mirror of
https://github.com/TREX-CoE/trexio.git
synced 2024-11-03 20:54:07 +01:00
29d927675e
* minor cleaning * create functions for dictionary parsers in the external file * remove files_exclude since post-processing does the job * no need to have group_group_dset when propagatin dsets * oneliner for paths to templates * add dset_per_group dict * add function to iteratively populate string based on triggers list * added recursive replacer for numbers * add recursive replaces for datasets * add function for text groups [iterative+recursive upd] * do not define triggers in the master script * transition to helper dictionaries * cleaning * comment main code blocks * rearrange parameters * add documentation strings to the functions * minor cleaning and changes * adapt build_trexio script * add consisteny check for dimensioning variables
559 lines
22 KiB
Python
559 lines
22 KiB
Python
from os import listdir
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from os.path import join, dirname, abspath, isfile
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from json import load as json_load
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def read_json(fname: str) -> dict:
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"""
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Read configuration from the input `fname` JSON file.
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Parameters:
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fname (str) : JSON file name
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Returns:
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config (dict) : full configuration dictionary loaded from the input file
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"""
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fileDir = dirname(abspath(__file__))
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parentDir = dirname(fileDir)
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with open(join(parentDir,fname), 'r') as f:
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config = json_load(f)
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return config
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def get_files_todo(source_files: dict) -> dict:
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"""
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Build dictionaries of templated files per objective.
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Parameters:
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source_files (dict) : dictionary with source files per source directory
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Returns:
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file_todo (dict) : dictionary with objective title : [list of files] as key-value pairs
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"""
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all_files = []
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for key in source_files.keys():
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all_files += source_files[key]
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files_todo = {}
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#files_todo['all'] = list(filter(lambda x: 'read' in x or 'write' in x or 'has' in x or 'hrw' in x or 'flush' in x or 'free' in x, all_files))
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files_todo['all'] = [f for f in all_files if 'read' in f or 'write' in f or 'has' in f or 'flush' in f or 'free' in f or 'hrw' in f]
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for key in ['dset', 'num', 'group']:
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files_todo[key] = list(filter(lambda x: key in x, files_todo['all']))
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files_todo['group'].append('struct_text_group_dset.h')
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# files that correspond to todo1 group (e.g. only iterative population within the function body)
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files_todo['auxiliary'] = ['def_hdf5.c', 'basic_hdf5.c', 'basic_text_group.c', 'struct_hdf5.h', 'struct_text_group.h']
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return files_todo
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def get_source_files(paths: dict) -> dict:
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"""
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Build dictionaries of all files per source directory.
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Parameters:
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paths (dict) : dictionary with paths to source directories
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Returns:
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file_dict (dict) : dictionary with source title : [list of files] as key-value pairs
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"""
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file_dict = {}
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for key in paths.keys():
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file_dict[key] = [f for f in listdir(paths[key]) if isfile(join(paths[key], f))]
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return file_dict
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def get_template_paths(source: list) -> dict:
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"""
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Build dictionary of the absolute paths to directory with templates per source.
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Parameters:
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source (list) : list of source titles, i.e. ['front', 'text', 'hdf5']
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Returns:
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path_dict (dict) : dictionary with source title : absolute path as key-value pairs
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"""
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fileDir = dirname(abspath(__file__))
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path_dict = {}
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for dir in source:
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path_dict[dir] = join(fileDir,f'templates_{dir}')
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return path_dict
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def recursive_populate_file(fname: str, paths: dict, detailed_source: dict) -> None:
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"""
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Populate files containing basic read/write/has functions.
