mirror of
https://github.com/TREX-CoE/trexio.git
synced 2024-11-03 20:54:07 +01:00
819 lines
35 KiB
Python
819 lines
35 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'] = [
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f for f in all_files
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if 'read' in f or 'write' in f or 'has' in f or 'flush' in f or 'free' in f or 'hrw' in f or 'delete' in f
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]
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for key in ['dset_data', 'dset_str', 'dset_sparse', 'attr_num', 'attr_str', 'group', 'buffered']:
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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 iterative population (e.g. the code is repeated within the function body but the function itself is unique)
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files_todo['auxiliary'] = [
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'def_hdf5.c', 'basic_hdf5.c', 'struct_hdf5.h',
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'basic_text_group.c', 'struct_text_group.h'
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]
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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
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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_py_dtype', 'group_dset_h5_dtype', 'default_prec', 'is_index',
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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_unique_rank', 'group_dset_dim_list', 'group_dset_f_dims',
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'group_num_f_dtype_default', 'group_num_f_dtype_double', 'group_num_f_dtype_single',
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'group_num_dtype_default', 'group_num_dtype_double', 'group_num_dtype_single',
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'group_num_h5_dtype', 'group_num_py_dtype', 'group_dset_format_scanf', 'group_dset_format_printf',
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'group_dset_sparse_indices_printf', 'group_dset_sparse_indices_scanf',
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'sparse_format_printf_8', 'sparse_format_printf_16', 'sparse_format_printf_32',
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'sparse_line_length_8', 'sparse_line_length_16', 'sparse_line_length_32',
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'group_dset', 'group_num', 'group_str', 'group']
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for item in detailed_source.keys():
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# special case to exclude write functions for readonly dimensions (like determinant_num) from the public API
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if 'write' in fname and 'front' in fname and ('.f90' in fname or '.py' in fname):
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if 'trex_json_int_type' in detailed_source[item].keys():
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if 'readonly' in detailed_source[item]['trex_json_int_type']:
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continue
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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 and (not 'fortran' in fname):
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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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# special case to uncomment check for positive dimensioning variables in templates
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elif 'uncommented by the generator for dimensioning' in line:
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# only uncomment and write the line if `dim` is in the name
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if 'dim' in detailed_source[item]['trex_json_int_type']:
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templine = line.replace('//', '')
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f_out.write(templine)
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# special case to get the max dimension of sparse datasets with different dimensions
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elif 'trexio_read_$group_dset_unique_dim$_64' in line:
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for i in range(int(detailed_source[item]['group_dset_unique_rank'])):
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templine = line.replace('$group_dset_unique_dim$', detailed_source[item]['unique_dims'][i]).replace('$dim_id$', str(i))
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f_out.write(templine)
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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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# special case to include some functions in the private header
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if 'trex_json_int_type' in detailed_source[item].keys():
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if 'readonly' in detailed_source[item]['trex_json_int_type'] and 'write' in line and 'front.h' in fname:
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with open(join(templ_path,'populated/private_pop_front.h'), 'a') as f_priv:
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f_priv.write(populated_line)
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else:
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f_out.write(populated_line)
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else:
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f_out.write(populated_line)
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f_out.write("\n")
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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, detailed_all: 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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detailed_all(dict) : dictionary with substitution details with the following keys:
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'groups' : dictionary of groups with substitution details
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'datasets' : dictionary of datasets with substitution details
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'numbers' : dictionary of numbers with substitution details
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'strings' : dictionary of strings 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, '$group_dset$', '$group_num$', '$group_str$', '$group$']
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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 deallocating 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, '$group$', detailed_all['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], detailed_all['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], detailed_all['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], detailed_all['strings'], None)
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f_out.write(populated_line)
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elif id == 4:
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populated_line = iterative_replace_line(line, triggers[id], detailed_all['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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f_out.write("\n")
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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, detailed_strings: 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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detailed_strings (dict) : dictionary of string attributes 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_format_printf', 'group_dset_format_scanf',
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'group_num_dtype_double', 'group_num_format_printf', 'group_num_format_scanf',
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'group_dset', 'group_num', 'group_str', '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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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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# this can be merged in one later using something like START REPEAT GROUP_ATTR in line
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elif 'START REPEAT GROUP_NUM' in line or 'START REPEAT GROUP_ATTR_STR' 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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if ('REPEAT GROUP_DSET_STR' in line) and (detailed_dset[dset]['group_dset_dtype'] != 'char*'):
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continue
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if ('REPEAT GROUP_DSET_NUM' in line) and (detailed_dset[dset]['group_dset_dtype'] == 'char*'):
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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_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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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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elif 'END REPEAT GROUP_ATTR_STR' in line:
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for str in detailed_strings.keys():
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if group != detailed_strings[str]['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_strings[str])
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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_str' in line:
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for str in detailed_strings.keys():
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if group != detailed_strings[str]['group']:
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continue
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populated_line = recursive_replace_line(line, triggers, detailed_strings[str])
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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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f_out.write("\n")
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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] = {'group' : k}
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return group_dict
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def get_dtype_dict (dtype: str, target: str, rank = None, int_len_printf = None) -> dict:
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"""
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Returns the dictionary of dtype-related templated variables set for a given `dtype`.
