def test_geometries(tmpdir):
test_file = '{}/test_file.shp'.format(tmpdir)
_create_test_input_files(test_file)
df = GeoDataFrame.from_file(test_file)
p = (ggplot(df) + aes(fill='geometry.bounds.miny') + geom_map() +
geom_map(draw='Point', size=4) + geom_map(draw='LineString', size=2) +
labs(fill='miny'))
assert p + _theme == 'geometries'
def test_geometries(tmpdir):
test_file = '{}/test_file.shp'.format(tmpdir)
_create_test_input_files(test_file)
df = GeoDataFrame.from_file(test_file)
p = (ggplot(df)
+ aes(fill='geometry.bounds.miny')
+ geom_map()
+ geom_map(draw='Point', size=4)
+ geom_map(draw='LineString', size=2)
+ labs(fill='miny')
)
assert p + _theme == 'geometries'
def test_geometries(tmpdir):
point_file = '{}/test_file_point.shp'.format(tmpdir)
polygon_file = '{}/test_file_polygon.shp'.format(tmpdir)
polyline_file = '{}/test_file_polyline.shp'.format(tmpdir)
_point_file(point_file)
_polygon_file(polygon_file)
_polyline_file(polyline_file)
df_point = GeoDataFrame.from_file(point_file)
df_polygon = GeoDataFrame.from_file(polygon_file)
df_polyline = GeoDataFrame.from_file(polyline_file)
p = (ggplot() + aes(fill='geometry.bounds.miny') + geom_map(df_polygon) +
geom_map(df_point, size=4) + geom_map(df_polyline, size=2) +
labs(fill='miny'))
assert p + _theme == 'geometries'
def test_facet_wrap(tmpdir):
polygon_file = '{}/test_file_polygon.shp'.format(tmpdir)
_polygon_file(polygon_file)
df_polygon = GeoDataFrame.from_file(polygon_file)
df_polygon['shape'] = ['rectangle', 'triangle']
p = (ggplot() + aes(fill='geometry.bounds.miny') + geom_map(df_polygon) +
facet_wrap('shape') + labs(fill='miny'))
assert p + _theme == 'facet_wrap'
def plot_restaurants_per_neighborhood(filepath, restaurant_data_file,
pittsburgh_shapefile):
mexican_restaurants = pd.read_csv(filepath + restaurant_data_file)
gdf = gpd.GeoDataFrame(
mexican_restaurants,
geometry=gpd.points_from_xy(mexican_restaurants.longitude,
mexican_restaurants.latitude),
)
restaurant_locations = gdf.filter(items=["geometry"])
# import Pittsburgh neighborhood shapefile
neighborhood_polygons = gpd.read_file(pittsburgh_shapefile).filter(
items=["hood", "hood_no", "geometry"])
# spatial join to figure out which neighborhood each restaurant is in
restaurants_in_polys = gpd.sjoin(restaurant_locations,
neighborhood_polygons,
how="inner",
op="intersects")
restaurants_counted = restaurants_in_polys.groupby(
"hood_no").count().reset_index()
restaurants_in_hoods = restaurants_counted.filter(
items=["hood_no", "hood"])
restaurants_in_hoods.rename(columns={"hood": "num_restaurants"},
inplace=True)
restaurants_per_shape = gpd.GeoDataFrame(
pd.merge(neighborhood_polygons, restaurants_in_hoods, how="left"))
restaurant_map = (p.ggplot(restaurants_per_shape) +
p.geom_map(p.aes(fill="num_restaurants")) +
p.scale_colour_gradient(low="white", high="black") +
p.theme(
panel_background=p.element_rect(fill="white"),
axis_text_x=p.element_blank(),
axis_text_y=p.element_blank(),
axis_ticks_major_x=p.element_blank(),
axis_ticks_major_y=p.element_blank(),
)) + p.scale_fill_gradient(
low="#efefef", high="#073763", name="# Restaurants")
restaurant_map.save("restaurant_map.png")