repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
bchappet/dnfpy | Rapport/Rapport.ipynb | gpl-2.0 | x = np.array([1, 2, 3, 4, 5, 6])
ir = np.array([0.000000000000000000e+00,
0.000000000000000000e+00,
6.056077528688350031e-03,
8.428876313973869550e-03,
0.000000000000000000e+00,
0.000000000000000000e+00])
ir=ir*100
dnf = np.array([-1.090321063995361328e+00,
-6.263688206672668457e-01,
2.505307266418066447e-03,
1.3928876... |
tpin3694/tpin3694.github.io | machine-learning/feature_importance.ipynb | mit | # Load libraries
from sklearn.ensemble import RandomForestClassifier
from sklearn import datasets
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Title: Feature Importance
Slug: feature_importance
Summary: How to identify important features in random forest in scikit-learn.
Date: 2017-09-21 12:00
C... |
awadalaa/DataSciencePractice | kaggle/titanic/TitanicPrediction.ipynb | mit | import csv as csv
import numpy as np
import pandas as pd
# We can use the pandas library in python to read in the csv file.
# This creates a pandas dataframe and assigns it to the titanic variable.
titanic = pd.read_csv("data/train.csv")
# Print the first 5 rows of the dataframe.
print(titanic.head(5))
print(titanic... |
landlab/landlab | notebooks/tutorials/tidal_flow/tidal_flow_calculator.ipynb | mit | # imports
import numpy as np
import matplotlib.pyplot as plt
from landlab import RasterModelGrid, imshow_grid
from landlab.components import TidalFlowCalculator
# set up the grid
grid = RasterModelGrid(
(3, 101), xy_spacing=2.0
) # only 1 row of core nodes, between 2 boundary rows
grid.set_closed_boundaries_at_gr... |
jo-c-2017/DS_Projects | JC_inferential_statistics_ex1.ipynb | apache-2.0 | import pandas as pd
df = pd.read_csv('data/human_body_temperature.csv')
df.head()
"""
Explanation: What is the True Normal Human Body Temperature?
Background
The mean normal body temperature was held to be 37$^{\circ}$C or 98.6$^{\circ}$F for more than 120 years since it was first conceptualized and reported by Carl ... |
ampl/amplpy | notebooks/hashcode/practice_problem.ipynb | bsd-3-clause | import os
if not os.path.isdir('input_data'):
os.system('git clone https://github.com/ampl/amplpy.git')
os.chdir('amplpy/notebooks/hashcode')
if not os.path.isdir('ampl_input'):
os.mkdir('ampl_input')
"""
Explanation: Google Hashcode 2022
Google Hashcode is a team programming competition to solve a complex... |
robblack007/clase-metodos-numericos | Practicas/P1/.ipynb_checkpoints/Practica 1 - Introduccion a Jupyter-checkpoint.ipynb | mit | 2 + 3
2*3
2**3
sin(pi)
"""
Explanation: Introducción a Jupyter
Expresiones aritmeticas y algebraicas
Empezaremos esta práctica con algo de conocimientos previos de programación. Se que muchos de ustedes no han tenido la oportunidad de utilizar Python como lenguaje de programación y mucho menos Jupyter como ambiente... |
skkandrach/foundations-homework | .ipynb_checkpoints/Homework6_Soma-checkpoint.ipynb | mit |
#api KEY = c9d64e80aa02ca113562a075e57256d7
https://api.forecast.io/forecast/c9d64e80aa02ca113562a075e57256d7/10.4806,66.9036
import requests
response = requests.get("https://api.forecast.io/forecast/c9d64e80aa02ca113562a075e57256d7/10.4806,66.9036")
forecast = response.json()
print(forecast.keys())
print(forecast... |
jbwhit/WSP-312-Tips-and-Tricks | notebooks/autoreload-example.ipynb | mit | import os
import sys
import time
sys.path.append("..")
%reload_ext autoreload
"""
Explanation: How to use autoreload
I have been confused on how to use autoreload IPython extension for a long time. The documentation simply wasn't clear to me. Or, rather, it seemed clear, but then I was surprised by the behavior.
After... |
bgruening/EDeN | examples/classification.ipynb | gpl-3.0 | from eden.util import load_target
y = load_target( 'http://www.bioinf.uni-freiburg.de/~costa/bursi.target' )
"""
Explanation: Classification
Consider a binary classification problem. The data and target files are available online. The domain of the problem is chemoinformatics. Data is about toxicity of 4K small molecu... |
nikodtbVf/aima-si | csp.ipynb | mit | from csp import *
"""
Explanation: Constraint Satisfaction Problems (CSPs)
This IPy notebook acts as supporting material for topics covered in Chapter 6 Constraint Satisfaction Problems of the book Artificial Intelligence: A Modern Approach. We make use of the implementations in csp.py module. Even though this noteboo... |
rubensfernando/mba-analytics-big-data | Python/2016-07-29/aula4-parte2-recuperar-tweets.ipynb | mit | import tweepy
consumer_key = ''
consumer_secret = ''
access_token = ''
access_token_secret = ''
"""
Explanation: Recuperando Tweets
Para utilizar qualquer API do Twitter temos que importar os módulos e definir as chaves e tokens de acesso.
