repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
kunaltyagi/SDES | notes/python/Python_Batteries.ipynb | gpl-3.0 | x = range(1, 20)
y = "The quick brown fox jumps over the lazy dog."
# print elements 2 to 4
print(x[2:5])
print(y[2:5])
# Print every third element starting from the first element
print(x[::3])
print(y[::3])
# Print the second last element
print(x[-2])
print(y[-2])
"""
Explanation: Subsets of elements
For lists and ... |
gena/earthengine-api | python/examples/ipynb/authorize_notebook_server.ipynb | apache-2.0 | import ee
"""
Explanation: Overview
This notebook guides you through process of testing if the Jupyter Notebook server is authorized to access the Earth Engine servers, and provides a way to authorize the server, if needed.
Testing if the Jupyter Notebook server is authorized to access Earth Engine
To begin, first ver... |
biof-309-python/BIOF309-2016-Fall | Week_06/Week 06 - 02 - Conditionals.ipynb | mit | print(3 == 5)
print(3 > 5)
print(3 <= 5)
print(len("ATGC") > 5)
print("GAATTC".count("T") > 1)
print("ATGCTT".startswith("ATG"))
print("ATGCTT".endswith("TTT"))
print("ATGCTT".isupper())
print("ATGCTT".islower())
print("V" in ["V", "W", "L"])
"""
Explanation: Conditions
Source: This material adapted from the ... |
JVillella/ml-playground | wiki2vec/wiki2vec.ipynb | mit | WIKIPEDIA_DUMP_PATH = './data/wiki-corpuses/enwiki-latest-pages-articles.xml.bz2'
# Choose a path that the word2vec model should be saved to
# (during training), and read from afterwards.
WIKIPEDIA_W2V_PATH = './data/enwiki.model'
"""
Explanation: Wiki2vec
Jupyter notebook for creating a Word2vec model from a Wikiped... |
ShubhamDebnath/Coursera-Machine-Learning | Course 4/Autonomous driving application - Car detection - v3.ipynb | mit | import argparse
import os
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
import scipy.io
import scipy.misc
import numpy as np
import pandas as pd
import PIL
import tensorflow as tf
from keras import backend as K
from keras.layers import Input, Lambda, Conv2D
from keras.models import load_model, Mo... |
kubeflow/examples | pipelines/simple-notebook-pipeline/Simple Notebook Pipeline.ipynb | apache-2.0 | # You may need to restart your notebook kernel after updating the kfp sdk
!python3 -m pip install kfp --upgrade --user
"""
Explanation: Simple notebook pipeline
Welcome to your first steps with Kubeflow Pipelines (KFP). This notebook demos:
Defining a Kubeflow pipeline with the KFP SDK
Creating an experiment and sub... |
edosedgar/xs-pkg | deep_learning/hw3/homework03_part3b_prd_score_advanced.ipynb | gpl-2.0 | import math
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
import matplotlib
import torch
import sklearn
CHANNEL_NUM = 3
PICTURE_SIZE = 36
class ParticleDataset():
def __init__(self, file):
self.data = np.load(file)
self.image = self.data['Pictures'].reshape(-... |
diegocavalca/Studies | deep-learnining-specialization/4. Convolutional Neural Networks/resources/Residual Networks - v1.ipynb | cc0-1.0 | import numpy as np
from keras import layers
from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D
from keras.models import Model, load_model
from keras.preprocessing import image
from keras.utils import layer_utils
... |
rvm-segfault/edx | python_for_data_sci_dse200x/week3/Satellite Image Analysis using numpy.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
from scipy import misc
import matplotlib.pyplot as plt
"""
Explanation: <p style="font-family: Arial; font-size:3.75em;color:purple; font-style:bold"><br>
Satellite Image Data <br><br><br>Analysis using numpy</p>
<p style="font-family: Arial; font-size:1.75em;color:#2462C0; font-... |
lmoresi/UoM-VIEPS-Intro-to-Python | Notebooks/SolveMathProblems/0 - IntroductionToNumericalSolutions.ipynb | mit | %pylab inline
import numpy as np
theta_0 = 1.0
time_values = np.linspace(0,1.0,1000)
for const_k in [1.0, 3.1, 10.0, 31, 100.0]:
exact_theta_values = theta_0 * np.exp(-const_k * time_values)
plot(time_values, exact_theta_values)
pass
"""
Explanation: Numerical models
We start with the numerical solution... |
ssunkara1/bqplot | examples/Marks/Object Model/Pie.ipynb | apache-2.0 | data = np.random.rand(3)
pie = Pie(sizes=data, display_labels='outside')
# Set `animation_duration` (in milliseconds) to have smooth transitions
fig = Figure(marks=[pie], animation_duration=1000)
fig
"""
Explanation: Basic Pie Chart
End of explanation
"""
n = np.random.randint(1, 10)
pie.sizes = np.random.rand(n)
"... |
harish-garg/Machine-Learning | udacity/enron/ud120-projects-master/final_project/.ipynb_checkpoints/Data Exploration and Cleanup-checkpoint.ipynb | mit | print "print out some values of the observation 'TOTAL'"
for name, person in data_dict.iteritems():
if name == 'TOTAL':
print person
salary = []
for name, person in data_dict.iteritems():
if float(person['salary']) > 0:
salary.append(float(person['salary']))
print "the sum of salary of all other person... |
tpin3694/tpin3694.github.io | python/matplotlib_pie_chart.ipynb | mit | %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Title: Pie Chart In MatPlotLib
Slug: matplotlib_pie_chart
Summary: Pie Chart In MatPlotLib
Date: 2016-05-01 12:00
Category: Python
Tags: Data Visualization
Authors: Chris Albon
Based on: Sebastian Raschka.
Preliminaries
End of ex... |
ereodeereigeo/dataTritiumWS22 | .ipynb_checkpoints/numero_de_datos_perdidos_slide-checkpoint.ipynb | gpl-2.0 | import pandas as pd
"""
Explanation: Número de datos obtenidos y perdidos
Importamos las librerías necesarias
End of explanation
"""
import ext_datos as ext
import procesar as pro
import time_plot as tplt
"""
Explanation: Importamos las librerías creadas para trabajar
End of explanation
"""
dia1 = ext.extraer_da... |
probml/pyprobml | notebooks/book1/15/kernel_regression_attention.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
np.random.seed(seed=1)
import math
import collections
try:
import torch
except ModuleNotFoundError:
%pip install -qq torch
import torch
from torch import nn
from torch.nn import functional as F
try:
from probml_utils import d2l
except ModuleNotFoundE... |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/ec-earth3-veg-lr/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'ec-earth3-veg-lr', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: EC-EARTH3-VEG-LR
Topic: Land
Sub-... |
nadvamir/deep-learning | sentiment-rnn/Sentiment_RNN.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural... |
brian-rose/ClimateModeling_courseware | Lectures/Lecture17 -- Heat transport.ipynb | mit | # Ensure compatibility with Python 2 and 3
from __future__ import print_function, division
"""
Explanation: ATM 623: Climate Modeling
Brian E. J. Rose, University at Albany
Lecture 17: Heat transport
Warning: content out of date and not maintained
You really should be looking at The Climate Laboratory book by Brian R... |
jenshnielsen/HJCFIT | exploration/CH82 -- optimization.ipynb | gpl-3.0 | from dcprogs import read_idealized_bursts
from dcprogs.likelihood import QMatrix
name = "CH82.scn"
tau = 1e-4
tcrit = 4e-3
graph = [["V", "V", "V", 0, 0],
["V", "V", 0, "V", 0],
["V", 0, "V", "V", "V"],
[ 0, "V", "V", "V", 0],
[ 0, 0, "V", 0, "V"]]
nop... |
mne-tools/mne-tools.github.io | stable/_downloads/f47934a488455dcef7b3567776837d1a/limo_data.ipynb | bsd-3-clause | # Authors: Jose C. Garcia Alanis <alanis.jcg@gmail.com>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt
from mne.datasets.limo import load_data
from mne.stats import linear_regression
from mne.viz import plot_events, plot_compare_evokeds
from mne import combine_evoked
print(__doc__)
# ... |
MRod5/pyturb | notebooks/ISA examples.ipynb | mit | # Import isa library
from pyturb.gas_models import isa
import numpy as np
from matplotlib import pyplot as plt
"""
Explanation: ISA: Example of usage
This Notebook serves as an example on how to use the Standard Atmosphere model provided with pyTurb. The isa functions can be found in the gas_models folder:
pyturb
ga... |
wbinventor/openmc | examples/jupyter/pincell.ipynb | mit | %matplotlib inline
import openmc
"""
Explanation: This notebook is intended to demonstrate the basic features of the Python API for constructing input files and running OpenMC. In it, we will show how to create a basic reflective pin-cell model that is equivalent to modeling an infinite array of fuel pins. If you have... |
dvirsamuel/MachineLearningCourses | Visual Recognision - Stanford/assignment2/.ipynb_checkpoints/FullyConnectedNets-checkpoint.ipynb | gpl-3.0 | # As usual, a bit of setup
import time
import numpy as np
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n... |
weiquan/notebook | notebook/BWT.ipynb | mit | seq = 'ACAGGAGAGATACCCAGG'
seq += '$'
suffixes = [seq[i:] for i in xrange(len(seq))]
suffixes
SA = range(len(seq))
print SA
SA.sort(key=lambda x:seq[x:])
SA
print 'suffix'.ljust(20), 'SA'
for i in SA:
print seq[i:].ljust(20), i
"""
Explanation: 2BWT
1. Suffix Array
End of explanation
"""
bwt = ''.join([seq... |
mohanprasath/Course-Work | coursera/python_for_data_science/2.2_Lists.ipynb | gpl-3.0 | L = ["Michael Jackson" , 10.1,1982]
L
"""
Explanation: <a href="http://cocl.us/topNotebooksPython101Coursera"><img src = "https://ibm.box.com/shared/static/yfe6h4az47ktg2mm9h05wby2n7e8kei3.png" width = 750, align = "center"></a>
<a href="https://www.bigdatauniversity.com"><img src = "https://ibm.box.com/shared/static/... |
CAChemE/curso-python-datos | notebooks/071-AEMET-Santander-weather.ipynb | bsd-3-clause | import requests
# Cargamos la api key
api_key = open("../../apikey-aemet.txt").read().rstrip()
querystring = {"api_key": api_key}
"""
Explanation: API-REST: AEMET OPEN DATA
En este notebook veremos otro ejemplo de uso de la api open data de AEMET. En este caso obtendremos parámetros medidos por una estación meteorol... |
kuo77122/deep-learning-nd | Lesson15-TFLearn/Handwritten Digit Recognition with TFLearn.ipynb | mit | # Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
"""
Explanation: Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
This... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-2/cmip6/models/sandbox-2/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-2', 'sandbox-2', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: TEST-INSTITUTE-2
Source ID: SANDBOX-2
Topic: Atmoschem
Sub-Topic... |
ueapy/ueapy.github.io | content/notebooks/2021-02-11-python-reuse.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: This tutorial shows various methods of reusing your Python code. The follow up tutorial on packaging code will explore ways to make code reusable by others.
End of explanation
"""
def go_figure():
figure, axes = plt.subplots(2, 2, figsize=(10,6)... |
quantopian/research_public | notebooks/lectures/Beta_Hedging/notebook.ipynb | apache-2.0 | # Import libraries
import numpy as np
from statsmodels import regression
import statsmodels.api as sm
import matplotlib.pyplot as plt
import math
# Get data for the specified period and stocks
start = '2014-01-01'
end = '2015-01-01'
asset = get_pricing('TSLA', fields='price', start_date=start, end_date=end)
benchmark ... |
ES-DOC/esdoc-jupyterhub | notebooks/bnu/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', 'bnu', 'sandbox-1', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: BNU
Source ID: SANDBOX-1
Sub-Topics: Radiative Forcings.
Properties: 85 (42 re... |
HUDataScience/StatisticalMethods2016 | notebooks/Basic_Python.ipynb | apache-2.0 | for i in range(10):
y = i*40 +i**2 + 2
print(y)
i = i+2
range(10)
for i in ["a","b","tt"]:
print i
x = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],[2, 1, 2, 3, 4, 5, 6, 7, 8, 9],[0,5, 2, 3, 4, 5, 6, 7, 8, 9]]
print(x)
np.mean(x)
x= np.linspace(-1,1,100)
x
x[[34,67]]
x[45:90]
x[::3]
x[10::3]
x[::-1]
x= [0... |
Kapani/BDDoctor | BMA data extraction notebook.ipynb | gpl-3.0 | #Load the necessary modules
from mechanize import Browser
import pandas as pd
from IPython.core.display import HTML
import requests
"""
Explanation: Bangladesh Medical Association(BMA) member data extraction
Version : 1.0<br>
Date : 2015-05-21
This notebook will illustrate the approach undertaken to extract the BMA do... |
daviddesancho/MasterMSM | examples/mueller_potential/mueller_potential.ipynb | gpl-2.0 | %matplotlib inline
%load_ext autoreload
%autoreload 2
import time
import numpy as np
import scipy.stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="ticks", color_codes=True, font_scale=1.5)
sns.set_style({"xtick.direction": "in", "ytick.direction": "in"})
from collections import OrderedDict
""... |
ramseylab/networkscompbio | class04_storage_python3_template.ipynb | apache-2.0 | import numpy as np
import igraph
import timeit
import itertools
"""
Explanation: Class Session 4 Exercise:
Comparing asymptotic running time for enumerating neighbors of all vertices in a graph
We will measure the running time for enumerating the neighbor vertices for three different data structures for representing a... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_corrmap_detection.ipynb | bsd-3-clause | # Authors: Jona Sassenhagen <jona.sassenhagen@gmail.com>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.io import Raw
from mne.preprocessing import ICA
from mne.preprocessing.ica import corrmap
from mne.datasets import sample
print(__doc__)
"""
Explanation: Iden... |
DB2-Samples/db2jupyter | Db2 Regular Expressions.ipynb | apache-2.0 | %run db2.ipynb
"""
Explanation: <a id='top'></a>
Db2 Regular Expressions
Db2 11.1 introduced extended support for regular
expressions. Regular expressions allow you to do very complex
pattern matching in character strings. Normal SQL LIKE
searches are limited to very specific patterns, but Regular
expression have a ri... |
eaton-lab/toytree | sandbox/26-highlight-clades.ipynb | bsd-3-clause | import toytree
import toyplot
# generate a random tree
tre = toytree.rtree.unittree(ntips=10, treeheight=100, seed=123)
"""
Explanation: Issue 26: highlight clades
I've created an early form of an Annotator class object that can be used to add highlights to a toytree drawing.
Possible extensions:
- apply linear gr... |
amitkaps/hackermath | Module_1b_linear_regression_ols.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (10, 6)
import ipywidgets as widgets
from ipywidgets import interact, interactive
price = np.array([199, 248, 302, 363, 418, 462, 523])
kmpl = np.array([23.9, 22.7, 21.1, 20.5, 19.... |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/ec-earth3-hr/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'ec-earth3-hr', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: EC-EARTH3-HR
Topic: Seaice
Sub-To... |
DawesLab/LabNotebooks | Variable pitch solenoid design-Copy1.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
mpl.rcParams['legend.fontsize'] = 10
from mpl_toolkits.mplot3d import Axes3D
from scipy.optimize import leastsq
from math import acos, atan2, cos, sin
from numpy import array, float64, zeros
from numpy.linalg import norm
#%matplotlib inline
"... |
michimichi/compartmentmodels | PracticalModelFitting.ipynb | bsd-3-clause | import numpy as np
# plotting and graphics settings
import matplotlib.pyplot as plt
%pylab inline
try:
import seaborn as sns # pretty graphics. not strictly necessary.
sns.set_context("notebook")
sns.set_style("whitegrid")
except:
pass # with less pretty graphics
"""
Explanation: Practical model f... |
pyro-ppl/numpyro | notebooks/source/ordinal_regression.ipynb | apache-2.0 | # !pip install -q numpyro@git+https://github.com/pyro-ppl/numpyro
from jax import numpy as np, random
import numpyro
from numpyro import sample, handlers
from numpyro.distributions import (
Categorical,
Dirichlet,
ImproperUniform,
Normal,
OrderedLogistic,
TransformedDistribution,
constraint... |
steinam/teacher | jup_notebooks/data-science-ipython-notebooks-master/deep-learning/theano-tutorial/scan_tutorial/scan_tutorial.ipynb | mit | import theano
import theano.tensor as T
import numpy as np
vector1 = T.vector('vector1')
vector2 = T.vector('vector2')
"""
Explanation: Introduction to Scan in Theano
Credits: Forked from summerschool2015 by mila-udem
In short
Mechanism to perform loops in a Theano graph
Supports nested loops and reusing results fro... |
marioberges/F16-12-752 | homeworks/homework3/homework3_solution_henningl.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pickle
%matplotlib inline
"""
Explanation: 12-752: Data-Driven Building Energy Management
Fall 2016, Carnegie Mellon University
Assignment #3
We will begin by unpickling the dataset containing the loadCurves that we had used in Assignment #... |
maxis42/ML-DA-Coursera-Yandex-MIPT | 1 Mathematics and Python/Homework/3 central limit theorem/Central limit theorem.ipynb | mit | #импортирование необходимых библиотек
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as sts
from math import sqrt
%matplotlib inline
"""
Explanation: Центральная предельная теорема
Для своего домашнего задания я выбрал распределение Парето, которое присутствует в scipy.stats.... |
wgm2111/wgm-optimization-sandbox | examples/radon_transform/plot_radon_transform.ipynb | gpl-2.0 |
from __future__ import print_function, division
import numpy as np
import matplotlib.pyplot as plt
from skimage.io import imread
from skimage import data_dir
from skimage.transform import radon, rescale
image = imread(data_dir + "/phantom.png", as_grey=True)
print(image.shape)
image = rescale(image, scale=0.4)
fi... |
srcole/qwm | sfn/.ipynb_checkpoints/Poster viewer distribution by country-checkpoint.ipynb | mit | %config InlineBackend.figure_format = 'retina'
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
import pandas as pd
# Load data
df = pd.DataFrame.from_csv('./posterviewers_by_country.csv')
key_N = 'Number of people'
"""
Explanatio... |
uranc/locmea | tutorials/Data_Analysis.ipynb | gpl-3.0 | # Data path/filename
t_ind = 38
data_path = '../data/'
file_name = data_path + 'data_sim_low.hdf5'
data_options = {'flag_cell': True, 'flag_electode': False}
data = data_in(file_name, **data_options)
"""
Explanation: The task in this tutorial is to load some data, visualize, and analyze it. Let's start by adding the ... |
Faris137/MachineLearningArabic | Pima Project/Pima Project.ipynb | mit | import numpy as np
import pandas as pd
df = pd.read_csv('diabetes.csv')
df.head() #لاستعراض ال5 السجلات الاولى من إطار البيانات
"""
Explanation: Loading the dataset into a pandas dataframe
تحميل قاعدة البيانات من ملف سي اس في الى إطاربيانات بانداس
End of explanation
"""
columns = ['Pregnancies', 'Glucose', 'BloodPr... |
ageron/ml-notebooks | 05_support_vector_machines.ipynb | apache-2.0 | # To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot ... |
hugues-talbot/optimization_textbook | Scripts/EEG.ipynb | cc0-1.0 | %matplotlib inline
# standard signal processing packages
import numpy as np
import scipy as sp
import scipy.signal as sig
import matplotlib.pyplot as plt
## learning
from sklearn.svm import SVC
sampling = 20 # Sampling rate in Hz
"""
Explanation: Electro-encephalogram simulation and analysis
Hugues Talbot, 17 Dec... |
NathanYee/ThinkBayes2 | code/.ipynb_checkpoints/chap07mine-checkpoint.ipynb | gpl-2.0 | from __future__ import print_function, division
% matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import math
import numpy as np
from thinkbayes2 import Pmf, Cdf, Suite, Joint
import thinkplot
"""
Explanation: Think Bayes: Chapter 7
This notebook presents code and exercises from Think Bayes, sec... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session09/Day3/LIGO_data_visualization.ipynb | mit | #! python3 -m pip install gwpy
"""
Explanation: Welcome to the LIGO data visualization tutorial!
Installation
Please make sure you have GWpy installed before you begin!
Only execute the below cell if you have not already installed GWpy
End of explanation
"""
from gwosc.datasets import find_datasets
find_datasets()
... |
CSB-book/CSB | regex/solutions/MapOfScience_solution.ipynb | gpl-3.0 | import re
import csv
"""
Explanation: Solution of 5.9.2, Map of Science
Read the file pubmed_results.txt, and extract all the US ZIP codes.
First, import the modules we'll need.
End of explanation
"""
with open('../data/MapOfScience/pubmed_results.txt') as f:
my_text = f.read()
len(my_text)
"""
Explanation: No... |
joshspeagle/dynesty | demos/Examples -- Gaussian Shells.ipynb | mit | # system functions that are always useful to have
import time, sys, os
# basic numeric setup
import numpy as np
import math
# inline plotting
%matplotlib inline
# plotting
import matplotlib
from matplotlib import pyplot as plt
# seed the random number generator
rstate = np.random.default_rng(715)
# re-defining pl... |
tmm/DS501 | lectures/05 Basic Statistics, Probability, and Linear Algebra/BaseRateFallacy.ipynb | mit | FailAndHasDisease = 1.0
NotFailAndHasDisease = 0.0
FailAndNotHasDisease = 0.01
NotFailAndNotHasDisease = 0.99
HasDisease = 1./1000
"""
Explanation: Base rate fallacy example
In this notenook we work an example of the base rate fallacy using Bayes Theorem.
Assume that we have two random variables $HasDisease$ and $Fa... |
phoebe-project/phoebe2-docs | 2.0/tutorials/l3.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.0,<2.1"
"""
Explanation: "Third" Light
Setup
Let's first make sure we have the latest version of PHOEBE 2.0 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
"""
%matplotlib inline
import... |
vanessajurtz/lasagne4bio | subcellular_localization/notebook tutorial/CNN.ipynb | gpl-3.0 | # Import all the necessary modules
import os
os.environ["THEANO_FLAGS"] = "mode=FAST_RUN,optimizer=None,device=cpu,floatX=float32"
import sys
sys.path.insert(0,'..')
import numpy as np
import theano
import theano.tensor as T
import lasagne
from confusionmatrix import ConfusionMatrix
from utils import iterate_minibatche... |
CPernet/LanguageDecision | notebooks/individuals/analysis_controls.ipynb | gpl-3.0 | """
Environment setup
"""
%matplotlib inline
%cd /lang_dec
import warnings; warnings.filterwarnings('ignore')
import hddm
import numpy as np
import matplotlib.pyplot as plt
from utils import model_tools
# Import control subject data (as pandas dataframe)
controls_data = hddm.load_csv('/lang_dec/data/controls_clean.cs... |
QInfer/qinfer-examples | Noisy Coin Example.ipynb | agpl-3.0 | from __future__ import division, print_function
"""
Explanation: Noisy Coin Example
License
Introduction
Preamble
We turn on the division feature first of all, since this is just a plain good idea when working in Python 2.x, as it is a more sensible default for scientific computing and is the default in Python 3.x any... |
yl565/statsmodels | examples/notebooks/mixed_lm_example.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf
%load_ext rpy2.ipython
%R library(lme4)
"""
Explanation: Linear Mixed Effects Models
End of explanation
"""
data = sm.datasets.get_rdataset('dietox', 'geepack').data
md = smf.mixedlm("Weight ~ Time", data, gro... |
azubiolo/itstep | it_step/intro_ml/lab_3/regression_lab.ipynb | mit | %matplotlib inline
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
"""
Explanation: Introduction to Machine Learning with scikit-learn
Lab 3: Regression
The goal of this lab session is to discover a few regression tools from scikit-learn. As for the classification lab, we will start wit... |
opesci/tutorial-hands-on | 01_introduction.ipynb | mit | from devito import *
from sympy import init_printing, symbols, solve
init_printing(use_latex=True)
"""
Explanation: Automated finite difference operators from symbolic equations
This notebook is the first in a series of hands-on tutorial notebooks that are intended to give a brief practical overview of the Devito fini... |
tensorflow/probability | tensorflow_probability/examples/jupyter_notebooks/Linear_Mixed_Effects_Models.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... |
TangqiFeng/Jupyter_numpy_pyplot | Fisher's_Iris.ipynb | apache-2.0 | # numpy efficiently deals with numerical multi-dimensional arrays.
import numpy as np
# matplotlib is a plotting library, and pyplot is its easy-to-use module.
import matplotlib.pyplot as pl
# This just sets the default plot size to be bigger.
pl.rcParams['figure.figsize'] = (16.0, 8.0)
"""
Explanation: Fisher's Iri... |
dani-lbnl/2017_ucberkeley_course | code/ZeissMicroscopyCenter2017_DaniUshizima_lecture.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
from skimage import data, io
fname = 'http://crl.berkeley.edu/files/2014/04/Holly-226x300.jpg'
imageFromWeb = data.imread(fname, as_grey=False, plugin=None, flatten=None)
plt.imshow(imageFromWeb)
"""
Explanation: Image exploration using Pytho... |
sisnkemp/deep-learning | sentiment-rnn/Sentiment_RNN.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural... |
phoebe-project/phoebe2-docs | development/examples/extinction_eclipse_depth_v_teff.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
"""
Explanation: Extinction: Eclipse Depth Difference as Function of Temperature
In this example, we'll reproduce Figure 3 in the extinction release paper (Jones et al. 2020).
NOTE: this script takes a long time to run.
<img src="jones+20_fig3.png" alt="Figure 3" width="800px"/>
Set... |
chemelnucfin/tensorflow | tensorflow/contrib/autograph/examples/notebooks/rnn_keras_estimator.ipynb | apache-2.0 | def parse(line):
"""Parses a line from the colors dataset."""
items = tf.string_split([line], ",").values
rgb = tf.string_to_number(items[1:], out_type=tf.float32) / 255.0
color_name = items[0]
chars = tf.one_hot(tf.decode_raw(color_name, tf.uint8), depth=256)
length = tf.cast(tf.shape(chars)[0], dtype=tf.i... |
Fifth-Cohort-Awesome/NightThree | three_agd.ipynb | mit |
with open('tmdb_5000_movies.csv','r') as f:
rtext=''
for line in f:
rtext += line
rtext[:500]
"""
Explanation: Goal One
Injest a csv file as pure text... (just 500 chars)
End of explanation
"""
with open('tmdb_5000_movies.csv','r') as f:
lines = [line for line in f]
lines[0]
"""
Explanation: The... |
snbhanja/A-Z-Machine-Learning---Udemy- | Part-2-Regression/P2-Multiple_Linear_Regression.ipynb | mit | # Import the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
#importing the datset
dataset = pd.read_csv('datasets/50_Startups.csv')
dataset.head()
X = dataset.iloc[:, :-1].values
Y = dataset.iloc[:, 4].values
X
Y
# But there is categorical variable. i.e. independent varaible Stat... |
tensorflow/docs-l10n | site/ko/probability/examples/Bayesian_Switchpoint_Analysis.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... |
csdms/pymt | notebooks/hydrotrend.ipynb | mit | # To start, import numpy and matplotlib.
import matplotlib.pyplot as plt
import numpy as np
# Then we import the package
import pymt.models
hydrotrend = pymt.models.Hydrotrend()
import pymt
pymt.__version__
"""
Explanation: HydroTrend
Link to this notebook: https://github.com/csdms/pymt/blob/master/notebooks/hydr... |
gjwo/nilm_gjw_data | notebooks/disaggregation-hart-active_only.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import pandas as pd
from os.path import join
from pylab import rcParams
import matplotlib.pyplot as plt
rcParams['figure.figsize'] = (13, 6)
plt.style.use('ggplot')
#import nilmtk
from nilmtk import DataSet, TimeFrame, MeterGroup, HDFDataStore
from nilmtk.disaggregate.hart_85 impor... |
marco-olimpio/ufrn | IMD0104 - PROGRAMAÇÃO ORIENTADA A OBJETOS E MAPEAMENTO OBJETO-RELACIONAL/assignments/3/IMD0104 - TerceiraUnidade.ipynb | gpl-3.0 | !pip install cufflinks
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
plt.style.use('ggplot')
import seaborn as sns # for making plots with seaborn
color = sns.color_palette()
sns.set(rc={'figure.figsize':(25,15)})
import plotly
plotly.offline.init_notebook_mode(connected=True)
import pl... |
swirlingsand/deep-learning-foundations | gans/batch-norm/Batch_Normalization_Lesson.ipynb | mit | # Import necessary packages
import tensorflow as tf
import tqdm
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Import MNIST data so we have something for our experiments
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
"... |
rbberger/lammps | python/examples/pylammps/simple.ipynb | gpl-2.0 | from lammps import IPyLammps
L = IPyLammps()
"""
Explanation: Example 1: Using LAMMPS with PyLammps
The LAMMPS Python package provides multiple interfaces. The PyLammps interface is a high-level abstration of the low-level lammps interface. IPyLammps further extends this interface with functions that are useful for Ju... |
dipanjank/ml | tensorflow/introduction.ipynb | gpl-3.0 | import tensorflow as tf
one = tf.constant(1, dtype=np.float64)
two = tf.constant(2, dtype=np.float64)
print(one)
print(two)
three = one + two
print(three)
three.op
"""
Explanation: The TensorFlow API
TensorFlow provides multiple APIs.
The lowest level API, TensorFlow Core, provides you with complete programmi... |
joelgrus/codefellows-data-science-week | ipynb/pandas bikes.ipynb | unlicense | from pandas import DataFrame, Series
import pandas as pd
import numpy as np
"""
Explanation: Standard pandas imports
End of explanation
"""
weather = pd.read_table('daily_weather.tsv', parse_dates=['date'])
stations = pd.read_table('stations.tsv')
usage = pd.read_table('usage_2012.tsv', parse_dates=['time_start', 't... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch1-Problem_1-08.ipynb | unlicense | %pylab notebook
%precision 4
from scipy import constants as c # we like to use some constants
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 1
Problem 1-8
End of explanation
"""
mu_r = 2000
mu = mu_r * c.mu_0
"""
Explanation: Description
A core with three legs is shown in Figure P1-5 below:
<... |
pdwyys20/deep-learning | intro-to-tflearn/TFLearn_Sentiment_Analysis_Solution.ipynb | mit | import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
"""
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network w... |
AllenDowney/ModSimPy | notebooks/jump.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... |
phockett/ePSproc | notebooks/LF_AF_verification_tests_260620_tidy.ipynb | gpl-3.0 | # Imports
import numpy as np
import pandas as pd
import xarray as xr
# Special functions
# from scipy.special import sph_harm
import spherical_functions as sf
import quaternion
# Performance & benchmarking libraries
# from joblib import Memory
# import xyzpy as xyz
import numba as nb
# Timings with ttictoc or time
#... |
deimagjas/qubits.cloud.AI | dlnd_image_classification.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
class DLProgress(tqdm):
last_block = 0
def hoo... |
gboeing/pynamical | examples/pynamical-demo-phase-diagrams.ipynb | mit | import IPython.display as IPdisplay
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pynamical
from pynamical import simulate, save_fig, phase_diagram, phase_diagram_3d
%matplotlib inline
title_font = pynamical.get_title_font()
label_font = pynamical.get_label_font()
"""
Explanation: Pyna... |
machinelearningdeveloper/lc101-kc | October 23, 2016/Studio Solution.ipynb | unlicense | days_of_week = [
# 0 1 2
'Sunday', 'Monday', 'Tuesday',
# 3 4 5
'Wednesday', 'Thursday', 'Friday',
# 6
'Saturday',
]
# Gather user input
# Need to use the `int` call so that it'll correctly be
# an integer for mathmatical operations
leaving_day = int(inp... |
