semvec/pypi-packages huggingface dataset:
lyonesez 1.1
lyonesez
License: MIT
Keywords: SOME, MEANINGFULL, KEYWORDS

# Python library quick start
This is a minimal template for uploading your python packages to pypi.org.

## To Use
* First clone this project.
```bash
# Clone this repository
git clone https://github.com/defartsa23/python-library-quick-start.git
# Go into the repository
cd python-library-quick-start
```
* Rename the folder `your-package` to the package name you want. then place all your files into a folder. Open the `__init__.py` file with a text editor of your choice. In this file, you write nothing but the import statement which has the following schema:
```python
from .Filename import Classname
```
* Replace the information in the `setup.py` file with the matching content.
* Upload your package to PyPi
```sh
python setup.py sdist
twine upload dist/*
```
rabbitproofz 1.1
rabbitproofz
License: MIT
Keywords: SOME, MEANINGFULL, KEYWORDS

# Python library quick start
This is a minimal template for uploading your python packages to pypi.org.

## To Use
* First clone this project.
```bash
# Clone this repository
git clone https://github.com/defartsa23/python-library-quick-start.git
# Go into the repository
cd python-library-quick-start
```
* Rename the folder `your-package` to the package name you want. then place all your files into a folder. Open the `__init__.py` file with a text editor of your choice. In this file, you write nothing but the import statement which has the following schema:
```python
from .Filename import Classname
```
* Replace the information in the `setup.py` file with the matching content.
* Upload your package to PyPi
```sh
python setup.py sdist
twine upload dist/*
```
spike-cluster-score 0.0.1
Spike Cluster Score (SCS) is a clustering performance metric designed with the purpose of spike sorting in mind
License: MIT
Requires Python: >=3.7

# Spike-Cluster-Score
Spike Cluster Score (SCS) is a clustering performance metric designed with the purpose of spike sorting in mind. Like Purity, it will evaluate the clustering of each sample as its own cluster as a perfect clustering. SCS was developed to not punish overclustering, as overclustering is acceptable for some applications. SCS was developed as underclustering was considered unacceptable and Purity did not penalise enough the underclustering of K-Means and DBSCAN. As a result, the score of Purity was similar across all algorithms. It is the user’s responsibility to evaluate how much overclustering is acceptable for each dataset. Clustering each sample as its own cluster will receive a perfect score but will not provide information. We recommend using SCS together with other performance evaluation metrics, while taking into consideration the weaknesses of each metric.

SCS was published in Frontiers in Computational Neuroscience, alongside with a clustering algorithm.

Install:
```
pip install spike-cluster-score
```

Usage example:
```
import numpy as np
from sklearn.cluster import KMeans

from spike_cluster_score import spike_cluster_score

for sim_nr in [1,4,21,22,30]:
    X = np.load(f"./data/sim{sim_nr}_data.npy")
    y = np.load(f"./data/sim{sim_nr}_labels.npy")

    nr_clusters = len(np.unique(y))
    kmeans = KMeans(n_clusters=nr_clusters, random_state=0).fit(X)

    print(f"{spike_cluster_score(y, kmeans.labels_):.3f}")

```

## Citation
We would appreciate it, if you cite the paper when you use this work:

- For Plain Text:
```
E.-R. Ardelean, A.-M. Ichim, M. Dînşoreanu, and R. C. Mureşan, “Improved space breakdown method – A robust clustering technique for spike sorting,” Frontiers in Computational Neuroscience, vol. 17, 2023, doi: 10.3389/fncom.2023.1019637.
```

- BibTex:
```
@ARTICLE{10.3389/fncom.2023.1019637,
AUTHOR={Ardelean, Eugen-Richard and Ichim, Ana-Maria and Dînşoreanu, Mihaela and Mureşan, Raul Cristian},   
TITLE={Improved space breakdown method – A robust clustering technique for spike sorting},      
JOURNAL={Frontiers in Computational Neuroscience},      
VOLUME={17},           
YEAR={2023},      
URL={https://www.frontiersin.org/articles/10.3389/fncom.2023.1019637},       
DOI={10.3389/fncom.2023.1019637}      
}
```
indiminishablez 1.1
indiminishablez
License: MIT
Keywords: SOME, MEANINGFULL, KEYWORDS

