Planet SciPy
Create an Intermittent Fan Controller With Python
About the Author Evelyn Boettcher founded and leads DiDacTex, LLC (a small woman-owned business). Her experience spans over 20 years in the field of electro-optics and remote sensing, with working experience with electronics, modeling, and algorithm development. She received an MS in physics from the University of Maryland and a BS in physics from the University of Florida. She has been head author in respected peer-reviewed journals, presented findings at international and national meetings, and received patents for electro-optical devices (# 6,738,536 #; 6,944,372). She enjoys supporting STEM activities for youth in her personal time.Propelling Python Into the Next Decade: Anaconda’s OSS Vision
This year marks Anaconda’s 10th anniversary, and we’ve been taking time to reflect on all we’ve accomplished as a company and as part of the Python open-source development community—and to think about where we are going from here. There is still so much to do, and in this blog post I’ll try to provide a snapshot of where we see some new challenges for Python and where Anaconda’s open-source development work is heading.Accelerating Innovation: Anaconda Announces Financing for the Future
As the most popular platform for Python data science, our team has always supported individual users and enterprises in their journey towards insight-based solutions. This year, we took that commitment even further with our acquisition of PythonAnywhere and our rollout of PyScript, a JavaScript framework that allows users to create Python applications in a browser. By extending the reach and accessibility of Python, PythonAnywhere and PyScript helps our industry take a giant leap toward democratizing data science—and we’re proud to be part of that change.State of Data Science 2022: Paving the Way for Innovation
Anaconda’s 2022 State of Data Science report is here! As with years prior, we conducted a survey to gather demographic information about our community, ascertain how that community works, and collect insights into big questions and trends that are top of mind within the community. As the impacts of COVID continue to linger and assimilate into our new normal, we decided to move away from covering COVID themes in our report and instead focus on more actionable issues within the data science, machine learning (ML), and artificial intelligence industries, like open-source security, the talent dilemma, ethics and bias, and more.How to Solve the Data Ingestion and Feature Store Component of the MLOps Stack
As every practitioner in the Data Science space knows, Data is the primary fuel for Machine Learning. A trustworthy data sourcing and high-quality data collection and processing can empower a vast range of potential ML use cases. But having a well-governed Data Warehouse requires a thorough devotion from every team in the organization to look […]
The post How to Solve the Data Ingestion and Feature Store Component of the MLOps Stack appeared first on neptune.ai.
Feature Selection Methods and How to Choose Them
Have you ever found yourself sitting in front of the screen wondering what kind of features will help your machine learning model learn its task best? I bet you have. Data preparation tends to consume vast amounts of data scientists’ and machine learning engineers’ time and energy, and making the data ready to be fed […]
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Exploratory Data Analysis for Tabular Data
Often on having a look at any dataset, we see a bunch of rows and columns filled with numbers or even with some alphabets, words, or abbreviations. Understanding this data and attempting to gain as many insights as possible is a smart strategy to begin the process of model development. In this article, we will […]
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Best ML Model Registry Tools
A model registry is a central repository that is used to version control Machine Learning (ML) models. It simply tracks the models while they move between training, production, monitoring, and deployment. It stores all the predominant information such as: metadata, lineage, model versions, annotations, and training jobs. As the model registry is shared by multiple […]
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Building ML Pipeline: 6 Problems & Solutions [From a Data Scientist’s Experience]
Long gone is the time where ML jobs start and end with a jupyter notebook. Since all companies want to deploy their models into production, having an efficient and rigorous MLOps pipeline to do so is a real challenge that ML engineers have to face nowadays. But creating such a pipeline is not an easy […]
The post Building ML Pipeline: 6 Problems & Solutions [From a Data Scientist’s Experience] appeared first on neptune.ai.
So! You want to search all the public metagenomes with a genome sequence!
Searching all the things - faster!
