7.1. Binary Search Trees (WORK IN PROGRESS)¶
7.1.1. Binary Search Tree Definition¶
A binary search tree (BST) is a binary tree that conforms to the following condition, known as the binary search tree property. All nodes stored in the left subtree of a node whose key value is \(K\) have key values less than or equal to \(K\). All nodes stored in the right subtree of a node whose key value is \(K\) have key values greater than \(K\). Figure 7.2.1 shows two BSTs for a collection of values. One consequence of the binary search tree property is that if the BST nodes are printed using an inorder traversal, then the resulting enumeration will be in sorted order from lowest to highest.
Here is a class declaration for the BST.
Recall that there are various ways to deal with
keys and
comparing records
Three typical approaches are key-value pairs,
a special comparison method such as using the Comparator
class,
and passing in a comparator function.
Our BST implementation will require that records implement the
Comparable
interface.
// Binary Search Tree implementation
class BST<E extends Comparable<E>> {
private BSTNode<E> root; // Root of the BST
private int nodecount; // Number of nodes in the BST
// constructor
BST() { root = null; nodecount = 0; }
// Reinitialize tree
public void clear() { root = null; nodecount = 0; }
// Insert a record into the tree.
// Records can be anything, but they must be Comparable
// e: The record to insert.
public void insert(E e) {
root = inserthelp(root, e);
nodecount++;
}
// Remove a record from the tree
// key: The key value of record to remove
// Returns the record removed, null if there is none.
public E remove(E key) {
E temp = findhelp(root, key); // First find it
if (temp != null) {
root = removehelp(root, key); // Now remove it
nodecount--;
}
return temp;
}
// Return the record with key value k, null if none exists
// key: The key value to find
public E find(E key) { return findhelp(root, key); }
// Return the number of records in the dictionary
public int size() { return nodecount; }
<IP_ADDRESS>. BST Search¶
The first operation that we will look at in detail will find the
record that matches a given key.
Notice that in the BST class, public member function
find
calls private member function findhelp
.
Method find
takes the search key as an explicit parameter
and its BST as an implicit parameter, and returns the record that
matches the key.
However, the find operation is most easily implemented as a
recursive function whose parameters are the root of a
subtree and the search key.
Member findhelp
has the desired form for this recursive
subroutine and is implemented as follows.
7.1.2. BST Insert¶
Now we look at how to insert a new node into the BST.
Note that, except for the last node in the path, inserthelp
will not actually change the child pointer for any of the nodes that
are visited.
In that sense, many of the assignments seem redundant.
However, the cost of these additional assignments is worth paying to
keep the insertion process simple.
The alternative is to check if a given assignment is necessary, which
is probably more expensive than the assignment!
We have to decide what to do when the node that we want to insert has a key value equal to the key of some node already in the tree. If during insert we find a node that duplicates the key value to be inserted, then we have two options. If the application does not allow nodes with equal keys, then this insertion should be treated as an error (or ignored). If duplicate keys are allowed, our convention will be to insert the duplicate in the left subtree.
The shape of a BST depends on the order in which elements are inserted. A new element is added to the BST as a new leaf node, potentially increasing the depth of the tree. Figure 7.2.1 illustrates two BSTs for a collection of values. It is possible for the BST containing \(n\) nodes to be a chain of nodes with height \(n\). This would happen if, for example, all elements were inserted in sorted order. In general, it is preferable for a BST to be as shallow as possible. This keeps the average cost of a BST operation low.
7.1.3. BST Remove¶
Removing a node from a BST is a bit trickier than inserting a node, but it is not complicated if all of the possible cases are considered individually. Before tackling the general node removal process, we will first see how to remove from a given subtree the node with the largest key value. This routine will be used later by the general node removal function.
The return value of the deletemax
method is the subtree of
the current node with the maximum-valued node in the subtree removed.
Similar to the inserthelp
method, each node on the path back to
the root has its right child pointer reassigned to the subtree
resulting from its call to the deletemax
method.
A useful companion method is getmax
which returns a
pointer to the node containing the maximum value in the subtree.
// Get the maximum valued element in a subtree
private BSTNode<E> getmax(BSTNode<E> rt) {
if (rt.right() == null) { return rt; }
return getmax(rt.right());
}
Now we are ready for the removehelp
method.
Removing a node with given key value \(R\) from the BST
requires that we first find \(R\) and then remove it from the
tree.
So, the first part of the remove operation is a search to find
\(R\).
Once \(R\) is found, there are several possibilities.
If \(R\) has no children, then \(R\)’s parent has its
pointer set to NULL.
If \(R\) has one child, then \(R\)’s parent has
its pointer set to \(R\)’s child (similar to deletemax
).
The problem comes if \(R\) has two children.
One simple approach, though expensive, is to set \(R\)’s parent to
point to one of \(R\)’s subtrees, and then reinsert the remaining
subtree’s nodes one at a time.
A better alternative is to find a value in one of the
subtrees that can replace the value in \(R\).
Thus, the question becomes: Which value can substitute for the one being removed? It cannot be any arbitrary value, because we must preserve the BST property without making major changes to the structure of the tree. Which value is most like the one being removed? The answer is the least key value greater than the one being removed, or else the greatest key value less than (or equal to) the one being removed. If either of these values replace the one being removed, then the BST property is maintained.
When duplicate node values do not appear in the tree, it makes no difference whether the replacement is the greatest value from the left subtree or the least value from the right subtree. If duplicates are stored in the left subtree, then we must select the replacement from the left subtree. 1 To see why, call the least value in the right subtree \(L\). If multiple nodes in the right subtree have value \(L\), selecting \(L\) as the replacement value for the root of the subtree will result in a tree with equal values to the right of the node now containing \(L\). Selecting the greatest value from the left subtree does not have a similar problem, because it does not violate the Binary Search Tree Property if equal values appear in the left subtree.
- 1
Alternatively, if we prefer to store duplicate values in the right subtree, then we must replace a deleted node with the least value from its right subtree.
7.1.4. BST Analysis¶
The cost for findhelp
and inserthelp
is the depth of
the node found or inserted.
The cost for removehelp
is the depth of the node being
removed, or in the case when this node has two children,
the depth of the node with smallest value in its right subtree.
Thus, in the worst case, the cost for any one of these operations is
the depth of the deepest node in the tree.
This is why it is desirable to keep BSTs
balanced, that is, with least possible
height.
If a binary tree is balanced, then the height for a tree of \(n\)
nodes is approximately \(\log n\).
However, if the tree is completely unbalanced, for example in the
shape of a linked list, then the height for a tree with \(n\)
nodes can be as great as \(n\).
Thus, a balanced BST will in the average case have operations costing
\(\Theta(\log n)\), while a badly unbalanced BST can have
operations in the worst case costing \(\Theta(n)\).
Consider the situation where we construct a BST of \(n\) nodes
by inserting records one at a time.
If we are fortunate to have them arrive in an order that results in a
balanced tree (a “random” order is likely to be good
enough for this purpose), then each insertion will cost on average
\(\Theta(\log n)\), for a total cost of
\(\Theta(n \log n)\).
However, if the records are inserted in order of increasing value,
then the resulting tree will be a chain of height \(n\).
The cost of insertion in this case will be
\(\sum_{i=1}^{n} i = \Theta(n^2)\).
Traversing a BST costs \(\Theta(n)\) regardless of the shape of the tree. Each node is visited exactly once, and each child pointer is followed exactly once.
Below is an example traversal, named printhelp
.
It performs an inorder traversal on the BST to print the node values
in ascending order.
private void printhelp(BSTNode<E> rt) {
if (rt == null) { return; }
printhelp(rt.left());
printVisit(rt.value());
printhelp(rt.right());
}
While the BST is simple to implement and efficient when the tree is balanced, the possibility of its being unbalanced is a serious liability. There are techniques for organizing a BST to guarantee good performance. Two examples are the AVL tree and the splay tree. There also exist other types of search trees that are guaranteed to remain balanced, such as the 2-3 Tree.