# Python library quick start
This is a minimal template for uploading your python packages to pypi.org.

## To Use
* First clone this project.
```bash
# Clone this repository
git clone https://github.com/defartsa23/python-library-quick-start.git
# Go into the repository
cd python-library-quick-start
```
* Rename the folder `your-package` to the package name you want. then place all your files into a folder. Open the `__init__.py` file with a text editor of your choice. In this file, you write nothing but the import statement which has the following schema:
```python
from .Filename import Classname
```
* Replace the information in the `setup.py` file with the matching content.
* Upload your package to PyPi
```sh
python setup.py sdist
twine upload dist/*
```
blessz 1.1
blessz
License: MIT
Keywords: SOME, MEANINGFULL, KEYWORDS

# Python library quick start
This is a minimal template for uploading your python packages to pypi.org.

## To Use
* First clone this project.
```bash
# Clone this repository
git clone https://github.com/defartsa23/python-library-quick-start.git
# Go into the repository
cd python-library-quick-start
```
* Rename the folder `your-package` to the package name you want. then place all your files into a folder. Open the `__init__.py` file with a text editor of your choice. In this file, you write nothing but the import statement which has the following schema:
```python
from .Filename import Classname
```
* Replace the information in the `setup.py` file with the matching content.
* Upload your package to PyPi
```sh
python setup.py sdist
twine upload dist/*
```
tensor-maximum-entropy 0.0.2
Python implementation of the Tensor Maximum Entropy (TME)
License: MIT
Requires Python: >=3.7

# TME
Python implementation of Tensor Maximum Entropy (TME)

Install:
```
pip install tensor-maximum-entropy
```

Usage example:
```
import numpy as np
import scipy.io
from tensor_maximum_entropy import TME

model_dim = 10

data = scipy.io.loadmat('./exampleData.mat')
dataTensor = data['dataTensor']

print(dataTensor.shape)
t = data['t']

mask = np.logical_and(t > - 50, t < 350)
TME(dataTensor, mask, model_dim)
```

The algorithm description can be found in the following article:
```
Elsayed, G.F.; Cunningham, J.P. Structure in Neural Population Recordings: An Expected Byproduct of Simpler Phenomena? Nat Neurosci 2017, 20, 1310â€“1318, doi:10.1038/nn.4617.
```

A matlab implementation can be found at the following link:
https://github.com/gamaleldin/TME
corrected-fisher-randomization 0.0.2
Python implementation of the Corrected Fisher Randomization (CFR)
License: MIT
Requires Python: >=3.7

# TME
Python implementation of Corrected Fisher Randomization (CFR)


Install:
```
pip install corrected-fisher-randomization
```

Usage example:
```
import numpy as np
import scipy.io
from corrected_fisher_randomization import CFR

model_dim = 10

data = scipy.io.loadmat('./exampleData.mat')
dataTensor = data['dataTensor']

print(dataTensor.shape)
t = data['t']

mask = np.logical_and(t > - 50, t < 350)
CFR(dataTensor, mask, model_dim)
```

The algorithm description can be found in the following article:
```
Elsayed, G.F.; Cunningham, J.P. Structure in Neural Population Recordings: An Expected Byproduct of Simpler Phenomena? Nat Neurosci 2017, 20, 1310â€“1318, doi:10.1038/nn.4617.
```

A matlab implementation can be found at the following link:
https://github.com/gamaleldin/CFR
pulse-coupled-nn 0.0.6
Python implementation of the Pulse Coupled Neural Network (PCNN)
License: MIT
Requires Python: >=3.7

# PCNN
Python implementation of the Pulse Coupled Neural Network (PCNN) alongside multiple variations:
- Classical PCNN
- Feature Linking Model (FLM)
- Intersecting Cortical Model (ICM)
- Multi Linking Model (MLM)
- Spiking Cortical Model (SCM)
- Sigmoidal Linking Model (SLM)

Install:
```
pip install pulse_coupled_nn
```

Usage example:
```
import numpy as np
import matplotlib.pyplot as plt

from pulse_coupled_nn import FLM
from pulse_coupled_nn import ICM
from pulse_coupled_nn import ClassicalPCNN
from pulse_coupled_nn import SCM
from pulse_coupled_nn import SLM


def run_image_segm(gamma=1, beta=2, v_theta=400, kernel_size=3, kernel='gaussian'):

    image = np.array(
        [[230, 230, 230, 230, 115, 115, 115, 115],
        [230, 230, 230, 230, 115, 115, 115, 115],
        [230, 230, 205, 205, 103, 103, 115, 115],
        [230, 230, 205, 205, 103, 103, 115, 115],
        [230, 230, 205, 205, 103, 103, 115, 115],
        [230, 230, 230, 230, 115, 115, 115, 115],
        [230, 230, 230, 230, 115, 115, 115, 115]]
    )

    model = ClassicalPCNN(image.shape, kernel, kernel_size=kernel_size)
    segm_image = model.segment_image(image, gamma=gamma, beta=beta, v_theta=v_theta, kernel_type='gaussian')

    plt.imshow(image)
    plt.colorbar()
    plt.show()

    plt.imshow(segm_image)
    plt.colorbar()
    plt.show()


run_image_segm()
```
kellyapi 0.0.2.5
A Project Made To Centralize Various APIs 📖 No Authorization Needed :)
License: MIT
Keywords: Kelly-API, Kelly, KellyAI, KellyAPI

# Kelly AI: A Versatile Language and Media Generation API

## Introduction:

Kelly AI is a comprehensive API that empowers developers to access powerful language and media generation services. With over 58 Stable Diffusion and 8 SDXL models, advanced LLMs, image upscaling capabilities, text-to-voice generation, and voice-to-text transcription, Kelly AI offers a wide range of capabilities to enhance any application or project.

## Image Generation:

* Stable Diffusion: Choose from 58 Stable Diffusion models, each with unique strengths and styles, to generate realistic and creative images from text prompts.
* SDXL: Leverage 8 specialized Stable Diffusion XL models for even higher quality and detailed image generation.

## Language Generation:

* ChatGPT: Utilize the advanced capabilities of ChatGPT, a dialogue-based AI model, to generate human-like text, answer questions, and assist with language tasks.
* Gemini: Access Gemini, a multimodal AI model, for tasks such as Q&A, summarization, translation, and more.
* Gemma: Employ Gemma, a specialized AI model for natural language processing, to perform tasks like text completion, language translation, and dialogue generation.
* RoastAI: Utilize RoastAI, a humorous AI model, to generate witty and entertaining responses.
* KellyAI: Engage with KellyAI, a general-purpose AI model, for a wide range of language-related tasks, including text generation, summarization, and translation.

## Image Upscaling:

* Upscale: Enhance the quality of your images by upscaling them up to 4x with our advanced image upscaling technology.

## Voice Generation:

* Text-to-Voice: Empower your applications with the ability to generate natural-sounding audio from text using 49 different voice models.

## Voice-to-Text Transcription:

* Voice-to-Text: Transcribe spoken audio into text with high accuracy, supporting multiple languages and accents.

### Benefits:

* Powerful API: Access a suite of cutting-edge AI models through a single, easy-to-use API.
* Versatile Capabilities: Enhance your applications with a wide range of language and media generation functionalities.
* High Quality: Generate realistic images, fluent text, natural-sounding audio, and accurate transcriptions.
* Customization: Choose from a variety of AI models and settings to suit your specific needs.
* Enhanced User Experience: Improve user engagement and satisfaction by integrating AI-powered features into your apps.

### Applications:

* Content Creation: Generate captivating images and text for social media, marketing materials, and e-commerce.
* Education: Create interactive learning experiences and personalized educational content.
* Customer Service: Provide virtual assistants with advanced AI capabilities for improved support.
* E-commerce: Enhance product descriptions, generate personalized recommendations, and improve customer interactions.
* Art and Design: Explore new artistic possibilities and unlock creative inspiration.