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Source code for galaxy.datatypes.msa
import abc
import logging
import os
import re
from galaxy.datatypes.binary import Binary
from galaxy.datatypes.data import get_file_peek, Text
from galaxy.datatypes.metadata import MetadataElement
from galaxy.datatypes.sniff import (
build_sniff_from_prefix,
FilePrefix,
)
from galaxy.datatypes.util import generic_util
from galaxy.util import (
nice_size,
unicodify,
)
log = logging.getLogger(__name__)
STOCKHOLM_SEARCH_PATTERN = re.compile(r'#\s+STOCKHOLM\s+1\.0')
[docs]@build_sniff_from_prefix
class InfernalCM(Text):
file_ext = "cm"
MetadataElement(name="number_of_models", default=0, desc="Number of covariance models",
readonly=True, visible=True, optional=True, no_value=0)
MetadataElement(name="cm_version", default="1/a", desc="Infernal Covariance Model version",
readonly=True, visible=True, optional=True, no_value=0)
[docs] def set_peek(self, dataset):
if not dataset.dataset.purged:
dataset.peek = get_file_peek(dataset.file_name)
if dataset.metadata.number_of_models == 1:
dataset.blurb = "1 model"
else:
dataset.blurb = f"{dataset.metadata.number_of_models} models"
dataset.peek = get_file_peek(dataset.file_name)
else:
dataset.peek = 'file does not exist'
dataset.blurb = 'file purged from disc'
[docs] def sniff_prefix(self, file_prefix: FilePrefix):
"""
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'infernal_model.cm' )
>>> InfernalCM().sniff( fname )
True
>>> fname = get_test_fname( '2.txt' )
>>> InfernalCM().sniff( fname )
False
"""
return file_prefix.startswith("INFERNAL")
[docs] def set_meta(self, dataset, **kwd):
"""
Set the number of models and the version of CM file in dataset.
"""
dataset.metadata.number_of_models = generic_util.count_special_lines('^INFERNAL', dataset.file_name)
with open(dataset.file_name) as f:
first_line = f.readline()
if first_line.startswith("INFERNAL"):
dataset.metadata.cm_version = (first_line.split()[0]).replace('INFERNAL', '')
[docs]@build_sniff_from_prefix
class Hmmer(Text):
edam_data = "data_1364"
edam_format = "format_1370"
[docs] def set_peek(self, dataset):
if not dataset.dataset.purged:
dataset.peek = get_file_peek(dataset.file_name)
dataset.blurb = "HMMER Database"
else:
dataset.peek = 'file does not exist'
dataset.blurb = 'file purged from disc'
[docs] def display_peek(self, dataset):
try:
return dataset.peek
except Exception:
return f"HMMER database ({nice_size(dataset.get_size())})"
[docs]class Hmmer2(Hmmer):
edam_format = "format_3328"
file_ext = "hmm2"
[docs] def sniff_prefix(self, file_prefix: FilePrefix):
"""HMMER2 files start with HMMER2.0
"""
return file_prefix.startswith('HMMER2.0')
[docs]class Hmmer3(Hmmer):
edam_format = "format_3329"
file_ext = "hmm3"
[docs] def sniff_prefix(self, file_prefix: FilePrefix):
"""HMMER3 files start with HMMER3/f
"""
return file_prefix.startswith('HMMER3/f')
[docs]class HmmerPress(Binary):
"""Class for hmmpress database files."""
file_ext = 'hmmpress'
composite_type = 'basic'
[docs] def set_peek(self, dataset):
"""Set the peek and blurb text."""
if not dataset.dataset.purged:
dataset.peek = "HMMER Binary database"
dataset.blurb = "HMMER Binary database"
else:
dataset.peek = 'file does not exist'
dataset.blurb = 'file purged from disk'
[docs] def display_peek(self, dataset):
"""Create HTML content, used for displaying peek."""
