import math
#import matplotlib.pyplot
#import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.collections import PolyCollection
from matplotlib.colors import colorConverter
from matplotlib.patches import FancyArrowPatch
from mpl_toolkits.mplot3d import proj3d
import mpl_toolkits.mplot3d.art3d as art3d
#import numpy as np
import matplotlib.pyplot as plt
#from numpy import *
from pylab import *
#import pylab
class vec(object):
def __init__(self, x, y, z):
self.vec=(x, y, z)
def unit(self, vec):
dist=math.sqrt(vec[0]**2+vec[1]**2+vec[2]**2)
unitvec=(vec[0]/dist,vec[1]/dist,vec[2]/dist)
return unitvec
def add(self, vector1, vector2):
sum=(vector1[0]+vector2[0], vector1[1]+vector2[1], vector1[2]+vector2[2])
return sum
def addfour(self, vec1, vec2, vec3, vec4):
comebine1=self.add(vec1,vec2)
comebine2=self.add(vec3,vec4)
outcome=self.add(comebine1, comebine2)
return outcome
def smultiply(self,scalar, vec):
product=(scalar*vec[0],scalar*vec[1],scalar*vec[2])
return product
def innerproduct(self, vec1, vec2):
result=(vec1[0]*vec2[0]+vec1[1]*vec2[1]+vec1[2]*vec2[2])
return result
def crossproduct(self, vec1, vec2):
res=(vec1[1]*vec2[2]-vec1[2]*vec2[1], vec1[2]*vec2[0]-vec1[0]*vec2[2], vec1[0]*vec2[1]-vec1[1]*vec2[0])
return res
def function(self, vec1, vec2, i):
coef1=c1*inner(vec2, vec2)
term1=self.smultiply(coef1, vec1)
term2=self.smultiply(c2,self.crossproduct(vec2,vec1))
term3=self.smultiply(c3*(1+c5*math.sin(5000*i*h)),(0,0,1))
term4=self.smultiply(c4*(1+c5*math.sin(5000*i*h))*vec1[2],vec1)
sumoffour=self.addfour(term1,term2,term3,term4)
out=self.smultiply(h, sumoffour)
return out
def rungekutta1(self,vec1,vec):
k11=self.smultiply(h,vec)
k11_half=self.smultiply(.5, k11)
y12=self.add(vec, k11_half)
k12=self.smultiply(h,y12)
k12_half=self.smultiply(.5, k12)
y13=self.add(vec, k12_half)
k13=self.smultiply(h,y13)
y14=self.add(vec, k13)
k14=self.smultiply(h,y14)
k12_double=self.smultiply(2.0, k12)
k13_double=self.smultiply(2.0, k13)
k1s=self.addfour(k11, k12_double, k13_double, k14)
n_ununit=self.add(vec1, self.smultiply(1.0/6, k1s))
n_next=self.unit(n_ununit)
return n_next
def rungekutta2(self, vec1, vec2, i):
k21=self.function(vec1, vec2, i)
n22=self.add(vec1,self.smultiply(.5*h,(1,1,1)))
d_n22=self.add(vec2,self.smultiply(.5,k21))
k22=self.function(n22,d_n22, i)
n23=self.add(vec1,self.smultiply(.5*h,(1,1,1)))
d_n23=self.add(vec2,self.smultiply(.5,k22))
k23=self.function(n23,d_n23, i)
n24=self.add(vec1,self.smultiply(h,(1,1,1)))
d_n24=self.add(vec2,k23)
k24=self.function(n24,d_n24, i)
k22_double=self.smultiply(2.0,k22)
k23_double=self.smultiply(2.0,k23)
k2s=self.addfour(k21, k22_double, k23_double, k24)
d_n_ununit=self.add(vec2, self.smultiply(1.0/6, k2s))
d_n_next=self.unit(d_n_ununit)
return d_n_next
class Arrow3D(FancyArrowPatch):
def __init__(self, xs, ys, zs, *args, **kwargs):
FancyArrowPatch.__init__(self, (0,0), (0,0), *args, **kwargs)
self._verts3d = xs, ys, zs
def draw(self, renderer):
xs3d, ys3d, zs3d = self._verts3d
xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, renderer.M)
self.set_positions((xs[0],ys[0]),(xs[1],ys[1]))
FancyArrowPatch.draw(self, renderer)
def drawbody(x, y, z):
u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]
x1=.4*np.cos(u)*np.sin(v)
y1=.4*np.sin(u)*np.sin(v)
z1=.1*np.cos(v)
theta = math.acos(math.sqrt(y**2+ z**2)/math.sqrt(x**2 + y**2 +z**2))
x2 = x1*math.cos(theta) + z1*math.sin(theta)
y2 = y1
z2 = -x1*math.sin(theta) + z1*math.cos(theta)
if theta <0:
theta1 = math.acos(z/math.sqrt(y**2+z**2))
