Search Results for "google-visualization-python" - Page 8
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Showing 3551 open source projects for "google-visualization-python"
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ResumableFunctions.jl
C# style generators a.k.a. semi-coroutines for Julia
C# has a convenient way to create iterators using the yield return statement. The package ResumableFunctions provides the same functionality for the Julia language by introducing the @resumable and the @yield macros. These macros can be used to replace the Task switching functions produce and consume which were deprecated in Julia v0.6. Channels are the preferred way for inter-task communication in Julia v0.6+, but their performance is subpar for iterator applications. -
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TensorBoardX
tensorboard for pytorch (and chainer, mxnet, numpy, etc.)
The SummaryWriter class provides a high-level API to create an event file in a given directory and add summaries and events to it. The class updates the file contents asynchronously. This allows a training program to call methods to add data to the file directly from the training loop, without slowing down training. TensorboardX now supports logging directly to Comet. Comet is a free cloud based solution that allows you to automatically track, compare and explain your experiments. It adds a... -
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WordCloud.jl
Word cloud generator in julia
...You have control over every aspect of generating a word cloud. You can customize the shape, color, angle, position, distribution, density, and spacing to align with your preferences and artistic style. This visualization solution guarantees precise results. Each word appears only once, and its font size is determined solely by the provided weight. Words are never repeated or shrunk artificially to fill empty spaces. It utilizes intelligent strategies and efficient nesting algorithms, implemented entirely in Julia (see Stuffing.jl). As a result, it can easily generate high-resolution word clouds. -
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LineSearches.jl
Line search methods for optimization and root-finding
Line search methods for optimization and root-finding. This package provides an interface to line search algorithms implemented in Julia. The code was originally written as part of Optim, but has now been separated out to its own package. -
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oneAPI.jl
Julia support for the oneAPI programming toolkit.
Julia support for the oneAPI programming toolkit. oneAPI.jl provides support for working with the oneAPI unified programming model. The package is verified to work with the (currently) only implementation of this interface that is part of the Intel Compute Runtime, only available on Linux. This package is still under significant development, so expect bugs and missing features. -
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BAT.jl
A Bayesian Analysis Toolkit in Julia
Welcome to BAT, a Bayesian analysis toolkit in Julia. BAT.jl offers a variety of posterior sampling, mode estimation and integration algorithms, supplemented by plotting recipes and I/O functionality. BAT.jl originated as a rewrite/redesign of BAT, the Bayesian Analysis Toolkit in C++. BAT.jl now offer a different set of functionality and a wider variety of algorithms than its C++ predecessor. -
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Gradle Task Tree
Gradle plugin that adds a 'taskTree' task that prints task dependency
A Gradle plugin that adds a taskTree task to your build. Running it prints out a hierarchical, easy‑to‑read task dependency tree, helping you visualize the build execution order. -
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FrankWolfe.jl
Julia implementation for various Frank-Wolfe and Conditional Gradient
This package is a toolbox for Frank-Wolfe and conditional gradient algorithms. Frank-Wolfe algorithms were designed to solve optimization problems where f is a differentiable convex function and C is a convex and compact set. They are especially useful when we know how to optimize a linear function over C in an efficient way. -
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Rocket.jl
Functional reactive programming extensions library for Julia
Rocket.jl is a Julia package for reactive programming using Observables, to make it easier to work with asynchronous data. Rocket.jl has been designed with a focus on performance and modularity. -
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PowerSystems.jl
Data structures in Julia to enable power systems analysis
The PowerSystems.jl package provides a rigorous data model using Julia structures to enable power systems analysis and modeling. In addition to stand-alone system analysis tools and data model building, the PowerSystems.jl package is used as the foundational data container for the PowerSimulations.jl and PowerSimulationsDynamics.jl packages. PowerSystems.jl supports a limited number of data file formats for parsing. -
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Clustering.jl
A Julia package for data clustering
Methods for data clustering and evaluation of clustering quality. -
