在端点处插入相等的值,主分支最好两个相等,然后,又去除了最后一个数,左右分支,都和主分支断开了
import numpy as np
import operator
import os
import copy
from matplotlib.font_manager import FontProperties
from scipy.interpolate import lagrange
import random
import matplotlib.pyplot as plt
import math
np.set_printoptions(suppress=True)
# 把opt文件内的逗号变为空格
#数据在我的百度云数据库txt文件,及opt文件
np.set_printoptions(threshold=np.inf) #输出全部矩阵不带省略号
random.seed(10)
##########################################
data = np.loadtxt('txt//final37.txt')
# data = data[0:100]#抽取一部分
x1 = data[:,5]#x起点坐标
x2 = data[:,9]#x终点坐标
y1 = data[:,6]#y起
y2 = data[:,10]#y起
z1 = data[:,4]#IDpart
z2 = data[:,8]#IDpart
diam = data[:,12]
s1 = [a1 for a1 in range(1,len(x1)-1) if z1[a1]==z2[a1-1]!=-1 or z1[a1]!= z2[a1-1]]#id相同不等于0,或id不同
# print(s1)
lx = []#x1,x2相同的部分组成的列表
lxqi = []
lxzg = []
for i1 in range(len(s1)-1):
b1 = x1[s1[i1]:s1[i1+1]]
b1 = b1.tolist()
b2 = x2[s1[i1+1]-1]#s1[i1]相当于a1
# b1 = b1 + [b2]#把与x2最后相连的一个数和x1拼接起来
b5 = z1[s1[i1]]#x,y起点id
b1qi_id = [b5]+b1 +[b2]
b6 = z2[s1[i1+1]-1]#x,y终点id
b1zg_id = [b6] + b1+[b2]
lx.append(b1)
lxqi.append(b1qi_id)
lxzg.append(b1zg_id)
###################################################
ly = []#y坐标以及管径大小
for i3 in range(len(s1)-1):
b3 = y1[s1[i3]:s1[i3+1]]
b3 = b3.tolist()
b4 = y2[s1[i3+1]-1]#y最后一个不相等的数
b3 = b3 + [b4]
dm = diam[s1[i3+1]-1]
b3 = b3 + [dm]#加上管径
ly.append(b3)
#####################################################
#带有起点id的x坐标与y坐标合并
for q1 in range(len(lxqi)):
for q2 in range(len(ly[q1])):
lxqi[q1].append(ly[q1][q2])
#带有终点id的x坐标与y坐标合并
for p1 in range(len(lxzg)):
for p2 in range(len(ly[p1])):
lxzg[p1].append(ly[p1][p2])
lxqi.sort(key=operator.itemgetter(0))#排序,只按照第一个索引大小排序
tou = lxqi
lxzg.sort(key=operator.itemgetter(0))
wei = lxzg
# #########################################
toudeng = []
weideng = []
for dwei in wei:
for i in range(len(tou)-1):
if dwei[0] ==tou[i][0] and dwei[0]==tou[i+1][0]:
toud = [dwei,tou[i],tou[i+1]]
toudeng.append(toud)
for dtou in tou:
for i in range(len(wei)-1):
if dtou[0] == wei[i][0] and dtou[0]==wei[i+1][0]:
weid = [wei[i],wei[i+1],dtou]
weideng.append(weid)
# ###################################################
datatoudeng = []
dataweideng = []
#去掉起点id
for i in range(len(toudeng)):
a = toudeng[i][0][1::]
b = toudeng[i][1][1::]
c = toudeng[i][2][1::]
d = [a]+[b]+[c]
datatoudeng.append(d)
for i in range(len(weideng)):
a1 = weideng[i][0][1::]
b1 = weideng[i][1][1::]
c1 = weideng[i][2][1::]
d1 = [a1]+[b1]+[c1]
dataweideng.append(d1)
# print(dataweideng)
####################################################################
#判断管径信息是否加进列表,若未加进则只为x,y坐标,为偶数
for i in range(len(dataweideng)):
a = dataweideng[i]
assert len(a[0])%2==1
assert len(a[1])%2==1
assert len(a[2])%2==1
for i in range(len(datatoudeng)):
a = datatoudeng[i]
assert len(a[0])%2==1
assert len(a[1])%2==1
assert len(a[2])%2==1
finaldata = datatoudeng +dataweideng#未插值
final = datatoudeng #所有分叉,头等分叉,尾等分叉
# print(final)
################################################################################
#插值方法:第二张策略,在端点处,补上与端点的值相等的数,然后使每个分支维度相等。
finaldata = []
for i in range(len(final)):#final[i]代表一个分叉,它有三个不同长度的分支
zhu = final[i][0]
zuo = final[i][1]
you = final[i][2]
zhu_diam = [zhu[-1]]
zuo_diam = [zuo[-1]]
you_diam = [you[-1]]
zhu_x = zhu[0:len(zhu)//2]
zuo_x = zuo[0:len(zuo)//2]
you_x = you[0:len(you)//2]
zhu_y = zhu[len(zhu)//2:(len(zhu)-1)]
zuo_y = zuo[len(zuo)//2:(len(zuo)-1)]
you_y = you[len(you)//2:(len(you)-1)]
#从这里开始插值,对于头部相等的数主分支,在头部插值,左右分支分别在尾部端点插值,对于final37希望每一个维度为60,所以,让他们每一个列表插入(30-len(zhu_x))个值
zhu_insert_x = zhu_x[0]#主分支的第一个坐标
zhu_insert_y = zhu_y[0]
zuo_insert_x = zuo_x[-1]#左分支的最后一个坐标
zuo_insert_y = zuo_y[-1]
you_insert_x = you_x[-1]#右分支的最后一个坐标
you_insert_y = you_y[-1]
for i in range(30-len(zhu_x)):
zhu_x.insert(0,zhu_insert_x)
zhu_y.insert(0,zhu_insert_y)
#左右分支在尾部插入与尾部端点坐标相等的值,因为左右分支头部要去掉一个值,所以应比主分支多插入一个
for i in range(31-len(zuo_x)):
zuo_x.append(zuo_insert_x)
zuo_y.append(zuo_insert_y)
for i in range(31-len(you_x)):
you_x.append(you_insert_x)
you_y.append(you_insert_y)
#从这里去掉左右分支开头与主分支相等的部分
zuo_x = zuo_x[1::]
you_x = you_x[1::]
zuo_y = zuo_y[1::]
you_y = you_y[1::]
#这里将x和y列表再接起来
zhu_xy = zhu_x + zhu_y
zuo_xy = zuo_x + zuo_y
you_xy = you_x + you_y
#这里再将坐标点与管径接起来
zhu = zhu_xy + zhu_diam
zuo = zuo_xy + zuo_diam
you = you_xy + you_diam
fencha = [zhu]+[zuo]+[you]
finaldata.append(fencha)
final = np.array(finaldata) #数组维度(-1,3,61)
print(final.shape)
#################################################################################
# final = final[0:2,:,:]#选取一个分叉测试旋转效果
x = final[:,:,0:30]
y = final[:,:,30:60]
diam = final[:,:,-1]
diam = diam.reshape(-1,3,1)
#########################################
#旋转
def rotate(angle,valuex,valuey):
rotatex = math.cos(angle)*valuex -math.sin(angle)*valuey
rotatey = math.cos(angle)*valuey + math.sin(angle)* valuex
return rotatex,rotatey
rotatedata = []
for i in range(0,360,3): #每隔3度旋转一次
x1,y1 = rotate(i,x,y)
rotate_final = np.concatenate((x1,y1,diam),axis=2)
rotatedata.append(rotate_final)
finaldata = []
for file in rotatedata:
for data in file:
finaldata.append(data)
finaldata = np.array(finaldata)
max1 = np.max(finaldata)
min1 = np.min(finaldata)
print(max1)
print(min1)
print(finaldata.shape)
##############################################################
#归一化前可视化单张图片
# final = finaldata
# for i in range(len(final)):
# # plt.figure(figsize=(128,128),dpi=1)
# plt.plot(final[i][0][0:30],final[i][0][30:60])
# plt.plot(final[i][1][0:30],final[i][1][30:60])
# plt.plot(final[i][2][0:30],final[i][2][30:60])
# # plt.axis('off')
# plt.show()
# # plt.savefig('C:\\Users\\Administrator\\Desktop\\调整分辨率\\原始图\\resouce%d.jpg' %(i),dpi=1)
# # plt.close()
################################################
#归一化处理不去除管径信息
finalSubCAM = []
final = finaldata
for i in range(len(final)):
finalx = final[i][:,0:30]#(7,10,30)
finaly = final[i][:,30:60]#(10,30,60)
diameter = final[i][:,-1]
diameter = diameter.reshape(3,1)
Xmax = np.max(finalx)
Xmin = np.min(finalx)
Ymax = np.max(finaly)
Ymin = np.min(finaly)
Dmax = np.max(diameter)
Dmin = np.min(diameter)
normx = (finalx-Xmin)/(Xmax-Xmin)
normy = (finaly-Ymin)/(Ymax-Ymin)
normd = (diameter-Dmin)/(Dmax-Dmin)
normxy = np.concatenate((normx,normy,diameter),axis=1) #加入原始管径diameter,或归一化管径normd
finalSubCAM.append(normxy)
finaldata = np.array(finalSubCAM)
np.random.shuffle(finaldata)
#####################################################
#final37主分支后两个数相等,那去掉主分支最后一个坐标
finaldata = finaldata.tolist()
final = []
for i in range(len(finaldata)):
zhu = finaldata[i][0]
zuo = finaldata[i][1]
you = finaldata[i][2]
#单独分开x,y,列表
zhu_x = zhu[0:30]
zhu_y = zhu[30:60]
zhu_diam = [zhu[-1]]
zuo_x = zuo[0:30]
zuo_y = zuo[30:60]
zuo_diam = [zuo[-1]]
you_x = you[0:30]
you_y = you[30:60]
you_diam = [you[-1]]
############################################
#去掉主分支倒数后两个基本相等,所以决定去掉主分支最后一个,然后头部再补一个相等的数,使维度相等
del zhu_x[-1]
del zhu_y[-1]
zhu_x.insert(0,zhu_x[0])
zhu_y.insert(0,zhu_y[0])
zhu_x.extend(zhu_y)
zuo_x.extend(zuo_y)
you_x.extend(you_y)
zhu_x.extend(zhu_diam)
zuo_x.extend(zuo_diam)
you_x.extend(you_diam)
fencha = [zhu_x] + [zuo_x] + [you_x]
final.append(fencha)
finaldata = np.array(final)
print(np.min(finaldata))
print(np.max(finaldata))
print(finaldata.shape)
######################################################
#一种普通的可视化方法,此时画出来的图端点都连在了原点位置
# finaldata = finaldata.tolist()
# for i in range(len(finaldata)):
# plt.plot(finaldata[i][0][0:30],finaldata[i][0][30:60],color='red',linewidth=np.log(finaldata[i][0][-1]))
# plt.plot([finaldata[i][0][29]]+finaldata[i][1][0:30],[finaldata[i][0][59]]+finaldata[i][1][30:60],color='blue',linewidth=np.log(finaldata[i][1][-1]))
# plt.plot([finaldata[i][0][29]]+finaldata[i][2][0:30],[finaldata[i][0][59]]+finaldata[i][2][30:60],color='green',linewidth=np.log(finaldata[i][2][-1]))
# plt.xticks(np.arange(0,1,0.1))
# plt.yticks(np.arange(0,1,0.1))
# plt.show()
#############################################################
np.save('C:\\Users\\Administrator\\Desktop\\重新整理血管网络\\final37端点补充相等值.npy',finaldata)
###################################################################
#每100张图片显示在一张图中
# rows,cols = 10, 10
# fig,axs = plt.subplots(rows,cols)
# cnt = 0
# for i in range(rows):
# for j in range(cols):
# xy = finaldata[cnt]#第n个分叉图,有三个分支,每个分支21个数
# for k in range(len(xy)):
# x = xy[k][0:30]
# y = xy[k][30:60]
# axs[i,j].plot(x,y,linewidth=2)
# axs[i,j].axis('off')
# cnt +=1
# plt.show()
依此判断两点之间的距离,然后在距离最大的两点之间插值
import numpy as np
import operator
import os
import copy
from matplotlib.font_manager import FontProperties
from scipy.interpolate import lagrange
import random
import matplotlib.pyplot as plt
import math
np.set_printoptions(suppress=True)
# 把opt文件内的逗号变为空格
#数据在我的百度云数据库txt文件,及opt文件
np.set_printoptions(threshold=np.inf) #输出全部矩阵不带省略号
random.seed(10)
##########################################
data = np.loadtxt('txt//final37.txt')
# data = data[0:1000]#抽取一部分
x1 = data[:,5]#x起点坐标
x2 = data[:,9]#x终点坐标
y1 = data[:,6]#y起
y2 = data[:,10]#y起
z1 = data[:,4]#IDpart
z2 = data[:,8]#IDpart
diam = data[:,12]
