mcell/utils/bng_analyzer_py/viz_reader.py

"""
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 sys
import os
import pandas as pd

MCELL_PATH = os.environ.get('MCELL_PATH', '')
if MCELL_PATH:
    sys.path.append(os.path.join(MCELL_PATH, 'lib'))
else:
    print("Error: variable MCELL_PATH that is used to find the mcell library was not set.")
    sys.exit(1)

import mcell as m


def load_counts_from_dat_file(file_name):
    # using pandas read_cvs because it is usually faster 
    # we could also read the file line by line and count the number of occurences 
    df = pd.read_csv(
        file_name, 
        sep=' ', 
        names=['name', 'id', 'x', 'y', 'z', 'nx', 'ny', 'nz'])

    # create a dataframe with counts per unique species
    counts = df['name'].value_counts().rename_axis('species').reset_index(name='count')
    
    # need to sort the data to get consistent results
    counts = counts.sort_values(['count','species'], ascending=(False, True))
    return counts


def parse_bngl_strings_to_complex_representations(counts_df):
    # returns a list of pairs (mcell.Complex, int), the second item is count 
    res = []
    for index, row in counts_df.iterrows():
        # constructor m.Complex parses the BNGL representaion into 
        # a MCell4 API representation 
        # (see https://cnl.salk.edu/~ahusar/mcell4_documentation/generated/subsystem.html#complex
        cplx = m.Complex(row['species'])
        res.append((cplx, row['count']))
    
    return res


def read_dat_file(file_name):
    # returns a list of pairs (mcell.Complex, int), the second item is count 
    
    # load the .dat file as a pandas dataframe
    counts_df = load_counts_from_dat_file(file_name)
    
    # parse the BNGL representations
    complex_counts = parse_bngl_strings_to_complex_representations(counts_df)
    
    return complex_counts