bugfix: date/timestamp conversions
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parent
febcaa5883
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0e4148d4e7
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@ -64,7 +64,7 @@ class Learner(Process):
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# self.logpath= _args['logpath'] if 'logpath' in _args else 'logs'
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# sel.max_epoc
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def log(self,**_args):
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self.lock.acquire()
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# self.lock.acquire()
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try:
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logger = transport.factory.instance(**self.store['logger']) if 'logger' in self.store else transport.factory.instance(provider='console',context='write',lock=True)
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_args = dict({'ndx':self.ndx,'module':self.name,'table':self.info['from'],'info':self.info['context'],**_args})
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@ -78,7 +78,8 @@ class Learner(Process):
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print (e)
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pass
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finally:
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self.lock.release()
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# self.lock.release()
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pass
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def get_schema(self):
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if self.store['source']['provider'] != 'bigquery' :
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return [{'name':self._df.dtypes.index.tolist()[i],'type':self._df.dtypes.astype(str).tolist()[i]}for i in range(self._df.dtypes.shape[0])]
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@ -222,7 +223,7 @@ class Generator (Learner):
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values[index] = values[index].astype(_type)
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x += values.tolist()
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if x :
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_log['input']['diff_pct'] = 100 * (1 - np.divide( (_df[name].dropna() == x).sum(),_df[name].dropna().size))
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_log['input']['identical_percentage'] = 100 * (1 - np.divide( (_df[name].dropna() == x).sum(),_df[name].dropna().size))
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_df[name] = x #np.array(x,dtype=np.int64) if 'int' in _type else np.arry(x,dtype=np.float64)
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self.log(**_log)
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@ -243,14 +244,15 @@ class Generator (Learner):
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day = np.random.randint(1,_end)
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#-- synthetic date
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_date = datetime(year=year,month=month,day=day)
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FORMAT = '%Y-%m-%d'
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if 'format' in self.info and 'field' in _args and _args['field'] in self.info['format']:
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_date = datetime(year=year,month=month,day=day) #,minute=0,hour=0,second=0)
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FORMAT = '%Y-%d-%m'
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_name = _args['field'] if 'field' in _args else None
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if 'format' in self.info and _name in self.info['format']:
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_name = _args['field']
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FORMAT = self.info['format'][_name]
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# print ([_name,FORMAT, _date.strftime(FORMAT)])
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r = []
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if offset :
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r = [_date.strftime(FORMAT)]
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@ -277,7 +279,7 @@ class Generator (Learner):
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r[name] = FORMAT
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_df[name] = pd.to_datetime(_df[name], format=FORMAT).astype(str) #.astype('datetime64[ns]')
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_df[name] = pd.to_datetime(_df[name], format=FORMAT).astype('datetime64[ns]')
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if r :
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self.log(**{'action':'format','input':r})
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return _df
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@ -314,6 +316,7 @@ class Generator (Learner):
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_schema = self.get_schema()
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_schema = [{'name':_item.name,'type':_item.field_type} for _item in _schema]
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_df = self.format(_df,_schema)
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writer.write(_df,schema=_schema)
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self.log(**{'action':'write','input':{'rows':N,'candidates':len(_candidates)}})
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