def testBasicUse(self):
silent_remove(SUB_SUB_DIR, dir_with_files=True)
make_dir(SUB_SUB_DIR)
fname_after_run = os.path.join(SUB_SUB_DIR, 'for_hartree',
'pet_mono_637_tzvp.log')
silent_remove(fname_after_run)
file1_to_copy = os.path.join(SUB_DATA_DIR, 'pet_mono_637_tzvp.tpl')
temp_fname1 = os.path.join(SUB_SUB_DIR, 'pet_mono_637_tzvp.log')
file2_to_copy = os.path.join(SUB_DATA_DIR, 'me2propprpnt_7.log')
temp_fname2 = os.path.join(SUB_SUB_DIR, 'me2propprpnt_7.log')
file3_to_copy = os.path.join(SUB_DATA_DIR, 'pet_mono_671_tzvp.log')
temp_fname3 = os.path.join(SUB_SUB_DIR, 'pet_mono_671_tzvp.log')
good_output = "The following files completed normally:\n" \
" tests/test_data/check_gauss/temp_dir/pet_mono_637_tzvp.log\n" \
"The following files may have failed:\n" \
" tests/test_data/check_gauss/temp_dir/me2propprpnt_7.log\n" \
"The following files may still be running:\n" \
" tests/test_data/check_gauss/temp_dir/pet_mono_671_tzvp.log\n"
try:
copyfile(file1_to_copy, temp_fname1)
copyfile(file2_to_copy, temp_fname2)
copyfile(file3_to_copy, temp_fname3)
test_input = ["-d", SUB_SUB_DIR]
# main(test_input)
with capture_stdout(main, test_input) as output:
self.assertTrue(good_output in output)
self.assertTrue(os.path.isfile(fname_after_run))
finally:
silent_remove(SUB_SUB_DIR,
dir_with_files=True,
disable=DISABLE_REMOVE)
pass
def main(argv=None):
args, ret = parse_cmdline(argv)
if ret != GOOD_RET or args is None:
return ret
try:
# start with copies of global variable dicts; then only the copies will be altered
if args.file_name:
mw_formula_dict = MW_FORM_DICT.copy()
mw_deprot_formula_dict = MW_DEPROT_FORM_DICT.copy()
mw_prot_formula_dict = MW_PROT_FORM_DICT.copy()
form_smi_dict = deepcopy(FORMULA_SMI_DICT)
form_dbe_dict = FORMULA_DBE_DICT.copy()
smi_name_dict = deepcopy(SMI_NAME_DICT)
smi_source_dict = deepcopy(SMI_SOURCE_DICT)
number_additions = process_input_file(
args.file_name, mw_formula_dict, mw_deprot_formula_dict,
mw_prot_formula_dict, form_smi_dict, form_dbe_dict,
smi_name_dict, smi_source_dict)
# Reading complete, now output
if number_additions:
pretty_print_dicts(mw_formula_dict, mw_deprot_formula_dict,
mw_prot_formula_dict, form_smi_dict,
form_dbe_dict, smi_name_dict,
smi_source_dict)
if args.image_library:
if args.mw_list:
mw_keys = [x.strip() for x in args.mw_list.split(",")]
else:
mw_keys = MW_FORM_DICT.keys()
if args.out_dir:
make_dir(args.out_dir)
image_grid_mult_mw(mw_keys,
MW_FORM_DICT,
FORMULA_SMI_DICT,
out_dir=args.out_dir)
except IOError as e:
warning(e)
return IO_ERROR
except InvalidDataError as e:
warning(e)
return INVALID_DATA
return GOOD_RET # success
def check_if_files_to_be_saved(cfg):
"""
Evaluate input for requests to save output and check for valid specified locations
:param cfg: dict of configuration values
:return: if the cfg designs that files should be created, returns an updated cfg dict, and raises errors if
invalid data in encountered
"""
if cfg[OUT_FORMAT_LIST]:
# remove any periods to aid comparison; might as well also change comma to space and then split on just space
out_format_list = cfg[OUT_FORMAT_LIST].replace(".", " ").replace(",", " ")
format_set = set(out_format_list.split())
else:
format_set = set()
if cfg[BASENAME] and (cfg[BASENAME] != DEF_BASENAME):
# If cfg[BASENAME] is not just the base name, make it so, saving a dir or ext in their spots
out_path, base_name = os.path.split(cfg[BASENAME])
if out_path and cfg[OUT_DIR]:
cfg[OUT_DIR] = os.path.join(cfg[OUT_DIR], out_path)
elif out_path:
cfg[OUT_DIR] = out_path
base, ext = os.path.splitext(base_name)
cfg[BASENAME] = base
format_set.add(ext.replace(".", ""))
if len(format_set) > 0:
for format_type in format_set:
if format_type in OUT_TYPE_LIST:
cfg[SAVE_FILES] = True
cfg[format_type] = True
else:
raise InvalidDataError(f"Invalid extension provided: '{format_type}'. The currently supported types "
f"are: '{OUT_TYPE_STR}'")
if cfg[PLOT_BONDS]:
cfg[SAVE_FILES] = True
# if out_dir does not already exist, recreate it, only if we will actually need it
if cfg[SAVE_FILES] and cfg[OUT_DIR]:
make_dir(cfg[OUT_DIR])
def collect_check_process_file_list(args):
