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Source code for galaxy.datatypes.text
""" Clearing house for generic text datatypes that are not XML or tabular.
"""
import gzip
import json
import logging
import os
import re
import subprocess
import tempfile
from typing import (
IO,
Optional,
Tuple,
)
import yaml
from galaxy.datatypes.data import (
get_file_peek,
Headers,
Text,
)
from galaxy.datatypes.metadata import (
MetadataElement,
MetadataParameter,
)
from galaxy.datatypes.protocols import (
DatasetHasHidProtocol,
DatasetProtocol,
HasCreatingJob,
HasExtraFilesAndMetadata,
)
from galaxy.datatypes.sniff import (
build_sniff_from_prefix,
FilePrefix,
iter_headers,
)
from galaxy.util import (
nice_size,
shlex_join,
string_as_bool,
unicodify,
)
log = logging.getLogger(__name__)
[docs]@build_sniff_from_prefix
class Html(Text):
"""Class describing an html file"""
edam_format = "format_2331"
file_ext = "html"
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
dataset.peek = "HTML file"
dataset.blurb = nice_size(dataset.get_size())
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Determines whether the file is in html format
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'complete.bed' )
>>> Html().sniff( fname )
False
>>> fname = get_test_fname( 'file.html' )
>>> Html().sniff( fname )
True
"""
headers = iter_headers(file_prefix, None)
for hdr in headers:
if hdr and hdr[0].lower().find("<html>") >= 0:
return True
return False
[docs]@build_sniff_from_prefix
class Json(Text):
edam_format = "format_3464"
file_ext = "json"
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
dataset.peek = get_file_peek(dataset.get_file_name())
dataset.blurb = "JavaScript Object Notation (JSON)"
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disc"
[docs] def get_mime(self) -> str:
"""Returns the mime type of the datatype"""
return "application/json"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Try to load the string with the json module. If successful it's a json file.
"""
return self._looks_like_json(file_prefix)
def _looks_like_json(self, file_prefix: FilePrefix) -> bool:
# Pattern used by SequenceSplitLocations
if file_prefix.file_size < 50000 and not file_prefix.truncated:
# If the file is small enough - don't guess just check.
try:
item = json.loads(file_prefix.contents_header)
# exclude simple types, must set format in these cases
assert isinstance(item, (list, dict))
return True
except Exception:
return False
else:
start = file_prefix.string_io().read(100).strip()
if start:
# simple types are valid JSON as well,
# but if necessary format has to be set explicitly
return start.startswith("[") or start.startswith("{")
return False
[docs] def display_peek(self, dataset: DatasetProtocol) -> str:
try:
return dataset.peek
except Exception:
return f"JSON file ({nice_size(dataset.get_size())})"
[docs]class ExpressionJson(Json):
"""Represents the non-data input or output to a tool or workflow."""
file_ext = "json"
MetadataElement(
name="json_type", default=None, desc="JavaScript or JSON type of expression", readonly=True, visible=True
)
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
""" """
if dataset.has_data():
json_type = "null"
file_path = dataset.get_file_name()
try:
with open(file_path) as f:
obj = json.load(f)
if isinstance(obj, int):
json_type = "int"
elif isinstance(obj, float):
json_type = "float"
elif isinstance(obj, list):
json_type = "list"
elif isinstance(obj, dict):
json_type = "object"
except json.decoder.JSONDecodeError:
with open(file_path) as f:
contents = f.read(512)
raise Exception(f"Invalid JSON encountered {contents}")
dataset.metadata.json_type = json_type
[docs]@build_sniff_from_prefix
class Ipynb(Json):
file_ext = "ipynb"
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
dataset.peek = get_file_peek(dataset.get_file_name())
dataset.blurb = "Jupyter Notebook"
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disc"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Try to load the string with the json module. If successful it's a json file.
