repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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ChristinaB/Observatory | tutorials/Observatory_usecase6_observationdata.ipynb | mit | #conda install -c conda-forge basemap-data-hires --yes
# data processing
import os
import pandas as pd, numpy as np, dask, json
import ogh
import geopandas as gpd
# data migration library
from utilities import hydroshare
# plotting and shape libraries
%matplotlib inline
# silencing warning
import warnings
warnings.... |
samstav/scipy_2015_sklearn_tutorial | notebooks/04.4 Model Evaluation and Scoring Metrics.ipynb | cc0-1.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
np.set_printoptions(precision=2)
from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
from sklearn.svm import LinearSVC
digits = load_digits()
X, y = digits.data, digits.target
X_train, X_test, y_train, y_t... |
CAChemE/curso-python-datos | notebooks/004-PythonBasico-EjerciciosBuclesCondicionales.ipynb | bsd-3-clause | def sumatorio(num):
"""Suma los `num` primeros números.
Ejemplos
--------
>>> sumatorio(4)
10
"""
suma = 0
for nn in range(1, num + 1):
suma = nn + suma
return suma
"""
Explanation: Ejercicios bucles y condicionales
Vamos a afianzar los conocimientos de Python que acabamos... |
parklab/PaSDqc | examples/07_example_region_blacklist/regions_blacklist.ipynb | mit | import PaSDqc
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pathlib
%matplotlib inline
%load_ext autoreload
%autoreload 2
sns.set_context('poster')
sns.set_style("ticks", {'ytick.minor.size': 0.0, 'xtick.minor.size': 0.0})
"""
Explanation: Introduction
In this no... |
openhep/ackp16 | LandauPole.ipynb | gpl-3.0 | %matplotlib inline
from scipy.integrate import odeint
import matplotlib.pyplot as plt
import numpy as np
#import math
from scipy import pi
from sympy import Rational, sqrt, Symbol, init_printing
# local packages
# from plothelp import label_line
from smgroup import *
from constants import *
init_printing(use_latex='m... |
sunilmallya/dl-twitch-series | E3_finetuning_randall_not_randall.ipynb | apache-2.0 | # helper functions
import mxnet as mx
import os, urllib
def download(url):
filename = url.split("/")[-1]
if not os.path.exists(filename):
urllib.urlretrieve(url, filename)
def get_model(prefix, epoch):
download(prefix+'-symbol.json')
download(prefix+'-%04d.params' % (epoch,))
get_mode... |
kern-lab/popGenMachineLearningExamples | ancPopSizeRegressionExample.ipynb | mit | #untar and compile ms and sample_stats
!tar zxf ms.tar.gz; cd msdir; gcc -o ms ms.c streec.c rand1.c -lm
#I get three compiler warnings from ms, but everything should be fine
#now I'll just move the program into the current working dir
!mv msdir/ms .;
"""
Explanation: Using a random forest to infer past population siz... |
georgetown-analytics/machine-learning | archive/notebook/ML Class Session III.ipynb | mit | import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
df = pd.read_csv('../data/energy/energy.csv')
df.shape
df.describe()
"""
Explanation: Energy Efficiency
What can you tell me about the data?
End of explanation
"""
import matplotlib.pyplot as plt
%matplotlib inline
from pandas.tools.plotting im... |
astro4dev/OAD-Data-Science-Toolkit | Teaching Materials/Machine Learning/Supervised Learning/Examples/PPC/Pulsar Problem Intro.ipynb | gpl-3.0 | # Plotting library.
import matplotlib.pyplot as plt
# For some math we need to do.
import numpy as np
# The HTRU 2 profile data is split - one file containing the real pulsar
# profiles, one file containing noise/interference profiles. We load both
# these data sources here. First we construct relative paths to the f... |
ioggstream/python-course | connexion-101/notebooks/03-connexion.ipynb | agpl-3.0 | # At first ensure connexion is installed
# together with the swagger module used to render the OAS3 spec
# in the web-ui
!pip install connexion[swagger-ui] connexion
"""
Explanation: Connexion
Connexion is a python framework based on Flask.
It streamlines the creation of contract-first REST APIs.
Once you have your O... |
UCBerkeleySETI/breakthrough | GBT/filterbank_tutorial/Filterbank Tutorial (public).ipynb | gpl-3.0 | from blimpy import Waterfall
fil = Waterfall('blc04_guppi_57563_69862_HIP35136_0011.gpuspec.0002.fil')
"""
Explanation: Introduction to Working with Filterbank Files
by Umran Haji, Undergraduate Research Intern
Welcome! In this tutorial I will demonstrate the basics of working with filterbank (.fil) files using bli... |
tuanavu/coursera-university-of-washington | machine_learning/4_clustering_and_retrieval/assigment/week5/.ipynb_checkpoints/module-8-boosting-assignment-2-graphlab-checkpoint.ipynb | mit | import sys
sys.path.append('C:\Anaconda2\envs\dato-env\Lib\site-packages')
import graphlab
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Boosting a decision stump
The goal of this notebook is to implement your own boosting module.