My Open-Source Journey From Graduation to Maintainership
About the Author Marcelo Trylesinski is a Brazilian software engineer and FastAPI expert, currently maintaining Starlette and Uvicorn.Notes on the Frank-Wolfe Algorithm, Part III: backtracking line-search
Backtracking step-size strategies (also known as adaptive step-size or approximate line-search) that set the step-size based on a sufficient decrease condition are the standard way to set the step-size on gradient descent and quasi-Newton methods. However, these techniques are much less common for Frank-Wolfe-like algorithms. In this blog post I …
Recommender Systems: Lessons From Building and Deployment
If you look at recommender systems papers, a large number of them come from the industry instead of academia. This is because RecSys is actually a practical problem. RecSys for e-commerce could be considerably different than RecSys for social media, as the business objectives differ. In addition, every novel idea needs to be tested in […]
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Pillars of MLOps and How to Implement Them
Machine Learning Operations or MLOps is a topic that is increasingly gaining traction over the last few years. As companies keep investing in Artificial Intelligence and Machine Learning after seeing the potential benefits of using ML applications in their products, the number of machine learning solutions is growing. Moreover, many projects that have been started, […]
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Introducing Numerically Speaking: The Anaconda Podcast
Anaconda is excited to introduce Numerically Speaking: The Anaconda Podcast, hosted by CEO Peter Wang. On the show, Peter and an impressive roster of guests including data science experts, creators of cutting-edge open-source tools, and executives pushing the boundaries of machine learning and artificial intelligence (AI) explore a variety of topics surrounding the dynamic world of data science, from quantitative computing to business and entrepreneurship and more.Deploying ML Models: How to Make Sure the New Model Is Better Than the One in Production? [Practical Guide]
Let’s assume that we’re working on an ML-related project and that the first ML model is successfully deployed in production, following most of the MLOps practices. Okay, but what now? Have we finished our work? Well, I assume that most of you know what the answer is, and of course, the answer is negative. We […]
The post Deploying ML Models: How to Make Sure the New Model Is Better Than the One in Production? [Practical Guide] appeared first on neptune.ai.
Leveraging Unlabeled Image Data With Self-Supervised Learning or Pseudo Labeling With Mateusz Opala
This article was originally an episode of MLOps Live, an interactive Q&A session where ML practitioners answer questions from other ML practitioners. Every episode is focused on one specific ML topic, and during this one, we talked to Mateusz Opala about leveraging unlabeled image data with self-supervised learning or pseudo-labeling. You can watch it on […]
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How to Solve the Model Serving Component of the MLOps Stack
Model serving and deployment is one of the pillars of the MLOps stack. In this article, I’ll dive into it and talk about what a basic, intermediate, and advanced setup for model serving look like. Let’s start by covering some basics. What is ML model serving? Training a machine learning model may seem like a […]
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Introducing Anaconda’s Safari Program
A safari is often characterized as an expedition, a time to explore, a time to observe. And while the word “safari” often conjures up images of wildlife in Africa, it refers to something different at Anaconda; here, the Safari program is a new part of our career development program.scikit-learn Sprint in Salta, Argentina
Author: Juan Martín LoyolaThere’s No Wrong Way to Become a Software Engineer: Part 2
So you or someone you know is interested in becoming a software engineer or pursuing one of the many adjacent careers (e.g., data scientist, system administrator, tech support, etc.). Well, here's the not-so-secret secret:Open-Source Tools for Graph Data Science
About the Author Janit Anjaria is a Senior Software Engineer at Aurora Innovation Inc., where he currently works on building high-definition 3-D maps for self-driving vehicles. Before joining Aurora, Janit worked on the Autonomous Vehicle Maps team at Uber Advanced Technology Group. Prior to Uber, he was at the University of Maryland, College Park Spatial Lab working on spatial data structures and machine learning. He has diverse professional and academic experience, and once worked on building out the Location Intelligence Platform at Flipkart Internet Pvt. Ltd. in India. Outside of professional and academic life, he is an open-source enthusiast and has contributed to Apache Solr and LibreOffice and has been a Linux user since 2011.New 2022 roadmap and grant funding
For the last couple of months, the Spyder team has been working on defining a new roadmap and submitting grant proposals to fund more features and improvements. We are pleased to announce our roadmap for the rest of 2022, and that two proposals were funded!
The roadmapConsidering the importance of sharing a clear perspective of where the Spyder project is going and where we will be focusing our efforts over the coming months, the team has created an initial roadmap for the rest of 2022. We prioritized the highlighted features and enhancements based on input from issues, face-to-face and virtual discussions, Stack Overflow, social media and other feedback, to try to best capture the interests of our users and community.
The proposalsTo help make our roadmap achievable, we wrote and submitted proposals to several different venues and organizations in the last couple of months. While we have yet to hear back from some of them, two have already been funded!
