smear | spatial interpolation | Machine Learning library

I bought a cheap chinese ip camera (GWIPC-26xxx/Yoosee). I want to record its stream with ffmpeg.

On FFMPEG I manage to make it work only using the RTSP/UDP transport protocol, like bellow. It also plays flawlessly on VLC.

Hi I created a program that will create deviations from a real trajectory, it is complicated and I do not have a simple example unfortunately.

It calculates a path with stochastic initial conditions from the real path and does this for x iterations, the goal is to show that the deviations become larger at greater times.

The real path and the deviations are showed below.

However I want to show that the deviations become greater the longer in time we are. Ofcourse I could just calculate the variance and plot mean+var and mean-var at each time step but I was wondering if I could plot something like this, using hist2d

You see that the blocks are not as smooth as a like and this is not that great to use.

Then I went and looked at python's kde and created the following.

This is also not preferable as I think it bins more points at the minima and maxima. Also it is 'too smeared out'. Especially in the beginning, all the points are the same so I want there just to be a straight line to really show that the deviations start later on.

I guess my question is; is what I want even possible and what package/command should I use. I haven't found what I am looking for on other questions. Or has anyone a suggestion to nicely show what I want in a any other way?

I have the following custom function that I am using to create a table of summary statistics in R.

I am trying to scrape some data from a website and the HTML code would look like as follows.

I have a ListView control and I want to add a checkbox in upper left corner of ListView.

The solution for adding the CheckBox control that I'm using is this:

This is a weird smearing effect I've never seen before in rendered matplotlib figures that looks like a printer error. I'm not clear on what might be causing it. I'm processing a sizeable dataframe (4.5gb) to render two copies of the same figure generated from each row, filenames are derived from the data which is why they're not simply copied and renamed after the process.

Here's a simpler version with np.random instead of the dataframe:

In the example below:

I am trying to rotate the major- and the minor axis within a multi-categorical plot in Bokeh (similar to what is done for major tick labels here and done within matplotlib here. When having several categories, their labels/text often get smeared together.

Taking some inspiration from the Bokeh documentation on Categorical Data the result, using Bokehs vbar functionality, would look something like

The major axis can be set using

I have the following image (please note the transparent background):

I also have a black/white mask of the same size:

I would like to "crop" the dress and get just the portion of the first image contained in the black circle. I tried many different methods but they didn't work or are too slow:

1) ImageMagick (command line) <== which command can I use to achieve this? I tried multiply and copyopacity but they didn't work

2) WideImage is working: $maskedImage = $source->applyMask($mask); but it takes more than 12 seconds.

I am interested in a ImageMagick solution if possible.

EDIT

The provided solutions work fine if the mask is smaller than the original image and if the original image is simple. With these source image and mask the result is "smeared":

Source:

Mask:

Command:

convert source.png \( mask.png -negate \) -alpha off -compose copy_opacity -composite result.png

Result (I added a grey background instead of the transparent one in order to show the wrong white):