def simple_plot(_G, _path):
# plot using the selected extents
plot.plot_nX(_G,
labels=False,
plot_geoms=True,
node_size=10,
edge_width=1,
x_lim=(min_x, max_x),
y_lim=(min_y, max_y),
dpi=200,
path=_path)
def simple_plot(_G, _path=None, plot_geoms=True):
# manual ax for plotting additional circles
util_funcs.plt_setup()
# create new plot
fig, target_ax = plt.subplots(1, 1)
plot.plot_nX(_G,
labels=False,
plot_geoms=plot_geoms,
node_size=10,
edge_width=1,
x_lim=(min_x, max_x),
y_lim=(min_y, max_y),
ax=target_ax)
if _path is not None:
plt.savefig(_path, facecolor='#2e2e2e')
else:
return target_ax
import numpy as np
import osmnx as ox
import utm
from matplotlib import colors
from shapely import geometry
from cityseer.metrics import networks, layers
from cityseer.tools import mock, graphs, plot
base_path = os.getcwd()
plt.style.use('matplotlibrc')
###
# INTRO PLOT
G = mock.mock_graph()
plot.plot_nX(G, labels=True, node_size=80, path='images/graph.png', dpi=150)
# INTRO EXAMPLE PLOTS
G = graphs.nX_simple_geoms(G)
G = graphs.nX_decompose(G, 20)
N = networks.NetworkLayerFromNX(G, distances=[400, 800])
N.segment_centrality(measures=['segment_harmonic'])
data_dict = mock.mock_data_dict(G, random_seed=25)
D = layers.DataLayerFromDict(data_dict)
D.assign_to_network(N, max_dist=400)
landuse_labels = mock.mock_categorical_data(len(data_dict), random_seed=25)
D.hill_branch_wt_diversity(landuse_labels, qs=[0])
G_metrics = N.to_networkX()
import utm
from matplotlib import colors
import matplotlib.pyplot as plt
from cityseer.metrics import networks, layers
from cityseer.tools import mock, graphs, plot
from src import util_funcs
util_funcs.plt_setup()
# INTRO PLOT
G = mock.mock_graph()
plot.plot_nX(G,
labels=True,
node_size=80,
path='../phd-doc/doc/images/cityseer/graph.pdf')
# INTRO EXAMPLE PLOTS
G = graphs.nX_simple_geoms(G)
G = graphs.nX_decompose(G, 20)
N = networks.NetworkLayerFromNX(G, distances=[400, 800])
N.segment_centrality(measures=['segment_harmonic'])
data_dict = mock.mock_data_dict(G, random_seed=25)
D = layers.DataLayerFromDict(data_dict)
D.assign_to_network(N, max_dist=400)
landuse_labels = mock.mock_categorical_data(len(data_dict), random_seed=25)
D.hill_branch_wt_diversity(landuse_labels, qs=[0])