No project description provided

Project description

TUnits - Fast Python Units

GCB CI status:

Implements unit of measurement arithmetic, where a number is associated with a product of powers of base units and values with compatible units can be added.

The library is written in Cython for speed. The library provides the ability to statically check dimensionality type (see below) and a limited protobuffer serialization support for select units (see below). Contributions to extend support for more units are welcome.

A precompiled wheel can be installed using pip install typedunits [--pre].

Example

>> import tunits
>> from tunits.units import meter, km, N, MHz

>> print(3*MHz)
Frequency(3, 'MHz')

>> print(5*meter + km)
Length(1005.0, 'm')

>> print(N/meter)
N/m

>> print((N/meter).in_base_units())
kg/s^2

>> print(2*km / tunits.Value(3, 's'))
0.666666666667 km/s

Static Type Check

TypedUnits provides the ability to statically check the dimensionality of variables and parameters. For example mypy would complain about incompatible types for the following code.

from tunits import Frequency, LengthArray
from tunits.units import meter, km, MHz

def frequency_fn(f: Frequency) -> Frequency:
    return 3*f

x = 2 * meter
x_arr = LengthArray([1, 2], km)
y = 3 * km
f = 7 * MHz

# okay
print(frequency_fn(f))
print(x + y)
print(x_arr - y)

# not okay
print(frequency_fn(x))
print(f + x)
print(x - f)
frequency_fn(x_arr)

$ mypy my_code.py
my_code.py:18: error: Argument 1 to "frequency_fn" has incompatible type "Length"; expected "Frequency"  [arg-type]
my_code.py:19: error: No overload variant of "__add__" of "Value" matches argument type "Length"  [operator]
my_code.py:19: note: Possible overload variants:
my_code.py:19: note:     def __add__(self, int | float | complex | number[Any], /) -> Frequency
my_code.py:19: note:     def __add__(self, ValueArray | list[Any] | tuple[Any] | ndarray[Any, dtype[Any]], /) -> ValueArray
my_code.py:19: note:     def __add__(self, Frequency, /) -> Frequency
my_code.py:20: error: No overload variant of "__sub__" of "Value" matches argument type "Frequency"  [operator]
my_code.py:20: note: Possible overload variants:
my_code.py:20: note:     def __sub__(self, int | float | complex | number[Any], /) -> Length
my_code.py:20: note:     def __sub__(self, list[Any] | tuple[Any] | ndarray[Any, dtype[Any]], /) -> ValueArray
my_code.py:20: note:     def __sub__(self, Length, /) -> Length
my_code.py:21: error: Argument 1 to "frequency_fn" has incompatible type "LengthArray"; expected "Frequency"  [arg-type]
Found 4 errors in 1 file (checked 1 source file)

Serialization support

TypedUnits provides protobuffer serialization support for selected units. Contributions are welcome to increase serialization coverage.

>> from tunits import Frequency
>> from tunits.units import MHz
>>
>> v = 3*MHz
>> msg = v.to_proto()
>> print(msg)
units {
  unit: HERTZ
  scale: MEGA
}
real_value: 3

>> Frequency.from_proto(msg)
Frequency(3.0, 'MHz')

Installation

To install a precompiled wheel (add --pre for prelease version)
```
pip install typedunits # [--pre] 
```

To locally build the latest version from the main branch

pip install git+https://github.com/quantumlib/TypedUnits

For a local editable copy

git clone https://github.com/quantumlib/TypedUnits
cd TypedUnits
pip install -e .

Development and Testing

Clone the repository.
```
git clone https://github.com/quantumlib/TypedUnits

cd TypedUnits
```
All future steps assume you are in the repository's directory.
Install dev environment dependencies.
```
pip install -r dev_tools/dev.env.txt
```
Install TUnits
```
pip install -e .
```
Test.
```
pytest
```

Formatting

dev_tools/format.sh  # to format
dev_tools/format.sh --check  # to verify format

Note: This is not an officially supported Google product

Project details

Release history Release notifications | RSS feed

0.0.1.dev20241120184900 pre-release

Nov 20, 2024

This version

0.0.1.dev20241114014153 pre-release

Nov 14, 2024

0.0.1.dev20241113200257 pre-release

Nov 13, 2024

0.0.1.dev20241112205618 pre-release

Nov 12, 2024

0.0.1.dev20241111230424 pre-release

Nov 11, 2024

0.0.1.dev20241105180513 pre-release

Nov 5, 2024

0.0.1.dev20241105175138 pre-release

Nov 5, 2024

0.0.1.dev20241018230039 pre-release

Oct 18, 2024

0.0.1.dev20241018223718 pre-release

Oct 18, 2024

0.0.1.dev20241018223705 pre-release

Oct 18, 2024

0.0.1.dev20241015222221 pre-release

Oct 15, 2024

0.0.1.dev20241015220558 pre-release

Oct 15, 2024

0.0.1.dev20240912144504 pre-release

Sep 12, 2024

0.0.1.dev20240912133457 pre-release

Sep 12, 2024

0.0.1.dev20240911153816 pre-release

Sep 11, 2024

0.0.1.dev20240911153237 pre-release

Sep 11, 2024

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

typedunits-0.0.1.dev20241114014153-cp312-cp312-win_amd64.whl (712.1 kB view details)

Uploaded Nov 14, 2024 CPython 3.12 Windows x86-64

typedunits-0.0.1.dev20241114014153-cp312-cp312-win32.whl (672.2 kB view details)

Uploaded Nov 14, 2024 CPython 3.12 Windows x86

typedunits-0.0.1.dev20241114014153-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.4 MB view details)

Uploaded Nov 14, 2024 CPython 3.12 manylinux: glibc 2.17+ x86-64

typedunits-0.0.1.dev20241114014153-cp312-cp312-macosx_11_0_arm64.whl (744.6 kB view details)

Uploaded Nov 14, 2024 CPython 3.12 macOS 11.0+ ARM64

typedunits-0.0.1.dev20241114014153-cp312-cp312-macosx_10_13_x86_64.whl (777.5 kB view details)

Uploaded Nov 14, 2024 CPython 3.12 macOS 10.13+ x86-64

typedunits-0.0.1.dev20241114014153-cp311-cp311-win_amd64.whl (739.5 kB view details)

Uploaded Nov 14, 2024 CPython 3.11 Windows x86-64

typedunits-0.0.1.dev20241114014153-cp311-cp311-win32.whl (705.0 kB view details)

Uploaded Nov 14, 2024 CPython 3.11 Windows x86

typedunits-0.0.1.dev20241114014153-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.5 MB view details)

Uploaded Nov 14, 2024 CPython 3.11 manylinux: glibc 2.17+ x86-64

typedunits-0.0.1.dev20241114014153-cp311-cp311-macosx_11_0_arm64.whl (755.8 kB view details)

Uploaded Nov 14, 2024 CPython 3.11 macOS 11.0+ ARM64

typedunits-0.0.1.dev20241114014153-cp311-cp311-macosx_10_9_x86_64.whl (813.8 kB view details)

Uploaded Nov 14, 2024 CPython 3.11 macOS 10.9+ x86-64

typedunits-0.0.1.dev20241114014153-cp310-cp310-win_amd64.whl (735.4 kB view details)

Uploaded Nov 14, 2024 CPython 3.10 Windows x86-64

typedunits-0.0.1.dev20241114014153-cp310-cp310-win32.whl (673.2 kB view details)

Uploaded Nov 14, 2024 CPython 3.10 Windows x86

typedunits-0.0.1.dev20241114014153-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB view details)

Uploaded Nov 14, 2024 CPython 3.10 manylinux: glibc 2.17+ x86-64

typedunits-0.0.1.dev20241114014153-cp310-cp310-macosx_11_0_arm64.whl (723.1 kB view details)

Uploaded Nov 14, 2024 CPython 3.10 macOS 11.0+ ARM64

typedunits-0.0.1.dev20241114014153-cp310-cp310-macosx_10_9_x86_64.whl (781.4 kB view details)

Uploaded Nov 14, 2024 CPython 3.10 macOS 10.9+ x86-64

File details

Details for the file typedunits-0.0.1.dev20241114014153-cp312-cp312-win_amd64.whl.

File metadata

Download URL: typedunits-0.0.1.dev20241114014153-cp312-cp312-win_amd64.whl
Upload date: Nov 14, 2024
Size: 712.1 kB
Tags: CPython 3.12, Windows x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.1.1 CPython/3.9.20

File hashes

Hashes for typedunits-0.0.1.dev20241114014153-cp312-cp312-win_amd64.whl
Algorithm	Hash digest
SHA256	`1268a7049d61675f0fd533badb3f59e85cdc3d60221831c1f5f96b692e4ec821`
MD5	`8480c70ae0ad11f1f0b9920f49b3daa3`
BLAKE2b-256	`cdd336b6d5e2efa9dd4f5b73920f1922066bd5cb5ada9c0c75f12a99104b9504`

Algorithm	Hash digest
SHA256	`b225dd2d61be85fa9517579e896d66f75d6d4c6c356251725615e4de825d1ab3`
MD5	`de9b26a2bcc6f5c98acb4efd9912b06f`
BLAKE2b-256	`0842657989ec6a107038f7b9f74a883e63a5c758f05aa30c7a8fff13616f3e40`

Algorithm	Hash digest
SHA256	`3a23bed6dfb2c6b79bdbb433a65828f55d9a8a8be3067474596200e75c4ad722`
MD5	`15d7b8843d79a3932a68a963aa11a7f3`
BLAKE2b-256	`4fa0fd92381160d7890dca3c4bcb337ec99387d021fef38a3aeadf6309172f42`

