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• Error Exception : You do not have ffmpeg installed on your machine when using python bar_chart_race with reticulate in rstudio

  27 septembre 2020, par Zhiqiang Wang

  I have run python bar_chart_race successfully in jupyter notebook.

  


  import bar_chart_race as bcr
bcr.bar_chart_race(df=wide_1, filename=None, title='Mortality (per million) by Country')


  


  I have ffmpeg installed on my machine.

  


  > Sys.which("ffmpeg")
                       ffmpeg 
"C:\\ffmpeg\\bin\\ffmpeg.exe" 


  


  However, when I tried the same code in rmarkdown with rstudio reticulate,

  


  import bar_chart_race as bcr
bcr.bar_chart_race(wide_1)


  


  I received the following message :

  


  Exception: You do not have ffmpeg installed on your machine. Download
                            ffmpeg from here: https://www.ffmpeg.org/download.html.
                            
                            Matplotlib's original error message below:

                            [WinError 6] The handle is invalid


  


  reticulate::py_config()

  


  > reticulate::py_config()
python:         C:/ProgramData/Anaconda3/python.exe
libpython:      C:/ProgramData/Anaconda3/python38.dll
pythonhome:     C:/ProgramData/Anaconda3
version:        3.8.3 (default, Jul  2 2020, 17:30:36) [MSC v.1916 64 bit (AMD64)]
Architecture:   64bit
numpy:          C:/ProgramData/Anaconda3/Lib/site-packages/numpy
numpy_version:  1.18.5


  


  ffmpeg works fine with R animated graph packages in rstudio, and with python bar_chart_race. I have tried to re-install ffmpeg several times. Any advice would be appreciated.

  


• What is data anonymization in web analytics ?

  11 février 2020, par Joselyn Khor — Analytics Tips, Privacy
  Collecting information via web analytics platforms is needed to help a website grow and improve. When doing so, it’s best to strike a balance between getting valuable insights, and keeping the trust of your users by protecting their privacy.

  This means not collecting or processing any personally identifiable information (PII). But what if your organisation requires you to collect PII ?

  That’s where data anonymization comes in.

  

  What is data anonymization ?

  Data anonymization makes identifiable information unidentifiable. This is done through data processing techniques which remove or modify PII data. So data becomes anonymous and can’t be linked to any individual.

  In the context of web analytics, data anonymization is handy because you can collect useful data while protecting the privacy of website visitors.

  Why is data anonymization important ?

  Through modern threats of identity theft, credit card fraud and the like, data anonymization is a way to protect the identity and privacy of individuals. As well as protect private and sensitive information of organisations. 

  Data anonymization lets you follow the many laws around the world which protect user privacy. These laws provide safeguards around collecting personal data or personally identifiable information (PII), so data anonymization is a good solution to ensure you’re not processing such sensitive information.

  In some cases, implementing data anonymization techniques means you can avoid having to show your users a consent screen. Which means you may not need to ask for consent in order to track data. This is a bonus as consent screens can annoy and stop people from engaging with your site.

  GDPR and data anonymization

  

  The GDPR is a law in the EU that limits the collection and processing of personal data. The aim is to give people more control over their online personal information. Which is why website owners need to follow certain rules to become GDPR compliant and protect user privacy. According to the GDPR, you can be fined up to 4% of your yearly revenue for data breaches or non-compliance. 

  In the case of web analytics, tools can be easily made compliant by following a number of steps. 

  This is why anonymizing data is a big deal.

  Anonymized data isn’t personal data according to the GDPR : 

  “The principles of data protection should therefore not apply to anonymous information, namely information which does not relate to an identified or identifiable natural person or to personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable.”

  This means, you still get the best of both worlds. By anonymizing data, you’re still able to collect useful information like visitor behavioural data.

  US privacy laws and data anonymization

  In the US, there isn’t one single law that governs the protection of personal data, called personally identifiable information (PII). There are hundreds of federal and state laws that protect the personal data of US residents. As well as, industry-specific statutes related to data privacy, like the California Consumer Privacy Act (CCPA) and the Health Insurance Portability and Accountability Act (HIPAA).

  Website owners in the US need to know exactly what laws govern their area of business in order to follow them.

  A general guideline is to protect user privacy regardless of whether you are or aren’t allowed to collect PII. This means anonymizing identifiable information so your website users aren’t put at risk.

  Data anonymization techniques in Matomo Analytics

  If you carry these out, you won’t need to ask your website visitors for tracking consent since anonymized data is no longer considered personal data under the GDPR.

  • Anonymize previously tracked raw data – like visitors/visits
  • Anonymize Visitor IP addresses
  • Anonymize geo-location information (country, region, city)
  • Pseudonymize User ID ( if you don’t want this data to land on your Matomo server, the solution is simply to not set the User ID. Then Matomo will use the normal anonymized IDs)
  • Data can also be anonymized before it’s even sent to Matomo
  • Learn how to further configure privacy settings in a web analytics tool like Matomo
  • 11 ways Matomo Analytics helps you to protect your visitors privacy

  The techniques listed above make it easy for you when using a tool like Matomo, as they are automatically anonymized.

  Tools like Google Analytics on the other hand don’t provide some of the privacy options and leave it up to you to take on the burden of implementation without providing steps.

  Data anonymization tools

  If you’re a website owner who wants to grow your business or learn more about your website visitors, privacy-friendly tools like Matomo Analytics are a great option. By following the easy steps to be GDPR compliant, you can anonymize all data that could put your visitors at risk.

• ffmpeg : video to aligned audio (audio too short)

  16 octobre 2020, par mcb

  I want to extract an aligned audio stream from a video. The goal is to obtain an audio sequence that is precisely aligned with the video.

  


  Issue : The video and audio sequences are not aligned. The output audio duration is shorter than the video input.

  


  Script to reproduce :

  


  fn=TV-20200617-2242-4900.websm.h264
url=https://download.media.tagesschau.de/video/2020/0617/$fn.mp4
wget -nc $url

ffmpeg -y -i "$fn.mp4" -vsync 1 -async 1 -map 0:a "$fn.wav" -map 0:v "$fn.flv"

ffprobe -i $fn.mp4  # Duration: 00:01:51.68
ffprobe -i $fn.flv  # Duration: 00:01:51.68
ffprobe -i $fn.wav  # Duration: 00:01:49.61


  


  What I have tried (without success) :

  


  

  • Adding -async 1 as suggested in this answer.

  


  • Adding -acodec copy and exporting the video at the same time (link).

  


  • Opening the mp4 in Audacity. The duration there is 00:01:49.61.

  


  • Opening the mp4 in VLC. Duration : 00:01:51.68.

  


  • Explicitly setting the framerate.

  


  • Other video files.

  


  


  ffmpeg version 4.2.4-1ubuntu0.1

  


  I would appreciate any hint on how to make this work. Thank you.