Scientific Visualization An Introduction

Transcription

1 Scientific Visualization An Introduction Featuring Vetria L. Byrd, PhD Assistant Professor Research and Technology Development Conference Missouri S&T September 13, 2016 RTD 2016 Thank You! Missouri S&T Mark Bookout Jennifer Nixon 1

2 Vetria L. Byrd, PhD Academic Preparation Computer Science (PhD, MS) Biomedical Engineering (MSMBE) Where I Am Now What I ve Done Visualization Initiatives Research Experience for Undergraduates in Collaborative Data Visualization Applications (2014/2015) BPViz: Broaden Participation in Visualization (2014/2016) Curriculum Development for Data Visualization Assistant Professor Purdue University Computer Graphics Technology CGT Advanced Data Visualization Laboratory, Director Agent for Insight AGENDA INTRODUCTION TO SCIENTIFIC VISUALIZATION FEATURING PARAVIEW High Level Overview Getting data into ParaView Creating a simple vtk file from scratch Running ParaView commands in the python shell Q&A 2

3 ASSUMPTIONS INTRODUCTION TO SCIENTIFIC VISUALIZATION FEATURING PARAVIEW You are familiar with ParaView At the very least have heard of it You are familiar with Python You are interested in utilizing the power of ParaView in your python scripts Data Visualization Process High Level Overview 3

4 What is the purpose of Visualization? The purpose of visualization is insight, not pictures. ~Ben Shneiderman 4

5 What does Insight lead to? Insight Leads to.. Spotting Differences Visualizing Patterns Spotting Differences How many 7 s do you see? 5

6 Insight Leads to Spotting Differences Insight Leads to.. Allows users to answer questions they didn t know they had Human Genome Project 6

7 Insight Leads to.. The Challenger Disaster File: Challenger_explosion.jpg Insight Leads to.. Visualizing Spatial Relationships Muehlenhaus, I. (2012). Chapter 8, Visualizing Spatial Relationships, Visualize This: The Flowing Data Guide to Design, Visualization, and Statistics, pp

8 Insight Tells a Story Explanation Insight Tells a Story 8

9 Visualization Applications BioVis SciVis The visualization of biological data; often grouped with computer animation InfoVis GeoVis Interdisciplinary study of the visual representation of large scale collections of non numerical information Communicates geospatial information in ways that, when combined with human understanding, allow for data exploration and decision making processes Primarily concerned with the visualization of three dimensional phenomena; Emphases on realistic renderings of volumes, surfaces, illumination sources, etc. 9

10 Scientific Visualization Pipeline Produce Input Data Input Data Analyze, Filter, Reformat Prepared Data Apply Sci Vis Techniques SciVis Model Data Computer Graphics Data Map to Geometry Render, Post process Image Data View Results to scientific visualization tutorial/the scientific visualization pipeline/ 20 Scientific Visualization Pipeline: Step 1... Simulated Data Images Numerical Some measured value Observed Phenomena Adopted from to scientific visualization tutorial/the scientific visualization pipeline/ 10

11 Scientific Visualization Pipeline: Step 2... Cleaning up the data Removing noise Replacing missing values Clamping values to be within a specific range of interest Performing operations to yield more useful data Adopted from to scientific visualization tutorial/the scientific visualization pipeline/ Scientific Visualization Pipeline: Step 3 Converts raw information into something more understandable Visually extracting meaning from a scientific data set using various techniques Contour Clip Threshold Glyphs Streamlines Adopted from to scientific visualization tutorial/the scientific visualization pipeline/ 11

12 Scientific Visualization Pipeline Step 4... Scalars, vectors, tensors 1D, 2D, 3D Mesh Adopted from to scientific visualization tutorial/the scientific visualization pipeline/ Scientific Visualization Pipeline: Step 5... Data Representation Visualization Primitives Iteration and Refinement Display Graphic Primitives Adopted from to scientific visualization tutorial/the scientific visualization pipeline/ 12

13 Scientific Visualization Pipeline: Step 6... Output from ParaView Adopted from to scientific visualization tutorial/the scientific visualization pipeline/ What s Missing? to scientific visualization tutorial/the scientific visualization pipeline/ 13

14 Visualization is an iterative process to scientific visualization tutorial/the scientific visualization pipeline/ Visualization is the tool that will take us forward from the traditional output of high performance computing (HPC) that we are used to into a visual medium that allows researchers to collaborate and elaborate on the finding's they ve got. Tim Carroll Director and Global Lead, Dell Research Computing Solutions HPC Source (Spring 2011) 14

