Planet SciPy
Pandas Plot: Deep Dive Into Plotting Directly with Pandas
Data Visualisation is an essential step in any data science pipeline. Exploring your data visually opens your mind to a lot of...
The post Pandas Plot: Deep Dive Into Plotting Directly with Pandas appeared first on neptune.ai.
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 …
Machine Learning Model Management in 2020 and Beyond – Everything That You Need to Know
Model management is a relatively new issue when it comes to Machine Learning. Since the technique is widely used in business, the...
The post Machine Learning Model Management in 2020 and Beyond – Everything That You Need to Know appeared first on neptune.ai.
Dive Into Football Analytics With TensorFlow Object Detection API
When it comes to football, it is surprising to see how a team is able to win a football match against a...
The post Dive Into Football Analytics With TensorFlow Object Detection API appeared first on neptune.ai.
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)
Image Classification: Tips and Tricks From 13 Kaggle Competitions (+ Tons of References)
Success in any field can be distilled into a set of small rules and fundamentals that produce great results when coupled together. ...
The post Image Classification: Tips and Tricks From 13 Kaggle Competitions (+ Tons of References) appeared first on neptune.ai.
TensorBoard vs Neptune: How Are They ACTUALLY Different
ML model development typically involves a tedious workflow of managing data, feature engineering, model training and evaluation. A data scientist could easily...
The post TensorBoard vs Neptune: How Are They ACTUALLY Different appeared first on neptune.ai.
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
The Best Tools for Reinforcement Learning in Python You Actually Want to Try
Nowadays, Deep Reinforcement Learning (RL) is one of the hottest topics in the Data Science community. The fast development of RL has...
The post The Best Tools for Reinforcement Learning in Python You Actually Want to Try appeared first on neptune.ai.
This Week in Machine Learning: Why ML Models Fail, AI & Plastic Waste, Vatican Library, and Bears
Machine learning is fascinating. New things happen every second while we’re busy performing our daily tasks. If you want to know what...
The post This Week in Machine Learning: Why ML Models Fail, AI & Plastic Waste, Vatican Library, and Bears appeared first on neptune.ai.
pyLDAvis: Topic Modelling Exploration Tool That Every NLP Data Scientist Should Know
Have you ever wanted to classify news, papers, or tweets based on their topics? Knowing how to do this can help you...
The post pyLDAvis: Topic Modelling Exploration Tool That Every NLP Data Scientist Should Know appeared first on neptune.ai.
Best Machine Learning as a Service Platforms (MLaaS) That You Want to Check as a Data Scientist
The availability of tremendous computing power in the cloud was one of the factors behind the machine learning revolution. Thus, it is...
The post Best Machine Learning as a Service Platforms (MLaaS) That You Want to Check as a Data Scientist appeared first on neptune.ai.
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
What Image Processing Techniques Are Actually Used in the ML Industry?
Processing can be used to improve the quality of your image, or to help you extract useful information from it. It’s useful...
The post What Image Processing Techniques Are Actually Used in the ML Industry? appeared first on neptune.ai.
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 […]
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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 […]
The post NumFOCUS Earns Transparency Recognition from GuideStar appeared first on NumFOCUS.
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 KDEIPython reproducible builds
Starting with IPython 7.16.1 (released in June 2020), you should be able to recreate the sdist (.tar.gz) and wheel
(.whl), and get byte for byte identical result to the wheels published on PyPI. This is a critical step toward being able
to trust your computing platforms, and a key component to improve efficiency of build and packaging platforms. It also
potentially impacts fast conda environment creation for users. The following goes into some reasons for why you should care.
Read more… (5 min remaining to read)
Introducing Versioned HDF5
The problem of storing and manipulating large amounts of data is a challenge in many scientific computing and industry applications. One of the standard data models for this is HDF5, an open technology that implements a hierarchical structure (similar to a file-system structure) for storing large amounts of possibly heterogeneous data within a single file. Data in an HDF5 file is organized into groups and datasets; you can think about these as the folders and files in your local file system, respectively. You can also optionally store metadata associated with each item in a file, which makes this a self-describing and powerful data storage model.
Read more… (3 min remaining to read)
CARLsim5 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.
NumFOCUS Projects helping combat the COVID-19 pandemic
Open source tools are uniquely positioned to help combat the ongoing COVID-19 pandemic through their adaptable and collaborative nature. NumFOCUS sponsored and affiliated projects are being used on a global scale to meet the needs of researchers and data scientists. Our projects are being used in groundbreaking scientific efforts to create response models, visualize and […]
The post NumFOCUS Projects helping combat the COVID-19 pandemic 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 2019Yellowbrick Update – April 2020
Yellowbrick released Version 1.1 on February 25, 2020. If you haven’t yet upgraded simply type pip install yellowbrick -U or conda install -c districtdatalabs yellow-brick into your terminal/command prompt to get it. The major improvement in v1.1 is introducing quick methods or one-liners to generate your favorite ML plots more quickly with Yellowbrick. Dr. Rebecca […]
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2020 PyData Conferences Update [COVID-19]
We wanted to give an update to our community regarding the upcoming 2020 PyData conferences. We have been closely monitoring the situation and to help ensure the safety of our community given the threat of the COVID-19 virus, the following in-person events have been postponed to 2021: PyData Miami PyData Amsterdam PyData LA PyData London PyData […]
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Scientific Software Developer- Contract Basis [SunPy Project]
Scientific Software Developer- Contract Basis NumFOCUS is seeking a Scientific Software Developer to support the SunPy project. SunPy is a Python-based open source scientific software package supporting solar physics data analysis. This is a 1 year contract. The successful applicant will work to improve SunPy’s functionality. There are four main tasks: Report on […]
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Creating 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.
LaKademy 2019
Past November 2019 KDE fellows from Latin-America arrived in Salvador – Brazil to attend an one more edition of LaKademy – the Latin American Akademy. That was the 7th edition of the event (or the 8th, if you count Akademy-BR as the first LaKademy) and the second one with Salvador as host city. No problem… Continue a ler »LaKademy 2019Is 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
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.
Uvas Gold 200
My poem about a rainy 200k was published in the Fall 2019 issue of American Randonneur (a quarterly magazine published by Randonneurs USA)
I've been doing samizdat poetry for as long as I've had a web presence (since 1999), but I am now officially a published poet! (I am deliberately not counting the embarrasing hackjob that was published in a youth anthology when I was in 8th grade.)
You can find "Uvas Gold 200" on page 26 - either directly on this skeuomorphic leafing viewer or the PDF, but I'm republishing both the exposition blurb and the poem below. If you prefer to listen, I recorded a reading of it that you can download in different flavors: a local audio only, a local video, or the embeded video version below.
(continued...)Uvas Gold 200k starts and ends in Fremont, CA and was held on Saturday, December 1st, 2018. The ride frontloads the climbing by going nearly half-way up Mount
Spyder 4.0: Kite integration is here
This blogpost was originally published on the Quansight Labs website.
Note: Kite is sponsoring the work discussed in this blog post, and in addition supports Spyder 4.0 development through a Quansight Labs Community Work Order.
As part of our next release, we are proud to announce an additional completion client for Spyder, Kite. Kite is a novel completion client that uses Machine Learning techniques to find and predict the best autocompletion for a given text. Additionally, it collects improved documentation for compiled packages, e.g. Matplotlib, NumPy and SciPy, that cannot be obtained easily by using traditional code analysis packages such as Jedi. Although Kite is not open source like Spyder, you can download it without charge at the Kite website.
By incorporating Kite into Spyder, we will improve and provide the ultimate autocompletion and signature retrieval experience for most of the scientific Python stack and beyond. For instance, let’s take a look at the following PyTorch completion. While
(continued...)An initial report on the Common Fund Data Ecosystem
Helping DCCs get FAIRer
Object Oriented Programming in Python : Learn by Examples
In the above example, car is an object. Functions are called methods in OOP world. Characteristics are attributes (properties). Technically attributes are variables or values related to the state of the object whereas methods
Precision Recall Curve Simplified
Comparing two genome binnings quickly with sourmash
Comparing two sets of MAGs, for fun and profit!
Calculate KS Statistic with Python
In data science, it compares the cumulative distribution of events and non-events and KS is where there is a maximum difference between the two distributions. In simple words, it helps us to understand how well our predictive model is able to discriminate between events and
A Complete Guide to Python DateTime Functions
- datetime
- date
- time
- timedelta
People who have no experience of working with real-world datasets might have not encountered date columns. They might be under impression that working with dates is rarely used and not so
What are *args and **kwargs and How to use them
args is a short form of arguments. With the use of *args python takes any number of arguments in user-defined function and converts user inputs to a tuple named args. In other words, *args means zero or more arguments which are stored in a tuple named args. When you define function without *args, it has a fixed number of inputs which means it cannot accept more (or less) arguments than you defined in the function.