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Keys are names of templated variables, values are strings to be used by the generator.
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Parameters:
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dtype (str) : dtype corresponding to the trex.json (i.e. int/dim/float/float sparse/str)
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target (str) : `num` or `dset`
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rank (int) : [optional] value of n in n-index (sparse) dset; needed to build the printf/scanf format string
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int_len_printf(dict): [optional]
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keys: precision (e.g. 32 for int32_t)
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values: lengths reserved for one index when printing n-index (sparse) dset (e.g. 10 for int32_t)
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|
|
Returns:
|
|
dtype_dict (dict) : dictionary dtype-related substitutions
|
|
"""
|
|
if not target in ['num', 'dset']:
|
|
raise Exception('Only num or dset target can be set.')
|
|
if 'sparse' in dtype:
|
|
if rank is None or int_len_printf is None:
|
|
raise Exception("Both rank and int_len_printf arguments has to be provided to build the dtype_dict for sparse data.")
|
|
if rank is not None and rank <= 1:
|
|
raise Exception('Rank of sparse quantity cannot be lower than 2.')
|
|
if int_len_printf is not None and not isinstance(int_len_printf, dict):
|
|
raise Exception('int_len_printf has to be a dictionary of lengths for different precisions.')
|
|
|
|
dtype_dict = {}
|
|
# set up the key-value pairs dependending on the dtype
|
|
if dtype == 'float':
|
|
dtype_dict.update({
|
|
'default_prec' : '64',
|
|
f'group_{target}_dtype' : 'double',
|
|
f'group_{target}_h5_dtype' : 'native_double',
|
|
f'group_{target}_f_dtype_default' : 'real(c_double)',
|
|
f'group_{target}_f_dtype_double' : 'real(c_double)',
|
|
f'group_{target}_f_dtype_single' : 'real(c_float)',
|
|
f'group_{target}_dtype_default' : 'double',
|
|
f'group_{target}_dtype_double' : 'double',
|
|
f'group_{target}_dtype_single' : 'float',
|
|
f'group_{target}_format_printf' : '24.16e',
|
|
f'group_{target}_format_scanf' : 'lf',
|
|
f'group_{target}_py_dtype' : 'float'
|
|
})
|
|
elif 'buffered' in dtype:
|
|
dtype_dict.update({
|
|
'default_prec' : '64',
|
|
f'group_{target}_dtype' : 'double',
|
|
f'group_{target}_h5_dtype' : 'native_double',
|
|
f'group_{target}_f_dtype_default' : 'real(c_double)',
|
|
f'group_{target}_f_dtype_double' : 'real(c_double)',
|
|
f'group_{target}_f_dtype_single' : 'real(c_float)',
|
|
f'group_{target}_dtype_default' : 'double',
|
|
f'group_{target}_dtype_double' : 'double',
|
|
f'group_{target}_dtype_single' : 'float',
|
|
f'group_{target}_format_printf' : '24.16e',
|
|
f'group_{target}_format_scanf' : 'lf',
|
|
f'group_{target}_py_dtype' : 'float'
|
|
})
|
|
elif dtype in ['int', 'dim', 'dim readonly', 'index']:
|
|
dtype_dict.update({
|
|
'default_prec' : '32',
|
|
f'group_{target}_dtype' : 'int64_t',
|
|
f'group_{target}_h5_dtype' : 'native_int64',
|
|
f'group_{target}_f_dtype_default' : 'integer(c_int32_t)',
|
|
f'group_{target}_f_dtype_double' : 'integer(c_int64_t)',
|
|
f'group_{target}_f_dtype_single' : 'integer(c_int32_t)',
|
|
f'group_{target}_dtype_default' : 'int32_t',
|
|
f'group_{target}_dtype_double' : 'int64_t',
