├── .github └── FUNDING.yml ├── .gitignore ├── AI.md ├── API.md ├── Algorithms.md ├── Biology.md ├── Build-Automation.md ├── Chemistry.md ├── Computer-Graphics.md ├── DataBase.md ├── Earth-Science.md ├── IO.md ├── LICENSE.md ├── Machines.md ├── Mathematics.md ├── OpenDataScience.md ├── Physics.md ├── Programming-Paradigms.md ├── Psychology.md ├── Publications.md ├── QA.md ├── README.md ├── Resources.md ├── Server.md ├── Statistics.md ├── Super-Computing.md ├── Utilities.md ├── db.csv ├── pythonidae.jl └── src ├── check_broken_links.jl └── scrape.jl /.github/FUNDING.yml: -------------------------------------------------------------------------------- 1 | # These are supported funding model platforms 2 | 3 | github: [svaksha] 4 | patreon: svaksha 5 | open_collective: svaksha 6 | ko_fi: svaksha 7 | tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel 8 | community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry 9 | liberapay: svaksha 10 | issuehunt: # Replace with a single IssueHunt username 11 | otechie: # Replace with a single Otechie username 12 | custom: #https://liberapay.com/svaksha 13 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | ############################################################################## 3 | #Author: @SVAKSHA, Saturday 12 April 2014 12:04:20 PM IST 4 | ############################################################################## 5 | 6 | #==================================================== 7 | # GENERAL 8 | #==================================================== 9 | az.md 10 | IGNORE/ 11 | 12 | # DATA folders 13 | #---------------------------------------------------- 14 | **/datum 15 | datum/ 16 | datum/** 17 | 18 | 19 | #==================================================== 20 | # Julia build config : ver-0.6.4 onwards 21 | #==================================================== 22 | *.buildconfig 23 | 24 | -------------------------------------------------------------------------------- /AI.md: -------------------------------------------------------------------------------- 1 | **AI :: Artificial Intelligence, Cognitive Science, Machine Learning {(Un)Supervised/RL}, Neural Nets, NLP, etc...** 2 | 3 | 4 | + [§1. AI](#1-AI) 5 | + [§2. DATA SCIENCE](#2-DATA-SCIENCE) 6 | + [§3. MACHINE LEARNING](#3-MACHINE-LEARNING) 7 | + [§3.1. Deep Learning](#3-1-Deep-Learning) 8 | + [§3.2. Classification Algorithms](#3-2-Classification-Algorithms) 9 | + [§3.3. Graph Theory](#3-3-Graph-Theory) 10 | + [§3.4. GPU](#3-4-GPU) 11 | + [§4. NLP](#4-NLP) 12 | + [§4.1. Computational Linguistics](#4-1-Computational-Linguistics) 13 | + [§4.1.1. Named Entity Recognition](#4-1-1-Named-Entity-Recognition) 14 | + [§4.2. Digital Humanities](#4-2-Digital-Humanities) 15 | + [§4.3. Screen Reading](#4-3-Screen-Reading) 16 | + [§4.4. Speech Recognition](#4-4-Speech-Recognition) 17 | + [§4.5. Transformers](#4-5-Transformers) 18 | + [§5. REINFORCEMENT LEARNING](#5-REINFORCEMENT-LEARNING) 19 | + [§6. Reproducibility](#6-Reproducibility) 20 | + [§7. SUPERVISED LEARNING](#7-SUPERVISED-LEARNING) 21 | + [§8. UNSUPERVISED LEARNING](#8-UNSUPERVISED-LEARNING) 22 | + [Neural Networks](#neural-networks) 23 | + [Artificial Neural Network](#artificial-neural-network) 24 | 25 | ---- 26 | ---- 27 | 28 | # §1. AI 29 | + [simpleai](https://github.com/simpleai-team/simpleai) :: Simple artificial intelligence utilities. 30 | 31 | 32 | ---- 33 | 34 | # §2. DATA SCIENCE 35 | + [engarde](https://github.com/TomAugspurger/engarde) :: A library for defensive data analysis. 36 | + [gqn-datasets](https://github.com/deepmind/gqn-datasets) :: Datasets used to train Generative Query Networks (GQNs) in the ‘Neural Scene Representation and Rendering’ paper. 37 | + [python-seminar](https://github.com/profjsb/python-seminar) :: Python Computing for Data Science. 38 | 39 | ##### Resources 40 | + General Assembly's Data Science course in Washington-DC: Jupyter notebooks for [DAT4](https://github.com/justmarkham/DAT4) and [DAT8](https://github.com/justmarkham/DAT8) 41 | + Public repository for course materials for the [Spring 2013 session of Introduction to Data Science](https://github.com/uwescience/datasci_course_materials), an online coursera course. 42 | + [General guidelines (table) for choosing a statistical analysis](http://www.ats.ucla.edu/stat/mult_pkg/whatstat/default.htm) which was adapted from [Choosing the Correct Statistic](http://bama.ua.edu/~jleeper/627/choosestat.html) developed by James D. Leeper, Ph.D. 43 | + [LearnDataScience](https://github.com/nborwankar/LearnDataScience) :: A collection of Data Science Learning materials in the form of IPython Notebooks with associated data sets. 44 | 45 | ---- 46 | 47 | # §3. MACHINE LEARNING. 48 | 49 | + [ConfidenceWeighted](https://github.com/echizentm/ConfidenceWeighted) :: Confidence weighted classifier. 50 | * Papers : (http://www.cs.jhu.edu/~mdredze/publications/icml_variance.pdf) and (http://icml.cc/2012/papers/86.pdf) 51 | + [Faceless](https://github.com/faithlessfriend/faceless) :: A port of ICAAM library by Luca Vezzaro to Python for Face Tracking based on Active Appearance Models. 52 | + [featureforge](https://github.com/machinalis/featureforge) :: A set of tools for creating and testing machine learning features, with a scikit-learn compatible API. 53 | + [Foxhound](https://github.com/IndicoDataSolutions/Foxhound) :: Scikit-learn inspired library for gpu-accelerated machine learning. 54 | + [fuel](https://github.com/mila-udem/fuel) :: A data pipeline framework for machine learning. 55 | + [hips-lib](https://github.com/HIPS/hips-lib) :: Library of common tools for machine learning research. 56 | + [MachineLearning](https://github.com/LocalGroupAstrostatistics2015/MachineLearning) :: Materials for the Wednesday Afternoon Machine Learning workshop. 57 | + [Machine Learning Video Library](http://work.caltech.edu/library/). 58 | + [Masque](https://github.com/faithlessfriend/masque) :: Experiments on Deep Learning and Emotion Classification. 59 | + [MILK](http://www.luispedro.org/software/milk) :: Machine Learning Toolkit. 60 | + [MLOSS.org](http://mloss.org/software/) 61 | + [MLTRP](https://github.com/thejat/mltrp) :: Machine Learning and the Traveling Repairman Problem. 62 | + [Morris_counter](https://github.com/ianozsvald/morris_counter) is a Probabilistic Morris Counter (counts 2^n using e.g. just a byte). 63 | + [MLTP](https://github.com/stefanv/MLTP) :: ML Timeseries Platform. 64 | + [ProFET](https://github.com/ddofer/ProFET) :: [Protein Feature Engineering Toolkit](http://www.protonet.cs.huji.ac.il) for Machine Learning. 65 | + [pyHANSO](https://github.com/dohmatob/pyHANSO) :: Python Implementation of Michael Overton's HANSO (Hybrid Algorithm for Non-Smooth Optimization). 66 | + [pyklsh](https://github.com/jakevdp/pyklsh) :: Python implementation of Kernelized Locality Sensitive Hashing 67 | + [PyML](http://sourceforge.net/projects/pyml/) is an interactive object oriented framework for machine learning written in Python, with support for classification and regression, including Support Vector Machines (SVM), feature selection, model selection, syntax for combining classifiers and methods for assessing classifier performance. 68 | + [PyML Tutorial](http://pyml.sourceforge.net/tutorial.html) 69 | + [Rambutan](https://github.com/jmschrei/rambutan) :: A python wrapper for caffe which aims at providing a simple, pythonic, interface for users so that users can define, train, and evaluate deep models in only a few lines of code. It requires that caffe and pycaffe are both built properly. 70 | + [RAMP](https://github.com/kvh/ramp) :: Rapid Machine Learning Prototyping in Python. 71 | + [python-recsys](https://github.com/ocelma/python-recsys) :: A python library 72 | for implementing a recommender system. 73 | + [Sixpack](http://sixpack.seatgeek.com) :: a language-agnostic a/b-testing framework. [Documentation](https://github.com/seatgeek/sixpack) 74 | + [TPOT](https://github.com/EpistasisLab/tpot) :: A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. A blog post explaining the same: http://www.randalolson.com/2016/05/08/tpot-a-python-tool-for-automating-data-science/ 75 | + [PyCM](https://github.com/sepandhaghighi/pycm) :: PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers. 76 | 77 | 78 | ###### Resources 79 | + [What I Learned Implementing a Classifier from Scratch](http://www.jeannicholashould.com/what-i-learned-implementing-a-classifier-from-scratch.html) in Python. 80 | + [DataSciencePython](https://github.com/ujjwalkarn/DataSciencePython) :: common data analysis and machine learning tasks using python. 81 | + [Examples from "Thoughtful Machine Learning"](https://github.com/thoughtfulml/examples-in-python). 82 | + [CIML](https://github.com/hal3/ciml) :: A Course in Machine Learning. This repository contains the source code for the CIML book (see http://ciml.info/) as well as any course materials that seem useful (slides, documents, labs, etc.). 83 | + [deepframeworks](https://github.com/zer0n/deepframeworks) :: An evaluation of Deep Learning Frameworks. 84 | + A [Machine Learning](http://work.caltech.edu/telecourse.html#lectures) course by Prof. Yaser Abu-Mostafa with videos on Youtube. 85 | + [study](https://github.com/codematician/study) :: A study of interesting algorithms. 86 | + [Machine Learning Algorithm Cheat Sheet](http://www.lauradhamilton.com/machine-learning-algorithm-cheat-sheet) by Laura D Hamilton. 87 | + [machine-learning-cheat-sheet](https://github.com/soulmachine/machine-learning-cheat-sheet) :: Classical equations and diagrams in machine learning by @soulmachine. 88 | + Cheatsheet for [choosing the right estimator](http://scikit-learn.org/stable/tutorial/machine_learning_map/). 89 | + [Machine Learning cheatsheet](http://eferm.com/machine-learning-cheat-sheet/). 90 | + [Big Data Machine Learning Patterns for Predictive Analytics](http://refcardz.dzone.com/refcardz/machine-learning-predictive) By Ricky Ho. 91 | + A HN site for [ML](http://news.startup.ml/). 92 | + [Source Code](https://github.com/luispedro/BuildingMachineLearningSystemsWithPython) for the book [Building Machine Learning Systems with Python](http://www.packtpub.com/building-machine-learning-systems-with-python/book). 93 | 94 | 95 | ## §3.1. [Deep Learning](https://en.wikipedia.org/wiki/Deep_learning). span id="3-1-Deep-Learning"> 96 | + https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software 97 | + [TRAINS](https://github.com/allegroai/trains) :: Auto-Magical Experiment Manager & Version Control for AI. Training production-grade deep learning models is a glorious but messy process. TRAINS tracks and controls the process by associating code version control, research projects, performance metrics, and model provenance. 98 | 99 | ##### Resources 100 | + [DeepLearningTutorials](https://github.com/lisa-lab/DeepLearningTutorials) :: Deep Learning Tutorial notes and code. See the [wiki](http://deeplearning.net/tutorial) for more info. 101 | + Deep Learning [Part 1: Comparison of Symbolic Deep Learning Frameworks](https://www.opendatascience.com/blog/deep-learning-part-1-comparison-of-symbolic-deep-learning-frameworks/). 102 | + [handson-ml](https://github.com/ageron/handson-ml) :: A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow. 103 | + [handson-ml2](https://github.com/ageron/handson-ml2) :: Version-2 of the series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2. 104 | 105 | 106 | 107 | ## §3.2. Classification Algorithms. span id="3-2-Classification-Algorithms"> 108 | + [K-Nearest-Neighbors-with-Dynamic-Time-Warping](https://github.com/markdregan/K-Nearest-Neighbors-with-Dynamic-Time-Warping) :: Python implementation of KNN and DTW classification algorithm. 109 | 110 | ###### Resources 111 | **Naive Bayes** 112 | + Blog on [How To Implement Naive Bayes From Scratch in Python](http://machinelearningmastery.com/naive-bayes-classifier-scratch-python/) 113 | 114 | 115 | ## §3.3. Graph Theory. span id="3-3-Graph-Theory"> 116 | + [fluffy-graph](https://github.com/vpavlenko/fluffy-graph) :: NP-hard game where you find isomorphic graphs. 117 | + [PyMarkovChain](https://github.com/TehMillhouse/PyMarkovChain) :: Simple markov chain implementation in python. 118 | + [python-igraph](http://igraph.org/python/) :: Python interface for igraph. The [code and issue tracker](https://github.com/igraph/python-igraph) is on github. 119 | 120 | ##### Resources 121 | + [Amazon Machine Learning: use cases and a real example in Python](http://cloudacademy.com/blog/aws-machine-learning/). 122 | + Some [machine learning libraries](http://daoudclarke.github.io/machine%20learning%20in%20practice/2013/10/08/machine-learning-libraries/) 123 | + [Visualizing Algorithms](http://bost.ocks.org/mike/algorithms/) 124 | + [Machine Learning HowTo](https://docs.google.com/document/d/1YN6BVdReNAYc8B0fjQ84yzDflqmeEPj7S0Xc-9_26R0/preview?sle=true) from HN. 125 | + [Alternating Least Squares Method for Collaborative Filtering](http://bugra.github.io/work/notes/2014-04-19/alternating-least-squares-method-for-collaborative-filtering/) 126 | + [Using Machine Learning To Pick Your Lottery Numbers](http://nbviewer.ipython.org/url/www.onewinner.me/en/devoxxML.ipynb) 127 | + [How a Russian mathematician constructed a decision tree - by hand - to solve a medical problem](http://fastml.com/how-a-russian-mathematician-constructed-a-decision-tree-by-hand-to-solve-a-medical-problem/) 128 | + [MST](http://healthyalgorithms.wordpress.com/2009/01/13/aco-in-python-pads-for-minimum-spanning-trees/) → python algorithms for minimum spanning trees. 129 | 130 | ## §3.4. GPU. span id="3-4-GPU"> 131 | * [cuML](https://github.com/rapidsai/cuml) :: is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. 132 | 133 | ---- 134 | 135 | 136 | # §4. NLP. span id="4-NLP"> 137 | 138 | + [Broca](https://github.com/ftzeng/broca) :: Various useful NLP algos and utilities for rapid NLP prototyping. 139 | + [commonast](https://github.com/almarklein/commonast) :: A common AST description for Python. 140 | + [Fairseq](https://github.com/pytorch/fairseq) :: A sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. 141 | + [Gensim](http://radimrehurek.com/gensim/) is a Python library for topic modelling, document indexing and similarity retrieval with large corpora for natural language processing (NLP) and information retrieval (IR). [Source Code](https://github.com/piskvorky/gensim). 142 | + [Geiger](https://github.com/ftzeng/geiger) :: An automated system for grouping similar comments and then identifying the best representative from each group. 143 | + [Glove-python](https://github.com/maciejkula/glove-python) :: Toy Python implementation of http://www-nlp.stanford.edu/projects/glove/ 144 | + [Gramformer](https://github.com/PrithivirajDamodaran/Gramformer) :: A framework for detecting, highlighting and correcting grammatical errors on natural language text. 145 | + [IEPY](https://github.com/machinalis/iepy) :: An open source tool for Information Extraction focused on Relation Extraction. 146 | + [JPKyteaTokenizer](https://github.com/chezou/JPKyteaTokenizer) :: A Japanese tokenizer with KyTea for nltk. 147 | + [Mykytea-python](https://github.com/chezou/Mykytea-python) :: Python wrapper for [KyTea](http://wp.me/pvR30-i1). 148 | + [NLTK](http://www.nltk.org/) :: Natural Language ToolKit to manipulate human language data. [Source Code](https://github.com/nltk/nltk) 149 | + [nupic.fluent](https://github.com/numenta/nupic.fluent) :: A platform for building language / NLP-based applications using NuPIC and CEPT. 150 | + [Quepy](https://github.com/machinalis/quepy) :: A python framework to transform natural language questions to queries in a database query language. 151 | + [Parrot_Paraphraser](https://github.com/PrithivirajDamodaran/Parrot_Paraphraser) :: Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. 152 | + [PLY](https://github.com/dabeaz/ply) :: Python Lex-Yacc. http://www.dabeaz.com/ply/index.html 153 | + [SAMR](https://github.com/rafacarrascosa/samr) :: An entry to kaggle's 'Sentiment Analysis on Movie Reviews' competition. 154 | + [Suggester](https://github.com/valyala/suggester) :: The heart for full-text auto-complete web services. 155 | + [TextGridTools](https://github.com/hbuschme/TextGridTools) :: Read, write, and manipulate Praat TextGrid files with Python. 156 | + [txtai](https://github.com/neuml/txtai):: builds an AI-powered index over sections of text & supports building text indices to perform similarity searches and create extractive question-answering based systems. 157 | + [word_cloud](https://github.com/amueller/word_cloud) :: A little word cloud generator in Python. 158 | 159 | 160 | ## §4.1. [Computational Linguistics](https://en.wikipedia.org/wiki/Category:Computational_linguistics) span id="4-1-Computational-Linguistics"> 161 | + [spaCy](https://github.com/explosion/spaCy) :: a library for advanced Natural Language Processing in Python and Cython; with pretrained pipelines and currently supports tokenization and training for 60+ languages that features neural network models for tagging, parsing, named entity recognition, text classification and more. 162 | 163 | ### §4.1.1. [Named Entity Recognition](https://en.wikipedia.org/wiki/Named-entity_recognition). span id="4-1-1-Named-Entity-Recognition"> 164 | + [CLNER](https://github.com/Alibaba-NLP/CLNER) :: The code is for the ACL-IJCNLP 2021 paper "Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning". 165 | + [mt-dnn](https://github.com/namisan/mt-dnn) :: This PyTorch package implements the Multi-Task Deep Neural Networks (MT-DNN) for Natural Language Understanding. 166 | 167 | 168 | ## §4.2. Digital Humanities. span id="4-2-Digital-Humanities"> 169 | + [NLP-Notebooks-Newspaper-Collections](https://github.com/NewsEye/NLP-Notebooks-Newspaper-Collections) :: NLP Notebooks for Newspaper Collections are aimed particularly at digital humanities scholars who use newspapers as a source. 170 | 171 | 172 | ## §4.3. Screen Reading 173 | + [wordgraph](https://github.com/tleeuwenburg/wordgraph) :: This project supports creating English-language text from a graph description for those doing screen reading for vision-impaired people, or just people who like to listen to graphs while jogging, or just to get a handle on what's going on. 174 | + __Resources__ 175 | + [STT with HMM](https://kastnerkyle.github.io/blog/2014/05/22/single-speaker-speech-recognition/) :: Single Speaker Speech Recognition with Hidden Markov Models. 176 | 177 | 178 | ## §4.4. Speech Recognition 179 | + [Speech recognition software for Linux](http://en.wikipedia.org/wiki/Speech_recognition_software_for_Linux) 180 | + [Dragonfly](https://code.google.com/p/dragonfly/) :: Dragonfly is a speech recognition framework. It is a Python package which offers a high-level object model and allows its users to easily write scripts, macros, and programs which use speech recognition. [Documentation](https://pythonhosted.org/dragonfly/). 181 | + [ParlAI](https://github.com/facebookresearch/ParlAI): A framework for training and evaluating AI models on a variety of openly available dialog datasets. http://parl.ai 182 | + [speech-processing](https://github.com/hbuschme/speech-processing) :: A Python framework for speech processing. 183 | 184 | ##### Resources 185 | + [An Introduction to Natural Language Processing](https://www.opendatascience.com/blog/an-introduction-to-natural-language-processing/) that introduces text based machine learning techniques (ex. N-grams, corpus,..) inorder to do text classification and analysis. 186 | 187 | 188 | ## §4.5. [Transformers](https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)). span id="4-5-Transformers"> 189 | + [BERT](https://github.com/google-research/bert) :: TensorFlow code and pre-trained models for 24 smaller BERT models (English only, uncased, trained with WordPiece masking) referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models. 190 | + [Transformers](https://github.com/huggingface/transformers) :: State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow. 191 | 192 | ---- 193 | 194 | # [REINFORCEMENT LEARNING](https://en.wikipedia.org/wiki/Reinforcement_learning) 195 | + [bsuite](https://github.com/deepmind/bsuite) :: A collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent. 196 | + [Tensortrade](https://github.com/notadamking/tensortrade) :: An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents. 197 | 198 | 199 | ---- 200 | 201 | # [Reproducibility](https://en.wikipedia.org/wiki/Reproducibility) 202 | 203 | * [AIQC](https://github.com/aiqc/aiqc) :: is an open source framework for rapid & reproducible deep learning. 204 | 205 | 206 | ---- 207 | 208 | # [SUPERVISED LEARNING](https://en.wikipedia.org/wiki/Supervised_learning) 209 | + [tensor2tensor](https://github.com/tensorflow/tensor2tensor) :: Tensor2Tensor (T2T) Transformers is a modular and extensible library and binaries for supervised learning with TensorFlow and with support for sequence tasks. It is actively used and maintained by researchers and engineers within the Google Brain team. 210 | 211 | 212 | ##### Resources 213 | + [ml_cheat_sheet](https://github.com/rcompton/ml_cheat_sheet) :: Supervised learning superstitions cheat sheet. 214 | 215 | 216 | 217 | ---- 218 | 219 | 220 | 221 | # [UNSUPERVISED LEARNING](https://en.wikipedia.org/wiki/Unsupervised_learning) 222 | 223 | ## [GAN](https://en.wikipedia.org/wiki/Generative_adversarial_network) 224 | * [Jokerise](https://github.com/junkwhinger/jokerise) :: Jokeriser with CycleGAN. 225 | 226 | ## Neural Networks 227 | + [BinaryConnect](https://github.com/MatthieuCourbariaux/BinaryConnect) :: Training Deep Neural Networks with binary weights during propagations. 228 | + [BinaryNet](https://github.com/MatthieuCourbariaux/BinaryNet) :: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. 229 | + [NAMAS](https://github.com/facebook/NAMAS) :: Neural Attention Model for Abstractive Summarization. 230 | + [SparkNet](https://github.com/amplab/SparkNet) :: Distributed Neural Networks for Spark. 231 | 232 | ### [Artificial Neural Network](https://en.wikipedia.org/wiki/Artificial_neural_network) 233 | + [pylearn2](https://github.com/lisa-lab/pylearn2) : A Machine Learning library based on Theano. 234 | + [Tensorflow](http://tensorflow.org) :: Open source software library for numerical computation using data flow graphs. [Source code](https://github.com/tensorflow/tensorflow) on GH. 235 | + [models](https://github.com/tensorflow/models) :: Models built with TensorFlow. 236 | + Resources: [TensorFlow-Tutorials](https://github.com/nlintz/TensorFlow-Tutorials) :: Simple tutorials using Google's TensorFlow Framework. 237 | + [theano-nlp](https://github.com/fchollet/theano-nlp) :: Tools and datasets for NLP in Theano. 238 | 239 | 240 | #### Pre-Trained Models 241 | + [Spiral](https://github.com/deepmind/spiral) :: A pre-trained model for unconditional 19-step generation of CelebA-HQ images. 242 | 243 | 244 | ###### Resources 245 | + An introduction to [Recurrent Neural Networks](https://www.opendatascience.com/blog/intro-to-recurrent-neural-networks-1/). 246 | + [TensorFlow-Book](https://github.com/BinRoot/TensorFlow-Book) :: Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations. http://www.tensorflowbook.com 247 | 248 | 249 | 250 | -------------------------------------------------------------------------------- /API.md: -------------------------------------------------------------------------------- 1 | + [API](#api) 2 | + [Git](#git) 3 | + [Telegram](#telegram) 4 | + [LANGUAGES](#languages) 5 | + [C-CXX](#cxx) 6 | + [FORTRAN](#fortran) 7 | + [PYTHON](#python) 8 | + [SCALA](#scala) 9 | 10 | ---- 11 | 12 | # API 13 | + [flow-python](https://github.com/SpiderOak/flow-python) :: Python API to the [Semaphor client backend](https://spideroak.com/solutions/semaphor) 14 | 15 | ## Git 16 | + [Dulwich](https://www.dulwich.io/) :: Pure-Python Git implementation. [Source code](https://github.com/jelmer/dulwich). 17 | + [GitPython](https://github.com/gitpython-developers/GitPython) :: A python library used to interact with Git repositories. http://gitpython.readthedocs.org 18 | + [git-fat](https://github.com/jedbrown/git-fat) :: Simple way to handle fat files without committing them to git, supports synchronization using rsync. 19 | + [gitsome](https://github.com/donnemartin/gitsome) :: A supercharged Git/shell autocompleter with GitHub integration. 20 | + [Homu](http://homu.io/) :: A bot that integrates with GitHub and your favorite continuous integration service. [Source Code](https://github.com/barosl/homu). 21 | + [python-gitlab](https://github.com/gpocentek/python-gitlab) :: Python wrapper for the GitLab API. 22 | 23 | ## Telegram 24 | + [python-telegram-bot](https://github.com/python-telegram-bot/python-telegram-bot) :: a pure Python interface for the Telegram Bot API, compatible with Python versions 2.7, 3.3+ and PyPy. 25 | + [munich-scripts](https://github.com/okainov/munich-scripts) :: Some useful scripts simplifying bureaucracy, especially when living in Munich, Germany. 