CrashRpt contains several demo programs intended to show how CrashRpt intercepts critical errors and how it delivers error reports to software vendor:
- WTLDemo application demonstrates how CrashRpt works with WTL-based applications;
- MFCDemo application demonstrates how CrashRpt works with MFC-based applications;
- ConsoleDemo application demonstrates how CrashRpt works with console applications.
WTLDemo application shows how CrashRpt intercepts exceptions in a WTL-based program and how it sends error reports to the software vendor. The application has simple dialog-based GUI that allows to choose what type of exception and in what thread to raise.
To run WTLDemo, open bin
directory and run WTLDemo.exe. You can also compile the demo yourself.
WTLDemo application (see the figure below) is designed to test the most probable crash situations. The Exception type combo box allows to select an exception type. For more information, see About Exceptions and Exception Handling. The Thread combo box allows to select the thread exception should be raised in.
WTLDemo Application Window
The demo does not upload error reports to any server, it just demonstrates how error report delivery works.To make this demo to upload error reports to your own server or to your E-mail address, you need to edit its configuration (see the
WTLDemo.cpp file inside of
/demos/WTLDemo folder) and then recompile the demo.
MFCDemo application shows how CrashRpt intercepts exceptions in an MFC-based program and how it sends error reports to the software vendor. To run MFCDemo, open
bin
directory and run
MFCDemo.exe. You can also compile the demo yourself.
MFCDemo application is shown in the figure below. To see what happens on crash, in the MFCDemo window, open menu File and choose Save. Then enter some file name and press the Save button. The application will terminate with an error message.
The MFCDemo application was created from scratch using the Visual Studio MFC wizard, and then it has been slightly modified to integrate it with CrashRpt. In MFCDemo.cpp file, CMFCDemoApp::Run() method was overridden, and in MFCDemoDoc.cpp, the method CMFCDemoDoc::Serialize() was modified to cause crash.
MFCDemo Application Window
The MFCDemo does not upload error reports to any server, it just demonstrates how error report delivery works.To make this demo to upload error reports to your own server or to your E-mail address, you need to edit its configuration (see the
MFCDemo.cpp file inside of
/demos/MFCDemo folder) and then recompile the demo.
ConsoleDemo application is a console application (presented in the figure below). It generates a SEH exception and shows how CrashRpt intercepts the exception and sends the error report to software support team.
ConsoleDemo Application Window
To run the demo, open
bin directory and run
ConsoleDemo.exe. You can also compile the demo yourself.
This demo does not upload error reports to any server, it just demonstrates how error report delivery works. To make this demo to upload your error reports, you need to edit its configuration (see ConsoleDemo.cpp file inside of /demos/ConsoleDemo folder) and then recompile the demo.
Further reading: Frequently Asked Questions.


Exodus 29:31
And thou shalt take the ram of the consecration, and seethe his flesh in the holy place.


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to test click the button
this the replace the html content
before to change
This is to change the image
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this is the to change to color
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1st edition
YottabyteX is a platform to discuss all there is to Data! This is a forum where the data community(within and outside Thoughtworks) can gather, share and discuss their learnings or practices. It is organized by a passionate group of data practitioners and sponsored by ThoughtWorks.
We love feedback! If you have any suggestions or cribs, feel free to fill out our feedback form. Don't worry, your feedback will remain completely anonymous.
YottabyteX Volunteers
15 Feb
11:00 - 11:15 am
Keynote
We'll talk about concurrency and parallelism with real world examples and the difference between them.
By: Kamal Kishore (Data Practice Head - ThoughtWorks)
11:15 - 12:00 pm
Leading indicators of success through north star KPIs
On our engagement with one of the world’s largest event organisers, we found success in implementing “Lean Analytics Cycle” -: Metrics Hypothesis Experiment Action, to strategise business wins & create a sustainable way to pick metrics that matter.This talk would walk you through this real world case study to help you in understanding the entire cycle in action.
By: Smitha Ganesh (Principal Data Scientist - ThoughtWorks)
12:00 - 12:15 pm
Tea Break
12:15 - 1:00 pm
Scaling Patterns for Real-time Streaming App
This talk is about our experiments and learnings on the way to create scalable streaming architectures. Some of them derive from the understanding of business domain while others on the understanding of streaming and engineering internals.
By: Shakti Garg (Lead Data Engineer - ThoughtWorks), Lalit Pandey (Developer -ThoughtWorks)
1:00 - 1:45 pm
Observability at Scale in Real time
This talk will explore how a universal dashboard can be created which can enable multiple stakeholders like business, developers etc. to explore, validate and control the platform behaviour. We will discuss how to achieve this kind of fine-grained observability with various latency requirements and what are the possible storage patterns for this use case.
By: Balvinder Khurana (Lead Data Engineer -ThoughtWorks), Sarang Vinayak Shinde (Senior Data Engineer - ThoughtWorks)
1:45 - 2:30 pm
Lunch
2:30 - 3:15 pm
Achieving human level accuracy through Deep NLP
This talk walks through all the recent breakthroughs in the deep NLP space and also talk about neural machine translation for a language pair in which we obtained SOTA results by using the most recent developments in the field.
By: Anirudh Gupta (Data Scientist - ThoughtWorks)
3:15 - 4:00 pm
Achieving Data Democratization
This talk explores ideas & characteristics of what 'Data Democracy' is and how do we attempt to achieve it. We touch upon certain tools that help enterprises become 'Data driven'.
By: Krishna Meduri (Lead Data Engineer - ThoughtWorks), Bharat Akkinepalli (Lead Developer - ThoughtWorks)
4:00 - 5:00 pm
Networking / Tea


books
Margaret Atwood is the latest literary dignitary to receive the honour of the 25,000 Peace Prize of the German Book Trade, which was announced on June 13, in Berlin.
books
Margaret Atwood is the latest literary dignitary to receive the honour of the 25,000 Peace Prize of the German Book Trade, which was announced on June 13, in Berlin.
books
Margaret Atwood is the latest literary dignitary to receive the honour of the 25,000 Peace Prize of the German Book Trade, which was announced on June 13, in Berlin.


Equation List
H
2
0
=5
X3
2
x4
2
=12
4
7
3
x9
2
=18
9
2
-5
3
=0
10
1
+7
2
=24
Department of Information Technology


Chapter 8 Simulation 2
In which we figure out some key statistical concepts through simulation and plotting. On the menu we have:
- Central limit theorem
- Sampling distributions
- p-value
- Confidence interval
8.1 Load packages and set plotting theme
library("knitr") # for knitting RMarkdown
library("NHANES") # data set
library("kableExtra") # for making nice tables
library("janitor") # for cleaning column names
library("tidyverse") # for wrangling, plotting, etc.
opts_chunk$set(
comment = "",
results = "hold",
fig.show = "hold"
)
theme_set(
theme_classic() + #set the theme
theme(text = element_text(size = 20)) #set the default text size
)
8.2 The central limit theorem
The Central Limit Theorem (CLT) states that the sample mean of a sufficiently large number of independent and identically distributed (i.i.d.) random variables is approximately normally distributed. The larger the sample, the better the approximation. The theorem is a key (“central”) concept in probability theory because it implies that statistical methods that work for normal distributions can be applicable to many problems involving other types of distributions.
Here are some nice interactive illustrations of the CLT:
8.2.1 Population distribution
Let’s first put the information we need for our population distribution in a data frame.
# the distribution from which we want to sample (aka the heavy metal distribution)
df.population = tibble(
numbers = 1:6,
probability = c(1/3, 0, 1/6, 1/6, 0, 1/3)
)
And then let’s plot it:
# plot the distribution
ggplot(data = df.population,
mapping = aes(x = numbers,
y = probability)) +
geom_bar(stat = "identity",
fill = "lightblue",
color = "black") +
scale_x_continuous(breaks = df.population$numbers,
labels = df.population$numbers,
limits = c(0.1, 6.9)) +
coord_cartesian(expand = F)
Here are the true mean and standard deviation of our population distribution:
# mean and standard deviation (see: https://nzmaths.co.nz/category/glossary/standard-deviation-discrete-random-variable)
df.population %>%
summarize(population_mean = sum(numbers * probability),
population_sd = sqrt(sum(numbers^2 * probability) - population_mean^2)) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| population_mean | population_sd |
|---|---|
| 3.5 | 2.06 |
8.2.2 Distribution of a single sample
Let’s draw a single sample of size \(n = 40\) from the population distribution and plot it:
# make example reproducible
set.seed(1)
# set the sample size
sample_size = 40
# create data frame
df.sample = sample(df.population$numbers,
size = sample_size,
replace = T,
prob = df.population$probability) %>%
enframe(name = "draw", value = "number")
# draw a plot of the sample
ggplot(data = df.sample,
mapping = aes(x = number, y = stat(density))) +
geom_histogram(binwidth = 0.5,
fill = "lightblue",
color = "black") +
scale_x_continuous(breaks = min(df.sample$number):max(df.sample$number)) +
scale_y_continuous(expand = expand_scale(mult = c(0, 0.01)))
Here are the sample mean and standard deviation:
# print out sample mean and standard deviation
df.sample %>%
summarize(sample_mean = mean(number),
sample_sd = sd(number)) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| sample_mean | sample_sd |
|---|---|
| 3.73 | 2.05 |
8.2.3 The sampling distribution
And let’s now create the sampling distribution (making the unrealistic assumption that we know the population distribution).
# make example reproducible
set.seed(1)
# parameters
sample_size = 40 # size of each sample
sample_n = 10000 # number of samples
# define a function that draws samples from a discrete distribution
fun.draw_sample = function(sample_size, distribution){
x = sample(distribution$numbers,
size = sample_size,
replace = T,
prob = distribution$probability)
return(x)
}
# generate many samples
samples = replicate(n = sample_n,
fun.draw_sample(sample_size, df.population))
# set up a data frame with samples
df.sampling_distribution = matrix(samples, ncol = sample_n) %>%
as_tibble() %>%
set_names(str_c(1:ncol(.))) %>%
gather("sample", "number") %>%
mutate(sample = as.numeric(sample)) %>%
group_by(sample) %>%
mutate(draw = 1:n()) %>%
select(sample, draw, number) %>%
ungroup()
Warning: `as_tibble.matrix()` requires a matrix with column names or a `.name_repair` argument. Using compatibility `.name_repair`.
This warning is displayed once per session.
# turn the data frame into long format and calculate the means of each sample
df.sampling_distribution_means = df.sampling_distribution %>%
group_by(sample) %>%
summarize(mean = mean(number)) %>%
ungroup()
And plot it:
# plot a histogram of the means with density overlaid
ggplot(data = df.sampling_distribution_means %>%
sample_frac(size = 1, replace = T),
mapping = aes(x = mean)) +
geom_histogram(aes(y = stat(density)),
binwidth = 0.05,
fill = "lightblue",
color = "black") +
stat_density(bw = 0.1,
size = 2,
geom = "line"
) +
scale_y_continuous(expand = expand_scale(mult = c(0, 0.01)))
That’s the central limit theorem in action! Even though our population distribution was far from normal (and much more heavy-metal like), the means of that distribution are normally distributed.
And here are the mean and standard deviation of the sampling distribution:
# print out sampling distribution mean and standard deviation
df.sampling_distribution_means %>%
summarize(sampling_distribution_mean = mean(mean),
sampling_distribution_sd = sd(mean)) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| sampling_distribution_mean | sampling_distribution_sd |
|---|---|
| 3.5 | 0.33 |
Here is a data frame that I’ve used for illustrating the idea behind how a sampling distribution is constructed from the population distribution.
# data frame for illustration in class
df.sampling_distribution %>%
filter(sample <= 10, draw <= 4) %>%
spread(draw, number) %>%
set_names(c("sample", str_c("draw_", 1:(ncol(.)-1)))) %>%
mutate(sample_mean = rowMeans(.[, -1])) %>%
head(10) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| sample | draw_1 | draw_2 | draw_3 | draw_4 | sample_mean |
|---|---|---|---|---|---|
| 1 | 1 | 6 | 6 | 4 | 4.25 |
| 2 | 3 | 6 | 3 | 6 | 4.50 |
| 3 | 6 | 3 | 6 | 1 | 4.00 |
| 4 | 4 | 6 | 6 | 1 | 4.25 |
| 5 | 1 | 4 | 6 | 3 | 3.50 |
| 6 | 1 | 1 | 6 | 1 | 2.25 |
| 7 | 1 | 6 | 4 | 1 | 3.00 |
| 8 | 1 | 6 | 4 | 6 | 4.25 |
| 9 | 6 | 1 | 6 | 4 | 4.25 |
| 10 | 1 | 1 | 4 | 1 | 1.75 |
<IP_ADDRESS> Bootstrapping a sampling distribution
Of course, in actuality, we never have access to the population distribution. We try to infer characteristics of that distribution (e.g. its mean) from our sample. So using the population distribution to create a sampling distribution is sort of cheating – helpful cheating though since it gives us a sense for the relationship between population, sample, and sampling distribution.
It urns out that we can approximate the sampling distribution only using our actual sample. The idea is to take the sample that we drew, and generate new samples from it by drawing with replacement. Essentially, we are treating our original sample like the population from which we are generating random samples to derive the sampling distribution.
# make example reproducible
set.seed(1)
# how many bootstrapped samples shall we draw?
n_samples = 1000
# generate a new sample from the original one by sampling with replacement
func.bootstrap = function(df){
df %>%
sample_frac(size = 1, replace = T) %>%
summarize(mean = mean(number)) %>%
pull(mean)
}
# data frame with bootstrapped results
df.bootstrap = tibble(
bootstrap = 1:n_samples,
average = replicate(n = n_samples, func.bootstrap(df.sample))
)
Let’s plot the bootstrapped sampling distribution:
# plot the bootstrapped sampling distribution
ggplot(data = df.bootstrap, aes(x = average)) +
geom_histogram(aes(y = stat(density)),
color = "black",
fill = "lightblue",
binwidth = 0.05) +
stat_density(geom = "line",
size = 1.5,
bw = 0.1) +
labs(x = "mean") +
scale_y_continuous(expand = expand_scale(mult = c(0, 0.01)))
And let’s calculate the mean and standard deviation:
# print out sampling distribution mean and standard deviation
df.sampling_distribution_means %>%
summarize(bootstrapped_distribution_mean = mean(mean),
bootstrapped_distribution_sd = sd(mean)) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| bootstrapped_distribution_mean | bootstrapped_distribution_sd |
|---|---|
| 3.5 | 0.33 |
Neat, as we can see, the mean and standard deviation of the bootstrapped sampling distribution are very close to the sampling distribution that we generated from the population distribution.
8.2.4 Exploring the CLT
<IP_ADDRESS> Distribution of height
In order for the CLT to apply, the following have to hold (approximately):
- sufficiently large number of variables that affect the outcome
- the variables are idependent and identically distributed
- the variables contribute additively, and none of the variables affects the outcome much more strongly than the rest
Let’s take a look at a situation where the CLT breaks down. We use survey data collected by the US National Center for Health Statistics (NCHS). The data is from 2009-2012. You can get more information about the NHANES data set by running help(NHANES)
.
Let’s load the data set into our environment first.
df.nhanes = NHANES %>%
clean_names() %>%
distinct(id, .keep_all = T) #drop duplicates
Let’s now plot a density of the distribution of womens’ height
df.plot = df.nhanes %>%
drop_na(height) %>% # remove missing values
filter(
age >= 18, # only look at adults
gender == "female"
)
ggplot(data = df.plot,
mapping = aes(x = height)) +
geom_density(size = 1,
fill = "red",
alpha = 0.5,
kernel = "gaussian",
bw = 2) +
stat_function(fun = "dnorm",
color = "red",
args = list(mean = mean(df.plot$height),
sd = sd(df.plot$height)),
size = 2) +
labs(title = "Women's height") +
coord_cartesian(expand = F, clip = "off")
Women’s height in the NHANES
data set is approximately normally distributed.
df.plot = df.nhanes %>%
drop_na(height) %>% # remove missing values
filter(
age >= 18, # only look at adults
gender == "male"
)
ggplot(data = df.plot,
mapping = aes(x = height)) +
geom_density(size = 1,
fill = "blue",
alpha = 0.5,
kernel = "gaussian",
bw = 2) +
stat_function(fun = "dnorm",
color = "blue",
args = list(mean = mean(df.plot$height),
sd = sd(df.plot$height)),
size = 2) +
labs(title = "Men's height") +
coord_cartesian(expand = F, clip = "off")
The same is true for men’s height.
df.plot = df.nhanes %>%
drop_na(height) %>% # remove missing values
filter(
age >= 18 # only look at adults
)
ggplot(data = df.plot,
mapping = aes(x = height))+
geom_density(size = 1,
fill = "gray50",
alpha = 0.5,
kernel = "gaussian",
bw = 2)+
stat_function(fun = "dnorm",
color = "black",
args = list(mean = mean(df.plot$height),
sd = sd(df.plot$height)),
size = 2)+
labs(title = "Adults' height") +
coord_cartesian(expand = F, clip = "off")
However, adults’ height is not quite normally distributed. Note that the distribution is too flat in the middle.
df.plot = df.nhanes %>%
drop_na(height) %>% # remove missing values
filter(
age >= 18
)
ggplot(data = df.plot, aes(x = height, group = gender, fill = gender))+
geom_density(size = 1,
alpha = 0.5,
kernel = "gaussian",
bw = 2)+
stat_function(fun = "dnorm", color = "blue",
args = df.plot %>%
filter(gender == "male") %>%
summarise(mean = mean(height),
sd = sd(height)) %>%
as.list(),
size = 2)+
stat_function(fun = "dnorm", color = "red",
args = df.plot %>%
filter(gender == "female") %>%
summarise(mean = mean(height),
sd = sd(height)) %>%
as.list(),
size = 2)+
labs(title = "Adults' height (separated by gender)")+
theme(legend.position = c(0.9, 0.8))
The fact that adults’ height overall is not normally distributed is because there is a single factor (gender) that accounts for much of the variation.
<IP_ADDRESS> Testing the limits
How do sample size and the number of samples affect what the sampling distribution looks like? Here are some simulations. Feel free to play around with:
- the population distributions to sample from
- the sample size for each sample
- the number of samples
ggplot(data = tibble(x = c(0, 20)), aes(x = x)) +
stat_function(fun = "dnorm",
args = list(mean = 10,
sd = 5),
size = 1,
color = "red") +
stat_function(fun = "dunif",
args = list(min = 0,
max = 20),
size = 1,
color = "green") +
stat_function(fun = "dexp",
args = list(rate = 0.1),
size = 1,
color = "blue") +
annotate(geom = "text",
label = "normal",
x = 0,
y = .03,
hjust = 0,
color = "red",
size = 6) +
annotate(geom = "text",
label = "uniform",
x = 0,
y = .055,
hjust = 0,
color = "green",
size = 6) +
annotate(geom = "text",
label = "exponential",
x = 0,
y = .105,
hjust = 0,
color = "blue",
size = 6)
# Parameters for the simulation
n_samples = c(10, 100, 1000, 10000)
sample_size = c(5, 10, 25, 100)
distributions = c("normal", "uniform", "exponential")
# take samples (of size n) from specified distribution and calculate the mean
fun.sample_mean = function(n, distribution){
if (distribution == "normal"){
tmp = rnorm(n, mean = 10, sd = 5)
}else if (distribution == "uniform"){
tmp = runif(n, min = 0, max = 20)
}else if (distribution == "exponential"){
tmp = rexp(n, rate = 0.1)
}
return(mean(tmp))
}
df.central_limit = tibble()
for (i in 1:length(n_samples)){
for (j in 1:length(sample_size)){
for (k in 1:length(distributions)){
# calculate sample mean
sample_mean = replicate(n_samples[i],
fun.sample_mean(sample_size[j],
distributions[k]))
df.tmp = tibble(n_samples = n_samples[i],
sample_size = sample_size[j],
distribution = distributions[k],
mean_value = list(sample_mean))
df.central_limit = rbind(df.central_limit, df.tmp)
}
}
}
# transform from list column
df.plot = df.central_limit %>%
unnest() %>%
mutate(sample_size = str_c("sample size = ", sample_size),
sample_size = factor(sample_size,
levels = str_c("sample size = ", c(5, 10, 25, 100))),
n_samples = str_c("n samples = ", n_samples),
distribution = factor(distribution,
levels = c("normal", "uniform", "exponential"))
)
# densities of sample means
ggplot(df.plot, aes(x = mean_value, color = distribution))+
stat_density(geom = "line", position = "identity")+
facet_grid(n_samples ~ sample_size, scales = "free")+
scale_x_continuous(breaks = c(0, 10, 20))+
coord_cartesian(xlim = c(0, 20))+
labs(x = "sample mean")+
theme(
axis.text.x = element_text(size = 10),
axis.text.y = element_text(size = 10),
strip.text.y = element_text(size = 6),
strip.text.x = element_text(size = 8),
legend.position = "bottom",
panel.background = element_rect(color = "black")
)
No matter where we start, as long as we draw samples that are independent and identically distributed, and these samples combine in an additive way, we end up with a normal distribution (note that this takes considerably longer when we start with an exponential distribution – shown in blue – compared to the other population distributions).
8.3 Understanding p-values
The p-value is the probability of finding the observed, or more extreme, results when the null hypothesis (\(H_0\)) is true.
\[ \text{p-value = p(observed or more extreme sample statistic} | H_{0}=\text{true}) \] What we are really interested in is the probability of a hypothesis given the data. However, frequentist statistics doesn’t give us this probability – we’ll get to Bayesian statistics later in the course.
Instead, we define a null hypothesis, construct a sampling distribution that tells us what we would expect the test statistic of interest to look like if the null hypothesis were true. We reject the null hypothesis in case our observed data would be unlikely if the null hypothesis were true.
An intutive way for illustrating (this rather unintuitive procedure) is the permutation test.
8.3.1 Permutation test
Let’s start by generating some random data from two different normal distributions (simulating a possible experiment).
# make example reproducible
set.seed(1)
# generate data from two conditions
df.permutation = tibble(
control = rnorm(25, mean = 5.5, sd = 2),
experimental = rnorm(25, mean = 4.5, sd = 1.5)
) %>%
gather("condition", "performance")
Here is a summary of how each group performed:
df.permutation %>%
group_by(condition) %>%
summarize(mean = mean(performance),
sd = sd(performance)) %>%
gather("statistic", "value", - condition) %>%
spread(condition, value) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| statistic | control | experimental |
|---|---|---|
| mean | 5.84 | 4.55 |
| sd | 1.90 | 1.06 |
Let’s plot the results:
ggplot(data = df.permutation,
mapping = aes(x = condition, y = performance)) +
geom_point(position = position_jitter(height = 0, width = 0.1),
alpha = 0.5) +
stat_summary(fun.data = mean_cl_boot,
geom = "linerange",
size = 1) +
stat_summary(fun.y = "mean",
geom = "point",
shape = 21,
color = "black",
fill = "white",
size = 4) +
scale_y_continuous(breaks = 0:10,
labels = 0:10,
limits = c(0, 10))
We are interested in the difference in the mean performance between the two groups:
# calculate the difference between conditions
difference_actual = df.permutation %>%
group_by(condition) %>%
summarize(mean = mean(performance)) %>%
pull(mean) %>%
diff()
The difference in the mean rating between the control and experimental condition is -1.2889834. Is this difference between conditions statistically significant? What we are asking is: what are the chances that a result like this (or more extreme) could have come about due to chance?
Let’s answer the question using simulation. Here is the main idea: imagine that we were very sloppy in how we recorded the data, and now we don’t remember anymore which participants were in the controld condition and which ones were in experimental condition (we still remember though, that we tested 25 participants in each condition).
set.seed(0)
df.permutation = df.permutation %>%
mutate(permutation = sample(condition)) #randomly assign labels
df.permutation %>%
group_by(permutation) %>%
summarize(mean = mean(performance),
sd = sd(performance)) %>%
ungroup() %>%
summarize(diff = diff(mean))
# A tibble: 1 x 1
diff
<dbl>
1 -0.0223
Here, the difference between the two conditions is 0.0223078.
After randomly shuffling the condition labels, this is how the results would look like:
ggplot(data = df.permutation, aes(x = permutation, y = performance))+
geom_point(aes(color = condition), position = position_jitter(height = 0, width = 0.1)) +
stat_summary(fun.data = mean_cl_boot, geom = 'linerange', size = 1) +
stat_summary(fun.y = "mean", geom = 'point', shape = 21, color = "black", fill = "white", size = 4) +
scale_y_continuous(breaks = 0:10,
labels = 0:10,
limits = c(0, 10))
The idea is now that, similar to bootstrapping above, we can get a sampling distribution of the difference in the means between the two conditions (assuming that the null hypothesis were true), by randomly shuffling the labels and calculating the difference in means (and doing this many times). What we get is a distribution of the differences we would expect, if there was no effect of condition.
set.seed(1)
n_permutations = 500
# permutation function
func_permutations = function(df){
df %>%
mutate(condition = sample(condition)) %>% #we randomly shuffle the condition labels
group_by(condition) %>%
summarize(mean = mean(performance)) %>%
pull(mean) %>%
diff()
}
# data frame with permutation results
df.permutations = tibble(
permutation = 1:n_permutations,
mean_difference = replicate(n = n_permutations, func_permutations(df.permutation))
)
#plot the distribution of the differences
ggplot(data = df.permutations, aes(x = mean_difference)) +
geom_histogram(aes(y = stat(density)),
color = "black",
fill = "lightblue",
binwidth = 0.05) +
stat_density(geom = "line",
size = 1.5,
bw = 0.2) +
geom_vline(xintercept = difference_actual, color = "red", size = 2) +
labs(x = "difference between means") +
scale_x_continuous(breaks = seq(-1.5, 1.5, 0.5),
labels = seq(-1.5, 1.5, 0.5),
limits = c(-2, 2)) +
coord_cartesian(expand = F, clip = "off")
Warning: Removed 2 rows containing missing values (geom_bar).
And we can then simply calculate the p-value by using some basic data wrangling (i.e. finding the proportion of differences that were as or more extreme than the one we observed).
#calculate p-value of our observed result
df.permutations %>%
summarize(p_value = sum(mean_difference <= difference_actual)/n())
# A tibble: 1 x 1
p_value
<dbl>
1 0.006
8.4 Confidence intervals
The definition of the confidence interval is the following:
“If we were to repeat the experiment over and over, then 95% of the time the confidence intervals contain the true mean.”
If we assume normally distributed data (and a large enough sample size), then we can calculate the confidence interval on the estimate of the mean in the following way: \(\overline X \pm Z \frac{s}{\sqrt{n}}\), where \(Z\) equals the value of the standard normal distribution for the desired level of confidence.
For smaller sample sizes, we can use the \(t\)-distribution instead with \(n-1\) degrees of freedom. For larger \(n\) the \(t\)-distribution closely approximates the normal distribution.
So let’s run a a simulation to check whether the definition of the confidence interval seems right. We will use our heavy metal distribution from above, take samples from the distribution, calculate the mean and confidende interval, and check how often the true mean of the population (\(M = 3.5\)) is contained within the confidence interval.
# make example reproducible
set.seed(1)
# parameters
sample_size = 25 # size of each sample
sample_n = 20 # number of samples
confidence_level = 0.95 # desired level of confidence
# define a function that draws samples and calculates means and CIs
fun.confidence = function(sample_size, distribution){
df = tibble(
values = sample(distribution$numbers,
size = sample_size,
replace = T,
prob = distribution$probability)) %>%
summarize(mean = mean(values),
sd = sd(values),
n = n(),
# confidence interval assuming a normal distribution
# error = qnorm(1-(1-confidence_level)*2) * sd / sqrt(n),
# assuming a t-distribution (more conservative, appropriate for smaller
# sample sizes)
error = qt(1-(1-confidence_level)/2, df = n-1) * sd / sqrt(n),
conf_low = mean - error,
conf_high = mean + error)
return(df)
}
# build data frame of confidence intervals
df.confidence = tibble()
for(i in 1:sample_n){
df.tmp = fun.confidence(sample_size, df.population)
df.confidence = df.confidence %>%
bind_rows(df.tmp)
}
# code which CIs contain the true value, and which ones don't
population_mean = 3.5
df.confidence = df.confidence %>%
mutate(sample = 1:n(),
conf_index = ifelse(conf_low > population_mean | conf_high < population_mean,
'outside',
'inside'))
# plot the result
ggplot(data = df.confidence, aes(x = sample, y = mean, color = conf_index))+
geom_hline(yintercept = 3.5, color = "red")+
geom_point()+
geom_linerange(aes(ymin = conf_low, ymax = conf_high))+
coord_flip()+
scale_color_manual(values = c("black", "red"), labels = c("inside", "outside"))+
theme(axis.text.y = element_text(size = 12),
legend.position = "none")
So, out of the 20 samples that we drew the 95% confidence interval of 1 sample did not contain the true mean. That makes sense!
Feel free to play around with the code above. For example, change the sample size, the number of samples, the confidence level.


