repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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pxcandeias/py-notebooks | DSP_FFT_psd.ipynb | mit | import sys
import numpy as np
import scipy as sp
import matplotlib as mpl
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
print(sys.version)
for package in (np, sp, mpl, pd):
print('{:.<15} {}'.format(package.__name__, package.__version__))
"""
Explanation: <a id='top'></a>
DSP using FFT a... |
google/starthinker | colabs/cm360_conversion_upload_from_sheets.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: CM360 Conversion Upload From Sheets
Move form Sheets to CM.
License
Copyright 2020 Google LLC,
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 ... |
ml4a/ml4a-guides | examples/dreaming/neural-net-painter.ipynb | gpl-2.0 | %matplotlib inline
import time
from PIL import Image
import numpy as np
import keras
from matplotlib.pyplot import imshow, figure
from keras.models import Sequential
from keras.layers import Dense
"""
Explanation: Neural net painter
This notebook demonstrates a fun experiment in training a neural network to do regress... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/sandbox-1/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'sandbox-1', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: NCC
Source ID: SANDBOX-1
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balance... |
martinjrobins/hobo | examples/toy/distribution-simple-egg-box.ipynb | bsd-3-clause | import pints
import pints.toy
import numpy as np
import matplotlib.pyplot as plt
# Create log pdf
sigma = 2
r = 4
log_pdf = pints.toy.SimpleEggBoxLogPDF(sigma, r)
# Contour plot of pdf
levels = np.linspace(-100, 0, 20)
x = np.linspace(-15, 15, 100)
y = np.linspace(-15, 15, 100)
X, Y = np.meshgrid(x, y)
Z = [[log_pdf(... |
SSQ/Coursera-UW-Machine-Learning-Classification | Programming Assignment 3/module-4-linear-classifier-regularization-assignment-blank.ipynb | mit | from __future__ import division
import graphlab
"""
Explanation: Logistic Regression with L2 regularization
The goal of this second notebook is to implement your own logistic regression classifier with L2 regularization. You will do the following:
Extract features from Amazon product reviews.
Convert an SFrame into a... |
tpin3694/tpin3694.github.io | machine-learning/dimensionality_reduction_with_kernel_pca.ipynb | mit | # Load libraries
from sklearn.decomposition import PCA, KernelPCA
from sklearn.datasets import make_circles
"""
Explanation: Title: Dimensionality Reduction With Kernel PCA
Slug: dimensionality_reduction_with_kernel_pca
Summary: How to reduce the dimensions of the feature matrix using kernels for machine learning in P... |
tensorflow/tensorflow | tensorflow/lite/g3doc/performance/post_training_float16_quant.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... |
mworles/capstone_one | notebooks/inferential_statistics.ipynb | bsd-3-clause | # import packages used in the notebook
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.linear_model import LogisticRegression
import statsmodels.api as sm
from scipy import stats
from sklearn.metrics import classification_report, f1... |
lehnertu/TEUFEL | scripts/TestCase_BackwardDiffractionRadiation.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import scipy.integrate
import scipy.special as func
from scipy import constants
from MeshedFields import *
"""
Explanation: Create a meshed screen to receive the emitted diffraction radiation
End of explanation
"""
mesh = MeshedField.CircularMesh(R=1.0, ratio=1.0, l... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch5-Problem_5-10.ipynb | unlicense | %pylab notebook
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 5
Problem 5-10
End of explanation
"""
Ea = 460 # [V]
EA_angle = -10/180*pi # [rad]
EA = Ea * (cos(EA_angle) + 1j*sin(EA_angle))
Vphi = 480 # [V]
VPhi_angle = 0/180*pi # [rad]
VPhi = V... |
tensorflow/docs-l10n | site/ko/probability/examples/A_Tour_of_TensorFlow_Probability.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... |
ML4DS/ML4all | U_lab1.Clustering/Lab_ShapeSegmentation_student/LabSessionClustering_student.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.misc import imread
"""
Explanation: Lab Session: Clustering algorithms for Image Segmentation
Author: Jesús Cid Sueiro
Jan. 2017
End of explanation
"""
name = "birds.jpg"
name = "Seeds.jpg"
birds = imread("Images/" + name)
birdsG = np.... |
SheffieldML/notebook | GPy/heteroscedastic_regression.ipynb | bsd-3-clause | import numpy as np
import pylab as pb
import GPy
%pylab inline
"""
Explanation: Heteroscedastic Regression
Updated on 27th November 2015
by Ricardo Andrade
In this Ipython Notebook we will look at how to implement a GP regression with different noise terms using GPy.