# find files to process, allowing any combination of a file name, a list of file names, searching dir...
# first look for mzML files
process_file_list = check_for_files(args.file_name, args.list_file, search_pattern=MZML_EXT,
search_dir=args.directory, search_sub_dir=args.sub_dir_flag,
warn_if_no_matches=False)
# if no mzml files, look for csv files
if len(process_file_list) == 0:
process_file_list = check_for_files(args.file_name, args.list_file, search_pattern=CSV_EXT,
search_dir=args.directory, search_sub_dir=args.sub_dir_flag,
warn_if_no_matches=False)
# Now check that didn't accidentally get program output or wrong file type
filtered_file_list = []
for fname in process_file_list:
fname_lower = os.path.basename(fname).lower()
if not (fname_lower.endswith(MZML_EXT.lower()) or fname_lower.endswith(CSV_EXT.lower())):
warning(f"The expected file extensions are '{MZML_EXT}' and '{CSV_EXT}'.\n"
f" Encountered file: {os.path.relpath(fname)}.\n Skipping file.")
continue
if not (fnmatch.fnmatch(fname_lower, "*matched.csv") or fnmatch.fnmatch(fname_lower, "*matched_ext.csv")):
filtered_file_list.append(fname)
if not len(filtered_file_list) and args.blank_file_name is None:
raise InvalidDataError("No files found to process. Exiting program.")
process_file_list = filtered_file_list
# Make sure that blank files are not processed twice by removing from this list if it is there
if args.blank_file_name:
if args.blank_file_name in process_file_list:
process_file_list.remove(args.blank_file_name)
fname_lower = os.path.basename(args.blank_file_name).lower()
if not (fname_lower.lower().endswith(MZML_EXT.lower()) or fname_lower.endswith(CSV_EXT.lower())):
raise InvalidDataError(f"The expected file extensions for MS output are '{MZML_EXT}' and '{CSV_EXT}'.\n"
f" Specified blank file: {os.path.relpath(args.blank_file_name)}\n"
f" Exiting program.")
# Now check names for protonated/deprotonated flags
for fname in process_file_list:
fname_lower = os.path.basename(fname).lower()
pos_match = fnmatch.fnmatch(fname_lower, "*+*")
neg_match = fnmatch.fnmatch(fname_lower, "*-*")
if pos_match and neg_match:
raise InvalidDataError(f"Found both a '+' and a '-' in the file name: {os.path.relpath(fname)}\n"
f" Only one of these characters can appear in a file name, as this "
f"program uses these characters to determine if matches should be attempted for "
f"protonated ('+') or deprotonated ('-') ion MWs.")
if not (pos_match or neg_match) and not args.quit_after_mzml_to_csv:
if args.omit_mol_ion:
raise InvalidDataError(f"The '-x'/'--omit_mol_ion' option was selection, although there is no '+' nor "
f"'-' in the file name: {os.path.relpath(fname)}\n Thus, no matching type "
f"has been selected. Program exiting.")
else:
warning(f"Since neither '+' nor '-' appear in the file name: {os.path.relpath(fname)}\n "
f"Only matches to the molecular ion will be reported.")
# While setting up, also make output directory if it does, so there is a place to save a CSV file
if args.out_dir:
make_dir(args.out_dir)
return process_file_list
def parse_cmdline(argv):
"""
Returns the parsed argument list and return code.
`argv` is a list of arguments, or `None` for ``sys.argv[1:]``.
"""
if argv is None:
argv = sys.argv[1:]
# initialize the parser object:
parser = argparse.ArgumentParser(
description=
'Creates Gaussian input files from SMILES strings, given a template '
'input file.')
parser.add_argument(
"-t",
"--" + GAU_TPL_FILE,
help=f"Required: the location of the Gaussian input template file. "
f"This file must contain the string '{REQ_STR}' in the "
f"location where the atom type and coordinates should be "
f"added.",
metavar='PATH',
default=None)
parser.add_argument(
"-l",
"--" + LIST_FNAME,
help=f"Option to specify a file with a list of SMILES strings "
f"(one file per line on the list).",
metavar='PATH',
default=None)
parser.add_argument(
"-m",
"--" + MAX_CONFS,
help=f"Option to specify the maximum number of conformations to be "
f"created for each SMILES string. The default value is "
f"{DEF_MAX_CONFS}.",
metavar='INT',
default=DEF_MAX_CONFS,
type=int)
parser.add_argument(
"-o",
"--" + OUT_DIR,
help="Directory where created files should be saved. The default "
"is the working directory. If a directory is specified and does not "
"yet exist, it will be created.",
metavar='PATH',
default=None)
parser.add_argument(
"-s",
"--" + SMI,
help=
"Option to specify a SMILES string. Multiple strings can be separated "
"by ','; if '.' is present the two molecules/fragments will be "
"considered together.",
metavar='STR',
default=None)
args = None
try:
args = parser.parse_args(argv)
if args.gau_tpl_file is None:
raise InvalidDataError(
f"A template Gaussian input file is required to be specified with the "
f"'-t'/'--{GAU_TPL_FILE}' option.")
if not os.path.isfile(args.gau_tpl_file):
raise InvalidDataError(
f"Could not locate the specified Gaussian input file.")
if args.list_file is None:
args.list_file = []
else:
args.list_file = file_rows_to_list(args.list_file)
if args.smiles is not None:
smi_list = [smi.strip() for smi in args.smiles.split(",")]
args.list_file.extend(smi_list)
if len(args.list_file) == 0:
raise InvalidDataError(
f"No SMILES input has been specified. Specify a single SMILES string with the "
f"'-s'/'--{SMI}' option or a files with a list of SMILES strings (one per line) "
f"with the '-l'/'--{LIST_FNAME}' option.")
args.list_file = list(set(args.list_file))
if args.out_dir is not None:
make_dir(args.out_dir)
except (IOError, KeyError, InvalidDataError, MissingSectionHeaderError,
SystemExit) as e:
if hasattr(e, 'code') and e.code == 0:
return args, GOOD_RET
warning(e)
parser.print_help()
return args, INPUT_ERROR
return args, GOOD_RET
def write_output(fname, ms_level, num_matches, short_output_list, long_output_list, matched_formulas,
combined_out_fname, omit_mol_ion_flag, deprot_flag, prot_flag, write_mode, out_dir):
"""
Print output from matching M/Z to lignin molecule library
:param fname: location of input file processed
:param ms_level: int, type of MS output, for output name so there are separate files from multiple-channel input
:param num_matches: the number of matches made between input M/Z and MW in lignin library
:param short_output_list: list of dicts of summary matching data (one list per match)
:param long_output_list: list of dicts of extended matching data (sorted by MZ values)
:param matched_formulas: set of formula names that were matched to M/Z values
:param combined_out_fname: None or string if output from multiple files is to be written to one file
:param omit_mol_ion_flag: boolean to indicate if molecular ion matches were not attempted (True) or sought (False)
:param deprot_flag: boolean to indicate if matches were found for molecular ions
:param prot_flag: flag to indicate if matches were found for molecular ions
:param write_mode: flag to indicate if matches were found for molecular ions
:param out_dir: location of output directory, or None if the current directory is the output directory
:return: n/a; several output files created
"""
# prepare string for txt output file
if write_mode == 'a':
short_txt_output_str = ''
else:
short_txt_output_str = MATCH_STR_HEADER
for mz_dict in short_output_list:
peak_str = MZ_STR_FMT.format(mz_dict[M_Z], mz_dict[INTENSITY], mz_dict[RET_TIME])
short_txt_output_str += MATCH_STR_FMT.format(peak_str, mz_dict[REL_INTENSITY], mz_dict[CALC_MW],
mz_dict[PPM_ERR], mz_dict[PARENT_FORMULA], mz_dict[DBE],
mz_dict[MATCH_TYPE])
ms_str = f"_ms{ms_level}"
if ms_str in fname:
suffix = DEF_SUFFIX
ext_suffix = DEF_LONG_SUFFIX
else:
suffix = ms_str + DEF_SUFFIX
ext_suffix = ms_str + DEF_LONG_SUFFIX
f_out_txt = create_out_fname(fname, suffix=suffix, base_dir=out_dir, ext="txt")
f_out_csv = create_out_fname(fname, suffix=suffix, base_dir=out_dir, ext="csv")
if combined_out_fname:
f_out_long = create_out_fname(combined_out_fname, suffix="_ext", base_dir=out_dir, ext="csv")
else:
f_out_long = create_out_fname(fname, suffix=ext_suffix, base_dir=out_dir, ext="csv")
# Print quick summary; first note which types of matches were investigated
if omit_mol_ion_flag:
match_str_list = []
else:
match_str_list = ["molecular ion"]
if deprot_flag:
match_str_list.append("deprotonated ion")
if prot_flag:
match_str_list.append("protonated ion")
print(f" {num_matches} of these matched a MW in our dictionaries for a {' or a '.join(match_str_list)}")
# save output to files
short_write_mode = 'w'
if num_matches == 0:
warning(f"No MW to MZ matches (within specified ppm error) found for file: {os.path.basename(fname)}\n "
f"Summary output will not be printed.")
