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Kitware logoParaView

VTK-based Data Analysis and Visualization Application

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Quick Overview

ParaView is an open-source, multi-platform data analysis and visualization application. It is designed to handle large datasets and provides a wide range of tools for data processing, analysis, and visualization. ParaView is primarily used in the scientific and engineering communities for tasks such as computational fluid dynamics, medical imaging, and geospatial data analysis.

Pros

  • Scalable and Efficient: ParaView can handle large datasets and provides efficient data processing and visualization capabilities.
  • Cross-Platform Compatibility: ParaView is available for Windows, macOS, and Linux, making it accessible to a wide range of users.
  • Extensive Visualization Capabilities: ParaView offers a comprehensive set of visualization tools, including 2D and 3D plotting, volume rendering, and advanced rendering techniques.
  • Customizable and Extensible: ParaView can be customized and extended through plugins and scripting, allowing users to tailor the application to their specific needs.

Cons

  • Steep Learning Curve: ParaView can be complex to use, especially for beginners, due to its extensive feature set and customization options.
  • Performance Limitations: While ParaView is designed to handle large datasets, it may still struggle with extremely large or complex data, particularly on older or less powerful hardware.
  • Limited Documentation: The documentation for ParaView, while comprehensive, can be challenging to navigate and may not always provide clear guidance for specific use cases.
  • Dependency on External Libraries: ParaView relies on several external libraries, which can make installation and configuration more complex, especially on non-standard platforms.

Code Examples

N/A (ParaView is not a code library)

Getting Started

N/A (ParaView is not a code library)

Competitor Comparisons

enthought logo

mayavi

1,297

3D visualization of scientific data in Python

Pros of Mayavi

  • Mayavi provides a more user-friendly and intuitive interface compared to ParaView, making it easier for users to visualize and interact with their data.
  • Mayavi is built on top of the NumPy and SciPy libraries, which are widely used in the scientific Python community, making it a natural choice for those already familiar with these tools.
  • Mayavi offers a more lightweight and flexible solution compared to ParaView, which can be more resource-intensive for certain use cases.

Cons of Mayavi

  • ParaView has a larger user community and more extensive documentation, which can make it easier to find support and resources for specific use cases.
  • ParaView offers a wider range of visualization and analysis tools compared to Mayavi, making it a more comprehensive solution for complex data analysis tasks.
  • ParaView has better support for large-scale, distributed data processing, which can be important for certain scientific and engineering applications.

Code Comparison

ParaView (Python):

from paraview.simple import *

# Load a dataset
data = OpenDataFile("path/to/dataset.vtk")

# Create a basic visualization
view = CreateView("RenderView")
Show(data, view)
Render()

Mayavi (Python):

from mayavi import mlab

# Load a dataset
data = mlab.pipeline.open("path/to/dataset.vtk")

# Create a basic visualization
mlab.figure()
mlab.pipeline.surface(data)
mlab.show()
Kitware logo

VTK

2,653

Mirror of Visualization Toolkit repository

Pros of VTK

  • VTK is a more low-level library, providing a wider range of functionality and customization options for developers.
  • VTK has a larger and more active community, with a wider range of available plugins and extensions.
  • VTK is more widely used in scientific and research communities, making it a more established and trusted choice.

Cons of VTK

  • VTK has a steeper learning curve, with a more complex API and a larger codebase.
  • VTK may be overkill for some simpler visualization tasks, where ParaView may be a more lightweight and user-friendly option.
  • VTK requires more manual configuration and setup compared to the more out-of-the-box experience of ParaView.

Code Comparison

VTK (C++):

vtkSmartPointer<vtkSphereSource> sphereSource = vtkSmartPointer<vtkSphereSource>::New();
sphereSource->SetCenter(0.0, 0.0, 0.0);
sphereSource->SetRadius(5.0);
sphereSource->Update();

vtkSmartPointer<vtkPolyDataMapper> mapper = vtkSmartPointer<vtkPolyDataMapper>::New();
mapper->SetInputConnection(sphereSource->GetOutputPort());

vtkSmartPointer<vtkActor> actor = vtkSmartPointer<vtkActor>::New();
actor->SetMapper(mapper);

ParaView (Python):

from paraview.simple import *

sphere = Sphere()
sphere.Center = [0.0, 0.0, 0.0]
sphere.Radius = 5.0

sphere_display = Show(sphere)
Kitware logo

CMake

6,748

Mirror of CMake upstream repository

Pros of CMake

  • CMake is a cross-platform build system that can generate native build files for various platforms and compilers, making it easier to build and distribute software across different environments.
  • CMake has a large and active community, with extensive documentation and a wide range of available modules and tools, making it a versatile and well-supported build system.
  • CMake's scripting language is powerful and flexible, allowing for complex build configurations and customizations.

Cons of CMake

  • The learning curve for CMake can be steep, especially for developers who are new to the tool or have limited experience with build systems.
  • CMake's syntax and configuration files can be complex and verbose, which can make it challenging to maintain and debug large or complex projects.
  • CMake's dependency management can be challenging, especially when dealing with third-party libraries or external dependencies.

Code Comparison

CMake (5 lines):

project(MyProject)
add_executable(myapp main.cpp)
target_link_libraries(myapp PUBLIC mylib)
add_library(mylib STATIC lib.cpp)
install(TARGETS myapp mylib DESTINATION bin)

ParaView (5 lines):

project(ParaView)
add_subdirectory(VTK)
add_subdirectory(Qt)
add_subdirectory(Rendering)
add_subdirectory(Plugins)

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README

ParaView

Introduction

ParaView is an open-source, multi-platform data analysis and visualization application based on Visualization Toolkit (VTK).

The first public release was announced in October 2002. Since then, the project has grown through collaborative efforts between Kitware Inc., Sandia National Laboratories, Los Alamos National Laboratory, Army Research Laboratory, and various other government and commercial institutions, and academic partners.

Learning Resources

Building

There are two ways to build ParaView:

  • The easiest method for begginners to build ParaView from source is by using our Getting Started compilation guide which includes commands to install the needed dependencies for most operating systems.

  • Another way to build ParaView, quite useful when trying to enable more specific options which requires to build dependencies yourself (ie osmesa, raytracing), would be the ParaView Superbuild. The superbuild downloads and builds all of ParaView's dependencies as well as ParaView itself.

Reporting Bugs

If you have found a bug:

  1. If you have a source-code fix, please read the CONTRIBUTING.md document.

  2. Otherwise, please join the ParaView Discourse forum and ask about the expected and observed behaviors to determine if it is really a bug.

  3. Finally, if the issue is not resolved by the above steps, open an entry in the ParaView Issue Tracker.

Contributing

See CONTRIBUTING.md for instructions to contribute.

For Github users

Github is a mirror of the official repository. We do not actively monitor issues or pull requests on Github. Please use the official repository to report issues or contribute fixes.

License

ParaView is distributed under the OSI-approved BSD 3-clause License. See Copyright.txt for details. For additional licenses, refer to ParaView Licenses.