repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
karolaya/PDI | PS-05/.ipynb_checkpoints/problem_set_5-checkpoint.ipynb | mit | '''This is a definition script, so we do not have to rewrite code'''
import numpy as np
import os
import cv2
import matplotlib.pyplot as mplt
import random
import json
# set matplotlib to print inline (Jupyter)
%matplotlib inline
# path prefix
pth = '../data/'
# files to be used as samples
# list *files* holds the... |
WomensCodingCircle/CodingCirclePython | Lesson10_Regexs/RegularExpressions.ipynb | mit | import re
# To run the examples we are going to use some of the logs from the
# django project, a web framework for python
django_logs = '''commit 722344ee59fb89ea2cd5b906d61b35f76579de4e
Author: Simon Charette <charette.s@gmail.com>
Date: Thu May 19 09:31:49 2016 -0400
Refs #24067 -- Fixed contenttypes renam... |
ES-DOC/esdoc-jupyterhub | notebooks/nims-kma/cmip6/models/sandbox-3/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nims-kma', 'sandbox-3', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: NIMS-KMA
Source ID: SANDBOX-3
Topic: Seaice
Sub-Topics: Dynamics, Thermodynami... |
skdaccess/skdaccess | skdaccess/examples/Demo_SRTM.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['figure.dpi'] = 150
import numpy as np
from getpass import getpass
from skdaccess.geo.srtm.cache import DataFetcher as SDF
"""
Explanation: The MIT License (MIT)<br>
Copyright (c) 2017 Massachusetts Institute of Technology<br>
Author: Cody Rude<br>
This ... |
cougarTech2228/Scouting-2016 | notebooks/robocop.ipynb | mit | # Object oriented approach, would have to feed csv data into objects
# maybe get rid of this and just use library analysis tools
class Robot(object):
def __init__(self, name, alliance, auto_points, points):
self.name = name
self.alliance = alliance
self.auto_points = auto_points
self.points = points
def p... |
california-civic-data-coalition/python-calaccess-notebooks | tutorials/first-python-notebook.ipynb | mit | 2+2
"""
Explanation: First Python Notebook: Scripting your way to the story
By Ben Welsh
A step-by-step guide to analyzing data with Python and the Jupyter Notebook.
This tutorial will teach you how to use computer programming tools to analyze data by exploring contributors to campaigns for and again Proposition 64, a... |
henchc/Rediscovering-Text-as-Data | 05-Intro-to-SpaCy/01-Intro-to-SpaCy.ipynb | mit | from datascience import *
import spacy
"""
Explanation: SpaCy: Industrial-Strength NLP
The tradtional NLP library has always been NLTK. While NLTK is still very useful for linguistics analysis and exporation, spacy has become a nice option for easy and fast implementation of the NLP pipeline. What's the NLP pipeline? ... |
phoebe-project/phoebe2-docs | 2.2/examples/legacy.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Comparing PHOEBE 2 vs PHOEBE Legacy
NOTE: PHOEBE 1.0 legacy is an alternate backend and is not installed with PHOEBE 2. In order to run this backend, you'll need to have PHOEBE 1.0 installed and manually build the python bindings in the phoebe-py directory.
Setup
Le... |
AllenDowney/ModSimPy | notebooks/jump2.ipynb | mit | # Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim.py module
from modsim import *
"""
Explanation: Modeling and Simulati... |
diegocavalca/Studies | deep-learnining-specialization/1. neural nets and deep learning/week2/Logistic+Regression+with+a+Neural+Network+mindset+v4.ipynb | cc0-1.0 | import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
from lr_utils import load_dataset
%matplotlib inline
"""
Explanation: Logistic Regression with a Neural Network mindset
Welcome to your first (required) programming assignment! You will build a ... |
emsi/ml-toolbox | random/catfish/TL_02-1_Fixed feature extraction (CNN Codes vel bottleneck) Max Pooling.ipynb | agpl-3.0 | from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import zipfile
from IPython.display import display, Image
from scipy import ndimage
from sklearn.linear_model import LogisticRegression
from six.moves.urllib.request import urlretrieve
from six.moves import cPi... |
ES-DOC/esdoc-jupyterhub | notebooks/ipsl/cmip6/models/sandbox-2/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ipsl', 'sandbox-2', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: IPSL
Source ID: SANDBOX-2
Topic: Landice
Sub-Topics: Glaciers, Ice.