End of explanation
"""
autorizar = tweepy.OAuthHandler(consumer_key, consume... |
mne-tools/mne-tools.github.io | stable/_downloads/c6baf7c1a2f53fda44e93271b91f45b8/50_beamformer_lcmv.ipynb | bsd-3-clause | # Authors: Britta Westner <britta.wstnr@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample, fetch_fsaverage
from mne.beamformer import make_lcmv, apply_lcmv
"""
Explanation: Source reconstruction using an LCM... |
samkennerly/fridge | cache_output.ipynb | bsd-3-clause | import datetime as dt
import time
import fridge
"""
Explanation: Test CacheOutput() class-based function decorator
End of explanation
"""
# This decorator just displays how long a function takes to run
def showtime(func):
def wrapper(*args,**kwargs):
start = dt.datetime.now()
result = func(*a... |
CoreSecurity/pysap | docs/protocols/SAPEnqueue.ipynb | gpl-2.0 | from pysap.SAPEnqueue import *
from IPython.display import display
"""
Explanation: SAP Enqueue
The following subsections show a graphical representation of the main protocol packets and how to generate them.
First we need to perform some setup to import the packet classes:
End of explanation
"""
for dest in enqueue... |
ML4DS/ML4all | P1.Python_intro/P1_Starting_with_Python_student.ipynb | mit | a = 'house'
print(a)
"""
Explanation: A Brief Tutorial of Basic Python
Author: Jesús Fernández Bes
Jerónimo Arenas García (jeronimo.arenas@uc3m.es)
Jesús Cid Sueiro (jcid@tsc.uc3m.es)
Vanessa Gómez Verdejo (vanessa@tsc.uc3m.es)
Óscar García Hinde (oghinnde@tsc.uc3m.es)
Simón Roc... |
mediagit2016/workcamp-maschinelles-lernen-grundlagen | 17-12-11-workcamp-ml/2017-12-11-arbeiten-mit-numpy-10.ipynb | gpl-3.0 | import numpy as np
x = np.array([1,2,3,4,5])
#Ausgabe des Arrays, des Speicherortes und der Länge des arrays
print(x,id(x),len(x))
"""
Explanation: <h1>Arbeiten mit numpy</h1>
<h2>Erzeugen eines numpy array</h2>
<h2>1. Beispiel</h2>
End of explanation
"""
y = np.array([6,8,3,2,5,4,7,6])
print(y)
print(len(y))
print... |
quantumlib/Cirq | examples/direct_fidelity_estimation.ipynb | apache-2.0 | try:
import cirq
except ImportError:
print("installing cirq...")
!pip install --quiet cirq
print("installed cirq.")
# Import Cirq, DFE, and create a circuit
import cirq
from cirq.contrib.svg import SVGCircuit
import examples.direct_fidelity_estimation as dfe
qubits = cirq.LineQubit.range(3)
circuit = ... |
DominikDitoIvosevic/Uni | STRUCE/SU-2019-LAB02-LDM-LR.ipynb | mit | # Učitaj osnovne biblioteke...
import numpy as np
import sklearn
import mlutils
import matplotlib.pyplot as plt
%pylab inline
"""
Explanation: Sveučilište u Zagrebu
Fakultet elektrotehnike i računarstva
Strojno učenje 2019/2020
http://www.fer.unizg.hr/predmet/su
Laboratorijska vježba 2: Linearni diskriminativni mod... |
SHDShim/pytheos | examples/10_pvt-eos_fit.ipynb | apache-2.0 | %config InlineBackend.figure_format = 'retina'
"""
Explanation: For high dpi displays.
End of explanation
"""
import numpy as np
import uncertainties as uct
import pandas as pd
from uncertainties import unumpy as unp
import matplotlib.pyplot as plt
import pytheos as eos
"""
Explanation: 0. General note
This noteb... |
ryan-leung/PHYS4650_Python_Tutorial | notebooks/Jan2018/pre_tutorial/CH2 Data Structures and Loops.ipynb | bsd-3-clause | (1, 'HKU', 3.0)
"""
Explanation: Ch2 Data Structures
In this part you will learn how to define data structure more than integer, tuples and boolean.
Tuple
Tuple in python is an 1D array that cannot change its content after declariation. The tuple can store differently type of elements. It is defined with parentheses.