try:
return dataset.peek
except Exception:
return "HMMER3 database (multiple files)"
[docs] def __init__(self, **kwd):
super().__init__(**kwd)
# Binary model
self.add_composite_file('model.hmm.h3m', is_binary=True)
# SSI index for binary model
self.add_composite_file('model.hmm.h3i', is_binary=True)
# Profiles (MSV part)
self.add_composite_file('model.hmm.h3f', is_binary=True)
# Profiles (remained)
self.add_composite_file('model.hmm.h3p', is_binary=True)
[docs]@build_sniff_from_prefix
class Stockholm_1_0(Text):
edam_data = "data_0863"
edam_format = "format_1961"
file_ext = "stockholm"
MetadataElement(name="number_of_models", default=0, desc="Number of multiple alignments", readonly=True, visible=True, optional=True, no_value=0)
[docs] def set_peek(self, dataset):
if not dataset.dataset.purged:
if (dataset.metadata.number_of_models == 1):
dataset.blurb = "1 alignment"
else:
dataset.blurb = f"{dataset.metadata.number_of_models} alignments"
dataset.peek = get_file_peek(dataset.file_name)
else:
dataset.peek = 'file does not exist'
dataset.blurb = 'file purged from disc'
[docs] def sniff_prefix(self, file_prefix: FilePrefix):
return file_prefix.search(STOCKHOLM_SEARCH_PATTERN)
[docs] def set_meta(self, dataset, **kwd):
"""
Set the number of models in dataset.
"""
dataset.metadata.number_of_models = generic_util.count_special_lines('^#[[:space:]+]STOCKHOLM[[:space:]+]1.0', dataset.file_name)
[docs] @classmethod
def split(cls, input_datasets, subdir_generator_function, split_params):
"""
Split the input files by model records.
"""
if split_params is None:
return None
if len(input_datasets) > 1:
raise Exception("STOCKHOLM-file splitting does not support multiple files")
input_files = [ds.file_name for ds in input_datasets]
chunk_size = None
if split_params['split_mode'] == 'number_of_parts':
raise Exception(f"Split mode \"{split_params['split_mode']}\" is currently not implemented for STOCKHOLM-files.")
elif split_params['split_mode'] == 'to_size':
chunk_size = int(split_params['split_size'])
else:
raise Exception(f"Unsupported split mode {split_params['split_mode']}")
def _read_stockholm_records(filename):
lines = []
with open(filename) as handle:
for line in handle:
lines.append(line)
if line.strip() == '//':
yield lines
lines = []
def _write_part_stockholm_file(accumulated_lines):
part_dir = subdir_generator_function()
part_path = os.path.join(part_dir, os.path.basename(input_files[0]))
with open(part_path, 'w') as part_file:
part_file.writelines(accumulated_lines)
try:
stockholm_records = _read_stockholm_records(input_files[0])
stockholm_lines_accumulated = []
for counter, stockholm_record in enumerate(stockholm_records, start=1):
stockholm_lines_accumulated.extend(stockholm_record)
if counter % chunk_size == 0:
_write_part_stockholm_file(stockholm_lines_accumulated)
stockholm_lines_accumulated = []
if stockholm_lines_accumulated:
_write_part_stockholm_file(stockholm_lines_accumulated)
except Exception as e:
log.error('Unable to split files: %s', unicodify(e))
raise
[docs]@build_sniff_from_prefix
class MauveXmfa(Text):
file_ext = "xmfa"
MetadataElement(name="number_of_models", default=0, desc="Number of alignmened sequences", readonly=True, visible=True, optional=True, no_value=0)
[docs] def set_peek(self, dataset):
if not dataset.dataset.purged:
if (dataset.metadata.number_of_models == 1):
dataset.blurb = "1 alignment"
else:
dataset.blurb = f"{dataset.metadata.number_of_models} alignments"
dataset.peek = get_file_peek(dataset.file_name)
else:
dataset.peek = 'file does not exist'
dataset.blurb = 'file purged from disc'
[docs] def sniff_prefix(self, file_prefix: FilePrefix):
return file_prefix.startswith('#FormatVersion Mauve1')
[docs] def set_meta(self, dataset, **kwd):
dataset.metadata.number_of_models = generic_util.count_special_lines('^#Sequence([[:digit:]]+)Entry', dataset.file_name)
[docs]class Msf(Text):
"""
Multiple sequence alignment format produced by the Accelrys GCG suite and
other programs.
"""
edam_data = "data_0863"
edam_format = "format_1947"
file_ext = 'msf'