$\bf N.B.:$ There is currently no implementation t... |
TiKeil/Master-thesis-LOD | notebooks/Figure_2.1-2.3_MsExampleFEM1d.ipynb | apache-2.0 | import os
import sys
import numpy as np
%matplotlib notebook
import matplotlib.pyplot as plt
from gridlod import util, world, fem
from gridlod.world import World
import femsolverCoarse
"""
Explanation: Multiscale example in one dimension
This script applies the FEM to a one dimensional example of a multiscale proble... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb | apache-2.0 | import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex AI Pipelines: Lightweight Python function-based components, and component I/O
<table ali... |
iRipVanWinkle/ml | Data Science UA - September 2017/Lecture 05 - Modeling Techniques and Regression/Linear_Regression.ipynb | mit | # imports
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
# read data into a DataFrame
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
data.head()
"""
Explanation: Introduction to Linear Regression
Adapted from Chapter 3 of An Introduction to Statistical L... |
dolittle007/dolittle007.github.io | notebooks/stochastic_volatility.ipynb | gpl-3.0 | import numpy as np
import pymc3 as pm
from pymc3.distributions.timeseries import GaussianRandomWalk
from scipy import optimize
%pylab inline
"""
Explanation: Stochastic Volatility model
End of explanation
"""
n = 400
returns = np.genfromtxt(pm.get_data("SP500.csv"))[-n:]
returns[:5]
plt.plot(returns)
"""
Explana... |
CeciliaShi/STA-663-Final-Project | simulation_fastfsr.ipynb | mit | url1 = 'http://www4.stat.ncsu.edu/~boos/var.select/sim/x.quad.0.txt'
url2 = 'http://www4.stat.ncsu.edu/~boos/var.select/sim/x.quad.70.txt'
url3 = 'http://www4.stat.ncsu.edu/~boos/var.select/sim/h0_0.rs35.txt'
url4 = 'http://www4.stat.ncsu.edu/~boos/var.select/sim/h1_0.rs35.txt'
url5 = 'http://www4.stat.ncsu.edu/~boos/v... |
juanshishido/experiments-guide | 03-statistical-inference.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
mpl.style.use('ggplot')
mpl.rc('savefig', dpi=100)
np.random.seed(42)
# data
mu, sigma = 0, 1
x = mu + sigma * np.random.randn(100000)
# plot
pd.Series(x).plot(kind='hist', bins=50,
c... |
QCaudron/pydata_pandas | coffee_analysis_solution.ipynb | mit | import pandas as pd
"""
Explanation: Introduction to data analytics with pandas
Quentin Caudron
PyData Seattle, July 2017
Systems check
Do you have a working Python installation, with the pandas package ?
End of explanation
"""
import pandas as pd
%matplotlib inline
"""
Explanation: Note : This cell should run with... |
SHDShim/pytheos | examples/6_p_scale_test_Dorogokupets2015_Au.ipynb | apache-2.0 | %config InlineBackend.figure_format = 'retina'
"""
Explanation: For high dpi displays.
End of explanation
"""
import matplotlib.pyplot as plt
import numpy as np
from uncertainties import unumpy as unp
import pytheos as eos
"""
Explanation: 0. General note
This example compares pressure calculated from pytheos and o... |
bashtage/statsmodels | examples/notebooks/robust_models_1.ipynb | bsd-3-clause | %matplotlib inline
from statsmodels.compat import lmap
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import statsmodels.api as sm
"""
Explanation: M-Estimators for Robust Linear Modeling
End of explanation
"""
norms = sm.robust.norms
def plot_weights(support, weights_func, xlabels, xt... |
el-ega/torneo-de-los-30 | Torneo.de.los.30.ipynb | mit | # imports iniciales
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# queremos que los gráficos se rendericen inline
%matplotlib inline
%pylab inline
# configuración para los gráficos, estilo y dimensiones
matplotlib.style.use('ggplot')
figsize(12, 12)
"""
Explanation: Torneo... |
johnhw/summerschool2015 | CrashCourse_ExerciseAudio.ipynb | mit | # standard imports
import numpy as np
import scipy.io.wavfile as wavfile
import scipy.signal as sig
import matplotlib.pyplot as plt
import sklearn.preprocessing, sklearn.cluster, sklearn.tree, sklearn.neighbors, sklearn.ensemble, sklearn.multiclass, sklearn.feature_selection
import ipy_table
import sklearn.svm, sklearn... |
GEMScienceTools/rmtk | notebooks/vulnerability/derivation_fragility/hybrid_methods/CSM/CSM.ipynb | agpl-3.0 | from rmtk.vulnerability.derivation_fragility.hybrid_methods.CSM import capacitySpectrumMethod
from rmtk.vulnerability.common import utils
%matplotlib inline
"""
Explanation: Capacity Spectrum Method (CSM)
The Capacity Spectrum Method (CSM) is a procedure capable of estimating the nonlinear response of structures, uti... |
fasiha/ebisu | EbisuHowto.ipynb | unlicense | import ebisu
defaultModel = (4., 4., 24.) # alpha, beta, and half-life in hours
"""
Explanation: Ebisu howto
A quick introduction to using the library to schedule spaced-repetition quizzes in a principled, probabilistically-grounded, Bayesian manner.
See https://fasiha.github.io/ebisu/ for details!
End of explanation... |
rsignell-usgs/python-training | web-services/Dust_Bowl_GDP-Pandas.ipynb | cc0-1.0 | from IPython.core.display import Image
Image('http://www-tc.pbs.org/kenburns/dustbowl/media/photos/s2571-lg.jpg')
"""
Explanation: Exploring Climate Data: Past and Future
Roland Viger, Rich Signell, USGS
First presented at the 2012 Unidata Workshop: Navigating Earth System Science Data, 9-13 July.
What if you were wat... |
rlopc/datcom-labs | ugr-datcom-ncc_ni-labs/ugr-datcom-ncc_ni-lab_00/ex_02-leaky-integrate-and-fire model.ipynb | gpl-3.0 | from neurodynex.leaky_integrate_and_fire import LIF
print("resting potential: {}".format(LIF.V_REST))
"""
Explanation: 2.1.1. Question: minimal current (calculation)
For the default neuron parameters (see above) compute the minimal amplitude i_min of a step current to elicitate a spike. You can access these default va... |
dataventures/workshops | 0/Pandas.ipynb | mit | %pylab inline
# Import pylab to provide scientific Python libraries (NumPy, SciPy, Matplotlib)
%pylab --no-import-all
#import pylab as pl
# import the Image display module
from IPython.display import Image
"""
Explanation: Pandas Dataframe Exploration - Restaurant Inspection
Modified from an IPython Notebook created ... |
Gordonei/MagicalTalkingTree | examples/TweetAnalysis.ipynb | gpl-3.0 | with open("search_output-2016-10-16.bin",'rb') as tweet_file:
results = []
while not tweet_file.closed:
try:
results += [pickle.load(tweet_file)]
except EOFError: tweet_file.close()
"""
Explanation: Data In
Reading in Tweepy data, and turning into a dictionary
End of expla... |
SN-Isotropy/Isotropy | examples/Example_MockDataCreation.ipynb | mit | mockDataFile = os.path.join(isotropy.example_data_dir, 'snFits.p.gz')
sampleData, totalSN = isotropy.read_mockDataPickle(mockDataFile)
sampleData.head()
# Total number of SN in the simulation (before we threw away bad points)
totalSN
sampleData['mu_err'] = sampleData.mu_var.apply(np.sqrt)
sampleData.head()
# mu_e... |
fluffy-hamster/A-Beginners-Guide-to-Python | A Beginners Guide to Python/Final Project (Minesweeper)/_01. Building the Board (HW).ipynb | mit | import random
def build_board(num_rows, num_cols, bomb_count=0, non_bomb_character="-"):
board_temp = ["B"] * bomb_count + [non_bomb_character] * (num_rows * num_cols - bomb_count)
if bomb_count:
random.shuffle(board_temp)
board = []
for i in range(0, num_rows*num_cols, num_cols):
bo... |
Yu-Group/scikit-learn-sandbox | jupyter/backup_deprecated_nbs/06_explore_binary_decision_tree.ipynb | mit | # Setup
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.datasets import load_iris
from sklearn import tree
import ... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/text_models/labs/rnn_encoder_decoder.ipynb | apache-2.0 | pip install nltk
import os
import pickle
import sys
import nltk
import numpy as np
import pandas as pd
import tensorflow as tf
import utils_preproc
from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import GRU, Dense, Embedding, Input
from tensorflow.keras.models import Model, load_mode... |
ES-DOC/esdoc-jupyterhub | notebooks/csir-csiro/cmip6/models/sandbox-3/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csir-csiro', 'sandbox-3', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: CSIR-CSIRO
Source ID: SANDBOX-3
Topic: Ocnbgchem
Sub-Topics: Tracers. ... |
Diyago/Machine-Learning-scripts | DEEP LEARNING/Pytorch from scratch/MLP/Part 3 - Training Neural Networks (Solution).ipynb | apache-2.0 | import torch
from torch import nn
import torch.nn.functional as F
from torchvision import datasets, transforms
# Define a transform to normalize the data
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/05_review/2_sample_dataset.ipynb | apache-2.0 | PROJECT = "cloud-training-demos" # Replace with your PROJECT
BUCKET = "cloud-training-bucket" # Replace with your BUCKET
REGION = "us-central1" # Choose an available region for Cloud MLE
TFVERSION = "1.14" # TF version for CMLE to use
import os
os.environ["BUCKET"] = BUCKET
os.environ["PROJ... |
ES-DOC/esdoc-jupyterhub | notebooks/cmcc/cmip6/models/cmcc-cm2-hr4/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cmcc', 'cmcc-cm2-hr4', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: CMCC
Source ID: CMCC-CM2-HR4
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy... |
JAmarel/LiquidCrystals | ElectroOptics/Plots V6.ipynb | mit | import numpy as np
from scipy.integrate import quad, dblquad
%matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: TO DO:
Need to be able to scatter plot measured values of Psi on top of the current Psi plot.
