From 5b174ea47ab3cbc338ce515a4f6c668c0e67e425 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Bart=C5=82omiej=20=C5=9Acise=C5=82?= Date: Tue, 20 May 2025 19:22:11 +0200 Subject: [PATCH 1/6] Initial version of base sequence quality --- Cargo.lock | 5 +- Cargo.toml | 1 + docs/notebooks/example.fastq | 800 +++++++++++++++++++++++++++++++++ docs/notebooks/example.parquet | Bin 0 -> 20352 bytes docs/notebooks/tutorial.ipynb | 211 ++++++--- polars_bio/__init__.py | 2 + polars_bio/quality_stats.py | 25 ++ src/lib.rs | 36 ++ src/operation.rs | 133 ++++++ 9 files changed, 1145 insertions(+), 68 deletions(-) create mode 100644 docs/notebooks/example.fastq create mode 100644 docs/notebooks/example.parquet create mode 100644 polars_bio/quality_stats.py diff --git a/Cargo.lock b/Cargo.lock index d84574f0..2527967b 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -5424,6 +5424,7 @@ dependencies = [ "pyo3-log", "rand", "sequila-core", + "serde_json", "tokio", "tracing", ] @@ -6346,9 +6347,9 @@ dependencies = [ [[package]] name = "serde_json" -version = "1.0.138" +version = "1.0.140" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d434192e7da787e94a6ea7e9670b26a036d0ca41e0b7efb2676dd32bae872949" +checksum = "20068b6e96dc6c9bd23e01df8827e6c7e1f2fddd43c21810382803c136b99373" dependencies = [ "indexmap", "itoa", diff --git a/Cargo.toml b/Cargo.toml index 47d326b7..919b38d5 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -43,3 +43,4 @@ coitrees = "0.4.0" fnv = "1.0.7" async-stream = "0.3.6" rand = "0.8.5" +serde_json = "1.0.140" diff --git a/docs/notebooks/example.fastq b/docs/notebooks/example.fastq new file mode 100644 index 00000000..4b357e52 --- /dev/null +++ b/docs/notebooks/example.fastq @@ -0,0 +1,800 @@ +@SRR9130495.1 D00236:723:HG32CBCX2:1:1108:1330:1935/1 +NCAATACAAAAGCAATATGGGAGAAGCTACCTACCATGCTTAAAAACGCCAATGAGCAGNGATTTGTCANCNNNNNNNNCNNNNNNNNTNNTANNANNCTC ++ +#4BDFDFFHGHGGJJJHIIIIGGIIJGJJGIIIIBHIJJJIIJIJJIJDHIGGGIJJJI#-@AEHGEFF#,########,########+##++##+##+2< +@SRR9130495.2 D00236:723:HG32CBCX2:1:1108:1472:1938/1 +NGTCAAAGATAAGATCAAAAGGCACTGGCTTACCTGATTAAGAAATTGTGTAGTCCAACATCAAAATACNTNTNNNNNAGAGNCANGNCAAGCNNANNAAT ++ +#1=DDDDD>DHFH@EFHHGHGGFGIIIGIGGGGIIGIIDDCHIIIIIID@FEGGGIIIIICHIIIIIIG#-#-#####,,;;#,5#,#,,85@A:AB@8>@:@A@9(:((+(834 +@SRR9130495.6 D00236:723:HG32CBCX2:1:1108:2392:1965/1 +CGATAAAGGACTTTCAGTCAACCAACTAGATAATGACCACTGGGCACCCATTCATTATGCATGCTGGTAAATAAATTATTCTGTTCAGGAACATTGAACTC ++ +CC@DDDBDFFHHHJJIJJIIJIJJJIIIHGIGGHCGGIGHHAACC??A< +@SRR9130495.11 D00236:723:HG32CBCX2:1:1108:4089:1977/1 +CATTCCAACCAGCCGCTTAAAGTTTCTAAAAGAAGCTGGTCATGGAACCCAGAAGGAGGAGATACCTGAGGAGGAATTAGCAGAGGATGTTGAAGAGATTG ++ +CCCFFFDFHHHHHJIJJJJIIJJJJJJJJJJJIIGJJJIGHGIJJIJJIJJIIFHGHIJJGHEHHFCEFFDEDDBDDDDDDDDDDDBACDDDDDDDDDDDD +@SRR9130495.12 D00236:723:HG32CBCX2:1:1108:6197:1936/1 +NCTTAAAGGCAAGGTGCTCGGCTTCCGCTATCAAGACCTCCGACAGAAAATCCGGCCTGNGGCTAAAGANCNNNNNNNNANNNNNNNNCNNGGNNCNNGGC ++ +#1BDFDEFGHHHHIJJJJJJIJJJJJJJJJJIJJIGIJJIJJJIJJJJHIJJHHHFFDC#,;?BBDDDD#,########+########+##++##+##++8 +@SRR9130495.13 D00236:723:HG32CBCX2:1:1108:6415:1939/1 +NTGTGTATGGGGATGAGGAAGGATATTAATATGTTCTATTTGAGATTTAGGGATTACATTTGTTTTTGCNCNCNNNNNTTTTNTCNTCATTTGNNGTNAAT ++ +#1:ADDDFHGGHFGGBHIGGIIJJEIIIJJIJIJJJFGJIIHGHGDGHJGHIHIIIIJJIIHFHIIJJJ#-#-#####,,;?#,;#,8?DDEE##,+#+2< +@SRR9130495.14 D00236:723:HG32CBCX2:1:1108:6361:1952/1 +TCAGATCTTATTTTAATAGTTGACTTTACCTCTTCTTTGACTTCCTCTTCCTCGGTCTCAGTAGATATAGATGGTACCTTGGGCTTATGCCATGAGATCTG ++ +CCCFFFFFHHHHDHIIIIJJJJJJJJJJHIIIJJJJJJJIHJHJII>GHIEGHIIIIJJJIJJIHHIJJJIJIGHIJJGJJHFHHFFDCFFEDCEDCCDEC +@SRR9130495.15 D00236:723:HG32CBCX2:1:1108:6263:1960/1 +CAATATCTGACTGAATGGGCCCATTTTCATAATATTCTGAAACTGTTCATACATGTCTCGCAATGTAAACTGACCTGAAATGCAATACAAAAAAATTCAGA ++ +CCCFFFFFHHGHGGGIIIIJJIJIJJIIIIIIIIEIHGGIEGGHGIJJIJJJCECGHIIIJJJJJIFGHIIHGIIJJHHGEFHDFFFFFCCCDDBBCCDCA +@SRR9130495.16 D00236:723:HG32CBCX2:1:1108:6338:1988/1 +AGACACTAAAATGCCATGTATGAGACTACATAGACATACCAATTTACAACACAAACACATGAAATATACATGAGAAAACATTAACTTACTTCCAGTTGGGA ++ +C@CFFDDDHGHHHIIIIHJJIIHIHGIJJJJJIJJJIJIIHEHIIJJJJJJIJJIIIIIJJDHIJIJGIJIJJIJJHHGGHFFFFFFFEEEDCCC@ACCBA +@SRR9130495.17 D00236:723:HG32CBCX2:1:1108:6742:1944/1 +NGAAACACTCTTTTTCTGGAATTTGCAAGTGGAGATTTCAGCCGCTTTGTGGTCAATGGTAGAAAAGGAAATATNTNCATATAAAAACTAGACAGAATGAT ++ +#1BDFDFFHGHGHJJJJJJJJHIJHHIJIIJJIJJGIJJIJIIJJGIJIHIJJJIIJJJHIEIIJJJJIHDEFH#,#,5=ADEEEDCDDDDDDDCDD5@CD +@SRR9130495.18 D00236:723:HG32CBCX2:1:1108:7076:1942/1 +NGTCTAAGAATGAAGTGCTTATGGTCAACATAGGCTCCCTGTCGACAGGAGGAAGAGTTAGTGCAGTCAAGGCTNANTTGGGCAAAATTGTTTNNACCAAT ++ +#1:B:BDDHHFFHIEGGIGIDEHGEEGIGHIIIFACHIGIGCFGEHIGGHHGHGH@DCGGIDEIIIIFFHHAEE#,#,5=@BBC@BCCCCCCC##,+8?BC +@SRR9130495.19 D00236:723:HG32CBCX2:1:1108:7440:1957/1 +CACCTGATGTCCCACAGTCCTCATAGACACTAGCACTGACTGCTGGCCATCGTCTCAGCCAGATGATGTTGACCTGCTAGCTTTTCAATTAAATTATTAAA ++ ++=144=DD>D4CCD?E@AECFFIIIIEI?E+??;3EBDECEIBEECDIEIIADDDDEIDCDCA;=A@CECE7ACD=(;;A@DA@A@A:ADAAAD>AADBB> +@SRR9130495.20 D00236:723:HG32CBCX2:1:1108:7363:1977/1 +ACTCATAGAGTTGAAGATTCCCTTTCATAGAGCAGGTTTGAAACACTCTTTCTGGAGTATCTGGATGTGGACATTTGGAGCGCTTTGATGCCTACGGTGGA ++ +CCCFFDDDFHHHHJJJIJJJJHHIIHGIJIJJJIGGHGIJIJJIIIJJJJJIJIGBFDHIIJIJGIJIGGCHGGIJHIIHHHFECDECEEDCDDCBD9?B? +@SRR9130495.21 D00236:723:HG32CBCX2:1:1108:7298:1979/1 +AACCGTCGCCAGGTACCATCCCAGAGAACTCTGTCTTCCTTACTTATAGCCAAGTTGCCGGCAGATCACAGCTGCATGCTTATCGGTCCATCCGTCATCGC ++ +BCCDDDFFGGFGFJIBBHHIJJIIHHIIIGJIIHIIJJJIIEIGHJIIJJJJIIHHGIJFGHGFDDCEECDDDDDDDDDDEDDDDDDDBDEDDDCCC +@SRR9130495.23 D00236:723:HG32CBCX2:1:1108:7307:1995/1 +TAATTTGGTATATGTCTTTTTAAAGGCATTTTTATTAGATATTTCCTTAATTTACATTTCAAATGTTATCCCCAAAGCCCCCTATAATCTACCCCTGCCTT ++ +;?7DD?;DBFHHFIA;A@AABBB@B?2<58+:?<<9<@: +@SRR9130495.25 D00236:723:HG32CBCX2:1:1108:7870:1955/1 +CTATCCCGTCGGGTGACTGTTTCCTGCTTTGCAGTTATTCAGTGGCAGAGCGTGGCGCTCTAATTTCTGCTTTCCTCTTTCCTGCAGATTGTGTGCTACAT ++ +CCCFFFDFFHHFHBHGJIIIHGIIIJJIJIIIIJHIIIIEIJJIIJJJJIJIJIJDFEDDDCEEEEEEDDDDDDDDCDDDCDDDDDDDCDDDCDDDDDDDD +@SRR9130495.26 D00236:723:HG32CBCX2:1:1108:8157:1994/1 +GAAGTGCTCTTCGTTACTACTTAAATCCCCCTGGGCATGTTTCATTATTTTACAATTTGTGCAGAACCCTATCCAAACACACATGGAGTACAAATGACTTC ++ +CCCFFDDDDHHDHHIGIIBIHHHJJJ@FGGA?@CCCCCC@CCCDD +@SRR9130495.27 D00236:723:HG32CBCX2:1:1108:8703:1937/1 +NATAAAAAAATAACATCCTTTCCTCCTAATAGCTTAATTATTTGAAAAAAAATATTTTCNAATCACATGNANNNNNNNNTNNNNNNNNTCTTTNNTNNCCT ++ +#1=DFDDDHHHHHIIJJJJJFIJJJIGIEGIIJJJHHIJJJJJIIJIIIIIJEHEHHHA#,;??AEDDC#,########,########++8??##+##(+2 +@SRR9130495.28 D00236:723:HG32CBCX2:1:1108:8702:1991/1 +CTCAGAGATTAAAAATGAATAACGCCTGCCGGCCAATGAGCGGACTCACAGTCCCTGTTTGTTTGTAAGCTAGGTGATTTTCAATCCACAGGGCAGGCTGA ++ +@@@DFDFFGGDHFGIIBGEHJIIIBGIIIJJIIJIJIIFGGEGIJJJHGGHHHDCFFDDDEDEDDDDDCDDDCBDDACDEDEDDDDCCCCDGII@GIFGFE???BFBBEHIGGIEEHIHIGGHHGIHA3?CH;?@CB@DCCEDEDD3 +@SRR9130495.34 D00236:723:HG32CBCX2:1:1108:10535:1962/1 +AATACAGAAAAGTTAAGAGCCAGCCCCAGGCGGATTGGATGAATAGGTTGCATCTCTTTCTTGCTTATATCAAATGCCTCTTGGCAGGCTCCTTGGGAATT ++ +???BD:A:3=?B:;?;;CA;;;A3-5>5-5:(::@8/2?8?9>>3(83(+ +@SRR9130495.35 D00236:723:HG32CBCX2:1:1108:11147:1968/1 +GCTGCCTTCTCCCCTCAAGGATGCAGTGGAAGTGTCAACCTGGAGAAGATGCTACACGATGCAGGAGGTGAACTCGGCCCTCAGTAAAATCCAGCTGGTGG ++ +CCCFFFDFHHHGHJFEGIHEGHIGIIGCCGGGIIJJIJJGGGGHCGIF>DGHIIIEHHGIIJJDBGHFHFCCFFFDDCDDDDBDDDCCDEDA:CACD@CDD +@SRR9130495.36 D00236:723:HG32CBCX2:1:1108:11124:1986/1 +GACAGGGTTTCACCATGTTAGCCAGGACGGTCTTGATCTCCTGACCTCGTGATCCGCCTGTCTCGGCCTCCCAAAGTCCTGGGATTACAGGCGTGAGCCAC ++ +CCCFFFFBFFFHGGHGHIHIIGIJFJJJIJHIIIFGHGGIIGEGHIH@@FGGIJIIGGIHIEEHEEFFDDCCCCBDDACCDCDBAACCDDDBDDDDBDDCC +@SRR9130495.37 D00236:723:HG32CBCX2:1:1108:11773:1947/1 +TACCTGCCTCTGCCTCTCGAGTGCTGGGATGAAAGATGTGCACACCCCCACCACCACCACCACCACTGCCTGGCNCNGTTTTTGATTTCTTATTCTCCAGA ++ +CCCFFFFFHGHHHJIJIJIIEHIJJIJJIJJIHIHJJIGFIIIIJBHHGFF>HGIJIEHHHFGDEEEEEECC?B#,#,,5?@HF1CGBGHCDDFF7=DEG.?AAC?3@EED;6@;>(-;@AC?31(,5?(4::A>AC +@SRR9130495.46 D00236:723:HG32CBCX2:1:1108:14226:1948/1 +AACCTGCACCCAGAATGGCAGGAGGTCCTGGTGGCCCAGGGGGTCCTGGTGGTCCAGGAACACCAGGTCTCCCANANCCAGGTGGCCCAGGCAGGCCTGGA ++ +??1D1BDDHFHHBBHGGGAFG)AF1:?CEGD@BGAFDGIGGB'5;CE77?=???>>CC3#+#++228>>28((2(>ACC?CCDDCCD@ +@SRR9130495.48 D00236:723:HG32CBCX2:1:1108:15049:1993/1 +TGACCAAGAACTCACAGAGATCCCCCCCCCCAGGGCTAAGATTAAAGGCATGTGCCACTGCCACTGGATAGATATTATCTTTTATTTTACCTGACTGGTTG ++ +@CCFFDDDHDFHGJJJJIJGIHHHIIJJIJFE=(5;?@>@A@;@C>CAB=@<:@C@CA:>CC3:>CB<@@?344>>@@A>CDC>ACDD:>4>>>ACD?CC( +@SRR9130495.49 D00236:723:HG32CBCX2:1:1108:15487:1951/1 +ACGGAGGGTGGGGCTGGGTGATTGTGGTTGTCTCCTTCTTCACCCAGTTCCTCTCTTACGGATCCCCGTTAGCTNTNGGGGTCTTGTATGTAGAATGGCTG ++ +BCCFD@DFDFHHGDGIIICGHIIJJIJGHIIJIIJJJIJIJIJEHIGHHHHHHHFFFFFFDDCDDCD@DDDDD:#+#+2<@8GHIIIEHGACFHIIGGIBHIIHHC??BDC@BBCECDCE63>@@CACCDD@ +@SRR9130495.51 D00236:723:HG32CBCX2:1:1108:15923:1957/1 +CCGCAACTGCCATGGAGCCACAGCCTGGTCCGTAATAGATGCAAAGCTTCTCAATAGTCAGGGGCGTGGTTTCGCGCAGCTTGGAGGCCAGCAACAGGCAG ++ +CCCFFFFFHFHHHIIFIIIGIIFIIGJIIJJIFHGHIIIIIJIIFIJIIIJJJJJJJGGIGJFFH@?BBBDBCDDDDDDDDDDDDDDDDDDDDDDDDDDD? +@SRR9130495.52 D00236:723:HG32CBCX2:1:1108:15793:1968/1 +GTTTTTCCACAGACCTCTGATCTCTTACATTCGAAAGTTCTACTACTATGATCCTCAGGAAGAGGTGTACCTGTCCCTAAAGGAAGCGCAGCTCATTTCCA ++ +BB@FFFFFFFHGDGFHJJJJIFIIGGG@FIIIGIBGGDHIIIJCHIIJJDHIIJJJIIIGIIJJFHHIJCHGDHIIHHHHHFFFFFDDDDDDCDDDDEDDE +@SRR9130495.53 D00236:723:HG32CBCX2:1:1108:16082:1968/1 +GAAACTTGTTTGTGACGTGTGTATTCAACTAACAGAGTTGAACCTTTCTTTTTACAGAGCAGCTTTGAAACACGCTTTTTGTAGAATCAGATCGGAAGAGC ++ +@<@ADDDECBFFHIFEGDG;EEHHB@FHGHIIGGEHGGHGGEGEGICGCEFFDB@D>AE>ADC@CBBFGIIJJJJIHHIJJGH?DGGHCFHHIJJEEIIJAHIJJFG=FGGGIFHHIIDHGEHIHHHHGHGEB;?CDCECEEDD +@SRR9130495.55 D00236:723:HG32CBCX2:1:1108:16048:2000/1 +AGGACAGGAAGGACGCTTTGAGATATGATTTCACAGGCGACAGTGAGAGAAAACCAATGTCTTTAATGCATTTCTCTGCAGCATGTGACAAACTTTCAACA ++ +CCCFFFFDFHGHHIIJJJJIFGGIJJIIIIJIJIHJIIJIHGIJJIJGGIIJIJJHHHHEDCDFFFEEEDDEDEEDDDDDDDDDDCDEDDCDDDDDDCCD@ +@SRR9130495.56 D00236:723:HG32CBCX2:1:1108:16580:1970/1 +GCCTGTACTCCCAGCTACTTGGGAGGCTGAGACAGGAGAATCACTTGAACCCAGGAGGTGGAGGTTGCAGTGAGCCAAGACCGGGCTATTGCACTAGATCG ++ +@@@FFD?DBFFHHIIEHCIJIFIJHEDCH@GGEBFHHIJJIIIIIJJJJJIFHFHF@G-6@AAEDFFF>AEC@@A?;=?BCD6/<@>BAAC>:@C:CDDA? +@SRR9130495.57 D00236:723:HG32CBCX2:1:1108:16594:1971/1 +TCCTCTGACTTTGACACTAGTGTTGACCTTGCATGAGGAGATGTTCTCCATTTGGACTAACCTGATGTACACAGACGTTACACTTATCACAGAATACCATA ++ +CCCFFDDFFHFHFHIJIJJIHIIIJJJJJIJJJJIIIIHIGIIJJJIGIIJJIIJEHIIJIJJJIGIJJJEIHJJIIHHEFFFFFFCCEEEEDCDDDDDDC +@SRR9130495.58 D00236:723:HG32CBCX2:1:1108:16808:1998/1 +ACCAATTTTCCCCTCCCCTTCCTCCCTCCCTCCCAGCCCCCTTCCTCTCTCTACCTCCTGTTATTGTTTTGTTCCTTGTTCTATGTAGGATTGAAGCATCT ++ +@@@FFDDFHDFHGIJJJJIIGGDFHI>DGC;DHI9??FHIJIIHG>>3>;ACCCC9:@>@:>AA +@SRR9130495.59 D00236:723:HG32CBCX2:1:1108:17428:1967/1 +TGCATGGTGCTGAAAGCTTTGTTGCAGCTTTTCTTGGGATTGCTTAGCTGCTCCGGGTCGATCCACTTGCAGATGAGCTCTTGCTTGATGCACTGCTGCCG ++ +?<A3>A>>AAAA?3 +@SRR9130495.60 D00236:723:HG32CBCX2:1:1108:17508:1988/1 +TTGTTCAGAAAAAAGTATCTTGAAACCAAAAGAACTGGGATCTTGTTAAATGCAGATTCTGTTCATTAGGTATAGGTATGCAGTCTTACAAAATGAGGTAG ++ +CCCFFEFFHHHHHJJJJJJJJJJJJJJJJJGIJJJJJJJIIJJJJJJJJIJJIJJJJJJJJJJJIJJIIIEHHBHGHHEFFFFFEEEEDEEDDCDCDDCDC +@SRR9130495.61 D00236:723:HG32CBCX2:1:1108:18425:1951/1 +CCACTTAATAAATCACCTATCAAGTTGAATTATTTGTGCAAAGGCACTAGGCTGAATAGAGACCACTCAGTAGCNTNTTTTTAATCTTGCTAAGAAAGAAT ++ +CCCFFDDFHFHHGJIIJJJJJJHGHEHHHIIJIJJIJGJIHIJHEFDHGHIFIJJGHIJJJFIGGIHIIIJIII#-#-5@DFFEEEDEEEDEDDACDCCBC +@SRR9130495.62 D00236:723:HG32CBCX2:1:1108:18468:1964/1 +CTATTGACTTTTATTAGAAAGGGTCTTGTTGCATAGGTAGGTCTTTAACAACCATCTCTTAAAGGGCTGGGATTGCCAGAGTAGGCCAACACGCCCAGCTA ++ +CCCFFFFFGGHHGIGIJIJJIIIJJJJIJJIJJJJIIJDGEHGIIJJJIJJJHIJJIGFHGIJGEGIJJJIIHHHHHHFFFFFEDEEDDDDDDDBDDBBD@ +@SRR9130495.63 D00236:723:HG32CBCX2:1:1108:18615:1941/1 +NAGCCGAGAGGCGCCGGCTCACCTGCCTGGGTCCCGGCCTTTCTCCTGCAGTGCCAGGGATTCACCTGANGNCNNNNNNTCTNCTAGGCAAGCNNATNCTT ++ +#1:DDFDDHHHHHIIJIIFGJJIEFHHIHHIIIJJIEHFFEEEEEEE?DFF;5:AAB9>29(#+#++8++4>>:@>AA:@1<@@A(:4? +@SRR9130495.72 D00236:723:HG32CBCX2:1:1108:1440:2047/1 +GCTGGTGCAGGACACCAGAATCCGCTCGATCATGCTCCCTAGAGAGGAGGGGCACAGTGAGTACACATAAGCACATGTACACACACACCCAGGACCCAAAG ++ +CCCFFFDEHHGHHHIIIIIIIIJJIJJJGEGHIJIJJJJIIJGHIIJJJIDFEDFFFFEEEEDEDDDDDDDDDDDDDEEEEDDDDDDDDDDDDDDDDDDDA +@SRR9130495.73 D00236:723:HG32CBCX2:1:1108:1468:2080/1 +ACTGTCTTTTTTTTAAAACAGGTGATTGCCCGTTGATTGTTCAGTTTGCTGCTAATGATGCAAGACTTTTATCTGATGCTGCCCTGCTAGTCTGTCCCTAT ++ +CCCFFDDEHFFDHIEIIJHGCGFHCIIHIIIHH@FGGIHIGHIJJIJGIIIJBHIGEIHGHHGGHFFFFFFEEEEDEDDDDDDDDDDDCDDDEDDDDDDDD +@SRR9130495.74 D00236:723:HG32CBCX2:1:1108:1333:2084/1 +ATGAGCACACAAGGGATGATCAGATTGATGGTGTAGAAGAGTGGCTTGCGCTTGATGATGAAGTCATAGGTCACGTCCACATAGCTGGGGTCCTGTGGGTT ++ +CCCFFFFFHHHHHJIJHDIIHHHIIJIIJJJJJIJJIIJIJJJIBDGIJJJJIJJJJGIIIJIAHEEHEFFFFF>EDCDDDDDCDDCDDDDDDDDDCDDDD +@SRR9130495.75 D00236:723:HG32CBCX2:1:1108:1447:2137/1 +TCCACTTGTACAAAAAATTACAAAAATTAGCTGGGCATGGTGGCACACACCTGTAGTCCCAGCTACTCGGGAGGCTGAAGTGGCAGGATCACTTGAGGCAG ++ +CCCFFEFFHFHGHJJJJIJJJJIIDIEHJIIJJJIGJJJHGJIIDHIDGIJIJJIGHIJJJJJHHHFEEBDCDBBDDDDDDDDDDDDDDDDDDDDDCDDD@ +@SRR9130495.76 D00236:723:HG32CBCX2:1:1108:1499:2151/1 +GAGAAAAAGCATCCCTTTAATAAGGCCGCCCCGGTTCCAAATCAATCCTGGCATTGCAGGAGGCAAGGGGGAAACACAGCCACGAAATTGGATTAGCTCTT ++ +CCCFFFFFGGHHHJIIJHIEIJJIIIIJIIGJJIHJIJJIGIJJJIIJJHHHHHFFFFFFDCDBDDDDDDDDDDDCDDDBDBDDBBDDDDDCDDDDCDDCD +@SRR9130495.77 D00236:723:HG32CBCX2:1:1108:1280:2166/1 +GCCTTCTTCCCAGCAGCAATATGGCTCTTTCTTCAGCTCTTATCAGTCACATCCATCAACGAGTGGCTTTTAAAAGGGTATGTTTAAACCTTTTGACGGGA ++ +CCCFFDEFHHHGGJIJJIJIJEIJJJJJIFGIIIIIIIJIIGIJJGIIBHIJJJJIJIEH>CG;CHCHGHICHFFFFFFDDC;@CCEEDDCDDCCDDDDDB +@SRR9130495.78 D00236:723:HG32CBCX2:1:1108:1458:2216/1 +TTTCTTTCCACACATCCCACCTAACACCCAAACTAAGCACTCAGTGCTTGGAATCTCCCCACCCATTCCCTCACCCCTGCTCTTCCATCATTTCCTCCAGC ++ +CCCFFFFFHHHHHIDHIIJJJJHIIJFHIJIJIGIJHGIIIIGJEHIGIIJCGEEHJJJJJJJDHEHHGFFFFFDCDDDDDDDDDDDECCDDEEEDDDDDD +@SRR9130495.79 D00236:723:HG32CBCX2:1:1108:1634:2001/1 +TGTGCATTTCTCATTTTTCACGATTTTCAGTGATTTCGTCATTTTTCAAGTCGTCAAGTGGATGTTTATGATTTTCCATGATTTTCAGTTTTCTTGCCATA ++ +CCCFFFFFHHHHGJJJJJJJJIJHGIJJJJIIIIJJIGIIJJJIJJJIIFIIJJJJJIJJJIJIIIIIIJIJGJJJJFHHGGHGFFFFCEFFDEEDEEDDD +@SRR9130495.80 D00236:723:HG32CBCX2:1:1108:1566:2120/1 +GGACGAAGTAAGGGAGGAGCAACTGACAACATTCATCTTGTCTGTCTCCTCCACGTCCCGAGGTACAAGGCGGATGTCATTCTTACTAATTTTTTTCTTCT ++ +CCCFFFFFHHHHHIIJJJJIJJJIJJJJJJJIFIJJJIJIJIJIIJJIJIJJJJIJJIIJHHFFEEEEEDDDDDDDCDEDEDEEDDDEDDEECDDDDDDDD +@SRR9130495.81 D00236:723:HG32CBCX2:1:1108:1863:2047/1 +AAATTCGGACCCCTTGGGTGGAATATTCCTTACGAATTCAATGAGACAGATCTAAGAATCAGTGTGCAGCAACTCCACATGTTCCTGGACCAGTATGAGGT ++ +@BCFFFFFHHFHHHIJIJGIIIJIJJJJIIJJIIIIJJJJGIJJJIJJIEGIIGIJIJJJEGHHHGEEHFFFFDEEEDDDDEEEDDDDDDDBBCDDCCCCC +@SRR9130495.82 D00236:723:HG32CBCX2:1:1108:1844:2145/1 +TAACTCTCTGCCTGCGATGTCCCTACCTTCCAGAATGGTGCCATGACAACGGTGTCAACTACAAGATCGGAGAGAAGTGGGATCGGCAGGGAGAAAATGGC ++ +@CCFFFFFHHGHHJJJJJJJIJJJJJIJIIIHIIIJIIJJJJIJIIJIJJJJJJJJJIJJIIJEHHGHHFDFDDDDDCDCDDDDDDDDDDD?>BDDDDDDD +@SRR9130495.83 D00236:723:HG32CBCX2:1:1108:1772:2188/1 +GAGGTAGGGGTGTGTGTGAATGGGTGAGTGTGTGCCTATGCTTGTATGCCATATGAGAGAAAATGCAGCATTTAAAATCAGTGGTTAACGGCCAGCACAGT ++ +B@BFDFDDHHDDHHGIGIJIJGIJ:CFHHIGGIJJIEHGIIIJIIIFHGIJBHIJJJJJJCHHIHHHHHGFDFFFFEEEECEEDDDDDDDDDDBDDDDDCA +@SRR9130495.84 D00236:723:HG32CBCX2:1:1108:2103:2085/1 +TACAAATGTGCCAGGCACTCTTCTAAGTCCTCACATGCATGAAGTTATACAACTCTACAACAAACCTAGGAATATAAACTGAGGGCAGGGACCCCCAGCAA ++ +CCCFFFFFHHHHHJIJIJJJJJJJJJJJJIJJJJJJIJJJIJJJFHIJJJJIIJJJJJIJIIHIICHHIJJJIIIJJHHHFHHFDDDDDBDDDDDBDDDDD +@SRR9130495.85 D00236:723:HG32CBCX2:1:1108:2067:2091/1 +ACCAGCCCTGCTGCCACCCAGCCCACGTCCCGCGCGCCACCCATGCTGCTGCCTCGGAGCTGCAGGGAGCCGGGGAGCCAGGGCCACACGCAGGTGCAGCT ++ +?@@D?A:BF8DDFFFFFFFFAECBF@GFECAEFIIIIIIFBE?DBBD;@CCCCBBBBBB@B::AABBBBBB7>BBB>@BB?B>B>BB?/?A?BCCCBDDDD>@ +@SRR9130495.87 D00236:723:HG32CBCX2:1:1108:2387:2038/1 +GGCTAACCACTGCCTTGTCAAGTTGTGTAGAGTGAGATTCAGGGGTGTTGAAGTAATGTCCTTGTTACTTGCTGTAGGGCATCTGTTTTCTGTGTATCCCA ++ +CCCFFDDDDHGHGJEIGHHIJIHGGIIHGIIIIIEGBEHGGHIGGAFHIJJJJJJJJIDGHIIGIJJJIIHGFHEHFDFEDCEECDCDDACCCAACDFCCC +@SRR9130495.88 D00236:723:HG32CBCX2:1:1108:2285:2075/1 +CTGAAAGCTGAGCGTGAGCGTGGTATCACTATTGACATCTCCCTGTGGAAATTCGAGACCAGCAAATACTATGTGACCATCATTGATGCCCCAGGACACAG ++ +?;@DDBDDDFFD>ACGED@D8@):E*::??FFC@;FEF>E;CC=CC=@DDD>?;>A>A>A;AB3;A(;@:??DFBCB4<CCD@=BB@-(4812>>> +@SRR9130495.93 D00236:723:HG32CBCX2:1:1108:2748:2098/1 +CATCATCTTTTTTTTTTTTTTCTCCTGAAAACTGTCTAGTAGTTTGATATATTTTGTCCGAGGTTATTTCAAGTGTTTTTTTTTTTTTTTTTAAAACGGTG ++ +@@@DDDDDHHHHHIIFEHIIH8))7)7CEF9).)7;;>B@>9BD;;(6(55>DDDCBCC@/8-084@CC>C(((+4>?CBBBBBBBBBBB>&23:A(5?(( +@SRR9130495.94 D00236:723:HG32CBCX2:1:1108:2733:2156/1 +GACTGAGAAGAACAGAAAGGGAGAGAGAGGCCAATGGAAATACATGAGAAGGGAGAGAGGGAGAGAGAGGGAGGGAGGGAGGGAAGGAGGGGGAGAGGGAG ++ +CCCFFFEFGHHHHJJGIIIJJHIIHHIIJJJJJIJIIHGJJJGIJJJJJJJJJHGGHHIHHHFDFCDCDDD>BDDBDDDDDDD>BDD?BDDDDBDBDBD@D?ABD@BD?BBDDDC +@SRR9130495.96 D00236:723:HG32CBCX2:1:1108:2818:2076/1 +ACACTTCATGGCAACCTGGCTTAGATTCTTCAAAATTTCTGATCCTATACCAAAGCCTCTGTAATCACTCATCACGAAGAAGTCTTCAAGATACAGTAACT ++ +CCCFFFDDHGHHHJJJJIIJIIHIJHGCHIFEGGJJJJIJIGIIJJJIIJCEHIIIIJIBGHGGIIJIIBFGGGGHHGHFFFFDEEDECEDDDDCD>CCDE +@SRR9130495.97 D00236:723:HG32CBCX2:1:1108:2848:2112/1 +AACCTCTTCTCTTTGTCTTTCTCTTTATCCTTCTCCCTCTTGCCAGGACTGGACTCGCTGGTGATGGTGACGACGCTGGTGGGTAAGGTCTGCGCCCGACT ++ +@@BFFFFEHGHHGIJDEHIJIIIIIIJIFHIIIJEIDHIIIIJJIIIGIEIIJJIIIJFEHFHIIIGIIJIFBEDDBD>=?BB@CACCCAACDDBDBB5;6@;ACBCCCC?@AA>C>?<<<9)?FHFCAG=GFFG>FGGEGHIEEBEDEFC>C@::=BB@BCACCC@CA3:(8@C<8?CC +@SRR9130495.100 D00236:723:HG32CBCX2:1:1108:3014:2117/1 +CCCTCCTGAAAAGGTCCAGCTCCAAAGCCTGACCCGTAGCTGCAGAGAAGAAAGCTTTTCCTCTAAAGGCTGAGGAAAAGATGAAAAATCACTGCTAGAAC ++ +CCCFFFFFHHHHGIJIJJJJJJJJJJJJJJJIJJJIJJIJJJJIJGIDHHIIIJJIJJJJJIJJJHHHHHFFFFDEEEEDDDDDDDDDDDDDDDDDDDDDD +@SRR9130495.101 D00236:723:HG32CBCX2:1:1108:3316:2011/1 +GCAGAGCTGAATGGGCAAGCCCAGGACCCTTTTCAGACATTCTGCTGGCCTTTGGAAAGTGTACTCCTGTTGTATTTGATTACTTTTAGAGGACAGTACAT ++ +CCCFFDDFHHHGHJJJJIJFGIGHJGIJIIIIIHIIGIIJIIIIJJJIIIIJIIIGIJGIJJJJIIJJJEEEE?ECFFFFFFCCEDEEEDDDDDDDDEDCD +@SRR9130495.102 D00236:723:HG32CBCX2:1:1108:3264:2036/1 +GGGTGCTGGAGATAGCCCACGTACACTCCTTCTTGCTGGGGTACTTGTCAGGCCAGTTGGGGCTGGTGATGGTGCCACTGGTGGATGTCACCTTGTGTTCA ++ +=@<=B+AD>BFDFIIDEDGEIGFIIIIIICDFGFFGIII;D?F>?*/9BF>DAF;CFFGI>/:=?>7@BAA:;@5=A5>@=,98?:>@(;:4>ABAB?ABD +@SRR9130495.103 D00236:723:HG32CBCX2:1:1108:3400:2065/1 +TCTGTCTGTCACCAGGTTGGAGTGCAGTGGTAGGATCATGGCTCACTGCAGCCTCGTCCTCTTGGGTTCAAGCAATCCTCCTGCCTCAGCCTCCCAGGTAG ++ +@@BDFFFFHHHGHIJJFHEG@GHHIIAFD@HGGGEHIJJJGIJJGGGIHFDAHHHHJFCGGGHGJI;CHFCEDDFFFCEDEEDDDDDDDD<C +@SRR9130495.104 D00236:723:HG32CBCX2:1:1108:3468:2219/1 +TGCACTTCGTTCTCTTAATGAAACCCTTTGACTTAACCATGACTCCGCTCTGCTCTTGAGTTTGCAAGTGTGTGCGAGTGCCCGAGAGACAGTTTTTTTTT ++ +CCCFFEFFHHHHHJIJIIJJIIJJJIIJJIHIHJJJJJFHFHIIJIJJJJIJIJJJJJGGIIJJJGDIJJJIHHHGFFDDEEEDDDDDDDCCDCEDDDDDD +@SRR9130495.105 D00236:723:HG32CBCX2:1:1108:3722:2006/1 +TCCATAGTTTCGCAGAAGACTTGGAAGGATGTTGATGTATATGCAGGTCCATTATCAGTTTTTAAATTAGATGGTTTTCCCCAAGCTGCCCATGCGTCTAA ++ +CCCFFFDDHHHHHJJIJJIIHGHIGIIDFHHIIIHHIJJIJJIJIJJJJJJIIJJIJG=DHHJJIJIIIJJJJJIGHGHHFFBFDDEEEDDDDDDBBDDDD +@SRR9130495.106 D00236:723:HG32CBCX2:1:1108:3517:2148/1 +CTCTGTTCTGTTCCATTGATCTATATCTCTGTTTTGGTACCAGTACCATGCTGTTTTGGTTACTGTAGCCTTGTAGTATAGTTTGAAGTCAGGTAACGTGA ++ +CCCFFEBFHHHHFGGGIEEHIIJIJJIJJIIJIJJHIIJIIJJIIFHGIIIIJJJJJJFIJIJJJJGIIJIJCHIJIJHGJIJHHHHHHFFFFFFEEDECA +@SRR9130495.107 D00236:723:HG32CBCX2:1:1108:3927:2234/1 +CTGTGCTCTATGTACACGCCCATCTGTTTGCCTGACTACCACAAGCCGCTACCACCGTGCCGTTCCGTGTGCGAGCGCGCCAAGGCCGGCTGCTCGCCGCT ++ +@C@FFFFFHHHHHJJJJGIIJIJJIJEHIIGHGJJIJJJJJJIIGHHIJIJIGHJIIJIHHGFFDEDE?BBDDBDCDDDBDDDDBDD>BDBDDDDBDDDB< +@SRR9130495.108 D00236:723:HG32CBCX2:1:1108:4124:2011/1 +GACTCAGAGCCAGGGCCCGGGAACAGAGATGACTCGAAGGCTAGGGCTCCAGCCAGACTTACCGGCACACGTACACCTCTAGGGGTGGCAGGGTGCTGGGT ++ +CCCFFDDEGGGHHIJIIIJIIIIJIHHJJIJIIJIJIGGIIGIIHIGGIGHBHGHFEFFEECEDDDDDDDDBDDDDDCBACCDDDDDDBDDDDDDDDCD?9 +@SRR9130495.109 D00236:723:HG32CBCX2:1:1108:4130:2090/1 +TTCTATTTCTATAAACTGGCCTATTTTGGGTATTTCATATATATGGAAATATATAATTTGATTTTTTTGTTCTCTTAGCTGTATGTTTTCAGGATTCTTTC ++ +@BBFDFFFHHGHHIJJJJJJJIIIJJJJIJJJIJJJJIJJIIIJJJIJJJJIJIIJJJJJJJJJIHJIJJHHHHHHFFFFFFEEEDECEEDDDDDCDDEDD +@SRR9130495.110 D00236:723:HG32CBCX2:1:1108:4176:2091/1 +AAATTGAAAGTAAATGTATACTGTAGTCCCACGCACGAGTGAATAAAGGGGTGTCTAAAAGGAGTGTGTTCTCTTCCAGGCTGCATCTCTCGGTACTCAGC ++ +;8;ABD?+AA=ADBHIGBHE?ACCCCC +@SRR9130495.111 D00236:723:HG32CBCX2:1:1108:4108:2121/1 +ATGCGGAAGTAGGCAAAAATGATGTGCTAGACTACAAGAATTCCTTTTACAGAAAGTAACAAATACAGAGCCAAGAAAGTTTTTGTTAATTATCACGGTGT ++ +@@@ADADA@AD>FIIBBBFGIBGHJDCIGEGGGHHHIJIIJJJJGHIIEHHEGHJIHGGGIFAGGGGIIG>=CHHFD?@;CCEDDDDCDD>>ACDCB@8<5 +@SRR9130495.112 D00236:723:HG32CBCX2:1:1108:4384:2110/1 +ACACAGGCAGCAATGATGTCTTTACTTCTTTATTTTTTTCGACTTCATCTACAGAGCTTAGCACAGCCATTGGAACAAAATTGGAGCTCAGTGCACAGTTA ++ +@@@FDEFDDFF3CCF?FHCH@DEEGEFHIIFGBGGGDGIHAFBGDDHHIBAGGDGHE@CHAHBFFFFBDD +@SRR9130495.115 D00236:723:HG32CBCX2:1:1108:4445:2247/1 +TCTGTATTCTGTGTCATCTGCCATTCCTTGACTCCCTGCGCCCTTCAGCCCACAGGAAACGTGTGGATGACACACGAGGAGATGGAGTCTCTGACGGCAGC ++ +CCCFFDDDHHHHGJJJJIJJJJBHHJJIIGIIJJJIIGIFIJJIIIIGIJJIIIJIJCHIIJJJIHHFHEFFFFDDDDDBDDCDDCDCDDDDCCCDBBDDB +@SRR9130495.116 D00236:723:HG32CBCX2:1:1108:4698:2005/1 +GAGGGAAGGAGGGAGGGAAAGAAGAAGGGAGAGAGGGAGGAAGGCAGGACTGTCGATGCAAGTACCTCGCTTCCTTGTTCTTAACTCATTTGATTCTTGCT ++ +C@BFFFFFGHHGHIIIJJIBGGDHC@FEGDHIIIHGEHHEGCGIHHHFFFFDEEDDDEDDDDCDCCCDDDDDDDDDDDDDEDCCCDDDCDDDED:CCDDDD +@SRR9130495.117 D00236:723:HG32CBCX2:1:1108:4588:2182/1 +CTGGGGTGCAGTGGTGCAATCATAGCTCACTGCAGCCTCAATTTCCTAGGCTTAAGCATTTCTTCCACCTCAACTTCCCAAGTAGCCAGGATTACAAGCAC ++ +CCCFFADDFFHHGGHHGIIJIJJJJJJHIIJIJJIFIJJEIIJIJIGIJIJJJJJJIJJJIIHIIJHIIFFHGHHFFFFFEDEECCCCBDBDDDDDDCDBC +@SRR9130495.118 D00236:723:HG32CBCX2:1:1108:4964:2029/1 +CCCCGTCTCTACTGAAACACACACACACACACACACACACACACACACACAATTAGCCAGGCGTGGCAGCGTCTGCCTGTAGTCCCAGCTACTCAGGAGGC ++ +;8=:DDDDFFFAFIIFFBEIIEFIFIFIEFFFIIBEGEF?BF<4;A@EE/?;AB>7;7;>@?>B?''5<@;@?;0((4:@>34@@>:4<@>BAB@(948&+ +@SRR9130495.119 D00236:723:HG32CBCX2:1:1108:4831:2078/1 +GCGAAGAAAACTGAAAAAGGTGGAAAATTTAGAAATGTCCACTGTAGGACGTGGAATATGGCAAGAAAACTGAAAATCATGGAAAATGAGAAACATCCACT ++ +CCCFFFFFGFHHFFHGIHDICFHIGGIDHIIJJJJIIIIIGIGHGIJJJJJJJJJJJIJJJIJJIHHHHHFFFFFEEEEEEEDDDDDDDDDDDDDDDDCDD +@SRR9130495.120 D00236:723:HG32CBCX2:1:1108:4877:2117/1 +GCATAATGTTGCCACTGCACTCCAGCTGGGACGACAAAGACTGTCTCTAAAAAAGTAATAAATAAATAAAAGTTTGAAATGCATTGTCCTAGGTTTTAGTC ++ +CCCFFDEFHHHHHIIJJJJJIJIIIGIJJJJJJIIJIJJJIJJJJJJJJJJJJJIIHHGHHHHHFDFFFFFEEEEEEDDDDDCEEDEEDDDDDCDDDDDDD +@SRR9130495.121 D00236:723:HG32CBCX2:1:1108:4918:2158/1 +AAACATGTCAATGGCCAAAAAAAACAGACAATCAAAAAATGGACAAATATATGAACAGACATTTCTCACAAGAGGACATACAAATGGCCAGCAAATATATA ++ +CCCFFFFDHHGHHIIJIJJJIIIJJJJJJJIJJIJJJIIJJJJIJJIJJIHGFHHHHFFFFFEFEEEEEDDDDDDDDCDDDDDDDCCBCDDDDDDDDEEEE +@SRR9130495.122 D00236:723:HG32CBCX2:1:1108:4939:2211/1 +CCTGGTCTCAGCATTCCTCACACGTCATAGCGAGGCCCATGGCTGTAGAAATCCCACCATTCTCTTCTCCCCAGGCCTGGCATCCGTAGAAGCCTACAGCT ++ +@CCFFFFFHHHHDHIDIHIJIJJJJJIGGEGIJIIGIIIJJJGGIJJJJIGGHGIIIJJGHHHHHFFFFEFDEDDDDDDBDDDDDDDDD?CDDDDDDDDAC +@SRR9130495.123 D00236:723:HG32CBCX2:1:1108:5169:2188/1 +TCTGACCCCATGTCCTCAGGCCAGAACCCGGGAGCCTGTCAGAAAAGGTCTCTCACCTAGAGTCCATGCTCTGGAAGCTCCAGGAGGACCTGCAGAGGGTG ++ +??@DD?DFHDFHFIIIIGFHEGGDFFHIBHGIAFIGGIIIFI@CHE@FGH@CDGGIFHEEFCCED@DEEEECCCCCCCCCCCCCCBB8ABC?ACAAABBBB +@SRR9130495.124 D00236:723:HG32CBCX2:1:1108:5192:2231/1 +ACTCTCCTGGCCCACGAGAGAGTCCACACAGGAGAGAAACCTTACCAGTGTCATGAGTGCGGCAAGAACTTTAGTCAGAAATCCTACCTTCAAAGCCATCA ++ +CCCFFFFFHFHHGJJGEFGHGIIEIIFJJJIHGIJIIJIIIJIIGIEHHIGIHIJIIJIIBDFDDDDDDDDDCDDDCCDDDDDDDCDDCDDDDDCDDDDDA +@SRR9130495.125 D00236:723:HG32CBCX2:1:1108:5408:2041/1 +TGTGTGCATCCTCATGTGTCCTTGATAAGTGGTGTGATAAATGAAGGCTTTGCCACATTCCTTACACATGTAGGGCTTCTCTCCAGTGTGAGTCCTCTCAT ++ +@@@DDDEBFHHHHIIIHIFHIDHIIIJHHJIHEGFIIHIJJIIJJEIJGHHIEIEGIIJIJJJGIGHGJJIIGIGIHJEHHHHHFFEFFFCC@CEEDDDDE +@SRR9130495.126 D00236:723:HG32CBCX2:1:1108:5351:2057/1 +CTCTATATATTTTAACAAATGCATAATGTCATGTGTTTACCATTACAGTAGGATAAAGAACAGTCTCATTGCCTTAAAAAGTTCCCTAACATTTTAATTGT ++ +CCCFFDEDFHHHHJIJIIJJJIJJJHJJJJIJIIIHHIJIJIJJJJIIHIJJIIJJJJJIJJJJJJIJIJIJIIJJJJJJJGGIGHHFHFFFFFFFEEEEE +@SRR9130495.127 D00236:723:HG32CBCX2:1:1108:5475:2108/1 +AGCCCAGAAGGCTGGACACACCTCCCCCTCACCCCATCCCGCTCCCCAATCAACCCAGTCCTCAAGAAGCACACTGTGGCTGCTTGCTCTCTTGCCCCCCT ++ +CCCFFDFFHGHGHJJIJJJIGIIIHIGIIJIIJJJIJJJHGIJIJIIIJHHGHHFFEDEEEECCDDDDDDDDDDDDDDDDDDDDDDDDDDCCDCDDDDDDB +@SRR9130495.128 D00236:723:HG32CBCX2:1:1108:5542:2138/1 +TGGCTAGCTACTGCTGCTGCTGCATCAAAGCCCAAATATTCACTGGCATCAGCTGTTTTGTTCTTTAGCATATTAGTAAAGTGCTCATTTAGAGACATCTT ++ +@CCFFFFFHHHHGJIJJIJJJIJJJJJJJJJJJJJJIGGIJJJJJHJJJJJJJJJHIEIIJJJIIJJJIJIIJJJJHHHHHHHF@DFFFFEDEEEEDDCDD +@SRR9130495.129 D00236:723:HG32CBCX2:1:1108:5707:2147/1 +CCAGCATCACTCATGGAACCGGAGGCACTAAGGCCCCTCGGGAGACGCTGAGCAGGTGGGTAGAGGCATACTTCTGGGAGATGGCATCAAGAGCCAGTCAA ++ +CC@FFDDFFDHG>FFFHGGIJIEIIIHIGIGHBHCEHGGGFH@FHGIHHFBFDEEDDEDDDDBDC?BBDCDDDDDC:?A?BDDDCCDC>ACDDDBDDACCC +@SRR9130495.130 D00236:723:HG32CBCX2:1:1108:5614:2168/1 +AAACCATGTCTCTACTAAAACTACAAAAATTAGCTGGGCAACATGGTGGGTGCCTGTATCCCAGCTACCTGGGAGGGTGAGGCACGAGAATCACTTGAACC ++ +CCCFFFFFHHHHHIJJJJIJGJIJJJIJJJJIJJJJJIJJJJIJJHIIHJIJJJJJJJJJJJJJJJJGHFHHF@DDDDDDDDDDDDDDDBDDDDDDDDDDD +@SRR9130495.131 D00236:723:HG32CBCX2:1:1108:5985:2027/1 +GCGGCAGCGGCCGCGATGGAAGAACTTACGGCGTTCGTCTCCAAGTCTTTTGACCAGAAAGTGAAGGAGAAGAAGGAGGCCATCACGTACCGGGAGGTGCT ++ +CCCFFFDDHHDGHFIJFHFFHHBFGHGEIJJJFFDDDDDDDDDDD@CCDCDDDDDAAACACDC@CCDBBBDBD@ +@SRR9130495.132 D00236:723:HG32CBCX2:1:1108:5816:2071/1 +TTTACATATAAGAACCTGATGACCTTTTGTTTTTGTCCAGGAGAGTCCTTCTTGTCTACGAAATGCAGCTATCACAGCAGCTGGACTTGTTTCCTGAATGC ++ +C@CFFDDAFFFHHHHHHGFIHJIJFHHHCAAEHIGHJGCCGHIHEHJJJEHGIIIFIGBBAEHGIJIGIIHHEHHHFFFFEECCABCDCCCCACDDACDA: +@SRR9130495.133 D00236:723:HG32CBCX2:1:1108:5835:2081/1 +CCAGGGCTCCAAGGGGCTGGTTACGAAGTGTCTCCTGCTGCATGAGGTCCCCACGGGAGAGATTGTGGTCCGCCTTGACCTGCAGTTGTTTGATGAGCCGT ++ +@CCDDDFFHHHGHGJGJFHIEHIGCGHIGJGIIIJJJJJJJJJIIIEIJFGIJJIJGCCDDBDDCDDDBDDDDCDDDDDDDD>BACDDDEDDCBD +@SRR9130495.134 D00236:723:HG32CBCX2:1:1108:5841:2101/1 +GGGCATGGTGGCATGCGCCTGTAGTCCCAGCTATTCGGGAAGCTGAGGCAGGAAAATCTCTTGAACCCAGGAGGCGGAGGTTGCAGTGAGCCAAGCTTGCA ++ +@CCF?BDDHFHGHIIIIIIIIIGHGHHIIIFIIIIIGGFGGIIIIEFGGEGHCD>CGGFHHHGGHFFFCDDADDDDDDDBDBCDCCCCA@CCCCBDDDDDD +@SRR9130495.135 D00236:723:HG32CBCX2:1:1108:6165:2044/1 +TTTATACCATTTTTTTTTTTAGCATATATCCTTGTACTTTATAGGAATTATTTGCTTTATTCTCTTGTGACTTGTAAATTGATGTACTTAATTAAATCTTT ++ +@@CBAB;DHFFHHIGIIGGHGFF@?BGB@8=FHEGHIGD=@CGHIA;@EHFHGHBFFFCBD;>(>@A@C>>;>CA;;35>@3;>5>,;3>:;(:@:>:@@C +@SRR9130495.136 D00236:723:HG32CBCX2:1:1108:6059:2069/1 +CTATGACCGCTATGTTGCCATCTGTAGCCCACTGCTTTATAACACTGTAATGTCCCACAAGGTCTGTTCCATAATGATGGCTGTGGTATACTCACTGGGCT ++ +CCBFFDBEHHHHFEGIIGHGIIIJIIHIJIFIJJJJJJJJJJIIJJIJJJJIIHHIJIIIFIHIJFIGIIIHGGHHHHFFFFDEEEEEDEFEDDDCDDDBC +@SRR9130495.137 D00236:723:HG32CBCX2:1:1108:6161:2181/1 +ATGAAGCAACAACCTTATAGGCATTTTAACTCATAGGTTTTAAAACTTAAGGTTATTTTCATAGGAGTCCCTTTTAGCAGAAATGCTCACCACAGGACCAG ++ +@@CGGDD@4???BDHCH@GH<=FHGEGGGIGCEG@E7ACH@:77?C@A@CBAA???2FFFIGIIGIIIIFDADBDDDDDDDBBBBBBCDD@CDDDD?BDDDDDDCDDBDDDBDDDCCCCCBBCCCDCCCCDDAC>ABDDDCDDDDA@C +@SRR9130495.144 D00236:723:HG32CBCX2:1:1108:6837:2146/1 +CCTGTTATTTTAGTTGTTAAAGGTGGCATTCTGTTCTTGTGGCTGTCTTCTTTTAGGTTTGTTGAGGGATTACCTTCTTGTTTTTTCTAGGGCATTGTTCC ++ +BCCFDDFDHFDHFGIIIIJIGIIJJIDGHIJIIJJJJJJGFIIIHFHHIJJJIGDGIGHGHIJICHGGEHGCHGFEHFHHFDDDDDDDDDDDC?CDDDDCC +@SRR9130495.145 D00236:723:HG32CBCX2:1:1108:6804:2189/1 +TGAATCTCTCTTGGCCTCCTCCCCTCTCATGTCCCCTCCTCCCTCCTCTCCACTTACTCCTCCTCCTCCCCTCCCTCCTCCCAGATGGTTCTGTGTCTTTT ++ +CCCFFEFFHHGHHJJJIJJIJJJJIJJJJJIJIFIJIGIIGGJIJJIIIIIGIIJIJIJGIIIJIJJHHHFDFFDCDCCDDDCDBCCDCDDD@ACCCDDDD +@SRR9130495.146 D00236:723:HG32CBCX2:1:1108:6940:2229/1 +CTTAATGCCACTATCACCACTTCCTTCAAGAGTGAGGGAGAGGAAGAGGAGGAAGAGGAGGAGGAAGAAGAGGAGGAGGAGGAAGAGGAGGGTGAAGGGGA ++ +@@@DBDDEFHHHHGIJIEGGIGECHIFIIJGFHHACCF?D:DFF;?FHH9DFCGGHFG@CA?AB?DD>?A@BDDBB=?B5@BBFCAA@A;;5?BB9;=>;BB?1>A?ABDCC +@SRR9130495.149 D00236:723:HG32CBCX2:1:1108:7167:2101/1 +TTTCATGTTTTAGGTCTTGTAAGCAAGATTTTTCCTGTTGAAAAACTGGTTGAAGAAGCCATCCAATGTGCAGAAAAAATTGCCAGCAATTCTAAAATCGT ++ +?@?DB?DDHHGDHIHHGBIECFDHHE>C7?BB7;@A(5==;88323>CDC9B +@SRR9130495.152 D00236:723:HG32CBCX2:1:1108:7449:2110/1 +GGCTTCAGGAGCTTCAGAAGTTAAGAGCTGCAAAAAAGAAGAAAAAGGATCGGCCAAGTAAAGACTGTTCCAAGTTGGACATGCTTGCTAGAAATTTCCAG ++ +CCCFFDFEHGHHGJIIBEEHHIIIHHIGGJIIJIIGIIGGHIIIJIGGGGGGGIIJJHHFHHGFFFFFEEEEEEEDDDDDDDCDDEDDDDDDDDDDDDEDC +@SRR9130495.153 D00236:723:HG32CBCX2:1:1108:7499:2197/1 +TTCTCATAGTTCAGCTTCCACTTGCGGTAGCTTGTTCCACTTGCGGAACATGTGGTGTTTGGTTTTTTGTACCTGCACTAGTTTGCTGAGAAAGATCGGAA ++ +@@@DDDDEHHFFHABECCAFHHHFDGIHJIIEFGHIIGIGEHEBGG0AEDHGFFFGIDG@EH=ADEBADDE@CCCBCACDDDEEDDCCCBCCC@CC>AB@@ +@SRR9130495.154 D00236:723:HG32CBCX2:1:1108:7309:2205/1 +GGAGGCTGAGGCAGGAGAATCGCTTGGACCCGGGAGGTGTAGGTTGCAGTGAGCCAAGATTGCGCCACTGCACTCCAGCCTGGGTGACAAAGTGAGATATT ++ +BC@FFFDFDHHGFIGIIIJIJJJJJIJJJIIJJJIIIHHIJIJJJIJHHHHGHFFFEBAEECEDDB:@CDDDDCDDDDDDDDDBCBDCDCDDDCDBDCC?ACDD34>C@BD<>>:CB<>@BA8 +@SRR9130495.156 D00236:723:HG32CBCX2:1:1108:7518:2119/1 +TTATCAAAGAGGCCCAAGAGAAACCACTTGTCTGACTTCTACCATATGAGTTTAGAATAAGATGATGGCTGCCTATGAGGAAGCAGGCCCTCAACAGATAC ++ +@@@DDBD4CFFAAHII=G9FDHG;?F@;EEBEFCF>BGBGDBECC@BBBBBBBCCABC@CC +@SRR9130495.157 D00236:723:HG32CBCX2:1:1108:7577:2169/1 +AGTTACTTAATATACCTTAGCCGAAACTTCTGCACTGATTTCCTCCTGTGTTTCAGCCAGCCGCTTTTTGGCAAGTTCGGTTCTCCGATCACACTCTGCAA ++ 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D00236:723:HG32CBCX2:1:1108:7898:2065/1 +GTTGGCTTCCCCCTCCCCTCTCCCGTGAGCTGAAAAGCAACAAGGGCTCCACCAGCCTGCAAAATAAGACTTGGGGGGGGGGGGGCAGGGATTGCTTTTTT ++ +@@@FDDDDHHFHFIJIFHIIJJJDHFGIGIGJAFHIDG>@GGFHGGHJIGCHIIGEEHFGF@CFEECC>;>CCABBD<99B@BD99&)&&+9(3(4>(+:0 +@SRR9130495.163 D00236:723:HG32CBCX2:1:1108:7872:2066/1 +CACGCTGGATGAGTTCCTGTTCAGCGACCTGCAGGCGCTGGAAGTGCTGTTGCTCTACAATAACCACATTGTGGTGGTGGACCGGAATGCCTTTGAGGACA ++ +CCCFFFFFGHGGHJJJJJJJJJJIJJJJJJHJJJJJIJJIJJJIGIJJIJJJJIHHHHHHFFDFFEDDEEEEDDDDBDDDDDDDDDDDDDDDDDDDDDDDD +@SRR9130495.164 D00236:723:HG32CBCX2:1:1108:7826:2191/1 +ATCTCTGGACCCAAACTGGAGGGTGACATTAAAGTTCCCAGGGTGGATTTGAAGGGCCCAGAAGTGGACATTTCTGCTCCCAAGGTCAATATTGATGGGAA ++ +CCCFFEFFGHHGHIHHIIJIIIJJIJIGIIJIHIGIJJJJIJJBFGGIJJIJJJJJIJIHFHHFFF@EEDEEFEEDDDDDDDDDBACDDF@CDEDEDDBDD +@SRR9130495.165 D00236:723:HG32CBCX2:1:1108:7791:2195/1 +GGAGAACAGCGTGTAGAGCACTCACAGTCTGCTGCCTTCAGGTGTGGGAGGCACTGCTCACACTGATCTTCTTCCCGGTGTGTGTGGTGTTTGCCTGGATG ++ +CCCFFDEFFGHHHJDIIJJIFJJJJJIJJJGIJJIIJIIIJIJIIGEGHHJJJJJIIFIJJJHHHHFHFFDDFFEDC>9;?BBDDD?CDDDDDDDCC?BDC +@SRR9130495.166 D00236:723:HG32CBCX2:1:1108:7767:2199/1 +GACTAGCCTGGCCAACATGGCAAAACCCAGTCTCTATTAAAAATACAAAAATTAGCTGGGCATGGTGGTGCACGCCTGTAGTCCCAGCTATTCAAGAGGCT ++ +@@@DDDFFFHHFBHBHBDEHHGGGHIIJIIHIHJIEEEIGHEIHIIGHIIHHGJIEHGI?G@CDHI=CA?BDFFACDCCDFCC32??A +@SRR9130495.167 D00236:723:HG32CBCX2:1:1108:7824:2210/1 +GCACCACCGTGCCTGGCTAATTTTTATATATTTAGTAGAGATTGGGTTTCACTGTGTTGGCCAGGCTGATCTTGAACTCTGGACCTCAGGTGATCCTCCCG ++ +@@@FFDDFDFAHHJJIJIIGHHHHHIIJIIDGG>GHHIGGCGHGIEHGGH>FFHJJJHDGBGHJCGGGHFEHHHHFFFFFECDECEDDDCDDDDCCDDCDD +@SRR9130495.168 D00236:723:HG32CBCX2:1:1108:8205:2084/1 +CTTAGCCGCTGGTGATGCTAAGGGCATGGTCAAAGTGTGGCAGCTGAGCACAGCCTTCACAGAACAAGGGCCCCGGGAGGTGGAGGACTTGGATCAGCTAG ++ +CCCFFDEDFFHHHJEGIDHHIIIIJJJIJGIJJJGHJIJIJJJIIIJIGGIIGIJJIGHIIJGEFHGBEFDDDBDDD;>B2<@BBDBBCCCCDDDDDDDDD +@SRR9130495.169 D00236:723:HG32CBCX2:1:1108:8202:2124/1 +GAGACTCTTGCACACATACCGGGGAGCTGGCTCACCCTGGCCCCTCCATCCTGTCAGACTGAAGAGAACAAGTGTCTTAATTTGGGTTTTTCTTATTATTA ++ +CCCFFEFDHFHGHGJIIIJJIJJJIIIGIJJJJFIJJHGIJGHIIJJEEEHFGFFEFECCEEEEDDDDCCAD>CCDDDDCCD?@@A=?8=?=BA93>CA??B@A????8CD(8&8?()(+224@?@>35 +@SRR9130495.179 D00236:723:HG32CBCX2:1:1108:8868:2131/1 +GTACATTGTATCTTTGTTCTCATTAGTTTCAGAGAAATTATTGATTTCTGCCTTTATTTCATTATTTACCCAAGAGTGATTTGGAAGCAGGTTGTTCAGTT ++ +<;;B?D>DFC:DBF@AEDHHAHHGH:A:AC4?BFFEDA?ABD@ACCD:> +@SRR9130495.183 D00236:723:HG32CBCX2:1:1108:9106:2031/1 +CTAGAAATCCTGGATTTTCAGCACAATAACTTAGCCAGGCTCTGGAAACGCGCAAACCCCGGTGGTCCCGTTAATTTCCTGAAGGGGCTGTCTCACCTCCA ++ +C@CFFDFFGGHHFJHIIJGIHCCEHHIGIFIIHIJJFIGJIGIJJIIJHIFGIJIJFFHHFDAD@BCCBDDDDBCC@CDDCCDDAEFC'8;&+)+((&28&&&+((&)&&++((++8((2(((25(&&&(++(0&&&(+((+4(+ +@SRR9130495.186 D00236:723:HG32CBCX2:1:1108:9230:2213/1 +ACTTAGTGCAGTACCCACTATTCCCGCTCAGGCTCCGAATAGTAGATAGAGGGTTCCGATATCTTTGTGATTGGTTGAGAATAATCAACGATTAATGAACA ++ +CCCFFFFEHGFFHFIICIIFIJHHGBHHGIGJJIJGGHIGFHIGIIJJJIIGJCFIJI@EFDFEDFFCCEEEEDDDBDDDDDDDECCDD@BDDDCDDDCDC +@SRR9130495.187 D00236:723:HG32CBCX2:1:1108:9264:2024/1 +AACATAAGGTTTCTCATAAAACAAAGAAAAATGTCAATTCAGTTGTGAATTCATATTGATACCTGGAACTCTCCTGCTAGACCACCTCTAAAGGCCCAGGG ++ +CCCFFFFFHHHGHIJJIDJIJJJDHIIJJJJIJJJJJJJIJJGCHIIJJJJJJJJJJIHJJJJJJGIJJIJIJIIIIJJHHHHGFFFFFECEEDDDDBBBB +@SRR9130495.188 D00236:723:HG32CBCX2:1:1108:9293:2034/1 +GTGGGGAGGTTTGGGAGTGAGCAGCACACCCCAGTTAGACTCCTGTTGGGTTTCATAGGAGCTGGCTGCTGAATGTAAGAGTGCAGGCTACCCCGGGACTT ++ +@@@FDA;1DAFHFIIFBGIGHGADHIGIIIIIIIIHBFEHIIIIIIIIHIACEEEHHBD@CDECECCBBCCACCCCEECCCCCCCCBBBCCCB?9>>>>BC +@SRR9130495.189 D00236:723:HG32CBCX2:1:1108:9484:2048/1 +ATGTAGAGAGAGGGAAAAAAGGAGAGAGAGAAGGATAAAGAGAAGGATGCACAAGAAGACCAAAATACCTGATCATGTAGGGGAGAGCCTCTGGGAGAAGG ++ +@@@DFDDDCDDFHICFHHDGIIHHCHCFFDGHHDCGIJIH@FFHFHGIIGIJIGIGF@CEBDFF@EECDCCCCCCDDEEEDDDDDDDDDDCDDCD<@BDCB +@SRR9130495.190 D00236:723:HG32CBCX2:1:1108:9388:2219/1 +AGCTGCTGCGAGATGGTGGCTTGCATCTCCTTGGACGGCCGCTTGTTCTCCTTGAAGATGGCAATCAGCGTGCGGCGCTGCAGGTCTGTGAACACGAGGCG ++ +B?>3=?>;@DF@>CACC>;>@A>',,88>>-09599(+8>C95>>>00 +@SRR9130495.191 D00236:723:HG32CBCX2:1:1108:9404:2245/1 +TCCGGCTGGTACCTTCATAACTACAGTAATAGAAGACATTGAGTGCCTCCACCGCAGCTGGCCCTCTCTGTTTGTAGCCAAAGATCAGATCTATCCATTCA ++ +CCCFFFFFHHHHHJJIJIIJEIIJJJJJIJJJIJJJIJIIIIIJJJIJJIIJJJJEIJJIHIGHHHFFFFFFDEEEEEDDDDDDDDDDDDDEDCDDDCDCC +@SRR9130495.192 D00236:723:HG32CBCX2:1:1108:9632:2134/1 +ATAGTGGCTGCTGATGGATGTGCTCTATGCAAGGGAGGTGCTCACTATTTCTGTTCGTCAATTTGTAACCCACGGGAGGAAAGGGAACAAAGAGTGAACAA ++ +CCCFFFFFHHHGAEFFHIICHGIJHGFIIIHIJDGIJJIIIEGIJJIIFHEGHGIICHIIFIJJJJIIJJHHHFFDBDD?BDDDDBDBDDBCDD:@CCCCD +@SRR9130495.193 D00236:723:HG32CBCX2:1:1108:9647:2175/1 +AATACCAGCCCAAGACTTTGGGAGAAGGGAAGAAAACAAAGTAAAATAACTTACCACTTTGGCCCAGTCCGAGAACAAGTGAAAATACCCAGGCTGCCCCA ++ +CCCFFFDFGHHFHIIJJGIIIJDIIJIJJJJIIHIIIIJJJHCFHHFHIJJJJJJGIHJJJIJJHGHHHFFFDDDDDCD>CCDDDDDDCDDDBDDDDDDDD +@SRR9130495.194 D00236:723:HG32CBCX2:1:1108:9552:2194/1 +CCATGCCGACACAGGTAGATGGTACGGGGCTGCACGTGGATGTTCATCAGGTAGTATACAATTCGGCTCTGGATGTGGTCCTGCACTCTGTTCACCAAGAA ++ +@@CFFFFFGHGHHJJJIJJJJIJJJIGHFEGGIFIJIJIIIIIJJIIJJIHHHHHHHFFFFFFFDCDDDC@@ACDDCDDDDDDDDDDDDCDDD@CCDDDDD +@SRR9130495.195 D00236:723:HG32CBCX2:1:1108:9620:2235/1 +CCAATGATGGCCAACTAGGCCATCTTCTACTATGTACGCAGCTAGAGGCACGAGCGCTGGGGGTACCGATTAGTTCATATTGGTGTTCCACCTATAGGGTT ++ +===BD?DBAABD8AC3?F?D9C6'5;:>7<<>8:(',&22>(((:>3>+:(+28(43>@B(>:>A((32 +@SRR9130495.196 D00236:723:HG32CBCX2:1:1108:9908:2124/1 +TCTTGTGAAGAAGATGCTGTTGGAAGCCTCTAAGAAGCCCGAACTGAATGCTCTTATAAACAATACCAGAGGAATTATTTTTTACAGTGTCCCTCACCATG ++ +CCCFFFFFHGDFGIIJJIJJIGGJJIIJJIIJHIIIIJJJJI>HHIFIJIJIJJIIHIHIJJJJIHIHHHGFDEFFEEEEEEDDDDDFDEDCCDCDDDDDD +@SRR9130495.197 D00236:723:HG32CBCX2:1:1108:9923:2206/1 +TGGGGCTGTGAACCGAAGTCTGCTCCTTTGCGTGAGCCACCCCTGCAGCCCCTCCCACAGTTCCTGAGGAGCCTTTAGTCCTCGTCCTTTCTCAGCTGTAT ++ +@BCFFDAFHHHHHIIIIHIIJIIJIIJJJIIIJJIGGIIIJJFGGGGGEIIIGFHHEFFFEECCCC@CB@BDDDCDDCCDCCDDABBBCDDCC@CCDCCCD +@SRR9130495.198 D00236:723:HG32CBCX2:1:1108:10131:2036/1 +ATGTGCTCAAAGGCTGGGTGGACCTTACCTCCAGTAAACCCCACGTTGTGAAGAAATCCATCAAGTACCTGGAACAAGGAACTCAAGACACCAAAGATGTG ++ +@CCFFFDDFFBHDHIIIBFEGFGHGHGDFHFGIJJIIHDGEGHIJHFHIIIIJJJJJIIIFHHHFHFFFFFFBAEAACBBCADDDDDDDDADBABCCC>AA +@SRR9130495.199 D00236:723:HG32CBCX2:1:1108:10246:2089/1 +GCCCTGGGATTGTCCCTCTGGGCACAGGGAGTCCTGGGGTTGTCCCTCTGAGTAGTTCTGTTGGGAGAGGAGGCCCTGGGATTGTCCCTCTGGGTACAGGG ++ +CCCFFBDDHHHFHJIIIJJJJIIIIIFIIEHGIGJIJDHJIJIIJJFJIJ@FFCHJJJBEGEHHHDFDCD@DBB;=?A?