26 | 27 | ---- 28 | 29 | # LANGUAGES 30 | 31 | ## C-CXX 32 | **wrappers for C and C++.** 33 | + [XDress](https://github.com/xdress/xdress) :: is a Cython-based, NumPy-aware automatic wrapper generator for C/C++ written in pure Python. Currently, [xdress](http://xdress.org/) may generate Python bindings (via Cython) for C++ classes & functions and in-memory wrappers for C++ standard library containers (sets, vectors, maps). 34 | 35 | ## FORTRAN 36 | + [f90wrap](https://github.com/jameskermode/f90wrap) :: Fortran to Python interface generator with derived type support. 37 | 38 | ###### Resources 39 | + Notes on [Calling Fortran from Python, Julia and Matlab](http://maurow.bitbucket.org/notes/calling_fortran_from_misc.html). 40 | 41 | ## PYTHON 42 | + [ParsePy](https://github.com/dgrtwo/ParsePy) :: A Python wrapper for the Parse.com API. 43 | + [github3.py](https://github.com/sigmavirus24/github3.py) :: Python library for interfacing with the GitHub APIv3. [Documentation](http://github3py.readthedocs.org/) 44 | + [vkAPI](https://github.com/ISosnovik/vkAPI) :: A Python wrapper for the [vk.com](https://vk.com/dev/) API. 45 | 46 | ## SCALA 47 | + [knit](https://github.com/dask/knit) :: Python interface to Scala for interacting with YARN. [Documentation](http://knit.readthedocs.org/en/latest/). 48 | 49 | -------------------------------------------------------------------------------- /Algorithms.md: -------------------------------------------------------------------------------- 1 | **[ALGORITHMS and Data Structures](https://en.wikipedia.org/wiki/Category:Algorithms_and_data_structures) : Data Mining, Clustering, Machine Learning, Neural, NLP, ...** 2 | 3 | + [COMPUTATIONAL PROBLEMS](#computational-problems) 4 | + [NP-complete](#np-complete) 5 | + [SAT](#sat) 6 | + [DATA STRUCTURES](#data-structures) 7 | + [HMM](#hmm) 8 | + [RESOURCES](#resources) 9 | 10 | ---- 11 | 12 | # [COMPUTATIONAL PROBLEMS](https://en.wikipedia.org/wiki/Category:Computational_problems) 13 | ## [NP-complete](https://en.wikipedia.org/wiki/Category:NP-complete_problems) 14 | ### [SAT](https://en.wikipedia.org/wiki/Satisfiability_modulo_theories) 15 | + [sat-solver](https://github.com/enthought/sat-solver) :: SAT solver for use in Enstaller, based on the MiniSat implementation. 16 | 17 | ---- 18 | 19 | # DATA STRUCTURES 20 | **Arrays / Hash tables / Matrix / Functional / Trees data structures** 21 | + [CGT](https://github.com/joschu/cgt) :: Computation Graph Toolkit (CGT) is a library for evaluation and differentiation of functions of multidimensional arrays. The author's [announcement](https://joschu.github.io/index.html#Announcing%20CGT) on his blog. 22 | + [Datarray](https://github.com/fperez/datarray) :: Prototyping numpy arrays with named axes for data management. Docs are available at: http://fperez.github.com/datarray-doc 23 | + [distarray](https://github.com/enthought/distarray) :: DistArray provides general multidimensional NumPy-like distributed arrays to Python and intends to bring the strengths of NumPy to data-parallel high-performance computing. DistArray has a similar API to NumPy. [Documentation](http://distarray.readthedocs.org). 24 | + [keras](https://github.com/fchollet/keras) :: Theano-based Deep Learning library. 25 | + [LA](https://pypi.python.org/pypi/la) :: Label the rows, columns, any dimension, of your NumPy arrays. The main class of the `la` package is a labeled array, larry. A larry consists of data and labels and data is stored as a NumPy array with labels as a list of lists (one list per dimension). 26 | + [Lasagne](https://github.com/Lasagne/Lasagne) :: A Lightweight library to build and train neural networks in Theano. [Documentation](http://lasagne.readthedocs.org/). 27 | + [netcdf4-python](https://github.com/Unidata/netcdf4-python) :: A python/numpy interface to the netCDF C library. http://unidata.github.io/netcdf4-python 28 | + [propagator.py](https://github.com/lucastx/propagator.py) :: A propagator network in Python, inspired by Radul & Sussman's The Art of the Propagator. 29 | + [PyGraphistry](https://github.com/graphistry/pygraphistry) :: A library to extract, transform, and visually explore big graphs. 30 | + [pymf](https://github.com/nils-werner/pymf) :: Python Matrix Factorization Module. [Source on Google](https://code.google.com/p/pymf/) 31 | + [PyTables](http://pytables.github.io/) is a package for managing hierarchical datasets and designed to efficiently and easily cope with extremely large amounts of data. 32 | + [python-graph](https://github.com/pmatiello/python-graph) :: A library for working with graphs in Python. 33 | + [sparray](https://github.com/jesolem/sparray) :: Sparse n-dimensional arrays in Python. 34 | + [Theano](http://deeplearning.net/software/theano/) :: A Python library for working with mathematical expressions involving multi-dimensional arrays efficiently. 35 | __Resources__ 36 | * [Official Documentation](http://deeplearning.net/software/theano/tutorial/). 37 | * [IPython Theano Tutorials](https://github.com/jaberg/IPythonTheanoTutorials) 38 | * [A brief IPython notebook-based tutorial on basic Theano concepts](http://nbviewer.ipython.org/github/craffel/theano-tutorial/blob/master/Theano%20Tutorial.ipynb), including a toy multi-layer perceptron example. 39 | + [Xray](https://github.com/xray/xray) :: Extended arrays for working with scientific datasets in Python. [Documentation](http://xray.readthedocs.org) 40 | 41 | ### scikit 42 | + [scikits-bootstrap](https://github.com/cgevans/scikits-bootstrap) :: Bootstrap Scikit for bootstrap confidence interval estimation. 43 | + [scikit-learn](http://scikit-learn.org/stable/) :: Machine Learning in Python. 44 | __Resources__ 45 | + [Riding on Large Data with Scikit-learn](https://www.opendatascience.com/blog/riding-on-large-data-with-scikit-learn/). 46 | + [odscon-sf-2015](https://github.com/amueller/odscon-sf-2015) :: Slides and Notebooks for Open Data Science Conference - ODSCON San Francisco 2015. 47 | + [scipy_2015_sklearn_tutorial](https://github.com/amueller/scipy_2015_sklearn_tutorial) :: Scikit-Learn tutorial material for Scipy 2015. 48 | + [Scikit-learn Tutorial at EuroPython 2014](https://github.com/GaelVaroquaux/sklearn_europython_2014) 49 | + [Using scikit-learn Pipelines and FeatureUnions](http://zacstewart.com/2014/08/05/pipelines-of-featureunions-of-pipelines.html) 50 | + [parallel_ml_tutorial](https://github.com/ogrisel/parallel_ml_tutorial) :: Tutorial on parallel Machine Learning with scikit-learn and IPython. 51 | + [postlearn](https://github.com/TomAugspurger/postlearn) :: Common post-estimation tasks for scikit-learn. 52 | + [sklearn_pycon2015](https://github.com/jakevdp/sklearn_pycon2015) :: Materials for @jakevdp's Pycon 2015 scikit-learn tutorial. 53 | + [scikits-sparse](https://github.com/njsmith/scikits-sparse) → Sparse matrix tools extending scipy.sparse, but with incompatible licenses. 54 | __Resources__ 55 | - [Gradient Boosted Regression Trees in scikit-learn](https://github.com/davidwhogg/DataAnalysisRecipes) 56 | - PyData-2014 Talk → [Know Thy Neighbor: Scikit-Learn and kNN Algorithm Tutorial and PyCon 2014 Talk](https://github.com/pkafei/Know_Thy_Neighbor) 57 | - [K-means Clustering with Scikit-Learn](https://github.com/sarguido/k-means-clustering) 58 | - @ageitgey gives an introduction to the basics of [Machine Learning](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471) 59 | - [The Computational Complexity of Machine Learning](https://www.cs.utexas.edu/~klivans/395t.html) 60 | - [Expensive lessons in Python performance tuning](http://blog.explainmydata.com/2012/07/expensive-lessons-in-python-performance.html) 61 | - [A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks](https://github.com/rasbt/pattern_classification) 62 | + [sklearn-pandas](https://github.com/paulgb/sklearn-pandas) :: This module provides a bridge between Scikit-Learn's machine learning methods and pandas-style Data Frames. 63 | + [sklearn-theano](https://github.com/sklearn-theano/sklearn-theano) :: Scikit-learn compatible tools using Theano. 64 | + Paper: [Fast Bird Part Localization for Fine-Grained Categorization](http://yassersouri.github.io/papers/fgvc-2015-fast-bird-part.pdf), Yaser Souri, Shohreh Kasaei, Sharif University of Technology. 65 | 66 | ###### Resources 67 | + [Problem Solving with Algorithms and Data Structures](http://interactivepython.org/courselib/static/pythonds/index.html) by Brad Miller and David Ranum, Luther College. 68 | 69 | ---- 70 | 71 | # [HMM](https://en.wikipedia.org/wiki/Hidden_Markov_model) 72 | + [YAHMM](https://github.com/jmschrei/yahmm) :: Yet Another Hidden Markov Model repository. 73 | 74 | 75 | ---- 76 | 77 | # RESOURCES 78 | + [bci-challenge-ner-2015](https://github.com/alexandrebarachant/bci-challenge-ner-2015) :: Code and documentation for the winning solution at the BCI Challenge @ NER 2015 : https://www.kaggle.com/c/inria-bci-challenge 79 | + [GraphLab_Practice](https://github.com/nyghtowl/GraphLab_Practice) 80 | 81 | 82 | -------------------------------------------------------------------------------- /Build-Automation.md: -------------------------------------------------------------------------------- 1 | **Build automation software, continuous integration (CI), continuous delivery (CD), Packaging, Virtualization, Release engineering and management tools, Software configuration management (SCM) software...** 2 | 3 | + [BUILD AUTOMATION](#build-automation) 4 | + [CONTINUOUS INTEGRATION](#continuous-integration) 5 | + [Operating System Technology](#operating-system-technology) 6 | + [Binaries](#binaries) 7 | + [Containers-Virtualization](#containers-virtualization) 8 | + [Docker](#docker) 9 | + [DistrOS](#distros) 10 | + [Packaging](#packaging) 11 | + [Sandbox](#sandbox) 12 | + [Anaconda](#anaconda) 13 | + [Conda](#conda) 14 | + [NEWS](#news) 15 | 16 | ---- 17 | 18 | # BUILD AUTOMATION 19 | + [Autopkg](https://github.com/autopkg) has more automation recipes: 20 | + [48kRAM-recipes](https://github.com/autopkg/48kRAM-recipes). 21 | + [Hansen-m-recipes](https://github.com/autopkg/hansen-m-recipes) :: More recipes for Autopkg. 22 | + [Jaharmi-recipes](https://github.com/autopkg/jaharmi-recipes) :: Recipes for the Mac OS X system administration tool. 23 | + [Jleggat-recipes](https://github.com/autopkg/jleggat-recipes) :: Recipes for [autopkg](http://autopkg.github.io/autopkg/). 24 | + [ScriptingOSX-recipes](https://github.com/autopkg/scriptingosx-recipes). 25 | + [Thenikola-recipes](https://github.com/autopkg/thenikola-recipes). 26 | + [Timsutton-recipes](https://github.com/autopkg/timsutton-recipes). 27 | + [BuildBot](https://en.wikipedia.org/wiki/Buildbot) :: Runs atop the Twisted libraries. 28 | + [gitian-builder](https://github.com/devrandom/gitian-builder) :: Build packages in a secure deterministic fashion inside a VM. https://gitian.org/ 29 | + [nupic-darwin64](https://github.com/numenta/nupic-darwin64) :: Python distribution for NuPIC on OSX. 30 | + [nupic-linux64](https://github.com/numenta/nupic-linux64) :: Python distribution for NuPIC on 64-bit linux. 31 | + [Pybuilder](http://pybuilder.github.io) :: Continuous build tool for Python. [Source code](https://github.com/pybuilder/pybuilder). 32 | + [Chef cookbook for Python](https://github.com/poise/python) :: Chef cookbook to install Python and related tools. http://community.opscode.com/cookbooks/python 33 | + [Diamond](https://github.com/python-diamond/Diamond) :: A python daemon that collects system metrics and publishes them to Graphite (and others). It is capable of collecting cpu, memory, network, i/o, load and disk metrics. Additionally, it features an API for implementing custom collectors for gathering metrics from almost any source. 34 | + [GRR](https://github.com/google/grr) Rapid Response :: remote live forensics for incident response. 35 | + [Pinball](https://github.com/pinterest/pinball) :: A scalable workflow manager. 36 | + [SCons](http://scons.org/) :: A build automation tool that automatically analyzes source code file dependencies and operating system adaptation requirements from a software project description and generates final binary executables for installation on the target operating system platform. [Wikipedia page](https://en.wikipedia.org/wiki/SCons). 37 | 38 | ### DevOps tools 39 | + [bake](https://github.com/kennethreitz/bake) :: a s☿rangely familiar workflow utlity - bash bake(make)file for shell-scripting. 40 | + [cloudpickle](https://github.com/cloudpipe/cloudpickle) :: Extended pickling support for Python objects. 41 | + [Fabric](http://www.fabfile.org) :: A Python library and command-line tool for streamlining the use of SSH for application deployment or systems administration tasks. [Source code and bug tracker](https://github.com/fabric/fabric/). 42 | + [patchwork](https://github.com/fabric/patchwork) :: Common deployment/sysadmin operations, built on Fabric. 43 | + [Plumbum](https://pypi.python.org/pypi/plumbum) :: Shell combinators library written in Python. Read the [documentation](https://plumbum.readthedocs.org/en/latest/) and [source code](https://github.com/tomerfiliba/plumbum). 44 | + [pyinvoke](http://pyinvoke.org) :: Simple Python task execution. [Source code](https://github.com/pyinvoke/invoke). 45 | + [Watcher](https://github.com/splitbrain/Watcher) :: A daemon that watches specified files/folders for changes and fires commands in response to those changes. It is similar to incron, however, configuration uses a simpler to read `ini` file instead of a plain text file. Unlike incron it can also recursively monitor directories. It's also written in Python, making it easier to hack. 46 | 47 | ---- 48 | 49 | # CONTINUOUS INTEGRATION 50 | 51 | ##### Resources 52 | * Blog post series on Continuous integration in Python by @jni: 53 | * [Volume 1: automated tests with pytest](http://ilovesymposia.com/2014/10/01/continuous-integration-0-automated-tests-with-pytest/) 54 | * [Volume 2: measuring test coverage](http://ilovesymposia.com/2014/10/02/continuous-integration-1-test-coverage/) 55 | * [3: set up your test configuration files](http://ilovesymposia.com/2014/10/13/continuous-integration-in-python-3-set-up-your-test-configuration-files/) 56 | * [4: set up Travis-CI](http://ilovesymposia.com/2014/10/15/continuous-integration-in-python-4-set-up-travis-ci/) 57 | * [5: report test coverage using Coveralls](http://ilovesymposia.com/2014/10/15/continuous-integration-in-python-5-report-test-coverage-using-coveralls/) 58 | * [6: Show off your work](http://ilovesymposia.com/2014/10/17/continuous-integration-in-python-6-show-off-your-work/) 59 | * [7: some helper tools and final thoughts](http://ilovesymposia.com/2014/10/27/continuous-integration-in-python-7-some-helper-tools-and-final-thoughts/) 60 | 61 | ---- 62 | 63 | # [Operating System Technology](http://en.wikipedia.org/wiki/Category:Operating_system_technology) 64 | + [klusta_process_manager](https://github.com/tymoreau/klusta_process_manager) :: A process manager. 65 | 66 | ## Containers-[Virtualization](http://en.wikipedia.org/wiki/Category:Virtualization_software) 67 | + [bootstrap-vz](https://github.com/andsens/bootstrap-vz) :: Bootstrap Debian images for virtualized environments. [Documentation](http://bootstrap-vz.readthedocs.org/). 68 | 69 | ### Docker 70 | + [Ansible and Docker](https://developer.rackspace.com/blog/ansible-and-docker/). 71 | + [docker-volume-backup](https://github.com/paimpozhil/docker-volume-backup) :: a python script to backup/restore the docker data containers / volumes. 72 | + [docker-mysql-scripts](https://github.com/luiselizondo/docker-mysql-scripts) :: A series of useful commands written in python to interact with a MySQL Server running on Docker. 73 | + [docker-notebook](https://github.com/ipython/docker-notebook) :: Docker containers for the [Jupyter IPython](https://registry.hub.docker.com/u/ipython/) notebook (+SciPy Stack). 74 | + [docker-scripts](https://github.com/goldmann/docker-scripts) :: A swiss-knife tool that could be useful for people working with Docker :: A swiss-knife tool that could be useful for people working with Docker. 75 | + [docker-AMI](https://github.com/johncosta/docker-AMI) :: Scripts used to create docker-AMI images. 76 | + [dockerspawner](https://github.com/jupyter/dockerspawner) :: Spawns JupyterHub user servers in Docker containers. 77 | + [hcp-neurodebian](https://github.com/yarikoptic/hcp-neurodebian) :: Scripts to create HCP environment containing basic Docker file to erect 10GB beast containing HCP500 full pipelines. 78 | + [Pythonista](https://github.com/ikalnitsky/pythonista) :: A Docker image for Python developers to use and test their applications. 79 | 80 | 81 | ## Packaging 82 | + [antipackage](https://github.com/ellisonbg/antipackage) :: Automagically import single file Python modules from GitHub. 83 | + [Changes](https://github.com/michaeljoseph/changes) :: Automates python library release tasks. 84 | + [cookiecutter-pylibrary](https://github.com/ionelmc/cookiecutter-pylibrary) :: Enhanced cookiecutter template for Python libraries. 85 | + [Flit](https://github.com/takluyver/flit) :: Simplified packaging of Python modules. [Documentation](http://flit.readthedocs.org/). 86 | + [picnic.py](https://github.com/Zulko/picnic.py) :: Easy Python packages creation. 87 | + [pip2pi](https://github.com/wolever/pip2pi) :: Builds a PyPI-compatible package repository from pip requirements. 88 | + [pip-tools](https://github.com/nvie/pip-tools) :: A set of tools to keep your pinned Python dependencies fresh. 89 | + [Punch](https://github.com/lgiordani/punch) :: A configurable version updater, that you can use to automate the management of your project's version number. 90 | + [pynsist](https://pypi.python.org/pypi/pynsist) :: Build Windows installers for Python apps. 91 | + [pythonize](https://github.com/princebot/pythonize) :: Download, install, and configure Python in one line. 92 | + [twine](https://github.com/pypa/twine) :: Utilities for interacting with PyPI. 93 | 94 | ###### Resources 95 | + AOSA book [Python Packaging](http://www.aosabook.org/en/packaging.html) by Tarek Ziade. 96 | + [Python Packaging User Guide](https://packaging.python.org/en/latest/index.html) and the github [source code](https://github.com/pypa/python-packaging-user-guide). 97 | + [python-cffi-example](https://github.com/wolever/python-cffi-example) :: An example project showing how to use Python's CFFI. 98 | + Another post explaining Python [Modules, Packages, and all that](https://python4astronomers.github.io/installation/packages.html). 99 | + How to [Port Python 2 Code to Python 3](https://docs.python.org/3/howto/pyporting.html) and write a proper [trove classifier](https://pypi.python.org/pypi?%3Aaction=list_classifiers) specifying what versions of Python are supported in your `setup.py` file. Here is the idioms [cheat-sheet](http://python-future.org/compatible_idioms.html) 100 | 101 | 102 | ## Sandbox 103 | + [autoenv](https://github.com/kennethreitz/autoenv) :: Directory-based environments. 104 | 105 | ### Anaconda 106 | + [anaconda-recipes](https://github.com/ContinuumIO/anaconda-recipes) :: Continuum managed recipes for free anaconda packages. 107 | + [dummypkg](https://github.com/bjodah/dummypkg) :: Using pytest --pyargs pythonpackagename under a conda environment does not work. This is a minimal example for reproducing this bug. 108 | + [Flonda](https://github.com/takluyver/flonda) :: An Anaconda package builder for `linux-64`, `linux-32`, `osx-64`, `win-64` and `win-32` platforms. 109 | + [sparkonda](https://github.com/moutai/sparkonda) :: Minimalistic utility library to manage conda environments for pyspark jobs on yarn clusters. 110 | + [xz-feedstock](https://github.com/scopatz/xz-feedstock) :: The conda recipe (raw material). 111 | 112 | ### Conda 113 | + [Conda](https://github.com/conda/conda) :: is a cross-platform, Python-agnostic [binary package manager](http://conda.pydata.org) 114 | + [Conda package for Python-3.2 for 64-bit Linux](https://binstar.org/cpcloud/python) 115 | + [ctox](https://github.com/hayd/ctox) :: Tox with conda. 116 | + [python-appveyor-conda-example](https://github.com/rmcgibbo/python-appveyor-conda-example) :: Demo project for building and shipping Python conda packages with Appveyor CI and Travis CI. 117 | + [CommandCI_conda](https://github.com/CommandCI/CommandCI_conda) :: Command CI services to build conda packages. 118 | + [conda build-all](https://github.com/SciTools/conda-build-all) :: A conda subcommand which allows multiple distributions to be built (and uploaded) in a single command. It makes use of the underlying machinery developed for conda build. 119 | + [conda-execute](https://github.com/pelson/conda-execute) : Execute scripts in their own temporary environment. 120 | + [conda-dev-recipes](https://github.com/omnia-md/conda-dev-recipes) :: Conda development recipes (*-dev packages), built [nightly](https://nightli.es/). 121 | + [conda-recipes](https://github.com/omnia-md/conda-recipes) :: conda build recipes for the [Omnia](http://omnia.md) project. 122 | + [recipes](https://github.com/bioconda/recipes) :: The bioconda channel is a Conda channel providing bioinformatics related packages. This repository hosts the corresponding recipes. 123 | 124 | #### [Conda-Forge](https://github.com/conda-forge) 125 | + [conda-smithy](https://github.com/conda-forge/conda-smithy) :: The tool for managing conda-forge feedstocks. 126 | + [libffi-feedstock](https://github.com/conda-forge/libffi-feedstock) :: A conda-smithy repository for libffi. 127 | + [pyopengl-feedstock](https://github.com/conda-forge/pyopengl-feedstock) :: A conda-smithy repository for pyopengl. 128 | 129 | ###### Resources 130 | + [Continuum Analytics: Documentation](http://docs.continuum.io) 131 | + @jakevdp blogs about [Conda: Myths and Misconceptions](https://jakevdp.github.io/blog/2016/08/25/conda-myths-and-misconceptions/). 132 | + [python-3 in anaconda](http://continuum.io/blog/anaconda-python-3) 133 | + [Conda cheatsheet](http://conda.pydata.org/docs/_downloads/conda-cheatsheet.pdf) 134 | 135 | ---- 136 | 137 | # NEWS 138 | + [Devops Weekly](http://www.devopsweekly.com/). 139 | 140 | ### Interesting Reads 141 | + [HN thread](https://news.ycombinator.com/item?id=9517392) on the blog post [Python 3 in Science: the great migration has begun](https://astrofrog.github.io/blog/2015/05/09/2015-survey-results/) by astrofrog. 142 | -------------------------------------------------------------------------------- /Chemistry.md: -------------------------------------------------------------------------------- 1 | **Analytical chemistry, cheminformatics, computational chemistry, crystallography, nanochemistry, nuclear chemistry ...** 2 | 3 | + [Computational Chemistry](#computational-chemistry) 4 | + [Combustion](#combustion) 5 | + [Org-Chemreac](#org-chemreac) 6 | + [Molecular Chemistry](#molecular-chemistry) 7 | + [Physical Chemistry](#physical-chemistry) 8 | + [Solid State Chemistry](#solid-state-chemistry) 9 | + [Quantum Chemistry](#quantum-chemistry) 10 | 11 | ---- 12 | 13 | + [Chemharp](https://github.com/Luthaf/Chemharp) :: An efficient IO library for chemistry file formats. 14 | + [ChemSpiPy](https://github.com/mcs07/ChemSpiPy) :: Python wrapper for the ChemSpider API. [Documentation](http://chemspipy.readthedocs.org) 15 | + [pyquante2](https://github.com/rpmuller/pyquante2/) :: PyQuante is a Quantum Chemistry suite written in Python, rewritten as PyQuante2 to clean up the old package. 16 | + [Pymatgen](http://pymatgen.org/) :: is the [Python materials analysis library](https://pypi.python.org/pypi/pymatgen/2.4.3) powering the [Materials Project](http://www.materialsproject.org). [Source code](https://github.com/materialsproject/pymatgen). 17 | + Paper : [Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis](https://www.sciencedirect.com/science/article/pii/S0927025612006295?np=y) 18 | + [Pymol-script-repo](https://github.com/Pymol-Scripts/Pymol-script-repo) :: Collected scripts for Pymol. [Pymol wiki](http://www.pymolwiki.org/index.php/Git_intro) 19 | 20 | ---- 21 | 22 | # Computational Chemistry 23 | * [BioStructMap](https://github.com/andrewguy/biostructmap ) :: a Python package for integration of protein structure and sequence-based features. Publications: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223362/ 24 | + [Cantera](https://github.com/Cantera/cantera) : An open-source suite of tools for problems involving chemical kinetics, thermodynamics, and transport processes. 25 | + [MDTraj](https://github.com/pandegroup/mdtraj) : A modern, open library for the analysis of molecular dynamics trajectories. 26 | + [PDBFixer](https://github.com/pandegroup/pdbfixer) :: An easy to use application for fixing problems in Protein Data Bank files in preparation for simulating them. 27 | 28 | ### Org-[Chemreac](https://github.com/chemreac) 29 | Open Source chemical kinetics library 30 | + [chemreac](https://github.com/chemreac/chemreac) :: Library for chemical kinetics in homogeneous or isotropically symmetric inhomogenous systems. 