... |
statsmodels/statsmodels.github.io | v0.12.2/examples/notebooks/generated/statespace_cycles.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from pandas_datareader.data import DataReader
endog = DataReader('UNRATE', 'fred', start='1954-01-01')
endog.index.freq = endog.index.inferred_freq
"""
Explanation: Trends and cycles in unemployment... |
tuanavu/python-cookbook-3rd | notebooks/ch01/01_unpacking_a_sequence_into_variables.ipynb | mit | # Example 1
p = (4, 5)
x, y = p
print x
print y
"""
Explanation: Unpacking a Sequence into Separate Variables
Problem
You have an N-element tuple or sequence that you would like to unpack into a collection of N variables.
Solution
Any sequence (or iterable) can be unpacked into variables using a simple assignment op... |
khalido/nd101 | mnist_keras_simple.ipynb | gpl-3.0 | from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
"""
Explanation: Keras is a high level wrapper (API) for Tensorflow and Theano which aims to make them easier to use. Tensorflow gets quite verbose and there is a lot of detail to handle, which Keras trys to abstract away to sane... |
Chilipp/psy-simple | examples/example_plot2d.ipynb | gpl-2.0 | import psyplot.project as psy
import xarray as xr
%matplotlib inline
%config InlineBackend.close_figures = False
import numpy as np
"""
Explanation: 2D plots
Demonstration of the 2D plot capabilities
The plot2d plot method make plots of 2-dimensional scalar data
using matplotlibs pcolormesh or the contourf functions.
... |
michaelneuder/image_quality_analysis | bin/calculations/ssim/SSIM.ipynb | mit | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from scipy import stats
from PIL import Image as im
%matplotlib inline
plt.rcParams['font.size'] = 20
"""
Explanation: ssim data
this notebook explores the data and makes sure everything looks correct.
End of explanation
"""
# take a peek at the ... |
prody/ProDy-website | _static/ipynb/MechStiff_tutorial.ipynb | mit | %matplotlib inline
from prody import *
import matplotlib.pylab as plt
gfp, header = parsePDB('1gfl', header=True)
gfp
calphas = gfp.select('protein and chain A and name CA')
calphas
"""
Explanation: MechStiff tutorial
This is an example how to use Mechanical Stiffness Calculations implemented in ProDy package.
The... |
catalystcomputing/DSIoT-Python-sessions | Session5/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... |
mturnbu/datascience | Thanksgiving-Dinner/Thanksgiving-Dinner.ipynb | mit | import pandas as pd
data = pd.read_csv("thanksgiving.csv", encoding = 'Latin-1')
data.head()
data.columns
data['Do you celebrate Thanksgiving?'].value_counts()
"""
Explanation: Analyzing Thanksgiving Dinner
This notebook analyzes Thanksgiving dinner in the US. The dataset contains 1058 responses to an online survey... |
mauriciogtec/PropedeuticoDataScience2017 | Alumnos/Arturo_Gonzalez/Tarea2-ArturoGonzalezBencomo.ipynb | mit | from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
#url = sys.argv[1]
url = 'Mario.png'
img = Image.open(url)
imggray = img.convert('LA')
"""
Explanation: Parte 1: Teoría de Algebra Lineal y Optimización
1. ¿Por qué una matriz equivale a una transformación lineal entre espacios vectoriales?<b... |
jhconning/Dev-II | notebooks/moralhazard.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from ipywidgets import interact, fixed
"""
Explanation: Risk Sharing and Moral Hazard
End of explanation
"""
alpha = 0.25
def u(c, alpha=alpha):
return (1/alpha)*c**alpha
def E(x,p):
return p*x[1] + (1-p)*x[0]
def EU(c, p):
return p... |
sorig/shogun | doc/ipython-notebooks/computer_vision/Scene_classification.ipynb | bsd-3-clause | #import Opencv library
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
try:
import cv2
except ImportError:
print "You must have OpenCV installed"
exit(1)
#check the OpenCV version
try:
v=cv2.__version__
assert (tuple(map(int,v.split(".")))>(2,4,2))
except (AssertionError, Va... |
kimegitee/deep-learning | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
diegocavalca/Studies | programming/Python/tensorflow/exercises/Control_Flow.ipynb | cc0-1.0 | from __future__ import print_function
import tensorflow as tf
import numpy as np
from datetime import date
date.today()
author = "kyubyong. https://github.com/Kyubyong/tensorflow-exercises"
tf.__version__
np.__version__
sess = tf.InteractiveSession()
"""
Explanation: Control Flow
End of explanation
"""
x = tf.c... |
Islast/BrainNetworksInPython | tutorials/global_measures_viz.ipynb | mit | import scona as scn
import scona.datasets as datasets
import numpy as np
import networkx as nx
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
%load_ext autoreload
%autoreload 2
"""
Explanation: Visualisation tutorial
In the introductor... |
NeuroDataDesign/seelviz | Jupyter/ClarityViz Tutorial.ipynb | apache-2.0 | from clarityviz import claritybase
token = 'Fear199'
source_directory = '/cis/home/alee/claritycontrol/code/data/raw'
