repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
idc9/law-net | vertex_metrics_experiment/chalboards/data_pipeline_scotus.ipynb | mit | setup_data_dir(data_dir)
"""
Explanation: set up the data directory
End of explanation
"""
%time download_op_and_cl_files(data_dir, network_name)
"""
Explanation: data download
get opinion and cluster files from CourtListener
opinions/cluster files are saved in data_dir/raw/court/
End of explanation
"""
%time dow... |
ecell/ecell4-notebooks | en/tutorials/tutorial10.ipynb | gpl-2.0 | %matplotlib inline
from ecell4.prelude import *
"""
Explanation: 10. Spatiocyte Simulations at Single-Molecule Resolution
We showed an example of E-Cell4 spatial representation.
Next let's simulate the models with more detailed spatial representation called "single molecule resolution".
End of explanation
"""
with s... |
tensorflow/docs-l10n | site/ko/lattice/tutorials/shape_constraints_for_ethics.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... |
SimplifyData/Codality | 2. Arrays/OddOccurances.ipynb | mit | A = [9,3,9,3,9,7,9]
print len(A)
print sorted(A)
"""
Explanation: Write a function:
def solution(A)
that, given an array A consisting of N integers fulfilling the above conditions, returns the value of the unpaired element.
For example, given array A such that:
A[0] = 9 A[1] = 3 A[2] = 9
A[3] = 3 A[4] = 9 A... |
fluxcapacitor/source.ml | jupyterhub.ml/notebooks/train_deploy/zz_under_construction/tensorflow/optimize/01a_Explore_GPU.ipynb | apache-2.0 | %%bash
nvidia-smi
"""
Explanation: Explore GPU
Sanity Check #1:
Run Standard nvidia-smi Tool
End of explanation
"""
%%bash
xla_device_test &> xla_device_test.log
tail -3 xla_device_test.log
"""
Explanation: Sanity Check #2:
Run Accelerated Linear Algebra (XLA) Tests
End of explanation
"""
%%bash
cat /root/... |
google-research/google-research | pairwise_fairness/regression_crime.ipynb | apache-2.0 | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
import sys
from sklearn import model_selection
import tensorflow as tf
!pip install git+https://github.com/google-research/tensorflow_constrained_optimization
import tensorflow_constrained_optimization as tfco
"""
Explanation: Copyr... |
transcranial/keras-js | notebooks/layers/pooling/AveragePooling2D.ipynb | mit | data_in_shape = (6, 6, 3)
L = AveragePooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(270)
data_in = 2 *... |
brettavedisian/phys202-2015-work | assignments/assignment12/FittingModelsEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Fitting Models Exercise 2
Imports
End of explanation
"""
f = np.load('decay_osc.npz')
tdata = np.array(f['tdata'])
ydata = np.array(f['ydata'])
dy = np.array(f['dy'])
plt.figure(figsize=(8,6))
plt.err... |
dnstanciu/masters-project | sources/notebooks/plot_MST_measures.ipynb | gpl-3.0 | from pprint import pprint
import scipy
import pandas as pd
from pandas.tools.plotting import parallel_coordinates
from pandas import concat
import matplotlib
# Set backend to pgf
matplotlib.use('pgf')
import matplotlib.pyplot as plt
import numpy as np
# Some nice default configuration for plots
plt.rcParams['figu... |
MostafaGazar/gladiator | fellowship_ai_challenge_final.ipynb | apache-2.0 | !pip install -U seaborn
!pip install xlrd>=0.9.0
!pip install pdpbox
!pip install eli5
import os.path
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
"""
Explanation: Global Terrorist Attacks
Global Terrorism Database (GTD) is an open-source ... |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/GUI/3 - Widget Events.ipynb | apache-2.0 | from __future__ import print_function
"""
Explanation: Widget Events
In this lecture we will discuss widget events, such as button clicks!
Special events
End of explanation
"""
import ipywidgets as widgets
print(widgets.Button.on_click.__doc__)
"""
Explanation: The Button is not used to represent a data type. Inste... |
cliburn/sta-663-2017 | notebook/18B_Spark.ipynb | mit | from pyspark import SparkContext
sc = SparkContext(master = 'local[*]')
"""
Explanation: Using Spark
With massive data, we need to load, extract, transform and analyze the data on multiple computers to overcome I/O and processing bottlenecks. However, when working on multiple computers (possibly hundreds to thousands)... |
NLeSC/noodles | notebooks/prime_numbers.ipynb | apache-2.0 | %load_ext cython
import multiprocessing
import threading
import queue
"""
Explanation: Real World Tutorial 3: Parallel Number crunching using Cython
Python was not designed to be very good at parallel processing. There are two major problems at the core of the language that make it hard to implement parallel algorithm... |
cjam/deep-onc | notebooks/tf-mnist-ml-pros.ipynb | mit | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/data/MNIST/",one_hot=True)
sess = tf.InteractiveSession()
"""
Explanation: MNIST ML Pros tutorial
This notebook is based on the tutorial found here
This tutorial is very similar to the beginners tutor... |
sdpython/code_beatrix | _doc/notebooks/scratch/scratch_dans_un_notebook.ipynb | mit | import code_beatrix.jsscripts.snap
%load_ext code_beatrix
import os, glob
js_path = os.path.dirname(code_beatrix.jsscripts.snap.__file__)
files = [ os.path.split(_)[-1] for _ in glob.glob(js_path + "/*.js") ]
print(",".join(files))
path = "/static/snap/"
js_libs = [path + _ for _ in files ]
import notebook
print("fic... |
bgroveben/python3_machine_learning_projects | NevadaDashboard/pythonic_pandas.ipynb | mit | import pandas as pd
pd.__version__
nrg = pd.read_csv('energy_consumption.csv'); nrg.describe(include='all')
"""
Explanation: Pythonic pandas
Using a tutorial about fast flexible pandas.
The pandas Python package is an effective way to examine and manipulate data.
The source code is available on Github, and be sure t... |
tensorflow/docs-l10n | site/ja/tutorials/generative/pix2pix.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... |
AllenDowney/ModSimPy | soln/chap10soln.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 *
from pandas import read_html
"""
Expl... |
sserkez/ocelot | test/workshop/1_introduction.ipynb | gpl-3.0 | from IPython.display import Image
#Image(filename='gui_example.png')
"""
Explanation: This notebook was created by Sergey Tomin for Workshop: Designing future X-ray FELs. Source and license info is on GitHub. August 2016.
An Introduction to Ocelot
Ocelot is a multiphysics simulation toolkit designed for studying FEL ... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session01/Day3/IntroToVisualization.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Introduction to Visualization:
Density Estimation, Dimensionality Reduction, and Data Exploration
Version 0.1
There are many flavors of data analysis that fall under the "visualization" umbrella in astronomy. Today, by way of examp... |
Vvkmnn/books | AutomateTheBoringStuffWithPython/lesson29.ipynb | gpl-3.0 | # Import Regex Module
import re
## Create Phone Regex
# Basic Phone Number: 415-555-0000
# Phone Numeber without Area Code: 555-0000
# Phone Number with Parenthesis: (415)-555-5000
# Phone Numbers with Extensions: ext /ext. /x12345
phoneRegex = re.compile(
r'''
( # entire number (stored as its o... |
QuantScientist/Deep-Learning-Boot-Camp | day03/2.2.2 CNN HandsOn - MNIST & CN Nets.ipynb | mit | #Import the required libraries
import numpy as np
np.random.seed(1338)
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.pooling import MaxPooling2D
from keras.utils i... |
omoju/hiphopathy | SnaptoPython/SnaptoPython.ipynb | gpl-3.0 | import turtle
wn = turtle.Screen() # creates a graphics window
"""
Explanation: A short Besides Blocks <i>Snap!</i> to Python Module
Previously in <i>Snap!</i> we have been used to programming with sprites. We will do something similar to help bridge your understanding of how computational thinking can be reali... |
noripyt/cours | algorithmique/Cours d’algorithmique.ipynb | mit | une_variable = 5
"""
Explanation: Pour compiler ce document en présentation, exécuter :
jupyter nbconvert Cours\ d’algorithmique.ipynb --to slides --post serve
Bertrand Bordage de NoriPyt
Programmeur Python, Django, PostgreSQL
Membre de l’équipe de développement du CMS Wagtail
Créateur de nombreux projets open source... |
parthasen/java-R | DS-ML_Nov_train_test.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import matplotlib.pylab as plt
import csv
import glob
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa.arima_model import ARIMAResults
import pickle
#from sklearn.cross_validation import train_test_split
from sklearn import linear_model
from sklearn.svm import ... |
Cyb3rWard0g/ThreatHunter-Playbook | docs/notebooks/windows/08_lateral_movement/WIN-201009183000.ipynb | gpl-3.0 | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: Remote DCOM IErtUtil DLL Hijack
Metadata
| | |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2020/10/09 |
| modification date | 2020/10/09 |
| playbook related | ['W... |
tensorflow/docs-l10n | site/ko/probability/examples/TensorFlow_Probability_on_JAX.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... |
ajrichards/notebook | munging/dealing-with-catagorical-variables.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder,OneHotEncoder, Binarizer
"""
Explanation: Dealing With Catagorical Features
Often features are not given as continuous values, but rather as categorical classes. For example, variables may be defined as ["male", "female"], ["Europe",... |
rajeshb/SelfDrivingCar | CarND-LeNet-Lab/LeNet-Lab-Solution.ipynb | mit | from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", reshape=False)
X_train, y_train = mnist.train.images, mnist.train.labels
X_validation, y_validation = mnist.validation.images, mnist.validation.labels
X_test, y_test = mnist.test.images, mn... |
telescopeuser/workshop_blog | wechat_tool_py3_local/lesson_6_py3_local.ipynb | mit | import IPython.display
IPython.display.YouTubeVideo('YSL--3j12VA')
"""
Explanation: <img src='https://www.iss.nus.edu.sg/Sitefinity/WebsiteTemplates/ISS/App_Themes/ISS/Images/branding-iss.png' width=15% style="float: right;">
<img src='https://www.iss.nus.edu.sg/Sitefinity/WebsiteTemplates/ISS/App_Themes/ISS/Images/br... |
the-deep-learners/TensorFlow-LiveLessons | notebooks/dense_sentiment_classifier.ipynb | mit | import keras
from keras.datasets import imdb
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense, Flatten, Dropout
from keras.layers import Embedding # new!
from keras.callbacks import ModelCheckpoint # new!
import os # new!
from sklearn.metrics im... |
rishuatgithub/MLPy | nlp/UPDATED_NLP_COURSE/02-Parts-of-Speech-Tagging/06-POS-Assessment-Solutions.ipynb | apache-2.0 | # RUN THIS CELL to perform standard imports:
import spacy
nlp = spacy.load('en_core_web_sm')
from spacy import displacy
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Parts of Speech Assessment - Solutions
For this assessment we'll be using the short story The Tale o... |
Lstyle1/Deep_learning_projects | batch-norm/Batch_Normalization_Exercises.ipynb | mit | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
"""
Explanation: Batch Normalization – Practice
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a con... |
mari-linhares/tensorflow-workshop | code_samples/RNN/sentiment_analysis/BagOfWords.ipynb | apache-2.0 | tf.reset_default_graph()
"""
Explanation: Catigorical Columns Don't just One-Hot, They Count.
End of explanation
"""
strings = np.array([['a','a','','b','c'],['a','c','zz','',''],['b','qq','qq','b','']])
"""
Explanation: Here we have 3 examples, each containing 5 strings.
Note: empty strings are ignored, and can b... |
karthikrangarajan/intro-to-sklearn | 01.ML 101 and Intro to scikit-learn.ipynb | bsd-3-clause | # Plot settings for notebook
# so that plots show up in notebook
%matplotlib inline
import matplotlib.pyplot as plt
# suppress future warning (some code is borrowed/adapted)
import warnings
warnings.filterwarnings('ignore')
# Import an example plot from the figures directory
from fig_code import plot_sgd_separator
p... |
zhuanxuhit/deep-learning | intro-to-tensorflow/intro_to_tensorflow_solution.ipynb | mit | # Problem 1 - Implement Min-Max scaling for grayscale image data
def normalize_grayscale(image_data):
"""
Normalize the image data with Min-Max scaling to a range of [0.1, 0.9]
:param image_data: The image data to be normalized
:return: Normalized image data
"""
a = 0.1
b = 0.9
grayscale... |
rcrehuet/Python_for_Scientists_2017 | notebooks/Random Numbers in NumPy Campus.ipynb | gpl-3.0 | import numpy as np
"""
Explanation: Random Numbers in NumPy
End of explanation
"""
samples = np.random.normal(size=(4,4))
samples
"""
Explanation: The numpy.random module adds to the standard built-in Python random functions for generating efficiently whole arrays of sample values with many kinds of probability dis... |
AstroHackWeek/AstroHackWeek2014 | day1/Version Control.ipynb | bsd-3-clause | ls
"""
Explanation: Version control for fun and profit: the tool you didn't know you needed. From personal workflows to open collaboration
Note: this tutorial was particularly modeled, and therefore owes a lot, to the excellent materials offered in:
"Git for Scientists: A Tutorial" by John McDonnell
Emanuele Olivett... |
ES-DOC/esdoc-jupyterhub | notebooks/cnrm-cerfacs/cmip6/models/sandbox-3/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'sandbox-3', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: SANDBOX-3
Topic: Ocean
Sub-Topics: Timestepping Fram... |
arnicas/eyeo_nlp | python/Word2Vec_Yelp.ipynb | cc0-1.0 | import json
import gensim
import numpy as np
import string
import tsne as ts # local copy in the repo
%matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: Word2Vec Whirlwind Tour Using Yelp Review Data
Lynn Cherny (@arnicas, arnicas@gmail.com)
Note: This repo requires a gzipped file of data to be unzi... |
csaladenes/aviation | code/airport_dest_parser2.ipynb | mit | L=json.loads(file('../json/L.json','r').read())
M=json.loads(file('../json/M.json','r').read())
N=json.loads(file('../json/N.json','r').read())
import requests
AP={}
for c in M:
if c not in AP:AP[c]={}
for i in range(len(L[c])):
AP[c][N[c][i]]=L[c][i]
"""
Explanation: Load airports of each country
En... |
cristhro/Machine-Learning | ejercicio 3/titanic.ipynb | gpl-3.0 | %matplotlib inline
import pandas as pd
import numpy as np
import random as rnd
import seaborn as sns
import matplotlib.pyplot as plt
"""
Explanation: Alumnos: Cristhian Rodriguez y Jesus Perucha
Practica 3: Titanic
End of explanation
"""
train_df = pd.read_csv('train.csv')
test_df = pd.read_csv('test.csv')
"""
Exp... |
MetricLearning/diplomado_datascience | 03_Visualizar_datos/Visualizacion de datos con Python.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
params = {'legend.fontsize': 'x-large',
'figure.figsize': (10, 7.5),
'axes.labelsize': 'x-large',
'axes.titlesize':'x-large',
'xtick.labelsize':'x-large',
'ytick... |
bashtage/statsmodels | examples/notebooks/glm_weights.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
import statsmodels.api as sm
"""
Explanation: Weighted Generalized Linear Models
End of explanation
"""
print(sm.datasets.fair.NOTE)
"""
Explanation: Weighted GLM: Poisson response data
Load data
In this example, we'll use the affair datas... |
autism-research-centre/Autism-Gradients | 1_SelectFiles.ipynb | gpl-3.0 | # imports
from __future__ import print_function
import numpy as np
import os
import nibabel as nib
from os import listdir
from os.path import isfile, join
import os.path
# little helper function to return the proper filelist with the full path but that skips hidden files
def listdir_nohidden(path):
for f in os.lis... |
ChileanVirtualObservatory/DISPLAY | src/experiments/DISPLAY - 2011.0.00419.S CH3OH7-6.ipynb | gpl-3.0 | file_path = '../data/2011.0.00419.S/sg_ouss_id/group_ouss_id/member_ouss_2013-03-06_id/product/IRAS16547-4247_Jet_CH3OH7-6.clean.fits'
noise_pixel = (15, 4)
train_pixels = [(133, 135),(134, 135),(133, 136),(134, 136)]
img = fits.open(file_path)
meta = img[0].data
hdr = img[0].header
# V axis
naxisv = hdr['NAXIS3']
... |
averyallen71/dcgan-denosing-autoencoder | dcgan_autoencoder_notebook.ipynb | mit | import sys
sys.path.append('..')
import os
import json
from time import time
import numpy as np
from tqdm import tqdm
import theano
import theano.tensor as T
from theano.sandbox.cuda.dnn import dnn_conv
from PIL import Image
"""
Explanation: Imports
End of explanation
"""
from lib import activations
from lib impo... |
Danghor/Algorithms | Python/Local-Search-Engine.ipynb | gpl-2.0 | import subprocess
"""
Explanation: Implementing a Local Search Engine
This notebook shows one important application of <em style="color:blue;">dictionaries</em> and <em style="color:blue;">sets</em>:
It implements a local search engine that can be used to index .pdf documents on the local file system. The index can t... |
kit-cel/wt | mloc/ch2_Machine_Learning/Learn_Names_Naive_Bayes.ipynb | gpl-2.0 | import numpy as np
import string
"""
Explanation: Name Competition with Naive Bayes Predictor
This code is provided as supplementary material of the lecture Machine Learning and Optimization in Communications (MLOC).<br>
This code illustrates
* Classifying names based on a simple naive Bayesian predictor with conditio... |
amitkaps/full-stack-data-science | credit-risk/notebooks/2-Building-an-ML-App.ipynb | mit | %%file sq.py
def square(n):
return n*n
"""
Explanation: Building an ML App
Now that we have a machine learning model to predict the defaults, let us try to build a web application to lend loans.
It'll have two parts:
a form to submit the loans
admin panel to look at the submitted loans and their probability of... |
jtwhite79/pyemu | examples/Schurexample_freyberg.ipynb | bsd-3-clause | %matplotlib inline
import os
import numpy as np
import pandas as pd
from matplotlib.patches import Rectangle as rect
import matplotlib.pyplot as plt
import warnings
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib as mpl
newparams = {'legend.fontsize':10, 'axes.labelsize':10,
'xtic... |
planetlabs/notebooks | jupyter-notebooks/analytics-snippets/roads_as_vector.ipynb | apache-2.0 | import os
import fiona
import matplotlib.pyplot as plt
from planet import api
import rasterio
from rasterio import features as rfeatures
from rasterio.enums import Resampling
from rasterio.plot import show
import shapely
from shapely.geometry import shape as sshape
# if your Planet API Key is not set as an environmen... |
LukasMosser/Jupetro | notebooks/Simple Decline Curve Analysis in Python.ipynb | gpl-3.0 | from scipy.optimize import curve_fit
"""
Explanation: Simple Decline Curve Analysis in Python
Created by Lukas Mosser, 2016
Introduction
We will look at how we can use python and related libraries to perform a decline curve analysis on
a number of example datasets.