15 Large data produced by large simulations produce large visualization results and require large visualization resources Terabytes of data AT LEAST Terabytes of Vis Gigapixel Images Resampling, Application,... Resolution to Capture Feature Detail Texas Advanced Computing Center Data visualization is becoming an increasingly important component of analytics in the age of big data (SAS: Five big data challenges and how to overcome them with visual analytics) Between now and 2020, the information in the Digital Universe will grow by a factor of 44; the number of files in it to be managed will grow by a factor of 67 Gantz, J., and Reinsel, D. (2012). The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East. IDC IVIEW, Sponsored by EMC Corporation 15

16 Getting Your Data Into ParaView Three Basic Steps: First your data must be read into ParaView Next, you may apply any number of filters that process the data to generate, extract, or derive features from the data Finally, a viewable image is rendered from the data 16

17 Open source, multiplatform Supports distributed computation models Extensible modular architecture Available for 3D computer graphics, image processing and visualization Collection of C++ libraries Leveraged by many applications Divided into logical areas Filtering Information Visualization Volume Rendering Cross platform, using OpenGL Wrapped in Python, Tool Command Language (Tcl) and Java ParaView is an end-user application with support for Parallel Data Archiving Parallel Reading Parallel Processing Parallel Rendering Single node, Client-Server, MPI Cluster Rendering 17

18 Multi-platform parallel data analysis and visualization application Mature, feature-rich interface Good for general purpose, rapid visualization Mac Windows Linux Open Source... It s Free! Built upon the Visualization Toolkit (VTK) library Primary contributors: Kitware, Inc. Sandia National Laboratory Los Alamos National Laboratory Army Research Laboratory 18

19 Grid regular structure, all voxels (cells) are the same size and shape Adopted from The ParaView Tutorial, The Basics of Visualization, version 3.98 Curvilinear regularly gridded mesh shaping function applied Adopted from The ParaView Tutorial, The Basics of Visualization, version

20 Unstructured grid irregular mesh typically composed of tetrahedra, prisms, pyramids, or hexahedra Adopted from The ParaView Tutorial, The Basics of Visualization, version 3.98 Point data Polygonal data Images Multi-block Adaptive Mesh Refinement (AMR) Time series support 20

21 Isosurfaces Cutting planes Streamlines Glyphs Volume rendering Clipping Height maps & more Supports derived variables Scriptable via Python Saves animations Can run in parallel / distributed mode for large data visualization 21

22 ParaView Let s get started.... Menu Bar Tool Bar Pipeline Browser Object Inspector 3D Viewer 22

23 Getting Data Into VTK File Format Sample File Many more... 23

24 VTK ( EnSight Plot3D Various polygonal formats Users can write data readers to extend support to other formats Conversion to the VTK format is straightforward VTK simple legacy format ( formats.pdf) ASCII or binary Supports all VTK grid types Easiest for data conversion 24

25 The data Simulated temperature values Sample size: 100 x 100 Rectilinear Grid # vtk DataFile Version 2.0 Rectilinear grid of temperature values ASCII DATASET RECTILINEAR_GRID 25

26 * * # vtk DataFile Version 2.0 Rectilinear grid of temperature values ASCII DATASET RECTILINEAR_GRID DIMENSIONS X_COORDINATES 100 float Y_COORDINATES 100 float Z_COORDINATES 1 float 0 * Although this is a 2D grid, the z coordinate must be included and represented in the DIMENSIONS # vtk DataFile Version 2.0 Rectilinear grid of temperature values ASCII DATASET RECTILINEAR_GRID DIMENSIONS X_COORDINATES 100 float Y_COORDINATES 100 float Z_COORDINATES 1 float 0 POINT_DATA x dimension * y dimension * z dimension SCALARS temperature float LOOKUP_TABLE default * * 26

27 # vtk DataFile Version 2.0 Rectilinear grid of temperature values ASCII DATASET RECTILINEAR_GRID DIMENSIONS X_COORDINATES 100 float Y_COORDINATES 100 float Z_COORDINATES 1 float 0 POINT_DATA SCALARS temperature float LOOKUP_TABLE default : : File Open Locate and open file you just saved Click Apply 27

28 EXERCISE: VISUALIZE SAMPLE DATA Add Contour Plot Filters Common Contour Set the range of values From To: 300 Step 10 Split Window 28

29 Delete all objects in the Pipeline Browser You should be here Select an object in the Pipeline Browser Click the Delete button (or right click, then Delete) To select multiple objects press and hold the CTRL key while selecting objects ParaView/Python Scripting A short introduction to ParaView s Python Interface In stand alone mode 29

