One outcome of the 2023 Scientific Python Developer Summit was the Scientific Python Development Guide, a comprehensive guide to modern Python package development, complete with a new project template supporting 10+ build backends and a WebAssembly-powered checker with checks linked to the guide. The guide covers topics like modern, compiled, and classic packaging, style checks, type checking, docs, task runners, CI, tests, and much more! There also are sections of tutorials, principles, and some common patterns.

This guide (along with cookie & repo-review) started in Scikit-HEP in 2020. During the summit, it was merged with the NSLS-II guidelines, which provided the basis for the principles section. I’d like to thank and acknowledge Dan Allan and Gregory Lee for working tirelessly during the summit to rework, rewrite, merge, and fix the guide, including writing most of the tutorials pages and first patterns page, and rewriting the environment page as a tutorial.

In mid-2018 I started learning Python by reading textbooks and watching online tutorials. I had absolutely zero background in computer science, but it seemed interesting so I continued to try. At some point, I decided I wanted to do a master’s degree in statistics, so I began to work on more statistics-based programming. That’s when I found SciPy. I became (and still am) fascinated by the idea of open-source software that is completely free to use and supported by a community of diligent programmers.

The first Scientific Python Developer Summit (May 22-26, 2023) brought together 34 developers at the eScience Institute at the University of Washington to develop shared infrastructure, documentation, tools, and recommendations for libraries in the Scientific Python ecosystem. Pre-summit planning Prior to the summit we held several hour-long planning meetings: General (2023-02-27) May 15, Package metrics, DevStats May 15, SPECs May 18, Community & Documentation May 19, Build Systems & CI Infrastructure May 19, PyTest plugins & Sphinx extensions Summit execution At the summit, we had a brief check-in and then split into several groups based on each developers time and interests.

Scientific-Python Developer Summit (May 22-26, 2023, Seattle WA) – The first Scientific Python Developer Summit provided an opportunity for core developers from the scientific Python ecosystem to come together to: improve joint infrastructure better coordinate core projects work on a shared strategic plan Related notes/sites: Worklog. Planning Meeting Notes and Info. One of the focuses of the summit was Sparse Arrays, and specifically their implementation in SciPy. This post attempts to recap what happened with “sparse” at the summit and a glimpse of plans for our continuing work.

This is the second part of a blog series where I talk about my experience during my Outreachy internship at NetworkX. If you haven’t read the first part you can find it here. As you advance through the contribution phase you may wonder how your internship is gonna be in case you get selected. Here is my experience as a NetworkX intern and some tips that could help you through the internship.

What’s Outreachy? Outreachy is a paid remote internship program for underrepresented groups in tech. All internships are in Open Source and Open Science. To be selected as an intern first you need to : Fill out an initial application: You’ll need to answer some questions about how you are affected by the systemic bias, and how being underrepresented in your local tech industry impacted your development. Maybe you don’t know how to answer some of these questions, especially if you are still not looking for a job, but it’s important to do some research first.

The NumPy team is excited to announce the launch of the NumPy Fellowship Program and the appointment of Sayed Adel (@seiko2plus) as the first NumPy Developer in Residence. This is a significant milestone in the history of the project: for the first time, NumPy is in a position to use its project funds to pay for a full year of maintainer time. We believe that this will be an impactful program that will contribute to NumPy’s long-term sustainability as a community-driven open source project.

We are delighted to announce a two-year grant from the Chan Zuckerberg Initiative (CZI) in support of the Scientific Python project. This grant will support work on common web themes, joint infrastructure and practices, accessibility, and interactivity of core library documentation. We are particularly excited that, through this work, we may expand global participation of scientific communities in using and contributing to Python tools. It is, to the best of our knowledge, the first time that a scientific open source community has received significant support for accessibility and internationalization efforts.

The Scientific Python project is an initiative to better coordinate and support the scientific Python ecosystem of libraries and to grow the surrounding community. It aims to improve communication between ecosystem projects, to better plan for their joint future, and to make that future a reality. Why is this important? Initially, the Scientific Python developer community was small, so that it was easy to discuss important ecosystem-wide decisions at events like the annual SciPy conference.

The last and final post discussing the VF2++ helpers can be found here. Now that we’ve figured out how to solve all the sub-problems that VF2++ consists of, we are ready to combine our implemented functionalities to create the final solver for the Graph Isomorphism problem. Introduction We should quickly review the individual functionalities used in the VF2++ algorithm: Node ordering which finds the optimal order to access the nodes, such that those that are more likely to match are placed first in the order.

The previous post can be found here, be sure to check it out so you can follow the process step by step. Since then, another two very significant features of the algorithm have been implemented and tested: node pair candidate selection and feasibility checks. Introduction As previously described, in the ISO problem we are basically trying to create a mapping such that, every node from the first graph is matched to a node from the second graph.

This post includes all the major updates since the last post about VF2++. Each section is dedicated to a different sub-problem and presents the progress on it so far. General progress, milestones and related issues can be found here. Node ordering The node ordering is one major modification that VF2++ proposes. Basically, the nodes are examined in an order that makes the matching faster by first examining nodes that are more likely to match.

Intro I got accepted as a GSoC contributor, and I am so excited to spend the summer working on such an incredibly interesting project. The mentors are very welcoming, communicative, fun to be around, and I really look forward to collaborating with them. My application for GSoC 2022 can be found here. About me My name is Konstantinos Petridis, and I am an Electrical Engineering student at the Aristotle University of Thessaloniki.

I was selected as an intern to work on SciPy build system. In this blog post, I will be describing my journey of this 10-months long internship at SciPy. I worked on a variety of topics starting from migrating the SciPy build system to Meson, cleaning up the public API namespaces and adding Uarray support to SciPy submodules. Experience Meson Build System The main reasons for switching to Meson include (in addition to distutils being deprecated):

The Scientific Python blog has just gotten a little more accessible! If you didn’t catch our invite on Twitter or run into the problem firsthand, there’s a good chance you might not have noticed the new descriptions for a number of blog post images. Since it’s not a flashy improvement, we wanted to make a point to highlight the community effort to to make a more accessible blog–and internet as a whole–last week.

Our first Contributor Spotlight interview is with Mukulika Pahari, our “go-to” person for Numpy documentation. Mukulika is a Computer Science student at Mumbai University. Her passions outside of computing involve things with paper, including reading books (fiction!), folding origami, and journaling. During our interview she discussed why she joined NumPy, what keeps her motivated, and how likely she would recommend becoming a NumPy contributor. Tell us something about yourself. Hi, I am Mukulika.

At the end of this article, my goal is to convince you that: if you need to use random numbers, you should consider using scipy.stats.qmc instead of np.random. In the following, we assume that SciPy, NumPy and Matplotlib are installed and imported: import numpy as np from scipy.stats import qmc import matplotlib.pyplot as plt Note that no seeding is used in these examples. This will be the topic of another article: seeding should only be used for testing purposes.

Create educational content for the Scientific Python Blog About your organization With an extensive and high-quality ecosystem of libraries, scientific Python has emerged as the leading platform for data analysis. This ecosystem is sustained largely by volunteers working on independent projects with separate mailing lists, websites, roadmaps, documentation, engineering and packaging solutions, and governance structures. The Scientific Python project aims to better coordinate the ecosystem and prepare the software projects in this ecosystem for the next decade of data science.

Introduction This tutorial will teach you how to create custom tables in Matplotlib, which are extremely flexible in terms of the design and layout. You’ll hopefully see that the code is very straightforward! In fact, the main methods we will be using are ax.text() and ax.plot(). I want to give a lot of credit to Todd Whitehead who has created these types of tables for various Basketball teams and players. His approach to tables is nothing short of fantastic due to the simplicity in design and how he manages to effectively communicate data to his audience.

As part of the University of North Carolina BIOL222 class, Dr. Catherine Kehl asked her students to “use matplotlib.pyplot to make art.” BIOL222 is Introduction to Programming, aimed at students with no programming background. The emphasis is on practical, hands-on active learning. The students completed the assignment with festive enthusiasm around Halloween. Here are some great examples: Harris Davis showed an affinity for pumpkins, opting to go 3D! # get library for 3d plotting from mpl_toolkits.

