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The JBehave Core requires the following dependencies to be in the classpath:
All dependencies are provided in the lib directory of binary distribution from the download page.
Attention: if you use junit.jar, be aware that from version 4.4 it comes bundled bundled with a copy of Hamcrest API, so you may experience unpredictable behaviours based on the relative position of the jars in the classpath. It is advisable to use the junit-dep.jar which is identitical, except that is come unbundled and any dependencies must be declared.
Note: JBehave Core is agnostic of any build tools, althought it does contain an optional dependency on the ant.jar for the implementaion of the ScenarioClassNameFinder. This is a utilily class which is used only by the JBehave Ant and JBehave Maven Plugin modules.
The JBehave Pico module requires the following dependencies (and their transitive dependencies) to be in the classpath (in addition to Core and its dependencies):
The JBehave Spring module requires the following dependencies (and their transitive dependencies) to be in the classpath (in addition to Core and its dependencies):
The JBehave Ant module requires the following dependencies to be in the classpath (in addition to Core and its dependencies):
The JBehave Maven Plugin module requires the following dependencies to be in the classpath (in addition to Core and its dependencies):


Methods
getProgram()
Returns program
Returns:
program and line number
getTokenName(matchGroups)
Extracts name of group of a given match
Parameters:
| Name | Type | Description |
|---|---|---|
matchGroups |
* | groups of matches |
Returns:
token name
parse(p) → {object}
Parses a program
Parameters:
| Name | Type | Description |
|---|---|---|
p |
* |
Returns:
tree - results
- Type
- object
parseApply(tree)
Parse and apply production rule
Parameters:
| Name | Type | Description |
|---|---|---|
tree |
* |
Returns:
object tree
parseExpression()
Parses a given expression
Returns:
Expression or parseApply
parseFromFile(fileName)
Compiles a program and writes it in other file
Parameters:
| Name | Type | Description |
|---|---|---|
fileName |
* |
regExpUnify(vector)
Unifies all regexs
Parameters:
| Name | Type | Description |
|---|---|---|
vector |
vector | of regexp |
run(program)
Runs a program
Parameters:
| Name | Type | Description |
|---|---|---|
program |
* |
Returns:
evaluated program
runFromEVM(fileName)
Runs a program from a compiled tree
Parameters:
| Name | Type | Description |
|---|---|---|
fileName |
* |
Returns:
evaluation of tree
runFromFile(fileName)
Runs a program from a given file
Parameters:
| Name | Type | Description |
|---|---|---|
fileName |
* |
Returns:
evaluated program
setProgram(prg) → {vector}
Sets program line at 1
Parameters:
| Name | Type | Description |
|---|---|---|
prg |
* |
Returns:
program and line number
- Type
- vector


CSPICE_FOVTRG determines if a specified ephemeris object is within
the field-of-view (FOV) of a specified instrument at a given time.
Given:
Parameters-
SPICE_GF_MAXVRT is the maximum number of vertices that may be used
to define the boundary of the specified instrument's
field of view. See SpiceGF.h for more details.
MARGIN is a small positive number used to constrain the
orientation of the boundary vectors of polygonal
FOVs. Such FOVs must satisfy the following
constraints:
1) The boundary vectors must be contained within
a right circular cone of angular radius less
than than (pi/2) - MARGIN radians; in
other words, there must be a vector A such that
all boundary vectors have angular separation
from A of less than (pi/2)-MARGIN radians.
2) There must be a pair of boundary vectors U, V
such that all other boundary vectors lie in
the same half space bounded by the plane
containing U and V. Furthermore, all other
boundary vectors must have orthogonal
projections onto a specific plane normal to
this plane (the normal plane contains the angle
bisector defined by U and V) such that the
projections have angular separation of at least
2*MARGIN radians from the plane spanned
by U and V.
MARGIN is currently set to 1.D-6.
Arguments-
instrument indicates the name of an instrument, such as a
spacecraft-mounted framing camera. The field of view
(FOV) of the instrument will be used to determine if
the target is visible with respect to the instrument.
[1,a] = size(instrument), char = class(instrument)
The position of the instrument is considered to
coincide with that of the ephemeris object 'observer' (see
description below).
The size of the instrument's FOV is constrained by the
following: There must be a vector A such that all of
the instrument's FOV boundary vectors have an angular
separation from A of less than (pi/2)-MARGIN radians
(see description above). For FOVs that are circular or
elliptical, the vector A is the boresight. For FOVs
that are rectangular or polygonal, the vector A is
calculated.
See the header of the CSPICE routine getfov_c for a
description of the required parameters associated with
an instrument.
Both object names and NAIF IDs are accepted. For
example, both 'CASSINI_ISS_NAC' and '-82360' are
accepted. Case and leading or trailing blanks are not
significant in the string.
target is the name of the target body. This routine determines
if the target body appears in the instrument's field of
view.
[1,b] = size(target), char = class(target)
Both object names and NAIF IDs are accepted. For
example, both 'Moon' and '301' are accepted. Case and
leading or trailing blanks are not significant in the
string.
target_shape is a string indicating the geometric model used to
represent the shape of the target body.
[1,c] = size(target_shape), char = class(target_shape)
The supported options are:
'ELLIPSOID' Use a triaxial ellipsoid model,
with radius values provided via the
kernel pool. A kernel variable
having a name of the form
'BODYnnn_RADII'
where nnn represents the NAIF
integer code associated with the
body, must be present in the kernel
pool. This variable must be
associated with three numeric
values giving the lengths of the
ellipsoid's X, Y, and Z semi-axes.
'POINT' Treat the body as a single point.
Case and leading or trailing blanks are not
significant in the string.
target_frame is the name of the body-fixed, body-centered reference
frame associated with the target body. Examples of
such names are 'IAU_SATURN' (for Saturn) and 'ITRF93'
(for the Earth).
[1,d] = size(target_frame), char = class(target_frame)
If the target body is modeled as a point, 'target_frame'
is ignored and should be left blank. (Ex: ' ').
Case and leading or trailing blanks bracketing a
non-blank frame name are not significant in the string.
abcorr indicates the aberration corrections to be applied
when computing the target's position and orientation.
[1,e] = size(abcorr), char = class(abcorr)
For remote sensing applications, where the apparent
position and orientation of the target seen by the
observer are desired, normally either of the
corrections:
'LT+S'
'CN+S'
should be used. These and the other supported options
are described below.
Supported aberration correction options for
observation (the case where radiation is received by
observer at 'et') are:
'NONE' No correction.
'LT' Light time only
'LT+S' Light time and stellar aberration.
'CN' Converged Newtonian (CN) light time.
'CN+S' CN light time and stellar aberration.
Supported aberration correction options for
transmission (the case where radiation is emitted from
observer at 'et') are:
'XLT' Light time only.
'XLT+S' Light time and stellar aberration.
'XCN' Converged Newtonian (CN) light time.
'XCN+S' CN light time and stellar aberration.
Case, leading and trailing blanks are not significant
in the string.
observer is the name of the body from which the target is
observed. The instrument 'instrument' is treated as if it
were co-located with the observer.
[1,f] = size(observer), char = class(observer)
Both object names and NAIF IDs are accepted. For
example, both 'CASSINI' and '-82' are accepted. Case and
leading or trailing blanks are not significant in the
string.
et is the observation time in seconds past the J2000
epoch.
[1,n] = size(et), double = class(et)
the call:
visibl = cspice_fovtrg ( instrument, target, target_shape, ...
target_frame, abcorr, observer, et )
returns:
visibl is true if 'target' is fully or partially in the
field-of-view of 'instrument' at the time 'et'. Otherwise,
'visibl' is false.
[1,n] = size(visibl), logical = class(visibl)
Any numerical results shown for this example may differ between
platforms as the results depend on the SPICE kernels used as input
and the machine specific arithmetic implementation.
Example(1):
A spectacular picture was taken by Cassini's
narrow-angle camera on Oct. 6, 2010 that shows
six of Saturn's moons. Let's verify that the moons
in the picture are Epimetheus, Atlas, Daphnis, Pan,
Janus, and Enceladus.
To see this picture, visit:
http://photojournal.jpl.nasa.gov/catalog/PIA12741
or go to the PDS Image Node's Image Atlas at
http://pds-imaging.jpl.nasa.gov/search/search.html.
Select Cassini as the mission, ISS as the instrument,
and enter 1_N1665078907.122 as the Product ID in the
Product tab. Note: these directions may change as the
PDS Imaging Node changes.
Use the meta-kernel shown below to load the required SPICE
kernels. For project meta-kernels similar to the one shown
below, please see the PDS section of the NAIF FTP server.
For example, look at the following path for Cassini
meta-kernels: ftp://naif.jpl.nasa.gov//pub/naif/pds/data/
co-s_j_e_v-spice-6-v1.0/cosp_1000/extras/mk
KPL/MK
File name: fovtrg_ex.tm
This meta-kernel is intended to support operation of SPICE
example programs. The kernels shown here should not be
assumed to contain adequate or correct versions of data
required by SPICE-based user applications.
In order for an application to use this meta-kernel, the
kernels referenced here must be present in the user's
current working directory.
The names and contents of the kernels referenced
by this meta-kernel are as follows:
File name Contents
--------- --------
naif0010.tls Leapseconds
cpck*.tpc Satellite orientation and
radii
pck00010.tpc Planet orientation and
radii
cas_rocks_v18.tf FK for small satellites
around Saturn
cas_v40.tf Cassini FK
cas_iss_v10.ti Cassini ISS IK
cas00149.tsc Cassini SCLK
*.bsp Ephemeris for Cassini,
planets, and satellites
10279_10284ra.bc Orientation for Cassini
\begindata
KERNELS_TO_LOAD = ( 'naif0010.tls'
'cpck14Oct2010.tpc'
'cpck_rock_21Jan2011_merged.tpc'
'pck00010.tpc'
'cas_rocks_v18.tf'
'cas_v40.tf'
'cas_iss_v10.ti'
'cas00149.tsc'
'110317AP_RE_90165_18018.bsp'
'110120BP_IRRE_00256_25017.bsp'
'101210R_SCPSE_10256_10302.bsp'
'10279_10284ra.bc' )
\begintext
End of meta-kernel
Example program starts here.
%
% Load the meta kernel.
%
cspice_furnsh ( 'fovtrg_ex.tm' );
%
% Retrieve Cassini's NAIF ID.
%
[cassini_id, found] = cspice_bodn2c ( 'cassini' );
if (~found)
fprintf ( 'Could not find ID code for Cassini.' );
return
end
%
% Convert the image tag SCLK to ET.
%
et = cspice_scs2e ( cassini_id, '1665078907.122' );
%
% Convert the ET to a string format for the output.
%
time_format = 'YYYY-MON-DD HR:MN:SC.###::TDB (TDB)';
time = cspice_timout ( et, time_format );
%
% Search through all of Saturn's moons to see if each
% satellite was in the ISS NAC's field-of-view at
% the image time. We're going to take advantage of the
% fact that all Saturn's moons have a NAIF ID of 6xx.
%
fprintf ( 'At time %s the following were\n', time );
fprintf ( 'in the field of view of CASSINI_ISS_NAC\n' );
for body_id = 600:699
%
% Check to see if the 'body_id' has a translation.
%
[body, found] = cspice_bodc2n ( body_id );
if (found)
%
% Check to see if a body-fixed frame for this ID exists.
% If the frame is not in the kernel pool, we cannot
% perform the visibility test. The main cause of a
% failure is a missing kernel.
%
[frame_code, frame_name, found] = cspice_cidfrm ( body_id );
if (found)
%
% Is this body in the field-of-view of Cassini's
% ISS narrow-angle camera?
%
visibl = cspice_fovtrg ( 'cassini_iss_nac', body, ...
'ellipsoid', frame_name, 'cn+s', ...
'cassini', et );
if ( visibl )
fprintf ( ' %s\n', body);
end
end
end
end
%
% Unload the kernels.
%
cspice_kclear
MATLAB outputs:
At time 2010-OCT-06 17:09:45.346 (TDB) the following were
in the field of view of CASSINI_ISS_NAC
ENCELADUS
JANUS
EPIMETHEUS
ATLAS
PAN
DAPHNIS
ANTHE
Note: there were actually 7 of Saturn's satellites in the
field-of-view of Cassini's narrow-angle camera. However, Anthe
is very small and was probably obscured by other objects or
shadow.
To treat the target as a ray rather than as an ephemeris object,
use the higher-level Mice routine cspice_fovray. cspice_fovray may be used
to determine if distant target objects such as stars are visible
in an instrument's FOV at a given time, as long as the direction
from the observer to the target can be modeled as a ray.
For important details concerning this module's function, please refer to
the CSPICE routine fovtrg_c.
MICE.REQ
DAF.REQ
-Mice Version 1.0.1, 13-APR-2015, EDW (JPL)
Edit to correct typos in "Usage" string.
-Mice Version 1.0.0, 16-FEB-2012, SCK (JPL)
Target in instrument FOV at specified time
Target in instrument field_of_view at specified time
|


Apt Pupil * * (out of * * * * )
Directed by Bryan Singer. Cast: Brad Renfro, Ian McKellen, Bruce Davison, Ann Dowd, David Schwimmer, Elias Koteas, Joshua Jackson, Heather McComb. 1998 - 112 minutes. Rated R (for violence, profanity, and brief nudity). Reviewed October 25, 1998.
By Dustin Putman < Dustin Putman's Film Reviews >
Bryan Singer made a big splash in 1995 with his surprising, critically-acclaimed, "The Usual Suspects," and after three years of hiatus, he is back with his sophomore effort, "Apt Pupil," based on a Stephen King novella, but I think his look has quickly run out.
The film is about an intelligent, but cold-hearted 16-year-old senior, Todd Bowden (Brad Renfro) who becomes interested in the Holocaust after he studies it in school, and discovers that a Nazi from the concentration camps in WWII is living in his own hometown under an alias name. That man is Kurt Dussander (Ian McKellen), whom Todd blackmails, telling him that he will turn him into the police if he does not do as he says. As Kurt begins to tell him horrific stories of killing the Jews and his experiences in the camps, Todd begins to become obsessed with it, starts to slack off in school, in which he was previously at the top of his class, and begins to look more and more likely of being capable of murder himself.
"Apt Pupil," has an intriguing story, and there are a few suspenseful moments that pop up every once in a while, but it is at the screenplay level that the film fails. There is no attempt at all to develop any of the supporting characters, and so they all come off as being underused and one-dimensional. Even Todd's parents, played by Bruce Davison and Ann Dowd, only appear in one scene in the first half, and the film would have you believe that Todd is practically living at Kurt's house. Todd's friend (Joshua Jackson) is also a wasted character, as is his potential girlfriend (Heather McComb). And so, because of this, what we have is basically a two-character film, which would be okay if there hadn't been so many open-ended subplots.
Renfro and McKellen are nothing short of spectacular in the film. Renfro especially could not have been any better, and he is thoroughly believable throughout, although his character is completely unlikable. They both work well together, and the dialogue between them is often well-written.
One subplot that I did kind of like, even though it also was not developed very well, concerns Todd's guidance counselor at school (David Shwimmer), that at first does not seem to be of much importance to the story until the climactic moments. Schwimmer usually plays the same character ("Friends," "The Pallbearer"), but here he is completely different, and shows that he is capable of actually having a lot of range as an actor.
But with all things considered, "Apt Pupil" just isn't a very well thought-out picture. The screenplay obviously should have went through a few more drafts, and it also should have decided what type of genre it wanted to fall into. Did it want to be a psychological drama, or a horror movie (as some scenes would have you believe, including a senseless murder in the last act)? Singer proved he was a talented director with "The Usual Suspects," but "Apt Pupil" is a highly inferior film in almost every way.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


|
buffer = system or image buffer
filename$ = string containing full path and filename of image |
|
There are a hundred and one uses for this command, but probably most often used would be to display a title screen or some other 'one time viewing only' image to the front buffer (as in our example).
You can also load to an image buffer or back buffer. The image is scaled to match the buffer size. This command returns 1 if the command was successful, 0 if there was an error. |
|
; LoadBuffer example
; Set graphics mode Graphics 800,600,16 ; Load an image directly to the front buffer (your location may be different) LoadBuffer (FrontBuffer(),"C:Program FilesBlitz Basicsampleslitzanoidgfx itle.bmp") ; wait for ESC so user gets to see the screen While Not KeyHit(1) Wend |


Simple shortcut for Rake.application.all_tasks
# File lib/hoe/rake.rb, line 42 42: def self.all_tasks 43: Rake.application.all_tasks 44: end
Hooks into rake and allows us to clear out a task by name or regexp. Use this if you want to completely override a task instead of extend it.
# File lib/hoe/rake.rb, line 50 50: def self.clear_tasks(*tasks) 51: tasks.flatten.each do |name| 52: case name 53: when Regexp then 54: all_tasks.delete_if { |k,_| k =~ name } 55: else 56: all_tasks.delete(name) 57: end 58: end 59: end
Removes the last action added to a task. Use this when two libraries define the same task and you only want one of the actions.
require 'hoe' require 'tasks/rails' Rake.undo("test") # rolls out rails' test task
# File lib/hoe/rake.rb, line 69 69: def self.undo(*names) 70: names.each do |name| 71: all_tasks[name].actions.delete_at(1) 72: end 73: end
Disabled; run with --debug to generate this.
Generated with the Darkfish Rdoc Generator 1.1.6.


| System | Era | Cost/MFLOPS | Cost/MB RAM | Cost/MB Disk |
|---|---|---|---|---|
| Mainframe | 1977-88 | US$ 1,000,000 | US$ 20,000 | US$ 1,000 |
| Parallel system | 1985-94 | US$ 10,000 | US$ 200 | US$ 10 |
| Commodity cluster | 1985-present | US$ 100 | US$ 2 | US$ 0.10 |
MFLOP = Millions of Floating-point Operations Per Second
(Return to white paper on seasonal climate forecasting.)


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Field
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Variables
Air
Asphalt
Basalt
Brick
Cobblestone
Concrete
Corroded
Metal
Cracked
Lava
Diamond
Plate
Fabric
Foil
Force
Field
Glacier
Glass
Granite
Grass
Ground
Ice
Leafy
Grass
Limestone
Marble
Metal
Mud
Neon
Pavement
Pebble
Plastic
Rock
Salt
Sand
Sandstone
Slate
Smooth
Plastic
Snow
Water
Wood
Wood
Planks
Functions
Get
Enum
Items
Variables
Const
Air
Air
:
Air
Const
Asphalt
Asphalt
:
Asphalt
Const
Basalt
Basalt
:
Basalt
Const
Brick
Brick
:
Brick
Const
Cobblestone
Cobblestone
:
Cobblestone
Const
Concrete
Concrete
:
Concrete
Const
Corroded
Metal
Corroded
Metal
:
CorrodedMetal
Const
Cracked
Lava
Cracked
Lava
:
CrackedLava
Const
Diamond
Plate
Diamond
Plate
:
DiamondPlate
Const
Fabric
Fabric
:
Fabric
Const
Foil
Foil
:
Foil
Const
Force
Field
Force
Field
:
ForceField
Const
Glacier
Glacier
:
Glacier
Const
Glass
Glass
:
Glass
Const
Granite
Granite
:
Granite
Const
Grass
Grass
:
Grass
Const
Ground
Ground
:
Ground
Const
Ice
Ice
:
Ice
Const
Leafy
Grass
Leafy
Grass
:
LeafyGrass
Const
Limestone
Limestone
:
Limestone
Const
Marble
Marble
:
Marble
Const
Metal
Metal
:
Metal
Const
Mud
Mud
:
Mud
Const
Neon
Neon
:
Neon
Const
Pavement
Pavement
:
Pavement
Const
Pebble
Pebble
:
Pebble
Const
Plastic
Plastic
:
Plastic
Const
Rock
Rock
:
Rock
Const
Salt
Salt
:
Salt
Const
Sand
Sand
:
Sand
Const
Sandstone
Sandstone
:
Sandstone
Const
Slate
Slate
:
Slate
Const
Smooth
Plastic
Smooth
Plastic
:
SmoothPlastic
Const
Snow
Snow
:
Snow
Const
Water
Water
:
Water
Const
Wood
Wood
:
Wood
Const
Wood
Planks
Wood
Planks
:
WoodPlanks
Functions
Get
Enum
Items
Get
Enum
Items
(
this
:
defined
)
:
Array
<
Material
>
Parameters
this:
defined
Returns
Array
<
Material
>
Globals
Air
Asphalt
Basalt
Brick
Cobblestone
Concrete
Corroded
Metal
Cracked
Lava
Diamond
Plate
Fabric
Foil
Force
Field
Glacier
Glass
Granite
Grass
Ground
Ice
Leafy
Grass
Limestone
Marble
Metal
Mud
Neon
Pavement
Pebble
Plastic
Rock
Salt
Sand
Sandstone
Slate
Smooth
Plastic
Snow
Water
Wood
Wood
Planks
Air
Asphalt
Basalt
Brick
Cobblestone
Concrete
Corroded
Metal
Cracked
Lava
Diamond
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Ocean forecast verification
Building a software system
Andy Ryan
Class 4 framework
- Same observations for all models
- Observations grouped into 24hr window
- Forecasts valid at noon on same day
- Forecasts mapped to observation space
- Spatial interpolation
- No temporal interpolation
- Further information
Ocean observations
- In-situ
- Sea surface temperature
- Temperature & salinity profiles
- Currents derived from floats
- Marine mammals
- Acoustic doppler current profilers
- HF radar
- Gliders
- Satellite
- Sea surface temperature
- Altimetry
- Sea ice concentration
- Chlorophyll
Ocean models
- Modelling infrastructure
- Tri-polar global models
- Eddy resolving regional models
- Shelf Seas models
- Coupled Ocean-Atmosphere models
- Biogeochemistry models
- Diagnostics
- Temperature
- Salinity
- Currents
- Sea surface height
- Vertical coordinates
- Fixed depth
- Terrain following
Verification metrics
- Match ups
- Observation: $\vec{o} = F_o(\lambda, \phi)$
- Forecast: $\vec{f} = F_f(\lambda, \phi)$
- Summary measures
- MSE: $\sum{(f_i - o_i)^2} / N$
- MAE: $\sum{\mid f_i - o_i \mid} / N$
- Bias: $\sum{(f_i - o_i)} / N$
- Pearson correlation: $\frac{covar(\vec{f}, \vec{o})}{\sqrt{var(\vec{f}) var(\vec{o})}}$
- Skill scores
[insert software system here]
Visualisations, publications, websites
Requirements
- Share data with collaborators
- Interpolate forecasts to observation space
- Support all model grids
- Support all ocean observations
- Compute verification metrics
- Visualise metrics
- Maintain archive
- Tie it all together in one system
Collaboration protocol
- File formats defined
- Forecast/observation pairs
- Summary statistics
- Files exchanged via a server
- Enable consistent comparison of data
- Tools build on top of file formats
Architecture
Requirements broken down into applications
> c4obs # Processes observations
> c4extract # Extracts Met Office forecasts
> c4interp # Interpolates forecasts
> c4stats # Computes summary statistics
> c4plot # Visualises statistics
> c4deliver # Sends files to collaborators
Each application has a single responsibility and
a problem specific interface
Development practices
To guarantee system integrity, resilience and backwards compatibility
- Version control
- Test driven development
- Refactoring
- Code review
- Semantic versioning
Unit tests
- Class 4 system spread over several packages
- Approx. 2,000 unit tests covering main packages
[17:12]: nosetests c4data
.................................................................
-----------------------------------------------------------------
Ran 724 tests in 2.418s
[17:12]: nosetests c4tools
.................................................................
-----------------------------------------------------------------
Ran 870 tests in 4.357s
[17:12]: nosetests obsoper
.................................................................
-----------------------------------------------------------------
Ran 376 tests in 0.167s
Code reviews
- Review styles
- Light weight using trac
- Over the shoulder
- Formal review meetings
User feedback
- Users have ideas for features
- Discussions take place with developers
- The ticket system logs the ideas
- Tickets often assigned to users for review/testing
Case study: Command line bloat
Visualisation
Basic command line interface
c4plot stats*.nc
Custom colors and markers
c4plot --colors red,green,blue --markers='.,x' stats*.nc
All bells and whistles
c4plot --title-y 0.9 --ylims 0,1 --metrics RMSE,Bias --regions='Indian Ocean' --colors red,green,blue --markers='.,x' --linestyles=':,-' stats*.nc
Command line bloat
- Features added frequently
- Customisations requested often
- Solutions:
- Abandon command line approach and build library
- Define a domain specific config format
Domain specific config files
Put options into a JSON structure
c4plot --config-file=file.json stats*.nc
{
"default": {
"colors": ["Red", "Blue"],
"ylims": [0, 0.5]
},
"runs": [
{
"metrics": ["RMSE"]
},
{
"metrics": ["Bias"],
"ylims": [-0.1, 0.1]
}
]
}
Final step
Glue applications together into a suite
- Task scheduler at the Met Office
- Deploys tasks across compute servers
- Declares dependencies between tasks
- Cycles and clock-triggers tasks
- Logs stdout and stderr
- Fits quite nicely with Class 4 architecture
- Each task runs independently
- Communication via files
Summary
- Ocean verification is performed routinely
- Output informs decision makers
- The system is scalable across machines
- Users suggest new features
- Developers follow good practices
- Clean scalable code follows


| Category: algorithms | Component type: function |
template <class InputIterator1, class InputIterator2> int lexicographical_compare_3way(InputIterator1 first1, InputIterator1 last1, InputIterator2 first2, InputIterator2 last2);
As with lexicographical_compare, lexicographical comparison means "dictionary" (element-by-element) ordering. That is, lexicographical_compare_3way returns a negative number if *first1 is less than *first2, and a positive number if *first1 is greater than *first2. If the two first elements are equivalent [2] then lexicographical_compare_3way compares the two second elements, and so on. Lexicographical_compare_3way returns 0 only if the two ranges [first1, last1) and [first2, last2) have the same length and if every element in the first range is equivalent to its corresponding element in the second.
int main() { int A1[] = {3, 1, 4, 2, 8, 5, 7}; int A2[] = {3, 1, 4, 1, 5, 9, 3}; int A3[] = {1, 2, 3, 4}; int A4[] = {1, 2, 3, 4, 5}; const int N1 = sizeof(A1) / sizeof(int); const int N2 = sizeof(A2) / sizeof(int); const int N3 = sizeof(A3) / sizeof(int); const int N4 = sizeof(A4) / sizeof(int); int C12 = lexicographical_compare_3way(A1, A1 + N1, A2, A2 + N2); int C34 = lexicographical_compare_3way(A3, A3 + N3, A4, A4 + N4); cout << "A1[] and A2[]: " << C12 << endl; cout << "A3[] and A4[]: " << C34 << endl; }
[1] Lexicographical_compare_3way is almost, but not quite, redundant: the call lexicographical_compare_3way(f1,l1, f2,l2) could be written as lexicographical_compare(f1,l1, f2,l2) ? -1 : (lexicographical_compare(f2,l2, f1,l1) ? 1 : 0). The single call to lexicographical_compare_3way, however, is much faster than the two calls to lexicographical_compare.
[2] "Equivalent", not "equal", because two equivalent elements (that is, two elements with the property that neither one is less than the other) are not necessarily equal. Operator< is required to induce a strict weak ordering, not necessarily a total ordering. See the LessThan Comparable requirements for a discussion.


|
ZPR Monitor
|
Class that represents state of the CPU usage. Contains information about percentage of CPU usage for system and this application. More...
#include <CpuState.hpp>
Public Member Functions | |
| CpuState () | |
| Construct new CpuState object with all usage values set to 0.0. | |
| CpuState (double _cpuInUsePercentage, double _monitorCpuInUsePercentage) | |
| Constructs new CpuState object with particular CPU usage given as arguments. | |
| virtual | ~CpuState () |
| Destructor of the CpuState object. | |
| double | currPercentageUsed (void) |
| Returns percentage of CPU used by the system. | |
| double | currMonitorPercentageUsed (void) |
| Returns percentage of CPU used by this process. | |
| void | setPercentageUsed (double _cpuInUsePercentage) |
| Sets percentage of CPU used by system. | |
| void | setMonitorPercentageUsed (double _monitorCpuInUsePercentage) |
| Sets percentage of CPU used by this process. | |
| bool | operator> (const TriggerType &triggerType) const |
| CpuState & | operator+= (const CpuState &other) |
| Addition operator. Used in mean computing. | |
| CpuState & | operator/= (double val) |
| Divide operator. Used in mean computing. | |
Friends | |
| std::ostream & | operator<< (std::ostream &stream, const CpuState &cpuState) |
Class that represents state of the CPU usage. Contains information about percentage of CPU usage for system and this application.


Alice Njambi
UI Developer|Graphics Designer
About me
My projects
Address:
P.O.Box 74938 00200, Nairobi Kenya
Email:
<EMAIL_ADDRESS>Phone:
O713987456


Sorry - Not Available on this Browser
Unfortunately the PaymentRequest API isn't available in your browser. Why not try this on Chrome for Android?
Payment Request API Demo
This demo allows you to play with the various options and inputs you can provide to the PaymentRequest API.
Please note that this demo will not charge you. It'll go through the process and print the details at the bottom of this page.
The values will not add up in the total since the Payment Request API does no calculations.
Select Supported Payment Methods
The Payment Request API has a range of support payment methods, this demo currently only supports basic credit cards and the Interledger protocol but the Payment Request API can support third party payment processors.
Basic Card - Supported Networks
Interledger Protocol
The Interledger Protocol is an open protocol that standardizes the interactions between the payer and payee irrespective of the payment network(s) that are used to make the payment.
The available addresses are determined by which accounts the server is listneing on for incoming payments.
Options
Order Summary
The order summary is the value that is shown most prominently to the user, it should be treated as the total of the users transaction.
Change the name, total and current to see how it effects the UI.
Display Items
It's possible to define any number of display items in the Payment Request UI.
It's up to you the developer to ensure that the list of items equals the total value shown in the order summary, the Payment Request API performs no calculations.
Try adding both valid and invalid amounts in the display items and the summary total to see the differences.
Options
The API can also request information for the user. The type of transaction will determine will often dictate what is needed.
Shipping Type
Shipping Options
If you require shipping information (i.e. you have the shipping option ticked above) you can provide a set of shipping options for the user to select from.
The UI will require the user to input an address to ship to which you listen to when it changes and accept or deny the address as valid.
Can Make Payment
You can call paymentRequest.canMakePayment()
before calling paymentRequest.show()
to determine if the PaymentRequest object can be used to make a payment.
paymentRequest.canMakePayment() Result
If you call canMakePayment()
, the result will be printed below.
Press the "Can Make Payment" button.
Buy Now
When you are ready to request payment from the user, you call paymentRequest.show()
, which is what
will happen when you click "Buy Now" below.
paymentRequest.show() Result
If you complete a "purchase" with the payment request API, the result will be printed below.
Press the "Buy Now" button.


PLANET SICK-BOY: http://www.sick-boy.com
With his latest film He Got Game, writer/director Spike Lee has crafted a film that's both lovely and enjoyable to watch as his previous effort, Girl 6. Like Girl 6, Lee has chosen a subject typically worthy of an eighty-five minute picture and fashioned it into a sweeping epic.
Pssssst…I'm joking. He Got Game is a long, boring, meandering, dog of a movie. One might compare watching it to walking forty acres with a mule, but I see it as more of pissing-a-kidney stone with a side order of numb-ass.
The main story is simple enough to barely fill an after school special – jailed Jake Shuttlesworth (Washington) gets a one week release from prison to persuade his estranged son and top basketball recruit, Jesus (NBA's Ray Allen), to attend the Governor's alma mater in an attempt to win a chance at early parole. Since Jake is in the big house for killing Jesus' mom, his task seems a formidable one.
Among other prominent problems, Lee takes over two hours to lead the viewer to the glaringly obvious one-on-one game between Jake and Jesus. In fact, the two only have about four scenes together. So what do you fill the two hours with?  There's also Jesus' relationship with plotting girlfriend, Lala (Rosario Dawson). But mostly there is a whole lot of Oliver Stone-style flashbacks and editing, teamed with a nails-across-the-blackboard score by Aaron Copeland.
 And there is a vomiting scene, too. But these are not nearly enough to make Game worthy of renting, let alone shelling out green for.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


| Main Page | My Favorite Things | Pet Page |
Isabelle is a dachshund/terrier mix. She loves to cuddle and bark at strange noises.
We adopted her on 1/28/17 and she has been a great addition to our family ever since.
Tulip is a dachshund/terrier mix. She loves to play with other dogs and run arond the house.
Her favorite playmate is Isabelle.


Aurobindo Pharma gets 14 observations from the USFDA for Hyderabad plant
The observations issued by the USFDA notifies the company's management of objectionable conditions at the inspected facility
by Press Trust of IndiaAurobindo Pharma has received 14 observations from the US health regulator for its Hyderabad-based plant, according to a regulatory filing.
The US Food and Drug Administration (USFDA) inspected company's Unit IV, a general injectable formulation manufacturing facility situated at Pashamylaram, Hyderabad from November 4-13, Aurobindo Pharma said.
"At the end of the inspection, we have been issued a Form 483 with 14 observations," it added.
The company believes that none of the observations are related to data integrity issues and, it will respond to the USFDA within the stipulated timeline, Aurobindo Pharma said.
The observations issued by the USFDA notifies the company's management of objectionable conditions at the inspected facility.


This paragraph has text in spans.
E1. Eat space
E2. Eat space
E3. Eat space
E4. Eat space
E5. Eat space
E6. Eat space
K1. Keep space
K2. Keep space
K3. Keep space
K4. Keep space
K5. Keep space
K6. Keep space
K7. Keep space
K8. Keep space


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That's right. Motherfucking albums, with your motherfucking pictures. Also, this ain't Pinterest.
Is it Pinterest though?Our albums have thumbnails, and your pictures could be in them, like this:
Jokes that aren't funny:
The husband puts, "Mypenis," and the wife falls on the ground laughing because on the screen it says, "Error. Not long enough."
"Because I heard my daddy tell my mommy, 'I am going to eat that p*ssy once Jimmy leaves for school today!'".
Husband: "ABCDEFGHIJK."
Wife: "What does that mean?"
Husband: "Adorable, beautiful, cute, delightful, elegant, fashionable, gorgeous, and hot."
Wife: "Aw, thank you, but what about IJK?"
Husband: "I'm just kidding!"
So... is this like imgur?
Ummm, is it digg then?
Looks like Google+ Photos to me.


JBL release a portable speaker with a 3D light show, the Pulse 4
by Scott PlowmanJBL have made some great portable speakers in recent times and today they have expanded and updated their lineup with a new Bluetooth speaker with some funky lights, the Pulse 4.
Taking over from the Pulse 3 the Pulse 4 also offers 12 hours of wireless Bluetooth playback and IPX7 waterproofing with 360 degrees of sound and light show.
The new Pulse 4 differs from the Pulse 3 with its sound. The sound is improved further with a new PartyBoost mode which allows multiple speakers to be connected to each other creating a louder, fuller sound. The new louder and better sound can be controlled via the JBL Connect+ app.
The Pulse 4 brings a full device speaker and light show and is visually stunning. For those who enjoy an outdoor party this is something that could light up the party making it a memorable night. The light show is provided by the 360 degree high-resolution LED lights underneath the surface of the device.
The new JBL Pulse 4 retails for $299.95 from leading retailers now and online at the JBL website from December 19.


