def create_scalemixin():
scalemixin = ScaleMixin()
# set props the mixin is responsible for
scalemixin.kwargs = {'scale': 'foo', 'limits': (1, 5), 'scale_func': None}
# set props set by the plot object initializer
scalemixin.ax = None
scalemixin.figsize = (8, 6)
scalemixin.extent = None
scalemixin.projection = None
np.random.seed(42)
scalemixin.df = gpd.GeoDataFrame(
{'foo': np.random.random(100), 'geometry': utils.gaussian_points(n=100)}
)
return scalemixin
def create_huemixin():
huemixin = HueMixin()
# set props the mixin is responsible for
huemixin.kwargs = {'hue': 'foo', 'cmap': 'viridis', 'norm': None}
# set props set by the plot object initializer
huemixin.ax = None
huemixin.figsize = (8, 6)
huemixin.extent = None
huemixin.projection = None
np.random.seed(42)
huemixin.df = gpd.GeoDataFrame(
{'foo': np.random.random(100), 'geometry': utils.gaussian_points(n=100)}
)
return huemixin
def create_legendmixin(legend_vars):
legendmixin = LegendMixin()
# set props the mixin is responsible for
legendmixin.kwargs = {
'legend': True,
'legend_labels': None,
'legend_values': None,
'legend_kwargs': None,
'legend_var': None
}
# set props controlled by the plot object initializer
_, ax = plt.subplots(figsize=(8, 6))
legendmixin.ax = ax
legendmixin.figsize = (8, 6)
legendmixin.extent = None
legendmixin.projection = None
# set data prop
np.random.seed(42)
legendmixin.df = gpd.GeoDataFrame({
'foo':
np.random.random(100),
'geometry':
utils.gaussian_points(n=100)
})
# set props controlled by the hue initializer, if appropriate
if 'hue' in legend_vars:
legendmixin.colors = ['black'] * 100
legendmixin.hue = legendmixin.df.foo
legendmixin.mpl_cm_scalar_mappable = ScalarMappable(
cmap='Reds')
legendmixin.k = None
legendmixin.categorical = False
legendmixin.categories = None
legendmixin.color_kwarg = 'color'
legendmixin.default_color = 'steelblue'
# set props controlled by the scale initializer, if appropriate
if 'scale' in legend_vars:
legendmixin.scale = legendmixin.df.foo
legendmixin.limits = (1, 5)
legendmixin.scale_func = None
legendmixin.dscale = lambda v: 1
legendmixin.sizes = [1] * 100
return legendmixin
def create_clipmixin():
clipmixin = ClipMixin()
clipmixin.kwargs = {
'clip': gpd.GeoSeries(Polygon([[0, 0], [0, 100], [100, 100], [100, 0]]))
}
clipmixin.ax = None
clipmixin.figsize = (8, 6)
clipmixin.extent = None
clipmixin.projection = None
np.random.seed(42)
points = utils.gaussian_points(n=2000)
geoms = np.hstack([
utils.gaussian_polygons(points, n=50), utils.gaussian_multi_polygons(points, n=50)
])
clipmixin.df = gpd.GeoDataFrame({
'foo': np.random.random(len(geoms)),
'geometry': geoms,
})
return clipmixin
import numpy as np
import geopandas as gpd
from shapely.geometry import Polygon
from matplotlib.colors import Normalize
import matplotlib.pyplot as plt
from geoplot import utils
from geoplot import (
pointplot, voronoi, kdeplot, polyplot, webmap, choropleth, cartogram, quadtree,
sankey
)
from geoplot.crs import AlbersEqualArea, WebMercator
np.random.seed(42)
p_srs = gpd.GeoSeries(utils.gaussian_points(n=100))
p_df = gpd.GeoDataFrame(geometry=p_srs)
p_df = p_df.assign(var=p_df.geometry.map(lambda p: abs(p.y) + abs(p.x)))
p_df = p_df.assign(var_cat=np.floor(p_df['var'] // (p_df['var'].max() / 5)).astype(str))
poly_df = gpd.GeoDataFrame(geometry=utils.gaussian_polygons(p_srs.geometry, n=10))
poly_df = poly_df.assign(
var=poly_df.geometry.centroid.x.abs() + poly_df.geometry.centroid.y.abs()
)
ls_df = gpd.GeoDataFrame(geometry=utils.gaussian_linestrings(p_srs.geometry))
ls_df = ls_df.assign(var=ls_df.geometry.centroid.x.abs() + ls_df.geometry.centroid.y.abs())
clip_geom = gpd.GeoSeries(Polygon([[-10, -10], [10, -10], [10, 10], [-10, 10]]))
non_clip_geom = gpd.GeoSeries(Polygon([[-30, -30], [30, -30], [30, 30], [-30, 30]]))
def setUp(self):
np.random.seed(42)
p_srs = gpd.GeoSeries(utils.gaussian_points(n=100))
self.p_df = gpd.GeoDataFrame(geometry=p_srs)
