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Source code for galaxy.datatypes.tabular
"""
Tabular datatype
"""
import abc
import binascii
import csv
import logging
import os
import re
import shutil
import subprocess
import tempfile
from json import dumps
from typing import (
cast,
Dict,
List,
Optional,
TYPE_CHECKING,
Union,
)
import pysam
from markupsafe import escape
from galaxy import util
from galaxy.datatypes import (
binary,
data,
metadata,
)
from galaxy.datatypes.binary import _BamOrSam
from galaxy.datatypes.data import (
DatatypeValidation,
Text,
)
from galaxy.datatypes.dataproviders.column import (
ColumnarDataProvider,
DictDataProvider,
)
from galaxy.datatypes.dataproviders.dataset import (
DatasetColumnarDataProvider,
DatasetDataProvider,
DatasetDictDataProvider,
GenomicRegionDataProvider,
)
from galaxy.datatypes.dataproviders.line import (
FilteredLineDataProvider,
RegexLineDataProvider,
)
from galaxy.datatypes.metadata import (
MetadataElement,
MetadataParameter,
)
from galaxy.datatypes.sniff import (
build_sniff_from_prefix,
FilePrefix,
get_headers,
iter_headers,
validate_tabular,
)
from galaxy.util import compression_utils
from galaxy.util.compression_utils import (
FileObjType,
FileObjTypeStr,
)
from galaxy.util.markdown import (
indicate_data_truncated,
pre_formatted_contents,
)
from . import dataproviders
if TYPE_CHECKING:
from galaxy.model import (
DatasetInstance,
HistoryDatasetAssociation,
)
log = logging.getLogger(__name__)
MAX_DATA_LINES = 100000
[docs]@dataproviders.decorators.has_dataproviders
class TabularData(Text):
"""Generic tabular data"""
edam_format = "format_3475"
# All tabular data is chunkable.
CHUNKABLE = True
data_line_offset = 0
max_peek_columns = 50
MetadataElement(
name="comment_lines", default=0, desc="Number of comment lines", readonly=False, optional=True, no_value=0
)
MetadataElement(
name="data_lines",
default=0,
desc="Number of data lines",
readonly=True,
visible=False,
optional=True,
no_value=0,
)
MetadataElement(name="columns", default=0, desc="Number of columns", readonly=True, visible=False, no_value=0)
MetadataElement(
name="column_types",
default=[],
desc="Column types",
param=metadata.ColumnTypesParameter,
readonly=True,
visible=False,
no_value=[],
)
MetadataElement(
name="column_names", default=[], desc="Column names", readonly=True, visible=False, optional=True, no_value=[]
)
MetadataElement(
name="delimiter", default="\t", desc="Data delimiter", readonly=True, visible=False, optional=True, no_value=[]
)
[docs] @abc.abstractmethod
def set_meta(self, dataset: "DatasetInstance", *, overwrite: bool = True, **kwd) -> None:
raise NotImplementedError
[docs] def set_peek(self, dataset: "DatasetInstance", **kwd) -> None:
kwd.setdefault("line_wrap", False)
super().set_peek(dataset, **kwd)
if dataset.metadata.comment_lines:
dataset.blurb = f"{dataset.blurb}, {util.commaify(str(dataset.metadata.comment_lines))} comments"
[docs] def displayable(self, dataset: "DatasetInstance"):
try:
return (
not dataset.dataset.purged
and dataset.has_data()
and dataset.state == dataset.states.OK
and dataset.metadata.columns > 0
and dataset.metadata.data_lines != 0
)
except Exception:
return False
[docs] def get_chunk(self, trans, dataset: "DatasetInstance", offset: int = 0, ck_size: Optional[int] = None) -> str:
ck_data, last_read = self._read_chunk(trans, dataset, offset, ck_size)
return dumps(
{
"ck_data": util.unicodify(ck_data),
"offset": last_read,
"data_line_offset": self.data_line_offset,
}
)
def _read_chunk(self, trans, dataset: "DatasetInstance", offset: int, ck_size: Optional[int] = None):
with compression_utils.get_fileobj(dataset.file_name) as f:
f.seek(offset)
ck_data = f.read(ck_size or trans.app.config.display_chunk_size)
if ck_data and ck_data[-1] != "\n":
cursor = f.read(1)
while cursor and cursor != "\n":
ck_data += cursor
cursor = f.read(1)
last_read = f.tell()
return ck_data, last_read
[docs] def display_data(
self,
trans,
dataset: "HistoryDatasetAssociation",
preview: bool = False,
filename: Optional[str] = None,
to_ext: Optional[str] = None,
offset: Optional[int] = None,
ck_size: Optional[int] = None,
**kwd,
):
headers = kwd.pop("headers", {})
preview = util.string_as_bool(preview)
if offset is not None:
return self.get_chunk(trans, dataset, offset, ck_size), headers
elif to_ext or not preview:
to_ext = to_ext or dataset.extension
return self._serve_raw(dataset, to_ext, headers, **kwd)
elif dataset.metadata.columns > 100:
# Fancy tabular display is only suitable for datasets without an incredibly large number of columns.
# We should add a new datatype 'matrix', with its own draw method, suitable for this kind of data.
# For now, default to the old behavior, ugly as it is. Remove this after adding 'matrix'.
max_peek_size = 1000000 # 1 MB
if os.stat(dataset.file_name).st_size < max_peek_size:
self._clean_and_set_mime_type(trans, dataset.get_mime(), headers)
return open(dataset.file_name, mode="rb"), headers
else:
headers["content-type"] = "text/html"
return (
trans.fill_template_mako(
"/dataset/large_file.mako",
truncated_data=open(dataset.file_name).read(max_peek_size),
data=dataset,
),
headers,
)
else:
column_names = "null"
if dataset.metadata.column_names:
column_names = dataset.metadata.column_names
elif hasattr(dataset.datatype, "column_names"):
column_names = dataset.datatype.column_names
column_types = dataset.metadata.column_types
if not column_types:
column_types = []
column_number = dataset.metadata.columns
if column_number is None:
column_number = "null"
return (
trans.fill_template(
"/dataset/tabular_chunked.mako",
dataset=dataset,
chunk=self.get_chunk(trans, dataset, 0),
column_number=column_number,
column_names=column_names,
column_types=column_types,
),
headers,
)
[docs] def display_as_markdown(self, dataset_instance: "DatasetInstance") -> str:
with open(dataset_instance.file_name) as f:
contents = f.read(data.DEFAULT_MAX_PEEK_SIZE)
markdown = self.make_html_table(dataset_instance, peek=contents)
if len(contents) == data.DEFAULT_MAX_PEEK_SIZE:
markdown += indicate_data_truncated()
return pre_formatted_contents(markdown)
[docs] def make_html_table(self, dataset: "DatasetInstance", **kwargs) -> str:
"""Create HTML table, used for displaying peek"""
try:
out = ['<table cellspacing="0" cellpadding="3">']
out.append(self.make_html_peek_header(dataset, **kwargs))
out.append(self.make_html_peek_rows(dataset, **kwargs))
out.append("</table>")
return "".join(out)
except Exception as exc:
return f"Can't create peek: {util.unicodify(exc)}"
[docs] def make_html_peek_header(
self,
dataset: "DatasetInstance",
skipchars: Optional[List] = None,
column_names: Optional[List] = None,
column_number_format: str = "%s",
column_parameter_alias: Optional[Dict] = None,
**kwargs,
) -> str:
if skipchars is None:
skipchars = []
if column_names is None:
column_names = []
if column_parameter_alias is None:
column_parameter_alias = {}
out = []
try:
if not column_names and dataset.metadata.column_names:
column_names = dataset.metadata.column_names
columns = dataset.metadata.columns
if columns is None:
columns = dataset.metadata.spec.columns.no_value
columns = min(columns, self.max_peek_columns)
column_headers = [None] * columns
# fill in empty headers with data from column_names
assert column_names is not None
for i in range(min(columns, len(column_names))):
if column_headers[i] is None and column_names[i] is not None:
column_headers[i] = column_names[i]
# fill in empty headers from ColumnParameters set in the metadata
for name, spec in dataset.metadata.spec.items():
if isinstance(spec.param, metadata.ColumnParameter):
try:
i = int(getattr(dataset.metadata, name)) - 1
except Exception:
i = -1
if 0 <= i < columns and column_headers[i] is None:
column_headers[i] = column_parameter_alias.get(name, name)
out.append("<tr>")
for i, header in enumerate(column_headers):
out.append("<th>")
if header is None:
out.append(column_number_format % str(i + 1))
else:
out.append(f"{str(i + 1)}.{escape(header)}")
out.append("</th>")
out.append("</tr>")
except Exception as exc:
log.exception("make_html_peek_header failed on HDA %s", dataset.id)
raise Exception(f"Can't create peek header: {util.unicodify(exc)}")
return "".join(out)
[docs] def make_html_peek_rows(self, dataset: "DatasetInstance", skipchars: Optional[List] = None, **kwargs) -> str:
if skipchars is None:
skipchars = []
out = []
try:
peek = kwargs.get("peek")
if peek is None:
if not dataset.peek:
dataset.set_peek()
peek = dataset.peek
columns = dataset.metadata.columns
if columns is None:
columns = dataset.metadata.spec.columns.no_value
columns = min(columns, self.max_peek_columns)
for i, line in enumerate(peek.splitlines()):
if i >= self.data_line_offset:
if line.startswith(tuple(skipchars)):
out.append(f'<tr><td colspan="100%">{escape(line)}</td></tr>')
elif line:
elems = line.split(dataset.metadata.delimiter)
elems = elems[: min(len(elems), self.max_peek_columns)]
# pad shortened elems, since lines could have been truncated by width
if len(elems) < columns:
elems.extend([""] * (columns - len(elems)))
# we may have an invalid comment line or invalid data
if len(elems) != columns:
out.append(f'<tr><td colspan="100%">{escape(line)}</td></tr>')
else:
out.append("<tr>")
for elem in elems:
out.append(f"<td>{escape(elem)}</td>")
out.append("</tr>")
except Exception as exc:
log.exception("make_html_peek_rows failed on HDA %s", dataset.id)
raise Exception(f"Can't create peek rows: {util.unicodify(exc)}")
return "".join(out)
[docs] def display_peek(self, dataset: "DatasetInstance") -> str:
"""Returns formatted html of peek"""
return self.make_html_table(dataset)
[docs] def is_int(self, column_text: str) -> bool:
# Don't allow underscores in numeric literals (PEP 515)
if "_" in column_text:
return False
try:
int(column_text)
return True
except ValueError:
return False
[docs] def is_float(self, column_text: str) -> bool:
# Don't allow underscores in numeric literals (PEP 515)
if "_" in column_text:
return False
try:
float(column_text)
return True
except ValueError:
if column_text.strip().lower() == "na":
return True # na is special cased to be a float
return False
[docs] def guess_type(self, text: str) -> str:
if self.is_int(text):
return "int"
if self.is_float(text):
return "float"
else:
return "str"
# ------------- Dataproviders
[docs] @dataproviders.decorators.dataprovider_factory("column", ColumnarDataProvider.settings)
def column_dataprovider(self, dataset: "DatasetInstance", **settings) -> ColumnarDataProvider:
"""Uses column settings that are passed in"""
dataset_source = DatasetDataProvider(dataset)
delimiter = dataset.metadata.delimiter
return ColumnarDataProvider(dataset_source, deliminator=delimiter, **settings)
[docs] @dataproviders.decorators.dataprovider_factory("dataset-column", ColumnarDataProvider.settings)
def dataset_column_dataprovider(self, dataset: "DatasetInstance", **settings) -> DatasetColumnarDataProvider:
"""Attempts to get column settings from dataset.metadata"""
delimiter = dataset.metadata.delimiter
return DatasetColumnarDataProvider(dataset, deliminator=delimiter, **settings)
[docs] @dataproviders.decorators.dataprovider_factory("dict", DictDataProvider.settings)
def dict_dataprovider(self, dataset: "DatasetInstance", **settings) -> DictDataProvider:
"""Uses column settings that are passed in"""
dataset_source = DatasetDataProvider(dataset)
delimiter = dataset.metadata.delimiter
return DictDataProvider(dataset_source, deliminator=delimiter, **settings)
[docs] @dataproviders.decorators.dataprovider_factory("dataset-dict", DictDataProvider.settings)
def dataset_dict_dataprovider(self, dataset: "DatasetInstance", **settings) -> DatasetDictDataProvider:
"""Attempts to get column settings from dataset.metadata"""
delimiter = dataset.metadata.delimiter
return DatasetDictDataProvider(dataset, deliminator=delimiter, **settings)
[docs]@dataproviders.decorators.has_dataproviders
class Tabular(TabularData):
"""Tab delimited data"""
file_ext = "tabular"
[docs] def set_meta(
self,
dataset: "DatasetInstance",
*,
overwrite: bool = True,
skip: Optional[int] = None,
max_data_lines: Optional[int] = MAX_DATA_LINES,
max_guess_type_data_lines: Optional[int] = None,
**kwd,
) -> None:
"""
Tries to determine the number of columns as well as those columns that
contain numerical values in the dataset. A skip parameter is used
because various tabular data types reuse this function, and their data
type classes are responsible to determine how many invalid comment
lines should be skipped. Using None for skip will cause skip to be
zero, but the first line will be processed as a header. A
max_data_lines parameter is used because various tabular data types
reuse this function, and their data type classes are responsible to
determine how many data lines should be processed to ensure that the
non-optional metadata parameters are properly set; if used, optional
metadata parameters will be set to None, unless the entire file has
already been read. Using None for max_data_lines will process all data
lines.
Items of interest:
1. We treat 'overwrite' as always True (we always want to set tabular metadata when called).
2. If a tabular file has no data, it will have one column of type 'str'.
3. We used to check only the first 100 lines when setting metadata and this class's
set_peek() method read the entire file to determine the number of lines in the file.
Since metadata can now be processed on cluster nodes, we've merged the line count portion
of the set_peek() processing here, and we now check the entire contents of the file.
