codeparrot/github-jupyter · Datasets at Hugging Face

repo_name stringlengths 6 92	path stringlengths 7 220	copies stringclasses 78 values	size stringlengths 2 9	content stringlengths 15 1.05M ⌀	license stringclasses 15 values
FRBs/FRB	docs/nb/Using_the_CIGALE_wrapper.ipynb	4	72255	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: UnitsWarning: 'km/s/Mpc' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]\n" ...	bsd-3-clause
joeladams/patternlets	patternlets/java-OpenMPI/notebook/Java_openmpi_patternlets.ipynb	1	112730	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Java openmpi_patternlets.ipynb", "provenance": [], "collapsed_sections": [ "kVWqN3EhJFbJ", "LJ_ThE3-KthZ", "Vww7AO93S8QD", "bXSiO5dGuJda", "49L8NojO6axL", "XxKI85d8LLll", ...	gpl-2.0
sfegan/calin	unit_tests/python/notebooks/Test air_shower - atmosphere.ipynb	2	13275	{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pyla...	gpl-2.0
gevann/mir_proj	code/project analysis.ipynb	1	3826975	null	mit
ES-DOC/esdoc-jupyterhub	notebooks/mpi-m/cmip6/models/mpi-esm-1-2-lr/seaice.ipynb	1	99815	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Seaice \n", "MIP Era: CMIP6 \n", "Institute: MPI-M \n", "Source ID: MPI-ESM-1-2-LR \n", "...	gpl-3.0
robertoalotufo/ia898	2S2018/Seminarios/C_OpenCV.ipynb	1	1922188	null	mit
clopeau/Julia_Introduction	1 - Prise en main-Julia.ipynb	1	24119	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Prise en main\n", "\n", "Pour un apprentissage rapide je recommande cette lecture (tout est dans le titre) http://learnxinyminutes.com/docs/julia/\n", "\n", "## Fonctions scientifiques\n", "\n", "Regardons les...	gpl-2.0
Upward-Spiral-Science/spect-team	Code/Assignment-10/SubjectSelectionExperiments (rCBF data).ipynb	1	66247	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Subject Selection Experiments disorder data - Srinivas (handle: thewickedaxe)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initial Data Cleaning" ] }, { "cell_type": "code", "exe...	apache-2.0
rajul/tvb-library	tvb/simulator/doc/tutorials/Tutorial_Surface_Stimuli/Tutorial_Surface_Stimuli.ipynb	2	223984	{ "metadata": { "name": "", "signature": "sha256:21534c90fb211a3704f8e4b21cb64475c5726890d0adea814577179e2f3a3243" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Tutorial: Surface Stimuli\n", "=====...	gpl-2.0
olgabot/prettyplotlib	ipython_notebooks/.ipynb_checkpoints/plot-checkpoint.ipynb.BASE.98994.ipynb	1	162318	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import prettyplotlib as ppl\n", "from prettyplotlib import plt\n", "from prettyplotlib import mpl\n", "from prettyp...	mit
saketkc/gencode_regions	notebooks/GRCg6.ipynb	1	15085	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from collections import defaultdict, OrderedDict\n", "import warnings\n", "import gffutils\n", "import pybedtools\n", "import pandas as pd\n", "import copy\n", "import os\n...	bsd-2-clause
IACS-CS-207/cs207-F17	lectures/L8/L8.ipynb	1	2647	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lecture 8: Chemical Kinetics\n", "Today's lecture will provide a rapid tour through chemical kinetics. The final result is that for a system consisting of $N$ species undergoing $M$ irreversible, elementary reactions of...	mit
biokit/biokit	notebooks/viz/anova.ipynb	1	10706	{ "cells": [ { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from biokit import viz" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ...	bsd-2-clause
aje/POT	notebooks/plot_barycenter_1D.ipynb	1	201530	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# 1D Wasserstein barycenter demo\n", "\n", "\n", ...	mit
swara-salih/Portfolio	Group Project--Chicago WNV Kaggle Competition/NileVirus_Prediction-kh (1).ipynb	1	2948954	null	mit
cosmoscalibur/herramientas_computacionales	Presentaciones/Notas/09_Extraccion_web.ipynb	1	60122	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Extracción de datos web (_Web scrapping_) \n", "\n", "Ante la generación masiva a traves de la red es importante tener herramientas que permitan la extracción de datos a partir de fuentes cuya ubicación es esta. De esto se t...	mit
awagner-mainz/notebooks	gallery/DHD2019_Azpilcueta.ipynb	1	203332	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Multimodale Versuche der Alignierung historischer Texte" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Andreas Wagner und Manuela Bragagnolo, Max-Planck-Institut für europäische Rechtsgeschichte, F...	mit
natronics/rust-fc	analysis/results.ipynb	1	10605	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparing `rust-fc` To Simulation Output\n", "\n", "The simulator proccessing code adds realisic noise to the IMU input before sending it to `rust-fc`.\n", "\n", "We'll compare the clean \"ideal\" simulator numbers to...	gpl-3.0
DillonNovak/Programming-for-Chemical-Engineering-Applications	SRK EOS.ipynb	1	37162	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import math as mm\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.integrate import odeint\n", "i...	gpl-3.0
EconForge/dolo	examples/notebooks/consumption_savings.ipynb	1	120884	{ "cells": [ { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [], "source": [ "from dolo import *" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [], "source": [ "model = yaml_import(\"../models/consumption_savings....	bsd-2-clause
bosscha/alma-calibrator	notebooks/sample/J0501_B6.ipynb	1	85943	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/scratch/home/rwibowo/anaconda3/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: The mpl_to...	gpl-2.0
gaufung/Data_Analytics_Learning_Note	DesignPattern/BridgePattern.ipynb	1	5403	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "桥梁模式(Bridge Pattern) \n", "# 1 代码 \n", "在一个画图程序中，常会见到这样的情况：有一些预设的图形，如矩形、圆形等，还有一个对象-画笔，调节画笔的类型（如画笔还是画刷，还是毛笔效果等）并设定参数（如颜色、线宽等），选定图形，就可以在画布上画出想要的图形了。要实现以上需求，先从最抽象的元素开始设计，即形状和画笔（暂时忽略画布，同时忽略画笔参数，只考虑画笔类型）。" ] }, { "cell_ty...	mit
llulai/deep-learning	transfer-learning/Transfer_Learning.ipynb	1	1342995	null	mit
joelowj/Udacity-Projects	Udacity-Deep-Learning-Foundation-Nanodegree/Project-4/dlnd_language_translation.ipynb	1	150646	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Language Translation\n", "In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and F...	apache-2.0
naoyak/Agile_Data_Code_2	ch07/Predicting flight delays with sklearn.ipynb	1	53524	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Predicting Flight Delays with sklearn\n", "\n", "In this notebook, we will be using features we've prepared in PySpark to predict flight delays via regression and classification." ] }, { "cell_type": "code", "exe...	mit
vallis/libstempo	demo/libstempo-demo.ipynb	2	330734	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## libstempo tutorial: basic functionality" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Michele Vallisneri, vallis@vallis.org; latest revision: 2016/10/12 for v2.3 revision" ] }, { "c...	mit
