Range reference handling, filling the range
Browse files- pmc_open_access_xml.py +73 -33
pmc_open_access_xml.py
CHANGED
@@ -84,10 +84,10 @@ def clean_raw(xml_text):
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"""
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#Some XML can't be parsed because they are not starting with the DOCTYPE declaration
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# Could be disabled if we handle the parsing error (TBD, how many files would be trashed)
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-
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begin_doc = begin_doc_rgx.search(xml_text)
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xml_text = xml_text[begin_doc.start():]
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-
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#Some XML are poisoned with consecutive tabs and new lines
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# xml_text = re.sub('\s+',' ',xml_text) # Commented because <code> requires those spacing
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return xml_text
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@@ -116,7 +116,7 @@ def get_ref_indexes(ref_el_l, refs_pmid, refs_nonpmid_keys):
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reference_d[k] = (v, " ##REF## ", "pmid_ref")
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for i, k in enumerate(refs_nonpmid_keys):
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reference_d[k] = (i, " ##UREF## ", "unknown_pub_ref")
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-
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refs_key_l = []
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for el in ref_el_l:
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keyword, ref_name = TAG_DIC[el.tag]
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@@ -137,20 +137,64 @@ def parseout_el_refs(el, rids):
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Extract then from the text all the references founds to the rids dictionnary,
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and replace them by keywords of the corresponding family (eg " ##FIG## " for a figure,
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" ##TAB## " for a table, or " ##MATHS## " for mathematical formulas)
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-
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Returns the parsed text, the identifiers for the references and the references text that
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were replaced by the keywords. (eg, "Figure 2" was a hypertext reference and got replaced by " ##FIG## ")
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"""
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res_rid = defaultdict(list)
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res_reftext = defaultdict(list)
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for xref in el.xpath(".//xref[not(ancestor::xref)]"): #Ignore innermost of imbricated references
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rid = xref.get("rid")
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if rid in rids.keys():
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ref_idx, ref_kword, ref_class = rids[rid]
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res_rid[ref_class].append(ref_idx)
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-
res_reftext[ref_class].append(
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-
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tail = xref.tail if xref.tail else ""
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prev_el = xref.getprevious()
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if prev_el is None:
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parent.text = "".join([(parent.text if parent.text else ""), ref_kword, tail])
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@@ -162,12 +206,12 @@ def parseout_el_refs(el, rids):
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#Removing the xml namespace, (otherwise they would be everywhere)
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tag_start = text.find(">")+1
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tag_txt = text[:tag_start]
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-
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for k, v in el.nsmap.items():
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tag_txt = tag_txt.replace(f' xmlns:{k}="{v}"', "", 1)
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text = "".join([tag_txt, text[tag_start:]])
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-
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return text, res_rid, res_reftext
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@@ -207,7 +251,6 @@ def get_references(article_tree):
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citation_d[el.tag].append(el.text)
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references_nonpmid.append(dict(citation_d))
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references_nonpmid_keys.append(ref_key)
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-
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return references_pmid, references_nonpmid, references_nonpmid_keys
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def construct_datadict(article_tree):
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@@ -220,26 +263,25 @@ def construct_datadict(article_tree):
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- Titles are used to identify ["introduction", "methods", "results" and "discussion"]
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- The path are then used to group paragraphs and titles into corresponding content.
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- Remaining p and title are put in three other section: front, body, back
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-
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Returns:
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- content_d: Dictionnary with the content result
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- reference_d: The references of each kind (figure, table, ...) for each content type (intro, figure caption, ...)
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- reference_text_d: The replaced text by the keywords of the references, with keys matching reference_d.
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- reference_count: The count of unique external-document references.
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-
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Useful information about the tags can be found here: https://jats.nlm.nih.gov/archiving/tag-library/1.3/
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"""
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-
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res_content_d, res_reference_d, res_reference_text_d = {}, defaultdict(dict), defaultdict(dict)
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-
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refs_pmid, refs_nonpmid, refs_nonpmid_keys = get_references(article_tree)
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reference_count = len(refs_pmid)+len(refs_nonpmid)
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-
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res_content_d["unknown_pub"] = json.dumps(refs_nonpmid)
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refs_el = article_tree.find(".//ref-list")
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if refs_el is not None:
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refs_el.getparent().remove(refs_el)
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-
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# Extracts the glossary if exists, and removes it from the tree
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glossary = {}
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def search_def(el):
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@@ -251,7 +293,7 @@ def construct_datadict(article_tree):
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definition = item.find(".//def")
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definition = "".join(definition.itertext()) if definition is not None else ""
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glossary[k] = definition
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-
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for el in article_tree.findall(".//glossary"):
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search_def(el)
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el.getparent().remove(el)
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@@ -259,7 +301,7 @@ def construct_datadict(article_tree):
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search_def(el) #There may be still more def-list outside of a glossary
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el.getparent().remove(el)
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res_content_d["glossary"] = glossary
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-
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# After testing, no question were found in the dataset, so I commented that part
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# question_l = []
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# for el in article_tree.xpath(".//question-preamble|.//question|.//answer|.//explanation"):
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@@ -268,7 +310,7 @@ def construct_datadict(article_tree):
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# res_content_d["question"] = "\n".join(question_l)
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# for el in article_tree.xpath(".//question-wrap-group|.//question-wrap|.//answer-set|.//explanation"):
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# el.getparent().remove(el)
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-
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# One big query is faster than multiple small ones
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ref_el_l = article_tree.xpath(".//fig|.//table-wrap|.//array|.//supplementary-material\
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|.//inline-supplementary-material|.//disp-formula\
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@@ -292,7 +334,7 @@ def construct_datadict(article_tree):
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repl_xref.tail = el.tail
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el.addprevious(repl_xref)
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el.getparent().remove(el)
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-
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# Finally, the discovered references and text are added to the result
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for ref_k in REFS_KEYS[2:]: #Slicing from 2, to not add pmid and unknown ref here
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res_content_d[ref_k[:-4]] = text_l_d[ref_k]#"\n".join(text_l_d[ref_k])
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@@ -312,7 +354,7 @@ def construct_datadict(article_tree):
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res_reference_d[part][ref_k] = list(chain(*tmp_l))
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tmp_l = [refs_d[ref_k] for refs_d in ref_texts_l]
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res_reference_text_d[part][ref_k] = list(chain(*tmp_l))
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-
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path_l, text_l, refs_l, refs_text_l = [], [], [], []
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t_paths, t_texts_lowcase = [], []
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for part in ["front", "body", "back"]: #Iterate parts and insert first front and back
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@@ -374,7 +416,7 @@ def construct_datadict(article_tree):
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res_reference_d = dict(res_reference_d)
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res_reference_text_d = dict(res_reference_text_d)
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-
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return (res_content_d, res_reference_d, res_reference_text_d, reference_count)
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class OpenAccessXMLConfig(datasets.BuilderConfig):
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@@ -408,7 +450,7 @@ class OpenAccessXML(datasets.GeneratorBasedBuilder):
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{
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"accession_id": datasets.Value("string"),
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"pmid": datasets.Value("string"),
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-
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"introduction": datasets.features.Sequence(datasets.Value("string")),
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"methods": datasets.features.Sequence(datasets.Value("string")),
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"results": datasets.features.Sequence(datasets.Value("string")),
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@@ -430,7 +472,7 @@ class OpenAccessXML(datasets.GeneratorBasedBuilder):
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"footnote": datasets.features.Sequence(datasets.Value("string")),
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"graphic": datasets.features.Sequence(datasets.Value("string")),
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"media": datasets.features.Sequence(datasets.Value("string")),
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-
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"unknown_pub": datasets.Value("string"),
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# "question": datasets.Value("string"),
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"glossary": datasets.features.Sequence(
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@@ -475,9 +517,9 @@ class OpenAccessXML(datasets.GeneratorBasedBuilder):
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"incremental_file_lists": [],
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"incremental_archives": []
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}
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-
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baseline_package_list = dl_manager.download(f"{_URL_ROOT}oa_file_list.csv")
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-
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baseline_file_lists = []
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baseline_archives = []
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for subset in self.config.subsets:
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@@ -494,12 +536,12 @@ class OpenAccessXML(datasets.GeneratorBasedBuilder):
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baseline_archive = dl_manager.download(baseline_archive_url)
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except FileNotFoundError: # non-commercial PMC000xxxxxx baseline does not exist
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continue
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-
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baseline_file_lists.append(baseline_file_list)
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baseline_archives.append(baseline_archive)
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baseline_file_list_url = f"{url}{basename}{baseline}.filelist.csv"
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-
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# Incremental commented because some articles are already in the main parts (updates?)
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# Need to find a way to add them to the dataset without duplicating the articles.
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# Also adding them would mean that each new day the dataset is loaded, the whole dataset is recreated.
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@@ -547,7 +589,7 @@ class OpenAccessXML(datasets.GeneratorBasedBuilder):
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incrementals = incrementals.join(oa_package_list).reset_index().set_index("Article File")
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incrementals.File = incrementals.File.fillna('')
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incrementals = incrementals.to_dict(orient="index")
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-
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for path, file in incremental_archive:
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data = incrementals.pop(path)
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pmcid = data["AccessionID"]
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@@ -563,7 +605,7 @@ class OpenAccessXML(datasets.GeneratorBasedBuilder):
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article_tree = etree.ElementTree(etree.fromstring(text))
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except etree.XMLSyntaxError: #In some files, xml is broken
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continue
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-
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content_d, reference_d, reference_text_d, n_ref = construct_datadict(article_tree)
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glossary = np.array([[k,v] for k,v in content_d["glossary"].items()])
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data = {
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@@ -611,7 +653,7 @@ class OpenAccessXML(datasets.GeneratorBasedBuilder):
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baselines = baselines.join(oa_package_list).reset_index().set_index("Article File")
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baselines.File = baselines.File.fillna('')
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baselines = baselines.to_dict(orient="index")
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-
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for path, file in baseline_archive:
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data = baselines.pop(path)
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pmcid = data["AccessionID"]
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@@ -627,7 +669,7 @@ class OpenAccessXML(datasets.GeneratorBasedBuilder):
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article_tree = etree.ElementTree(etree.fromstring(text))
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except etree.XMLSyntaxError: #In some files, xml is broken
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continue
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-
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content_d, reference_d, reference_text_d, n_ref = construct_datadict(article_tree)
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glossary = np.array([[k,v] for k,v in content_d["glossary"].items()])
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data = {
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@@ -669,5 +711,3 @@ class OpenAccessXML(datasets.GeneratorBasedBuilder):
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#except FileNotFoundError: # non-commercial PMC000xxxxxx baseline does not exist
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# continue
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-
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-
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"""
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#Some XML can't be parsed because they are not starting with the DOCTYPE declaration
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# Could be disabled if we handle the parsing error (TBD, how many files would be trashed)
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+
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begin_doc = begin_doc_rgx.search(xml_text)
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xml_text = xml_text[begin_doc.start():]
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+
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#Some XML are poisoned with consecutive tabs and new lines
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# xml_text = re.sub('\s+',' ',xml_text) # Commented because <code> requires those spacing
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return xml_text
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reference_d[k] = (v, " ##REF## ", "pmid_ref")
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for i, k in enumerate(refs_nonpmid_keys):
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reference_d[k] = (i, " ##UREF## ", "unknown_pub_ref")
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+
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refs_key_l = []
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for el in ref_el_l:
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keyword, ref_name = TAG_DIC[el.tag]
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Extract then from the text all the references founds to the rids dictionnary,
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and replace them by keywords of the corresponding family (eg " ##FIG## " for a figure,
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" ##TAB## " for a table, or " ##MATHS## " for mathematical formulas)
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+
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+
The range reference (e.g. 1-3 or 15-17) are replaced by their range (1,2,3 or 15,16,17)
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+
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Returns the parsed text, the identifiers for the references and the references text that
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were replaced by the keywords. (eg, "Figure 2" was a hypertext reference and got replaced by " ##FIG## ")
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"""
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+
for xref in el.xpath(".//xref"):
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+
inner_text = "".join(xref.itertext())
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+
if inner_text == "": # Removing "empty" references
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+
tail = xref.tail if xref.tail else ""
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+
prev_el = xref.getprevious()
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+
parent = xref.getparent()
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+
if prev_el is None:
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+
parent.text = "".join([(parent.text if parent.text else ""), tail])
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+
else:
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prev_el.tail = "".join([(prev_el.tail if prev_el.tail else ""), tail])
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parent.remove(xref)
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+
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res_rid = defaultdict(list)
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res_reftext = defaultdict(list)
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+
ref_rstart, ref_rstop = None, None
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+
has_ref_range = None
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for xref in el.xpath(".//xref[not(ancestor::xref)]"): #Ignore innermost of imbricated references
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+
inner_text = "".join(xref.itertext())
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+
parent = xref.getparent()
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rid = xref.get("rid")
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if rid in rids.keys():
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ref_idx, ref_kword, ref_class = rids[rid]
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res_rid[ref_class].append(ref_idx)
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+
res_reftext[ref_class].append(inner_text)
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+
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tail = xref.tail if xref.tail else ""
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+
#### START HANDLING REF RANGE ########
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try:
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if has_ref_range is None:
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if ref_kword in [" ##UREF## ", " ##REF## "]: # Otherwise it's a year
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has_ref_range = res_reftext[ref_class][-1].isnumeric() and int(res_reftext[ref_class][-1]) < 500
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+
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if has_ref_range and ref_kword in [" ##UREF## ", " ##REF## "]:
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if tail=="-":
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ref_rstart = int(res_reftext[ref_class][-1])
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tail = ", "
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elif ref_rstart is not None:
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ref_rstop = int(res_reftext[ref_class][-1])
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ref_kword = [ref_kword]
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+
for i in range(ref_rstart+1, ref_rstop):
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new_rid = re.sub(str(ref_rstop), str(i), rid, count=1)
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ref_idx_, ref_kword_, ref_class_ = rids[new_rid]
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res_rid[ref_class_].insert(-1, ref_idx_)
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res_reftext[ref_class_].insert(-1, str(i))
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ref_kword.insert(-1, ref_kword_)
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ref_kword = ", ".join(ref_kword)
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ref_rstart = None
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except (KeyError, ValueError):
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ref_rstart = None
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continue # The substitution failed, happen when text don't match the rid
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+
#### END HANDLING REF RANGE ########
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+
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prev_el = xref.getprevious()
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if prev_el is None:
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parent.text = "".join([(parent.text if parent.text else ""), ref_kword, tail])
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#Removing the xml namespace, (otherwise they would be everywhere)
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tag_start = text.find(">")+1
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tag_txt = text[:tag_start]
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+
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for k, v in el.nsmap.items():
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tag_txt = tag_txt.replace(f' xmlns:{k}="{v}"', "", 1)
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text = "".join([tag_txt, text[tag_start:]])
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+
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return text, res_rid, res_reftext
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citation_d[el.tag].append(el.text)
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references_nonpmid.append(dict(citation_d))
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references_nonpmid_keys.append(ref_key)
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return references_pmid, references_nonpmid, references_nonpmid_keys
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def construct_datadict(article_tree):
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- Titles are used to identify ["introduction", "methods", "results" and "discussion"]
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- The path are then used to group paragraphs and titles into corresponding content.
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- Remaining p and title are put in three other section: front, body, back
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+
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Returns:
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- content_d: Dictionnary with the content result
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- reference_d: The references of each kind (figure, table, ...) for each content type (intro, figure caption, ...)
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- reference_text_d: The replaced text by the keywords of the references, with keys matching reference_d.
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- reference_count: The count of unique external-document references.
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+
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Useful information about the tags can be found here: https://jats.nlm.nih.gov/archiving/tag-library/1.3/
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"""
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res_content_d, res_reference_d, res_reference_text_d = {}, defaultdict(dict), defaultdict(dict)
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+
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refs_pmid, refs_nonpmid, refs_nonpmid_keys = get_references(article_tree)
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reference_count = len(refs_pmid)+len(refs_nonpmid)
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+
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res_content_d["unknown_pub"] = json.dumps(refs_nonpmid)
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refs_el = article_tree.find(".//ref-list")
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if refs_el is not None:
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refs_el.getparent().remove(refs_el)
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+
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# Extracts the glossary if exists, and removes it from the tree
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glossary = {}
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def search_def(el):
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definition = item.find(".//def")
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definition = "".join(definition.itertext()) if definition is not None else ""
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glossary[k] = definition
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+
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for el in article_tree.findall(".//glossary"):
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search_def(el)
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el.getparent().remove(el)
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search_def(el) #There may be still more def-list outside of a glossary
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el.getparent().remove(el)
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res_content_d["glossary"] = glossary
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+
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# After testing, no question were found in the dataset, so I commented that part
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# question_l = []
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# for el in article_tree.xpath(".//question-preamble|.//question|.//answer|.//explanation"):
|
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|
310 |
# res_content_d["question"] = "\n".join(question_l)
|
311 |
# for el in article_tree.xpath(".//question-wrap-group|.//question-wrap|.//answer-set|.//explanation"):
|
312 |
# el.getparent().remove(el)
|
313 |
+
|
314 |
# One big query is faster than multiple small ones
|
315 |
ref_el_l = article_tree.xpath(".//fig|.//table-wrap|.//array|.//supplementary-material\
|
316 |
|.//inline-supplementary-material|.//disp-formula\
|
|
|
334 |
repl_xref.tail = el.tail
|
335 |
el.addprevious(repl_xref)
|
336 |
el.getparent().remove(el)
|
337 |
+
|
338 |
# Finally, the discovered references and text are added to the result
|
339 |
for ref_k in REFS_KEYS[2:]: #Slicing from 2, to not add pmid and unknown ref here
|
340 |
res_content_d[ref_k[:-4]] = text_l_d[ref_k]#"\n".join(text_l_d[ref_k])
|
|
|
354 |
res_reference_d[part][ref_k] = list(chain(*tmp_l))
|
355 |
tmp_l = [refs_d[ref_k] for refs_d in ref_texts_l]
|
356 |
res_reference_text_d[part][ref_k] = list(chain(*tmp_l))
|
357 |
+
|
358 |
path_l, text_l, refs_l, refs_text_l = [], [], [], []
|
359 |
t_paths, t_texts_lowcase = [], []
|
360 |
for part in ["front", "body", "back"]: #Iterate parts and insert first front and back
|
|
|
416 |
|
417 |
res_reference_d = dict(res_reference_d)
|
418 |
res_reference_text_d = dict(res_reference_text_d)
|
419 |
+
|
420 |
return (res_content_d, res_reference_d, res_reference_text_d, reference_count)
|
421 |
|
422 |
class OpenAccessXMLConfig(datasets.BuilderConfig):
|
|
|
450 |
{
|
451 |
"accession_id": datasets.Value("string"),
|
452 |
"pmid": datasets.Value("string"),
|
453 |
+
|
454 |
"introduction": datasets.features.Sequence(datasets.Value("string")),
|
455 |
"methods": datasets.features.Sequence(datasets.Value("string")),
|
456 |
"results": datasets.features.Sequence(datasets.Value("string")),
|
|
|
472 |
"footnote": datasets.features.Sequence(datasets.Value("string")),
|
473 |
"graphic": datasets.features.Sequence(datasets.Value("string")),
|
474 |
"media": datasets.features.Sequence(datasets.Value("string")),
|
475 |
+
|
476 |
"unknown_pub": datasets.Value("string"),
|
477 |
# "question": datasets.Value("string"),
|
478 |
"glossary": datasets.features.Sequence(
|
|
|
517 |
"incremental_file_lists": [],
|
518 |
"incremental_archives": []
|
519 |
}
|
520 |
+
|
521 |
baseline_package_list = dl_manager.download(f"{_URL_ROOT}oa_file_list.csv")
|
522 |
+
|
523 |
baseline_file_lists = []
|
524 |
baseline_archives = []
|
525 |
for subset in self.config.subsets:
|
|
|
536 |
baseline_archive = dl_manager.download(baseline_archive_url)
|
537 |
except FileNotFoundError: # non-commercial PMC000xxxxxx baseline does not exist
|
538 |
continue
|
539 |
+
|
540 |
baseline_file_lists.append(baseline_file_list)
|
541 |
baseline_archives.append(baseline_archive)
|
542 |
|
543 |
baseline_file_list_url = f"{url}{basename}{baseline}.filelist.csv"
|
544 |
+
|
545 |
# Incremental commented because some articles are already in the main parts (updates?)
|
546 |
# Need to find a way to add them to the dataset without duplicating the articles.
|
547 |
# Also adding them would mean that each new day the dataset is loaded, the whole dataset is recreated.
|
|
|
589 |
incrementals = incrementals.join(oa_package_list).reset_index().set_index("Article File")
|
590 |
incrementals.File = incrementals.File.fillna('')
|
591 |
incrementals = incrementals.to_dict(orient="index")
|
592 |
+
|
593 |
for path, file in incremental_archive:
|
594 |
data = incrementals.pop(path)
|
595 |
pmcid = data["AccessionID"]
|
|
|
605 |
article_tree = etree.ElementTree(etree.fromstring(text))
|
606 |
except etree.XMLSyntaxError: #In some files, xml is broken
|
607 |
continue
|
608 |
+
|
609 |
content_d, reference_d, reference_text_d, n_ref = construct_datadict(article_tree)
|
610 |
glossary = np.array([[k,v] for k,v in content_d["glossary"].items()])
|
611 |
data = {
|
|
|
653 |
baselines = baselines.join(oa_package_list).reset_index().set_index("Article File")
|
654 |
baselines.File = baselines.File.fillna('')
|
655 |
baselines = baselines.to_dict(orient="index")
|
656 |
+
|
657 |
for path, file in baseline_archive:
|
658 |
data = baselines.pop(path)
|
659 |
pmcid = data["AccessionID"]
|
|
|
669 |
article_tree = etree.ElementTree(etree.fromstring(text))
|
670 |
except etree.XMLSyntaxError: #In some files, xml is broken
|
671 |
continue
|
672 |
+
|
673 |
content_d, reference_d, reference_text_d, n_ref = construct_datadict(article_tree)
|
674 |
glossary = np.array([[k,v] for k,v in content_d["glossary"].items()])
|
675 |
data = {
|
|
|
711 |
|
712 |
#except FileNotFoundError: # non-commercial PMC000xxxxxx baseline does not exist
|
713 |
# continue
|
|
|
|