semvec/pypi-packages huggingface dataset:
fs2 0.1
A cmd tool based on pyfilesystem2 lib
License: MIT
Requires Python: >=3.6

# fs2

### [fs2](https://github.com/pythonwood/fs2/) is a cmd tool based on [pyfilesystem2](https://github.com/PyFilesystem/pyfilesystem2)

### Usage

    Usage: fs2 [OPTIONS] COMMAND [ARGS]...
    
      This script is pyfilesystem2 command line tool.
    
      example:
          fs2 ls .
          fs2 -u file://c:/windows ls system32
          fs2 -u zip:///tmp/a.zip ls /
          fs2 -u tar:///etc/bak.tar.gz  ls opkg config
          fs2 -u temp:// ls .
          fs2 -u s3:// ls .                                 # pip install fs-s3fs
          fs2 -u dropbox:// ls .                            # pip install fs.dropboxfs
          fs2 -u webdav://user:pass@127.0.0.1/web/dav/ ls . # pip install fs.webdavfs
          fs2 -u ssh://my.vps.com/home/ ls .                # pip install fs.sshfs
          fs2 --listopener                                  # list all support filesystem
    
      mutil url is supported:
          fs2 -u /tmp -u ssh://vps/tmp ls .
    
    Options:
      -l, --listopener  list supported file system
      -u, --url TEXT    filesystem url: default is "."
      --help            Show this message and exit.
    
    Commands:
      cat    read file and print content.
      cp     copy file (same fs).
      dl     download file to local disk.
      info   get info of resource.
      ls     list files and dirs.
      mkdir  create directory.
      mv     move file (same fs).
      rm     delete file and dirs.
      rmdir  get info of resource.
      tree   list files and dirs as tree view.
      up     upload local disk file to remote filesystem.
coglist 1.0.1
A collection of cogs to create Discord bots with.
License: MIT
Requires Python: >=3.6

# botlist/cogs

## A collection of cogs for use with [templatebot](https://github.com/vcokltfre/template-bot-v2)

### What's this?

This is a repository to store discord.py cogs that you're able to use with templatebot.

### Links

● [discord server](https://discord.gg/tWfaeNftAS)
django-graphql-jwt-reload 1.3.6
JSON Web Token for Django GraphQL
License: MIT

Django GraphQL JWT
==================

|Pypi| |Build Status| |Codecov| |Codacy|


`JSON Web Token <https://jwt.io/>`_ authentication for `Django GraphQL <https://github.com/graphql-python/graphene-django>`_


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

Install last stable version from Pypi:

::

    pip install django-graphql-jwt

Add ``AuthenticationMiddleware`` middleware to your *MIDDLEWARE* settings:

.. code:: python

    MIDDLEWARE = [
        ...
        'django.contrib.auth.middleware.AuthenticationMiddleware',
        ...
    ]

Add ``JSONWebTokenMiddleware`` middleware to your *GRAPHENE* settings:

.. code:: python

    GRAPHENE = {
        'SCHEMA': 'mysite.myschema.schema',
        'MIDDLEWARE': [
            'graphql_jwt.middleware.JSONWebTokenMiddleware',
        ],
    }

Add ``JSONWebTokenBackend`` backend to your *AUTHENTICATION_BACKENDS*:

.. code:: python

    AUTHENTICATION_BACKENDS = [
        'graphql_jwt.backends.JSONWebTokenBackend',
        'django.contrib.auth.backends.ModelBackend',
    ]


Schema
------

Add *django-graphql-jwt* mutations to the root schema:

.. code:: python

    import graphene
    import graphql_jwt


    class Mutation(graphene.ObjectType):
        token_auth = graphql_jwt.ObtainJSONWebToken.Field()
        verify_token = graphql_jwt.Verify.Field()
        refresh_token = graphql_jwt.Refresh.Field()


    schema = graphene.Schema(mutation=Mutation)


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

Fantastic documentation is available at https://django-graphql-jwt.domake.io.


.. |Pypi| image:: https://img.shields.io/pypi/v/django-graphql-jwt.svg
   :target: https://pypi.python.org/pypi/django-graphql-jwt
   :alt: Pypi

.. |Build Status| image:: https://travis-ci.org/flavors/django-graphql-jwt.svg?branch=master
   :target: https://travis-ci.org/flavors/django-graphql-jwt
   :alt: Build Status

.. |Codecov| image:: https://codecov.io/gh/flavors/django-graphql-jwt/branch/master/graph/badge.svg
   :target: https://codecov.io/gh/flavors/django-graphql-jwt
   :alt: Codecov

.. |Codacy| image:: https://api.codacy.com/project/badge/Grade/4f9fd439fbc74be88a215b9ed2abfcf9
   :target: https://app.codacy.com/gh/flavors/django-graphql-jwt?utm_source=github.com&utm_medium=referral&utm_content=flavors/django-graphql-jwt&utm_campaign=Badge_Grade_Dashboard
   :alt: Codacy
vantetider-scraper 0.2.0
A scraper of statistical data from Vantetider.se built on top of Statscraper.
License: MIT

This is a scraper for statistical data from http://www.vantetider.se built on top of the `Statscraper package <https://github.com/jplusplus/statscraper>`.

Install
-------

  pip install -r requirements.txt

The scraper has to do a lot of requests and uses `requests-cache <https://pypi.python.org/pypi/requests-cache>` to store queries.

