removed ' from horses, added weapon, added .gitignore

Author: matthewkumar

.gitignore

@@ -0,0 +1 @@
+.DS_Store

digital_manuscript.py

@@ -9,25 +9,24 @@ class BnF():
 
   def __init__(self, entry_list = [], apply_corrections = True):
     """ Initialize entire manuscript. If a list of IDs is given, narrow it down to them. """
-    complete_manuscript = generate_complete_manuscript(complete=False, apply_corrections=apply_corrections)
+    complete_manuscript = generate_complete_manuscript(apply_corrections=apply_corrections)
+    # complete_manuscript2 = generate_complete_manuscript2(apply_corrections=apply_corrections)
     if entry_list:
-      self.entries = [page for page in complete_manuscript if page.identity in entry_list]
+      self.entries = {i:e for i, e in complete_manuscript.items() if e.identity in entry_list}
     else:
       self.entries = complete_manuscript
 
-  def entry(self, identity: str):
+  def entry(self, identity: str = ''):
     """ Return entry with the given identity. """
-    for entry in self.entries:
-      if entry.identity == identity:
-        return entry
+    return self.entries.get(identity)
 
   search_type = Optional[Union[str, bool]]
   def search(self, animal: search_type = None, body_part: search_type = None, currency: search_type = None,
              definition: search_type = None, environment: search_type = None, material: bool = None,
              medical: search_type = None, measurement: search_type = None, music: search_type = None,
              plant: search_type = None, place: search_type = None, personal_name: search_type = None,
              profession: search_type = None, sensory: search_type = None, tool: search_type = None,
-             time: search_type = None) -> List[str]:
+             time: search_type = None, weapon: search_type = None) -> List[str]:
     """
     Search through each entry and return the identities that satisfy the criterion. Arguments are each of the element
     types for a manuscript entry, which can be a string, bool, or None. If the argument is a string, then results must have
@@ -38,24 +37,28 @@ def search(self, animal: search_type = None, body_part: search_type = None, curr
     args = {'animal': animal, 'body_part': body_part, 'currency': currency, 'definition': definition,
             'environment': environment, 'material': material, 'medical': medical, 'measurement': measurement,
             'music': music, 'plant': plant, 'place': place, 'personal_name': personal_name, 'profession': profession,
-            'sensory': sensory, 'tool': tool, 'time': time}
+            'sensory': sensory, 'tool': tool, 'time': time, 'weapon': weapon}
 
     versions = ['tc', 'tcn', 'tl']
     results = self.entries # initialize results
     search_bools = [k for k, v in args.items() if isinstance(v, bool)] # select bool element categories
     search_strings = [k for k, v in args.items() if isinstance(v, str)] # select string element categories
 
     for s in search_bools: # filter by each bool
-      results = [r for r in results if any(r.get_prop(s, v) for v in versions)]
+      results = {i: r for i, r in results.items() if any(r.get_prop(s, v) for v in versions)}
     for s in search_strings: # filter by each string
-      results = [r for r in results if any(args[s] in r.get_prop(s, v) for v in versions)]
+      results = {i: r for i, r in results if any(args[s] in r.get_prop(s, v) for v in versions)}
 
-    return([r.identity for r in results]) # return identities
+    return([i for i, r in results.items()]) # return identities
+
+  def search_margins(self, version: str, term: str, placement: str) -> List[str]:
+    return [i for i, entry in self.entries.items()
+            if any(margin[0] == placement and term in margin[1] for margin in entry.margins[version])]
 
   def tablefy(self):
     # id, head, no. words, category, amount of each tag, margins
     # include figure margins?
-    df = pd.DataFrame(columns=['entry'], data=self.entries)
+    df = pd.DataFrame(columns=['entry'], data=self.entries.values())
     df['identity'] = df.entry.apply(lambda x: x.identity)
     df['title'] = df.entry.apply(lambda x: x.title['tl'])
     df['length'] = df.entry.apply(lambda x: x.length['tl'])
@@ -64,3 +67,6 @@ def tablefy(self):
     df['del_tags'] = df.entry.apply(lambda x: len(x.del_tags))
     df = df.drop(columns=['entry'])
     return df
+
+manuscript = BnF()
+# print(manuscript.search_margins('tl', 'gold', 'left-middle'))

jsonify.py

@@ -14,7 +14,7 @@
 
 properties = ['animal', 'body_part', 'currency', 'definition', 'environment', 'material',
               'medical', 'measurement', 'music', 'plant', 'place', 'personal_name',
-              'profession', 'sensory', 'tool', 'time']
+              'profession', 'sensory', 'tool', 'time', 'weapon']
 
 def read_csvs() -> Dict[str, pd.DataFrame]:
   """
@@ -39,8 +39,8 @@ def read_manuscript(manuscript: BnF, df_dict: Dict[str, pd.DataFrame]):
   Output: df_dict -- a dict where they keys are an element of properties and the value is the thesaurus
   DataFrame for that property.
   """
-  for i in tqdm(range(len(manuscript.entries))):
-    entry = manuscript.entries[i]
+  for identity in tqdm(manuscript.entries.keys()):
+    entry = manuscript.entries[identity]
     for prop in properties:
       df = df_dict[prop]
       prop_list = entry.get_prop(prop, 'tl')
@@ -129,7 +129,6 @@ def jsonify():
   if not os.path.exists('thesaurus'):
     print('Thesaurus not found. Generating now.')
     os.system('python thesaurus.py')
-    print('Finished Generating Thesaurus')
 
   manuscript = BnF(apply_corrections=False)
   df_dict = read_csvs()