eds-uga/csci1360-fa16	assignments/A0/A0_header.ipynb	1	2759	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "a0_header", "locked": true, "solution": false } }, "source": [ "# Assignment 0\n", "CSCI 1360E: Foundations for Informatics and Analytics" ] }, { "cell_type": "ma...	mit
griffinfoster/fundamentals_of_interferometry	0_Introduction/0_introduction.ipynb	1	38281	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fundamentals of Radio Interferometry for Aperture Synthesis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interferometry is a fundamental technique used in radio astronomy but is often presented in...	gpl-2.0
chongxi/spiketag	demo/GT/unit_test_spiketag.ipynb	1	90300	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:32:24.519803Z", "start_time": "2019-03-13T19:32:24.489777Z" } }, "outputs": [], "source": [ "%load_ext autoreload" ] }, { "cell_type": "code", "exec...	bsd-3-clause
mne-tools/mne-tools.github.io	0.12/_downloads/plot_read_and_write_raw_data.ipynb	1	2438	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "execution_count": null, "cell_type": "code", "source": [ "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "\n# Reading and writin...	bsd-3-clause
empet/Plotly-plots	Chord-diagram.ipynb	1	210208	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotly plot of chord diagrams ##" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Circular layout or [Chord diagram](https://en.wikipedia.org/wiki/Chord_diagram) is a method of visualizing data that...	gpl-3.0
Kaggle/learntools	notebooks/ml_intermediate/raw/tut3.ipynb	1	13711	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial, you will learn what a categorical variable is, along with three approaches for handling this type of data.\n", "\n", "\n", "# Introduction\n", "\n", "A categorical variable takes only a lim...	apache-2.0
analysiscenter/dataset	examples/experiments/squeeze_and_excitation/se_different_ratio.ipynb	1	67858	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import torch.nn as nn\n", "import matplotlib.pyplot as plt\n", "from matplotlib import colors as mcolors\n", "\n", "\n", ...	apache-2.0
radu941208/DeepLearning	Intro_to_Neural_Networks/Building+your+Deep+Neural+Network+-+Step+by+Step+v5.ipynb	1	56732	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Building your Deep Neural Network: Step by Step\n", "\n", "Welcome to your week 4 assignment (part 1 of 2)! You have previously trained a 2-layer Neural Network (with a single hidden layer). This week, you will build a deep n...	mit
Saytiras/StalkerML	Generate Topic Models LDA.ipynb	2	9253	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import logging\n", "from os import path\n", "from pprint import pprint\n", "import pandas as pd\n", "from corputil import ListCorpus\n", "from corputil.utils import load_sto...	gpl-2.0
cliburn/sta-663-2017	homework/05_Making_Python_Faster_Solutions.ipynb	1	226105	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "%load_ext Cython" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": ...	mit
nravi89/nlphu	HW8.ipynb	1	5957	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "###Exercise from http://www.nltk.org/book_1ed/ch09.html\n", "###Author : Nirmal kumar Ravi" ] }, { "cell_typ...	bsd-2-clause
GoogleCloudPlatform/vertex-ai-samples	community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/mlops_pipeline_tf_agents_bandits_movie_recommendation.ipynb	1	69546	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "ur8xi4C7S06n" }, "outputs": [], "source": [ "# Copyright 2021 Google LLC\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", ...	apache-2.0
pbnator18/labs	Spring Oscillation Lab.ipynb	1	32077	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Spring Oscillation Relationship of Time and Mass" ] }, { ...	gpl-3.0
Unidata/unidata-python-workshop	notebooks/Surface_Data/Surface Data with Siphon and MetPy.ipynb	1	16315	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<a name=\"top\"></a>\n", "<div style=\"width:1000 px\">\n", "\n", "<div style=\"float:right; width:98 px; height:98px;\">\n", "<img src=\"https://raw.githubusercontent.com/Unidata/MetPy/master/metpy/plots/_static/unidat...	mit
Cyb3rWard0g/HELK	docker/helk-jupyter/notebooks/sigma/win_susp_procdump.ipynb	1	3589	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Suspicious Use of Procdump\n", "Detects suspicious uses of the SysInternals Procdump utility by using a special command line parameter in combination with the lsass.exe process. This way we're also able to catch cases in which th...	gpl-3.0
calico/basenji	manuscripts/akita/tutorial.ipynb	1	55506	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Required inputs for Akita are:\n", "* binned Hi-C or Micro-C data stored in cooler format (https://github.com/mirnylab/cooler)\n", "* Genome FASTA file\n", "\n", "First, make sure you have a FASTA file available consist...	apache-2.0
jts/nanocorrect	nicks_notebooks/2014-12-08-try-r7-data-again-for-more-coverage.ipynb	1	96499	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Currently the assemblies are topping out at around N50 ~100kb. Not bad, but not where we need to be.\n", "\n", "Although...	mit
balmandhunter/jupyter-tips-and-tricks	notebooks/04-More_basics.ipynb	4	748309	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Jupyter Notebook Basics " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "names = ['alice', 'jonathan', 'bobby']\n", "ages = [24, 3...	mit
tensorflow/docs	site/en/tutorials/images/cnn.ipynb	2	12130	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "x4HI2mpwlrcn" }, "source": [ "##### Copyright 2019 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "i...	apache-2.0
dsteurer/cs4814fa15	turing/turing.ipynb	1	28866	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/david/anaconda3/lib/python3.5/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package ...	mit
mne-tools/mne-tools.github.io	stable/_downloads/64e3b6395952064c08d4ff33d6236ff3/evoked_whitening.ipynb	1	4928	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Whitening ...	bsd-3-clause
gutouyu/cs231n	cs231n/assignment/assignment2/TensorFlow.ipynb	3	31003	{ "nbformat_minor": 1, "nbformat": 4, "cells": [ { "source": [ "## What's this TensorFlow business?\n", "\n", "You've written a lot of code in this assignment to provide a whole host of neural network functionality. Dropout, Batch Norm, and 2D convolutions are some of the workhorses of deep lear...	mit
pfschus/fission_bicorrelation	methods/build_det_df_angles_pairs.ipynb	1	471349	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 id=\"tocheading\">Table of Contents</h1>\n", "<div id=\"toc\"></div>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "appl...	mit
OzFlux/Peters-iPython-notebooks	compare_OzFluxQC_EddyPro.ipynb	1	10324	{ "metadata": { "name": "", "signature": "sha256:74a9d0b68127f1e40ead95526b68137ae493073dd11c07e5a8aa3cee3bf7f1f0" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%run basics\n", "%matplotlib" ],...	gpl-3.0
tchmiel/TJC-CarND-BehavioralTraining-P3	P3_PreprocessModel.ipynb	1	765611	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Behaviorial Cloning Project\n", "## Thomas J. Chmielenski\n", "### September 2017\n", "\n", "This notebook is a utility notebook to help with experimenting and preprocessing the training set data.\n", "Utility fun...	mit
InsightSoftwareConsortium/SimpleITK-Notebooks	Python/02_Pythonic_Image.ipynb	2	9185	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pythonic Syntactic Sugar <a href=\"https://mybinder.org/v2/gh/InsightSoftwareConsortium/SimpleITK-Notebooks/master?filepath=Python%2F02_Pythonic_Image.ipynb\"><img style=\"float: right;\" src=\"https://mybinder.org/badge_logo.svg\"><...	apache-2.0
DAInamite/programming-humanoid-robot-in-python	kinematics/inverse_kinematics_2d_autograd.ipynb	1	428735	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Inverse Kinematics (2D) \n", "with https://github.com/HIPS/autograd" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "from matpl...	gpl-2.0
WMD-group/SMACT	examples/Inverse_perovskites/Inverse_formate_perovskites.ipynb	1	36334	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Formate perovskites\n", "\n", "Perovskites are an important class of inorganic materials, particularly for solar energy applications.\n", "Usually, the perovskite structure contains an A cation, a B cation and an...	mit
ultrareality/mat201a	hw1/samplingdemo.ipynb	1	20474	{ "metadata": { "name": "", "signature": "sha256:8ebf1e53c6d8e364ad9b913822b1e4941edcd22e3fbbc22d83136cd1cff42b41" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "py...	mit
StingraySoftware/notebooks	Simulator/Lag Analysis.ipynb	1	102867	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Contents" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook analyses lag-frequency spectrums of the light curves simulated through impulse response approach. First, a simple case with del...	mit
moizumi99/CVBookExercise	Chapter-5/CV Book Ch 5 Ex 6.ipynb	1	534564	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from PIL import Image\n", "from numpy import \n", "from pylab import \n", "import scipy.misc\n", "from scipy import ndimage\n", "import numpy as np"...	unlicense
theandygross/TCGA_differential_expression	Notebooks/OneOffs/James_DX_methy.ipynb	1	113902	{ "cells": [ { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'/cellar/users/agross/TCGA_Code/DX/Notebooks'" ] }, "execution_count": 122, "metadata": {}, "output_type": "exe...	mit
dereneaton/ipyrad	newdocs/API-analysis/cookbook-vcf2hdf5.ipynb	1	243371	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <span style=\"color:gray\">ipyrad-analysis toolkit:</span> vcf_to_hdf5\n", "\n", "[View as notebook](https://nbviewer.jupyter.org/github/dereneaton/ipyrad/blob/master/newdocs/API-analysis/cookbook-vcf2hdf5.ipynb)\n", "\n"...	gpl-3.0
PytLab/catplot	examples/energy_profile_examples/simple_use.ipynb	1	17701	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# catplot 的简单使用" ] }, { "cell_type": "code", "execution_...	mit
therealAJ/python-sandbox	data-science/learning/ud1/DataScience/Python101.ipynb	1	13272	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python Basics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Whitespace Is Important" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, ...	gpl-3.0
Unidata/MetPy	talks/MetPy Exercise.ipynb	5	10734	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from datetime import datetime, timedelta\n", "from siphon.catalog import TDSCatalog\n", "from siphon.ncss import NCSS\n", "import numpy as np\n", "impo...	bsd-3-clause
james-prior/cohpy	20160826-dojo-regex-travis.ipynb	1	12106	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function\n", "import re" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start with the original r...	mit
GuidoBR/python-for-finance	Markowitz+-+Bovespa.ipynb	1	228330	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'numpy'", "output_type": "error", "traceback": [ "\u001b[0;31m-----------------------------------------------------------------...	mit
walchko/pygecko	dev/images/image-message.ipynb	1	1075642	null	mit
ShantanuKamath/PythonWorkshop	2. Python Advanced.ipynb	1	51409	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python Advanced\n", "\n", "*Disclaimer* - *This document is only meant to serve as a reference for the attendees of the workshop. It does not cover all the concepts or implementation details discussed during the actual ...	mit
fcollonval/coursera_data_visualization	Making_Data_Management.ipynb	1	141471	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Assignment: Making Data Management Decisions - Python\n", "\n", "Following is the Python program I wrote to fulfill the third assignment of the [Data Management and Visualization online course]( https://www.coursera.org/learn...	mit
amueller/advanced_training	05.1 Trees and Forests.ipynb	1	374567	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Trees and Forests" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib notebook\n", "from preamble import *" ] }, {...	bsd-2-clause
jrbourbeau/composition	analysis/parameter-tuning/KS-test.ipynb	1	60789	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jbourbeau/.local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is ...	mit
Jwuthri/Mozinor	mozinor/example/Mozinor example Class.ipynb	1	114565	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Use mozinor for classification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Import the main module" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outpu...	mit
aidiss/disciplines	ipynb/1 Theories.ipynb	1	10545	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook introduces major package in disciplines - theory.\n", "Theory is a package dedicated to implement theories.\n", "It enables easy reading, and supports developer with tools to implement theories into Python cod...	mit
QuLogic/folium	examples/GeodedeticImageOverlay.ipynb	1	31335	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5.0+105.g065f6f3.dirty\n" ] } ], "source": [ "import os\n", "import folium\n", "\n", "print(folium.__ver...	mit
danresende/deep-learning	autoencoder/Convolutional_Autoencoder.ipynb	1	408854	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Convolutional Autoencoder\n", "\n", "Sticking with the MNIST dataset, let's improve our autoencoder's performance using convolutional layers. Again, loading modules and the data." ] }, { "cell_type": "code", "exe...	mit
avtlearns/automatic_text_summarization	TextRank_Automatic_Summarization_for_Medical_Articles.ipynb	1	19243	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Automatic Summarization of Medical Articles\n", "### Author: Abhijit V Thatte" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Solution:__ We will use TextRank for automatic summarization of med...	gpl-3.0
mne-tools/mne-tools.github.io	0.24/_downloads/b7659d33d6ffe8531d004e9d6051f16f/forward_sensitivity_maps.ipynb	1	6052	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Display se...	bsd-3-clause
rohinkumar/galsurveystudy	APTest/.ipynb_checkpoints/DR72_VAGC_correl_AP_MI_V06-fast-cdist-rr200k-opt-checkpoint.ipynb	1	80132	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Correlation function of DR72 SDSS VAGC Catalog" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First import all the modules such as healpy and astropy needed for analyzing the structure" ] }, ...	mit
phungkh/phys202-2015-work	assignments/assignment05/InteractEx03.ipynb	1	39699	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Interact Exercise 3" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 105, "meta...	mit
mysticPrince/hcpf	algos/.ipynb_checkpoints/plot_graphs-checkpoint.ipynb	1	62704	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, ...	mit
daniorerio/trackpy	benchmarks/maxima_benchmarks.ipynb	2	1054	# must be run in ipython import numpy as np import trackpy as tp from trackpy.preprocessing import scale_to_gamut def b(command): get_ipython().magic(command) dummy_noise_image = scale_to_gamut(np.random.randint(0, 100, (100, 100)), np.uint8) real_image_raw = tp.ImageSequence('../trackpy/tests/video/image_sequ...	bsd-3-clause
CalPolyPat/Python-Workshop	Python Workshop/Containers.ipynb	2	15684	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##Containers\n", "Containers are set of objects including lists, sets, tuples, and dictionaries. They hold other variables in various different ways following different rules. These are arguably the most used, and m...	mit
drphilmarshall/LocalGroupHaloProps	Notebooks/Untitled1.ipynb	1	36373	{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "h = ...	gpl-2.0
Jim00000/Numerical-Analysis	2_Systems_Of_Equations.ipynb	1	55943	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# CHAPTER 2 - Systems Of Equations" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import modules\n", "import sys\n", "import numpy as np\n", "impor...	unlicense
jeicher/cobrapy	documentation_builder/solvers.ipynb	3	15731	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Solver Interface\n", "\n", "Each cobrapy solver must expose the following API. The solvers all will have their own distinct LP object types, but each can be manipulated by these functions. This API can be used directly when i...	lgpl-2.1
ucsc-astro/coffee	15_01_27_sqlalchemy/Learning SQLAlchemy.ipynb	1	39280	{ "metadata": { "name": "", "signature": "sha256:1834ef0c2b3c1bae82186376fd5217f01a31d10dde6b09c086f67e4f5ca1eda6" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1>Learning SQLAlchemy</h1>\n", "<t...	gpl-3.0
kfollette/ASTR200-Spring2017	Labs/Lab9/Lab 9.ipynb	2	27075	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### * Names: [Insert Your Names Here]*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Lab 9 - Data Investigation 1 (Week 1) - Educational Research Data\n", "\n", "## Lab 9 Contents\n", ...	mit
mikemcfarlane/TickleMeNAO	Idea_Development/Microphone_anomaly_analysis_from_Jonny.ipynb	1	263204	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from pysoundfile import SoundFile\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "import nu...	gpl-2.0
racu10/emapy	notebooks/test funcions.ipynb	1	2234134	null	bsd-3-clause
cathalmccabe/PYNQ	boards/Pynq-Z1/base/notebooks/video/opencv_face_detect_hdmi.ipynb	4	5510529	null	bsd-3-clause
weikang9009/pysal	notebooks/explore/spaghetti/Spaghetti_Pointpatterns_Empirical.ipynb	5	9410	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "---------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# $SPA$tial $G$rap$H$s: n$ET$works, $T$opology, & $I$nference\n", "\n", "## Tutorial for `pysal.spaghetti...	bsd-3-clause
JudoWill/ResearchNotebooks	Dream2Problem.ipynb	1	6048	{ "metadata": { "name": "Dream2Problem" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import division\n", "import os, os.path\n", "from pandas import read_csv, DataFrame, Series,...	mit
ahuang11/ahh	examples/vis/plot_map_CENTRAL_LONGITUDE.ipynb	1	480370	{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from ahh import vis, exp" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ ...	mit
jldinh/multicell	examples/06 - Growth and divisions.ipynb	1	821916	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Preparation" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "%matplotlib notebook" ] }, { "cell_type":...	mit
barakovic/COMMIT	doc/tutorials/AdvancedSolvers/tutorial_solvers.ipynb	1	8442	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "You can find the text version of this tutorial [at this link](README.md)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Advanced solvers\n", "\n", "This tutorial shows how to exploit the ad...	gpl-3.0
jquacinella/IS608_Project	.ipynb_checkpoints/IS608 Project-checkpoint.ipynb	1	16263	{ "metadata": { "name": "", "signature": "sha256:47dcac1f1f7021b0cabfdc81fc1049387953feb3760ca4ba3486debafbf6cf98" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# IS608 Project -- James Quacinella\n", ...	gpl-2.0
darkomen/TFG	medidas/20072015/FILAEXTRUDER/.ipynb_checkpoints/Analisis-checkpoint.ipynb	1	287636	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Análisis de los datos obtenidos " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Producción del día 20 de Julio de 2015\n", "\n", "Los datos del experimento:\n", "* Hora de inicio: 16:08\...	cc0-1.0
barbagroup/JITcode-MechE	module00_Introduction_to_Python/01_Lesson01_Playing_with_data.ipynb	2	431062	{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "Content provided under a Creative Commons Attribution license, CC-BY 4.0.\n", "(c) 2014 L.Barba, P.Bardet, A.Wickenheiser (The George Washington University).\n", "Thanks: A. Ahmadia, G. Forsyth, A. Golding, NSF for support via CAREE...	mit
gvanhorn38/nobal	index.ipynb	1	274118	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook implements the decision making algorithm presented in the paper [Near Optimal Bayesian Active Learning for Decision Making](http://www.ri.cmu.edu/pub_files/2014/4/javdani14hec_extended.pdf) by [Shervin Javdani](http://www...	mit
ecervera/Baxter-Vision	00 00 Lowercase Folder and File Names.ipynb	1	1306	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import glob, json, os.path\n", "\n", "with open('parameters.json', 'r') as infile:\n", " params = json.load(infile)\n", "\n", "ITEM_FOLDER = pa...	mit
amueller/nyu_ml_lectures	Unsupervised Transformers.ipynb	1	8022	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib nbagg\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ...	bsd-2-clause
airanmehr/bio	Scripts/MultiLociSelection/.ipynb_checkpoints/MultiLociSelection-checkpoint.ipynb	1	722912	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "simuPOP Version 1.1.7 : Copyright (c) 2004-2016 Bo Peng\n", "Revision 5000 (Jan 21 2016) for Python 2.7....	mit

FRBs/FRB

docs/nb/Using_the_CIGALE_wrapper.ipynb

4

72255

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: UnitsWarning: 'km/s/Mpc' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]\n" ...

bsd-3-clause

joeladams/patternlets

patternlets/java-OpenMPI/notebook/Java_openmpi_patternlets.ipynb

1

112730

{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Java openmpi_patternlets.ipynb", "provenance": [], "collapsed_sections": [ "kVWqN3EhJFbJ", "LJ_ThE3-KthZ", "Vww7AO93S8QD", "bXSiO5dGuJda", "49L8NojO6axL", "XxKI85d8LLll", ...

gpl-2.0

sfegan/calin

unit_tests/python/notebooks/Test air_shower - atmosphere.ipynb

2

13275

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pyla...

gpl-2.0

gevann/mir_proj

code/project analysis.ipynb

1

3826975

null

mit

ES-DOC/esdoc-jupyterhub

notebooks/mpi-m/cmip6/models/mpi-esm-1-2-lr/seaice.ipynb

1

99815

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Seaice \n", "**MIP Era**: CMIP6 \n", "**Institute**: MPI-M \n", "**Source ID**: MPI-ESM-1-2-LR \n", "...

gpl-3.0

robertoalotufo/ia898

2S2018/Seminarios/C_OpenCV.ipynb

1

1922188

null

mit

clopeau/Julia_Introduction

1 - Prise en main-Julia.ipynb

1

24119

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Prise en main\n", "\n", "Pour un apprentissage rapide je recommande cette lecture (tout est dans le titre) http://learnxinyminutes.com/docs/julia/\n", "\n", "## Fonctions scientifiques\n", "\n", "Regardons les...

gpl-2.0

Upward-Spiral-Science/spect-team

Code/Assignment-10/SubjectSelectionExperiments (rCBF data).ipynb

1

66247

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Subject Selection Experiments disorder data - Srinivas (handle: thewickedaxe)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initial Data Cleaning" ] }, { "cell_type": "code", "exe...

apache-2.0

rajul/tvb-library

tvb/simulator/doc/tutorials/Tutorial_Surface_Stimuli/Tutorial_Surface_Stimuli.ipynb

2

223984

{ "metadata": { "name": "", "signature": "sha256:21534c90fb211a3704f8e4b21cb64475c5726890d0adea814577179e2f3a3243" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Tutorial: Surface Stimuli\n", "=====...

gpl-2.0

olgabot/prettyplotlib

ipython_notebooks/.ipynb_checkpoints/plot-checkpoint.ipynb.BASE.98994.ipynb

1

162318

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import prettyplotlib as ppl\n", "from prettyplotlib import plt\n", "from prettyplotlib import mpl\n", "from prettyp...

mit

saketkc/gencode_regions

notebooks/GRCg6.ipynb

1

15085

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from collections import defaultdict, OrderedDict\n", "import warnings\n", "import gffutils\n", "import pybedtools\n", "import pandas as pd\n", "import copy\n", "import os\n...

bsd-2-clause

IACS-CS-207/cs207-F17

lectures/L8/L8.ipynb

1

2647

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lecture 8: Chemical Kinetics\n", "Today's lecture will provide a rapid tour through chemical kinetics. The final result is that for a system consisting of $N$ species undergoing $M$ **irreversible**, **elementary** reactions of...

mit

biokit/biokit

notebooks/viz/anova.ipynb

1

10706

{ "cells": [ { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from biokit import viz" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ...

bsd-2-clause

aje/POT

notebooks/plot_barycenter_1D.ipynb

1

201530

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# 1D Wasserstein barycenter demo\n", "\n", "\n", ...

mit

swara-salih/Portfolio

Group Project--Chicago WNV Kaggle Competition/NileVirus_Prediction-kh (1).ipynb

1

2948954

null

mit

cosmoscalibur/herramientas_computacionales

Presentaciones/Notas/09_Extraccion_web.ipynb

1

60122

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Extracción de datos web (_Web scrapping_) \n", "\n", "Ante la generación masiva a traves de la red es importante tener herramientas que permitan la extracción de datos a partir de fuentes cuya ubicación es esta. De esto se t...

mit

awagner-mainz/notebooks

gallery/DHD2019_Azpilcueta.ipynb

1

203332

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Multimodale Versuche der Alignierung historischer Texte" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Andreas Wagner und Manuela Bragagnolo, Max-Planck-Institut für europäische Rechtsgeschichte, F...

mit

natronics/rust-fc

analysis/results.ipynb

1

10605

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparing `rust-fc` To Simulation Output\n", "\n", "The simulator proccessing code adds realisic noise to the IMU input before sending it to `rust-fc`.\n", "\n", "We'll compare the clean \"ideal\" simulator numbers to...

gpl-3.0

DillonNovak/Programming-for-Chemical-Engineering-Applications

SRK EOS.ipynb

1

37162

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import math as mm\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.integrate import odeint\n", "i...

gpl-3.0

EconForge/dolo

examples/notebooks/consumption_savings.ipynb

1

120884

{ "cells": [ { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [], "source": [ "from dolo import *" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [], "source": [ "model = yaml_import(\"../models/consumption_savings....

bsd-2-clause

bosscha/alma-calibrator

notebooks/sample/J0501_B6.ipynb

1

85943

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/scratch/home/rwibowo/anaconda3/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: The mpl_to...

gpl-2.0

gaufung/Data_Analytics_Learning_Note

DesignPattern/BridgePattern.ipynb

1

5403

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "桥梁模式(Bridge Pattern) \n", "# 1 代码 \n", "在一个画图程序中，常会见到这样的情况：有一些预设的图形，如矩形、圆形等，还有一个对象-画笔，调节画笔的类型（如画笔还是画刷，还是毛笔效果等）并设定参数（如颜色、线宽等），选定图形，就可以在画布上画出想要的图形了。要实现以上需求，先从最抽象的元素开始设计，即形状和画笔（暂时忽略画布，同时忽略画笔参数，只考虑画笔类型）。" ] }, { "cell_ty...

mit

llulai/deep-learning

transfer-learning/Transfer_Learning.ipynb

1

1342995

null

mit

joelowj/Udacity-Projects

Udacity-Deep-Learning-Foundation-Nanodegree/Project-4/dlnd_language_translation.ipynb

1

150646

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Language Translation\n", "In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and F...

apache-2.0

naoyak/Agile_Data_Code_2

ch07/Predicting flight delays with sklearn.ipynb

1

53524

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Predicting Flight Delays with sklearn\n", "\n", "In this notebook, we will be using features we've prepared in PySpark to predict flight delays via regression and classification." ] }, { "cell_type": "code", "exe...

mit

vallis/libstempo

demo/libstempo-demo.ipynb

2

330734

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## libstempo tutorial: basic functionality" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Michele Vallisneri, vallis@vallis.org; latest revision: 2016/10/12 for v2.3 revision" ] }, { "c...

mit

eds-uga/csci1360-fa16

assignments/A0/A0_header.ipynb

1

2759

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "a0_header", "locked": true, "solution": false } }, "source": [ "# Assignment 0\n", "CSCI 1360E: Foundations for Informatics and Analytics" ] }, { "cell_type": "ma...

mit

griffinfoster/fundamentals_of_interferometry

0_Introduction/0_introduction.ipynb

1

38281

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fundamentals of Radio Interferometry for Aperture Synthesis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interferometry is a fundamental technique used in radio astronomy but is often presented in...

gpl-2.0

chongxi/spiketag

demo/GT/unit_test_spiketag.ipynb

1

90300

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:32:24.519803Z", "start_time": "2019-03-13T19:32:24.489777Z" } }, "outputs": [], "source": [ "%load_ext autoreload" ] }, { "cell_type": "code", "exec...

bsd-3-clause

mne-tools/mne-tools.github.io

0.12/_downloads/plot_read_and_write_raw_data.ipynb

1

2438

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "execution_count": null, "cell_type": "code", "source": [ "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "\n# Reading and writin...

bsd-3-clause

empet/Plotly-plots

Chord-diagram.ipynb

1

210208

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotly plot of chord diagrams ##" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Circular layout or [Chord diagram](https://en.wikipedia.org/wiki/Chord_diagram) is a method of visualizing data that...

gpl-3.0

Kaggle/learntools

notebooks/ml_intermediate/raw/tut3.ipynb

1

13711

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial, you will learn what a **categorical variable** is, along with three approaches for handling this type of data.\n", "\n", "\n", "# Introduction\n", "\n", "A **categorical variable** takes only a lim...

apache-2.0

analysiscenter/dataset

examples/experiments/squeeze_and_excitation/se_different_ratio.ipynb

1

67858

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import torch.nn as nn\n", "import matplotlib.pyplot as plt\n", "from matplotlib import colors as mcolors\n", "\n", "\n", ...

apache-2.0

radu941208/DeepLearning

Intro_to_Neural_Networks/Building+your+Deep+Neural+Network+-+Step+by+Step+v5.ipynb

1

56732

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Building your Deep Neural Network: Step by Step\n", "\n", "Welcome to your week 4 assignment (part 1 of 2)! You have previously trained a 2-layer Neural Network (with a single hidden layer). This week, you will build a deep n...

mit

Saytiras/StalkerML

Generate Topic Models LDA.ipynb

2

9253

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import logging\n", "from os import path\n", "from pprint import pprint\n", "import pandas as pd\n", "from corputil import ListCorpus\n", "from corputil.utils import load_sto...

gpl-2.0

cliburn/sta-663-2017

homework/05_Making_Python_Faster_Solutions.ipynb

1

226105

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "%load_ext Cython" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": ...

mit

nravi89/nlphu

HW8.ipynb

1

5957

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "###Exercise from http://www.nltk.org/book_1ed/ch09.html\n", "###Author : Nirmal kumar Ravi" ] }, { "cell_typ...

bsd-2-clause

GoogleCloudPlatform/vertex-ai-samples

community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/mlops_pipeline_tf_agents_bandits_movie_recommendation.ipynb

1

69546

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "ur8xi4C7S06n" }, "outputs": [], "source": [ "# Copyright 2021 Google LLC\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", ...

apache-2.0

pbnator18/labs

Spring Oscillation Lab.ipynb

1

32077

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Spring Oscillation Relationship of Time and Mass" ] }, { ...

gpl-3.0

Unidata/unidata-python-workshop

notebooks/Surface_Data/Surface Data with Siphon and MetPy.ipynb

1

16315

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<a name=\"top\"></a>\n", "<div style=\"width:1000 px\">\n", "\n", "<div style=\"float:right; width:98 px; height:98px;\">\n", "<img src=\"https://raw.githubusercontent.com/Unidata/MetPy/master/metpy/plots/_static/unidat...

mit

Cyb3rWard0g/HELK

docker/helk-jupyter/notebooks/sigma/win_susp_procdump.ipynb

1

3589

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Suspicious Use of Procdump\n", "Detects suspicious uses of the SysInternals Procdump utility by using a special command line parameter in combination with the lsass.exe process. This way we're also able to catch cases in which th...

gpl-3.0

calico/basenji

manuscripts/akita/tutorial.ipynb

1

55506

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Required inputs for Akita are:\n", "* binned Hi-C or Micro-C data stored in cooler format (https://github.com/mirnylab/cooler)\n", "* Genome FASTA file\n", "\n", "First, make sure you have a FASTA file available consist...

apache-2.0

jts/nanocorrect

nicks_notebooks/2014-12-08-try-r7-data-again-for-more-coverage.ipynb

1

96499

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Currently the assemblies are topping out at around N50 ~100kb. Not bad, but not where we need to be.\n", "\n", "Although...

mit

balmandhunter/jupyter-tips-and-tricks

notebooks/04-More_basics.ipynb

4

748309

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Jupyter Notebook Basics " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "names = ['alice', 'jonathan', 'bobby']\n", "ages = [24, 3...

mit

tensorflow/docs

site/en/tutorials/images/cnn.ipynb

2

12130

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "x4HI2mpwlrcn" }, "source": [ "##### Copyright 2019 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "i...

apache-2.0

dsteurer/cs4814fa15

turing/turing.ipynb

1

28866

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/david/anaconda3/lib/python3.5/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package ...

mit

mne-tools/mne-tools.github.io

stable/_downloads/64e3b6395952064c08d4ff33d6236ff3/evoked_whitening.ipynb

1

4928

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Whitening ...

bsd-3-clause

gutouyu/cs231n

cs231n/assignment/assignment2/TensorFlow.ipynb

3

31003

{ "nbformat_minor": 1, "nbformat": 4, "cells": [ { "source": [ "## What's this TensorFlow business?\n", "\n", "You've written a lot of code in this assignment to provide a whole host of neural network functionality. Dropout, Batch Norm, and 2D convolutions are some of the workhorses of deep lear...

mit

pfschus/fission_bicorrelation

methods/build_det_df_angles_pairs.ipynb

1

471349

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 id=\"tocheading\">Table of Contents</h1>\n", "<div id=\"toc\"></div>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "appl...

mit

OzFlux/Peters-iPython-notebooks

compare_OzFluxQC_EddyPro.ipynb

1

10324

{ "metadata": { "name": "", "signature": "sha256:74a9d0b68127f1e40ead95526b68137ae493073dd11c07e5a8aa3cee3bf7f1f0" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%run basics\n", "%matplotlib" ],...

gpl-3.0

tchmiel/TJC-CarND-BehavioralTraining-P3

P3_PreprocessModel.ipynb

1

765611

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Behaviorial Cloning Project\n", "## Thomas J. Chmielenski\n", "### September 2017\n", "\n", "This notebook is a utility notebook to help with experimenting and preprocessing the training set data.\n", "Utility fun...

mit

InsightSoftwareConsortium/SimpleITK-Notebooks

Python/02_Pythonic_Image.ipynb

2

9185

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pythonic Syntactic Sugar <a href=\"https://mybinder.org/v2/gh/InsightSoftwareConsortium/SimpleITK-Notebooks/master?filepath=Python%2F02_Pythonic_Image.ipynb\"><img style=\"float: right;\" src=\"https://mybinder.org/badge_logo.svg\"><...

apache-2.0

DAInamite/programming-humanoid-robot-in-python

kinematics/inverse_kinematics_2d_autograd.ipynb

1

428735

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Inverse Kinematics (2D) \n", "with https://github.com/HIPS/autograd" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "from matpl...

gpl-2.0

WMD-group/SMACT

examples/Inverse_perovskites/Inverse_formate_perovskites.ipynb

1

36334

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Formate perovskites\n", "\n", "Perovskites are an important class of inorganic materials, particularly for solar energy applications.\n", "Usually, the perovskite structure contains an A cation, a B cation and an...

mit

ultrareality/mat201a

hw1/samplingdemo.ipynb

1

20474

{ "metadata": { "name": "", "signature": "sha256:8ebf1e53c6d8e364ad9b913822b1e4941edcd22e3fbbc22d83136cd1cff42b41" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "py...

mit

StingraySoftware/notebooks

Simulator/Lag Analysis.ipynb

1

102867

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Contents" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook analyses lag-frequency spectrums of the light curves simulated through impulse response approach. First, a simple case with del...

mit

moizumi99/CVBookExercise

Chapter-5/CV Book Ch 5 Ex 6.ipynb

1

534564

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from PIL import Image\n", "from numpy import *\n", "from pylab import *\n", "import scipy.misc\n", "from scipy import ndimage\n", "import numpy as np"...

unlicense

theandygross/TCGA_differential_expression

Notebooks/OneOffs/James_DX_methy.ipynb

1

113902

{ "cells": [ { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'/cellar/users/agross/TCGA_Code/DX/Notebooks'" ] }, "execution_count": 122, "metadata": {}, "output_type": "exe...

mit

dereneaton/ipyrad

newdocs/API-analysis/cookbook-vcf2hdf5.ipynb

1

243371

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <span style=\"color:gray\">ipyrad-analysis toolkit:</span> vcf_to_hdf5\n", "\n", "[View as notebook](https://nbviewer.jupyter.org/github/dereneaton/ipyrad/blob/master/newdocs/API-analysis/cookbook-vcf2hdf5.ipynb)\n", "\n"...

gpl-3.0

PytLab/catplot

examples/energy_profile_examples/simple_use.ipynb

1

17701

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# catplot 的简单使用" ] }, { "cell_type": "code", "execution_...

mit

therealAJ/python-sandbox

data-science/learning/ud1/DataScience/Python101.ipynb

1

13272

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python Basics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Whitespace Is Important" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, ...

gpl-3.0

Unidata/MetPy

talks/MetPy Exercise.ipynb

5

10734

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from datetime import datetime, timedelta\n", "from siphon.catalog import TDSCatalog\n", "from siphon.ncss import NCSS\n", "import numpy as np\n", "impo...

bsd-3-clause

james-prior/cohpy

20160826-dojo-regex-travis.ipynb

1

12106

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function\n", "import re" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start with the original r...

mit

GuidoBR/python-for-finance

Markowitz+-+Bovespa.ipynb

1

228330

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'numpy'", "output_type": "error", "traceback": [ "\u001b[0;31m-----------------------------------------------------------------...

mit

walchko/pygecko

dev/images/image-message.ipynb

1

1075642

null

mit

ShantanuKamath/PythonWorkshop

2. Python Advanced.ipynb

1

51409

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python Advanced\n", "\n", "***Disclaimer*** *-* *This document is only meant to serve as a reference for the attendees of the workshop. It does not cover all the concepts or implementation details discussed during the actual ...

mit

fcollonval/coursera_data_visualization

Making_Data_Management.ipynb

1

141471

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Assignment: Making Data Management Decisions - Python\n", "\n", "Following is the Python program I wrote to fulfill the third assignment of the [Data Management and Visualization online course]( https://www.coursera.org/learn...

mit

amueller/advanced_training

05.1 Trees and Forests.ipynb

1

374567

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Trees and Forests" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib notebook\n", "from preamble import *" ] }, {...

bsd-2-clause

jrbourbeau/composition

analysis/parameter-tuning/KS-test.ipynb

1

60789

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jbourbeau/.local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is ...

mit

Jwuthri/Mozinor

mozinor/example/Mozinor example Class.ipynb

1

114565

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Use mozinor for classification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Import the main module" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outpu...

mit

aidiss/disciplines

ipynb/1 Theories.ipynb

1

10545

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook introduces major package in disciplines - *theory*.\n", "*Theory* is a package dedicated to implement theories.\n", "It enables easy reading, and supports developer with tools to implement theories into Python cod...

mit

QuLogic/folium

examples/GeodedeticImageOverlay.ipynb

1

31335

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5.0+105.g065f6f3.dirty\n" ] } ], "source": [ "import os\n", "import folium\n", "\n", "print(folium.__ver...

mit

danresende/deep-learning

autoencoder/Convolutional_Autoencoder.ipynb

1

408854

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Convolutional Autoencoder\n", "\n", "Sticking with the MNIST dataset, let's improve our autoencoder's performance using convolutional layers. Again, loading modules and the data." ] }, { "cell_type": "code", "exe...

mit

avtlearns/automatic_text_summarization

TextRank_Automatic_Summarization_for_Medical_Articles.ipynb

1

19243

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Automatic Summarization of Medical Articles\n", "### Author: Abhijit V Thatte" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Solution:__ We will use TextRank for automatic summarization of med...

gpl-3.0

mne-tools/mne-tools.github.io

0.24/_downloads/b7659d33d6ffe8531d004e9d6051f16f/forward_sensitivity_maps.ipynb

1

6052

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Display se...

bsd-3-clause

rohinkumar/galsurveystudy

APTest/.ipynb_checkpoints/DR72_VAGC_correl_AP_MI_V06-fast-cdist-rr200k-opt-checkpoint.ipynb

1

80132

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Correlation function of DR72 SDSS VAGC Catalog" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First import all the modules such as healpy and astropy needed for analyzing the structure" ] }, ...

mit

phungkh/phys202-2015-work

assignments/assignment05/InteractEx03.ipynb

1

39699

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Interact Exercise 3" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 105, "meta...

mit

mysticPrince/hcpf

algos/.ipynb_checkpoints/plot_graphs-checkpoint.ipynb

1

62704

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, ...

mit

daniorerio/trackpy

benchmarks/maxima_benchmarks.ipynb

2

1054

# must be run in ipython import numpy as np import trackpy as tp from trackpy.preprocessing import scale_to_gamut def b(command): get_ipython().magic(command) dummy_noise_image = scale_to_gamut(np.random.randint(0, 100, (100, 100)), np.uint8) real_image_raw = tp.ImageSequence('../trackpy/tests/video/image_sequ...

bsd-3-clause

CalPolyPat/Python-Workshop

Python Workshop/Containers.ipynb

2

15684

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##Containers\n", "Containers are set of objects including **lists**, **sets**, **tuples**, and **dictionaries**. They hold other variables in various different ways following different rules. These are arguably the most used, and m...

mit

drphilmarshall/LocalGroupHaloProps

Notebooks/Untitled1.ipynb

1

36373

{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "h = ...

gpl-2.0

Jim00000/Numerical-Analysis

2_Systems_Of_Equations.ipynb

1

55943

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# CHAPTER 2 - Systems Of Equations" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import modules\n", "import sys\n", "import numpy as np\n", "impor...

unlicense

jeicher/cobrapy

documentation_builder/solvers.ipynb

3

15731

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Solver Interface\n", "\n", "Each cobrapy solver must expose the following API. The solvers all will have their own distinct LP object types, but each can be manipulated by these functions. This API can be used directly when i...

lgpl-2.1

ucsc-astro/coffee

15_01_27_sqlalchemy/Learning SQLAlchemy.ipynb

1

39280

{ "metadata": { "name": "", "signature": "sha256:1834ef0c2b3c1bae82186376fd5217f01a31d10dde6b09c086f67e4f5ca1eda6" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1>Learning SQLAlchemy</h1>\n", "<t...

gpl-3.0

kfollette/ASTR200-Spring2017

Labs/Lab9/Lab 9.ipynb

2

27075

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### *** Names: [Insert Your Names Here]***" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Lab 9 - Data Investigation 1 (Week 1) - Educational Research Data\n", "\n", "## Lab 9 Contents\n", ...

mit

mikemcfarlane/TickleMeNAO

Idea_Development/Microphone_anomaly_analysis_from_Jonny.ipynb

1

263204

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from pysoundfile import SoundFile\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "import nu...

gpl-2.0

racu10/emapy

notebooks/test funcions.ipynb

1

2234134

null

bsd-3-clause

cathalmccabe/PYNQ

boards/Pynq-Z1/base/notebooks/video/opencv_face_detect_hdmi.ipynb

4

5510529

null

bsd-3-clause

weikang9009/pysal

notebooks/explore/spaghetti/Spaghetti_Pointpatterns_Empirical.ipynb

5

9410

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "---------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# $SPA$tial $G$rap$H$s: n$ET$works, $T$opology, & $I$nference\n", "\n", "## Tutorial for `pysal.spaghetti...

bsd-3-clause

JudoWill/ResearchNotebooks

Dream2Problem.ipynb

1

6048

{ "metadata": { "name": "Dream2Problem" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import division\n", "import os, os.path\n", "from pandas import read_csv, DataFrame, Series,...

mit

ahuang11/ahh

examples/vis/plot_map_CENTRAL_LONGITUDE.ipynb

1

480370

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from ahh import vis, exp" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ ...

mit

jldinh/multicell

examples/06 - Growth and divisions.ipynb

1

821916

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Preparation" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "%matplotlib notebook" ] }, { "cell_type":...

mit

barakovic/COMMIT

doc/tutorials/AdvancedSolvers/tutorial_solvers.ipynb

1

8442

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "You can find the text version of this tutorial [at this link](README.md)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Advanced solvers\n", "\n", "This tutorial shows how to exploit the ad...

gpl-3.0

jquacinella/IS608_Project

.ipynb_checkpoints/IS608 Project-checkpoint.ipynb

1

16263

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gpl-2.0

darkomen/TFG

medidas/20072015/FILAEXTRUDER/.ipynb_checkpoints/Analisis-checkpoint.ipynb

1

287636

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Análisis de los datos obtenidos " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Producción del día 20 de Julio de 2015\n", "\n", "Los datos del experimento:\n", "* Hora de inicio: 16:08\...

cc0-1.0

barbagroup/JITcode-MechE

module00_Introduction_to_Python/01_Lesson01_Playing_with_data.ipynb

2

431062

{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "Content provided under a Creative Commons Attribution license, CC-BY 4.0.\n", "(c) 2014 L.Barba, P.Bardet, A.Wickenheiser (The George Washington University).\n", "Thanks: A. Ahmadia, G. Forsyth, A. Golding, NSF for support via CAREE...

mit

gvanhorn38/nobal

index.ipynb

1

274118

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook implements the decision making algorithm presented in the paper [Near Optimal Bayesian Active Learning for Decision Making](http://www.ri.cmu.edu/pub_files/2014/4/javdani14hec_extended.pdf) by [Shervin Javdani](http://www...

mit

ecervera/Baxter-Vision

00 00 Lowercase Folder and File Names.ipynb

1

1306

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import glob, json, os.path\n", "\n", "with open('parameters.json', 'r') as infile:\n", " params = json.load(infile)\n", "\n", "ITEM_FOLDER = pa...

mit

amueller/nyu_ml_lectures

Unsupervised Transformers.ipynb

1

8022

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib nbagg\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ...

bsd-2-clause

airanmehr/bio

Scripts/MultiLociSelection/.ipynb_checkpoints/MultiLociSelection-checkpoint.ipynb

1

722912

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mit