def plot_flow(od_matrix, title, chengdu):
colors = [cm.Spectral_r(x) for x in linspace(0, 1, 5)]
df = od_matrix.copy()
df_class = mpc.NaturalBreaks(df['ridership'])
df['class'] = df_class.yb
bins = df_class.bins
fig, ax = plt.subplots(1, 1, figsize=(5, 5))
chengdu.plot(figsize=(5, 5),
facecolor='none',
edgecolor='black',
linewidth=0.6,
ax=ax,
alpha=1)
for i in range(len(bins)):
sub = df[df['class'] == i]
for j in sub.index:
ax.annotate('',
xy=(sub.loc[j, 'ori_x'], sub.loc[j, 'ori_y']),
xytext=(sub.loc[j, 'end_x'], sub.loc[j, 'end_y']),
arrowprops=dict(arrowstyle="->",
color=colors[i],
linewidth=0.5,
alpha=0.8,
shrinkA=0,
shrinkB=0,
patchA=None,
patchB=None,
connectionstyle='arc3,rad=-0.3'))
ax.set_title(title, fontsize=16)
ax.set_xlim(103.89, 104.24)
ax.set_ylim(30.54, 30.8)
line1, _ = ax.plot(((0, 0), (1, 1)), color=colors[0], alpha=0.8)
line2, _ = ax.plot(((0, 0), (1, 1)), color=colors[1], alpha=0.8)
line3, _ = ax.plot(((0, 0), (1, 1)), color=colors[2], alpha=0.8)
line4, _ = ax.plot(((0, 0), (1, 1)), color=colors[3], alpha=0.8)
line5, _ = ax.plot(((0, 0), (1, 1)), color=colors[4], alpha=1)
plt.legend((line1, line2, line3, line4, line5), [
'{} - {}'.format(0.0, round(bins[0], 1)), '{} - {}'.format(
round(bins[0], 1), round(bins[1], 1)), '{} - {}'.format(
round(bins[1], 1), round(bins[2], 1)), '{} - {}'.format(
round(bins[2], 1), round(bins[3], 1)), '{} - {}'.format(
round(bins[3], 1), round(bins[4], 1))
],
framealpha=1,
title='Trips',
facecolor='whitesmoke')
plt.xticks([])
plt.yticks([])
# plt.savefig(name, dpi=300, transparent=True, pad_inches=0)
# plt.close(fig=fig)
plt.show()
def add_coa(option):
#Get Bounds
x0, x1 = ax.get_xlim()
y0, y1 = ax.get_ylim()
#Plot COA
gplt.choropleth(coas,
ax=ax,
projection=geoplot.crs.WebMercator(),
hue=option,
zorder=1,
alpha=0.7,
scheme=mc.NaturalBreaks(coas[option], k=5),
cmap=option,
legend=False,
edgecolor='lightgray')
#Set Extent
ax.set_xlim(left=x0, right=x1)
ax.set_ylim(bottom=y0, top=y1)
def add_legend(option):
#Decide Format
if option == 'SimpleCO2': frmt = '{:.1f}'
else: frmt = '{:.0f}'
#Legend Labels
classification = mc.NaturalBreaks(coas[option], k=5)
legend_labels = []
for i, c in enumerate(classification.counts):
if i == 0:
str_ = "{0} - {1}".format(str(frmt.format(coas[option].min())),
str(frmt.format(classification.bins[i])))
else:
str_ = "{0} - {1}".format(
str(frmt.format(classification.bins[i - 1])),
str(frmt.format(classification.bins[i])))
str_ += "({0})".format(c)
legend_labels.append(str_)
#Legend Handles
cmap = matplotlib.cm.get_cmap(option)
handles = [
Patch(edgecolor='black', facecolor=cmap(0.)),
Patch(edgecolor='black', facecolor=cmap(.25)),
Patch(edgecolor='black', facecolor=cmap(.5)),
Patch(edgecolor='black', facecolor=cmap(.75)),
Patch(edgecolor='black', facecolor=cmap(1.))
