brain | the brain of public science | Machine Learning library

You can write PromisedArgs like this:

It looks like, in calculate_community_modularity, you use greedy_modularity_communities to create a dict, modularity_dict, which maps a node in your graph to a community. If I understand correctly, you can take each subgraph community in modularity_dict and pass it into shannon_entropy to calculate the entropy for that community.

this is pseudo code, so there may be some errors. This should convey the principle, though.

after running calculate_community_modularity, you have a dict like this, where the key is each node, and the value is that which the community belongs to

• In your situation, it seems that the values of sku of container is not existing in the values of sku of data_obj. I thought that the reason for your issue might be due to this.
• As a script for checking this, you can use const res = container.filter(e => !data_obj[e.sku]) for your script. In this case, [ { sku: 'L4943-0ULT', id: 3195, price: '5083.33' } ] is returned. When this value is searched from your sample Spreadsheet, l4943-0ult is found. In this case, the character case is different. By this, your issue occurs. I resulted in the reason of your issue is due to this.

When this issue was removed, how about the following modification?

The interesting part of the question is of course finding the nearest point on the desired path to the actual position; distance is easy. The hard part of that is in turn identifying the nearest element (line segment) of the path; projecting onto it is also straightforward.

If the path is simple enough (in particular, if it doesn’t branch and it’s impossible/disallowed to skip sections at a self-intersection), you can finesse that part by just maintaining that current element in a variable and updating it to the previous or next element when the projection onto one of them is closer than the projection onto the current one. This is a typical algorithm used by racing games to determine the instantaneous order of racers.

In many cases it helps to expand an expression manually:

To start, this code is heavily borrowed from Matteo Mancini, which he describes here and he has released under the MIT license.

In the original code, networkx is not used, so it's clear you don't actually need networkx to accomplish your goal. If this is not a strict requirement, I would consider using his original code and reworking it to fit your input data.

Since you listed networkx as a requirement, I simply reworked his code to take a networkx Graph object with certain node attributes such as 'color' and 'coord' to be used for those marker characteristics in the final plotly scatter. I just chose the first ten points in the dataset to color red, which is why they aren't grouped.

The full copy-pasteable code is below. The screenshot here obviously isn't interactive, but you can try the demo here on Google Colab.

To download files if in Jupyter notebook on Linux/Mac:

In future, please consider the Stack Overflow guidelines provided under How to create a Minimal, Reproducible Example. Well, minimal and reproducible the code in your question is, but it shall also be complete...

…Complete – Provide all parts someone else needs to reproduce your problem in the question itself

That being said, when given the following definitions, your code will compile:

It sounds like you're trying to calculate the colors in the fragment shader independently of the mesh underneath. If so, you should decouple the color calculation from the mesh.

Assuming your occupancy is stored in a texture, use textureGather to get the four nearby occupancy values; determine the equation of the boundary; then use the fractional part of the texture coordinates to determine its position relative to the boundary. (The devil here is in the details -- in particularly the ambiguous checker-board pattern case.)

Once you implement the above approach, it's very likely you won't even need the triangle strip mesh anymore -- simply fill your entire drawing area with a single large quad and let the fragment shader to do the rest.

For is_in(b, { "a", "b", "c" });, template parameter Element is deduced as std::string on the 1st argument b, and deduced as const char* on the 2nd argument { "a", "b", "c" }; they don't match.

You can give two template parameters for is_in, e.g.