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Source code for galaxy.datatypes.data
import abc
import logging
import mimetypes
import os
import shutil
import string
import tempfile
from inspect import isclass
from typing import (
Any,
Dict,
List,
Optional,
Tuple,
TYPE_CHECKING,
)
from markupsafe import escape
from galaxy import util
from galaxy.datatypes.metadata import (
MetadataElement, # import directly to maintain ease of use in Datatype class definitions
)
from galaxy.datatypes.sniff import (
build_sniff_from_prefix,
FilePrefix,
)
from galaxy.exceptions import ObjectNotFound
from galaxy.util import (
compression_utils,
file_reader,
FILENAME_VALID_CHARS,
inflector,
iter_start_of_line,
smart_str,
unicodify,
)
from galaxy.util.bunch import Bunch
from galaxy.util.sanitize_html import sanitize_html
from galaxy.util.zipstream import ZipstreamWrapper
from . import dataproviders as p_dataproviders
from . import metadata
if TYPE_CHECKING:
from galaxy.model import DatasetInstance
XSS_VULNERABLE_MIME_TYPES = [
'image/svg+xml', # Unfiltered by Galaxy and may contain JS that would be executed by some browsers.
'application/xml', # Some browsers will evalute SVG embedded JS in such XML documents.
]
DEFAULT_MIME_TYPE = 'text/plain' # Vulnerable mime types will be replaced with this.
log = logging.getLogger(__name__)
# Valid first column and strand column values vor bed, other formats
col1_startswith = ['chr', 'chl', 'groupun', 'reftig_', 'scaffold', 'super_', 'vcho']
valid_strand = ['+', '-', '.']
DOWNLOAD_FILENAME_PATTERN_DATASET = "Galaxy${hid}-[${name}].${ext}"
DOWNLOAD_FILENAME_PATTERN_COLLECTION_ELEMENT = "Galaxy${hdca_hid}-[${hdca_name}__${element_identifier}].${ext}"
DEFAULT_MAX_PEEK_SIZE = 1000000 # 1 MB
Headers = Dict[str, Any]
[docs]class DatatypeValidation:
[docs] @staticmethod
def validated():
return DatatypeValidation("ok", "Dataset validated by datatype validator.")
[docs] @staticmethod
def unvalidated():
return DatatypeValidation("unknown", "Dataset validation unimplemented for this datatype.")
def __repr__(self):
return f"DatatypeValidation[state={self.state},message={self.message}]"
[docs]def validate(dataset_instance):
try:
datatype_validation = dataset_instance.datatype.validate(dataset_instance)
except Exception as e:
datatype_validation = DatatypeValidation.invalid(f"Problem running datatype validation method [{str(e)}]")
return datatype_validation
[docs]def get_params_and_input_name(converter, deps, target_context=None):
# Generate parameter dictionary
params = {}
# determine input parameter name and add to params
input_name = 'input1'
for key, value in converter.inputs.items():
if deps and value.name in deps:
params[value.name] = deps[value.name]
elif value.type == 'data':
input_name = key
# add potentially required/common internal tool parameters e.g. '__job_resource'
if target_context:
for key, value in target_context.items():
if key.startswith('__'):
params[key] = value
return params, input_name
[docs]class DataMeta(abc.ABCMeta):
"""
Metaclass for Data class. Sets up metadata spec.
"""
[docs] def __init__(cls, name, bases, dict_):
cls.metadata_spec = metadata.MetadataSpecCollection()
for base in bases: # loop through bases (class/types) of cls
if hasattr(base, "metadata_spec"): # base of class Data (object) has no metadata
cls.metadata_spec.update(base.metadata_spec) # add contents of metadata spec of base class to cls
metadata.Statement.process(cls)
[docs]@p_dataproviders.decorators.has_dataproviders
class Data(metaclass=DataMeta):
"""
Base class for all datatypes. Implements basic interfaces as well
as class methods for metadata.
>>> class DataTest( Data ):
... MetadataElement( name="test" )
...
>>> DataTest.metadata_spec.test.name
'test'
>>> DataTest.metadata_spec.test.desc
'test'
>>> type( DataTest.metadata_spec.test.param )
<class 'galaxy.model.metadata.MetadataParameter'>
"""
edam_data = "data_0006"
edam_format = "format_1915"
file_ext = 'data'
# Data is not chunkable by default.
CHUNKABLE = False
#: Dictionary of metadata fields for this datatype
metadata_spec: metadata.MetadataSpecCollection
# Add metadata elements
MetadataElement(name="dbkey", desc="Database/Build", default="?", param=metadata.DBKeyParameter, multiple=False, optional=True, no_value="?")
