visualization of modis data in the blender ......blender is a 3-d(three-dimensional) animation tool...

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

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 coor-dinator, 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 11.1 Visualization of large amounts of data . . . . . . . . . . . . . . . . . 21.2 Current methods of displaying satellite data . . . . . . . . . . . . . . 21.3 Why a new method? . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 History 52.1 Modis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 Blender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.3 HDF 4 and HDF 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3 Procedures 9

4 Results 114.1 Analysis of h4toh5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114.2 Analysis of a Blender rendering of a sweep . . . . . . . . . . . . . . . 124.3 Theory of Data display . . . . . . . . . . . . . . . . . . . . . . . . . . 13

5 Conclusion 155.1 Did Blender work to a satisfactory level at displaying the data? . . . 165.2 Did Blender render in a satisfactory amount of time? . . . . . . . . . 165.3 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Bibliography 21

A Instructions for use 23A.1 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23A.2 Use of Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25A.3 Methods of interacting with the software . . . . . . . . . . . . . . . . 28A.4 Computer Specifications . . . . . . . . . . . . . . . . . . . . . . . . . 29

xi

xii CONTENTS

B Python Code 33B.1 Version 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33B.2 Version 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40B.3 Version 0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

List of Figures

4.1 File conversion times . . . . . . . . . . . . . . . . . . . . . . . . . . . 124.2 An example of a rendered image . . . . . . . . . . . . . . . . . . . . . 13

5.1 An example of a rendered image . . . . . . . . . . . . . . . . . . . . . 175.2 An example of a rendered image . . . . . . . . . . . . . . . . . . . . . 175.3 Times for importing the MODIS file(first version) . . . . . . . . . . . 185.4 Times for importing the MODIS file(third version) . . . . . . . . . . 18

A.1 The first thing you see . . . . . . . . . . . . . . . . . . . . . . . . . . 25A.2 Results from running the code . . . . . . . . . . . . . . . . . . . . . . 26A.3 Highlighting how to change to a different view . . . . . . . . . . . . . 26A.4 The contents of the editor types menu . . . . . . . . . . . . . . . . . 27

xiii

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 three-

dimensional analysis into them. They are coming from a history of two-dimensional

representation and trying to convert to a three-dimensional representation. This fun-

damental 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 lan-

guage 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 coordi-

nates 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 dif-

ferent 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 re-

leased 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 pro-

cesses 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 mak-

ing 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 sci-

entific 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 par-

allel 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.

http://www.python.org/getit/releases/3.0/

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 environ-

ment 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 compli-

cated, 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

http://reverb.echo.nasa.gov/reverb/

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

12 Chapter 4 Results

Figure 4.1 The time taken to convert is consistent with the size of the fileand 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 13

Figure 4.2 This is the resulting image when rendered. This is an earlyimage where the lighting is not in place.

The NumPy functions that handle the trigonometric functions do return differ-

ences 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 avail-

able. 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 analyz-

ing two-dimensional data representations, the conclusion can be drawn that a simple

14 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.

15

16 Chapter 5 Conclusion

5.1 Did Blender work to a satisfactory level at dis-

playing 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? 17

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.


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

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

5.3 Future Directions 19

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


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 after-

wards to provide either different views for the same time frame, or to render many

overlapping time-frames, or view information in different wavelengths simultaneously.

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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 repos-

itories.

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

http://sourceforge.net/projects/numpy/files/NumPy/




http://code.google.com/p/h5py/downloads/list




24 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 pack-

age 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-serial-

dev, 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.

www.blender.org

http://www.scipy.org/Installing_SciPy/Linux

http://www.scipy.org/Installing_SciPy/Linux

A.2 Use of Software 25

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


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

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

A.2 Use of Software 27

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


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 29

A.4 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


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

Appendix B

Python Code

B.1 Version 2

1 #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. If the code breaks this

16 # is something to check.

17

18 if [s for s in sys.path if systemPath in s] == []:

19 sys.path.append(systemPath)

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. The other data set that is the

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.

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. The newer version of Blender

39 # will take longer names up to 63 characters in length. 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. This might be because the hdf-eos extension is not

48 # recognized because it has nested attributes. Either way these values can

49 # be found using HDFView and entered here. n2 and m2 is the shape of the

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

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


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. If this level of accuracy is unnecessary

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

70 # section.

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’]

75 [’Latitude’],dtype=float)*numpy.pi)/180)

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’]

79 [’Longitude’],dtype=float)*numpy.pi)/180)

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. I don’t know which direction this puts the

90 # norm of the face facing.

