![]() ![]() If you require a rather specific plotting feature, feel free to request it. Nevertheless, extreme configurability is not a goal of Plots. Please add wishlist items, bugs, or any other comments/questions to the issues list, and join the conversation on zulip. This preprocessing maintains modularity and allows for efficient separation of front end code, algorithms, and backend graphics. Use the preprocessing pipeline in Plots to describe your visualization completely before it calls the backend code. It's not quite AGI, but Plots should figure out what you want it to do. Very few dependencies, since backends are loaded and initialized dynamically. Use the same code and access the strengths of all backends. Produce your favorite plots from your favorite package, only quicker and simpler. Less code means fewer mistakes and more efficient development and analysis. Start generating plots without reading volumes of documentation. Plots might be the last plotting package you ever learn. If one backend does not support your desired features or make the right trade-offs, you can just switch to another backend with one command. It sits above other backends, like GR, PythonPlot, PGFPlotsX, or Plotly, connecting commands with implementation. Plots is a visualization interface and toolset. Some packages make a display and never change it, while others make updates in real-time. Plotting software makes trade-offs between features and simplicity, speed and beauty, and a static and dynamic interface. Intro to Plots in Juliaĭata visualization has a complicated history. Tap into the extensive visualization functionality enabled by the Plots ecosystem, and easily build your own complex graphics components with recipes. ![]() Fortunately, since the purpose is only to draw graphs, the basic functions can be satisfied by one-way output to a file, and I think the performance is within the acceptable range with some latency. Edit on GitHub Plots - powerful convenience for visualization in JuliaĪuthor: Thomas Breloff get started, see the tutorial.Īlmost everything in Plots is done by specifying plot attributes. Of course, since it is executed via a file, I had to do some tricks in how I pass variables and use return values. So in the end I have decided to implement it independently of these libraries at all. However, we had to install a separate environment-dependent library to use JNI, and we also had to install the library from pip on the Python side, which was too much work for something as simple as drawing graphs. There is a library in the world that allow you to use CPython from Java code, and this one was a candidate because we can use both Python3 and numpy. Initially, I wanted to implement it using Jython, but it only supported the Python version up to 2.7, and since numpy wasn't supported, the Matplotlib which depends on it wouldn't work either, so I decided to abandon this path. Matplotlib4j calls Matplotlib in a way that generates Python code without using JNI or Jython. So I decided to use Matplotlib, which is a familiar Python library, as the reason to create the library. However, implementing the graphing tool in Scala from scratch is too hard. But found that there are no good graphing tools in Scala. Of course, the common practice to tackle this is to thicken the tests, but I'd like to see what's going on first quickly by displaying a graph like in the book. I was happy to be able to write it in a functional way in Scala, but when I got to the backpropagation using the steepest descent method, I encountered a situation where the loss was not dropping at all, and I thought, "What's wrong?" ![]() I recently started reading a book of Deep Learning and decided to try to implement it in Scala which I've often touched lately, since it was not interesting to copy the code on the book in Python as it is. In the Tutorial page, you can find more cases step by step in Java, Scala and Kotlin. Enter fullscreen mode Exit fullscreen mode
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