from os import listdir from os.path import join, dirname, abspath, isfile from json import load as json_load def read_json(fname: str) -> dict: """ Read configuration from the input `fname` JSON file. Parameters: fname (str) : JSON file name Returns: config (dict) : full configuration dictionary loaded from the input file """ fileDir = dirname(abspath(__file__)) parentDir = dirname(fileDir) with open(join(parentDir,fname), 'r') as f: config = json_load(f) return config def get_files_todo(source_files: dict) -> dict: """ Build dictionaries of templated files per objective. Parameters: source_files (dict) : dictionary with source files per source directory Returns: file_todo (dict) : dictionary with objective title : [list of files] as key-value pairs """ all_files = [] for key in source_files.keys(): all_files += source_files[key] files_todo = {} 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 or 'delete' in f ] for key in ['dset_data', 'dset_str', 'dset_sparse', 'attr_num', 'attr_str', 'group']: files_todo[key] = list(filter(lambda x: key in x, files_todo['all'])) files_todo['group'].append('struct_text_group_dset.h') # files that correspond to iterative population (e.g. the code is repeated within the function body but the function itself is unique) files_todo['auxiliary'] = [ 'def_hdf5.c', 'basic_hdf5.c', 'struct_hdf5.h', 'basic_text_group.c', 'struct_text_group.h' ] return files_todo def get_source_files(paths: dict) -> dict: """ Build dictionaries of all files per source directory. Parameters: paths (dict) : dictionary with paths to source directories Returns: file_dict (dict) : dictionary with source title : [list of files] as key-value pairs """ file_dict = {} for key in paths.keys(): file_dict[key] = [f for f in listdir(paths[key]) if isfile(join(paths[key], f))] return file_dict def get_template_paths(source: list) -> dict: """ Build dictionary of the absolute paths to directory with templates per source. Parameters: source (list) : list of source titles, i.e. ['front', 'text', 'hdf5'] Returns: path_dict (dict) : dictionary with source title : absolute path as key-value pairs """ fileDir = dirname(abspath(__file__)) path_dict = {} for dir in source: path_dict[dir] = join(fileDir,f'templates_{dir}') return path_dict def recursive_populate_file(fname: str, paths: dict, detailed_source: dict) -> None: """ Populate files containing basic read/write/has functions. Parameters: filename (str) : template file to be populated paths (dict) : dictionary of paths per source directory detailed_source (dict) : dictionary of variables with substitution details Returns: None """ fname_new = join('populated',f'pop_{fname}') templ_path = get_template_path(fname, paths) triggers = ['group_dset_dtype', 'group_dset_py_dtype', 'group_dset_h5_dtype', 'default_prec', 'is_index', 'group_dset_f_dtype_default', 'group_dset_f_dtype_double', 'group_dset_f_dtype_single', 'group_dset_dtype_default', 'group_dset_dtype_double', 'group_dset_dtype_single', 'group_dset_rank', 'group_dset_dim_list', 'group_dset_f_dims', 'group_num_f_dtype_default', 'group_num_f_dtype_double', 'group_num_f_dtype_single', 'group_num_dtype_default', 'group_num_dtype_double', 'group_num_dtype_single', 'group_num_h5_dtype', 'group_num_py_dtype', 'group_dset_format_scanf', 'group_dset_format_printf', 'group_dset_sparse_dim', 'group_dset_sparse_indices_printf', 'group_dset_sparse_indices_scanf', 'sparse_format_printf_8', 'sparse_format_printf_16', 'sparse_format_printf_32', 'sparse_line_length_8', 'sparse_line_length_16', 'sparse_line_length_32', 'group_dset', 'group_num', 'group_str', 'group'] for item in detailed_source.keys(): # special case to exclude write functions for readonly dimensions (like determinant_num) from the public API if 'write' in fname and 'front' in fname and ('.f90' in fname or '.py' in fname): if 'trex_json_int_type' in detailed_source[item].keys(): if 'readonly' in detailed_source[item]['trex_json_int_type']: continue with open(join(templ_path,fname), 'r') as f_in : with open(join(templ_path,fname_new), 'a') as f_out : num_written = [] for line in f_in : # special case to add error handling for read/write of dimensioning variables if '$group_dset_dim$' in line: rc_line = 'if (rc != TREXIO_SUCCESS) return rc;\n' indentlevel = len(line) - len(line.lstrip()) for dim in detailed_source[item]['dims']: if not dim.isdigit() and not dim in num_written: num_written.append(dim) templine = line.replace('$group_dset_dim$', dim) if '_read' in templine and (not 'fortran' in fname): line_toadd = indentlevel*" " + rc_line templine += line_toadd f_out.write(templine) num_written = [] continue # special case to uncomment check for positive dimensioning variables in templates elif 'uncommented by the generator for dimensioning' in line: # only uncomment and write the line if `dim` is in the name if 'dim' in detailed_source[item]['trex_json_int_type']: templine = line.replace('//', '') f_out.write(templine) # general case of recursive replacement of inline triggers else: populated_line = recursive_replace_line(line, triggers, detailed_source[item]) # special case to include some functions in the private header if 'trex_json_int_type' in detailed_source[item].keys(): if 