Mongoose Add Update: Mongoose 1.0 has been released, and we recommend going to our dedicated website for the most up-to-date information and code At LearnBoost, we like to consider ourselves hackers. The team is very proud to release Mongoose and we hope you like it! What is Mongoose? Mongoose is a javascript library that makes working with MongoDB a breeze. Why MongoDB? While looking into storage solutions we decided that using a NoSQL engine was the best fit for our needs. MongoDB We ended up choosing Mongo for a variety of reasons: commercial support leaders in the industry (founder was CTO at doubleclick ) VMM mapping is a good idea horizontal scaling (sharding at a collection level, soon at the db level) fast large set of features (query language has power and is constantly expanding) javascript (a great fit with Nodejs) runs on spidermonkey but a port is in the works for v8 as well Quick Intro to Mongoose It is first very important to understand what Nodejs is: Example The current state: if(doc ! },hydrate);
Automate the Boring Stuff with Python PDF and Word documents are binary files, which makes them much more complex than plaintext files. In addition to text, they store lots of font, color, and layout information. If you want your programs to read or write to PDFs or Word documents, you’ll need to do more than simply pass their filenames to open(). Fortunately, there are Python modules that make it easy for you to interact with PDFs and Word documents. This chapter will cover two such modules: PyPDF2 and Python-Docx. PDF stands for Portable Document Format and uses the .pdf file extension. The module you’ll use to work with PDFs is PyPDF2. Extracting Text from PDFs PyPDF2 does not have a way to extract images, charts, or other media from PDF documents, but it can extract text and return it as a Python string. Figure 13-1. Download this PDF from and enter the following into the interactive shell: First, import the PyPDF2 module. PyPDF2 doesn’t allow you to directly edit a PDF. Note #! #! #! #!
aubio, a library for audio labelling Functional Javascript var Functional; Functional is the namespace for higher-order functions. Functional.install = function(except) This function copies all the public functions in Functional except itself into the global namespace. If the optional argument is present, functions named by its property names are not copied. Higher-order functions Functional.compose = function(fn...) Type: (a2 → a1) (a3 -> a2)… (a… -> an) -> a… -> a1 Returns a function that applies the last argument of this function to its input, and the penultimate argument to the result of the application, and so on. (1, 2, 3…, )() =def 1(2(3(…((…))))) compose('1+', '2*')(2)→ 5 Functional.sequence = function(fn...) Type: (a… → a1) (a1 -> a2) (a2 -> a3)… (an-1 -> an) -> a… -> an Same as compose, except applies the functions in argument-list order. (1, 2, 3…, )(…) =def (…(3(2(1(…))))) sequence('1+', '2*')(2)→ 6 Functional.map = function(fn, sequence, object) Type: (a ix → boolean) [a] -> [a] Applies fn to each element of sequence. map('1+', [1,2,3])→ [2, 3, 4]
Docs: Pandas Recipes This page contains code snippets that focus on working with pandas, a popular Python library that is used throughout the Quantopian API. Most Quantopian API functions that output data or results do so in the form of a pandas data structure like a Series or a DataFrame. The recipes below illustrate some of the techniques you can use to manipulate these data structures. Working With Pipeline Outputs in Research¶ The following code snippets provide tips for working with MultiIndex DataFrames, which are the output type of pipeline runs in research. Looking at results for one asset in research¶ This code snippet illustrates how to index into a particular level of a pandas MultiIndex DataFrame. Looking at results for a subset of dates in a pipeline output¶ This code snippet illustrates how to look at a subset of a MultiIndex DataFrame. Note Count the number of assets returned by pipeline each day¶ This code snippet plots the number of US equities trading on a supported exchange every day.
A Speech Recognition Toolkit based on Python (EuroSciPy) SRTk is an open-source software framework for speech recognition implemented by using Python . Automatic speech recognition (ASR), which converts recorded speech audio signals to text transcriptions, is one of the most promising technologies for advanced human-machine interaction. Although the algorithms and heuristics used in this technology are still under investigation and are in need of rapid implementation of state-of-the-art methods, the current frameworks are mainly implemented by using "heavyweight" languages. This module installs several shell commands to manipulate probabilistic models and data used in ASR, and several python modules to enable investigations of ASR. Continuous-density hidden Markov models (CD-HMMs) and their training methodsIn current ASR methods, CD-HMMs are widely accepted as a model of speech feature sequences.
