semvec/pypi-packages huggingface dataset:
ncvis 1.5.13
Noise contrastive data visualization
License: MIT
Requires Python: >=3

[![Conda](https://anaconda.org/alartum/ncvis/badges/version.svg)](https://anaconda.org/alartum/ncvis)
[![PyPI](https://img.shields.io/pypi/v/ncvis.svg)](https://pypi.python.org/pypi/ncvis/)
[![GitHub](https://img.shields.io/github/license/alartum/ncvis.svg)](https://github.com/alartum/ncvis/blob/master/LICENSE)
[![Build Status](https://dev.azure.com/stat-ml/ncvis/_apis/build/status/stat-ml.ncvis?branchName=master)](https://dev.azure.com/stat-ml/ncvis/_build/latest?definitionId=1&branchName=master)

# ncvis

**NCVis** is an efficient solution for data visualization and dimensionality reduction. It uses [HNSW](https://github.com/nmslib/hnswlib) to quickly construct the nearest neighbors graph and a parallel (batched) approach to build its embedding. Efficient random sampling is achieved via [PCGRandom](https://github.com/imneme/pcg-cpp). Detailed application examples can be found [here](https://github.com/alartum/ncvis-examples).

# Using

```python
import ncvis

vis = ncvis.NCVis()
Y = vis.fit_transform(X)
```

More detailed examples can be found [here](https://github.com/alartum/ncvis-examples).

# Installation

## Conda [recommended]

You do not need to setup the environment if using *conda*, all dependencies are installed automatically. 
```bash
$ conda install alartum::ncvis 
```

## Pip [not recommended]

**Important**: be sure to have a compiler with *OpenMP* support. *GCC* has it by default, which is not the case with *clang*. You may need to install *llvm-openmp* library beforehand.  

1. Install **numpy**, **cython** and **pybind11** packages (compile-time dependencies):
    ```bash
    $ pip install numpy cython pybind11
    ```
2. Install **ncvis** package:
    ```bash
    $ pip install ncvis
    ```

## From source [not recommended]

**Important**: be sure to have *OpenMP* available.

First of all, download the *pcg-cpp* and *hnswlib* libraries:
```bash
$ make libs
``` 
### Python Wrapper 

If *conda* environment is used, it replaces library search paths. To prevent compilation errors, you either need to use compilers provided by *conda* or switch to *pip*  and system compilers. 

* Conda
    ```bash
    $ conda install -c conda-forge cxx-compiler c-compiler conda-build numpy cython pybind11 scipy
    $ conda-develop -bc .
    ``` 

* Pip
    ```bash
    $ pip install numpy cython pybind11
    $ make wrapper
    ```

You can then use *pytest* to run some basic checks
```bash
$ pytest -v recipe/test.py
```


### C++ Binary

* Release
    ```bash
    $ make ncvis
    ```

* Debug
    ```bash
    $ make debug
    ```
hpc_helper 0.3.2
Python package with helper functions for working with FAU's High Performance Cluster (HPC).
License: MIT
Requires Python: >=3.8,<3.11

# hpc-helper

[![PyPI](https://img.shields.io/pypi/v/hpc-helper)](https://pypi.org/project/hpc-helper/)
![GitHub](https://img.shields.io/github/license/mad-lab-fau/hpc-helper)
[![Test and Lint](https://github.com/mad-lab-fau/hpc-helper/actions/workflows/test-and-lint.yml/badge.svg)](https://github.com/mad-lab-fau/hpc-helper/actions/workflows/test-and-lint.yml)
[![codecov](https://codecov.io/gh/mad-lab-fau/hpc-helper/branch/main/graph/badge.svg?token=GOUBR5KPQF)](https://codecov.io/gh/mad-lab-fau/hpc-helper)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
![PyPI - Downloads](https://img.shields.io/pypi/dm/hpc-helper)
![GitHub commit activity](https://img.shields.io/github/commit-activity/m/mad-lab-fau/hpc-helper)

Python package with helper functions for working with FAU's [High Performance Cluster](https://hpc.fau.de/) (HPC).

## Installation

``hpc-helper`` requires Python >=3.7. First, install a compatible version of Python. 
Then you can install ``hpc-helper`` via pip. 

Installation from [PyPi](https://pypi.org/): 
```bash
pip install hpc-helper
```

Installation from local repository copy:
```bash
git clone https://github.com/mad-lab-fau/hpc-helper.git
cd hpc-helper
pip install .
```