VISUALIZATION OF MODIS DATA IN THE BLENDER ...

VISUALIZATION OF MODIS DATA IN THE BLENDER ENVIRONMENT: OR SCIENCE IN A BLENDER

by Jonathan D. Wilson

A senior thesis submitted to the faculty of Brigham Young University - Idaho in partial fulfillment of the requirements for the degree of

Bachelor of Science

Department of Physics Brigham Young University - Idaho July 2012

c 2012 Jonathan D. Wilson Copyright All Rights Reserved

BRIGHAM YOUNG UNIVERSITY - IDAHO

DEPARTMENT APPROVAL

of a senior thesis submitted by Jonathan D. Wilson

This thesis has been reviewed by the research committee, senior thesis coordinator, and department chair and has been found to be satisfactory.

Date

Todd Lines, Advisor

Date

David Oliphant, Senior Thesis Coordinator

Date

Kevin Kelley, Committee Member

Date

Stephen Turcotte, Chair

ABSTRACT

VISUALIZATION OF MODIS DATA IN THE BLENDER ENVIRONMENT: OR SCIENCE IN A BLENDER

Jonathan D. Wilson Department of Physics Bachelor of Science

Modis data files are visualized by way of Blender. Care is used to maintain the accuracy of the files data and geolocation information. The Modis teams georeferencing is used as the basis for locating the data within an x,y,z space.

ACKNOWLEDGMENTS

I would like to thank the advisors who have assisted me in the process of writing this thesis and the code for Blender. I would also like to thank Kyle for the insights during the editing process. I would also like to thank my family for their patience while I talked their ears off on this subject.

Contents Table of Contents

xi

List of Figures

xiii

1 Introduction 1.1 Visualization of large amounts of data . . . . . . . . . . . . . . . . . 1.2 Current methods of displaying satellite data . . . . . . . . . . . . . . 1.3 Why a new method? . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 2 2 4

2 History 2.1 Modis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Blender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 HDF 4 and HDF 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 5 6 7

3 Procedures

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4 Results 4.1 Analysis of h4toh5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Analysis of a Blender rendering of a sweep . . . . . . . . . . . . . . . 4.3 Theory of Data display . . . . . . . . . . . . . . . . . . . . . . . . . .

11 11 12 13

5 Conclusion 5.1 Did Blender work to a satisfactory level at displaying the data? . . . 5.2 Did Blender render in a satisfactory amount of time? . . . . . . . . . 5.3 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15 16 16 19

Bibliography

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A Instructions for use A.1 Installation . . . . . . . . . . . . . . . . A.2 Use of Software . . . . . . . . . . . . . . A.3 Methods of interacting with the software A.4 Computer Specifications . . . . . . . . .

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CONTENTS

B Python Code B.1 Version 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.2 Version 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.3 Version 0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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List of Figures 4.1 4.2

File conversion times . . . . . . . . . . . . . . . . . . . . . . . . . . . An example of a rendered image . . . . . . . . . . . . . . . . . . . . .

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An example of a rendered image An example of a rendered image Times for importing the MODIS Times for importing the MODIS

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The first thing you see . . . . . Results from running the code . Highlighting how to change to a The contents of the editor types

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Chapter 1 Introduction A challenge that exists is that of displaying information about a three-dimensional object onto a two-dimensional plane. A map is an example of this. Over the years there have been many developments that have tried to overcome the challenge of displaying information about the globe onto flat sheets. This has resulted in numerous types of maps to answer the call for greater accuracy of one aspect or other, or increase the ease of use. The venerable Cartesian coordinate system has been used with great success to show the relationship between two different sets of data. This does not work as well with multiple data sets being compared simultaneously. With computer technology we can start to explore the idea of creating a three-dimensional space that we can explore and interact with like the real world. This paper will discuss the methodology and philosophy behind using Blender [1] as a visualization program for scientific data sets. Blender is a 3-D(three-dimensional) animation tool that is open source. The benefits of using Blender are that it is free to use and easily modified to suit a particular purpose. This will be demonstrated through the specific application of Blender to MODIS [2] data sets taken from the 1

2

Chapter 1 Introduction

Aqua [3] and Terra [4] satellites. In addition, the use of the software written to achieve this visualization will be discussed.

1.1

Visualization of large amounts of data

A significant challenge of the era of satellite-based research is the task of extracting meaning from the vast amount of data that is gathered. One method is to automate the detection of events that are significant to the researchers involved. Another method is to provide a visual representation of the data in a manner that can be clearly seen; both the events that you are looking for, as well as understanding the context in which the event is found. The second method is the one that is necessary to research because this is the method that will aid in the communication of the ideas and evidences that are found to someone who is not intimately familiar with the conventions that are used for that satellite. This challenge has been faced in the past and this is where we get the Cartesian coordinate system. This was a method created to show the relationship between two separate quantities in a clear manner. As we go forward we are seeking meaning and relationships between larger data sets and larger numbers of quantities. There are now two-dimensional graphs that can include the relation between three or more quantities. Being able to represent data accurately within a 3-D space will increase the clarity of the relationships.

