Purchase Medium: {{purchase.medium}}
Purchase Date: {{purchase.purchase_date}}
Purchase Amount: {{purchase.amount}}
Purchase ID: {{purchase.merchant_id}}
Merchant ID: {{merchant._id}}
Merchant Address: {{merchant.address.street_number}} {{merchant.address.street_name}}, {{merchant.address.city}}, {{ merchant.address.state}} {{merchant.address.zip}}
Merchant Geocode: lat = {{merchant.geocode.lat}} lng = {{merchant.geocode.lng}}
{{category}}


An
Action
can be used to separate functionality and state from a component. For
example, if you have two or more components that perform the same function,
consider using an Action
object to implement the function.
An Action
object is an
action listener
that provides not only action-event handling, but also centralized handling
of the state of action-event-firing components such as
tool bar buttons,
menu items,
common buttons, and
text fields. The state that an action can handle includes text, icon, mnemonic, enabled,
and selected status.
You typically attach an action to a component using the
setAction
method. Here's what happens when setAction
is invoked on a component:
Action
.
For example, if the Action
's text and icon values
were set,
the component's text and icon are set to those values.
Action
object is registered as an action listener
on the component.
Action
changes,
the component's state is updated to match the Action
.
For example, if you change the enabled status of the action,
all components it's attached to change their enabled states
to match the action.
Here's an example of creating a tool-bar button and menu item that perform the same function:
Action leftAction = new LeftAction(); //LeftAction code is shown later ... button = new JButton(leftAction) ... menuItem = new JMenuItem(leftAction);
To create an Action
object,
you generally create a subclass of
AbstractAction
and then instantiate it.
In your subclass,
you must implement the actionPerformed
method
to react appropriately
when the action event occurs.
Here's an example of creating and instantiating
an AbstractAction
subclass:
leftAction = new LeftAction("Go left", anIcon, "This is the left button.", new Integer(KeyEvent.VK_L)); ... class LeftAction extends AbstractAction { public LeftAction(String text, ImageIcon icon, String desc, Integer mnemonic) { super(text, icon); putValue(SHORT_DESCRIPTION, desc); putValue(MNEMONIC_KEY, mnemonic); } public void actionPerformed(ActionEvent e) { displayResult("Action for first button/menu item", e); } }
When the action created by the preceding code is attached to a
button and a menu item, the button and menu item display the
text and icon associated with the action. The L
character is used for mnemonics on the button and menu item, and
their tool-tip text is set to the SHORT_DESCRIPTION
string followed by a representation of the mnemonic key.
For example, we have provided a simple example,
ActionDemo.java
, which defines three actions. Each action is attached to a button
and a menu item.
Thanks to the mnemonic values set for each button's action, the key
sequence Alt-L
activates the left button, Alt-M
the middle button, and Alt-R
the right button. The tool tip
for the left button displays This is the left button. Alt-L.
All of this configuration occurs automatically, without the program making
explicit calls to set the mnemonic or tool-tip text. As we'll show
later, the program does make calls to set the button text,
but only to avoid using the values already set by the actions.
Click the Launch button to run ActionDemo using Java™ Web Start (download JDK 6). Or, to compile and run the example yourself, consult the example index.
Choose the top item from the left menu (Menu > Go left).
The text area displays some text identifying both the event source and
the action listener that received the event.
Click the leftmost button in the tool bar.
The text area again displays information about the event. Note that although
the source of the events is different, both events were detected by the same
action listener: the Action
object attached to the
components.
Choose the top item from the Action State menu.
This disables the "Go left" Action
object, which in turn
disables its associated menu item and button.
|
|
|
Here's the code that disables the "Go left" action:
boolean selected = ...//true if the action should be enabled; //false, otherwise leftAction.setEnabled(selected);
Action
,
you might well need to customize them.
For example, you might want to customize the
appearance of one of the components
by adding or deleting the icon or text.
For example,
ActionDemo.java
has no icons in its menus, and no text in its buttons.
Here's the code that accomplishes this:
menuItem = new JMenuItem(); menuItem.setAction(leftAction); menuItem.setIcon(null); //arbitrarily chose not to use icon in menu ... button = new JButton(); button.setAction(leftAction); button.setText(""); //an icon-only button
We chose to create an icon-only button and
a text-only menu item from the same action by setting
the icon property to null
and the text
to an empty string.
However, if a property of the Action
changes, the
widget may try to reset the icon and text from the
Action
again.
Action
constructors and methods.
The API for using Action
objects
falls into three categories:
| Class | Purpose |
|---|---|
|
AbstractButton JComboBox JTextField |
These components and their subclasses may have an action directly assigned
to them via setAction .
For further information about components that are often
associated with actions, see the sections on
tool bar buttons,
menu items,
common buttons, and
text fields.
For details on which properties
each component takes from the Action ,
see the API documentation for the relevant class's
configurePropertiesFromAction
method. Also refer to the
buttonActions
table.
|
| Constructor or Method | Purpose |
|---|---|
|
AbstractAction() AbstractAction(String) AbstractAction(String, Icon) |
Create an Action object.
Through arguments,
you can specify the text and icon
to be used in the components that the action is attached to. |
|
void setEnabled(boolean) boolean isEnabled() |
Set or get whether the components the action controls are enabled.
Invoking setEnabled(false)
disables all the components that the action controls.
Similarly, invoking setEnabled(true)
enables the action's components. |
|
void putValue(String, Object) Object getValue(String) |
Set or get an object associated with a specified key. Used for setting and getting properties associated with an action. |
This table defines the properties that can be set on an
action. The second column lists which components automatically
use the properties (and what method is specifically called).
For example,
setting the ACCELERATOR_KEY
on an action that
is then attached to a menu item, means that
JMenuItem.setAccelerator(KeyStroke)
is called automatically.
| Property | Auto-Applied to: Class (Method Called) |
Purpose |
|---|---|---|
| ACCELERATOR_KEY | JMenuItem
(setAccelerator) |
The KeyStroke to be used as the accelerator for
the action.
For a discussion of accelerators versus mnemonics, see
Enabling Keyboard Operation. Introduced in 1.3.
|
| ACTION_COMMAND_KEY | AbstractButton , JCheckBox ,
JRadioButton
(setActionCommand) |
The command string associated with the
ActionEvent .
|
| LONG_DESCRIPTION | None | The longer description for the action. Can be used for context-sensitive help. |
| MNEMONIC_KEY | AbstractButton , JMenuItem ,
JCheckBox , JRadioButton
(setMnemonic) |
The mnemonic for the action. For a discussion of accelerators versus mnemonics, see Enabling Keyboard Operation. Introduced in 1.3. |
| NAME | AbstractButton , JMenuItem ,
JCheckBox , JRadioButton
(setText) |
The name of the action.
You can set this property when creating the action using
the AbstractAction(String) or
AbstractAction(String, Icon) constructors.
|
| SHORT_DESCRIPTION | AbstractButton ,
JCheckBox , JRadioButton
(setToolTipText) |
The short description of the action. |
| SMALL_ICON | AbstractButton , JMenuItem
(setIcon) |
The icon for the action used in the tool bar or on
a button.
You can set this property when creating the action using
the AbstractAction(name, icon) constructor.
|
Action
objects.
| Example | Where Described | Notes |
|---|---|---|
ActionDemo |
This section | Uses actions to bind buttons and menu items to the same function. |
TextComponentDemo |
Text Component Features | Uses text actions to create menu items
for text editing commands, such as cut, copy, and paste,
and to bind key strokes to caret movement.
Also implements custom AbstractAction subclasses
to implement undo and redo.
The text action discussion begins in
Concepts:
About Editor Kits. |


{% extends "base.html" %} {% import "bootstrap/wtf.html" as wtf %} {% import "_macros.html" as macros %} {% block title %}SimCore{% endblock %} {% block page_content %}
Here we will have a list of available projects or sessions here ...
{% else %}Please log-in to access available projects/sessions.
{% endif %}So far {{ user_count }} users and growing ....


---
layout: post
title: Three open Rails positions
categories:
- jobs
author: admin
published: true
date: 2005-05-15 17:43:36.000000000 +01:00
---
It’s a great time to have skills with Ruby on Rails. People are dying to hire you:
- Illanti Industries: Canadian Development company looking for a junior-intermediate RoR developer (read: inexpensive) to work on some small applications with us. Good change to experiment and have fun, with plenty of projects coming in the near future. For more information, contact<EMAIL_ADDRESS>- Unknown: We’re looking for a developer with significant rails and Javascript experience for a contract project. The fundamentals of the project are relatively straightforward; we’d like to build a working foundation quickly, and then go forwards from there. We’re looking for someone with very strong web application UI and client-side Javascript development skills. Please contact Adam Marsh — adamjmarsh at yahoo.com


Do you have any questions? Please do not hesitate to contact us directly.
Minneapolis, MN 55455, USA
+ 1<PHONE_NUMBER>
contact@mattscooking.com


Please follow each step completely. Use the other lessons and resources (Mozilla Developer Network) to help you. You may also get help from your classmates. Kevin and Adam will be able to help, so if you get stuck, raise your hand to let them know.
We are going to use a concept called nested for loops to create a 4 by 4 grid of tiles for our memory game.
First, inside our jQuery function (below the stub functions, below the call
to .empty()
, let's add a call to renderTiles()
.
Inside of our renderTiles
function, declare a const
variable called gridSize
. Set it equal to 4.
Now, start a for-loop. Name the variable for the for-loop row
and set it equal to 0. Have the for-loop run as long as the row
variable is less than the gridSize
variable. Increment the
row
variable by 1 using the +=
operator. When you
are finished, it should look like this:
Inside the braces of this for loop, create a new

element using jQuery and store it in a variable.
When using a variable to store a jQuery object, it is a good idea to prefix
the variable with $
. This will help you remember that the variable
value is a jQuery object. This is just a convention and is not required.
When you have finished, your code should look like the code below:
Important: At this point, your tile row has not been added to
the page. The div inside $tileRow
is said to be detached
from the page.  Our tile row exists, it just hasn't been
stuck on the "wall" (HTML page) yet.
Right after the $tileRow
variable you just created, create
another for-loop
, which we will refer to as the inner for-loop.
Name the for-loop variable column
, set it to equal 0, and have the
loop run as long as column
is less than gridSize
.
Increment column
by 1 each iteration.
See if you can figure out how to write this for loop on your own. If you get stuck, ask for help.
The overall outer/inner loop structure should look like this:
Inside the braces for the inner for-loop, we will again create a new

element using the same syntax as in Step 4. Name
your variable $tile
. The HTML code you should have is below:
We now should append our $tile
div to our $tileRow
div. We can use the .append()
function for this. The contents of
your inner for-loop should look like the code below:
Immediately after the inner for-loop, outside of the braces for it, append
the contents of $tileRow
to the tiles div. Again, you should use
the .append()
function here. You can pass $tileRow
right into append, no HTML is needed here. Once you have done this, refresh
your page.
The append code should look something like this:
You should now see a 4 by 4 grid of tiles when you save your files and refresh the page.
The overall structure of the renderTiles
function should look
something like this:
Once you have finished, please raise your hand so either Kevin or Adam can check your work.


