Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/numpy/lib/format.py: 12%

1"""

2Binary serialization

4NPY format

5==========

7A simple format for saving numpy arrays to disk with the full

8information about them.

10The ``.npy`` format is the standard binary file format in NumPy for

11persisting a *single* arbitrary NumPy array on disk. The format stores all

12of the shape and dtype information necessary to reconstruct the array

13correctly even on another machine with a different architecture.

14The format is designed to be as simple as possible while achieving

15its limited goals.

17The ``.npz`` format is the standard format for persisting *multiple* NumPy

18arrays on disk. A ``.npz`` file is a zip file containing multiple ``.npy``

19files, one for each array.

21Capabilities

22------------

24- Can represent all NumPy arrays including nested record arrays and

25 object arrays.

27- Represents the data in its native binary form.

29- Supports Fortran-contiguous arrays directly.

31- Stores all of the necessary information to reconstruct the array

32 including shape and dtype on a machine of a different

33 architecture. Both little-endian and big-endian arrays are

34 supported, and a file with little-endian numbers will yield

35 a little-endian array on any machine reading the file. The

36 types are described in terms of their actual sizes. For example,

37 if a machine with a 64-bit C "long int" writes out an array with

38 "long ints", a reading machine with 32-bit C "long ints" will yield

39 an array with 64-bit integers.

41- Is straightforward to reverse engineer. Datasets often live longer than

42 the programs that created them. A competent developer should be

43 able to create a solution in their preferred programming language to

44 read most ``.npy`` files that they have been given without much

45 documentation.

47- Allows memory-mapping of the data. See `open_memmap`.

49- Can be read from a filelike stream object instead of an actual file.

51- Stores object arrays, i.e. arrays containing elements that are arbitrary

52 Python objects. Files with object arrays are not to be mmapable, but

53 can be read and written to disk.

55Limitations

56-----------

58- Arbitrary subclasses of numpy.ndarray are not completely preserved.

59 Subclasses will be accepted for writing, but only the array data will

60 be written out. A regular numpy.ndarray object will be created

61 upon reading the file.

63.. warning::

65 Due to limitations in the interpretation of structured dtypes, dtypes

66 with fields with empty names will have the names replaced by 'f0', 'f1',

67 etc. Such arrays will not round-trip through the format entirely

68 accurately. The data is intact; only the field names will differ. We are

69 working on a fix for this. This fix will not require a change in the

70 file format. The arrays with such structures can still be saved and

71 restored, and the correct dtype may be restored by using the

72 ``loadedarray.view(correct_dtype)`` method.

74File extensions

75---------------

77We recommend using the ``.npy`` and ``.npz`` extensions for files saved

78in this format. This is by no means a requirement; applications may wish

79to use these file formats but use an extension specific to the

80application. In the absence of an obvious alternative, however,

81we suggest using ``.npy`` and ``.npz``.

83Version numbering

84-----------------

86The version numbering of these formats is independent of NumPy version

87numbering. If the format is upgraded, the code in `numpy.io` will still

88be able to read and write Version 1.0 files.

90Format Version 1.0

91------------------

93The first 6 bytes are a magic string: exactly ``\\x93NUMPY``.

95The next 1 byte is an unsigned byte: the major version number of the file

96format, e.g. ``\\x01``.

98The next 1 byte is an unsigned byte: the minor version number of the file

99format, e.g. ``\\x00``. Note: the version of the file format is not tied

100to the version of the numpy package.

101

102The next 2 bytes form a little-endian unsigned short int: the length of

103the header data HEADER_LEN.

104

105The next HEADER_LEN bytes form the header data describing the array's

106format. It is an ASCII string which contains a Python literal expression

107of a dictionary. It is terminated by a newline (``\\n``) and padded with

108spaces (``\\x20``) to make the total of

109``len(magic string) + 2 + len(length) + HEADER_LEN`` be evenly divisible

110by 64 for alignment purposes.

111

112The dictionary contains three keys:

113

114 "descr" : dtype.descr

115 An object that can be passed as an argument to the `numpy.dtype`

116 constructor to create the array's dtype.