1.2

Current methods of displaying satellite data

At this time there are several pieces of software that can be used to visualize the data in a satellite data product; ArcGIS [5], Erdas [6], ENVI [7] and Google Earth [8]. Ar-

1.2 Current methods of displaying satellite data

3

cgis, Erdas and Envi are predominately used to generate two-dimensional maps from the data for visualization purposes, although they are working to incorporate a threedimensional analysis into them. They are coming from a history of two-dimensional representation and trying to convert to a three-dimensional representation. This fundamental shift will require a great deal of work on their part. Since the data is three-dimensional, this leads to visual warping as a large enough data set is translated from a two-dimensional representation to a three-dimensional one. Google Earth is more appropriate for large volumes of data in terms of design because it is inherently three-dimensional and thus provides the data in a form that gives context, as well as including a way to tag data as coming from a certain time. Unfortunately Google Earth, at least the free edition, is not a good choice for large data sets due to its loading everything into memory, and thus, at a certain point, it exceeds its resources and crashes. Another limitation of Google Earth is that it does not have fine controls for the display of the time sensitive data, which is necessary for maintaining the time aspect. There are challenges when dealing with combining satellites’ data sets from the georeferencing. Satellite data sets do not use a uniform reference system for defining latitude and longitude. Modis data itself comes in various levels of processing. This project uses level 1A geolocation and level 1B data products which are georeferenced to the WGS84 Geoid [9] and contain the height relative to said geoid. Levels 2 and beyond use different coordinate systems that are more appropriate to the intended application. When trying to combine data sets there can be confusion from the differences that results in improperly aligned data due to the different methods of referencing.

4

Chapter 1 Introduction

1.3

Why a new method?

There are several reasons a new method is needed to display information using Blender. Using open source software olves the issues created by evolving software. As future technology changes the methods are freely available to be updated. There is a great deal of effort expended on updating libraries that were programmed in Fortran and highly optimized. This project is itself built on this very idea. The NumPy [10] library is built on a collection of libraries that were programmed in the Fortran language and highly optimized. By using NumPy from within Python [11], I was able to decrease the amount of time it took to perform the longest section of code from 77 seconds to a fifth of a second. The next reason this method is needed is Blender is designed to make time series of three-dimensional objects and thus contains a large number of tools for working with those time-resolved data sets. All of the separate methods of georeferencing can also be coded to convert to a standard x,y,z coordinate system using whichever references are needed and the separate shapes can be included within the space. These make for an easy-to-use environment, bringing all of these data sets within and viewing them concurrently if desired. It is also possible in Blender to create multiple views of the different data sets that are all viewed from the same universal perspective over time. The final reason for using Blender is because it contains a number of functions that are for smoothing three-dimensional shapes and the coloring of them. These tools use in some fashion linear and non-linear extrapolation. If in the future someone could vet these functions they could be applied directly to the data. This final reason is partially addressed in section 4.3 where the future works are described.

Chapter 2 History This project covers several different areas that would benefit from a bit of background for better context. Firstly, there is the sensor itself that flies on the Aqua and Terra satellites. Secondly, is the piece of software called Blender. Finally, there is the HDF file format. Each one of these elements brings something to the project that makes it possible for this to work together. The background for the satellite is complex and deserves a close reading of the the Algorithm Theoretical Basis Documents for the Level 1A geolocation and Level 1B data products [9] [12]. Blender is also complicated. However, it is more user-friendly and can be learned by trial and error [13]. HDF is used in this research as a black box that enables a unix-like directory access to the data.

2.1

Modis

Modis has been flying on Aqua and Terra for a decade. These satellites are in sun synchronous flight at an altitude of 705 km. The Terra satellite is flying at 10:30 am and 10:30 pm local time descending and Aqua is flying at 1:30 pm and 1:30 am 5

6

Chapter 2 History

local time ascending. The sensor sweeps out an area at the rate of 20.3 rotations per minute perpendicular to the orbit path. The swath that is viewed in a pass is approximately 2330 km (perpendicular to orbit) by 10 km (parallel to orbit). The data that comes from Modis [12] is processed and packaged into 10 minute slices of data. The level 1A geolocation set contains the latitude and longitude coordinates to the 1km resolution level. The level 1B contains the measured radiances of the instrument. The level 1B comes in three versions: the 250m contains the data from the sensors that have the highest resolution, the 500m contains the mid resolution data as well as a consolidated data from the 250m data set, and the 1km contains the lowest resolution data, as well as consolidated data from the 500m and 250m data. In addition there are several methods of georeferencing that are used by the different data products for the MODIS instrument. Data products that are labelled “swath” use WGS84. Other data products are labelled with “SIN” or “SIN Grid”, and they use as a reference a sphere, 6,371,007.181 m in radius.

2.2

Blender

Blender started its life as an in-house tool to create 3-D animations for movies and other forms of entertainment. The tool was created in 1995 and since then was released as an open source tool. Coming from a field where all revenue is based on the ability to create compelling images and animations has lead to the refinement of processes to take an idea and visualize it. One of the latest refinements that has enabled this project was the inclusion of the Python language for scripting purposes and making most of the commands in the Blender software accessible from the Python script itself. Most of what the user can do in the Blender environment can be reproduced in a script in Python. This allows the rapid testing of methods in an environment de-

2.3 HDF 4 and HDF 5

7

signed to work with 3-D objects. Once a method is found that works, the conversion to a script to repeat the task over and over again is a simple process. Blender has decided to use Python version 3.x in it’s scripting. This choice has implications for using scientific Python packages because they are slowly being ported from version 2.x to version 3.x. For this project, this has prevented the use of PyHDF [14]. PyHDF is only compatible with Python version 2.x. This library would have allowed for the use of the HDF4 format, which is the native format for MODIS data, thus skipping the step of translating from HDF4 to HDF5. PyHDF has not released a new version since October of 2008, two months before Python 3.0 was first released. H5py [15] is Python 3.x compatible as well as NumPy and SciPy. Going forward, additional packages will become compatible which will expand the libraries that are available.

