glr | Great Language RIIR challenge entry

I am reading the example: https://matplotlib.org/stable/gallery/mplot3d/voxels_rgb.html#sphx-glr-gallery-mplot3d-voxels-rgb-py about creating a 3d sphere. But I don't understand how the indexing works in the example. Can any one help me to understand. Thanks

I'm having trouble with a VTK file I've created not being loaded by ParaView. The file looks ok and I can't seen any obvious errors but ParaView throws the following warnings when trying to view the data.

Imagine you have a data set in three dimensions, x, y and z, and you want to show their relation. You could do this for example using a scatter plot in x and y and adding information about z with the help of a colormap:

But such a plot can be hard to read or even missleading, so I would like to use a 2d-histogram in x and y instead and weigh each data point by their z value:

However, as can be seen by the plot above, the magnitude of bin values can now be much higher than the maximum in z, which makes sense of course, as the bin values are usually the sums several z values.

So weighing by their z value is not enough, I also need to "normalize" each bin value by the number of data points within it. But as can be seen on the right plot above, for some reason, this doesn't seem to work. The color value range remains unchanged.

What am I doing wrong and is there a better approach to do this?

Code for reproduction (loosely based on this example):

Inspired by this ROC curve using cross validation I attempted to create a PR-ROC curve using cross-validation. However, the PR-ROC curve produced seems strange and not what a PR-ROC curve usually looks like when I've used it without CV. Here it is:

The code is below:

As you might know, when you make a doc2vec model, you might do model.build_vocab(corpus_file='...') first, then do model.train(corpus_file='...', total_examples=..., total_words=..., epochs=10).

I am making the model w/ huge wikipedia data file. So, I have to designate the 'total_examples' and the 'total_words' for parameters of train(). Gensim's Tutorial says that I can get the first one as total_examples=model.corpus_count. This is fine. But I don't know how to get second one, total_words. I can see the # of total words in the last log from model.build_vocab() as below. So, I directory put the number, like total_words=1304592715, but I'd like to designate it like model.corpus_count manner. Can someone tell me how to obtain the number? Thank you,

I have data that I'd like to plot. I think the best way to do this is with a series of rectangles. I'd like each rectangle to span a width delta_t (each time interval is the same) and a height delta_f (the frequency intervals may differ) and each rectangle's color is given by the log(z). This example seems to have some hints, but I'm not able to put it all together. Here's what I've gotten so far

I have this code snippet taken from here. What i'm failing to do is to extract the parameters from the predicted data. i.e. for the cubic function i would like to know the a, b, c and d from the ax^3 + bx^2 + cx + d equation. How can this be done in the pipeline, especially for the RANSAC estimator?

This is a follow-up question to this question in the astronomy SO.

Following this example in sunpy (very similar to the answer in the linked post above), I am trying to access and download a time-series dataset spanning a few decades of X-Ray flux (XRS). The parameters tstart and tend define the boundaries of the range of datetimes to be considered.

Using sunpy 3.0.1, the following code snippet works successfully:

I'm new to matplotlib, and trying to plot something quite difficult.

I would like to plot something like (taken from the matplotlib docs):

Except, I want the timeline (x-axis) and stems to have labels in time, like: