sns.set(font_scale=1.5,font='STSong')
flights_long = pd.read_csv(fu.getCurPathAndName('data.csv'), encoding="utf-8")
flights = flights_long.pivot(u"from", u"to", u"rate")
order=[u'三里屯',u'后海',u'雍和宫',u'故宫',u'天安门广场',u'798艺术区',u'鸟巢',u'颐和园',u'国际机场',u'世贸天阶']
flights = flights.reindex(index=order, columns=order)
#flights.index = pd.CategoricalIndex(flights.index, categories= )
#flights.sortlevel(level=0, inplace=True)
print flights
# 绘制x-y-z的热力图，比如 年-月-销量 的热力图
f, ax = plt.subplots(figsize=(9, 8))
#绘制热力图，还要将数值写到热力图上
#每个网格上用线隔开
#sns.heatmap(flights, annot=True, annot_kws={"size": 7})
sns.heatmap(flights, annot=True, fmt=".2f", linewidths=.1, ax=ax, cmap="YlOrRd")#, cbar=False)
#设置坐标字体方向
label_y = ax.get_yticklabels()
plt.setp(label_y, rotation=360, horizontalalignment='right')
label_x = ax.get_xticklabels()
plt.setp(label_x, rotation=30, horizontalalignment='right')
plt.show()
plt.tight_layout()
fig=plt.figure()
mc.saveFig(fig)
X = np.random.randn(833, 1) for i in range(len(X)): X[i] = i + 0.1 print X #fclust1 = fclusterdata(X, 1.0, metric=mydist2) Z = linkage(X, 'average', metric=mydist2) c, coph_dists = cophenet(Z, pdist(X)) print c #print Z #print Z[-2:,2] plt.figure(figsize=(8, 6)) #plt.title(u'外地游客访问路径层次聚类结果') plt.xlabel(u'路径下标') plt.ylabel(u'类间距离') #dendrogram(Z, leaf_rotation=90., leaf_font_size=8.) dendrogram(Z, truncate_mode='lastp', p=300, leaf_rotation=0., leaf_font_size=12., show_contracted=True) max_d = 0.4 #plt.axhline(y=max_d, c='r') #fancy_dendrogram(Z, truncate_mode='lastp', p=20, leaf_rotation=90., leaf_font_size=12., show_contracted=True, annotate_above=10, max_d=0.3) plt.tight_layout() plt.show() mc.saveFig(plt) clusters = fcluster(Z, max_d, criterion='distance') print clusters curDir=fu.getCurDir() np.savetxt(curDir+"/clusters.txt", clusters, fmt="%d", delimiter=","); #参考网址：https://haojunsui.github.io/2016/07/16/scipy-hac/
linewidth=0.1)
fig.colorbar(surf)
surf.set_clim(vmin=np.min(z), vmax=2)
#surf.set_edgecolors(surf.to_rgba(surf._A))
#surf.set_facecolors("white")
#ax.scatter(x, y, min(z), s=30, marker='o', cmap=cm)
#ax.scatter(x, y, z, s=30, marker='*', cmap=cm)
#for index in range(len(x)):
#pz=np.linspace(np.min(z), z[index], 50)
#px=[x[index]]*len(pz)
#py=[y[index]]*len(pz)
#ax.plot(px, py, pz)
#ax.set_axis_off()
ax.set_zlim3d(np.min(z), np.max(z))
#ax.set_xlim3d(0,1),
ax.set_xticks([])
#ax.set_ylim3d(0,1),
ax.set_yticks([])
#ax.set_zticks([])
plt.grid(True)
#ax.set_xlabel(u'位置', linespacing=3.2)
#zLabel = ax.set_zlabel('Local Density', linespacing=3.4)
#plt.title('Number of models processed per block')
mc.saveFig(plt, 'fig_choose_block.pdf')
plt.show()