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train · 59.3k rows

repo_name stringlengths 6 77	path stringlengths 8 215	license stringclasses 15 values	cells list	types list
antoniomezzacapo/qiskit-tutorial	community/games/game_engines/Making_your_own_hello_quantum.ipynb	apache-2.0	[ "Hello Quantum for Jupyter notebook\nHello Quantum is a project based on the idea of visualizing two qubit states and gates, and making them accessible to a non-specialist audience.\nIn the hello_quantum.py file you'll find some tools with which the 'Hello Quantum' visualizations and puzzles can be implemented in J...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
arcyfelix/Courses	17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/03-General Pandas/02-Series.ipynb	apache-2.0	[ "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n\nSeries\nThe first main data type we will learn about for pandas is the Series data type. Let's import Pandas and explore the Series object.\nA Series is very similar to a NumPy array (in fact it is built on top of the NumPy array o...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
ramseylab/networkscompbio	class21_reveal_python3.ipynb	apache-2.0	[ "Class 21: joint entropy and the REVEAL algorithm\nWe'll use the bladder cancer gene expression data to test out the REVEAL algorithm. First, we'll load the data and filter to include only genes for which the median log2 expression level is > 12 (as we did in class session 20). That should give us 164 genes to wo...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
elenduuche/deep-learning	seq2seq/sequence_to_sequence_implementation.ipynb	mit	[ "Character Sequence to Sequence\nIn this notebook, we'll build a model that takes in a sequence of letters, and outputs a sorted version of that sequence. We'll do that using what we've learned so far about Sequence to Sequence models.\n<img src=\"images/sequence-to-sequence.jpg\"/>\nDataset\nThe dataset lives in t...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
swails/mdtraj	examples/principal-components.ipynb	lgpl-2.1	[ "scikit-learn is a machine learning library for python, with a very easy to use API and great documentation.", "%matplotlib inline\nfrom __future__ import print_function\nimport mdtraj as md\nimport matplotlib.pyplot as plt\nfrom sklearn.decomposition import PCA", "Lets load up our trajectory. This is the traje...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
ajaybhat/DLND	Project 1/Project-1.ipynb	apache-2.0	[ "Your first neural network\nIn this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
EducationalTestingService/rsmtool	rsmtool/notebooks/summary/header.ipynb	apache-2.0	[ "# Setting options for the plots\n%matplotlib inline\n%config InlineBackend.figure_formats={'retina', 'svg'}\n%config InlineBackend.rc={'savefig.dpi': 150}", "Summary Report", "import itertools\nimport json\nimport os\nimport re\nimport pickle\nimport platform\nimport time\n\nfrom collections import defaultdict...	[ "code", "markdown", "code", "markdown", "code" ]
ES-DOC/esdoc-jupyterhub	notebooks/pcmdi/cmip6/models/sandbox-3/land.ipynb	gpl-3.0	[ "ES-DOC CMIP6 Model Properties - Land\nMIP Era: CMIP6\nInstitute: PCMDI\nSource ID: SANDBOX-3\nTopic: Land\nSub-Topics: Soil, Snow, Vegetation, Energy Balance, Carbon Cycle, Nitrogen Cycle, River Routing, Lakes. \nProperties: 154 (96 required)\nModel descriptions: Model description details\nInitialized From: -- \n...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
mne-tools/mne-tools.github.io	0.19/_downloads/a1ab4842a5aa341564b4fa0a6bf60065/plot_dipole_orientations.ipynb	bsd-3-clause	[ "%matplotlib inline", "The role of dipole orientations in distributed source localization\nWhen performing source localization in a distributed manner\n(MNE/dSPM/sLORETA/eLORETA),\nthe source space is defined as a grid of dipoles that spans a large portion of\nthe cortex. These dipoles have both a position and an...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
cavestruz/MLPipeline	notebooks/anomaly_detection/sample_anomaly_detection.ipynb	mit	[ "Let us first explore an example that falls under novelty detection. Here, we train a model on data with some distribution and no outliers. The test data, has some \"novel\" subset of data that does not follow that distribution.", "import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import svm\n%m...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
mzszym/oedes	examples/scl/scl-trapping.ipynb	agpl-3.0	[ "Steady-state space-charge-limited current with traps\nThis example shows how to simulate effects of a single trap level on current-voltage characteristics of a single carrier device.", "%matplotlib inline\nimport matplotlib.pylab as plt\nimport oedes\nimport numpy as np\noedes.init_notebook() # for displaying pr...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
ptpro3/ptpro3.github.io	Projects/Project2/Project2_Prashant.ipynb	mit	[ "Project: Project 2: Luther\nDate: 02/03/2017\nName: Prashant Tatineni\nProject Overview\nFor Project Luther, I gathered the set of all films listed under movie franchises on boxofficemojo.com. My goal was to predict the success of a movie sequel (i.e., domestic gross in USD) based on the performance of ot...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
PyLCARS/PythonUberHDL	myHDL_DigLogicFundamentals/myHDL_Combinational/Multiplexers(MUX).ipynb	bsd-3-clause	[ "\\title{myHDL Combinational Logic Elements: Multiplexers (MUXs))}\n\\author{Steven K Armour}\n\\maketitle\n<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n<div class=\"toc\" style=\"margin-top: 1em;\"><ul class=\"toc-item\"><li><span><a href=\"#Refrances\" data-toc-modified-id=\"Refrances-1\"><span clas...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
dfm/emcee3	docs/user/parallel.ipynb	mit	[ "Parallelization\nemcee supports parallelization out of the box. The algorithmic details are given in the paper but the implementation is very simple. The parallelization is applied across the walkers in the ensemble at each step and it must therefore be synchronized after each iteration. This means that you will r...	[ "markdown", "code", "markdown", "code", "markdown" ]
whitead/numerical_stats	unit_7/hw_2018/Homework_7_Key.ipynb	gpl-3.0	[ "Homework 7 Key\nCHE 116: Numerical Methods and Statistics\n3/8/2018", "%matplotlib inline\nimport random\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy\nimport scipy.stats\nimport seaborn as sns\nplt.style.use('seaborn-whitegrid')\n\nimport pydataset", "1. Conceptual Questions (8 Points)\nA...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
thewtex/SimpleITK-Notebooks	62_Registration_Tuning.ipynb	apache-2.0	[ "<h1 align=\"center\">Registration Settings: Choices, Choices, Choices</h1>\n\nThe performance of most registration algorithms is dependent on a large number of parameter settings. For optimal performance you will need to customize your settings, turning all the knobs to their \"optimal\" position:<br>\n<img src=\"...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
bloomberg/bqplot	examples/Interactions/Interaction Layer.ipynb	apache-2.0	[ "import pandas as pd\nimport numpy as np\n\nsymbol = \"Security 1\"\nsymbol2 = \"Security 2\"\n\nprice_data = pd.DataFrame(\n np.cumsum(np.random.randn(150, 2).dot([[0.5, 0.4], [0.4, 1.0]]), axis=0) + 100,\n columns=[\"Security 1\", \"Security 2\"],\n index=pd.date_range(start=\"01-01-2007\", periods=150),...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "cod...
crystalzhaizhai/cs207_yi_zhai	homeworks/HW6/HW6_finished.ipynb	mit	[ "Homework 6\nDue: Tuesday, October 10 at 11:59 PM\nProblem 1: Bank Account Revisited\nWe are going to rewrite the bank account closure problem we had a few assignments ago, only this time developing a formal class for a Bank User and Bank Account to use in our closure (recall previously we just had a nonlocal vari...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
VUInformationRetrieval/IR2016_2017	04_analysis.ipynb	gpl-2.0	[ "Mini-Assignment 4: Link Analysis\nIn this mini-assignment, we will exploit graph algorithms to improve search results. For our dataset of scientific papers, we look at two graphs in particular: the co-authorship network and the citation network.\nThe citation network is similar to the link network of the web: Cita...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
shashank14/Asterix	1-Python Crash course/Python-Crash-Course/Python Crash Course Exercises .ipynb	apache-2.0	[ "Python Crash Course Exercises\nThis is an optional exercise to test your understanding of Python Basics. If you find this extremely challenging, then you probably are not ready for the rest of this course yet and don't have enough programming experience to continue. I would suggest you take another course more gea...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
chusine/dlnd	autoencoder/Convolutional_Autoencoder.ipynb	mit	[ "Convolutional Autoencoder\nSticking with the MNIST dataset, let's improve our autoencoder's performance using convolutional layers. Again, loading modules and the data.", "%matplotlib inline\n\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\nfrom tensorflow.examples.tutorials.mnis...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
OSGeoLabBp/tutorials	english/data_processing/lessons/ml_clustering.ipynb	cc0-1.0	[ "<a href=\"https://colab.research.google.com/github/OSGeoLabBp/tutorials/blob/master/english/data_processing/lessons/ml_clustering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\nClustering with Machine Learning\nWhat is Machine Learning?...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
eds-uga/csci4360-fa17	workshops/w7/Workshop6_ Auto-Differentiation.ipynb	mit	[ "Autograd", "import time", "Have to install autograd module first: pip install autograd", "import autograd.numpy as np # Thinly-wrapped version of Numpy\nfrom autograd import grad", "EX1, Normal Numpy", "def tanh(x):\n y = np.exp(-x)\n return (1.0 - y) / (1.0 + y)\n\nstart = time.time()\n\ngrad_ta...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
ioam/scipy-2017-holoviews-tutorial	notebooks/08-deploying-bokeh-apps.ipynb	bsd-3-clause	[ "<a href='http://www.holoviews.org'><img src=\"assets/hv+bk.png\" alt=\"HV+BK logos\" width=\"40%;\" align=\"left\"/></a>\n<div style=\"float:right;\"><h2>08. Deploying Bokeh Apps</h2></div>\n\nIn the previous sections we discovered how to use a HoloMap to build a Jupyter notebook with interactive visualizations th...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
tanmay987/deepLearning	gan_mnist/Intro_to_GANs_Exercises.ipynb	mit	[ "Generative Adversarial Network\nIn this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!\nGANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exp...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
zuphilip/ocropy	doc/line-normalization.ipynb	apache-2.0	[ "Line Normalization (dewarping)\n( These notes are based on: https://github.com/tmbdev/ocropy/blob/758e023f808d88e5995af54034c155621eb087b2/OLD/normalization-api.ipynb from 2014 )\nThe line normalization is performed before the actual text recognition and before the actual training. Therefore, the same line normali...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
dsacademybr/PythonFundamentos	Cap05/Notebooks/DSA-Python-Cap05-02-Objetos.ipynb	gpl-3.0	[ "<font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 5</font>\nDownload: http://github.com/dsacademybr", "# Versão da Linguagem Python\nfrom platform import python_version\nprint('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())", "Objetos\nEm Python, tudo é objet...	[ "markdown", "code", "markdown", "code", "markdown" ]
marcinofulus/ProgramowanieRownolegle	CUDA/iCSE_PR_map2d.ipynb	gpl-3.0	[ "Zastosowanie indeksowania wielowymiarowego\nZadanie: Oblicz wartości funkcji $\\sin(x^2+y^2)$ na siatce w zadanym obszarze.\nKrok pierwszy\nNapiszemy jądro obliczające wartości funkcji $\\sin(x^2)$ na zadanym wektorze danych. Jest to zadanie, które można by wykonać używając gpuarray, ale dla celów dydaktycznych w...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
btw2111/intro-numerical-methods	0_intro_numerical_methods.ipynb	mit	[ "Introduction and Motivation: Modeling and methods for scientific computing\nWhy are we here?\nCannot solve everything\n$$x^5 + 3x^2+ 2x + 3 = 0$$\n$$f(x,y,z,t) = 0$$\nProblems can be too big...\n\nActually want an answer...\nNumerics compliment analytical methods\nWhy should I care?\nThe Retirement Problem\n$$A = ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
hande-qmc/hande	tools/pyhande/tutorials/3_custom_get_results_ccmc.ipynb	lgpl-2.1	[ "This demonstration shows how CCMC [1] data (analysis) results can be analysed in a more customised way. \nThis applies to FCIQMC [2] as well.", "from pyhande.data_preparing.hande_ccmc_fciqmc import PrepHandeCcmcFciqmc\nfrom pyhande.extracting.extractor import Extractor\nfrom pyhande.error_analysing.blocker impor...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
ComputationalModeling/spring-2017-danielak	past-semesters/fall_2016/day-by-day/day15-Schelling-1-dimensional-segregation-day2/Day_15_Pre_Class_Notebook.ipynb	agpl-3.0	[ "Getting ready to implement the Schelling model\nGoal for this assignment\nThe goal of this assignment is to finish up the two functions that you started in class on the first day of this project, to ensure that you're ready to hit the ground running when you get back to together with your group. \nYou are welcome...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
UWSEDS/LectureNotes	Fall2018/09_UnitTests/unit-tests.ipynb	bsd-2-clause	[ "import numpy as np", "Unit Tests\nOverview and Principles\nTesting is the process by which you exercise your code to determine if it performs as expected. The code you are testing is referred to as the code under test. \nThere are two parts to writing tests.\n1. invoking the code under test so that it is exercis...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
xmnlab/notebooks	jupyter/Introducción.ipynb	mit	[ "Table of Contents\n<p><div class=\"lev1 toc-item\"><a href=\"#Introducción-a-Jupyter-Notebook\" data-toc-modified-id=\"Introducción-a-Jupyter-Notebook-1\"><span class=\"toc-item-num\">1  </span>Introducción a Jupyter Notebook</a></div><div class=\"lev2 toc-item\"><a href=\"#¿Qué-es-Jupyter-Notebook?\" da...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
yl565/statsmodels	examples/notebooks/ols.ipynb	bsd-3-clause	[ "Ordinary Least Squares", "%matplotlib inline\n\nfrom __future__ import print_function\nimport numpy as np\nimport statsmodels.api as sm\nimport matplotlib.pyplot as plt\nfrom statsmodels.sandbox.regression.predstd import wls_prediction_std\n\nnp.random.seed(9876789)", "OLS estimation\nArtificial data:", "nsa...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
GoogleCloudPlatform/cloudml-samples	notebooks/xgboost/TrainingWithXGBoostInCMLE.ipynb	apache-2.0	[ "XGBoost Training on AI Platform\nThis notebook uses the Census Income Data Set to demonstrate how to train a model on Ai Platform.\nHow to bring your model to AI Platform\nGetting your model ready for training can be done in 3 steps:\n1. Create your python model file\n 1. Add code to download your data from Goo...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
google/jax-cfd	notebooks/ml_model_inference_demo.ipynb	apache-2.0	[ "! pip install -U -q jax-cfd[complete]==0.1.0\n\ndataset_name = 'kolmogorov_re_1000' #@param ['kolmogorov_re_1000', 'decaying', 'kolmogorov_re_4000'] {type: \"string\"}\n\n%time ! gsutil -m cp gs://gresearch/jax-cfd/public_eval_datasets/{dataset_name}/eval_*.nc /content\n\n%time ! gsutil -m cp gs://gresearch/jax-c...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
GoogleCloudPlatform/training-data-analyst	courses/machine_learning/deepdive/01_bigquery/labs/c_extract_and_benchmark.ipynb	apache-2.0	[ "Extract Datasets and Establish Benchmark\nLearning Objectives\n- Divide into Train, Evaluation and Test datasets\n- Understand why we need each\n- Pull data out of BigQuery and into CSV\n- Establish Rules Based Benchmark\nIntroduction\nIn the previous notebook we demonstrated how to do ML in BigQuery. However BQML...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
mne-tools/mne-tools.github.io	0.14/_downloads/plot_sensors_decoding.ipynb	bsd-3-clause	[ "%matplotlib inline", "Decoding sensor space data\nDecoding, a.k.a MVPA or supervised machine learning applied to MEG\ndata in sensor space. Here the classifier is applied to every time\npoint.", "import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.cros...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
karlstroetmann/Artificial-Intelligence	Python/6 Classification/Polynomial-Logistic-Regression.ipynb	gpl-2.0	[ "from IPython.core.display import HTML\nwith open (\"../style.css\", \"r\") as file:\n css = file.read()\nHTML(css)", "Polynomial Logistic Regression", "import numpy as np\nimport pandas as pd", "The data we want to investigate is stored in the file 'fake-data.csv'. It is data that I have found somewher...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "cod...
