hypers | hyperspectral data structure , data analysis | Machine Learning library

 by   priyankshah7 Python Version: 0.1.1 License: BSD-3-Clause

X-RayKey FeaturesCode SnippetsCommunity Discussions(1)VulnerabilitiesInstallSupport

kandi X-RAY | hypers Summary

kandi X-RAY | hypers Summary

hypers is a Python library typically used in Artificial Intelligence, Machine Learning, Numpy applications. hypers has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install hypers' or download it from GitHub, PyPI.

hypers provides a data structure in python for hyperspectral data. The data structure includes:. 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.
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              hypers has a low active ecosystem.
              It has 17 star(s) with 10 fork(s). There are 2 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 0 open issues and 4 have been closed. On average issues are closed in 118 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of hypers is 0.1.1

            kandi-Quality Quality

              hypers has 0 bugs and 0 code smells.

            kandi-Security Security

              hypers has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              hypers code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              hypers is licensed under the BSD-3-Clause License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              hypers releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              hypers saves you 481 person hours of effort in developing the same functionality from scratch.
              It has 632 lines of code, 55 functions and 20 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed hypers and discovered the below as its top functions. This is intended to give you an instant insight into hypers implemented functionality, and help decide if they suit your requirements.
            • Setup the UI
            • Translate the UI
            • Plot an HSI plot
            • Calculate the expectation of the fit
            • Convert numpy array to CVXopt matrix
            • Concatenate two numpy arrays
            • Variant of numpy arrays
            • Updates the data spectrum
            • Plot the data spectrum
            • Convert input array to hparray
            • Create a new instance from an input array
            • Calculate the map for a given fit
            • The number of samples
            • Calculate the covariance matrix
            • R Calculate vertex components
            • Estimate the SNR
            • Number of features
            • Number of spatial dimensions
            • Calculate the inverse map
            • Update image
            • Set image data
            • Update the image
            • Reset the data
            • Load data
            Get all kandi verified functions for this library.

            hypers Key Features

            No Key Features are available at this moment for hypers.

            hypers Examples and Code Snippets

            No Code Snippets are available at this moment for hypers.

            Community Discussions

            Trending Discussions on hypers

            Raku Ambiguous call to infix(Hyper: Dan::Series, Int)

            QUESTION

            Raku Ambiguous call to infix(Hyper: Dan::Series, Int)
            Asked 2022-Mar-31 at 13:17

            I am writing a model Series class (kinda like the one in pandas) - and it should be both Positional and Associative.

            ...

            ANSWER

            Answered 2022-Mar-31 at 13:17
            Take #1

            First, an MRE with an emphasis on the M1:

            Source https://stackoverflow.com/questions/70643520

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install hypers

            To install using pip:.
            numpy
            scipy
            PyQt5
            pyqtgraph

            Support

            The docs are hosted here.
            Find more information at:

            Reuse Trending Solutions

            build-a-realtime-voice-to-image-generator-using-generative-ai
            build-a-realtime-voice-to-image-generator-using-generative-ai
            Build a Realtime Voice-to-Image Generator using Generative AI
            image-resizing-using-opencv-in-python
            image-resizing-using-opencv-in-python
            Image Resizing using OpenCV in Python
            build-your-own-gpt4all-content-generator
            build-your-own-gpt4all-content-generator
            Build your own Custom GPT Content Generator (Open-Source ChatGPT Alternative)
            validate-an-email-address-in-javascript
            validate-an-email-address-in-javascript
            How to Validate an Email Address in JavaScript
            age-calculator-using-javascript
            age-calculator-using-javascript
            Age Calculator using JavaScript
            addressing-bias-in-ai---toolkit-for-fairness,-explainability-and-privacy
            addressing-bias-in-ai---toolkit-for-fairness,-explainability-and-privacy
            Addressing Bias in AI - Toolkit for Fairness, Explainability and Privacy
            javascript-node-js-payment-processing
            javascript-node-js-payment-processing
            15 best JavaScript Node.js Payment libraries
            build-credit-risk-predictor-using-federated-learning
            build-credit-risk-predictor-using-federated-learning
            Build Credit Risk predictor using Federated Learning
            top-10-javascript-tours-and-guides-libraries-in-2023
            top-10-javascript-tours-and-guides-libraries-in-2023
            10 Best JavaScript Tours and Guides Libraries in 2023
            disease-predictor
            disease-predictor
            Disease Predictor using Pandas & Scikit
            python-face-recognition
            python-face-recognition
            28 best Python Face Recognition libraries

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            Install
          • PyPI

            pip install hypers

            • CLONE
          • HTTPS

            https://github.com/priyankshah7/hypers.git

          • CLI

            gh repo clone priyankshah7/hypers

          • sshUrl

            git@github.com:priyankshah7/hypers.git

            • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link

            Open Weaver – Develop Applications Faster with Open Source

            kandi

            Community and Support

            Company

            Follow

            • © 2023 Open Weaver Inc.