# pcts
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
## Developer Guide
### Setup
``` sh
# create conda environment
$ mamba env create -f env.yml
# update conda environment
$ mamba env update -n pcts --file env.yml
```
### Install
``` sh
pip install -e .
# install from pypi
pip install pcts
```
### nbdev
``` sh
# activate conda environment
$ conda activate pcts
# make sure the pcts package is installed in development mode
$ pip install -e .
# make changes under nbs/ directory
# ...
# compile to have changes apply to the pcts package
$ nbdev_prepare
```
### Publishing
``` sh
# publish to pypi
$ nbdev_pypi
# publish to conda
$ nbdev_conda --build_args '-c conda-forge'
$ nbdev_conda --mambabuild --build_args '-c conda-forge -c dsm-72'
```
# Usage
## Installation
Install latest from the GitHub
[repository](https://github.com/dsm-72/pcts):
``` sh
$ pip install git+https://github.com/dsm-72/pcts.git
```
or from [conda](https://anaconda.org/dsm-72/pcts)
``` sh
$ conda install -c dsm-72 pcts
```
or from [pypi](https://pypi.org/project/pcts/)
``` sh
$ pip install pcts
```
## Documentation
Documentation can be found hosted on GitHub
[repository](https://github.com/dsm-72/pcts)
[pages](https://dsm-72.github.io/pcts/). Additionally you can find
package manager specific guidelines on
[conda](https://anaconda.org/dsm-72/pcts) and
[pypi](https://pypi.org/project/pcts/) respectively.
## NumPy Documentation:
- [`np.DataSource`](https://numpy.org/doc/stable/reference/generated/numpy.DataSource.html)
## PyTorch Documentation:
- [`TorchData`](https://pytorch.org/data/beta/index.html)
- [How to Package PyTorch
Models](https://pytorch.org/docs/stable/package.html)
- [`torch.monitor.Event`](https://pytorch.org/docs/stable/monitor.html#torch.monitor.Event)
- [`torchvision`](https://pytorch.org/vision/stable/index.html)
- [`torchvision.Datasets.VisionDataset`](https://pytorch.org/vision/stable/generated/torchvision.datasets.VisionDataset.html#torchvision.datasets.VisionDataset)
- [`torchvision.utils.flow_to_image`](https://pytorch.org/vision/stable/generated/torchvision.utils.flow_to_image.html)
## PyTorch Models to Consider:
- [Diffusion Video
AutoEncoders](https://github.com/man805/Diffusion-Video-Autoencoders)
- [ConvLSTM
AutoEncoder](https://holmdk.github.io/2020/04/02/video_prediction.html)
- [Recurrent All Pairs Field Transforms for Optical
Flow](https://github.com/princeton-vl/RAFT/blob/master/core/raft.py)
- [Optical Flow Toolbox
(`mmflow`)](https://github.com/open-mmlab/mmflow/blob/master/docs/en/intro.md)
- [`torchvision.models.optical_flow.raft`](https://github.com/pytorch/vision/blob/main/torchvision/models/optical_flow/raft.py)
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