# Initialize the claritybase object, the initial basis for all operations.
# After you initialize with a token and source directory, a folder will be created in your current directory
# with the token... |
ruleva1983/udacity-mle | exploratory_project/Titanic_Survival_Exploration.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
# RMS Titanic data visualization code
from titanic_visualizations import survival_stats
from IPython.display import display
%matplotlib inline
# Load the dataset
in_file = 'titanic_data.csv'
full_data = pd.read_csv(in_file)
# Print the first few entries of the RMS Titanic dat... |
PyladiesMx/Empezando-con-Python | 2. Lists_and_conditionals/.ipynb_checkpoints/lists and conditionals-checkpoint.ipynb | mit | lista1 = ["Hola", 1, 2.0, True]
lista1
lista2 = [2+3, 5+1, 4**2]
lista2
"""
Explanation: ¡Bienvenidas nuevamente!
Hoy veremos otro tipo de objetos en python llamados listas y además empezaremos a tomar decisiones con expresiones condicionales.
¿Y qué pasa si queremos coleccionar valores? Acerca de las listas...
En ... |
AlphaGit/deep-learning | autoencoder/Simple_Autoencoder.ipynb | mit | %matplotlib inline
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', validation_size=0)
"""
Explanation: A Simple Autoencoder
We'll start off by building a simple autoencoder to compres... |
tuanavu/coursera-university-of-washington | machine_learning/2_regression/lecture/week4/Overfitting_Demo_Ridge_Lasso.ipynb | mit | import sys
sys.path.append('C:\Anaconda2\envs\dato-env\Lib\site-packages')
import graphlab
import math
import random
import numpy
from matplotlib import pyplot as plt
%matplotlib inline
"""
Explanation: Overfitting demo
Create a dataset based on a true sinusoidal relationship
Let's look at a synthetic dataset consisti... |
csaladenes/aviation | code/.ipynb_checkpoints/airport_arrv_parser_old-checkpoint.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]
sch={}
"""
Explanation: Load airports of each co... |
adityaka/misc_scripts | python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/05_03/Begin/Multiple.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
"""
Explanation: <h1>Multiples Lines, Single Plot</h1>
End of explanation
"""
data_set_size = 15
low_mu, low_sigma = 50, 4.3
low_data_set = low_mu + low_sigma * np.random.randn(data_set_size)
high_mu, high_sigma = 57, 5.2
... |
AtmaMani/pyChakras | udemy_ml_bootcamp/Machine Learning Sections/Decision-Trees-and-Random-Forests/Decision Trees and Random Forest Project - Solutions.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Random Forest Project - Solutions
For this project we will be exploring publicly available data from Lending... |
deculler/DataScienceTableDemos | BerkeleySalary.ipynb | bsd-2-clause | # This useful nonsense just goes at the top
from datascience import *
import numpy as np
import matplotlib.pyplot as plots
plots.style.use('fivethirtyeight')
%matplotlib inline
# datascience version number of last run of this notebook
version.__version__
"""
Explanation: Illustration of datascience Tables on Open Data... |
lia-statsletters/notebooks | Copula Packages Notebook.ipynb | gpl-3.0 | #The first assert makes sure that you are getting a 1D in X and Y
#replace this code around line 58 in site-packages/copulalib/copulalib.py
try:
if X.shape[0] != Y.shape[0]:
raise ValueError('The size of both arrays should be same.')
except:
raise ... |
tensorflow/docs-l10n | site/ja/addons/tutorials/tqdm_progress_bar.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... |
sdss/marvin | docs/sphinx/tutorials/notebooks/VAC_MVM_Marvin_tutorial.ipynb | bsd-3-clause | import marvin
from marvin.tools import Cube
from marvin.tools import Maps
from marvin.tools.vacs import VACs
"""
Explanation: MaNGA Visual Morphology Catalogue (MVM-VAC)
Credit: Jose Antonio Vazquez Mata
See SDSS DR16 MVM for more information about this VAC.
This catalogue contains a direct visual morphological classi... |
alberto-antonietti/nest-simulator | doc/model_details/noise_generator.ipynb | gpl-2.0 | import sympy
sympy.init_printing()
x = sympy.Symbol('x')
sympy.series((1-sympy.exp(-x))/(1+sympy.exp(-x)), x)
"""
Explanation: The NEST noise_generator
Hans Ekkehard Plesser, 2015-06-25
This notebook describes how the NEST noise_generator model works and what effect it has on model neurons.
