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mirror of https://github.com/TREX-CoE/trexio.git synced 2024-12-23 12:56:53 +01:00
trexio/tools/generator_tools.py

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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)
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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():
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 `num` 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])
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 deallocting 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 = ''
dset_allocated = []
str_allocated = []
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
dset_allocated.append(dset)
if 'FREE($group$->$group_dset$)' in loop_body:
tmp_string = ''
for dset_alloc in dset_allocated:
tmp_string += f'FREE({group}->{dset_alloc});\n '
tmp_body = loop_body.replace('FREE($group$->$group_dset$);', tmp_string)
populated_body = recursive_replace_line(tmp_body, triggers, detailed_dset[dset])
f_out.write(populated_body)
else:
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 = ''
dset_allocated = []
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
str_allocated.append(str)
if 'FREE($group$->$group_str$)' in loop_body:
tmp_string = ''
for str_alloc in str_allocated:
tmp_string += f'FREE({group}->{str_alloc});\n '
tmp_body = loop_body.replace('FREE($group$->$group_str$);', tmp_string)
populated_body = recursive_replace_line(tmp_body, triggers, detailed_strings[str])
f_out.write(populated_body)
else:
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 = ''
str_allocated = []
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', '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 i 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}'
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if not 'str' in v2[0]:
tmp_dict = {}
tmp_dict['group'] = k1
tmp_dict['group_num'] = tmp_num
num_dict[tmp_num] = tmp_dict
tmp_dict.update(get_dtype_dict(v2[0], 'num'))
if v2[0] in ['int', 'dim']:
tmp_dict['trex_json_int_type'] = v2[0]
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else:
tmp_dict['trex_json_int_type'] = ''
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]
# 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
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# add flag to detect index types
if 'index' in datatype:
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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 specs['trex_json_int_type']=='dim']
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")