"""
# Store original skip value to check with later
requested_skip = skip
if skip is None:
skip = 0
column_type_set_order = ["int", "float", "list", "str"] # Order to set column types in
default_column_type = column_type_set_order[-1] # Default column type is lowest in list
column_type_compare_order = list(column_type_set_order) # Order to compare column types
column_type_compare_order.reverse()
def type_overrules_type(column_type1, column_type2):
if column_type1 is None or column_type1 == column_type2:
return False
if column_type2 is None:
return True
for column_type in column_type_compare_order:
if column_type1 == column_type:
return True
if column_type2 == column_type:
return False
# neither column type was found in our ordered list, this cannot happen
raise ValueError(f"Tried to compare unknown column types: {column_type1} and {column_type2}")
def is_int(column_text):
# Don't allow underscores in numeric literals (PEP 515)
if "_" in column_text:
return False
try:
int(column_text)
return True
except ValueError:
return False
def is_float(column_text):
# Don't allow underscores in numeric literals (PEP 515)
if "_" in column_text:
return False
try:
float(column_text)
return True
except ValueError:
if column_text.strip().lower() == "na":
return True # na is special cased to be a float
return False
def is_list(column_text):
return "," in column_text
def is_str(column_text):
# anything, except an empty string, is True
if column_text == "":
return False
return True
is_column_type = {} # Dict to store column type string to checking function
for column_type in column_type_set_order:
is_column_type[column_type] = locals()[f"is_{column_type}"]
def guess_column_type(column_text):
for column_type in column_type_set_order:
if is_column_type[column_type](column_text):
return column_type
return None
data_lines = 0
comment_lines = 0
column_names = None
column_types: List = []
first_line_column_types = [default_column_type] # default value is one column of type str
if dataset.has_data():
# NOTE: if skip > num_check_lines, we won't detect any metadata, and will use default
with compression_utils.get_fileobj(dataset.file_name) as dataset_fh:
i = 0
for line in iter(dataset_fh.readline, ""):
line = line.rstrip("\r\n")
if i == 0:
column_names = self.get_column_names(first_line=line)
if i < skip or not line or line.startswith("#"):
# We'll call blank lines comments
comment_lines += 1
else:
data_lines += 1
if max_guess_type_data_lines is None or data_lines <= max_guess_type_data_lines:
fields = line.split("\t")
for field_count, field in enumerate(fields):
if field_count >= len(
column_types
): # found a previously unknown column, we append None
column_types.append(None)
column_type = guess_column_type(field)
if type_overrules_type(column_type, column_types[field_count]):
column_types[field_count] = column_type
if i == 0 and requested_skip is None:
# This is our first line, people seem to like to upload files that have a header line, but do not
# start with '#' (i.e. all column types would then most likely be detected as str). We will assume
# that the first line is always a header (this was previous behavior - it was always skipped). When
# the requested skip is None, we only use the data from the first line if we have no other data for
# a column. This is far from perfect, as
# 1,2,3 1.1 2.2 qwerty
# 0 0 1,2,3
# will be detected as
# "column_types": ["int", "int", "float", "list"]
# instead of
# "column_types": ["list", "float", "float", "str"] *** would seem to be the 'Truth' by manual
# observation that the first line should be included as data. The old method would have detected as
# "column_types": ["int", "int", "str", "list"]
first_line_column_types = column_types
column_types = [None for col in first_line_column_types]
if max_data_lines is not None and data_lines >= max_data_lines:
if dataset_fh.tell() != dataset.get_size():
# Clear optional data_lines metadata value
data_lines = None # type: ignore [assignment]
# Clear optional comment_lines metadata value; additional comment lines could appear below this point
comment_lines = None # type: ignore [assignment]
break
i += 1
# we error on the larger number of columns
# first we pad our column_types by using data from first line
if len(first_line_column_types) > len(column_types):
for column_type in first_line_column_types[len(column_types) :]:
column_types.append(column_type)
# Now we fill any unknown (None) column_types with data from first line
for i in range(len(column_types)):
if column_types[i] is None:
if len(first_line_column_types) <= i or first_line_column_types[i] is None:
column_types[i] = default_column_type
else:
column_types[i] = first_line_column_types[i]
# Set the discovered metadata values for the dataset
dataset.metadata.data_lines = data_lines
dataset.metadata.comment_lines = comment_lines
dataset.metadata.column_types = column_types
dataset.metadata.columns = len(column_types)
dataset.metadata.delimiter = "\t"
if column_names is not None:
dataset.metadata.column_names = column_names
[docs] def as_gbrowse_display_file(self, dataset: "DatasetInstance", **kwd) -> Union[FileObjType, str]:
return open(dataset.file_name, "rb")
[docs] def as_ucsc_display_file(self, dataset: "DatasetInstance", **kwd) -> Union[FileObjType, str]:
return open(dataset.file_name, "rb")
[docs]class SraManifest(Tabular):
"""A manifest received from the sra_source tool."""
file_ext = "sra_manifest.tabular"
data_line_offset = 1
[docs] def set_meta(self, dataset: "DatasetInstance", overwrite: bool = True, **kwd) -> None:
super().set_meta(dataset, overwrite=overwrite, **kwd)
dataset.metadata.comment_lines = 1
[docs] def get_column_names(self, first_line: str) -> Optional[List[str]]:
return first_line.strip().split("\t")
[docs]class Taxonomy(Tabular):
file_ext = "taxonomy"
[docs] def __init__(self, **kwd):
"""Initialize taxonomy datatype"""
super().__init__(**kwd)
self.column_names = [
"Name",
"TaxId",
"Root",
"Superkingdom",
"Kingdom",
"Subkingdom",
"Superphylum",
"Phylum",
"Subphylum",
"Superclass",
"Class",
"Subclass",
"Superorder",
"Order",
"Suborder",
"Superfamily",
"Family",
"Subfamily",
"Tribe",
"Subtribe",
"Genus",
"Subgenus",
"Species",
"Subspecies",
]
[docs] def display_peek(self, dataset: "DatasetInstance") -> str:
"""Returns formated html of peek"""
return self.make_html_table(dataset, column_names=self.column_names)
[docs]@dataproviders.decorators.has_dataproviders
@build_sniff_from_prefix
class Sam(Tabular, _BamOrSam):
edam_format = "format_2573"
edam_data = "data_0863"
file_ext = "sam"
track_type = "ReadTrack"
data_sources = {"data": "bam", "index": "bigwig"}
MetadataElement(
name="bam_version",
default=None,
desc="BAM Version",
param=MetadataParameter,
readonly=True,
visible=False,
optional=True,
)
MetadataElement(
name="sort_order",
default=None,
desc="Sort Order",
param=MetadataParameter,
readonly=True,
visible=False,
optional=True,
)
MetadataElement(
name="read_groups",
default=[],
desc="Read Groups",
param=MetadataParameter,
readonly=True,
visible=False,
optional=True,
no_value=[],
)
MetadataElement(
name="reference_names",
default=[],
desc="Chromosome Names",
param=MetadataParameter,
readonly=True,
visible=False,
optional=True,
no_value=[],
)
MetadataElement(
name="reference_lengths",
default=[],
desc="Chromosome Lengths",
param=MetadataParameter,
readonly=True,
visible=False,
optional=True,
no_value=[],
)
MetadataElement(
name="bam_header",
default={},
desc="Dictionary of BAM Headers",
param=MetadataParameter,
readonly=True,
visible=False,
optional=True,
no_value={},
)
[docs] def __init__(self, **kwd):
"""Initialize sam datatype"""
super().__init__(**kwd)
self.column_names = [
"QNAME",
"FLAG",
"RNAME",
"POS",
"MAPQ",
"CIGAR",
"MRNM",
"MPOS",
"ISIZE",
"SEQ",
"QUAL",
"OPT",
]
[docs] def display_peek(self, dataset: "DatasetInstance") -> str:
"""Returns formated html of peek"""
return self.make_html_table(dataset, column_names=self.column_names)
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Determines whether the file is in SAM format
A file in SAM format consists of lines of tab-separated data.
The following header line may be the first line::
@QNAME FLAG RNAME POS MAPQ CIGAR MRNM MPOS ISIZE SEQ QUAL
or
@QNAME FLAG RNAME POS MAPQ CIGAR MRNM MPOS ISIZE SEQ QUAL OPT
Data in the OPT column is optional and can consist of tab-separated data
For complete details see http://samtools.sourceforge.net/SAM1.pdf
Rules for sniffing as True::
There must be 11 or more columns of data on each line
Columns 2 (FLAG), 4(POS), 5 (MAPQ), 8 (MPOS), and 9 (ISIZE) must be numbers (9 can be negative)
We will only check that up to the first 5 alignments are correctly formatted.
