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Publier sur MédiaSpip
13 juin 2013Puis-je poster des contenus à partir d’une tablette Ipad ?
Oui, si votre Médiaspip installé est à la version 0.2 ou supérieure. Contacter au besoin l’administrateur de votre MédiaSpip pour le savoir -
Que fait exactement ce script ?
18 janvier 2011, parCe script est écrit en bash. Il est donc facilement utilisable sur n’importe quel serveur.
Il n’est compatible qu’avec une liste de distributions précises (voir Liste des distributions compatibles).
Installation de dépendances de MediaSPIP
Son rôle principal est d’installer l’ensemble des dépendances logicielles nécessaires coté serveur à savoir :
Les outils de base pour pouvoir installer le reste des dépendances Les outils de développements : build-essential (via APT depuis les dépôts officiels) ; (...) -
Contribute to a better visual interface
13 avril 2011MediaSPIP is based on a system of themes and templates. Templates define the placement of information on the page, and can be adapted to a wide range of uses. Themes define the overall graphic appearance of the site.
Anyone can submit a new graphic theme or template and make it available to the MediaSPIP community.
Sur d’autres sites (5511)
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Use FFmpeg concat two video, is output video level mistake ?
27 février, par 哇哈哈video1
{
 "index": 0,
 "codec_name": "hevc",
 "codec_long_name": "H.265 / HEVC (High Efficiency Video Coding)",
 "profile": "Main",
 "codec_type": "video",
 "codec_tag_string": "hev1",
 "codec_tag": "0x31766568",
 "width": 1920,
 "height": 1080,
 "coded_width": 1920,
 "coded_height": 1080,
 "has_b_frames": 2,
 "sample_aspect_ratio": "1:1",
 "display_aspect_ratio": "16:9",
 "pix_fmt": "yuv420p",
 "level": 120,
 "color_range": "tv",
 "chroma_location": "left",
 "field_order": "progressive",
 "refs": 1,
 "view_ids_available": "",
 "view_pos_available": "",
 "id": "0x1",
 "r_frame_rate": "30/1",
 "avg_frame_rate": "30/1",
 "time_base": "1/15360",
 "start_pts": 0,
 "start_time": "0.000000",
 "duration_ts": 200192,
 "duration": "13.033333",
 "bit_rate": "10794613",
 "nb_frames": "391",
 "extradata_size": 2496,
 "disposition": {
 "default": 1,
 "dub": 0,
 "original": 0,
 "comment": 0,
 "lyrics": 0,
 "karaoke": 0,
 "forced": 0,
 "hearing_impaired": 0,
 "visual_impaired": 0,
 "clean_effects": 0,
 "attached_pic": 0,
 "timed_thumbnails": 0,
 "non_diegetic": 0,
 "captions": 0,
 "descriptions": 0,
 "metadata": 0,
 "dependent": 0,
 "still_image": 0,
 "multilayer": 0
 },
 "tags": {
 "language": "eng",
 "handler_name": "VideoHandler",
 "vendor_id": "[0][0][0][0]",
 "encoder": "Lavc61.33.100 libx265",
 "timecode": "00:00:00;00"
 }
}

video2 
{
 "index": 0,
 "codec_name": "hevc",
 "codec_long_name": "H.265 / HEVC (High Efficiency Video Coding)",
 "profile": "Main",
 "codec_type": "video",
 "codec_tag_string": "hev1",
 "codec_tag": "0x31766568",
 "width": 1920,
 "height": 1080,
 "coded_width": 1920,
 "coded_height": 1080,
 "has_b_frames": 2,
 "sample_aspect_ratio": "1:1",
 "display_aspect_ratio": "16:9",
 "pix_fmt": "yuv420p",
 "level": 120,
 "color_range": "tv",
 "chroma_location": "left",
 "field_order": "progressive",
 "refs": 1,
 "view_ids_available": "",
 "view_pos_available": "",
 "id": "0x1",
 "r_frame_rate": "25/1",
 "avg_frame_rate": "25/1",
 "time_base": "1/12800",
 "start_pts": 0,
 "start_time": "0.000000",
 "duration_ts": 1309696,
 "duration": "102.320000",
 "bit_rate": "1024122",
 "nb_frames": "2558",
 "extradata_size": 2496,
 "disposition": {
 "default": 1,
 "dub": 0,
 "original": 0,
 "comment": 0,
 "lyrics": 0,
 "karaoke": 0,
 "forced": 0,
 "hearing_impaired": 0,
 "visual_impaired": 0,
 "clean_effects": 0,
 "attached_pic": 0,
 "timed_thumbnails": 0,
 "non_diegetic": 0,
 "captions": 0,
 "descriptions": 0,
 "metadata": 0,
 "dependent": 0,
 "still_image": 0,
 "multilayer": 0
 },
 "tags": {
 "language": "und",
 "handler_name": "VideoHandler",
 "vendor_id": "[0][0][0][0]",
 "encoder": "Lavc61.33.100 libx265"
 }
}