117 "fortran_order" : bool

118 Whether the array data is Fortran-contiguous or not. Since

119 Fortran-contiguous arrays are a common form of non-C-contiguity,

120 we allow them to be written directly to disk for efficiency.

121 "shape" : tuple of int

122 The shape of the array.

123

124For repeatability and readability, the dictionary keys are sorted in

125alphabetic order. This is for convenience only. A writer SHOULD implement

126this if possible. A reader MUST NOT depend on this.

127

128Following the header comes the array data. If the dtype contains Python

129objects (i.e. ``dtype.hasobject is True``), then the data is a Python

130pickle of the array. Otherwise the data is the contiguous (either C-

131or Fortran-, depending on ``fortran_order``) bytes of the array.

132Consumers can figure out the number of bytes by multiplying the number

133of elements given by the shape (noting that ``shape=()`` means there is

1341 element) by ``dtype.itemsize``.

135

136Format Version 2.0

137------------------

138

139The version 1.0 format only allowed the array header to have a total size of

14065535 bytes. This can be exceeded by structured arrays with a large number of

141columns. The version 2.0 format extends the header size to 4 GiB.

142`numpy.save` will automatically save in 2.0 format if the data requires it,

143else it will always use the more compatible 1.0 format.

144

145The description of the fourth element of the header therefore has become:

146"The next 4 bytes form a little-endian unsigned int: the length of the header

147data HEADER_LEN."

148

149Format Version 3.0

150------------------

151

152This version replaces the ASCII string (which in practice was latin1) with

153a utf8-encoded string, so supports structured types with any unicode field

154names.

155

156Notes

157-----

158The ``.npy`` format, including motivation for creating it and a comparison of

159alternatives, is described in the

160:doc:`"npy-format" NEP <neps:nep-0001-npy-format>`, however details have

161evolved with time and this document is more current.

162

163"""

164import numpy

165import warnings

166from numpy.lib.utils import safe_eval

167from numpy.compat import (

168 os_fspath, pickle

169 )

170from numpy.compat.py3k import _isfileobj

173__all__ = []

176EXPECTED_KEYS = {'descr', 'fortran_order', 'shape'}

177MAGIC_PREFIX = b'\x93NUMPY'

178MAGIC_LEN = len(MAGIC_PREFIX) + 2

179ARRAY_ALIGN = 64 # plausible values are powers of 2 between 16 and 4096

180BUFFER_SIZE = 2**18 # size of buffer for reading npz files in bytes

181# allow growth within the address space of a 64 bit machine along one axis

182GROWTH_AXIS_MAX_DIGITS = 21 # = len(str(8*2**64-1)) hypothetical int1 dtype

183

184# difference between version 1.0 and 2.0 is a 4 byte (I) header length

185# instead of 2 bytes (H) allowing storage of large structured arrays

186_header_size_info = {

187 (1, 0): ('<H', 'latin1'),

188 (2, 0): ('<I', 'latin1'),

189 (3, 0): ('<I', 'utf8'),

190}

191

192# Python's literal_eval is not actually safe for large inputs, since parsing

193# may become slow or even cause interpreter crashes.

194# This is an arbitrary, low limit which should make it safe in practice.

195_MAX_HEADER_SIZE = 10000

196

197def _check_version(version):

198 if version not in [(1, 0), (2, 0), (3, 0), None]:

199 msg = "we only support format version (1,0), (2,0), and (3,0), not %s"

200 raise ValueError(msg % (version,))

201

202def magic(major, minor):

203 """ Return the magic string for the given file format version.

204

205 Parameters

206 ----------

207 major : int in [0, 255]

208 minor : int in [0, 255]

209

210 Returns

211 -------

212 magic : str

213

214 Raises

215 ------

216 ValueError if the version cannot be formatted.

217 """

218 if major < 0 or major > 255:

219 raise ValueError("major version must be 0 <= major < 256")

220 if minor < 0 or minor > 255:

221 raise ValueError("minor version must be 0 <= minor < 256")

222 return MAGIC_PREFIX + bytes([major, minor])

223

224def read_magic(fp):

225 """ Read the magic string to get the version of the file format.

226

227 Parameters

228 ----------

229 fp : filelike object

230

231 Returns

232 -------

233 major : int

234 minor : int

235 """

236 magic_str = _read_bytes(fp, MAGIC_LEN, "magic string")

237 if magic_str[:-2] != MAGIC_PREFIX:

238 msg = "the magic string is not correct; expected %r, got %r"

239 raise ValueError(msg % (MAGIC_PREFIX, magic_str[:-2]))

240 major, minor = magic_str[-2:]

241 return major, minor

242

243def _has_metadata(dt):

244 if dt.metadata is not None:

245 return True

246 elif dt.names is not None:

247 return any(_has_metadata(dt[k]) for k in dt.names)

248 elif dt.subdtype is not None:

249 return _has_metadata(dt.base)

250 else:

251 return False

252

253def dtype_to_descr(dtype):

254 """

255 Get a serializable descriptor from the dtype.

256

257 The .descr attribute of a dtype object cannot be round-tripped through

258 the dtype() constructor. Simple types, like dtype('float32'), have

259 a descr which looks like a record array with one field with '' as

260 a name. The dtype() constructor interprets this as a request to give

261 a default name. Instead, we construct descriptor that can be passed to

262 dtype().

263

264 Parameters

265 ----------

266 dtype : dtype

267 The dtype of the array that will be written to disk.

268

269 Returns

270 -------

271 descr : object

272 An object that can be passed to `numpy.dtype()` in order to

273 replicate the input dtype.

274

275 """

276 if _has_metadata(dtype):

277 warnings.warn("metadata on a dtype may be saved or ignored, but will "

278 "raise if saved when read. Use another form of storage.",

279 UserWarning, stacklevel=2)

280 if dtype.names is not None:

281 # This is a record array. The .descr is fine. XXX: parts of the

282 # record array with an empty name, like padding bytes, still get

283 # fiddled with. This needs to be fixed in the C implementation of

284 # dtype().

285 return dtype.descr

286 else:

287 return dtype.str

288

289def descr_to_dtype(descr):

290 """

291 Returns a dtype based off the given description.

292

293 This is essentially the reverse of `dtype_to_descr()`. It will remove

294 the valueless padding fields created by, i.e. simple fields like

295 dtype('float32'), and then convert the description to its corresponding

296 dtype.

297

298 Parameters

299 ----------

300 descr : object

301 The object retrieved by dtype.descr. Can be passed to

302 `numpy.dtype()` in order to replicate the input dtype.

303

304 Returns

305 -------

306 dtype : dtype

307 The dtype constructed by the description.

308

309 """

310 if isinstance(descr, str):

311 # No padding removal needed

312 return numpy.dtype(descr)

313 elif isinstance(descr, tuple):

314 # subtype, will always have a shape descr[1]

315 dt = descr_to_dtype(descr[0])

316 return numpy.dtype((dt, descr[1]))

317

318 titles = []

319 names = []

320 formats = []

321 offsets = []

322 offset = 0

323 for field in descr:

324 if len(field) == 2:

325 name, descr_str = field

326 dt = descr_to_dtype(descr_str)

327 else:

328 name, descr_str, shape = field

329 dt = numpy.dtype((descr_to_dtype(descr_str), shape))

330

331 # Ignore padding bytes, which will be void bytes with '' as name

332 # Once support for blank names is removed, only "if name == ''" needed)

333 is_pad = (name == '' and dt.type is numpy.void and dt.names is None)

334 if not is_pad:

335 title, name = name if isinstance(name, tuple) else (None, name)

336 titles.append(title)

337 names.append(name)

338 formats.append(dt)

339 offsets.append(offset)

340 offset += dt.itemsize

341

342 return numpy.dtype({'names': names, 'formats': formats, 'titles': titles,

343 'offsets': offsets, 'itemsize': offset})

344

345def header_data_from_array_1_0(array):

346 """ Get the dictionary of header metadata from a numpy.ndarray.

347

348 Parameters

349 ----------

350 array : numpy.ndarray

351

352 Returns

353 -------

354 d : dict

355 This has the appropriate entries for writing its string representation

356 to the header of the file.

