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Récupération d’informations sur le site maître à l’installation d’une instance
26 novembre 2010, parUtilité
Sur le site principal, une instance de mutualisation est définie par plusieurs choses : Les données dans la table spip_mutus ; Son logo ; Son auteur principal (id_admin dans la table spip_mutus correspondant à un id_auteur de la table spip_auteurs)qui sera le seul à pouvoir créer définitivement l’instance de mutualisation ;
Il peut donc être tout à fait judicieux de vouloir récupérer certaines de ces informations afin de compléter l’installation d’une instance pour, par exemple : récupérer le (...) -
Use, discuss, criticize
13 avril 2011, parTalk to people directly involved in MediaSPIP’s development, or to people around you who could use MediaSPIP to share, enhance or develop their creative projects.
The bigger the community, the more MediaSPIP’s potential will be explored and the faster the software will evolve.
A discussion list is available for all exchanges between users. -
XMP PHP
13 mai 2011, parDixit Wikipedia, XMP signifie :
Extensible Metadata Platform ou XMP est un format de métadonnées basé sur XML utilisé dans les applications PDF, de photographie et de graphisme. Il a été lancé par Adobe Systems en avril 2001 en étant intégré à la version 5.0 d’Adobe Acrobat.
Étant basé sur XML, il gère un ensemble de tags dynamiques pour l’utilisation dans le cadre du Web sémantique.
XMP permet d’enregistrer sous forme d’un document XML des informations relatives à un fichier : titre, auteur, historique (...)
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How to measure the performance of a newsletter (or any email) with Matomo
19 décembre 2017, par InnoCraftTo be able to grow your business, it is crucial to track all your marketing efforts. This includes all newsletters and emails that you share with people outside of your business. Otherwise, you won’t be able to know which of your daily efforts are yielding results.
Are you wondering if it is possible to track the performance of an emailing campaign in Matomo (Piwik) efficiently ? Would you like to know if it is technically easy ? No worries, here is a “How to” tutorial showing you how easily you can track an emailing in Matomo properly.
Different tracking levels for different needs
There are many things that you may be interested to track, for example :
- How many users opened your email
- How many users interacted with the links in your email
- How many users interacted on your website through your email
Let’s have a look at each of these levels.
Step 1 – Tracking email and newsletter openings in Matomo
Tracking email openings requires to add an HTML code to your newsletter. It works through what we call a tracking pixel, a tiny image of 1×1 that is transparent so the user will not be able to see it.
In order to install it, here is an example of what this code looks like :<img src="https://piwik.example.com/piwik.php?idsite=YOUR_PIWIK_WEBSITE_ID&rec=1&bots=1&url=https%3A%2F%2Fexample.com%2Femail-opened%2Fnewsletter_XYZ&action_name=Email%20opened&_rcn=internal%20email%20name&_rck=newsletter_XYZ" style="border:0;” alt="" />
The Matomo tracking pixel explained
The above URL is composed of the following URL parameters which are part of our Tracking API :
- idsite : Corresponds to the ID of the website you would like to track.
- rec : You need to have rec=1 in order for the request to be actually recorded.
- bots : Set it to 1 to include all the connections made to this request, bots included.
- url : corresponds to the URL you would like to display in Matomo (Piwik) every time the email is opened.
- action_name : This is the page name you would like to be tracked when the email is opened.
- _rcn : The name you would like to give to your campaign.
- _rck : The keyword you may like to use in order to summarize the content of your newsletter.
You may have noticed some special characters here such as “%20”, “%2F”. That’s because the URL is encoded. We strongly recommend you to do so in order for your tracking not to break. Many tools are available on the web in order to encode your URLs such as https://www.urlencoder.org/.
If you would like to access the previous tracking code easily, keep in mind that you can always find the tracking code generator within the “Matomo admin panel → Tracking code” :
You can find more information about it on our guide at : How do I track how many users open and read my newsletter emails (using a pixel / beacon) ?
As a result, the information will be pushed as following for any user who opens your email :
To not bias your regular page views on your website with newsletter openings, we recommend tracking newsletter openings into a new website.
Tracking even more data : the user ID example
You can go deeper in your URL tracking by inserting other parameters such as the user id if you have this information within your emailing database. One of the main benefit of tracking the User ID is to connect data across multiple devices and browsers for a given user.
You only need to add the following parameter &uid=XXX where XXX equals the dynamic value of the user ID :
Make sure that UID from your emailing provider is the same as the one used on your website in order for your data to be consistent.
Important note : some email providers are loading email messages by default which results in an opening even if the user did not actually open the email.
Step 2 – Measure the clicks within your emailing
Tracking clicks within an email lets you know with which content readers interacted the most. We recommend tracking all links in all your emails as a campaign, whether it is a newsletter, a custom support email, an email invoice, etc. You might be surprised to see which of your emails lead to conversions and if they don’t, try to tweak those emails, so they might in the future.
Tracking clicks This works thanks to URL campaign tracking. In order to perform this action, you will need to add Matomo (Piwik) URL parameters to all your existing link URLs :
- Website URL : for example “www.your-website.com”.
- Campaign name : for example “pk_campaign=emailing”. Represents the name you would like to give to your campaign.
- Campaign keyword : for example “pk_keyword=name-of-your-article”. Represents the name you would like to give to your content.
- Campaign source : for example “pk_source=newsletter”. Represents the name of the referrer.
- Campaign medium : for example “pk_medium=email”. Represents the type of referrer you are using.
- Campaign content : for example “pk_content=title”. Represents the type of content.
You can find more information about campaign url tracking in our “Tracking marketing campaigns with Matomo” guide.
Here is a sample showing you how you can differentiate some links in a newsletter, all pointing to the same URL :
Once you have added these URL parameters to each of your link, Matomo (Piwik) will clearly indicate the referrer of this specific campaign when a user clicks on a link in the newsletter and visits your website.
Important note : if you do not track your campaigns, it will result in a bad interpretation of your data within Matomo (Piwik) as you will get webmail services or direct entries as referrer instead of your newsletter campaign.
Step 3 – Measure emailing performances on your website
Thanks to Matomo (Piwik) URL campaign parameters, you can now clearly identify the traffic brought through your emailing. You can now specifically isolate users who come from emails by creating a segment :
Once done, you can either have a look at each user specifically through the visitor log report or analyze it as a whole within the rest of the reports.
You can even measure your return on investment directly if goals have been defined. In order to know more about how to track goals within Matomo (Piwik).
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The post How to measure the performance of a newsletter (or any email) with Matomo appeared first on Analytics Platform - Matomo.
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FFmpeg transcode GIF into Mp4 and Mp4 to AVI using GPU
9 octobre 2023, par CristianI'm trying to convert GIF animated to mp4 and mp4 to AVI with FFmpeg.


