Planet SciPy
How to Make your TensorBoard Projects Easy to Share and Collaborate on
We all know that teamwork is an essential part of every machine learning project. Although each engineer has their own piece of...
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micro:bit Space Invaders — Playable retro game in just 25 pixels
How much game can you fit into 25 pixels? Quite a bit it turns out.
This is a mini clone of arcade classic Space Invaders for the BBC micro:bit microcomputer. Using the accelerometer and two buttons for input, to can beat off wave after wave of aliens that advance …
Best Machine Learning Model Management Tools That You Need to Know
Developing your model is an essential part of working on ML projects. And it’s usually a tough challenge. Every data scientist has...
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How to Keep Track of Experiments in PyTorch Using Neptune
Machine Learning development seems a lot like conventional software development since both of them require us to write a lot of code....
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How to Organize Your LightGBM ML Model Development Process – Examples of Best Practices
LightGBM is a distributed and efficient gradient boosting framework that uses tree-based learning. It’s known for its fast training, accuracy, and efficient...
The post How to Organize Your LightGBM ML Model Development Process – Examples of Best Practices appeared first on neptune.ai.
Normalizing Images in PyTorch
You can use the torchvision Normalize() transform to subtract the mean and divide by the standard deviation for image tensors in PyTorch. But it's important to understand how the transform works and how to reverse it.How to Organize Deep Learning Projects – Examples of Best Practices
For a successful deep learning project, you need a lot of iterations, a lot of time, and a lot of effort. To...
The post How to Organize Deep Learning Projects – Examples of Best Practices appeared first on neptune.ai.
MLOps: What It Is, Why it Matters, and How To Implement it (from a Data Scientist Perspective)
What is this MLOps thing? It was the question I had on my mind, but until recently, I had only heard about...
The post MLOps: What It Is, Why it Matters, and How To Implement it (from a Data Scientist Perspective) appeared first on neptune.ai.
Dropping columns and rows in Pandas
There are a few ways to drop columns and rows in Pandas. This post describes the easiest way to do it and provides a few alternatives that can sometimes be useful.MLflow vs. TensorBoard vs. Neptune – What Are the Differences?
You see endless columns and rows, random colors, and don’t know where to find any values? Ahh, the beautifully chaotic spreadsheet of...
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This Week in Machine Learning: Language & Robotics, 10 Underappreciated Python Packages, Avocado Armchair, and More
The new year brings new opportunities, news, and discoveries. Today, we’re bringing you the first weekly roundup in the new year 2021....
The post This Week in Machine Learning: Language & Robotics, 10 Underappreciated Python Packages, Avocado Armchair, and More appeared first on neptune.ai.
Best 8 Machine Learning Model Deployment Tools That You Need to Know
Machine learning is nothing new in the tech world. It brought about a revolutionary change for many industries, with the ability to...
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The easiest way to rename a column in Pandas
Two easy recipes for renaming column(s) in a Pandas DataFrame.How to Organize Your XGBoost Machine Learning (ML) Model Development Process – Best Practices
XGBoost is a top gradient boosting library that is available in Python, Java, C++, R, and Julia. The library offers support for GPU...
The post How to Organize Your XGBoost Machine Learning (ML) Model Development Process – Best Practices appeared first on neptune.ai.
Reshaping arrays: How the NumPy reshape operation works
This post explains how the NumPy reshape operation works, how to use it and gotchas to watch out for.Calculating the norm of an array in NumPy: all about np.linalg.norm()
You can calculate the L1 and L2 norms of a vector or the Frobenius norm of a matrix in NumPy with np.linalg.norm(). This post explains the API and gives a few concrete usage examples.Run SAS in Python without Installation
Do you wish
(continued...)Welcoming Tania Allard as Quansight Labs co-director
Today I'm incredibly excited to welcome Tania Allard to Quansight as Co-Director of Quansight Labs. Tania (GitHub, Twitter, personal site) is a well-known and prolific PyData community member. In the past few years she has been involved as a conference organizer (JupyterCon, SciPy, PyJamas, PyCon UK, PyCon LatAm, JuliaCon and more), as a community builder (PyLadies, NumFOCUS, RForwards), as a contributor to Matplotlib and Jupyter, and as a regular speaker and mentor. She also brings relevant experience in both industry and academia - she joins us from Microsoft where she was a senior developer advocate, and has a PhD in computational modelling.
Read more… (4 min remaining to read)
Scientific notation in Python and NumPy
Using and suppressing scientific notation in Python and NumPy.NumPy tile
The unofficial guide to np.tile() with examplesThe NumPy square operation
You can get the element-wise square of an input with np.square(). This is not exactly the same as x**2.NumPy random uniform number generator
The unofficial guide to np.random.uniform()Sorting a list of tuples in Python
The best way to sort a list of tuples in Python.The Keras Dense layer
The Keras dense layer can be a little confusing. This post will give you everything you need to start using it.Disnatia X/Potências de X
Nenhuma equipe de heróis me é tão querida quanto X-Men. Lá pelo final dos anos 90 comecei a colecionar por alguns anos, mas em seguida veio o fatídico aumento de preço com as Super-Heróis Premium, o que me acabou desmotivando a comprar. De lá para cá, acompanho esporadicamente, lendo notícias sobre, comprando uma ou outra… Continue a ler »Disnatia X/Potências de XThe NumPy square root operation
Everything you ever wanted to know about the numpy square root operation...Combinations in Python
There are lots of ways to generate combinations in Python. This post will show you all of them.The Keras custom layer explained
Sometimes you need to define your own Keras custom layer. This tutorial explains how custom layers work for tensorflow>=1.7.0 (up to at least 2.4.0) which includes a fairly stable version of the Keras API.Maximum likelihood estimate for the uniform distribution
If you have a random sample drawn from a continuous uniform(a, b) distribution stored in an array x, the maximum likelihood estimate (MLE) of a is...Develop a JupyterLab Winter Theme
JupyterLab 3.0 is about to be released and provides many improvements to the extension system. Theming is a way to extend JupyterLab and benefits from those improvements.
While theming is often disregarded as a purely cosmetic endeavour, it can greatly improve software. Theming can be great help for accessibility, and the Jupyter team pays attention to making the default appearance accessibility-aware by using sufficient contrast. For users with a high visual acuity you may also choose to increase the information density.
Theming can also be a great way to improve communication by increasing or decreasing emphasis of the user interface, which can be of use for teaching or presenting. Theming may also help with security, for example, by having a clear distinction between staging and production.
Finally Theming can be a great way to express oneself, for example, by using a branded version of software that fits well into a context, or expressing one's artistic preferences or opinions.
In the following blog post, we will show you step-by-step how you
Wish Christmas with Python and R
This post is dedicated to all the Python and R Programming Lovers...Flaunt your knowledge in your peer group with the following programs. As a data science professional, you want your wish to be special on eve of christmas. If you observe the code, you may also learn 1-2 tricks which you can use later in your daily tasks.
Method 1 : Run the following program and see what I mean
R Code
paste(intToUtf8(acos(log(1))*180/pi-13),
toupper(substr(month.name[2],2,2)),
paste(rep(intToUtf8(acos(exp(0)/2)*180/pi+2^4+3*2),2), collapse = intToUtf8(0)),
LETTERS[5^(3-1)], intToUtf8(atan(1/sqrt(3))*180/pi+2),
toupper(substr(month.abb[10],2,2)),
intToUtf8(acos(log(1))*180/pi-(2*3^2)),
toupper(substr(month.name[4],3,4)),
intToUtf8(acos(exp(0)/2)*180/pi+2^4+3*2+1),
intToUtf8(acos(exp(0)/2)*180/pi+2^4+2*4),
intToUtf8(acos(log(1))*180/pi-13),
LETTERS[median(0:2)],
intToUtf8(atan(1/sqrt(3))*180/pi*3-7),
sep = intToUtf8(0)
)
Python Code
import math
import datetime
(chr(int(math.acos(math.log(1))*180/math.pi-13)) \
+ datetime.date(1900, 2, 1).strftime('%B')[1] \
+ 2 * datetime.date(1900, 2, 1).strftime('%B')[3] \
+ datetime.date(1900, 2, 1).strftime('%B')[7] \
+ chr(int(math.atan(1/math.sqrt(3))*180/math.pi+2)) \
+ datetime.date(1900, 10, 1).strftime('%B')[1] \
+ chr(int(math.acos(math.log(1))*180/math.pi-18)) \
+ datetime.date(1900, 4, 1).strftime('%B')[2:4] \
+ chr(int(math.acos(math.exp(0)/2)*180/math.pi+2**4+3*2+1)) \
+ chr(int(math.acos(math.exp(0)/2)*180/math.pi+2**4+2*4)) \
+ chr(int(math.acos(math.log(1))*180/math.pi-13)) \
+ "{:c}".format(97) \
+ chr(int(math.atan(1/math.sqrt(3))*180/math.pi*3-7))).upper()
Turn on computer speakers before running the code.
R Code
install.packages("audio")
library(audio)
christmas_file <- tempfile()
download.file("https://sites.google.com/site/pocketecoworld/merrychristmas1.wav", christmas_file, mode = "wb")
xmas
On the Link Between Optimization and Polynomials, Part 2
An analysis of momentum can be tightened using a combination Chebyshev polynomials of the first and second kind. Through this connection we'll derive one of the most iconic methods in optimization: Polyak momentum.
How to use variable in a query in pandas
Suppose you want to reference a variable in a query in pandas package in Python. This seems to be a straightforward task but it becomes daunting sometimes. Let's discuss it with examples in the article below.Let's create a sample dataframe having 3 columns and 4 rows. This dataframe is used for demonstration purpose.
import pandas as pd
df = pd.DataFrame({"col1" : range(1,5),
"col2" : ['A A','B B','A A','B B'],
"col3" : ['A A','A A','B B','B B']
})
A A in column col2@. NumFOCUS hires Open Source Developer Advocate!
