semvec/pypi-packages huggingface dataset:
tooncher 1.0.3
automates toontown rewritten's login process
Keywords: game,launcher,toontown rewritten,ttr

# tooncher
automates toontown rewritten's login process

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Last Release](https://img.shields.io/pypi/v/tooncher.svg)](https://pypi.org/project/tooncher/#history)
[![Python Version](https://img.shields.io/pypi/pyversions/tooncher.svg)](https://pypi.org/project/tooncher/)

## Installation

```sh
$ pip3 install --user --upgrade tooncher
# or
$ pip3 install --user git+https://github.com/fphammerle/tooncher@master
```

Optional: Install cpulimit to enable use of parameter `--cpu-limit`

```sh
$ sudo apt-get install cpulimit
```

## Configuration

```yaml
# default path: $HOME/.tooncher
accounts:
- username: toon
  password: secret
- username: ceo
  password: golf
- username: cfo
  password: train
engine_path: '/opt/Toontown Rewritten/TTREngine'
```

## Usage

```sh
$ tooncher [username]
```

`tooncher --help` shows all available options.

### Examples

```sh
$ tooncher toon
$ tooncher ceo
$ tooncher --cpu-limit 70 cfo
```

### Python Interface

```python
import pathlib
import tooncher

tooncher.launch(
    engine_path=pathlib.Path('/somewhere/toontown-rewritten/TTREngine'),
    username='toon',
    password='secret',
)
```
sklearn-xarray 0.4.0
xarray integration with sklearn

.. -*- mode: rst -*-

|Travis|_ |Coverage|_ |PyPI|_ |Black|_

.. |Travis| image:: https://travis-ci.org/phausamann/sklearn-xarray.svg?branch=master
.. _Travis: https://travis-ci.org/phausamann/sklearn-xarray

.. |Coverage| image:: https://coveralls.io/repos/github/phausamann/sklearn-xarray/badge.svg?branch=master
.. _Coverage: https://coveralls.io/github/phausamann/sklearn-xarray?branch=master

.. |PyPI| image:: https://badge.fury.io/py/sklearn-xarray.svg
.. _PyPI: https://badge.fury.io/py/sklearn-xarray

.. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
.. _Black: https://github.com/psf/black

sklearn-xarray
==============

**sklearn-xarray** is an open-source python package that combines the
n-dimensional labeled arrays of xarray_ with the machine learning and model
selection tools of scikit-learn_. The package contains wrappers that allow
the user to apply scikit-learn estimators to xarray types without losing their
labels.

.. _scikit-learn: http://scikit-learn.org/stable/
.. _xarray: http://xarray.pydata.org


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

The package documentation can be found at
https://phausamann.github.io/sklearn-xarray/


Features
----------

- Makes sklearn estimators compatible with xarray DataArrays and Datasets.
- Allows for estimators to change the number of samples.
- Adds a large number of pre-processing transformers.


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

The package can be installed with ``pip``::

    $ pip install sklearn-xarray

or with ``conda``::

    $ conda install -c phausamann -c conda-forge sklearn-xarray


Example
-------

The `activity recognition example`_ demonstrates how to use the
package for cross-validated grid search for an activity recognition task.
You can also download the example as a jupyter notebook.

.. _activity recognition example: https://phausamann.github.io/sklearn-xarray/auto_examples/plot_activity_recognition.html


Contributing
------------

Please read the `contribution guide <https://github.com/phausamann/sklearn-xarray/blob/master/.github/CONTRIBUTING.rst>`_
if you want to contribute to this project.
plyflatten 0.2.0
Take a series of ply files and produce a digital elevation map

# PlyFlatten

Take a series of ply files and produce a digital elevation map

## Installation

```
pip install plyflatten
```

## Usage

This package comes with a command-line tool:
```
$ plyflatten --help
usage: plyflatten [-h] [--resolution RESOLUTION]
                  list_plys [list_plys ...] dsm_path

plyflatten: Take a series of ply files and produce a digital elevation map

positional arguments:
  list_plys             Space-separated list of .ply files
  dsm_path              Path to output DSM file

optional arguments:
  -h, --help            show this help message and exit
  --resolution RESOLUTION
                        Resolution of the DSM in meters (defaults to 1m)
```