Properties:... |
minireference/noBSLAnotebooks | chapter04_problems.ipynb | mit | # helper code needed for running in colab
if 'google.colab' in str(get_ipython()):
print('Downloading plot_helpers.py to util/ (only neded for colab')
!mkdir util; wget https://raw.githubusercontent.com/minireference/noBSLAnotebooks/master/util/plot_helpers.py -P util
# setup SymPy
from sympy import *
init_pri... |
charlesreid1/empirical-model-building | ipython/Factorial - Two-Level Six-Factor Design.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
from numpy.random import rand, seed
import seaborn as sns
import scipy.stats as stats
from matplotlib.pyplot import *
seed(10)
"""
Explanation: A Two-Level, Six-Factor Full Factorial Design
<br />
<br />
<br />
Table of Contents
Introduction
Factorial Experi... |
srcole/qwm | burrito/Burrito_nonlinear.ipynb | mit | %config InlineBackend.figure_format = 'retina'
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import pandas as pd
import statsmodels.api as sm
import pandasql
import seaborn as sns
sns.set_style("white")
"""
Explanation: San Diego Burrito Analytics: Data characterization
Sco... |
esa-as/2016-ml-contest | JesperDramsch/Facies_classification_NMM_Split-Jesper.ipynb | apache-2.0 | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable
from pandas import set_option
set_option("display.max_rows", 10)
pd.set_option('display.width', 1000)
pd.options.... |
ioggstream/python-course | ansible-101/notebooks/05_inventories.ipynb | agpl-3.0 | cd /notebooks/exercise-05
!cat inventory
"""
Explanation: Inventories
Inventories are a fundamental doc entrypoint for our infrastructures.
They contain a lot of informations, including:
- ansible_user
- configuration variables in [group_name:vars]
- host grouping eg. by geographical zones in [group_name:children]
... |
StingraySoftware/notebooks | Simulator/Simulator Tutorial.ipynb | mit | %load_ext autoreload
%autoreload 2
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
"""
Explanation: Contents
This notebook covers the basics of initializing and using the functionalities of simulator class. Various ways of simulating light curves that include 'power law distribution', 'user-... |
jdvelasq/pytimeseries | pytimeseries/PyTimeSeries.ipynb | mit | import pytimeseries
import pandas
import matplotlib
"""
Explanation: PyTimeSeries
Test for pytimeseries package
Importamos la librería
End of explanation
"""
tserie = pandas.read_csv('champagne.csv', index_col='Month')
print(tserie)
"""
Explanation: Preparación de los datos
pytimeseries recibe una serie de pandas p... |
phoebe-project/phoebe2-docs | 2.2/examples/contact_spots.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
%matplotlib inline
"""
Explanation: Contact Binary with Spots
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
... |
zaqwes8811/micro-apps | self_driving/deps/Kalman_and_Bayesian_Filters_in_Python_master/03-Gaussians.ipynb | mit | from __future__ import division, print_function
%matplotlib inline
#format the book
import book_format
book_format.set_style()
"""
Explanation: Table of Contents
Probabilities, Gaussians, and Bayes' Theorem
End of explanation
"""
import numpy as np
import kf_book.book_plots as book_plots
belief = np.array([1, 4, 2... |
FiryZeplin/deep-learning | dcgan-svhn/DCGAN.ipynb | mit | %matplotlib inline
import pickle as pkl
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat
import tensorflow as tf
!mkdir data
"""
Explanation: Deep Convolutional GANs
In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a De... |
tensorflow/examples | courses/udacity_intro_to_tensorflow_lite/tflite_c01_linear_regression.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
tkurfurst/deep-learning | gan_mnist/Intro_to_GANs_Exercises.ipynb | mit | %matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
newsapps/public-notebooks | Red Light Camera Locations.ipynb | mit | few_crashes_url = 'http://www.arcgis.com/sharing/rest/content/items/5a8841f92e4a42999c73e9a07aca0c23/data?f=json&token=lddNjwpwjOibZcyrhJiogNmyjIZmzh-pulx7jPD9c559e05tWo6Qr8eTcP7Deqw_CIDPwZasbNOCSBHfthynf-8WRMmguxHbIFptbZQvnpRupJHSY8Abrz__xUteBS93MitgvoU6AqSN5eDVKRYiUg..'
removed_url = 'http://www.arcgis.com/sharing/re... |
LucaCanali/Miscellaneous | PLSQL_Neural_Network/MNIST_tensorflow_exp_to_oracle.ipynb | apache-2.0 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# Import data
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string('data_dir', '/tmp/data/', 'Directory for storing da... |
ssanderson/pstats-view | examples/ExampleView.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
import cProfile
from pstatsviewer import StatsViewer
from qgrid import nbinstall
nbinstall()