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'sequence.maf' )
>>> Sam().sniff( fname )
False
>>> fname = get_test_fname( '1.sam' )
>>> Sam().sniff( fname )
True
"""
count = 0
for line in file_prefix.line_iterator():
line = line.strip()
if line:
if line[0] != "@":
line_pieces = line.split("\t")
if len(line_pieces) < 11:
return False
try:
int(line_pieces[1])
int(line_pieces[3])
int(line_pieces[4])
int(line_pieces[7])
int(line_pieces[8])
except ValueError:
return False
count += 1
if count == 5:
return True
if count < 5 and count > 0:
return True
return False
[docs] def set_meta(
self,
dataset: "DatasetInstance",
overwrite: bool = True,
skip: Optional[int] = None,
max_data_lines: Optional[int] = 5,
**kwd,
) -> None:
"""
>>> from galaxy.datatypes.sniff import get_test_fname
>>> from galaxy.datatypes.registry import example_datatype_registry_for_sample
>>> from galaxy.model import Dataset, set_datatypes_registry
>>> from galaxy.model import History, HistoryDatasetAssociation
>>> from galaxy.model.mapping import init
>>> sa_session = init("/tmp", "sqlite:///:memory:", create_tables=True).session
>>> hist = History()
>>> sa_session.add(hist)
>>> sa_session.flush()
>>> set_datatypes_registry(example_datatype_registry_for_sample())
>>> fname = get_test_fname( 'sam_with_header.sam' )
>>> samds = Dataset(external_filename=fname)
>>> hda = hist.add_dataset(HistoryDatasetAssociation(id=1, extension='sam', create_dataset=True, sa_session=sa_session, dataset=samds))
>>> Sam().set_meta(hda)
>>> hda.metadata.comment_lines
2
>>> hda.metadata.reference_names
['ref', 'ref2']
"""
if dataset.has_data():
with open(dataset.file_name) as dataset_fh:
comment_lines = 0
if (
self.max_optional_metadata_filesize >= 0
and dataset.get_size() > self.max_optional_metadata_filesize
):
# If the dataset is larger than optional_metadata, just count comment lines.
for line in dataset_fh:
if line.startswith("@"):
comment_lines += 1
else:
# No more comments, and the file is too big to look at the whole thing. Give up.
dataset.metadata.data_lines = None
break
else:
# Otherwise, read the whole thing and set num data lines.
for i, line in enumerate(dataset_fh): # noqa: B007
if line.startswith("@"):
comment_lines += 1
dataset.metadata.data_lines = i + 1 - comment_lines
dataset.metadata.comment_lines = comment_lines
dataset.metadata.columns = 12
dataset.metadata.column_types = [
"str",
"int",
"str",
"int",
"int",
"str",
"str",
"int",
"int",
"str",
"str",
"str",
]
_BamOrSam().set_meta(dataset, overwrite=overwrite, **kwd)
[docs] @staticmethod
def merge(split_files: List[str], output_file: str) -> None:
"""
Multiple SAM files may each have headers. Since the headers should all be the same, remove
the headers from files 1-n, keeping them in the first file only
"""
shutil.move(split_files[0], output_file)
if len(split_files) > 1:
cmd = ["egrep", "-v", "-h", "^@"] + split_files[1:] + [">>", output_file]
subprocess.check_call(cmd, shell=True)
# Dataproviders
# sam does not use '#' to indicate comments/headers - we need to strip out those headers from the std. providers
# TODO:?? seems like there should be an easier way to do this - metadata.comment_char?
[docs] @dataproviders.decorators.dataprovider_factory("line", FilteredLineDataProvider.settings)
def line_dataprovider(self, dataset: "DatasetInstance", **settings) -> FilteredLineDataProvider:
settings["comment_char"] = "@"
return super().line_dataprovider(dataset, **settings)
[docs] @dataproviders.decorators.dataprovider_factory("regex-line", RegexLineDataProvider.settings)
def regex_line_dataprovider(self, dataset: "DatasetInstance", **settings) -> RegexLineDataProvider:
settings["comment_char"] = "@"
return super().regex_line_dataprovider(dataset, **settings)
[docs] @dataproviders.decorators.dataprovider_factory("column", ColumnarDataProvider.settings)
def column_dataprovider(self, dataset: "DatasetInstance", **settings) -> ColumnarDataProvider:
settings["comment_char"] = "@"
return super().column_dataprovider(dataset, **settings)
[docs] @dataproviders.decorators.dataprovider_factory("dataset-column", ColumnarDataProvider.settings)
def dataset_column_dataprovider(self, dataset: "DatasetInstance", **settings) -> DatasetColumnarDataProvider:
settings["comment_char"] = "@"
return super().dataset_column_dataprovider(dataset, **settings)
[docs] @dataproviders.decorators.dataprovider_factory("dict", DictDataProvider.settings)
def dict_dataprovider(self, dataset: "DatasetInstance", **settings) -> DictDataProvider:
settings["comment_char"] = "@"
return super().dict_dataprovider(dataset, **settings)
[docs] @dataproviders.decorators.dataprovider_factory("dataset-dict", DictDataProvider.settings)
def dataset_dict_dataprovider(self, dataset: "DatasetInstance", **settings) -> DatasetDictDataProvider:
settings["comment_char"] = "@"
return super().dataset_dict_dataprovider(dataset, **settings)
[docs] @dataproviders.decorators.dataprovider_factory("header", RegexLineDataProvider.settings)
def header_dataprovider(self, dataset: "DatasetInstance", **settings) -> RegexLineDataProvider:
dataset_source = DatasetDataProvider(dataset)
headers_source = RegexLineDataProvider(dataset_source, regex_list=["^@"])
return RegexLineDataProvider(headers_source, **settings)
[docs] @dataproviders.decorators.dataprovider_factory("id-seq-qual", dict_dataprovider.settings)
def id_seq_qual_dataprovider(self, dataset: "DatasetInstance", **settings) -> DictDataProvider:
# provided as an example of a specified column dict (w/o metadata)
settings["indeces"] = [0, 9, 10]
settings["column_names"] = ["id", "seq", "qual"]
return self.dict_dataprovider(dataset, **settings)
[docs] @dataproviders.decorators.dataprovider_factory("genomic-region", GenomicRegionDataProvider.settings)
def genomic_region_dataprovider(self, dataset: "DatasetInstance", **settings) -> GenomicRegionDataProvider:
settings["comment_char"] = "@"
return GenomicRegionDataProvider(dataset, 2, 3, 3, **settings)
[docs] @dataproviders.decorators.dataprovider_factory("genomic-region-dict", GenomicRegionDataProvider.settings)
def genomic_region_dict_dataprovider(self, dataset: "DatasetInstance", **settings) -> GenomicRegionDataProvider:
settings["comment_char"] = "@"
return GenomicRegionDataProvider(dataset, 2, 3, 3, True, **settings)
# @dataproviders.decorators.dataprovider_factory( 'samtools' )
# def samtools_dataprovider( self, dataset, **settings ):
# dataset_source = dataproviders.dataset.DatasetDataProvider( dataset )
# return dataproviders.dataset.SamtoolsDataProvider( dataset_source, **settings )
[docs]@dataproviders.decorators.has_dataproviders
@build_sniff_from_prefix
class Pileup(Tabular):
"""Tab delimited data in pileup (6- or 10-column) format"""
edam_format = "format_3015"
file_ext = "pileup"
line_class = "genomic coordinate"
data_sources = {"data": "tabix"}
MetadataElement(name="chromCol", default=1, desc="Chrom column", param=metadata.ColumnParameter)
MetadataElement(name="startCol", default=2, desc="Start column", param=metadata.ColumnParameter)
MetadataElement(name="endCol", default=2, desc="End column", param=metadata.ColumnParameter)
MetadataElement(name="baseCol", default=3, desc="Reference base column", param=metadata.ColumnParameter)
[docs] def init_meta(self, dataset: "DatasetInstance", copy_from: Optional["DatasetInstance"] = None) -> None:
super().init_meta(dataset, copy_from=copy_from)
[docs] def display_peek(self, dataset: "DatasetInstance") -> str:
"""Returns formated html of peek"""
return self.make_html_table(
dataset, column_parameter_alias={"chromCol": "Chrom", "startCol": "Start", "baseCol": "Base"}
)
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Checks for 'pileup-ness'
There are two main types of pileup: 6-column and 10-column. For both,
the first three and last two columns are the same. We only check the
first three to allow for some personalization of the format.
