mcell/utils/react_data_analyzer/reac_data_analyzer.py

#!/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
from itertools import count

# for now a simple scripts that prints averages of last n values of react_data files
# usage:
# python3 reac_data_analyzer.py N
#     - N is the number of the last observable counts from react_data outputs that will be averaged
# 

# function collectreturns 
def get_last_observable_counts(num_last_samples_to_avg=1):
    counts = {}
    num_seeds = 0
    seed_dirs = os.listdir()
        
    # go through all seed_* directories
    for seed_dir in seed_dirs:
        if not seed_dir.startswith('seed_'):
            continue
        
        # we need the total number of directories to compute an average later
        num_seeds += 1
        
        # go through all *.dat files in the seed directory
        file_list = os.listdir(seed_dir)
        for file in file_list:
            file_path = os.path.join(seed_dir, file)
            
            # we care only about .dat files
            if os.path.isfile(file_path) and file.endswith('.dat'):
                print("Processing " + file_path)
                observable = os.path.splitext(file)[0]
                if observable.endswith('_MDLString'):
                    observable = observable[:-len('_MDLString')]
                
                # read the .dat file into a pandas dataframe
                df = pd.read_csv(file_path, sep=' ', names=['time', 'count'])
                
                #print(df.tail(num_last_samples_to_avg)['count'].mean())
                #print("From " + observable)
                #sys.exit()
                
                # get average of the last N items
                avg_cnt = df.tail(num_last_samples_to_avg)['count'].mean()
                
                # and accumulate the observable count
                if observable in counts:
                    counts[observable] += avg_cnt
                else:
                    counts[observable] = avg_cnt
    
    # compute average of the sums of averages we computed above
    res = {}
    for v,c in sorted(counts.items()):
        res[v] = c / num_seeds
    
    return res
    

# process argument
num_last_samples_to_avg = 1    
if len(sys.argv) == 2:
    num_last_samples_to_avg = int(sys.argv[1])
    
# read all *.dat files in the current directory and return a  
# dictionary observable -> average count
avg_counts = get_last_observable_counts(num_last_samples_to_avg)


for v,c in sorted(avg_counts.items()):
    print(v + ": " + str(c))