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| author | Nico Schlömer <nico.schloemer@gmail.com> | 2021-04-26 22:23:25 +0200 |
|---|---|---|
| committer | Nico Schlömer <nico.schloemer@gmail.com> | 2021-04-26 22:23:25 +0200 |
| commit | d5fd9cc4927c19eaabf576e1c043b261799657a5 (patch) | |
| tree | ae2f3c674dc3173b94093c5af3203e4d87c2aaa6 | |
| parent | 65a3e7d2d07844ec20aaba5912798ec27a3662f4 (diff) | |
| download | numpy-d5fd9cc4927c19eaabf576e1c043b261799657a5.tar.gz | |
change CRLF line terminators to Unix
| -rw-r--r-- | .github/PULL_REQUEST_TEMPLATE.md | 32 | ||||
| -rwxr-xr-x | doc/release/upcoming_changes/17727.performance.rst | 14 | ||||
| -rw-r--r-- | doc/source/_static/numpylogo.svg | 46 | ||||
| -rw-r--r-- | doc/source/user/how-to-how-to.rst | 236 | ||||
| -rw-r--r-- | doc/source/user/how-to-io.rst | 656 | ||||
| -rw-r--r-- | doc/source/user/install.rst | 12 | ||||
| -rw-r--r-- | doc/source/user/who_covid_19_sit_rep_time_series.csv | 228 | ||||
| -rw-r--r-- | numpy/core/src/common/npy_cpuinfo_parser.h | 524 |
8 files changed, 874 insertions, 874 deletions
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 508c8c034..704d2d16f 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -1,16 +1,16 @@ -<!-- ----------------------------------------------------------------
- MAKE SURE YOUR PR GETS THE ATTENTION IT DESERVES!
- ----------------------------------------------------------------
-
-* FORMAT IT RIGHT:
- http://www.numpy.org/devdocs/dev/development_workflow.html#writing-the-commit-message
-
-* IF IT'S A NEW FEATURE OR API CHANGE, TEST THE WATERS:
- http://www.numpy.org/devdocs/dev/development_workflow.html#get-the-mailing-list-s-opinion
-
-* HIT ALL THE GUIDELINES:
- https://numpy.org/devdocs/dev/index.html#guidelines
-
-* WHAT TO DO IF WE HAVEN'T GOTTEN BACK TO YOU:
- http://www.numpy.org/devdocs/dev/development_workflow.html#getting-your-pr-reviewed
--->
+<!-- ---------------------------------------------------------------- + MAKE SURE YOUR PR GETS THE ATTENTION IT DESERVES! + ---------------------------------------------------------------- + +* FORMAT IT RIGHT: + http://www.numpy.org/devdocs/dev/development_workflow.html#writing-the-commit-message + +* IF IT'S A NEW FEATURE OR API CHANGE, TEST THE WATERS: + http://www.numpy.org/devdocs/dev/development_workflow.html#get-the-mailing-list-s-opinion + +* HIT ALL THE GUIDELINES: + https://numpy.org/devdocs/dev/index.html#guidelines + +* WHAT TO DO IF WE HAVEN'T GOTTEN BACK TO YOU: + http://www.numpy.org/devdocs/dev/development_workflow.html#getting-your-pr-reviewed +--> diff --git a/doc/release/upcoming_changes/17727.performance.rst b/doc/release/upcoming_changes/17727.performance.rst index 7b447a3b2..5dd23b285 100755 --- a/doc/release/upcoming_changes/17727.performance.rst +++ b/doc/release/upcoming_changes/17727.performance.rst @@ -1,7 +1,7 @@ -Improved performance in integer division of NumPy arrays
---------------------------------------------------------
-Integer division of NumPy arrays now uses `libdivide <https://libdivide.com/>`
-when the divisor is a constant. With the usage of libdivde and
-other minor optimizations, there is a large speedup.
-The ``//`` operator and ``np.floor_divide`` makes use
-of the new changes.
+Improved performance in integer division of NumPy arrays +-------------------------------------------------------- +Integer division of NumPy arrays now uses `libdivide <https://libdivide.com/>` +when the divisor is a constant. With the usage of libdivde and +other minor optimizations, there is a large speedup. +The ``//`` operator and ``np.floor_divide`` makes use +of the new changes. diff --git a/doc/source/_static/numpylogo.svg b/doc/source/_static/numpylogo.svg index 5f0dac700..a566851b8 100644 --- a/doc/source/_static/numpylogo.svg +++ b/doc/source/_static/numpylogo.svg @@ -1,23 +1,23 @@ -<?xml version="1.0" standalone="no"?>
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-
-##############################################################################
-How to write a NumPy how-to
-##############################################################################
-
-How-tos get straight to the point -- they
-
- - answer a focused question, or
- - narrow a broad question into focused questions that the user can
- choose among.
-
-******************************************************************************
-A stranger has asked for directions...
-******************************************************************************
-
-**"I need to refuel my car."**
-
-******************************************************************************
-Give a brief but explicit answer
-******************************************************************************
-
- - `"Three kilometers/miles, take a right at Hayseed Road, it's on your left."`
-
-Add helpful details for newcomers ("Hayseed Road", even though it's the only
-turnoff at three km/mi). But not irrelevant ones:
-
- - Don't also give directions from Route 7.
- - Don't explain why the town has only one filling station.
-
-If there's related background (tutorial, explanation, reference, alternative
-approach), bring it to the user's attention with a link ("Directions from Route 7,"
-"Why so few filling stations?").
-
-
-******************************************************************************
-Delegate
-******************************************************************************
-
- - `"Three km/mi, take a right at Hayseed Road, follow the signs."`
-
-If the information is already documented and succinct enough for a how-to,
-just link to it, possibly after an introduction ("Three km/mi, take a right").
-
-******************************************************************************
-If the question is broad, narrow and redirect it
-******************************************************************************
-
- **"I want to see the sights."**
-
-The `See the sights` how-to should link to a set of narrower how-tos:
-
-- Find historic buildings
-- Find scenic lookouts
-- Find the town center
-
-and these might in turn link to still narrower how-tos -- so the town center
-page might link to
-
- - Find the court house
- - Find city hall
-
-By organizing how-tos this way, you not only display the options for people
-who need to narrow their question, you also have provided answers for users
-who start with narrower questions ("I want to see historic buildings," "Which
-way to city hall?").
-
-******************************************************************************
-If there are many steps, break them up
-******************************************************************************
-
-If a how-to has many steps:
-
- - Consider breaking a step out into an individual how-to and linking to it.
- - Include subheadings. They help readers grasp what's coming and return
- where they left off.
-
-******************************************************************************
-Why write how-tos when there's Stack Overflow, Reddit, Gitter...?
-******************************************************************************
-
- - We have authoritative answers.
- - How-tos make the site less forbidding to non-experts.
- - How-tos bring people into the site and help them discover other information
- that's here .
- - Creating how-tos helps us see NumPy usability through new eyes.
-
-******************************************************************************
-Aren't how-tos and tutorials the same thing?
-******************************************************************************
-
-People use the terms "how-to" and "tutorial" interchangeably, but we draw a
-distinction, following Daniele Procida's `taxonomy of documentation`_.
-
- .. _`taxonomy of documentation`: https://documentation.divio.com/
-
-Documentation needs to meet users where they are. `How-tos` offer get-it-done
-information; the user wants steps to copy and doesn't necessarily want to
-understand NumPy. `Tutorials` are warm-fuzzy information; the user wants a
-feel for some aspect of NumPy (and again, may or may not care about deeper
-knowledge).
-
-We distinguish both tutorials and how-tos from `Explanations`, which are
-deep dives intended to give understanding rather than immediate assistance,
-and `References`, which give complete, autoritative data on some concrete
-part of NumPy (like its API) but aren't obligated to paint a broader picture.
-
-For more on tutorials, see the `tutorial how-to`_.
-
-.. _`tutorial how-to`: https://github.com/numpy/numpy-tutorials/blob/master/tutorial_style.ipynb
-
-
-******************************************************************************
-Is this page an example of a how-to?
-******************************************************************************
-
-Yes -- until the sections with question-mark headings; they explain rather
-than giving directions. In a how-to, those would be links.
+.. _how-to-how-to: + +############################################################################## +How to write a NumPy how-to +############################################################################## + +How-tos get straight to the point -- they + + - answer a focused question, or + - narrow a broad question into focused questions that the user can + choose among. + +****************************************************************************** +A stranger has asked for directions... +****************************************************************************** + +**"I need to refuel my car."** + +****************************************************************************** +Give a brief but explicit answer +****************************************************************************** + + - `"Three kilometers/miles, take a right at Hayseed Road, it's on your left."` + +Add helpful details for newcomers ("Hayseed Road", even though it's the only +turnoff at three km/mi). But not irrelevant ones: + + - Don't also give directions from Route 7. + - Don't explain why the town has only one filling station. + +If there's related background (tutorial, explanation, reference, alternative +approach), bring it to the user's attention with a link ("Directions from Route 7," +"Why so few filling stations?"). + + +****************************************************************************** +Delegate +****************************************************************************** + + - `"Three km/mi, take a right at Hayseed Road, follow the signs."` + +If the information is already documented and succinct enough for a how-to, +just link to it, possibly after an introduction ("Three km/mi, take a right"). + +****************************************************************************** +If the question is broad, narrow and redirect it +****************************************************************************** + + **"I want to see the sights."** + +The `See the sights` how-to should link to a set of narrower how-tos: + +- Find historic buildings +- Find scenic lookouts +- Find the town center + +and these might in turn link to still narrower how-tos -- so the town center +page might link to + + - Find the court house + - Find city hall + +By organizing how-tos this way, you not only display the options for people +who need to narrow their question, you also have provided answers for users +who start with narrower questions ("I want to see historic buildings," "Which +way to city hall?"). + +****************************************************************************** +If there are many steps, break them up +****************************************************************************** + +If a how-to has many steps: + + - Consider breaking a step out into an individual how-to and linking to it. + - Include subheadings. They help readers grasp what's coming and return + where they left off. + +****************************************************************************** +Why write how-tos when there's Stack Overflow, Reddit, Gitter...? +****************************************************************************** + + - We have authoritative answers. + - How-tos make the site less forbidding to non-experts. + - How-tos bring people into the site and help them discover other information + that's here . + - Creating how-tos helps us see NumPy usability through new eyes. + +****************************************************************************** +Aren't how-tos and tutorials the same thing? +****************************************************************************** + +People use the terms "how-to" and "tutorial" interchangeably, but we draw a +distinction, following Daniele Procida's `taxonomy of documentation`_. + + .. _`taxonomy of documentation`: https://documentation.divio.com/ + +Documentation needs to meet users where they are. `How-tos` offer get-it-done +information; the user wants steps to copy and doesn't necessarily want to +understand NumPy. `Tutorials` are warm-fuzzy information; the user wants a +feel for some aspect of NumPy (and again, may or may not care about deeper +knowledge). + +We distinguish both tutorials and how-tos from `Explanations`, which are +deep dives intended to give understanding rather than immediate assistance, +and `References`, which give complete, autoritative data on some concrete +part of NumPy (like its API) but aren't obligated to paint a broader picture. + +For more on tutorials, see the `tutorial how-to`_. + +.. _`tutorial how-to`: https://github.com/numpy/numpy-tutorials/blob/master/tutorial_style.ipynb + + +****************************************************************************** +Is this page an example of a how-to? +****************************************************************************** + +Yes -- until the sections with question-mark headings; they explain rather +than giving directions. In a how-to, those would be links. diff --git a/doc/source/user/how-to-io.rst b/doc/source/user/how-to-io.rst index ca9fc41f0..d238ccbb6 100644 --- a/doc/source/user/how-to-io.rst +++ b/doc/source/user/how-to-io.rst @@ -1,328 +1,328 @@ -.. _how-to-io:
-
-##############################################################################
-Reading and writing files
-##############################################################################
-
-This page tackles common applications; for the full collection of I/O
-routines, see :ref:`routines.io`.
-
-
-******************************************************************************
-Reading text and CSV_ files
-******************************************************************************
-
-.. _CSV: https://en.wikipedia.org/wiki/Comma-separated_values
-
-With no missing values
-==============================================================================
-
-Use :func:`numpy.loadtxt`.
-
-With missing values
-==============================================================================
-
-Use :func:`numpy.genfromtxt`.
-
-:func:`numpy.genfromtxt` will either
-
- - return a :ref:`masked array<maskedarray.generic>`
- **masking out missing values** (if ``usemask=True``), or
-
- - **fill in the missing value** with the value specified in
- ``filling_values`` (default is ``np.nan`` for float, -1 for int).
-
-With non-whitespace delimiters
-------------------------------------------------------------------------------
-::
-
- >>> print(open("csv.txt").read()) # doctest: +SKIP
- 1, 2, 3
- 4,, 6
- 7, 8, 9
-
-
-Masked-array output
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-::
-
- >>> np.genfromtxt("csv.txt", delimiter=",", usemask=True) # doctest: +SKIP
- masked_array(
- data=[[1.0, 2.0, 3.0],
- [4.0, --, 6.0],
- [7.0, 8.0, 9.0]],
- mask=[[False, False, False],
- [False, True, False],
- [False, False, False]],
- fill_value=1e+20)
-
-Array output
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-::
-
- >>> np.genfromtxt("csv.txt", delimiter=",") # doctest: +SKIP
- array([[ 1., 2., 3.],
- [ 4., nan, 6.],
- [ 7., 8., 9.]])
-
-Array output, specified fill-in value
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-::
-
- >>> np.genfromtxt("csv.txt", delimiter=",", dtype=np.int8, filling_values=99) # doctest: +SKIP
- array([[ 1, 2, 3],
- [ 4, 99, 6],
- [ 7, 8, 9]], dtype=int8)
-
-Whitespace-delimited
--------------------------------------------------------------------------------
-
-:func:`numpy.genfromtxt` can also parse whitespace-delimited data files
-that have missing values if
-
-* **Each field has a fixed width**: Use the width as the `delimiter` argument.
- ::
-
- # File with width=4. The data does not have to be justified (for example,
- # the 2 in row 1), the last column can be less than width (for example, the 6
- # in row 2), and no delimiting character is required (for instance 8888 and 9
- # in row 3)
-
- >>> f = open("fixedwidth.txt").read() # doctest: +SKIP
- >>> print(f) # doctest: +SKIP
- 1 2 3
- 44 6
- 7 88889
-
- # Showing spaces as ^
- >>> print(f.replace(" ","^")) # doctest: +SKIP
- 1^^^2^^^^^^3
- 44^^^^^^6
- 7^^^88889
-
- >>> np.genfromtxt("fixedwidth.txt", delimiter=4) # doctest: +SKIP
- array([[1.000e+00, 2.000e+00, 3.000e+00],
- [4.400e+01, nan, 6.000e+00],
- [7.000e+00, 8.888e+03, 9.000e+00]])
-
-* **A special value (e.g. "x") indicates a missing field**: Use it as the
- `missing_values` argument.
- ::
-
- >>> print(open("nan.txt").read()) # doctest: +SKIP
- 1 2 3
- 44 x 6
- 7 8888 9
-
- >>> np.genfromtxt("nan.txt", missing_values="x") # doctest: +SKIP
- array([[1.000e+00, 2.000e+00, 3.000e+00],
- [4.400e+01, nan, 6.000e+00],
- [7.000e+00, 8.888e+03, 9.000e+00]])
-
-* **You want to skip the rows with missing values**: Set
- `invalid_raise=False`.
- ::
-
- >>> print(open("skip.txt").read()) # doctest: +SKIP
- 1 2 3
- 44 6
- 7 888 9
-
- >>> np.genfromtxt("skip.txt", invalid_raise=False) # doctest: +SKIP
- __main__:1: ConversionWarning: Some errors were detected !
- Line #2 (got 2 columns instead of 3)
- array([[ 1., 2., 3.],
- [ 7., 888., 9.]])
-
-
-* **The delimiter whitespace character is different from the whitespace that
- indicates missing data**. For instance, if columns are delimited by ``\t``,
- then missing data will be recognized if it consists of one
- or more spaces.
- ::
-
- >>> f = open("tabs.txt").read() # doctest: +SKIP
- >>> print(f) # doctest: +SKIP
- 1 2 3
- 44 6
- 7 888 9
-
- # Tabs vs. spaces
- >>> print(f.replace("\t","^")) # doctest: +SKIP
- 1^2^3
- 44^ ^6
- 7^888^9
-
- >>> np.genfromtxt("tabs.txt", delimiter="\t", missing_values=" +") # doctest: +SKIP
- array([[ 1., 2., 3.],
- [ 44., nan, 6.],
- [ 7., 888., 9.]])
-
-******************************************************************************
-Read a file in .npy or .npz format
-******************************************************************************
-
-Choices:
-
- - Use :func:`numpy.load`. It can read files generated by any of
- :func:`numpy.save`, :func:`numpy.savez`, or :func:`numpy.savez_compressed`.
-
- - Use memory mapping. See `numpy.lib.format.open_memmap`.
-
-******************************************************************************
-Write to a file to be read back by NumPy
-******************************************************************************
-
-Binary
-===============================================================================
-
-Use
-:func:`numpy.save`, or to store multiple arrays :func:`numpy.savez`
-or :func:`numpy.savez_compressed`.
-
-For :ref:`security and portability <how-to-io-pickle-file>`, set
-``allow_pickle=False`` unless the dtype contains Python objects, which
-requires pickling.
-
-Masked arrays :any:`can't currently be saved <MaskedArray.tofile>`,
-nor can other arbitrary array subclasses.
-
-Human-readable
-==============================================================================
-
-:func:`numpy.save` and :func:`numpy.savez` create binary files. To **write a
-human-readable file**, use :func:`numpy.savetxt`. The array can only be 1- or
-2-dimensional, and there's no ` savetxtz` for multiple files.
-
-Large arrays
-==============================================================================
-
-See :ref:`how-to-io-large-arrays`.
-
-******************************************************************************
-Read an arbitrarily formatted binary file ("binary blob")
-******************************************************************************
-
-Use a :doc:`structured array <basics.rec>`.
-
-**Example:**
-
-The ``.wav`` file header is a 44-byte block preceding ``data_size`` bytes of the
-actual sound data::
-
- chunk_id "RIFF"
- chunk_size 4-byte unsigned little-endian integer
- format "WAVE"
- fmt_id "fmt "
- fmt_size 4-byte unsigned little-endian integer
- audio_fmt 2-byte unsigned little-endian integer
- num_channels 2-byte unsigned little-endian integer
- sample_rate 4-byte unsigned little-endian integer
- byte_rate 4-byte unsigned little-endian integer
- block_align 2-byte unsigned little-endian integer
- bits_per_sample 2-byte unsigned little-endian integer
- data_id "data"
- data_size 4-byte unsigned little-endian integer
-
-The ``.wav`` file header as a NumPy structured dtype::
-
- wav_header_dtype = np.dtype([
- ("chunk_id", (bytes, 4)), # flexible-sized scalar type, item size 4
- ("chunk_size", "<u4"), # little-endian unsigned 32-bit integer
- ("format", "S4"), # 4-byte string, alternate spelling of (bytes, 4)
- ("fmt_id", "S4"),
- ("fmt_size", "<u4"),
- ("audio_fmt", "<u2"), #
- ("num_channels", "<u2"), # .. more of the same ...
- ("sample_rate", "<u4"), #
- ("byte_rate", "<u4"),
- ("block_align", "<u2"),
- ("bits_per_sample", "<u2"),
- ("data_id", "S4"),
- ("data_size", "<u4"),
- #
- # the sound data itself cannot be represented here:
- # it does not have a fixed size
- ])
-
- header = np.fromfile(f, dtype=wave_header_dtype, count=1)[0]
-
-This ``.wav`` example is for illustration; to read a ``.wav`` file in real
-life, use Python's built-in module :mod:`wave`.
-
-(Adapted from Pauli Virtanen, :ref:`advanced_numpy`, licensed
-under `CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/>`_.)
-
-.. _how-to-io-large-arrays:
-
-******************************************************************************
-Write or read large arrays
-******************************************************************************
-
-**Arrays too large to fit in memory** can be treated like ordinary in-memory
-arrays using memory mapping.
