repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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tensorflow/tpu | tools/colab/profiling_tpus_in_colab.ipynb | apache-2.0 | import os
IS_COLAB_BACKEND = 'COLAB_GPU' in os.environ # this is always set on Colab, the value is 0 or 1 depending on GPU presence
if IS_COLAB_BACKEND:
from google.colab import auth
# Authenticates the Colab machine and also the TPU using your
# credentials so that they can access your private GCS buckets.
au... |
kevinracso/01Tarea | Copia_de_Copia_de_Preprocesamiento_y_Red_test.ipynb | mit | from google.colab import drive
drive.mount('/content/drive')
"""
Explanation: <a href="https://colab.research.google.com/github/kevinracso/01Tarea/blob/master/Copia_de_Copia_de_Preprocesamiento_y_Red_test.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/><... |
mne-tools/mne-tools.github.io | 0.17/_downloads/c5956ed7b8d9cbc581fc863a3aba47e1/plot_mne_inverse_coherence_epochs.ipynb | bsd-3-clause | # Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import (apply_inverse, apply_inverse_epochs,
read_inverse_operator)
from mne.connectivity import seed_target_indices, spec... |
edarin/ENSAE_projects | SemiParametricTilting/implementation.ipynb | gpl-3.0 | from ols import ols
from logit import logit
from att import att
%pylab inline
import warnings
warnings.filterwarnings('ignore') # Remove pandas warnings
import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.nonparametric.kde import KDEUnivariate
import seaborn as sns
from __future__... |
sibirbil/HesKit | Fonksiyonlar.ipynb | gpl-2.0 | meyva = "ARMUT"
print meyva.lower()
"""
Explanation: Fonksiyonlar
Şu ana kadar zengin Python kütüphaneleri sayesinde pek çok fonksiyonu kolayca kullandık. Öte yandan bazı durumlarda kendi fonksiyonlarımızı yazmak isteyebiliriz. Mesela Python'da kullanılan standart dize fonksiyonları Türkçe harfler ile başa çıkamıyorla... |
UWSEDS/short-course | LectureNotes/ProceduralPython/Completed-ProceduralPython.ipynb | mit | import this
"""
Explanation: Procedural Python and Unit Tests
In this section, our main goal will be to outline how to go from the kind of trial-and-error exploratory data analysis we explored this morning, into a nice, linear, reproducible analysis.
End of explanation
"""
URL = "https://s3.amazonaws.com/pronto-data... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/structured/solutions/5a_train_keras_ai_platform_babyweight.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip3 install cloudml-hypertune
"""
Explanation: LAB 5a: Training Keras model on Cloud AI Platform
Learning Objectives
Setup up the environment
Create trainer module's task.py to hold hyperparameter argparsing code
Create trainer module's model.py t... |
ShantanuKamath/PythonWorkshop | 1. Python Basics.ipynb | mit | print("This is an example of Python code.")
print()
a = int(input("Enter a value for a: "))
b = int(input("Enter a value for b: "))
print("The sum of a & b is: " + str(a + b)) # print the sum of a & b
"""
Explanation: Python Basics
Disclaimer - This document is only meant to serve as a reference for the attendees of ... |
icaoberg/falcon | examples/human_protein_atlas/human_protein_atlas.ipynb | gpl-3.0 | import cPickle as pickle
from IPython.display import Image
import halcon
data = pickle.load( open( 'dataset.pkl', 'r' ) )
"""
Explanation: Human Protein Atlas Notebook
This notebook uses a fraction of the content database built for OMERO.searcher Local client
http://murphylab.web.cmu.edu/software/searcher/
The databa... |
bhargavchippada/randomfun | NeuralEquationFinder/NeuralEquationFinder_Part_1.ipynb | mit | # Let's try to find the equation y = 2 * x
# We have 6 examples:- (x,y) = (0.1,0.2), (1,2), (2, 4), (3, 6), (-4, -8), (25, 50)
# Let's assume y is a linear combination of the features x, x^2, x^3
# We know that Normal Equation gives us the exact solution so let's first use that
N = 6
x = np.array([0.1, 1, 2, 3, -4, 2... |
NYUDataBootcamp/Projects | UG_S16/Ou-GDP Predictor.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import pandas.io.data as web
import datetime
import numpy as np
from scipy import stats
from patsy import dmatrices
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import train_test_split
from sklearn import metrics... |
atcemgil/notes | fe588/Pandas Examples.ipynb | mit | import pandas as pd
ids = [100, 200, 300, 301, 308]
names = ['Ali', 'Veli', 'Ayse', 'Fatma', 'Gamze']
surnames = ['Yilmaz', 'Gorali', 'Tasci', 'Bakkaloglu', 'Yilmaz']
ages = [27,32,19,28,32]
gender = ['M','M','F','F','F']
city = ['Istanbul', 'Istanbul', 'Ankara', 'Istanbul', 'Izmir']
number_plate = [('Adana','01'... |
InsightLab/data-science-cookbook | 2020/05-geographic-information-system/Notebook_Geopandas_Basics.ipynb | mit | # Import necessary modules
import geopandas as gpd
# Set filepath
fp = "data/limitebairro.json"
# Read file using gpd.read_file()
data = gpd.read_file(fp, driver='GeoJSON')
"""
Explanation: 1. Introdução a Geopandas
Fonte:
este material é uma tradução e adaptação do notebook: <br/> https://github.com/Automating-GIS... |
ES-DOC/esdoc-jupyterhub | notebooks/mpi-m/cmip6/models/sandbox-2/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mpi-m', 'sandbox-2', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: MPI-M
Source ID: SANDBOX-2
Topic: Ocnbgchem
Sub-Topics: Tracers.
Propertie... |
shaunharker/DSGRN | Tutorials/PatternMatchTutorial.ipynb | mit | from DSGRN import *
"""
Explanation: DSGRN Pattern Match Tutorial
This tutorial presents the pattern matching features in DSGRN.
Functions demonstrated
In this tutorial the following classes/functions are demonstrated:
Network
DrawGraph
ParameterGraph
ParameterGraph::parameter
DomainGraph
SearchGraph
PosetOfExtrema
P... |
drrelyea/SPGL1_python_port | examples/Official_demo.ipynb | lgpl-2.1 | %load_ext autoreload
%autoreload 2
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import matplotlib.pyplot as plt
from scipy.sparse import spdiags
from scipy.sparse.linalg import lsqr as splsqr
from spgl1.lsqr import lsqr
from spgl1 import spgl1, spg_lasso, spg_bp, spg_bpdn, ... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_read_noise_covariance_matrix.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
from os import path as op
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
fname_cov = op.join(data_path, 'MEG', 'sample', 'sample_audvis-cov.fif')
fname_evo = op.join(data_path,... |
jseabold/statsmodels | examples/notebooks/predict.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
plt.rc("figure", figsize=(16,8))
plt.rc("font", size=14)
"""
Explanation: Prediction (out of sample)
End of explanation
"""
nsample = 50
sig = 0.25
x1 = np.linspace(0, 20, nsample)
X = np.column_stack((x1, np.sin(x1... |
JanetMatsen/Neo4j_meta4 | jupyter/old/neo4j_test.ipynb | gpl-3.0 | # http://neo4j.com/docs/developer-manual/current/cypher/#query-load-csv
command = """
LOAD CSV WITH HEADERS FROM "https://gist.githubusercontent.com/jexp/d788e117129c3730a042/raw/1bd8c19bf8b49d9eb7149918cc11a34faf996dd8/people.tsv"
AS line
FIELDTERMINATOR '\t'
CREATE (:Artist)
"""
#CREATE (:Artist ... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_run_ica.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.preprocessing import ICA, create_ecg_epochs
from mne.datasets import sample
print(__doc__)
"""
Explanation: Compute ICA components on epochs
ICA is fit to MEG raw data.
We assume that the non-stationary EOG artifacts... |
ajgpitch/qutip-notebooks | examples/piqs_superradiance.ipynb | lgpl-3.0 | import matplotlib as mpl
from matplotlib import cm
import matplotlib.pyplot as plt
from qutip import *
from qutip.piqs import *
#TLS parameters
N = 6
ntls = N
nds = num_dicke_states(ntls)
[jx, jy, jz] = jspin(N)
jp = jspin(N,"+")
jm = jp.dag()
w0 = 1
gE = 0.1
gD = 0.01
h = w0 * jz
#photonic parameters
nphot = 20
wc =... |
ethen8181/machine-learning | python/logging.ipynb | mit | # code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', 'notebook_format'))
from formats import load_style
load_style(plot_style=False)
os.chdir(path)
# 1. magic to print version
# 2. magic so that the notebo... |
phronesis-mnemosyne/census-schema-alignment | wit/wit/notebooks/simple-forum-notebook.ipynb | apache-2.0 | import keras
import urllib2
import pandas as pd
from hashlib import md5
from pprint import pprint
from bs4 import BeautifulSoup
from sklearn.cluster import DBSCAN
import sys
sys.path.append('/Users/BenJohnson/projects/what-is-this/wit/')
from wit import *
"""
Explanation: Schema Alignment Example
End of explanation... |
gregcaporaso/short-read-tax-assignment | ipynb/mock-community/evaluate-classification-accuracy-nb-extra.ipynb | bsd-3-clause | %matplotlib inline
from os.path import join, exists, expandvars
import pandas as pd
from IPython.display import display, Markdown
import seaborn.xkcd_rgb as colors
from tax_credit.plotting_functions import (pointplot_from_data_frame,
boxplot_from_data_frame,
... |
SN-Isotropy/Isotropy | doc/Maddi/Hubble+Diagram.ipynb | mit | import sys
import gzip, pickle
if sys.version.startswith('2'):
snFits = pickle.load(gzip.GzipFile('snFits.p.gz'))
else:
snFits = pickle.load(gzip.GzipFile('snFits.p.gz'),
encoding='latin1')
print(len(snFits))
snf = [s for s in snFits.values() if s is not None]
print(len(snf))
snf[0]
"""
E... |
szitenberg/ReproPhyloVagrant | notebooks/Tutorials/Basic/3.6 Producing and accessing sequence alignment.ipynb | mit | mafft_linsi = AlnConf(pj, # The Project