12 Sep 2021 » On Saturdays
21 Aug 2021 » How to accept alipay payments on your Nuxtjs app using Stripe and Deta
13 Jun 2021 » On Slowness
20 Apr 2021 » Remembering Dadaji
31 Mar 2021 » Suggested reading list for Masters students at IDC -II
27 Mar 2021 » Personal Update and Restart
31 Mar 2019 » Suggested reading list for Masters students at IDC -I
01 Mar 2019 » missingMenu- Understanding food menus as a language
04 Nov 2018 » Getting a Spanish student visa
04 Jul 2018 » The delta of design
30 Jun 2018 » Clearance Processes at IDC
30 May 2018 » Spreadmaker- A tool to generate spreads for hand binding
04 May 2018 » Pinchphone- Exertoys for kids affected by cerebral palsy
04 May 2018 » Jalajap- A band to encourage water drinking behaviour
02 May 2018 » Togetherness
02 May 2018 » Points of View
02 May 2018 » Playful surfaces in a hackathon
02 May 2018 » Blowables
02 May 2018 » Birds on a Wire
01 May 2018 » Day and Night
04 Apr 2018 » Repurposing mass market addressable LEDs
03 Dec 2017 » FAND - shareable haptic foot interface device
01 Dec 2017 » Notes on being a department placement co-ordinator at IDC, IIT Bombay
10 Sep 2017 » Visualizing the Indian Railways in 3D
03 Sep 2017 » Who you are is what you get
31 Aug 2017 » Contributing to OSM using JOSM
03 Aug 2017 » Paisa Stick- A tangible cashless payment interface
12 Jun 2017 » The CHI 17 Experience
03 Jun 2017 » Local - Gamification in participatory GIS
02 Jun 2017 » Sleeping Dawn
03 Apr 2017 » How does this city sound ?
03 Apr 2017 » Hey Happy !
03 Apr 2017 » City Bot, may i take your order please
03 Apr 2017 » How do I look honey?
01 Apr 2017 » Hacking with Bamboo in the Field
22 Mar 2017 » Beaches,Bicycles and Pagoda
12 Mar 2017 » Me Dinojh and 100kms on cycle
12 Feb 2017 » Making processing and fluidsynth talk
12 Feb 2017 » Men and Three Worlds
26 Dec 2016 » How Do I Look Honey !
01 Dec 2016 » The Semester that was
01 Dec 2016 » Junior MDes Sem 1 Exhibitions
16 Oct 2016 » Typesetting ePUBs in LaTex
01 Sep 2016 » Dapoli mein kya chalta hai?
15 Aug 2016 » Making Dynamic Indicators for Ubuntu using pyGTK
09 Aug 2016 » DLogs-How to watch movies better
27 Jul 2016 » DIY Vacuum Pickup tool
18 Jun 2016 » Exif Data on Linux
11 Apr 2016 » Getting started with WIO Link
03 Apr 2016 » ConnectNeXt App Ecosystem
19 Mar 2016 » Augmenting Reality with Visiting Cards
12 Mar 2016 » ReactorX- The Arc Reactor from the movies
04 Feb 2016 » SpectraVUlizer- Lights,Music and Magic
02 Feb 2016 » Incar Navigation
31 Jan 2016 » RXing into the HAM World using RTL-SDR
10 Jan 2016 » Interactive Standies
01 Jan 2016 » Maker’s Rule-The Feature Packed PCB Multi-Tool
30 Dec 2015 » StarMAT-Star Wars Themed Interactive Doormat
11 Dec 2015 » CNCs & Me 4 - Fume Extractor
10 Dec 2015 » CNCs & Me 3 - Karigar
20 Nov 2015 » Doormate- Runner’s Up at Intel IOT Roadshow
16 Nov 2015 » PlantPal-Garden Light Watering Reminder
15 Nov 2015 » A Wifi DataLogger
14 Nov 2015 » Electronic Heart
11 Nov 2015 » GreenWali Diwali:A social interactivity experiment
08 Nov 2015 » Cricket Sentiment Analysis using nodejs
23 Oct 2015 » TiltStick:Trinket Makes You a Driver
22 Oct 2015 » Open Badge:You’da Maker
14 Oct 2015 » CNCs & Me 2 - Sketchy:The TableTop Pen Artist
12 Oct 2015 » Getting Started with RTL SDR- Playing some old school FM
06 Oct 2015 » Tamhini Ghats
26 Sep 2015 » Fiddle Mini - The DIY ‘Portable’ Music System
24 Sep 2015 » CNCs & Me 1 - Easy Peasy Stepper System Tester
15 Sep 2015 » PiTuber- The Low Power Raspberry Pi Youtube Offliner
08 Aug 2015 » Unbricking a MotoX, Resurrecting Smiles
01 Aug 2015 » LIVE BLOG - Intel IOT Roadshow Pune
27 Jul 2015 » Kalam Sir
12 Jul 2015 » CricPi- The (probably) MOST Minimalistic Live Cricket Ever
05 Jul 2015 » Juggad Supply- Saving a power supply from planned obsolescence
29 Jun 2015 » LiTiny- A Tiny LiIon charger for most things battery
29 May 2015 » TruPlay- Hacking an OVC3860 A2DP Adapter
17 May 2015 » FM430 - FM using MSP430
01 May 2015 » RoomMote- Control the color and sockets of your Room
17 Apr 2015 » BoardMerge- The command line single page PDF merge utility for PCBs
12 Apr 2015 » The Noob’s Guide to Pune Relocation
05 Apr 2015 » Kindify- Making PDFs readable on the Kindle
02 Apr 2015 » Improving UX of FM/AM Audio
01 Apr 2015 » Vehicle Acoustics Strategy
26 Feb 2015 » MacroCASE- A DIY phone case to see, up close !
17 Jan 2015 » MakerFest 2015 Ahe !
14 Dec 2014 » TinkerRule- The Maker’s Swiss Army Knife
13 Nov 2014 » Fiddle - The DIYed Music System
09 Nov 2014 » Nokia 5110 LCDs and MSP430
03 Nov 2014 » Arduino Library for Freescale’s MMA7361 3 axis accelerometer
28 Oct 2014 » Inside a Cheap LED Bulb
21 Oct 2014 » Implementing Tags/Categories on Jekyll using Liquid !
16 Oct 2014 » Welcome to the new home!
16 Oct 2014 » Mozilla Maker Party-Pune
24 Jul 2014 » The Hyderabad Quickie !
22 Jul 2014 » UberCubeX4- The portable LED Cube !
16 Apr 2014 » Tiano- The Touchscreen Piano
07 Apr 2014 » Open Agitator
01 Apr 2014 » Indian Solar Car Project
31 Mar 2014 » Ahemdabad Che!
29 Apr 2013 » The Electronic Inaugural Lamp
25 Aug 2012 » Arduino Controlware- Play with your duino without frequent code uploads


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- Right click to show/hide the options.
- Drag on an area to zoom.
- Shift click to approximate the function at f(x-a)
- Control click to multiply by f(a)
- Shift right click to add a mandelbrot variation
- Control right click to recurse by f(x-a)
- Middle click to change the function [ex, ln( (1+x) / (1-x) ) sinh(x)]
Fractal Home


| Tel: +1<PHONE_NUMBER> |
|<EMAIL_ADDRESS>|
| oganm.com |
MSc, Bioinformatics; University of British Columbia (Vancouver, Canada)
Thesis title: Identification/validation of cell type marker genes of the brain and their use in estimating cell type proportions in brain samples.
MSc Student at UBC Micheal Smith Laboratories - Supervisor: Dr. Paul Pavlidis
Rotation at BC Children’s Hospital - Supervisor: Dr. Wyeth Wasserman
Rotation at Simon Fraser University - Supervisor: Dr. Fiona Brinkman
Summer internship at University of Zurich - Supervisor: Dr. Barbara Tschirren
Summer internship at Harvard Medical School - Supervisor: Dr. George Daley
Lim, N., Tesar, S., Belmadani, M., Poirier-Morency, G., Mancarci, B.O., Sicherman, J., Jacobson, M., Leong, J., Tan, P. and Pavlidis, P. (2021). Curation of over 10 000 transcriptomic studies to enable data reuse. Database, 2021.
Zhuang, B., Mancarci, B.O., Toker, L. and Pavlidis, P. (2019). Mega-analysis of gene expression in mouse models of Alzheimer’s Disease. Eneuro, 6(6).
Tripathy, S.J., Toker, L., Bomkamp, C., Mancarci, B.O., Belmadani, M., and Pavlidis, P. (2018). Assessing Transcriptome Quality in Patch-Seq Datasets. Front. Mol. Neurosci. 11.
Toker, L., Mancarci, B.O., Tripathy, S., and Pavlidis, P. (2018). Transcriptomic evidence for alterations in astrocytes and parvalbumin interneurons in bipolar disorder and schizophrenia subjects. Biological Psychiatry.
Mancarci, B.O., Toker, L., Tripathy, S.J., Li, B., Rocco, B., Sibille, E., and Pavlidis, P. (2017). Cross-Laboratory Analysis of Brain Cell Type Transcriptomes with Applications to Interpretation of Bulk Tissue Data. ENeuro 4.
Tripathy, S.J., Toker, L., Li, B., Crichlow, C.-L., Tebaykin, D., Mancarci, B.O., and Pavlidis, P. (2017). Transcriptomic correlates of neuron electrophysiological diversity. PLOS Computational Biology 13, e1005814.
Horvath, G.A., Demos, M., Shyr, C., Matthews, A., Zhang, L., Race, S., Stockler-Ipsiroglu, S., Van Allen, M.I., Mancarci, O., Toker, L., et al. (2016). Secondary neurotransmitter deficiencies in epilepsy caused by voltage-gated sodium channelopathies: A potential treatment target? Mol. Genet. Metab. 117, 42–48.
Onder, T.T., Kara, N., Cherry, A., Sinha, A.U., Zhu, N., Bernt, K.M., Cahan, P., Mancarci, B.O., Unternaehrer, J., Gupta, P.B., et al. (2012). Chromatin-modifying enzymes as modulators of reprogramming. Nature 483, 598–602.
NeuroExpresso: A web application for visualization of gene expression data in brain cell types. Available at neuroexpresso.org.
markerGeneProfile: An R package for calculation of marker gene profiles as described in Mancarci et al. (2017). Available at github.com/PavlidisLab/markerGeneProfile.
VASCO: A web application for visualization of gene expression data from single cell RNA sequencing experiments. Developed for HackSeq 2016. Available at hackseq.github.io/vasco.
Viral Voyager: A web application for visualization and analysis of metavirome data collected for the Tara Oceans Project. Developed for HackSeq 2018. Available at oganm.com/shiny/taracyc.
Impact Replays: A web application for visualization play-by-play data from football games. Developed for SportsHack hackaton. Available at daattali.com/shiny/cfl.
eggstractor: Software to automate extraction of shape and pigmentation related information from quail eggs. Developed for 2012 summer internship under Barbara Tschirren. Available at github.com/oganm/eggstractor.
Misc. Bioinformatics related R packages: I maintain R packages aimed at making life easier for myslef and other bioinformaticians. See github.com/topics/ogan-bio for a full list.
Misc. D&D related repositories: I maintain R packages and web applications to be used while playing Dungeon’s and Dragons, a popular tabletop roleplaying game. See github.com/topics/ogan-dnd for a full list.


|
GIFMOD
|
Public Attributes | |
| vector< QColor > | colors |
| vector< QColor > | legendColors |
| vector< QString > | legendTexts |
| QGraphicsView * | view = 0 |
| QGraphicsScene * | scene = 0 |
| vector< QString > | middleText |
| int | x = 10 |
| int | y = 10 |
| int | dy = 20 |
| int | columnOffset = 120 |
| int | boxHeight = 20 |
| int | boxWidth = 50 |
| vector< QGraphicsTextItem * > | texts |
| vector< QGraphicsRectItem * > | boxes |
| vector< CBTC > | data |
| vector< float > | factors |
| vector< float > | shifts |
| QString | propertyName |
| double | time = -1 |
| QStringList | nodeNames |
| QStringList | edgeNames |
| QMainWindow * | window = 0 |
Definition at line 17 of file colorScheme.h.


Valoria Zhou
Project Portfolio
Valoria is a graduate student at Computer Science & Engineering Department, UC San Diego.
Projects
CSE 222
CSE 255


Back-End Architecture
By iCareer Climber Team on 6 July 2018
[1] The Backend Application
The backend is a Flask application whose primary purpose is to accept job description inputs and output job similarity scores using the model. This allows us to serve up model results for consumption by the frontend application.
The application is built as a docker container which can easily be tested locally and then uploaded to Google Cloud Container Registry and deployed to a Google Cloud cluster with Kubernetes.
The models are stored in a Google Cloud Storage bucket, which is mounted to the application container using FUSE. The models are stored as pickled Scikit Learn models which can be loaded into the Flask application.
Next Steps
- Serve data for visualizations through api
- Add processes to automate scraping and data procoessing
- Store data received from front end for use in updating of models
[2] The API Requests
The backend is a basic REST api which accepts and serves JSON data.
Job similarity model:
$ curl -XPOST -H 'Content-Type: application/json' -d '{"experience":[{"description": "organizing projects with agile and scrum methodologies"}]}' <IP_ADDRESS>/model/similar_jobs | python -m json.tool | head -22
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 7535 100 7445 100 90 8638 104 --:--:-- --:--:-- --:--:-- 8636
{
"results": [
{
"probability": 0.24560086752139812,
"title": "technical project manager"
},
{
"probability": 0.09362546705168302,
"title": "quality assurance engineer"
},
{
"probability": 0.07340485510020563,
"title": "quality assurance analyst"
},
{
"probability": 0.05443605668438692,
"title": "ux engineer"
},
{
"probability": 0.05370892876346319,
"title": "program manager"
},
Top skills model
$ curl <IP_ADDRESS>/model/skills/data%20scientist | jq
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 501 100 501 0 0 20293 0 --:--:-- --:--:-- --:--:-- 21782
{
"results": [
"learning model predict",
"utilize machine learning",
"time series model",
"random forest svm",
"market basket analysis",
"forest gradient boosting",
"http github com",
"principal component analysis",
"using python sql",
"data analysis data",
"using logistic regression",
"structure unstructured data",
"large data set",
"various data source",
"learning predictive analytics",
"language processing nlp",
"using kmeans clustering",
"learning algorithm python",
"data science bootcamp",
"amazon web service"
]
}


--- layout: post authors: ["Greg Wilson"] title: "Translating Software Carpentry into Spanish" date: 2014-06-22 time: "07:00:00" tags: ["Translation"] ---
We are very pleased to announce that a group of volunteers have begun translating our lessons into Spanish. If you would like to help, please head over to this GitHub repository, which has instructions and contact information. Our thanks to Fran Navarro, David Perez-Suarez, Javier J. Gutiérrez, Iván González, Daniel Domene y Zuri Bauer, and Jorge Bernabé for organizing this, and to everyone else for lending a hand.


Rare variant association test integrating variant position information. It aims to identify the presence of clusters of disease-risk variants in specific gene regions. For more details, please read the publication from Persyn et al. (2017) <doi:10.1371/journal.pone.0179364>.
| Version: | 0.2 |
| Depends: | R (≥ 2.15) |
| Published: | 2017-08-01 |
| Author: | Elodie Persyn |
| Maintainer: | Elodie Persyn <elodie.persyn at univ-nantes.fr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | DoEstRare results |
| Reference manual: | DoEstRare.pdf |
| Package source: | DoEstRare_0.2.tar.gz |
| Windows binaries: | r-devel: DoEstRare_0.2.zip, r-release: DoEstRare_0.2.zip, r-oldrel: DoEstRare_0.2.zip |
| OS X binaries: | r-release: DoEstRare_0.2.tgz, r-oldrel: DoEstRare_0.2.tgz |
Please use the canonical form https://CRAN.R-project.org/package=DoEstRare to link to this page.


Taught by
Alexandra Ackerman
Code available at
https://github.com/newschool-webfundamentals
Presentations available at
newschool-webfundamentals.github.io
Today we will have a short lesson so that you can spend a majority of the class focusing on lab and improving your CSS skills.
Cascading Style Sheets
Let's do a recap
In the early days of the web, there was no CSS.
No separation between HTML content and presentation
Creators of the web realized this wasn't scalable. They created a separate langague to define how HTML tags should look.
CSS creates rules for how different HTML elements (the boxes) are presented
Headers have a black background and white Helvetica fontYou use selectors and delcaration to define css rules.
Properties describe what aspect of the element should be styled, and values describe how.
To have CSS rules show up in your HTML page, you need to "link" them together. You do this by adding a link tag to your stylesheet file in the head of your HTML document
In the class, you should keep all CSS in stylesheets. Why?
There are many different styles you can control in these boxes:
Now that we have the basics down, let's talk more about the box model and how to position our content using floats.
Margin is the space between the element's border and other elements. Padding is the space between the content and the element's border.
Here's a closer look
Now that we can basically style our websites and position our elements on the page, let's talk about a simple effect you can use to spice things up: hover.
Pseudo-classes are keywords added to selectors that specify a special state of the element to be selected. For example :hover will apply a style when the user hovers over the element specified by the selector.
Here's a closer look
For today's lab, continue to focus on your portfolio project and getting your HTML and CSS organized.


Second edition of national energy quiz begins on Wednesday
Accra, Nov. 19, GNA - Ninety-six schools nationwide are set to contest for the Nana Sir Ofori-Atta I National Energy Quiz (NEQ) trophy.
A statement issued by the organisers of the quiz competition said it aims at educating students in Senior High Schools (SHS) and Technical, Vocational Education, and Training (TVET) on the energy sector of the country.
It said the competition was designed to empower students with knowledge in the entire energy value chain, including petroleum upstream and downstream, power generation, transmission and distribution, with focus on Power, Petroleum and Culture.
The statement said the preliminaries would begin on Wednesday, November 20, 2019 at the Krobo Girls Senior High School and end in December.
It noted that the contest that would run at six zones, would see the schools going through stages including the quarterfinals, semi-finals and the grand finals.
The statement said the NEQ is sponsored by the Ghana Oil Company Limited, Ghana Grid Company Limited, The Energy Commission, Ghana Maritime Authority, Ghana National Gas Company Limited and the Petroleum Commission.
It said the annual competition is run under the auspices of the Nananom of the Eastern Regional House of Chiefs, jointly organized by Eastern Star Awards Co. Limited in partnership with the Conference of Heads of Assisted Secondary Schools (CHASS) and coordinated in collaboration with the Ghana Education Service and the Ministry of Energy.
GNA


Harmony Core Parameter Loop Extensions
Expansion Tokens
<HARMONYCORE_BRIDGE_PARAMETER_DATAOBJECT>
For structure parameters, this token inserts the name of the associated Harmony Core data object. Unlike other tokens that produce similar results, such as <ParameterStructureNoplural>, this token is immune from the effects of the PARAMDEFSTR tweak.
<HARMONYCORE_BRIDGE_PARAMETER_DEFINITION>
Similar to the parameter loop token <PARAMETER_DEFINITION> in that it inserts the parameter data type definition of the current parameter, but with one key difference. If an implied decimal field has the same size and precision (e.g. D8.8) then this token increments the size of the field by one byte (e.g. inserting D9.8.
<HARMONYCORE_BRIDGE_PARAMETER_TYPE>
Inserts the Synergy .NET data type to be used when passing data to a method parameter via Harmony Core Traditional Bridge. The logic used when determining the Synergy .NET type to use follows the guidelines defined in the Synergy Method Catalog for the parameter, including any coercion rules that may be defined. In addition any optional parameters that result in value types will be converted to a nullable type.
<HARMONYCORE_BRIDGE_PARAMETER_TYPE_CS>
Inserts the C# data type to be used when passing data to a method parameter via Harmony Core Traditional Bridge. The logic used when determining the C# type to use follows the guidelines defined in the Synergy Method Catalog for the parameter, including any coercion rules that may be defined. In addition any optional parameters that result in value types will be converted to a nullable type. For structure parameters the structure name is inserted in Pascal case, for example ORDER_ITEM will be inserted as OrderItem.
<HARMONYCORE_BRIDGE_PARAMETER_TYPE_CS_XF>
Inserts the C# data type to be used when passing data to a method parameter via Harmony Core Traditional Bridge. The logic used when determining the C# type to use follows the guidelines defined in the Synergy Method Catalog for the parameter, including any coercion rules that may be defined. In addition any optional parameters that result in value types will be converted to a nullable type. For structure parameters the structure name is inserted in "xf" case, for example ORDER_ITEM will be inserted as Order_item.
<HARMONYCORE_PARAMETER_SAMPLE_DATA>
Inserts sample data for the parameter.
Expressions
Copyright © 2021 Synergex International, Inc.


*** User 2017-05-05 21:52:26.061 ***
21:52:26.061 [info] Disabling blocking sync...
*** User 2017-05-05 21:52:26.066 ***
21:52:26.066 [info] Configured peer_service_manager partisan_default_peer_service_manager on node 'node-1@MacBook-Pro-de-Anne-France'
*** User 2017-05-05 21:52:27.567 ***
21:52:27.567 [info] Enabling membership...
*** User 2017-05-05 21:52:27.569 ***
21:52:27.569 [info] Disabling blocking sync...
*** User 2017-05-05 21:52:27.574 ***
21:52:27.574 [info] Configured peer_service_manager partisan_default_peer_service_manager on node 'node-2@MacBook-Pro-de-Anne-France'
*** User 2017-05-05 21:52:29.089 ***
21:52:29.089 [info] Enabling membership...
*** User 2017-05-05 21:52:29.091 ***
21:52:29.091 [info] Disabling blocking sync...
*** User 2017-05-05 21:52:29.096 ***
21:52:29.096 [info] Configured peer_service_manager partisan_default_peer_service_manager on node 'node-3@MacBook-Pro-de-Anne-France'
*** User 2017-05-05 21:52:29.363 ***
Joining node: 'node-3@MacBook-Pro-de-Anne-France' to 'runner@MacBook-Pro-de-Anne-France' at port 51952
*** User 2017-05-05 21:52:29.363 ***
Joining issued: 'node-3@MacBook-Pro-de-Anne-France' to 'runner@MacBook-Pro-de-Anne-France' at port 51952
*** User 2017-05-05 21:52:29.364 ***
Joining node: 'node-2@MacBook-Pro-de-Anne-France' to 'runner@MacBook-Pro-de-Anne-France' at port 51936
*** User 2017-05-05 21:52:29.364 ***
Joining issued: 'node-2@MacBook-Pro-de-Anne-France' to 'runner@MacBook-Pro-de-Anne-France' at port 51936
*** User 2017-05-05 21:52:29.365 ***
Joining node: 'node-1@MacBook-Pro-de-Anne-France' to 'runner@MacBook-Pro-de-Anne-France' at port 51923
*** User 2017-05-05 21:52:29.365 ***
Joining issued: 'node-1@MacBook-Pro-de-Anne-France' to 'runner@MacBook-Pro-de-Anne-France' at port 51923
*** User 2017-05-05 21:52:29.426 ***
Waiting for join to complete with manager: partisan_default_peer_service_manager
*** User 2017-05-05 21:52:29.428 ***
Finished.
*** CT 2017-05-05 21:52:29.428 *** COVER INFO
Adding nodes to cover test: ['runner@MacBook-Pro-de-Anne-France']
*** CT 2017-05-05 21:52:29.437 *** COVER INFO
Successfully added nodes to cover test: []
*** User 2017-05-05 21:52:29.857 ***
Case finished: product_test
*** User 2017-05-05 21:52:36.925 ***
21:52:36.925 [info] Application lasp exited with reason: stopped
*** User 2017-05-05 21:52:36.926 ***
21:52:36.926 [info] Application partisan exited with reason: stopped
=== Ended at 2017-05-05 21:52:36
=== successfully completed test case
=== Returned value: ok
Test run history
| Top level test index


