#Lab 5 Group 1 #Zoe Woolf, Jose Hopkins, Michael Sandoval, Nick Scheier #Romeo and Juliet s=var('R,J') #R and J are the variables t=srange(0,20,0.05) # we will use t for desolve_odeint. It is the range of time values # for which we will solve the differential equations. ic1=[.5,.6] # our 4 initial conditions. Play around with different values for these. ic2=[.5,-.2] ic3=[-.9,.6] ic4=[-.5,-.7] @interact def Romeo_Juliet(a=slider(-2,2,0.5,default=-1), b=slider(-2,2,0.5,default=0), c=slider(-2,2,0.5,default=0), d=slider(-2,2,0.5,default=-1)): # use 4 sliders for the coefficients, set the default values # for a,d to -1, b and c to 0. titleeq= ("$R'= "+'%2.1f'%(a)+" R "+"%+2.1f"%(b)+" J$\n" +"$J' = "+'%2.1f'%(c)+" R "+"%+2.1f"%(d)+" J$") # some fancy formatting stuff to create a title with our differential equations vector_field=[a*R+b*J,c*R+d*J] # our differential equations pv=plot_vector_field(vector_field,(R,-1,1),(J,-1,1),plot_points=11,axes_labels=["R","J"]) # plot the vector field,R and J both going from -1 to 1, using 11x11 arrows sol1=desolve_odeint(vector_field,times=t,ics=ic1,dvars=[R,J]) # solve the differential equation starting at ic1, for times in t ps1=list_plot(sol1,plotjoined=True,color='blue')+disk(ic1,.03,(0,2*pi),color='blue') # plot a trajectory starting at ic2, with a starting dot sol2=desolve_odeint(vector_field,times=t,ics=ic2,dvars=[R,J]) # solve the differential equation starting at ic2, for times in t ps2=list_plot(sol2,plotjoined=True,color='red')+disk(ic2,.03,(0,2*pi),color='red') # plot a trajectory starting at ic2, with a starting dot sol3=desolve_odeint(vector_field,times=t,ics=ic3,dvars=[R,J]) # solve the differential equation starting at ic3, for times in t ps3=list_plot(sol3,plotjoined=True,color='green')+disk(ic3,.03,(0,2*pi),color='green') #plot a trajectory starting at ic3, with a starting dot sol4=desolve_odeint(vector_field,times=t,ics=ic4,dvars=[R,J]) # solve the differential equation starting at ic4, for times in t ps4=list_plot(sol4,plotjoined=True,color='purple')+disk(ic4,.03,(0,2*pi),color='purple') #a trajectory starting at ic2, with a starting dot show(pv+ps1+ps2+ps3+ps4,xmin=-1,xmax=1,ymin=-1,ymax=1,aspect_ratio=1,title=titleeq,figsize=8,fontsize=20) #"R'=aR+bJ\n J'=cR+dJ") # combine and show the plots but limit the range of x and of y, add the title etc.
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#Deer and Moose Code 1 vs=var("D,M") t=srange(0,50,0.05) Dmax=3.1 Mmax=2.5 @interact # use the slider function to specify defaults and fancy labels def Deer_Moose(r_D=slider(0,5,0.5,default=3,label="$r_D$"), r_M=slider(0,5,0.5,default=2,label="$r_M$"), alpha=slider(0,2,0.5,default=0.5,label="$\\alpha$"), D0=slider(0,Dmax,0.1,default=1,label="$D_0$"), M0=slider(0,Mmax,0.1,default=1,label="$M_0$")): titleeq=("$D'= " + '%2.1f'%(r_D) + " D - M D - D^2$\n " + "$M'= " + '%2.1f'%(r_M) + " M - " + '%2.1f'%(alpha) + " M D - M^2$") # make a title for the plot which has the differential equations. vector_field=[r_D*D-M*D-D^2,r_M*M-alpha*M*D-M^2] initial_value=[D0,M0] pv=plot_vector_field(vector_field,(D,0,Dmax),(M,0,Mmax), plot_points=11,axes_labels=["D","M"]) sol1=desolve_odeint(vector_field,times=t,ics=initial_value,dvars=[D,M]) ps1=list_plot(sol1,plotjoined=True,color='green',thickness=2) d1=disk(initial_value,.035,(0,2*pi),color='green') nullclines=( plot(r_D-D,(D,0,r_D),color="red",thickness=3) +plot(r_M-alpha*D,(D,0,r_M/alpha),color="blue",thickness=3)) # make plots of the nullcline lines (excluding D=0 and M=0) show(pv+ps1+d1+nullclines,xmin=0,xmax=Dmax,ymin=0,ymax=Mmax, aspect_ratio=1,title=titleeq,figsize=8,fontsize=20,svg=False)