]
#Create Legend
legend = ax.legend(handles=handles,
labels=legend_labels,
loc='upper left',
title=get_title(option),
framealpha=1,
facecolor='white',
edgecolor='black',
fancybox=False,
handlelength=1)
legend._legend_box.align = 'left' #align text
#Make Draggable
legend.set_draggable(True, update='loc')
import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import matplotlib.colors
import mapclassify
# get coefficient surface w_sh
legend_classes = mapclassify.NaturalBreaks(
gwr_map_2.loc[(~gwr_map_2['w_sh'].isna()), 'w_sh'], k=5)
legend_classes = mapclassify.UserDefined(
gwr_map_2.loc[(~gwr_map_2['w_sh'].isna()), 'w_sh'],
bins=[-5.55, 0, 5.64, 8.93, 12.69])
mapping = dict([(i, s)
for i, s in enumerate(legend_classes.get_legend_classes())])
# plot
fig = plt.figure(figsize=(10, 8))
ax = fig.add_axes([0, 0, 0.95, 0.95])
gwr_map_2.loc[(~gwr_map_2['w_sh'].isna())].assign(cl = legend_classes.yb).plot(column = 'cl', categorical = True,\
k = 6, cmap = 'coolwarm', norm = matplotlib.colors.Normalize(-1.8,3.5),\
linewidth = 0.3,\
alpha = 0.7,\
ax = ax, edgecolor = 'black', legend = True,\
legend_kwds = {'loc':'lower left',\
'fontsize': 10,\
'frameon': False,\
'title': 'GWR coefficients\nw_sh\n',\
'title_fontsize': 12,\
'edgecolor': 'gray'})
import matplotlib.pyplot as plt
import geopandas
import equal_area_breaks
import mapclassify
from demo_utils import *
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
world = world[(world.pop_est > 0) & (world.name != "Antarctica")]
world['gdp_per_cap'] = 1000000 * (world.gdp_md_est / world.pop_est)
world = world.to_crs({'proj': 'wintri'})
world['area'] = world.geometry.area
data_col = 'gdp_per_cap'
cmap = cmap_yl_or_rd()
fig, axes = plt.subplots(nrows=2, ncols=2)
plot_classes(world, mapclassify.Quantiles(world[data_col]), fig, axes[0, 0],
cmap)
plot_classes(world, mapclassify.EqualInterval(world[data_col]), fig,
axes[1, 0], cmap)
plot_classes(world, mapclassify.NaturalBreaks(world[data_col]), fig,
axes[0, 1], cmap)
plot_classes(
world, equal_area_breaks.Equal_Area_DP(world[data_col],
area=world['area']), fig,
axes[1, 1], cmap)
plt.show()
def test_classify():
# data
link_to_data = examples.get_path('columbus.shp')
gdf = gpd.read_file(link_to_data)
x = gdf['HOVAL'].values
# box_plot
a = classify(x, 'box_plot')
b = mapclassify.BoxPlot(x)
_assertions(a, b)
# EqualInterval
a = classify(x, "EqualInterval", k=3)
b = mapclassify.EqualInterval(x, k=3)
_assertions(a, b)
# FisherJenks
a = classify(x, "FisherJenks", k=3)
b = mapclassify.FisherJenks(x, k=3)
_assertions(a, b)
a= classify(x, "FisherJenksSampled", k=3, pct_sampled=0.5, truncate=False)
b = mapclassify.FisherJenksSampled(x, k=3, pct=0.5,truncate=False)
_assertions(a, b)
# headtail_breaks
a = classify(x, 'headtail_breaks')
b = mapclassify.HeadTailBreaks(x)
_assertions(a, b)
# quantiles
a = classify(x, 'quantiles',k=3)
b = mapclassify.Quantiles(x, k=3)
_assertions(a, b)
# percentiles
a = classify(x, 'percentiles', pct=[25,50,75,100])
b = mapclassify.Percentiles(x, pct=[25,50,75,100])
_assertions(a, b)
#JenksCaspall
a = classify(x, 'JenksCaspall', k=3)
b = mapclassify.JenksCaspall(x, k=3)
_assertions(a, b)
a = classify(x, 'JenksCaspallForced', k=3)
b = mapclassify.JenksCaspallForced(x, k=3)
_assertions(a, b)
a = classify(x, 'JenksCaspallSampled', pct_sampled=0.5)
b = mapclassify.JenksCaspallSampled(x, pct=0.5)
_assertions(a, b)
# natural_breaks, max_p_classifier
a = classify(x, 'natural_breaks')
b = mapclassify.NaturalBreaks(x)
_assertions(a, b)
a = classify(x, 'max_p', k=3, initial=50)
b = mapclassify.MaxP(x, k=3, initial=50)
_assertions(a, b)
# std_mean
a = classify(x, 'std_mean', multiples=[-1,-0.5,0.5,1])
b = mapclassify.StdMean(x, multiples=[-1,-0.5,0.5,1])
_assertions(a, b)
# user_defined
a = classify(x, 'user_defined', bins=[20, max(x)])
b = mapclassify.UserDefined(x, bins=[20, max(x)])
_assertions(a, b)