wktplot 2.0.dev0

Well-known-text and shapely object visualiation library

WKTPlot

Wrapper around the Bokeh library for plotting well-known-text strings and shapely geometries!

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Installation

pip install wktplot

Description

The well-known-text standard is very common for working with and representing geospatial data, however it is painful to visualize them programatically. The Shapely library extends the functionality of the well-known-text standard with a rich assortment of geometry objects and operations, but it doesn't make it any easier to visualize.

WKTPlot is a library provides an easy-to-use API for visualizing well-known-text strings and shapely objects programatically. This library wraps around the Bokeh library, which is a powerful plotting library for generating interactive visualizations. Bokeh also provides a rich assortment of stylizing options which are all usable through WKTPlot's add_shape method.

Basic Usage

from shapely.geometry import Polygon

from wktplot import WKTPlot



# Create plot object

plot = WKTPlot(title="My first plot!", save_dir="/path/to/directory")



# Define shapes either through well-known-text (WKT) string, or shapely object

shape_1 = "POINT (30 10)"

shape_2 = Polygon([[30, 10], [40, 40], [20, 40], [10, 20], [30, 10]])



# Add shapes to the plot

plot.add_shape(shape_1, fill_color="green", line_width=3)

plot.add_shape(shape_2, fill_color="cyan", fill_alpha=0.7)



# Save the plot to disk [/path/to/directory/my_first_plot.html]

plot.save()

Supported datatypes

WKTPlot supports majority of shapely objects including:

• Point

• MultiPoint

• LineString

• MultiLineString

• LinearRing

• Polygon

• MultiPolygon

• GeometryCollection

Advanced Usage

Example for plotting from shapefile. Shapefile is of California's county boundaries from here.

import shapefile  # pyshp module



from random import randrange

from shapely.geometry import Polygon

from wktplot import WKTPlot



def get_rand_color():

    return f"#{randrange(0, 0xffffff):0>6x}"



plot = WKTPlot(title="California Counties", save_dir="~/scratch")

with shapefile.Reader("~/scratch/CA_Counties_TIGER2016.shp") as shp:

    for shape in shp.shapes():

        p = Polygon(shape.points)

        plot.add_shape(p, fill_color=get_rand_color())

plot.save()

Which will result in this output:

Dependency Limitations

• Shapely version 1.8 is not yet compatible with Python 3.9 on M1 MacOS Big Sur: issue

• Shapely version 1.8 is not yet compatible with Python 3.9 on Windows: issue

Future Plans

• Add native support for visualizing GeoDataframes and shapefiles.

• Make web view more interactive.