31 | + [chemreac_anaconda](https://github.com/chemreac/chemreac_anaconda) :: Conda recipes for conda packages published under `conda.anaconda.org/chemreac`. 32 | + [chemreac-benchmarks](https://github.com/chemreac/chemreac-benchmarks) :: benchmarks for chemreac. 33 | 34 | ---- 35 | 36 | # Molecular Chemistry 37 | + [FRETUtils](https://github.com/martinhoefling/fretutils) :: FRET simulation and distance reconstruction utilities written in Python. [Website](http://www.schwabenlan.de/en/science/distance-measurement-on-the-nanometer-scale-with-fret-and-md). 38 | + [MDAnalysis](https://code.google.com/p/mdanalysis/) :: An object-oriented python toolkit to analyze molecular dynamics trajectories generated by CHARMM, Gromacs, NAMD, LAMMPS, or Amber. 39 | + [TEA](https://github.com/dzesmin/TEA) :: Thermochemical Equilibrium Abundances (TEA), a code to calculate gaseous molecular abundances under thermochemical equilibrium conditions. 40 | 41 | ---- 42 | 43 | # [Physical Chemistry](https://en.wikipedia.org/wiki/Category:Physical_chemistry) 44 | 45 | ## [Solid State Chemistry](https://en.wikipedia.org/wiki/Solid-state_chemistry) 46 | + [GSAS_Bayes](https://github.com/AntonGagin/GSAS_Bayes) :: Extension to the Rietveld package GSAS-II. 47 | + [tomopy](https://github.com/tomopy/tomopy) :: An open-sourced Python toolbox to perform tomographic data processing and image reconstruction tasks at the Advanced Photon Source. [Documentation](https://tomopy.readthedocs.org/). 48 | 49 | ---- 50 | 51 | # Quantum Chemistry 52 | + [Clusterchem](https://github.com/jiahao/clusterchem) :: Q-Chem Grid Engine handler. 53 | 54 | 55 | -------------------------------------------------------------------------------- /Computer-Graphics.md: -------------------------------------------------------------------------------- 1 | **Graphics and other Visualization libraries and packages. Also includes relevant documentation/tutorials and blog links narrating user experiences and howto's.** 2 | 3 | + [ARTS & CRAFTS](#arts-&-crafts) 4 | + [GRAPHICS](#graphics) 5 | + [CUDA](#cuda) 6 | + [Games](#games) 7 | + [GPU](#gpu) 8 | + [OCR](#ocr) 9 | + [Video](#video) 10 | + [INFOGRAPHICS](#infographics) 11 | + [Org-Enthought](#org-enthought) 12 | 13 | ---- 14 | 15 | # ARTS & CRAFTS 16 | ## Crochet 17 | + [Crystallocrafty](https://github.com/PerlJam/Crystallocrafty) :: Crochet patterns generated from protein structural data. 18 | + [img2pattern](https://github.com/g19fanatic/img2pattern) :: Takes an image and helps break it down so that it can be crocheted in a "paint by numbers" fashion 19 | 20 | ## Knitting 21 | + [patternknitt](https://github.com/Timian/patternknitt) :: Pattern making to knitting purposes. 22 | 23 | ## Sewing 24 | + [Embroidermodder](https://github.com/Embroidermodder/Embroidermodder) :: A free machine embroidery software program supporting a variety of formats. 25 | 26 | ###### Resources-Sewing 27 | + Information on [computerized embroidery](http://edutechwiki.unige.ch/en/Computerized_embroidery). 28 | + An old blog post on machine [embroidery designs with Linux](http://libregraphicsworld.org/blog/entry/embroidery-design-on-linux-now-possible-with-thred). 29 | + [Comparison of embroidery software](https://en.wikipedia.org/wiki/Comparison_of_embroidery_software). 30 | 31 | ---- 32 | 33 | # GRAPHICS 34 | + [bitarray](https://github.com/ilanschnell/bitarray) :: This module provides an object type which efficiently represents an array of booleans. 35 | + [CoDrawing](https://github.com/charlyoleg/CoDrawing) :: A workspace attempting to make easier the co-creation of drawings. 36 | + [colour-analysis](https://github.com/colour-science/colour-analysis) :: Image analysis tools based on Colour and Vispy. 37 | + [EigenFace](https://github.com/bugra/EigenFace) :: EigenFace implementation in Python for Yale FaceDatabase. Eigenvectors are used in the computer vision problem of human face recognition. 38 | + [Gizeh](https://github.com/Zulko/gizeh) :: Simple Vector Graphics for Python. 39 | + [Vector Animations With Python](https://zulko.github.io/blog/2014/09/20/vector-animations-with-python/) using Gizeh. 40 | + [ImageAI](https://github.com/OlafenwaMoses/ImageAI) :: A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities. https://commons.specpal.science 41 | + The blog post [Object Detection with 10 lines of code](https://towardsdatascience.com/object-detection-with-10-lines-of-code-d6cb4d86f606). 42 | + [MoviePy](http://zulko.github.io/moviepy/) :: Video editing with Python. 43 | + [Data Animations With Python and MoviePy](https://zulko.github.io/blog/2014/11/29/data-animations-with-python-and-moviepy/) 44 | + [PCV](https://github.com/jesolem/PCV) :: An open source pure Python library for computer vision based on the book "Programming Computer Vision with Python" by Jan Erik Solem. 45 | + [PIL](http://www.pythonware.com/products/pil/) :: Python Imaging Library, now forked as [pillow](http://pillow.readthedocs.org/) to support Python3. 46 | + [pydeface](https://github.com/poldracklab/pydeface) :: A defacing utility for MRI images. 47 | + [pygfx](https://github.com/pygfx/pygfx) :: A render engine, inspired by ThreeJS, but for Python and targeting Vulkan/Metal/DX12 (via wgpu). 48 | + [python-colormath](https://github.com/gtaylor/python-colormath) :: A python module that abstracts common color math operations. For example, converting from CIE L*a*b to XYZ, or from RGB to CMYK, with [Documentation](http://python-colormath.readthedocs.org). 49 | 50 | 51 | ## CUDA 52 | 53 | 54 | ## Games 55 | + [Panda3D](https://www.panda3d.org/) :: The Panda3D free open-source cross-platform game engine with excellent Python support. [Source code](https://github.com/panda3d/panda3d). 56 | + [PySoy](http://www.pysoy.org/) is a 3D cloud game engine for Python. [Source code](http://hg.pysoy.org/pysoy) 57 | 58 | ## GPU 59 | + [wgpu-py](https://github.com/pygfx/wgpu-py): Next generation GPU API for Python. 60 | 61 | ## OCR 62 | + [EAST](https://github.com/argman/EAST) :: A tensorflow implementation of EAST text detector. 63 | + [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR):: aims to create multilingual and practical OCR tools that provide data annotation and synthesis tools, support training and helps users train better models and deploy on server, mobile, embedded and IoT devices. 64 | + [Strabo text recognition deep learning](https://github.com/jamespjh/strabo-text-recognition-deep-learning): This project detects and recognizes text in maps. Input to the system is an image (`jpeg or png`). The output is a `geo json` image which contains the coordinates of the text in the map along with the recognized text in that area. 65 | + Forks: 66 | + https://github.com/spatial-computing/strabo-text-recognition-deep-learning 67 | 68 | ## Video 69 | + [submerger](https://github.com/grote/submerger) :: Merges subtitle files into one unified video file. 70 | 71 | ###### Resources 72 | + [Bokeh Snake](https://github.com/akloster/bokeh-snake) :: This is an example on how to use Jupyter widgets and the Bokeh plotting library to implement a simple Snake Game. 73 | + [OpenCV panorama stitching](http://www.pyimagesearch.com/2016/01/11/opencv-panorama-stitching/) :: Image stitching and panorama construction using Python and OpenCV. 74 | + 5 Python Libraries for Creating [Interactive Plots](https://blog.modeanalytics.com/python-interactive-plot-libraries/). 75 | 76 | ---- 77 | 78 | # INFOGRAPHICS 79 | __Visualization software__ 80 | + [abstract_rendering](https://github.com/ContinuumIO/abstract_rendering) :: Bin based rendering for the win (in python)! 81 | + [Arfview](https://github.com/kylerbrown/arfview) :: A data visualization program for use with data in the arf format. 82 | + [Biggles](https://github.com/nolta/biggles) : Scientific (simple, elegant) plotting in Python. 83 | + [Bokeh](http://bokeh.pydata.org) :: is a Python library for visualizing large and realtime datasets on the web. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity to thin clients. Bokeh includes its own Javascript library (BokehJS) that implements a reactive scenegraph representation of the plot, and renders efficiently to HTML5 Canvas. Bokeh works well with IPython Notebook, but can generate standalone graphics that embed into regular HTML. If you are a Matplotlib user, you can just use %bokeh magic to start interacting with your plots in the notebook immediately! 84 | + [Bokeh-notebooks](https://github.com/ContinuumIO/bokeh-notebooks) - Interactive web plotting samples with Bokeh in IPython notebook. 85 | + [brewer2mpl](https://github.com/jiffyclub/brewer2mpl) :: Connect colorbrewer2.org color maps to Python and matplotlib. [Wiki](https://github.com/jiffyclub/brewer2mpl/wiki) 86 | + [d3py](https://github.com/mikedewar/d3py) :: is a plotting library for Python based on the JavaScript library "[D3.js](http://d3js.org/)". 87 | + [Glue](https://github.com/glue-viz/glue) :: is a python project to link visualizations of scientific datasets across many files. 88 | + [GmtPy](http://emolch.github.com/gmtpy) :: A Python interface to GMT. [Tutorial](http://emolch.github.io/gmtpy/tutorial.html) 89 | + [Healpy](https://github.com/healpy/healpy) :: A python package to manipulate healpix maps, based on the standard numeric and visualisation tools for Python, Numpy and matplotlib. 90 | + [HoloViews](http://ioam.github.io/holoviews) :: A Python package for scientific and engineering data visualization that provides composable, sliceable, declarative data structures for building complex visualizations easily. 91 | + [napari](https://github.com/napari/napari) :: is a fast, interactive, multi-dimensional image viewer for Python. It's designed for browsing, annotating, and analyzing large multi-dimensional images. It's built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (numpy, scipy). 92 | + [Matplotlib](http://matplotlib.org/) :: is a 2D/3D plotting library that can be used in python scripts, the python and ipython shell (ala MATLAB or Mathematica), web application servers, etc.. to generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code. 93 | __RESOURCES__ 94 | + [mplot3d tutorial](http://matplotlib.org/1.3.1/mpl_toolkits/mplot3d/tutorial.html) 95 | + [Inset plots in Matplotlib](http://scipython.com/blog/inset-plots-in-matplotlib/). 96 | + [Orange](http://orange.biolab.si) :: An Open source data visualization and analysis suite for data mining through visual programming or Python scripting. 97 | + [object_detection_trainer](https://github.com/seattle-police/object_detection_trainer) :: A web interface for creating histogram-of-oriented-gradient (HOG) based object detectors. 98 | + [Pygal](http://pygal.org) :: PYthon SVG GrAph plotting Library. [Source code](https://github.com/Kozea/pygal). 99 | + [Pylab](http://wiki.scipy.org/PyLab) :: is Matplotlib's pylab interface for interactive mode. 100 | + [Plotly](https://plot.ly/python) :: is an API and webhost for all your Python web plotting needs. 101 | + [SnakeViz](https://github.com/jiffyclub/snakeviz) :: A viewer for Python profiling data that runs as a web application in your browser. 102 | + [Seaborn](http://stanford.edu/~mwaskom/software/seaborn/) :: Statistical data visualization using matplotlib. [Source code](https://github.com/mwaskom/seaborn) 103 | + [Toyplot](https://github.com/sandialabs/toyplot) :: Interactive plotting for Python. [Documentation](http://toyplot.readthedocs.org) 104 | + [PyWaffle](https://github.com/gyli/PyWaffle) :: An open source, MIT-licensed Python package for plotting waffle charts. 105 | + [Yellowbrick](https://github.com/DistrictDataLabs/yellowbrick) :: A suite of custom matplotlib visualizers for scikit-learn estimators to support visual feature analysis, model selection, evaluation, and diagnostics. [Documentation](http://www.scikit-yb.org/en/latest/) 106 | + [YT](http://yt-project.org/):: A python package for analyzing and visualizing volumetric, multi-resolution data from astrophysical simulations, radio telescopes, and a burgeoning interdisciplinary community. 107 | 108 | #### Non-Python tools 109 | + [36 Excellent Data Visualization Tools](http://www.smashingapps.com/2014/06/30/36-excellent-data-visualization-tools.html) in JavaScript. 110 | 111 | ---- 112 | 113 | # Org-Enthought 114 | + [Enstaller](https://github.com/enthought/enstaller) :: A package management and installation tool for egg-based Python distributions. 115 | + [MayaVi2](http://github.com/enthought/mayavi) :: 3D visualization of scientific data in Python. 116 | + [Okonomiyaki](https://github.com/enthought/okonomiyaki) :: An experimental library aimed at consolidating a lot of our low-level code used for Enthought's eggs. 117 | + [Pyface](https://github.com/enthought/pyface) :: Traits-capable windowing framework. 118 | + [SciMath](https://github.com/enthought/scimath) :: This project includes packages to support scientific and mathematical calculations, beyond the capabilities offered by SciPy. 119 | -------------------------------------------------------------------------------- /DataBase.md: -------------------------------------------------------------------------------- 1 | For downloading various data sets, visit the [OpenData.md](https://github.com/svaksha/pythonidae/blob/master/OpenData.md) page. 2 | 3 | + [Distributed Storage](#distributed-storage) 4 | + [SciDB](#scidb) 5 | + [HDF](#hdf) 6 | + [NoSQL](#nosql) 7 | + [ArangoDB](#arangodb) 8 | + [Neo4J](#neo4j) 9 | + [RDBMS](#rdbms) 10 | + [PostgreSQL](#postgresql) 11 | + [ORM](#orm) 12 | + [TSDB](#tsdb) 13 | + [InfluxDB](#influxdb) 14 | + [RESOURCES](#resources) 15 | 16 | ---- 17 | 18 | # [Distributed](https://en.wikipedia.org/wiki/Category:Distributed_data_stores) Storage 19 | 20 | ## SciDB 21 | + [SciDB-py](https://github.com/Paradigm4/SciDB-py) :: Python wrapper for [SciDB](http://scidb.org) queries. 22 | * __DOCS__: [SciDB-py documentation](http://scidb-py.readthedocs.org/). 23 | + [bio_scidb](https://github.com/ChrisBeaumont/bio_scidb) : ScidDB-Py Biology Demo. 24 | + [Dev tools](https://github.com/Paradigm4/dev_tools) :: Tools for installing SciDB plugins from GitHub. 25 | + [gdal2scidb](https://github.com/albhasan/gdal2scidb) :: Python scripts for exporting a raster (supported by GDAL) to SciDB binary format. 26 | + [modis2scidb](https://github.com/albhasan/modis2scidb) :: Python scripts for uploading MODIS images to SciDB. 27 | + [scidb4py](https://github.com/artyom-smirnov/scidb4py) :: Pure python SciDB client library. The [@Paradigm4 fork](https://github.com/Paradigm4/scidb4py) 28 | + [scidbbackup](https://github.com/nicksteiner/scidbbackup) :: Script to backup/manage SciDB arrays during upgrades. 29 | + [scidbload](https://github.com/nicksteiner/scidbload) :: Load scidb arrays to python. 30 | + [scidbpy-aflgen](https://github.com/ChrisBeaumont/scidbpy-aflgen) :: SciDB AFL Parser Generate data for SciDBpy AFL bindings. 31 | + [spheredb](https://github.com/jakevdp/spheredb) :: Python utilities for storage and manipulation of spherical data in SciDB. 32 | + [TAQ](https://github.com/Paradigm4/TAQ) :: Example SciDB queries for finance trade and quote data. 33 | 34 | ---- 35 | 36 | # HDF 37 | + [h5io](https://github.com/h5io/h5io) :: Read and write simple Python objects using HDF5. 38 | 39 | ###### Resources 40 | + [hdf5-is-for-lovers](https://github.com/scopatz/hdf5-is-for-lovers) :: HDF5 Tutorial notebooks. 41 | 42 | ---- 43 | 44 | # NoSQL 45 | 46 | ## [ArangoDB](http://www.arangodb.org/) 47 | + [ArangoPy](https://github.com/saeschdivara/ArangoPy) :: Python driver Framework to access https://github.com/triAGENS/ArangoDB 48 | + [pyArango](https://github.com/tariqdaouda/pyArango) :: Python Object Wrapper for ArangoDB with built-in validation. http://pyArango.tariqdaouda.com 49 | + [python-arango](https://github.com/joowani/python-arango) by @joowani :: Python Driver for ArangoDB REST API. 50 | + [python-arango](https://github.com/linkux-it/python-arango) by @linkux-it :: Driver for ArangoDB. 51 | + [python-arangodb](https://github.com/diefans/python-arangodb) :: One of many arangodb rest adaptors for python. 52 | 53 | ## Neo4J 54 | + [Neomodel](https://github.com/robinedwards/neomodel) :: An Object Graph Mapper (OGM) for the neo4j graph database. [Documentation](http://neomodel.rtfd.org) 55 | 56 | ---- 57 | 58 | # RDBMS 59 | + [DBNormalizer](https://github.com/humbertog/DBNormalizer) :: DB normalizer project DMKM. 60 | + [Tangerine](https://github.com/jmschrei/tangerine) :: A lightweight embedded Python RDBMS. 61 | 62 | ## PostgreSQL 63 | + [governor](https://github.com/compose/governor) :: Runners to orchestrate a high-availability PostgreSQL. 64 | + [postgresql-metrics](https://github.com/spotify/postgresql-metrics) :: Tool that extracts and provides metrics on your PostgreSQL database. 65 | 66 | ---- 67 | 68 | # ORM 69 | + [peewee](https://github.com/coleifer/peewee) :: A small, expressive ORM that supports Postgresql, Mysql and Sqlite. [Documentation](http://docs.peewee-orm.com/). 70 | 71 | ---- 72 | 73 | # [TSDB](https://en.wikipedia.org/wiki/Time_series_database) 74 | 75 | ## InfluxDB 76 | + [influxdb-python](https://github.com/influxdata/influxdb-python) :: Python client for InfluxDB. 77 | 78 | ---- 79 | 80 | # RESOURCES 81 | + [The Lesser Known Normal Forms of Database Design](http://www.johnmyleswhite.com/notebook/2014/09/10/the-lesser-known-normal-forms/) 82 | -------------------------------------------------------------------------------- /Earth-Science.md: -------------------------------------------------------------------------------- 1 | + [AERONAUTICS](#aeronautics) 2 | + [ASTRONOMY](#astronomy) 3 | + [GIS](#gis) 4 | + [Geolocation](#geolocation) 5 | 6 | ---- 7 | 8 | # AERONAUTICS 9 | + [av3-fc](https://github.com/psas/av3-fc) :: PSAS Flight Computer's avionics version 3 flight computer. 10 | 11 | ###### Resources 12 | + [AeroPython](https://github.com/barbagroup/AeroPython) :: Classical Aerodynamics of potential flow using Python, for [Prof. Barba](http://lorenabarba.com/)'s course at GW (1st version:Spring 2014, 2nd run:2015). 13 | 14 | ---- 15 | 16 | # ASTRONOMY 17 | **Astronautics, Astronomy** 18 | + [Astropy](https://github.com/astropy) → Astronomy Python Library. 19 | + [astroscrappy](https://github.com/astropy/astroscrappy) :: Speedy Cosmic Ray Annihilation Package. 20 | + [ASV | Airspeed Velocity](http://spacetelescope.github.io/asv) :: A simple Python benchmarking tool with web-based reporting. 21 | + [Caput](https://github.com/radiocosmology/caput) :: Cluster Astronomical Python Utilities. 22 | + [Celestialbody](https://github.com/jsbj/celestialbody) is a library for calculating the climate and weather on existing and hypothetical planets and moons. 23 | + [EchelleTools](https://github.com/iancze/EchelleTools) are command-line tools to process high resolution echelle spectra from astronomical instruments (TRES, HIRES, Spex, etc...) into a common HDF5 format. 24 | + [Enrico](https://github.com/gammapy/enrico) helps you with your Fermi data analysis. [Documentation](http://enrico.readthedocs.org/) 25 | + [Gammapy](https://github.com/gammapy/gammapy) :: A Python package for gamma-ray astronomy. [Documentation](https://gammapy.readthedocs.org/) 26 | + [Gamma-speed](https://github.com/gammapy/gamma-speed) :: Measure gamma-ray data analysis speed (focus on multi-core likelihood fitting speed-ups). [Documentation](https://gamma-speed.readthedocs.org/). 27 | + [gatspy](https://github.com/jakevdp/gatspy) :: General tools for Astronomical Time Series in Python. 28 | + [klima](https://github.com/wuan/klima) :: Python modules for building a climate data logger. 29 | + [LRG_redshifts](https://github.com/jakevdp/LRG_redshifts) :: Finding double redshifts in SDSS LRGs. 30 | + [oec_outreach](https://github.com/hannorein/oec_outreach) :: A variant of the Open Exoplanet Catalogue (a database of all discovered extra-solar planets) with additional data and images mainly useful for outreach projects. 31 | + [periodogram](https://github.com/astroML/periodogram) :: Unified implementation for period-finding in astronomical time series. 32 | + [PyFITS](https://github.com/spacetelescope/PyFITS) :: Git repository for the PyFITS project; PyFITS is still actively maintained in SVN, but this repository exists to ease the migration into AstroPy (see above). http://www.stsci.edu/resources/software_hardware/pyfits 33 | + [Pylades](https://github.com/helgee/plyades) :: Plyades is an astrodynamics library, written in Python and based on Numpy and Scipy. [Documentation for the astrodynamics library](http://plyades.readthedocs.org) 34 | + [pyMarsTime](https://github.com/ashima/pyMarsTime) :: Mars Time Calculation in Python. 35 | + [ProbabilisticLensing](https://github.com/jakevdp/ProbabilisticLensing) :: A probabilistic formalism of weak lensing, based on Bayes Nets/Probabilistic Graphical Models. 36 | + [ShutterCorrect](https://github.com/iancze/ShutterCorrect) calculates a shutter correction map for digital images. Primarily designed for correcting astronomical images to achieve the desired illumination. 37 | + [SunPy](http://sunpy.org/) is a free and open-source software library for solar physics based on Python. [Source code](https://github.com/sunpy/sunpy) 38 | + [utsc-ptcs](https://github.com/hannorein/utsc-ptcs) :: University of Toronto Scarborough - Python Telescope Control System. 39 | 40 | ###### Resources 41 | + [AAS227Workshop](https://github.com/jakevdp/AAS227Workshop) :: Materials for the Bayesian Methods in Astronomy workshop at AAS227. 42 | 43 | ---- 44 | 45 | # GIS 46 | + [Atmosphere](https://github.com/iPlantCollaborativeOpenSource/atmosphere) :: An integrative, private, self-service cloud computing platform designed to provide easy access to preconfigured, frequently used analysis routines, relevant algorithms, and data sets in an available-on-demand environment designed to accommodate computationally and data-intensive bioinformatics tasks. 47 | + [biggus](https://github.com/SciTools/biggus) :: Virtual large arrays and lazy evaluation. [Documentation](http://biggus.readthedocs.org/) 48 | + [BurnMan](http://www.burnman.org) :: is a library for modeling mantle thermodynamics and thermoelasticity constrained by mineral physics experiments. Get the [source code](https://github.com/geodynamics/burnman) from github. 49 | + [Cartopy](http://scitools.org.uk/cartopy/) :: A cartographic python library with matplotlib support. [Source code](https://github.com/SciTools/cartopy) on github. 50 | + [Cora](https://github.com/radiocosmology/cora) :: Code for simulating skies for 21cm Intensity Mapping, including galactic and extragalactic foregrounds. 51 | + [Driftscan](https://github.com/radiocosmology/driftscan) :: A Python project for simulating and analysing the transit radio interferometers telescopes, with a particular focus on 21cm Cosmology. 52 | + [Iris](http://scitools.org.uk/iris/) :: A Python library for Meteorology and Climatology. The Iris library implements a data model to create a data abstraction layer which isolates analysis and visualisation code from data format specifics. 53 | + [Karta](https://github.com/njwilson23/karta) → A tidy Python package for geospatial computation. 54 | + [MASSACR](https://github.com/navahnavahnavah/massacr) :: Modeling Altered Seafloor: Simulation and Climatic Response. 55 | + [MetPy](https://github.com/metpy/MetPy) :: A Python Package for Meteorological Data. 56 | + [PitchWeather](https://github.com/roryk/PitchWeather) :: Links Weather Underground weather data to a PitchFx database. 57 | + [PyLith](https://github.com/geodynamics/pylith) :: is a finite element code for the solution of dynamic and quasi-static tectonic deformation problems. 58 | + [Pyshp](https://github.com/GeospatialPython/pyshp) :: library to read and write ESRI Shapefiles in pure Python. 59 | + [ulmo](https://github.com/ulmo-dev/ulmo) :: clean, simple and fast access to public hydrology and climatology data. 60 | 61 | ## Geolocation 62 | __Libraries for geolocation.__ 63 | + [geoip-api-python](https://github.com/maxmind/geoip-api-python) :: A Python API for MaxMind GeoIP Legacy Database. 64 | + [geopandas](https://github.com/geopandas/geopandas) :: Python tools for geographic data. 65 | + [geoplotlib](https://github.com/andrea-cuttone/geoplotlib) :: A toolbox for visualizing geographical data. 66 | + [GeoPy](https://github.com/geopy/geopy) → is a Geocoding Toolbox for Python 2 and 3 for several popular geocoding web services. 67 | + [geo-tools](https://github.com/jesolem/geo-tools) :: Collection of Python modules for working with geo data. 68 | + [pygeoip](https://github.com/appliedsec/pygeoip) → Pure Python API for Maxmind's binary GeoIP databases. 69 | + [python-geojson](https://github.com/frewsxcv/python-geojson) :: Python bindings and utilities for GeoJSON. On [PyPI](https://pypi.python.org/pypi/geojson/). 70 | + [reverse-geocoder](https://github.com/thampiman/reverse-geocoder) :: A fast, offline reverse geocoder in Python. 