Final Portfolio
NANO 230
Scott Mason
6/9/13
Nano-characterization
Final Portfolio
NANO 230
Scott Mason
6/9/13
Scott Mason
Spring 2013
Nano-characterization
Final Portfolio
NANO 230
Scott Mason
6/9/13
SCOTT MASON
Qzzxt4@gmail.com/(719)331-9013
EDUCATION
North Seattle Community College, Seattle, WA
Certificate in Nanotechnology (Sep. 2012-Current)
University of Washington, Seattle, WA
B.S. in Biochemistry (Aug. 2005-Jun. 2009)
GPA 3.14 (Dean’s List: Fall 2005, Fall 2006)
TECHNICAL SKILLS
Animal models: Intraperitoneal hormone injection; Maintain mouse colony
Cell Biology: RNA and DNA FISH (fluorescence in situ hybridization); Transformation (E.coli and
S.cerevisiae); Making chemically competent cells
DNA: PCR; PCR primer design; Restriction enzyme digestion; Ligation; Gel electrophoresis;
Subcloning; DNA sequencing
Mammalian cell culture: Mouse bone marrow extraction; Transfection; culturing: 293T, 293F, BJAB,
Mono Mac 6, Thp-1, WeHi, 3T3, B-cells
Material techniques: Salinize glass with aminosilane; UV spectroscopy; Limited experience with Instron
Micro-fabrication: Spin coating; Lithography; Thermal evaporation; Vacuum systems; Class 10k clean
room; PDMS microfluidics chambers
Microscopy: Light; Phase; Fluorescence; Live-cell; Limited experience with Scanning Electron,
Confocal, and Atomic Force microscopes
Protein analysis: SDS-Page; Western blot; Silver stain; Luciferase assay; FACs; Mass spectroscopy; Coimmunoprecipitation; Tandem affinity purification
COMPUTER SKILLS
Computer experience: Mac; PC; Photoshop; Microsoft Office; VectorNTI
Programming experience: Smoldyn; Limited experience with Bash, Python, HTML, and CSS
Website experience: Wordpress
TRAINING
Equipment: NanoDrop; Nikon Compound Fluorescence Microscope; Limited experience with Aspex
Scanning Electron Microscope and Fluoveiw Confocal Microscope
Lab: Radiation safety; Mouse work; Blood-borne pathogens; Familiar with MSDS and SOPs
ACADEMIC EXPERIENCE
North Seattle Community College (NSCC)
Independent research and class projects (Sept. 2012-Aug. 2013)
 Developed a program to convert images from Scanning Electron and Atomic Force microscopes into
3D printer format to create easily visualized educational materials
 Created an optical sensor using nanolithography techniques
 Produced microfluidics chambers using nanolithography
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International Genetically Engineered Machines competition (iGEM)
Undergraduate researcher (Jun.-Nov. 2008)
 Worked on creating a system of bacterial plasmids to control bacteria-yeast conjugation resulting in
successful transfer of DNA from bacteria to yeast
 Educated team members in basic lab techniques improving overall project success
ACHIEVEMENTS AND AWARDS
Phi Theta Kappa (North Seattle Community College)
National Society of Collegiate Scholars (University of Washington, 2006-present)
PROFESSIONAL EXPERIENCE
University of Washington (UW): Ailion Lab [Start-up lab: began Sept. 2011]
Research Scientist/Engineer I (Dec. 2011-Sept. 2012); General Lab Manager (Dec. 2011-Sept. 2012)
 Developed, catalogued, stocked, and maintained database of frozen biological samples resulting in
successful implementation of lab sample storage system
 Envisioned and created lab website with Wordpress
 Purchased, organized, and inventoried laboratory chemicals, buffers, and other supplies for orders
under $500
 Assisted principal investigator in organizing purchasing orders over $500
 Collaborated with supervisor to design and perform experiments in order to advance scientific
understanding and aid in the ability to acquire future funding
 Understood and enforced university policy on lab waste disposal, proper chemical storage, and proper
safety protocols
 Learned C. elegans culturing techniques to assist in scientific research
 Critiqued scientific papers in a group in order to better understand current research and get ideas for
future project directions.
