I am a software engineer, who enjoys bridging the gap between engineering and design — combining my technical knowledge with my keen eye for design to create a beautiful product.
.
Experience
Data Analyst / Developer
Mediachoice
Extensive Data Analysis using Advanced Excel and SQL.
Advanced Database design and development – PLSQL.
Data Analysis for Billboards related to traffic count on Google Earth/ Google Maps.
Design/Develop Wordpress websites for clients with advanced HTML and CSS.
Advanced Google Analytics Analysis and report dashboards.
SEO and page load optimization for websites.
Manage social media content, marketing, IT and cybersecurity policies, firewall and system security.
February 2018 - Present
Software Engineer
CDK Global
Development and Testing of internal enterprise application tools to deliver to client facing products.
Database SQL development, design, Query Tuning, Procedures, Triggers and Views.
Significant database knowledge and ability to write queries that perform optimally.
Experience working on build and deployment process of various applications using Java, Javascript, SQL, Bamboo.
Hands on experience with full software development life cycle process from dev, staging to production environments.
Experience working in Agile Development.
Experience with version control systems like Git and Subversion.
Experience working in DevOps environment, Business Analysis and Requirement Gathering, Quality Control and Quality Assurance.
Experience working as a scrum master.
January 2016 - August 2017
Software Engineer Intern
CDK Global
Internal application bug fixes, enhancements, reporting and documenting in a Agile environment.
Worked with team through entire process of Software Development Life Cycle including analysis and design, business and functional requirements analysis, data analysis, system design, development, testing and implementation.
Designed, built and tested the core components of the platform and applications.
June 2015 - December 2015
Education
Texas State University
Master of Science
Computer Science
GPA: 3.5
June 2014 - December 2015
Jawaharlal Nehru Technological University
Electrical & Electronics Engineering
GPA: 3.8
August 2008 - May 2012
Skills
Programming Languages & Tools
Java, Javascript, C,C++, HTML, CSS, Unix, Junit, Rest Web Services
SQL, AngularJS,Requirement Gathering, SDLC, API Integration


About Mariah!!!
Mariah is best friends with Darth Vader Their hobbies include :
- taking over the universe
- Fighting jedi
- They both would love to start a study group if others in the class are interested?
Fun facts about Mariah
- She is the best TIE pilot in the universe
- She has a Double Bladed Light saber
- She can fly


Reduce your credit score by an extra 1 per click
Repay Late!
0
Apply for Credit Card
Max score: 650
Because you can never have too many...
Reduce your credit score by an extra 5 per click
Apply!
0
Max Out Credit Card
Max score: 500
Did you really need that many bananas?
Reduce your credit score by an extra 10 per click
Max Out!
0
Hire Monkeys To Apply For More Loans
Max score: 300
Oh, so that's what the bananas were for...
Reduce your credit score by 5 per second
Monkeys!


norfolk-southern
Businessmodel of Norfolk Southern
Customer Segments
Norfolk Southern has a mass market business model, with no significant differentiation between customer groups. The company targets its offerings at firms of all industries and sizes. That said, most of its customers are in the coal, metals & construction, agriculture, chemicals, automotive, and paper/clay/forest sectors.
Value Proposition
Norfolk Southern offers three primary value propositions: innovation, risk reduction, and brand/status.
The company embraces innovation as part of its culture. Its groundbreaking efforts include:
- Building the NS 999, a first-of-its-kind battery-powered switch locomotive
- Creating the “virtual” ethanol inventory system, a software program that tracks the amount of ethanol pumped and shipped for each customer; it is the first of its kind among Class 1 railroads
-
Developing a container door tool that can close the doors of intermodal containers when employees are standing on the ground; it has helped keep freight moving and prevented injuries for employees mounting and dismounting rail equipment The company reduces risk by maintaining high safety standards, as follows:
- It conducts behavior-based safety training for employees, which emphasizes using positive reinforcement in supporting safe workplace habits and continued safety process improvements
- It participates in Operation Lifesaver, a national organization focused on eliminating trespasser incidents and highway-rail grade crossing crashes
- It operates the “Protect the Line” reporting program, which makes it easier for rail fans, businesses, and communities to report issues to the police
- It offers a mobile app for the “Protect the Line” program which also enables reporting The company has established a strong brand due to its success. It operates 20,000 route miles in 22 states and the District of Columbia. It provides service to every major Eastern seaport, 10 river ports, and nine lake ports, and operates the most extensive intermodal network in the East. It transports overseas freight through numerous Atlantic and Gulf Coast ports. Lastly, it has won a number of honors, including recognition as a top Military-Friendly Employer by Victory Media and the E.H. Harriman Award, which recognized railroads with low casualty rates, for 24 years in a row.
Channels
Norfolk Southern’s main channel is its direct sales team. The company promotes its offering through its website, social media pages, and participation in conferences.
Customer Relationships
Norfolk Southern’s customer relationship is primarily of a personal assistance nature. The company helps its customers transport their goods while having limited interactions with them. It also operates the Norfolk Southern Training Center, which is an Accredited Testing Facility (ATF) for the American Welding Society.
The center administers testing programs for procedure qualification and maintenance renewal for welding codes; they are available to customers and the public.
Key Activities
Norfolk Southern’s business model entails developing and delivering its services for customers.
Key Partners
Norfolk Southern’s key partners are the suppliers that provide it with the equipment, material, and services it needs to run its railroad operations. It also maintains public-private partnerships with various government entities through which it has completed major projects.
These include partnering with 13 states to build the Crescent Corridor, a 2,500-mile rail infrastructure from the Gulf Coast to the East Coast; and participating in the Chicago Region Environment And Transportation Efficiency Program (CREATE), a partnership to add freight and passenger capacity and reduce congestion in the metropolitan Chicago area.
Key Resources
Norfolk Southern’s main resources are its physical resources, which include its 4,353 owned and leased locomotives, its 76,641 owned and leased freight cars, and its 64 automotive facilities.
Its Juniata locomotive shop is the largest locomotive repair facility in North America. It also maintains important human resources in the form of the employees that manage its railroad operations.
Cost Structure
Norfolk Southern has a cost-driven structure, aiming to minimize expenses through significant automation and low-price value propositions. Its biggest cost driver is compensation/benefits, a variable expense. Other major drivers are in the areas of purchased services/rents and fuel.
Revenue Streams
Norfolk Southern has one revenue stream: revenues it generates from fees paid by clients for the transportation of their raw materials, intermediate products, and finished goods.


Git-2.26.0
Introduction to Git
Git is a free and open source,
distributed version control system designed to handle everything
from small to very large projects with speed and efficiency. Every
Git clone is a full-fledged
repository with complete history and full revision tracking
capabilities, not dependent on network access or a central server.
Branching and merging are fast and easy to do. Git is used for version control of files, much
like tools such as Mercurial-5.3.1, Bazaar, Subversion-1.13.0, CVS, Perforce, and Team
Foundation Server.
This package is known to build and work properly using an LFS-9.1
platform.
Package Information
-
Download (HTTP):
https://www.kernel.org/pub/software/scm/git/git-2.26.0.tar.xz
-
Download MD5 sum: 251c26b32398db3551c24dc806845855
-
Download size: 5.7 MB
-
Estimated disk space required: 319 MB (with downloaded
documentation)
-
Estimated build time: 0.3 SBU (with parallelism=4; add 2.5
SBU for tests with parallelism=4)
Additional Downloads
Git Dependencies
Recommended
cURL-7.69.1 (needed to use Git over http, https, ftp or ftps)
Optional
pcre2-10.34 (or the deprecated PCRE-8.44), in either case
configured with --enable-jit
,
Subversion-1.13.0 with Perl bindings (for
git svn), Tk-8.6.10 (gitk, a
simple Git repository viewer, uses
Tk at runtime), and Valgrind-3.15.0
Optional (to create the man pages, html docs and other docs)
xmlto-0.0.28 and asciidoc-8.6.9 or
AsciiDoctor,
and also dblatex (for the PDF version
of the user manual), and docbook2x to create info
pages
User Notes: http://wiki.linuxfromscratch.org/blfs/wiki/git
Installation of Git
Install Git by running the
following commands:
./configure --prefix=/usr \
--with-gitconfig=/etc/gitconfig \
--with-python=python3 &&
make
You can build the man pages and/or html docs, or use downloaded
ones. If you choose to build them, use next two following
instructions.
If you have installed asciidoc-8.6.9 you can create the html version
of the man pages and other docs:
make html
If you have installed asciidoc-8.6.9 and xmlto-0.0.28 you can
create the man pages:
make man
The test suite can be run in parallel mode. To run the test suite,
issue: make test. If
run as a normal user, 0 tests should be reported as failed in the
final summary.
Now, as the root
user:
make install
If you created the man pages and/or html docs
Install the man pages as root
user:
make install-man
Install the html docs as root
user:
make htmldir=/usr/share/doc/git-2.26.0 install-html
If you downloaded the man pages and/or html docs
If you downloaded the man pages untar them as the root
user:
tar -xf ../git-manpages-2.26.0.tar.xz \
-C /usr/share/man --no-same-owner --no-overwrite-dir
If you downloaded the html docs untar them as the root
user:
mkdir -vp /usr/share/doc/git-2.26.0 &&
tar -xf ../git-htmldocs-2.26.0.tar.xz \
-C /usr/share/doc/git-2.26.0 --no-same-owner --no-overwrite-dir &&
find /usr/share/doc/git-2.26.0 -type d -exec chmod 755 {} \; &&
find /usr/share/doc/git-2.26.0 -type f -exec chmod 644 {} \;
Reorganize text and html in the html-docs (both methods)
For both methods, the html-docs include a lot of plain text files.
Reorganize the files as the root
user:
mkdir -vp /usr/share/doc/git-2.26.0/man-pages/{html,text} &&
mv /usr/share/doc/git-2.26.0/{git*.txt,man-pages/text} &&
mv /usr/share/doc/git-2.26.0/{git*.,index.,man-pages/}html &&
mkdir -vp /usr/share/doc/git-2.26.0/technical/{html,text} &&
mv /usr/share/doc/git-2.26.0/technical/{*.txt,text} &&
mv /usr/share/doc/git-2.26.0/technical/{*.,}html &&
mkdir -vp /usr/share/doc/git-2.26.0/howto/{html,text} &&
mv /usr/share/doc/git-2.26.0/howto/{*.txt,text} &&
mv /usr/share/doc/git-2.26.0/howto/{*.,}html &&
sed -i '/^<a href=/s|howto/|&html/|' /usr/share/doc/git-2.26.0/howto-index.html &&
sed -i '/^\* link:/s|howto/|&html/|' /usr/share/doc/git-2.26.0/howto-index.txt
Command Explanations
--with-gitconfig=/etc/gitconfig
: This
sets /etc/gitconfig
as the file that
stores the default, system wide, Git settings.
--with-python=python3
: Use this switch
to use Python 3, instead of the
EOL'ed Python 2. Python is used
for the git p4
interface to Perforce repositories, and also used in some tests.
--with-libpcre2
: Use this switch if
PCRE2 is installed and has been
built with the non-default JIT enabled.
--with-libpcre
: As an alternative to
PCRE2, use this switch if the deprecated PCRE is installed and has been built with the
non-default JIT enabled.
tar -xf ../git-manpages-2.26.0.tar.gz
-C /usr/share/man --no-same-owner: This untars
git-manpages-2.26.0.tar.gz
. The
-C
option makes tar change directory to
/usr/share/man
before it starts to
decompress the docs. The --no-same-owner
option stops tar from preserving
the user and group details of the files. This is useful as that
user or group may not exist on your system; this could
(potentially) be a security risk.
mv /usr/share/doc/git-2.26.0
...: These commands move some of the files into
subfolders to make it easier to sort through the docs and find what
you're looking for.
find ... chmod ...:
These commands correct the permissions in the shipped documentation
tar file.
Configuring Git
Config Files
~/.gitconfig
and /etc/gitconfig
Contents
Installed Programs:
git, git-receive-pack and
git-upload-archive (hardlinked to each other), git-cvsserver,
git-shell, git-upload-pack, and gitk
Installed Libraries:
None
Installed Directories:
/usr/share/perl5/Git,
/usr/libexec/git-core and
/usr/share/{doc/git-2.26.0,git-core,git-gui,gitk,gitweb}
Short Descriptions
|
git
|
is the stupid content tracker.
|
|
git-cvsserver
|
is a CVS server emulator for Git.
|
|
gitk
|
is a graphical Git
repository browser (needs Tk-8.6.10).
|
|
git-receive-pack
|
is invoked by git
send-pack and updates the repository with
the information fed from the remote end.
|
|
git-shell
|
is a login shell for SSH accounts to provide restricted
Git access.
|
|
git-upload-archive
|
is invoked by git archive
--remote and sends a generated archive to
the other end over the git protocol.
|
|
git-upload-pack
|
is invoked by git
fetch-pack, it discovers what objects the
other side is missing, and sends them after packing.
|
Last updated on 2020-03-23 15:21:56 -0500


