from java.lang import *
from java.io import File,FileReader
from net.sf.picard.metrics import MetricsFile
from org.broadinstitute.sting.datasources.pipeline import Pipeline
from org.broadinstitute.sting.utils.yaml import YamlUtils

import os,string,sys

def median(l):
    return sorted(l)[(len(l)+1)/2]
def mean(l):
    return float(sum(l))/len(l)

def get_metrics(filename):
    if not os.path.exists(filename):
        return None
    file_reader = FileReader(filename)
    metrics_file = MetricsFile()
    metrics_file.read(file_reader)
    metrics = metrics_file.getMetrics()
    file_reader.close()
    return metrics

if len(sys.argv) != 2:
    print 'USAGE: %s <pipeline_file.yaml>'
    sys.exit(1)
if not os.path.exists(sys.argv[1]):
    print 'Pipeline file %s not found' % sys.argv[1]
    sys.exit(1)

pipeline_file = sys.argv[1]
pipeline = YamlUtils.load(Pipeline,File(pipeline_file))

header = ['SAMPLE','HAPLOTYPES_CONFIDENTLY_MATCHING.MIN','HAPLOTYPES_CONFIDENTLY_MATCHING.MAX','HAPLOTYPES_CONFIDENTLY_MATCHING.MEDIAN',
          'BAIT_SET','GENOME_SIZE','PCT_SELECTED_BASES','MEAN_TARGET_COVERAGE','ZERO_CVG_TARGETS_PCT','FOLD_80_BASE_PENALTY','HS_LIBRARY_SIZE',
          'PCT_PF_READS_ALIGNED','PF_HQ_ERROR_RATE','MEAN_READ_LENGTH','BAD_CYCLES','STRAND_BALANCE','PCT_CHIMERAS','PCT_ADAPTER','MEDIAN_INSERT_SIZE',
          'TOTAL_SNPS']
data = ['%s'] * len(header)

print string.join(header,'\t')

# get a representative BAM file for each sample, to use as a base path.  Note that this assumes every sample corresponds to the same base path.
samples = dict()
for sample in pipeline.getSamples():
    if sample.getBamFiles().size() > 0:
        samples[sample.getId()] = sample.getBamFiles().values().iterator().next()

for sample_id,filename in samples.items():
    basepath = filename.getAbsolutePath()[0:filename.getAbsolutePath().rindex('.bam')]
    
    fingerprinting_summary_metrics = get_metrics('%s.%s' % (basepath,'fingerprinting_summary_metrics'))

    if fingerprinting_summary_metrics != None:
        haplotypes_confidently_matching = [metric.HAPLOTYPES_CONFIDENTLY_MATCHING for metric in fingerprinting_summary_metrics]
        min_haplotypes_confidently_matching = str(min(haplotypes_confidently_matching))
        max_haplotypes_confidently_matching = str(max(haplotypes_confidently_matching))
        median_haplotypes_confidently_matching = str(median(haplotypes_confidently_matching))
    else:
        min_haplotypes_confidently_matching = 'NA'
        max_haplotypes_confidently_matching = 'NA'
        median_haplotypes_confidently_matching = 'NA'

    hybrid_selection_metrics = get_metrics('%s.%s' % (basepath,'hybrid_selection_metrics'))[0]
    alignment_summary_metrics = get_metrics('%s.%s' % (basepath,'alignment_summary_metrics'))[0]
    insert_size_metrics = get_metrics('%s.%s' % (basepath,'insert_size_metrics'))[0]
    dbsnp_matches = get_metrics('%s.%s' % (basepath,'dbsnp_matches'))[0]

    print string.join(data,'\t')%(sample_id,min_haplotypes_confidently_matching,max_haplotypes_confidently_matching,median_haplotypes_confidently_matching,
                                  hybrid_selection_metrics.BAIT_SET,hybrid_selection_metrics.GENOME_SIZE,hybrid_selection_metrics.PCT_SELECTED_BASES,
                                  hybrid_selection_metrics.MEAN_TARGET_COVERAGE,hybrid_selection_metrics.ZERO_CVG_TARGETS_PCT,hybrid_selection_metrics.FOLD_80_BASE_PENALTY,
                                  hybrid_selection_metrics.HS_LIBRARY_SIZE,alignment_summary_metrics.PCT_PF_READS_ALIGNED,alignment_summary_metrics.PF_HQ_ERROR_RATE,
                                  alignment_summary_metrics.MEAN_READ_LENGTH,alignment_summary_metrics.BAD_CYCLES,alignment_summary_metrics.STRAND_BALANCE,
                                  alignment_summary_metrics.PCT_CHIMERAS,alignment_summary_metrics.PCT_ADAPTER,insert_size_metrics.MEDIAN_INSERT_SIZE,dbsnp_matches.TOTAL_SNPS)