357 """

358 d = {'shape': array.shape}

359 if array.flags.c_contiguous:

360 d['fortran_order'] = False

361 elif array.flags.f_contiguous:

362 d['fortran_order'] = True

363 else:

364 # Totally non-contiguous data. We will have to make it C-contiguous

365 # before writing. Note that we need to test for C_CONTIGUOUS first

366 # because a 1-D array is both C_CONTIGUOUS and F_CONTIGUOUS.

367 d['fortran_order'] = False

368

369 d['descr'] = dtype_to_descr(array.dtype)

370 return d

371

372

373def _wrap_header(header, version):

374 """

375 Takes a stringified header, and attaches the prefix and padding to it

376 """

377 import struct

378 assert version is not None

379 fmt, encoding = _header_size_info[version]

380 header = header.encode(encoding)

381 hlen = len(header) + 1

382 padlen = ARRAY_ALIGN - ((MAGIC_LEN + struct.calcsize(fmt) + hlen) % ARRAY_ALIGN)

383 try:

384 header_prefix = magic(*version) + struct.pack(fmt, hlen + padlen)

385 except struct.error:

386 msg = "Header length {} too big for version={}".format(hlen, version)

387 raise ValueError(msg) from None

388

389 # Pad the header with spaces and a final newline such that the magic

390 # string, the header-length short and the header are aligned on a

391 # ARRAY_ALIGN byte boundary. This supports memory mapping of dtypes

392 # aligned up to ARRAY_ALIGN on systems like Linux where mmap()

393 # offset must be page-aligned (i.e. the beginning of the file).

394 return header_prefix + header + b' '*padlen + b'\n'

395

396

397def _wrap_header_guess_version(header):

398 """

399 Like `_wrap_header`, but chooses an appropriate version given the contents

400 """

401 try:

402 return _wrap_header(header, (1, 0))

403 except ValueError:

404 pass

405

406 try:

407 ret = _wrap_header(header, (2, 0))

408 except UnicodeEncodeError:

409 pass

410 else:

411 warnings.warn("Stored array in format 2.0. It can only be"

412 "read by NumPy >= 1.9", UserWarning, stacklevel=2)

413 return ret

414

415 header = _wrap_header(header, (3, 0))

416 warnings.warn("Stored array in format 3.0. It can only be "

417 "read by NumPy >= 1.17", UserWarning, stacklevel=2)

418 return header

419

420

421def _write_array_header(fp, d, version=None):

422 """ Write the header for an array and returns the version used

423

424 Parameters

425 ----------

426 fp : filelike object

427 d : dict

428 This has the appropriate entries for writing its string representation

429 to the header of the file.

430 version : tuple or None

431 None means use oldest that works. Providing an explicit version will

432 raise a ValueError if the format does not allow saving this data.

433 Default: None

434 """

435 header = ["{"]

436 for key, value in sorted(d.items()):

437 # Need to use repr here, since we eval these when reading

438 header.append("'%s': %s, " % (key, repr(value)))

439 header.append("}")

440 header = "".join(header)

441

442 # Add some spare space so that the array header can be modified in-place

443 # when changing the array size, e.g. when growing it by appending data at

444 # the end.

445 shape = d['shape']

446 header += " " * ((GROWTH_AXIS_MAX_DIGITS - len(repr(

447 shape[-1 if d['fortran_order'] else 0]

448 ))) if len(shape) > 0 else 0)

449

450 if version is None:

451 header = _wrap_header_guess_version(header)

452 else:

453 header = _wrap_header(header, version)

454 fp.write(header)

455

456def write_array_header_1_0(fp, d):

457 """ Write the header for an array using the 1.0 format.

458

459 Parameters

460 ----------

461 fp : filelike object

462 d : dict

463 This has the appropriate entries for writing its string

464 representation to the header of the file.

465 """

466 _write_array_header(fp, d, (1, 0))

467

468

469def write_array_header_2_0(fp, d):

470 """ Write the header for an array using the 2.0 format.

471 The 2.0 format allows storing very large structured arrays.

472

473 .. versionadded:: 1.9.0

474

475 Parameters

476 ----------

477 fp : filelike object

478 d : dict

479 This has the appropriate entries for writing its string

480 representation to the header of the file.

481 """

482 _write_array_header(fp, d, (2, 0))

483

484def read_array_header_1_0(fp, max_header_size=_MAX_HEADER_SIZE):

485 """

486 Read an array header from a filelike object using the 1.0 file format

487 version.

488

489 This will leave the file object located just after the header.

490

491 Parameters

492 ----------

493 fp : filelike object

494 A file object or something with a `.read()` method like a file.

495

496 Returns

497 -------

498 shape : tuple of int

499 The shape of the array.

500 fortran_order : bool

501 The array data will be written out directly if it is either

502 C-contiguous or Fortran-contiguous. Otherwise, it will be made

503 contiguous before writing it out.

504 dtype : dtype

505 The dtype of the file's data.

506 max_header_size : int, optional

507 Maximum allowed size of the header. Large headers may not be safe

508 to load securely and thus require explicitly passing a larger value.

509 See :py:meth:`ast.literal_eval()` for details.

510

511 Raises

512 ------

513 ValueError

514 If the data is invalid.

515

516 """

517 return _read_array_header(

518 fp, version=(1, 0), max_header_size=max_header_size)

519

520def read_array_header_2_0(fp, max_header_size=_MAX_HEADER_SIZE):

521 """

522 Read an array header from a filelike object using the 2.0 file format

523 version.

524

525 This will leave the file object located just after the header.

526

527 .. versionadded:: 1.9.0

528

529 Parameters

530 ----------

531 fp : filelike object

532 A file object or something with a `.read()` method like a file.

533 max_header_size : int, optional

534 Maximum allowed size of the header. Large headers may not be safe

535 to load securely and thus require explicitly passing a larger value.

536 See :py:meth:`ast.literal_eval()` for details.

537

538 Returns

539 -------

540 shape : tuple of int

541 The shape of the array.

542 fortran_order : bool

543 The array data will be written out directly if it is either

544 C-contiguous or Fortran-contiguous. Otherwise, it will be made

545 contiguous before writing it out.

546 dtype : dtype

547 The dtype of the file's data.

548

549 Raises

550 ------

551 ValueError

552 If the data is invalid.

553

554 """

555 return _read_array_header(

556 fp, version=(2, 0), max_header_size=max_header_size)

557

558

559def _filter_header(s):

560 """Clean up 'L' in npz header ints.

561

562 Cleans up the 'L' in strings representing integers. Needed to allow npz

563 headers produced in Python2 to be read in Python3.

564

565 Parameters

566 ----------

567 s : string

568 Npy file header.

569

570 Returns

571 -------

572 header : str

573 Cleaned up header.

574

575 """

576 import tokenize

577 from io import StringIO

578

579 tokens = []

580 last_token_was_number = False

581 for token in tokenize.generate_tokens(StringIO(s).readline):

582 token_type = token[0]

583 token_string = token[1]

584 if (last_token_was_number and

585 token_type == tokenize.NAME and

586 token_string == "L"):

587 continue

588 else:

589 tokens.append(token)

590 last_token_was_number = (token_type == tokenize.NUMBER)

591 return tokenize.untokenize(tokens)

592

593

594def _read_array_header(fp, version, max_header_size=_MAX_HEADER_SIZE):

595 """

596 see read_array_header_1_0

597 """

598 # Read an unsigned, little-endian short int which has the length of the

599 # header.

600 import struct

601 hinfo = _header_size_info.get(version)

602 if hinfo is None:

603 raise ValueError("Invalid version {!r}".format(version))

604 hlength_type, encoding = hinfo

605

606 hlength_str = _read_bytes(fp, struct.calcsize(hlength_type), "array header length")

607 header_length = struct.unpack(hlength_type, hlength_str)[0]

608 header = _read_bytes(fp, header_length, "array header")

609 header = header.decode(encoding)

610 if len(header) > max_header_size:

611 raise ValueError(

612 f"Header info length ({len(header)}) is large and may not be safe "

613 "to load securely.\n"

614 "To allow loading, adjust `max_header_size` or fully trust "

615 "the `.npy` file using `allow_pickle=True`.\n"

616 "For safety against large resource use or crashes, sandboxing "

617 "may be necessary.")

618

619 # The header is a pretty-printed string representation of a literal

620 # Python dictionary with trailing newlines padded to a ARRAY_ALIGN byte

621 # boundary. The keys are strings.