Carbon source reset
(share of circle: cost increase)
Click on a source to limit the analysis to this source. The part of the circle shows the average cost increase over all filtered results.
GHG scenario reset
(Value: GHG increase/decrease)
Click on a scenario to limit the analysis to this scenario. The bar length shows the average GHG increase/decrease over all filtered results. Click to the left of the "zero" for "empty" pathways. Those have no GHG change compared to the reference.
Syngas route
reset
(Value: cost increase)
Click on a syngas route to limit the analysis to this route. The bar length shows the average cost increase over all filtered results.
Costs scenario reset
(Value: cost increase)
Click on a scenario to limit the analysis to this scenario. The bar length shows the average cost increase over all filtered results.
Results
(Relative cost increase over absolute GHG difference. Values below and left of absolute zero are best.)
reset
Each dot represents one source / pathway / cost scenario / GHG scenario / syngas route combination. By clicking on the other charts, you can filter the results. By hovering over a dot, you can see detailed information. By hovering over a pathway in a legend, you can highlight all dots from this pathway.
Pathways reset
(Value: GHG increase/decrease)
Click on a pathway to limit the analysis to this pathway. The bar length shows the average GHG increase/decrease over all filtered results. Click to the left of the "zero" for "empty" pathways. Those have no GHG change compared to the reference.


1 What’s Binary Search?
Binary search is a search algorithm that finds the position of the target value within a given logic list. Binary search compares the target value to the middle of the list. If they are equal, the position of the middle value will be returned. Otherwise, the half which the target value can’t lie in will be eliminated, again taking the middle value of the remaining half to compare to the target value. Repeat this process until the target value is found. If the search ends with the remaining half is empty, the target value is not on the list.
2 Classical Version and Variants
1） Classical version
Return the position of the target value within a sorted array. If the target value is not in the array, return -1.
I. Code Rush
public class BinarySearch {
public int binarySearch(int target, int[] arr) {
if(arr == null || arr.length == 0) {
return -1;
}else {
int left = 0;
int right = arr.length - 1;
while(left <= right) {
int middle = (right + left) / 2;
if (target == arr[middle]) {
return middle;
}else if (target > arr[middle]) {
left = middle + 1;
}else {
right = middle - 1;
}
}
return -1;
}
}
public static void main(String[] args) {
BinarySearch b = new BinarySearch();
int[] arr = new int[]{-1, 2, 3, 49, 100};
int target = 3;
System.out.println(b.binarySearch(target, arr));
}
}
II. Time & Space complexity
The time complexity is because binary search divides the array into two parts each time until there is one element remaining. This process takes log(n) times. The space complexity is .
2） Apply binary search to a 2D matrix.
Question: Given a m*n matrix and a target value x, find the position of x in the matrix. Each row is sorted. The first element of the next row is smaller or equal than the last element of the previous row.
Solution 1:
We can apply binary search to column and row separately. The first binary search is used to locate the row in which the target value may lie. The second aims to find the position of target value within that row.
import java.util.Arrays;
public class BinarySearch2D {
public int[] binarySearch2D(int target, int[][] metrix) {
int[] res = new int[]{-1, -1};
if(metrix == null || (metrix.length == 0 && metrix[0].length == 0)) {
return res;
}
// first-time binary search
int top = 0;
int bottom = metrix.length - 1;
int middle = 0;
while(top < bottom - 1) {
middle = (bottom + top) / 2;
if(metrix[middle][0] == target) {
res[0] = middle;
res[1] = 0;
return res;
}else if(metrix[middle][0] > target) {
bottom = middle;
}else {
top = middle;
}
}
// post progress
if(top == bottom) {
res[0] = top;
}else {
if(metrix[bottom][0] < target) {
res[0] = bottom;
}else {
res[0] = top;
}
}
// second-time binary search
int left = 0;
int right = metrix[0].length - 1;
middle = 0;
while(left <= right) {
middle = (right + left) / 2;
if(metrix[res[0]][middle] == target) {
res[1] = middle;
return res;
}else if(metrix[res[0]][middle] > target) {
right = middle - 1;
}else{
left = middle + 1;
}
}
res[0] = -1;
res[1] = -1;
return res;
}
public static void main(String[] args) {
BinarySearch2D b = new BinarySearch2D();
int metrix[][] = new int[][] {
{1, 2, 3, 4},
{4, 4, 5, 6},
{7, 8, 9, 10}
};
int target = 5;
System.out.println(Arrays.toString(b.binarySearch2D(target, metrix)));
}
}


Introduction to AXS¶
AXS is a framework for processing data from astronomical catalogs based on Apache Spark. AXS stands for Astronomy eXtensions for Spark.
AXS was designed to make life easier for astronomers when they have to cruch data from astronomical catalogs (which is close to “all the time”). The way this process usually works is that an astronomer selects some data from a public or a private catalog using an SQL query, exports that data to some location and then writes custom (most likely Python) scripts to further analyze the data. Relying on the underlying Spark engine, AXS simplifies this process by allowing astronomers to query the data and run arbitrary processing on the results all within the same framework. There is usually no need for data export and separate programs.
Another thing that astronomers often need to do is to cross-match several catalogs: find the same objects from different catalogs based on distance calculations. This is computationally very expensive operation. AXS partitions the data in a way that makes cross-matching extremely efficient.
Spark specifics¶
If you wish to use AXS and you have never worked with Spark, you will have some adjusting to do because AXS is a
wrapper around Spark, or Spark SQL to be exact. AxsFrame
class is a wrapper around Spark’s DataFrame so
all operations available on DataFrames are also available on AxsFrame
.
There are a few concepts that a new Spark user might find confusing.
First of all, DataFrames (and AxsFrames) are lazy and will be evaluated and executed only when an action (such as show or count) is called.
Secondly, DataFrames are immutable. When you call a transformation (such as select or where) you obtain a new DataFrame. Stacking up transformations builds the DataFrame “program” to be executed later.
Because of all this, if this program results in an error, you will have to examine all the transformations that went into building the DataFrame and not just the last one (which would be the first, natural reaction).
Spark is a complex framework and execution engine and you would do well to invest some time into learning Spark basics if you intend to use AXS.
AXS data partitioning¶
AXS partitions the sky into horizontal strips of fixed height, called zones. Data is physically partitioned into buckets, which are ordinary (Parquet) files containing part of the data of the full dataset. Zones are sequentially placed into buckets. Since there are many more zones than buckets, a single bucket contains data from various non-neighboring zones. Within a bucket, data is sorted by zone (derived from the Dec coordinate) and then by the RA coordinate.
This setup enables fast spatial searches and fast cross-matching of different catalogs.


|
MXQuery 0.6.0 API Documentation - Copyright 2006-2009 ETH Zurich | ||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object ch.ethz.mxquery.sms.btree.LeafArrayMap
public class LeafArrayMap
Helper for b-tree leaves. Has to be taylored to different key and value types manually as Generics would use Complex type (inefficent) instead of native types.
| Constructor Summary | |
|---|---|
LeafArrayMap(int n)
Instantiates a Leaf Array Map |
| Method Summary | |
|---|---|
void |
addAtPos(DeweyIdentifier key,
LinguisticToken value,
int pos)
Adds the given mapping at the given position in this array. |
int |
binarySearch(java.util.Vector a,
DeweyIdentifier key,
int from,
int to)
This binary search method is modified to guarantee that, in the presence of duplicate keys, we will always return the first occurrence of a found key in the portion of the array being searched (from-to). |
int |
continueGet(int pos,
DeweyIdentifier key,
BtreePushOperator results)
|
int |
continueScan(int pos,
DeweyIdentifier highKey,
BtreePushOperator results)
|
void |
deleteAtPos(int i)
Deletes the value and key at position i |
int |
get(DeweyIdentifier key,
BtreePushOperator results)
Gets the values for the specified key and pushes them in the given push operator. |
int |
getFirstKeyAfter(DeweyIdentifier key,
int[] atPos)
|
int |
getLastKeyBefore(DeweyIdentifier key,
int[] atPos)
|
DeweyIdentifier |
getMidKey()
|
int |
queryRange(DeweyIdentifier lowKey,
DeweyIdentifier highKey,
BtreePushOperator results)
Obtains all values mapped to the given key range in this array. |
boolean |
remove(DeweyIdentifier key,
LinguisticToken value)
Remove mappings with the given key. |
int |
size()
|
LeafArrayMap |
split()
Splits this map. |
java.lang.String |
toString()
|
boolean |
tryAdd(DeweyIdentifier key,
LinguisticToken value)
Associates the given key with the given value in this array map, if the insertion point for the key may be found here. |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Constructor Detail |
|---|
public LeafArrayMap(int n)
n
- the size of the LeafArrayMap| Method Detail |
|---|
public int binarySearch(java.util.Vector a, DeweyIdentifier key, int from, int to)
a
- key
- from
- to
-
public DeweyIdentifier getMidKey()
public LeafArrayMap split()
public boolean tryAdd(DeweyIdentifier key, LinguisticToken value)
key
- value
-
public void addAtPos(DeweyIdentifier key, LinguisticToken value, int pos)
key
- value
- pos
- public int get(DeweyIdentifier key, BtreePushOperator results)
public int getFirstKeyAfter(DeweyIdentifier key, int[] atPos)
public int getLastKeyBefore(DeweyIdentifier key, int[] atPos)
public int continueGet(int pos, DeweyIdentifier key, BtreePushOperator results)
public boolean remove(DeweyIdentifier key, LinguisticToken value)
key
- value
-
public java.lang.String toString()
toString
in class java.lang.Object
public int size()
public int queryRange(DeweyIdentifier lowKey, DeweyIdentifier highKey, BtreePushOperator results)
lowKey
- highKey
- results
-
CONTINUE_WITH_SCAN
, CONTINUE_WITH_BINSEARCH
public int continueScan(int pos, DeweyIdentifier highKey, BtreePushOperator results)
public void deleteAtPos(int i)
i
- the position the key and value is deleted at
|
MXQuery 0.6.0 API Documentation - Copyright 2006-2009 ETH Zurich | ||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


McASP frame object params. More...
#include <mcasp_drv.h>
Data Fields | |
| Mcasp_QueueElem | link |
| void * | addr |
| uint32_t | size |
| uintptr_t | arg |
| Mcasp_IOcmd_e | cmd |
| int32_t | status |
| uint32_t | misc |
McASP frame object params.
Mcasp_IOBuf structures are managed by the driver. MCASP buffers are the basis for all I/O operations. 'cmd' field contains the command id for the driver. 'status' is filled in by the driver and contains the status of the commmand.
| void* Mcasp_IOBuf_t::addr |
buffer address
| uintptr_t Mcasp_IOBuf_t::arg |
arg to be used by end app
| Mcasp_IOcmd_e Mcasp_IOBuf_t::cmd |
command for driver - READ, WRITE, ABORT or FLUSH
| Mcasp_QueueElem Mcasp_IOBuf_t::link |
queue link
| uint32_t Mcasp_IOBuf_t::misc |
reserved for driver
| uint32_t Mcasp_IOBuf_t::size |
buffer size
| int32_t Mcasp_IOBuf_t::status |
status of command


CrimPapers
Articles published on 28 August 2018
On 28 August 2018, 4 new articles were published in 3 journals.
Predicting Fear of Crime: Results From A Community Survey of A Small City
Aboriginal Women’s Experiences With Intimate Partner Sexual Violence and the Dangerous Lives They Live As A Result of Victimization by Marika Guggisberg
Partners As Possession: A Qualitative Exploration of Intimate Partner Sexual Violence in Freetown, Sierra Leone by Luisa Theresia Schneider
Intergenerational Educational Pathways and Instrumental Crime, Violent Crime, and Illegal Drug Use Across the Life Course by Christopher R. Dennison


Jones AV, Cross NC. Inherited predisposition to myeloproliferative neoplasms. Ther Adv Hematol. 2013;4(4):237-253.
Jakafi [package insert]. Wilmington, DE: Incyte Corporation; 2016.
Bogani C, Bartalucci N, Martinelli S, et al. mTOR inhibitors alone and in combination with JAK2 inhibitors effectively inhibit cells of myeloproliferative neoplasms. PLoS One. 2013;8(1):e54826.
Shin N, Koblish H, Covington M, et al. Abstract 2671: INCB050465, a novel PI3Kδ inhibitor, synergizes with PIM protein kinase inhibition to cause tumor regression in a model of DLBCL. August 2015. 10.1158/1538-7445.AM2015-2671
MPN=myeloproliferative neoplasm; MF=myelofibrosis; JAK=Janus kinase; STAT=signal transduction and activator of transcription; PI3K=phosphatidylinositol-3-Kinase


Chapter 6 Exploratory Data Analysis
6.1 Descriptive statistics
The first task to do with any dataset is to characterize it in terms of summary statistics and graphics.
Displaying information graphically will help us to identify the main characteristics of the data. To describe a distribution we often want to know where it is centered and and what the spread is (mean, median, quantiles)
6.2 Basic Plots
Histogram defines a sequence of breaks and then counts the number of observations in the bins formed by the breaks.
Boxplot used to summarize data succinctly, quickly displaying if the data is symmetric or has suspected outliers.
- Q-Q plot is used to determine if the data is close to being normally distributed. The quantiles of the standard normal distribution is represented by a straight line. The normality of the data can be evaluated by observing the extent in which the points appear on the line. When the data is normally distributed around the mean, then the mean and the median should be equal. Quantiles are cut points dividing the range of a probability distribution into continuous intervals with equal probabilities, or dividing the observations in a sample in the same way.
Scatterplot provides a graphical view of the relationship between two sets of numbers: one numerical variable against another.
Kernel Density plot visualizes the underlying distribution of a variable. Kernel density estimation is a non-parametric method of estimating the probability density function of continuous random variable. It helps to identify the distribution of the variable.
Violin plot is a combination of a boxplot and a kernel density plot.
6.3 Normality
- A normal distribution is an arrangement of a data set in which most values cluster in the middle of the range
- A graphical representation of a normal distribution is sometimes called a bell curve because of its shape.
- Many procedures in statistics are based on this property. Parametric procedures require the normality property.
- In a normal distribution about 95% of the probability lies within 2 Standard Deviations of the mean.
- Two examples: one population with mean 60 and the standard deviation of 1, and the other with mean 60 and \(sd=4\) (means shifted to 0)
# Area within 2SD of the mean
par(mfrow=c(1,2))
plot(function(x) dnorm(x, mean = 0, sd = 1),
xlim = c(-3, 3), main = "SD 1", xlab = "x",
ylab = "", cex = 2)
segments(-2, 0, -2, 0.4)
segments(2, 0, 2, 0.4)
# Area within 4SD of the mean
plot(function(x) dnorm(x, mean = 0, sd = 4),
xlim = c(-12, 12), main = "SD 4", xlab = "x",
ylab = "", cex = 2)
segments(-8, 0, -8, 0.1)
segments(8, 0, 8, 0.1)
- if we sample from this population we get “another population”:
#tidy uses the package formatR to format the code
<- rep(NA, 1000)
sample.means for (i in 1:1000) {
.40 <- rnorm(40, mean = 60, sd = 4)
sample#rnorm generates random numbers from normal distribution
<- mean(sample.40)
sample.means[i]
}<- mean(sample.means)
means40 <- sd(sample.means)
sd40 means40
## [1] 60.02732
sd40
## [1] 0.6185025
- These sample means are another “population.” The sampling distribution of the sample mean is normally distributed meaning that the “mean of a representative sample provides an estimate of the unknown population mean.” This is shown in Figure 6.2
hist(sample.means)
6.4 Using a running Example to visualise the different plots
As a running example we do next:
Set the path to to the file
Read the Telecom1 dataset and print out the summary statistics with the command
summary
options(digits=3)
<- read.table("./datasets/effortEstimation/Telecom1.csv", sep=",",header=TRUE, stringsAsFactors=FALSE, dec = ".") #read data
telecom1 summary(telecom1)
## size effort EstTotal
## Min. : 3.0 Min. : 24 Min. : 30
## 1st Qu.: 37.2 1st Qu.: 119 1st Qu.:142
## Median : 68.5 Median : 222 Median :289
## Mean :100.3 Mean : 284 Mean :320
## 3rd Qu.:164.0 3rd Qu.: 352 3rd Qu.:472
## Max. :284.0 Max. :1116 Max. :777
- We see that this dataset has three variables (or parameters) and few data points (18)
- size: the independent variable
- effort: the dependent variable
- EstTotal: the estimates coming from an estimation method
- Basic Plots
par(mfrow=c(1,2)) #n figures per row
<- telecom1$size
size_telecom1 <- telecom1$effort
effort_telecom1
hist(size_telecom1, col="blue", xlab='size', ylab = 'Probability', main = 'Histogram of project Size')
lines(density(size_telecom1, na.rm = T, from = 0, to = max(size_telecom1)))
plot(density(size_telecom1))
hist(effort_telecom1, col="blue")
plot(density(effort_telecom1))
boxplot(size_telecom1)
boxplot(effort_telecom1)
# violin plots for those two variables
library(vioplot)
vioplot(size_telecom1, names = '')
title("Violin Plot of Project Size")
vioplot(effort_telecom1, names = '')
title("Violin Plot of Project Effort")
par(mfrow=c(1,1))
qqnorm(size_telecom1, main="Q-Q Plot of 'size'")
qqline(size_telecom1, col=2, lwd=2, lty=2) #draws a line through the first and third quartiles
qqnorm(effort_telecom1, main="Q-Q Plot of 'effort'")
qqline(effort_telecom1)
We can observe the non-normality of the data.
We may look the possible relationship between size and effort with a scatter plot
plot(size_telecom1, effort_telecom1)
6.4.1 Example with the China dataset
library(foreign)
<- read.arff("./datasets/effortEstimation/china.arff")
china <- china$AFP
china_size summary(china_size)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9 100 215<PHONE_NUMBER>8
<- china$Effort
china_effort summary(china_effort)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 26 704 1829 3921 3826 54620
par(mfrow=c(1,2))
hist(china_size, col="blue", xlab="Adjusted Function Points", main="Distribution of AFP")
hist(china_effort, col="blue",xlab="Effort", main="Distribution of Effort")
boxplot(china_size)
boxplot(china_effort)
qqnorm(china_size)
qqline(china_size)
qqnorm(china_effort)
qqline(china_effort)
* We observe the non-normality of the data.
<IP_ADDRESS> Normality. Galton data
It is the data based on the famous 1885 Francis Galton’s study about the relationship between the heights of adult children and the heights of their parents.
<IP_ADDRESS> Normalization
Take \(log\)s in both independent variables. For example, with the China dataset.
- If the \(log\) transformation is used, then the estimation equation is: \[y= e^{b_0 + b_1 log(x)} \]
6.5 Correlation
Correlation is a statistical relationship between two sets of data. With the whole dataset we may check for the linear Correlation of the variables we are interested in.
As an example with the China dataset
par(mfrow=c(1,1))
plot(china_size,china_effort)
cor(china_size,china_effort)
## [1] 0.685
cor.test(china_size,china_effort)
##
## Pearson's product-moment correlation
##
## data: china_size and china_effort
## t = 21, df = 497, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.635 0.729
## sample estimates:
## cor
## 0.685
cor(china_size,china_effort, method="spearman")
## [1] 0.649
cor(china_size,china_effort, method="kendall")
## [1] 0.468
6.6 Confidence Intervals. Bootstrap
- Until now we have generated point estimates
- A confidence interval (CI) is an interval estimate of a population parameter. The parameter can be the mean, the median or other. The frequentist CI is an observed interval that is different from sample to sample. It frequently includes the value of the unobservable parameter of interest if the experiment is repeated. The confidence level is the value that measures the frequency that the constructed intervals contain the true value of the parameter.
- The construction of a confidence interval with an exact value of confidence level for a distribution requires some statistical properties. Usually, normality is one of the properties required for computing confidence intervals.
- Not all confidence intervals contain the true value of the parameter.
- Simulation of confidence intervals
An example from Ugarte et al. (Ugarte, Militino, and Arnholt 2015)
set.seed(10)
norsim(sims = 100, n = 36, mu = 100, sigma = 18, conf.level = 0.95)
- The range defined by the confidence interval will vary with each sample, because the sample size will vary each time and the standard deviation will vary too.
- 95% confidence interval: it is the probability that the hypothetical confidence intervals (that would be computed from the hypothetical repeated samples) will contain the population mean.
- the particular interval that we compute on one sample does not mean that the population mean lies within that interval with a probability of 95%.
- Recommended reading: (Hoekstra et al. 2014) Robust misinterpretation of confidence intervals
6.7 Nonparametric Bootstrap
- For computing CIs the important thing is to know the assumptions that are made to “know” the distribution of the statistic.
- There is a way to compute confidence intervals without meeting the requirements of parametric methods.
- Resampling or bootstraping is a method to calculate estimates of a parameter taking samples from the original data and using those resamples to calculate statistics. Using the resamples usually gives more accurate results than using the original single sample to calculate an estimate of a parameter.
- An example of bootstrap CI can be found in Chapter 13, “Evaluation in Software Engineering”