It’s my great pleasure to announce that I’ve finished my book on matplotlib and it is now freely available at www.labri.fr/perso/nrougier/scientific-visualization.html while sources for the book are hosted at github.com/rougier/scientific-visualization-book. Abstract The Python scientific visualisation landscape is huge. It is composed of a myriad of tools, ranging from the most versatile and widely used down to the more specialised and confidential. Some of these tools are community based while others are developed by companies.

Introduction I have been creating common visualisations like scatter plots, bar charts, beeswarms etc. for a while and thought about doing something different. Since I’m an avid football fan, I thought of ideas to represent players’ usage or involvement over a period (a season, a couple of seasons). I have seen some cool visualisations like donuts which depict usage and I wanted to make something different and simple to understand. I thought about representing batteries as a form of player usage and it made a lot of sense.

Matplotlib: Revisiting Text/Font Handling To kick things off for the final report, here’s a meme to nudge about the previous blogs. About Matplotlib Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations, which has become a de-facto Python plotting library. Much of the implementation behind its font manager is inspired by W3C compliant algorithms, allowing users to interact with font properties like font-size, font-weight, font-family, etc. However, the way Matplotlib handled fonts and general text layout was not ideal, which is what Summer 2021 was all about.

“Matplotlib, I want 多个汉字 in between my text.” Let’s say you asked Matplotlib to render a plot with some label containing 多个汉字 (multiple Chinese characters) in between your English text. Or conversely, let’s say you use a Chinese font with Matplotlib, but you had English text in between (which is quite common). Assumption: the Chinese font doesn’t have those English glyphs, and vice versa With this short writeup, I’ll talk about how does a migration from a font-first to a text-first approach in Matplotlib looks like, which ideally solves the above problem.

Data visualization is a key step in a data science pipeline. Python offers great possibilities when it comes to representing some data graphically, but it can be hard and time-consuming to create the appropriate chart. The Python Graph Gallery is here to help. It displays many examples, always providing the reproducible code. It allows to build the desired chart in minutes. About 400 charts in 40 sections The gallery currently provides more than 400 chart examples.

“Well? Did you get it working?!” Before I answer that question, if you’re missing the context, check out my previous blog’s last few lines.. promise it won’t take you more than 30 seconds to get the whole problem! With this short writeup, I intend to talk about what we did and why we did, what we did. XD Ostrich Algorithm Ring any bells? Remember OS (Operating Systems)? It’s one of the core CS subjects which I bunked then and regret now.

"Aitik, how is your GSoC going?" Well, it’s been a while since I last wrote. But I wasn’t spending time watching Loki either! (that’s a lie.) During this period the project took on some interesting (and stressful) curves, which I intend to talk about in this small writeup. New Mentor! The first week of coding period, and I met one of my new mentors, Jouni. Without him, along with Tom and Antony, the project wouldn’t have moved an inch.

The day of result, was a very, very long day. With this small writeup, I intend to talk about everything before that day, my experiences, my journey, and the role of Matplotlib throughout! About Me I am a third-year undergraduate student currently pursuing a Dual Degree (B.Tech + M.Tech) in Information Technology at Indian Institute of Information Technology, Gwalior. During my sophomore year, my interests started expanding in the domain of Machine Learning, where I learnt about various amazing open-source libraries like NumPy, SciPy, pandas, and Matplotlib!

In May 2020, Alexandre Morin-Chassé published a blog post about the stellar chart. This type of chart is an (approximately) direct alternative to the radar chart (also known as web, spider, star, or cobweb chart) — you can read more about this chart here. In this tutorial, we will see how we can create a quick-and-dirty stellar chart. First of all, let’s get the necessary modules/libraries, as well as prepare a dummy dataset (with just a single record).

Background Cover of the IPCC SR15 The IPCC’s Special Report on Global Warming of 1.5°C (SR15), published in October 2018, presented the latest research on anthropogenic climate change. It was written in response to the 2015 UNFCCC’s “Paris Agreement” of holding the increase in the global average temperature to well below 2 °C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5 °C […]". cf. Article 2.