Hi, this is Mohammad Aasim and i am Computer Science student in ITS Enginnering College Greater Noida. I am passionated about technologies. In my spare time i love to learn and polish my skills.
I am thinker and dedicated toward my work. I am fascinated about competitve programming, web and desktop application development. I try to learn something new each day to achieve excellence. I believe in hard working in smart way to cope up the stuff.
Acedemic work is as important as practicle work because it strenthens and enables you to tackle the risk and opportunities. My academic work helped me to grab the opportunities and enabled me to find the truth about the topics.
My work simply means to grab the honour and pleasing words which makes everybody happy.
Being a Computer Science student i have basic knowledge about multiple things. However, i developed following skills at some level to accomplish the stuff.
To shape my ideas i have done some projects. My projects are open source and can be found on my GibHub account. My work is dedicated to my skills.
It is like a BIOS for 3G SIM card. I worked on it during my training by Syscom Corporation Limited and implemented some administrative commands of 3G SIM operating system.I worked on the File Structure of the real time SIM card which has a Master File(MF) as a root of file system. Though it was confidential and can not be mentioned on the public forum.
CaloAssist is a GUI based application for simple computing developed in Java programming language. It has a calculator to perform simple calculation. Other features include simple and compound interest calculation. It is open source and can be found on my Github account.
What's up!
Hey, gear up if you want to work with me, i will be glad to do the same.


{% import "macros/policy_portfolio.html" as portfolio %} {% extends "layouts/dwp_layout.html" %} {% block header_global %} {{ serviceName }} {% endblock %} {% block main %}


Alleged Top Lieutenant in Sinaloa Cartel Indicted
ALEXANDRIA, Va. – A man the U.S. government has identified as a narcotics kingpin and top lieutenant in the Sinaloa Cartel has been indicted by a federal grand jury in Alexandria, Va.
Neil H. MacBride, United States Attorney for the Eastern District of Virginia; Mythili Raman, Acting Assistant Attorney General for the Criminal Division; and Karl Colder, Special Agent in Charge for the Drug Enforcement Administration’s (DEA) Washington Division Office, made the announcement after the indictment was unsealed.
Damaso Lopez Nunez, aka “El Licensiado,” 47, of Mexico, was indicted on Nov. 23, 2011, and accused of conspiring to distribute cocaine and conspiring to commit money laundering. He faces a maximum penalty of life imprisonment, if convicted. The indictment was unsealed on March 7, 2013.
On Jan. 9, 2013, the U.S. Department of the Treasury’s Office of Foreign Assets Control (OFAC) designated Lopez Nunez under the Foreign Narcotics Kingpin Designation Act, which prohibits U.S. persons from conducting financial or commercial transactions with the designated individual and freezes any assets they have under U.S. jurisdiction. According to OFAC, Lopez Nunez is a top lieutenant of Joaquin “Chapo” Guzman Loera of the Sinaloa Cartel. A chart of the Chapo Guzman organization can be viewed here: http://www.treasury.gov/resource-center/sanctions/Programs/Documents/20130109_sinaloa_operatives.pdf.
The indictment provides a forfeiture notice of $280 million representing the proceeds of the alleged offenses.
This ongoing investigation is being conducted by the DEA’s Washington Division Office and Mazatlan Resident Office. Assistant United States Attorney Daniel J. Grooms of the Eastern District of Virginia’s National Security and International Crime Unit and Trial Attorney Darrin L. McCullough of the Narcotic and Dangerous Drug Section of the Justice Department’s Criminal Division are prosecuting the case on behalf of the United States.
Criminal indictments are only charges and not evidence of guilt. A defendant is presumed to be innocent until and unless proven guilty.
A copy of this press release may be found on the website of the United States Attorney's Office for the Eastern District of Virginia at http://www.justice.gov/usao/vae. Related court documents and information may be found on the website of the District Court for the Eastern District of Virginia at http://www.vaed.uscourts.gov or on https://pcl.uscourts.gov.


STEELO has been established as a manufacturing company in India. Our traditional business model is based on the accomplishment of PET (Poly Ethylene Threpthalate) containers and Bottles. Our Directors having an experience of 20 plus years in this field In 2007 we have started our new brand Steelo™ and marketing
throughout India. After launching this product within 3 months we
have created a brand in the market available throughout India approximately in 5000 outlets. Steelo is a combination of PET and Steel. We are taking keen caution in supplying quality material even the PET containers are made in single stretch blown machine and high quality steel is used to make the lid. Even for the printing we are using food grade materials. In addition to PET products, we have introduced TRITAN material first time in India.

Mission Statement : To build long term relationships with our customers and clients and provide exceptional customer services by pursuing business through innovation and advanced technology.
Vision : To provide quality services that exceeds the expectations of our esteemed customers
Core Values : We believe in treating our customers with respect and faith• We grow through creativity, invention and innovation.• We integrate honesty, integrity and business ethics into all aspects of our business functioning
Goals : To build good reputation in the field of PET industry
and become a key player in the industry.
At Steelo we are mainly catering in distribution network and supplying to various companies in different segments like F M C G, Pharmaceuticals, Distilleries and Electronic Companies for their consumer promotion scheme. Our products are available through TV channels like HS18,Shop CJ, all the major online channels like Amazon, Flipkart, Snapdeal, Pepperfry, Paytm, Shopclues, Ebay and also avaialble in all Hyper Markets and Marts.
  A/226, Synthofine Industrial Estate,
Off Aarey Road, Goregaon(E)
Mumbai - 400063
  +91-22-29272965/29272095
  +91-22-29272117
  bettly@steelo.in


For some reason, it's available in javascript on your device.
<%= htmlWebpackPlugin.options.serviceWorkerLoader %>