The first was for the
(continued...)10 Years of Data Science Innovation: Anaconda’s Commitment to the Open-Source Python Community
For the last decade, Anaconda has helped define the guard rails and infrastructure of Python programming to empower a new generation of data science professionals and enthusiasts. Over the years, we’ve watched as the open-source Python community has burgeoned into a thriving network of creators, contributors, and dedicated maintainers. Because of the human ecology that has formed around Python, it is now the fastest-growing programming language in the world.Pollution in India : Real-time AQI Data
Air pollution has become a serious problem in recent years across the world. Effects of Air Pollution is devastating and its harmful effects are not just limited to Humans but also animals and plants as well. It also leads to global warming which is esentially increasing air and ocean temperatures around the world.
Indian cities have been topping the list of polluted cities. In order to solve the problem of air pollution the most important thing is to track air pollution on real-time basis first which alerts people to avoid outdoor activities during high air Pollution. This post explains how you can fetch real-time Air Quality Index (AQI) of Indian cities using Python and R code. It allows both Python and R programmers to pull pollution data.
You can download the dataset which contains static information about Indian states, cities and AQI stations. Variables stored in this dataset will be used further to fetch real-time data.
My Mayavi story: discovering open source communities
The Mayavi Python software, and my personal history: A thread on Python and scipy ecosystems, building open source codebase, and meeting really cool and friendly people
I am writing today as a goodbye to the project: I used to be one of the core contributors and maintainers but have been …
Pointwise mutual information (PMI) in NLP
Natural Language Processing (NLP) has secured so much acceptance recently as there are many live projects running and now it's not just limited to academics only. Use cases of NLP can be seen across industries like understanding customers' issues, predicting the next word user is planning to type in the keyboard, automatic text summarization etc. Many researchers across the world trained NLP models in several human languages like English, Spanish, French, Mandarin etc so that benefit of NLP can be seen in every society. In this post we will talk about one of the most useful NLP metric called Pointwise mutual information (PMI) to identify words that can go together along with its implementation in Python and R.
PMI helps us to find related words. In other words, it explains how likely the co-occurrence of two words than we would expect by chance. For example the word "Data Science" has a specific meaning when
How to import your data into Acoular
Acoular is a Python library that processes multichannel data (up to a few hundred channels) from acoustic measurements with a microphone array which is stored in an HDF5 file. This blog post explains how to convert data available in other formats into this file format. As examples for other file formats we will use both .csv (comma separated text files) and .mat (Matlab files).Checking for accessibility: thoughts and a checklist!
On the Link Between Optimization and Polynomials, Part 5
Six: All of this has happened before.
Baltar: But the question remains, does all of this have to happen again?
Six: This time I bet no.
Baltar: You know, I've never known you to play the optimist. Why the change of heart?
Six: Mathematics. Law of averages. Let a complex …
Announcing ribbity - a hacky project to build Web sites from GitHub issue trackers
Munging GitHub issue trackers for fun!
Interview with Norbert Preining, scikit-learn Team Member
Author: Reshama Shaikh , Norbert PreiningThe Value of Open Source Sprints, the scikit-learn Experience
Author: Reshama Shaikh5 Years, 10 Sprints, A scikit-learn Open Source Journey
Author: Reshama ShaikhOnly size-1 arrays can be converted to Python scalars
Numpy is one of the most used module in Python and it is used in a variety of tasks ranging from creating array to mathematical and statistical calculations. Numpy also bring efficiency in Python programming. While using numpy you may encounter this errorTypeError: only size-1 arrays can be converted to Python scalars It is one of the frequently appearing error and sometimes it becomes a daunting challenge to solve it. 
Interview with Lucy Liu, scikit-learn Team Member
Author: Reshama Shaikh , Lucy LiuThe second Common Fund Data Ecosystem hackathon - May 9-13, 2022!
We're running another hackathon!
Storing 64-bit unsigned integers in SQLite databases, for fun and profit
Storing unsigned longs in SQLite is possible, and can be fast.
Interview with Maren Westermann: Extending the Impact of the scikit-learn Sprints to the Community
Author: Reshama Shaikh , Maren WestermannBehind the Scenes of Data Umbrella scikit-learn Open Source Sprints
Author: Reshama Shaikh , Angela OkuneWomen in Machine Learning - A WiMLDS Paris sprint and contribution workshop
Author: François GoupilThe First Common Fund Data Ecosystem Hackathon
We ran a successful pilot hackathon, and we will run a second one soon!