Experimenting with Node.js If you see extra mouse cursors moving around: don’t worry, they’re part of the demo. You can always disable them if you want. I’ve written a follow-up on this article, in which I improved a lot of the code. Be sure to read that one too! If you’re using a browser that supports web sockets, you might see some extra mouse cursors moving around. This is an experiment I did to play around with Node.js and web sockets. Web socket server Using @miksago‘s node-websocket-server made it extremely easy to send and receive messages from a web socket. After including the node-websocket-server library and creating the server, I add some listeners to know when clients disconnect or send a message and make sure messages get sent to the other clients. I saved it as server.js, so starting the server is as simple as running node server.js. Receiving messages Now, in a regular javascript file — with some jQuery — I included into this page, I connect to the web socket like this: Sending messages Blew your mind?
Où trouver les meilleurs ensembles de données gratuits et publics Skip to main content Où trouver les meilleurs ensembles de données gratuits et publics Partager Jacob Olsufka Data Visualization Specialist chez Spotify 26 févr., 2019 S'entraîner à créer des visualisations en dehors du travail est un bon moyen de vous sortir du train-train de l'analyse de données métier et de donner libre cours à votre créativité pour progresser. Vous avez certainement déjà eu envie de créer une visualisation, sans pourtant savoir où trouver les données à analyser. Source 1 : Données provenant de sites d'actualité et d'organes de presse Si vous recherchez des données sur un large éventail de sujets, vous pouvez regarder du côté des sites d'actualités. FiveThirtyEight : une véritable mine de plus de 100 ensembles de données sur le sport et la politique. Source 2 : Projets animés par la communauté La passion et les compétences analytiques de la communauté Tableau sont inégalées, et vous pourrez facilement y trouver des sources de données prêtes à être analysées.
God - A Process Monitoring Framework in Ruby Initiation au Machine Learning avec Python - La pratique Scikits-Learn est une librairie d'apprentissage automatique couvrant l'ensemble de la discipline: Découverte par la pratique - Classer une fleur selon des critères observables Nous allons utiliser pour ce tutoriel la base de données d'Iris de la librairie scikit-learn Mais quel est donc cet Iris ? Cet exemple est très souvent repris sur Internet. Cette base contient des Iris qu'un botaniste, Ronald Fisher, a classés en 1936 à l'aide d'une clef d'identification des plantes (type de pétales, sépale, type des feuilles, forme des feuilles, ...). Puis, pour chaque fleur classée il a mesuré les longueurs et largeurs des sépales et pétales. L'idée qui nous vient alors, consiste à demander à l'ordinateur de déterminer automatiquement l'espèce d'une nouvelle plante en fonction de la mesure des dimensions de ses sépales et pétales que nous aurions réalisée sur le terrain. C'est un cas d'apprentissage supervisé (mais nous le transformerons aussi en non supervisé). Botanistes en herbe, à vos claviers ! x
DracoBlue/node-facebook-client - GitHub Présentation de l'écosystème Python scientifique Sommaire Introduction J'ai découvert l'écosystème scientifique de Python il y a près de 4 ans maintenant, avec la « Scipy stack » dans un projet de simulation statistiques ayant de forts besoins en parallélisation. La prise en main de ces librairies n'a pas été des plus simples, l'environnement étant très riche et très varié, il était difficile d'y voir clair et de trouver les bons tutoriels présentant les concepts de base au novice que j'étais. Et que je reste encore. Avec la pratique j'ai commencé à être plus à l'aise, mais plus je creusais les sujets plus je découvrais de nouvelles fonctionnalités et librairies, riches, variées, souvent concurrentes, généralement de grande qualité. Cet ensemble de librairies scientifiques ressemblait à une galaxie qui se voulait interminable et dont les contours n'arrivaient jamais à se dessiner. Pourquoi utiliser Python pour le calcul scientifique ? Il offre plusieurs avantages sur ces outils : Les distributions Python pour les « data scientists » Anaconda
Facebook Connect with Node Static Version A big part of building a new web application is repurposing common patterns, one such pattern is the ability for users to sign in and out. One way of solving this quickly is by using Facebook Connect. Background Unlike some APIs, the Facebook API is very Javascript friendly, but unfortunately it can be very time consuming to go through the maze of misdirected Facebook documentation. Facebook's developer tools are increasingly going into the direction of more Javascript on the client-side. Communication As you can see in this totally unnecessary diagram, most of the integration takes place on the client-side: Dependencies Note: For this article I've been using NodeJS version 0.1.31 and Express version 0.7.1 You need to install both NodeJS and the Express Web Framework. mkdir -p lib/supportgit submodule add lib/support/expresscd lib/support/expressgit submodule initgit submodule update 1. 2. 3. Now let's implement a basic UI: index.html <! 4.
Image Deconvolution — skimage v0.18.dev0 docs In this example, we deconvolve an image using Richardson-Lucy deconvolution algorithm (, ). The algorithm is based on a PSF (Point Spread Function), where PSF is described as the impulse response of the optical system. The blurred image is sharpened through a number of iterations, which needs to be hand-tuned.