-
-- Raw array data written with :func:`numpy.ndarray.tofile` or
- :func:`numpy.ndarray.tobytes` can be read with :func:`numpy.memmap`::
-
- array = numpy.memmap("mydata/myarray.arr", mode="r", dtype=np.int16, shape=(1024, 1024))
-
-- Files output by :func:`numpy.save` (that is, using the numpy format) can be read
- using :func:`numpy.load` with the ``mmap_mode`` keyword argument::
-
- large_array[some_slice] = np.load("path/to/small_array", mmap_mode="r")
-
-Memory mapping lacks features like data chunking and compression; more
-full-featured formats and libraries usable with NumPy include:
-
-* **HDF5**: `h5py <https://www.h5py.org/>`_ or `PyTables <https://www.pytables.org/>`_.
-* **Zarr**: `here <https://zarr.readthedocs.io/en/stable/tutorial.html#reading-and-writing-data>`_.
-* **NetCDF**: :class:`scipy.io.netcdf_file`.
-
-For tradeoffs among memmap, Zarr, and HDF5, see
-`pythonspeed.com <https://pythonspeed.com/articles/mmap-vs-zarr-hdf5/>`_.
-
-******************************************************************************
-Write files for reading by other (non-NumPy) tools
-******************************************************************************
-
-Formats for **exchanging data** with other tools include HDF5, Zarr, and
-NetCDF (see :ref:`how-to-io-large-arrays`).
-
-******************************************************************************
-Write or read a JSON file
-******************************************************************************
-
-NumPy arrays are **not** directly
-`JSON serializable <https://github.com/numpy/numpy/issues/12481>`_.
-
-
-.. _how-to-io-pickle-file:
-
-******************************************************************************
-Save/restore using a pickle file
-******************************************************************************
-
-Avoid when possible; :doc:`pickles <python:library/pickle>` are not secure
-against erroneous or maliciously constructed data.
-
-Use :func:`numpy.save` and :func:`numpy.load`. Set ``allow_pickle=False``,
-unless the array dtype includes Python objects, in which case pickling is
-required.
-
-******************************************************************************
-Convert from a pandas DataFrame to a NumPy array
-******************************************************************************
-
-See :meth:`pandas.DataFrame.to_numpy`.
-
-******************************************************************************
- Save/restore using `~numpy.ndarray.tofile` and `~numpy.fromfile`
-******************************************************************************
-
-In general, prefer :func:`numpy.save` and :func:`numpy.load`.
-
-:func:`numpy.ndarray.tofile` and :func:`numpy.fromfile` lose information on
-endianness and precision and so are unsuitable for anything but scratch
-storage.
-
+.. _how-to-io: + +############################################################################## +Reading and writing files +############################################################################## + +This page tackles common applications; for the full collection of I/O +routines, see :ref:`routines.io`. + + +****************************************************************************** +Reading text and CSV_ files +****************************************************************************** + +.. _CSV: https://en.wikipedia.org/wiki/Comma-separated_values + +With no missing values +============================================================================== + +Use :func:`numpy.loadtxt`. + +With missing values +============================================================================== + +Use :func:`numpy.genfromtxt`. + +:func:`numpy.genfromtxt` will either + + - return a :ref:`masked array<maskedarray.generic>` + **masking out missing values** (if ``usemask=True``), or + + - **fill in the missing value** with the value specified in + ``filling_values`` (default is ``np.nan`` for float, -1 for int). + +With non-whitespace delimiters +------------------------------------------------------------------------------ +:: + + >>> print(open("csv.txt").read()) # doctest: +SKIP + 1, 2, 3 + 4,, 6 + 7, 8, 9 + + +Masked-array output +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +:: + + >>> np.genfromtxt("csv.txt", delimiter=",", usemask=True) # doctest: +SKIP + masked_array( + data=[[1.0, 2.0, 3.0], + [4.0, --, 6.0], + [7.0, 8.0, 9.0]], + mask=[[False, False, False], + [False, True, False], + [False, False, False]], + fill_value=1e+20) + +Array output +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +:: + + >>> np.genfromtxt("csv.txt", delimiter=",") # doctest: +SKIP + array([[ 1., 2., 3.], + [ 4., nan, 6.], + [ 7., 8., 9.]]) + +Array output, specified fill-in value +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +:: + + >>> np.genfromtxt("csv.txt", delimiter=",", dtype=np.int8, filling_values=99) # doctest: +SKIP + array([[ 1, 2, 3], + [ 4, 99, 6], + [ 7, 8, 9]], dtype=int8) + +Whitespace-delimited +------------------------------------------------------------------------------- + +:func:`numpy.genfromtxt` can also parse whitespace-delimited data files +that have missing values if + +* **Each field has a fixed width**: Use the width as the `delimiter` argument. + :: + + # File with width=4. The data does not have to be justified (for example, + # the 2 in row 1), the last column can be less than width (for example, the 6 + # in row 2), and no delimiting character is required (for instance 8888 and 9 + # in row 3) + + >>> f = open("fixedwidth.txt").read() # doctest: +SKIP + >>> print(f) # doctest: +SKIP + 1 2 3 + 44 6 + 7 88889 + + # Showing spaces as ^ + >>> print(f.replace(" ","^")) # doctest: +SKIP + 1^^^2^^^^^^3 + 44^^^^^^6 + 7^^^88889 + + >>> np.genfromtxt("fixedwidth.txt", delimiter=4) # doctest: +SKIP + array([[1.000e+00, 2.000e+00, 3.000e+00], + [4.400e+01, nan, 6.000e+00], + [7.000e+00, 8.888e+03, 9.000e+00]]) + +* **A special value (e.g. "x") indicates a missing field**: Use it as the + `missing_values` argument. + :: + + >>> print(open("nan.txt").read()) # doctest: +SKIP + 1 2 3 + 44 x 6 + 7 8888 9 + + >>> np.genfromtxt("nan.txt", missing_values="x") # doctest: +SKIP + array([[1.000e+00, 2.000e+00, 3.000e+00], + [4.400e+01, nan, 6.000e+00], + [7.000e+00, 8.888e+03, 9.000e+00]]) + +* **You want to skip the rows with missing values**: Set + `invalid_raise=False`. + :: + + >>> print(open("skip.txt").read()) # doctest: +SKIP + 1 2 3 + 44 6 + 7 888 9 + + >>> np.genfromtxt("skip.txt", invalid_raise=False) # doctest: +SKIP + __main__:1: ConversionWarning: Some errors were detected ! + Line #2 (got 2 columns instead of 3) + array([[ 1., 2., 3.], + [ 7., 888., 9.]]) + + +* **The delimiter whitespace character is different from the whitespace that + indicates missing data**. For instance, if columns are delimited by ``\t``, + then missing data will be recognized if it consists of one + or more spaces. + :: + + >>> f = open("tabs.txt").read() # doctest: +SKIP + >>> print(f) # doctest: +SKIP + 1 2 3 + 44 6 + 7 888 9 + + # Tabs vs. spaces + >>> print(f.replace("\t","^")) # doctest: +SKIP + 1^2^3 + 44^ ^6 + 7^888^9 + + >>> np.genfromtxt("tabs.txt", delimiter="\t", missing_values=" +") # doctest: +SKIP + array([[ 1., 2., 3.], + [ 44., nan, 6.], + [ 7., 888., 9.]]) + +****************************************************************************** +Read a file in .npy or .npz format +****************************************************************************** + +Choices: + + - Use :func:`numpy.load`. It can read files generated by any of + :func:`numpy.save`, :func:`numpy.savez`, or :func:`numpy.savez_compressed`. + + - Use memory mapping. See `numpy.lib.format.open_memmap`. + +****************************************************************************** +Write to a file to be read back by NumPy +****************************************************************************** + +Binary +=============================================================================== + +Use +:func:`numpy.save`, or to store multiple arrays :func:`numpy.savez` +or :func:`numpy.savez_compressed`. + +For :ref:`security and portability <how-to-io-pickle-file>`, set +``allow_pickle=False`` unless the dtype contains Python objects, which +requires pickling. + +Masked arrays :any:`can't currently be saved <MaskedArray.tofile>`, +nor can other arbitrary array subclasses. + +Human-readable +============================================================================== + +:func:`numpy.save` and :func:`numpy.savez` create binary files. To **write a +human-readable file**, use :func:`numpy.savetxt`. The array can only be 1- or +2-dimensional, and there's no ` savetxtz` for multiple files. + +Large arrays +============================================================================== + +See :ref:`how-to-io-large-arrays`. + +****************************************************************************** +Read an arbitrarily formatted binary file ("binary blob") +****************************************************************************** + +Use a :doc:`structured array <basics.rec>`. + +**Example:** + +The ``.wav`` file header is a 44-byte block preceding ``data_size`` bytes of the +actual sound data:: + + chunk_id "RIFF" + chunk_size 4-byte unsigned little-endian integer + format "WAVE" + fmt_id "fmt " + fmt_size 4-byte unsigned little-endian integer + audio_fmt 2-byte unsigned little-endian integer + num_channels 2-byte unsigned little-endian integer + sample_rate 4-byte unsigned little-endian integer + byte_rate 4-byte unsigned little-endian integer + block_align 2-byte unsigned little-endian integer + bits_per_sample 2-byte unsigned little-endian integer + data_id "data" + data_size 4-byte unsigned little-endian integer + +The ``.wav`` file header as a NumPy structured dtype:: + + wav_header_dtype = np.dtype([ + ("chunk_id", (bytes, 4)), # flexible-sized scalar type, item size 4 + ("chunk_size", "<u4"), # little-endian unsigned 32-bit integer + ("format", "S4"), # 4-byte string, alternate spelling of (bytes, 4) + ("fmt_id", "S4"), + ("fmt_size", "<u4"), + ("audio_fmt", "<u2"), # + ("num_channels", "<u2"), # .. more of the same ... + ("sample_rate", "<u4"), # + ("byte_rate", "<u4"), + ("block_align", "<u2"), + ("bits_per_sample", "<u2"), + ("data_id", "S4"), + ("data_size", "<u4"), + # + # the sound data itself cannot be represented here: + # it does not have a fixed size + ]) + + header = np.fromfile(f, dtype=wave_header_dtype, count=1)[0] + +This ``.wav`` example is for illustration; to read a ``.wav`` file in real +life, use Python's built-in module :mod:`wave`. + +(Adapted from Pauli Virtanen, :ref:`advanced_numpy`, licensed +under `CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/>`_.) + +.. _how-to-io-large-arrays: + +****************************************************************************** +Write or read large arrays +****************************************************************************** + +**Arrays too large to fit in memory** can be treated like ordinary in-memory +arrays using memory mapping. + +- Raw array data written with :func:`numpy.ndarray.tofile` or + :func:`numpy.ndarray.tobytes` can be read with :func:`numpy.memmap`:: + + array = numpy.memmap("mydata/myarray.arr", mode="r", dtype=np.int16, shape=(1024, 1024)) + +- Files output by :func:`numpy.save` (that is, using the numpy format) can be read + using :func:`numpy.load` with the ``mmap_mode`` keyword argument:: + + large_array[some_slice] = np.load("path/to/small_array", mmap_mode="r") + +Memory mapping lacks features like data chunking and compression; more +full-featured formats and libraries usable with NumPy include: + +* **HDF5**: `h5py <https://www.h5py.org/>`_ or `PyTables <https://www.pytables.org/>`_. +* **Zarr**: `here <https://zarr.readthedocs.io/en/stable/tutorial.html#reading-and-writing-data>`_. +* **NetCDF**: :class:`scipy.io.netcdf_file`. + +For tradeoffs among memmap, Zarr, and HDF5, see +`pythonspeed.com <https://pythonspeed.com/articles/mmap-vs-zarr-hdf5/>`_. + +****************************************************************************** +Write files for reading by other (non-NumPy) tools +****************************************************************************** + +Formats for **exchanging data** with other tools include HDF5, Zarr, and +NetCDF (see :ref:`how-to-io-large-arrays`). + +****************************************************************************** +Write or read a JSON file +****************************************************************************** + +NumPy arrays are **not** directly +`JSON serializable <https://github.com/numpy/numpy/issues/12481>`_. + + +.. _how-to-io-pickle-file: + +****************************************************************************** +Save/restore using a pickle file +****************************************************************************** + +Avoid when possible; :doc:`pickles <python:library/pickle>` are not secure +against erroneous or maliciously constructed data. + +Use :func:`numpy.save` and :func:`numpy.load`. Set ``allow_pickle=False``, +unless the array dtype includes Python objects, in which case pickling is +required. + +****************************************************************************** +Convert from a pandas DataFrame to a NumPy array +****************************************************************************** + +See :meth:`pandas.DataFrame.to_numpy`. + +****************************************************************************** + Save/restore using `~numpy.ndarray.tofile` and `~numpy.fromfile` +****************************************************************************** + +In general, prefer :func:`numpy.save` and :func:`numpy.load`. + +:func:`numpy.ndarray.tofile` and :func:`numpy.fromfile` lose information on +endianness and precision and so are unsuitable for anything but scratch +storage. + diff --git a/doc/source/user/install.rst b/doc/source/user/install.rst index e05cee2f1..b9425701f 100644 --- a/doc/source/user/install.rst +++ b/doc/source/user/install.rst @@ -1,7 +1,7 @@ -:orphan:
-
-****************
-Installing NumPy
-****************
-
+:orphan: + +**************** +Installing NumPy +**************** + See `Installing NumPy <https://numpy.org/install/>`_.
\ No newline at end of file diff --git a/doc/source/user/who_covid_19_sit_rep_time_series.csv b/doc/source/user/who_covid_19_sit_rep_time_series.csv index ebf670b8c..8ad5c2c23 100644 --- a/doc/source/user/who_covid_19_sit_rep_time_series.csv +++ b/doc/source/user/who_covid_19_sit_rep_time_series.csv @@ -1,115 +1,115 @@ -Province/States,Country/Region,WHO region,1/21/20,1/22/20,1/23/20,1/24/20,1/25/20,1/26/20,1/27/20,1/28/20,1/29/20,1/30/20,1/31/20,2/1/20,2/2/20,2/3/20,2/4/20,2/5/20,2/6/20,2/7/20,2/8/20,2/9/20,2/10/20,2/11/20,2/12/20,2/13/20,2/14/20,2/15/20,2/16/20,2/17/20,2/18/20,2/19/20,2/20/20,2/21/20,2/22/20,2/23/20,2/24/20,2/25/20,2/26/20,2/27/20,2/28/20,2/29/20,3/1/20,3/2/20,3/3/20
-Confirmed,Globally,,282,314,581,846,1320,2014,2798,4593,6065,7818,9826,11953,14557,17391,20630,24554,28276,31481,34886,37558,40554,43103,45171,46997,49053,50580,51857,71429,73332,75204,75748,76769,77794,78811,79331,80239,81109,82294,83652,85403,87137,88948,90870
-Confirmed,Mainland China,Western Pacific Region,278,309,571,830,1297,1985,2741,4537,5997,7736,9720,11821,14411,17238,20471,24363,28060,31211,34598,37251,40235,42708,44730,46550,48548,50054,51174,70635,72528,74280,74675,75569,76392,77042,77262,77780,78191,78630,78961,79394,79968,80174,80304
-Confirmed,Outside of China,,4,5,10,16,23,29,57,56,68,82,106,132,146,153,159,191,216,270,288,307,319,395,441,447,505,526,683,794,804,924,1073,1200,1402,1769,2069,2459,2918,3664,4691,6009,7169,8774,10566
-Suspected,Mainland China,Western Pacific Region,,,,,,,5794,6973,9239,12167,15238,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-Severe,Mainland China,Western Pacific Region,,,,,,,461,976,1239,1370,1527,1795,2110,2296,2788,3219,3859,4821,6101,6188,6484,7333,8204,,,,,,,,,,,,,,,,,,,,
-Deaths,Mainland China,Western Pacific Region,,,,,,,80,106,132,170,213,259,304,361,425,491,564,637,723,812,909,1017,1114,1260,1381,1524,1666,1772,1870,2006,2121,2239,2348,2445,2595,2666,2718,2747,2791,2838,2873,2915,2946
-Hubei ,China,Western Pacific Region,258,270,375,375,,,,,,,,7153,9074,11177,13522,16678,19665,22112,24953,27100,29631,31728,33366,34874,51968,54406,56249,58182,59989,61682,62031,62662,63454,64084,64287,64786,65187,65596,65914,66337,66907,67103,67217
-Guangdong,China,Western Pacific Region,14,17,26,32,,,,,,,,520,604,683,797,870,944,1018,1075,1120,1151,1177,1219,1241,1261,1295,1316,1322,1328,1331,1332,1333,1339,1342,1345,1347,1347,1347,1348,1349,1349,1350,1350
-Henan,China,Western Pacific Region,,1,1,1,,,,,,,,422,493,566,675,764,851,914,981,1033,1073,1105,1135,1169,1184,1212,1231,1246,1257,1262,1265,1267,1270,1271,1271,1271,1271,1272,1272,1272,1272,1272,1272
-Zhejiang,China,Western Pacific Region,,5,5,5,,,,,,,,599,661,724,829,895,954,1006,1048,1075,1104,1117,1131,1145,1155,1162,1167,1171,1172,1173,1175,1203,1205,1205,1205,1205,1205,1205,1205,1205,1205,1206,1213
-Hunan,China,Western Pacific Region,,1,1,1,,,,,,,,389,463,521,593,661,711,772,803,838,879,912,946,968,988,1001,1004,1006,1007,1008,1010,1011,1013,1016,1016,1016,1016,1017,1017,1018,1018,1018,1018
-Anhui,China,Western Pacific Region,,,,,,,,,,,,297,340,408,480,530,591,665,733,779,830,860,889,910,934,950,962,973,982,986,987,988,989,989,989,989,989,989,990,990,990,990,990
-Jiangxi,China,Western Pacific Region,,1,2,2,,,,,,,,286,333,391,476,548,600,661,698,740,771,804,844,872,900,913,925,930,933,934,934,934,934,934,934,934,934,934,935,935,935,935,935
-Shandong,China,Western Pacific Region,,1,1,1,,,,,,,,202,225,246,270,298,343,379,407,435,459,486,497,506,519,530,537,541,543,544,546,748,750,754,755,755,756,756,756,756,756,758,758
-Jiangsu,China,Western Pacific Region,,,,,,,,,,,,202,231,271,308,341,373,408,439,468,492,515,543,570,593,604,617,626,629,631,631,631,631,631,631,631,631,631,631,631,631,631,631
-Chongqing,China,Western Pacific Region,,1,5,5,,,,,,,,238,262,300,337,366,389,411,426,446,468,486,505,518,529,537,544,551,553,555,560,567,572,573,575,576,576,576,576,576,576,576,576
-Sichuan,China,Western Pacific Region,,1,2,2,,,,,,,,207,236,254,282,301,321,344,363,386,405,417,436,451,463,470,481,495,508,514,520,525,526,526,527,529,531,534,538,538,538,538,538
-Heilongjiang,China,Western Pacific Region,,,,,,,,,,,,80,95,118,155,190,227,277,282,307,331,360,378,395,418,425,445,457,464,470,476,479,479,480,480,480,480,480,480,480,480,480,480
-Beijing,China,Western Pacific Region,5,5,10,10,,,,,,,,156,183,212,228,253,274,297,315,326,337,342,352,366,372,375,380,381,387,393,395,396,399,399,399,400,400,410,410,411,413,414,414
-Shanghai,China,Western Pacific Region,1,2,9,9,,,,,,,,153,177,193,208,233,254,269,281,292,295,302,306,313,318,326,328,331,333,333,333,334,334,335,335,335,336,337,337,337,337,337,338
-Hebei,China,Western Pacific Region,,,,,,,,,,,,96,104,113,126,135,157,171,195,206,218,239,251,265,283,291,300,301,302,306,307,308,309,311,311,311,312,317,318,318,318,318,318
-Fujian,China,Western Pacific Region,,,,,,,,,,,,144,159,179,194,205,215,224,239,250,261,267,272,279,281,285,287,290,292,293,293,293,293,293,293,294,294,296,296,296,296,296,296
-Guangxi,China,Western Pacific Region,,,,,,,,,,,,100,111,127,139,150,168,172,183,195,210,215,222,222,226,235,237,238,242,244,245,246,249,249,251,252,252,252,252,252,252,252,252
-Shaanxi,China,Western Pacific Region,,,,,,,,,,,,101,116,128,142,165,173,184,195,208,213,219,225,229,230,232,236,240,240,242,245,245,245,245,245,245,245,245,245,245,245,245,245
-Yunnan,China,Western Pacific Region,,1,1,1,,,,,,,,91,99,109,117,122,128,135,138,140,141,149,154,155,162,168,169,171,172,172,172,174,174,174,174,174,174,174,174,174,174,174,174
-Hainan,China,Western Pacific Region,,,,,,,,,,,,57,63,70,79,89,100,111,123,128,136,142,145,157,157,162,162,162,163,163,168,168,168,168,168,168,168,168,168,168,168,168,168
-Guizhou,China,Western Pacific Region,,,,,,,,,,,,29,38,46,56,64,69,77,89,96,109,118,131,135,140,143,144,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146
-Tianjin,China,Western Pacific Region,,2,2,2,,,,,,,,34,40,49,63,67,70,94,81,88,91,96,106,112,119,120,122,124,125,128,130,131,133,135,135,135,135,135,136,136,136,136,136
-Shanxi,China,Western Pacific Region,,,,,,,,,,,,47,56,66,74,81,90,96,104,115,119,122,124,126,126,127,128,129,130,131,131,132,132,132,132,133,133,133,133,133,133,133,133
-Liaoning,China,Western Pacific Region,,,,,,,,,,,,60,64,70,74,81,89,94,99,105,107,108,111,116,117,119,120,121,121,121,121,121,121,121,121,121,121,121,121,121,122,122,125
-Hong Kong,China,Western Pacific Region,,,1,2,5,5,8,8,8,10,12,13,14,15,15,18,21,24,26,26,36,42,49,50,53,56,56,57,60,62,65,68,68,70,74,81,85,91,93,94,95,98,101
-Jilin,China,Western Pacific Region,,,,,,,,,,,,17,21,31,42,54,59,65,69,78,80,81,83,84,86,88,88,89,89,90,91,91,91,91,93,93,93,93,93,93,93,93,93
-Gansu,China,Western Pacific Region,,,,,,,,,,,,35,45,51,56,57,62,70,71,81,85,86,86,87,90,90,90,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91
-Xinjiang,China,Western Pacific Region,,,,,,,,,,,,18,23,24,29,32,36,39,42,45,49,55,59,63,65,70,71,73,76,76,76,76,76,75,76,76,76,76,76,76,76,76,76
-Inner Mongolia,China,Western Pacific Region,,,,,,,,,,,,23,26,33,37,42,46,49,50,54,58,58,60,61,63,68,70,72,73,75,75,75,75,75,75,75,75,75,75,75,75,75,75
-Ningxia,China,Western Pacific Region,,,,,,,,,,,,26,28,31,34,34,40,43,45,45,49,53,58,64,67,70,70,70,70,71,71,71,71,71,71,71,71,72,72,73,73,74,74
-Taiwan,China,Western Pacific Region,,1,1,1,3,3,4,7,8,8,9,10,10,10,10,11,11,16,16,17,18,18,18,18,18,18,18,20,22,23,24,26,26,23,28,31,32,32,34,39,39,40,42
-Qinghai,China,Western Pacific Region,,,,,,,,,,,,8,9,13,15,17,18,18,18,18,18,18,18,18,18,18,18,18,18,12,18,18,18,18,18,18,18,18,18,18,18,18,18
-Macau,China,Western Pacific Region,,,1,2,2,2,5,7,7,7,7,7,7,8,8,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10
-Xizang,China,Western Pacific Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-Unspecified*,China,Western Pacific Region,,,131,384,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-,Japan,Western Pacific Region,1,1,1,1,3,3,4,6,7,11,14,17,20,20,20,33,25,25,25,26,26,26,28,29,33,41,53,59,65,73,85,93,105,132,144,157,164,186,210,230,239,254,268
-,Republic of Korea,Western Pacific Region,1,1,1,2,2,2,4,4,4,4,11,12,15,15,16,18,23,24,24,27,27,28,28,28,28,28,29,30,31,51,104,204,346,602,763,977,1261,1766,2337,3150,3736,4212,4812
-,Thailand,South-East Asia Region,2,2,2,4,4,5,5,14,14,14,14,19,19,19,19,25,25,25,32,32,32,33,33,33,33,34,34,35,35,35,35,35,35,35,35,37,40,40,40,42,42,42,43
-,United States of America,Region of the Americas,,,1,1,2,2,5,5,5,5,6,7,8,11,11,11,12,12,12,12,12,13,13,14,15,15,15,15,15,15,15,15,35,35,35,53,53,59,59,62,62,62,64
-,Vietnam,Western Pacific Region,,,,2,2,2,2,2,2,2,5,6,7,8,9,10,10,12,13,14,14,15,15,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16
-,Singapore,Western Pacific Region,,,,1,3,3,4,7,7,10,13,16,18,18,18,24,28,30,33,40,43,45,47,50,58,67,72,75,77,81,84,85,86,89,89,90,91,93,96,98,102,106,108
-,Italy,European Region,,,,,,,,,,,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,9,76,124,229,322,400,650,888,1128,1689,2036
-,Nepal,South-East Asia Region,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-,Australia,Western Pacific Region,,,,,3,3,4,5,7,7,9,12,12,12,12,13,14,15,15,15,15,15,15,15,15,15,15,15,15,15,15,17,21,21,21,22,23,23,23,24,25,27,33
-,Malaysia,Western Pacific Region,,,,,,3,4,4,4,7,8,8,8,8,10,10,12,14,15,17,18,18,18,18,19,21,22,22,22,22,22,22,22,22,22,22,22,22,24,24,24,24,29
-,Canada,Region of the Americas,,,,,,,1,2,3,3,3,4,4,4,4,5,5,7,7,7,7,7,7,7,7,7,7,7,8,8,8,8,8,9,9,10,10,11,11,14,19,19,27
-,Cambodia,Western Pacific Region,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-,France,European Region,,,,,3,3,3,3,4,5,6,6,6,6,6,6,6,6,6,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12,18,38,57,100,100,191
-,Sri Lanka,South-East Asia Region,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-,Iran,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,5,18,28,43,61,95,141,245,388,593,978,1501
-,India,South-East Asia Region,,,,,,,,,,1,1,1,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,5
-,Germany,European Region,,,,,,,,1,4,4,5,7,8,10,12,12,12,13,14,14,14,14,16,16,16,16,16,16,16,16,16,16,16,16,16,16,18,21,26,57,57,129,157
-,Philippines,Western Pacific Region,,,,,,,,,,1,1,1,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3
-,Spain,European Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,12,25,32,45,45,114
-,United Kingdom,European Region,,,,,,,,,,,,2,2,2,2,2,2,3,3,3,4,8,8,9,9,9,9,9,9,9,9,9,9,9,9,13,13,13,16,20,23,36,39
-,Sweden,European Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,7,12,13,14,15
-,Switzerland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,6,10,18,26,30
-,Austria,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,4,5,10,10,18
-,Norway,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,4,6,15,19,25
-,Kuwait,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,8,12,43,43,45,45,56,56
-,Bahrain,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,26,33,33,38,40,47,49
-,United Arab Emirates,Eastern Mediterranean Region,,,,,,,,,4,4,4,4,5,5,5,5,5,5,7,7,7,8,8,8,8,8,8,9,9,9,9,9,11,13,13,13,13,13,19,19,19,21,21
-,Israel,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,2,2,2,3,5,7,7,10
-,Iraq,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,5,6,7,8,13,19,26
-,Oman,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,4,4,6,6,6,6,6
-,Lebanon,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,2,2,2,2,10,13
-,Pakistan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,2,4,4,5
-,Egypt,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2
-,Croatia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,3,3,5,7,7,9
-,Greece,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,3,3,3,7,7
-,Finland,European Region,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,6,7
-,Algeria,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,5
-,Brazil,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,2,2,2
-,Russian,European Region,,,,,,,,,,,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3
-,Belgium,European Region,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,8
-,Denmark,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,2,3,4,5
-,Estonia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1
-,Georgia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,2,3,3,3
-,North Macedonia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1
-,Romania,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,3,3,3,3
-,Afghanistan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1
-,New Zealand,Western Pacific Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,2
-,Belarus,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1
-,Lithuania,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1
-,Netherlands,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,7,13,18
-,Nigeria,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1
-,Mexico,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,5,5
-,San Marino,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,8
-,Azerbaijan,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,3,3
-,Ireland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1
-,Monaco,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1
-,Qatar,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,3,7
-,Ecuador,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,6
-,Czechia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,3
-,Iceland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,9
-,Armenia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1
-,Luxembourg,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1
-,Indonesia,South-East Asia Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2
-,Dominican Republic,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1
-,Portugal,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2
-,Andorra,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-,Latvia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-,Jordan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-,Morocco,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-,Saudi Arabia,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-,Tunisia,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-,Senegal,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
