semvec/pypi-packages huggingface dataset:
idsim 0.0.1
Face Recognition Metric
Requires Python: >=3.7

# Identity Similarity

This repository can helps researchers that want to use face recognition in their researches. You can easly implement powerfull face recognition in your project. I motivated for this repository from 
[LPIPS](https://github.com/richzhang/PerceptualSimilarity). The models are borrowed from [Insigtface](https://github.com/deepinsight/insightface/tree/master/recognition/arcface_torch).

**Warning :** Please, be careful when chosing your criterion. Lower is more similar in MSE while higher is more similar in CosineSimilarity.


<img src="docs/similarity_distance_figure.png" >

## Usage

### 1. Training with preprocessed dataset.

In this case, we assume that you have aligned images using a keypoint template and you want to calculate identity similarity between two aligned images or a image and a saved identity vector.
```python

import torch
import numpy as np
from idsim import IdentitySimilarity

idsim = IdentitySimilarity()
template = np.array([[35.066223, 34.23266],
                  [84.1586, 33.96113],
                  [59.768444, 62.152763],
                  [39.60066, 90.89288],
                  [80.255, 90.66802]], dtype=np.float32)
idsim.set_ref_point(template)

# dummy variables
v1 = torch.rand(1, 512)
im1 = torch.rand(5, 3, 128, 128)

# useful functions
sim_v2v = idsim.forward_v2v(v1, v1)
sim_im2im = idsim.forward_img2img(im1, im1)
sim_v2im = idsim.forward_v2img(v1, im1)
print("\nsim_v2v :", sim_v2v, "\nsim_im2im :", sim_im2im, "\nsim_v2im :", sim_v2im)
```


### 2. Face recognition 

In this case, Idsim can caculate identity similarity of your images.

```python
import cv2
from idsim import IdentitySimilarity

idsim = IdentitySimilarity(criterion="Cosine")
img1 = cv2.imread("a.jpg")
img2 = cv2.imread("b.jpg")
v1 = idsim.extract_identity(img1) 
v2 = idsim.extract_identity(img2)
sim = idsim.forward_v2v(v1,v2)
print("Similarity :", sim)
```

**Note:** You can check the [proving_differentiability.ipynb](proving_differentiability.ipynb) for an example training.

## Todo

- [] Release pypi package

## Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are **greatly appreciated**.

1. Fork the Project
1. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
1. Run `make style && make quality` in the root repo directory, to ensure code quality.
1. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
1. Push to the Branch (`git push origin feature/AmazingFeature`)
1. Open a Pull Request
neuralbridge 1.0.2
Easy-to-use, one-liner AI model conversion tool between AI frameworks to ensure faster model development and portability with ONNX.
Requires Python: >=3.11.7

# NeuralBridge

Easy-to-use, one-liner AI model conversion tool between AI frameworks to ensure faster model development and portability with Open Neural Network Exchange (ONNX).

## How to Install

To install NeuralBridge simple use:

` pip install neuralbridge `

In case you would like to build this project from source please consult the ` requirements.txt ` file and the ` src ` directory.

## How to Use

The main use case of NeuralBridge is to convert model runtime. It converts TensorFlow and PyTorch models from one framework to another absolutely automatically. You can access runtime conversion like this:

```Python
from neuralbridge import convert_model

model_in_other_framework = convert_model(model_in_one_framework)
```

## Examples

You can find additional examples in the ` examples ` directory.

### Converting PyTorch model into TensorFlow

This example is in the ` example_tf_to_torch.py ` file.

### Converting TensorFlow model into PyTorch

This example is in the ` example_torch_to_tf.py ` file.

### Old design patterns

You can find the old design patterns in the following files:

- ` old_way_onnx_to_tf.py `

- ` old_way_onnx_to_torch.py `

- ` old_way_tf_to_onnx.py `

- ` old_way_tf_to_torch.py `

- ` old_way_torch_to_onnx.py `

- ` old_way_torch_to_tf.py `

## Requirements

The list of all requirements are in the [requirements.txt](https://gitlab.com/neuralbridge/neuralbridge-python/-/blob/main/requirements.txt).