622 # "shape" : tuple of int

623 # "fortran_order" : bool

624 # "descr" : dtype.descr

625 # Versions (2, 0) and (1, 0) could have been created by a Python 2

626 # implementation before header filtering was implemented.

627 if version <= (2, 0):

628 header = _filter_header(header)

629 try:

630 d = safe_eval(header)

631 except SyntaxError as e:

632 msg = "Cannot parse header: {!r}"

633 raise ValueError(msg.format(header)) from e

634 if not isinstance(d, dict):

635 msg = "Header is not a dictionary: {!r}"

636 raise ValueError(msg.format(d))

637

638 if EXPECTED_KEYS != d.keys():

639 keys = sorted(d.keys())

640 msg = "Header does not contain the correct keys: {!r}"

641 raise ValueError(msg.format(keys))

642

643 # Sanity-check the values.

644 if (not isinstance(d['shape'], tuple) or

645 not all(isinstance(x, int) for x in d['shape'])):

646 msg = "shape is not valid: {!r}"

647 raise ValueError(msg.format(d['shape']))

648 if not isinstance(d['fortran_order'], bool):

649 msg = "fortran_order is not a valid bool: {!r}"

650 raise ValueError(msg.format(d['fortran_order']))

651 try:

652 dtype = descr_to_dtype(d['descr'])

653 except TypeError as e:

654 msg = "descr is not a valid dtype descriptor: {!r}"

655 raise ValueError(msg.format(d['descr'])) from e

656

657 return d['shape'], d['fortran_order'], dtype

658

659def write_array(fp, array, version=None, allow_pickle=True, pickle_kwargs=None):

660 """

661 Write an array to an NPY file, including a header.

662

663 If the array is neither C-contiguous nor Fortran-contiguous AND the

664 file_like object is not a real file object, this function will have to

665 copy data in memory.

666

667 Parameters

668 ----------

669 fp : file_like object

670 An open, writable file object, or similar object with a

671 ``.write()`` method.

672 array : ndarray

673 The array to write to disk.

674 version : (int, int) or None, optional

675 The version number of the format. None means use the oldest

676 supported version that is able to store the data. Default: None

677 allow_pickle : bool, optional

678 Whether to allow writing pickled data. Default: True

679 pickle_kwargs : dict, optional

680 Additional keyword arguments to pass to pickle.dump, excluding

681 'protocol'. These are only useful when pickling objects in object

682 arrays on Python 3 to Python 2 compatible format.

683

684 Raises

685 ------

686 ValueError

687 If the array cannot be persisted. This includes the case of

688 allow_pickle=False and array being an object array.

689 Various other errors

690 If the array contains Python objects as part of its dtype, the

691 process of pickling them may raise various errors if the objects

692 are not picklable.

693

694 """

695 _check_version(version)

696 _write_array_header(fp, header_data_from_array_1_0(array), version)

697

698 if array.itemsize == 0:

699 buffersize = 0

700 else:

701 # Set buffer size to 16 MiB to hide the Python loop overhead.

702 buffersize = max(16 * 1024 ** 2 // array.itemsize, 1)

703

704 if array.dtype.hasobject:

705 # We contain Python objects so we cannot write out the data

706 # directly. Instead, we will pickle it out

707 if not allow_pickle:

708 raise ValueError("Object arrays cannot be saved when "

709 "allow_pickle=False")

710 if pickle_kwargs is None:

711 pickle_kwargs = {}

712 pickle.dump(array, fp, protocol=3, **pickle_kwargs)

713 elif array.flags.f_contiguous and not array.flags.c_contiguous:

714 if _isfileobj(fp):

715 array.T.tofile(fp)

716 else:

717 for chunk in numpy.nditer(

718 array, flags=['external_loop', 'buffered', 'zerosize_ok'],

719 buffersize=buffersize, order='F'):

720 fp.write(chunk.tobytes('C'))

721 else:

722 if _isfileobj(fp):

723 array.tofile(fp)

724 else:

725 for chunk in numpy.nditer(

726 array, flags=['external_loop', 'buffered', 'zerosize_ok'],

727 buffersize=buffersize, order='C'):

728 fp.write(chunk.tobytes('C'))

729

730

731def read_array(fp, allow_pickle=False, pickle_kwargs=None, *,

732 max_header_size=_MAX_HEADER_SIZE):

733 """

734 Read an array from an NPY file.

735

736 Parameters

737 ----------

738 fp : file_like object

739 If this is not a real file object, then this may take extra memory

740 and time.

