def test_proportion_overlap(loader):
# Calculate proportion of each parcel overlapped by water.
parcels = loader.tables.sample.heather_farms
water = loader.tables.sample.hf_water
assert not hasattr(parcels, 'proportion_water')
spatialtoolz.proportion_overlap(parcels, water, 'proportion_water')
assert hasattr(parcels, 'proportion_water')
# Build DataFrame from columns of parcels table.
columns = [parcels.parcel_id, parcels.geom.ST_Area(),
parcels.proportion_water]
parcels_df = db_to_df(columns, index_col='parcel_id')
# Assert that proportion overlap values are between 0 and 1.
assert parcels_df.proportion_water.dtype == float
assert not (parcels_df.proportion_water < 0).any()
assert not (parcels_df.proportion_water > 1).any()
# Assert that sum of overlapped parcel area is <= total water area.
with loader.database.session() as sess:
overlapped_area = sess.query(
func.sum(parcels.proportion_water * parcels.geom.ST_Area())
).scalar()
water_area = sess.query(func.sum(water.geom.ST_Area())).scalar()
assert overlapped_area <= water_area
def test_proportion_overlap(loader):
# Calculate proportion of each parcel overlapped by water.
parcels = loader.tables.sample.heather_farms
water = loader.tables.sample.hf_water
assert not hasattr(parcels, 'proportion_water')
spatialtoolz.proportion_overlap(parcels, water, 'proportion_water')
assert hasattr(parcels, 'proportion_water')
# Build DataFrame from columns of parcels table.
columns = [
parcels.parcel_id,
parcels.geom.ST_Area(), parcels.proportion_water
]
parcels_df = db_to_df(columns, index_col='parcel_id')
# Assert that proportion overlap values are between 0 and 1.
assert parcels_df.proportion_water.dtype == float
assert not (parcels_df.proportion_water < 0).any()
assert not (parcels_df.proportion_water > 1).any()
# Assert that sum of overlapped parcel area is <= total water area.
with loader.database.session() as sess:
overlapped_area = sess.query(
func.sum(parcels.proportion_water *
parcels.geom.ST_Area())).scalar()
water_area = sess.query(func.sum(water.geom.ST_Area())).scalar()
assert overlapped_area <= water_area
def test_tag(loader):
# Tag parcels with block group ID.
parcels = loader.tables.sample.heather_farms
bg = loader.tables.sample.hf_bg
assert not hasattr(parcels, 'bg_id')
spatialtoolz.tag(parcels, 'bg_id', bg, 'objectid')
assert hasattr(parcels, 'bg_id')
# Build DataFrame from parcels and block groups tables.
parcels_df = db_to_df(parcels, index_col='parcel_id')
bg_df = db_to_df(bg, index_col='objectid')
# Assert that all parcels have integer block groups.
assert not parcels_df.bg_id.isnull().any()
assert np.issubdtype(parcels_df.bg_id.dtype, int)
# Assert that there are at least 10 unique parcel block groups.
parcels_bg_ids = parcels_df.bg_id.unique()
assert len(parcels_bg_ids) >= 10
# Assert that parcel block groups are a subset of all block groups.
assert np.all([bg_id in bg_df.index for bg_id in parcels_bg_ids])
def test_tag(loader):
# Tag parcels with block group ID.
parcels = loader.tables.sample.heather_farms
bg = loader.tables.sample.hf_bg
assert not hasattr(parcels, 'bg_id')
spatialtoolz.tag(parcels, 'bg_id', bg, 'objectid')
assert hasattr(parcels, 'bg_id')
# Build DataFrame from parcels and block groups tables.
parcels_df = db_to_df(parcels, index_col='parcel_id')
bg_df = db_to_df(bg, index_col='objectid')
# Assert that all parcels have integer block groups.
assert not parcels_df.bg_id.isnull().any()
assert np.issubdtype(parcels_df.bg_id.dtype, int)
# Assert that there are at least 10 unique parcel block groups.
parcels_bg_ids = parcels_df.bg_id.unique()
assert len(parcels_bg_ids) >= 10
# Assert that parcel block groups are a subset of all block groups.
assert np.all([bg_id in bg_df.index for bg_id in parcels_bg_ids])
def test_sim_export(loader):
# Try importing the UrbanSim simulation framework, otherwise skip test.
sim = pytest.importorskip('urbansim.sim.simulation')
# Register input parcels table.
parcels = loader.tables.sample.heather_farms
parcels_in = TableFrame(parcels, index_col='gid')
sim.add_table('parcels_in', parcels_in, copy_col=False)
# Register output parcels table.
@sim.table()
def parcels_out(parcels_in):
return pd.DataFrame(index=parcels_in.parcel_id)
# Specify default table for output columns as decorator.
out = sim.column('parcels_out')
# Specify some output columns.
@out
def apn(apn='parcels_in.puid'):
return apn.groupby(parcels_in.parcel_id).first().astype(str)
@out
def county_id():
return 13
@out
def area(acr='parcels_in.parcel_acr'):
return 4047. * acr.groupby(parcels_in.parcel_id).median()
# Register model to export output table to database.
@sim.model()
def export(parcels_out):
schema = loader.tables.sample
df_to_db(parcels_out.to_frame(), 'parcels_out', schema=schema)
# Inspect output table.
column_names = ['apn', 'county_id', 'area']
parcels_out_df1 = sim.get_table('parcels_out').to_frame()
assert set(parcels_out_df1.columns) == set(column_names)
assert parcels_out_df1.county_id.unique() == [13]
# Export table to database and import back to compare.
sim.run(['export'])
parcels_out_table = loader.tables.sample.parcels_out
parcels_out_df2 = db_to_df(parcels_out_table, index_col='parcel_id')
pdt.assert_frame_equal(parcels_out_df1[column_names],
parcels_out_df2[column_names])