# Construct two 5000 x 8 frames with random floats.
df1 = pd.DataFrame(
np.random.randn(5000, 8),
columns=[chr(ord('A') + i) for i in range(8)],
index=rang... |
nickdavidhaynes/python-data-science-intro | week_2/dealing_with_data.ipynb | mit | def to_integer(x):
the_sum = 0
for index, val in enumerate(x[::-1]):
the_sum += val * 2 ** index
return the_sum
# [1, 1] == 3
to_integer([1, 0, 0, 0, 1, 1, 0, 1])
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Week-2:-Dealing-with-data" data-toc-modified-id="Week-2... |
CAChemE/curso-python-datos | notebooks_vacios/022-matplotlib-GeoData-cartopy.ipynb | bsd-3-clause | # Inicializamos una figura con el tamaño que necesitemos
# si no la queremos por defecto
# Creamos unos ejes con la proyección que queramos
# por ejemplo, Mercator
# Y lo que queremos representar en el mapa
# Tierra
# Océanos
# Líneas de costa (podemos modificar el color)
# Fronteras
# Ríos y lagos
# Por último, podemo... |
dxl0632/deeplearning_nd_udacity | embeddings/Skip-Gram_word2vec.ipynb | mit | import time
import numpy as np
import tensorflow as tf
import utils
"""
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language p... |
computational-class/computational-communication-2016 | code/03.python_intro.ipynb | mit | import random, datetime
import numpy as np
import pylab as plt
import statsmodels.api as sm
from scipy.stats import norm
from scipy.stats.stats import pearsonr
"""
Explanation: 数据科学的编程工具
Python使用简介
王成军
wangchengjun@nju.edu.cn
计算传播网 http://computational-communication.com
人生苦短，我用Python。
Python（/ˈpaɪθən/）是一种面向对象、解释型计... |
brettavedisian/phys202-2015-work | assignments/assignment05/InteractEx04.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 4
Imports
End of explanation
"""
def random_line(m, b, sigma, size=10):
"""Create a line y = m*x + b + N(0,si... |
enakai00/jupyter_tfbook | Chapter02/MNIST softmax estimation.ipynb | gpl-3.0 | import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
np.random.seed(20160604)
"""
Explanation: [MSE-01] モジュールをインポートして、乱数のシードを設定します。
End of explanation
"""
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
"""
Explanation: ... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session04/Day1/LSSTC-DSFP4-Juric-FrequentistAndBayes-03-Credibility.ipynb | mit | import numpy as np
N = 5
Nsamp = 10 ** 6
sigma_x = 2
np.random.seed(0)
x = np.random.normal(0, sigma_x, size=(Nsamp, N))
mu_samp = x.mean(1)
sig_samp = sigma_x * N ** -0.5
print("{0:.3f} should equal {1:.3f}".format(np.std(mu_samp), sig_samp))
"""
Explanation: Frequentism and Bayesianism III: Confidence, Credibilit... |
karenlmasters/ComputationalPhysicsUnit | IntroductiontoPython/UserDefinedFunction.ipynb | apache-2.0 | import numpy as np
import scipy.constants as constants
print('Pi = ', constants.pi)
h = float(input("Enter the height of the tower (in metres): "))
t = float(input("Enter the time interval (in seconds): "))
s = constants.g*t**2/2
print("The height of the ball is",h-s,"meters")
"""
Explanation: User Defined Functions
... |
ilyankou/passport-index-dataset | Update.ipynb | mit | import requests
import pandas as pd
import json
codes = pd.read_csv(
'https://gist.githubusercontent.com/ilyankou/b2580c632bdea4af2309dcaa69860013/raw/420fb417bcd17d833156efdf64ce8a1c3ceb2691/country-codes',
dtype=str
).fillna('NA').set_index('ISO2')
def fix_iso2(x):
o = {
'UK': 'GB',
'RK'... |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/cmip6/models/sandbox-1/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'sandbox-1', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: MOHC
Source ID: SANDBOX-1
Sub-Topics: Radiative Forcings.
