Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Test/nodes #57

Merged
merged 10 commits into from
Jul 14, 2023
Merged

Test/nodes #57

Changes from all commits
Commits
Show all changes
10 commits
Select commit Hold shift + click to select a range
b4a66f3
added test_buildings_intersecting_plan_area
levisweetbreu Jul 12, 2023
67732cd
added test_building_plan_area
levisweetbreu Jul 12, 2023
10b1ec8
fixed input
levisweetbreu Jul 12, 2023
defa6bc
fixed test function
levisweetbreu Jul 12, 2023
a6ab249
Merge branch 'feature/nodes' into test/nodes
levisweetbreu Jul 14, 2023
ae91158
fixed join style
levisweetbreu Jul 14, 2023
ea5be95
fixed shapely imports
levisweetbreu Jul 14, 2023
be861f6
fixed shapely imports
levisweetbreu Jul 14, 2023
e86a524
fixed shapely imports
levisweetbreu Jul 14, 2023
8bbdbc5
fixed missing element, column names
levisweetbreu Jul 14, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
315 changes: 313 additions & 2 deletions tests/test_nodes.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
import numpy as np
import geopandas as gpd
import pandas as pd
from shapely.geometry import Point, Polygon
from shapely.geometry import Point, Polygon, JOIN_STYLE
from typing import List


Expand Down Expand Up @@ -319,13 +319,324 @@ def test_average_distance_between_buildings(self):
default_street_width = Settings.DEFAULT_STREET_WIDTH

expected = pd.DataFrame(
{"id": ids, "distance": pd.Series([1.5, 2.0, 1.0, 3.0, default_street_width])}
{
Settings.id_field: ids,
Settings.average_distance_between_buildings: pd.Series(
[1.5, 2.0, 1.0, 3.0, default_street_width]
),
}
)

actual = nodes.average_distance_between_buildings(ids, distance)

pd.testing.assert_frame_equal(expected, actual)

def test_buildings_intersecting_plan_area(self):
"""Test that the function buildings_intersecting_plan_area returns the correct intersecting buildings."""

polygon1 = Polygon([[0, 0], [0, 1], [1, 1], [1, 0]])
polygon2 = Polygon([[3, 3], [3, 4], [4, 4], [4, 3]])
building_id = pd.Series([0, 1])
building_height = pd.Series([5, 10])
building_geometry = pd.Series([polygon1, polygon2])
building_area = pd.Series([polygon1.area, polygon2.area])
crs = "epsg:3857"

total_plan_area_geometry_no_overlap = pd.Series([polygon1.buffer(1), polygon2.buffer(1)])
total_plan_area_geometry_some_overlap = pd.Series(
[polygon1.buffer(3.5), polygon2.buffer(3.5)]
)
total_plan_area_geometry_total_overlap = pd.Series([polygon1.buffer(5), polygon2.buffer(5)])

no_overlap_output_gdf = gpd.GeoDataFrame(
{
"building_id_neighbor": building_id,
"building_id_target": building_id,
"building_height_target": building_height,
"building_area_target": building_area,
"building_geometry": building_geometry,
"building_buffered_target": gpd.GeoSeries(total_plan_area_geometry_no_overlap),
"index_neighbor": building_id,
"building_height_neighbor": building_height,
"building_area_neighbor": building_area,
"building_buffered_neighbor": total_plan_area_geometry_no_overlap,
"building_geometry_neighbor": gpd.GeoSeries(building_geometry),
},
geometry="building_geometry",
).set_index("building_id_neighbor")