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by Max Scheinin Read more of Max's reviews at http://www.garfieldlib.com/yanews/july97/max/max.html
I think we should, as responsible citizens, all get together and stop Jim Carrey from making another movie. He's changing the cinema as we know it--and for the worst. I would willingly rewatch Batman And Robin rather then again sit through this miserable collection of filmic moments--prologues, epilogues, etc. The premise: A lawyer can't lie for a day. Ho, ho. Normally a movie made from this premise would be terrible--mr. Carrey suceeds in making it unwatchable. I laughed once--and I was laughing at the sight of everyone else laughing. Grinding doesn't begin to describe.   At best. This movie is intended to make people laugh, yes, not to be voted best picture of all time by Sight And Sound. But, it fails on every possible level. It fails to be funny. It fails to be insightful. It fails to be suspensful. It fails to be fun. They should pay people to see it.
It suceeds on one level--it makes me want to get down on the ground and start thanking Orson Welles, for having lived.
 Yes, literally insane. Stark, raving mad. It is boring, stupid, melodramatic and in the end ugly. I reccemond it strongly. To Ed Wood.
Grade: F
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Reporting is a general problem within the domain of web applications. Usually three Methods of reporting are applied within a web application.
All of the above mentioned methods can be covered by Weblets via separate access methods!
The easiest method, basically an on demand report, is generated on the fly within a single request.
As we can see in Figure 1 Weblets supports this reporting case with its own frontend apis. Weblets always serves a valid URL String to the resource, which then can be processed by the standard java URL api of Java for further processing.
The following picture displays the execution flow for this reporting case:
As displayed, only the calls to the frontend API are utilized. The other parts are pure Java calls!! To enable the reporting capabilities in this case the WebletUtils class can be used with its helper methods.
WebletUtils.getResource(<webletname>, <resource>) WebletUtils.getURL(<webletname>, <resource>)
See also Users Documentation: Servlet Weblets Usage Guide for further descriptions.
In server reporting is a common reporting case. Longer running reports are triggered and executed within the same process space which also hosts the running web application. This is very common in small to medium size web applications, delivering reports which take longer, than tolerable for a single user request.
Also asynchronously periodically generated reports (like static pages of a CMS system or downloadable monthly PDF reports) are usecases for this reporting type. Often it is not needed to run a separate reporting server or running one one would be overkill for the web application installation.
As of Weblets 1.1 support for in server reporting has been added. Currently this feature only works with Packaged Weblets . Additional Weblet implementations might be enabled for this feature in future revisions!
< img src="../images/2-weblets-reporting-3.png" alt="Figure 3. In Server Reporting " />This kind of reporting causes one major problem: The reports run most of the times asynchronously, hence there is no servlet API available, although the container itself serves the servlet API!
Weblets adds support via the addition of a serviceStream method on the Weblet api level. Additionally on the copy provider level the input chain creation method wrapInputStream is provided.
Note if you do not program your own Weblets, only the serviceStream method must be a concern to you.
(See Programmers Documentation: Programming Weblets for further reference on both API calls)
As we said before only the serviceStream method has to be applied internally by the user trying to trigger weblets over asynchronous reporting calls. However, Weblets shields the user from the proper application of the entire internal call chain. As usual, a helper method defined in the WebletUtils can be called to obtain the proper behavior.
Following code example displays how to call apply the Weblets API to asynchronous reporting calls:
InputStream istr = WebletUtils.getResourceAsStream(<weblet name>, <resource path>, mimetype);
Code 1: Resource Streaming via weblets
Note that in Weblets 1.1 the mime types still have to be determined manually for the reporting case! This limitation might be removed for later versions!
The advantage is, you can get a stream which points to the post processed resource and you can do the processing in your report like you would do with any other datasource. The call can be done outside of a regular request it is not dependent on an actual request being present. However Weblets itself must be preinitialized (aka. being started within a web container!)
A dedicated reporting server is a very common use case for big installations or cluster systems. The idea behind it is to outsource the reporting to a dedicated installation only doing reporting. Advantages of such systems are:
Admittably currently Weblets support for this usecase is not very extensive. There are no dedicated helper classes which make things easier for us.
But, since Weblets 1.0 there haven been several constructs in place aiding in the integration of this reporting case, although probably adjustments on the build level are required!
This is arguably the worst solution which can be applied to the above mentioned problem. Most modern reporting engines have neutral datasource constructs which serve data from any given source. By utilizing those APIs we can hook into weblets. The probably easiest way is simply to handcode the resource URLs for the resources hosted on the remote server into our datasource.
This method can be used but is error prone. Every version change of your resource hosting source needs readjustments on the reporting server part! But this solution is the only viable one, if you cannot alter the web configuration of your reporting server, or if your reporting server does not run within a web container!
This solution works, if your reporting server runs within the scope of a dedicated web application and you have write access to the configuration!
To overcome the versioning problem we can use the URL Weblet which has been introduced in Weblets 1.0 as proxying mechanism to our resources. This would overcome the problem of code adjustments. If the source changes its versioning and would push the problem to one central location, which then can be handled via build processes!
What does the URL Weblet do. To sum it up shortly, it is a proxy mechanism which allows to utilize the Weblets frontend API to gather access to remotely hosted resources. For the application programmer utilizing this Weblet, it looks exactly like a local one, only the generated URL patterns redirect to remotely hosted resources!
The advantage of this method is, resources can be outsourced to a dedicated resource serving server, without having to change a single line of code.
(See also Users Documentation: Weblets Packaging Guide for further documentation)
So how does this aid in our problem. Very simple we can use this weblet to point towards anther server delivering our resources. We then can use our frontend APIs to gain access to the correct resources and then use, the IO and URL apis from java to get a proper input stream on the resource!
By application of this solution we have pushed the entire configuration aspects down to one single point of configuration which theoretically could be handled by a centralized build process!
The resource post processing done by Weblets can either be handled by the resource server itself (if it utilizes Weblets for its reporting) or by the URL Weblet proxing into the reporting server!
As we can see neither solution to this problem is ideal, however solution 2 is recommendable over the direct url generation solution. If you can gain access to the web configuration and can add Weblets to your reporting server configuration, use the proxying solution !