|
|
f'group_{target}_dtype_single' : 'int32_t',
|
|
f'group_{target}_format_printf' : '" PRId64 "',
|
|
f'group_{target}_format_scanf' : '" SCNd64 "',
|
|
f'group_{target}_py_dtype' : 'int'
|
|
})
|
|
elif dtype == 'str':
|
|
dtype_dict.update({
|
|
'default_prec' : '',
|
|
f'group_{target}_dtype' : 'char*',
|
|
f'group_{target}_h5_dtype' : '',
|
|
f'group_{target}_f_dtype_default': '',
|
|
f'group_{target}_f_dtype_double' : '',
|
|
f'group_{target}_f_dtype_single' : '',
|
|
f'group_{target}_dtype_default' : 'char*',
|
|
f'group_{target}_dtype_double' : '',
|
|
f'group_{target}_dtype_single' : '',
|
|
f'group_{target}_format_printf' : 's',
|
|
f'group_{target}_format_scanf' : 's',
|
|
f'group_{target}_py_dtype' : 'str'
|
|
})
|
|
elif 'sparse' in dtype:
|
|
# build format string for n-index sparse quantity
|
|
item_printf_8 = f'%{int_len_printf[8]}" PRIu8 " '
|
|
item_printf_16 = f'%{int_len_printf[16]}" PRIu16 " '
|
|
item_printf_32 = f'%{int_len_printf[32]}" PRId32 " '
|
|
item_scanf = '%" SCNd32 " '
|
|
group_dset_format_printf_8 = '"'
|
|
group_dset_format_printf_16 = '"'
|
|
group_dset_format_printf_32 = '"'
|
|
group_dset_format_scanf = ''
|
|
for _ in range(rank):
|
|
group_dset_format_printf_8 += item_printf_8
|
|
group_dset_format_printf_16 += item_printf_16
|
|
group_dset_format_printf_32 += item_printf_32
|
|
group_dset_format_scanf += item_scanf
|
|
# append the format string for float values
|
|
group_dset_format_printf_8 += '%24.16e" '
|
|
group_dset_format_printf_16 += '%24.16e" '
|
|
group_dset_format_printf_32 += '%24.16e" '
|
|
group_dset_format_scanf += '%lf'
|
|
|
|
# set up the dictionary for sparse
|
|
dtype_dict.update({
|
|
'default_prec' : '',
|
|
f'group_{target}_dtype' : 'double',
|
|
f'group_{target}_h5_dtype' : '',
|
|
f'group_{target}_f_dtype_default': '',
|
|
f'group_{target}_f_dtype_double' : '',
|
|
f'group_{target}_f_dtype_single' : '',
|
|
f'group_{target}_dtype_default' : '',
|
|
f'group_{target}_dtype_double' : '',
|
|
f'group_{target}_dtype_single' : '',
|
|
f'sparse_format_printf_8' : group_dset_format_printf_8,
|
|
f'sparse_format_printf_16' : group_dset_format_printf_16,
|
|
f'sparse_format_printf_32' : group_dset_format_printf_32,
|
|
f'group_{target}_format_scanf' : group_dset_format_scanf,
|
|
f'group_{target}_py_dtype' : ''
|
|
})
|
|
|
|
return dtype_dict
|
|
|
|
|
|
|
|
def get_detailed_num_dict (configuration: dict) -> dict:
|
|
"""
|
|
Returns the dictionary of all `num`-suffixed variables.
|
|
Keys are names, values are subdictionaries containing corresponding group and group_num names.
|
|
|
|
Parameters:
|
|
configuration (dict) : configuration from `trex.json`
|
|
|
|
Returns:
|
|
num_dict (dict) : dictionary of all numerical attributes (of types int, float, dim)
|
|
"""
|
|
num_dict = {}
|
|
for k1,v1 in configuration.items():
|
|
for k2,v2 in v1.items():
|
|
if len(v2[1]) == 0:
|
|
tmp_num = f'{k1}_{k2}'
|
|
if not 'str' in v2[0]:
|
|
tmp_dict = {}
|
|
|
|
tmp_dict['group'] = k1
|
|
tmp_dict['group_num'] = tmp_num
|
|
tmp_dict.update(get_dtype_dict(v2[0], 'num'))
|
|
if v2[0] in ['int', 'dim', 'dim readonly']:
|
|
tmp_dict['trex_json_int_type'] = v2[0]
|
|
else:
|
|
tmp_dict['trex_json_int_type'] = ''
|
|
|
|
num_dict[tmp_num] = tmp_dict
|
|
|
|
return num_dict
|
|
|
|
|
|
def get_detailed_str_dict (configuration: dict) -> dict:
|
|
"""
|
|
Returns the dictionary of all `str`-like attributes.