NEST needs to be in your PY... |
ivukotic/ML_platform_tests | tutorial/DeepLearningImages/DeepLearning-MNIST.ipynb | gpl-3.0 | # Function to import the dataset
# (if 'do_handwritten_digit_mnist = True' the standard handwritten digit MNIST dataset will be used instead of the fashion dataset)
import h5py
def import_data(do_handwritten_digit_mnist = False):
# Load the data from hdf5 files
if do_handwritten_digit_mnist:
h5_fi... |
harishkrao/DSE200x | Week-3-Numpy/03_Numpy_Notebook.ipynb | mit | import numpy as np
an_array = np.array([3, 33, 333]) # Create a rank 1 array
print(type(an_array)) # The type of an ndarray is: "<class 'numpy.ndarray'>"
# test the shape of the array we just created, it should have just one dimension (Rank 1)
print(an_array.shape)
# because this is a 1-rank array, we... |
cgivre/oreilly-sec-ds-fundamentals | Notebooks/Intro/Two Dimensional Data Worksheet - Python Answers.ipynb | apache-2.0 | %pylab inline
import pandas as pd
import numpy as np
pd.options.mode.chained_assignment = None
#Create a dataframe called twitter data from the CSV file
#Note if this is breaking your machine there is a smaller data set in the data file called twitter1-small.csv
twitterData = pd.read_csv( '../../data/twitter1.csv', en... |
arsenovic/clifford | docs/tutorials/cga/robotic-manipulators.ipynb | bsd-3-clause | class AddMethodsAsWeGo:
@classmethod
def _add_method(cls, m):
if isinstance(m, property):
name = (m.fget or m.fset).__name__
else:
name = m.__name__
setattr(cls, name, m)
"""
Explanation: This notebook is part of the clifford documentation: https://clifford.readt... |
astroumd/GradMap | notebooks/Haiti2016/orbits.ipynb | gpl-3.0 | %matplotlib inline
# python 2-3 compatibility
from __future__ import print_function
"""
Explanation: Gas Streaming in Disks: orbit approach
The gas streaming around a young star, or in a galactic disk is dominated by gravity. So we can simply compute the orbits of a point mass around a star, or in the more complex po... |
joshspeagle/dynesty | demos/Examples -- 200-D Multivariate Normal.ipynb | mit | # system functions that are always useful to have
import time, sys, os
import pickle
# basic numeric setup
import numpy as np
from numpy import linalg
from scipy import stats
# inline plotting
%matplotlib inline
# plotting
import matplotlib
from matplotlib import pyplot as plt
# seed the random number generator
rst... |
SHDShim/pytheos | examples/4_calculate_thermal_terms.ipynb | apache-2.0 | %config InlineBackend.figure_format = 'retina'
"""
Explanation: For high dpi displays.
End of explanation
"""
import uncertainties as uct
import numpy as np
from uncertainties import unumpy as unp
import matplotlib.pyplot as plt
import pytheos as eos
"""
Explanation: 0. General note
There exist different formulati... |
rashikaranpuria/Machine-Learning-Specialization | Clustering_&_Retrieval/Week5/5_lda_blank.ipynb | mit | import graphlab as gl
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# import wiki data
wiki = gl.SFrame('people_wiki.gl/')
wiki
"""
Explanation: Latent Dirichlet Allocation for Text Data
In this assignment you will
apply standard preprocessing techniques on Wikipedia text data
use GraphLab ... |
awhite40/pymks | notebooks/cahn_hilliard.ipynb | mit | %matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Cahn-Hilliard Example
This example demonstrates how to use PyMKS to solve the Cahn-Hilliard equation. The first section provides some background information about the Cahn-Hilliard equation as we... |
oscarmore2/deep-learning-study | 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... |
exowanderer/ExoplanetTSO | Exoplanet_TSO_Pipeline_Median.ipynb | gpl-3.0 | from wanderer import wanderer
def clipOutlier2D(arr2D, nSig=10):
arr2D = arr2D.copy()
medArr2D = median(arr2D,axis=0)
sclArr2D = np.sqrt(((scale.mad(arr2D)**2.).sum()))
outliers = abs(arr2D - medArr2D) > nSig*sclArr2D
inliers = abs(arr2D - medArr2D) <= nSig*sclArr2D
arr2D[outliers] = ... |
NeuroDataDesign/pan-synapse | pipeline_1/background/Net_Registration.ipynb | apache-2.0 | def knn_filter(volume, n):
#neighborList = []
outVolume = np.zeros_like(volume)
#for all voxels in volume
for z in range(volume.shape[0]):
for y in range(volume.shape[1]):
for x in range(volume.shape[2]):
#get all valid neighbors
neighbors = []
... |
jasonding1354/PRML_Notes | 2.LINEAR_MODELS_FOR_REGRESSION/Old_Faithful_Geyser_Experiment.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
faithfulData = pd.read_csv("faithful.csv", index_col=0, dtype=np.float64)
faithfulData.head()
waiting = faithfulData['waiting']
eruptions = faithfulData['eruptions']
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot()
plt.p... |
cgpotts/cs224u | vsm_04_contextualreps.ipynb | apache-2.0 | __author__ = "Christopher Potts"
__version__ = "CS224u, Stanford, Spring 2022"
"""
Explanation: Vector-space models: Static representations from contextual models
End of explanation
"""
import os
import pandas as pd
import torch
from transformers import BertModel, BertTokenizer
from transformers import RobertaModel,... |
andrebell/winproxy | winproxy/__init__.ipynb | bsd-2-clause | import re, six
from six.moves import winreg
"""
Explanation: Python 2.7 compatibility
To achieve Python 2.7 compatibility we will import the "_winreg" module
from six.moves, since it has been renamed to winreg in Python 3.
End of explanation
"""
if six.PY2:
FileNotFoundError = WindowsError
"""
Explanation: The ... |
TariqAHassan/BioVida | tutorials/3_domain_unification_and_data_management.ipynb | bsd-3-clause | from biovida.images import OpeniInterface
opi = OpeniInterface()
opi.search(query='lung cancer')
pull_df1 = opi.pull()
"""
Explanation: BioVida: Domain Unification and Data Management
This tutorial will cover the facilities BioVida offers to:
integrate images data against other kinds of biomedical data
manage cac... |
ivannz/study_notes | year_14_15/spring_2015/netwrok_analysis/notebooks/assignments/networks_ha2.ipynb | mit | ## As usual, attach the numpy and the netwrokx modules
import networkx as nx
import numpy as np
import numpy.random as rnd
import numpy.matlib as ml
from scipy.stats import chisquare
import matplotlib.pyplot as plt
%matplotlib inline
## Use direct HTML output capabilities of iPython
from IPython.display import HTML
... |
nickgorgone/AstroHackWeek2015 | Parallel-Profile.ipynb | gpl-2.0 | import multiprocessing as mp
import time
import numpy as np
npoints = 5
input_arr = np.random.rand(npoints)
nproc=int(np.floor(npoints/2.))
def makedistance(i):
output_arr = i**i
return output_arr
t1 = time.time()
pool = mp.Pool(processes=nproc)
out = pool.map(makedistance, input_arr)
pool.close()
pool.joi... |
mne-tools/mne-tools.github.io | 0.22/_downloads/03c9d71de135994dbf45db72856a1f9a/plot_mne_inverse_envelope_correlation.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
# Sheraz Khan <sheraz@khansheraz.com>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.connectivity import envelope_correlation
from mn... |
brandoncgay/deep-learning | dcgan-svhn/DCGAN_Exercises.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... |
CORE-GATECH-GROUP/serpent-tools | examples/XSPlot.ipynb | mit | import os
xfile = os.path.join(
os.environ["SERPENT_TOOLS_DATA"],
"plut_xs0.m")
"""
Explanation: Copyright (c) 2017-2020 Serpent-Tools developer team, GTRC
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS... |
richlewis42/scikit-chem | docs/tutorial/io.ipynb | bsd-3-clause | df = pd.read_csv('https://archive.org/download/scikit-chem_example_files/iris.csv',
header=None); df
"""
Explanation: Input/Output
Pandas objects are the main data structures used for collections of molecules. scikit-chem provides convenience functions to load objects into pandas.DataFrames from comm... |
gte620v/PythonTutorialWithJupyter | examples/2017-03-05-Graph_Entity_Resolution.ipynb | mit | import pandas as pd
df = pd.read_csv(
'https://github.com/gte620v/graph_entity_resolution/raw/master/data/scraped_data.csv.gz',
converters={'name': lambda x: str(x).lower(),
'number': str,
'oid': str,
'post_id': str},
parse_dates=['postdate'],
compression=... |
squishbug/DataScienceProgramming | DataScienceProgramming/04-Pandas-Data-Tables/PandasExercise_solution.ipynb | cc0-1.0 | import pandas as pd
import numpy as np
%%time
Employees = pd.read_excel('/home/data/AdventureWorks/Employees.xls')
print("shape:", Employees.shape)
%%time
Territory = pd.read_excel('/home/data/AdventureWorks/SalesTerritory.xls')
print("shape:", Territory.shape)
%%time
Customers = pd.read_excel('/home/data/AdventureW... |
xdnian/pyml | assignments/ex05_ch0607_xdnian.ipynb | mit | # Added version check for recent scikit-learn 0.18 checks
from distutils.version import LooseVersion as Version
from sklearn import __version__ as sklearn_version
"""
Explanation: Assignment 5
This assignment has weighting $1.5$.
