pylops | PyLops – A Linear-Operator Library for Python | Machine Learning library

 by   PyLops Python Version: v2.0.0 License: LGPL-3.0

kandi X-RAY | pylops Summary

kandi X-RAY | pylops Summary

pylops is a Python library typically used in Artificial Intelligence, Machine Learning applications. pylops has no bugs, it has no vulnerabilities, it has build file available, it has a Weak Copyleft License and it has low support. You can download it from GitHub.

This Python library is inspired by the MATLAB Spot – A Linear-Operator Toolbox project. Linear operators and inverse problems are at the core of many of the most used algorithms in signal processing, image processing, and remote sensing. When dealing with small-scale problems, the Python numerical scientific libraries numpy and scipy allow to perform many of the underlying matrix operations (e.g., computation of matrix-vector products and manipulation of matrices) in a simple and compact way. Many useful operators, however, do not lend themselves to an explicit matrix representation when used to solve large-scale problems. PyLops operators, on the other hand, still represent a matrix and can be treated in a similar way, but do not rely on the explicit creation of a dense (or sparse) matrix itself. Conversely, the forward and adjoint operators are represented by small pieces of codes that mimic the effect of the matrix on a vector or another matrix. Luckily, many iterative methods (e.g. cg, lsqr) do not need to know the individual entries of a matrix to solve a linear system. Such solvers only require the computation of forward and adjoint matrix-vector products as done for any of the PyLops operators.
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              pylops has a low active ecosystem.
              It has 318 star(s) with 82 fork(s). There are 14 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 21 open issues and 89 have been closed. On average issues are closed in 182 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of pylops is v2.0.0

            kandi-Quality Quality

              pylops has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              pylops is licensed under the LGPL-3.0 License. This license is Weak Copyleft.
              Weak Copyleft licenses have some restrictions, but you can use them in commercial projects.

            kandi-Reuse Reuse

              pylops releases are available to install and integrate.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed pylops and discovered the below as its top functions. This is intended to give you an instant insight into pylops implemented functionality, and help decide if they suit your requirements.
            • Apply one point to a given trajectory
            • Return the name of the module
            • Convert x into a cupy condition
            • Linear linear operator
            • Wrapper for CGLS
            • Return condense cond
            • Generate the eigenvalues of the matrix A
            • Linear interpolation
            • Filter data using linter
            • Compute the travel time series for the given sources
            • Identify the coordinates of the geometries
            • Predict a linear regression
            • Predict the trace of the trace
            • Calculate slope dipole
            • Estimate slope and slope
            • Describe an operator
            • Make the axis for a given parameter
            • Convert to a sparse sparse matrix
            • Run scalability test
            • Generate a random ricks
            • Perform step of step model
            • 2d hyperbolic interpolation function
            • Calculate the slope estimate of the slope
            • Linear solve op
            • Linear linear interpolation
            • Compute the prediction for the given traces
            • Return the eigenvalues of the matrix A
            • Create a 2d ndarray
            • Run the step
            Get all kandi verified functions for this library.

            pylops Key Features

            No Key Features are available at this moment for pylops.

            pylops Examples and Code Snippets

            No Code Snippets are available at this moment for pylops.

            Community Discussions

            QUESTION

            cvxpy: best strategy for enforcing smoothness in second dimension for large problems
            Asked 2020-May-14 at 21:29

            There might be an answer for that floating around somewhere but I wasn't able to find it.

            I want to minimize with variable X >= 0 and 1st derivative matrix D, so that X is smooth in column-direction and with relatively large data.

            In the past I have used various ways of solving this (e.g. with scipy.optimize.lsq_linear or pylops) and now wanted to try out cvxpy with the generic approach below (using SCS, because the problem wouldn't fit into memory otherwise):

            ...

            ANSWER

            Answered 2020-May-12 at 12:24
            SCS-based

            Check what SCS is configured with in your option-free call. I suspect it's started in direct-mode and you should also try the indirect-mode (use_indirect=True). Turning on verbosity (verbose=True) should show you what's currently being used

            Alternatives

            This looks like smooth unconstrained optimization with bound-constraints only. I doubt, that SCS is the right approach here (it's just too powerful; too general).

            I would fire up L-BFGS-B (which supports bound-constraints) (reference impl), e.g. through scipy. For a prototype, you might get away with automatic numerical-diff, but for your final version, you should provide a customized gradient then. I suspect, it will be much more efficient than SCS. But that also depends on the accuracy you are striving for.

            Source https://stackoverflow.com/questions/61751485

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

            Vulnerabilities

            No vulnerabilities reported

            Install pylops

            You need Python 3.6 or greater.
            To ensure that further development of PyLops is performed within the same environment (i.e., same dependencies) as that defined by requirements-dev.txt or environment-dev.yml files, we suggest to work off a new Conda enviroment.

            Support

            We advise using the Anaconda Python distribution to ensure that all the dependencies are installed via the Conda package manager. Follow the following instructions and read carefully the CONTRIBUTING file before getting started.
            Find more information at:

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

            Find more libraries
            CLONE
          • HTTPS

            https://github.com/PyLops/pylops.git

          • CLI

            gh repo clone PyLops/pylops

          • sshUrl

            git@github.com:PyLops/pylops.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