We have a complete autonomous water pumping and distribution solution based on our wisenet. This solution can be directly applicable to any gas and liquid pumping and distribution application.
Our electrical utility management solutioncombined with water management makes full-fledged facility management system.
Our platform is applicable to all non-realtime and soft realtime control systems
Our solution for large industrial A/C facility management is in works. Development and deployment is going on
WiSeNet provides breakthrough PLC specification interface. It facilitates automation logic to be specified transparently to the industrial domain and it's end application.
WiSeNet wireless nodes have various analog & digital peripheral interfaces to talk to digital sensors, actuators, and analog transducers. Our wireless nodes have multiple ADC channels for data logging applications
WiSeNet is designed with most unreliable power supply conditions in mind. We are proud of the result. Synchronization, and logic gating provides a safe, reliable and predictable execution of automation logic at all times. WiSeNet came out with flying colors in the tests conducted in most challenging power conditions of rural India.
We have cloud interface for remote monitoring and control capability from anywhere and any device. Our WiSeNet is fully functional with or without cloud/internet access.
WiSeNet's application layer is seamlessly extendable to additional modules at any stage of deployment, even after post deployment. Many of our different solutions can be clubbed together into a single installation.
We have a solution for managing various electrical utilities. We have devices to interact HVAC systems, motors, consumer utilities. Together with water management solution, we have complete large facility management solution. HVAC Solution
We have a set of sensors and actuators for managing large industrical HVAC faclities. We take feedback from our sensor devices and control actuators depending on the user's requirements.
We have low power data logging solution. Our wireless devices have interfaces to connect to various temperature, humidity, proximity, vibration, and pressure sensors. Our platform is applicable to all non real-time and soft real-time control systems.
We always had an idea of starting our own company.But how to start ?when to start??and where to start? are the three questions we always had in back of our minds.Finally we got inspired by the field of automation and that inspiration lead us to start a company named visual sensing and automation.
In 2013 we started our company and after that there was no looking back.We had many rest less nights, Infact we enjoyed a lot while spending hours and hours on the work which we like the most.We were a group of three when we started building our platform WISENET and it keeps on increasing till date.
Once we thought that our platform is ready we started looking for applications.The main four fields which we opted were water management,Facility management,AC management,Industrial automation and we started provinding services to several organizations in those areas.
Now we are trying to extend our platform to other applications.
Lead Marketer
Lead Marketer
Lead Marketer
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Susannah Lowe
Web Developer
Portland, OR
Susannah Lowe is a web developer living in Portland, OR. She has blended her background in sociology and psychology with her interest in art and techology, to become a web developer passionate about the user's experience.


Home
Rotation 3
T-Shirt Printing
This week, we started by working with printing t-shirts. I made a design on good drawings and then, using Corel Draw, converted it into a GT-3 file.
Pictured below is the final result.
UV Printing
The UV printer is a machine that uses UV light to cure ink on a surface. It is a very cheap printer to run, but there are some disadvantages.
These include: high startup costs, hard to clean up ink, and the ink can cause irritation
The next project I worked on was a puzzle. I made a design on Google Draw, and then I printed it on the UV printer. Shown below is my design.
I had some initial difficulty, because I had not used the printer before. These design was not difficult.
The other thing that I printed on the UV printer was a golfball with a design pictured on the left below. I did not have any difficulty designing this


jQuery Dom removing values into various elements
This is first paragraph with Simple text
- Delete list - Existing line1
- Delete list - Existing line2
- Delete list - Existing line3
- Delete list - Existing line1
- Delete list - Existing line2
- Delete list - Existing line3


{% extends 'admin/master.html' %} {% block body %}
The pages here let you directly edit the database of the site. The editing interface is somewhat low-level, so before you use it you should probably know more about the internals of the site.
The MeieRaha database consists of four tables, which you might or might not need to edit: Settings, Users, Datasets and Visualizations.
The Settings table currently contains some global parameters used in the application:
DEFAULT_VISUALIZATION_ID
parameter determines what visualization is shown when the user enters the main page of the website. You may edit the value of this parameter
to any integer, which is a valid id of one of the Visualization entries.
INFO
and INFO_<lang>
parameters contain HTML code that is displayed in the "Info" box of the site.The Users table is what you expect it to be - a list of users. The user credentials are only checked when this
administration interface is accessed, and no matter what username you log in under, you will have full access. Hence, although
you may in principle add users, a single admin
user should suffice. You may want use the interface to edit the password, though.
Note that the password is stored in the database in encrypted form. However, when you edit the record, you should enter it in plaintext, and it will be automatically encrypted as necessary.
The entries of the Datasets table represent separate panels in the MeieRaha visualization. A single visualization references three Datasets: the left panel, the right panel and the "comparison bubbles".
The Dataset record contains two fields: title
and data
. The first one is just a descriptive string,
that is useful to identify the meaning of the dataset in the table of the admin interface (it is not displayed anywhere else).
The second stores the content of a dataset is stored as a JSON record. The record must contain the fields:
id
: integer ID of the bubble. All ids of the bubbles must be differentlabel
: textual label of the corresponding bubble. Make it reasonably short.
If you need a longer explanation, provide it in the description
field (see below).amount
: monetary amount, which also determines the size of the bubbleThe record may in addition, optionally contain the fields:
label_<lang>
: localized value of the label for a given language.description
and description_<lang>
: a longer description (possibly localized) for the given bubble. It is shown on
the tooltip that appears when mouse is hovered over a bubble.color
: a valid HTML color string. Forces the corresponding bubble to have that color.actualFillAmount
: a positive number, which depicts how much of a bubble is "filled".plannedFillAmount
: a positive number, which depicts how much of a bubble is "planned to be filled".
This value makes most sense in the context of "revisions" (see below). Whenever for some revision the actualFillAmount
is not specified,
the corresponding plannedFillAmount
is looked up instead.children
: a list of child records, each itself a valid record.
A dataset may contain several "revisions".
If it is the case, all the fields of the record except children
may contain instead of a single value a list of values or an object of values. For example, suppose that the visualization
has revisions "R1"
, "R2"
, and "R3"
. In this case, the following are valid examples of an amount
field:
amount: 1000
, indicates the amount is 1000 for all three revisionsamount: [100, 200, 300]
, indicates the amount has value 100 for revision R1
, 200 for revision R2
and 300 for revision R3
amount: {"R1":100, "R2":200, "R3": 300}
, is equivalent in meaning to the above notation,amount: [100, 200]
is equivalent to amount: [100, 200, 200]
and amount: {"R1":100, "R2":200}
,amount: {"R1":100, "R3":300}
is equivalent to amount: [100, 100, 300]
,amount: {"R2":100}
is equivalent to amount: [0, 100, 100]
.
Special logic is used to interpret revisioned values of the actualFillAmount
and plannedFillAmount
.
As noted above, actualFillAmount
is used to determine the displayed "filling" of the bubble, however if for some revision it is
not specified, value of plannedFillAmount
will be used if available.
You can also use revisions for label
and color
fields, however note that if you specify
label: {"R2": "Something"}
, the system will not be able to guess a reasonable value for the label at revision R1
.
The list of revisions of a multi-revision visualization must be specified within the meta
field of the Visualization object (see below).
The Dataset database entry also has a title
field, which is only used to identify the meaning of the dataset in the database for convenience.
The entries in Visualization correspond to actual visualizations, available on the site. Each entry is specified by providing
references to three Dataset entries (the left, the right and the comparison panels) a title
(to help identify
the record in the admin interface for the DB administrator) and a meta_data
JSON record.
The meta_data
record must have the field title
which will be shown on the website to identify the visualization.
In addition, the following optional fields are supported:
title_<lang>
: localised title.hide
: when this is "true", the resulting dataset is not shown in the list of available datasets on the main page.multiyear
: when this is "true", the revisions are assumed to correspond to different years rather than modifications of the same budget for one year.
The difference is in how the tooltip is displayed (for a single year budget revisions you will see things like "initial/planned" and "current/adjusted" values).show_info_panel
: when this is "true", an info panel is displayed automatically whenever the visualization is opened (the Armenia guys wanted this behaviour).revisions
: a list of objects, describing revisions. Each object must contain fields id
, label
and, possibly,
label_<lang>
. If such field is available, both left and right datasets are assumed to have multiple versions,
corresponding to those revisions (see above). The visualization will allow switching between the versions.


All strings used to create the message can be unicode strings
(or UTF-8 bytestrings). The SafeMIMEText class will handle any
necessary encoding conversions.
| def google.appengine._internal.django.core.mail.message.EmailMessage.attach |
( |
|
self, |
|
|
|
filename = None , |
|
|
|
content = None , |
|
|
|
mimetype = None |
|
) |
| |
Attaches a file with the given filename and content. The filename can
be omitted and the mimetype is guessed, if not provided.
If the first parameter is a MIMEBase subclass it is inserted directly
into the resulting message attachments.
| def google.appengine._internal.django.core.mail.message.EmailMessage.attach_file |
( |
|
self, |
|
|
|
path, |
|
|
|
mimetype = None |
|
) |
| |
Attaches a file from the filesystem.
| def google.appengine._internal.django.core.mail.message.EmailMessage.recipients |
( |
|
self | ) |
|
Returns a list of all recipients of the email (includes direct
addressees as well as Bcc entries).
| def google.appengine._internal.django.core.mail.message.EmailMessage.send |
( |
|
self, |
|
|
|
fail_silently = False |
|
) |
| |
The documentation for this class was generated from the following file:
- code/googleappengine-read-only/python/google/appengine/_internal/django/core/mail/message.py


Inria is the French national research institute for digital science and technology. As a technological institute, Inria supports the diversity of innovation pathways: from open source software publishing to the creation of technological startups (Deeptech).
FrostForecast is a collaboration between Inria Chile and the Inria-Eva team.
Created in 2012, Inria Chile is the first Inria center outside France. It is a driving force for technological innovation and knowledge transfer, collaborating effectively with companies, universities, public institutions, and startups to meet the challenges of the digital revolution. It is also a means to promote the R&D activities of companies and startups in Chile and Latin America.
Inria-EVA is a leading research team in low-power wireless communications. The team pushes the limits of low-power wireless mesh networking by applying them to critical applications such as industrial control loops, with harsh reliability, scalability, security and energy constraints.