Introduction This year’s Google Season of Docs (GSoD) provided me the opportunity to work with the open source organization, Matplotlib. In early summer, I submitted my proposal of Developing Matplotlib Entry Paths with the goal of improving the documentation with an alternative approach to writing. I had set out to identify with users more by providing real world contexts to examples and programming. My purpose was to lower the barrier of entry for others to begin using the Python library with an expository approach.

Introduction Code-switching is the practice of alternating between two or more languages in the context of a single conversation, either consciously or unconsciously. As someone who grew up bilingual and is currently learning other languages, I find code-switching a fascinating facet of communication from not only a purely linguistic perspective, but also a social one. In particular, I’ve personally found that code-switching often helps build a sense of community and familiarity in a group and that the unique ways in which speakers code-switch with each other greatly contribute to shaping group dynamics.

Google Summer of Code 2020 is completed. Hurray!! This post discusses about the progress so far in the three months of the coding period from 1 June to 24 August 2020 regarding the project Baseline Images Problem under matplotlib organisation under the umbrella of NumFOCUS organization. Project Details: This project helps with the difficulty in adding/modifying tests which require a baseline image. Baseline images are problematic because Baseline images cause the repo size to grow rather quickly.

Welcome! This post is not going to be discussing technical implementation details or theortical work for my Google Summer of Code project, but rather serve as a summary and recap for the work that I did this summer. I am very happy with the work I was able to accomplish and believe that I successfully completed my project. Overview My project was titled NetworkX: Implementing the Asadpour Asymmetric Traveling Salesman Problem Algorithm.

My implementation of asadpour_atsp is now working! Recall that my pseudo code for this function from my last post was def asadpour_tsp Input: A complete graph G with weight being the attribute key for the edge weights. Output: A list of edges which form the approximate ATSP solution. z_star = held_karp(G) # test to see if z_star is a graph or dict if type(z_star) is nx.DiGraph return z_star.edges z_support = nx.

Google Summer of Code 2020’s second evaluation is completed. I passed!!! Hurray! Now we are in the mid way of the last evaluation. This post discusses about the progress so far in the first two weeks of the third coding period from 26 July to 9 August 2020. Completion of the modification logic for the matplotlib_baseline_images package We successfully created the matplotlib_baseline_image_generation command line flag for baseline image generation for matplotlib and mpl_toolkits in the previous months.

Well, we’re finally at the point in this GSoC project where the end is glimmering on the horizon. I have completed the Held Karp relaxation, generating a spanning tree distribution and now sampling from that distribution. That means that it is time to start thinking about how to link these separate components into one algorithm. Recall that from the Asadpour paper the overview of the algorithm is Algorithm 1 An \(O(\log n / \log \log n)\)-approximation algorithm for the ATSP

The heavy lifting I did in the preliminary post certainly paid off here! In just one day I was able to implement sample_spanning_tree and its two helper functions. krichhoffs This was a very easy function to implement. It followed exactly from the pesudo code and was working with spanning_tree_distribution before I started on sample_spanning_tree. sample_spanning_tree This function was more difficult than I originally anticipated. The code for the main body of the function only needed minor tweaks to work with the specifics of python such as shuffle being in place and returning None and some details about how sets work.

Google Summer of Code 2020’s second evaluation is about to complete. Now we are about to start with the final coding phase. This post discusses about the progress so far in the last two weeks of the second coding period from 13 July to 26 July 2020. Modular approach towards removal of matplotlib baseline images We have divided the work in two parts as discussed in the previous blog. The first part is the generation of the baseline images discussed below.

In order to test the exponential distribution that I generate using spanning_tree_distribution, I need to be able to sample a tree from the distribution. The primary citation used in the Asadpour paper is Generating Random Combinatorial Objects by V. G. Kulkarni (1989). While I was not able to find an online copy of this article, the Michigan Tech library did have a copy that I was able to read. Does the Kulkarni Algorithm work with Asadpour?

