"""
Visualizing NYC Boroughs
------------------------

Visualize the Boroughs of New York City with Geopandas.

This example generates many images that are used in the documentation. See
the `Geometric Manipulations <geometric_manipulations>` example for more
details.

First we'll import a dataset containing each borough in New York City. We'll
use the ``datasets`` module to handle this quickly.
"""
import numpy as np
import matplotlib.pyplot as plt
from shapely.geometry import Point
from geopandas import GeoSeries, GeoDataFrame
import geopandas as gpd

np.random.seed(1)
DPI = 100

path_nybb = gpd.datasets.get_path('nybb')
boros = GeoDataFrame.from_file(path_nybb)
boros = boros.set_index('BoroCode')
boros

##############################################################################
# Next, we'll plot the raw data
ax = boros.plot()
plt.xticks(rotation=90)
plt.savefig('nyc.png', dpi=DPI, bbox_inches='tight')

##############################################################################
# We can easily retrieve the convex hull of each shape. This corresponds to
# the outer edge of the shapes.
boros.geometry.convex_hull.plot()
plt.xticks(rotation=90)

# Grab the limits which we'll use later
xmin, xmax = plt.gca().get_xlim()
ymin, ymax = plt.gca().get_ylim()

plt.savefig('nyc_hull.png', dpi=DPI, bbox_inches='tight')

##############################################################################
# We'll generate some random dots scattered throughout our data, and will
# use them to perform some set operations with our boroughs. We can use
# GeoPandas to perform unions, intersections, etc.

N = 2000  # number of random points
R = 2000  # radius of buffer in feet

#xmin, xmax, ymin, ymax = 900000, 1080000, 120000, 280000
xc = (xmax - xmin) * np.random.random(N) + xmin
yc = (ymax - ymin) * np.random.random(N) + ymin
pts = GeoSeries([Point(x, y) for x, y in zip(xc, yc)])
mp = pts.buffer(R).unary_union
boros_with_holes = boros.geometry - mp
boros_with_holes.plot()
plt.xticks(rotation=90)
plt.savefig('boros_with_holes.png', dpi=DPI, bbox_inches='tight')

##############################################################################
# Finally, we'll show the holes that were taken out of our boroughs.

holes = boros.geometry & mp
holes.plot()
plt.xticks(rotation=90)
plt.savefig('holes.png', dpi=DPI, bbox_inches='tight')
plt.show()

"""
Visualizing NYC Boroughs
------------------------

Visualize the Boroughs of New York City with Geopandas.

This example generates many images that are used in the documentation. See
the `Geometric Manipulations <geometric_manipulations>` example for more
details.

First we'll import a dataset containing each borough in New York City. We'll
use the ``datasets`` module to handle this quickly.
"""
import numpy as np
import matplotlib.pyplot as plt
from shapely.geometry import Point
from geopandas import GeoSeries, GeoDataFrame
import geopandas as gpd

np.random.seed(1)
DPI = 100

path_nybb = gpd.datasets.get_path('nybb')
boros = GeoDataFrame.from_file(path_nybb)
boros = boros.set_index('BoroCode')
boros

##############################################################################
# Next, we'll plot the raw data
ax = boros.plot()
plt.xticks(rotation=90)
plt.savefig('nyc.png', dpi=DPI, bbox_inches='tight')

##############################################################################
# We can easily retrieve the convex hull of each shape. This corresponds to
# the outer edge of the shapes.
boros.geometry.convex_hull.plot()
plt.xticks(rotation=90)

# Grab the limits which we'll use later
xmin, xmax = plt.gca().get_xlim()
ymin, ymax = plt.gca().get_ylim()

plt.savefig('nyc_hull.png', dpi=DPI, bbox_inches='tight')

##############################################################################
# We'll generate some random dots scattered throughout our data, and will
# use them to perform some set operations with our boroughs. We can use
# GeoPandas to perform unions, intersections, etc.

N = 2000  # number of random points
R = 2000  # radius of buffer in feet

#xmin, xmax, ymin, ymax = 900000, 1080000, 120000, 280000
xc = (xmax - xmin) * np.random.random(N) + xmin
yc = (ymax - ymin) * np.random.random(N) + ymin
pts = GeoSeries([Point(x, y) for x, y in zip(xc, yc)])
mp = pts.buffer(R).unary_union
boros_with_holes = boros.geometry - mp
boros_with_holes.plot()
plt.xticks(rotation=90)
plt.savefig('boros_with_holes.png', dpi=DPI, bbox_inches='tight')

##############################################################################
# Finally, we'll show the holes that were taken out of our boroughs.

holes = boros.geometry & mp
holes.plot()
plt.xticks(rotation=90)
plt.savefig('holes.png', dpi=DPI, bbox_inches='tight')
plt.show()

Visualizing NYC Boroughs ------------------------  Visualize the Boroughs of New York City with Geopandas.  This example generates many images that are used in the documentation. See the `Geometric Manipulations <geometric_manipulations>` example for more details.  First we'll import a dataset containing each borough in New York City. We'll use the ``datasets`` module to handle this quickly. ############################################################################## # Next, we'll plot the raw data ############################################################################## # We can easily retrieve the convex hull of each shape. This corresponds to # the outer edge of the shapes. # Grab the limits which we'll use later ############################################################################## # We'll generate some random dots scattered throughout our data, and will # use them to perform some set operations with our boroughs. We can use # GeoPandas to perform unions, intersections, etc. # number of random points # radius of buffer in feet #xmin, xmax, ymin, ymax = 900000, 1080000, 120000, 280000 ############################################################################## # Finally, we'll show the holes that were taken out of our boroughs.