+Province/States,Country/Region,WHO region,1/21/20,1/22/20,1/23/20,1/24/20,1/25/20,1/26/20,1/27/20,1/28/20,1/29/20,1/30/20,1/31/20,2/1/20,2/2/20,2/3/20,2/4/20,2/5/20,2/6/20,2/7/20,2/8/20,2/9/20,2/10/20,2/11/20,2/12/20,2/13/20,2/14/20,2/15/20,2/16/20,2/17/20,2/18/20,2/19/20,2/20/20,2/21/20,2/22/20,2/23/20,2/24/20,2/25/20,2/26/20,2/27/20,2/28/20,2/29/20,3/1/20,3/2/20,3/3/20 +Confirmed,Globally,,282,314,581,846,1320,2014,2798,4593,6065,7818,9826,11953,14557,17391,20630,24554,28276,31481,34886,37558,40554,43103,45171,46997,49053,50580,51857,71429,73332,75204,75748,76769,77794,78811,79331,80239,81109,82294,83652,85403,87137,88948,90870 +Confirmed,Mainland China,Western Pacific Region,278,309,571,830,1297,1985,2741,4537,5997,7736,9720,11821,14411,17238,20471,24363,28060,31211,34598,37251,40235,42708,44730,46550,48548,50054,51174,70635,72528,74280,74675,75569,76392,77042,77262,77780,78191,78630,78961,79394,79968,80174,80304 +Confirmed,Outside of China,,4,5,10,16,23,29,57,56,68,82,106,132,146,153,159,191,216,270,288,307,319,395,441,447,505,526,683,794,804,924,1073,1200,1402,1769,2069,2459,2918,3664,4691,6009,7169,8774,10566 +Suspected,Mainland China,Western Pacific Region,,,,,,,5794,6973,9239,12167,15238,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Severe,Mainland China,Western Pacific Region,,,,,,,461,976,1239,1370,1527,1795,2110,2296,2788,3219,3859,4821,6101,6188,6484,7333,8204,,,,,,,,,,,,,,,,,,,, +Deaths,Mainland China,Western Pacific Region,,,,,,,80,106,132,170,213,259,304,361,425,491,564,637,723,812,909,1017,1114,1260,1381,1524,1666,1772,1870,2006,2121,2239,2348,2445,2595,2666,2718,2747,2791,2838,2873,2915,2946 +Hubei ,China,Western Pacific Region,258,270,375,375,,,,,,,,7153,9074,11177,13522,16678,19665,22112,24953,27100,29631,31728,33366,34874,51968,54406,56249,58182,59989,61682,62031,62662,63454,64084,64287,64786,65187,65596,65914,66337,66907,67103,67217 +Guangdong,China,Western Pacific Region,14,17,26,32,,,,,,,,520,604,683,797,870,944,1018,1075,1120,1151,1177,1219,1241,1261,1295,1316,1322,1328,1331,1332,1333,1339,1342,1345,1347,1347,1347,1348,1349,1349,1350,1350 +Henan,China,Western Pacific Region,,1,1,1,,,,,,,,422,493,566,675,764,851,914,981,1033,1073,1105,1135,1169,1184,1212,1231,1246,1257,1262,1265,1267,1270,1271,1271,1271,1271,1272,1272,1272,1272,1272,1272 +Zhejiang,China,Western Pacific Region,,5,5,5,,,,,,,,599,661,724,829,895,954,1006,1048,1075,1104,1117,1131,1145,1155,1162,1167,1171,1172,1173,1175,1203,1205,1205,1205,1205,1205,1205,1205,1205,1205,1206,1213 +Hunan,China,Western Pacific Region,,1,1,1,,,,,,,,389,463,521,593,661,711,772,803,838,879,912,946,968,988,1001,1004,1006,1007,1008,1010,1011,1013,1016,1016,1016,1016,1017,1017,1018,1018,1018,1018 +Anhui,China,Western Pacific Region,,,,,,,,,,,,297,340,408,480,530,591,665,733,779,830,860,889,910,934,950,962,973,982,986,987,988,989,989,989,989,989,989,990,990,990,990,990 +Jiangxi,China,Western Pacific Region,,1,2,2,,,,,,,,286,333,391,476,548,600,661,698,740,771,804,844,872,900,913,925,930,933,934,934,934,934,934,934,934,934,934,935,935,935,935,935 +Shandong,China,Western Pacific Region,,1,1,1,,,,,,,,202,225,246,270,298,343,379,407,435,459,486,497,506,519,530,537,541,543,544,546,748,750,754,755,755,756,756,756,756,756,758,758 +Jiangsu,China,Western Pacific Region,,,,,,,,,,,,202,231,271,308,341,373,408,439,468,492,515,543,570,593,604,617,626,629,631,631,631,631,631,631,631,631,631,631,631,631,631,631 +Chongqing,China,Western Pacific Region,,1,5,5,,,,,,,,238,262,300,337,366,389,411,426,446,468,486,505,518,529,537,544,551,553,555,560,567,572,573,575,576,576,576,576,576,576,576,576 +Sichuan,China,Western Pacific Region,,1,2,2,,,,,,,,207,236,254,282,301,321,344,363,386,405,417,436,451,463,470,481,495,508,514,520,525,526,526,527,529,531,534,538,538,538,538,538 +Heilongjiang,China,Western Pacific Region,,,,,,,,,,,,80,95,118,155,190,227,277,282,307,331,360,378,395,418,425,445,457,464,470,476,479,479,480,480,480,480,480,480,480,480,480,480 +Beijing,China,Western Pacific Region,5,5,10,10,,,,,,,,156,183,212,228,253,274,297,315,326,337,342,352,366,372,375,380,381,387,393,395,396,399,399,399,400,400,410,410,411,413,414,414 +Shanghai,China,Western Pacific Region,1,2,9,9,,,,,,,,153,177,193,208,233,254,269,281,292,295,302,306,313,318,326,328,331,333,333,333,334,334,335,335,335,336,337,337,337,337,337,338 +Hebei,China,Western Pacific Region,,,,,,,,,,,,96,104,113,126,135,157,171,195,206,218,239,251,265,283,291,300,301,302,306,307,308,309,311,311,311,312,317,318,318,318,318,318 +Fujian,China,Western Pacific Region,,,,,,,,,,,,144,159,179,194,205,215,224,239,250,261,267,272,279,281,285,287,290,292,293,293,293,293,293,293,294,294,296,296,296,296,296,296 +Guangxi,China,Western Pacific Region,,,,,,,,,,,,100,111,127,139,150,168,172,183,195,210,215,222,222,226,235,237,238,242,244,245,246,249,249,251,252,252,252,252,252,252,252,252 +Shaanxi,China,Western Pacific Region,,,,,,,,,,,,101,116,128,142,165,173,184,195,208,213,219,225,229,230,232,236,240,240,242,245,245,245,245,245,245,245,245,245,245,245,245,245 +Yunnan,China,Western Pacific Region,,1,1,1,,,,,,,,91,99,109,117,122,128,135,138,140,141,149,154,155,162,168,169,171,172,172,172,174,174,174,174,174,174,174,174,174,174,174,174 +Hainan,China,Western Pacific Region,,,,,,,,,,,,57,63,70,79,89,100,111,123,128,136,142,145,157,157,162,162,162,163,163,168,168,168,168,168,168,168,168,168,168,168,168,168 +Guizhou,China,Western Pacific Region,,,,,,,,,,,,29,38,46,56,64,69,77,89,96,109,118,131,135,140,143,144,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146 +Tianjin,China,Western Pacific Region,,2,2,2,,,,,,,,34,40,49,63,67,70,94,81,88,91,96,106,112,119,120,122,124,125,128,130,131,133,135,135,135,135,135,136,136,136,136,136 +Shanxi,China,Western Pacific Region,,,,,,,,,,,,47,56,66,74,81,90,96,104,115,119,122,124,126,126,127,128,129,130,131,131,132,132,132,132,133,133,133,133,133,133,133,133 +Liaoning,China,Western Pacific Region,,,,,,,,,,,,60,64,70,74,81,89,94,99,105,107,108,111,116,117,119,120,121,121,121,121,121,121,121,121,121,121,121,121,121,122,122,125 +Hong Kong,China,Western Pacific Region,,,1,2,5,5,8,8,8,10,12,13,14,15,15,18,21,24,26,26,36,42,49,50,53,56,56,57,60,62,65,68,68,70,74,81,85,91,93,94,95,98,101 +Jilin,China,Western Pacific Region,,,,,,,,,,,,17,21,31,42,54,59,65,69,78,80,81,83,84,86,88,88,89,89,90,91,91,91,91,93,93,93,93,93,93,93,93,93 +Gansu,China,Western Pacific Region,,,,,,,,,,,,35,45,51,56,57,62,70,71,81,85,86,86,87,90,90,90,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91 +Xinjiang,China,Western Pacific Region,,,,,,,,,,,,18,23,24,29,32,36,39,42,45,49,55,59,63,65,70,71,73,76,76,76,76,76,75,76,76,76,76,76,76,76,76,76 +Inner Mongolia,China,Western Pacific Region,,,,,,,,,,,,23,26,33,37,42,46,49,50,54,58,58,60,61,63,68,70,72,73,75,75,75,75,75,75,75,75,75,75,75,75,75,75 +Ningxia,China,Western Pacific Region,,,,,,,,,,,,26,28,31,34,34,40,43,45,45,49,53,58,64,67,70,70,70,70,71,71,71,71,71,71,71,71,72,72,73,73,74,74 +Taiwan,China,Western Pacific Region,,1,1,1,3,3,4,7,8,8,9,10,10,10,10,11,11,16,16,17,18,18,18,18,18,18,18,20,22,23,24,26,26,23,28,31,32,32,34,39,39,40,42 +Qinghai,China,Western Pacific Region,,,,,,,,,,,,8,9,13,15,17,18,18,18,18,18,18,18,18,18,18,18,18,18,12,18,18,18,18,18,18,18,18,18,18,18,18,18 +Macau,China,Western Pacific Region,,,1,2,2,2,5,7,7,7,7,7,7,8,8,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10 +Xizang,China,Western Pacific Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +Unspecified*,China,Western Pacific Region,,,131,384,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,Japan,Western Pacific Region,1,1,1,1,3,3,4,6,7,11,14,17,20,20,20,33,25,25,25,26,26,26,28,29,33,41,53,59,65,73,85,93,105,132,144,157,164,186,210,230,239,254,268 +,Republic of Korea,Western Pacific Region,1,1,1,2,2,2,4,4,4,4,11,12,15,15,16,18,23,24,24,27,27,28,28,28,28,28,29,30,31,51,104,204,346,602,763,977,1261,1766,2337,3150,3736,4212,4812 +,Thailand,South-East Asia Region,2,2,2,4,4,5,5,14,14,14,14,19,19,19,19,25,25,25,32,32,32,33,33,33,33,34,34,35,35,35,35,35,35,35,35,37,40,40,40,42,42,42,43 +,United States of America,Region of the Americas,,,1,1,2,2,5,5,5,5,6,7,8,11,11,11,12,12,12,12,12,13,13,14,15,15,15,15,15,15,15,15,35,35,35,53,53,59,59,62,62,62,64 +,Vietnam,Western Pacific Region,,,,2,2,2,2,2,2,2,5,6,7,8,9,10,10,12,13,14,14,15,15,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16 +,Singapore,Western Pacific Region,,,,1,3,3,4,7,7,10,13,16,18,18,18,24,28,30,33,40,43,45,47,50,58,67,72,75,77,81,84,85,86,89,89,90,91,93,96,98,102,106,108 +,Italy,European Region,,,,,,,,,,,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,9,76,124,229,322,400,650,888,1128,1689,2036 +,Nepal,South-East Asia Region,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +,Australia,Western Pacific Region,,,,,3,3,4,5,7,7,9,12,12,12,12,13,14,15,15,15,15,15,15,15,15,15,15,15,15,15,15,17,21,21,21,22,23,23,23,24,25,27,33 +,Malaysia,Western Pacific Region,,,,,,3,4,4,4,7,8,8,8,8,10,10,12,14,15,17,18,18,18,18,19,21,22,22,22,22,22,22,22,22,22,22,22,22,24,24,24,24,29 +,Canada,Region of the Americas,,,,,,,1,2,3,3,3,4,4,4,4,5,5,7,7,7,7,7,7,7,7,7,7,7,8,8,8,8,8,9,9,10,10,11,11,14,19,19,27 +,Cambodia,Western Pacific Region,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +,France,European