Alpha and rho LaTeX not working in plots.
Legend needs to be move in the Psi plot.
Consider also... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/03_tensorflow/labs/b_estimator.ipynb | apache-2.0 | import tensorflow as tf
import pandas as pd
import numpy as np
import shutil
print(tf.__version__)
"""
Explanation: <h1>2b. Machine Learning using tf.estimator </h1>
In this notebook, we will create a machine learning model using tf.estimator and evaluate its performance. The dataset is rather small (7700 samples),... |
DeepLearningUB/DeepLearningMaster | 4. Tensorflow first learning models.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
# Load data.
import numpy as np
data = pd.read_csv('data/Advertising.csv',index_col=0)
train_X = data[['TV']].values
train_Y = data.Sales.values
train_Y = train_Y[:,np.newaxis]
n_samples = train_X.shape[0]
print n_samples
pr... |
alexandrnikitin/algorithm-sandbox | courses/DAT256x/Module01/01-02-Linear Equations.ipynb | mit | import pandas as pd
# Create a dataframe with an x column containing values from -10 to 10
df = pd.DataFrame ({'x': range(-10, 11)})
# Add a y column by applying the solved equation to x
df['y'] = (3*df['x'] - 4) / 2
#Display the dataframe
df
"""
Explanation: Linear Equations
The equations in the previous lab inclu... |
yvesdubief/UVM-ME249-CFD | ME249-Lecture-3.ipynb | gpl-2.0 | %matplotlib inline
# plots graphs within the notebook
%config InlineBackend.figure_format='svg' # not sure what this does, may be default images to svg format
from IPython.display import Image
from IPython.core.display import HTML
def header(text):
raw_html = '<h4>' + str(text) + '</h4>'
return raw_html
def... |
xR86/ml-stuff | labs-python/PythonLab-1-3.ipynb | mit | def gcd(a,b):
while b:
a,b = b,a%b
return a
def gcdMultiple(*args):
#print(len(args))
#for i in args:
#print(i)
if len(args) < 2:
return -1
for i in range(2,len(args)+1,2):
res = gcd(args[i-2],args[i-1])
fin = gcd(res,args[i-2])
return fin
'''
def a... |
california-civic-data-coalition/python-calaccess-notebooks | project-management/mooc-students.ipynb | mit | import bs4
import numpy as np
import pandas as pd
from iso3166 import countries as iso3166
%matplotlib inline
pd.options.display.max_rows = None
"""
Explanation: Python for Data Journalists MOOC participant analysis
By Ben Welsh
Import Python tools
End of explanation
"""
html = open("./input/PDJ0517_ Participants.... |
althonos/pronto | docs/source/examples/ms.ipynb | mit | import pronto
ms = pronto.Ontology.from_obo_library("ms.obo")
"""
Explanation: Exploring MzML files with the MS Ontology
In this example, we will learn how to use pronto to extract a hierarchy from the MS Ontology, a controlled vocabulary developed by the Proteomics Standards Initiative to hold metadata about Mass Sp... |
sdrogers/lda | notebooks/experimental_pipeline.ipynb | gpl-3.0 | import luigi as lg
import json
import pickle
import sys
basedir = '/Users/joewandy/git/lda/code/'
sys.path.append(basedir)
from multifile_feature import SparseFeatureExtractor
from lda import MultiFileVariationalLDA
"""
Explanation: New experimental MS2LDA workflow
Based on Luigi, a Python-based pipeline engine. Als... |
enbanuel/phys202-2015-work | assignments/assignment03/NumpyEx04.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
"""
Explanation: Numpy Exercise 4
Imports
End of explanation
"""
import networkx as nx
K_5=nx.complete_graph(5)
nx.draw(K_5)
"""
Explanation: Complete graph Laplacian
In discrete mathematics a Graph is a set of vertices or n... |
aleph314/K2 | Getting and Cleaning Data/cleaning_exercises_all-advanced.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
sets = ['station', 'trip', 'weather']
cycle = {}
for s in sets:
cycle[s] = pd.read_csv('cycle_share/' + s + '.csv')
cycle['trip'].head()
"""
Explanation: Cleaning: Cycle Share
There are 3 datasets that provide data on the stations, trips, and weather from 2014-2016.
