def plot(self,
xys,
neighborhoods=None,
shpfile=None,
outfile=None,
title=None):
if self.happy_ending:
cm = get_cmap('RdBu')
cm = get_cmap('Accent')
f = plt.figure()
ax = f.add_subplot(111)
grid = ax.scatter(xys[:, 0], xys[:, 1], alpha=0.75, linewidths=0, \
s=80, marker=None, color='0.6', cmap=cm)
axys = xys[self.agent_xyids, :]
ax.scatter(axys[:, 0], axys[:, 1], alpha=0.8, linewidths=0, \
s=20, marker='o', c=self.group_map, cmap=cm)
props = self.prop_groups + [1. - sum(self.prop_groups)]
if title == None:
title = "%i agents | %s share | %.2f similar wanted"\
%(self.pop_size, '_'.join(map(str, props)), \
self.pct_similar_wanted)
plt.title(title, color='0.3')
ax.set_frame_on(False)
ax.axes.get_yaxis().set_visible(False)
ax.axes.get_xaxis().set_visible(False)
if neighborhoods:
nr, nc = neighborhoods
r = len(set(xys[:, 1]))
ir = int(np.round((r * 1.) / nr))
nrb = [i + ir for i in range(-ir, r, ir)]
nrb[-1] += 0.5
nrb[0] -= 0.5
c = len(set(xys[:, 0]))
ic = int(np.round((c * 1.) / nc))
ncb = [i + ic for i in range(-ic, c, ic)]
ncb[0] -= 0.5
ncb[-1] += 0.5
# Horizontal lines
for i in nrb:
plt.hlines(i - 0.5, xmin=-1, xmax=c, color='k')
# Vertical lines
for i in ncb:
plt.vlines(i - 0.5, ymin=-1, ymax=r, color='k')
elif shpfile:
from pysal.contrib.viz import mapping as viz
shp = ps.open(shpfile)
patchco = viz.map_poly_shp(shp)
patchco.set_facecolor('none')
patchco.set_edgecolor('0.3')
patchco.set_linewidth(0.3)
ax = viz.setup_ax([patchco], ax)
fc = '1'
f.set_facecolor(fc)
if not outfile:
plt.show()
else:
plt.savefig(outfile, facecolor=fc)
else:
print 'No use in plotting a bad ending'
def plot(self, xys, neighborhoods=None, shpfile=None, outfile=None,
title=None):
if self.happy_ending:
cm = get_cmap('RdBu')
cm = get_cmap('Accent')
f = plt.figure()
ax = f.add_subplot(111)
grid = ax.scatter(xys[:, 0], xys[:, 1], alpha=0.75, linewidths=0, \
s=80, marker=None, color='0.6', cmap=cm)
axys = xys[self.agent_xyids, :]
ax.scatter(axys[:, 0], axys[:, 1], alpha=0.8, linewidths=0, \
s=20, marker='o', c=self.group_map, cmap=cm)
props = self.prop_groups + [1. - sum(self.prop_groups)]
if title == None:
title = "%i agents | %s share | %.2f similar wanted"\
%(self.pop_size, '_'.join(map(str, props)), \
self.pct_similar_wanted)
plt.title(title, color='0.3')
ax.set_frame_on(False)
ax.axes.get_yaxis().set_visible(False)
ax.axes.get_xaxis().set_visible(False)
if neighborhoods:
nr, nc = neighborhoods
r = len(set(xys[:, 1]))
ir = int(np.round((r*1.)/nr))
nrb = [i+ir for i in range(-ir, r, ir)]
nrb[-1] += 0.5
nrb[0] -= 0.5
c = len(set(xys[:, 0]))
ic = int(np.round((c*1.)/nc))
ncb = [i+ic for i in range(-ic, c, ic)]
ncb[0] -= 0.5
ncb[-1] += 0.5
# Horizontal lines
for i in nrb:
plt.hlines(i-0.5, xmin=-1, xmax=c, color='k')
# Vertical lines
for i in ncb:
plt.vlines(i-0.5, ymin=-1, ymax=r, color='k')
elif shpfile:
from pysal.contrib.viz import mapping as viz
shp = ps.open(shpfile)
patchco = viz.map_poly_shp(shp)
patchco.set_facecolor('none')
patchco.set_edgecolor('0.3')
patchco.set_linewidth(0.3)
ax = viz.setup_ax([patchco], ax)
fc = '1'
f.set_facecolor(fc)
if not outfile:
plt.show()
else:
plt.savefig(outfile, facecolor=fc)
else:
print 'No use in plotting a bad ending'
def lisa_mapa(variavel, shapefile, p_thres=0.05, **kws):
w = ps.queen_from_shapefile(shapefile)
lisa = ps.Moran_Local(variavel, w)
fig = plt.figure(figsize=(9, 7))
shp = ps.open(shapefile)
base = maps.map_poly_shp(shp)
base = maps.base_lisa_cluster(base, lisa, p_thres=p_thres)
base.set_edgecolor('1')
base.set_linewidth(0.1)
ax = maps.setup_ax([base], [shp.bbox])
boxes, labels = maps.lisa_legend_components(lisa, p_thres=p_thres)
plt.legend(boxes, labels, fancybox=True, **kws)
plt.show();
def lisa_mapa(variavel, shapefile, p_thres=0.05, **kws):
w = ps.queen_from_shapefile(shapefile)
lisa = ps.Moran_Local(variavel, w)
fig = plt.figure(figsize=(9, 7))
shp = ps.open(shapefile)
base = maps.map_poly_shp(shp)
