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• Introducing the Matomo Connector for Looker Studio (Formerly Google Data Studio)

  26 janvier 2024, par Erin — Community

  Explore Matomo data like never before with the official Matomo Connector for Looker Studio. Matomo users can now securely display accurate web analytics data in Looker Studio for free.

  Connect Matomo to Looker Studio (formerly known as Google Data Studio) in a few clicks and start building dashboards instantly. Get access to a range of data visualisation capabilities and chart types in Looker Studio’s easy-to-use interface. 

  Leave behind manual, error-prone spreadsheet entries and disparate data. With the Matomo Connector for Looker Studio, you get unified, automated reporting and interactive dashboards for faster insights and smoother collaboration.

  What sets the official Matomo Connector for Looker Studio apart ?

  Our open-source connector puts security first by providing a reliable connection without relying on third-party intermediaries. It’s free, with no hidden charges, and no limits on the number of users or Matomo instances. Connect as many instances as you need. 

  Plus, our Support team is here anytime you need help.

  

  Who is this connector made for ?

  The Matomo Connector for Looker Studio is a good fit for institutions and corporations using Looker Studio, NGOs handling multiple entities, marketing agencies with various clients, and small to medium-sized businesses with advanced data practices.

  When is this connector not the best fit ?

  If you prioritise privacy and compliance, this might not be the right fit. The Looker Studio app operates on Google servers, and while we don’t log or store any data, privacy considerations should be carefully evaluated. Transferring data, especially visitor data, to external platforms can have privacy implications.

  Getting started

  Check out our documentation for an easy setup.

  To help, we’ve also created a template report so you can visualise your Matomo data instantly.

  Here’s how to get started :

  1. Visit the demo template report in Looker Studio
  2. Click the more options button then Make a copy

  

  3. Click Create data source within the New Data Source dropdown.

  

  4. Connect your Matomo (Full Connection Guide)
  5. Select the API > Main Metrics report
  6. Click Connect and then Add to Report
  7. Click Copy Report to finalise

  For additional support, visit our Matomo Looker Studio forum or reach out to our Looker Studio support team via email at support-lookerstudio@matomo.org

• Python-FFMPEG Corruption Problems

  11 juillet 2023, par Gabriel Ruben Guzman

  I'm repurposing some python code to generate gifs/mp4s showcasing nba player movements dot form. (With the 'frames' used in the gifs being generated by matplotlib).

  


  The repo comes with two different functions for generating the gifs, watch_play and animate_play. Both of which use python command line functionalities to run ffmpeg and generate the mp4s.
I've been able to use the watch_play succesfully, bot every time I try using animate_play, which according to the documention is meant to be significantly faster than watch play, I run into the error showcased here.(I printed the cmd string being passed into the pipe, in the hopes it would make debugging easier)

  


  I've tried generating gifs/mp4s of various size and added a decent bit of code to lessen the volume of data being processed. (I'm essentially repurposing the code just to generate clips, so I've been able to remove a lot of the pbp/tracking data logs to speed up the run time) But no matter what I've done, gotten some variation of the screenshotted error.

  


  pipe: : corrupt input packet in stream 0&#xA;[rawvideo @ 0x55ccc0e2bb80] Invalid buffer size, packet size 691200 < expected frame_size 921600&#xA;Error while decoding stream #0:0 : Invalid argument

  &#xA;

  The code for animate_play

  &#xA;

  def animate_play(self, game_time=None, length=None, highlight_player=None,
                 commentary=True, show_spacing=None):
    """
    Method for animating plays in game.
    Outputs video file of play in {cwd}/temp.
    Individual frames are streamed directly to ffmpeg without writing them
    to the disk, which is a great speed improvement over watch_play

    Args:
        game_time (int): time in game to start video
            (seconds into the game).
            Currently game_time can also be an tuple of length two
            with (starting_frame, ending_frame)if you want to
            watch a play using frames instead of game time.
        length (int): length of play to watch (seconds)
        highlight_player (str): If not None, video will highlight
            the circle of the inputed player for easy tracking.
        commentary (bool): Whether to include play-by-play commentary in
            the animation
        show_spacing (str) in ['home', 'away']: show convex hull
            spacing of home or away team.
            If None, does not show spacing.