|
|
Keys are names, values are subdictionaries containing corresponding group and group_str names.
|
|
|
|
Parameters:
|
|
configuration (dict) : configuration from `trex.json`
|
|
|
|
Returns:
|
|
str_dict (dict) : dictionary of string attributes
|
|
"""
|
|
str_dict = {}
|
|
for k1,v1 in configuration.items():
|
|
for k2,v2 in v1.items():
|
|
if len(v2[1]) == 0:
|
|
tmp_str = f'{k1}_{k2}'
|
|
if 'str' in v2[0]:
|
|
tmp_dict = {}
|
|
tmp_dict['group'] = k1
|
|
tmp_dict['group_str'] = tmp_str
|
|
str_dict[tmp_str] = tmp_dict
|
|
|
|
return str_dict
|
|
|
|
|
|
def get_dset_dict (configuration: dict) -> dict:
|
|
"""
|
|
Returns the dictionary of datasets.
|
|
Keys are names, values are lists containing datatype, list of dimensions and group name
|
|
|
|
Parameters:
|
|
configuration (dict) : configuration from `trex.json`
|
|
|
|
Returns:
|
|
dset_dict (dict) : dictionary of datasets
|
|
"""
|
|
dset_dict = {}
|
|
for k1,v1 in configuration.items():
|
|
for k2,v2 in v1.items():
|
|
if len(v2[1]) != 0:
|
|
tmp_dset = f'{k1}_{k2}'
|
|
dset_dict[tmp_dset] = v2
|
|
# append a group name for postprocessing
|
|
dset_dict[tmp_dset].append(k1)
|
|
|
|
return dset_dict
|
|
|
|
|
|
def split_dset_dict_detailed (datasets: dict) -> tuple:
|
|
"""
|
|
Returns the detailed dictionary of datasets.
|
|
Keys are names, values are subdictionaries containing substitutes for templated variables
|
|
|
|
Parameters:
|
|
configuration (dict) : configuration from `trex.json`
|
|
|
|
Returns:
|
|
(tuple) : dictionaries corresponding to all types of datasets in trexio.
|
|
"""
|
|
dset_numeric_dict = {}
|
|
dset_string_dict = {}
|
|
dset_sparse_dict = {}
|
|
dset_buffer_dict = {}
|
|
for k,v in datasets.items():
|
|
|
|
# create a temp dictionary
|
|
tmp_dict = {}
|
|
rank = len(v[1])
|
|
datatype = v[0]
|
|
|
|
# skip the data which has 'special' datatype (e.g. determinants for which the code is not templated)
|
|
if 'special' in datatype:
|
|
continue
|
|
|
|
# define whether the dset is sparse
|
|
is_sparse = False
|
|
int_len_printf = {}
|
|
if 'sparse' in datatype:
|
|
is_sparse = True
|
|
int_len_printf[32] = 10
|
|
int_len_printf[16] = 5
|
|
int_len_printf[8] = 3
|
|
|
|
# get the dtype-related substitutions required to replace templated variables later
|
|
if not is_sparse:
|
|
dtype_dict = get_dtype_dict(datatype, 'dset')
|
|
else:
|
|
dtype_dict = get_dtype_dict(datatype, 'dset', rank, int_len_printf)
|
|
|
|
tmp_dict.update(dtype_dict)
|
|
|
|
# set the group_dset key to the full name of the dset
|
|
tmp_dict['group_dset'] = k
|
|
# add flag to detect index types
|
|
if 'index' in datatype:
|
|
tmp_dict['is_index'] = 'file->one_based'
|
|
else:
|
|
tmp_dict['is_index'] = 'false'
|
|
|
|
# add the list of dimensions
|
|
tmp_dict['dims'] = [dim.replace('.','_') for dim in v[1]]
|
|
|
|
# get a list of unique dimensions for sparse datasets
|
|
if is_sparse:
|
|
tmp_dict['unique_dims'] = list(set(tmp_dict['dims']))
|
|
tmp_dict['group_dset_unique_rank'] = str(len(tmp_dict['unique_dims']))
|
|
|
|
# add the rank
|
|
tmp_dict['rank'] = rank
|
|