"""
if self._looks_like_json(file_prefix):
try:
with open(file_prefix.filename) as f:
ipynb = json.load(f)
if ipynb.get("nbformat", False) is not False and ipynb.get("metadata", False):
return True
else:
return False
except Exception:
return False
return False
[docs] def display_data(
self,
trans,
dataset: DatasetHasHidProtocol,
preview: bool = False,
filename: Optional[str] = None,
to_ext: Optional[str] = None,
**kwd,
):
config = trans.app.config
trust = getattr(config, "trust_jupyter_notebook_conversion", False)
if trust:
return self._display_data_trusted(trans, dataset, preview=preview, filename=filename, to_ext=to_ext, **kwd)
else:
return super().display_data(trans, dataset, preview=preview, filename=filename, to_ext=to_ext, **kwd)
def _display_data_trusted(
self,
trans,
dataset: DatasetHasHidProtocol,
preview: bool = False,
filename: Optional[str] = None,
to_ext: Optional[str] = None,
**kwd,
) -> Tuple[IO, Headers]:
headers = kwd.pop("headers", {})
preview = string_as_bool(preview)
if to_ext or not preview:
return self._serve_raw(dataset, to_ext, headers, **kwd)
else:
with tempfile.NamedTemporaryFile(delete=False) as ofile_handle:
ofilename = ofile_handle.name
try:
cmd = [
"jupyter",
"nbconvert",
"--to",
"html",
"--template",
"full",
dataset.get_file_name(),
"--output",
ofilename,
]
subprocess.check_call(cmd)
ofilename = f"{ofilename}.html"
except subprocess.CalledProcessError:
ofilename = dataset.get_file_name()
log.exception(
'Command "%s" failed. Could not convert the Jupyter Notebook to HTML, defaulting to plain text.',
shlex_join(cmd),
)
return open(ofilename, mode="rb"), headers
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
"""
Set the number of models in dataset.
"""
[docs]@build_sniff_from_prefix
class Biom1(Json):
"""
BIOM version 1.0 file format description
http://biom-format.org/documentation/format_versions/biom-1.0.html
"""
file_ext = "biom1"
edam_format = "format_3746"
MetadataElement(
name="table_rows",
default=[],
desc="table_rows",
param=MetadataParameter,
readonly=True,
visible=False,
optional=True,
no_value=[],
)
MetadataElement(
name="table_matrix_element_type",
default="",
desc="table_matrix_element_type",
param=MetadataParameter,
readonly=True,
visible=False,
optional=True,
no_value="",
)
MetadataElement(
name="table_format",
default="",
desc="table_format",
param=MetadataParameter,
readonly=True,
visible=False,
optional=True,
no_value="",
)
MetadataElement(
name="table_generated_by",
default="",
desc="table_generated_by",
param=MetadataParameter,
readonly=True,
visible=True,
optional=True,
no_value="",
)
MetadataElement(
name="table_matrix_type",
default="",
desc="table_matrix_type",
param=MetadataParameter,
readonly=True,
visible=False,
optional=True,
no_value="",
)
MetadataElement(
name="table_shape",
default=[],
desc="table_shape",
param=MetadataParameter,
readonly=True,
visible=False,
optional=True,
no_value=[],
)
MetadataElement(
name="table_format_url",
default="",
desc="table_format_url",
param=MetadataParameter,
readonly=True,
visible=False,
optional=True,
no_value="",
)
MetadataElement(
name="table_date",
default="",
desc="table_date",
param=MetadataParameter,
readonly=True,
visible=True,
optional=True,
no_value="",
)
MetadataElement(
name="table_type",
default="",
desc="table_type",
param=MetadataParameter,
readonly=True,
visible=True,
optional=True,
no_value="",
)
MetadataElement(
name="table_id",
default=None,
desc="table_id",
param=MetadataParameter,
readonly=True,
visible=True,
optional=True,
)
MetadataElement(
name="table_columns",
default=[],
desc="table_columns",
param=MetadataParameter,
readonly=True,
visible=False,
optional=True,
no_value=[],
)
MetadataElement(
name="table_column_metadata_headers",
default=[],
desc="table_column_metadata_headers",
param=MetadataParameter,
readonly=True,
visible=True,
optional=True,
no_value=[],
)
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
super().set_peek(dataset)
if not dataset.dataset.purged:
dataset.blurb = "Biological Observation Matrix v1"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
is_biom = False
if self._looks_like_json(file_prefix):
is_biom = self._looks_like_biom(file_prefix)
return is_biom
def _looks_like_biom(self, file_prefix: FilePrefix, load_size: int = 50000) -> bool:
"""
@param filepath: [str] The path to the evaluated file.
@param load_size: [int] The size of the file block load in RAM (in
bytes).
"""
is_biom = False
segment_size = int(load_size / 2)
try:
with open(file_prefix.filename) as fh:
prev_str = ""
segment_str = fh.read(segment_size)
if segment_str.strip().startswith("{"):
while segment_str:
current_str = prev_str + segment_str
if '"format"' in current_str:
current_str = re.sub(r"\s", "", current_str)
if '"format":"BiologicalObservationMatrix' in current_str:
is_biom = True
break
prev_str = segment_str
segment_str = fh.read(segment_size)
except Exception:
pass
return is_biom
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
"""
Store metadata information from the BIOM file.