# Stores the set of display applications, and viewing methods, supported by this datatype
supported_display_apps: Dict[str, Any] = {}
# If False, the peek is regenerated whenever a dataset of this type is copied
copy_safe_peek = True
# The dataset contains binary data --> do not space_to_tab or convert newlines, etc.
# Allow binary file uploads of this type when True.
is_binary = True
# Composite datatypes
composite_type: Optional[str] = None
composite_files: Dict[str, Any] = {}
primary_file_name = 'index'
# Allow user to change between this datatype and others. If left to None,
# datatype change is allowed if the datatype is not composite.
allow_datatype_change: Optional[bool] = None
# A per datatype setting (inherited): max file size (in bytes) for setting optional metadata
_max_optional_metadata_filesize = None
# Trackster track type.
track_type: Optional[str] = None
# Data sources.
data_sources: Dict[str, str] = {}
dataproviders: Dict[str, Any]
[docs] def __init__(self, **kwd):
"""Initialize the datatype"""
self.supported_display_apps = self.supported_display_apps.copy()
self.composite_files = self.composite_files.copy()
self.display_applications = {}
[docs] @classmethod
def is_datatype_change_allowed(cls):
"""
Returns the value of the `allow_datatype_change` class attribute if set
in a subclass, or True iff the datatype is not composite.
"""
if cls.allow_datatype_change is not None:
return cls.allow_datatype_change
return cls.composite_type is None
[docs] def get_raw_data(self, dataset):
"""Returns the full data. To stream it open the file_name and read/write as needed"""
try:
return open(dataset.file_name, 'rb').read(-1)
except OSError:
log.exception('%s reading a file that does not exist %s', self.__class__.__name__, dataset.file_name)
return ''
[docs] def dataset_content_needs_grooming(self, file_name):
"""This function is called on an output dataset file after the content is initially generated."""
return False
[docs] def groom_dataset_content(self, file_name):
"""This function is called on an output dataset file if dataset_content_needs_grooming returns True."""
[docs] def init_meta(self, dataset, copy_from=None):
# Metadata should be left mostly uninitialized. Dataset will
# handle returning default values when metadata is not set.
# copy_from allows metadata to be passed in that will be
# copied. (although this seems ambiguous, see
# Dataset.set_metadata. It always copies the rhs in order to
# flag the object as modified for SQLAlchemy.
if copy_from:
dataset.metadata = copy_from.metadata
[docs] def set_meta(self, dataset: Any, overwrite=True, **kwd):
"""Unimplemented method, allows guessing of metadata from contents of file"""
return True
[docs] def missing_meta(self, dataset, check=None, skip=None):
"""
Checks for empty metadata values.
Returns False if no non-optional metadata is missing and the missing metadata key otherwise.
Specifying a list of 'check' values will only check those names provided; when used, optionality is ignored
Specifying a list of 'skip' items will return True even when a named metadata value is missing; when used, optionality is ignored
"""
if skip is None:
skip = []
if check:
to_check = check
else:
to_check = dataset.metadata.keys()
for key in to_check:
if key in skip:
continue
if not check and len(skip) == 0 and dataset.metadata.spec[key].get("optional"):
continue # we skip check for optional and nonrequested values here
if not dataset.metadata.element_is_set(key) and (check or dataset.metadata.spec[key].check_required_metadata):
# FIXME: Optional metadata isn't always properly annotated,
# so skip check if check_required_metadata is false on the datatype that defined the metadata element.
# See https://github.com/galaxyproject/tools-iuc/issues/4367
return key
return False
[docs] def set_max_optional_metadata_filesize(self, max_value):
try:
max_value = int(max_value)
except (TypeError, ValueError):
return
self.__class__._max_optional_metadata_filesize = max_value
[docs] def get_max_optional_metadata_filesize(self):
rval = self.__class__._max_optional_metadata_filesize
if rval is None:
return -1
return rval
max_optional_metadata_filesize = property(get_max_optional_metadata_filesize, set_max_optional_metadata_filesize)
[docs] def set_peek(self, dataset):
"""
Set the peek and blurb text
"""
if not dataset.dataset.purged:
dataset.peek = ''
dataset.blurb = 'data'
else:
dataset.peek = 'file does not exist'
dataset.blurb = 'file purged from disk'
[docs] def display_peek(self, dataset):
"""Create HTML table, used for displaying peek"""
out = ['<table cellspacing="0" cellpadding="3">']
try:
if not dataset.peek:
dataset.set_peek()
data = dataset.peek
lines = data.splitlines()
for line in lines:
line = line.strip()
if not line:
continue
out.append(f"<tr><td>{escape(unicodify(line, 'utf-8'))}</td></tr>")
out.append('</table>')
return "".join(out)
except Exception as exc:
return f"Can't create peek: {unicodify(exc)}"
def _archive_main_file(self, archive, display_name, data_filename):
"""Called from _archive_composite_dataset to add central file to archive.
Unless subclassed, this will add the main dataset file (argument data_filename)
to the archive, as an HTML file with its filename derived from the dataset name
(argument outfname).