Stati... |
mne-tools/mne-tools.github.io | 0.23/_downloads/548b4fc45f1ed79527138879cd79d3c8/muscle_detection.ipynb | bsd-3-clause | # Authors: Adonay Nunes <adonay.s.nunes@gmail.com>
# Luke Bloy <luke.bloy@gmail.com>
# License: BSD (3-clause)
import os.path as op
import matplotlib.pyplot as plt
import numpy as np
from mne.datasets.brainstorm import bst_auditory
from mne.io import read_raw_ctf
from mne.preprocessing import annotate_muscle_... |
Diyago/Machine-Learning-scripts | DEEP LEARNING/Pytorch from scratch/TODO/Autoencoders/convolutional-autoencoder/Upsampling_Solution.ipynb | apache-2.0 | import torch
import numpy as np
from torchvision import datasets
import torchvision.transforms as transforms
# convert data to torch.FloatTensor
transform = transforms.ToTensor()
# load the training and test datasets
train_data = datasets.MNIST(root='data', train=True,
download=True... |
zomansud/coursera | ml-classification/week-7/module-10-online-learning-assignment-blank.ipynb | mit | from __future__ import division
import graphlab
"""
Explanation: Training Logistic Regression via Stochastic Gradient Ascent
The goal of this notebook is to implement a logistic regression classifier using stochastic gradient ascent. You will:
Extract features from Amazon product reviews.
Convert an SFrame into a Num... |
sidazhang/udacity-dlnd | 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... |
glouppe/scikit-optimize | examples/hyperparameter-optimization.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (10, 6)
"""
Explanation: Tuning a scikit-learn estimator with skopt
Gilles Louppe, July 2016.
End of explanation
"""
from sklearn.datasets import load_boston
from sklearn.ensemble import GradientBoostingRegressor
f... |
minesh1291/Practicing-Kaggle | MNIST_2017/dump_/MNIST_TensorFlow_script.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
from sklearn.model_selection import ShuffleSplit
from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder
"""
Explanation: A Convolutional Neural Network for MNIST Classification. This... |
southpaw94/MachineLearning | TextExamples/3547_04_Code.ipynb | gpl-2.0 | %load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,scikit-learn
# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
"""
Explanation: Sebastian Raschka, 2015
Python Machine Learning Essentia... |
shirtsgroup/physical-validation | doc/examples/kinetic_energy_distribution.ipynb | lgpl-2.1 | # enable plotting in notebook
%matplotlib notebook
"""
Explanation: Kinetic energy distribution
Note: This notebook can be run locally by cloning the
Github repository.
The notebook is located in doc/examples/kinetic_energy_distribution.ipynb. Be aware
that probabilistic quantities such as error estimates based on boo... |
karlstroetmann/Artificial-Intelligence | Python/1 Search/Sliding-Puzzle.ipynb | gpl-2.0 | def find_tile(tile, State):
n = len(State)
for row in range(n):
for col in range(n):
if State[row][col] == tile:
return row, col
"""
Explanation: The Sliding Puzzle
<img src="8-puzzle.png">
The picture above shows an instance of the $3 \times 3$
<a href="https://en.wikipedi... |
phoebe-project/phoebe2-docs | development/tutorials/mpi.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
import phoebe
"""
Explanation: Advanced: Running PHOEBE in MPI
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
"""
print(phoebe.mpi.enabled)
print(phoe... |
ChadFulton/statsmodels | examples/notebooks/discrete_choice_example.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import print_function
import numpy as np
import pandas as pd
from scipy import stats
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.formula.api import logit, probit, poisson, ols
print(sm.datasets.fair.SOURCE)
print( sm.datasets.fair.NOTE)
dta = sm.d... |
antoniomezzacapo/qiskit-tutorial | community/games/game_engines/Making_your_own_hello_quantum.ipynb | apache-2.0 | %matplotlib notebook
import hello_quantum
"""
Explanation: Hello Quantum for Jupyter notebook
Hello Quantum is a project based on the idea of visualizing two qubit states and gates, and making them accessible to a non-specialist audience.
In the hello_quantum.py file you'll find some tools with which the 'Hello Quantu... |
tensorflow/docs | site/en/tutorials/audio/music_generation.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... |
ucsc-astro/coffee | 18_01_26_optimizing_python/Speeding up python code.ipynb | gpl-3.0 | import numpy as np
"""
Explanation: Overview
Pre-mature optimization is the root of all evil
A good framework to keep in mind is:
Make it work -- you should first make sure your code is working properly, and make sure you save/version-control the correctly copy. Fast, but buggy code doesn't help anyone.