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'interval.interval' )
>>> Pileup().sniff( fname )
False
>>> fname = get_test_fname( '6col.pileup' )
>>> Pileup().sniff( fname )
True
>>> fname = get_test_fname( '10col.pileup' )
>>> Pileup().sniff( fname )
True
>>> fname = get_test_fname( '1.excel.xls' )
>>> Pileup().sniff( fname )
False
>>> fname = get_test_fname( '2.txt' )
>>> Pileup().sniff( fname ) # 2.txt
False
>>> fname = get_test_fname( 'test_tab2.tabular' )
>>> Pileup().sniff( fname )
False
"""
found_non_comment_lines = False
try:
headers = iter_headers(file_prefix, "\t")
for hdr in headers:
if hdr and not hdr[0].startswith("#"):
if len(hdr) < 5:
return False
# chrom start in column 1 (with 0-based columns)
# and reference base is in column 2
chrom = int(hdr[1])
assert chrom >= 0
assert hdr[2] in ["A", "C", "G", "T", "N", "a", "c", "g", "t", "n"]
found_non_comment_lines = True
except Exception:
return False
return found_non_comment_lines
# Dataproviders
[docs] @dataproviders.decorators.dataprovider_factory("genomic-region", GenomicRegionDataProvider.settings)
def genomic_region_dataprovider(self, dataset: "DatasetInstance", **settings) -> GenomicRegionDataProvider:
return GenomicRegionDataProvider(dataset, **settings)
[docs] @dataproviders.decorators.dataprovider_factory("genomic-region-dict", GenomicRegionDataProvider.settings)
def genomic_region_dict_dataprovider(self, dataset: "DatasetInstance", **settings) -> GenomicRegionDataProvider:
settings["named_columns"] = True
return self.genomic_region_dataprovider(dataset, **settings)
[docs]@dataproviders.decorators.has_dataproviders
@build_sniff_from_prefix
class BaseVcf(Tabular):
"""Variant Call Format for describing SNPs and other simple genome variations."""
edam_format = "format_3016"
track_type = "VariantTrack"
data_sources = {"data": "tabix", "index": "bigwig"}
column_names = ["Chrom", "Pos", "ID", "Ref", "Alt", "Qual", "Filter", "Info", "Format", "data"]
MetadataElement(name="columns", default=10, desc="Number of columns", readonly=True, visible=False)
MetadataElement(
name="column_types",
default=["str", "int", "str", "str", "str", "int", "str", "list", "str", "str"],
param=metadata.ColumnTypesParameter,
desc="Column types",
readonly=True,
visible=False,
)
MetadataElement(
name="viz_filter_cols",
desc="Score column for visualization",
default=[5],
param=metadata.ColumnParameter,
optional=True,
multiple=True,
visible=False,
)
MetadataElement(
name="sample_names", default=[], desc="Sample names", readonly=True, visible=False, optional=True, no_value=[]
)
def _sniff(self, fname_or_file_prefix: Union[str, FilePrefix]) -> bool:
# Because this sniffer is run on compressed files that might be BGZF (due to the VcfGz subclass), we should
# handle unicode decode errors. This should ultimately be done in get_headers(), but guess_ext() currently
# relies on get_headers() raising this exception.
headers = get_headers(fname_or_file_prefix, "\n", count=1)
return headers[0][0].startswith("##fileformat=VCF")
[docs] def display_peek(self, dataset: "DatasetInstance") -> str:
"""Returns formated html of peek"""
return self.make_html_table(dataset, column_names=self.column_names)
[docs] def set_meta(self, dataset: "DatasetInstance", overwrite: bool = True, **kwd) -> None:
super().set_meta(dataset, overwrite=overwrite, **kwd)
line = None
with compression_utils.get_fileobj(dataset.file_name) as fh:
# Skip comments.
for line in fh:
if not line.startswith("##"):
break
if line and line.startswith("#"):
# Found header line, get sample names.
dataset.metadata.sample_names = line.split()[9:]
[docs] @staticmethod
def merge(split_files: List[str], output_file: str) -> None:
stderr_f = tempfile.NamedTemporaryFile(prefix="bam_merge_stderr")
stderr_name = stderr_f.name
command = ["bcftools", "concat"] + split_files + ["-o", output_file]
log.info(f"Merging vcf files with command [{' '.join(command)}]")
exit_code = subprocess.call(args=command, stderr=open(stderr_name, "wb"))
with open(stderr_name, "rb") as f:
stderr = f.read().strip()
# Did merge succeed?
if exit_code != 0:
raise Exception(f"Error merging VCF files: {stderr!r}")
[docs] def validate(self, dataset: "DatasetInstance", **kwd) -> DatatypeValidation:
def validate_row(row):
if len(row) < 8:
raise Exception("Not enough columns in row %s" % row.join("\t"))
validate_tabular(dataset.file_name, sep="\t", validate_row=validate_row, comment_designator="#")
return DatatypeValidation.validated()
# Dataproviders
[docs] @dataproviders.decorators.dataprovider_factory("genomic-region", GenomicRegionDataProvider.settings)
def genomic_region_dataprovider(self, dataset: "DatasetInstance", **settings) -> GenomicRegionDataProvider:
return GenomicRegionDataProvider(dataset, 0, 1, 1, **settings)
[docs] @dataproviders.decorators.dataprovider_factory("genomic-region-dict", GenomicRegionDataProvider.settings)
def genomic_region_dict_dataprovider(self, dataset: "DatasetInstance", **settings) -> GenomicRegionDataProvider:
settings["named_columns"] = True
return self.genomic_region_dataprovider(dataset, **settings)
[docs]class VcfGz(BaseVcf, binary.Binary):
# This class name is a misnomer, should be VcfBgzip
file_ext = "vcf_bgzip"
file_ext_export_alias = "vcf.gz"
compressed = True
compressed_format = "gzip"
MetadataElement(
name="tabix_index",
desc="Vcf Index File",
param=metadata.FileParameter,
file_ext="tbi",
readonly=True,
visible=False,
optional=True,
)
[docs] def sniff(self, filename: str) -> bool:
if not self._sniff(filename):
return False
# Check that the file is compressed with bgzip (not gzip), i.e. the
# compressed format is BGZF, as explained in
# http://samtools.github.io/hts-specs/SAMv1.pdf
with open(filename, "rb") as fh:
fh.seek(-28, 2)
last28 = fh.read()
return binascii.hexlify(last28) == b"1f8b08040000000000ff0600424302001b0003000000000000000000"
[docs] def set_meta(
self, dataset: "DatasetInstance", overwrite: bool = True, metadata_tmp_files_dir: Optional[str] = None, **kwd
) -> None:
super().set_meta(dataset, overwrite=overwrite, **kwd)