NumFOCUS is pleased to announce that Arliss Collins has been hired as our organization’s first Open Source Developer Advocate. Founded in 2012, NumFOCUS has finally grown beyond just providing non-technical needs for our 40+ sponsored projects! As our first technical hire, Arliss will work to help understand our projects from a technical perspective and […]
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A Pivotal Time in NumFOCUS’s Project Aimed DEI Efforts
NumFOCUS is pleased to announce the launch of our Contributor Diversification & Retention Research Project funded by a grant from the Gordon and Betty Moore Foundation. “We were eager to support NumFOCUS’s diversity initiative because it aims to get to the heart of what is preventing greater participation in data science. We are hopeful that […]
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Anaconda Announces Multi-Year Partnership with NumFOCUS
A key stakeholder in the open source scientific computing ecosystem has further formalized their long-standing partnership with NumFOCUS. Anaconda, the Austin, Texas-based software development and consulting company which provides global distribution of Python and R software packages, last month introduced their Anaconda Dividend Program. Through this initiative, Anaconda plans to direct a portion of their […]
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What's in a model
During the coronavirus epidemic, the belgian federal group of scientific experts came up regularly in the official communication of the government. How can scientists understand the spread of an epidemic? By using a model: a mathematical description of a phenomenon. By varying the parameters of the model, one can test …
A second CZI grant for NumPy and OpenBLAS
I am happy to announce that NumPy and OpenBLAS have once again been awarded a grant from the Chan Zuckerberg Initiative through Cycle 3 of the Essential Open Source Software for Science (EOSS) program. This new grant totaling $140,000 will fund part of our efforts to improve usability and sustainability in both projects and is excellent news for the scientific computing community, which will certainly benefit from this work downstream.
Read more… (4 min remaining to read)
NumFOCUS Receives Support from Heising-Simons
NumFOCUS is grateful to announce that we received a grant award of $50,000 in October from the Heising-Simons Foundation. This generous grant funding will provide general support resources to NumFOCUS and will benefit all of our Sponsored and Affiliated Projects as well as our organization’s several programs and initiatives. “This grant award from Heising-Simons will […]
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Introduction to Design in Open Source
This blog post is a conversation. Portions lead by Tim George are marked with TG, and those lead by Isabela Presedo-Floyd are marked with IPF.
TG: When I speak with other designers, one common theme I see concerning why they chose this career path is they want to make a difference in the world. We design because we imagine a better world and we want to help make it real. Part of the reason we design as a career is we're unable to go through life without designing; we're always thinking about how things are and how they could be better. This ethos also exists in many open-source communities. It seems like it ought to be an ideal match.
So what's the disconnect? I'm still exploring that myself, but after a few years in open source I want to share my observations, experiences, and hope for a stronger collaboration between design and development. I don't think I have a complete solution, and some days I'm not even sure I grasp the entire
Querying multiple backends with Ibis
In our recent Ibis post, we discussed querying & retrieving data using a familiar Pandas-like interface. That discussion focused on the fluent API that Ibis provides to query structure from a SQLite database—in particular, using a single specific backend. In this post, we'll explore Ibis's ability to answer questions about data using two different Ibis backends.
import ibis.omniscidb, dask, intake, sqlalchemy, pandas, pyarrow as arrow, altair, h5py as hdf5
Before we delve into the technical details of using Ibis, we'll consider Ibis in the greater historical context of the scientific Python ecosystem. It was started by Wes McKinney, the creator of Pandas, as way to query information on the Hadoop distributed file system and PySpark. More backends were added later as Ibis became a general tool for data queries.
Throughout the rest of this post, we'll highlight the ability of Ibis to generically prescribe query expressions across different data storage systems.
Read more… (3 min remaining to
Manylinux1 is obsolete, manylinux2010 is almost EOL, what is next?
The basic installation format for users who install packages via pip is
the wheel format. Wheel names are composed of four parts: a
package-name-and-version tag (which can be further broken down), a Python tag,
an ABI tag, and a platform tag. More information on the tags can be found in
PEP 425. So a package like NumPy
will be available on PyPI as numpy-1.19.2-cp36-cp36m-win_amd64.whl for 64-bit
windows and numpy-1.19.2-cp36-cp36m-macosx_10_9_x86_64.whl for macOS. Note
that only the plaform tag win_amd64 or macosx_10_9_x86_64 differs.
But what about Linux? There is no single, vendor controlled, "Linux platform" e.g., Ubuntu, RedHat, Fedora, Debian, FreeBSD all package software at slightly different versions. What most Linux distributions do have in common is the glibc runtime library, and a smattering of various additional system libraries. So it is possible to define a least common denominator (LCD) of software expected to be on a Linux platform (exceptions apply, e.g. non-glibc distributions).
The decision to converge on a LCD common platform gave birth to the manylinux1 standard. Going back to our example, numpy
Bate-papo com Vivi Reis sobre tecnologia e política
Hoje à noite (5 de novembro) às 20h conversarei com Vivi Reis, candidata a vereadora pelo PSOL em Belém. No bate-papo vamos focar bastante sobre temas que entrelaçam tecnologia e política. Entre os pontos, teremos o Escritório de Dados, dados e políticas públicas, software livre na administração pública, conectividade em Belém, inclusão digital, aplicativos cidadãos,… Continue a ler »Bate-papo com Vivi Reis sobre tecnologia e políticaNew features in Spyder 4's new debugger!
IPython is a great improvement over the standard Python interpreter, bringing many enhancements such as autocompletion and "magic" commands. When debugging, however, many of these features become inaccessible. With Spyder, we aim to bring back these capabilities and more for a truly premium debugging experience! (And believe me, I use this debugger a lot, and not only because I write code that might contain bugs :p).
In this post, I will describe the debugger improvements we've already made in Spyder 4, as well as those that are already implemented or under review for Spyder 4.2 and beyond.
Make the debugger more like IPythonIPython improves on the stock Python interpreter by adding syntax highlighting, completion, and history. We have done the same for the debugger!
The output is prettier (and easier to read) than plain black text, as it was in Spyder 3!
Code completion and history for the debugger use the same functionality as the IPython console, so you should not notice any difference in behaviour. Just press
(continued...)JupyterCon 2020: Code of Conduct Reports
Following the reports to the NumFOCUS Code-of-Conduct committee on Jeremy Howard’s keynote at JupyterCon 2020, and the controversy that followed, the NumFOCUS Code of Conduct Committee issued a public apology to Jeremy Howard and escalated the case to the board of directors. The context In his keynote at JupyterCon 2020, Jeremy Howard gave a point-by-point rebuttal of […]
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Public Apology to Jeremy Howard
We, the NumFOCUS Code of Conduct Enforcement Committee, issue a public apology to Jeremy Howard for our handling of the JupyterCon 2020 reports. We should have done better. We thank you for sharing your experience and we will use it to improve our policies going forward. We acknowledge that it was an extremely stressful experience, […]
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Money and California Propositions (2020)
Ten years ago, I made some plots for how much money was contributed to and spent by the various proposition campaigns in California.
I decided to update these for this election, and here's the result:
Just in case you didn't get the full picture, here is the same data plotted on a common scale:
So, whereas 10 years ago, we had a total of ~$58 million on the election, the overwhelming amount of in support, this time, we had ~$662 million, an 11 fold increase!
The Cal-Access Campaign Finance Activity: Propositions & Ballot Measures source I used last time was still there, but there are way more propositions this time (12 vs 5), and the money details are broken out by committee, with some propositions have a dozen committees. Another wrinkle is that website has protected by some fancy scraping protection. I could browse it just fine in Firefox, even with Javascript turned off, but couldn't download it using wget, curl,
(continued...)TARDIS Joins NumFOCUS as a Sponsored Project
NumFOCUS is pleased to announce the newest addition to our fiscally sponsored projects: TARDIS TARDIS is an open-source, Monte Carlo based radiation transport simulator for supernovae ejecta. TARDIS simulates photons traveling through the outer layers of an exploded star including relevant physics like atomic interactions between the photons and the expanding gas. The TARDIS collaboration […]
The post TARDIS Joins NumFOCUS as a Sponsored Project appeared first on NumFOCUS.
Por um Escritório de Dados para Políticas Públicas em Belém
Dados sempre foram determinantes para a concepção e implementação de políticas públicas nas mais diferentes esferas governamentais. Acompanhamentos de indicadores econômicos, de saúde, de violência, de deslocamentos urbanos, de distribuição espacial da população, de áreas de cobertura de locais de lazer, entre outros, são apenas alguns dos dados que podem embasar o desenho de políticas… Continue a ler »Por um Escritório de Dados para Políticas Públicas em BelémTranslating Web Page while Scraping
Suppose you need to scrape data from a website after translating the web page in R and Python. In google chrome, there is an option (or functionality) to translate any foreign language. If you are an english speaker and don't know any other foreign language and you want to extract data from the website which does not have option to convert language to English, this article would help you how to perform translation of a webpage.
NumFOCUS Earns Transparency Recognition from GuideStar
Earlier this week, NumFOCUS earned our first-ever Silver Seal of Transparency from GuideStar, an independent organization which classifies nonprofit organizations based on multiple metrics pertaining to transparency and accountability. Fewer than 5% of US-based nonprofits have received this type of recognition. “This respected acknowledgment comes as we prepare to enter our year-end fundraising season,” said […]
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Learn Python for Data Science
 |
| Data Science with Python Tutorial |
aka: also known as
I was chatting with Anthony Scopatz last week, and one of the things we covered
was how it'd be cool to have a subcommand launcher, kind of like git, where
the subcommands were swappable. If you're not familiar, git automatically
calls out to git-something (note the dash) whenever you run
$ git something
and something is not one of the builtin git commands. For me, ~/bin is in my PATH, so
$ git lost git: 'lost' is not a git command. See 'git --help'. $ echo "echo how rude!" > ~/bin/git-lost; chmod +x ~/bin/git-lost $ git lost how rude!
And so what Anthony was talking about was having two commands that are supposed
to do the same thing, and being able to switch between them. For example: maybe
we have git-away and git-gone and both
of them perform a similar function, and we wish call our preferred one when we run git lost.
One way to do this would be to copy or symlink our chosen version as git-lost,
and replace that file whenever
Design of the Versioned HDF5 Library
In a previous post, we introduced the Versioned HDF5 library and described some of its features. In this post, we'll go into detail on how the underlying design of the library works on a technical level.
Read more… (6 min remaining to read)
How to rename columns in Pandas Dataframe
pandas which simplifies doing these kind of data operations. 
First step is to install pandas package if it is not already installed. You can check if the package is installed on your machine by running
Performance of the Versioned HDF5 Library
In several industry and science applications, a filesystem-like storage model such as HDF5 is the more appropriate solution for manipulating large amounts of data. However, suppose that data changes over time. In that case, it's not obvious how to track those different versions, since HDF5 is a binary format and is not well suited for traditional version control systems and tools.
In a previous post, we introduced the Versioned HDF5 library, which implements a mechanism for storing binary data sets in a versioned way that feels natural to users of other version control systems, and described some of its features. In this post, we'll show some of the performance analysis we did while developing the library, hopefully making the case that reading and writing versioned HDF5 files can be done with a nice, intuitive API while being as efficient as possible. The tests presented here show that using the Versioned HDF5 library results in reduced disk space usage,
PyTorch-Ignite: training and evaluating neural networks flexibly and transparently
Read more… (28 min remaining to read)
Traitlets - an introduction & use in Jupyter configuration management
You have probably seen Traitlets in applications, you likely even use it. The package has nearly 5 million downloads on conda-forge alone.
But, what is Traitlets ?In this post we'll answer this question along with where Traitlets came from, its applications, and a bit of history.
Read more… (8 min remaining to read)
Seqtembro de eventos virtuais e gratuitos sobre Qt e KDE
(Ok a piada com seqtembro funciona melhor na versão em inglês, seqtember, mas simbora) Por uma grande coincidência, obra do destino, ou nada disso, teremos um Setembro de 2020 repleto de eventos virtuais e gratuitos de alta qualidade sobre Qt e KDE. Começando de 4 à 11 do referido mês teremos o Akademy 2020, o… Continue a ler »Seqtembro de eventos virtuais e gratuitos sobre Qt e KDECARLsim5 Released!
IntroductionCARLsim5 is an efficient, easy-to-use, GPU-accelerated library for simulating large-scale spiking neural network (SNN) models with a high degree of biological detail. It allows execution of networks of Izhikevich spiking neurons with realistic synaptic dynamics using multiple off-the-shelf GPUs and x86 CPUs. The simulator provides a PyNN-like programming interface in C/C++, which allows for details and parameters to be specified at the synapse, neuron, and network level.
The present release, CARLsim 5, builds on the efficiency and scalability of earlier releases (Nageswaran et al., 2009; Richert et al., 2011, and Beyeler et al., 2015; Chou et al., 2018). The functionality of the simulator has been greatly expanded by the addition of a number of features that enable and simplify the creation, tuning, and simulation of complex networks with spatial structure.
New Features
pyCARL is a interface between the simulator-independent language PyNN and a CARLsim5 based back-end. In other words, you can write the code for a SNN model once, using the
(continued...)Improved workflows-as-applications: tips and tricks for building applications on top of snakemake
Writing applications around workflow systems, take 2.
O que será do Lev com o “fim” da Saraiva?
Disclaimer: apesar do sobrenome, não tenho qualquer relação com a Saraiva. E também não tenho respostas para a pergunta do título. Como usuário do Lev acompanho com interesse a agonia da Saraiva. A rede de livrarias, uma das maiores do Brasil, está há anos em um imbróglio judicial devendo diversas editoras, em um processo que… Continue a ler »O que será do Lev com o “fim” da Saraiva?Dask Life Sciences Fellow [Open Job]
Dask is an open-source library for parallel computing in Python that interoperates with existing Python data science libraries like Numpy, Pandas, Scikit-Learn, and Jupyter. Dask is used today across many different scientific domains. Recently, we’ve observed an increase in use in a few life sciences applications: Large scale imaging in microscopy Single cell analysis Genomics […]
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STX Next, Python development company, uses Spyder to improve their workflow
STX Next, one of Europe's largest Python development companies, has shared with us how Spyder has been a powerful tool for them when performing data analysis. It is a pleasure for us on the Spyder team to work every day to improve the workflow of developers, scientists, engineers and data analysts. We are very glad to receive and share a STX Next testimonial about Spyder, along with an interview with one of their developers, Michael Wiśniewski, who has found Spyder very useful in his job.
In an era of a continuously growing demand for analysis of vast amounts of data, we are facing increasingly complex tasks to perform. Sure, we are not alone—there are many great tools designed for scientists and data analysts. We have NumPy, SciPy, Matplotlib, Pandas, and others. But, wouldn't it be nice to have one extra tool that could combine all the required packages into one compact working environment? Asking this question is precisely how
(continued...)NumFOCUS Introduces New Supporter Program
Today NumFOCUS is pleased to introduce a new program for our individual supporters, called Open Science Champions. Each year, our community members generously support NumFOCUS and our Projects in several ways; this program is intended to connect these various forms of support so that we can engage with our community most effectively and offer our […]
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Educação Vigiada
Essa época de pandemia tem sido de produção em muitas frentes, o que infelizmente implica na redução de tempo para divulgação das mesmas aqui no blog. Nesse post quero me redimir dessa falta falando de um dos projetos que acho dos mais importantes que contribui recentemente, o Educação Vigiada. Há alguns meses o projeto Educação… Continue a ler »Educação VigiadaOpen Source Developer Advocate
Position Overview The primary role of the Open Source Developer Advocate is to represent and support developers of NumFOCUS open source projects by serving as a link to internal and external stakeholders as well as the global user community. You will generate attention and support by applying your technical knowledge, passion for open source data […]
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Engrenagem Ep. 04 – Aplicações KDE favoritas dos KDErs brasileiros
Nesse sábado dia 11/07 às 10h o KDE Brasil vai voltar com episódios do Engrenagem, o videocast da comunidade brasileira (que está há 4 anos sem episódios inéditos 🙂 ). Para retomar os trabalhos, o episódio trará 6 colaboradores brasileiros (Ângela, Aracele, Caio, Filipe (eu), Fred e Tomaz) falando de suas aplicações KDE favoritas –… Continue a ler »Engrenagem Ep. 04 – Aplicações KDE favoritas dos KDErs brasileirosWriting docs is not just writing docs
This blogpost was originally published on the Quansight Labs website.
I joined the Spyder team almost two years ago, and I never thought I was going to end up working on docs. Six months ago I started a project with CAM Gerlach and Carlos Cordoba to improve Spyder’s documentation. At first, I didn’t actually understand how important docs are for software, especially for open source projects. However, during all this time I’ve learned how documentation has a huge impact on the open-source community and I’ve been thankful to have been able to do this. But, from the beginning, I asked myself “why am I the ‘right person’ for this?”
Improving Spyder’s documentation started as part of a NumFOCUS Small Development Grant awarded at the end of last year. The goal of the project was not only to update the documentation for Spyder 4, but also to make it more user-friendly, so users can understand Spyder’s key concepts and get started with it more
(continued...)Sobre o livro “Uma História de Desigualdade”
Finalizei a leitura do premiado livro do Pedro de Souza, “Uma História de Desigualdade – A Concentração de Renda entre os Ricos no Brasil 1926 – 2013“, baseado na tese que defendeu no programa de sociologia da UnB. É um livro de fôlego e que faz jus a todos os elogios que recebeu desde o… Continue a ler »Sobre o livro “Uma História de Desigualdade”Thanking the people behind Spyder 4
This blogpost was originally published on the Quansight Labs website.
After more than three years in development and more than 5000 commits from 60 authors around the world, Spyder 4 finally saw the light on December 5, 2019! I decided to wait until now to write a blogpost about it because shortly after the initial release, we found several critical performance issues and some regressions with respect to Spyder 3, most of which are fixed now in version 4.1.3, released on May 8th 2020.
This new release comes with a lengthy list of user-requested features aimed at providing an enhanced development experience at the level of top general-purpose editors and IDEs, while strengthening Spyder's specialized focus on scientific programming in Python. The interested reader can take a look at some of them in previous blog posts, and in detail in our Changelog. However, this post is not meant to describe those improvements, but to acknowledge all people that contributed
(continued...)Technical discussions are hard; a few tips
Note
This post discuss the difficulties of communicating while developing open-source projects and tries to gives some simple advice.
A large software project is above all a social exercise in which technical experts try to reach good decisions together, for instance on github pull requests. But communication is difficult, in …
Tidynamics, what use?
In 2018 I published small Python library, tidynamics. The scope was deliberately limited: compute the typical correlation functions for stochastic and molecular dynamics: the autocorrelation and the mean-square displacement. Two years later, I wonder about its usage.
Moderna, IMC Renew NumFOCUS Corporate Sponsorships
Monday, May 18th, 2020 Two NumFOCUS corporate supporters recently made fresh commitments to our open source mission. Trading firm IMC and biotechnology company Moderna Therapeutics each renewed their corporate sponsorships earlier this month. Both companies have supported NumFOCUS since 2018 at our Silver and Bronze sponsorship levels, respectively. Asked about his company’s decision to partner […]
The post Moderna, IMC Renew NumFOCUS Corporate Sponsorships appeared first on NumFOCUS.
Lazy River of Curious Content 0
This is the first post of what I'm calling a Lazy
River of Curious Content.
