How to use the geoplot.pointplot function in geoplot

def test_scale_params(kwargs):
    return pointplot(p_df, **kwargs).get_figure()

def test_pointplot(self):
        try:
            gplt.pointplot(list_gaussian_points, projection=gcrs.PlateCarree(), color='white')

            gplt.pointplot(list_gaussian_points, projection=gcrs.PlateCarree(), s=5)

            gplt.pointplot(list_gaussian_points, projection=gcrs.PlateCarree(),
                           legend_kwargs={'fancybox': False})
        finally: plt.close()

import sys; sys.path.insert(0, '../')
import geoplot as gplt
from geoplot import crs as gcrs
import geopandas as gpd


# cf. https://github.com/Toblerity/Shapely/issues/435

# Fiona/Shapely/Geopandas test.
cities = gpd.read_file("../data/cities/citiesx010g.shp")
census_tracts = gpd.read_file("../data/nyc_census_tracts/census_tracts_2010.geojson")


# Cartopy test.
gplt.pointplot(cities.head(50), extent=(10, 20, 10, 20))

def test_pointplot(self):
        try:
            gplt.pointplot(list_gaussian_points, projection=gcrs.PlateCarree(), color='white')

            gplt.pointplot(list_gaussian_points, projection=gcrs.PlateCarree(), s=5)

            gplt.pointplot(list_gaussian_points, projection=gcrs.PlateCarree(),
                           legend_kwargs={'fancybox': False})
        finally: plt.close()

ax=axarr[1][0], scale_func=log_scale, **pointplot_kwargs
)
axarr[1][0].set_title("Log Scale")


def power_scale(minval, maxval):
    def scalar(val):
        val = val + abs(minval) + 1
        return (val/1000)**2
    return scalar

gplt.polyplot(
    contiguous_usa.geometry, 
    ax=axarr[1][1], **polyplot_kwargs
)
gplt.pointplot(
    continental_usa_cities.query("POP_2010 > 10000"),
    ax=axarr[1][1], scale_func=power_scale, **pointplot_kwargs
)
axarr[1][1].set_title("Power Scale")

plt.suptitle('Continental US Cities by Elevation, 2016', fontsize=16)

plt.subplots_adjust(top=0.95)
plt.savefig("usa-city-elevations.png", bbox_inches='tight')

import geopandas as gpd
import geoplot as gplt
import geoplot.crs as gcrs
import matplotlib.pyplot as plt

nyc_boroughs = gpd.read_file(gplt.datasets.get_path('nyc_boroughs'))
collisions = gpd.read_file(gplt.datasets.get_path('nyc_collision_factors'))

ax = gplt.quadtree(
    collisions, nmax=1,
    projection=gcrs.AlbersEqualArea(), clip=nyc_boroughs,
    facecolor='lightgray', edgecolor='white', zorder=0
)
gplt.pointplot(collisions, s=1, ax=ax)

plt.title("New York Ciy Traffic Collisions, 2016")
plt.savefig("nyc-collisions-quadtree.png", bbox_inches='tight', pad_inches=0)

`_
"""

import geopandas as gpd
import geoplot as gplt
import geoplot.crs as gcrs
import matplotlib.pyplot as plt
import mplleaflet

boston_airbnb_listings = gpd.read_file(gplt.datasets.get_path('boston_airbnb_listings'))

ax = gplt.kdeplot(
    boston_airbnb_listings, cmap='viridis', projection=gcrs.WebMercator(), figsize=(12, 12),
    shade=True
)
gplt.pointplot(boston_airbnb_listings, s=1, color='black', ax=ax)
gplt.webmap(boston_airbnb_listings, ax=ax)
plt.title('Boston AirBnB Locations, 2016', fontsize=18)

fig = plt.gcf()
plt.savefig("boston-airbnb-kde.png", bbox_inches='tight', pad_inches=0.1)
# mplleaflet.show(fig)

continental_usa_cities = gpd.read_file(gplt.datasets.get_path('usa_cities'))
continental_usa_cities = continental_usa_cities.query('STATE not in ["AK", "HI", "PR"]')
contiguous_usa = gpd.read_file(gplt.datasets.get_path('contiguous_usa'))


proj = gcrs.AlbersEqualArea(central_longitude=-98, central_latitude=39.5)
f, axarr = plt.subplots(2, 2, figsize=(12, 8), subplot_kw={'projection': proj})

polyplot_kwargs = {'facecolor': (0.9, 0.9, 0.9), 'linewidth': 0}
pointplot_kwargs = {
    'scale': 'ELEV_IN_FT', 'edgecolor': 'white', 'linewidth': 0.5, 'color': 'black'
}


gplt.polyplot(contiguous_usa.geometry, ax=axarr[0][0], **polyplot_kwargs)
gplt.pointplot(
    continental_usa_cities.query("POP_2010 > 10000"),
    ax=axarr[0][0], limits=(0.1, 10), **pointplot_kwargs
)
axarr[0][0].set_title("Linear Scale")


def identity_scale(minval, maxval):
    def scalar(val):
        return 2
    return scalar

gplt.polyplot(contiguous_usa.geometry, ax=axarr[0][1], **polyplot_kwargs)
gplt.pointplot(
    continental_usa_cities.query("POP_2010 > 10000"),
    ax=axarr[0][1], scale_func=identity_scale, **pointplot_kwargs
)

ax2 = plt.subplot(122, projection=proj)

ax1 = gplt.pointplot(
    nyc_fatal_collisions, projection=proj,
    hue='BOROUGH', cmap='Set1',
    edgecolor='white', linewidth=0.5,
    scale='NUMBER OF PERSONS KILLED', limits=(8, 24),
    legend=True, legend_var='scale',
    legend_kwargs={'loc': 'upper left', 'markeredgecolor': 'black'},
    legend_values=[2, 1], legend_labels=['2 Fatalities', '1 Fatality'],
    ax=ax1
)
gplt.polyplot(nyc_boroughs, ax=ax1)
ax1.set_title("Fatal Crashes in New York City, 2016")

gplt.pointplot(
    nyc_injurious_collisions, projection=proj,
    hue='BOROUGH', cmap='Set1',
    edgecolor='white', linewidth=0.5,
    scale='NUMBER OF PERSONS INJURED', limits=(4, 20),
    legend=True, legend_var='scale',
    legend_kwargs={'loc': 'upper left', 'markeredgecolor': 'black'},
    legend_values=[20, 15, 10, 5, 1],
    legend_labels=['20 Injuries', '15 Injuries', '10 Injuries', '5 Injuries', '1 Injury'],
    ax=ax2
)
gplt.polyplot(nyc_boroughs, ax=ax2, projection=proj)
ax2.set_title("Injurious Crashes in New York City, 2016")

plt.savefig("nyc-collisions-map.png", bbox_inches='tight', pad_inches=0)

continental_usa_cities = gpd.read_file(gplt.datasets.get_path('usa_cities'))
continental_usa_cities = continental_usa_cities.query('STATE not in ["AK", "HI", "PR"]')
contiguous_usa = gpd.read_file(gplt.datasets.get_path('contiguous_usa'))
scheme = mc.Quantiles(continental_usa_cities['POP_2010'], k=5)

ax = gplt.polyplot(
    contiguous_usa, 
    zorder=-1,
    linewidth=1,
    projection=gcrs.AlbersEqualArea(),
    edgecolor='white',
    facecolor='lightgray',
    figsize=(8, 12)
)
gplt.pointplot(
    continental_usa_cities, 
    scale='POP_2010',
    limits=(2, 30),
    hue='POP_2010',
    cmap='Blues',
    scheme=scheme,
    legend=True,
    legend_var='scale',
    legend_values=[8000000, 2000000, 1000000, 100000],
    legend_labels=['8 million', '2 million', '1 million', '100 thousand'],
    legend_kwargs={'frameon': False, 'loc': 'lower right'},
    ax=ax
)


plt.title("Large cities in the contiguous United States, 2010")