|
2010 / bwmon: Application-level bandwidth monitoring bwmon User Manualbwmon is a per-application bandwith monitoring framework written in Python. It can monitor generated traffic and bandwidth (traffic over time) per process or application (group of processes). bwmon has two different means of monitoring: the Monitor which relies on data gathered from the /proc filesystem and and Pipe which accounts data passed through it. Both monitoring types can be used standalone or be combined through the Aggregator. The Aggregator can group processes to applications and issue notifications if one process/application passes its configured threshold. The MonitorThe Monitor is used for general-purpose traffic and bandwith monitoring. It uses data from the /proc filesystem, especially from /proc/net/ip_conntrack which is provided by the Kernel module ip_conntrack. If this file does not exist, try to run sudo modprobe ip_conntrack Using the MonitorThe Monitor can be used standalone by running sudo python runmonitor.py [--no-lookback] [--bandwidth] [--ignore-local] [--include= When started without arguments, Monitor displays the accumulated traffic since monitoring for the process was started by ip_conntrack. However this timespan can vary between processes and can (currently) not be determined. To display only traffic generated after the start of the Monitor, use the --no-lookback switch. To display bandwith usage instead of accumulated traffic, you can use --bandwidth switch. Monitor will then calculate the bandwidth usage per second. By default Monitor also includes traffic via the loopback interface. By using the --ignore-local switch loopback traffic can be ignored. To select the processes that are monitored by Monitor you can use the --include and --exclude options to pass regular expressions. Both flags can be combined and each can be used multiple times. When --include is used, only processes that match at least one of the "include" regular expressions are monitored. When --exclude is used, processes that match the "exclude" regular expressions are excluded. Exclude overrides include. The PipeThe Pipe opens a local port and connects to a remote host. It them forwards and accounts all traffic passed through it. Using the PipeThe Pipe is started with python runpipe.py port remote-host python runpipe.py local-port remote-host remote-port When started with two arguments, Pipe opens port locally and establishes a connection to remote-host on the same port. When started with three arguments, different local and remote ports can be used. The AggregatorThe aggregator combines multiple instances of Monitor and Pipe to provide a consistant configuration and notification interface. Multiple processes (even with traffic data coming from different monitors) can be grouped to applications. Notification thresholds can then be assigned to applications instead of simple processes. Using the AggregatorThe aggregator is started with python runaggregator.py [--app-config= It then prints one line per application that has used bandwidth during the last interval (1 second). The output can be divided into three columns. The first containins the incoming and the second the bandwidth. The third columns is used to distinguish the processes and contains the commandline (for processes) or the application name (for grouped processes). Specifying MonitorsBy default the Aggregator starts one Monitor and no Pipes. To override this behaviour, the switch --monitor-config can be used to specify a monitor configuration file. The configuration file is expected to have the following format. [Monitor Name] type=monitor-type parameter=value Monitor Name is a description of the monitoring instance and is not processed by the Aggregator. The type can either be "monitor" or pipe. The available parameters depend on the type. Paramters for Monitors:
The parameters correspond to the commandline switches of the standalone monitor. Only one include and exclude regular expression can be given per Monitor instance. Parameters for Pipes
The parameters correspond to the commandline switches of the standalone pipe. A sample configuration can be found in monitors.sample.cfg Grouping ProcessesThe Aggregator can aggregate the bandwidth usage for a group of processes. The grouping can either happen automatically or through a supplied configuration. The switch --auto-group activates automatic grouping based on the basename of the processes. Grouping is tried for all processes without exceptions. If only some processes shall be grouped or different rules shall be applied, a configuration file can be specified with the --app-config switch. The supplied configuration file is expected to have the following format: [Application Name] shortname1=regex shortname2=regex Where Application Name is the name under which the aggregated bandwidth of the matched processes will be shown. Each application section can have multiple rules labeled with a "shortname" each with a regular expression that is matched against the processes' commandline. The shortname is used to describe the rule and is not used by the Aggregator. A sample configuration file can be found in group.sample.cfg. NotificationsThe Aggregator can issue notifications if a process or application exceeds its configured bandwidth threshold. Aggregator does not use default thresholds and does therefor not issue notifications unless explicitly configured. The configuration file is provided via the parameter --notification-config and is expected to have the following format. [Application Name] process_filter=regex notification_threshold=bandwidth in kB/s interval=average timestamp in s notification_command=cmd Application Name is a label that is used to describe the following entry and is not processed by the Aggregator. process_filter specifies a regular expression that is used to limit the scope of the rule. The regular expression is matched against
The rule is only valid for matching processes/groups. Incoming and outgoing bandwidth thresholds are specified in kB/s. Notifications are triggered if the average bandwidth over the given interval (in seconds) exceeds the configured threshold. A triggered notification writes its information to stderr. If a notification_command is configured, the given program is started. Thomas Perl (thp at this domain), jabber<EMAIL_ADDRESS>|
|


