semvec/pypi-packages huggingface dataset:
mathbox 0.0.8
A math toolbox.
License: MIT
Requires Python: >=3.8.0
Keywords: math,data-processing

# Mathbox

## Overview

A math toolbox without numpy and scipy.

## Usage

1. `pip install mathbox`
2. Download from source code, `export PYTHONPATH='/<path>/<to>/mathbox-py'`. And then get into examples folder `cd examples`, install dependencies from requirements.txt (recommended in virtualenv) and run `python xxx.py`. 

## APIs

### Time-series 1D real data

* Prerequisities

    All the signals must be well interpolated.

* Cross-correlation
  * max_corr(obj, candidates, num, lag_max)
    * obj - input: a list. The objective list whose correlated lists are concerned.
    * cadidates - input: list of lists.
    * num - input: a uint. Default is 1.
    * lag_max - input: a uint. Default is 20. The lag refers to how far the series are offset, and its sign determines which series is shifted.

    The function returns **num** pieces of list from **candidates** that are most correlated to the **obj** list. And the function will calculate the maximum correlation from -lag_max to +lag_max.
* Filters
  * moving_median(signal, window)
    * signal - input: a list.
    * window - input: an int and the odd is recommended.
  * moving_average(signal, window)
    * signal - input: a list.
    * window - input: an int.
  * f_lowpass_filter(signal, T, n=3, f_min)
    * signal - input: a list.
    * T - input: sample period time.
    * n - input: a unit. Get the **n** pieces of frequencies with the maximum amplitude.
    * f_min - input: cut-off frequency.
* Outliers detection
  * noise_outlier(noise, level)
    * noise - input: a list. Signal should obey normal distribution. So it's better to choose a white noise as input.
    * level - input: a unit. Default is 3 which means 3 sigma. Usually, it should be 1, 2, 3 or 6.
* Change points detection
  * e_divisive(signal,jump,pvalue=0.05,permutations=100)
    * signal - input: a list.
    * jump - input: a unit. Jump controls the minimum distance between change points.
    * pvalue - a float from 0~1.
    * permutations - a uint. Default is 100 which means 100 times permutation test will be done to check whether the change point is really a chnage point.

## Utils
purus 0.1.1
parse parameters
License: MIT
Requires Python: >=3.8,<3.11
Keywords: aws,parameters,parser

# purus

[![Pytest](https://github.com/acxelerator/purus/actions/workflows/pytest.yml/badge.svg)](https://github.com/acxelerator/purus/actions/workflows/pytest.yml)
[![codecov](https://codecov.io/gh/acxelerator/purus/branch/main/graph/badge.svg?token=2X3BA0RCER)](https://codecov.io/gh/acxelerator/purus)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)
[![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)

[![PythonVersion](https://img.shields.io/pypi/pyversions/purus.svg)](https://pypi.org/project/purus/)
[![PiPY](https://img.shields.io/pypi/v/purus.svg)](https://pypi.org/project/purus/)


`purus` parses parameters provided from Lambda@Edge on CloudFront, and it manipulates request and response.
It is named after The Purus River, or a tributary of The Amazon River.


## Install

```shell
$ pip install purus
```

## Usage

### CloudFront:Lambda@Edge

`purus` can parse parameters of

- viewer-request
- origin-request
- origin-response
- viewer-response

```python
# Lambda@Edge
from purus.amazon_cloudfront import CloudFrontLambdaEdge

# on viewer-request or origin-request
def lambda_handler(event: dict, _):
    # load data
    lambda_edge = CloudFrontLambdaEdge.from_event(event=event)
    
    # return on error
    if some_error_occurred:
        pseudo_payload = lambda_edge.add_pseudo_response(
            status="400",
            status_description="error_occurred"
        )
        return pseudo_payload.response.format()
    
    # redirect
    if redirect_condition:
        pseudo_payload = lambda_edge.add_pseudo_redirect_response(
            status="307",
            status_description="Redirect",
            location_url="https://redirect.example.com"
        )
        return pseudo_payload.response.format()
    
    # add headers to request
    modified_request = lambda_edge.append_request_header(
        key="X-Original-Header",
        value="Your data"
    ).append_request_header(
        key="X-Original-Header",
        value="Your additional data"
    )
    
    # to request
    return  modified_request.request.format()


```
aop2db 0.3.1
AOP2DB - Python parser for converting importing adverse outcome pathway data into a relational database.
License: MIT
Requires Python: >=3.8,<4.0
Keywords: Biomedical Database

====================================================
AOP2DB |docs| |python_versions| |travis| |coverage|
====================================================

Package for compiling the adverse outcome pathway (AOP) data into a relational database. The data is publicly available at the `AOP website <https://aopwiki.org/>`_.

Installation |pypi_version| |pypi_license|
==========================================

``aop2db`` can be directly installed from PyPi with pip::

    $ pip install aop2db

Usage
=====

To load the data into a relational database::

    $ aop2db load

To set the driver of your database::

    $ aop2db conn mysql+pymysql://<user>:<password>@<server>/<database>

Disclaimer
==========
AOP2DB is a scientific software that has been developed in an academic capacity, and thus comes with no warranty or
guarantee of maintenance, support, or back-up of data.