Returns a tuple of boolean, string, string: (error, msg, messagetype)
"""
error, msg, messagetype = False, "", ""
archname = f'{display_name}.html' # fake the real nature of the html file
try:
archive.write(data_filename, archname)
except OSError:
error = True
log.exception("Unable to add composite parent %s to temporary library download archive", data_filename)
msg = "Unable to create archive for download, please report this error"
messagetype = "error"
return error, msg, messagetype
def _archive_composite_dataset(self, trans, data, headers: Headers, do_action='zip'):
# save a composite object into a compressed archive for downloading
outfname = data.name[0:150]
outfname = ''.join(c in FILENAME_VALID_CHARS and c or '_' for c in outfname)
archive = ZipstreamWrapper(
archive_name=outfname,
upstream_mod_zip=trans.app.config.upstream_mod_zip,
upstream_gzip=trans.app.config.upstream_gzip
)
error = False
msg = ''
ext = data.extension
path = data.file_name
efp = data.extra_files_path
# Add any central file to the archive,
display_name = os.path.splitext(outfname)[0]
if not display_name.endswith(ext):
display_name = f'{display_name}_{ext}'
error, msg = self._archive_main_file(archive, display_name, path)[:2]
if not error:
# Add any child files to the archive,
for fpath, rpath in self.__archive_extra_files_path(extra_files_path=efp):
try:
archive.write(fpath, rpath)
except OSError:
error = True
log.exception("Unable to add %s to temporary library download archive", rpath)
msg = "Unable to create archive for download, please report this error"
continue
if not error:
headers.update(archive.get_headers())
return archive, headers
return trans.show_error_message(msg), headers
def __archive_extra_files_path(self, extra_files_path):
"""Yield filepaths and relative filepaths for files in extra_files_path"""
for root, _, files in os.walk(extra_files_path):
for fname in files:
fpath = os.path.join(root, fname)
rpath = os.path.relpath(fpath, extra_files_path)
yield fpath, rpath
def _serve_raw(self, dataset, to_ext, headers: Headers, **kwd):
headers['Content-Length'] = str(os.stat(dataset.file_name).st_size)
headers["content-type"] = "application/octet-stream" # force octet-stream so Safari doesn't append mime extensions to filename
filename = self._download_filename(dataset, to_ext, hdca=kwd.get("hdca"), element_identifier=kwd.get("element_identifier"), filename_pattern=kwd.get("filename_pattern"))
headers["Content-Disposition"] = f'attachment; filename="{filename}"'
return open(dataset.file_name, mode='rb'), headers
[docs] def to_archive(self, dataset, name=""):
"""
Collect archive paths and file handles that need to be exported when archiving `dataset`.
:param dataset: HistoryDatasetAssociation
:param name: archive name, in collection context corresponds to collection name(s) and element_identifier,
joined by '/', e.g 'fastq_collection/sample1/forward'
"""
rel_paths = []
file_paths = []
if dataset.datatype.composite_type or dataset.extension.endswith('html'):
main_file = f"{name}.html"
rel_paths.append(main_file)
file_paths.append(dataset.file_name)
for fpath, rpath in self.__archive_extra_files_path(dataset.extra_files_path):
rel_paths.append(os.path.join(name, rpath))
file_paths.append(fpath)
else:
rel_paths.append(f"{name or dataset.file_name}.{dataset.extension}")
file_paths.append(dataset.file_name)
return zip(file_paths, rel_paths)
[docs] def display_data(self, trans, data, preview=False, filename=None, to_ext=None, **kwd):
"""
Displays data in central pane if preview is `True`, else handles download.
Datatypes should be very careful if overridding this method and this interface
between datatypes and Galaxy will likely change.
TOOD: Document alternatives to overridding this method (data
providers?).
"""
headers = kwd.get("headers", {})
# Relocate all composite datatype display to a common location.
composite_extensions = trans.app.datatypes_registry.get_composite_extensions()
composite_extensions.append('html') # for archiving composite datatypes
# Prevent IE8 from sniffing content type since we're explicit about it. This prevents intentionally text/plain
# content from being rendered in the browser
headers['X-Content-Type-Options'] = 'nosniff'
if isinstance(data, str):
return smart_str(data), headers
if filename and filename != "index":
# For files in extra_files_path
extra_dir = data.dataset.extra_files_path_name
file_path = trans.app.object_store.get_filename(data.dataset, extra_dir=extra_dir, alt_name=filename)
if os.path.exists(file_path):
if os.path.isdir(file_path):
with tempfile.NamedTemporaryFile(mode='w', delete=False, dir=trans.app.config.new_file_path, prefix='gx_html_autocreate_') as tmp_fh:
tmp_file_name = tmp_fh.name
dir_items = sorted(os.listdir(file_path))
base_path, item_name = os.path.split(file_path)
tmp_fh.write('<html><head><h3>Directory %s contents: %d items</h3></head>\n' % (escape(item_name), len(dir_items)))
tmp_fh.write('<body><p/><table cellpadding="2">\n')
for index, fname in enumerate(dir_items):
if index % 2 == 0:
bgcolor = '#D8D8D8'
else:
bgcolor = '#FFFFFF'
# Can't have an href link here because there is no route
# defined for files contained within multiple subdirectory
# levels of the primary dataset. Something like this is
# close, but not quite correct:
# href = url_for(controller='dataset', action='display',
# dataset_id=trans.security.encode_id(data.dataset.id),
# preview=preview, filename=fname, to_ext=to_ext)
tmp_fh.write(f'<tr bgcolor="{bgcolor}"><td>{escape(fname)}</td></tr>\n')
tmp_fh.write('</table></body></html>\n')
return self._yield_user_file_content(trans, data, tmp_file_name, headers), headers
mime = mimetypes.guess_type(file_path)[0]
if not mime:
try:
mime = trans.app.datatypes_registry.get_mimetype_by_extension(".".split(file_path)[-1])
except Exception:
mime = "text/plain"
self._clean_and_set_mime_type(trans, mime, headers)
return self._yield_user_file_content(trans, data, file_path, headers), headers
else:
raise ObjectNotFound(f"Could not find '{filename}' on the extra files path {file_path}.")
self._clean_and_set_mime_type(trans, data.get_mime(), headers)
trans.log_event(f"Display dataset id: {str(data.id)}")
from galaxy.datatypes import ( # DBTODO REMOVE THIS AT REFACTOR
binary,
images,
text,
)
if to_ext or isinstance(
data.datatype, binary.Binary
): # Saving the file, or binary file
if data.extension in composite_extensions:
return self._archive_composite_dataset(trans, data, headers, do_action=kwd.get('do_action', 'zip'))
else:
headers['Content-Length'] = str(os.stat(data.file_name).st_size)
filename = self._download_filename(data, to_ext, hdca=kwd.get("hdca"), element_identifier=kwd.get("element_identifier"), filename_pattern=kwd.get("filename_pattern"))
headers['content-type'] = "application/octet-stream" # force octet-stream so Safari doesn't append mime extensions to filename
headers["Content-Disposition"] = f'attachment; filename="{filename}"'
return open(data.file_name, 'rb'), headers
if not os.path.exists(data.file_name):
raise ObjectNotFound(f"File Not Found ({data.file_name}).")
max_peek_size = DEFAULT_MAX_PEEK_SIZE # 1 MB
if isinstance(data.datatype, text.Html):
max_peek_size = 10000000 # 10 MB for html
preview = util.string_as_bool(preview)
if (
not preview
or isinstance(data.datatype, images.Image)
or os.stat(data.file_name).st_size < max_peek_size
):
return self._yield_user_file_content(trans, data, data.file_name, headers), headers
else:
headers["content-type"] = "text/html"
return trans.fill_template_mako("/dataset/large_file.mako",
truncated_data=open(data.file_name, 'rb').read(max_peek_size),
data=data), headers
[docs] def display_as_markdown(self, dataset_instance, markdown_format_helpers):
"""Prepare for embedding dataset into a basic Markdown document.
This is a somewhat experimental interface and should not be implemented
on datatypes not tightly tied to a Galaxy version (e.g. datatypes in the
Tool Shed).
Speaking very losely - the datatype should should load a bounded amount
of data from the supplied dataset instance and prepare for embedding it
into Markdown. This should be relatively vanilla Markdown - the result of
this is bleached and it should not contain nested Galaxy Markdown
directives.
If the data cannot reasonably be displayed, just indicate this and do
not throw an exception.
"""
if self.file_ext in {'png', 'jpg'}:
return self.handle_dataset_as_image(dataset_instance)
if self.is_binary:
result = "*cannot display binary content*\n"
else:
with open(dataset_instance.file_name) as f:
contents = f.read(DEFAULT_MAX_PEEK_SIZE)
result = markdown_format_helpers.literal_via_fence(contents)
if len(contents) == DEFAULT_MAX_PEEK_SIZE:
result += markdown_format_helpers.indicate_data_truncated()
return result
def _yield_user_file_content(self, trans, from_dataset, filename, headers: Headers):
"""This method is responsible for sanitizing the HTML if needed."""
if trans.app.config.sanitize_all_html and headers.get("content-type", None) == "text/html":
# Sanitize anytime we respond with plain text/html content.
# Check to see if this dataset's parent job is allowlisted
# We cannot currently trust imported datasets for rendering.
if not from_dataset.creating_job.imported and from_dataset.creating_job.tool_id.startswith(tuple(trans.app.config.sanitize_allowlist)):
return open(filename, mode='rb')
# This is returning to the browser, it needs to be encoded.
# TODO Ideally this happens a layer higher, but this is a bad
# issue affecting many tools
with open(filename) as f:
return sanitize_html(f.read()).encode('utf-8')
return open(filename, mode='rb')
def _download_filename(self, dataset, to_ext, hdca=None, element_identifier=None, filename_pattern=None):
def escape(raw_identifier):
return ''.join(c in FILENAME_VALID_CHARS and c or '_' for c in raw_identifier)[0:150]
if not to_ext or to_ext == "data":
# If a client requests to_ext with the extension 'data', they are
# deferring to the server, set it based on datatype.
to_ext = dataset.extension
template_values = {
"name": escape(dataset.name),
"ext": to_ext,
"hid": dataset.hid,
}
if not filename_pattern:
if hdca is None:
filename_pattern = DOWNLOAD_FILENAME_PATTERN_DATASET
else:
filename_pattern = DOWNLOAD_FILENAME_PATTERN_COLLECTION_ELEMENT
if hdca is not None:
# Use collection context to build up filename.
template_values["element_identifier"] = element_identifier
template_values["hdca_name"] = escape(hdca.name)
template_values["hdca_hid"] = hdca.hid
return string.Template(filename_pattern).substitute(**template_values)
[docs] def display_name(self, dataset):
"""Returns formatted html of dataset name"""
try:
return escape(unicodify(dataset.name, 'utf-8'))
except Exception:
return "name unavailable"
[docs] def display_info(self, dataset):
"""Returns formatted html of dataset info"""
try:
# Change new line chars to html
info: str = escape(dataset.info)
if info.find('\r\n') >= 0:
info = info.replace('\r\n', '<br/>')
if info.find('\r') >= 0:
info = info.replace('\r', '<br/>')
if info.find('\n') >= 0:
info = info.replace('\n', '<br/>')
info = unicodify(info, 'utf-8')
return info
except Exception:
return "info unavailable"
[docs] def repair_methods(self, dataset):
"""Unimplemented method, returns dict with method/option for repairing errors"""
return None
[docs] def get_mime(self):
"""Returns the mime type of the datatype"""
return 'application/octet-stream'
[docs] def add_display_app(self, app_id, label, file_function, links_function):
"""
Adds a display app to the datatype.
app_id is a unique id
label is the primary display label, e.g., display at 'UCSC'
file_function is a string containing the name of the function that returns a properly formatted display
links_function is a string containing the name of the function that returns a list of (link_name,link)
"""
self.supported_display_apps = self.supported_display_apps.copy()
self.supported_display_apps[app_id] = {'label': label, 'file_function': file_function, 'links_function': links_function}
[docs] def remove_display_app(self, app_id):
"""Removes a display app from the datatype"""
self.supported_display_apps = self.supported_display_apps.copy()
try:
del self.supported_display_apps[app_id]
except Exception:
log.exception('Tried to remove display app %s from datatype %s, but this display app is not declared.', type, self.__class__.__name__)
[docs] def add_display_application(self, display_application):
"""New style display applications"""
assert display_application.id not in self.display_applications, 'Attempted to add a display application twice'
self.display_applications[display_application.id] = display_application
[docs] def get_display_application(self, key, default=None):
return self.display_applications.get(key, default)
[docs] def get_display_applications_by_dataset(self, dataset, trans):
rval = {}
for key, value in self.display_applications.items():
value = value.filter_by_dataset(dataset, trans)
if value.links:
rval[key] = value
return rval
[docs] def get_display_types(self):
"""Returns display types available"""
return list(self.supported_display_apps.keys())
[docs] def get_display_label(self, type):
"""Returns primary label for display app"""
try:
return self.supported_display_apps[type]['label']
except Exception:
return 'unknown'
[docs] def as_display_type(self, dataset, type, **kwd):
"""Returns modified file contents for a particular display type """
try:
if type in self.get_display_types():
return getattr(self, self.supported_display_apps[type]['file_function'])(dataset, **kwd)
except Exception:
log.exception('Function %s is referred to in datatype %s for displaying as type %s, but is not accessible', self.supported_display_apps[type]['file_function'], self.__class__.__name__, type)
return f"This display type ({type}) is not implemented for this datatype ({dataset.ext})."
[docs] def get_display_links(self, dataset, type, app, base_url, target_frame='_blank', **kwd):
"""
Returns a list of tuples of (name, link) for a particular display type. No check on
'access' permissions is done here - if you can view the dataset, you can also save it
or send it to a destination outside of Galaxy, so Galaxy security restrictions do not
apply anyway.
"""
try:
if app.config.enable_old_display_applications and type in self.get_display_types():
return target_frame, getattr(self, self.supported_display_apps[type]['links_function'])(dataset, type, app, base_url, **kwd)
except Exception:
log.exception('Function %s is referred to in datatype %s for generating links for type %s, but is not accessible',
self.supported_display_apps[type]['links_function'], self.__class__.__name__, type)
return target_frame, []
[docs] def get_converter_types(self, original_dataset, datatypes_registry):
"""Returns available converters by type for this dataset"""
return datatypes_registry.get_converters_by_datatype(original_dataset.ext)
[docs] def find_conversion_destination(
self, dataset, accepted_formats: List[str], datatypes_registry, **kwd
) -> Tuple[bool, Optional[str], Optional["DatasetInstance"]]:
"""Returns ( direct_match, converted_ext, existing converted dataset )"""
return datatypes_registry.find_conversion_destination_for_dataset_by_extensions(dataset, accepted_formats, **kwd)
[docs] def convert_dataset(self, trans, original_dataset, target_type, return_output=False, visible=True, deps=None, target_context=None, history=None):
"""This function adds a job to the queue to convert a dataset to another type. Returns a message about success/failure."""
converter = trans.app.datatypes_registry.get_converter_by_target_type(original_dataset.ext, target_type)
if converter is None:
raise DatatypeConverterNotFoundException(f"A converter does not exist for {original_dataset.ext} to {target_type}.")
params, input_name = get_params_and_input_name(converter, deps, target_context)
params[input_name] = original_dataset
# Make the target datatype available to the converter
params['__target_datatype__'] = target_type
# Run converter, job is dispatched through Queue
job, converted_datasets, *_ = converter.execute(trans, incoming=params, set_output_hid=visible, history=history)
trans.app.job_manager.enqueue(job, tool=converter)
if len(params) > 0:
trans.log_event(f"Converter params: {str(params)}", tool_id=converter.id)
if not visible:
for value in converted_datasets.values():
value.visible = False
if return_output:
return converted_datasets
return f"The file conversion of {converter.name} on data {original_dataset.hid} has been added to the Queue."
# We need to clear associated files before we set metadata
# so that as soon as metadata starts to be set, e.g. implicitly converted datasets are deleted and no longer available 'while' metadata is being set, not just after
# We'll also clear after setting metadata, for backwards compatibility
[docs] def after_setting_metadata(self, dataset):
"""This function is called on the dataset after metadata is set."""
dataset.clear_associated_files(metadata_safe=True)
[docs] def before_setting_metadata(self, dataset):
"""This function is called on the dataset before metadata is set."""
dataset.clear_associated_files(metadata_safe=True)
def __new_composite_file(
self,
name,
optional=False,
mimetype=None,
description=None,
substitute_name_with_metadata=None,
is_binary=False,
to_posix_lines=True,
space_to_tab=False,
**kwds,
):
kwds["name"] = name
kwds["optional"] = optional
kwds["mimetype"] = mimetype
kwds["description"] = description
kwds["substitute_name_with_metadata"] = substitute_name_with_metadata
kwds["is_binary"] = is_binary
kwds["to_posix_lines"] = to_posix_lines
kwds["space_to_tab"] = space_to_tab
composite_file = Bunch(**kwds)
return composite_file
[docs] def add_composite_file(self, name, **kwds):
# self.composite_files = self.composite_files.copy()
self.composite_files[name] = self.__new_composite_file(name, **kwds)
@property
def writable_files(self):
files = {}
if self.composite_type != 'auto_primary_file':
files[self.primary_file_name] = self.__new_composite_file(self.primary_file_name)
for key, value in self.get_composite_files().items():
files[key] = value
return files
[docs] def get_writable_files_for_dataset(self, dataset):
files = {}
if self.composite_type != "auto_primary_file":
files[self.primary_file_name] = self.__new_composite_file(self.primary_file_name)
for key, value in self.get_composite_files(dataset).items():
files[key] = value
return files
[docs] def get_composite_files(self, dataset=None):
def substitute_composite_key(key, composite_file):
if composite_file.substitute_name_with_metadata:
if dataset:
meta_value = str(dataset.metadata.get(composite_file.substitute_name_with_metadata))
else:
meta_value = self.metadata_spec[composite_file.substitute_name_with_metadata].default
return key % meta_value
return key
files = {}
for key, value in self.composite_files.items():
files[substitute_composite_key(key, value)] = value
return files
[docs] def generate_primary_file(self, dataset=None):
raise Exception("generate_primary_file is not implemented for this datatype.")
@property
def has_resolution(self):
return False
[docs] def matches_any(self, target_datatypes: List[Any]) -> bool:
"""
Check if this datatype is of any of the target_datatypes or is
a subtype thereof.
"""
datatype_classes = tuple(datatype if isclass(datatype) else datatype.__class__ for datatype in target_datatypes)
return isinstance(self, datatype_classes)
[docs] @staticmethod
def merge(split_files, output_file):
"""
Merge files with copy.copyfileobj() will not hit the
max argument limitation of cat. gz and bz2 files are also working.
"""
if not split_files:
raise ValueError(f'Asked to merge zero files as {output_file}')
elif len(split_files) == 1:
shutil.copyfileobj(open(split_files[0], 'rb'), open(output_file, 'wb'))
else:
with open(output_file, 'wb') as fdst:
for fsrc in split_files:
shutil.copyfileobj(open(fsrc, 'rb'), fdst)
[docs] def get_visualizations(self, dataset):
"""
Returns a list of visualizations for datatype.
"""
if self.track_type:
return ['trackster', 'circster']
return []
# ------------- Dataproviders
[docs] def has_dataprovider(self, data_format):
"""
Returns True if `data_format` is available in `dataproviders`.
"""
return data_format in self.dataproviders
[docs] def dataprovider(self, dataset, data_format, **settings):
"""
Base dataprovider factory for all datatypes that returns the proper provider
for the given `data_format` or raises a `NoProviderAvailable`.
"""
if self.has_dataprovider(data_format):
return self.dataproviders[data_format](self, dataset, **settings)
raise p_dataproviders.exceptions.NoProviderAvailable(self, data_format)
[docs] @p_dataproviders.decorators.dataprovider_factory("base")
def base_dataprovider(self, dataset, **settings):
dataset_source = p_dataproviders.dataset.DatasetDataProvider(dataset)
return p_dataproviders.base.DataProvider(dataset_source, **settings)
[docs] @p_dataproviders.decorators.dataprovider_factory(
"chunk", p_dataproviders.chunk.ChunkDataProvider.settings
)
def chunk_dataprovider(self, dataset, **settings):
dataset_source = p_dataproviders.dataset.DatasetDataProvider(dataset)
return p_dataproviders.chunk.ChunkDataProvider(dataset_source, **settings)
[docs] @p_dataproviders.decorators.dataprovider_factory(
"chunk64", p_dataproviders.chunk.Base64ChunkDataProvider.settings
)
def chunk64_dataprovider(self, dataset, **settings):
dataset_source = p_dataproviders.dataset.DatasetDataProvider(dataset)
return p_dataproviders.chunk.Base64ChunkDataProvider(dataset_source, **settings)
def _clean_and_set_mime_type(self, trans, mime, headers: Headers):
if mime.lower() in XSS_VULNERABLE_MIME_TYPES:
if not getattr(trans.app.config, "serve_xss_vulnerable_mimetypes", True):
mime = DEFAULT_MIME_TYPE
headers["content-type"] = mime
[docs]@p_dataproviders.decorators.has_dataproviders
class Text(Data):
edam_format = "format_2330"
file_ext = 'txt'
line_class = 'line'
is_binary = False
# Add metadata elements
MetadataElement(name="data_lines", default=0, desc="Number of data lines", readonly=True, optional=True, visible=False, no_value=0)
[docs] def set_meta(self, dataset, **kwd):
"""
Set the number of lines of data in dataset.
