Short for "Spatial Layers," Slayer is a Python wrapper around deck.gl.
Features
- Scale. Built on deck.gl, Slayer plots hundreds of thousands of points.
- Focus on Data Science, Analytics, and GIS. API treats Pandas as a first class citizen.
- Time. Built for plotting data over time.
For example, 1.6M LIDAR points visualized here:
Plotting all registered businesses in San Francisco by neighborhood:
import slayer as sly
import pandas as pd
# A data frame of all the businesses registered in SF in the last 50+ years
DATA_URL = 'https://raw.githubusercontent.com/ajduberstein/sf_growth/master/public/data/business.csv'
businesses = pd.read_csv(DATA_URL)
# Automatically fit to the center 95% of your data
auto_view = sly.Viewport.autocompute(businesses[['lng', 'lat']], view_proportion=0.95)
# Create a random set of 100 colors, on the variable `neighborhood`
color_scale = sly.ColorScale(
num_classes=100,
palette='random',
variable_name='neighborhood',
scale_type='categorical_random')
# Create a geospatial scatterplot
s = sly.Slayer(auto_view, drag_boxes=True)
L = sly.Scatterplot(
businesses,
position=['lng', 'lat'],
radius=50,
color=color_scale)
# Write it out to disk and visualize it
# If in a Jupyter notebook, a pane will open
(s + L).to_html('demo.html', interactive=True)Get a Mapbox API key and set it as an environment variable (or list it in your script as above).
This package isn't on Pip yet.
In the shell, type:
>>> git clone https://github.com/ajduberstein/slayer
>>> cd slayer
>>> python setup.py install