"""
if dataset.has_data():
with open(dataset.get_file_name()) as fh:
try:
json_dict = json.load(fh)
except Exception:
return
def _transform_dict_list_ids(dict_list):
if dict_list:
return [x.get("id", None) for x in dict_list]
return []
b_transform = {"rows": _transform_dict_list_ids, "columns": _transform_dict_list_ids}
for m_name, b_name in [
("table_rows", "rows"),
("table_matrix_element_type", "matrix_element_type"),
("table_format", "format"),
("table_generated_by", "generated_by"),
("table_matrix_type", "matrix_type"),
("table_shape", "shape"),
("table_format_url", "format_url"),
("table_date", "date"),
("table_type", "type"),
("table_id", "id"),
("table_columns", "columns"),
]:
try:
metadata_value = json_dict.get(b_name, None)
if b_name == "columns" and metadata_value:
keep_columns = set()
for column in metadata_value:
if column["metadata"] is not None:
for k, v in column["metadata"].items():
if v is not None:
keep_columns.add(k)
final_list = sorted(list(keep_columns))
dataset.metadata.table_column_metadata_headers = final_list
if b_name in b_transform:
metadata_value = b_transform[b_name](metadata_value)
setattr(dataset.metadata, m_name, metadata_value)
except Exception:
log.exception("Something in the metadata detection for biom1 went wrong.")
[docs]@build_sniff_from_prefix
class ImgtJson(Json):
"""
https://github.com/repseqio/library-imgt/releases
Data coming from IMGT server may be used for academic research only,
provided that it is referred to IMGT®, and cited as:
"IMGT®, the international ImMunoGeneTics information system®
http://www.imgt.org (founder and director: Marie-Paule Lefranc, Montpellier, France)."
"""
file_ext = "imgt.json"
MetadataElement(name="taxon_names", default=[], desc="taxonID: names", readonly=True, visible=True, no_value=[])
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
super().set_peek(dataset)
if not dataset.dataset.purged:
dataset.blurb = "IMGT Library"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Determines whether the file is in json format with imgt elements
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( '1.json' )
>>> ImgtJson().sniff( fname )
False
>>> fname = get_test_fname( 'imgt.json' )
>>> ImgtJson().sniff( fname )
True
"""
is_imgt = False
if self._looks_like_json(file_prefix):
is_imgt = self._looks_like_imgt(file_prefix)
return is_imgt
def _looks_like_imgt(self, file_prefix: FilePrefix, load_size: int = 5000) -> bool:
"""
@param filepath: [str] The path to the evaluated file.
@param load_size: [int] The size of the file block load in RAM (in
bytes).
"""
is_imgt = False
try:
with open(file_prefix.filename) as fh:
segment_str = fh.read(load_size)
if segment_str.strip().startswith("["):
if '"taxonId"' in segment_str and '"anchorPoints"' in segment_str:
is_imgt = True
except Exception:
pass
return is_imgt
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
"""
Store metadata information from the imgt file.
"""
if dataset.has_data():
with open(dataset.get_file_name()) as fh:
try:
json_dict = json.load(fh)
tax_names = []
for entry in json_dict:
if "taxonId" in entry:
names = "%d: %s" % (entry["taxonId"], ",".join(entry["speciesNames"]))
tax_names.append(names)
dataset.metadata.taxon_names = tax_names
except Exception:
return
[docs]@build_sniff_from_prefix
class GeoJson(Json):
"""
GeoJSON is a geospatial data interchange format based on JavaScript Object Notation (JSON).
https://tools.ietf.org/html/rfc7946
"""
file_ext = "geojson"
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
super().set_peek(dataset)
if not dataset.dataset.purged:
dataset.blurb = "GeoJSON"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Determines whether the file is in json format with imgt elements
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( '1.json' )
>>> GeoJson().sniff( fname )
False
>>> fname = get_test_fname( 'gis.geojson' )
>>> GeoJson().sniff( fname )
True
"""
is_geojson = False
if self._looks_like_json(file_prefix):
is_geojson = self._looks_like_geojson(file_prefix)
return is_geojson
def _looks_like_geojson(self, file_prefix: FilePrefix, load_size: int = 5000) -> bool:
"""
One of "Point", "MultiPoint", "LineString", "MultiLineString", "Polygon", "MultiPolygon", and "GeometryCollection" needs to be present.
