Update README.md. Changed git clone url to gitea

This commit is contained in:
2025-05-26 14:42:01 +02:00
parent 37dc1d390d
commit b4d07bc2b4

166
README.md
View File

@ -1,84 +1,84 @@
# QC/QA Data Flagging Application
This repository hosts a Dash Plotly data flagging app for ACSM data structured in HDF5 format using the DIMA submodule. The provided Jupyter notebooks walk you through the steps to append metadata about diagnostic and target channels, which are necessary for the app to run properly.
## Getting Started
### Requirements
For Windows users, the following are required:
1. **Git Bash**: Git Bash will be used to run shell scripts (`.sh` files).
2. **Conda**: You must have [Anaconda](https://www.anaconda.com/products/individual) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html) installed on your system. Git Bash needs access to Conda to set up the environment properly. Ensure that Conda is added to your systems PATH during installation.
3. **PSI Network Access (for data retrieval)**: Real data retrieval can only be performed when connected to the PSI network and with the appropriate access rights to the source network drive.
## Clone the Repository
Open **Git Bash** and run:
```bash
cd GitLab
git clone --recurse-submodules https://gitlab.psi.ch/apog/acsmnode.git
cd acsmnode
```
## Run the Data Chain App
1. Open **PowerShell as Administrator** and navigate to the `acsmnode` repository.
2. Create a `.env` file in the root of `acsmnode/`.
3. **Securely store your network drive access credentials** in the `.env` file by adding the following lines:
```plaintext
CIFS_USER=<your-username>
CIFS_PASS=<your-password>
```
**To protect your credentials:**
- Do not share the .env file with others.
- Ensure the file is excluded from version control by adding .env to your .gitignore and .dockerignore files.
4. Open **Docker Desktop**, then build the container image:
```bash
docker build -t datachain_processor .
```
5. Run the app:
```bash
docker compose --file docker-compose.yaml up datachain_processor
6. Access:
- **Jupyter Lab**: [http://localhost:8889/lab/tree/notebooks/](http://localhost:8889/lab/tree/notebooks/)
7. Stop the app:
In the previously open PowerShell terminal, enter:
```bash
Ctrl + C
```
After the container is properly Stopped, remove the container process as:
```bash
docker rm $(docker ps -aq --filter ancestor=datachain_processor)
```
## Set Up the Python Environment
If **Git Bash** lacks a suitable Python interpreter, run:
```bash
bash env_setup.sh
```
## Run the Dashboard App
Run the following command to start the dashboard app:
```bash
python data_flagging_app.py
```
This command will launch the data flagging app.
## Stop the Dashboard App
Run the following command to stop the dashboard app:
```bash
CTRL + C
```
# QC/QA Data Flagging Application
This repository hosts a Dash Plotly data flagging app for ACSM data structured in HDF5 format using the DIMA submodule. The provided Jupyter notebooks walk you through the steps to append metadata about diagnostic and target channels, which are necessary for the app to run properly.
## Getting Started
### Requirements
For Windows users, the following are required:
1. **Git Bash**: Git Bash will be used to run shell scripts (`.sh` files).
2. **Conda**: You must have [Anaconda](https://www.anaconda.com/products/individual) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html) installed on your system. Git Bash needs access to Conda to set up the environment properly. Ensure that Conda is added to your systems PATH during installation.
3. **PSI Network Access (for data retrieval)**: Real data retrieval can only be performed when connected to the PSI network and with the appropriate access rights to the source network drive.
## Clone the Repository
Open **Git Bash** and run:
```bash
cd GitLab
git clone --recurse-submodules https://gitea.psi.ch/APOG/acsmnode.git
cd acsmnode
```
## Run the Data Chain App
1. Open **PowerShell as Administrator** and navigate to the `acsmnode` repository.
2. Create a `.env` file in the root of `acsmnode/`.
3. **Securely store your network drive access credentials** in the `.env` file by adding the following lines:
```plaintext
CIFS_USER=<your-username>
CIFS_PASS=<your-password>
```
**To protect your credentials:**
- Do not share the .env file with others.
- Ensure the file is excluded from version control by adding .env to your .gitignore and .dockerignore files.
4. Open **Docker Desktop**, then build the container image:
```bash
docker build -t datachain_processor .
```
5. Run the app:
```bash
docker compose --file docker-compose.yaml up datachain_processor
6. Access:
- **Jupyter Lab**: [http://localhost:8889/lab/tree/notebooks/](http://localhost:8889/lab/tree/notebooks/)
7. Stop the app:
In the previously open PowerShell terminal, enter:
```bash
Ctrl + C
```
After the container is properly Stopped, remove the container process as:
```bash
docker rm $(docker ps -aq --filter ancestor=datachain_processor)
```
## Set Up the Python Environment
If **Git Bash** lacks a suitable Python interpreter, run:
```bash
bash env_setup.sh
```
## Run the Dashboard App
Run the following command to start the dashboard app:
```bash
python data_flagging_app.py
```
This command will launch the data flagging app.
## Stop the Dashboard App
Run the following command to stop the dashboard app:
```bash
CTRL + C
```
This command will terminate the server process running the app.