# Creates the index for the VCF file.
# These metadata values are not accessible by users, always overwrite
index_file = dataset.metadata.tabix_index
if not index_file:
index_file = dataset.metadata.spec["tabix_index"].param.new_file(
dataset=dataset, metadata_tmp_files_dir=metadata_tmp_files_dir
)
try:
pysam.tabix_index(
dataset.file_name, index=index_file.file_name, preset="vcf", keep_original=True, force=True
)
except Exception as e:
raise Exception(f"Error setting VCF.gz metadata: {util.unicodify(e)}")
dataset.metadata.tabix_index = index_file
[docs]@build_sniff_from_prefix
class Eland(Tabular):
"""Support for the export.txt.gz file used by Illumina's ELANDv2e aligner"""
compressed = True
compressed_format = "gzip"
file_ext = "_export.txt.gz"
MetadataElement(name="columns", default=0, desc="Number of columns", readonly=True, visible=False)
MetadataElement(
name="column_types",
default=[],
param=metadata.ColumnTypesParameter,
desc="Column types",
readonly=True,
visible=False,
no_value=[],
)
MetadataElement(name="comment_lines", default=0, desc="Number of comments", readonly=True, visible=False)
MetadataElement(
name="tiles",
default=[],
param=metadata.ListParameter,
desc="Set of tiles",
readonly=True,
visible=False,
no_value=[],
)
MetadataElement(
name="reads",
default=[],
param=metadata.ListParameter,
desc="Set of reads",
readonly=True,
visible=False,
no_value=[],
)
MetadataElement(
name="lanes",
default=[],
param=metadata.ListParameter,
desc="Set of lanes",
readonly=True,
visible=False,
no_value=[],
)
MetadataElement(
name="barcodes",
default=[],
param=metadata.ListParameter,
desc="Set of barcodes",
readonly=True,
visible=False,
no_value=[],
)
[docs] def __init__(self, **kwd):
"""Initialize eland datatype"""
super().__init__(**kwd)
self.column_names = [
"MACHINE",
"RUN_NO",
"LANE",
"TILE",
"X",
"Y",
"INDEX",
"READ_NO",
"SEQ",
"QUAL",
"CHROM",
"CONTIG",
"POSITION",
"STRAND",
"DESC",
"SRAS",
"PRAS",
"PART_CHROM" "PART_CONTIG",
"PART_OFFSET",
"PART_STRAND",
"FILT",
]
[docs] def make_html_table(
self, dataset: "DatasetInstance", skipchars: Optional[List] = None, peek: Optional[List] = None, **kwargs
) -> str:
"""Create HTML table, used for displaying peek"""
skipchars = skipchars or []
try:
out = ['<table cellspacing="0" cellpadding="3">']
# Generate column header
out.append("<tr>")
for i, name in enumerate(self.column_names):
out.append(f"<th>{str(i + 1)}.{name}</th>")
# This data type requires at least 11 columns in the data
if dataset.metadata.columns - len(self.column_names) > 0:
for i in range(len(self.column_names), max(dataset.metadata.columns, self.max_peek_columns)):
out.append(f"<th>{str(i + 1)}</th>")
out.append("</tr>")
out.append(self.make_html_peek_rows(dataset, skipchars=skipchars, peek=peek))
out.append("</table>")
return "".join(out)
except Exception as exc:
return f"Can't create peek {exc}"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Determines whether the file is in ELAND export format
A file in ELAND export format consists of lines of tab-separated data.
There is no header.
Rules for sniffing as True::
- There must be 22 columns on each line
- LANE, TILEm X, Y, INDEX, READ_NO, SEQ, QUAL, POSITION, *STRAND, FILT must be correct
- We will only check that up to the first 5 alignments are correctly formatted.
"""
count = 0
for line in file_prefix.line_iterator():
line = line.strip()
if not line:
break # Had a EOF comment previously, but this does not indicate EOF. I assume empty lines are not valid and this was intentional.
if line:
line_pieces = line.split("\t")
if len(line_pieces) != 22:
return False
if int(line_pieces[1]) < 0:
raise Exception("Out of range")
if int(line_pieces[2]) < 0:
raise Exception("Out of range")
if int(line_pieces[3]) < 0:
raise Exception("Out of range")
int(line_pieces[4])
int(line_pieces[5])
# can get a lot more specific
count += 1
if count == 5:
break
if count > 0:
return True
return False
[docs] def set_meta(
self,
dataset: "DatasetInstance",
overwrite: bool = True,
skip: Optional[int] = None,
max_data_lines: Optional[int] = 5,
**kwd,
) -> None:
if dataset.has_data():
with compression_utils.get_fileobj(dataset.file_name, compressed_formats=["gzip"]) as dataset_fh:
dataset_fh = cast(FileObjTypeStr, dataset_fh)
lanes = {}
tiles = {}
barcodes = {}
reads = {}
# Should always read the entire file (until we devise a more clever way to pass metadata on)
# if self.max_optional_metadata_filesize >= 0 and dataset.get_size() > self.max_optional_metadata_filesize:
# If the dataset is larger than optional_metadata, just count comment lines.
# dataset.metadata.data_lines = None
# else:
# Otherwise, read the whole thing and set num data lines.
for i, line in enumerate(dataset_fh):
if line:
line_pieces = line.split("\t")
if len(line_pieces) != 22:
raise Exception("%s:%d:Corrupt line!" % (dataset.file_name, i))
lanes[line_pieces[2]] = 1
tiles[line_pieces[3]] = 1
barcodes[line_pieces[6]] = 1
reads[line_pieces[7]] = 1
dataset.metadata.data_lines = i + 1
dataset.metadata.comment_lines = 0
dataset.metadata.columns = 21
dataset.metadata.column_types = [
"str",
"int",
"int",
"int",
"int",
"int",
"str",
"int",
"str",
"str",
"str",
"str",
"str",
"str",
"str",
"str",
"str",
"str",
"str",
"str",
"str",
]
dataset.metadata.lanes = list(lanes.keys())
dataset.metadata.tiles = ["%04d" % int(t) for t in tiles.keys()]
dataset.metadata.barcodes = [_ for _ in barcodes.keys() if _ != "0"] + [
"NoIndex" for _ in barcodes.keys() if _ == "0"
]
dataset.metadata.reads = list(reads.keys())
[docs]class FeatureLocationIndex(Tabular):
"""
An index that stores feature locations in tabular format.
"""
file_ext = "fli"
MetadataElement(name="columns", default=2, desc="Number of columns", readonly=True, visible=False)
MetadataElement(
name="column_types",
default=["str", "str"],
param=metadata.ColumnTypesParameter,
desc="Column types",
readonly=True,
visible=False,
no_value=[],
)
[docs]@dataproviders.decorators.has_dataproviders
class BaseCSV(TabularData):
"""
Delimiter-separated table data.
This includes CSV, TSV and other dialects understood by the
Python 'csv' module https://docs.python.org/2/library/csv.html
Must be extended to define the dialect to use, strict_width and file_ext.
See the Python module csv for documentation of dialect settings
"""
@property
def dialect(self):
raise NotImplementedError
@property
def strict_width(self):
raise NotImplementedError
delimiter = ","
peek_size = 1024 # File chunk used for sniffing CSV dialect
big_peek_size = 10240 # Large File chunk used for sniffing CSV dialect
[docs] def sniff(self, filename: str) -> bool:
"""Return True if if recognizes dialect and header."""
# check the dialect works
with open(filename, newline="") as f:
reader = csv.reader(f, self.dialect)
# Check we can read header and get columns
header_row = next(reader)
if len(header_row) < 2:
# No columns so not separated by this dialect.
return False
# Check that there is a second row as it is used by set_meta and
# that all rows can be read
if self.strict_width:
num_columns = len(header_row)
found_second_line = False
for data_row in reader:
found_second_line = True
# All columns must be the same length
if num_columns != len(data_row):
return False
if not found_second_line:
return False
else:
data_row = next(reader)
if len(data_row) < 2:
# No columns so not separated by this dialect.
return False
# ignore the length in the rest
for _ in reader:
pass
# Optional: Check Python's csv comes up with a similar dialect
with open(filename) as f:
big_peek = f.read(self.big_peek_size)
auto_dialect = csv.Sniffer().sniff(big_peek)
if auto_dialect.delimiter != self.dialect.delimiter:
return False
if auto_dialect.quotechar != self.dialect.quotechar:
return False
# Not checking for other dialect options
# They may be mis detected from just the sample.
# Or not effect the read such as doublequote
# Optional: Check for headers as in the past.
# Note: No way around Python's csv calling Sniffer.sniff again.
# Note: Without checking the dialect returned by sniff
# this test may be checking the wrong dialect.
if not csv.Sniffer().has_header(big_peek):
return False
return True
[docs] def set_meta(self, dataset: "DatasetInstance", overwrite: bool = True, **kwd) -> None:
column_types = []
header_row = []
data_row = []
data_lines = 0
if dataset.has_data():
with open(dataset.file_name, newline="") as csvfile:
# Parse file with the correct dialect
reader = csv.reader(csvfile, self.dialect)
try:
header_row = next(reader)
data_row = next(reader)
for _ in reader:
pass
except StopIteration:
pass
except csv.Error as e:
raise Exception("CSV reader error - line %d: %s" % (reader.line_num, e))
else:
data_lines = reader.line_num - 1
# Guess column types
for cell in data_row:
column_types.append(self.guess_type(cell))
# Set metadata
dataset.metadata.data_lines = data_lines
dataset.metadata.comment_lines = int(bool(header_row))
dataset.metadata.column_types = column_types
dataset.metadata.columns = max(len(header_row), len(data_row))
dataset.metadata.column_names = header_row
dataset.metadata.delimiter = self.dialect.delimiter
[docs]@dataproviders.decorators.has_dataproviders
class CSV(BaseCSV):
"""
Comma-separated table data.
Only sniffs comma-separated files with at least 2 rows and 2 columns.
"""
file_ext = "csv"
dialect = csv.excel # This is the default
strict_width = False # Previous csv type did not check column width
[docs]@dataproviders.decorators.has_dataproviders
class TSV(BaseCSV):
"""
Tab-separated table data.
Only sniff tab-separated files with at least 2 rows and 2 columns.
Note: Use of this datatype is optional as the general tabular datatype will
handle most tab-separated files. This datatype is only required for datasets
with tabs INSIDE double quotes.
This datatype currently does not support TSV files where the header has one
column less to indicate first column is row names. This kind of file is
handled fine by the tabular datatype.
"""
file_ext = "tsv"
dialect = csv.excel_tab
strict_width = True # Leave files with different width to tabular
[docs]@build_sniff_from_prefix
class ConnectivityTable(Tabular):
edam_format = "format_3309"
file_ext = "ct"
header_regexp = re.compile("^[0-9]+(?: |[ ]+).*?(?:ENERGY|energy|dG)[ ].*?=")
structure_regexp = re.compile(
"^[0-9]+(?: |[ ]+)[ACGTURYKMSWBDHVN]+(?: |[ ]+)[^ ]+(?: |[ ]+)[^ ]+(?: |[ ]+)[^ ]+(?: |[ ]+)[^ ]+"
)
[docs] def __init__(self, **kwd):
super().__init__(**kwd)
self.columns = 6
self.column_names = ["base_index", "base", "neighbor_left", "neighbor_right", "partner", "natural_numbering"]
self.column_types = ["int", "str", "int", "int", "int", "int"]
[docs] def set_meta(self, dataset: "DatasetInstance", overwrite: bool = True, **kwd) -> None:
data_lines = 0
with open(dataset.file_name) as fh:
for _ in fh:
data_lines += 1
dataset.metadata.data_lines = data_lines
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
The ConnectivityTable (CT) is a file format used for describing
RNA 2D structures by tools including MFOLD, UNAFOLD and
the RNAStructure package. The tabular file format is defined as
follows::
5 energy = -12.3 sequence name
1 G 0 2 0 1
2 A 1 3 0 2
3 A 2 4 0 3
4 A 3 5 0 4
5 C 4 6 1 5
The links given at the edam ontology page do not indicate what
type of separator is used (space or tab) while different
implementations exist. The implementation that uses spaces as
separator (implemented in RNAStructure) is as follows::
10 ENERGY = -34.8 seqname
1 G 0 2 9 1
2 G 1 3 8 2
3 G 2 4 7 3
4 a 3 5 0 4
5 a 4 6 0 5
6 a 5 7 0 6
7 C 6 8 3 7
8 C 7 9 2 8
9 C 8 10 1 9
10 a 9 0 0 10
"""
i = 0
j = 1
handle = file_prefix.string_io()
for line in handle:
line = line.strip()
if len(line) > 0:
if i == 0:
if not self.header_regexp.match(line):
return False
else:
length = int(re.split(r"\W+", line, 1)[0])
else:
if not self.structure_regexp.match(line.upper()):
return False
else:
if j != int(re.split(r"\W+", line, 1)[0]):
return False
elif j == length: # Last line of first sequence has been reached
return True
else:
j += 1
i += 1
return False
[docs] def get_chunk(self, trans, dataset: "DatasetInstance", offset: int = 0, ck_size: Optional[int] = None) -> str:
ck_data, last_read = self._read_chunk(trans, dataset, offset, ck_size)
try:
# The ConnectivityTable format has several derivatives of which one is delimited by (multiple) spaces.
# By converting these spaces back to tabs, chunks can still be interpreted by tab delimited file parsers
ck_data_header, ck_data_body = ck_data.split("\n", 1)
ck_data_header = re.sub("^([0-9]+)[ ]+", r"\1\t", ck_data_header)
ck_data_body = re.sub("\n[ \t]+", "\n", ck_data_body)
ck_data_body = re.sub("[ ]+", "\t", ck_data_body)
ck_data = f"{ck_data_header}\n{ck_data_body}"
except ValueError:
pass # 1 or 0 lines left
return dumps(
{
"ck_data": util.unicodify(ck_data),
"offset": last_read,
"data_line_offset": self.data_line_offset,
}
)
[docs]@build_sniff_from_prefix
class MatrixMarket(TabularData):
"""
The Matrix Market (MM) exchange formats provide a simple mechanism
to facilitate the exchange of matrix data. MM coordinate format is
suitable for representing sparse matrices. Only nonzero entries need
be encoded, and the coordinates of each are given explicitly.
The tabular file format is defined as follows:
.. code-block::
%%MatrixMarket matrix coordinate real general <--- header line
% <--+
% comments |-- 0 or more comment lines
% <--+
M N L <--- rows, columns, entries
I1 J1 A(I1, J1) <--+
I2 J2 A(I2, J2) |
I3 J3 A(I3, J3) |-- L lines
. . . |
IL JL A(IL, JL) <--+
Indices are 1-based, i.e. A(1,1) is the first element.
>>> from galaxy.datatypes.sniff import get_test_fname
>>> MatrixMarket().sniff( get_test_fname( 'sequence.maf' ) )
False
>>> MatrixMarket().sniff( get_test_fname( '1.mtx' ) )
True
>>> MatrixMarket().sniff( get_test_fname( '2.mtx' ) )
True
>>> MatrixMarket().sniff( get_test_fname( '3.mtx' ) )
True
"""
file_ext = "mtx"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
return file_prefix.startswith("%%MatrixMarket matrix coordinate")
[docs] def set_meta(
self,
dataset: "DatasetInstance",
overwrite: bool = True,
skip: Optional[int] = None,
max_data_lines: Optional[int] = 5,
**kwd,
) -> None:
if dataset.has_data():
# If the dataset is larger than optional_metadata, just count comment lines.
with open(dataset.file_name) as dataset_fh:
line = ""
data_lines = 0
comment_lines = 0
# If the dataset is larger than optional_metadata, just count comment lines.
count_comments_only = (
self.max_optional_metadata_filesize >= 0
and dataset.get_size() > self.max_optional_metadata_filesize
)
for line in dataset_fh:
if line.startswith("%"):
comment_lines += 1
elif count_comments_only:
data_lines = None # type: ignore [assignment]
break
else:
data_lines += 1
if " " in line:
dataset.metadata.delimiter = " "
else:
dataset.metadata.delimiter = "\t"
dataset.metadata.comment_lines = comment_lines
dataset.metadata.data_lines = data_lines
dataset.metadata.columns = 3
dataset.metadata.column_types = ["int", "int", "float"]
[docs]@build_sniff_from_prefix
class CMAP(TabularData):
"""
# CMAP File Version: 2.0
# Label Channels: 1
# Nickase Recognition Site 1: cttaag;green_01
# Nickase Recognition Site 2: cctcagc;red_01
# Number of Consensus Maps: 459
# Values corresponding to intervals (StdDev, HapDelta) refer to the interval between current site and next site
#h CMapId ContigLength NumSites SiteID LabelChannel Position StdDev Coverage Occurrence ChimQuality SegDupL SegDupR FragileL FragileR OutlierFrac ChimNorm Mask
#f int float int int int float float float float float float float float float float float Hex
182 58474736.7 10235 1 1 58820.9 35.4 13.5 13.5 -1.00 -1.00 -1.00 3.63 0.00 0.00 -1.00 0
182 58474736.7 10235 1 1 58820.9 35.4 13.5 13.5 -1.00 -1.00 -1.00 3.63 0.00 0.00 -1.00 0
182 58474736.7 10235 1 1 58820.9 35.4 13.5 13.5 -1.00 -1.00 -1.00 3.63 0.00 0.00 -1.00 0
"""
file_ext = "cmap"
MetadataElement(
name="cmap_version",
default="0.2",
desc="version of cmap",
readonly=True,
visible=True,
optional=False,
no_value="0.2",
)
MetadataElement(
name="label_channels",
default=1,
desc="the number of label channels",
readonly=True,
visible=True,
optional=False,
no_value=1,
)
MetadataElement(
name="nickase_recognition_site_1",
default=[],
desc="comma separated list of label motif recognition sequences for channel 1",
readonly=True,
visible=True,
optional=False,
no_value=[],
)
MetadataElement(
name="number_of_consensus_nanomaps",
default=0,
desc="the total number of consensus genome maps in the CMAP file",
readonly=True,
visible=True,
optional=False,
no_value=0,
)
MetadataElement(
name="nickase_recognition_site_2",
default=[],
desc="comma separated list of label motif recognition sequences for channel 2",
readonly=True,
visible=True,
optional=True,
no_value=[],
)
MetadataElement(
name="channel_1_color",
default=[],
desc="channel 1 color",
readonly=True,
visible=True,
optional=True,
no_value=[],
)
MetadataElement(
name="channel_2_color",
default=[],
desc="channel 2 color",
readonly=True,
visible=True,
optional=True,
no_value=[],
)
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
handle = file_prefix.string_io()
for line in handle:
if not line.startswith("#"):
return False
if line.startswith("# CMAP File Version:"):
return True
return False
[docs] def set_meta(
self,
dataset: "DatasetInstance",
overwrite: bool = True,
skip: Optional[int] = None,
max_data_lines: Optional[int] = 7,
**kwd,
) -> None:
if dataset.has_data():
with open(dataset.file_name) as dataset_fh:
comment_lines = 0
column_headers = None
cleaned_column_types = None
number_of_columns = 0
for i, line in enumerate(dataset_fh):
line = line.strip("\n")
if line.startswith("#"):
if line.startswith("#h"):
column_headers = line.split("\t")[1:]
elif line.startswith("#f"):
cleaned_column_types = []
for column_type in line.split("\t")[1:]:
if column_type == "Hex":
cleaned_column_types.append("str")
else:
cleaned_column_types.append(column_type)
comment_lines += 1
fields = line.split("\t")
if len(fields) == 2:
if fields[0] == "# CMAP File Version:":
dataset.metadata.cmap_version = fields[1]
elif fields[0] == "# Label Channels:":
dataset.metadata.label_channels = int(fields[1])
elif fields[0] == "# Nickase Recognition Site 1:":
fields2 = fields[1].split(";")
if len(fields2) == 2:
dataset.metadata.channel_1_color = fields2[1]
dataset.metadata.nickase_recognition_site_1 = fields2[0].split(",")
elif fields[0] == "# Number of Consensus Maps:":
dataset.metadata.number_of_consensus_nanomaps = int(fields[1])
elif fields[0] == "# Nickase Recognition Site 2:":
fields2 = fields[1].split(";")
if len(fields2) == 2:
dataset.metadata.channel_2_color = fields2[1]
dataset.metadata.nickase_recognition_site_2 = fields2[0].split(",")
elif (
self.max_optional_metadata_filesize >= 0
and dataset.get_size() > self.max_optional_metadata_filesize
):
# If the dataset is larger than optional_metadata, just count comment lines.
# No more comments, and the file is too big to look at the whole thing. Give up.
dataset.metadata.data_lines = None
break
elif i == comment_lines + 1:
number_of_columns = len(line.split("\t"))
if not (
self.max_optional_metadata_filesize >= 0
and dataset.get_size() > self.max_optional_metadata_filesize
):
dataset.metadata.data_lines = i + 1 - comment_lines
dataset.metadata.comment_lines = comment_lines
dataset.metadata.column_names = column_headers
dataset.metadata.column_types = cleaned_column_types
dataset.metadata.columns = number_of_columns
dataset.metadata.delimiter = "\t"
[docs]@build_sniff_from_prefix
class Psl(Tabular):
"""Tab delimited data in psl format."""
edam_format = "format_3007"
file_ext = "psl"
line_class = "assemblies"
data_sources = {"data": "tabix"}
[docs] def __init__(self, **kwd):
"""Initialize psl datatype"""
super().__init__(**kwd)
self.column_names = [
"matches",
"misMatches",
"repMatches",
"nCount",
"qNumInsert",
"qBaseInsert",
"tNumInsert",
"tBaseInsert",
"strand",
"qName",
"qSize",
"qStart",
"qEnd",
"tName",
"tSize",
"tStart",
"tEnd",
"blockCount",
"blockSizes",
"qStarts",
"tStarts",
]
[docs] def sniff_prefix(self, file_prefix: FilePrefix):
"""
PSL lines represent alignments, and are typically generated
by BLAT. Each line consists of 21 required fields, and track
lines may optionally be used to provide more information.
Fields are tab-separated, and all 21 are required.
Although not part of the formal PSL specification, track lines
may be used to further configure sets of features. Track lines
are placed at the beginning of the list of features they are
to affect.
Rules for sniffing as True::
- There must be 21 columns on each fields line
- matches, misMatches repMatches, nCount, qNumInsert,
qBaseInsert, tNumInsert, tBaseInsert, strand, qSize, qStart,
qEnd, tName, tSize, tStart, tEnd, blockCount, blockSizes,
qStarts, tStarts must be correct
- We will only check that up to the first 10 alignments are
correctly formatted.
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( '1.psl' )
>>> Psl().sniff( fname )
True
>>> fname = get_test_fname( '2.psl' )
>>> Psl().sniff( fname )
True
>>> fname = get_test_fname( 'interval.interval' )
>>> Psl().sniff( fname )
False
>>> fname = get_test_fname( '2.txt' )
>>> Psl().sniff( fname )
False
>>> fname = get_test_fname( 'test_tab2.tabular' )
>>> Psl().sniff( fname )
False
>>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.ref.taxonomy' )
>>> Psl().sniff( fname )
False
"""
def check_items(s):
s_items = s.split(",")
for item in s_items:
if int(item) < 0:
raise Exception("Out of range")
count = 0
for line in file_prefix.line_iterator():
line = line.strip()
if not line:
break
if line:
if line.startswith("browser") or line.startswith("track"):
# Skip track lines.
continue
items = line.split("\t")
if len(items) != 21:
return False
# tName is a string
items.pop(13)
# qName is a string
items.pop(9)
# strand
if items.pop(8) not in ["-", "+", "+-", "-+"]:
raise Exception("Invalid strand")
# blockSizes
s = items.pop(15).rstrip(",")
check_items(s)
# qStarts
s = items.pop(15).rstrip(",")
check_items(s)
# tStarts
s = items.pop(15).rstrip(",")
check_items(s)
if any(int(item) < 0 for item in items):
raise Exception("Out of range")
count += 1
if count == 10:
break
if count > 0:
return True