This is a way to review stuff that I've been doing, dealing with, or find
interesting during the week recently (This was
originally written two weeks ago, May 3rd, my shoddy internet connectivity kept
me from posting it.). I'm loosely following the format that Justin Sherrill
uses with great effect over at
https://dragonflydigest.com
Learn NixOS by turning a Raspberry Pi into a Wireless Router Friend of the show, Anthony Scopatz, tried NixOS for the first time and provides a detailed report:
"While I had read the NixOS pamphlets, and listened politely when the faithful came knocking on my door at inconvenient times, I had never walked the path of functional Linux enlightenment myself"
Reading through that made me file away a todo of writing up how I use propellor (and why). But those todo sometimes just pile up for a while...
An interview of one of my long time nerd-crushes, Rob Pike. The questions focus on the Go programming
(continued...)sourmash databases as zip files, in sourmash v3.3.0
Use compressed databases directly!
LaKademy 2019
Em novembro passado, colaboradores latinoamericanos do KDE desembarcaram em Salvador/Brasil para participarem de mais uma edição do LaKademy – o Latin American Akademy. Aquela foi a sétima edição do evento (ou oitava, se você contar o Akademy-BR como o primeiro LaKademy) e a segunda com Salvador como a cidade que hospedou o evento. Sem problemas… Continue a ler »LaKademy 2019Akademy 2019
Em setembro de 2019 a cidade italiana de Milão sediou o principal encontro mundial dos colaboradores do KDE – o Akademy, onde membros de diferentes áreas como tradutores, desenvolvedores, artistas, pessoal de promo e mais se reúnem por alguns dias para pensar e construir o futuro dos projetos e comunidade(s) do KDE Antes de chegar… Continue a ler »Akademy 2019Creating the ultimate terminal experience in Spyder 4 with Spyder-Terminal
This blogpost was originally published on the Quansight Labs website.
The Spyder-Terminal project is revitalized! The new 0.3.0 version adds numerous features that improve the user experience, and enhances compatibility with the latest Spyder 4 release, in part thanks to the improvements made in the xterm.js project.
First, we were able to update all the old JavaScript files to use ES6/JSX syntax and the tests for the client terminal. This change simplified the code base and maintenance and allows us to easily extend the project to new functionalities that the xterm.js API offers. In order to compile this code and run it inside Spyder, we migrated our deployment to Webpack.
Multiple shells per operating systemIn the new release, you now have the ability to configure which shell to use in the terminal. On Linux and UNIX systems, bash, sh, ksh, zsh, csh, pwsh, tcsh, screen, tmux, dash and rbash are supported, while cmd and powershell are the
Software and workflow development practices (April 2020 update)
How we develop software and workflows in the DIB Lab, in 2020.
Is it getting better yet? — An optimistic visual guide to the Coronavirus pandemic
As the apocalypse rumbles on, I found myself wondering "Is it getting any better?"
Daily updates of spiralling case numbers (and worse, deaths) does little to give a sense of whether we're getting to, or already past, the worst of it.
To answer that question for myself and you, I …
On the Link Between Polynomials and Optimization, Part 1
There's a fascinating link between minimization of quadratic functions and polynomials. A link that goes deep and allows to phrase optimization problems in the language of polynomials and vice versa. Using this connection, we can tap into centuries of research in the theory of polynomials and shed new light on …
pheriday 3: infrastructure
Looks like we can't inline audio for your browser. That's cool, just find the direct file links below.
paul's habitual errant ramblings (on Fr)idays
pheridays: 3
2020-04-10: A week ago, I recorded a 5 minute audio segment of some stuff I've been thinking about, but when I started to write it up I stumbled into and kept dropping down a deep technostalgic hole.
fall down along with me:
https://pirsquared.org/blog/pheriday-infrastructure.html
The recording is just shy of five minutes long, you can also download it in different formats, depending on your needs, if the audio tag above doesn't suit you:
https://pirsquared.org/pheridays/2020-04-03.ogg
(2.9 Mb)
https://pirsquared.org/pheridays/2020-04-03.mp3
(4.5 Mb)
https://pirsquared.org/pheridays/2020-04-03.m4a
(6.3 Mb)
--
Stuff I mentioned in the audio:
Propellor - "configuration management system using Haskell and Git" by Joey Hess
OpenWRT - specifically - reducing Bufferbloat
Mumble - "a free, open source, low latency, high quality voice chat application."
sourcehut.org - "the hacker's forge" also know as sr.ht by Drew DeVault
Jitsi - "Multi-platform open-source video conferencing"
OpenFire - "real time collaboration (RTC) server licensed under the
(continued...)Some snakemake hacks for dealing with large collections of files
snakemake4life
Two talks at JGI in May: sourmash, spacegraphcats, and disease associations in the human microbiome.
Using k-mers and taxonomy to find contamination in metagenomes
Advancing research software in the UK through an SSI fellowship
I have been selected as part of the 2020 cohort of Fellows of the Software Sustainability Institute!
