semvec/pypi-packages huggingface dataset:
simiotics-lambada 0.2.0
Manage AWS Lambda functions using a Simiotics Function Registry

# lambada
Manage AWS Lambda functions using a Simiotics Function Registry

## Install

```
pip install -U simiotics-lambada
```

## Use

This section lets you try out `lambada` using our
[example `hello` function](./examples/hello/hello.py).

First, export your `SIMIOTICS_FUNCTION_REGISTRY` environment variable. This tells `lambada` which
Simiotics Function Registry to work with. If you do not have a private Simiotics account, you can
use our free, public, and totally open registry:
```
export SIMIOTICS_FUNCTION_REGISTRY=registry-alpha.simiotics.com:7011
```

Make sure that your AWS credentials are available either by exporting `AWS_ACCESS_KEY_ID` and
`AWS_SECRET_ACCESS_KEY` or by ensuring that the profile you want to use is `default` in your
AWS credentials file, or by exporting `AWS_PROFILE`. If you are not familiar with AWS
authentication, you can read more here:
https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html

Decide on a key under which you would like to register the `hello` function into the Simiotics
Function Registry. For example:
```
HELLO_KEY=hello-$(date -u +%s)
```

Register the function against the Simiotics Function Registry using `lambada`:
```
lambada register --key $HELLO_KEY \
    --code examples/hello/hello.py \
    --handler hello \
    --requirements examples/hello/requirements.txt
```

Check that the function was successfully registered (if the output of the following command is
empty, there is a problem):
```
lambada list | grep "$HELLO_KEY"
```

Create an AWS IAM role capable of executing the `hello` function and logging to CloudWatch:
```
lambada create_role --key $HELLO_KEY --name iam-lambada-demo
```

Deploy the function as an AWS Lambda:
```
lambada deploy --key $HELLO_KEY --name lambda-lambada-demo
```

This should return an AWS Lambda ARN of the form:
```
arn:aws:lambda:<REGION>:<PROJECT ID>:function:lambda-lambada-demo
```

Now you can invoke the deployed Lambda using the AWS command-line interface:
```
OUTFILE=$(mktemp)
aws lambda invoke --function-name lambda-lambada-demo \
    --payload '{"target": "Sophia"}' \
    $OUTFILE
cat $OUTFILE
rm $OUTFILE
```

To take down the AWS Lambda:
```
lambada down --key $HELLO_KEY
```

This only removes the AWS Lambda. It doesn't delete the IAM role. If you would like to get rid of
that, as well:
```
lambada down --key $HELLO_KEY --teardown
```

Kick back, [enjoy some smooth tunes](https://www.youtube.com/watch?v=csaUvkYOkLY), and let the
Lambdas proliferate.

## Support

If you experience any problems with this tool, please add an issue on this repository.
build-subpfam 0.1.1
Build clade HMMs from a multiple sequence alignment file
Requires Python: >=3.6

# build_subpfam
Build per-clade pHMM models from a collection of
aligned protein sequences (in a multifasta format).

Clades are programmatically defined by cutting a
mid-rooted, laderized, and ultrametricized phylogenetic
tree of the aligned sequences by a specified length
point.

![subpfam](subpfam.png)

**Usage**
under construction
tf-jieba 0.3
Tensorflow Operation Wrapper of cppjieba (Chinese Word Segamentation)

# Jieba Op for Tensorflow

## Intro

This is a tensorflow wrapper for the cpp version of the famous Chinese
word segmentation tool, [cppjieba](https://github.com/yanyiwu/cppjieba).

I made some changes from the original cppjieba. This is 
[my fork of cppjieba](https://github.com/applenob/cppjieba).

Most code of this repo are from Didi's deep learning language platform:
[Delta](https://github.com/didi/delta)

## Install

```bash
pip install tf-jieba
```

## Usage

1. Clone the cppjieba repository for resource files like model and dict
   files.

```bash
git clone https://github.com/applenob/cppjieba.git
```

2. Download or copy the test python script `custom_test.py` to the same
   project. You should have `cppjieba` and `custom_test.py` under your
   test directory now.

```
python3 custom_test.py
```

Demo output:

![](imgs/jieba_res.png)

## Support Platform 

- python == 3.6
- tensorflow >= 1.14.0
- OS: MacOS / Linux

Please refer to [my Chinese blog post](https://applenob.github.io/tensorflow/tf_jieba/) for more detail.
fstrans 1.0.1
Performs transactional modification on directory trees
Requires Python: >=3.5

# fstrans - Python  module for filesystem transactions

Description
-----------

This module allows to manipulate some directory tree in safe way,
making sure that in case of error tree would left intact, and 
no other process such as webserver would access partially modified tree
(although you can use external programs from your script to work with
tree)

It also serializes write tree access - if one program  starts transaction
with some directory, other one, if started, would wait for some time for it to
complete.

Module is Unix-only. It heavily relies on hard links to quickly make
copies of big trees.

As working tree is populated with symlinks from final tree, user
should make sure that file is different in local copy before modifying
it.

For this module provides following methods:

1. **open** - works just like builtin function **open**, but makes sure
that original file would not be clobbered before commit.
2. **putfile** - safely replaces file in tree with other file
3. **clonefile**, **clonetree** - make exact copy of original file with
   link count = 1.

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

You can install released version with `pip3 install fstrans`.
If you prefer cutting age software, recommended way is

```
python3 setup.py sdist
pip3 install ./dist/fstrans-1.0.tar.gz
```

Of course, you can install system-wide build your OS packages and so on.
But if you are planning to do so, you probably don't need special
instructions.

Testing
-------

There is unittest-based test suite in the test directory.


Run

```
python3 setup.py test
```

to run test suite.
pacurses 0.1.1
A curses graphical interface to the PulseAudio sound system for the terminal.
Requires Python: >=3.7,<4.0

# Pacurses

A curses graphical interface to the
[PulseAudio](https://www.freedesktop.org/wiki/Software/PulseAudio) sound system
for the terminal.

![Main Menu](docs/screenshots/menu_main.png)
![Output Volume Menu](docs/screenshots/menu_volume_outputs.png)

## Installation

### PulseAudio

In the end, `pacurses` is only a graphical interface which is using `pacmd`
to communicate with the _PulseAudio_ daemon. Therefore make sure that both are
installed and running on your machine.

- [ArchLinux](https://wiki.archlinux.org/index.php/PulseAudio#Installation)
- [Ubuntu](https://wiki.ubuntu.com/PulseAudio)
- ...

### Pacurses

Install it globally with [pip](https://pip.pypa.io/en/stable):

```shell
$ pip install pacurses
```

Execute always the most recent version with [pipx](https://pipxproject.github.io/pipx/):

```shell
$ pipx run pacurses
```
not-much-fire 0.1.2
A simple Notmuch notification tool
Requires Python: >=3.7,<4.0

# Not Much Fire

A simple [Notmuch](https://notmuchmail.org) notification tool

- [Installation](#installation)
- [Usage](#usage)
- [Notmuch Hook](#notmuch-hook)

## Installation

Install it globally with [pip](https://pip.pypa.io/en/stable):

```shell
$ pip install not-much-fire
```

Execute always the most recent version with [pipx](https://pipxproject.github.io/pipx/):

```shell
$ pipx run not-much-fire
```

## Usage

```shell
$ not-much-fire --help

Usage: not-much-fire [OPTIONS]

  A simple Notmuch notification tool.

  Requests Notmuch for new unread messages and send notifications to the
  desktop environment. Already notified messages get not shown again for a
  whole day. If they remain unread, they get are handled again on the next
  day.

Options:
  --notmuch-query <query>  Used to query the unread messages from the Notmuch
                          database  [default: is:unread and is:inbox]
  --help                   Show this message and exit.
```

## Notmuch Hook

To get notification each time after `notmuch` has updated its database, add
a new hook. Therefore add a new line like the following into
`$DATABASE/.notmuch/hooks/post-new`.

```shell
#!/bin/bash
not-much-fire
# or: pipx run not-much-fire
```

Checkout `man notmuch-hooks` to get further information about hooks and
`notmuch`. I recommend to set the `$NOTMUCH_CONFIG` environment variable to
reach compatibility with the [XDG base directory
standard](https://specifications.freedesktop.org/basedir-spec/basedir-spec-latest.html).
aftershoq 1.0
A Flexible Tool for QCL/QCD Opitmization
Requires Python: >=3.6

# aftershoq
A Flexible Tool for Em-Radiation-emitting Semiconductor Heterostructure Optimization using Quantum models

This Tool aims to aid in the simulation of quantum cascade structures (such as QC lasers, detectors, or QWIPs)
using a variety of different simulation models. It also contains routines for optimization of such structures.
It contains a libraty of common materials and structures used in the Litterature, and provides a framework
for simulations. It does not contain any simualation code, this has to be provided by the users themselves
(for now). The respective simulation code can be linked to [aftershoq] by the implementation of a subclass
to Interface, which writes input files, executes the model, computes the merit function, and gathers the
results data.

This is a program written for Python 3.6. You need to have Python 3 installed to use and modify this software
to your needs. The current implentation also uses numpy, scipy, matplotlib, and lxml for some features.


# Installation

When cloning, use the --recursive option:

git clone --recursive https://github.com/mfranckie/aftershoq.git

(or

git clone --recurse-submodules https://github.com/mfranckie/aftershoq.git

depending on your git version) so that the project "hilbert_curve" appears in the base directory of aftershoq.
To install aftershoq and all its dependencies, execute

python setup.py install

from the aftershoq/ directory. To install on a system without root privileges, run

python setyp.py install --user

insead.

# Tutorials

For a demonstration, see the Jupyter notebooks located in examples/notebooks. To install Jupyter, run

python -m pip install jupyter

then run with

jupyter notebook

[Materials_guide.ipynb](https://github.com/mfranckie/aftershoq/blob/master/examples/notebooks/Materials_guide.ipynb) Shows how to create materials and alloys with varying composition and strain.

[QCL_guide.ipynb](https://github.com/mfranckie/aftershoq/blob/master/examples/notebooks/QCL_guide.ipynb) Shows how to generate structures from scratch, how to load them from the [library](https://github.com/mfranckie/aftershoq/blob/master/aftershoq/qcls/qcls.py) and how to generate them automatically.

[Opt_guide.ipynb](https://github.com/mfranckie/aftershoq/blob/master/examples/notebooks/Opt_guide.ipynb) Shows how to setup and run an optimization with Gaussian Processs (GP) regression for a test function and for a real QCL (requires ownership of a separate QCL simulation model).


If you don't want to/can't use jupyter, the following examples have a similar content:

1) "QCLexample.py" (Requires a supported simulation program)
2) "example_sewself.py" (Requires the sewself program)
3) "example_sewlab.py" (Requires sewlab version 4.6.4 or later)
4) "test_optim.py" (No requirements, this is a test of the optimization scheme)

Good luck!
netwell 0.4.0
Checker to determine if all is well
Keywords: network ping check test

===================
Welcome to Netwell!
===================

.. image:: https://badge.fury.io/py/netwell.svg
   :target: http://badge.fury.io/py/netwell

.. image:: https://travis-ci.org/pennersr/netwell.svg
   :target: http://travis-ci.org/pennersr/netwell

.. image:: https://img.shields.io/pypi/v/netwell.svg
   :target: https://pypi.python.org/pypi/netwell

.. image:: https://pennersr.github.io/img/bitcoin-badge.svg
   :target: https://blockchain.info/address/1AJXuBMPHkaDCNX2rwAy34bGgs7hmrePEr

.. image:: https://img.shields.io/badge/code%20style-pep8-green.svg
   :target: https://www.python.org/dev/peps/pep-0008/


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

Python 3 is required::

   $ pip install netwell


Quickstart
==========

Given a netwell checkup file `checks.py`:
::

    from netwell.checkers import URL, DNS, Port, Repo

    URL('http://fsf.org') \
        .redirects_to('http://www.fsf.org/') \
        .title_matches('Free Software Foundation') \
        .has_header('Content-Type', 'text/html;charset=utf-8')
    Port('fsf.org', 443).ssl_valid_for(days=3000)
    DNS('fsf.org', 'www.fsf.org').resolves_to('208.118.235.131')
    Path('/').free_space(gb=1)
    Path('/var/log/syslog').modified_within(hours=1)
    Repo('/home/deploy/src/project').is_clean()

    def custom_check(response, outcome):
        data = response.json()
        if data:
            outcome.fail('Other data expected')

