2021国赛A题代码记录
预先要使用的函数库
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import sys
import re
from mpl_toolkits.mplot3d import Axes3D
import math as m
import random
数据读取
date1 = pd.read_excel("C://AppData//image//guosai//data (1).xlsx",sheet_name='附件1',encoding = 'gbk')#
date2 = pd.read_excel("C://AppData//image//guosai//data (1).xlsx",sheet_name='附件2',encoding = 'gbk')#
date3 = pd.read_excel("C://AppData//image//guosai//data (1).xlsx",sheet_name='附件3',encoding = 'gbk')#
数据预览
date1 = date1.set_index('节点编号')
date1.head()
节点提取（好像没意义）
class SANJ():
def __init__(self):
self.name = ""
self.A = None
self.B = None
self.C = None
sanks = []
for i in range(0,len(date3)):
san = SANJ()
san.A = date3.at[i, '主索节点1']
san.B = date3.at[i, '主索节点2']
san.C = date3.at[i, '主索节点3']
sanks.append(san)
网格可视化
fig=plt.figure(figsize=(18,8))
ax1 = Axes3D(fig)
for i in sanks:
#date1.at['i.A', 'Q1']
xx = []
yy = []
zz = []
xx.append(date1.at[i.A, 'X坐标（米）'])
yy.append(date1.at[i.A, 'Y坐标（米）'])
zz.append(date1.at[i.A, 'Z坐标（米）'])
xx.append(date1.at[i.B, 'X坐标（米）'])
yy.append(date1.at[i.B, 'Y坐标（米）'])
zz.append(date1.at[i.B, 'Z坐标（米）'])
xx.append(date1.at[i.C, 'X坐标（米）'])
yy.append(date1.at[i.C, 'Y坐标（米）'])
zz.append(date1.at[i.C, 'Z坐标（米）'])
xx.append(date1.at[i.A, 'X坐标（米）'])
yy.append(date1.at[i.A, 'Y坐标（米）'])
zz.append(date1.at[i.A, 'Z坐标（米）'])
ax1.plot3D(xx,yy,zz,'blue')
for i in range(0,len(date2)):
xx = []
yy = []
zz = []
xx.append(date2.at[i, '下端点X坐标（米）'])
xx.append(date2.at[i, '基准态时上端点X坐标（米）'])
yy.append(date2.at[i, '下端点Y坐标（米）'])
yy.append(date2.at[i, '基准态时上端点Y坐标（米）'])
zz.append(date2.at[i, '下端点Z坐标（米）'])
zz.append(date2.at[i, '基准态时上端点Z坐标（米）'])
ax1.plot3D(xx,yy,zz,'red')
plt.show()
牵引装置可视化
fig1=plt.figure(figsize=(50,30))
ax2 = Axes3D(fig1)
#ax1.scatter3D(xd,yd,zd, cmap='Blues') #绘制散点图
for i in range(0,len(date1)):
xx = []
yy = []
zz = []
xx.append(date1.reset_index().at[i, 'X坐标（米）'])#X坐标（米）
yy.append(date1.reset_index().at[i, 'Y坐标（米）'])
zz.append(date1.reset_index().at[i, 'Z坐标（米）'])
ax2.scatter3D(xx,yy,zz, cmap='Blues')
for i in range(0,len(date2)):
xx = []
yy = []
zz = []
xx.append(date2.at[i, '下端点X坐标（米）'])
xx.append(date2.at[i, '基准态时上端点X坐标（米）'])
yy.append(date2.at[i, '下端点Y坐标（米）'])
yy.append(date2.at[i, '基准态时上端点Y坐标（米）'])
zz.append(date2.at[i, '下端点Z坐标（米）'])
zz.append(date2.at[i, '基准态时上端点Z坐标（米）'])
ax2.plot3D(xx,yy,zz,'red')
plt.show()
数据转化与计算
def cart2sph(x,y,z):
XsqPlusYsq = x**2 + y**2
r = m.sqrt(XsqPlusYsq + z**2) # r
elev = m.atan2(z,m.sqrt(XsqPlusYsq)) # theta
az = m.atan2(y,x) # phi
return r, elev, az
def jiaodu(vec1,vec2):#vec1=[x1,y1,z1] vec2=[x2,y2,z2]
angle=np.arccos(np.dot(vec1,vec2)/(np.linalg.norm(vec1)*np.linalg.norm(vec2)))
return angle
datehead = heas1.copy()
uint0 = heas1.copy()
Az = np.pi*(36.795/180)
Elev = np.pi*(78.169/180)
x0 = -m.cos(Elev)*m.cos(Az)
y0 = -m.cos(Elev)*m.sin(Az)
z0 = -m.sin(Elev)
vec1 = [x0,y0,z0]
print(vec1)
for i in range(0,len(date1)):
uint0.at[0,'节点编号'] = date1.reset_index().at[i, '节点编号']
xx = date1.reset_index().at[i, 'X坐标（米）']
yy = date1.reset_index().at[i, 'Y坐标（米）']
zz = date1.reset_index().at[i, 'Z坐标（米）']
uint0.at[0,'X坐标'] = xx
uint0.at[0,'Y坐标'] = yy
uint0.at[0,'Z坐标'] = zz
r, elev, az = cart2sph(xx,yy,zz)
uint0.at[0,'景象长度'] = r
uint0.at[0,'偏角'] = az
uint0.at[0,'俯仰'] = elev
vec2 = [xx,yy,zz]
uint0.at[0,'偏离度数'] = jiaodu(vec1,vec2)
datehead = datehead.append(uint0,ignore_index=True)
datehead.head()
工作面提取与可视化
fig=plt.figure(figsize=(30,15))
ax1 = Axes3D(fig)
for i in sanks:
#date1.at['i.A', 'Q1']
xx = []
yy = []
zz = []
xx.append(date1.at[i.A, 'X坐标（米）'])
yy.append(date1.at[i.A, 'Y坐标（米）'])
zz.append(date1.at[i.A, 'Z坐标（米）'])
xx.append(date1.at[i.B, 'X坐标（米）'])
yy.append(date1.at[i.B, 'Y坐标（米）'])
zz.append(date1.at[i.B, 'Z坐标（米）'])
xx.append(date1.at[i.C, 'X坐标（米）'])
yy.append(date1.at[i.C, 'Y坐标（米）'])
zz.append(date1.at[i.C, 'Z坐标（米）'])
xx.append(date1.at[i.A, 'X坐标（米）'])
yy.append(date1.at[i.A, 'Y坐标（米）'])
zz.append(date1.at[i.A, 'Z坐标（米）'])
#ax1.plot3D(xx,yy,zz,'blue')
#ax1.scatter3D(xx,yy,zz, 'b')
xs = list(datehead[(datehead['偏离度数']<np.pi*(30/180))]['X坐标'])
ys = list(datehead[(datehead['偏离度数']<np.pi*(30/180))]['Y坐标'])
zs = list(datehead[(datehead['偏离度数']<np.pi*(30/180))]['Z坐标'])
