shell: """

    echo '''{params.dataframe}''' > {output}

    # TODO: Write something clever to the benchmarks/ directory, so we can infer relationship between hardware allocations, input size and running time.

"""

shell: """

    mkdir -p output/{config_batch}/
    >&2 echo "Concatenating database ..."
    cat {input.glob} > output/{config_batch}/cat_database_sources.faa

    mkdir -p output/{config_batch}/
    cd output/{config_batch}/

    {params.philosopher} workspace --init

    # https://github.com/Nesvilab/philosopher/wiki/Database
    {params.philosopher} database \
        --custom cat_database_sources.faa \
        --contam 

    echo "Existing database pattern-matched files:"
    ls *-decoys-contam-cat_database_sources.faa.fas

    mv *-decoys-contam-cat_database_sources.faa.fas philosopher_database.faa # rename database file.
    # rm cat_database_sources.faa # remove unneccessary .faa file. # No, keep it for annotation purposes.

    {params.philosopher} workspace --clean




"""

shell: """

    echo "Create manifest ..."
    echo '''{params.manifest}''' > {output.manifest} 
    tail {output.manifest}


    echo "Modifying workflow with runtime parameters ..." # TODO: Check if it matters to overwrite or not.
    # Copy and modify parameter file with dynamic content.
    cp {params.original_fragpipe_workflow} {output.fragpipe_workflow}
    echo -e "\n# Added by mspipeline1 in rule fragpipe in snakefile below ..." >> {output.fragpipe_workflow}

    echo "num_threads={threads}" >> {output.fragpipe_workflow}
    echo "database_name={input.database}" >> {output.fragpipe_workflow}
    echo "database.db-path={input.database}" >> {output.fragpipe_workflow}

    echo "output_location={params.fragpipe_workdir}" >> {output.fragpipe_workflow}

    # These settings minimize memory usage. 
    echo "msfragger.misc.slice-db={params.slice_db}" >> {output.fragpipe_workflow} # Default 1
    echo "msfragger.calibrate_mass=0" >> {output.fragpipe_workflow} # Default 2
    echo "msfragger.digest_max_length=35" >> {output.fragpipe_workflow} # Default 50
    # echo "msfragger.allowed_missed_cleavage_1=1" >> {output.fragpipe_workflow} # Default 2
    # echo "msfragger.allowed_missed_cleavage_2=1" >> {output.fragpipe_workflow} # Default 2

    # Debug, presentation of the bottom of the modified workflow
    echo "" >> {output.fragpipe_workflow}
    tail {output.fragpipe_workflow}


    # Convert mem_mb into gb (I'm not sure if fragpipe reads GiB or GB?)
    mem_gib=$(({resources.mem_mib}/1024-2)) # Because there is some overhead, we subtract a few GBs. Everytime fragpipe runs out of memory, I subtract another one: that should be more effective than doing a series of tests ahead of time.
    echo "Fragpipe will be told not to use more than $mem_gib GiB. In practice it usually uses a bit more." # Or maybe there just is an overhead when using a conda environment?

    echo "Fragpipe ..."
    # https://fragpipe.nesvilab.org/docs/tutorial_headless.html
    {params.fragpipe_executable} \
        --headless \
        --workflow {output.fragpipe_workflow} \
        --manifest {output.manifest} \
        --workdir {params.fragpipe_workdir} \
        --ram $mem_gib \
        --threads {threads} \
        --config-msfragger {params.msfragger_jar} \
        --config-ionquant {params.ionquant_jar} \
        --config-philosopher {params.philosopher_executable} \
    | tee {output.fragpipe_stdout} # Write the log, so we can later extract the number of "scans"



"""

shell: """

    # Extract scans from the fragpipe stdout log. Will later be compared to the individual psm files.
    grep -E ": Scans = [0-9]+" {input.fragpipe_stdout} \
    > {output.scans}


"""

shell: """

    cp scripts/QC.Rmd rmarkdown_template.rmd
    Rscript -e 'rmarkdown::render("rmarkdown_template.rmd", "html_document", output_file = "{output.report}", knit_root_dir = "output/{config_batch}/", quiet = F)' 
    rm rmarkdown_template.rmd

"""

shell: """

    zip {output} {input} 

"""