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Parameters:
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filename (str) : template file to be populated
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paths (dict) : dictionary of paths per source directory
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detailed_source (dict) : dictionary of variables with substitution details (usually either datasets or numbers)
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Returns:
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None
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"""
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fname_new = join('populated',f'pop_{fname}')
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templ_path = get_template_path(fname, paths)
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triggers = ['group_dset_dtype', 'group_dset_h5_dtype', 'default_prec',
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'group_dset_f_dtype_default', 'group_dset_f_dtype_double', 'group_dset_f_dtype_single',
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'group_dset_dtype_default', 'group_dset_dtype_double', 'group_dset_dtype_single',
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'group_dset_rank', 'group_dset_dim_list', 'group_dset_f_dims',
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'group_dset', 'group_num', 'group']
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for item in detailed_source.keys():
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with open(join(templ_path,fname), 'r') as f_in :
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with open(join(templ_path,fname_new), 'a') as f_out :
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num_written = []
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for line in f_in :
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# special case to add error handling for read/write of dimensioning variables
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if '$group_dset_dim$' in line:
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rc_line = 'if (rc != TREXIO_SUCCESS) return rc;\n'
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indentlevel = len(line) - len(line.lstrip())
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for dim in detailed_source[item]['dims']:
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if not dim.isdigit() and not dim in num_written:
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num_written.append(dim)
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templine = line.replace('$group_dset_dim$', dim)
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if '_read' in templine:
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line_toadd = indentlevel*" " + rc_line
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templine += line_toadd
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f_out.write(templine)
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num_written = []
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continue
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# general case of recursive replacement of inline triggers
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else:
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populated_line = recursive_replace_line(line, triggers, detailed_source[item])
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f_out.write(populated_line)
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def recursive_replace_line (input_line: str, triggers: list, source: dict) -> str:
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"""
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Recursive replacer. Recursively calls itself as long as there is at least one "$" present in the `input_line`.
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Parameters:
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input_line (str) : input line
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triggers (list) : list of triggers (templated variables to be replaced)
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source (dict) : dictionary of variables with substitution details (usually either datasets or numbers)
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Returns:
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output_line (str) : processed (replaced) line
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"""
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is_triggered = False
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output_line = input_line
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if '$' in input_line:
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for case in triggers:
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test_case = f'${case}$'
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if test_case in input_line:
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output_line = input_line.replace(test_case, source[case])
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is_triggered = True
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break
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elif test_case.upper() in input_line:
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output_line = input_line.replace(test_case.upper(), source[case].upper())
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is_triggered = True
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break
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if is_triggered:
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return recursive_replace_line(output_line, triggers, source)
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else:
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print(output_line)
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raise ValueError('Recursion went wrong, not all cases considered')
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return output_line
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def iterative_populate_file (filename: str, paths: dict, groups: dict, datasets: dict, numbers: dict) -> None:
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"""
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Iteratively populate files with unique functions that contain templated variables.
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Parameters:
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filename (str) : template file to be populated
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paths (dict) : dictionary of paths per source directory
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groups (dict) : dictionary of groups
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datasets (dict) : dictionary of datasets with substitution details
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numbers (dict) : dictionary of numbers with substitution details
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Returns:
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None
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"""
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add_trigger = 'rc = trexio_text_free_$group$'
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triggers = [add_trigger, '$group_dset$', '$group_num$', '$group$']
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templ_path = get_template_path(filename, paths)
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filename_out = join('populated',f'pop_{filename}')
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# Note: it is important that special conditions like add_trigger above will be checked before standard triggers
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# that contain only basic $-ed variable (like $group$). Otherwise, the standard triggers will be removed
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# from the template and the special condition will never be met.
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with open(join(templ_path,filename), 'r') as f_in :
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with open(join(templ_path,filename_out), 'a') as f_out :
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for line in f_in :
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id = check_triggers(line, triggers)
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if id == 0:
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# special case for proper error handling when deallocting text groups
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error_handler = ' if (rc != TREXIO_SUCCESS) return rc;\n'
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populated_line = iterative_replace_line(line, triggers[3], groups, add_line=error_handler)
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f_out.write(populated_line)
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elif id == 1:
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populated_line = iterative_replace_line(line, triggers[id], datasets, None)
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f_out.write(populated_line)
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elif id == 2:
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populated_line = iterative_replace_line(line, triggers[id], numbers, None)
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f_out.write(populated_line)
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elif id == 3:
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populated_line = iterative_replace_line(line, triggers[id], groups, None)
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f_out.write(populated_line)
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else:
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f_out.write(line)
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def iterative_replace_line (input_line: str, case: str, source: dict, add_line: str) -> str:
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"""
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Iterative replacer. Iteratively copy-pastes `input_line` each time with a new substitution of a templated variable depending on the `case`.
