semvec/pypi-packages huggingface dataset:
cli-sender 0.0.3
use OS terminal
Requires Python: >=3.6
Keywords: cli,cli user,cli sender,os terminal,os terminal sender,os terminal user

# cli_sender
Designed to send commands into OS terminal

## Features
1. send commands into OS terminal
2. check if cli commands are accessible (special utilities is installed)
3. access to standard parts of result in a simple ready-to-use form (stdout/stderr/retcode/full state)

## License
See the [LICENSE](LICENSE) file for license rights and limitations (MIT).


## Release history
See the [HISTORY.md](HISTORY.md) file for release history.


## Installation
```commandline
pip install cli-sender
```

## Import

```python
from cli_sender import *
```


## GUIDE

### USAGE

```python
from cli_sender import *

victim = CliSender()

cmd_line = "ping localhost"
victim.send(cmd_line, timeout=0.1)
victim.print_state()
"""
==================================================
self.last_cmd='ping -n 1 localhost'
self.last_finished_success=True
self.last_finished=True
self.last_retcode=0
--------------------------------------------------
self.last_stdout=
	
    ###STDOUT PING!!!
	
--------------------------------------------------
self.last_stderr=
--------------------------------------------------
self._last_exx_timeout=None
==================================================
"""
```
UmbrellaRL 0.0.0
Package to handle Reinforcement Learning functions. This package is nowhere near complete.
Requires Python: >=3.12

# UmbrellaRL

## This package is nowhere near complete.

## Structure

- ### Agent

The Agent represents the logic for reinforcement learning.
It contains functions for policy evaluation & calculating a State's value.

- ### Policy

The Policy object determines the actions taken by the agent.
The Policy object must inherit from the Base Policy class.

- ### Environment

The Environment represents the State that the Agent is in. It describes which
states are accessible following actions & what their probabilities are.

The Environment depends on the State Space.

- #### State Space

The State Space describes the states that the agent can be in.
Each State is of type State.

The State Space depends on the State.

- ##### State

The State represents a singular State that the agent can be in.
The State contains the information that describes each State.
numpy-ext 0.9.9
numpy extension
Requires Python: >=3.6

# NumPy Extensions
[![Build Status - GitHub](https://github.com/3jane/numpy_ext/workflows/Python%20package/badge.svg)](https://github.com/3jane/numpy_ext/actions?query=workflow%3A%22Python+package%22) [![Build Status - GitHub](https://github.com/3jane/numpy_ext/workflows/Deploy%20docs/badge.svg)](https://github.com/3jane/numpy_ext/actions?query=workflow%3A%22Deploy+docs%22) ![Deploy PYPI](https://github.com/3jane/numpy_ext/workflows/Deploy%20PYPI/badge.svg)  [![Coverage Status](https://coveralls.io/repos/github/3jane/numpy_ext/badge.svg)](https://coveralls.io/github/3jane/numpy_ext)

An extension library for [NumPy](https://github.com/numpy/numpy) that implements common array operations not present in NumPy.

* `npext.fill_na(...)`
* `npext.drop_na(...)`
* `npext.rolling(...)`
* `npext.expanding(...)`
* `npext.rolling_apply(...)`
* `npext.expanding_apply(...)`
* `# etc`

---
## Documentation

* [API Reference](http://3jane.github.io/numpy_ext/)

## Installation
Regular installation:
```bash
pip install numpy_ext
```

For development:
```bash
git clone https://github.com/3jane/numpy_ext.git
cd numpy_ext
pip install -e .[dev]  # note: make sure you are using pip>=20
```

## Examples
Here are few common examples of how the library is used. The rest is available in the [documentation](http://3jane.github.io/numpy_ext/).

1) Apply a function to a rolling window over the provided array

```python
import numpy as np
import numpy_ext as npext

a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
window = 3

npext.rolling_apply(np.sum, window, a)

> array([nan, nan,  3.,  6.,  9., 12., 15., 18., 21., 24.])
```

2) Same as the above, but with a custom function, two input arrays and parallel computation using `joblib`:

```python
def func(array_first, array_second, param):
    return (np.min(array_first) + np.sum(array_second)) * param


a = np.array([0, 1, 2, 3])
b = np.array([3, 2, 1, 0])

npext.rolling_apply(func, 2, a, b, n_jobs=2, param=-1)

> array([nan, -5., -4., -3.])
```

3) Same as the first example, but using **rolling** function:

```python
a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
window = 3

rolls = npext.rolling(a, window, as_array=True)

np.sum(rolls, axis=1)

> array([nan, nan,  3.,  6.,  9., 12., 15., 18., 21., 24.])
```

4) Apply a function with multiple output to a rolling window over the provided array, with no nans prepend

```python

res = npext.rolling_apply(
        lambda x: (max(x), min(x)),
        3,
        np.array([1, 2, 5, 1, 6, 4, 0]),
        prepend_nans=False,
    )

> array([[5, 1],
       [5, 1],
       [6, 1],
       [6, 1],
       [6, 0]])
```

## License
[![MIT Licence](https://badges.frapsoft.com/os/mit/mit.svg?v=103)](https://tldrlegal.com/license/mit-license)

The software is distributed under MIT license.
UmbrellaRL-ctorrington 0.0.0
Reiforcement Learning package description.
Requires Python: >=3.12

# Gradient-MC-1000-State-Random-Walk

## Structure

- ### Agent

The Agent represents the logic for reinforcement learning.
It contains functions for policy evaluation & calculating a State's value.

- ### Policy

The Policy object determines the actions taken by the agent.
The Policy object must inherit from the Base Policy class.

- ### Environment

The Environment represents the State that the Agent is in. It describes which
states are accessible following actions & what their probabilities are.

The Environment depends on the State Space.

- #### State Space

The State Space describes the states that the agent can be in.
Each State is of type State.

The State Space depends on the State.

- ##### State

The State represents a singular State that the agent can be in.
The State contains the information that describes each State.
django-vrsns 0.3.0
Provide an Django view that lists installed package versions
Requires Python: >=3.11,<4.0

Django versions
===============

Provides an endpoint that lists versions of installed packages. The endpoint is
only activated if `DEBUG` is set to `true`.

# Usage

Add the package to your installed apps:
```Python
INSTALLED_APPS.append('django_vrsns')
```

Add the shipped ulrpatterns to your urlpatterns:
```Python
from django_vrsns.urls import urlpatterns as dvurlpatterns
urlpatterns = dvurlpatterns + urlpatterns
```
spirit-extras 0.0.35
Package to be used with the magnetism simulation tool spirit
Requires Python: >=3.7

# spirit-extras
Package with some useful scripts, to be used together with the Spirit python API (`github.com/spirit-code/spirit/`).


## Data structure for spin systems

### Parse data from a Spirit state pointer
```python
from spirit import state
import spirit_extras.data
with state.State("input.cfg") as p_state:
    spin_sytem = spirit_extras.data.spin_system_from_p_state()

    # Information about the bravais lattice is automatically parsed from the spirit API
    print(spin_system)

    # The spins can be set
    ib, a, b, c = 1, 5, 3, 4
    idx = spin_system.idx(ib, a, b, c) # computes the linear index from a bravais tupel
    spin_system.spins[idx] = [0,0,-1]

    ib, a, b, c = spin_system.tupel(5) # computes the bravais tupel from a linear index

    # A shaped view into the spin system is also available
    spin_system.shaped().spins[ib,a,b,c] = [0,0,1]

    # Slicing syntax works
    spin_system.shaped().spins[ib,:,b,c] = [0,1,0]

    mask = np.linalg.norm(spin_system.positions - spin_system.center() < 5, radius=1)
    spin_sytem.spins[mask] = [1,0,0]
```

### Create from your own buffers
```python
# Since spirit_extras.Spin_System is needed for many functions in spirit_extras, it can also be created from a spins and positions arrays
spins = [ [1,0,0] for i in range(10) ]

# The default behaviour is to expect an unordered system, meaning some functions will throw an exception
my_spin_system = spirit_extras.data.Spin_System(positions=None, spins=spins) # positions dont _have_ to be specified
try:
    my_spin_sytem.shaped() # needs an ordered system
except:
    print("Throws :(")

# To create an ordered system pass `unordered=False` and specify `n_cells`
my_spin_system = spirit_extras.data.Spin_System(positions=None, spins=spins, n_cells=[10,1,1], unordered=False)
my_spin_sytem.shaped() # works!
```