Author: Nian Xiaodong (3035087112)
Model tuning and evaluation
End of explanation
"""
i... |
M-R-Houghton/euroscipy_2015 | scikit_image/lectures/stackoverflow_challenges.ipynb | mit | from scipy.signal import convolve2d
img = color.rgb2gray(io.imread('../images/snakes.png'))
# Reduce all lines to one pixel thickness
snakes = morphology.skeletonize(img < 1)
# Find pixels with only one neighbor
corners = convolve2d(snakes, [[1, 1, 1],
[1, 0, 1],
... |
asurve/arvind-sysml2 | samples/jupyter-notebooks/.ipynb_checkpoints/Linear_Regression_Algorithms_Demo-checkpoint.ipynb | apache-2.0 | !pip show systemml
"""
Explanation: Linear Regression Algorithms using Apache SystemML
This notebook shows:
- Install SystemML Python package and jar file
- pip
- SystemML 'Hello World'
- Example 1: Matrix Multiplication
- SystemML script to generate a random matrix, perform matrix multiplication, and compute th... |
geektoni/shogun | doc/ipython-notebooks/converter/Tapkee.ipynb | bsd-3-clause | import numpy as np
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
def generate_data(curve_type, num_points=1000):
if curve_type=='swissroll':
tt = np.array((3*np.pi/2)*(1+2*np.random.rand(num_points)))
height = np.array((np.random.rand(num_points)-0.5))
X = np.array([tt*np.cos(tt), 10*h... |
biof-309-python/BIOF309-2016-Fall | Week_06/Week06 - 01 - Homework Solutions.ipynb | mit | # %load data.csv
Drosophila melanogaster,atatatatatcgcgtatatatacgactatatgcattaattatagcatatcgatatatatatcgatattatatcgcattatacgcgcgtaattatatcgcgtaattacga,kdy647,264
Drosophila melanogaster,actgtgacgtgtactgtacgactatcgatacgtagtactgatcgctactgtaatgcatccatgctgacgtatctaagt,jdg766,185
Drosophila simulans,atcgatcatgtcgatcgatgatgc... |
Ccaccia73/semimonocoque | 03_Multiconnected_section.ipynb | mit | from pint import UnitRegistry
import sympy
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
import sys
%matplotlib inline
from IPython.display import display
"""
Explanation: Semi-Monocoque Theory
End of explanation
"""
from Section import Section
"""
Explanation: Import Section class, which... |
tensorflow/probability | tensorflow_probability/examples/jupyter_notebooks/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... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/miroc6/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'miroc6', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: MIROC
Source ID: MIROC6
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radiativ... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_left_cerebellum_volume_source.ipynb | bsd-3-clause | # Author: Alan Leggitt <alan.leggitt@ucsf.edu>
#
# License: BSD (3-clause)
import numpy as np
from scipy.spatial import ConvexHull
from mayavi import mlab
from mne import setup_source_space, setup_volume_source_space
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_pa... |
NicWayand/xray | examples/xarray_multidimensional_coords.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import pandas as pd
import xarray as xr
import cartopy.crs as ccrs
from matplotlib import pyplot as plt
print("numpy version : ", np.__version__)
print("pandas version : ", pd.__version__)
print("xarray version : ", xr.version.version)
"""
Explanation: Working with Multidimens... |
MegaShow/college-programming | Homework/Principles of Artificial Neural Networks/Week 17 Robustness/Robustness.ipynb | mit | %matplotlib inline
%load_ext autoreload
%autoreload 2
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import resnet
import numpy as np
import matplotlib.pyplot as plt
# start
"""
Explanation: Week 16. Robustness
Deep learn... |
google/jax | cloud_tpu_colabs/JAX_demo.ipynb | apache-2.0 | import jax.tools.colab_tpu
jax.tools.colab_tpu.setup_tpu()
"""
Explanation: Colab JAX TPU Setup
End of explanation
"""
import jax
import jax.numpy as jnp
from jax import random
key = random.PRNGKey(0)
key, subkey = random.split(key)
x = random.normal(key, (5000, 5000))
print(x.shape)
print(x.dtype)
y = jnp.dot(x... |
woobe/odsc_h2o_machine_learning | py_03a_regression_basics.ipynb | apache-2.0 | # Start and connect to a local H2O cluster
import h2o
h2o.init(nthreads = -1)
"""
Explanation: Machine Learning with H2O - Tutorial 3a: Regression Models (Basics)
<hr>
Objective:
This tutorial explains how to build regression models with four different H2O algorithms.