@BDDDCACDDDDD@?CCCCDCB +@SRR9130495.200 D00236:723:HG32CBCX2:1:1108:10051:2156/1 +ATGGAGGATGGCACCCTGCAGGCTGGCCCAGGAGGTGCCAGTGGGCCTCGTGCCCTGGAAATAAATAAAATGATTTCTTTTTGGAGGAATGCTCATAAACG ++ +@@CDFFDFGFHFFIJGGIICCDDADDB diff --git a/docs/notebooks/example.parquet b/docs/notebooks/example.parquet new file mode 100644 index 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+ ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" } ], - "execution_count": 1 + "source": [ + "import polars_bio as pb\n", + "import pandas as pd\n", + "\n", + "a_lazyframe = pb.read_fastq(\"./example.fastq\")\n", + "a_dataframe = a_lazyframe.collect()\n", + "print(type(a_lazyframe))\n", + "# display types of columns\n", + "print(a_lazyframe.dtypes)\n", + "print(type(a_dataframe))\n", + "a_pandas_dataframe = a_lazyframe.collect().to_pandas()\n", + "print(type(a_pandas_dataframe))\n", + "pb.base_sequence_quality(a_lazyframe)\n", + "pb.base_sequence_quality(a_dataframe)\n", + "pb.base_sequence_quality(a_pandas_dataframe)\n", + "pb.base_sequence_quality(\"./example.csv\")\n", + "pb.base_sequence_quality(\"./example.parquet\")\n" + ] }, { "cell_type": "markdown", "id": "d2bb8c193890f27f", "metadata": {}, - "source": "### Sample data" + "source": [ + "### Sample data" + ] }, { "cell_type": "code", + "execution_count": null, "id": "86fe039c3780140e", "metadata": { "ExecuteTime": { @@ -46,27 +107,44 @@ "start_time": "2025-02-24T16:59:37.452650Z" } }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "argument 'df_path_or_table': 'DataFrame' object cannot be converted to 'PyString'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 1\u001b[39m df1 = pd.DataFrame(\n\u001b[32m 2\u001b[39m [[\u001b[33m\"\u001b[39m\u001b[33mchr1\u001b[39m\u001b[33m\"\u001b[39m, \u001b[32m1\u001b[39m, \u001b[32m5\u001b[39m], [\u001b[33m\"\u001b[39m\u001b[33mchr1\u001b[39m\u001b[33m\"\u001b[39m, \u001b[32m3\u001b[39m, \u001b[32m8\u001b[39m], [\u001b[33m\"\u001b[39m\u001b[33mchr1\u001b[39m\u001b[33m\"\u001b[39m, \u001b[32m8\u001b[39m, \u001b[32m10\u001b[39m], [\u001b[33m\"\u001b[39m\u001b[33mchr1\u001b[39m\u001b[33m\"\u001b[39m, \u001b[32m12\u001b[39m, \u001b[32m14\u001b[39m]],\n\u001b[32m 3\u001b[39m columns=[\u001b[33m\"\u001b[39m\u001b[33mchrom\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mstart\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mend\u001b[39m\u001b[33m\"\u001b[39m],\n\u001b[32m 4\u001b[39m )\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m \u001b[43mpb\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbase_sequance_quality\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf1\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 8\u001b[39m df2 = pd.DataFrame(\n\u001b[32m 9\u001b[39m [[\u001b[33m\"\u001b[39m\u001b[33mchr1\u001b[39m\u001b[33m\"\u001b[39m, \u001b[32m4\u001b[39m, \u001b[32m8\u001b[39m], [\u001b[33m\"\u001b[39m\u001b[33mchr1\u001b[39m\u001b[33m\"\u001b[39m, \u001b[32m10\u001b[39m, \u001b[32m11\u001b[39m]], columns=[\u001b[33m\"\u001b[39m\u001b[33mchrom\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mstart\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mend\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 10\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.12.9/lib/python3.12/site-packages/polars_bio/quality_stats.py:9\u001b[39m, in \u001b[36mbase_sequance_quality\u001b[39m\u001b[34m(df)\u001b[39m\n\u001b[32m 8\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mbase_sequance_quality\u001b[39m(df: Union[\u001b[38;5;28mstr\u001b[39m, pl.DataFrame, pl.LazyFrame, pd.DataFrame]):\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpy_base_sequence_quality\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[31mTypeError\u001b[39m: argument 'df_path_or_table': 'DataFrame' object cannot be converted to 'PyString'" + ] + } + ], "source": [ "df1 = pd.DataFrame(\n", " [[\"chr1\", 1, 5], [\"chr1\", 3, 8], [\"chr1\", 8, 10], [\"chr1\", 12, 14]],\n", " columns=[\"chrom\", \"start\", \"end\"],\n", ")\n", "\n", + "pb.base_sequence_quality(df1)\n", + "\n", "df2 = pd.DataFrame(\n", " [[\"chr1\", 4, 8], [\"chr1\", 10, 11]], columns=[\"chrom\", \"start\", \"end\"]\n", ")" - ], - "outputs": [], - "execution_count": 2 + ] }, { "cell_type": "markdown", "id": "a884cd2960796fdb", "metadata": {}, - "source": "### Overlap" + "source": [ + "### Overlap" + ] }, { "cell_type": "code", + "execution_count": 3, "id": "304f3aa6fcdc9650", "metadata": { "ExecuteTime": { @@ -74,9 +152,6 @@ "start_time": "2025-02-24T16:59:37.538707Z" } }, - "source": [ - "overlapping_intervals = pb.overlap(df1, df2, output_type=\"pandas.DataFrame\")" - ], "outputs": [ { "name": "stderr", @@ -86,10 +161,13 @@ ] } ], - "execution_count": 3 + "source": [ + "overlapping_intervals = pb.overlap(df1, df2, output_type=\"pandas.DataFrame\")" + ] }, { "cell_type": "code", + "execution_count": 4, "id": "61c9254622598622", "metadata": { "ExecuteTime": { @@ -97,17 +175,9 @@ "start_time": "2025-02-24T16:59:37.552440Z" } }, - "source": [ - "display(overlapping_intervals)" - ], "outputs": [ { "data": { - "text/plain": [ - " chrom_1 start_1 end_1 chrom_2 start_2 end_2\n", - "0 chr1 1 5 chr1 4 8\n", - "1 chr1 3 8 chr1 4 8" - ], "text/html": [ "
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+ average GC content + + 46.15999984741211 +
+ average read length + + 101 +
+ canonical + + True +
+ file name + + example.fastq +
+ k + + 5 +
+ total reads + + 200 +
+
+
+ + \ No newline at end of file diff --git a/docs/notebooks/tutorial.ipynb b/docs/notebooks/tutorial.ipynb index 42771c6a..5b98a620 100644 --- a/docs/notebooks/tutorial.ipynb +++ b/docs/notebooks/tutorial.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "7b173024d3e8f76", "metadata": { "ExecuteTime": { @@ -18,75 +18,36 @@ "start_time": "2025-02-24T16:59:36.960817Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:polars_bio:Table: example registered for path: ./example.fastq\n", - "200rows [00:00, 46795.76rows/s]\n", - "/tmp/ipykernel_220539/1600797315.py:8: PerformanceWarning: Determining the data types of a LazyFrame requires resolving its schema, which is a potentially expensive operation. Use `LazyFrame.collect_schema().dtypes()` to get the data types without this warning.\n", - " print(a_lazyframe.dtypes)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "[String, String, String, String]\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "200rows [00:00, 67012.37rows/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "200rows [00:00, 138084.08rows/s]\n" - ] - }, - { - "data": { - "text/plain": [ - 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- ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import polars_bio as pb\n", "import pandas as pd\n", "\n", - "a_lazyframe = pb.read_fastq(\"./example.fastq\")\n", - "a_dataframe = a_lazyframe.collect()\n", - "print(type(a_lazyframe))\n", - "# display types of columns\n", - "print(a_lazyframe.dtypes)\n", - "print(type(a_dataframe))\n", - "a_pandas_dataframe = a_lazyframe.collect().to_pandas()\n", - "print(type(a_pandas_dataframe))\n", - "pb.base_sequence_quality(a_lazyframe)\n", - "pb.base_sequence_quality(a_dataframe)\n", - "pb.base_sequence_quality(a_pandas_dataframe)\n", - "pb.base_sequence_quality(\"./example.csv\")\n", - "pb.base_sequence_quality(\"./example.parquet\")\n" + "# print(type(a_lazyframe))\n", + "# # display types of columns\n", + "# print(a_lazyframe.dtypes)\n", + "# print(type(a_dataframe))\n", + "# print(type(a_pandas_dataframe))\n", + "# print(pb.sql(\"SHOW TABLES\").collect())\n", + "print(pb.sql(\"SHOW TABLES\").collect())\n", + "\n", + "# a_lazyframe = pb.read_fastq(\"./example.fastq\")\n", + "# a_dataframe = a_lazyframe.collect()\n", + "# a_pandas_dataframe = a_lazyframe.collect().to_pandas()\n", + "# print(pb.base_sequence_quality(a_lazyframe))\n", + "# print(pb.base_sequence_quality(a_dataframe))\n", + "# print(pb.base_sequence_quality(a_pandas_dataframe))\n", + "\n", + "# result = pb.sql(\"SELECT base_sequence_quality(quality_scores) FROM example\").collect()\n", + "# print(result.item())\n", + "# print(pb.base_sequence_quality(\"./example.csv\"))\n", + "print(pb.base_sequence_quality(\"./example.fastq\"))\n", + "# print(pb.base_sequence_quality(\"./example.parquet\"))\n", + "\n", + "# sql display all tables and print it\n", + "print(pb.sql(\"SHOW TABLES\").collect())\n", + "# use sql and display result (it is aggregate function that returns string)\n", + "# print(result.item())\n" ] }, { @@ -107,20 +68,7 @@ "start_time": "2025-02-24T16:59:37.452650Z" } }, - "outputs": [ - { - "ename": "TypeError", - "evalue": "argument 'df_path_or_table': 'DataFrame' object cannot be converted to 'PyString'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 1\u001b[39m df1 = pd.DataFrame(\n\u001b[32m 2\u001b[39m [[\u001b[33m\"\u001b[39m\u001b[33mchr1\u001b[39m\u001b[33m\"\u001b[39m, \u001b[32m1\u001b[39m, \u001b[32m5\u001b[39m], [\u001b[33m\"\u001b[39m\u001b[33mchr1\u001b[39m\u001b[33m\"\u001b[39m, \u001b[32m3\u001b[39m, \u001b[32m8\u001b[39m], [\u001b[33m\"\u001b[39m\u001b[33mchr1\u001b[39m\u001b[33m\"\u001b[39m, \u001b[32m8\u001b[39m, \u001b[32m10\u001b[39m], [\u001b[33m\"\u001b[39m\u001b[33mchr1\u001b[39m\u001b[33m\"\u001b[39m, \u001b[32m12\u001b[39m, \u001b[32m14\u001b[39m]],\n\u001b[32m 3\u001b[39m columns=[\u001b[33m\"\u001b[39m\u001b[33mchrom\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mstart\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mend\u001b[39m\u001b[33m\"\u001b[39m],\n\u001b[32m 4\u001b[39m )\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m \u001b[43mpb\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbase_sequance_quality\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf1\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 8\u001b[39m df2 = pd.DataFrame(\n\u001b[32m 9\u001b[39m [[\u001b[33m\"\u001b[39m\u001b[33mchr1\u001b[39m\u001b[33m\"\u001b[39m, \u001b[32m4\u001b[39m, \u001b[32m8\u001b[39m], [\u001b[33m\"\u001b[39m\u001b[33mchr1\u001b[39m\u001b[33m\"\u001b[39m, \u001b[32m10\u001b[39m, \u001b[32m11\u001b[39m]], columns=[\u001b[33m\"\u001b[39m\u001b[33mchrom\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mstart\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mend\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 10\u001b[39m )\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.12.9/lib/python3.12/site-packages/polars_bio/quality_stats.py:9\u001b[39m, in \u001b[36mbase_sequance_quality\u001b[39m\u001b[34m(df)\u001b[39m\n\u001b[32m 8\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mbase_sequance_quality\u001b[39m(df: Union[\u001b[38;5;28mstr\u001b[39m, pl.DataFrame, pl.LazyFrame, pd.DataFrame]):\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpy_base_sequence_quality\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[31mTypeError\u001b[39m: argument 'df_path_or_table': 'DataFrame' object cannot be converted to 'PyString'" - ] - } - ], + "outputs": [], "source": [ "df1 = pd.DataFrame(\n", " [[\"chr1\", 1, 5], [\"chr1\", 3, 8], [\"chr1\", 8, 10], [\"chr1\", 12, 14]],\n", @@ -144,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "304f3aa6fcdc9650", "metadata": { "ExecuteTime": { @@ -152,22 +100,14 @@ "start_time": "2025-02-24T16:59:37.538707Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:polars_bio.operation:Running Overlap operation with algorithm Coitrees and 1 thread(s)...\n" - ] - } - ], + "outputs": [], "source": [ "overlapping_intervals = pb.overlap(df1, df2, output_type=\"pandas.DataFrame\")" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "61c9254622598622", "metadata": { "ExecuteTime": { @@ -175,76 +115,14 @@ "start_time": "2025-02-24T16:59:37.552440Z" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
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chrom_1start_1end_1chrom_2start_2end_2
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1chr138chr148
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chrom_1start_1end_1chrom_2start_2end_2distance
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1chr138chr1480
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3chr11214chr110111
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" - ], - "text/plain": [ - " chrom_1 start_1 end_1 chrom_2 start_2 end_2 distance\n", - "0 chr1 1 5 chr1 4 8 0\n", - "1 chr1 3 8 chr1 4 8 0\n", - "2 chr1 8 10 chr1 4 8 0\n", - "3 chr1 12 14 chr1 10 11 1" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "display(nearest_intervals)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "5f69f700b50f58e2", "metadata": { "ExecuteTime": { @@ -421,48 +183,7 @@ "start_time": "2025-02-24T16:59:37.673937Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "visualize_intervals(nearest_intervals, \"nearest pair\")" ] diff --git a/polars_bio/quality_stats.py b/polars_bio/quality_stats.py index 6cf85c46..c18ec841 100644 --- a/polars_bio/quality_stats.py +++ b/polars_bio/quality_stats.py @@ -4,17 +4,20 @@ import pandas as pd import pyarrow as pa from .context import ctx -from polars_bio.polars_bio import my_scan, my_frame +from polars_bio.polars_bio import ( + base_sequance_quality_scan, + base_sequance_quality_frame, +) def base_sequence_quality(df: Union[str, pl.DataFrame, pl.LazyFrame, pd.DataFrame]): if isinstance(df, str): - supported_exts = set([".parquet", ".csv", ".bed", ".vcf"]) + supported_exts = set([".parquet", ".csv", ".bed", ".vcf", ".fastq"]) ext = set(Path(df).suffixes) assert ( len(supported_exts.intersection(ext)) > 0 or len(ext) == 0 - ), "Dataframe1 must be a Parquet, a BED or CSV or VCF file" - return my_scan(ctx, df) + ), "Dataframe1 must be a Parquet, CSV, BED, VCF, or FASTQ file." + return base_sequance_quality_scan(ctx, df) else: if isinstance(df, pl.DataFrame): df = df.to_arrow().to_reader() @@ -22,4 +25,4 @@ def base_sequence_quality(df: Union[str, pl.DataFrame, pl.LazyFrame, pd.DataFram df = pa.Table.from_pandas(df) elif isinstance(df, pl.LazyFrame): df = df.collect().to_arrow().to_reader() - return my_frame(ctx, df) + return base_sequance_quality_frame(ctx, df) diff --git a/src/context.rs b/src/context.rs index 5f47f30e..afc02d4e 100644 --- a/src/context.rs +++ b/src/context.rs @@ -1,6 +1,9 @@ use std::collections::HashMap; +use std::sync::Arc; +use datafusion::arrow::datatypes::DataType; use datafusion::config::ConfigOptions; +use datafusion::logical_expr::{create_udaf, AggregateUDF, Volatility}; use datafusion::prelude::SessionConfig; use exon::config::ExonConfigExtension; use exon::ExonSession; @@ -8,6 +11,8 @@ use log::debug; use pyo3::{pyclass, pymethods, PyResult}; use sequila_core::session_context::SequilaConfig; +use crate::udaf::{base_quality_result_type, QualityScoresStats}; + #[pyclass(name = "BioSessionContext")] // #[derive(Clone)] pub struct PyBioSessionContext { @@ -25,7 +30,7 @@ impl PyBioSessionContext { pub fn new(seed: String, catalog_dir: String) -> PyResult { let ctx = create_context().unwrap(); let session_config: HashMap = HashMap::new(); - + ctx.session.register_udaf(make_base_sequence_quality_udaf()); Ok(PyBioSessionContext { ctx, session_config, @@ -87,3 +92,14 @@ fn create_context() -> exon::Result { ExonSession::with_config_exon(config) } + +pub fn make_base_sequence_quality_udaf() -> AggregateUDF { + create_udaf( + "base_sequence_quality", // nazwa funkcji w SQL + vec![DataType::Utf8], // typ wejściowy + Arc::new(base_quality_result_type()), + Volatility::Immutable, + Arc::new(|_| Ok(Box::new(QualityScoresStats::new()))), + Arc::new(vec![]), + ) +} diff --git a/src/lib.rs b/src/lib.rs index 629a9205..d92eb0c4 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -4,12 +4,14 @@ mod option; mod query; mod scan; mod streaming; +mod udaf; mod udtf; mod utils; use std::string::ToString; use std::sync::{Arc, Mutex}; +use arrow::array::*; use datafusion::arrow::ffi_stream::ArrowArrayStreamReader; use datafusion::arrow::pyarrow::PyArrowType; use datafusion::datasource::MemTable; @@ -21,6 +23,8 @@ use polars_lazy::prelude::{LazyFrame, ScanArgsAnonymous}; use polars_python::error::PyPolarsErr; use polars_python::lazyframe::PyLazyFrame; use pyo3::prelude::*; +use pyo3::types::{PyDict, PyList}; +use scan::deregister_table; use tokio::runtime::Runtime; use crate::context::PyBioSessionContext; @@ -32,11 +36,9 @@ use crate::scan::{maybe_register_table, register_frame, register_table}; use crate::streaming::RangeOperationScan; use crate::utils::convert_arrow_rb_schema_to_polars_df_schema; -use pyo3::types::PyString; -use serde_json::Value; - const LEFT_TABLE: &str = "s1"; const RIGHT_TABLE: &str = "s2"; +const DEFAULT_TABLE_NAME: &str = "unnamed_table"; const DEFAULT_COLUMN_NAMES: [&str; 3] = ["contig", "start", "end"]; #[pyfunction] @@ -407,34 +409,128 @@ fn py_from_polars( }) } -#[pyfunction] -#[pyo3(signature = (py_ctx, path))] -fn my_scan(py: Python<'_>, py_ctx: &PyBioSessionContext, path: String) -> PyResult { - let rt = Runtime::new()?; +fn struct_array_to_pydict(py: Python<'_>, struct_array: &StructArray) -> PyResult { + let warn_array = struct_array + .column_by_name("base_quality_warn") + .and_then(|a| a.as_any().downcast_ref::()) + .unwrap(); + + let base_per_pos_array = struct_array + .column_by_name("base_per_pos_data") + .and_then(|a| a.as_any().downcast_ref::()) + .unwrap(); + + let struct_array = base_per_pos_array + .values() + .as_any() + .downcast_ref::() + .unwrap(); + + let mut base_per_pos_data = Vec::new(); + for i in 0..base_per_pos_array.value_length(0) { + let mut row = std::collections::HashMap::new(); + + let pos = struct_array + .column_by_name("pos") + .and_then(|a| a.as_any().downcast_ref::()) + .unwrap() + .value(i as usize); + let average = struct_array + .column_by_name("average") + .and_then(|a| a.as_any().downcast_ref::()) + .unwrap() + .value(i as usize); + let median = struct_array + .column_by_name("median") + .and_then(|a| a.as_any().downcast_ref::()) + .unwrap() + .value(i as usize); + let q1 = struct_array + .column_by_name("q1") + .and_then(|a| a.as_any().downcast_ref::()) + .unwrap() + .value(i as usize); + let q3 = struct_array + .column_by_name("q3") + .and_then(|a| a.as_any().downcast_ref::()) + .unwrap() + .value(i as usize); + let lower = struct_array + .column_by_name("lower") + .and_then(|a| a.as_any().downcast_ref::()) + .unwrap() + .value(i as usize); + let upper = struct_array + .column_by_name("upper") + .and_then(|a| a.as_any().downcast_ref::()) + .unwrap() + .value(i as usize); + + row.insert("pos", pos.into_py(py)); + row.insert("average", average.into_py(py)); + row.insert("median", median.into_py(py)); + row.insert("q1", q1.into_py(py)); + row.insert("q3", q3.into_py(py)); + row.insert("lower", lower.into_py(py)); + row.insert("upper", upper.into_py(py)); + + base_per_pos_data.push(row.into_py(py).to_object(py)); + } + + let result_dict = PyDict::new_bound(py); + result_dict.set_item("base_quality_warn", warn_array.value(0))?; + result_dict.set_item( + "base_per_pos_data", + PyList::new_bound(py, base_per_pos_data), + )?; + + Ok(result_dict.to_object(py)) +} + +fn handle_base_sequence_quality<'a, F>( + py: Python<'a>, + py_ctx: &PyBioSessionContext, + table_name: &str, + register_fn: F, +) -> PyResult +where + F: FnOnce(&PyBioSessionContext, &str, &Runtime), +{ let ctx = &py_ctx.ctx; - let _table = maybe_register_table(path, &LEFT_TABLE.to_string(), None, ctx, &rt); - let result: Value = rt.block_on(do_base_sequence_quality(ctx, LEFT_TABLE.to_string())); + let rt = Runtime::new().unwrap(); + register_fn(py_ctx, table_name, &rt); + let result_opt = rt.block_on(do_base_sequence_quality(ctx, &table_name.to_string())); + deregister_table(ctx, table_name); + if let Some(struct_array) = result_opt { + struct_array_to_pydict(py, &struct_array) + } else { + Ok(py.None()) + } +} - let json_str = serde_json::to_string(&result).unwrap(); - let py_str = PyString::new_bound(py, &json_str); - Ok(py_str.into_py(py)) +#[pyfunction] +#[pyo3(signature = (py_ctx, path))] +fn base_sequance_quality_scan( + py: Python<'_>, + py_ctx: &PyBioSessionContext, + path: String, +) -> PyResult { + handle_base_sequence_quality(py, py_ctx, DEFAULT_TABLE_NAME, |py_ctx, table_name, rt| { + let ctx = &py_ctx.ctx; + maybe_register_table(path, &table_name.to_string(), None, ctx, rt); + }) } #[pyfunction] #[pyo3(signature = (py_ctx, df))] -fn my_frame( +fn base_sequance_quality_frame( py: Python<'_>, py_ctx: &PyBioSessionContext, df: PyArrowType, ) -> PyResult { - let rt = Runtime::new().unwrap(); - let ctx = &py_ctx.ctx; - register_frame(py_ctx, df, LEFT_TABLE.to_string()); - let result: Value = rt.block_on(do_base_sequence_quality(ctx, LEFT_TABLE.to_string())); - - let json_str = serde_json::to_string(&result).unwrap(); - let py_str = PyString::new_bound(py, &json_str); - Ok(py_str.into_py(py)) + handle_base_sequence_quality(py, py_ctx, DEFAULT_TABLE_NAME, |py_ctx, table_name, _rt| { + register_frame(py_ctx, df, table_name.to_string()); + }) } #[pymodule] @@ -451,9 +547,8 @@ fn polars_bio(_py: Python, m: &Bound) -> PyResult<()> { m.add_function(wrap_pyfunction!(py_describe_vcf, m)?)?; m.add_function(wrap_pyfunction!(py_register_view, m)?)?; m.add_function(wrap_pyfunction!(py_from_polars, m)?)?; - m.add_function(wrap_pyfunction!(my_frame, m)?)?; - m.add_function(wrap_pyfunction!(my_scan, m)?)?; - // m.add_function(wrap_pyfunction!(unary_operation_scan, m)?)?; + m.add_function(wrap_pyfunction!(base_sequance_quality_frame, m)?)?; + m.add_function(wrap_pyfunction!(base_sequance_quality_scan, m)?)?; m.add_class::()?; m.add_class::()?; m.add_class::()?; diff --git a/src/operation.rs b/src/operation.rs index 9e5c8cdf..808c55a9 100644 --- a/src/operation.rs +++ b/src/operation.rs @@ -1,10 +1,10 @@ use std::sync::Arc; +use arrow_array::{Array, StructArray}; use datafusion::catalog_common::TableReference; use exon::ExonSession; use log::{debug, info}; use sequila_core::session_context::{Algorithm, SequilaConfig}; -use serde_json::json; use tokio::runtime::Runtime; use crate::context::set_option_internal; @@ -194,134 +194,33 @@ async fn do_count_overlaps_coverage_naive( pub(crate) async fn do_base_sequence_quality( ctx: &ExonSession, - table: String, -) -> serde_json::Value { - let query = format!("SELECT quality_scores FROM {}", table); + table: &String, +) -> Option { + let query = format!( + "SELECT base_sequence_quality(quality_scores) as result FROM {}", + table + ); debug!("Query: {}", query); let batches = ctx.sql(&query).await.unwrap().collect().await.unwrap(); - let mut base_quality_count: std::collections::HashMap> = - std::collections::HashMap::new(); - - use std::any::type_name_of_val; - - for batch in batches { + if let Some(batch) = batches.get(0) { let col_idx = batch .schema() .fields() .iter() - .position(|f| f.name() == "quality_scores") - .expect("Column 'quality_scores' not found"); + .position(|f| f.name() == "result") + .expect("Column 'result' not found"); let array = batch.column(col_idx); - for i in 0..array.len() { - if array.is_null(i) { - continue; - } - let quality_str = if let Some(string_array) = - array.as_any().downcast_ref::() - { - string_array.value(i) - } else if let Some(large_string_array) = array - .as_any() - .downcast_ref::() - { - large_string_array.value(i) - } else if let Some(generic_string_array_i64) = array - .as_any() - .downcast_ref::>( - ) { - generic_string_array_i64.value(i) - } else if let Some(generic_string_array_i32) = array - .as_any() - .downcast_ref::>( - ) { - generic_string_array_i32.value(i) - } else if let Some(string_view_array) = array - .as_any() - .downcast_ref::() - { - string_view_array.value(i) - } else { - panic!( - "Column 'quality_scores' has unsupported array type: {:?} (concrete Rust type: {})", - array.data_type(), - type_name_of_val(array) - ); - }; - - for (pos, qchar) in quality_str.chars().enumerate() { - let qscore = qchar as usize - 33; - let rec = base_quality_count - .entry(pos) - .or_insert_with(|| vec![0_usize; 94]); - if qscore < 94 { - rec[qscore] += 1; - } - } - } - } - fn quartiles(counts: &[usize]) -> Vec { - let mut expanded = Vec::new(); - for (q, &c) in counts.iter().enumerate() { - for _ in 0..c { - expanded.push(q as f32 + 33.0); + if array.len() > 0 && !array.is_null(0) { + if let Some(struct_array) = array.as_any().downcast_ref::() { + return Some(struct_array.clone()); + } else { + panic!("Unsupported result type: {:?}", array.data_type()); } } - if expanded.is_empty() { - return vec![0.0; 5]; - } - expanded.sort_by(|a, b| a.partial_cmp(b).unwrap()); - let n = expanded.len(); - let q = |p: f32| { - let idx = (p * (n - 1) as f32).round() as usize; - expanded[idx] - }; - vec![ - q(0.0), // min - q(0.25), // Q1 - q(0.5), // median - q(0.75), // Q3 - q(1.0), // max - ] - } - - let mut base_quality_warn = "pass"; - let mut base_per_pos_data = Vec::new(); - for (position, qualities) in base_quality_count.iter() { - let (sum, len) = qualities - .iter() - .enumerate() - .fold((0_usize, 0_usize), |(s, l), (q, c)| { - (s + (q + 33) * c, l + c) - }); - let avg = if len > 0 { - sum as f64 / len as f64 - } else { - 0.0 - }; - let values = quartiles(qualities); - let median = values[2]; - if median <= 20.0 { - base_quality_warn = "fail"; - } else if median <= 25.0 && base_quality_warn != "fail" { - base_quality_warn = "warn"; - } - base_per_pos_data.push(json!({ - "pos": position, - "average": avg, - "upper": values[4], - "lower": values[0], - "q1": values[1], - "q3": values[3], - "median": values[2], - })); } - - json!({ - "base_quality_warn": base_quality_warn, - "base_per_pos_data": base_per_pos_data - }) + None } async fn get_non_join_columns( diff --git a/src/scan.rs b/src/scan.rs index 4d5b288e..e7795a99 100644 --- a/src/scan.rs +++ b/src/scan.rs @@ -61,6 +61,8 @@ pub(crate) fn get_input_format(path: &str) -> InputFormat { InputFormat::Bed } else if path.ends_with(".vcf") || path.ends_with(".vcf.gz") || path.ends_with(".vcf.bgz") { InputFormat::Vcf + } else if path.ends_with(".fastq") { + InputFormat::Fastq } else { panic!("Unsupported format") } @@ -154,3 +156,7 @@ pub(crate) fn maybe_register_table( } .to_string() } + +pub(crate) fn deregister_table(ctx: &ExonSession, table_name: &str) { + let _ = ctx.session.deregister_table(table_name); +} diff --git a/src/udaf.rs b/src/udaf.rs new file mode 100644 index 00000000..a26ac237 --- /dev/null +++ b/src/udaf.rs @@ -0,0 +1,210 @@ +use std::collections::HashMap; +use std::sync::Arc; + +use arrow::buffer::OffsetBuffer; +use arrow_array::{Array, ArrayRef, Float64Array, Int64Array, ListArray, StringArray, StructArray}; +use arrow_schema::{DataType, Field, Fields}; +use datafusion::error::{DataFusionError, Result}; +use datafusion::physical_plan::Accumulator; +use datafusion::scalar::ScalarValue; + +#[derive(Debug)] +pub(crate) struct QualityScoresStats { + values_per_pos: HashMap>, // key: position, value: decoded quality scores +} + +impl QualityScoresStats { + pub fn new() -> Self { + Self { + values_per_pos: HashMap::new(), + } + } + + fn decode_score(c: char) -> Option { + let ascii = c as u8; + if ascii >= 33 { + Some(ascii - 33) + } else { + None + } + } + + fn calc_stats(values: &mut Vec) -> (f64, f64, f64, f64, f64, f64) { + values.sort_unstable(); + let n = values.len(); + let average = values.iter().map(|&v| v as f64).sum::() / n as f64; + let median = if n % 2 == 0 { + (values[n / 2 - 1] as f64 + values[n / 2] as f64) / 2.0 + } else { + values[n / 2] as f64 + }; + let q1 = values[n / 4] as f64; + let q3 = values[(3 * n) / 4] as f64; + let iqr = q3 - q1; + let lower = q1 - 1.5 * iqr; + let upper = q3 + 1.5 * iqr; + (average, median, q1, q3, lower, upper) + } +} + +impl Accumulator for QualityScoresStats { + fn state(&mut self) -> Result> { + Ok(vec![]) + } + + fn evaluate(&mut self) -> Result { + #[derive(Default)] + struct StatColumns { + pos: Vec, + avg: Vec, + median: Vec, + q1: Vec, + q3: Vec, + lower: Vec, + upper: Vec, + } + + let mut cols = StatColumns::default(); + let mut base_quality_warn = "pass"; + + for (&pos, values) in &mut self.values_per_pos { + if values.is_empty() { + continue; + } + + let (avg, median, q1, q3, lower, upper) = Self::calc_stats(values); + + cols.pos.push(pos as i64); + cols.avg.push(avg); + cols.median.push(median); + cols.q1.push(q1); + cols.q3.push(q3); + cols.lower.push(lower); + cols.upper.push(upper); + + base_quality_warn = match (q1 <= 20.0, q1 <= 25.0, base_quality_warn) { + (true, _, _) => "fail", + (false, true, "pass") => "warn", + _ => base_quality_warn, + }; + } + + let result_type = base_quality_result_type(); + + let fields = match result_type { + DataType::Struct(ref fields) => fields.clone(), + _ => { + return Err(DataFusionError::Execution( + "Unexpected result type".to_string(), + )) + }, + }; + + let base_quality_warn_field = fields[0].clone(); + let base_per_pos_data_field = fields[1].clone(); + + let base_per_pos_data_element_field = match base_per_pos_data_field.data_type() { + DataType::List(inner_field) => inner_field.as_ref().clone(), + _ => return Err(DataFusionError::Execution("Expected List type".to_string())), + }; + + let struct_fields = match base_per_pos_data_element_field.data_type() { + DataType::Struct(inner_fields) => inner_fields.clone(), + _ => { + return Err(DataFusionError::Execution( + "Expected Struct type inside list".to_string(), + )) + }, + }; + + let to_array = |vec: Vec| Arc::new(Float64Array::from(vec)) as ArrayRef; + + let struct_array = Arc::new(StructArray::new( + struct_fields.clone(), + vec![ + Arc::new(Int64Array::from(cols.pos)) as ArrayRef, + to_array(cols.avg), + to_array(cols.median), + to_array(cols.q1), + to_array(cols.q3), + to_array(cols.lower), + to_array(cols.upper), + ], + None, + )) as ArrayRef; + + let list_array = Arc::new(ListArray::new( + Arc::new(base_per_pos_data_element_field), + OffsetBuffer::new(vec![0, struct_array.len() as i32].into()), + struct_array, + None, + )); + + Ok(ScalarValue::Struct(Arc::new(StructArray::from(vec![ + ( + base_quality_warn_field, + Arc::new(StringArray::from(vec![base_quality_warn])) as ArrayRef, + ), + (base_per_pos_data_field, list_array), + ])))) + } + + fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> { + let arr = values[0] + .as_any() + .downcast_ref::() + .ok_or_else(|| { + datafusion::error::DataFusionError::Internal("Expected StringArray".to_string()) + })?; + + for i in 0..arr.len() { + if arr.is_null(i) { + continue; + } + let val = arr.value(i); + + for (j, c) in val.chars().enumerate() { + if let Some(decoded) = Self::decode_score(c) { + self.values_per_pos.entry(j).or_default().push(decoded); + } + } + } + + Ok(()) + } + + fn merge_batch(&mut self, _states: &[ArrayRef]) -> Result<()> { + Ok(()) + } + + fn size(&self) -> usize { + std::mem::size_of_val(self) + } +} + +pub fn base_quality_result_type() -> DataType { + let per_pos_fields = Fields::from(vec![ + Field::new("pos", DataType::Int64, false), + Field::new("average", DataType::Float64, false), + Field::new("median", DataType::Float64, false), + Field::new("q1", DataType::Float64, false), + Field::new("q3", DataType::Float64, false), + Field::new("lower", DataType::Float64, false), + Field::new("upper", DataType::Float64, false), + ]); + + let base_per_pos_element_field = Field::new( + "base_per_pos_data_element", + DataType::Struct(per_pos_fields), + false, + ); + + DataType::Struct(Fields::from(vec![ + Field::new("base_quality_warn", DataType::Utf8, false), + Field::new( + "base_per_pos_data", + DataType::List(Arc::new(base_per_pos_element_field)), + false, + ), + ])) +} From 5a64fe13c7219ace43e83997e233d5a1f2169ab3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Bart=C5=82omiej=20=C5=9Acise=C5=82?= Date: Fri, 30 May 2025 21:46:33 +0200 Subject: [PATCH 3/6] Remove serde_json dependency from Cargo files --- Cargo.lock | 1 - Cargo.toml | 1 - 2 files changed, 2 deletions(-) diff --git a/Cargo.lock b/Cargo.lock index 2527967b..946f46a3 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -5424,7 +5424,6 @@ dependencies = [ "pyo3-log", "rand", "sequila-core", - "serde_json", "tokio", "tracing", ] diff --git a/Cargo.toml b/Cargo.toml index 919b38d5..47d326b7 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -43,4 +43,3 @@ coitrees = "0.4.0" fnv = "1.0.7" async-stream = "0.3.6" rand = "0.8.5" -serde_json = "1.0.140" From dbc09494a94f0e6c4de961aef0396f65dd0e13b6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Bart=C5=82omiej=20=C5=9Acise=C5=82?= Date: Tue, 3 Jun 2025 14:47:09 +0200 Subject: [PATCH 4/6] Added examples of using the base_sequence_quality function --- docs/notebooks/base_sequence_quality.ipynb | 4587 ++++++++++++++++++++ 1 file changed, 4587 insertions(+) create mode 100644 docs/notebooks/base_sequence_quality.ipynb diff --git a/docs/notebooks/base_sequence_quality.ipynb b/docs/notebooks/base_sequence_quality.ipynb new file mode 100644 index 00000000..35d8bed5 --- /dev/null +++ b/docs/notebooks/base_sequence_quality.ipynb @@ -0,0 +1,4587 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "83d7ccd7", + "metadata": {}, + "source": [ + "### Import dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "58b40aa6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/user/.pyenv/versions/3.12.9/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "INFO:polars_bio:Creating BioSessionContext\n" + ] + } + ], + "source": [ + "import polars_bio as pb\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "2e29cd0a", + "metadata": {}, + "source": [ + "### Usage examples" + ] + }, + { + "cell_type": "markdown", + "id": "f9aedeb9", + "metadata": {}, + "source": [ + "#### Usage example - calling UDAF directly in SQL" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5f6fccf3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:polars_bio:Table: example registered for path: ./example.fastq\n", + "1rows [00:00, 327.48rows/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'base_quality_warn': 'pass', 'base_per_pos_data': [{'pos': 89, 'average': 32.44, 'median': 35.0, 'q1': 33.0, 'q3': 35.0, 'lower': 30.0, 'upper': 38.0}, {'pos': 84, 'average': 32.415, 'median': 35.0, 'q1': 33.0, 'q3': 35.0, 'lower': 30.0, 'upper': 38.0}, {'pos': 96, 'average': 31.315, 'median': 34.0, 'q1': 32.0, 'q3': 35.0, 'lower': 27.5, 'upper': 39.5}, {'pos': 31, 'average': 38.795, 'median': 40.0, 'q1': 38.0, 'q3': 41.0, 'lower': 33.5, 'upper': 45.5}, {'pos': 49, 'average': 37.21, 'median': 39.0, 'q1': 36.0, 'q3': 40.0, 'lower': 30.0, 'upper': 46.0}, {'pos': 69, 'average': 33.7, 'median': 36.0, 'q1': 34.0, 'q3': 39.0, 'lower': 26.5, 'upper': 46.5}, {'pos': 63, 'average': 36.25, 'median': 38.0, 'q1': 35.0, 'q3': 40.0, 'lower': 27.5, 'upper': 47.5}, {'pos': 65, 'average': 35.995, 'median': 37.0, 'q1': 35.0, 'q3': 39.0, 'lower': 29.0, 'upper': 45.0}, {'pos': 70, 'average': 33.565, 'median': 35.0, 'q1': 34.0, 'q3': 38.0, 'lower': 28.0, 'upper': 44.0}, {'pos': 68, 'average': 35.91, 'median': 36.0, 'q1': 34.0, 'q3': 39.0, 'lower': 26.5, 'upper': 46.5}, {'pos': 55, 'average': 37.55, 'median': 39.0, 'q1': 36.0, 'q3': 40.0, 'lower': 30.0, 'upper': 46.0}, {'pos': 24, 'average': 38.265, 'median': 40.0, 'q1': 38.0, 'q3': 41.0, 'lower': 33.5, 'upper': 45.5}, {'pos': 21, 'average': 38.445, 'median': 40.0, 'q1': 38.0, 'q3': 40.0, 'lower': 35.0, 'upper': 43.0}, {'pos': 60, 'average': 35.985, 'median': 38.0, 'q1': 35.0, 'q3': 40.0, 'lower': 27.5, 'upper': 47.5}, {'pos': 45, 'average': 37.45, 'median': 40.0, 'q1': 36.0, 'q3': 41.0, 'lower': 28.5, 'upper': 48.5}, {'pos': 30, 'average': 38.245, 'median': 40.0, 'q1': 38.0, 'q3': 40.0, 'lower': 35.0, 'upper': 43.0}, {'pos': 20, 'average': 38.625, 'median': 40.0, 'q1': 38.0, 'q3': 41.0, 'lower': 33.5, 'upper': 45.5}, {'pos': 82, 'average': 31.525, 'median': 35.0, 'q1': 33.0, 'q3': 36.0, 'lower': 28.5, 'upper': 40.5}, {'pos': 85, 'average': 32.195, 'median': 35.0, 'q1': 33.0, 'q3': 35.0, 'lower': 30.0, 'upper': 38.0}, {'pos': 35, 'average': 38.385, 'median': 40.0, 'q1': 38.0, 'q3': 41.0, 'lower': 33.5, 'upper': 45.5}, {'pos': 57, 'average': 37.35, 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'average': 31.21, 'median': 34.0, 'q1': 31.0, 'q3': 34.0, 'lower': 26.5, 'upper': 38.5}, {'pos': 80, 'average': 32.61, 'median': 35.0, 'q1': 32.0, 'q3': 36.0, 'lower': 26.0, 'upper': 42.0}]}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "a_lazyframe = pb.read_fastq(\"./example.fastq\")\n", + "result = pb.sql(\"SELECT base_sequence_quality(quality_scores) FROM example\").collect()\n", + "print(result.item())" + ] + }, + { + "cell_type": "markdown", + "id": "b238193d", + "metadata": {}, + "source": [ + "#### Usage example - .fastq file" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0420c240", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'base_quality_warn': 'pass',\n", + " 'base_per_pos_data': [{'lower': 33.5,\n", + " 'q3': 41.0,\n", + " 'q1': 38.0,\n", + " 'median': 40.0,\n", + " 'average': 38.205,\n", + " 'upper': 45.5,\n", + " 'pos': 34},\n", + " {'average': 38.47,\n", + " 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'average': 38.11,\n", + " 'q1': 38.0,\n", + " 'median': 40.0},\n", + " {'lower': 35.0,\n", + " 'average': 38.445,\n", + " 'median': 40.0,\n", + " 'q1': 38.0,\n", + " 'q3': 40.0,\n", + " 'upper': 43.0,\n", + " 'pos': 21},\n", + " {'q1': 38.0,\n", + " 'pos': 16,\n", + " 'q3': 41.0,\n", + " 'average': 38.48,\n", + " 'lower': 33.5,\n", + " 'upper': 45.5,\n", + " 'median': 40.0}]}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a_lazyframe = pb.read_fastq(\"./example.fastq\")\n", + "print(type(a_lazyframe))\n", + "pb.base_sequence_quality(a_lazyframe)" + ] + }, + { + "cell_type": "markdown", + "id": "cdb4aad6", + "metadata": {}, + "source": [ + "#### Usage example - `polars.dataframe.frame.DataFrame` object" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7830b8aa", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:polars_bio:Table: example registered for path: ./example.fastq\n", + "200rows [00:00, 80924.25rows/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "{'base_quality_warn': 'pass',\n", + " 'base_per_pos_data': [{'lower': 33.5,\n", + " 'upper': 45.5,\n", + " 'median': 40.0,\n", + " 'average': 38.265,\n", + " 'pos': 24,\n", + " 'q3': 41.0,\n", + " 'q1': 38.0},\n", + " {'median': 35.0,\n", + " 'average': 30.83,\n", + " 'pos': 74,\n", + " 'q1': 32.0,\n", + " 'lower': 24.5,\n", + " 'q3': 37.0,\n", + " 'upper': 