#ax1.plot3D(xs,ys,zs,'red')
ax1.scatter3D(xs,ys,zs, 'r')
plt.show()
一推几何计算的处理公式函数
model1 = pd.read_excel("C://AppData//image//guosai//bans.xlsx")
model1.head()
dateS = pd.read_excel("C://AppData//image//guosai//附件4.xlsx",sheet_name='促动器顶端伸缩量',encoding = 'gbk')
#dateS = dateS.set_index('对应主索节点编号')
def sph2cart(r, phi,theta):
x = r * m.sin(theta) * m.cos(phi)
y = r * m.sin(theta) * m.sin(phi)
z = r * m.cos(theta)
return x,y,z
def cart2sph(x,y,z):
XsqPlusYsq = x**2 + y**2
r = m.sqrt(XsqPlusYsq + z**2) # r
elev = np.pi/2-m.atan2(z,m.sqrt(XsqPlusYsq)) # theta
az = m.atan2(y,x) # phi
return r, az, elev
def cal_area(p1,p2,p3):
a = float(m.sqrt((p2[0]-p3[0])*(p2[0]-p3[0])+(p2[1]-p3[1])*(p2[1]-p3[1])))
b = float(m.sqrt((p1[0]-p3[0])*(p1[0]-p3[0])+(p1[1]-p3[1])*(p1[1]-p3[1])))
c = float(m.sqrt((p1[0]-p2[0])*(p1[0]-p2[0])+(p1[1]-p2[1])*(p1[1]-p2[1])))
s = (a+b+c)/2
S = (s*(s-a)*(s-b)*(s-c))**0.5
return S,a,b,c
def dch1(x,y,z,x1,y1,z1,x2,y2,z2,x3,y3,z3):
a=(y2-y1)*(z3-z1)-(z2-z1)*(y3-y1)
b=(z2-z1)*(x3-x1)-(z3-z1)*(x2-x1)
c=(x2-x1)*(y3-y1)-(x3-x1)*(y2-y1)
px = (a*b*y1+b*b*x-a*b*y+a*z1*c+c*c*x-a*c*z+a*a*x1)/(a*a+b*b+c*c)
py = (b*c*z1+c*c*y-b*c*z+b*a*x1+a*a*y-a*b*x+b*b*y1)/(a*a+b*b+c*c)
pz = ((a*c*x1+a*a*z-a*x*c+b*y1*c+b*b*z-b*c*y+c*c*z1)/(a*a+b*b+c*c))
return px,py,pz,a,b,c,
def dch(x,y,z,x1,y1,z1,x2,y2,z2,x3,y3,z3):
a=(y2-y1)*(z3-z1)-(z2-z1)*(y3-y1)
b=(z2-z1)*(x3-x1)-(z3-z1)*(x2-x1)
c=(x2-x1)*(y3-y1)-(x3-x1)*(y2-y1)
px = (a*b*y1+b*b*x-a*b*y+a*z1*c+c*c*x-a*c*z+a*a*x1)/(a*a+b*b+c*c)
py = (b*c*z1+c*c*y-b*c*z+b*a*x1+a*a*y-a*b*x+b*b*y1)/(a*a+b*b+c*c)
pz = ((a*c*x1+a*a*z-a*x*c+b*y1*c+b*b*z-b*c*y+c*c*z1)/(a*a+b*b+c*c))
px = px+(px-x)
py = py+(py-y)
pz = pz+(pz-z)
return px,py,pz,a,b,c,
def get_point_line_distance(point_x, point_y,point_z,line_a_x,line_a_y,line_a_z,line_b_x ,line_b_y,line_b_z):
ap_x = point_x-line_a_x
ap_y = point_y-line_a_y
ap_z = point_z-line_a_z
ab_x = line_b_x-line_a_x
ab_y = line_b_y-line_a_y
ab_z = line_b_z-line_a_z
lap = np.sqrt(ap_x**2+ap_y**2+ap_z**2)
lab = np.sqrt(ab_x**2+ab_y**2+ab_z**2)