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Parameters:
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input_line (str) : input line
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case (str) : single trigger case (templated variable to be replaced)
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source (dict) : dictionary of variables with substitution details
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add_line (str) : special line to be added (e.g. for error handling)
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Returns:
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output_block (str) : processed (replaced) block of text
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"""
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output_block = ""
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for item in source.keys():
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templine1 = input_line.replace(case.upper(), item.upper())
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templine2 = templine1.replace(case, item)
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if add_line != None:
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templine2 += add_line
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output_block += templine2
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return output_block
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def check_triggers (input_line: str, triggers: list) -> int:
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"""
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Check the presence of the trigger in the `input_line`.
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Parameters:
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input_line (str) : string to be checked
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triggers (list) : list of triggers (templated variables)
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Returns:
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out_id (int) : id of the trigger item in the list
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"""
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out_id = -1
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for id,trig in enumerate(triggers):
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if trig in input_line or trig.upper() in input_line:
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out_id = id
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return out_id
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return out_id
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def special_populate_text_group(fname: str, paths: dict, group_dict: dict, detailed_dset: dict, detailed_numbers: dict) -> None:
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"""
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Special population for group-related functions in the TEXT back end.
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Parameters:
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fname (str) : template file to be populated
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paths (dict) : dictionary of paths per source directory
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group_dict (dict) : dictionary of groups
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detailed_dset (dict) : dictionary of datasets with substitution details
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detailed_numbers (dict) : dictionary of numbers with substitution details
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Returns:
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None
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"""
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fname_new = join('populated',f'pop_{fname}')
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templ_path = get_template_path(fname, paths)
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triggers = ['group_dset_dtype', 'group_dset_std_dtype_out', 'group_dset_std_dtype_in',
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'group_dset', 'group_num', 'group']
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for group in group_dict.keys():
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with open(join(templ_path,fname), 'r') as f_in :
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with open(join(templ_path,fname_new), 'a') as f_out :
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subloop_dset = False
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subloop_num = False
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loop_body = ''
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dset_allocated = []
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for line in f_in :
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if 'START REPEAT GROUP_DSET' in line:
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subloop_dset = True
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continue
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elif 'START REPEAT GROUP_NUM' in line:
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subloop_num = True
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continue
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if 'END REPEAT GROUP_DSET' in line:
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for dset in detailed_dset.keys():
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if group != detailed_dset[dset]['group']:
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continue
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dset_allocated.append(dset)
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if 'FREE($group$->$group_dset$)' in loop_body:
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tmp_string = ''
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for dset_alloc in dset_allocated:
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tmp_string += f'FREE({group}->{dset_alloc});\n '
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tmp_body = loop_body.replace('FREE($group$->$group_dset$);',tmp_string)
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populated_body = recursive_replace_line(tmp_body, triggers, detailed_dset[dset])
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f_out.write(populated_body)
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else:
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save_body = loop_body
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populated_body = recursive_replace_line(save_body, triggers, detailed_dset[dset])
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f_out.write(populated_body)
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subloop_dset = False
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loop_body = ''
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dset_allocated = []
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continue
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elif 'END REPEAT GROUP_NUM' in line:
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for dim in detailed_numbers.keys():
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if group != detailed_numbers[dim]['group']:
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continue
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save_body = loop_body
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populated_body = recursive_replace_line(save_body, triggers, detailed_numbers[dim])
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f_out.write(populated_body)
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subloop_num = False
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loop_body = ''
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continue
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if not subloop_num and not subloop_dset:
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# NORMAL CASE WITHOUT SUBLOOPS
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if '$group_dset' in line:
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for dset in detailed_dset.keys():
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if group != detailed_dset[dset]['group']:
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continue
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populated_line = recursive_replace_line(line, triggers, detailed_dset[dset])
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f_out.write(populated_line)
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elif '$group_num$' in line:
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for dim in detailed_numbers.keys():
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if group != detailed_numbers[dim]['group']:
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continue
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populated_line = recursive_replace_line(line, triggers, detailed_numbers[dim])
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f_out.write(populated_line)
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elif '$group$' in line:
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populated_line = line.replace('$group$', group)
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f_out.write(populated_line)
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else:
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f_out.write(line)
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else:
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loop_body += line
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def get_template_path (filename: str, path_dict: dict) -> str:
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"""
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Returns the absolute path to the directory with indicated `filename` template.
