semvec/pypi-packages huggingface dataset:
python-adc-eval 0.4.0
ADC Evaluation Library
License: MIT
Requires Python: >=3.9.0
Keywords: adc, analog-to-digital, evaluation, eval, spectrum

python-adc-eval |Lint| |PyPi Version| |Codecov| |Codestyle|
=============================================================

A python-based ADC evaluation tool, suitable for standalone or library-based usage

Details
--------

Inspired by
`esynr3z/adc-eval <https://github.com/esynr3z/adc-eval>`__

Performs spectral analysis of a dataset utilizing the Bartlett method. Calculates SFDR, SNDR, as well as harmonics.

.. figure:: analyser.png
   :alt: analyser

   analyser


USAGE
=======

To load the library in a module:

.. code-block:: python

    import adc_eval


Given an array of values representing the output of an ADC, the spectrum can be analyzed with the following:

.. code-block:: python

    import adc_eval

    adc_eval.spectrum.analyze(
        <data>,
        <fft bins>,
        fs=<sample frequency>,
        dr=<dynamicrange/vref>,
        harmonics=<num of harmonics to find>,
        leak=<adjacent bins to filter>,
        window=<window type (rectangular/hanning)>,
        no_plot=<True/False>,
        yaxis=<"power"/"fullscale"/"magnitude">,
        single_sided=<True/False>
    )


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   :target: https://github.com/fronzbot/python-adc-eval/actions?query=workflow%3ALint
.. |PyPi Version| image:: https://img.shields.io/pypi/v/python-adc-eval.svg
   :target: https://pypi.org/project/python-adc-eval
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   :target: https://github.com/psf/black
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 :target: https://codecov.io/gh/fronzbot/python-adc-eval
auconfig 0.1.0
Framework para pipelines de procesamiento de datos con Spark
License: MIT
Requires Python: <4.0,>=3.8

# AU_DATA Framework

A Python framework for building Spark-based data processing pipelines with support for both local and Databricks environments.

## Features

- Unified Spark session management (local/Databricks)
- YAML-based configuration system
- Pipeline/node configuration validation
- Environment-aware path resolution
- Automatic fallback mechanisms

## Installation

### From PyPI (recommended)
```bash
pip install auconfig
mixgarden 0.1.3
Python SDK for the Mixgarden API
License: MIT
Requires Python: >=3.9
Keywords: ai, llm, mixgarden, sdk

# Mixgarden Python SDK

```bash
pip install mixgarden
```

- Use `get_models()` to get a list of available models and providers.
- Use `get_plugins()` to get a list of available plugins.
- Use `get_conversations()` to get a list of available conversations.
- Use `get_conversation(id)` to get a specific conversation.
- Use `chat(model, messages)` when you’re building a conversational UI, or want the platform to maintain context for you.
- Use `get_mg_completion(model, content)` when you need a one-off, stateless generation enhanced by Mixgarden plugins.
- Use `get_completion(model, messages)` when you need a one-off, stateless generation directly from LLM (Raw LLM Output). 

```python
import os
from mixgarden import MixgardenSDK

sdk = MixgardenSDK(api_key=os.getenv("MIXGARDEN_API_KEY"))

# List available models
models = sdk.get_models()

# Stateful chat (conversation is stored server-side)
chat = sdk.chat(
    model="openai/gpt-4o-mini",
    # Optionally add conversationId to add to an existing conversation
    # conversationId: "your-convo-id",
    content="Hello mixgarden!",
    pluginId="tone-pro",
    pluginSettings={
        "emotion-type": "neutral",
        "emotion-intensity": 6,
        "personality-type": "friendly",
    },
)

# Other helper calls
plugins = sdk.get_plugins()
conversations = sdk.get_conversations()
conversation = sdk.get_conversation(conversations[0]["id"])

# One-shot completion (Mixgarden Enhanced)
completion = sdk.get_mg_completion(
    model="openai/gpt-4o-mini",
    content="hello mixgarden!",
    maxTokens=100,
    temperature=0.7,
    pluginId="tone-pro",
    pluginSettings={
        "emotion-type": "neutral",
        "emotion-intensity": 6,
        "personality-type": "friendly"
    }
)

# One-shot completion (Raw LLM Output)
completion = sdk.get_completion(
    model= "openai/gpt-4o-mini",
    messages=[{"role": "user", "content": "Hi there!"}],
    maxTokens=100,
    temperature=0.7,
)

print(chat)
print(completion)
```