Source code for

# Copyright 2019-2022 The ASReview Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import io
import logging
import re
from urllib.request import urlopen

import pandas
import rispy

from import _standardize_dataframe
from asreview.utils import is_url

[docs]class RISReader: """RIS file reader.""" read_format = [".ris", ".txt"] write_format = [".csv", ".tsv", ".xlsx", ".ris"] def _strip_zotero_p_tags(note_list): """Converter function for removing the XHTML <p></p> tags from Zotero export. Arguments --------- note_list: list A list of notes, coming from the Dataframe's "notes" column. Returns ------- new_notes: list A list of updated notes, where XHTML <p></p> tags have been stripped. note_list: list The original note_list, when no XHTML <p></p> tags have been found. """ if isinstance(note_list, list): new_notes = [] for v in note_list: try: new_notes.append(re.sub(r"^<p>|<\/p>$", "", v)) except Exception: new_notes.append(v) return new_notes else: return note_list def _label_parser(note_list): """Parse "included" and "notes" columns. Arguments --------- note_list: list A list of notes, coming from the Dataframe's "notes" column. Returns ------- asreview_new_notes: list A list of updated notes, where internal label has been added. note_list: list The original note_list, when no labels have been found. 1,0,-1: int Labels in case they are still needed from the internal representation. """ regex = r"ASReview_relevant|ASReview_irrelevant|ASReview_not_seen" # Check whether note_list is actually a list and not NaN # Return -1 and an empty list if not isinstance(note_list, list): return -1, [] # Create lists of lists for ASReview references asreview_refs = [re.findall(regex, note) for note in note_list] asreview_refs_list = [item for sublist in asreview_refs for item in sublist] if len(asreview_refs_list) > 0: # Create lists of lists for notes without references asreview_new_notes = [re.sub(regex, "", note) for note in note_list] # Remove empty elements from list asreview_new_notes[:] = [item for item in asreview_new_notes if item != ""] label = asreview_refs_list[-1] # Check for the label and return proper values for internal representation if label == "ASReview_relevant": return 1, asreview_new_notes elif label == "ASReview_irrelevant": return 0, asreview_new_notes elif label == "ASReview_not_seen": return -1, asreview_new_notes else: return -1, note_list
[docs] @classmethod def read_data(cls, fp): """Import dataset. Arguments --------- fp: str, pathlib.Path File path to the RIS file. note_list: list A list of notes, coming from the Dataframe's "notes" column. Returns ------- pandas.DataFrame: Dataframe with entries. Raises ------ ValueError File with unrecognized encoding is used as input. """ encodings = ["utf-8", "utf-8-sig", "ISO-8859-1"] entries = None if entries is None: if is_url(fp): url_input = urlopen(fp) for encoding in encodings: if is_url(fp): try: bibliography_file = io.StringIO( ) entries = list( rispy.load(bibliography_file, skip_unknown_tags=True) ) bibliography_file.close() break except UnicodeDecodeError: pass else: try: with open(fp, "r", encoding=encoding) as bibliography_file: entries = list( rispy.load(bibliography_file, skip_unknown_tags=True) ) break except UnicodeDecodeError: pass except IOError as e: logging.warning(e) if entries is None: raise ValueError("Cannot find proper encoding for data file.") # Turn the entries dictionary into a Pandas dataframe df = pandas.DataFrame(entries) # Check if "notes" column is present if "notes" in df: # Strip Zotero XHTML <p> tags on "notes" df["notes"] = df["notes"].apply(cls._strip_zotero_p_tags) # Split "included" from "notes" df[["included", "notes"]] = pandas.DataFrame( df["notes"].apply(cls._label_parser).tolist(), columns=["included", "notes"], ) # Return the standardised dataframe with label and notes separated return _standardize_dataframe(df) else: # Return the standardised dataframe return _standardize_dataframe(df)