30 Can run in two modes: [1] Stand-alone [2] Client Server where the server is usually a visualization cluster Rich scripting support through Python. Available As part of the ParaView Client (ParaView) An MPI-enabled batch application (pvbatch) The ParaView python client (pvpython) or Any other Python-enabled application Using Python, users and developers can gain access to the ParaView engine called Server Manager 30

31 SERVER MANAGER Library Designed to make it easy to build distributed client-server applications PYTHON SHELL USING PARAVIEW CLIENT Start ParaView Open Python Shell: Tools Python Shell 31

32 CREATING A PIPELINE Create a Cone Object type: >>> cone = Cone() CREATING A PIPELINE Create a Cone Object: >>> cone = Cone() 32

33 CREATING A PIPELINE Create a Cone Object type: >>> cone = Cone() >>> help(cone) CREATING A PIPELINE Create a Cone Object: >>> cone = Cone() >>> help(cone) This gives you the full list of properties. 33

34 CREATING A PIPELINE Create a Cone Object: >>> cone = Cone() >>> help(cone) OUTPUT >>> cone.resolution 6 >>> Check what the resolution property is set to type: >>> cone.resolution CREATING A PIPELINE Create a Cone Object: >>> cone = Cone() >>> help(cone) >>> cone.resolution OUTPUT >>> cone.resolution 6 >>> You can increase the resolution, type: >>> cone.resolution = 32 34

35 CREATING A PIPELINE Create a Cone Object: >>> cone = Cone() >>> help(cone) >>> cone.resolution OUTPUT >>> cone.resolution 6 >>> cone.resolution = 32 >>> You can increase the resolution: >>> cone.resolution = 32 You could have specified a value for resolution when creating the object >>> cone = Cone(Resolution=32) CREATING A PIPELINE Create a Cone Object: >>> cone = Cone() >>> help(cone) >>> cone.resolution You can assign values to any number of properties during construction using keyword arguments: OUTPUT >>> cone.resolution 6 >>> cone.resolution = 32 >>> cone.center [0.0, 0.0, 0.0] Type: >>> cone.center [0.0, 0.0, 0.0] 35

36 CREATING A PIPELINE Create a Cone Object: >>> cone = Cone() >>> help(cone) >>> cone.resolution >>> cone.center >>> cone.center = [1, 2, 3] CREATING A PIPELINE Create a Cone Object: >>> cone = Cone() >>> help(cone) >>> cone.resolution >>> cone.center >>> cone.center = [1, 2, 3] >>> cone.center[0:2] = [2, 4] >>> cone.center [2.0, 4.0, 3.0] Vector properties such as this one support setting and retrieval of individual elements, as well as slices (ranges of elements). 36

37 CREATING A PIPELINE Create a Cone Object: >>> cone = Cone() >>> help(cone) >>> cone.resolution >>> cone.center >>> cone.center = [1, 2, 3] >>> cone.center[0:2] = [2, 4] >>> cone.center [2.0, 4.0, 3.0] Apply a shrink filter to the cone >>> shrinkfilter = Shrink(cone) CREATING A PIPELINE Create a Cone Object: >>> cone = Cone() >>> help(cone) >>> cone.resolution >>> cone.center >>> cone.center = [1, 2, 3] >>> cone.center[0:2] = [2, 4] >>> cone.center [2.0, 4.0, 3.0] Apply a shrink filter to the cone >>> shrinkfilter = Shrink(cone) >>> shrinkfilter.input 37

38 CREATING A PIPELINE Create a Cone Object: >>> cone = Cone() >>> help(cone) >>> cone.resolution >>> cone.center >>> cone.center = [1, 2, 3] >>> cone.center[0:2] = [2, 4] >>> cone.center [2.0, 4.0, 3.0] >>> shrinkfilter = Shrink(cone) >>> shrinkfilter.input <paraview.servermanager.cone object at 0x EEB8> >>> CREATING A PIPELINE Create a Cone Object: >>> cone = Cone() >>> help(cone) >>> cone.resolution >>> cone.center >>> cone.center = [1, 2, 3] >>> cone.center[0:2] = [2, 4] >>> cone.center [2.0, 4.0, 3.0] >>> shrinkfilter = Shrink(cone) >>> shrinkfilter.input <paraview.servermanager.cone object at 0x EEB8> >>> 38