2.3

HDF 4 and HDF 5

HDF has seen twenty years of development and use in storing and distributing scientific data. Over the years it has grown to take into account more generic ideas. The change from HDF4 to HDF5 marks the addition of the ability to perform parallel IO. The HDF format allows for self-description, which for MODIS includes the measurements of the height, range, angle to the sun as well as descriptions of what range these values should have. Several aspects of HDF make it useful for Blender visualization. Because the data can be accessed in subsets the entirety of the file does not need to be loaded into memory. This has a large impact on performance in the pipeline.

8

Chapter 2 History

Chapter 3 Procedures The procedure is to go to the website http://reverb.echo.nasa.gov/reverb/ and select the appropriate settings including; satellite, instrument, level of processing, time period and geographic location. With these selected, you will get a list of granules to download via ftp. With the files downloaded, convert the HDF4 files to HDF5 using h4toh5 [16]. Once the files are converted, set the path parameter in the script to point to the directory in which you have the files, as well as the file name parameter. Once the script has run, you can apply further effects from the Blender environment to smooth the object generated. You have the option to output the results as an image or video. Image output has the options for bmp, png, jpeg and tiff as well as other formats with settings for the file output contents. For the image, the recommended setting is to output in RGBA so that it includes the alpha channels, which is part of the generated object. Alpha channel is the information about the transparency of the object. Video output comes in a variety of formats as well, with avi, h.264, mpeg, ogg theora, and xvid. The settings for the video are rather complicated, and should be fine with the default settings. However, some of these settings will affect the render-time and the quality of the output visually. 9

10

Chapter 3 Procedures

Chapter 4 Results The result of this research is a tool that can handle a known data format that is robust. Multiple satellite data sets can be used within the environment, so long as they use HDF4 or HDF5 format and the appropriate code is written to include it. The scripts included in Appendix B can be used as a template for the steps necessary to import the data and create an object. The system can be implemented and automated to generate video over specified sections of the earths surface, or to follow the satellite through its flight and give an accurate recreation of its view. The time that it takes for the rendering is good for use in a real-time rendering system. This process is not yet completed. There are additional steps that could be taken to improve the efficacy of the process.

4.1

Analysis of h4toh5

The tool h4toh5 used to convert the data files from the HDF4 format to HDF5 format worked wonderfully. The time it took to complete the conversion is within the desired time frame, with delays of under a minute for the size of files that MODIS produces. 11

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Chapter 4 Results

Figure 4.1 The time taken to convert is consistent with the size of the file and follows a linear trend. The conversion does alter the internal file structure in a consistent fashion. The alterations that the program h4toh5 introduce are relatively minor. The original data path is still intact and can be used with h5py to access the data. There are some artifacts that are generated but they can be ignored and will not affect the operation of the script at all.

4.2

Analysis of a Blender rendering of a sweep

Blender takes a short time to render for the small data sets used. The length of time to render, and the accuracy of the render can be controlled intimately through several settings. This allows for the software to be configured to do either a fast, close to real time, or a slower more accurate view of the scene that is easier on the eyes. This greater control could become necessary as either the number of data sets imported increases or the resolution of data imported increases. See figure 4.2 to see what the default settings will give you for an image.

4.3 Theory of Data display

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Figure 4.2 This is the resulting image when rendered. This is an early image where the lighting is not in place. The NumPy functions that handle the trigonometric functions do return differences in values based on the smallest possible change in the lattitude and longitude values. The color values range in Blender handles a range of 0.000000 to 1.000000 and thus will handle the conversion from an integer range of 0 to 32767 which is what the MODIS data is scaled to.

4.3

Theory of Data display

The user is tempted to place within view all of the information that they have available. This temptation is best avoided due to the increased risk of misrepresenting what is actually there as others view the image. From Tufte and his work analyzing two-dimensional data representations, the conclusion can be drawn that a simple

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Chapter 4 Results

style is needed for this complex topic [17]. There is the concept of space, time, and intensity of light to all display in the same image. The animation helps to provide context for space and time through a smoothly changing reference frames. Limiting the displayed data to a single value for each point, instead of trying to put all of the frequencies measured into a single point, allows for the highest contrast of grayscale to be used. Additionally, there is error in the data and error ranges that should be conveyed at the same time so that a false view is not presented and accepted. In order to achieve this, a transparency layer is added to the data layer which will allow a third color to show through based on the error value. A small error will allow a small amount of the other color through the data object, while a large error will allow a large amount through and immediately lets the viewer know that there is something wrong in the data object. In order to display information about all of the frequencies, there needs to be a consistent function that is applied across the view. This can be seen in graphs where the axis are labeled in logarithmic scale. Tufte points out that if a graph has the axis abbreviated it can exaggerate the difference in the relationship. Data displayed in a three-dimensional space can have a similar problem because of the chosen view point. As an example, terrain data can be taken and -using a view that is just above the surface- will exaggerate the height differences. A good study of this would be optical illusions to find where the presentation method will break down.

Chapter 5 Conclusion There is a great deal of potential in exploring Blender for the visualization of data. There are the smoothing functions and the subdivide functions that can potentially take a data set and estimate intermediate values for an entire space and present the results. In addition, the standardized three-dimensional space can be used to bring in disparate data sets for display over each other. With the standardized file format access there is the very real possibility that Blender can perform visualization for a large number of different projects. A super computer can handle the heavy lifting of the simulating and a script can be written to view the data quickly and easily from multiple angles over time. In addition, the data can be rendered quickly by only pulling the data that is to be viewed at a certain angle and time.

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Chapter 5 Conclusion

5.1

Did Blender work to a satisfactory level at displaying the data?

