import urban_mapper as um

# Get UrbanMapper rolling
mapper = um.UrbanMapper()

# Load data
# Note: For the documentation interactive mode, we only query 5000 records from the dataset.  Feel free to remove for a more realistic analysis.
data = (
    mapper
    .loader
    .from_huggingface("oscur/pluto", number_of_rows=5000, streaming=True).with_columns("longitude", "latitude").load()
    # From the loader module, from the following file within the HuggingFace OSCUR datasets hub and with the `longitude` and `latitude` or only `geometry`
)

# Create urban layer
layer = (
    mapper.urban_layer.with_type("streets_intersections")  # From the urban_layer module and with type streets_intersections
    .from_place("Downtown Brooklyn, New York City, USA") # From a place
    .build()
)

# Load CSV data
data1 = (
    mapper
    .loader
    .from_huggingface("oscur/pluto", number_of_rows=1000, streaming=True).with_columns("longitude", "latitude").load()
    # From the loader module, from the following file and with the `longitude` and `latitude` or only `geometry`
)

# Load Parquet data
data2 = (
    mapper
    .loader
    .from_huggingface("oscur/taxisvis1M", number_of_rows=1000, streaming=True) # To update with your own path
    .with_columns("pickup_longitude", "pickup_latitude").load() # Inform your long and lat columns or only geometry
)

data = {
  "pluto_data": data1,
  "taxi_data": data2,
}

# Apply filter
filtered_data = (
    mapper
    .filter # From the filter module
    .with_type("BoundingBoxFilter") # With type BoundingBoxFilter which is a filter that filters out your data points based on the bounding box of the layer
    .transform(data, layer) # Transform the data with the layer previously queried
)