semvec/pypi-packages huggingface dataset:
vip4model 1.0.0
Python implementation of the VIP4 model

# vip4model
VIP4 model (Connerney et al. 1998) implementation using Python.

## Installation

Install using `pip`:

```bash
pip3 install vip4model --user
```

Or by cloning this repo:

```bash
git clone https://github.com/mattkjames7/vip4model.git
cd vip4model

#EITHER create a wheel and install (replace X.X.X with the version number):
python3 setup.py bdist_wheel
pip3 install dist/vip4model-X.X.X-py3-none-any.whl --user

#OR install directly using setup.py
python3 setup.py install --user
```

## Usage

The model accepts right-handed System III coordinates either in Cartesian form (`vip4model.ModelCart()`) or in spherical polar form (`vip4model.Model()`), e.g.:

```python
import vip4model

#get some Cartesian field vectors (MaxDeg keyword is optional)
Bx,By,Bz = vip4model.ModelCart(x,y,z,MaxDeg=4)

#or spherical polar ones
Br,Bt,Bp = vip4model.Model(r,theta,phi,MaxDeg=4)
```

Please read the docstrings for `vip4model.Model()` and `vip4model.ModelCart()` using `help` or `?` e.g. `help(vip4model.Model)` .

There is also a test function which requires `matplotlib` to be installed:

```python
#evaluate the model at some R
vip4model.Test(R=0.85)
```

which produces this (based on figure 4 of Connerney et al. 2018):

![vip4test.png](vip4test.png)





## References

Connerney, J. E. P., Acuña, M. H., Ness, N. F., and Satoh, T. (1998), New models of Jupiter's magnetic field constrained by the Io flux 	tube footprint, J. Geophys. Res., 103( A6), 11929– 11939, doi:10.1029/97JA03726.

Connerney, J. E. P., Kotsiaros, S., Oliversen, R. J., Espley, J. R.,  Joergensen, J. L., Joergensen, P. S., et al. (2018). A new model of Jupiter's magnetic field from Juno's first nine orbits. Geophysical Research Letters, 45, 2590– 2596. https://doi.org/10.1002/2018GL077312
cowherd 0.3.0
Partially-observed visual reinforcement learning domain.

# CowHerd

[![PyPI](https://img.shields.io/pypi/v/cowherd.svg)](https://pypi.python.org/pypi/cowherd/#history)

CowHerd is a partially-observed reinforcement learning environment, where the
player walks around an area and is rewarded for milking cows. The cows try to
escape and the player can place fences to help capture them. The implementation
of CowHerd is based on the [Crafter][crafter] environment.

![Cow Herd Video](https://github.com/danijar/cowherd/raw/main/media/video.gif)

[crafter]: https://github.com/danijar/crafter

## Play Yourself

You can play the game yourself with an interactive window and keyboard input.
The mapping from keys to actions, health level, and inventory state are printed
to the terminal.

```sh
# Install with GUI
pip3 install 'cowherd[gui]'

# Start the game
cowherd

# Alternative way to start the game
python3 -m cowherd.run_gui
```

The following optional command line flags are available:

| Flag | Default | Description |
| :--- | :-----: | :---------- |
| `--window <width> <height>` | 800 800 | Window size in pixels, used as width and height. |
| `--fps <integer>` | 5 | How many times to update the environment per second. |
| `--record <filename>.mp4` | None | Record a video of the trajectory. |
| `--num_cows` | 3 | The number of cows in the environment. |
| `--view <width> <height>` | 7 7 | The layout size in cells; determines view distance. |
| `--length <integer>` | None | Time limit for the episode. |
| `--seed <integer>` | None | Determines world generation and creatures. |

## Training Agents

Installation: `pip3 install -U cowherd`

The environment follows the [OpenAI Gym][gym] interface:

```py
import cowherd

env = cowherd.Env(seed=0)
obs = env.reset()
assert obs.shape == (64, 64, 3)

done = False
while not done:
  action = env.action_space.sample()
  obs, reward, done, info = env.step(action)
```

[gym]: https://github.com/openai/gym

## Environment Details

### Reward

A reward of +1 is given every time the player milks one of the cows.

### Termination

Episodes terminate after 1000 steps.

### Observation Space

Each observation is an RGB image that shows a local view of the world around
the player, as well as the inventory state of the agent.

### Action Space

The action space is categorical. Each action is an integer index representing
one of the possible actions:

| Integer | Name | Description |
| :-----: | :--- | :---------- |
| 0 | `noop` | Do nothing. |
| 1 | `move_left` | Walk to the left. |
| 2 | `move_right` | Walk to the right. |
| 3 | `move_up` | Walk upwards. |
| 4 | `move_down` | Walk downwards. |
| 5 | `do` | Pick up a placed fence or milk a cow. |
| 6 | `place_fence` | Place a fence in front of the player. |

## Questions

Please [open an issue][issues] on Github.

[issues]: https://github.com/danijar/cowherd/issues
jrm09 1.0.0
Python implementation of the JRM09 model

# jrm09

JRM09 model (Connerney et al. 2018) implementation using Python.

## Installation

Install using `pip`:

```bash
pip3 install jrm09 --user
```

Or by cloning this repo:

```bash
git clone https://github.com/mattkjames7/jrm09.git
cd jrm09

#EITHER create a wheel and install (replace X.X.X with the version number):
python3 setup.py bdist_wheel
pip3 install dist/jrm09-X.X.X-py3-none-any.whl --user

#OR install directly using setup.py
python3 setup.py install --user
```

## Usage

The model accepts right-handed System III coordinates either in Cartesian form (`jrm09.ModelCart()`) or in spherical polar form (`jrm09.Model()`), e.g.:

```python
import jrm09

#get some Cartesian field vectors (MaxDeg keyword is optional)
Bx,By,Bz = jrm09.ModelCart(x,y,z,MaxDeg=10)

#or spherical polar ones
Br,Bt,Bp = jrm09.Model(r,theta,phi,MaxDeg=10)
```

Please read the docstrings for `jrm09.Model()` and `jrm09.ModelCart()` using `help` or `?` e.g. `help(jrm09.Model)` .

There is also a test function which requires `matplotlib` to be installed:

```python
#evaluate the model at some R
jrm09.Test(R=0.85)
```

which produces this (based on figure 4 of Connerney et al. 2018):

![jrm09test.png](jrm09test.png)

## References

Connerney, J. E. P., Kotsiaros, S., Oliversen, R. J., Espley, J. R.,  Joergensen, J. L., Joergensen, P. S., et al. (2018). A new model of Jupiter's magnetic field from Juno's first nine orbits. Geophysical Research Letters, 45, 2590– 2596. https://doi.org/10.1002/2018GL077312
leftpadx 0.0.1
A library that pads strings to the left. Feel free to make it your main dependency for your project.
Requires Python: >=3.0

# leftpad
`leftpad` is a Python library for padding the left of strings.

# Installation
`pip install leftpadx`

# Usage
```
from leftpad import leftpad

print(leftpad("epic", 100, "@"))
```
Expected output:
```
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@epic
```