What permissions does code.pyret.org ask for and why?
code.pyret.org asks for several permissions when you log in:
- Know who you are on Google and View your email address: The site needs to know your Google identity because this allows us to give persistent access to saving to Drive that doesn't expire or require popping up new windows while you're editing.
- Manage your photos and videos and View the photos, videos, and albums in your Google Drive: This is used to import images into programs from your Drive (which can be useful for customizing games, for example).
- View and manage your spreadsheets on Google Drive: This enables importing tables and working with data sources in your Drive.
- Add itself to Google Drive: This lets you right-click on Pyret programs in Google Drive and "Open with Pyret."
- View and manage Google Drive files and folders that you have opened or created with this app: This lets the editor see, save, publish, and re-open programs you create.
Where are things stored in my Drive?
The site initially makes a single folder in your Google Drive, called
code.pyret.org
, and stores all of your programs there, with the
names you choose for them. They are all created with you as the owner, and
private to your account. There is also a directory called
code.pyret.org.compiled
that is used for caching compiled copies
of programs.
If you publish programs, the site creates a new folder, called
code.pyret.org.shared
, and makes publicly-readable
copies of programs you publish in that directory. If you delete this
files in this folder, links for those programs you have shared with others
will stop working, but any copies others have made will be theirs to keep.
Each time you publish a program, a new copy is created from its current
contents.
How can I remove the access code.pyret.org has to my data?
You can always go to https://security.google.com/settings/security/permissions?pli=1 and remove all access that code.pyret.org has to your account. Your programs and the created folders will not be deleted if you do so. You can always manually delete the folder and its contents yourself.
How can I see old versions of my programs?
Google Drive automatically stores old versions of your programs as they are saved (as it does for all files). You can follow the instructions at Google's answer to manage old versions of files that you may have saved over or renamed.
What information is sent back to the Pyret team?
We collect basic information about your use of code.pyret.org to help us improve the language and editor, and enable some debugging. We send back information about errors, authentication, whether features such as the type-checker are used, and preferences (such as which mode you choose to display highlights with).
We don't log the contents of your programs by default.
Contributing Detailed Information
If you opt to contribute detailed usage information under the "More" menu (off by default), we will also receive information about the contents of your programs and the edits you make.


Contact Information
- Office: TBD
- Office Hours:
- Monday: 2:45-3:45 PM
- Thursday: 5:30-6:30 PM
- By Appointment
- Contact Information:
- mlevet who is in the domain math dot sc dot edu
- mlevet who is in the domain email dot sc dot edu
Research Interests
- Pure Math:
- Algebraic Combinatorics
- Algebraic Graph Theory
- Theoretical Computer Science
- HW5
- Interdisciplinary:
- Microeconomic Theory
- Game Theory
Publications:
- “ A Mechanism Design Approach For Influence Maximization ” - (with Siddharth Krishnan, 2016 )
- “ Network Structure and Activity in Boolean Networks ” -
(with Abhijin Adiga, Hilton Galyean, Chris J. Kuhlman, Henning S. Mortveit, and Sichao Wu),
Cellular Automata and Discrete Complex Systems (2014).
Teaching Experience:
- Virginia Tech:
- Fall 2014: CS 4124 Theory of Computation (UTA)
- Spring 2015: CS 4114 Formal Languages and Automata Theory (UTA)
- University of South Carolina- Columbia:
- Fall 2015:
- CSCE 145 Algorithmic Design I (GTA for one lab section)
- CSCE 355 Foundations of Computation (GTA)
- Spring 2016:
- CSCE 146 Algorithmic Design II (GTA for one lab section)
- CSCE 551 Theory of Computation (GTA)
- Summer 2016:
- CSCE 355 Foundations of Computation (Instructor of Record)
- Graduate Tutor for Math Tutoring Center
- Fall 2016: Three sections of Math 142 Calculus II
My Notes
Book Recommendations
- A First Course in Proofs
- Favorite Combinatorics and Graph Theory Books
- Theory of Computation and Algorithms
- Introduction to Algorithms- Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein
- Computational Complexity: A Modern Approach- Sanjeev Arora and Boaz Barak
- Models of Computation- John E. Savage
- Introduction to the Theory of Computation - Michael Sipser
- Introduction to Automata Theory, Languages and Computation (1st Edition)-
John E. Hopcroft and Jeffrey D. Ullman.
- Introduction to Automata Theory, Languages and Computation (3rd Edition)-
John E. Hopcroft, Rajeev Motwani, and Jeffrey D. Ullman
- Remark: The 1st Edition of Hopcroft and Ullman's text is a beautifully written book full of a lot of important material.
However, it is really geared at a mathematically mature audience. The 3rd edition is more watered down in terms of the material,
but the authors emphasize proof strategy. The later edition is more appropriate for an audience with
minimal proofs-based mathematics.
- Linear Algebra
- Advanced Linear Algebra- Nicholas Loehr
- Abstract Algebra
- Abstract Algebra: Theory and Applications- Thomas Judson
- A Book of Abstract Algebra- Charles C. Pinter
- Abstract Algebra- David Dummit and Richard Foote
- Algebra: Chapter 0- Paolo Aluffi
- Remark: The Pinter and Judson texts are excellent and gentle introductions for students to study abstract algebra.
Dummit and Foote is a very thorough senior and graduate text on the subject. Aluffi's text uses category theory as a framework, which
makes it better as a second read for someone with previous exposure to Algebra.
- Microeconomics and Game Theory
Helpful Links


NAVIGATION
Quiz
Segment Title
Course Title
Playlist
Overview
About
Instructor
Next
Segment Description
Next
1h30
-
Debutant
-
12 May 2014
Course Description


In the part below, the needed libraries are imported.
pandas helps us to read data from ".csv" file in pandas dataframe. dataframe is a map which maps name of each column in input file to its corresponding vector. Each vector stores the data available in each column of .csv file.
"matplotlib.pyplot" helps us to show our plots in the notebook.
numpy help us in some iterating operations.
#import
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# %matplotlib inline
In this part, at first we read the data from file in pandas dataframe by using "pd.read_csv()" function.
pd.read_csv(): This function is located in pandas library, it puts the address of the .csv file as input and gives back dataframe produced from the data in file, the output is a dataframe which contains a dictionary mapping column names to their corresponding vector, produced from .csv file columns data.
Then we call some function which gives summary of data.
head(): The function gives us the first five rows fo data, meaning, column names and five first rows of each column.
describe(): With calling this function on the dataframe, we give a dataframe, containing 8 statistic parameters for each column in the source dataframe. Theses statistics parameters are, count, mean, std, min, first & second & third quartile and max of each column.
info(): This function tells us the memory related information about the data, including datatype and the number of valid data for each column, num of entries in the dataframe, and amount of memory usage
#Part 1.1
data = pd.read_csv("/content/AdmissionPredict.csv")
print("head >>>>")
print(data.head())
print()
print("description >>>>")
print(data.describe())
print()
print("info >>>>")
print(data.info())
print()
In this part we call "pd.isnull()" function.
pd.isnull(): The function gets a vector as input and gives a vector containing true/false on the output. So by putting a column of data on its input and then calculate a sum over output we would have num of "true"s on the output which represents the number of "Nan"s on each column.
So we iterate over all of data columns and add the expressed sum value to the vector containing ans.
enumerate(): This function gets an iterable object as input and, returns another iterable oject, which has the object entries with an index sticked with them.
At last, with an iterate over data.columns we print the sum value of each column of dataframe with its column name on each row.
#Part 1.2
nan_counts = [sum(pd.isnull(data[i])) for i in data.columns]
print("Nan counts:\n")
for index, col in enumerate(data.columns):
print(col, nan_counts[index])
In this part, with an iteration over all of columns unless last column, we replace mean of column with "Nan" values with use of "fillna()" function and "mean()" function calculating the mean of each column.
mean(): The function gives us the mean value of the vector that is called on.
fillna(): The function puts the given value on its input in the rows containt "Nan" in the vector, that it is called on.
data.columns[:-1]: this expression, includes list of all columns of data, except the last column, which corresponds to "Chance of Admit" column.
So, we have the data cleaned and we name the new data, cleaned_data.
And with printing the num of "Nan"s of each column, we observe that, they are zero in clean_data except for the last column.
copy(): The function called on any object in python gives us a deep copy of that object in the output.
#Part 1.3
clean_data = data.copy()
for i in data.columns[:-1]:
clean_data[i].fillna((data[i].mean()), inplace=True)
print("Nan counts:\n")
nan_counts = [sum(pd.isnull(clean_data[i])) for i in clean_data.columns]
for index, col in enumerate(clean_data.columns):
print(col, nan_counts[index])
At first, with an iteration over all of columns except the last one, we plot the scatter of each column with the last column which represents the connection between every column datapoints and their corresponding "Chance of Admit" values.
plt.subplot(): The function gets three integer values on input, the first one represents the num of columns of plots that we want to plot near each other and, the second one, represents the num of rows of plots that we want to plot near each other, And the third input value, represents the location of current on plot on the table of plots.
plt.scatter(): The function gets two vector of data on its input and plots the scatter plot of them on output.
plt.xlabel(): sets the label of x-axis for each plot.
plt.ylabel(): sets the label of y-axis for each plot.
plt.show(): The function, shows all of the plot that we prepared to be plotted with function scatter() or other plot functions.
In this part, we are to find the paramtere which correlates the most to the "Chance of Admit".
Choose parameter: In order to find the quested parameter, that make it possible for us to forecast the amount of "Chance of Admit" for every specific datapoint through a linear function. Among the plots we observe that CPGA datapoints are better fitted on an imaginary line, than others, and we are searching for this aim, because the plots that are not linear like University Rating gives us no information about any relation between themselves and "Chance of Admit".
Conclude: The answer is a plot which represents a line that points fit on it with a high level of accuracy, because the plots that are not fitted on a line, don't get us any information about the quested relation.
So we we chose CPGA as it is showing a line on its plot with a higher accuracy rather than others.So the Chance of Admit is mostly dependant on CGPA rahter than others.
#Part 2.1
plt.figure(figsize=(25, 12))
for idx, col in enumerate(clean_data.columns[:-1]):
plt.subplot(2, 4, idx+1)
plt.scatter(clean_data[col], clean_data[clean_data.columns[-1]])
plt.xlabel(col)
plt.ylabel(clean_data.columns[-1])
plt.show()
We know that, if we put a vector of numbers in an inequality the output result is a vector of true/false's with the same length of that vector, where every element represents the result of inequality for that indexed number on vector.
So, We got the result of two given inequalities seperately, and then, with an "and" operation on them, we would give true just on indices that have both conditions, and at last, with putting that filter vector as index input of data frame of data we would get the entries that satify the both conditions from the cleaned_data and we name that data, selected_data.
With print length of selected_data, we see that, they are 97 entries of cleaned_dat which had 400 entries, at first.
#Part 3.1
higher_CPGA = clean_data['CGPA'] >= 9
higher_TOEFL = clean_data['TOEFL Score'] >= 110
selected_students = clean_data[higher_CPGA & higher_TOEFL]
print('num of selected students : ', selected_students.shape[0])
We know that, possible values for University Rating are integer numbers from 1 to 5, so we make an iteration from 1 to 5 and, at every iteration, at first select the entries those have that, Rating University through a quality and then, we add mean value of GRE Score of selected entries to the array, storing the mean values.
then, with an iteration over enumerated mean_GRE_uni_rating, we print the university rating and its mean GRE Score on every line.
#Part 3.2
mean_GRE_uni_ratings = []
for rating in range(1, 6):
filt = clean_data['University Rating'] == rating
mean_GRE_uni_ratings.append(clean_data['GRE Score'][filt].mean())
for idx, mean in enumerate(mean_GRE_uni_ratings):
print("GRE mean in rating ", idx+1, " = ", mean)
In order to store the quested columns in a dataframe, we put an array of strings representing the quested columns in the column index input of dataframe, so it would get us a dataframe with having just "CGPA" and "Chance of Admit", columns of the source dataframe.
At last we print the head() of the derivated dataframe, to observe the columns of that dataframe, and their data.
#Part 4.1
cgpa_data = clean_data[['CGPA', 'Chance of Admit']]
print(cgpa_data.head())
In this part, at first we define the hypothesis function, which gives theta array and X array as input and gives array of predicted y's as output.
hypothesis_func(): In this function, the x on input is a vector of input data, and we should multiply all of the elements of the vector with theta1 which is stored in theta[1] and then add theta0 stored in theta[0] to all of them. We perform these operations with .mul() and .add() functions called on the x as input vector of data.
cost_func(): In order to calculate the cost of the hypothesis function, we implement this function in this way: square power of (Y_P - Y) gives us every element of subtracted vector squared, then with calling mean() function on this vector we get the mean value of errors, but we know that, according to the given formula, we have to divide sum of the squared diffrences by (2*m) but through mean() function this sum is divided by m, so we divide the mean() output by 2.
In order to find theta with an appropirate value, gives us a low cost: We know that, the cost function, totally is a sum of squared terms consists of theta0 and theta1, so we conclude that, cost function is a convex function with variables of theta, so its local minimum and global minimum are the at the same point, and we see that, for example when we put theta values between -1 and 1, the cost amount get lower and then decreases, so we conclude that, the local point is between -1 and 1, so we follow this algorithm to find a point near, global minimum give us a low level of cost.
Algorithm: with iteration from i = -1 to -5, and giving the inital values of theta to be 0, at every step, we examine values between the found minimum point with step length of 10 i from theta - 10 (i+1) to theta + 10 ** (i+1), so at last, we would find a point near global optimum, because at every step we find the nearest point to optimum and choose it ot explore near it for the next step.
#Part 4.2
cgpa_X = cgpa_data['CGPA']
cgpa_Y = cgpa_data['Chance of Admit']
def hypothesis_func(theta, x):
return x.mul(theta[1]).add(theta[0])
minError = np.inf
minTheta = [0, 0]
def cost_func(Y_p, Y):
return ((Y_p - Y) ** 2).mean() ** 0.5
for i in range(-1, -5, -1):
# print(i)
for theta0 in np.arange(minTheta[0] - 10 ** (i+1), minTheta[0] + 10 ** (i+1), 10 ** i):
for theta1 in np.arange(minTheta[1] - 10 ** (i+1), minTheta[1] + 10 ** (i+1), 10 ** i):
Y_P = hypothesis_func([theta0, theta1], cgpa_X)
err = cost_func(Y_P, cgpa_Y)
if(err < minError):
# print(theta0, theta1, "err = ", err)
minTheta = [theta0, theta1]
minError = err
print("min theta = ", minTheta)
Y_P = hypothesis_func(minTheta, cgpa_X)
error = cost_func(Y_P, cgpa_Y)
print(error)
plt.scatter(cgpa_X, cgpa_Y)
plt.plot(cgpa_X, Y_p)
In this part, after filtering the students with "Nan" value of Chance of Admit, calculate the forecasted values output from hypothesis function and put them, in filtered dataframe and then we get the two wanted columns to print.
#part 4.3
Nan_chance = clean_data[pd.isnull(clean_data["Chance of Admit"])]
Nan_chance["Chance of Admit"] = hypothesis_func(minTheta, Nan_chance["CGPA"])
final_list = Nan_chance[["Serial No.", "Chance of Admit"]]
print(final_list)


| Top |
| void * | vips_thread_state_set () |
| VipsThreadState * | vips_thread_state_new () |
| VipsThreadState * | (*VipsThreadStartFn) () |
| int | (*VipsThreadpoolAllocateFn) () |
| int | (*VipsThreadpoolWorkFn) () |
| int | (*VipsThreadpoolProgressFn) () |
| int | vips_threadpool_run () |
| void | vips_get_tile_size () |
vips_threadpool_run() loops a set of threads over an image. Threads take it in turns to allocate units of work (a unit might be a tile in an image), then run in parallel to process those units. An optional progress function can be used to give feedback.
int (*VipsThreadpoolAllocateFn) (VipsThreadState *state
,void *a
,gboolean *stop
);
This function is called to allocate a new work unit for the thread. It is always single-threaded, so it can modify per-pool state (such as a counter).
a
, b
, c
are the values supplied to the call to
vips_threadpool_run()
.
It should set stop
to TRUE
to indicate that no work could be allocated
because the job is done.
See also: vips_threadpool_run()
.
int (*VipsThreadpoolWorkFn) (VipsThreadState *state
,void *a
);
This function is called to process a work unit. Many copies of this can run at once, so it should not write to the per-pool state. It can write to per-thread state.
a
, b
, c
are the values supplied to the call to
vips_threadpool_run()
.
See also: vips_threadpool_run()
.
int
(*VipsThreadpoolProgressFn) (void *a
);
This function is called by the main thread once for every work unit processed. It can be used to give the user progress feedback.
See also: vips_threadpool_run()
.
int vips_threadpool_run (VipsImage *im
,VipsThreadStartFn start
,VipsThreadpoolAllocateFn allocate
,VipsThreadpoolWorkFn work
,VipsThreadpoolProgressFn progress
,void *a
);
This function runs a set of threads over an image. Each thread first calls
start
to create new per-thread state, then runs
allocate
to set up a new work unit (perhaps the next tile in an image, for
example), then work
to process that work unit. After each unit is
processed, progress
is called, so that the operation can give
progress feedback. progress
may be NULL
.
The object returned by start
must be an instance of a subclass of
VipsThreadState. Use this to communicate between allocate
and work
.
allocate
and start
are always single-threaded (so they can write to the
per-pool state), whereas work
can be executed concurrently. progress
is
always called by
the main thread (ie. the thread which called vips_threadpool_run()
).
See also: vips_concurrency_set()
.
im |
image to loop over |
|
start |
allocate per-thread state |
|
allocate |
allocate a work unit |
|
work |
process a work unit |
|
progress |
give progress feedback about a work unit, or |
|
a |
client data |
void vips_get_tile_size (VipsImage *im
,int *tile_width
,int *tile_height
,int *n_lines
);
Pick a tile size and a buffer height for this image and the current
value of vips_concurrency_get()
. The buffer height
will always be a multiple of tile_height.


§462A-9 Exhibition of license. A pilot, on boarding a vessel, and if required by the master thereof, shall exhibit the pilot's license or a photostatic copy thereof. [L 1978, c 231, pt of §2; gen ch 1985]


Hello World!
Tutorial Assignment: 1_1_getting_started
This is Tomiko's assignment
1_1_getting_started
Here's a cat video on YouTube


screen as: conversation
Create your own dialogue between this screen and another screen
by pointing their webcams displays to each other.
Press "take screenshot" to capture stills and view them below the camera.
gallery


DfE enquiries
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Press release
New advice to schools will make clear that they will no longer be able to use pupils’ biometric data without parental consent. The advice, launched today for consultation, comes into effect from September 2013.
New advice to schools will make clear that they will no longer be able to use pupils’ biometric data without parental consent. The advice, launched today for consultation, comes into effect from September 2013.
Schools Minister Nick Gibb said children’s biometric data was sensitive personal information and parents must have the right to prevent its use by schools and colleges. Pupils also have the right to refuse to participate and these provisions are explained in the guidance.
The advice has been updated to take into account new measures in the Protection of Freedoms Act 2012, which has now gained Royal Assent. It will clearly set out to schools and colleges that use biometric recognition systems, such as fingerprint identification and facial scanning, that:
Frequently asked questions and optional templates for notification and consent will also be included in the advice.
The Government has been clear that parents should have the right to prevent the use of their child’s personal data in automated biometric recognition systems. This commitment was underlined in the Coalition’s manifesto, Our programme for government.
Schools Minister Nick Gibb said:
Biometrics in schools is a sensitive issue. We want schools to be in no doubt of their responsibilities when it comes to young people’s personal data.
I have heard from many angry parents after they have learned that their children’s personal data was being used by schools without their knowledge. The new legislation gives the power back to parents, as it requires parental consent before the information can be collected.
In the age of the internet, identity and the integrity of biometric data are of increasing importance. Young people need to understand from an early age the sensitivity of such personal data. The provisions of the Protection of Freedoms Act 2012 and the accompanying advice to schools will help to reinforce that message.
Some schools and colleges use biometric technologies such as fingerprint identification and facial scanning. These may be used to record attendance, grant access to libraries and to process cashless payments. The benefits to schools include site safety and the speed and ease of access to services.
The consultation is aimed at proprietors, governing bodies, head teachers, principals and staff. The Department wants schools and colleges to be able to accommodate the new duties without increasing the burden on them. The consultation seeks feedback on the clarity of the Department’s advice ahead of its final publication later in the year. It runs for 12 weeks and closes on 3 August 2012.
Further Education Minister John Hayes said:
It is absolutely right that what we do in schools is consistent with the approach in colleges and, in that spirit, I welcome this consultation.
The consultation on the draft advice is available on the consultations pages of the Department for Education’s website.
The Department for Education is providing the advice for schools, sixth form colleges and 16 - 19 Academies. The Department for Business, Innovation and Skills will provide advice to FE institutions with under-18 students. Please contact the BIS press office on 020 7215 5982 for more information on its advice.
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Published: 15 May 2012


{% extends 'pages/home.html' %} {% load staticfiles %} {% load flatblocks %} {% block content %}
TaxBrain is an interface to open source economic models for tax policy analysis. The code for the TaxBrain webapp interface is itself open source.
Throughout this process, if you have a question about how to use TaxBrain or interpret the results, if you want to make a suggestion for making the interface or underlying models better, or if you discover a bug, please join our mailing list and send a message or leave a note for the OSPC team.
Static tax analysis entails computing individuals' tax changes under the assumption that behavior does not change in response to tax policy. Static analyses are useful for understanding the mechanistic effects of tax policy changes, and they form the basis to which behavior is applied for dynamic analyses.
TaxBrain's static modeling capabilities rely on several open source economic models and other packages:
Transparency and Replicability
In addition to relying on open source models, we are devoted to making it easy for reviewers to understand the models even if they can't understand the source code or don't have access to the underlying data. Toward that end we produce several additional reports to enhance transparency, peer review, collaboration and a scientific advancement.
Note that these reports currently rely on the latest versions of tax-calculator and TaxData, which might not correspond perfectly to TaxBrain.
Accuracy notes
The Python code that performs the tax calculations has been validated in a number of ways. First, Tax-Calculator results for a number of tax filing units have been compared to hand calculations performed using IRS tax forms. Second, Tax-Calculator results for a large sample of tax filing units have been compared to results for the same sample generated by a detailed SAS program developed by Dan Feenberg and Ina Shapiro of NBER. Third, a subset of input variables has been used to compare the results of Tax-Calculator to Internet TAXSIM as well as against the Policy Simulation Group's PENSIM tax module
Bugs aside, the static modelingresults from TaxBrain might differ in comparison to those produced by Congress or the Administration for other reasons. Modeling requires many assumptions, and neither Congress nor the executive branch publicize all of their assumptions. For example, the distribution of wages in TaxData is assumed to stay the same in real terms for all years after the last year we have available data (2013). We know that Congress assumes this distribution changes over time, but it doesn't publish by how much. These assumptions are all flexible in TaxData, so please conduct sensitivity analyses. Other assumptions can be made flexible in TaxBrain based on user requests.
Core Maintainers (static modeling)*:
*These members have "write access" to one or both of the core static modeling repositories, Tax-Calculator and TaxData, and work as a team to determine which open source contributions are accepted.
Proper use of ospc.org tools and description of that use is ultimately your responsibility. If you plan on publishing your results, it is recommended that you confirm with the community that you are using the tools properly and interpreting the results correctly before you publish them.
Results will change as the underlying models improve. A fundamental reason for adopting open source methods in this project is to let people from all backgrounds contribute to the models that our society uses to assess economic policy; when community-contributed improvements are incorporated, the models will produce different results.
Neither the Open Source Policy Center nor the American Enterprise Institute maintain institutional positions, and the results from models accessible via the ospc.org interfaces should not be attributed to OSPC or AEI.


The child's wonder
At the old moon
Comes back nightly.
She points her finger
To the far silent yellow thing
Shining through the branches
Filtering on the leaves a golden sand,
Crying with her little tongue, "See the moon!"


#include <datastruct.h>
Data Fields | |
| char * | host |
| char * | port |
| char * | name |
| char * | pwd |
| PROXY_TYPE | type |
| char* _PROXY_ITEM::host |
Proxy host.
| char* _PROXY_ITEM::port |
Proxy port.
| char* _PROXY_ITEM::name |
User name.
| char* _PROXY_ITEM::pwd |
User password.
Proxy type, which can be HTTP, SOCKS4, or SOCKS5.


Score free tickets to U2 concert in Manila
by Manila Standard LifestyleHeads up, U2 fans!
Telecommunications company Smart gives subscribers an opportunity to score tickets to this year’s biggest concert, the Joshua Tree Tour, the first ever concert of U2 in Manila.
The one-night only show on Dec. 11 at the Philippine Arena is expected to bring together music fans across generations with the iconic Irish rock band’s hits including “Where The Streets Have No Name,” “Pride (In The Name of Love),” “I Still Haven’t Found What I’m Looking For,” “With or Without You,” and a lot more.
Through the new Smart MVP Rewards program, prepaid and postpaid subscribers can win free tickets.
Simply download the MVP Rewards app in the App Store or Play Store and register your details. Then enroll your Smart account to start earning stars.
Click on your stars to visit the Marketplace where you can click on the U2 raffle icon and participate for free.
All customers who click on the U2 raffle icon will only have one e-raffle ticket for the entire promo duration.
Those who will enter the free Smart MVP Rewards U2 Raffle Promo get a chance to win two tickets to the U2 The Joshua Tree Tour concert, plus access to the Smart Music Live VIP Lounge during the concert proper.
The promo is ongoing until Dec. 5. Winners will be drawn on Dec. 6.


Containers and virtual machines are two of the top approaches to improve scalability and reduce overhead costs. Both can help a software team become responsive to business demands.
Virtualization enables you to run multiple operating systems on a single physical server while containerization enables you to deploy multiple applications using the same operating system on a single server.
Both containers and virtual machines are software that create self-contained virtual packages. Although they share this commonality, they do differ in their characteristics.
It depends on your objective!
Virtual machines are great for supporting applications that require an operating system’s full functionality. Containers are a better choice when your biggest priority is to minimize the number of servers you’re using for multiple applications.
Containerization offers an elective strategy for Virtualization in which a single working framework on a host can run a wide range of users from the cloud.
The use case makes a big difference in deciding which is better for you. Because of their fast set up time, containers offer a better choice for tasks with a much shorter lifecycle. Virtualization, however, has a longer lifecycle and are used for longer periods of time. Read here if you want more information.