71 | 72 | ###### Resources 73 | + [UW_Python_for_Geosciences](https://github.com/jhamman/UW_Python_for_Geosciences) :: Talks from the UW Python for Geosciences Seminar. 74 | + [SciPy-Tutorial-2015](https://kjordahl.github.io/SciPy-Tutorial-2015/) :: TUTORIAL on Geospatial Data with Open Source Tools in Python. 75 | 76 | -------------------------------------------------------------------------------- /IO.md: -------------------------------------------------------------------------------- 1 | + [Data Formats](#data-formats) 2 | + [CSV](#csv) 3 | + [ELAN-EAF](#elan-eaf) 4 | + [Data Models](#data-models) 5 | + [Data Type](#data-type) 6 | + [Parser](#parser) 7 | + [Markdown](#markdown) 8 | 9 | ---- 10 | 11 | # Data Formats 12 | **Libraries for Data/File formats.** 13 | + [Arf](https://github.com/melizalab/arf) :: Advanced recording format for physiology and behavior. 14 | + [Feather](https://github.com/wesm/feather) :: A fast, interoperable binary columnar serialization data frame storage for Python, R, and more powered by Apache Arrow. 15 | + [hdfs](https://github.com/mtth/hdfs) :: API and command line interface for HDFS. [Documentation](http://hdfscli.readthedocs.org) 16 | + [lbl](https://github.com/kylerbrown/lbl) :: A python library for reading/writing and manipulating lbl files, represented within python as a numpy record array with fields 'name', 'start', and 'stop'. This format is useful for writing data to the arf file format. 17 | + [loompy](https://github.com/linnarsson-lab/loompy) :: Python implementation of the `Loom` file format - http://loompy.org 18 | + [PyCifRW](https://pypi.python.org/pypi/PyCifRW/3.6.2) :: Library support for reading and writing CIF (Crystallographic Information Format) files using Python. [Source code on Bitucket](https://bitbucket.org/jamesrhester/pycifrw/). 19 | + [PyCIF](https://github.com/quantumjockey/pycif) :: A python module for reading from, writing to, and manipulating International Union of Crystallography (IUCr) CIF-formatted data files. 20 | + [pymarkdown](https://github.com/mrocklin/pymarkdown) :: Evaluate code in markdown. 21 | + [RDFLib](https://github.com/RDFLib/rdflib/) :: A Python library for working with RDF, a simple yet powerful language for representing information. [Documentation](https://rdflib.readthedocs.org/). 22 | + [sam](https://github.com/smdabdoub/sam) :: Simple parsing tools in python for SAM-format files. 23 | + [stapler](https://github.com/hellerbarde/stapler) :: A small utility making use of the pypdf library to provide a (somewhat) lighter alternative to pdftk. 24 | + [snakebite](https://github.com/spotify/snakebite) :: A pure python HDFS client. 25 | + [xlrd3](https://github.com/joidegn/xlrd3) :: Port of xlrd for reading excel files to python-3. 26 | + [x_x](https://github.com/krockode/x_x) :: A command line reader that displays either Excel files or CSVs in your terminal. 27 | 28 | ## CSV 29 | + [pyexcel](https://github.com/pyexcel/pyexcel) :: Single API for reading, manipulating and writing data in csv, ods, xls, xlsx and xlsm files. Documentation: http://pyexcel.readthedocs.io 30 | + [tabulator-py](https://github.com/frictionlessdata/tabulator-py) :: Consistent interface for stream reading and writing tabular data (csv/xls/json/etc). 31 | 32 | ## ELAN-EAF 33 | + [eaf_tools](https://github.com/LongSeanSilvr/eaf_tools) :: Collection of scripts to help in reviewing and preparing ELAN .eaf` files for FAVE alignment 34 | + [pyelan](https://github.com/jonkeane/pyelan) :: A python module that makes interacting with ELAN files easier. 35 | 36 | ---- 37 | 38 | # Data Models 39 | + Documentation for [working with RDFLib and RDFExtras](http://rdfextras.readthedocs.org/en/latest/working_with.html). 40 | + [pySesame](http://pysesame.projects.semwebcentral.org) :: a Python wrapper for Sesame. 41 | 42 | ---- 43 | 44 | # [Data Type](http://en.wikipedia.org/wiki/Data_type) 45 | + [typeshed](https://github.com/python/typeshed) :: Collection of library stubs for Python, with static types. 46 | 47 | ---- 48 | 49 | # Parser 50 | + [numparser](https://pypi.python.org/pypi/numparser) :: Python library for parsing numbers from strings. [Source Code](https://github.com/sadovnychyi/numparser). 51 | + [Poyo](https://github.com/hackebrot/poyo) :: A lightweight YAML Parser for Python. 52 | 53 | ## Markdown 54 | + [CommonMark-py](https://github.com/rtfd/CommonMark-py) :: Python CommonMark parser. 55 | -------------------------------------------------------------------------------- /LICENSE.md: -------------------------------------------------------------------------------- 1 | # LICENSE 2 | 3 | This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/legalcode), [CC-BY-NC-SA 4.0 legalcode](http://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). 4 | 5 | THE WORK (AS DEFINED BELOW) IS PROVIDED UNDER THE TERMS OF THIS CREATIVE COMMONS PUBLIC LICENSE ("CCPL" OR "LICENSE"). THE WORK IS PROTECTED BY COPYRIGHT AND/OR OTHER APPLICABLE LAW. ANY USE OF THE WORK OTHER THAN AS AUTHORIZED UNDER THIS LICENSE OR COPYRIGHT LAW IS PROHIBITED. 6 | 7 | BY EXERCISING ANY RIGHTS TO THE WORK PROVIDED HERE, YOU ACCEPT AND AGREE TO BE BOUND BY THE TERMS OF THIS LICENSE. TO THE EXTENT THIS LICENSE MAY BE CONSIDERED TO BE A CONTRACT, THE LICENSOR GRANTS YOU THE RIGHTS CONTAINED HERE IN CONSIDERATION OF YOUR ACCEPTANCE OF SUCH TERMS AND CONDITIONS. 8 | 9 | 10 | # Attribution-NonCommercial-ShareAlike 4.0 International 11 | 12 | Creative Commons Corporation (“Creative Commons”) is not a law firm and does not provide legal services or legal advice. Distribution of Creative Commons public licenses does not create a lawyer-client or other relationship. Creative Commons makes its licenses and related information available on an “as-is” basis. Creative Commons gives no warranties regarding its licenses, any material licensed under their terms and conditions, or any related information. Creative Commons disclaims all liability for damages resulting from their use to the fullest extent possible. 13 | 14 | # Using Creative Commons Public Licenses 15 | 16 | Creative Commons public licenses provide a standard set of terms and conditions that creators and other rights holders may use to share original works of authorship and other material subject to copyright and certain other rights specified in the public license below. The following considerations are for informational purposes only, are not exhaustive, and do not form part of our licenses. 17 | 18 | ### Considerations for licensors: 19 | Our public licenses are intended for use by those authorized to give the public permission to use material in ways otherwise restricted by copyright and certain other rights. Our licenses are irrevocable. Licensors should read and understand the terms and conditions of the license they choose before applying it. Licensors should also secure all rights necessary before applying our licenses so that the public can reuse the material as expected. Licensors should clearly mark any material not subject to the license. This includes other CC-licensed material, or material used under an exception or limitation to copyright. More considerations for licensors. 20 | 21 | ### Considerations for the public: 22 | By using one of our public licenses, a licensor grants the public permission to use the licensed material under specified terms and conditions. If the licensor’s permission is not necessary for any reason–for example, because of any applicable exception or limitation to copyright–then that use is not regulated by the license. Our licenses grant only permissions under copyright and certain other rights that a licensor has authority to grant. Use of the licensed material may still be restricted for other reasons, including because others have copyright or other rights in the material. A licensor may make special requests, such as asking that all changes be marked or described. Although not required by our licenses, you are encouraged to respect those requests where reasonable. More considerations for the public. 23 | 24 | # Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License 25 | 26 | By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions. 27 | 28 | ## Section 1 – Definitions. 29 | 30 | * Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image. 31 | * Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public License. 32 | * BY-NC-SA Compatible License means a license listed at creativecommons.org/compatiblelicenses, approved by Creative Commons as essentially the equivalent of this Public License. 33 | * Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights. 34 | * Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements. 35 | * Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material. 36 | * License Elements means the license attributes listed in the name of a Creative Commons Public License. The License Elements of this Public License are Attribution, NonCommercial, and ShareAlike. 37 | * Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License. 38 | Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license. 39 | * Licensor means the individual(s) or entity(ies) granting rights under this Public License. 40 | * NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange. 41 | * Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them. 42 | * Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world. 43 | You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning. 44 | 45 | ## Section 2 – Scope. 46 | 47 | #### License grant. 48 | * Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to: 49 | * reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and 50 | * produce, reproduce, and Share Adapted Material for NonCommercial purposes only. 51 | * Exceptions and Limitations. For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions. 52 | * Term. The term of this Public License is specified in Section 6(a). 53 | * Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material. 54 | * Downstream recipients. 55 | * Offer from the Licensor – Licensed Material. Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License. 56 | * Additional offer from the Licensor – Adapted Material. Every recipient of Adapted Material from You automatically receives an offer from the Licensor to exercise the Licensed Rights in the Adapted Material under the conditions of the Adapter’s License You apply. 57 | * No downstream restrictions. You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material. 58 | * No endorsement. Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i). 59 | 60 | #### Other rights. 61 | 62 | Moral rights, such as the right of integrity, are not licensed under this Public License, nor are publicity, privacy, and/or other similar personality rights; however, to the extent possible, the Licensor waives and/or agrees not to assert any such rights held by the Licensor to the limited extent necessary to allow You to exercise the Licensed Rights, but not otherwise. 63 | Patent and trademark rights are not licensed under this Public License. 64 | To the extent possible, the Licensor waives any right to collect royalties from You for the exercise of the Licensed Rights, whether directly or through a collecting society under any voluntary or waivable statutory or compulsory licensing scheme. In all other cases the Licensor expressly reserves any right to collect such royalties, including when the Licensed Material is used other than for NonCommercial purposes. 65 | 66 | ## Section 3 – License Conditions. 67 | 68 | Your exercise of the Licensed Rights is expressly made subject to the following conditions. 69 | 70 | Attribution. 71 | 72 | If You Share the Licensed Material (including in modified form), You must: 73 | 74 | retain the following if it is supplied by the Licensor with the Licensed Material: 75 | identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated); 76 | a copyright notice; 77 | a notice that refers to this Public License; 78 | a notice that refers to the disclaimer of warranties; 79 | a URI or hyperlink to the Licensed Material to the extent reasonably practicable; 80 | indicate if You modified the Licensed Material and retain an indication of any previous modifications; and 81 | indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License. 82 | You may satisfy the conditions in Section 3(a)(1) in any reasonable manner based on the medium, means, and context in which You Share the Licensed Material. For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information. 83 | If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable. 84 | ShareAlike. 85 | In addition to the conditions in Section 3(a), if You Share Adapted Material You produce, the following conditions also apply. 86 | 87 | The Adapter’s License You apply must be a Creative Commons license with the same License Elements, this version or later, or a BY-NC-SA Compatible License. 88 | You must include the text of, or the URI or hyperlink to, the Adapter's License You apply. You may satisfy this condition in any reasonable manner based on the medium, means, and context in which You Share Adapted Material. 89 | You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, Adapted Material that restrict exercise of the rights granted under the Adapter's License You apply. 90 | 91 | ## Section 4 – Sui Generis Database Rights. 92 | 93 | Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material: 94 | 95 | for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only; 96 | if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material, including for purposes of Section 3(b); and 97 | You must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database. 98 | For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights. 99 | 100 | ## Section 5 – Disclaimer of Warranties and Limitation of Liability. 101 | 102 | Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You. 103 | To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You. 104 | The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability. 105 | 106 | ## Section 6 – Term and Termination. 107 | 108 | This Public License applies for the term of the Copyright and Similar Rights licensed here. However, if You fail to comply with this Public License, then Your rights under this Public License terminate automatically. 109 | Where Your right to use the Licensed Material has terminated under Section 6(a), it reinstates: 110 | 111 | automatically as of the date the violation is cured, provided it is cured within 30 days of Your discovery of the violation; or 112 | upon express reinstatement by the Licensor. 113 | For the avoidance of doubt, this Section 6(b) does not affect any right the Licensor may have to seek remedies for Your violations of this Public License. 114 | For the avoidance of doubt, the Licensor may also offer the Licensed Material under separate terms or conditions or stop distributing the Licensed Material at any time; however, doing so will not terminate this Public License. 115 | Sections 1, 5, 6, 7, and 8 survive termination of this Public License. 116 | 117 | ## Section 7 – Other Terms and Conditions. 118 | 119 | The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed. 120 | Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License. 121 | 122 | ## Section 8 – Interpretation. 123 | 124 | For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License. 125 | To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions. 126 | No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor. 127 | Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority. 128 | Creative Commons is not a party to its public licenses. Notwithstanding, Creative Commons may elect to apply one of its public licenses to material it publishes and in those instances will be considered the “Licensor.” Except for the limited purpose of indicating that material is shared under a Creative Commons public license or as otherwise permitted by the Creative Commons policies published at creativecommons.org/policies, Creative Commons does not authorize the use of the trademark “Creative Commons” or any other trademark or logo of Creative Commons without its prior written consent including, without limitation, in connection with any unauthorized modifications to any of its public licenses or any other arrangements, understandings, or agreements concerning use of licensed material. For the avoidance of doubt, this paragraph does not form part of the public licenses. 129 | 130 | 131 | -------------------------------------------------------------------------------- /Machines.md: -------------------------------------------------------------------------------- 1 | **Hardware arch (ARM, MIPS), API for Compilers, other software libraries,..** 2 | 3 | + [CAD-CAM](#cad-cam) 4 | + [Control System](#control-system) 5 | + [Embedded Systems](#embedded-systems) 6 | + [Microcontrollers](#microcontrollers) 7 | + [GUI](#gui) 8 | + [Robots](#robots) 9 | + [Tools](#tools) 10 | 11 | ---- 12 | 13 | + [aravisGigE](https://github.com/areaDetector/aravisGigE) :: An EPICS area Detector driver using the glib aravis library for video acquisition with Genicam cameras. Python is used to create databases and edm screens from the XML file. 14 | + [Migen](https://github.com/m-labs/migen) :: A Python toolbox for building complex digital hardware by [M-Labs](http://m-labs.hk). 15 | + [PeachPy](https://github.com/Maratyszcza/PeachPy) :: x86-64 assembler embedded in Python. An [online IDE](http://www.peachpy.io) for PeachPy is also available. 16 | 17 | ---- 18 | 19 | # CAD-CAM 20 | + [OSCAD](https://github.com/hardythe1/OSCAD) :: An open source CAD tool for electronics & electrical engineering. 21 | 22 | ---- 23 | 24 | # Control System 25 | **Implementations of numerical algorithms for computations in systems and control theory.** 26 | 27 | + [Adskalman](https://github.com/astraw/adskalman) :: Kalman filtering routine in Python/numpy. 28 | 29 | ---- 30 | 31 | # [Embedded Systems](https://en.wikipedia.org/wiki/Category:Embedded_systems) 32 | 33 | ## [Microcontrollers](https://en.wikipedia.org/wiki/Category:Microcontrollers) 34 | + [Raspberry_Pi](https://github.com/nineties/py-videocore) :: Python library for GPGPU on [Raspberry Pi](https://en.wikipedia.org/wiki/Raspberry_Pi). 35 | 36 | ---- 37 | 38 | # GUI 39 | + [PyGMI](https://github.com/Argonne-National-Laboratory/PyGMI) :: Open source graphical interface for taking generic measurements over GPIB, USB, ethernet, serial ports. 40 | 41 | ---- 42 | 43 | # Robots 44 | + [baldr](https://github.com/motet/baldr) :: A flight simulator for quadrotor drones written entirely in Python. 45 | + [DroneKit-Python](https://github.com/dronekit/dronekit-python) :: A library for communicating with Drones via MAVLink. 46 | + TUT : [Study of Exploration Algorithms in developmental robotics with Explauto](http://nbviewer.jupyter.org/github/sebastien-forestier/ExplorationAlgorithms/blob/master/main.ipynb) 47 | + [Caesar.jl](https://github.com/JuliaRobotics/Caesar.jl):: The Robot toolkit - Towards non-parametric and parametric navigation solutions. 48 | 49 | ---- 50 | 51 | # Tools 52 | + [xlang](https://github.com/darpa-xdata/xlang) :: Tools for Cross-language data interoperability. 53 | 54 | -------------------------------------------------------------------------------- /Mathematics.md: -------------------------------------------------------------------------------- 1 | + [CRYPTOGRAPHY](#cryptography) 2 | + [MATH](#math) 3 | + [Algebra](#algebra) 4 | + [Calculus & Applied Math](#calculus-&-applied-math) 5 | + [Geometry](#geometry) 6 | + [Numerical Analysis](#numerical-analysis) 7 | + [Numerical Linear Algebra](#numerical-linear-algebra) 8 | + [Scipy](#scipy) 9 | + [RESOURCES](#resources) 10 | 11 | ---- 12 | 13 | # CRYPTOGRAPHY 14 | + [Cryptography](https://cryptography.io/) :: A package designed to expose cryptographic primitives and recipes to Python developers. [Source code](https://github.com/pyca/cryptography) 15 | + [ed25519](https://github.com/pyca/ed25519) :: Optimized version of the reference implementation of Ed25519. 16 | 17 | ---- 18 | 19 | # MATH 20 | + [bcolz](https://blosc.org) :: A columnar data container that can be compressed. [Source code and bug tracker](https://github.com/Blosc/bcolz) 21 | + Notebook showing the [Benchmark for querying the MovieLens dataset using pandas and bcolz](http://nbviewer.ipython.org/github/Blosc/movielens-bench/blob/master/querying-ep14.ipynb) 22 | + [beta_ntf](https://github.com/nils-werner/beta_ntf) :: Nonnegative Matrix and Tensor Factorization in Python. https://code.google.com/p/beta-ntf/ 23 | + [bquery](https://github.com/visualfabriq/bquery) :: A query and aggregation framework for Bcolz. 24 | + [complex-numbers](https://github.com/j2kun/complex-numbers) :: The code respository for the post "Learning to Love Complex Numbers". 25 | + [impyla](https://github.com/cloudera/impyla) :: A Python client and Numba-based UDFs for Cloudera Impala, which allows you to rapidly analyze large, distributed data sets, but doesn't integrate easily with your ad hoc (Python) analytical tools (pandas, scikit-learn), which impyla aims to remedy. 26 | + [nimfa](https://github.com/marinkaz/nimfa) :: A Python Library for Nonnegative Matrix Factorization. http://nimfa.biolab.si 27 | + [Smolyak](https://github.com/EconForge/Smolyak) :: Efficient implementations of Smolyak's algorithm for function approxmation in Python and Julia. 28 | + [symengine.py](https://github.com/symengine/symengine.py) :: Python wrappers for SymEngine. 29 | + [zero-modform](https://github.com/haochenuw/zero-modform) :: Polynomials satisfied by j-invariants of zeros of atkin-lehner eigenforms. 30 | 31 | ###### Resources 32 | + [Elementary Number Theory](https://github.com/williamstein/ent) :: Primes, Congruences, and Secrets. 33 | 34 | ---- 35 | 36 | # Algebra 37 | + [boolprob](https://github.com/cvxgrp/boolprob) :: A Python tool to analyze joint distributions of boolean random variables. 38 | + [galgebra](https://github.com/brombo/galgebra) :: Geometric algebra/calculus modules for sympy. 39 | + [Mathics](https://github.com/mathics/Mathics) :: A general-purpose computer algebra system (CAS). It is meant to be a free, light-weight alternative to Mathematica. 40 | + [pykrylov](http://dpo.github.com/pykrylov) :: A library of Krylov methods in pure Python. 41 | + [Sympy](http://sympy.org/) :: A computer algebra system for symbolic mathematics written in pure Python. [Source code](https://github.com/sympy/sympy). 42 | + [sympy-notebooks](https://github.com/sympy/sympy-notebooks) :: Some Jupyter notebook examples for SymPy. 43 | + [Thea](https://github.com/SciTools/thea):: Python GUI to visualise a cube. 44 | 45 | #### Non-Linear Equations 46 | + [pyneqsys](https://github.com/bjodah/pyneqsys) :: Solving of symbolic systems of non-linear equations numerically. 47 | 48 | ###### Resources 49 | + [adeles](https://github.com/williamstein/adeles) :: Ideles adeles algebraic number theory. 50 | + Learn the [theory of linear algebra](https://github.com/ULAFF/notebooks) hand-in-hand with the practice of [software library development](https://www.edx.org/course/linear-algebra-foundations-frontiers-utaustinx-ut-5-02x) 51 | + Dr. Juan H Klopper has created an IPython notebook for each MIT-OCW lecture on [Linear Algebra (18.06)](http://www.juanklopper.com/opencourseware/mathematics-2/ipython-lecture-notes/) by [Prof. Gilbert Strang](http://www-math.mit.edu/~gs/). 52 | + [Coding_the_matrix](https://github.com/branner-courses/coding_the_matrix) is a self-Study of Linear Algebra Using Philip Klein's Materials. 53 | 54 | ---- 55 | 56 | # Calculus & Applied Math 57 | + [finitediff](https://github.com/bjodah/finitediff) → A Fortran-90 version of Begnt Fornberg's formulae for optimized inter-/extrapolation of data series for up to N-th order derivative with C/C++/Python bindings. 58 | + [HyperPython](https://github.com/ketch/HyperPython) :: A brief and practical introduction to the solution of [hyperbolic conservation laws](http://en.wikipedia.org/wiki/Hyperbolic_partial_differential_equation) 59 | 60 | ###### Resources 61 | + [Riemann Hypothesis book](http://wstein.org/rh/) with the [source code on github](https://github.com/williamstein/rh). 62 | 63 | ---- 64 | 65 | # Geometry 66 | * [functional-differential-geometry](https://github.com/jtauber/functional-differential-geometry) :: Sussman and Wisdom's Functional Differential Geometry in Python. 67 | * [Shapely](https://github.com/Toblerity/Shapely) :: Python package for manipulation and analysis of geometric objects in the Cartesian plane. [Documentation](http://toblerity.github.com/shapely/) 68 | 69 | 70 | ---- 71 | 72 | # [Numerical Analysis](https://en.wikipedia.org/wiki/Category:Numerical_analysis) 73 | 74 | + [awkward-array](https://github.com/scikit-hep/awkward-array) :: Manipulate arrays of complex data structures as easily as Numpy. 75 | + [Blaze](http://blaze.pydata.org) :: The next-generation of NumPy and Pandas for BigData. 76 | + [Boost.NumPy](https://github.com/ndarray/Boost.NumPy) :: The Boost.Python interface for NumPy; in preparation for eventual proposal to Boost (manual mirror of Boost Sandbox SVN). 77 | + [castra](https://github.com/blaze/castra) :: A partitioned storage system based on blosc. 78 | + [distributed](https://github.com/blaze/distributed) :: A library for distributed computation. 79 | + [ignition](https://github.com/IgnitionProject/ignition) :: A python automation project producing low-level optimized scientific code from high level language descriptions. A [numerical code generator](http://ignitionproject.github.io/ignition/). 80 | + [irlbpy](https://github.com/bwlewis/irlbpy) :: Truncated SVD by implicitly restarted Lanczos bidiagonalization for Python Numpy. 81 | + [LASS](https://github.com/cvxgrp/lass) :: Linear Algebra for Structured Sparse Matrices. 82 | + [La](http://pypi.python.org/pypi/la) :: Larry, the labeled numpy array. The main class of the la package is a labeled array, larry. A larry consists of data and labels. The data is stored as a NumPy array and the labels as a list of lists (one list per dimension). [Source Code](https://github.com/kwgoodman/la). 83 | + [minpy](https://github.com/dmlc/minpy) :: Pure numpy practice with third party operator Integration. 84 | + [Multiuserblazeserver](https://github.com/ContinuumIO/multiuserblazeserver) 85 | + [Numba](http://numba.pydata.org/) → is a pure Python JIT(ted) complier to LLVM to improve and optimize NumPy. 86 | + [numexpr](https://github.com/pydata/numexpr) is a fast numerical array expression evaluator for Python, NumPy, PyTables, pandas, BLZ. 87 | + [NumPy](http://www.numpy.org) has support for linear algebra, large multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. 88 | + __Blogs, Books, Cookbooks, IPYNB's, Slides, Talks, Tutorials, Videos__ 89 | + [CS231n Convolutional Neural Networks for Visual Recognition](https://cs231n.github.io/python-numpy-tutorial/) 90 | + [100 Numpy tricks](http://www.loria.fr/~rougier/teaching/numpy.100/index.html) 91 | + A quick reference guide to the commonly used [NumPy functions](http://people.duke.edu/~ccc14/pcfb/numpympl/NumpyBasics.html). 92 | + [Proteus](http://proteus.usace.army.mil) :: A Python package for rapidly developing computer models and numeric simulation methods. Get the [source code](https://github.com/erdc-cm/proteus) from github. 93 | + [IPython notebooks involving proteus](https://github.com/erdc-cm/proteus-notebooks). 94 | + [pyeq2](https://github.com/zunzun/pyeq2) :: A large collection of Python equations that can fit themselves to 2D and 3D data sets, output source code in several computing languages, and run a genetic algorithm for initial parameter estimation. Comes with cluster, parallel processing, GUI, NodeJS, and web-based graphical examples. Includes orthogonal distance and relative error regressions. 95 | + [python-flint/](http://fredrik-johansson.github.com/python-flint/) :: Python bindings for FLINT (Fast Library for Number Theory). 96 | + [SAGE](http://www.sagemath.org) → System for Algebra and Geometry Experimentation, is a mathematical software with features covering many aspects of mathematics, including algebra, combinatorics, numerical mathematics, number theory, and calculus. [Source code on github](https://github.com/sagemath/sage) 97 | + [Tinynumpy](https://github.com/wadetb/tinynumpy) :: A lightweight, pure Python, numpy compliant ndarray class. 98 | 99 | 100 | ###### Resources 101 | + [Benchmarking HDF5 and BLZ for genotype data storage and access](http://nbviewer.ipython.org/gist/alimanfoo/67fdcf58e364763fd0b6/benchmark_hdf5_blz.ipynb) 102 | + Matthew Rocklin's blog post on the [`into` function](http://matthewrocklin.com/blog/work/2015/02/03/Into/) being spun off into (pun intended) a separate project that encodes the conversions explicitly as a network. 103 | + [scipy2015-blaze-bokeh](https://github.com/chdoig/scipy2015-blaze-bokeh) :: Building Python Data Applications with Blaze and Bokeh Tutorial, SciPy 2015. 104 | 105 | 106 | ### Numerical Linear Algebra (NLA) 107 | + [cvxpy](https://github.com/cvxgrp/cvxpy) :: A Python-embedded modeling language for convex optimization problems. [Elemental](http://libelemental.org) is a distributed-memory dense and sparse-direct linear algebra and optimization library with third-party Python interfaces. [Source code](https://github.com/elemental/Elemental). 108 | 109 | 110 | 111 | ### [SciPy](http://www.scipy.org) 112 | A signal and image processing library that contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering. 113 | + [Official Documentation](http://www.scipy.org/docs.html) 114 | + [scipy-lectures](http://scipy-lectures.github.io) :: Tutorial material on the scientific Python ecosystem. 115 | + Download the [Python Scientific lecture notes](http://www.scipy-lectures.org/_downloads/PythonScientific.pdf), Release 2013.2 beta (euroscipy 2013. 116 | 117 | ---- 118 | 119 | # RESOURCES 120 | + [Teaching numerical methods with IPython notebooks](https://github.com/ketch/teaching-numerics-with-notebooks) :: a tutorial prepared for Scipy 2014. 121 | + IPython Notebooks training material for [ML, Numpy, Pandas and IPython](https://github.com/addfor/tutorials) distributed by Addfor s.r.l. 122 | + [PyData Seattle 2015](https://github.com/wrobstory/pydataseattle2015) :: Python Data Bikeshed. 123 | 124 | ### Coursera.org 125 | + [High Performance Scientific Computing](https://www.coursera.org/course/scicomp) 126 | 127 | -------------------------------------------------------------------------------- /OpenDataScience.md: -------------------------------------------------------------------------------- 1 | __NB :: OpenScience listings are in the `software` section. Not all the DB's are freely available.__ 2 | 3 | + [SOFTWARE - Open Science](#software-open-science) 4 | + [OpenScience - OpenResearch](#openscience-openresearch) 5 | + [ACTUARIAL SCIENCE](#actuarial-science) 6 | + [Econometrics](#econometrics) 7 | + [Finance](#finance) 8 | + [ASTRONOMY](#astronomy) 9 | + [BIOLOGY](#biology) 10 | + [Genome](#genome) 11 | + [Genetics-Medicine](#genetics-medicine) 12 | + [Molecular Biology](#molecular-biology) 13 | + [Neuroscience](#neuroscience) 14 | + [Pharma](#pharma) 15 | + [CHEMISTRY](#chemistry) 16 | + [Crystallography](#crystallography) 17 | + [DATA](#data) 18 | + [DATA-DataScience](#data-datascience) 19 | + [DATA-General](#data-general) 20 | + [DATA-Research](#data-scientific) 21 | + [DATA-Scientific](#data-scientific) 22 | + [Earth Science](#earth-science) 23 | + [Gender Violence](#gender-violence) 24 | + [MACHINE LEARNING](#machine-learning) 25 | + [Learning Resources](#learning-resources) 26 | + [MATH](#math) 27 | + [PHYSICS](#physics) 28 | + [VIDEO](#video) 29 | 30 | ---- 31 | 32 | # SOFTWARE - Open Science 33 | + [aristotle-metadata-registry](https://github.com/aristotle-mdr/aristotle-metadata-registry) :: Aristotle-MDR is an open-source metadata registry as laid out by the requirements of the ISO/IEC 11179 specification. 34 | + [camlipy](https://github.com/tsileo/camlipy) :: The unofficial Python client for [Camlistore](http://camlistore.org/). [Documentation](http://camlipy.readthedocs.org). 35 | + [caffe-oxford102](https://github.com/jimgoo/caffe-oxford102) :: Caffe CNNs for the [Oxford 102 flower dataset](http://jimgoo.com/flower-power/). 36 | + [datacleaner](https://github.com/rhiever/datacleaner) :: A Python tool that automatically cleans data sets and readies them for analysis. 37 | + [eggo](https://github.com/bigdatagenomics/eggo) :: Ready-to-go Parquet-formatted public Genomics datasets. 38 | + [data-projects](https://github.com/voxmedia/data-projects) :: Scripts and data for various Vox Media stories and news projects. 39 | + [scrapi](https://github.com/fabianvf/scrapi) :: A data processing pipeline that schedules and runs content harvesters, normalizes their data, and outputs that normalized data to a variety of output streams. 40 | + [simmetrica](https://github.com/o/simmetrica) :: Lightweight framework for collecting and aggregating event metrics as timeseries data. 41 | 42 | ## OpenScience - OpenResearch 43 | + [ActivePapers](https://github.com/activepapers/activepapers-python) :: A tool for working with executable papers, which combine data, code, and documentation in single-file packages, suitable for publication as supplementary material or on sites such as figshare. 44 | + [pytextbooks](https://github.com/xSetrox/pytextbooks) :: Python 3.7 program to search Library Genesis via ISBN and fetch direct download links. 45 | + [Sumatra](https://github.com/open-research/sumatra) :: is a tool for managing and tracking projects based on numerical simulation and/or analysis, with the aim of supporting reproducible research. It can be thought of as an automated electronic lab notebook for computational projects. 46 | 47 | ---- 48 | 49 | # ACTUARIAL SCIENCE 50 | 51 | ## Econometrics 52 | + The [FRED](https://en.wikipedia.org/wiki/Federal_Reserve_Economic_Data) economic [time series data](https://research.stlouisfed.org/fred2/). 53 | 54 | ## Finance 55 | + Fannie Mae [Single-Family Loan Performance Data](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html). 56 | + [NASDAQ](https://data.nasdaq.com/) data. 57 | + [OANDA](http://www.oanda.com/) data. 58 | 59 | ---- 60 | 61 | # [ASTRONOMY](https://en.wikipedia.org/wiki/Category:Astronomy) 62 | + [NASA Open DataSets](https://open.nasa.gov/) :: To solve looming challenges here on Earth using NASA data, [tools, and resources](https://github.com/nasa). 63 | + [Open Exoplanet Catalogue](https://github.com/OpenExoplanetCatalogue/open_exoplanet_catalogue) 64 | 65 | ## AstroPhysics 66 | + [sndatasets](https://github.com/kbarbary/sndatasets) :: Download and normalize published supernova photometric data. 67 | 68 | ---- 69 | 70 | # BIOLOGY 71 | + WP's list of [biological databases](https://en.wikipedia.org/wiki/List_of_biological_databases). 72 | 73 | ## Genome 74 | + [ChromosomeMappings](https://github.com/dpryan79/ChromosomeMappings) :: This repository contains chromosome/contig name mappings between UCSC <-> Ensembl <-> Gencode for a variety of genomes. 75 | + [DataLad](http://datalad.org) :: aims to provide access to scientific data available from various sources (e.g. lab or consortium web-sites such as Human connectome; data sharing portals such as OpenFMRI and CRCNS) through a single convenient interface and integrated with your software package managers (such as APT in Debian). Although initially targeting neuroimaging and neuroscience data in general, it will not be limited by the domain and a wide range of contributions are welcome. Get the [source code](https://github.com/datalad) on github. 76 | + Download [Gene](https://www.ncbi.nlm.nih.gov/gene) data (via ftp) which integrates information from a wide range of species. A record may include nomenclature, Reference Sequences (RefSeqs), maps, pathways, variations, phenotypes, and links to genome-, phenotype-, and locus-specific resources worldwide. 77 | + [RefSeqGene](https://www.ncbi.nlm.nih.gov/refseq/rsg/) defines genomic sequences to be used as reference standards for well-characterized genes and is part of the Locus Reference Genomic (LRG) Project. 78 | + The [3000 Rice Genomes Project Data](http://dx.doi.org/10.5524/200001), GigaScience Database and and [blog article in BMC](See also: http://blogs.biomedcentral.com/gigablog/2014/05/29/publish-data-fight-world-hunger/). 79 | + NCBI's [Sequence Read Archive (SRA)](http://www.ncbi.nlm.nih.gov/sra) 80 | + [Saccharomyces Genome Database](https://secure.wikimedia.org/wikipedia/en/wiki/Saccharomyces_Genome_Database) 81 | 82 | ### Worms, Virus, Nematodes 83 | + The central [MANUELA database](http://www.marbef.org/projects/Manuela/data.php), a.k.a. _M_eiobenthic _A_nd _N_ematode biodiversity _U_nravelling _E_cological and _L_atitudinal _A_spects database is compiled by capturing the available data on meiobenthos on a broad European scale. 84 | + [Nematodes DB](http://www.nematodes.org/bioinformatics/databases.shtml) from the Blaxter Lab, based on analyses of ESTs or GSSs from "neglected taxa" using the PartiGene suite of programmes. 85 | + [Nematode Transcriptome Analyses](http://www.nematodes.org/nembase4/). 86 | + [WormBase](http://www.wormbase.org/species/all#0--10) :: Species genomes with standardized sequence and annotations. 87 | 88 | ## Genetics-Medicine 89 | + NCBI Resources for [Genetics and Medicine](https://www.ncbi.nlm.nih.gov/guide/genetics-medicine/). 90 | + [HIV-1, Human Protein Interaction Database](http://www.ncbi.nlm.nih.gov/genome/viruses/retroviruses/hiv-1/interactions/) :: A database of known interactions of HIV-1 proteins with proteins from human hosts. It provides annotated bibliographies of published reports of protein interactions, with links to the corresponding PubMed records and sequence data. 91 | 92 | ## Medical Imaging 93 | + [Computed Tomography Emphysema Database](http://image.diku.dk/emphysema_database/). 94 | + Cornell's Public [Medical Image Databases](http://www.via.cornell.edu/databases/). 95 | 96 | ## Molecular Biology 97 | + [SASBDB](http://www.sasbdb.org/) ::Small Angle Scattering Biological Data Bank. 98 | 99 | ## Neuroscience 100 | + [Codeneuro-Datasets](https://github.com/codeneuro/datasets) :: Shared data sets for collaborating, testing, and benchmarking. 101 | + [Neuroscience Databases](http://en.wikipedia.org/wiki/List_of_neuroscience_databases) list. 102 | + [NeuroVault](http://neurovault.org/) :: A web database for human brain statistical maps, atlases and parcellation maps that researchers can publicly store and share their unthresholded statistical maps produced by MRI and PET studies. [Source code](https://github.com/NeuroVault/NeuroVault). 103 | + [OpenfMRI.org](https://openfmri.org) :: A project dedicated to the free and open sharing of functional magnetic resonance imaging (fMRI) datasets, including raw data. 104 | 105 | ## Pharma 106 | + [Open PHACTS](http://www.openphacts.org) search service. 107 | + [OSDD - open source drug discovery](http://www.osdd.net/). 108 | 109 | ---- 110 | 111 | # CHEMISTRY 112 | + [Chemical DB](http://en.wikipedia.org/wiki/Category:Chemical_databases) list on WP. 113 | 114 | ## Crystallography 115 | + [Crystallographic databases](http://en.wikipedia.org/wiki/Category:Crystallographic_databases) list on WP. 116 | + [Crystallography](http://www.crystallography.net/) Open Database. 117 | + [Protein Data Bank](http://en.wikipedia.org/wiki/Protein_Data_Bank) (PDB) on WP. 118 | + [Inorganic Crystal Structure Database](http://en.wikipedia.org/wiki/Inorganic_Crystal_Structure_Database) 119 | 120 | ---- 121 | 122 | # DATA 123 | 124 | ## DATA-DataScience 125 | * [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/) by @jakevdp. 126 | 127 | ## DATA-General 128 | + [awesome-public-datasets](https://github.com/caesar0301/awesome-public-datasets) :: A collection of large-scale public datasets on the Internet. 129 | + [common-workflow-language](https://github.com/common-workflow-language/common-workflow-language) :: Repository for CWL Specfications. 130 | + [datasets](https://github.com/ali-ce/datasets) :: Original data or Aggregated / cleaned / restructured existing datasets. Released under Creative Commons Attribution-ShareAlike 4.0 International License. 131 | + [Freebase](http://www.freebase.com) :: A community-curated database of well-known people, places, and things. 132 | + [Wikidata](https://www.wikidata.org/) :: A free linked database that acts as central storage for the structured data of its Wikimedia sister projects including Wikipedia, Wikivoyage, Wikisource, and others, that can be read and edited by both humans and machines. 133 | + [SPARQL endpoints to Wikidata](https://www.wikidata.org/wiki/Wikidata:Data_access#SPARQL_endpoints) with a [general introduction](http://www.slideshare.net/_Emw/an-ambitious-wikidata-tutorial) to Wikidata and its data model. 134 | + [World Bank Open Data](http://data.worldbank.org) :: Free and open access to data about development in countries around the globe. 135 | 136 | ## DATA-Research 137 | + [Registry of Research Data Repositories](http://www.re3data.org/) :: provides researchers, funding organisations, libraries and publishers with over 1,000 listed research data repositories from all over the world making it the largest and most comprehensive online catalog of research data repositories on the web. 138 | 139 | ## DATA-Scientific 140 | + [Scientific Databases](http://en.wikipedia.org/wiki/Category:Scientific_databases) list on WP. 141 | 142 | ---- 143 | 144 | # [Earth Science](https://en.wikipedia.org/wiki/Outline_of_Earth_sciences) 145 | + [British Geological Survey dataset](http://www.bgs.ac.uk/) and a [blog post](http://britgeopeople.blogspot.de/2016/08/our-new-data-search-applicationby-lorna.html) linking 900 Gb of digital and electronic data deposited with the British Geological Survey since July 2014. 146 | O 147 | ---- 148 | 149 | # Gender Violence 150 | + https://weaponizedsocial.aspirationtech.org/Data_Analysis 151 | 152 | ---- 153 | 154 | # MACHINE LEARNING 155 | + [Machine learning datasets](https://www.datasetlist.com/) :: A list of the biggest machine learning datasets from across the web. 156 | + [Celeb-DF](http://www.cs.albany.edu/~lsw/celeb-deepfakeforensics.html) :: A New Dataset for DeepFake Forensics that contains real and DeepFake synthesized videos having similar visual quality on par with those circulated online. The Celeb-DF dataset includes 408 original videos collected from YouTube with subjects of different ages, ethic groups and genders, and 795 DeepFake videos synthesized from these real videos. 157 | + [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/) 158 | 159 | ## Learning Resources 160 | * [The Mechanics of Machine Learning](https://mlbook.explained.ai/) by Terence Parr and Jeremy Howard. 161 | 162 | ---- 163 | 164 | # MATH 165 | + [Juliaset.jl](https://github.com/tanmaykm/Juliaset.jl) :: Generate Julia set images. This is created primarily as an example for JuliaBox hosted REST APIs. 166 | 167 | ---- 168 | 169 | # PHYSICS 170 | + [CERN OpenData](http://opendata.cern.ch/) Portal. 171 | 172 | ---- 173 | 174 | # VIDEO 175 | + [Databrary](https://nyu.databrary.org) :: A video data library for developmental science. Share videos, audio files, and related metadata. The source codes on [github](https://nyu.databrary.org). 176 | -------------------------------------------------------------------------------- /Physics.md: -------------------------------------------------------------------------------- 1 | + [AstroPhysics](#astropysics) 2 | + [Computational Physics](#computational-physics) 3 | + [Electromagnetism](#electromagnetism) 4 | + [Nuclear Physics](#nuclear-physics) 5 | + [Plasma Physics](#plasma-physics) 6 | + [Quantum Mechanics](#quantum-mechanics) 7 | + [Resources-Physics](#resources-physics) 8 | 9 | ---- 10 | 11 | + [Python-for-Signal-Processing](https://github.com/unpingco/Python-for-Signal-Processing) :: Notebooks for "Python for Signal Processing" book. 12 | 13 | ---- 14 | 15 | # AstroPhysics 16 | + [Astrophysics](https://github.com/eteq/astropysics) :: A python library for astronomy/astrophysics calculations and data analysis, available as a [python package](http://packages.python.org/Astropysics/). 17 | 18 | ---- 19 | 20 | # Computational Physics 21 | + [NUFFT](https://github.com/jakevdp/nufft) :: A pure-python implementation of the [NUFFT](http://www.cims.nyu.edu/cmcl/nufft/nufft.html) (Non-Uniform Fast Fourier Transform) and related algorithms. 22 | + [ToPy](https://github.com/williamhunter/topy) :: High performance topology optimization framework. Based on modified [SIMP](http://www.topopt.dtu.dk/files/matlab.pdf) algorithm. 23 | 24 | ---- 25 | 26 | # Electromagnetism 27 | * [Dipole](https://github.com/manuamador/Dipole) :: Compute E and B of an Hertz Dipole (3 functions: full fields, far fields only, near fields only). 28 | * [RandomEUT](https://github.com/manuamador/RandomEUT) :: Computes the radiation pattern of a random equipment under test made of Hertzian dipoles on a sphere. 29 | 30 | ---- 31 | 32 | # Nuclear Physics 33 | + [PyNE](http://pyne.io/) : The Nuclear Engineering Toolkit from the PyNE project, which aims to provide a common set of tools for nuclear science and engineering needs and depends on many scientific python packages like, NumPy, SciPy, and PyTables. Join the mailing list by emailing [pyne-dev@googlegroups.com](https://groups.google.com/forum/#!forum/pyne-dev) ) or [file bugs on github](https://github.com/pyne/pyne). 34 | + [Sandbox](https://github.com/ergs/sandbox) :: for [ERGS]( http://www.ergs.sc.edu) (Energy Research Group: Scopatz) projects. 35 | + [xsgen](https://github.com/bright-dev/xsgen) :: Cross section generation. 36 | 37 | ###### Resources 38 | + [2015-12-15-msu](https://github.com/katyhuff/2015-12-15-msu) :: Modeling and Simulation Approaches to Challenges in Nuclear Engineering. 39 | 40 | ---- 41 | 42 | # Plasma Physics 43 | + [PIConGPU](http://picongpu.hzdr.de) :: A fully relativistic, many GPGPU, 3D3V particle-in-cell (PIC) code. [Source code](https://github.com/ComputationalRadiationPhysics/picongpu) 44 | 45 | ---- 46 | 47 | # Quantum Mechanics 48 | + [Carlfg](http://pypi.python.org/pypi/carlfg) :: Python package to calculate Clebsch-Gordan coefficients. Get the [source code](https://github.com/psachin/carlfg). 49 | 50 | ---- 51 | 52 | # Resources 53 | + [Physics.codes](http://physics.codes) website with [Examples of exercises that accompany Effective Computation in Physics](https://github.com/physics-codes/examples) 54 | 55 | 56 | 57 | -------------------------------------------------------------------------------- /Programming-Paradigms.md: -------------------------------------------------------------------------------- 1 | + [Programming Paradigms](#programming-paradigms) 2 | + [Functional Programming](#functional-programming) 3 | + [RE](#re) 4 | + [Software Optimization](#software-optimization) 5 | 6 | ---- 7 | 8 | # Programming Paradigms 9 | + [Boltons](https://github.com/mahmoud/boltons) :: A set of over 100 BSD-licensed, pure-Python utilities in the same spirit as the standard library. 10 | + [me-al](https://github.com/dabeaz/me-al) :: Meẗal - The Decorator. 11 | + [metamodule](https://github.com/njsmith/metamodule) :: Experiment with Python "metamodules". 12 | + [Programming Language Theory](https://github.com/steshaw/plt-study) : Mathematics, Compilers, Functional Programming, and other papers. 13 | 14 | ### Functional Programming 15 | + [fn.py](https://github.com/kachayev/fn.py) :: Implementation of missing features to enjoy Functional programming in Python. 16 | + [Pixie](https://github.com/pixie-lang/pixie) :: A small, fast, native lisp with "magical" powers. 17 | 18 | ### RE 19 | + [refo](https://github.com/machinalis/refo) :: Regular expressions for objects. 20 | 21 | ### [Software Optimization](https://en.wikipedia.org/wiki/Category:Software_optimization) 22 | + [line_profiler](https://github.com/rkern/line_profiler) :: Line-by-line profiling for Python. 23 | + [memory_profiler](https://pypi.python.org/pypi/memory_profiler) :: A module for monitoring memory usage of a python program. 24 | + [plop](https://github.com/bdarnell/plop) :: Python Low-Overhead Profiler for stack sampling. 25 | + [vprof](https://github.com/nvdv/vprof) :: Visual Python profiler. 26 | + [yappi](https://code.google.com/p/yappi/) :: Yet Another Python Profiler, but this time support Multithread/CPU time profiling. 27 | 28 | 29 | ###### Resources for Software Optimization 30 | + [Plop: Low-overhead profiling for Python](https://blogs.dropbox.com/tech/2012/07/plop-low-overhead-profiling-for-python/) 31 | + [Python Profilers](http://pansop.com/1003/) with some usecases. 32 | 33 | 34 | 35 | -------------------------------------------------------------------------------- /Psychology.md: -------------------------------------------------------------------------------- 1 | [Psychology](https://en.wikipedia.org/wiki/Category:Psychology) is a collection of academic, clinical and industrial disciplines concerned with the explanation and prediction of behavior, thinking, emotions, motivations, relationships, potentials and pathologies. 2 | 3 | + [Behavioral Neuroscience‎)(#behavioral-neuroscience‎) 4 | 5 | ---- 6 | 7 | # Behavioral Neuroscience‎ 8 | + [OpenEXP](https://github.com/openexp/OpenEXP):: An open-source desktop app for running experiments and collecting behavioral and physiological data. 9 | 10 | # Resources 11 | + Python Programming in Psychology – [From Data Collection to Analysis](http://blog.efpsa.org/2016/07/12/python-programming-in-psychology-from-data-collection-to-analysis/). 12 | -------------------------------------------------------------------------------- /Publications.md: -------------------------------------------------------------------------------- 1 | * [API WRAPPER](#api-wrapper) 2 | * [RESEARCH PAPERS](#research-papers) 3 | 4 | ---- 5 | 6 | # API WRAPPER 7 | * [Pyalm](https://github.com/articlemetrics/pyalm) :: A Python Wrapper for the PLOS Article Level Metrics App API. 8 | 9 | ---- 10 | 11 | # RESEARCH PAPERS 12 | + [ABigSurvey](https://github.com/NiuTrans/ABigSurvey) :: A Survey of hundreds of survey papers on Natural Language Processing (NLP) and Machine Learning (ML) papers, categorized into popular topics with simple counting for some interesting problems and a list of the papers with urls (441 papers). 13 | + [MT-paper-lists](https://github.com/NiuTrans/MT-paper-lists) :: It contains machine translation papers from recent conferences including ACL, EMNLP, NAACL and COLING and some interesting research works grouped by different areas, such as deep network, neural architecture search, etc.. 14 | + [How to write, publish, and review scientific papers in the early 21st Century](https://github.com/swcarpentry/modern-scientific-authoring) 15 | + Louppe Gilles, 2014, [Understanding Random Forests: From Theory to Practice](http://orbi.ulg.ac.be/handle/2268/170309). Citation: http://hdl.handle.net/2268/170309 16 | 17 | ### Open Portals 18 | + [GitXiv](http://gitxiv.com) :: Collaborative Open Computer Science Portal. The website code on [github](https://github.com/samim23/GitXiv). 19 | + The [ReScience journal](https://github.com/ReScience/ReScience) and [Wiki](https://github.com/ReScience/ReScience/wiki). 20 | -------------------------------------------------------------------------------- /QA.md: -------------------------------------------------------------------------------- 1 | __Anything "Software Quality" related goes here: Quality-Analysis(QA), Test-Driven-Development (TDD), Behaviour-Driven-Development (BDD), Unit testing, Regression testing,...__ 2 | 3 | + [Documentation Driven Development](#documentation-driven-development) 4 | + [FUNCTIONAL](#functional) 5 | + [LOGGING](#logging) 6 | + [SANDBOX](#sandbox) 7 | + [UNITTEST](#unittest) 8 | + [Resources](#resources) 9 | 10 | ---- 11 | 12 | # [Documentation Driven Development](http://collectiveidea.com/blog/archives/2014/04/21/on-documentation-driven-development/) 13 | + [doctr](https://github.com/gforsyth/doctr) :: A tool for automatically building Sphinx docs on Travis CI, and deploying them to GitHub pages. 14 | + [nbsphinx](https://github.com/spatialaudio/nbsphinx) :: Sphinx source parser for `*.ipynb` files. [Documentation](http://nbsphinx.rtfd.io/) 15 | + [travis_docs_builder](https://github.com/gforsyth/travis_docs_builder) :: A tool for automatically building Sphinx docs on Travis CI, and deploying them to GitHub pages. 16 | 17 | ---- 18 | 19 | # FUNCTIONAL 20 | + [Boom!](https://github.com/tarekziade/boom) :: A simple command line tool to send some load to a web app. 21 | + [FunkLoad](https://github.com/nuxeo/FunkLoad) :: Functional and load testing framework for web applications, written in Python http://funkload.nuxeo.org/ 22 | 23 | ---- 24 | 25 | # LOGGING 26 | + [logbook](http://logbook.pocoo.org/) :: A cool logging replacement for Python. [Source code](https://github.com/getlogbook/logbook). 27 | 28 | ---- 29 | 30 | # SANDBOX 31 | + [pymola](https://github.com/jgoppert/pymola) :: A python/modelica based simulation environment. 32 | 33 | ---- 34 | 35 | # UNITTEST 36 | + [assertpy](https://github.com/ActivisionGameScience/assertpy) :: Dead simple assertions framework for unit testing in Python with a nice fluent API. Supports both Python 2 and 3. 37 | + [Hypothesis](https://github.com/DRMacIver/hypothesis) :: An advanced Quickcheck style testing library for Python. 38 | + [nupic.regression](https://github.com/numenta/nupic.regression) :: Regression test suite for NuPIC. 39 | + [Prospector](https://github.com/landscapeio/prospector) :: A tool to inspect and analyse Python source code files and provide output information about errors, potential problems, convention violations, complexity, information about type and location of classes, methods etc.. 40 | + [python-anti-patterns](https://github.com/quantifiedcode/python-anti-patterns) :: An open collection of Python anti-patterns and "worst practices", many of which can be checked automatically on QuantifiedCode http://docs.quantifiedcode.com/python-anti-patterns 41 | + [Pypete](https://github.com/Artimi/pypete) :: Write python performance tests with this plugin in the traditional nosetests way. 42 | + [PyTest](http://pytest.org/) :: The pytest framework makes it easy to write small tests, yet scales to support complex functional testing. [Source code](https://github.com/pytest-dev/pytest). 43 | + [PyZen](http://pypi.python.org/pypi/PyZen) :: A continuous test runner for Python. [Source code](https://github.com/coderanger/pyzen) on github. 44 | + [testkraut](https://github.com/neurodebian/testkraut) :: a framework for software testing, that minimizes the overlap with the scopes of unit testing, regression testing, and continuous integration testing. 45 | 46 | ---- 47 | 48 | # Resources 49 | + [python-testing](https://github.com/swcarpentry/python-testing) :: An introduction to testing based on Scopatz and Huff's "Effective Computation in Physics". 50 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Pythonidae 2 | 3 | [Pythonidae](http://svaksha.github.io/pythonidae) curates decibans of Python libraries and resources for scientific programming and mathematical and statistical computing. Some resources listed here may also use other languages like Go, Java, Julia, R-language, etc.. in their codebase. 4 | 5 | + [INDEX](#index) 6 | + [LICENSE](#license) 7 | + [Mirrors](#mirrors) 8 | + [CONTRIBUTE](#contribute) 9 | + [BugReport-PullRequest](#bugreport-pullrequest) 10 | + [Stargazers](#stargazers) 11 | 12 | ---- 13 | 14 | # INDEX 15 | + [AI.md](https://github.com/svaksha/pythonidae/blob/master/AI.md) :: Artificial Intelligence, Cognitive Science, Machine Learning, Neural, NLP. 16 | + [Algorithms.md](https://github.com/svaksha/pythonidae/blob/master/Algorithms.md) :: Algorithms, Data Mining, Clustering, Data Structures, Linguistics, ... 17 | + [API.md](https://github.com/svaksha/pythonidae/blob/master/API.md) :: API wrapper generators, other tools and bindings. 18 | + [Biology.md](https://github.com/svaksha/pythonidae/blob/master/Biology.md) :: Bioinformatics, genomics, agriculture, food science, medicine, genetic engineering, Neuroscience, et. al... 19 | + [Build-Automation.md](https://github.com/svaksha/pythonidae/blob/master/Build-Automation.md) :: Tools for continuous integration (CI), continuous delivery (CD), Packaging, release engineering (RE), release management (RM), software configuration management (SCM), etc... 20 | + [Chemistry.md](https://github.com/svaksha/pythonidae/blob/master/Chemistry.md) :: Analytical chemistry, cheminformatics, crystallography, nanochemistry, nuclear chemistry, etc... 21 | + [Computer-Graphics.md](https://github.com/svaksha/pythonidae/blob/master/Computer-Graphics.md) :: Plotting, Graphics and other Visualization tools. 22 | + [DataBase.md](https://github.com/svaksha/pythonidae/blob/master/DataBase.md) :: NoSQL, RDBMS and Middleware API's. 23 | + [Earth-Science.md](https://github.com/svaksha/pythonidae/blob/master/Earth-Science.md) :: software related to the subcategories of cartography, climatology, geobiology, geochemistry, geography, geoinformatics, geology‎, geophysics‎, geoscience/GIS, geomathematics, meteorology, oceanography, etc... 24 | + [IO.md](https://github.com/svaksha/pythonidae/blob/master/IO.md) :: Input/Output functionality and support for file formats. 25 | + [Machines.md](https://github.com/svaksha/pythonidae/blob/master/Machines.md) :: Cross-Platform Software and API for Hardware arch. (ARM, MIPS, GPU, CUDA), language API, HPC, and other libraries for all kinds of Machine related software. 26 | + [Mathematics.md](https://github.com/svaksha/pythonidae/blob/master/Mathematics.md) :: Mathematical software libraries, blogs, etc.. 27 | + [OpenDataScience.md](https://github.com/svaksha/pythonidae/blob/master/OpenDataScience.md) :: OpenData + OpenScience + OpenResearch, Reproducible research, Free Data Sets, et al. 28 | + [Physics.md](https://github.com/svaksha/pythonidae/blob/master/Physics.md) :: software related to Physics. 29 | + [Programming-Paradigms.md](https://github.com/svaksha/pythonidae/blob/master/Programming-Paradigms.md) :: Programming Paradigms and language concepts that are used in the type system, data types, file formats, etc.. 30 | + [Psychology.md](https://github.com/svaksha/pythonidae/blob/master/Psychology.md) :: software related to the explanation and prediction of behavior, thinking, emotions, motivations, relationships, potentials and pathologies. 31 | + [Publications.md](https://github.com/svaksha/pythonidae/blob/master/Publications.md) :: Research papers, etc... 32 | + [QA.md](https://github.com/svaksha/pythonidae/blob/master/QA.md) :: Test Driven Development, Sandbox, Functional/Regression/Unit testing, and other Quality-related tools. 33 | + [Resources.md](https://github.com/svaksha/pythonidae/blob/master/Resources.md) :: blogs, cookbooks, cheatsheets, IPython NoteBooks, and other non-standard resources. 34 | + [Server.md](https://github.com/svaksha/pythonidae/blob/master/Server.md) :: HTTP/Web Frameworks, Networking, and other server-side utils... 35 | + [Statistics.md](https://github.com/svaksha/pythonidae/blob/master/Statistics.md) :: Statistical software, MOOC, tutorials, ... 36 | + [Super-Computing.md](https://github.com/svaksha/pythonidae/blob/master/Super-Computing.md) :: Software for HPC, Distributed and parallel Computing, Cloud computing/ infrastructure, Cluster computing, Grid computing, ... 37 | + [Utilities.md](https://github.com/svaksha/pythonidae/blob/master/Utilities.md) :: Handy toolkits, User Interface software in Python and other general utilities for your Desktop. 38 | 39 | ---- 40 | 41 | # LICENSE 42 | + COPYRIGHT © 2012-Now [SVAKSHA](http://svaksha.com/pages/Bio), All Rights Reserved. 43 | + This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License - (CC BY-NC-SA 4.0) as detailed in the [LICENSE.md](LICENSE.md) file and ALL references, citations, copies and forks of this work must retain the Copyright, Licence (LICENSE.md file), this permission notice and must [attribute credit](https://en.wikipedia.org/wiki/Creative_Commons_license#Attribution). 44 | 45 | 46 | ## Mirrors 47 | + [Bitbucket](https://bitbucket.org/svaksha/pythonidae) :: git clone git@bitbucket.org:svaksha/pythonidae.git 48 | + [GitLab](https://gitlab.com/svaksha/pythonidae) :: git clone git@gitlab.com:svaksha/pythonidae.git 49 | 50 | 51 | ---- 52 | 53 | # CONTRIBUTE 54 | 55 | [Contributions](https://github.com/svaksha/pythonidae/graphs/contributors) are welcome in the form of pull requests (PR). Please follow the [Python Community Code of Conduct](https://www.python.org/psf/conduct/). 56 | 57 | 58 | ## BugReport-PullRequest 59 | 60 | __To submit a [PR](https://github.com/svaksha/pythonidae/pulls)__: 61 | 62 | 1. Add your link as per the top-level Category page within the topic sub-section(s), in _alphabetical order_, with notes (if any) in the markdown files. Before creating a __new__ top-level tag within the various categories, please check wikipedia or other resources first. If you are unable to decide, discuss it via a BR (not in a PR ;-)). 63 | 2. Fire a CLI and install [JuliaLang](https://github.com/JuliaLang). Then run the [pythonidae.jl](https://github.com/svaksha/pythonidae/blob/master/pythonidae.jl) script with `julia pythonidae.jl` which will run the [scraper](https://github.com/svaksha/pythonidae/blob/master/src/scrape.jl) file. Commit the `db.csv` file and open a PR. 64 | 3. Fix broken links or outdated information and submit a bug report. 65 | 4. For Documentation and cookbooks, check if it matches the categories listed, else, list it on the [Resources.md](Resources.md) page. 66 | 5. For those unable to use git, create a github account, fork the 'pythonidae' repo and edit the page by [clicking on the "pencil" icon on the markdown page](https://help.github.com/articles/editing-files-in-your-repository), then click on save and submit a PR. Github does this [automatically in 8 steps](https://help.github.com/articles/editing-files-in-another-user-s-repository). 67 | 68 | 69 | 70 | ## Stargazers 71 | [![Stargazers over time](https://starchart.cc/svaksha/pythonidae.svg)](https://starchart.cc/svaksha/pythonidae) 72 | 73 | -------------------------------------------------------------------------------- /Resources.md: -------------------------------------------------------------------------------- 1 | + [Ask for Help](#ask-for-help) 2 | + [Free](#free) 3 | + [Non-Free](#non-free) 4 | + [Books](#books) 5 | + [Free](#free) 6 | + [Non-Free](#non-free) 7 | + [Bootstrapping](#bootstrapping) 8 | + [Blogs](#blogs) 9 | + [Cheatsheets](#cheatsheets) 10 | + [CONFERENCES](#conferences) 11 | + [GENERAL RESOURCES](#general-resources) 12 | + [NEWS](#news) 13 | + [Blogs](#blogs) 14 | + [Media](#media) 15 | + [Yellow Press](#yellow-press) 16 | + [Organisations](#organisations) 17 | + [Tools](#tools) 18 | + [Videos](#videos) 19 | + [Workshop Tutorials](#workshop-tutorials) 20 | 21 | ---- 22 | 23 | # Ask for Help 24 | _Spaces where one can ask python-related questions and get help from other programmers._ 25 | 26 | ### Free 27 | + The [Tutor](https://mail.python.org/mailman/listinfo/tutor) list. 28 | + [Cracking Codes with Python](http://inventwithpython.com/cracking/) :: teaches complete beginners how to program in the Python programming language and features the source code to several ciphers and hacking programs for these ciphers. The programs include the Caesar cipher, transposition cipher, simple substitution cipher, multiplicative & affine ciphers, Vigenere cipher, and hacking programs for each of these ciphers. The final chapters cover the modern RSA cipher and public key cryptography. Cracking Codes with Python is the 2nd edition of the previously-titled book, `Hacking Secret Ciphers with Python`. 29 | 30 | 31 | ### Non-Free 32 | + [Treehouse](http://teamtreehouse.com) :: Kenneth Love teaches Python, where plans start around $25/month with access to their entire library. 33 | + [Real Python](https://realpython.com/) :: Michael Herman, Fletcher Heisler, and Jeremy Johnson teach Python courses with a project driven learning approach. 34 | 35 | ---- 36 | 37 | # Books 38 | ### Free 39 | + List of of free [Python eBooks](https://pythonbooks.revolunet.com/) and the [PythonBooks code](https://github.com/revolunet/PythonBooks). 40 | + [Think Python](http://greenteapress.com/wp/think-python-2e/) : [How to Think Like a Computer Scientist](http://greenteapress.com/thinkpython2/thinkpython2.pdf) by Allen Downey, 2nd Edition, Version 2.2.18. Example programs and solutions to some exercises are in this [GitHub repository](https://github.com/AllenDowney/ThinkPython2/tree/master/code). 41 | + [Intermediate Python](http://book.pythontips.com) :: The [source](https://github.com/yasoob/intermediatePython) on github. 42 | + [Python 3 Tutorial](http://www.python-course.eu) by Bernd Klein. 43 | + [Free Data Mining Books](http://christonard.com/12-free-data-mining-books/) and the [HN thread with more links](https://news.ycombinator.com/item?id=7760969) 44 | + [List of free programming books](https://github.com/vhf/free-programming-books) 45 | + [List of free software testing books](https://github.com/ligurio/free-software-testing-books) 46 | + [A Byte of Python](http://www.swaroopch.com/notes/python/) :: A free book on programming using the Python language, with the [source](https://github.com/swaroopch/byte_of_python) on github. 47 | 48 | ### Non-Free 49 | + [Python for Kids](https://www.amazon.com/dp/1593274076/ref=cm_sw_r_awd_e5ybwb2BHG9JN) :: A Playful Introduction to Programming. 50 | + [The Quick Python Book 3rd Edition](https://www.manning.com/books/the-quick-python-book-third-edition) :: A clear, crisp updated introduction. 51 | + [The Well-Grounded Python Developer](https://www.manning.com/books/the-well-grounded-python-developer?a_aid=the_well_grounded_python_developer&a_bid=e505681b) 52 | 53 | ---- 54 | 55 | # Bootstrapping 56 | __Resources and guides for Scientists new to Python.__ 57 | 58 | ### Begin-Python 59 | + To start programming with Python you need to [setup your local Python environment](http://www.datarobot.com/blog/getting-up-and-running-with-python), then work your way through this workshop material for "[Introduction to Data Processing with Python](http://opentechschool.github.io/python-data-intro/)". 60 | + [Learn Python the Hard Way](http://learnpythonthehardway.org/book/). 61 | + [How to Think Like a Computer Scientist](http://interactivepython.org/runestone/static/thinkcspy/index.html) 62 | + [python-guide](https://github.com/kennethreitz/python-guide) :: Python best practices guidebook, written for Humans. [Documentation](http://docs.python-guide.org) 63 | + [python-intro](http://bennuttall.github.io/python-intro/) :: A set of Python exercises led by hints in comments - suitable for beginner / intermediate level. 64 | + [Python Koans](https://github.com/gregmalcolm/python_koans) :: Learn Python through TDD. 65 | + [Code Academy](http://www.codecademy.com/en/tracks/python) :: A beginner course. 66 | + [Google’s Python Class](https://developers.google.com/edu/python/) 67 | + [PythonCompilerWorkbench](https://github.com/pgbovine/PythonCompilerWorkbench) :: Live coding environment to develop intuition about how Python source code compiles to ASTs and bytecodes. 68 | + [Snakify](https://snakify.org/) :: An introductory tutorial with 100+ exercises and a step-by-step debugger. 69 | 70 | ### Begin-Git 71 | + [Flight rules for git](https://github.com/k88hudson/git-flight-rules) 72 | 73 | ---- 74 | 75 | # Blogs 76 | + [Graphs, Databases and Graphlab](http://bugra.github.io/work/notes/2014-04-06/graphs-databases-and-graphlab/) 77 | + [Neural Networks, Manifolds, and Topology](http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/) 78 | + [Jake Van derPlas explains why Python is difficult to optimize, hence slow](http://jakevdp.github.io/blog/2014/05/09/why-python-is-slow/) 79 | + @randyzwitch's [Fun With Just-In-Time Compiling: Julia, Python, R and pqR](http://randyzwitch.com/python-pypy-julia-r-pqr-jit-just-in-time-compiler/) 80 | + [Getting started with Python in HPC](http://andy.terrel.us/blog/2012/09/27/starting-with-python/) by Andy R. Terrel. 81 | + Gael Varoquaux on [Improving your programming style in Python](http://gael-varoquaux.info/programming/improving-your-programming-style-in-python.html). 82 | + @bugra muses "[I wish I knew these things when I learned Python](https://bugra.github.io/work/notes/2015-01-03/i-wish-i-knew-these-things-when-i-first-learned-python/)". 83 | 84 | ### CS Education 85 | + [Software Carpentry bootcamps](https://github.com/swcarpentry/bc) for scientists. 86 | + [List of free online programming and CS courses](https://github.com/fffaraz/free-programming-courses) 87 | + [Intro CS notebooks](https://github.com/alanedelman/ExploringIntroCS) :: Playing around with codes for Intro CS. 88 | 89 | ###### Data Science 90 | + [How To Choose A Data Science Project For Your Data Science Portfolio](http://www.getadatasciencejob.com/advice/how-to-choose-a-data-science-project-for-your-data-science-portfolio) 91 | + [Data Science Specialization](https://datasciencespecialization.github.io) :: A community curated content directory. 92 | + [Big Data vs. Ed Psychology : Work harder vs. work smarter](https://computinged.wordpress.com/2014/01/31/big-data-vs-old-psychology-work-harder-vs-work-smarter/) 93 | 94 | ---- 95 | 96 | # Cheatsheets 97 | + [Python Basic Operators](http://www.tutorialspoint.com/python/python_basic_operators.htm) 98 | + The python [decorator cheatsheet](http://www.pydanny.com/python-decorator-cheatsheet.html) by @PyDanny. 99 | + [30 Essential Python Tips and Tricks for Programmers](http://www.techbeamers.com/essential-python-tips-tricks-programmers/) 100 | 101 | ---- 102 | 103 | # CONFERENCES 104 | 105 | ### PyCon 106 | + [Xonsh Talk for PyCon 2016](https://github.com/xonsh/pycon2016). 107 | 108 | ### [PyData](http://pydata.org/) 109 | + [Determining Skill Levels](https://github.com/khwilson/pydata2014) talk at PyData2NYC 2014. 110 | + 2014/February/21-23 [London-2014](http://pydata.org/ldn2014) 111 | + 2013/November/08-10 [NewYork-2013](http://pydata.org/nyc2013) 112 | + 2013/July/27-28 [Boston-2013](http://pydata.org/bos2013) 113 | + 2013/March/18-20 [Silicon-Valley-2013](http://pydata.org/sv2013) 114 | + 2013/October/26-27 [NewYork-2012](http://pydata.org/nyc2012) 115 | 116 | ### [SciPy](https://conference.scipy.org/) 117 | 118 | ### [StrataConf](http://strataconf.com/) 119 | 120 | ---- 121 | 122 | # GENERAL RESOURCES 123 | + [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) 124 | + [A curated list of awesome big data frameworks, ressources and other awesomeness](https://github.com/onurakpolat/awesome-bigdata#distributed-programming) 125 | + [The definitive list of (awesome) lists curated on GitHub](https://github.com/jnv/lists#technical) 126 | + [A curated list of awesome awesomeness - scroll down to the Python section](https://github.com/bayandin/awesome-awesomeness) 127 | + [The Open-Source Data Science Masters](http://datasciencemasters.org) 128 | + [LectureNotes](https://github.com/UWSEDS/LectureNotes) :: Lecture content for UW Software Engineering for Data Scientists. 129 | + [The Elements of Python Style](https://github.com/amontalenti/elements-of-python-style). 130 | + [Python Quick Reference](https://github.com/justmarkham/python-reference) 131 | 132 | ---- 133 | 134 | # NEWS 135 | * [DataTau.com](http://www.datatau.com/) is a data mining news aggregator. 136 | * [Data Science Weekly Newsletter](http://www.datascienceweekly.org/newsletters) 137 | 138 | ### Blogs 139 | + [Why becoming a data scientist is NOT actually easier than you think](https://medium.com/cs-math/5b65b548069b), and the [HN thread](https://news.ycombinator.com/item?id=4658391). 140 | + [10 Myths of Enterprise Python](https://www.paypal-engineering.com/2014/12/10/10-myths-of-enterprise-python/) 141 | 142 | ### INTERVIEWS 143 | * [18 Data Scientist Interviews - Vol 1, April 2014](http://www.datascienceweekly.org/blog/18-data-scientist-interviews-volume-1-april-2014) 144 | 145 | 146 | ---- 147 | 148 | # Organisations 149 | + [Myria](http://myria.cs.washington.edu) :: is a distributed, shared-nothing Big Data management system and Cloud service from the University of Washington, deriving requirements from real users and complex workflows, especially in science. 150 | + [OSF.io](https://github.com/CenterForOpenScience/osf.io) :: The Open Science Framework (OSF) supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery. 151 | + A list of [UK-based professional/ scientific organisations](https://docs.google.com/spreadsheets/d/1haSsyhY6bqjXJHIiCNaOCIqgCqmQqUVQEv55vewu93I/edit#gid=0) 152 | 153 | ---- 154 | 155 | # Tools 156 | + [About-Code-Tool](https://github.com/dejacode/about-code-tool) :: It provides a simple way to document the provenance (origin and license) and other important or interesting information about third-party software components that you use in your project. [Website](http://aboutcode.org/) 157 | 158 | ---- 159 | 160 | # Videos 161 | __For Conference talk videos go to the `Community.md` page__ 162 | + [PyData Tutorial videos](http://vimeo.com/channels/612789) 163 | + EuroSciPy-2014 Video: [Crossing Language Barriers with Julia, SciPy, IPython](https://www.youtube.com/watch?v=jhlVHoeB05A&list=PLYx7XA2nY5GfavGAILg08spnrR7QWLimi) by Stephen G. Johnson. 164 | + [Highlights from EuroSciPy](http://www.agilegeoscience.com/journal/2014/9/2/highlights-from-euroscipy.html) 165 | + [Videos](https://www.youtube.com/user/europython2014/videos) from EuroPython-2014/July/21~27 @ Berlin. 166 | + [PyData@StrataNYC](http://strataconf.com/stratany2014/public/schedule/detail/37035?cmp=pe-strata-confreg-home-stny14_pydata)-2014/Oct/15 :: [Introduction to NumPy and Matplotlib](https://github.com/jakevdp/PyData2014) talk by Jake Vanderplas. 167 | + PyData-[Berlin-2014](http://pydata.org/berlin2014/) talk :: [Fast Serialization of Numpy Arrays with Bloscpack](https://github.com/esc/PyDataBerlin2014-bloscpack-talk), [Video](http://www.youtube.com/watch?v=TZdqeEd7iTM), [slides as PDF](http://slides.zetatech.org/haenel-bloscpack-talk-2014-PyDataBerlin.pdf) and [nbviewer](http://nbviewer.ipython.org/github/esc/PyDataBerlin2014-bloscpack-talk/blob/master/2014-07-results/neuronal.ipynb) NB's. 168 | + PyData-[Silicon-Valley-2014](http://pydata.org/sv2014)-2014/May/02-04 :: [Videos](https://www.youtube.com/user/PyDataTV). 169 | - [pydata-silicon-valley-2014](http://bugra.github.io/work/notes/2014-05-12/pydata-silicon-valley-2014/). 170 | + [SciPy](https://conference.scipy.org/scipy2014/)-2014/July/06-12 :: [Videos are online](http://pyvideo.org/category/51/scipy-2014). 