The Not So Secret Life of C
The idea of this class is to understand how C++ is translated
into assembly (how assembly is translated into object code is
left as an exercise for the student).
So first we will talk about how C is translated into assembly,
because C++ is mostly a superset of C, and because it a good way
to learn techniques for achieving this understanding.
Hello World
First lets look at what hello world looks like in assembly.
Our source code, as you might expect, looks like this (click here for the source in a file.):
#include
int
main (int argc, char **argv)
{
printf("Hello World.\n");
return 0;
}
We turn that into assembly, and it looks like hello-world.s.
How do we do that? We run cc -s -o hello-world.s hello-world.c
We can also see it with source interpolated by running
cc -g -c -Wa,-adhls=hello-world.listing hello-world.c
The output of that will look like hello-world.listing
Some notes about memory
In a running process there are 5 parts to memory:
- Text
- The area where the program code is found.
- Data
- Area where initialized global variables are loaded.
- BSS
- Area for uninitialized global variables, part of your free store.
- Heap
- Area where memory allocators get their memory.
- Stack
- Place were stack frames and local variables are allocated.
Global Variable
We can look at how global variables are defined in assembly. They
are basically symbols which point into these memory areas and
never change. Sometimes the are exposed to LD, sometimes not. See
this assembly or this listing based on global-variables.c for more detail.
Functions
Functions are chuncks of code that get called using the
call
instruction. The basic idea is that we jump into
that location, run the code, and jump back to where we came from.
We use the stack to store where we came from, so that we can get
back. But since the code we call might trash the registers, we
also want to save some registers on the stack. And we need to pass
arguments, so we put those on the stack too, unless if they don't
fit in the registers.
We also pass return values back through a register,
eax
on Intel.
Calling Convention
Calling convention just means identifying which registers the
caller saves, which registers the callee saves, where to put the
arguments and where to put the return value.
The example function.c (function.s, function.listing)
Control Structures
Control structures such as if/else, while and do-while are all
implemented in terms of conditional jumps in assembly. This is
fairly straightforward. Check out if-then.c (if-then.s,
if-then.listing) for a simple
example.
Pointers
You may have noticed that assembly already deals with pointers a
great deal. So its obvious how pointers and pointer arithmatic are
dealt with when they are compiled.
Array
An array is just a pointer to the beginning of the array. To mess
with a value to it, you just calculate the offset.
TODO: Write a short example.
Structures
Like arrays, structures are just going to be pointers to the start
of a memory area. To access the contents, you just calculate the
offset.
Since they are of a fixed size, you can put them on the stack easily enough.
TODO: Write a short example.
Unions
And of course Unions are like structures, but with slightly more complex sorts of offsets.
TODO: Write a short example.
That's it for our introduction to how C is compiled. As you can
see, C is very simple to compile, asssuming you aren't doing any
optimization.
Richard Tibbetts
Last modified: Mon Jan 19 19:06:34 EST 2004


Primer3 params (for verification primers)
| OPT_SIZE: | MIN_SIZE: | MAX_SIZE: | |||
| OPT_TM: | MIN_TM: | MAX_TM: | |||
| MIN_GC: | MAX_GC: | ||||
| SALT_MONOVALENT: | DNA_CONC nM: |
Waiting for server to respond ...
No matching region
PCR primers to verify 5' junction
(product on modified genome only, no product on WT)
PCR primers to verify 3' junction
(product on modified genome only, no product on WT)
PCR primers to verify whole cassette
(product on modified genome and WT)
Inherited and new annotations on cassette


class: center, middle, inverse, title-slide # Efficient programming ### The R Bootcamp
Twitter:
@therbootcamp
### January 2018 --- # What is efficient code? .pull-left4[ >"Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered."
 >-- Donald Knuth ] .pull-right5[
Donald E. Knuth
Author of The Art of Programming
source http://www-cs-faculty.stanford.edu/ ] --- # Why is R slow? And, is it? .pull-left45[ >R is not a fast language. This is not an accident. R was purposely designed to make data analysis and statistics easier for you to do. It was not designed to make life easier for your computer. While R is slow compared to other programming languages, for most purposes, it’s fast enough. >-- Hadley Wickham ] .pull-right45[ *Reasons for R being slow*

 **Extreme dynamism** - allows you to code flexibly. - weak dynamic typing - copy-on-modify semantics - Pass-by-value <br2> **Name lookup** (in environments/namespaces) - allows you to import packages and name your objects flexibly. ] --- # Profiling The first step to making your code efficient is to **identify critical parts** of your code. Do this using one of the following: | Function| Package| Description| |:------|:------|:------------| | `proc.time()` |`base`|Returns the time.| | `system.time()`| `base`|Runs one expression once and returns elapsed CPU time| | `microbenchmark()`|`microbenchmark`| Runs one or many expressions multiple times and returns statistics on elapsed time.| | `profvis()`|`profvis`| Evaluates larger code chunks and entire scripts.| | `lineprof(), shine()`|`lineprof`| Similar to `profvis` but deprecated (From Hadley's Github)| --- # Profiling: Example Often some small part of your code takes orders of magnitudes longer than everything else. Profiling is about figuring out which parts of your code take so long. .pull-left45[ ```r profvis({ # load data data <- read_csv('data/happiness.csv') # mutate data <- data %>% mutate(happy_num = case_when( happy == 'not too happy' ~ 0, happy == 'pretty happy' ~ 1, happy == 'very happy' ~ 2)) # multiple regression model <- lm(happy_num ~ tvhours + owngun, data = data) # evaluate model summary(model) }, interval = .005) ``` ] .pull-right5[  ] --- # Improving performance beginner
  1. Look for existing solutions
 2. Do less work
 3. Vectorise
 4. Parallelise
 5. Avoid copies
 6. Byte-code compile

 advanced
  7. Rcpp
 8. Using a different R
  --- # Look for an existing solution .pull-left3[ Almost always your problem has been solved by someone else. Look for solutions in: **Base R** which can be amazingly fast. **Other packages** which often provide faster versions of one and the same function. **google**, **stackoverflow**, **rseek** ] .pull-right6[

https://stackoverflow.com

http://rseek.org

 ] --- # Do as little as possible .pull-left3[ **Do everything only once** (or exactly as often as needed). Don't repeated yourself. Use **tailor-made** functions, e.g., `data.table::fread()`. Use **primitive** functions, e.g., `sum(x)/length(x)` rather than `mean(x)`. **Be specific**, e.g., `unlist(x, use.names = F)` vs. `unlist()`. ] .pull-right65[ ```r # load package library(microbenchmark, quietly = T) # define link to data link <- 'http://tinyurl.com/y99aj5ed' # microbenchmark microbenchmark( web = read_csv(link), local = read_csv('data/titanic.csv'), fread = fread('data/titanic.csv'), times = 10) ``` ``` ## Unit: microseconds ## expr min lq mean median uq max neval cld ## web<PHONE_NUMBER>50<PHONE_NUMBER>65<PHONE_NUMBER>96 10 b ## local 4369 4642 16737 4987 5434 122567 10 a ## fread<PHONE_NUMBER> 1040 1253 2751 10 a ``` ] --- # Vectorise .pull-left35[ Whenever possible **use vector operations** or functions that do vectorized operations. In other words, **don't use loops**, stay away from all **apply idoms**, such as `apply()`, `sapply()`, `tapply()`, etc. Yet in other words, **use functions** that have been **implemented in C/C++**. ] .pull-right6[ ```r # create data my_data <- matrix(rnorm(1000000), ncol = 10) # microbenchmark microbenchmark( colMeans = colMeans(my_data), apply = apply(my_data, 2, mean), times = 10) ``` ``` ## Unit: microseconds ## expr min lq mean median uq max neval cld ## colMeans 776 816 877<PHONE_NUMBER> 10 a ## apply 13211 14684 46848 17957<PHONE_NUMBER>31 10 b ``` ] --- .pull-left3[ # Avoid copies **R always copies.** whenever using c(), cbind(), rbind(), paste() R creates a copy large enough to contain its inputs. ] .pull-right65[ ```r # define vectors short_vec <- runif(10) long_vec <- runif(100) # define collapse function combine <- function(x) { my_vec = c() for(i in 1:length(x)) my_vec = c(my_vec, x[i]) } # microbenchmark microbenchmark( loop10 = combine(short_vec), loop100 = combine(long_vec), vec10 = c(short_vec), vec100 = c(long_vec) ) ``` ``` ## Unit: nanoseconds ## expr min lq mean median uq max neval cld ## loop10 2732 3234 3553 3506 3802 4572 100 a ## loop100 38152 56063 94253 61125 66352 3390715 100 b ## vec10 142 208 273 250 303 979 100 a ## vec100<PHONE_NUMBER><PHONE_NUMBER>5 100 a ``` ] --- .pull-left2[ # Byte-code
compilation R can be compiled to byte-code, to create a faster, **lower-level version of the code**. R does this automatically with the first execution of a function. Thus, the second time a function is executed it will be faster. ] .pull-right7[ ```r # define unneccessarily complex function and compile my_fun <- function(x, f) sapply(x, f) my_fun_c <- compiler::cmpfun(my_fun) # define some awfully complex data x <- list(1:10, letters, c(F, T), NULL) # microbenchmark microbenchmark(my_fun(x, is.null), my_fun_c(x, is.null), unit='us') ``` ``` ## Unit: microseconds ## expr min lq mean median uq max neval cld ## my_fun(x, is.null) 7.67 7.96 19.85 8.16 8.82 1109.2 100 a ## my_fun_c(x, is.null) 7.64 7.97 8.53 8.17 8.77 16.7 100 a ``` ```r # microbenchmark again microbenchmark(my_fun(x, is.null), my_fun_c(x, is.null), unit='us') ``` ``` ## Unit: microseconds ## expr min lq mean median uq max neval cld ## my_fun(x, is.null) 10.6 11.0 12.3 11.6 12.4 61.1 100 a ## my_fun_c(x, is.null) 10.8 11.2 12.3 11.8 12.9 19.9 100 a ``` ] --- .pull-left2[ # Parallel
computing When working with large data one of the **best ways to speed up** execution is parallel execution. Parallel execution means splitting the data in many **jobs** and having many **workers** (separate R instances equipped with a worker function) complete the jobs in parallel. ] .pull-right7[ ```r # define data and splitted data (jobs) data <- matrix(rnorm(10000000), ncol = 10) split_data <- lapply(1:10, function(i) data[(1:1000)+(i-1)*1000, ]) # open cluster require(parallel) clu <- makeCluster(5) # my cluster fun my_cluster_fun <- function(split_data){ # apply cluster function out <- clusterApplyLB(clu, split_data, colMeans) # combine results colMeans(do.call(rbind, out)) } # microbenchmark microbenchmark(vectorz = colMeans(data), cluster = my_cluster_fun(split_data)) ``` ``` ## Unit: milliseconds ## expr min lq mean median uq max neval cld ## vectorz 7.67 8.01 8.37 8.14 8.45 11.2 100 b ## cluster 4.02 4.37 4.69 4.58 4.87 7.4 100 a ``` ] --- .pull-left2[ # Rcpp Another very effective, but quite advanced option is to write essential code chunks in C++ using Rcpp - **R's C++ interface**. Many functions are already implemented in C++ or Fortran. Thus, large benefits will only be seen for **for custom functions**. **Quick-Guide** ] .pull-right7[ ```r # define data my_data <- matrix(rnorm(10000000),ncol = 10) # define function my_Rcpp_fun = "NumericVector colMeans_c(NumericMatrix& mat) { int n_rows = mat.nrow(), n_cols= mat.ncol() ; NumericVector means(n_cols); for(int j = 0; j < n_cols; ++j) { double sum = 0; for(int i = 0; i < n_rows; ++i) sum += mat(i, j); means[j] = sum / n_rows; } return means; }" # compile function require(Rcpp) ; cppFunction(my_Rcpp_fun) # microbenchmark microbenchmark(vector_implementation = colMeans(my_data), rcpp_implementation = colMeans_c(my_data)) ``` ``` ## Unit: milliseconds ## expr min lq mean median uq max neval cld ## vector_implementation 7.63 7.93 8.44 8.16 8.53 11.1 100 a ## rcpp_implementation 7.55 7.89 8.51 8.14 8.71 11.4 100 a ``` ] --- # Alternative R implementations from  **Advanced R** | R implementation| Author | Description| |:------|:------|:------------| | [`pqR`](http://www.pqr-project.org/) |Radford Neal|This *p*retty *q*uick version of *R* builds on R 2.15.0; it fixes several performance issues, provides better memory management and some support for automatic multithreading.| | [`Renjin`](http://www.renjin.org/) |BeDataDriven| Renjin uses the Java virtual machine.| | [`FastR`](http://www.oracle.com/technetwork/java/jvmls2013vitek-2013524.pdf) |Purdue University| FastR is similar to Renjin. Optimisation is more ambitious, but at this point less mature.| | [`Riposte`](https://research.tableau.com/sites/default/files/pact2012-talbot-riposte.pdf) |Justin Talbot and Zachary DeVito| Riposte is experimental and ambitious. It's work in progress, but the existing implementations are extremely fast. Riposte is described in more detail in Riposte: A Trace-Driven Compiler and Parallel VM for Vector Code in R.| --- # Microsoft R Open .pull-left4[ Microsoft [R Open](https://mran.microsoft.com/) is the enhanced distribution of R from Microsoft Corporation. Open R interfaces with the high-performance, multi-threaded [**BLAS/LAPACK**](http://www.netlib.org/lapack/lug/node11.html) linear algebra libraries for superior performance. **Maximize reproducibility** by freezing the set of base packages with every version of Open R. Sort of R's Matlab. ] .pull-right45[  ] --- # Efficient code is readable and maintainable A lot of time in programming is consumed by reading, understanding, debugging your own code and that of others, and this time often far outweighs the benefits of making code faster. .pull-left45[ ```r library(readr ) library( magrittr) library( dplyr) syc = read_csv('https://tinyurl.com/y8gqcht3') syc = syc%>%mutate(fcm=father/2.54,mcm=mother/2.54) a = syc$father b = syc[['mother']] t.test(a,b) ``` ] .pull-right45[ ```r # load packages library(readr) library(magrittr) library(dplyr) # read galton data galton_data <- read_csv( 'https://tinyurl.com/y8gqcht3' ) # transform to metric system galton_data = galton_data %>% mutate(father_cm = father / 2.54, mother_cm = mother / 2.54) #conduct t-test father <- galton_data$father mother <- galton_data$mother t.test(father, mother) ``` ] --- # Practical
Link to practical


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§490:4A-102 Subject matter. Except as otherwise provided in section 490:4A-108, this article applies to funds transfers defined in section 490:4A-104. [L 1991, c 41, pt of §1]


My name is Michael J. Bondra, and I create high-quality experiences for the web's users and makers.
I am a full-stack engineer who has been developing websites and applications for more than fifteen years. In that time I have seen the web transform from a blinking, scrolling, table-filled, frame-organized, perl-script-driven guestbook, to a world of respectable architectures and unquestioned utility.
I am primarily a JavaScript developer. The hastily devised language is far from perfect — an obvious statement for those who have lived through the nightmares of loaded operators, unintuitive coercion, and 0.1 + 0.2 != 0.3. But a server-side runtime, evolving standards, and a myriad of excellent frameworks have given new life to the imperfect child of necessity.
The rise of Node.js has given JavaScript a reliable engine upon which complex applications can be built. The preeminent framework used for developing these applications is Express. It abstracts away the complexity of handling the request-response cycle with the powerful concept of sequentially executed functions known as middleware. The Koa framework will undoubtedly inherit the success of Express, and not just because they share an overlapping set of contributors. Koa extends the concept of middleware by layering functions known as generators, which can yield their execution to other generators, functions, and objects, before resuming their own.
On the front-end, JQuery has long been the standard by virtue of its simple, intuitive API for interacting with HTML documents. Utility libraries like Underscore and its forked sibling Lo-Dash provide an additional set of useful functions capable of standing on their own or in concert with others. But most exciting has been the advent of libraries and frameworks like AngularJS, Ember, Backbone.js, and React, with AngularJS and React having perhaps the greatest mindshare of this latest generation. AngularJS is complete framework that extends HTML as a templating language, with powerful two-way data-binding that synchronizes values between the document and the underlying JavaScript engine. React is an ultra-performative library that focuses on creating abstractions for interface functionality, and is often paired with an architectural pattern called Flux.
When I am not developing, you might find me exploring Seattle with my long-time partner, Vanessa, and our five-year-old German Shepherd mix, Stella. Being an avid homebrewer and craft beer enthusiast, you might also see me perusing the shelves and chalkboards of the local homebrew supply shops, beer stores and taprooms.
Not long ago I earned my second degree from the University of Michigan — an MSI in Human-Computer Interaction to go with my BA in Social Anthropology. While I take pride in my academic achievements, I take even greater pride in my passion, and the attention to detail that put into everything that I do.
Are you interested in collaborating with someone who has an unrelenting drive and a deep understanding of the technology that will guide your ideas and goals from concept to production? Let's have a conversation.


GarageDoorOpener(GDO) - System Level Test Procedures
- INIT - Initialization
- DISPLAY - User Interface Display
- COMMAND - Commands
- CA - Controller Authentication
- PROV - Provisioning
- PERF - Performance Requirements
- Requirements to Test Case ID Trace
- Test Case ID to Requirements Trace
- Test Log Entry
- Test Log


08.Write a script that converts a number in the range [0...999] to a text corresponding to its English pronunciation.
Examples:
0 -> "Zero"
273 -> "Two hundred seventy three"
Input number between 0 and 999:
Calculate


{% extends "base.html" %} {% block title %} Welcome {% endblock %} {% block stylesheets %}{% endblock stylesheets %} {% block content %}
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Joe Geigel is a Professor of Computer Science at the Rochester Institute of Technology and co-director of the CS Graphics and Applied Perception Lab at RIT.
He received his doctorate in Computer Science in 2000 from the George Washington University in Washington D.C. Previous to his academic career, Joe held a variety of research and development positions in industry at companies including Eastman Kodak, Bell Laboratories, Bellcore, and RCA Solid State. Just prior to joining the faculty at RIT, Joe was a Senior Web technologist at theatre.com / BroadwayOnLine.com where he applied 3D Web technologies to theatrical applications.
For the past decade, his main research efforts have focused on interdisciplinary multimedia projects that that combine computer science, real-time graphics, mobile devices, art, music, and theatre to create interactive live experiences, collaborating with artists, designers, musician and photographers from both within RIT and externally.
He uses these projects as a springboard for interdisciplinary education promoting collaboration and teamwork between artists and technologists. In addition to his work on these experiences, Joe has published in the areas of tone reproduction, virtual reality, facial motion capture, sound synthesis, multimedia systems, applied perception, and curricular design for graphics education.
He currently teaches graduate and undergraduate classes focusing on Computer Graphics, Global Illumination, Animation, Virtual Reality, and Interactive Musical Experiences.
Joe is an active and long time member of ACM/SIGGRAPH and a SIGGRAPH Pioneer. He is also an avid fan of Broadway musicals and pop music culture.
Research Areas


 Median Mean 3rd Qu. Max.
## 3.00 27.00 34.00 37.41 45.00 1189.00
summary(trials$past_donations)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 2.000 6.916 5.000 2500.000
Average participant was 34 years old and had given 2 donations in the past 12-months.
A quick summary statistic of donations for the presence of metaphor and each metaphor condition.
with(trials, tapply(donation, cond_anymet, summary))
## $`FALSE`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 10.0 25.0 23.6 39.0 50.0
##
## $`TRUE`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 10.00 25.00 23.67 35.00 50.00
with(trials, tapply(donation, cond_metaphor, summary))
## $literal
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 10.0 25.0 23.6 39.0 50.0
##
## $battle
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 10.0 25.0 24.2 39.0 50.0
##
## $journey
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 10.00 25.00 23.13 35.00 50.00
cm <- model.matrix(~ 0 + age + past_donations + gender + self_cancer + ff_cancer + education + socioeconomic + sympathy + urgent, data=trials) %>%
cor(use="pairwise.complete.obs")
rownames(cm) <- paste(paste(1:nrow(cm), ".", sep = ""), rownames(cm))
colnames(cm) <- 1:nrow(cm)
heatmap(cm, symm = T, margins = c(1,1), Rowv = NA, col = colorRampPalette(c("blue", "white", "red"))(20))
There don’t appear to be any strong inter-variable correlations (naturally, we do see some strong intra-variable correlations, e.g. male gender and female gender).
2.2 Visualization Summaries
Visualize the distributions of the conditions and demographic variables against donation amounts
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
# g = ggplot(trials) + ptheme +
# theme(axis.ticks = element_line(color = "white")) +
# geom_line(aes(donation, linetype=cond_metaphor), stat = "density") +
# scale_linetype_manual(values = c(1,2,9), labels = c("literal" = "Literal", "battle" = "Battle", "journey" = "Journey")) +
# scale_x_continuous(labels = scales::dollar) +
# coord_cartesian(ylim = c(0.01, 0.028)) +
# labs(x = "Donation Amount", y = "Density", linetype = element_blank(), title = element_blank())
#
# g
# ggsave("metaphor_donation_density.png", g, width = pwidth, height = 2.7, bg = "transparent")
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
## Warning: Removed 7 rows containing missing values (geom_smooth).
## Warning: Removed 19 rows containing missing values (geom_smooth).
2.3 Duration Statistics
How long did participants spend on the main stimulus and overall?
summary(trials$rt_trial) / (1000) # in seconds
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.061 26.736 45.612 59.613 71.813 3598.752
summary(trials$total_time) / (1000*60) # in minutes
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.8248 3.1519 4.4563 5.5495 6.4615 110.5542
ggplot(trials) + itheme +
geom_density(aes(total_time/(1000*60))) +
labs(x = "Total Duration (mins)", y = "Density", title = "Total Duration Distribution")
Participants spent a median time of 45.6 seconds on the main stimulus and a median time of 4.43 minutes overall.
Take a look at the fastest and slowest participants to see if they are completely unreasonable outliers
# only useful when run interactively as an Rmd
# trials %>% arrange(rt_trial) %>% head()
# only useful when run interactively as an Rmd
# trials %>% arrange(desc(rt_trial)) %>% head()
Some of the responses do look a little fishy, but excluding them would be nontrivial. Perhaps set aside for future work.
2.4 Coarse Donation Behavior
First, the count and proportion of participants, grouped by gender, then sex condition, then metaphor condition, with donations at three natural breaks: $0, $25, and $50. These natural breaks (minimum, default/middle, maximum) might signal certain behavior (e.g. people often giving nothing, staying with the default value).
Summary statistics of donation amounts:
summary(trials$donation)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 10.00 25.00 23.64 36.00 50.00
The mean donation was $23.53 and the median was $25. However, the donation distribution plot above appears to be somewhat trimodal, so this statistics may be unreliable.
Participants, by demand of the experiment, may have had mental representations of possible donations, perhaps small, medium, and large amounts. These may correspond to standard values, falling onto certain donation values. Based on inspection, participants tended to go toward donations at $5 increments. Therefore, the smallest standard values they may donate were $0 and $5. The medium standard values were $20, $25, and $30, surround the defaut value of $25. The large standard values were $45 and $50.
trials$donation_narrow_group = ""
trials[trials$donation %in% c(0, 5), "donation_narrow_group"] = "small"
cat("Proportion of small donations: ", round(mean(trials$donation_narrow_group == "small"), 3), "\n")
## Proportion of small donations: 0.179
trials[trials$donation %in% c(20, 25, 30), "donation_narrow_group"] = "medium"
cat("Proportion of medium donations: ", round(mean(trials$donation_narrow_group == "medium"), 3), "\n")
## Proportion of medium donations: 0.287
trials[trials$donation %in% c(45, 50), "donation_narrow_group"] = "large"
cat("Proportion of large donations: ", round(mean(trials$donation_narrow_group == "large"), 3), "\n")
## Proportion of large donations: 0.152
# the proportion of participants who donated one of the above amounts
with(trials, mean(trials$donation_narrow_group %in% c("small", "medium", "large")))
## [1] 0.6178844
18% of participants donated one of the coarse small donation amounts ($0 and $5). 29% of participants donated one of the coarse medium donation amounts ($20, $25, $30). 15% of participants donated one of the coarse large donation amounts ($45 and $50).
In total, 62% of participants donated one of these coarse donations.
trials %>%
ggplot(aes(donation_narrow_group, fill = cond_metaphor)) + itheme +
geom_bar(position = position_dodge()) +
labs(title = "Number of Standard Donations given Metaphor Condition", subtitle = "e.g. \"small\" donations are $0 or $5", x = "Donation Group", y = "# of Participants") +
scale_fill_brewer(palette = "Pastel1")
Metaphor may dictate small differences here between groups of standard donations, we’ll model these in a bit.


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FoodChain
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Sharing the meal experience, for better or worse


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Donations will help us in our mission. We gratefully acknowledge the support of the following benefactors: No Company (knowledgeATpeoplepcDOTcom) |
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EVENTS
WORKSHOPS
SHOWS AND EXHIBITIONS
PRE-SHAASTRA ACTIVITIES
LECTURE SERIES
MAKER SUMMIT
PLEDGE-A-BOOK


In this project, we're going to make a dark webpage that you can draw on with bright, random colors.
1. Give your page a title.
2. Set the width
and height
of the HTML
and body
to 100% each.
3. In JavaScript, create 100 div
elements and store them in a new array.
(Hint: don't create the div
's one by one. Instead, use a for
loop).
5. Set the background color of the div
's to black or dark gray.
6. Append the div
's to the page when the page loads.
(If the div
's are all in a line, make them into a grid using the CSS display
property).
7. Set the height and width of the div
's so that they take up the whole page.
8. Choose a bunch of bright colors that you want to use -- at least five -- and store them in an array.
9. Make a function that chooses a random color from your array.
10. When the mouse hovers over the first div
, run that function, and change the background of the div
to the new color.
11. Now make that code work with any div
, not just the first one.
Hint: To do this, you will need to use a forEach
loop.
12. Keep working on your project until you're happy with it. You could change the number of div
's, allow the user to pick which color the div
's change to, etc.
13. Save and submit your project!


EEP 520 Final Project
Random maze generator for final project
Class Hierarchy
Go to the textual class hierarchy
Generated by
1.8.13


Adelaide swelters but misses Nov record
Adelaide has fallen just short of setting a November temperature record as South Australia sweltered through a day of scorching temperatures and dangerous bushfires.
Late on Wednesday after the Country Fire Service continued to fight a number of fires across the state, with emergency warning messages in place for some centres.
The hot dry northerly winds that fanned the flames also sent temperatures in some regional towns soaring.
Nullarbor and Ceduna, on SA's west coast, were the hottest with tops of 46.6C and 46.2C respectively while Port Augusta had 45.1C and Renmark 45C.
Nullarbor's maximum was a November record, but it wasn't alone with a string of other towns posting their hottest temperatures for this time of the year.
Among those were Keith (44.3C), Clare (40.8), Strathalbyn (43.1C), Hindmarsh Island (40.7C), Murray Bridge (44C) and Stenhouse Bay (40.1C).
In Adelaide, the mercury peaked at 42.2C at the Bureau of Meteorology's Kent Town observatory and 41.6C at its official West Terrace headquarters.
Both were just shy of the record 42.7C recorded at Kent Town in 1962.
The hottest spot across the Adelaide suburbs was Edinburgh with 44.5C.
The heat was not expected to last with the weather bureau forecasting a cool change to sweep across the state by early on Thursday morning.
Temperatures were expected to drop back to the low 20s with Adelaide's maximum tipped to fall by half to just 21C.
That change also prompted the bureau to also put a severe weather warning in place for potentially damaging winds across a wide area including most of York and Eyre Peninsulas and the Adelaide Hills.
It said wind gusts of up to 90km/h were possible as the front moved through.
The unusually hot conditions on Wednesday prompted authorities to close more than 100 schools along with most national parks and reserves.
On the lower Eyre Peninsula, SA Power Networks also decided to cut power to more than 10,000 properties as a safety precaution.
By late afternoon, most of those properties remained offline with the company warning of the prospect of extended outages.
In Port Lincoln, the local council opened an arts centre to provide a refuge from the heat and also urged people to seek out commercial venues with generators such as hotels or restaurants.
The heat and high winds also prompted a warning from health authorities about raised dust and other issues.
Chief Public Health Officer Nicola Spurrier said people with pre-existing chest or heart conditions should stay indoors, avoid exposure to the conditions and to follow their personal management plans.


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Tonto Dikeh Reveals How Bobrisky Spent 47k Dollars On One Shoe In Dubai(+Screenshot)
by vanessa nordziWhen you have money spending it becomes easy. Whiles some of us have been crying over the hardship of the economy, Nigerian cross dresser Bobrisky doesn’t seem to feel the heat at all.
According to Nollywood actress Tonto Dikeh who is currently in Dubai shopping, she was shocked to the core when she went shopping with Bobrisky and saw him spend 47k Dollars on one shoe, two bags and a necklace.
Explaining what happened in a post on her Instagram page, Tonto Dikeh said that instead of spending that huge amount of money on a single shoe and bag, Bobrisky should rather donate it to her charity organization to put smiles on the faces of the less privileged this Christmas.
She wrote ; Bob spent 47k dollars on 1 shoe and 2bags and necklace in my front..ooo I am holding myself back from crying…. Can you all tell Bobrisky to pls Donate for #10,000 SmilesforChristmas by the Tonto Dikeh Foundation.
#PS- I miss my single girls days when I was wild, money careless and free egennnn(pikin cry lol)”.
Well, whatever Bobrisky is doing is definitely putting huge sums of money in his pocket for him to be able for afford such luxury.
See post below;


Bookmarks
A Pen by Rodrigo Muniz.
simple on/off button
URL Rewriting for Beginners - Web Development in Brighton - Added Bytes