Kamlesh Tiwari murder case: Man held for providing weapon
Tiwari was shot dead at his residence in Lucknow on October 18.
by PTIThe UP anti-terrorism squad (ATS) along with its Gujarat counterpart arrested a person from Kanpur for providing the pistol used in the murder of Hindu Samaj Party leader Kamlesh Tiwari. Tiwari was shot dead at his residence in Lucknow on October 18.
"On October 18, the president of Hindu Samaj Party Kamlesh Tiwari was murdered by two persons. The prime accused in the case, Ashfaq, alias Moinuddin, and Fareed Pathan have been arrested and sent to jail. Yusuf Khan, who provided the pistol, which was used in the murder, has been arrested from Kanpur in a joint operation by the UP and Gujarat ATS," the UP ATS said in a statement, adding that detailed interrogation was on.
Two mobile phones had also been recovered from the accused, the ATS said.Special Thursday Expiry on 10th 7th Nov
Early Closure to The Great Diwali Discount. Last Chance to get Moneycontrol PRO for a year @ Rs. 289/- only
Coupon code: DIWALI


Google Earth Enterprise Documentation Home | Install Google Earth Enterprise
Hostname and Fully Qualified Domain Name Configuration
In order to make sure that the Apache server correctly determines the Fully Qualified Domain Name (FQDN) of the
server, and that the virtual hosts use the correct server URL the hostname should be configured correctly. This is
preferred instead of modifying the Apache server name configuration so that all the software looks to the same
source for determining FQDNs. If the command hostname -f
returns the correct FQDN hostname then
the system is correctly configured to run Open GEE. If not the following instructions may help.
Ideally the network where GEE is installed has a DNS set up to resolve hostnames so that IP traffic can be
routed appropriately. However in cases where there is no DNS, static hostnames can be used as well. With static
hostnames the hostname
or hostnamectl
command should be used to set the hostname to its
FQDN. The /etc/hostname
file should be updated to match as well so the name stays after reboots. If
setting the hostname to the FQDN is not possible then setting the FQDN in the /etc/hosts
file is
necessary. When using a static hostname some operating systems may still give a warning on Apache startup even if
it correctly determined the FQDN. Adding the FQDN to /etc/hosts
would fix this as well. If adding the
FQDN to /etc/hosts
is needed, then the FQDN followed by just the host portion should be added to the
line that contains the IP address the server will be listening on. This should be the first line of the file:
<IP_ADDRESS> myserver.mydomainname.com myserver localhost
If GEE is not using DNS other clients that connect to the server non-locally would need to have the same static mapping set up in their hosts files since the server hostname would not be resolved dynamically.
When determining where to look for the correct FQDN Apache calls hostname
which will use a resolver.
The system configuration determines what the order is for resolving the FQDN. If the /etc/nsswitch.conf
file is being used there is a line like this that defines the order:
hosts: dns myhostname files
This example shows that the DNS on the network would be queried first. If that does not define the FQDN then the
hostname would be queried. If the FQDN is still not defined then configuration files would be queried, mainly
/etc/hosts
.
If the /etc/host.conf
file is being used to resolve the hostname, then there is a line like this
that defines the order:
order hosts,bind
In this example hosts refers to the /etc/hosts
file and is checked first, and bind refers to the
DNS.
Here is another note on the server hostname.


--- layout: page title: About header-img: "img/about-bg.png" ---
Currently a medical supply delivery driver, I decided to start my coding career when working as an electrician at an Eli Lilly data hub. I enjoy long walks to the fridge, my two pups, and the occasional marvel tv series on netflix.


--- layout: post title: Notes from my E-VAN talk on Sagas date: 2011-03-12 17:34:02.000000000 +01:00 categories: - NServiceBus - Presenting tags: [] status: publish type: post published: true meta: dsq_thread_id: '299892789' _wp_old_slug: '' _edit_last: '1' author: login: andreas.ohlund email<EMAIL_ADDRESS>display_name: Andreas Öhlund first_name: Andreas last_name: "Öhlund" ---
Last week I did a online presentation for the European Virtual Alt.net on Sagas. The talk gives a basic introduction to sagas and how they are supported in NServiceBus. There was a Q & A at the end where we covered the details on how timeout are managed for sagas.This lead us to discuss how the NServiceBus timeout manager works and considerations for deployment. We also went into how sagas are unit tested and the NServiceBus support in this area.
The recording can be found here:
Slides here:
http://www.slideshare.net/andreasohlund/evan-sagas
Finally I like to give credit to Jan Van Ryswyck for his great effort in keeping the E-VAN talks coming at a steady pace. Priceless efforts for the community, thanks Jan!


Small lightweight loading indicator components.
To install this library, run: npm install @ngx-lite/loaders --save and then from your Angular AppModule:
npm install @ngx-lite/loaders --save
AppModule


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--- title: "Purrr Tutorial: Understanding Nested Dataframes" author: "Corey Pembleton" date: '2018-11-11' output: html_document slug: purrr-and-nesting-dataframes categories: ["R", "Tutorial"] tags: ["tidyverse", "purrr"] thumbnailImagePosition: "left" thumbnailImage: "/img/alicerabbitholefeaturesmall.jpg" ---
I’m still learning the functionality behind purrr
, the tidyverse package which helps with the commonly found challenges in data management relating to splitting, applying analysis, and recombining the data. In this post, I walk through my progress in various purrr tutorials, and a few use cases of the purrr::map()
functions. I will be doing some of this with a Canadian census dataset I created in a previous post. Here we go down the purrr rabbit hole…
purrr, and its map_*()
functions are fundamental to executing functions in the tidyverse. I have used map()
and different purrr functions in the past, but really didn’t understand the underlying mechanisms behind the functions.
To understand map()
, I realized I need to understand lists, and tidyr::nest()
. Starting with Hadley’s R For Data Science (r4ds) section on vectors, and this note detailing the nest()
tidyr
function, I could gain some understanding around lists, lists of lists, and lists of dataframes.
From r4ds, I learned that lists (also known as recursive vectors) are a type of vector which unlike atomic vectors (e.g. logical, integer, character, etc.) can contain a mix of different types of atomic vectors. The structure of lists can be viewed using str()
:
y <- list("Canada", 32500, TRUE, 1L)
str(y)
## List of 4
## $ : chr "Canada"
## $ : num 32500
## $ : logi TRUE
## $ : int 1
Lists can contain other lists, which is structured hierarchically:
z <- list(list("Canada", 32500), list(TRUE, 1L) )
str(z)
## List of 2
## $ :List of 2
## ..$ : chr "Canada"
## ..$ : num 32500
## $ :List of 2
## ..$ : logi TRUE
## ..$ : int 1
The hierarchical structure of the nested list is important when considering the composition of a list containing multiple dataframes in a nested manner, and subsequently how the purrr::map()
family of functions work. Interestingly, dataframes and tibbles are also lists, or augmented lists, with tibbles having the classes tbl_df, tbl, and data.frame, as can be seen with the attributes()
function (not shown).
#dataframe of a .csv derived dataset:
census_small_df <- Census_2011[,3:10]
typeof(census_small_df)
## [1] "list"
#tibble of a .csv-derived dataset
census_small <- as.tibble(census_small_df)
## Warning: `as.tibble()` is deprecated, use `as_tibble()` (but mind the new semantics).
## This warning is displayed once per session.
typeof(census_small)
## [1] "list"
Hadley Wickham describes a nested dataframe as:
“In a grouped data frame, you have one row per observation, and additional metadata define the groups. In a nested data frame, you have one row per group, and the individual observations are stored in a column that is a list of data frames.”
And to see what the looks like I can take the census dataset, group it by province, and see how the nested dataframe looks:
(nested_census <- census_small %>%
group_by(PRNAME) %>%
nest())
## # A tibble: 9 x 2
## PRNAME data
## <chr> <list>
## 1 Manitoba <tibble [173 × 7]>
## 2 Ontario <tibble [2,273 × 7]>
## 3 Quebec / Québec <tibble [1,371 × 7]>
## 4 British Columbia / Colombie-Britannique <tibble [711 × 7]>
## 5 Newfoundland and Labrador / Terre-Neuve-et-Labrador <tibble [47 × 7]>
## 6 Alberta <tibble [573 × 7]>
## 7 New Brunswick / Nouveau-Brunswick <tibble [102 × 7]>
## 8 Saskatchewan <tibble [109 × 7]>
## 9 Nova Scotia / Nouvelle-Écosse <tibble [93 × 7]>
Aside from being neat, something interesting is added - a new column “data”, which is a list class class. Looking at Newfoundland and Labrador’s dataframe can be done through the same operators as with lists: with [, [[, or $, which shows the ungrouped data, in this case of the province:
nested_census$data[[5]]
## # A tibble: 47 x 7
## CMANAME POP_PERC_CHANGE POP_DENSITY_KM2 PERC_POP_OVER_15 MEDIAN_AGE
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 St. Jo… 98 22.9 74.5 34.7
## 2 St. Jo… 232. 81.8 82.9 30.7
## 3 St. Jo… 3 204. 84.3 40.3
## 4 St. Jo… 11.1 40.3 81.5 39
## 5 St. Jo… 18.8 174 78.5 37
## 6 St. Jo… -7.5 1268 86.8 42.3
## 7 St. Jo… 6.4 885. 81.7 40.5
## 8 St. Jo… 13.1 299. 82.2 39.2
## 9 St. Jo… -1.7 2537 86.8 42.5
## 10 St. Jo… 0.3 2997. 87.5 42.3
## # … with 37 more rows, and 2 more variables: YEAR <dbl>, Population <dbl>
Nesting and pulling out one of the “nested” dataframes in its ungrouped format is helpful for seeing how data is structured. Now I’d like to look at how this process can be used in analysis, particularly when combined with the purr::map()
family of functions.
Now that I have my head wrapped around these structures, I can get back to the point: purrr::map()
. On CRAN, the function family of map_()
is titled as “apply a function to each element of a vector” - which for me is the simplest way to understand what the function does.
The rdocumentation gives some examples which I’d like to breakdown further, and refer to some other helpful stackoverflow and rdocumentation ntoes to expand on what I see happening.
In rdocumentation the first use of map()
, is presented as:
1:10 %>%
map(rnorm, n = 10) %>%
map_dbl(mean)
The first line and map()
give a randomly generated normal distribution of each vector in the 1:10 sequence, with the result looking like:
## [[1]]
## [1] 0.07794699 0.57283287 -0.61036166 -0.02172565 2.16254669
## [6] -0.22964343 -0.71100144 0.24052083 -1.51981458 1.46839613
##
## [[2]]
## [1] 1.6956291 0.6685591 1.8500667 2.8520690 3.2504057 2.5747697
## [7] 1.4511755 3.2623793 -0.6024948 1.4050053
##
## [[3]]
## [1] 3.3321520 0.5760959 3.2950726 1.5054192 3.6112089 4.0412260 2.6709625
## [8] 1.7469497 2.7019538 5.1608684
##
## [[4]]
## [1] 2.678901 3.158115 4.260420 4.280214 3.177970 4.326018 5.050615
## [8] 4.749415 3.675177 3.668754
##
## [[5]]
## [1] 5.267240 5.701283 3.734860 3.992918 6.429126 4.898337 4.096238
## [8] 6.498313 5.582025 5.737893
##
## [[6]]
## [1] 8.162955 5.571917 7.747532 6.133565 5.572346 6.311682 7.556221
## [8] 4.471871 4.006520 4.672951
##
## [[7]]
## [1] 8.766733 9.064169 8.376088 7.095315 7.925380 7.138724 6.055886
## [8] 7.607645 8.276201 4.896866
##
## [[8]]
## [1] 6.535975 8.715075 8.733495 6.622027 8.862002 6.685298 7.279018
## [8] 8.681013 7.523140 6.864805
##
## [[9]]
## [1] 9.690216 8.022188 8.458355 9.470875 8.648643 10.934756 7.419015
## [8] 7.417690 9.423873 8.632644
##
## [[10]]
## [1] 9.959626 9.898772 9.591644 11.422263 10.562875 10.265946 8.696758
## [8] 10.014282 10.682052 10.903074
Based on the structure map()
passes a function (rnorm()
) to each vector in 1:10.
In the third line another function, mean()
is passed through each of the 10 rnorm generated values in the list:
## [1] 0.8933956 1.7723813 2.5335300 4.0198242 4.5247112 6.2349205
## [7] 6.4516677 7.9534776 9.2499696 10.1123457
Another use-case which I see often is to pass a formula through various pieces of a dataframe. In the rdocumentation script sample below, we see three uses of map()
, a formula object being passed through a split dataframe, a function summary()
being called, and a named vector from a nested list “r.squared” being called:
mtcars %>%
split(.$cyl) %>%
map(~ lm(mpg ~ wt, data = .x)) %>%
map(summary) %>%
map_dbl("r.squared")
The second line, split(.$cyl)
, uses the base r function split()
to split the dataframe based upon the 3 cyl groups, 4, 6, and 8.
In the third line, map()
is being used to pass a formula object across each of the three groups created, which is a regression between the mpg and wt variables. Formulas in map()
can take three arguments:
.
;.x
and .y
and;..1
, ..2
, etc.Hence, map(~ lm(mpg ~ wt, data = .x))
will pass the formula, with .x
meaning that map
will pass the formula across all unused data variables present in the data frame. It will have the same result using map(~ lm(mpg ~ wt, data = .))
.
Note on the tilde (~): tildes are used in formula objects in R, such as within the lm() formula used above. with map() there is an additional ~ before the lm() function when used in map() to identify the expression as being a formula object.
In the subsequent lines, map passed thesummary()
function over each of the resultant groups’ linear model, and the map_dbl()
does what map()
does best: it passes through a nested list (summary output) and extracts based on the character vector(list) named “r.squared”. We could replace this with other summary variables in the list, such as adj.r.squared, sigma, fstatistic, and more.
In the above examples, there is a combination of base R functions and “tidyverse” tools. Using nest()
over split()
offers a few benefits such as smoother organization of the workflow, especially when combining other operations. The below example introduces broom::glance()
, which is a function well paired with nest()
and map()
, and plan to explore in detail in a future post.
With a nesting and mapping approach, the workflow would instead look something like this:
(model_summary <- mtcars %>%
group_by(cyl) %>%
nest() %>%
mutate(
model = map(data, ~lm(mpg ~ wt, data = .)),
model_summaries = map(model, broom::glance),
model_coef = map(model, broom::tidy))
)
## # A tibble: 3 x 5
## cyl data model model_summaries model_coef
## <dbl> <list> <list> <list> <list>
## 1 6 <tibble [7 × 10]> <S3: lm> <tibble [1 × 11]> <tibble [2 × 5]>
## 2 4 <tibble [11 × 10]> <S3: lm> <tibble [1 × 11]> <tibble [2 × 5]>
## 3 8 <tibble [14 × 10]> <S3: lm> <tibble [1 × 11]> <tibble [2 × 5]>
Which gives us a nested dataframe with the tidied broom results and glances. Like in the nest example above, these results can then be unnested, to reveal the statistical summaries from each model:
model_summary %>%
unnest(model_summaries, .drop = TRUE)
## # A tibble: 3 x 12
## cyl r.squared adj.r.squared sigma statistic p.value df logLik AIC
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
## 1 6 0.465 0.357 1.17 4.34 0.0918 2 -9.83 25.7
## 2 4 0.509 0.454 3.33 9.32 0.0137 2 -27.7 61.5
## 3 8 0.423 0.375 2.02 8.80 0.0118 2 -28.7 63.3
## # … with 3 more variables: BIC <dbl>, deviance <dbl>, df.residual <int>
By nesting instead of splitting, the subsequent mutate and map functions could more smoothly fit into a workflow, especially when the desire is to create a quick graphic to check results:
Learning a single additional tool in R for a beginner often means needing to learn or brush up on many more concepts to fully grasp the functionality behind the scenes. Wanting to explore a single topic opened the doors to multiple concepts and functions, and I’m glad I entered that rabbit hole.