some_overlap_output_gdf = gpd.GeoDataFrame(
{
"building_id_neighbor": pd.Series([0, 0, 1, 1]),
"building_id_target": pd.Series([0, 1, 0, 1]),
"building_height_target": pd.Series([5, 10, 5, 10]),
"building_area_target": pd.Series([1.0, 1.0, 1.0, 1.0]),
"building_geometry": gpd.GeoSeries(
[
building_geometry[0],
building_geometry[1],
building_geometry[0],
building_geometry[1],
]
),
"building_buffered_target": gpd.GeoSeries(
[
total_plan_area_geometry_some_overlap[0],
total_plan_area_geometry_some_overlap[1],
total_plan_area_geometry_some_overlap[0],
total_plan_area_geometry_some_overlap[1],
]
),
"index_neighbor": pd.Series([0, 0, 1, 1]),
"building_height_neighbor": pd.Series([5, 5, 10, 10]),
"building_area_neighbor": pd.Series([1.0, 1.0, 1.0, 1.0]),
"building_buffered_neighbor": pd.Series(
[
total_plan_area_geometry_some_overlap[0],
total_plan_area_geometry_some_overlap[0],
total_plan_area_geometry_some_overlap[1],
total_plan_area_geometry_some_overlap[1],
]
),
"building_geometry_neighbor": gpd.GeoSeries(
[
building_geometry[0],
building_geometry[0],
building_geometry[1],
building_geometry[1],
]
),
},
geometry="building_geometry",
).set_index("building_id_neighbor")

total_overlap_output_gdf = gpd.GeoDataFrame(
{
"building_id_neighbor": pd.Series([0, 0, 1, 1]),
"building_id_target": pd.Series([0, 1, 0, 1]),
"building_height_target": pd.Series([5, 10, 5, 10]),
"building_area_target": pd.Series([1.0, 1.0, 1.0, 1.0]),
"building_geometry": gpd.GeoSeries(
[
building_geometry[0],
building_geometry[1],
building_geometry[0],
building_geometry[1],
]
),
"building_buffered_target": gpd.GeoSeries(
[
total_plan_area_geometry_total_overlap[0],
total_plan_area_geometry_total_overlap[1],
total_plan_area_geometry_total_overlap[0],
total_plan_area_geometry_total_overlap[1],
]
),
"index_neighbor": pd.Series([0, 0, 1, 1]),
"building_height_neighbor": pd.Series([5, 5, 10, 10]),
"building_area_neighbor": pd.Series([1.0, 1.0, 1.0, 1.0]),
"building_buffered_neighbor": pd.Series(
[
total_plan_area_geometry_total_overlap[0],
total_plan_area_geometry_total_overlap[0],
total_plan_area_geometry_total_overlap[1],
total_plan_area_geometry_total_overlap[1],
]
),
"building_geometry_neighbor": gpd.GeoSeries(
[
building_geometry[0],
building_geometry[0],
building_geometry[1],
building_geometry[1],
]
),
},
geometry="building_geometry",
).set_index("building_id_neighbor")

@dataclass
class TestCase:
name: str
input: pd.Series
expected: gpd.GeoDataFrame

testcases = [
TestCase(
name="no overlap",
input=total_plan_area_geometry_no_overlap,
expected=no_overlap_output_gdf,
),
TestCase(
name="some overlap",
input=total_plan_area_geometry_some_overlap,
expected=some_overlap_output_gdf,
),
TestCase(
name="total overlap",
input=total_plan_area_geometry_total_overlap,
expected=total_overlap_output_gdf,
),
]

for case in testcases:
actual = nodes.buildings_intersecting_plan_area(
building_id, building_height, building_geometry, building_area, case.input, crs
)
expected = case.expected
pd.testing.assert_frame_equal(
expected,
actual,
"failed test {} expected {}, actual {}".format(case.name, expected, actual),
)

def test_building_plan_area(self):
"""Test that the function building_plan_area returns the correct building plan area."""

polygon1 = Polygon([[0, 0], [0, 1], [1, 1], [1, 0]])
polygon2 = Polygon([[3, 3], [3, 4], [4, 4], [4, 3]])
building_id = pd.Series([0, 1])
building_height = pd.Series([5, 10])
building_geometry = pd.Series([polygon1, polygon2])
building_area = pd.Series([polygon1.area, polygon2.area])

total_plan_area_geometry_no_overlap = pd.Series([polygon1.buffer(1), polygon2.buffer(1)])
total_plan_area_geometry_some_overlap = pd.Series(
[
polygon1.buffer(2.5, join_style=JOIN_STYLE.mitre),
polygon2.buffer(2.5, join_style=JOIN_STYLE.mitre),
]
)
total_plan_area_geometry_total_overlap = pd.Series([polygon1.buffer(5), polygon2.buffer(5)])