44.5},\n", + " {'pos': 93,\n", + " 'upper': 39.5,\n", + " 'average': 31.05,\n", + " 'median': 34.0,\n", + " 'q1': 32.0,\n", + " 'q3': 35.0,\n", + " 'lower': 27.5},\n", + " {'q1': 37.0,\n", + " 'pos': 10,\n", + " 'q3': 39.0,\n", + " 'lower': 34.0,\n", + " 'upper': 42.0,\n", + " 'median': 39.0,\n", + " 'average': 37.675},\n", + " {'lower': 28.5,\n", + " 'upper': 48.5,\n", + " 'median': 39.0,\n", + " 'average': 37.425,\n", + " 'pos': 50,\n", + " 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+ " 'average': 38.425},\n", + " {'pos': 11,\n", + " 'average': 37.71,\n", + " 'median': 39.0,\n", + " 'q1': 37.0,\n", + " 'q3': 39.0,\n", + " 'lower': 34.0,\n", + " 'upper': 42.0}]}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a_lazyframe = pb.read_fastq(\"./example.fastq\")\n", + "a_dataframe = a_lazyframe.collect()\n", + "print(type(a_dataframe))\n", + "pb.base_sequence_quality(a_dataframe)" + ] + }, + { + "cell_type": "markdown", + "id": "ddf5da9d", + "metadata": {}, + "source": [ + "#### Usage example - `pandas.core.frame.DataFrame` object" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "56817174", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:polars_bio:Table: example registered for path: ./example.fastq\n", + "200rows [00:00, 120508.66rows/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "{'base_quality_warn': 'pass',\n", + " 'base_per_pos_data': [{'upper': 41.0,\n", + " 'q3': 35.0,\n", + " 'lower': 25.0,\n", + " 'pos': 97,\n", + " 'average': 30.67,\n", + " 'median': 34.0,\n", + " 'q1': 31.0},\n", + " {'q1': 38.0,\n", + " 'pos': 14,\n", + " 'average': 38.965,\n", + " 'lower': 33.5,\n", + " 'median': 40.0,\n", + " 'upper': 45.5,\n", + " 'q3': 41.0},\n", + " {'pos': 23,\n", + " 'upper': 45.5,\n", + " 'q1': 38.0,\n", + " 'q3': 41.0,\n", + " 'average': 38.635,\n", + " 'lower': 33.5,\n", + " 'median': 40.0},\n", + " {'q1': 37.0,\n", + " 'lower': 34.0,\n", + " 'average': 37.71,\n", + " 'upper': 42.0,\n", + " 'q3': 39.0,\n", + " 'median': 39.0,\n", + " 'pos': 11},\n", + " {'average': 38.425,\n", + " 'q1': 38.0,\n", + " 'q3': 41.0,\n", + " 'lower': 33.5,\n", + " 'upper': 45.5,\n", + " 'median': 40.0,\n", + " 'pos': 19},\n", + " {'q3': 34.0,\n", + " 'upper': 38.5,\n", + 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+0200 Subject: [PATCH 5/6] Refactor base sequence quality computation and integrate DataFusion - Updated `base_sequence_quality` function to accept a quality scores column and output type. - Introduced `BaseSequenceQualityProvider` and `BaseSequenceQualityExec` in Rust for efficient execution plans. - Removed the custom UDAF for quality scores and replaced it with a DataFusion table provider. - Simplified data handling by directly using DataFrames from DataFusion. - Cleaned up unnecessary code and files related to UDAF implementation. - Enhanced error handling and type checking for input data. --- docs/notebooks/base_sequence_quality.ipynb | 4461 +------------------- docs/notebooks/tutorial.ipynb | 336 +- polars_bio/quality_stats.py | 48 +- src/base_sequence_quality.rs | 247 ++ src/context.rs | 17 - src/lib.rs | 138 +- src/operation.rs | 41 +- src/scan.rs | 4 - src/udaf.rs | 210 - 9 files changed, 776 insertions(+), 4726 deletions(-) create mode 100644 src/base_sequence_quality.rs delete mode 100644 src/udaf.rs diff --git a/docs/notebooks/base_sequence_quality.ipynb b/docs/notebooks/base_sequence_quality.ipynb index 35d8bed5..cac9a2ce 100644 --- a/docs/notebooks/base_sequence_quality.ipynb +++ b/docs/notebooks/base_sequence_quality.ipynb @@ -47,37 +47,15 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "5f6fccf3", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:polars_bio:Table: example registered for path: ./example.fastq\n", - "1rows [00:00, 327.48rows/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'base_quality_warn': 'pass', 'base_per_pos_data': [{'pos': 89, 'average': 32.44, 'median': 35.0, 'q1': 33.0, 'q3': 35.0, 'lower': 30.0, 'upper': 38.0}, {'pos': 84, 'average': 32.415, 'median': 35.0, 'q1': 33.0, 'q3': 35.0, 'lower': 30.0, 'upper': 38.0}, {'pos': 96, 'average': 31.315, 'median': 34.0, 'q1': 32.0, 'q3': 35.0, 'lower': 27.5, 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"stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ "a_lazyframe = pb.read_fastq(\"./example.fastq\")\n", - "result = pb.sql(\"SELECT base_sequence_quality(quality_scores) FROM example\").collect()\n", - "print(result.item())" + "# not implemented yet\n", + "result = pb.sql(\"???\").collect()\n", + "print(result)" ] }, { @@ -95,725 +73,29 @@ "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "{'base_quality_warn': 'pass',\n", - " 'base_per_pos_data': [{'lower': 33.5,\n", - " 'q3': 41.0,\n", - " 'q1': 38.0,\n", - " 'median': 40.0,\n", - " 'average': 38.205,\n", - " 'upper': 45.5,\n", - " 'pos': 34},\n", - " {'average': 38.47,\n", - " 'q1': 38.0,\n", - " 'lower': 33.5,\n", - " 'median': 40.0,\n", - " 'q3': 41.0,\n", - " 'pos': 18,\n", - " 'upper': 45.5},\n", - " {'q3': 35.0,\n", - " 'average': 31.815,\n", - " 'median': 35.0,\n", - " 'q1': 33.0,\n", - " 'pos': 86,\n", - " 'lower': 30.0,\n", - " 'upper': 38.0},\n", - " {'q3': 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output_type=\"pandas.DataFrame\")\n", + "print(result)" ] }, { @@ -832,715 +114,35 @@ "outputs": [ { "data": { + "text/html": [ + "
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posscore
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" + ], "text/plain": [ - "{'base_quality_warn': 'pass',\n", - " 'base_per_pos_data': [{'average': 32.195,\n", - " 'pos': 85,\n", - " 'q3': 35.0,\n", - " 'lower': 30.0,\n", - " 'upper': 38.0,\n", - " 'median': 35.0,\n", - " 'q1': 33.0},\n", - " {'average': 38.245,\n", - " 'median': 40.0,\n", - " 'q3': 40.0,\n", - " 'lower': 35.0,\n", - " 'pos': 30,\n", - " 'upper': 43.0,\n", - " 'q1': 38.0},\n", - " {'pos': 2,\n", - " 'average': 32.015,\n", - " 'q3': 34.0,\n", - " 'lower': 26.5,\n", - " 'upper': 38.5,\n", - " 'q1': 31.0,\n", - " 'median': 34.0},\n", - " {'lower': 30.0,\n", - " 'pos': 89,\n", - " 'upper': 38.0,\n", - " 'average': 32.44,\n", - " 'median': 35.0,\n", - " 'q1': 33.0,\n", - " 'q3': 35.0},\n", - " {'q3': 39.0,\n", - " 'average': 37.5,\n", - " 'q1': 37.0,\n", - " 'pos': 12,\n", - " 'median': 39.0,\n", - " 'lower': 34.0,\n", - " 'upper': 42.0},\n", - " {'median': 40.0,\n", - " 'pos': 23,\n", - " 'lower': 33.5,\n", - " 'q3': 41.0,\n", - " 'q1': 38.0,\n", - " 'upper': 45.5,\n", - 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"source": [ - "### Import dependencies" - ] + "source": "### Import dependencies" }, { "cell_type": "code", - "execution_count": null, "id": "7b173024d3e8f76", "metadata": { "ExecuteTime": { @@ -18,49 +15,30 @@ "start_time": "2025-02-24T16:59:36.960817Z" } }, - "outputs": [], "source": [ "import polars_bio as pb\n", "import pandas as pd\n", - "\n", - "# print(type(a_lazyframe))\n", - "# # display types of columns\n", - "# print(a_lazyframe.dtypes)\n", - "# print(type(a_dataframe))\n", - "# print(type(a_pandas_dataframe))\n", - "# print(pb.sql(\"SHOW TABLES\").collect())\n", - "print(pb.sql(\"SHOW TABLES\").collect())\n", - "\n", - "# a_lazyframe = pb.read_fastq(\"./example.fastq\")\n", - "# a_dataframe = a_lazyframe.collect()\n", - "# a_pandas_dataframe = a_lazyframe.collect().to_pandas()\n", - "# print(pb.base_sequence_quality(a_lazyframe))\n", - "# print(pb.base_sequence_quality(a_dataframe))\n", - "# print(pb.base_sequence_quality(a_pandas_dataframe))\n", - "\n", - "# result = pb.sql(\"SELECT base_sequence_quality(quality_scores) FROM example\").collect()\n", - "# print(result.item())\n", - "# print(pb.base_sequence_quality(\"./example.csv\"))\n", - "print(pb.base_sequence_quality(\"./example.fastq\"))\n", - "# print(pb.base_sequence_quality(\"./example.parquet\"))\n", - "\n", - "# sql display all tables and print it\n", - "print(pb.sql(\"SHOW TABLES\").collect())\n", - "# use sql and display result (it is aggregate function that returns string)\n", - "# print(result.item())\n" - ] + "from polars_bio.range_viz import visualize_intervals" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:polars_bio:Creating BioSessionContext\n" + ] + } + ], + "execution_count": 1 }, { "cell_type": "markdown", "id": "d2bb8c193890f27f", "metadata": {}, - "source": [ - "### Sample data" - ] + "source": "### Sample data" }, { "cell_type": "code", - "execution_count": null, "id": "86fe039c3780140e", "metadata": { "ExecuteTime": { @@ -68,31 +46,27 @@ "start_time": "2025-02-24T16:59:37.452650Z" } }, - "outputs": [], "source": [ "df1 = pd.DataFrame(\n", " [[\"chr1\", 1, 5], [\"chr1\", 3, 8], [\"chr1\", 8, 10], [\"chr1\", 12, 14]],\n", " columns=[\"chrom\", \"start\", \"end\"],\n", ")\n", "\n", - "pb.base_sequence_quality(df1)\n", - "\n", "df2 = pd.DataFrame(\n", " [[\"chr1\", 4, 8], [\"chr1\", 10, 11]], columns=[\"chrom\", \"start\", \"end\"]\n", ")" - ] + ], + "outputs": [], + "execution_count": 2 }, { "cell_type": "markdown", "id": "a884cd2960796fdb", "metadata": {}, - "source": [ - "### Overlap" - ] + "source": "### Overlap" }, { "cell_type": "code", - "execution_count": null, "id": "304f3aa6fcdc9650", "metadata": { "ExecuteTime": { @@ -100,14 +74,22 @@ "start_time": "2025-02-24T16:59:37.538707Z" } }, - "outputs": [], "source": [ "overlapping_intervals = pb.overlap(df1, df2, output_type=\"pandas.DataFrame\")" - ] + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:polars_bio.operation:Running Overlap operation with algorithm Coitrees and 1 thread(s)...\n" + ] + } + ], + "execution_count": 3 }, { "cell_type": "code", - "execution_count": null, "id": "61c9254622598622", "metadata": { "ExecuteTime": { @@ -115,14 +97,76 @@ "start_time": "2025-02-24T16:59:37.552440Z" } }, - "outputs": [], "source": [ "display(overlapping_intervals)" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + " chrom_1 start_1 end_1 chrom_2 start_2 end_2\n", + "0 chr1 1 5 chr1 4 8\n", + "1 chr1 3 8 chr1 4 8" + ], + "text/html": [ + "
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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 5 }, { "cell_type": "markdown", "id": "8e2509b9cb5237d8", "metadata": {}, - "source": [ - "### Nearest" - ] + "source": "### Nearest" }, { "cell_type": "code", - "execution_count": null, "id": "bc0f8689c31221b3", "metadata": { "ExecuteTime": { @@ -153,14 +216,22 @@ "start_time": "2025-02-24T16:59:37.652480Z" } }, - "outputs": [], "source": [ "nearest_intervals = pb.nearest(df1, df2, output_type=\"pandas.DataFrame\")" - ] + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:polars_bio.operation:Running Nearest operation with algorithm Coitrees and 1 thread(s)...\n" + ] + } + ], + "execution_count": 6 }, { "cell_type": "code", - "execution_count": null, "id": "aad83ab53e1294fc", "metadata": { "ExecuteTime": { @@ -168,14 +239,101 @@ "start_time": "2025-02-24T16:59:37.665033Z" } }, - "outputs": [], "source": [ "display(nearest_intervals)" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + " chrom_1 start_1 end_1 chrom_2 start_2 end_2 distance\n", + "0 chr1 1 5 chr1 4 8 0\n", + "1 chr1 3 8 chr1 4 8 0\n", + "2 chr1 8 10 chr1 4 8 0\n", + "3 chr1 12 14 chr1 10 11 1" + ], + "text/html": [ + "
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chrom_1start_1end_1chrom_2start_2end_2distance
0chr115chr1480
1chr138chr1480
2chr1810chr1480
3chr11214chr110111
\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 7 }, { "cell_type": "code", - "execution_count": null, "id": "5f69f700b50f58e2", "metadata": { "ExecuteTime": { @@ -183,29 +341,71 @@ "start_time": "2025-02-24T16:59:37.673937Z" } }, - "outputs": [], "source": [ "visualize_intervals(nearest_intervals, \"nearest pair\")" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 8 } ], "metadata": { "kernelspec": { - "display_name": "3.12.9", + "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 3 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.9" + "pygments_lexer": "ipython2", + "version": "2.7.6" } }, "nbformat": 4, diff --git a/polars_bio/quality_stats.py b/polars_bio/quality_stats.py index c18ec841..d19daec9 100644 --- a/polars_bio/quality_stats.py +++ b/polars_bio/quality_stats.py @@ -1,5 +1,6 @@ from pathlib import Path from typing import Union +import datafusion import polars as pl import pandas as pd import pyarrow as pa @@ -10,19 +11,42 @@ ) -def base_sequence_quality(df: Union[str, pl.DataFrame, pl.LazyFrame, pd.DataFrame]): +def base_sequence_quality( + df: Union[str, pl.DataFrame, pl.LazyFrame, pd.DataFrame], + quality_scores_column: str = "quality_scores", + output_type: str = "polars.DataFrame", +) -> Union[pl.DataFrame, pd.DataFrame]: + """ + Compute base sequence quality statistics from various dataframe/file types. + + Args: + df: Input data as a file path or dataframe. + quality_scores_column: Name of the column with quality scores. + output_type: Output type, either "polars.DataFrame" or "pandas.DataFrame". + + Returns: + DataFrame with base sequence quality statistics. + """ if isinstance(df, str): - supported_exts = set([".parquet", ".csv", ".bed", ".vcf", ".fastq"]) + supported_exts = {".parquet", ".csv", ".bed", ".vcf", ".fastq"} ext = set(Path(df).suffixes) - assert ( - len(supported_exts.intersection(ext)) > 0 or len(ext) == 0 - ), "Dataframe1 must be a Parquet, CSV, BED, VCF, or FASTQ file." - return base_sequance_quality_scan(ctx, df) + if not (supported_exts & ext or not ext): + raise ValueError("Input file must be a Parquet, CSV, BED, VCF, or FASTQ file.") + result: datafusion.DataFrame = base_sequance_quality_scan(ctx, df, quality_scores_column) else: - if isinstance(df, pl.DataFrame): - df = df.to_arrow().to_reader() + if isinstance(df, pl.LazyFrame): + arrow_table = df.collect().to_arrow() + elif isinstance(df, pl.DataFrame): + arrow_table = df.to_arrow() elif isinstance(df, pd.DataFrame): - df = pa.Table.from_pandas(df) - elif isinstance(df, pl.LazyFrame): - df = df.collect().to_arrow().to_reader() - return base_sequance_quality_frame(ctx, df) + arrow_table = pa.Table.from_pandas(df) + else: + raise TypeError("Unsupported dataframe type.") + result: datafusion.DataFrame = base_sequance_quality_frame(ctx, arrow_table, quality_scores_column) + + if output_type == "polars.DataFrame": + return result.to_polars() + elif output_type == "pandas.DataFrame": + return result.to_pandas() + else: + raise ValueError("output_type must be 'polars.DataFrame' or 'pandas.DataFrame'") diff --git a/src/base_sequence_quality.rs b/src/base_sequence_quality.rs new file mode 100644 index 00000000..1ca4d1e4 --- /dev/null +++ b/src/base_sequence_quality.rs @@ -0,0 +1,247 @@ +use std::fmt::{Debug, Formatter}; +use std::sync::Arc; + +use arrow_array::{Array, StringArray}; +use async_trait::async_trait; +use datafusion::arrow::datatypes::{DataType, Field, Schema, SchemaRef}; +use datafusion::arrow::record_batch::RecordBatch; +use datafusion::catalog::{Session, TableProvider}; +use datafusion::error::{DataFusionError, Result}; +use datafusion::execution::context::TaskContext; +use datafusion::physical_expr::EquivalenceProperties; +use datafusion::physical_plan::repartition::RepartitionExec; +use datafusion::physical_plan::stream::RecordBatchStreamAdapter; +use datafusion::physical_plan::{ + DisplayAs, DisplayFormatType, ExecutionMode, ExecutionPlan, Partitioning, PlanProperties, + SendableRecordBatchStream, +}; +use datafusion::prelude::SessionContext; +use futures::stream::BoxStream; +use futures::{StreamExt, TryStreamExt}; + +pub struct BaseSequenceQualityProvider { + session: Arc, + table_name: String, + column_name: String, + schema: SchemaRef, +} + +impl BaseSequenceQualityProvider { + pub fn new(session: Arc, table_name: String, column_name: String) -> Self { + let schema = Arc::new(Schema::new(vec![ + Field::new("pos", DataType::Int64, false), + Field::new("score", DataType::Int8, false), + ])); + Self { + session, + table_name, + column_name, + schema, + } + } +} + +impl Debug for BaseSequenceQualityProvider { + fn fmt(&self, _f: &mut Formatter<'_>) -> std::fmt::Result { + Ok(()) + } +} + +#[async_trait] +impl TableProvider for BaseSequenceQualityProvider { + fn as_any(&self) -> &dyn std::any::Any { + self + } + + fn schema(&self) -> SchemaRef { + self.schema.clone() + } + + fn table_type(&self) -> datafusion::datasource::TableType { + datafusion::datasource::TableType::Base + } + + async fn scan( + &self, + _state: &dyn Session, + _projection: Option<&Vec>, + _filters: &[datafusion::prelude::Expr], + _limit: Option, + ) -> Result> { + let target_partitions = self.session.state().config().target_partitions(); + + Ok(Arc::new(BaseSequenceQualityExec { + schema: self.schema.clone(), + session: Arc::clone(&self.session), + table_name: self.table_name.clone(), + column_name: self.column_name.clone(), + cache: PlanProperties::new( + EquivalenceProperties::new(self.schema.clone()), + Partitioning::UnknownPartitioning(target_partitions), + ExecutionMode::Bounded, + ), + })) + } +} + +pub struct BaseSequenceQualityExec { + schema: SchemaRef, + session: Arc, + table_name: String, + column_name: String, + cache: PlanProperties, +} + +impl Debug for BaseSequenceQualityExec { + fn fmt(&self, _f: &mut Formatter<'_>) -> std::fmt::Result { + Ok(()) + } +} + +impl DisplayAs for BaseSequenceQualityExec { + fn fmt_as(&self, _t: DisplayFormatType, _f: &mut Formatter) -> std::fmt::Result { + Ok(()) + } +} + +impl ExecutionPlan for BaseSequenceQualityExec { + fn name(&self) -> &str { + "BaseSequenceQualityExec" + } + + fn as_any(&self) -> &dyn std::any::Any { + self + } + + fn schema(&self) -> SchemaRef { + self.schema.clone() + } + + fn properties(&self) -> &PlanProperties { + &self.cache + } + + fn children(&self) -> Vec<&Arc> { + vec![] + } + + fn with_new_children( + self: Arc, + _children: Vec>, + ) -> Result> { + Ok(self) + } + + fn execute( + &self, + partition: usize, + context: Arc, + ) -> Result { + let fut = get_stream( + Arc::clone(&self.session), + self.table_name.clone(), + self.column_name.clone(), + self.cache.partitioning.partition_count(), + partition, + context, + self.schema.clone(), + ); + let stream = futures::stream::once(fut).try_flatten(); + let schema = self.schema.clone(); + Ok(Box::pin(RecordBatchStreamAdapter::new(schema, stream))) + } +} + +fn decode_score(c: char) -> Option { + let ascii = c as u8; + if ascii >= 33 { + Some(ascii - 33) + } else { + None + } +} + +fn calc_stats(values: &mut Vec) -> (f64, f64, f64, f64, f64, f64) { + values.sort_unstable(); + let n = values.len(); + let average = values.iter().map(|&v| v as f64).sum::() / n as f64; + let median = if n % 2 == 0 { + (values[n / 2 - 1] as f64 + values[n / 2] as f64) / 2.0 + } else { + values[n / 2] as f64 + }; + let q1 = values[n / 4] as f64; + let q3 = values[(3 * n) / 4] as f64; + let iqr = q3 - q1; + let lower = q1 - 1.5 * iqr; + let upper = q3 + 1.5 * iqr; + (average, median, q1, q3, lower, upper) +} + +async fn get_stream( + session: Arc, + table_name: String, + column_name: String, + target_partitions: usize, + partition: usize, + context: Arc, + new_schema: SchemaRef, +) -> Result { + let table_stream = session.table(table_name).await?; + let plan = table_stream.create_physical_plan().await?; + let repartition_stream = + RepartitionExec::try_new(plan, Partitioning::RoundRobinBatch(target_partitions))?; + let partition_stream = repartition_stream.execute(partition, context)?; + let new_schema_out = new_schema.clone(); + let iter = partition_stream.map(move |batch| match batch { + Ok(batch) => { + let index = match batch.schema().index_of(&column_name) { + Ok(idx) => idx, + Err(_) => { + return Err(DataFusionError::Internal(format!( + "Column '{}' not found in schema", + column_name + ))) + }, + }; + let col = batch.column(index); + + // Try to cast to StringArray if possible + let col = arrow::compute::cast(col, &DataType::Utf8) + .map_err(|e| DataFusionError::Internal(format!("Cast error: {e}")))?; + + let col = col + .as_any() + .downcast_ref::() + .ok_or_else(|| DataFusionError::Internal("Expected StringArray".into()))?; + + let mut positions = Vec::new(); + let mut scores = Vec::new(); + + for row in 0..col.len() { + if col.is_null(row) { + continue; + } + let s = col.value(row); + for (pos, byte) in s.bytes().enumerate() { + if let Some(score) = decode_score(byte as char) { + positions.push(pos as i64); + scores.push(score as i8); + } + } + } + + let pos_array = Arc::new(arrow_array::Int64Array::from(positions)); + let score_array = Arc::new(arrow_array::Int8Array::from(scores)); + let new_batch = + RecordBatch::try_new(new_schema.clone(), vec![pos_array, score_array]).unwrap(); + + Ok(new_batch) + }, + Err(e) => Err(e), + }); + + let adapted_stream = + RecordBatchStreamAdapter::new(new_schema_out, Box::pin(iter) as BoxStream<_>); + Ok(Box::pin(adapted_stream)) +} diff --git a/src/context.rs b/src/context.rs index afc02d4e..1525ef05 100644 --- a/src/context.rs +++ b/src/context.rs @@ -1,9 +1,6 @@ use std::collections::HashMap; -use std::sync::Arc; -use datafusion::arrow::datatypes::DataType; use datafusion::config::ConfigOptions; -use datafusion::logical_expr::{create_udaf, AggregateUDF, Volatility}; use datafusion::prelude::SessionConfig; use exon::config::ExonConfigExtension; use exon::ExonSession; @@ -11,8 +8,6 @@ use log::debug; use pyo3::{pyclass, pymethods, PyResult}; use sequila_core::session_context::SequilaConfig; -use crate::udaf::{base_quality_result_type, QualityScoresStats}; - #[pyclass(name = "BioSessionContext")] // #[derive(Clone)] pub struct PyBioSessionContext { @@ -30,7 +25,6 @@ impl PyBioSessionContext { pub fn new(seed: String, catalog_dir: String) -> PyResult { let ctx = create_context().unwrap(); let session_config: HashMap = HashMap::new(); - ctx.session.register_udaf(make_base_sequence_quality_udaf()); Ok(PyBioSessionContext { ctx, session_config, @@ -92,14 +86,3 @@ fn create_context() -> exon::Result { ExonSession::with_config_exon(config) } - -pub fn make_base_sequence_quality_udaf() -> AggregateUDF { - create_udaf( - "base_sequence_quality", // nazwa funkcji w SQL - vec![DataType::Utf8], // typ wejściowy - Arc::new(base_quality_result_type()), - Volatility::Immutable, - Arc::new(|_| Ok(Box::new(QualityScoresStats::new()))), - Arc::new(vec![]), - ) -} diff --git a/src/lib.rs b/src/lib.rs index d92eb0c4..ac78e9b3 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -1,17 +1,16 @@ +mod base_sequence_quality; mod context; mod operation; mod option; mod query; mod scan; mod streaming; -mod udaf; mod udtf; mod utils; use std::string::ToString; use std::sync::{Arc, Mutex}; -use arrow::array::*; use datafusion::arrow::ffi_stream::ArrowArrayStreamReader; use datafusion::arrow::pyarrow::PyArrowType; use datafusion::datasource::MemTable; @@ -23,8 +22,6 @@ use polars_lazy::prelude::{LazyFrame, ScanArgsAnonymous}; use polars_python::error::PyPolarsErr; use polars_python::lazyframe::PyLazyFrame; use pyo3::prelude::*; -use pyo3::types::{PyDict, PyList}; -use scan::deregister_table; use tokio::runtime::Runtime; use crate::context::PyBioSessionContext; @@ -409,128 +406,65 @@ fn py_from_polars( }) } -fn struct_array_to_pydict(py: Python<'_>, struct_array: &StructArray) -> PyResult { - let warn_array = struct_array - .column_by_name("base_quality_warn") - .and_then(|a| a.as_any().downcast_ref::()) - .unwrap(); - - let base_per_pos_array = struct_array - .column_by_name("base_per_pos_data") - .and_then(|a| a.as_any().downcast_ref::()) - .unwrap(); - - let struct_array = base_per_pos_array - .values() - .as_any() - .downcast_ref::() - .unwrap(); - - let mut base_per_pos_data = Vec::new(); - for i in 0..base_per_pos_array.value_length(0) { - let mut row = std::collections::HashMap::new(); - - let pos = struct_array - .column_by_name("pos") - .and_then(|a| a.as_any().downcast_ref::()) - .unwrap() - .value(i as usize); - let average = struct_array - .column_by_name("average") - .and_then(|a| a.as_any().downcast_ref::()) - .unwrap() - .value(i as usize); - let median = struct_array - .column_by_name("median") - .and_then(|a| a.as_any().downcast_ref::()) - .unwrap() - .value(i as usize); - let q1 = struct_array - .column_by_name("q1") - .and_then(|a| a.as_any().downcast_ref::()) - .unwrap() - .value(i as usize); - let q3 = struct_array - .column_by_name("q3") - .and_then(|a| a.as_any().downcast_ref::()) - .unwrap() - .value(i as usize); - let lower = struct_array - .column_by_name("lower") - .and_then(|a| a.as_any().downcast_ref::()) - .unwrap() - .value(i as usize); - let upper = struct_array - .column_by_name("upper") - .and_then(|a| a.as_any().downcast_ref::()) - .unwrap() - .value(i as usize); - - row.insert("pos", pos.into_py(py)); - row.insert("average", average.into_py(py)); - row.insert("median", median.into_py(py)); - row.insert("q1", q1.into_py(py)); - row.insert("q3", q3.into_py(py)); - row.insert("lower", lower.into_py(py)); - row.insert("upper", upper.into_py(py)); - - base_per_pos_data.push(row.into_py(py).to_object(py)); - } - - let result_dict = PyDict::new_bound(py); - result_dict.set_item("base_quality_warn", warn_array.value(0))?; - result_dict.set_item( - "base_per_pos_data", - PyList::new_bound(py, base_per_pos_data), - )?; - - Ok(result_dict.to_object(py)) -} - fn handle_base_sequence_quality<'a, F>( - py: Python<'a>, + _py: Python<'a>, py_ctx: &PyBioSessionContext, table_name: &str, + column: &str, register_fn: F, -) -> PyResult +) -> PyResult where F: FnOnce(&PyBioSessionContext, &str, &Runtime), { let ctx = &py_ctx.ctx; let rt = Runtime::new().unwrap(); register_fn(py_ctx, table_name, &rt); - let result_opt = rt.block_on(do_base_sequence_quality(ctx, &table_name.to_string())); - deregister_table(ctx, table_name); - if let Some(struct_array) = result_opt { - struct_array_to_pydict(py, &struct_array) - } else { - Ok(py.None()) - } + let data_frame = rt.block_on(do_base_sequence_quality( + ctx, + table_name.to_string(), + column.to_string(), + )); + // deregister_table(ctx, table_name); + Ok(PyDataFrame::new(data_frame)) } #[pyfunction] -#[pyo3(signature = (py_ctx, path))] +#[pyo3(signature = (py_ctx, path, column))] fn base_sequance_quality_scan( py: Python<'_>, py_ctx: &PyBioSessionContext, path: String, -) -> PyResult { - handle_base_sequence_quality(py, py_ctx, DEFAULT_TABLE_NAME, |py_ctx, table_name, rt| { - let ctx = &py_ctx.ctx; - maybe_register_table(path, &table_name.to_string(), None, ctx, rt); - }) + column: String, +) -> PyResult { + handle_base_sequence_quality( + py, + py_ctx, + DEFAULT_TABLE_NAME, + &column, + |py_ctx, table_name, rt| { + let ctx = &py_ctx.ctx; + maybe_register_table(path, &table_name.to_string(), None, ctx, rt); + }, + ) } #[pyfunction] -#[pyo3(signature = (py_ctx, df))] +#[pyo3(signature = (py_ctx, df, column))] fn base_sequance_quality_frame( py: Python<'_>, py_ctx: &PyBioSessionContext, df: PyArrowType, -) -> PyResult { - handle_base_sequence_quality(py, py_ctx, DEFAULT_TABLE_NAME, |py_ctx, table_name, _rt| { - register_frame(py_ctx, df, table_name.to_string()); - }) + column: String, +) -> PyResult { + handle_base_sequence_quality( + py, + py_ctx, + DEFAULT_TABLE_NAME, + &column, + |py_ctx, table_name, _rt| { + register_frame(py_ctx, df, table_name.to_string()); + }, + ) } #[pymodule] diff --git a/src/operation.rs b/src/operation.rs index 808c55a9..d4481184 100644 --- a/src/operation.rs +++ b/src/operation.rs @@ -1,12 +1,12 @@ use std::sync::Arc; -use arrow_array::{Array, StructArray}; use datafusion::catalog_common::TableReference; use exon::ExonSession; use log::{debug, info}; use sequila_core::session_context::{Algorithm, SequilaConfig}; use tokio::runtime::Runtime; +use crate::base_sequence_quality::BaseSequenceQualityProvider; use crate::context::set_option_internal; use crate::option::{FilterOp, RangeOp, RangeOptions}; use crate::query::{count_overlaps_query, nearest_query, overlap_query}; @@ -194,33 +194,20 @@ async fn do_count_overlaps_coverage_naive( pub(crate) async fn do_base_sequence_quality( ctx: &ExonSession, - table: &String, -) -> Option { - let query = format!( - "SELECT base_sequence_quality(quality_scores) as result FROM {}", - table - ); + table: String, + column: String, +) -> datafusion::dataframe::DataFrame { + let session = &ctx.session; + let provider = + BaseSequenceQualityProvider::new(Arc::new(session.clone()), table.clone(), column.clone()); + let table_name = format!("{}_base_sequence_quality", table); + ctx.session.deregister_table(table_name.clone()).ok(); + ctx.session + .register_table(table_name.clone(), Arc::new(provider)) + .unwrap(); + let query = format!("SELECT * FROM {}", table_name); debug!("Query: {}", query); - let batches = ctx.sql(&query).await.unwrap().collect().await.unwrap(); - - if let Some(batch) = batches.get(0) { - let col_idx = batch - .schema() - .fields() - .iter() - .position(|f| f.name() == "result") - .expect("Column 'result' not found"); - let array = batch.column(col_idx); - - if array.len() > 0 && !array.is_null(0) { - if let Some(struct_array) = array.as_any().downcast_ref::() { - return Some(struct_array.clone()); - } else { - panic!("Unsupported result type: {:?}", array.data_type()); - } - } - } - None + ctx.sql(&query).await.unwrap() } async fn get_non_join_columns( diff --git a/src/scan.rs b/src/scan.rs index e7795a99..34412780 100644 --- a/src/scan.rs +++ b/src/scan.rs @@ -156,7 +156,3 @@ pub(crate) fn maybe_register_table( } .to_string() } - -pub(crate) fn deregister_table(ctx: &ExonSession, table_name: &str) { - let _ = ctx.session.deregister_table(table_name); -} diff --git a/src/udaf.rs b/src/udaf.rs deleted file mode 100644 index a26ac237..00000000 --- a/src/udaf.rs +++ /dev/null @@ -1,210 +0,0 @@ -use std::collections::HashMap; -use std::sync::Arc; - -use arrow::buffer::OffsetBuffer; -use arrow_array::{Array, ArrayRef, Float64Array, Int64Array, ListArray, StringArray, StructArray}; -use arrow_schema::{DataType, Field, Fields}; -use datafusion::error::{DataFusionError, Result}; -use datafusion::physical_plan::Accumulator; -use datafusion::scalar::ScalarValue; - -#[derive(Debug)] -pub(crate) struct QualityScoresStats { - values_per_pos: HashMap>, // key: position, value: decoded quality scores -} - -impl QualityScoresStats { - pub fn new() -> Self { - Self { - values_per_pos: HashMap::new(), - } - } - - fn decode_score(c: char) -> Option { - let ascii = c as u8; - if ascii >= 33 { - Some(ascii - 33) - } else { - None - } - } - - fn calc_stats(values: &mut Vec) -> (f64, f64, f64, f64, f64, f64) { - values.sort_unstable(); - let n = values.len(); - let average = values.iter().map(|&v| v as f64).sum::() / n as f64; - let median = if n % 2 == 0 { - (values[n / 2 - 1] as f64 + values[n / 2] as f64) / 2.0 - } else { - values[n / 2] as f64 - }; - let q1 = values[n / 4] as f64; - let q3 = values[(3 * n) / 4] as f64; - let iqr = q3 - q1; - let lower = q1 - 1.5 * iqr; - let upper = q3 + 1.5 * iqr; - (average, median, q1, q3, lower, upper) - } -} - -impl Accumulator for QualityScoresStats { - fn state(&mut self) -> Result> { - Ok(vec![]) - } - - fn evaluate(&mut self) -> Result { - #[derive(Default)] - struct StatColumns { - pos: Vec, - avg: Vec, - median: Vec, - q1: Vec, - q3: Vec, - lower: Vec, - upper: Vec, - } - - let mut cols = StatColumns::default(); - let mut base_quality_warn = "pass"; - - for (&pos, values) in &mut self.values_per_pos { - if values.is_empty() { - continue; - } - - let (avg, median, q1, q3, lower, upper) = Self::calc_stats(values); - - cols.pos.push(pos as i64); - cols.avg.push(avg); - cols.median.push(median); - cols.q1.push(q1); - cols.q3.push(q3); - cols.lower.push(lower); - cols.upper.push(upper); - - base_quality_warn = match (q1 <= 20.0, q1 <= 25.0, base_quality_warn) { - (true, _, _) => "fail", - (false, true, "pass") => "warn", - _ => base_quality_warn, - }; - } - - let result_type = base_quality_result_type(); - - let fields = match result_type { - DataType::Struct(ref fields) => fields.clone(), - _ => { - return Err(DataFusionError::Execution( - "Unexpected result type".to_string(), - )) - }, - }; - - let base_quality_warn_field = fields[0].clone(); - let base_per_pos_data_field = fields[1].clone(); - - let base_per_pos_data_element_field = match base_per_pos_data_field.data_type() { - DataType::List(inner_field) => inner_field.as_ref().clone(), - _ => return Err(DataFusionError::Execution("Expected List type".to_string())), - }; - - let struct_fields = match base_per_pos_data_element_field.data_type() { - DataType::Struct(inner_fields) => inner_fields.clone(), - _ => { - return Err(DataFusionError::Execution( - "Expected Struct type inside list".to_string(), - )) - }, - }; - - let to_array = |vec: Vec| Arc::new(Float64Array::from(vec)) as ArrayRef; - - let struct_array = Arc::new(StructArray::new( - struct_fields.clone(), - vec![ - Arc::new(Int64Array::from(cols.pos)) as ArrayRef, - to_array(cols.avg), - to_array(cols.median), - to_array(cols.q1), - to_array(cols.q3), - to_array(cols.lower), - to_array(cols.upper), - ], - None, - )) as ArrayRef; - - let list_array = Arc::new(ListArray::new( - Arc::new(base_per_pos_data_element_field), - OffsetBuffer::new(vec![0, struct_array.len() as i32].into()), - struct_array, - None, - )); - - Ok(ScalarValue::Struct(Arc::new(StructArray::from(vec![ - ( - base_quality_warn_field, - Arc::new(StringArray::from(vec![base_quality_warn])) as ArrayRef, - ), - (base_per_pos_data_field, list_array), - ])))) - } - - fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> { - let arr = values[0] - .as_any() - .downcast_ref::() - .ok_or_else(|| { - datafusion::error::DataFusionError::Internal("Expected StringArray".to_string()) - })?; - - for i in 0..arr.len() { - if arr.is_null(i) { - continue; - } - let val = arr.value(i); - - for (j, c) in val.chars().enumerate() { - if let Some(decoded) = Self::decode_score(c) { - self.values_per_pos.entry(j).or_default().push(decoded); - } - } - } - - Ok(()) - } - - fn merge_batch(&mut self, _states: &[ArrayRef]) -> Result<()> { - Ok(()) - } - - fn size(&self) -> usize { - std::mem::size_of_val(self) - } -} - -pub fn base_quality_result_type() -> DataType { - let per_pos_fields = Fields::from(vec![ - Field::new("pos", DataType::Int64, false), - Field::new("average", DataType::Float64, false), - Field::new("median", DataType::Float64, false), - Field::new("q1", DataType::Float64, false), - Field::new("q3", DataType::Float64, false), - Field::new("lower", DataType::Float64, false), - Field::new("upper", DataType::Float64, false), - ]); - - let base_per_pos_element_field = Field::new( - "base_per_pos_data_element", - DataType::Struct(per_pos_fields), - false, - ); - - DataType::Struct(Fields::from(vec![ - Field::new("base_quality_warn", DataType::Utf8, false), - Field::new( - "base_per_pos_data", - DataType::List(Arc::new(base_per_pos_element_field)), - false, - ), - ])) -} From 1bff752c6caa4ea7e01d5a7a54501079c4b8131a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Bart=C5=82omiej=20=C5=9Acise=C5=82?= Date: Thu, 5 Jun 2025 17:58:00 +0200 Subject: [PATCH 6/6] Implement sequence quality histogram and quantile statistics - Added `SequenceQualityHistogramProvider` and `SequenceQualityHistogramExec` to compute quality histograms from sequence data. - Introduced `QuantileStatsTableProvider` and `QuantileStatsExec` for calculating quantile statistics based on histogram data. --- docs/notebooks/base_sequence_quality.ipynb | 267 ++++++++---------- polars_bio/quality_stats.py | 17 +- src/lib.rs | 66 ++--- src/operation.rs | 42 ++- src/quantile_stats.rs | 266 +++++++++++++++++ ...ality.rs => sequence_quality_histogram.rs} | 172 ++++++----- 6 files changed, 544 insertions(+), 286 deletions(-) create mode 100644 src/quantile_stats.rs rename src/{base_sequence_quality.rs => sequence_quality_histogram.rs} (53%) diff --git a/docs/notebooks/base_sequence_quality.ipynb b/docs/notebooks/base_sequence_quality.ipynb index cac9a2ce..ac49dd6f 100644 --- a/docs/notebooks/base_sequence_quality.ipynb +++ b/docs/notebooks/base_sequence_quality.ipynb @@ -37,27 +37,6 @@ "### Usage examples" ] }, - { - "cell_type": "markdown", - "id": "f9aedeb9", - "metadata": {}, - "source": [ - "#### Usage example - calling UDAF directly in SQL" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f6fccf3", - "metadata": {}, - "outputs": [], - "source": [ - "a_lazyframe = pb.read_fastq(\"./example.fastq\")\n", - "# not implemented yet\n", - "result = pb.sql(\"???\").collect()\n", - "print(result)" - ] - }, { "cell_type": "markdown", "id": "b238193d", @@ -68,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "0420c240", "metadata": {}, "outputs": [ @@ -76,25 +55,25 @@ "name": "stdout", "output_type": "stream", "text": [ - " pos score\n", - "0 0 2\n", - "1 1 19\n", - "2 2 33\n", - "3 3 35\n", - "4 4 37\n", - "... ... ...\n", - "20195 96 35\n", - "20196 97 32\n", - "20197 98 35\n", - "20198 99 35\n", - "20199 100 33\n", + " pos avg q1 median q3 lower upper\n", + "0 47 37.665 37.535714 39.921053 41.060185 32.249008 46.346892\n", + "1 38 37.640 37.964286 40.067308 41.024038 33.374657 45.613668\n", + "2 65 35.995 35.190000 37.433333 39.810000 28.260000 46.740000\n", + "3 3 35.690 35.483333 37.208661 37.600394 32.307743 40.775984\n", + "4 41 37.870 37.678571 40.116071 41.004902 32.689076 45.994398\n", + ".. ... ... ... ... ... ... ...\n", + "96 42 37.780 37.583333 40.139344 40.954918 32.525956 46.012295\n", + "97 43 37.775 38.114583 40.126866 40.869403 33.982354 45.001632\n", + "98 15 38.725 38.226190 40.352459 41.168033 33.813427 45.580796\n", + "99 46 37.790 37.479167 39.975000 41.042453 32.134237 46.387382\n", + "100 24 38.265 38.397059 40.095745 41.125000 34.305147 45.216912\n", "\n", - "[20200 rows x 2 columns]\n" + "[101 rows x 7 columns]\n" ] } ], "source": [ - "result = pb.base_sequence_quality(\"example.fastq\", output_type=\"pandas.DataFrame\")\n", + "result = pb.base_sequence_quality(\"example.fastq\", output_type=\"pandas.DataFrame\", target_partitions=2)\n", "print(result)" ] }, @@ -108,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "66c3af24", "metadata": {}, "outputs": [ @@ -122,30 +101,30 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (20_200, 2)
