bug fix: uploading data

data/maker/__init__.py

@@ -96,14 +96,17 @@ class Learner(Process):
         #
         # Below is a source of inefficiency, unfortunately python's type inference doesn't work well in certain cases
         # - The code below tries to address the issue (Perhaps better suited for the reading components)
+        _log = {}
         for name in columns :
             _index = np.random.choice(np.arange(self._df[name].size),5,False)
             no_value = [type(value) in [int,float,np.int64,np.int32,np.float32,np.float64] for value in self._df[name].values[_index]]            
             no_value = 0 if np.sum(no_value) > 0 else ''
             
             self._df[name] = self._df[name].fillna(no_value)
-
+            
-
+            _log[name] = self._df[name].dtypes.name
+        _log = {'action':'structure','input':_log}
+        self.log(**_log)
         #
         # convert the data to binary here ...
         _schema = self.get_schema()       
@@ -293,46 +296,52 @@ class Generator (Learner):
             name = _item['name']
             
             if _item['type'].upper() in ['DATE','DATETIME','TIMESTAMP'] :
-                FORMAT = '%Y-%m-%d'
+                FORMAT = '%m-%d-%Y'
                 
-                try:
+                # try:
-                    #
+                #     #
-                    #-- Sometimes data isn't all it's meant to be
+                #     #-- Sometimes data isn't all it's meant to be
-                    SIZE = -1
+                #     SIZE = -1
-                    if 'format' in self.info and name in self.info['format'] :
+                #     if 'format' in self.info and name in self.info['format'] :
-                        FORMAT = self.info['format'][name]
+                #         FORMAT = self.info['format'][name]
-                        SIZE = 10
+                #         SIZE = 10
-                    elif _item['type'] in ['DATETIME','TIMESTAMP'] :
+                #     elif _item['type'] in ['DATETIME','TIMESTAMP'] :
-                            FORMAT = '%Y-%m-%d %H:%M:%S'
+                #             FORMAT = '%m-%d-%Y %H:%M:%S'
-                            SIZE = 19
+                #             SIZE = 19
                     
-                    if SIZE > 0 :
+                #     if SIZE > 0 :
+                        
+                #         values = pd.to_datetime(_df[name], format=FORMAT).astype(str)
+                #         _df[name] = [_date[:SIZE].strip() for _date in values]
                         
-                        values = pd.to_datetime(_df[name], format=FORMAT).astype(str)
-                        _df[name] = [_date[:SIZE] for _date in values]
                        
+                #     # _df[name] = _df[name].astype(str)
+                #     r[name] = FORMAT
+                #     # _df[name] = pd.to_datetime(_df[name], format=FORMAT) #.astype('datetime64[ns]')
+                #     if _item['type'] in ['DATETIME','TIMESTAMP']:                   
+                #         pass #;_df[name] = _df[name].fillna('').astype('datetime64[ns]')
                     
-                    r[name] = FORMAT
+                # except Exception as e:
-                    # _df[name] = pd.to_datetime(_df[name], format=FORMAT) #.astype('datetime64[ns]')
+                #     pass
-                    if _item['type'] in ['DATETIME','TIMESTAMP']:                   
+                # finally:
-                        pass #;_df[name] = _df[name].fillna('').astype('datetime64[ns]')
+                #     pass
-                    
-                except Exception as e:
-                    pass
-                finally:
-                    pass
             else:
                 
                 #
                 # Because types are inferred on the basis of the sample being processed they can sometimes be wrong
                 #   To help disambiguate we add the schema information
                 _type = None
+                
                 if 'int' in _df[name].dtypes.name or 'int' in _item['type'].lower():                    
                     _type = np.int
+                    
                 elif 'float' in _df[name].dtypes.name or 'float' in _item['type'].lower():
                     _type = np.float
                 if _type :
-                    _df[name] = _df[name].fillna(0).replace('',0).astype(_type)
+                    
+                    _df[name] = _df[name].fillna(0).replace('',0).replace('NA',0).replace('nan',0).astype(_type)
+                # else:
+                #     _df[name] = _df[name].astype(str)
         # _df = _df.replace('NaT','').replace('NA','')
         
         if r :
@@ -373,10 +382,19 @@ class Generator (Learner):
             _schema = self.get_schema()
             _schema = [{'name':_item.name,'type':_item.field_type} for _item in _schema]
             _df = self.format(_df,_schema)
+            _log = [{"name":_schema[i]['name'],"dataframe":_df[_df.columns[i]].dtypes.name,"schema":_schema[i]['type']} for i in np.arange(len(_schema)) ]
+            self.log(**{"action":"consolidate","input":_log})
+
+            # w = transport.factory.instance(doc='observation',provider='mongodb',context='write',db='IOV01_LOGS',auth_file='/home/steve/dev/transport/mongo.json')
+            # w.write(_df)
+            # print (_df[cols])
             
             writer = transport.factory.instance(**_store)
             writer.write(_df,schema=_schema)
-            # _df.to_csv('foo.csv')
+            
+            
+
+           
         
         self.log(**{'action':'write','input':{'rows':N,'candidates':len(_candidates)}})
 class Shuffle(Generator):