Note to self on catching exceptions in multithreaded Haskell code. Literate Haskell source and build scripts and cabal stuff is at https://github.com/carlohamalainen/playground/tree/master/haskell/exceptions-in-parallel.
For my use cases there are two scenarios when running a list of worker threads:
First, imports that we’ll use:
> {-# LANGUAGE DeriveDataTypeable, ScopedTypeVariables #-}
>
> module Main where
>
> import Data.Conduit
> import Data.Conduit.List
> import Data.Traversable (traverse)
> import Control.Applicative
> import Control.Monad.Catch
> import Control.Concurrent
> import Control.Concurrent.Async
> import Control.Concurrent.ParallelIO.Local
> import Control.Monad hiding (mapM, mapM_)
> import Control.Monad.Catch
> import Data.Typeable
> import Prelude hiding (map, mapM, mapM_)
> import System.IO
We will use code from parallel-io and async for running worker threads. For a pipeline we’ll also use conduit.
Here’s our exception type, which we throw using throwM
from Control.Monad.Catch:
> data MyException = MyException String deriving (Show, Typeable)
>
> instance Exception MyException
Our two tasks. The first task immediately throws an exception; the second waits for 5 seconds and completes happily.
> task1 :: IO String
> task1 = throwM $ MyException "task1 blew up"
>
> task2 :: IO String
> task2 = do
> threadDelay $ 5 * 10^6
> return $ "task2 finished"
Example: parallel_
> main1 :: IO ()
> main1 = do
>
> x <- withPool 2 $ \pool -> parallel_ pool [task1, task2]
> print (x :: ())
Output:
*Main> main1 *** Exception: MyException "task1 blew up"
Example: parallelE_
> main2 :: IO ()
> main2 = do
>
> x <- withPool 2 $ \pool -> parallelE_ pool [task1, task2]
> print x
Output:
*Main> main2 [Just (MyException "task1 blew up"),Nothing]
Example: parallel
> main3 :: IO ()
> main3 = do
> x <- withPool 2 $ \pool -> parallel pool [task1, task2]
> print x
Output:
*Main> main3 *** Exception: MyException "task1 blew up"
Example: parallelE
> main4 :: IO ()
> main4 = do
> x <- withPool 2 $ \pool -> parallelE pool [task1, task2]
> print x
Output:
*Main> main4 [Left (MyException "task1 blew up"),Right "task2 finished"]
Example: async/wait
> main5 :: IO ()
> main5 = do
> a1 <- async task1
> a2 <- async task2
> result1 <- wait a1
> result2 <- wait a2
>
> print [result1, result2]
Output:
*Main> main5 *** Exception: MyException "task1 blew up"
Example: async/waitCatch
> main6 :: IO ()
> main6 = do
> a1 <- async task1
> a2 <- async task2
> result1 <- waitCatch a1
> result2 <- waitCatch a2
>
> print [result1, result2]
Output:
*Main> main6 [Left (MyException "task1 blew up"),Right "task2 finished"]
Example: concurrently
> main7 :: IO ()
> main7 = do
> result <- concurrently task1 task2
>
> print result
Output:
*Main> main7 *** Exception: MyException "task1 blew up"
Example: throwM in a conduit sink
> main8 :: IO ()
> main8 = do
> sourceList [1..5] $$ (throwM $ MyException "main8 in conduit exploded")
> print "this is never printed"
Output:
*** Exception: MyException "main8 in conduit exploded"
Example: throwM in a conduit sink (on one value)
> main9 :: IO ()
> main9 = do
>
> let foo x = if x == 3 then throwM $ MyException "got a 3 in main9"
> else print x
>
> sourceList [1..5] $$ (mapM_ foo)
> print "this is never printed"
The conduit processes values 1 and 2, throws an exception on 3, and never sees 4 and 5.
*Main> main9 1 2 *** Exception: MyException "got a 3 in main9"
Example: throwM/catchC
> main10 :: IO ()
> main10 = do
>
> let foo x = if x == 3 then throwM $ MyException "got a 3 in main10"
> else print x
>
> let sink = catchC (mapM_ foo)
> (\(e :: SomeException) -> mapM_ $ \x -> putStrLn $ "When processing " ++ show x ++ " caught exception: " ++ show e)
>
> sourceList [1..5] $$ sink
> print "main10 finished"
The output is not what I expected. Values 1 and 2 are processed as expected, then the 3 throws an exception, but the effect of catchC
is that the rest of the values (4 and 5) are processed using the second argument to catchC
. In this situation, a conduit can’t be used to process a stream with independently failing components. You have to catch all exceptions before they bubble up to the conduit code.
1 2 When processing 4 caught exception: MyException "got a 3 in main10" When processing 5 caught exception: MyException "got a 3 in main10" "main10 finished"
Example: catchAll in conduit
A combinator that runs an IO action and catches any exception:
> catchBlah :: Show a => (a -> IO ()) -> a -> IO ()
> catchBlah action = \x -> catchAll (action x)
> (\(e :: SomeException) -> putStrLn $ "On value " ++ show x ++ " caught exception: " ++ show e)
Using catchBlah
in the sink:
> main11 :: IO ()
> main11 = do
>
> let foo x = if x == 3 then throwM $ MyException "got a 3 in main11"
> else print x
>
> sourceList [1..5] $$ (mapM_ $ catchBlah foo)
>
> print "main11 finished"
Now the conduit processes every value, because the exception is caught and dealt with at a lower level.
*Main> main11 1 2 On value 3 caught exception: MyException "got a 3 in main11" 4 5 "main11 finished"
Example: catchBlah’ in conduit
Now, suppose we have a few stages in the conduit and the first stage blows up. Use catchAll
to catch the exception and return a IO (Maybe b)
instead of IO b
:
> catchBlah' :: Show a => (a -> IO b) -> a -> IO (Maybe b)
> catchBlah' action = \x -> do
> catchAll (action x >>= (return . Just))
> (\(e :: SomeException) -> do putStrLn $ "On value " ++ show x ++ " caught exception: " ++ show e
> return Nothing)
> main12 :: IO ()
> main12 = do
>
> let src = [1..5] :: [Int]
>
> let stage1 x = do when (x == 3) $ throwM $ MyException "Got a 3 in stage1"
> putStrLn $ "First print: " ++ show x
> return x
>
> sourceList src $$ (mapM $ catchBlah' stage1) =$= (mapM_ print)
>
> print "main12 finished"
Output:
First print: 1 Just 1 First print: 2 Just 2 On value 3 caught exception: MyException "Got a 3 in stage1" Nothing First print: 4 Just 4 First print: 5 Just 5 "main12 finished"
Example: catchBlah’ in conduit (tweaked)
Same as the previous example but with nicer printing in the sink:
> main13 :: IO ()
> main13 = do
>
> let src = [1..5] :: [Int]
>
> let stage1 x = do when (x == 3) $ throwM $ MyException "Got a 3 in stage1"
> putStrLn $ "First print: " ++ show x
> return x
> stage2 x = case x of
> Just x' -> do putStrLn $ "Second print: " ++ show (x' + 1)
> putStrLn ""
> Nothing -> do putStrLn $ "Second print got Nothing..."
> putStrLn ""
>
> sourceList src $$ (mapM $ catchBlah' stage1) =$= (mapM_ stage2)
>
> print "main13 finished"
Output:
*Main> main13 First print: 1 Second print: 2 First print: 2 Second print: 3 On value 3 caught exception: MyException "Got a 3 in stage1" Second print got Nothing... First print: 4 Second print: 5 First print: 5 Second print: 6 "main13 finished"


SEGA Genesis/MD "Cannon Fodder" Password Generator
Level:
Soldiers Alive:
Password:
Hangs up the game:
*Level_Number < 24, Soldiers_Alive_Quantity < 361;
*If Soldiers_Alive_Quantity > (Level_Number x 15), password will hang up the game;
*If Soldiers_Alive_Quantity < (Soldiers_On_Mission + 1), password will hang up the game.
By
Griever
. [2008]


On the occasion of Hacktoberfest starting from 1st October, a month-long celebration of open source software Mozilla Campus Club is providing a great opportunity for students to improve their competitive coding skills and also to start making open source contributions and get a chance of working in a community.
The session will include an introduction to Git & GitHub and a hands-on session explaining basic git commands and the use of GitHub platform while making open source contributions and projects.
This webinar was conducted by Mr. Prathamesh Chavan, where he guided us on how one can get started with making contributions in both technical and non-technical fields. This beginner-friendly session also covered the ways to explore open-source with Mozilla and other great opportunities.
The speaker for this session was Ankitkumar Singh. The speaker promoted open-source software development. In this session, he guided students on how to get started with web development..
Provided a great opportunity to learn about important topics for ML enthusiasts, the track of Machine Learning and Artificial Intelligence in open source technologies and to learn about the latest Mozilla speech to text recognition technology ..


This page is designed to roll 5 six sided dices. You can click on each one to roll seperately or, for your convenience, you can also press the enter key to roll all dices as well. You can use this this for any game that requires to roll a dice
You can also click the 'Roll All' button to roll all five dices at the same time!


interface IServiceConnector
Connects with a service and returns a Binder through the suspendable getService function
See Also
disconnect |
Disconnect from the service abstract fun disconnect(): Unit |
getService |
Returns the IBinder for the service, by binding to it as needed abstract suspend fun getService(): IBinder |
ServiceConnector |
Implements IServiceConnector class ServiceConnector : IServiceConnector, CoroutineScope |


Bilbao ends Osasuna run of 31 home games without losing
by JOSEPH WILSONBARCELONA, Spain (AP) - Athletic Bilbao won 2-1 at Osasuna in the Spanish league on Sunday, ending the hosts’ club record streak of 31 home games without a loss.
Striker Iñaki Williams struck first for Bilbao in the 21st minute. Chimy Avila leveled in the 76th, only for Kenan Kodro to restore the visitors’ advantage three minutes later.
Bilbao’s first away win of the season lifted it into fifth place - five points behind leader Barcelona and second-place Real Madrid.
Osasuna hadn’t lost at its El Sadar Stadium in Pamplona since a second-tier defeat to Tenerife on April 1, 2018. From that loss it went unbeaten in 24 second-division games and seven home games this season.
Also, China forward Wu Lei scored to salvage a 1-1 draw for Espanyol at home with Getafe.
Sevilla visits Valladolid later Sunday, while Villarreal hosts Celta Vigo and Alavés is at Eibar.
More AP soccer https://apnews.com/Soccer and https://twitter.com/AP_Sports
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Copyright © 2019 The Washington Times, LLC.
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Garden is a modern, trendy and stylish Ghost Theme. We have developed Garden as simple as possible with a highly user experience so that you can create and facelift and your site easily. Garden is suitable for Blogging,autobiography. However the possibilities have no limit.
Garden/ ├── assets/ │ ├── css/ │ │ ├── bootstrap.min.css │ │ ├── custom.css │ │ └── font-awesome.min.css │ │ ├── main.css │ │ └── responsive.css │ ├── fonts/ │ │ ├── fontawesome-webfont.eot │ │ ├── fontawesome-webfont.svg │ │ ├── fontawesome-webfont.woff │ │ └── fontawesome-webfont.ttf │ ├── images/ │ │ ├── logo.png │ │ │ │ ├── js/ │ │ ├──jquery-2.1.0.min.js │ │ ├──main.js │ ├── less/ │ │ ├── main.less │ │ ├── responsive.less ├── partials/ │ ├── footer/ │ │ ├── tx-footer.hbs │ ├── home-page/ │ │ ├── tx-mainbody.hbs │ │ ├── tx-showcase.hbs │ ├── post-page/ │ │ ├── tx-mainbody.hbs │ ├── widgets/ │ │ ├── tx-disqus.hbs │ │ ├── tx-post-header.hbs │ ├── static-page/ │ │ ├── tx-mainbody.hbs │ │ ├── tx-showcase.hbs │ ├── pagination.hbs ├── author.hbs ├── default.hbs ├── index.hbs ├── package.json ├── page.hbs ├── post.hbs
If you want to replicate the Garden demo site, there are 2 ways to do this, by using GhostData. We provide the data source we use on Garden demo side inside "Demobuilder" directory. Unzip Garden-demobuilder.zip and you will a GhostData.json inside,To import GhostData.json to your new Ghost installation, please follow these steps:
1 - Navigate to http://www.my-blog-site.com/ghost/debug/ . Please replace www.my-blog-site.com with your actual site url. You might need to login with your administrator details.
2 - Press the red Delete button. This will delete all posts and tags from your Ghost database. 3 - Now, browse GhostData.json and press Import button, to load all Ghostion demo data and configurations
Upload the new logo via your Ghost dashboard Settings - General - Blog Logo.
You can skip this step if you are happy with the default color or style of Garden. However, if you would like to do some minor change, you can use the custom stylesheet. The following guide will take you through the steps needed to load the custom stylesheet: 1 - Inside your Garden theme folder, find the following code in /garden/default.hbs .
2 - Remove the block to make your theme load the custom.css file.
stylesheet. 3 - Inside your Garden theme folder, find /Garden/assets/css/custom.css . You can add your custom rules and properties in that
We are using the google web font, if you would like to font for the Garden, open file assets/css/main.css, change google web font css url at line 1 and change font-family at line 3,10,12.
In this section you can change Social Network Icons on the bottom or author page ..
Open File: partials/home-page/tx-showcase.hbs and edit the item,Its for homepage
for the single post page just open partials/widgets/tx-post-header.hbs edit the item;


Trump Locked Out Of White House After Accidentally Revoking Own Security Clearance
Growing increasingly flustered while impotently pounding on the front door and pleading to come inside, President Trump was reportedly locked out of the White House Thursday after accidentally revoking his own security clearance.
Read More...


Material Design Extensions features styles and attached properties for styling a WPF TabControl
to match the tabs styles in the
Material Design specification. The following predefined
styles are available:
MaterialDesignTabControl
MaterialDesignAppBarTabControl
MaterialDesignAppBarAccentTabControl
Styling a TabControl
in Material Design is as easy as applying a style to a control in XAML:
<TabControl Style="{StaticResource MaterialDesignTabControl}">
<TabItem Header="ANIMALS"></TabItem>
<TabItem Header="PLANTS"></TabItem>
<TabItem Header="VEHICLES"></TabItem>
</TabControl>


{% block style %} {% endblock %} {% block navbar %} Sign In Sign Up {% endblock %} {% block content %} Secure your documents with the thing that makes you unique - your face! How does it work? Create an Account When you sign up, you will be asked to upload a picture of yourself. This will act as your "password." On Our End To login you have to upload any picture that shows your face, our face recognition software checks to make sure its you! Upload your Documents All uploaded documents are saved in our database so that you can access them anytime, safely and securely! {% endblock %}
When you sign up, you will be asked to upload a picture of yourself. This will act as your "password."
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"STEM"...
A central part of something from which other parts grow;
A fundamental line from which others have arisen;
To restrain oneself
Copyright © 2014 Stem Capital Partners. All rights reserved. | Disclaimer


