semvec/pypi-packages huggingface dataset:
gdrivedl-parallel 0.1.0
Python library that allows you to download filers/folders from Google Drive
Requires Python: >=3.7

# GDriveDL-Parallel

Google Drive Download Script

-   Python 2 / 3 compatible
-   No API keys / credentials required
-   Supports all operating systems
-   No external dependencies
-   Works with shared files or folders
-   Works with large files
-   Files / folders must have been shared via link
-   Files are downloaded concurrently. 

## Usage

```bash
python gdrivedl-parallel.py <URL>
```
-   URL is a shared Google Drive URL

Multiple urls can be used by seperating them with a space. eg. ```python gdrivedl-parallel.py <URL1> <URL2> <URL3>```<br>
On some systems you may need to enclose the url within quotes. eg ```python gdrivedl-parallel.py "<URL>"```

### Options
- `-P` `--directory-prefix` Output directory (default is current directory)
- `-O` `--output-document` Download to a particular filename (defaults to the
  GDrive filename). Only valid when downloading a single file.
- `-q` `--quiet` Disable console output
- `-d` `--debug` Show more vebose debug console output
- `-m` `--mtimes` Try use modified times to check for changed files
- `-f` `--urlfile` Text file containing Google Drive URLS to download (one per line)
catwallpaper 0.11
Download and apply random cat wallpapers on every platform (windows, mac or even linux) automatically
Keywords: cat,catwallpaper,wallpaper,meow,cats

What it does?
===============
Web Scraping Using Python to Download and Apply Random Cat Images as Wallpaper According to Your Operating System

Supported platforms
===============
Presently, it provides support the following platforms mentioned below:

1. Windows
2. Mac
3. Linux with desktop environment: Gnome, Xfce, Mate, Kde plasma

Installation
===============

It's available on pypi as https://pypi.org/project/catwallpaper/


``pip install catwallpaper``
orthanc-mock 0.1.0
Mock for the python orthanc plugin intended for development
Requires Python: >=3.8,<4.0

# orthanc-mock
__Mock for the python orthanc plugin intended for development.__

This library is intended to be used while developing python scripts
with the Python plugin for Orthanc. It simply provides the 
functions/classes/enums signatures for the autocomplete.

## Installation
```bash
pip install orthanc-mock
```

## Usage


Note that the `orthanc-mock` library should be installed as a
__dev__ dependency so that it does not interfere while running inside Orthanc.

```python
import orthanc

# Functions are accessible
orthanc.RegisterRestCallback()
orthanc.RestApiGet()
...

# Classes are as well
orthanc.DicomInstance.GetInstanceMetadata()
orthanc.DicomInstance.HasInstanceMetadata()
...

# And the enum
orthanc.ChangeType.NEW_STUDY
orthanc.ChangeType.ORTHANC_STOPPED
...
```

## Credits
The `orthanc.py` has been generated from the `data/python-sdk.txt` file,
which is from the [Python Orthanc Plugin](https://github.com/orthanc-server/orthanc-setup-samples/blob/master/python-samples/python-sdk.txt)
quantum-distance-based-classifier 0.0.4
The Quantum Distance-based classifier is a technique inspired by the classical k-Nearest Neighbors that leverage quantum properties to perform prediction. The package has been implemented in Qiskit

The Quantum Distance-based classifier is a technique inspired by the classical k-Nearest Neighbors that leverage quantum properties to perform prediction. The package has been implemented in Qiskit.

        ```
        from quantum_distance_based_classifier.quantum_distance_based_classifier import QuantumDistaceBasedClassifier
        from sklearn import preprocessing
        from sklearn.datasets import load_iris
        from sklearn.preprocessing import StandardScaler
        import numpy as np


        X, y = load_iris(return_X_y=True)

        n_features = 2
        X = X[:, :n_features] # Keep only n_features

        # Standardize and normalize the features
        X = StandardScaler().fit_transform(X)
        X = preprocessing.normalize(X, axis=1)

        # Initialize variables to store sampled instances
        sampled_X = []
        sampled_y = []

        # Loop through each class to sample instances
        for class_label in np.unique(y):
            class_indices = np.where(y == class_label)[0]
            sampled_indices = np.random.choice(class_indices, size=instances_per_class, replace=False)
            sampled_X.extend(X[sampled_indices])
            sampled_y.extend(y[sampled_indices])

        # Convert lists to numpy arrays
        sampled_X = np.array(sampled_X)
        sampled_y = np.array(sampled_y)

        qdbc = QuantumDistaceBasedClassifier()
        qdbc.fit(sampled_X, sampled_y)
        result = qdbc.predict(sampled_X[0])
        print(f"Classification result: {result}")
        ```