Shoping Cart
×
Shopping Cart
Nothing in cart
Your order is received. We will contact you as soon as possible.
...
Purchase


<!--mksite:updatevars--> <!--mksite:noupdatevars-->
The option can be selected in the sitefile, per default we do
update content prefixed with these comment variables.
mksite:nocommentvars
disables this feature as well.
input: derived<!--$varname:=-->
2004/04 output: derived 2004-04-16 default: derived 2004/04 input: derived<!--on $varname:=-->
2004/04 output: derived on 2004-04-16 default: derived 2004/04 input: derived<!--on $varname:?-->
2004/04 output: derived on 2004-0416 default: derived 2004/04 input: derived<!--$varname:?-->
2004/04 output: derived - 2004-0416 output: derived (2004-0416) default: derived 2004/04
This is the preferred method of updating content in the htm file. The comment itself is invisible while a default representation is visible (usually in a similar formatting). The expansion will cut away all text following the comment variable up to the next markup or endofline. That text is replaced with the value of the selected variable. If no variable of such a name exists then nothing is done and the default value persists.
The variant expansions can be selected via "?" after the
colon instead of the "=". This syntax derives directly
from shell variable name expansion which knows the two
variants as well ${varname:=default}
and
${varname:?default}
. A similar syntax was
picked up for the attribvars
expansion which update markup attribute text intead of
page content text.
The variant expansion scheme has two modes. In the direct variant expansion we pick the same as for the expandvars being hyphenated for toplevel variables ("$title" and "$author") and parenthesed for secondary variables ("$modified" and "$issued"). This is not recommended to be used with modern mksite.sh, and instead prefix the variable name with some whitespace to select the core expansion. (The direct variant expansion is a heritage of simplevars).
The variant expansion scheme with some prefix inside the
comment markup does only use the core expansion for
variable values. The variant value is internally already
(a) lowercased from the original value and (b) any
additional markup has been cut out. This core modification
of variants is very useful to paste variable values into
<title>
markups - in those the values are
officially marked text content but many browsers will treat it
as CDATA
thereby showing any markup verbatim instead
of interpreting them.


|
My Project
|
alogrithm to divide 2 big number More...
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <stdbool.h>
#include <math.h>
#include "multiply.c"
#include "minus.c"
Functions | |
| int | numlify (char number) |
| Turn a chracter number into a number it correspond. | |
| char * | single_mul1 (char *num1, char num2) |
| A function to multiply a big number with a single digit number. More... | |
| char * | single_mul2 (char *num1, char num2) |
| A function to multiply a big number with a single digit number. More... | |
| char * | short_division (char *num1, char num2, bool return_remainder) |
| divsion of a big number by a single digit number More... | |
| char * | division (char *num1, char *num2) |
alogrithm to divide 2 big number
| char* short_division | ( | char * | num1, |
| char | num2, | ||
| bool | return_remainder | ||
| ) |
divsion of a big number by a single digit number
Let the dividend be with , the divisor is a single number between 0 and 10 the quotient is . If divisor is 0 return error, if not do the following:
| *num1 | the pointer the a big number |
| num2 | a number that is between 0 and 10 |
num2
is zero then it will terminate the function and return back ERROR: CANNOT DIVIDE BY 0
| char* single_mul1 | ( | char * | num1, |
| char | num2 | ||
| ) |
A function to multiply a big number with a single digit number.
It gonna loop through every element of the list and divide the each digit of the dividend by divisor
Let the multplier be {with} result quotient be and the remainder
From i = 0 to n + 1 do:
| *num1 | the pointer to a big number |
| num2 | a single digit number |
| char* single_mul2 | ( | char * | num1, |
| char | num2 | ||
| ) |
A function to multiply a big number with a single digit number.
Like single_mul1 but no leading 0 ( ). It gonna loop through every element of the list and divide the each digit of the dividend by divisor
Let the multplier be {with} result quotient be and the remainder
From i = 0 to n + 1 do:
| *num1 | the pointer to a big number |
| num2 | a single digit number |


Scrapbooking is a method, an art for preserving, presenting, arranging personal and family history in the form of a book, box, card. Typical memorabilia (objects treasured for their memories or historical interest) include photographs, printed media, and artwork. Scrapbooking is a hobby commonly practiced in many parts of the world.
A number of years ago, scrapbooks were no more than clippings from newspapers and stuff. There is, however, one thing that all good scrapbook pages do: they tell a story. A scrapbook is a story and scrapbooking is storytelling at a precious, personal level. It is organizing moments of the past so they have meaning for future generations. It is a recognition that events make up lives, and it is a celebration of living. Scrapbookers today are preserving yesterday for tomorrow.
Click on the image for more information


All trains cancelled between Dundee and Edinburgh due to broken rail
Trains between Dundee and Edinburgh have been cancelled after the discovery of a broken rail at Leuchars on Monday morning.
by Claire WarrenderScotrail, LNER and Cross Country services are all affected and replacement buses have been put in place.
Disruption is expected to last until noon.
Scotrail announced at 9.30am that a safety inspection was taking place near Leuchars and that no southbound trains would run.
The line towards Edinburgh was then closed completely pending further examination.
National Rail Enquiries said trains running between Dundee, Fife and Edinburgh may be cancelled, delayed or revised.
Stagecoach buses are accepting tickets to stations at Dundee, Arbroath, Invergowrie, Cupar, Ladybank, Markinch, Glenrothes, Kirkcaldy and Cowdenbeath.
Passenger Alan Morrison said he had boarded a London-bound train at Dundee, but had to disembark to get on a bus to Edinburgh before boarding a train there.
He said station staff were very organised but warned of long queues to board the replacement buses.
More later.


I am currently in 3B Systems Design Engineering at the University of Waterloo.  I am currently looking for a four month co-op position for the summer 2020 term. Feel free to checkout my Github to see what I've been up to!


{% extends "layout.html" %} {% block page_title %} GOV.UK prototyping kit {% endblock %} {% block content %} ALPHA This is a new service – your feedback will help us to improve it. Pollution inventory reportingChoose waste codes (EWC and D&R codes) Collaborative filteringChoices given based on similar users' previous choices. This prototype represents the possible choices but the design isn't finished Finished designThis design shows one pathway with the correct UI design finish {% endblock %}
ALPHA This is a new service – your feedback will help us to improve it.


This is where the fun begins
Anakin
Consent requested
Do you want to allow this app to access your data?
Yes
No