Three Components for Reviewing a Pull Request
Author: Thomas J. FanPerformances and scikit-learn
Author: Julien JerphanionMestrado em Ciência da Computação 2022: Metaheurísticas
Estamos ainda com algumas vagas abertas para o Mestrado em Ciência da Computação na UFPA, Belém. Os interessados, favor olhar as instruções para submissão na página de seleção do programa. Desde meu ingresso no programa venho orientando alunos em diferentes pesquisas sobre inteligência computacional aplicados a problemas de smart grids. Já tivemos trabalhos sobre sistemas multiagentes… Continue a ler »Mestrado em Ciência da Computação 2022: MetaheurísticasDiffCast: Hands-free Python Screencast Creator — Create reproducible programming screencasts without typos or edits
Programming screencasts are a popular way to teach programming and demo tools. Typically people will open up their favorite editor and record themselves tapping away. But this has a few problems. A good setup for coding isn't necessarily a good setup for video -- with text too small, a window too …
On minimum metagenome covers, and calculating them for your own data.
You, too, can run our software!
Optimization Nuggets: Implicit Bias of Gradient-based Methods
When an optimization problem has multiple global minima, different algorithms can find different solutions, a phenomenon often referred to as the implicit bias of optimization algorithms. In this post we'll characterize the implicit bias of gradient-based methods on a class of regression problems that includes linear least squares and Huber …
Optimization Nuggets: Exponential Convergence of SGD
This is the first of a series of blog posts on short and beautiful proofs in optimization (let me know what you think in the comments!). For this first post in the series I'll show that stochastic gradient descent (SGD) converges exponentially fast to a neighborhood of the solution.
A bioinformatics training career panel in the DIB Lab
Careers in training!
Hiring an engineer and post-doc to simplify data science on dirty data
Note
Join us to work on reinventing data-science practices and tools to produce robust analysis with less data curation.
It is well known that data cleaning and preparation are a heavy burden to the data scientist.
In the dirty data project, we have been conducting machine-learning research …
TorchVision Datasets: Getting Started
The TorchVision datasets subpackage is a convenient utility for accessing well-known public image and video datasets. You can use these tools to start training new computer vision models very quickly. TorchVision Datasets Example To get started, all you have to do is import one of the Dataset classes. Then, instantiate it and access one of ... Read more
The post TorchVision Datasets: Getting Started appeared first on Sparrow Computing.
NumPy Any: Understanding np.any()
The np.any() function tests whether any element in a NumPy array evaluates to true: The input can have any shape and the data type does not have to be boolean (as long as it’s truthy). If none of the elements evaluate to true, the function returns false: Passing in a value for the axis argument ... Read more
The post NumPy Any: Understanding np.any() appeared first on Sparrow Computing.
PyTorch DataLoader Quick Start
PyTorch comes with powerful data loading capabilities out of the box. But with great power comes great responsibility and that makes data loading in PyTorch a fairly advanced topic. One of the best ways to learn advanced topics is to start with the happy path. Then add complexity when you find out you need it. ... Read more
The post PyTorch DataLoader Quick Start appeared first on Sparrow Computing.
How the NumPy append operation works
Understanding the np.append() operation and when you might want to use it.
The post How the NumPy append operation works appeared first on Sparrow Computing.
Hiring someone to develop scikit-learn community and industry partners
Note
With the growth of scikit-learn and the wider PyData ecosystem, we want to recruit in the Inria scikit-learn team for a new role. Departing from our usual focus on excellence in algorithms, statistics, or code, we want to add to the team someone with some technical understanding, but an …
Using snakemake to do simple wildcard operations on many, many, many files
snakemake is awesome
A paper on the Lees-Edwards method
A few years ago1, Sebastian contacted me to help with simulations. Great, I like simulation studies, so we start discussing the details. The idea: use an established method, the Lees-Edwards boundary condition, to study colloids under shear.
A biotech career panel in the DIB Lab
Careers outside of universities!
Scaling sourmash to millions of samples
Bigger and better!
Poetry for Package Management in Machine Learning Projects
When you’re building a production machine learning system, reproducibility is a proxy for the effectiveness of your development process. But without locking all your Python dependencies, your builds are not actually repeatable. If you work in a Python project without locking long enough, you will eventually get a broken build because of a transitive dependency ... Read more
The post Poetry for Package Management in Machine Learning Projects appeared first on Sparrow Computing.
Development containers in VS Code: a quick start guide
If you’re building production ML systems, dev containers are the killer feature of VS Code. Dev containers give you full VS Code functionality inside a Docker container. This lets you unify your dev and production environments if production is a Docker container. But even if you’re not targeting a Docker deployment, running your code in ... Read more
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New sourmash databases are available!
Databases are now available for GTDB!
Colunando no O Estado do Piauí
O Estado do Piauí é um novo jornal que surgiu recentemente pelas bandas de lá. Com um foco maior em reportagens longas e densas, misturando jornalismo investigativo e literário, o projeto pretende discutir em profundidade os temas de interesse do estado, descobrir histórias piauienses únicas, repercutir situações problemáticas, apontar alternativas e muito mais. Não se… Continue a ler »Colunando no O Estado do PiauíCiclo de Entrevistas sobre as Pesquisas no PPGCC da UFPA – Inteligência Computacional
A Faculdade de Computação e o Programa de Pós-Graduação em Ciência da Computação da UFPA estão desenvolvendo um projeto que pretende atingir dois objetivos: o primeiro, fazer uma melhor divulgação para o público externo à universidade do que produzimos em nossas pesquisas; o segundo, uma melhor divulgação INTERNA da mesma coisa – o que desenvolvemos… Continue a ler »Ciclo de Entrevistas sobre as Pesquisas no PPGCC da UFPA – Inteligência ComputacionalMoving sourmash towards more community engagement - a funding application
CZI EOSS4 application for sourmash support
Searching all public metagenomes with sourmash
Searching all the things!
Is your software ready for the Journal of Open Source Software?
For the unaware reader, the Journal of Open Source Software (JOSS) is an open-access scientific journal founded in 2016 and aimed at publishing scientific software. A JOSS article in itself is short and its publication contributes to recognize the work on the software. I share here my point of view on what makes some software tools more ready to be published in JOSS. I do not comment on the size or the relevance for research which are both documented on JOSS' website.
Basic Counting in Python
I love fancy machine learning algorithms as much as anyone. But sometimes, you just need to count things. And Python’s built-in data structures make this really easy. Let’s say we have a list of strings: With a list like this, you might care about a few different counts. What’s the count of all items? What’s ... Read more
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How to Use the PyTorch Sigmoid Operation
The PyTorch sigmoid function is an element-wise operation that squishes any real number into a range between 0 and 1. This is a very common activation function to use as the last layer of binary classifiers (including logistic regression) because it lets you treat model predictions like probabilities that their outputs are true, i.e. p(y ... Read more
The post How to Use the PyTorch Sigmoid Operation appeared first on Sparrow Computing.
On the Link Between Optimization and Polynomials, Part 4
While the most common accelerated methods like Polyak and Nesterov incorporate a momentum term, a little known fact is that simple gradient descent –no momentum– can achieve the same rate through only a well-chosen sequence of step-sizes. In this post we'll derive this method and through simulations discuss its practical …
NumFOCUS Welcomes Tesco Technology to Corporate Sponsors
NumFOCUS is pleased to announce our new partnership with Tesco Technology. A long-time PyData event sponsor, Tesco Technology joined NumFOCUS as a Silver Corporate Sponsor in December 2020. “We are very excited to formalize our partnership with Tesco Technology,” said Leah Silen, NumFOCUS Executive Director. “Tesco Technology has partnered with NumFOCUS for the past several […]
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Job Posting | Communications and Marketing Manager
Job Title: Communications and Marketing Manager Position Overview The primary role of the Communications & Marketing Manager is to manage the NumFOCUS brand by overseeing all outgoing communications between NumFOCUS and our stakeholders. You will serve the project communities by playing a key role in their event marketing management and assist with project promotional and […]
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Getting started with Acoular - Part 2
This is the second in a series of three blog posts about the basic use of Acoular. It assumes that you already have read the first post and continues by explaining some more concepts and additional methods. Acoular is a Python library that processes multichannel data (up to a few hundred channels) from acoustic measurements with a microphone array. The focus of the processing is on the construction of a map of acoustic sources. This is somewhat similar to taking an acoustic photograph of some sound sources.Getting started with Acoular - Part 3
This is the third and final in a series of three blog posts about the basic use of Acoular. It assumes that you already have read the first two posts and continues by explaining additional concepts to be used with time domain methods. Acoular is a Python library that processes multichannel data (up to a few hundred channels) from acoustic measurements with a microphone array. The focus of the processing is on the construction of a map of acoustic sources. This is somewhat similar to taking an acoustic photograph of some sound sources. To continue, we do the same set up as in Part 1. However, as we are setting out to do some signal processing in time domain, we define only TimeSamples, MicGeom, RectGrid and SteeringVector objects but no PowerSpectra or BeamformerBase. import acoular ts = acoular.TimeSamples( name="three_sources.h5" ) mg = acoular.MicGeom( from_file="array_64.xml" ) rg = acoular.RectGrid( x_min=-0.2, x_max=0.2, y_min=-0.2, y_max=0.2, z=0.3, increment=0.01 ) st = acoular.SteeringVector( grid=rg, mics=mg (continued...)Getting started with Acoular - Part 1
This is the first in a series of three blog posts about the basic use of Acoular. It explains some fundamental concepts and walks through a simple example. Acoular is a Python library that processes multichannel data (up to a few hundred channels) from acoustic measurements with a microphone array. The focus of the processing is on the construction of a map of acoustic sources. This is somewhat similar to taking an acoustic photograph of some sound sources.PyTorch Tensor to NumPy Array and Back
You can easily convert a NumPy array to a PyTorch tensor and a PyTorch tensor to a NumPy array. This post explains how it works.
