public interface MediaItemQueue
MediaItems
.| Modifier and Type | Method and Description |
|---|---|
void |
add(int index,
MediaItem... items)
Adds the given sequence of items to the queue at the given position, so that the first of
items is placed at the given index. |
void |
add(MediaItem... items)
Appends the given sequence of items to the queue.
|
void |
clear()
Removes all items in the queue.
|
MediaItem |
get(int index)
Returns the item at the given index.
|
int |
getSize()
Returns the number of items in this queue.
|
void |
move(int indexFrom,
int indexTo)
Moves an existing item within the playlist.
|
void |
remove(int index)
Removes an item from the queue.
|
void |
removeRange(int from,
int exclusiveTo)
Removes a range of items from the queue.
|
MediaItem get(int index)
index
- The index of the item to retrieve.IndexOutOfBoundsException
- If index < 0 || index >= getSize()
.int getSize()
void add(MediaItem... items)
items
- The sequence of items to append.void add(int index, MediaItem... items)
items
is placed at the given index.index
- The index at which items
will be inserted.items
- The sequence of items to append.IndexOutOfBoundsException
- If index < 0 || index > getSize()
.void move(int indexFrom, int indexTo)
Calling this method is equivalent to removing the item at position indexFrom
and
immediately inserting it at position indexTo
. If the moved item is being played at the
moment of the invocation, playback will stick with the moved item.
indexFrom
- The index of the item to move.indexTo
- The index at which the item will be placed after this operation.IndexOutOfBoundsException
- If for either index, index < 0 || index >= getSize()
.void remove(int index)
index
- The index of the item to remove from the queue.IndexOutOfBoundsException
- If index < 0 || index >= getSize()
.void removeRange(int from, int exclusiveTo)
Does nothing if an empty range (from == exclusiveTo
) is passed.
from
- The inclusive index at which the range to remove starts.exclusiveTo
- The exclusive index at which the range to remove ends.IndexOutOfBoundsException
- If from < 0 || exclusiveTo > getSize() || from >
exclusiveTo
.void clear()


Basscss
/
Docs
User Interface
A guide to creating user interface elements with Basscss
Navigation
Navbars
Breadcrumbs
Pagination
Boxes
Navigation


Configuring Submission and Security Settings
How to submission settings and optionally enable CAPTCHA security on the Feedback module. CAPTCHA is disabled by default.
-
- Select the Feedback Settings tab.
- Expand the Submission and Security Settings section.
- At Captcha Visibility, set when or if the Security Code field is displayed on the Feedback module (as shown in the below image). CAPTCHA requires users to enter the displayed code in order the submit feedback. Select from these options:
- All Users: CAPTCHA is displayed to all users.
- Anonymous Users Only: CAPTCHA is displayed to anonymous users only.
- Disabled: Select to disable CAPTCHA. This is the default setting.
- At Repeat Submission Filter, select the type of filtering used to limit repeat submissions by the same user. The following options are available:
- No Filtering: Skip to Step 7.
- DotNetNuke User ID: Do not filter by DotNetNuke UserID if anonymous users are allowed to submit feedback. Filtering by User IP Address may block legitimate uses behind proxy or firewall sharing same IP.
- User IP Address: Filtering by this option may block legitimate uses behind proxy or firewall sharing same IP.
- User Email Address
- In the Minimum Submission Interval text box, enter the time (in minutes) the same user must wait before submitting additional feedback. The default setting is 0.
- At Redirect To Page, select the page users are redirected to upon submitting feedback. This allows you to create a custom experience for your users - OR - Select <None Specified> for no redirect page.
- Click the Update button
CAPTCHA enabled on the Feedback Mode


?h£w
yo*>
BOTANICAL
New
York
Botanical
Garden
lb241S7
EX
HERB.
HORT.
BOT.
REG.
KEW.
Rhliophora
raoeraoaa
&,F*W.
Mey.
Grenadines:
Baquia*
Spring
Bay*
Mangrove
swampÈ
sea
level*
A
single
tree
35
ft*
tallÈ
midribs
reddish
beneath*
fruits
not
seen*
With
tall
Lagunoularia
raoemoaa
and
Conooarpus
ereotua*
J.R*
Charter
11
17-4-1966
EA
1624127
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


The true roots of Kickboxing can be found to date back 2000 years ago in Far East Asia, where Muay Thai Kickboxing was commonly practiced as a self-defense discipline. However it gradually became more of a sport over the years. Thai boxing soon became the most common and popular fighting sport in Asia.(columbia.edu)
When full contact karate (now kickboxing) first began as a sport in the US in the early seventies, the fighters of that time had to learn through a process of trial and error. The fighters all came from ranks of traditional karate or other traditional martial arts, and when they fought in professional full contact bouts certain shortcomings and defects became apparent. They discovered that they were not as fit or conditioned as they had thought and they struggled to fight 10 rounds in the professional ring. The full contact fighters also discovered to their dismay that their punches were not as effective in the ring as they had expected. (columbia.edu)
Kickboxing had started to gain in popularity all over the world, to the point where it had become both an internationally recognized sport and martial arts discipline. The dynamic modern version of kickboxing had arrived on the international sport circuit and was expanding and spreading all over the world.(columbia.edu)


removing nitrites from aquariums
Aquarium Fish Disease - Nitrite Poisoning
Nitrite poisoning follows closely on the heels of ammonia as a major killer of aquarium fish. Just when you think you are home free after losing half your fish to .
http://freshaquarium.about.com/cs/disease/p/nitritepoison.htm
Aquarium Nitrogen Cycle and Cycling. Methods for Ammonia, Nitrite ...
Inorganic nitrogenous compounds from our tap or well water also enter our aquarium, often as Nitrites or Nitrates. Reverse Osmosis can remove much of this .
http://www.americanaquariumproducts.com/nitrogen_cycle.html
How to Remove Nitrites, Nitrates and Ammonia From A Freshwater ...
Oct 17, 2010 . Here are some recommendations on how to remove nitrites, nitrates and ammonia from your freshwater aquarium. If you have any other .
http://www.youtube.com/watch?v=k9o6rXndvVE
Things You'll Need
- Certified copies of public records evidence in your case
Instructions
-
-
1
Basic Water Chemistry Part 3: Ammonia, Nitrites and Nitrates
Ammonia, nitrites, and nitrates are all toxic to fish and must be quickly and effectively removed from the aquarium. A proper functioning biological filter will do this .
http://www.liveaquaria.com/PIC/article.cfm?aid=62High Nitrite Levels!!? | My Aquarium Club
Water changes are the only way to remove nitrites. Products . Kees Aquarium in shelby township MI, That was the last time I will be going there... If you have .
http://www.myaquariumclub.com/high-nitrite-levels-915954.html -
2
Nitrite - The Aquarium Wiki
Feb 24, 2011 . Add a chemical treatment that removes or neutralises the nitrites. . by Fresh Aquarium; Nitrite in the aquarium and safe levels by the OATA .
http://www.theaquariumwiki.com/NitriteNitrite and the Reef Aquarium by Randy Holmes-Farley
Finally, in a reef aquarium, nitrite can be removed by reaction with ozone, presumably to produce nitrate.18. NO2- + O3 à NO3- + O2. Effects of Nitric Oxide .
http://reefkeeping.com/issues/2005-06/rhf/index.php -
-
3
Nitrates in the Aquarium
Aug 27, 2012 . Aquarium nitrate is the result of the bacterial breakdown of accumulated . The removal of nitrate, if not fulfilled by plants, takes place in an .
http://www.algone.com/articles/aquarium-filtration/nitrates-in-the-aquariumAlternative Marine Aquarium Nitrate Removal
An alternative for saltwater aquarium nitrate removal.
http://www.fishchannel.com/saltwater-aquariums/aquarium-frontiers/nitrate-removal-alternative.aspx -
4
Aquarium Water Quality: Nitrate Removal Options for Complete ...
High nitrate levels can cause stress to fish and invertebrates. In addition, they can quickly alter aquarium water chemistry and are often the source of serious .
http://www.drsfostersmith.com/pic/article.cfm?aid=2224Can live plants remove nitrate nitrite and ammonia from a fish ...
Yes. A properly balanced tank is a mini eco-sytem. Fish droppings feed the plants and the plants provide oxygen,food,and cover for the fish,and can .
http://uk.answers.yahoo.com/question/index?qid=20090423162937AAMIJtO
-
1
References
Resources
- Photo Credit Justice image by MVit from ecommerce innovation 2007 inventions


pywinEA.visualization¶
pywinEA.visualization.evaluation module¶
-
class
pywinEA.visualization.evaluation.
GAevaluator
(algorithm: pywinEA.algorithm.basic.GA)¶ Bases:
object
Class that allows evaluating the performance of the best combination of features found by the GA algorithm.
- plot_learning_curve(): Function that shows the performance of the algorithm using training and validation
data based on the size of the training data.
- metrics(): Function that computes several metrics. Currently available accuracy, recall, precision,
and f1-score for train and test data.
training_confusion_matrix(): Method that computes the confusion matrix on training data.
test_confusion_matrix(): Method that computes the confusion matrix using cross-validation.
roc(): Method that computes the ROC curve.
-
metrics
(cv: int = 5, reps: int = 5, n_jobs: int = -1, **kwargs)¶ Function that computes several metrics. Currently computed accuracy, recall, precision, and f1-score for train and test data. Metrics are computed by several stratified cross-validation.
- Parameters
cv – int Number of K for stratified cross-validation. By default 5.
reps – int Number of repetitions for k-cv. By default 5.
- :param n_jobs <optional> int
Number of cores used for compute the repeated stratified cross-validation.
-
plot_learning_curve
(cv: int = 5, n_jobs: int = -1)¶ Function that shows the performance of the algorithm using training and validation data based on the size of the training data. This allows to analyze the variance and the bias of the algorithm. This method apply cross validation with stratification.
- Parameters
cv – int Number of k-folds. It must be a number appropriate to the number of instances in each of the classes.
n_jobs – int Number of threads to compute cross validation.
-
roc
(estimator=None, cv: int = 5, reps: int = 1, positive_class: int = None, random_state: int = 0)¶ Function that allows to represent the ROC curve on the best solution found using cross validation with repetitions.
- Parameters
estimator – <optional> sklearn.base.BaseEstimator If none is provided the algorithm estimator will be used. This must support predictions with probabilities, otherwise it will throw an exception.
cv – <optional> int Default 5
reps – <optional> int Default 1
positive_class – <optional> int By default the class selected as positive in the algorithm. In the case that the algorithm does not have a positive class and one is not provided, an exception will be thrown.
random_state – <optional> int
-
test_confusion_matrix
(cv: int = 5, reps: int = 5, random_state: int = None)¶ Function that computes the confusion matrix making repetitions of cross-validation (the results given are the average of each fold)
- :param cv <optional> int
Default 5.
- :param reps <optional> int
Number of repetitions. Default 5.
- :param random_state <optional> int
Random seed.
-
training_confusion_matrix
()¶ Function that computes the confusion matrix on the training data.
-
class
pywinEA.visualization.evaluation.
MOAevaluator
(algorithm: pywinEA.interface.algorithm.MOAbase)¶ Bases:
object
Class that allows evaluating the performance of the best solutions found by a multi-objective algorithm.
- metrics(): Function that computes several metrics. Currently available accuracy, recall,
precision, and f1-score for train and test data.
training_confusion_matrix(): Method that computes the confusion matrix on training data.
test_confusion_matrix(): Method that computes the confusion matrix using cross-validation.
roc(): Method that computes the ROC curve.
-
metrics
(idx: int = None, cv: int = 5, reps: int = 5, n_jobs: int = -1, **kwargs)¶ Method that computes several metrics. Currently computed accuracy, recall, precision, and f1-score for train and test data. Metrics are computed by several stratified cross-validation.
- Parameters
idx – int Solution index.
cv – int Number of K for stratified cross-validation. By default 5.
reps – int Number of repetitions for k-cv. By default 5.
- :param n_jobs <optional> int
Number of cores used for compute the repeated stratified cross-validation.
-
roc
(idx: int = None, estimator=None, fitness_idx: int = 0, cv: int = 5, reps: int = 1, positive_class: int = None, random_state: int = 0)¶ Function that allows to represent the ROC curve on the solutions found in the non-dominated front using cross validation with repetitions.
- Parameters
idx – int Index of the solution to represent.
fitness_idx – int Index of the fitness function to represent.
estimator – <optional> sklearn.base.BaseEstimator If none is provided the algorithm estimator will be used. This must support predictions with probabilities, otherwise it will throw an exception.
cv – <optional> int Default 5
reps – <optional> int Default 1
positive_class – <optional> int By default the class selected as positive in the algorithm. In the case that the algorithm does not have a positive class and one is not provided, an exception will be thrown.
random_state – <optional> int
-
test_confusion_matrix
(idx: int = None, cv: int = 5, reps: int = 5, fitness_idx: int = 0, random_state: int = None)¶ Compute the confusion matrix using cross-validation repeats. By indicating an index (idx parameter) you can select one of the solutions from the non-dominated front. If the algorithm has more than one fitness function with estimators (in addition to optimization in the number of features), the estimator can be selected with the parameter fitness_idx.
- Parameters
idx – int Index of the solution to represent.
cv – int Number of folds in which to split the data.
reps – int Number of repetitions for CV.
fitness_idx – int Index of the fitness function to represent.
random_state – int
-
training_confusion_matrix
(idx: int = None, fitness_idx: int = 0)¶ Function that computes the confusion matrix on the training data. By indicating an index (idx parameter) you can select one of the solutions from the non-dominated front. If the algorithm has more than one fitness function with estimators (in addition to optimization in the number of features), the estimator can be selected with the parameter fitness_idx.
- Parameters
idx – int Index of the solution to represent.
fitness_idx – int Index of the fitness function to represent.
pywinEA.visualization.evolution module¶
-
class
pywinEA.visualization.evolution.
Plotter
¶ Bases:
object
Class that allows to represent the genetic algorithm evolution and performance.
- plot_evolution(): Represents information of the number of features and performance of the model in
each generation.
-
classmethod
plot_evolution
(*algorithms, **kwargs)¶ Function that performs the graphic representation of the training process of the algorithm(s). Based on the type of algorithm received as a parameter it will make a different representation.


[§708-8303] Unlicensed contractor fraud in the first degree. (1) A person commits the offense of unlicensed contractor fraud in the first degree if the person commits unlicensed contractor fraud and the total value of the property over which the person obtains control is equal to or greater than $20,000.
(2) Unlicensed contractor fraud in the first degree is a class B felony. [L 2012, c 244, pt of §1]


| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| ERC20Interface.sol | 100% | 0/0 | 100% | 0/0 | 100% | 0/0 | 100% | 0/0 | |
| ERC20Token.sol | 62.5% | 15/24 | 100% | 0/0 | 70% | 7/10 | 62.5% | 15/24 | |
| Math.sol | 55.56% | 5/9 | 33.33% | 2/6 | 50% | 2/4 | 55.56% | 5/9 | |
| Owned.sol | 15.79% | 3/19 | 8.33% | 1/12 | 50% | 3/6 | 20% | 4/20 | |
| PortalNetworkToken.sol | 96.08% | 49/51 | 50% | 12/24 | 100% | 12/12 | 96.15% | 50/52 | |
| PortalNetworkTokenConfig.sol | 100% | 0/0 | 100% | 0/0 | 100% | 0/0 | 100% | 0/0 | |
| Registry.sol | 100% | 0/0 | 100% | 0/0 | 100% | 0/0 | 100% | 0/0 | |
| UniversalRegistrar.sol | 98.31% | 116/118 | 57.45% | 54/94 | 100% | 15/15 | 97.56% | 120/123 | |
| UniversalRegistry.sol | 42.11% | 16/38 | 23.33% | 7/30 | 54.55% | 6/11 | 43.9% | 18/41 | |
| strings.sol | 5.88% | 12/204 | 1.04% | 1/96 | 13.33% | 4/30 | 7.63% | 19/249 |


Policy
The staff of Green River PC Repair Shop will exercise due care to secure your personal information/computer data. We do not sell, trade, or transfer to outside parties any of your personal information or computer data.
The staff of Green River PC Repair Shop will do everything within our power to secure your personal property.We do not sell, trade, or transfer to outside parties any of the equipment you bring in for repair.
A) The staff of Green River PC Repair Shop will do everything within our abilities to repair your equipment, however not all computer equipment can be repaired at this location. We are a student run facility and have limited access to diagnostic/repair equipment. If we cannot repair a computer, we will give a full explanation of the situation and provide information detailing our findings. B) We do not have the ability to order computer parts/software if required for a repair. We are happy to provide you with the information needed to purchase computer parts/ software for your computer. We are willing to install the computer parts/software you provide at no further cost to you. C) The staff of Green River PC Repair Shop is not liable for any voided warranties that may arise as a result of any work done on any equipment brought into the shop for repair. D) The services provided by the Green River PC Repair Shop are intended for individual and owners of the equipment to be repaired. All repairs are done on a first come, first served basis. The staff of the Green River PC Repair Shop cannot guarantee or accommodate a specific date/time for completion of repairs.
Payment is a suggested donation and is not required before any work will be done on equipment. The suggested donation pays for our time spent searching, troubleshooting, and diagnosing computer problems. A further fee is not charged for any repairs we are able to complete.
The suggested donation is a non-refundable fee to investigate computer problems.
Upon completion of work, Green River PC Repair Shop will contact the owner of the equipment. If equipment is not picked up within 90 days after this phone call, all equipment will be considered abandoned, and must be recycled. Green River PC Repair Shop will make due effort to ensure you are contacted when your equipment has been repaired.
In order to do backups of your files the Green River PC Repair Shop will need you to supply the storage media needed. Example: An external Hard Drive
The Green River PC Repair Shop will only work on legitimate installations of Windows operating system. Any computer found to have an illegitimate copy of Windows operating system will be return immediately without and further repair.


"Less Code" Print Design
The quote in the design is heavily inspired by StackExchange user
quentin-starin
's
answer:
How many lines of code can a C# developer produce per month?
I hope he won't mind me stealing his quote, but I'm not earning a dime from it anyway. It isn't a direct translation either, I ended up adapting it.
At first I wanted to put the quote and, right below it, a snippet of the
dreaded
"Hello World" converted into a design pattern. But, after seing
The Futur's prints
The thumbnail of this article is getting cropped and I
can't do much about it, or at least I don't care to. The real designs
are below and they feature variations of two characteristics: having
labels/watermarks; and having different proportions, either squared, 2x3
or 3x2.
You can download everything
The text is in pure white, however, it's considered better practice to use light grey instead because the contrast is usually too high. In The Futur's prints, I think the background is a slab of stone, so it actually looks kind of cool to have pure white. Anyway, if you would like to change the color, feel free to open the AI file and edit it yourself.


Home
(current)
Status
Gate
Contact
Boys
Girls
Approver
Boys
Girls
Hostel Leave Form
Name of the student
ROLL NO
Valid Roll No. is required.
Gender
Please select a valid Gender.
Email :
Please enter your address.
Phone Number
+91
Your Moile Numer is required.
Hostel
Choose...
Ashwatha
Ashoka
Banyan
Lotus
Jasmine
ROOM NO
Parent Name
Valid Name is required.
Parent Phone Number
+91
Your Moile Numer is required.
Parent Email :
Address for communication:
Please enter your address.
OUTGOING DATE
INCOMING DATE
OUTGOING TIME
INCOMING TIME
Reason for leave
My parent is fully aware of my travel plan and assures you that I will abide by the hostel rules. In the event of violation, I lose my eligibility to stay in hostel.
Submit
LoggedIn
© IIITDM Kancheepuram 2018
Designed by CED16I024 and CED15I033


MR & MRS PETER WILLIAMS EMAIL<EMAIL_ADDRESS><EMAIL_ADDRESS>KINDEST ATTENTION, PLEASE PERMIT ME TO MAKE YOUR ACQUAINTANCE IN SO INFORMAL A MANNER. THIS IS NECESSITATED BY MY URGENT NEED TO REACH A DEPENDABLE AND TRUSTWORTHY FOREIGN PARTNER.    ON THE 17TH OF SEPTEMBER 2002. THE REBEL LEADERS, UNTIL THEN WERE LOYAL SOLDIERS TO THE GOVERNMENT OF PRESIDENT LAURENT GBAGBO CONTACTED ME IN MY OFFICE WITH THE BUSINESS OF IMPORTATION OF ARMOURIES. WITHOUT THE PREMONITION OF WAR, I ACCEPTED THE JOB. THE SOLDIERS PAID ME THE SUM OF TWENTY MILLION FIVE HUNDRED THOUSAND UNITED STATES DOLLARS (US$ 20.5) ON THE SPOT AND TOLD ME TO EXPEDITE ACTION. SINCE I AM NOT INTO IMPORTATION OF ARMUNITIONS AND BECAUSE OF THE URGENCY, I IMMEDIATELY STARTED MAKING SEVERAL CONTACTS WITH BUSINESSMEN ACROSS THE GLOBE. TWO DAYS LATER, HEAVY FIGHTING BROKE UP IN CENTRAL BAOUKE AND KORHOGO, EVERYWHERE WAS IN DISSARRAY AND I LOST EVERYTHING THAT TOOK ME SO MANY YEARS TO ACQUIRE. I HAVE NO OPTION THAN TO DO AWAY WITH THE US$20.5M. MY WIFE AND I QUICKLY MADE AN ARRANGEMENT WITH A RED CROSS RELIEF WORKER WHO USED HIS OFFICIAL VAN TO MOVE THIS MONEY TO ABIDJAN, ALTHOUGH HE DID NOT KNOW THE REAL CONTENT OF THE BOX. PRESENTLY, THIS FUND HAS BEEN DEPOSITED WITH A SAFE RELIABLE SECURITY COMPANY IN ABIDJAN WHERE I AM HIDING FROM THE REBEL WHO DECLEARED ME WANTED. I DO NOT WANT TO INVEST THIS MONEY IN ABIDJAN DUE TO UNFAVORABLE ECONOMIC CLIMATE. AS A RESULT, I KEPT THE MONEY IN A CUSTOM BONDED WAREHOUSE WITH THE SECURITY COMPANY. SO THAT I CAN NOT LAY MY HANDS ON IT UNTIL IT GETS TO YOUR COUNTRY, WHERE I WILL COMMENCE INVESTMENT.  MAKE IMMEDIATE ARRANGEMENT TO TAKE THE DELIVERY OF THE CRATE (FUND) AT THEIR EUROPE OR AMERICAN COMMISSION AGENTS. 2.      KINDLY TREAT AS URGENT AND CONTACT ME IMMEDIATELY THROUGH THE ABOVE E-MAIL ADDRESS<EMAIL_ADDRESS>OR<EMAIL_ADDRESS>I HAVE NOT ACQUIRED A NEW PHONE AS I LOST ALL MY PROPERTIES IN BOAUKE YOURS FAITHFULLY, MR PETER WILLIAMS


