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load.py
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load.py
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import sys
import click
import numpy as np
import pandas as pd
import simplejson as json
from ..create import (
BIN_DTYPE,
COUNT_DTYPE,
create_from_unordered,
sanitize_pixels,
sanitize_records,
)
from . import cli, get_logger
from ._util import parse_bins, parse_field_param, parse_kv_list_param
@cli.command()
@click.argument("bins_path", metavar="BINS_PATH")
@click.argument(
"pixels_path", type=click.Path(exists=True, allow_dash=True), metavar="PIXELS_PATH"
)
@click.argument("cool_path", metavar="COOL_PATH")
@click.option(
"--format",
"-f",
help="'coo' refers to a tab-delimited sparse triplet file "
"(bin1, bin2, count). "
"'bg2' refers to a 2D bedGraph-like file "
"(chrom1, start1, end1, chrom2, start2, end2, count).",
type=click.Choice(["coo", "bg2"]),
required=True,
)
@click.option("--metadata", help="Path to JSON file containing user metadata.")
@click.option("--assembly", help="Name of genome assembly (e.g. hg19, mm10)")
@click.option(
"--field",
help="Add supplemental value fields or override default field numbers for "
"the specified format. "
"Specify quantitative input fields to aggregate into value columns "
"using the syntax ``--field <field-name>=<field-number>``. "
"Optionally, append ``:`` followed by ``dtype=<dtype>`` to specify "
"the data type (e.g. float). Field numbers are 1-based. "
"Repeat the ``--field`` option for each additional field.",
type=str,
multiple=True,
)
@click.option(
"--count-as-float",
is_flag=True,
default=False,
help="Store the 'count' column as floating point values instead of as "
"integers. Can also be specified using the `--field` option.",
)
@click.option(
"--one-based",
is_flag=True,
default=False,
help="Pass this flag if the bin IDs listed in a COO file are one-based "
"instead of zero-based.",
)
@click.option(
"--comment-char",
type=str,
default="#",
show_default=True,
help="Comment character that indicates lines to ignore.",
)
@click.option(
"--no-symmetric-upper",
"-N",
help="Create a complete square matrix without implicit symmetry. "
"This allows for distinct upper- and lower-triangle values",
is_flag=True,
default=False,
)
@click.option(
"--input-copy-status",
type=click.Choice(["unique", "duplex"]),
default="unique",
help="Copy status of input data when using symmetric-upper storage. | "
"`unique`: Incoming data comes from a unique half of a symmetric "
"matrix, regardless of how element coordinates are ordered. "
"Execution will be aborted if duplicates are detected. "
"`duplex`: Incoming data contains upper- and lower-triangle duplicates. "
"All lower-triangle input elements will be discarded! | "
"If you wish to treat lower- and upper-triangle input data as "
"distinct, use the ``--no-symmetric-upper`` option instead. ",
show_default=True,
)
@click.option(
"--chunksize",
"-c",
help="Size in number of lines/records of data chunks to read and process "
"from the input stream at a time. These chunks will be saved as temporary "
"partial coolers and then merged.",
type=int,
default=20_000_000,
)
@click.option(
"--mergebuf",
help="Total number of records to buffer per epoch of merging data. Defaults "
"to the same value as `chunksize`.",
type=int,
)
@click.option(
"--temp-dir",
help="Create temporary files in a specified directory. Pass ``-`` to use "
"the platform default temp dir.",
type=click.Path(exists=True, file_okay=False, dir_okay=True, allow_dash=True),
)
@click.option(
"--max-merge",
help="Maximum number of chunks to merge in a single pass.",
type=int,
default=200,
show_default=True,
)
@click.option(
"--no-delete-temp",
help="Do not delete temporary files when finished.",
is_flag=True,
default=False,
)
@click.option(
"--storage-options",
help="Options to modify the data filter pipeline. Provide as a "
"comma-separated list of key-value pairs of the form 'k1=v1,k2=v2,...'. "
"See http://docs.h5py.org/en/stable/high/dataset.html#filter-pipeline "
"for more details.",
)
@click.option(
"--append",
"-a",
is_flag=True,
default=False,
help="Pass this flag to append the output cooler to an existing file "
"instead of overwriting the file.",
)
def load(
bins_path,
pixels_path,
cool_path,
format,
metadata,
assembly,
field,
count_as_float,
one_based,
comment_char,
input_copy_status,
no_symmetric_upper,
chunksize,
mergebuf,
max_merge,
temp_dir,
no_delete_temp,
storage_options,
append,
**kwargs,
):
"""
Create a cooler from a pre-binned matrix.
BINS_PATH : One of the following
<TEXT:INTEGER> : 1. Path to a chromsizes file, 2. Bin size in bp
<TEXT> : Path to BED file defining the genomic bin segmentation.
PIXELS_PATH : Text file containing nonzero pixel values. May be gzipped.
Pass '-' to use stdin.
COOL_PATH : Output COOL file path or URI.
