semvec/pypi-packages huggingface dataset:
foureg 1.0.0
Fourier transform based image registration
Requires Python: <3.12,>=3.8
Keywords: image registration, image

Foureg
======
Image registration using discrete Fourier transform.


Given two images, `foureg` calculates a similarity transformation that
transforms one image into the other.

Example
-------
The example transforms an image with a user defined transformation and then rediscovers
it using `foureg`.

```python
import matplotlib.pyplot as plt
import numpy as np
import torch
from PIL import Image

from foureg import (Constraints, frame_img, similarity, similarity_matrix,
                    transform_img)

# Generate the test images
transformation = similarity_matrix(0.8, -10, (60, 20))
master = np.asarray(Image.open("./resources/examples/sample1.png"))
master = torch.from_numpy(master.copy()).type(torch.float32)

slave = transform_img(master, transformation, invert=True)

# Define some constraints and coregister
constraints = Constraints(angle=(-10, 5), scale=(0.8, 0.2), tx=(60, 3), ty=(20, 1))
imreg_result = similarity(
    master, slave, constraints=constraints, numiter=5, filter_pcorr=5
)

# Transform the slave image
slave_transformed = transform_img(slave, imreg_result.transformation, invert=False)

_, axs = plt.subplots(1, 4, figsize=(13, 8))
im_0 = axs[0].imshow(master)
plt.colorbar(im_0, ax=axs[0])
im_1 = axs[1].imshow(slave)
plt.colorbar(im_1, ax=axs[1])
im_2 = axs[2].imshow(slave_transformed)
plt.colorbar(im_2, ax=axs[2])
im_3 = axs[3].imshow(np.abs(slave_transformed - master))
plt.colorbar(im_3, ax=axs[3])

plt.show()
```

Features
--------
* Image pre-processing options (frequency filtration, image extension).
* Under-the-hood options exposed (iterations, phase correlation filtration).
* Permissive open-source license (3-clause BSD).
* GPU accelerated

Origin story
------------
This is a fork of the [imreg_dft](https://github.com/matejak/imreg_dft) borned of the
desire to achieve the following goals:
- Ability to return the final transformation in matrix form as opposed to the angle,
translation and scaling factor separately. The original code makes obtaining that
matrix really hard because it does it performs using scipy in  away that each transformation
resizes the image.
- Better performance powered by pytorch
- A more focused codebase. The only goal here is to estimate similarity transformations
between pairs of images.


Acknowledgements
----------------
The code was originally developed by Christoph Gohlke (University of California, Irvine, USA)
and later on developed further by Matěj Týč (Brno University of Technology, CZ). This
repo wouldn't exist without them.
midi-neural-processor 1.0.3
Tokenize MIDI files for neural network processing
Requires Python: >=3.8
Keywords: midi, midi-processor, midi-tokenizer, music, neural, processor, tokenizer

# Midi Neural Processor (Fork)

**This repository is a fork of [this](https://github.com/jason9693/midi-neural-processor) repo with the main objective of structuring as an installable python package via pip.**

* Repository for midi-based machine learning model's {pre/post} processing.
* You can use this processor in any machine learnig library like tensorflow, pytorch, etc...

* This processor's algorithm is based on [PerformanceRNN](https://magenta.tensorflow.org/performance-rnn) & [Music Transformer (Polyphonic Music)](https://arxiv.org/abs/1809.04281) Model's preprocessing algorithm suggested by Google Magenta.

## Simple Usage

### Install

```bash
$ pip install midi-neural-processor
```

### Encoding & Load midi file

* You can load & encode your midi file just one line
* encode_midi() is a role of pre-processing.

```python
from midi_neural_processor.processor import encode_midi
encoded = encode_midi('bin/ADIG04.mid') ## 'bin/AIDG04.mid' is midi file path.
## output: [int, int, int, int, ... ].
## int range is range(0,388). 388 = NOTE_ON + NOTE_OFF + TIME_SHIFT + VELOCITY 
```

### Decoding

* decode_midi is convert integer array to midi form.
* you can gave method to ***file_path*** as a second args in that if you want to save midi as .mid file. 
* all elements in integer array should be range(0,388). 

```python
from midi_neural_processor.processor import decode_midi
decode_midi(encoded, 'bin/test.mid') ## 'bin/test.mid' is midi file path.
```

## Comming Soon

1. Pedal Control
2. Midi Converter to .tfrecords

## License

Project is published under the MIT licence. Feel free to clone and modify repo as you want, but don't forget to add reference to authors :)
conda-forge-only 0.1.0
A pre-commit hook to check that conda environment files use conda-forge only.
Requires Python: >=3.7

conda-forge-only
================

.. image:: https://github.com/sfinkens/conda-forge-only/workflows/CI/badge.svg
    :target: https://github.com/sfinkens/conda-forge-only/actions/workflows/ci.yml

.. image:: https://codecov.io/gh/sfinkens/conda-forge-only/graph/badge.svg?token=5MMOU081WQ
    :target: https://codecov.io/gh/sfinkens/conda-forge-only


A pre-commit hook to check that conda environment files use conda-forge only.

Usage
-----

Add the following lines to your project's ``.pre-commit-config.yaml``:

.. code-block:: yaml

    repos:
      - repo: https://github.com/sfinkens/conda-forge-only
        rev: v0.1.0
        hooks:
          - id: conda-forge-only
bskyshell 0.1.0
Add your description here
Requires Python: >=3.10

# BlueSky Shell

A python CLI to BlueSky.

Never leave your shell! Check-in on BlueSky or automate using BlueSky Shell.

## Installation

- `pip install bskyshell`
- `pipx install bskyshell`

## Usage

- `bsky` to open application.
- `bsky post` to post.