semvec/pypi-packages huggingface dataset:
highlighter 0.2.0
Easy-to-use PEP 508 markers
License: MIT
Requires Python: >=3.10

# highlighter

Easy to use PEP 508 markers.  These are the strings in requirements after a
semicolon, such as `enum34; python_version < '3.5'` means the package `enum34`
but only on some versions of python.


```py
from packaging.markers import Marker
from packaging.requirements import Requirement

from highlighter import EnvironmentMarkers

req = Requirement("enum34; python_version < '3.5'")

env = EnvironmentMarkers.for_python("3.7.5", "win32")
env.match(req.marker)  # False

# Lower-level interface, you can use Marker directly
m = Marker("python_version < '3.5'")
env.match(m)  # False

# What about extras?
req = Requirement("somepackage[foo,bar]")
m = Marker("extra == 'foo'")
env.match(m, extras=req.extras) # True
```

## Egg-info Extras

This also includes a function that can convert a requires.txt (as found in
egg-info dirs, the only kind we have in sdists) into an equivalent list of
requirements.txt lines using PEP 508 markers.

```py
from highlighter.sdist import convert_sdist_requires

convert_sdist_requires("""\
[socks]
PySocks!=1.5.7,>=1.5.6

[socks:sys_platform == "win32" and python_version == "2.7"]
win_inet_pton
""") == """\
PySocks!=1.5.7,>=1.5.6; extra == 'socks'
win_inet_pton; (sys_platform == "win32" and python_version == "2.7") and extra == 'socks'
"""
```

# License

highlighter is copyright [Tim Hatch](https://timhatch.com/), and licensed under
the MIT license.  I am providing code in this repository to you under an open
source license.  This is my personal repository; the license you receive to
my code is from me and not from my employer. See the `LICENSE` file for details.
autodmri 0.2.7
Implementation of "Automated characterization of noise distributions in diffusion MRI data".
License: MIT
Requires Python: >=3.7

# Automated characterization of noise distributions in diffusion MRI data

[miccai]: https://arxiv.org/abs/1805.12071
[miccai_publisher]: https://link.springer.com/chapter/10.1007%2F978-3-030-00928-1_35
[media]: https://www.sciencedirect.com/science/article/pii/S1361841520301225

###  The example and documentation

The latest version can be installed with
~~~
pip install autodmri
~~~

You can find a quick example and datasets over [here](example) and the full documentation at http://autodmri.rtfd.io/.

An example, super basic call to the script would be

~~~bash
autodmri_get_distribution dwi.nii.gz sigma.nii.gz N.nii.gz mask.nii.gz
~~~

Be sure to check the options by passing `--help` to the script.

###  The manuscript and references

You can read the [journal version][media] in Medical Image Analysis and the datasets are available here https://zenodo.org/record/2483105.

The conference version of the manuscript (as published in MICCAI 2018) is available [here][miccai] for free
and from the [publisher][miccai_publisher].

Here is a bibtex entry for the journal version

~~~
@article{St-jean2020_media,
author = {St-Jean, Samuel and {De Luca}, Alberto and Tax, Chantal M.W. and Viergever, Max A. and Leemans, Alexander},
doi = {10.1016/j.media.2020.101758},
eprint = {1906.12121},
issn = {13618415},
journal = {Medical Image Analysis},
month = {jun},
pages = {101758},
title = {{Automated characterization of noise distributions in diffusion MRI data}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1361841520301225},
year = {2020}
}
~~~

and for the conference manuscript in MICCAI

~~~
@InProceedings{St-jean2018_miccai,
author="St-Jean, Samuel
and De Luca, Alberto
and Viergever, Max A.
and Leemans, Alexander",
editor="Frangi, Alejandro F.
and Schnabel, Julia A.
and Davatzikos, Christos
and Alberola-L{\'o}pez, Carlos
and Fichtinger, Gabor",
title="Automatic, Fast and Robust Characterization of Noise Distributions for Diffusion MRI",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2018",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="304--312",
isbn="978-3-030-00928-1"
}
~~~

###  Referencing the code itself

The code is also autoarchived on zenodo for those wanting to refer to a specific version over here

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3339157.svg)](https://doi.org/10.5281/zenodo.3339157)
pylemon8 0.1.1
Lemon8 Wrapper in Python
License: MIT
Keywords: python,lemon8,pylemon8,bytedance,api,lemon,8,wrapper,py-lemon8,pythonlemon8,eight

# Lemon8 Wrapper for Python

A lightweight, zestful Python library for integrating with the Lemon8 API, providing an intuitive interface for developers to access the Lemon8 social media platform.
    
## Getting Started
    
### Installation
Pip
```
pip install pylemon8
```

From source
```python
https://github.com/shotnothing/pylemon8.git
python setup.py install
```

### Example Usage
```python
from lemon8 import Lemon8

lemon8 = Lemon8()
print(lemon8.feed('foryou').get_items())
```

## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details