method_name='mafftLinsi', # Any unique method name,
# 'mafftDefault' by default
CDSAlign=True, ... |
ematvey/tensorflow-seq2seq-tutorials | 3-seq2seq-native-new.ipynb | mit | %matplotlib inline
import numpy as np
import tensorflow as tf
from tensorflow.contrib.rnn import LSTMCell, GRUCell
from model_new import Seq2SeqModel, train_on_copy_task
import pandas as pd
import helpers
import warnings
warnings.filterwarnings("ignore")
tf.__version__
"""
Explanation: Playing with new 2017 tf.cont... |
sofmonk/aima-python | learning.ipynb | mit | from learning import *
"""
Explanation: Learning
This notebook serves as supporting material for topics covered in Chapter 18 - Learning from Examples , Chapter 19 - Knowledge in Learning, Chapter 20 - Learning Probabilistic Models from the book Artificial Intelligence: A Modern Approach. This notebook uses implementa... |
JelleAalbers/xeshape | notebooks/extraction/extract_s1s.ipynb | mit | # Get SR1 krypton datasets
dsets = hax.runs.datasets
dsets = dsets[dsets['source__type'] == 'Kr83m']
dsets = dsets[dsets['trigger__events_built'] > 10000] # Want a lot of Kr, not diffusion mode
dsets = hax.runs.tags_selection(dsets, include='sciencerun0')
# Sample ten datasets randomly (with fixed seed, so the anal... |
sameersingh/uci-statnlp | tutorials/intro_to_pytorch.ipynb | apache-2.0 | import numpy as np
import torch
# Create a 3 x 2 array
np.ndarray((3, 2))
# Create a 3 x 2 Tensor
torch.Tensor(3, 2)
"""
Explanation: Introduction to PyTorch
PyTorch is a Python package for performing tensor computation, automatic differentiation, and dynamically defining neural networks. It makes it particularly ea... |
xiongzhenggang/xiongzhenggang.github.io | AI/ML/week5_code.ipynb | gpl-3.0 | import numpy as np
import scipy.io as sio
import scipy.optimize as opt
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data = sio.loadmat('../data/andrew_ml_ex55139/ex5data1.mat')
X, y, Xval, yval, Xtest, ytest = map(np.ravel,[data['X'], data['y'], data['Xval'], data['yval'], data['Xtest'], d... |
gfrubi/electrodinamica | notebooks/campo_electrico_disco_cargado-Vpython.ipynb | gpl-3.0 | import vpython as vp
#Code
def charge_color(charge):
if charge>0:
charge_color = vp.color.red
elif charge <0:
charge_color = vp.color.blue
else:
charge_color = vp.color.white
return charge_color
#
def getfield(position):
r = position
field = vp.vec(0,0,0)
for charge ... |
AeroPython/Taller-PyConEs-2015 | Teoria I - Algoritmos geneticos.ipynb | mit | from IPython.core.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_toggle()"><input type="submit" value="Click... |
scienceguyrob/Docker | Images/music/samples/libROSA/LibROSA_Demo.ipynb | gpl-3.0 | from __future__ import print_function
# We'll need numpy for some mathematical operations
import numpy as np
# matplotlib for displaying the output
import matplotlib.pyplot as plt
import matplotlib.style as ms
ms.use('seaborn-muted')
%matplotlib inline
# and IPython.display for audio output
import IPython.display
... |
sony/nnabla | tutorial/model_finetuning.ipynb | apache-2.0 | !pip install nnabla-ext-cuda100
!git clone https://github.com/sony/nnabla.git
%cd nnabla/tutorial
"""
Explanation: NNabla Models Finetuning Tutorial
Here we demonstrate how to perform finetuning using nnabla's pre-trained models.
End of explanation
"""
from nnabla.models.imagenet import ResNet18
model = ResNet18()
... |
eyaltrabelsi/my-notebooks | Lectures/practical_optimisations_for_pandas/PyconIL2021-Optimizing Pandas.ipynb | mit | ! pip install numba numexpr
import math
import time
import warnings
from dateutil.parser import parse
import janitor
import numpy as np
import pandas as pd
from numba import jit
from sklearn import datasets
from pandas.api.types import is_datetime64_any_dtype as is_datetime
warnings.filterwarnings("ignore", category... |
kwinkunks/rainbow | notebooks/Guessing_colourmaps_HULL.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: App-ifying 'recovering data from images'
See the other notebook for the grisly details and dead-ends.
Requirements:
numpy
scipy
scikit-learn
pillow
I recommend installing them with conda install.
End of explanation
"""
from sci... |
TESScience/FPE_Test_Procedures | HK_Variance_Frames_Running.ipynb | mit | from tessfpe.dhu.fpe import FPE
from tessfpe.dhu.unit_tests import check_house_keeping_voltages
fpe1 = FPE(1, debug=False, preload=True, FPE_Wrapper_version='6.1.1')
print fpe1.version
fpe1.cmd_start_frames()
fpe1.cmd_stop_frames()
if check_house_keeping_voltages(fpe1):
print "Wrapper load complete. Interface volta... |
tpin3694/tpin3694.github.io | machine-learning/visualize_a_decision_tree.ipynb | mit | # Load libraries
from sklearn.tree import DecisionTreeClassifier
from sklearn import datasets
from IPython.display import Image
from sklearn import tree
import pydotplus
"""
Explanation: Title: Visualize A Decision Tree
Slug: visualize_a_decision_tree
Summary: How to visualize a decision tree regression in scikit-le... |
mathLab/RBniCS | tutorials/17_navier_stokes/tutorial_navier_stokes_2_exact.ipynb | lgpl-3.0 | from ufl import transpose
from dolfin import *
from rbnics import *
"""
Explanation: Tutorial 17 - Navier Stokes equations
Keywords: exact parametrized functions, supremizer operator
1. Introduction
In this tutorial, we will study the Navier-Stokes equations over the two-dimensional backward-facing step domain $\Omega... |
mne-tools/mne-tools.github.io | stable/_downloads/9bd293f49554a21d68d4f2a842cc6cc2/59_head_positions.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
# Richard Höchenberger <richard.hoechenberger@gmail.com>
# Daniel McCloy <dan@mccloy.info>
#
# License: BSD-3-Clause
from os import path as op
import mne
data_path = op.join(mne.datasets.testing.data_path(verbose=True), 'SSS')
fname_raw = op.join(data... |
daniestevez/jupyter_notebooks | dslwp/DSLWP GMSK SSDV 2.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import scipy.signal
import matplotlib.pyplot as plt
"""
Explanation: Analysis of DSLWP-B 2018-08-12 SSDV transmission
This notebook analyzes SSDV transmissions made by DSLWP-B from the Moon.
End of explanation
"""
x = np.fromfile('/home/daniel/Descargas/DSLWP-B_PI9CAM_2018-08-1... |
mne-tools/mne-tools.github.io | 0.19/_downloads/6684371ec2bc8e72513b3bdbec0d3a9f/plot_20_events_from_raw.ipynb | bsd-3-clause | import os
import numpy as np
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
raw.crop(tmax=60).load_data()
"""
Explanati... |
JeffAbrahamson/MLWeek | practicum/09_TensorFlow/TensorFlow_intro.ipynb | gpl-3.0 | from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
"""
Explanation: Ne pas faire un "execute all" : la dernière cellule est très lourde.
Introduction à TensorFlow
Ce code est basé sur des tutoriel à tensorflow.org.
Nous allons utiliser _softmax ... |
metpy/MetPy | v1.0/_downloads/62a1acd718d4c5b9717787544d4cf09f/Gradient.ipynb | bsd-3-clause | import numpy as np
import metpy.calc as mpcalc
from metpy.units import units
"""
Explanation: Gradient
Use metpy.calc.gradient.
This example demonstrates the various ways that MetPy's gradient function
can be utilized.
End of explanation
"""
data = np.array([[23, 24, 23],
[25, 26, 25],
... |
hetland/python4geosciences | materials/1_core.ipynb | mit | a = 5
b = a + 3.1415
c = a / b
print(a, b, c)
"""
Explanation: Core language
A. Variables
Variables are used to store and modify values.
End of explanation
"""
s = 'Ice cream' # A string
f = [1, 2, 3, 4] # A list
d = 3.1415928 # A floating point number
i = 5 # ... |
tensorflow/docs-l10n | site/en-snapshot/hub/tutorials/retrieval_with_tf_hub_universal_encoder_qa.ipynb | apache-2.0 | # Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
gwtsa/gwtsa | examples/notebooks/1_basic_model.ipynb | mit | # First perform the necessary imports
import pandas as pd
import matplotlib.pyplot as plt
import pastas as ps
%matplotlib inline
"""
Explanation: A Basic Model
In this example application it is shown how a simple time series model can be developed to simulate groundwater levels. The recharge (calculated as preciptatio... |
AllenDowney/ThinkStats2 | workshop/hypothesis_soln.ipynb | gpl-3.0 | %matplotlib inline
import numpy
import scipy.stats
import matplotlib.pyplot as plt
import first
"""
Explanation: Hypothesis Testing
Copyright 2016 Allen Downey
License: Creative Commons Attribution 4.0 International
End of explanation
"""
live, firsts, others = first.MakeFrames()
"""
Explanation: Part One
Suppos... |
PrairieLearn/PrairieLearn | exampleCourse/questions/demo/annotated/MarkovChainGroupActivity/MarkovChains-Intro/workspace/Markov-Chains-1.ipynb | agpl-3.0 | x1 = M @ x
x1
"""
Explanation: Introduction to Markov Chains
A Markov chain is a mathematical model used to describe a set of states and the probability of transitioning between them. In this simple example, we use Markov chain to model the weather. We have two states to represent the possible weather for a day: Sunny... |
tensorflow/docs-l10n | site/ja/lattice/tutorials/custom_estimators.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... |
jasonding1354/PRML_Notes | 1.PROBABILITY_DISTRIBUTIONS/1.2 Multinomial_Variables.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from scipy.stats import dirichlet
import matplotlib.tri as tri
from matplotlib import cm
corners = np.array([[0, 0], [1, 0], [0.5, 0.75**0.5]])
triangle = tri.Triangulation(corners[:, 0], corners[:, 1])
refi... |
CivicKnowledge/metatab-py | examples/Pandas Reporter Example.ipynb | bsd-3-clause | [e for e in b17001.columns if '65 to 74' in str(e) or '75 years' in str(e) ]
# Now create a subset dataframe with just the columns we need.
b17001s = b17001[['geoid', 'B17001015', 'B17001016','B17001029','B17001030']]
b17001s.head()
"""
Explanation: B17001 Poverty Status by Sex by Age
For the Poverty Status by Sex b... |
vbarua/PythonWorkshop | Code/Introduction To Python/3 - Dictionaries.ipynb | mit | numbers = {1: "one", 2: "two", 3: "three"}
numbers
"""
Explanation: Dictionaries
A Python dictionary is a mutable data structure that can be used to associate keys with values. They are created using {} braces. You can think of dictionaries as lists, except that instead of extracting elements by their position you ext... |
mlhy/ResNet-50-for-Cats.Vs.Dogs | Preprocessing train dataset.ipynb | apache-2.0 | from sklearn.model_selection import train_test_split
import seaborn as sns
import os
import shutil
%matplotlib inline
"""
Explanation: Preprocessing train dataset
Divide the train folder into two folders mytrain and myvalid
mytrain ---- including two folders
cat ---- including about 11250 cat images
dog ---- incl... |
phoebe-project/phoebe2-docs | development/tutorials/LC_estimators_tutorial.ipynb | gpl-3.0 | b = phoebe.default_binary()
# set parameter values
b.set_value('q', value = 0.6)
b.set_value('incl', component='binary', value = 84.5)
b.set_value('ecc', 0.2)
b.set_value('per0', 63.7)
b.set_value('requiv', component='primary', value=1.)
b.set_value('requiv', component='secondary', value=0.6)
b.set_value('teff', compon... |
brianoleary15/Hands-On-Machine-Learning-with-ScikitLearn-and-TensorFlow | 11_deep_learning.ipynb | apache-2.0 | # To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
def reset_graph(seed=42):
tf.reset_default_graph()
tf.set_random_seed(seed)
np.random.seed(seed)
# To... |
mne-tools/mne-tools.github.io | 0.24/_downloads/1abc74aa28d845859c3852be5f0bdd21/30_forward.ipynb | bsd-3-clause | import os.path as op
import mne
from mne.datasets import sample
data_path = sample.data_path()
# the raw file containing the channel location + types
sample_dir = op.join(data_path, 'MEG', 'sample',)
raw_fname = op.join(sample_dir, 'sample_audvis_raw.fif')
# The paths to Freesurfer reconstructions
subjects_dir = op.jo... |
anhquan0412/deeplearning_fastai | deeplearning1/nbs/lesson5.ipynb | apache-2.0 | from keras.datasets import imdb
idx = imdb.get_word_index()
"""
Explanation: Setup data
We're going to look at the IMDB dataset, which contains movie reviews from IMDB, along with their sentiment. Keras comes with some helpers for this dataset.
End of explanation
"""
idx_arr = sorted(idx, key=idx.get)
idx_arr[:10]
... |
maxrose61/GA_DS | FInal_Project/Quantifying_Influence_Analysis_maxrose_DSFinal.ipynb | gpl-3.0 | ### Import as many items as possible to have available.
### Import data from CSV
%matplotlib inline
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegres... |
udacity/deep-learning | first-neural-network/Your_first_neural_network.ipynb | mit | %matplotlib inline
%load_ext autoreload
%autoreload 2
%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 ridersh... |
quantumlib/Cirq | docs/tutorials/google/xeb_calibration_example.ipynb | apache-2.0 | try:
import cirq
except ImportError:
!pip install --quiet cirq --pre
# The Google Cloud Project id to use.
project_id = "" #@param {type:"string"}
processor_id = "" #@param {type:"string"}
from cirq_google.engine.qcs_notebook import get_qcs_objects_for_notebook
device_sampler = get_qcs_objects_for_notebook(pr... |
iagapov/ocelot | demos/ipython_tutorials/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 a... |
lfairchild/PmagPy | data_files/notebooks/Importing and using the 3.0 data model.ipynb | bsd-3-clause | # import req'd modules
import json
import os
import pandas as pd
from pandas import DataFrame, Series
import numpy as np
import pmagpy.builder2 as builder
"""
Explanation: This notebook was used to develop functionality that is now in pmagpy/data_model3.py. Examples of how to use the data_model3 module can be found i... |
ktmud/deep-learning | intro-to-tensorflow/intro_to_tensorflow.ipynb | mit | import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfile import ZipFile
print('All m... |
pycrystem/pycrystem | doc/demos/11 Accelerated orientation mapping with template matching.ipynb | gpl-3.0 | %matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
import hyperspy.api as hs
"""
Explanation: Fast template matching
Background
This notebook describes how the new accelerated orientation mapping facilities in Pyxem can be used.
Orientation mapping with template matching is illustrated in example... |
maartenbreddels/ipyvolume | docs/source/examples/lighting.ipynb | mit | import ipyvolume as ipv
import numpy as np
def scene():
f = ipv.figure()
ipv.xyzlim(-1, 1)
x = np.array([0.1, 0.5], dtype=np.float32)
ipv.material_phong()
s = ipv.scatter(x, x, x, marker="sphere", size=10);
k = ipv.examples.klein_bottle(show=False)
ipv.xyzlim(2)
m = ipv.plot_plane('bott... |
neutronimaging/imagingsuite | notebooks/MorphSpotCleanDemo.ipynb | gpl-3.0 | import sys, os
sys.path.insert(0, "/Users/kaestner/git/scripts/python/")
sys.path.insert(0, "/Users/kaestner/git/install/lib/")
if 'LD_LIBRARY_PATH' not in os.environ:
os.environ['LD_LIBRARY_PATH'] = '/Users/kaestner/git/install/lib'
os.environ['LD_LIBRARY_PATH'] = '/Users/kaestner/git/install/lib'
os.environ['... |
tensorflow/fairness-indicators | g3doc/tutorials/Fairness_Indicators_TensorBoard_Plugin_Example_Colab.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... |
arnoldlu/lisa | ipynb/android/antutu/Android_antutu_hikey.ipynb | apache-2.0 | import logging
reload(logging)
log_fmt = '%(asctime)-9s %(levelname)-8s: %(message)s'
logging.basicConfig(format=log_fmt)
# Change to info once the notebook runs ok
logging.getLogger().setLevel(logging.INFO)
%pylab inline
import copy
import os
from time import sleep
from subprocess import Popen
import pandas as pd
... |
justanr/notebooks | hexagonal/refactoring_and_interfaces.ipynb | mit | @app.route('/register', methods=['GET', 'POST'])
def register():
form = RegisterUserForm()
if form.validate_on_submit():
user = User()
form.populate_obj(user)
db.session.add(user)
db.session.commit()
return redirect('homepage')
return render_template('regist... |
tensorflow/docs-l10n | site/en-snapshot/tfx/tutorials/tfx/components_keras.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... |
obulpathi/datascience | pandas/3. Data Wrangling with Pandas.ipynb | apache-2.0 | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Set some Pandas options
pd.set_option('display.notebook_repr_html', False)
pd.set_option('display.max_columns', 20)
pd.set_option('display.max_rows', 25)
"""
Explanation: Data Wrangling with Pandas
Now that we have been expose... |
LimeeZ/phys292-2015-work | assignments/assignment07/AlgorithmsEx02.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
"""
Explanation: Algorithms Exercise 2
Imports
End of explanation
"""
def find_peaks(a):
"""Find the indices of the local maxima in a sequence."""
maxima = []
for x in range(0, len(a)):
if(x==len(a)-1... |
tensorflow/docs-l10n | site/zh-cn/tensorboard/migrate.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... |
Amarchuk/2FInstability | notebooks/2f/photometry.ipynb | gpl-3.0 | from IPython.display import Image
import numpy as np
import math
%pylab
%matplotlib inline
"""
Explanation: Фотометрия
Ноутбук с функциями для работы с фотометрией.
