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• How can I speed up the generation of an MP4 using matplotlib's Animation Writer ?

  18 février 2019, par Victor 'Chris' Cabral

  I am using matplotlib to generate a graphical animation of some data. The data has about 4 hours of collection time so I expect the animation to be about 4 hours. However, generating a smaller 60 second video takes approximately 15 minutes. Thus, the total estimated run time for generating the 4 hour video is 2.5 days. I assume I am doing something incredibly inefficient. How can I speed up the creation of an animation with matplotlib ?

  create_graph.py

  import matplotlib.pyplot as plt
  
  import matplotlib.animation as animation
  
  import matplotlib
  
  import pandas as pd
  
  import numpy as np
  
  
  
  matplotlib.use("Agg")
  
  
  
  frame = pd.read_csv("tmp/total.csv")
  
  min_time = frame.iloc[0]["time"]
  
  max_time = frame.iloc[-1]["time"]
  
  total_time = max_time - min_time
  
  
  
  hertz_rate = 50
  
  window_length = 5
  
  save_count = hertz_rate * 100
  
  
  
  def data_gen():
  
      current_index_of_matching_ts = 0
  
      t = data_gen.t
  
      cnt = 0
  
      while cnt < save_count:
  
          print("Done: {}%".format(cnt/save_count*100.0))
  
          predicted = cnt * (1.0/hertz_rate)
  
          while frame.iloc[current_index_of_matching_ts]["time"] - min_time <= predicted and current_index_of_matching_ts < len(frame) - 1:
  
              current_index_of_matching_ts = current_index_of_matching_ts + 1
  
  
  
          y1 = frame.iloc[current_index_of_matching_ts]["var1"]
  
          y2 = frame.iloc[current_index_of_matching_ts]["var2"]
  
          y3 = frame.iloc[current_index_of_matching_ts]["var3"]
  
          y4 = frame.iloc[current_index_of_matching_ts]["var4"]
  
          y5 = frame.iloc[current_index_of_matching_ts]["var5"]
  
          y6 = frame.iloc[current_index_of_matching_ts]["var6"]
  
          y7 = frame.iloc[current_index_of_matching_ts]["var7"]
  
          y8 = frame.iloc[current_index_of_matching_ts]["var8"]
  
          y9 = frame.iloc[current_index_of_matching_ts]["var9"]
  
          t = frame.iloc[current_index_of_matching_ts]["time"] - min_time
  
          # adapted the data generator to yield both sin and cos
  
          yield t, y1, y2, y3, y4, y5, y6, y7, y8, y9
  
          cnt+=1
  
  
  
  data_gen.t = 0
  
  
  
  # create a figure with two subplots
  
  fig, (ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8, ax9) = plt.subplots(9,1,figsize=(7,14)) # produces a video of 700 × 1400
  
  
  
  # intialize two line objects (one in each axes)
  
  line1, = ax1.plot([], [], lw=2, color='b')
  
  line2, = ax2.plot([], [], lw=2, color='b')
  
  line3, = ax3.plot([], [], lw=2, color='b')
  
  line4, = ax4.plot([], [], lw=2, color='g')
  
  line5, = ax5.plot([], [], lw=2, color='g')
  
  line6, = ax6.plot([], [], lw=2, color='g')
  
  line7, = ax7.plot([], [], lw=2, color='r')
  
  line8, = ax8.plot([], [], lw=2, color='r')
  
  line9, = ax9.plot([], [], lw=2, color='r')
  
  line = [line1, line2, line3, line4, line5, line6, line7, line8, line9]
  
  
  
  # the same axes initalizations as before (just now we do it for both of them)
  
  for ax in [ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8,  ax9]:
  
      ax.set_ylim(-1.1, 1.1)
  
      ax.grid()
  
  
  
  # initialize the data arrays 
  
  xdata, y1data, y2data, y3data, y4data, y5data, y6data, y7data, y8data, y9data = [], [], [], [], [], [], [], [], [], []
  
  
  
  my_gen = data_gen()
  
  for index in range(hertz_rate*window_length-1):
  
      t, y1, y2, y3, y4, y5, y6, y7, y8, y9 = my_gen.__next__()
  
      xdata.append(t)
  
      y1data.append(y1)
  
      y2data.append(y2)
  
      y3data.append(y3)
  
      y4data.append(y4)
  
      y5data.append(y5)
  
      y6data.append(y6)
  
      y7data.append(y7)
  
      y8data.append(y8)
  
      y9data.append(y9)
  
  
  
  
  
  def run(data):
  
      # update the data
  
      t, y1, y2, y3, y4, y5, y6, y7, y8, y9 = data
  
      xdata.append(t)
  
      y1data.append(y1)
  
      y2data.append(y2)
  
      y3data.append(y3)
  
      y4data.append(y4)
  
      y5data.append(y5)
  
      y6data.append(y6)
  
      y7data.append(y7)
  
      y8data.append(y8)
  
      y9data.append(y9)
  
  
  
      # axis limits checking. Same as before, just for both axes
  
      for ax in [ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8, ax9]:
  
          ax.set_xlim(xdata[-1]-5.0, xdata[-1])
  
  
  
      # update the data of both line objects
  
      line[0].set_data(xdata, y1data)
  
      line[1].set_data(xdata, y2data)
  
      line[2].set_data(xdata, y3data)
  
      line[3].set_data(xdata, y4data)
  
      line[4].set_data(xdata, y5data)
  
      line[5].set_data(xdata, y6data)
  
      line[6].set_data(xdata, y7data)
  
      line[7].set_data(xdata, y8data)
  
      line[8].set_data(xdata, y9data)
  
  
  
      return line
  
  
  
  ani = animation.FuncAnimation(fig, run, my_gen, blit=True, interval=20, repeat=False, save_count=save_count)
  
  
  
  Writer = animation.writers['ffmpeg']
  
  writer = Writer(fps=hertz_rate, metadata=dict(artist='Me'), bitrate=1800)
  
  ani.save('lines.mp4', writer=writer)

• java Runtime.getRuntime().exec(ffmpeg)

  6 janvier 2016, par metzojack

  I’m using Java Runtime.getRuntime().exec(ffmpeg) to execute an ffmpeg command to transcode video from any format to mp4 format. When command starts, ps -ef shows ffmpeg process and top shows that ffmpeg uses most of cpu (90%). When input video for ffmpeg is short (less than 4 mn) the Runtime.getRuntime().exec(ffmpeg) command runs well. But when trying to transcode full HD video that ffmpeg command need more than 10 minutes to be complete, an unknown problem happens around 5 minutes after ffmpeg command starts. ps -ef command still shows ffmpeg process even after many hours, but top command shows that ffmpeg process doesn’t use more that 1% of cpu. So Runtime.getRuntime().exec(ffmpeg) never finishes.

• Mute parts of Wave file using Python/FFMPEG/Pydub

  20 avril 2020, par Adil Azeem

  I am new to Python, please bear with me. I have been able to get so far with the help of Google/StackOverflow and youtube :). So I have a long (2 hours) *.wav file. I want to mute certain parts of that file. I have all of those [start], [stop] timestamps in the "Timestamps.txt" file in seconds. Like this :

  



     0001.000 0003.000
   0744.096 0747.096
   0749.003 0750.653
   0750.934 0753.170
   0753.210 0754.990
   0756.075 0759.075
   0760.096 0763.096
   0810.016 0811.016
   0815.849 0816.849


  



  What I have been able to do is read the file and isolate each tuple. I have just output the first tuple and printed it to check if everything looks good. It seems that the isolation of tuple works :) I plan to count the number of tuples (which is 674 in this case) and put in a 'for loop' according to that count and change the start and stop time according to the tuple. Perform the loop on that single *.wav file and output on file with muted sections as the timestamps. I have no idea how to implement my thinking with FFMPEG or any other utility in Python e.g pydub. Please help me.

  



     with open('Timestamps.txt') as f:
   data = [line.split() for line in f.readlines()]
   out = [(float(k), float(v)) for k, v in data]

   r = out[0] 
   x= r[0]
   y= r[1]
   #specific x and y values
   print(x)
   print(y)