out:
{
 "index": 0,
 "codec_name": "hevc",
 "codec_long_name": "H.265 / HEVC (High Efficiency Video Coding)",
 "profile": "Main",
 "codec_type": "video",
 "codec_tag_string": "hev1",
 "codec_tag": "0x31766568",
 "width": 1920,
 "height": 1080,
 "coded_width": 1920,
 "coded_height": 1080,
 "has_b_frames": 2,
 "sample_aspect_ratio": "1:1",
 "display_aspect_ratio": "16:9",
 "pix_fmt": "yuv420p",
 "level": 186,
 "color_range": "tv",
 "chroma_location": "left",
 "field_order": "progressive",
 "refs": 1,
 "view_ids_available": "",
 "view_pos_available": "",
 "id": "0x1",
 "r_frame_rate": "30/1",
 "avg_frame_rate": "147450/5767",
 "time_base": "1/1000000",
 "start_pts": 0,
 "start_time": "0.000000",
 "duration_ts": 115340000,
 "duration": "115.340000",
 "bit_rate": "1060604",
 "nb_frames": "2949",
 "extradata_size": 2500,
 "disposition": {
 "default": 1,
 "dub": 0,
 "original": 0,
 "comment": 0,
 "lyrics": 0,
 "karaoke": 0,
 "forced": 0,
 "hearing_impaired": 0,
 "visual_impaired": 0,
 "clean_effects": 0,
 "attached_pic": 0,
 "timed_thumbnails": 0,
 "non_diegetic": 0,
 "captions": 0,
 "descriptions": 0,
 "metadata": 0,
 "dependent": 0,
 "still_image": 0,
 "multilayer": 0
 },
 "tags": {
 "language": "und",
 "handler_name": "VideoHandler",
 "vendor_id": "[0][0][0][0]",
 "encoder": "Lavc61.33.100 libx265"
 }
}


output video level is 6.2 ? i wiki level refer to fps resolusion or bitrate,but not suit this output video.
0。0 ! Could Someone HELP me ?


ffmpeg -i .\HEVC_1080p_30P_yellowtree.mp4 -i .\HEVC_1080p_24fps_happy.mp4 -filter_complex "[0:v][1:v]concat=n=2:v=1:a=0[outv]" -map "[outv]" -c:v libx265 concat_output.mp4


ffmpeg version N-118448-g43be8d0728-20250209 Copyright (c) 2000-2025 the FFmpeg developers
built with gcc 14.2.0 (crosstool-NG 1.26.0.120_4d36f27)
configuration : —prefix=/ffbuild/prefix —pkg-config-flags=—static —pkg-config=pkg-config —cross-prefix=x86_64-w64-mingw32- —arch=x86_64 —target-os=mingw32 —enable-gpl —enable-version3 —disable-debug —enable-shared —disable-static —disable-w32threads —enable-pthreads —enable-iconv —enable-zlib —enable-libfreetype —enable-libfribidi —enable-gmp —enable-libxml2 —enable-lzma —enable-fontconfig —enable-libharfbuzz —enable-libvorbis —enable-opencl —disable-libpulse —enable-libvmaf —disable-libxcb —disable-xlib —enable-amf —enable-libaom —enable-libaribb24 —enable-avisynth —enable-chromaprint —enable-libdav1d —enable-libdavs2 —enable-libdvdread —enable-libdvdnav —disable-libfdk-aac —enable-ffnvcodec —enable-cuda-llvm —enable-frei0r —enable-libgme —enable-libkvazaar —enable-libaribcaption —enable-libass —enable-libbluray —enable-libjxl —enable-libmp3lame —enable-libopus —enable-librist —enable-libssh —enable-libtheora —enable-libvpx —enable-libwebp —enable-libzmq —enable-lv2 —enable-libvpl —enable-openal —enable-libopencore-amrnb —enable-libopencore-amrwb —enable-libopenh264 —enable-libopenjpeg —enable-libopenmpt —enable-librav1e —enable-librubberband —enable-schannel —enable-sdl2 —enable-libsnappy —enable-libsoxr —enable-libsrt —enable-libsvtav1 —enable-libtwolame —enable-libuavs3d —disable-libdrm —enable-vaapi —enable-libvidstab —enable-vulkan —enable-libshaderc —enable-libplacebo —disable-libvvenc —enable-libx264 —enable-libx265 —enable-libxavs2 —enable-libxvid —enable-libzimg —enable-libzvbi —extra-cflags=-DLIBTWOLAME_STATIC —extra-cxxflags= —extra-libs=-lgomp —extra-ldflags=-pthread —extra-ldexeflags= —cc=x86_64-w64-mingw32-gcc —cxx=x86_64-w64-mingw32-g++ —ar=x86_64-w64-mingw32-gcc-ar —ranlib=x86_64-w64-mingw32-gcc-ranlib —nm=x86_64-w64-mingw32-gcc-nm —extra-version=20250209
libavutil 59. 56.100 / 59. 56.100
libavcodec 61. 33.100 / 61. 33.100
libavformat 61. 9.107 / 61. 9.107
libavdevice 61. 4.100 / 61. 4.100
libavfilter 10. 9.100 / 10. 9.100
libswscale 8. 13.100 / 8. 13.100
libswresample 5. 4.100 / 5. 4.100
libpostproc 58. 4.100 / 58. 4.100