Entity Framework v4.1-v6 Code First
EF v4.1 with Code First was released in 2011. EF v6 was released in 2013.
EF v6 was still the recommended EF after EF Core v1 (originally EF7) was released in 2016 (EF Core was missing some critical features, including lazy loading)
Links
- ADO.Net team introduction )blogs.msdn.com)
- MSDN
- Glimpse has MVC plugins for profiling
- EF Code First generic repository
- EF Code First Databases
You can't have a code first model in the same project as an EDMX model.
DbContext (Unit of Work)
DbContext = EF 4 ObjectContext
- Default ctor: uses the namespace qualified name of the class (or, if app.config defines one, a connection with name or namespace.name)
- ctor(string): uses the connection string with that name (also: "name=MyDb")
- The context name should match the connection string name (if the database doesn't exist, it creates it - default SqlExpress- and adds an EdmMetadata table)
It's better to expose DbSets as IDbSet. DbSet properties can be readonly: get { return Set<T>(); }
public IDbSet<Product> Products
{
get { return Set<Product>(); }
}
DbSet
DbSet<T> = EF 4 ObjectSet
| Query | set.Find(1) //Get by id (from 1st level cache, then store, or null) |
| Insert | set.Add(entity) |
| Update |
Either load and change... var entity = context.Products.Find(1); (DO NOT set.Add ... you'll create a new entity) ... OR... //create a detached entity and populate *all* fields |
| Attach |
var entity = new Product(); |
| InsertOrUpdate |
You need to know the primary key. public void InsertOrUpdate(DbContext context, Station entity) |
Exceptions
- DbEntityValidationException - validation
- DbUpdateConcurrencyException - concurrency
- DbUpdateException - updates (sometimes validation)
try
{
context.SaveChanges();
}
catch (System.Data.Entity.Validation.DbEntityValidationException validationException)
{
foreach (var error in validationException.EntityValidationErrors)
{
var entry = error.Entry;
foreach (var err in error.ValidationErrors)
{
Debug.WriteLine(err.PropertyName + " " + err.ErrorMessage);
}
}
}
catch (DbUpdateConcurrencyException concurrencyException)
{
//assume just one
var dbEntityEntry = concurrencyException.Entries.First();
//store wins
dbEntityEntry.Reload();
//OR client wins
var dbPropertyValues = dbEntityEntry.GetDatabaseValues();
dbEntityEntry.OriginalValues.SetValues(dbPropertyValues); //orig = db
}
catch (DbUpdateException updateException)
{
//often in innerException
if (updateException.InnerException != null)
Debug.WriteLine(updateException.InnerException.Message);
//which exceptions does it relate to
foreach (var entry in updateException.Entries)
{
Debug.WriteLine(entry.Entity);
}
}
Lazy/Eager loading
- Lazy load with .Include(x=>x.Reference)
- Collections can be non-lazy if they are not virtual, or in the context Configuration.LazyLoadingEnabled = false;
- Eager loading:
context.Entry(entity).Reference(x => x.Reference).Load();
context.Entry(entity).Collection(x => x.Children).Load(); - Filtered loads:
context.Entry(entity)
.Collection(x => x.Children)
.Query()
.Where(x=> x.DateOfBirth > DateTime.Now)
.Load();
var count = context.Entry(entity)
.Collection(x => x.Children)
.Query()
.Count();
//load all into memory
context.Categories.Load();
//get all without tracking (NOT in dbContext)
var cachableCategories = context.Categories.AsNoTracking().ToList();
WCF/WebAPI may have problems with proxies so:
context.Configuration.ProxyCreationEnabled = false;
context.Configuration.LazyLoadingEnabled = false;
You may have to have [System.Runtime.Serialization.IgnoreDataMember] on association properties to foil the DataContractSerializer.
Other DbSet properties
- context.Categories.Local - an ObservableCollection of the first level cache.
//what's changed...
foreach (var dbEntityEntry in context.ChangeTracker.Entries<Category>())
{
Debug.WriteLine(dbEntityEntry.State);
}
Entities
- Entities should have a primary key (by default "Id" or "classId" or [Key] attribute)
- Can be lazy loading proxies or change tracking proxies depending on entity definition and how they are created.
- For simple lazy loading proxies, only navigation properties have to be virtual.
 NOT IList<T>
- For change tracking proxies, navigation collections are EntityCollection<T> at runtime. If your code initializes them (as List<T> in the constructor, for instance), you get a runtime error. This makes them awkward for use in test vs runtime code as the collection magically appears when using CodeFirst.
- If context.Configuration.ProxyCreationEnabled = true (default) proxies are created over the pocos for tracking.
- You can also have readonly non-tracking) entities, use DbSet.AsNoTracking() -
eg context.Products.AsNoTracking().Where(x => x.CategoryId == 2) (using System.Data.Entity)
To get a proxy, you can't new it up, use DbSet.Create() - eg context.Products.Create().
Also you can create derived classes: context.Products.Create<FoodProduct>()
//create a proxy (NOT ADDED TO SET)
var entity = context.Categories.Create();
Change tracking
For POCOs/lazy loading proxies, DetectChanges is used (implicitly or explicitly); change tracking proxies have it built in.
DbEntityEntry<Category> entry = context.Entry(entity);
//entry.State
var prop = entry.Property(x => x.Name);
//prop.IsModified;
//prop.OriginalValue vs prop.CurrentValue
entry.GetDatabaseValues(); //force a db update
//automap
var clone = new Category();
context.Entry(entity).CurrentValues.SetValues(clone);
Raw SQL
//can grab DbSet entities (by default tracked)
//or any type incl. primitive types
context.Categories.SqlQuery("select * from categories"); //only DbSet, not IDbSet
var categoriesByRawSql = context.Database.SqlQuery<Category>(
"exec mysproc @p1, @p2",
new SqlParameter("p1", 1),
new SqlParameter("p2", 2));
context.Database.ExecuteSqlCommand("drop database");


10 #ifndef GUARD_IMAGESEL_H_INCLUDE
11 #define GUARD_IMAGESEL_H_INCLUDE
13 #include <imagesel/imagesel-config.h>
20 class IMAGESEL_EXPORT
ImageSel :
public QWidget {
43 QWidget * parent = NULL);
51 const QPixmap & value) {
106 void mousePressEvent (QMouseEvent *);
107 void mouseReleaseEvent (QMouseEvent *);
108 void mouseMoveEvent (QMouseEvent *);
109 void paintEvent (QPaintEvent *);
117 #endif // GUARD_IMAGESEL_H_INCLUDE
LayoutInterf * lay() const
Get the layout interface.
Definition: imagesel.h:84
void setLay(LayoutInterf *value)
Set the layout interface; ownership transfered to this instance.
Definition: imagesel.h:76
const QPixmap & image() const
The image that's being presented to the user.
Definition: imagesel.h:65
void clearImage()
Remove the image that's being presented to the user.
Definition: imagesel.h:58
brief description
Definition: layoutinterf.h:20
brief description
Definition: activegrip.h:19
void setImage(const QPixmap &value)
Change the image that's being presented to the user.
Definition: imagesel.h:50
BuildInLay
The list of build-in layouts.
Definition: imagesel.h:28
brief description
Definition: imagesel.h:20
LayoutInterf * takeLay()
Take ownership of the layout interface.
Definition: imagesel.h:90


Yurlungur documentation master file, created by sphinx-quickstart on Wed Apr 11 23:48:01 2018. You can adapt this file completely to your liking, but it should at least contain the root toctree directive.
Yurlungur for Universal DCC Scripting API¶
Yurlungur is universal scripting environment with Python which Maya, Houdini and Unreal.
DCC tool scripting is almost used Python, but these api isn’t similarly anything. If you make lightweight tool, need to remember each application manners. Yurlungur is common interface which adapted each application for universal wrapper.


The package.json must contain the keyword node-red
eg
"keywords": [ "node-red" ],
This identifies the package as a Node-RED module rather than a more generic node package.
Without this keyword set, it will not be listed on the Flow Library, but it will still be npm installable.
If the module is not yet ready for wider use by the Node-RED community, you should not include the keyword.
https://nodered.org/docs/creating-nodes/packaging#packagejson


Item Divider
Divider by itself
Divider in List
Danger Divider in List
Divider in Item Group
Secondary Divider in Item Group
Secondary header
Plain Ol' div with some text
Item Divider Danger Span
Multiline text that should wrap when it is too long
to fit on one line in the item. Attribute on .item
Dark


First week of Phase 0 @ DBC
I am so excited about this program, because I am looking forward to see the brand new chapter in my life. During the first week, I have more chances to work on projects with use of GitHub and Google+, which I barely use when I work as individual. I pretty enjoy it. I love to see other classmates comments and opinions on Google+ community, and I am looking forward to pair coding with them. I didn't feel nervous at all.


Ipsita Banerjee
Ph.D, Chemical and Biochemical Engineering, Rutgers University, 2000 - 2005
M.S, Chemical Engineering, Indian Institute of Science, 1996 - 1998
B.Che, Chemical Engineering, Jadavpur University, 1992 - 1996
Ravikumar K
Ph.D, Indian Institute of Science, Bangalore, 2011 - 2017
B.S, Materials Science and Engineering, National Institute of Technology, Surathkal, 2007 - 2011
Islet-on-a-Chip
Microwell fabrication
Connor Wiegand
B.S. Chemical Engineering,
Ohio State University
PhD. Candidate
Chemical and Petroleum Engineering
Islet Differentiation
Islet-on-a-Chip
Vascularization
Miranda Poklar
B.S. Chemical Engineering,
Ohio State University
PhD. Candidate
Chemical and Petroleum Engineering
Bioprinting Islet Organoids
Kevin Pietz
Bioengineering
Islet Differentiation
and Islet-on-a-Chip
Abigail Wuenschel
Bioengineering
Bioprinting Islet Organoids
Benjamin Mizerak
Bioengineering
iPSC-derived Thymus
Thomas Richardson
Joseph Candiello
Nadine Humphrey
Remziye Erdogen