Try using it on the test data provided with the repository:
```
plyflatten tests/data/{1,2}.ply out.tiff --resolution 2
```

## Contributing

To work on this project, install the development requirements by running:
```
make install
```

The tests can be run with:
```
make test
```
mypassword 0.1.2
Tools for generating strong passwords
Keywords: password,pass,password-manager

# MyPassword Package

## Installation
mypassword requires Python 3.* to work

```sh
$ pip install mypassword
```
or

```sh
$ pip3 install mypassword
```

## Password
### Default Use
```python
from mypassword import Password

password = Password()

print(password)          # Ex. print xvcpdudi
print(password.password) # Ex. print xvcpdudi
```

### Stronger passwords
There are 4 levels for the password strength level
1. Level One: Includes only lower case characters
2. Level Two: Includes lower and upper case characters
3. Level Three: Includes lower and upper case characters and digits
4. Level Four: Includes lower and upper case characters, digits and punctuation characters

If you need more security, you can specify the level in the creation of the object

```python
from mypassword import Password, PasswordLevel

password_level_one = Password()
password_level_two = Password(level=PasswordLevel.TWO)
password_level_three = Password(level=PasswordLevel.THREE)
password_level_four = Password(level=PasswordLevel.FOUR)

print(password_level_one)   # Ex. print amzvlepp  
print(password_level_two)   # Ex. print RbHnbpMa
print(password_level_three) # Ex. print 9hS7c1Tw
print(password_level_four)  # Ex. print l8:N7@.1
```

### Length
Additionally you can specify the password length, by default the length is 8

```python
from mypassword import Password, PasswordLevel

password_level_one = Password(length=16)
password_level_four = Password(length=32, level=PasswordLevel.FOUR)

print(password_level_one)   # Ex. print enxhyrpgwecyboyn  
print(password_level_four)  # Ex. print lX7a`DMN$e\09<i|0U93r6Lj7bg1m0Z/
```

## Pasworify
### Default use
```python
from mypassword import Passworify

my_string = 'my secure password'
passworify = Passworify(my_string)
passworify.parse()
print(passworify.password) # Ex. print My+SEcUR3!PASSw0rd
```

`passworify.parse()` converts the string into a `Password` instance. The object is in the attribute `passworify.password`
topo2geo 0.4.4
CLI for converting TopoJSON files to GeoJSON


# topo2geo

CLI for converting TopoJSON files to GeoJSON

### Installation

```
pip install topo2geo
```

Depends on [shapely](https://pypi.org/project/Shapely/) and [click](https://pypi.org/project/click/)

### Usage

```
[~]$ topo2geo input.topojson output.geojson
```

### Multilayered Topojsons
If the topojson contains mulitple layers (i.e. there are multiple values in the "objects" key). Then seperate geojson files will be output with the layer name. 

For example a topojson containing states and counties:
```
{
    "type": "Topology",
    "objects": {
        "county": {
            "type": "GeometryCollection",
            "geometries": [...]
        },
        "state": {
            "type": "GeometryCollection",
            "geometries": [...]
        }
    }
}
```
would produce two geojson files, `output_geo_county.json` and `output_geo_state.json`

### Troubleshooting
If you experience a "segmentation fault" one thing to try is explained [here](https://github.com/Toblerity/Shapely#source-distributions):
```
pip install shapely --no-binary shapely
```

### Credits
Originally written by [sgillies](https://github.com/sgillies) and [perrygeo](https://github.com/perrygeo). Converted to Python3 and packaged into a CLI by [kylepollina](https://github.com/kylepollina).

Sources:
* https://gist.github.com/perrygeo/1e767e42e8bc54ad7262
* https://github.com/sgillies/topojson/blob/master/topojson.py

-------

License - https://github.com/topojson/topojson/blob/master/LICENSE.md