| Package | Description |
|---|---|
| odata.msgraph.client.beta.entity | |
| odata.msgraph.client.beta.enums |
| Modifier and Type | Field and Description |
|---|---|
protected ServiceStartType |
Windows10EndpointProtectionConfiguration.xboxServicesAccessoryManagementServiceStartupMode |
protected ServiceStartType |
Windows10EndpointProtectionConfiguration.xboxServicesLiveAuthManagerServiceStartupMode |
protected ServiceStartType |
Windows10EndpointProtectionConfiguration.xboxServicesLiveGameSaveServiceStartupMode |
protected ServiceStartType |
Windows10EndpointProtectionConfiguration.xboxServicesLiveNetworkingServiceStartupMode |
| Modifier and Type | Method and Description |
|---|---|
Optional<ServiceStartType> |
Windows10EndpointProtectionConfiguration.getXboxServicesAccessoryManagementServiceStartupMode()
“This setting determines whether the Accessory management service's start type is
Automatic(2), Manual(3), Disabled(4).
|
Optional<ServiceStartType> |
Windows10EndpointProtectionConfiguration.getXboxServicesLiveAuthManagerServiceStartupMode()
“This setting determines whether Live Auth Manager service's start type is
Automatic(2), Manual(3), Disabled(4).
|
Optional<ServiceStartType> |
Windows10EndpointProtectionConfiguration.getXboxServicesLiveGameSaveServiceStartupMode()
“This setting determines whether Live Game save service's start type is Automatic
(2), Manual(3), Disabled(4).
|
Optional<ServiceStartType> |
Windows10EndpointProtectionConfiguration.getXboxServicesLiveNetworkingServiceStartupMode()
“This setting determines whether Networking service's start type is Automatic(2),
Manual(3), Disabled(4).
|
| Modifier and Type | Method and Description |
|---|---|
Windows10EndpointProtectionConfiguration |
Windows10EndpointProtectionConfiguration.withXboxServicesAccessoryManagementServiceStartupMode(ServiceStartType xboxServicesAccessoryManagementServiceStartupMode)
Returns an immutable copy of
this with just the xboxServicesAccessoryManagementServiceStartupMode field changed. |
Windows10EndpointProtectionConfiguration |
Windows10EndpointProtectionConfiguration.withXboxServicesLiveAuthManagerServiceStartupMode(ServiceStartType xboxServicesLiveAuthManagerServiceStartupMode)
Returns an immutable copy of
this with just the xboxServicesLiveAuthManagerServiceStartupMode field changed. |
Windows10EndpointProtectionConfiguration |
Windows10EndpointProtectionConfiguration.withXboxServicesLiveGameSaveServiceStartupMode(ServiceStartType xboxServicesLiveGameSaveServiceStartupMode)
Returns an immutable copy of
this with just the xboxServicesLiveGameSaveServiceStartupMode field changed. |
Windows10EndpointProtectionConfiguration |
Windows10EndpointProtectionConfiguration.withXboxServicesLiveNetworkingServiceStartupMode(ServiceStartType xboxServicesLiveNetworkingServiceStartupMode)
Returns an immutable copy of
this with just the xboxServicesLiveNetworkingServiceStartupMode field changed. |
Windows10EndpointProtectionConfiguration.Builder |
Windows10EndpointProtectionConfiguration.Builder.xboxServicesAccessoryManagementServiceStartupMode(ServiceStartType xboxServicesAccessoryManagementServiceStartupMode)
“This setting determines whether the Accessory management service's start type is
Automatic(2), Manual(3), Disabled(4).
|
Windows10EndpointProtectionConfiguration.Builder |
Windows10EndpointProtectionConfiguration.Builder.xboxServicesLiveAuthManagerServiceStartupMode(ServiceStartType xboxServicesLiveAuthManagerServiceStartupMode)
“This setting determines whether Live Auth Manager service's start type is
Automatic(2), Manual(3), Disabled(4).
|
Windows10EndpointProtectionConfiguration.Builder |
Windows10EndpointProtectionConfiguration.Builder.xboxServicesLiveGameSaveServiceStartupMode(ServiceStartType xboxServicesLiveGameSaveServiceStartupMode)
“This setting determines whether Live Game save service's start type is Automatic
(2), Manual(3), Disabled(4).
|
Windows10EndpointProtectionConfiguration.Builder |
Windows10EndpointProtectionConfiguration.Builder.xboxServicesLiveNetworkingServiceStartupMode(ServiceStartType xboxServicesLiveNetworkingServiceStartupMode)
“This setting determines whether Networking service's start type is Automatic(2),
Manual(3), Disabled(4).
|
| Modifier and Type | Method and Description |
|---|---|
static ServiceStartType |
ServiceStartType.valueOf(String name)
Returns the enum constant of this type with the specified name.
|
static ServiceStartType[] |
ServiceStartType.values()
Returns an array containing the constants of this enum type, in
the order they are declared.
|
Copyright © 2018–2020. All rights reserved.


In this vignette, we’ll walk through conducting an analysis of variance (ANOVA) test using infer
. ANOVAs are used to analyze differences in group means.
Throughout this vignette, we’ll make use of the gss
dataset supplied by infer
, which contains a sample of data from the General Social Survey. See ?gss
for more information on the variables included and their source. Note that this data (and our examples on it) are for demonstration purposes only, and will not necessarily provide accurate estimates unless weighted properly. For these examples, let’s suppose that this dataset is a representative sample of a population we want to learn about: American adults. The data looks like this:
dplyr::glimpse(gss)
## Rows: 500
## Columns: 11
## $ year <dbl> 2014, 1994, 1998, 1996, 1994, 1996, 1990, 2016, 2000, 1998, 20…
## $ age <dbl> 36, 34, 24, 42, 31, 32, 48, 36, 30, 33, 21, 30, 38, 49, 25, 56…
## $ sex <fct> male, female, male, male, male, female, female, female, female…
## $ college <fct> degree, no degree, degree, no degree, degree, no degree, no de…
## $ partyid <fct> ind, rep, ind, ind, rep, rep, dem, ind, rep, dem, dem, ind, de…
## $ hompop <dbl> 3, 4, 1, 4, 2, 4, 2, 1, 5, 2, 4, 3, 4, 4, 2, 2, 3, 2, 1, 2, 5,…
## $ hours <dbl> 50, 31, 40, 40, 40, 53, 32, 20, 40, 40, 23, 52, 38, 72, 48, 40…
## $ income <ord> $25000 or more, $20000 - 24999, $25000 or more, $25000 or more…
## $ class <fct> middle class, working class, working class, working class, mid…
## $ finrela <fct> below average, below average, below average, above average, ab…
## $ weight <dbl> 0.8960, 1.0825, 0.5501, 1.0864, 1.0825, 1.0864, 1.0627, 0.4785…
To carry out an ANOVA, we’ll examine the association between age and political party affiliation in the United States. The age
variable is a numerical variable measuring the respondents’ age at the time that the survey was taken, and partyid
is a factor variable with unique values ind, rep, dem, other.
This is what the relationship looks like in the observed data:
If there were no relationship, we would expect to see the each of these boxplots lining up along the y-axis. It looks like the average age of democrats and republicans seems to be a bit larger than independent and other American voters. Is this difference just random noise, though?
First, to calculate the observed statistic, we can use specify()
and calculate()
.
# calculate the observed statistic
observed_f_statistic <- gss %>%
specify(age ~ partyid) %>%
hypothesize(null = "independence") %>%
calculate(stat = "F")
The observed \(F\) statistic is 2.4842. Now, we want to compare this statistic to a null distribution, generated under the assumption that age and political party affiliation are not actually related, to get a sense of how likely it would be for us to see this observed statistic if there were actually no association between the two variables.
We can generate
an approximation of the null distribution using randomization. The randomization approach permutes the response and explanatory variables, so that each person’s party affiliation is matched up with a random age from the sample in order to break up any association between the two.
# generate the null distribution using randomization
null_dist <- gss %>%
specify(age ~ partyid) %>%
hypothesize(null = "independence") %>%
generate(reps = 1000, type = "permute") %>%
calculate(stat = "F")
Note that, in the line specify(age ~ partyid)
above, we could use the equivalent syntax specify(response = age, explanatory = partyid)
.
To get a sense for what this distribution looks like, and where our observed statistic falls, we can use visualize()
:
# visualize the null distribution and test statistic!
null_dist %>%
visualize() +
shade_p_value(observed_f_statistic,
direction = "greater")
We could also visualize the observed statistic against the theoretical null distribution. To do so, use the assume()
verb to define a theoretical null distribution and then pass it to visualize()
like a null distribution outputted from generate()
and calculate()
.
# visualize the theoretical null distribution and test statistic!
null_dist_theory <- gss %>%
specify(age ~ partyid) %>%
assume(distribution = "F")
visualize(null_dist_theory) +
shade_p_value(observed_f_statistic,
direction = "greater")
To visualize both the randomization-based and theoretical null distributions to get a sense of how the two relate, we can pipe the randomization-based null distribution into visualize()
, and then further provide method = "both"
to visualize()
.
# visualize both null distributions and the test statistic!
null_dist %>%
visualize(method = "both") +
shade_p_value(observed_f_statistic,
direction = "greater")
Either way, it looks like our observed test statistic would be quite unlikely if there were actually no association between age and political party affiliation. More exactly, we can approximate the p-value from the randomization-based approximation to the null distribution:
# calculate the p value from the observed statistic and null distribution
p_value <- null_dist %>%
get_p_value(obs_stat = observed_f_statistic,
direction = "greater")
p_value
## # A tibble: 1 × 1
## p_value
## <dbl>
## 1 0.055
Thus, if there were really no relationship between age and political party affiliation, our approximation of the probability that we would see a statistic as or more extreme than 2.4842 is approximately 0.055.
To calculate the p-value using the true \(F\) distribution, we can use the pf
function from base R. This function allows us to situate the test statistic we calculated previously in the \(F\) distribution with the appropriate degrees of freedom.
pf(observed_f_statistic$stat, 3, 496, lower.tail = FALSE)
## [1] 0.06006
Note that, while the observed statistic stays the same, the resulting p-value differs slightly between these two approaches since the randomization-based empirical \(F\) distribution is an approximation of the true \(F\) distribution.
The package currently does not supply a wrapper for tidy ANOVA tests.


An implementation of the original Webhookr by Matt W. completely re-written in Go
Test HTTP/S callbacks quickly by creating new Webhookr and pointing your callback to it.
Webhookr.go collects no data, ever.


What is Code?
Coding allows us to create things such as computer software, websites, apps and video games. There are various types of different code, depending on what you want to develop. And different “programming languages” that each have their own set of rules. But basically, coding is giving instructions to a computer in order to produce a desired outcome.
But why is it important to learn how to code?
It increases problem-solving skills, job opportunities, critical thinking, and creativity, there are so many reasons to learn programming. Understanding computers and learning the basics of coding helps children to develop an appreciation of how things work. It also teaches them how software engineers use math in order to solve problems in a logical and creative way. This is an important reason that coding should be taught in schools.
Coding teaches you how to think
“ A computer is a bicycle for your mind” – Steve Jobs
Learning to code teaches children how to think. Computer programming isn’t just about teaching how to type lines of code.
It is more about teaching children how to think differently. Being able to code effectively, a programmer needs to use logical thinking.
They need to be able to see a large problem and break it down into smaller pieces in order to solve it in an effective manner.
This is called decomposition and is one of the key features of computational thinking.


- Game - Class in pak27
-
The game of patience main class
- Game() - Constructor for class pak27.Game
-
- getAsChar() - Method in enum pak27.playingCards.Suit
-
Getsthe Suit as a single char.
- getAsChar() - Method in enum pak27.playingCards.Value
-
Returns the value represented by a single char.
- getBoard() - Method in class pak27.scoreKeeping.ScoreBoard
-
Gets the scoreboard as an array of Scores.
- getByNum(int) - Static method in enum pak27.playingCards.Value
-
Returns a Value from the provided num.
- getCards() - Method in class pak27.playingCards.Stack
-
Getter for the cards in this stack.
- getCode() - Method in enum pak27.util.ANSIColour
-
Gets the string that needs to be inserted so the text is coloured.
- getColour() - Method in enum pak27.playingCards.Suit
-
Gets the colour of the cards that have this suit.
- getColour() - Method in class pak27.scoreKeeping.Score
-
Gets colour.
- getColoured(String, ANSIColour) - Static method in class pak27.util.Util
-
Returns the input text surrounded by the appropriete ANSI codes to have it be the colour.
- getImg() - Method in class pak27.playingCards.Card
-
Gets img
- getName() - Method in class pak27.scoreKeeping.Score
-
Gets name.
- getNum() - Method in enum pak27.playingCards.Value
-
Gets the Value of a card as a number.
- getScore() - Method in class pak27.scoreKeeping.Score
-
Gets score.
- getSuit() - Method in class pak27.playingCards.Card
-
Gets the suit of the card
- getUseColor() - Static method in class pak27.util.Util
-
Gets the value of the useColor value.
- getValue() - Method in class pak27.playingCards.Card
-
Gets the value of the card
- getWorst() - Method in class pak27.scoreKeeping.ScoreBoard
-
Gives the last score on the board (so the worst one).