Make it ... |
vrbala/vrbala.github.io | machine-learning-nanodegree/student_intervention/student_intervention/new/.ipynb_checkpoints/student_intervention-checkpoint.ipynb | mit | # Import libraries
import numpy as np
import pandas as pd
# Read student data
student_data = pd.read_csv("student-data.csv")
print "Student data read successfully!"
# Note: The last column 'passed' is the target/label, all other are feature columns
"""
Explanation: Project 2: Supervised Learning
Building a Student In... |
parrt/dtreeviz | notebooks/dtreeviz_sklearn_pipeline_visualisations.ipynb | mit | random_state = 1234
dataset = pd.read_csv("../data/titanic/titanic.csv")
# Fill missing values for Age
dataset.fillna({"Age":dataset.Age.mean()}, inplace=True)
# Encode categorical variables
dataset["Sex_label"] = dataset.Sex.astype("category").cat.codes
dataset["Cabin_label"] = dataset.Cabin.astype("category").cat.cod... |
SudiptaBiswas/moose | modules/tensor_mechanics/test/tests/torque/validation.ipynb | lgpl-2.1 | import math
d = 2*0.95
D = 2
Iz = math.pi*(D**4-d**4)/32
md(f"$$I_z = {Iz}$$")
"""
Explanation: Hollow cylinder torsion validation
Polar moment of inertia
For a hollow cylinder with inner diameter $d$ and outer diameter $D$ the polar moment of inertia $I_z$ id
$$
I_z = \frac{\pi\left(D^4-d^4\right)}{32}
$$
Analytic... |
hungiyang/StatisticalMethods | examples/SDSScatalog/quasars_jsb.ipynb | gpl-2.0 | ## get the data locally ... I put this on a gist
!curl -k -O https://gist.githubusercontent.com/anonymous/53781fe86383c435ff10/raw/4cc80a638e8e083775caec3005ae2feaf92b8d5b/qso10000.csv
!curl -k -O https://gist.githubusercontent.com/anonymous/2984cf01a2485afd2c3e/raw/964d4f52c989428628d42eb6faad5e212e79b665/star1000.csv... |
grantjenks/pyannote-core | notebook/pyannote.core.segment.ipynb | mit | from pyannote.core import Segment
"""
Explanation: Segment (pyannote.core.segment.Segment)
End of explanation
"""
# start time in seconds
s = 1.
# end time in seconds
e = 9.
segment = Segment(start=s, end=e)
segment
"""
Explanation: Segment instances are used to describe temporal fragments (e.g. of an audio file).
... |
MrKriss/ThinkStatsToolbox | stats_toolbox/examples/Histogram Example.ipynb | gpl-3.0 | # Imports
import os
import sys
import pandas as pd
import seaborn as sb
# Custom Imports
sys.path.insert(0, '../../')
import stats_toolbox as st
from stats_toolbox.utils.data_loaders import load_fem_preg_2002
# Graphics setup
%pylab inline --no-import-all
sb.set_context('notebook', font_scale=1.5)
"""
Explanation: ... |
cesarcontre/Simulacion2017 | Modulo3/Clase21_AjusteCurvas.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
P1 = [0, 1]
P2 = [1, 0]
X = np.array([[1, 0], [1, 1]])
y = np.array([1, 0])
b0, b1 = np.linalg.inv(X).dot(y)
b0, b1
x = np.linspace(-0.2, 1.2, 100)
y = b1*x+b0
plt.figure(figsize=(8,6))
plt.plot([P1[0], P2[0]], [P1[1], P2[1]], 'r*', label = 'puntos')
plt.plot(x, y,... |
emiliom/ODM2PythonAPI | Examples/WaterQualityMeasurements_RetrieveVisualize.ipynb | bsd-3-clause | %matplotlib inline
import sys
import os
import sqlite3
import matplotlib.pyplot as plt
from shapely.geometry import Point
import pandas as pd
import geopandas as gpd
import folium
from folium.plugins import MarkerCluster
import odm2api
from odm2api.ODMconnection import dbconnection
import odm2api.services.readServic... |
Yu-Group/scikit-learn-sandbox | jupyter/backup_deprecated_nbs/25_wrapper_stability-BL.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from sklearn.datasets import load_breast_cancer
import numpy as np
from functools import reduce
# Needed for the scikit-learn wrapper function
from sklearn.utils import resample
from sklearn.ensemble import RandomForestClassifier
from math import ceil
# Import our c... |
goodwordalchemy/thinkstats_notes_and_exercises | code/.ipynb_checkpoints/chap03ex-checkpoint.ipynb | gpl-3.0 | %matplotlib inline
import chap01soln
resp = chap01soln.ReadFemResp()
"""
Explanation: Exercise from Think Stats, 2nd Edition (thinkstats2.com)<br>
Allen Downey
Read the female respondent file.