Patsy: Contrast Coding Systems for categorical variables¶
Note
This document is based on this excellent resource from UCLA.
A categorical variable of K categories, or levels, usually enters a regression as a sequence of K-1 dummy variables. This amounts to a linear hypothesis on the level means. That is, each test statistic for these variables amounts to testing whether the mean for that level is statistically significantly different from the mean of the base category. This dummy coding is called Treatment coding in R parlance, and we will follow this convention. There are, however, different coding methods that amount to different sets of linear hypotheses.
In fact, the dummy coding is not technically a contrast coding. This is because the dummy variables add to one and are not functionally independent of the model’s intercept. On the other hand, a set of contrasts for a categorical variable with k levels is a set of k-1 functionally independent linear combinations of the factor level means that are also independent of the sum of the dummy variables. The dummy coding is not wrong per se. It captures all of the coefficients, but it complicates matters when the model assumes independence of the coefficients such as in ANOVA. Linear regression models do not assume independence of the coefficients and thus dummy coding is often the only coding that is taught in this context.
To have a look at the contrast matrices in Patsy, we will use data from UCLA ATS. First let’s load the data.
Example Data¶
In [1]: import pandas
In [2]: url = 'https://stats.idre.ucla.edu/stat/data/hsb2.csv'
In [3]: hsb2 = pandas.read_csv(url)
It will be instructive to look at the mean of the dependent variable, write, for each level of race ((1 = Hispanic, 2 = Asian, 3 = African American and 4 = Caucasian)).
In [4]: hsb2.groupby('race')['write'].mean()
Out[4]:
race
1 46.458333
2 58.000000
3 48.200000
4 54.055172
Name: write, dtype: float64
Treatment (Dummy) Coding¶
Dummy coding is likely the most well known coding scheme. It compares each level of the categorical variable to a base reference level. The base reference level is the value of the intercept. It is the default contrast in Patsy for unordered categorical factors. The Treatment contrast matrix for race would be
In [5]: from patsy.contrasts import Treatment
In [6]: levels = [1,2,3,4]
In [7]: contrast = Treatment(reference=0).code_without_intercept(levels)
In [8]: print(contrast.matrix)
[[0. 0. 0.]
[1. 0. 0.]
[0. 1. 0.]
[0. 0. 1.]]
Here we used reference=0, which implies that the first level, Hispanic, is the reference category against which the other level effects are measured. As mentioned above, the columns do not sum to zero and are thus not independent of the intercept. To be explicit, let’s look at how this would encode the race variable.
In [9]: contrast.matrix[hsb2.race-1, :][:20]
Out[9]:
array([[0., 0., 1.],
[0., 0., 1.],
[0., 0., 1.],
[0., 0., 1.],
[0., 0., 1.],
[0., 0., 1.],
[0., 1., 0.],
[0., 0., 0.],
[0., 0., 1.],
[0., 1., 0.],
[0., 0., 1.],
[0., 0., 1.],
[0., 0., 1.],
[0., 0., 1.],
[0., 1., 0.],
[0., 0., 1.],
[0., 0., 1.],
[0., 0., 1.],
[0., 0., 1.],
[0., 0., 1.]])
This is a bit of a trick, as the race category conveniently maps to zero-based indices. If it does not, this conversion happens under the hood, so this will not work in general but nonetheless is a useful exercise to fix ideas. The below illustrates the output using the three contrasts above
In [10]: from statsmodels.formula.api import ols
In [11]: mod = ols("write ~ C(race, Treatment)", data=hsb2)
In [12]: res = mod.fit()
In [13]: print(res.summary())
OLS Regression Results
==============================================================================
Dep. Variable: write R-squared: 0.107
Model: OLS Adj. R-squared: 0.093
Method: Least Squares F-statistic: 7.833
Date: Tue, 02 Feb 2021 Prob (F-statistic): 5.78e-05
Time: 07:06:52 Log-Likelihood: -721.77
No. Observations: 200 AIC: 1452.
Df Residuals: 196 BIC: 1465.
Df Model: 3
Covariance Type: nonrobust
===========================================================================================
coef std err t P>|t| [0.025 0.975]
-------------------------------------------------------------------------------------------
Intercept 46.4583 1.842 25.218 0.000 42.825 50.091
C(race, Treatment)[T.2] 11.5417 3.286 3.512 0.001 5.061 18.022
C(race, Treatment)[T.3] 1.7417 2.732 0.637 0.525 -3.647 7.131
C(race, Treatment)[T.4] 7.5968 1.989 3.820 0.000 3.675 11.519
==============================================================================
Omnibus: 10.487 Durbin-Watson: 1.779
Prob(Omnibus): 0.005 Jarque-Bera (JB): 11.031
Skew: -0.551 Prob(JB): 0.00402
Kurtosis: 2.670 Cond. No. 8.25
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
We explicitly gave the contrast for race; however, since Treatment is the default, we could have omitted this.
Simple Coding¶
Like Treatment Coding, Simple Coding compares each level to a fixed reference level. However, with simple coding, the intercept is the grand mean of all the levels of the factors. See User-Defined Coding for how to implement the Simple contrast.
In [14]: contrast = Simple().code_without_intercept(levels)
In [15]: print(contrast.matrix)
[[-0.25 -0.25 -0.25]
[ 0.75 -0.25 -0.25]
[-0.25 0.75 -0.25]
[-0.25 -0.25 0.75]]
In [16]: mod = ols("write ~ C(race, Simple)", data=hsb2)
In [17]: res = mod.fit()
In [18]: print(res.summary())
OLS Regression Results
==============================================================================
Dep. Variable: write R-squared: 0.107
Model: OLS Adj. R-squared: 0.093
Method: Least Squares F-statistic: 7.833
Date: Tue, 02 Feb 2021 Prob (F-statistic): 5.78e-05
Time: 07:06:52 Log-Likelihood: -721.77
No. Observations: 200 AIC: 1452.
Df Residuals: 196 BIC: 1465.
Df Model: 3
Covariance Type: nonrobust
===========================================================================================
coef std err t P>|t| [0.025 0.975]
-------------------------------------------------------------------------------------------
Intercept 51.6784 0.982 52.619 0.000 49.741 53.615
C(race, Simple)[Simp.1] 11.5417 3.286 3.512 0.001 5.061 18.022
C(race, Simple)[Simp.2] 1.7417 2.732 0.637 0.525 -3.647 7.131
C(race, Simple)[Simp.3] 7.5968 1.989 3.820 0.000 3.675 11.519
==============================================================================
Omnibus: 10.487 Durbin-Watson: 1.779
Prob(Omnibus): 0.005 Jarque-Bera (JB): 11.031
Skew: -0.551 Prob(JB): 0.00402
Kurtosis: 2.670 Cond. No. 7.03
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Sum (Deviation) Coding¶
Sum coding compares the mean of the dependent variable for a given level to the overall mean of the dependent variable over all the levels. That is, it uses contrasts between each of the first k-1 levels and level k In this example, level 1 is compared to all the others, level 2 to all the others, and level 3 to all the others.
In [19]: from patsy.contrasts import Sum
In [20]: contrast = Sum().code_without_intercept(levels)
In [21]: print(contrast.matrix)
[[ 1. 0. 0.]
[ 0. 1. 0.]
[ 0. 0. 1.]
[-1. -1. -1.]]
In [22]: mod = ols("write ~ C(race, Sum)", data=hsb2)
In [23]: res = mod.fit()
In [24]: print(res.summary())
OLS Regression Results
==============================================================================
Dep. Variable: write R-squared: 0.107
Model: OLS Adj. R-squared: 0.093
Method: Least Squares F-statistic: 7.833
Date: Tue, 02 Feb 2021 Prob (F-statistic): 5.78e-05
Time: 07:06:52 Log-Likelihood: -721.77
No. Observations: 200 AIC: 1452.
Df Residuals: 196 BIC: 1465.
Df Model: 3
Covariance Type: nonrobust
=====================================================================================
coef std err t P>|t| [0.025 0.975]
-------------------------------------------------------------------------------------
Intercept 51.6784 0.982 52.619 0.000 49.741 53.615
C(race, Sum)[S.1] -5.2200 1.631 -3.200 0.002 -8.437 -2.003
C(race, Sum)[S.2] 6.3216 2.160 2.926 0.004 2.061 10.582
C(race, Sum)[S.3] -3.4784 1.732 -2.008 0.046 -6.895 -0.062
==============================================================================
Omnibus: 10.487 Durbin-Watson: 1.779
Prob(Omnibus): 0.005 Jarque-Bera (JB): 11.031
Skew: -0.551 Prob(JB): 0.00402
Kurtosis: 2.670 Cond. No. 6.72
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
This corresponds to a parameterization that forces all the coefficients to sum to zero. Notice that the intercept here is the grand mean where the grand mean is the mean of means of the dependent variable by each level.
In [25]: hsb2.groupby('race')['write'].mean().mean()
Out[25]: 51.67837643678162
Backward Difference Coding¶
In backward difference coding, the mean of the dependent variable for a level is compared with the mean of the dependent variable for the prior level. This type of coding may be useful for a nominal or an ordinal variable.
In [26]: from patsy.contrasts import Diff
In [27]: contrast = Diff().code_without_intercept(levels)
In [28]: print(contrast.matrix)
[[-0.75 -0.5 -0.25]
[ 0.25 -0.5 -0.25]
[ 0.25 0.5 -0.25]
[ 0.25 0.5 0.75]]
In [29]: mod = ols("write ~ C(race, Diff)", data=hsb2)
In [30]: res = mod.fit()
In [31]: print(res.summary())
OLS Regression Results
==============================================================================
Dep. Variable: write R-squared: 0.107
Model: OLS Adj. R-squared: 0.093
Method: Least Squares F-statistic: 7.833
Date: Tue, 02 Feb 2021 Prob (F-statistic): 5.78e-05
Time: 07:06:52 Log-Likelihood: -721.77
No. Observations: 200 AIC: 1452.
Df Residuals: 196 BIC: 1465.
Df Model: 3
Covariance Type: nonrobust
======================================================================================
coef std err t P>|t| [0.025 0.975]
--------------------------------------------------------------------------------------
Intercept 51.6784 0.982 52.619 0.000 49.741 53.615
C(race, Diff)[D.1] 11.5417 3.286 3.512 0.001 5.061 18.022
C(race, Diff)[D.2] -9.8000 3.388 -2.893 0.004 -16.481 -3.119
C(race, Diff)[D.3] 5.8552 2.153 2.720 0.007 1.610 10.101
==============================================================================
Omnibus: 10.487 Durbin-Watson: 1.779
Prob(Omnibus): 0.005 Jarque-Bera (JB): 11.031
Skew: -0.551 Prob(JB): 0.00402
Kurtosis: 2.670 Cond. No. 8.30
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
For example, here the coefficient on level 1 is the mean of write at level 2 compared with the mean at level 1. Ie.,
In [32]: res.params["C(race, Diff)[D.1]"]
Out[32]: 11.541666666666657
In [33]: hsb2.groupby('race').mean()["write"][2] - \
....: hsb2.groupby('race').mean()["write"][1]
....:
Out[33]: 11.541666666666664
Helmert Coding¶
Our version of Helmert coding is sometimes referred to as Reverse Helmert Coding. The mean of the dependent variable for a level is compared to the mean of the dependent variable over all previous levels. Hence, the name ‘reverse’ being sometimes applied to differentiate from forward Helmert coding. This comparison does not make much sense for a nominal variable such as race, but we would use the Helmert contrast like so:
In [34]: from patsy.contrasts import Helmert
In [35]: contrast = Helmert().code_without_intercept(levels)
In [36]: print(contrast.matrix)
[[-1. -1. -1.]
[ 1. -1. -1.]
[ 0. 2. -1.]
[ 0. 0. 3.]]
In [37]: mod = ols("write ~ C(race, Helmert)", data=hsb2)
In [38]: res = mod.fit()
In [39]: print(res.summary())
OLS Regression Results
==============================================================================
Dep. Variable: write R-squared: 0.107
Model: OLS Adj. R-squared: 0.093
Method: Least Squares F-statistic: 7.833
Date: Tue, 02 Feb 2021 Prob (F-statistic): 5.78e-05
Time: 07:06:52 Log-Likelihood: -721.77
No. Observations: 200 AIC: 1452.
Df Residuals: 196 BIC: 1465.
Df Model: 3
Covariance Type: nonrobust
=========================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------------
Intercept 51.6784 0.982 52.619 0.000 49.741 53.615
C(race, Helmert)[H.2] 5.7708 1.643 3.512 0.001 2.530 9.011
C(race, Helmert)[H.3] -1.3431 0.867 -1.548 0.123 -3.054 0.368
C(race, Helmert)[H.4] 0.7923 0.372 2.130 0.034 0.059 1.526
==============================================================================
Omnibus: 10.487 Durbin-Watson: 1.779
Prob(Omnibus): 0.005 Jarque-Bera (JB): 11.031
Skew: -0.551 Prob(JB): 0.00402
Kurtosis: 2.670 Cond. No. 7.26
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
To illustrate, the comparison on level 4 is the mean of the dependent variable at the previous three levels taken from the mean at level 4
In [40]: grouped = hsb2.groupby('race')
In [41]: grouped.mean()["write"][4] - grouped.mean()["write"][:3].mean()
Out[41]: 3.169061302681982
As you can see, these are only equal up to a constant. Other versions of the Helmert contrast give the actual difference in means. Regardless, the hypothesis tests are the same.
In [42]: k = 4
In [43]: 1./k * (grouped.mean()["write"][k] - grouped.mean()["write"][:k-1].mean())
Out[43]: 0.7922653256704955
In [44]: k = 3
In [45]: 1./k * (grouped.mean()["write"][k] - grouped.mean()["write"][:k-1].mean())
Out[45]: -1.3430555555555561
Orthogonal Polynomial Coding¶
The coefficients taken on by polynomial coding for k=4 levels are the linear, quadratic, and cubic trends in the categorical variable. The categorical variable here is assumed to be represented by an underlying, equally spaced numeric variable. Therefore, this type of encoding is used only for ordered categorical variables with equal spacing. In general, the polynomial contrast produces polynomials of order k-1. Since race is not an ordered factor variable let’s use read as an example. First we need to create an ordered categorical from read.
In [46]: _, bins = np.histogram(hsb2.read, 3)
In [47]: try: # requires numpy master
....: readcat = np.digitize(hsb2.read, bins, True)
....: except:
....: readcat = np.digitize(hsb2.read, bins)
....:
In [48]: hsb2['readcat'] = readcat
In [49]: hsb2.groupby('readcat').mean()['write']
Out[49]:
readcat
0 46.000000
1 44.980392
2 53.356436
3 60.127660
Name: write, dtype: float64
In [50]: from patsy.contrasts import Poly
In [51]: levels = hsb2.readcat.unique().tolist()
In [52]: contrast = Poly().code_without_intercept(levels)
In [53]: print(contrast.matrix)
[[-0.6708 0.5 -0.2236]
[-0.2236 -0.5 0.6708]
[ 0.2236 -0.5 -0.6708]
[ 0.6708 0.5 0.2236]]
In [54]: mod = ols("write ~ C(readcat, Poly)", data=hsb2)
In [55]: res = mod.fit()
In [56]: print(res.summary())
OLS Regression Results
==============================================================================
Dep. Variable: write R-squared: 0.320
Model: OLS Adj. R-squared: 0.309
Method: Least Squares F-statistic: 30.73
Date: Tue, 02 Feb 2021 Prob (F-statistic): 2.51e-16
Time: 07:06:52 Log-Likelihood: -694.54
No. Observations: 200 AIC: 1397.
Df Residuals: 196 BIC: 1410.
Df Model: 3
Covariance Type: nonrobust
==============================================================================================
coef std err t P>|t| [0.025 0.975]
----------------------------------------------------------------------------------------------
Intercept 51.1161 2.018 25.324 0.000 47.135 55.097
C(readcat, Poly).Linear 11.3501 5.348 2.122 0.035 0.803 21.897
C(readcat, Poly).Quadratic 3.8954 4.037 0.965 0.336 -4.066 11.857
C(readcat, Poly).Cubic -2.4598 1.998 -1.231 0.220 -6.400 1.480
==============================================================================
Omnibus: 9.741 Durbin-Watson: 1.699
Prob(Omnibus): 0.008 Jarque-Bera (JB): 10.263
Skew: -0.535 Prob(JB): 0.00591
Kurtosis: 2.703 Cond. No. 13.7
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
As you can see, readcat has a significant linear effect on the dependent variable write but not a significant quadratic or cubic effect.
User-Defined Coding¶
If you want to use your own coding, you must do so by writing a coding class that contains a code_with_intercept and a code_without_intercept method that return a patsy.contrast.ContrastMatrix instance.
In [57]: from patsy.contrasts import ContrastMatrix
....:
....: def _name_levels(prefix, levels):
....: return ["[%s%s]" % (prefix, level) for level in levels]
....:
In [58]: class Simple(object):
....: def _simple_contrast(self, levels):
....: nlevels = len(levels)
....: contr = -1./nlevels * np.ones((nlevels, nlevels-1))
....: contr[1:][np.diag_indices(nlevels-1)] = (nlevels-1.)/nlevels
....: return contr
....:
....: def code_with_intercept(self, levels):
....: contrast = np.column_stack((np.ones(len(levels)),
....: self._simple_contrast(levels)))
....: return ContrastMatrix(contrast, _name_levels("Simp.", levels))
....:
....: def code_without_intercept(self, levels):
....: contrast = self._simple_contrast(levels)
....: return ContrastMatrix(contrast, _name_levels("Simp.", levels[:-1]))
....:
File "<tokenize>", line 13
def code_without_intercept(self, levels):
^
IndentationError: unindent does not match any outer indentation level
In [60]: mod = ols("write ~ C(race, Simple)", data=hsb2)
....: res = mod.fit()
....: print(res.summary())
....:
OLS Regression Results
==============================================================================
Dep. Variable: write R-squared: 0.107
Model: OLS Adj. R-squared: 0.093
Method: Least Squares F-statistic: 7.833
Date: Tue, 02 Feb 2021 Prob (F-statistic): 5.78e-05
Time: 07:06:52 Log-Likelihood: -721.77
No. Observations: 200 AIC: 1452.
Df Residuals: 196 BIC: 1465.
Df Model: 3
Covariance Type: nonrobust
===========================================================================================
coef std err t P>|t| [0.025 0.975]
-------------------------------------------------------------------------------------------
Intercept 51.6784 0.982 52.619 0.000 49.741 53.615
C(race, Simple)[Simp.1] 11.5417 3.286 3.512 0.001 5.061 18.022
C(race, Simple)[Simp.2] 1.7417 2.732 0.637 0.525 -3.647 7.131
C(race, Simple)[Simp.3] 7.5968 1.989 3.820 0.000 3.675 11.519
==============================================================================
Omnibus: 10.487 Durbin-Watson: 1.779
Prob(Omnibus): 0.005 Jarque-Bera (JB): 11.031
Skew: -0.551 Prob(JB): 0.00402
Kurtosis: 2.670 Cond. No. 7.03
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.


