semvec/pypi-packages huggingface dataset:
aklite 0.0.4
AKLite is lite version fo AKShare
Requires Python: >=3.8

## Introduction

AKLite is a lite version of AKShare, which will be used in the future to support the AKShare project.

AKLite Features:
1. Small, fast and powerful
2. High performance
3. Easy to ues

## Installation

```shell
pip install aklite --upgrade -i https://pypi.org/simple
```

## Usage

```python
import aklite as ai

stock_zh_a_hist_obj = ai.stock_zh_a_hist(symbols=["000001", "000002"], period="daily", start_date="20220101", 
                                         end_date="20230601", adjust="hfq", timeout=5, proxies={})
print(stock_zh_a_hist_obj.data)
print(stock_zh_a_hist_obj.columns)
print(stock_zh_a_hist_obj.url)
print(stock_zh_a_hist_obj.desc)
print(stock_zh_a_hist_obj.symbols)
print(stock_zh_a_hist_obj.start_date)
print(stock_zh_a_hist_obj.end_date)
print(stock_zh_a_hist_obj.adjust)
```

```shell
           date  symbol  ...  price_change  turnover_rate
0    2022-01-04  000001  ...         29.26           0.60
1    2022-01-05  000001  ...         79.63           1.01
2    2022-01-06  000001  ...         -4.87           0.57
3    2022-01-07  000001  ...         13.00           0.58
4    2022-01-10  000001  ...         -1.62           0.47
..          ...     ...  ...           ...            ...
677  2023-05-26  000002  ...          2.62           0.42
678  2023-05-29  000002  ...        -23.56           0.46
679  2023-05-30  000002  ...         43.20           0.91
680  2023-05-31  000002  ...        -15.71           0.46
681  2023-06-01  000002  ...        -31.42           0.48
[682 rows x 12 columns]
```

## Contributing

## Translate

```shell
cd docs
sphinx-build -b gettext ./source build/gettext
sphinx-intl update -p ./build/gettext -l zh_CN
```

## Publish

```shell
hatch build
hatch publish
```
tb-mean-field-hubbard 2.2.0
Package to run tight-binding mean field hubbard calculations
Requires Python: >=3.6

[![PyPI version](https://badge.fury.io/py/tb-mean-field-hubbard.svg)](https://badge.fury.io/py/tb-mean-field-hubbard)
[![DOI](https://zenodo.org/badge/314531046.svg)](https://zenodo.org/badge/latestdoi/314531046)

# tb-mean-field-hubbard 

Python library to perform tight-binding mean field Hubbard calculations on the conjugated π-networks of organic systems.
Only carbon atoms are supported and each atom is modelled by a single p<sub>z</sub> orbital hosting a single electron.

The modelled Hamiltonian is the following:

![](https://latex.codecogs.com/svg.latex?\dpi{280}\large{\hat{H}_\text{MFH}=-t\sum\limits_{\langle{i,j}\rangle,\sigma}\left(\hat{c}^{\dag}_{i,\sigma}\hat{c}_{j,\sigma}+\text{h.c.}\right)+U\sum\limits_{i,\sigma}\langle{\hat{n}_{i,\sigma}}\rangle%20\hat{n}_{i,\overline{\sigma}}-U\sum\limits_{i}\langle{\hat{n}_{i,\uparrow}}\rangle\langle{\hat{n}_{i,\downarrow}}\rangle,})

where c<sup>†</sup>, c and n are respectively the creation, annihiliation and number operators, t is the hopping integral and U denotes the on-site Coulomb repulsion.

### System requirements

Python 3 (tested with 3.6 - 3.9) environment is required with the following libraries (parenthesis indicate tested versions; but the library should work with all recent versions):
* Standard python libraries: `numpy` (1.17.2), `scipy` (1.3.1), `matplotlib` (3.2.1)
* Atomistic simulation environment: `ase` (3.18.1)
* Python Tight Binding: `pythtb` (1.7.2)

Note: the dependencies will be automatically installed via `pip`.

### Installation

Option 1) Install the library and dependencies hosted on PyPI:

```
pip install tb-mean-field-hubbard
```

Option 2) Manual installation, such that you have direct access to the code and the notebook:

```
git clone https://github.com/eimrek/tb-mean-field-hubbard.git
cd tb-mean-field-hubbard
pip install -e .
```

On a normal desktop computer, installation of the python dependencies can take some minutes, while the `tb-mean-field-hubbard` should install in seconds.

### Example usage

Example jupyter notebook `example.ipynb` is provided that performs the calculation for the Clar's goblet molecule. The geometry is read from a `xyz` file. The whole notebook should run in a matter of seconds on a normal desktop computer.
The following image demonstrates a selection of the output for the calculation for parameters `t=2.7` and `U=3.0` (both in electronvolts).

<p align="center"><img class="marginauto" src="misc/example-output.png" width="700"></p>

In addition to the structure of Clar's goblet, the `geom/` folder contains input geometries for triangulene, 4- and 5-rhombene, and several other systems.