semvec/pypi-packages huggingface dataset:
iq 0.0.2
IQ SDK.
License: MIT
Keywords: iq sdk

Pay
===

.. image:: https://img.shields.io/pypi/v/iq.svg
    :target: https://pypi.python.org/pypi/iq/
    :alt: Latest Version

.. image:: https://img.shields.io/pypi/wheel/iq.svg
    :target: https://pypi.python.org/pypi/iq/

.. image:: https://img.shields.io/pypi/pyversions/iq.svg
    :target: https://pypi.python.org/pypi/iq/

.. image:: https://img.shields.io/pypi/l/iq.svg
    :target: https://pypi.python.org/pypi/iq/



IQ SDK.


Installing
----------

Install and update using `pip`_:

.. code-block:: text

    pip install -U iq



.. _pip: https://pip.pypa.io/en/stable/quickstart/
pay 0.0.1
Pay SDK.
License: MIT
Keywords: pay sdk

Pay
===

.. image:: https://img.shields.io/pypi/v/pay.svg
    :target: https://pypi.python.org/pypi/pay/
    :alt: Latest Version

.. image:: https://img.shields.io/pypi/wheel/pay.svg
    :target: https://pypi.python.org/pypi/pay/

.. image:: https://img.shields.io/pypi/pyversions/pay.svg
    :target: https://pypi.python.org/pypi/pay/

.. image:: https://img.shields.io/pypi/l/pay.svg
    :target: https://pypi.python.org/pypi/pay/



Pay SDK.


Installing
----------

Install and update using `pip`_:

.. code-block:: text

    pip install -U pay



.. _pip: https://pip.pypa.io/en/stable/quickstart/
too 0.0.1
A bundle of most common useful class.
License: MIT
Keywords: too python class lib library

Too
========

.. image:: https://img.shields.io/pypi/v/too.svg
    :target: https://pypi.python.org/pypi/too/
    :alt: Latest Version

.. image:: https://img.shields.io/pypi/wheel/too.svg
    :target: https://pypi.python.org/pypi/too/

.. image:: https://img.shields.io/pypi/pyversions/too.svg
    :target: https://pypi.python.org/pypi/too/

.. image:: https://img.shields.io/pypi/l/too.svg
    :target: https://pypi.python.org/pypi/too/



A bundle of most common useful class.


Installing
----------

Install and update using `pip`_:

.. code-block:: text

    pip install -U too



.. _pip: https://pip.pypa.io/en/stable/quickstart/
pointer 0.0.2
SDK For Pointer.
License: MIT
Keywords: pointer sdk

Torstack
========

.. image:: https://travis-ci.org/longniao/pointer.svg
    :target: https://travis-ci.org/longniao/pointer
    :alt: Travis CI

.. image:: https://img.shields.io/pypi/v/pointer.svg
    :target: https://pypi.python.org/pypi/pointer/
    :alt: Latest Version

.. image:: https://img.shields.io/pypi/wheel/pointer.svg
    :target: https://pypi.python.org/pypi/pointer/

.. image:: https://img.shields.io/pypi/pyversions/pointer.svg
    :target: https://pypi.python.org/pypi/pointer/

.. image:: https://img.shields.io/pypi/l/pointer.svg
    :target: https://pypi.python.org/pypi/pointer/


SDK for pointer.


Installing
----------

Install and update using `pip`_:

.. code-block:: text

    pip install -U pointer



.. _pip: https://pip.pypa.io/en/stable/quickstart/
term2md 0.3.4
convert ANSI terminal control characters to Markdown
License: MIT
Keywords: text terminal ansi control markdown

# term2md

This is a text processing utility that will convert input containing
[ANSI escape sequences](https://en.wikipedia.org/wiki/ANSI_escape_code)
into nearly-equivalent Markdown.

This is not currently a universal converter, since Markdown cannot
express all of the formatting possible with the ANSI control
characters. However, it does a reasonable job of handling green, red,
and bold text.

`term2md` was written originally so that output from a tool designed
for terminal output could be added via continuous integration as
comments on pull requests.

## Installation

```
$ pip install term2md
```

In addition to installing the core Python module, it will also install
a `term2md` executable script for you.
dcma 0.1.2
Deep Codon Mutation Analyser
License: MIT

# Deep Codon Mutation Analyser (DCMA)

DCMA analyses each column of codons within .fasta files, from a target file, generating a report with all possible mutations listed.

## Installation
```bash
pip install dcma
```

## Examples

- [Simple Execution](examples/example1-simple.md)
- [Group Execution](examples/example2-group.md)

## Usage
### Command Line

```bash
usage: run-dcma [-h] [--reportName REPORTNAME] [--reportPath REPORTPATH]
                [--searchKP SEARCHKP] [--debug]
                TARGET REPORTTYPE

Analyse mutation on all nucleotide alignment .fasta files from a target.

positional arguments:
  TARGET                Target .fasta file to be analysed.
  REPORTTYPE            Output report file type.

optional arguments:
  -h, --help            show this help message and exit
  --reportName REPORTNAME
                        Output report custom file name.
  --reportPath REPORTPATH
                        Output report custom file path.
  --searchKP SEARCHKP   Custom keyphrase to detect searchable sequences.
  --debug               Turn debug messages on.
```

### Python

```python
import dcma.core as solver

target_path = 'example.fasta'
report_name = 'myrep'
report_type = 'all' # 'xls', 'csv' or 'all'
report_path = 'results-folder' # optional

solver.set_debug_mode(True) # optional

# results[0] -> polarity results dataframe
# results[1] -> alerts dataframe
results = solver.run(target_path)

# option 1: export to the current folder
solver.export(results, report_type, report_name)
# option 2: export to custom folder
solver.export(results, report_type, report_name, report_path)
```
dppa 0.1.2
Deep Protein Polarity Analyser
License: MIT

# Deep Protein Polarity Analyser (DPPA)

DPPA analyses each column of amino acids within .fasta files, from a target file, generating a report with all possible polarities listed.

## Installation
```bash
pip install dppa
```

## Examples

- [Simple Execution](examples/example1-simple.md)
- [Group Execution](examples/example2-group.md)

## Usage
### Command Line

```bash
usage: run-dppa [-h] [--reportName REPORTNAME] [--reportPath REPORTPATH]
                [--searchKP SEARCHKP] [--debug]
                TARGET REPORTTYPE

Analyse polarity on all protein alignment .fasta files from a target.

positional arguments:
  TARGET                Target .fasta file to be analysed.
  REPORTTYPE            Output report file type.

optional arguments:
  -h, --help            show this help message and exit
  --reportName REPORTNAME
                        Output report custom file name.
  --reportPath REPORTPATH
                        Output report custom file path.
  --searchKP SEARCHKP   Custom keyphrase to detect searchable sequences.
  --debug               Turn debug messages on.
```

### Python

```python
import dppa.core as solver

target_path = 'example.fasta'
report_name = 'myrep'
report_type = 'all' # 'xls', 'csv' or 'all'
report_path = 'results-folder' # optional

solver.set_debug_mode(True) # optional

# results[0] -> polarity results dataframe
# results[1] -> alerts dataframe
results = solver.run(target_path)

# option 1: export to the current folder
solver.export(results, report_type, report_name)
# option 2: export to custom folder
solver.export(results, report_type, report_name, report_path)
```
djangocms-lang-chooser 0.1.0
Language chooser as a Django CMS plugin
License: MIT

djangocms-lang-chooser
======================

Installation
------------

To get started using ``djangocms-lang-chooser``:

- install it with ``pip``::

    $ pip install djangocms-lang-chooser

- add the app to ``INSTALLED_APPS``::

    INSTALLED_APPS = (
        ...
        'djangocms_lang_chooser',
        ...
    )
pyaiocrawler 0.5.0
Asynchronous web crawler built on asyncio
License: MIT

AIOCrawler
==========
[![Build Status](https://travis-ci.org/tapanpandita/aiocrawler.svg?branch=master)](https://travis-ci.org/tapanpandita/aiocrawler)
[![Codacy Badge](https://api.codacy.com/project/badge/Grade/eab04685503c490082f1c6a545c4016e)](https://www.codacy.com/app/tapanpandita/aiocrawler?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=tapanpandita/aiocrawler&amp;utm_campaign=Badge_Grade)
[![PyPI version](https://badge.fury.io/py/pyaiocrawler.svg)](https://badge.fury.io/py/pyaiocrawler)

Asynchronous web crawler built on [asyncio](https://docs.python.org/3/library/asyncio.html)

Installation
------------
```shell
pip install pyaiocrawler
```
Usage
-----
### Generating sitemap
```python
import asyncio
from aiocrawler import SitemapCrawler

crawler = SitemapCrawler('https://www.google.com', depth=3)
sitemap = asyncio.run(crawler.get_results())
```
### Configuring the crawler
```python
from aiocrawler import SitemapCrawler

crawler = SitemapCrawler(
    init_url='https://www.google.com', # The base URL to start crawling from
    depth=3,                           # The maximum depth to crawl till
    concurrency=300,                   # Maximum concurrent requests to make
    max_retries=3,                     # Maximum times the crawler will retry to get a response from a URL
    user_agent='My Crawler',           # Use a custom user agent for requests
)
```
### Extending the crawler
To create your own crawler, simply subclass `AIOCrawler` and implement the `parse` method. For every page crawled, the `parse` method is executed with the url of the page, the links in that page and the html of the page. The return of the `parse` method is appended to an array which is then available when the `get_results` method is called. We have implemented an example crawler here that extracts the title from the page.
```python
import asyncio
from aiocrawler import AIOCrawler
from bs4 import BeautifulSoup          # We will use beautifulsoup to extract the title from the html
from typing import Set, Tuple


class TitleScraper(AIOCrawler):
    '''
    Subclasses AIOCrawler to extract titles for the pages on the given domain
    '''
    timeout = 10
    max_redirects = 2

    def parse(self, url: str, links: Set[str], html: bytes) -> Tuple[str, str]:
        '''
        Returns the url and the title of the url
        '''
        soup = BeautifulSoup(html, 'html.parser')
        title = soup.find('title').string
        return url, title


crawler = TitleScraper('https://www.google.com', 3)
titles = asyncio.run(crawler.get_results())
```
Contributing
------------
### Installing dependencies
```shell
pipenv install --dev
```
### Running tests
```shell
pytest --cov=aiocrawler
```