include: 'common.snakemake'

import os

def get_all_inputs(wildcards):

    available_inputs = dict(

        fastqc=expand('{output_dir}/fastqc/{sample_id}_fastqc.zip',

            output_dir=output_dir, sample_id=sample_ids),

        summarize_fastqc=expand('{output_dir}/summary/fastqc.txt',

            output_dir=output_dir),

        summarize_fastqc_html=expand('{output_dir}/summary/fastqc.html',

            output_dir=output_dir)

    )

    enabled_inputs = list(available_inputs.keys())

    inputs = []

    for key, l in available_inputs.items():

        if key in enabled_inputs:

            inputs += l

    return inputs

rule all:

    input:

        get_all_inputs

rule fastqc:

    input:

        auto_gzip_input(data_dir + '/fastq/{sample_id}.fastq')

    output:

        html='{output_dir}/fastqc/{sample_id}_fastqc.html',

        zip='{output_dir}/fastqc/{sample_id}_fastqc.zip'

    params:

        output_prefix='{output_dir}/fastqc/',

        temp_dir=config['temp_dir']

    log:

        '{output_dir}/log/fastqc/{sample_id}'

    shell:

        '''fastqc -q -o {params.output_prefix} -d {params.temp_dir} {input} > {log} 2>&1

        '''

rule summarize_fastqc:

    input:

        zip=lambda wildcards: expand('{output_dir}/{fastqc_step}/{sample_id}_fastqc.zip',

            output_dir=wildcards.output_dir, fastqc_step=wildcards.fastqc_step, sample_id=sample_ids)

    output:

        '{output_dir}/summary/{fastqc_step}.txt'

    wildcard_constraints:

        fastqc_step='fastqc.*'

    run:

        import pandas as pd

        from zipfile import ZipFile

        import os

        from collections import OrderedDict

        summary = OrderedDict()

        columns = None

        for filename in input.zip:

            sample_id = os.path.splitext(os.path.basename(filename))[0][:-7]

            with ZipFile(filename, 'r') as zf:

                with zf.open(sample_id + '_fastqc/fastqc_data.txt', 'r') as f:

                    summary[sample_id] = parse_fastqc_data(f)

                    if columns is None:

                        columns = list(summary[sample_id].keys())

        summary = pd.DataFrame.from_records(summary)

        summary = summary.T

        summary = summary.reindex(columns=columns)

        summary.index.name = 'sample_id'

        summary.to_csv(output[0], sep='\t', index=True, header=True) 

"""

rule summarize_fastqc_jupyter:

    input:

        fastqc='{output_dir}/summary/{fastqc_step}.txt',

        jupyter=root_dir + '/templates/fastqc.ipynb'

    output:

        jupyter='{output_dir}/summary/{fastqc_step}.ipynb',

        html='{output_dir}/summary/{fastqc_step}.html'

    wildcard_constraints:

        fastqc_step='fastqc.*'

    run:

        shell(nbconvert_command)

"""

rule multiqc:

    input:

        fastqc=expand('{output_dir}/fastqc/{sample_id}_fastqc.zip',

            output_dir=output_dir, sample_id=sample_ids)

    output:

        html='{output_dir}/summary/fastqc.html',

        data=directory('{output_dir}/summary/fastqc_data')

    params:

        fastqc_dir='{output_dir}/fastqc'

    shell:

        '''multiqc -m fastqc -n {output.html} {params.fastqc_dir}

        '''

rule prinseq_clean_se:

    input:

        fastq='{output_dir}/cutadapt/{sample_id}.fastq.gz'

    output:

        graph_data='{output_dir}/prinseq_clean/{sample_id}.gd'

    shell:

        '''perl {tools_dir}/prinseq/prinseq-lite.pl -verbose -fastq <(zcat {input.fastq}) \

            -ns_max_n 0 -graph_data {output.graph_data} -out_good null -out_bad null

        '''

rule prinseq_graph_clean_se:

    input:

        '{output_dir}/prinseq_clean/{sample_id}.gd'

    output:

        '{output_dir}/prinseq_clean/{sample_id}.html'

    shell:

        '''perl {tools_dir}/prinseq/prinseq-graphs.pl -i {input} -html_all -o {output}

        '''