semvec/pypi-packages huggingface dataset:
pycubescrambler 0.1.7
A scrambling package
Requires Python: >=3.6

Pycubescrambler is a scrambling module that can generate scrambles for most wca events.
Here is how it works:




from pycubescrambler import *

#gets 1x1 scramble and prints it out
scramble=nxn.get1()
print(scramble)

#gets 2x2 scramble and prints it out
scramble=nxn.get2()
print(scramble)

#gets 3x3 scramble and prints it out
scramble=nxn.get3()
print(scramble)

#gets 4x4 scramble and prints it out
scramble=nxn.get4()
print(scramble)

#gets 5x5 scramble and prints it out
scramble=nxn.get5()
print(scramble)

#gets 6x6 scramble and prints it out
scramble=nxn.get6()
print(scramble)

#gets 7x7 scramble and prints it out
scramble=nxn.get7()
print(scramble)

#gets a big cube of size 100 (100x100)
scramble=nxn.get_big_cube(100)
print(scramble)

#gets fmc scramble and prints it out
scramble=nxn.get_fmc()
print(scramble)

#gets skewb scramble and prints it out
scramble=side.get_skewb()
print(scramble)

#gets pyraminx scramble and prints it out
scramble=side.get_pyra()
print(scramble)

#gets clock scramble and prints it out
scramble=side.get_clock()
print(scramble)

#gets megaminx scramble and prints it out
scramble=side.get_mega()
print(scramble)

#gets 3bld scramble and prints it out
scramble=bld.get3()
print(scramble)

#gets 4bld scramble and prints it out
scramble=bld.get4()
print(scramble)

#gets 5bld scramble and prints it out
scramble=bld.get5()
print(scramble)
mc2-symmetry 0.6.0
Symmetry - A cluster management system for micro-clusters

symmetry
========

Cluster management tool for the MC2.

Build
-----

To create a virtual environment and install the requirements, run (by default it uses `python3.7`, you can change that
in the `Makefile` variable `VENV_BIN`)

    make venv


Run Symmetry components manually
--------------------------------

### Prerequisites

Make sure the virtualenv is activated

    source .venv/bin/activate

Symmetry publishes and reads telemetry data to and from [redis](http://redis.io),
so you need a running redis instance first:

    redis-server

### Start the telemetry deamon

Download and start the [telemetry daemon](https://github.com/edgerun/telemd) 

    ./telemd

You can verify that the telemetry daemon sends data by suscribing to the topic
via the redis CLI:

    redis-cli psubscribe "telem/*"


### Start the cluster deamon

Start the cluster daemon (which controls the cluster state)

    python -m symmetry.cli.clusterd


### Websocket server

To start the websocket server for the dashboard:

    python -m symmetry.cli.websocket

The websocket is running on port ```8082``` and automatically connects to the local redis-server on `localhost:6379`.
Connect with ```ws://ip:8082/topic``` to receive all published messages for the given ```topic```. 


### Run the dashboard

Serve the app with gunicorn

    gunicorn -w 4 --preload -b 0.0.0.0:5000 \
        -c symmetry/webapp/gunicorn.conf.py \
        symmetry.webapp.wsgi:api

Point your browser to http://localhost:5000/ui/dashboard.

Using Symmetry
--------------

### Creating services

To create a new service in the cluster a `POST` request must be sent to the
`api/services/{service_id}` endpoint, with `service_id` being the new service.

The `POST` request has one optional 
([tar-exported docker image](https://docs.docker.com/engine/reference/commandline/save/)) and one mandatory (service 
description) body element.

The service description is structured this way:

```yaml
%YAML 1.2
---
id: #must be same id as in endpoint
maintainer: #the uploader of the service
name: #"Pretty" version of the name
version: #any version number
description: #simple description
image_tag: #if docker image uploaded then this tag must be provided by the image. otherwise it is pulled 
port: #the port which is exposed by the service
```  

After correct execution the answer should look somewhat like this:

```json
{
    "name": "Test Service",
    "version": "1.0.0",
    "desc": "This is a simple test_service",
    "maintainer": "silvio",
    "10.0.0.3": "1"
}
```


Other Client Scripts
--------------------

### Recording telemetry data

You can use the telemetry client `cli.telemc` to consume telemetry data in
various ways.

    # prints to system out
    python -m symmetry.cli.telemc

    # writes to a csv file
    python -m symmetry.cli.telemc --file /tmp/telemetry-results.csv
typekev-mlnd-probability 1.1
Gaussian and Binomial distributions

# MLND Probability Classes

This package exposes two probability distribution classes, Gaussian and Binomial, and was made as part of the Udacity Machine Learning Engineer Nanodegree.

This package was created for educational purposes.

# Use of `Gaussian` and `Binomial`

The classes can be imported as so: `from typekev_mlnd_probability import Gaussian` and `from typekev_mlnd_probability import Binomial`

Both classes are initialized with the following attributes:

- `mean`: The mean or average
- `stdev`: Standard deviation
- `data`: The list of data in which to calculate the other attributes

Both classes also contain a `read_data_file` method.

The `Gaussian` and `Binomial` classes also contain unique attributes and methods. Though names may overlap, implementation will differ.

## `Gaussian` attributes and methods

The `Gaussian` class contains the following additional methods:

- `calculate_mean`
- `calculate_stdev`
- `plot_histogram`
- `pdf`
- `plot_histogram_pdf`

The magic methods `__add__` and `__repr__` have also been implemented.

## `Binomial` attributes and methods

The `Binomial` class contains the following additional attributes:

- `n`: The size of the data set
- `p`: The probability

The `Binomial` class contains the following additional methods:

- `calculate_mean`
- `calculate_stdev`
- `replace_stats_with_data`
- `plot_bar`
- `pdf`
- `plot_bar_pdf`

The magic methods `__add__` and `__repr__` have also been implemented.