semvec/pypi-packages huggingface dataset:
xicam.SAXS 2.2.2
SAXS GUI Interface
License: BSD
Keywords: synchrotron analysis x-ray scattering tomography

# Xi-CAM SAXS

Xi-CAM GUI plugin that facilitates data exploration and processing for SAXS experiments.

(Work in progress, release 1.0.0 will be more stable.)

## Installation

The latest build of Xi-cam.SAXS is available on pypi as `xicam.SAXS`.

### Details

If unsure about where to install, we recommend creating an environment.
There are several tools that provide this, such as virtualenv or conda.

For more details about setting up an environment for installation, see the
[Xi-CAM Installation Instructions](https://xi-cam.readthedocs.io/en/latest/quickstart.html#install-xi-cam)
for your operating system.

Once your environment is activated, install with the following command:

```
pip install xicam.SAXS
```

(this will install xicam for you as well).


## Resources

For more information about Xi-CAM, see the [main Xi-CAM repository's README](https://github.com/xi-cam/xi-cam)

## Release Notes

* 0.3.0 - Refactoring; provides Calibration and Correlation stages.
xicam 2.4.6
The CAMERA platform for synchrotron data management, visualization, and reduction.
License: BSD
Keywords: synchrotron analysis x-ray scattering tomography

# Xi-CAM
[![PyPI](https://badgen.net/pypi/v/xicam)](https://pypi.org/project/xicam/)
[![License](https://badgen.net/pypi/license/xicam)](https://github.com/Xi-cam/Xi-cam)
[![Build Status](https://img.shields.io/travis/Xi-cam/Xi-cam/master.svg)](https://travis-ci.org/Xi-cam/Xi-cam)
[![Documentation Status](https://readthedocs.org/projects/xi-cam/badge/?version=latest)](https://xi-cam.readthedocs.io/en/latest/?badge=latest)
[![Test Coverage](https://img.shields.io/codecov/c/github/Xi-cam/Xi-cam/master.svg)](https://codecov.io/github/Xi-cam/Xi-cam?branch=master)
[![Slack Status](https://img.shields.io/badge/slack-@ronpandolfi/nikea-yellow.svg?logo=slack)](https://nikea.slack.com/messages/U7Q1N42F6)

Xi-cam is a graphical environment for synchrotron data analysis,
management, and visualization developed by the Advanced Light Source at
Lawrence Berkeley National Laboratory. This is a cross-platform
open-source Python 3 project licensed under BSD.

## Installation

The latest stable Xi-CAM is available on pypi

```bash
pip install xicam
```

For more information, see the [installation documentation](https://xi-cam.readthedocs.io/en/latest/quickstart.html).

## Resources

* Documentation: https://xi-cam.readthedocs.io/en/latest
* Report an issue in Xi-CAM: [New Bug Report](https://github.com/Xi-CAM/Xi-cam/issues/new?labels=bug&template=bug_report.md)
commonroad-vehicle-models 3.0.2
Implementation of vehicle models with varying abstraction levels ranging from kinematic single track model to a multi-body model.
License: BSD
Requires Python: >=3.7
Keywords: autonomous automated vehicles driving motion planning

# Python Vehicle Models of CommonRoad

This package contains all vehicle models of the [CommonRoad benchmarks](https://commonroad.in.tum.de/).

We provide implementations of the vehicle dynamics, routines to convert initial states, and vehicle parameters.

## Documentation

For a detailed explanation of the vehicle models, please have a look at the [documentation](https://gitlab.lrz.de/tum-cps/commonroad-vehicle-models/blob/master/vehicleModels_commonRoad.pdf).

## Installation

To use vehicle models and parameters, run
```
pip install commonroad-vehicle-models
```

## Code examples

For an extended simulation example demonstrating the advantages of more complicated models, we refer to our [gitlab repository](https://gitlab.lrz.de/tum-cps/commonroad-vehicle-models/-/tree/master/PYTHON/scripts). A simple simulation example for using the single-track model in combination with an odeint solver would be

```python3
from scipy.integrate import odeint
import numpy

from vehiclemodels.init_ks import init_ks
from vehiclemodels.parameters_vehicle1 import parameters_vehicle1
from vehiclemodels.vehicle_dynamics_ks import vehicle_dynamics_ks

def func_KS(x, t, u, p):
    f = vehicle_dynamics_ks(x, u, p)
    return f

tStart = 0  # start time
tFinal = 1  # start time

# load vehicle parameters
p = parameters_vehicle1()

# initial state for simulation
delta0 = 0
vel0 = 15
Psi0 = 0
sy0 = 0
initialState = [0, sy0, delta0, vel0, Psi0]
x0_KS = init_ks(initialState)

t = numpy.arange(0, tFinal, 0.01)
u = [0, 5]
x = odeint(func_KS, x0_KS, t, args=(u, p))

```



## Contribute

If you want to contribute new vehicle models, you can create a merge request in our [repository](https://gitlab.lrz.de/tum-cps/commonroad-vehicle-models/), or contact via our [forum](https://commonroad.in.tum.de/forum/).


## Changelog
Compared to version 2.0.0 the following features were added/changed:
* linearized kinematic single-track model added as an additional vehicle model
* vehicle parameters are stored in YAML-files
* parameter configuration of vehicles are generated from YAML-files using [OmegaConf](https://omegaconf.readthedocs.io/en/2.2_branch/) (backwards compatible) 


## Referencing

If you use CommonRoad for your research, please cite [our paper](http://mediatum.ub.tum.de/doc/1379638/776321.pdf): 

```
@inproceedings{Althoff2017a,
	author = {Althoff, Matthias and Koschi, Markus and Manzinger, Stefanie},
	title = {CommonRoad: Composable benchmarks for motion planning on roads},
	booktitle = {Proc. of the IEEE Intelligent Vehicles Symposium},
	year = {2017},
}
```