741 allow_pickle : bool, optional

742 Whether to allow writing pickled data. Default: False

743

744 .. versionchanged:: 1.16.3

745 Made default False in response to CVE-2019-6446.

746

747 pickle_kwargs : dict

748 Additional keyword arguments to pass to pickle.load. These are only

749 useful when loading object arrays saved on Python 2 when using

750 Python 3.

751 max_header_size : int, optional

752 Maximum allowed size of the header. Large headers may not be safe

753 to load securely and thus require explicitly passing a larger value.

754 See :py:meth:`ast.literal_eval()` for details.

755 This option is ignored when `allow_pickle` is passed. In that case

756 the file is by definition trusted and the limit is unnecessary.

757

758 Returns

759 -------

760 array : ndarray

761 The array from the data on disk.

762

763 Raises

764 ------

765 ValueError

766 If the data is invalid, or allow_pickle=False and the file contains

767 an object array.

768

769 """

770 if allow_pickle:

771 # Effectively ignore max_header_size, since `allow_pickle` indicates

772 # that the input is fully trusted.

773 max_header_size = 2**64

774

775 version = read_magic(fp)

776 _check_version(version)

777 shape, fortran_order, dtype = _read_array_header(

778 fp, version, max_header_size=max_header_size)

779 if len(shape) == 0:

780 count = 1

781 else:

782 count = numpy.multiply.reduce(shape, dtype=numpy.int64)

783

784 # Now read the actual data.

785 if dtype.hasobject:

786 # The array contained Python objects. We need to unpickle the data.

787 if not allow_pickle:

788 raise ValueError("Object arrays cannot be loaded when "

789 "allow_pickle=False")

790 if pickle_kwargs is None:

791 pickle_kwargs = {}

792 try:

793 array = pickle.load(fp, **pickle_kwargs)

794 except UnicodeError as err:

795 # Friendlier error message

796 raise UnicodeError("Unpickling a python object failed: %r\n"

797 "You may need to pass the encoding= option "

798 "to numpy.load" % (err,)) from err

799 else:

800 if _isfileobj(fp):

801 # We can use the fast fromfile() function.

802 array = numpy.fromfile(fp, dtype=dtype, count=count)

803 else:

804 # This is not a real file. We have to read it the

805 # memory-intensive way.

806 # crc32 module fails on reads greater than 2 ** 32 bytes,

807 # breaking large reads from gzip streams. Chunk reads to

808 # BUFFER_SIZE bytes to avoid issue and reduce memory overhead

809 # of the read. In non-chunked case count < max_read_count, so

810 # only one read is performed.

811

812 # Use np.ndarray instead of np.empty since the latter does

813 # not correctly instantiate zero-width string dtypes; see

814 # https://github.com/numpy/numpy/pull/6430

815 array = numpy.ndarray(count, dtype=dtype)

816

817 if dtype.itemsize > 0:

818 # If dtype.itemsize == 0 then there's nothing more to read

819 max_read_count = BUFFER_SIZE // min(BUFFER_SIZE, dtype.itemsize)

820

821 for i in range(0, count, max_read_count):

822 read_count = min(max_read_count, count - i)

823 read_size = int(read_count * dtype.itemsize)

824 data = _read_bytes(fp, read_size, "array data")

825 array[i:i+read_count] = numpy.frombuffer(data, dtype=dtype,

826 count=read_count)

827

828 if fortran_order:

829 array.shape = shape[::-1]

830 array = array.transpose()

831 else:

832 array.shape = shape

833

834 return array

835

836

837def open_memmap(filename, mode='r+', dtype=None, shape=None,

838 fortran_order=False, version=None, *,

839 max_header_size=_MAX_HEADER_SIZE):

840 """

841 Open a .npy file as a memory-mapped array.

842

843 This may be used to read an existing file or create a new one.

844

845 Parameters

846 ----------

847 filename : str or path-like

848 The name of the file on disk. This may *not* be a file-like

849 object.

