The aim of this work is to deliver a tool to a consortium around QuotaClimat, Climat Medias allowing them to quantify the media coverage of the climate crisis.
Radio and TV data are collected thanks to Mediatree API.
And webpress is currently at work in progress (as for 04/2024)
- 2022-09-28, Introduction by Eva Morel (Quota Climat): from 14:10 to 32:00 https://www.youtube.com/watch?v=GMrwDjq3rYs
- 2022-11-29 Project status and prospects by Estelle Rambier (Data): from 09:00 to 25:00 https://www.youtube.com/watch?v=cLGQxHJWwYA
- 2024-03 Project tech presentation by Paul Leclercq (Data) : https://www.youtube.com/watch?v=zWk4WLVC5Hs
- Learn about the project by watching the introduction videos mentioned above.
- Create an issue or/and join https://dataforgood.fr/join and the Slack #offseason_quotaclimat.
- Introduce yourself on Slack #offseason_quotaclimat
Doing the following step will enable your local environement to be aligned with the one of any other collaborator.
First install pyenv:
| OS | Command |
| MacOS |
cd -
brew install pyenv # pyenv itself
brew install pyenv-virtualenv # integration with Python virtualenvsec |
| Ubuntu |
sudo apt-get update; sudo apt-get install make build-essential libssl-dev zlib1g-dev \
libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm \
libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev
curl https://pyenv.run | bash |
| Windows | An installation using miniconda is generally simpler than a pyenv one on Windows. |
Make the shell pyenv aware:
| OS | Command |
| MacOS |
eval "$(pyenv init --path)"
eval "$(pyenv init -)"
eval "$(pyenv virtualenv-init -)" |
| Ubuntu |
export PYENV_ROOT="$HOME/.pyenv"
command -v pyenv >/dev/null || export PATH="$PYENV_ROOT/bin:$PATH"
eval "$(pyenv init -)"
eval "$(pyenv virtualenv-init -)" |
| Windows |
🇫🇷 Dans Propriétés systèmes > Paramètres système avancés > Variables d'environnement... Choisissez la variable "Path" > Modifier... et ajoutez le chemin de votre installation python, où se trouve le python.exe. (par défaut, C:\Users\username\AppData\Roaming\Python\Scripts\ ) 🇬🇧 In System Properties > Advanced > Environment Variables... Choose the variable "Path" > Edit... et add the path to your python's installation, where is located the pyhton.exe (by default, this should be at C:\Users\username\AppData\Roaming\Python\Scripts\ ) In the console, you can now try : poetry --version |
Let's install a python version (for windows, this step have been done with miniconda):
pyenv install 3.11.6 # this will take timeCheck if it works properly, this command:
pyenv versionsshould return:
system
3.11.6Then you are ready to create a virtual environment. Go in the project folder, and run:
pyenv virtualenv 3.11.6 quotaclimat
pyenv local quotaclimatIn case of a version upgrade you can perform this command to switch
eval "$(pyenv init --path)"
pyenv activate 3.11.6/envs/quotaclimat
You now need a tool to manage dependencies. Let's use poetry. On windows, if not already installed, you will need a VS installation.
pip install poetry
poetry update
poetry lock --no-updateNLDA : I have not been able to work with wordcloud on windows.
When you need to install a new dependency (use a new package, e.g. nltk), run
poetry add ntlkUpdate dependencies
poetry self update
After commiting to the repo, other team members will be able to use the exact same environment you are using.
First, have docker and compose installed on your computer
Then to start the different services
## To run only one service, have a look to docker-compose.yml and pick one service :
docker compose up metabase
docker compose up ingest_to_db
docker compose up mediatree
docker compose up test
If you add a new dependency, don't forget to rebuild
docker compose build test # or ingest_to_db, mediatree etc
docker compose up metabase -d
Will give you access to Metabase to explore the SQL table sitemap table or keywords here : http://localhost:3000/
To connect to it you have use the variables used inside docker-compose.yml :
- password: password
- username: user
- db: barometre
- host : postgres_db
If we encounter a OOM error, we can set this env variable : JAVA_OPTS=-Xmx2g
The scrapping of sitemap.xml is done using the library advertools.
A great way to discover sitemap.xml is to check robots.txt page of websites : https://www.midilibre.fr/robots.txt
What medias to parse ? This document is a good start.
Learn more about site maps here.
By default, we use a env variable ENV to only parse from localhost. If you set this value to another thing that docker or dev, it will parse everything.
Thanks to the nginx container, we can have a local server for sitemap :
docker compose up -d nginx # used to scrap sitemap locally - a figaro like website with only 3 news
# docker compose up test with entrypoint modified to sleep
# docker exec test bash
pytest -vv --log-level DEBUG test # "test" is the folder containing tests
# Only one test
pytest -vv --log-level DEBUG -k detect
# OR
docker compose up test # test is the container name running pytest test
Every commit on the main branch will build an deploy to the Scaleway container registry a new image that will be deployed. Have a look to .github/deploy-main.yml.
Learn more here.
With Sentry, with env variable SENTRY_DSN.
Learn more here : https://docs.sentry.io/platforms/python/configuration/options/
Mediatree Documentation API : https://keywords.mediatree.fr/docs/
You must contact QuotaClimat team to 2 files with the API's username and password inside :
- secrets/pwd_api.txt
- secrets/username_api.txt
Otherwise, a mock api response is available at https://github.com/dataforgoodfr/quotaclimat/blob/main/test/sitemap/mediatree.json
You can check the API with
curl -X POST https://keywords.mediatree.fr/api/auth/token/ \
-H "Content-Type: application/x-www-form-urlencoded" \
-d "grant_type=password" \
-d "username=USERNAME" \
-d "password=PASSWORD"
curl -X GET "https://keywords.mediatree.fr/api/epg/?channel=tf1&start_gte=2024-09-01T00:00:00&start_lte=2024-09-01T23:59:59&token=TOKEN_RECEIVED_FROM_PREVIOUS_QUERY"
docker compose up mediatree
If our media perimeter evolves, we have to reimport it all using env variable START_DATE like in docker compose (epoch second format : 1705409797).
Otherwise, default is yesterday midnight date (default cron job)
As pandas to_sql does not enable upsert (update/insert), if we want to update already saved rows, we have to delete first the rows and then start the program with START_DATE :
DELETE FROM keywords
WHERE start BETWEEN '2024-05-01' AND '2024-05-30';
Use env variable CHANNEL like in docker compose (string: tf1)
Otherwise, default is all channels
In case we have a new word detection logic - and already saved data from Mediatree inside our DB (otherwise see Batch import based on time or channel) - we can re-apply it to all saved keywords inside our database.
We should use env variable UPDATE like in docker compose (should be set to "true")
In order to see actual change in the local DB, run the test first docker compose up test and then these commands :
docker exec -ti quotaclimat-postgres_db-1 bash
psql -h localhost --port 5432 -d barometre -U user
--> enter password : password
UPDATE keywords set number_of_keywords=1000 WHERE id = '71b8126a50c1ed2e5cb1eab00e4481c33587db478472c2c0e74325abb872bef6';
UPDATE keywords set number_of_keywords=1000 WHERE id = '975b41e76d298711cf55113a282e7f11c28157d761233838bb700253d47be262';
After having updated UPDATE env variable to true inside docker-compose.yml and running docker compose up mediatree you should see these logs :
update_pg_keywords.py:20 | Difference old 1000 - new_number_of_keywords 0
We can adjust batch update with these env variables (as in the docker-compose.yml):
BATCH_SIZE: 50000 # number of records to update in one batch
Use env variable CHANNEL like in docker compose (string: tf1) with UPDATE to true
UPDATE_PROGRAM_ONLY to true will only update program metadata, otherwise, it will update program metadata and all theme/keywords calculations.
UPDATE_PROGRAM_CHANNEL_EMPTY_ONLY to true will only update program metadata with empty value : "".
With +1 millions rows, we can update from an offset to fix a custom logic by using START_DATE_UPDATE (YYYY-MM-DD), the default will use the end of the month otherwise you can specifyEND_DATE (optional) (YYYY-MM-DD) to batch update PG from a date range.
~55 minutes to update 50K rows on a mVCPU 2240 - 4Gb RAM on Scaleway.
Every month has ~80K rows.
Example inside the docker-compose.yml mediatree service -> START_DATE_UPDATE: 2024-04-01 - default END_DATE will be 2024-04-30
We can use a Github actions to start multiple update operations with different date, set it using the matrix
Using Alembic Auto Generating Migrations¶ we can add a new column inside models.py and it will automatically make the schema evolution :
# If changes have already been applied (on your feature vranch) and you have to recreate your alembic file by doing :
# 1. change to your main branch
git switch main
# 2. start test container (docker compose up testconsole -d / docker compose exec testconsole bash) and run "pytest -vv -k api" to rebuild the state of the DB (or drop table the table you want) - just let it run a few seconds.
# 3. rechange to your WIP branch
git switch -
# 4. connect to the test container : docker compose up testconsole -d / docker compose exec testconsole bash
# 5. reapply the latest saved state :
poetry run alembic stamp head
# 6. Save the new columns
poetry run alembic revision --autogenerate -m "Add new column test for table keywords"
# this should generate a file to commit inside "alembic/versions"
# 7. to apply it we need to run, from our container
poetry run alembic upgrade head
Inside our Dockerfile_api_import, we call this line
# to migrate SQL tables schema if needed
RUN alembic upgrade head
In order to maintain channel perimeter (weekday, hours) up to date, we save the current version inside postgres/channel_metadata.json, if we modify this file the next deploy will update every lines of inside Postgresql table channel_metadata.
How to update quotaclimat/data_processing/mediatree/keyword/keyword.py from shared excel files ?
Download files locally to "document-experts" from Google Drive (ask on Slack) then :
# Be sure to have updated the folder "document-experts" before running it :
poetry run python3 quotaclimat/transform_excel_to_json.py
The media perimeter is defined here : "quotaclimat/data_processing/mediatree/channel_program_data.py"
To evolve the media perimeter, we use program_grid_start and program_grid_end columns to version all evolutions.
To calculate the right total duration for each channel, after updating "quotaclimat/data_processing/mediatree/channel_program_data.py" you need to execute this command to update postgres/program_metadata.json
poetry run python3 transform_program.py
The SQL queries are based on this file that generate the Program Metadata table.
Program data will not be updated to avoid lock concurrent issues when using UPDATE=true for keywords logic. Note: The default case will update them.
With the docker-entrypoint.sh this command is done automatically, so for production uses, you will not have to run this command.
- Use scaleway
- Use [Ray dashboard] on port 8265
Before committing, make sure that the line of codes you wrote are conform to PEP8 standard by running:
poetry run black .
poetry run isort .
poetry run flake8 .There is a debt regarding the cleanest of the code right now. Let's just not make it worth for now.