The methodology shown here can be readily extended t... |
junhwanjang/DataSchool | Lecture/05. 기초 선형 대수 1 - 행렬의 정의와 연산/5) 행렬의 연산과 성질.ipynb | mit | A = (np.arange(9) - 4).reshape((3, 3))
A
np.linalg.norm(A)
"""
Explanation: 행렬의 연산과 성질
행렬에는 곱셈, 전치 이외에도 지수 함수 등의 다양한 연산을 정의할 수 있다. 각각의 정의와 성질을 알아보자.
행렬의 부호
행렬은 복수의 실수 값을 가지고 있으므로 행렬 전체의 부호는 정의할 수 없다. 하지만 행렬에서도 실수의 부호 정의와 유사한 기능을 가지는 정의가 존재한다. 바로 행렬의 양-한정(positive definite) 특성이다.
모든 실수 공간 $\mathbb{R}^n$ 의 0벡터가 아닌 벡터 $... |
phungkh/phys202-2015-work | assignments/assignment04/MatplotlibEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Matplotlib Exercise 2
Imports
End of explanation
"""
!head -n 30 open_exoplanet_catalogue.txt
"""
Explanation: Exoplanet properties
Over the past few decades, astronomers have discovered thousands of extrasolar planets. The follo... |
VectorBlox/PYNQ | Pynq-Z1/notebooks/examples/asyncio_buttons.ipynb | bsd-3-clause | from pynq import Overlay, PL
from pynq.board import LED, Switch, Button
Overlay('base.bit').download()
buttons = [Button(i) for i in range(4)]
leds = [LED(i) for i in range(4)]
switches = [Switch(i) for i in range(2)]
"""
Explanation: Using Interrupts and asyncio for Buttons and Switches
This notebook provides a sim... |
mssalvador/Fifa2018 | Teknisk Tirsdag - Data Cleaning.ipynb | apache-2.0 | #PURE PYTHON!!!!
from IPython.display import display, Markdown
import numpy as np
import pandas as pd
import os
import re
# path = %pwd
# path += '/fifa-18-demo-player-dataset/CompleteDataset.csv'
# Til Windows
path = '.\\Downloads\\Fifa2018-master\\Fifa2018-master'
path += '\\fifa-18-demo-player-dataset\\CompleteDat... |
mayank-johri/LearnSeleniumUsingPython | Section 1 - Core Python/Chapter 09 - Classes & OOPS/OOPs Fundamentals - Inheritance.ipynb | gpl-3.0 | class P1():
def __init__(super):
print("P-1 init")
class P2():
def __init__(super):
print("P_2 init")
class C1(P1, P2):
pass
class C2(P2, P1):
pass
c1 = C1()
print("*"*20)
c2 = C2()
"""
Explanation: OOPS Fundamentals
What is Inheritance?
Inheritance is used to indicate that one cla... |
liganega/Gongsu-DataSci | previous/y2017/W10-stats-correlation/.ipynb_checkpoints/GongSu23_Statistics_Correlation-checkpoint.ipynb | gpl-3.0 | from GongSu22_Statistics_Population_Variance import *
"""
Explanation: 자료 안내: 여기서 다루는 내용은 아래 사이트의 내용을 참고하여 생성되었음.
https://github.com/rouseguy/intro2stats
상관분석
안내사항
지난 시간에 다룬 21장과 22장 내용을 활용하고자 한다.
따라서 아래와 같이 21장과 22장 내용을 모듈로 담고 있는 파이썬 파일을 임포트 해야 한다.
주의: 아래 두 개의 파일이 동일한 디렉토리에 위치해야 한다.
* GongSu21_Statistics_Averages.py ... |
tensorflow/agents | docs/tutorials/1_dqn_tutorial.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... |
ThierryMondeel/FBA_python_tutorial | FBA_tutorials/1_introduction_to_python.ipynb | mit | # ignore the first two lines: they allow you to show multiple outputs per cell
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
import this
"""
Explanation: A crash course in Python
<p>**Authors**: Thierry D.G.A Mondeel, Stefania Astrologo, Ewelina Weglarz-To... |
PMBio/mtSet | notebooks/demo_arabidopsis.ipynb | apache-2.0 | import ipdb
import sys
import limix
sys.path.append('./..')
import mtSet.pycore.modules.splitter as SPLIT
import mtSet.pycore.modules.multiTraitSetTest as MTST
import mtSet.pycore.modules.chi2mixture as C2M
from mtSet.pycore.utils.utils import smartAppend
from mtSet.pycore.utils.utils import smartDumpDictHdf5
import ... |
ES-DOC/esdoc-jupyterhub | notebooks/ncar/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', 'ncar', 'sandbox-3', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: NCAR
Source ID: SANDBOX-3
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radi... |
iurilarosa/thesis | scritti/slides/review code/Review.ipynb | gpl-3.0 | binh_df0ORIG=zeros(nbin_d,nbin_f0); % HM matrix container
for it = 1:nTimeSteps
kf=(peaks(2,ii0:ii(it))-inifr)/ddf; % normalized frequencies
w=peaks(5,ii0:ii(it)); % wiener weights
t=peaks(1,ii0)*Day_inSeconds; % time conversion days to s
tddf=t/ddf;
f0_a=kf-deltaf2;
for id = 1... |
catalystcomputing/DSIoT-Python-sessions | Session201811/code/09 Pandas - Part 2.ipynb | apache-2.0 | import pandas as pd
df_temp = pd.read_csv('../data/airquality.csv',
usecols = ["Ozone", "Solar.R", "Wind", "Temp", "Month", "Day"])
# We exclude the first column (= index) because we don't need it.
# To do that, just specify the columns of interest in usecols
#Let's add a year column
df_temp["Y... |
mne-tools/mne-tools.github.io | 0.24/_downloads/ead9220acec394667b95e490359e08e7/70_point_spread.ipynb | bsd-3-clause | import os.path as op
import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, apply_inverse
from mne.simulation import simulate_stc, simulate_evoked
"""
Explanation: Corrupt known signal with point spread
The aim of this tutorial is to demonstrate how to put ... |
theandygross/TCGA_differential_expression | Notebooks/Global_Parameters.ipynb | mit | GENE_SETS = '/cellar/users/agross/Data/GeneSets/c2.cp.v5.csv'
HUGO_TO_REFSEQ = '/cellar/users/agross/Data/GeneSets/hugo_to_refseq.csv'
"""
Explanation: GLOBAL Parameters
These are gene sets from mSigDB, downloading them is free but you need to create a login. Here is the link to the gene sets page, yI am using the ca... |
harmsm/pythonic-science | chapters/00_inductive-python/03_conditionals.ipynb | unlicense | x = 5
print(x > 2)
x = 5
print(x < 2)
"""
Explanation: Conditional Execution
Conditional execution allows a program to only execute code if some condition is met.
Predict what this code will do.
End of explanation
"""
x = 20
print (x > 2)
"""
Explanation: Summarize
What does x < y do? What does it spit out?
M... |
letsgoexploring/teaching | winter2017/econ129/python/Econ129_Class_12.ipynb | mit | # Create an array with 5 draws from the normal(0,1) distribution and print
# Create an array with 5 draws from the normal(0,1) distribution and print
"""
Explanation: Class 12: Stochastic Time Series Processes
Simulating normal random variables with Numpy
The numpy.random module has bunch of functions for generatin... |
edno/udacity-sandbox | ud170/ipython_notebook_tutorial.ipynb | unlicense | # Hit shift + enter or use the run button to run this cell and see the results
print 'hello world'
# The last line of every code cell will be displayed by default,
# even if you don't print it. Run this cell to see how this works.
2 + 2 # The result of this line will not be displayed
3 + 3 # The result of this line... |
Zhenxingzhang/AnalyticsVidhya | Articles/Python_List_Comprehension/List and Dictionary Comprehension-final.ipynb | apache-2.0 | [x**2 for x in range(0,10)]
[x for x in range(1,20) if x%2==0 ]
[x for x in 'MATHEMATICS' if x in ['A','E','I','O','U']]
for i in range(1,101):
if int(i**0.5)==i**0.5:
print i
[i for i in range(1,101) if int(i**0.5)==i**0.5]
import numpy as np
"""
Explanation: Python List Comprehension
Note: The run t... |
eds-uga/csci1360-fa16 | assignments/A3/A3_Q3.ipynb | mit | import numpy as np
np.random.seed(8948394)
list_of_numbers = np.random.randint(10, size = 100000).tolist()
index = -1
### BEGIN SOLUTION
### END SOLUTION
"""
Explanation: Q3
This question will focusing on indexing lists and dictionaries directly, no loops needed.
A
Reassign index to be the middle index of the list ... |
DOsinga/notebooks | Style_Transfer_Interpolation.ipynb | apache-2.0 | from keras.preprocessing.image import load_img, img_to_array
from scipy.misc import imsave
import numpy as np
from scipy.optimize import fmin_l_bfgs_b
import time
import argparse
import os
import imageio
from IPython.display import Image, display, HTML
from keras.applications import vgg16
from keras import backend as ... |
pastas/pastas | concepts/handling_timeseries.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pastas as ps
ps.show_versions()
"""
Explanation: Time series in Pastas
R.A. Collenteur, University of Graz, 2020
Time series are at the heart of time series analysis, and therefore need to be considered carefully when dealing with time seri... |
ga7g08/ga7g08.github.io | _notebooks/Harmonically-related-sinusoids.ipynb | mit | def fEven(x, omega, x0, R):
return np.sin(omega*x+x0) - R*np.cos(2*(omega*x+x0))
"""
Explanation: Two harmonically related sinusoids
This notebook looks at properties of two functions which can be described by
\begin{equation}
f_E(x) = A\sin(x) - B\cos(2 x)
\end{equation}
\begin{equation}
f_O(x) = A\cos(x)... |
dolittle007/dolittle007.github.io | notebooks/Model Comparison.ipynb | gpl-3.0 | %matplotlib inline
import pymc3 as pm
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context('notebook')
"""
Explanation: Model comparison
To demonstrate the use of model comparison criteria in PyMC3, we implement the 8 schools example from Section 5.5 of Gelman et al (2003), which at... |
tritemio/multispot_paper | out_notebooks/usALEX-5samples-E-corrected-all-ph-out-12d.ipynb | mit | ph_sel_name = "None"
data_id = "12d"
# data_id = "7d"
"""
Explanation: Executed: Mon Mar 27 11:39:24 2017
Duration: 7 seconds.