Implementing spanning_tree_distribution proved to have some NetworkX difficulties and one algorithmic difficulty. Recall that the algorithm for creating the distribution is given in the Asadpour paper as Set \(\gamma = \vec{0}\). While there exists an edge \(e\) with \(q_e(\gamma) > (1 + \epsilon) z_e\): Compute \(\delta\) such that if we define \(\gamma’\) as \(\gamma_e’ = \gamma_e - \delta\), and \(\gamma_f’ = \gamma_f\) for all \(f \in E\ \backslash {e}\), then \(q_e(\gamma’) = (1 + \epsilon/2)z_e\).

Cellular automata are discrete models, typically on a grid, which evolve in time. Each grid cell has a finite state, such as 0 or 1, which is updated based on a certain set of rules. A specific cell uses information of the surrounding cells, called it’s neighborhood, to determine what changes should be made. In general cellular automata can be defined in any number of dimensions. A famous two dimensional example is Conway’s Game of Life in which cells “live” and “die”, sometimes producing beautiful patterns.

Finally moving on from the Held Karp relaxation, we arrive at the second step of the Asadpour asymmetric traveling salesman problem algorithm. Referencing the Algorithm 1 from the Asadpour paper, we are now finally on step two. Algorithm 1 An \(O(\log n / \log \log n)\)-approximation algorithm for the ATSP Input: A set \(V\) consisting of \(n\) points and a cost function \(c\ :\ V \times V \rightarrow \mathbb{R}^+\) satisfying the triangle inequality.

Google Summer of Code 2020’s first evaluation is completed. I passed!!! Hurray! Now we are in the mid way of the second evaluation. This post discusses about the progress so far in the first two weeks of the second coding period from 30 June to 12 July 2020. Completion of the matplotlib_baseline_images package We successfully created the matplotlib_baseline_images package. It contains the matplotlib and the matplotlib toolkit baseline images. Symlinking is done for the baseline images, related changes for Travis, appvoyer, azure pipelines etc.

This should be my final post about the Held-Karp relaxation! Since my last post titled Implementing The Held Karp Relaxation, I have been testing both the ascent method as well as the branch and bound method. My first test was to use a truly asymmetric graph rather than a directed graph where the cost in each direction happened to be the same. In order to create such a test, I needed to know the solution to any such proposed graphs.

Imagine zooming an image over and over and never go out of finer details. It may sound bizarre but the mathematical concept of fractals opens the realm towards this intricating infinity. This strange geometry exhibits the same or similar patterns irrespectively of the scale. We can see one fractal example in the image above. The fractals may seem difficult to understand due to their peculiarity, but that’s not the case. As Benoit Mandelbrot, one of the founding fathers of the fractal geometry said in his legendary TED Talk:

I have now completed my implementation of the ascent and the branch and bound method detailed in the 1970 paper The Traveling-Salesman Problem and Minimum Spanning Trees by Micheal Held and Richard M. Karp. In my last post, titled Understanding the Ascent Method, I completed the first iteration of the ascent method and found an important bug in the find_epsilon() method and found a more efficient way to determine substitutes in the graph.

Google Summer of Code 2020’s first evaluation is about to complete. This post discusses about the progress so far in the last two weeks of the first coding period from 15 June to 30 June 2020. Completion of the demo package We successfully created the demo app and uploaded it to the test.pypi. It contains the main and the secondary package. The main package is analogous to the matplotlib and secondary package is analogous to the matplotlib_baseline_images package as discussed in the previous blog.

It has been far longer than I would have preferred since I wrote a blog post. As I expected in my original GSoC proposal, the Held-Karp relaxation is proving to be quite difficult to implement. My mentors and I agreed that the branch and bound method discussed in Held and Karp’s 1970 paper The Traveling-Salesman Problem and Minimum Spanning Trees which first required the implementation of the ascent method because it is used in the branch and bound method.

The ocean is a key component of the Earth climate system. It thus needs a continuous real-time monitoring to help scientists better understand its dynamic and predict its evolution. All around the world, oceanographers have managed to join their efforts and set up a Global Ocean Observing System among which Argo is a key component. Argo is a global network of nearly 4000 autonomous probes or floats measuring pressure, temperature and salinity from the surface to 2000m depth every 10 days.