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IoT LED Horticulture Lighting System.
By
Rohit Bhardwaj
Mayur Hadole
Kunal Malhotra


For launching Solar System Model managed with ASTRA press the button below. Loading textures and objects may take some time, depending on your connection speed.
Launch AstraCommand terminal is the access to all functions and methods of Astra system. Next you will find a brief introduction to the usage of terminal command of Astra Solaris main model.
Move and controll the camera of the scene
goto <object>
goto Earth
jump to <object>
jump to Earth
target <object>
target Earth
around
-around
approach
away
zoom
-zoom
Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Aenean commodo ligula eget dolor. Aenean massa.
hide <object>
hide Earth atmosphere
show <object>
show Earth atmosphere


London's best mate
Avoid the typical British weather. Dubbledecker gives you access to London's entire bus network - No more waiting around.
The entire London bus network. Real time.
Gain access to live bus arrival times and use your iPhone's GPS technology to find nearby bus stops.
Your dubbledecker
Save your favourite stops to dubbledecker's home screen for immediate access.
Download the App
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Contact Us
Press Kit
© 2014 Kish Patel. All rights reserved. iPhone is a trademark of Apple Inc. App Store is a service mark of Apple inc.
Designed and built in London by
Kish Patel
.


This package implements small helper functions usefull in infectious disease modelling and epidemics analysis.
The main features of the package include:
gamma_shapescale2mucv
: convert shape and scale of a Gamma distribution to mean and CV
gamma_mucv2shapescale
: convert mean and CV of a Gamma distribution to shape and scale
gamma_log_likelihood
: Gamma log-likelihood using mean and CV
r2R0
: convert growth rate into a reproduction number
lm2R0_sample
: generates a distribution of R0 from a log-incidence linear model
fit_disc_gamma
: fits a discretised Gamma distribution to data (typically useful for describing delays)
clean_labels
: generate portable labels by removing non-standard characters or replacing them with their closest alphanumeric matches, standardising separators, etc.
hash_names
: generate unique, anonymised, reproducible labels from various data fields (e.g. First name, Last name, Date of birth).
emperical_incubation_dist()
will estimate the empirical incubation distribution if given a data frame with dates of onset and a range of exposure dates.
fit_gamma_incubation_dist()
wraps empirical_incubation_dist()
and fit_disc_gamma()
to fit a discretized gamma distribution to an empirical incubation distribution
In this example, we simulate data which replicate the serial interval (SI), i.e. the delays between primary and secondary symptom onsets, in Ebola Virus Disease (EVD). We start by converting previously estimates of the mean and standard deviation of the SI (WHO Ebola Response Team (2014) NEJM 371:1481–1495) to the parameters of a Gamma distribution:
library(epitrix)
mu <- 15.3 # mean in days days
sigma <- 9.3 # standard deviation in days
cv <- sigma/mu # coefficient of variation
cv
## [1] 0.6078431
## $shape
## [1] 2.706556
##
## $scale
## [1] 5.652941
The shape and scale are parameters of a Gamma distribution we can use to generate delays. However, delays are typically reported per days, which implies a discretisation (from continuous time to discrete numbers). We use the package distcrete to achieve this discretisation. It generates a list of functions, including one to simulate data ($r
), which we use to simulate 500 delays:
si <- distcrete::distcrete("gamma", interval = 1,
shape = param$shape,
scale = param$scale, w = 0)
si
## A discrete distribution
## name: gamma
## parameters:
## shape: 2.70655567117586
## scale: 5.65294117647059
## [1] 8 10 15 28 7 27 32 17 16 4
hist(x, col = "grey", border = "white",
xlab = "Days between primary and secondary onset",
main = "Simulated serial intervals")
x
contains simulated data, for illustrative purpose. In practice, one would use real data from an ongoing outbreaks. Now we use fit_disc_gamma
to estimate the parameters of a dicretised Gamma distribution from the data:
## $mu
## [1] 15.21914
##
## $cv
## [1] 0.5851581
##
## $sd
## [1] 8.905604
##
## $ll
## [1] -1741.393
##
## $converged
## [1] TRUE
##
## $distribution
## A discrete distribution
## name: gamma
## parameters:
## shape: 2.92047557759351
## scale: 5.21118646429829
The package incidence can fit a log-linear model to incidence curves (function fit
), which produces a growth rate (r). This growth rate can in turn be translated into a basic reproduction number (R0) using r2R0
. We illustrate this using simulated Ebola data from the outbreaks package, and using the serial interval from the previous example:
## <incidence object>
## [5888 cases from days 2014-04-07 to 2015-04-30]
##
## $counts: matrix with 389 rows and 1 columns
## $n: 5888 cases in total
## $dates: 389 dates marking the left-side of bins
## $interval: 1 day
## $timespan: 389 days
## $cumulative: FALSE
## Warning in fit(i[1:150]): 22 dates with incidence of 0 ignored for fitting
## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang
## [1] 1.348624
## 2.5 % 97.5 %
## [1,] 1.314055 1.383674
In addition, we can also use the function lm2R0_sample
to generate samples of R0 values compatible with a model fit:
## [1] 1.350970 1.347374 1.350076 1.358523 1.341549 1.341634
If you want to use labels that will work across different computers, independent of local encoding and operating systems, clean_labels
will make your life easier. The function transforms character strings by replacing diacritic symbols with their closest alphanumeric matches, setting all characters to lower case, and replacing various separators with a single, consistent one.
For instance:
## [1] " Thîs- is A wêïrD LäBeL .."
## [1] "this_is_a_weird_label"
variables <- c("Date.of.ONSET ",
"/ date of hôspitalisation /",
"-DäTÈ--OF___DîSCHARGE-",
"GEndèr/",
" Location. ")
variables