s1 = [a1 for a1 in range(1,len(x1)-1) if z1[a1]==z2[a1-1]!=-1 or z1[a1]!= z2[a1-1]]#id相同不等于0,或id不同
# print(s1)
lx = []#x1,x2相同的部分组成的列表
lxqi = []
lxzg = []
for i1 in range(len(s1)-1):
b1 = x1[s1[i1]:s1[i1+1]]
b1 = b1.tolist()
b2 = x2[s1[i1+1]-1]#s1[i1]相当于a1
# b1 = b1 + [b2]#把与x2最后相连的一个数和x1拼接起来
b5 = z1[s1[i1]]#x,y起点id
b1qi_id = [b5]+b1 +[b2]
b6 = z2[s1[i1+1]-1]#x,y终点id
b1zg_id = [b6] + b1+[b2]
lx.append(b1)
lxqi.append(b1qi_id)
lxzg.append(b1zg_id)
###################################################
ly = []#y坐标以及管径大小
for i3 in range(len(s1)-1):
b3 = y1[s1[i3]:s1[i3+1]]
b3 = b3.tolist()
b4 = y2[s1[i3+1]-1]#y最后一个不相等的数
b3 = b3 + [b4]
dm = diam[s1[i3+1]-1]
b3 = b3 + [dm]#加上管径
ly.append(b3)
#####################################################
#带有起点id的x坐标与y坐标合并
for q1 in range(len(lxqi)):
for q2 in range(len(ly[q1])):
lxqi[q1].append(ly[q1][q2])
#带有终点id的x坐标与y坐标合并
for p1 in range(len(lxzg)):
for p2 in range(len(ly[p1])):
lxzg[p1].append(ly[p1][p2])
lxqi.sort(key=operator.itemgetter(0))#排序,只按照第一个索引大小排序
tou = lxqi
lxzg.sort(key=operator.itemgetter(0))
wei = lxzg
# #########################################
toudeng = []
weideng = []
for dwei in wei:
for i in range(len(tou)-1):
if dwei[0] ==tou[i][0] and dwei[0]==tou[i+1][0]:
toud = [dwei,tou[i],tou[i+1]]
toudeng.append(toud)
for dtou in tou:
for i in range(len(wei)-1):
if dtou[0] == wei[i][0] and dtou[0]==wei[i+1][0]:
weid = [wei[i],wei[i+1],dtou]
weideng.append(weid)
# ###################################################
datatoudeng = []
dataweideng = []
#去掉起点id
for i in range(len(toudeng)):
a = toudeng[i][0][1::]
b = toudeng[i][1][1::]
c = toudeng[i][2][1::]
d = [a]+[b]+[c]
datatoudeng.append(d)
for i in range(len(weideng)):
a1 = weideng[i][0][1::]
b1 = weideng[i][1][1::]
c1 = weideng[i][2][1::]
d1 = [a1]+[b1]+[c1]
dataweideng.append(d1)
# print(dataweideng)
####################################################################
#判断管径信息是否加进列表,若未加进则只为x,y坐标,为偶数
for i in range(len(dataweideng)):
a = dataweideng[i]
assert len(a[0])%2==1
assert len(a[1])%2==1
assert len(a[2])%2==1
for i in range(len(datatoudeng)):
a = datatoudeng[i]
assert len(a[0])%2==1
assert len(a[1])%2==1
assert len(a[2])%2==1
finaldata = datatoudeng +dataweideng#未插值
final = datatoudeng #所有分叉,头等分叉,尾等分叉
##############################################################
#计算两点之间距离
def get_len(x1,x2,y1,y2):
diff_x = (x1-x2)**2
diff_y = (y1-y2)**2
length = np.sqrt(diff_x+diff_y)
return length
######################################################################
#插值方法三:在相邻两点坐标距离最大的地方插值
finaldata = []
for i in range(len(final)):
zhu = final[i][0]
zuo = final[i][1]
you = final[i][2]
zhu_diam = [zhu[-1]]
zuo_diam = [zuo[-1]]
you_diam = [you[-1]]
zhu_x = zhu[0:len(zhu)//2]
zuo_x = zuo[0:len(zuo)//2]
you_x = you[0:len(you)//2]
zhu_y = zhu[len(zhu)//2:(len(zhu)-1)]
zuo_y = zuo[len(zuo)//2:(len(zuo)-1)]
you_y = you[len(you)//2:(len(you)-1)]
# plt.plot(zhu_x,zhu_y,color='red')
# plt.plot(zuo_x,zuo_y,color='blue')
# plt.plot(you_x,you_y,color='green')
while len(zhu_x)< 31:
zhu_lin_list = []
for j in range(1,len(zhu_x)):
zhu_lin_len = get_len(zhu_x[j-1],zhu_x[j],zhu_y[j-1],zhu_y[j])
zhu_lin_list.append(zhu_lin_len)
zhu_max_index = zhu_lin_list.index(max(zhu_lin_list)) #j-1
#不处理的话会出现nan
if abs(zhu_x[zhu_max_index]-zhu_x[zhu_max_index+1])==0:
zhu_x[zhu_max_index+1] = zhu_x[zhu_max_index+1] +1
zhu_insert_x = np.linspace(zhu_x[zhu_max_index],zhu_x[zhu_max_index+1],3)
#插入的点
zhu_insert_x = zhu_insert_x[1]
f_zhu = lagrange([zhu_x[zhu_max_index],zhu_x[zhu_max_index+1]],[zhu_y[zhu_max_index],zhu_y[zhu_max_index+1]])
zhu_insert_y = f_zhu(zhu_insert_x)
zhu_x.insert(zhu_max_index+1,zhu_insert_x)
zhu_y.insert(zhu_max_index+1,zhu_insert_y)
while len(zuo_x) < 31:
zuo_lin_list = []
for j in range(1,len(zuo_x)):
zuo_lin_len = get_len(zuo_x[j-1],zuo_x[j],zuo_y[j-1],zuo_y[j])
zuo_lin_list.append(zuo_lin_len)
zuo_max_index = zuo_lin_list.index(max(zuo_lin_list)) #对应j-1
if abs(zuo_x[zuo_max_index]-zuo_x[zuo_max_index+1])==0:
zuo_x[zuo_max_index+1] = zuo_x[zuo_max_index+1] + 1
zuo_insert_x = np.linspace(zuo_x[zuo_max_index],zuo_x[zuo_max_index+1],3)
# #插入的点
zuo_insert_x = zuo_insert_x[1]
f_zuo = lagrange([zuo_x[zuo_max_index],zuo_x[zuo_max_index+1]],[zuo_y[zuo_max_index],zuo_y[zuo_max_index+1]])
zuo_insert_y = f_zuo(zuo_insert_x)
zuo_x.insert(zuo_max_index+1,zuo_insert_x)
zuo_y.insert(zuo_max_index+1,zuo_insert_y)
while len(you_x) < 31:
you_lin_list = []
for j in range(1,len(you_x)):
#计算相邻两坐标的距离
you_lin_len = get_len(you_x[j-1],you_x[j],you_y[j-1],you_y[j])
#添加进列表中
you_lin_list.append(you_lin_len)
#计算距离最大的值对应的索引,对应x坐标的j-1和j之间的距离,最大
you_max_index = you_lin_list.index(max(you_lin_list)) #对应j-1
#然后,在两个最大点之间,平均插入一个数,作为插入x点
if abs(you_x[you_max_index]-you_x[you_max_index+1]) == 0:
you_x[you_max_index+1] = you_x[you_max_index+1] + 1
you_insert_x = np.linspace(you_x[you_max_index],you_x[you_max_index+1],3)
#插入的点
you_insert_x = you_insert_x[1]
#拉格朗日计算直线方程
f_you = lagrange([you_x[you_max_index],you_x[you_max_index+1]],[you_y[you_max_index],you_y[you_max_index+1]])
#插入的y点
you_insert_y = f_you(you_insert_x)
#将求得的x,y点插入对应位置
you_x.insert(you_max_index+1,you_insert_x)
you_y.insert(you_max_index+1,you_insert_y)
################################################
#可视化插值点
# plt.scatter(zhu_x,zhu_y,marker='*',color='red')
# plt.scatter(zuo_x,zuo_y,marker='*',color='blue')
# plt.scatter(you_x,you_y,marker='*',color='green')
# plt.show()
#####################################################################
#处理头部相等,删除主分支最后一个,删除左右分支第一个
del zhu_x[-1]
del zhu_y[-1]
zuo_x = zuo_x[1::]
you_x = you_x[1::]
zuo_y = zuo_y[1::]
you_y = you_y[1::]
#这里将x和y列表再接起来
zhu_xy = zhu_x + zhu_y
zuo_xy = zuo_x + zuo_y
you_xy = you_x + you_y
#这里再将坐标点与管径接起来
zhu = zhu_xy + zhu_diam
zuo = zuo_xy + zuo_diam
you = you_xy + you_diam
fencha = [zhu] + [zuo] + [you]
finaldata.append(fencha)
final = np.array(finaldata) #数据维度为(-1,3,61)
print(final.shape)
########################################################################
# final = final[0:2,:,:]#选取一个分叉测试旋转效果
x = final[:,:,0:30]
