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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,
    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.protocols import (
    DatasetHasHidProtocol,
    DatasetProtocol,
    HasFileName,
    HasMetadata,
)
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

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: DatasetProtocol, *, overwrite: bool = True, **kwd) -> None: raise NotImplementedError
[docs] def set_peek(self, dataset: DatasetProtocol, **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: DatasetProtocol) -> bool: 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: HasFileName, 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: HasFileName, offset: int, ck_size: Optional[int] = None): with compression_utils.get_fileobj(dataset.get_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: DatasetHasHidProtocol, 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.get_file_name()).st_size < max_peek_size: self._clean_and_set_mime_type(trans, dataset.get_mime(), headers) return open(dataset.get_file_name(), mode="rb"), headers else: headers["content-type"] = "text/html" return ( trans.fill_template_mako( "/dataset/large_file.mako", truncated_data=open(dataset.get_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: DatasetProtocol) -> str: with open(dataset_instance.get_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: DatasetProtocol, **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: DatasetProtocol, 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: DatasetProtocol, 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: DatasetProtocol) -> 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: DatasetProtocol, **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: DatasetProtocol, **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: DatasetProtocol, **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: DatasetProtocol, **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 get_column_names(self, first_line: str) -> Optional[List[str]]: return None
[docs] def set_meta( self, dataset: DatasetProtocol, *, 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.get_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: HasFileName, **kwd) -> Union[FileObjType, str]: return open(dataset.get_file_name(), "rb")
[docs] def as_ucsc_display_file(self, dataset: DatasetProtocol, **kwd) -> Union[FileObjType, str]: return open(dataset.get_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: DatasetProtocol, 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: DatasetProtocol) -> 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: DatasetProtocol) -> 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: DatasetProtocol, 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() >>> with sa_session.begin(): ... sa_session.add(hist) >>> 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.get_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: DatasetProtocol, **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: DatasetProtocol, **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: DatasetProtocol, **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: DatasetProtocol, **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: DatasetProtocol, **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: DatasetProtocol, **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: DatasetProtocol, **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: DatasetProtocol, **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: DatasetProtocol, **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: DatasetProtocol, **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: HasMetadata, copy_from: Optional[HasMetadata] = None) -> None: super().init_meta(dataset, copy_from=copy_from)
[docs] def display_peek(self, dataset: DatasetProtocol) -> 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: DatasetProtocol, **settings) -> GenomicRegionDataProvider: return GenomicRegionDataProvider(dataset, **settings)
[docs] @dataproviders.decorators.dataprovider_factory("genomic-region-dict", GenomicRegionDataProvider.settings) def genomic_region_dict_dataprovider(self, dataset: DatasetProtocol, **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: DatasetProtocol) -> str: """Returns formated html of peek""" return self.make_html_table(dataset, column_names=self.column_names)
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None: super().set_meta(dataset, overwrite=overwrite, **kwd) line = None with compression_utils.get_fileobj(dataset.get_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: DatasetProtocol, **kwd) -> DatatypeValidation: def validate_row(row): if len(row) < 8: raise Exception("Not enough columns in row %s" % row.join("\t")) validate_tabular(dataset.get_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: DatasetProtocol, **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: DatasetProtocol, **settings) -> GenomicRegionDataProvider: settings["named_columns"] = True return self.genomic_region_dataprovider(dataset, **settings)
[docs]class Vcf(BaseVcf): file_ext = "vcf"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool: return self._sniff(file_prefix)
[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: DatasetProtocol, 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.get_file_name(), index=index_file.get_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: DatasetProtocol, 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: DatasetProtocol, 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.get_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.get_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]@build_sniff_from_prefix class ElandMulti(Tabular): file_ext = "elandmulti"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool: return False
[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: DatasetProtocol, overwrite: bool = True, **kwd) -> None: column_types = [] header_row = [] data_row = [] data_lines = 0 if dataset.has_data(): with open(dataset.get_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: DatasetProtocol, overwrite: bool = True, **kwd) -> None: data_lines = 0 with open(dataset.get_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, maxsplit=1)[0]) else: if not self.structure_regexp.match(line.upper()): return False else: if j != int(re.split(r"\W+", line, maxsplit=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: HasFileName, 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 __init__(self, **kwd): super().__init__(**kwd)
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool: return file_prefix.startswith("%%MatrixMarket matrix coordinate")
[docs] def set_meta( self, dataset: DatasetProtocol, 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.get_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: DatasetProtocol, overwrite: bool = True, skip: Optional[int] = None, max_data_lines: Optional[int] = 7, **kwd, ) -> None: if dataset.has_data(): with open(dataset.get_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