Blender did do a good job at visualizing the data. There are some aspects that did not work out as anticipated. The biggest drawback being that currently Alpha layer information is not used at the vertex level, but at a face level which makes for difficulty in displaying the error as proposed earlier in this paper. That can be overcome mostly by taking the vertex errors and blending them to generate a face alpha quantity. This is by no means perfect, and there is the chance that in the future the vertex alpha may be implemented. This should only be a problem for data that has large amounts of space between the data points. There is a need for a visual reference to anchor where people think they are when viewing the videos or pictures. This will be the subject of further research and development. At the time of writing, the image produced will be based on where you have moved the camera to view. The figures 5.1 and 5.2 are results from renderings of a file. These files are a 5 km subsampling of an entire granule of 1 km data from the MODIS instrument.

5.2

Did Blender render in a satisfactory amount of time?

The time that it took Blender to import the data was a bottle neck to the process. In these graphs the script’s major actions were benchmarked. Step 1 is where the latitude and longitude are read in and converted from WGS84 to x,y,z coordinate triplets. Step 2 is generating the face list using a “for” loop. Step 3 is generating the object and linking it to the scene. Step 4 is generating the material and adding

5.2 Did Blender render in a satisfactory amount of time?

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Figure 5.1 Produced from file MYD02SSH.A2012001.0840.005.2012001181210.2.h5. This is the red channel taken at night.

Figure 5.2 Produced from file MYD02SSH.A2012001.1945.005.2012002174518.1.h5. This is the red channel taken during the day.

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Figure 5.3 The time taken to import the file and set up the environment is dominated by the first step.

Figure 5.4 The time taken to import the file and set up the environment is now dominated by the painting of vertices with the data.

5.3 Future Directions

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the vertex paint layer to the object. Step 5 is reading in the data from the HDF file. Step 6 is checking for error codes in the data. (The data has a range of values from 0 to 32767 with values higher than this indicating errors in the data which must be handled.) Step 7 is painting the vertices with the data done with a “for” loop. As can be seen from the graphs, the time taken for importing the data can be decreased significantly. Version 0 did not use the NumPy libraries trigonometric routines while Version 2 did. Steps 3 and 4 are built-in functions that will not be able to be sped up with changes to the script. The rest of the steps can be improved in the future in regards to time and features. For the rendering to an image, once the data is imported to Blender, the time taken is three to five seconds. I have not had the chance to benchmark this part thoroughly, because the render time was previously a fraction of the time spent. With the improvements to import time, render time has again become a consideration for whether or not this is a good process for realtime rendering. Better hardware for rendering would speed this step immensely. Video rendering has not yet been tested, but should be a nearly linear extrapolation from the time it takes to render a single image.

5.3

Future Directions

In the future, Blender could automatically take in data and render from the cues in the HDF file. A standardized set of formats for the marking up within the HDF file will allow parameters to be passed in from an external program. Gaining an understanding of how some of the smoothing functions work so that, if some of our standard data-fitting routines are already programmed in, we can use them with a single command to process large sets of data. Finally, there is the challenge of

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integrating several data sources either through a layer approach within Blender, or in the Python script before it renders. There is also the pursuit of efficiency in the pipeline, so that it can handle the large amounts of data that are out there. The amount of data that is imported could be limited by knowing what will be viewed which would use less memory and less rendering time. Improvements are possible in the coding for Python. Additionally the rendering could be split up among different segments and stitched together afterwards to provide either different views for the same time frame, or to render many overlapping time-frames, or view information in different wavelengths simultaneously.

Bibliography [1] Blender http://www.blender.org/ (Accessed July 16, 2012). [2] Brandon Maccharone, Shannell Frazier, Modis Website http://modis.gsfc.nasa. gov/index.php (Accessed July 16, 2012). [3] Steven Graham, Claire Parkinson, Aqua Project Science http://aqua.nasa.gov/ (Accessed July 16, 2012). [4] Holly Zell, Brian Dunbar, NASA - Terra http://www.nasa.gov/mission pages/ terra/index.html [5] Esri Inc., ArcGIS [Software], http://www.esri.com/software/arcgis [6] Erdas Inc.,

Erdas Imagine [Software],

http://geospatial.intergraph.com/

products/ERDASIMAGINE/ERDASIMAGINE/Details.aspx [7] Exelis Visual Information Solutions Inc., ENVI [Software], http://www.exelisvis. com/ProductsServices/ENVI.aspx [8] Google Inc., Google Earth [Software], http://www.google.com/earth/index.html [9] Nishihama, R. Wolfe, D. Solomon, F. Patt, J Blanchette, A. Fleig, and E. Masuoka, MODIS Level 1A earth location: Algorithm Theoretical Basis Document ,

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BIBLIOGRAPHY Version 3.0 (Report SDST-092, Laboratory for Terrestrial Physics, NASA Goddard Space Flight Center, Greenbelt, MD, USA, 1997).

[10] D. Ascher et al., Numerical Python, tech. report UCRL-MA-128569, Lawrence Livermore National Laboratory, 2001; http://numpy.scipy.org. [11] G. van Rossum and F. L. Drake(eds), Python Reference Manual, (Python Software Foundation, 2006), http://docs.python.org/ref/ref.html. [12] J. Xiong, G. Toller, V. Chiang, J. Sun, J. Esposito, and W. Barnes, MODIS Level 1B Algorithm Theoretical Basis Document , Version 3.0 (Prepared for: National Aeronautics and Space Administration) [13] J. v. Gumster, Blender for Dummies, 2nd ed. (Wiley, Indianapolis, 2011). [14] Andre

Gosselin,

PyHDF,

2008

(http://pysclint.sourceforge.net/

pyhdf/)(Accessed July 16, 2012). [15] A. Collette, HDF5 for Python, 2008 (http://h5py.alfven.org)(Accessed July 10, 2012) [16] H4toH5 Conversion Library API Reference Manual with H4/H5 Command-line Conversion Utilities , Release 2.2.1 (The HDF Group, 2012 http://www. hdfgroup.org/ftp/HDF5/tools/h4toh5/src/unpacked/doc/h4toh5lib RM.pdf) (Accessed July 16, 2012). [17] E. R. Tufte, The Visual Display of Quantitative Information (Graphics Press, Cheshire, 1983). [18] Blender

2.6

Python

API

Documentation

http://www.blender.org/

documentation/blender python api 2 63 5/ (Accessed July 10, 2012).