All of "type", "geometry", and "coordinates" needs to be present.
"""
is_geojson = False
try:
with open(file_prefix.filename) as fh:
segment_str = fh.read(load_size)
if any(
x in segment_str
for x in [
"Point",
"MultiPoint",
"LineString",
"MultiLineString",
"Polygon",
"MultiPolygon",
"GeometryCollection",
]
):
if all(x in segment_str for x in ["type", "geometry", "coordinates"]):
return True
except Exception:
pass
return is_geojson
[docs]@build_sniff_from_prefix
class Obo(Text):
"""
OBO file format description
https://owlcollab.github.io/oboformat/doc/GO.format.obo-1_2.html
"""
edam_data = "data_0582"
edam_format = "format_2549"
file_ext = "obo"
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
dataset.peek = get_file_peek(dataset.get_file_name())
dataset.blurb = "Open Biomedical Ontology (OBO)"
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disc"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Try to guess the Obo filetype.
It usually starts with a "format-version:" string and has several stanzas which starts with "id:".
"""
stanza = re.compile(r"^\[.*\]$")
handle = file_prefix.string_io()
first_line = handle.readline()
if not first_line.startswith("format-version:"):
return False
for line in handle:
if stanza.match(line.strip()):
# a stanza needs to begin with an ID tag
if next(handle).startswith("id:"):
return True
return False
[docs]@build_sniff_from_prefix
class Arff(Text):
"""
An ARFF (Attribute-Relation File Format) file is an ASCII text file that describes a list of instances sharing a set of attributes.
http://weka.wikispaces.com/ARFF
"""
edam_format = "format_3581"
file_ext = "arff"
MetadataElement(
name="comment_lines", default=0, desc="Number of comment lines", readonly=True, optional=True, no_value=0
)
MetadataElement(name="columns", default=0, desc="Number of columns", readonly=True, visible=True, no_value=0)
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
dataset.peek = get_file_peek(dataset.get_file_name())
dataset.blurb = "Attribute-Relation File Format (ARFF)"
dataset.blurb += f", {dataset.metadata.comment_lines} comments, {dataset.metadata.columns} attributes"
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disc"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Try to guess the Arff filetype.
It usually starts with a "format-version:" string and has several stanzas which starts with "id:".
"""
handle = file_prefix.string_io()
relation_found = False
attribute_found = False
for line_count, line in enumerate(handle):
if line_count > 1000:
# only investigate the first 1000 lines
return False
line = line.strip()
if not line:
continue
start_string = line[:20].upper()
if start_string.startswith("@RELATION"):
relation_found = True
elif start_string.startswith("@ATTRIBUTE"):
attribute_found = True
elif start_string.startswith("@DATA"):
# @DATA should be the last data block
if relation_found and attribute_found:
return True
return False
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
"""
Trying to count the comment lines and the number of columns included.
A typical ARFF data block looks like this:
@DATA
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
"""
comment_lines = column_count = 0
if dataset.has_data():
first_real_line = False
data_block = False
with open(dataset.get_file_name()) as handle:
for line in handle:
line = line.strip()
if not line:
continue
if line.startswith("%") and not first_real_line:
comment_lines += 1
else:
first_real_line = True
if data_block:
if line.startswith("{"):
# Sparse representation
"""
@data
0, X, 0, Y, "class A", {5}
or
@data
{1 X, 3 Y, 4 "class A"}, {5}
"""
token = line.split("}", 1)
first_part = token[0]
last_column = first_part.split(",")[-1].strip()
numeric_value = last_column.split()[0]
column_count = int(numeric_value)
if len(token) > 1:
# we have an additional weight
column_count -= 1
else:
columns = line.strip().split(",")
column_count = len(columns)
if columns[-1].strip().startswith("{"):