**Notes**
Two input format options (tab-delimited).
Input pixel file may be compressed.
COO: COO-rdinate sparse matrix format (a.k.a. ijv triple).
3 columns: "bin1_id, bin2_id, count",
BG2: 2D version of the bedGraph format.
7 columns: "chrom1, start1, end1, chrom2, start2, end2, count"
**Examples**
cooler load -f bg2 <chrom.sizes>:<binsize> in.bg2.gz out.cool
"""
logger = get_logger(__name__)
chromsizes, bins = parse_bins(bins_path)
if mergebuf is None:
mergebuf = chunksize
symmetric_upper = not no_symmetric_upper
tril_action = None
if symmetric_upper:
if input_copy_status == "unique":
tril_action = "reflect"
elif input_copy_status == "duplex":
tril_action = "drop"
# User-supplied JSON file
if metadata is not None:
with open(metadata) as f:
metadata = json.load(f)
# Initialize the output schema. We don't include 'count' yet.
output_field_names = ["bin1_id", "bin2_id"]
output_field_dtypes = {
"bin1_id": BIN_DTYPE,
"bin2_id": BIN_DTYPE,
"count": COUNT_DTYPE,
}
# Initialize the input schema and create the input santizer.
if format == "bg2":
input_field_names = [
"chrom1",
"start1",
"end1",
"chrom2",
"start2",
"end2",
# We don't include 'count' yet.
]
input_field_dtypes = {
"chrom1": str,
"start1": int,
"end1": int,
"chrom2": str,
"start2": int,
"end2": int,
"count": output_field_dtypes["count"],
}
input_field_numbers = {
"chrom1": kwargs.get("chrom1", 0),
"start1": kwargs.get("start1", 1),
"end1": kwargs.get("end1", 2),
"chrom2": kwargs.get("chrom2", 3),
"start2": kwargs.get("start2", 4),
"end2": kwargs.get("end2", 5),
"count": 6,
}
pipeline = sanitize_records(
bins,
schema="bg2",
is_one_based=one_based,
tril_action=tril_action,
sort=True,
)
elif format == "coo":
input_field_names = [
"bin1_id",
"bin2_id",
# We don't include 'count' yet.
]
input_field_dtypes = {
"bin1_id": int,
"bin2_id": int,
"count": output_field_dtypes["count"],
}
input_field_numbers = {"bin1_id": 0, "bin2_id": 1, "count": 2}
pipeline = sanitize_pixels(
bins, is_one_based=one_based, tril_action=tril_action, sort=True
)
# Include input value columns
if len(field):
for arg in field:
name, colnum, dtype, _ = parse_field_param(arg, includes_agg=False)
# Special cases: omit field number to change standard dtypes.
if colnum is None:
if name in {"bin1_id", "bin2_id"} and dtype is not None:
# No input field
output_field_dtypes[name] = dtype
continue
elif name == "count" and dtype is not None:
input_field_names.append("count")
output_field_names.append("count")
input_field_dtypes[name] = dtype
output_field_dtypes[name] = dtype
continue
else:
raise click.BadParameter(
"A field number is required.", param_hint=arg
)
if name not in input_field_names:
input_field_names.append(name)
if name not in output_field_names:
output_field_names.append(name)
input_field_numbers[name] = colnum
if dtype is not None:
input_field_dtypes[name] = dtype
output_field_dtypes[name] = dtype
else:
# If no other fields are given, 'count' is implicitly included.
# Default dtype and field number are assumed.
input_field_names.append("count")
output_field_names.append("count")
if "count" in input_field_names and count_as_float:
input_field_dtypes["count"] = np.float64
output_field_dtypes["count"] = np.float64
# Customize the HDF5 filters
if storage_options is not None:
h5opts = parse_kv_list_param(storage_options)
for key in h5opts:
if isinstance(h5opts[key], list):
h5opts[key] = tuple(h5opts[key])
else:
h5opts = None
# Initialize the input stream
if pixels_path == "-":
f_in = sys.stdin
else:
f_in = pixels_path
reader = pd.read_csv(
f_in,
sep="\t",
usecols=[input_field_numbers[name] for name in input_field_names],
names=input_field_names,
dtype=input_field_dtypes,
comment=comment_char,
iterator=True,
chunksize=chunksize,
)
logger.info(f"fields: {input_field_numbers}")
logger.info(f"dtypes: {input_field_dtypes}")
logger.info(f"symmetric-upper: {symmetric_upper}")
create_from_unordered(
cool_path,
bins,
map(pipeline, reader),
columns=output_field_names,
dtypes=output_field_dtypes,
metadata=metadata,
assembly=assembly,
mergebuf=mergebuf,
max_merge=max_merge,
temp_dir=temp_dir,
delete_temp=not no_delete_temp,
ensure_sorted=False,
# boundscheck=True,
# dupcheck=True,
triucheck=True if symmetric_upper else False,
symmetric_upper=symmetric_upper,
h5opts=h5opts,
mode="a" if append else "w",
)