Example usage
-------------

.. code:: python

  from vantetider import VantetiderScraper

  scraper = VantetiderScraper()
  scraper.items  # List _implemeted_ datasets
  # [<VantetiderDataset: VantatKortareAn60Dagar (Väntat kortare än 60 dagar )>, <VantetiderDataset: Overbelaggning (Överbeläggningar)>, <VantetiderDataset: PrimarvardTelefon (Telefontillgänglighet)>, <VantetiderDataset: PrimarvardBesok (Läkarbesök)>, <VantetiderDataset: SpecialiseradBesok (Förstabesök)>, <VantetiderDataset: SpecialiseradOperation (Operation/åtgärd)>]

  dataset = scraper.get("Overbelaggning")  # Get a specific dataset

  # List all available dimensions
  print dataset.dimensions

  print datatset.regions  # List available region
  print datatset.years  # List available years

  # Make a query, you have to explicitly define all dimension values you want
  # to query. By default the scraper will fetch default values.
  res = dataset.fetch({
    "region": "Blekinge",
    "year": "2016",
    "period": "Februari",
    # Currenty we can only query by id of dimension value
    "type_of_overbelaggning": ["0", "1"], # "Somatik" and "Psykiatri"
    })

  # Do something with the result
  df = res.pandas

Practical application, using dataset.py for storege.

.. code:: python

  from vantetider import VantetiderScraper
  from vantetider.allowed_values import TYPE_OF_OVERBELAGGNING, PERIODS
  import dataset

  db = dataset.connect('sqlite:///vantetider.db')

  TOPIC = "Overbelaggning"

  # Set up local db
  table = db.create_table(TOPIC)
  scraper = VantetiderScraper()

  dataset = scraper.get(TOPIC)

  # Get all available regions and years for query
  years = [x.value for x in dataset.years]
  regions = [x.value for x in dataset.regions]

  # Query in chunks to be able to store to database on the run
  for region in regions:
      for year in years:
          res = dataset.fetch({
              "year": year,
              "type_of_overbelaggning": [x[0] for x in TYPE_OF_OVERBELAGGNING],
              "period": PERIODS,
              "region": region,
              })
          df = res.pandas
          data = res.list_of_dicts
          table.insert_many(data)

TODO
----

- Implement scraping of "Aterbesok", "Undersokningar", "BUPdetalj", "BUP".
- Enable querying on label names on all dimensions
- Add more allowed values to `vantetider/allowed_values.py`
- Make requests-cache optional.

Devlop
------

Run tests:

  make tests
flightplan 0.1.2
Code based description for Concourse pipelines
License: MIT
Requires Python: >=3.8,<4.0
Keywords: concourse,yaml,ci/cd



# Flight plan - Plan your Concourse Pipeline with ease

AWS CDK like tool to code Concourse pipelines (with autocompletion.)

## Why not stick with YAML

Writing YAML files feels not as heavy as XML, 
but still lacks the comfort of autocompletion and some kind of structuring 
(Beside anchors or tools like [YTT](https://get-ytt.io/)).

The vision of Flightplan does not stop with replacing YAML, the real benefit
starts with component libraries, which ease the setup of pipelines.

Furthermore these components can be updated, which make all improvements 
automatically available to all pipelines.  

## Features

* Convert:
  * YAML -> Python
  * Python -> YAML
* Fly integration
  * Set pipeline
  * Get pipeline
* Shiped examples
  * Hello world
  * more to come

## Disclaimer - Alpha

> The package is still in alpha. Upcoming versions may include breaking changes. 

## Upcoming

* Provide high level components that handle common use cases

## Setup 

Flightplan requires Python 3.8 and higher.

### Install FlightPlan

Flightplan requires `fly` to be installed on path.

```bash
pip3 install flightplan
```

## Usage

If you start with Flightplan it is recommended to have a look on the quickstart examples, 
which are shipped within the cli.

If you want to migrate an existing pipeline you can use 
 * `fp import` - to convert YAML to Python
 * `fp get ...` - to get and convert a running pipeline 

### Quickstart
Generate a basic pipeline example.

```bash
fp quickstart
```

### Import existing pipeline file
Convert a pipeline yaml and render a flightplan `.py` file.

```bash
fp import <src.yaml> <target.py>
```

### Import existing pipeline from fly
Convert a pipeline from fly and render a flightplan `.py` file.

```bash
fp get <fly_target> <pipeline_name> <target.py>
```

> Static and dynamic vars will be imported as `Var(str)`, if the type of the field is limited to an int or Enum type.

### Synthesize yaml from flightplan `.py` file

```bash
fp synth <src.py> <target.yaml>
```

### Direct Fly Set Pipeline

```bash
fp set <fly-target> <pipeline_name> <src.py>
```



## Examples

Quickstart hello world example:

```python
from flightplan.render import *

pipe = Pipeline(
    resource_types=[],
    resources=[],
    jobs=[
        Job(
            name="job-hello-world",
            public=True,
            plan=[
                Task(
                    task="hello-world",
                    config=TaskConfig(
                        platform="linux",
                        image_resource=ImageResource(
                            type="docker-image",
                            source=dict(repository="busybox", tag="latest"),
                        ),
                        run=Command(path="echo", args=["hello world"]),
                        inputs=[],
                        outputs=[],
                    ),
                )
            ],
        )
    ],
)

if __name__ == "__main__":
    print(pipe.synth())
```