Fred Hutchinson Cancer Research Center (FHCRC): Groudine Lab
Research Technician I (Nov. 2009-Oct. 2011)
 Designed and tested protein expression from DNA vectors for visualization in in vivo and in vitro
mammalian cell expression systems allowing for successful imaging of protein localization
 Bonded antibodies to glass using aminosilane and glutathione conjugation to successfully capture
cells on glass surface
 Assisted supervisor with purchasing and inventorying lab expenses under $500
 Performed 4 color live and fixed cell fluorescence microscopy to analyze protein and DNA
localization
 Prepared fixed cells via DNA and RNA FISH to obtain high resolution images of intranuclear colocalization and obtained high quality data of c-Myc and IgH localization
 Extended a computer simulation that models nuclear protein dynamics to help direct wetlab research
 Conceptualized new research projects to validate computer simulations opening up new avenues of
research
 Developed a robust and efficient way to collect data from a sample size exceeding 1000 data points in
a fast, reproducible manner with data output 100 times faster than previous method
 Helped maintain mouse colony; changed bedding, fed, and injected hormones for mating
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NANO 230
Scott Mason
6/9/13
Viral Logic Systems Technology (VLST)
Lab Assistant / Proteomics and Informatics (Oct. 2005-Oct. 2009)
 Developed gadgets to improve high throughput laboratory techniques resulting in a 15% decrease in
product production time
 Led research in creating a DNA vector to screen non-classically secreted proteins used a dualluciferase/SEAP-luciferase reporter assay
 Constructed and tested expression plasmids for viral proteins for use in downstream processed
 Implemented, arranged, validated, and managed DNA stocks generated by the Proteomics and
Informatics group in the company-wide DNA database leading to efficient company-wide DNA
distribution
 Maintained mammalian cell cultures for transfections and assays creating a stock for my research
group
 Supported senior lab personnel by helping with experiments on an as needed basis
 Trained new hires in basic lab work and lab protocols
 Organized, stocked, monitored, and inventoried lab supplies and chemicals
 Logged tasks performed and hours worked to communicate accomplished tasks to management
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AFM
1. A picture of our AFM with the manufacture name and model number
Scott Mason
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Scott Mason
6/9/13
2. A short description of the instrument specifications
[1]
Measurement channels
16 bit A/D converters, up to seven signals depending on configuration
Scan generator
16 bit D/A converter for all axes
Scan speed
Up to 60ms / line at 128 datapoints / line
Scan area and data points
Individual width / height, up to 2048 x 2048 points
Scan image rotation
0° - 360°
Sample tilt compensation
Hardware X/Y-slope compensation
Spectroscopy modes
Single point measurements or multiple measurements along a vector
Spectroscopy measurement averaging
1-255
Spectroscopy data points
Up to 2048
AFM Scan Head:
110µm
Maximum Scan range 1)
110 µm
Maximum Z-range 1)
22 µm
Drive resolution Z 2)
0.34 nm
Drive resolution XY 2)
1.7 nm
XY-Linearity Mean Error
< 0.6%
Z measurement noise level
(RMS, Static Mode)
0.4 nm
(max. 0.55 nm)
Z measurement noise level
(RMS, Dynamic Mode)
0.3 nm
(max. 0.55 nm)
3. A diagram showing the labeled parts of an AFM
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4. A short description, numbered list, or bulleted list detailing how an AFM works
In tapping mode:

Cantilever with a sharp tip resonates near the surface of a sample, lightly tapping the surface

Laser is reflected off the back of the cantilever to a photodetector

Cantilever is rastered across the surface of the sample and the position of the reflected laser
is recorded

Changes in the position of the reflected laser light are caused by changes in the oscillation of
the cantilever and this is correlated to changes in the height of the sample
5. One AFM image labeled and with a scale bar and z-scale from one of our standard samples
6. One additional AFM image of a standard sample or a sample of your choosing labeled and with a
scale bar and z-scale
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7. Optional: SEM image of an AFM cantilever and tip including label and scalebar.
Works Cited
1. Nanoscience Instruments, Inc. "The Nanosurf® EasyScan 2 AFM Specifications." EasyScan 2 AFM
Specifications. Nanoscience Instruments, Inc., 2012. Web. 25 Apr. 2013.
<http://www.nanoscience.com/products/easyScan2/easyScan2AFM_specs.html>.
2. Murray, Richard M. "Atomic Force Microscope." - FBSwiki. Caltech, n.d. Web. 25 Apr. 2013.
<http://www.cds.caltech.edu/~murray/amwiki/index.php/Atomic_force_microscope>.
Final Portfolio
NANO 230
Scott Mason
6/9/13
SEM
1. A picture of our SEM with the manufacture name and model number
Aspex – Explorer
Guaranteed Performance**
Particle Detection Efficiency
Greater than 95%
Particle Sizing Precision
0.25 microns or better
Particle Sizing Accuracy
0.5 microns or better
Occurrence of False Positives
Less than 1 per mm2
Particles Sized per Hour
Up to 33,000
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Particles Sized per Hour
Up to 10,000
**Performance as measured using the Performance Grading System™ for features 1 to 100 μm.
Performance Specifications
Particle Detection Range
30nm to 5mm
Detectors
SED, Quad BSED, SDD EDX
Accelerating Voltage
0.2 to 25 KeV Continuous
Stage Movement
80mm x 100mm
Lightest Element Detection
Boron
EDX Resolution
135 eV
Sizing Algorithms Available RCA and CFA
FDA Compliance Level
cGMP
Data Security
21 CFR part 11 Compliant*
Stage Motorization
80mm x 100mm XY, Manual Z
Chamber Dimensions
180mm x 230mm x 140mm
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2. A diagram showing the labeled parts of an SEM used for imaging and EDS analysis
a short description, numbered list, or bulleted list
detailing how an SEM works
 A Tungsten filament is heated to eject electrons
 Ejected electrons are focused using
electromagnetic coils
 The electrons interact with the sample in one of
three ways[2,3]
o The sample can eject low energy electrons from
surface electron orbitals leaving a low energy
vacancy for another electron to fill. This causes
high-energy wavelengths to be emitted from the
sample.
o They can bump electrons in the material to
higher orbital energy levels; the electrons then fall
back to a lower energy orbital and release
characteristic X-rays (EDX).
o They can interact directly with the nucleus of an
atom in the material and be scattered in any
direction. This results in backscattered electrons.
[1]
4 Images
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-1 SEM image labeled and with a scale bar from one of our standard samples
-2 SEM images labeled and with scale bars from samples of your choosing
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-A macro picture of a sample used for EDS, the EDS spectra with KLM markers and the elemental
composition
6600 ×
10 µm
Counts
1,500
1,000
500
0
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
keV
Works Cited
1. Bahadorimehr, Alireza. "SEM, FESEM." Blogspot.com, 12 July 2010. Web. 09 May
2013. <http://bahadorimehr.blogspot.com/2010/07/sem-fesem.html>.
2. Wittke, James H. "[SEM] Types of Signals." [SEM] Signals. Northern Arizona
University, 2008. Web. 24 Apr. 2013. <http://www4.nau.edu/microanalysis/MicroprobeSEM/Signals.html>.
3. Versluijs, Marjan. "Inorganic Chemistry UU - Research: Programme Design." Inorganic
Chemistry UU - Research: Programme Design. Utrecht University, n.d. Web. 24 Apr.
2013. <http://www.anorg.chem.uu.nl/people/staff/MarjanVersluijsHelder/home.htm>.
Final Portfolio
NANO 230
Scott Mason
6/9/13
Profilometer
1. A picture of our profilometer with the manufacture name and model number
Bruker - DektakXT
a short description of the instrument specifications
Measurement Technique
Stylus profilometry (contact measurement)
Measurement Capability
Two-dimensional surface profile measurements
Stylus Force
1 to 15mg with LIS 3 sensor
Stylus Options
Stylus radius options from 50nm to 25μm
Scan Length
Range 55mm (2in.); 200mm (8in.) with scan
stitching capability
Data Points Per Scan
120,000 maximum
Max. Sample Thickness
50mm (1.95in.)
Step Height Repeatability
<5Å, 1sigma on 0.1μm step
Vertical Range
1mm (0.039in.)
Vertical Resolution
1Å max. (@ 6.55-m range)
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Temperature Range Operating Range
20 and 25°C (68 to 77°F)
2. A diagram showing the labeled parts of an profilometer
Deflection detector
Stylus
Cantilever
[1]
a short description, numbered list, or bulleted list detailing how a profilometer works

A stylus attached to a cantilever is dragged across the surface of a sample with a constant
contact force.

On the other end of the cantilever is a permanent magnet inside a pair of solenoids.

As the tip moves across the sample, vertical deflection is detected by measuring the change in
current in two solenoids.
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Images
- 1 screen capture or replotted line scan showing a step height and a picture of the sample
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- A one page study showing a process measured with the profilometer. It could be a measurement of
sputter coated films, spin coated films, doctor blade films, graphite exfoliation, RMS surface roughness
etc. The study needs to include a plot of data with labeled axis and error bars, thickness data, and a
camera screen capture. A very short description needs to be included describing the study and the
results.

Profilometer data of the height of three different colors of PLA
Blue
White
Yellow
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Average Surface Variation of Different
Colored PLA
800
Average Variation
700
600
500
400
300
200
100
0
Blue
White
Yellow
Color
Works Cited
1. Lee, Dong-Hyeok, and Nahm-Gyoo Cho. "Assessment of Surface Profile Data Acquired by a Stylus
Profilometer." Measurement Science and Technology 23.10 (2012): 105601. Print.
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Scott Mason
6/9/13
Confocal
1. A picture of our confocal with the manufacture name and model number
[1]
2. A short description of the instrument specifications (laser lines etc).
FLUOVIEW FV10i SPECIFICATIONS
FV10i-LIV
Laser Light
Source
Detection
Focus
FV10i-DOC
Ultraviolet/Visible light
LD lasers
405nm (18mW), 473nm (12.5mW), 635nm (10mW), 559nm
Modulation:
Continuously variable by the LD direct modulation (0.1%-100%, 0.1% inclement) Line return
period-laser OFF
Detector module
Fluorescence: 2 channels, Phase Contrast: 1 channel Variable barrier filter mechanism for
fluorescence channel by diffraction grating and slit
Detection method
Analog integration detection by Photomultiplier
Pinhole
Single motorized pinhole. Pinhole diameter: ø50-800μm automatic setting (adjustable to ×1.0,
×1.5, ×2.0, and ×2.5)
Field number
18
Optical zoom
10× objective: 1× – 6× in 0.1x increments
60× objective: 1× – 10× in 0.1x increments
Automatic Exposure
Automatic setting of the laser intensity and photomultiplier sensitivity to fluorescence intensity.
Z-drive
Motorized focus with minimum increment: 0.01μm
Objectives
Exclusively designed 10× phase contrast
objective NA 0.4 (equivalent to UPLSAPO
10x)
Exclusively designed 60× phase contrast
water-immersion objective NA 1.2 (equivalent
Exclusively designed 10× phase contrast
objective NA 0.4 (equivalent to UPLSAPO
10x)
Exclusively designed 60× phase contrast oilimmersion objective NA 1.35 (equivalent to
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to UPLSAPO 60× W)
Remote switching from software by electric
revolver
UPLSAPO 60× O)
Remote switching from software by electric
revolver
3. A diagram showing the labeled parts of a laser scanning confocal microscope
[2]
4. A short description, numbered list, or bulleted list detailing how a laser scanning confocal microscope
works

Laser light is reflected off a dichromatic mirror (A mirror that reflects only certain wavelengths
of light, while allowing other wavelengths to pass through)

The reflected light interacts with the sample causing fluorescence

The emitted light travels back through the dichromatic mirror to a pinhole aperature where out
of focus light is blocked and only in focus light is allowed to reach the photo detector.
5. 3 Confocal images labeled and with scale bars- at least one of the images must be a 3D rendering.
You may include both a 2D and a 3D image of the same sample, but this only counts as one i
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Works Cited
1. "New FluoView FV10i Confocal Laser Scanning Microscope from Olympus." New FluoView FV10i
Confocal Laser Scanning Microscope from Olympus. Nanowerk.com, 17 Nov. 2009. Web. 09 June
2013.
2. Rice, George. "Fluorescent Microscopy." Fluorescent Microscopy. Carleton College, 16 Mar.
2013. Web. 21 May 2013.
<http://serc.carleton.edu/microbelife/research_methods/microscopy/fluromic.html>.
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6/9/13
'''
Created on Feb 24, 2013
@author: qzzxt
'''
import linecache
# The following block of code must be edited for each run to run properly
filename = "Ultra High resolution" #The name of the text file created in processing
filesuffix = "_3d rounding"
#Suffix to provide a unique filename file will save in path listed in main
code below as 'filename + filesuffix + .stl'
dimX = 256
#The X-resolution of the picture
dimY = 256
#The Y-resolution of the picture
inverse = False
#If True, black values become high points
isflat = True
#If False, creates a back-to-back mirror 3D image. If True, creates a flat plane
with the 3D figure protruding out
issmooth = True
#If True, runs smoothing algorithm
minheight = 5
#Determines the minimum height of the surface (Good starting value ~5)
scale = 1/10
#Scales down the Z-heights so that there is less difference between the highs
and lows (Good starting value ~'1/10')
offset = -50
#Adds depth to the 3D figure (Should be negative with a good starting value ~50)
slopethreshold = 50
#The maximum difference in height allowed for averaging (Lower values
give sharper edges, higher values give smoother curves. Good starting value ~50)
binning = 1
#determines the bin size (pixel distance) used for averaging. A value of 1 uses
the 8 nearest neighbors. A value of 2 uses the 22 nearest pixels.
class Surface_Draw():
#Creates the normal vectors for the two triangles that connect four points
def calculatesurface():
normalize1 = 1
normalize2 = 1
A1 = ((y1-y3)*(z1-z2))-((z1-z3)*(y1-y2))
B1 = ((z1-z3)*(x1-x2))-((x1-x3)*(z1-z2))
C1 = ((x1-x3)*(y1-y2))-((y1-y3)*(x1-x2))
A2 = ((y2-y3)*(z2-z4))-((z2-z3)*(y2-y4))
B2 = ((z2-z3)*(x2-x4))-((x2-x3)*(z2-z4))
C2 = ((x2-x3)*(y2-y4))-((y2-y3)*(x2-x4))
if abs(A1) >= abs(B1) and abs(C1):
normalize1 = A1
if abs(B1) >= abs(A1) and abs(C1):
normalize1 = B1
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if abs(C1) >= abs(A1) and abs(B1):
normalize1 = C1
if abs(A2) >= abs(B2) and abs(C2):
normalize2 = A2
if abs(B2) >= abs(A2) and abs(C2):
normalize2 = B2
if abs(C2) >= abs(A2) and abs(B2):
normalize2 = C2
if normalize1 == 0:
normalize1 = 1
if normalize2 == 0:
normalize2 = 1
A1 = A1/normalize1
B1 = B1/normalize1
C1 = C1/normalize1
A2 = A2/normalize2
B2 = B2/normalize2
C2 = C2/normalize2
surfacenormal = [A1, B1, C1, A2, B2, C2]
imagenormal.append(surfacenormal)
#Writes the planes that make up the 3D surface
def surfacedraw(wz1, wz2, wz3, wz4):
filepath.write("facet normal " + str(A1) + " " + str(B1) + " " + str(C1) + "\n")
filepath.write("outer loop \n")
filepath.write("vertex " + str(x1) + " " + str(y1) + " " + str(wz1) + "\n")
filepath.write("vertex " + str(x3) + " " + str(y3) + " " + str(wz3) + "\n")
filepath.write("vertex " + str(x2) + " " + str(y2) + " " + str(wz2) + "\n")
filepath.write("endloop \n")
filepath.write("endfacet \n")
filepath.write("facet normal " + str(A2) + " " + str(B2) + " " + str(C2) + "\n")
filepath.write("outer loop \n")
filepath.write("vertex " + str(x2) + " " + str(y2) + " " + str(wz2) + "\n")
filepath.write("vertex " + str(x3) + " " + str(y3) + " " + str(wz3) + "\n")
filepath.write("vertex " + str(x4) + " " + str(y4) + " " + str(wz4) + "\n")
filepath.write("endloop \n")
filepath.write("endfacet \n")
#Writes the planes that create the sides of the 3D figure
def sidedraw(A1, B1, q1, q2, r1, r2, s1, s2, s3, s4):
filepath.write("facet normal " + str(A1) + " " + str(B1) + " 0\n")
filepath.write("outer loop \n")
filepath.write("vertex " + str(q1) + " " + str(r1) + " " + str(s1) + "\n")
filepath.write("vertex " + str(q1) + " " + str(r1) + " " + str(s3) + "\n")
filepath.write("vertex " + str(q2) + " " + str(r2) + " " + str(s2) + "\n")
filepath.write("endloop \n")
filepath.write("endfacet \n")
filepath.write("facet normal " + str(A2) + " " + str(B2) + " 0\n")
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filepath.write("outer loop \n")
filepath.write("vertex " + str(q2) + " " + str(r2) + " " + str(s2) + "\n")
filepath.write("vertex " + str(q1) + " " + str(r1) + " " + str(s3) + "\n")
filepath.write("vertex " + str(q2) + " " + str(r2) + " " + str(s4) + "\n")
filepath.write("endloop \n")
filepath.write("endfacet \n")
#Writes the planes that make up the bottom of the Flat surface
def bottomdraw(x1, x2, y1, y2):
filepath.write("facet normal 0 0 1\n")
filepath.write("outer loop \n")
filepath.write("vertex " + str(x1) + " " + str(y1) + " " + str(offset) + "\n")
filepath.write("vertex " + str(x2) + " " + str(y2) + " " + str(offset) + "\n")
filepath.write("vertex " + str(dimX/2) + " " + str(dimY/2) + " " + str(offset) + "\n")
filepath.write("endloop \n")
filepath.write("endfacet \n")
class Image_Store():
#Reads the X, Y, and Z positions from the text file defined by the 'filename' variable
#'minheight' determines the cutoff threashold for the lower end of the Z values
def gatherposition():
inputstring1 = str(str(positionnum % dimX) + ";" + str(positionnum//dimX) + ";" +
str(inputfile[positionnum]))
inputsplit1 = inputstring1.split(';')
x1 = float(inputsplit1[0])
y1 = float(inputsplit1[1])
if inverse:
if float(inputsplit1[2]) <= minheight:
z1 = minheight
else:
z1 = (256 - float(inputsplit1[2]))
else:
z1 = (float(inputsplit1[2]))
if z1 <= minheight:
currentposition = [x1, y1, z1, False, z1, avg, 0, values, height, templimit]
else:
currentposition = [x1, y1, z1, True, z1, avg, 0, values, height, templimit]
imageposition.append(currentposition)
#Smooths the image by averaging local values:
#The 'binning' value determines how many pixels away the averaging function goes
#The 'slopethreshold' determines how close the height values must be to be averaged (small values