850 mode : str, optional

851 The mode in which to open the file; the default is 'r+'. In

852 addition to the standard file modes, 'c' is also accepted to mean

853 "copy on write." See `memmap` for the available mode strings.

854 dtype : data-type, optional

855 The data type of the array if we are creating a new file in "write"

856 mode, if not, `dtype` is ignored. The default value is None, which

857 results in a data-type of `float64`.

858 shape : tuple of int

859 The shape of the array if we are creating a new file in "write"

860 mode, in which case this parameter is required. Otherwise, this

861 parameter is ignored and is thus optional.

862 fortran_order : bool, optional

863 Whether the array should be Fortran-contiguous (True) or

864 C-contiguous (False, the default) if we are creating a new file in

865 "write" mode.

866 version : tuple of int (major, minor) or None

867 If the mode is a "write" mode, then this is the version of the file

868 format used to create the file. None means use the oldest

869 supported version that is able to store the data. Default: None

870 max_header_size : int, optional

871 Maximum allowed size of the header. Large headers may not be safe

872 to load securely and thus require explicitly passing a larger value.

873 See :py:meth:`ast.literal_eval()` for details.

874

875 Returns

876 -------

877 marray : memmap

878 The memory-mapped array.

879

880 Raises

881 ------

882 ValueError

883 If the data or the mode is invalid.

884 OSError

885 If the file is not found or cannot be opened correctly.

886

887 See Also

888 --------

889 numpy.memmap

890

891 """

892 if _isfileobj(filename):

893 raise ValueError("Filename must be a string or a path-like object."

894 " Memmap cannot use existing file handles.")

895

896 if 'w' in mode:

897 # We are creating the file, not reading it.

898 # Check if we ought to create the file.

899 _check_version(version)

900 # Ensure that the given dtype is an authentic dtype object rather

901 # than just something that can be interpreted as a dtype object.

902 dtype = numpy.dtype(dtype)

903 if dtype.hasobject:

904 msg = "Array can't be memory-mapped: Python objects in dtype."

905 raise ValueError(msg)

906 d = dict(

907 descr=dtype_to_descr(dtype),

908 fortran_order=fortran_order,

909 shape=shape,

910 )

911 # If we got here, then it should be safe to create the file.

912 with open(os_fspath(filename), mode+'b') as fp:

913 _write_array_header(fp, d, version)

914 offset = fp.tell()

915 else:

916 # Read the header of the file first.

917 with open(os_fspath(filename), 'rb') as fp:

918 version = read_magic(fp)

919 _check_version(version)

920

921 shape, fortran_order, dtype = _read_array_header(

922 fp, version, max_header_size=max_header_size)

923 if dtype.hasobject:

924 msg = "Array can't be memory-mapped: Python objects in dtype."

925 raise ValueError(msg)

926 offset = fp.tell()

927

928 if fortran_order:

929 order = 'F'

930 else:

931 order = 'C'

932

933 # We need to change a write-only mode to a read-write mode since we've

934 # already written data to the file.

935 if mode == 'w+':

936 mode = 'r+'

937

938 marray = numpy.memmap(filename, dtype=dtype, shape=shape, order=order,

939 mode=mode, offset=offset)

940

941 return marray

942

943

944def _read_bytes(fp, size, error_template="ran out of data"):

945 """

946 Read from file-like object until size bytes are read.

947 Raises ValueError if not EOF is encountered before size bytes are read.

948 Non-blocking objects only supported if they derive from io objects.

949

950 Required as e.g. ZipExtFile in python 2.6 can return less data than

951 requested.

952 """

953 data = bytes()

954 while True:

955 # io files (default in python3) return None or raise on

956 # would-block, python2 file will truncate, probably nothing can be

957 # done about that. note that regular files can't be non-blocking

958 try:

959 r = fp.read(size - len(data))

960 data += r

961 if len(r) == 0 or len(data) == size:

962 break

963 except BlockingIOError:

964 pass

965 if len(data) != size:

966 msg = "EOF: reading %s, expected %d bytes got %d"

967 raise ValueError(msg % (error_template, size, len(data)))

968 else:

969 return data