"""
dataset.metadata.data_lines = self.count_data_lines(dataset)
[docs] def estimate_file_lines(self, dataset):
"""
Perform a rough estimate by extrapolating number of lines from a small read.
"""
sample_size = 1048576
try:
with compression_utils.get_fileobj(dataset.file_name) as dataset_fh:
dataset_read = dataset_fh.read(sample_size)
sample_lines = dataset_read.count('\n')
return int(sample_lines * (float(dataset.get_size()) / float(sample_size)))
except UnicodeDecodeError:
log.error(f'Unable to estimate lines in file {dataset.file_name}')
return None
[docs] def count_data_lines(self, dataset):
"""
Count the number of lines of data in dataset,
skipping all blank lines and comments.
"""
CHUNK_SIZE = 2 ** 15 # 32Kb
data_lines = 0
with compression_utils.get_fileobj(dataset.file_name) as in_file:
# FIXME: Potential encoding issue can prevent the ability to iterate over lines
# causing set_meta process to fail otherwise OK jobs. A better solution than
# a silent try/except is desirable.
try:
for line in iter_start_of_line(in_file, CHUNK_SIZE):
line = line.strip()
if line and not line.startswith('#'):
data_lines += 1
except UnicodeDecodeError:
log.error(f'Unable to count lines in file {dataset.file_name}')
return None
return data_lines
[docs] def set_peek(self, dataset, line_count=None, WIDTH=256, skipchars=None, line_wrap=True, **kwd):
"""
Set the peek. This method is used by various subclasses of Text.
"""
if not dataset.dataset.purged:
# The file must exist on disk for the get_file_peek() method
dataset.peek = get_file_peek(dataset.file_name, WIDTH=WIDTH, skipchars=skipchars, line_wrap=line_wrap)
if line_count is None:
# See if line_count is stored in the metadata
if dataset.metadata.data_lines:
dataset.blurb = f"{util.commaify(str(dataset.metadata.data_lines))} {inflector.cond_plural(dataset.metadata.data_lines, self.line_class)}"
else:
# Number of lines is not known ( this should not happen ), and auto-detect is
# needed to set metadata
# This can happen when the file is larger than max_optional_metadata_filesize.
if int(dataset.get_size()) <= 1048576:
# Small dataset, recount all lines and reset peek afterward.
lc = self.count_data_lines(dataset)
if lc is not None:
dataset.metadata.data_lines = lc
dataset.blurb = f"{util.commaify(str(lc))} {inflector.cond_plural(lc, self.line_class)}"
else:
dataset.blurb = "Error: Cannot count lines in dataset"
else:
est_lines = self.estimate_file_lines(dataset)
if est_lines is not None:
dataset.blurb = f"~{util.commaify(util.roundify(str(est_lines)))} {inflector.cond_plural(est_lines, self.line_class)}"
else:
dataset.blurb = "Error: Cannot estimate lines in dataset"
else:
dataset.blurb = f"{util.commaify(str(line_count))} {inflector.cond_plural(line_count, self.line_class)}"
else:
dataset.peek = 'file does not exist'
dataset.blurb = 'file purged from disk'
[docs] @classmethod
def split(cls, input_datasets, subdir_generator_function, split_params):
"""
Split the input files by line.
"""
if split_params is None:
return
if len(input_datasets) > 1:
raise Exception("Text file splitting does not support multiple files")
input_files = [ds.file_name for ds in input_datasets]
lines_per_file = None
chunk_size = None
if split_params['split_mode'] == 'number_of_parts':
lines_per_file = []
