Metadata-Version: 2.1
Name: hypers
Version: 0.0.12.2
Summary: Hyperspectral data analysis and machine learning
Home-page: https://github.com/priyankshah7/hypers
Author: Priyank Shah
Author-email: priyank.shah@kcl.ac.uk
License: BSD 3-Clause
Download-URL: https://github.com/priyankshah7/hypers/archive/v0.0.11.tar.gz
Description: # hypers
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        hypers provides a data structure in python for hyperspectral data. The data structure includes:
        
        + Tools for processing and exploratory analysis of hyperspectral data
        + Interactive hyperspectral viewer (using PyQt) that can be accessed as a method from the object
        + Allows for unsupervised machine learning directly on the object
        
        The data structure is built on top of the numpy `ndarray`, and this package simply adds additional functionality that 
        allows for quick analysis of hyperspectral data. Importantly, this means that the object can still be used as a 
        normal numpy array.
        
        <p align="center"><img src="/docs/source/images/hyperspectral_image.png" width="300"></p>
        
        **Please note that this package is currently in pre-release. It can still be used, however there is likely to be 
        significant changes to the API. The first public release will be v0.1.0.**
        
        ## Contents
        1. [Installation](#installation)
        2. [Features](#features)
        3. [Examples](#examples)
        4. [Documentation](#documentation)
        5. [License](#license)
        6. [References](#references)
           
        ## Installation
        To install using `pip`:
        ```
        pip install hypers
        ```
        
        The following packages will also be installed:
        
        + numpy
        + matplotlib
        + scipy
        + scikit-learn
        + PyQt5
        + pyqtgraph
        
        ## Features
        Features implemented in ``hypers`` include:
        
        + Clustering
        + Decomposition (e.g. PCA, ICA, NMF)
        + Hyperspectral viewer
        + Vertex component analysis
        + Gaussian mixture models
        
        A full list of features can be found [here](http://hypers.readthedocs.io/en/latest/).
        	
        ## Examples
        
        ### Hyperspectral dimensionality reduction and clustering
        Below is a quick example of using some of the features of the package on a randomized hyperspectral array. 
        For an example using the IndianPines dataset, see the Jupyter notebook in the [examples](/examples/indian_pines.ipynb) directory.
        
        ```python
        import numpy as np
        import hypers as hp
        
        # Generating a random 4-d dataset and creating a Dataset instance
        # The test dataset here has spatial dimensions (x=200, y=200, z=10) and spectral dimension (s=1024)
        test_data = np.random.rand(200, 200, 10, 1024)
        X = hp.array(test_data)
        
        # Using Principal Components Analysis to reduce to first 5 components
        # The variables ims, spcs are arrays of the first 5 principal components for the images, spectra respectively
        ims, spcs = X.decompose.pca.calculate(n_components=5)
        
        # Clustering using K-means (with and without applying PCA first)
        # The cluster method will return the labeled image array and the spectrum for each cluster
        lbls_nodecompose, spcs_nodecompose = X.cluster.kmeans.calculate(
            n_clusters=3,
            decomposed=False
        )
        
        # Clustering on only the first 5 principal components
        lbls_decomposed, spcs_decomposed = X.cluster.kmeans.calculate(
            n_clusters=3,
            decomposed=True,
            pca_comps=5
        )
        ```
        
        ### Interactive viewer
        The interactive viewer can be particularly helpful for exploring a completely new dataset for the first time to get 
        a feel for the type of data you are working with. An example from a coherent anti-Stokes Raman (CARS) dataset is 
        shown below:
         
         <p align="center"><img src="/docs/source/images/hyperspectral_view.png" width="400"></p>
        
        ## Documentation
        The docs are hosted [here](http://hypers.readthedocs.io/en/latest/?badge=latest).
        
        ## License
        hypers is licensed under the OSI approved BSD 3-Clause License.
        
        ## References
        1. VCA algorithm  
        J. M. P. Nascimento and J. M. B. Dias, "Vertex component analysis: a fast algorithm to unmix hyperspectral data," 
        in IEEE Transactions on Geoscience and Remote Sensing, 2005  
        Adapted from [repo](https://github.com/Laadr/VCA).
Keywords: hyperspectral,data-analysis,clustering,matrix-decompositions,hyperspectral-analysis,machine learning
Platform: UNKNOWN
Requires-Python: >=3.5.0
Description-Content-Type: text/markdown