WINDS GATE
HOME
ABOUT US
CAREER
CONTACT
SERVICES
Biometrics
Software Development
Philippine Software Development Inc.
Oopss! Sorry page not found..
Click here


| 1 | package fr.sii.ogham.testing.extension.common; | |
| 2 | ||
| 3 | import java.io.PrintWriter; | |
| 4 | import java.io.StringWriter; | |
| 5 | ||
| 6 | /** | |
| 7 | * Simple logger that writes header, success and failure using a | |
| 8 | * {@link StringWriter}. The {@link StringWriter} is passed to the constructor | |
| 9 | * to ba able to read the written strings from the external. | |
| 10 | * | |
| 11 | * <p> | |
| 12 | * This is mainly used for tests | |
| 13 | * | |
| 14 | * @author Aurélien Baudet | |
| 15 | * | |
| 16 | */ | |
| 17 | public class StringPrinter implements Printer { | |
| 18 | private final StringWriter writer; | |
| 19 | private final boolean writeMarker; | |
| 20 | ||
| 21 | /** | |
| 22 | * | |
| 23 | * @param writer | |
| 24 | * the writer that can be shared | |
| 25 | * @param writeMarker | |
| 26 | * true to write marker | |
| 27 | */ | |
| 28 | public StringPrinter(StringWriter writer, boolean writeMarker) { | |
| 29 | super(); | |
| 30 | this.writer = writer; | |
| 31 | this.writeMarker = writeMarker; | |
| 32 | } | |
| 33 | ||
| 34 | @Override | |
| 35 | public void printHeader(String marker, String header) { | |
| 36 |
2
1. printHeader : removed call to fr/sii/ogham/testing/extension/common/StringPrinter::printMarker → SURVIVED 2. printHeader : removed call to fr/sii/ogham/testing/extension/common/StringPrinter::printMarker → NO_COVERAGE |
printMarker(marker); |
| 37 | writer.append(header + "\n\n"); | |
| 38 | } | |
| 39 | ||
| 40 | @Override | |
| 41 | public void printSucess(String marker, String success) { | |
| 42 |
2
1. printSucess : removed call to fr/sii/ogham/testing/extension/common/StringPrinter::printMarker → NO_COVERAGE 2. printSucess : removed call to fr/sii/ogham/testing/extension/common/StringPrinter::printMarker → SURVIVED |
printMarker(marker); |
| 43 | writer.append(success + "\n\n"); | |
| 44 | } | |
| 45 | ||
| 46 | @Override | |
| 47 | public void printFailure(String marker, String failure, Throwable e) { | |
| 48 |
2
1. printFailure : removed call to fr/sii/ogham/testing/extension/common/StringPrinter::printMarker → SURVIVED 2. printFailure : removed call to fr/sii/ogham/testing/extension/common/StringPrinter::printMarker → NO_COVERAGE |
printMarker(marker); |
| 49 | writer.append("Test failure:" + e + "\n"); | |
| 50 |
2
1. printFailure : removed call to java/lang/Throwable::printStackTrace → NO_COVERAGE 2. printFailure : removed call to java/lang/Throwable::printStackTrace → SURVIVED |
e.printStackTrace(new PrintWriter(writer)); |
| 51 | writer.append(failure + "\n\n"); | |
| 52 | } | |
| 53 | ||
| 54 | ||
| 55 | private void printMarker(String marker) { | |
| 56 |
2
1. printMarker : negated conditional → SURVIVED 2. printMarker : negated conditional → NO_COVERAGE |
if (writeMarker) { |
| 57 | writer.append("[marker:" + marker + "]\n"); | |
| 58 | } | |
| 59 | } | |
| 60 | } | |
Mutations | ||
| 36 |
1.1 2.2 |
|
| 42 |
1.1 2.2 |
|
| 48 |
1.1 2.2 |
|
| 50 |
1.1 2.2 |
|
| 56 |
1.1 2.2 |


Before Change
error not in mutant.get_original_response_body():
desc = "A possible (detection is really hard...) format"\
" string vulnerability was found at: %s"
desc = desc % mutant.found_at()
v = Vuln.from_mutant("Format string vulnerability", desc,
severity.MEDIUM, response.id,
self.get_name(), mutant)
After Change
desc = ("A possible (detection is really hard...) format"
" string vulnerability was found at: %s")
desc %= mutant.found_at()
v = Vuln.from_mutant("Format string vulnerability", desc,
severity.MEDIUM, response.id,
self.get_name(), mutant)


Alex Salmond due in court over sexual assault and attempted rape charges
The former First Minister denies the allegations and said he will defend himself 'to the utmost' in court.
by Conor RiordanFormer First Minister Alex Salmond is due to appear in court over a series of sexual assault and attempted rape charges.
The preliminary hearing is due to take place at the High Court in Edinburgh on Thursday.
Salmond faced a total of 14 charges, including two counts of attempted rape, when he appeared at Edinburgh Sheriff Court later this year on January 24.
He has previously denied the allegations and said he will defend himself "to the utmost" in court.
The 64-year-old was Scotland's First Minister from 2007 to 2014.
The result of the 2014 referendum - a 55% to 45% vote to stay in the UK - led to him stepping down as First Minister and SNP leader.
A full trial is expected to take place early next year.


--- layout: post title: "Comcast's Torrent Filtering: Criminal Acts?" date: 2007-09-04 23:00:48 +0000 permalink: /2007/09/04/comcasts-torrent-filtering-criminal-acts/ category: Misc redirect_from: - /blog/comcasts-torrent-filtering-criminal-acts/ ---
According to an article on cnet.com, the manner in which Comcast is filtering BitTorrent traffic may, in fact, be criminal. Comcast is sending forged RST (reset) packets to the end-user, which may qualify as impersonating with the intent to profit. (Criminal Impersonation in the 2nd Degree). Whether or not this plays out in court remains to be seen.


Emma Worthington
Outline
(thermo = temperature; haline = salinity)
(AMOC)
Components
Geostrophic transport
Before 2004, measurements were taken by research ships crossing the Atlantic
RRS Discovery II leaving Woods Hole Oceanographic Institute in 1957
(Data from Bryden et al., (2005))
Climate simulations show that the AMOC is very likely to slow down by the end of this century
(Intergovernmental Panel on Climate Change (IPCC), 5th Annual Report, 2013)
So what?
Northern Hemisphere freezes over in days!
Sea level rises 25 m!
Climate model shows NH air temperatures cool by up to 8°C
(Jackson et al., 2009)
Sea level rose by 128 mm during a 30% AMOC downturn
(Goddard et al., 2015)
(from www.rapid.ac.uk)
Instruments on the moorings measure temperature, salinity, pressure and current velocity - twice a day
Data is collected by a research ship every 18 months
(Photos: Ben Moat, NOC)
What have RAPID results shown?
(Data from Bryden et al., (2005))
(Data from www.rapid.ac.uk)
(Smeed et al., 2018)
Reduced LNADW
(3000 to 5000 m)
2008 – 2017 is 30% less than 2004 - 2008
Detecting long-term change in the AMOC
(Baehr et al., 2008)
Using the AMOC time series alone means a change takes 40-60 years to detect
Reduce variability by combining AMOC with a less noisy climate signal
Using deep cross-basin density difference reduces the detection time to 30 years.
(Baehr et al., 2007)
Variability due to mesoscale (~100 km, 1 month) eddies is suppressed close to steep continental slopes, e.g., off the Bahamas
AMOC noise is suppressed slightly by using measurements closer to the boundary
Variability of density with distance from boundary
Identifying where variability is lowest may allow us to say more about historical 'snapshot' data, i.e., increase the length of the timeseries.
Summary
Thank you!
Any questions?
<EMAIL_ADDRESS>@E_L_Worthington
@emma_at_sea
If you were wondering how a 5 km long mooring is deployed from a ship...


Description
Note
Edit
Add
Log in
to add notes to lectures!
Delete
Cancel
Save
Video Information
Lecturer
Added on
Community rating
Your rating
Log in
to rate this lecture!
Information
Channel Title
Video on Youtube
Go to Youtube


exact_cols
returns the exact column names, including the case.
exact_cols(tbl, cols)
A character vector of column names with the appropriate case.
This particularly useful when working with Microsoft SQL Server tables as the column names may be case sensitive. This is also useful when working with table metadata and the exact column names are required for matching purposes.
tbl_vars does not return case sensitive columns, thus the the need for this function.
## create tibble my_tibble <- tibble::tribble( ~MixedCase, ~lowercase, ~UPPERCASE, ~aLtCaSe, 1, 11, 111, 1111, 2, 22, 222, 2222, 3, 33, 333, 3333 ) ## column names to search for, ignoring case my_cols <- c("mixedcase", "altcase") my_cols_case <- my_tibble %>% exact_cols(my_cols) ## particularly useful when combined with dplyr::one_of my_tibble %>% dplyr::select(dplyr::one_of(my_cols_case))#> # A tibble: 3 × 2 #> MixedCase aLtCaSe #> <dbl> <dbl> #> 1 1 1111 #> 2 2 2222 #> 3 3 3333 #>


AG_fft_tools Package¶
AG_fft_tools Package¶
Wrappers around numpy, scipy, and pyfftw tools to perform 2D convolution in general, smoothing with a set of ‘standard’ kernels, and computing power spectra and PSDs.
convolve_nd Module¶
- AG_fft_tools.convolve_nd.convolvend(array, kernel, boundary='fill', fill_value=0, crop=True, return_fft=False, fftshift=True, fft_pad=True, psf_pad=False, interpolate_nan=False, quiet=False, ignore_edge_zeros=False, min_wt=0.0, normalize_kernel=False, use_numpy_fft=False, nthreads=1)[source]¶
Convolve an ndarray with an nd-kernel. Returns a convolved image with shape = array.shape. Assumes image & kernel are centered.
Parameters : array: `numpy.ndarray` :
Array to be convolved with kernel
kernel: `numpy.ndarray` :
Will be normalized if normalize_kernel is set. Assumed to be centered (i.e., shifts may result if your kernel is asymmetric)
Returns : default: `array` convolved with `kernel` :
if return_fft: fft(`array`) * fft(`kernel`) :
- if fftshift: Determines whether the fft will be shifted before returning
if not(`crop`) : Returns the image, but with the fft-padded size
instead of the input size
Examples
>>> convolve_fft([1,0,3],[1,1,1]) array([ 1., 4., 3.])
>>> convolve_fft([1,np.nan,3],[1,1,1]) array([ 1., 4., 3.])
>>> convolve_fft([1,0,3],[0,1,0]) array([ 1., 0., 3.])
>>> convolve_fft([1,2,3],[1]) array([ 1., 2., 3.])
>>> convolve_fft([1,np.nan,3],[0,1,0], interpolate_nan=True) array([ 1., 0., 3.])
>>> convolve_fft([1,np.nan,3],[0,1,0], interpolate_nan=True, min_wt=1e-8) array([ 1., nan, 3.])
>>> convolve_fft([1,np.nan,3],[1,1,1], interpolate_nan=True) array([ 1., 4., 3.])
>>> convolve_fft([1,np.nan,3],[1,1,1], interpolate_nan=True, normalize_kernel=True) array([ 1., 2., 3.])
correlate2d Module¶
- AG_fft_tools.correlate2d.correlate2d(im1, im2, **kwargs)[source]¶
Cross-correlation of two images of arbitrary size. Returns an image cropped to the largest of each dimension of the input images
Options: return_fft - if true, return fft(im1)*fft(im2[::-1,::-1]), which is the power
spectral density- fftshift - if true, return the shifted psd so that the DC component is in
- the center of the image
pad - Default on. Zero-pad image to the nearest 2^n crop - Default on. Return an image of the size of the largest input image.
If the images are asymmetric in opposite directions, will return the largest image in both directions.WARNING: Normalization may be arbitrary if you use the PSD
psds Module¶
Power Spectra
- AG_fft_tools.psds.PSD2(image, image2=None, oned=False, fft_pad=False, real=False, imag=False, binsize=1.0, radbins=1, azbins=1, radial=False, hanning=False, wavnum_scale=False, twopi_scale=False, **kwargs)[source]¶
Two-dimensional Power Spectral Density. NAN values are treated as zero.
- image2 - can specify a second image if you want to see the cross-power-spectrum instead of the
- power spectrum.
- oned - return radial profile of 2D PSD (i.e. mean power as a function of spatial frequency)
- freq,zz = PSD2(image); plot(freq,zz) is a power spectrum
- fft_pad - Add zeros to the edge of the image before FFTing for a speed
- boost? (the edge padding will be removed afterwards)
real - Only compute the real part of the PSD (Default is absolute value) imag - Only compute the complex part of the PSD (Default is absolute value) hanning - Multiply the image to be PSD’d by a 2D Hanning window before performing the FTs.
Reduces edge effects. This idea courtesy Paul Ricchiazzia (May 1993), author of the IDL astrolib psd.prowavnum_scale - multiply the FFT^2 by the wavenumber when computing the PSD? twopi_scale - multiply the FFT^2 by 2pi? azbins - Number of azimuthal (angular) bins to include. Default is 1, or
all 360 degrees. If azbins>1, the data will be split into [azbins] equally sized pie pieces. Azbins can also be a numpy array. See AG_image_tools.azimuthalAverageBins for details- radial - An option to return the azimuthal power spectrum (i.e., the spectral power as a function
- of angle). Not commonly used.
radbins - number of radial bins (you can compute the azimuthal power spectrum in different annuli)
- AG_fft_tools.psds.hanning2d(M, N)[source]¶
A 2D hanning window, as per IDL’s hanning function. See numpy.hanning for the 1d description
- AG_fft_tools.psds.power_spectrum(*args, **kwargs)[source]¶
Thin wrapper of PSD2. Returns the 1D power spectrum in stead of the 2D Power Spectral Density
- AG_fft_tools.psds.pspec(psd2, return_index=True, wavenumber=False, return_stddev=False, azbins=1, binsize=1.0, view=False, **kwargs)[source]¶
Create a Power Spectrum (radial profile of a PSD) from a Power Spectral Density image
return_index - if true, the first return item will be the indexes wavenumber - if one dimensional and return_index set, will return a normalized wavenumber instead view - Plot the PSD (in logspace)?
smooth_tools Module¶
- AG_fft_tools.smooth_tools.make_kernel(kernelshape, kernelwidth=3, kerneltype='gaussian', trapslope=None, normalize_kernel=<function sum at 0x10637bcf8>, force_odd=False)¶
Create a smoothing kernel for use with convolve or convolve_fft
Parameters : kernelshape : n-tuple
A tuple (or list or array) defining the shape of the kernel. The length of kernelshape determines the dimensionality of the resulting kernel
Returns : An N-dimensional float array :
- AG_fft_tools.smooth_tools.smooth(image, kernelwidth=3, kerneltype='gaussian', trapslope=None, silent=True, psf_pad=True, interp_nan=False, nwidths='max', min_nwidths=6, return_kernel=False, normalize_kernel=<function sum at 0x10637bcf8>, downsample=False, downsample_factor=None, **kwargs)[source]¶
Returns a smoothed image using a gaussian, boxcar, or tophat kernel
Options: kernelwidth:
width of kernel in pixels (see definitions below)- kerneltype:
gaussian, boxcar, or tophat. For a gaussian, uses a gaussian with sigma = kernelwidth (in pixels)
out to [nwidths]-sigmaA boxcar is a kernelwidth x kernelwidth square A tophat is a flat circle with radius = kernelwidth
- Default options:
- psf_pad: [True]
- will pad the input image to be the image size + PSF. Slows things down but removes edge-wrapping effects (see convolve) This option should be set to false if the edges of your image are symmetric.
- interp_nan: [False]
- Will replace NaN points in an image with the smoothed average of its neighbors (you can still simply ignore NaN values by setting ignore_nan=True but leaving interp_nan=False)
- silent: [True]
- turn it off to get verbose statements about kernel types
- return_kernel: [False]
- If set to true, will return the kernel as the second return value
- nwidths: [‘max’]
- number of kernel widths wide to make the kernel. Set to ‘max’ to match the image shape, otherwise use any integer
- min_nwidths: [6]
- minimum number of gaussian widths to make the kernel (the kernel will be larger than the image if the image size is < min_widths*kernelsize)
- normalize_kernel:
- Should the kernel preserve the map sum (i.e. kernel.sum() = 1) or the kernel peak (i.e. kernel.max() = 1) ? Must be a function that can operate on a numpy array
- downsample:
- downsample after smoothing?
- downsample_factor:
- if None, default to kernelwidth
Note that the kernel is forced to be even sized on each axis to assure no offset when smoothing.
convolve Module¶
- AG_fft_tools.convolve.convolve(array, kernel, boundary=None, fill_value=0.0, normalize_kernel=False)[source]¶
Convolve an array with a kernel
This routine differs from scipy.ndimage.convolve because it includes a special treatment for NaN values. Rather than including NaNs in the convolution calculation, which causes large NaN holes in the convolved image, NaN values are replaced with interpolated values using the kernel as an interpolation function.
Parameters : array: np.ndarray :
The array to convolve. This should be a 1, 2, or 3-dimensional array or a list or a set of nested lists representing a 1, 2, or 3-dimensional array.
kernel: np.ndarray :
The convolution kernel. The number of dimensions should match those for the array, and the dimensions should be odd in all directions.
boundary: str, optional :
- A flag indicating how to handle boundaries:
- None : set the result values to zero where the kernel
extends beyond the edge of the array (default)
‘fill’ : set values outside the array boundary to fill_value
‘wrap’ : periodic boundary
- ‘extend’ : set values outside the array to the nearest array
value
fill_value: float, optional :
The value to use outside the array when using boundary=’fill’
normalize_kernel: bool, optional :
Whether to normalize the kernel prior to convolving
Returns : result, np.ndarray :
An array with the same dimensions and type as the input array, convolved with kernel.
Notes
Masked arrays are not supported at this time.


Abstract
Actually the Catalyst driver was released yesterday (15/8/12) but the official link was broken, although many users find out the mispelling and download the driver without problem -- currently the link is fixed.
Introduction
There are two ways to install the AMD drivers:
- Easy Way with Xorg-Edgers
- Hard way (Manual) from binaries
Also, there are two ways to uninstall them if you need so:
- PPA purge if you used Xorg-Edgers
- Purge Autoremove if you used the manual way
Materias and Methods
Here we will see how to install, upgrade and uninstall the AMD drivers
The easy way: Xorg-Edgers PPA for Catalyst
Do you want to install a PPA once, then stay back and relax for life? If so, then follow the easy path that most Ubuntu users do.
Xorg-Edgers offers you bleeding-edge drivers directly from upstream git, but in case of AMD Catalyst they always provide the latest stable AMD release, since AMD is not giving beta drivers in public (in contrast to NVIDIA).
If you go to Xorg-Edgers Lauchpad profile, you will see the 2:8.980-0ubuntu1\~xedgers\~precise1 is already available. Additionally, these guys are so crazy that they even provide the driver for the Ubuntu 12.10 Alpha 3 release via fglrx 2:8.980-0ubuntu1\~xedgers\~quantal1.
PPA Installation
Anyway, add the PPA into your source and update/upgrade your packages. If you had already add Xorg-Edgers into your Ubuntu, there is no need to do it again. Just update your system and reboot.
sudo add-apt-repository ppa:xorg-edgers/ppa
sudo apt-get update
sudo apt-get dist-upgrade
sudo apt-get install fglrx-installer
PPA Uninstallation
In case you want to upgrade Ubuntu up to a new version (eg from Ubuntu 12.04 upgrade into Ubuntu 12.10) you have to remove this PPA or strange things will happen and maybe render unstable the whole system. So before you upgrade to the newer Ubuntu distro, please act wise and remove it..
sudo apt-get install ppa-purge
sudo ppa-purge ppa:xorg-edgers/ppa
So far, we have covered the easy method and now you should be able to install and remove them properly. You can stop reading now and reboot your PC. Have fun!
The hard (manual) way
First you will need to download the driver
Click to download the drivers for 64/32 bit (the package contains both the 32-bit and 64-bit driver.).
If you install the AMD Catalyst for the first time then follow the following procedure
Binary Installation
Install prerequisite packages
sudo apt-get install build-essential cdbs dh-make dkms
sudo apt-get install execstack dh-modaliases fakeroot libqtgui4
If you are 64 bit, install these too
sudo apt-get install ia32-libs ia32-libs-multiarch:i386
sudo apt-get install lib32gcc1 libc6-i386
cd /usr ; sudo ln -svT lib /usr/lib64
Now go into your Downloads folder (I suppose you have already download the driver), make the package executable, create and install deb packages
cd Downloads
chmod +x amd-driver-installer-8.982-x86.x86_64.run
sudo sh ./amd-driver-installer-8.982-x86.x86_64.run --buildpkg Ubuntu/precise
sudo dpkg -i fglrx*.deb
Now enable new settings
sudo aticonfig --initial -f
Binary Upgrade
If you are to Upgrade from AMD Catalyst 12.6 to 12.8 then follow the following procedure:
Go into your Downloads folder (I suppose you have already download the driver), make the package executable, create deb packages, force overwrite the older packages with the new ones
cd Downloads
chmod +x amd-driver-installer-8.982-x86.x86_64.run
sudo sh ./amd-driver-installer-8.982-x86.x86_64.run --buildpkg Ubuntu/precise
sudo dpkg --force-overwrite -i *.deb
Now reboot you PC.
Binary Uninstall
If want to unistall them
sudo apt-get --purge autoremove fglrx fglrx_* fglrx-amdcccle* fglrx-dev*
So what do you think? Did I miss something? Is any part unclear? Please leave your comments below


Main Page
Surviving Grad School
FAQ
Resources
Name
Title:
PhD Student
Advisor:
Someone
Lab:
Some Lab
Email Address:
email
Website:
website
Birthday:
B-Day
Bio


EXIV2(1) EXIV2(1) NAME exiv2 - Image metadata manipulation tool SYNOPSIS exiv2 [options] [action] file ... DESCRIPTION exiv2 is a program to read and write Exif, IPTC and XMP image metadata and image comments.  Type Exif IPTC XMP Image comments JPEG Read/Write Read/Write Read/Write Read/Write EXV Read/Write Read/Write Read/Write Read/Write CR2 Read/Write Read/Write Read/Write - CRW Read/Write - - Read/Write MRW Read Read Read - TIFF Read/Write Read/Write Read/Write - DNG Read/Write Read/Write Read/Write - NEF Read/Write Read/Write Read/Write - PEF Read/Write Read/Write Read/Write - ARW Read Read Read - RW2 Read Read Read - SR2 Read Read Read - SRW Read/Write Read/Write Read/Write - ORF Read/Write Read/Write Read/Write - PNG Read/Write Read/Write Read/Write Read/Write PGF Read/Write Read/Write Read/Write Read/Write RAF Read Read Read - EPS - - Read/Write - XMP - - Read/Write - GIF - - - - PSD Read/Write Read/Write Read/Write - TGA - - - - BMP - - - - JP2 Read/Write Read/Write Read/Write - · Support for GIF, TGA and BMP images is minimal: the image format is recognized, a MIME type assigned to it and the height and width of the image are determined. · Reading other TIFF-like RAW image formats, which are not listed in the table, may also work. ACTIONS The action argument is only required if it is not clear from the options which action is implied. pr | print Print image metadata. This is the default action, i.e., the com‐ mand exiv2 image.jpg will print a summary of the image Exif meta‐ data. ex | extract Extract metadata to *.exv, XMP sidecar (*.xmp) and thumbnail image files. Modification commands can be applied on-the-fly. in | insert Insert metadata from corresponding *.exv, XMP sidecar (*.xmp) and thumbnail files. Use option -S .suf to change the suffix of the input files. Since files of any supported format can be used as input files, this command can be used to copy the metadata between files of different formats. Modification commands can be applied on-the-fly. rm | delete Delete image metadata from the files. ad | adjust Adjust Exif timestamps by the given time. Requires at least one of the options -a time, -Y yrs, -O mon or -D day. mo | modify Apply commands to modify (add, set, delete) the Exif, IPTC and XMP metadata of image files. Requires option -c, -m or -M. mv | rename Rename files and/or set file timestamps according to the Exif cre‐ ate timestamp. Uses the value of tag Exif.Photo.DateTimeOriginal or, if not present, Exif.Image.DateTime to determine the time‐ stamp. The filename format can be set with -r fmt, timestamp options are -t and -T. fi | fixiso Copy the ISO setting from one of the proprietary Nikon or Canon makernote ISO tags to the regular Exif ISO tag, Exif.Photo.ISOSpeedRatings. Does not overwrite an existing stan‐ dard Exif ISO tag. fc | fixcom Fix the character encoding of Exif Unicode user comments. Decodes the comment using the auto-detected or specified character encod‐ ing and writes it back in UCS-2. Use option -n to specify the cur‐ rent encoding of the comment if necessary. OPTIONS -h Display help and exit. -V Show the program version and exit. -v Be verbose during the program run. -q Silence warnings and error messages from the Exiv2 library during the program run (quiet). Note that options -v and -q can be used at the same time. -Q lvl Set the log-level to 'd'(ebug), 'i'(nfo), 'w'(arning), 'e'(rror) or 'm'(ute). The default log-level is 'w'. -Qm is equivalent to -q. All log messages are written to standard error. -b Show large binary values (default is to suppress them). -u Show unknown tags (default is to suppress tags which don't have a name). -g key Only output info for this Exiv2 key (grep). Multiple -g options can be used to grep info for several keys. -n enc Charset to use to decode Exif Unicode user comments. enc is a name understood by iconv_open(3), e.g., 'UTF-8'. -k Preserve file timestamps when updating files (keep). Can be used with all options which update files. The flag is ignored by read- only options. -t Set the file timestamp according to the Exif create timestamp in addition to renaming the file (overrides -k). This option is only used with the 'rename' action. -T Only set the file timestamp according to the Exif create time‐ stamp, do not rename the file (overrides -k). This option is only used with the 'rename' action. Note: On Windows you may have to set the TZ environment variable for this option to work correctly. -f Do not prompt before overwriting existing files (force overwrite). -F Do not prompt before renaming files (Force rename). Appends '_1' ('_2', ...) to the name of the new file. -a time Time adjustment in the format [-]HH[:MM[:SS]]. This option is only used with the 'adjust' action. Examples: 1 adds one hour, 1:01 adds one hour and one minute, -0:00:30 subtracts 30 seconds. -Y yrs Time adjustment by a positive or negative number of years, for the 'adjust' action. -O mon Time adjustment by a positive or negative number of months, for the 'adjust' action. -D day Time adjustment by a positive or negative number of days, for the 'adjust' action. -p mode Print mode for the 'print' action. Possible modes are: s : print a summary of the Exif metadata (the default) a : print Exif, IPTC and XMP metadata (shortcut for -Pkyct) t : interpreted (translated) Exif tags (-PEkyct) v : plain Exif tag values (-PExgnycv) h : hexdump of the Exif data (-PExgnycsh) i : IPTC datasets (-PIkyct) x : XMP properties (-PXkyct) c : JPEG comment p : list available image previews, sorted by preview image size in pixels -P flgs Print flags for fine control of the tag list ('print' action). Allows control of the type of metadata as well as data columns included in the print output. Valid flags are: E : include Exif tags in the list I : IPTC datasets X : XMP properties x : print a column with the tag number g : group name k : key l : tag label n : tag name y : type c : number of components (count) s : size in bytes v : plain data value t : interpreted (translated) data h : hexdump of the data -d tgt Delete target(s) for the 'delete' action. Possible targets are: a : all supported metadata (the default) e : Exif section t : Exif thumbnail only i : IPTC data x : XMP packet c : JPEG comment -i tgt Insert target(s) for the 'insert' action. Possible targets are the same as those for the -d option, plus a modifier: X : Insert metadata from an XMP sidecar file <file>.xmp. The remaining insert targets determine what metadata to insert from the sidecar file. Possible are Exif, IPTC and XMP and the default is all of these. Note that the inserted XMP properties include those converted to Exif and IPTC. Only JPEG thumbnails can be inserted (not TIFF thumbnails), they need to be named file-thumb.jpg. -e tgt Extract target(s) for the 'extract' action. Possible targets are the same as those for the -d option, plus a target to extract pre‐ view images and a modifier to generate an XMP sidecar file: p[<n>[,<m> ...]] : Extract preview images. The optional comma sep‐ arated list of preview image numbers is used to determine which preview images to extract. The available preview images and their numbers are displayed with the 'print' option -pp. X : Extract metadata to an XMP sidecar file <file>.xmp. The remaining extract targets determine what metadata to extract to the sidecar file. Possible are Exif, IPTC and XMP and the default is all of these. -r fmt Filename format for the 'rename' action. The format string follows strftime(3) and supports the following keywords: :basename: - original filename without extension :dirname: - name of the directory holding the original file :parentname: - name of parent directory Default filename format is %Y%m%d_%H%M%S. -c txt JPEG comment string to set in the image ('modify' action). This option can also be used with the 'extract' and 'insert' actions to modify metadata on-the-fly. -m file Command file for the 'modify' action. This option can also be used with the 'extract' and 'insert' actions to modify metadata on-the- fly. -M cmd Command line for the 'modify' action. This option can also be used with the 'extract' and 'insert' actions to modify metadata on-the- fly. The format for the commands is the same as that of the lines of a command file. -l dir Location (directory) for files to be inserted or extracted. -S .suf Use suffix .suf for source files in 'insert' action. COMMANDS Commands for the 'modify' action can be read from a command file, e.g., exiv2 -m cmd.txt image.jpg or given on the command line, as in exiv2 -M"add Iptc.Application2.Credit String Mr. Smith" image.jpg Note the quotes. Multiple -m and -M options can be combined. When writing Exif, IPTC and XMP metadata, exiv2 enforces only a correct metadata structure. It is possible to write tags with types and values different from those specified in the standards, duplicate Exif tags, undefined tags, or incomplete metadata. While exiv2 is able to read all metadata that it can write, other programs may have difficulties with images that contain non standard-conforming metadata. Command format The format of a command is set | add | del key [[type] value] set Set the value of an existing tag with a matching key or add the tag. add Add a tag (unless key is a non-repeatable IPTC key; nothing pre‐ vents you from adding duplicate Exif tags). del Delete all occurrences of a tag (requires only a key). key Exiv2 Exif, IPTC or XMP key. type Byte | Ascii | Short | Long | Rational | Undefined | SShort | SLong | SRational | Comment for Exif keys, String | Date | Time | Short | Undefined for IPTC keys, and XmpText | XmpAlt | XmpBag | XmpSeq | LangAlt for XMP keys. A default type is used if none is explicitly given. The default is determined based on key. value The remaining text on the line is the value. It can optionally be enclosed in single quotes ('value') or double quotes ("value"). The value is optional. Not providing any value is equivalent to an empty value ("") and is mainly useful to create an XMP array prop‐ erty, e.g., a bag. The format of Exif Comment values includes an optional charset specification at the beginning: [charset=Ascii|Jis|Unicode|Undefined ]comment Undefined is used by default if the value doesn't start with a charset definition. The format for IPTC Date values is YYYY-MM-DD (year, month, day), that for IPTC Time values is HH:MM:SS+|-HH:MM, where HH:MM:SS refers to local hour, minute and seconds and +|-HH:MM refers to hours and minutes ahead or behind Universal Coordinated Time (+|- means either a + or a - sign is required). The format of XMP LangAlt values includes an optional language qualifier: [lang=language-code ]text x-default is used by default if the value doesn't start with a language qualifier. An additional command is available to register XMP namespaces: reg prefix namespace Command file format Empty lines and lines starting with # in a command file are ignored (com‐ ments). Remaining lines are commands as described above. EXAMPLES exiv2 *.jpg Prints a summary of the Exif information for all JPEG files in the directory. exiv2 -pi image.jpg Prints the IPTC metadata of the image. exiv2 rename img_1234.jpg Renames img_1234.jpg (taken on 13-Nov-05 at 22:58:31) to 20051113_225831.jpg exiv2 -r':basename:_%Y%m' rename img_1234.jpg Renames img_1234.jpg to img_1234_200511.jpg exiv2 -et img1.jpg img2.jpg Extracts the Exif thumbnails from the two files into img1-thumb.jpg and img2-thumb.jpg. exiv2 -it img1.jpg img2.jpg Inserts (copies) metadata from img1.exv to img1.jpg and from img2.exv to img2.jpg. exiv2 -ep1,2 image.jpg Extracts previews 1 and 2 from the image to the files image-pre‐ view1.jpg and image-preview2.jpg. exiv2 -eiX image.jpg Extracts IPTC datasets into an XMP sidecar file image.xmp and in the process converts them to "IPTC Core" XMP schema. exiv2 -iixX image.jpg Inserts IPTC and XMP metadata from an XMP sidecar file image.xmp into image.jpg. The resulting IPTC datasets are converted from the "IPTC Core" XMP schema properties in the sidecar file to the older IPTC IIM4 format. The inserted XMP properties include those in the "IPTC Core" XMP schema. exiv2 -M"set Exif.Photo.UserComment charset=Ascii New Exif comment" image.jpg Sets the Exif comment to an ASCII string. exiv2 -M"set Exif.GPSInfo.GPSLatitude 4/1 15/1 33/1" \ -M"set Exif.GPSInfo.GPSLatitudeRef N" image.jpg Sets the latitude to 4 degrees, 15 minutes and 33 seconds north. The Exif standard stipulates that the GPSLatitude tag consists of three Rational numbers for the degrees, minutes and seconds of the latitude and GPSLatitudeRef contains either 'N' or 'S' for north or south latitude respectively. exiv2 insert -l/tmp -S.CRW /data/*.JPG Copy all metadata from CRW files in the /tmp directory to JPG files with corresponding basenames in the /data directory. Note that this copies metadata as is, without any modifications to adapt it to the requirements of the target format. Some tags copied like this may not make sense in the target image. SEE ALSO http://www.exiv2.org/sample.html#modify Sample command files. http://www.exiv2.org/metadata.html Taglists with key and default type values. AUTHORS exiv2 was written by Andreas Huggel<EMAIL_ADDRESS>This manual page was originally written by KELEMEN Peter<EMAIL_ADDRESS>for the Debian project. Mar 23, 2012 EXIV2(1)