171 | + Strata-2014 :: [Notebooks from @ellisonbg's talk at Strata](https://github.com/ellisonbg/talk-2014-strata-sc) in Santa Clara, CA. [Link to the talk](http://strataconf.com/strata2014/public/schedule/detail/32033) on the Strata website. 172 | + VIS-2013 :: [Supporting the "Visualization and Analytics in Python" tutorial at VIS 2013](https://github.com/ContinuumIO/PythonVIS2013) by @pzwang. 173 | + OpenVisConf-2014/April/24-25 :: [@jakevdp's talk at OpenVisConf, Boston, MA](https://github.com/jakevdp/OpenVisConf2014) 174 | 175 | 176 | ---- 177 | 178 | # Workshop Tutorials 179 | + [python-bootcamp](https://github.com/profjsb/python-bootcamp) :: Bootcamp docs and lectures. 180 | + Scipy-2015 [tutorial workshops](http://www.scipy2015.scipy.org/ehome/115969/304620/?&) list. 181 | + [data-science-ipython-notebooks](https://github.com/donnemartin/data-science-ipython-notebooks) :: Continually updated Data Science Python Notebooks: Spark, Hadoop MapReduce, HDFS, AWS, Kaggle, scikit-learn, matplotlib, pandas, NumPy, SciPy, and various command lines. 182 | + [Python-data-intro](http://opentechschool.github.io/python-data-intro/) :: Workshop material for "Introduction to Data Processing with Python". 183 | + [San Diego Python Users Group Presentations](https://github.com/pythonsd/presentations) 184 | 185 | -------------------------------------------------------------------------------- /Server.md: -------------------------------------------------------------------------------- 1 | Frameworks and other Web-related software used in scientific computing, cloud computing and high-performance computing. 2 | 3 | + [CRAWLERS](#crawlers) 4 | + [BROWSER](#browser) 5 | + [JAVASCRIPT](#javascript) 6 | + [Network](#network) 7 | + [FTP](#ftp) 8 | + [TEMPLATES](#templates) 9 | + [WEB-FRAMEWORKS](#web-frameworks) 10 | + [Django](#django) 11 | + [WEB-SERVER](#web-server) 12 | + [Security](#security) 13 | + [Utilities](#utilities) 14 | 15 | ---- 16 | 17 | # CRAWLERS 18 | + [scrapy](http://scrapy.org/) :: A fast high-level web crawling and screen scraping framework for Python. [Source code](https://github.com/scrapy/scrapy). 19 | + [scrapy-cluster](https://github.com/istresearch/scrapy-cluster) :: This Scrapy project uses Redis and Kafka to create a distributed on demand scraping cluster. [Documentation](http://scrapy-cluster.readthedocs.org/). 20 | + [Soupy](https://github.com/ChrisBeaumont/soupy) :: A wrapper around BeautifulSoup that makes it easier to build complex queries when wrangling web data. 21 | 22 | ###### Resources 23 | + [image_project](https://github.com/IEPUG/image_project) :: A group learning project to scrape flickr, extract some GPS EXIF info from the images, save the images to local storage, and insert some meta data into an sqlite database using SQLAlchemy. 24 | + [Python Web Scraping Tutorial using BeautifulSoup](https://www.dataquest.io/blog/web-scraping-tutorial-python/). 25 | 26 | ---- 27 | 28 | # BROWSER 29 | + [Air](https://github.com/audreyr/air) :: An ultra-lightweight static site generator. 30 | + [Brython](https://github.com/brython-dev/brython) :: a.k.a. `Browser Python` is an implementation of Python 3 running in the browser. 31 | + [Ghost.py](https://github.com/jeanphix/Ghost.py) :: Webkit based scriptable web browser for python. Read the [Documentation](http://ghost-py.readthedocs.org/en/latest/). 32 | + [pyotherside](http://thp.io/2011/pyotherside/) :: Asynchronous Python-3 Bindings for Qt-5. [Source code](https://github.com/thp/pyotherside) 33 | + [Python-Progressbar](https://github.com/WoLpH/python-progressbar) :: A text progress bar library for Python. [Documentation](http://progressbar-2.readthedocs.org/en/latest/). 34 | 35 | ---- 36 | 37 | # JAVASCRIPT 38 | + [jiphy](https://github.com/timothycrosley/jiphy) :: Your client side done in a jiphy. Python to JavaScript 2-way converter. 39 | 40 | ---- 41 | 42 | # [Network](https://en.wikipedia.org/wiki/Category:Network_protocols) 43 | + [RxPY](https://github.com/ReactiveX/RxPY) :: A library for composing asynchronous and event-based programs using observable collections and LINQ-style query operators in Python. 44 | 45 | # [FTP](https://en.wikipedia.org/wiki/File_Transfer_Protocol) 46 | + [pyftpdlib](https://github.com/giampaolo/pyftpdlib) :: Extremely fast and scalable Python FTP server library. 47 | 48 | 49 | ---- 50 | 51 | # TEMPLATES 52 | + [cookiecutter-tapioca](https://github.com/vintasoftware/cookiecutter-tapioca) :: A cookiecutter template for new tapioca wrappers. 53 | 54 | ---- 55 | 56 | # WEB FRAMEWORKS 57 | + [Falcon](http://falconframework.org) :: A high-performance Python framework for building cloud APIs and web app backends. 58 | + [palladium](https://github.com/ottogroup/palladium) :: A pluggable framework for easily setting up predictive analytics services as web services and developing real-world machine learning solutions. 59 | + [redactvideo](https://github.com/seattle-police/redactvideo) :: A Web application for redacting and publishing police body worn video as efficiently as possible. 60 | + [Spyre](https://github.com/adamhajari/spyre) : A Web Application Framework for providing a simple user interface for Python data projects and data visualization, so you'll also need pandas and matplotlib in addition to cherrypy, with jinja2 templating. 61 | + [Tornado](http://www.tornadoweb.org/) :: A Python web framework and asynchronous networking library, originally developed at FriendFeed. [Source code](https://github.com/tornadoweb/tornado). 62 | 63 | ## Django 64 | + The [Django](https://djangoproject.com)-project. 65 | + [django-adminplus](https://github.com/jsocol/django-adminplus) :: Easily add custom views to the Django admin. 66 | + [django-mailgun](https://github.com/BradWhittington/django-mailgun) :: A Django email backend for Mailgun. 67 | 68 | ###### Resources 69 | + [Hello Web App](http://hellowebapp.com) :: An Introduction to web app development using Python and Django with notes on how to package and install necessary files for [deployment on Heroku](https://github.com/hellowebapp/hellowebapp-deploy) and the [code repository](https://github.com/hellowebapp/hellowebapp-code) for Hello Web App. 70 | 71 | ---- 72 | 73 | # WEB-SERVER 74 | + [Just-Metadata](https://github.com/ChrisTruncer/Just-Metadata) :: A tool that gathers and analyzes metadata about IP addresses. It attempts to find relationships between systems within a large dataset. https://www.christophertruncer.com/just-metadata-intel-gathering-and-analysis-of-ip-metadata/ 75 | + [python-webpack](https://github.com/markfinger/python-webpack) :: Python bindings to webpack. 76 | + [restcommander](https://github.com/eBay/restcommander) :: Fast Parallel Async HTTP client as a Service to monitor and manage 10,000 web servers. 77 | + [thriftpy](https://github.com/eleme/thriftpy) :: Pure python implementation of Apache Thrift. [Documentation](https://thriftpy.readthedocs.org/). 78 | + [werkzeug](http://werkzeug.pocoo.org/) :: A flexible WSGI implementation and toolkit. [Source code](https://github.com/mitsuhiko/werkzeug). 79 | 80 | ## Security 81 | + [PenTestScripts](https://github.com/ChrisTruncer/PenTestScripts) :: Scripts that are useful for pen tests. 82 | + [PBP](https://github.com/stef/pbp) :: A simple python wrapper and a command line interface around libsodium, to provide basic functionality resembling PGP. 83 | + [Simple-RBAC](https://github.com/tonyseek/simple-rbac) :: A simple role based access control utility for Python. 84 | + [SQLMap](http://sqlmap.org) :: Automatic SQL injection and database takeover tool. [Source code](https://github.com/sqlmapproject/sqlmap) on github. 85 | 86 | ## Utilities 87 | + [confix](https://github.com/giampaolo/confix) :: A language-agnostic configuration parser for Python. 88 | + [EyeWitness](https://github.com/ChrisTruncer/EyeWitness) :: This utility is designed to take screenshots of websites, RDP services, and open VNC servers, provide some server header info, and identify default credentials if possible. 89 | 90 | 91 | -------------------------------------------------------------------------------- /Statistics.md: -------------------------------------------------------------------------------- 1 | **Statistics, etc...** 2 | 3 | + [COOKBOOKS](#cookbooks) 4 | + [ACTUARIAL SCIENCE](#actuarial-science) 5 | + [Econometrics](#econometrics) 6 | + [Financial Accounting](#financial-accounting) 7 | + [OPERATIONS RESEARCH](#operations-research) 8 | + [Optimization](#optimization) 9 | + [STATISTICS](#statistics) 10 | + [Bayesian](#bayesian) 11 | + [TimeSeries Analysis](#timeseries-analysis) 12 | + [Pandas](#pandas) 13 | + [RESOURCES](#resources) 14 | 15 | ---- 16 | 17 | # COOKBOOKS 18 | + [Little Inference Book](https://github.com/bcaffo/LittleInferenceBook) :: A book for the coursera statistical inference class. 19 | + [General guidelines (table) for choosing a statistical analysis](http://www.ats.ucla.edu/stat/mult_pkg/whatstat/default.htm) which was adapted from [Choosing the Correct Statistic](http://bama.ua.edu/~jleeper/627/choosestat.html) developed by James D. Leeper, Ph.D. 20 | + [Rational and Irrational Thought: The Thinking that IQ Tests Miss](http://www.scientificamerican.com/article/rational-and-irrational-thought-the-thinking-that-iq-tests-miss/) 21 | 22 | ---- 23 | 24 | # ACTUARIAL SCIENCE 25 | 26 | ## Econometrics 27 | + [HARK](https://github.com/econ-ark/HARK) :: Heterogenous Agents Resources & toolKit. 28 | + [NYUecondata](https://github.com/NYUEcon/NYUecondata) :: This is a repository for handling certain datasets and doing simple plots. Supervised by Dave Backus. 29 | + [QuantEcon.py](https://github.com/QuantEcon/QuantEcon.py) :: A community based Python library for quantitative economics, the code is described at http://quant-econ.net/, a website dedicated to providing lectures that teach economics and programming authored by Thomas J. Sargent and [John Stachurski](http://jstac.github.io/quant-econ/) 30 | + [Quantitative economic modelling](http://quant-econ.net/py/index.html) lecture series in Python language, designed and written by Thomas J. Sargent and John Stachurski, that is freely available as a [PDF](http://quant-econ.net/_static/pdfs/py-quant-econ.pdf) file too. 31 | 32 | ###### Resources 33 | + [QuantEcon applications](https://github.com/QuantEcon/QuantEcon.applications) :: A repository that houses example code, applications and teaching material related to QuantEcon. 34 | + [Greene Econometrics](https://github.com/TomAugspurger/Greene_Econometrics) :: Working through the examples in the wonderful Econometric Analysis by William Greene. 35 | + [An Introduction to Stock Market Data Analysis with Python (Part 1)](https://ntguardian.wordpress.com/2016/09/19/introduction-stock-market-data-python-1/) 36 | 37 | 38 | ## Financial Accounting 39 | + [autograd](https://github.com/HIPS/autograd) :: Computes derivatives of complicated numpy code. 40 | + [blpapi-py](https://github.com/filmackay/blpapi-py) :: Bloomberg Python API. 41 | + [DX](https://github.com/yhilpisch/dx) :: DX Analytics is a Python-based financial analytics library which allows the modeling of rather complex derivatives instruments and portfolios. 42 | + [Graphical-Lasso-in-Finance](https://github.com/CamDavidsonPilon/Graphical-Lasso-in-Finance) :: Implementations of the graphical lasso method to estimation of covariance matrices in finance. 43 | + [i3](https://github.com/stuhlmueller/i3) :: Learning stochastic inverses for amortized inference in Bayesian networks. 44 | + [kcbo](https://github.com/HHammond/kcbo) :: A Bayesian testing framework written in Python. 45 | + [Pyfin](https://github.com/opendoor-labs/pyfin) :: Basic options pricing in Python with basic Greeks calculation across valuation models, discrete dividends support in the lattice (binomial tree) and Monte Carlo simulation models. 46 | + [pyfolio](https://github.com/quantopian/pyfolio) :: Portfolio and risk analytics in Python. 47 | + [PyMC](https://github.com/pymc-devs/pymc) :: A python module for Bayesian statistical modeling and model fitting which focuses on advanced Markov chain Monte Carlo fitting algorithms. Its flexibility and extensibility make it applicable to a large suite of problems. 48 | + __Talk__ : [Bayesian Data Analysis with PyMC3](https://github.com/twiecki/pymc3_talk) by @twiecki. 49 | + [Bayesian pymc3 europy 2014 slides](http://twiecki.github.io/bayesian_pymc3_europy_ab.slides.html#/) 50 | + [PyMC3](https://github.com/pymc-devs/pymc3) :: A python module for Bayesian statistical modeling and model fitting which focuses on advanced Markov chain Monte Carlo fitting algorithms. 51 | + [pysdmx](https://github.com/widukind/pysdmx) :: Python interface to SDMX endpoint provided by Eurostat. 52 | + [Zipline](https://github.com/quantopian/zipline) :: A Pythonic Algorithmic Trading Library. 53 | + [zipline-tensorboard](https://github.com/jimgoo/zipline-tensorboard) :: TensorBoard as a Zipline [dashboard](http://jimgoo.com/tensorboard-and-zipline/). 54 | 55 | ###### Resources 56 | + [ISLR-python](https://github.com/JWarmenhoven/ISLR-python) :: Python code for "An Introduction to Statistical Learning", by James, Witten, Hastie, Tibshirani, 2013. 57 | + [ISLR chapters from R implemented in numpy](https://github.com/TomAugspurger/StatLearning/tree/master/python) 58 | + [538model](https://github.com/jseabold/538model) - 538 Election Forecasting Model :: Python scripts that replicates some features of Nate Silver's 538 Election Forecasting Model. 59 | 60 | ---- 61 | 62 | # OPERATIONS RESEARCH 63 | 64 | ### Optimization 65 | + [Mystic](https://github.com/uqfoundation/mystic) :: highly-constrained non-convex optimization and uncertainty quantification. 66 | + [nelder-mead](https://github.com/fchollet/nelder-mead) :: Pure Python/Numpy implementation of the Nelder-Mead algorithm. 67 | + [hyperopt](http://hyperopt.github.io/hyperopt) :: Distributed Asynchronous Hyperparameter Optimization in Python. 68 | 69 | 70 | ---- 71 | 72 | # STATISTICS 73 | + [distcan](https://github.com/spencerlyon2/distcan) :: Probability distributions for python in their canonical form. 74 | + [emcee](http://dan.iel.fm/emcee) :: The Python ensemble sampling toolkit for affine-invariant MCMC. [Documentation](https://github.com/dfm/emcee) 75 | + [hypergrad](https://github.com/HIPS/hypergrad) :: Exploring differentiation w.r.t hyperparameters. 76 | + [mcnets](https://github.com/stuhlmueller/mcnets) :: Adaptive Markov chain networks. 77 | + [pandaSDMX](https://github.com/dr-leo/pandaSDMX) :: An Apache 2.0-licensed Python package to retrieve and acquire statistical data and metadata disseminated in SDMX format. It works well with the SDMX services of the European statistics office (Eurostat) and the European Central Bank (ECB). 78 | + [Patsy](https://github.com/pydata/patsy) :: Describing statistical models in Python using symbolic formulas. 79 | + [permute](https://github.com/statlab/permute) :: Permutation tests and confidence sets. 80 | + [pymbar](https://github.com/choderalab/pymbar) :: Statistically optimal analysis of samples from multiple equilibrium states. 81 | + [PyMix](https://pypi.python.org/pypi/pymix) :: The Python mixture package. 82 | + [pystatsd](https://github.com/jsocol/pystatsd) :: A Python client for statsd. [Documentation](http://statsd.readthedocs.org/en/latest/index.html) 83 | + [Statsmodels](http://statsmodels.sourceforge.net) is a Python library package for econometrics, plotting functions, statistical modeling and tests, that provides a complement to SciPy for statistical computations including descriptive statistics, and estimation and inference for statistical models. [Source Code](https://github.com/statsmodels/statsmodels). 84 | 85 | ## Bayesian 86 | + [bayesian-belief-networks](https://github.com/eBay/bayesian-belief-networks) :: Pythonic Bayesian Belief Network Package, supporting creation of and exact inference on Bayesian Belief Networks specified as pure python functions. 87 | + [Bumps](https://github.com/bumps/bumps) :: It provides data fitting and Bayesian uncertainty modeling for inverse problems. 88 | + [DBDA-python](https://github.com/JWarmenhoven/DBDA-python) :: Python code for the book `Doing Bayesian Data Analysis`, 2nd Edition (Kruschke, 2015). 89 | + [dora](https://github.com/NICTA/dora) :: A library for Bayesian active sampling with non-parametric models. 90 | + [PRIMO](https://github.com/mbaumBielefeld/PRIMO) :: An implementation of a Bayesian Network in Python. 91 | + [Pomegranate](https://github.com/jmschrei/pomegranate) :: A package for graphical models and Bayesian statistics for Python, implemented in cython. [Documentation](http://pomegranate.readthedocs.org/en/latest/). 92 | + [SAMCNet](https://github.com/binarybana/samcnet) :: A toolkit and demonstration for Bayesian model averaging over objective functions defined over model classes of interest using advanced MCMC techniques. 93 | + [Spearmint](https://github.com/HIPS/Spearmint) :: Bayesian optimization codebase. 94 | + [YABN](https://github.com/jmschrei/yabn) :: Yet Another Bayesian Network. 95 | 96 | ###### Resources 97 | + [Kalman-and-Bayesian-Filters-in-Python](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python) :: Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions. 98 | + [Bayesian-Modelling-in-Python](https://github.com/markdregan/Bayesian-Modelling-in-Python) :: A python tutorial on bayesian modeling techniques (PYMC3). 99 | + [Biostat 778](https://github.com/rdpeng/biostat778_notes) :: Advanced Statistical Computing. 100 | + [stats_in_python_tutorial](http://gaelvaroquaux.github.io/stats_in_python_tutorial) :: Material for the statistics in Python tutorial. [Source](https://github.com/GaelVaroquaux/stats_in_python_tutorial) on github. 101 | + [Probabilistic-Programming-and-Bayesian-Methods-for-Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) :: Bayesian Methods for Hackers using Python and PyMC. 102 | + [Frequentism and Bayesianism: What's the Big Deal?](https://speakerdeck.com/jakevdp/frequentism-and-bayesianism-whats-the-big-deal-scipy-2014) by Jake Vanderplas at SciPy 2014. 103 | + [Frequentism and Bayesianism IV: How to be a Bayesian in Python](http://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/) by @jakevdp. 104 | + [Doing bayesian data analysis](https://github.com/aloctavodia/Doing_bayesian_data_analysis) :: This repository contains the Python/PyMC3 version of the R programs described in the great book Doing bayesian data analysis (first edition) by John K. Kruschke, A.K.A, the puppy book. 105 | + [ThinkX](https://github.com/AllenDowney/ThinkX) :: This package contains support code for books by Allen B. Downey. 106 | + _Book_ "thinkbayes" :: [Bayesian Statistics Made Simple](http://www.greenteapress.com/thinkbayes/) by Allen B.Downey. 107 | + [ThinkStats2](https://github.com/AllenDowney/ThinkStats2) :: Text and supporting code for Think Stats, 2nd Edition. 108 | + [ThinkPython2](https://github.com/AllenDowney/ThinkPython2) :: LaTeX source and supporting code for Think Python, 2nd edition, by Allen Downey. 109 | 110 | ## [TimeSeries](https://en.wikipedia.org/wiki/Time_series) Analysis 111 | + [Time-series-classification-and-clustering](https://github.com/alexminnaar/time-series-classification-and-clustering) :: Time series classification and clustering code written in Python. Mostly based on the work of Dr. Eamonn Keogh at University of California Riverside. 112 | 113 | ### Pandas 114 | + [Pandas](http://pandas.pydata.org/) :: A flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. 115 | + [Pandas Documentation](http://pandas.pydata.org/pandas-docs/stable/) 116 | + [How to contribute to Pandas](http://pandas.pydata.org/pandas-docs/stable/contributing.html) 117 | + [Source code](https://github.com/pydata/pandas) and [Source tarballs, windows binaries are available on PyPI](https://pypi.python.org/pypi/pandas). 118 | + [pandas-finance](https://github.com/davidastephens/pandas-finance) :: High level API for access to and analysis of financial data. 119 | + [Pandashells](https://github.com/robdmc/pandashells) :: An attempt to marry the expressive, concise workflow of the shell pipeline with the statistical and visualization tools of the python data-stack. 120 | + [pandasql](https://github.com/yhat/pandasql) :: Query pandas DataFrames using SQL syntax with SQLite. 121 | + [pandas_talib](https://github.com/femtotrader/pandas_talib) :: A Python Pandas implementation of technical analysis indicators. 122 | + [sandals](https://github.com/jbochi/sandals) :: Query pandas dataframes and objects using SQL. 123 | + [sql4pandas](https://github.com/keeganmccallum/sql4pandas) :: Efficient SQL bindings for the pandas data analysis library implemented entirely in python. Compile and execute SQL queries directly on pandas data frames without copying to an external database. It uses [sqlparse](https://pypi.python.org/pypi/sqlparse) at the backend. 124 | 125 | ###### Resources 126 | + [modern-pandas](https://github.com/TomAugspurger/modern-pandas) :: A collection of notebooks behind @TomAugspurger's series on writing idiomatic pandas. 127 | + [2015-EuroScipy-pandas-tutorial](https://github.com/jorisvandenbossche/2015-EuroScipy-pandas-tutorial) :: Material for the pandas tutorial at EuroScipy 2015. 128 | + [Cohort Analysis with Python](http://www.gregreda.com/2015/08/23/cohort-analysis-with-python/). 129 | + Materials for ["Python for Data Analysis" by Wes McKinney](https://github.com/pydata/pydata-book), published by O'Reilly Media. 130 | + [Half day pandas tutorial](http://nbviewer.ipython.org/urls/gist.github.com/wesm/5773719/raw/1399562c0a02b9edc3d13c71a70387a31d87260b/tutorial.ipynb) at Pycon Singapore 2013 by Wes McKinney. 131 | + [MultiIndex_Drop](https://www.wakari.io/sharing/bundle/quasiben_tr/MultiIndex_Drop) 132 | + [Learn-Pandas](https://bitbucket.org/hrojas/learn-pandas) via ipython notebooks OR use the [Pandas Bootcamp App](https://pandasbootcamp.herokuapp.com/). 133 | + [Pandas101](http://2014.pycon-au.org/wiki/Miniconfs/Miniconf/Pandas101) miniconf talk at pycon-au, 2014. Talk [Video](http://youtu.be/1QOMk2k9aI8) and the talk [slides](https://slideviewer.herokuapp.com/github/lexual/2014-PyConAU-Pandas-talk/blob/master/slides.ipynb#/). 134 | + [Baby steps in Python – Exploratory analysis in Python (using Pandas)](http://www.analyticsvidhya.com/blog/2014/08/baby-steps-python-performing-exploratory-analysis-python/) 135 | + Baypiggies meetup on Pandas Data Analysis [slides](https://www.youtube.com/watch?v=3Chgzna2nMg) and [talk video](https://www.youtube.com/watch?v=3Chgzna2nMg) with accompanying IPython notebooks while listening to the audio in the video (that's what was on the screen): 136 | + https://www.wakari.io/sharing/bundle/hayd/baypiggies 137 | + https://www.wakari.io/sharing/bundle/hayd/vbench 138 | + https://www.wakari.io/sharing/bundle/hayd/pandorable 139 | + [Apache log analysis with Pandas](http://nbviewer.ipython.org/github/koldunovn/nk_public_notebooks/blob/master/Apache_log.ipynb) 140 | + Common Excel Tasks Demonstrated in Pandas :: [Part-ONE](http://pbpython.com/excel-pandas-comp.html) and [Part-TWO](http://pbpython.com/excel-pandas-comp-2.html) 141 | + Data Science in Python: [Pandas Cheat Sheet](http://www.datasciencecentral.com/profiles/blogs/data-science-in-python-pandas-cheat-sheet) 142 | 143 | ---- 144 | 145 | # RESOURCES 146 | + _Book_ "thinkstats" :: [Probability and Statistics for Programmers](http://greenteapress.com/thinkstats/) by Allen B.Downey. 147 | + [Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/): Data Mining, Inference, and Prediction. 148 | + [fpp](https://www.otexts.org/fpp/) :: Forecasting principles and practice - a comprehensive introduction to forecasting methods. 149 | + MCMC :: Testing MCMC code, [part 1: unit tests](https://hips.seas.harvard.edu/blog/2013/05/20/testing-mcmc-code-part-1-unit-tests/) 150 | + [scipy-lectures](http://scipy-lectures.github.io) :: Tutorial material on the scientific Python ecosystem 151 | + [statlearning-notebooks](https://github.com/sujitpal/statlearning-notebooks) :: IPython notebooks for exercises covered in Stanford's online StatLearning class. 152 | + [Statistics and Data Mining with Open Source Tools](http://oswco.com/2013/mar/14/statistics-and-data-mining-open-source-tools/) 153 | + Talk :: [IPython and Sympy to Develop a Kalman Filter for Multisensor Data Fusion](https://github.com/balzer82/PyData-Berlin-2014-Kalman) by Paul Balzer at PyData-Berlin-2014 Conference. [Video](https://www.youtube.com/watch?v=XSRr2HHedrY) #StochasticDifferentialEquations. 154 | + [Statistical Data Mining Tutorials](http://www.autonlab.org/tutorials/) 155 | + [Introduction to SciPy](http://www.johndcook.com/blog/2013/03/29/new-introduction-to-scipy/) 156 | + FIFA predictions : A notebook on [World Cup Learning predictions for world cup matches results since 1950](http://nbviewer.ipython.org/github/fisadev/world_cup_learning/blob/master/learn.ipynb) 157 | + Class repository for [Fall-2013 Statistics-243](https://github.com/paciorek/stat243-fall-2013) (Intro to Statistical Computing) at UC Berkeley. 