#
# Reads in selected Picard metrics, generating an R-compatible TSV suitable for pre-QC analysis.
#
# To run:
#   /humgen/gsa-hpprojects/software/bin/jython2.5.2/jython \
#     -J-classpath $STING_HOME/dist/sam-1.47.869.jar:$STING_HOME/dist/picard-1.47.869.jar:$STING_HOME/dist/picard-private-parts-1941.jar \
#     $STING_HOME/python/generate_per_sample_metrics.py <bam.list> > <output_metrics_file.tsv>
#
# To add a new metric:
#   - If the metric file is new to Picard, add the relevant parser to the picard-private jar
#     (see http://www.broadinstitute.org/gsa/wiki/index.php/Adding_and_updating_dependencies for details).
#   - Add the field name to the header array.
#   - Add the field data to the statement printing the data array.
#
from java.lang import *
from java.io import File,FileReader

from edu.mit.broad.picard.genotype.concordance import DbSnpMatchMetrics
from net.sf.picard.analysis import AlignmentSummaryMetrics,InsertSizeMetrics
from net.sf.picard.analysis.directed import HsMetrics
from net.sf.picard.metrics import MetricsFile

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_all_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

def get_sample_summary_metrics_fields(type):
    return [field.getName() for field in type.getFields() if not field.getName().startswith('__')]

def get_sample_summary_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()[0]
    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 'BAM list %s not found' % sys.argv[1]
    sys.exit(1)

bam_list_filename = sys.argv[1]

sample_summary_metrics_types = [ (HsMetrics,'hybrid_selection_metrics'),
                                 (AlignmentSummaryMetrics, 'alignment_summary_metrics'),
                                 (InsertSizeMetrics, 'insert_size_metrics'),
                                 (DbSnpMatchMetrics, 'dbsnp_matches') ]

header = ['sample','FINGERPRINT_LODS','HAPLOTYPES_CONFIDENTLY_MATCHING']
for metric_type in sample_summary_metrics_types:
    header.extend(get_sample_summary_metrics_fields(metric_type[0]))
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.
bam_list = open(bam_list_filename,'r')
samples = dict()

for bam_filename in bam_list:
    bam_filename = bam_filename.strip()
    if bam_filename == '':
        continue
    bam_filename_tokens = bam_filename.split('/')
    sample_id = bam_filename_tokens[len(bam_filename_tokens)-3]
    samples[sample_id] = bam_filename
bam_list.close()

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

    if fingerprinting_summary_metrics != None:
        haplotypes_confidently_matching = [str(metric.HAPLOTYPES_CONFIDENTLY_MATCHING) for metric in fingerprinting_summary_metrics]
        fingerprint_lods = [str(metric.LOD_EXPECTED_SAMPLE) 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:
        haplotypes_confidently_matching = []
        fingerprint_lods = []
#        min_haplotypes_confidently_matching = 'NA'
#        max_haplotypes_confidently_matching = 'NA'
#        median_haplotypes_confidently_matching = 'NA'

    data = [sample_id,'c('+string.join(fingerprint_lods,',')+')','c('+string.join(haplotypes_confidently_matching,',')+')']

    for metrics_type,metrics_extension in sample_summary_metrics_types:
        metrics = get_sample_summary_metrics('%s.%s' % (basepath,metrics_extension))
        data.extend([getattr(metrics, metrics_field_name) for metrics_field_name in get_sample_summary_metrics_fields(metrics_type)])
    print string.join(['%s']*len(header),'\t')%tuple(data)