'readonly' in detailed_source[item]['trex_json_int_type'] and 'write' in line and 'front.h' in fname: with open(join(templ_path,'populated/private_pop_front.h'), 'a') as f_priv: f_priv.write(populated_line) else: f_out.write(populated_line) else: f_out.write(populated_line) f_out.write("\n") def recursive_replace_line (input_line: str, triggers: list, source: dict) -> str: """ Recursive replacer. Recursively calls itself as long as there is at least one "$" present in the `input_line`. Parameters: input_line (str) : input line triggers (list) : list of triggers (templated variables to be replaced) source (dict) : dictionary of variables with substitution details (usually either datasets or numbers) Returns: output_line (str) : processed (replaced) line """ is_triggered = False output_line = input_line if '$' in input_line: for case in triggers: test_case = f'${case}$' if test_case in input_line: output_line = input_line.replace(test_case, source[case]) is_triggered = True break elif test_case.upper() in input_line: output_line = input_line.replace(test_case.upper(), source[case].upper()) is_triggered = True break if is_triggered: return recursive_replace_line(output_line, triggers, source) else: print(output_line) raise ValueError('Recursion went wrong, not all cases considered') return output_line def iterative_populate_file (filename: str, paths: dict, detailed_all: dict) -> None: """ Iteratively populate files with unique functions that contain templated variables. Parameters: filename (str) : template file to be populated paths (dict) : dictionary of paths per source directory detailed_all(dict) : dictionary with substitution details with the following keys: 'groups' : dictionary of groups with substitution details 'datasets' : dictionary of datasets with substitution details 'numbers' : dictionary of numbers with substitution details 'strings' : dictionary of strings with substitution details Returns: None """ add_trigger = 'rc = trexio_text_free_$group$' triggers = [add_trigger, '$group_dset$', '$group_num$', '$group_str$', '$group$'] templ_path = get_template_path(filename, paths) filename_out = join('populated',f'pop_{filename}') # Note: it is important that special conditions like add_trigger above will be checked before standard triggers # that contain only basic $-ed variable (like $group$). Otherwise, the standard triggers will be removed # from the template and the special condition will never be met. with open(join(templ_path,filename), 'r') as f_in : with open(join(templ_path,filename_out), 'a') as f_out : for line in f_in : id = check_triggers(line, triggers) if id == 0: # special case for proper error handling when deallocating text groups error_handler = ' if (rc != TREXIO_SUCCESS) return rc;\n' populated_line = iterative_replace_line(line, '$group$', detailed_all['groups'], add_line=error_handler) f_out.write(populated_line) elif id == 1: populated_line = iterative_replace_line(line, triggers[id], detailed_all['datasets'], None) f_out.write(populated_line) elif id == 2: populated_line = iterative_replace_line(line, triggers[id], detailed_all['numbers'], None) f_out.write(populated_line) elif id == 3: populated_line = iterative_replace_line(line, triggers[id], detailed_all['strings'], None) f_out.write(populated_line) elif id == 4: populated_line = iterative_replace_line(line, triggers[id], detailed_all['groups'], None) f_out.write(populated_line) else: f_out.write(line) f_out.write("\n") def iterative_replace_line (input_line: str, case: str, source: dict, add_line: str) -> str: """ Iterative replacer. Iteratively copy-pastes `input_line` each time with a new substitution of a templated variable depending on the `case`. Parameters: input_line (str) : input line case (str) : single trigger case (templated variable to be replaced) source (dict) : dictionary of variables with substitution details add_line (str) : special line to be added (e.g. for error handling) Returns: output_block (str) : processed (replaced) block of text """ output_block = "" for item in source.keys(): templine1 = input_line.replace(case.upper(), item.upper()) templine2 = templine1.replace(case, item) if add_line != None: templine2 += add_line output_block += templine2 return output_block def check_triggers (input_line: str, triggers: list) -> int: """ Check the presence of the trigger in the `input_line`. Parameters: input_line (str) : string to be checked triggers (list) : list of triggers (templated variables) Returns: out_id (int) : id of the trigger item in the list """ out_id = -1 for id,trig in enumerate(triggers): if trig in input_line or trig.upper() in input_line: out_id = id return out_id return out_id def special_populate_text_group(fname: str, paths: dict, group_dict: dict, detailed_dset: dict, detailed_numbers: dict, detailed_strings: dict) -> None: """ Special population for group-related functions in the TEXT back end. Parameters: fname (str) : template file to be populated paths (dict) : dictionary of paths per source directory group_dict (dict) : dictionary of groups detailed_dset (dict) : dictionary of datasets with substitution details detailed_numbers (dict) : dictionary of numbers with substitution