End of explanation
"""
Image('../Bell_2003.png')
"""
Explanation: Калибровки Bell et al. 2003
Калибровки Bell et al. (2003) https://ui.adsabs.harvard.... |
JaviMerino/lisa | ipynb/tutorial/05_TrappyUsage.ipynb | apache-2.0 | import logging
reload(logging)
logging.basicConfig(
format='%(asctime)-9s %(levelname)-8s: %(message)s',
datefmt='%I:%M:%S')
# Enable logging at INFO level
logging.getLogger().setLevel(logging.INFO)
"""
Explanation: Tutorial Goal
This tutorial aims to show some example of data analysis and visualization
from a... |
blua/deep-learning | tv-script-generation/olds_ipnbs/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... |
marcinofulus/teaching | ML_SS2017/zajecia_MJ_21.4.2017.ipynb | gpl-3.0 | def read_data(filename_queue):
reader = tf.TFRecordReader()
_, se = reader.read(filename_queue)
f = tf.parse_single_example(se,features={'image/encoded':tf.FixedLenFeature([],tf.string),
'image/class/label':tf.FixedLenFeature([],tf.int64),
... |
intel-analytics/BigDL | apps/dogs-vs-cats/transfer-learning.ipynb | apache-2.0 | import re
from bigdl.dllib.nn.criterion import CrossEntropyCriterion
from pyspark.ml import Pipeline
from pyspark.sql.functions import col, udf
from pyspark.sql.types import DoubleType, StringType
from bigdl.dllib.nncontext import *
from bigdl.dllib.feature.image import *
from bigdl.dllib.keras.layers import Dense, I... |
stefanseefeld/numba | examples/notebooks/LinearRegr.ipynb | bsd-2-clause | %pylab inline
def gradient_descent_numpy(X, Y, theta, alpha, num_iters):
m = Y.shape[0]
theta_x = 0.0
theta_y = 0.0
for i in range(num_iters):
predict = theta_x + theta_y * X
err_x = (predict - Y)
err_y = (predict - Y) * X
theta_x = theta_x - alpha * (1.0 / m) * err_x.... |
gutouyu/cs231n | cs231n/assignment/assignment1/knn.ipynb | mit | # Run some setup code for this notebook.
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a new window.
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.... |
European-XFEL/h5tools-py | docs/dask_averaging.ipynb | bsd-3-clause | from karabo_data import open_run
import dask.array as da
from dask.distributed import Client, progress
from dask_jobqueue import SLURMCluster
import numpy as np
"""
Explanation: Averaging detector data with Dask
We often want to average large detector data across trains, keeping the pulses within each train separate,... |
kingsgeocomp/code-camp | notebook-05-truth-and-conditions.ipynb | mit | myBoolean = True
print(myBoolean)
print("This statement is: '" + str(myBoolean) + "'")
"""
Explanation: Notebook-5: Truth & Conditions
Lesson Content
Comparisons
Booleans
"Not equal" operator
"< > <= >=" operators
Conditions pt.1
IF
ELSE
ELIF
Boolean Logic
AND
OR
NOT
In this lesson we'll learn how to c... |
sussexwearlab/OpenEnded | preprocessing/JSI-preprocess2.ipynb | mit | import numpy as np
import scipy
import scipy.stats
filename = 'raw_data_example.txt'
"""
Explanation: Preprocessing
This notebook contains an example code for preprocessing raw acceleration data.
It contains takes as input raw 3 axias acceleration signal, and outputs a file with extracted features.
It uses overlapping... |
carltoews/tennis | notebooks/extract_features.ipynb | gpl-3.0 | import sqlalchemy # pandas-mysql interface library
import sqlalchemy.exc # exception handling
from sqlalchemy import create_engine # needed to define db interface
import sys # for defining behavior under errors
import numpy as np # numerical libraries
import scipy as sp
import pandas as pd # for data analysis
import... |
mzwiessele/topslam | notebooks/ExampleWorkflow.ipynb | bsd-3-clause | from topslam.simulation import qpcr_simulation
seed_differentiation = 5001
seed_gene_expression = 0
Xsim, simulate_new, t, c, labels, seed = qpcr_simulation(seed=seed_differentiation)
np.random.seed(seed_gene_expression)
Y = simulate_new()
"""
Explanation: Example Workflow
In this notebook we will look at an exampl... |
seth2000/chinesepoem | PrepareData.ipynb | mit | # -*- coding: utf-8 -*-
import os
import re
import time
import codecs
import argparse
TIME_FORMAT = '%Y-%m-%d %H:%M:%S'
BASE_FOLDER = os.getcwd() # os.path.abspath(os.path.dirname(__file__))
DATA_FOLDER = os.path.join(BASE_FOLDER, 'data')
DEFAULT_FIN = os.path.join(DATA_FOLDER, '唐诗语料库.txt')
DEFAULT_FOUT = os.path.jo... |
rmsouza01/iamxt | jupyter-notebook/iamxt_getting_started.ipynb | bsd-2-clause | # This makes plots appear in the notebook
%matplotlib inline
import numpy as np # numpy is the major library in which iamxt was built upon
# we like the array programming style =)
# We are using PIL to read images
from PIL import Image
# and matplotlib to display images
import matplotlib.pyp... |
ellamil/bubblepopper | bubblepopper_2topicextraction.ipynb | mit | from gensim import corpora, models
import gensim
import numpy as np
import random
import pickle
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
"""
Explanation: TOPIC EXTRACTION
Topic Assignment Consistency
End of explanation
"""
texts = pickle.load(open('pub_articles_cleaned_super.pkl','r... |
ComputationalModeling/spring-2017-danielak | past-semesters/spring_2016/day-by-day/day21-monte-carlo-integration/MonteCarlo_Integration_SOLUTIONS.ipynb | agpl-3.0 | # Put your code here!