39 CREATING A PIPELINE Create a Cone Object: >>> cone = Cone() >>> help(cone) >>> cone.resolution >>> cone.center >>> cone.center = [1, 2, 3] >>> cone.center[0:2] = [2, 4] >>> cone.center [2.0, 4.0, 3.0] >>> shrinkfilter = Shrink(cone) >>> shrinkfilter.input <paraview.servermanager.cone object at 0x EEB8> >>> At this point you can force ParaView to update, which will also cause the execution of the cone source CREATING A PIPELINE Create a Cone Object: >>> shrinkfilter.updatepipeline() >>> shrinkfilter.getdatainformation().getnumberofcells() 33L >>> shrinkfilter.getdatainformation().getnumberofpoints() 128L >>> 39

40 CREATING A PIPELINE Create Cone Object Set Cone Resolution Set Cone Center Properties Apply Shrink Filter to the Cone Updated Pipeline RENDERING Two objects are needed to render the output A representation takes a data object and renders it in a view A view responsible for managing a render context and a collection of representations 40

41 RENDERING Type at prompt: >>> Show(shrinkFilter) >>> Render() OUTPUT >>> Show(shrinkFilter) <paraview.servermanager.unstructuredgridre presentation object at 0x BE85B70> >>> Render() <paraview.servermanager.renderview object at 0x C26D278> >>> RENDERING Should see something similar to this 41

42 CREATING A PIPELINE WHAT DID WE DO? # Create a cone and assign it as the active object # Set a property of the active object # Apply the shrink filter to the active object # Shrink is now active # Show shrink # Render the active view CREATING A PIPELINE The value returned by Cone() and Shrink() was assigned to Python variables and used to build the pipeline ParaView keeps track of the last pipeline object created by the user. This allows you to accomplish everything that was just done 42

43 CREATING A PIPELINE >>> from paraview.simple import * # Create a cone and assign it as the active object >>> Cone() <paraview.servermanager.cone object at 0x2910f0> # Set a property of the active object >>> SetProperties(Resolution=32) # Apply the shrink filter to the active object # Shrink is now active >>> Shrink() <paraview.servermanager.shrink object at 0xaf64050> # Show shrink >>> Show() <paraview.servermanager.unstructuredgridrepresentation object at 0xaf57f90> # Render the active view >>> Render() <paraview.servermanager.renderview object at 0xaf57ff0> doc/quick start.html Type the following code in a text editor Cone() SetProperties(Resolution=32) Shrink() Show() Render() Save file as testscript.py Click RUN SCRIPT from Python Shell Locate and select script Click OK Should see New objects in Pipeline Browser Cone rendering in 3D Viewer 43

44 Locate and select pvpython (Python Shell) from ParaView application folder Type (text in red): >>> from paraview.simple import* >>> SetProperties(Resolution=32) >>> Shrink() >>> Show() >>> Render() Should see a new visualization Toolkit window with output Will not have ability to rotate output TRY THIS >>> sphere = Sphere() >>> help(sphere) >>> sphere.thetaresolution >>> sphere.phiresolution >>> sphere = Sphere(PhiResolution=32) >>> sphere = Sphere(ThetaResolution=32) >>> sphere.center = [1,2,3] >>> shrinkfilter = Shrink(sphere) >>> shrinkfilter.input >>> shrinkfilter.updatepipeline() >>> shrinkfilter.getdatainformation().getnumberofcells() >>> shrinkfilter.getdatainformation().getnumberofpoints() >>> Show(shrinkFilter) >>> Render() 44

45 The simple module is a ParaView component written using Python on top of the Server Manager C++ Library. Can be loaded from Python interpreters running in several applications pvpython: The python application, distributed with the ParaView application suite, is a Python client to the ParaView severs. Supports interactive and batch execution pvbatch: Also distributed with the ParaView application suite, is a Python application designed to run batch scripts on distributed servers paraview: Python scripts can be run from the paraview client using the Python shell that is invoked from Tools Python Shell Supports interactive mode as well as loading of scripts from files. WHAT DID WE DO? INTRODUCTION TO SCIENTIFIC VISUALIZATION FEATURING PARAVIEW High Level Overview Getting data into ParaView Creating a simple vtk file from scratch Running ParaView commands in the python shell 45

46 ADDITIONAL RESOURCES ParaView User s Guide: Downloaded with ParaView ParaView Quick Start ParaView Sample Data ParaView/Python Scripting KitwarePublic ParaView Server Manager g{tç~ çéâ4 Vetria L. Byrd Assistant Professor Computer Graphics Technology @VisREU Purdue Polytechnic Institute polytechnic.purdue.edu / TechPurdue 46