# we have an additional weight at the end
column_count -= 1
# We have now the column_count and we know the initial comment lines. So we can terminate here.
break
if line[:5].upper() == "@DATA":
data_block = True
dataset.metadata.comment_lines = comment_lines
dataset.metadata.columns = column_count
[docs]class SnpEffDb(Text):
"""Class describing a SnpEff genome build"""
edam_format = "format_3624"
file_ext = "snpeffdb"
MetadataElement(name="genome_version", default=None, desc="Genome Version", readonly=True, visible=True)
MetadataElement(name="snpeff_version", default="SnpEff4.0", desc="SnpEff Version", readonly=True, visible=True)
MetadataElement(
name="regulation", default=[], desc="Regulation Names", readonly=True, visible=True, no_value=[], optional=True
)
MetadataElement(
name="annotation", default=[], desc="Annotation Names", readonly=True, visible=True, no_value=[], optional=True
)
# The SnpEff version line was added in SnpEff version 4.1
[docs] def getSnpeffVersionFromFile(self, path: str) -> Optional[str]:
snpeff_version = None
try:
with gzip.open(path, "rt") as fh:
buf = fh.read(100)
lines = buf.splitlines()
m = re.match(r"^(SnpEff)\s+(\d+\.\d+).*$", lines[0].strip())
if m:
snpeff_version = m.groups()[0] + m.groups()[1]
except Exception:
pass
return snpeff_version
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
super().set_meta(dataset, overwrite=overwrite, **kwd)
data_dir = dataset.extra_files_path
# search data_dir/genome_version for files
regulation_pattern = "regulation_(.+).bin"
# annotation files that are included in snpEff by a flag
annotations_dict = {"nextProt.bin": "-nextprot", "motif.bin": "-motif", "interactions.bin": "-interaction"}
regulations = []
annotations = []
genome_version = None
snpeff_version = None
if data_dir and os.path.isdir(data_dir):
for root, _, files in os.walk(data_dir):
for fname in files:
if fname.startswith("snpEffectPredictor"):
# if snpEffectPredictor.bin download succeeded
genome_version = os.path.basename(root)
dataset.metadata.genome_version = genome_version
# read the first line of the gzipped snpEffectPredictor.bin file to get the SnpEff version
snpeff_version = self.getSnpeffVersionFromFile(os.path.join(root, fname))
if snpeff_version:
dataset.metadata.snpeff_version = snpeff_version
else:
m = re.match(regulation_pattern, fname)
if m:
name = m.groups()[0]
regulations.append(name)
elif fname in annotations_dict:
value = annotations_dict[fname]
name = value.lstrip("-")
annotations.append(name)
dataset.metadata.regulation = regulations
dataset.metadata.annotation = annotations
try:
with open(dataset.get_file_name(), "w") as fh:
fh.write(f"{genome_version}\n" if genome_version else "Genome unknown")
fh.write(f"{snpeff_version}\n" if snpeff_version else "SnpEff version unknown")
if annotations:
fh.write(f"annotations: {','.join(annotations)}\n")
if regulations:
fh.write(f"regulations: {','.join(regulations)}\n")
except Exception:
pass
[docs]class SnpSiftDbNSFP(Text):
"""
Class describing a dbNSFP database prepared fpr use by SnpSift dbnsfp
The dbNSFP file is a tabular file with 1 header line.
The first 4 columns are required to be: chrom pos ref alt
These match columns 1,2,4,5 of the VCF file
SnpSift requires the file to be block-gzipped and the indexed with samtools tabix
Example:
- Compress using block-gzip algorithm:
$ bgzip dbNSFP2.3.txt
- Create tabix index
$ tabix -s 1 -b 2 -e 2 dbNSFP2.3.txt.gz
"""
file_ext = "snpsiftdbnsfp"
composite_type = "auto_primary_file"
MetadataElement(
name="reference_name",
default="dbSNFP",
desc="Reference Name",
readonly=True,
visible=True,
set_in_upload=True,
no_value="dbSNFP",
)
MetadataElement(name="bgzip", default=None, desc="dbNSFP bgzip", readonly=True, visible=True)
MetadataElement(name="index", default=None, desc="Tabix Index File", readonly=True, visible=True)
MetadataElement(name="annotation", default=[], desc="Annotation Names", readonly=True, visible=True, no_value=[])
[docs] def __init__(self, **kwd):
super().__init__(**kwd)
self.add_composite_file(
"%s.gz", description="dbNSFP bgzip", substitute_name_with_metadata="reference_name", is_binary=True
)
self.add_composite_file(
"%s.gz.tbi", description="Tabix Index File", substitute_name_with_metadata="reference_name", is_binary=True
)
[docs] def generate_primary_file(self, dataset: HasExtraFilesAndMetadata) -> str:
"""
This is called only at upload to write the html file
cannot rename the datasets here - they come with the default unfortunately
"""
return "<html><head><title>SnpSiftDbNSFP Composite Dataset</title></head></html>"
