semvec/pypi-packages huggingface dataset:
libtetrabz 0.1.2
Optimized tetrahedron method to perform brillouin-zone integral
Requires Python: >=3.6

# libtetrabz

Libtetrabz is a library which perform efficiently the Brillouin-zone 
integration in the electronic structure calculation in a solid by 
using the tetrahedron method.

## Quick start guide

To experience libtetrabz, please open the following python notebook with jupyter, Google colab, etc.

https://github.com/mitsuaki1987/libtetrabz/blob/main/python/src/libtetrabz_tutorial.ipynb

## Manual

https://mitsuaki1987.github.io/libtetrabz/python/_build/html/index.html

## For citing libtetrabz
We would appreciate if you cite the following article in your 
research with libtetrabz.

"Improved tetrahedron method for the Brillouin-zone integration applicable to response functions",

M. Kawamura, Y. Gohda, and S. Tsuneyuki, Phys. Rev. B 89, 094515 (2014).
arXiv:2203.15648

https://journals.aps.org/prb/abstract/10.1103/PhysRevB.89.094515
https://arxiv.org/abs/2203.15648

## Git repository

https://github.com/mitsuaki1987/libtetrabz
smlr-learn 0.1.3
Python Package of Simplex-based Multinomial Logistic Regression
Requires Python: >=3.10

# SMLR: Simplex-based Multinomial Logistic Regression

> Currently in v0.1.2

## Introduction

This is a project of SMLR (Simplex-based Multinomial Logistic Regression) + Penalty. You may learn this model from paper by Fu et al.  [1]

## Starter Guidance

### By `pip`

To create a virtual env in your device, use:

```shell
python3 -m venv .venv
```

Then use pip to install:

```shell
pip install smlr-learn
```

### Fit SMLR

You can use SMLR by `sklearn`-like API:

```python
from smlr import SMLR
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report


model = SMLR(penalty='l1', gamma=1.0)

X, y = make_classification(
    n_informative=10,
    n_samples=1000,
    random_state=42
)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=42)

model.fit(X_train, y_train)
y_pred = model.predict(X_test)

print(classification_report(y_pred, y_test))
```

Then you can see:

```
Iteration 1000, loss: 0.335493
Converged after 1786 iterations, loss: 0.335087
              precision    recall  f1-score   support

           0       0.78      0.88      0.83       135
           1       0.89      0.79      0.84       165

    accuracy                           0.83       300
   macro avg       0.83      0.84      0.83       300
weighted avg       0.84      0.83      0.83       300
```



## References

[1] Fu, S., Chen, P., Liu, Y., & Ye, Z. (2023). Simplex-based multinomial logistic regression with diverging numbers of categories and covariate. Statistica Sinica, 33(4), 2463-2493.


## Update Notice

### 0.1.2

- Fix `CMLR1` and `CMLR2`. Now `CMLR1` performs correctly.
- Enhance `SMLR` computation by change gradient descent optimization method to **proximal gradient descent**.
- Add examples in repository.
streamlit-dateinput-intl 0.0.2
DateInput internationalization
Requires Python: >=3.10

# streamlit-dateinput-intl

DateInput internationalization

## Installation instructions

```sh
uv pip install streamlit-dateinput-intl
```

## Usage instructions

```python
import streamlit as st

from streamlit_dateinput_intl import streamlit_dateinput_intl

selected_date = streamlit_dateinput_intl(
   value="today",
   min=date.today() - timedelta(days=30),
   max=date.today() + timedelta(days=30),
   key="date_input_full",
   format="YYYY/MM/DD",
   disabled=False,
   width="stretch",
   locale="ja"
)

st.write(selected_date)
```