tpin3694/tpin3694.github.io	machine-learning/.ipynb_checkpoints/loading_scikit-learns_digits-dataset-checkpoint.ipynb	mit	[ "Title: Loading Scikit-Learn's Digits Dataset\nSlug: loading_scikit-learns_digits-dataset\nSummary: Loading the built-in digits datasets of Scikit-Learn. \nDate: 2016-08-31 12:00\nCategory: Machine Learning\nTags: Basics\nAuthors: Chris Albon \nPreliminaries", "# Load libraries\nfrom sklearn import datasets\nimpo...	[ "markdown", "code", "markdown", "code", "markdown", "code" ]
tensorflow/docs-l10n	site/en-snapshot/tfx/tutorials/tfx/penguin_simple.ipynb	apache-2.0	[ "Copyright 2021 The TensorFlow Authors.", "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
mne-tools/mne-tools.github.io	0.17/_downloads/d25fdfa446b06c82b756855681845935/plot_mne_dspm_source_localization.ipynb	bsd-3-clause	[ "%matplotlib inline", "Source localization with MNE/dSPM/sLORETA/eLORETA\nThe aim of this tutorial is to teach you how to compute and apply a linear\ninverse method such as MNE/dSPM/sLORETA/eLORETA on evoked/raw/epochs data.", "# sphinx_gallery_thumbnail_number = 10\n\nimport numpy as np\nimport matplotlib.pypl...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
PyPSA/PyPSA	examples/notebooks/scigrid-sclopf.ipynb	mit	[ "Security-Constrained Optimisation\nIn this example, the dispatch of generators is optimised using the security-constrained linear OPF, to guaranteed that no branches are overloaded by certain branch outages.", "import pypsa, os\nimport numpy as np\n\nnetwork = pypsa.examples.scigrid_de(from_master=True)", "The...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
dominikgrimm/ridge_and_svm	Toy-Example-Solution.ipynb	mit	[ "Toy Example: Ridge Regression vs. SVM\n<p></p>\n\n<div style=\"text-align:justify\">\n In this toy example we will compare two machine learning models: <em>Ridge Regression</em> and <em>C-SVM</em>. The data is generated <em>in silico</em> and is only used to illustrate how to use <em>Ridge Regression</em> and <...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
catalystcomputing/DSIoT-Python-sessions	Session3/code/03 Supervised Learning - 00 Python basics and Logistic Regression.ipynb	apache-2.0	[ "# Here we introduce Data science by starting with a common regression model(logistic regression). The example uses the Iris Dataset\n# We also introduce Python as we develop the model. (The Iris dataset section is adatped from an example from Analyics Vidhya) \n# Python uses some libraries which we load first. \n#...	[ "code", "markdown", "code", "markdown", "code" ]
kmunve/APS	aps/notebooks/ml_varsom/linear_regression.ipynb	mit	[ "LINEAR REGRESSION\n\nis the simplest machine learning model\nis used for finding linear relationship between target and one or more predictors\nthere are two types of linear regression:\nSimple (one feature)\nMultiple (two or more features) \n\n\nThe main idea of linear regression is to obtain a line that best fit...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
ES-DOC/esdoc-jupyterhub	notebooks/cas/cmip6/models/sandbox-3/ocean.ipynb	gpl-3.0	[ "ES-DOC CMIP6 Model Properties - Ocean\nMIP Era: CMIP6\nInstitute: CAS\nSource ID: SANDBOX-3\nTopic: Ocean\nSub-Topics: Timestepping Framework, Advection, Lateral Physics, Vertical Physics, Uplow Boundaries, Boundary Forcing. \nProperties: 133 (101 required)\nModel descriptions: Model description details\nInitializ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
EmuKit/emukit	notebooks/Emukit-tutorial-bayesian-optimization-external-objective-evaluation.ipynb	apache-2.0	[ "External objective function evaluation in Bayesian optimization with Emukit\nOverview\nThe Bayesian optimization component of Emukit allows for objective functions to be evaluated externally. If users opt for this approach, they can use Emukit to suggest the next point for evaluation, and then evaluate the objecti...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
cdt15/lingam	examples/VARMALiNGAM.ipynb	mit	[ "VARMALiNGAM\nImport and settings\nIn this example, we need to import numpy, pandas, and graphviz in addition to lingam.", "import numpy as np\nimport pandas as pd\nimport graphviz\nimport lingam\nfrom lingam.utils import make_dot, print_causal_directions, print_dagc\n\nimport warnings\nwarnings.filterwarnings('i...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
mdiaz236/DeepLearningFoundations	tensorboard/Anna_KaRNNa.ipynb	mit	[ "Anna KaRNNa\nIn this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.\nThis network is based off of Andrej Karpathy's post on RNNs and implementation in Torch. Also, some information here at r2...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
tensorflow/docs-l10n	site/ja/lite/performance/post_training_integer_quant_16x8.ipynb	apache-2.0	[ "Copyright 2020 The TensorFlow Authors.", "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
GoogleCloudPlatform/vertex-ai-samples	notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb	apache-2.0	[ "# Copyright 2021 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "cod...
telescopeuser/uat_shl	rnd03/shl_sm_NoOCR_v010.ipynb	mit	[ "SHL Project\n\nsimulation module: shl_sm\n\nshl_sm required data feeds:\n\nlive bidding price, per second, time series\n\nprediction module parameters/csv\n\n\nparm_si.csv (seasonality index per second)\n\n\nparm_month.csv (parameter like alpha, beta, gamma, etc. per month)\n\n\nSHL Simulation Module: shl_sm\nImpo...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
jnarhan/Breast_Cancer	src/models/JN_BC_Threshold_Diagnosis.ipynb	mit	[ "<hr>\n<h1>Predicting Benign and Malignant Classes in Mammograms Using Thresholded Data</h1>\n\n<p>Jay Narhan</p>\nJune 2017\nThis is an application of the best performing models but using thresholded data instead of differenced data. See JN_DC_Diff_Diagnosis.ipynb for more background and details on the problem.\n<...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
chapmanbe/RadNLP	notebooks/radnlp_demo.ipynb	apache-2.0	[ "RadNLP\nRadiology NLP or\nRad (as in cool) NLP or\n[Fill in the Blank] NLP\n© Brian E. Chapman, PhD\nRadNLP is a package that builds upon the pyConTextNLP algorithm's sentence-level text processing to perform simple document-level classification. The package also contains a number of functions for identifying...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
google/jax	docs/notebooks/Common_Gotchas_in_JAX.ipynb	apache-2.0	[ "🔪 JAX - The Sharp Bits 🔪\n\nlevskaya@ mattjj@\nWhen walking about the countryside of Italy, the people will not hesitate to tell you that JAX has \"una anima di pura programmazione funzionale\".\nJAX is a language for expressing and composing transformations of numerical programs. JAX is also able to compile num...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
jhamilius/chain	notebooks/chain-clustering.ipynb	mit	[ "Clustering and outlier detection\n<hr>\n\n<u>Objectives</u>\n- Test clustering methods on the features extracted from the graph for nodes and transactions\n- Test outlier detection methds on the features extracted from the graph for nodes and transactions\n- Detect if the clustering is splitting some publicy known...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
ES-DOC/esdoc-jupyterhub	notebooks/csiro-bom/cmip6/models/sandbox-1/landice.ipynb	gpl-3.0	[ "ES-DOC CMIP6 Model Properties - Landice\nMIP Era: CMIP6\nInstitute: CSIRO-BOM\nSource ID: SANDBOX-1\nTopic: Landice\nSub-Topics: Glaciers, Ice. \nProperties: 30 (21 required)\nModel descriptions: Model description details\nInitialized From: -- \nNotebook Help: Goto notebook help page\nNotebook Initialised: 2018-0...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
landlab/landlab	notebooks/tutorials/agent_based_modeling/groundwater/landlab_mesa_groundwater_pumping.ipynb	mit	[ "Coupling a Landlab groundwater with a Mesa agent-based model\nThis notebook shows a toy example of how one might couple a simple groundwater model (Landlab's GroundwaterDupuitPercolator, by Litwin et al. (2020)) with an agent-based model (ABM) written using the Mesa Agent-Based Modeling (ABM) package.\nThe purpose...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
joshnsolomon/phys202-2015-work	assignments/assignment05/InteractEx04.ipynb	mit	[ "Interact Exercise 4\nImports", "%matplotlib inline\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom IPython.html.widgets import interact, interactive, fixed\nfrom IPython.display import display", "Line with Gaussian noise\nWrite a function named random_line that creates x and y data for a line with...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
oseledets/fastpde	lecture-7.ipynb	cc0-1.0	[ "Lecture 7: Fast sparse solvers\nSparse matrix\nDEF: Sparse matrix is a matrix that contains $\\mathcal{O}(n)$ nonzero elements.\nSparse matrices are ubiquitous in PDEs\nConsider for example a 3D Poisson equation:\n$$\\Delta T = \\frac{\\partial^2T}{\\partial x^2}+\\frac{\\partial^2T}{\\partial y^2}+\\frac{\\partia...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
nholtz/structural-analysis	Devel/Old/v04-old/Milestones/Frame2D-v04-Milestone2.ipynb	cc0-1.0	[ "Milestone 2 - this version has all the input completed, individually and each tested.\n2-Dimensional Frame Analysis - Version 04\nThis program performs an elastic analysis of 2-dimensional structural frames. It has the following features:\n1. Input is provided by a set of CSV files (and cell-magics exist so you c...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