else:
str_to_file(short_txt_output_str, os.path.relpath(f_out_txt), print_info=True, mode=short_write_mode)
write_csv(short_output_list, os.path.relpath(f_out_csv), SHORT_OUTPUT_HEADERS, extrasaction="ignore",
mode=short_write_mode)
if out_dir:
struct_dir = os.path.join(out_dir, STRUCT_DIR)
else:
struct_dir = STRUCT_DIR
make_dir(struct_dir)
for formula in matched_formulas:
my_formula = formula.replace("*", "")
make_image_grid(formula, list(FORMULA_SMI_DICT[my_formula]), out_dir=struct_dir, write_output=False)
# print long output even if no matches
write_csv(long_output_list, os.path.relpath(f_out_long), OUTPUT_HEADERS, extrasaction="ignore", mode=write_mode)
def process_ms_run_file(args, fname, blank_data_array_dict):
"""
Read ms_data and remove low intensity peaks
:param args: command-line input and default values for program options
:param fname: the file name of the file with ms data
:param blank_data_array_dict: dictionary of blank data (keys are ms level); empty dict if no blank data provided
:return:
"""
base_name = os.path.basename(fname)
print(f"\nReading file: {os.path.basename(fname)}")
if args.out_dir is None:
# if out_dir was specified, the directory has already been created; otherwise, make a directory just for this
# file's output
args.out_dir = get_fname_root(fname) + DEF_SUFFIX
make_dir(args.out_dir)
fname_lower = base_name.lower()
# Reading file
if fname_lower.endswith(MZML_EXT.lower()):
data_array_dict = process_mzml_input(fname, args.num_decimals_ms_accuracy, args.ms_accuracy,
args.ret_time_accuracy, args.direct_injection)
else:
# previously screened that only CSV_EXT or MZML_EXT get to this point
data_array_dict = read_validate_csv_data(fname, fname_lower, args.ms_accuracy, args.direct_injection)
# not all mz entries in data_array_dict will be rounded, but wait to round until needed
if len(data_array_dict) == 0:
warning(f"Found no spectra to analyze in file: {os.path.relpath(fname)}\n Skipping file.")
return None
# Clean up noise and save, if not already clean
for ms_level, data_array in data_array_dict.items():
print(f"Read MS Level {ms_level}")
# # todo: it does not look like rounding below is needed
# if not np.isnan(data_array_dict[ms_level][0][2]):
# data_array_dict[ms_level][:, 2] = round_to_fraction(data_array_dict[ms_level][:, 2],
# args.ret_time_accuracy)
if args.direct_injection or not ("clean" in fname_lower):
comment = ""
if ms_level in blank_data_array_dict:
data_array, ret_type = compare_blank(data_array, blank_data_array_dict[ms_level], args.ms_accuracy,
args.ret_time_accuracy, args.num_decimals_ms_accuracy,
args.threshold)
if ret_type == 1:
warning(f"No common retention times found for blank file: {os.path.relpath(args.blank_file_name)}\n"
f" and ms run file: {os.path.relpath(fname)}\n")
elif ret_type == 2:
warning(f"No common M/Z values found for blank file: {os.path.relpath(args.blank_file_name)}\n"
f" and ms run file: {os.path.relpath(fname)}\n")
else:
comment = f" Subtracted blank run data provided in file: {os.path.relpath(args.blank_file_name)}"
print(" " + comment)
comment = "#" + comment + "\n"
# whether or not remove blank, want to prune and print
data_array, comment = prune_intensity(data_array, args.min_rel_intensity, comment)
# removing blanks changes sorting *if* there is retention time; change back (to be consistent with
# other output) now that pruned (rather than before), because it is faster to sort a shorter array
if not np.isnan(data_array[-1][2]):
# sorting needed; arrives sorted first by retention time
data_array = data_array[np.lexsort((-data_array[:, 1], data_array[:, 0]))]
print_clean_csv(fname, fname_lower, ms_level, data_array, comment, args.direct_injection,
args.unlabeled_csvs, args.numpy_save_fmt, args.out_dir)
data_array_dict[ms_level] = data_array
return data_array_dict