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PartitionedArrays.jl
Vectors and sparse matrices partitioned into pieces
This package provides distributed (a.k.a. partitioned) vectors and sparse matrices in Julia. See the documentation for further details. -
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Clapeyron
Framework for the development and use of fluid-thermodynamic models
Welcome to Clapeyron! This module provides both a large library of thermodynamic models and a framework for one to easily implement their own models. Clapeyron provides a framework for the development and use of fluid-thermodynamic models, including SAFT, cubic, activity, multi-parameter, and COSMO-SAC. -
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ResultTypes.jl
A Result type for Julia—it's like Nullables for Exceptions
ResultTypes provides a Result type that can hold either a value or an error. This allows us to return a value or an error in a type-stable manner without throwing an exception. -
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FreqTables.jl
Frequency tables in Julia
This package allows computing one- or multi-way frequency tables (a.k.a. contingency or pivot tables) from any type of vector or array. It includes support for CategoricalArray and Tables.jl compliant objects, as well as for weighted counts. -
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Chain.jl
A Julia package for piping a value through transformation expressions
A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality. -
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Surrogates.jl
Surrogate modeling and optimization for scientific machine learning
A surrogate model is an approximation method that mimics the behavior of a computationally expensive simulation. In more mathematical terms: suppose we are attempting to optimize a function f(p), but each calculation of f is very expensive. It may be the case we need to solve a PDE for each point or use advanced numerical linear algebra machinery, which is usually costly. The idea is then to develop a surrogate model g which approximates f by training on previous data collected from... -
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Chart.js
Simple yet flexible JavaScript charting for designers & developers
Chart.js is a Javascript library that allows designers and developers to draw all kinds of charts using the HTML5 canvas element. Chart js offers a great array of simple, clean charts including animated and interactive versions. Chartjs is an easy way to include beautiful and engaging charts into your website for free. -
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EvoTrees.jl
Boosted trees in Julia
A Julia implementation of boosted trees with CPU and GPU support. Efficient histogram-based algorithms with support for multiple loss functions, including various regressions, multi-classification and Gaussian max likelihood. -
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Manopt.jl
Optimization on Manifolds in Julia
Optimization Algorithm on Riemannian Manifolds. A framework to implement arbitrary optimization algorithms on Riemannian Manifolds. Library of optimization algorithms on Riemannian manifolds. Easy-to-use interface for (debug) output and recording values during an algorithm run. Several tools to investigate the algorithms, gradients, and optimality criteria. -
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MAT.jl
Julia module for reading MATLAB files
Julia module for reading MATLAB files. Read and write MATLAB files in Julia. This library can read MATLAB .mat files, both in the older v4/v5/v6/v7 format, as well as the newer v7.3 format. -
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QuantumClifford.jl
Clifford circuits, graph states, and other quantum Stabilizer tools
A Julia package for working with quantum stabilizer states and Clifford circuits that act on them. Graphs states are also supported. The package is already very fast for the majority of common operations, but there are still many low-hanging fruits performance-wise. See the detailed suggested readings & references page for background on the various algorithms. -
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SCIP.jl
Julia interface to SCIP solver
SCIP.jl is a Julia interface to the SCIP solver. This wrapper is maintained by the SCIP project with the help of the JuMP community. -
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ImplicitDifferentiation.jl
Automatic differentiation of implicit functions
ImplicitDifferentiation.jl is a package for automatic differentiation of functions defined implicitly, i.e., forward mappings. Those for which automatic differentiation fails. Reasons can vary depending on your backend, but the most common include calls to external solvers, mutating operations or type restrictions. Those for which automatic differentiation is very slow. A common example is iterative procedures like fixed point equations or optimization algorithms. -
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ProximalAlgorithms.jl
Proximal algorithms for nonsmooth optimization in Julia
A Julia package for non-smooth optimization algorithms. This package provides algorithms for the minimization of objective functions that include non-smooth terms, such as constraints or non-differentiable penalties.