robblack007/clase-dinamica-robot	Practicas/practica2/numerico.ipynb	mit	[ "Práctica 2 - Cinemática directa y dinámica de manipuladores\nUna vez obtenida la dinámica del manipulador, tenemos la necesidad de construir una función f para poder simular el comportamiento del manipulador, empecemos escribiendo la ecuación:\n$$\n\\tau =\n\\begin{bmatrix}\nJ_1 + J_2 + m_1 l_1^2 + m_2 l_1^2 + \\m...	[ "markdown", "code", "markdown", "code", "markdown" ]
phungkh/phys202-2015-work	assignments/assignment11/OptimizationEx01.ipynb	mit	[ "Optimization Exercise 1\nImports", "%matplotlib inline\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.optimize as opt", "Hat potential\nThe following potential is often used in Physics and other fields to describe symmetry breaking and is often known as the \"hat potential\":\n$$ V(x) = -a ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
ES-DOC/esdoc-jupyterhub	notebooks/thu/cmip6/models/sandbox-3/seaice.ipynb	gpl-3.0	[ "ES-DOC CMIP6 Model Properties - Seaice\nMIP Era: CMIP6\nInstitute: THU\nSource ID: SANDBOX-3\nTopic: Seaice\nSub-Topics: Dynamics, Thermodynamics, Radiative Processes. \nProperties: 80 (63 required)\nModel descriptions: Model description details\nInitialized From: -- \nNotebook Help: Goto notebook help page\nNote...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
fmaschler/networkit	Doc/uploads/docs/SpectralCentrality.ipynb	mit	[ "Centrality\nThis evaluates the Eigenvector Centrality and PageRank implemented in Python against C++-native EVZ and PageRank. The Python implementation uses SciPy (and thus ARPACK) to compute the eigenvectors, while the C++ method implements a power iteration method itself.", "cd ../../\n\nimport networkit\n\nG ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
jrg365/gpytorch	examples/02_Scalable_Exact_GPs/Simple_GP_Regression_With_LOVE_Fast_Variances_and_Sampling.ipynb	mit	[ "GP Regression with LOVE for Fast Predictive Variances and Sampling\nOverview\nIn this notebook, we demonstrate that LOVE (the method for fast variances and sampling introduced in this paper https://arxiv.org/abs/1803.06058) can significantly reduce the cost of computing predictive distributions. This can be especi...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
seniosh/StatisticalMethods	notes/InferenceSandbox.ipynb	gpl-2.0	[ "Inference Sandbox\nIn this notebook, we'll mock up some data from the linear model, as reviewed here. Then it's your job to implement a Metropolis sampler and constrain the posterior distriubtion. The goal is to play with various strategies for accelerating the convergence and acceptance rate of the chain. Remembe...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
PyDataMallorca/WS_Introduction_to_data_science	ml_miguel/Crackeando el guess who.ipynb	gpl-3.0	[ "Cual es la mejor estrategia para adivinar?\nPor Miguel Escalona", "import pandas as pd\nimport matplotlib\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\n\nfrom IPython.display import Image", "¡Adivina Quién es!\nEl juego de adivina quién es, consiste en adivinar el personaje que tu oponente ha selecci...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
NathanYee/ThinkBayes2	code/.ipynb_checkpoints/chap07mine-checkpoint.ipynb	gpl-2.0	[ "Think Bayes: Chapter 7\nThis notebook presents code and exercises from Think Bayes, second edition.\nCopyright 2016 Allen B. Downey\nMIT License: https://opensource.org/licenses/MIT", "from __future__ import print_function, division\n\n% matplotlib inline\nimport warnings\nwarnings.filterwarnings('ignore')\n\nim...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
GPflow/GPflowOpt	doc/source/notebooks/constrained_bo.ipynb	apache-2.0	[ "Bayesian Optimization with black-box constraints\nJoachim van der Herten\nIntroduction\nThis notebook demonstrates the optimization of an analytical function using the well known Expected Improvement (EI) function. The problem is constrained by a black-box constraint function. The feasible regions are learnt joint...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
mtchem/Twitter-Politics	EDA.ipynb	mit	[ "# imports\nimport pandas as pd\nimport numpy as np\nimport pickle\nimport re\nimport pandas as pd", "The following data was generated using code that can be found on GitHub\nhttps://github.com/mtchem/Twitter-Politics/blob/master/data_wrangle/Data_Wrangle.ipynb", "# load federal document data from pickle file\n...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "cod...
