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Create the conda environment
# the environment's name will be `carbonseq_vaud`
conda env create -f environment.yml
Follow this instructions to set the environment variables AWS_ACCESS_KEY_ID
and AWS_SECRET_ACCESS_KEY
for the fresh floodreg_vaud
conda environment (in order for boto3 to find your S3-like credentials for DigitalOcean Spaces). They correspond to the DigitalOcean Spaces access and secret key respectively. Important note: the command jupyter-notebook
must be launched from within the floodreg_vaud
environment so the access keys can be extracted from the environment variables
Enter the fresh environment
conda activate carbonseq_vaud
Already within the environment, make it available as a jupyter
kernel as in:
python -m ipykernel install --user --name carbonseq_vaud --display-name "Python (carbonseq_vaud)"
From the repository's root, create a folder named papermill_outputs
Pull the data from the dvc remote
dvc pull
Reproduce the land data frame
dvc repro data/vaud_ldf.csv.dvc
Now you can execute the Notebook invest.ipynb
Press p or to see the previous file or, n or to see the next file
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?