End of explanation
"""
def BiasPmf(pmf, label=''):
"""Returns the Pmf with oversampling proportional to value.
If ... |
testedminds/sand | docs/Visualization with Cytoscape.ipynb | apache-2.0 | from py2cytoscape.data.cynetwork import CyNetwork
from py2cytoscape.data.cyrest_client import CyRestClient
from py2cytoscape.data.style import StyleUtil
import py2cytoscape.util.cytoscapejs as cyjs
import py2cytoscape.cytoscapejs as renderer
from IPython.display import Image
import igraph as igraph
import sand
impor... |
josephcslater/mousai | docs/algorithm/Algorithm.ipynb | bsd-3-clause | # Define our function (Python)
def duff_osc_ss(x, params):
omega = params['omega']
t = params['cur_time']
xd = np.array([[x[1]],
[-x[0] - 0.1 * x[0]**3 - 0.1 * x[1] + 1 * sin(omega * t)]])
return xd
# Arguments are name of derivative function, number of states, driving frequency,
# f... |
google/empirical_calibration | notebooks/survey_calibration_cvxr.ipynb | apache-2.0 | from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_style('whitegrid')
%config InlineBackend.figure_format='retina'
# install and import ec
!pip install -q git+https://github.com/google/empirical_calibration
import empirical_calibration as ec
# install and import ... |
psci2195/espresso-ffans | 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()
# ... |
agushman/coursera | src/cours_2/week_2/OverfittingTask.ipynb | mit | import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
"""
Explanation: Практическое задание к уроку 1 (2 неделя).
Линейная регрессия: переобучение и регуляризация
В этом задании мы на примерах увидим, как переобучаются линейные модели, разберем, почему так происходит, и выясним... |
Heerozh/deep-learning | first-neural-network/Your_first_neural_network.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
xuanhan863/polyglot | notebooks/CLI.ipynb | gpl-3.0 | !polyglot --help
"""
Explanation: Command Line Interface
polyglot package offer a command line interface along with the library access.
For each task in polyglot, there is a subcommand with specific options for that task.
Common options are gathered under the main command polyglot
End of explanation
"""
!polyglot --... |
NelisW/ComputationalRadiometry | 02-PythonWhirlwindCheatSheet.ipynb | mpl-2.0 | from IPython.display import display
from IPython.display import Image
from IPython.display import HTML
from IPython.core.display import display, HTML # display(HTML(df.to_html()))
import numpy as np
import os.path
"""
Explanation: 2 Python and Numpy whirlwind cheat sheet
This notebook forms part of a series on compu... |
datactive/bigbang | examples/experimental_notebooks/IETF Participants.ipynb | mit | %matplotlib inline
import bigbang.ingress.mailman as mailman
import bigbang.analysis.graph as graph
import bigbang.analysis.process as process
from bigbang.parse import get_date
from bigbang.archive import Archive
import bigbang.utils as utils
import pandas as pd
import datetime
import matplotlib.pyplot as plt
import n... |
pyreaclib/pyreaclib | examples/pp-CNO-example.ipynb | bsd-3-clause | %matplotlib inline
import pynucastro as pyrl
"""
Explanation: Interactive Network Exploration with pynucastro
This notebook shows off the interactive RateCollection network plot.
You must have widgets enabled, e.g., via:
jupyter nbextension enable --py --user widgetsnbextension
for a user install or
jupyter nbextensi... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/reduction_server/distributed-training-reduction-server.ipynb | apache-2.0 | import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG = "--user"
"""
Explanation:... |
agile-geoscience/welly | tutorial/03_Plotting.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import welly
welly.__version__
"""
Explanation: Plotting
Some preliminaries...
