semvec/pypi-packages huggingface dataset:
MSKR-tiktok 1.0.0
TikTok username checker
Requires Python: >=3.7

# MSKR_tiktok

TikTok username checker library.

## Install
```
pip install MSKR_tiktok
```

## Usage
```python
from MSKR_tiktok import TikTokChecker

c = TikTokChecker()
print(c.check_user("muslim"))
```
jupyter-serial-scheduler 0.2.0
A tool to execute Jupyter Notebooks sequentially with resource isolation.
Requires Python: >=3.7

# Jupyter Notebook Scheduler

A simple Python script to execute multiple Jupyter Notebooks sequentially. This tool ensures that each notebook runs in a separate process, guaranteeing that GPU memory and other resources are fully released between runs.

## Features

- **Sequential Execution**: Runs notebooks one after another.
- **Resource Isolation**: Each notebook runs in a fresh process, clearing the kernel and freeing GPU memory upon completion.
- **Detailed Logging**: 
    - Real-time cell-level progress in the console (clean output).
    - Automatic log file generation in `logs/` for each run.
    - **Captures Cell Outputs**: Actual output from code cells (print statements, etc.) is saved to the log file but **hidden from the console** to keep it tidy.
- **Robust Error Handling**: 
    - Stops execution if a notebook fails.
    - **Saves the failed notebook** with the error traceback, allowing for easy debugging.

## Installation

**Option A: Install from PyPI (Recommended)**

```bash
pip install jupyter-serial-scheduler
```

**Option B: Install from Source**

1.  Clone the repository:
    ```bash
    git clone https://github.com/chandan2300/Serial-Notebook-Executor.git
    cd Serial-Notebook-Executor
    ```

2.  Install the package:
    ```bash
    pip install .
    ```

## Usage

Once installed, you can use the `nb-scheduler` command from anywhere.

1.  **Place Notebooks**:
    Put your `.ipynb` files in a directory (e.g., `notebooks/`).

2.  **Run the Scheduler**:
    
    **Option A: Run default list (defined in script)**
    ```bash
    nb-scheduler
    ```

    **Option B: Run specific notebooks**
    ```bash
    nb-scheduler notebooks/test_nb_1.ipynb notebooks/test_nb_2.ipynb
    ```

## Configuration

You can modify `scheduler.py` to change the list of notebooks or the directory they are located in.
nba-sheet-cleaner 0.1.1
Automate cleaning NBA projection sheets.
Requires Python: >=3.9

# NBA Sheet Cleaner

Utility for cleaning NBA projection spreadsheets.

## Usage

### Default behavior

```bash
nba-sheet-clean
```

- Processes the current working directory (same as `nba-sheet-clean .`).
- If a `sheets/` subfolder exists in the current directory, it is preferred automatically (equivalent to `nba-sheet-clean ./sheets`).
- Cleaned files are written to `<source>/sheets_cleaned/` (e.g., `./sheets/sheets_cleaned/`) with the `<name>_cleaned.<ext>` naming convention.

### Custom folder

```bash
nba-sheet-clean path/to/folder
```

Processes only the Excel files found inside `path/to/folder` and writes results to `path/to/folder/sheets_cleaned`.

### Single file

```bash
nba-sheet-clean path/to/file.xlsx
```

Processes only that Excel file and writes the cleaned version to `path/to/sheets_cleaned/file_cleaned.xlsx`.

### Notes

- Files ending with `_cleaned` are automatically skipped.
- The script requires the columns `Name`, `Team`, `p`, `3`, `r`, `a`, `s`, `b`. Missing columns cause the file to be skipped with a log message.
sensirion-shdlc-sensorbridge 1.0.0
Driver for Communicating With Sensirion SEK-SensorBridge
Requires Python: <4.0,>=3.8
Keywords: sensirion, sensor bridge, sensorbridge driver, shdlc

# Python Driver for Sensirion SEK-SensorBridge

This repository contains the SHDLC driver for the
[Sensirion SEK-SensorBridge](https://www.sensirion.com/sensorbridge/)
as a Python package. For details, please read the package description in
[README.rst](https://github.com/Sensirion/python-shdlc-sensorbridge/blob/master/README.rst).

## Usage

See package description in [README.rst](https://github.com/Sensirion/python-shdlc-sensorbridge/blob/master/README.rst) and user manual at
https://sensirion.github.io/python-shdlc-sensorbridge/.

## Development

We develop and test this driver using our company internal tools (version
control, continuous integration, code review etc.) and automatically
synchronize the `master` branch with GitHub. But this doesn't mean that we
don't respond to issues or don't accept pull requests on GitHub. In fact,
you're very welcome to open issues or create pull requests :)

### Check coding style

The coding style can be checked with [`flake8`](http://flake8.pycqa.org/):

```bash
pip install -e .[test]  # Install requirements
flake8                  # Run style check
```

In addition, we check the formatting of files with
[`editorconfig-checker`](https://editorconfig-checker.github.io/):

```bash
pip install editorconfig-checker==2.0.3   # Install requirements
editorconfig-checker                      # Run check
```

### Run tests

Unit tests can be run with [`pytest`](https://pytest.org/):

```bash
pip install -e .[test]          # Install requirements
pytest -m "not needs_device"    # Run tests without hardware
pytest                          # Run all tests
```

The tests with the marker `needs_device` have following requirements:

- A SensorBridge must be connected to the computer:
  - Firmware version must be 5.8
  - Default settings (baudrate 460800, address 0)
  - Port 0: SHTC3 connected
  - Port 1: No device connected
- Pass the serial port where the SensorBridge is connected with `--serial-port`,
  e.g. `pytest --serial-port=COM7`


### Build documentation

The documentation can be built with [Sphinx](http://www.sphinx-doc.org/):

```bash
python setup.py install                        # Install package
pip install -r docs/requirements.txt           # Install requirements
sphinx-versioning build docs docs/_build/html  # Build documentation
```

## License

See [LICENSE](https://github.com/Sensirion/python-shdlc-sensorbridge/blob/master/LICENSE).