F#!KIN' A! Two f#!kin' Welsh sociopaths f#!k s#!t up in f#!kin' TWIN TOWN --A review by Justin Siegel
TWIN TOWN is the story of two brothers: Julian and Jeremy Lewis (Llyrs Ifans and Rhys Evans), who aren't twins but are thought to be by just about everyone. They a little bit poorer than TRAINSPOTTING's Mark Renton, but a little bit crazier, so it works out even. When their father, Fatty, falls off a roof and breaks his leg while working for Brynne Cartright, the "twins" want a little compensation. It doesn't take a genius to figure out that they aren't going to get it.
So what do they do? They urinate on Brynne's daughter's head while she sings at a Karaoke bar. And what does Brynne then do to get back at the twins? He gets a crooked cop to beat them up. And what do the twins then do to--well, the list goes on. Let' just say that decapitated poodles, sheep-molesting farmers, and exploding trailor parks all make cameos along the way.
TWIN TOWN really wants to be TRAINSPOTTING. In fact, TRAINSPOTTING's director and producer exec-produced TWIN TOWN, and director Keith Allen had a supporting role in TRAINSPOTTING. TWIN TOWN is considerably funnier than TRAINSPOTTING, but not as powerful -- not by a longshot.
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


{% extends "dixy/master.html" %} {% block content %}
Dixy is a web application for producing Dynamic, Interactive XY plots of data.
To use and interact with the data, click Datasets in the menu bar above.
Alternatively, here is an example visualisation of a number of QFA datasets.
Quantitative Fitness Analysis (QFA) is an experimental and computational workflow for comparing fitnesses of microbial cultures grown in parallel. QFA can be applied to focused observations of single cultures but is most useful for genome-wide genetic interaction or drug screens investigating up to thousands of independent cultures. The central experimental method is the inoculation of independent, dilute liquid microbial cultures onto solid agar plates which are incubated and regularly photographed. Photographs from each time-point are analyzed, producing quantitative cell density estimates, which are used to construct growth curves, allowing quantitative fitness measures to be derived. Culture fitnesses can be compared to quantify and rank genetic interaction strengths or drug sensitivities. The effect on culture fitness of any treatments added into substrate agar (e.g. small molecules, antibiotics or nutrients) or applied to plates externally (e.g. UV irradiation, temperature) can be quantified by QFA.
For more information about QFA, and the data presented on this site, see the following publications:
An R package that implements Quantitative Fitness Analysis:
{% endblock %}


Jira(+)
Available functionality:
- Extra fields styling for distinguish tickets for SWDC
- Definition of Done for WEBDEV project
- Definition of Ready quick action for Description field
- Quick actions for story points and impediment flag
- Quick jump navigation for backlog view


Chapter 23 – FRIENDS
GOOD FRIENDS FROM OGDEN, MOUNT FORT WARD
We have had many friends while living in our several wards. Some are very special. Bishop and June Moulton will always hold a place in our hearts. They are very spiritual, humble people. They were like our second parents. Ken was Delmar’s first counselor in the bishopric. Larry was the 2nd counselor. We loved their children also. I taught three of their daughters in YWMIA – Luwana, Ardel, and Debra. Myrna was the 2nd oldest in the family and we became good friends and since she was a beautician, I would go to her, and while she was doing my hair, we would have wonderful visits. Delmar & June were wonderful parents, and wonderful friends. We visited them occasionally whenever we lived close enough to do so.
Our dearest truest friends are Larry and Tammy Braithwaite. Tammy is just like a sister to me, except as she has often said, we never fight like sisters do. We were together a lot, either just the two of us with our children or with our husbands or with our families. We ate at each other’s homes, played games, went on trips together, went camping together a lot, we had so much fun together. Either she or I would call and say “What are you doing today?”  We had so much in common that we could talk about anything. Pat Bradfield & I were always best friends, but I think Tammy & I were even closer, we really enjoyed being together.
We were good friends with Pete and Grace Mooney. Pete was so much fun. He was really a character. We enjoyed getting together with them. Sometime after we moved, Pete was put in the Bishopric. We went over to the ward as they were having something special - missionary farewell or something (can’t remember for sure), anyway, we were five or so minutes late and when we walked in we saw a row in the middle isle partway up in the chapel and as we walked up there, Pete was conducting and he said “Well, if it isn’t our good friends Ken & Mae Browning and their family.  You take up an entire row.” Everyone chuckled. We were a little embarrassed, but that was just Pete. Grace had some bad health problems, and we were saddened to hear that years after we had moved, Pete had an affair with a younger woman, he couldn’t forgive himself so he took his life. We were so sorry to hear that.
Our other goodfriends are Bob & Pauline Hein, and John and Linda Willerton. When we were in the Mount Fort Ward, we got together in the Young Marrieds Program and had a lot of fun together. Since we enjoyed each other’s company, we started going to the temple together, getting together for family home evenings and eating at each other’s homes. John & Linda moved into a home in Syracuse. Bob & Pauline bought a home in Salt Lake, we moved to Clearfield and Larry & Tammy to Liberty. We still get together once or twice a month at each other’s homes for family night and Sunday dinners. Now Larry & Tammy & Ken & I have moved to Arizona, but we write to the others and hope to get together with them occasionally. We have been fortunate to have wonderful friends. I am also thankful for my wonderful brothers and sister, and my wonderful sisters-in-law. Jean and I have been very close and I miss not being able to get together like we used to, but we write and get together whenever Ken and I get up. John & Linda Willerton are the first couple in the picture on the left, and Pauline & Bob Hein are just behind them. The same is true on the picture on the right. Then Tammy &Larry Braithwaite. The Arvey’s are the other couple are the left picture and then me as Ken is taking the picture. I am taking the picture on the right and Hal & Jean are sitting by Ke.
GOOD FRIENDS FROM CLEARFIELD – CLEARFIELD 1ST WARD
Bishop and Shirley Murdock will always be good friends. Ken loved being Kent’s counselor. I write to Ione & Carolyn, who were my counselors when I was Primary President in Clearfield and we go out for lunch when we are living close by. RaNae and I call or get together when we can also. I think a lot of them, they are great friends. RaNae & Richard are the second couple from the left – next to Bishop & Shirley. I am on the end, holding our son, David, and Mary and Dale Cottrell are next to me. I don’t remember who the couple on the other end are.
Our friends from the other wards and long time friends are put in other chapters, mainly they are in the chapters when we moved to their neighborhoods and wards.


|
utf8rewind
1.2.1
Cross-platform library for UTF-8 encoded text
|
Suppose you maintain a client application written in C. In order for clients to login, the application needs to check their credentials. This is accomplished by hashing the specified password with a salt:
We want to improve the security of the application by allowing the full range of Unicode codepoints in the passwords. We can accomplish this by encoding the codepoints using UTF-8. The beauty of this change is that the password algorithm does not have to be modified. Because all functions already work on char
arrays, you can encode Unicode as UTF-8, but treat the string as ASCII.
A password like "MôntiPythônikdenHôlieGrâilen" would be encoded as
"M\xC3\xB4Pyth\xC3\xB4" "denH\xC3\xB4Gr\xC3\xA2" "en"
which is backwards-compatible with ASCII. Calls to strcpy
and strcat
still work as expected, because the string does not contain NUL-terminators (\0
), except to signify the end of data.
When converting your project to work with UTF-8 encoded text, there are only two surface areas you will have to concern yourself with: input and display.
We'll look at these individually.
In the previous example, the password field in the client application only accepted ASCII. This is how the password field is currently implemented:
What we want to do is make sure the password field can accept Unicode input. To that end, we'll change the input type to unicode_t, which can encode every valid Unicode codepoint.
Every location that calls PasswordField_EnterCharacter
will have to cast the parameter to unicode_t, but luckily this is backwards-compatible. All ASCII codepoints are valid in Unicode as well.
Inside the function, we'll want to convert the UTF-32 codepoint to UTF-8. To that end, we'll use utf32toutf8.
Although the call to utf32toutf8 is guaranteed not to fail, because the maximum length of a Unicode codepoint in UTF-8 is 4 bytes, it's good practice to check for and respond to error conditions.
Because the converted string can now consist of more than one byte, we'll have to change the check to see if we're out of bounds of the password input array.
The rewritten version is now fully compatible with Unicode.
With only a few changes, we've upgraded a text field that previously only accepted ASCII to accept the full range of Unicode. And we didn't have to change the algorithm at all.
Even though the user is now able to enter Unicode text, it won't show up right on the screen. That's because the font rendering implementation expects ASCII instead of Unicode.
Let's take a look at the offending function:
The issue here is that FontBatch_AddCharacter
expects codepoints encoded as one byte per codepoint. Because UTF-8 is a variable-length encoding, this isn't necessarily true anymore.
In order for the font renderer to display Unicode text, we'll need to convert the UTF-8 encoded text to UTF-32. To that end, we'll use utf8toutf32.
After this conversion, the FontBatch_AddCharacter
function would have to be modified in order to handle Unicode codepoints. Fortunately this is a string handling library, not a font rendering one, so I can get away with saying I'm leaving it as an exercise to the reader.
One thing to keep in mind is that even though we convert the entire input string to UTF-32 before rendering it in this example, it's equally valid to read only one codepoint at a time. However, you'll need to use utf8seek to move the cursor to the next codepoint.


To use this simple example, just start 'server.js' using Node and open this page in multiple tabs.
Each tab will create a peer that is connected to the most recently connected peer.
Click on an "available peer" to request a connection with that peer.


F# is a beautiful language that is concise, expressive, and efficient. I’m obsessed with making it run as fast as possible. On this page, you will find the resources I find most helpful in learning to write high-performance code. I have arranged it by content type and provided a summary of my thoughts so you can find what will be most helpful for you. If you have any suggestions, please email me at<EMAIL_ADDRESS>or message me on Twitter @McCrews.
Books
I have found all of these books incredibly helpful and have them in my library. The heading is a link to the book’s webpage or a link where you can purchase them. I am not an affiliate of any website, so I make no money on purchases.
The Essentials
These first four are what I would consider the essentials for any .NET developer serious about writing high-performance code.
Pro .NET Memory Management
“Pro .NET Memory Management” is a tome detailing how the CLR manages memory. Much of .NET performance comes down to memory access patterns and understanding how garbage collection works in .NET. Konrad Kokosa has done an incredible job documenting how the CLR manages memory and what you can do to maximize performance.
Pro .NET Benchmarking
“Pro .NET Benchmarking” is essential for understanding how to measure your code’s performance accurately. Many gotchas can lead you to draw incorrect conclusions on how your code will perform in real-world scenarios. Andrey Akinshin does an excellent job walking you through how to create good benchmarks that will allow you to see how well your code will perform.
Writing High-Performance .NET
Writing High-Performance .NET by Ben Watson, provides excellent advice on patterns and practices to get the most performance out of .NET. The book offers many practical examples of what you can do to write code that will run fast on the CLR. I re-read regularly and each time I learn more.
The Algorithm Design Manual
This book is not .NET specific but has been so helpful for me that I believe it belongs in the Essentials list. The Algorithm Design Manual by Steven Skiena is my first stop when I’m confronted with a new problem. It does an incredible job of talking through real-world problems and then mapping them to the algorithm that best solves that problem. Possibly the best part of the book is the extensive citations. You can quickly find the literature that deals with your specific situation. I recommend reading through it even if you don’t have a current problem you are working on because it may give you insight into how to tackle issues you’ve dealt with in the past.
Extra Credit
The next set of books is not .NET specific, but I think it provides significant value for those serious about writing fast code.
Data-Oriented Design
There is a new paradigm developing in the software community called Data-Oriented Programming. We need to think about how our data is laid out and accessed to make our code fast. Even the best algorithms fall apart when the data layout is poorly designed. Therefore, data and how it is arranged should be of primary importance. In his book, Data-Oriented Design, Richard Fabian does an excellent job laying out the principles of this new paradigm and discussing practical implications.
Game Programming Patterns
Bob Nystrom is possibly one of the funniest tech authors I have come across. His book “Game Programming Patterns” is both hilarious and immensely practical. Games have long had some of the most demanding performance requirements, and Bob has done an excellent job cataloging the most useful patterns. I enjoy that he starts with a problem and then unpacks the pattern that addresses it. It is an approachable style and makes the need for the pattern clear. At the very least, you should read this for the humor.
The Art of Writing Efficient Programs
This book is targeted to a C++ developer crowd, but there is plenty in here that any dev would find helpful. Fedor Pikus provides excellent insight into how to write programs that will most efficiently run on today’s CPUs. About 1/3 of this book is about concurrent C++, which does not apply to me, but the rest of the content was good enough that I thought it bore mentioning. I especially like the section on branchless programming, for which there are not many resources.
Game Engine Architecture
This book is an absolute monster at just over 1,200 pages. I have not read through all of it, but I found what I read helpful. Jason Gregory has led game engine development on several of the top AAA games in the last decade, so he has some excellent insight into designing large software projects that must perform at the highest level. I recommend this for someone who needs to architect an extensive system or program that needs ultimate performance. I think of this as less of a low-level optimization book and more of a guide to creating a more extensive program.
Modern Computer Architecture and Organization
I want to understand how computers work at the lowest level, and this book has been an excellent guide to how CPUs work from the ground up. It starts with binary logic, builds up to circuits, and from there processors. At some point, everyone serious about high-performance code wants to understand how hardware works. I have found this to be the best resource to go from the absolute fundamentals and build up to the complexity of modern CPUs.
Videos
These videos run the gamut of languages and technologies, but I have found them all helpful in expanding my understanding of writing fast code. The title is a link to where the video can be found.
CppCon 2014: Mike Acton “Data-Oriented Design and C++” - Mike Acton
There isn’t enough good I can say about this talk. Mike Acton reminds us that code is meant to solve problems, and the problem is fundamentally one of data transformations. He’s channeling years of frustration with people obsessing over design patterns or class hierarchies. Every time I watch this video, I smile. The comments on this video are gold as well. Mike is not alone in his frustration with the industry.
code::dive conference 2014 - Scott Meyers: Cpu Caches and Why You Care
Scott Meyers provides an excellent talk detailing CPU caches and why we care about them. Cache access patterns have large implications on performance and Scott makes a perfect case why they should be top of mind when writing performant code.
DConf 2017 Day 2 Keynote: Things that Matter – Scott Meyers
Another talk by Scott Meyers but vital because he unpacks the phrase “premature optimization is the root of all evil.” Scott explains how this quote has been taken out of context, overly applied, and caused undue harm. I like it because it faces this dogma and shows how even the paper from whence it came defies this prevailing wisdom.
Everything (maybe too much) about stackalloc With Kevin Jones | Hampton Roads .NET Users Group
There are not many videos that detail stackalloc,
and when I came across this one, I was pleased. It explains this feature in .NET and some of the gotchas to be concerned about when using it in your code.
CppCon 2018: Fedor Pikus “Design for Performance”
A great talk by Fedor Pikus about how to design for performance: I enjoy this because he shows several different ways of attacking a problem and how performance can be impacted.
Bartosz Adamczewski — Data-oriented design for business applications
An excellent talk by Bartosz Adamczewski on how Data-Oriented Design can be applied to business applications. He does a great job of taking a practical problem you would face in any enterprise CRUD app and shows you how Data-Oriented Design can be applied. This helped pivot some of my thinking about how widely applicable the design methodology is.
Branchless Programming in C++ - Fedor Pikus - CppCon 2021
Branchless Programming is not something I have dived into much, but I still find it fascinating. Fedor surprises you with how far you can push the concept and what performance gains can be had.
Workshops and Classes
This course can be thought of as the companion to Konrad’s book. The approach in teaching is very different, though. The book is more of an academic approach to memory management in .NET, while this course focuses on application. Suppose you are someone who likes the hands-on, application-heavy approach to learning a subject. I highly recommend this course. Before I took this course, I read the book, and I still found it incredibly useful and enlightening. If you found that the book was too heavy on theory, this is probably the content you are looking for.


2 references to SHFILEINFO
System.Management.Automation (2)
engine\NativeCommandProcessor.cs (2)
1115
SHFILEINFO
shinfo = new SHFILEINFO();
1672
ref
SHFILEINFO
psfi, uint cbSizeFileInfo, uint uFlags);


{% extends "base.html" %} {% block title %} - Home{% endblock %} {% block content %}
This dialog will close automatically when the log file has been completely loaded.


[1] Lint is a common programming tool for detecting anomalies in C programs. S. C. Johnson developed the original lint in the late seventies, mainly because early versions of C did not support function prototypes. Splint was originally named LCLint because it was originally intended to check for inconsistencies between LCL specifications and C implementations. To reflect divergence from LCL and increased focus on detecting security vulnerabilities, the name was changed to Splint, short for “Specification Lint” and “Secure Programming Lint”.
[2] The meta-notation, item,+ is used to denote a comma separated list of items. For example, /*@access mstring, intSet@*/
allows access to the representations of both mstring and intSet.)
[3] This section is largely based on [Evans96]. It semi-formally defines some of the terms needed to describe memory management checking; if you are satisfied with an intuitive understanding of these terms, this section may be skipped.
[4] This is similar to the LISP storage model, except that objects are typed.
[5] Except sizeof, which does not need the value of its argument.
[6] If the storage is not assigned to a reference, an internal reference is created to track the storage.
[7] The declaration of free has a null annotation on the parameter to indicate that the argument may be NULL. According to [ISO, <IP_ADDRESS>], NULL may be passed to free without no action. On some UNIX platforms, passing NULL to free causes a program crash so the UNIX version of the standard library specifies free without the null annotation on its parameter. To check that allocated objects are completely destroyed (e.g., all unshared objects inside a structure are deallocated before the structure is deallocated), Splint checks that any parameter passed as an out only void * does not contain references to live, unshared objects. This makes sense, since such a parameter could not be used sensibly in any way other than deallocating its storage.
[8] In versions of Splint before 3.0, the noreturn annotation was named exits. The noreturn annotation means the same thing, but is a more appropriate name. For legacy code, Splint still supports the exits annotations. Similarly, maynotreturn replaces mayexit, noreturnwhentrue replaces truexit and noreturnwhenfalse replaces falseexit.
[9]The sef annotation denotes a parameter as side effect free (see Section 11.2.1). We use bool /*@alt int@*/ as the type of the parameter, to indicate that it may be either a Boolean or an integer.
[10] Peter van der Linden estimates that default fall through is the wrong behavior 97% of the time. [vdL95, p. 37]
[11] “Software Glitch Cripples AT&T Network”, Telephony, 22 January 1990.
[12] See [Larochelle01] for information on internal aspects of the checking.
[13] This section is largely based on [Evans02].
[14] C. Cowan et al., FormatGuard: Automatic Protection from printf Format String Vulnerabilities. 10th Usenix Security Symposium, 2001.
[15] To be completely correct, all the macro parameters should be evaluated before the macro has any side effects. Splint does not check this.
[16] Functions that do not produce to the same result each time they are called with the same arguments should be declared to modify internalState so they will lead to errors if they are passed as sef parameters.
[17] The most renowned C naming convention is the Hungarian naming convention, introduced by Charles Simonyi [Simonyi, Charles, and Martin Heller. “The Hungarian Revolution.” BYTE, August 1991, p. 131-38]. The names for Splint naming conventions follow the tradition of using Central European nationalities as mnemonics for naming conventions. The Splint conventions are similar to the Hungarian naming convention in that they encode type information in names, except that the Splint conventions encode the names of accessible abstract types instead of the type of the declaration of return value. Prefixes used in the Hungarian naming convention are not supported by Splint.
[18] Of course, namespace prefixes should really be described by regular expressions. If there is sufficient interest (that is, someone volunteers to program it), regular expressions will be supported in a future version of Splint.
[19] POSIX library was contributed by Jens Schweikhardt.