INSERT GOOD
Bowl:
New Mexico
Las Vegas
Camellia
Cure
New Orleans
Miami Beach
Boca Raton
Poinsettia
Location:
Albequerque, NM
Las Vegas, NV
Montgomery, AL
Orlando, FL
New Orleans, LA
Miami, FL
Boca Raton, FL
San Diego, CA
Date:Time:
Dec. 1702:00 pm
Dec. 1703:30 pm
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Dec. 1705:30 pm
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Dec. 1902:30 pm
Dec. 2007:00 pm
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Channel:
ESPN
ABC
CBS S
Teams:
Texas - San Antonio
San Diego State
Houston
Appalachian State
Toledo
UCF
Arkansas State
UL Lafayette
Southern Miss
Tulsa
Central Michigan
Western Kentucky
Memphis
BYU
Wyoming
Record:
(6-6)
(8-4)
(10-3)
(9-3)
(7-5)
(8-5)
Winner:
Array
Famous Idaho Potato
Bahamas
Armed Forces
Dollar General
Hawaii
St. Pete
Quick Lane
Independence
Boise, ID
Nassau
Fort Worth, TX
Mobile, AL
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Detroit, MI
Shreveport, LA
Dec. 2207:00 pm
Dec. 2301:00 pm
Dec. 2304:30 pm
Dec. 2308:00 pm
Dec. 2408:00 pm
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ESPN2
Idaho
Colorado State
Old Dominion
Eastern Michigan
Navy
Louisiana Tech
Ohio
Troy
Middle Tennessee
Miami (Ohio)
Mississippi State
Boston College
Maryland
Vanderbilt
NC State
(6-7)
(5-7)
Heart of Dallas
Military
Holiday
Cactus
Pinstripe
Russell Athletic
Foster Farms
Texas
Dallas, TX
Annapolis, MD
Phoenix, AZ
Bronx, NY
Santa Clara, CA
Houston, TX
Dec. 2712:00 pm
Dec. 2703:30 pm
Dec. 2707:00 pm
Dec. 2710:15 pm
Dec. 2802:00 pm
Dec. 2805:30 pm
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Dec. 2809:00 pm
North Texas
Army
Temple
Wake Forest
Minnesota
Washington State
Boise State
Baylor
Pittsburgh
Northwestern
West Virginia
Miami (FL)
Utah
Indiana
Texas A&M
Kansas State
(6-5)
(10-2)
Birmingham
Belk
Alamo
Liberty
Sun
Music City
Arizona
Orange
Birmingham, AL
Charlotte, NC
San Antonio, TX
Memphis, TN
El Pasco, TX
Nashville, TN
Tucson, AZ
Dec. 2902:00 pm
Dec. 2905:30 pm
Dec. 2909:00 pm
Dec. 3012:00 pm
Dec. 3002:00 pm
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CBS
ASN
South Carolina
South Florida
Virginia Tech
Arkansas
Colorado
Oklahoma State
Georgia
TCU
Stanford
North Carolina
Tennessee
Nebraska
South Alabama
Air Force
Michigan
Florida State
(9-4)
Citrus
TaxSlayer
Peach
Fiesta
Outback
Cotton
Rose
Sugar
Jacksonville, FL
Atlanta, GA
Glendale, AZ
Tampa, FL
Arlington, TX
Pasadena, CA
Dec. 3111:00 am
Dec. 3111:15 am
Dec. 3103:00 pm
Dec. 3107:00 pm
Jan. 0201:00 pm
Jan. 0205:00 pm
Jan. 0208:30 pm
Louisville
LSU
Georgia Tech
Kentucky
Alabama
Washington
Clemson
Ohio State
Florida
Iowa
Wisconsin
Western Michigan
Penn State
USC
Oklahoma
Auburn
(7-4)
(13-0)
(12-1)
(11-1)
(11-2)
Tie Breaker:
Instructions:
Pick the winner of each bowl game by clicking the corresponding radio button . Assign each pick a "le


Union govt shifts Telugu studies centre from Mysuru
by Prakash KumarThe Centre of Excellence for Studies in Classical Telugu has been shifted from the Central Institute of Indian Languages (CIIL), Mysuru, to Nellore in Andhra Pradesh, the government told the Lok Sabha.
“The centre of excellence for studies in classical Telugu has been shifted to Andhra Pradesh in view of it being a Telugu-speaking state.
Allocation of budget for it (institution) has also been made,” Human Resources Development Minister Ramesh Pokhriyal Nishank told the House, while replying to a question.
The centre of excellence had been set up in Mysuru, after bifurcation of Andhra Pradesh in 2014.
The HRD minister told the Lok Sabha that the Union government was committed to support and strengthen the Telugu language.
To a question on the steps taken by his ministry to promote studies in Telugu language, the minister said the University Grants Commission (UGC) has approved a centre for classical languages in Telugu in the University of Hyderabad and allocated Rs 1.5 crore to it.
“Aligarh Muslim University, Banaras Hindu University, University of Delhi, University of Hyderabad and the Central University of Andhra Pradesh are offering Telugu language courses,” he added.


@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import config.AppConfig @this(mainContent: testonly.views.html.main, errorFeedback: partials.error_feedback) @()(implicit request : Request[_], lang : Lang, messages : Messages, appConfig : AppConfig) @getHelpForm = { @errorFeedback( form = controllers.ProblemReportForm.emptyForm(service = None), actionUrl=appConfig.externalReportProblemSecureUrl, csrfToken=play.filters.csrf.CSRF.getToken(request).map(_.value), service=None, referrer=None) } @mainContent(title = messages("testPage.title"), getHelpForm = getHelpForm) {


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Sometimes it might be necessary to move a Plone instance to another host. This article describes how do this without almost any downtime.
Read MoreRhodeCode uses Beaker for session management. By default the Beaker backend is set to file storage. Cleaning up those files by time is necessary.
Read MoreInstalling Apple's Xcode 4 can cause some trouble when compiling Zope packages via buildout. This article shows the solution for this problem.
Read MoreOver the past few days I had some time to finish what was planned for so long: integrate prettyPhoto (written by Stéphane Caron) into Plone. Here it is..."
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Our design practice offers a full range of services including brand strategy,interaction and visual design and user experience testing.
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Kanye West Steps Out Dripping In Silver (Photos)
by Oyindamola OlubajoAmerican rapper, Kanye West has showcased his creative side as he steps out painting his entire Body silver for his Opera show, “Mary” which took place at Miami Marine Stadium.
Concise News understands that the premiere of “Mary” comes after West’s first opera “Nebuchadnezzar” debuted in Los Angeles, last month.
Mary was Jesus’s virgin mom, a big deal in Christianity and elsewhere.
West performed with members of the Sunday School collective choir, all of them also covered in silver from head to toe.
Based on a quote Ye included in his description of the opera, it appears this is will portray the Nativity story, in which Mary miraculously became pregnant and gave birth to Jesus in Bethlehem.Judging from the rapper’s silver outfit, he may be playing the role of angels in “Mary”https://twitter.com/DatPiff/status/1203864180526219265?s=20
Meanwhile, the rapper has been in the news since his conversion to Christianity.
He earlier declared that he is now a christian, specifically a converted one and has taken up the role of spreading the gospel.


This is my sincere and serious first attempt to create a blog page and complete some Html and CSS coding.
I have been trying to learn to do this for a long time however kept pushing it back due to one reason or the another.
I would find excuse evertime and blame it on lack of not having enough time in my life or maybe it was somethign else (I would
never know).
One day I woke up and decided enough is enough and to practice what I try and preach my kids hence this decision to
enrol in Dev Academy. It has been a satisying journey so far and I am enjoying being pushed and challenged.
Next on the agenda is Learning Python and Java scripts.
About me
I am a 40 year old father of two kids, a teenage daughter and a school aged son. Life is busy and full of challenges
especially with my dear teenager...haha.
My whole family has been very supportive of my efforts to try and learn something new and embark on this new nd exciting chapter
in my life.
I fully embrace the concept of life begining at 40 and am looking forward to what life brings next.


The Pinscape unit can send and receive IR commands if you have the
necessary extra components attached. To send commands, you need an
IR LED; to receive, you need an IR sensor (TSOP38438 or equivalent).
The transmit and receive capabilities are independent, so you can
attach either or both components. The Build Guide has details on
what you need and how to attach everything to the KL25Z.
You can program the Pinscape device with a list of IR commands
learned from the remote controls for your TV(s) and other devices.
An IR "command" is the signal sent when you push a button on one
of your remotes. Each button generally has a unique IR signal
associated with it. Pinscape can learn these signals and store
them in the command list, and then use the stored commands to
repeat the same commands via its IR transmitter, and to recognize
the commands when they come in on its receiver.
Each entry in the IR Command List has the following elements:
- IR Code: this is a magic number representing a particular
IR command. In most cases, you'll want to obtain these codes
using the "learn" function.
- Learn ():
Click this button to bring up the "learn" dialog, which lets
Pinscape learn a code by reading the IR signal from one of
your remotes through the Pinscape IR receiver.
- Test ():
Click this button to test a code by transmitting it through the
Pinscape IR LED. This lets you confirm that the target device
gets the signal when Pinscape sends the code.
- TV ON ():
Click this to include the IR command in the TV ON sequence when
the system powers up. This lets you turn on your cabinet TV(s)
with IR commands.
- Key ():
Click this to select a keyboard key or joystick button to send
to the PC whenever Pinscape receives the IR code. This lets you
use a remote control to send key presses to the PC.
- Description: You can fill this in with the name of the remote
control button programmed into the slot ("Vizio TV ON", say).
This is purely optional. It's just a memory jogger to help you
keep track of what's programmed where, since the "IR Code" field
is pretty cryptic by itself.
IR Code Numbers
Each IR Code is a magic number that tells the Pinscape firmware how
to read and send a particular remote control code. It's "magic" in
that it's really only meaningful within the Pinscape software. The
number encodes the type of infrared signal protocol the remote uses
(these vary by manufacturer, and there are many) and the data bits
contained in the IR signal.
The best way to determine a code is to use the "learn" function,
which lets Pinscape determine the code for a particular remote button
by reading the signal that your remote sends when you press the
button. You can also simply type in a code if you happen to know the
number, such as by getting a code from another Pinscape user.
But you can't generally use code numbers you might see published in
third-party IR code tables on the Internet, since every table uses its
own numbering system that probably doesn't match the Pincsape format.
Things Pinscape can do with IR
You can use the IR capabilities in several ways:
- Turn on your cabinet TV(s) when the system starts up
- Send key presses to the PC on receiving an IR command
- Transmit IR commands when you push a cabinet button
These are all described in more detail below.
TV ON with IR
The IR transmitter can be used to turn on your cabinet TVs when
the system starts up. This is a less invasive alternative to the
hard-wired TV ON feature (where you have to open the TV's case
and solder wires to your TV's "on" switch). With IR, you don't
have to do any surgery on the TV.
If you haven't already configured the basic TV ON settings, you'll
have to set that up first. Go to the TV ON section in the
settings, enable it, set the delay timer, and set up the input pins.
If you're not using the Pinscape expansion boards, you'll need to
build some external circuitry, which is explained in the Build
Guide. The "Power status input" and "Status latch output" parts
are required. The "Relay output" isn't required if you're only
using IR (but it's also okay to use both IR and the relay; they're
happy to work together or separately).
Next, set up the Pinscape IR transmitter LED and position it so
that your TV can see the commands it emits. See the Build Guide
for instructions on wiring the IR LED. Position the LED so that
it's near to and pointing at the remote control sensor window on
the TV.
All that remains is to program the TV's "ON" command into a slot in
the IR code list. Get out your TV's remote, and program the ON button
into a slot as described above. Click the TV ON icon
(
)
to include the command in the power-up sequence. Pinscape will
now transmit this command through its IR transmitter when the
power comes on, after the delay timer finishes.
Send a key press on receiving an IR command
You can program the Pinscape unit to send a key press to the PC on
receiving an IR command.
For example, suppose you want to be able to adjust the audio volume
on your PC, but you don't have any spare buttons on your cabinet.

You can program Pinscape so that it sends a keyboard "Volume Up" key
to the PC when you press the Up button on the remote, and a keyboard
"Volume Down" when you press Down on the remote.
To use this feature, you'll need to set up the Pinscape IR sensor,
and position it so that it can see IR light from your remote. This
means positioning the sensor outside the cabinet, or through a hole
in the cabinet. You can't place it entirely inside the cabinet, since
IR light from the remote won't penetrate the cabinet walls.
Now go to the IR Command List in the settings. Program in the
remote control button codes, as described above, for the buttons
that you want to turn into keystrokes. For each one, click the
"Key" icon (
)
to select the key to send. Once that's all set up, Pinscape will
"press" the programmed key whenever it receives the corresponding
IR command.
Send an IR command when you press a cabinet button
You can program Pinscape to transmit an IR command whenever
you press a cabinet button.
For example, suppose that you want to be able to adjust your TV's
volume via cabinet buttons. To do this, you can configure buttons
on your cabinet to send Volume Up and Volume Down commands
to the TV via IR.
To make this work, you need to set up the Pinscape IR transmitter LED,
as explained in the Build Guide, and position the LED so that the
TV's remote control sensor can see it. This is the same arrangement
needed for the TV ON feature via IR, so you probably already
set that up.
Now you just need to assign an IR command to each button that you want
to use to send IR signals to the TV. The first step is to program the
IR command codes that you want to send. Program the desired commands
into slots in the IR command list as described above.
Once you have the IR commands programmed, go to the Buttons section in
the settings. Find the row for the button that you want to use to send
an IR command, and click the IR Command icon in the row
(
). Select
the desired IR command from the menu.


The Marvel Cinematic Universe (often referred to as the MCU) is comprised of 20 interconnected movies (with more on the way), organized into three separate "phases," centered on the stories of over 20 different comic book characters (with more on the way) who originate in the pages of Marvel Comics
While other movies have been made by different movie studios based on comic book characters who also originate in Marvel Comics, the MCU has thus far been the most successful attempt at creating a series of interconnected movies that both tell the individual stories of their characters while simultaneously connecting those stories to a larger narrative that expands across multiple movies
The 20+ movies of the MCU are made by the Disney owned company, Marvel Studios. Marvel Studios is led by current president and all around awesome guy, Kevin Feige.


outline
outline: red dashed 5px
:hover { outline: red dashed 5px; }
outline-style
solid
double
dashed
dotted
inset
outset
groove
ridge
outline-offset
outline-offset: 15px
outline-offset: 0
outline-offset: -15px


This section explains how to find and work with the output from your BigJob scripts.
It explains the output using the default scripts and default directory names. Please note that the actual directory names can be changed in your scripts.
Steps to interpreting your output:
1.) cd into your “working directory.” In the examples on this tutorial, this would be the $HOME/agent directory.
2.) The agent directory contains a directory that is named after the Pilot Service. This directory begins with bj-####, where ### is a unique identifier. cd into the bj-#### directory.
3.) If you do a ls in this directory, you should see a number of directories that begin with sj-### where ### is a unique identifier. These sj-directories represent each compute unit that is associated with the pilot-service. For example, if you ran 4 ‘single’ jobs, you will have 4 sj-directories.
4.) Contained within each sj-directory is a stdout.txt (which contains the output of your executable) and stderr.txt (which contains the output of standard error on the command line – it will be blank if there were no errors when running your executable) file. Please note that if you defined different names for the “output” and “error” fields in your compute unit description, then the names stdout and stderr will vary.