Properties: 85 (42 ... |
awjuliani/DeepRL-Agents | Policy-Network.ipynb | mit | from __future__ import division
import numpy as np
try:
import cPickle as pickle
except:
import pickle
import tensorflow as tf
%matplotlib inline
import matplotlib.pyplot as plt
import math
try:
xrange = xrange
except:
xrange = range
"""
Explanation: Simple Reinforcement Learning in Tensorflow Part 2... |
CLEpy/CLEpy-MotM | Scrapy_nb/Quotes base case.ipynb | mit | # Settings for notebook
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
# Show Python version
import platform
platform.python_version()
try:
import scrapy
except:
!pip install scrapy
import scrapy
from scrapy.crawler import CrawlerProcess
"""
Ex... |
scraperwiki/databaker | databaker/tutorial/Introduction.ipynb | agpl-3.0 | from databaker.framework import *
tab = loadxlstabs("example1.xls", "beatles", verbose=False)[0]
savepreviewhtml(tab, verbose=False)
"""
Explanation: Introduction
Databaker is an Open Source Python library for converting semi-structured spreadsheets into computer-friendly datatables. The resulting data can be store... |
rishuatgithub/MLPy | torch/PYTORCH_NOTEBOOKS/03-CNN-Convolutional-Neural-Networks/06-CNN-Exercises-Solutions.ipynb | apache-2.0 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from torchvision.utils import make_grid
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
transform = transforms.ToTensor()
t... |
AllenDowney/ThinkStats2 | code/chap04ex.ipynb | gpl-3.0 | import numpy as np
from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename):
from urllib.request import urlretrieve
local, _ = urlretrieve(url, filename)
print("Downloaded " + local)
download("https://github.com/AllenDowney/ThinkStats... |
computational-class/computational-communication-2016 | code/09.machine_learning_with_sklearn.ipynb | mit | %matplotlib inline
from sklearn import datasets
from sklearn import linear_model
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report
from sklearn.preprocessing import scale
import sklearn
print sklearn.__version__
# boston data
boston = datasets.load_boston()
y = boston.target
X = bosto... |
saturn77/CythonBootstrap | .ipynb_checkpoints/CythonBootstrap-checkpoint.ipynb | gpl-2.0 | %%file ./src/helloCython.pyx
import cython
import sys
def message():
print(" Hello World ....\n")
print(" Hello Central Ohio Python User Group ...\n")
print(" The 614 > 650::True")
print(" Another line ")
print(" The Python version is %s" % sys.version)
print(" The Cython version is %s" % cyt... |
brinkar/real-world-machine-learning | Chapter 3 - Modeling and prediction.ipynb | mit | %pylab inline
"""
Explanation: Chapter 3 - Modeling and prediction
End of explanation
"""
import pandas
data = pandas.read_csv("data/titanic.csv")
data[:5]
# We make a 80/20% train/test split of the data
data_train = data[:int(0.8*len(data))]
data_test = data[int(0.8*len(data)):]
"""
Explanation: The Titanic datas... |
tritemio/PyBroMo | notebooks/PyBroMo - 2. Generate smFRET data, including mixtures.ipynb | gpl-2.0 | %matplotlib inline
from pathlib import Path
import numpy as np
import tables
import matplotlib.pyplot as plt
import seaborn as sns
import pybromo as pbm
print('Numpy version:', np.__version__)
print('PyTables version:', tables.__version__)
print('PyBroMo version:', pbm.__version__)
"""
Explanation: PyBroMo - 2. Genera... |
GoogleCloudPlatform/tensorflow-without-a-phd | tensorflow-mnist-tutorial/keras_01_mnist.ipynb | apache-2.0 | BATCH_SIZE = 128
EPOCHS = 10
training_images_file = 'gs://mnist-public/train-images-idx3-ubyte'
training_labels_file = 'gs://mnist-public/train-labels-idx1-ubyte'
validation_images_file = 'gs://mnist-public/t10k-images-idx3-ubyte'
validation_labels_file = 'gs://mnist-public/t10k-labels-idx1-ubyte'
"""
Explanation... |
BrentDorsey/pipeline | gpu.ml/notebooks/09_Deploy_Optimized_Model.ipynb | apache-2.0 | from tensorflow.python.tools import freeze_graph
optimize_me_parent_path = '/root/models/optimize_me/linear/cpu'
fully_optimized_model_graph_path = '%s/fully_optimized_cpu.pb' % optimize_me_parent_path
fully_optimized_frozen_model_graph_path = '%s/fully_optimized_frozen_cpu.pb' % optimize_me_parent_path
model_checkp... |
ant0nisk/pybrl | docs/Samples/pdf_translation/Notebook.ipynb | gpl-3.0 | # Load our dependencies
import pybrl as brl
filename = "lorem_ipsum.pdf" # of course :P
pdf_password = None
language = 'english'
# Let's translate the PDF file.
translated = brl.translatePDF(filename, password = pdf_password, language = language) # Easy, right?
# Let's explore what this object looks like:
print... |
colour-science/colour-hdri | colour_hdri/examples/examples_variance_minimization_light_probe_sampling.ipynb | bsd-3-clause | import os
from pprint import pprint
import colour
from colour_hdri import (
EXAMPLES_RESOURCES_DIRECTORY,
light_probe_sampling_variance_minimization_Viriyothai2009,
)
from colour_hdri.sampling.variance_minimization import (
find_regions_variance_minimization_Viriyothai2009,
highlight_regions_variance_... |
AlJohri/DAT-DC-12 | notebooks/10_linear_regression_ml.ipynb | mit | # read the data and set the datetime as the index
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (8, 6)
plt.rcParams['font.size'] = 14
import pandas as pd
urls = ['../data/KDCA-201601.csv', '../data/KDCA-201602.csv', '../data/KDCA-201603.csv']
frames = [pd.read... |
drcgw/bass | Single Wave- Basic.ipynb | gpl-3.0 | from bass import *
"""
Explanation: Welcome to BASS!
Single Wave Analysis Notebook. This is the basic version.
BASS: Biomedical Analysis Software Suite for event detection and signal processing.
Copyright (C) 2015 Abigail Dobyns
This program is free software: you can redistribute it and/or modify
it under the terms ... |
KMFleischer/PyEarthScience | Data_Analysis/convert_ascii_to_netcdf.ipynb | mit | import numpy as np
from cdo import *
import csv
"""
Explanation: Convert a CSV data to netCDF
Read the CSV file, generate the gridfile from the CSV lon and lat data,
write data to file. Then use cdo to write the data to an netCDF file.
read the ASCII file
generate the gridfile
write netcdf file
Input data data/1901... |
OpenAstronomy/workshop_sunpy_astropy | 03-python2-functions-instructors.ipynb | mit | # Let's get our import statements out of the way first
from __future__ import division, print_function
import numpy as np
import glob
import matplotlib.pyplot as plt
%matplotlib inline
def kelvin_to_celsius(temp):
return temp - 273.15
"""
Explanation: Introduction to Python 2
Creating Functions
<section class="ob... |
cod3licious/simec | 00_matrix_factorization.ipynb | mit | from __future__ import unicode_literals, division, print_function, absolute_import
import numpy as np
np.random.seed(28)
import matplotlib.pyplot as plt
import tensorflow as tf
tf.set_random_seed(28)
import keras
from simec import SimilarityEncoder
%matplotlib inline
%load_ext autoreload
%autoreload 2
def msqe(A, B)... |
piklprado/ode_examples | Qualitative analysis and Bifurcation diagram Tutorial.ipynb | mit | %matplotlib inline
from numpy import *
from scipy.integrate import odeint
from matplotlib.pyplot import *
ion()
def RM(y, t, r, K, a, h, e, d):
return array([ y[0] * ( r*(1-y[0]/K) - a*y[1]/(1+a*h*y[0]) ),
y[1] * (e*a*y[0]/(1+a*h*y[0]) - d) ])
t = arange(0, 1000, .1)
y0 = [1, 1.]
pars = (1., 1... |
kongjy/hyperAFM | Tutorials/Image Registration Tutorial.ipynb | mit | #for igor files:
!curl -o util.py https://raw.githubusercontent.com/kongjy/hyperAFM/master/hyperAFM/util.py
#for image alignment:
!curl -o imagealignment.py https://raw.githubusercontent.com/kongjy/hyperAFM/master/hyperAFM/imagealignment.py
#the above will download the files at the specified URL and save them as the... |
ALEXKIRNAS/DataScience | Python_for_data_analysis/Chapter_11/Chapter_11.ipynb | mit | from pandas import Series, DataFrame
import pandas as pd
from numpy.random import randn
import numpy as np
pd.options.display.max_rows = 12
np.set_printoptions(precision=4, suppress=True)
import matplotlib.pyplot as plt
plt.rc('figure', figsize=(12, 6))
%matplotlib inline
"""
Explanation: Financial and Economic Data ... |
fazzolini/fast_ai | deeplearning1/nbs/lesson4.ipynb | apache-2.0 | ratings = pd.read_csv(path+'ratings.csv')
ratings.head()
len(ratings)
"""
Explanation: Set up data
We're working with the movielens data, which contains one rating per row, like this:
End of explanation
"""
movie_names = pd.read_csv(path+'movies.csv').set_index('movieId')['title'].to_dict
users = ratings.userId.un... |
usantamaria/iwi131 | ipynb/01-Intro1/Introduccion.ipynb | cc0-1.0 | a, b = 2, 3
while b < 300:
print b,
a, b = b, a+b
"""
Explanation: <header class="w3-container w3-teal">
<img src="images/utfsm.png" alt="" align="left"/>
<img src="images/inf.png" alt="" align="right"/>
</header>
<br/><br/><br/><br/><br/>
IWI131
Programación de Computadores
Sebastián Flores
¿Qué contenido apr... |
bxin/cwfs | examples/AuxTel.ipynb | gpl-3.0 | from lsst.cwfs.instrument import Instrument
from lsst.cwfs.algorithm import Algorithm
from lsst.cwfs.image import Image, readFile, aperture2image, showProjection
import lsst.cwfs.plots as plots
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Patrick provided a pair of images fr... |
luctrudeau/DaalaNotebooks | CFL/DCT-Domain Subsampling.ipynb | mpl-2.0 | %matplotlib inline
import sys
import y4m
import matplotlib.pyplot as plt
import numpy as np
def decode_y4m_buffer(frame):
W, H = frame.headers['W'], frame.headers['H']
Wdiv2, Hdiv2 = W // 2, H // 2
C, buf = frame.headers['C'], frame.buffer
A, Adiv2, div2 = W * H, Hdiv2 * Wdiv2, (Hdiv2, Wdiv2)
dtyp... |
thaophung/Udacity_deep_learning | tv-script-generation/dlnd_tv_script_generation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scrip... |
VectorBlox/PYNQ | docs/source/9_base_overlay_video.ipynb | bsd-3-clause | from pynq import Overlay
from pynq.drivers.video import HDMI
# Download bitstream
Overlay("base.bit").download()
# Initialize HDMI as an input device
hdmi_in = HDMI('in')
"""
Explanation: Video using the Base Overlay