import random as rand
import math
def f(x):
return 2.0*(x**2) + 3.0
# x min, max: -2, 4 (delta_x = 6)
# y min, max: 0, 35
Area = (35-0)*(4+2)
real_area = 66.0
samples = []
errors = []
for i in range(1,7):
N_samples = 10**i
N_below = 0
for j in range(N_samples):... |
lia-statsletters/notebooks | mv_kecdf_frechet.ipynb | gpl-3.0 | from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as spst
import statsmodels.api as sm
from scipy import optimize
from statsmodels.nonparametric import kernels
kernel_func = dict(wangryzin=kernels.wang_ryzin,
aitchisonaitken=kernels.aitchison_ai... |
dnstanciu/masters-project | sources/notebooks/testing_connectivity.ipynb | gpl-3.0 | %load_ext pymatbridge
%%matlab
addpath /home/dragos/src/fieldtrip-20160526
addpath /home/dragos/Projects/SummerProject
ft_defaults
"""
Explanation: Testing Connectivity
Here we explore how different paddings of the MEG recordings affect the phase after applying the Hilbert transform.
Start Matlab session:
End of exp... |
patrick-kidger/diffrax | examples/neural_ode.ipynb | apache-2.0 | import time
import diffrax
import equinox as eqx # https://github.com/patrick-kidger/equinox
import jax
import jax.nn as jnn
import jax.numpy as jnp
import jax.random as jrandom
import matplotlib.pyplot as plt
import optax # https://github.com/deepmind/optax
"""
Explanation: Neural ODE
This example trains a Neural ... |
aphearin/AstroHackWeek2015 | inference/straightline.ipynb | gpl-2.0 | %load_ext autoreload
%autoreload 2
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (6.0, 6.0)
plt.rcParams['savefig.dpi'] = 100
from straightline_utils import *
"""
Explanation: Bayesian Inference II: Fitting a Straight Li... |
GoogleCloudPlatform/training-data-analyst | quests/sparktobq/05_functions.ipynb | apache-2.0 | %%bash
wget http://kdd.ics.uci.edu/databases/kddcup99/kddcup.data_10_percent.gz
gunzip kddcup.data_10_percent.gz
BUCKET='cloud-training-demos-ml' # CHANGE
gsutil cp kdd* gs://$BUCKET/
bq mk sparktobq
"""
Explanation: Migrating from Spark to BigQuery via Dataproc -- Part 5
Part 1: The original Spark code, now running... |
tommytwoeyes/continuity | 11_Infinite_Seq_and_Series/Lab_III__Infinite_Series.ipynb | gpl-3.0 | import sympy as sp
from matplotlib import pyplot as plt
%matplotlib inline
# Customize figure size
plt.rcParams['figure.figsize'] = 25, 15
#plt.rcParams['lines.linewidth'] = 1
#plt.rcParams['lines.color'] = 'g'
#plt.rcParams['font.family'] = 'monospace'
plt.rcParams['font.size'] = '16.0'
plt.rcParams['font.monospace'... |
seanjmcm/TrafficSign | Traffic_Sign_Classifier.ipynb | mit | # Load pickled data
import pickle
import cv2 # for grayscale and normalize
# TODO: Fill this in based on where you saved the training and testing data
training_file ='traffic-signs-data/train.p'
validation_file='traffic-signs-data/valid.p'
testing_file = 'traffic-signs-data/test.p'
with open(training_file, mode='rb'... |
tensorflow/docs-l10n | site/ja/tutorials/load_data/numpy.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... |
QinetiQ-datascience/Docker-Data-Science | WooWeb-Presentation/Workspace/Widgets/Lorenz Differential Equations.ipynb | mit | %matplotlib inline
from ipywidgets import interact, interactive
from IPython.display import clear_output, display, HTML
import numpy as np
from scipy import integrate
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.colors import cnames
from matplotlib import animation
""... |
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