leech | download user that uses rtorrent

I'm making a first-time website and wanted to have a 0-100% progress bar which has a delay of a few seconds to first fade in at the start (which I can do in CSS), but the progress bar script doesn't seem to delay when I put the following:

HTML:

So I have a file called pokemon.json, it is a rather large list of pokemon and all of their stats. I am trying to read the data from the json file, generate a random number, then send the pokemon's information who's id matches the random number.

Currently I am trying this

I have a json data link for my markers on leaflet based website. When I clicked on a marker, on the map , I want it to show live data which is leeched from json file. I want it to be refreshed every sec or so. If you can guide me i would be pleased. I tried other methods but they direct me to website which have the live data.

I have the most frustrating issue ever with docker-compose:

I push a new image to gitlab, run docker compose on the server and.. the old image runs.

• I have restarted docker
• I have cleaned everything (all images and all containers)
• I have done docker-compose pull follow by up
• I have tried docker-compose --force-recreate
• I have wiped /var/lib/docker/image

and.. I'm still getting a container with the old image. I don't even know where it gets the image from since I have wiped them with docker rmi...

now, if I do a docker run gitlabimagepath, the new image runs properly. so, on the same computer the issue is specifically happening with docker-compose, not with docker.

docker-compose pull is getting an image with the proper digest. when I run it with docker-compose and get the old image, or I run with docker run, the inspection gives the same image sha256. So it looks like there is some old container that keeps being resurrected for docker-compose and never gets rebuilt

I can't think of a better question than 'what the hell?'

nothing from this similar question docker-compose keeps using old image content helps at all.

The docker compose is very basic:

I am would like to edit my R chunks from RMarkdown files the way org-edit-special does. I found generic-edit-special from jonathan leech-pepin that does a similar thing for js, css and ruby in html files. I figured I could tweak it as suggested by the author to make it work for my case but I was not able to make it work even in its original form.

I was able to run the function ges/org-edit-special from a html file with a js script block but nothing happened (no error and no new buffer). I used the Internal Script example from here as html file for this test.

Here is the init.el I made for testing:

I'm totally new to Python just started learning a few days ago, and I tried to make a simple RPG code, but I have recently ran into a hiccup, it keep returning me TypeError: generate_damage() missing 1 required positional argument: 'self'. Does anyone know what's going on?

Main.py

Is it possible to convert a character vector into a data.frame with a max row length of 5, and the character vector just fills the rows up, making as many new columns as necessary to lay all the data out to cater towards the 5 max row limit?

Here's an example of a vector I wanna work with

I have a "seed" GeoDataFrame (GDF)(RED) which contains a 0.5 arc minutes global grid ((180*2)*(360*2) = 259200). Each cell contains an absolute population estimate. In addition, I have a "leech" GDF (GREEN) with roughly 8250 adjoining non-regular shapes of various sizes (watersheds).

I wrote a script to allocate the population estimates to the geometries in the leech GDF based on the overlapping area between grid cells (seed GDF) and the geometries in the leech GDF. The script works perfectly fine for my sample data (see below). However, once I run it on my actual data, it is very slow. I ran it overnight and the next morning only 27% of the calculations had been performed. I will have to run this script many times and waiting for two days each time, is simply not an option.

After doing a bit of literature research, I already replaced (?) for loops with for index i in df.iterrows() (or is this the same as "conventional" python for loops) but it didn't bring about the performance imporvement I had hoped for.

Any suggestion son how I can speed up my code? In twelve hours, my script only processed only ~30000 rows out of ~200000.

My expected output is the column leech_df['leeched_values'].

I have a container based application that is used to receive load from a benchmark tool and generate memory and cpu utilization. So I created this deployment file to the app: