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4 changes: 3 additions & 1 deletion micov/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,5 @@
"""micov: microbiome coverage."""

from . import _version
__version__ = _version.get_versions()['version']

__version__ = _version.get_versions()["version"]
119 changes: 76 additions & 43 deletions micov/_constants.py
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Original file line number Diff line number Diff line change
@@ -1,47 +1,56 @@
COLUMN_GENOME_ID = 'genome_id'
COLUMN_GENOME_ID = "genome_id"
COLUMN_GENOME_ID_DTYPE = str
COLUMN_SAMPLE_ID = 'sample_id'
COLUMN_SAMPLE_ID = "sample_id"
COLUMN_SAMPLE_ID_DTYPE = str
COLUMN_START = 'start'
COLUMN_START = "start"
COLUMN_START_DTYPE = int
COLUMN_STOP = 'stop'
COLUMN_STOP = "stop"
COLUMN_STOP_DTYPE = int
COLUMN_READ_ID = 'read'
COLUMN_READ_ID = "read"
COLUMN_READ_ID_DTYPE = str
COLUMN_FLAG = 'flag'
COLUMN_FLAG = "flag"
COLUMN_FLAG_DTYPE = int
COLUMN_CIGAR = 'cigar'
COLUMN_CIGAR = "cigar"
COLUMN_CIGAR_DTYPE = str
COLUMN_LENGTH = 'length'
COLUMN_LENGTH = "length"
COLUMN_LENGTH_DTYPE = int
COLUMN_TAXONOMY = 'taxonomy'
COLUMN_TAXONOMY = "taxonomy"
COLUMN_TAXONOMY_DTYPE = str
COLUMN_COVERED = 'covered'
COLUMN_PERCENT_COVERED = 'percent_covered'
COLUMN_COVERED = "covered"
COLUMN_PERCENT_COVERED = "percent_covered"
COLUMN_COVERED_DTYPE = int
COLUMN_PERCENT_COVERED = 'percent_covered'
COLUMN_PERCENT_COVERED = "percent_covered"
COLUMN_PERCENT_COVERED_DTYPE = float

### should really probably just use a dataclass, and type annotations?


class _SCHEMA:
def __init__(self):
self.dtypes_dict = dict(self.dtypes_flat)
self.columns = tuple([c for c, _ in self.dtypes_flat])


class _BED_COV_SCHEMA(_SCHEMA):
dtypes_flat = [(COLUMN_GENOME_ID, COLUMN_GENOME_ID_DTYPE),
(COLUMN_START, COLUMN_START_DTYPE),
(COLUMN_STOP, COLUMN_STOP_DTYPE)]
dtypes_flat = [
(COLUMN_GENOME_ID, COLUMN_GENOME_ID_DTYPE),
(COLUMN_START, COLUMN_START_DTYPE),
(COLUMN_STOP, COLUMN_STOP_DTYPE),
]


BED_COV_SCHEMA = _BED_COV_SCHEMA()


class _BED_COV_WITH_SAMPLEID_SCHEMA(_SCHEMA):
dtypes_flat = [(COLUMN_GENOME_ID, COLUMN_GENOME_ID_DTYPE),
(COLUMN_START, COLUMN_START_DTYPE),
(COLUMN_STOP, COLUMN_STOP_DTYPE),
(COLUMN_SAMPLE_ID, COLUMN_SAMPLE_ID_DTYPE)]
dtypes_flat = [
(COLUMN_GENOME_ID, COLUMN_GENOME_ID_DTYPE),
(COLUMN_START, COLUMN_START_DTYPE),
(COLUMN_STOP, COLUMN_STOP_DTYPE),
(COLUMN_SAMPLE_ID, COLUMN_SAMPLE_ID_DTYPE),
]


BED_COV_SAMPLEID_SCHEMA = _BED_COV_WITH_SAMPLEID_SCHEMA()


Expand All @@ -51,48 +60,72 @@ class _SAM_SUBSET_SCHEMA(_SCHEMA):
# for binary coverage, we don't care about the flag, but we're parsing it
# now so we can care in the future.
column_indices = [0, 1, 2, 3, 5]
dtypes_flat = [(COLUMN_READ_ID, COLUMN_READ_ID_DTYPE),
(COLUMN_FLAG, COLUMN_FLAG_DTYPE),
(COLUMN_GENOME_ID, COLUMN_GENOME_ID_DTYPE),
(COLUMN_START, COLUMN_START_DTYPE),
(COLUMN_CIGAR, COLUMN_CIGAR_DTYPE)]
dtypes_flat = [
(COLUMN_READ_ID, COLUMN_READ_ID_DTYPE),
(COLUMN_FLAG, COLUMN_FLAG_DTYPE),
(COLUMN_GENOME_ID, COLUMN_GENOME_ID_DTYPE),
(COLUMN_START, COLUMN_START_DTYPE),
(COLUMN_CIGAR, COLUMN_CIGAR_DTYPE),
]