arasdar/DL	udacity-dl/CNN/cnn_bp-learning-curves.ipynb	unlicense	[ "Image Classification\nIn this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
hdesmond/StatisticalMethods	examples/SDSScatalog/CorrFunc.ipynb	gpl-2.0	[ "\"Spatial Clustering\" - the Galaxy Correlation Function\n\n\nThe degree to which objects positions are correlated with each other - \"clustered\" - is of great interest in astronomy. \n\n\nWe expect galaxies to appear in groups and clusters, as they fall together under gravity: the statistics of galaxy clustering...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
tensorflow/docs-l10n	site/zh-cn/tutorials/distribute/custom_training.ipynb	apache-2.0	[ "Copyright 2019 The TensorFlow Authors.", "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
to266/hyperspy	hyperspy/tests/drawing/test_plot_image.ipynb	gpl-3.0	[ "Testing (and demonstrating) plot_images()", "# %hyperspy -r inline\nimport numpy as np\nimport hyperspy.api as hs\n%matplotlib inline\nimport matplotlib.pyplot as plt\n\n", "plot_images() is used to plot several images in the same figure. It supports many configurations and has many options available to custom...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
tensorflow/docs-l10n	site/ko/tutorials/keras/regression.ipynb	apache-2.0	[ "Copyright 2018 The TensorFlow Authors.", "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
phoebe-project/phoebe2-docs	2.3/tutorials/ltte.ipynb	gpl-3.0	[ "Rømer and Light Travel Time Effects (ltte)\nSetup\nLet's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab).", "#!pip install -I \"phoebe>=2.3,<2.4\"", "As always, let's do imports and initialize a logger and a new Bun...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
mrcinv/matpy	oma/kolokviji/OMA, 2. kolokvij 2012_2013.ipynb	gpl-2.0	[ "import math\nimport sympy\nfrom sympy import latex, solve, Eq\nfrom IPython.display import HTML, display\nfrom sympy.abc import x, a, b\n\n%matplotlib notebook\n%install_ext https://raw.githubusercontent.com/meduz/ipython_magics/master/tikzmagic.py\n%load_ext tikzmagic\n \nsympy.init_printing()", "OMA 2. kolo...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
statsmodels/statsmodels.github.io	v0.12.1/examples/notebooks/generated/markov_autoregression.ipynb	bsd-3-clause	[ "Markov switching autoregression models\nThis notebook provides an example of the use of Markov switching models in statsmodels to replicate a number of results presented in Kim and Nelson (1999). It applies the Hamilton (1989) filter the Kim (1994) smoother.\nThis is tested against the Markov-switching models from...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
bioinformatica-corso/lezioni	laboratorio/lezione17-09dic21/esercizio4-biopython.ipynb	cc0-1.0	[ "Biopython - Esercizio4\nMAFFT è un tool di allineamento multiplo sviluppato da EMBL-EBI (European Bioinformatics Institute - European Molecular Biology Laboratory) per sequenze di DNA.\nUsare MAFFT (scegliendo ClustalW come formato di output) per allineare i 14 genomi completi di SARS-CoV-2 presenti nel file covid...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
erikdrysdale/erikdrysdale.github.io	_rmd/extra_unequalvar/unequalvar.ipynb	mit	[ "Vectorizing t-test and F-tests for unequal variances\nAlmost all modern data science tasks begin with exploratory data analysis (EDA) phase. Visualizing summary statistics and testing for associations forms the basis of hypothesis generation and subsequent exploration and modeling. Applied statisticians need to be...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
tuanavu/python-cookbook-3rd	notebooks/ch01/01_unpacking_a_sequence_into_variables.ipynb	mit	[ "Unpacking a Sequence into Separate Variables\nProblem\n\nYou have an N-element tuple or sequence that you would like to unpack into a collection of N variables.\n\nSolution\nAny sequence (or iterable) can be unpacked into variables using a simple assignment operation. The only requirement is that the number of var...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
proinsias/gilbert-shannon-reeds	Gilbert-Shannon-Reeds.ipynb	mit	[ "import multiprocessing as mp\nimport typing\n\nimport matplotlib\nmatplotlib.use('nbagg')\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker\nimport numpy as np\nimport scipy as sp\nimport sklearn.utils\n\nfrom IPython import get_ipython # For automatically-generated python file.\n\n%matplotlib inline\n%l...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
phoebe-project/phoebe2-docs	2.3/examples/eccentric_ellipsoidal.ipynb	gpl-3.0	[ "Eccentric Ellipsoidal (Heartbeat)\nSetup\nLet's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab).", "#!pip install -I \"phoebe>=2.3,<2.4\"", "As always, let's do imports and initialize a logger and a new bundle.", ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
shengshuyang/PCLCombinedObjectDetection	TheanoLearning/TheanoLearning/theano_demo.ipynb	gpl-2.0	[ "Basics about Theano\nFirst let's do the standard import", "import time\nimport numpy as np\n#import matplotlib.pyplot as plt\nimport theano\n# By convention, the tensor submodule is loaded as T\nimport theano.tensor as T", "The following are all Theano defined types:", "A = T.matrix('A')\nb = T.scalar('b')\n...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
tylere/earthengine-api	python/examples/ipynb/EarthEngineColabInstall.ipynb	apache-2.0	[ "Copyright 2018 Google LLC.\nSPDX-License-Identifier: Apache-2.0\nEarth Engine Colab installation\nThis notebook demonstrates a simple installation of Earth Engine to a Colab notebook.\nColab setup\nThis notebook section installs the Earth Engine Python API on your Colab virtual machine (VM) and will need to be exe...	[ "markdown", "code", "markdown", "code", "markdown", "code" ]
ForestClaw/forestclaw	applications/clawpack/advection/2d/disk/swirl.ipynb	bsd-2-clause	[ "Swirl\n\nScalar advection problem with swirling velocity field.\n\nRun code in serial mode (will work, even if code is compiled with MPI)", "!swirl ", "Or, run code in parallel mode (command may need to be customized, depending your on MPI installation.)", "!mpirun -n 4 swirl", "Create PNG files for web-...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
leoferres/prograUDD	labs/ejercicios_for.ipynb	mit	[ "Una contraseña es valida si cumple con lo siguiente:", "passwd = input(\"Ingrese su contraseña: \")\n\nif len(passwd) < 8:\n print(\"Contraseña no valida, faltan caracteres\")\nelse:\n cantnum = 0\n cantsimb = 0\n for i in passwd:\n if i.isdigit():\n cantnum += 1\n elif not i...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
xMyrst/BigData	python/howto/005_Estructuras de control.ipynb	gpl-3.0	[ "ESTRUCTURAS DE CONTROL\n<br />\n INSTRUCCIONES IF, ELIF, ELSE\nEl intérprete de Python ejecuta un programa ejecutando una instrucción cada vez.\nif <condition>:\n <do something>\nelif <condition2>:\n <do other thing>\nelse:\n <do other thing>\n\nRecordar que en Python los blo...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
Upward-Spiral-Science/team1	code/Assignment11_Group.ipynb	apache-2.0	[ "Group", "from mpl_toolkits.mplot3d import axes3d\nimport matplotlib.pyplot as plt\n%matplotlib inline \nimport numpy as np\nimport urllib2\nimport scipy.stats as stats\n\nnp.set_printoptions(precision=3, suppress=True)\nurl = ('https://raw.githubusercontent.com/Upward-Spiral-Science'\n '/data/master/syn-de...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
leriomaggio/python-in-a-notebook	08 Classes and OOP.ipynb	mit	[ "Classes\nSo far you have learned about Python's core data types: strings, numbers, lists, tuples, and dictionaries. In this section you will learn about the last major data structure, classes. Classes are quite unlike the other data types, in that they are much more flexible. Classes allow you to define the inform...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
ekostat/ekostat_calculator	notebooks/lv_notebook_kustzon.ipynb	mit	[ "# coding: utf-8\n\n# In[1]:\n\n\nimport os \nimport sys\npath = \"../\"\npath = \"D:/github/w_vattenstatus/ekostat_calculator\"\nsys.path.append(path)\n#os.path.abspath(\"../\")\nprint(os.path.abspath(path))\n\nimport pandas as pd\nimport numpy as np\nimport json\nimport timeit\nimport time\nimport core\nimport im...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
cuttlefishh/emp	methods/figure-data/fig-1/Fig1_data_files.ipynb	bsd-3-clause	[ "import pandas as pd", "Figure 1 csv data generation\nFigure data consolidation for Figure 1, which maps samples and shows distribution across EMPO categories\nFigure 1a and 1b\nfor these figure, we just need the samples, EMPO level categories, and lat/lon coordinates", "# Load up metadata map\n\nmetadata_fp = ...	[ "code", "markdown", "code", "markdown", "code" ]
tiegz/ThreatExchange	ipynb/ThreatExchange Data Dashboard.ipynb	bsd-3-clause	[ "ThreatExchange Data Dashboard\nPurpose\nThe ThreatExchange APIs are designed to make consuming threat intelligence from multiple sources easy. This notebook will walk you through:\n\nbuilding an initial dashboard for assessing the data visible to your appID;\nfiltering down to a subset you consider high value; an...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
Ykharo/notebooks	C elemental, querido Cython..ipynb	bsd-2-clause	[ "Cython, que no CPython\nNo, no nos hemos equivocado en el título, hoy vamos a hablar de Cython.\n¿Qué es Cython?\nCython son dos cosas:\n\nPor una parte, Cython es un lenguaje de programación (un superconjunto de Python) que une Python con el sistema de tipado estático de C y C++.\nPor otra parte, cython es un com...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
kaushikpavani/neural_networks_in_python	src/linear_regression/linear_regression.ipynb	mit	[ "import numpy as np\nimport matplotlib.pyplot as plt\n%matplotlib inline ", "Given a 2D set of points spanned by axes $x$ and $y$ axes, we will try to fit a line that best approximates the data. The equation of the line, in slope-intercept form, is defined by: $y = mx + b$.", "def generate_random_points_along_...	[ "code", "markdown", "code", "markdown", "code" ]
AtmaMani/pyChakras	udemy_ml_bootcamp/Big-Data-and-Spark/Introduction to Spark and Python.ipynb	mit	[ "Introduction to Spark and Python\nLet's learn how to use Spark with Python by using the pyspark library! Make sure to view the video lecture explaining Spark and RDDs before continuing on with this code.\nThis notebook will serve as reference code for the Big Data section of the course involving Amazon Web Service...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
mlund/kirkwood-buff	nacl-water/nacl.ipynb	mit	[ "Kirkwood-Buff example: NaCl in water\nIn this example we calculate Kirkwood-Buff integrals in a solute (c) and solvent (w) system and correct for finite size effects as described at http://dx.doi.org/10.1073/pnas.0902904106 (see Supporting Information).", "%matplotlib inline\nimport matplotlib\nimport matplotlib...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
kimkipyo/dss_git_kkp	통계, 머신러닝 복습/160502월_1일차_분석 환경, 소개/14.Pandas 고급 인덱싱.ipynb	mit	[ "Pandas 고급 인덱싱\npandas는 numpy 행렬과 같이 comma를 사용한 복수 인덱싱을 지원하기 위해 다음과 같은 특별한 인덱서 속성을 제공한다.\n\nix : 라벨과 숫자를 동시에 지원하는 복수 인덱싱\nloc : 라벨 기반의 복수 인덱싱\niloc : 숫자 기반의 복수 인덱싱\n\nix 인덱서\n\n행(Row)/열(Column) 양쪽에서 라벨 인덱싱, 숫자 인덱싱, 불리언 인덱싱(행만) 동시 가능\n단일 숫자 인덱싱 가능\n열(column)도 라벨이 아닌 숫자 인덱싱 가능\n열(column)도 라벨 슬라이싱(label slicing) 가능", ...	[ "markdown", "code", "markdown", "code", "markdown", "code" ]

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