The post PyTorch Tensor to NumPy Array and Back appeared first on Sparrow Computing.
TorchVision Transforms: Image Preprocessing in PyTorch
TorchVision, a PyTorch computer vision package, has a great API for image pre-processing in its torchvision.transforms module. This post gives some basic usage examples, describes the API and shows you how to create and use custom image transforms.
The post TorchVision Transforms: Image Preprocessing in PyTorch appeared first on Sparrow Computing.
sourmash 4.0 is now available! Low low cost if you buy now!
sourmash v4.0.0 is here!
On the Link Between Optimization and Polynomials, Part 3
I've seen things you people wouldn't believe.
Valleys sculpted by trigonometric functions.
Rates on fire off the shoulder of divergence.
Beams glitter in the dark near the Polyak gate.
All those landscapes will be lost in time, like tears in rain.
Time to halt.
A momentum optimizer *
Introducing PyMC Labs: Saving the World with Bayesian Modeling
After I left Quantopian in 2020, something interesting happened: various companies contacted me inquiring about consulting to help them with their PyMC3 models.
Usually, I don't hear how people are using PyMC3 -- they mostly show up on GitHub or Discourse when something isn't working right. So, hearing about all these …
Using MicroPython and uploading libraries on Raspberry Pi Pico — Using rshell to upload custom code
MicroPython is an implementation of the Python 3 programming language, optimized to run microcontrollers. It's one of the options available for programming your Raspberry Pi Pico and a nice friendly way to get started with microcontrollers.
MicroPython can be installed easily on your Pico, by following the instructions on the …
sourmash v4.0.0 release candidate 1 is now available for comment!
sourmash v4.0.0 is coming!
Job Posting | Events and Digital Marketing Coordinator
Job Title: Events and Digital Marketing Coordinator Position Overview The primary role of the Events and Digital Marketing Coordinator is to support and assist the Events Manager and the Community Communications and Marketing Manager to advance one of NumFOCUS’s primary missions of educating and building the community of users and developers of open source scientific […]
The post Job Posting | Events and Digital Marketing Coordinator appeared first on NumFOCUS.
Transition your Python project to use pyproject.toml and setup.cfg! (An example.)
Updating old Python packages, in this year of the PSF 2021!
Writing a SAM Coupé SCREEN$ Converter in Python — Interrupt optimizing image converter
The SAM Coupé was a British 8 bit home computer that was pitched as a successor to the ZX Spectrum, featuring improved graphics and sound and higher processor speed.
The SAM Coupé's high-color MODE4 could manage 256x192 resolution graphics, with 16 colors from a choice of 128. Each pixel can …
A snakemake hack for checkpoints
snakemake checkpoints r awesome
Squeezing Space Invaders onto the BBC micro:bit's 25 pixels — MicroPython 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 …
Run SAS in Python without Installation
Do you wish
(continued...)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 XWish 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://github.com/deepanshu88/Datasets/raw/master/UploadedFiles/merrychristmas1.wav", christmas_file, mode = "wb")
xmas
On the Link Between Optimization and Polynomials, Part 2
We can tighten the analysis of gradient descent with momentum through a cobination of Chebyshev polynomials of the first and second kind. Following 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 …
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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