Asset Description¶
This section describes how a large-scale building inventory was constructed in two phases. The initial phase of this work involved identifying the attributes needed. The second phase comprised obtaining these attributes for each building using machine learning and computer vision techniques to create a core set of attributes. The remaining attributes were then obtained using other data sources as discussed below.
Phase I: Attribute Definition¶
All the attributes required for loss estimation were first identified to develop the Building Inventory
data model. This Building Inventory data model presented in tab-bldg_inv_data_model_ac
provides a set of attributes that are assigned to each asset to form the building inventory file
serving as input to the workflow. For each attribute a row in the table is provided. Each row has a number
of columns: the attribute name, description, format (such as alphanumeric, floating-point number), the data source used to define that attribute.
An expanded version of tab-bldg_inv_data_model_ac
with the full details of this data
model is available on DesignSafe PRJ-3314.
Attribute |
Description |
Format |
|---|---|---|
BldgID |
Building unique ID. The first four characters will be “NJBF,” followed by a 9-digit zero-padded number. |
NJBF######### |
Address |
Typically assessor field for property location. This is distinct from Street Address in MODIV which is associated with the billing address |
Alphanumeric |
City |
Typically assessor field for property location city. |
Alphanumeric |
State |
Typically assessor field for property location - state abbreviation. |
Alphanumeric |
Latitude |
Latitude of the Building Centroid (inside polygon). |
Floating point number (Decimal Degrees) |
Longitude |
Longitude of the Building Centroid (inside polygon). |
Floating point number (Decimal Degrees) |
OccupancyClass |
Subclassifications of buildings across various categories of Residential (RES), Commercial (COM), Industrial (IND), Agriculture (AGR), Government (GOV), Education (EDU), Religious/Non-Profit (REL). |
Choices: RES1, RES2, RES3A, RES3B, RES3C, RES3D, RES3E, RES3F, RES4, RES5, RES6, COM1, COM2, COM3, COM4, COM5, COM6, COM7, COM8, COM9, COM10, IND1, IND2, IND3, IND4, IND5, IND6, AGR1, REL1, GOV1, GOV2, EDU1, EDU2 |
BuildingType |
Core construction material type; Wood, Concrete, Steel, Masonry, Manufactured Housing. |
Choices: 3001, 3002, 3003, 3004, 3005 |
UseCode |
Class 4 Use Codes (Field 67) applicable to commercial buildings only, describing specific use of commercial properties. |
Integer (3-digit) |
BldgClass |
Building class according to NJ Appraisal Manual (see Vol 2) |
Integer (3-digit) |
EssentialClass |
Designates several classes of essential facilities in the region based on open data to ensure they are properly modeled. |
Choices: PS, EOC, HO, HS, SCH |
DesignLevel |
Classification of level of engineering used in design process: Choices: Engineered (E), Pre-Engineered, (PE) Marginally Engineered (ME), Non-Engineered (NE) |
Choices: E, PE, ME, NE |
YearBuiltNJDEP |
Assessor-provided Year of Construction from NJDEP Footprints |
Integer (4-digit) |
YearBuiltMODIV |
Assessor-provided Year of Construction from MODIV |
Integer (4-digit) |
NumberofStories0 |
Assessor-provided number of stories |
Integer |
NumberofStories1 |
Number of stories estimated via image processing |
Integer |
NoUnits |
Number of units in the property (commercial or residential) |
Integer |
PlanArea0 |
Plan area in square feet from assessor databases |
Floating point number |
PlanArea1 |
Plan area in square feet from footprint data |
Floating point Number |
FoundationType |
Classification using 7 types referenced by flood model |
Integer (4-digit) |
SplitLevel |
Specifies if residential construction is split-level |
Choices: Yes, No |
ElevationR0 |
Elevation of the bottom plane of the roof (lowest edge of roof line) relative to grade (ft) |
Floating point number |
ElevationR1 |
Elevation of highest point of the roof (peak of gable or apex of hip) relative to grade (ft) |
Floating point Number |
FirstFloorHt0 |
Height above grade estimated from elevation certificate or inferred from foundation type (in feet): Defined as top of lowest/bottom floor |
Floating point Number |
FirstFloorHt1 |
Height of top of floor as estimated from base of door height above grade, based on streetview imagery (in feet) |
Floating point Number |
FloodZone |
Flood zone specified on FEMA FIRM based on building location |
Choices: 6101, 6102, 6103, 6104, 6105, 6106, 6107, 6108, 6109, 6110, 6111, 6112, 6113, 6114, 6115, 6199 |
DWSI |
DesignWindSpeed I in mph |
Floating point number |
DWSII |
DesignWindSpeed II in mph |
Floating point number |
DWSIII |
DesignWindSpeed III in mph |
Floating point number |
DWSIV |
DesignWindSpeed IV in mph |
Floating point number |
WindZone |
HAZUS-defined Wind Zone (For Manuactured homes, based on HUD Designation) |
Choices: I, II, III |
AvgJanTemp |
Average temperature in January below or above critial value of 25F. |
Choices: Above, Below |
RoofShape |
Roof classified into equivalent hip, gable or flat |
Choices: Hip, Gable, Flat |
RoofSlope |
Slope of roof covering the majority of the dwelling |
Floating point number |
RoofCover |
Roof covering, specified only for residential construction. |
Choices: 5701, 5702, 5703, 5704 |
MeanRoofHt |
Mean height of roof system in ft |
Floating point number |
WindowArea |
Percentage of walls defined by window openings |
Floating point number (decimal<1) |
Garage |
Assessor-provided type of garage. |
Floating point number |
TerrainRoughness |
HAZUS-defined terrain classifications (x100) based on LULC data |
Choices: 3, 15, 35, 70, 100 |
AnalysisDefault |
Defines the default level of fidelity for analysis |
Choices: 1, 2, 3 |
AnalysisAdopted |
Defines the adopted level of fidelity for analysis |
Choices: 1, 2, 3 |
Modifications |
Record of manual updates, corrections or modifications to record |
Alphanumeric (freeform) |
HazusClass-W |
Hazus building classes as defined for wind hazards |
CHOICES: WSF1, WSF2, WMUH1, WMUH2, WMUH3, WMUH1NE, WMUH2NE, WMUH3NE, WMUH4NE, MSF1, MSF2, MMUH1, MMUH2, MMUH3, MLRM1, MLRM2, MLRI, MERBL, MERBM, MERBH, MECBL, MECBM, MECBH, MMUH1NE, MMUH2NE, MMUH3NE, CERBL, CERBM, CERBH, CECBL, CECBM, CECBH, SPMBS, SPMBM, SPMBL, SERBL, SERBM, SERBH, SECBL, SECBM, SECBH, MHPHUD, MH76HUD, MH94HUD-I, MH94HUD-II, MH94HUD-III, HUEFFS, HUEFSS, HUEFSM, HUEFSL, HUEFHS, HUEFHM, HUEFHL, HUEFPS, HUEFEO |
RoofSystem |
Underlying roof structure, applies only to masonry buildings |
Choices: Wood, OWSJ |
HazardProneRegion |
Defines Hazard Prone Regions (HPR) for the purposes of Hazus wind vulnerability assignments for WSF1-2 |
Choices: yes, no |
WindBorneDebris |
Defines Wind Borne Debris (WBD) for the purposes of Hazus wind vulnerability assignments for WSF1-2 |
Choices: yes, no |
SecondaryWaterResistance |
Defines Secondary Water Resistance (SWR) for the purposes of Hazus wind vulnerability assignments for WSF1-2, WMUH1-3, MSF1-2, MMUH1-3 |
Choices: yes, no |
RoofCover |
Defines roof cover for the purposes of Hazus wind vulnerability assignments for WMUH1-3, MMUH1-3, MERBL-M-H, MECBL-M-H, MLRI, MLRM1, MLRM2, SERBL-M-H, SECBL-M-H, CECBL-M-H, CERBL-M-H and Fire Stations (HUEFFS), Elementary Schools (HUEFSS), 2-story High School (HUEFSM) and 3-story High School (HUEFSL) and Hospitals (small - HUEFHS, medium - HUEFHM, large - HUEFHL) and Police Stations (HUEFPS), Emergency Operation Centers (HUEFEO) |
Choices: N/A, BUR, SPM |
RoofQuality |
Defines roof cover quality for the purposes of Hazus wind vulnerability assignments for WMUH1-3, MMUH1-3, MLRI |
Choices: N/A, poor, good |
RoofDeckAttachmentW |
Defines Roof Deck Attachment (RDA) for wood for the purposes of Hazus wind vulnerability assignments for WSF1-2, WMUH1-3, MMUH1-3, MSF1-2, MLRM1, MLRM2 |
Choices: A, B, C, D |
RDA-OWSJ |
Defines Roof Deck Attachment (RDA) for OWSJ for the purposes of Hazus wind vulnerability assignments for MSF1-2 |
Choices: smtl standard, smtl superior, cshl standard, cshl superior |
RoofToWallConnection |
Defines Roof to Wall Connection (R2WC) for the purposes of Hazus wind vulnerability assignments for WSF1-2, WMUH1-3, MMUH1-3, MSF1-2, MLRM1, MLRM2 |
Choices: strap, toe-nail |
Shutters |
Defines use of window opening protection for the purposes of Hazus wind vulnerability assignments for WSF1-2, WMUH1-3, MMUH1-3, MSF1-2, MERBL-M-H, MECBL-M-H, MMUH1-3,MLRM1, MLRM2, SERBL-M-H, SECBL-M-H, CECBL-M-H, CERBL-M-H, SPMBS-M-L, MH94HUDI-II-III, MH76HUD, MHPHUD and Fire Stations (HUEFFS), Elementary Schools (HUEFSS), 2-story High School (HUEFSM) and 3-story High School (HUEFSL) and Hospitals (small - HUEFHS, medium - HUEFHM, large - HUEFHL) and Police Stations (HUEFPS), Emergency Operation Centers (HUEFEO) |
Choices: yes, no |
AugmentedGarage |
Defines presence of attached garage for the purposes of Hazus wind vulnerability assignments for WSF1-2, MSF1-2 |
Choices: none, SFBC 1994, standard, weak |
MasonryReinforcing |
Defines presence of reinforcement in masonry walls for the purposes of Hazus wind vulnerability assignments for MSF1-2, MLRI, MLRM1, MLRM2, MMUH1-3 |
Choices: yes, no |
OWSJ-r |
Defines property of OWSJ required for Hazus wind vulnerability assignments for MSF1-2 |
Choices: cshl, smtl |
RoofDeckAttachmentM |
Defines Metal Roof Deck Attachment (RDA) for purposes of Hazus wind vulnerability assessments for MLRI, MERBL-M-H, MECBL-M-H, MLRM1, MLRM2, SERBL-M-H, SECBL-M-H, SPMBS-M-L and Fire Stations (HUEFFS), Elementary Schools (HUEFSS), 2-story High School (HUEFSM) and 3-story High School (HUEFSL) and Hospitals (small - HUEFHS, medium - HUEFHM, large - HUEFHL) and Police Stations (HUEFPS), Emergency Operation Centers (HUEFEO) |
Choices: standard, superior |
RoofDeckAge |
Defines roof deck age for the purposes of Hazus wind vulnerability assessments for MLRM1, MLRM2, SPMBS-M-L and Fire Stations (HUEFFS), Elementary Schools (HUEFSS) |
Choices: new/avg, old |
UnitClass |
Defines number of units in strip mall for purposes of Hazus wind vulnerability assessments for MLRM2 |
Choices: single, multi |
JoistSpace |
Defines joist spacing for multi-unit strip malls for purposes of Hazus wind vulnerability assessments for MLRM2 |
Choices: N/A, 4, 6 |
WindDebris |
Defines likely sources of wind debris for purpose of Hazus wind vulnerability assessments for MERBL-M-H, MECBL-M-H, MLRM1, MLRM2, SERBL-M-H, SECBL-M-H, CECBL-M-H, CERBL-M-H and Fire Stations (HUEFFS), Elementary Schools (HUEFSS), 2-story High School (HUEFSM) and 3-story High School (HUEFSL) and Hospitals (small - HUEFHS, medium - HUEFHM, large - HUEFHL) and Police Stations (HUEFPS), Emergency Operation Centers (HUEFEO) |
Choices: Res/Comm, Varies by Direction, Residential, None, A, B, C, D |
WindowAreaRatio |
Defines window to wall ratio (WWR) for purpose of Hazus wind vulnerability assessments for MERBL-M-H, MECBL-M-H, SERBL-M-H, SECBL-M-H, CECBL-M-H, CERBL-M-H and Police Stations (HUEFPS), Emergency Operation Centers (HUEFEO) |
Choices: low, medium, high |
TieDowns |
Defines use of ties to connect mobile homes to foundations per HUD guidelnes for purpose of Hazus wind vulnerability assessments for MH94HUDI-II-III, MH76HUD, MHPHUD |
Choices: yes, no |
HazusClass-IN |
Hazus building classes as defined for inundation (flooding) |
Choices: SF1XA, SF1XV, SF2XA, SF2XV, SF2BA, SF2BV, SF2SA, SF2SV, MH, APT, HOT, NURSE, RETAIL, WHOLE, SERVICE, OFFICE, BANK, HOSP, MED, REC, THEAT, GARAGE, INDH, INDL, CHEM, PROC, CONST, AGRI, RELIG, CITY, EMERG, SCHOOL |
HazusClass-WA |
Hazus building classes as defined for wave action |
Choices: W1, W2, W3, MC1, MC2, MC3, S1, S2, S3, MH |
FloodType |
Assignment to flood zones as defined for Hazus damage/loss descriptions |
Choices: Riverine/A-Zone, Coastal/A-Zone, Coastal/V-Zone |
FirstFloorElev |
Assignment of first floor height as defined by Hazus |
Floating Point Number |
PostFIRM |
Assignment of FIRM phasing as defined by Hazus |
Choices: Yes, No |
NumberofStories |
Initalizing number of stories for Hazus analysis |
integer |
BasementType |
Assignement of basement type for Hazus analysis |
Choices: Basement, Split-Level Basement, No Basement |
OccupancyType |
Assignment of Occupancy type for Hazus analysis |
Choices: SF1XA, SF1XV, SF2XA, SF2XV, SF2BA, SF2BV, SF2SA, SF2SV, MH, APT, HOT, NURSE, RETAIL, WHOLE, SERVICE, OFFICE, BANK, HOSP, MED, REC, THEAT, GARAGE, INDH, INDL, CHEM, PROC, CONST, AGRI, RELIG, CITY, EMERG, SCHOOL |
Duration |
Assignment of storm suration for Hazus Analysis |
Short, Long |
WaveVelocity |
Definition of wave velocity in ft/s for Hazus Analysis |
Floating Point Number |
Phase II: Inventory Generation¶
This section describes how the large-scale building inventory was constructed for Atlantic County using a phased approach that used machine learning, computer vision algorithm and data distributions to generate all attributes required for the corresponding loss assessment. It is emphasized that the intent is to demonstrate how an inventory could be constructed and not to address potential errors, omissions or inaccuracies in the source data, i.e., source data are assumed to be accurate, and no additional quality assurance was conducted outside of addressing glaring omissions or errors.
For each of the attributes identified in tab-bldg_inv_data_model_ac
,
a description of the attribute and information on how the data was identified and validated is presented.
Phase II: Footprint Selection¶
Inventory development began with the footprint Data generated by the New Jersey Department of Environmental Protection (NJDEP). These NJDEP footprints include flood-exposed properties cataloged in two geodatabases encompassing approximately 453,000 footprints across the entire state:
1. BF_NJDEP_20190612: all building footprints within 1% annual chance (AC) floodplain, as defined by FEMA Flood Insurance Rate Maps (FIRMs).
2. 02pct_20190520 Building_Footprints_02pct: buildings that are not in the first dataset but fall within a 200-ft buffer of the 1% AC floodplain boundary.
These databases were then combined, with only properties within the limits of Atlantic County retained to form the Flood-Exposed Inventory. This inventory was then extended to include other footprints within the county boundaries. Microsoft (MS) Footprint Database was utilized as the primary source of Non-NJDEP footprint polygons. One observed shortcoming of the MS Footprint Database is it incorrectly lumps together the footprints of closely spaced buildings. This issue was resolved by a combination of manual inspections and applying a separate roof segmentation algorithm to the satellite images obtained for the buildings. This resulted in the Atlantic County Inventory.
AI/ML Techniques combined with Computer Vision¶
Most of the key building attributes were generated using SimCenter’s BRAILS. The following
is a brief description of how these attributes were obtained and the utilized methods were validated.
Attribute: NumberOfStories¶
Number of floors of each building is determined using an object detection procedure implemented in BRAILS. A detection model that
can automatically detect rows of building windows was established to generate the image-based detections
of visible floor locations from street-level images. The model was trained on the
EfficientDet-D7 architecture with a dataset of 60,000 images,
using 80% for training, 15% for validation, and 5% testing of the model. In order to ensure faster model
convergence, initial weights of the model were set to model weights of the (pretrained) object detection
model that, at the time, achieved state-of-the-art performance on the
2017 COCO Detection set. For this
specific implementation, the peak model performance was achieved using the Adam optimizer at a learning
rate of 0.0001 (batch size: 2), after 50 epochs. num_stories_detection
shows examples of the
floor detections performed by the model.
For an image, the described floor detection model generates the bounding box output for its
detections and calculates the confidence level associated with each detection
(see num_stories_detection
). A post-processor that converts stacks of neighboring
bounding boxes into floor counts was developed to convert this output into floor counts.
Recognizing an image may contain multiple buildings at a time, this post-processor was
designed to perform counts at the individual building level.
For a random image dataset of buildings captured using arbitrary camera orientations (also
termed in the wild images), the developed floor detection model was determined to capture
the number of floors information of buildings with an accuracy of 86%.
num_stories_vali
(a) provides a breakdown of this accuracy measure for
different prediction classes (i.e. the confusion matrix of model classifications).
It was also observed that if the image dataset is established such that building images
are captured with minimal obstructions, the building is at the center of the image, and
perspective distortions are limited, the number of floors detections were performed at an
accuracy level of 94.7% by the model. num_stories_vali
(b)
shows the confusion matrix for the model predicting on the “cleaned” image data.
In quantifying both accuracy levels, a test set of 3,000 images randomly selected
across all counties of a companion testbed in New Jersey, excluding Atlantic County (site of that
testbed), was utilized.
Attribute: MeanRoofHt¶
The elevation of the bottom plane of the roof (lowest edge of roof line) and elevation of the roof
(peak of gable or apex of hip) are estimated with respect to grade (in feet) from street-level imagery.
These geometric properties are defined visually for common residential coastal typologies in
building_elevation
. The mean height of the roof system is then derived as
the average of these dimensions.
The MeanRoofHt is based on the following AI technique. mean_roof_ht_app
plots the predicted roof height versus the number of floors of the inventory.
As in any single-image metrology application, extracting the building elevations from imagery requires:
Rectification of image perspective distortions, typically introduced during capturing of an image capture.
Determining the pixel counts representing the distances between ends of the objects or surfaces of interest (e.g., for first-floor height, the orthogonal distance between the ground and first-floor levels).
Converting these pixel counts to real-world dimensions by matching a reference measurement with the corresponding pixel count.
Given that the number of street-level images available for a building can be limited and sparsely spaced, a single image rectification approach was deemed most applicable for regional-scale inventory development. The first step in image rectification requires detecting line segments on the front face of the building. This is performed by using the L-CNN end-to-end wireframe parsing method. Once the segments are detected, vertical and horizontal lines on the front face of the building are automatically detected using RANSAC line fitting based on the assumptions that line segments on this face are the predominant source of line segments in the image and the orientation of these line segments change linearly with their horizontal or vertical position depending on their predominant orientation. The Another support vector model implemented for image rectification focuses on the street-facing plane of the building in an image, and, based on the Manhattan World assumption, (i.e., all surfaces in the world are aligned with two horizontal and one vertical dominant directions) iteratively transforms the image such that horizontal edges on the facade plain lie parallel to each other, and its vertical edges are orthogonal to the horizontal edges.
In order to automate the process of obtaining the pixel counts for the ground elevations, a facade segmentation model was trained to automatically label ground, facade, door, window, and roof pixels in an image. The segmentation model was trained using DeepLabV3 architecture on a ResNet-101 backbone, pretrained on PASCAL VOC 2012 segmentation dataset, using a facade segmentation dataset of 30,000 images supplemented with relevant portions of ADE20K segmentation dataset. The peak model performance was attained using the Adam optimizer at a learning rate of 0.001 (batch size: 4), after 40 epochs. The conversion between pixel dimensions and real-world dimensions were attained by use of field of view and camera distance information collected for each street-level imagery.
mean_roof_ht_app
shows a scatter plot of the AI predicted mean roof heights vs AI-predicted number of floors.
A general trend observed in this plot is that the roof height increases with the number of floors,
which is in line with the general intuition.
Attribute: FirstFloorElevation¶
FirstFloorElevation is calculated using the distance between the bottom of the facade mask and the bottom of the fron door of the building. The process of obtaining these masks follow the same approach used for roof mean height and is not repeated here.
Attribute: RoofSlope¶
RoofSlope is calculated as the ratio between the roof height and the roof run. Roof height is obtained
by determining the difference between the bottom plane and apex elevations of the roof as defined in the
Attribute: MeanRoofHt
section. Roof run is determined as half the smaller dimension of the building, as determined from
the dimensions of the building footprint. mean_slope_app
displays the AI-predicted mean roof height versus the
AI-precited roof pitch ratios. As expected, very little correlation between these two parameters are observed.
Attribute: RoofShape¶
The RoofShape is obtained by CityBuilder using the BRAILS Roof shape module. The roof shape module
determines roof shape based on a satellite image obtained for the building. The module uses machine
learning, specifically it utilizes a convolutional neural network that has been trained on satellite
images. In AI/ML terminology the Roof Shape module is an image classifier: it takes an image and
classifies it into one of three categories used in HAZUS: gable, hip, or flat as shown in
roof_shape
. The original training of the AI model utilized 6,000 images obtained from google
satellite imagery in conjunction with roof labels obtained from
Open Street Maps. As many roofs have more complex shapes, a
similitude measure is used to determine which of these roof geometries is the best match to a given roof.
More details of the classifier can be found
here.
The trained classifier was employed here to classify the roof information for Atlantic County.
The performance of the roof shape classifier was validated against two ground truth datasets.
The first is comprised of 125 manually labeled satellite images sampled from OpenStreetMap from
across the US, retaining only those with unobstructed views of building roofs (a cleaned dataset).
The second is 56 residences assessed by StEER for which roof types were one of the three HAZUS classes,
e.g., removing all roofs labeled as “Complex” according to StEER’s distinct image labeling standards.
The validation process is documented
here.
The confusion matrices are presented in roof_shape_vali
. These matrices visually present
the comparison between the predictions and actual data and should have values of 1.0 along the diagonal
if the classification is perfect, affirming the accuracy of the classification by the roof shape classifier.
Attribute: RoofSlope¶
RoofSlope is calculated as the ratio between the roof height and the roof run. Roof height is obtained
by determining the difference between the bottom plane and apex elevations of the roof as defined in the
Attribute: MeanRoofHt
section. Roof run is determined as half the smaller dimension of the building, as determined from
the dimensions of the building footprint. mean_slope_app
displays the AI-predicted mean roof height versus the
AI-precited roof pitch ratios. As expected, very little correlation between these two parameters are observed.
Attribute: WindowArea¶
- The proportion of windows to the overall surface area is not available in inventory and
assessor datasets though required for wind loss modeling. Generally, window area can be assumed based on the building occupancy class given Department of Energy industry databases. This property can also be estimated from street-level imagery, by taking advantage of the window masks generated as part of the segmentation performed when determining building elevations. For this application, window area is defined as a percentage of the total facade area as the ratio of the area of windows masks to the area of the front facade of the building. The underlying assumption is that the proportion of surface area occupied by windows at the front of the building is representative of the amount of window openings on the sides and rear of the building. This enables the ratio calculated for the front face of the building to be used for the whole building. This assumption may hold for single family residential buildings, but possibly not for other commercial construction where street fronts have higher proportions of glass. In lieu of this computer vision approach, users may choose to adopt industry norms for their window areas (see callout box below).
Note
Industry Norms on Window Area: Engineered residential buildings can be assumed to have low window to wall area ratios (WWR) based on the information for Reference Buildings in Baltimore, MD from the Office of Energy Efficiency and Renewable Energy. Reference Buildings were created for select cities based on climate profile; of the available cities, Baltimore is selected since its climate is most similar to Atlantic City, NJ. Office buildings (used as a test case for commercial), have WWR of 33% and apartments (used as a test case for residential) have WWR of 15%.
Note
The process of constructing the Atlantic County Inventory for footprints beyond those in the Flood-Exposed Inventory underscored a number of tasks/issues that are commonly encountered when constructing an inventory in a location with sparse inventory data. Recommended best practices are summarized in Best Practices.
Phase III: Augmentation Using Third-Party Data¶
Attributes were then parsed from third-party data providers to populate all required attributes in the Building
Inventory data model. For the Flood-Exposed Inventory, NJDEP had already enriched these footprints with various
attributes necessary to conduct standard FEMA risk assessments. Specifically, all footprints included a set of
Basic Attributes (tab-basicAttri
). A subset of the data, including Atlantic County, had additional Advanced
Attributes required by HAZUS User Defined Facilities (UDF) Module (tab-udfAttri
) and FEMA Substantial Damage
Estimator (SDE) Tool (tab-sdeAttri
).
Field |
Description |
|---|---|
BldgUniqueID |
Building unique ID. The first four characters will be followed by a 9-digit zero-padded number. |
ParcelID |
ParcelID from MOD-IV. |
LAG |
Lowest Adjacent Grade. |
HAG |
Highest Adjacent Grade. |
AreaSqFt |
Area square feet. |
PamsPIN |
MunicipalityCode_Block_Lot_Qual. |
CentroidX |
X coordinate in State Plane Feet. |
CentroidY |
Y coordinate in State Plane Feet. |
DemoYear |
Year demolished. |
BldgNum |
Unique ID for multiple structures in one lot (PAMS_PIN). |
BldgType |
Tag with domain name (i.e., Apartment, Condo). |
Elevation |
Numeric value. |
ElevUnits |
Units of elevation (i.e., feet). |
County |
County name |
Field |
