semvec/pypi-packages huggingface dataset:
macimg 0.0.3
A package for manipulating images using Objective-C frameworks
Requires Python: >=3.10

# macimg
A Python package for manipulating images using Objective-C frameworks.

# Examples

## Example 1: Applying Image Filters

```python
from macimg import Image
from macimg.filters import (
    Bloom,
    Comic,
    Crystallize,
    Edges,
    Invert,
    Sepia,
    Vignette
)

# Load an image
img = Image("assets/Iceland1.jpg")
img.show_in_preview()

# Apply a single filter
Vignette(intensity=10).apply_to(img)
img.show_in_preview()

# Apply multiple filters
filters = [Comic(), Edges(), Invert(), Sepia(), Bloom(), Crystallize(crystal_size=50)]
for index, filter in enumerate(filters):
    filter.apply_to(img)
    img.show_in_preview()
```

The code above produces the following sequence of images:
![Images produced by applying several filters sequentially](./assets/FilterExample.png)

## Example 2: Applying Distortions

```python
from macimg import Image
from macimg.distortions import (
    Bump,
    CircleSplash,
    Hole,
    LightTunnel,
    Pinch,
    TorusLens,
    Vortex,
)

# Load image
img = Image("assets/Iceland2.jpg")
img.show_in_preview()

# Apply multiple distortions
distortions = [Bump(radius=500, curvature=1), Vortex(radius=750, angle=1000.0), TorusLens(radius=1000, width=250), Hole(), Pinch(intensity=1), CircleSplash(), LightTunnel()]
for index, distortion in enumerate(distortions):
    distortion.apply_to(img)
    img.show_in_preview()
```

The code above produces the following sequence of images:
![Images produced by applying several distortions sequentially](./assets/DistortionExample.png)

## Example 3: Applying Transformations

```python
from macimg import Image
from macimg.transforms import (
    Flip,
    Rotate,
)

# Load image
img = Image("assets/Iceland3.jpg")
img.show_in_preview()

# Apply multiple transformations
transforms = [Flip("horizontal"), Flip("vertical"), Rotate(degrees=30)]
for index, transform in enumerate(transforms):
    transform.apply_to(img)
    img.show_in_preview()
```

The code above produces the following sequence of images:
![Images produced by applying multiple transformations sequentially](./assets/TransformExample.png)
pydislib 0.7
Tool box for python
Keywords: python

Pytool Package
==============

`pytool` is a collection of often used helper methods and classes that make
life easier or fill in gaps in the Python Standard Library.

Pytool is compatible with Python 2 (tested against 2.7) or Python 3.3 and
higher. *Compatibility added August 4, 2015.*

*Note*: Python 2 is now `End of Life <https://www.python.org/doc/sunset-python-2/>`_
and support in Pytool is deprecated and will be removed in Pytool 4.x.

Pytool's source is hosted on Github: `<https://github.com/shakefu/pytool>`_

Any comments, issues or requests should be submitted via Github:
`<https://github.com/shakefu/pytool/issues>`_

.. image:: https://travis-ci.com/shakefu/pytool.svg?branch=master
   :target: https://travis-ci.com/shakefu/pytool

.. image:: https://coveralls.io/repos/shakefu/pytool/badge.svg?branch=master&service=github
  :target: https://coveralls.io/github/shakefu/pytool?branch=master


Documentation
-------------

Read the `complete documentation <https://pytool.readthedocs.org/en/latest/>`_
on `Read the Docs <https://readthedocs.org>`_!

Installation
""""""""""""

Pytool is available on PyPI for your installation ease.

.. code-block:: bash

   $ pip install pytool

Usage
=====

Pytool is a collection of common and useful functions designed to help your
code be easier to read, write and maintain. The API is well documented with
examples of how they might be used.

See the `complete documentation <https://pytool.readthedocs.org/en/latest/>`_
on Read The Docs for more information.

Contributors
============

* `shakefu <https://github.com/shakefu>`_ (creator, maintainer)
* `dshen109 <https://github.com/dshen109>`_ (contributor)
* `abendig <https://github.com/abendig>`_
lancium-compute-api 1.0.2
Client Library for Lancium API
Requires Python: >=3.6

# lancium-library

This is the Python library for Lancium Compute's API. 

# Notes: 
* Must upgrade requests module if you are running Python 3.10 or later.

`pip3 --install --upgrade requests`

* If switching from Python3.9 and under to Python3.10 and over, must uninstall and reinstall the library with the version of pip corresponding to the version of Python you are on.

## Setting up a Lancium Compute Account

Please head to `https://portal.lancium.com/` to sign-up for a Lancium Compute account and retrieve an API key.

