def test_ExclusionCalculator_excludeVectorType():
# exclude all features directly
ec = gl.ExclusionCalculator(aachenShape)
ec.excludeVectorType(cddaVector)
assert np.isclose(np.nanmean(ec.availability), 76.47581482)
assert np.isclose(np.nanstd(ec.availability), 42.41498947)
# exclude all features directly, new srs
ec = gl.ExclusionCalculator(aachenShape, srs='latlon', pixelRes=0.005)
ec.excludeVectorType(cddaVector)
assert np.isclose(np.nanmean(ec.availability), 76.31578827)
assert np.isclose(np.nanstd(ec.availability), 42.51445770)
# exclude a selection of features
ec = gl.ExclusionCalculator(aachenShape)
ec.excludeVectorType(cddaVector, where="YEAR>2000")
assert np.isclose(np.nanmean(ec.availability), 86.89811707)
assert np.isclose(np.nanstd(ec.availability), 33.74209595)
# exclude a selection of features with buffer
ec = gl.ExclusionCalculator(aachenShape)
ec.excludeVectorType(cddaVector, where="YEAR>2000", buffer=400)
assert np.isclose(np.nanmean(ec.availability), 77.95021057)
assert np.isclose(np.nanstd(ec.availability), 41.45823669)
def calculate_pv_area(region):
# Quaschning, Volker. Systemtechnik einer klimaverträglichen Elektrizitätsversorgung in Deutschland für
# das 21. Jahrhundert. Düsseldorf, 2000
# Fraunhofer Institut für Windenergie und Energiesystemtechnik (IWES). Vorstudie zur Integration großer
# Anteile Photovoltaik in die elektrische Energieversorgung – Studie im Auftrag des BSW - Bundesverband
# Solarwirtschaft e.V. – ergänzte Fassung vom 29.05.2012. 2012
# WG: 818.31 km² und NWG: 698 km² Gesamt: 1516 km²
# Divide Potential by eligible area in Germany
share_pv = 1516 / 43677
# Intialise ExclusionCalculator object
ecPV = gl.ExclusionCalculator(region,
srs=3035,
pixelSize=100,
limitOne=False)
# Perform exclusion
ecPV.excludePrior("settlement_proximity", value=0)
ecPV.excludePrior("settlement_urban_proximity", value=0)
# Calculate area eligible for pv
area_total = (float(ecPV.maskPixels) / 100) * 1e6 # Fläche in m²
area_excluded = ecPV.areaAvailable
area_available = area_total - area_excluded
area_pv = area_available * share_pv
return area_pv
def calculate_potential(gid, save_map=None):
feature = gk.vector.extractFeature(paths["regions"], where=gid)
ec = gl.ExclusionCalculator(feature.geom)
corine = config.get("corine", {})
if isinstance(corine, list):
corine = {'grid_codes': corine}
if "grid_codes" in corine:
ec.excludeRasterType(clc, value=corine["grid_codes"], invert=True)
if corine.get("distance", 0.) > 0.:
ec.excludeRasterType(clc, value=corine["distance_grid_codes"], buffer=corine["distance"])
if config.get("natura", False):
ec.excludeRasterType(natura, value=1)
if "max_depth" in config:
ec.excludeRasterType(gebco, (None, -config["max_depth"]))
# TODO compute a distance field as a raster beforehand
if 'max_shore_distance' in config:
ec.excludeVectorType(paths["country_shapes"], buffer=config['max_shore_distance'], invert=True)
if 'min_shore_distance' in config:
ec.excludeVectorType(paths["country_shapes"], buffer=config['min_shore_distance'])
if save_map is not None:
ec.draw()
plt.savefig(save_map, transparent=True)
plt.close()
availability = downsample_to_coarse_grid(bounds, dx, dy, ec.region, np.where(ec.region.mask, ec._availability, 0))
return csr_matrix(gk.raster.extractMatrix(availability).flatten() / 100.)
def test_ExclusionCalculator_excludePrior():
# make a prior source
pr = gl.core.priors.PriorSource(priorSample)
# test same srs
ec = gl.ExclusionCalculator(aachenShape)
ec.excludePrior(pr, value=(400, None))
assert np.isclose(np.nanmean(ec.availability), 24.77587891)
assert np.isclose(np.nanstd(ec.availability), 43.17109680)
# test different srs and resolution
ec = gl.ExclusionCalculator(aachenShape, srs='latlon', pixelRes=0.001)
ec.excludePrior(pr, value=(400, None))
assert np.isclose(np.nanmean(ec.availability), 24.83173180)
assert np.isclose(np.nanstd(ec.availability), 41.84893036)
def test_ExclusionCalculator_save():
ec = gl.ExclusionCalculator(aachenShape)
ec.save(join(RESULTDIR, "save1.tif"))
mat = gk.raster.extractMatrix(join(RESULTDIR, "save1.tif"))
assert np.nansum(mat - ec.availability) == 0
assert np.isclose(np.nansum(mat), 28461360)
assert np.isclose(np.nanstd(mat), 77.2323849648)
def test_ExclusionCalculator_draw():
ec = gl.ExclusionCalculator(aachenShape)
ec._availability[:, 140:160] = 0
ec._availability[140:160, :] = 0
ec.draw()
plt.savefig(join(RESULTDIR, "DrawnImage.png"), dpi=200)
plt.close()
def test_ExclusionCalculator_pruneIsolatedAreas():
# make a prior source
pr = gl.core.priors.PriorSource(priorSample)
ec = gl.ExclusionCalculator(aachenShape)
ec.excludePrior(pr, value=(None, 400))
ec.pruneIsolatedAreas(12000000)
assert np.isclose(np.nanmean(ec.availability), 65.41215515)
assert np.isclose(np.nanstd(ec.availability), 47.56538391)
def test_ExclusionCalculator_shrinkAvailability():
# make a prior source
pr = gl.core.priors.PriorSource(priorSample)
ec = gl.ExclusionCalculator(aachenShape)
ec.excludePrior(pr, value=(None, 400))
ec.shrinkAvailability(500)
assert np.isclose(np.nanmean(ec.availability), 41.88655853)
assert np.isclose(np.nanstd(ec.availability), 49.33732986)
def test_ExclusionCalculator_excludeRegionEdge():
# make a prior source
pr = gl.core.priors.PriorSource(priorSample)
ec = gl.ExclusionCalculator(aachenShape)
ec.excludePrior(pr, value=(None, 400))
ec.excludeRegionEdge(500)
assert np.isclose(np.nanmean(ec.availability), 63.68544388)
assert np.isclose(np.nanstd(ec.availability), 48.09062958)
def test_multiple_exclusions():
pr = gl.core.priors.PriorSource(priorSample)
ec = gl.ExclusionCalculator(aachenShape)
# apply exclusions
ec.excludePrior(pr, value=(None, 400))
ec.excludeVectorType(cddaVector, where="YEAR>2000")
ec.excludeRasterType(clcRaster, value=(None, 12))
assert np.isclose(np.nanmean(ec.availability), 37.1109619141, 1e-6)
assert np.isclose(np.nanstd(ec.availability), 48.3101692200, 1e-6)
def test_ExclusionCalculator_excludeVectorType():
# exclude all features directly
ec = gl.ExclusionCalculator(aachenShape)
ec.excludeVectorType(cddaVector)
assert np.isclose(np.nanmean(ec.availability), 76.47581482)
assert np.isclose(np.nanstd(ec.availability), 42.41498947)
# exclude all features directly, new srs
ec = gl.ExclusionCalculator(aachenShape, srs='latlon', pixelRes=0.005)
ec.excludeVectorType(cddaVector)
assert np.isclose(np.nanmean(ec.availability), 76.31578827)
assert np.isclose(np.nanstd(ec.availability), 42.51445770)
# exclude a selection of features
ec = gl.ExclusionCalculator(aachenShape)
ec.excludeVectorType(cddaVector, where="YEAR>2000")
assert np.isclose(np.nanmean(ec.availability), 86.89811707)
assert np.isclose(np.nanstd(ec.availability), 33.74209595)
# exclude a selection of features with buffer
ec = gl.ExclusionCalculator(aachenShape)
ec.excludeVectorType(cddaVector, where="YEAR>2000", buffer=400)
assert np.isclose(np.nanmean(ec.availability), 77.95021057)
assert np.isclose(np.nanstd(ec.availability), 41.45823669)
# test with intermediate functionality
for i in range(2):
ec = gl.ExclusionCalculator(aachenShape)
ec.excludeVectorType(
cddaVector,
where="YEAR>2000",
buffer=400,
intermediate=join(RESULTDIR, "exclude_vector_intermediate.tif"))
assert isfile(join(RESULTDIR, "exclude_vector_intermediate.tif"))
assert np.isclose(np.nanmean(ec.availability), 77.95021057)
assert np.isclose(np.nanstd(ec.availability), 41.45823669)
def test_ExclusionCalculator_excludeSet():
ec = gl.ExclusionCalculator(aachenShape)
exclusion_set = pd.read_csv(gl._test_data_["sample_exclusion_set.csv"])
ec.excludeSet(
exclusion_set=exclusion_set,
clc=gl._test_data_['clc-aachen_clipped.tif'],
osm_roads=gl._test_data_["aachenRoads.shp"],
verbose=False,
)
assert np.isclose(np.nanmean(ec.availability), 15.231732)
assert np.isclose(np.nanstd(ec.availability), 35.93282)
def test_ExclusionCalculator___init__():
# Test by giving a shapefile
ec = gl.ExclusionCalculator(aachenShape)
assert ec.region.mask.shape == (509, 304)
assert np.isclose(ec.region.mask.sum(), 70944)
assert np.isclose(ec.region.mask.std(), 0.498273451386)
# Test by giving a region mask
rm = gk.RegionMask.load(aachenShape, padExtent=5000)
ec = gl.ExclusionCalculator(rm)
assert ec.region.mask.shape == (609, 404)
assert np.isclose(ec.region.mask.sum(), 70944)
assert np.isclose(ec.region.mask.std(), 0.45299387483)
# Test by giving a region mask with different resolution and srs
rm = gk.RegionMask.load(aachenShape, srs=gk.srs.EPSG4326, pixelRes=0.001)
ec = gl.ExclusionCalculator(rm)
assert ec.region.mask.shape == (457, 446)
assert np.isclose(ec.region.mask.sum(), 90296)
assert np.isclose(ec.region.mask.std(), 0.496741981394)
def test_ExclusionCalculator_distributeItems():
# make a prior source
pr = gl.core.priors.PriorSource(priorSample)
ec = gl.ExclusionCalculator(aachenShape)
ec.excludePrior(pr, value=(400, None))
# Do a regular distribution
ec.distributeItems(1000,
output=join(RESULTDIR, "distributeItems1.shp"),
outputSRS=3035)
geoms = gk.vector.extractFeatures(join(RESULTDIR, "distributeItems1.shp"))
assert geoms.shape[0] == 287
minDist = 1000000
for gi in range(geoms.shape[0] - 1):
for gj in range(gi + 1, geoms.shape[0]):
d = geoms.geom[gi].Distance(geoms.geom[gj])
if d < minDist:
minDist = d
I = (gi, gj)
assert minDist >= 999
# Do an axial distribution
ec.distributeItems((1000, 300),
axialDirection=180,
output=join(RESULTDIR, "distributeItems2.shp"),
outputSRS=3035)
geoms = gk.vector.extractFeatures(join(RESULTDIR, "distributeItems2.shp"))
assert geoms.shape[0] == 882
x = np.array([g.GetX() for g in geoms.geom])
y = np.array([g.GetY() for g in geoms.geom])
for gi in range(geoms.shape[0] - 1):
d = (x[gi] - x[gi + 1:])**2 / 1000**2 + (y[gi] -
y[gi + 1:])**2 / 300**2
assert (d >= 1).all() # Axial objects too close
# Do make areas
ec.distributeItems(2000,
asArea=True,
output=join(RESULTDIR, "distributeItems3.shp"),
outputSRS=4326)
geoms = gk.vector.extractFeatures(join(RESULTDIR, "distributeItems3.shp"))
assert np.isclose(geoms.shape[0], 97)
assert np.isclose(geoms.area.mean(), 0.000230714164474)
assert np.isclose(geoms.area.std(), 8.2766693979e-05)
def calculate_PV_sites(region, invert=True, separation=1000, name='NoRegion', convert2epsg=True):
# Choose Region
ecPV = gl.ExclusionCalculator(region, srs=3035, pixelSize=100, limitOne=False)
# Apply selected exclusion criteria
ecPV.excludePrior(pr.settlement_proximity, value=None)
ecPV.excludePrior(pr.settlement_urban_proximity, value=None)
ecPV.excludePrior(pr.industrial_proximity, value=None)
# Placement Algorithm
ecPV.distributeItems(separation=1000, invert=invert, outputSRS=4326)
# Extract and convert site coords of turbines
site_coords = pd.DataFrame(ecPV.itemCoords)
site_coords.columns = ['latitude','longitude']
site_coords_gdf = geom.create_geo_df(site_coords, wkt_column=None, lon_column="longitude", lat_column='latitude')
# Convert2epsg for plotting purposes
if convert2epsg==True:
trsf= site_coords_gdf["geometry"]
site_coords_gdf_epsg3857 = trsf.to_crs(epsg=3857)
# Save coords in EPSG3587 to hard disk
site_coords_gdf_epsg3857.to_file("site_coordsPV_epsg3857_" + name + ".geojson", driver='GeoJSON')
site_coords_gdf.to_file("site_coordsPV_WGS84_" + name + ".geojson", driver='GeoJSON')
elif convert2epsg==False:
site_coords_gdf.to_file("site_coordsPV_WGS84_" + name + ".geojson", driver='GeoJSON')
# Calculate Power per Site in MW
p_mean = 300000 / len(site_coords) #Total possible PV-Power in MW divided by site count
# Write turbines to power plants df
res_df_PV = pd.DataFrame(columns=["energy_source_level_1", "energy_source_level_2", "technology",
"electrical_capacity", "lon", "lat", "data_source"])
res_df_PV["lon"] = site_coords["latitude"]
res_df_PV["lat"] = site_coords["longitude"]
res_df_PV["energy_source_level_1"] = 'Renewable energy'
res_df_PV["energy_source_level_2"] = 'Solar'
res_df_PV["technology"] = 'Photovoltaics'
res_df_PV["electrical_capacity"] = p_mean
res_df_PV["data_source"] = 'GLAES'
return res_df_PV, ecPV
def test_ExclusionCalculator_distributeItems():
# make a prior source
pr = gl.core.priors.PriorSource(priorSample)
ec = gl.ExclusionCalculator(aachenShape)
ec.excludePrior(pr, value=(400, None))
# Do a regular distribution
ec.distributeItems(1000,
output=join(RESULTDIR, "distributeItems1.shp"),
outputSRS=3035)
geoms = gk.vector.extractFeatures(join(RESULTDIR, "distributeItems1.shp"))
assert geoms.shape[0] == 287
minDist = 1000000
for gi in range(geoms.shape[0] - 1):
for gj in range(gi + 1, geoms.shape[0]):
d = geoms.geom[gi].Distance(geoms.geom[gj])
if d < minDist:
minDist = d
I = (gi, gj)
assert minDist >= 999
# Do an axial distribution
ec.distributeItems((1000, 300),
axialDirection=180,
output=join(RESULTDIR, "distributeItems2.shp"),
outputSRS=3035)
geoms = gk.vector.extractFeatures(join(RESULTDIR, "distributeItems2.shp"))
assert geoms.shape[0] == 882
x = np.array([g.GetX() for g in geoms.geom])
y = np.array([g.GetY() for g in geoms.geom])
for gi in range(geoms.shape[0] - 1):
d = (x[gi] - x[gi + 1:])**2 / 1000**2 + (y[gi] -
y[gi + 1:])**2 / 300**2
assert (d >= 1).all() # Axial objects too close
# Do make areas
ec.distributeItems(2000,
asArea=True,
output=join(RESULTDIR, "distributeItems3.shp"),
outputSRS=4326)
geoms = gk.vector.extractFeatures(join(RESULTDIR, "distributeItems3.shp"))
assert np.isclose(geoms.shape[0], 97)
assert np.isclose(geoms.area.mean(), 0.000230714164474)
assert np.isclose(geoms.area.std(), 8.2766693979e-05)
# Do a variable separation distance placement
ec = gl.ExclusionCalculator(gl._test_data_['aachenShapefile.shp'],
pixelRes=25,
srs="LAEA")
ec.excludeRasterType(gl._test_data_['clc-aachen_clipped.tif'],
value=(1, 2),
invert=True)
mat = np.zeros_like(ec.region.mask, dtype=np.uint16)
for i in range(mat.shape[0]):
mat[i, :] = (300 - 50) * i / mat.shape[0] + 100
ras = ec.region.createRaster(data=mat)
points = ec.distributeItems(separation=5, sepScaling=ras, _stamping=False)
assert points.shape[0] == 335
points = ec.distributeItems(separation=(8, 3),
sepScaling=ras,
axialDirection=0)
assert points.shape[0] == 389
def calculate_wind_sites(region, invert=False, separation=700, name='NoRegion', convert2epsg=True):
# Choose Region
ecWind = gl.ExclusionCalculator(region, srs=3035, pixelSize=100, limitOne=False)
# Define Exclusion Criteria
selExlWind = {
"access_distance": (5000, None ),
#"agriculture_proximity": (None, 50 ),
#"agriculture_arable_proximity": (None, 50 ),
#"agriculture_pasture_proximity": (None, 50 ),
#"agriculture_permanent_crop_proximity": (None, 50 ),
#"agriculture_heterogeneous_proximity": (None, 50 ),
"airfield_proximity": (None, 1760 ), # Diss WB
"airport_proximity": (None, 5000 ), # Diss WB
"connection_distance": (10000, None ),
#"dni_threshold": (None, 3.0 ),
"elevation_threshold": (1500, None ),
#"ghi_threshold": (None, 3.0 ),
"industrial_proximity": (None, 250 ), # Diss Wingenbach / UBA 2013
"lake_proximity": (None, 0 ),
"mining_proximity": (None, 100 ),
"ocean_proximity": (None, 10 ),
"power_line_proximity": (None, 120 ), # Diss WB
"protected_biosphere_proximity": (None, 5 ), # UBA 2013
"protected_bird_proximity": (None, 200 ), # UBA 2013
"protected_habitat_proximity": (None, 5 ), # UBA 2013
"protected_landscape_proximity": (None, 5 ), # UBA 2013
"protected_natural_monument_proximity": (None, 200 ), # UBA 2013
"protected_park_proximity": (None, 5 ), # UBA 2013
"protected_reserve_proximity": (None, 200 ), # UBA 2013
"protected_wilderness_proximity": (None, 200 ), # UBA 2013
"camping_proximity": (None, 900), # UBA 2013)
#"touristic_proximity": (None, 800),
#"leisure_proximity": (None, 1000),
"railway_proximity": (None, 250 ), # Diss WB
"river_proximity": (None, 5 ), # Abweichung vom standardwert (200)
"roads_proximity": (None, 80 ), # Diss WB
"roads_main_proximity": (None, 80 ), # Diss WB
"roads_secondary_proximity": (None, 80 ),# Diss WB
#"sand_proximity": (None, 5 ),
"settlement_proximity": (None, 600 ), # Diss WB
"settlement_urban_proximity": (None, 1000 ),
"slope_threshold": (10, None ),
#"slope_north_facing_threshold": (3, None ),
"wetland_proximity": (None, 5 ), # Diss WB / UBA 2013
"waterbody_proximity": (None, 5 ), # Diss WB / UBA 2013
"windspeed_100m_threshold": (None, 4.5 ),
"windspeed_50m_threshold": (None, 4.5 ),
"woodland_proximity": (None, 0 ), # Abweichung vom standardwert (300) / Diss WB
"woodland_coniferous_proximity": (None, 0 ), # Abweichung vom standardwert (300)
"woodland_deciduous_proximity": (None, 0 ), # Abweichung vom standardwert (300)
"woodland_mixed_proximity": (None, 0 ) # Abweichung vom standardwert (300)
}
# Apply selected exclusion criteria
for key in selExlWind:
ecWind.excludePrior(pr[key], value=ecWind.typicalExclusions[key])
# Placement Algorithm
ecWind.distributeItems(separation=separation, outputSRS=4326)
# Extract and convert site coords of turbines
site_coords = pd.DataFrame(ecWind.itemCoords)
site_coords.columns = ['latitude','longitude']
site_coords_gdf = geom.create_geo_df(site_coords, wkt_column=None, lon_column="longitude", lat_column='latitude')
# Convert2epsg for plotting purposes
if convert2epsg==True:
trsf= site_coords_gdf["geometry"]
site_coords_gdf_epsg3857 = trsf.to_crs(epsg=3857)
# Save coords in EPSG3587 to hard disk
site_coords_gdf_epsg3857.to_file("site_coordsWind_epsg3857_" + name + ".geojson", driver='GeoJSON')
site_coords_gdf.to_file("site_coordsWind_WGS84_" + name + ".geojson", driver='GeoJSON')
elif convert2epsg==False:
site_coords_gdf.to_file("site_coordsWind_WGS84_" + name + ".geojson", driver='GeoJSON')
# Write turbines to power plants df
res_df_Wind = pd.DataFrame(columns=["energy_source_level_1", "energy_source_level_2", "technology",
"electrical_capacity", "lon", "lat", "data_source"])
res_df_Wind["lon"] = site_coords["latitude"]
res_df_Wind["lat"] = site_coords["longitude"]
res_df_Wind["energy_source_level_1"] = 'Renewable energy'
res_df_Wind["energy_source_level_2"] = 'Wind'
res_df_Wind["technology"] = 'Onshore'
res_df_Wind["electrical_capacity"] = 3.5
res_df_Wind["data_source"] = 'GLAES'
return res_df_Wind, ecWind
import glaes as gl
import pandas as pd
PARAMS = pd.read_csv('input_data/landEligibilityParameters.csv',
index_col='name')['value']
gl.Priors.loadDirectory("input_data")
# INPUTS
regionFile = "output_data/gadm36_DEU_1.shp"
regionID = int(PARAMS.loc['regionID'])
settlement_proximity = int(PARAMS.loc['exc_settlement_proximity'])
# OUTPUTS
outputFile = "output_data/eligibility_result.tif"
# Initiate Exclusion Calculator object
ec = gl.ExclusionCalculator(regionFile, where=regionID)
# Do a simple exclusion
ec.excludePrior("settlement_proximity",
value=(0, settlement_proximity)) # Exclude urban areas
# TODO: Add into inputs
ec.excludePrior("roads_main_proximity", value=(0, 200)) # Exclude urban areas
ec.excludePrior("roads_secondary_proximity",
value=(0, 200)) # Exclude urban areas
ec.excludePrior("protected_landscape_proximity",
value=(0, 1000)) # Exclude urban areas
# Save result
ec.save(outputFile)
def calculate_wind_area(region):
# Choose Region
ecWind = gl.ExclusionCalculator(region,
srs=3035,
pixelSize=100,
limitOne=False)
# Define Exclusion Criteria
selExlWind = {
"access_distance": (5000, None),
#"agriculture_proximity": (None, 50 ),
#"agriculture_arable_proximity": (None, 50 ),
#"agriculture_pasture_proximity": (None, 50 ),
#"agriculture_permanent_crop_proximity": (None, 50 ),
#"agriculture_heterogeneous_proximity": (None, 50 ),
"airfield_proximity": (None, 1760), # Diss WB
"airport_proximity": (None, 5000), # Diss WB
"connection_distance": (10000, None),
#"dni_threshold": (None, 3.0 ),
"elevation_threshold": (1500, None),
#"ghi_threshold": (None, 3.0 ),
"industrial_proximity": (None, 250), # Diss Wingenbach / UBA 2013
"lake_proximity": (None, 0),
"mining_proximity": (None, 100),
"ocean_proximity": (None, 10),
"power_line_proximity": (None, 120), # Diss WB
"protected_biosphere_proximity": (None, 5), # UBA 2013
"protected_bird_proximity": (None, 200), # UBA 2013
"protected_habitat_proximity": (None, 5), # UBA 2013
"protected_landscape_proximity": (None, 5), # UBA 2013
"protected_natural_monument_proximity": (None, 200), # UBA 2013
"protected_park_proximity": (None, 5), # UBA 2013
"protected_reserve_proximity": (None, 200), # UBA 2013
"protected_wilderness_proximity": (None, 200), # UBA 2013
"camping_proximity": (None, 900), # UBA 2013)
#"touristic_proximity": (None, 800),
#"leisure_proximity": (None, 1000),
"railway_proximity": (None, 250), # Diss WB
"river_proximity": (None, 5), # Abweichung vom standardwert (200)
"roads_proximity": (None, 80), # Diss WB
"roads_main_proximity": (None, 80), # Diss WB
"roads_secondary_proximity": (None, 80), # Diss WB
#"sand_proximity": (None, 5 ),
"settlement_proximity": (None, 600), # Diss WB
"settlement_urban_proximity": (None, 1000),
"slope_threshold": (10, None),
#"slope_north_facing_threshold": (3, None ),
"wetland_proximity": (None, 5), # Diss WB / UBA 2013
"waterbody_proximity": (None, 5), # Diss WB / UBA 2013
"windspeed_100m_threshold":
(None, 5.5), # Wert angepasst. Bei Nabenhöhe >100m realistisch?
#"windspeed_50m_threshold": (None, 4.5),
"woodland_proximity":
(None, 0), # Abweichung vom standardwert (300) / Diss WB
"woodland_coniferous_proximity":
(None, 0), # Abweichung vom standardwert (300)
"woodland_deciduous_proximity":
(None, 0), # Abweichung vom standardwert (300)
"woodland_mixed_proximity": (None, 0
) # Abweichung vom standardwert (300)
}
# Apply selected exclusion criteria
# for key in selExlWind:
# ecWind.excludePrior(pr[key], value=ecWind.typicalExclusions[key])
for key in selExlWind.keys():
ecWind.excludePrior(key, value=selExlWind[key])
area = ecWind.areaAvailable
return area
def Holtinger2016_max(reg):
"""
SOURCE: Holtinger2016
EXCLUSIONS:
- Areas above the alpine forest line
- Maximum slope (11.3 degrees)
- Water bodies
- Settlement areas - 1000m buffer
- Buildings outside of settlement areas - 750m buffer
- Building land outside of settlement areas - 750m buffer
- Built-up areas - 300m buffer
* Industrial, commercial, and mining areas
- Railways - 300m buffer
- Motorways, primary, and secondary roads - 300m buffer
- Airport public safety zones - 5100m buffer
- Power grid( >110kV) - 250m buffer
- National parks - 1000m buffer
- Natura 2000 - habitats directive sites
* Potentially
- Natura 2000 - birds directive sites
- Other protected areas
* Biosphere reserves, landscape protection areas, natural monuments and sites,
- Important birdlife areas
* Potentially
- Major migration routes for wild animals
* Potentially
- Lakes (> 50 ha) - 1000m buffer
"""
ec = gl.ExclusionCalculator(reg)
##### do exclusions #####
# "Areas above the alpine forest line
ec.excludePrior("elevation_threshold",
value=(1750,
None)) # alpine forest line assumed at 1750 m
# "maximum slope (degrees)"
ec.excludePrior("slope_threshold", value=(11.3, None))
# "water bodies"
ec.excludePrior("river_proximity", value=0)
#ec.excludePrior("lake_proximity", value=0 ) # commented out since it is included late with a buffer
# "settlement areas - 1000m buffer"
ec.excludePrior("settlement_proximity", value=(None, 1000))
# "buildings outside of settlement areas - 750m buffer"
# "building land outside of settlement areas - 750m buffer"
# "built up areas - 300m buffer"
# " * industrial, commercial, and mining areas"
ec.excludePrior("industrial_proximity", value=(None, 300))
ec.excludePrior("mining_proximity", value=(None, 300))
# "railways - 300m buffer"
ec.excludePrior("railway_proximity", value=(None, 300))
# "motorways, primary, and secondary roads - 300m buffer"
ec.excludePrior("roads_main_proximity", value=(None, 300))
ec.excludePrior("roads_secondary_proximity", value=(None, 300))
# "airport public safety zones - 5100m buffer"
ec.excludePrior("airport_proximity", value=(None, 5100))
# "power grid( >110kV) - 250m buffer"
ec.excludePrior("power_line_proximity", value=(None, 250))
# "national parks - 1000m buffer"
ec.excludePrior("protected_park_proximity", value=(None, 1000))
# "Natura 2000 - habitats directive sites"
# "*potentially"
ec.excludePrior("protected_habitat_proximity", value=0)
# "Natura 2000 - birds directive sites"
ec.excludePrior("protected_bird_proximity", value=0)
# "Other protected areas"
# "*Biosphere reserves, landscape protection areas, natural monuments and sites,
# protected habitats, and landscape section"
ec.excludePrior("protected_biosphere_proximity", value=0)
ec.excludePrior("protected_landscape_proximity", value=0)
ec.excludePrior("protected_natural_monument_proximity", value=0)
# "important birdlife areas"
# "*potentially"
# "major migration routes for wild animals"
# "*potentially"
# "lakes (> 50 ha) - 1000m buffer"
ec.excludePrior("lake_proximity", value=(None, 1000))
# All done
return ec
def compute_land_availability(shape: Union[Polygon, MultiPolygon]) -> float:
"""
Compute land availability in (km2) of a geographical shape.
Parameters
----------
shape: Union[Polygon, MultiPolygon]
Geographical shape
Returns
-------
float
Available area in (km2)
Notes
-----
spatial_ref, filters, natura, clc and gebco must have been previously initialized as global variables
"""
import matplotlib.pyplot as plt
poly_wkt = shapely.wkt.dumps(shape)
poly = ogr.CreateGeometryFromWkt(poly_wkt, spatial_ref_)
# Compute rooftop area using ESM (European Settlement Map)
if filters_.get("esm", 0):
path_cop = f"{data_path}generation/vres/potentials/source/ESM/ESM_class50_100m/ESM_class50_100m.tif"
ec = gl.ExclusionCalculator(poly, pixelRes=100, initialValue=path_cop)
return ec.areaAvailable / 1e6
ec = gl.ExclusionCalculator(poly, pixelRes=100)
# GLAES priors
if 'glaes_prior_defaults' in filters_:
prior_defaults_params = filters_['glaes_prior_defaults']
prior_config = prior_defaults_params["config"]
priors = prior_defaults_params[
"priors"] if 'priors' in prior_defaults_params else None
filters_["glaes_priors"] = get_glaes_prior_defaults(
prior_config, priors)
if 'glaes_priors' in filters_:
priors_filters = filters_["glaes_priors"]
for prior_name in priors_filters.keys():
prior_value = priors_filters[prior_name]
if isinstance(prior_value, str):
prior_value = literal_eval(prior_value)
# Can receive a tuple or a list of tuples
if not isinstance(prior_value, list):
prior_value = [prior_value]
for value in prior_value:
ec.excludePrior(prior_name, value=value)
#ec.draw()
#plt.title(prior_name)
#print(prior_name)
#plt.show()
# Exclude protected areas
if 'natura' in filters_:
ec.excludeRasterType(natura_, value=filters_["natura"])
# Depth and altitude filters
if 'depth_thresholds' in filters_:
depth_thresholds = filters_["depth_thresholds"]
# Keep points between two depth thresholds
for gebco in gebcos_:
ec.excludeRasterType(gebco, (-1e4, depth_thresholds["low"]))
ec.excludeRasterType(gebco, (depth_thresholds["high"], 0))
elif 'altitude_threshold' in filters_ and filters_[
'altitude_threshold'] > 0.:
for gebco in gebcos_:
ec.excludeRasterType(gebco, (filters_['altitude_threshold'], 1e6))
# Corine filters
if 'clc' in filters_:
clc_filters = filters_["clc"]
if "remove_codes" in clc_filters and "remove_distance" in clc_filters and clc_filters[
"remove_distance"] > 0.:
# for remove_code in clc_filters["remove_codes"]:
# ec.excludeRasterType(clc_, value=remove_code, buffer=clc_filters["remove_distance"])
ec.excludeRasterType(clc_,
value=clc_filters["remove_codes"],
buffer=clc_filters["remove_distance"])
if "keep_codes" in clc_filters:
# Invert True indicates code that need to be kept
# for keep_code in clc_filters["keep_codes"]:
# ec.excludeRasterType(clc_, value=keep_code, mode='include')
ec.excludeRasterType(clc_,
value=clc_filters["keep_codes"],
mode='include')
if 'shipping' in filters_:
value = filters_['shipping']
value = literal_eval(value) if isinstance(value, str) else value
ec.excludeRasterType(cargo_, value=value)
ec.excludeRasterType(tanker_, value=value)
if 'cables' in filters_:
ec.excludeVectorType(cables_, buffer=filters_['cables'])
if 'pipelines' in filters_:
ec.excludeVectorType(pipelines_, buffer=filters_['pipelines'])
# ax = ec.draw()
# ec.pruneIsolatedAreas(0.9*1e6, 50)
# ec.draw()
# plt.show()
# plt.savefig("test.png")
return ec.areaAvailable / 1e6
def test_ExclusionCalculator_excludeRasterType():
# exclude single value
ec = gl.ExclusionCalculator(aachenShape)
ec.excludeRasterType(clcRaster, 12)
assert np.isclose(np.nanmean(ec.availability), 82.8033)
assert np.isclose(np.nanstd(ec.availability), 37.73514175)
# exclude value range
ec = gl.ExclusionCalculator(aachenShape)
ec.excludeRasterType(clcRaster, (5, 12))
assert np.isclose(np.nanmean(ec.availability), 81.16260529)
assert np.isclose(np.nanstd(ec.availability), 39.10104752)
# Exclude iterable (should have the same result as the test above)
ec = gl.ExclusionCalculator(gl._test_data_["aachenShapefile.shp"], srs=gk.srs.EPSG3035, pixelRes=100)
ec.excludeRasterType(
gl._test_data_['clc-aachen_clipped.tif'],
value=[5, 6, 7, 8, 9, 10, 11, 12])
assert np.isclose(np.nanmean(ec.availability), 81.16260529)
assert np.isclose(np.nanstd(ec.availability), 39.10104752)
# exclude value maximum
ecMax12 = gl.ExclusionCalculator(aachenShape)
ecMax12.excludeRasterType(clcRaster, (None, 12))
assert np.isclose(np.nanmean(ecMax12.availability), 58.52362442)
assert np.isclose(np.nanstd(ecMax12.availability), 49.26812363)
# exclude value minimum
ecMin13 = gl.ExclusionCalculator(aachenShape)
ecMin13.excludeRasterType(clcRaster, (13, None))
assert np.isclose(np.nanmean(ecMin13.availability), 41.47637558)
assert np.isclose(np.nanstd(ecMin13.availability), 49.26812363)
# Make sure min and max align
s1 = ecMax12.availability[ecMax12.region.mask] > 0
s2 = ecMin13.availability[ecMax12.region.mask] > 0
assert np.logical_xor(s1, s2).all()
# Test with a different projection system
# exclude single value
ec = gl.ExclusionCalculator(aachenShape, srs='latlon', pixelRes=0.005)
ec.excludeRasterType(clcRaster, 12)
print("AVAIL MEAN:", np.nanmean(ec.availability))
assert np.isclose(np.nanmean(ec.availability), 82.95262909)
assert np.isclose(np.nanstd(ec.availability), 32.26681137)
# Test with complex value input
ec = gl.ExclusionCalculator(gl._test_data_["aachenShapefile.shp"], srs='latlon', pixelRes=0.005)
ec.excludeRasterType(
gl._test_data_['clc-aachen_clipped.tif'],
value="[-2),[5-7),12,(22-26],29,33,[40-]")
assert np.isclose(np.nanmean(ec.availability), 49.5872573853)
assert np.isclose(np.nanstd(ec.availability), 41.2754364014)
# Test with intermediate functionaliy (creation and re-use)
for i in range(2):
ec = gl.ExclusionCalculator(
gl._test_data_["aachenShapefile.shp"],
srs='latlon',
pixelRes=0.005,)
ec.excludeRasterType(
gl._test_data_['clc-aachen_clipped.tif'],
value="[-2),[5-7),12,(22-26],29,33,[40-]",
intermediate=join(RESULTDIR, "exclude_raster_intermediate.tif"))
assert isfile(join(RESULTDIR, "exclude_raster_intermediate.tif"))
assert np.isclose(np.nanmean(ec.availability), 49.5872573853)
assert np.isclose(np.nanstd(ec.availability), 41.2754364014)
import glaes as gl import geokit as gk from os.path import join from matplotlib import pyplot as plt # Choose a region to operate on (Here, a predefined region for Aachen, Germany is used) regionPath = '/home/dbeier/git-projects/glaes/glaes/test/data/aachenShapefile.shp' #['aachenShapefile.shp'] # Initialize ExclusionCalculator object ec = gl.ExclusionCalculator(regionPath, srs=3035, pixelSize=100) # Visualize avilability plot = ec.draw() # Set a path to a local copy of the CLC raster dataset (Here, a small sample is provided around Aachen) clcRasterPath = gl._test_data_["clc-aachen_clipped.tif"] # Apply exclusion ec.excludeRasterType(clcRasterPath, value=(12,22)) # Visualize ec.draw() # Apply exclusion ec.excludeRasterType(clcRasterPath, value=(1,2), buffer=1000) # Visualize ec.draw() # Set a path to a local copy of the OSM roads dataset (Here, a small sample is provided around Aachen) roadwaysPath = gl._test_data_["aachenRoads.shp"] # Apply Exclusion whereStatement = "type='motorway' OR type='primary' OR type='trunk'" ec.excludeVectorType(roadwaysPath, where=whereStatement, buffer=200) # Visualize
