import argparse
import os
import subprocess


def main():

    parser = argparse.ArgumentParser(
                description='Run Jupyter notebook, create Markdown output')
    parser.add_argument('nb', help='Name of Jupyter notebook.')
    parser.add_argument('md_output', help='Name of created Markdown output.')
    args = parser.parse_args()

    nb = args.nb
    if not (os.path.isfile(nb) and os.path.splitext(nb)[-1] == '.ipynb'):
        raise IOError(f"not a valid Jupyter notebook: {nb}")

    md_output = args.md_output
    if os.path.splitext(md_output)[-1] not in {'.md', '.markdown'}:
        raise IOError(f"not a valid Markdown file: {md_output}")

    if os.path.isfile(md_output):
        os.remove(md_output)

    subprocess.check_call(['jupyter', 'nbconvert',
                           '--to', 'notebook',
                           '--execute',
                           '--inplace',
                           '--ExecutePreprocessor.timeout=-1',
                           nb,
                           ])

    subprocess.check_call(['jupyter', 'nbconvert',
                           '--output-dir', os.path.dirname(md_output),
                           '--output', os.path.basename(md_output),
                           '--to', 'markdown',
                           nb,
                           ])


if __name__ == '__main__':
    main()

run:
    def path(f):
        """Get path relative to `summary_dir`."""
        return os.path.relpath(f, config['summary_dir'])
    with open(output.summary, 'w') as f:
        f.write(textwrap.dedent(f"""
        # Summary

        Analysis run by [Snakefile]({path(workflow.snakefile)})
        using [this config file]({path(workflow.configfiles[0])}).
        See the [README in the top directory]({path('README.md')})
        for details.

        Here is the DAG of the computational workflow:
        ![{path(input.dag)}]({path(input.dag)})

        Here is the Markdown output of each Jupyter notebook in the
        workflow:

        1. Process PacBio CCSs for [PDCoV libraries]({path(input.process_ccs_PDCoV)}). Creates barcode-variant lookup table, which can be found [here]({path(input.barcode_variant_table_PDCoV)}),.

        2. [Count variants by barcode]({path(input.count_variants)}).
           Creates a [variant counts file]({path(input.variant_counts_file)})
           giving counts of each barcoded variant in each condition.

        3. [Fit gAPN titration curves]({path(input.fit_gAPN_titrations)}) to calculate per-barcode K<sub>D</sub>, recorded in [this file]({path(input.gAPN_Kds_file)}).

        4. Analyze single-concentration sort-seq experients for [hAPN]({path(input.fit_hAPN_meanF)}) and [pAPN]({path(input.fit_pAPN_meanF)}) to calculate per-barcode binding MFI, recorded in these files for [hAPN]({path(input.hAPN_meanF_file)}) and [pAPN]({path(input.pAPN_meanF_file)}).

        5. [Derive final genotype-level phenotypes from replicate barcoded sequences]({path(input.collapse_scores)}).
           Generates final phenotypes, recorded in [this file]({path(input.mut_phenos_file)}).

        6. Make interactive data visualizations, available [here](https://jbloomlab.github.io/PD-CoV-RBD_DMS/)

        """
        ).strip())

shell:
    """
    R -e \"rmarkdown::render(input=\'{params.nb}\')\";
    mv {params.md} {output.md};
    mv {params.md_files} {output.md_files}
    """

shell:
    """
    R -e \"rmarkdown::render(input=\'{params.nb}\')\";
    mv {params.md} {output.md};
    mv {params.md_files} {output.md_files}
    """

shell:
    """
    R -e \"rmarkdown::render(input=\'{params.nb}\')\";
    mv {params.md} {output.md};
    mv {params.md_files} {output.md_files}
    """

shell:
    """
    R -e \"rmarkdown::render(input=\'{params.nb}\')\";
    mv {params.md} {output.md};
    mv {params.md_files} {output.md_files}
    """

shell:
    "python scripts/run_nb.py {params.nb} {output.nb_markdown}"

shell:
    "python scripts/run_nb.py {params.nb} {output.nb_markdown}"

run:
    os.symlink(input.ccs_fastq, output.ccs_fastq)