Brace yourselves! This is going to be a fun and challenging assign... |
usdivad/fibonaccistretch | nbs/fibonaccistretch_using_module.ipynb | mit | import IPython.display as ipd
ipd.Audio("../data/out_humannature_90s_stretched.mp3", rate=44100)
"""
Explanation: Fibonacci Stretch: An Exploration Through Code
by David Su
This notebook and its associated code are also available on GitHub.
Contents
Introduction
A sneak peek at the final result
Part 1 - Representin... |
ueapy/ueapy.github.io | content/notebooks/2017-06-30-pdf-scraping.ipynb | mit | stars_file = '../pdfs/stars-dat_user-manual_p2d-4_v3p1.pdf'
moore2016_file = '../pdfs/moore_bromwich_qjrms_2016.pdf'
"""
Explanation: Some organisations still release their data in PDF format
PDF was not designed as a data format. It was designed as an "electronic paper" format.
Main purpose: presenting elements exact... |
antoniomezzacapo/qiskit-tutorial | community/hello_world/laurel_or_yanny.ipynb | apache-2.0 | from qiskit import ClassicalRegister, QuantumRegister
from qiskit import QuantumCircuit, execute
from qiskit.tools.visualization import plot_histogram
from qiskit import IBMQ, available_backends, get_backend
from pydub import AudioSegment
from pydub.playback import play
#Set up the quantum and classical registers, and... |
NuGrid/NuPyCEE | regression_tests/temp/RTS_custom_imf.ipynb | bsd-3-clause | import matplotlib
matplotlib.use('nbagg')
import matplotlib.pyplot as plt
#matplotlib.use('nbagg')
import numpy as np
%matplotlib inline
import sygma as s
reload(s)
"""
Explanation: Introduction to SYGMA parameter
Tips at the beginning:
$\odot$ You can find the documentation <a href="DOC/sygma.html">here</a>.
$\odot$... |
crystalzhaizhai/cs207_yi_zhai | lectures/L6/L6-slides.ipynb | mit | from IPython.display import HTML # Allows us to embed HTML into our notebook.
HTML('<iframe width="800" height="400" frameborder="0" src="http://pythontutor.com/iframe-embed.html#code=a%20%3D%20%5B1,%203,%205%5D%0Ab%20%3D%20a%0Aprint%28%22a%20%3D%20%7B0%7D%20and%20has%20id%20%7B1%7D%22.format%28a,%20id%28a%29%29%29%0Ap... |
minesh1291/Practicing-Kaggle | MNIST_2017/dump_/Mens_script.ipynb | gpl-3.0 | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... |
mne-tools/mne-tools.github.io | dev/_downloads/c37ac181bfe2eb2f1fa69c3fab30417d/mne_cov_power.ipynb | bsd-3-clause | # Author: Denis A. Engemann <denis-alexander.engemann@inria.fr>
# Luke Bloy <luke.bloy@gmail.com>
#
# License: BSD-3-Clause
import os.path as op
import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse_cov
data_path = sample.data_path()
s... |
PhilipReschke/TensorFlow-Code-Examples | Recurring Neural Network.ipynb | gpl-3.0 | import tensorflow as tf
import numpy as np
"""
Explanation: TensorFlow - Recurring Neural Neural Network Example
About this notebook
Author: Philip Reschke (http://www.philipreschke.com).
Project: https://github.com/PhilipReschke/TensorFlow-Code-Examples
I will build a Recurring Neural Network using a number of LSTM l... |
alexandrnikitin/algorithm-sandbox | courses/DAT256x/Module03/03-04-More Matrices.ipynb | mit | import numpy as np
A = np.array([[1,2,3],
[4,5,6]])
print(2 * A)
"""
Explanation: More Matrices
This notebook continues your exploration of matrices.