## [1] "Date.of.ONSET " "/ date of hôspitalisation /"
## [3] "-DäTÈ--OF___DîSCHARGE-" "GEndèr/"
## [5] " Location. "
## [1] "date_of_onset" "date_of_hospitalisation"
## [3] "date_of_discharge" "gender"
## [5] "location"
hash_names
can be used to generate hashed labels from linelist data. Based on pre-defined fields, it will generate anonymous labels. This system has the following desirable features:
given the same input, the output will always be the same, so this encoding system generates labels which can be used by different people and organisations
given different inputs, the output will always be different; even minor differences in input will result in entirely different outputs
given an output, it is very hard to infer the input (it requires hacking skills); if security is challenged, the hashing algorithm can be ‘salted’ to strengthen security
first_name <- c("Jane", "Joe", "Raoul", "Raoul")
last_name <- c("Doe", "Smith", "Dupont", "Dupond")
age <- c(25, 69, 36, 36)
## detailed output by default
hash_names(first_name, last_name, age)
## label hash_short
## 1 jane_doe_25 6485f2
## 2 joe_smith_69 ea1ccc
## 3 raoul_dupont_36 f60676
## 4 raoul_dupond_36 cd7104
## hash
## 1 6485f29654c5a9d55625cd6efeb96d569917e1c272790959ad3fa132c6d51648
## 2 ea1cccce320aa45a0d694ea12c30ff6b4b52c67f69d58b23dad5441ea17c5807
## 3 f60676d1c11ae5badc0e5ec4dfde06eaba817a78f3d54eb327a25df485ec1efd
## 4 cd7104e7e7009bfd988d5a4b46a930424908736065573e51a85d16575ed7c2a5
## [1] "6485f296" "ea1cccce" "f60676d1" "cd7104e7"
The function empirical_incubation_dist()
computes the discrete probability distribution by giving equal weight to each patient. Thus, in the case of N
patients, the n
possible exposure dates of a given patient get the overall weight 1/(n*N)
. The function returns a data frame with column incubation_period
containing the different incubation periods with a time step of one day and their relative_frequency
.
Load environment:
Make a linelist object containing toy data with several possible exposure dates for each case:
ll <- messy_data() %>% clean_data()
x <- 0:15
y <- distcrete("gamma", 1, shape = 12, rate = 3, w = 0)$d(x)
mkexposures <- function(i) i - sample(x, size = sample.int(5, size = 1), replace = FALSE, prob = y)
exposures <- sapply(ll$date_of_onset, mkexposures)
ll$dates_exposure <- exposures
print(ll)
## id date_of_onset discharge gender epi_case_definition messy_dates
## 1 zhugcj 2018-01-08 2018-01-18 male not_a_case 1989-12-24
## 2 aeedki 2018-01-10 2018-01-20 female suspected 2018-10-17
## 3 bjbblf 2018-01-07 2018-01-17 female not_a_case 1989-12-24
## 4 nzigse 2018-01-11 2018-01-21 male confirmed 2018-10-17
## 5 qivlvy 2018-01-09 2018-01-19 male probable 2018-10-19
## 6 mlchbw 2018-01-06 2018-01-16 female suspected 2001-12-01
## 7 zvipzg 2018-01-10 2018-01-20 female suspected 2001-12-01
## 8 ewggll 2018-01-07 2018-01-17 female probable 2018-10-19
## 9 zbttys 2018-01-07 2018-01-17 female not_a_case 2018-10-19
## 10 ithycv 2018-01-09 2018-01-19 female probable 2018-10-19
## 11 uupgyj 2018-01-06 2018-01-16 female probable 1989-12-24
## 12 szcymp 2018-01-09 2018-01-19 male suspected 2001-12-01
## 13 snsjbp 2018-01-04 2018-01-14 male suspected <NA>
## 14 ksxcgv 2018-01-09 2018-01-19 female not_a_case 2018-10-19
## 15 wxlhwm 2018-01-06 2018-01-16 male confirmed 2001-12-01
## 16 turjbi 2018-01-04 2018-01-14 female not_a_case <NA>
## 17 sljxly 2018-01-04 2018-01-14 female not_a_case <NA>
## 18 wfhwmk 2018-01-06 2018-01-16 female probable 2018-10-17
## 19 mbhlsv 2018-01-04 2018-01-14 female suspected 2018-10-19
## 20 wtcduu 2018-01-10 2018-01-20 female suspected <NA>
## lat lon dates_exposure
## 1 10.416562 48.68798 17538, 17533, 17537
## 2 14.819341 48.59420 17538
## 3 10.784489 47.72968 17534
## 4 12.231752 49.55408 17540, 17537, 17539, 17538
## 5 13.165470 47.48926 17536, 17538
## 6 12.032235 47.70816 17532, 17534
## 7 8.830518 49.10144 17539, 17538, 17537, 17536
## 8 15.335174 48.45867 17534, 17533, 17535, 17536, 17537
## 9 12.846348 48.85191 17534, 17532, 17531, 17535
## 10 14.060843 49.46620 17535, 17537, 17536, 17538, 17534
## 11 13.009818 48.24386 17535
## 12 11.939537 48.64942 17537
## 13 16.029733 47.35888 17531, 17532, 17530
## 14 14.630919 48.46446 17537, 17536
## 15 9.986956 47.69080 17532, 17535, 17534, 17533, 17536
## 16 10.685640 49.27602 17531
## 17 15.603110 47.95239 17532, 17531, 17529
## 18 11.188718 48.03010 17531, 17532
## 19 13.031127 47.99642 17531, 17530, 17533, 17532
## 20 10.143990 45.96543 17538, 17537, 17539, 17540, 17536
Empirical distribution:
incubation_period_dist <- empirical_incubation_dist(ll, date_of_onset, dates_exposure)
print(incubation_period_dist)
## # A tibble: 8 x 2
## incubation_period relative_frequency
## <dbl> <dbl>
## 1 0 0
## 2 1 0.0467
## 3 2 0.169
## 4 3 0.273
## 5 4 0.273
## 6 5 0.144
## 7 6 0.0808
## 8 7 0.0125
Fit discrete gamma:
## $mu
## [1] 4.068634
##
## $cv
## [1] 0.3322478
##
## $sd
## [1] 1.351795
##
## $ll
## [1] -1714.162
##
## $converged
## [1] TRUE
##
## $distribution
## A discrete distribution
## name: gamma
## parameters:
## shape: 9.05890591579835
## scale: 0.449130877853043
x = c(0:10)
y = fit$distribution$d(x)
ggplot(data.frame(x = x, y = y), aes(x, y)) +
geom_col(data = incubation_period_dist, aes(incubation_period, relative_frequency)) +
geom_point(stat="identity", col = "red", size = 3) +
geom_line(stat="identity", col = "red")
Note that if the possible exposure dates are consecutive for all patients then empirical_incubation_dist()
and fit_gamma_incubation_dist()
can take date ranges as inputs instead of lists of individual exposure dates (see help for details).
The overview vignette essentially replicates the content of this README
. To request or contribute other vignettes, see the section “getting help, contributing”.
The estimate incubation vignette contains worked examples for the emperical_incubation_dist()
fit_gamma_incubation_dist()
.
Click here for the website dedicated to epitrix.
Bug reports and feature requests should be posted on github using the issue system. All other questions should be posted on the RECON forum.
Contributions are welcome via pull requests.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.


