semvec/pypi-packages huggingface dataset:
ILThermoPy 1.1.0
A simple Python wrapper around the ILThermo 2.0 database
Requires Python: >=3.7
Keywords: ionic liquids ilthermo nist api chemistry cheminformatics

# ILThermoPy: Python API for the ILThermo 2.0 database

**ILThermoPy** is a Python library to access [ILThermo 2.0](https://ilthermo.boulder.nist.gov/), which is the NIST standard reference database, containing measured physico-chemical properties for a wide spectrum of ionic liquids. Despite the significant amount of stored data, ILThermo 2.0 cannot be used for machine learning, parametrization of empirical physical models, and other data-driven approaches as is. The reason is that ILThermo 2.0 describes molecular structure of IL's components with a chemical name only, whereas SMILES, InChI and other structure identifiers are not available.

**ILThermoPy** solves this problem via the preliminary semi-automatic conversion of compound names to SMILES with subsequent manual validation. This allows one to conduct a substructural search and to immediately generate chemoinformatic descriptors for the extracted data.


## Please note:
 
- There is no official web API available to access ILThermo 2.0, therefore the stability of this library depends on the stability of the JSON/javascript framework of the webservice.

- ILThermo 2.0 database is regularly updated, at least once a year. Those updates change internal compound IDs, which are used to add SMILES to the extracted data. Therefore, after database's update old versions of **ILThermoPy** can fail to retrieve structural data at least for some of the new entries.


## Installation

**ILThermoPy** can be installed via [PyPI](https://pypi.org/project/ilthermopy/):

```ssh
> pip install ilthermopy
```


## Requirements

1. Python 3.7+;

2. requests;

3. pandas;

4. importlib_resources (for Python 3.7 and 3.8).


## Useful links

1. [ILThermo 2.0](https://ilthermo.boulder.nist.gov/): webapp accessing ILThermo 2.0 database.

2. [PyPI package](https://pypi.org/project/ilthermopy/): PyPI page of the package.

3. [Documentation](https://ilthermopy.readthedocs.io/): cookbook, descriptive API, and other useful information.

4. [Update tools](update/): script for semi-automatic update of structural information of new ILThermo compounds after database's update.
wonderpatch 0.1.0
A better patch tool for python test.
Requires Python: >=3.5
Keywords: wonderpatch

===========
wonderpatch
===========


.. image:: https://img.shields.io/pypi/v/wonderpatch.svg
        :target: https://pypi.python.org/pypi/wonderpatch


Better patch for test.


Usage
------

patch module method
^^^^^^^^^^^^^^^^^^^

.. code:: python

    import sys

    from wonderpatch import wonder

    with wonder(os).cpu_count.called_once(return_value=12345):
        assert os.cpu_count() == 12345


Use path is ok:

.. code:: python

    import sys

    from wonderpatch import wonder

    with wonder('os.cpu_count').called_once(return_value=12345):
        assert os.cpu_count() == 12345


patch object
^^^^^^^^^^^^

.. code:: python

    from wonderpatch import wonder

    class TestObject:

        def name(self):
            return self.__class__.__name__


    with wonder(TestObject.name).called_once(return_value='x'):
        assert TestObject().name() == 'x'


It is also works for property:

.. code:: python

    from wonderpatch import wonder

    class TestObject:

        @property
        def name(self):
            return self.__class__.__name__


    with wonder(TestObject.name).called_once(return_value='x'):
        assert TestObject().name == 'x'


Multiple patch in one function
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

If you want to use multiple patches in one place, and do not want to write a verify long statement, use `wonder.together`:

.. code:: python

    from wonderpatch import wonder

    with wonder.together():
        wonder(TestObject.name).called_once(return_value='x')
        wonder(TestObject.age).called_once(return_value='x')

        assert TestObject().name() == 'x'
        assert TestObject().age == 'x'


Or as a decorator:

.. code:: python

    from wonderpatch import wonder

    @wonder.together
    def test_name():
        wonder(TestObject.name).called_once(return_value='x')
        assert TestObject().name() == 'x'


the wonder api
^^^^^^^^^^^^^^

- wonder(...).then_return(value: Any)

  patch and set return value, works for called 0+ times

- wonder(...).then_raise(exception)

  patch and set raise value, works for called 0+ times

- wonder(...).called(*times=None, min=1, max=None, return_value=_empty, side_effect=_empty, only_self=False*)

  patch and set min or max times call, default is at least 1 time.

- wonder(...).called_with(`*args, **kwargs`)

  patch and set called at least once with `args, kwargs`

- wonder(...).called_once_with(`*args, **kwargs`)

  patch and set called once with `args, kwargs`

- wonder(...).never_called()

  patch and set never called.




Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
gmlp-tinygrad 0.9.2
An implementation of gated MLPs in tinygrad, as an alternative to transformers.
Requires Python: >=3.8

# Gated MLPs (gMLP) Implementation in Tinygrad

An implementation of gated MLPs in tinygrad, as an alternative to transformers.

## What is a gMLP?

A Gated Multi-Layer Perceptron (gMLP) is a type of deep learning model that uses basic multi-layer perceptrons combined with gating mechanisms to capture spatial interactions between sequence elements, achieving comparable performance to Transformers on various language and vision tasks without relying on self-attention mechanisms.

## Installation

To get started, simply install gmlp_tinygrad using pip:

```bash
pip install gmlp_tinygrad
```

## Usage
```bash
from gmlp_tinygrad import gMLP
```
factor-analyser 1.1.1
A tool for factor development and analysis.
Requires Python: >=3.6

# FactorAnalyser

FactorAnalyser is a powerful toolkit designed to analyze cross-sectional factors and optimize alpha strategies. It comprises two core components: **FactorBacktester** and **FactorFactory**, each providing essential functionalities for comprehensive factor analysis.

## FactorBacktester

FactorBacktester is dedicated to backtesting cross-sectional factors. Key features include:
- Computing rankICs, quantile returns, and factor returns through various methods.
- **Planned Enhancements**:
  1. **Risk Evaluation Module**: Implement a risk assessment framework (e.g., Barra) to evaluate factor risks.
  2. **Base Correlation Integration**: Add correlation analysis with baseline factors to improve model robustness.
  3. **IC Attenuation Testing**: Develop lead-lag tests to measure IC variation over time, evaluating IC attenuation over n days.

## FactorFactory

FactorFactory is designed to streamline the creation and management of cross-sectional factors. It is tightly integrated with FactorBacktester, offering advanced capabilities for factor refinement and analysis.

## Example

For detailed usage, please refer to the [functional demo](Functional%20Demo.ipynb) included in the repository.

## License

This project is released under the [MIT License](LICENSE). Please note that **FactorAnalyser** is still in active development, and feedback or contributions are welcome!
NLIF 0.0.5
using natural language as condition
Requires Python: >=3.7

# NLIF
ChatGPT API를 이용하여 자연어 문장을 조건문으로써 활용할 수 있도록 해주는 프로젝트
(https://pypi.org/project/NLIF/)

# 목차
- [설치](#설치)
- [활용 예시](#활용-예시)
- [Contact](#contact)

# 설치
```
pip install NLIF
```

# 활용 예시
```python
from NLIF import *

ni = NLIF('Your OpenAI API key')

if ni.nlif('Whales are mammals'):
    print('True')
else:
    print('False')
```

# Contact
| Maintainer | e-mail |
|---------|---------|
| marmot8080 | marmot8080@gmail.com |