158 | 159 | ### MOOC - Coursera.org 160 | + [Data Science](https://www.coursera.org/specialization/jhudatascience/1) 161 | + [The Data Scientist’s Toolbox](https://www.coursera.org/course/datascitoolbox) 162 | + [Statistics: Making Sense of Data](https://www.coursera.org/course/introstats) 163 | 164 | ---- 165 | -------------------------------------------------------------------------------- /Super-Computing.md: -------------------------------------------------------------------------------- 1 | __HPC, Distributed Computing, Cloud computing, Cluster computing, Grid computing, Parallel computing, etc..__ 2 | 3 | + [Computer Performance](#computer-performance) 4 | + [CONCURRENCY](#concurrency) 5 | + [Cloud-Cluster](#cloud-cluster) 6 | + [AWS](#aws) 7 | + [APACHE](#apache) 8 | + [Distributed Computing](#distributed-computing) 9 | + [Grid Computing](#grid-computing) 10 | + [HPC](#hpc) 11 | + [Job Scheduler](#job-scheduler) 12 | + [Parallel Computing](#parallel-computing) 13 | 14 | ---- 15 | 16 | # Computer Performance 17 | + [Boxfish](https://github.com/scalability-llnl/boxfish) :: A platform for visualizing performance data across the domains the data was collected, (e.g. communication groups) to another (e.g. hardware processes). 18 | + [PeachPy](https://github.com/Maratyszcza/PeachPy) :: A Python framework for writing high-performance assembly kernels - an efficient Assembly Code-generator in Higher-level Python. 19 | 20 | ---- 21 | 22 | # CONCURRENCY 23 | 24 | ## Cloud-Cluster 25 | + [arrayfire-python](https://github.com/arrayfire/arrayfire-python) :: A high performance library for parallel computing with an easy-to-use API. 26 | + [clusterlib](https://github.com/clusterlib/clusterlib) :: Tools to manage jobs on supercomputer. [Documentation](http://clusterlib.readthedocs.org/) 27 | + [Curio](https://github.com/dabeaz/curio) :: The coroutine concurrency library. 28 | + [lustre_kvm_quickstart](https://github.com/suykerbuyk/lustre_kvm_quickstart) :: Support scripts for the Lustre KVM. Quick Start Guide at : http://wiki.lustre.org/KVM_Quick_Start_Guide 29 | + [Pathos](https://github.com/uqfoundation/pathos) :: A framework for parallel graph management and execution in heterogeneous computing. It primarily provides the communication mechanisms for configuring and launching parallel computations across heterogenous resources. Pathos provides configurable launchers for parallel and distributed computing, where each launcher contains the syntactic logic to configure and launch jobs in an execution environment. 30 | + [Pyina](https://github.com/uqfoundation/pyina) :: a MPI-based parallel mapper and launcher. 31 | + [Ufora](https://github.com/ufora/ufora) :: A compiled, automatically parallel subset of python for data science and numerical computing. 32 | 33 | ### AWS 34 | + [AmazonEC2scripts](https://github.com/swederik/AmazonEC2scripts) :: Useful scripts to setup Amazon EC2 Cloud Compute servers for Neuroimaging. 35 | + [Anaconda-ec2](https://github.com/ContinuumIO/anaconda-ec2) :: Anaconda plugin for StarCluster. 36 | + [Boto](https://pypi.python.org/pypi/boto) :: A Python package that provides interfaces to Amazon Web Services. The [source code](https://github.com/boto/boto/) on github. 37 | + [Cloudman](https://bitbucket.org/chapmanb/cloudman) :: Easily create a compute cluster on top of a cloud computing infrastructure. 38 | + [kappa](https://github.com/garnaat/kappa) :: Kappa is a command line tool that makes it easier to deploy, update, and test functions for AWS Lambda. 39 | + [StarCluster](http://star.mit.edu/cluster) is an open source cluster-computing toolkit for Amazon's Elastic Compute Cloud (EC2). [Source code on Github](https://github.com/jtriley/StarCluster) 40 | 41 | ### APACHE 42 | + [SparklingPandas](https://github.com/holdenk/sparklingpandas) :: Make it easy to use the distributed computing power of [PySpark](http://spark.apache.org/) to scale your data anlysis with Pandas. 43 | + [Kazoo](https://github.com/python-zk/kazoo) :: A high-level Python library that makes it easier to use Apache Zookeeper. [Documentation](https://kazoo.readthedocs.org). 44 | 45 | #### Hadoop 46 | + [Hue](https://github.com/cloudera/hue) :: A Web application for interacting with Apache Hadoop that supports a file and job browser, Hive, Pig, Impala, Spark, Oozie, HBase, Solr, Sqoop2, ZooKeeper and more. 47 | 48 | ###### Resources 49 | + [Native Hadoop file system (HDFS) connectivity in Python](http//wesmckinney.com/blog/python-hdfs-interfaces/) . 50 | + Blog post on [Querying 1.7 Billion Reddit Comments with Anaconda Platform Tools on Hadoop](https://www.continuum.io/blog/developer-blog/querying-17-billion-reddit-comments-anaconda-platform). 51 | 52 | ## HPC 53 | + [Dask](http://dask.pydata.org) :: Task scheduling and blocked algorithms for parallel processing. [Source code](https://github.com/ContinuumIO/dask) and [Documentation](https://dask.pydata.org/en/latest/). 54 | + [Luigi](https://github.com/spotify/luigi) :: A Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in. 55 | + [Pyston](https://github.com/dropbox/pyston) : An open-source Python implementation using JIT techniques, built using LLVM for performance speed. Their blog [introducing Pyston](https://tech.dropbox.com/2014/04/introducing-pyston-an-upcoming-jit-based-python-implementation/) 56 | + [PySGE](https://github.com/jiahao/PySGE) : Low-level Python interface to Sun Grid Engine. 57 | + [Pythran](https://pypi.python.org/pypi/pythran) : A Static Compilation of Parallel Scientific Kernels a.k.a. Python/Numpy compiler for the mass. [Source Code](https://github.com/serge-sans-paille/pythran) on Github. 58 | 59 | ###### Resources 60 | + PyData Paris [talk slides](https://serge-sans-paille.github.io/talks/pydata-2015-04-03.html#/0/1). 61 | 62 | 63 | -------------------------------------------------------------------------------- /Utilities.md: -------------------------------------------------------------------------------- 1 | + [2to3](#2to3) 2 | + [DOCUMENTATION](#documentation) 3 | + [ENVIRONMENTS](#environments) 4 | + [IDE](#ide) 5 | + [Zoof](#zoof) 6 | + [Jupyter](#jupyter) 7 | + [TERMINAL](#terminal) 8 | + [UTILS](#utils) 9 | + [Backups](#backups) 10 | + [Communication](#communication) 11 | + [Desktop Document Processing](#desktop-document-processing) 12 | + [File Compression](#file-compression) 13 | + [MTA](#mta) 14 | + [Open-Research-Tools](#open-research-tools) 15 | + [Scientific Workflow](#scientific-workflow) 16 | + [TimeTracker](#timetracker) 17 | 18 | 19 | ---- 20 | 21 | # 2to3 22 | __Python 2&3 tools__ 23 | + [py3readiness](http://py3readiness.org) :: Python 3 support graph for most popular packages. [Source code](https://github.com/chhantyal/py3readiness). 24 | + [python-future](http://python-future.org) :: Easy, clean, reliable Python 2/3 compatibility. [Source code](https://github.com/PythonCharmers/python-future) and a [cheat sheet for writing Python 2-3 compatible code](http://python-future.org/compatible_idioms.html). 25 | + [Six](https://pypi.python.org/pypi/six) :: A Python 2-3 porting utility. 26 | 27 | ---- 28 | 29 | # DOCUMENTATION 30 | **Documentation tools** 31 | + [PythonTeX](https://github.com/gpoore/pythontex) :: A LaTeX package that provides fast, user-friendly access to Python code entered within a TeX document to be executed, and the output to be included in the original document. It also provides syntax highlighting for code within LaTeX documents via the Pygments syntax highlighter. PythonTeX also provides support for Ruby, Julia, and Octave programming languages. 32 | + [Scisphinx](https://github.com/numfocus/scisphinx) :: Sphinx extensions common to the scientific computing ecosystem. 33 | 34 | ---- 35 | 36 | # ENVIRONMENTS 37 | **Braille, REPL shells, GUI, IDE/Text editors, WIDE, Desktop User Interface, GUI, &c..** 38 | 39 | ## IDE 40 | + [anaconda-mode](https://github.com/proofit404/anaconda-mode) :: Code navigation, documentation lookup and completion for Python. 41 | + [autocomplete-python](https://github.com/sadovnychyi/autocomplete-python) :: Jedi-based Python autocomplete for [Atom](https://atom.io/packages/autocomplete-python). 42 | + [Bugjar](https://github.com/pybee/bugjar) :: A interactive graphical debugger for Python code. 43 | + [jedi](https://github.com/davidhalter/jedi) :: Awesome autocompletion and static analysis library for python. 44 | + [MagicPython](https://github.com/MagicStack/MagicPython) :: Syntax highlighter for cutting edge Python for Sublime Text and Atom. 45 | + [pyzo](https://github.com/almarklein/pyzo) :: The Interactive editor for Python. 46 | 47 | ### Zoof 48 | + [Zoof](http://zoof.io) :: The Official repository - The IDE for dynamic coding. [Source code](https://github.com/zoofIO/zoof). 49 | + [flexx](https://github.com/zoofIO/flexx) :: A Python UI tookit based on web technology. [Documentation](http://flexx.readthedocs.org). 50 | + [flexx-notebooks](https://github.com/zoofIO/flexx-notebooks) :: Jupyter notebooks with Flexx examples. 51 | 52 | ## [Jupyter](https://github.com/jupyter) 53 | + [Jupyter](http://jupyter.org), 'nee [IPython](http://ipython.org/) provides a rich architecture for interactive computing with interactive shells (terminal and Qt-based) and browser-based notebook support for code, text, mathematical expressions, inline plots and support for interactive data visualization and use of GUI toolkits. The Project Jupyter has a [governance process and model](https://github.com/jupyter/governance) for their organization. 54 | + [bookmarkd](https://github.com/ryansb/bookmarkd) :: Markdown -> IPython conversion tool. 55 | + [callipy](https://github.com/damiendr/callipy) :: Call IPython notebooks with arguments. 56 | + [cite2c](https://github.com/takluyver/cite2c) :: Citations in the IPython Notebook. 57 | + [Colaboratory](https://github.com/jupyter/colaboratory) :: Create and store notebooks in Google Drive and allow for collaborative editing of notebooks. 58 | + [cookbook](http://ipython-books.github.io/cookbook/) :: Recipes of the IPython Cookbook, the definitive guide to high-performance scientific computing and data science in Python. Get the [source code](https://github.com/ipython-books/cookbook-code). 59 | + [ipydb](https://github.com/jaysw/ipydb) :: Turn your IPython console into a cross-database SQL client. [Documentation](http://ipydb.readthedocs.org). 60 | + [ipymd](https://github.com/rossant/ipymd) : Use the IPython notebook as an interactive Markdown editor. 61 | + [ipyparallel](https://github.com/ipython/ipyparallel) :: Interactive Parallel Computing in Python. [Documentation](http://ipyparallel.readthedocs.org/). 62 | + [Ipython_memory_usage](https://github.com/ianozsvald/ipython_memory_usage) :: This tool reports memory usage deltas for every command you type. 63 | + [IPythonblocks](http://ipythonblocks.org) :: Practice Python with colored grids in the IPython Notebook. [Source code](https://github.com/jiffyclub/ipythonblocks) on GH. 64 | + [ipython-sql](https://github.com/catherinedevlin/ipython-sql) :: Introduces a `%sql` (or `%%sql`) magic for Jupyter/IPython. Connect to a database, using SQLAlchemy connect strings, then issue SQL commands within IPython or IPython Notebook. 65 | + [jupyter_kernel_test](https://github.com/jupyter/jupyter_kernel_test) :: A tool for testing Jupyter kernels. 66 | + [Multiuser-server](https://github.com/jupyter/multiuser-server) :: Multi-user server for Jupyter notebooks. 67 | + [NBDiff](http://nbdiff.org) :: A diffing and merging tool for the IPython Notebook. 68 | + [nbdime](https://github.com/jupyter/nbdime) :: Tools for diffing and merging of Jupyter notebooks. [Documentation](http://nbdime.readthedocs.io) 69 | + [qtconsole](https://github.com/jupyter/qtconsole) :: Jupyter Qt Console - [stable release](http://jupyter.org/qtconsole/stable/). 70 | + [RunIPy](https://github.com/paulgb/runipy) :: Run IPython notebooks as command-line scripts, generate HTML reports. 71 | + [Rodeo](https://github.com/yhat/rodeo/) :: A data science IDE for Python - an alternative UI to the notebook for the IPython Kernel, heavily inspired by great projects like Sublime Text and Eclipse. 72 | + [Introducing Rodeo](http://blog.yhathq.com/posts/introducing-rodeo.html). 73 | + [sudospawner](https://github.com/jupyter/sudospawner) :: Spawn JupyterHub single-user servers with sudo. 74 | + [vIPer](https://github.com/damianavila/vIPer) :: A new tool for IPython notebooks. 75 | 76 | ### JupyterLab 77 | + [JupyterLab](https://github.com/jupyter/jupyterlab) :: The computational environment. 78 | + A blog post [Walkthrough of Jupyter Lab Alpha](https://veekaybee.github.io/jupyter-lab/) 79 | 80 | 81 | ### Jupyter Themes 82 | + [jupyter-themer](https://github.com/transcranial/jupyter-themer) :: Apply custom CSS styling to your jupyter notebooks. 83 | 84 | ###### Resources 85 | + [Jupyter notebook beginner guide](https://github.com/tritemio/jupyter_notebook_beginner_guide) :: A brief guide to install and run Jupyter/IPython notebooks. 86 | + [Run Jupyterhub on a Supercomputer](http://zonca.github.io/2015/04/jupyterhub-hpc.html). 87 | + [NotebookNameTest](http://nbviewer.ipython.org/github/staticfloat/notebooks/blob/master/julia_notebooks/NotebookNameTest.ipynb) from the julia-users thread on [How to find the name of the current IJulia notebook](https://groups.google.com/forum/#!topic/julia-users/mnCEQNd7ew0) 88 | + [IPython quick reference sheets](http://damontallen.github.io/IPython-quick-ref-sheets/) 89 | + D3 in IPython Notebook-2 :: [Here is an example of getting information back from d3 into the kernel](http://nbviewer.ipython.org/gist/anonymous/9975962) and another [eventful_graph and eventful_dict datastructures written for a demo with live-updating graphs (NetworkX -> D3)](https://gist.github.com/takluyver/9619942351cdc571a302). Mailing list discussion: http://mail.scipy.org/pipermail/ipython-dev/2014-June/014109.html 90 | + [ipython-in-depth](https://github.com/ipython/ipython-in-depth) :: IPython in-depth Tutorial, first presented at PyCon 2012, is meant to be executed with IPython 2.0 or newer. 91 | + Talk: [Project Jupyter](https://speakerdeck.com/fperez/project-jupyter) by Fernando Perez at Scipy-2014. 92 | + [IPython Interactive Computing and Visualization Cookbook](http://ipython-books.github.io/cookbook/) and the [recipes used in the IPython Cookbook](https://github.com/ipython-books/cookbook-code) :: Your definitive guide to high-performance scientific computing and data science in Python. 93 | + [A gallery of interesting IPython Notebooks](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks) 94 | + [7 favorite IPython Notebooks](http://beautifuldata.net/2014/03/datalicious-notebookmania-my-favorite-7-ipython-notebooks/) 95 | + The EuroPython 2014 training session "[Topic Modeling for Fun and Profit](https://github.com/piskvorky/topic_modeling_tutorial)". 96 | + [Networks meet Finance in Python - July 27 2014](https://github.com/mvaz/PyData2014-Berlin) 97 | + [OsloWorkshop2014](https://github.com/jakevdp/OsloWorkshop2014) :: Material for @jakevdp 's lectures at the University of Oslo, Dec 2014. 98 | + [Running Jupyter with multiple Python and iPython paths](http://stackoverflow.com/questions/39007571/running-jupyter-with-multiple-python-and-ipython-paths/). 99 | + Kirill Pomogajko blogs on [Why I Don't Like Jupyter (FKA IPython Notebook)](http://opiateforthemass.es/articles/why-i-dont-like-jupyter-fka-ipython-notebook/). 100 | 101 | ---- 102 | 103 | # TERMINAL 104 | + [cookiecutter](https://github.com/audreyr/cookiecutter) :: A command-line utility that creates projects from cookiecutters (project templates). E.g. Python package projects, jQuery plugin projects. 105 | + [dotfiler](https://github.com/svetlyak40wt/dotfiler) :: Shell agnostic git-based dotfiles package manager, written in Python. 106 | + [python-prompt-toolkit](https://github.com/jonathanslenders/python-prompt-toolkit) :: Library for building powerful interactive command lines in Python. 107 | + [pythonpy](https://github.com/Russell91/pythonpy) :: command line kung-fu with python. 108 | + [Xonsh_Kernel](https://github.com/Calysto/xonsh_kernel) :: Jupyter Kernel for Xonsh. 109 | + [xonda](https://github.com/gforsyth/xonda) :: This is a thin wrapper around conda for use with xonsh. 110 | + [xontrib-prompt-ret-code](https://github.com/Siecje/xontrib-prompt-ret-code) :: Adds return code info to xonsh prompt. 111 | 112 | ---- 113 | 114 | # UTILS 115 | __General tools & useful utilities.__ 116 | + [free-labour](https://github.com/brettcannon/free-labour) :: Calculate your open source contributions. 117 | + [pandocfilters](https://github.com/jgm/pandocfilters) :: A python module for writing pandoc filters, with a collection of examples. 118 | + [podoc](https://github.com/podoc/podoc) :: A minimalistic pure Python pandoc clone, i.e. a markup document conversion library. Currently, it supports Markdown, Jupyter notebook, OpenDocument, O'Reilly Atlas, Python + comments. Support for ReST, LaTeX, HTML, AsciiDoc is planned. 119 | + [python-progressbar](https://github.com/niltonvolpato/python-progressbar) :: Text progress bar library for Python. 120 | + [Slides](https://github.com/mrocklin/slides) :: A template for slides using markdown. 121 | 122 | ## Backups 123 | + [attic](https://github.com/jborg/attic) :: Deduplicating backup program. 124 | + [rotate-backups](https://github.com/xolox/python-rotate-backups) :: Simple command line interface for backup rotation, also available via [PyPI](https://pypi.python.org/pypi/rotate-backups). 125 | 126 | ## Communication 127 | #### WhatsApp 128 | + [whatsapp-bot-seed](https://github.com/joaoricardo000/whatsapp-bot-seed) :: A 129 | small python framework to create a whatsapp bot, with regex-callback message 130 | routing. 131 | + [whatsappcli](https://github.com/KarimJedda/whatsappcli) :: Control your server from Whatsapp. 132 | + [yowsup](https://github.com/tgalal/yowsup) :: The python WhatsApp library. 133 | 134 | #### Twitter 135 | + [followtrack](https://github.com/grote/followtrack) :: Keeps track of your Twitter followers and lets you know who unfollowed you. 136 | + [twitter-unfav](https://github.com/grote/twitter-unfav) :: A script to remove favorited tweets from Twitter for privacy reasons. 137 | 138 | ## Desktop Document processing 139 | + [Pweave](https://github.com/mpastell/Pweave) :: A scientific report generator and a literate programming tool for Python. Pweave can capture the results and plots from data analysis and works well with numpy, scipy and matplotlib. Pweave is inspired by Sweave, an excellent tool for R programmers, and the syntax is mostly compatible with it. 140 | 141 | ## File Compression 142 | + [bitshuffle]((https://github.com/kiyo-masui/bitshuffle) :: Filter for improving compression of typed binary data. 143 | 144 | ## MTA 145 | + [python-slimta](http://slimta.org/) :: Lightweight, asynchronous SMTP libraries. [Source code](https://github.com/slimta/python-slimta). 146 | 147 | ## Open-Research-Tools 148 | + [arxiv-sanity-preserver](https://github.com/karpathy/arxiv-sanity-preserver) :: Web interface for browsing, search and filtering recent arxiv submissions. [Website](http://www.arxiv-sanity.com/). 149 | + [autograde](https://github.com/asutton/autograde) :: A set of tools that help automate the more mechanical aspects of grading. 150 | + [Citer](https://github.com/mangecoeur/Citer) :: Citations from bibtex for Sublime Text 3. 151 | + [depsy](http://depsy.org) :: Track the impact of research software. [Source code](https://github.com/impactstory/depsy). 152 | + [depsy-research](https://github.com/Impactstory/depsy-research) :: Documentation and research output for Depsy. 153 | + [duecredit](https://github.com/yarikoptic/duecredit) :: Automated collection and reporting of citations for used software/methods. 154 | + [gradebook](https://github.com/jarrodmillman/gradebook) :: Tools for grading. 155 | + [habanero](https://github.com/sckott/habanero) :: crossref search low level client. 156 | + [sempervirens](https://github.com/njsmith/sempervirens) :: An experimental prototype for gathering anonymous, opt-in usage data for open scientific software. 157 | 158 | ## [Scientific Workflow](https://en.wikipedia.org/wiki/Scientific_workflow_system) 159 | + [incubator-airflow](https://github.com/apache/incubator-airflow) :: Airflow is a platform to programmatically author, schedule and monitor workflows. 160 | 161 | 162 | ## TimeTracker 163 | + [Meistercharts](https://github.com/mribeirodantas/meistercharts) :: Command line tool to generate charts out of CSV files exported from MeisterTask. 164 | + [Watson](http://tailordev.github.io/Watson/) :: A wonderful CLI to track your time! [Source code](https://github.com/TailorDev/Watson). 165 | -------------------------------------------------------------------------------- /pythonidae.jl: -------------------------------------------------------------------------------- 1 | module Julia 2 | export scrape 3 | 4 | # Run CAT2csv scraper 5 | const jl = joinpath(@__DIR__,"src/scrape.jl") 6 | 7 | scrape() = run(`julia $jl`) 8 | 9 | __init__() = scrape() 10 | 11 | end # module 12 | 13 | -------------------------------------------------------------------------------- /src/check_broken_links.jl: -------------------------------------------------------------------------------- 1 | ## simple check if there are links 404 2 | 3 | import Requests: get, statuscode 4 | 5 | function scrape_links() 6 | _dir = joinpath(Base.source_dir(), "..") 7 | 8 | for file in readdir(_dir) 9 | endswith(file, ".md") || continue 10 | 11 | for (i, line) in enumerate(eachline(joinpath(_dir, file))) 12 | for m in eachmatch(r"]\((https?://.*?)\)", line) 13 | produce((file, i, m[1])) 14 | end 15 | end 16 | end 17 | end 18 | 19 | # `nrunning` is the number of concurrent requests, and `nlimit` is it's limit 20 | cond, nrunning, nlimit = Condition(), Ref(0), try parse(Int, ARGS[1]) catch 20 end 21 | 22 | for (file, line, link) in Task(scrape_links) 23 | nrunning[] < nlimit || wait(cond) 24 | 25 | nrunning[] += 1 26 | 27 | @schedule try 28 | status = get(link) |> statuscode 29 | status != 200 && println("In file $file, line: $line, link $link responses $status") 30 | catch e 31 | print("In file $file, line: $line, request to link $link failed with exception:\n", e) 32 | finally 33 | nrunning[] -= 1 34 | notify(cond) 35 | end 36 | end 37 | 38 | while nrunning[] != 0 wait(cond) 39 | close(file) 40 | end # do not exit before all requests done 41 | 42 | print("Finished checking all the markdown files for broken URIs !") 43 | -------------------------------------------------------------------------------- /src/scrape.jl: -------------------------------------------------------------------------------- 1 | module ScrapeJuliajl 2 | 3 | function scrape_md(filename) 4 | 5 | # get the category, don't process some files 6 | category = split(splitdir(filename)[end], ".")[1] 7 | category in ("LICENSE", "README") && return [] 8 | 9 | subcategory = "" 10 | records = NTuple{5,String}[] 11 | 12 | subcategory = "" 13 | records = NTuple{5,String}[] 14 | 15 | # process the lines 16 | f = open(filename) 17 | for l in eachline(f) 18 | 19 | l = strip(l) 20 | if length(l) == 0 21 | continue 22 | 23 | # handle subcategories 24 | elseif l[1] == '#' 25 | subcategory = join(split(l)[2:end]) 26 | 27 | # collect repo info 28 | elseif l[1] == '+' 29 | tmp = split(l, " :: ") 30 | repotuple = split(tmp[1], "](") 31 | length(repotuple) == 2 || continue 32 | reponame = repotuple[1][4:end] 33 | repourl = repotuple[2][1:end-1] 34 | length(repourl) > 4 || continue 35 | repourl[1:4] == "http" || continue 36 | desc = length(tmp) > 1 ? join(tmp[2:end]...) : "" 37 | push!(records, (category, subcategory, reponame, repourl, desc)) 38 | end 39 | end 40 | close(f) 41 | println("Processed $(length(records)) records in category $category.") 42 | records 43 | end 44 | 45 | function write_csv_line(io::IO, record::NTuple{N,T}) where {N, T<:AbstractString} 46 | record = map(x->replace(x, '"'=>"\"\""), record) 47 | 48 | for (i, el) in enumerate(record) 49 | any(x->x in el, (',', '"')) ? write(io, '"', el, '"') : 50 | write(io, el) 51 | 52 | write(io, i == N ? '\n' : ',') 53 | end 54 | end 55 | 56 | # ----------------------------------------------------------------- 57 | 58 | import Glob: glob 59 | const _dir = joinpath(relpath(Base.source_dir()), "../") 60 | 61 | # loop over all markdown files in the root directory, appending the records to the list 62 | records = [] 63 | for filename in glob(joinpath(_dir, "*.md")) 64 | append!(records, scrape_md(filename)) 65 | end 66 | 67 | # save a csv file 68 | println("Writing out $(length(records)) records.") 69 | f = open(joinpath(_dir, "db.csv"), "w") 70 | for record in records 71 | write_csv_line(f, record) 72 | end 73 | close(f) 74 | 75 | end # module 76 | --------------------------------------------------------------------------------