|
QuantLib: a free/open-source library for quantitative finance
Fully annotated sources - version 1.30
|
#include <ql/models/marketmodels/callability/triggeredswapexercise.hpp>
Public Member Functions | |
| TriggeredSwapExercise (const std::vector< Time > &rateTimes, const std::vector< Time > &exerciseTimes, std::vector< Rate > strikes) | |
| Size | numberOfExercises () const override |
| const EvolutionDescription & | evolution () const override |
| void | nextStep (const CurveState &) override |
| void | reset () override |
| std::valarray< bool > | isExerciseTime () const override |
| void | values (const CurveState &, std::vector< Real > &results) const override |
| std::vector< Size > | numberOfVariables () const override |
| std::vector< Size > | numberOfParameters () const override |
| bool | exercise (Size exerciseNumber, const std::vector< Real > ¶meters, const std::vector< Real > &variables) const override |
| void | guess (Size exerciseNumber, std::vector< Real > ¶meters) const override |
| std::unique_ptr< MarketModelParametricExercise > | clone () const override |
| Public Member Functions inherited from MarketModelParametricExercise | |
| std::vector< Size > | numberOfData () const override |
| virtual std::unique_ptr< MarketModelParametricExercise > | clone () const =0 |
| Public Member Functions inherited from MarketModelNodeDataProvider | |
| virtual | ~MarketModelNodeDataProvider ()=default |
| virtual Size | numberOfExercises () const =0 |
| virtual std::vector< Size > | numberOfData () const =0 |
| virtual const EvolutionDescription & | evolution () const =0 |
| virtual void | nextStep (const CurveState &)=0 |
| virtual void | reset ()=0 |
| virtual std::valarray< bool > | isExerciseTime () const =0 |
| virtual void | values (const CurveState &, std::vector< Real > &results) const =0 |
| Public Member Functions inherited from ParametricExercise | |
| virtual | ~ParametricExercise ()=default |
| virtual std::vector< Size > | numberOfVariables () const =0 |
| virtual std::vector< Size > | numberOfParameters () const =0 |
| virtual bool | exercise (Size exerciseNumber, const std::vector< Real > ¶meters, const std::vector< Real > &variables) const =0 |
| virtual void | guess (Size exerciseNumber, std::vector< Real > ¶meters) const =0 |
Private Attributes | |
| std::vector< Time > | rateTimes_ |
| std::vector< Time > | exerciseTimes_ |
| std::vector< Rate > | strikes_ |
| Size | currentStep_ = 0 |
| std::vector< Size > | rateIndex_ |
| EvolutionDescription | evolution_ |
Definition at line 28 of file triggeredswapexercise.hpp.
| TriggeredSwapExercise | ( | const std::vector< Time > & | rateTimes, |
| const std::vector< Time > & | exerciseTimes, | ||
| std::vector< Rate > | strikes | ||
| ) |
Definition at line 27 of file triggeredswapexercise.cpp.
|
overridevirtual |
Implements MarketModelNodeDataProvider.
Definition at line 40 of file triggeredswapexercise.cpp.
|
overridevirtual |
Implements MarketModelNodeDataProvider.
Definition at line 44 of file triggeredswapexercise.cpp.
|
overridevirtual |
Implements MarketModelNodeDataProvider.
Definition at line 48 of file triggeredswapexercise.cpp.
|
overridevirtual |
Implements MarketModelNodeDataProvider.
Definition at line 52 of file triggeredswapexercise.cpp.
|
overridevirtual |
Implements MarketModelNodeDataProvider.
Definition at line 56 of file triggeredswapexercise.cpp.
|
overridevirtual |
Implements MarketModelNodeDataProvider.
Definition at line 60 of file triggeredswapexercise.cpp.
|
overridevirtual |
Implements ParametricExercise.
Definition at line 67 of file triggeredswapexercise.cpp.
|
overridevirtual |
Implements ParametricExercise.
Definition at line 71 of file triggeredswapexercise.cpp.
|
overridevirtual |
Implements ParametricExercise.
Definition at line 75 of file triggeredswapexercise.cpp.
Implements ParametricExercise.
Definition at line 82 of file triggeredswapexercise.cpp.
|
overridevirtual |
Implements MarketModelParametricExercise.
Definition at line 89 of file triggeredswapexercise.cpp.
|
private |
Definition at line 53 of file triggeredswapexercise.hpp.
|
private |
Definition at line 53 of file triggeredswapexercise.hpp.
|
private |
Definition at line 54 of file triggeredswapexercise.hpp.
|
private |
Definition at line 55 of file triggeredswapexercise.hpp.
|
private |
Definition at line 56 of file triggeredswapexercise.hpp.
|
private |
Definition at line 57 of file triggeredswapexercise.hpp.


American Pie (R) ** 1/2 Starring Jason Biggs, Thomas Ian Nicholas Directed by Paul Weitz A Review by Frankie Paiva
First of all, let me explain why this movie will do so well. Should I go see a terrorist thriller or see a movie where a boy gets freaky with some apple pie? Should I see a western action movie where I see Salma Hayek bear her rear, or a movie where a foreign exchange student bears her enlarged chest?
American Pie is like this in many ways, possibly the most outrageous movie this year, it has several plot holes, but other good stuff to pull itself out of them. Jim, (Biggs) Finch, (Eddie Kay Thomas) Kevin, (Ian Nicholas) and Oz (Chris Klein) vow not to keep their penises "flaccid and unused" and thus they begin their conquets to end their virginity. Jim goes after a foreign exchange student (Shannon Elizabeth) and broadcasts his experiences over the internet. Finch has Jessica (Natasha Lyonne) spread rumors about how great he was in bed. Oz tries to score on the girls in the choir by putting on a sensitive side that he ends up liking, and Kevin's having trouble with the "l" word.
 Whereas, There's Something About Mary (obviously a major inspiration) centered on love and romance (no matter how sick and twisted it may be) this attacks us with the same stuff over, and over, until it decides in it's final minutes to change. Yes, this change comes way too late and it came off to me as an easy way to end the film. American Pie's message is that sex isn't everything...but if that's true...what was all the 80-minute masturbation, oral sex, and porno magazines about?
A Review by Frankie Paiva The 12 Year-Old Movie Reviewer E-Mail me at<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


- Source:
Methods
emit(event, …argsopt) → {array}
Emit an event synchronously. This emit will execute all matching handlers and return an array of their return values.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
event |
string | The name of the event to emit (use full-stop '.' as a namespace delimiter). | |
args |
* |
<optional> <repeatable> |
Any number of arguments to pass to all event handlers. |
- Source:
Returns:
An array of all the values returned by event handlers in the order they were executed.
- Type
- array
emitAsync(event, …argsopt) → {Promise.<array>}
Emit an event asynchronously. This emit will execute all matching handlers concurrently (in parallel) and return an
array of their return values.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
event |
string | The name of the event to emit (use full-stop '.' as a namespace delimiter). | |
args |
* |
<optional> <repeatable> |
Any number of arguments to pass to all event handlers. |
- Source:
Returns:
A Promise that will resolve to an array of all the values returned by the event handlers in the order they were matched.
- Type
- Promise.<array>
emitWaterfall(event, …argsopt)
Emit an event synchronously and in order where the output of each handler in the chain becomes the input to the next.
The exception to this rule is that returning undefined from a handler will be interpreted as "leave the data unchanged".
If the intention of a handler is to continue execution, but replace the data of previous steps with undefined, return "event.continueWithUndefined" instead
of simply returning undefined. If the intention is to halt execution of subsequent steps and return undefined overall, return "event.returnUndefined"
where "event" is the first parameter passed to each handler.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
event |
string | The name of the event to emit (use full-stop '.' as a namespace delimiter). | |
args |
* |
<optional> <repeatable> |
Any number of arguments to pass to the first event handler. |
- Source:
Returns:
The return value of the last event handler that executed and returned a value.
emitWaterfallAsync(event, …argsopt)
An asynchronous version of EventEmitter#emitWaterfall
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
event |
string | The name of the event to emit (use full-stop '.' as a namespace delimiter). | |
args |
* |
<optional> <repeatable> |
Any number of arguments to pass to the first event handler. |
- Source:
- See:
Returns:
The return value of the last event handler that executed and returned a value.
on(event, handler)
Register an event handler. The event handler will be called when any event matching the event string is emitted. The order of execution and parameters
passed to the handler can change based on which emit was used. EventEmitter#emit EventEmitter#emitWaterfall.
Parameters:
| Name | Type | Description |
|---|---|---|
event |
string | The name (or matching string) of the event you want the handler to be registered against. |
handler |
function | The event handler to register. |
- Source:


The {medicaldata} package is purely a data package, for the purpose of collecting well-documented medical datasets in one package for teaching to medical students, residents, fellows, nurses, pharmacists, physician assistants, and anyone else who wants to learn R for use with medical data.
The deidentified datasets included in this package come from a variety of sources, including:
- donations by generous authors
- sourcing from other R packages
- the TSHS (Teaching Statistics in the Health Sciences) project, which can be found at link, and is sponsored by the American Statistical Association.
- reconstruction of datasets from historically important clinical studies, like the scurvy study by James Lind, and the first clinical trial of Streptomycin in Pulmonary Tuberculosis.
Each dataset is documented in three ways:
- a help file, which can be accessed with
help(datasetname)
from the Console pane
- a pdf document describing the study, found at the Github README page here
- a pdf codebook, found at the Github README page here
Currently, the datasets included are:
- Streptomycin in Tuberculosis (
strep_tb
)
- Scurvy RCT (
scurvy
)
- Indomethacin RCT to Prevent Post-ERCP Pancreatitis (
indo_rct
)
- Sulindac RCT for polyps (
polyps
)
- Covid Testing (
covid_testing
)
- Blood Storage (prostate cancer) (
blood_storage
)
- Cytomegalovirus in BMT (
cytomegalovirus
)
- Esophageal cancer (
esoph_ca
)
- Laryngoscope (
laryngoscope
)
- Licorice gargle (
licorice_gargle
)
- OPT (
opt
)
- Smartpill (
smartpill
)
- Supraclavicular (
supraclavicular
)
- Tumor growth (
tumor_growth
)
- Indomethacin Pharmacokinetics (
indometh
)
- Theophylline Pharmacokinetics (
theoph
)


Ftest test test test ar far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean. Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. A small river named Duden flows by their place and supplies it with the necessary regelialia. It is a paradisematic country, in which roasted parts of sentences fly into your mouth. Even the all-powerful Pointing has no control about the blind texts it is an almost unorthographic life.
Matar Paneer, Palak Paneer, Mix Veg, Bhaigan Bharta, Mashroom, Aalu Baigan, Bhindi Masala, Parval Aalu, Karela Masala, Dal Tadka, Plain Rice, Jira Rice.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains.
Far far away, behind the word mountains.
Far far away, behind the word mountains.
Far far away, behind the word mountains.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.


India has proposed a draft bill banning cryptocurrency trading, which could impose severe criminal penalties for violations, Bloomberg Quint writes. If the ‘Banning Cryptocurrencies and Regulation of Official Digital Currency Bill 2019’ becomes law, mining, generating, ho...


25:00
Pomodoro Counter: 0
What Is It?
This is the "Pomodoro Technique" and this is a time management method developed by
Francesco Cirillo in the late 1980s.
This method use a timer set to 25 minutes separeted by short
breaks of 5 minutes and after 100 minutes you can take a break of 15 or 30 minutes.
Simple right? The answer is YES, but when the timer is on, you can't use your phone to do other
tasks that makes you to distract, so get in work and have fun!
About Me
Let me introduce myself. Hello my name is Stefano, I am 17 years old and I am an italian student.
This is my first public project about the "Pomodoro Technique", a very useful techique to increase your
productivity and time management.
I have a lot of passions, but I really enjoy doing some IT programs and I have a lot of them to do in the future.
In this period I want to learn more thinks about JavaScript, Node.js and Electron to performe one of the main project I want to do.
GitHub
If you want to see the complete source code, you can check my repository on GitHub.
Don't worry, my project is free and open source. I hope you enjoy and support my work with some feedback.


"HORITES "cave-men, a race of Troglodytes who dwelt in the limestone caves which abounded in Edom. Their ancestor was “Seir,” who probably gave his name to the district where he lived. They were a branch of the Hivites (Genesis 14:6; 36:20-30; 1 Chronicles 1:38, 39). They were dispossessed by the descendants of Esau, and as a people gradually became extinct (Deuteronomy 2:12-22).
"HORITES "cave-men, a race of Troglodytes who dwelt in the limestone caves which abounded in Edom. Their ancestor was “Seir,” who probably gave his name to the district where he lived. They were a branch of the Hivites (Genesis 14:6; 36:20-30; 1 Chronicles 1:38, 39). They were dispossessed by the descendants of Esau, and as a people gradually became extinct (Deuteronomy 2:12-22).


|
Nexmo .NET Library
5.0.0
Nexmo REST API client for .NET, ASP.NET, ASP.NET MVC written in C#. API support for SMS, Voice, Text-to-Speech, Numbers, Verify (2FA) and more. https://developer.nexmo.com/
|
Classes | |
| class | AdvancedInsightsAsyncResponse |
| class | AdvancedInsightsResponse |
| class | AdvancedNumberInsightAsynchronousRequest |
| class | AdvancedNumberInsightRequest |
| class | BasicInsightResponse |
| class | BasicNumberInsightRequest |
| class | CallerId |
| class | Carrier |
| interface | INumberInsightClient |
| class | NexmoNumberInsightResponseException |
| class | NumberInsightClient |
| class | NumberInsightResponseBase |
| class | Roaming |
| class | StandardInsightResponse |
| class | StandardNumberInsightRequest |


|
Glfwx
0.1.0
C++ wrapper for GLFW
|
C++ wrapper for a GLFW window. More...
#include <Glfwx/Window.h>
Public Types | |
| enum | Hint { FOCUSED = GLFW_FOCUSED, ICONIFIED = GLFW_ICONIFIED, RESIZABLE = GLFW_RESIZABLE, VISIBLE = GLFW_VISIBLE, DECORATED = GLFW_DECORATED, AUTO_ICONIFY = GLFW_AUTO_ICONIFY, FLOATING = GLFW_FLOATING, MAXIMIZED = GLFW_MAXIMIZED, CENTER_CURSOR = GLFW_CENTER_CURSOR, TRANSPARENT_FRAMEBUFFER = GLFW_TRANSPARENT_FRAMEBUFFER, HOVERED = GLFW_HOVERED, FOCUS_ON_SHOW = GLFW_FOCUS_ON_SHOW, RED_BITS = GLFW_RED_BITS, GREEN_BITS = GLFW_GREEN_BITS, BLUE_BITS = GLFW_BLUE_BITS, ALPHA_BITS = GLFW_ALPHA_BITS, DEPTH_BITS = GLFW_DEPTH_BITS, STENCIL_BITS = GLFW_STENCIL_BITS, ACCUM_RED_BITS = GLFW_ACCUM_RED_BITS, ACCUM_GREEN_BITS = GLFW_ACCUM_GREEN_BITS, ACCUM_BLUE_BITS = GLFW_ACCUM_BLUE_BITS, ACCUM_ALPHA_BITS = GLFW_ACCUM_ALPHA_BITS, AUX_BUFFERS = GLFW_AUX_BUFFERS, STEREO = GLFW_STEREO, SAMPLES = GLFW_SAMPLES, SRGB_CAPABLE = GLFW_SRGB_CAPABLE, REFRESH_RATE = GLFW_REFRESH_RATE, DOUBLEBUFFER = GLFW_DOUBLEBUFFER, CLIENT_API = GLFW_CLIENT_API, CONTEXT_VERSION_MAJOR = GLFW_CONTEXT_VERSION_MAJOR, CONTEXT_VERSION_MINOR = GLFW_CONTEXT_VERSION_MINOR, CONTEXT_REVISION = GLFW_CONTEXT_REVISION, CONTEXT_ROBUSTNESS = GLFW_CONTEXT_ROBUSTNESS, OPENGL_FORWARD_COMPAT = GLFW_OPENGL_FORWARD_COMPAT, OPENGL_DEBUG_CONTEXT = GLFW_OPENGL_DEBUG_CONTEXT, OPENGL_PROFILE = GLFW_OPENGL_PROFILE, CONTEXT_RELEASE_BEHAVIOR = GLFW_CONTEXT_RELEASE_BEHAVIOR, CONTEXT_NO_ERROR = GLFW_CONTEXT_NO_ERROR, CONTEXT_CREATION_API = GLFW_CONTEXT_CREATION_API, SCALE_TO_MONITOR = GLFW_SCALE_TO_MONITOR, COCOA_RETINA_FRAMEBUFFER = GLFW_COCOA_RETINA_FRAMEBUFFER, COCOA_FRAME_NAME = GLFW_COCOA_FRAME_NAME, COCOA_GRAPHICS_SWITCHING = GLFW_COCOA_GRAPHICS_SWITCHING, X11_CLASS_NAME = GLFW_X11_CLASS_NAME, X11_INSTANCE_NAME = GLFW_X11_INSTANCE_NAME, JOYSTICK_HAT_BUTTONS = GLFW_JOYSTICK_HAT_BUTTONS, COCOA_CHDIR_RESOURCES = GLFW_COCOA_CHDIR_RESOURCES, COCOA_MENUBAR = GLFW_COCOA_MENUBAR } |
| Window hints. More... | |
| enum | Attribute { FOCUSED = GLFW_FOCUSED, ICONIFIED = GLFW_ICONIFIED, VISIBLE = GLFW_VISIBLE, DECORATED = GLFW_DECORATED, AUTO_ICONIFY = GLFW_AUTO_ICONIFY, RESIZABLE = GLFW_RESIZABLE, FLOATING = GLFW_FLOATING, MAXIMIZED = GLFW_MAXIMIZED, TRANSPARENT_FRAMEBUFFER = GLFW_TRANSPARENT_FRAMEBUFFER, HOVERED = GLFW_HOVERED, FOCUS_ON_SHOW = GLFW_FOCUS_ON_SHOW, CLIENT_API = GLFW_CLIENT_API, CONTEXT_VERSION_MAJOR = GLFW_CONTEXT_VERSION_MAJOR, CONTEXT_VERSION_MINOR = GLFW_CONTEXT_VERSION_MINOR, CONTEXT_REVISION = GLFW_CONTEXT_REVISION, CONTEXT_ROBUSTNESS = GLFW_CONTEXT_ROBUSTNESS, OPENGL_FORWARD_COMPAT = GLFW_OPENGL_FORWARD_COMPAT, OPENGL_DEBUG_CONTEXT = GLFW_OPENGL_DEBUG_CONTEXT, OPENGL_PROFILE = GLFW_OPENGL_PROFILE, CONTEXT_RELEASE_BEHAVIOR = GLFW_CONTEXT_RELEASE_BEHAVIOR, CONTEXT_NO_ERROR = GLFW_CONTEXT_NO_ERROR, CONTEXT_CREATION_API = GLFW_CONTEXT_CREATION_API } |
| Window attributes. More... | |
Public Member Functions | |
| Window (int width, int height, char const *title, Monitor *monitor=nullptr, GLFWwindow *share=nullptr) | |
| Constructor. More... | |
| virtual | ~Window () |
| Destructor. More... | |
| void | setTitle (char const *title) |
| Sets the title. More... | |
| void | setIcon (std::vector< GLFWimage > images=std::vector< GLFWimage >()) |
| Sets the window's icon. More... | |
| void | setPosition (int x, int y) |
| Sets the window's position. More... | |
| void | position (int &x, int &y) const |
| Returns the window's position. More... | |
| void | setSize (int width, int height) |
| Sets the size of the window. More... | |
| void | size (int &width, int &height) const |
| Returns the width and height of the window. More... | |
| void | contentScale (float &x, float &y) const |
| Returns the content scale. More... | |
| void | setSizeLimits (int minWidth=-1, int minHeight=-1, int maxWidth=-1, int maxHeight=-1) |
| Limits the size of the window. More... | |
| void | setAspectRatio (int n, int d) |
| Sets the window's aspect ratio. More... | |
| void | framebufferSize (int &width, int &height) const |
| Returns the width and height of the window's framebuffer. More... | |
| void | frame (int &left, int &top, int &right, int &bottom) const |
| Returns the locations of the edges of the window's frame. More... | |
| void | iconify () |
| Reduces the window to an icon. | |
| void | restore () |
| Displays the window normally. | |
| void | maximize () |
| Fits the window to the size of the screen. | |
| void | show (bool shown=true) |
| Shows or hides the window. More... | |
| void | focus () |
| Sets the focus to the window. | |
| Monitor | monitor () const |
| Returns the window's monitor. More... | |
| void | setMonitor (GLFWmonitor *monitor, int x, int y, int width, int height, int refreshRate) |
| Sets the window's monitor parameters. More... | |
| void | setAttribute (Attribute id, int value) |
| Sets the value of the specified attribute. More... | |
| int | attribute (Attribute id) const |
| Returns the value of the specified attribute. More... | |
| void | setContext (void *context) |
| Saves a context for the window. More... | |
| void * | context () const |
| Returns the previously set context, or nullptr. | |
| int | processEvents () |
| Processes any window activity. More... | |
| int | waitEvents () |
| Waits for any window activity and processes it. More... | |
Vulkan-specific | |
These methods are specific to Vulkan and defined only when GLFW_INCLUDE_VULKAN is defined | |
| VkSurfaceKHR | createSurface (VkInstance instance, VkAllocationCallbacks *allocator) const |
| Creates a device-specific Vulkan surface. More... | |
Static Public Member Functions | |
| static void | resetHints () |
| Resets all window hints back to default values. More... | |
| static void | hint (Hint id, int value) |
| Provides some window initialization context. More... | |
Private Member Functions | |
Overridable Notifications | |
These methods must be overridden in order to receive the appropriate notifications. | |
| virtual void | onPositionChanged (int x, int y) |
| Called when the window position is changed. More... | |
| virtual void | onSizeChanged (int width, int height) |
| Called when the size of the window is changed. More... | |
| virtual void | onClose () |
| Called before the window is closed. More... | |
| virtual void | onRefresh () |
| Called when the contents of the window need to be refreshed. More... | |
| virtual void | onFocusChanged (int gained) |
| Called when the window focused has changed to or from the window. More... | |
| virtual void | onIconifyChanged (int iconified) |
| Called when the window has changed to or from the icon state. More... | |
| virtual void | onMaximizeChanged (int maximized) |
| Called when the when changes to or from maximized state. More... | |
| virtual void | onFramebufferSizeChanged (int width, int height) |
| Called when the size of the window's framebuffer changes. More... | |
| virtual void | onContentScaleChanged (float x, float y) |
| Called when the window's content scale changes. More... | |
C++ wrapper for a GLFW window.
|
strong |
Window attributes.
|
strong |
Window hints.
| Glfwx::Window::Window | ( | int | width, |
| int | height, | ||
| char const * | title, | ||
| Monitor * | monitor = nullptr , |
||
| GLFWwindow * | share = nullptr |
||
| ) |
Constructor.
| width | The desired width of the window (must be > 0) |
| height | The desired height of the window (must be > 0) |
| title | The initial UTF-8 window title |
| monitor | The monitor to use for full screen mode, or nullptr for windowed mode (optional) |
| share | The window whose context to share resources with, or nullptr to not share resources (optional) |
|
virtual |
Destructor.
| int Glfwx::Window::attribute | ( | Attribute | id | ) | const |
Returns the value of the specified attribute.
| id | The attribute to query |
| void Glfwx::Window::contentScale | ( | float & | x, |
| float & | y | ||
| ) | const |
Returns the content scale.
| [out] | x | Scale |
| [out] | y | Scale |
| VkSurfaceKHR Glfwx::Window::createSurface | ( | VkInstance | instance, |
| VkAllocationCallbacks * | allocator | ||
| ) | const |
Creates a device-specific Vulkan surface.
| instance | Vulkan instance |
| allocator | Vulkan allocator |
| void Glfwx::Window::frame | ( | int & | left, |
| int & | top, | ||
| int & | right, | ||
| int & | bottom | ||
| ) | const |
Returns the locations of the edges of the window's frame.
| [out] | left | Location of the left edge |
| [out] | top | Location of the top edge |
| [out] | right | Location of the right edge |
| [out] | bottom | Location of the bottom edge |
| void Glfwx::Window::framebufferSize | ( | int & | width, |
| int & | height | ||
| ) | const |
Returns the width and height of the window's framebuffer.
| [out] | width | Width |
| [out] | height | Height |
|
static |
Provides some window initialization context.
| id | Hint ID |
| value | Value |
| Monitor Glfwx::Window::monitor | ( | ) | const |
Returns the window's monitor.
|
privatevirtual |
Called before the window is closed.
|
privatevirtual |
Called when the window's content scale changes.
| x | New scale |
| y | New scale |
|
privatevirtual |
Called when the window focused has changed to or from the window.
| gained | 1 if the window gains focus. 0 if the window loses focus. |
|
privatevirtual |
Called when the size of the window's framebuffer changes.
| width | New framebuffer size |
| height | New framebuffer size |
|
privatevirtual |
Called when the window has changed to or from the icon state.
| iconified | 1 if the window is iconified. 0 if the window is opened. |
|
privatevirtual |
Called when the when changes to or from maximized state.
| maximized | 1 if the window is maximized, 0 if the window returns to normal |
|
privatevirtual |
Called when the window position is changed.
| x | New location |
| y | New location |
|
privatevirtual |
Called when the contents of the window need to be refreshed.
|
privatevirtual |
Called when the size of the window is changed.
| width | New size |
| height | New size |
| void Glfwx::Window::position | ( | int & | x, |
| int & | y | ||
| ) | const |
Returns the window's position.
| [out] | x | Horizontal position |
| [out] | y | Vertical position |
| int Glfwx::Window::processEvents | ( | ) |
Processes any window activity.
|
static |
Resets all window hints back to default values.
| void Glfwx::Window::setAspectRatio | ( | int | n, |
| int | d | ||
| ) |
Sets the window's aspect ratio.
The aspect ratio is set the value of the fraction n/d.
| n | numerator |
| d | denominator |
| void Glfwx::Window::setAttribute | ( | Attribute | id, |
| int | value | ||
| ) |
Sets the value of the specified attribute.
| id | The attribute to change |
| value | THe value to set |
| void Glfwx::Window::setContext | ( | void * | context | ) |
Saves a context for the window.
| context | Value to set |
| void Glfwx::Window::setIcon | ( | std::vector< GLFWimage > | images = std::vector<GLFWimage>() | ) |
Sets the window's icon.
| images | icon images (default: reset to default) |
| void Glfwx::Window::setMonitor | ( | GLFWmonitor * | monitor, |
| int | x, | ||
| int | y, | ||
| int | width, | ||
| int | height, | ||
| int | refreshRate | ||
| ) |
Sets the window's monitor parameters.
| monitor | Monitor to display window |
| x | Location |
| y | Location |
| width | Size |
| height | Size |
| refreshRate | Refresh rate |
| void Glfwx::Window::setPosition | ( | int | x, |
| int | y | ||
| ) |
Sets the window's position.
| x | Horizontal position |
| y | Vertical position |
| void Glfwx::Window::setSize | ( | int | width, |
| int | height | ||
| ) |
Sets the size of the window.
| width | Size |
| height | Size |
| void Glfwx::Window::setSizeLimits | ( | int | minWidth = -1 , |
| int | minHeight = -1 , |
||
| int | maxWidth = -1 , |
||
| int | maxHeight = -1 |
||
| ) |
Limits the size of the window.
| minWidth | Minimum size (-1 = no limit, optional, default is -1) |
| minHeight | Minimum size (-1 = no limit, optional, default is -1) |
| maxWidth | Maximum size (-1 = no limit, optional, default is -1) |
| maxHeight | Maximum size (-1 = no limit, optional, default is -1) |
| void Glfwx::Window::setTitle | ( | char const * | title | ) |
Sets the title.
| title | New title |
| void Glfwx::Window::show | ( | bool | shown = true | ) |
Shows or hides the window.
| shown | Shows if true, hides if false (optional, default is true) |
| void Glfwx::Window::size | ( | int & | width, |
| int & | height | ||
| ) | const |
Returns the width and height of the window.
| [out] | width | Size |
| [out] | height | Size |
| int Glfwx::Window::waitEvents | ( | ) |
Waits for any window activity and processes it.