dis = np.sin(m.acos((ap_x*ab_x+ap_y*ab_y+ap_z*ab_z)/(lap*lab)))*lap
return dis
#dch(1,6,5,1,0,0,0,0,1,0,0,0)
def jiance(x1,y1,z1,x2,y2,z2,a,b,c,d):
p1 = np.array([x1,y1,z1])#
p2 = np.array([x2,y2,z2])
plane_normal = np.array([a,b,c]) #a,b,c,d平面方程系数
n = np.abs((np.vdot(p1,plane_normal)+d)/(np.vdot(p2-p1,plane_normal)))
p = p1 + n*(p2- p1)#所求交点
return p
def isPointinPolygon(px,py,pz,ax,ay,az,bx,by,bz,cx,cy,cz): #[[0,0],[1,1],[0,1],[0,0]] [1,0.8]
ap = np.array([px-ax,py-ay,pz-az])
ab = np.array([bx-ax,by-ay,bz-az])
ca = np.array([ax-cx,ay-cy,az-cz])
cp = np.array([px-cx,py-cy,pz-cz])
bp = np.array([px-bx,py-by,pz-bz])
bc = np.array([cx-bx,cy-by,cz-bz])
c1 = np.cross(ab,ap)
c2 = np.cross(ca,cp)
c3 = np.cross(bc,bp)
temp1 = np.dot(c1,c2)
temp2 = np.dot(c2,c3)
temp3 = np.dot(c3,c1)
#print(c1,c2,c3)
#print(c2)
#print(c3)
#print(temp1,temp2,temp3)
if temp1>=0 and temp2>=0 and temp3>=0:
return True
elif temp1<=0 and temp2<=0 and temp3<=0:
return True
else:
return False
def peng(ax,ay,bx,by,cx,cy,k):
'''
x0 = (ax+bx+cx)/2+random.randint(-30,30) *k
y0 = (ay+by+cy)/2+random.randint(-30,30) *k
ax = x0+(ax-x0)*random.randint(-20,20)
ay = y0+(ay-y0)*random.randint(-20,20)
bx = x0+(bx-x0)*random.randint(-20,20)
by = y0+(by-y0)*random.randint(-20,20)
cx = x0+(cx-x0)*random.randint(-20,20)
cy = y0+(cy-y0)*random.randint(-20,20)
'''
x0 = (ax+bx+cx)/2
y0 = (ay+by+cy)/2
ax = x0+(ax-x0)*k
ay = y0+(ay-y0)*k
bx = x0+(bx-x0)*k
by = y0+(by-y0)*k
cx = x0+(cx-x0)*k
cy = y0+(cy-y0)*k
return ax,ay,bx,by,cx,cy
#isPointinPolygon(-2,0.1,0, 1,0,0, 0,1,0, 0,0,0) randrange(20,100,5)
#jiance(0,0,0 ,0.1,0.1,0.2 ,1,1,1,-1)
peng(1,1,1,3,8,6,30)
(-115.0, -115.0, -115.0, -55.0, 95.0, 35.0)
这一堆计算是将反射光从三角板反射后投影到吸收面版的投影（可能太细致了），代码有些些凌乱
dateS = pd.read_excel("C://AppData//image//guosai//附件4.xlsx",sheet_name='促动器顶端伸缩量',encoding = 'gbk')
datess = model1.copy()
uint0 = model1.copy()
n = 1
#######
fig=plt.figure(figsize=(180,80))
ax3 = Axes3D(fig)
Az = np.pi*(36.795/180)
Elev = np.pi*(89/180)
RR = 300*0.536*0.001
x0 = -m.cos(Elev)*m.cos(Az)*RR
y0 = -m.cos(Elev)*m.sin(Az)*RR
z0 = -m.sin(Elev)*RR