Simple step for sending a Mattermost message to specified channel.
Use the advanced settings to override the Mattermost Plugin global configuration to include: endpoint
, channel
and icon
.
Please see the Mattermost Plugin global configuration for more details on the fields.
Usage Example:
mattermostSend "Build Started - ${env.JOB_NAME} ${env.BUILD_NUMBER} (<${env.BUILD_URL}|Open>)"


COLUMBIA PICTURES PRESENTS AN S. SYLVAN SIMON PRODUCTION JACK CARSON IS 'THE GOOD HUMOR MAN" LOLA ALBRIGHT JEAN WALLACE GEORGE REEVES FRANK FERGUSON CHICK COLLINS JACK OVERMAN RICHARD EGAN AND EDDIE PARKER CINEMATOGRAPHY BY LESTER WHITE EDITED BY JEROME THOMS MUSIC BY HEINZ ROEMHELD PRODUCED BY S. SYLVAN SIMON SCREENPLAY BY FRANK TASHLIN BASED ON A STORY BY ROY HUGGINS DIRECTED BY LLOYD BACON
Review by R. L. Strong The Dungeonmaster's Cornucopia of Film
The 1950's were a period of Americana that many of us have embraced and cherished. The innocents and sense of discovery during that era were both frightening and comforting. Many of the films released during that decade reflected both the political and societal changes taking place in the U.S. But there was also escapism and fun to be had. The many shorts featurette staring the Three Stooges (with Shemp Howard), Red Skelton's later films and Howard Hawks' attempts at screwball comedy all managed to soften the blow brought about by the troubling times of the 1950's.
One of those films is the unheralded classic "THE GOOD HUMOR MAN" a slapstick gem that manages to make nostalgic the innocents of the 1930's while taking place in 1950. The story is a simple little caper about a good natured Ice Cream Salesman who is caught up in a scheme to steal money from a payroll.
It what could have been an offshoot of the classic Red Skelton vehicle "The Fuller Brush Man", "The Good Humor Man" manages to both charm and enthrall with it's simple and witty comedy. Jack Carter stars as Biff Jones, a mild mannered milquetoast who is in love with Margie Bellew (Lola Albright). While driving home one night, Biff is confronted by a Bonnie Conroy, a young woman who is being pursued by three hoods. Biff, caught in the middle of the struggle is knocked unconscious by the hoods, giving Bonnie an opportunity to escape.
When Biff is fired from his job, Bonnie reappears asking for his help. Terrified, she pleads with the Good Humor Man to stay the night with her for protection. Biff agrees on the condition that Bonnie buy all of his ice cream. In the morning, Biff wakes to find Bonnie dead in the living room. Convinced that he murdered the girl in his sleep, he calls the police to turn himself in. When the police arrive, Biff is dumbfounded to find that the scene of the crime has been completely clean of all evidence. Situations change when Biff's Ice Cream truck is reported to have been seen at the scene of a robbery of $300,000.00. With Biff the primary suspect, it is up to him, Margie, Her little brother and the Captain Marvel fan club to rescue him from the bad guys and prove his innocents.
"The Good Humor Man" is a marvelously charming and good natured comedy. The slapstick and mayhem in the film is of the Red Skelton, 3 Stooges, Laurel and Hardy school of comedy. Lots of double takes and double entendre litter the plot and are so prolific that they create second pulse for the film. There are a couple of instances of racial humor, but nothing really offensive and is of the time and place the film was made. The climatic chase and battle in the school classrooms is a showstopper. Wire gags, sped up photography and just outright silly situations create a loving tribute to the silent films of Buster Keaton and Max Sennet. The last twenty minutes of the film is so full of situations and comedic stunts as to be almost draining. The filmmakers go all out to place one deadly and hilarious situation after another, creating a sort of Rube Goldbergian series of captures and escapes that is absolutely joyous in both it's complexity and inventiveness.
Written by Frank Tashlin (formerly an Animation Director at Warner Brothers), "The Good Humor Man" moves at a brisk pace. It's 79 minute running time seeming a lot shorter. Using the plot from the thriller "Appointment with Fear" by Roy Higgins, Mr. Tashlin has crafted a grand piece of entertainment.
Director Lloyd Bacon, uses every trick in the book on this moderately budgeted production. The use of wire gags and props is so expert in their timing and execution, that one can't help but be reminded of the classic Laurel and Hardy shorts. The performances by all of the cast are perfectly tuned to the film, with larger than life villains and damsels in distress. The surprising turn here is by future Superman, George Reeves as the villainous Stuart Nagle. Never really appreciated as a character actor, Mr. Reeves sense of timing is perfect, showing him to be an actor of more range than ever given credit for. It's really a shame that his part is so small in this film.
The best turn is from, of course Jack Carson. Jack Carson began his career at RKO studios as an extra. He grew into on of the industries most recognized character actors, working in such diverse films as "The Strawberry Blonde" (with James Cagney) and "Mildred Peirce" (with Joan Crawford in which he received his best notices). Mr. Carson uses his famed double take to new heights here, playing Biff Jones as an innocent man, still caught up in his childhood. That childlike innocents is what makes the character so charmingly funny, much like Red Skelton's character in "The Fuller Brush Man. He is not a buffoon. He is a grown child, an essence that helps define (or defend) the innocents of the time. This is Jack Carson's film. His demeanor and stature (he stood 6 foot 2 inches), combine to make the character both charming and believable.
"The Good Humor Man" is one of the industries most underrated comedies. A charming and enjoyable romp that with give you some really healthy belly laughs, if you're of the mind to succumb to it's simplistic charms.
On a side note, watching the film again recently; I've come to the conclusion that some of today's film makers are more than aware of the talents involved in this film. Such diverse projects as Tim Burton's "Pee Wee's Big Adventure", and any of Jackie Chan's recent vehicles all show an appreciation and love for this type of comedy. Not as drawn out as some of Blake Edwards work (i.e. 'The Pink Panther, The Party), but more in line with the work of Producer S. Sylvan Simon, and the many other talents that endeavored to make comedy something universal.
Unavailable on Home Video presently, "The Good Humor Man" does turn up on television from time to time. It would be well worth your while to seek this film out. It is one of the best comedies of its time.
Owned and Copyright © 1999, 2000 R. L. Strong Nothing in this article may be quoted or re-printed without the expressed written permission of the author.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Agents Module Documentation¶
Occupant¶
Fire¶
-
class
projects.seba.fire.
Fire
(model, pos)[source]¶ This class enables to create fire object on a position. The objects of this class are controlled by one FireControl object.
- Attributes:
- grade: Intensity level of the fire. pos: Fire position.
-
class
projects.seba.fire.
FireControl
(unique_id, model, posInit, expansionRate=0.7, growthRate=0.7)[source]¶ This class enables to create agents that control the fire expansión, representing the emergency threat.
- Attributes:
- fireExpansion: Set of Fire objects belonging to this FireControl. limitFire: Fire objects that are in the limit to make the expansion. expansionRate: Rate of expansion of the threat. growthRate: Value of growth in intensity of the fire.
- Methods:
- createFirePos: Create a Fire object in a given position. getFirePos: Get a Fire object in a position given. expansionFire: Make the expansion of fire limits. growthFire: Make the growth in intensity of the fire. step: Method invoked by the Model scheduler in each step.
-
createFirePos
(pos)[source]¶ - Create a Fire object in a given position.
- Args:
- pos: Position to put the Fire object as (x, y)
Avatar¶
-
class
projects.seba.avatar.
EmergencyAvatar
(unique_id, model, initial_pos, color='red', initial_state='walking')[source]¶ This class enables to create avatars that represent virtual occupants, that is, they are not controlled by the simulation but by an API Rest. This class inherits from the avatar class of SOBA.
- Attributes:
- Those Inherited from the Avatar class of SOBA. alive: Current state of an avatar, live or not. life: Number of remaining life points of the avatar.
- Methods:
- getExitGate: Obtain the optimal way to evacuate the building according to an evacuation strategy. getPosFireFOV: Obtain the positions in the avatar’s field of vision where there is fire. makeEmergencyAction: Method that is invoked when initiating an emergency to make the decision of response.
-
getExitGate
()[source]¶ - Obtain the optimal way to evacuate the building according to an evacuation strategy.
- Return: List of positions (x, y)


Thank you to our sponsors
Space Engineering is a difficult and somewhat expensive game, and we are grateful to our parters and sponsors who share our passion for this developing field.
The University of Sydney
Zero Robotics Australia is proudly sponsored by The University of Sydney.
The only University offering a Space Engineering undergraduate course in Australia - they are committed to investing in the bright minds of the future and developing Australia's long term space capability.


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/Lean.204.20tc.20order.html ---
@Daniel Selsam could you comment on https://github.com/leanprover/lean4/pull/130/? I thought the Lean 4 TC system was avoiding every loop problem.
It might not be a loop problem. We do that in mathlib to put the fast fail typeclasses before the ones with many instances
The name of the branch is dselsam:fix-tc-loop
...
And even with Mario's explanation, I kind of hoped that all the tc subtleties without any mathematical content would be gone in Lean 4.
@Patrick Massot I have been using the old frontend with the old typeclass resolution procedure. The new frontend is still missing a few necessary features, e.g. an equation compiler. The fix is only relevant for using the old procedure.
Thanks!
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


WITNESS LIST Jurisprudence Committee April 23, 2003 - 1:30P HB 227 FOR: Green, Bob (Texas Fathers Alliance), Austin, TX HB 233 FOR: Tindall, Harry (Family Law Section, State Bar), Houston, TX ON: Sampson, John (Self), Austin, TX HB 234 FOR: Lambert, Tim (Texas Home School Coalition), Lubbock, TX Tindall, Harry (Family Law Section, State Bar), Houston, TX ON: Sampson, John (Self), Austin, TX HB 1536 FOR: Gaither, Jr., Harold "Hal" (Self), Quinlan, TX SB 1011 FOR: Hampton, Keith (Texas Criminal Defense Lawyers Association), Austin, TX Morris, Gerry (Texas Criminal Defense Lawyers Assn.), Austin, TX Wischkaemper, Bill (Self), Lubbock, TX AGAINST: Macha, Barry (Wichita County District Attorney), Wichita Falls, TX ON: Van Horn, Jeff (State Prosecuting Attorney), Austin, TX SB 1207 ON: Baron, Steve (Texas Attorney General), Austin, TX SB 1424 ON: Otto, Kingsbury (Texas Department of Protective and Regulatory Services), Austin, TX Sampson, John (Self), Austin, TX Registering, but not testifying: For: Bower, Bruce (Texas Kincare Task Force), Austin, TX SB 1691 Registering, but not testifying: For: Hamlin, Jim Dallas County District Clerk (Self and The County and District Clerk's Association), Dallas, TX Hillier, King (Harris County Commissioners Court), Houston, TX Lemon, W. Clyde (Charles Bacarisse - Harris County District Clerk), Houston, TX On: Wolbrueck, Bonnie Williamson County District Clerk (Texas District Court Alliance), Georgetown, TX SB 1834 ON: Underwood, Olen (Montgomery County Texas), Conroe, TX SCR 22 ON: Aston, LaNell (Texas General Land Office), Austin, TX Enquist, Marshall (General Land Office), Austin, TX Goldsmith, Bob (Self), Austin, TX


---
layout: post
title: 1/14
date: '2013-01-14T22:26:00.001-07:00'
author: Jacob .
tags:
modified_time: '2013-01-14T22:26:18.409-07:00'
blogger_id: tag:blogger.com,1999:blog-6400085873105072120.post-6544546734673615746
blogger_orig_url: http://www.svlark.com/2013/01/114.html
---
I picked up a bunch of stuff we need for the house fix-up list: a light fixture for the kitchen; towel ring for the upstairs bathroom; slides for the kitchen drawer; locking knob for the front door; flat white interior paint.
Audrey did a great job with lessons 50 and 51 tonight. We've finally broken out of the morse code business from the first 50 lessons.
My withholding went up $3900 this year. Can't wait to stop working for a paycheck...