details detailed_strings (dict) : dictionary of string attributes with substitution details Returns: None """ fname_new = join('populated',f'pop_{fname}') templ_path = get_template_path(fname, paths) triggers = ['group_dset_dtype', 'group_dset_format_printf', 'group_dset_format_scanf', 'group_num_dtype_double', 'group_num_format_printf', 'group_num_format_scanf', 'group_dset', 'group_num', 'group_str', 'group'] for group in group_dict.keys(): with open(join(templ_path,fname), 'r') as f_in : with open(join(templ_path,fname_new), 'a') as f_out : subloop_dset = False subloop_num = False loop_body = '' for line in f_in : if 'START REPEAT GROUP_DSET' in line: subloop_dset = True continue # this can be merged in one later using something like START REPEAT GROUP_ATTR in line elif 'START REPEAT GROUP_NUM' in line or 'START REPEAT GROUP_ATTR_STR' in line: subloop_num = True continue if 'END REPEAT GROUP_DSET' in line: for dset in detailed_dset.keys(): if group != detailed_dset[dset]['group']: continue if ('REPEAT GROUP_DSET_STR' in line) and (detailed_dset[dset]['group_dset_dtype'] != 'char*'): continue if ('REPEAT GROUP_DSET_NUM' in line) and (detailed_dset[dset]['group_dset_dtype'] == 'char*'): continue save_body = loop_body populated_body = recursive_replace_line(save_body, triggers, detailed_dset[dset]) f_out.write(populated_body) subloop_dset = False loop_body = '' continue elif 'END REPEAT GROUP_NUM' in line: for dim in detailed_numbers.keys(): if group != detailed_numbers[dim]['group']: continue save_body = loop_body populated_body = recursive_replace_line(save_body, triggers, detailed_numbers[dim]) f_out.write(populated_body) subloop_num = False loop_body = '' continue elif 'END REPEAT GROUP_ATTR_STR' in line: for str in detailed_strings.keys(): if group != detailed_strings[str]['group']: continue save_body = loop_body populated_body = recursive_replace_line(save_body, triggers, detailed_strings[str]) f_out.write(populated_body) subloop_num = False loop_body = '' continue if not subloop_num and not subloop_dset: # NORMAL CASE WITHOUT SUBLOOPS if '$group_dset' in line: for dset in detailed_dset.keys(): if group != detailed_dset[dset]['group']: continue populated_line = recursive_replace_line(line, triggers, detailed_dset[dset]) f_out.write(populated_line) elif '$group_str' in line: for str in detailed_strings.keys(): if group != detailed_strings[str]['group']: continue populated_line = recursive_replace_line(line, triggers, detailed_strings[str]) f_out.write(populated_line) elif '$group_num$' in line: for dim in detailed_numbers.keys(): if group != detailed_numbers[dim]['group']: continue populated_line = recursive_replace_line(line, triggers, detailed_numbers[dim]) f_out.write(populated_line) elif '$group$' in line: populated_line = line.replace('$group$', group) f_out.write(populated_line) else: f_out.write(line) else: loop_body += line f_out.write("\n") def get_template_path (filename: str, path_dict: dict) -> str: """ Returns the absolute path to the directory with indicated `filename` template. Parameters: filename (str) : template file to be populated path_dict (dict) : dictionary of paths per source directory Returns: path (str) : resulting path """ for dir_type in path_dict.keys(): if dir_type in filename: path = path_dict[dir_type] return path raise ValueError('Filename should contain one of the keywords') def get_group_dict (configuration: dict) -> dict: """ Returns the dictionary of all groups. Parameters: configuration (dict) : configuration from `trex.json` Returns: group_dict (dict) : dictionary of groups """ group_dict = {} for k in configuration.keys(): group_dict[k] = {'group' : k} return group_dict def get_dtype_dict (dtype: str, target: str, rank = None, int_len_printf = None) -> dict: """ Returns the dictionary of dtype-related templated variables set for a given `dtype`. Keys are names of templated variables, values are strings to be used by the generator. Parameters: dtype (str) : dtype corresponding to the trex.json (i.e. int/dim/float/float sparse/str) target (str) : `num` or `dset` rank (int) : [optional] value of n in n-index (sparse) dset; needed to build the printf/scanf format string int_len_printf(dict): [optional] keys: precision (e.g. 32 for int32_t) values: lengths reserved for one index when printing n-index (sparse) dset (e.g. 10 for int32_t) 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 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: dset_numeric_dict, dset_string_dict (tuple) : dictionaries corresponding to all numeric- and string-based datasets, respectively. """ dset_numeric_dict = {} dset_string_dict = {} dset_sparse_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 rank tmp_dict['rank'] = rank tmp_dict['group_dset_rank'] = str(rank) # add the list of dimensions tmp_dict['dims'] = [dim.replace('.','_') for dim in v[1]] # 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: # store the max possible dim of the sparse dset (e.g. mo_num) tmp_dict['group_dset_sparse_dim'] = tmp_dict['dims'][0] # 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 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) 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")