Conflict Free Computing 2016 Laptop Buyers Guide
Info
Some corporations fund human rights abuses to obtain essential minerals. While no company is perfect, many are working to improve their practices. This guide tries to make the right choice simple.
Method
Every computer listed here has at least one report of someone online running Linux as well as at least one HDMI and USB ports. I've tried to slim down the list of excellent developer laptops by their maker's conflict mineral policies rather than shoehorn possibly great machines into preexisting conflict-free manufacturers.
I have bought Thinkpads, but at this point the issues with the newest Lenovo P & T lines, as well as the long-standing conflict-mineral, Superfish and Chinese BIOS backdoor privacy scandal, as well as generally shitty machines.
The details presented here are based on the details of the highest-priced model that is less than $3000 within each family. Most of these machines can be priced lower than $1100 if you don't want or need some of their more expensive capabilities.
Each of these is from a manufacturer that is has demonstrated a good faith, proactive stance on stopping the illegal coltan and wolframite trade.
Macbook Pro
The Macbook Pro is well-regarded by many developers, designers and engineers. I have omitted it from this guide because of substantial usability concerns that have emerged in response to their latest Macbook Pro product announcement.
If you are considering a MBP in spite of this, Apple has done a considerable work in the past 5 years to rehabilitate their foundries and factories labor practices and are well-regarded in their policies and has actively pressured smelters to abandon conflict-derived mineral sources.
NVidia vs AMD?
If you are on the fence between these manufacturers, I'd recommend AMD.
AMD is a world-leader and a founder of the Public-Private Alliance for Responsible Mineral Trade. NVidia's status is less clear, but does not have any notable violations and is a US company that should be compliant with Dodd-Frank.
Dell XPS 13 9550
Developer Reviews
I have xps 9550 FHD with 512GB SSD and 16GB RAM. I installed Ubuntu gnome 16.10. Everything worked out if the box. I even played steam games on Linux (like firewatch).
– vasilakisfil
I have xps 9550 FHD with 512GB SSD and 16GB RAM. I installed Ubuntu gnome 16.10. Everything worked out if the box. I even played steam games on Linux (like firewatch).
Details
- Price
- $1,149.99
- CPU
- 6th Generation Intel® Core™ i5-6300HQ
- Clock
- 3.2Ghz
- Cores
- 4
- Cache
- 6MB
- Memory
- 8GB 2133MHz DDR4 (1x8)
- Slots
- 2 SODIMM slots
- Disk
- 256GB PCIe Solid State Drive (M.2)
- Drive Bays
- 1 M.2 Slot
- Display
- 15.6” 4K Ultra HD (3840 x 2160)
- GPU
- NVIDIA® GeForce® GTX 960M with 2GB GDDR5
- CUDA
- Yes
- Cores
- 640
- Memory Bandwidth
- 80GB/sec
- OpenGL
- 4.5
- Linux
- Out-of-the-box. Manufacturer Supported.
- Servicability
- Standard (No Soldered RAM/HDD, Torx Screws)
- Tenkeyless
- NOG
- Weight
- 4 lbs
- UEFI & Shell
- Yes
- Ports
- 1 Thunderbolt 3
- 2 USB 3.0 / 1 USB 3.1
- HDMI
- SD card slot
HP ZBook 17 G3 Mobile Workstation
Ok, here's the honest truth. I haven't found very much about these guys HN, cat-v or freenode.
Developer Reviews
Ok, I'll put in a word for the HP ZBook Studio. You can have i7 quad-core, dual 1 TB SSD, 32 GBytes RAM (maybe more), backlit keyboard, thin(ish) form factor, and a fantastic 3840 x 2160 screen. Graphics are NVidia M1000M which is desktop class and runs CineBench 11(?) OpenGL test at 93 fps (faster than my NVidia GTX 970). This is by FAR the nicest and thinnest portable workstation-class laptop I've ever used. It's easily carryable-around, which is more than I can say for any previous laptop such as the larger Dell Precisions, HP Elitebooks I've previously used in this class.
– watmough
Not sure on the battery life, but check out the HP ZBook G3. We have the G2 at work and it's quite nice. It's got a lot of horsepower and I can go 4-6 hours on battery.
– greggyb
Details
- Price
- $2,799.00
- CPU
- Intel® Core™ i7-6700HQ
- Clock
- 3.5Ghz
- Cores
- 4
- Cache
- 6 MB
- Memory
- 16 GB DDR4-2133 SDRAM (1 X 16 GB)
- Slots
- 4 SODIMM slots
- Disk
- 512 GB PCIe SSD
- Drive Bays
- 2 M.2 slots & 2 2.5”
- Display
- 17.3" diagonal FHD Touch UWVA IPS LED-backlit (1920 x 1080)
- GPU
NVIDIA® Quadro® M3000M (4 GB dedicated GDDR5)
HP also services an alternative: the AMD FirePro™ W6150M configuration exists
- CUDA
- Yes
- Cores
- 1024
- Memory Bandwidth
- 160GB/s
- OpenGL
- 4.5
- Linux
- Manufacturer Supported
- Servicability
- Standard (No Soldered RAM/HDD or Major Components)
- Tenkeyless
- No
- Weight
- 6.2 Lbs
- UEFI & Shell
- Yes
- Ports
- 2 Thunderbolt 3
- 1 USB 3.0 (charging)
- 2 USB 3.0
- 3 USB 3.0
- HDMI
- VGA
Acer TravelMate P6 (658/648)
Developer Reviews
As for me the only decent laptops on the market are HP Spectre x360 (second generation, recently updated, comes with Kaby Lake) and Acer TravelMate P648. Unfortunately not the Dell XPS laptops due to the widely known coil whine problem and in general a quality control issue.
– vladimir-y
Details
- Price
- $2,799.00
- CPU
- Intel® Core™ i7-6500U
- Clock
- 2.5Ghz
- Cores
- 2
- Cache
- 4 MB
- Memory
- 8 GB DDR4-2133 SDRAM (1 X 8 GB)
- Disk
- 256 GB SATA
- Display
- 15.6" LCD (1920x1080)
- GPU
- NVIDIA® GeForce® 940M (Up to 2GB)
- CUDA
- Yes
- Cores
- 384
- Memory Bandwidth
- 16GB/s
- OpenGL
- 4.5
- Linux
- Reports of Successful Out-Of-Box Install
- Servicability
- ???
- Tenkeyless
- No
- Weight
- 4.6 Lbs
- UEFI & Shell
- Yes
- Ports
- 1 Thunderbolt 3
- 3 USB 3.0
- HDMI
- VGA


请参阅: 说明
| 接口 | 说明 |
|---|---|
| CallMetaDataProvider |
Interface specifying the API to be implemented by a class providing call meta-data.
|
| TableMetaDataProvider |
Interface specifying the API to be implemented by a class providing table meta-data.
|
| 类 | 说明 |
|---|---|
| CallMetaDataContext |
Class to manage context meta-data used for the configuration
and execution of a stored procedure call.
|
| CallMetaDataProviderFactory |
Factory used to create a
CallMetaDataProvider implementation
based on the type of database being used. |
| CallParameterMetaData |
Holder of meta-data for a specific parameter that is used for call processing.
|
| Db2CallMetaDataProvider |
DB2 specific implementation for the
CallMetaDataProvider interface. |
| DerbyCallMetaDataProvider |
Derby specific implementation for the
CallMetaDataProvider interface. |
| DerbyTableMetaDataProvider |
The Derby specific implementation of
TableMetaDataProvider . |
| GenericCallMetaDataProvider |
A generic implementation of the
CallMetaDataProvider interface. |
| GenericTableMetaDataProvider |
A generic implementation of the
TableMetaDataProvider interface
which should provide enough features for all supported databases. |
| HanaCallMetaDataProvider |
SAP HANA specific implementation for the
CallMetaDataProvider interface. |
| HsqlTableMetaDataProvider |
The HSQL specific implementation of
TableMetaDataProvider . |
| OracleCallMetaDataProvider |
Oracle-specific implementation for the
CallMetaDataProvider interface. |
| OracleTableMetaDataProvider |
Oracle-specific implementation of the
TableMetaDataProvider . |
| PostgresCallMetaDataProvider |
Postgres-specific implementation for the
CallMetaDataProvider interface. |
| PostgresTableMetaDataProvider |
The PostgreSQL specific implementation of
TableMetaDataProvider . |
| SqlServerCallMetaDataProvider |
SQL Server specific implementation for the
CallMetaDataProvider interface. |
| SybaseCallMetaDataProvider |
Sybase specific implementation for the
CallMetaDataProvider interface. |
| TableMetaDataContext |
Class to manage context meta-data used for the configuration
and execution of operations on a database table.
|
| TableMetaDataProviderFactory |
Factory used to create a
TableMetaDataProvider implementation
based on the type of database being used. |
| TableParameterMetaData |
Holder of meta-data for a specific parameter that is used for table processing.
|


Source code for src.model_code.agent
import numpy as np
[docs]class Agent:
"""An Agent as in the Schelling (1969, :cite:`Schelling69`)
segregation model. Move each period until enough neighbours
of the same type are found or the maximum number of moves
is reached.
Code is based on the example in the Stachurski and Sargent
Online Course :cite:`StachurskiSargent13`.
"""
def __init__(self, typ, initial_location, n_neighbours, require_same_type, max_moves):
self.type = typ
self.location = np.asarray(initial_location)
self._n_neighbours = n_neighbours
self._require_same_type = require_same_type
self._max_moves = max_moves
def _draw_new_location(self):
self.location = np.random.uniform(size=self.location.shape)
def _get_distance(self, other):
"""Return the Euclidean distance between self and other agent."""
return np.linalg.norm(self.location - other.location)
def _happy(self, agents):
"""True if sufficient number of nearest neighbours are of the same type."""
# Create a sorted list of pairs (d, agent), where d is distance from self
distances = [(self._get_distance(other), other) for other in agents if not self == other]
distances.sort()
# Extract the types of neighbouring agents
neighbour_types = [other.type for d, other in distances[:self._n_neighbours]]
# Count how many neighbours have the same type as self
n_same_type = sum(self.type == nt for nt in neighbour_types)
return n_same_type >= self._require_same_type
[docs] def move_until_happy(self, agents):
"""If not happy, then randomly choose new locations until happy."""
if self._happy(agents):
return
else:
for m in range(self._max_moves):
self._draw_new_location()
if self._happy(agents):
return


All transactions incur a 0.25% fee. This fee is split evenly among the verifier of this block and the verifiers of the previous nine blocks. For blocks before block 9, the fee is split evenly among the verifier of this block and all previous blocks. Transaction fees that cannot be divided evenly are rounded down to the nearest micronyzo, and the remainder is rolled over to the next block.


Back to the Main Page
Sonic Gesture Challenge
Sound design 3
Try to mimic the stored sound by making gestures on the play area!
1. Play sound to mimic
3. Play my last sound and compare
2. Tap and slide to play


This won't work if you load this page from an HTTP server, because it attempts to send requests via XMLHttpRequest to term.ie or madgex.com. Most browsers forbid this for security reasons. You can make it work with Microsoft Internet Explorer, if you copy this page to a file and direct your browser to load from the file.


workbox-runtime-caching
workbox-runtime-caching
A service worker helper library that implements various runtime caching strategies.
You can learn more about each caching strategy on Jake Archibald's blog post which covers various ways of handling fetch events with a service worker.
These strategies can be used as the handler parameter in the Route constructor or as the handler parameter in workboxSW.router.registerRoute().
Classes
CacheFirst
An implementation of a cache-first request strategy.
The important thing to note with this caching strategy is that once a response is cached, it will not be updated. This is useful for assets that are revisioned since it caches the asset long term and doesn't waste the user's data.
CacheOnly
An implementation of a cache-only request strategy.
The advantage to using this versus directly calling caches.match()
is that it will use the cache configuration and trigger the plugins defined in the underlying RequestWrapper
which accounts for behaviors like cache
expiration.
Handler
This a base class which each caching strategy extends.
NetworkFirst
An implementation of a network first request strategy.
By default, NetworkFirst
will cache responses with a 200 status code as well as opaque responses (responses from cross-origin servers which don't support
CORS). You can override this default by passing in a RequestWrapper
that includes an appropriately-configured
CacheableResponsePlugin
.
NetworkOnly
An implementation of a network-only request strategy.
The advantage to using this versus directly calling fetch()
is that it will trigger the plugins defined in the underlying RequestWrapper
.
RequestWrapper
This class is used by the various subclasses of Handler to configure the cache name and any desired plugins, which is to say classes that implement request lifecycle callbacks.
It automatically triggers any registered callbacks at the appropriate time.
StaleWhileRevalidate
An implementation of a stale-while-revalidate request strategy.
Resources are requested from both the cache and the network in parallel, then responds with the cached version. The cache is replaced with whatever returns from the network. In addition to updating the appropriate caches, it will also trigger
any appropriate plugins defined in the underlying
RequestWrapper
.
By default, StaleWhileRevalidate
will cache responses with a 200 status code as well as opaque responses (responses from cross-origin servers which don't support
CORS). You can override this default by passing in a RequestWrapper
that includes an appropriately-configured
CacheableResponsePlugin
.
Method
getDefaultCacheName
getDefaultCacheName(input) returns string
The default cache name, used by RequestWrapper when there's no name provided.
It combines a constant prefix with the service worker scope
, ensuring that multiple service workers used on the same origin will have different default caches.
Calling this method without any parameters will return
workbox-runtime-caching-<service worker scope>
.
If you pass in a cacheId, it will be prepended, returning:
<cacheid>-workbox-runtime-caching-<service worker scope>
.
Parameter |
|
|---|---|
|
input |
Object |
- Returns
-
string
Returns the default cache name used based on optional inputs.


All functions
|
|
analyse()
|
Sensory data analysis |
|
create_template()
|
Create template for sensory table |
|
inspect_attribute()
|
Inspect sensory attribute |
|
inspect_panel()
|
Inspect panel |
|
inspect_panelist()
|
Inspect panelist |
|
inspect_preference_panelist()
|
Inspect preference |
|
inspect_preference_product()
|
Inspect preference |
|
inspect_product()
|
Inspect product |
|
inspect_space()
|
Inspect sensory space |
|
perform_anova()
|
Analysis of variance |
|
perform_ca()
|
Correspondance Analysis |
|
perform_cochran()
|
Cochran's Q test |
|
perform_friedman()
|
Friedman rank sum test |
|
perform_liking_analysis()
|
Liking analysis |
|
perform_mdpref()
|
Internal preference mapping |
|
perform_panel_analysis()
|
Panel analysis |
|
perform_pca()
|
Principal Component Analysis |
|
perform_penalty()
|
Penalty analysis |
|
perform_prefmap()
|
External preference mapping |
|
perfume_jar
|
Perfume QDA Consumers |
|
perfume_qda_consumers
|
Perfume QDA Consumers |
|
perfume_qda_experts
|
Perfume QDA Experts |
|
prepare()
|
Prepare sensory experiment |
|
sensochoc
|
Sensory data for 6 chocolates |
|
specify()
|
Specify sensory table |
|
visualise(<tbl_sensory_global>)
|
Visualise sensory space |
|
visualise(<tbl_sensory_local>)
|
Visualise sensory properties |
|
visualise(<tbl_sensory_penalty>)
|
Visualise penalty |
|
visualise()
|
Visualise product or sensory attributes |
|
visualise(<tbl_sensory_prefmap>)
|
Visualise External Preference Map |


Z-checker
Data Properties
Compression Results
Comparison Results
Variables:
Properties:
Compressors:
Error Bounds:
Variables:
Properties:


[§124A-142] Military property; loss, damage, destruction, or wrongful disposition. Any person subject to this chapter who, without proper authority:
(1) Sells or otherwise disposes of;
(2) Wilfully or through neglect damages, destroys, or loses; or
(3) Wilfully or through neglect suffers to be damaged, destroyed, sold, or wrongfully disposed of;
any military property of the United States or of the State shall be punished as a court-martial may direct. [L 1982, c 171, pt of §2]
Revision Note
"Or" substituted for "of" in section heading.


Batch of sentences represented as word indices with masking.
More...
#include <corpus_base.h>
|
| | SubBatch (int size, int width, const Ptr< Vocab > &vocab) |
| | Creates an empty subbatch of specified size. More...
|
| |
| std::vector< Word > & | data () |
| | Flat vector of word indices. More...
|
| |
| std::vector< float > & | mask () |
| | Flat masking vector; 0 is used for masked words. More...
|
| |
|
const Ptr< Vocab > & | vocab () const |
| | Accessors to the vocab_ field.
|
| |
|
size_t | batchSize () |
| | The number of sentences in the batch.
|
| |
|
size_t | batchWidth () |
| | The number of words in the longest sentence in the batch.
|
| |
|
size_t | batchWords () |
| | The total number of words in the batch, considering the mask.
|
| |
| std::vector< Ptr< SubBatch > > | split (size_t n) |
| | Splits the subbatch into subbatches of equal size. More...
|
| |
|
void | setWords (size_t words) |
| |
Batch of sentences represented as word indices with masking.
◆ SubBatch()
| marian::data::SubBatch::SubBatch |
( |
int |
size, |
|
|
int |
width, |
|
|
const Ptr< Vocab > & |
vocab |
|
) |
| |
|
inline |
Creates an empty subbatch of specified size.
- Parameters
-
| size | Number of sentences |
| width | Number of words in the longest sentence |
◆ data()
| std::vector<Word>& marian::data::SubBatch::data |
( |
| ) |
|
|
inline |
Flat vector of word indices.
The order of indices is , where is the number of words (width) and is the number of sentences (size).
◆ mask()
| std::vector<float>& marian::data::SubBatch::mask |
( |
| ) |
|
|
inline |
Flat masking vector; 0 is used for masked words.
- See also
- data()
◆ split()
| std::vector<Ptr<SubBatch> > marian::data::SubBatch::split |
( |
size_t |
n | ) |
|
|
inline |
Splits the subbatch into subbatches of equal size.
- Parameters
-
- Returns
- Vector of pointers to new subbatches.
- See also
- marian::data::Batch::split(size_t n)
The documentation for this class was generated from the following file:


Class: RSpec::Mocks::Constant
- Inherits:
-
Object
- Object
- RSpec::Mocks::Constant
- Extended by:
- Support::RecursiveConstMethods
- Defined in:
- lib/rspec/mocks/mutate_const.rb
Overview
Provides information about constants that may (or may not) have been mutated by rspec-mocks.
Instance Attribute Summary collapse
-
#name ⇒ String
readonly
The fully qualified name of the constant.
-
#original_value ⇒ Object?
The original value (e.g. before it was mutated by rspec-mocks) of the constant, or nil if the constant was not previously defined.
Class Method Summary collapse
-
.original(name) ⇒ Constant
Queries rspec-mocks to find out information about the named constant.
Instance Method Summary collapse
-
#hidden? ⇒ Boolean
Whether or not rspec-mocks has hidden this constant.
-
#initialize(name) {|_self| ... } ⇒ Constant
constructor
private
A new instance of Constant.
-
#mutated? ⇒ Boolean
Whether or not rspec-mocks has mutated (stubbed or hidden) this constant.
-
#previously_defined? ⇒ Boolean
Whether or not the constant was defined before the current example.
-
#stubbed? ⇒ Boolean
Whether or not rspec-mocks has stubbed this constant.
-
#to_s ⇒ Object
(also: #inspect)
The default
to_s
isn't very useful, so a custom version is provided. -
#valid_name? ⇒ Boolean
Whether or not the provided constant name is a valid Ruby constant name.
Constructor Details
#initialize(name) {|_self| ... } ⇒ Constant
This method is part of a private API. You should avoid using this method if possible, as it may be removed or be changed in the future.
Returns a new instance of Constant
11 12 13 14 15 16 17 18 |
# File 'lib/rspec/mocks/mutate_const.rb', line 11 def initialize(name) @name = name @previously_defined = false @stubbed = false @hidden = false @valid_name = true yield self if block_given? end |
Instance Attribute Details
#name ⇒ String (readonly)
Returns The fully qualified name of the constant.
21 22 23 |
# File 'lib/rspec/mocks/mutate_const.rb', line 21 def name @name end |
#original_value ⇒ Object?
Returns The original value (e.g. before it was mutated by rspec-mocks) of the constant, or nil if the constant was not previously defined.
26 27 28 |
# File 'lib/rspec/mocks/mutate_const.rb', line 26 def original_value @original_value end |
Class Method Details
Instance Method Details
#hidden? ⇒ Boolean
Returns Whether or not rspec-mocks has hidden this constant.
51 52 53 |
# File 'lib/rspec/mocks/mutate_const.rb', line 51 def hidden? @hidden end |
#mutated? ⇒ Boolean
Returns Whether or not rspec-mocks has mutated (stubbed or hidden) this constant.
39 40 41 |
# File 'lib/rspec/mocks/mutate_const.rb', line 39 def mutated? @stubbed || @hidden end |
#previously_defined? ⇒ Boolean
Returns Whether or not the constant was defined before the current example.
33 34 35 |
# File 'lib/rspec/mocks/mutate_const.rb', line 33 def previously_defined? @previously_defined end |
#stubbed? ⇒ Boolean
Returns Whether or not rspec-mocks has stubbed this constant.
45 46 47 |
# File 'lib/rspec/mocks/mutate_const.rb', line 45 def stubbed? @stubbed end |
#to_s ⇒ Object Also known as: inspect
The default to_s
isn't very useful, so a custom version is provided.
62 63 64 |
# File 'lib/rspec/mocks/mutate_const.rb', line 62 def to_s "#<#{self.class.name} #{name}>" end |
#valid_name? ⇒ Boolean
Returns Whether or not the provided constant name is a valid Ruby constant name.
57 58 59 |
# File 'lib/rspec/mocks/mutate_const.rb', line 57 def valid_name? @valid_name end |


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API Reference¶
proxalgs.core¶
Proximal algorithms
A proximal consensus optimization algorithm
- class proxalgs.core.Optimizer(objfun, **kwargs)¶
Bases: object
Optimizer class for running proximal algorithms
Notes
- TODO: Add citations
- TODO: Add a demo notebook
Examples
To initialize an Optimizer object, pass the name of the desired objective function from the operators module (and any additional arguments needed for that function). Then, add any desired regularizers along with the necessary hyperparameters for those functions. Finally, use the minimize() function to run a proximal consensus algorithm for your problem.
>>> opt = Optimizer('squared_error', x_obs=x_obs) >>> opt.add_regularizer('sparse', gamma=0.1) >>> opt.add_regularizer('nucnorm', gamma=0.5) >>> x_hat = opt.minimize(x_init)
Methods
add_regularizer(proxfun, **kwargs) Add a regularizer from the operators module to the list of objectives clear_regularizers() Clear any added regularizers (only retains the first objective) hyperopt(regularizers, validation_loss, ...) Learn hyperparameters minimize(theta_init[, max_iter, callback, ...]) Minimize a list of objectives using a proximal consensus algorithm update_display(iteration, disp_level[, ...]) Prints information about the optimization procedure to standard output - add_regularizer(proxfun, **kwargs)¶
Add a regularizer from the operators module to the list of objectives
Parameters: proxfun : string or function
If a string, then it must be the name of a corresponding function in the operators module. If a function, then it must apply a proximal update given an initial point x0, momentum parameter rho, and optional arguments given in **kwargs.
**kwargs : keyword arguments
Any optional arguments required for the given function
- clear_regularizers()¶
Clear any added regularizers (only retains the first objective)
- hyperopt(regularizers, validation_loss, theta_init, num_runs, num_iter=50)¶
Learn hyperparameters
Warning
Work in progress
Parameters: regularizers : list
A list of tuples. Each tuple contains four items. The first is a string (the name of the regularizer, which can be anything you want). The second is EITHER a string and the name of a function in the operators module which has three arguments (x0, rho, gamma) OR a custom function that accepts three arguments (x0, rho, and gamma) and applies a proximal operator to the point x0. The final two parameters are floats which define the bounds of the log-transformed search space for the regularization parameters (e.g bounds of -3 and 0 would correspond to searching values from 0.001 to 1)
validation_loss : function
A callback function that takes a single argument, a value for the parameters, and evaluates the error or loss on some held out data.
theta_init : array_like
An array corresponding to the initial parameter values for optimization
num_runs : int
The number of different hyperparameter combinations to search through
Returns: gamma_opt : dict
a dictionary containing the optimal hyperparameters found. Each key is a regularizer with a corresponding function in the operators module, and each value is the learned hyperparameter value
trials : dict
Results object from hyperopt.fmin()
- minimize(theta_init, max_iter=50, callback=None, disp=0, mu=10.0, tau_inc=2.0, tau_dec=2.0, tol=0.001)¶
Minimize a list of objectives using a proximal consensus algorithm
Parameters: theta_init : ndarray
Initial parameter vector (numpy array)
max_iter : int, optional
Maximum number of iterations to run (default: 50)
callback : function, optional
a function that gets called on each iteration with the following arguments: the current parameter value (ndarray), and a dictionary that contains a information about the status of the algorithm
disp : integer, optional
determines how much information to display when running. Ranges from 0 (nothing) to 3 (lots of information)
Returns: theta : ndarray
The parameters found after running the optimization procedure
res : dict
A dictionary containing results and other information about convergence of the algorithm
Other Parameters: mu : int, optional
initial value of the momentum term, larger values take smaller steps (default: 10)
tau_inc : int, optional
increment parameter for the momentum scheduler (default: 2)
tau_dec : int, optional
decrement parameter for the momentum scheduler (default: 2)
tol : float, optional
residual tolerance for assessing convergence. if both the primal and dual residuals are less than this value, then the algorithm has converged
- update_display(iteration, disp_level, col_width=20)¶
Prints information about the optimization procedure to standard output
Parameters: iteration : int
The current iteration. Must either a positive integer or -1, which indicates the end of the algorithm
disp_level : int
An integer which controls how much information to display, ranging from 0 (nothing) to 3 (lots of stuff)
col_width : int
The width of each column in the data table, used if disp_level > 1
proxalgs.operators¶
Proximal operators
Evaluates proximal operators for various functions.
TODO: flesh out details
Notes¶
evaluates expressions of the form:
- proxalgs.operators.bfgs(x0, rho, f, fgrad)¶
Proximal operator for minimizing an arbitrary function using BFGS
Uses the BFGS algorithm to find the proximal update for an arbitrary function, f, whose gradient is known.
Parameters: x0 : array_like
The starting or initial point used in the proximal update step
rho : float
Momentum parameter for the proximal step (larger value -> stays closer to x0)
f : function
The function to use when applying the proximal operator. Must take as input a parameter vector (array_like) and return a real number (floating point value)
df : function
A function that computes the gradient of f with respect to the parameters. Must take as input a parameter vector (array_like) and returns another ndarray of the same size.
Returns: theta : array_like
The parameter vector found after running the proximal update step
- proxalgs.operators.linsys(x0, rho, P, q)¶
Proximal operator for the linear approximation Ax = b
Minimizes the function:
\[f(x) = (1/2)||Ax-b||_2^2 = (1/2)x^TA^TAx - (b^TA)x + b^Tb\]Parameters: x0 : array_like
The starting or initial point used in the proximal update step
rho : float
Momentum parameter for the proximal step (larger value -> stays closer to x0)
P : array_like
The symmetric matrix A^TA, where we are trying to approximate Ax=b
q : array_like
The vector A^Tb, where we are trying to approximate Ax=b
Returns: theta : array_like
The parameter vector found after running the proximal update step
- proxalgs.operators.nonneg(x0, rho)¶
Proximal operator for enforcing non-negativity (indicator function over the set x >= 0)
Parameters: x0 : array_like
The starting or initial point used in the proximal update step
rho : float
Unused parameter
Returns: theta : array_like
The parameter vector found after running the proximal update step
- proxalgs.operators.nucnorm(x0, rho, gamma, mode=None)¶
Proximal operator for the nuclear norm (sum of the singular values of a matrix)
Parameters: x0 : array_like
The starting or initial point used in the proximal update step
rho : float
Momentum parameter for the proximal step (larger value -> stays closer to x0)
gamma : float
A constant that weights how strongly to enforce the constraint
model : int, optional
If None (default), then the input is treated as a numpy array. If an integer, it is treated as a tensor object and the nuclear norm is applied to an unfolding of the tensor (using the array axis given by mode).
Returns: theta : array_like
The parameter vector found after running the proximal update step
- proxalgs.operators.poissreg(x0, rho, x, y)¶
Proximal operator for Poisson regression
Computes the proximal operator of the negative log-likelihood loss assumping a Poisson noise distribution.
Parameters: x0 : array_like
The starting or initial point used in the proximal update step
rho : float
Momentum parameter for the proximal step (larger value -> stays closer to x0)
x : (n, k) array_like
A design matrix consisting of n examples of k-dimensional features (or input).
y : (n,) array_like
A vector containing the responses (outupt) to the n features given in x.
Returns: theta : array_like
The parameter vector found after running the proximal update step
- proxalgs.operators.sfo(x0, rho, optimizer, num_steps=5)¶
Proximal operator for an arbitrary function minimized via the Sum-of-Functions optimizer (SFO)
Parameters: x0 : array_like
The starting or initial point used in the proximal update step
rho : float
Momentum parameter for the proximal step (larger value -> stays closer to x0)
optimizer : SFO instance
Instance of the SFO object in SFO_admm.py
num_steps : int, optional
Number of SFO steps to take
Returns: theta : array_like
The parameter vector found after running num_steps iterations of the SFO optimizer
Notes
SFO is a function optimizer for the case where the target function breaks into a sum over minibatches, or a sum over contributing functions. It is described in more detail in [R1].
References
[R1] (1, 2) Jascha Sohl-Dickstein, Ben Poole, and Surya Ganguli. Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods. International Conference on Machine Learning (2014). arXiv preprint arXiv:1311.2115 (2013).
- proxalgs.operators.sgd(x0, rho, fgrad, alpha)¶
Takes a stochastic gradient step
Parameters: x0 : array_like
The starting or initial point used in the proximal update step
rho : float
Momentum parameter for the proximal step (larger value -> stays closer to x0)
fgrad : function
Computes the gradient of the objective at the given set of parameters
alpha : float
Learning rate parameter
- proxalgs.operators.smooth(x0, rho, gamma, mode=None)¶
Proximal operator for a smoothing function enforced via the discrete laplacian operator
Parameters: x0 : array_like
The starting or initial point used in the proximal update step
rho : float
Momentum parameter for the proximal step (larger value -> stays closer to x0)
gamma : float
A constant that weights how strongly to enforce the constraint
model : int, optional
If None (default), then the input is treated as a numpy array. If an integer, it is treated as a tensor object and the smoothing is applied to the columns of an unfolding of the tensor (using the array axis given by mode).
Returns: theta : array_like
The parameter vector found after running the proximal update step
- proxalgs.operators.sparse(x0, rho, gamma)¶
Proximal operator for the l1 norm (induces sparsity)
Parameters: x0 : array_like
The starting or initial point used in the proximal update step
rho : float
Momentum parameter for the proximal step (larger value -> stays closer to x0)
gamma : float
A constant that weights how strongly to enforce the constraint
Returns: theta : array_like
The parameter vector found after running the proximal update step
- proxalgs.operators.squared_error(x0, rho, x_obs)¶
Proximal operator for the pairwise difference between two matrices (Frobenius norm)
Parameters: x0 : array_like
The starting or initial point used in the proximal update step
rho : float
Momentum parameter for the proximal step (larger value -> stays closer to x0)
x_obs : array_like
The true matrix that we want to approximate. The error between the parameters and this matrix is minimized.
Returns: x0 : array_like
The parameter vector found after running the proximal update step
- proxalgs.operators.tvd(x0, rho, gamma)¶
Proximal operator for the total variation denoising penalty
Requires scikit-image be installed
Parameters: x0 : array_like
The starting or initial point used in the proximal update step
rho : float
Momentum parameter for the proximal step (larger value -> stays closer to x0)
gamma : float
A constant that weights how strongly to enforce the constraint
Returns: theta : array_like
The parameter vector found after running the proximal update step
Raises: ImportError
If scikit-image fails to be imported


public class RayTracingExample extends ApplicationBase
| Modifier and Type | Field and Description |
|---|---|
(package private) static Camera |
camera |
(package private) static Transformation |
cameraTransform |
(package private) static ComputeShader |
comp |
(package private) TransformController |
controller |
(package private) FramerateManager |
frm |
(package private) Input |
input |
(package private) static ShadedGameObject |
renderingQuad |
(package private) static VFShader |
renderQuadShader |
(package private) static Texture2D |
renderTexture |
(package private) Time |
time |
static Window |
window |
| Constructor and Description |
|---|
RayTracingExample() |
| Modifier and Type | Method and Description |
|---|---|
void |
cleanup()
called just before exit, for deallocation of memory.
|
void |
init()
called first, for initialization
|
void |
loop()
this will be the game loop
|
static void |
main(java.lang.String[] args) |
run
public static Window window
static VFShader renderQuadShader
static ShadedGameObject renderingQuad
static Texture2D renderTexture
static ComputeShader comp
static Camera camera
static Transformation cameraTransform
Input input
Time time
FramerateManager frm
TransformController controller
public void init()
ApplicationBase
init
in class ApplicationBase
public void loop()
ApplicationBase
loop
in class ApplicationBase
public void cleanup()
ApplicationBase
cleanup
in class ApplicationBase
public static void main(java.lang.String[] args)


In this chapter, you will use statistical programming tools like R and Python notebooks to analyze large amounts of police data. These are datasets that would either be extremely slow or impossible to open in Excel or Google Docs.


Elie Michal Zwiebel
Democrat
Attorney (Elie Zwiebel, LLC; Colorado Juvenile Defender Center)
University of Denver Sturm College of Law -- JD, 2016 University of Beijing -- Teaching English as a Foreign Language Certification, 2010 University of Michigan -- BA, 2009
80219
As a juvenile civil rights attorney, I have worked in several capacities to collaborate and support community organizers and grassroots organizations. For example, I provided legal research and organizing support for Padres y Jovenes Unidos' efforts to lobby the Denver Public Schools' Board to cancel its contract with the Denver Police Department. In my current role as board member of several organizations and groups, I work to encourage and promote civic engagement as well as wider engagement with education--specifically around individual civil liberties.
To my knowledge, I have not donated to any campaigns or political organizations. I have made donations to several nonprofits (e.g. ACLU of Colorado, Learn Your Rights in the Community, etc.), but I do not think I have given any money to political campaigns, action committees, or organizations.
AUL Denver Board of Directors Learn Your Rights in the Community Board of Directors, Volunteer Colorado Juvenile Defender Center Education First Program Director Colorado Criminal Defense Initiative Board of Directors Words Beyond Bars Board of Directors
Civic engagement is the highest form of democracy and community service. I was born and raised in Colorado and value a truly representative democratic future for our state. I am proud of the direction our state has taken: Moving closer and closer to being a leader in one-person-one-vote and equitable, free-and-fair elections. I want to be a part of the next step in this process--this is a calling, an opportunity, and a privilege.
As an attorney, my job requires the ability to be a neutral assessor of facts and law. This is invaluable in building consensus and bridging differences of opinions. My work also requires high-level listening and communication skills--hearing what others are saying (even when it requires "reading between the lines") and articulately and precisely conveying my positions as well as my understanding of others' positions. Additionally, lawyers typically convey power and consume attention and focus in the room--people often defer to lawyers' expertise; my humbling experience working with community organizers, grassroots organizations, and several nonprofit boards has helped me understand how to effectively step-back and allow space for others to speak, take on leadership, and engage effectively as equal partners in a project.
As previously mentioned, my career as a lawyer requires daily application of logic and reason to all sorts of complex issues. I regularly reconcile detailed factual circumstances with nuanced law so as to resolve issues my clients face. At times, this requires issue-spotting--i.e. identifying problems that my clients may not initially label but may become significant barriers as we move forward. I must also think critically and analytically about how opposing parties, potential partners, or the court will address issues.
Male


Back to Tips
Title Insurance - Where Does Your Dollar Go?
Title Insurance: As a homebuyer, the term is probably familiar - but is
it understood? What is your dollar actually paying for when you purchase
a title policy?
Title Insurers, unlike property or casualty insurance companies, operate
under the theory of risk elimination. Title companies spend a high
percentage of their operating income each year collecting, storing,
maintaining and analyzing official records for information that affects
title to real property. Their technical experts are trained to identify
the rights others may have in your property, such as recorded liens,
legal actions, disputed interests, rights of way or other encumbrances
on your title. Before closing your transaction, the title company will
proceed to clear those encumbrances which you do not wish to assume.
This theory is different from that of most other insurance where, for
example, rates and anticipated losses are based on actuarial studies and
premiums are pooled on the assumption that a certain number of claims
will be made. The distinction is important: title insurance premiums are
paid to identify and eliminate potential risks and claims before they
happen. Medical and casualty insurance premiums, for example, are paid
to insure against an unpredictable future event, knowing that risks
exist and claims will occur. Furthermore, title insurance involves a
one-time premium, paid when you close the real estate transaction, while
property, casualty and medical insurance require regular renewal
premiums.
The goal of title companies is to conduct such a thorough search and
evaluation of public records that no claims will ever arise. Of course,
this is impossible -- we live in an imperfect world, where human error
and changing legal interpretations make 100% risk elimination
impossible. When claims arise, professional claims personnel are
assigned to handle them according to the terms of the title insurance
policy.
As in all competitive business environments, rates vary from company to
company, so you should make comparisons before deciding on a particular
title company. Your real estate professional can help you do this. In
addition, there are many helpful customer services provided by title
companies which you and your real estate professional may find helpful
to your transaction.
The issuance of a title insurance policy is highly labor-intensive. It
is based upon the maintenance of a title plant, or library of title
records, in many cases dating back over a hundred years. Each day,
recorded documents affecting real property and property owners are
posted to these title plants so that when a title search on a particular
parcel is requested, the information is already organized for rapid and
accurate retrieval. This investment in skilled personnel and advanced
data processing represents a major part of the title insurance premium
dollar.
Article by CLTA
Back to Tips


Mdt 2012 Deployment Guide Step by Step 10 of 11
Automating the Deployment Process
- Right-click on the deployment share and select Properties
- Select the “Rules” tab
- In the main window, edit the settings as required. A sample CUSTOMSETTINGS.INI is below but feel free to change it as needed…
| [Settings]
Priority=Default [Default ] _SMSTSORGNAME=Customer SkipBDDWelcome=YES SkipProductKey=YES ProductKey= XXXX-XXXX-XXXXX-XXXXX OverrideProductKey=XXXX-XXXX-XXXXX-XXXXX SkipDeploymentType=YES DeploymentType=NEWCOMPUTER SkipDomainMembership=NO JoinDomain=domain.com DomainAdminDomain=domain.com DomainAdmin=MDTAddComputer DomainAdminPassword=Pa55w0rd MachineObjectOU=ou=example,dc=domain,dc=com OrgName=Customer SkipUserData=YES SkipTaskSequence=NO TaskSequenceID=TASKSEQID SkipPackageDisplay=YES SkipLocaleSelection=NO UILanguage=en-US UserLocale=en-GB KeyboardLocale=0809: 00000809 SystemLocale=en-US SkipTimeZone=NO TimeZoneName=GMT Standard Time TimeZone=085 SkipBitLocker=YES SkipSummary=YES SkipCapture=YES SkipAdminPassword=YES SkipApplications=NO SkipFinalSummary=NO WsusServer=http: //wsus.domain.com Home_page=http: //intranet.domain.com FinishAction=SHUTDOWN |
Sets the Company Name during the deployment
Skips unneeded wizard pages Skips unneeded wizard pages The Windows product key to use (only required if using MAK keys) Sets the deployment type Skip the domain membership screen \ Credentials to use to join computers to the domain / Do not perform a user state migration Do not ask which task sequence to run Defautl to the task sequenceID you created earlier Don’t display a list of possible OS packages to install Ask which regional settings to configure Set the default UI language to English Set the default user locale to GB English Set the default keyboard to UK Ask which time zone the computer is in Set the default time zone to GMT Set the default time zone to GMT (legacy support) Skip the installation of BitLocker Skip the Summary screen before deployment starts Skip the “Build and Capture” options during deployment Don’t ask for a local admin password for new PCs Do not request local admin password during OS deployment Pre-sets the admin password to the value specified Allows you to set the local WSUS server for updates Sets the default IE homepage for all users of new computers Tells the computer to shut down once task sequence is completed |
If you want to specify the Timezone, Locale or other settings during the deployment, simply change the YES to a NO for the relevant option and you’ll be prompted during deployment. These settings are injected automatically during deployment into the unattend.xml so you don’t have to worry about messing around with it usually.


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{% load i18n %}
| Starts | {% blocktrans with occurrence.start|date:_("DATETIME_FORMAT") as start_date %}{{ start_date }}{% endblocktrans %} |
| Ends | {% blocktrans with occurrence.end|date:_("DATETIME_FORMAT") as end_date %}{{ end_date }}{% endblocktrans %} |
| Reoccurs | {{occurrence.event.rule}} |
| Until | {% blocktrans with occurrence.event.end_recurring_period|date:_("DATETIME_FORMAT") as end_date %}{{ end_date }}{% endblocktrans %} |
{{occurrence.description}}
{% endif %}


kafka-cascade is a lightweight library built on top of kafkajs to provide automatic message reprocessing from services utilizing Kafka.
The basic flow of how kafka-cascade operates is as follows:
Basic Usage
To create a kafka-cascade service you need to import the library:
const cascade = require('kafka');
const service = cascade.service(kafka, 'example-topic', 'example-group', serviceCB, successCB, dlqCB);
The kafka
object is created from kafkajs
and is passed into the service to create the consumer and producer required to process messages.
Callbacks
The service callback should have the following signature:
serviceCB(msg, resolve, reject)
msg
is the kafka message that the consumer is listening for. resolve
and reject
are the callbacks to provide message reprocessing.
resolve
takes the msg
as an argument and will call the success callback which can receive the msg
as it's argument.
reject
has the following signature: reject(msg, status = '')
. Reject will either reprocess the message or call the dead letter queue (DLQ) callback which can receive the msg
as it's argument.
Retry Strategies
kafka-cascade supports two different retry strategies that can be set by calling .setDefaultRoute
on the returned service.
Levels: specifies how many times a message should be retried before calling the DLQ callback. Each level can have it's own retry strategy.
- Timeout Limit: specifies how long to wait before retrying the message
- Batch LimitL specifies how many messages the producer should wait for before sending all of the messages to be retried at once
Routes
kafka-cascade supports setting routes on the message retries for different status codes. By calling .setRoute
on the service, kafka-cascade can utilize that route by suppling the status code when calling reject
from the service callback
Example
const cascade = require('kafka');
// create the service callback to simulate some time consumming task
const serviceCB = (msg, resolve, reject) => {
performTimeConsumingTask(msg)
.then(res => resolve(res))
.catch(error => {
if(error === 'timeout') reject(msg, error);
else reject(msg);
});
};
// the service callback will be called with any arguments passed in through the resolve callback
const successCB = (res) => {
console.log('success:', res);
};
// the dead letter queue call back will be called when a message is out of retries
const dlqCB = (msg) => {
console.log('failed:', msg);
}
// create a service with previously established kafka object
const service = await cascade.service(kafka, 'example-topic', 'example-group', serviceCB, successCB, dlqCB);
// establish a default route with a 5 levels and a timeout strategy
await service.setDefaultRoute(5, {timeoutLimit: [500, 1000, 2000, 4000, 8000]});
// establish a 'timeout' route which goes directly to the DLQ
await service.setRoute('timeout', 0);
// connect and start the service
await service.connect();
await service.run();


% include header.html %>
Welcome to the horizon of games
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<% include footer.html %>


@Retention(value=RUNTIME) @Target(value={ANNOTATION_TYPE,METHOD}) @Documented public @interface FollowPath
ReferencePath
.
This annotation can be used as a convenience to let Permazen auto-generate reference path traversal code. A common use case is inverting references, e.g., from a parent back to a child in a one-to-many relationship.
Annotating an abstract method with @FollowPath("some.path")
is just short-hand for providing an implementation
that creates and traverses the reference path "some.path"
.
See ReferencePath
for details on reference paths. The specified reference path is assumed to not
have any target field.
For example, the following declaration:
@FollowPath("^TreeNode:parent^") public abstract NavigableSet<TreeNode> getChildren();is functionally equivalent to, and slightly more efficient than:
public NavigableSet<TreeNode> getChildren() { final JTransaction jtx = this.getTransaction(); final ReferencePath path = jtx.getPermazen().parseReferencePath(this.getClass(), "^TreeNode:parent^", false); return jtx.followReferencePath(path, Collections.singleton(this)); }
Method Return Type
The annotated method should return NavigableSet<T>
, where T
is any super-type of
the type(s) at the other end of the reference path.
However, if firstOnly
is true then the method returns only the first object found (if any), and the method's
return type should instead be Optional
<T>
; this is always appropriate when the reference path
contains only forward traversals of many-to-one relationships.
For example:
@PermazenType public interface TreeNode extends JObject { /** * Get the parent of this node, or null if node is a root. */ TreeNode getParent(); void setParent(TreeNode parent); /** * Get the children of this node. */ @FollowPath("^TreeNode:parent^") NavigableSet<TreeNode> getChildren(); /** * Get the siblings of this node (including this node). */ @FollowPath("parent.^TreeNode:parent^") NavigableSet<TreeNode> getSiblings(); /** * Get the grandparent of this node. */ @FollowPath(value = "parent.parent", firstOnly = true) Optional<TreeNode> getGrandparent(); }
Inverse Paths
Inverse paths may be specified using the combination of inverseOf()
and startingFrom()
properties.
So the following two declarations are functionally equivalent:
/** * Get the children of this node. */ @FollowPath("^TreeNode:parent^") NavigableSet<TreeNode> getChildren(); /** * Get the children of this node. */ @FollowPath(inverseOf = "parent", startingFrom = TreeNode.class) NavigableSet<TreeNode> getChildren();
Meta-Annotations
This annotation may be configured indirectly as a Spring
meta-annotation
when spring-core
is on the classpath.
| Modifier and Type | Optional Element and Description |
|---|---|
boolean |
firstOnly
Whether only the first, if any, of the set of objects should be returned, or the entire set.
|
String |
inverseOf
The reference path to follow in the inverse direction.
|
Class<?> |
startingFrom
The starting model type for the reference path to follow in the inverse direction.
|
String |
value
The reference path to follow in the forward direction.
|
public abstract String value
The starting type for the reference path is the class containing the annotated method.
If this property is used, both of inverseOf()
and startingFrom()
must be left unset.
public abstract String inverseOf
If this property is used, startingFrom()
must also be specified and value()
must be left unset.
public abstract Class<?> startingFrom
When an inverse reference path is specified, the starting type for the reference path is not implied, because the path starts from the "remote end", instead of from the class containing the annotated method. Therefore, it must be explicitly specified via this property.
If this property is used, inverseOf()
must also be specified and value()
must be left unset.
public abstract boolean firstOnly
If this property is true, then the annotated method's return type must be java.util.Optional
<T>
,
or if false, it must be NavigableSet
<T>
, where T
is some super-type of the type(s)
at the remote end of the reference path.
Copyright © 2020. All rights reserved.


Star this game @ GitHub Play again
Either all of your ships ran out of fuel or your station's shield broke down. Let's see what you achieved:
You can code if:
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--- title: Integrating MS-Word in .NET tags: meta: abstract: This article will explain how we can use Microsoft Word in your .NET application. This will enable you to use your favorite word-processor within your .NET application. For some cases, you will find that most of the document that you create is redundant work and only few things need to be filled up. E.g. the Client's ID, Current Date, Record Number, etc. thumbnail: date: 2005-09-30 ---
This article will explain how we can use Microsoft Word in your .NET application. This will enable you to use your favorite word-processor within your .NET application.
Background
I needed a MS-Word control similar to the HTML editor that can become a part of the application. Unfortunately, there was no such way to do that at that time.
While searching at Google, I came across the project which shows the trick of making any window the part of your application.
This article is based on the article posted at http://www.codeproject.com/cs/miscctrl/winwordcontrol.asp. I would HIGHLY RECOMMEND reading this article too.
Just when I was writing this article, I found one more article suggesting this trick at: http://www.codeproject.com/vb/net/ByPassAutomation.asp
Using the code
When we contain a window within our application, we do not want that the window should be allowed to close through its Close (X) button. Similarly, we do not want the Minimize, Maximize and Restore buttons to work.
One way to solve the problem is to hide the title bar itself. This can be done using the SystemInformation class, as shown below
int borderWidth = SystemInformation.Border3DSize.Width; int borderHeight = SystemInformation.Border3DSize.Height; int captionHeight = SystemInformation.CaptionHeight; int statusHeight = SystemInformation.ToolWindowCaptionHeight; // Move the window in the container so that the container borders hide the window's title bar. MoveWindow( wordWnd, -2*borderWidth, -2*borderHeight - captionHeight, this.Bounds.Width + 4*borderWidth, this.Bounds.Height + captionHeight + 4*borderHeight + statusHeight, true);
But what would happen if the user presses "Alt+Spacebar"? This will open up the System Menu for the user, where the user can select Restore, Minimize, Close, etc. We don't even want that to happen. We can disable this System Menu using the Windows API. This is shown below:
// We want to remove the system menu also. // The title bar is not visible, but we want to avoid // accidental minimize, maximize, etc ..by // disabling the system menu (Alt+Space) int hMenu = GetSystemMenu(wordWnd,false); if(hMenu>0) { int menuItemCount = GetMenuItemCount(hMenu); RemoveMenu(hMenu, menuItemCount - 1, MF_REMOVE | MF_BYPOSITION); RemoveMenu(hMenu, menuItemCount - 2, MF_REMOVE | MF_BYPOSITION); RemoveMenu(hMenu, menuItemCount - 3, MF_REMOVE | MF_BYPOSITION); RemoveMenu(hMenu, menuItemCount - 4, MF_REMOVE | MF_BYPOSITION); RemoveMenu(hMenu, menuItemCount - 5, MF_REMOVE | MF_BYPOSITION); RemoveMenu(hMenu, menuItemCount - 6, MF_REMOVE | MF_BYPOSITION); RemoveMenu(hMenu, menuItemCount - 7, MF_REMOVE | MF_BYPOSITION); RemoveMenu(hMenu, menuItemCount - 8, MF_REMOVE | MF_BYPOSITION); DrawMenuBar(wordWnd); }
You may also want to have only some command bars in the MS-Word window. E.g. you may not want the "Menu Bar" (which is also a command bar in MS-Word). This is shown below.
objWinWordControl.document.ActiveWindow.Application.CommandBars["Menu Bar"].Enabled=false;
The "Standard" command bar in MS-Word has the "New" and "Open" buttons also. We wanted to disable those buttons also. We did that as shown below
wd.ActiveWindow.Application.CommandBars["Standard"].Controls[1].Enabled=false; wd.ActiveWindow.Application.CommandBars["Standard"].Controls[2].Enabled=false;
In some cases, you will need to have the "Menu Bar", but not all the sub-menu options. You can disable those options by using the CommandBarPopUp class
Office.CommandBarPopup c; c = (Office.CommandBarPopup)wd.ActiveWindow.Application.CommandBars["Menu Bar"].Controls[0]; c.Controls["Close Window"].Enabled=false;
Attached code
The code attached is part of the larger transcription workflow. It is developed using .NET 1.1 framework and MS-Word 2000.
In most transcription applications, you will have a scenario like "Transcription --> QA1 --> QA2....." In these cases, you will have to give an error-marking facility to the QAs. I have just shown a demo in a very basic manner. Say, when a transcript goes from QA1 to QA2, the errors marked by QA2 should not be shown. Therefore we need to remove the error-marking before displaying the transcript to QA2.
We need to have specific kind of templates for different clients of the transcription division. These templates can be pre-configured and used to create new documents (through the "LoadDocument" method). An example of this is shown through the "Load Document" button.
In most cases, you will require that the part of the document being transcribe is automatically filled up. E.g. the Client's ID, Current Date, Record Number, etc. This kind of information will come from the database, therefore must be automatically filled up. I have accomplished this using "Bookmarks" in the word document. A sample can be seen using the "Sample.dot" file.
You can do much more than what is shown in the attached code. Please find more about Microsoft Word Automation and Windows APIs at MSDN.
Enhancements
While this application just integrates (contains) MS-Word in .NET application, it was quite interesting to do so with other applications also. We did that with an audio player and found that the concept of containing windows was really useful the second time too.
Happy coding!


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Edit page content here. I've put text in a separate div because I don't like it centered. Also, I've chosen about for this page name because I don't think it's a website without an 'About' section.


Josh lagu
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Looking for room in Thung Khru district
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| OR |
Detail of finding a room
| Room type | 2 people |
| Prices | 7501 - 10000 bath |
| Rent periods | 12 months |
| Moving date | 1/10/2016 |
| Domestic animal | Yes |
| Smoke | No |
Detail of finding roommate
| Gender | Male |
| Age | 25 - 35 years |
| Cleanliness | Very clean |
| Party habits | Not at all |
| Habit | Friendly |
Contact


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Total Space -1100 Square Feet
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Painted -Yes
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JavaScript syntax highlighter that generates html that is compatible with pygments styles.
This project is maintained by thlorenz


{% include head.html %} {% include facebook-sdk.html %} {% include coverpage.html %} {% include hero.html %} {% include features.html %} {% include footer.html %}