    URL('http://httpbin.org/get').check_response(custom_check)

Then, run:

::

    $ netwell /some/where/checks.py
    Checking that http://fsf.org redirects to http://www.fsf.org/... OK
    Checking that http://fsf.org title matches "Free Software Foundation"... OK
    Checking that http://fsf.org has header "Content-Type": "text/html;charset=utf-8"... OK
    Checking that SSL at fsf.org:443 is valid for at least 3000 days... ERROR
    ERROR: Not valid after 2016-10-13
    Checking that fsf.org resolves to 208.118.235.131... OK
    Checking that www.fsf.org resolves to 208.118.235.131... OK
    Checking that / has 1 GB free space... ERROR
    ERROR: Only 0.5 GB free
    Checking that /var/log/syslog was modified after 2015-12-27 22:21:05.873355... OK
    Checking that http://httpbin.org/get passes custom_check... ERROR
    ERROR: Other data expected
    Checking that repository /home/deploy/src/project is clean... ERROR
    ERROR: Untracked files found


Use `--quiet` to only output the error messages, if any.
cloud-push-client 1.0.3
A Python client for the Bluemix/IBM push notifications service
Requires Python: >=3.4

# Cloud-Push-Notifications Python Client
This repository contains a simple Python client for the Bluemix/IBM push notification service

Installation
===
```bash
pip install cloud-push-client
```

Getting Started
===
Log into IBM/Bluemix cloud and retrieve api_key, app_id and client_secret from your push service
```python
import cloudpush.Client as cp
push_client = cp.Client("api_key", "app_id", "client_secret")
```

Methods
===

devices
---
```python
register_device(token, platform, device_id=None)

delete_device(device_id)

get_device(device_id=None,token=None)

get_devices()

get_device_stats()
```

topics
---
```python
register_topic(name,description=None)

delete_topic(name)
```

subscriptions
---
```python
subscribe( topic, device_id)

unsubscribe(self, topic, device_id)

get_subscriptions( topic, device_id=None)
```

push messages
---
```python
get_push_stats()

get_messages(offset=0)

get_message(message_id)

get_message_status(message_id)

get_message_delivery_status(message_id,device_id="")

send_message(alert, url=None,
                     platforms=["A", "G"],
                     payload="", 
                     device_ids=None,
                     topics=None,
                     badge=1,
                     ttl=3600,
                     type='DEFAULT',
                     title=None,
                     subtitle=None,
                     validate=True)
```
photo-dl 0.0.20
A photo album downloader
Requires Python: >=3.5

# photo-dl

photo-dl is a command-line downloader which helps to crawl photo albums from [Supported sites](#supported-sites)



## Installation

#### Dependencies

- python >= 3.5
- requests >= 2.12.0
- lxml >= 3.7.0

#### Install via Pip

$ pip install --upgrade photo-dl

#### Install from source

$ git clone https://github.com/windrises/photo-dl.git

$ cd photo-dl

$ python setup.py install



## Usage

Assign a url or .txt file (one url per line)

$ photo-dl  url

$ photo-dl xxx.txt

#### Examples

$ photo-dl  https://www.meituri.com/t/1820/

$ photo-dl urls.txt



## Configuration

modify photo_dl/config.py

| configuration | default       |
| :------------ | :------------ |
| headers       | User-Agent... |
| timeout       | 15 sec        |
| max_retries   | 3             |
| log_file      | ./log.txt     |
| threads       | 4             |



## Supported sites

Temporarily only supports two sites

| site                     | feature             | example                                                      |
| :----------------------- | :------------------ | :----------------------------------------------------------- |
| https://www.meituri.com/ | model               | https://www.meituri.com/t/1820/                              |
|                          | album               | https://www.meituri.com/a/7893/                              |
|                          | search              | https://www.meituri.com/search/鏈夋潙鏋剁磾                      |
| https://www.jav.ink/     | tag/search/category | https://www.jav.ink/category/graphis-collection-2002-2018/ [NSFW] |
|                          | album               | https://www.jav.ink/graphis-collection-2002-2018/yura-kano-銆巗weet-memories銆弙ol-2/ [NSFW] |