parameters: (self, x_col, y_col, mapping_distance=None)
return_statement: return df.node_id
docstring: Assign node_ids to data specified by x_col and y_col.
  
          Parameters
          ----------
          x_col : pandas.Series (float)
              A Pandas Series where values specify the x (e.g. longitude)
              location of dataset.
          y_col : pandas.Series (float)
              A Pandas Series where values specify the y (e.g. latitude)
              location of dataset.  x_col and y_col should use the same index.
          mapping_distance : float, optional
              The maximum distance that will be considered a match between the
              x, y data and the nearest node in the network.  This will usually
              be a distance unit in meters however if you have customized the
              impedance this could be in other units such as utility or time
              etc. If not specified, every x, y coordinate will be mapped to
              the nearest node.
  
          Returns
          -------
          node_ids : pandas.Series (int)
              Returns a Pandas Series of node_ids for each x, y in the
              input data. The index is the same as the indexes of the
              x, y input data, and the values are the mapped node_ids.
              If mapping distance is not passed and if there are no nans in the
              x, y data, this will be the same length as the x, y data.
              If the mapping is imperfect, this function returns all the
              input x, y's that were successfully mapped to node_ids.
docstring_summary: Assign node_ids to data specified by x_col and y_col.
docstring_tokens: Assign node_ids to data specified by x_col and y_col .
function: def get_node_ids(self, x_col, y_col, mapping_distance=None):
          """
          Assign node_ids to data specified by x_col and y_col.
  
          Parameters
          ----------
          x_col : pandas.Series (float)
              A Pandas Series where values specify the x (e.g. longitude)
              location of dataset.
          y_col : pandas.Series (float)
              A Pandas Series where values specify the y (e.g. latitude)
              location of dataset.  x_col and y_col should use the same index.
          mapping_distance : float, optional
              The maximum distance that will be considered a match between the
              x, y data and the nearest node in the network.  This will usually
              be a distance unit in meters however if you have customized the
              impedance this could be in other units such as utility or time
              etc. If not specified, every x, y coordinate will be mapped to
              the nearest node.
  
          Returns
          -------
          node_ids : pandas.Series (int)
              Returns a Pandas Series of node_ids for each x, y in the
              input data. The index is the same as the indexes of the
              x, y input data, and the values are the mapped node_ids.
              If mapping distance is not passed and if there are no nans in the
              x, y data, this will be the same length as the x, y data.
              If the mapping is imperfect, this function returns all the
              input x, y's that were successfully mapped to node_ids.
          """
          xys = pd.DataFrame({'x': x_col, 'y': y_col})
  
          distances, indexes = self.kdtree.query(xys.values)
          indexes = np.transpose(indexes)[0]
          distances = np.transpose(distances)[0]
  
          node_ids = self.nodes_df.iloc[indexes].index
  
          df = pd.DataFrame({"node_id": node_ids, "distance": distances},
                            index=xys.index)
  
          if mapping_distance is not None:
              df = df[df.distance <= mapping_distance]
  
          return df.node_id
function_tokens: def get_node_ids ( self , x_col , y_col , mapping_distance = None ) : xys = pd . DataFrame ( { 'x' : x_col , 'y' : y_col } ) distances , indexes = self . kdtree . query ( xys . values ) indexes = np . transpose ( indexes ) [ 0 ] distances = np . transpose ( distances ) [ 0 ] node_ids = self . nodes_df . iloc [ indexes ] . index df = pd . DataFrame ( { "node_id" : node_ids , "distance" : distances } , index = xys . index ) if mapping_distance is not None : df = df [ df . distance <= mapping_distance ] return df . node_id