<hr>
Wine Quality Dataset:
Source: https://ar... |
smorton2/think-stats | code/chap11soln.ipynb | gpl-3.0 | from __future__ import print_function, division
%matplotlib inline
import numpy as np
import pandas as pd
import random
import thinkstats2
import thinkplot
"""
Explanation: Examples and Exercises from Think Stats, 2nd Edition
http://thinkstats2.com
Copyright 2016 Allen B. Downey
MIT License: https://opensource.org... |
Sasanita/nmt-keras | examples/4_nmt_model_tutorial.ipynb | mit | from keras.layers import *
from keras.models import model_from_json, Model
from keras.optimizers import Adam, RMSprop, Nadam, Adadelta, SGD, Adagrad, Adamax
from keras.regularizers import l2
from keras_wrapper.cnn_model import Model_Wrapper
from keras_wrapper.extra.regularize import Regularize
"""
Explanation: NMT-Ker... |
m2dsupsdlclass/lectures-labs | labs/09_triplet_loss/triplet_loss_totally_looks_like.ipynb | mit | import os
import os.path as op
from urllib.request import urlretrieve
from pathlib import Path
URL = "https://github.com/m2dsupsdlclass/lectures-labs/releases/download/totallylookslike/dataset_totally.zip"
FILENAME = "dataset_totally.zip"
if not op.exists(FILENAME):
print('Downloading %s to %s...' % (URL, FILENAM... |
fullmetalfelix/ML-CSC-tutorial | GeneticAlgorithm.ipynb | gpl-3.0 | # IMPORTS
import os
import math
import random
import numpy
from GA import GAEngine
import matplotlib.pyplot as plt
"""
Explanation: Genetic Algorithm
GA is a simple optimisation algorithm that mimics evolution. It starts with a random population of elements, and evaluates how <i>fit for survival</i> they are. Elements... |
KaiSzuttor/espresso | doc/tutorials/12-constant_pH/12-constant_pH.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
import numpy as np
import scipy.constants # physical constants
import espressomd
import pint # module for working with units and dimensions
from espressomd import electrostatics, polymer, reaction_ensemble
from espressomd.interactions import HarmonicBond
ureg = pint.UnitRegistry()
# ... |
tensorflow/docs | site/en/guide/migrate/upgrade.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... |
eriksalt/jupyter | Python Quick Reference/Metaprogramming.ipynb | mit | def hello_world():
print('hello world')
# wrap hello world in a function that does logging
def wrap_hello():
print('Enter: hello_world')
hello_world()
print('Exit: hello_world')
wrap_hello()
# to wrap any function at all, write a generic wrapper that takes the a function as input
def logthis(... |
dusenberrymw/incubator-systemml | samples/jupyter-notebooks/Image_Classify_Using_VGG_19_Transfer_Learning.ipynb | apache-2.0 | !pip show systemml
"""
Explanation: Image Classification using Caffe VGG-19 model (Transfer Learning)
This notebook demonstrates importing VGG-19 model from Caffe to SystemML and use that model to do an image classification. VGG-19 model has been trained using ImageNet dataset (1000 classes with ~ 14M images). If an i... |
epfl-lts2/pyunlocbox | examples/playground.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from pyunlocbox import functions, solvers
plt.rcParams['figure.figsize'] = (17, 5)
"""
Explanation: Playing with the PyUNLocBoX
https://github.com/epfl-lts2/pyunlocbox
End of explanation
"""
f1 = functions.norm_l2(y=[4, 5, 6, 7])
f2 = functions.... |
allafort/StatisticalMethods | examples/XrayImage/Modeling.ipynb | gpl-2.0 | import astropy.io.fits as pyfits
import astropy.visualization as viz
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 10.0)
"""
Explanation: Forward Modeling the X-ray Image data
In this notebook, we'll take a closer look at the X-ray image data products, an... |
YzPaul3/h2o-3 | h2o-py/demos/EEG_eyestate_sklearn_NOPASS.ipynb | apache-2.0 | import pandas as pd
import numpy as np
from collections import Counter
"""
Explanation: Scikit-Learn singalong: EEG Eye State Classification
Author: Kevin Yang
Contact: kyang@h2o.ai
This tutorial replicates Erin LeDell's oncology demo using Scikit Learn and Pandas, and is intended to provide a comparison of the syntac... |
patrick-kidger/equinox | examples/frozen_layer.ipynb | apache-2.0 | import functools as ft
import jax
import jax.numpy as jnp
import jax.random as jrandom
import optax # https://github.com/deepmind/optax
import equinox as eqx
# Toy data
def get_data(dataset_size, *, key):
x = jrandom.normal(key, (dataset_size, 1))
y = 5 * x - 2
return x, y
# Toy dataloader
def dataloa... |
anhquan0412/deeplearning_fastai | deeplearning1/nbs/imagenet_batchnorm.ipynb | apache-2.0 | from __future__ import division, print_function
%matplotlib inline
from importlib import reload
import utils; reload(utils)
from utils import *
"""
Explanation: This notebook explains how to add batch normalization to VGG. The code shown here is implemented in vgg_bn.py, and there is a version of vgg_ft (our fine tun... |
lknelson/text-analysis-2017 | 03-Pandas_and_DTM/00-PandasAndTextAnalysis.ipynb | bsd-3-clause | import pandas
import nltk
import string
import matplotlib.pyplot as plt #note this last import statement. Why might we use "import as"?
#read in our data
df = pandas.read_csv("../Data/childrens_lit.csv.bz2", sep = '\t', encoding = 'utf-8', compression = 'bz2', index_col=0)
df
"""
Explanation: Combining Pandas and Tex... |
Kreiswolke/gensim | docs/notebooks/word2vec.ipynb | lgpl-2.1 | # import modules & set up logging
import gensim, logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
sentences = [['first', 'sentence'], ['second', 'sentence']]
# train word2vec on the two sentences
model = gensim.models.Word2Vec(sentences, min_count=1)
"""
Explanation:... |
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