y = final[:,:,30:60]
diam = final[:,:,-1]
diam = diam.reshape(-1,3,1)
#########################################
#旋转
def rotate(angle,valuex,valuey):
rotatex = math.cos(angle)*valuex -math.sin(angle)*valuey
rotatey = math.cos(angle)*valuey + math.sin(angle)* valuex
return rotatex,rotatey
rotatedata = []
for i in range(0,360,360): #每隔3,30度旋转一次
x1,y1 = rotate(i,x,y)
rotate_final = np.concatenate((x1,y1,diam),axis=2)
rotatedata.append(rotate_final)
finaldata = []
for file in rotatedata:
for data in file:
finaldata.append(data)
finaldata = np.array(finaldata)
max1 = np.max(finaldata)
min1 = np.min(finaldata)
print(max1)
print(min1)
print(finaldata.shape)
################################################################
#归一化前可视化单张图片
# for i in range(len(finaldata)):
# plt.scatter(finaldata[i][0][0:30],finaldata[i][0][30:60],marker='*',color='red')
# plt.scatter(finaldata[i][1][0:30],finaldata[i][1][30:60],marker='*',color='blue')
# plt.scatter(finaldata[i][2][0:30],finaldata[i][2][30:60],marker='*',color='green')
# plt.show()
##################################################################
#归一化处理不去除管径信息
finalSubCAM = []
final = finaldata
for i in range(len(final)):
finalx = final[i][:,0:30]#(7,10,30)
finaly = final[i][:,30:60]#(10,30,60)
diameter = final[i][:,-1]
diameter = diameter.reshape(3,1)
Xmax = np.max(finalx)
Xmin = np.min(finalx)
Ymax = np.max(finaly)
Ymin = np.min(finaly)
Dmax = np.max(diameter)
Dmin = np.min(diameter)
normx = (finalx-Xmin)/(Xmax-Xmin)
normy = (finaly-Ymin)/(Ymax-Ymin)
normd = (diameter-Dmin)/(Dmax-Dmin)
normxy = np.concatenate((normx,normy,diameter),axis=1) #加入原始管径diameter,或归一化管径normd
finalSubCAM.append(normxy)
finaldata = np.array(finalSubCAM)
np.random.shuffle(finaldata)
print(np.min(finaldata))
print(np.max(finaldata))
print(finaldata.shape)
######################################################
#一种普通的可视化方法,此时画出来的图端点都连在了原点位置
# finaldata = finaldata.tolist()
# for i in range(len(finaldata)):
# plt.plot(finaldata[i][0][0:30],finaldata[i][0][30:60],color='red',linewidth=np.log(finaldata[i][0][-1]))
# plt.plot([finaldata[i][0][29]]+finaldata[i][1][0:30],[finaldata[i][0][59]]+finaldata[i][1][30:60],color='blue',linewidth=np.log(finaldata[i][1][-1]))
# plt.plot([finaldata[i][0][29]]+finaldata[i][2][0:30],[finaldata[i][0][59]]+finaldata[i][2][30:60],color='green',linewidth=np.log(finaldata[i][2][-1]))
# plt.xticks(np.arange(0,1,0.1))
# plt.yticks(np.arange(0,1,0.1))
# plt.show()
############################################################
np.save('C:\\Users\\Administrator\\Desktop\\重新整理血管网络\\final37逐个判断最大距离不旋转.npy',finaldata)
###################################################################
#每100张图片显示在一张图中
# rows,cols = 10, 10
# fig,axs = plt.subplots(rows,cols)
# cnt = 0
# for i in range(rows):
# for j in range(cols):
# xy = finaldata[cnt]#第n个分叉图,有三个分支,每个分支21个数
# for k in range(len(xy)):
# x = xy[k][0:30]
# y = xy[k][30:60]
# axs[i,j].plot(x,y,linewidth=2)
# axs[i,j].axis('off')
# cnt +=1
# plt.show()