posscore
i64i8
02
119
233
335
437
……
9635
9732
9835
9935
10033
" + "shape: (101, 7)
posavgq1medianq3lowerupper
u64f64f64f64f64f64f64
4737.66537.53571439.92105341.06018532.24900846.346892
9032.27533.18333334.98684235.6396129.49891839.324026
2637.85538.02884640.07894740.95175433.64448445.336117
735.435.46590936.9062537.48737432.43371240.519571
335.6935.48333337.20866137.60039432.30774340.775984
…………………
1938.42538.237540.23846241.00490234.08639745.156005
9430.77532.05769234.65151535.5126.87923140.688462
1738.50538.12540.12745141.0937533.67187545.546875
7932.4631.9687535.20535736.437525.26562543.140625
4137.8737.67857140.11607141.00490232.68907645.994398
" ], "text/plain": [ - "shape: (20_200, 2)\n", - "┌─────┬───────┐\n", - "│ pos ┆ score │\n", - "│ --- ┆ --- │\n", - "│ i64 ┆ i8 │\n", - "╞═════╪═══════╡\n", - "│ 0 ┆ 2 │\n", - "│ 1 ┆ 19 │\n", - "│ 2 ┆ 33 │\n", - "│ 3 ┆ 35 │\n", - "│ 4 ┆ 37 │\n", - "│ … ┆ … │\n", - "│ 96 ┆ 35 │\n", - "│ 97 ┆ 32 │\n", - "│ 98 ┆ 35 │\n", - "│ 99 ┆ 35 │\n", - "│ 100 ┆ 33 │\n", - "└─────┴───────┘" + "shape: (101, 7)\n", + "┌─────┬────────┬───────────┬───────────┬───────────┬───────────┬───────────┐\n", + "│ pos ┆ avg ┆ q1 ┆ median ┆ q3 ┆ lower ┆ upper │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ u64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", + "╞═════╪════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡\n", + "│ 47 ┆ 37.665 ┆ 37.535714 ┆ 39.921053 ┆ 41.060185 ┆ 32.249008 ┆ 46.346892 │\n", + "│ 90 ┆ 32.275 ┆ 33.183333 ┆ 34.986842 ┆ 35.63961 ┆ 29.498918 ┆ 39.324026 │\n", + "│ 26 ┆ 37.855 ┆ 38.028846 ┆ 40.078947 ┆ 40.951754 ┆ 33.644484 ┆ 45.336117 │\n", + "│ 7 ┆ 35.4 ┆ 35.465909 ┆ 36.90625 ┆ 37.487374 ┆ 32.433712 ┆ 40.519571 │\n", + "│ 3 ┆ 35.69 ┆ 35.483333 ┆ 37.208661 ┆ 37.600394 ┆ 32.307743 ┆ 40.775984 │\n", + "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", + "│ 19 ┆ 38.425 ┆ 38.2375 ┆ 40.238462 ┆ 41.004902 ┆ 34.086397 ┆ 45.156005 │\n", + "│ 94 ┆ 30.775 ┆ 32.057692 ┆ 34.651515 ┆ 35.51 ┆ 26.879231 ┆ 40.688462 │\n", + "│ 17 ┆ 38.505 ┆ 38.125 ┆ 40.127451 ┆ 41.09375 ┆ 33.671875 ┆ 45.546875 │\n", + "│ 79 ┆ 32.46 ┆ 31.96875 ┆ 35.205357 ┆ 36.4375 ┆ 25.265625 ┆ 43.140625 │\n", + "│ 41 ┆ 37.87 ┆ 37.678571 ┆ 40.116071 ┆ 41.004902 ┆ 32.689076 ┆ 45.994398 │\n", + "└─────┴────────┴───────────┴───────────┴───────────┴───────────┴───────────┘" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -164,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "a2cb9c97", "metadata": {}, "outputs": [ @@ -178,30 +157,30 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (20_200, 2)
posscore
i64i8
02
119
233
335
437
……
9635
9732
9835
9935
10033
" + "shape: (101, 7)
posavgq1medianq3lowerupper
u64f64f64f64f64f64f64
4437.56537.7187540.13392941.02403832.76081745.981971
2738.4438.23437540.2741.20703133.77539145.666016
7233.26533.86111135.687537.92391327.76690844.018116
8631.81533.36538535.13291135.76265829.76947439.358569
9032.27533.18333334.98684235.6396129.49891839.324026
…………………
3438.20537.97916740.15957441.16803333.19586745.951332
8432.41533.2535.18243235.8547329.34290539.761824
4937.2136.67857139.64583340.76388930.55059546.891865
6336.2535.21590938.02631640.27527.62727347.863636
8032.6132.62535.28947436.42045526.93181842.113636
" ], "text/plain": [ - "shape: (20_200, 2)\n", - "┌─────┬───────┐\n", - "│ pos ┆ score │\n", - "│ --- ┆ --- │\n", - "│ i64 ┆ i8 │\n", - "╞═════╪═══════╡\n", - "│ 0 ┆ 2 │\n", - "│ 1 ┆ 19 │\n", - "│ 2 ┆ 33 │\n", - "│ 3 ┆ 35 │\n", - "│ 4 ┆ 37 │\n", - "│ … ┆ … │\n", - "│ 96 ┆ 35 │\n", - "│ 97 ┆ 32 │\n", - "│ 98 ┆ 35 │\n", - "│ 99 ┆ 35 │\n", - "│ 100 ┆ 33 │\n", - "└─────┴───────┘" + "shape: (101, 7)\n", + "┌─────┬────────┬───────────┬───────────┬───────────┬───────────┬───────────┐\n", + "│ pos ┆ avg ┆ q1 ┆ median ┆ q3 ┆ lower ┆ upper │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ u64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", + "╞═════╪════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡\n", + "│ 44 ┆ 37.565 ┆ 37.71875 ┆ 40.133929 ┆ 41.024038 ┆ 32.760817 ┆ 45.981971 │\n", + "│ 27 ┆ 38.44 ┆ 38.234375 ┆ 40.27 ┆ 41.207031 ┆ 33.775391 ┆ 45.666016 │\n", + "│ 72 ┆ 33.265 ┆ 33.861111 ┆ 35.6875 ┆ 37.923913 ┆ 27.766908 ┆ 44.018116 │\n", + "│ 86 ┆ 31.815 ┆ 33.365385 ┆ 35.132911 ┆ 35.762658 ┆ 29.769474 ┆ 39.358569 │\n", + "│ 90 ┆ 32.275 ┆ 33.183333 ┆ 34.986842 ┆ 35.63961 ┆ 29.498918 ┆ 39.324026 │\n", + "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", + "│ 34 ┆ 38.205 ┆ 37.979167 ┆ 40.159574 ┆ 41.168033 ┆ 33.195867 ┆ 45.951332 │\n", + "│ 84 ┆ 32.415 ┆ 33.25 ┆ 35.182432 ┆ 35.85473 ┆ 29.342905 ┆ 39.761824 │\n", + "│ 49 ┆ 37.21 ┆ 36.678571 ┆ 39.645833 ┆ 40.763889 ┆ 30.550595 ┆ 46.891865 │\n", + "│ 63 ┆ 36.25 ┆ 35.215909 ┆ 38.026316 ┆ 40.275 ┆ 27.627273 ┆ 47.863636 │\n", + "│ 80 ┆ 32.61 ┆ 32.625 ┆ 35.289474 ┆ 36.420455 ┆ 26.931818 ┆ 42.113636 │\n", + "└─────┴────────┴───────────┴───────────┴───────────┴───────────┴───────────┘" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -220,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "1899ca01", "metadata": {}, "outputs": [ @@ -242,7 +221,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "200rows [00:00, 85580.58rows/s]\n" + "200rows [00:00, 62156.25rows/s]\n" ] }, { @@ -255,30 +234,30 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (20_200, 2)
posscore
i64i8
02
119
233
335
437
……
9635
9732
9835
9935
10033
" + "shape: (101, 7)
posavgq1medianq3lowerupper
u64f64f64f64f64f64f64
6436.09535.06481537.740.14583327.44328747.767361
435.6835.55288537.20866137.60039432.48162140.671657
6835.9134.94047636.93181839.53947428.0419846.43797
7233.26533.86111135.687537.92391327.76690844.018116
5836.7735.33823538.9062540.71710527.2699348.78541
…………………
2637.85538.02884640.07894740.95175433.64448445.336117
937.3637.3437539.01470639.50245134.10569942.740502
7133.00533.79166735.85135138.58333326.60416745.770833
1137.7137.78448339.09459539.54279335.14701842.180258
2438.26538.39705940.09574541.12534.30514745.216912
" ], "text/plain": [ - "shape: (20_200, 2)\n", - "┌─────┬───────┐\n", - "│ pos ┆ score │\n", - "│ --- ┆ --- │\n", - "│ i64 ┆ i8 │\n", - "╞═════╪═══════╡\n", - "│ 0 ┆ 2 │\n", - "│ 1 ┆ 19 │\n", - "│ 2 ┆ 33 │\n", - "│ 3 ┆ 35 │\n", - "│ 4 ┆ 37 │\n", - "│ … ┆ … │\n", - "│ 96 ┆ 35 │\n", - "│ 97 ┆ 32 │\n", - "│ 98 ┆ 35 │\n", - "│ 99 ┆ 35 │\n", - "│ 100 ┆ 33 │\n", - "└─────┴───────┘" + "shape: (101, 7)\n", + "┌─────┬────────┬───────────┬───────────┬───────────┬───────────┬───────────┐\n", + "│ pos ┆ avg ┆ q1 ┆ median ┆ q3 ┆ lower ┆ upper │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ u64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", + "╞═════╪════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡\n", + "│ 64 ┆ 36.095 ┆ 35.064815 ┆ 37.7 ┆ 40.145833 ┆ 27.443287 ┆ 47.767361 │\n", + "│ 4 ┆ 35.68 ┆ 35.552885 ┆ 37.208661 ┆ 37.600394 ┆ 32.481621 ┆ 40.671657 │\n", + "│ 68 ┆ 35.91 ┆ 34.940476 ┆ 36.931818 ┆ 39.539474 ┆ 28.04198 ┆ 46.43797 │\n", + "│ 72 ┆ 33.265 ┆ 33.861111 ┆ 35.6875 ┆ 37.923913 ┆ 27.766908 ┆ 44.018116 │\n", + "│ 58 ┆ 36.77 ┆ 35.338235 ┆ 38.90625 ┆ 40.717105 ┆ 27.26993 ┆ 48.78541 │\n", + "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", + "│ 26 ┆ 37.855 ┆ 38.028846 ┆ 40.078947 ┆ 40.951754 ┆ 33.644484 ┆ 45.336117 │\n", + "│ 9 ┆ 37.36 ┆ 37.34375 ┆ 39.014706 ┆ 39.502451 ┆ 34.105699 ┆ 42.740502 │\n", + "│ 71 ┆ 33.005 ┆ 33.791667 ┆ 35.851351 ┆ 38.583333 ┆ 26.604167 ┆ 45.770833 │\n", + "│ 11 ┆ 37.71 ┆ 37.784483 ┆ 39.094595 ┆ 39.542793 ┆ 35.147018 ┆ 42.180258 │\n", + "│ 24 ┆ 38.265 ┆ 38.397059 ┆ 40.095745 ┆ 41.125 ┆ 34.305147 ┆ 45.216912 │\n", + "└─────┴────────┴───────────┴───────────┴───────────┴───────────┴───────────┘" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -299,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "7830b8aa", "metadata": {}, "outputs": [ @@ -308,7 +287,7 @@ "output_type": "stream", "text": [ "INFO:polars_bio:Table: example registered for path: ./example.fastq\n", - "200rows [00:00, 99332.24rows/s]" + "200rows [00:00, 66234.57rows/s]" ] }, { @@ -335,30 +314,30 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (20_200, 2)
posscore
i64i8
02
119
233
335
437
……
9635
9732
9835
9935
10033
" + "shape: (101, 7)
posavgq1medianq3lowerupper
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335.6935.48333337.20866137.60039432.30774340.775984
2938.59538.3540.25833341.0937534.23437545.209375
4437.56537.7187540.13392941.02403832.76081745.981971
7630.26530.91666735.41489437.07352921.68137346.308824
6735.9634.72368436.97222239.8127.09421147.439474
…………………
1538.72538.2261940.35245941.16803333.81342745.580796
2637.85538.02884640.07894740.95175433.64448445.336117
3937.89538.02777840.12540.85661833.78451845.099877
9730.6731.57534.89062535.55722925.60165741.530572
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" ], "text/plain": [ - "shape: (20_200, 2)\n", - "┌─────┬───────┐\n", - "│ pos ┆ score │\n", - "│ --- ┆ --- │\n", - "│ i64 ┆ i8 │\n", - "╞═════╪═══════╡\n", - "│ 0 ┆ 2 │\n", - "│ 1 ┆ 19 │\n", - "│ 2 ┆ 33 │\n", - "│ 3 ┆ 35 │\n", - "│ 4 ┆ 37 │\n", - "│ … ┆ … │\n", - "│ 96 ┆ 35 │\n", - "│ 97 ┆ 32 │\n", - "│ 98 ┆ 35 │\n", - "│ 99 ┆ 35 │\n", - "│ 100 ┆ 33 │\n", - "└─────┴───────┘" + "shape: (101, 7)\n", + "┌─────┬────────┬───────────┬───────────┬───────────┬───────────┬───────────┐\n", + "│ pos ┆ avg ┆ q1 ┆ median ┆ q3 ┆ lower ┆ upper │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ u64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", + "╞═════╪════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡\n", + "│ 3 ┆ 35.69 ┆ 35.483333 ┆ 37.208661 ┆ 37.600394 ┆ 32.307743 ┆ 40.775984 │\n", + "│ 29 ┆ 38.595 ┆ 38.35 ┆ 40.258333 ┆ 41.09375 ┆ 34.234375 ┆ 45.209375 │\n", + "│ 44 ┆ 37.565 ┆ 37.71875 ┆ 40.133929 ┆ 41.024038 ┆ 32.760817 ┆ 45.981971 │\n", + "│ 76 ┆ 30.265 ┆ 30.916667 ┆ 35.414894 ┆ 37.073529 ┆ 21.681373 ┆ 46.308824 │\n", + "│ 67 ┆ 35.96 ┆ 34.723684 ┆ 36.972222 ┆ 39.81 ┆ 27.094211 ┆ 47.439474 │\n", + "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", + "│ 15 ┆ 38.725 ┆ 38.22619 ┆ 40.352459 ┆ 41.168033 ┆ 33.813427 ┆ 45.580796 │\n", + "│ 26 ┆ 37.855 ┆ 38.028846 ┆ 40.078947 ┆ 40.951754 ┆ 33.644484 ┆ 45.336117 │\n", + "│ 39 ┆ 37.895 ┆ 38.027778 ┆ 40.125 ┆ 40.856618 ┆ 33.784518 ┆ 45.099877 │\n", + "│ 97 ┆ 30.67 ┆ 31.575 ┆ 34.890625 ┆ 35.557229 ┆ 25.601657 ┆ 41.530572 │\n", + "│ 51 ┆ 37.53 ┆ 36.71875 ┆ 39.447368 ┆ 41.024038 ┆ 30.260817 ┆ 47.481971 │\n", + "└─────┴────────┴───────────┴───────────┴───────────┴───────────┴───────────┘" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -380,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "56817174", "metadata": {}, "outputs": [ @@ -389,7 +368,7 @@ "output_type": "stream", "text": [ "INFO:polars_bio:Table: example registered for path: ./example.fastq\n", - "200rows [00:00, 91638.72rows/s]" + "200rows [00:00, 91799.17rows/s]" ] }, { @@ -416,30 +395,30 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (20_200, 2)
posscore
i64i8
02
119
233
335
437
……
9635
9732
9835
9935
10033
" + "shape: (101, 7)
posavgq1medianq3lowerupper
u64f64f64f64f64f64f64
7133.00533.79166735.85135138.58333326.60416745.770833
7430.8332.7535.53260937.48529425.64705944.588235
4537.4536.5937540.07142941.07727329.86846647.802557
4937.2136.67857139.64583340.76388930.55059546.891865
7531.0630.95833335.41836737.28333321.47083346.770833
…………………
5037.42535.97539.77083341.06018528.34722248.687963
335.6935.48333337.20866137.60039432.30774340.775984
030.13531.21363633.72222234.48214326.31087739.384903
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" ], "text/plain": [ - "shape: (20_200, 2)\n", - "┌─────┬───────┐\n", - "│ pos ┆ score │\n", - "│ --- ┆ --- │\n", - "│ i64 ┆ i8 │\n", - "╞═════╪═══════╡\n", - "│ 0 ┆ 2 │\n", - "│ 1 ┆ 19 │\n", - "│ 2 ┆ 33 │\n", - "│ 3 ┆ 35 │\n", - "│ 4 ┆ 37 │\n", - "│ … ┆ … │\n", - "│ 96 ┆ 35 │\n", - "│ 97 ┆ 32 │\n", - "│ 98 ┆ 35 │\n", - "│ 99 ┆ 35 │\n", - "│ 100 ┆ 33 │\n", - "└─────┴───────┘" + "shape: (101, 7)\n", + "┌─────┬────────┬───────────┬───────────┬───────────┬───────────┬───────────┐\n", + "│ pos ┆ avg ┆ q1 ┆ median ┆ q3 ┆ lower ┆ upper │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ u64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", + "╞═════╪════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡\n", + "│ 71 ┆ 33.005 ┆ 33.791667 ┆ 35.851351 ┆ 38.583333 ┆ 26.604167 ┆ 45.770833 │\n", + "│ 74 ┆ 30.83 ┆ 32.75 ┆ 35.532609 ┆ 37.485294 ┆ 25.647059 ┆ 44.588235 │\n", + "│ 45 ┆ 37.45 ┆ 36.59375 ┆ 40.071429 ┆ 41.077273 ┆ 29.868466 ┆ 47.802557 │\n", + "│ 49 ┆ 37.21 ┆ 36.678571 ┆ 39.645833 ┆ 40.763889 ┆ 30.550595 ┆ 46.891865 │\n", + "│ 75 ┆ 31.06 ┆ 30.958333 ┆ 35.418367 ┆ 37.283333 ┆ 21.470833 ┆ 46.770833 │\n", + "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", + "│ 50 ┆ 37.425 ┆ 35.975 ┆ 39.770833 ┆ 41.060185 ┆ 28.347222 ┆ 48.687963 │\n", + "│ 3 ┆ 35.69 ┆ 35.483333 ┆ 37.208661 ┆ 37.600394 ┆ 32.307743 ┆ 40.775984 │\n", + "│ 0 ┆ 30.135 ┆ 31.213636 ┆ 33.722222 ┆ 34.482143 ┆ 26.310877 ┆ 39.384903 │\n", + "│ 37 ┆ 38.0 ┆ 38.215278 ┆ 40.080357 ┆ 40.96875 ┆ 34.085069 ┆ 45.098958 │\n", + "│ 65 ┆ 35.995 ┆ 35.19 ┆ 37.433333 ┆ 39.81 ┆ 28.26 ┆ 46.74 │\n", + "└─────┴────────┴───────────┴───────────┴───────────┴───────────┴───────────┘" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } diff --git a/polars_bio/quality_stats.py b/polars_bio/quality_stats.py index d19daec9..501bd44a 100644 --- a/polars_bio/quality_stats.py +++ b/polars_bio/quality_stats.py @@ -15,6 +15,7 @@ def base_sequence_quality( df: Union[str, pl.DataFrame, pl.LazyFrame, pd.DataFrame], quality_scores_column: str = "quality_scores", output_type: str = "polars.DataFrame", + target_partitions: int = 8, ) -> Union[pl.DataFrame, pd.DataFrame]: """ Compute base sequence quality statistics from various dataframe/file types. @@ -27,12 +28,20 @@ def base_sequence_quality( Returns: DataFrame with base sequence quality statistics. """ + ctx.set_option( + "datafusion.execution.target_partitions", str(target_partitions), False + ) + if isinstance(df, str): supported_exts = {".parquet", ".csv", ".bed", ".vcf", ".fastq"} ext = set(Path(df).suffixes) if not (supported_exts & ext or not ext): - raise ValueError("Input file must be a Parquet, CSV, BED, VCF, or FASTQ file.") - result: datafusion.DataFrame = base_sequance_quality_scan(ctx, df, quality_scores_column) + raise ValueError( + "Input file must be a Parquet, CSV, BED, VCF, or FASTQ file." + ) + result: datafusion.DataFrame = base_sequance_quality_scan( + ctx, df, quality_scores_column + ) else: if isinstance(df, pl.LazyFrame): arrow_table = df.collect().to_arrow() @@ -42,7 +51,9 @@ def base_sequence_quality( arrow_table = pa.Table.from_pandas(df) else: raise TypeError("Unsupported dataframe type.") - result: datafusion.DataFrame = base_sequance_quality_frame(ctx, arrow_table, quality_scores_column) + result: datafusion.DataFrame = base_sequance_quality_frame( + ctx, arrow_table, quality_scores_column + ) if output_type == "polars.DataFrame": return result.to_polars() diff --git a/src/lib.rs b/src/lib.rs index ac78e9b3..152b0736 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -1,7 +1,8 @@ -mod base_sequence_quality; +mod sequence_quality_histogram; mod context; mod operation; mod option; +mod quantile_stats; mod query; mod scan; mod streaming; @@ -406,28 +407,6 @@ fn py_from_polars( }) } -fn handle_base_sequence_quality<'a, F>( - _py: Python<'a>, - py_ctx: &PyBioSessionContext, - table_name: &str, - column: &str, - register_fn: F, -) -> PyResult -where - F: FnOnce(&PyBioSessionContext, &str, &Runtime), -{ - let ctx = &py_ctx.ctx; - let rt = Runtime::new().unwrap(); - register_fn(py_ctx, table_name, &rt); - let data_frame = rt.block_on(do_base_sequence_quality( - ctx, - table_name.to_string(), - column.to_string(), - )); - // deregister_table(ctx, table_name); - Ok(PyDataFrame::new(data_frame)) -} - #[pyfunction] #[pyo3(signature = (py_ctx, path, column))] fn base_sequance_quality_scan( @@ -436,16 +415,17 @@ fn base_sequance_quality_scan( path: String, column: String, ) -> PyResult { - handle_base_sequence_quality( - py, - py_ctx, - DEFAULT_TABLE_NAME, - &column, - |py_ctx, table_name, rt| { - let ctx = &py_ctx.ctx; - maybe_register_table(path, &table_name.to_string(), None, ctx, rt); - }, - ) + py.allow_threads(|| { + let ctx = &py_ctx.ctx; + let rt = Runtime::new().unwrap(); + maybe_register_table(path, &DEFAULT_TABLE_NAME.to_string(), None, ctx, &rt); + let data_frame = rt.block_on(do_base_sequence_quality( + ctx, + DEFAULT_TABLE_NAME.to_string(), + column.to_string(), + )); + Ok(PyDataFrame::new(data_frame)) + }) } #[pyfunction] @@ -456,15 +436,17 @@ fn base_sequance_quality_frame( df: PyArrowType, column: String, ) -> PyResult { - handle_base_sequence_quality( - py, - py_ctx, - DEFAULT_TABLE_NAME, - &column, - |py_ctx, table_name, _rt| { - register_frame(py_ctx, df, table_name.to_string()); - }, - ) + py.allow_threads(|| { + let ctx = &py_ctx.ctx; + let rt = Runtime::new().unwrap(); + register_frame(py_ctx, df, DEFAULT_TABLE_NAME.to_string()); + let data_frame = rt.block_on(do_base_sequence_quality( + ctx, + DEFAULT_TABLE_NAME.to_string(), + column.to_string(), + )); + Ok(PyDataFrame::new(data_frame)) + }) } #[pymodule] diff --git a/src/operation.rs b/src/operation.rs index d4481184..bd50e182 100644 --- a/src/operation.rs +++ b/src/operation.rs @@ -6,9 +6,10 @@ use log::{debug, info}; use sequila_core::session_context::{Algorithm, SequilaConfig}; use tokio::runtime::Runtime; -use crate::base_sequence_quality::BaseSequenceQualityProvider; +use crate::sequence_quality_histogram::SequenceQualityHistogramProvider; use crate::context::set_option_internal; use crate::option::{FilterOp, RangeOp, RangeOptions}; +use crate::quantile_stats::QuantileStatsTableProvider; use crate::query::{count_overlaps_query, nearest_query, overlap_query}; use crate::udtf::CountOverlapsProvider; use crate::utils::default_cols_to_string; @@ -197,16 +198,37 @@ pub(crate) async fn do_base_sequence_quality( table: String, column: String, ) -> datafusion::dataframe::DataFrame { - let session = &ctx.session; - let provider = - BaseSequenceQualityProvider::new(Arc::new(session.clone()), table.clone(), column.clone()); - let table_name = format!