Transport properties of the reference state¶
These files define the radial transport properties of the reference state.
These arrays are calculated in the subroutines radial
and transportProperties
. The output files are written in the
subroutine preCalc
.
anel.TAG
¶
Note
This output is only calculated when an anelastic model is run, that is when
l_anel=.true.
or l_anelastic_liquid=.true.
.
This file contains the radial profiles of the reference state (density, temperature, gravity, etc.).
No. of column
Contents
1
radial level: \(r\)
2
temperature: \(\tilde{T}(r)\)
3
density: \(\tilde{\rho}(r)\)
4
radial derivative of the log of the density: \(\beta={\rm d} \ln\tilde{\rho}/{\rm d} r\)
5
radial derivative of \(\beta\): \({\rm d} \beta/{\rm d} r\)
6
gravity: \(g(r)\)
7
entropy gradient: \({\rm d} s_0/{\rm d} r\)
8
thermal diffusion operator: \(\nabla \cdot (K(r)\tilde{T}(r)\nabla s_0)\)
9
inverse of the Gruneisen parameter :math`1/Gamma`: \((\partial\ln\tilde{\rho}/\partial\ln\tilde{T})_S\)
10
radial derivative of the log of temperature: \(\beta={\rm d} \ln\tilde{T}/{\rm d} r\)
This file can be read using MagicRadial
with the following options:
>>> rad = MagicRadial(field='anel')
>>> # print radius and density
>>> print(rad.radius, rad.rho0)
varCond.TAG
¶
Note
This output is only calculated when the electrical conductivity varies with radius, i.e. when nVarCond /= 0
This file contains the radial profiles of the electrical conductivity, the electrical diffusivity and its radial derivative.
No. of column
Contents
1
radial level: \(r\)
2
electrical conductivity: \(\sigma(r)\)
3
electrical diffusivity: \(\lambda(r)=1/\sigma(r)\)
4
radial derivative of the electrical diffusivity: \({\rm d} \ln\lambda/{\rm d} r\)
This file can be read using MagicRadial
with the following options:
>>> rad = MagicRadial(field='varCond')
>>> print(rad.conduc) # Electrical conductivity
varDiff.TAG
¶
Note
This output is only calculated when the thermal diffusivity varies with radius, i.e. when nVarDiff /= 0
This file contains the radial profiles of the thermal conductivity, the thermal diffusivity and its radial derivative.
No. of column
Contents
1
radial level: \(r\)
2
thermal conductivity: \(K(r)\)
3
thermal diffusivity: \(\kappa(r)=K(r)/\tilde{\rho}(r)\)
4
radial derivative of the electrical diffusivity: \({\rm d} \ln\kappa/{\rm d} r\)
5
Prandtl number: \(Pr(r)=\nu(r)/\kappa(r)\)
This file can be read using MagicRadial
with the following options:
>>> rad = MagicRadial(field='varDiff')
>>> print(rad.kappa) # Thermal diffusivity
varVisc.TAG
¶
Note
This output is only calculated when the kinematic viscosity varies with radius, i.e. when nVarVisc /= 0
This file contains the radial profiles of the dynamic viscosity, the kinematic viscosity and its radial derivative.
No. of column
Contents
1
radial level: \(r\)
2
dynamic viscosity: \(\mu(r)\)
3
kinetmatic viscosity: \(\nu(r)=\mu(r)/\tilde{\rho}(r)\)
4
radial derivative of the kinematic viscosity: \({\rm d} \ln\nu/{\rm d} r\)
5
Prandtl number: \(Pr(r)=\nu(r)/\kappa(r)\)
6
magnetic Prandtl number \(Pm(r)=\nu(r)/\lambda(r)\)
This file can be read using MagicRadial
with the following options:
>>> rad = MagicRadial(field='varVisc')
>>> # print kinematic viscosity and Ekman
>>> print(rad.kinVisc, rad.ekman)
Nonlinear mapping of the Chebyshev grid¶
rNM.TAG
¶
Note
This file is only written when l_newmap=.true..
This file contains the profile of the radial mapping and its derivatives:
No. of column
Contents
1
Grid point index
2
Radius of a grid point
3
First derivative of the mapping at a grid point
4
Second derivative of the mapping at a grid point
5
Third derivative of the mapping at a grid point


Art
work created while at MIT Visible Language Workshop 1983-84.
Stills for a 3-D
computer generated animation "Launch", created at
the Visible Language Workshop department at MIT during the
period 1983-84. I wrote the modeling software, built the
hardware controller to connect the main computer to the 1-inch
Apex video tape recorder, and created the animation. I spent
several months building the entire system. The computer would
run over night for about 8 hours to render and record
frame-by-frame a few seconds of animation.
Stills from a
music video "Color Dance" created at the Visible
Language Workshop at MIT during the period 1983-84. The
dancers pictured are Countney Grey, an MIT student at the
time, and his troupe. I programmed the computer frame buffer
to create colorized video in real-time. I also directed and
edited the video.
Frame from
"Challenge" animation.
Frame from
"Meeting" animation.
Frame from
"Blow" animation. My last frame-by-frame
computer rendered animation. A video captured frame is
combined with computer rendered objects.


{% extends 'fpiweb/base.html' %} {% load bootstrap4 %} {% comment %} CONTEXT VARIABLES ----------------- mode: The mode this template is running in. Defined in view class view_class: The class of the View from_location_form: ExistingLocationForm for from Location. May be None depending on mode to_location_form: MoveToLocationForm. May be None depending on mode confirm_merge_form: ConfirmMergeForm. May be None depending on mode boxes_moved: Number of boxes moved. Only used in MODE_COMPLETE. to_location: Location boxes moved to. Only used in MODE_COMPLETE. errors: Iterable of non-form errors {% endcomment %} {% block title %} Move Pallet {% endblock %} {% block content %}


TABBED(1) TABBED(1) NAME tabbed - simple generic tabbed interface SYNOPSIS tabbed [-d] [-h] [-v] DESCRIPTION tabbed is a simple container for applications which support XEmbed, such as xterm or surf. OPTIONS -d detaches tabbed from the terminal and prints xid to standard out‐ put. Useful for scripting. -h prints usage information to standard error, then exits. -v prints version information to standard error, then exits. USAGE Ctrl-Shift-Return new surf window Ctrl-Shift-h previous tab Ctrl-Shift-l next tab Ctrl-Tab toggles between tabs Ctrl-q closes the tab Ctrl-[0..9] jumps to nth tab SEE ALSO xterm(1), surf(1). BUGS Please report them! tabbed-0.3 TABBED(1)


{% macro displayPitches(pitches) %} {% for pitch in pitches %}
Category: {{pitch.category}}
Pitch Contents: {{pitch.content}}
Comments: {{comment.comment}}
User: {{comment.users.username}}


