zs | Absolutely minimal static site generator in Go ( powers | Static Site Generator library

I have to do the clique problem where the graph is defined like that:

[1-[2,3,4,5,6], 2-[1,3,4,5,6], 3-[1,2,4,5,6],4-[1,2,3,5,6], 5-[1,2,3,4,6], 6-[1,2,3,4,5]]

so I started to do this

Say that I would like to replace all as that are after 2 initial as and that only have as in between it and the first 2 as. I can do this in Vim using the (very magic \v) regex s:\v(^a{2}a{-})@<=a:X:g:

I have a query that it's been written a while ago. Basically a Materialized View that uses json_table function.

Recently since we moved to Oracle 19c that MV sometimes works and other times doesn't. I rewrite that query by using oracle json_value function. Looking at the query plan, I see that the query that is using json_table is much slower but I don't understand all that data.

Can someone explain what means the bytes, CPU, time etc.

This is using json_value

How to match these string: Ks 1

I have used this pattern ^((?!\*).)*$!

As mentioned above, the function below works, however its very slow. I am very interested in using faster/optimised numpy (or other) vectorized alternatives. I have not posted the entire script here due to it being too large.

My specific question is - are there suitable numpy (or other) functions that I can use to 1) reduce run time and 2) reduce code volume of this function, specifically the for loop?

Edit: mass, temp, U and dpdh are functions that carry out simple algebraic calculations and return constants

I am using a fastify server, containing a typescript file that calls a function, which make sure people won't send unwanted characters. Here is the function :

I have the following question and I have not been able to find an answer that works I have multiple dataframes (35 to be exact) and I want to add another dataframe containing demographics to each one of the 35 dataframes.

To make it simple, I have the following example:

When an IFC model is uploaded and translated by Autodesk Forge, some weird "shadow lines" are generated and appear in the Forge viewer. These lines mostly are IFC wall elements and correspond to real walls in the model, but they are drawn under the actual model. Here is an example of such lines:

Now, if I try to add another model, which should align with the first one and I set both Global Offset Zs to 0, the added model fits into the shadow lines at the bottom and not the actual model. Thus, it seems that the shadow lines are the considered to be a part of the model. Here is a visualisation of the problem:

When I load the model with other IFC viewers, these shadow lines don't appear:

Thus, I guess these lines are generated during the Forge translation process. Where are these lines coming from and how can I get rid of these? And even more important, how can I align the models correctly?

Thanks for any kind of help!

EDIT: When I use legacy conversion, the alignment works! Here is an image:

The problem is, that I need to have spaces and opening elements, thus I need to use the modern conversion.

I have a dataframe below and I want to remove duplicates based on columns country and year, and keep the non NA values for the columns 3 to the last column. If all rows within (country, year) are NA, the value for the row should be an NA as well.