SAM_SUBSET_SCHEMA = _SAM_SUBSET_SCHEMA()


class _SAM_SUBSET_SCHEMA_PARSED(_SCHEMA):
dtypes_flat = [(COLUMN_READ_ID, COLUMN_READ_ID_DTYPE),
(COLUMN_FLAG, COLUMN_FLAG_DTYPE),
(COLUMN_GENOME_ID, COLUMN_GENOME_ID_DTYPE),
(COLUMN_START, COLUMN_START_DTYPE),
(COLUMN_CIGAR, COLUMN_CIGAR_DTYPE),
(COLUMN_STOP, COLUMN_STOP_DTYPE)]
dtypes_flat = [
(COLUMN_READ_ID, COLUMN_READ_ID_DTYPE),
(COLUMN_FLAG, COLUMN_FLAG_DTYPE),
(COLUMN_GENOME_ID, COLUMN_GENOME_ID_DTYPE),
(COLUMN_START, COLUMN_START_DTYPE),
(COLUMN_CIGAR, COLUMN_CIGAR_DTYPE),
(COLUMN_STOP, COLUMN_STOP_DTYPE),
]


SAM_SUBSET_SCHEMA_PARSED = _SAM_SUBSET_SCHEMA_PARSED()


class _GENOME_LENGTH_SCHEMA(_SCHEMA):
dtypes_flat = [(COLUMN_GENOME_ID, COLUMN_GENOME_ID_DTYPE),
(COLUMN_LENGTH, COLUMN_LENGTH_DTYPE)]
dtypes_flat = [
(COLUMN_GENOME_ID, COLUMN_GENOME_ID_DTYPE),
(COLUMN_LENGTH, COLUMN_LENGTH_DTYPE),
]


GENOME_LENGTH_SCHEMA = _GENOME_LENGTH_SCHEMA()


class _GENOME_TAXONOMY_SCHEMA(_SCHEMA):
dtypes_flat = [(COLUMN_GENOME_ID, COLUMN_GENOME_ID_DTYPE),
(COLUMN_TAXONOMY, COLUMN_TAXONOMY_DTYPE)]
dtypes_flat = [
(COLUMN_GENOME_ID, COLUMN_GENOME_ID_DTYPE),
(COLUMN_TAXONOMY, COLUMN_TAXONOMY_DTYPE),
]


GENOME_TAXNOMY_SCHEMA = _GENOME_TAXONOMY_SCHEMA()


class _GENOME_COVERAGE_SCHEMA(_SCHEMA):
dtypes_flat = [(COLUMN_GENOME_ID, COLUMN_GENOME_ID_DTYPE),
(COLUMN_COVERED, COLUMN_COVERED_DTYPE),
(COLUMN_LENGTH, COLUMN_LENGTH_DTYPE),
(COLUMN_PERCENT_COVERED, COLUMN_PERCENT_COVERED_DTYPE)]
dtypes_flat = [
(COLUMN_GENOME_ID, COLUMN_GENOME_ID_DTYPE),
(COLUMN_COVERED, COLUMN_COVERED_DTYPE),
(COLUMN_LENGTH, COLUMN_LENGTH_DTYPE),
(COLUMN_PERCENT_COVERED, COLUMN_PERCENT_COVERED_DTYPE),
]


GENOME_COVERAGE_SCHEMA = _GENOME_COVERAGE_SCHEMA()


class _GENOME_COVERAGE_WITH_SAMPLEID_SCHEMA(_SCHEMA):
dtypes_flat = [(COLUMN_GENOME_ID, COLUMN_GENOME_ID_DTYPE),
(COLUMN_COVERED, COLUMN_COVERED_DTYPE),
(COLUMN_LENGTH, COLUMN_LENGTH_DTYPE),
(COLUMN_PERCENT_COVERED, COLUMN_PERCENT_COVERED_DTYPE),
(COLUMN_SAMPLE_ID, COLUMN_SAMPLE_ID_DTYPE)]
dtypes_flat = [
(COLUMN_GENOME_ID, COLUMN_GENOME_ID_DTYPE),
(COLUMN_COVERED, COLUMN_COVERED_DTYPE),
(COLUMN_LENGTH, COLUMN_LENGTH_DTYPE),
(COLUMN_PERCENT_COVERED, COLUMN_PERCENT_COVERED_DTYPE),
(COLUMN_SAMPLE_ID, COLUMN_SAMPLE_ID_DTYPE),
]


GENOME_COVERAGE_WITH_SAMPLEID_SCHEMA = _GENOME_COVERAGE_WITH_SAMPLEID_SCHEMA()
10 changes: 5 additions & 5 deletions micov/_convert.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,14 +29,14 @@ def cigar_to_lens(cigar):

"""
align, offset = 0, 0
n = '' # current step size
n = "" # current step size
for c in cigar:
if c in 'MDIHNPSX=':
if c in 'M=X':
if c in "MDIHNPSX=":
if c in "M=X":
align += int(n)
elif c in 'DN':
elif c in "DN":
offset += int(n)
n = ''
n = ""
else:
n += c

Expand Down
87 changes: 56 additions & 31 deletions micov/_cov.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,28 +2,44 @@

import numba
import polars as pl
from ._constants import (COLUMN_GENOME_ID, COLUMN_START, COLUMN_STOP,
COLUMN_COVERED, BED_COV_SCHEMA,
COLUMN_LENGTH, COLUMN_PERCENT_COVERED)
from ._constants import (
COLUMN_GENOME_ID,
COLUMN_START,
COLUMN_STOP,
COLUMN_COVERED,
BED_COV_SCHEMA,
COLUMN_LENGTH,
COLUMN_PERCENT_COVERED,
)


def coverage_percent(coverages, lengths):
missing = (set(coverages[COLUMN_GENOME_ID]) -
set(lengths[COLUMN_GENOME_ID]))
missing = set(coverages[COLUMN_GENOME_ID]) - set(lengths[COLUMN_GENOME_ID])
if len(missing) > 0:
raise ValueError(f"{len(missing)} genome(s) appear unrepresented in "
f"the length information, examples: "
f"{sorted(missing)[:5]}")
raise ValueError(
f"{len(missing)} genome(s) appear unrepresented in "
f"the length information, examples: "
f"{sorted(missing)[:5]}"
)

return (coverages
.lazy()
.with_columns((pl.col(COLUMN_STOP) -
pl.col(COLUMN_START)).alias(COLUMN_COVERED))
.group_by([COLUMN_GENOME_ID, ])
.agg(pl.col(COLUMN_COVERED).sum())
.join(lengths.lazy(), on=COLUMN_GENOME_ID)
.with_columns(((pl.col(COLUMN_COVERED) /
pl.col(COLUMN_LENGTH)) * 100).alias(COLUMN_PERCENT_COVERED))) # noqa
return (
coverages.lazy()
.with_columns(
(pl.col(COLUMN_STOP) - pl.col(COLUMN_START)).alias(COLUMN_COVERED)
)
.group_by(
[
COLUMN_GENOME_ID,
]
)
.agg(pl.col(COLUMN_COVERED).sum())
.join(lengths.lazy(), on=COLUMN_GENOME_ID)
.with_columns(
((pl.col(COLUMN_COVERED) / pl.col(COLUMN_LENGTH)) * 100).alias(
COLUMN_PERCENT_COVERED
)
)
) # noqa


@numba.jit(nopython=True)
Expand Down Expand Up @@ -59,22 +75,31 @@ def _compress(rows):

def compress(df):
compressed = []
for (genome, ), grp in df.group_by([COLUMN_GENOME_ID, ]):
rows = (grp
.lazy()
.select([COLUMN_START, COLUMN_STOP])
.sort(COLUMN_START)
.collect()
.to_numpy(order='c'))
for (genome,), grp in df.group_by(
[
COLUMN_GENOME_ID,
]
):
rows = (
grp.lazy()
.select([COLUMN_START, COLUMN_STOP])
.sort(COLUMN_START)
.collect()
.to_numpy(order="c")
)

grp_compressed = _compress(rows)
grp_compressed_df = pl.LazyFrame(grp_compressed,
schema=[BED_COV_SCHEMA.dtypes_flat[1],
BED_COV_SCHEMA.dtypes_flat[2]],
orient='row')
grp_compressed_df = (grp_compressed_df
.with_columns(pl.lit(genome).alias(COLUMN_GENOME_ID))
.select(BED_COV_SCHEMA.columns))
grp_compressed_df = pl.LazyFrame(
grp_compressed,
schema=[
BED_COV_SCHEMA.dtypes_flat[1],
BED_COV_SCHEMA.dtypes_flat[2],
],
orient="row",
)
grp_compressed_df = grp_compressed_df.with_columns(
pl.lit(genome).alias(COLUMN_GENOME_ID)
).select(BED_COV_SCHEMA.columns)

compressed.append(grp_compressed_df.collect())

Expand Down
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