Apply Pre-commit fixes

Author: [email protected]

hypernetx/hif.py

@@ -29,22 +29,27 @@ def normalize_dataframe(df):
     pd.DataFrame
         allowed columns are limited to HIF keys
     """
-    default_cols = ['weight'] + list(set(df.columns).intersection(['direction'])) + ['misc_properties']
+    default_cols = (
+        ["weight"]
+        + list(set(df.columns).intersection(["direction"]))
+        + ["misc_properties"]
+    )
     cols = list(set(df.columns).difference(default_cols))
     dfdict = df[cols].T.to_dict()
     newdf = df[default_cols]
     for uid in newdf.index:
-        newdf.loc[uid]['misc_properties'].update(dfdict[uid])
-    return newdf.fillna('nil')
-                          
-def to_hif(hg,filename=None, network_type='undirected', metadata=None):
+        newdf.loc[uid]["misc_properties"].update(dfdict[uid])
+    return newdf.fillna("nil")
+
+
+def to_hif(hg, filename=None, network_type="undirected", metadata=None):
     """
     Returns a dictionary object valid for the HIF Json schema
 
     Parameters
     ----------
     hg : hnx.Hypergraph
-        
+
     filename : str, optional
         filepath where json object is to be stored, by default None
     network_type : str, optional
@@ -57,51 +62,58 @@ def to_hif(hg,filename=None, network_type='undirected', metadata=None):
     hif : dict
         format is defined by HIF schema
     """
-    hyp_objs = ['nodes','edges','incidences']
-    defaults = {part:dict(getattr(hg,part).property_store._defaults) for part in hyp_objs}
+    hyp_objs = ["nodes", "edges", "incidences"]
+    defaults = {
+        part: dict(getattr(hg, part).property_store._defaults) for part in hyp_objs
+    }
     for part in hyp_objs:
-        misc_properties = defaults[part].pop('misc_properties',{})
-        defaults[part]['attrs'] = dict(misc_properties)
-    
+        misc_properties = defaults[part].pop("misc_properties", {})
+        defaults[part]["attrs"] = dict(misc_properties)
+
     incj = deepcopy(hg.incidences.to_dataframe)
-    incj.index.names = ['edge','node']
+    incj.index.names = ["edge", "node"]
     incj = normalize_dataframe(incj)
-    incj = incj.rename(columns={"misc_properties":"attrs"})
+    incj = incj.rename(columns={"misc_properties": "attrs"})
     incj = incj.reset_index().to_dict(orient="records")
-    
+
     edgj = deepcopy(hg.edges.to_dataframe)
-    edgj.index.names = ['edge']
+    edgj.index.names = ["edge"]
     edgj = normalize_dataframe(edgj)
-    edgj = edgj.rename(columns={"misc_properties":"attrs"})
+    edgj = edgj.rename(columns={"misc_properties": "attrs"})
     edgj = edgj.reset_index().to_dict(orient="records")
-    
+
     nodj = deepcopy(hg.nodes.to_dataframe)
-    nodj.index.names = ['node']
+    nodj.index.names = ["node"]
     nodj = normalize_dataframe(nodj)
-    nodj = nodj.rename(columns={"misc_properties":"attrs"})
+    nodj = nodj.rename(columns={"misc_properties": "attrs"})
     nodj = nodj.reset_index().to_dict(orient="records")
 
-    if isinstance(metadata,dict):
-        metadata = metadata.update({'default_attrs':defaults})
+    if isinstance(metadata, dict):
+        metadata = metadata.update({"default_attrs": defaults})
     else:
-        metadata = {'default_attrs':defaults}
+        metadata = {"default_attrs": defaults}
     if hg.name is not None:
-        metadata['name'] = hg.name
-    
-    hif = {"edges": edgj, "nodes": nodj, "incidences": incj, "network-type": network_type, 
-           "metadata": metadata}
+        metadata["name"] = hg.name
+
+    hif = {
+        "edges": edgj,
+        "nodes": nodj,
+        "incidences": incj,
+        "network-type": network_type,
+        "metadata": metadata,
+    }
     try:
         validator(hif)
-        if filename is  not None:
-            json.dump(hif,open(filename,'w'))
+        if filename is not None:
+            json.dump(hif, open(filename, "w"))
         return hif
     except Exception as ex:
         HyperNetXError(ex)
 
 
 def from_hif(hif=None, filename=None):
     """
-    Reads HIF formatted string or dictionary and returns corresponding 
+    Reads HIF formatted string or dictionary and returns corresponding
     hnx.Hypergraph
 
     Parameters
@@ -116,7 +128,7 @@ def from_hif(hif=None, filename=None):
     Returns
     -------
     hnx.Hypergraph
-        
+
     """
     if hif is not None:
         try:
@@ -125,48 +137,59 @@ def from_hif(hif=None, filename=None):
             HyperNetXError(ex)
             return None
     elif filename is not None:
-        hif = json.load(open(filename,'r'))
+        hif = json.load(open(filename, "r"))
         try:
             validator(hif)
         except Exception as ex:
             HyperNetXError(ex)
-            return None      
+            return None
     else:
-        print('No data given')  
-    
-    mkdd = lambda : {'weight':1, 'attrs':{}}
+        print("No data given")
+
+    mkdd = lambda: {"weight": 1, "attrs": {}}
     hifex = deepcopy(hif)
-    parts = {part:deepcopy(pd.DataFrame(hifex.get(part,{}))) for part in ['nodes','edges','incidences']} 
-    metadata = hifex.get('metadata',{})   
-    defaults = metadata.get('default_attrs',{})
-    defaults = {part: defaults.get(part,mkdd()) for part in parts}
+    parts = {
+        part: deepcopy(pd.DataFrame(hifex.get(part, {})))
+        for part in ["nodes", "edges", "incidences"]
+    }
+    metadata = hifex.get("metadata", {})
+    defaults = metadata.get("default_attrs", {})
+    defaults = {part: defaults.get(part, mkdd()) for part in parts}
     # cols = dict()
-    default_weights = {part:defaults[part].get('weight',1) for part in parts}
+    default_weights = {part: defaults[part].get("weight", 1) for part in parts}
     for part in parts:
         if len(part) == 0:
             continue
         thispart = parts[part]
         d = deepcopy(defaults[part])
-        dkeys = [k for k in d.keys() if k not in ['weight','attrs']]
+        dkeys = [k for k in d.keys() if k not in ["weight", "attrs"]]
         # cols[part] = ['weight'] + dkeys + ['attrs']
         if len(dkeys) > 0:
             for attr in dkeys:
-                thispart[attr] = [row.attrs.pop(attr,d[attr]) for row in thispart.itertuples()]
+                thispart[attr] = [
+                    row.attrs.pop(attr, d[attr]) for row in thispart.itertuples()
+                ]
     hyp_objects = dict()
-    for part in ['nodes','edges']:
+    for part in ["nodes", "edges"]:
         if len(parts[part]) > 0:
             uid = part[:-1]
             cols = [uid] + list(set(parts[part].columns).difference([uid]))
             hyp_objects[part] = parts[part][cols]
         else:
             hyp_objects[part] = None
-    cols = ['edge','node'] + list(set(parts['incidences'].columns).difference(['edge','node']))
-    incidences = parts['incidences'][cols]
-    name = metadata.get('name',None)          
-    return hnx.Hypergraph(incidences, default_cell_weight=default_weights['incidences'],
-                          misc_cell_properties_col='attrs',
-                          node_properties=hyp_objects['nodes'], default_edge_weight=default_weights['edges'],
-                          edge_properties=hyp_objects['edges'], default_node_weight=default_weights['nodes'],
-                          misc_properties_col='attrs',
-                          name=name
-                          )
+    cols = ["edge", "node"] + list(
+        set(parts["incidences"].columns).difference(["edge", "node"])
+    )
+    incidences = parts["incidences"][cols]
+    name = metadata.get("name", None)
+    return hnx.Hypergraph(
+        incidences,
+        default_cell_weight=default_weights["incidences"],
+        misc_cell_properties_col="attrs",
+        node_properties=hyp_objects["nodes"],
+        default_edge_weight=default_weights["edges"],
+        edge_properties=hyp_objects["edges"],
+        default_node_weight=default_weights["nodes"],
+        misc_properties_col="attrs",
+        name=name,
+    )

hypernetx/utils/toys/lesmis.py

@@ -42,7 +42,7 @@ def __init__(self):
     @property
     def dnames(self):
         return self.df_names.set_index("Symbol")
-    
+
     def hypergraph_example(self):
 
         names = self.df_names
@@ -62,11 +62,11 @@ def hypergraph_example(self):
             cell_weight_col="weight",
             node_properties=nprops,
             node_weight_prop_col="weight",
-            name="LesMis example from HNX"
+            name="LesMis example from HNX",
         )
-        lm.nodes['JV'].job = 'mayor'
-        lm.nodes['MY'].avocation = 'to be kind'
-        lm.nodes['BS'].vocation = 'explorer'
+        lm.nodes["JV"].job = "mayor"
+        lm.nodes["MY"].avocation = "to be kind"
+        lm.nodes["BS"].vocation = "explorer"
         return lm
 
 
@@ -80,6 +80,7 @@ def lesmis_hypergraph_from_df(df, by="Chapter", on="Characters"):
         }
     )
 
+
 def lesmis_hypergraph():
     lesmis = LesMis()
     names = lesmis.df_names
@@ -100,11 +101,10 @@ def lesmis_hypergraph():
         node_properties=nprops,
         node_weight_prop_col="weight",
     )
-    lm.nodes['JV'].job = 'mayor'
-    lm.nodes['MY'].avocation = 'to be kind'
-    lm.nodes['BS'].vocation = 'explorer'
+    lm.nodes["JV"].job = "mayor"
+    lm.nodes["MY"].avocation = "to be kind"
+    lm.nodes["BS"].vocation = "explorer"
     return lm
-     
 
 
 def book_tour(df, xlabel="Book", ylabel="Volume", s=3.5):