[docs] def regenerate_primary_file(self, dataset: DatasetProtocol) -> None:
"""
cannot do this until we are setting metadata
"""
annotations = f"dbNSFP Annotations: {','.join(dataset.metadata.annotation)}\n"
with open(dataset.get_file_name(), "a") as f:
if dataset.metadata.bgzip:
bn = dataset.metadata.bgzip
f.write(bn)
f.write("\n")
f.write(annotations)
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
try:
efp = dataset.extra_files_path
if os.path.exists(efp):
flist = os.listdir(efp)
for fname in flist:
if fname.endswith(".gz"):
dataset.metadata.bgzip = fname
try:
with gzip.open(os.path.join(efp, fname), "rt") as fh:
buf = fh.read(5000)
lines = buf.splitlines()
headers = lines[0].split("\t")
dataset.metadata.annotation = headers[4:]
except Exception as e:
log.warning("set_meta fname: %s %s", fname, unicodify(e))
if fname.endswith(".tbi"):
dataset.metadata.index = fname
self.regenerate_primary_file(dataset)
except Exception as e:
log.warning(
"set_meta fname: %s %s",
dataset.get_file_name() if dataset and dataset.get_file_name() else "Unkwown",
unicodify(e),
)
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
dataset.peek = f"{dataset.metadata.reference_name} : {','.join(dataset.metadata.annotation)}"
dataset.blurb = f"{dataset.metadata.reference_name}"
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disc"
[docs]@build_sniff_from_prefix
class IQTree(Text):
"""IQ-TREE format"""
file_ext = "iqtree"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Detect the IQTree file
Scattered text file containing various headers and data
types.
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('example.iqtree')
>>> IQTree().sniff(fname)
True
>>> fname = get_test_fname('temp.txt')
>>> IQTree().sniff(fname)
False
>>> fname = get_test_fname('test_tab1.tabular')
>>> IQTree().sniff(fname)
False
"""
return file_prefix.startswith("IQ-TREE")
[docs]@build_sniff_from_prefix
class Paf(Text):
"""
PAF: a Pairwise mApping Format
https://github.com/lh3/miniasm/blob/master/PAF.md
"""
file_ext = "paf"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('A-3105.paf')
>>> Paf().sniff(fname)
True
"""
found_valid_lines = False
for line in iter_headers(file_prefix, "\t"):
if len(line) < 12:
return False
for i in (1, 2, 3, 6, 7, 8, 9, 10, 11):
int(line[i])
if line[4] not in ("+", "-"):
return False
if not (0 <= int(line[11]) <= 255):
return False
# Check that the optional columns after the 12th contain SAM-like typed key-value pairs
for i in range(12, len(line)):
if len(line[i].split(":")) != 3:
return False
found_valid_lines = True
return found_valid_lines
[docs]@build_sniff_from_prefix
class Gfa1(Text):
"""
Graphical Fragment Assembly (GFA) 1.0
http://gfa-spec.github.io/GFA-spec/GFA1.html
"""
file_ext = "gfa1"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('big.gfa1')
>>> Gfa1().sniff(fname)
True
>>> Gfa2().sniff(fname)
False
"""
found_valid_lines = False
for line in iter_headers(file_prefix, "\t"):
if line[0].startswith("#"):
continue
if line[0] == "H":
return len(line) == 2 and line[1] == "VN:Z:1.0"
elif line[0] == "S":
if len(line) < 3:
return False
elif line[0] == "L":
if len(line) < 6:
return False
for i in (2, 4):
if line[i] not in ("+", "-"):
return False
elif line[0] == "C":
if len(line) < 7:
return False
for i in (2, 4):
if line[i] not in ("+", "-"):
return False
int(line[5])
elif line[0] == "P":
if len(line) < 4:
return False
else:
return False
found_valid_lines = True
return found_valid_lines
[docs]@build_sniff_from_prefix
class Gfa2(Text):
"""
Graphical Fragment Assembly (GFA) 2.0
https://github.com/GFA-spec/GFA-spec/blob/master/GFA2.md
"""
file_ext = "gfa2"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('sample.gfa2')
>>> Gfa2().sniff(fname)
True
>>> Gfa1().sniff(fname)
False
"""
found_valid_lines = False
for line in iter_headers(file_prefix, "\t"):
if line[0].startswith("#"):
continue
if line[0] == "H":
return len(line) >= 2 and line[1] == "VN:Z:2.0"
elif line[0] == "S":
if len(line) < 3:
return False
elif line[0] == "F":
if len(line) < 8:
return False
elif line[0] == "E":
if len(line) < 9:
return False
elif line[0] == "G":
if len(line) < 6:
return False
elif line[0] == "O" or line[0] == "U":
if len(line) < 3:
return False
else:
return False
found_valid_lines = True
return found_valid_lines
[docs]@build_sniff_from_prefix
class Yaml(Text):
"""Yaml files"""
file_ext = "yaml"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Try to load the string with the yaml module. If successful it's a yaml file.
