import numpy as np import pysal as ps import random as rdm from pysal.contrib.viz import mapping as maps
This document describes the main structure, components and usage of the mapping module in
PySAL. The is organized around three main layers:
This includes basic functionality to read spatial data from a file (currently only shapefiles supported) and produce rudimentary Matplotlib objects. The main methods are:
map_poly_shape: to read in polygon shapefiles
map_line_shape: to read in line shapefiles
map_point_shape: to read in point shapefiles
These methods all support an option to subset the observations to be plotted (very useful when missing values are present). They can also be overlaid and combined by using the
setup_ax function. the resulting object is very basic but also very flexible so, for minds used to matplotlib this should be good news as it allows to modify pretty much any property and attribute.
shp_link = ps.examples.get_path('columbus.shp') shp = ps.open(shp_link) some = [bool(rdm.getrandbits(1)) for i in ps.open(shp_link)] fig = figure() base = maps.map_poly_shp(shp) base.set_facecolor('none') base.set_linewidth(0.75) base.set_edgecolor('0.8') some = maps.map_poly_shp(shp, which=some) some.set_alpha(0.5) some.set_linewidth(0.) cents = np.array([poly.centroid for poly in ps.open(shp_link)]) pts = scatter(cents[:, 0], cents[:, 1]) pts.set_color('red') ax = maps.setup_ax([base, some, pts]) fig.add_axes(ax) show()
This layer comprises functions that perform usual transformations on matplotlib objects, such as color coding objects (points, polygons, etc.) according to a series of values. This includes the following methods:
net_link = ps.examples.get_path('eberly_net.shp') net = ps.open(net_link) values = np.array(ps.open(net_link.replace('.shp', '.dbf')).by_col('TNODE')) pts_link = ps.examples.get_path('eberly_net_pts_onnetwork.shp') pts = ps.open(pts_link) fig = figure() netm = maps.map_line_shp(net) netc = maps.base_choropleth_unique(netm, values) ptsm = maps.map_point_shp(pts) ptsm = maps.base_choropleth_classif(ptsm, values) ptsm.set_alpha(0.5) ptsm.set_linewidth(0.) ax = maps.setup_ax([netc, ptsm]) fig.add_axes(ax) show()
This currently includes the following end-user functions:
plot_poly_lines: very quick shapefile plotting.
plot_choropleth: for quick plotting of several types of chocopleths.
shp_link = ps.examples.get_path('columbus.shp') values = np.array(ps.open(ps.examples.get_path('columbus.dbf')).by_col('HOVAL')) types = ['classless', 'unique_values', 'quantiles', 'fisher_jenks', 'equal_interval'] for typ in types: maps.plot_choropleth(shp_link, values, typ, title=typ)
General concepts and specific ideas to implement over time, with enough description so they can be brought to life.
Support for points (dots) is still not quite polished. Ideally, one would like to create a
PathCollection from scratch so it is analogue to the creation of a
LineCollection. However, for the time being, we are relying on the wrapper
plt.scatter, which makes it harder to extract the collection and plug it in a different figure. For that reason, it is recommended that, for the time being, one creates the line and/or polygon map as shown in this notebook and then grabs the output axis and uses
ax.scatter to overlay the points.
PathCollection created by
plt.scatter is detailed on line 3142 of
_axes.py. Maybe we can take some inspiration from there to create our own
PathCollection for points so they live at the same level as polygons.