"""
Plotting with Geoplot and GeoPandas
-----------------------------------

`Geoplot <https://residentmario.github.io/geoplot/index.html>`_ is a Python
library providing a selection of easy-to-use geospatial visualizations. It is
built on top of the lower-level `CartoPy <http://scitools.org.uk/cartopy/>`_,
covered in a separate section of this tutorial, and is designed to work with
GeoPandas input.

This example is a brief tour of the `geoplot` API. For more details on the
library refer to `its documentation
<https://residentmario.github.io/geoplot/index.html>`_.

First we'll load in the data using GeoPandas.
"""
import geopandas

path = geopandas.datasets.get_path('naturalearth_lowres')
df = geopandas.read_file(path)
# Add a column we'll use later
df['gdp_pp'] = df['gdp_md_est'] / df['pop_est']

boroughs = geopandas.read_file(geopandas.datasets.get_path('nybb')).to_crs(epsg='4326')
injurious_collisions = geopandas.read_file(
    "https://github.com/ResidentMario/geoplot-data/raw/master/nyc-injurious-collisions.geojson")


###############################################################################
# Plotting with Geoplot
# =====================
#
# We start out by replicating the basic GeoPandas world plot using Geoplot.
import geoplot

geoplot.polyplot(df, figsize=(8, 4))

###############################################################################
# Geoplot can re-project data into any of the map projections provided by
# CartoPy (see the list
# `here <http://scitools.org.uk/cartopy/docs/latest/crs/projections.html>`_).

import geoplot.crs as gcrs
ax = geoplot.polyplot(df, projection=gcrs.Orthographic(), figsize=(8, 4))
ax.set_global()
ax.outline_patch.set_visible(True)

###############################################################################
# ``polyplot`` is trivial and can only plot the geometries you pass to it. If
# you want to use color as a visual variable, specify a ``choropleth``. Here
# we sort GDP per person by country into five buckets by color.

geoplot.choropleth(df, hue='gdp_pp', cmap='Greens', figsize=(8, 4))

###############################################################################
# If you want to use size as a visual variable, you want a ``cartogram``. Here
# are population estimates for countries in Africa.

geoplot.cartogram(df[df['continent'] == 'Africa'],
                  scale='pop_est', limits=(0.2, 1), figsize=(7, 8))

###############################################################################
# If we have data in the shape of points in space, we may generate a
# three-dimensional heatmap on it using ``kdeplot``. This example also
# demonstrates how easy it is to stack plots on top of one another.

ax = geoplot.kdeplot(injurious_collisions.sample(1000),
                     shade=True, shade_lowest=False,
                     clip=boroughs.geometry)
geoplot.polyplot(boroughs, ax=ax)

###############################################################################
# Alternatively, we may partition the space into neighborhoods automatically,
# using Voronoi tessellation.

ax = geoplot.voronoi(
    injurious_collisions.sample(1000),
    hue='NUMBER OF PERSONS INJURED', cmap='Reds', scheme='fisher_jenks',
    clip=boroughs.geometry,
    linewidth=0)
geoplot.polyplot(boroughs, ax=ax)

###############################################################################
# Again, these are just some of the plots you can make with Geoplot. There are
# several other possibilities not covered in this brief introduction. For more
# examples, refer to the
# `Gallery <https://residentmario.github.io/geoplot/gallery.html>`_ in the
# Geoplot documentation.