semvec/pypi-packages huggingface dataset:
utilhttp 2.3.5
The lightweight yet powerful Python library.
License: MIT
Requires Python: >=3.8

# 🚀 UtilHTTP

Simplify your HTTP requests with UtilHTTP - the lightweight yet powerful Python library.

## 🌟 Quick Start

```python
import utilhttp

# Make a GET request with basic authentication
response = utilhttp.get('https://httpbin.org/basic-auth/user/pass', auth=('user', 'pass'))

# Check the response
print(f"Status Code: {response.status_code}")
print(f"Content Type: {response.headers['content-type']}")
print(f"Encoding: {response.encoding}")
print(f"Text: {response.text[:20]}...")  # First 20 characters
print(f"JSON: {response.json()}")
```

## 🛠️ Installation

Get UtilHTTP up and running in seconds:

```bash
pip install utilhttp
```

> 📌 Note: UtilHTTP supports Python 3.8 and above.

## 🔥 Key Features

- **Effortless Requests**: No need to manually handle query strings or form-encode data
- **JSON Support**: Seamlessly work with JSON data using the `json` method
- **Connection Magic**: Keep-Alive & Connection Pooling for optimal performance
- **Global Ready**: Full support for International Domains and URLs
- **Session Handling**: Cookie persistence across requests
- **Secure by Default**: Browser-style TLS/SSL Verification
- **Authentication**: Basic & Digest methods supported
- **Cookie Management**: Intuitive dict-like interface
- **Content Handling**: Automatic decompression and decoding
- **File Uploads**: Hassle-free multi-part file uploads
- **Proxy Support**: SOCKS proxy compatibility
- **Timeout Control**: Set connection timeouts with ease
- **Streaming**: Efficient streaming downloads
- **Config Integration**: Automatic .netrc file recognition
- **Chunked Requests**: Support for Chunked HTTP requests
---
modernreqs 2.3.5
The lightweight yet powerful Python library.
License: MIT
Requires Python: >=3.8

# 🚀 ModernReqs

Simplify your HTTP requests with ModernReqs - the lightweight yet powerful Python library.

## 🌟 Quick Start

```python
import modernreqs

# Make a GET request with basic authentication
response = modernreqs.get('https://httpbin.org/basic-auth/user/pass', auth=('user', 'pass'))

# Check the response
print(f"Status Code: {response.status_code}")
print(f"Content Type: {response.headers['content-type']}")
print(f"Encoding: {response.encoding}")
print(f"Text: {response.text[:20]}...")  # First 20 characters
print(f"JSON: {response.json()}")
```

## 🛠️ Installation

Get ModernReqs up and running in seconds:

```bash
pip install modernreqs
```

> 📌 Note: ModernReqs supports Python 3.8 and above.

## 🔥 Key Features

- **Effortless Requests**: No need to manually handle query strings or form-encode data
- **JSON Support**: Seamlessly work with JSON data using the `json` method
- **Connection Magic**: Keep-Alive & Connection Pooling for optimal performance
- **Global Ready**: Full support for International Domains and URLs
- **Session Handling**: Cookie persistence across requests
- **Secure by Default**: Browser-style TLS/SSL Verification
- **Authentication**: Basic & Digest methods supported
- **Cookie Management**: Intuitive dict-like interface
- **Content Handling**: Automatic decompression and decoding
- **File Uploads**: Hassle-free multi-part file uploads
- **Proxy Support**: SOCKS proxy compatibility
- **Timeout Control**: Set connection timeouts with ease
- **Streaming**: Efficient streaming downloads
- **Config Integration**: Automatic .netrc file recognition
- **Chunked Requests**: Support for Chunked HTTP requests
---
miso 3.1.25
Python scripts for training CNNs for particle classification
License: MIT
Requires Python: <=3.11,>=3.9
Keywords: microfossil, cnn

# Particle Classification
Python scripts for particle classification

Used by particle trieur to perform model training.

## Installation

```
conda create -n miso python=3.9
conda activate miso
conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
pip install tensorflow==2.10.1
pip install miso
```

## Updating

```
conda activate miso
pip install -U miso
```

## Command line interface (CLI)

### Train a model

TBD

### Classify a folder of images

Classifies a folder of images and saves the result in a CSV. This CSV can be imported into Particle Trieur. If the images are organised by sample into subfolders it will use the subfolder name as the sample name, else specify the sample name manually.

```
Usage: python -m miso classify-folder [OPTIONS]

  Classify images in a folder and output the results to a CSV file.

Options:
  -m, --model PATH              Path to the model information.  [required]
  -i, --input PATH              Path to the directory containing images.
                                [required]
  -o, --output PATH             Path where the output CSV will be saved.
                                [required]
  -b, --batch_size INTEGER      Batch size for processing images.  [default:
                                32]
  -s, --sample TEXT             Default sample name if not using
                                subdirectories.  [default: unknown]
  -u, --unsure_threshold FLOAT  Threshold below which predictions are
                                considered unsure.  [default: 0.0]
```

### Morphology of a folder of plankton images

Does morphology on a folder of plankton images and saves the result in a CSV. This CSV can be imported into Particle Trieur as parameters. If the images are organised by sample into subfolders it will use the subfolder name as the sample name, else specify the sample name manually.

The plankton model must first be downloaded from here: https://1drv.ms/f/s!AiQM7sVIv7fanuNzfcw2O8kAnDU26Q?e=NA5ztG

Make sure to put the `model.onnx` and the `model_info.xml` files in the same folder

```
Usage: python -m miso segment-folder [OPTIONS]

  Segment images in a folder and output the results.

Options:
  -m, --model PATH          Path to the model information.  [required]
  -i, --input PATH          Path to the folder containing images.  [required]
  -o, --output PATH         Path where the morphology csv will be saved.
                            [required]
  -b, --batch_size INTEGER  Batch size for processing images.  [default: 32]
  -s, --sample TEXT         Default sample name  [default: unknown]
  -t, --threshold FLOAT     Threshold for segmentation.  [default: 0.5]
  --save-contours           Whether to save contours or not.
```
pymodaq-plugins-oceaninsight 1.1.1
Set of PyMoDAQ plugins for OceanInsight (OceanOptics) spectrometers
License: MIT

pymodaq_plugins_oceaninsight (Ocean Insight (Optics) Spectrometers)
###################################################################

.. image:: https://img.shields.io/pypi/v/pymodaq_plugins_oceaninsight.svg
   :target: https://pypi.org/project/pymodaq_plugins_oceaninsight/
   :alt: Latest Version

.. image:: https://readthedocs.org/projects/pymodaq/badge/?version=latest
   :target: https://pymodaq.readthedocs.io/en/stable/?badge=latest
   :alt: Documentation Status

.. image:: https://github.com/PyMoDAQ/pymodaq_plugins_oceaninsight/workflows/Upload%20Python%20Package/badge.svg
    :target: https://github.com/PyMoDAQ/pymodaq_plugins_oceaninsight

PyMoDAQ plugin for OceanInsight (OceanOptics) spectrometers


Authors
=======

* Sebastien J. Weber
* Nicolas Tappy

Instruments
===========
Below is the list of instruments included in this plugin.
Url: https://www.oceaninsight.com/products/spectrometers/

Viewer1D
++++++++

* **Omnidriver**: Control of Spectrometer using the Omnidriver library (should be installed)
* **Seabreeze** : If the Omnidriver library is not available, a plugin implementation based on seabreeze is provided: https://python-seabreeze.readthedocs.io/en/latest/index.html