Description |
|---|---|
BldgUniqueID |
Building unique ID. The first four characters will be followed by a 9-digit zero-padded number. |
Name |
Typically assessor attribute for owner. |
Address |
Typically assessor field for property location. |
City |
Typically assessor field for property location city. |
State |
Typically assessor field for property location - state abbreviation. |
ZipCode |
Typically assessor field for property location Zip Code. |
Contact |
Typically assessor attribute for owner. |
Tract |
Value for Census Tract or an area roughly equal to a neighborhood established by the Census Bureau. |
PhoneNumber |
Typically assessor attribute for owner. |
OccupancyClass |
Residential, Commercial, Industrial, Agriculture, Government, Education, and Religious/Non-Profit. |
BuildingType |
Core construction material type; Wood, Concrete, Steel, Masonry, Manufactured Housing. |
Cost |
Replacement value; assessor data does not often include replacement cost. It is usually derived by considering heated or livable space and multiplied by cost per square foot. |
YearBuilt |
Typically assessor attribute. |
Area |
Heated or livable space. May or may not exist in typically assessor attributes. Can potentially be derived from building footprints. |
NumberofStories |
Typically assessor attribute. Indicates number of stories. |
DesignLevel |
Must have the year built to establish; see Hazus Flood Model User Manual, Table 6.2. |
FoundationType |
Flood model needs to distinguish which of seven types. |
FirstFloorHt |
Flood model needs height (in feet) above grade. Can be based on default values assigned to foundation types or other preferred methods. |
ContentCost |
Can be calculated from formula to be applied to final cost per Hazus Flood Model User Manual. |
BldgDmgFnID |
The damage function ID from Hazus would be entered in this field if anything other than the default was to be used. The damage function is based on the building characteristics defined in the items above. |
ContDmgFnID |
The damage function ID from Hazus would be entered in this field if anything other than the default was to be used. The damage function is based on the building characteristics defined in the items above. |
InvDmgFnID |
The damage function ID from Hazus would be entered in this field if anything other than the default was to be used. The damage function is based on the building characteristics defined in the items above. |
FloodProtection |
Does protection exist, and if yes to what frequency? (e.g., 100-year). |
ShelterCapacity |
Number of persons that can be sheltered. |
BackupPower |
Does backup power exist? [yes (1) or no (0)] |
Latitude |
Latitude of the Building Centroid (inside polygon). |
Longitude |
Longitude of the Building Centroid (inside polygon). |
Comment |
As needed. |
InventoryCost |
Can be calculated from formula to be applied to final cost per Hazus Flood Model User Manual. |
RiverineCoastal |
Can be used to identify if building is in riverine or coastal areas. Can be used to help designate the Damage Function IDs. |
HzSourceCit |
Source Citation details. |
Field |
Description |
|---|---|
BldgUniqueID |
Building unique ID. The first four characters will be followed by a 9-digit zero-padded number. |
StructureType |
Residential or Non-Residential. |
ResidenceType |
Residential Type Only: Single Family Residence, Town or Row House, Manufactured House. |
StructureUse |
Non-Residential Type Only: Apartments, Commercial Retail, Mini-Warehouse, etc. |
FoundationType |
Residential Type Only: Select from domain. |
SuperStructure |
Residential Type Only: Select from domain. |
ExteriorFinish |
Residential Type Only: Select from domain. |
ElevationLowestFloor |
Typically assessor attribute. |
NFIPCommunityID |
Typically assessor attribute. |
NFIPCommunityName |
Typically assessor attribute. |
YearConstruction |
Typically assessor attribute. |
Story |
Select from domain. |
RoofCovering |
Select from domain. |
HVACSystem |
Select from domain. |
Quality |
Select from domain. |
SDESourceCit |
Source citation details. |
FloodZone |
From FIRM. |
FIRMPanelID |
FIRM Panel number from S_FIRM_PAN |
For the Atlantic County Inventory, any building not included in the NJDEP footprints had attributes encompassed
by NJDEP Basic, UDF or SDE fields assigned by parsing New Jersey Tax Assessor Data (called MODIV) ([MODIV]) as defined in
the MODIV User Manual ([MODIV18]). This notably affected attributes such as OccupancyClass, BuildingType and FoundationType,
whose rulesets (PDFs and Python scripts) are cross-referenced in addinfo_ruleset_njdep
.
In all cases where attributes were derived from MODIV data, whose fields can be sparsely populated, default
values were initially assigned to ensure that every footprint would have the attributes required to execute
the workflow. These default values were selected using engineering judgment to represent the most common/likely
attribute expected or conservatively from the perspective of anticipated losses (i.e., picking the more
vulnerable attribute option). These initial assignments were then updated if additional data is available in
MODIV to make a more faithful attribute assignment.
Ruleset Name |
Ruleset Definition Table |
Python script |
|---|---|---|
Building Type Rulesets |
To be released |
|
Foundation Type Rulesets |
To be released |
|
Occupancy Type Rulesets |
To be released |
Some attributes in the Building Inventory Data Model were not encompassed by NJDEP Basic, UDF or SDE fields, thus remaining attributes in both the Flood-Exposed and Atlantic County Inventories were assigned using data from the following third-party sources: 1. Locations of essential facilities were sourced from NJ Office of Information Technology (part of NJGIN Open Data [NJGIN20]) 2. ATC Hazards by Location API ([ATC20]) was used to query Design Wind Speeds as defined in ASCE 7 3. Terrain features (roughness length associated with different exposure classes) was derived from Land Use Land Cover data (part of NJGIN Open Data [NJGIN20])
See the Transformation and Detail columns in the PDFs listed in tab-bldgInventory
for specifics of how each attribute
was assigned using these various third-party data sources.
Populated Inventories¶
Executing this four-phase process resulted in the assignment of all required attributes at the asset description
stage of the workflow for both the Atlantic County Inventory and the Flood-Exposed Inventory.
bldg_inv_ac
and bldg_inv_fei
provide respective examples of each of these inventories.
The Flood-Exposed Inventory then was used to extract out the subset of buildings defining the
Exploration Inventory (see example in bldg_inv_ei
). The full inventories can be downloaded
here.
Longitude |
Latitude |
Address |
City |
BldgClass |
YearBuilt |
NumberofStories1 |
OccupancyClass |
BuildingType |
-74.52 |
39.39 |
368 UPLAND AVE |
ABSECON CITY |
17 |
1986 |
1 |
RES1 |
3001 |
-74.69 |
39.65 |
368 UPLAND AVE |
ABSECON CITY |
17 |
1986 |
1 |
RES1 |
3001 |
-74.52 |
39.39 |
135 TENTH AVE |
ABSECON CITY |
17 |
1988 |
1 |
RES1 |
3001 |
-74.71 |
39.65 |
135 TENTH AVE |
ABSECON CITY |
17 |
1988 |
1 |
RES1 |
3004 |
-74.52 |
39.39 |
358 UPLAND AVE |
ABSECON CITY |
17 |
1986 |
1 |
REL1 |
3004 |
-74.71 |
39.65 |
358 UPLAND AVE |
ABSECON CITY |
17 |
1986 |
1 |
RES1 |
3001 |
-74.53 |
39.39 |
360 UPLAND AVE |
ABSECON CITY |
17 |
1986 |
1 |
RES1 |
3001 |
-74.78 |
39.64 |
360 UPLAND AVE |
ABSECON CITY |
17 |
1986 |
1 |
RES1 |
3001 |
-74.53 |
39.39 |
362 UPLAND AVE |
ABSECON CITY |
17 |
1986 |
1 |
RES1 |
3001 |
Address |
City |
State |
Latitude |
Longitude |
OccupancyClass |
BuildingType |
UseCode |
BldgClass |
DesignLevel |
YearBuiltNJDEP |
YearBuiltMODIV |
NumberofStories0 |
NumberofStories1 |
NoUnits |
PlanArea0 |
PlanArea1 |
FoundationType |
SplitLevel |
ElevationR0 |
ElevationR1 |
FirstFloorHt0 |
FirstFloorHt1 |
FloodZone |
DSWII |
WindZone |
AvgJanTemp |
RoofShape |
RoofSlope |
RoofCover |
RoofSystem |
MeanRoofHt |
WindowArea |
Garage |
z0 |
14 W LEE AVE |
Absecon |
NJ |
39.42 |
-74.5 |
RES1 |
3001 |
17 |
NE |
1956 |
1956 |
3101 |
2 |
1 |
1311 |
1311 |
3505 |
NO |
19.64 |
33.89 |
3 |
-2.97 |
6112 |
122 |
I |
Above |
Gable |
0 |
5701 |
26.8 |
0 |
0 |
0.35 |
||
28 W SUMMIT AVE |
Absecon |
NJ |
39.42 |
-74.5 |
RES1 |
3001 |
17 |
NE |
1948 |
1948 |
3101 |
2 |
1 |
1061 |
1061 |
3505 |
NO |
33.52 |
47.46 |
3 |
6.72 |
6112 |
122 |
I |
Above |
Gable |
0 |
5701 |
40.5 |
0 |
0 |
0.35 |
||
6353 MONMOUTH DRIVE |
Hamilton |
NJ |
39.45 |
-74.75 |
RES1 |
3001 |
17 |
NE |
1954 |
1954 |
3101 |
1 |
1 |
1047 |
1047 |
3507 |
NO |
18.92 |
31.75 |
1 |
2.02 |
6112 |
118 |
I |
Above |
Gable |
0 |
5701 |
25.3 |
0 |
0 |
0.35 |
||
2004 SYCAMORE LANE |
Hamilton |
NJ |
39.53 |
-74.79 |
RES1 |
3001 |
17 |
NE |
1977 |
1977 |
3102 |
2 |
1 |
1234 |
1234 |
3507 |
NO |
31.43 |
43.06 |
1 |
1.96 |
6112 |
117 |
I |
Above |
Hip |
0 |
5701 |
37.2 |
0 |
1.1 |
0.35 |
||
35 PENNINGTON AVENUE |
Hamilton |
NJ |
39.45 |
-74.73 |
RES1 |
3001 |
17 |
NE |
1924 |
1924 |
3103 |
2 |
1 |
936 |
936 |
3505 |
NO |
34.55 |
47.39 |
3 |
6.76 |
6112 |
119 |
I |
Above |
Gable |
0 |
5701 |
41 |
0 |
0 |
0.35 |
||
135 GARNETT LANE |
Egg Harbor |
NJ |
39.38 |
-74.59 |
RES1 |
3001 |
16 |
NE |
2005 |
2005 |
3102 |
2 |
1 |
2579 |
2579 |
3507 |
NO |
29 |
37.43 |
1 |
2.6 |
6112 |
121 |
I |
Above |
Gable |
0 |
5701 |
33.2 |
0 |
0 |
0.35 |
||
122 GARNETT LANE |
Egg Harbor |
NJ |
39.38 |
-74.59 |
RES1 |
3001 |
16 |
NE |
2005 |
2005 |
3102 |
2 |
1 |
2441 |
2441 |
3507 |
NO |
27.38 |
33.19 |
1 |
0.27 |
6112 |
121 |
I |
Above |
Hip |
0 |
5701 |
30.3 |
0 |
0 |
0.35 |
||
332 MONTCLAIR DR |
Pleasantville |
NJ |
39.41 |
-74.5 |
RES3C |
3001 |
16 |
E |
1954 |
1954 |
3303 |
1 |
1 |
1602 |
1602 |
3507 |
NO |
16.67 |
24.08 |
1 |
0.87 |
6112 |
122 |
I |
Above |
Gable |
0 |
20.4 |
0 |
1.1 |
0.35 |
|||
128 N FRANKLIN BLVD |
Pleasantville |
NJ |
39.39 |
-74.52 |
RES1 |
3001 |
15 |
NE |
1943 |
1943 |
3101 |
2 |
1 |
1062 |
1062 |
3505 |
NO |
32.89 |
42.43 |
3 |
8.08 |
6112 |
122 |
I |
Above |
Hip |
0 |
5701 |
37.7 |
0 |
0 |
0.35 |
Address |
City |
State |
Latitude |
Longitude |
OccupancyClass |
BuildingType |
UseCode |
BldgClass |
DesignLevel |
YearBuiltNJDEP |
YearBuiltMODIV |
NumberofStories0 |
NumberofStories1 |
NoUnits |
PlanArea0 |
PlanArea1 |
FoundationType |
SplitLevel |
ElevationR0 |
ElevationR1 |
FirstFloorHt0 |
FirstFloorHt1 |
FloodZone |
DSWII |
WindZone |
AvgJanTemp |
RoofShape |
RoofSlope |
RoofCover |
RoofSystem |
MeanRoofHt |
WindowArea |
Garage |
z0 |
425 N MARYLAND AVE |
Atlantic City |
NJ |
39.37 |
-74.43 |
RES1 |
3001 |
210 |
NaN |
NE |
1969 |
0 |
3102 |
1 |
1 |
906.2940373 |
906.2940373 |
3505 |
NO |
12.09 |
25.16 |
3 |
-0.83 |
6106 |
124 |
I |
Above |
Flat |
0 |
5701 |
Wood |
18.6 |
0 |
0 |
0.03 |
125 N FLORIDA AVE |
Atlantic City |
NJ |
39.36 |
-74.44 |
RES3B |
3001 |
NaN |
ME |
1969 |
0 |
3301 |
2 |
1 |
746.77695 |
746.77695 |
3505 |
NO |
20.8 |
28.38 |
3 |
-1.12 |
6106 |
124 |
I |
Above |
Flat |
0 |
NaN |
Wood |
24.6 |
0 |
0 |
0.03 |
|
600 HURON AVE |
Atlantic City |
NJ |
39.38 |
-74.43 |
GOV2 |
3001 |
NaN |
E |
1969 |
0 |
3401 |
3 |
1 |
1513.12497 |
1513.12497 |
3507 |
NO |
53.51 |
64.92 |
1 |
2.61 |
6102 |
124 |
I |
Above |
Hip |
0 |
NaN |
Wood |
59.2 |
0 |
0 |
0.03 |
|
332 N RHODE ISLAND AVE |
Atlantic City |
NJ |
39.37 |
-74.42 |
IND1 |
3003 |
100 |
NaN |
E |
1969 |
0 |
3401 |
1 |
1 |
1000.334181 |
1000.334181 |
3507 |
NO |
12.94 |
25.08 |
1 |
0.65 |
6106 |
124 |
I |
Above |
Flat |
0 |
NaN |
Wood |
19 |
0 |
0 |
0.03 |
1 ATLANTIC OCEAN |
Atlantic City |
NJ |
39.35 |
-74.43 |
COM8 |
3003 |
738 |
NaN |
ME |
1969 |
0 |
3401 |
1 |
1 |
177963.6629 |
177963.6629 |
3502 |
NO |
23.82 |
31.38 |
5 |
9.26 |
6102 |
124 |
I |
Above |
Flat |
0 |
NaN |
Wood |
27.6 |
0 |
0 |
0.03 |
11 N MORRIS AVE |
Atlantic City |
NJ |
39.35 |
-74.45 |
RES3B |
3001 |
45 |
ME |
1900 |
1900 |
3301 |
2 |
3 |
835.133255 |
835.133255 |
3504 |
NO |
22.28 |
31.19 |
-4 |
0 |
6106 |
124 |
I |
Above |
Hip |
0 |
NaN |
Wood |
26.7 |
0 |
0 |
0.03 |
|
601 N ALBANY AVE |
Atlantic City |
NJ |
39.36 |
-74.46 |
COM1 |
3003 |
NaN |
ME |
1969 |
0 |
3401 |
1 |
1 |
654.0515616 |
654.0515616 |
3507 |
NO |
12.92 |
22.85 |
1 |
0.48 |
6106 |
124 |
I |
Above |
Flat |
0 |
NaN |
Wood |
17.9 |
0 |
0 |
0.03 |
|
1500-1538 ADRIATIC AVE |
Atlantic City |
NJ |
39.37 |
-74.43 |
RES3C |
3004 |
NaN |
ME |
1969 |
0 |
3301 |
1 |
1 |
938.5675163 |
938.5675163 |
3505 |
NO |
22.15 |
30.29 |
3 |
8.37 |
6106 |
124 |
I |
Above |
Gable |
0 |
NaN |
Wood |
26.2 |
0 |
0 |
0.03 |
|
900 BEACH THOROFARE |
Atlantic City |
NJ |
39.38 |
-74.42 |
GOV2 |
3001 |
NaN |
E |
1969 |
0 |
3401 |
2 |
1 |
606.967372 |
606.967372 |
3507 |
NO |
36.33 |
45.23 |
1 |
0.84 |
6106 |
124 |
I |
Above |
Flat |
0 |
NaN |
Wood |
40.8 |
0 |
0 |
0.03 |
Inventory Name |
DesignSafe Document |
Number of Assets |
Typical Run Time |
|---|---|---|---|
Atlantic County Inventory |
100,721 |
~ 1,500 CPU-Hour |
|
Flood-Exposed Inventory |
32,828 |
~ 440 CPU-Hour |
|
Exploration Inventory |
1,000 |
~ 12 CPU-Hour |
- ATC20
ATC (2020b), ATC Hazards By Location, https://hazards.atcouncil.org/, Applied Technology Council, Redwood City, CA.
- NJGIN20(1,2)
NJ Geographic Information Network, State of New Jersey, https://njgin.nj.gov/njgin/#!/
- Wang19
Wang C. (2019), NHERI-SimCenter/SURF: v0.2.0 (Version v0.2.0). Zenodo. http://doi.org/10.5281/zenodo.3463676
- Microsoft2018
Microsoft (2018) US Building Footprints. https://github.com/Microsoft/USBuildingFootprints
- MODIV
Parcels and MOD-IV of Atlantic County, NJ. NJGIN Open Data, https://njogis-newjersey.opendata.arcgis.com/datasets/680b02ff9b4348409a2f4ccd4c238215.
- MODIV18
Department of the Treasury, State of New Jersey (2018), MOD IV User Manual. https://www.state.nj.us/treasury/taxation/pdf/lpt/modIVmanual.pdf


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