class_docstr: A traffic forecasting dataset as described in Diffusion Convolution Layer Paper.
  
  This traffic dataset is collected by California Transportation Agencies (CalTrans)
  Performance Measurement System (PeMS). It is represented by a network of 325 traffic sensors
  in the Bay Area with 6 months of traffic readings ranging from Jan 1st 2017 to May 31th 2017
  in 5 minute intervals.
  
  For details see: `"Diffusion Convolutional Recurrent Neural Network:
  Data-Driven Traffic Forecasting" <https://arxiv.org/abs/1707.01926>`_
code: def _read_web_data(self):
      url = "https://graphmining.ai/temporal_datasets/PEMS-BAY.zip"
  
      # Check if zip file is in data folder from working directory, otherwise download
      if not os.path.isfile(
          os.path.join(self.raw_data_dir, "PEMS-BAY.zip")
      ):  # pragma: no cover
          if not os.path.exists(self.raw_data_dir):
              os.makedirs(self.raw_data_dir)
          self._download_url(
              url, os.path.join(self.raw_data_dir, "PEMS-BAY.zip")
          )
  
      if not os.path.isfile(
          os.path.join(self.raw_data_dir, "pems_adj_mat.npy")
      ) or not os.path.isfile(
          os.path.join(self.raw_data_dir, "pems_node_values.npy")
      ):  # pragma: no cover
          with zipfile.ZipFile(
              os.path.join(self.raw_data_dir, "PEMS-BAY.zip"), "r"
          ) as zip_fh:
              zip_fh.extractall(self.raw_data_dir)
  
      A = np.load(os.path.join(self.raw_data_dir, "pems_adj_mat.npy"))
      X = np.load(
          os.path.join(self.raw_data_dir, "pems_node_values.npy")
      ).transpose((1, 2, 0))
      X = X.astype(np.float32)
  
      # Normalise as in DCRNN paper (via Z-Score Method)
      means = np.mean(X, axis=(0, 2))
      X = X - means.reshape(1, -1, 1)
      stds = np.std(X, axis=(0, 2))
      X = X / stds.reshape(1, -1, 1)
  
      self.A = torch.from_numpy(A)
      self.X = torch.from_numpy(X)
  
file_path: pytorch_geometric_temporal/torch_geometric_temporal/dataset/pems_bay.py
imports: import os
  import urllib
  import zipfile
  import numpy
  import torch
  from torch_geometric.utils import dense_to_sparse
  from ..signal import StaticGraphTemporalSignal
label_desc: Use image node_id 3 for calling the PemsBayDatasetLoader obj's underlying member method code with example usage: obj._read_web_data() without return types