## License

See details in the [LICENSE](https://gitlab.com/neuralbridge/neuralbridge-python/-/blob/main/LICENSE).
pypv 0.3.0
pypv is a state-of-the-art Python library designed to empower engineers and developers in the field of solar energy. This library simplifies the use of complex physical models, making it easier to calculate key solar parameters and optimize solar energy systems.
Requires Python: >=3.11
Keywords: photovoltaic,pv panel,pv inverter,mppt,pv-calculator

<div align="center">
    <a href="https://github.com/patrickpasquini/PyPv" target="_blank">
      <img src="./docs/assets/logo.png" alt="PyPv Logo">
    </a>
</div>

<div align="center">
    <a href="https://pypi.org/project/pypv/" target="_blank">
      <img src="https://img.shields.io/pypi/v/pypv" alt="PyPv Version">
    </a>
    <a href="https://pypi.org/project/pypv/" target="_blank">
      <img src="https://img.shields.io/badge/Python-3.11-blue" alt="Python Version">
    </a>
    <!-- <a href="LINK_TO_YOUR_COVERAGE_REPORT" target="_blank">
      <img src="https://img.shields.io/endpoint?url=URL_TO_YOUR_COVERAGE_BADGE_JSON&logo=pytest" alt="Coverage">
    </a> -->
    <a href="https://pepy.tech/project/pypv" target="_blank">
      <img src="https://static.pepy.tech/badge/pypv" alt="Downloads">
    </a>
</div>

---

**Documentation**: Building...

**Source Code**: <a href="https://github.com/patrickpasquini/PyPv" target="_blank">https://github.com/patrickpasquini/PyPv</a>

---

## Installation

You can install `pypv` using pip:
```bash
pip install pypv
```

## What is it?

**pypv** is a state-of-the-art Python library designed to empower engineers and developers in the field of solar energy. This library simplifies the use of complex physical models, making it easier to calculate key solar parameters and optimize solar energy systems. 

## Key Features

Here's a simple example of how to use the `calculate_pv_system` method:

```python
from pypv import PvSystem

# Config module and inverter
pv_module = PvModule(
    p_max=545,
    vmp=41.8,
    imp=13.09,
    voc=49.65,
    isc=13.92,
    efficiency=21.1,
    p_max_coefficient=-0.35,
    voc_coefficient=-0.27,
    isc_coefficient=0.048,
)

pv_inverter = PvInverter(
    start_voltage=90,
    max_voltage=600,
    nominal_voltage=360,
    nominal_power=5,
    efficiency=98,
    max_pv_power=6.5,
    MPPTs=[
        MPPT(min_voltage=90, max_voltage=560, isc=15, dc_inputs=1),
        MPPT(min_voltage=90, max_voltage=560, isc=15, dc_inputs=1),
    ],
)

# Creating an instance of PvSystem
pv_system = PvSystem(pv_module=pv_module, pv_inverter=pv_inverter, target_power=6, max_temperature=75, min_temperature=0)
pv_system.calculate()

# Displaying the results
PvSystem(inverter_quantity=2, ROS=0.8333333333333334, layouts=[Layout(line_quantity=2, modules_per_line=6), Layout(line_quantity=2, modules_per_line=5)])
```


## Contributing

We warmly welcome contributions to **pypv** Whether you're fixing a bug, adding a feature, or improving documentation, your help is invaluable.
azcam-webtools 24.0
A azcam web application for interacting with azcamserver.
Keywords: ccd,imaging,astronomy,sensors

# azcam-webtools

*azcam-webtools* contains azcam web applications which interact with azcamserver over the web interface. Current functions include a status page and an exposure control page.

## Installation

`pip install azcam-webtools`

Or download from github: https://github.com/mplesser/azcam-webtools.git.

## Usage:

ToDo


# Working - In Progress

## Webserver Tool

This tool implements a FastAPI-based web server.  See https://fastapi.tiangolo.com.

```python
from azcam.tools.webserver.fastapi_server import WebServer
webserver = WebServer()
webserver.index = f"index_mysystem.html"
webserver.start()
```

## Browser-based Tools

These tools implements various browser-based tools which connect to an azcam web server.

### Usage

Open a web browser to an address such as http://localhost:2403/status, replacing `localhost`, `2403`, and `status` with the appropriate hostname, web server port number, and tool name.

### Supported Browser Tools
 - status - display current exposure status
 - exptool - a simple exposure control tool
azcam-console 24.0
Console package for the AzCam acquisition and analysis package
Requires Python: >= 3.10
Keywords: ccd,imaging,astronomy,sensors

# AzCam Console

*azcam-console* is a console application for the *azcam* acquisition and analysis package. It usually runs in an IPython window and is used to both acquire and analyze data in a python scripting environment.

## Documentation

See https://azcam.readthedocs.io/.

## Installation

`pip install azcam-console`

Or download the latest version from from github: https://github.com/mplesser/azcam-console.git.

You may need to install `python3-tk` on Linux systems [`sudo apt-get install python3-tk`].

## Configuration and startup 

An example code snippet to start an *azcamconsole* process is:

```
ipython -m azcam_itl.console --profile azcamconsole -i -- -system DESI
```

and then in the IPython window:

```python
instrument.set_wavelength(450)
wavelength = instrument.get_wavelength()
print(f"Current wavelength is {wavelength}")
exposure.expose(2., 'flat', "a 450 nm flat field image")
```