Solar mask model

This notebook demonstrates how to use the solar mask model

from buildingmodel import data_path
from buildingmodel.main import Parameters, load_data, run_inference, run_creation
from buildingmodel.io.gis import load_data as load_gis_data
from buildingmodel.io.climate import load_data as load_climate_data
from buildingmodel.models.climate import run_models as run_climate_models
from buildingmodel.creation.boundary import run_creation as run_boundary_creation
from buildingmodel.models.solar_masks import run_models as run_solar_mask_models
from buildingmodel.models.solar_gains import run_models as run_solar_gain_models
from pathlib import Path
import pandas as pd
import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt

Loading gis data and running the boundary creation model are required before running the solar mask model (the masks are calculated at the center of each exterior wall and roof)

parameters = Parameters(
    districts="districts_test_sample.gpkg",
    district_level_census="district_level_census_test_sample.hdf",
    district_level_diagnosis="district_level_diagnosis_data_test_sample.hdf",
    gas_network_route="gas_network_route_sample.gpkg",
    simplification_tolerance=0.1,
)

building_data = Path(data_path["gis"]) / "testing" / "test_sample.gpkg"
climate_data = Path(data_path["climate"]) / "Brest.epw"

buildings, climate, metadata = load_data(building_data, climate_data, parameters)
buildings, boundaries, dwellings = run_creation(buildings, parameters)
dwellings, buildings = run_inference(buildings, dwellings, boundaries, parameters)
run_climate_models(climate, metadata, parameters)

/home/yassine/miniconda3/envs/buildingmodel_dev/lib/python3.8/site-packages/topojson/core/cut.py:112: ShapelyDeprecationWarning: STRtree will be changed in 2.0.0 and will not be compatible with versions < 2.
  tree_splitter = STRtree(mp)

The mask model takes as input the building and boundary GeoDataframes, as well as optional parameters grid_resolution (in meters) and angular_resolution (in degrees)

parameters.grid_resolution = 0.2
parameters.angular_resolution = 10.0
parameters.bbox_filter = 100.0

boundaries = run_solar_mask_models(buildings, boundaries, parameters)

The results of the mask model are concatenated to the boundary GeoDataframe passed as input. The number of columns containing mask results depends on the angular resolution.

mask_columns = [
    col_name for col_name in boundaries.columns if col_name.startswith("mask_")
]

boundaries[mask_columns].head(10)

	mask_0	mask_1	mask_2	mask_3	mask_4	mask_5	mask_6	mask_7	mask_8	mask_9	...	mask_27	mask_28	mask_29	mask_30	mask_31	mask_32	mask_33	mask_34	mask_35	mask_36
0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	90.000000	90.000000	90.000000	19.655833	20.737675	21.306570	85.532880	0.000000	0.000000	0.0
1	90.0	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	...	7.725851	7.884824	9.086201	10.014632	10.645363	10.931181	10.964663	14.798831	22.270093	0.0
2	90.0	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	...	28.571591	11.547256	9.941625	9.764441	10.362783	10.653227	10.679292	10.616735	10.262963	0.0
3	90.0	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	...	45.177657	50.875262	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.0
4	90.0	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	...	37.754538	34.215829	30.585186	25.044680	18.883746	13.159557	13.197851	13.121730	12.668176	0.0
5	90.0	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	...	0.000000	0.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.0
6	90.0	90.000000	0.000000	0.000000	0.000000	0.000000	89.330561	14.502335	14.015015	13.060362	...	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.0
36	0.0	10.607839	12.195332	13.307794	14.199923	14.601427	14.665498	14.578485	13.989155	13.228434	...	90.000000	90.000000	90.000000	13.717217	14.618307	14.993473	82.732568	0.000000	0.000000	0.0
37	90.0	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	...	9.772777	8.805085	7.559253	6.932708	7.345726	7.537268	7.552021	7.488912	7.221574	0.0
38	90.0	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	...	1.943871	7.511871	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.000000	90.0

10 rows × 37 columns

The following function will plot side by side the geometries of the buildings as well as the mask for the i-th boundary

def plot_mask(buildings, boundaries, i):
    fig, ax = plt.subplots(1, 2, sharex=False, sharey=False, figsize=(15, 7))

    buildings.plot(ax=ax[0], color="red", alpha=0.5)
    gpd.GeoDataFrame(geometry=[boundaries.iloc[i].geometry]).plot(
        ax=ax[0], color="blue"
    )
    mask_columns = [
        col_name for col_name in boundaries.columns if col_name.startswith("mask_")
    ]
    boundaries[mask_columns].iloc[i].transpose().plot(ax=ax[1])
    plt.show()

plot_mask(buildings, boundaries, 52)

The following code will import a 3d plotting utility that relies on additional dependencies mapbox_earcut and k3d. Make sure to install them before executing the next cells.

from buildingmodel.io.plot_3D import plot_3D_district

boundaries["average_mask"] = boundaries[mask_columns].mean(axis=1)

plot_3D_district(buildings, boundaries, "average_mask", [0.0, 90.0])

/home/yassine/miniconda3/envs/buildingmodel/lib/python3.8/site-packages/traittypes/traittypes.py:97: UserWarning: Given trait value dtype "float64" does not match required type "float32". A coerced copy has been created.
  warnings.warn(
/home/yassine/miniconda3/envs/buildingmodel/lib/python3.8/site-packages/traittypes/traittypes.py:97: UserWarning: Given trait value dtype "float64" does not match required type "uint32". A coerced copy has been created.
  warnings.warn(