xn0 = -m.cos(Elev)*m.cos(Az)
yn0 = -m.cos(Elev)*m.sin(Az)
zn0 = -m.sin(Elev)
#print(x0,y0,z0)
#print([xn0,yn0,zn0])
tt = 0
ka = 1
kb = 1/(np.sin(Az))
kc = np.tan(Elev)/np.cos(Az)
kd = -(-x0-kb*y0-kc*z0)
#print(ka,kb,kc,kd)
#########
for i in range(0,len(date3)):
A = date3.at[i, '主索节点1']
B = date3.at[i, '主索节点2']
C = date3.at[i, '主索节点3']
uint0.at[0,'编号'] = str(A) + str(B) + str(C)
uint0.at[0,'顶点A'] = A
uint0.at[0,'顶点B'] = B
uint0.at[0,'顶点C'] = C
#
Ax = date1.at[A, 'X坐标（米）']
Ay = date1.at[A, 'Y坐标（米）']
Az = date1.at[A, 'Z坐标（米）']
Bx = date1.at[B, 'X坐标（米）']
By = date1.at[B, 'Y坐标（米）']
Bz = date1.at[B, 'Z坐标（米）']
Cx = date1.at[C, 'X坐标（米）']
Cy = date1.at[C, 'Y坐标（米）']
Cz = date1.at[C, 'Z坐标（米）']
uint0.at[0,'Ax'] = Ax
uint0.at[0,'Ay'] = Ay
uint0.at[0,'Az'] = Az
uint0.at[0,'Bx'] = Bx
uint0.at[0,'By'] = By
uint0.at[0,'Bz'] = Bz
uint0.at[0,'Cx'] = Cx
uint0.at[0,'Cy'] = Cy
uint0.at[0,'Cz'] = Cz
#
Ar, Aa, Ab = cart2sph(Ax,Ay,Az)
uint0.at[0,'Ar'] = Ar
uint0.at[0,'Aa'] = Aa
uint0.at[0,'Ab'] = Ab
Br, Ba, Bb = cart2sph(Bx,By,Bz)
uint0.at[0,'Br'] = Br
uint0.at[0,'Ba'] = Ba
uint0.at[0,'Bb'] = Bb
Cr, Ca, Cb = cart2sph(Cx,Cy,Cz)
uint0.at[0,'Cr'] = Cr
uint0.at[0,'Ca'] = Ca
uint0.at[0,'Cb'] = Cb
###
AS = 0
BS = 0
CS = 0
uint0.at[0,'垂线X'] = 0
if A in list(dateS['对应主索节点编号']):
if B in list(dateS['对应主索节点编号']):
if C in list(dateS['对应主索节点编号']):
uint0.at[0,'垂线X'] = 1
'''
if A in list(dateS['对应主索节点编号']):
AS = dateS.set_index('对应主索节点编号').at[A, '伸缩量（米）']
else:
AS = 0
if B in list(dateS['对应主索节点编号']):
BS = dateS.set_index('对应主索节点编号').at[B, '伸缩量（米）']
else:
BS = 0
if C in list(dateS['对应主索节点编号']):
CS = dateS.set_index('对应主索节点编号').at[C, '伸缩量（米）']
else:
CS = 0'''
uint0.at[0,'AS'] = AS
uint0.at[0,'BS'] = BS
uint0.at[0,'CS'] = CS
###
#
Anr = Ar-AS
Ana = Aa
Anb = Ab
uint0.at[0,'Anr'] = Anr
uint0.at[0,'Ana'] = Ana
uint0.at[0,'Anb'] = Anb
Bnr = Br-BS
Bna = Ba
Bnb = Bb
uint0.at[0,'Bnr'] = Bnr
uint0.at[0,'Bna'] = Bna
uint0.at[0,'Bnb'] = Bnb
Cnr = Cr-CS
Cna = Ca
Cnb = Cb
uint0.at[0,'Cnr'] = Cnr
uint0.at[0,'Cna'] = Cna
uint0.at[0,'Cnb'] = Cnb
#
Anx,Any,Anz = sph2cart(Anr,Ana,Anb)