Appendix A Instructions for use The procedures outlined here include a list of the packages used in this work, as well as a step-by-step set of instructions for installing the Python packages. In addition, the scripts for Blender will also be described. Finally, the basic methods of interacting will be described.

A.1

Installation

The following is a step-by-step set of instructions for the installation of the software libraries that will be used. All of the commands to be entered at the command line are surrounded by double quotes. 1. Install Blender, libatlas-dev, libhdf5-serial-dev, libhdf5-serial-1.8.4, libblas-dev, libblas3gf, liblapack3gf, liblapack-dev and gfortran from your distributions repositories. 2. Download the source code for NumPy and h5py from http://sourceforge.net/ projects/numpy/files/NumPy/ and http://code.google.com/p/h5py/downloads/ list 23

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Chapter A Instructions for use 3. Unzip the source code packages. 4. Open the terminal and change to the directory you unzipped NumPy to. 5. From the command line run “python3.2mu setup.py build –fcompiler=gnu95”. 6. After the code has compiled, this will take a while if succesful for the first time, then run “sudo python3.2mu setup.py install”. 7. From the command line run “find /usr -name numpy”. This will tell you where your software is installed. Take note of the directory structure before the first occurence of NumPy. This will be used later. An example of the output is /usr/local/lib/python3.2/dist-packages/numpy /usr/local/lib/python3.2/dist-packages/numpy/core/include/numpy /usr/local/lib/python3.2/dist-packages/numpy/numarray/include/numpy 8. Move to the directory in which you unzipped h5py and run “python3.2mu setup.py build”. 9. Run “sudo python3.2mu setup.py install”.

10. Run “find /usr -name h5py” to double check that h5py is installed in the same directory. Installation will use the standard Blender install. In linux either use a package manager to install Blender or go to the website www.blender.org to obtain the latest stable package. In addition, you will need to install libatlas-dev, libhdf5-serialdev, libhdf5-serial-1.8.4, libblas-dev, libblas3gf, liblapack3gf, liblapack-dev, as well as gfortran. The directions at http://www.scipy.org/Installing SciPy/Linux should be followed with one exception. The reason for this change is that Blender uses Python 3 with wide unicode support which is not the standard settings.

A.2 Use of Software

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Figure A.1 When you first start Blender this is the starting view. In order to find out where Python is searching for the libraries open Blender. Select the Python console from the editor menu. This menu is found in the lower left of the work space and is one of these images based on the editing mode for the window. From the drop down menu select the Python console. In the console type in “import sys” and then “sys.path”. This will list off the different directories in which Blender’s Python is looking for libraries. The default directory is set by the system. You can either append onto the sys.path the directory where your libraries are stored or move the libraries into one of the searched directories. If you choose to leave them in the default location then you will need to set the path in the script. Any changes made to the sys.path variable are lost when Blender is closed and reopened.

A.2

Use of Software

Once the program, scripts and libraries are installed you can use them from within Blender by supplying the script with a list of files. You will also need to give it a location to track for the rendering. For this you will need to specify a position in

26

Chapter A Instructions for use

Figure A.2 After the code you will see the object colored if you switch to vertex paint mode in the object viewing area.

Figure A.3 By selecting the image in the bottom left of a window you can change the view

A.2 Use of Software

Figure A.4 The ones in use for this are the Python console, Text editor and the 3-D view.

27

28


space by giving it a latitude and longitude coordinates and a height above the geoid in WGS84. In addition, you need to supply the viewing parameters for the camera. Once these are set, run the script and it will generate a video file. There will be several options for control of the camera once the script is set up. There is the possibility of the video following the satellite’s track or to remain located above a certain location for the duration of the video. There will also be settings for the amount of time that is rendered. The data file contains the data for ten minutes of flight time and the video could recreate faithfully the time that it takes to view, or it could generate a time lapse at a set rate.

A.3

Methods of interacting with the software

At this time, the methods of interacting with the code is by settings within the script file itself. This is a little ungainly and ultimately the program should be accessible by command-line interface. That has not been worked out just yet. There are some challenges to interfacing that have not been addressed. When a Python script is run in Blender, it does lock out the other controls as intended [18]. There are methods of updating in the middle of a script so that the process can be viewed. However, it is not recommended by the documentation. It would be preferable to run this script without the user interface running to preserve more system resources for the rendering itself, and because this will be repetitive and should be left to a machine to crank through. For more information look, in the section “Gotcha’s” in Blender’s Python api documentation.

