""" 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