Algorithm	Hash digest
SHA256	`b44e10477b6bb2e0dce618f70d3ea975f15a7f4e5513224636ed3943141309f3`
MD5	`30048ede982056e4d82d686dd6d2e7e3`
BLAKE2b-256	`d40b016561d88eef49e27cd89875c307ed9f1e8b5a6ec001b65e2ad4d28cccb4`

Algorithm	Hash digest
SHA256	`d466d3221672030f2bca4876f64c3f600404e1433f94539a7ac6cbb4946ab58e`
MD5	`1f361f1cf949f78becebbd640afca8a4`
BLAKE2b-256	`d1189a711419d07c5063de0c7656776d355342210d0cbe29ad7d73b97a2cfae5`

Algorithm	Hash digest
SHA256	`1f99da94e1834b7f6b26fa9e8ac3f1022ecef5fa8e92dd4364adfb203de9778b`
MD5	`a5242b089d71bc1517f10d3ef1efc83a`
BLAKE2b-256	`4c181fabdc7b319cabfe4e5f91888f190f1261baa3a0934f1f5ec5a44358099e`

Algorithm	Hash digest
SHA256	`8499c5757fd810560cb32a5fcd9ef59f19ce2cadeb71ac9b626f06d337b9b74c`
MD5	`a6710bf563be6a11418744cad5c21be4`
BLAKE2b-256	`2d23669cc2f9dca263670742fc72a234b491c31fd8a5d0062fea007b51ec665c`

Algorithm	Hash digest
SHA256	`d4908d0fb697ef23ced1b5d8420ee1750452715481d2c0a400257a913d603d86`
MD5	`79cfe70fbe4d8b2690d5bb10a5bd2bf2`
BLAKE2b-256	`7412a324c840d85c0c40006c5f01be3b3bd2661fcf3f3e012dd5b549f81cdd49`

Algorithm	Hash digest
SHA256	`03749e98611f3a3b8f98540deb1455f52267cd56e22e7e41c576be835060ce99`
MD5	`5f71770652be06fec8597f62dccdab27`
BLAKE2b-256	`b0d0b8073f1a240a0be3eb8a87e96291e4ee18b17ba51a5dd4b35158bbcb3ef9`

Algorithm	Hash digest
SHA256	`e47bd039579518cb1693fc3a75f710eefcb0d497c2fa00ae217f396b10f19907`
MD5	`b7ba20db402608d0b9c2c4c06f39bc35`
BLAKE2b-256	`2813a09f565ae209feea33efc8148cefe4a7474e4a5f483f2ebf63e09ff5f346`

Algorithm	Hash digest
SHA256	`c054651922b12ae36ba50b3b6515b905b4d946ce812f2010bb2e9a734b34db97`
MD5	`aedf176d684fe56ee4dfd297c4f2c8fe`
BLAKE2b-256	`a6cd842f72b5bf582da7ede0a4b64ef6721850e9f719c24c98117045eb65b061`

Algorithm	Hash digest
SHA256	`7f9297d5640dbb96382f153fba0a39675823cf9f5a31bf44e4ab4e48736367c2`
MD5	`2f2c1f2691d8fcc9448b4b036e55b693`
BLAKE2b-256	`de0ffec187720fb44448d5f1a49862f42d13693e74c111a0858c46c2838824d8`

Algorithm	Hash digest
SHA256	`9b270508fab6419973af5a4558c32b25e3b73190ec6c7ded7decbb799c47a70b`
MD5	`e6b9d5da770ce0393dd30b15a01273b9`
BLAKE2b-256	`0eede0de9bda197bca84ec570c0135da8f2453722d9b3f8ca15d7237e6ef602d`

Algorithm	Hash digest
SHA256	`af35625ad2a21a37de5fff2af845de15d95248a78a0765a3667ef79e19ca709a`
MD5	`b72f5572f3756e39241874ebbf16a4f8`
BLAKE2b-256	`e3be28bda69a0e55e0df421e197dd22bd53fe9ee6b65fed6cbd2a4cf95bb79a6`

Algorithm	Hash digest
SHA256	`27f61a6d2cd46f099170eb15d088df518fa2cbeca95c4a0b33cb82c5dd55d28d`
MD5	`f7c81331efd72c69c9a3f407c37e2992`
BLAKE2b-256	`c30c8ea6772df79e0b9ed3380a5c7656eadf7735eebeb51add2ca294b56a857f`

typedunits 0.0.1.dev20241114014153

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Project description

TUnits - Fast Python Units

Example

Static Type Check

Serialization support

Installation

Development and Testing

Formatting

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Release history Release notifications | RSS feed

Download files

Source Distributions

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes