semvec/pypi-packages huggingface dataset:
livox-lvx 0.2.1
Library for reading, processing and writing .lvx files
Requires Python: >=3.7

# livox_lvx

Library for reading, processing and writing `.lvx` files.

## Usage

```py
from lvx import LvxFileReader, LvxFileWriter

INPUT = '370132276011LIDAR.lvx'

with open(INPUT, 'rb') as fi, open(INPUT + '.other', 'wb') as fo:
    lvx_in = LvxFileReader(fi)
    header = lvx_in.header
    lvx_out = LvxFileWriter(header, fo)

    for frame in lvx_in:
        lvx_out.write_frame(frame, True)
```

### lvx.clean_file

The `lvx.clean_file` will remove any duplicate devices in the header device
block and updates the frame offsets while writing.

```py
from lvx import clean_devices

clean_devices('input.lvx', 'output.lvx')
```

#### Script

```sh
python -m lvx.clean_devices -h  # Show script help
python -m lvx.clean_devices input.lvx -o output.lvx
```

### lvx.diff

The `lvx.diff` function will write a plain text file with information about the
structure of the input file.

```py
from lvx import diff

diff('input.lvx', 'output.diff', header_only=False)
```

#### Script

```sh
python -m lvx.diff -h  # Show script help
python -m lvx.diff input.lvx -o output.diff
```

## Install

```sh
git clone https://gitlab.com/twh2898/livox_lvx
cd livox_lvx
pip install .
```

### Development Install

Replace the pip command in the above code with the following (note the `-e`).

```sh
pip install -e .
```

## Building

```sh
git clone https://gitlab.com/twh2898/livox_lvx
cd livox_lvx
pip install build
python -m build
```

## License

livox_lvx uses the [MIT](LICENSE) License.
mcroute 1.1.0
Markov chain modelling of transportation networks.
Requires Python: >=3.8

# MCRoute
Markov chain modelling of transportation networks.

This package enables the modelling of networks (transportation or otherwise) in
which movement through the network is represented by discrete state Markov
chains.

MCRoute extends the [NetworkX](https://networkx.org/documentation/stable/index.html)
module, requiring each node and edge to have an accompanying transition
probability matrix that can be used during network traversal calculations. The
package also incldues utility functions for constructing transition matrices
from data and theoretical distributions.

## Get Started
You can take a look at the example workbooks available on 
[GitHub](https://github.com/wklumpen/mcroute/tree/main/examples).

## Documentation
Documentation can be found for the latest release on [Read the Docs](http://mcroute.readthedocs.io/)

## Citing This Package
Please cite the following paper (currently under preparation):

**W. Klumpenhouwer** Modelling Stochastic Transportation Networks with Markov 
Chains. (Under Preparation)