Processing of the increments, without duplicate

pmc_open_access_xml.py

@@ -538,16 +538,21 @@ class OpenAccessXML(datasets.GeneratorBasedBuilder):
         oa_package_list = pd.read_csv(baseline_package_list, index_col="Accession ID")
         oa_package_list = oa_package_list[["File"]]
         oa_package_list.sort_index(inplace=True)
-        
-        # Baselines
-        for baseline_file_list, baseline_archive in zip(baseline_file_lists, baseline_archives):
+        processed_ids = set()
 
-            try:
-                baselines = pd.read_csv(baseline_file_list, index_col="AccessionID")
-                baselines = baselines.join(oa_package_list).reset_index().set_index("Article File")
-                baselines = baselines.to_dict(orient="index")
-                for path, file in baseline_archive:
-                    data = baselines.pop(path)
+        # Incrementals
+        if incremental_file_lists:
+            for incremental_file_list, incremental_archive in zip(incremental_file_lists[::-1], incremental_archives[::-1]):
+                incrementals = pd.read_csv(incremental_file_list, index_col="AccessionID")
+                incrementals = incrementals.join(oa_package_list).reset_index().set_index("Article File")
+                incrementals.File = incrementals.File.fillna('')
+                incrementals = incrementals.to_dict(orient="index")
+                
+                for path, file in incremental_archive:
+                    data = incrementals.pop(path)
+                    pmcid = data["AccessionID"]
+                    if pmcid in processed_ids: #oa_package_list.loc[pmcid, "yet_processed"]:
+                        continue
                     content = file.read()
                     try:
                         text = content.decode("utf-8").strip()
@@ -561,7 +566,6 @@ class OpenAccessXML(datasets.GeneratorBasedBuilder):
                         
                     content_d, reference_d, reference_text_d, n_ref = construct_datadict(article_tree)
                     glossary = np.array([[k,v] for k,v in content_d["glossary"].items()])
-                    pmcid    = data["AccessionID"]
                     data = {
                         "introduction":  content_d["introduction"],
                         "methods":       content_d["methods"],
@@ -582,7 +586,6 @@ class OpenAccessXML(datasets.GeneratorBasedBuilder):
                         "footnote":      content_d["footnote"],
                         "graphic":       content_d["graphic"],
                         "media":         content_d["media"],
-                        
                         # "question":         content_d["question"],
                         "unknown_pub": content_d["unknown_pub"],
                         "references": reference_d,
@@ -597,17 +600,74 @@ class OpenAccessXML(datasets.GeneratorBasedBuilder):
                         "citation": data["Article Citation"],
                         "package_file": data["File"],
                     }
-
+                    processed_ids.add(pmcid)
                     yield pmcid, data
 
-            except FileNotFoundError:  # non-commercial PMC000xxxxxx baseline does not exist
-                continue
+        # Baselines
+        for baseline_file_list, baseline_archive in zip(baseline_file_lists, baseline_archives):
+
+            #try:
+            baselines = pd.read_csv(baseline_file_list, index_col="AccessionID")
+            baselines = baselines.join(oa_package_list).reset_index().set_index("Article File")
+            baselines.File = baselines.File.fillna('')
+            baselines = baselines.to_dict(orient="index")
+            
+            for path, file in baseline_archive:
+                data = baselines.pop(path)
+                pmcid = data["AccessionID"]
+                if pmcid in processed_ids:
+                    continue
+                content = file.read()
+                try:
+                    text = content.decode("utf-8").strip()
+                except UnicodeDecodeError as e:
+                    text = content.decode("latin-1").strip()
+                text = clean_raw(text)
+                try:
+                    article_tree = etree.ElementTree(etree.fromstring(text))
+                except etree.XMLSyntaxError: #In some files, xml is broken
+                    continue
+                    
+                content_d, reference_d, reference_text_d, n_ref = construct_datadict(article_tree)
+                glossary = np.array([[k,v] for k,v in content_d["glossary"].items()])
+                data = {
+                    "introduction":  content_d["introduction"],
+                    "methods":       content_d["methods"],
+                    "results":       content_d["results"],
+                    "discussion":    content_d["discussion"],
+                    "conclusion":    content_d["conclusion"],
+                    "front":         content_d["front"],
+                    "body":          content_d["body"],
+                    "back":          content_d["back"],
+                    "figure":        content_d["figure"],
+                    "table":         content_d["table"],
+                    "formula":       content_d["formula"],
+                    "box":           content_d["box"],
+                    "code":          content_d["code"],
+                    "quote":         content_d["quote"],
+                    "chem":          content_d["chem"],
+                    "supplementary": content_d["supplementary"],
+                    "footnote":      content_d["footnote"],
+                    "graphic":       content_d["graphic"],
+                    "media":         content_d["media"],
+                    # "question":         content_d["question"],
+                    "unknown_pub": content_d["unknown_pub"],
+                    "references": reference_d,
+                    "references_text": reference_text_d,
+                    "glossary": {"acronym":glossary[:,0], "definition":glossary[:,1]} if len(glossary)>0 else {"acronym":[], "definition":[]},
+                    "n_references": n_ref,
+                    "pmid": data["PMID"],
+                    "accession_id": pmcid,
+                    "license": data["License"],
+                    "last_updated": data["LastUpdated (YYYY-MM-DD HH:MM:SS)"],
+                    "retracted": data["Retracted"],
+                    "citation": data["Article Citation"],
+                    "package_file": data["File"],
+                }
+                processed_ids.add(pmcid)
+                yield pmcid, data
+
+            #except FileNotFoundError:  # non-commercial PMC000xxxxxx baseline does not exist
+            #    continue
                 
-        # Incrementals
-        # if incremental_file_lists:
-            # for incremental_file_list, incremental_archive in zip(incremental_file_lists, incremental_archives):
-                # incrementals = pd.read_csv(incremental_file_list, index_col="Article File").to_dict(orient="index")
-                # for path, file in incremental_archive:
-                    # ...
-                    # 
-                    # yield key, data
+