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Parameters:
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filename (str) : template file to be populated
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path_dict (dict) : dictionary of paths per source directory
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Returns:
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path (str) : resulting path
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"""
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for dir_type in path_dict.keys():
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if dir_type in filename:
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path = path_dict[dir_type]
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return path
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raise ValueError('Filename should contain one of the keywords')
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def get_group_dict (configuration: dict) -> dict:
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"""
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Returns the dictionary of all groups.
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Parameters:
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configuration (dict) : configuration from `trex.json`
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Returns:
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group_dict (dict) : dictionary of groups
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"""
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group_dict = {}
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for k in configuration.keys():
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group_dict[k] = 0
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return group_dict
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def get_detailed_num_dict (configuration: dict) -> dict:
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"""
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Returns the dictionary of all `num`-suffixed variables.
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Keys are names, values are subdictionaries containing corresponding group and group_num names.
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Parameters:
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configuration (dict) : configuration from `trex.json`
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Returns:
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num_dict (dict) : dictionary of num-suffixed variables
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"""
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num_dict = {}
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for k1,v1 in configuration.items():
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for k2,v2 in v1.items():
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if len(v2[1]) == 0:
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tmp_num = f'{k1}_{k2}'
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if 'str' not in v2[0]:
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tmp_dict = {}
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tmp_dict['group'] = k1
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tmp_dict['group_num'] = tmp_num
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num_dict[tmp_num] = tmp_dict
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return num_dict
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def get_dset_dict (configuration: dict) -> dict:
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"""
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Returns the dictionary of datasets.
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Keys are names, values are lists containing datatype, list of dimensions and group name
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Parameters:
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configuration (dict) : configuration from `trex.json`
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Returns:
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dset_dict (dict) : dictionary of datasets
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"""
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dset_dict = {}
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for k1,v1 in configuration.items():
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for k2,v2 in v1.items():
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if len(v2[1]) != 0:
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tmp_dset = f'{k1}_{k2}'
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dset_dict[tmp_dset] = v2
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# append a group name for postprocessing
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dset_dict[tmp_dset].append(k1)
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return dset_dict
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def split_dset_dict_detailed (datasets: dict) -> tuple:
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"""
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Returns the detailed dictionary of datasets.
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Keys are names, values are subdictionaries containing substitutes for templated variables
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Parameters:
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configuration (dict) : configuration from `trex.json`
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Returns:
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dset_numeric_dict, dset_string_dict (tuple) : dictionaries corresponding to all numeric- and string-based datasets, respectively.
