semvec/pypi-packages huggingface dataset:
jaxfss 0.1.0
JAX/Flax implementation of finite-size scaling
Requires Python: >=3.7

# jaxfss
![github workflow](https://github.com/yonesuke/jaxfss/actions/workflows/book.yml/badge.svg)
[![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](https://yonesuke.github.io/jaxfss/)

JAX/Flax implementation of finite-size scaling analysis
![ising_binder](https://user-images.githubusercontent.com/12659790/191948671-dc28959d-0e24-4197-baca-0c2ef0aad311.png)


## Installation
`jaxfss` can be installed with pip with the following command:
```
pip install jaxfss
```

## Quickstart
Check out the [documentation](https://yonesuke.github.io/jaxfss/)!!

## Other packages
- Finite-size scaling package by Gaussian process with C++

    https://kenjiharada.github.io/BSA/
- Finite-size scaling package by neural network and Gaussian process with Python (PyTorch)

    https://github.com/KenjiHarada/FSS-tools

## Citation
Please cite this paper when you use this package for your research!!
- [Full paper] Ryosuke Yoneda and Kenji Harada, Neural Network Approach to Scaling Analysis of Critical Phenomena, [arXiv: 2209.01777](https://arxiv.org/abs/2209.01777).

    ```tex
    @article{yoneda2022neural,
        title={Neural Network Approach to Scaling Analysis of Critical Phenomena},
        author={Yoneda, Ryosuke and Harada, Kenji},
        url={https://arxiv.org/abs/2209.01777},
        journal={arXiv preprint arXiv:2209.01777},
        year={2022}
    }
    ```

- [Conference paper] Currently preparing!!
softclip 0.1.0
JAX/Flax implementation of softclip
Requires Python: >=3.7

# softclip

Simple JAX implementation of softclip, inspired by tensorflow probability

- <https://www.tensorflow.org/probability/api_docs/python/tfp/substrates/jax/bijectors/SoftClip>

softclip is a differentiable bijector from the real number space to some interval.
This is useful when you want to optimize a parameter that is assumed to be inside the interval [low, high].

## Installation

softclip can be installed with pip directly from GitHub, with the following command:

```
pip install git+https://github.com/yonesuke/softclip.git
```

## QuickStart

The `forward` method is the function from the real number space to the interval [low, high].
The `inverse` method is the function from the interval [low, high] to the real number space, and is the inverse function of `forward`.

```python
from softclip import SoftClip

bij = SoftClip(low=1.0, high=3.0, hinge_softness=0.5)
y = bij.forward(2.0) # y = 2.9640274
bij.inverse(y) # 1.9999975 ≒ 2.0
```

Simply set `low=0.0` or `high=0.0` to create a bijector to a positive/negative number domain.

```python
bij_positive = SoftClip(low=0.0)
bij_negative = SoftClip(high=0.0)
```

By transforming softclip to distrax with `to_distrax`, you can create distrax bijectors:

```python
bij_distrax = bij.to_distrax()
```
pycpt 0.0.1
Python Competitive Programming Tools

# Python Competitive Programming Tools

This is an example project demonstrating how to publish a python module to PyPI.

## Installation

Run the following to install:

```bash
$ pip install pycpt
```

## Usage

```python
from cpin import si,mi

# Get single int input value
t = si()

for _ in range(t):
    # Get multiple int input values
    a, b, c = mi()
    print("yay!")
```

## Development

To install pycpt, along with the tools you need to develop and run tests, run the following in your virtualenv:

```bash
$ pip install -e .[dev]
```
rationalnets 0.1.0
JAX/Flax implementation of rational neural nets
Requires Python: >=3.7

# rationalnets
JAX/Flax implementation of rational neural nets.

![](images/activation_fn.png)

Original
- paper: Nicolas Boullé, Yuji Nakatsukasa, and Alex Townsend, [Rational neural networks](https://arxiv.org/abs/2004.01902), arXiv preprint arXiv:2004.01902 (2020).
- github: https://github.com/NBoulle/RationalNets


## Installation
`rationalnets` can be installed with pip with the following command:
```
python -m pip install rationalnets
```

Or you can install the latest version with the following command:
```
python -m pip install git+https://github.com/yonesuke/RationalNets.git
```

## QuickStart
Rational activation function
```python
import jax.numpy as jnp
from jax import random
from rationalnets import RationalMLP

xs = jnp.arange(-2.0, 2.0, 0.01)
act = Rational()
params = model.init(random.PRNGKey(0), xs)
ys = act.apply(params, xs) # values of rational activation function for -2.0 ~ 2.0
```

Rational MLP
```python
import jax.numpy as jnp
from jax import random
from rationalnets import RationalMLP

model = RationalMLP([12, 8, 4])
batch = jnp.ones((32, 10))
variables = model.init(random.PRNGKey(0), batch)
output = model.apply(variables, batch)
```
dcmstack 0.9.0
Stack DICOM images into volumes and convert to Nifti
Requires Python: >=2.7

========
dcmstack
========

This package provides DICOM to Nifti conversion with the added ability 
to extract and summarize meta data from the source DICOMs. The meta data
can be injected it into a Nifti header extension or written out as a JSON 
formatted text file.


Installing
----------

You can the latest release from PyPI by doing:

.. code-block:: console

  $ pip install dcmstack


Documentation
-------------

Documentation can be read online: https://dcmstack.readthedocs.org/

You can build the HTML documentation under build/sphinx/html with:

If you have the *sphinx* and *numpydoc* packages and a *make* command you 
can build the documentation by running the *make* command in the *doc/* 
directory. For example, to create the HTML documentation you would do:

.. code-block:: console
  
  $ python setup.py build_sphinx
  $ make html

And then view doc/_build/html/index.html with a web browser.


Running Tests
-------------

You can install dcmstack along with any test dependencies by installing the ``test`` 
extra. Then you can run the ``pytest`` command to run the test-suite:

.. code-block:: console
  
  $ pip install dcmstack[test]
  $ pytest
SafeDriver 1.3.1
自动检测并更新driver文件，提高selenium代码的稳定性

# 介绍
浏览器的更新和driver文件的版本异常，经常是影响代码稳定性的一个原因，而每次driver文件的更新也是比较繁杂的工作，此次的safe-driver意在帮助维护driver的稳定性

# 作用
safe-driver导入后，可替换selenium的webdriver，使用方式和方法同selenium的webdriver
当启动浏览器driver文件出错时，程序将自动下载并更新浏览器驱动，并重新返回driver，提高代码的稳定性


# 导入
```pycon
from SafeDriver.drivers import driver
from SafeDriver.drivers import option
```
# 使用
此操作同selenium的webdriver.Chrome()
```pycon
driver = driver()
driver.get("https://www.baidu.com")
```

# 放入options参数
首先确定导入option配置参数
使用同selenium的ChromeOptions，可直接添加，添加后，无需再次写入到driver中
```pycon
from SafeDriver.drivers import option
option.add_argument('--headless')
```
# 路径和系统配置
路径和系统保存在option中，配置需要导入option
```pycon
from SafeDriver.drivers import option
option.pypath = 'xxx' # python的根目录路径
option.os_ = 'xxx'  # 当前使用的操作系统
option.chromedriver_path = 'xxx'    # 设置chromedriver保存路径，只写到文件夹
```

# 注意事项：
1、option.os_：参数不赋值，则视为自动查找当前操作系统
2、option.pypath：参数不赋值，则视为自动查到python路径(目前自动查找操作系统仅支持windows系统，linux和mac用户请手动配置option.pypath属性) 
3、option.chromedriver_path：参数不赋值，则选择option.pypath作为保存路径

# 目前仅支持chrome浏览器，暂未更新其他浏览器

# 使用问题快查
## 在mac和linux上，SafeDriver不好使
SafeDriver自动查找的路径是python的路径，这个查找，在linux和mac上面并不一定能正常进行，linux和mac的用户，最好是手动配置好option.os_和option.pypath
## 如果更好保持稳定性？
建议代码中，指定driver的保存路径，此时的option.pypath则需要是你保存driver的路径
## mac用户不能移动文件
这属于mac系统的限制，某些路径需要进行授权才能访问和显示，所以可以将driver文件放在可直接访问的目录，在代码中设置好driver路径，并将这个路径赋值给option.pypath
elan-scissors 0.0.2
Cut ELAN audio into snippets.
Keywords: elan,linguistics

# elan-scissors

Cut ELAN audio into snippets. You may need to [install an audio library usable by pydub](https://github.com/jiaaro/pydub#getting-ffmpeg-set-up).
Usage:

`elanscissors <SRC> <TARGET>`, where `SRC` is a flextext file and `TARGET` a wav file.

Documentation: https://elan-scissors.readthedocs.io

![License](https://img.shields.io/github/license/fmatter/elan-scissors)
[![Documentation Status](https://readthedocs.org/projects/elan-scissors/badge/?version=latest)](https://elan-scissors.readthedocs.io/en/latest/?badge=latest)
[![Linting](https://img.shields.io/github/workflow/status/fmatter/elan-scissors/lint?label=linting)](https://github.com/fmatter/elan-scissors/actions/workflows/lint.yml)
[![PyPI](https://img.shields.io/pypi/v/elan-scissors.svg)](https://pypi.org/project/elan-scissors)
![Versions](https://img.shields.io/pypi/pyversions/elan-scissors)
Opentrons-HTTP-API-client 0.1.0
A Python library for interacting with the Opentrons HTTP API

# Opentrons Python API

Simple (and currently incomplete) Python wrapper around the Opentrons HTTP API. Like https://github.com/Opentrons/opentrons/tree/edge/api-client, but in Python. This API is atomic and interactive.

This project is created for use with [PyLabRobot](https://github.com/pylabrobot/pylabrobot), but can be used however you like.

## Installation

- from pip

```
pip install opentrons-python-api
```

- from source

```
git clone http://github.com/rickwierenga/opentrons-python-api
```

## Usage

Minimal example for simple liquid handling:

```py
import ot_api
ot_api.set_host("x.x.x.x") # find in OT app
ot_api.set_port(31950)     # default, so not really necessary

# Creating a run
run_id = ot_api.runs.create()
ot_api.set_run(run_id) # set run globally, alternative to `run_id` parameter for functions

# Add pipettes that are detected in hardware to the software
left_pipette, right_pipette = ot_api.labware.add_mounted_pipettes()
left_pipette_id = left_pipette["pipetteId"]

# Defining labware
labware_def = ot_api.labware.define(labware_definition) # json from opentrons-shared-data

# Adding labware
labware_id = ot_api.labware.add(labware_def, slot=1)

# Picking up a tip
ot_api.lh.pick_up_tip(labware_id=labware_id, well_name="A1", pipette_id=left_pipette_id)

# Aspirating
ot_api.lh.aspirate(labware_id=labware_id, well_name="A1", pipette_id=left_pipette_id,
                   flow_rate=10, volume=10)

# Dispensing
ot_api.lh.dispense(labware_id=labware_id, well_name="A1", pipette_id=left_pipette_id,
                   flow_rate=10, volume=10)

# Tip drop
ot_api.lh.drop_tip(labware_id=labware_id, well_name="A1", pipette_id=left_pipette_id)
```

**Note: the ot_api is blocking!**

## Notice

This project is not affiliated with Opentrons.

This project is created for the Sculpting Evolution group at the MIT Media Lab.

See [`LICENSE`](/LICENSE)