vignettes/how-to-use-this-package.Rmd
how-to-use-this-package.Rmd
To install the package, type the following commands into the R console:
# install.packages("devtools")
devtools::install_github("hauselin/hausekeep") # you might have to install devtools first (see above)
summaryh()
generates formatted results and effect sizes for manuscriptsGenerate model summaries that can be copied and pasted straight into your manuscript (no more copy-paste frustrations and errors!). Summaries are formatted according to American Psychological Association (APA) guidelines (get in touch if you require other formats). Example APA summaries generated by summaryh()
:
b = 1.41, SE = 0.56, t(30) = 2.53, p = .017, r = 0.42
F(1, 30) = 9.00, p = .005, r = 0.48
t(23) = −4.67, p < .001, r = 0.70
See documentation for optional parameters.
model_lm <- lm(mpg ~ cyl, mtcars)
summary(model_lm) # base R summary()
summaryh(model_lm) # returns APA-formatted output in a data.table
# linear mixed effects regression
library(lme4); library(lmerTest) # load packages to fit mixed effects models
model <- lmer(weight ~ Time * Diet + (1 + Time | Chick), data = ChickWeight)
summary(model) # standard summary
summaryh(model)
# ANOVA
summaryh(aov(mpg ~ gear, mtcars))
# correlation
cor.test(mtcars$mpg, mtcars$cyl)
summaryh(cor.test(mtcars$mpg, mtcars$cyl))
es()
converts between effect size measuresThe es
function converts one effect size into other effect sizes (e.g., d, r, R2, f, odds ratio, log odds ratio, area-under-curve [AUC]). Also available at https://www.escal.site.
es(d = 0.2)
#> d: 0.2
#> d r R2 f oddsratio logoddsratio auc fishersz
#> 1 0.2 0.1 0.01 0.1 1.437 0.363 0.556 0.1
es(r = c(0.1, 0.4, 0.7))
#> r: 0.1 r: 0.4 r: 0.7
#> d r R2 f oddsratio logoddsratio auc fishersz
#> 1 0.201 0.1 0.01 0.101 1.440 0.365 0.557 0.100
#> 2 0.873 0.4 0.16 0.436 4.871 1.583 0.731 0.424
#> 3 1.960 0.7 0.49 0.980 35.014 3.556 0.917 0.867
outliersMAD()
identifies outliers using robust median absolute deviation approach
example <- c(1, 3, 3, 6, 8, 10, 10, 1000) # 1000 is an outlier
outliersMAD(example) # MAD approach
#> 1 outliers detected.
#> Outliers replaced with NA
#> [1] 1 3 3 6 8 10 10 NA
outliersZ()
identifies outliers using Z-score cut-off
example <- c(1, 3, 3, 6, 8, 10, 10, 1000) # 1000 is an outlier
outliersZ(example) # SD approach
#> 1 outliers detected.
#> Outliers replaced with NA
#> [1] 1 3 3 6 8 10 10 NA
# compare with MAD approach from above
outliersZ(example) # SD approach
#> 1 outliers detected.
#> Outliers replaced with NA
#> [1] 1 3 3 6 8 10 10 NA
fit_ezddm()
fits EZ-diffusion model for two-choice response time tasks
library(rtdists) # load package to help us simulate some data
data1 <- rdiffusion(n = 100, a = 2, v = 0.3, t0 = 0.5, z = 0.5 * 2) # simulate data
data2 <- rdiffusion(n = 100, a = 2, v = -0.3, t0 = 0.5, z = 0.5 * 2) # simulate data
dataAll <- rbind(data1, data2) # join data
dataAll$response <- ifelse(dataAll$response == "upper", 1, 0) # convert responses to 1 and 0
dataAll$subject <- rep(c(1, 2), each = 100) # assign subject id
dataAll$cond1 <- sample(c("a", "b"), 200, replace = T) # randomly assign conditions a/b
dataAll$cond2 <- sample(c("y", "z"), 200, replace = T) # randomly assign conditions y/z
# fit model to just entire data set (assumes all data came from 1 subject)
fit_ezddm(data = dataAll, rts = "rt", responses = "response")
# fit model to each subject (no conditions)
fit_ezddm(data = dataAll, rts = "rt", responses = "response", id = "subject")
# fit model to each subject by cond1
fit_ezddm(data = dataAll, rts = "rt", responses = "response", id = "subject", group = "cond1")
# fit model to each subject by cond1,cond2
fit_ezddm(data = dataAll, rts = "rt", responses = "response", id = "subject", group = c("cond1", "cond2"))
sca_lm()
fits every possible linear regression model given a set of predictors and covariatessca_lm()
is a basic implementation of specification curve analysis for linear regression.
# models to fit: mpg ~ cyl; mpg ~ carb; mpg ~ cyl + carb
sca_lm(data = mtcars, dv = "mpg", ivs = c("cyl", "carb")) # default no covariates
# models to fit (with and without covariate vs): mpg ~ cyl; mpg ~ carb; mpg ~ cyl + carb
sca_lm(data = mtcars, dv = "mpg", ivs = c("cyl", "carb"), covariates = "vs")


Please note that you are not viewing the latest version of this documentation. You can select the version through the dropdown in the top left. NextPrevious NextPrevious Created with Lightdoc


Join us in lovely Seattle to tie the knot and raise a glass this Labor Day Weekend, 2015.
Our wedding ceremony will be on the afternoon of Saturday, September 5th aboard a retired ferry boat on Lake Union named {% if safe %} The MV Skansonia. {% else %} The MV Skansonia. {% endif %} Saturday evening we will host a great big house party at The Purple, our home in Ballard. It is the purple one with the chickens.
For our East Coast friends and family, please join us for a reception in Bruce and Barbara Davis' backyard. On Saturday, September 12th we'll have music and wild, wonderful West Virginia festivities.
While we know the country is big, we encourage all of our friends and family to join us at any or all of the celebrations. We'd love to see the Charlestonians basking in the Seattle sun and the Kelso folks eating barbecue in Scott Depot.
Invitation Code: {{ invitation.get_code }}
{% if safe %}
Wedding Website: jacobandkatiegetmarried.com
{% else %}
Wedding Website: http://jacobandkatiegetmarried.com
Your Personal Invitation: {{ invitation.public_url }}
{% endif %}
Thank you,
Jacob and Katie


The mailing list for SubEthaSMTP, including a searchable archive, is run on SubEthaMail at this address:
http://www.subethamail.org/smtp


{% extends "dform/base.html" %} {% load staticfiles %} {% block title %}{{title}}{% endblock title %} {% block contents %}
The following is a link to a stand-alone survey version page which has been answered before that a user can use to edit their answers.
{{version.survey.name}}


Hi! I'm Molly,
Owner and operator of Nychol's Sweets & Things. Thank you for visiting my site to learn more about my small business.
I'm a single momma, work full-time at my "regular" job during the week and if I'm not off watching my daughter dance somewhere, you will find me in the kitchen baking and decorating cakes and things!
I started making cakes a little over a year ago as a creative outlet and hobby. I have learned much along the way!
It all started by accident one day when I made a cake for a friend's birthday, shared it on Facebook, then other people started asking me to make their cakes. The rest is history, as they say.


London Eye
Situated on the banks of the River Thames, the London Eye is an enormous,
135 meter (443 ft) high observation wheel carrying 32 exterior glass-walled capsules.
It offers fantastic views over central London.
The wheel does not usually stop to take on passengers as the rotation rate
is slow enough to allow passengers to walk on and off the moving capsules at ground level.
It is the most popular paid tourist attraction in the United Kingdom, visited by over 3.5 million people annually..
Tower Bridge
Tower Bridge is a combined bascule and suspension bridge in London,
over the River Thames.
It is close to the Tower of London, which gives it its name and has become an iconic symbol of London.
Construction started in 1886 and took eight years to build.
The bridge consists of two towers which are tied together at the upper level
by means of two horizontal walkways which are designed
to withstand the forces of the suspended sections of the bridge..
Traflaglar Square
Trafalgar Square is a large city square commemorating Lord Horatio Nelson’s victory against
Napoleon’s navy at the Battle of Trafalgar in 1805.
The central monument within the square is a single tall column on which the figure of Nelson
stands gazing over London.
His monument is surrounded by four colossal lions and a series of large fountains.
Much more than just an open plaza, Trafalgar Square is one of the most famous city squares
in the world and has become a social and political location for tourists and Londoners alike...


