Source code for pymatgen.io.qchem.utils

import re
import numpy as np
from collections import defaultdict


[docs]def read_pattern(text_str, patterns, terminate_on_match=False, postprocess=str): """ General pattern reading on an input string Args: text_str (str): the input string to search for patterns patterns (dict): A dict of patterns, e.g., {"energy": r"energy\\(sigma->0\\)\\s+=\\s+([\\d\\-.]+)"}. terminate_on_match (bool): Whether to terminate when there is at least one match in each key in pattern. postprocess (callable): A post processing function to convert all matches. Defaults to str, i.e., no change. Renders accessible: Any attribute in patterns. For example, {"energy": r"energy\\(sigma->0\\)\\s+=\\s+([\\d\\-.]+)"} will set the value of matches["energy"] = [[-1234], [-3453], ...], to the results from regex and postprocess. Note that the returned values are lists of lists, because you can grep multiple items on one line. """ compiled = { key: re.compile(pattern, re.MULTILINE | re.DOTALL) for key, pattern in patterns.items() } matches = defaultdict(list) for key, pattern in compiled.items(): for match in pattern.finditer(text_str): matches[key].append([postprocess(i) for i in match.groups()]) if terminate_on_match: break return matches
[docs]def read_table_pattern(text_str, header_pattern, row_pattern, footer_pattern, postprocess=str, attribute_name=None, last_one_only=False): """ Parse table-like data. A table composes of three parts: header, main body, footer. All the data matches "row pattern" in the main body will be returned. Args: text_str (str): the input string to search for patterns header_pattern (str): The regular expression pattern matches the table header. This pattern should match all the text immediately before the main body of the table. For multiple sections table match the text until the section of interest. MULTILINE and DOTALL options are enforced, as a result, the "." meta-character will also match "\n" in this section. row_pattern (str): The regular expression matches a single line in the table. Capture interested field using regular expression groups. footer_pattern (str): The regular expression matches the end of the table. E.g. a long dash line. postprocess (callable): A post processing function to convert all matches. Defaults to str, i.e., no change. attribute_name (str): Name of this table. If present the parsed data will be attached to "data. e.g. self.data["efg"] = [...] last_one_only (bool): All the tables will be parsed, if this option is set to True, only the last table will be returned. The enclosing list will be removed. i.e. Only a single table will be returned. Default to be True. Returns: List of tables. 1) A table is a list of rows. 2) A row if either a list of attribute values in case the the capturing group is defined without name in row_pattern, or a dict in case that named capturing groups are defined by row_pattern. """ table_pattern_text = header_pattern + \ r"\s*(?P<table_body>(?:" + row_pattern + r")+)\s*" + footer_pattern table_pattern = re.compile(table_pattern_text, re.MULTILINE | re.DOTALL) rp = re.compile(row_pattern) data = {} tables = [] for mt in table_pattern.finditer(text_str): table_body_text = mt.group("table_body") table_contents = [] for ml in rp.finditer(table_body_text): d = ml.groupdict() if len(d) > 0: processed_line = {k: postprocess(v) for k, v in d.items()} else: processed_line = [postprocess(v) for v in ml.groups()] table_contents.append(processed_line) tables.append(table_contents) if last_one_only: retained_data = tables[-1] else: retained_data = tables if attribute_name is not None: data[attribute_name] = retained_data return data return retained_data
[docs]def lower_and_check_unique(dict_to_check): """ Takes a dictionary and makes all the keys lower case. Also replaces "jobtype" with "job_type" just so that key specifically can be called elsewhere without ambiguity. Finally, ensures that multiple identical keys, that differed only due to different capitalizations, are not present. If there are multiple equivalent keys, an Exception is raised. Args: dict_to_check (dict): The dictionary to check and standardize Returns: to_return (dict): An identical dictionary but with all keys made lower case and no identical keys present. """ if dict_to_check == None: return None else: to_return = {} for key in dict_to_check: new_key = key.lower() if new_key == "jobtype": new_key = "job_type" if new_key in to_return: raise Exception( "Multiple instances of key " + new_key + " found!") else: try: to_return[new_key] = dict_to_check.get(key).lower() except AttributeError: to_return[new_key] = dict_to_check.get(key) return to_return
[docs]def process_parsed_coords(coords): """ Takes a set of parsed coordinates, which come as an array of strings, and returns a numpy array of floats. """ geometry = np.zeros(shape=(len(coords), 3), dtype=float) for ii, entry in enumerate(coords): for jj in range(3): geometry[ii, jj] = float(entry[jj]) return geometry