Ink input is stored in data blocks. The Input Manager stores up Ink events into a data block and then transfers the block to the proper window or application with
MSG_META_NOTIFY_WITH_DATA_BLOCK
. (This is a general change notification message.) The type of the data block passed is NT_INK, the manufacturer ID is MANUFACTURER_ID_GEOWORKS.
The data block is headed by an
InkHeader
structure (defined below). The header is followed by a list of points, all in screen coordinates (not points as with normal graphics commands). The block will contain as many points as are registered during the Ink input sequence.
The
InkHeader
structure has the following format:
typedef struct { word IH_count; Rectangle IH_bounds; optr IH_destination; dword IH_reserved; Point IH_data; } InkHeader;
The fields of the structure are listed below with the information they contain.
IH_count
IH_bounds
IH_destination
IH_reserved
IH_data
Point
structures, each one detailing a single Ink point.


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|
Mackey
V3.1
A C++ library for computing RO(G) graded homology
|
Stores Massey products and their indeterminacy. More...
#include <Massey.hpp>
Public Member Functions | |
| Massey () | |
| Set 0 by default. More... | |
| Massey (const chains_t< group_t > &, const chains_t< group_t > &, const chains_t< group_t > &, int, int, int, int, int, int, int, bool) | |
| Compute Massey product given level, generator degrees and selections. More... | |
Public Attributes | |
| rank_t | basis |
| Expresses the Massey product as a linear combination of the generators of the box product. More... | |
| rank_t | normalBasis |
| Same as basis, but normalized. More... | |
| AbelianGroup< rank_t > | Groups |
| The homology group the product lives in. More... | |
| std::array< rank_t, 2 > | indeterminacy |
| The indeterminacy of the Massey product, stored as two bases. More... | |
| IDGenerators< rank_t > | boxID |
| Identification data for the generators of the Massey product. More... | |
| bool | exists |
| If the Massey product exists. More... | |
| bool | noIndeterminacy |
| If the Massey product has no indeterminacy. More... | |
| bool | isZero |
| If the Massey product is 0. More... | |
Stores Massey products and their indeterminacy.
| Massey | ( | ) |
Set 0 by default.
| Massey | ( | const chains_t< group_t > & | , |
| const chains_t< group_t > & | , | ||
| const chains_t< group_t > & | , | ||
| int | , | ||
| int | , | ||
| int | , | ||
| int | , | ||
| int | , | ||
| int | , | ||
| int | , | ||
| bool | |||
| ) |
Compute Massey product given level, generator degrees and selections.
| rank_t basis |
Expresses the Massey product as a linear combination of the generators of the box product.
| rank_t normalBasis |
Same as basis, but normalized.
| AbelianGroup<rank_t> Groups |
The homology group the product lives in.
| std::array<rank_t, 2> indeterminacy |
The indeterminacy of the Massey product, stored as two bases.
| IDGenerators<rank_t> boxID |
Identification data for the generators of the Massey product.
| bool exists |
If the Massey product exists.
| bool noIndeterminacy |
If the Massey product has no indeterminacy.
| bool isZero |
If the Massey product is 0.


Time for Vehicle To Travel Between Lights (1-60 seconds):
Time for Vehicle To Move Up Queue (1-5 seconds):
Time for Simulation to Run for each Setup (seconds)
Range of Time Traffic Light Green (1-240 seconds):
to
Range of Chance Vehicle Added (0-1 per second):


Internet of Things
Register
Report
Username
Password
REpassword
check password :{{mode = (input.password === repassword)}}
Name
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E-mail
Submit
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A lightweight front-end framework for building accessible, bespoke interfaces.
Explore the documentation on codyhouse.co
The framework is composed of:
Some of the advantages of working with CodyFrame:
codyhouse-framework/
└── main/
├── assets/
│ ├── css/
│ │ ├── base/*
│ │ │── custom-style/
│ │ │ ├── _buttons.scss
│ │ │ ├── _colors.scss
│ │ │ ├── _forms.scss
│ │ │ ├── _icons.scss
│ │ │ ├── _shared-styles.scss
│ │ │ ├── _spacing.scss
│ │ │ └── _typography.scss
│ │ ├── _base.scss
│ │ ├── _custom-style.scss
│ │ ├── style-fallback.css
│ │ ├── style.css
│ │ └── style.scss
│ └── js/
│ └── util.js
└── index.html
If you're using the Components, make sure to include the following script in the <head>
of your document:
<script>document.getElementsByTagName("html")[0].className += " js";</script>
The script is used in CSS to target that JavaScript is enabled and apply additional style accordingly. If you don't include the script, part of the style of the components won't be visible.
CodyFrame includes a Gulp configuration file. To start a project that runs on Gulp, once you have downloaded the source files, navigate to the framework folder, and run the following commands in your command line:
npm install
npm run gulp watch
The first command will install the modules the framework requires for compiling SCSS into CSS; the second will launch your project on a development server.
⚠ Note: if you receive error messages while trying to run the npm install
command, you may need to download and install Git first.
The Gulp configuration file is set to compile the SCSS into two separate CSS files: style.css includes the CSS variables; in the style-fallback.css file, the CSS variables are replaced by their fallbacks (generated by the PostCSS plugin). A script in the
of the index.html file is used to deliver only one file, according to whether the browser supports CSS variables or not.<link id="codyframe" rel="stylesheet" href="assets/css/style.css">
<!-- browsers not supporting CSS variables -->
<script>
if(!('CSS' in window) || !CSS.supports('color', 'var(--color-var)')) {var cfStyle = document.getElementById('codyframe');if(cfStyle) {var href = cfStyle.getAttribute('href');href = href.replace('style.css', 'style-fallback.css');cfStyle.setAttribute('href', href);}}
</script>
📝 How to use the Framework with Webpack
CodyHouse's Components are accessible, progressively enhanced, HTML, CSS, JS components that work seamlessly with the framework.
You can use the Gulp configuration file to auto-import the .scss of the CodyHouse's Components.
Learn more about importing the CSS and JS of the CodyHouse's Components
The Global Editors are web design tools that allow you to set the style of typography elements, color themes, spacing rules, buttons, and forms directly in the browser. They generate SCSS code that is compatible with CodyFrame.
Explore the Editors:
Explore our autocomplete extensions for VSCode, Sublime Text and Atom:


Way To Health specific documents & templates for IRB submission
All research studies need to submit documentation around the appropriate policies and procedures implemented to protect the rights and welfare of humans participating as subjects in the research. To accomplish this, IRBs use a group process to review research protocols and related materials (e.g., informed consent documents and investigator brochures) to ensure protection of the rights and welfare of human subjects of research. For research studies using the Way To Health platform, we have prepared a set of documents and templates to accompany the submission.
Variants of these documents have been reviewed and approved by the University of Pennsylvania's Institutional Review Board.
Click the title to download {{ with .Params.irb }}{{ .description }}


Invite for ${test.title}
Dear student,
You can take the test by clicking
here
or to copy this into your browser:
${absolute_url}index.html#/answer/${test_session.id}


Citing TorchMPS¶
If you found TorchMPS useful for your work, you can cite it by adding the following entry to your BibTeX bibliography (.bib) file:
author = {Miller, Jacob}, title = {TorchMPS}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {url{https://github.com/jemisjoky/torchmps}},


io_submit - submit asynchronous I/O blocks for processing
#include <linux/aio_abi.h> /* Defines needed types */
int io_submit(aio_context_t ctx_id, long nr, struct iocb **iocbpp);
Note: There is no glibc wrapper for this system call; see
NOTES.
The io_submit() system call queues nr I/O request blocks for
processing in the AIO context ctx_id. The iocbpp argument should
be an array of nr AIO control blocks, which will be submitted to
context ctx_id.
The iocb (I/O control block) structure defined in
linux/aio_abi.h defines the parameters that control the I/O
operation.
#include <linux/aio_abi.h>
struct iocb {
__u64 aio_data;
__u32 PADDED(aio_key, aio_rw_flags);
__u16 aio_lio_opcode;
__s16 aio_reqprio;
__u32 aio_fildes;
__u64 aio_buf;
__u64 aio_nbytes;
__s64 aio_offset;
__u64 aio_reserved2;
__u32 aio_flags;
__u32 aio_resfd;
};
The fields of this structure are as follows:
- aio_data
- This data is copied into the data field of the io_event
structure upon I/O completion (see io_getevents(2)).
- aio_key
- This is an internal field used by the kernel. Do not modify this field
after an io_submit() call.
- aio_rw_flags
- This defines the R/W flags passed with structure. The valid values
are:
- RWF_APPEND (since Linux 4.16)
- Append data to the end of the file. See the description of the flag of the
same name in pwritev2(2) as well as the description of
O_APPEND in open(2). The aio_offset field is ignored.
The file offset is not changed.
- RWF_DSYNC (since Linux 4.13)
- Write operation complete according to requirement of synchronized I/O data
integrity. See the description of the flag of the same name in
pwritev2(2) as well the description of O_DSYNC in
open(2).
- RWF_HIPRI (since Linux 4.13)
- High priority request, poll if possible
- RWF_NOWAIT (since Linux 4.14)
- Don't wait if the I/O will block for operations such as file block
allocations, dirty page flush, mutex locks, or a congested block device
inside the kernel. If any of these conditions are met, the control block
is returned immediately with a return value of -EAGAIN in the
res field of the io_event structure (see
io_getevents(2)).
- RWF_SYNC (since Linux 4.13)
- Write operation complete according to requirement of synchronized I/O file
integrity. See the description of the flag of the same name in
pwritev2(2) as well the description of O_SYNC in
open(2).
- aio_lio_opcode
- This defines the type of I/O to be performed by the iocb structure.
The valid values are defined by the enum defined in
linux/aio_abi.h:
-
enum {
IOCB_CMD_PREAD = 0,
IOCB_CMD_PWRITE = 1,
IOCB_CMD_FSYNC = 2,
IOCB_CMD_FDSYNC = 3,
IOCB_CMD_NOOP = 6,
IOCB_CMD_PREADV = 7,
IOCB_CMD_PWRITEV = 8,
};
- aio_reqprio
- This defines the requests priority.
- aio_fildes
- The file descriptor on which the I/O operation is to be performed.
- aio_buf
- This is the buffer used to transfer data for a read or write
operation.
- aio_nbytes
- This is the size of the buffer pointed to by aio_buf.
- aio_offset
- This is the file offset at which the I/O operation is to be
performed.
- aio_flags
- This is the set of flags associated with the iocb structure. The
valid values are:
- IOCB_FLAG_RESFD
- Asynchronous I/O control must signal the file descriptor mentioned in
aio_resfd upon completion.
- IOCB_FLAG_IOPRIO (since Linux 4.18)
- Interpret the aio_reqprio field as an IOPRIO_VALUE as
defined by linux/ioprio.h.
- aio_resfd
- The file descriptor to signal in the event of asynchronous I/O
completion.
On success, io_submit() returns the number of iocbs submitted
(which may be less than nr, or 0 if nr is zero). For the failure
return, see NOTES.
- EAGAIN
- Insufficient resources are available to queue any iocbs.
- EBADF
- The file descriptor specified in the first iocb is invalid.
- EFAULT
- One of the data structures points to invalid data.
- EINVAL
- The AIO context specified by ctx_id is invalid. nr is less
than 0. The iocb at *iocbpp[0] is not properly initialized,
the operation specified is invalid for the file descriptor in the
iocb, or the value in the aio_reqprio field is invalid.
- ENOSYS
- io_submit() is not implemented on this architecture.
- EPERM
- The aio_reqprio field is set with the class IOPRIO_CLASS_RT,
but the submitting context does not have the CAP_SYS_ADMIN
capability.
The asynchronous I/O system calls first appeared in Linux 2.5.
io_submit() is Linux-specific and should not be used in programs that are
intended to be portable.
Glibc does not provide a wrapper function for this system call. You could invoke
it using syscall(2). But instead, you probably want to use the
io_submit() wrapper function provided by libaio.
Note that the libaio wrapper function uses a different type
(io_context_t) for the ctx_id argument. Note also that the
libaio wrapper does not follow the usual C library conventions for
indicating errors: on error it returns a negated error number (the negative
of one of the values listed in ERRORS). If the system call is invoked via
syscall(2), then the return value follows the usual conventions for
indicating an error: -1, with errno set to a (positive) value that
indicates the error.
io_cancel(2), io_destroy(2), io_getevents(2),
io_setup(2), aio(7)
This page is part of release 5.05 of the Linux man-pages project. A
description of the project, information about reporting bugs, and the latest
version of this page, can be found at https://www.kernel.org/doc/man-pages/.


