How to use the flowmachine.core.make_spatial_unit function in flowmachine

def __init__(
        self,
        *,
        meaningful_locations: MeaningfulLocations,
        spatial_unit: Optional[AnySpatialUnit] = None,
    ) -> None:
        self.meaningful_locations = meaningful_locations
        if spatial_unit is None:
            self.spatial_unit = make_spatial_unit("admin", level=3)
        else:
            self.spatial_unit = spatial_unit
        self.spatial_unit.verify_criterion("is_polygon")
        super().__init__()

def __init__(
        self,
        *,
        meaningful_locations_a: MeaningfulLocations,
        meaningful_locations_b: MeaningfulLocations,
        spatial_unit: Optional[AnySpatialUnit] = None,
    ) -> None:
        self.flow = meaningful_locations_a.join(
            meaningful_locations_b,
            on_left="subscriber",
            left_append="_from",
            right_append="_to",
        )
        if spatial_unit is None:
            self.spatial_unit = make_spatial_unit("admin", level=3)
        else:
            self.spatial_unit = spatial_unit
        self.spatial_unit.verify_criterion("is_polygon")
        super().__init__()

# The purpose is to speed up execution of the example notebooks,
# by caching some of the queries beforehand (which is currently
# not possible using flowclient directly).

import flowmachine
import pandas as pd
import networkx as nx
import concurrent.futures
from flowmachine.core.errors import UnstorableQueryError

flowmachine.connect()

print("Constructing query objects")

admin1_spatial_unit = flowmachine.core.make_spatial_unit("admin", level=1)
admin3_spatial_unit = flowmachine.core.make_spatial_unit("admin", level=3)
vsite_spatial_unit = flowmachine.core.make_spatial_unit("versioned-site")
vcell_spatial_unit = flowmachine.core.make_spatial_unit("versioned-cell")

# FlowClient example usage
example_usage_queries = [
    flowmachine.features.utilities.spatial_aggregates.SpatialAggregate(
        locations=flowmachine.features.daily_location(
            date="2016-01-01", spatial_unit=admin3_spatial_unit, method="last"
        )
    ),
    flowmachine.features.utilities.spatial_aggregates.SpatialAggregate(
        locations=flowmachine.features.ModalLocation(
            *[
                flowmachine.features.daily_location(
                    date=dl_date, spatial_unit=admin3_spatial_unit, method="last"
                )

# This file is executed in build.sh before building the docs.
# The purpose is to speed up execution of the example notebooks,
# by caching some of the queries beforehand (which is currently
# not possible using flowclient directly).

import flowmachine
import pandas as pd
import networkx as nx
import concurrent.futures
from flowmachine.core.errors import UnstorableQueryError

flowmachine.connect()

print("Constructing query objects")

admin1_spatial_unit = flowmachine.core.make_spatial_unit("admin", level=1)
admin3_spatial_unit = flowmachine.core.make_spatial_unit("admin", level=3)
vsite_spatial_unit = flowmachine.core.make_spatial_unit("versioned-site")
vcell_spatial_unit = flowmachine.core.make_spatial_unit("versioned-cell")

# FlowClient example usage
example_usage_queries = [
    flowmachine.features.utilities.spatial_aggregates.SpatialAggregate(
        locations=flowmachine.features.daily_location(
            date="2016-01-01", spatial_unit=admin3_spatial_unit, method="last"
        )
    ),
    flowmachine.features.utilities.spatial_aggregates.SpatialAggregate(
        locations=flowmachine.features.ModalLocation(
            *[
                flowmachine.features.daily_location(
                    date=dl_date, spatial_unit=admin3_spatial_unit, method="last"

def __init__(
        self,
        start,
        stop,
        *,
        spatial_unit: AnySpatialUnit = make_spatial_unit("cell"),
        hours="all",
        tables="all",
        subscriber_identifier="msisdn",
        ignore_nulls=True,
        subscriber_subset=None,
    ):

        self.ul = UniqueLocations(
            SubscriberLocations(
                start=start,
                stop=stop,
                spatial_unit=spatial_unit,
                hours=hours,
                table=tables,
                subscriber_identifier=subscriber_identifier,
                ignore_nulls=ignore_nulls,

# by caching some of the queries beforehand (which is currently
# not possible using flowclient directly).

import flowmachine
import pandas as pd
import networkx as nx
import concurrent.futures
from flowmachine.core.errors import UnstorableQueryError

flowmachine.connect()

print("Constructing query objects")

admin1_spatial_unit = flowmachine.core.make_spatial_unit("admin", level=1)
admin3_spatial_unit = flowmachine.core.make_spatial_unit("admin", level=3)
vsite_spatial_unit = flowmachine.core.make_spatial_unit("versioned-site")
vcell_spatial_unit = flowmachine.core.make_spatial_unit("versioned-cell")

# FlowClient example usage
example_usage_queries = [
    flowmachine.features.utilities.spatial_aggregates.SpatialAggregate(
        locations=flowmachine.features.daily_location(
            date="2016-01-01", spatial_unit=admin3_spatial_unit, method="last"
        )
    ),
    flowmachine.features.utilities.spatial_aggregates.SpatialAggregate(
        locations=flowmachine.features.ModalLocation(
            *[
                flowmachine.features.daily_location(
                    date=dl_date, spatial_unit=admin3_spatial_unit, method="last"
                )
                for dl_date in pd.date_range("2016-01-01", "2016-01-03", freq="D")

# Rename the columns to match what we'll join to
            # sort the dataframe so we'll have a consistent md5
            self.departure_rate = departure_rate.rename(
                columns=lambda x: x if x == "rate" else f"{x}_from"
            ).apply(lambda x: x.sort_values().values)
            self.departure_rate = self.departure_rate.reindex(
                columns=sorted(self.departure_rate.columns)
            )
        elif isinstance(departure_rate, float):
            self.departure_rate = departure_rate
        else:
            raise TypeError(f"{departure_rate} must be a float or dataframe")
        self.start = standardise_date(start)
        self.stop = standardise_date(stop)
        if spatial_unit is None:
            self.spatial_unit = make_spatial_unit("versioned-site")
        else:
            self.spatial_unit = spatial_unit
        self.distance_matrix = DistanceMatrix(
            spatial_unit=self.spatial_unit, return_geometry=True
        )

        self.population_object = ModalLocation(
            *[
                daily_location(
                    d,
                    spatial_unit=self.spatial_unit,
                    hours=hours,
                    method=method,
                    table=table,
                    subscriber_identifier=subscriber_identifier,
                    ignore_nulls=True,

self,
        start,
        stop,
        spatial_unit: Optional[AnySpatialUnit] = None,
        hours="all",
        table="all",
        subscriber_identifier="msisdn",
        *,
        ignore_nulls=True,
        subscriber_subset=None,
    ):

        self.start = standardise_date(start)
        self.stop = standardise_date(stop)
        if spatial_unit is None:
            self.spatial_unit = make_spatial_unit("admin", level=3)
        else:
            self.spatial_unit = spatial_unit
        self.hours = hours
        self.table = table
        self.subscriber_identifier = subscriber_identifier
        self.subscriber_locs = SubscriberLocations(
            start=self.start,
            stop=self.stop,
            spatial_unit=self.spatial_unit,
            hours=self.hours,
            table=self.table,
            subscriber_identifier=self.subscriber_identifier,
            ignore_nulls=ignore_nulls,
            subscriber_subset=subscriber_subset,
        )
        super().__init__()

ignore_nulls=True,
        subscriber_subset=None,
    ):

        self.unit = unit.lower()
        if unit not in self.allowed_units:
            raise ValueError(
                f"Unrecognised unit {unit}, use one of {self.allowed_units}"
            )

        self.start = standardise_date(start)
        self.stop = standardise_date(stop)
        self.ul = SubscriberLocations(
            self.start,
            self.stop,
            spatial_unit=make_spatial_unit("lon-lat"),
            hours=hours,
            table=table,
            subscriber_subset=subscriber_subset,
            subscriber_identifier=subscriber_identifier,
            ignore_nulls=ignore_nulls,
        )

        super().__init__()