/usr/lib/python2.7/dist-packages/pbcommand/models/common.py is in python-pbcommand 0.2.17-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 | """Core models used in the ToolContract and Resolved ToolContract
Large parts of this are pulled from pbsmrtpipe.
Author: Michael Kocher
"""
import json
import logging
import os
import re
import warnings
import functools
import datetime
log = logging.getLogger(__name__)
REGISTERED_FILE_TYPES = {}
class PacBioNamespaces(object):
# File Types
# PBSMRTPIPE_FILE_PREFIX = 'pbsmrtpipe.files'
# NEW File Type Identifier style Prefix
NEW_PBSMRTPIPE_FILE_PREFIX = "PacBio.FileTypes"
# New DataSet Identifier Prefix
DATASET_FILE_PREFIX = "PacBio.DataSet"
PB_INDEX = "PacBio.Index"
# Task Ids
PBSMRTPIPE_TASK_PREFIX = 'pbsmrtpipe.tasks'
PB_TASK_TYPES = 'pbsmrtpipe.task_types'
# Task Options
PBSMRTPIPE_TASK_OPTS_PREFIX = 'pbsmrtpipe.task_options'
# Workflow Level Options
PBSMRTPIPE_OPTS_PREFIX = 'pbsmrtpipe.options'
# Constants
PBSMRTPIPE_CONSTANTS_PREFIX = 'pbsmrtpipe.constants'
# Pipelines
PBSMRTPIPE_PIPELINES = "pbsmrtpipe.pipelines"
def __to_type(prefix, name):
return ".".join([prefix, name])
to_constant_ns = functools.partial(__to_type, PacBioNamespaces.PBSMRTPIPE_CONSTANTS_PREFIX)
to_file_ns = functools.partial(__to_type, PacBioNamespaces.NEW_PBSMRTPIPE_FILE_PREFIX)
to_ds_ns = functools.partial(__to_type, PacBioNamespaces.DATASET_FILE_PREFIX)
to_task_option_ns = functools.partial(__to_type, PacBioNamespaces.PBSMRTPIPE_TASK_OPTS_PREFIX)
to_task_ns = functools.partial(__to_type, PacBioNamespaces.PBSMRTPIPE_TASK_PREFIX)
to_task_types_ns = functools.partial(__to_type, PacBioNamespaces.PB_TASK_TYPES)
to_workflow_option_ns = functools.partial(__to_type, PacBioNamespaces.PBSMRTPIPE_OPTS_PREFIX)
to_pipeline_ns = functools.partial(__to_type, PacBioNamespaces.PBSMRTPIPE_PIPELINES)
to_index_ns = functools.partial(__to_type, PacBioNamespaces.PB_INDEX)
class TaskTypes(object):
# This is really TC types
STANDARD = to_task_types_ns("standard")
SCATTERED = to_task_types_ns("scattered")
GATHERED = to_task_types_ns("gathered")
class SymbolTypes(object):
"""*Symbols* that are understood during resolving, such as max number of
processors, Max Chunks"""
MAX_NPROC = '$max_nproc'
MAX_NCHUNKS = '$max_nchunks'
TASK_TYPE = '$task_type'
RESOLVED_OPTS = '$ropts'
SCHEMA_OPTS = '$opts_schema'
OPTS = '$opts'
NCHUNKS = '$nchunks'
NPROC = '$nproc'
class ResourceTypes(object):
"""Resources such as tmp dirs and files, log files"""
TMP_DIR = '$tmpdir'
TMP_FILE = '$tmpfile'
LOG_FILE = '$logfile'
# tasks can write output to this directory
OUTPUT_DIR = '$outputdir'
# Not sure this is a good idea
#TASK_DIR = '$taskdir'
@classmethod
def ALL(cls):
return cls.TMP_DIR, cls.TMP_FILE, cls.LOG_FILE, cls.OUTPUT_DIR
@classmethod
def is_tmp_resource(cls, name):
return name in (cls.TMP_FILE, cls.TMP_DIR)
@classmethod
def is_valid(cls, attr_name):
return attr_name in cls.ALL()
class _RegisteredFileType(type):
def __init__(cls, name, bases, dct):
super(_RegisteredFileType, cls).__init__(name, bases, dct)
def __call__(cls, *args, **kwargs):
if len(args) != 4:
log.error(args)
raise ValueError("Incorrect initialization for {c}".format(c=cls.__name__))
file_type_id, base_name, file_ext, mime_type = args
file_type = REGISTERED_FILE_TYPES.get(file_type_id, None)
if file_type is None:
file_type = super(_RegisteredFileType, cls).__call__(*args)
#log.debug("Registering file type '{i}'".format(i=file_type_id))
REGISTERED_FILE_TYPES[file_type_id] = file_type
else:
# print warning if base name, ext, mime type aren't the same
attrs_names = [('base_name', base_name),
('ext', file_ext),
('mime_type', mime_type)]
for attrs_name, value in attrs_names:
v = getattr(file_type, attrs_name)
if v != value:
_msg = "Attempting to register a file with a different '{x}' -> {v} (expected {y})".format(x=attrs_name, v=v, y=value)
log.warn(_msg)
warnings.warn(_msg)
return file_type
class FileType(object):
__metaclass__ = _RegisteredFileType
def __init__(self, file_type_id, base_name, ext, mime_type):
self.file_type_id = file_type_id
self.base_name = base_name
self.ext = ext
self.mime_type = mime_type
if file_type_id not in REGISTERED_FILE_TYPES:
REGISTERED_FILE_TYPES[file_type_id] = self
@property
def default_name(self):
return ".".join([self.base_name, self.ext])
def __eq__(self, other):
if isinstance(other, self.__class__):
if self.file_type_id == other.file_type_id:
if self.base_name == other.base_name:
if self.ext == other.ext:
return True
return False
def __ne__(self, other):
return not self.__eq__(other)
def __repr__(self):
_d = dict(k=self.__class__.__name__,
i=self.file_type_id,
n=self.default_name)
return "<{k} id={i} name={n} >".format(**_d)
class MimeTypes(object):
JSON = 'application/json'
TXT = 'text/plain'
CSV = 'text/csv'
XML = 'application/xml'
BINARY = 'application/octet-stream'
PICKLE = 'application/python-pickle'
class FileTypes(object):
"""Registry of all PacBio Files types
This needs to be cleaned up and solidified. The old pre-SA3 file types need to be deleted.
"""
# generic Txt file
TXT = FileType(to_file_ns('txt'), 'file', 'txt', MimeTypes.TXT)
# Generic Log file
LOG = FileType(to_file_ns('log'), 'file', 'log', MimeTypes.TXT)
# THIS NEEDS TO BE CONSISTENT with scala code. When the datastore
# is written to disk the file type id's might be translated to
# the DataSet style file type ids.
REPORT = FileType(to_file_ns('JsonReport'), "report", "json", MimeTypes.JSON)
# this will go away soon in favor of using a more type based model to
# distinguish between scatter and gather file types
CHUNK = FileType(to_file_ns("CHUNK"), "chunk", "json", MimeTypes.JSON)
GCHUNK = FileType(to_file_ns("GCHUNK"), 'gather_chunk', "json", MimeTypes.JSON)
SCHUNK = FileType(to_file_ns("SCHUNK"), "scatter_chunk", "json", MimeTypes.JSON)
FASTA = FileType(to_file_ns('Fasta'), "file", "fasta", MimeTypes.TXT)
FASTQ = FileType(to_file_ns('Fastq'), "file", "fastq", MimeTypes.TXT)
# Not sure this should be a special File Type?
INPUT_XML = FileType(to_file_ns('input_xml'), "input", "xml", MimeTypes.XML)
FOFN = FileType(to_file_ns("generic_fofn"), "generic", "fofn", MimeTypes.TXT)
MOVIE_FOFN = FileType(to_file_ns('movie_fofn'), "movie", "fofn", MimeTypes.TXT)
RGN_FOFN = FileType(to_file_ns('rgn_fofn'), "region", "fofn", MimeTypes.TXT)
RS_MOVIE_XML = FileType(to_file_ns("rs_movie_metadata"), "file", "rs_movie.metadata.xml", MimeTypes.XML)
REF_ENTRY_XML = FileType(to_file_ns('reference_info_xml'), "reference.info.xml", "xml", MimeTypes.XML)
ALIGNMENT_CMP_H5 = FileType(to_file_ns('alignment_cmp_h5'), "alignments", "cmp.h5", MimeTypes.BINARY)
# I am not sure this should be a first class file
BLASR_M4 = FileType(to_file_ns('blasr_file'), 'blasr', 'm4', MimeTypes.TXT)
BAM = FileType(to_file_ns('bam'), "alignments", "bam", MimeTypes.BINARY)
BAMBAI = FileType(to_file_ns('bam_bai'), "alignments", "bam.bai", MimeTypes.BINARY)
BED = FileType(to_file_ns('bed'), "file", "bed", MimeTypes.TXT)
SAM = FileType(to_file_ns('sam'), "alignments", "sam", MimeTypes.BINARY)
VCF = FileType(to_file_ns('vcf'), "file", "vcf", MimeTypes.TXT)
GFF = FileType(to_file_ns('gff'), "file", "gff", MimeTypes.TXT)
CSV = FileType(to_file_ns('csv'), "file", "csv", MimeTypes.CSV)
XML = FileType(to_file_ns('xml'), "file", "xml", 'application/xml')
# Generic Json File
JSON = FileType(to_file_ns("json"), "file", "json", MimeTypes.JSON)
# Generic H5 File
H5 = FileType(to_file_ns("h5"), "file", "h5", MimeTypes.BINARY)
# Generic Python pickle XXX EVIL
PICKLE = FileType(to_file_ns("pickle"), "file", "pickle", MimeTypes.PICKLE)
# ******************* NEW SA3 File Types ********************
# DataSet Types. The default file names should have well-defined agreed
# upon format. See what Dave did for the bam files.
# https://github.com/PacificBiosciences/PacBioFileFormats
DS_SUBREADS_H5 = FileType(to_ds_ns("HdfSubreadSet"), "file", "hdfsubreadset.xml", MimeTypes.XML)
DS_SUBREADS = FileType(to_ds_ns("SubreadSet"), "file", "subreadset.xml", MimeTypes.XML)
DS_CCS = FileType(to_ds_ns("ConsensusReadSet"), "file", "consensusreadset.xml", MimeTypes.XML)
DS_REF = FileType(to_ds_ns("ReferenceSet"), "file", "referenceset.xml", MimeTypes.XML)
DS_ALIGN = FileType(to_ds_ns("AlignmentSet"), "file", "alignmentset.xml", MimeTypes.XML)
DS_CONTIG = FileType(to_ds_ns("ContigSet"), "file", "contigset.xml", MimeTypes.XML)
DS_BARCODE = FileType(to_ds_ns("BarcodeSet"), "file", "barcodeset.xml", MimeTypes.XML)
DS_ALIGN_CCS = FileType(to_ds_ns("ConsensusAlignmentSet"), "file",
"consensusalignmentset.xml", MimeTypes.XML)
# Index Files
I_SAM = FileType(to_index_ns("SamIndex"), "file", "sam.index", MimeTypes.BINARY)
I_SAW = FileType(to_index_ns("SaWriterIndex"), "file", "sa", MimeTypes.BINARY)
# PacBio Defined Formats
FASTA_BC = FileType("PacBio.BarcodeFile.BarcodeFastaFile", "file", "barcode.fasta", MimeTypes.TXT)
# No ':' or '"' in the id
FASTA_REF = FileType("PacBio.ReferenceFile.ReferenceFastaFile", "file", "pbreference.fasta", MimeTypes.TXT)
# FIXME. Add Bax/Bam Formats here. This should replace the exiting pre-SA3 formats.
BAM_ALN = FileType("PacBio.AlignmentFile.AlignmentBamFile", "file", "alignment.bam", MimeTypes.BINARY)
BAM_SUB = FileType("PacBio.SubreadFile.SubreadBamFile", "file", "subread.bam", MimeTypes.BINARY)
BAM_CCS = FileType("PacBio.ConsensusReadFile.ConsensusReadBamFile", "file", "ccs.bam", MimeTypes.BINARY)
BAX = FileType("PacBio.SubreadFile.BaxFile", "file", "bax.h5", MimeTypes.BINARY)
# THIS IS EXPERIMENT for internal analysis. DO NOT use
COND = FileType(to_file_ns("COND"), "file", "conditions.json", MimeTypes.JSON)
@staticmethod
def is_valid_id(file_type_id):
return file_type_id in REGISTERED_FILE_TYPES
@staticmethod
def ALL():
return REGISTERED_FILE_TYPES
class DataStoreFile(object):
def __init__(self, uuid, source_id, type_id, path, is_chunked=False):
# adding this for consistency. In the scala code, the unique id must be
# a uuid format
self.uuid = uuid
# this must globally unique. This is used to provide context to where
# the file originated from (i.e., the tool author
self.file_id = source_id
# Consistent with a value in FileTypes
self.file_type_id = type_id
self.path = path
self.file_size = os.path.getsize(path)
self.created_at = datetime.datetime.fromtimestamp(os.path.getctime(path))
self.modified_at = datetime.datetime.fromtimestamp(os.path.getmtime(path))
# Was the file produced by Chunked task
self.is_chunked = is_chunked
def __repr__(self):
_d = dict(k=self.__class__.__name__,
i=self.file_id,
t=self.file_type_id,
p=os.path.basename(self.path))
return "<{k} {i} type:{t} filename:{p} >".format(**_d)
def to_dict(self):
return dict(sourceId=self.file_id,
uniqueId=str(self.uuid),
fileTypeId=self.file_type_id,
path=self.path,
fileSize=self.file_size,
createdAt=_datetime_to_string(self.created_at),
modifiedAt=_datetime_to_string(self.modified_at),
isChunked=self.is_chunked)
@staticmethod
def from_dict(d):
# FIXME. This isn't quite right.
to_a = lambda x: x.encode('ascii', 'ignore')
to_k = lambda x: to_a(d[x])
is_chunked = d.get('isChunked', False)
return DataStoreFile(to_k('uniqueId'),
to_k('sourceId'),
to_k('fileTypeId'),
to_k('path'), is_chunked=is_chunked)
def _datetime_to_string(dt):
return dt.strftime('%Y-%m-%dT%H:%M:%S')
class DataStore(object):
version = "0.2.2"
def __init__(self, ds_files, created_at=None):
"""
:type ds_files: list[DataStoreFile]
"""
self.files = {f.uuid: f for f in ds_files}
self.created_at = datetime.datetime.now() if created_at is None else created_at
self.updated_at = datetime.datetime.now()
def __repr__(self):
_d = dict(n=len(self.files), k=self.__class__.__name__)
return "<{k} nfiles={n} >".format(**_d)
def add(self, ds_file):
if isinstance(ds_file, DataStoreFile):
self.files[ds_file.uuid] = ds_file
self.updated_at = datetime.datetime.now()
else:
raise TypeError("DataStoreFile expected. Got type {t} for {d}".format(t=type(ds_file), d=ds_file))
def to_dict(self):
fs = [f.to_dict() for i, f in self.files.iteritems()]
_d = dict(version=self.version,
createdAt=_datetime_to_string(self.created_at),
updatedAt=_datetime_to_string(self.updated_at), files=fs)
return _d
def _write_json(self, file_name, permission):
with open(file_name, permission) as f:
s = json.dumps(self.to_dict(), indent=4, sort_keys=True)
f.write(s)
def write_json(self, file_name):
# if the file exists is should raise?
self._write_json(file_name, 'w')
def write_update_json(self, file_name):
"""Overwrite Datastore with current state"""
self._write_json(file_name, 'w+')
@staticmethod
def load_from_json(path):
with open(path, 'r') as reader:
d = json.loads(reader.read())
ds_files = [DataStoreFile.from_dict(x) for x in d['files']]
return DataStore(ds_files)
def _is_chunk_key(k):
return k.startswith(PipelineChunk.CHUNK_KEY_PREFIX)
class MalformedChunkKeyError(ValueError):
"""Chunk Key does NOT adhere to the spec"""
pass
class PipelineChunk(object):
CHUNK_KEY_PREFIX = "$chunk."
RX_CHUNK_KEY = re.compile(r'^\$chunk\.([A-z0-9_]*)')
def __init__(self, chunk_id, **kwargs):
"""
kwargs is a key-value store. keys that begin "$chunk." are considered
to be semantically understood by workflow and can be "routed" to
chunked task inputs.
Values that don't begin with "$chunk." are considered metadata.
:param chunk_id: Chunk id
:type chunk_id: str
"""
if self.RX_CHUNK_KEY.match(chunk_id) is not None:
raise MalformedChunkKeyError("'{c}' expected {p}".format(c=chunk_id, p=self.RX_CHUNK_KEY.pattern))
self.chunk_id = chunk_id
# loose key-value pair
self._datum = kwargs
def __repr__(self):
_d = dict(k=self.__class__.__name__, i=self.chunk_id, c=",".join(self.chunk_keys))
return "<{k} id='{i}' chunk keys={c} >".format(**_d)
def set_chunk_key(self, chunk_key, value):
"""Overwrite or add a chunk_key => value to the Chunk datum
the chunk-key can be provided with or without the '$chunk:' prefix
"""
if not chunk_key.startswith(PipelineChunk.CHUNK_KEY_PREFIX):
chunk_key = PipelineChunk.CHUNK_KEY_PREFIX + chunk_key
self._datum[chunk_key] = value
def set_metadata_key(self, metadata_key, value):
"""Set chunk metadata key => value
metadata key must NOT begin with $chunk. format
"""
if metadata_key.startswith(PipelineChunk.CHUNK_KEY_PREFIX):
raise ValueError("Cannot set chunk-key values. {i}".format(i=metadata_key))
self._datum[metadata_key] = value
@property
def chunk_d(self):
return {k: v for k, v in self._datum.iteritems() if _is_chunk_key(k)}
@property
def chunk_keys(self):
return self.chunk_d.keys()
@property
def chunk_metadata(self):
return {k: v for k, v in self._datum.iteritems() if not _is_chunk_key(k)}
def to_dict(self):
return {'chunk_id': self.chunk_id, 'chunk': self._datum}
|