In the example code below, we are creating a very basic function which adds two numbers. At the same time, we created a
Python : 10 Ways to Filter Pandas DataFrame
- Select all the active customers whose accounts were opened
Python Dictionary Comprehension with Examples
- Keys : Think about columns in tables. It must be unique (like column names cannot be duplicate)
- Values : It is similar to rows in tables. It can be duplicate.
To extract keys, values and structure of dictionary, you can submit the following commands.
d = {'a': [1,2], 'b': [3,4], 'c': [5,6]}
d.keys() # 'a', 'b', 'c'
d.values() # [1, 2], [3, 4], [5,
HTML outputs in Jupyter
SummaryUser interaction in data science projects can be improved by adding a small amount of visual deisgn.
To motivate effort around visual design we show several simple-yet-useful examples. The code behind these examples is small and accessible to most Python developers, even if they don’t have much HTML experience.
This post in particular focuses on Jupyter’s ability to add HTML output to any object. This can either be full-fledged interactive widgets, or just rich static outputs like tables or diagrams. We hope that by showing examples here we will inspire some throughts in other projects.
This post was supported by replies to this tweet. The rest of this post is just examples.
IrisI originally decided to write this post after reading another blogpost from the UK Met office, where they included the HTML output of their library Iris in a a blogpost
(work by Peter Killick, post by Theo McCaie)
The fact that the output provided by an interactive session is the same output that you would provide in a published result helps everyone. The interactive
(continued...)Python list comprehension : Learn by Examples
f(x) = x2, f(x) will always return the same result with the same x value. The function has no "side-effect" which means an operation has no effect on a variable/object that is outside the intended usage. "Side-effect" refers to leaks in your code which can modify a mutable data structure or variable. Functional programming is also good for parallel computing as there is no
Don't cite the No Free Lunch Theorem
Tldr; You probably shouldn’t be citing the "No Free Lunch" Theorem by Wolpert. If you’ve cited it somewhere, you might have used it to support the wrong conclusion. What it actually (vaguely) says is “You can’t learn from data without making assumptions”.The paper on the “No Free Lunch Theorem”, actually called "The Lack of A Priori Distinctions Between Learning Algorithms" is one of these papers that are often cited and rarely read, and I hear many people in the ML community refer to it when supporting the claim that “one model can’t be the best at everything” or “one model won’t always be better than another model”. The point of this post is to convince you that this is not what the paper or theorem says (at least not the one usually cited by Wolpert), and you should not cite this theorem in this context; and also that common versions cited of the "No Free Lunch" Theorem (continued...)
15 ways to read CSV file with pandas
pip install pandas. Next step is to load the package by running the following command. pd is an alias of pandas package. We will use it instead of full name "pandas". import pandas as pd
Matplotlib Tutorial : Learn by Examples
MATLAB programming language and provides a similar MATLAB like interface for graphics. The beauty of this library is that it integrates well with pandas package which is used for data manipulation. With the combination of these two libraries, you can easily perform data wrangling along with visualization and get valuable insights out of data. Like ggplot2 library in R, matplotlib library is the grammar of graphics in Python and most used library for charts in Python. 
Write Short Blogposts
I encourage my colleagues to write blogposts more frequently. This is for a few reasons:
-
It informs your broader community what you’re up to, and allows that community to communicate back to you quickly.
You communicating to the community fosters a sense of collaboration, openness, and trust. You gain collaborators, build momentum behind your work, and curate a body of knowledge that early adopters can consume to become experts quickly.
Getting feedback from your community helps you to course-correct early in your work, and stops you from wasting time in inefficient courses of action.
You can only work for a long time without communicating if you are either entirely confident in what you’re doing, or reckless, or both.
-
It increases your visibility, and so is good for your career.
I have a great job. I find my work to be both
How to encourage participation in teleconferences
Participation is good!
How to drop one or multiple columns from Pandas Dataframe
- Drop or Keep rows and columns
- Aggregate data by one or more columns
- Sort or reorder data
- Merge or append multiple dataframes
- String Functions to handle text data
- DateTime Functions to handle date or time format columns
import command. import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(6, 4), columns=list('ABCD'))
A B C D
0 -1.236438 -1.656038
String Functions in Python with Examples
