def test_voronoi_geopandas_with_plot():
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
# focus on South America, convert to World Mercator (unit: meters)
south_am = world[world.continent == 'South America'].to_crs(epsg=3395)
cities = cities.to_crs(
south_am.crs) # convert city coordinates to same CRS!
# create the bounding shape as union of all South American countries' shapes
south_am_shape = unary_union(south_am.geometry)
south_am_cities = cities[cities.geometry.within(
south_am_shape)] # reduce to cities in South America
# convert the pandas Series of Point objects to NumPy array of coordinates
coords = points_to_coords(south_am_cities.geometry)
# calculate the regions
region_polys, region_pts = voronoi_regions_from_coords(coords,
south_am_shape,
per_geom=False)
# full checks for voronoi_regions_from_coords() are done in test_voronoi_regions_from_coords_italy()
assert isinstance(region_polys, dict)
assert isinstance(region_pts, dict)
assert len(region_polys) == len(region_pts) == len(coords)
# generate plot
fig, ax = subplot_for_map(show_spines=True)
plot_voronoi_polys_with_points_in_area(ax, south_am_shape, region_polys,
coords, region_pts)
return fig
def test_issue_7b():
centroids = np.array([[496712, 232672], [497987, 235942], [496425, 230252],
[497482, 234933], [499331, 238351], [496081, 231033],
[497090, 233846], [496755, 231645], [498604,
237018]])
polygon = Polygon([[495555, 230875], [496938, 235438], [499405, 239403],
[499676, 239474], [499733, 237877], [498863, 237792],
[499120, 237335], [498321, 235010], [497295, 233185],
[497237, 231359], [496696, 229620], [495982, 230047],
[496154, 230347], [496154, 230347], [495555, 230875]])
poly_shapes, pts, poly_to_pt_assignments = voronoi_regions_from_coords(
centroids, polygon)
assert isinstance(poly_shapes, list)
assert 0 < len(poly_shapes) <= len(centroids)
assert all([isinstance(p, (Polygon, MultiPolygon)) for p in poly_shapes])
assert np.array_equal(points_to_coords(pts), centroids)
assert isinstance(poly_to_pt_assignments, list)
assert len(poly_to_pt_assignments) == len(poly_shapes)
assert all([isinstance(assign, list) for assign in poly_to_pt_assignments])
assert all([len(assign) == 1 for assign in poly_to_pt_assignments
]) # in this case there is a 1:1 correspondance
fig, ax = subplot_for_map()
plot_voronoi_polys_with_points_in_area(ax, polygon, poly_shapes, centroids,
poly_to_pt_assignments)
return fig
def createVoronoi(boundary, cities):
"""process the boundary (polygon border) and points to create the voronoi diagram.
Params:
boundary (geoDataFrame) : the polygon representing the container of the diagram
cities (geoDataFrame) : the points that represent the "seeds" for the diagram
Returns:
All the datastructures needed to plot the voronoi diagram. Most important for us however,
is the "regionPolys" needed for us to determine which ufos are each of the polygons.
cityCoords - the seeds converted to proper coordinate system for the diagram
boundaryShape - the boundary simplified or converted to a single outer ring polygon
regionPolys - a dict of the internal polygons created around each seed
regionPoints - a dict of the points used in the creation of the polygon
"""
# pre-process data so it works with voronoi
boundaryProj = boundary.to_crs(epsg=3395)
citiesProj = cities.to_crs(boundaryProj.crs)
boundaryShape = unary_union(boundaryProj.geometry)
cityCoords = points_to_coords(citiesProj.geometry)
# create the polygons and such
regionPolys, regionPoints = voronoi_regions_from_coords(cityCoords, boundaryShape)
# return all the things created so we can use / plot
return cityCoords, boundaryShape, regionPolys, regionPoints
def update_inner(inner_pos, area_shape):
pts = [p for p in coords_to_points(inner_pos)
if p.within(area_shape)] # converts to shapely Point
coords = points_to_coords(
pts) # convert back to simple NumPy coordinate array
return coords
def test_voronoi_sweden_duplicate_points_with_plot():
area_shape = _get_country_shape('Sweden')
coords = _rand_coords_in_shape(area_shape, 20)
# duplicate a few points
rand_dupl_ind = np.random.randint(len(coords), size=10)
coords = np.concatenate((coords, coords[rand_dupl_ind]))
poly_shapes, pts, poly_to_pt_assignments = voronoi_regions_from_coords(
coords, area_shape, accept_n_coord_duplicates=10)
assert isinstance(poly_shapes, list)
assert 0 < len(poly_shapes) <= 20
assert all([isinstance(p, (Polygon, MultiPolygon)) for p in poly_shapes])
assert np.array_equal(points_to_coords(pts), coords)
assert isinstance(poly_to_pt_assignments, list)
assert len(poly_to_pt_assignments) == len(poly_shapes)
assert all([isinstance(assign, list) for assign in poly_to_pt_assignments])
assert all([0 < len(assign) <= 10 for assign in poly_to_pt_assignments
]) # in this case there is not
# everywhere a 1:1 correspondance
pts_to_poly_assignments = np.array(
get_points_to_poly_assignments(poly_to_pt_assignments))
# make point labels: counts of duplicates per points
count_per_pt = [
sum(pts_to_poly_assignments == i_poly)
for i_poly in pts_to_poly_assignments
]
pt_labels = list(map(str, count_per_pt))
# highlight voronoi regions with point duplicates
count_per_poly = np.array(list(map(len, poly_to_pt_assignments)))
vor_colors = np.repeat('blue', len(poly_shapes)) # default color
vor_colors[count_per_poly > 1] = 'red' # hightlight color
fig, ax = subplot_for_map()
plot_voronoi_polys_with_points_in_area(
ax,
area_shape,
poly_shapes,
coords,
plot_voronoi_opts={'alpha': 0.2},
plot_points_opts={'alpha': 0.4},
voronoi_color=list(vor_colors),
point_labels=pt_labels,
points_markersize=np.array(count_per_pt) * 10)
return fig
def _rand_coords_in_shape(area_shape, n_points):
# generate some random points within the bounds
minx, miny, maxx, maxy = area_shape.bounds
randx = np.random.uniform(minx, maxx, n_points)
randy = np.random.uniform(miny, maxy, n_points)
coords = np.vstack((randx, randy)).T
# use only the points inside the geographic area
pts = [p for p in coords_to_points(coords)
if p.within(area_shape)] # converts to shapely Point
return points_to_coords(pts)
def test_voronoi_geopandas_with_plot():
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
# focus on South America, convert to World Mercator (unit: meters)
south_am = world[world.continent == 'South America'].to_crs(epsg=3395)
cities = cities.to_crs(
south_am.crs) # convert city coordinates to same CRS!
# create the bounding shape as union of all South American countries' shapes
south_am_shape = cascaded_union(south_am.geometry)