## Using the Python Library

Simply `pip install lancium-compute-api`.

Create an API key for your Lancium Compute account and export the key prior to using any modules like so:

`export LANCIUM_API_KEY=$KEY_FROM_WEBSITE`

## Python Modules

* `lancium.api.Job`
* `lancium.api.JobInput`
* `lancium.api.Data`
* `lancium.api.Image`
* `lancium.api.Resources`

## Lancium Compute Library / API Documentation
Documentation for the Lancium Library and API can be found here:

* https://docs.lanciumcompute.com/


## Support

Please contact support@lancium.com for support.
streamlit-apexjs 0.0.3
streamlit_apexcharts React component for Streamlit
Requires Python: >=3.7

# Streamlit - ApexCharts ReactJS

A Streamlit simple component to display [ApexChart](https://apexcharts.com/).

![](./img/demo2.png)

## Install

```shell script
pip install streamlit_apexjs
```

## Usage
```python
from streamlit_apexjs import st_apexcharts
options = {
    "chart": {
        "toolbar": {
            "show": False
        }
    },

    "labels": [1991, 1992, 1993, 1994, 1995]
    ,
    "legend": {
        "show": True,
        "position": "bottom",
    }
}

series = [44, 55, 41, 17, 15]

st_apexcharts(options, series, 'donut', '600', 'title')
```

### st_apexcharts params


```
st_apexcharts(
    options: Dict
    series: List
    type: str
    width: str, number
    title: str
)
```

## Credit
Thank you for inspiration :) [andfanilo](https://github.com/andfanilo).
medipy 0.0.6
Code Collection for Medical Data Processing and Analysis
Requires Python: >=3.8
Keywords: medical,signals,analysis,processing

# medipy
Code Collection for Medical Data Processing and Analysis in Python

<!--![Version](https://img.shields.io/pypi/v/your-package-name.svg) -->
[![License](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)

This Python package provides a broad collection of code for processing and analyzing medical data (like ECG, EEG, ACC, EMG, GYRO, ..). Every modul reflects a model, algorithm or concept published in scientific journal (or similiar) and is named after its main author. The publications are cited in the docstring of each modul.

## Implemented Code
These are the current modules which are implemented in this package:
- R-Peak-Detection for 1D ECG Signals by [Hamilton](src/medipy/hamilton.py)
- RR-Interval-Error-Detection for 1D ECG Signals by [Lipponen](src/medipy/lipponen.py)

## Installation
You can install the package using pip:

```bash
pip install medipy
```

## Usage
You can use any function of this package by either importing the complete package
```python
import medipy as mp
r_peaks= mp.hamilton.detect_r_peaks(ecg, sampling_rate)
```
or single modules

```python
from medipy import hamilton
r_peaks= hamilton.detect_r_peaks(ecg, sampling_rate)
```

## Example
```python
import math
from medipy import hamilton
from medipy import lipponen
from matplotlib import pyplot as plt
from scipy.misc import electrocardiogram

# Load ECG Data using scipy
ecg = electrocardiogram()
sample_rate = 360

# Detect R-Peaks
ecg_preprocessed = hamilton.preprocessing(ecg, sample_rate)
r_peaks = hamilton.detect_r_peaks(ecg, ecg_preprocessed, sample_rate, least_distance=0.2, th_coefficient=0.189, th_search_back=0.3)
r_peaks_refined = hamilton.refine_r_peaks(ecg, r_peaks, refine_samples=30)

# Calculate RR-Intervals
rr_intervals = [math.ceil(r_peaks[k] - r_peaks[k - 1]) for k in range(1, len(r_peaks))]

# Detect RR-Errors
rr_errors, ectopic_intervals, missed_intervals, extra_intervals, long_short_intervals = lipponen.rr_interval_error_detection(rr_intervals)

# Print Results
print("R-Peaks: ", len(r_peaks))
print("RR-Intervals: ", len(rr_intervals))
print("RR-Errors: ", sum(rr_errors))
print("Ectopic Intervals: ", sum(ectopic_intervals))
print("Missed Intervals: ", sum(missed_intervals))
print("Extra Intervals: ", sum(extra_intervals))
print("Long Short Intervals: ", sum(long_short_intervals))

# Plot
fig = plt.figure()
plt.plot(ecg)
plt.plot(r_peaks, ecg[r_peaks], 'rx')
plt.plot(r_peaks_refined, ecg[r_peaks_refined], 'go')
plt.show()
```

## Contribution 
If you want to support the project or contribute to the code, feel free to contact.

## License
This project is licensed under the MIT License - see the [License](LICENSE) file for details.