The Institute cultivates world-class research with software. It's based at the universities of Edinburgh, Manchester, Southampton, and Oxford in the UK. Their motto says it all:
The SSI has a yearly fellowship program to fund the organization of communities around scientific software (creating of local user groups, workshops, hackathons, etc). Even more importantly, they organize several events to get current and past fellows in the same place doing awesome stuff. I'm really looking forward to this year's Collaborations Workshop (registration is open to all, not just fellows). I applied at the end of last year and was selected to join the 2020 cohort of fellows along with some truly amazing people.
My plan for the fellowship is to
(continued...)Don't fund Software that doesn't exist
sourmash-oddify: a workflow for exploring contamination in metagenome-assembled genomes
Using k-mers and taxonomy to find contamination in metagenomes
Finding problematic bacterial/archaeal genomes using k-mers and taxonomy
Some things in Genbank look ...odd.
How does sourmash's lca classification routine compare with GTDB classifications?
GTDB databases again!
Sourmash LCA databases now available for the GTDB taxonomy
GTDB databases!
Two PhD studentships at the University of Liverpool
I have two open positions for funded studentships at the University of Liverpool. Applications are open until 10 January 2020.
Project descriptionsFollow the links for more detailed versions.
Bringing machine learning techniques to geophysical data processing
The goal of this project is to investigate the use of existing machine learning techniques to process gravity and magnetics data using the Equivalent Layer Method. The methods and software developed during this project can be applied to process large amounts of gravity and magnetics data, including airborne and satellite surveys, and produce data products that can enable further scientific investigations. Examples of such data products include global gravity gradient grids from GOCE satellite measurements, regional magnetic grids for the UK, gravity grids for the Moon and Mars, etc.
Large-scale mapping of the thickness of the
(continued...)Getting a big scientific prize for open-source software
Note
An important acknowledgement for a different view of doing science: open, collaborative, and more than a proof of concept.
A few days ago, Loïc Estève, Alexandre Gramfort, Olivier Grisel, Bertrand Thirion, and myself received the “Académie des Sciences Inria prize for transfer”, for our contributions to the scikit-learn project …
Variable Explorer improvements in Spyder 4
This blogpost was originally published on the Quansight Labs website.
Spyder 4 will be released very soon with lots of interesting new features that you'll want to check out, reflecting years of effort by the team to improve the user experience. In this post, we will be talking about the improvements made to the Variable Explorer.
These include the brand new Object Explorer for inspecting arbitrary Python variables, full support for MultiIndex dataframes with multiple dimensions, and the ability to filter and search for variables by name and type, and much more.
It is important to mention that several of the above improvements were made possible through integrating the work of two other projects. Code from gtabview was used to implement the multi-dimensional Pandas indexes, while objbrowser was the foundation of the new Object Explorer.
New viewer for arbitrary Python objectsFor Spyder 4 we added a long-requested feature: full support for inspecting any kind of Python object through the Variable
(continued...)File management improvements in Spyder 4
This blogpost was originally published on the Quansight Labs website.
Version 4.0 of Spyder is almost ready! It has been in the making for well over two years, and it contains lots of interesting new features. We will focus on the Files pane in this post, where we've made several improvements to the interface and file management tools.
Simplified interfaceIn order to simplify the Files pane's interface, the columns corresponding to size and kind are hidden by default. To change which columns are shown, use the top-right pane menu or right-click the header directly.
First, we added the ability to associate different external applications with specific file extensions they can open. Under the File associations tab of the Files preferences pane, you can add file types and set the external program used to open each of them by default.
Once you've set this up, files will automatically launch in the associated application when opened from the Files pane in Spyder.
(continued...)This Week in Glean: November 1, 2019
When data goes wrong
Loan Amortisation Schedule using R and Python
When you take a loan from bank at x% annual interest rate for N number of years. Bank calculates monthly (or quarterly) instalments based on the following factors :
- Loan Amount
- Annual Interest Rate
- Number of payments per year
- Number of years for loan to be repaid in instalments
Introducing napari: a fast n-dimensional image viewer in Python
I'm really excited to finally, officially, share a new(ish) project called napari with the world. We have been developing napari in the open from the very first commit, but we didn't want to make any premature fanfare about it… Until now. It's still alpha software, but for months now, both the core napari team and a few collaborators/early adopters have been using napari in our daily work. I've found it life-changing.
The backgroundI've been looking for a great nD volume viewer in Python for the better part of
a decade. In 2009, I joined Mitya Chklovskii's lab and the FlyEM team at the
Janelia [Farm] Research Campus to work on the segmentation of 3D electron
microscopy (EM) volumes. I started out in Matlab, but moved to Python pretty
quickly and it was a very smooth transition (highly recommended! ;). Looking at
my data was always annoying though. I was either looking at single 2D slices
using matplotlib.pyplot.imshow, or saving the volumes in VTK format and
loading them into ITK-SNAP — which worked ok
How to Evaluate the Logistic Loss and not NaN trying
A naive implementation of the logistic regression loss can results in numerical indeterminacy even for moderate values. This post takes a closer look into the source of these instabilities and discusses more robust Python implementations.