    Returns: an instance of self, and outputs video file of play
    """
    if type(game_time) == tuple:
        starting_frame = game_time[0]
        ending_frame = game_time[1]
    else:
        game_time= self.start +(self.quarter*720)
        end_time= self.end +(self.quarter*720)
        length = end_time-game_time
        # Get starting and ending frame from requested 
        # game_time and length
        print('hit')
        print(len(self.moments))
        print(game_time)
        print(end_time)
        print(length)
        print(game_time+length)
        
        print(self.moments.game_time.min())
        print(self.moments.game_time.max())

        sys.exit()
        starting_frame = self.moments[self.moments.game_time.round() ==
                                      game_time].index.values[0]
        ending_frame = self.moments[self.moments.game_time.round() ==
                                    game_time + length].index.values[0]

    # Make video of each frame
    filename = "./temp/{game_time}.mp4".format(game_time=game_time)
    if commentary:
        size = (960, 960)
    else:
        size = (480, 480)
    cmdstring = ('ffmpeg',
                 '-y', '-r', '20',  # fps
                 '-s', '%dx%d' % size,  # size of image string
                 '-pix_fmt', 'argb',  # Stream argb data from matplotlib
                 '-f', 'rawvideo','-i', '-',
                 '-vcodec', 'libx264', filename)
    #print(pipe)
    #print(cmdstring)
    
    

    # Stream plots to pipe
    pipe = Popen(cmdstring, stdin=PIPE)
    print(cmdstring)
    for frame in range(starting_frame, ending_frame):
        print(frame)
        self.plot_frame(frame, highlight_player=highlight_player,
                        commentary=commentary, show_spacing=show_spacing,
                        pipe=pipe)
    print(cmdstring)
    pipe.stdin.close()
    pipe.wait()
    return self


  &#xA;

  The code for watch play

  &#xA;

  def watch_play(self, game_time=None, length=None, highlight_player=None,
               commentary=True, show_spacing=None):

    """
    DEPRECIATED.  See animate_play() for similar (fastere) method

    Method for viewing plays in game.
    Outputs video file of play in {cwd}/temp

    Args:
        game_time (int): time in game to start video
            (seconds into the game).
            Currently game_time can also be an tuple of length
            two with (starting_frame, ending_frame) if you want
            to watch a play using frames instead of game time.
        length (int): length of play to watch (seconds)
        highlight_player (str): If not None, video will highlight
            the circle of the inputed player for easy tracking.
        commentary (bool): Whether to include play-by-play
            commentary underneath video
        show_spacing (str in ['home', 'away']): show convex hull
            of home or away team.
            if None, does not display any convex hull

    Returns: an instance of self, and outputs video file of play
    """
    print('hit this point ')
    warnings.warn(("watch_play is extremely slow. "
                   "Use animate_play for similar functionality, "
                   "but greater efficiency"))

    if type(game_time) == tuple:
        starting_frame = game_time[0]
        ending_frame = game_time[1]
    else:
        # Get starting and ending frame from requested game_time and length
        game_time= self.start +(self.quarter*720)
        end_time= self.end +(self.quarter*720)
        length = end_time-game_time


        starting_frame = self.moments[self.moments.game_time.round() ==
                                      game_time].index.values[0]
        ending_frame = self.moments[self.moments.game_time.round() ==
                                    game_time + length].index.values[0]
    #print(self.moments.head(2))
    #print(starting_frame)
    #print(ending_frame)
    print(len(self.moments))
    # Make video of each frame
    title = str(starting_frame)+'-'+str(ending_frame)
    for frame in range(starting_frame, ending_frame):
        print(frame)
        self.plot_frame(frame, highlight_player=highlight_player,
                        commentary=commentary, show_spacing=show_spacing)
    command = ('ffmpeg -framerate 20 -start_number {starting_frame} '
               '-i %d.png -c:v libx264 -r 30 -pix_fmt yuv420p -vf '
               '"scale=trunc(iw/2)*2:trunc(ih/2)*2" {title}'
               '.mp4').format(starting_frame=starting_frame,title=title)
    os.chdir('temp')
    os.system(command)
    os.chdir('..')

    # Delete images
    for file in os.listdir('./temp'):
        if os.path.splitext(file)[1] == '.png':
            os.remove('./temp/{file}'.format(file=file))

    return self'


  &#xA;

• Anomalie #2381 : Corriger le niveau d’intertitre

  9 juin 2018, par b b

  Il y a des H2 dans la dist, qui introduisent des listes d’articles, de résultats, ou des blocs (forum), je pense qu’ils peuvent rester comme ça.

  Si le bloc de forum sous le texte de l’article reste en h2, il va se retrouver "au même niveau" qu’un intertitre en h2 donc, c’est pas top non ?