91 for i in range(0,n2):

B.1 Version 2 37

92 for j in range(0,m2):

93 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:

112 bpy.ops.material.new()

113

114 #Takes the name of the first material

115 MaterialName = bpy.data.materials.keys()[0]

116

117 #This next line sets the active object to the one we just created, necessary


118 # for the ops commands to work

119 bpy.context.scene.objects.active = ob

120

121 #Add a new material slot to the active object

122 bpy.ops.object.material_slot_add()

123

124 #Set the Material for the object to the first one

125 ob.material_slots[’’].material = bpy.data.materials[MaterialName]

126

127 #Set the first material to use Vertex color painting

128 bpy.data.materials[MaterialName].use_vertex_color_paint = True

129

130 #Finally generate the data structure that stores the vertex paint information

131 bpy.data.objects[ShortName].data.vertex_colors.new()

132

133 #Take in the information about the recorded radiances and scale to between 0

134 # and 1 This is also where you can set what channel to use

135 Edata1 = (H5File[’MODIS_SWATH_Type_L1B’][’Data Fields’][’EV_1KM_RefSB’][7]

136 /32767)


138 /32767)

139 Edata3 = (H5File[’MODIS_SWATH_Type_L1B’][’Data Fields’]

140 [’EV_500_Aggr1km_RefSB’][0]/32767)

141

142 #This for loop structure is to make sure that the elements are between 0 and 1

143 # because outside of that indicates error, for now I am zeroing the data for

B.1 Version 2 39

144 # testing purposes. This will need to be addressed in greater depth later.

145 for i in range(0,n):

146 for j in range(0,m):

147 if Edata1[i][j] > 1:

148 Edata1[i][j] = 0

149 if Edata2[i][j] > 1:

150 Edata2[i][j] = 0

151 if Edata3[i][j] > 1:

152 Edata3[i][j] = 0

153



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])


B.2 Version 1

1 #Libraries used

2

3 import bpy

4 import math

5 import sys

6 import time

7


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


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. This does not account for any rotation of the coordinate

23 # system.

24 def WGS84toXYZ(phi,theta):

B.2 Version 1 41



27 p = (phi*numpy.pi)/180

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

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

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


48


50 name = ’MYD02SSH.A2012001.1945.005.2012002174518.1.h5’


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


53 path = ’’





58 # be found using HDFView and entered here. n2 and m2 are referenced so often

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

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




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



86 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


92

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

94 +’ seconds\n’)

95


97




101



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

104








112



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

116




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


122





127


B.2 Version 1 45


130




134



137



140



143



146


148 Time.write(’Generating the material and adding the vertex paint layer took ’

149 +str(Time2-Time1)+’ seconds\n’)

150



153 # and 1. This is also where you can set what channel to use.



155 /32767)


157 /32767)


159 [’EV_500_Aggr1km_RefSB’][0]/32767)


161 Time.write(’Reading in the data took ’+str(Time2-Time1)+’ seconds\n’)

162




166 # testing purposes



169 if Edata1[i][j] > 1:

170 Edata1[i][j] = 0

171 if Edata2[i][j] > 1:

172 Edata2[i][j] = 0

173 if Edata3[i][j] > 1:

174 Edata3[i][j] = 0

175


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


179


B.3 Version 0 47



183 index = i*m2+j


185 Edata2[i][j],

186 Edata3[i][j])


188 Edata2[i+1][j],

189 Edata3[i+1][j])


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

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


194 Edata2[i][j+1],

195 Edata3[i][j+1])

196


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

199 Time.close()

B.3 Version 0

1 #Libraries used

2

3 import bpy


4 import math

5 import sys

6 import time

7


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


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. This does not account for any rotation of the coordinate

23 # system

24

25 def WGS84toXYZ(phi,theta):



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

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

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


49


51 name = ’MYD02SSH.A2012001.1945.005.2012002174518.1.h5’

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


54 path = ’’






59 # be found using HDFView and entered here. n2 and m2 are referenced so often

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

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




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 51

82 #Take the time before starting the loop


84



87 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


93

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


96


98




102


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

105









113



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

117




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


123





128



131



B.3 Version 0 53


135



138



141



144



147


149 Time.write(’Generating the material and adding the vertex paint layer took ’

150 +str(Time2-Time1)+’ seconds\n’)

151



154 # and 1. This is also where you can set what channel to use.


156 /32767)


158 /32767)



160 [’EV_500_Aggr1km_RefSB’][0]/32767)


162 Time.write(’Reading in the data took ’+str(Time2-Time1)+’ seconds\n’)

163




167 # testing purposes



170 if Edata1[i][j] > 1:

171 Edata1[i][j] = 0

172 if Edata2[i][j] > 1:

173 Edata2[i][j] = 0

174 if Edata3[i][j] > 1:

175 Edata3[i][j] = 0

176


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


180




184 index = i*m2+j


B.3 Version 0 55

186 Edata2[i][j],

187 Edata3[i][j])


189 Edata2[i+1][j],

190 Edata3[i+1][j])


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

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


195 Edata2[i][j+1],

196 Edata3[i][j+1])

197


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

200 Time.close()