"""
return self._looks_like_yaml(file_prefix)
[docs] def get_mime(self) -> str:
"""Returns the mime type of the datatype"""
return "application/yaml"
def _yield_user_file_content(self, trans, from_dataset: HasCreatingJob, filename: str, headers: Headers) -> IO:
# Override non-standard application/yaml mediatype with
# text/plain, so preview is shown in preview iframe,
# instead of downloading the file.
headers["content-type"] = "text/plain"
return super()._yield_user_file_content(trans, from_dataset, filename, headers)
def _looks_like_yaml(self, file_prefix: FilePrefix) -> bool:
# Pattern used by SequenceSplitLocations
if file_prefix.file_size < 50000 and not file_prefix.truncated:
# If the file is small enough - don't guess just check.
try:
item = yaml.safe_load(file_prefix.contents_header)
assert isinstance(item, (list, dict))
return True
except yaml.YAMLError:
return False
else:
# If file is too big, load the first part. Trim the current line, in case it cut off in the middle of a key.
file_start = file_prefix.string_io().read(50000).strip().rsplit("\n", 1)[0]
try:
item = yaml.safe_load(file_start)
assert isinstance(item, (list, dict))
return True
except yaml.YAMLError:
return False
return False
[docs]@build_sniff_from_prefix
class BCSLmodel(Text):
"""BioChemical Space Language model file"""
file_ext = "bcsl.model"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Determines whether the file is in .bcsl.model format
"""
reg = r"^#! rules|^#! inits|^#! definitions"
return re.search(reg, file_prefix.contents_header, re.MULTILINE) is not None
[docs]@build_sniff_from_prefix
class BCSLts(Json):
"""BioChemical Space Language transition system file"""
file_ext = "bcsl.ts"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Determines whether the file is in .bcsl.ts format
"""
is_bcsl_ts = False
if self._looks_like_json(file_prefix):
is_bcsl_ts = self._looks_like_bcsl_ts(file_prefix)
return is_bcsl_ts
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
lines = "States: {}\nTransitions: {}\nUnique agents: {}\nInitial state: {}"
ts = json.load(open(dataset.get_file_name()))
dataset.peek = lines.format(len(ts["nodes"]), len(ts["edges"]), len(ts["ordering"]), ts["initial"])
dataset.blurb = nice_size(dataset.get_size())
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
def _looks_like_bcsl_ts(self, file_prefix: FilePrefix) -> bool:
content = open(file_prefix.filename).read()
keywords = ['"edges":', '"nodes":', '"ordering":', '"initial":']
if all(keyword in content for keyword in keywords):
return self._looks_like_json(file_prefix)
return False
[docs]@build_sniff_from_prefix
class StormSample(Text):
"""
Storm PCTL parameter synthesis result file
containing probability function of parameters.
"""
file_ext = "storm.sample"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Determines whether the file is in .storm.sample format
"""
keywords = ["Storm-pars", "Result (initial states)"]
return all(keyword in file_prefix.contents_header for keyword in keywords)
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
dataset.peek = "Storm-pars sample results."
dataset.blurb = nice_size(dataset.get_size())
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
[docs]@build_sniff_from_prefix
class StormCheck(Text):
"""
Storm PCTL model checking result file
containing boolean or numerical result.
