import avro.protocol as avpr

ADAM_formats = avpr.parse(open("adam.avpr", "r").read()) 
sorted([(v.type, k) for k, v in ADAM_formats.types_dict.items()])

from avro.datafile import DataFileReader, DataFileWriter
from avro.io import DatumReader, DatumWriter

ADAM_contig_schema = ADAM_formats.types_dict['ADAMContig']
ADAM_contig_schema.to_json()

# this is the Python representation of the Avro object. 
ADAM_contig = {'contigId': 9230,
               'contigName':"1Nabc", 
               'contigLength':7781,
               'contigMD5':"8743b52063cd84097a65d1633f5c74f5",
               'referenceURL': 'http://data.dna/223'} # mock data

with DataFileWriter(open("contigs.avro", "w"), DatumWriter(), ADAM_contig_schema) as contig_writer:
    contig_writer.append(ADAM_contig)

with DataFileReader(open("contigs.avro", "r"), DatumReader()) as contigs:
    for contig in contigs:
        print contig

ADAM_record = {"referenceName": "20", "referenceId": 19, "start": 19893804, "mapq": 60, 
               "readName": "20GAVAAXX100126:4:64:6132:191287", 
               "sequence": "GTTTTCTATGAAGTTATTTTCTAGGGATTCTGTTTTGTTGTCGTTGTTCACACTGTAGCTCTCAGATCTTACTGTTTTTTTTTTAATTGTGATAAAGCATA", 
               "mateReference": "20", "mateAlignmentStart": 19893476, "cigar": "101M", 
               "qual": "EHHHFGDGHFHF8EEFB=B@=GHFFBAA@8??>IHHEIHEH>EHH@HFG>GEGHEGHFA<GDGHFFHGHGFCFCE@EEHHHHHGGFHGEGFFFGGGFBDDD", 
               "recordGroupName": "20GAV.4", "recordGroupId": 11, "readPaired": True, "properPair": True, "readMapped": True, 
               "mateMapped": True, "readNegativeStrand": True, "mateNegativeStrand": False, "firstOfPair": True, 
               "secondOfPair": False, "primaryAlignment": True, "failedVendorQualityChecks": False, 
               "duplicateRead": False, "mismatchingPositions": "101", 
               "attributes": "XT:A:U\tOQ:Z:HHHHFHGHHHHG=FGGC?DC@HHHHFDDA:?E>HHHHHHHHHHHHFHGHGHGHHHHHHF@HFHHHHHHHHHGGGGGGGHHHHHHHHHHHHHHHHHHHGHHH\tMQ:i:60\tBQ:Z:@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\tXO:i:0\tXM:i:0\tSM:i:37\tNM:i:0\tAM:i:37\tXG:i:0\tRG:Z:20GAV.4\tX1:i:0\tX0:i:1", 
               "recordGroupSequencingCenter": "BI", "recordGroupDescription": None, "recordGroupRunDateEpoch": None, 
               "recordGroupFlowOrder": None, "recordGroupKeySequence": None, "recordGroupLibrary": "Solexa-18484", 
               "recordGroupPredictedMedianInsertSize": None, "recordGroupPlatform": "illumina", "recordGroupPlatformUnit": "20GAVAAXX100126.4", 
               "recordGroupSample": "NA12878", 
               "mateReferenceId": 19, "referenceLength": 63025520, "referenceUrl": None, 
               "mateReferenceLength": 63025520, "mateReferenceUrl": None, "origQual": None}

20GAVAAXX100126:4:64:6132:191287     83     20     19893805     60     101M     =     19893477     -428    GTTTTCTATGAAGTTATTTTCTAGGGATTCTGTTTTGTTGTCGTTGTTCACACTGTAGCTCTCAGATCTTACTGTTTTTTTTTTAATTGTGATAAAGCATA   
EHHHFGDGHFHF8EEFB=B@=GHFFBAA@8??>IHHEIHEH>EHH@HFG>GEGHEGHFA<GDGHFFHGHGFCFCE@EEHHHHHGGFHGEGFFFGGGFBDDD   
X0:i:1  X1:i:0  MD:Z:101  RG:Z:20GAV.4  XG:i :0  AM:i:37  NM:i:0  SM:i:37  XM:i:0  XO:i:0  MQ:i:60  XT:A:U BQ:Z:@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
ADAM_formats.types_dict['ADAMRecord'].to_json()