base = maps.base_lisa_cluster(base, lisa, p_thres=p_thres)
base.set_edgecolor('1')
base.set_linewidth(0.1)
ax = maps.setup_ax([base], [shp.bbox])
boxes, labels = maps.lisa_legend_components(lisa, p_thres=p_thres)
plt.legend(boxes, labels, fancybox=True, **kws)
plt.show()
def visualize_adjacency_graph(mggg_graph, out_dir=None):
''' Visualize an adjacency graph
Input:
mggg_graph (Graph): A graph object from adjacency_graphs.algorithms
out_dir (string, optional): Path to the output file. If none provided,
no file will be created
Output:
fig (matplotlib.pyplot): An object which can be plotted
'''
# open the file and obtain pysal geometries
shp = mggg_graph.loaded_geodata
# setting up matplot figure
fig = plt.figure(figsize=(9, 9))
fig.set_facecolor('white')
base = maps.map_poly_shp(shp)
base.set_linewidth(0.75)
base.set_facecolor('none')
base.set_edgecolor('0.8')
# Build a dictionary to associate geoid and index.
data = mggg_graph.shape_df
gti = {}
for index, row in data.iterrows():
gti[row[mggg_graph.id_column]] = index
# graph contains polygons matched to their neighbors, uses polygon identifiers
graph = mggg_graph.neighbors
# obtain the centroids of polygons
polygon_centroids = {x: y.centroid for x, y in enumerate(shp)}
# connect centroids of the polygons using LineCollection
edge_list = [(polygon_centroids[gti[poly1]], polygon_centroids[gti[poly2]])
for poly1, neighbors in graph.items() for poly2 in neighbors]
edge_list = LineCollection(edge_list)
edge_list.set_linewidth(0.20)
ax = maps.setup_ax([base, edge_list], [shp.bbox, shp.bbox])
fig.add_axes(ax)
# save your output
if (out_dir is not None):
savefig(out_dir)
return fig
def choropleth(shp, bbox, data, title=None):
data = 1.0 * np.nan_to_num(np.array(data))
normed = (data - data.min()) / data.ptp()
if np.min(data) < 0:
cmap = 'PRGn'
else:
cmap = 'viridis'
shapes = maps.map_poly_shp(shp)
zips = maps.base_choropleth_classless(shapes,
values=normed,
cmap=cmap)
zips.set_linewidth(0.75)
zips.set_edgecolor('0.8')
fig = plt.figure(figsize=(7, 7))
ax = maps.setup_ax([zips], [bbox])
fig.add_axes(ax)
if title:
plt.title(title)
m = mpl.cm.ScalarMappable(cmap=cmap)
m.set_array(np.unique(data))
plt.colorbar(m)
plt.show()
def visualize_adjacency_graph(mggg_graph, fig=None, out_dir=None):
"""
visualize_adjacency_graph
create and visualize an adjacency graph of geometries in an shp file.
@param file_dir a string directory for the shp file which one wishes to
analyze.
@param out_dir a string directory where one wishes to save the output
image. Image will not be saved if this is None.
"""
if not fig:
fig = plt.figure(figsize=(9, 9))
fig.set_facecolor('white')
base = maps.map_poly_shp(mggg_graph.loaded_geodata)
base.set_linewidth(0.75)
base.set_facecolor('none')
base.set_edgecolor('0.8')
# obtain the centroids of polygons
polygon_centroids = {
x: y.centroid
for x, y in enumerate(mggg_graph.loaded_geodata)
}
# connect centroids of the polygons using LineCollection
edge_list = ((polygon_centroids[poly1], polygon_centroids[poly2])
for poly1, neighbors in mggg_graph.neighbors.items()
for poly2 in neighbors)
edge_list = LineCollection(edge_list)
edge_list.set_linewidth(0.20)
ax = maps.setup_ax(
[base, edge_list],
[mggg_graph.loaded_geodata.bbox, mggg_graph.loaded_geodata.bbox])
fig.add_axes(ax)
return fig
def visualize_adjacency_graph(mggg_graph, bokeh_graph=False, out_dir=None):
''' Visualize an adjacency graph
Input:
mggg_graph (Graph): A graph object from adjacency_graphs.algorithms
out_dir (string, optional): Path to the output file. If none provided,
no file will be created
Output:
fig (matplotlib.pyplot): An object which can be plotted
'''
# open the file and obtain pysal geometries
shp = mggg_graph.loaded_geodata
# setting up matplot figure
fig = plt.figure(figsize=(9, 9))
fig.set_facecolor('white')
base = maps.map_poly_shp(shp)
base.set_linewidth(0.75)
base.set_facecolor('none')
base.set_edgecolor('0.8')