usALEX-5samples - Template
This notebook is executed through 8-spots paper analysis.
For a direct execution, uncomment the cell below.
End of explanation
"""
from fretbursts import *
ini... |
ramseylab/networkscompbio | class05_find_python3_template.ipynb | apache-2.0 | import numpy as np
import igraph
import timeit
import itertools
import bintrees
"""
Explanation: CS446/546 Class Session 5 - Adjacency Forests
Exploring the running time for testing if there is an edge between a pair of vertices
In this exercise, we'll compare the asymptotic computational running time for testing if ... |
fluffy-hamster/A-Beginners-Guide-to-Python | A Beginners Guide to Python/15. Indexing & Slicing.ipynb | mit | # flipping signs of numbers...
a = 5
b = -5
print(-a, -b)
# len function
x1 = []
x2 = "12"
x3 = [1,2,3]
print(len(x1), len(x2), len(x3))
x = [1,2,3]
print(x[100]) # <--- IndexError! 100 is waayyy out of bounds
"""
Explanation: Indexing
Okay guys today's lecture is indexing.
What is indexing?
At heart, indexing i... |
abhi1509/deep-learning | intro-to-tflearn/TFLearn_Sentiment_Analysis.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... |
mne-tools/mne-tools.github.io | dev/_downloads/b89584de6ec99a847868d7b80a32cf50/80_dics.ipynb | bsd-3-clause | # Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
#
# License: BSD-3-Clause
"""
Explanation: DICS for power mapping
In this tutorial, we'll simulate two signals originating from two
locations on the cortex. These signals will be sinusoids, so we'll be looking
at oscillatory activity (as opposed to evoked activity).
... |
arcyfelix/Courses | 18-11-22-Deep-Learning-with-PyTorch/07-Deploying PyTorch Models/.ipynb_checkpoints/Deploying_PyTorch_models_in_production-checkpoint.ipynb | apache-2.0 | torch.__version__
"""
Explanation: Requirements
End of explanation
"""
# Let's create an example model using ResNet-18
model = torchvision.models.resnet18()
model
# Creating a sample of the input
# It will be used to pass it to the network to build the dimensions
sample = torch.rand(size=(1, 3, 224, 224))
# Creat... |
Bio204-class/bio204-notebooks | 2016-04-13-association-categorical-variables.ipynb | cc0-1.0 | %matplotlib inline
import numpy as np
import scipy.stats as stats
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sbn
# load the bumpus dat set
dataurl = "https://github.com/Bio204-class/bio204-datasets/raw/master/bumpus-data.txt"
bumpus = pd.read_table(dataurl)
# for clarity of presentation let... |
phoebe-project/phoebe2-docs | development/tutorials/ui_jupyter.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
import phoebe
from phoebe import u # units
logger = phoebe.logger()
b = phoebe.default_binary()
"""
Explanation: Advanced: Interacting with the UI in Jupyter
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running in an onlin... |
UltronAI/Deep-Learning | CS231n/assignment2/PyTorch.ipynb | mit | import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torch.utils.data import sampler
import torchvision.datasets as dset
import torchvision.transforms as T
import numpy as np
import timeit
"""
Explanation: Training a ConvNet ... |
ml4a/ml4a-guides | examples/dreaming/neural-synth.ipynb | gpl-2.0 | #Grab inception model from online and unzip it (you can skip this step if you've already downloaded the model.