End of explanation
"""
from welly import Well
w = Well.from_las('data/P-130_out.LAS')
w.data.keys()
w.data['GR'].plot()
"""
Explanation: Load a well and add deviation and a striplog
... |
param411singh/inf1340-2015-notebooks | Week 5.ipynb | mit | # This program displays a rectangular pattern of asterisks
width = 2
height = 2
for h in range(height):
for w in range(width):
print ("*"),
print("")
# This program displays a triangle pattern of asterisks
# *
# * *
# * * *
height = 10
for h in range(height):
for w in range(h + 1):
... |
materialsvirtuallab/matgenb | notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 1 - Structure Generation.ipynb | bsd-3-clause | from pymatgen.core import Structure
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
from pymatgen.transformations.advanced_transformations import EnumerateStructureTransformation
from pymatgen.io.vasp.sets import batch_write_input, MPRelaxSet
"""
Explanation: Introduction
This notebook demonstrates how to pe... |
landlab/landlab | notebooks/tutorials/overland_flow/soil_infiltration_green_ampt/infilt_green_ampt_with_overland_flow.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
from landlab import imshow_grid, RasterModelGrid
from landlab.io import read_esri_ascii
from landlab.components import SoilInfiltrationGreenAmpt, KinwaveImplicitOverlandFlow
"""
Explanation: Green-Ampt infiltration and kinematic wave overland flow
This tutorial shows ... |
geodynamics/burnman | tutorial/tutorial_02_composition_class.ipynb | gpl-2.0 | from burnman import Composition
olivine_composition = Composition({'MgO': 1.8,
'FeO': 0.2,
'SiO2': 1.}, 'molar')
"""
Explanation: <h1>The BurnMan Tutorial</h1>
Part 2: The Composition Class
This file is part of BurnMan - a thermoelastic and thermo... |
AssembleSoftware/IoTPy | examples/FunctionsStreamToStream.ipynb | bsd-3-clause | import os
import sys
sys.path.append("../")
from IoTPy.core.stream import Stream, run
from IoTPy.agent_types.op import map_element
from IoTPy.agent_types.basics import fmap_e
from IoTPy.helper_functions.recent_values import recent_values
@fmap_e
def f(v): return v+10
# f is a function that maps a stream to a stream
... |
rflamary/POT | notebooks/plot_otda_mapping_colors_images.ipynb | mit | # Authors: Remi Flamary <remi.flamary@unice.fr>
# Stanislas Chambon <stan.chambon@gmail.com>
#
# License: MIT License
import numpy as np
from scipy import ndimage
import matplotlib.pylab as pl
import ot
r = np.random.RandomState(42)
def im2mat(I):
"""Converts and image to matrix (one pixel per line)"""... |
tritemio/multispot_paper | out_notebooks/usALEX-5samples-PR-raw-out-Dex-22d.ipynb | mit | ph_sel_name = "Dex"
data_id = "22d"
# ph_sel_name = "all-ph"
# data_id = "7d"
"""
Explanation: Executed: Mon Mar 27 11:36:04 2017
Duration: 8 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 ... |
grfiv/MNIST | svm.scikit/svm_rbf_pca.scikit_benchmark.ipynb | mit | from __future__ import division
import os, time, math
import cPickle as pickle
import matplotlib.pyplot as plt
import numpy as np
import scipy
import csv
from operator import itemgetter
from tabulate import tabulate
from print_imgs import print_imgs # my own function to print a grid of square images
from sklearn.pr... |
hankcs/HanLP | plugins/hanlp_demo/hanlp_demo/zh/amr_stl.ipynb | apache-2.0 | !pip install hanlp[amr] -U
"""
Explanation: <h2 align="center">点击下列图标在线运行HanLP</h2>
<div align="center">
<a href="https://colab.research.google.com/github/hankcs/HanLP/blob/doc-zh/plugins/hanlp_demo/hanlp_demo/zh/amr_stl.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt... |
mne-tools/mne-tools.github.io | 0.23/_downloads/e51cf7d76ca7b5745c35997ababd9c86/covariance_whitening_dspm.ipynb | bsd-3-clause | # Author: Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import spm_face
from mne.minimum_norm import apply_inverse, make_inverse_operator
from mne.cov import compute_covariance
print(__doc__)... |
rileyrustad/pdxapartmentfinder | analysis/First_Analysis.ipynb | mit | # start with imports
import numpy as np
import pandas as pd
from pandas import DataFrame
import json
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: This is my first attempt at creating a model using sklearn alogithms
The algorithms I am most familiar ... |
phoebe-project/phoebe2-docs | 2.2/tutorials/ORB.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: 'orb' Datasets and Options
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
"""
%matplotlib i... |
gcgruen/homework | data-databases-homework/Homework_5_Gruen.ipynb | mit | from bs4 import BeautifulSoup
from urllib.request import urlopen
html = urlopen("http://static.decontextualize.com/cats.html").read()
document = BeautifulSoup(html, "html.parser")
"""
Explanation: Homework #5
This homework presents a sophisticated scenario in which you must design a SQL schema, insert data into it, an... |
willsa14/ras2las | data/kgs/DownloadLogs_v2.ipynb | mit | elogs = pd.read_csv('temp/ks_elog_scans.txt', parse_dates=True)
lases = pd.read_csv('temp/ks_las_files.txt', parse_dates=True)
elogs_mask = elogs['KID'].isin(lases['KGS_ID']) # Create mask for elogs
both_elog = elogs[elogs_mask] # select items elog that fall in both
both_elog.drop_duplicates('KID') # remove duplicate... |
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