# Build a dictionary to associate geoid and index.
data = mggg_graph.shape_df
gti = {}
for index, row in data.iterrows():
gti[row[mggg_graph.id_column]] = index
# graph contains polygons matched to their neighbors, uses polygon identifiers
graph = mggg_graph.neighbors
# obtain the centroids of polygons
polygon_centroids = {x: y.centroid for x, y in enumerate(shp)}
if bokeh_graph == False:
edge_list = [
(polygon_centroids[gti[poly1]], polygon_centroids[gti[poly2]])
for poly1, neighbors in graph.items() for poly2 in neighbors
]
edge_list = LineCollection(edge_list)
edge_list.set_linewidth(0.20)
ax = maps.setup_ax([base, edge_list], [shp.bbox, shp.bbox])
fig.add_axes(ax)
# save your output
if (out_dir is not None):
savefig(out_dir)
return fig
else:
edge_list_bokeh_x = [[
polygon_centroids[gti[poly1]][0], polygon_centroids[gti[poly2]][0]
] for poly1, neighbors in graph.items() for poly2 in neighbors]
edge_list_bokeh_y = [[
polygon_centroids[gti[poly1]][1], polygon_centroids[gti[poly2]][1]
] for poly1, neighbors in graph.items() for poly2 in neighbors]
lons, lats = gpd_bokeh(mggg_graph.shape_df)
source = ColumnDataSource(
data=dict(x=lons, y=lats, name=mggg_graph.shape_df.NAME))
TOOLS = "pan, wheel_zoom, box_zoom, reset, hover, save"
p = figure(title="Adjecency Graph",
tools=TOOLS,
plot_width=900,
plot_height=900)
p.patches('x',
'y',
source=source,
fill_alpha=0.7,
color='white',
line_color='black',
line_width=0.5)
hover = p.select_one(HoverTool)
hover.point_policy = 'follow_mouse'
hover.tooltips = [
("Name", "@name"),
("(Long, Lat)", "($x, $y)"),
]
p.multi_line(edge_list_bokeh_x, edge_list_bokeh_y, line_width=1)
output_file("adjacency.html", title="Adjacency Graph")
show(p)
import numpy as np
import pysal as ps
import random as rdm
from pysal.contrib.viz import mapping as maps
import matplotlib.pyplot as plt
shp_link = 'transportation.shp'
shp = ps.open(shp_link)
fig = plt.figure()
base = maps.map_poly_shp(shp)
base.set_facecolor('none')
base.set_linewidth(0.75)
base.set_edgecolor('0.8')
ax = maps.setup_ax([base])
fig.add_axes(ax)
plt.show()
def plot_lisa(lisa, st, msa, outfile=None, thr=0.05, title=''):
'''
Plot LISA results for MSAs on background map of US states
NOTE: shapefiles hardcoded linked to paths inside the function
...
Arguments
---------
lisa : Moran_Local
LISA object from PySAL
st : str
Path to states shape
msa : str
Path to MSA points shape
outfile : str
[Optional] Path to png to be written
thr : float
[Optional] Significance value to identify clusters
title : str
[Optional] Title for the figure
title : str
Returns
-------
None
'''
sig = (lisa.p_sim < thr) * 1
vals = pd.Series(lisa.q * sig)
states = ps.open(st)
pts = ps.open(msa)
fig = plt.figure(figsize=(9, 5))
base = maps.map_poly_shp(states)
base.set_facecolor('0.85')
base.set_linewidth(0.75)
base.set_edgecolor('0.95')
msas = pd.np.array([pt for pt in ps.open(msa)])
sizes = vals.apply(lambda x: 4 if x==0 else 50)
colors = vals.map(q_mapper)
colors = pd.np.array(list(colors))
pts = []
for clas in q_mapper:
i = vals[vals==clas].index
p = plt.scatter(msas[i, 0], msas[i, 1], s=sizes[i], \
c=colors[i, :], label=q_names[clas])
p.set_linewidth(0)
pts.append(p)
plt.legend(loc=3, ncol=2, fontsize=14, scatterpoints=1, frameon=False)
ax = maps.setup_ax([base] + pts)
#ax = maps.setup_ax(pts)
fig.add_axes(ax)
if title:
plt.title(title)
if outfile:
plt.savefig(outfile)
else:
plt.show()
return None