If NgForm is bound in a component, <form> elements in that component will be
upgraded to use the Angular form system.
Typical Use
Include FORM_DIRECTIVES in the directives section of a View annotation
to use NgForm and its associated controls.
Structure
An Angular form is a collection of Controls in some hierarchy.
Controls can be at the top level or can be organized in ControlGroups
or ControlArrays. This hierarchy is reflected in the form's value, a
JSON object that mirrors the form structure.
Submission
The ngSubmit event signals when the user triggers a form submission.
If
NgForm
is bound in a component,<form>
elements in that component will be upgraded to use the Angular form system.Typical Use
Include
FORM_DIRECTIVES
in thedirectives
section of a View annotation to useNgForm
and its associated controls.Structure
An Angular form is a collection of
Control
s in some hierarchy.Control
s can be at the top level or can be organized inControlGroup
s orControlArray
s. This hierarchy is reflected in the form'svalue
, a JSON object that mirrors the form structure.Submission
The
ngSubmit
event signals when the user triggers a form submission.Example (live demo)
```typescript
({ selector: 'my-app', template:
<div> <p>Submit the form to see the data object Angular builds</p> <h2>NgForm demo</h2> <form #f="ngForm" (ngSubmit)="onSubmit(f.value)"> <h3>Control group: credentials</h3> <div ngControlGroup="credentials"> <p>Login: <input type="text" ngControl="login"></p> <p>Password: <input type="password" ngControl="password"></p> </div> <h3>Control group: person</h3> <div ngControlGroup="person"> <p>First name: <input type="text" ngControl="firstName"></p> <p>Last name: <input type="text" ngControl="lastName"></p> </div> <button type="submit">Submit Form</button> <p>Form data submitted:</p> </form> <pre>{{data}}</pre> </div>
, directives: [CORE_DIRECTIVES, FORM_DIRECTIVES] }) export class App { constructor() {}data: string;
onSubmit(data) { this.data = JSON.stringify(data, null, 2); } } ```


--- layout: post title: "My Week in Review" date: 2020-01-12 16:50:00 +0000 image: https://s3.amazonaws.com/hackedu/2018-01-05-pigeon.jpg imagecredits: https://www.flickr.com/photos/birkancaghan/16612869791 ---
I am finally hitting my groove with the revisions to the manuscript, and it feels great.
HEWN, No. 338 via substack.com
This past week: Berkeley, CA. Upcoming week: Home.
I had lunch with the amazing Rose E.
Icon credits: The Noun Project


2 killed in shooting outside of North Carolina medical center
by Scripps NationalTwo people were killed Monday in a shooting outside of a medical center in Durham, North Carolina, according to WRAL-TV in Raleigh.
Citing sources with the Durham police department, WRAL reported that two people — a man and a woman — were shot and killed outside of the University of North Carolina Medical Center at about 8 a.m.
Police have not given any information about a suspect.
The medical center remains on lockdown.
WRAL reports that Monday morning's shooting was the third in the city in a 24-hour span.


Game maps are Unity Scenes.
1- Create your map in an empty scene. 'Empty' means these scene must not have a Camera object.
IMPORTANT NOTE
MAPS CANNOT HAS NEGATIVE POSITIONS.  If you want to walkable static colliders, just set it layer to 'Ground'..
All collider types are supported
3- After creating your map, click on the EasyMOBA button on Top menu and select Map Exporter.
Export your map. Your exported map will be placed at Resources/Maps/
4- Now let's define this map it on the server side.
Go to ServerData folder. ServerData folders are placed at the server application folder. If your server is a Unity Project (not builded yet) you can find that folder in the same folder with Assets, Project Settings etc.
Enter ServerData/Maps/ folder. You will see a sample maps. Create new one by copying one of them. Name it as you wish.
Now go to that folder, you will see the a .png file. Replace it with your map export (See the step 3)
But don't delete your maps from client project. Client also uses the map exports.
The name of the .png file is not important. Server will read the first .png file in the folder.
Open the MapData.json file. It's setting file of your map. You will add your spawn points, creature spawns, team size, round time etc.
MAP SETTINGS
"mapSize":64, // MapSize is deprecated. The server reads the map size automatically from file name.
"clientLanguageId":55, // All connected client will see this map on the list. clientLanguageId will be the map name. Check Languages section for more information
"clientSceneName":"TeamDeathmatch", // Client scene name is the name of your scene you have created on the client project. Don't forget to add your scene to the build settings->scene list.
"teamsize":2,
// teamsize:0 means Deathmatch. Team spawn points will be readed from spawnPoints array no 0.
teamsize:1 means co-op. Team spawn points will be readed from spawnPoints array no 0.
If the teamsize is higher than 1 means team teathmatch. Team spawn points will be readed from spawnPoints array no [TeamNo]. Don't create a new spawnPoints object in the json file for every team.
"roundCount":8,// round count of the session (seconds)
"roundTime":60,// round time of per round (seconds)
"lobbyTime":2,// Lobby waiting time for other players or preparing (seconds)
"experienceModifier":2, // You know what it means :) Every received exp will be multiplied with this value. (float value, it can be etc 1.5)
"maxPlayers":8, // Maximum players
"minPlayers":4, // Minimum players. If you make it 1, the map will be played as singleplayer. Probably againts creatures. Or Traning ground etc.
After the waiting lobby time, if the minimum players doesn't meet, the session owner can add bots.
"teamData": // Spawn points, add spawnObject {} object for every team. If you face parsing errors, see: https://www.w3schools.com/js/js_json_arrays.asp
"creatureSpawns": // The spawns of creatures.
[
{
"spawnPoint": { "x":20.98, "y":0, "z":20.49}, // The spawn point
"spawnTime":4, // Spawn time
"spawnCount":4, // Max creatures will be spawned by this spawner.
"spawnRange":30, // Range of the spawner
"spawnObject": // Spawn objects. High value of rate will be spawned mostly.
[
{
"clientPrefab":"Skeleton", // The creature Id, check Adding a Creature section for more information
"spawnRate":20
},
{
"clientPrefab":"SkeletonKing",
"spawnRate":1
}
]
}
]


User Contribution
Month Contribution
Month Contribution
15 Kg
Total Amount Recycled
1024
Points collected
Data From "SmartBin"
Weekly Contribution from the nearest bin
| Paper | Can | Glass | Plastic | Cloth | Garden | misc | |
| Current week (kg) | 10 | 5 | 7 | 4 | 6 | 1 | 1 |
| Last Week (kg) | 40 | 14 | 6 | 9 | 4 | 3 | 2 |


An entity component system, as the name implies, manages entities and components. Entities are your game objects, however unlike many other architectures, no actual data is stored in the entity abstraction. An entity is a simple integer ID. The data of the game object is instead separated into components, objects that can be associated with entities. Instead of using game object hierarchies via inheritance (which can get clunky), game objects can be composed by adding different combinations of components onto entities.
When game objects have been created, the entity component architecture provides a simple way of performing logic on them as well, via an abstraction called processors. A processor will subscribe to all entities that have a specific set of components and will get a list of them each update. These components are then guaranteed to be on these entities and the data in them can be modified.
This tutorial will show a simple example to get you started and step through the library interface.
In order to get started with ecs.js you will first need to include the file into your project. This module is not yet node.js or require.js compatible and only a script tag has been tested so far. When the module has been included, the first step is to create an entity manager:
var entityManager = new ECS.EntityManager();
The entity manager is the interface to all functionality in the library. The first thing we need to be able to use this entity manager is a few component objects:
var PositionComponent =
{
position: [0, 0]
};
var PhysicsComponent =
{
velocity: [0, 0],
acceleration: [0, 0]
};
var RenderComponent =
{
vertex_buffer: 0,
index_buffer: 0,
vertex_count: 0,
shader_program: 0
};
entityManager.registerComponent('PositionComponent', PositionComponent);
entityManager.registerComponent('PhysicsComponent', PhysicsComponent);
entityManager.registerComponent('RenderComponent', RenderComponent);
Note that these objects need to be registered with the entity manager; the name you choose is your choice, but no two components can have the same name. These components can be arbitrary objects with data you might want to have on game objects. Remember that you can have any combination of components on an entity, so it is usually a good idea to try to reuse components.
Next up, we need to create a few actual entities:
var entity1 = entityManager.createEntity(['PositionComponent', 'PhysicsComponent', 'RenderComponent']);
var entity2 = entityManager.createEntity(['PositionComponent', 'RenderComponent']);
var entity3 = entityManager.createEntity(['PositionComponent']);
You can specify what components you want initially on the entities in the array parameter, however components can be added and removed from entities at a later point as well (in case, for instance, you don't want an entity to be renderer anymore). Often, we want to initialize the data on these entities. To do so, we first need to access the component we just added to it:
var physics = entityManager.getComponent(entity1, 'PhysicsComponent');
physics.velocity[0] = 5.5f;
physics.acceleration[1] = 1.0f;
Finally, to be able to perform logic on these entities, we need to create a processor class:
function RenderProcessor(entityManager)
{
this.entityManager = entityManager;
}
RenderProcessor.prototype.update = function()
{
var entities = this.entityManager.getEntitiesByProcessor(this);
for (var i = 0; i < entities.length; i++)
{
var entity = entities[i];
var position = this.entityManager.getComponent(entity, 'PositionComponent');
var render = this.entityManager.getComponent(entity, 'RenderComponent');
// render the entity using the data in the position and render component objects.
}
}
entityManager.registerProcessor(new RenderProcessor(entityManager), ['PositionComponent', 'RenderComponent']);
The update method is a required method on processors and it will be called every frame by the entity component framework as long as the following method is called every frame:
entityManager.update();
Every processor has a list of entities associated with them, which is accessed through getEntitiesByProcessor
. These are guaranteed to have at least the components specified in the array sent to registerProcessor
. Hence, we can be guaranteed that the entities we loop through in the update method has the PositionComponent
and the RenderComponent
which we needed to render the entities. Note that in the above case, the RenderProcessor
would not process entity3
, since it does not have a RenderComponent
. It would, however, process entity1
, since it has all the required components.
Through this functionality, we can build extensible and decoupled systems for managing game objects and game logic.


Garbage Collection in CPython
01:45 PM - 02:10 PM on August 15, 2015, Room 701Andrée Monette
- Audience level:
- intermediate
- Watch:
- http://youtu.be/P-8Z0-MhdQs
Description
This talk is an overview of CPython's approach to memory management - how the life cycle of an object progresses, how objects are flagged for collection, and the tools that Python provides for manipulating the garbage collector's behavior. Along the way, we'll explore how to learn about Python language features through experimentation.
Abstract
First, we'll explore what the role of a garbage collector is. We'll touch on the two major types of garbage collectors, tracing vs. reference counting, and convince ourselves that Python uses the latter through code that behaves differently under the two models.
We'll explore how the del statement works, and when it might not have the behavior one expects. (Hint: if objects are deleted when their reference counts hit 0, what happens if you try to delete an object with more than one reference?) In exploring this, we'll encounter __del__ and its ability to provide a window into the behavior of objects when they're being garbage collected.
We'll then look at the gc module, when manual memory management might be required and how it works in Python, and which aspects of Python garbage collection are implementation specific. We'll take a digression into why CPython's garbage collection features led to the development of the global interpreter lock. Along the way, we'll see an interesting interaction between the depth of the call stack and CPython's specific garbage collection implementation, and construct a simple fractal whose maximum size actually elucidates the value of a constant in the CPython source code.
Finally, we'll use the garbage collector's behavior to implement depth-first search, and solve the eight queens problem using the Python finalizer.


--- layout: post status: publish published: true title: Completed coding of recursive crawler author: display_name: vishvendra login: vishvendra email<EMAIL_ADDRESS>url: '' author_login: vishvendra author_email<EMAIL_ADDRESS>wordpress_id: 165 wordpress_url: http://www.vishvendrasingh.com/?p=165 date: '2016-04-19 16:41:26 +0530' date_gmt: '2016-04-19 16:41:26 +0530' categories: - Projects tags: - Common - Crawler comments: [] ---
Completed coding of recursive crawler, it was fun and a lot of hard work, some meditation, and lots of google. I finally did it. My friend Abhijeet asked to make recursive crawler and I was thinking how can I do that. So came up with this idea wo making two lists
1. processed list (All crawled urls are stored here)
2. unprocessed list (All new url are stored here)
Now if a new url exists in any of these lists then skip it and move furthur. Happy crawling guys.....:)
This program do the following thing
Here is the link https://github.com/vishvendrasingh/crawler.git


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/What's.20the.20fastest.20way.20to.20prove.20this.3F.html ---
example : ∀ m n : ℕ, m = m * n → m = 0 ∨ n = 1 := sorry
I would do by_cases m = 0
and eq_of_mul_eq_mul_left
(after mul_one
)
or maybe even by_cases n = 1
and eq_zero_of_mul_eq_self_right
(both of those long theorems are from integral domains https://github.com/leanprover/lean/blob/master/library/init/algebra/ring.lean#L290 )
but they don't apply to natural numbers
oh whoops, you’re right ... rings and all
https://github.com/leanprover/lean/blob/master/library/init/data/nat/lemmas.lean#L332
thanks
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


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