!wget -P . https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip
!unzip inception5h.zip -d inception5h/
!rm inception5h.zip
"""
Explanation: Neural synthesis, feature visualization, an... |
Flaviolib/dx | 01_dx_frame.ipynb | agpl-3.0 | from dx import *
np.set_printoptions(precision=3)
"""
Explanation: <img src="http://hilpisch.com/tpq_logo.png" alt="The Python Quants" width="45%" align="right" border="4">
Framework Classes and Functions
This section explains the usage of some basic framework classes and functions of DX Analytics. Mainly some helper... |
AlJohri/DAT-DC-12 | notebooks/intro-pandas.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Introduction to Pandas
Forked from 10 Minutes to pandas
End of explanation
"""
s = pd.Series([1,3,5,np.nan,6,8])
s
"""
Explanation: Object Creation
http://pandas.pydata.org/pandas-docs/stable/10min.html#objec... |
pagutierrez/tutorial-sklearn | notebooks-spanish/17-modelos_lineales.ipynb | cc0-1.0 | from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
X, y, true_coefficient = make_regression(n_samples=200, n_features=30, n_informative=10, noise=100, coef=True, random_state=5)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=5, train_size=60, test_... |
jtempkin/enhanced-sampling-toolkit | docs/data/NEUS-basic-usage.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from neus import pyramid
from neus import partition
"""
Explanation: The NEUS application toolkit provides three modules:
A Window object
A partition object
An Entry Point collection
The Window object represents the data strutures and routin... |
googleinterns/bizview-semi-supervised-learning | Supervised_learning/resnet_test_create_dataset.ipynb | apache-2.0 | from keras.utils import np_utils
from keras.datasets import cifar10
import numpy as np
from resnet_lib import create_dataset, create_model, test_callback, tune_model, visualize_model
# Global constants
NUM_CLASSES = 10
IMAGE_SIZE = 32
BATCH_SIZE = 64
EPOCHS = 10
# Directories
INPUT_TF_TRAIN = '../Mixmatch/ML_DATA/cif... |
srnas/barnaba | manuscript_figures/02_figure.ipynb | gpl-3.0 | import pickle
import numpy as np
# read ermsd from pickle
fname = "ermsd.p"
print "# reading pickle %s" % fname,
ermsd = pickle.load(open(fname, "r"))
print " shape ", ermsd.shape
print "####"
# define thresholds for folded and unfolded states, in eRMSD values.
thr_u = 1.9
thr_f = 0.6
ll = ermsd.shape[0]
# make assi... |
phoebe-project/phoebe2-docs | 2.2/examples/diher_misaligned.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: DI Her: Misaligned Binary
In this example, we'll reproduce Figure 8 in the misalignment release paper (Horvat et al. 2018).
<img src="horvat+18_fig8.png" alt="Figure 8" width="400px"/>
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (Y... |
ctroupin/OceanData_NoteBooks | PythonNotebooks/PlatformPlots/Read_drifter_data_1.ipynb | gpl-3.0 | %matplotlib inline
import netCDF4
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
"""
Explanation: In this application we will read a netCDF file containing a surface drifter trajectory.
1. Reading the data
We use the netCDF4 module.
End of explanation
"""
drifterfile = "/data_loca... |
caseresearch/code-review | tutorials/jupyter_notebook_emcee/.ipynb_checkpoints/emcee_notebook-checkpoint.ipynb | mit | %matplotlib inline
"""
Explanation: The rad-ness of notebooks
I use notebooks more often than I use an executable .py script. This is partially because notebooks were my first major introduction to python, but my continued use relates back to the fact that it allows me to break up problems I'm solving into different b... |
slundberg/shap | notebooks/tabular_examples/tree_based_models/Force Plot Colors.ipynb | mit | import xgboost
import shap
# load JS visualization code to notebook
shap.initjs()
# train XGBoost model
X,y = shap.datasets.boston()
bst = xgboost.train({"learning_rate": 0.01}, xgboost.DMatrix(X, label=y), 100)
# explain the model's predictions using SHAP values
explainer = shap.TreeExplainer(bst)
shap_values = ex... |
janpipek/physt | doc/tutorial.ipynb | mit | # Necessary import evil
from physt import binnings, h1, h2, h3
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1337) # Have always the same data
"""
Explanation: Get started with physt
This tutorial describes some of the basic features of physt.
End of explanation
"""
# Basic dataset
heights =... |
volgy/notebook | PSD.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
# constants
FS = 1e4 # sampling rate (Hz)
SIG_F = 1e3 # signal frequency (Hz)
SIG_DB = 0 # signal amplitude (dB)
NOISE_DB = -15 # noise amplitude (dB)
T = 1 # signal length (s)
dT = 1 / FS
t = np.... |
ChristinaB/Observatory | tutorials/Observatory_usecase7_xmapLandlab.ipynb | mit | #!conda install -c conda-forge ogh libgdal gdal pygraphviz ncurses matplotlib=2.2.3 --yes
# silencing warning
import warnings
warnings.filterwarnings("ignore")
# data processing
import os
import pandas as pd, numpy as np, dask
# data migration library
import ogh
import ogh_xarray_landlab as oxl
from utilities import... |
RomanSC/algorithms | chapter-2/.ipynb_checkpoints/Chapter-2-Analysis-checkpoint.ipynb | gpl-3.0 | # Importing some things
import matplotlib.patches as patches
from matplotlib import pyplot
from time import time
import math
from executiontime import execution_time
from issubstring import *
from random_string import *
"""
Explanation: <h1>Chapter 2: Analysis</h1>
<span>
My first jupyter notebook, demonstrating some... |
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