start_date = datetime.strptime("2020-04-01 00:00:30", "%Y-%m-%d %H:%M:%S")
end_date = datetime.strptime("2020-05-01 00:00:00", "%Y-%m-%d %H:%M:%S")
interval = timedelta(minutes=60)

xs = []
edge_indices = []
ys = []
y_indices = []
edge_features = []


while start_date <= end_date:
    # 0 - 60 min 
    current_snapshot = trips[((start_date + interval) >= trips["end_date"])
                                & (start_date <= trips["end_date"])]
    # 60 - 120 min
    subsequent_snapshot = trips[((start_date + 2*interval) >= trips["end_date"])
                                & (start_date + interval <= trips["end_date"])]


    current_snapshot = current_snapshot.groupby(["start_station_id", "end_station_id"]).mean().reset_index()
    subsequent_snapshot = subsequent_snapshot.groupby(["start_station_id", "end_station_id"]).mean().reset_index()

    edge_feats, additional_edge_index = extract_dynamic_edges(current_snapshot)
    exteneded_edge_index = np.concatenate([edge_index, additional_edge_index], axis=1)
    extended_edge_feats = np.concatenate([edge_feats, static_edge_features], axis=0)


    y = subsequent_snapshot["duration"].values
    y_index = subsequent_snapshot[["start_station_id", "end_station_id"]].values

    xs.append(node_features) 
    edge_indices.append(exteneded_edge_index)
    edge_features.append(extended_edge_feats)
    ys.append(y) 
    y_indices.append(y_index.transpose())

    start_date += interval