PicoPlanet is a procedurally generated series of PCBs. The three planets act as capacitive touch buttons. The board also has a RGB LED on top, a USB-C connector and 4 more pin pads on the bottom. The board’s brain is a powerful SAMD21. The design also has stars that are not covered by copper or solder mask and are perfect spots to place more LEDs.

PicoPlanet is compatible with CircuitPython and arduino.

PicoPlanet boards can be used as small additional Keyboards. Control your PC Media, OBS, your room light or anything else you’d like. Use the RGB LED as little status notification. Maybe add a small vibration motor for haptic feedback or built a nice case utilizing the M2 mounting holes. Reach for the stars!

What makes it special?

The board design is generated procedurally and each version of the board is only produced 10 times. You will exactly know which design number (also printed on the back side) and which board from the batch you receive.

See the video below and the Tindie site for more.

 

• PyPy2.7, which is an interpreter supporting the syntax and the features of Python 2.7 including the stdlib for CPython 2.7.13
• PyPy3.6: which is an interpreter supporting the syntax and the features of Python 3.6, including the stdlib for CPython 3.6.9.
• PyPy3.7 alpha: which is our first release of an interpreter supporting the syntax and the features of Python 3.7, including the stdlib for CPython 3.7.9. We call this an alpha release since it is our first. It is based off PyPy 3.6 so issues should be around compatibility and not stability. Please try it out and let us know what is broken or missing. We have not implemented some of the documented changes in the re module, and other pieces are also missing. For more information, see the PyPy 3.7 wiki page

The interpreters are based on much the same codebase, thus the multiple release. This is a micro release, all APIs are compatible with the 7.3.0 (Dec 2019) and 7.3.1 (April 2020) releases, but read on to find out what is new.

Conda Forge now supports PyPy as a python interpreter. The support is quite complete for linux and macOS. This is the result of a lot of hard work and good will on the part of the Conda Forge team. A big shout out to them for taking this on.

Development of PyPy has transitioning to https://foss.heptapod.net/pypy/pypy. This move was covered more extensively in this blog post. We have seen an increase in the number of drive-by contributors who are able to use gitlab + mercurial to create merge requests.

The CFFI backend has been updated to version 1.14.2. We recommend using CFFI rather than c-extensions to interact with C, and using cppyy for performant wrapping of C++ code for Python.

NumPy has begun shipping wheels on PyPI for PyPy, currently for linux 64-bit only. Wheels for PyPy windows will be available from the next NumPy release. Thanks to NumPy for their support.

A new contributor took us up on the challenge to get windows 64-bit support. The work is proceeding on the win64 branch, more help in coding or sponsorship is welcome.

As always, this release fixed several issues and bugs. We strongly recommend updating. Many of the fixes are the direct result of end-user bug reports, so please continue reporting issues as they crop up.

You can find links to download the v7.3.2 releases here:

https://pypy.org/download.html

We would like to thank our donors for the continued support of the PyPy project. Please help support us at Open Collective. If PyPy is not yet good enough for your needs, we are available for direct consulting work.

We would also like to thank our contributors and encourage new people to join the project. PyPy has many layers and we need help with all of them: PyPy and RPython documentation improvements, tweaking popular modules to run on pypy, or general help with making RPython’s JIT even better. Since the previous release, we have accepted contributions from 8 new contributors, thanks for pitching in.

If you are a python library maintainer and use c-extensions, please consider making a cffi / cppyy version of your library that would be performant on PyPy. In any case both cibuildwheel and the multibuild system support building wheels for PyPy.

 

 

Version 5.0 of the Calibre electronic-book manager has been released. "There has been a lot of work on the calibre E-book viewer. It now supports Highlighting. The highlights can be colors, underlines, strikethrough, etc. and have added notes. All highlights can be both stored in EPUB files for easy sharing and centrally in the calibre library for easy browsing. Additionally, the E-book viewer now supports both vertical and right-to-left text." Another significant change is a port to Python 3; that was a necessary change but it means that there are a number of plugins that have not yet been ported and thus won't work. The status of many plugins can be found on this page.

The CDC recently released some new data on mortality counts by state and cause of death in the U.S., allowing us to get a look at excess mortality patterns due to the COVID-19 pandemic. I’ve folded the data into the covdata package. As an illustration of the sort of thing you can do with it—and of the sort of thing you can do with ggplot and R—here’s a graph of various aspects of mortality in the U.S. so far this year.

This figure has three panels. At the top is the weekly count of deaths from all causes in the United States. Counts for 2020 so far are highlighted in red. In gray are the equivalent counts for the years 2015 to 2019. More or less reliable data is available for about the first thirty weeks of the year so far, so we stop there. If you’re not familiar with mortality data of this sort, one thing that will jump out at you is its strongly seasonal character. People are more likely to die in the Winter than in the Summer. You’ll also note the relative stability of these patterns. The grey lines over the past five years are pretty steady, as the ordinary cycle of things continues. It’s this patterned character to the data that lets us infer excess mortality, when things are worse than usual for some reason. Not everything is fixed, of course. For example, the flu season in the Winter of 2017-2018 was exceptionally severe and is the reason there’s a high peak for one of the gray lines. The severity of the flu is easy to underestimate.

The bottom left panel shows the same weekly data as the upper panel, but broken out by major cause of death. The causes are ordered from highest to lowest by prevalence, with Malignant Neoplasms (that is, cancer) and heart disease being the leading causes in the country in these data. The bottom right panel shows the CDC’s own calculation of the percentage difference between each cause of death so far this year as compared to its average in the five previous years. The ordering of the panels is the same, from highest to lowest overall number of deaths. But because the column charts show weekly changes, you can see where excess deaths are being registered within each cause.

I think the data make some patterns quite clear. Most obviously, deaths attributed to influenza and pneumonia surge upwards beginning about ten weeks into the year. But so, too, do deaths from Alzheimer’s, hypertension, and diabetes. While I’m not a public health expert, I think the distribution of these surges is clearly suggestive of the differential impact on various groups of people, such as the elderly and those more likely to suffer from various diseases.

As I say, these data are available at the state as well as the national level. Here, for example, is the same graph for New York:

And here, for contrast, is Georgia:

I imagine a serious dive into this data would reveal not just structural variation across states but also evidence of differences in reporting and attribution. The data for states with smaller populations is of course much noisier than for bigger ones, as breaking things down by fourteen causes of death on a week by week basis causes you to run out of degrees of freedom pretty quickly.

These plots were all made in R and ggplot, and assembling the multiple panels was made much easier thanks to Thomas Lin Pedersen’s fabulous patchwork package. The combination of patchwork and purrr makes the production of a whole lot of plots quite efficient. I’ll put a repository with the code on GitHub once I’ve cleaned it up a little. In the meantime, here are links to graphs for all the jurisdictions in the data. Bear in mind that the graphs have different y-axes, each appropriate to the range of variation within each state and directly connected to the number of people that live in that jurisdiction. So you can’t just overlay one on top of another. For a PDF version of any one of these, replace the .png extension in the file name with a .pdf.

• Alabama
• Alaska
• Arizona
• Arkansas
• California
• Colorado
• Connecticut
• Delaware
• District of Columbia
• Florida
• Georgia
• Hawaii
• Idaho
• Illinois
• Indiana
• Iowa
• Kansas
• Kentucky
• Louisiana
• Maine
• Maryland
• Massachusetts
• Michigan
• Minnesota
• Mississippi
• Missouri
• Montana
• Nebraska
• Nevada
• New Hampshire
• New Jersey
• New Mexico
• New York
• New York City
• North Carolina
• North Dakota
• Ohio
• Oklahoma
• Oregon
• Pennsylvania
• Puerto Rico
• Rhode Island
• South Carolina
• South Dakota
• Tennessee
• Texas
• Utah
• Vermont
• Virginia
• Washington
• Wisconsin
• Wyoming

Nintendo wasn’t always in the videogames business. Long before Mario, the company was one of the foremost producers of Hanafuda playing cards in Japan. From 1930 until 1959, Nintendo ran its printing business from a four-story art deco style building that featured distinctive plaques at the front entrance. We now have a chance to print those former Nintendo HQ plaques at home thanks to [Mr. Talida] who shared some 3D models on Twitter. Talida, a self-described “retro video game archivist”, recreated the plaques via photogrammetry from a number of reference photos he took from a visit to the Kyoto site late last year.

These 3D models come at a crucial time as the old Nintendo HQ building, which sat dormant for years, is set to be turned into a boutique hotel next year. According to JPC, the hotel will feature twenty rooms, a restaurant, and a gym and is expected to be completed by summer 2021 (although that estimate was from the “before” times). The renovation is expected to retain as much of the original exterior’s appearance as possible, but the Nintendo plaques almost assuredly will not be included. For a first-person tour of the former Nintendo headquarters building, there is a video from the world2529 YouTube channel provided below.

It is encouraging to see examples of this DIY-style of historical preservation. Many companies have proven themselves to be less-than-stellar stewards of their own history. Though if his Twitter timeline is any indication, [Mr. Talida] is up to something further with this photogrammetry project. A video export exhibiting a fully textured 3D model of the old Nintendo headquarters’ entrance was published recently along with the words, “What have I done.”

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