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public class PolygonArea
extends java.lang.Object
This computes the area of a geodesic polygon using the method given Section 6 of
This class lets you add vertices one at a time to the polygon. The area and perimeter are accumulated at two times the standard floating point precision to guard against the loss of accuracy with many-sided polygons. At any point you can ask for the perimeter and area so far. There's an option to treat the points as defining a polyline instead of a polygon; in that case, only the perimeter is computed.
Example of use:
// Compute the area of a geodesic polygon.
// This program reads lines with lat, lon for each vertex of a polygon.
// At the end of input, the program prints the number of vertices,
// the perimeter of the polygon and its area (for the WGS84 ellipsoid).
import java.util.*;
import net.sf.geographiclib.*;
public class Planimeter {
public static void main(String[] args) {
PolygonArea p = new PolygonArea(Geodesic.WGS84, false);
try {
Scanner in = new Scanner(System.in);
while (true) {
double lat = in.nextDouble(), lon = in.nextDouble();
p.AddPoint(lat, lon);
}
}
catch (Exception e) {}
PolygonResult r = p.Compute();
System.out.println(r.num + " " + r.perimeter + " " + r.area);
}
}
| Constructor and Description |
|---|
PolygonArea(Geodesic earth,
boolean polyline)
Constructor for PolygonArea.
|
| Modifier and Type | Method and Description |
|---|---|
void |
AddEdge(double azi,
double s)
Add an edge to the polygon or polyline.
|
void |
AddPoint(double lat,
double lon)
Add a point to the polygon or polyline.
|
void |
Clear()
Clear PolygonArea, allowing a new polygon to be started.
|
PolygonResult |
Compute()
Return the results so far.
|
PolygonResult |
Compute(boolean reverse,
boolean sign)
Return the results so far.
|
Pair |
CurrentPoint()
Report the previous vertex added to the polygon or polyline.
|
double |
Flattening() |
double |
MajorRadius() |
PolygonResult |
TestEdge(double azi,
double s,
boolean reverse,
boolean sign)
Return the results assuming a tentative final test point is added via an
azimuth and distance; however, the data for the test point is not saved.
|
PolygonResult |
TestPoint(double lat,
double lon,
boolean reverse,
boolean sign)
Return the results assuming a tentative final test point is added;
however, the data for the test point is not saved.
|
public PolygonArea(Geodesic earth, boolean polyline)
earth
- the Geodesic object to use for geodesic calculations.polyline
- if true that treat the points as defining a polyline
instead of a polygon.public void Clear()
public void AddPoint(double lat, double lon)
lat
- the latitude of the point (degrees).lon
- the latitude of the point (degrees).
lat should be in the range [−90°, 90°].
public void AddEdge(double azi, double s)
azi
- azimuth at current point (degrees).s
- distance from current point to next point (meters).
This does nothing if no points have been added yet. Use PolygonArea.CurrentPoint to determine the position of the new vertex.
public PolygonResult Compute()
Counter-clockwise traversal counts as a positive area.
public PolygonResult Compute(boolean reverse, boolean sign)
reverse
- if true then clockwise (instead of counter-clockwise)
traversal counts as a positive area.sign
- if true then return a signed result for the area if
the polygon is traversed in the "wrong" direction instead of returning
the area for the rest of the earth.More points can be added to the polygon after this call.
public PolygonResult TestPoint(double lat, double lon, boolean reverse, boolean sign)
lat
- the latitude of the test point (degrees).lon
- the longitude of the test point (degrees).reverse
- if true then clockwise (instead of counter-clockwise)
traversal counts as a positive area.sign
- if true then return a signed result for the area if
the polygon is traversed in the "wrong" direction instead of returning
the area for the rest of the earth.lat should be in the range [−90°, 90°].
public PolygonResult TestEdge(double azi, double s, boolean reverse, boolean sign)
azi
- azimuth at current point (degrees).s
- distance from current point to final test point (meters).reverse
- if true then clockwise (instead of counter-clockwise)
traversal counts as a positive area.sign
- if true then return a signed result for the area if
the polygon is traversed in the "wrong" direction instead of returning
the area for the rest of the earth.public double MajorRadius()
public double Flattening()
public Pair CurrentPoint()
If no points have been added, then Double.NaN is returned. Otherwise, lon will be in the range [−180°, 180°).


--- layout: post title: no title date: '2011-03-31T23:27:55+02:00' tags: [] tumblr_url: http://dotmr.tumblr.com/post/4250481861 ---


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RONIN
*** (out of 4) - a good movie
Release Date: September 25, 1998 Starring: Robert De Niro, Jean Reno, Jonathan Pryce, Natascha McElhone, Stellan Skarsgard, Sean Bean, Katarina Witt Directed by: John Frankenheimer Distributed by: Metro-Goldwyn-Mayer MPAA Rating: R (strong violence, some language) URL: http://www.execpc.com/~kinnopio/reviews/1998/ronin.htm
There exists a litany of differences between a successful action movie and a successful suspense movie. Action movies are typically devoid of plot other than a simple byline which can string together several explosive sequences, while suspense movies hinge on plot and subtlety and the ability to bring everything full-circle. For fans of both genres, however, realism is key. Action fans want to know that the weapons and methods their heroes are using are authentic, and suspense fans want everything to fall into an explainable framework by the movie's end. On this advice, Director John Frankenheimer (THE ISLAND OF DR. MOREAU) strives for realism in an interestingly dynamic balance of suspense and action for his latest thriller, RONIN.
The depth of intrigue in RONIN is quite amazing, considering the fare that's fallen into the same category over the last few years. The script, written by J.D. Zeik in his big-screen debut, approaches levels of complexity that had people spinning away from MISSION: IMPOSSIBLE two years ago. A certain number of people will be lost by this, but afficianados will appreciate it all the more and return multiple times. Frankenheimer masters the script with poise, and tells us a dark but stoically realistic tale of a group of post-Cold War mercenaries working to earn their paycheck. His ability to convey feeling and character while promoting explosiveness and tense atmosphere works well here, and RONIN becomes a better movie because of it.
Robert De Niro headlines this cast of noteworthies in a performance that is the most outstanding of his in quite some time. He takes command and offers much gravity as the ex-spy Sam, a man who's called to France to help a shady operations controller named Dierdre (Natascha McElhone) recover a package. The package becomes the central motivator for the entire cast, especially once the requisite double-crossing begins, and its role is skillfully penned by Zeik. At times, however, Zeik's tale becomes almost too reminiscent of MISSION: IMPOSSIBLE - De Niro might easily become Tom Cruise, the package might become Cruise's computer disk. Fortunately, the focus here is almost entirely on the package, whereas in MISSION the script was more concerned with the double-cross and why it happened.
The action content is impressive, and it lives up to the hints given in previews. The only fans who will be disappointed are those who expect full-blown combat scenes and instead are forced to suffer through moments of slowness. RONIN does take a degree of intelligence to follow all of the nuance from start to finish, but even the uninitiated will be able to keep a grasp on most characters mand subplots. The suspense tapers off considerably by the end, a bit of a drawback because of its height at the movie's open. Considering these factors it's good to see that RONIN was a project that turned out well, and it's another good bet this month. Since quality is usually sparse during the early fall months, it's definitely worth the visit.
-- Craig Roush<EMAIL_ADDRESS>-- Kinnopio's Movie Reviews http://www.execpc.com/~kinnopio
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.
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Related links: index of all rec.arts.movies.reviews reviews


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Monday, August 26, 2013Klez Contra 4!Contradances, Yiddish Dances, Klezmer Music!Presented in collaboration with the Montreal Jewish Music Festival |
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A clarinet virtuoso based in Berlin, renowned
for his ground-breaking work in Yiddish and other
east-Europe styles, Christian has played with Brave
Old World, Budowitz, Frank London, and Theodore
Bikel, and directs Konsonans Retro, the Ukrainian
hipster brass band.
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Strauss/Warshcauer DuoJeff Warschauer (mandolin)Deborah Strauss (fiddle)
"Terrific." — New York Times
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3/5 of ShtreimlThierry Arsenault (drums)Rachel Lemisch (trombone) Jason Rosenblatt (piano) "Rosenblatt and the band burned the house down ... "— Klezmershack |
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Dance leadersDeborah Strauss (Yiddish dances)Ric Goldman (contras) Sala Rossa4848 boul. St. Laurent
Advance: $13
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