tmlib.datasets¶
This module includes some classes and utility functions which help us work with the dataset
class DataSet¶
This is the main class storing the information about your corpus such as: number of documents, size of vocabulary set, etc. You also can load the mini-batches data to implement your learning algorithm.
tmlib.datasets.DataSet(data_path=None, batch_size=None, passes=1, shuffle_every=None, vocab_file=None)
Parameters¶
data_path: string,
Path of file input (corpus)
batch_size: int
size of mini-batch in each sampling from corpus.
passes: int, default: 1
passes controls how often we train the model on the entire corpus. Another word for passes might be “epochs” (in training neural network). iterations is somewhat technical, but essentially it controls how often we repeat a particular loop over each document. It is important to set the number of “passes” and “iterations” high enough.
For example, if you set passes = 5, assume that batch_size = 100 and size of corpus is 10000 then number of training iterations is 10000/100*5 = 5000
shuffle_every: int,
This parameter help us shuffle the samples (documents) in corpus at each pass (epoch)
If you set shuffle_every=2, it means after passing over corpus 2 times, corpus will be shuffled
vocab_file: string, default: None
File vocabulary of corpus
If corpus is raw text format, file vocabulary is non-necessary. Otherwise, if corpus is tf or sq format, user must set it
Attributes¶
batch_size: int
vocab_file: string,
num_docs: int,
Return number of document in corpus
data_path: string,
path of file corpus which is the term-frequency or term-sequence format
data_format: attribute of class
DataFormat
The class
DataFormat
stores name of formats data: DataFormat.RAW_TEXT, DataFormat.TERM_SEQUENCE or DataFormat.TERM_FREQUENCYoutput_format: attribute of class
DataFormat
, default is DataFormat.TERM_FREQUENCYformat of mini-batch. User change the format by use method set_output_format
passes: int
shuffle_every: int
work_path: string
This path is different from data_path. If corpus is shuffled then work_path is path of the shuffled file, not the original file
Methods¶
__init__(data_path=None, batch_size=None, passes=1, shuffle_every=None, vocab_file=None)
load_mini_batch ()
loading a mini-batch from corpus with specific format (controlled by output_format) Return: object class
Corpus
storing mini-batchload_new_document (path_file, vocab_file=None)
You can load new document from
path_file
. If format of file is raw text, you need addvocab_file
Return: objectCorpus
check_end_of_data ()
To check out that whether we visit to the last mini-batch or not.
Return True if the last mini-batch is loaded and the training is done
set_output_format (output_format)
set format for the loaded mini-batch
- Parameters: output_format (DataFormat.TERM_SEQUENCE or DataFormat.TERM_FREQUENCY)
get_total_docs ()
Return number of documents which have been analyzed until the present
get_num_tokens ()
Return number of tokens in corpus
get_num_terms ()
Return number of unique terms in corpus (size of vocabulary set)
Example¶
- Load mini-batch with term-frequency format
from tmlib.datasets import DataSet
#AP corpus in folder examples/ap/data
data = DataSet(data_path='data/ap_train_raw.txt', batch_size=100, passes=4, shuffle_every=2)
minibatch = data.load_mini_batch() # The format is term-frequency by default
- Load mini-batch with term-sequence format
from tmlib.datasets import DataSet
from tmlib.datasets.utilities import DataFormat
#AP corpus in folder examples/ap/data
data = DataSet(data_path='data/ap_train_raw.txt', batch_size=100, passes=4, shuffle_every=2)
data.set_output_format(DataFormat.TERM_SEQUENCE)
minibatch = data.load_mini_batch()
In these examples, we set passes=4 and shuffle_every=2, it means: 4 times of passing over data and after every 2 times, corpus is shuffled again. Assume that size of corpus is 5000 documents, batch_size = 100, then number of iterators is: 5000/100*4 = 2000. We can check the last iterator by using method check_end_of_data().
class DataFormat¶
This is class which contains 3 data-format types of library is: raw text, term_sequence, term-frequency
tmlib.datasets.utilities.DataFormat
Static Attributes¶
- RAW_TEXT: string, value is ‘txt’
- TERM_FREQUENCY: string, value is ‘tf’
- TERM_SEQUENCE: string, value is ‘sq’
Example¶
This example allows checking data format for: corpus examples/ap/ap_train_raw.txt
from tmlib.datasets.utilities import DataFormat, check_input_format
input_format = check_input_format('examples/ap/ap_train_raw.txt')
print(input_format)
if input_format == DataFormat.RAW_TEXT:
print('Corpus is raw text')
elif input_format == DataFormat.TERM_SEQUENCE:
print('Corpus is term-sequence format')
else:
print(Corpus is term-frequency format')
Output:
txt
Corpus is raw text
class Corpus¶
This class is used to store the corpus with 2 formats: term-frequency and term-sequence
tmlib.datasets.utilities.Corpus(format_type)
Parameters¶
- format_type: DataFormat.TERM_SEQUENCE or DataFormat.TERM_FREQUENCY
Attributes¶
format_type: format of corpus
word_ids_tks: list of list,
Each element in this list is a list which include the words of a document in corpus (words is unique terms if format is term-frequency and is list of tokens if format is term-sequence)
cts_lens: list
if format is term-frequency, each element in list is a list frequency of unique terms in respectly document of corpus. If format is term-sequence, each element in list is the number of tokens in document (number of tokens in each doc).
Methods¶
append_doc (ids_tks, cts_lens)
Add a document to corpus. If format of this document is term-frequency, this method will append list of unique terms to word_ids_tks and append list of frequency to cts_lens. If format is term-sequence, the list of tokens and number of tokens will be appended respectly
Parameters: ids_tks and cts_lens is format (tf or sq) of added document
ids_tks: list of unique terms (term-frequency format) or list of tokens (term-sequence format) cts_lens: list of frequency of unique terms (term-frequency format) or number tokens in document (term-sequence format)
Utility functions¶
These functions below are in module tmlib.datasets.utilities
get_data_home¶
tmlib.datasets.utilities.get_data_home(data_home=None)
This folder is used by some large dataset loaders to avoid downloading the data several times.
By default the data dir is set to a folder named ‘tmlib_data’ in the user home folder. We can change it by change value of data_home parameter The ‘~’ symbol is expanded to the user home folder.
If the folder does not already exist, it is automatically created.
- Return: path of the tmlib data dir.
>>> from tmlib.datasets import utilities
>>> print 100.get_data_home()
/home/kde/tmlib_data
clear_data_home¶
tmlib.datasets.utilities.clear_data_home(data_home=None)
Delete all the content of the data home cache.
check_input_format¶
tmlib.datasets.utilities.check_input_format(file_path)
Check format of input file(text formatted or raw text)
Parameters: file_path (string)
Path of file input
Return: format of input (DataFormat.RAW_TEXT, DataFormat.TERM_FREQUENCY or DataFormat.TERM_SEQUENCE)
>>> from tmlib.datasets import utilities
>>> file_path = '/home/kde/Desktop/topicmodel-lib/examples/ap/ap_train.txt'
>>> print utilities.check_input_format(file_path)
tf
>>> file_path = '/home/kde/Desktop/topicmodel-lib/examples/ap/ap_train_raw.txt'
>>> print utilities.check_input_format(file_path)
txt
load_batch_raw_text¶
tmlib.datasets.utilities.load_batch_raw_text(file_raw_text_path)
load all of documents and store as a list. Each element in this list is a document with raw text format (string)
Parameters: file_raw_text_path (string)
Path of file input
Return: list, each element in list is string type and also is text of a document
>>> from tmlib.datasets import utilities
>>> path_file_raw_text = '/home/kde/Desktop/topicmodel-lib/examples/ap/ap_infer_raw.txt'
>>> list_docs = utilities.load_batch_raw_text(path_file_raw_text)
>>> print 'number of documents: ', len(list_docs)
number of documents: 50
>>> print list_docs[8]
Here is a summary of developments in forest and brush fires in Western states:
pre_process¶
tmlib.datasets.utilities.pre_process(file_path)
Preprocessing for file input if format of data is raw text
Paremeter: file_path (string)
Path of file input
Return: list which respectly includes path of vocabulary file, term-frequency file, term-sequence file after preprocessing
>>> from tmlib.datasets import utilities
>>> path_file = '/home/kde/Desktop/topicmodel-lib/examples/ap/ap_train_raw.txt'
>>> path_vocab, path_tf, path_sq = utilities.pre_process(path_file)
Waiting...
>>> print 'path to file vocabulary extracted: ', path_vocab
path to file vocabulary extracted: /home/kde/tmlib_data/ap_train_raw/vocab.txt
>>> print 'path to file with term-frequency format: ', path_tf
path to file with term-frequency format: /home/kde/tmlib_data/ap_train_raw/ap_train_raw.tf
>>> print 'path to file with term-sequence format: ', path_sq
path to file with term-sequence format: /home/kde/tmlib_data/ap_train_raw/ap_train_raw.sq
load_batch_formatted_from_file¶
tmlib.datasets.utilities.load_batch_formatted_from_file(data_path, output_format=DataFormat.TERM_FREQUENCY)
- load all of documents in file which is formatted as term-frequency format or term-sequence format and return a corpus with format is output_format
- Parameters:
- data_path: path of file data input which is formatted
- output_format: format data of output, default: term-frequence format
- Return: object corpus which is the data input for learning
>>> path_file_tf = '/home/kde/Desktop/topicmodel-lib/examples/ap/ap_train.txt'
>>> corpus_tf = utilities.load_batch_formatted_from_file(path_file_tf)
>>> print 'Unique terms in the 9th documents: ', corpus_tf.word_ids_tks[8]
Unique terms in the 9th documents: [5829 4040 2891 14 1783 381 2693]
>>> print 'Frequency of unique terms in the 9th documents: ', corpus_tf.cts_lens[8]
Frequency of unique terms in the 9th documents: [1 1 1 1 1 1 1]
>>> corpus_sq = utilities.load_batch_formatted_from_file(path_file_tf, output_format=utilities.DataFormat.TERM_SEQUENCE)
>>> print 'List of tokens in the 9th documents: ', corpus_sq.word_ids_tks[8]
List of tokens in the 9th documents: [5829 4040 2891 14 1783 381 2693]
>>> print 'Number of tokens in the 9th document: ', corpus_sq.cts_lens[8]
Number of tokens in the 9th document: 7
reformat_file_to_term_sequence¶
tmlib.datasets.utilities.reformat_file_to_term_sequence(file_path)
convert the formatted file input (tf or sq) to file with format term-sequence
Parameter: file_path (string)
Path of file input
Return: path of file which is formatted to term-sequence
>>> from tmlib.datasets import utilities
>>> path_file_tf = tmlib
>>> path_file_sq = utilities.reformat_file_to_term_sequence(path_file_tf)
>>> print 'path to file term-sequence: ', path_file_sq
path to file term-sequence: /home/kde/tmlib_data/ap_train/ap_train.sq
reformat_file_to_term_frequency¶
tmlib.datasets.utilities.reformat_file_to_term_sequence(file_path)
convert the formatted file input (tf or sq) to file with format term-frequency
Parameter: file_path (string)
Path of file input
Return: path of file which is formatted to term-frequency
>>> from tmlib.datasets import utilities
>>> path_file = '/home/kde/Desktop/topicmodel-lib/examples/ap/ap_train.txt'
>>> path_file_tf = utilities.reformat_file_to_term_sequence(path_file)
>>> print 'path to file term-frequency: ', path_file_tf
path to file term-frequency: /home/kde/tmlib_data/ap_train/ap_train.tf
convert_corpus_format¶
tmlib.datasets.utilities.convert_corpus_format(corpus, data_format)
- convert corpus (object of class
Corpus
) to desired format - Parameters:
- corpus: object of class Corpus,
- data_format: format type desired (DataFormat.TERM_SEQUENCE or DataFormat.TERM_FREQUENCY)
- Return: object corpus with desired format
>>> from tmlib.datasets import utilities
>>> path_file_tf = '/home/kde/Desktop/topicmodel-lib/examples/ap/ap_train.txt'
>>> corpus = utilities.load_batch_formatted_from_file(path_file_tf)
>>> corpus_sq = utilities.convert_corpus_format(corpus, utilities.DataFormat.TERM_SEQUENCE)
>>> print 'Unique terms in the 22th documents: ', corpus.word_ids_tks[21]
Unique terms in the 22th documents: [ 32 396 246 87 824 3259 316 285]
>>> print 'Frequency of unique terms in the 22th documents: ', corpus.cts_lens[21]
Frequency of unique terms in the 22th documents: [1 1 1 2 1 1 2 1]
>>> print 'List of tokens in the 22th documents: ', corpus_sq.word_ids_tks[21]
List of tokens in the 22th documents: [32, 396, 246, 87, 87, 824, 3259, 316, 316, 285]
>>> print 'Number of tokens in the 22th document: ', corpus_sq.cts_lens[21]
Number of tokens in the 22th document: 10
compute_sparsity¶
tmlib.datasets.utilities.compute_sparsity(doc_tp, num_docs, num_topics, _type)
- Compute document sparsity.
- Parameters:
- doc_tp: numpy.array, 2-dimention, the estimated topic mixtures of all documents in corpus
- num_docs: int, the number of documents in corpus
- num_topics: int, is the number of requested latent topics to be extracted from the training corpus.
- _type: string, if the value is ‘z’, the topic mixtures is estimated by the sampling method as CGS or CVB0, so we have the individual caculation for this. Otherwise, if the value of it isn’t ‘z’, this is for the methods as: VB, OPE or FW
- Return: float, sparsity of documents
>>> import numpy as np
>>> from tmlib.datasets import utilities
>>> theta = np.array([[0.1, 0.3, 0.2, 0.2, 0.1, 0.1], [0.02, 0.05, 0.03, 0.5, 0.2, 0.2]], dtype='float32')
>>> utilities.compute_sparsity(theta, theta.shape[0], theta.shape[1], _type='t')
1.0
write_topic_proportions¶
tmlib.datasets.utilities.write_topic_proportions(theta, file_name)
- save topic mixtures (theta) to a file
- Parameters:
- theta: numpy.array, 2-dimention
- file_name: name (path) of file which is written


Cody Fajardo not playing; Isaac Harker to start at quarterback for Saskatchewan Roughriders
An oblique injury to Cody Fajardo will prevent him from starting Saturday against Edmonton. Rookie Isaac Harker will start in place of Fajardo.
by Regina Leader-PostWith Cody Fajardo sidelined by an oblique injury, Isaac Harker will start at quarterback for the Saskatchewan Roughriders on Saturday.
CKRM’s Michael Ball reported the news to open the station’s pre-game show.
Fajardo, the Roughriders’ nominee for the CFL’s most-outstanding-player award, was injured in practice on Wednesday.
Harker, 23, is poised to make his first CFL start. A victory by the home team over the Edmonton Eskimos at Mosaic Stadium will give Saskatchewan first place in the West Division for the first time since 2009. Kickoff is scheduled for 2 p.m.
If the Roughriders lose, they can also finish first if the B.C. Lions defeat the Calgary Stampeders on Saturday night.


Your offer has been successfully registered. Our team will get back to you as soon as possible to follow up on it and get right holders’ feedback.


| Number of Classes | Line Coverage | Mutation Coverage | Test Strength |
|---|---|---|---|
| 6 | 93% | 72% | 100% |
| Name | Line Coverage | Mutation Coverage | Test Strength |
|---|---|---|---|
| AlwaysOverride.java | 100% |
100% |
100% |
| ConfigurationValueBuilderDelegate.java | 82% |
50% |
100% |
| ConfigurationValueBuilderHelper.java | 94% |
65% |
100% |
| CurrentValueNotSetOverride.java | 100% |
100% |
100% |
| MayOverride.java | 100% |
100% |
100% |
| NonNullOverride.java | 100% |
100% |
100% |


Images are based on different geometric shapes and patterns: squares, triangles, ...
Images can have 2 to 4 colors from a set of color palettes.
Use Tinygraphs to generate avatars and banners for your users and websites.


Splash Designer
Saving...
Upload a background image. Please note, large images will delay page load times.
Upload your logo which must be 200px by 106px. Upload your banner image which must be 851px by 315px.


Filled with regret, Waiting to die alone
this is just a line of words(fixed a typo)
This is the second line of words


Network Resource Monitor
Home
My machine
System Management
User Management
Real Time
Real time monitoring of network


Consistency semantics define the outcome of multiple accesses to a single file. All file accesses in MPI are relative to a specific file handle created from a collective open. MPI provides three levels of consistency: sequential consistency among all accesses using a single file handle, sequential consistency among all accesses using file handles created from a single collective open with atomic mode enabled, and user-imposed consistency among accesses other than the above. Sequential consistency means the behavior of a set of operations will be as if the operations were performed in some serial order consistent with program order; each access appears atomic, although the exact ordering of accesses is unspecified. User-imposed consistency may be obtained using program order and calls to MPI_FILE_SYNC.
Let FH1 be the set of file handles created from one particular collective open of the file FOO, and FH2 be the set of file handles created from a different collective open of FOO. Note that nothing restrictive is said about FH1 and FH2: the sizes of FH1 and FH2 may be different, the groups of processes used for each open may or may not intersect, the file handles in FH1 may be destroyed before those in FH2 are created, etc. Consider the following three cases: a single file handle (e.g., ), two file handles created from a single collective open (e.g., and ), and two file handles from different collective opens (e.g., and ).
For the purpose of consistency semantics, a matched pair (Section Split Collective Data Access Routines , page Split Collective Data Access Routines ) of split collective data access operations (e.g., MPI_FILE_READ_ALL_BEGIN and MPI_FILE_READ_ALL_END) compose a single data access operation. Similarly, a nonblocking data access routine (e.g., MPI_FILE_IREAD) and the routine which completes the request (e.g., MPI_WAIT) also compose a single data access operation. For all cases below, these data access operations are subject to the same constraints as blocking data access operations.
Advice to users.
For an MPI_FILE_IREAD and MPI_WAIT pair,
the operation begins when MPI_FILE_IREAD is called and
ends when MPI_WAIT returns.
( End of advice to users.)
Assume that A1 and A2 are two data access operations.
Let D1 (D2) be the set of absolute byte displacements
of every byte accessed in A1 (A2).
The two data accesses overlap
if
.
The two data accesses conflict
if they overlap and at least one is a write access.
Let SEQfh be a sequence of file operations on a single file handle, bracketed by MPI_FILE_SYNCs on that file handle. (Both opening and closing a file implicitly perform an MPI_FILE_SYNC.) SEQfh is a ``write sequence'' if any of the data access operations in the sequence are writes or if any of the file manipulation operations in the sequence change the state of the file (e.g., MPI_FILE_SET_SIZE or MPI_FILE_PREALLOCATE). Given two sequences, SEQ1 and SEQ2, we say they are not concurrent if one sequence is guaranteed to completely precede the other (temporally).
The requirements for guaranteeing sequential consistency among all accesses to a particular file are divided into the three cases given below. If any of these requirements are not met, then the value of all data in that file is implementation dependent.
Case 1: $fh_1 FH_1$ All operations on fh1 are sequentially consistent if atomic mode is set. If nonatomic mode is set, then all operations on fh1 are sequentially consistent if they are either nonconcurrent, nonconflicting, or both.
Case 2: $fh_1a FH_1$ and $fh_1b FH_1$ Assume A1 is a data access operation using fh1a, and A2 is a data access operation using fh1b. If for any access A1, there is no access A2 that conflicts with A1, then MPI guarantees sequential consistency.
However, unlike POSIX semantics, the default MPI semantics for conflicting accesses do not guarantee sequential consistency. If A1 and A2 conflict, sequential consistency can be guaranteed by either enabling atomic mode via the MPI_FILE_SET_ATOMICITY routine, or meeting the condition described in Case 3 below.
Case 3: $fh_1 FH_1$ and $fh_2 FH_2$
Consider access to a single file using file handles from distinct collective opens. In order to guarantee sequential consistency, MPI_FILE_SYNC must be used (both opening and closing a file implicitly perform an MPI_FILE_SYNC).
Sequential consistency is guaranteed among accesses to a single file if for any write sequence SEQ1 to the file, there is no sequence SEQ2 to the file which is concurrent with SEQ1. To guarantee sequential consistency when there are write sequences, MPI_FILE_SYNC must be used together with a mechanism that guarantees nonconcurrency of the sequences.
See the examples in Section Examples , page Examples , for further clarification of some of these consistency semantics.
| MPI_FILE_SET_ATOMICITY(fh, flag) | |
| INOUT fh | file handle (handle) |
| IN flag | true to set atomic mode, false to set nonatomic mode (logical) |
int MPI_File_set_atomicity(MPI_File fh, int flag)
MPI_FILE_SET_ATOMICITY(FH, FLAG, IERROR)
INTEGER FH, IERROR
LOGICAL FLAG
void MPI::File::Set_atomicity(bool flag)
Let FH be the set of file handles created by one collective open. The consistency semantics for data access operations using FH is set by collectively calling MPI_FILE_SET_ATOMICITY on FH. MPI_FILE_SET_ATOMICITY is collective; all processes in the group must pass identical values for fh and flag. If flag is true, atomic mode is set; if flag is false, nonatomic mode is set.
Changing the consistency semantics for an open file only affects new data accesses. All completed data accesses are guaranteed to abide by the consistency semantics in effect during their execution. Nonblocking data accesses and split collective operations that have not completed (e.g., via MPI_WAIT) are only guaranteed to abide by nonatomic mode consistency semantics.
Advice
to implementors.
Since the semantics guaranteed by atomic mode are stronger than
those guaranteed by nonatomic mode, an implementation is
free to adhere to the more stringent atomic mode semantics
for outstanding requests.
( End of advice to implementors.)
| MPI_FILE_GET_ATOMICITY(fh, flag) | |
| IN fh | file handle (handle) |
| OUT flag | true if atomic mode, false if nonatomic mode (logical) |
int MPI_File_get_atomicity(MPI_File fh, int *flag)
MPI_FILE_GET_ATOMICITY(FH, FLAG, IERROR)
INTEGER FH, IERROR
LOGICAL FLAG
bool MPI::File::Get_atomicity() const
MPI_FILE_GET_ATOMICITY returns the current consistency semantics for data access operations on the set of file handles created by one collective open. If flag is true, atomic mode is enabled; if flag is false, nonatomic mode is enabled.
| MPI_FILE_SYNC(fh) | |
| INOUT fh | file handle (handle) |
int MPI_File_sync(MPI_File fh)
MPI_FILE_SYNC(FH, IERROR)
INTEGER FH, IERROR
void MPI::File::Sync()
Calling MPI_FILE_SYNC with fh causes all previous writes to fh by the calling process to be transferred to the storage device. If other processes have made updates to the storage device, then all such updates become visible to subsequent reads of fh by the calling process. MPI_FILE_SYNC may be necessary to ensure sequential consistency in certain cases (see above).
MPI_FILE_SYNC is a collective operation.
The user is responsible for ensuring that all nonblocking requests and split collective operations on fh have been completed before calling MPI_FILE_SYNC---otherwise, the call to MPI_FILE_SYNC is erroneous.


{% extends 'base.html' %} {% block page_title %}Update your Profile{% endblock %} {% load static from static %} {% block css %} {% endblock %} {% block content %} Profile Details Customise your public profile This is your opportunity to let your personality shine. A well crafted profile can increase your engagement 4x so try to make it an honest expression of who you are! {% csrf_token %} {{ profile_form }} Profile Photos: {{ formset.management_form }} {% for form in formset %} {{form}} {% if form.initial.image %} {% else %} {% endif %} {% for hidden in form %} {% if not 'image' in hidden.auto_id %} {% endif %} {% endfor %} {% endfor %} {% endblock %} {% block js %} {% endblock %}
Customise your public profile
This is your opportunity to let your personality shine. A well crafted profile can increase your engagement 4x so try to make it an honest expression of who you are!


We would like to here your opinion about the website or if you have any inquiry. Do not hesitate to contact by filling in the form or by dropping-in in the garage or by calling us!
Mobile:
07123456789
Telephone:
01234567890
Email add:
<EMAIL_ADDRESS>Address
123 Fake,
Fake Town,
Fake City.
F4 K35


| HADOOP-8346 |
Changes to support Kerberos with non Sun JVM (HADOOP-6941) broke SPNEGO |
Blocker |
security |
Alejandro Abdelnur |
Devaraj Das |
| HADOOP-8338 |
Can't renew or cancel HDFS delegation tokens over secure RPC |
Major |
security |
Owen O'Malley |
Owen O'Malley |
| HADOOP-8294 |
IPC Connection becomes unusable even if server address was temporarilly unresolvable |
Critical |
ipc |
Kihwal Lee |
Kihwal Lee |
| HADOOP-8293 |
The native library's Makefile.am doesn't include JNI path |
Major |
build |
Owen O'Malley |
Owen O'Malley |
| HADOOP-8251 |
SecurityUtil.fetchServiceTicket broken after HADOOP-6941 |
Blocker |
security |
Todd Lipcon |
Todd Lipcon |
| HADOOP-8151 |
Error handling in snappy decompressor throws invalid exceptions |
Major |
io, native |
Todd Lipcon |
Matt Foley |
| HADOOP-7381 |
FindBugs OutOfMemoryError |
Major |
build |
Joep Rottinghuis |
Joep Rottinghuis |
| HADOOP-6941 |
Support non-SUN JREs in UserGroupInformation |
Major |
. |
Stephen Watt |
Devaraj Das |
| HADOOP-6924 |
Build fails with non-Sun JREs due to different pathing to the operating system architecture shared libraries |
Major |
. |
Stephen Watt |
Devaraj Das |
| HDFS-3374 |
hdfs' TestDelegationToken fails intermittently with a race condition |
Major |
namenode |
Owen O'Malley |
Owen O'Malley |
| HDFS-3310 |
Make sure that we abort when no edit log directories are left |
Major |
. |
Colin Patrick McCabe |
Colin Patrick McCabe |
| HDFS-3265 |
PowerPc Build error. |
Major |
build |
Kumar Ravi |
Kumar Ravi |
| HDFS-3127 |
failure in recovering removed storage directories should not stop checkpoint process |
Major |
namenode |
Brandon Li |
Brandon Li |
| HDFS-3061 |
Backport HDFS-1487 to branch-1 |
Blocker |
namenode |
Alex Holmes |
Kihwal Lee |
| HDFS-1041 |
DFSClient does not retry in getFileChecksum(..) |
Major |
hdfs-client |
Tsz Wo Nicholas Sze |
Tsz Wo Nicholas Sze |
| HDFS-119 |
logSync() may block NameNode forever. |
Major |
namenode |
Konstantin Shvachko |
Suresh Srinivas |
| MAPREDUCE-4207 |
Remove System.out.println() in FileInputFormat |
Major |
mrv1 |
Kihwal Lee |
Kihwal Lee |
| MAPREDUCE-4154 |
streaming MR job succeeds even if the streaming command fails |
Major |
. |
Thejas M Nair |
Devaraj Das |
| MAPREDUCE-4003 |
log.index (No such file or directory) AND Task process exit with nonzero status of 126 |
Major |
task-controller, tasktracker |
toughman |
Koji Noguchi |
| MAPREDUCE-3857 |
Grep example ignores mapred.job.queue.name |
Major |
examples |
Jonathan Eagles |
Jonathan Eagles |
| MAPREDUCE-3377 |
Compatibility issue with 0.20.203. |
Major |
. |
Jane Chen |
Jane Chen |
| MAPREDUCE-1238 |
mapred metrics shows negative count of waiting maps and reduces |
Major |
jobtracker |
Ramya Sunil |
Thomas Graves |


| Process primitives: |
| fork | tbd (Process) |
| execl, execv, execle, ... | tbd (Process) |
| wait, waitpid | tbd (Process) |
| _exit | Process::fatalError |
| errno | ??? |
| environ | ??? |
| Process primitives (signals): |
| kill | tbd (Signal?) |
| sig*set, sigismember | tbd |
| sigaction | tbd |
| sigprocmask | tbd |
| sigpending | tbd |
| sigsuspend | tbd |
| alarm | tbd |
| pause | tbd |
| sleep | tbd |
| Process environment: |
| getpid, getppid | tbd |
| getuid, geteuid, getgid, getegid | na |
| setuid, setgid | na |
| getgroups | na |
| getlogin, cuserid | tbd |
| getpgrp, setsid, setpgid | na |
| uname | tbd |
| time | tbd |
| times | tbd |
| getenv | ??? |
| ctermid | na |
| ttyname, isatty | na |
| sysconf | na |
| Files and directories: |
| opendir, readdir, rewindir, closedir | bw::Directory Class |
| chdir | tbd (FileName) |
| getcwd | FileName::currentDirectory |
| open, creat bw::BFile object |
| umask | na |
| link | na |
| mkdir | tbd (Directory) |
| mkfifo | na |
| unlink | tbd (FileName) |
| rmdir | tbd (Directory) |
| rename | tbd (FileName) |
| stat, fstat | FileName::getFileType |
| access | tbd (FileName) |
| chmod | na |
| chown | na |
| utime | na |
| pathconf, fpathconf | na |
| I/O primitives: |
| pipe tbd |
| dup, dup2 na |
| close bw::BFile object |
| read bw::BFile object |
| write bw::BFile object |
| fcntl tbd (BFile) |
| lseek bw::BFile object |
| Device control functions (terminals): |
| cf*speed na |
| tcgetattr, tcsetattr na |
| tcsendbreak, tcdrain, tcflush, tcflow na |
| tcgetpgrp, tcsetpgrp na |
| C Language services (locales): |
| setlocale bw::Process object |
| fileno na |
| fdopen na |
| sigsetjmp, siglongjmp na |
| tzset tbd (Process) |
| System databases (users and groups): |
| getgrgid, getgrnam na |
| getpwuid, getpwnam na |
|


Class: Citygate::User
- Inherits:
-
ActiveRecord::Base
- Object
- ActiveRecord::Base
- Citygate::User
- Includes:
- Gravatar
- Defined in:
- app/models/citygate/user.rb
Overview
Instance Attribute Summary (collapse)
-
- (String) email
The email of the user.
-
- (String) name
The name of the user.
Instance Method Summary (collapse)
-
- (Hash) as_json(options = {})
Get the json object for an user.

-
- (String) name_or_email
Get the name if it is present or else get the email.
-
- (Object) to_s
Alias for name_or_email.
Methods included from Gravatar
Instance Attribute Details
- (String) email
The email of the user
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
# File 'app/models/citygate/user.rb', line 8 class User < ActiveRecord::Base include Gravatar # @!group Devise configurations devise :database_authenticatable, :registerable, :encryptable, :recoverable, :rememberable, :trackable, :validatable, :confirmable, :omniauthable devise :encryptor => :sha1 # @!endgroup attr_accessible :first_name, :last_name, :email, :password, :password_confirmation, :remember_me, :confirmed_at, :role_id has_many :authorizations, :dependent => :destroy belongs_to :role before_create :check_no_of_users # Get the json object for an user. Used by to_json. # @example # { # "email"<EMAIL_ADDRESS># "name": "First User", # "link": null, # "image": "https://secure.gravatar.com/avatar/b58996c504c5638798eb6b511e6f49af.png/rating=g" # } # @return [Hash] the json object in the form of an hash def as_json( = {}) = self..first { email: email, name: first_name || (.name if ), link: (.link if ), image: (.image_url if ) || self.gravatar_url } end # Get the name if it is present or else get the email # @return [String] the name or email of the user def name_or_email self.full_name || self.email end # Get the user's full name (the concatenation of his first and last names) # @return [String] the user's full name def full_name name = "#{self.first_name} #{self.last_name}".strip (name.blank?) ? nil : name end # Alias for name_or_email def to_s self.name_or_email end protected def check_no_of_users if Citygate::Engine.no_of_users > 0 && Citygate::User.count >= Citygate::Engine.no_of_users self.errors.add(:base,I18n::t('users.errors.too_many')) return false else return true end end end |
- (String) name
The name of the user
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
# File 'app/models/citygate/user.rb', line 8 class User < ActiveRecord::Base include Gravatar # @!group Devise configurations devise :database_authenticatable, :registerable, :encryptable, :recoverable, :rememberable, :trackable, :validatable, :confirmable, :omniauthable devise :encryptor => :sha1 # @!endgroup attr_accessible :first_name, :last_name, :email, :password, :password_confirmation, :remember_me, :confirmed_at, :role_id has_many :authorizations, :dependent => :destroy belongs_to :role before_create :check_no_of_users # Get the json object for an user. Used by to_json. # @example # { # "email"<EMAIL_ADDRESS># "name": "First User", # "link": null, # "image": "https://secure.gravatar.com/avatar/b58996c504c5638798eb6b511e6f49af.png/rating=g" # } # @return [Hash] the json object in the form of an hash def as_json( = {}) = self..first { email: email, name: first_name || (.name if ), link: (.link if ), image: (.image_url if ) || self.gravatar_url } end # Get the name if it is present or else get the email # @return [String] the name or email of the user def name_or_email self.full_name || self.email end # Get the user's full name (the concatenation of his first and last names) # @return [String] the user's full name def full_name name = "#{self.first_name} #{self.last_name}".strip (name.blank?) ? nil : name end # Alias for name_or_email def to_s self.name_or_email end protected def check_no_of_users if Citygate::Engine.no_of_users > 0 && Citygate::User.count >= Citygate::Engine.no_of_users self.errors.add(:base,I18n::t('users.errors.too_many')) return false else return true end end end |
Instance Method Details
- (Hash) as_json(options = {})
Get the json object for an user. Used by to_json.
34 35 36 37 38 39 40 41 42 |
# File 'app/models/citygate/user.rb', line 34 def as_json( = {}) = self..first { email: email, name: first_name || (.name if ), link: (.link if ), image: (.image_url if ) || self.gravatar_url } end |
- (Object) check_no_of_users (protected)
64 65 66 67 68 69 70 71 |
# File 'app/models/citygate/user.rb', line 64 def check_no_of_users if Citygate::Engine.no_of_users > 0 && Citygate::User.count >= Citygate::Engine.no_of_users self.errors.add(:base,I18n::t('users.errors.too_many')) return false else return true end end |
- (String) full_name
Get the user’s full name (the concatenation of his first and last names)
52 53 54 55 |
# File 'app/models/citygate/user.rb', line 52 def full_name name = "#{self.first_name} #{self.last_name}".strip (name.blank?) ? nil : name end |
- (String) name_or_email
Get the name if it is present or else get the email
46 47 48 |
# File 'app/models/citygate/user.rb', line 46 def name_or_email self.full_name || self.email end |
- (Object) to_s
Alias for name_or_email
58 59 60 |
# File 'app/models/citygate/user.rb', line 58 def to_s self.name_or_email end |


AC Milan fan stabbed in fight over players’ shorts – reports
by AFPAn AC Milan supporter was stabbed outside Bologna stadium Sunday in a fight over who had caught a players’ shorts, according to media reports in Italy.
The dispute broke out between the two visiting fans after Milan players threw shirts and shorts into the stands after the final whistle following their 3-2 away win.
It continued outside the Stadio Dall’Ara with one fan being stabbed in the abdomen and taken to hospital in a serious condition.
The argument was over who had caught one of the players’ shorts, Gazzetto Dello Sport said, quoting local reports.
Former European giants AC Milan got back-to-back Serie A wins for just the second time this season to move up to 10th place in the Italian league.


This page is appropriate timing for me as I picked up a pretty nasty tummy bug that has me wiped. I haven’t training since yesterday morning.  I still swam. I didn’t do my planned set. I did a super easy session. I woke up today feeling much worse. I learned my mistake from yesterday and training was definitely a no no. I’ll use my HR readings from HRV to determine if I will do an easy session tomorrow. If it is still super high, then I will leave it and try and rest as much as I can.
Here are some rules:
If you’re sick or on the verge of becoming sick, immediately take a day off. Then see how you feel the following day and make the decision to train.
When coming back to training, it is essential that you ease the body into the training. 2-3 days that would look like this:
I understand you may be highly driven to get back into training but doing so too quickly will see both your performance (consistency) and health suffer. You could potentially lose 1-2 weeks fitness if your train through it. Play it smart and don’t gamble with your health.
Another reason to re-learn to listen what your body is telling you.


A sample application that demonstrates how to create an interactive Android home screen widget that launches an application activity.
When installed, this adds a "Wiktionary" option to the widget installation menu. The word of the day is downloaded from Wiktionary and displayed in a frame. Touching the widget will launch a custom application activity showing more details.


Your gift card claim code may also be entered when prompted during checkout. To redeem your gift card using the Amazon.com 1-Click® service, first add the gift card funds to Your Account.
If you have questions about redeeming your gift card, please visit www.amazon.com/gc-redeem. If you have questions regarding the BitPay Introductory offer, please contact BitPay.


Clack
Menu
Home
Categories
Login
All
Albums
albumName
Image
Image
{{_photoToCreateT.AlbumName}}
View


DeVry University
Menu
Why DeVry
Colleges & Degree Programs
Locations
Online Options
Financial Aid
Admissions
Career Services


---
layout: post
title: Single-Step Debugging the Uno ATMEGA328 (aka Arduino Uno) for the M5Stack LoRa
date: '2018-03-10T11:57:00.000-08:00'
author: gojimmypi
tags:
- M5Stack
- Atmel
- LoRa
- Arduino
- ATMEGA328
- Atmel ICE
- SPI
- Debugger
- ISP
- Debug
- Embedded Debugging
- Espressif
modified_time: '2018-03-12T10:08:10.766-07:00'
thumbnail: https://4.bp.blogspot.com/-3Ms885AV0DI/WqQXMNKTLGI/AAAAAAAAA7o/UFFziDa2Xe043k2hYXmdVoJ1SJT-wM6fwCLcBGAs/s72-c/LoRa%2BV2.PNG
blogger_id: tag:blogger.com,1999:blog-4109066286647243251.post-936036907833975522
blogger_orig_url: https://gojimmypi.blogspot.com/2018/03/single-step-debugging-atmega328-arduino-uno.html
---
Today I revisit my old friend, the Microchip Atmel ATMEGA328P
(aka Arduino Uno), although this is unlike any Arduino I've had before!
This is a continuation of my
previous blog, where I am working on improving the range of LoRa on the ESP32-based M5Stack
module. Many thanks to Jimmy Lai that sent me a prototype of the next version of LoRa module!
Note my LoRa board arrived with no headers soldered on. There's not really room for a typical install of the usual jumper headers due to how thin this the module is. I still used the standard header, but given the limited space, I inserted the plastic on the reverse side of the board and snipped off the pins on that side.
I soldered from above. I can't remember the last time I did this - if ever. One thing to keep in mind is to use as little solder as possible, as quickly as possible. The solder will want to wick up the post, making them ever-so-slightly thicker. Also - if there's a glob of solder at the bottom, the jumper wire won't fit all the way, and we're already a little on the short side from installing the header on the other side.
Ready to proceed with the fun stuff...
Upon close inspection of this new LoRa board, there's a tiny ATMEGA328P sitting there! (yes, the same micro-controller in the famous
Arduino Uno (see typical Uno schematic
).
Unlike a "regular" Arduino, there's no need to cut the trace for "Reset EN". (regular Arduinos programmed with the serial port have DTR connected to reset with a capacitor that otherwise interferes with ISP).
This Mega328 has the 28 pin, MLF form factor:
See the full
ATMega328 spec sheet here
. This Mega 328 LoRa board is connected like this:
As shown, the Mega328 sits between the ESP32 core and the LoRa RA-02 transceiver. So the plan is interrupt driven LoRa that the Mega328 handles, while I2C will be used to exchange data from the ESP32 core and the Mega328. So before doing anything, this sounds complex enough to need a serious debugger.
Fortunately I already had an
Atmel ICE debugger and Atmel Studio 7
installed. As a reminder, here are the instructions:
First: ensure the latest version of Atmel Studio is installed via Help - Check for Updates (I am using Version 7.0.1645). It may also prompt you to update the firmware in the ICE Debugger. (1.27 in my case). See
Tools - Device Programming
.
The most difficult thing is figuring out the pins to connect. Unfortunately the LoRa module has the ISP connections in a line rather than the standard connection. Adding to the frustration is the fact that the DuPont pins on the octopus cable have taped-on numbers. Although it is handy having the connections labeled, the thickness of the tape makes having 6 of them in a row a
very
tight fit:
As a feature request, I hope the M5Stack folks consider implementing a standard SPI / ICSP connector:
In the meantime, it would probably be worthwhile to create a custom inline Dupont header using a kit such as this and this crimping tool
. (this is incredibly handy to have)
The single most important thing is that: MO = MOSI = TDI (I wasted a bunch of time when I had assumed MO = MisO = TDO; which is not correct!). Also note that nTRST != nSRST. (yes, I made that mistake, too; my original screen clip did not include the SPI column which makes this more obvious).
These are the correct connections for debugWIRE, aka SPI, aka ISP programming of the M5Stack LoRa ATMEGA328P:
Note that VTG is not suited to power a remote device. This is a power sensor to determine logic voltage levels. Devices being debugged, including this LoRa module most be self powered!
For more information on the ICE Debugger,
read the fine manual
.
I started with a simple Uno sketch file:
{% include code_header.html %}
void setup() {
// put your setup code here, to run once:
Serial.begin(115200);
}
void loop() {
// put your main code here, to run repeatedly:
Serial.println("hello");
}
Then in Atmel Studio: File - New - Project:
Select "Create project from Arduino sketch", edit directories as needed, then press ok. Next select the file location of the Arduino sketch. Be sure to select Arduino/Genuino Uno for Board:
It may take a short while to create a project.
Once the project is created, click Project - {
name
} Properties (Alt-F7). Select Atmel ICE Debugger and ensure Debug Wire is selected for interface:
Next - click Debug - Step Into. The first time, there will likely be a warning about fuse settings:
Press Yes to set the fuses appropriately. If you get any other errors, check the wiring. Then recheck the wiring.
If successful, there will be a message that debugWIRE is enabled. BE SURE TO READ THE MESSAGE:
Note that you MUST power cycle the Mega328 (in our case the M5Stack & LoRa). Do not power cycle the Atmel ICE debugger.
Press ok, and VOILA!
I recall reading that the ATMega328 *must* have debugging properly disabled upon completion or Really Bad Things might happen (really?). I still do this, although I've never actually tested it to be sure. While paused at a breakpoint, click Disable debugWIRE and Close.
So the next step will be seeing if I can get the RadioHead drivers working on this new LoRa module... but first I'd like to get the LoRa
AVR serial port working
.
Resources, Inspiration, Credits, and Other Links:
Copyright (c) gojimmypi all rights reserved. Blogger Image Move Cleaned: 5/3/2021 1:35:53 PM


CSAPI(Subscribers) Category Reference
| Declared in | CSAPI+Subscribers.h |
Tasks
-
– subscribeToListWithID:emailAddress:name:shouldResubscribe:customFields:completionHandler:errorHandler:
Adds a subscriber to an existing subscriber list, including custom field data if provided. If the subscriber (email address) already exists, their name and any custom field values are updated.
-
– updateSubscriptionWithListID:currentEmailAddress:newEmailAddress:name:shouldResubscribe:customFields:completionHandler:errorHandler:
Update an existing subscriber, including email address, name, and custom field data if supplied.
-
– unsubscribeFromListWithID:emailAddress:completionHandler:errorHandler:
Change the status of a subscriber from
Active
toUnsubscribed
. -
– getSubscriberDetailsWithListID:emailAddress:completionHandler:errorHandler:
Get a subscribers details including their email address, name, active/inactive state and any custom field data.
-
– getSubscriberHistoryWithListID:emailAddress:completionHandler:errorHandler:
Get a list of all campaigns or autoresponder emails, to which a subscriber has made some trackable action. For each campaign or autoresponder email, all actions are provided, including the event type, date and IP address from which the event occurred.
-
– importSubscribersToListWithID:subscribers:shouldResubscribe:shouldQueueSubscriptionBasedAutoresponders:shouldRestartSubscriptionBasedAutoresponders:completionHandler:errorHandler:
Import many subscribers into a subscriber list, including any custom field data if provided.
Instance Methods
getSubscriberDetailsWithListID:emailAddress:completionHandler:errorHandler:
Get a subscribers details including their email address, name, active/inactive state and any custom field data.
- (void)getSubscriberDetailsWithListID:(NSString *)listID emailAddress:(NSString *)emailAddress completionHandler:(void ( ^ ) ( CSSubscriber *subscriber ))completionHandler errorHandler:(CSAPIErrorHandler)errorHandler
Parameters
- listID
The ID of the subscriber list to which the subscriber belongs
- emailAddress
The ID of the subscriber whose details should be retrieved
- completionHandler
Completion callback, with a
CSSubscriber
instance as the first and only argument.
- errorHandler
Error callback
Discussion
http://www.campaignmonitor.com/api/subscribers/#getting_a_subscribers_details
Declared In
CSAPI+Subscribers.h
getSubscriberHistoryWithListID:emailAddress:completionHandler:errorHandler:
Get a list of all campaigns or autoresponder emails, to which a subscriber has made some trackable action. For each campaign or autoresponder email, all actions are provided, including the event type, date and IP address from which the event occurred.
- (void)getSubscriberHistoryWithListID:(NSString *)listID emailAddress:(NSString *)emailAddress completionHandler:(void ( ^ ) ( NSArray *historyItems ))completionHandler errorHandler:(CSAPIErrorHandler)errorHandler
Parameters
- listID
The ID of the subscriber list to which the subscriber belongs
- emailAddress
The email address of the subscriber whose history should be retrieved
- completionHandler
Completion callback, with an array of
CSSubscriberHistoryItem
objects
- errorHandler
Error callback
Discussion
http://www.campaignmonitor.com/api/subscribers/#getting_a_subscribers_history
Declared In
CSAPI+Subscribers.h
importSubscribersToListWithID:subscribers:shouldResubscribe:shouldQueueSubscriptionBasedAutoresponders:shouldRestartSubscriptionBasedAutoresponders:completionHandler:errorHandler:
Import many subscribers into a subscriber list, including any custom field data if provided.
- (void)importSubscribersToListWithID:(NSString *)listID subscribers:(NSArray *)subscribers shouldResubscribe:(BOOL)shouldResubscribe shouldQueueSubscriptionBasedAutoresponders:(BOOL)shouldQueueSubscriptionBasedAutoresponders shouldRestartSubscriptionBasedAutoresponders:(BOOL)shouldRestartSubscriptionBasedAutoresponders completionHandler:(void ( ^ ) ( CSSubscriberImportResult *subscriberImportResult ))completionHandler errorHandler:(CSAPIErrorHandler)errorHandler
Parameters
- listID
The ID of the subscriber list to which the subscribers should be added
- subscribers
An array of
CSSubscriber
objects
- shouldResubscribe
Whether or not to resubscribe inactive subscribers
- shouldQueueSubscriptionBasedAutoresponders
By default, Autoresponder emails that are based on the subscription date will not be sent for subscribers imported with this method. This can be overridden by setting the shouldQueueSubscriptionBasedAutoResponders parameter to true.
- shouldRestartSubscriptionBasedAutoresponders
By default, resubscribed subscribers will not restart any subscription-based autoresponder sequences, but they will receive any remaining emails. However, if you specify the shouldRestartSubscriptionBasedAutoresponders input value as true, any sequences will be restarted.
- completionHandler
Completion callback, with a
CSSubscriberImportResult
object containing information about the import.
- errorHandler
Error callback
Discussion
http://www.campaignmonitor.com/api/subscribers/#importing_many_subscribers
Declared In
CSAPI+Subscribers.h
subscribeToListWithID:emailAddress:name:shouldResubscribe:customFields:completionHandler:errorHandler:
Adds a subscriber to an existing subscriber list, including custom field data if provided. If the subscriber (email address) already exists, their name and any custom field values are updated.
- (void)subscribeToListWithID:(NSString *)listID emailAddress:(NSString *)emailAddress name:(NSString *)name shouldResubscribe:(BOOL)shouldResubscribe customFields:(NSArray *)customFields completionHandler:(void ( ^ ) ( NSString *subscribedAddress ))completionHandler errorHandler:(CSAPIErrorHandler)errorHandler
Parameters
- listID
The ID of the subscriber list to which the subscriber should be added
- emailAddress
Email address of the subscriber
- name
Name of the subscriber
- shouldResubscribe
Whether to resubscribe subscribers in the inactive or unsubscribed state
- customFields
An array of
CSCustomField
objects
- completionHandler
Completion callback, with the email address of the new subscriber as the first and only argument
- errorHandler
Error callback
Discussion
http://www.campaignmonitor.com/api/subscribers/#adding_a_subscriber
Declared In
CSAPI+Subscribers.h
unsubscribeFromListWithID:emailAddress:completionHandler:errorHandler:
Change the status of a subscriber from Active
to Unsubscribed
.
- (void)unsubscribeFromListWithID:(NSString *)listID emailAddress:(NSString *)emailAddress completionHandler:(void ( ^ ) ( void ))completionHandler errorHandler:(CSAPIErrorHandler)errorHandler
Parameters
- listID
The ID of the subscriber list from which the subscriber should be unsubscribed
- emailAddress
Email address of the subscriber you’d like to unsubscribe
- completionHandler
Completion callback
- errorHandler
Error callback
Discussion
http://www.campaignmonitor.com/api/subscribers/#unsubscribing_a_subscriber
Declared In
CSAPI+Subscribers.h
updateSubscriptionWithListID:currentEmailAddress:newEmailAddress:name:shouldResubscribe:customFields:completionHandler:errorHandler:
Update an existing subscriber, including email address, name, and custom field data if supplied.
- (void)updateSubscriptionWithListID:(NSString *)listID currentEmailAddress:(NSString *)currentEmailAddress newEmailAddress:(NSString *)newEmailAddress name:(NSString *)name shouldResubscribe:(BOOL)shouldResubscribe customFields:(NSArray *)customFields completionHandler:(void ( ^ ) ( void ))completionHandler errorHandler:(CSAPIErrorHandler)errorHandler
Parameters
- listID
The ID of the subscriber list containing the subscriber you’d like to update
- currentEmailAddress
Existing email address of the subscriber
- newEmailAddress
New email address of the subscriber
- name
New name of the subscriber
- shouldResubscribe
Whether or not to resubscribe inactive subscribers
- customFields
An array of
CSCustomField
objects to update
- completionHandler
Coompletion callback
- errorHandler
Error callback
Discussion
http://www.campaignmonitor.com/api/subscribers/#updating_a_subscriber
Declared In
CSAPI+Subscribers.h


JS Quiz
This is a quiz designed to help you understand some of the fundamentals of JavaScript, press the button below to start the Quiz.
This is a quiz designed to help you understand some of the fundamentals of JavaScript, press the button below to start the Quiz.


START OVER
1 CTA
Two CTA's
- Go to "What"
- Set template to "Custom HTML"
- Again, change to "Custom HTML"
- Click "Source" and paste the HTML there
- Click "Source" again to save, and double check to make sure everything fits within the mock-up - check
different phone sizes as well
- Next set overlay to "Dark"
- Make sure Javascript is enabled
- Uncheck the "Include close button"
- Done 😃


CrimPapers
Articles published on 1 June 2019
On 1 June 2019, 6 new articles were published in 4 journals.
Blurring the Distinction Between Empirical and Normative Legitimacy? A Methodological Commentary on ‘police Legitimacy and Citizen Cooperation in China’
Social Disorganization Theory in Contemporary China: A Review of the Evidence and Directions For Future Research
Understanding the Expression of Grievances in the Arabic Twitter-Sphere Using Machine Learning
The Abusive Behaviour By Children- Indices (abc-I): A Measure to Discriminate Between Normative and Abusive Child Behaviour
Terror in France: the Rise of Jihad in the West by John C. Zimmerman
Secessionism and Terrorism: Bombs, Blood, and Independence in Europe and Eurasia by Brandon Stewart


and the with was for {background patients remdesivir respiratory margin {border-style COVID- al., were day px;} #ffffff;} The SARS-CoV- from March clinical five viral Case February January acute coronavirus nasopharyngeal samples severe this treated Remdesivir after case had lower patient px; started treatment two virological {font-family any because been but care days due failure font-size have renal study syndrome that All centre disease double; -year-old Bichat-Claude Elsevier ICU admitted antiviral between border-color border-width chest died discontinued first load novel oxygen patients, patients. report solid; tract {font-style {font-weight Bernard Engl French IU/l Infect Med PCR Paris, University approved available before bold cannula courier; data despite diagnosed did discharged distress em; fever has illness infectious infusion intensive kidney lopinavir/ritonavir mechanical not observed other presented pt;} randomized replication requiring research resource showed upper value


|
DSQMKryST
0.2
|
Massively parallel implementation and approaches to simulate quantum dynamics using Krylov subspace techniques
Current version : v0.2 (January, 2018)
DSQMKryST is an application based on implemented distributed memory parallel algorithms in order to provide a computational framework suitable for massively parallel supercomputers to study the dynamics of one-dimensional quantum systems.
The main idea is to approximate the solutions to the time-dependent Schroedinger equation by means of Krylov subspace techniques using a carefully constructed and distributed basis for the Hilbert subspace and a sparse matrix representation of the Hamiltonian of the system.
Currently calculates estimations on the Loschmidt echo to show the basic scope of the application, but adding estimation to other quantum observables can be integrated with ease (such as magnetization and imbalance, or even entanglement entropy).
The application depends on PETSc, SLEPc and Boost
This in-depth documentation refers to the DSQMKryST project and it's composed of two different modules: NodeComm and RingComm. Further information about the difference between each of the approaches documented here can be found on the manuscript located in /docs.