else:
theta1=-math.acos(z/math.sqrt(y**2+z**2))
x3 = x2
y3 = y2*math.cos(theta1) - z2*math.sin(theta1)
z3 = y2*math.sin(theta1) + z2*math.cos(theta1)
ax.plot_wireframe(x3+x/2, y3+y/2, z3+z/2, color="#254117")
ax.scatter([x3+x/2],[y3+y/2],[0], marker='_', color='black')
matplotlib.pyplot.show()
def drawgroundandlabelaxis(z, n_o):
rectangle = Rectangle((-1,-1),2,2, color='grey')
ax.add_patch(rectangle)
art3d.pathpatch_2d_to_3d(rectangle, z=z- n_o)
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')
ax.set_zlabel('Z-axis')
matplotlib.pyplot.show()
def plotvel(dx, dy, dz, x, y, z):
a = Arrow3D([x,dx+x],[y,dy+y],[z,dz+z], mutation_scale=20, lw=1, arrowstyle="-|>", color='r')
ax.add_artist(a)
plt.show()
#ax.scatter(dx, dy, dz, c='#330099')
matplotlib.pyplot.show()
def plotpos(x ,y ,z, n_o, w):
ax.cla()
#drawbody(x,y,z)
line = ax.plot([x,0],[y,0],[z,z- n_o],color='#000066', marker= 'o')
line = ax.plot([0,0],[0,0],[1.1,-.5],color='g')
ax.scatter([0,0],[0,0],[0,1], marker='8', color='g')
drawgroundandlabelaxis(z, n_o)
matplotlib.pyplot.show()
def plotthetaphi(x,y,z):
phi= math.acos(z/math.sqrt(x**2+y**2+z**2))
theta = math.acos(x/math.sqrt(x**2 + y**2))
plot(theta, phi,'.',color='red')
def drawstaticplot(m, n, d_n, n_o):
counter=0
for i in range(0,m):
n=vector.rungekutta1(n, d_n)
d_n=vector.rungekutta2(n, d_n, i)
x1 = n[0]
y1 = n[1]
z1 = n[2]
if i%20==0:
xarray.append(x1)
yarray.append(y1)
zarray.append(z1)
x_m = xarray[::20]
y_m = yarray[::20]
z_m = zarray[::20]
for j in range(0,6):
print x_m[j], y_m[j], z_m[j]
# if (((zarray[j]-n_o)>0) and ((zarray[j+1]-n_o)<0)): #or (((zarray[j]-n_o)<0) and ((zarray[j+20]-n_o)>0)):
# counter= counter +1
# print zarray[j]-n_o,counter
# plotthetaphi(xarray[j],yarray[j],zarray[j])
#ax.plot([xarray[j],xarray[j+20]],[yarray[j],yarray[j+20]],[zarray[j],zarray[j+20]],color='#0000FF' ,marker='.')
#ax.plot([xarray[j],0],[yarray[j],0],[zarray[j],zarray[j]-n_o],color='#0000FF',marker='.')
#matplotlib.pyplot.show()
def setboundary():
ax.set_xlim3d(-1,1)
ax.set_ylim3d(-1,1)
ax.set_zlim3d(0,1)
def drawdynamicplot(m,n,d_n, n_o):
for i in range(0,m):
n=vector.rungekutta1(n, d_n)
d_n=vector.rungekutta2(n, d_n, i)
setboundary()
if i%20 == 0:
print "-------------" ,i , "----------------"
print n[0]
plotpos(n[0],n[1],n[2], n_o, i)
plotvel(d_n[0],d_n[1],d_n[2],n[0],n[1],n[2])
#if i%320 ==0:
#plotthetaphi(n[0],n[1],n[2])
def realtimeconnectline(m,n,d_n):
for i in range(0,m):
n=vector.rungekutta1(n, d_n)
d_n=vector.rungekutta2(n, d_n, i)
x1=n[0]
y1=n[1]
z1=n[2]
print x1,y1,z1
if i%10 == 0:
x1=n[0]
y1=n[1]
z1=n[2]
if i%20==0:
x2=n[0]
y2=n[1]
z2=n[2]
ax.plot([x1,x2],[y1,y2],[z1,z2],color = 'blue',marker='.')
matplotlib.pyplot.show()
#fig = plt.figure()
#ax = fig.add_subplot(111, projection='3d')
c1=-1.0
c2=6.6
c3=-5.4
c4=-c3
c5=1
#[c1,c2,c3,c4,c5]=[-1.0,-2.4,-3.67,-c3,0]
h=.005
vector=vec(1,2,3)
allone=vec(1.0,1.0,1.0)
n=(0.0,.5,.5)
d_n=(5.0,5.0,0.0)
ion()
n=vector.unit(n)
#d_n = vec.unit(vec, d_n)
m=5000
xarray=[]
yarray=[]
zarray=[]
drawstaticplot(m,n, d_n, n[2])
#drawdynamicplot(m,n, d_n, n[2])
#realtimeconnectline(m,n,d_n)
import math
#import matplotlib.pyplot
#import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.collections import PolyCollection
from matplotlib.colors import colorConverter
from matplotlib.patches import FancyArrowPatch
from mpl_toolkits.mplot3d import proj3d
import mpl_toolkits.mplot3d.art3d as art3d
#import numpy as np
import matplotlib.pyplot as plt
#from numpy import *
from pylab import *
#import pylab