This is a simple calculator
This calculator can perform addition, subtraction, multiplication and division of integer numbers
C
←
+
-
*
/
1
2
3
4
5
6
7
8
9
0
=


I'm Lucas and I'm a TAM major and CS minor. I love anything that has to do with computers or music – especially together. Besides taking the WEB class, I interned as a software engineer developing a data analytics and visualization web application for Polycom. From that experience, I learned about Javascript, React.js, REST APIs, and Agile Scrum. I'm interested in learning about DevOps, Music Tech, D3.js and advanced CSS design.


Before Change
nomad_running_jobs = {}
nomad_pending_jobs = {}
for (aggregate_key, group) in aggregated_jobs:
if not aggregate_key: continue
aggregated_pending = 0
aggregated_running = 0
for job in group:
children = job["JobSummary"]["Children"]
aggregated_pending = aggregated_pending + children["Pending"]
aggregated_running = aggregated_running + children["Running"]
nomad_pending_jobs[aggregate_key] = aggregated_pending
After Change
nomad_running_jobs = {}
nomad_pending_jobs = {}
for (aggregate_key, group) in aggregated_jobs:
nomad_pending_jobs[aggregate_key] = sum(job["JobSummary"]["Children"]["Pending"] for job in group)
nomad_running_jobs[aggregate_key] = sum(job["JobSummary"]["Children"]["Running"] for job in group)
return nomad_pending_jobs, nomad_running_jobs


--- layout: default ---
mdserve
A minimalistic HTTP server used to render markdown files.
mdserve
is a minimalistic HTTP serve used to render markdown files into HTML, which are then served
to users. Requires minimal configuration, but provides complete customization of the generated HTML.
roxy
A simple reverse proxy for forwarding requests based on the (sub) domain.
roxy
is used to reverse proxy requests based on the (sub) domain being requested. It
is based off autocert,
allowing it to generate certs for SSL automatically for the server.


This blog is about technology and pan-Africanism. Even better this blog is about Quilombism. In this blog, There will be posts in the three most used languages in the pan-African world: English, French and Portuguese.
Topics
- Black culture
- Black history
- Pan-Africanism
- Technology
What is Pan-Africanism?
Pan-Africanism is a movement that emerged within the diaspora, an organizing political tool that was designed to bring people of African descent together to fight for their rights, whether under colonialism in Africa and the Caribbean or under discrimination in the United States, Brazil or other countries, and to promote the unification of black people, especially those living on the continent of Africa.
What about Quilombism?
Quilombism is a concept of pan-Africanism with political system written by Abdias Nascimento in his essay [1] called Quilombism.
You said technology right?
Yes, I am a software developer. ;)
I believe that technology can be an efficient tool for pan-Africanism.
Tools
The source code for the blog is available at github.com.
| [1] | Quilombismo - An Afro-Brazilian Political Alternative |


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Placehoder for Browser does not support
This is border-box demo
This is content-box demo
add/remove placeholder
content box
padding box(valid in firefox only)
border box


Financial Services
We offer a broad range of financial services including capital optimisation, financial restructuring and other advisory services for financial stability and maximising the returns. At Outbak Ventures, we are continually looking for new opportunities to reduce operational costs and increase efficiencies of businesses, which in return reflects as better performances across all dimensions.
Outbak Ventures also advises on infrastructure, transactions, valuations, merger, acquisition and integration. Our knowledge of the local and international investment market allows us to help our clients gain a better return on their investment.


Management Pane:
Strategy:
OVERALL_ELASTICITY
COST_ELASTICITY
QUALITY_ELASTICITY
RESOURCE_ELASTICITY
SERVICE_UNITS_ASSOCIATIONS_ELASTICITY
OVERALL_REQUIREMENTS
COST_REQUIREMENTS
QUALITY_REQUIREMENTS
RESOURCE_REQUIREMENTS
MINIMUM_COST
MINIMUM_RESOURCES;
Name:
Requirement metric type:
COST
QUALITY
RESOURCE
Requirement metric:
Condition:
LESS_THAN
LESS_EQUAL
GREATER_THAN
GREATER_EQUAL
EQUAL
RANGE
ENUMERATION


--- layout: page title: Just Stop tagline: " Overthinking. It isn't thinking at all" permalink: /philosophy/overthinking.html categories: [philosophy, overthinking] tags: [advice, wit, wisdom, experience] ---
99% of the harm is caused in your head, by you and your thoughts. 1% of the harm is caused by reality, what actually happens and the outcome. Most of the time, the problem isn't the problem. The way you think about the problem is.
Don't think you deserve that opportunity? Apply for it anyways. Don't think your article is good enough? Publish it anyways. Don't think they'll reply to your email? Send it anyways. Never overthink yourself into self-rejection.
The truth is, most problems aren't solved with more thinking, they're solved with less. You'll find most of the answers you're looking for in silence, in time band with a clear mind. If you can't solve a problem, stop trying to. Sometimes doing nothing can do more for you, than you're might expect.
When you start criticizing yourself for past mistakes, or seeing disaster around every corner, ask yourself:
Is there anything I can do right now to change the past or to
positively influence the future?
If the answer is Yes, do it take action. If the answer is No, be at peace let it go. You have to take action or let it go, everything else is self-harm.
You're not going to overthink your way to a better future. You're not going to overthink your way to a better past. All you have is NOW. And what you do with NOW can make right of your past and make good of your future. Make peace with yesterday, let go of tomorrow, grab hold of now.
 So it's important to always fact-check your thoughts before accepting them because, in highly emotional situations, your thoughts will tell you stories that aren't true.
Fact-check yourself.
No amount of anxiety will change your future, no amount of regret will change your past.
Peace is found in acceptance:
You don't have to understand, tolerate or even forget something, but if you want peace, you must accept it.
You can go to the gym, eat healthy, do yoga, drink water and take vitamins, but if you don't directly confront the negativity in your thoughts, you will never truly be 'healthy'. Our health isn't measured on scales, by the size of our muscules or by the width of our waist. True health is measured by the quality of our thougts and the peacefulness of our minds. Health starts in your mind.