semvec/pypi-packages huggingface dataset:
Spin-dependent-d-band-center 1.0.5
Package for spin-dependent d-band center app
License: MIT
Requires Python: >=3.9

# Spin Dependent d-band Center 

A simple Python/Tkinter GUI to compute spin-resolved and effective d-band centers directly from a VASP `DOSCAR` (ISPIN=2, LORBIT=11). 

## Features
- Load a `DOSCAR` (ISPIN=2, LORBIT=11).
- Select atom range (1-based, inclusive).
- Compute:
  - Spin-up d-band center ($\varepsilon_{d\uparrow}$)
  - Spin-down d-band center ($\varepsilon_{d\downarrow}$)
  - Effective d-band center ($\varepsilon_{\mathrm{eff}}$)
  - Hammer–Nørskov d-band center, $\varepsilon_{\mathrm{HN}}$
  - Fractional occupations $f_\uparrow$, $f_\downarrow$

## Equations
Let energies be aligned such that \(E_F = 0\), i.e., we use \(E - E_F\) internally.

- Spin-resolved d-band centers (integrated over the full energy grid):
  
$$
  \varepsilon_{d\sigma} 
  = \frac{\int_{-\infty}^{+\infty} E D_{d\sigma}(E - E_F) dE}
         {\int_{-\infty}^{+\infty} D_{d\sigma}(E - E_F) dE}
$$

- Fractional occupations (integrated up to the Fermi level):

 
 $$
  f_\sigma = \frac{1}{N_{\text{atoms}}\times 5} \int_{-\infty}^{E_F} D_{d\sigma}(E) dE
  \quad (E_F = 0)
  $$

- Effective d-band center (**Bhattacharjee, S.**, Waghmare, U. & Lee, SC. _An improved d-band model of the catalytic activity of magnetic transition metal surfaces_. Sci Rep 6, 35916 (2016). https://doi.org/10.1038/srep35916):

$$
  \varepsilon_{\mathrm{eff}} =
  \frac{f_\uparrow\varepsilon_{d\uparrow} + f_\downarrow \varepsilon_{d\downarrow}}
       {f_\uparrow + f_\downarrow}-(\varepsilon_{d\downarrow} - \varepsilon_{d\uparrow})
    \frac{f_\uparrow - f_\downarrow}{f_\uparrow + f_\downarrow}
  $$

- Hammer–Nørskov d-band center (spin-summed, full energy range):

$$
  \varepsilon_{\mathrm{HN}} =
  \frac{\int E \big(D_{d\uparrow}(E)+D_{d\downarrow}(E)\big) dE}
       {\int \big(D_{d\uparrow}(E)+D_{d\downarrow}(E)\big) dE}
  $$



Optionally, the reduced fractional occupation is

$$
\mu = \frac{f_\uparrow - f_\downarrow}{f_\uparrow + f_\downarrow}.
$$

## Installation
- Python 3.8+ (standard library only).
- Tkinter must be available (usually bundled with Python). On Debian/Ubuntu:
  - `sudo apt-get install python3-tk`

## For ubuntu
- Just type in your terminal:
  
  **sudo apt update**
  
  **pip install --upgrade Spin-dependent-d-band-center**
- Then in bash type:
  
  **d-band-satadeep**
  
 --> This  will launch the application (see the pic below)

## Usage
```
python3 d-band.py
```
- Click “Browse…” and select your `DOSCAR` (generated with `ISPIN=2`, `LORBIT=11`).
- Enter the atom range (e.g., `17` to `24`).
- Click “Compute”.
- Once executed the window will look like the following:
<img width="872" height="889" alt="image" src="https://github.com/user-attachments/assets/10e663de-2404-43e4-a107-549ae1cfeeab" />


## Notes
- The app shifts energies so that \(E_F=0\). Integrals for \(f_\sigma\) run from the grid minimum to 0; centers use the full grid range.
- The code aggregates the five d-orbitals (dxy, dyz, dz2, dxz, dx2) for each spin channel.
- Results shown in eV.

## Credits
Created by Satadeep Bhattacharjee, IKST, Bangalore
sample-loggers 0.0.5
This package provides sample loggers.
License: MIT
Requires Python: >=3.9
Keywords: python, tools, logger

# Deep Learning Tools for Pytorch

[![Python >= 3.8](https://img.shields.io/badge/python->=3.8-blue.svg)](https://www.python.org/downloads/release/)

This package provides some common configurations to quickly create your own logger through the logging library. 

## Installation

```bash
pip install sample_loggers
```

## Example for using
To ues the logger directly, you can use the `logger` object provided by the package. 
```python
import os
from sample_loggers import logger

# you can set the log dir and log file name through environment variables.
os.environ["LOG_DIR"] = "./output/log"
os.environ["LOG_FILE_NAME"] = "log.txt"

logger.info("hello world")
```

To define your own logger, you can use the `create_logger` method. This method allows you to specify the name of the logger and the log level.
```python
from sample_loggers import create_logger, LEVEL

# The format can be defined as a string, 
# you can refer to the official website 
# https://docs.python.org/3/library/logging.html#logrecord-attributes.
define_format = "Define your own format here, you can use the default format or define your own format."

logger = create_logger(
    logger_name="logger",
    log_level=LEVEL.INFO,
    log_file_name="log.txt",
    output_dir="./output/log",
    format_type="default",
    define_format=define_format
)
```

## Update
- 0.0.5 - improve the default logger.
- 0.0.4post1 - Fix the bug.
- 0.0.4 - Samplify thr method to create logger and provide the default logger that can be used directly.
- 0.0.3post1 - Fix the bug.
- 0.0.3 - Add the level definition.
- 0.0.2post2 - Fix the bug.
- 0.0.2post1 - Fix the bug.
- 0.0.2 - Add the summery and throughput method.
- 0.0.1 - We provide the common config for logging to create logger.

## License

Sample Loggers is MIT licensed. See the [LICENSE](LICENSE) for details.
openhands-aci 0.3.2
An Agent-Computer Interface (ACI) designed for software development agents OpenHands.
License: MIT
Requires Python: <4.0,>=3.12

# Agent-Computer Interface (ACI) for OpenHands

An Agent-Computer Interface (ACI) designed for software development agents [OpenHands](https://github.com/All-Hands-AI/OpenHands). This package provides essential tools and interfaces for AI agents to interact with computer systems for software development tasks.

## Features

- **Code Editor Interface**: Sophisticated editing capabilities through the `editor` module
  - File creation and modification
  - Code editing
  - Configuration management

- **Code Linting**: Built-in linting capabilities via the `linter` module
  - Tree-sitter based code analysis
  - Python-specific linting support

- **Utility Functions**: Helper modules for common operations
  - Shell command execution utilities
  - Diff generation and analysis
  - Logging functionality

## Installation

```bash
pip install openhands-aci
```

Or using Poetry:

```bash
poetry add openhands-aci
```

## Project Structure

```
openhands_aci/
├── editor/           # Code editing functionality
├── linter/           # Code linting capabilities
└── utils/            # Utility functions
```

## Development

1. Clone the repository:
```bash
git clone https://github.com/All-Hands-AI/openhands-aci.git
cd openhands-aci
```

2. Install development dependencies:
```bash
poetry install --extras llama
```

3. Configure pre-commit-hooks
```bash
make install-pre-commit-hooks
```

4. Run tests:
```bash
poetry run pytest
```

## License

This project is licensed under the MIT License.