We are coming into the end of the first week of coding for the Summer of Code, and I have implemented two new, but related, features in NetworkX. In this post, I will discuss how I implemented them, some of the challenges and how I tested them. Those two new features are a spanning tree iterator and a spanning arborescence iterator. The arborescence iterator is the feature that I will be using directly in my GSoC project, but I though that it was a good idea to implement the spanning tree iterator first as it would be easier and I could directly refer back to the research paper as needed.

I Sidharth Bansal, was waiting for the coding period to start from the March end so that I can make my hands dirty with the code. Finally, coding period has started. Two weeks have passed. This blog contains information about the progress so far from 1 June to 14 June 2020. Movement from mpl-test and mpl packages to mpl and mpl-baseline-images packages Initially, we thought of creating a mpl-test and mpl package.

There is only one thing that I need to figure out before the first coding period for GSoC starts on Monday: how to find all of the minimum arborescences of a graph. This is the set \(K(\pi)\) in the Held and Karp paper from 1970 which can be refined down to \(K(\pi, d)\) or \(K_{X, Y}(\pi)\) as needed. For more information as to why I need to do this, please see my last post here.

After talking with my GSoC mentors about what we all believe to be the most difficult part of the Asadpour algorithm, the Held-Karp relaxation, we came to several conclusions: The Asadpour paper recommends using the ellipsoid method so that their algorithm runs in polynomial time. We do not need a polynomial time, just an algorithm with reasonable execution time. An example of this would be the ellipsoid algorithm versus the simplex algorithm.

Generating the data points To get acquainted with the basics of plotting with matplotlib, let’s try plotting how much distance an object under free-fall travels with respect to time and also, its velocity at each time step. If, you have ever studied physics, you can tell that is a classic case of Newton’s equations of motion, where $$ v = a \times t $$ $$ S = 0.5 \times a \times t^{2} $$

A while back, I came across this cool repository to create emoji-art from images. I wanted to use it to transform my mundane Facebook profile picture to something more snazzy. The only trouble? It was written in Rust. So instead of going through the process of installing Rust, I decided to take the easy route and spin up some code to do the same in Python using matplotlib. Because that’s what anyone sane would do, right?

Now that my proposal was accepted by NetworkX for the 2021 Google Summer of Code (GSoC), I can get more into the technical details of how I plan to implement the Asadpour algorithm within NetworkX. In this post I am going to outline my thought process for the control scheme of my implementation and create function stubs according to my GSoC proposal. Most of the work for this project will happen in netowrkx.

Continuing the theme of my last post, we know that the Held-Karp relaxation in the Asadpour Asymmetric Traveling Salesman Problem cannot be practically written into the standard matrix form of a linear program. Thus, we need a different method to solve the relaxation, which is where the ellipsoid method comes into play. The ellipsoid method can be used to solve semi-infinite linear programs, which is what the Held-Karp relaxation is.

The other day I was homeschooling my kids, and they asked me: “Daddy, can you draw us all possible non-isomorphic graphs of 3 nodes”? Or maybe I asked them that? Either way, we happily drew all possible graphs of 3 nodes, but already for 4 nodes it got hard, and for 5 nodes - plain impossible! So I thought: let me try to write a brute-force program to do it! I spent a few hours sketching some smart dynamic programming solution to generate these graphs, and went nowhere, as apparently the problem is quite hard.

When I, Sidharth Bansal, heard I got selected in Google Summer of Code(GSOC) 2020 with Matplotlib under Numfocus, I was jumping and dancing. In this post, I talk about my past experiences, how I got selected for GSOC with Matplotlib, and my project details. I am grateful to the community :) About me: I am currently pursuing a Bachelor’s in Technology in Software Engineering at Delhi Technological University, Delhi, India. I started my journey of open source with Public Lab, an open-source organization as a full-stack Ruby on Rails web developer.

In linear programming, we sometimes need to take what would be a integer program and ‘relax’ it, or unbound the values of the variables so that they are continuous. One particular application of this process is Held-Karp relaxation used the first part of the Asadpour algorithm for the Asymmetric Traveling Salesman Problem, where we find the lower bound of the approximation. Normally the relaxation is written as follows. \[ \begin{array}{c l l} \text{min} & \sum_{a} c(a)x_a \\\ \text{s.

1 - The Basis Let’s make up some numbers, put them in a Pandas dataframe and plot them: import pandas as pd import matplotlib.pyplot as plt df = pd.DataFrame({'A': [1, 3, 9, 5, 2, 1, 1], 'B': [4, 5, 5, 7, 9, 8, 6]}) df.plot(marker='o') plt.show() 2 - The Darkness Not bad, but somewhat ordinary. Let’s customize it by using Seaborn’s dark style, as well as changing background and font colors:

As has been discussed in detail in Nadia Eghbal’s Roads and Bridges, the CZI EOSS program announcement, and in the NumFocus sustainability program goals, much of the critical software that science and industry are built on is maintained by a primarily volunteer community. While this has worked, it is not sustainable in the long term for the health of many projects or their contributors. We are happy to announce that we have hired Elliott Sales de Andrade (QuLogic) as the Matplotlib Software Research Engineering Fellow supported by the Chan Zuckerberg Initiative Essential Open Source Software for Science effective March 1, 2020!

Matplotlib for diagrams This is my first post for the Matplotlib blog so I wanted to lead with an example of what I most love about it: How much control Matplotlib gives you. I like to use it as a programmable drawing tool that happens to be good at plotting data. The default layout for Matplotlib works great for a lot of things, but sometimes you want to exert more control.

Introduction This post will outline how we can leverage gridspec to create ridgeplots in Matplotlib. While this is a relatively straightforward tutorial, some experience working with sklearn would be beneficial. Naturally it being a vast undertaking, this will not be an sklearn tutorial, those interested can read through the docs here. However, I will use its KernelDensity module from sklearn.neighbors. Packages import pandas as pd import numpy as np from sklearn.

My name is Ted Petrou, founder of Dunder Data, and in this tutorial you will learn how to create the new Tesla Cybertruck using Matplotlib. I was inspired by the image below which was originally created by Lynn Fisher (without Matplotlib). Before going into detail, let’s jump to the results. Here is the completed recreation of the Tesla Cybertruck that drives off the screen. Tutorial A tutorial now follows containing all the steps that creates a Tesla Cybertruck that drives.

Preliminaries import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl A Top-Down runnable Jupyter Notebook with the exact contents of this blog can be found here An interactive version of this guide can be accessed on Google Colab A word before we get started… Although a beginner can follow along with this guide, it is primarily meant for people who have at least a basic knowledge of how Matplotlib’s plotting functionality works.

Matplotlib has a really nice 3D interface with many capabilities (and some limitations) that is quite popular among users. Yet, 3D is still considered to be some kind of black magic for some users (or maybe for the majority of users). I would thus like to explain in this post that 3D rendering is really easy once you’ve understood a few concepts. To demonstrate that, we’ll render the bunny above with 60 lines of Python and one Matplotlib call.

Search Engine Optimization (SEO) is a process that aims to increase quantity and quality of website traffic by ensuring a website can be found in search engines for phrases that are relevant to what the site is offering. Google is the most popular search engine in the world and presence in top search results is invaluable for any online business since click rates drop exponentially with ranking position. Since the beginning, specialized entities have been decoding signals that influence position in search engine result page (SERP) focusing on e.

Earth’s temperatures are rising and nothing shows this in a simpler, more approachable graphic than the “Warming Stripes”. Introduced by Prof. Ed Hawkins they show the temperatures either for the global average or for your region as colored bars from blue to red for the last 170 years, available at #ShowYourStripes. The stripes have since become the logo of the Scientists for Future. Here is how you can recreate this yourself using Matplotlib.

Postdocs are the workers of academia. They are the main players beyond the majority of scientific papers published in journals and conferences. Yet, their effort is often not recognized in terms of salary and benefits. A few years ago, the NIH has established stipend levels for undergraduate, predoctoral and postdoctoral trainees and fellows, the so-called NIH guidelines. Many universities and research institutes currently adopt these guidelines for deciding how much to pay postdocs.