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Animating.py
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Animating.py
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import numpy as np
import pylab as pp
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import matplotlib.animation as animation
def normal_dist(mean,variance,Min,Max):
stddev = np.sqrt(variance)
#x = np.linspace(mean-3*stddev,mean+3*stddev,100)
x = np.arange(Min,Max,1./1000)
y = 1./(np.sqrt(2 * np.pi * variance)) * np.exp(- (x-mean)**2 / (2*variance))
return x,y
def auto_normal_dist(mean,variance):
stddev = np.sqrt(variance)
x = np.linspace(mean-6*stddev,mean+6*stddev,10000)
#x = np.arange(Min,Max,1./1000)
y = 1./(np.sqrt(2 * np.pi * variance)) * np.exp(- (x-mean)**2 / (2*variance))
return x,y
class SubplotAnimation(animation.TimedAnimation):
def __init__(self):
fig = pp.figure(figsize=(10,6))
self.States = np.load('States.npy')
self.Covariances = np.load('Covariances.npy')
self.times = np.load('Times.npy')
self.IncomingTMass = np.load('Incoming.npy',)
self.tau_s0s = np.load('tau_s.npy',)
self.Q = np.load('Q.npy')
self.T = np.load('Turbidity.npy')
ax1 = pp.subplot2grid((4, 3), (0, 0),colspan = 2,rowspan = 2)
ax1.plot(self.times,self.Q,'k--',label = 'Measured Flow')
ax1.set_ylabel('Applied Shear (Pa)')
self.line1 = Line2D([],[],color='red')
self.line1a = Line2D([],[],color='red',alpha = 0.5)
self.line1b = Line2D([],[],color='red',alpha = 0.5)
ax1.add_line(self.line1)
ax1.add_line(self.line1a)
ax1.add_line(self.line1b)
ax2 = pp.subplot2grid((4, 3), (2, 0),colspan = 2,rowspan = 2)
ax2.plot(self.times,self.T,'k--',label = 'Measured Turbidity')
ax2.set_ylabel('Turbidity (turbidity units)')
ax2.set_xlabel('Time (time units)')
self.line2 = Line2D([],[],color='red')
self.line2a = Line2D([],[],color='red',alpha = 0.5)
self.line2b = Line2D([],[],color='red',alpha = 0.5)
ax2.add_line(self.line2)
ax2.add_line(self.line2a)
ax2.add_line(self.line2b)
self.ax3 = pp.subplot2grid((4, 3), (0, 2))
self.ax3.set_xlabel('Upstream Turbidity (turbidity units)')
self.ax3.set_ylim(0,10)
self.line3 = Line2D([],[],color='red')
self.line3a = Line2D([],[],color = 'black',linestyle = '--')
self.ax3.add_line(self.line3)
self.ax3.add_line(self.line3a)
self.ax4 = pp.subplot2grid((4, 3), (1, 2))
self.ax4.set_xlabel('tau_s (N/m2)')
self.ax4.set_ylim(0,10.)
self.line4 = Line2D([],[],color='red')
self.line4a = Line2D([],[],color = 'black',linestyle = '--')
self.ax4.add_line(self.line4)
self.ax4.add_line(self.line4a)
self.ax5 = pp.subplot2grid((4, 3), (2, 2))
self.ax5.plot([5,5],[0,1000000000],'k--')
self.ax5.set_xlabel('alpha (mass/m2)')
self.line5 = Line2D([],[],color='red')
self.ax5.add_line(self.line5)
self.ax6 = pp.subplot2grid((4, 3), (3, 2))
self.ax6.plot([0.2,0.2],[0,100000000],'k--')
self.ax6.set_xlabel('beta (1/s)')
self.line6 = Line2D([],[],color='red')
self.ax6.add_line(self.line6)
pp.tight_layout()
animation.TimedAnimation.__init__(self, fig, interval=50, blit=False)
def _draw_frame(self, framedata):
i = framedata
self.line1.set_data(self.times[:i], self.States[-4,:i])
self.line1a.set_data(self.times[:i],self.States[-4,:i]+self.Covariances[-4,-4,:i])
self.line1b.set_data(self.times[:i],self.States[-4,:i]-self.Covariances[-4,-4,:i])
self.line2.set_data(self.times[:i], self.States[-6,:i])
self.line2a.set_data(self.times[:i],self.States[-6,:i]+self.Covariances[-6,-6,:i])
self.line2b.set_data(self.times[:i],self.States[-6,:i]-self.Covariances[-6,-6,:i])
x,y = auto_normal_dist(self.States[-5,i],self.Covariances[-5,-5,i]**2)
self.line3.set_data(x,y)
self.ax3.set_ylim(0,max(y))
self.ax3.set_xlim(0,+0.5)
self.line3a.set_data([self.IncomingTMass[i],self.IncomingTMass[i]],[0,max(y)])
x,y = auto_normal_dist(self.States[-3,i],self.Covariances[-3,-3,i]**2)
self.line4.set_data(x,y)
self.ax4.set_ylim(0,max(y))
self.ax4.set_xlim(0,5.0)
self.line4a.set_data([self.tau_s0s[i],self.tau_s0s[i]],[0,max(y)])
x,y = auto_normal_dist(self.States[-2,i],self.Covariances[-2,-2,i]**2)
self.line5.set_data(x,y)
self.ax5.set_ylim(0,max(y))
self.ax5.set_xlim(0.,10.)
x,y = auto_normal_dist(self.States[-1,i],self.Covariances[-1,-1,i]**2)
self.line6.set_data(x,y)
self.ax6.set_ylim(0,max(y))
self.ax6.set_xlim(0.,1.0)
self._drawn_artists = [self.line1,self.line1a,self.line1b, self.line2,self.line2a,self.line2b,self.line3,self.line3a,self.line4,self.line4a,self.line5,self.line6]
def new_frame_seq(self):
return iter(range(self.times.size))
def _init_draw(self):
lines = [self.line1,self.line1a,self.line1b, self.line2,self.line2a,self.line2b,self.line3,self.line3a,self.line4,self.line4a,self.line5,self.line6]
for l in lines:
l.set_data([], [])
ani = SubplotAnimation()
#ani.save('Alpha_Beta_Train_OnlinePODDS1.mp4')
pp.show()