burnout | DRY interface for building Selenium 2 WebDriver scripts | Functional Testing library
kandi X-RAY | burnout Summary
kandi X-RAY | burnout Summary
Burnout is an asynchronous, chainable and DRY interface for building Selenium 2 WebDriver scripts in Node. It was written primarily for interfacing with Sauce Labs, but it should work with most Selenium 2 setups. Burnout builds on top of the excellent Selenium 2 WebDriver library wd.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of burnout
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burnout Examples and Code Snippets
Community Discussions
Trending Discussions on burnout
QUESTION
I am a rather new user of lavaan and have been trying to build a moderator model with a continuous moderator and an interaction term with a latent variable. I would like to hear your feedback on my code and especially whether my approach seems appropriate regarding adding the interaction term afterwards (as it requires saving the latent variable in the data frame). Just to give a short description of my study: I investigate the relationship between stress and burnout, and whether social support moderates this association. Unfortunately, I don’t have the actual data yet, so I cannot give information on the possible warning/error messages.
...ANSWER
Answered 2021-Mar-12 at 14:19Since you did not provide actual data, I will produce an example using the HolzingerSwineford1939 data frame. The library semTools has a function to make products of indicators using no centering, mean centering, double-mean centering, or residual centering:
QUESTION
Running brew doctor the output is too long for the shell. Below is what I can still reach.
Any idea what the warning (or error) for these might be and how to fix it?
Some system info:
...ANSWER
Answered 2021-Mar-12 at 01:53Try doing brew update-reset. Do make a note of the following, however:
QUESTION
When I unnest_tokens for a list I enter manually; the output includes the row number each word came from.
...ANSWER
Answered 2020-May-19 at 14:10I guess if you import the text using text <- read.csv("TextSample.csv", stringsAsFactors=FALSE), text is a data frame while if you enter it manually it is a vector.
If you would alter the code to: text_df <- tibble(text = text$col_name) to select the column from the data frame (which is a vector) in the csv case, I think you should get the same result as before.
QUESTION
I have a search function, and I need to add id of posts ( from table opinions, column id). In my view I already have subject, and I get it from database with $user->subject, but at $user->id, I get id of user, not of the post. So, I need to fetch data correctly. I need to get id of posts based on subject .
Now, I've made a new variable which returns something like that :
...ANSWER
Answered 2019-Oct-23 at 09:33You can add select function in your query builder to fetch opinion id like this
QUESTION
Array
(
[0] => Array
(
[count] => 9
[slug] => concediat-reangajat
)
[1] => Array
(
[count] => 7
[slug] => salarii-5togo
)
[2] => Array
(
[count] => 10
[slug] => piata-fortei-munca
)
[3] => Array
(
[count] => 3
[slug] => productivitate-angajati
)
[4] => Array
(
[count] => 1
[slug] => stocare-date
)
[5] => Array
(
[count] => 4
[slug] => infrastructura-leadership
)
[6] => Array
(
[count] => 2
[slug] => airbnb-uber
)
[7] => Array
(
[count] => 5
[slug] => salarii-productivitate
)
[8] => Array
(
[count] => 2
[slug] => ceo-resurse-umane
)
[9] => Array
(
[count] => 3
[slug] => hr-ceo
)
[10] => Array
(
[count] => 1
[slug] => burnout-tratament
)
[11] => Array
(
[count] => 1
[slug] => angajati-vanzarea-afacerii
)
[12] => Array
(
[count] => 1
[slug] => job-linkedin
)
[13] => Array
(
[count] => 1
[slug] => primul-faliment
)
[14] => Array
(
[count] => 3
[slug] => salariu-mic
)
[15] => Array
(
[count] => 1
[slug] => varsta-programatori
)
)
i want to sort this array based on decending order of count
ANSWER
Answered 2019-Oct-19 at 16:03usort with callback will work for you.
QUESTION
I am a highschool student and am conducting primary research on "To what extent do variances in the performance of CPU, RAM, and storage affect the performance of the merge sort algorithm when it is run on large data sets?"
Methodology
The research question will be investigated by running the merge sort algorithm on various hardware components (CPU, RAM, and primary drive) and evaluating which hardware component is the most effective at increasing the efficiency of the algorithm. The performance of hardware components is going to be changed by underclocking and overclocking the CPU and RAM, whilst storing the program running the algorithm on SSD vs. HDD and recording the time it takes for the algorithm to sort 500 randomly generated integers in an array. A small data set was used in comparison to the Big Data used by major companies, to avoid taxing the limited available resources by constantly reaching the limits of the hardware through bottlenecks or by causing a burnout. To truly understand the efficacy of the merge sort algorthim on a big data set, the size of the data set should ideally be around billion data elements, but that would require superior CPU, RAM, storage, and cooling solutions to protect the hardware and keep up with processing large amounts of data. To reiterate the merge sort algorithm is very popular in large corporations to easily locate items in large lists and thus it is likely that in reality the merge sort algorithm has been modified to be more efficient and to better handle billions of data elements. Before conducting this experiment, it is important to control various extraneous variables which could skew the accuracy of the results presented in this essay. Firstly, it is important that the operating system on which the algorithm is run is the same during all trials so that the way the OS allocates and prioritizes memory is the same during all tests. Additionally, all the hardware components such as the CPU, RAM, graphics, motherboard, and the cooling solution must be the same for all tests to avoid any manufacturing differences in protocols or specifications such as the available cache, latency, number of cores, or multithreading performance. All of these differences can directly improve or deteriorate the performance of the merge sort algorithm and thus lead to distortions in the results. Lastly, no other programs must be open during the testing process to avoid other programs to use memory or processing power that was intended for sorting purposes.
Here is the algorithm I am going to be running:
...ANSWER
Answered 2019-Oct-15 at 18:46To benchmark Java code, you should use a proper benchmarking framework like JMH. JMH makes sure to warm up the JVM and to run your code enough times to get consistent results. Without it, you might be simply measuring the performance of JVM startup and compilation, not the sorting. This means that you'll be measuring something completely different from what you meant to measure - the results will be just noise, no signal.
500 integers is a ridiculously low number. If every integer is 4 bytes long, that's only 2000 bytes of data. This means a few things:
The entire "dataset" will be sorted in a very short time - we're talking microseconds here. This will be very difficult to measure accurately. On the whole, general purpose operating systems aren't great for accuracies below 10-20ms, which is probably x100 - x1000 the time it'll take to sort 500 ints. So you'll need to sort those numbers a whole bunch of times (say 1000), see how long that takes, and then divide by 1000 to see how long a single run took. This brings us to the next problem:
The entire "dataset" will probably fit into a single memory page. Moreover, it'll fit into the CPU cache in its entirety. Heck, it can all fit into L1, the smallest (and fastest) of the CPU caches. This means that during sorting, everything will be done within the CPU, so no memory accesses, no disk access. The size and clock speed of RAM will therefore impact only the initial loading of the 500 integers, and even then, the impact will be negligible. And since you'll need to run the test thousands of times for a single benchmark, you won't even see any loading times in your result, since the data will only be loaded once for all those runs.
In other words using 500 ins, is like comparing different types of engine oil by measuring the speed of a car over a distance of one meter (3.3 feet, if you're from that side of the ocean).
For any meaningful result, you need to vastly increase the amount of data you're sorting, and of course use JMH. I'd also use different data sizes and throw in some additional sorting algorithms to compare against. It would be interesting to show how the size of the input and the different hardware choices affect the results.
QUESTION
I have an object and I am trying to set up (eventually) the capacity to edit each key value in that object by opening a form with a toggle and editing that property via a small form.
I have tried using a key pipe, but the data is structured differently for each k:v, as there is quite a lot of relational stuff going on with different tables. I applied this, thinking that I could then index the k:v and thus, open each based on its index. Maybe I should look into this more?
To start with, I need to get the toggle(*ngIf"=show_form" | openForm()) to work, as currently if I click one button to show that k:v, all the buttons open as the condition is set to true across the board.
This is a simplistic view of the HTML:
...ANSWER
Answered 2019-Sep-11 at 15:55Store show_form as an object of all titles (or anything else that is unique)
Typescript:
QUESTION
I have this questionnaire that has a score for answers in 3 rows (3 questionnaires). I need to find the ones that have either Q1 >=30 or Q2 >12 or Q3 <=33. I've been googling and trying solutions but failed when compared by counting manually.
See screenshot (rows AN / AT / BC would be the answers for Q1 / Q2 / Q3):
On rows BD/BE you can see my manual count, which was a PITA and is prone to error.
This formula helped me count ALL values including Q1+Q2+Q3:
ANSWER
Answered 2019-Jul-21 at 18:37Here is one way of doing it :
QUESTION
I'll be setting up an webapp with Flask in an old Raspberrypi B+ running raspbian. The pi will also handle the desktop fuzz, so I'll try to keep it as light as possible.
The point of this question is mainly 1- what DB should I use? But I'm also wondering if 2- keeping it in a external usbstick would help? Let's take it step by step.
What DB: Consideration points
- I rather do the programming using SQLAlchemy, so restrictions apply
- The schema is not complex (around 10 tables)
- Only one local user at first, probably forever, so a few querys and connections
- Low overhead, the pi will most likely struggle, I'm just trying to minimize it.
The second point is about sd cards burnout. I read somewhere that any db should hit sd cards pretty hard and it got me thinking.
I'll set up some kind of external backup to this db anyway, but should I also keep the path to it in an stick? This should be really simple if I choose to use SQLite.
TYA
...ANSWER
Answered 2019-Apr-18 at 15:38SQLite sounds like a perfect fit for this sort of use-case with embedded systems where you need a light-weight, yet full featured database. Many folks use SQLite databases on mobile devices as well for this reason: fairly limited cpu / memory resources, simple storage as a single file.
QUESTION
I have a react native app with the state:
...ANSWER
Answered 2019-Mar-20 at 09:32You can use the spread operator, like so:
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