similar to: ldap authentication and useradd

How do I do the following: if a user is logged in, then opens another browser tab and logs in again (either as himself or as someone else), I want to invalidate the session in the first tab and throw up a "logged in from another browser window" screen. RIght now, user1''s session silently becomes a duplicate user2 session instead (from what I understand, sessions are bound to

Is there a wiki around anywhere? martin

I have the following quick-and-dirty hack in my model: def after_destroy # search for orphaned tags and delete them orphans = Resource.tags_count :count => ''= 0'' orphans.keys.each {|tag| Tags.find_by_name(''tag'').destroy} end It''s nice from a readable code perspective, but it seems inefficient - is there some way to do this with only one

I was thinking, if I don''t manage to figure out the problem with acts_as_taggable, that I could untag objects via a direct SQL DELETE on the tag join table (something like "where tag_id = tag_to_delete and user_id = current_user"). Are there any dangers to this approach? martin

I want to set my app up to prevent multiple simultaneous logins by the same user. What strategies are people using? Is it worth going to database session storage just for this? martin

Setup as follows: class Resource < ActiveRecord::Base belongs_to :user validates_presence_of :filename validates_uniqueness_of :filename, :scope => "user_id", :message => "already exists, try uploading another file or deleting first." acts_as_taggable :join_class_name => ''TagResource''

Hi all It like my visitors to be able to upload a zip file (or any other possible compressed file archive) to my server. After upload, my web app should extract the content to the server and delete the zip file. Is this possible somehow? I''m very thankful for help. Have a nice weekend, Joshua -- Posted via http://www.ruby-forum.com/.

Le 23/01/2018 ? 08:47, Martin Maechler a ?crit?: >>>>>> Serguei Sokol <sokol at insa-toulouse.fr> >>>>>> on Mon, 22 Jan 2018 17:57:47 +0100 writes: > > Le 22/01/2018 ? 17:40, Keith O'Hara a ?crit?: > >> This behavior is noted in the qr documentation, no? > >> > >> rank - the rank of x as

Le 22/01/2018 ? 17:40, Keith O'Hara a ?crit?: > This behavior is noted in the qr documentation, no? > > rank - the rank of x as computed by the decomposition(*): always full rank in the LAPACK case. For a me a "full rank matrix" is a matrix the rank of which is indeed min(nrow(A), ncol(A)) but here the meaning of "always is full rank" is somewhat confusing. Does it

I''d like to monitor a folder and when someone drops a file in there I can take the files name and add it to a database. My dream is to have a folder always being watched. and if someone drops a file or folders with files into that watched folder, I take the filename(s) and insert them into a db and if the files are in folder I use the folder name(s) as tags for that file. make sense?

Le 05/07/2017 ? 13:09, Duncan Murdoch a ?crit : > On 05/07/2017 5:26 AM, January W. wrote: >> I tried the newest patch, but it does not seem to work for me (on >> Linux). Despite the check in Rconn_printf, the write.csv happily writes >> to /dev/full and does not report an error. When I added a printf("%d\n", >> res); to the Rconn_printf() definition, I see

Le 10/01/2023 ? 10:44, RICHET Yann a ?crit?: > Dear R-devel people, > > We are working to submit a package which is mainly a binding over a C++ lib (https://github.com/libKriging) using armadillo. > It is _not_ a standard RcppArmadillo package, because we also had to provide a python binding... so there is a huge layer of cmake & scripting to make it work with a standard armadillo

Le 10/01/2023 ? 11:37, Serguei Sokol a ?crit?: > Le 10/01/2023 ? 10:44, RICHET Yann a ?crit?: >> Dear R-devel people, >> >> We are working to submit a package which is mainly a binding over a >> C++ lib (https://github.com/libKriging) using armadillo. >> It is _not_ a standard RcppArmadillo package, because we also had to >> provide a python binding... so

Here is an attached patch. Best, Serguei. Le 29/05/2017 ? 12:21, Serguei Sokol a ?crit : > The problem or actual R implementation relies on an assumption > that median(x[i] | x[i] <= quantile(x, 1/3)) == quantile(x, 1/6) > which reveals not to be true despite very trustful appearance. > > If we continue with the example of x=y=1:9 > then quantile(x, 1/6)=2.5 (here quantile()

Le 04/12/2018 ? 11:27, I?aki Ucar a ?crit?: > On Tue, 4 Dec 2018 at 11:12, <qweytr1 at mail.ustc.edu.cn> wrote: >> function ppois is a function calculate the CDF of Poisson distribution, it should generate a non-decreasing result, but what I got is: >> >>> any(diff(ppois(0:19,lambda=0.9))<0) >> [1] TRUE >> >> Actually, >> >>>

I''ve been using script/runner to add and delete files from the database in response to inotify events. This works in theory, but is too slow to keep up with a large number of events in succession; I''m suspecting that the bottleneck is that script/runner has a huge startup cost. One thing I thought of was to queue inotify events, and call script/runner once a minute to clear the

Hi I started studying Bioinformatics at my engineering school (INSA de lyon!) in september. We started learning statistics and did two classes of R hands-on, but I want to learn to use R better. (As in for my every day calculations - I'm in front of a computer anyway so why should I have to take out my TI-30?) I'm having trouble finding some of the simpler stat functions though.

I'm sure this exists elsewhere, but, as a trade-off, could you achieve what you want with a separate helper function F(expr) that constructs the function you want to pass to [lsv]apply()? Something that would allow you to write: sapply(split(mtcars, mtcars$cyl), F(summary(lm(mpg ~ wt,.))$r.squared)) Such an F() function would apply elsewhere too. /Henrik On Thu, Apr 16, 2020 at 9:30 AM

Hello for my password recovery system I want to recover the users password and send it to them. In the DB its saved via SHA1 (login generator) -- Posted via http://www.ruby-forum.com/.

Le 31/08/2018 ? 16:25, Mark van der Loo a ?crit?: > Ah, my bad, you're right of course. > > sum(abs(diff(diff( sort(x))))) < eps > > for some reasonable eps then, would do as a oneliner, or > > all(abs(diff(diff(sort(x)))) < eps) > > or > > max(abs(diff(diff(sort(x))))) < eps Or with only four function calls: diff(range(diff(sort(x)))) < eps