A.4 Computer Specifications

A.4

29

Computer Specifications

A set of tables containing hardware descriptions of the computer on which all of the timing tests were performed. A table of the software packages as listed in the software repositories is also included here. Table A.1 Cpu Brand

AMD

Name

Athlon II X2 260

Model

ADX260OCGMBOX

Core

Regor

Multi-Core

Dual-Core

Operating Frequency

3.2GHz

Hyper Transports

4000MHz

L1 Cache

2 x 128KB

L2 Cache

2 x 1MB

Manufacturing Tech

45 nm

64 bit Support

Yes

Hyper-Transport Support

Yes

30

Chapter A Instructions for use Table A.2 Harddrive Brand

Seagate

Series

Barracuda Green

Model

ST1500DL003

Interface

SATA 6.0Gb/s

RPM

5900 RPM

Cache

64MB

Average Seek Time

12ms

Average Write Time

13ms

Average Latency

4.16ms

Table A.3 Graphics Card Brand

ZOTAC

Model

ZT-40704-10L

Interface

PCI Express 2.0 x16

Chipset Manufacturer

NVIDIA

GPU

GeForce GT 440 (Fermi)

Core Clock

810MHz

Shader Clock

1620MHz

CUDA Cores

96

Effective Memory Clock

1600MHz

Memory Size

1GB

Memory Interface

128-bit

Memory Type

GDDR3

3-D API

DirectX, DirectX 11, OpenGL, OpenGL 4.1

Features

NVIDIA PhysX , NVIDIA CUDA

A.4 Computer Specifications

31

Table A.4 Memory Brand

CORSAIR

Series

Vengeance

Model

CML16GX3M4A1600C9B

Type

240-Pin DDR3 SDRAM

Capacity

16GB (4 x 4GB)

Speed

DDR3 1600 (PC3 12800)

Cas Latency

9

Timing

9-9-9-24

Multi-channel Kit

Dual Channel Kit

Table A.5 Software Package

Version

Ubuntu

11.10(12.04)

Blender

2.58-svn37702-1ubuntu1

libblas3gf

1.2.20110419-2ubuntu1

libblas-dev

1.2.20110419-2ubuntu1

libatlas-dev

3.8.4-3build1

liblapack3gf

3.3.1-1

liblapack-dev

3.3.1-1

libhdf5-serial-dev

1.8.4-patch1-2ubuntu4

libhdf5-serial-1.8.4

1.8.4-patch1-2ubuntu4

gfortran

4:4.6.1-2ubuntu5

32


Appendix B Python Code B.1

1

Version 2

#Libraries used

2

3

import bpy

4

import sys

5

6

#The following string is used to indicate where the library packages are stored

7

# change to match the location on your system This is necessary to find numpy

8

# and h5py if the libraries are already in one of the searched directories then

9

# comment out this section of code.

10

11

systemPath = ’/usr/local/lib/python3.2/dist-packages’

12

13

#The following checks against each element of the system path list and if the 33

34

Chapter B Python Code

14

# variable systemPath is not found adds it. This could break if your sys.path

15

# variable contains elements that are not strings.

16

# is something to check.

If the code breaks this

17

18

if [s for s in sys.path if systemPath in s] == []: sys.path.append(systemPath)

19

20

21

#

22

import numpy

23

import h5py

24

25

#Configuration values

26

27

#Name is the name of the hdf file and the path is the location to it.

28

#In the case of the MYD02SSH you can use the internal latitude and longitude

29

# coordinates because they map one to one.

30

# 1km resolution does not include the one to one coordinate points to data

31

# points therefore you need to include an additional data file that contains

32

# those. Which is where name2 comes from.

The other data set that is the

33

34

name = ’MYD02SSH.A2012001.1945.005.2012002174518.1.h5’

35

#name2 = ’MYD03.A2012001.1945.005.2012002170952.1.h5’

36

37

#ShortName is to get around the name size limitation in Blender.

38

#Otherwise I would use the name of the file.

39

# will take longer names up to 63 characters in length.

The newer version of Blender So this will be

B.1 Version 2

35

40

# phased out in later versions but will necessitate using Blender 2.62

41

# and up.

42

ShortName = ’First’

43

path = ’’

44

45

#These are the dimensions of the data set.

This data does exist within the

46

# data file itself but I have not taken the time to learn how to access that

47

# specific attribute yet.

48

# recognized because it has nested attributes.

49

# be found using HDFView and entered here.

50

# faces which is one row and column less and referenced so often that it makes

51

# more sense to compute once and reference.

52

n = 406

53

n2 = n-1

54

m = 271

55

m2 = m-1

This might be because the hdf-eos extension is not Either way these values can

n2 and m2 is the shape of the

56

57

#Creating the file handle

58

H5File = h5py.File(path+name,’r’)

59

#H5File2 = h5py.File(path+name2,’r’)

60

61

#these are the list objects where the data for the mesh frame will be stored

62

vertex_list = []

63

face_list = []

64

65

#This block of code computes the x,y,z coordinates from the latitude and

36


66

# longitude included in the hdf file Numpy makes this much faster.

The dtype

67

# is included to make the accuracy achieved match my previous method of using

68

# the math package and a for loop.

69

# then that can be removed for a speed increase of three times for this

70

# section.

If this level of accuracy is unnecessary

71

72

a = 6.378137 #This is the semi major axisreference datum for wgs 84

73

b = 6.356752314245 #This is the semi minor axis

74

p = ((numpy.array(H5File[’MODIS_SWATH_Type_L1B’][’Geolocation Fields’] [’Latitude’],dtype=float)*numpy.pi)/180)

75

76

r = numpy.sqrt(1/((numpy.square(numpy.sin(p))/(a*a))

77

+(numpy.square(numpy.cos(p))/(b*b))))

78

t = ((numpy.array(H5File[’MODIS_SWATH_Type_L1B’][’Geolocation Fields’] [’Longitude’],dtype=float)*numpy.pi)/180)

79

80

x = numpy.multiply(numpy.multiply(r,numpy.sin(p)),numpy.cos(t))

81

y = numpy.multiply(numpy.multiply(r,numpy.sin(p)),numpy.sin(t))

82

z = numpy.multiply(r,numpy.cos(p))

83

vertex_list = zip(x.flatten().tolist(),y.flatten().tolist(),

84

z.flatten().tolist())

85

86

#This section could also be sped up a bit with some work.