The PYNQ-Z1 board contains a HDMI input port, and a HDMI output port connected to the FPGA fabric of... |
jdhp-docs/python-notebooks | python_super_fr.ipynb | mit | help(super)
"""
Explanation: Python's super()
TODO
* https://docs.python.org/3/library/functions.html#super
* https://rhettinger.wordpress.com/2011/05/26/super-considered-super/
* https://stackoverflow.com/questions/904036/chain-calling-parent-constructors-in-python
* https://stackoverflow.com/questions/2399307/how-to... |
FishingOnATree/deep-learning | seq2seq/sequence_to_sequence_implementation.ipynb | mit | import helper
source_path = 'data/letters_source.txt'
target_path = 'data/letters_target.txt'
source_sentences = helper.load_data(source_path)
target_sentences = helper.load_data(target_path)
"""
Explanation: Character Sequence to Sequence
In this notebook, we'll build a model that takes in a sequence of letters, an... |
oscarmore2/deep-learning-study | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/training-giga-fren/giga-fren.release2.fixed.en'
target_path = 'data/training-giga-fren/giga-fren.release2.fixed.fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""... |
ktaneishi/deepchem | examples/tutorials/Uncertainty.ipynb | mit | import deepchem as dc
import numpy as np
import matplotlib.pyplot as plot
tasks, datasets, transformers = dc.molnet.load_sampl()
train_dataset, valid_dataset, test_dataset = datasets
model = dc.models.MultitaskRegressor(len(tasks), 1024, uncertainty=True)
model.fit(train_dataset, nb_epoch=200)
y_pred, y_std = model.p... |
lenovor/notes-on-dirichlet-processes | 2015-08-03-nonparametric-latent-dirichlet-allocation.ipynb | mit | %matplotlib inline
%precision 2
"""
Explanation: I wrote this in an IPython Notebook. You may prefer to view it on nbviewer.
End of explanation
"""
vocabulary = ['see', 'spot', 'run']
num_terms = len(vocabulary)
num_topics = 2 # K
num_documents = 5 # M
mean_document_length = 5 # xi
term_dirichlet_parameter = 1 # bet... |
Startupsci/data-science-notebooks | .ipynb_checkpoints/titanic-data-science-solutions-refactor-checkpoint.ipynb | mit | # data analysis and wrangling
import pandas as pd
import numpy as np
import random as rnd
# visualization
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
# machine learning
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import ... |
martinggww/lucasenlights | MachineLearning/DataScience-Python3/CovarianceCorrelation.ipynb | cc0-1.0 | %matplotlib inline
import numpy as np
from pylab import *
def de_mean(x):
xmean = mean(x)
return [xi - xmean for xi in x]
def covariance(x, y):
n = len(x)
return dot(de_mean(x), de_mean(y)) / (n-1)
pageSpeeds = np.random.normal(3.0, 1.0, 1000)
purchaseAmount = np.random.normal(50.0, 10.0, 1000)
sca... |
ES-DOC/esdoc-jupyterhub | notebooks/uhh/cmip6/models/sandbox-3/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'uhh', 'sandbox-3', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: UHH
Source ID: SANDBOX-3
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbulen... |
cvxgrp/cvxpylayers | examples/tf/data_poisoning_attack.ipynb | apache-2.0 | import cvxpy as cp
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from cvxpylayers.tensorflow.cvxpylayer import CvxpyLayer
"""
Explanation: Data poisoning attack
In this notebook, we use a convex optimization layer to perform a data poisoning attack; i.e., we show how to perturb the data ... |
google-research/ott | ott/tools/gaussian_mixture/gmm_pair_demo.ipynb | apache-2.0 | import typing
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import jax
import jax.numpy as jnp
from colabtools import adhoc_import
import importlib
import ott
from ott.tools.gaussian_mixture import gaussian_mixture
from ott.tools.gaussian_mixture import gaussian_mixture_pair
from ott.tools.ga... |
osplanning-data-standards/GTFS-PLUS | tools/Tutorial - GTFS to GTFS-PLUS.ipynb | apache-2.0 | import os,datetime,shutil
import pandas as pd
"""
Explanation: Tutorial: Quick Translation of GTFS to GTFS-PLUS
End of explanation
"""
GTFS_LINK = r"http://admin.gotransitnc.org/sites/default/files/developergtfs/GoRaleigh_GTFS_0.zip"
BASE_DIR = os.getcwd()
NEW_FOLDER = "GoRaleigh_GTFS"
GTFS_LOC = os.path.join(... |
fweik/espresso | doc/tutorials/lattice_boltzmann/lattice_boltzmann_part2.ipynb | gpl-3.0 | import numpy as np
import logging
import sys
import espressomd
import espressomd.accumulators
import espressomd.observables
