reframe | powerful Python framework for writing | Testing library

I'm trying to simulate a deliberate deadlock on sql server, where I could test a piece of code which would do retries. Need a query/SP/Func which I can execute, later this query fails with 1205/deadlock and trigger my retry logic.

constraints: -Within a single client n single session. (Kind of reading a meta data n locking itself in a single session, may be)

Tried with success: -mocking custom SQL exception n successful recovery. -multithreaded approaches.

Now, need a SQL component which does this in a single session.

Edit: reframed the question for better suggestions.

I am using Python 3.9 and Jupyter Notebook to make inferences with an object detection model. I'm pretty new to this process so I'm having trouble exporting the images after the objects are detected. Here is my code:

As the title says; AuthenticationWebFilter has it's own set of matchers to determine if a request needs authentication. This seems to work against spring security's way of doing things.

If an endpoint is set in spring securty config to .permitAll() it would also have to be excluded in the AuthenticationWebFilter, why doesn't the filter just let the request through and let the rest of spring security handle it?

EDIT: To reframe my question in response answer by Steve Riesenberg:

Why does AuthenticationWebFilter, an authentication filter, control access to resources? Shouldn't that be handled by the authorization filters?

EDIT: I just figured out that the filter actually doesn't block access when there is not authentication, only when verification fails, which makes sense.

I am having this code and its running as expected. However I am trying to find a better way to rewrite the following query as it can be seen that the dates and account codes are repeated all the time.

The data is being extracted from 3 Databases i.e. Db1, Db2 and Db3. The tables of each Database are similar. Even the AcctCodes to be extracted are similar.

So,I am wondering if the following code can be rewritten in few lines.

Since, the AcctCodes are similar, so adding an empty row with database name as Headers between each query helps me to identify them.

Select 'Outlet1','0','0' from Dummy

So if there is a better version of the following code, please let me know. Thanks.

EDIT :

i added the folliwing in my .csproj

What I am trying to code

1. Getting buffer from a h264 encoded mp4 file
2. Passing the buffer to an appsink
3. Then separately in another pipeline, the appsrc would read in the buffer
4. The buffer would be h264parse and then send out through rtp using GstRTSPServer

Would want to simulate this for a CLI pipeline to make sure the video caps is working:

My attempts as follows: gst-launch-1.0 filesrc location=video.mp4 ! appsink name=mysink ! appsrc name=mysrc ! video/x-h264 width=720 height=480 framerate=30/1 ! h264parse config-interval=1 ! rtph264pay name=pay0 pt=96 ! udpsink host=192.168.x.x port=1234

But this doesnt really works and I not too sure this is how appsrc and appsink is used

Can some one enlighten me

EDIT: The file i am trying to play has the following property

General Complete name : video3.mp4 Format : AVC Format/Info : Advanced Video Codec File size : 45.4 MiB

I want to create some sort of html-tag cheatsheet in [R] using markdown. I thought this would be a good idea as I could easily show the tag and the result. Turns out it is not that easy. Let's reframe the sentence: I think it should be easy, but still I am stuck when it comes to printing the results. I would really appreciate some hints :)

What I am doing right now:

Say you have a large array which stores arbitrary objects. Every now and then an object is removed here and removed there. Now you want to keep track of the empty slots in the array so they can be filled up and don't go to waste. What is the best way to keep track of the empty space in a compact yet efficient way?

By compact I mean, instead of storing every removed index in another array, it should store the removed index and the size of slots available after that index. How can it do that efficiently without using a hash table?

So for example:

I'm working on a supervised binary classification problem for predictive maintenance that is phrased as the following question: "What's the probability that this piece of equipment will fail in the next N months?"

I have a dataset of continuous and categorical features that are taken at a single point in time. The status of that machine was then tracked over a period of time to see if it had any failures. From this, my target is either a numerical value (the time of failure in months) or a null (it didn't fail).

Currently, I'm modeling this as a pure binary classification - 0 if it failed > N months or didn't fail and 1 if it failed < N months. Then, I train a model that has a calibrated probability output and I'm done. But intuitively, I feel that there must be a way to include the actual numerical information of the failure date to help improve the probability prediction. Should I try to reframe this as a regression problem? If so, how do I handle the null values (where it didn't fail)?

Cheers!