(self)
return self.response
List available models.
        This function is only meant to be used as a chart expression in Qlik.
List available models.
        This function is only meant to be used as a chart expression in Qlik.
List available models . This function is only meant to be used as a chart expression in Qlik .
def list_models(self):
        """
        List available models.
        This function is only meant to be used as a chart expression in Qlik.
        """
        
        # Interpret the request data based on the expected row and column structure
        row_template = ['strData']
        col_headers = ['search_pattern']
        
        # Create a Pandas Data Frame for the request data
        self.request_df = utils.request_df(self.request, row_template, col_headers)
        
        # Get the list of models based on the search pattern
        search_pattern = self.request_df.loc[0, 'search_pattern']
        
        # If the search pattern is empty default to all models
        if not search_pattern.strip():
            search_pattern = '*'
        
        # Get the list of models as a string
        models = "\n".join([str(p).split("\\")[-1] for p in list(pathlib.Path(self.path).glob(search_pattern))])
        
        # Prepare the output
        self.response = pd.Series(models)
        
        # Finally send the response
        return self.response
def list_models ( self ) : # Interpret the request data based on the expected row and column structure row_template = [ 'strData' ] col_headers = [ 'search_pattern' ] # Create a Pandas Data Frame for the request data self . request_df = utils . request_df ( self . request , row_template , col_headers ) # Get the list of models based on the search pattern search_pattern = self . request_df . loc [ 0 , 'search_pattern' ] # If the search pattern is empty default to all models if not search_pattern . strip ( ) : search_pattern = '*' # Get the list of models as a string models = "\n" . join ( [ str ( p ) . split ( "\\" ) [ - 1 ] for p in list ( pathlib . Path ( self . path ) . glob ( search_pattern ) ) ] ) # Prepare the output self . response = pd . Series ( models ) # Finally send the response return self . response