1.0 Introduction
1.1 Objectives
1.2 What is Computer?
1.2.1 History of Computers
1.2.2 Characteristics Of Computer System
1.2.3 Basic Applications of Computer
1.3 Components of Computer System
1.3.1 Central Processing Unit
1.3.2 Keyboard, mouse and VDU
1.3.3 Input devices
1.3.4 Output devices
1.3.5 Computer Memory
1.4 Concept of Hardware and Software
1.4.1 Hardware
1.4.2 Software
<IP_ADDRESS> Application Software
<IP_ADDRESS> Systems software
1.4.3 Programming Languages
1.5 Representation of Data/Information
1.6 Concept of Data processing
1.7 Applications of IECT
1.7.1 e-governance
1.7.2 Multimedia and Entertainment
1.8 Summary


2018 results
Governor
The race for governor was a near tie with Republican Jane Doe winning by 0.9 points.
It was an important victory for Republicans. The race one of six key races identified by JamaicaWatch ahead of the Nov. 6 election. Those races broke overwhelmingly for Doe's party.
County by County results
Votes cast by county and party
Doe won in 70 of Jamaica's 99 counties. That included three of the 10 biggest counties. Doe's largest margin was in Woodbury County -- beating Smith by 8,517 votes out of 42,300 cast.
The result was in line with others in Jamaica, which saw Republicans chalking up victories in most tight races. It keeps the governor's mansion in Republican hands. Prior to this the job has been held by Republicans for more than two decades.
Historical results
Republicans have WON THREE of the past FIVE elections. DOE will succeed DEMOCRAT SCOTTY PIPPIN, who WAS NOT A CANDIDATE in the general election.
Recent gubernatorial elections
Voter turnout over time
Turnout was down from four years earlier and lower than in statewide races two years ago. Over the past TWO DECADES higher turnout in JAMAICA has GENERALLY FAVORED Republicans.


Deep Learning researcher at AZmed, I am fascinated by AI, Deep Learning and Computer Vision. I truly believe in the #AIforGood movement, that aims to place the AI at the service of the human and the common good. In the years to come, I aspire to join major projects in e-health, that will revolutionize the current practices.
A powerful detection algorithm for one medical center, but unsuitable for another. This is one of the major problems faced by research teams wishing to deploy their models on a large scale. In this article, I introduce this challenge called Domain Adaptation in the context of medical imaging, based on my experience at AZmed.
Research report comparing two main approaches - Radiomics and Deep Learning - to detect malignant soft tissue tumors directly on magnetic resonance imaging.
Utilities to download (CLI) and load an MRI brain tumor dataset with Python, providing 2D slices, tumor masks and tumor classes.
Application of object detection / segmentation (MaskRCNN and RetinaNet) on Mars satellite images. The goal was to detect automatically craters.
Implementation of the reinforcement learning technique Q-Learning. The environment was MountainCar (OpenAI Gym), where the goal is to teach a car to swing from left to right so that it can climb the mountain.
Development of various visualizations to respond to the VAST mini-challenge 1 of 2018. The goal was to discover if the actions of a company had led to the migration of birds. Spatio-temporal data analysis led to the development of an interactive map to observe the movements of different species. Spectral analysis of bird songs has also revealed interesting facts.
Maximum a posteriori (MAP) estimation on NMF (non-negative matrix factorization), applied on faces.
Development of an Open Source archiving system allowing the storage of any type of data. The users can interact with web-based system through the website or directly from scripts (using the developed Python library). Originally, the application was designed and developed within SoftBank Robotics to store tests performed on NAO and Pepper robots.
Participation in two editions of the French Robotics Cup with DaVinciBot, association of the Pôle Universitaire Léonard de Vinci.
A web application that makes it easy to find all Michelin starred restaurants that currently offer promotions in France.
Web application to easily explore a MongoDB database, containing the 5000 best selling movies on Amazon.
The goal of this project was to develop an Android application similar to Youtube, using their API.
Classes followed during the 1st semester:
Classes followed during the 2nd semester:
Major: Computer Science, Big Data and connected objects
Activities and associations : Member and Communication manager of the association of robotics DaVinciBot.
Example of the classes followed:
Major: Computer Science
Thanks to an exchange program, I had the opportunity to study in this american university for one semester.
Article about my experience (by ESILV)
Classes followed:
GPA: 4.0 (A)
Scientific Baccalaureat, with high honours
FishFriender is an online platform that aims to modernize the angling industry.
➢ Mission: automate data recovery and process partner data to adapt and insert them into the application database.
The partner data were either in Excel sheets or in web pages. I extracted the data using web scraping techniques.
✹ Tech used: Node.js, JavaScript, SQL, JSON.
✹ Environment: Mac OS
DaVinciBot is an association of Leonard De Vinci University, which aims to gather all the students interested in robotics. It was first created to participate to the French Cup of Robotics. Now we work on several projects like InMoov, a life size humanoid robot we are printing in 3D.
Summer Job in a sports shop.
Summer Job in an amusement park.
- Python # Numpy, Pandas, Scikit-Learn, TensorFlow, Keras, Matplotlib
- Web development # HTML, CSS, JavaScript, jQuery, Node.js, Express.js
- Databases # SQL (MySQL, PostgreSQL), NoSQL (MongoDB), ElasticStack
- C# / Java
- R
- Git
- Jupyter
- LaTeX
- Unix
- RStudio
- VS Code
- Android Studio
- Adobe Photoshop
- 🇫🇷 French # native
- 🇬🇧 English # fluent
- 🇪🇸 Spanish # notions


evothings.iotsensor. instance_ble
Source: evothings-libraries/
Abstract IoT Sensor instance object. This object specifies the interface common to BLE IoT Sensors
Methods
connectToClosestSensor(scanTime, callbackFun, disconnectFun)
Connect to nearest IoT Sensor device.
Example
iotsensor.connectToClosestSensor(
3000, // Scan for 3000 ms
function()
{
// Connected and device is ready
},
function(error)
{
console.log('Disconnect error ' + error);
}
);
Parameters
| Name | Type | Optional | Description |
|---|---|---|---|
|
scanTime |
integer |
|
Time (in ms) to scan for nearby IoT Sensors (optional, default 2 seconds) |
|
callbackFun |
function() |
|
Callback called when connected and device is initalized |
|
disconnectFun |
function() |
|
Callback called when connection is lost (optional) |
connectToDevice(device, callbackFun, disconnectFun)
Connect to an IoT Sensor device
Example
iotsensor.connectToDevice(
device,
function()
{
// Connected and device is ready
},
function(error)
{
console.log('Disconnect error ' + error);
}
);
Parameters
| Name | Type | Optional | Description |
|---|---|---|---|
|
device |
|
A evothings.easyble.device object |
|
|
callbackFun |
function() |
|
Callback called when connected and device is initialized |
|
disconnectFun |
function() |
|
Callback called when connection is lost (optional) |
disableAllSensors()
RAW and SFL. Implementation of evothings.iotsensor.instance#disableAllSensors
Example
iotsensor.disableAllSensors();
disconnectDevice()
Disconnect from the connect device
Example
iotsensor.disconnectDevice();
isIoTSensor(device) → boolean
Determine if a BLE device is a IoT Sensor.
Checks the general case using the advertised name.
Specific implementation is added for SFL and RAW.
Example
if(iotsensor.isIoTSensor(device))
{
// connect to device
}
Parameter
| Name | Type | Optional | Description |
|---|---|---|---|
|
device |
|
easyble device object |
- Returns
-
boolean
startScanningForDevices(callbackFun)
Start a manual scan for physical devices.
This process can be automated by using connectToClosestSensor(scanTime, callbackFun, disconnectFun)
The device (evothings.easyble.EasyBLEDevice) that is found is passed to callbackFun and can be used to inspect device fields to determine properties
such as RSSI, name etc.
You can call isIoTSensor(device) to determine if this is an IoT sensor. To connect, call connectToDevice(device, callbackFun, disconnectFun).
Example
iotsensor.startScanningForDevices(
function(device)
{
console.log('Device found: ' + device.name + ' RSSI: ' + device.rssi);
if(iotsensor.isIoTSensor(device))
{
// We have an IoT Sensor, let's connect!
iotsensor.connectToDevice(
device,
function()
{
// Connected and device is ready
},
function(error)
{
console.log('Disconnect error ' + error);
}
);
}
}
);
Parameter
| Name | Type | Optional | Description |
|---|---|---|---|
|
callbackFun |
function() |
|
Callback called with found device: callbackFun(device). |
stopScanningForDevices()
Stop scanning for physical devices and call statusCallback with STOPPED_SCANNING message.
Example
iotsensor.stopScanningForDevices();


Within the C++ standard iostreams, locale is a class. Class locale is used for two distinct purposes:
The formatting layer of iostreams uses locale facilities for numeric formatting and character classification.
The transport layer uses locale facilities for code conversion.
In the standard iostream class templates, the formatting (stream) layer uses only the ctype, num_put, and num_get facets, while the transport (stream buffer) layer uses only the codecvt facet. For more information on locales and facets, see Chapter 24 and the sections of the Apache C++ Standard Library Reference Guide devoted to the locale class and the various facets.
Each layer contains its own locale, so the code conversion facility can be changed independent of numeric formatting. We simply change the locale associated with a stream buffer without altering the locale contained by the stream using that buffer. On the other hand, changing a stream's locale also changes the locale contained by the associated stream buffer.
The base class for all streams, ios_base, contains a locale object. This locale object can be accessed with the getloc() member function and replaced with the imbue() member function. Similarly, basic_streambuf also contains a locale object and it, too, defines getloc() and imbue() member functions (the latter is protected and virtual and called from the publicly accessible pubimbue() member function).


| Interface | Description |
|---|---|
| FreqBuilder |
Every type of frequency builder will have these methods.
|
| Tokenizer |
Interface tokenizer used to tokenize sentences.
|
| Class | Description |
|---|---|
| Body |
Body holds an ArrayList of strings,
where the strings represent predicates/descriptions.
|
| Box |
Box is made up of a Head and a Body,
where the Head is a single word and
the Body is the description of a Head.
|
| BoxBuilder |
The BoxBuilder's purpose to instantiate Box objects.
|
| BoxMerger | |
| Head |
Represents the subject of a sentence, held as a string.
|
| MapBuilder | |
| ParaMap | |
| PoSFrequency |
Builds a HashMap out of the frequencies of parts of speech appearing
in the input String.
|
| SentenceTokenizer | |
| SubPredSeparator | |
| Tagger |
This class tags a given sentence
|
| TextSimplifier |
TextSimplifier uses interfaces and instances of other classes to break down and simplify the input.
|
| WhiteSpaceTokenizer |
The WhiteSpaceTokenizer's purpose is to tokenize a String input based on
OpenNLP's whitespace tokenizer model (i.e.
|
| WordFrequency |
WordFrequency implements FreqBuilder and The its purpose is to map
how many times each token occurs in a String input.
|