class vec(object):
    def __init__(self, x, y, z):
        self.vec=(x, y, z)
        
    def unit(self, vec):
        dist=math.sqrt(vec[0]**2+vec[1]**2+vec[2]**2)
        unitvec=(vec[0]/dist,vec[1]/dist,vec[2]/dist)
        return unitvec
    
    def add(self, vector1, vector2):
        sum=(vector1[0]+vector2[0], vector1[1]+vector2[1], vector1[2]+vector2[2])
        return sum
    
    def addfour(self, vec1, vec2, vec3, vec4):
        comebine1=self.add(vec1,vec2)
        comebine2=self.add(vec3,vec4)
        outcome=self.add(comebine1, comebine2)
        return outcome
    
    def smultiply(self,scalar, vec):
        product=(scalar*vec[0],scalar*vec[1],scalar*vec[2])
        return product
    
    def innerproduct(self, vec1, vec2):
        result=(vec1[0]*vec2[0]+vec1[1]*vec2[1]+vec1[2]*vec2[2])
        return result
    
    def crossproduct(self, vec1, vec2):
        res=(vec1[1]*vec2[2]-vec1[2]*vec2[1], vec1[2]*vec2[0]-vec1[0]*vec2[2], vec1[0]*vec2[1]-vec1[1]*vec2[0])
        return res
    
    def function(self, vec1, vec2, i):
        coef1=c1*inner(vec2, vec2)
        term1=self.smultiply(coef1, vec1)
        term2=self.smultiply(c2,self.crossproduct(vec2,vec1))
        term3=self.smultiply(c3*(1+c5*math.sin(5000*i*h)),(0,0,1))
        term4=self.smultiply(c4*(1+c5*math.sin(5000*i*h))*vec1[2],vec1)
        sumoffour=self.addfour(term1,term2,term3,term4)
        out=self.smultiply(h, sumoffour)
        return out
    def rungekutta1(self,vec1,vec):
        k11=self.smultiply(h,vec)
        k11_half=self.smultiply(.5, k11)
        y12=self.add(vec, k11_half)
        k12=self.smultiply(h,y12)
        k12_half=self.smultiply(.5, k12)
        y13=self.add(vec, k12_half)
        k13=self.smultiply(h,y13)
        y14=self.add(vec, k13)
        k14=self.smultiply(h,y14)
        k12_double=self.smultiply(2.0, k12)
        k13_double=self.smultiply(2.0, k13)
        k1s=self.addfour(k11, k12_double, k13_double, k14)
        n_ununit=self.add(vec1, self.smultiply(1.0/6, k1s))
        n_next=self.unit(n_ununit)
        return n_next
    def rungekutta2(self, vec1, vec2, i):
        k21=self.function(vec1, vec2, i)
        n22=self.add(vec1,self.smultiply(.5*h,(1,1,1)))
        d_n22=self.add(vec2,self.smultiply(.5,k21))
        k22=self.function(n22,d_n22, i)
        n23=self.add(vec1,self.smultiply(.5*h,(1,1,1)))
        d_n23=self.add(vec2,self.smultiply(.5,k22))
        k23=self.function(n23,d_n23, i)
        n24=self.add(vec1,self.smultiply(h,(1,1,1)))
        d_n24=self.add(vec2,k23)
        k24=self.function(n24,d_n24, i)
        k22_double=self.smultiply(2.0,k22)
        k23_double=self.smultiply(2.0,k23)
        k2s=self.addfour(k21, k22_double, k23_double, k24)
        d_n_ununit=self.add(vec2, self.smultiply(1.0/6, k2s))
        d_n_next=self.unit(d_n_ununit)
        return d_n_next

class Arrow3D(FancyArrowPatch):
    def __init__(self, xs, ys, zs, *args, **kwargs):
        FancyArrowPatch.__init__(self, (0,0), (0,0), *args, **kwargs)
        self._verts3d = xs, ys, zs

    def draw(self, renderer):
        xs3d, ys3d, zs3d = self._verts3d
        xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, renderer.M)
        self.set_positions((xs[0],ys[0]),(xs[1],ys[1]))
        FancyArrowPatch.draw(self, renderer)

def drawbody(x, y, z):
        u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]
        x1=.4*np.cos(u)*np.sin(v)
        y1=.4*np.sin(u)*np.sin(v)
        z1=.1*np.cos(v)
        theta = math.acos(math.sqrt(y**2+ z**2)/math.sqrt(x**2 + y**2 +z**2))
        x2 = x1*math.cos(theta) + z1*math.sin(theta)
        y2 = y1
        z2 = -x1*math.sin(theta) + z1*math.cos(theta)
        if theta <0:
            theta1 = math.acos(z/math.sqrt(y**2+z**2))
        else:
            theta1=-math.acos(z/math.sqrt(y**2+z**2))
            x3 = x2
            y3 = y2*math.cos(theta1) - z2*math.sin(theta1)
            z3 = y2*math.sin(theta1) + z2*math.cos(theta1)
        ax.plot_wireframe(x3+x/2, y3+y/2, z3+z/2, color="#254117")           
        ax.scatter([x3+x/2],[y3+y/2],[0], marker='_', color='black')
        matplotlib.pyplot.show()

def drawgroundandlabelaxis(z, n_o):
        rectangle = Rectangle((-1,-1),2,2, color='grey')
        ax.add_patch(rectangle)
        art3d.pathpatch_2d_to_3d(rectangle, z=z- n_o)
        ax.set_xlabel('X-axis')
        ax.set_ylabel('Y-axis')
        ax.set_zlabel('Z-axis')     
        matplotlib.pyplot.show()

def plotvel(dx, dy, dz, x, y, z):
        a = Arrow3D([x,dx+x],[y,dy+y],[z,dz+z], mutation_scale=20, lw=1, arrowstyle="-|>", color='r')
        ax.add_artist(a)
        plt.show()
        #ax.scatter(dx, dy, dz, c='#330099')
        matplotlib.pyplot.show()
    