qutip/qutip-notebooks | examples/photon-emission.ipynb | lgpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from qutip import *
"""
Explanation: QuTiP Lecture: Particle emission from a photon cascade
D. Lukin, Stanford University
In this Jupyter notebook, we use QuTiP: The Quantum Toolbox in Python to study a cascaded three level system excited by a clas... |
qwertzuhr/2015_Data_Analyst_Project_3 | .ipynb_checkpoints/Data Analysis Project 3 - Data Wrangle OpenStreetMaps Data-checkpoint.ipynb | agpl-3.0 | from Project.notebook_stub import project_coll
import pprint
# Query used - see function Project.audit_stats_map.stats_general
pipeline = [
{"$group": {"_id": "$type", "count": {"$sum": 1}}},
{"$match": {"_id": {"$in": ["node", "way"]}}}
]
l = list(project_coll.aggregate(pipeline))
pprint.pprint(l)... |
InsightLab/data-science-cookbook | 2019/09-clustering/cl_Franklyn.ipynb | mit | # import libraries
# linear algebra
import numpy as np
# data processing
import pandas as pd
# data visualization
from matplotlib import pyplot as plt
# load the data with pandas
dataset = pd.read_csv('dataset.csv', header=None)
dataset = np.array(dataset)
plt.scatter(dataset[:,0], dataset[:,1], s=10)
plt.show()
... |
zunio/python-recipes | 00-BestPractices/Iterator.ipynb | apache-2.0 | numbers = [1, 2, 3]
for n in numbers:
print(n)
iterator=iter(numbers)
next(iterator)
next(iterator)
next(iterator)
next(iterator)
"""
Explanation: iterable is an object that it can be used in iteration (with a for loop) by using iterator
iterator is an object that define how actually do the iteration: specifi... |
ignaciomsarmiento/ignaciomsarmiento.github.io | teaching/BDML/Lecture08_example.ipynb | mit | #cargar librerias
import pandas as pd
import numpy as np
"""
Explanation: Clase 8: Big Data and Machine Learning en el Mercado Inmobiliario. Educación Continua
El siguiente cuaderno limpia los datos que use durante la clase teórica e implementa lo mostrado en clase.
End of explanation
"""
df = pd.read_csv('co_prop... |
tensorflow/docs-l10n | site/en-snapshot/probability/examples/Linear_Mixed_Effects_Model_Variational_Inference.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... |
SheffieldML/GPyOpt | manual/GPyOpt_integrating_model_hyperparameters.ipynb | bsd-3-clause | %pylab inline
import GPyOpt
"""
Explanation: GPyOpt: Integrating the model hyperparameters
Written by Javier Gonzalez and Zhenwen Dai, University of Sheffield.
Reference Manual index
Last updated Friday, 11 March 2016.
Integrating the model hyperparameters of a GP is easy with GPyOpt. To see how to use this feature ... |
tranlyvu/autonomous-vehicle-projects | Finding Lane Lines/notebook/Fiding_Lane_Lines.ipynb | apache-2.0 | #importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
import math
import os
%matplotlib inline
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
"""
Explanation: Se... |
atlury/deep-opencl | DL0110EN/4.3.2mist1layer.ipynb | lgpl-3.0 | !conda install -y torchvision
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as dsets
import torch.nn.functional as F
import matplotlib.pylab as plt
import numpy as np
"""
Explanation: <div class="alert alert-block alert-info" style="margin-top: 20px">
<a h... |
chetan51/nupic.research | projects/dynamic_sparse/notebooks/kWinners.ipynb | gpl-3.0 | dataset = Dataset(config=dict(dataset_name='MNIST', data_dir='~/nta/datasets',
batch_size_train=256, batch_size_test=1024))
# torch cross_entropy is log softmax activation + negative log likelihood
loss_func = F.cross_entropy
# a custom Lambda module
class Lambda(nn.Module):
def __ini... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/image_models/labs/2_mnist_models.ipynb | apache-2.0 | import os
from datetime import datetime
REGION = "us-central1"
PROJECT = !(gcloud config get-value core/project)
PROJECT = PROJECT[0]
BUCKET = PROJECT
MODEL_TYPE = "cnn" # "linear", "dnn", "dnn_dropout", or "cnn"
# Do not change these
os.environ["PROJECT"] = PROJECT
os.environ["BUCKET"] = BUCKET
os.environ["REGION"]... |
phoebe-project/phoebe2-docs | development/examples/binary_spots.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
"""
Explanation: Binary with Spots
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
"""
import phoebe
from phoebe import u # units
import numpy as np
impo... |
g-weatherill/oq-risklib | doc/lite-course.ipynb | agpl-3.0 | import os; print os.getcwd()
print [f for f in os.listdir('.') if f.endswith(('.py', '.ini', '.xml'))]
print open('job.ini').read()
from openquake.baselib.general import import_all; import_all('my_calculators')
from openquake.commonlib import readinput
oqparam = readinput.get_oqparam('job.ini'); oqparam
for name, ... |
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