give sharper edges, large values give smother features)
def heightsmooth():
if imageposition[positionnum][3]:
binningX = -binning
binningY = -binning
while binningX <= binning:
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while binningY <= binning:
if imageposition[positionnum][0] >= binningX and imageposition[positionnum][0] <= dimX - 2
- binningX:
if imageposition[positionnum][1] >= binningY and imageposition[positionnum][1] <= dimY 2 - binningY:
if abs(imageposition[positionnum][2] - imageposition[positionnum + dimX * binningY + 1
* binningX][2]) < slopethreshold:
imageposition[positionnum + dimX * binningY + 1 * binningX][6] = 1
imageposition[positionnum][8] = imageposition[positionnum][8] +
imageposition[positionnum + dimX * binningY + 1 * binningX][2] * imageposition[positionnum + dimX *
binningY + 1 * binningX][6]
imageposition[positionnum][7] = imageposition[positionnum][7] + 1
binningY = binningY + 1
binningY = -binning
binningX = binningX + 1
imageposition[positionnum][5] = imageposition[positionnum][8]/imageposition[positionnum][7]
imageposition[positionnum][4] = imageposition[positionnum][5]
else:
imageposition[positionnum][5] = 0
imageposition[positionnum][4] = 0
#Draws all necessary planes for a given set of four points
def imagedraw(x1, x2, x3, x4, y1, y2, y3, y4, z1, z2, z3, z4, w1, w2, w3, w4):
if isflat:
if imageposition[positionnum][0] <= dimX - 2:
Surface_Draw.surfacedraw(z1, z2, z3, z4)
if imageposition[positionnum][1] <= 0:
Surface_Draw.sidedraw(0, 1, x1, x2, y1, y2, z1, z2, w1, w2)
Surface_Draw.bottomdraw(x1, x2, y1, y2)
if imageposition[positionnum][0] <= 0:
Surface_Draw.sidedraw(1, 0, x3, x1, y3, y1, z3, z1, w3, w1)
Surface_Draw.bottomdraw(x3, x1, y3, y1)
if imageposition[positionnum][1] >= dimY - 2:
Surface_Draw.sidedraw(0, 1, x4, x3, y4, y3, z4, z3, w4, w3)
Surface_Draw.bottomdraw(x4, x3, y4, y3)
if imageposition[positionnum][0] >= dimX - 2:
Surface_Draw.sidedraw(1, 0, x2, x4, y2, y4, z2, z4, w2, w4)
Surface_Draw.bottomdraw(x2, x4, y2, y4)
else:
if imageposition[positionnum][3]:
if imageposition[positionnum][0] <= dimX - 2:
Surface_Draw.surfacedraw(z1, z2, z3, z4)
Surface_Draw.surfacedraw(w1, w2, w3, w4)
if imageposition[positionnum][0] <= dimX - 2:
if not(imageposition[positionnum + 1][3]):
Surface_Draw.sidedraw(1, 0, x2, x4, y2, y4, z2, z4, w2, w4)
if imageposition[positionnum][1] <= dimY - 2:
if not(imageposition[positionnum + dimX][3]):
Final Portfolio
NANO 230
Scott Mason
6/9/13
Surface_Draw.sidedraw(0, 1, x4, x3, y4, y3, z4, z3, w4, w3)
if imageposition[positionnum][0] != 0:
if not(imageposition[positionnum - 1][3]):
Surface_Draw.sidedraw(1, 0, x3, x1, y3, y1, z3, z1, w3, w1)
if imageposition[positionnum][1] != 0:
if not(imageposition[positionnum - dimX][3]):
Surface_Draw.sidedraw(0, 1, x1, x2, y1, y2, z1, z2, w1, w2)
if imageposition[positionnum][1] <= 0:
Surface_Draw.sidedraw(0, 1, x1, x2, y1, y2, z1, z2, w1, w2)
if imageposition[positionnum][0] <= 0:
Surface_Draw.sidedraw(1, 0, x3, x1, y3, y1, z3, z1, w3, w1)
if imageposition[positionnum][1] >= dimY - 2:
Surface_Draw.sidedraw(0, 1, x4, x3, y4, y3, z4, z3, w4, w3)
if imageposition[positionnum][0] >= dimX - 2:
Surface_Draw.sidedraw(1, 0, x2, x4, y2, y4, z2, z4, w2, w4)
#The main program that calls the above functions
if __name__ == '__main__':
filepath = open('C:\\Users\\qzzxt\\North Seattle Community College\\Nano Lab\\image converter\\' +
filename + filesuffix + '.stl', 'w')
#The filepath that determines where the file is saved
inputfile = linecache.getlines('C:\\Users\\qzzxt\\North Seattle Community College\\Nano Lab\\\image
converter\\Pixel_converter\\' + filename + '.txt') #The filepath for the input text file from Processing
filepath.write("solid " + filename + "\n")
#Initializes the .stl file
imagenormal = []
#Initializes the variable for use later
imageposition = []
#Initializes the variable for use later
positionnum = 0
#Initializes the variable for use later
currentmax = 0
#Initializes the variable for use later
templimit = 0
#Initializes the variable for use later
slope = 0
#Initializes the variable for use later
height = 0
#Initializes the variable for use later
values = 1
#Initializes the variable for use later
avg = 0
#Initializes the variable for use later
while positionnum < dimX * dimY:
Image_Store.gatherposition()
positionnum = positionnum + 1
positionnum = 0
if issmooth:
while positionnum < dimX * dimY:
Image_Store.heightsmooth()
positionnum = positionnum + 1
positionnum = 0
while positionnum < dimX * dimY - dimX - 1:
x1 = q1 = imageposition[positionnum][0]
x2 = q2 = imageposition[positionnum + 1][0]
x3 = imageposition[positionnum + dimX][0]
Final Portfolio
NANO 230
x4 = imageposition[positionnum + 1 + dimX][0]
y1 = r1 = imageposition[positionnum][1]
y2 = r2 = imageposition[positionnum + 1][1]
y3 = imageposition[positionnum + dimX][1]
y4 = imageposition[positionnum + 1 + dimX][1]
w1 = z1 = minheight * scale
w2 = z2 = minheight * scale
w3 = z3 = minheight * scale
w4 = z4 = minheight * scale
if imageposition[positionnum][3]:
wz1 = z1 = s1 = imageposition[positionnum][4] * scale
if imageposition[positionnum + 1][3]:
wz2 = z2 = s2 = imageposition[positionnum + 1][4] * scale
if imageposition[positionnum + dimX][3]:
wz3 = z3 = s3 = imageposition[positionnum + dimX][4] * scale
if imageposition[positionnum + dimX + 1][3]:
wz4 = z4 = s4 = imageposition[positionnum + 1 + dimX][4] * scale
if isflat:
if z1 < minheight:
wz1 = z1 = s1 = minheight
if z2 < minheight:
wz2 = z2 = s2 = minheight
if z3 < minheight:
wz3 = z3 = s3 = minheight
if z4 < minheight:
wz4 = z4 = s4 = minheight
if imageposition[positionnum][3]:
w1 = s1 = float(offset) - (float(z1))
w2 = s2 = float(offset) - (float(z2))
w3 = s3 = float(offset) - (float(z3))
w4 = s4 = float(offset) - (float(z4))
if isflat:
w1 = s1 = float(offset)
w2 = s2 = float(offset)
w3 = s3 = float(offset)
w4 = s4 = float(offset)
Surface_Draw.calculatesurface()
A1 = imagenormal[positionnum][0]
B1 = imagenormal[positionnum][1]
C1 = imagenormal[positionnum][2]
A2 = imagenormal[positionnum][3]
B2 = imagenormal[positionnum][4]
C2 = imagenormal[positionnum][5]
Image_Store.imagedraw(x1, x2, x3, x4, y1, y2, y3, y4, z1, z2, z3, z4, w1, w2, w3, w4)
positionnum = positionnum + 1
Scott Mason
6/9/13
Final Portfolio
NANO 230
filepath.write(("endsolid").format("a"))
print("End")
Scott Mason
6/9/13