Among Us, the popular multiplayer game that rose to popularity this year, will be available on Xbox consoles in 2021. The announcement came soon after the game joined Xbox Game Pass for PC. Among Us will be available on Xbox One, Xbox Series X, and Xbox Series S in 2021, although the launch date has not been revealed yet. The 2018 game has been expanding and was launched for Nintendo Switch earlier this week as well. The game can be played next year onwards on Xbox consoles using Microsoft's Xbox Game Pass service. Among Us just joined Xbox Game Pass for PC as well. Xbox Game Pass will offer subscribers access to over 100 popular games on PC. Subscriptions can be availed at just Rs. 50 per month. The company updated its blog post to reflect the future availability of Among Us on Xbox consoles next year on. Among Us on Xbox One Xbox Series X, and Xbox Series S will likely feature cross-platform play. Among Us was launched on Nintendo Switch₹ 31,249 just a few days ago. It features cross-platform play on the Switch, allowing mobile, PC, and Switch users to play the game together. It can be purchased at $5 (roughly Rs. 400) through Nintendo's online store.


Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Makes 6 cakes
Dietary
One of my favourite bakes is a recipe for apple cake I created many years ago. This is a version of the same recipe but made up into individual cakes, which could be served warm as a dinner party dessert or cold as a delicious picnic treat.
Equipment and preparation: for this recipe you will need six 7cm/2¾in cooking rings.
Preheat the oven to 180C/160C Fan/Gas 4.
Grease the inside of the cooking rings with a little butter and dust with flour. Arrange the cooking rings on a baking sheet lined with baking paper.
Pour the melted butter into a large bowl. Add the sugar, flour, egg and almond extract and mix together until combined.
Spoon a little of the mixture into the base of each ring, arrange some of the apple slices over the batter and spoon the remaining cake mixture on top, levelling with the back of a teaspoon.
Scatter each cake with flaked almonds. Bake for 25–30 minutes, or until well risen and golden-brown.
Set aside to cool for about 10 minutes before removing the rings. Serve warm with a dollop of crème fraîche.


Having had the experience of taking care of their grandmother and helping her move into an assisted living facility, Caleb and Josh understand the daily needs and challenges of seniors. They have seen their grandmother and her friends often not having transportation when needed. Available transportation is often not ideal (uncomfortable taxis, uncourteous drivers, or crowded vans). Engler Brothers provides safe, dependable, and trustworthy transportation that seniors deserve, while understanding their need to keep as much independence as possible.
Relax and focus on the business at hand. We’ll take care of you and your business associates, out of town guests, and clients, right down to your baggage and a cold bottle of water. When it comes to business, timing is everything. Corporate transportation should be no different. We pride ourselves on being on time every time.
Our Lincoln Town Car Executive Sedans are perfect for airport transportation. The sedans seat up to three passengers with plenty of space for luggage. Features include: tinted windows, leather seating, smart phone chargers and more.


The custom (non-jquery-terminal) ProscriptLS interpreter console may be used to query programs defined in the 'Prolog source' area.


| Enumerator |
|---|
| INT_Initial_Stack_Pointer |
Initial stack pointer
|
| INT_Initial_Program_Counter |
Initial program counter
|
| INT_NMI |
Non-maskable interrupt
|
| INT_Hard_Fault |
Hard fault exception
|
| INT_Mem_Manage_Fault |
Memory Manage Fault
|
| INT_Bus_Fault |
Bus fault exception
|
| INT_Usage_Fault |
Usage fault exception
|
| INT_Reserved7 |
Reserved interrupt 7
|
| INT_Reserved8 |
Reserved interrupt 8
|
| INT_Reserved9 |
Reserved interrupt 9
|
| INT_Reserved10 |
Reserved interrupt 10
|
| INT_SVCall |
A supervisor call exception
|
| INT_DebugMonitor |
Debug Monitor
|
| INT_Reserved13 |
Reserved interrupt 13
|
| INT_PendableSrvReq |
PendSV exception - request for system level service
|
| INT_SysTick |
SysTick interrupt
|
| INT_DMA0_DMA16 |
DMA channel 0/16 transfer complete interrupt
|
| INT_DMA1_DMA17 |
DMA channel 1/17 transfer complete interrupt
|
| INT_DMA2_DMA18 |
DMA channel 2/18 transfer complete interrupt
|
| INT_DMA3_DMA19 |
DMA channel 3/19 transfer complete interrupt
|
| INT_DMA4_DMA20 |
DMA channel 4/20 transfer complete interrupt
|
| INT_DMA5_DMA21 |
DMA channel 5/21 transfer complete interrupt
|
| INT_DMA6_DMA22 |
DMA channel 6/22 transfer complete interrupt
|
| INT_DMA7_DMA23 |
DMA channel 7/23 transfer complete interrupt
|
| INT_DMA8_DMA24 |
DMA channel 8/24 transfer complete interrupt
|
| INT_DMA9_DMA25 |
DMA channel 9/25 transfer complete interrupt
|
| INT_DMA10_DMA26 |
DMA channel 10/26 transfer complete interrupt
|
| INT_DMA11_DMA27 |
DMA channel 11/27 transfer complete interrupt
|
| INT_DMA12_DMA28 |
DMA channel 12/28 transfer complete interrupt
|
| INT_DMA13_DMA29 |
DMA channel 13/29 transfer complete interrupt
|
| INT_DMA14_DMA30 |
DMA channel 14/30 transfer complete interrupt
|
| INT_DMA15_DMA31 |
DMA channel 15/31 transfer complete interrupt
|
| INT_DMA_Error |
DMA error interrupt
|
| INT_MCM |
Normal interrupt
|
| INT_FTFE |
FTFE interrupt
|
| INT_Read_Collision |
Read collision interrupt
|
| INT_LVD_LVW |
Low Voltage Detect, Low Voltage Warning
|
| INT_LLW |
Low Leakage Wakeup
|
| INT_Watchdog |
WDOG interrupt
|
| INT_RNG |
RNGA interrupt
|
| INT_I2C0 |
I2C0 interrupt
|
| INT_I2C1 |
I2C1 interrupt
|
| INT_SPI0 |
SPI0 interrupt
|
| INT_SPI1 |
SPI1 interrupt
|
| INT_SPI2 |
SPI2 interrupt
|
| INT_CAN0_ORed_Message_buffer |
CAN0 OR'd message buffers interrupt
|
| INT_CAN0_Bus_Off |
CAN0 bus off interrupt
|
| INT_CAN0_Error |
CAN0 error interrupt
|
| INT_CAN0_Tx_Warning |
CAN0 Tx warning interrupt
|
| INT_CAN0_Rx_Warning |
CAN0 Rx warning interrupt
|
| INT_CAN0_Wake_Up |
CAN0 wake up interrupt
|
| INT_I2S0_Tx |
I2S0 transmit interrupt
|
| INT_I2S0_Rx |
I2S0 receive interrupt
|
| INT_CAN1_ORed_Message_buffer |
CAN1 OR'd message buffers interrupt
|
| INT_CAN1_Bus_Off |
CAN1 bus off interrupt
|
| INT_CAN1_Error |
CAN1 error interrupt
|
| INT_CAN1_Tx_Warning |
CAN1 Tx warning interrupt
|
| INT_CAN1_Rx_Warning |
CAN1 Rx warning interrupt
|
| INT_CAN1_Wake_Up |
CAN1 wake up interrupt
|
| INT_Reserved59 |
Reserved interrupt 59
|
| INT_UART0_LON |
UART0 LON interrupt
|
| INT_UART0_RX_TX |
UART0 receive/transmit interrupt
|
| INT_UART0_ERR |
UART0 error interrupt
|
| INT_UART1_RX_TX |
UART1 receive/transmit interrupt
|
| INT_UART1_ERR |
UART1 error interrupt
|
| INT_UART2_RX_TX |
UART2 receive/transmit interrupt
|
| INT_UART2_ERR |
UART2 error interrupt
|
| INT_UART3_RX_TX |
UART3 receive/transmit interrupt
|
| INT_UART3_ERR |
UART3 error interrupt
|
| INT_UART4_RX_TX |
UART4 receive/transmit interrupt
|
| INT_UART4_ERR |
UART4 error interrupt
|
| INT_UART5_RX_TX |
UART5 receive/transmit interrupt
|
| INT_UART5_ERR |
UART5 error interrupt
|
| INT_ADC0 |
ADC0 interrupt
|
| INT_ADC1 |
ADC1 interrupt
|
| INT_CMP0 |
CMP0 interrupt
|
| INT_CMP1 |
CMP1 interrupt
|
| INT_CMP2 |
CMP2 interrupt
|
| INT_FTM0 |
FTM0 fault, overflow and channels interrupt
|
| INT_FTM1 |
FTM1 fault, overflow and channels interrupt
|
| INT_FTM2 |
FTM2 fault, overflow and channels interrupt
|
| INT_CMT |
CMT interrupt
|
| INT_RTC |
RTC interrupt
|
| INT_RTC_Seconds |
RTC seconds interrupt
|
| INT_PIT0 |
PIT timer channel 0 interrupt
|
| INT_PIT1 |
PIT timer channel 1 interrupt
|
| INT_PIT2 |
PIT timer channel 2 interrupt
|
| INT_PIT3 |
PIT timer channel 3 interrupt
|
| INT_PDB0 |
PDB0 interrupt
|
| INT_USB0 |
USB0 interrupt
|
| INT_USBDCD |
USBDCD interrupt
|
| INT_ENET_1588_Timer |
Ethernet MAC IEEE 1588 timer interrupt
|
| INT_ENET_Transmit |
Ethernet MAC transmit interrupt
|
| INT_ENET_Receive |
Ethernet MAC receive interrupt
|
| INT_ENET_Error |
Ethernet MAC error and miscelaneous interrupt
|
| INT_Reserved95 |
Reserved interrupt 95
|
| INT_SDHC |
SDHC interrupt
|
| INT_DAC0 |
DAC0 interrupt
|
| INT_DAC1 |
DAC1 interrupt
|
| INT_TSI0 |
TSI0 interrupt
|
| INT_MCG |
MCG interrupt
|
| INT_LPTimer |
LPTimer interrupt
|
| INT_Reserved102 |
Reserved interrupt 102
|
| INT_PORTA |
Port A interrupt
|
| INT_PORTB |
Port B interrupt
|
| INT_PORTC |
Port C interrupt
|
| INT_PORTD |
Port D interrupt
|
| INT_PORTE |
Port E interrupt
|
| INT_PORTF |
Port F interrupt
|
| INT_DDR |
DDR interrupt
|
| INT_SWI |
Software interrupt
|
| INT_NFC |
NAND flash controller interrupt
|
| INT_USBHS |
USB high speed OTG interrupt
|
| INT_LCD |
Graphical LCD interrupt
|
| INT_CMP3 |
CMP3 interrupt
|
| INT_Reserved115 |
Reserved interrupt 115
|
| INT_Reserved116 |
Reserved interrupt 116
|
| INT_FTM3 |
FTM3 fault, overflow and channels interrupt
|
| INT_ADC2 |
ADC2 interrupt
|
| INT_ADC3 |
ADC3 interrupt
|
| INT_I2S1_Tx |
I2S1 transmit interrupt
|
| INT_I2S1_Rx |
I2S1 receive interrupt
|