87

#The face list is a set of four numbers that are indexes into the list of

88

# vertex points and should be in the order of upper left, lower left,

89

# lower right, and upper right.

90

# norm of the face facing.

91

for i in range(0,n2):

I don’t know which direction this puts the

B.1 Version 2 92

93

37

for j in range(0,m2): face_list.append(((i*m+j),(i*m+j+m),(i*m+j+m+1),(i*m+j+1)))

94

95

#Generate the mesh object and link with the object

96

me = bpy.data.meshes.new(ShortName+’Mesh’)

97

ob = bpy.data.objects.new(ShortName, me)

98

ob.location = (0,0,0)

99

ob.show_name = True

100

# Link object to scene

101

bpy.context.scene.objects.link(ob)

102

103

# Create mesh from given verts, edges, faces. Either edges or

104

# faces should be [], or you ask for problems

105

me.from_pydata(list(vertex_list), [], face_list)

106

107

# Update mesh with new data

108

me.update(calc_edges=True)

109

110

#The following generates a new material if there is none available

111

if len(bpy.data.materials.keys()) 1:

148

Edata1[i][j] = 0

149

if Edata2[i][j] > 1:

150

Edata2[i][j] = 0

151


152

Edata3[i][j] = 0

153

154

155

for i in range(0,n2): for j in range(0,m2):

156

index = i*m2+j

157

ob.data.vertex_colors[’Col’].data[index].color1 = (Edata1[i][j],

158

Edata2[i][j],

159

Edata3[i][j])

160

ob.data.vertex_colors[’Col’].data[index].color2 = (Edata1[i+1][j],

161

Edata2[i+1][j],

162

Edata3[i+1][j])

163

ob.data.vertex_colors[’Col’].data[index].color3 = (Edata1[i+1][j+1],

164

Edata2[i+1][j+1],

165

Edata3[i+1][j+1])

166

ob.data.vertex_colors[’Col’].data[index].color4 = (Edata1[i][j+1],

167

Edata2[i][j+1],

168

Edata3[i][j+1])

40

B.2

1


Version 1

#Libraries used

2

3

import bpy

4

import math

5

import sys

6

import time

7

8


9

# change to match the location on your system This is necessary to find numpy

10


11

# comment it out

12

13

sys.path.append(’/usr/local/lib/python3.2/dist-packages’)

14

15

import numpy

16

import h5py

17

18

#Internal functions

19

20

#WGS84toXYZ takes in the latitude and longitude in terms of degrees with

21

# latitude and longitude and computes the x,y,z coordinates for them based on

22

# the WGS84 Geoide.

23

# system.

24

def WGS84toXYZ(phi,theta):

This does not account for any rotation of the coordinate

B.2 Version 1

41

25


26


27

p = (phi*numpy.pi)/180

28

r = math.sqrt(1/((numpy.sin(p)*numpy.sin(p)/(a*a))+((numpy.cos(p)* numpy.cos(p))/(b*b))))

29

30

t = ((theta)*math.pi)/180

31

x = r*numpy.sin(p)*numpy.cos(t)

32

y = r*numpy.sin(p)*numpy.sin(t)

33

z = r*numpy.cos(p)

34

return x,y,z

35

36

#This function takes in the latitude and longitude and computes the x,y,z

37

# coordinate for them based on the Sinusoidal projection system used by some

38

# MODIS gridded data products

39

def MODISSINtoXYZ(phi,theta):

40

r = 6.371007181

41

p = (phi*math.pi)/180

42

x = r*math.sin(p)*math.cos(t)

43

y = r*math.sin(p)*math.sin(t)

44

z = r*math.cos(p)

45

return x,y,z

46

47


48

49


50

name = ’MYD02SSH.A2012001.1945.005.2012002174518.1.h5’

42


51

#name2 = ’MYD03.A2012001.1945.005.2012002170952.1.h5’

52


53

path = ’’

54


55


56


57


58


59

# that it makes more sense to compute once and reference.

60

n = 406

61

n2 = n-1

62

m = 271

63

m2 = m-1



n2 and m2 are referenced so often

64

65

66

#for profiling the for loops to see where the most time is spent is handled

67

# through time.clock and written to file every time it is run.

68

Time = open(’time’,’at’)

69

Time.write(’This is the time for a run of ’+name+’\n’)

70

Time1 = 0

71

Time2 = 0

72

73


74


75


76

B.2 Version 1

43

77

#These are the list objects where the data for the mesh frame will be stored

78

vertex_list = []

79

face_list = []

80

81

#Take the time before starting the loop

82

Time1 = time.clock()

83

84

85

86

for i in range(0,n): for j in range(0,m): vertex_list.append(WGS84toXYZ(

87

H5File[’MODIS_SWATH_Type_L1B’][’Geolocation Fields’][’Latitude’][i][j],

88

H5File[’MODIS_SWATH_Type_L1B’][’Geolocation Fields’][’Longitude’][i][j]))

89

90

#Take the time after finishing the loop

91


92

93

94

Time.write(’Converting from WGS84 to XYZ took using numpy’+str(Time2-Time1) +’ seconds\n’)

95

96


97

98

99

100

for i in range(0,n2): for j in range(0,m2): face_list.append(((i*m+j),(i*m+j+m),(i*m+j+m+1),(i*m+j+1)))

