mirror of
https://github.com/TREX-CoE/trexio.git
synced 2024-12-23 04:43:57 +01:00
563 lines
22 KiB
Python
563 lines
22 KiB
Python
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'] = list(filter(lambda x: 'read' in x or 'write' in x or 'has' in x or 'hrw' in x or 'flush' in x or 'free' in x, all_files))
|
|
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]
|
|
for key in ['dset_data', 'dset_str', 'num', '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 todo1 group (e.g. only iterative population within the function body)
|
|
files_todo['auxiliary'] = ['def_hdf5.c', 'basic_hdf5.c', 'basic_text_group.c', 'struct_hdf5.h', '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 (usually either datasets or numbers)
|
|
|
|
Returns:
|
|
None
|
|
"""
|
|
fname_new = join('populated',f'pop_{fname}')
|
|
templ_path = get_template_path(fname, paths)
|
|
|
|
triggers = ['group_dset_dtype', 'group_dset_h5_dtype', 'default_prec',
|
|
'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_dset', 'group_num', '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:
|
|
line_toadd = indentlevel*" " + rc_line
|
|
templine += line_toadd
|
|
|
|
f_out.write(templine)
|
|
num_written = []
|
|
continue
|
|
# general case of recursive replacement of inline triggers
|
|
else:
|
|
populated_line = recursive_replace_line(line, triggers, detailed_source[item])
|
|
f_out.write(populated_line)
|
|
|
|
|
|
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, groups: dict, datasets: dict, numbers: 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
|
|
groups (dict) : dictionary of groups
|
|
datasets (dict) : dictionary of datasets with substitution details
|
|
numbers (dict) : dictionary of numbers with substitution details
|
|
|
|
Returns:
|
|
None
|
|
"""
|
|
add_trigger = 'rc = trexio_text_free_$group$'
|
|
triggers = [add_trigger, '$group_dset$', '$group_num$', '$group$']
|
|
|
|
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, triggers[3], groups, add_line=error_handler)
|
|
f_out.write(populated_line)
|
|
elif id == 1:
|
|
populated_line = iterative_replace_line(line, triggers[id], datasets, None)
|
|
f_out.write(populated_line)
|
|
elif id == 2:
|
|
populated_line = iterative_replace_line(line, triggers[id], numbers, None)
|
|
f_out.write(populated_line)
|
|
elif id == 3:
|
|
populated_line = iterative_replace_line(line, triggers[id], groups, None)
|
|
f_out.write(populated_line)
|
|
else:
|
|
f_out.write(line)
|
|
|
|
|
|
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) -> 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
|
|
|
|
Returns:
|
|
None
|
|
"""
|
|
fname_new = join('populated',f'pop_{fname}')
|
|
templ_path = get_template_path(fname, paths)
|
|
|
|
triggers = ['group_dset_dtype', 'group_dset_std_dtype_out', 'group_dset_std_dtype_in',
|
|
'group_dset', 'group_num', '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 = []
|
|
|
|
for line in f_in :
|
|
|
|
if 'START REPEAT GROUP_DSET' in line:
|
|
subloop_dset = True
|
|
continue
|
|
elif 'START REPEAT GROUP_NUM' 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
|
|
|
|
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
|
|
|
|
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_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
|
|
|
|
|
|
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] = 0
|
|
|
|
return group_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 num-suffixed variables
|
|
"""
|
|
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 'str' not in v2[0]:
|
|
tmp_dict = {}
|
|
tmp_dict['group'] = k1
|
|
tmp_dict['group_num'] = tmp_num
|
|
num_dict[tmp_num] = tmp_dict
|
|
|
|
return num_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 = {}
|
|
for k,v in datasets.items():
|
|
# create a temp dictionary
|
|
tmp_dict = {}
|
|
# specify details required to replace templated variables later
|
|
if v[0] == 'float':
|
|
datatype = 'double'
|
|
group_dset_h5_dtype = 'native_double'
|
|
group_dset_f_dtype_default= 'real(8)'
|
|
group_dset_f_dtype_double = 'real(8)'
|
|
group_dset_f_dtype_single = 'real(4)'
|
|
group_dset_dtype_default= 'double'
|
|
group_dset_dtype_double = 'double'
|
|
group_dset_dtype_single = 'float'
|
|
default_prec = '64'
|
|
group_dset_std_dtype_out = '24.16e'
|
|
group_dset_std_dtype_in = 'lf'
|
|
elif v[0] == 'int':
|
|
datatype = 'int64_t'
|
|
group_dset_h5_dtype = 'native_int64'
|
|
group_dset_f_dtype_default= 'integer(4)'
|
|
group_dset_f_dtype_double = 'integer(8)'
|
|
group_dset_f_dtype_single = 'integer(4)'
|
|
group_dset_dtype_default= 'int32_t'
|
|
group_dset_dtype_double = 'int64_t'
|
|
group_dset_dtype_single = 'int32_t'
|
|
default_prec = '32'
|
|
group_dset_std_dtype_out = '" PRId64 "'
|
|
group_dset_std_dtype_in = '" SCNd64 "'
|
|
elif v[0] == 'str':
|
|
datatype = 'char*'
|
|
group_dset_h5_dtype = 'c_s1'
|
|
group_dset_f_dtype_default = 'character(len=*)'
|
|
group_dset_dtype_default = 'char*'
|
|
group_dset_std_dtype_out = 's'
|
|
group_dset_std_dtype_in = 's'
|
|
|
|
# add the dset name for templates
|
|
tmp_dict['group_dset'] = k
|
|
# add the datatypes for templates
|
|
tmp_dict['dtype'] = datatype
|
|
tmp_dict['group_dset_dtype'] = datatype
|
|
tmp_dict['group_dset_h5_dtype'] = group_dset_h5_dtype
|
|
tmp_dict['group_dset_f_dtype_default'] = group_dset_f_dtype_default
|
|
tmp_dict['group_dset_f_dtype_double'] = group_dset_f_dtype_double
|
|
tmp_dict['group_dset_f_dtype_single'] = group_dset_f_dtype_single
|
|
tmp_dict['group_dset_dtype_default'] = group_dset_dtype_default
|
|
tmp_dict['group_dset_dtype_double'] = group_dset_dtype_double
|
|
tmp_dict['group_dset_dtype_single'] = group_dset_dtype_single
|
|
tmp_dict['default_prec'] = default_prec
|
|
tmp_dict['group_dset_std_dtype_in'] = group_dset_std_dtype_in
|
|
tmp_dict['group_dset_std_dtype_out'] = group_dset_std_dtype_out
|
|
# add the rank
|
|
tmp_dict['rank'] = len(v[1])
|
|
tmp_dict['group_dset_rank'] = str(tmp_dict['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 tmp_dict['rank'] > 1:
|
|
for i in range(1, tmp_dict['rank']):
|
|
dim_toadd = tmp_dict['dims'][i]
|
|
dim_list += f', {dim_toadd}'
|
|
|
|
tmp_dict['group_dset_dim_list'] = dim_list
|
|
|
|
if tmp_dict['rank'] == 0:
|
|
dim_f_list = ""
|
|
else:
|
|
dim_f_list = "(*)"
|
|
tmp_dict['group_dset_f_dims'] = dim_f_list
|
|
|
|
# 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 (datatype == 'char*'):
|
|
dset_string_dict[k] = tmp_dict
|
|
else:
|
|
dset_numeric_dict[k] = tmp_dict
|
|
|
|
return (dset_numeric_dict, dset_string_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 num-suffixed variables
|
|
dset (dict) : dictionary of datasets
|
|
|
|
Returns:
|
|
None
|
|
"""
|
|
dim_tocheck = []
|
|
for v in dset.values():
|
|
tmp_dim_list = [dim.replace('.','_') for dim in v[1] if not dim.isdigit()]
|
|
for dim in tmp_dim_list:
|
|
if dim not in dim_tocheck:
|
|
dim_tocheck.append(dim)
|
|
|
|
for dim in dim_tocheck:
|
|
if not dim in num.keys():
|
|
raise ValueError(f"Dimensioning variable {dim} is not a num attribute of any group.\n")
|