ADAM_genotype = {"variant": None, # c.f. ADAMVariant section
                 "variantCallingAnnotations": None, # c.f. VariantCallingAnnocations section
                 "sampleId": "GENOTYPE", "sampleDescription": None, 
                 "processingDescription": None, "alleles": ["Ref", "Alt"], 
                 "referenceReadDepth": None, "alternateReadDepth": None, 
                 "readDepth": None, "genotypeQuality": None, "genotypeLikelihoods": [], 
                 "expectedAlleleDosage": None, "readsMappedForwardStrand": None, 
                 "splitFromMultiAllelic": False, "isPhased": False, "phaseSetId": None, 
                 "phaseQuality": None}

##fileformat=VCFv4.1
##fileDate=20140416
##source=23andme_to_vcf.pl
##reference=file://23andme_hg19ref_20121017.txt.gz
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
#CHROM  POS     ID      REF     ALT     QUAL    FILTER  INFO    FORMAT  GENOTYPE
chr17   7621777 rs1544724       T       G       .       .       .       GT      0/1
chr1    82154   rs4477212       a       .       .       .       .       GT      1/1
chr1    752566  rs3094315       g       A       .       .       .       GT      0/1
chr1    752721  rs3131972       A       G       .       .       .       GT      0/0
chr1    776546  rs12124819      A       .       .       .       .       GT      0/0
variant = {"contig": {"contigId": 0, "contigName": "chr17", "contigLength": None, 
                      "contigMD5": None, "referenceURL": None}, 
                      "position": 7621776, "referenceAllele": "T", "variantAllele": "G"}

ADAM_variant_schema = ADAM_formats.types_dict['ADAMVariant']
ADAM_variant_schema.to_json()

variant_calling_annotation  = {
    "readDepth": None,
    "downsampled": None,
    "baseQRankSum": None,
    "clippingRankSum": None, 
    "fisherStrandBiasPValue": None,
    "haplotypeScore": None,
    "inbreedingCoefficient": None,
    "alleleCountMLE": [], 
    "alleleFrequencyMLE": [],
    "rmsMapQ": None,
    "mapq0Reads": None,
    "mqRankSum": None, 
    "usedForNegativeTrainingSet": None,
    "usedForPositiveTrainingSet": None,
    "variantQualityByDepth": None, 
    "readPositionRankSum": None,
    "vqslod": None, "culprit": None, 
    "variantCallErrorProbability": None,
    "variantIsPassing": True,
    "variantFilters": [],
} 

ADAM_variant_calling_annotations_schema = ADAM_formats.types_dict['VariantCallingAnnotations']
ADAM_variant_calling_annotations_schema.to_json()

ADAM_nucleotide_contig_fragment_schema = ADAM_formats.types_dict['ADAMNucleotideContigFragment']
ADAM_nucleotide_contig_fragment_schema.to_json()
contigName = 20
contigId = 0
fragmentSequence = TTCG……………AACCGGCTCGA
contigLength = 20000000
fragmentNumber = 4
fragmentStartPosition = 40000
numberOfFragmentsInContig = 200020 9999944 A 35 ,,,....,.,,..,,...,.,,,,,,,.,,.,...
20 9999945 C 37 ,,,....,.,,..,,...,.,,,,,,,.,,.,....,
20 9999946 T 37 ,,,....,.,,..,,...,.,,,,,,,.,,.,....,
20 9999947 C 38 ,,,....,.,,..,,...,.,,,,,,,.,,.,....,,
20 9999948 T 40 ,,,....,.,,..,,...,.,,,,,,,.,,.,....,,.,
20 9999949 T 41 ,,,....,.,,..,,...,.,,,,,,,.,,.,....,,.,.
20 9999950 A 42 ,,,....,.,,..,,...,.,,,,,,,.,,.,....,,.,..
20 9999951 G 43 ,,,....,.,,..,,...,.,,,,,,,.,,.,....,,.,...
20 9999952 T 46 ,,,....,.,,..,,...,.,,,,,,,.,,.,....,,.,.....,
ADAM_pileup_schema = ADAM_formats.types_dict['ADAMPileup']
ADAM_pileup_schema.to_json()

ADAM_variant_effect_schema = ADAM_formats.types_dict['VariantEffect']
ADAM_variant_effect_schema.to_json()

ADAM_nested_pileup_schema = ADAM_formats.types_dict['ADAMNestedPileup']
ADAM_nested_pileup_schema.to_json()