tmp_dict['group_dset_rank'] = str(rank)
|
|
|
|
# build a list of dimensions to be inserted in the dims array initialization, e.g. {ao_num, ao_num}
|
|
dim_list = tmp_dict['dims'][0]
|
|
if rank > 1:
|
|
for i in range(1, rank):
|
|
dim_toadd = tmp_dict['dims'][i]
|
|
dim_list += f', {dim_toadd}'
|
|
|
|
tmp_dict['group_dset_dim_list'] = dim_list
|
|
|
|
if rank == 0:
|
|
dim_f_list = ""
|
|
else:
|
|
dim_f_list = "(*)"
|
|
tmp_dict['group_dset_f_dims'] = dim_f_list
|
|
|
|
if is_sparse:
|
|
# build printf/scanf sequence and compute line length for n-index sparse quantity
|
|
index_printf = f'*(index_sparse + {str(rank)}*i'
|
|
index_scanf = f'index_sparse + {str(rank)}*i'
|
|
# one index item consumes up to index_length characters (int32_len_printf for int32 + 1 for space)
|
|
group_dset_sparse_indices_printf = index_printf + ')'
|
|
group_dset_sparse_indices_scanf = index_scanf
|
|
sparse_line_length_32 = int_len_printf[32] + 1
|
|
sparse_line_length_16 = int_len_printf[16] + 1
|
|
sparse_line_length_8 = int_len_printf[8] + 1
|
|
# loop from 1 because we already have stored one index
|
|
for index_count in range(1,rank):
|
|
group_dset_sparse_indices_printf += f', {index_printf} + {index_count})'
|
|
group_dset_sparse_indices_scanf += f', {index_scanf} + {index_count}'
|
|
sparse_line_length_32 += int_len_printf[32] + 1
|
|
sparse_line_length_16 += int_len_printf[16] + 1
|
|
sparse_line_length_8 += int_len_printf[8] + 1
|
|
|
|
# add 24 chars occupied by the floating point value of sparse dataset + 1 char for "\n"
|
|
sparse_line_length_32 += 24 + 1
|
|
sparse_line_length_16 += 24 + 1
|
|
sparse_line_length_8 += 24 + 1
|
|
|
|
tmp_dict['sparse_line_length_32'] = str(sparse_line_length_32)
|
|
tmp_dict['sparse_line_length_16'] = str(sparse_line_length_16)
|
|
tmp_dict['sparse_line_length_8'] = str(sparse_line_length_8)
|
|
tmp_dict['group_dset_sparse_indices_printf'] = group_dset_sparse_indices_printf
|
|
tmp_dict['group_dset_sparse_indices_scanf'] = group_dset_sparse_indices_scanf
|
|
|
|
# add group name as a key-value pair to the dset dict
|
|
tmp_dict['group'] = v[2]
|
|
|
|
# split datasets in numeric- and string- based
|
|
if 'str' in datatype:
|
|
dset_string_dict[k] = tmp_dict
|
|
elif 'buffered' in datatype:
|
|
dset_buffer_dict[k] = tmp_dict
|
|
elif is_sparse:
|
|
dset_sparse_dict[k] = tmp_dict
|
|
else:
|
|
dset_numeric_dict[k] = tmp_dict
|
|
|
|
return (dset_numeric_dict, dset_string_dict, dset_sparse_dict, dset_buffer_dict)
|
|
|
|
|
|
def check_dim_consistency(num: dict, dset: dict) -> None:
|
|
"""
|
|
Consistency check to make sure that each dimensioning variable exists as a num attribute of some group.
|
|
|
|
Parameters:
|
|
num (dict) : dictionary of numerical attributes
|
|
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:
|
|
dim_tocheck.append(dim)
|
|
|
|
num_onlyDim = [
|
|
attr_name for attr_name, specs in num.items()
|
|
if 'dim' in specs['trex_json_int_type']
|
|
]
|
|
|
|
for dim in dim_tocheck:
|
|
if not dim in num_onlyDim:
|
|
raise ValueError(f"Dimensioning variable {dim} is not a num attribute of any group.\n")
|