semvec/pypi-packages huggingface dataset:
MAVdataflash 2.5.3
Read, analyze and visualize *.bin flight data logs recorded by ArduPilot
Requires Python: >=3.6

# MAVdataflash
Read, analyze and visualize *.bin flight data logs recorded by ArduPilot

## Installation
#### Installing via [PyPI](https://pypi.org/project/MAVdataflash/)
Install the latest version with:
```
$ pip install --upgrade MAVdataflash
```
#### Installing via [GitHub](https://github.com/generalaeronautics/MAVdataflash)
Install directly from the source code:

    $ pip install https://github.com/generalaeronautics/MAVdataflash/archive/refs/heads/latest.zip

If you have git installed, you can also try:

    $ pip install git+https://github.com/generalaeronautics/MAVdataflash.git

You can also install by cloning or downloading the repo:

    $ git clone https://github.com/generalaeronautics/MAVdataflash
    $ cd MAVdataflash
    $ pip install .

If you get any installation or compilation errors, make sure you have the latest pip and setuptools::

    $ pip install --upgrade pip setuptools

## Usage
```python
from MAVdataflash import DataFlash
```
```python
# Create a new instance with DataFlash class
analysis = DataFlash("flight_data.bin")
```

```python
# Return data in pandas dataframe
analysis.GetData('IMU')

# Return data of specific instance in pandas dataframe
analysis.GetData('IMU', instance=1)

# Return data in polars dataframe
analysis.GetData('IMU', in_polars=True)
```

```python
# Plot any Parameter and Subparameter VS Time
analysis.Plot('GPS', 'Alt')

# Plot with instance, if parameter data type has more than 1 instance
analysis.Plot('IMU', 'GyrX', instance=1)
```

```python
# Return the pandas dataframe of EV(Events) data type with Message
analysis.GetEvents()

# Return in polars dataframe
analysis.GetEvents(in_polars=True)
```

```python
# Return the pandas dataframe of MODE(Modes) data type with Message
analysis.GetModes()

# Return in polars dataframe
analysis.GetModes(in_polars=True)
```

```python
# Return the pandas dataframe of PARM(Commands) with Values
analysis.GetPARAMS()

# Return Params with Date and Time included in dataframe
analysis.GetPARAMS(with_datetime=True)

# Return Params in dictionary data type
analysis.GetPARAMS(in_dict=True)

# Return in polars dataframe
analysis.GetPARAMS(in_polars=True)
```

```python
# Return PARM(Command) with Value
analysis.GetPARAM('EK3_IMU_MASK')
```

```python
# Return the list of Subparameter(Columns) of the Parameter data type
analysis.GetColumns('GPS')
```

```python
# Return True if Parameter data type is plottable otherwise return as False
analysis.isPlotable('GPS')
analysis.isPlotable('GPS', column='Alt')
```
sherlock-scanner 1.0.1
A Python security scanner for hardcoded secrets and API keys.
Requires Python: >=3.6

# Sherlock Scanner

Sherlock is a Python-based security scanner designed to detect hardcoded secrets and API keys in files and directories.

## Features
- ✅ Scans for API keys, passwords, and secrets in codebases.
- ✅ CLI-based tool for easy use in CI/CD pipelines.
- ✅ Recursively scans files and directories.

## Installation
### You can install Sherlock directly from PyPI:
```
pip install sherlock-scanner
```

### Scan the current directory for secrets
```
sherlock .
```

### Scan a specific folder
```
sherlock /path/to/code
```

## Example Output:
```
⚠️ Security issues found:
[
  {
    "file": "example.py",
    "issue": "API Key found",
    "severity": "HIGH"
  }
]
```
lite-llm-client 0.2.0
A small example package
Requires Python: >=3.8

# Lite LLM Client

This project made for very light llm client.
the main idea is `do not use any llm client library`.

# setup

## How to pass `API_KEY`

1. use parameter of LLMConfig
```python
from lite_llm_client import LiteLLMClient, OpenAIConfig, LLMMessage, LLMMessageRole

client = LiteLLMClient(OpenAIConfig(api_key="YOUR API KEY"))
answer = client.chat_completions(messages=[LLMMessage(role=LLMMessage.USER, content="hello ai?")])

print(answer)
```
2. use .env
    - rename `.env_example` to `.env`
    - replace YOUR KEY to real api_key

```bash
OPENAI_API_KEY=YOUR KEY
ANTHROPIC_API_KEY=YOUR KEY
GEMINI_API_KEY=YOUR KEY
```


# Known issue

- gemini path may not stable. guide code has `/v1beta/...`. sometimes gemini returns http 500 error

# Roadmap

## Future version
- [ ] support multimodal (image and text)
- [ ] apply opentelemetry in async functions

## 0.2.0
- [x] `2025-01-11` support json mode(OpenAI)
- [x] `2024-11-01` apply opentelemetry in sync functions

## 0.1.2
- [x] `2024-10-25` fix exception when use new model name as str type

## 0.1.0
- [x] `2024-07-21` support OpenAI
- [x] `2024-07-25` support Anthropic
- [x] `2024-07-27` add options for inference
- [x] `2024-07-28` support Gemini
- [x] `2024-07-30` support streaming (OpenAI). simple SSE implement.
- [x] `2024-07-31` support streaming (Anthropic).
- [x] `2024-08-01` support streaming (Gemini). unstable google gemini.
- [x] `2024-08-13` support inference result(token count, stop readon)



# Reference

- [OpenAI REST API](https://platform.openai.com/docs/api-reference/chat/create)
- [Gemini REST API](https://ai.google.dev/gemini-api/docs/get-started/tutorial?lang=rest)
nsi25Perlin 0.0.2
nsi25Perlin is a small module that implements Perlin's noise.
Requires Python: >=3.10

# nsi25Perlin

nsi25Perlin is a small module that implements Perlin's noise.
nsi25Perlin has been designed for the [Trophées NSI 2025](https://trophees-nsi.fr).

## Warning

This package is not maintained and is not intended to be used.