no_overlap_gdf = gpd.GeoDataFrame(
{
"building_id_neighbor": building_id,
"building_id_target": building_id,
"building_height_target": building_height,
"building_area_target": building_area,
"building_geometry": building_geometry,
"building_buffered_target": gpd.GeoSeries(total_plan_area_geometry_no_overlap),
"index_neighbor": building_id,
"building_height_neighbor": building_height,
"building_area_neighbor": building_area,
"building_buffered_neighbor": total_plan_area_geometry_no_overlap,
"building_geometry_neighbor": gpd.GeoSeries(building_geometry),
},
geometry="building_geometry",
).set_index("building_id_neighbor")

some_overlap_gdf = gpd.GeoDataFrame(
{
"building_id_neighbor": pd.Series([0, 0, 1, 1]),
"building_id_target": pd.Series([0, 1, 0, 1]),
"building_height_target": pd.Series([5, 10, 5, 10]),
"building_area_target": pd.Series([1.0, 1.0, 1.0, 1.0]),
"building_geometry": gpd.GeoSeries(
[
building_geometry[0],
building_geometry[1],
building_geometry[0],
building_geometry[1],
]
),
"building_buffered_target": gpd.GeoSeries(
[
total_plan_area_geometry_some_overlap[0],
total_plan_area_geometry_some_overlap[1],
total_plan_area_geometry_some_overlap[0],
total_plan_area_geometry_some_overlap[1],
]
),
"index_neighbor": pd.Series([0, 0, 1, 1]),
"building_height_neighbor": pd.Series([5, 5, 10, 10]),
"building_area_neighbor": pd.Series([1.0, 1.0, 1.0, 1.0]),
"building_buffered_neighbor": pd.Series(
[
total_plan_area_geometry_some_overlap[0],
total_plan_area_geometry_some_overlap[0],
total_plan_area_geometry_some_overlap[1],
total_plan_area_geometry_some_overlap[1],
]
),
"building_geometry_neighbor": gpd.GeoSeries(
[
building_geometry[0],
building_geometry[0],
building_geometry[1],
building_geometry[1],
]
),
},
geometry="building_geometry",
).set_index("building_id_neighbor")

total_overlap_gdf = gpd.GeoDataFrame(
{
"building_id_neighbor": pd.Series([0, 0, 1, 1]),
"building_id_target": pd.Series([0, 1, 0, 1]),
"building_height_target": pd.Series([5, 10, 5, 10]),
"building_area_target": pd.Series([1.0, 1.0, 1.0, 1.0]),
"building_geometry": gpd.GeoSeries(
[
building_geometry[0],
building_geometry[1],
building_geometry[0],
building_geometry[1],
]
),
"building_buffered_target": gpd.GeoSeries(
[
total_plan_area_geometry_total_overlap[0],
total_plan_area_geometry_total_overlap[1],
total_plan_area_geometry_total_overlap[0],
total_plan_area_geometry_total_overlap[1],
]
),
"index_neighbor": pd.Series([0, 0, 1, 1]),
"building_height_neighbor": pd.Series([5, 5, 10, 10]),
"building_area_neighbor": pd.Series([1.0, 1.0, 1.0, 1.0]),
"building_buffered_neighbor": pd.Series(
[
total_plan_area_geometry_total_overlap[0],
total_plan_area_geometry_total_overlap[0],
total_plan_area_geometry_total_overlap[1],
total_plan_area_geometry_total_overlap[1],
]
),
"building_geometry_neighbor": gpd.GeoSeries(
[
building_geometry[0],
building_geometry[0],
building_geometry[1],
building_geometry[1],
]
),
},
geometry="building_geometry",
).set_index("building_id_neighbor")

@dataclass
class TestCase:
name: str
input: gpd.GeoDataFrame
expected: List[float]

testcases = [
TestCase(name="no overlap", input=no_overlap_gdf, expected=[1.0, 1.0]),
TestCase(name="some overlap", input=some_overlap_gdf, expected=[1.25, 1.25]),
TestCase(name="total overlap", input=total_overlap_gdf, expected=[2.0, 2.0]),
]

for case in testcases:
actual = nodes.building_plan_area(case.input)
expected = pd.Series(case.expected)
pd.testing.assert_series_equal(
expected,
actual,
"failed test {} expected {}, actual {}".format(case.name, expected, actual),
)


if __name__ == "__main__":
unittest.main()