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"""
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dset_numeric_dict = {}
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dset_string_dict = {}
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for k,v in datasets.items():
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# create a temp dictionary
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tmp_dict = {}
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# specify details required to replace templated variables later
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if v[0] == 'float':
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datatype = 'double'
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group_dset_h5_dtype = 'double'
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group_dset_f_dtype_default= 'real(8)'
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group_dset_f_dtype_double = 'real(8)'
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group_dset_f_dtype_single = 'real(4)'
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group_dset_dtype_default= 'double'
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group_dset_dtype_double = 'double'
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group_dset_dtype_single = 'float'
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default_prec = '64'
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group_dset_std_dtype_out = '24.16e'
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group_dset_std_dtype_in = 'lf'
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elif v[0] == 'int':
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datatype = 'int64_t'
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group_dset_h5_dtype = 'int64'
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group_dset_f_dtype_default= 'integer(4)'
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group_dset_f_dtype_double = 'integer(8)'
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group_dset_f_dtype_single = 'integer(4)'
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group_dset_dtype_default= 'int32_t'
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group_dset_dtype_double = 'int64_t'
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group_dset_dtype_single = 'int32_t'
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default_prec = '32'
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group_dset_std_dtype_out = '" PRId64 "'
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group_dset_std_dtype_in = '" SCNd64 "'
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elif v[0] == 'str':
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# TODO
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datatype = 'string'
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# add the dset name for templates
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tmp_dict['group_dset'] = k
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# add the datatypes for templates
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tmp_dict['dtype'] = datatype
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tmp_dict['group_dset_dtype'] = datatype
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tmp_dict['group_dset_h5_dtype'] = group_dset_h5_dtype
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tmp_dict['group_dset_f_dtype_default'] = group_dset_f_dtype_default
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tmp_dict['group_dset_f_dtype_double'] = group_dset_f_dtype_double
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tmp_dict['group_dset_f_dtype_single'] = group_dset_f_dtype_single
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tmp_dict['group_dset_dtype_default'] = group_dset_dtype_default
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tmp_dict['group_dset_dtype_double'] = group_dset_dtype_double
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tmp_dict['group_dset_dtype_single'] = group_dset_dtype_single
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tmp_dict['default_prec'] = default_prec
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tmp_dict['group_dset_std_dtype_in'] = group_dset_std_dtype_in
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tmp_dict['group_dset_std_dtype_out'] = group_dset_std_dtype_out
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# add the rank
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tmp_dict['rank'] = len(v[1])
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tmp_dict['group_dset_rank'] = str(tmp_dict['rank'])
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# add the list of dimensions
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tmp_dict['dims'] = [dim.replace('.','_') for dim in v[1]]
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# build a list of dimensions to be inserted in the dims array initialization, e.g. {ao_num, ao_num}
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dim_list = tmp_dict['dims'][0]
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if tmp_dict['rank'] > 1:
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for i in range(1, tmp_dict['rank']):
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dim_toadd = tmp_dict['dims'][i]
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dim_list += f', {dim_toadd}'
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|
|
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tmp_dict['group_dset_dim_list'] = dim_list
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|
|
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if tmp_dict['rank'] == 0:
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dim_f_list = ""
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else:
|
|
dim_f_list = "(*)"
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tmp_dict['group_dset_f_dims'] = dim_f_list
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|
|
|
# add group name as a key-value pair to the dset dict
|
|
tmp_dict['group'] = v[2]
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|
|
|
# split datasets in numeric- and string- based
|
|
if (datatype == 'string'):
|
|
dset_string_dict[k] = tmp_dict
|
|
else:
|
|
dset_numeric_dict[k] = tmp_dict
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|
|
|
return (dset_numeric_dict, dset_string_dict)
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|
|
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|
|
def check_dim_consistency(num: dict, dset: dict) -> None:
|
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"""
|
|
Consistency check to make sure that each dimensioning variable exists as a num attribute of some group.
|
|
|
|
Parameters:
|
|
num (dict) : dictionary of num-suffixed variables
|
|
dset (dict) : dictionary of datasets
|
|
|
|
Returns:
|
|
None
|
|
"""
|
|
dim_tocheck = []
|
|
for v in dset.values():
|
|
tmp_dim_list = [dim.replace('.','_') for dim in v[1] if not dim.isdigit()]
|
|
for dim in tmp_dim_list:
|
|
if dim not in dim_tocheck:
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|
dim_tocheck.append(dim)
|
|
|
|
for dim in dim_tocheck:
|
|
if not dim in num.keys():
|
|
raise ValueError(f"Dimensioning variable {dim} is not a num attribute of any group.\n")
|