def plotpos(x ,y ,z, n_o, w):
        ax.cla()
        #drawbody(x,y,z)
        line = ax.plot([x,0],[y,0],[z,z- n_o],color='#000066', marker= 'o')
        line = ax.plot([0,0],[0,0],[1.1,-.5],color='g')
        ax.scatter([0,0],[0,0],[0,1], marker='8', color='g')
        drawgroundandlabelaxis(z, n_o)
        matplotlib.pyplot.show()

def plotthetaphi(x,y,z):
    phi= math.acos(z/math.sqrt(x**2+y**2+z**2))
    theta = math.acos(x/math.sqrt(x**2 + y**2))
    plot(theta, phi,'.',color='red')
    

    
def drawstaticplot(m, n, d_n, n_o):
    counter=0
    for i in range(0,m):
        n=vector.rungekutta1(n, d_n)
        d_n=vector.rungekutta2(n, d_n, i)
        x1 = n[0]
        y1 = n[1]
        z1 = n[2]
        if i%20==0:
            xarray.append(x1)
            yarray.append(y1)
            zarray.append(z1)
        x_m = xarray[::20]
        y_m = yarray[::20]
        z_m = zarray[::20]
        
    for j in range(0,6):
        print x_m[j], y_m[j], z_m[j]
    #    if (((zarray[j]-n_o)>0) and ((zarray[j+1]-n_o)<0)): #or (((zarray[j]-n_o)<0) and ((zarray[j+20]-n_o)>0)):
    #        counter= counter +1
    #        print zarray[j]-n_o,counter
    #        plotthetaphi(xarray[j],yarray[j],zarray[j])
            
            #ax.plot([xarray[j],xarray[j+20]],[yarray[j],yarray[j+20]],[zarray[j],zarray[j+20]],color='#0000FF' ,marker='.')
            #ax.plot([xarray[j],0],[yarray[j],0],[zarray[j],zarray[j]-n_o],color='#0000FF',marker='.')
            #matplotlib.pyplot.show()



    
def setboundary():
    ax.set_xlim3d(-1,1)
    ax.set_ylim3d(-1,1)
    ax.set_zlim3d(0,1)
    

def drawdynamicplot(m,n,d_n, n_o):
    for i in range(0,m):        
            n=vector.rungekutta1(n, d_n)
            d_n=vector.rungekutta2(n, d_n, i)
            setboundary()
            if i%20 == 0:
                print "-------------" ,i , "----------------"
                print n[0]                
                plotpos(n[0],n[1],n[2], n_o, i)
                plotvel(d_n[0],d_n[1],d_n[2],n[0],n[1],n[2])
                #if i%320 ==0:               
                #plotthetaphi(n[0],n[1],n[2])
                
def realtimeconnectline(m,n,d_n):
    for i in range(0,m):
        n=vector.rungekutta1(n, d_n)
        d_n=vector.rungekutta2(n, d_n, i)
        x1=n[0]
        y1=n[1]
        z1=n[2]
        print x1,y1,z1
        if i%10 == 0:
                x1=n[0]
                y1=n[1]
                z1=n[2]
                if i%20==0:
                    x2=n[0]
                    y2=n[1]
                    z2=n[2]
                ax.plot([x1,x2],[y1,y2],[z1,z2],color = 'blue',marker='.')
                matplotlib.pyplot.show()
                


                
#fig = plt.figure()
#ax = fig.add_subplot(111, projection='3d')

c1=-1.0
c2=6.6
c3=-5.4
c4=-c3
c5=1
#[c1,c2,c3,c4,c5]=[-1.0,-2.4,-3.67,-c3,0]
h=.005
vector=vec(1,2,3)
allone=vec(1.0,1.0,1.0)
n=(0.0,.5,.5)
d_n=(5.0,5.0,0.0)
ion()
n=vector.unit(n)
#d_n = vec.unit(vec, d_n)

m=5000
xarray=[]
yarray=[]
zarray=[]

drawstaticplot(m,n, d_n, n[2])
#drawdynamicplot(m,n, d_n, n[2])
#realtimeconnectline(m,n,d_n)