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#Deer and Moose Code 2 def Scale_vf(xy): #Defining the code # a function that takes a vector input and returns a scaled # version of the vector to keep arrows from getting too long scale=sqrt(5+xy[0]^2+xy[1]^2) return [xy[0]/scale,xy[1]/scale] vs=var("D,M") #creating the labels t=srange(0,50,0.05) #setting the range Dmax=4.0 #setting max for graph Mmax=3.5 #Setting min for graph @interact #calling the code def Deer_Moose_norm(r_D=slider(0,5,0.5,default=3,label="$r_D$"), #setting the sliders and their range with labels r_M=slider(0,5,0.5,default=2,label="$r_M$"), alpha=slider(0,2,0.5,default=0.5,label="$\\alpha$"), D0=slider(0,Dmax,0.1,default=1,label="$D_0$"), M0=slider(0,Mmax,0.1,default=1,label="$M_0$")): titleeq=("$D'= " + '%2.1f'%(r_D) + " D - M D - D^2$\n " + #setting the title with the equations "$M'= " + '%2.1f'%(r_M) + " M - " + '%2.1f'%(alpha) + " M D - M^2$") vector_field=[r_D*D-M*D-D^2,r_M*M-alpha*M*D-M^2] #setting the vector field initial_value=[D0,M0] #initial values pv=plot_vector_field(Scale_vf(vector_field),(D,0,Dmax),(M,0,Mmax), plot_points=11,axes_labels=["D","M"]) #setting the plots with labels and vector sizes and axes labels # plot the scaled vector field sol1=desolve_odeint(vector_field,times=t,ics=initial_value,dvars=[D,M]) ps1=list_plot(sol1,plotjoined=True,color='green',thickness=2) d1=disk(initial_value,.035,(0,2*pi),color='green') nullclines=(plot(r_D-D,(D,0,r_D),color="red",thickness=3) +plot(r_M-alpha*D,(D,0,r_M/alpha),color="blue",thickness=3)) show(pv+ps1+d1+nullclines,xmin=0,xmax=Dmax,ymin=0,ymax=Mmax, #what is being showed on the graph aspect_ratio=1,title=titleeq,figsize=8,fontsize=20,svg=False)
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#Shark Tuna Code def Scale_vf(xy): #Defining the code # a function that takes a vector input and returns a scaled # version of the vector to keep arrows from getting too long # scale=sqrt(5+xy[0]^2+xy[1]^2) return [xy[0]/scale,xy[1]/scale] vs=var('T,S') #creating the labels t=srange(0,150,0.05) #setting the range Tmax=100 #setting max for graph Smax=40 #Setting min for graph @interact #calling the code def Shark_Tuna_norm(r_T=slider(0,0.4,0.01,default=0.1,label="$r_T$"), #setting the sliders and their range with labels r_S=slider(-0.5,0,0.1,default=-0.2,label="$r_S$"), beta=slider(0,0.1,0.005,default=0.01,label="$\\beta$"), m=slider(0,1,0.01,default=0.5,label="$m$"), T0=slider(0,Tmax,0.1,default=20,label="$T_0$"), S0=slider(0,Smax,0.1,default=10,label="$S_0$")): titleeq=("$T'= "+'%4.3f'%(r_T)+" T - "'%4.3f'%(beta)+" S T$\n " #setting the title with the equations +"$S' = "+'%4.3f'%(r_S)+" S + "+'%4.3f'%(beta*m)+" S T$") vector_field=[r_T*T-beta*T*S-0.001*T^2,r_S*S+m*beta*T*S] #setting the vector field initial_value=[T0,S0] #initial values pv=plot_vector_field(Scale_vf(vector_field),(T,0,Tmax), #setting the plots with labels and vector sizes (S,0,Smax),plot_points=11,axes_labels=["T","S"]) sol1=desolve_odeint(vector_field,times=t,ics=initial_value,dvars=[T,S]) ps1=list_plot(sol1,plotjoined=True,color='green',thickness=2) d1=disk(initial_value,1,(0,2*pi),color='green') show(pv+ps1+d1,xmin=0,xmax=Tmax,ymin=0,ymax=Smax, #what is being showed on the graph aspect_ratio=1,title=titleeq,figsize=8,fontsize=20,svg=False)
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