Snakefile

# Note that if you are working on the analysis development locally, i.e. outside
# of the REANA platform, you can proceed as follows:
#
#   $ mkdir snakemake-local-run
#   $ cd snakemake-local-run
#   $ virtualenv ~/.virtualenvs/worldpopulation-snakemake
#   $ source ~/.virtualenvs/worldpopulation-snakemake/bin/activate
#   $ pip install snakemake papermill ipykernel pandas matplotlib
#   $ cp -a ../code ../data .
#   $ snakemake -s ../workflow/snakemake/Snakefile \
#               --configfile ../workflow/snakemake/inputs.yaml
#               --config notebook=code/worldpopulation.ipynb -p --cores 1
#   $ open results/plot.png

rule all:
    input:
        config["notebook"],
        config["output_file"]

rule worldpopulation:
    input:
        notebook=config["notebook"],
        input_file=config["input_file"]
    params:
        region=config["region"],
        year_max=config["year_max"],
        year_min=config["year_min"]
    output:
        output_file=config["output_file"]
    container:
        "docker://docker.io/reanahub/reana-env-jupyter:2.0.0"
    shell:
        "mkdir -p results && papermill {input.notebook} /dev/null -p "
        "input_file {input.input_file} -p output_file {output.output_file} "
        "-p region {params.region} -p year_min {params.year_min} -p year_max {params.year_max}"