I started to use just the CPU, but I have to process millions of GIFs/mp4 content pieces. So, I started to have a lot of errors processing them, and it ended as a bottleneck. Therefore, I'm trying to use GPU to process the videos.


Converting GIF to mp4 with CPU, I run the following command :


ffmpeg -i animated.gif -movflags faststart -pix_fmt yuv420p -vf "scale=trunc(iw/2)*2:trunc(ih/2)*2" video.mp4



Using the GPU I'm trying the following :


ffmpeg
 -y
 -hwaccel nvdec
 -hwaccel_output_format cuda
 -i gifInputPath
 -threads 1
 -filter_threads 1
 -c:v h264_nvenc
 -vf hwupload_cuda,scale_cuda=-2:320:240:format=yuv420p
 -gpu 0
 mp4VideoPath



The above command generates an exit status 1.


The following is the dmesg command log


Converting mp4 videos to AVI videos I'm running the following command


ffmpeg
-i videoInputPath
-vcodec rawvideo
-pix_fmt yuv420p
-acodec pcm_s16le
-ar 44100
-ac 2
-s 320x240
-r 4
-f avi
aviOutputVideoPath



For GPU I tried :


ffmpeg
 -y
 -hwaccel cuda
 -hwaccel_output_format cuda
 -i videoInputPath
 -threads 1
 -filter_threads 1
 -c:a pcm_s16le
 -ac 2
 -ar 44100
 -c:v h264_nvenc
 -vf hwupload_cudascale_cuda=-2:320:240:format=yuv420p
 -r 4
 -f avi
 -gpu 0
 aviOutputVideoPath



The following is the dmseg output is log


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What should be the best command for converting the GIF into Mp4 and Mp4 into AVI based on CPU configuration using the GPU(Amazon Nvidia t4) for best performance, low CPU, and moderated GPU consumption ?


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What are the best suggestions to Process these content pieces concurrently using GPU ?








Note : I'm using Golang to execute the FFmpeg commands.


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swscale/aarch64 : use multiply accumulate and increase vector factor to 4
17 novembre 2019, par Sebastian Popswscale/aarch64 : use multiply accumulate and increase vector factor to 4
This patch implements ff_hscale_8_to_15_neon with NEON fused multiply accumulate
and bumps the vectorization factor from 2 to 4.
The speedup is of 25% on Graviton1 A1 instances based on A-72 cpus :$ ffmpeg -nostats -f lavfi -i testsrc2=4k:d=2 -vf bench=start,scale=1024x1024,bench=stop -f null -
before : t:0.040303 avg:0.040287 max:0.040371 min:0.039214
after : t:0.032168 avg:0.032215 max:0.033081 min:0.032146The speedup is of 39% on Graviton2 m6g instances based on Neoverse-N1 cpus :
$ ffmpeg -nostats -f lavfi -i testsrc2=4k:d=2 -vf bench=start,scale=1024x1024,bench=stop -f null -
before : t:0.019446 avg:0.019423 max:0.019493 min:0.019181
after : t:0.014015 avg:0.014096 max:0.015018 min:0.013971Tested with `make check` on aarch64-linux.
Signed-off-by : Sebastian Pop <spop@amazon.com>
Reviewed-by : Jean-Baptiste Kempf <jb@videolan.org>
Signed-off-by : Michael Niedermayer <michael@niedermayer.cc>