uint0.at[0,'Anx'] = Anx
uint0.at[0,'Any'] = Any
uint0.at[0,'Anz'] = Anz
Bnx,Bny,Bnz = sph2cart(Bnr,Bna,Bnb)
uint0.at[0,'Bnx'] = Bnx
uint0.at[0,'Bny'] = Bny
uint0.at[0,'Bnz'] = Bnz
Cnx,Cny,Cnz = sph2cart(Cnr,Cna,Cnb)
uint0.at[0,'Cnx'] = Cnx
uint0.at[0,'Cny'] = Cny
uint0.at[0,'Cnz'] = Cnz
#
p1 = [Ax,Ay,Az]
p2 = [Bx,By,Bz]
p3 = [Cx,Cy,Cz]
SS,Lab,Lac,Lbc = cal_area(p1,p2,p3)
uint0.at[0,'板面积'] = SS
uint0.at[0,'Lab'] = Lab
uint0.at[0,'Lac'] = Lac
uint0.at[0,'Lbc'] = Lbc
pn1 = [Anx,Any,Anz]
pn2 = [Bnx,Bny,Bnz]
pn3 = [Cnx,Cny,Cnz]
SnS,Lnab,Lnac,Lnbc = cal_area(pn1,pn2,pn3)
uint0.at[0,'新版面积'] = SnS
uint0.at[0,'Lnab'] = Lnab
uint0.at[0,'Lnac'] = Lnac
uint0.at[0,'Lnbc'] = Lnbc
#####
'''
Ajx,Ajy,Ajz,nx,ny,nz = dch(Anx+xn0,Any+yn0,Anz+zn0,Anx,Any,Anz,Bnx,Bny,Bnz,Cnx,Cny,Cnz)
Bjx,Bjy,Bjz,nx,ny,nz = dch(Bnx+xn0,Bny+yn0,Bnz+zn0,Anx,Any,Anz,Bnx,Bny,Bnz,Cnx,Cny,Cnz)
Cjx,Cjy,Cjz,nx,ny,nz = dch(Cnx+xn0,Cny+yn0,Cnz+zn0,Anx,Any,Anz,Bnx,Bny,Bnz,Cnx,Cny,Cnz)
'''
Ajx,Ajy,Ajz,nx,ny,nz = dch(Anx-xn0,Any-yn0,Anz-zn0,Anx,Any,Anz,Bnx,Bny,Bnz,Cnx,Cny,Cnz)
Bjx,Bjy,Bjz,nx,ny,nz = dch(Bnx-xn0,Bny-yn0,Bnz-zn0,Anx,Any,Anz,Bnx,Bny,Bnz,Cnx,Cny,Cnz)
Cjx,Cjy,Cjz,nx,ny,nz = dch(Cnx-xn0,Cny-yn0,Cnz-zn0,Anx,Any,Anz,Bnx,Bny,Bnz,Cnx,Cny,Cnz)
lost = np.abs((nx*x0+ny*y0+nz*z0)/(np.sqrt(nx**2+ny**2+nz**2)*np.sqrt(x0**2+y0**2+z0**2)))
uint0.at[0,'密度折损率'] = lost
[AApx,AApy,AApz] = jiance(Anx,Any,Anz ,Ajx,Ajy,Ajz ,ka,kb,kc,kd)
[BBpx,BBpy,BBpz] = jiance(Bnx,Bny,Bnz ,Bjx,Bjy,Bjz ,ka,kb,kc,kd)
[CCpx,CCpy,CCpz] = jiance(Cnx,Cny,Cnz ,Cjx,Cjy,Cjz ,ka,kb,kc,kd)
#uint0.at[0,'垂线X'] = np.array([Ajx,Ajy,Ajz])-np.array([Anx,Any,Anz])
uint0.at[0,'垂线Y'] = np.array([xn0,yn0,zn0])
uint0.at[0,'垂线Z'] = np.array([nx,ny,nz])
#XQx = [AApx,BBpx,CCpx,AApx]
#YQy = [AApy,BBpy,CCpy,AApy]
YQz = [0,0,0,0]
#AApx,AApy,BBpx,BBpy,CCpx,CCpy = peng(AApx,AApy,BBpx,BBpy,CCpx,CCpy,3)
XQx = [AApx,BBpx,CCpx,AApx]
YQy = [AApy,BBpy,CCpy,AApy]
if uint0.at[0,'垂线X'] == 1:
ax3.plot3D(XQx,YQy,YQz,'red')
ax3.scatter3D(x0,y0,0, 'r')
if isPointinPolygon(x0,y0,0,AApx,AApy,0,BBpx,BBpy,0,CCpx,CCpy,0):
p1 = np.array([Anx,Any,Anz])
p2 = -(np.array([Ajx,Ajy,Ajz])-np.array([Anx,Any,Anz]))*140+np.array([Anx,Any,Anz])
#p2 = np.array([AApx,AApy,AApz])
p3 = np.array([Anx,Any,Anz])
p4 = -(np.array([xn0,yn0,zn0]))*120+np.array([Anx,Any,Anz])
#ax3.plot3D([p1[0],p2[0]],[p1[1],p2[1]],[p1[2],p2[2]],'red')
if tt%60 == 0:
#ax3.plot3D([p1[0],p2[0]],[p1[1],p2[1]],[p1[2],p2[2]],'red')
tt = tt+1
#ax3.plot3D([p3[0],p4[0]],[p3[1],p4[1]],[p3[2],p4[2]],'blue')
else:
tt = tt+1
SgS,Lab,Lac,Lbc = cal_area(p1,p2,p3)
uint0.at[0,'吸收量'] = (np.pi/4)*lost
uint0.at[0,'是否接收'] = 'yes'
else:
uint0.at[0,'吸收量'] = 0
uint0.at[0,'是否接收'] = 'no'
''' px,py,pz,nx,ny,nz = dch(x0,y0,z0,Anx,Any,Anz,Bnx,Bny,Bnz,Cnx,Cny,Cnz)
dis = get_point_line_distance(px,py,pz,xo,yo,zo,xo+x0,yo+y0,zo+z0)
uint0.at[0,'垂线X'] = dis
if dis<=5:
SgS = ((nx*x0+ny*y0+nz*z0)/(np.sqrt(nx**2+ny**2+nz**2)*np.sqrt(x0**2+y0**2+z0**2)))*SnS
uint0.at[0,'吸收面积'] = SgS
else:
uint0.at[0,'吸收面积'] = 0'''
#####
datess = datess.append(uint0,ignore_index=True)
n = n+1
if n%500 == 0:
#plt.show()
#break
print(n)
#ax3.scatter3D(xs,ys,zs, 'r')
plt.show()
print("总板面积")
print(datess['板面积'].sum())
print("总有效板数")
print(datess[((datess['是否接收'] == 'yes'))]['吸收量'].count())
print("工作区有效板数")
print(datess[((datess['是否接收'] == 'yes')&(datess['垂线X'] == 1))]['吸收量'].count())
print("工作区板面积")
print(datess[((datess['是否接收'] == 'yes')&(datess['垂线X'] == 1))]['板面积'].sum())
print("总收益")
print(datess['吸收量'].sum())
print("工作区收益")
print(datess[((datess['是否接收'] == 'yes')&(datess['垂线X'] == 1))]['吸收量'].sum())
'''
print("总板面积")
print(datess['板面积'].sum())
print("总有效板数")
print(datess[((datess['是否接收'] == 'yes'))]['吸收量'].count())
print("工作区有效板数")
print(datess[((datess['是否接收'] == 'yes')&(datess['垂线X'] == 1))]['吸收量'].count())
print("工作区板面积")
print(datess[((datess['是否接收'] == 'yes')&(datess['垂线X'] == 1))]['板面积'].sum())
print("总收益")
print(datess['吸收量'].sum())
print("工作区收益")
print(datess[((datess['是否接收'] == 'yes')&(datess['垂线X'] == 1))]['吸收量'].sum())
'''
初始反射锅投影接受平面
调整之后的反射面（看着不太好）
通过判断接受面板中心是否在反射三角形里判断是否接受。
结果放在了一个表格里
只需要5个积分
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