101

102


44 103


Time.write(’Generating the face list took ’+str(Time2-Time1)+’ seconds\n’)

104

105


106


107


108


109

ob.show_name = True

110


111


112

113


114


115

me.from_pydata(vertex_list, [], face_list)

116

117


118


119


120

Time.write(’Generating the object and linking it took ’+str(Time2-Time1)

121

+’ seconds\n’)

122

123


124


125


170

Edata1[i][j] = 0

171


172

Edata2[i][j] = 0

173


174

Edata3[i][j] = 0

175

176


177

Time.write(’Checking for error codes in the data took ’+str(Time2-Time1)

178

+’ seconds\n’)

179

180


B.3 Version 0 181

182

47

for i in range(0,n2): for j in range(0,m2):

183

index = i*m2+j

184


185

Edata2[i][j],

186

Edata3[i][j])

187


188

Edata2[i+1][j],

189

Edata3[i+1][j])

190


191

Edata2[i+1][j+1],

192

Edata3[i+1][j+1])

193


194

Edata2[i][j+1],

195

Edata3[i][j+1])

196

197


198

Time.write(’Painting the vertices took ’+str(Time2-Time1)+’ seconds\n’)

199

Time.close()

B.3

1

Version 0

#Libraries used

2

3

import bpy

48


4

import math

5

import sys

6

import time

7

8


9

# change to match the location on your system. This is necessary to find numpy

10


11

# comment it out

12

13

sys.path.append(’/usr/local/lib/python3.2/dist-packages’)

14

15

import numpy

16

import h5py

17

18

#Internal functions

19

20

#WGS84toXYZ takes in the latitude and longitude in terms of degrees with

21

# latitude and longitude and computes the x,y,z coordinates for them based on

22

# the WGS84 Geoide.

23

# system

This does not account for any rotation of the coordinate

24

25

def WGS84toXYZ(phi,theta):

26


27


28

p = (phi*numpy.pi)/180

29

r = math.sqrt(1/((numpy.sin(p)*numpy.sin(p)/(a*a))+((numpy.cos(p)*

B.3 Version 0

49 numpy.cos(p))/(b*b))))

30

31

t = ((theta)*math.pi)/180

32

x = r*numpy.sin(p)*numpy.cos(t)

33

y = r*numpy.sin(p)*numpy.sin(t)

34

z = r*numpy.cos(p)

35

return x,y,z

36

37

#This function takes in the latitude and longitude and computes the x,y,z

38

# coordinate for them based on the Sinusoidal projection system used by some

39

# MODIS gridded data products

40

def MODISSINtoXYZ(phi,theta):

41

r = 6.371007181

42

p = (phi*math.pi)/180

43

x = r*math.sin(p)*math.cos(t)

44

y = r*math.sin(p)*math.sin(t)

45

z = r*math.cos(p)

46

return x,y,z

47

48


49

50


51

name = ’MYD02SSH.A2012001.1945.005.2012002174518.1.h5’

52

#name2 = ’MYD03.A2012001.1945.005.2012002170952.1.h5’

53


54

path = ’’

55



50


56


57


58


59


60

# that it makes more sense to compute once and reference.

61

n = 406

62

n2 = n-1

63

m = 271

64

m2 = m-1


n2 and m2 are referenced so often

65

66

67

#for profiling the for loops to see where the most time is spent is handled

68

# through time.clock and written to file every time it is run

69

Time = open(’time’,’at’)

70

Time.write(’This is the time for a run of ’+name+’\n’)

71

Time1 = 0

72

Time2 = 0

73

74


75


76


77

78

#These are the list objects where the data for the mesh frame will be stored

79

vertex_list = []

80

face_list = []

81

B.3 Version 0 82

#Take the time before starting the loop

83


51

84

85

86

87

for i in range(0,n): for j in range(0,m): vertex_list.append(WGS84toXYZ(

88

H5File[’MODIS_SWATH_Type_L1B’][’Geolocation Fields’][’Latitude’][i][j],

89

H5File[’MODIS_SWATH_Type_L1B’][’Geolocation Fields’][’Longitude’][i][j]))

90

91

#Take the time after finishing the loop

92


93

94

95

Time.write(’Converting from WGS84 to XYZ took using numpy’+str(Time2-Time1) +’ seconds\n’)

96

97


98

99

100

101

for i in range(0,n2): for j in range(0,m2): face_list.append(((i*m+j),(i*m+j+m),(i*m+j+m+1),(i*m+j+1)))

102

103


104

Time.write(’Generating the face list took ’+str(Time2-Time1)+’ seconds\n’)

105

106


107


52


108


109


110

ob.show_name = True

111


112


113

114


115


116

me.from_pydata(vertex_list, [], face_list)

117

118


119


120


121

Time.write(’Generating the object and linking it took ’+str(Time2-Time1)

122

+’ seconds\n’)

123

124


125


126


171

Edata1[i][j] = 0

172


173

Edata2[i][j] = 0

174


175

Edata3[i][j] = 0

176

177


178

Time.write(’Checking for error codes in the data took ’+str(Time2-Time1)

179

+’ seconds\n’)

180

181


182

for i in range(0,n2):

183

for j in range(0,m2):

184

index = i*m2+j

185


B.3 Version 0

55

186

Edata2[i][j],

187

Edata3[i][j])

188


189

Edata2[i+1][j],

190

Edata3[i+1][j])

191


192

Edata2[i+1][j+1],

193

Edata3[i+1][j+1])

194


195

Edata2[i][j+1],

196

Edata3[i][j+1])

197

198


199

Time.write(’Painting the vertices took ’+str(Time2-Time1)+’ seconds\n’)

200

Time.close()

56