"""
file_ext = "storm.check"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Determines whether the file is in .storm.check format
"""
keywords = ["Storm ", "Result (for initial states)"]
return all(keyword in file_prefix.contents_header for keyword in keywords)
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
with open(dataset.get_file_name()) as result:
answer = ""
for line in result:
if "Result (for initial states):" in line:
answer = line.split()[-1]
break
dataset.peek = f"Model checking result: {answer}"
dataset.blurb = nice_size(dataset.get_size())
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
[docs]@build_sniff_from_prefix
class CTLresult(Text):
"""CTL model checking result"""
file_ext = "ctl.result"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Determines whether the file is in .ctl.result format
"""
keywords = ["Result:", "Number of satisfying states:"]
return all(keyword in file_prefix.contents_header for keyword in keywords)
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
with open(dataset.get_file_name()) as result:
answer = ""
for line in result:
if "Result:" in line:
answer = line.split()[-1]
dataset.peek = f"Model checking result: {answer}"
dataset.blurb = nice_size(dataset.get_size())
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
[docs]@build_sniff_from_prefix
class PithyaProperty(Text):
"""Pithya CTL property format"""
file_ext = "pithya.property"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Determines whether the file is in .pithya.property format
"""
return re.search(r":\?[a-zA-Z0-9_]+[ ]*=", file_prefix.contents_header) is not None
[docs]@build_sniff_from_prefix
class PithyaModel(Text):
"""Pithya model format"""
file_ext = "pithya.model"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Determines whether the file is in .pithya.model format
"""
keywords = ["VARS", "EQ", "THRES"]
return all(keyword in file_prefix.contents_header for keyword in keywords)
[docs]@build_sniff_from_prefix
class PithyaResult(Json):
"""Pithya result format"""
file_ext = "pithya.result"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Determines whether the file is in .pithya.result format
"""
is_pithya_result = False
if self._looks_like_json(file_prefix):
is_pithya_result = self._looks_like_pithya_result(file_prefix)
return is_pithya_result
def _looks_like_pithya_result(self, file_prefix: FilePrefix) -> bool:
content = open(file_prefix.filename).read()
keywords = ['"variables":', '"states":', '"parameter_values":', '"results":']
if all(keyword in content for keyword in keywords):
return self._looks_like_json(file_prefix)
return False
[docs]@build_sniff_from_prefix
class Castep(Text):
"""Report on a CASTEP calculation"""
file_ext = "castep"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""Determines whether the file is a CASTEP log
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('Si.castep')
>>> Castep().sniff(fname)
True
>>> fname = get_test_fname('Si.param')
>>> Castep().sniff(fname)
False
"""
castep_header = [
"+-------------------------------------------------+",
"| |",
"| CCC AA SSS TTTTT EEEEE PPPP |",
"| C A A S T E P P |",
"| C AAAA SS T EEE PPPP |",
"| C A A S T E P |",
"| CCC A A SSS T EEEEE P |",
"| |",
"+-------------------------------------------------+",
]
handle = file_prefix.string_io()
for header_line in castep_header:
if handle.readline().strip() != header_line:
return False
return True
[docs]@build_sniff_from_prefix
class Param(Yaml):
"""CASTEP parameter input file"""
file_ext = "param"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Modified version of the normal Yaml sniff that also checks
for a valid CASTEP task key-value pair, which is not case
sensitive
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('Si.param')
>>> Param().sniff(fname)
True
>>> fname = get_test_fname('Si.castep')
>>> Param().sniff(fname)
False
"""
valid_tasks = [
"SINGLEPOINT",
"BANDSTRUCTURE",
"GEOMETRYOPTIMIZATION",
"GEOMETRYOPTIMISATION",
"MOLECULARDYNAMICS",
"OPTICS",
"PHONON",
"EFIELD",
"PHONON+EFIELD",
"TRANSITIONSTATESEARCH",
"MAGRES",
"ELNES",
"ELECTRONICSPECTROSCOPY",
]
# check it looks like YAML
if not super().sniff_prefix(file_prefix):
return False
# check the TASK keyword is present
# and that it is set to a valid CASTEP task
pattern = re.compile(r"^TASK ?: ?([A-Z\+]*)$", flags=re.IGNORECASE | re.MULTILINE)
task = file_prefix.search(pattern)
return task and task.group(1).upper() in valid_tasks
[docs]@build_sniff_from_prefix
class FormattedDensity(Text):
"""Final electron density from a CASTEP calculation written to an ASCII file"""
file_ext = "den_fmt"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""Determines whether the file contains electron densities in the CASTEP den_fmt format
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('Si.den_fmt')
>>> FormattedDensity().sniff(fname)
True
>>> fname = get_test_fname('YbCuAs2.den_fmt')
>>> FormattedDensity().sniff(fname)
True
>>> fname = get_test_fname('Si.param')
>>> FormattedDensity().sniff(fname)
False
"""
begin_header = "BEGIN header"
end_header = 'END header: data is "<a b c> charge" in units of electrons/grid_point * number'
grid_points = "of grid_points"
end_header_spin = 'END header: data is "<a b c> charge spin" in units of electrons/grid_point * nu'
grid_points_spin = "mber of grid_points"
handle = file_prefix.string_io()
lines = handle.readlines()
return lines[0].strip() == begin_header and (
(lines[9].strip() == end_header and lines[10].strip() == grid_points)
or (lines[9].strip() == end_header_spin and lines[10].strip() == grid_points_spin)
)