.. |pypi_version| image:: https://img.shields.io/pypi/v/aop2db.svg
    :target: https://pypi.python.org/pypi/aop2db
    :alt: Current version on PyPI

.. |python_versions| image:: https://img.shields.io/pypi/pyversions/aop2db.svg
    :alt: Stable Supported Python Versions

.. |travis| image:: https://app.travis-ci.com/brucetony/aop2db.svg?branch=master
    :alt: Current build status

.. |docs| image:: https://readthedocs.org/projects/aop2db/badge/?version=latest
        :target: https://readthedocs.org/projects/aop2db/badge/?version=latest
        :alt: Documentation Status

.. |pypi_license| image:: https://img.shields.io/pypi/l/aop2db.svg
    :alt: MIT

.. |coverage| image:: https://codecov.io/gh/brucetony/aop2db/branch/master/graphs/badge.svg?
    :alt: Coverage Status
pymcd 0.2.1
Calculate Mel-Cepstral Distortion (MCD)
License: MIT
Keywords: python,Mel-Cepstral Distortion (MCD)

## Project description

This package (pymcd) computes Mel Cepstral Distortion (MCD) in python, which is used to assess the quality of the generated speech by comparing the discrepancy between generated and ground-truth speeches.

## Overview

Mel Cepstral Distortion (MCD) is a measure of how different two sequences of mel cepstra are, which is widely used to evaluate the performance of speech synthesis models. The MCD metric compares k-th (default k=13) Mel Frequency Cepstral Coefficient (MFCC) vectors derived from the generated speech and ground truth, respectively.

The pymcd package provides scripts to compute a variety of forms of MCD score:

- MCD (plain): the conventional MCD metric, which requires the lengths of two input speeches to be the same. Otherwise, it would simply extend the shorted speech to the length of longer one by padding zero for the time-domain waveform.
- MCD-DTW: an improved MCD metric that adopts the Dynamic Time Warping (DTW) algorithm to find the minimum MCD between two speeches.
- MCD-DTW-SL: MCD-DTW weighted by Speech Length (SL) evaluates both the length and the quality of alignment between two speeches. Based on the MCD-DTW metric, the MCD-DTW-SL incorporates an additional coefficient w.r.t. the difference between the lengths of two speeches. 

More details of the above three types of MCD can be found in [V2C: Visual Voice Cloning](https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_V2C_Visual_Voice_Cloning_CVPR_2022_paper.pdf).

## License

Please see the file License for details of the license and warranty for mcd.

## Installation

Require Python 3, the package can be installed and updated using pip, i.e.,

```
pip install -U pymcd
```

## Example

```
from pymcd.mcd import Calculate_MCD

# instance of MCD class
# three different modes "plain", "dtw" and "dtw_sl" for the above three MCD metrics
mcd_toolbox = Calculate_MCD(MCD_mode="plain")

# two inputs w.r.t. reference (ground-truth) and synthesized speeches, respectively
mcd_value = mcd_toolbox.calculate_mcd("001.wav", "002.wav")

```
Image2Story 0.0.6
Create Short Story from image caption
License: MIT
Requires Python: >=3.6
Keywords: CLIP,GPT3,BLIP

# Image2ShortStroy
This is a Python package for easy Create Short Story from image captions using blip models by getting image captions.
 to get some information for any artworks, portraits, any images, and convert image captions to short stories using GPT-3 models
 
## Installation

```
pip install Image2Story==0.0.6

or in colab google cloud
!pip install Image2Story==0.0.6


```

## Tutorial
[Colab Google Drive](https://colab.research.google.com/drive/1ZR0cfKkOHvFi7E4IchLdc2S3JP83kvEp?usp=sharing)
```
u can see tutorial in colab google drive

```
vaz 0.0.7
Testing installation of Package
License: MIT
Keywords: pandas,manipulation,vaz

Vaz has been created to open and enter any kind of data file into the development area and make it so easy for anyone to handle the dataframe after reading.  
It was written using pandas and tried to use very basic steps which can be understood by anyone, even Python beginners.
Please send me your feedback to this email: siadatisaman@gmail.com



....................................................................

Vaz means 'open' in the Mazani accent(Persian) in the northern regions.
ontogram 0.1.1
Ontogram - an OWL ontology diagram generator.
License: MIT
Requires Python: >=3.7,<4.0

# Ontogram

[![PyPI version](https://badge.fury.io/py/ontogram.svg)](https://badge.fury.io/py/ontogram)

An OWL ontology diagram generator.

Currently it supports `owl:class`, `rdfs:subClassOf`, `owl:equivalentClass`, datatype properties and domain and range relationships. I am planning to add support for `owl:subClassOf` restrictions soon. 


## Example output

The output of [examples/tern-org.ttl](examples/tern-org.ttl).

![generated ontology diagram](examples/tern-org.ttl.txt.png)


## Installation

Install via PyPI for Python 3.

```
pip3 install ontogram
```


## Usage

### Command line application

```
$ ontogram --help

Usage: ontogram [OPTIONS] ONTOLOGY_FILEPATH

  Ontogram CLI is a tool to generate a diagram from an OWL ontology file.

Options:
  --format ['turtle', 'xml', 'nt', 'n3']
                                  RDF serialization of input file. Default is
                                  turtle.
  --help                          Show this message and exit.
```

Use Ontogram's CLI to generate diagrams of an OWL ontology.
```
ontogram ontology.ttl
```

Output will be 3 files, `ontology.ttl.txt`, `ontology.ttl.png`, `ontology.ttl.svg`.

Use the --format option to specify the RDF serialization of the ontology if it is not Turtle.


### Python library

Ontogram is a Python library and can be easily integrated with any existing Python application.

```python
from ontogram import Ontogram

# First parameter accepts a file path to the OWL ontology. 
# Second parameter tells Ontogram what RDF format the OWL ontology is in.
ontogram = Ontogram('ontology.ttl', format='turtle')

# Generate a PNG diagram from the OWl ontology and write to disk as 'ontology.ttl.txt'.
ontogram.png_file('ontology.ttl.txt')

# Same as above, but as an SVG diagram. 
ontogram.svg_file('ontology.ttl.svg')
```

See the [examples](examples) directory for example outputs.