DUST OFF THE WINGS 1997 A film review by Timothy Voon Copyright 1997 Timothy Voon 2 :-) :-) for the life of a surfie
Directed by Lee Rogers Cast Lee Rogers, Ward Stevens, Kate Ceberano Written by Lee Rogers, Ward Stevens
If you're wandering what the title of this movie means, it has something to do with the mating habits of moths, and the dust that falls off their wings which act as pheromones. Now who would have chosen such an unusual title for a movie about drugs, sex, surfing, marriage and infidelity? It would have to be ex-junky, now happily married, still surfing, Australian director Lee Rogers. The material on which the movie is based hangs loosely on the type of lifestyle he lived as a Bondi Beach surfer. Now married to Australian singer Kate Cebrano, who is also in the movie, he closes a chapter on the youthful wild days of his life.
In this movie "fuck" is a word more commonly found in a sentence than "and" or "the". He creates images of carefree surfers who do the average crazy things to get a thrill outside riding waves. There's a bit of coke snorting in the toilets, the occasional joint being smoked.  It's no wonder Rogers has decided to get married and settle down. Such excessive roller coaster riding couldn't be too good for anybody's physical or mental health.
So Roger's story circles about his getting married and the problems that arise from within his world of confusion. He finds out that his fiancee has been seduced by another surfer whilst on drugs. Can he forgive her and go through with the wedding? He finds it hard to accept what has happened, despite being the one to get a blow job the night before the wedding. Whether the marriage will last isn't the issue here. Rogers just wants to make a point about how difficult it is to stay faithful to one person, when everybody around him is doing a lot of f-king and jerking off.
Some salient points are made when Rogers and his best friend Ward Steven's discuss how many women they have slept with in their short lives. It doesn't surprise them that they have lost count, but the figure hovers somewhere in the triple digits. An interesting comparison is also made between a "Buck's Night" and a "Hen's Party". One party settles for an enormously obese stripper, whilst the other chooses a Don Juan impersonator. The film has a feel and look of a documentary, with amateurish angles and views to serve the style of movie.
This isn't a story about morals, but it does have a lot to say about choice of lifestyle. So after watching a lot of men my age chasing cheap thrills and meaningless relationships, made me feel relieved that I had chosen a life more ordinary.
Timothy Voon e-mail<EMAIL_ADDRESS>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


| 1 | /* | |
| 2 | * Licensed to the Apache Software Foundation (ASF) under one or more | |
| 3 | * contributor license agreements. See the NOTICE file distributed with | |
| 4 | * this work for additional information regarding copyright ownership. | |
| 5 | * The ASF licenses this file to You under the Apache License, Version 2.0 | |
| 6 | * (the "License"); you may not use this file except in compliance with | |
| 7 | * the License. You may obtain a copy of the License at | |
| 8 | * | |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 | |
| 10 | * | |
| 11 | * Unless required by applicable law or agreed to in writing, software | |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, | |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| 14 | * See the License for the specific language governing permissions and | |
| 15 | * limitations under the License. | |
| 16 | */ | |
| 17 | package org.apache.commons.math3.exception; | |
| 18 | ||
| 19 | import org.apache.commons.math3.exception.util.Localizable; | |
| 20 | import org.apache.commons.math3.exception.util.LocalizedFormats; | |
| 21 | ||
| 22 | /** | |
| 23 | * Exception to be thrown when function values have the same sign at both | |
| 24 | * ends of an interval. | |
| 25 | * | |
| 26 | * @since 3.0 | |
| 27 | * @version $Id: NoBracketingException.java 1364378 2012-07-22 17:42:38Z tn $ | |
| 28 | */ | |
| 29 | public class NoBracketingException extends MathIllegalArgumentException { | |
| 30 | /** Serializable version Id. */ | |
| 31 | private static final long serialVersionUID = -3629324471511904459L; | |
| 32 | /** Lower end of the interval. */ | |
| 33 | private final double lo; | |
| 34 | /** Higher end of the interval. */ | |
| 35 | private final double hi; | |
| 36 | /** Value at lower end of the interval. */ | |
| 37 | private final double fLo; | |
| 38 | /** Value at higher end of the interval. */ | |
| 39 | private final double fHi; | |
| 40 | ||
| 41 | /** | |
| 42 | * Construct the exception. | |
| 43 | * | |
| 44 | * @param lo Lower end of the interval. | |
| 45 | * @param hi Higher end of the interval. | |
| 46 | * @param fLo Value at lower end of the interval. | |
| 47 | * @param fHi Value at higher end of the interval. | |
| 48 | */ | |
| 49 | public NoBracketingException(double lo, double hi, | |
| 50 | double fLo, double fHi) { | |
| 51 | this(LocalizedFormats.SAME_SIGN_AT_ENDPOINTS, lo, hi, fLo, fHi); | |
| 52 | } | |
| 53 | ||
| 54 | /** | |
| 55 | * Construct the exception with a specific context. | |
| 56 | * | |
| 57 | * @param specific Contextual information on what caused the exception. | |
| 58 | * @param lo Lower end of the interval. | |
| 59 | * @param hi Higher end of the interval. | |
| 60 | * @param fLo Value at lower end of the interval. | |
| 61 | * @param fHi Value at higher end of the interval. | |
| 62 | * @param args Additional arguments. | |
| 63 | */ | |
| 64 | public NoBracketingException(Localizable specific, | |
| 65 | double lo, double hi, | |
| 66 | double fLo, double fHi, | |
| 67 | Object ... args) { | |
| 68 | super(specific, lo, hi, fLo, fHi, args); | |
| 69 | this.lo = lo; | |
| 70 | this.hi = hi; | |
| 71 | this.fLo = fLo; | |
| 72 | this.fHi = fHi; | |
| 73 | } | |
| 74 | ||
| 75 | /** | |
| 76 | * Get the lower end of the interval. | |
| 77 | * | |
| 78 | * @return the lower end. | |
| 79 | */ | |
| 80 | public double getLo() { | |
| 81 | 1 | return lo; |
| 82 | } | |
| 83 | /** | |
| 84 | * Get the higher end of the interval. | |
| 85 | * | |
| 86 | * @return the higher end. | |
| 87 | */ | |
| 88 | public double getHi() { | |
| 89 | 1 | return hi; |
| 90 | } | |
| 91 | /** | |
| 92 | * Get the value at the lower end of the interval. | |
| 93 | * | |
| 94 | * @return the value at the lower end. | |
| 95 | */ | |
| 96 | public double getFLo() { | |
| 97 | 1 | return fLo; |
| 98 | } | |
| 99 | /** | |
| 100 | * Get the value at the higher end of the interval. | |
| 101 | * | |
| 102 | * @return the value at the higher end. | |
| 103 | */ | |
| 104 | public double getFHi() { | |
| 105 | 1 | return fHi; |
| 106 | } | |
| 107 | } | |
Mutations | ||
| 81 |
replaced return of double value with -(x + 1) for org/apache/commons/math3/exception/NoBracketingException::getLo : NO_COVERAGE |
|
| 89 |
replaced return of double value with -(x + 1) for org/apache/commons/math3/exception/NoBracketingException::getHi : NO_COVERAGE |
|
| 97 |
replaced return of double value with -(x + 1) for org/apache/commons/math3/exception/NoBracketingException::getFLo : NO_COVERAGE |
|
| 105 |
replaced return of double value with -(x + 1) for org/apache/commons/math3/exception/NoBracketingException::getFHi : NO_COVERAGE |


It was a chilly morning as we met at the South Gate parking lot. Discussions of the Super Bowl taking place the next day, four wheeling, and tails of who shot John were plentiful as we went through the ritual of greeting all new arrivals and waiting on folks to get ready for our trek south. We departed a respectful half hour late, around 8:30 a.m., being led by yours truly as co-pilot for Curtis. Yes, Muddancer was without his trusty mount (still playing transfer case games).
Those folks in attendance were - Curtis and myself, Chris Long, Dave and Kelly Nothstein, Terry Howe, Jim Hope and son with friend, Bill Dixon, Matt Peterson, Roger Wild and Linda with her two daughters Robin and Erin. Guests in attendance where Peka Aalto, Ed Magoffin, and R.J. Robinson.
We arrived at the Mad Dog trail head, aired down, locked ‘em in, moved out to conquer. First up to conquer was 4HOOFIN Bronco ridden by Curtis. Complete with newly installed T-18, he hardly spun a wheel, making it look easy. Next came Dave in his Jeep Cherokee.  After drive shaft removal, Curtis attempted to winch the injured Jeep to level ground. One winch proved not enough and had to be joined by a second. After much time and effort, the task was completed and the conquering continued. The Mad Dog seemed content with one Jeep, until Terry woke it up again trying to get over the upper wall.  This time it was the rear axle of the tasty CJ-7. After much work by all, both axles were removed, the third member dropped, and a spare axle replaced the bitten one.
A short trail was made long by the Mad Dog. As we limped away, vowing to return another day to conquer this nasty cur, we could hear a deep growl that sounded like a sadistic chuckle daring us to try.
We returned home just after dark, secure in the knowledge that a good time was had by all, even with the mishaps of the day. After all, we were able to commune with nature and challenge another of her beasts instead of sitting at home or working.


TunnelBeast
login
Powered by
TunnelBeast
Current Routes:
Select external port:
Add
Your Favorite Routes:
LOGOUT


50GB / 100GB
Picnic Time
2 MB
Waiting for the train
1.3 MB
Little friends
1.5 MB
Awesome
4 MB
Gymnastics Time.
Developer Script Time
1 MB
Developer Wheels
Steam Train
Nature
Waiting Train
2.2 MB
Friends fishing
1.2 MB
NASA satellite
Saw Blade
Island
Riding Time.


GET CLOSER USING GOOGLE MAPS!
Culinary Journeys.
Get Started!
send
AFRICAN CUP OF NATIONS
The AFCON is a...
See Stats!
send
THE MONGOLS
The 'Mongol' definition originated by...
Know their History!
send


Syndicate Drive
Syndicate runs as a FUSE filesystem on UNIX-like operating systems (Linux, Mac OS X, etc.). Our source code and documentation are on GitHub. Our first stable release will be for OpenCloud users.
Syndicate integrates cloud services into a scalable coherent storage system. This puts you in control of your data, not the services.
With Syndicate, you are not tied down to any single service provider, and you can seamlessly move between them. You control where and how your data is stored.
Syndicate offers a secure programmable storage layer over untrusted providers. You control what Syndicate does with your data, and who can access it.
Syndicate can use content distribution networks and scalable PaaS providers to boost the speed of your data downloads, while also ensuring you always read fresh, correct data.
Syndicate gives developers end-to-end control over data consistency and trust boundaries, so you never worry about hosting user data or scaling access to it.
Syndicate is scalable software-defined storage. With Syndicate, you control end-to-end data consistency, dataflow processing, and admin/trust boundaries across a changing set of existing cloud services.
With Syndicate, each user gets their own decentralized storage volumes that can host a scalable amount of data and handle a scalable number of reads and writes.
Volumes are always online and are not tied to any service providers--users do not need to run or manage their own servers, nor do they need to leave any of their devices turned on 24/7.
Syndicate instead incorporates existing cloud storage, content distribution networks, publicly-routable datasets, PaaS providers, and authentication services to create volumes.
Please read our extended abstract or whitepaper for a technical overview of the Syndicate architecture.
Applications that are being built using Syndicate include:
Storing emails in the cloud is convenient and enables access from multiple devices. However, users have to trust cloud providers for not leaking or processing their emails. Syndicate enables private encrypted email that is secure as PGP email, but without forcing users to manage keys.
Scientific data storage is the ultimate data silo. Syndicate helps integrate scientific storage with commodity CDNs and cloud storage to enable multiple labs to efficiently share data between their computing facilities.
Dropbox showed the world the value of having a shared folder sync'ed across all your devices. With Syndicate, your shared folder can not only scale to millions of users, but also offer end-to-end encryption and arbitrary replication policies.
Syndicate is developed at the Computer Science department of Princeton University. Syndicate started off as an academic research project by Jude Nelson under the supervision of Larry Peterson. Syndicate is currently deployed using PlanetLab and VICCI and contributors include: Muneeb Ali, Wathsala Vithanage, John Whelchel, and Illyoung Choi.


Tasty `American Pie' by Homer Yen (1999)
I recall the trials and tribulations of my high school years. It was a period that would mercilessly force me to exit boyhood and thrust me into the unwelcome arms of adulthood. But of all the experiences that I collected throughout those tender years, I can honestly say that the toughest and the scariest thing that I had to cope with was my raging hormones and my crazed curiosity about girls and sex. Yes, more scarier than my seventh grade English teacher and even scarier than the bullies that constantly hounded me, trying to deal with my personal wonderment about sex was like trying to fight an invisible monster. For all teenagers, it's almost like a rite of passage. And what we did was sometimes downright nasty, sometimes funny, and sometimes shocking. And all of that is terrifically and shamelessly brought to light in what might be the funniest movie this summer.
`American Pie' is a comedy about four sexually frustrated seniors that enter into a pact to lose their virginity by the end of the school year. Just how great are their hormones raging? One of them notes, after watching Disney's The Little Mermaid, that Ariel is soooo hot. The four schoolmates are Kevin, Oz, Finch and Jim. Kevin is the only one with a steady girlfriend, but doesn't want to have sex until the time is just right. But what is the perfect time? There is just no way to know, and that frightens him.  He joins up with a jazz choir and discovers that getting in touch with his feminine side has some tremendous advantages. Finch is the phobic hermit who doesn't stand a chance with any girl. So, he pays someone $200 to start spreading rumors about his prowess. Suddenly, girls are lining up to go out with him. But among these friends, it is Jim that is the most curious and the most fearful. When he has a chance to romp with a pretty exchange student, his inexperience comes shining through. He has much to learn, and will do anything to seek out this kind of knowledge. This may include watching scrambled porn channels or experimenting with Mom's apple pie in a scene that is completely shocking, raunchy, yet unbelievably funny.
The one thing that I want to say about this film is that it well deserves its `R' rating. There is stuff in here that is completely gross in the same manner that `There's Something about Mary' was gloriously ribald. In fact, at first this film received a sinful NC-17 rating before a couple of more cuts finally brought it down to a teen-accessible rating. But I guess after seeing the no-holds-barred coarseness of South Park, this seems tame by comparison.  But unlike many teen sex comedies, there is a genuine intelligence at work here.  Their plans of action are thoughtful and they think about the consequences. They know what they want, yet temper it with responsibility. But almost always, it's impossible to know how to fight the invisible monster, and it's these episodes that make `American Pie' such a carnal delight to watch.
Grade: B+
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


This example demonstrates a tool that can be built with an intermediate-level knowledge of the MyGeotab system. It is suitable for those who have a good understanding of the Geotab ecosystem and are comfortable with the MyGeotab SDK and leveraging the MyGeotab APIs within their projects.
| Date | Diagnostic | Value |
|---|


WITNESS LIST Civil Practices Committee April 21, 1999-2:00P HB 35 For: Green, Charles (Self) Against: Schneider, Rob (Consumers Union - Southwest Regional Office) On: Durden, David (Texas Department of Insurance) HB 185 For: Shirk, Michael (Self) Against: Kennard, Karen (Texas Civil Justice League) HB 331 For: Jackson, Gary D. (Self) Taylor, Jay (Self) HB 759 For: Shirk, Michael (Self) Against: Kennard, Karen (Texas Municipal League) HB 1529 Against: Hundere, Nan (Self and Texas Association of School Boards (TASB)) HB 1558 For: Ratliff, Shannon (Texas Civil Justice League) Against: Hampton, Hartley (Texas Trial Lawyers Association) HB 1679 Against: Ramsey, Mike (Texas Trial Lawyers Association) HB 2733 For: McBeth, John M. (Texas Transit Association) Against: Hampton, Hartley (Texas Trial Lawyers Association) On: Temple, Larry (Texas Workforce Commission) HB 3479 For: Gourley, Gail (Self) Gourley, Kevin (Self) Lovelace, Joe (National Alliance for the Mentally Ill of Texas) On: Green, Wayne (Texas Commission on Law Enforcement Officer Standards and Education (TCLEOSE)) HB 3564 For: Cameron, Douglas G. (Self and World Association of Electroshock Survivors) Martin, Jennifer Harris (Self and RESPECT) Novak, Dianna Loper (Self) Novak, Henry J. (Self) Against: Haralson, Hal (Self) Kern, Diana (Self) Leatherman, M.D., Martha E. (Self and Texas Association of Psychiatric Physicians) Lovelace, Joe (National Alliance for the Mentally Ill of Texas) Swinney, James C. (Self and Texas Depressive and Manic Depressive Association) HCR 83 For: Anderson, Darrell (Paul Herbig) Herbig, Paul (Self) On: Cain, Delmar L. (Texas A & M University System, General Counsel) HCR 127 For: Stuart, Earnest (AFEX Corporation) HCR 153 For: Aubin, Richard (Self and Banowsky, Betz & Levine, P.C.) SB 509 For: Glenn, Eric (Texas Association of Business & Chambers of Commerce)


EDA and Data Visualization¶
Exploratory data analysis is comprised of three parts:
Data Inspection:
This will start by looking at the raw data. This can be done by using functions such as head(), describe(), info(), etc.
The head() function will print the first n rows of the dataset.
random_df.head(5)
The tail() function is similar to the head() function; it will print the last n rows of the dataset.
random_df.tail(5)
The sample function randomly selects n rows to display, where n is a specified argument
random_df.sample(5)
The describe() function computes summary statistics of your data, i.e. mean, count, median, quartile 1 value, quartile 3 value, and more.
random_df.describe()
The info() function prints a summary of your dataset. Specifically, it identifies the number of non-null observations per variable as well as the type of each variable.
random_df.info()
Data Exploration:
Data exploration calls for exploring further relationships between variables. Examples of ways to explore these relationships are by cross-tabulation (which is used for analysis of categorical data) and dimension reduction (which is used to reduce the amount of unnecessary data in your model), both of which improve accuracy and speed up computing.
Data Visualization:
Data visualization calls for visualizing relationships between variables. In this chapter, we will review some common data visualization methods.
Basic Charts¶
These charts will display two variables (or columns) of data.
Ex: Bar charts, scatter plots, line graphs
Bar Charts - compare a single statistic across groups
Horizontal (x-axis): categorical variable
Vertical (y-axis): “statistic” of a numerical variable (ex. count, mean, median, etc.)
ax = random_df.groupby('categorical_variable_of_interest').categorical_variable_of_interest.mean().plot(kind='bar', figsize=[10, 6], color='orangered')
ax.set_ylabel('label_here')
plt.tight_layout()
plt.show()
(Bar chart example)
Scatter plot - comparing two numerical variables
Horizontal (x-axis): numerical variable
Vertical (y-axis): also a numerical variable
random_df.plot.scatter(x= "variable_1", y="variable_2", legend=False, color='orange')
(Scatter plot example)
Line graph - display numerical variable (or statistic) over time
Horizontal (x-axis): date/time variable
Vertical (y-axis): numerical variable
First, add Date/Time variable
random_df['Date'] = pd.to_datetime(random_df.dteday, format='%m/%d/%Y')
Next, set Date column as index. This is a time series, not a data frame.
random_ts = pd.Series(random_df.cnt.to_numpy(), index=random_df["Date"])
Lastly, plot your Line Graph.
random_ts.plot(ylim=[0, 10000],
legend=False,
figsize=[6, 4],
color='darkorange')
plt.xlabel('x_label_here') # set x-axis label
plt.ylabel('y_label_here') # set y-axis label
plt.tight_layout()
plt.show()
(Line graph example)
Distribution Plots
Display distribution of a single numerical variable
Ex. Histograms, Box plots
Histogram - represents frequency of values with vertical bars
Horizontal (x-axis): numerical variable, potentially “binned”
Vertical (y-axis): frequency for that same variable
Note: For continuous data or data with many possible values, group values into bins or a series of ranges (ex. One bin contains all values between 20 and 40)
fig, ax = plt.subplots(1,1)
sns.histplot(random_df.numerical_variable_of_interest, bins=10, color='orange')
ax.set_title('Variable Distribution', fontsize=20)
ax.set(xlabel='x_label', ylabel='count')
(Histogram Example)
Box plots - Display range and partial distribution of a numerical value
Quartile 1: 25% of the data below/to the left of this point
Median: 50% of the data below/to the left of this point; “Middle” value of a variable
Quartile 3: 75% of the data below/to the left of this point
Interquartile Range (IQR): IQR = Q3 - Q1; measure of dispersion
ax = data_df.boxplot(column='numerical_variable')
ax.set_ylabel('y_label')
plt.suptitle('')
plt.title('')
plt.show()
(Basic box plot example)
Box plot for comparison - Often used to compare distribution across groups (against categorical variables)
Horizontal (x-axis): categorical variable
Vertical (y-axis): numerical variable
ax = random_df.boxplot(column='numerical_variable', by='categorical_variable')
ax.set_ylabel('y_label')
plt.suptitle('') # Suppress the overall title
plt.title('')
plt.show()
(comparison box plots examples)
Special charts¶
Correlation table:
Correlation tables are a (relatively) concise way to display the correlation between different variables in our dataframe. However, they show a lot of information and can be difficult to digest. We solve this by using a heat map.
random_df.corr()
(Example of correlation table here)
Correlation heatmap:
Below, we use the correlation table defined above, but we add a heatmap. Let’s breakdown the code:
sns: using the seaborn library
heatmap: call heatmap function
corr: first positional argument, the correlation table
annot: if True, write data values in each cell
fmt: string formatting code for each value, # indicates decimal places
cmap: colormap to use, RdBu is another
corr = random_df.corr() # create correlation table
fig, ax = plt.subplots()
fig.set_size_inches(11, 7)
sns.heatmap(corr, annot=True, fmt=".1f", cmap="RdBu_r", center=0, ax=ax)
plt.show()
(Example of heatmap here)
Missing Value Analysis:
We can generate a bar chart to quickly see which variables are missing values.
msno.bar(random_df, color='deepskyblue')
(Example of graph here)
Pivot Tables:
A pivot table is used to summarize a large data set by grouping data together via multiple columns. This can be done using the pivot_table function, first taking one argument which will be the aggregated column. The index column then takes the row indices, specifying how the table will be grouped and the column arguments specifies the columns that are displayed in the table. Finally, the aggfunc argument specifies what functions should be computed and displayed, with average being the default.
random_df.pivot_table(index='var1', columns='var2', aggfunc={'totall': ['max', 'median', 'min']})
References:¶
Glossary:¶
Data Inspection:
Data Exploration:
Data Visualization:
Bar Charts:
Scatter Plots:
Line Graph:
Distribution Plots:
Histograms:
Box Plots:
Correlation Tables:
Correlation Heatmap:
Missing Value Analysis:
Pivot Tables:


I added search to this blog. If you're viewing at http://wtwf.com/scripts/ you can see it over there on the right.
Awesome!
--- comments: false date: 2008-04-22 layout: post permalink: /2008/04/search-results.php tags: - google title: Search Results ---


(This example assumes that you have created a menu named "myMenu" in the database via the admin interface)
This is a very basic example of how to use django-treemenus. Extend it to make your menus look good!