south_am_cities = cities[cities.geometry.within(
south_am_shape)] # reduce to cities in South America
# convert the pandas Series of Point objects to NumPy array of coordinates
coords = points_to_coords(south_am_cities.geometry)
# calculate the regions
poly_shapes, pts, poly_to_pt_assignments = voronoi_regions_from_coords(
coords, south_am_shape)
assert isinstance(poly_shapes, list)
assert 0 < len(poly_shapes) <= len(coords)
assert all([isinstance(p, (Polygon, MultiPolygon)) for p in poly_shapes])
assert np.array_equal(points_to_coords(pts), coords)
assert isinstance(poly_to_pt_assignments, list)
assert len(poly_to_pt_assignments) == len(poly_shapes)
assert all([isinstance(assign, list) for assign in poly_to_pt_assignments])
assert all([len(assign) == 1 for assign in poly_to_pt_assignments
]) # in this case there is a 1:1 correspondance
fig, ax = subplot_for_map()
plot_voronoi_polys_with_points_in_area(ax, south_am_shape, poly_shapes,
pts, poly_to_pt_assignments)
return fig
def test_issue_7a():
centroids = np.array([[537300, 213400], [538700, 213700], [536100,
213400]])
polygon = Polygon([[540000, 214100], [535500, 213700], [535500, 213000],
[539000, 213200]])
poly_shapes, pts, poly_to_pt_assignments = voronoi_regions_from_coords(
centroids, polygon)
assert isinstance(poly_shapes, list)
assert 0 < len(poly_shapes) <= len(centroids)
assert all([isinstance(p, (Polygon, MultiPolygon)) for p in poly_shapes])
assert np.array_equal(points_to_coords(pts), centroids)
assert isinstance(poly_to_pt_assignments, list)
assert len(poly_to_pt_assignments) == len(poly_shapes)
assert all([isinstance(assign, list) for assign in poly_to_pt_assignments])
assert all([len(assign) == 1 for assign in poly_to_pt_assignments
]) # in this case there is a 1:1 correspondance
def create_voronoi_polygons(self):
"""
creates Voronoi polygons with `self.antennas_data` as centers, bounded by `self.contour`
:return: GeoPandas DF with VCs
"""
if self.antennas_data.crs == self.contour.crs:
coords = points_to_coords(self.antennas_data.geometry)
poly_shapes, pts = voronoi_regions_from_coords(
coords, self.contour.geometry[0])
voronoi_polygons = gpd.GeoDataFrame({'geometry': poly_shapes},
crs=SWEREF_EPSG)
return voronoi_polygons
else:
logger.error('Objects have different CRSs: %s and %s ' %
(self.antennas_data.crs, self.contour.crs))
def test_voronoi_spain_area_with_plot():
area_shape = _get_country_shape('Spain')
coords = _rand_coords_in_shape(area_shape, 20)
poly_shapes, pts, poly_to_pt_assignments = voronoi_regions_from_coords(
coords, area_shape)
assert isinstance(poly_shapes, list)
assert 0 < len(poly_shapes) <= 20
assert all([isinstance(p, (Polygon, MultiPolygon)) for p in poly_shapes])
assert np.array_equal(points_to_coords(pts), coords)
assert isinstance(poly_to_pt_assignments, list)
assert len(poly_to_pt_assignments) == len(poly_shapes)
assert all([isinstance(assign, list) for assign in poly_to_pt_assignments])
assert all([len(assign) == 1 for assign in poly_to_pt_assignments
]) # in this case there is a 1:1 correspondance
poly_areas = calculate_polygon_areas(poly_shapes,
m2_to_km2=True) # converts m² to km²
assert isinstance(poly_areas, np.ndarray)
assert np.issubdtype(poly_areas.dtype, np.float_)
assert len(poly_areas) == len(poly_shapes)
assert np.all(poly_areas > 0)
fig, ax = subplot_for_map(show_x_axis=True, show_y_axis=True)
voronoi_labels = ['%d km²' % round(a) for a in poly_areas]
plot_voronoi_polys_with_points_in_area(ax,
area_shape,
poly_shapes,
coords,
poly_to_pt_assignments,
voronoi_labels=voronoi_labels,
voronoi_label_fontsize=7,
