first_line.split()[1:] df = pd.read_csv(file, delim_whitespace=True, comment='#', names=header) return df def process_nsfim_gdat_file(full_dir, counts): df = get_bng_observables_counts(os.path.join(full_dir, 'test.gdat'), counts) # transform into separate dataframes based on observable for i in range(1, df.shape[1]): observable = df.columns[i] if observable not in counts: col_name = 'count0' # select time and the current observable counts[observable] = pd.DataFrame() counts[observable]['time'] = df.iloc[:, 0] counts[observable][col_name] = df.iloc[:, i] else: col_name = 'count' + str(counts[observable].shape[1] - 1) counts[observable][col_name] = df.iloc[:, i] def get_nfsim_observables_counts(opts): single_bng_run = opts.single_bng_run dir = opts.bng_dir counts = {} if not single_bng_run: nf_dirs = os.listdir(dir) for nf_dir in nf_dirs: full_dir = os.path.join(dir, nf_dir) if not nf_dir.startswith('nf_') or not os.path.isdir(full_dir): continue process_nsfim_gdat_file(full_dir, counts) else: process_nsfim_gdat_file(dir, counts) return counts def main(): opts = process_opts() counts = [] if opts.mcell4_dir: if os.path.exists(opts.mcell4_dir): print("Reading MCell data from " + opts.mcell4_dir) counts.append(get_mcell_observables_counts(opts.mcell4_dir)) else: print("Directory " + opts.mcell4_dir + " with MCell4 data not found, ignored") sys.exit(1) else: counts.append({}) if opts.mcell3_dir: if os.path.exists(opts.mcell3_dir): print("Reading MCell data from " + opts.mcell3_dir) counts.append(get_mcell_observables_counts(opts.mcell3_dir)) else: print("Error: directory " + opts.mcell3_dir + " with MCell3 data not found, ignored") sys.exit(1) else: counts.append({}) # get_nfsim_observables_counts may return an empty dict if opts.bng_dir: if os.path.exists(opts.bng_dir): print("Reading BNG data from " + opts.bng_dir) counts.append(get_nfsim_observables_counts(opts)) else: print("Error: directory " + opts.bng_dir + " with BNG data not found, ignored") sys.exit(1) else: counts.append({}) clrs = ['b', 'g', 'r'] all_observables = set(counts[0].keys()) all_observables = all_observables.union(set(counts[1].keys())) all_observables = all_observables.union(set(counts[2].keys())) for obs in sorted(all_observables): print("Processing observable " + obs) fig,ax = plt.subplots() ax.set_title(obs) legend_names = [] for i in range(len(counts)): if obs not in counts[i]: continue data = counts[i][obs] df = pd.DataFrame() df['time'] = data.iloc[:, 0] df['means'] = data.iloc[:, 1:].mean(axis=1) print(opts.labels[i], df['means']) df['mean_minus_std'] = df['means'] - data.iloc[:, 1:].std(axis=1) df['mean_plus_std'] = df['means'] + data.iloc[:, 1:].std(axis=1) # free collected data to decrease memory consumption del data ax.plot(df['time'], df['means'], label=obs + "1", c=clrs[i]) ax.fill_between( df['time'], df['mean_minus_std'], df['mean_plus_std'], alpha=0.1, facecolor=clrs[i]) legend_names.append(opts.labels[i]) plt.legend(legend_names) plt.xlabel("time [s]") plt.ylabel("N(t)") plt.savefig(obs + '.png', dpi=600) plt.close(fig) if __name__ == '__main__': main()