Region,,,,,3,3,3,3,4,5,6,6,6,6,6,6,6,6,6,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12,18,38,57,100,100,191 +,Sri Lanka,South-East Asia Region,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +,Iran,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,5,18,28,43,61,95,141,245,388,593,978,1501 +,India,South-East Asia Region,,,,,,,,,,1,1,1,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,5 +,Germany,European Region,,,,,,,,1,4,4,5,7,8,10,12,12,12,13,14,14,14,14,16,16,16,16,16,16,16,16,16,16,16,16,16,16,18,21,26,57,57,129,157 +,Philippines,Western Pacific Region,,,,,,,,,,1,1,1,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3 +,Spain,European Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,12,25,32,45,45,114 +,United Kingdom,European Region,,,,,,,,,,,,2,2,2,2,2,2,3,3,3,4,8,8,9,9,9,9,9,9,9,9,9,9,9,9,13,13,13,16,20,23,36,39 +,Sweden,European Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,7,12,13,14,15 +,Switzerland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,6,10,18,26,30 +,Austria,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,4,5,10,10,18 +,Norway,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,4,6,15,19,25 +,Kuwait,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,8,12,43,43,45,45,56,56 +,Bahrain,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,26,33,33,38,40,47,49 +,United Arab Emirates,Eastern Mediterranean Region,,,,,,,,,4,4,4,4,5,5,5,5,5,5,7,7,7,8,8,8,8,8,8,9,9,9,9,9,11,13,13,13,13,13,19,19,19,21,21 +,Israel,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,2,2,2,3,5,7,7,10 +,Iraq,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,5,6,7,8,13,19,26 +,Oman,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,4,4,6,6,6,6,6 +,Lebanon,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,2,2,2,2,10,13 +,Pakistan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,2,4,4,5 +,Egypt,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2 +,Croatia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,3,3,5,7,7,9 +,Greece,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,3,3,3,7,7 +,Finland,European Region,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,6,7 +,Algeria,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,5 +,Brazil,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,2,2,2 +,Russian,European Region,,,,,,,,,,,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3 +,Belgium,European Region,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,8 +,Denmark,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,2,3,4,5 +,Estonia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1 +,Georgia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,2,3,3,3 +,North Macedonia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1 +,Romania,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,3,3,3,3 +,Afghanistan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1 +,New Zealand,Western Pacific Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,2 +,Belarus,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1 +,Lithuania,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1 +,Netherlands,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,7,13,18 +,Nigeria,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1 +,Mexico,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,5,5 +,San Marino,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,8 +,Azerbaijan,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,3,3 +,Ireland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1 +,Monaco,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1 +,Qatar,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,3,7 +,Ecuador,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,6 +,Czechia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,3 +,Iceland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,9 +,Armenia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1 +,Luxembourg,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1 +,Indonesia,South-East Asia Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2 +,Dominican Republic,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1 +,Portugal,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2 +,Andorra,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +,Latvia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +,Jordan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +,Morocco,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +,Saudi Arabia,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +,Tunisia,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +,Senegal,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 Case on an international conveyance,Other,Other,,,,,,,,,,,,,,,,,20,61,64,64,70,135,175,174,218,218,355,454,454,542,621,634,634,634,695,691,691,705,705,705,706,706,706
\ No newline at end of file diff --git a/numpy/core/src/common/npy_cpuinfo_parser.h b/numpy/core/src/common/npy_cpuinfo_parser.h index 4c00c847b..f4540f6ab 100644 --- a/numpy/core/src/common/npy_cpuinfo_parser.h +++ b/numpy/core/src/common/npy_cpuinfo_parser.h @@ -1,262 +1,262 @@ -/*
- * Copyright (C) 2010 The Android Open Source Project
- * All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- * * Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.
- * * Redistributions in binary form must reproduce the above copyright
- * notice, this list of conditions and the following disclaimer in
- * the documentation and/or other materials provided with the
- * distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
- * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
- * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
- * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
- * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
- * OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED
- * AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
- * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
- * OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
- * SUCH DAMAGE.
- */
-#ifndef __NPY_CPUINFO_PARSER_H__
-#define __NPY_CPUINFO_PARSER_H__
-#include <errno.h>
-#include <stdio.h>
-#include <fcntl.h>
-#include <string.h>
-#include <stddef.h>
-
-#define NPY__HWCAP 16
-#define NPY__HWCAP2 26
-
-// arch/arm/include/uapi/asm/hwcap.h
-#define NPY__HWCAP_HALF (1 << 1)
-#define NPY__HWCAP_NEON (1 << 12)
-#define NPY__HWCAP_VFPv3 (1 << 13)
-#define NPY__HWCAP_VFPv4 (1 << 16)
-#define NPY__HWCAP2_AES (1 << 0)
-#define NPY__HWCAP2_PMULL (1 << 1)
-#define NPY__HWCAP2_SHA1 (1 << 2)
-#define NPY__HWCAP2_SHA2 (1 << 3)
-#define NPY__HWCAP2_CRC32 (1 << 4)
-// arch/arm64/include/uapi/asm/hwcap.h
-#define NPY__HWCAP_FP (1 << 0)
-#define NPY__HWCAP_ASIMD (1 << 1)
-#define NPY__HWCAP_FPHP (1 << 9)
-#define NPY__HWCAP_ASIMDHP (1 << 10)
-#define NPY__HWCAP_ASIMDDP (1 << 20)
-#define NPY__HWCAP_ASIMDFHM (1 << 23)
-/*
- * Get the size of a file by reading it until the end. This is needed
- * because files under /proc do not always return a valid size when
- * using fseek(0, SEEK_END) + ftell(). Nor can they be mmap()-ed.
- */
-static int
-get_file_size(const char* pathname)
-{
- int fd, result = 0;
- char buffer[256];
-
- fd = open(pathname, O_RDONLY);
- if (fd < 0) {
- return -1;
- }
-
- for (;;) {
- int ret = read(fd, buffer, sizeof buffer);
- if (ret < 0) {
- if (errno == EINTR) {
- continue;
- }
- break;
- }
- if (ret == 0) {
- break;
- }
- result += ret;
- }
- close(fd);
- return result;
-}
-
-/*
- * Read the content of /proc/cpuinfo into a user-provided buffer.
- * Return the length of the data, or -1 on error. Does *not*
- * zero-terminate the content. Will not read more
- * than 'buffsize' bytes.
- */
-static int
-read_file(const char* pathname, char* buffer, size_t buffsize)
-{
- int fd, count;
-
- fd = open(pathname, O_RDONLY);
- if (fd < 0) {
- return -1;
- }
- count = 0;
- while (count < (int)buffsize) {
- int ret = read(fd, buffer + count, buffsize - count);
- if (ret < 0) {
- if (errno == EINTR) {
- continue;
- }
- if (count == 0) {
- count = -1;
- }
- break;
- }
- if (ret == 0) {
- break;
- }
- count += ret;
- }
- close(fd);
- return count;
-}
-
-/*
- * Extract the content of a the first occurence of a given field in
- * the content of /proc/cpuinfo and return it as a heap-allocated
- * string that must be freed by the caller.
- *
- * Return NULL if not found
- */
-static char*
-extract_cpuinfo_field(const char* buffer, int buflen, const char* field)
-{
- int fieldlen = strlen(field);
- const char* bufend = buffer + buflen;
- char* result = NULL;
- int len;
- const char *p, *q;
-
- /* Look for first field occurence, and ensures it starts the line. */
- p = buffer;
- for (;;) {
- p = memmem(p, bufend-p, field, fieldlen);
- if (p == NULL) {
- goto EXIT;
- }
-
- if (p == buffer || p[-1] == '\n') {
- break;
- }
-
- p += fieldlen;
- }
-
- /* Skip to the first column followed by a space */
- p += fieldlen;
- p = memchr(p, ':', bufend-p);
- if (p == NULL || p[1] != ' ') {
- goto EXIT;
- }
-
- /* Find the end of the line */
- p += 2;
- q = memchr(p, '\n', bufend-p);
- if (q == NULL) {
- q = bufend;
- }
-
- /* Copy the line into a heap-allocated buffer */
- len = q - p;
- result = malloc(len + 1);
- if (result == NULL) {
- goto EXIT;
- }
-
- memcpy(result, p, len);
- result[len] = '\0';
-
-EXIT:
- return result;
-}
-
-/*
- * Checks that a space-separated list of items contains one given 'item'.
- * Returns 1 if found, 0 otherwise.