Known as "Crazy Dave" to neighborhood kids. While in the U.S. Army, apparently killed a boy in Germany. Killed another boy in Illinois, and then stabbed one in Texas. The legal system knew this guy was a psychopath who liked to kill boys -- comparing him even to John Wayne Gacy -- yet somehow he was released after only a few years. And, surprise, Crazy Dave gets a job at a trophy shop where he has contact with young boys, and can't resist the delicious temptations.
| 5 Apr 1954 |
David Edward Maust born. |
| 9 Aug 1981 |
Stabs and drowns boy Donald Jones, age 15, in a quarry near Elgin IL. |
| 10 Dec 1981 |
Stabs a child, Galveston County TX. Sentenced to five years. |
| 1982 |
Charged with the murder of Donald Jones. |
| 17 Jan 1983 |
Extradition paperwork refers to Maust as a "Gacey type" (sic.) |
| 22 Jul 1986 |
Date of the deposition made by detectives Behrens and Bettiker, to whom Maust confessed. Maust refused to sign a confession, and later tried to have the statements to Behrens and Bettiker suppressed from evidence, but a judge refused. |
| Jun 1985 |
Maust declared mentally unfit to stand trial. |
| 15 Aug 1986 |
"[V]ery long and significant mental history with multiple suicide attempts and homicidal activities... He has a pathological preoccupation with death, mutilation, dying and other bizarre ideations. He is currently unstable and unpredictable and runs a high suicidal, homicidal risk." Cook County psychiatrist Dr. Matthew Marcos. |
| 30 Mar 1987 |
Maust declared mentally fit to stand trial. |
| 1994 |
Pleads guilty to the murder of Donald Jones. |
| 6 May 1994 |
Sentenced to 35 years prison, with 11 years credit for time already served. |
| 25 Jun 1999 |
Paroled. Yikes! |
| 27 Jun 2002 |
Supervised release ends. |
| 2 May 2003 |
A boy who works at the Trophys Are Us -- the same place Maust does -- Nick James, age 19, reported missing. |
| 10 Sep 2003 |
Michael Dennis and James Raganyi last seen. |
| 5 Dec 2003 |
Police begin excavating beneath concrete in Maust's rented house at 4333 Ash Ave, Hammond IL. |
| 9 Dec 2003 |
Two bodies recovered beneath the concrete. |
| 10 Dec 2003 |
A third body recovered beneath the concrete. The bodies are identified as: Michael Dennis, age 13; James Raganyi, age 16; and Nick James, age 19. |
| 11 Dec 2003 |
David Edward Maust charged with one count of murder. |
| 20 Jan 2006 |
Serial killer David Edward Maust dies at St. Anthony Medical Center in Crown Point, Indiana, 27 hours after hanging himself in a botched though successful jail-cell suicide attempt. |


MH Dashboard
Top Priority
Example
Priorities
Priority 1
Priority 2
Priority 3
Habits
Habit 1
Habit 2
Habit 3
Habit 4
Obstacles
Obstacle 1
Obstacle 2
Obstacle 3
Life Foundations
Mind
Body
Relationships
Well Being
Finance
Gspanth


| 002:129 Khan | : |
"Our Lord! Send amongst them a Messenger of their own (and indeed Allah answered their invocation by sending Muhammad Peace be upon him ), who shall recite unto them Your Verses and instruct them in the Book (this Qur'an) and Al- Hikmah (full knowledge of the Islamic laws and jurisprudence or wisdom or Prophethood, etc.), and sanctify them. Verily! You are the All- Mighty, the All-Wise." |
| 002:129 Maulana | : |
Our Lord, and raise up in them a Messenger from among them who shall recite to them Thy messages and teach them the Book and the Wisdom, and purify them. Surely Thou art the Might, the Wise. |
| 002:129 Pickthal | : |
Our Lord! And raise up in their midst a messenger from among them who shall recite unto them Thy revelations, and shall instruct them in the Scripture and in wisdom and shall make them grow. Lo! Thou, only Thou, art the Mighty, Wise. |
| 002:129 Rashad | : |
"Our Lord, and raise among them a messenger to recite to them Your revelations, teach them the scripture and wisdom, and purify them. You are the Almighty, Most Wise." |
| 002:129 Sarwar | : |
Lord, send to them (our descendants) a Messenger of their own who will recite to them Your revelations, teach them the Book, give them wisdom, and purify them. You alone are the Majestic and the Most Wise." |
| 002:129 Shakir | : |
Our Lord! and raise up in them a Messenger from among them who shall recite to them Thy communications and teach them the Book and the wisdom, and purify them; surely Thou art the Mighty, the Wise. |
| 002:129 Sherali | : |
'And, Our Lord, raise up among them a Messenger from among themselves who may recite to them THY Signs and teach them the Book and Wisdom and may purify them; surely Thou art the Mighty the Wise. |
| 002:129 Yusufali | : |
"Our Lord! send amongst them a Messenger of their own, who shall rehearse Thy Signs to them and instruct them in scripture and wisdom, and sanctify them: For Thou art the Exalted in Might, the Wise." |
Concepts/Themes Covered: أركان الإسلام > التوحيد > توحيد الله تعالي > صفات الله تعالى > العزيز Pillars of Islam> Islamic> Oneness of Allah> His Glory's Characteristics> The All-Mighty أركان الإسلام > (محمد (صلى الله عليه وسلم > بعثته Pillars of Islam> The Blessed Muhammad(PBUH)> His Mission(PBUH) الإنسان والعلاقات الأخلاقية > الأخلاق الحميدة > الحكمة Man and The Moral Relations> Good Morals>Wisdom
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Problem: I had to write some code that would go through a list of numbers and return either Fizz, Bang or FizzBang depending on if the number was divisible by 5 or 3. While I had a fairly clear understanding of the logic and steps involved in solving this, I stumbled on the numbers that were divisible by both 3 and 5. Eventually I discovered that I had my step order the wrong way around and managed to solve the problem with a quick cut and paste!
Techniques Used: Rubber Ducky, Pseudocode, Trying something, trying something again, and again. Googling and finally good old console.logging to keep track of what was going on.
During the Process: Initially I was quite confident that I could easily solve the problem and when I ran up against issues I searched for wrong problems. This lead to a bit of frustration and I got fairly bogged down. Luckily I trusted my gut at one stage and tried the order swap and it worked!
Lessons Learnt: Mistakes can crop up in various stages of the process, and even if I feel fairly confident in how it all comes ogether I should still check out all avenues instead of leaving blind spots because I think what I did there was correct.
Problem: I was asked to write code to automate a gradebook for students. While I do not think myself qualified in even the aguest sense to be calling my work elegant, I managed to get a good grasp of what was required and what steps to follow to achieve my goals. My pseudocode worked well and I was able to finish up the task without too many issues - which was a very exciting and hugely encouraging.
Techniques Used: Rubber Ducky, PseudoCode, Trying Something and Google.
During the Process: During the process I felt quite calm, not sure of myself at all, but certain enough that the answer was just around the corner to get into the focus zone and put it all together efficiently.
Lessons Learnt: Not a technical or problem solving skill, but rather than I can do this and that coding can be as much fun as I had hoped it would be!
PseudoCode: I enjoy using pseudocode and have tried to make it the second step in all the problems I have to solve. That being said there is a lot of room for improvement in the way I plan and lay the pseudocode out.
Try Something: I do this a lot, but I don’t find it as effective at this stage. Almost as if it is a bit of a dice roll considering my low level of coding skills, but sometimes that quick attempt yields some pretty pleasing results!
Rubber Ducky: My go-to first step, I feel that the process of pulling apart a problem during the story telling phase of RubberDucky gives a good understanding of what needs to be done before the brain kicks in with recons and plans. This understanding works really well with pseudocode as I can use them together to stop myself from going down rabbit holes which might not even yield the results I was hoping for.
Error Messages: I feel I make good use of error messages. Or at least I try to, as they can be pretty inscrutable at times still. I am however very confident that my skills in reading them and using the information to quickly hone in on an issue will only grow experience.
Console.log: Another technique I use all the time. Not entirely sure if it is good practice to do so but I am constantly checking the results of functions or logging out alerts when my code reaches certain spots. To me it feels like a very valuable window into what my code is doing, or let’s be honest - what it ISN’T doing.
Google: Where would I be without googling - when I started this course I was blown away by how granular you can get with your search terms and still get solid results. There is truth to the statement that every problem you have, someone else already had and probably solved. The hardest part would be sifting through them all to find the applicable solutions - but this is also a skill I expect will develop as I gain experience.
Peer Assistance: A bit of a gap in my arsenal here as I have not had to do this yet. There is just so much help available online that I have not had to resort to asking my peers for help. I suspect that this will change once we hit bootcamp and are all together.
Coach Assistance: I have not had to do this yet either - but feel that I would be pretty confident in asking assistance or clarification if I need to do so.
Improving through Reflection: This is a pretty vital step to learning and almost takes place despite what I’m doing. I often find myself thinking of different ways of tackling a coding task while cooking or just running through a problem in my head while driving. I feel that this mulling over helps me incorporate new skills into my matrix of meaning.
Understanding the principles of neuroplasticity brings two main benefits. The first benefit aligns extremely closely with that of the Growth Mindset, in that it opens that mental door inside a person that leads to development through all stages of life. The second, and more practical benefit it that by understanding how neuroplasticity works, is that once a person becomes familiar with how it takes place and what kind of actions and behaviours encourage neuroplasticity, they can actively seek out, and embrace such situations and behaviours to harness this incredible ability for their own continued improvement.
Principles of neuroplasticity which may be extremely useful in a learning environment is knowing that there are two distinct stages of learning (short term and long term) and that each of these stages have techniques you can use improve performance. Short term learning can be encouraged through the use of novelty and by applying short bursts of fully focussed engagement. The second phase of neuroplastic development involves the physical growth and development of brain structures and as such thrives on repeated practice over a time, and from the continued integration of new knowledge into a cohesive contextual model.
The following methods are some of the ways by which you can increase the effectiveness of neuroplasticity: Intermittent Fasting offers a range of cognitive benefits such as the promotion of neuron growth and synaptic adaptation. Learning a new skill, such as juggling or playing an instrument triggers activity across a number of brain areas and in so doing enhances their interconnectivity which in turn can lead to the formation of new neural networks. Non-dominant hand exercises encourages the growth of entirely new neural pathways, and strengthens the connections between neurons. Sleeping well aids in the retention of what you have learnt as it encourages the growth of new connections between neurons, and thereby increasing the transfer of information.
Source: What is Neuroplasticity? A Psychologist Explains.Growth mindset is the faith you have in your own ability to grow and develop. This is hugely important as it forms he motivational backbone for virtually all human development. Having a growth mindset is also extremely relevant in our current situation as there would be no purpose in even trying to learn something new if you didn’t believe that you had the ability to do so.
This belief isn’t a simple on/off switch though and by integrating the principles of a growth mindset in a mindful way you are able to find areas where you might be holding yourself back due to a lack of self confidence, or to give you that final morale boost to keep cracking away at problem which seems unsolvable.
It was this last realisation that I found most surprising during my research and I will be focusing a lot of effort during my learning journey towards finding such self limiting beliefs in myself and swapping them out for more complete and resilient growth mindset versions.
Source: Growth Mindset vs. Fixed.


This page is to represent the work related to assignments for course Social Data Analysis and Visualization.
These exercises has been performed by DTU students:
This page is to represent the work related to assignments for course Social Data Analysis and Visualization.
These exercises has been performed by DTU students:
Here we have a small introduction video, that shows the idea behind of data analysis.
As explained in the video above, the following page will look into vehicle collisions in NYC. Additionally, we will try to explore where, how and why collisions happen.
One of our hypothesis is that when; the weather in NYC is bad (e.g. Snowy), there are more collisions. We will investigate if this actually is true, or not. Additionally, we want to investigate if there is any particular places in NYC are affected by bad weather compared to others.
Our main dataset is the NYPD Motor Vehicle Collisions. It contains records for every reported incident in the NYC area. Records are available from July 2012 till today. For each record there is also specified, where the collision took place, the cause of the collision, injuries, fatalities and more.
The dataset consists of more than;
The secondary dataset used is an extraction of weather conditions for the NYC area. This was pulled from Weather Underground using their csv service for hourly updates. For each hour, there are measurements of the temperature, visibility, windspeed as well as the overall weather conditions such as Rain, Snow, Clear etc. You can download our scrape from the service here.
This means that we have more than;
For the analysis the two datasets were combined. The combined dataset can be downloaded here.
If you want to know, how we extracted all of the information from the two datasets. You can take a look backstage, in our explainer notebook.
NYC is a big city. A lot of traffic means a high chance for a collision to occur. Over 500 incidents are reported everyday. Therefore, we are providing an opportunity for you to discover the amount of vehicle collisions that has happened throughout the city in the period 2012-2015. See if you can find the safer commute. Go discover!
The size of each point indicates the accumulated amount of incidents that have happened for that intersection. The color red shows if there is any fatalities. Click on a point to get the numeric values.
Note that all intersections with less that 10 collisions and without fatalities have been omitted to increase performance.
This is all good. Looking at the worst offenders regarding the number of collisions, can we find out why there are worse than the others? Is there any underlying reason for this?
Let's begin by locating the top 10 intersections with the most collisions (Click a bar to view the intersections).
Title
Location: 100
Collisions:
Click to view
By hovering over each bar in the top 10 bar chart. We can find that the intersection Tillary Street / Flatbush Avenue Extension is the most dangerous intesection - by some margin - based on the 585 collisions happening.
For now, we can only guess, but clicking on the Tillary Street & Flatbush Avenue Extension bar, or looking below one could assume that the intersection is the perfect storm. The intersection is right before a highway access/exit ramp and extends into Manhattan by the Manhattan Bridge.
What we see is in the top 10. Is that most of the intersections have a few things in common; they are next to a highway access/exit ramp or one block from one.
This means that there are a right thouht put of cars, every day. Resulting in the high amount of car collisions.
We all assume that weather has a big impact on the traffic anywhere. Collisions are far more likely to happen in bad weather. So what is bad weather really? Is there any condition that is whose than others? And if so can we found out which so you can be more aware in traffic during one of these?
By combining the Motor Vehicle Collisions dataset with weather data from Weather Underground we are able to find out which weather condition on average produces the most collisions per hour. The data has been normalized with the most common weather condition, Mostly Cloudy. However you can change this yourself in the below dropdown, to better understand our data.
Title
% difference from
From the visualization we see a clear uptick in the collision frequency rainy and snowy conditions. Up to 60%, more collisions compared to Mostly Cloudy conditions. In completely Clear weather seems to hover around 40% less. It does however also raises some questions. Why are the conditions Blowing Snow and Heavy Snow so far down the list?
In early 2015 and 2016, NYC experienced two blizzards. In both cases, NYPD issued Travel Bans meaning that only emergency vehicles were allowed on the roads. This could be a tell in why we in the very bad weather we do not see the uptick in collision frequency in very bad weather.
At 11pm tonight, streets will only be available to emergency vehicles. #SafeNYC
— NYPD NEWS (@NYPDnews) January 26, 2015
After 2:30 p.m and you're on the road, we will arrest you @NYPDChiefofDept says
— NYPD NEWS (@NYPDnews) January 23, 2016
Okay, now that we have confirmed that the frequency of collisions is higher for same conditions relative to Mostly Cloudy. Can we find any causes in the Motor Vehicle Collisions dataset that might have been affected by the weather conditions?
Title
Amount:
As it is clear in the chart above. Accidents happening due to a slippery pavement, were as predicted indeed more frequent at light rain and snow. This shows that some accidents are sensitive to the weather. However this was the general data. There might be more information hidden here. For instance, is there some peticular intersections in NYC that are overly sensitive to this condition?
If we look at the slippery pavement cause for collisions. We can find som interesting information. Looking at the frequency of accidents, we found perticular sensitive intersection.
As we see in the above heat map, the spread is very broad. This is in fact very similar to what the overall incident spread. However looking a the intersections, we can find some interesting discoveries.
As shown above we have some intersections, that are particular prone to accidents, with slippery pavements.
But why do people have accidents here? Let's take a look at the intersections thought the wonders of Street view:
As we can see on the image above. The top intersection is just by the graveyard at the church of St. Andrew. This can be no of coincidence!
As it should be visible from the image, this intersection is down hill, and with a sharp turn. We belive that this is why there are so many accidents here. Looking further up the road, it is clear that the city council have tried to take some actions to increase the safety. However, this seems not to be sufficient. We would recommend that the speed limit was lowered.
The next intersection one the list, is located in the Bronx, not Staten Island. Here again, the oncoming traffic is lead down a steep hill. Leading into a small but significant turn. Here the driver is not able to see the light beforehand, as of this turn. And when visible he might be unable to stop as of the slope. Additionally, we can see that the pavement here is made of concrete, meaning that it is going to be extra slippery even in light rain or snow.
Let's try to apply a different approach, for finding these sensitive intersections.
As intersections sometimes can be located very close together. How do you then determine what intersections, the collisions happened in? Therefore, we will apply a clustering method, in order locate small groups of intersections, that might be extra sensitive.
A perticular good method creating these things of clusters, is the Density-based spatial clustering of applications with noise (DBSCAN). It allows for clustering intersections by distrance, and remove noise (intersections with small amounts of collisions).
We have made this cluster analysis for all weather conditions. Below is a snippet of what we found based on the conditions:
The dendrogram shows these the clusters for each condition. As well as the conditions for each cluster. Try clicking an intersection (the points in the outer circle).
Title
The three intersections on 3rd Ave, Manhattan seem to be very sensitive to heavy rainfall. Looking at the street view (clicking on the intersection node, choosing streetview), it is not hard to see why. Especially on 3rd Ave & E 58th St:
As we found with collisions caused by slippery pavement sloped roads did have an impact on the collision rate. This intersection Groove St & Hygeia Pl, Staten Island appears to be sensitive to snowy conditions:
Looking at the historical data is great as it can tell us a lot if interesting facts. However could we also use it to say something about the future?
Looking into the future is hard. However it is not completly impossible, thanks to machine learning. Therefore we have applied some machine learning techniques to our data. In order to try to predict, where collisions will happen. So given a hour of day, a weather condition as well as an approximated location. We can predict the specific postal area, with upto 35% precision.
This means, that if you know the current weather condition in NYC (just try to look outside), as well as the current hour (just look at the clock on your computer or phone), we can say where collisions might happen with a 35% precision.
By applying a K-Mean clustering and Random Forest Classification. As explained in detail in the explainer notebook. We were able to train our model, so that we can make these predictions, with such high accuracy.
Below, we have a visualization, the different clusters that we used in our analysis. Here you can see for yourself, how the different amount of clusters affect the precision of the model.
Note that the shown shapes, are the convexes of clusters of collisions, hence the shape gaps in the visualization.


PLANET SICK-BOY: http://www.sick-boy.com "We Put the SIN in Cinema"
Remember when Johnny Depp used to make good films and Michael Stipe used to make good music? At some point over the last couple of years, the two seemed to undergo some sort of swap. Depp has struck out with roles in awful films like The Astronaut's Wife and The Ninth Gate, while playing guitar for a band called P (which, ironically, has a song about Stipe). Meanwhile, Stipe produced three of 1999's best films (Being John Malkovich, American Movie and The Limey), but R.E.M.'s sales figures have plummeted.
Stipe has also produced Spring Forward – an action fan's worst nightmare. There's nothing a knuckle-dragging, mouth-breathing Van Damme fan hates more than a character study, and that's just what they'll get here. But Forward is so much more than a film heavy in dialogue and deficient in superfluous action. In fact, you'd be hard-pressed to find a picture that bests Forward in the arenas of acting, directing and writing. It is clearly one of the year's best films, but, sadly, the lack of a major distributor will likely hamper any shot at well-deserved year-end accolades.
Already the winner of numerous film festival awards, Forward is set over the course of one year in a small Connecticut town. The film is comprised of seven sections that focus on two Parks & Recreation Department workers. Murph (Ned Beatty, Cookie's Fortune) has been married for nearly half a century and is less than a year away from retirement. Paul (Liev Schreiber, Scream 3) is a young high school dropout who has just been released from prison.
As you would expect, their relationship doesn't start off on the right foot, but as the film progresses, the two men begin to open up to each other despite the differences in their age and backgrounds. Surprising parts of their lives surface, and Murph and Paul begin to bond as New England's seasons change around them. By the time the closing credits roll, the seasons have made a complete arc, as has the relationship between Murph and Paul.
Although it sounds like it, Forward isn't an Odd Couple ripoff.  In a film this finely written, cinematography hardly matters, but Forward is handsomely photographed by Terry Stacey, from television's hastily cancelled medical drama Wonderland.
Forward is an astonishing debut from writer/director Tom Gilroy, who previously played bit parts in a bunch of independent films. Some people may have big problems with Gilroy's style, which leaves a lot of unanswered questions instead of neatly wrapping up every loose end. Murph and Paul meet various people while painting fences and fixing gazebos, but once the scenes end, those characters disappear from the film. It's a bold move, especially when you consider that some of those roles are played by some pretty established actors – like Ian Hart (Wonderland), Campbell Scott (The Impostors) and Peri Gilpin (Frasier). But this technique allows viewers to concentrate on the fantastically realistic performances given by Beatty and Schreiber.
1:40 – R for adult language, sex talk and some drug content
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