semvec/pypi-packages huggingface dataset:
rocketflightsim 0.2.0
A lightweight rocket flight simulator.
License: MIT
Requires Python: >=3.6

# RocketFlightSim

RocketFlightSim (RFS) is a light-weight flight simulator for experimental rockets. It is designed to be simple to use, understand, and modify, with the goal of providing a platform for students and hobbyists to learn about rocket flight dynamics.

The simulator currently supports simulation of single-stage rockets in the troposphere, from ignition to apogee. Simple airbrakes can added to the rocket to induce drag. 

A large emphasis is placed on computational efficiency, with the goal of being lightweight enough to run real-time during flight on an onboard microcontroller.
dinjectorr 0.1.8
A simple dependency injector for Python
License: MIT

# dinjectorr
A simple dependency injector for Python.

## Installation
```bash
pip install dinjectorr
```

## Usage
Example 1:
```python
from dinjectorr import inject

class A:
    def print_hello(self):
        print("Hello from A")

class B:
    @inject
    def __init__(self, a: A):
        self._a = a

    def print_hello(self):
        self._a.print_hello()

b = B()
b.print_hello()
# Output:
# Hello from A
```
Example 2:
```python
from dinjectorr import Injector

class Client:
    def __init__(self, api_key: str):
        self._api_key = api_key

    def print_api_key(self):
        print(self._api_key)

Injector.register(Client, api_key="some_api_key")
client = Injector.get_instance(Client)
client.print_api_key()
# Output:
# some_api_key
```
Example 3:
```python
from dinjectorr import inject, Injector

class Client:
    def __init__(self, api_key: str):
        self._api_key = api_key

    def print_api_key(self):
        print(self._api_key)

class CustomClient(Client):
    @inject
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

Injector.register(CustomClient, api_key="some_api_key")
custom_client = CustomClient()
custom_client.print_api_key()
# Output:
# some_api_key
```
Example 4:
```python
from dinjectorr import inject, Injector

class Client:
    def __init__(self, api_key: str):
        self._api_key = api_key

    def print_api_key(self):
        print(self._api_key)

class CustomClient(Client):
    @inject
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

Injector.register(CustomClient, api_key="some_api_key")
custom_client = CustomClient(api_key="another_api_key")
custom_client.print_api_key()
# Output:
# another_api_key
```
sdcd 0.1.4
Stable differentiable causal discovery for interventional data.
License: MIT
Requires Python: >=3.9

# SDCD: Stable Differentiable Causal Discovery

SDCD is a method for inferring causal graphs from labeled interventional data.\
You can read the associated preprint, ["Stable Differentiable Causal Discovery"](https://arxiv.org/abs/2311.10263), on arXiv.

![sdci-cartoon](https://github.com/azizilab/sdcd/assets/14086852/c2b34538-c5e0-48f2-b092-9167c02101a7)

If you find this work useful, please consider citing our work:

```bibtex
@article{nazaret2023stable,
  title={Stable Differentiable Causal Discovery},
  author={Achille Nazaret and Justin Hong and Elham Azizi and David Blei},
  journal={arXiv preprint arXiv:2311.10263},
  year={2023}
}
```

---

## Quick Start

You can install the package via `pip install sdcd`.

For the main implementation of the method, see the [SDCD](sdcd/models/_sdcd.py) class.

For a tutorial on the basic usage of SDCD, see [this notebook](tutorials/SDCD_basic_usage.ipynb).

Code used to generate paper figures can be found in [this folder](paper_experiments/).