- */
-static int
-has_list_item(const char* list, const char* item)
-{
- const char* p = list;
- int itemlen = strlen(item);
-
- if (list == NULL) {
- return 0;
- }
-
- while (*p) {
- const char* q;
-
- /* skip spaces */
- while (*p == ' ' || *p == '\t') {
- p++;
- }
-
- /* find end of current list item */
- q = p;
- while (*q && *q != ' ' && *q != '\t') {
- q++;
- }
-
- if (itemlen == q-p && !memcmp(p, item, itemlen)) {
- return 1;
- }
-
- /* skip to next item */
- p = q;
- }
- return 0;
-}
-
-static void setHwcap(char* cpuFeatures, unsigned long* hwcap) {
- *hwcap |= has_list_item(cpuFeatures, "neon") ? NPY__HWCAP_NEON : 0;
- *hwcap |= has_list_item(cpuFeatures, "half") ? NPY__HWCAP_HALF : 0;
- *hwcap |= has_list_item(cpuFeatures, "vfpv3") ? NPY__HWCAP_VFPv3 : 0;
- *hwcap |= has_list_item(cpuFeatures, "vfpv4") ? NPY__HWCAP_VFPv4 : 0;
-
- *hwcap |= has_list_item(cpuFeatures, "asimd") ? NPY__HWCAP_ASIMD : 0;
- *hwcap |= has_list_item(cpuFeatures, "fp") ? NPY__HWCAP_FP : 0;
- *hwcap |= has_list_item(cpuFeatures, "fphp") ? NPY__HWCAP_FPHP : 0;
- *hwcap |= has_list_item(cpuFeatures, "asimdhp") ? NPY__HWCAP_ASIMDHP : 0;
- *hwcap |= has_list_item(cpuFeatures, "asimddp") ? NPY__HWCAP_ASIMDDP : 0;
- *hwcap |= has_list_item(cpuFeatures, "asimdfhm") ? NPY__HWCAP_ASIMDFHM : 0;
-}
-
-static int
-get_feature_from_proc_cpuinfo(unsigned long *hwcap, unsigned long *hwcap2) {
- char* cpuinfo = NULL;
- int cpuinfo_len;
- cpuinfo_len = get_file_size("/proc/cpuinfo");
- if (cpuinfo_len < 0) {
- return 0;
- }
- cpuinfo = malloc(cpuinfo_len);
- if (cpuinfo == NULL) {
- return 0;
- }
- cpuinfo_len = read_file("/proc/cpuinfo", cpuinfo, cpuinfo_len);
- char* cpuFeatures = extract_cpuinfo_field(cpuinfo, cpuinfo_len, "Features");
- if(cpuFeatures == NULL) {
- return 0;
- }
- setHwcap(cpuFeatures, hwcap);
- *hwcap2 |= *hwcap;
- *hwcap2 |= has_list_item(cpuFeatures, "aes") ? NPY__HWCAP2_AES : 0;
- *hwcap2 |= has_list_item(cpuFeatures, "pmull") ? NPY__HWCAP2_PMULL : 0;
- *hwcap2 |= has_list_item(cpuFeatures, "sha1") ? NPY__HWCAP2_SHA1 : 0;
- *hwcap2 |= has_list_item(cpuFeatures, "sha2") ? NPY__HWCAP2_SHA2 : 0;
- *hwcap2 |= has_list_item(cpuFeatures, "crc32") ? NPY__HWCAP2_CRC32 : 0;
- return 1;
-}
-#endif
+/* + * Copyright (C) 2010 The Android Open Source Project + * All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in + * the documentation and/or other materials provided with the + * distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS + * OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED + * AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, + * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT + * OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF + * SUCH DAMAGE. + */ +#ifndef __NPY_CPUINFO_PARSER_H__ +#define __NPY_CPUINFO_PARSER_H__ +#include <errno.h> +#include <stdio.h> +#include <fcntl.h> +#include <string.h> +#include <stddef.h> + +#define NPY__HWCAP 16 +#define NPY__HWCAP2 26 + +// arch/arm/include/uapi/asm/hwcap.h +#define NPY__HWCAP_HALF (1 << 1) +#define NPY__HWCAP_NEON (1 << 12) +#define NPY__HWCAP_VFPv3 (1 << 13) +#define NPY__HWCAP_VFPv4 (1 << 16) +#define NPY__HWCAP2_AES (1 << 0) +#define NPY__HWCAP2_PMULL (1 << 1) +#define NPY__HWCAP2_SHA1 (1 << 2) +#define NPY__HWCAP2_SHA2 (1 << 3) +#define NPY__HWCAP2_CRC32 (1 << 4) +// arch/arm64/include/uapi/asm/hwcap.h +#define NPY__HWCAP_FP (1 << 0) +#define NPY__HWCAP_ASIMD (1 << 1) +#define NPY__HWCAP_FPHP (1 << 9) +#define NPY__HWCAP_ASIMDHP (1 << 10) +#define NPY__HWCAP_ASIMDDP (1 << 20) +#define NPY__HWCAP_ASIMDFHM (1 << 23) +/* + * Get the size of a file by reading it until the end. This is needed + * because files under /proc do not always return a valid size when + * using fseek(0, SEEK_END) + ftell(). Nor can they be mmap()-ed. + */ +static int +get_file_size(const char* pathname) +{ + int fd, result = 0; + char buffer[256]; + + fd = open(pathname, O_RDONLY); + if (fd < 0) { + return -1; + } + + for (;;) { + int ret = read(fd, buffer, sizeof buffer); + if (ret < 0) { + if (errno == EINTR) { + continue; + } + break; + } + if (ret == 0) { + break; + } + result += ret; + } + close(fd); + return result; +} + +/* + * Read the content of /proc/cpuinfo into a user-provided buffer. + * Return the length of the data, or -1 on error. Does *not* + * zero-terminate the content. Will not read more + * than 'buffsize' bytes. + */ +static int +read_file(const char* pathname, char* buffer, size_t buffsize) +{ + int fd, count; + + fd = open(pathname, O_RDONLY); + if (fd < 0) { + return -1; + } + count = 0; + while (count < (int)buffsize) { + int ret = read(fd, buffer + count, buffsize - count); + if (ret < 0) { + if (errno == EINTR) { + continue; + } + if (count == 0) { + count = -1; + } + break; + } + if (ret == 0) { + break; + } + count += ret; + } + close(fd); + return count; +} + +/* + * Extract the content of a the first occurence of a given field in + * the content of /proc/cpuinfo and return it as a heap-allocated + * string that must be freed by the caller. + * + * Return NULL if not found + */ +static char* +extract_cpuinfo_field(const char* buffer, int buflen, const char* field) +{ + int fieldlen = strlen(field); + const char* bufend = buffer + buflen; + char* result = NULL; + int len; + const char *p, *q; + + /* Look for first field occurence, and ensures it starts the line. */ + p = buffer; + for (;;) { + p = memmem(p, bufend-p, field, fieldlen); + if (p == NULL) { + goto EXIT; + } + + if (p == buffer || p[-1] == '\n') { + break; + } + + p += fieldlen; + } + + /* Skip to the first column followed by a space */ + p += fieldlen; + p = memchr(p, ':', bufend-p); + if (p == NULL || p[1] != ' ') { + goto EXIT; + } + + /* Find the end of the line */ + p += 2; + q = memchr(p, '\n', bufend-p); + if (q == NULL) { + q = bufend; + } + + /* Copy the line into a heap-allocated buffer */ + len = q - p; + result = malloc(len + 1); + if (result == NULL) { + goto EXIT; + } + + memcpy(result, p, len); + result[len] = '\0'; + +EXIT: + return result; +} + +/* + * Checks that a space-separated list of items contains one given 'item'. + * Returns 1 if found, 0 otherwise. + */ +static int +has_list_item(const char* list, const char* item) +{ + const char* p = list; + int itemlen = strlen(item); + + if (list == NULL) { + return 0; + } + + while (*p) { + const char* q; + + /* skip spaces */ + while (*p == ' ' || *p == '\t') { + p++; + } + + /* find end of current list item */ + q = p; + while (*q && *q != ' ' && *q != '\t') { + q++; + } + + if (itemlen == q-p && !memcmp(p, item, itemlen)) { + return 1; + } + + /* skip to next item */ + p = q; + } + return 0; +} + +static void setHwcap(char* cpuFeatures, unsigned long* hwcap) { + *hwcap |= has_list_item(cpuFeatures, "neon") ? NPY__HWCAP_NEON : 0; + *hwcap |= has_list_item(cpuFeatures, "half") ? NPY__HWCAP_HALF : 0; + *hwcap |= has_list_item(cpuFeatures, "vfpv3") ? NPY__HWCAP_VFPv3 : 0; + *hwcap |= has_list_item(cpuFeatures, "vfpv4") ? NPY__HWCAP_VFPv4 : 0; + + *hwcap |= has_list_item(cpuFeatures, "asimd") ? NPY__HWCAP_ASIMD : 0; + *hwcap |= has_list_item(cpuFeatures, "fp") ? NPY__HWCAP_FP : 0; + *hwcap |= has_list_item(cpuFeatures, "fphp") ? NPY__HWCAP_FPHP : 0; + *hwcap |= has_list_item(cpuFeatures, "asimdhp") ? NPY__HWCAP_ASIMDHP : 0; + *hwcap |= has_list_item(cpuFeatures, "asimddp") ? NPY__HWCAP_ASIMDDP : 0; + *hwcap |= has_list_item(cpuFeatures, "asimdfhm") ? NPY__HWCAP_ASIMDFHM : 0; +} + +static int +get_feature_from_proc_cpuinfo(unsigned long *hwcap, unsigned long *hwcap2) { + char* cpuinfo = NULL; + int cpuinfo_len; + cpuinfo_len = get_file_size("/proc/cpuinfo"); + if (cpuinfo_len < 0) { + return 0; + } + cpuinfo = malloc(cpuinfo_len); + if (cpuinfo == NULL) { + return 0; + } + cpuinfo_len = read_file("/proc/cpuinfo", cpuinfo, cpuinfo_len); + char* cpuFeatures = extract_cpuinfo_field(cpuinfo, cpuinfo_len, "Features"); + if(cpuFeatures == NULL) { + return 0; + } + setHwcap(cpuFeatures, hwcap); + *hwcap2 |= *hwcap; + *hwcap2 |= has_list_item(cpuFeatures, "aes") ? NPY__HWCAP2_AES : 0; + *hwcap2 |= has_list_item(cpuFeatures, "pmull") ? NPY__HWCAP2_PMULL : 0; + *hwcap2 |= has_list_item(cpuFeatures, "sha1") ? NPY__HWCAP2_SHA1 : 0; + *hwcap2 |= has_list_item(cpuFeatures, "sha2") ? NPY__HWCAP2_SHA2 : 0; + *hwcap2 |= has_list_item(cpuFeatures, "crc32") ? NPY__HWCAP2_CRC32 : 0; + return 1; +} +#endif |
