rule estimateKmerCoverage:
    input:
        read1 = 'data/auxiliary/{dataset}/{id}'+'.qc_internal_R1.fq',
        read2 = 'data/auxiliary/{dataset}/{id}'+'.qc_internal_R2.fq'
    output:
        histogram = report('data/output/{dataset}/kmers/{kmer}/{id}/kmers.histo.png',category='Coverage-Based Method',caption='../report/kmerCoverageHistogram.rst'),
        histogramRaw = 'data/auxiliary/{dataset}/kmers/{kmer}/{id}/kmers.histo.raw.png',
        mean = 'data/auxiliary/{dataset}/kmers/{kmer}/{id}/coverage_estimate_kmercountbased.txt',
        kmererror = 'data/auxiliary/{dataset}/kmers/{kmer}/{id}/kmer_error.txt'
    params:
        k = lambda wildcards: wildcards.kmer,
        #cluster execution
        cpus = '1', #TODO: This could in theory be sped up significantly using a shared cache and multithreading
        gpus = '0',
        mem = '64G',
        walltime = '00:45:00'
    log:
        'logs/{dataset}/kmers/{kmer}/{id}/estimateKmerCoverage.log'
    benchmark:
        'benchmarks/{dataset}/kmers/{kmer}/{id}/estimateKmerCoverage.log'
    conda:
        '../envs/biopythonworkbench.yaml'
    script:
        '../scripts/estimateKmerCoverage.py'


'''
rule estimateKmerCoverageFiltered:
    input:
        reads = 'data/auxiliary/{dataset}/{id}/filteredReads.fastq'
    output:
        histogram = 'data/auxiliary/{dataset}/kmers/{kmer}/{id}/kmers.histo.regionXOnly.png'
    params:
        k = lambda wildcards: wildcards.kmer
    #TODO: Threads = 2 ?
    log:
        'logs/{dataset}/kmers/{kmer}/{id}/estimateKmerCoverageFiltered.log'
    conda:
        '../envs/main.yaml'
    script:
        '../scripts/estimateKmerCoverageFiltered.py'
'''


rule estimateKmerCoverageAlignment:
    input:
        coverageEstimate = 'data/auxiliary/{dataset}/{id}/coverageEstimate.txt',
        readLengthEstimate = 'data/auxiliary/{dataset}/{id}/readLengthEstimate.txt',
        baseErrorEstimate = 'data/auxiliary/{dataset}/{id}/base_error_estimate.txt'
    output:
        kmerCoverage = 'data/auxiliary/{dataset}/kmers/{kmer}/{id}/coverage_estimate_alignmentbased.txt'
    params:
        k = lambda wildcards: wildcards.kmer,
        # cluster execution
        cpus = '1',
        gpus = '0',
        mem = '16G',
        walltime = '00:30:30'
    log:
        'logs/{dataset}/kmers/{kmer}/{id}/estimateKmerCoverage.log'
    conda:
        '../envs/biopythonworkbench.yaml'
    script:
        '../scripts/estimateKmerCoverage_alignment.py'

rule estimateCoverageAlignment:
    input:
        filteredAlignment = 'data/auxiliary/{dataset}/{id}/alignment.sorted.bam'
    output:
        coverageEstimate = 'data/auxiliary/{dataset}/{id}/coverageEstimate.txt'
    params:
        # cluster execution
        cpus = '1',
        gpus = '0',
        mem = '16G',
        walltime = '00:30:30'
    log:
        'logs/{dataset}/{id}/estimateKmerCoverage_alignment.log'
    conda:
        '../envs/biopythonworkbench.yaml'
    shell:
        'samtools depth  {input.filteredAlignment} |  awk \' $1 == "maskref" {{sum+=$3}} END {{ print "Average = ",sum/NR}}\' | grep -Eo \'[+-]?[0-9]+([.][0-9]+)?\' > {output.coverageEstimate}'

rule calcPriorProbabilitiesCoverage:
    input:
        likelihoods = 'data/auxiliary/{dataset}/{iterset}/kmers/{kmer}/{id}/likelihoods_cov.json'
    output:
        priorFilePath = 'data/auxiliary/{dataset}/{iterset}/kmers/{kmer}/{id}/prior_cov.txt'
    params:
        k = lambda wildcards: wildcards.kmer,
        dps = config['dps'],
        # cluster execution
        cpus = '1',
        gpus = '0',
        mem = '2G',
        walltime = '00:05:30'
    log:
        'logs/{dataset}/{iterset}/probabilistic/kmers/{kmer}/{id}/calcPrior_cov.log'
    conda:
        '../envs/biopythonworkbench.yaml'
    script:
        '../scripts/calcPriorProbabilities.py'

rule calcProbabilitiesCoverage:
    input:
        likelihoods = 'data/auxiliary/{dataset}/{iterset}/kmers/{kmer}/{id}/likelihoods_cov.json',
        prior = 'data/auxiliary/{dataset}/{iterset}/kmers/{kmer}/{id}/prior_cov.txt'
    output:
        probabilities = 'data/auxiliary/{dataset}/{iterset}/kmers/{kmer}/{id}/scores.probabilistic_cov.tsv'
    params:
        dps = config['dps'],
        # cluster execution
        cpus = '1',
        gpus = '0',
        mem = '8G',
        walltime = '00:10:30'
    conda:
        '../envs/biopythonworkbench.yaml'
    log:
        'logs/{dataset}/{iterset}/probabilistic/kmers/{kmer}/{id}/probabilities_cov.log'
    script:
        '../scripts/calcSpaTypeProbabilities.py'


rule createFitnessPlots:
    input:
        counts = 'data/auxiliary/{dataset}/kmers/{kmer}/{id}/alignment.counts.json',
        probabilities = 'data/auxiliary/{dataset}/{iterset}/kmers/{kmer}/{id}/scores.probabilistic_cov.tsv',
        ratios = 'data/auxiliary/kmers/{kmer}/spaSequencesRatios.json'
    output:
        report('data/output/{dataset}/{iterset}/kmers/{kmer}/{id}_top3fit.svg',category='Coverage-Based Method',caption='../report/fitnessPlot.rst')
    params:
        dps = config['dps'],
        # cluster execution
        cpus = '1',
        gpus = '0',
        mem = '1G',
        walltime = '00:20:30'
    conda:
        '../envs/biopythonworkbench.yaml'
    script:
        '../scripts/createFitnessPlots.py'


rule calcExpectedCounts:
    input:
        kmerCoverageEstimate = determineKmerCoverageEstimateFile(),
        counts = 'data/auxiliary/kmers/{kmer}/spaSequences.counts.json'
    output:
        'data/auxiliary/{dataset}/kmers/{kmer}/{id}/expected_counts.json'
    params:
        k = lambda wildcards: wildcards.kmer,
        # cluster execution
        cpus = '1',
        mem = '8G',
        gpus = '0',
        walltime = '00:30:00' 
    conda:
        '../envs/biopythonworkbench.yaml'
    script:
        '../scripts/calcExpectedCounts.py'