logging.basicConfig(level=logging.INFO, stream=sys.stdout)
# Constants
KT = 1.1
STEPS = 400000
# System setup
system = espressomd.System(box_l=[16] * 3)
system.time_step = 0.01
system.cell_sys... |
avehtari/BDA_py_demos | demos_ch2/demo2_4.ipynb | gpl-3.0 | # Import necessary packages
import numpy as np
from scipy.stats import beta
%matplotlib inline
import matplotlib.pyplot as plt
import arviz as az
# add utilities directory to path
import os, sys
util_path = os.path.abspath(os.path.join(os.path.pardir, 'utilities_and_data'))
if util_path not in sys.path and os.path... |
BrainIntensive/OnlineBrainIntensive | resources/matplotlib/Examples/specialplots.ipynb | mit | %load_ext watermark
%watermark -u -v -d -p matplotlib,numpy
"""
Explanation: Sebastian Raschka
back to the matplotlib-gallery at https://github.com/rasbt/matplotlib-gallery
End of explanation
"""
%matplotlib inline
"""
Explanation: <font size="1.5em">More info about the %watermark extension</font>
End of explanati... |
vasco-da-gama/ros_hadoop | doc/Rosbag larger than 2 GB.ipynb | apache-2.0 | %%bash
ls -tralFh /root/project/doc/el_camino_north.bag
%%bash
# same size, no worries, just the -h (human) formating differs in rounding
hdfs dfs -ls -h
"""
Explanation: Let us have a look at a 20 GB Rosbag file
Note data can be found for instance at https://github.com/udacity/self-driving-car/tree/master/dataset... |
thewtex/ieee-nss-mic-scipy-2014 | 4_Cython.ipynb | apache-2.0 | import numpy as np
"""
Explanation: Cython
The Cython language is a superset of the Python language that additionally
supports calling C functions and declaring C types on variables and class
attributes.
This allows the compiler to generate very efficient C code from Cython code.
Write Python code that calls back... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/introduction_to_tensorflow/solutions/adv_logistic_reg_TF2.0.ipynb | apache-2.0 | # You can use any Python source file as a module by executing an import statement in some other Python source file.
# The import statement combines two operations; it searches for the named module, then it binds the
# results of that search to a name in the local scope.
import tensorflow as tf
from tensorflow import ke... |
google/tf-quant-finance | tf_quant_finance/experimental/notebooks/Cashflows_Rate_Curves.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, sof... |
mne-tools/mne-tools.github.io | 0.24/_downloads/b36af73820a7a52a4df3c42b66aef8a5/source_power_spectrum_opm.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
# Luke Bloy <luke.bloy@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
import os.path as op
from mne.filter import next_fast_len
import mne
print(__doc__)
data_path = mne.datasets.opm.data_path()
subject = 'OPM_sam... |
xpmanoj/content | HW5.ipynb | mit | %matplotlib inline
import json
import numpy as np
import networkx as nx
import requests
from pattern import web
import matplotlib.pyplot as plt
from bs4 import BeautifulSoup as bs
# set some nicer defaults for matplotlib
from matplotlib import rcParams
#these colors come from colorbrewer2.org. Each is an RGB triple... |
Nathx/think_stats | resolved/chap05ex.ipynb | gpl-3.0 | from __future__ import print_function, division
import thinkstats2
import thinkplot
from brfss import *
import populations as p
import random
import pandas as pd
import test_models
%matplotlib inline
"""
Explanation: Exercise from Think Stats, 2nd Edition (thinkstats2.com)<br>
Allen Downey
End of explanation
"""
i... |
datascience-practice/data-quest | python_introduction/beginner/.ipynb_checkpoints/Functions and Debugging-checkpoint.ipynb | mit | # The story is stored in the file "story.txt".
f = open("story.txt", "r")
story = f.read()
print(story)
"""
Explanation: 2: Reading the file in
Instructions
The story is stored in the "story.txt" file. Open the file and read the contents into the story variable.
Answer
End of explanation
"""
# We can split strings i... |
texib/spark_tutorial | 2.ProcessText Data.ipynb | gpl-2.0 | urllist = ['http://chahabi77.pixnet.net/blog/post/436715527',
'http://chahabi77.pixnet.net/blog/post/403682269',
'http://chahabi77.pixnet.net/blog/post/354943724',
'http://chahabi77.pixnet.net/blog/post/386442944',
'http://chahabi77.pixnet.net/blog/post/235296791',
... |
angelmtenor/deep-learning | tensorboard/Anna_KaRNNa.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In 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.
