This captured my interest for long enough to produce a little test. The code is below, and you can see it in action It lists all the spans on the page (might want to restrict that to just those with ids starting with T if that is suitable), and uses the ‘ids’ attribute to build … Read more
Updating slightly, with seaborn 0.11.1: Seaborn’s relplot function creates a FacetGrid and gives each subplot its own explanatory title. You can add a title over the whole thing: import seaborn as sns tips = sns.load_dataset(‘tips’) rp = sns.relplot(data=tips, x=’total_bill’, y=’tip’, col=”sex”, row=’smoker’, kind=’scatter’) # rp is a FacetGrid; # relplot is a nice organized way … Read more
There are several solutions out there, but basically every one is using the same approach: create layer on top of your source to let you query at high level create a front end layer to talk with the level explained above use the visualization tool you want As miro marchi pointed, there are several solutions … Read more
The answer from las3rjock, which somehow is the answer accepted by the OP, is incorrect–the code doesn’t run, nor is it valid matplotlib syntax; that answer provides no runnable code and lacks any information or suggestion that the OP might find useful in writing their own code to solve the problem in the OP. Given … Read more
In general colormaps in matplotlib are 1D, i.e. they map a scalar to a color. In order to obtain a 2D colormap one would need to somehow invent a mapping of two scalars to a color. While this is in principle possible, it’s not as convenient as the usual colormaps. An example is below, where … Read more
simply use stat = “summary” and fun.y = “mean” ggplot(test2) + geom_bar(aes(label, X2, fill = as.factor(groups)), position = “dodge”, stat = “summary”, fun.y = “mean”)
Use Valgrind and its tool Massif. Its example output (a part of it): 99.48% (20,000B) (heap allocation functions) malloc/new/new[], –alloc-fns, etc. ->49.74% (10,000B) 0x804841A: main (example.c:20) | ->39.79% (8,000B) 0x80483C2: g (example.c:5) | ->19.90% (4,000B) 0x80483E2: f (example.c:11) | | ->19.90% (4,000B) 0x8048431: main (example.c:23) | | | ->19.90% (4,000B) 0x8048436: main (example.c:25) | ->09.95% … Read more
Creating a single combined plot with your current data set up would look something like this p <- ggplot() + # blue plot geom_point(data=visual1, aes(x=ISSUE_DATE, y=COUNTED)) + geom_smooth(data=visual1, aes(x=ISSUE_DATE, y=COUNTED), fill=”blue”, colour=”darkblue”, size=1) + # red plot geom_point(data=visual2, aes(x=ISSUE_DATE, y=COUNTED)) + geom_smooth(data=visual2, aes(x=ISSUE_DATE, y=COUNTED), fill=”red”, colour=”red”, size=1) however if you could combine the data sets … Read more
I develop ETE, which is a python package intended, among other stuff, for programmatic tree rendering and visualization. You can create your own layout functions and produce custom tree images: It has a focus on phylogenetics, but it can actually deal with any type of hierarchical tree (clustering, decision trees, etc.)
Making something audio reactive is pretty simple. Here’s an open source site with lots audio reactive examples. As for how to do it you basically use the Web Audio API to stream the music and use its AnalyserNode to get audio data out. “use strict”; const ctx = document.querySelector(“canvas”).getContext(“2d”); ctx.fillText(“click to start”, 100, 75); ctx.canvas.addEventListener(‘click’, … Read more