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• Révision 20305 : Regression fonctionnelle : dans SPIP 3 une recherche sur un nombre entier prod...

  15 mars 2013, par cedric -

• even though my code is running without an error, it does not generate plot or video. I have xming runing

  23 octobre 2017, par Amin Abbasi

  This is my first code and I am really new to coding. I am trying to create a video or just plot my code.Eeven though my code is running without an error, I can’t get the video or the plot to generate. I have xming running and I have plotted a sample to make sure it not a computer Issue. I have also tried the following on GitHub but no success :
  https://jakevdp.github.io/blog/2013/05/19/a-javascript-viewer-for-matplotlib-animations/

  # -*- coding: utf-8 -*-
  
  import numpy as np
  
  def solver(I, V, f, c, L, dt, cc, T, user_action=None):
  
      """Solve u_tt=c^2*u_xx + f on (0,L)x(0,T]."""
  
      Nt = int(round(T/dt))
  
      t = np.linspace(0, Nt*dt, Nt+1) # Mesh points in time
  
      dx = dt*c/float(cc)
  
      Nx = int(round(L/dx))
  
      x = np.linspace(0, L, Nx+1) # Mesh points in space
  
      C2 = cc**2 # Help variable in the scheme
  
      # Make sure dx and dt are compatible with x and t
  
      dx = x[1] - x[0]
  
      dt = t[1] - t[0]
  
      if f is None or f == 0 :
  
          f = lambda x, t: 0
  
      if V is None or V == 0:
  
          V = lambda x: 0
  
      u       = np.zeros(Nx+1) # Solution array at new time level
  
      u_n     = np.zeros(Nx+1) # Solution at 1 time level back
  
      u_nm1   = np.zeros(Nx+1) # Solution at 2 time levels back
  
      import time; t0 = time.clock() # Measure CPU time
  
      # Load initial condition into u_n
  
      for i in range(0,Nx+1):
  
          u_n[i] = I(x[i])
  
      if user_action is not None:
  
          user_action(u_n, x, t, 0)
  
      # Special formula for first time step
  
      n = 0
  
      for i in range(1, Nx):
  
          u[i] = u_n[i] + dt*V(x[i]) + \
  
              0.5*C2*(u_n[i-1] - 2*u_n[i] + u_n[i+1]) + \
  
              0.5*dt**2*f(x[i], t[n])
  
      u[0] = 0; u[Nx] = 0
  
      if user_action is not None:
  
          user_action(u, x, t, 1)
  
      # Switch variables before next step
  
      u_nm1[:] = u_n; u_n[:] = u
  
      for n in range(1, Nt):
  
          # Update all inner points at time t[n+1]
  
          for i in range(1, Nx):
  
              u[i] = - u_nm1[i] + 2*u_n[i] + \
  
                      C2*(u_n[i-1] - 2*u_n[i] + u_n[i+1]) + \
  
                      dt**2*f(x[i], t[n])
  
          # Insert boundary conditions
  
          u[0] = 0; u[Nx] = 0
  
          if user_action is not None:
  
              if user_action(u, x, t, n+1):
  
                  break
  
          # Switch variables before next step
  
          u_nm1[:] = u_n; u_n[:] = u
  
      cpu_time = time.clock() - t0
  
      return u, x, t,
  
  def test_quadratic():
  
      """Check that u(x,t)=x(L-x)(1+t/2) is exactly reproduced."""
  
      def u_exact(x, t):
  
          return x*(L-x)*(1 + 0.5*t)
  
      def I(x):
  
          return u_exact(x, 0)
  
      def V(x):
  
          return 0.5*u_exact(x, 0)
  
      def f(x, t):
  
          return 2*(1 + 0.5*t)*c**2
  
      L = 2.5
  
      c = 1.5
  
      cc = 0.75
  
      Nx = 6 # Very coarse mesh for this exact test
  
      dt = cc*(L/Nx)/c
  
      T = 18
  
      def assert_no_error(u, x, t, n):
  
          u_e = u_exact(x, t[n])
  
          diff = np.abs(u - u_e).max()
  
          tol = 1E-13
  
          assert diff < tol
  
      solver(I, V, f, c, L, dt, cc, T,
  
              user_action=assert_no_error)
  
  def viz(
  
      I, V, f, c, L, dt, C, T,umin, umax, animate=True, tool='matplotlib'):
  
      """Run solver and visualize u at each time level."""
  
      def plot_u_st(u, x, t, n):
  
          """user_action function for solver."""
  
          plt.plot(x, u, 'r-')
  
  #                 xlabel='x', ylabel='u',
  
  #                 axis=[0, L, umin, umax],
  
  #                 title='t=%f' % t[n], show=True)
  
          # Let the initial condition stay on the screen for 2
  
          # seconds, else insert a pause of 0.2 s between each plot
  
          time.sleep(2) if t[n] == 0 else time.sleep(0.2)
  
          plt.savefig('frame_%04d.png' % n) # for movie making
  
      class PlotMatplotlib:
  
          def __call__(self, u, x, t, n):
  
              """user_action function for solver."""
  
              if n == 0:
  
                  plt.ion()
  
                  self.lines = plt.plot(x, u, 'r-')
  
                  plt.xlabel('x'); plt.ylabel('u')
  
                  plt.axis([0, L, umin, umax])
  
                  plt.legend(['t=%f' % t[n]], loc='lower left')
  
              else:
  
                  self.lines[0].set_ydata(u)
  
                  plt.legend(['t=%f' % t[n]], loc='lower left')
  
                  plt.draw()
  
              time.sleep(2) if t[n] == 0 else time.sleep(0.2)
  
              plt.savefig('tmp_%04d.png' % n) # for movie making
  
      if tool == 'matplotlib':
  
          import matplotlib.pyplot as plt
  
          plot_u = PlotMatplotlib()
  
      elif tool == 'scitools':
  
          import scitools.std as plt # scitools.easyviz interface
  
          plot_u = plot_u_st
  
      import time, glob, os
  
      # Clean up old movie frames
  
      for filename in glob.glob('tmp_*.png'):
  
          os.remove(filename)
  
      # Call solver and do the simulaton
  
      user_action = plot_u if animate else None
  
      u, x, t, cpu = solver_function(
  
          I, V, f, c, L, dt, C, T, user_action)
  
      # Make video files
  
      fps = 4 # frames per second
  
      codec2ext = dict(flv='flv', libx264='mp4', libvpx='webm',
  
                       libtheora='ogg') # video formats
  
      filespec = 'tmp_%04d.png'
  
      movie_program = 'ffmpeg' # or 'avconv'
  
      for codec in codec2ext:
  
          ext = codec2ext[codec]
  
          cmd = '%(movie_program)s -r %(fps)d -i %(filespec)s '\
  
                '-vcodec %(codec)s movie.%(ext)s' % vars()
  
          os.system(cmd)
  
      if tool == 'scitools':
  
          # Make an HTML play for showing the animation in a browser
  
          plt.movie('tmp_*.png', encoder='html', fps=fps,
  
                    output_file='movie.html')
  
      return cpu

• Animating a 2D plot (2D brownian motion) not working in Python

  8 avril 2020, par Thamu Mnyulwa

  I am trying to plot a 2D Brownian motion in Python but my plot plots the grid but does not animate the line.

  



  Attempted at performing this plot is below,

  



  !apt install ffmpeg

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import matplotlib.animation as animation


np.random.seed(5)


# Set up formatting for the movie files
Writer = animation.writers['ffmpeg']
writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)


def generateRandomLines(dt, N):
    dX = np.sqrt(dt) * np.random.randn(1, N)
    X = np.cumsum(dX, axis=1)

    dY = np.sqrt(dt) * np.random.randn(1, N)
    Y = np.cumsum(dY, axis=1)

    lineData = np.vstack((X, Y))

    return lineData


# Returns Line2D objects
def updateLines(num, dataLines, lines):
    for u, v in zip(lines, dataLines):
        u.set_data(v[0:2, :num])

    return lines

N = 501 # Number of points
T = 1.0
dt = T/(N-1)


fig, ax = plt.subplots()

data = [generateRandomLines(dt, N)]

ax = plt.axes(xlim=(-2.0, 2.0), ylim=(-2.0, 2.0))

ax.set_xlabel('X(t)')
ax.set_ylabel('Y(t)')
ax.set_title('2D Discretized Brownian Paths')

## Create a list of line2D objects
lines = [ax.plot(dat[0, 0:1], dat[1, 0:1])[0] for dat in data]


## Create the animation object
anim = animation.FuncAnimation(fig, updateLines, N+1, fargs=(data, lines), interval=30, repeat=True, blit=False)

plt.tight_layout()
plt.show()

## Uncomment to save the animation
#anim.save('brownian2d_1path.mp4', writer=writer)


  



  However, instead of performing the plot the program is printing,


  



  How do I animate this plot ? I am new to python so I apologize in advanced if this is an easy question.

  



  I found this question on http://people.bu.edu/andasari/courses/stochasticmodeling/lecture5/stochasticlecture5.html , there is a walkthrough of how this code came to being.