This network is base... |
NEONScience/NEON-Data-Skills | tutorials/Python/Hyperspectral/indices/Calc_NDVI_Extract_Spectra_Masks_Tiles_py/Calc_NDVI_Extract_Spectra_Masks_Tiles_py.ipynb | agpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings('ignore') #don't display warnings
# %load ../neon_aop_hyperspectral.py
"""
Created on Wed Jun 20 10:34:49 2018
@author: bhass
"""
import matplotlib.pyplot as plt
import numpy as np
import h5py, os, copy
de... |
KitwareMedical/ITKUltrasound | examples/PlotPowerSpectra.ipynb | apache-2.0 | import sys
!"{sys.executable}" -m pip install itk matplotlib scipy numpy
import os
import itk
import matplotlib.pyplot as plt
from scipy import signal
import numpy as np
"""
Explanation: Plot Power Spectra
Power spectra are used to analyze the average frequency content across signals in an RF image such as that prod... |
joelowj/Udacity-Projects | Udacity-Deep-Learning-Foundation-Nanodegree/Project-1/dlnd-your-first-neural-network.ipynb | apache-2.0 | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In 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... |
mauroalberti/geocouche | pygsf/docs/notebooks/General 2 - orientations.ipynb | gpl-2.0 | %load_ext autoreload
%autoreload 1
"""
Explanation: pygsf 2: vectors and orientations
March-April, 2018, Mauro Alberti, alberti.m65@gmail.com
Developement code:
End of explanation
"""
%matplotlib inline
"""
Explanation: 1. Introduction
Since we will plot geometric data into stereonets, prior to any other operation,... |
bollwyvl/ipylivecoder | examples/Three Little Circles.ipynb | bsd-2-clause | from livecoder.widgets import Livecoder
from IPython.utils import traitlets as T
"""
Explanation: Three Little Circles
The "Hello World" (or Maxwell's Equations) of d3, Three Little Circles introduces all of the main concepts in d3, which gives you a pretty good grounding in data visualization, JavaScript, and SVG. Le... |
hainesr/tdd-fibonacci-example | walkthrough-notebook.ipynb | bsd-3-clause | import unittest
def run_tests():
suite = unittest.TestLoader().loadTestsFromTestCase(TestFibonacci)
unittest.TextTestRunner().run(suite)
"""
Explanation: Agile and Test-Driven Development
TDD Worked Example
Robert Haines, University of Manchester, UK
Adapted from "Test-Driven Development By Example", Kent Bec... |
unpingco/Python-for-Probability-Statistics-and-Machine-Learning | chapters/statistics/notebooks/Hypothesis_Testing.ipynb | mit | from __future__ import division
%pylab inline
"""
Explanation: Python for Probability, Statistics, and Machine Learning
End of explanation
"""
%matplotlib inline
from matplotlib.pylab import subplots
import numpy as np
fig,ax=subplots()
fig.set_size_inches((6,3))
xi = np.linspace(0,1,50)
_=ax.plot(xi, (xi)**5,'-k',... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/kubeflow_pipelines/pipelines/solutions/kfp_pipeline_vertex_automl_batch_predictions.ipynb | apache-2.0 | import os
from google.cloud import aiplatform
REGION = "us-central1"
PROJECT = !(gcloud config get-value project)
PROJECT = PROJECT[0]
os.environ["PROJECT"] = PROJECT
# Set `PATH` to include the directory containing KFP CLI
PATH = %env PATH
%env PATH=/home/jupyter/.local/bin:{PATH}
"""
Explanation: Continuous Trai... |
ColeLab/informationtransfermapping | MasterScripts/ManuscriptS5b_PerformanceDecoding_withITE.ipynb | gpl-3.0 | import sys
import os
sys.path.append('utils/')
import numpy as np
import loadGlasser as lg
import scipy.stats as stats
import matplotlib.pyplot as plt
import statsmodels.sandbox.stats.multicomp as mc
import sys
import multiprocessing as mp
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
import nib... |
SeismicPi/SeismicPi | Lessons/Stethoscope/Piezo Stethoscope.ipynb | mit | import SensDisLib as s
plot = s.SensorDisplay()
"""
Explanation: Piezo Stethoscope
In this module we will be creating a sthethoscope to monitor our heartbeats via a piezosensor and learn how log and read data. The idea is that if you tape a contact microphone directly to your skin, it will pick up on your pulse and ge... |
mne-tools/mne-tools.github.io | 0.22/_downloads/59a29cf7eb53c7ab95857dfb2e3b31ba/plot_40_sensor_locations.ipynb | bsd-3-clause | import os
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # noqa
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io... |
NuGrid/NuPyCEE | regression_tests/Stellab_tests.ipynb | bsd-3-clause | # Import the needed packages
import matplotlib
import matplotlib.pyplot as plt
# Import the observational data module
import stellab
import sys
# Trigger interactive or non-interactive depending on command line argument
__RUNIPY__ = sys.argv[0]
if __RUNIPY__:
%matplotlib inline
else:
%pylab nbagg
"""
Explan... |
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