("{}_base_sequence_quality", table); - ctx.session.deregister_table(table_name.clone()).ok(); - ctx.session - .register_table(table_name.clone(), Arc::new(provider)) + let session = Arc::new(ctx.session.clone()); + let base_provider = Arc::new(SequenceQualityHistogramProvider::new( + session.clone(), + table.clone(), + column, + )); + + let base_table_name = format!("{}_decoded", table); + session.deregister_table(base_table_name.clone()).unwrap(); + session + .register_table(&base_table_name, base_provider) .unwrap(); - let query = format!("SELECT * FROM {}", table_name); - debug!("Query: {}", query); + + let query = format!( + "SELECT pos, score, SUM(count) as count FROM {} GROUP BY pos, score", + base_table_name + ); + let base_df = ctx.sql(&query).await.unwrap(); + let base_plan = base_df.create_physical_plan().await.unwrap(); + + let quantile_provider = Arc::new(QuantileStatsTableProvider::new(base_plan)); + + let quantile_table_name = format!("{}_quantiles", table); + session + .deregister_table(quantile_table_name.clone()) + .unwrap(); + session + .register_table(&quantile_table_name, quantile_provider) + .unwrap(); + + let query = format!("SELECT * FROM {}", quantile_table_name); ctx.sql(&query).await.unwrap() } diff --git a/src/quantile_stats.rs b/src/quantile_stats.rs new file mode 100644 index 00000000..36bf01b2 --- /dev/null +++ b/src/quantile_stats.rs @@ -0,0 +1,266 @@ +use std::any::Any; +use std::collections::HashMap; +use std::fmt::{Debug, Formatter}; +use std::sync::Arc; + +use arrow::array::{Array, Float64Builder, UInt64Array, UInt64Builder, UInt8Array}; +use arrow::compute::concat_batches; +use arrow::datatypes::{DataType, Field, Schema, SchemaRef}; +use arrow::record_batch::RecordBatch; +use async_trait::async_trait; +use datafusion::catalog::{Session, TableProvider}; +use datafusion::datasource::TableType; +use datafusion::error::Result; +use datafusion::execution::context::TaskContext; +use datafusion::physical_expr::EquivalenceProperties; +use datafusion::physical_plan::memory::MemoryStream; +use datafusion::physical_plan::{ + collect, DisplayAs, DisplayFormatType, ExecutionMode, ExecutionPlan, Partitioning, + PlanProperties, SendableRecordBatchStream, +}; + +pub struct QuantileStatsTableProvider { + input: Arc, + schema: SchemaRef, +} + +impl QuantileStatsTableProvider { + pub fn new(input: Arc) -> Self { + let schema = Arc::new(Schema::new(vec![ + Field::new("pos", DataType::UInt64, false), + Field::new("avg", DataType::Float64, true), + Field::new("q1", DataType::Float64, true), + Field::new("median", DataType::Float64, true), + Field::new("q3", DataType::Float64, true), + Field::new("lower", DataType::Float64, true), + Field::new("upper", DataType::Float64, true), + ])); + Self { + input, + schema, + } + } +} + +impl Debug for QuantileStatsTableProvider { + fn fmt(&self, _f: &mut Formatter<'_>) -> std::fmt::Result { + Ok(()) + } +} + +#[async_trait] +impl TableProvider for QuantileStatsTableProvider { + fn as_any(&self) -> &dyn std::any::Any { + self + } + + fn schema(&self) -> SchemaRef { + self.schema.clone() + } + + fn table_type(&self) -> TableType { + TableType::Base + } + + async fn scan( + &self, + _state: &dyn Session, + _projection: Option<&Vec>, + _filters: &[datafusion::logical_expr::Expr], + _limit: Option, + ) -> datafusion::error::Result> { + Ok(Arc::new(QuantileStatsExec::new(self.input.clone()))) + } +} + +#[derive(Debug)] +pub struct QuantileStatsExec { + input: Arc, + schema: SchemaRef, + properties: PlanProperties, +} + +impl QuantileStatsExec { + pub fn new(input: Arc) -> Self { + let schema = Arc::new(Schema::new(vec![ + Field::new("pos", DataType::UInt64, false), + Field::new("avg", DataType::Float64, true), + Field::new("q1", DataType::Float64, true), + Field::new("median", DataType::Float64, true), + Field::new("q3", DataType::Float64, true), + Field::new("lower", DataType::Float64, true), + Field::new("upper", DataType::Float64, true), + ])); + + let schema_clone = schema.clone(); + + Self { + input, + schema, + properties: PlanProperties::new( + EquivalenceProperties::new(schema_clone), + Partitioning::UnknownPartitioning(1), + ExecutionMode::Bounded, + ), + } + } +} + +impl ExecutionPlan for QuantileStatsExec { + fn name(&self) -> &str { + "QuantileAggregateExec" + } + + fn as_any(&self) -> &dyn Any { + self + } + + fn schema(&self) -> SchemaRef { + Arc::clone(&self.schema) + } + + fn properties(&self) -> &PlanProperties { + &self.properties + } + + fn children(&self) -> Vec<&Arc> { + vec![&self.input] + } + + fn with_new_children( + self: Arc, + _children: Vec>, + ) -> Result> { + Ok(self) + } + + fn execute( + &self, + _partition: usize, + context: Arc, + ) -> Result { + let input = self.input.clone(); + let schema = self.schema.clone(); + + let batches = futures::executor::block_on(collect(input, context.clone()))?; + let combined = concat_batches(&self.input.schema(), &batches)?; + let pos_array = combined + .column_by_name("pos") + .expect("Column 'pos' not found") + .as_any() + .downcast_ref::() + .expect("Expected UInt64 for pos"); + + let score_array = combined + .column_by_name("score") + .expect("Column 'score' not found") + .as_any() + .downcast_ref::() + .expect("Expected UInt8 for score"); + + let count_array = combined + .column_by_name("count") + .expect("Column 'count' not found") + .as_any() + .downcast_ref::() + .expect("Expected UInt64 for count"); + + let mut groups: HashMap> = HashMap::new(); + for i in 0..combined.num_rows() { + if pos_array.is_valid(i) && score_array.is_valid(i) && count_array.is_valid(i) { + let pos = pos_array.value(i); + let score = score_array.value(i); + let count = count_array.value(i); + let entry = groups.entry(pos).or_insert_with(|| vec![0; 256]); + entry[score as usize] += count; + } + } + + let mut pos_builder = UInt64Builder::with_capacity(groups.len()); + let mut avg_builder = Float64Builder::with_capacity(groups.len()); + let mut q1_builder = Float64Builder::with_capacity(groups.len()); + let mut median_builder = Float64Builder::with_capacity(groups.len()); + let mut q3_builder = Float64Builder::with_capacity(groups.len()); + let mut lower_builder = Float64Builder::with_capacity(groups.len()); + let mut upper_builder = Float64Builder::with_capacity(groups.len()); + + for (pos, hist) in groups { + if let Some((average, q1, median, q3, lower, upper)) = calculate_histogram_stats(&hist) + { + pos_builder.append_value(pos); + avg_builder.append_value(average); + q1_builder.append_value(q1); + median_builder.append_value(median); + q3_builder.append_value(q3); + lower_builder.append_value(lower); + upper_builder.append_value(upper); + } + } + + let result_batch = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(pos_builder.finish()), + Arc::new(avg_builder.finish()), + Arc::new(q1_builder.finish()), + Arc::new(median_builder.finish()), + Arc::new(q3_builder.finish()), + Arc::new(lower_builder.finish()), + Arc::new(upper_builder.finish()), + ], + )?; + let mem_stream = MemoryStream::try_new(vec![result_batch], schema, None)?; + Ok(Box::pin(mem_stream)) + } +} + +impl DisplayAs for QuantileStatsExec { + fn fmt_as(&self, _t: DisplayFormatType, _f: &mut Formatter) -> std::fmt::Result { + Ok(()) + } +} + +fn calculate_histogram_stats(hist: &[u64]) -> Option<(f64, f64, f64, f64, f64, f64)> { + let total_count: u64 = hist.iter().sum(); + if total_count == 0 { + return None; + } + + let weighted_sum: u64 = hist + .iter() + .enumerate() + .map(|(score, &count)| score as u64 * count) + .sum(); + let average = weighted_sum as f64 / total_count as f64; + + fn quantile(hist: &[u64], quantile: f64, total: u64) -> f64 { + let target = quantile * (total - 1) as f64; + let mut acc = 0u64; + let mut prev_idx = 0usize; + for (idx, &count) in hist.iter().enumerate() { + if count == 0 { + continue; + } + if (acc as f64) <= target && (acc + count) as f64 > target { + let delta = target - acc as f64; + if count > 1 && delta > 0.0 { + return idx as f64 + delta / count as f64; + } else { + return idx as f64; + } + } + acc += count; + prev_idx = idx; + } + prev_idx as f64 + } + + let q1 = quantile(hist, 0.25, total_count); + let median = quantile(hist, 0.5, total_count); + let q3 = quantile(hist, 0.75, total_count); + let iqr = q3 - q1; + let lower = q1 - 1.5 * iqr; + let upper = q3 + 1.5 * iqr; + + Some((average, q1, median, q3, lower, upper)) +} diff --git a/src/base_sequence_quality.rs b/src/sequence_quality_histogram.rs similarity index 53% rename from src/base_sequence_quality.rs rename to src/sequence_quality_histogram.rs index 1ca4d1e4..38c922aa 100644 --- a/src/base_sequence_quality.rs +++ b/src/sequence_quality_histogram.rs @@ -1,3 +1,5 @@ +use std::any::Any; +use std::collections::HashMap; use std::fmt::{Debug, Formatter}; use std::sync::Arc; @@ -6,6 +8,7 @@ use async_trait::async_trait; use datafusion::arrow::datatypes::{DataType, Field, Schema, SchemaRef}; use datafusion::arrow::record_batch::RecordBatch; use datafusion::catalog::{Session, TableProvider}; +use datafusion::datasource::TableType; use datafusion::error::{DataFusionError, Result}; use datafusion::execution::context::TaskContext; use datafusion::physical_expr::EquivalenceProperties; @@ -15,22 +18,22 @@ use datafusion::physical_plan::{ DisplayAs, DisplayFormatType, ExecutionMode, ExecutionPlan, Partitioning, PlanProperties, SendableRecordBatchStream, }; -use datafusion::prelude::SessionContext; +use datafusion::prelude::{col, SessionContext}; use futures::stream::BoxStream; use futures::{StreamExt, TryStreamExt}; - -pub struct BaseSequenceQualityProvider { +pub struct SequenceQualityHistogramProvider { session: Arc, table_name: String, column_name: String, schema: SchemaRef, } -impl BaseSequenceQualityProvider { +impl SequenceQualityHistogramProvider { pub fn new(session: Arc, table_name: String, column_name: String) -> Self { let schema = Arc::new(Schema::new(vec![ - Field::new("pos", DataType::Int64, false), - Field::new("score", DataType::Int8, false), + Field::new("pos", DataType::UInt64, false), + Field::new("score", DataType::UInt8, false), + Field::new("count", DataType::UInt64, false), ])); Self { session, @@ -41,14 +44,14 @@ impl BaseSequenceQualityProvider { } } -impl Debug for BaseSequenceQualityProvider { +impl Debug for SequenceQualityHistogramProvider { fn fmt(&self, _f: &mut Formatter<'_>) -> std::fmt::Result { Ok(()) } } #[async_trait] -impl TableProvider for BaseSequenceQualityProvider { +impl TableProvider for SequenceQualityHistogramProvider { fn as_any(&self) -> &dyn std::any::Any { self } @@ -57,8 +60,8 @@ impl TableProvider for BaseSequenceQualityProvider { self.schema.clone() } - fn table_type(&self) -> datafusion::datasource::TableType { - datafusion::datasource::TableType::Base + fn table_type(&self) -> TableType { + todo!() } async fn scan( @@ -69,13 +72,12 @@ impl TableProvider for BaseSequenceQualityProvider { _limit: Option, ) -> Result> { let target_partitions = self.session.state().config().target_partitions(); - - Ok(Arc::new(BaseSequenceQualityExec { + Ok(Arc::new(SequenceQualityHistogramExec { schema: self.schema.clone(), - session: Arc::clone(&self.session), + session: self.session.clone(), table_name: self.table_name.clone(), column_name: self.column_name.clone(), - cache: PlanProperties::new( + properties: PlanProperties::new( EquivalenceProperties::new(self.schema.clone()), Partitioning::UnknownPartitioning(target_partitions), ExecutionMode::Bounded, @@ -84,32 +86,32 @@ impl TableProvider for BaseSequenceQualityProvider { } } -pub struct BaseSequenceQualityExec { +pub struct SequenceQualityHistogramExec { schema: SchemaRef, session: Arc, table_name: String, column_name: String, - cache: PlanProperties, + properties: PlanProperties, } -impl Debug for BaseSequenceQualityExec { +impl Debug for SequenceQualityHistogramExec { fn fmt(&self, _f: &mut Formatter<'_>) -> std::fmt::Result { Ok(()) } } -impl DisplayAs for BaseSequenceQualityExec { +impl DisplayAs for SequenceQualityHistogramExec { fn fmt_as(&self, _t: DisplayFormatType, _f: &mut Formatter) -> std::fmt::Result { Ok(()) } } -impl ExecutionPlan for BaseSequenceQualityExec { +impl ExecutionPlan for SequenceQualityHistogramExec { fn name(&self) -> &str { "BaseSequenceQualityExec" } - fn as_any(&self) -> &dyn std::any::Any { + fn as_any(&self) -> &dyn Any { self } @@ -118,7 +120,7 @@ impl ExecutionPlan for BaseSequenceQualityExec { } fn properties(&self) -> &PlanProperties { - &self.cache + &self.properties } fn children(&self) -> Vec<&Arc> { @@ -138,10 +140,10 @@ impl ExecutionPlan for BaseSequenceQualityExec { context: Arc, ) -> Result { let fut = get_stream( - Arc::clone(&self.session), + self.session.clone(), self.table_name.clone(), self.column_name.clone(), - self.cache.partitioning.partition_count(), + self.properties.partitioning.partition_count(), partition, context, self.schema.clone(), @@ -161,23 +163,6 @@ fn decode_score(c: char) -> Option { } } -fn calc_stats(values: &mut Vec) -> (f64, f64, f64, f64, f64, f64) { - values.sort_unstable(); - let n = values.len(); - let average = values.iter().map(|&v| v as f64).sum::() / n as f64; - let median = if n % 2 == 0 { - (values[n / 2 - 1] as f64 + values[n / 2] as f64) / 2.0 - } else { - values[n / 2] as f64 - }; - let q1 = values[n / 4] as f64; - let q3 = values[(3 * n) / 4] as f64; - let iqr = q3 - q1; - let lower = q1 - 1.5 * iqr; - let upper = q3 + 1.5 * iqr; - (average, median, q1, q3, lower, upper) -} - async fn get_stream( session: Arc, table_name: String, @@ -187,61 +172,74 @@ async fn get_stream( context: Arc, new_schema: SchemaRef, ) -> Result { - let table_stream = session.table(table_name).await?; - let plan = table_stream.create_physical_plan().await?; + let df = session + .table(table_name.clone()) + .await? + .select(vec![col(&column_name)])?; + + let plan = df.create_physical_plan().await?; + let repartition_stream = RepartitionExec::try_new(plan, Partitioning::RoundRobinBatch(target_partitions))?; - let partition_stream = repartition_stream.execute(partition, context)?; - let new_schema_out = new_schema.clone(); - let iter = partition_stream.map(move |batch| match batch { - Ok(batch) => { - let index = match batch.schema().index_of(&column_name) { - Ok(idx) => idx, - Err(_) => { - return Err(DataFusionError::Internal(format!( - "Column '{}' not found in schema", - column_name - ))) - }, - }; - let col = batch.column(index); - - // Try to cast to StringArray if possible - let col = arrow::compute::cast(col, &DataType::Utf8) - .map_err(|e| DataFusionError::Internal(format!("Cast error: {e}")))?; - - let col = col - .as_any() - .downcast_ref::() - .ok_or_else(|| DataFusionError::Internal("Expected StringArray".into()))?; - - let mut positions = Vec::new(); - let mut scores = Vec::new(); - - for row in 0..col.len() { - if col.is_null(row) { - continue; - } - let s = col.value(row); - for (pos, byte) in s.bytes().enumerate() { - if let Some(score) = decode_score(byte as char) { - positions.push(pos as i64); - scores.push(score as i8); + + let mut partition_stream = repartition_stream.execute(partition, context)?; + + let mut pos_map: HashMap> = HashMap::new(); + + while let Some(batch_result) = partition_stream.next().await { + let batch = batch_result?; + let col = batch.column(0); // tylko jedna kolumna + + let col = arrow::compute::cast(col, &DataType::Utf8) + .map_err(|e| DataFusionError::Internal(format!("Cast error: {e}")))?; + + let col = col + .as_any() + .downcast_ref::() + .ok_or_else(|| DataFusionError::Internal("Expected StringArray".into()))?; + + for row in 0..col.len() { + if col.is_null(row) { + continue; + } + let s = col.value(row); + for (pos, byte) in s.bytes().enumerate() { + if let Some(score) = decode_score(byte as char) { + let entry = pos_map.entry(pos).or_insert_with(|| vec![0u64; 94]); + if (score as usize) < entry.len() { + entry[score as usize] += 1; } } } + } + } - let pos_array = Arc::new(arrow_array::Int64Array::from(positions)); - let score_array = Arc::new(arrow_array::Int8Array::from(scores)); - let new_batch = - RecordBatch::try_new(new_schema.clone(), vec![pos_array, score_array]).unwrap(); + let mut positions = Vec::new(); + let mut scores = Vec::new(); + let mut counts = Vec::new(); - Ok(new_batch) - }, - Err(e) => Err(e), - }); + for (pos, counts_vec) in pos_map { + for (score, &count) in counts_vec.iter().enumerate() { + if count > 0 { + positions.push(pos as u64); + scores.push(score as u8); + counts.push(count as u64); + } + } + } + let pos_array = Arc::new(arrow_array::UInt64Array::from(positions)); + let score_array = Arc::new(arrow_array::UInt8Array::from(scores)); + let count_array = Arc::new(arrow_array::UInt64Array::from(counts)); + let new_batch = RecordBatch::try_new( + new_schema.clone(), + vec![pos_array, score_array, count_array], + ) + .unwrap(); + + let iter = futures::stream::once(async move { Ok(new_batch) }); let adapted_stream = - RecordBatchStreamAdapter::new(new_schema_out, Box::pin(iter) as BoxStream<_>); + RecordBatchStreamAdapter::new(new_schema.clone(), Box::pin(iter) as BoxStream<_>); + Ok(Box::pin(adapted_stream)) }