voronoi_label_color='gray')
return fig
def test_voronoi_italy_with_plot():
area_shape = _get_country_shape('Italy')
coords = _rand_coords_in_shape(area_shape, 100)
poly_shapes, pts, poly_to_pt_assignments = voronoi_regions_from_coords(
coords, area_shape)
assert isinstance(poly_shapes, list)
assert 0 < len(poly_shapes) <= 100
assert all([isinstance(p, (Polygon, MultiPolygon)) for p in poly_shapes])
assert np.array_equal(points_to_coords(pts), coords)
assert isinstance(poly_to_pt_assignments, list)
assert len(poly_to_pt_assignments) == len(poly_shapes)
assert all([isinstance(assign, list) for assign in poly_to_pt_assignments])
assert all([len(assign) == 1 for assign in poly_to_pt_assignments
]) # in this case there is a 1:1 correspondance
fig, ax = subplot_for_map()
plot_voronoi_polys_with_points_in_area(ax, area_shape, poly_shapes, coords,
poly_to_pt_assignments)
return fig
def computeCrossProduct(self, reference_old_entries, second_old_entries):
# 1. Reference distribution is regions, second dist is regions
# - uniform distribute overlap of regions
# 2. Reference distribution is regions, second dist is points
# - map points to regions and perform as usual
# 3. Reference distribution is points, second dist is regions
# - create regions from reference dist. points with Voronoi diagram
# 4. Reference distribution is points, second dist is points
# - " "
if 'POLYGON' in reference_old_entries[0]:
if 'POLYGON' in second_old_entries[0]:
# For 1. compute cross product for new regions
new_entries = SpatialHelper.computeNewRegions(
reference_old_entries, second_old_entries)
#print('inside spatial handler')
#print(len(reference_old_entries))
#print(len(second_old_entries))
#print(len(new_entries))
#print('leaving spatial handler')
elif 'POINT' in second_old_entries[0] or \
type(second_old_entries[0]) is tuple:
# For 2., don't need to compute cross product
# Just use Regions of reference distribution
new_entries = reference_old_entries
elif 'POINT' in reference_old_entries[0] or \
type(reference_old_entries[0]) is tuple:
# For 3. compute Voronoi
ref_points_obj = SpatialHelper.getGeoObjectsFromString(
list(set(reference_old_entries)))
sec_regions_obj = SpatialHelper.getGeoObjectsFromString(
second_old_entries)
sec_regions_obj_union = unary_union(sec_regions_obj)
for point in ref_points_obj:
#Remove points from reference that are outside sec_region_union
if not point.within(sec_regions_obj_union):
raise ValueError(
"Points from reference distribution lie outside union of regions from secondary distribution."
)
#ref_points_obj.remove(point)
#train_df = train_df[train_df.Region != (point.x,point.y)]
coords = points_to_coords(ref_points_obj)
ref_vor_regions_obj, pts, poly_to_pt_assignments = voronoi_regions_from_coords(
coords, sec_regions_obj_union)
#Convert back to string
ref_vor_regions = SpatialHelper.convertGeoObjectsToString(
ref_vor_regions_obj)
#print(ref_vor_regions[0])
new_entries = SpatialHelper.computeNewRegions(
ref_vor_regions, second_old_entries)
# For 3. and 4., return error for now
#raise ValueError("Invalid input for the {} variable. Spatial\
# mismatch variables of the reference distribution\
# must be 'Multipolygon' or 'Polygon.'"
# .format(str(self.node)))
#print('inside spatial handler')
#print(len(reference_old_entries))
#print(len(second_old_entries))
#print(len(new_entries))
#print('leaving spatial handler')
else:
raise ValueError("Invalid input for {} variable. Spatial mismatch\
variables must be a 'Multipolygon', 'Polygon'\
or 'Point', or be a tuple of (X,Y) coordinates.".
format(str(self.node)))
return reference_old_entries, second_old_entries, new_entries
def update(self, outer_pos, inner_pos, UPDATE, EPS=0.1):
global t
t = time.time() - start
outputs = []
global FLAG
if FLAG:
fig, ax = subplot_for_map()
global ax, fig
FLAG = False
def reshape_coords(coords):
new_coords = []
for p in poly_shapes:
for n in coords:
m = Point(n)
if m.within(p):
new_coords.append(n)
return new_coords
def reshape_centroids(centroids):
new_centroids = []
for p in poly_shapes:
for n in centroids:
m = Point(n)
if m.within(p):
new_cent
roids.append(n)
return new_centroids
def match_pair(poly_shapes, coords, new_centroids):
sorted_coords = []
points = coords_to_points(coords)
for i, p in enumerate(points):
c = coords[i]
#print("c: ", c[0],c[1])
for j, poly in enumerate(poly_shapes):
if p.within(poly):
pair = new_centroids[j]
sorted_coords.append(pair)
return sorted_coords
N = 4 #len(inner_pos)
area_shape = Polygon(outer_pos) #update_outer(outer_pos)
# generate some random points within the bounds
minx, miny, maxx, maxy = area_shape.bounds
pts = [p for p in coords_to_points(inner_pos)
if p.within(area_shape)] # converts to shapely Point
while len(pts) < N: #isinstance(compensated, int):
inner_pos = points_to_coords(pts)
print('%d of %d drone"s pos is available' % (len(pts), N))
#print("compensated!!", compensated, type(compensated))
randx = np.random.uniform(minx, maxx, N - len(pts))
randy = np.random.uniform(miny, maxy, N - len(pts))
compensated = np.vstack((randx, randy)).T
inner_pos = np.append(inner_pos, compensated, axis=0)
#print(inner_pos)
#inner_pos = inner_pos[sorted(np.random.choice(inner_pos.shape[0], N, replace=False)), :]
pts = [
p for p in coords_to_points(inner_pos) if p.within(area_shape)
] # converts to shapely Point
ax.clear() # comment out if you want to plot trajectory
coords = points_to_coords(
pts) # convert back to simple NumPy coordinate array
poly_shapes, pts, poly_to_pt_assignments = voronoi_regions_from_coords(
coords, area_shape, accept_n_coord_duplicates=0)
poly_centroids = np.array([p.centroid.coords[0] for p in poly_shapes])
#new_centroids = reshape_centroids(poly_centroids)
# plotting
EPS = EPS
err = 99999
#old_coords = coords
new_centroids = match_pair(poly_shapes, coords, poly_centroids)
for i in range(len(coords)):
xo = coords[i][0]
yo = coords[i][1]
#old_coords[i][0] = xo
#old_coords[i][1] = yo
xc = new_centroids[i][0]
yc = new_centroids[i][1]
#err = np.sqrt((xo-xc)**2 + (yo-yc)**2)
data = [xc, yc]
outputs.append(data) #(np.array((xc, yc)).astype(np.float64))
#if err > EPS:
# # print("UPDARED!!")
# coords[i][0] = xc#xo + 0.2*(xc-xo)
# coords[i][1] = yc#yo + 0.2*(yc-yo)
# draw centroid that each drone follow
for i, centroid in enumerate(new_centroids):
c1 = centroid
ax.plot(c1[0], c1[1], '*', label=str(i))
for coord in coords:
c = coord
ax.plot(c[0], c[1], 'o', alpha=0.5)
fig = plot_voronoi_polys_with_points_in_area(ax, area_shape,
poly_shapes, coords,
poly_to_pt_assignments)
plt.title(str(t) + "[s]")
plt.pause(0.00001)
return outputs
# focus on South America, convert to World Mercator (unit: meters)
south_am = world[world.continent == 'South America'].to_crs(epsg=3395)
cities = cities.to_crs(south_am.crs) # convert city coordinates to same CRS!
# create the bounding shape as union of all South American countries' shapes
south_am_shape = cascaded_union(south_am.geometry)
south_am_cities = cities[cities.geometry.within(south_am_shape)] # reduce to cities in South America
#
# calculate the Voronoi regions, cut them with the geographic area shape and assign the points to them
#
# convert the pandas Series of Point objects to NumPy array of coordinates
coords = points_to_coords(south_am_cities.geometry)
# calculate the regions
poly_shapes, pts, poly_to_pt_assignments = voronoi_regions_from_coords(coords, south_am_shape)
#
# Plotting
#
fig, ax = subplot_for_map()
plot_voronoi_polys_with_points_in_area(ax, south_am_shape, poly_shapes, pts, poly_to_pt_assignments)
ax.set_title('Cities data for South America from GeoPandas\nand Voronoi regions around them')
AoI = FILE_PATHS["AoI_file"]
boundary = gpd.read_file(AoI)
# need to change the projection from 32633 to 4326
gdf = gdf.set_crs(epsg=32633, inplace = True)
gdf_proj =gdf.to_crs(epsg=4326)
# set the projection of the AoI shapefile
boundary = boundary.set_crs(epsg=4326, inplace = True)
boundary_shape = cascaded_union(boundary.geometry)
coords = points_to_coords(gdf_proj.geometry)
# Calculate Voronoi Regions
poly_shapes, pts, poly_to_pt_assignments = voronoi_regions_from_coords(coords, boundary_shape)
# plot the voronoi regions in a map
fig, ax = subplot_for_map()
plot_voronoi_polys_with_points_in_area(ax, boundary_shape, poly_shapes, pts, poly_to_pt_assignments)
ax.set_title('Voronoi regions')
plt.tight_layout()
plt.savefig(shpdir + 'Voronoi_polygons_validation_attributes3.png')
# save the polygon objects in a shapefile
def test_coords_to_points_and_points_to_coords(coords):
# test for bijectivity of points_to_coords and coords_to_points
assert np.array_equal(points_to_coords(coords_to_points(coords)), coords)
def Within(pts, shape):
pts = coords_to_points(pts)
pts = [p for p in pts if p.within(shape)]
return [(x, y) for x, y in points_to_coords(pts)]
def test_coords_to_points_and_points_to_coords(coords):
assert np.array_equal(points_to_coords(coords_to_points(coords)), coords)
# generate some random points within the bounds
minx, miny, maxx, maxy = area_shape.bounds
randx = np.random.uniform(minx, maxx, N_POINTS)
randy = np.random.uniform(miny, maxy, N_POINTS)
coords = np.vstack((randx, randy)).T
# use only the points inside the geographic area
pts = [p for p in coords_to_points(coords)
if p.within(area_shape)] # converts to shapely Point
n_pts = len(pts)
print(
'will use %d of %d randomly generated points that are inside geographic area'
% (n_pts, N_POINTS))
coords = points_to_coords(pts) # convert back to simple NumPy coordinate array
del pts
#%%
#
# calculate the Voronoi regions, cut them with the geographic area shape and assign the points to them
#
region_polys, region_pts = voronoi_regions_from_coords(coords, area_shape)
# calculate area in km², too
poly_areas = calculate_polygon_areas(region_polys,
m2_to_km2=True) # converts m² to km²
def updated_geography_plot(
self, CRS=4326, attribute="EUE", line_attribute="utilization", plot_type="fills"
):
if attribute != "LOLE" and attribute != "EUE":
raise ValueError("can only plot LOLE or EUE")
boundary = self.iso_map[self.iso_map["NAME"] == self.iso_map.at[0, "NAME"]]
boundary = boundary.to_crs(epsg=CRS)
gdf_proj = self.miso_seam_zone_gdf.to_crs(boundary.crs)
# re-assignment due to different zone naming conventions
gdf_proj.at[
gdf_proj[gdf_proj.Seams_Region == "WAPA_DK"].index.values[0], "Seams_Region"
] = "CBPC-NIPCO" # [0] = "CBPC-NIPCO"
gdf_proj.at[
gdf_proj[gdf_proj.Seams_Region == "BREC"].index.values[0], "Seams_Region"
] = "AECIZ"
gdf_proj.at[
gdf_proj[gdf_proj.Seams_Region == "LA-Gulf"].index.values[0], "Seams_Region"
] = "LA-GULF"
# end re-assignment
gdf_merge = pd.merge(
gdf_proj,
self.region_df,
how="left",
left_on="Seams_Region",
right_on="names",
)
self.gdf_merge = gdf_merge
line_gdf = self.create_lines(line_attribute)
labs = list(gdf_merge["Seams_Region"])
attribute_max = gdf_merge[attribute].max()
boundary.geometry = boundary.geometry.buffer(0)
boundary_shape = cascaded_union(boundary.geometry)
coords = points_to_coords(gdf_proj.geometry)
poly_shapes, pts, poly_to_pt_assignments = voronoi_regions_from_coords(
coords, boundary_shape
)
# run plotting
fig, ax = subplot_for_map()
myaxes = plt.axes()
myaxes.set_ylim([20, 50])
myaxes.set_xlim([-104, -82])
# for i,s in enumerate(poly_shapes):
# gdf_merge.at[i,'geometry'] = s
divider = make_axes_locatable(myaxes)
cax = divider.append_axes("bottom", size="5%", pad=0.1)
if plot_type == "bubbles":
gdf_merge.plot(
ax=myaxes,
column=attribute,
cmap="Blues",
legend=True,
cax=cax,
alpha=1.0,
markersize=100,
legend_kwds={
"label": attribute + " (MWh (EUE) or Hours (LOLE) /y)",
"orientation": "horizontal",
},
)
plot_points(
myaxes, pts, 2, labels=labs, alpha=0.0
) # mostly just adds the zonal labels
elif plot_type == "fills":
for i, s in enumerate(poly_shapes):
plot_voronoi_polys(
myaxes,
s,
color="g",
alpha=gdf_merge.at[i, attribute] / attribute_max,
)
gdf_merge.plot(
ax=myaxes,
column=attribute,
cmap="Greens",
legend=True,
cax=cax,
alpha=0.0,
legend_kwds={
"label": attribute + " (MWh (EUE) or Hours (LOLE) /y)",
"orientation": "horizontal",
},
)
plot_points(
myaxes, pts, 2, labels=labs
) # mostly just adds the zonal labels
else:
raise ValueError("plot_type must be either fills or bubbles")
linewidths = list(line_gdf.MW)
linewidths_2 = list(line_gdf.capacity)
# finally, add the tx lines
for lw, lw2 in zip(linewidths, linewidths_2):
line_gdf[line_gdf.MW == lw].plot(
lw=lw2 * 0.001, ax=myaxes, color="k", zorder=2, alpha=0.3
)
line_gdf[line_gdf.MW == lw].plot(
lw=lw * 0.001, ax=myaxes, color="r", zorder=3
)
# could also add a MISO boundary if it seems useful
self.iso_map[self.iso_map["NAME"] == self.iso_map.at[0, "NAME"]].plot(
ax=myaxes, facecolor="b", edgecolor="y", alpha=0.04, linewidth=2, zorder=1
)
# last big thing would be a helpful legend....
self.states_map.plot(ax=myaxes, edgecolor="k", facecolor="None", alpha=0.3)
# states_map.plot(ax=myaxes, edgecolor="k", facecolor="None")
myaxes.set_title("MISO regions polygons \n (fill based on " + attribute + ")")
# add manual legends to help interpret plot
cap_1 = round(max(linewidths_2), -3)
cap_2 = round(max(linewidths_2), -3) * 2.0 / 3.0
cap_3 = round(max(linewidths_2), -3) * 1.0 / 3.0
utilization_1 = round(max(linewidths), -2)
utilization_2 = round(max(linewidths), -2) * 2.0 / 3.0
utilization_3 = round(max(linewidths), -2) * 1.0 / 3.0
custom_capacity_lines = [
Line2D([0], [0], color="k", lw=cap_1 * 0.001, alpha=0.3),
Line2D([0], [0], color="k", lw=cap_2 * 0.001, alpha=0.3),
Line2D([0], [0], color="k", lw=cap_3 * 0.001, alpha=0.3),
Line2D([0], [0], color="r", lw=utilization_1 * 0.001),
Line2D([0], [0], color="r", lw=utilization_2 * 0.001),
Line2D([0], [0], color="r", lw=utilization_3 * 0.001),
]
myaxes.legend(
custom_capacity_lines,
[
str(int(cap_1)) + " MW",
str(int(cap_2)) + " MW",
str(int(cap_3)) + " MW",
str(int(utilization_1)) + " MW",
str(int(utilization_2)) + " MW",
str(int(utilization_3)) + " MW",
],
loc="lower left",
title="Line Capacity Line " + line_attribute.capitalize(),
fontsize="x-small",
title_fontsize="small",
frameon=False,
ncol=2,
)
# custom_utilization_lines = []
# myaxes.legend(custom_utilization_lines, [],
# loc="lower right",title="Line "+line_attribute, fontsize="x-small",title_fontsize="small",frameon=False)
print("plotted")
plt.savefig(
os.path.join(
self.results_folder, "voronoi" + plot_type + self.casename + ".jpg"
),
dpi=300,
)
# eventually create values for loading EUE, lole, etc
return None