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Introducing the Data Warehouse Connector feature
30 janvier, par Matomo Core TeamMatomo is built on a simple truth : your data belongs to you, and you should have complete control over it. That’s why we’re excited to launch our new Data Warehouse Connector feature for Matomo Cloud, giving you even more ways to work with your analytics data.
Until now, getting raw data from Matomo Cloud required APIs and custom scripts, or waiting for engineering help.
Our new Data Warehouse Connector feature removes those barriers. You can now access your raw, unaggregated data and schedule regular exports straight to your data warehouse.
The feature works with all major data warehouses including (but not limited to) :
- Google BigQuery
- Amazon Redshift
- Snowflake
- Azure Synapse Analytics
- Apache Hive
- Teradata
You can schedule exports, combine your Matomo data with other data sources in your data warehouse, and easily query data with SQL-like queries.
Direct raw data access for greater data portability
Waiting for engineering support can delay your work. Managing API connections and writing scripts can be time-consuming. This keeps you from focusing on what you do best—analysing data.
With the Data Warehouse Connector feature, you get direct access to your raw Matomo data without the technical setup. So, you can spend more time analysing data and finding insights that matter.
Bringing your data together
Answering business questions often requires data from multiple sources. A single customer interaction might span your CRM, web analytics, sales systems, and more. Piecing this data together manually is time-consuming—what starts as a seemingly simple question from stakeholders can turn into hours of work collecting and comparing data across different tools.
This feature lets you combine your Matomo data with data from other business systems in your data warehouse. Instead of switching between tools or manually comparing spreadsheets, you can analyse all your data in one place to better understand how customers interact with your business.
Easy, custom analysis with SQL-like queries
Standard, pre-built reports often don’t address the specific, detailed questions that analysts need to answer.
When you use the Data Warehouse Connector feature, you can use SQL-like queries in your data warehouse to do detailed, customised analysis. This flexibility allows you to explore your data in depth and uncover specific insights that aren’t possible with pre-built reports.
Here is an example of how you might use SQL-like query to compare the behaviours of paying vs. non-paying users :
<xmp>SELECT
custom_dimension_value AS user_type, -- Assuming 'user_type' is stored in a custom dimension
COUNT(*) AS total_visits,
AVG(visit_total_time) AS avg_duration,
SUM(conversion.revenue) AS total_spent
FROM
`your_project.your_dataset.matomo_log_visit` AS visit
LEFT JOIN
`your_project.your_dataset.matomo_log_conversion` AS conversion
ON
visit.idvisit = conversion.idvisit
GROUP BY
custom_dimension_value; </xmp>
This query helps you compare metrics such as the number of visits, average session duration, and total amount spent between paying and non-paying users. It provides a full view of behavioural differences between these groups.
Advanced data manipulation and visualisation
When you need to create detailed reports or dive deep into data analysis, working within the constraints of a fixed user interface (UI) can limit your ability to draw insights.
Exporting your Matomo data to a data warehouse like BigQuery provides greater flexibility for in-depth manipulation and advanced visualisations, enabling you to uncover deeper insights and tailor your reports more effectively.
Getting started
To set up data warehouse exports in your Matomo :
- Go to System Admin (cog icon in the top right corner)
- Select ‘Export’ from the left-hand menu
- Choose ‘Data Warehouse Connector’
You’ll find detailed instructions in our data warehouse exports guide.
Please note, enabling this feature will cost an additional 10% of your current subscription. You can view the exact cost by following the steps above.
New to Matomo ? Start your 21-day free trial now (no credit card required), or request a demo.
Try Matomo for Free
21 day free trial. No credit card required.
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configure : update copyright year
1er janvier, par Lynne
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