# Computing the length is expensive!
def _file_len(fname):
with open(fname) as f:
return sum(1 for _ in f)
length = _file_len(input_files[0])
parts = int(split_params['split_size'])
if length < parts:
parts = length
len_each, remainder = divmod(length, parts)
while length > 0:
chunk = len_each
if remainder > 0:
chunk += 1
lines_per_file.append(chunk)
remainder -= 1
length -= chunk
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']}")
f = open(input_files[0])
try:
chunk_idx = 0
file_done = False
part_file = None
while not file_done:
if lines_per_file is None:
this_chunk_size = chunk_size
elif chunk_idx < len(lines_per_file):
this_chunk_size = lines_per_file[chunk_idx]
chunk_idx += 1
lines_remaining = this_chunk_size
part_file = None
while lines_remaining > 0:
a_line = f.readline()
if a_line == '':
file_done = True
break
if part_file is None:
part_dir = subdir_generator_function()
part_path = os.path.join(part_dir, os.path.basename(input_files[0]))
part_file = open(part_path, 'w')
part_file.write(a_line)
lines_remaining -= 1
except Exception as e:
log.error('Unable to split files: %s', unicodify(e))
raise
finally:
f.close()
if part_file:
part_file.close()
# ------------- Dataproviders
[docs] @p_dataproviders.decorators.dataprovider_factory(
"line", p_dataproviders.line.FilteredLineDataProvider.settings
)
def line_dataprovider(self, dataset, **settings):
"""
Returns an iterator over the dataset's lines (that have been stripped)
optionally excluding blank lines and lines that start with a comment character.
"""
dataset_source = p_dataproviders.dataset.DatasetDataProvider(dataset)
return p_dataproviders.line.FilteredLineDataProvider(dataset_source, **settings)
[docs] @p_dataproviders.decorators.dataprovider_factory(
"regex-line", p_dataproviders.line.RegexLineDataProvider.settings
)
def regex_line_dataprovider(self, dataset, **settings):
"""
Returns an iterator over the dataset's lines
optionally including/excluding lines that match one or more regex filters.
"""
dataset_source = p_dataproviders.dataset.DatasetDataProvider(dataset)
return p_dataproviders.line.RegexLineDataProvider(dataset_source, **settings)
[docs]class GenericAsn1(Text):
"""Class for generic ASN.1 text format"""
edam_data = "data_0849"
edam_format = "format_1966"
file_ext = 'asn1'
[docs]class LineCount(Text):
"""
Dataset contains a single line with a single integer that denotes the
line count for a related dataset. Used for custom builds.
"""
[docs]class Newick(Text):
"""New Hampshire/Newick Format"""
edam_data = "data_0872"
edam_format = "format_1910"
file_ext = "newick"
[docs] def sniff(self, filename):
""" Returning false as the newick format is too general and cannot be sniffed."""
return False
[docs] def get_visualizations(self, dataset):
"""
Returns a list of visualizations for datatype.
"""
return ['phyloviz']
[docs]@build_sniff_from_prefix
class Nexus(Text):
"""Nexus format as used By Paup, Mr Bayes, etc"""
edam_data = "data_0872"
edam_format = "format_1912"
file_ext = "nex"
[docs] def sniff_prefix(self, file_prefix: FilePrefix):
"""All Nexus Files Simply puts a '#NEXUS' in its first line"""
return file_prefix.string_io().read(6).upper() == "#NEXUS"
[docs] def get_visualizations(self, dataset):
"""
Returns a list of visualizations for datatype.
"""
return ['phyloviz']
# ------------- Utility methods --------------
# nice_size used to be here, but to resolve cyclical dependencies it's been
# moved to galaxy.util. It belongs there anyway since it's used outside
# datatypes.
nice_size = util.nice_size
[docs]def get_test_fname(fname):
"""Returns test data filename"""
path = os.path.dirname(__file__)
full_path = os.path.join(path, 'test', fname)
return full_path
[docs]def get_file_peek(file_name, WIDTH=256, LINE_COUNT=5, skipchars=None, line_wrap=True):
"""
Returns the first LINE_COUNT lines wrapped to WIDTH.
>>> def assert_peek_is(file_name, expected, *args, **kwd):
... path = get_test_fname(file_name)
... peek = get_file_peek(path, *args, **kwd)
... assert peek == expected, "%s != %s" % (peek, expected)
>>> assert_peek_is('0_nonewline', u'0')
>>> assert_peek_is('0.txt', u'0\\n')
>>> assert_peek_is('4.bed', u'chr22\\t30128507\\t31828507\\tuc003bnx.1_cds_2_0_chr22_29227_f\\t0\\t+\\n', LINE_COUNT=1)
>>> assert_peek_is('1.bed', u'chr1\\t147962192\\t147962580\\tCCDS989.1_cds_0_0_chr1_147962193_r\\t0\\t-\\nchr1\\t147984545\\t147984630\\tCCDS990.1_cds_0_0_chr1_147984546_f\\t0\\t+\\n', LINE_COUNT=2)
"""
# Set size for file.readline() to a negative number to force it to
# read until either a newline or EOF. Needed for datasets with very
# long lines.
if WIDTH == 'unlimited':
WIDTH = -1
if skipchars is None:
skipchars = []
lines = []
count = 0
last_line_break = False
with compression_utils.get_fileobj(file_name) as temp:
while count < LINE_COUNT:
try:
line = temp.readline(WIDTH)
except UnicodeDecodeError:
return "binary file"
if line == "":
break
last_line_break = False
if line.endswith('\n'):
line = line[:-1]
last_line_break = True
elif not line_wrap:
for i in file_reader(temp, 1):
if i == '\n':
last_line_break = True
if not i or i == '\n':
break
skip_line = False
for skipchar in skipchars:
if line.startswith(skipchar):
skip_line = True
break
if not skip_line:
lines.append(line)
count += 1
return '\n'.join(lines) + ('\n' if last_line_break else '')