Matrix Multiplication
Multiplying matrices is a little more complex than the operations we've seen so far. There are two cases to consider, scalar multipl... |
whitead/numerical_stats | unit_7/hw_2017/problem_set_1.ipynb | gpl-3.0 | data = [-4.4,-10.1,1.6,4.0,3.4,-2.0,-5.8,-1.9,-3.1,1.8,-2.9,4.9,2.7,-9.5,2.8,4.3,-1.1,3.9,-9.1,5.6,1.1,-1.0,0.7,9.7,-1.6,-2.3,1.4,2.5,-9.0,1.1]
### BEGIN SOLUTION
import scipy.stats as stats
x = np.mean(data)
s = np.std(data, ddof=1)
Z = stats.norm.ppf(0.975)
print('{:.2f} +/- {:.2f}'.format(x, s * Z / np.sqrt(len(dat... |
tanyaschlusser/office-nfl-pool | Fantasy (first steps).ipynb | mit | ##
# Setup -- import the modules we want and set up inline plotting
#
from __future__ import print_function
import datetime
import matplotlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
# Bigger fonts and figures for the demo
matplotlib.rcParams.update({
'font.size... |
hfoffani/deep-learning | gan_mnist/Intro_to_GANs.ipynb | mit | %matplotlib inline
import pickle as pkl
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')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
xdze2/thermique_appart | get_data_and_preprocess.ipynb | mit | coords_grenoble = (45.1973288, 5.7139923)
startday = pd.to_datetime('12/07/2017', format='%d/%m/%Y').tz_localize('Europe/Paris')
lastday = pd.to_datetime('now').tz_localize('Europe/Paris')
"""
Explanation: Téléchargement des données et premier traitement
End of explanation
"""
# routine pour construire automatiquem... |
weleen/mxnet | example/notebooks/tutorials/finetune-CNN-catsvsdogs.ipynb | apache-2.0 | # Data Iterators for cats vs dogs dataset
import mxnet as mx
def get_iterators(batch_size, data_shape=(3, 224, 224)):
train = mx.io.ImageRecordIter(
path_imgrec = './cats_dogs_train.rec',
data_name = 'data',
label_name = 'softmax_label',
batch_size ... |
JoseGuzman/myIPythonNotebooks | Optimization/linear_regression.ipynb | gpl-2.0 | %pylab inline
from numpy import poly1d, polyfit
"""
Explanation: <H1>Linear regression</H1>
End of explanation
"""
# generate some data
np.random.seed(1000)
mysize = 1000
xdata = np.random.normal(3.0, 1.0, mysize)
m = 2.0 + np.random.normal(0,.2,mysize)
a = 0.05 + np.random.normal(0, .01, size=mysize)
ydata = m*xdat... |
keshr3106/ThinkStats2 | code/chap03soln.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
"""
import thinkstats2
pmf = thinkstats2.Pmf(resp.numkdhh)
pmf
"""
Explanation: Make a PMF of <tt>numkd... |
cathywu/flow | tutorials/tutorial09_environments.ipynb | mit | # import the base environment class
from flow.envs import Env
# define the environment class, and inherit properties from the base environment class
class myEnv(Env):
pass
"""
Explanation: Tutorial 09: Creating Custom Environments
This tutorial walks you through the process of creating custom environments in Flow... |
mrcslws/nupic.research | projects/archive/dynamic_sparse/notebooks/mcaporale/2019-10-10-ExperimentAnalysis-GSCSparser.ipynb | agpl-3.0 | from IPython.display import Markdown, display
%load_ext autoreload
%autoreload 2
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import tabulate
import pprint
import click
import numpy as np
import pandas as pd
from ray.tune.commands i... |
AeroPython/Taller-PyConEs-2015 | Ejercicios/El problema de los matrimonios estables/matrimonios_estables.ipynb | mit | from IPython.display import HTML
HTML('<iframe src="http://youtube.com" width="700" height="400"></iframe>')
"""
Explanation: El problema de los matrimonios estables
Dados un cierto número de hombres y mujeres heterosexuales, organízalos por parejas de tal manera que su matrimonio sea estable. Cada persona ha ordenad... |
yandexdataschool/Practical_RL | week05_explore/week5.ipynb | unlicense | class BernoulliBandit:
def __init__(self, n_actions=5):
self._probs = np.random.random(n_actions)
@property
def action_count(self):
return len(self._probs)
def pull(self, action):
if np.any(np.random.random() > self._probs[action]):
return 0.0
return 1.0
... |
dmolina/es_intro_python | 08-Defining-Functions.ipynb | gpl-3.0 | print('abc')
"""
Explanation: <!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="fig/cover-small.jpg">
This notebook contains an excerpt from the Whirlwind Tour of Python by Jake VanderPlas; the content is available on GitHub.
The text and code are released under the CC0 license; see also the c... |
yonetaniryo/corrsearch | ipython_notebook/example.ipynb | gpl-2.0 | sys.path.append('../src/')
import CorrSearchSV as CSSV # main module
rootdir = '<set your path to the example directory>'
# Input data
tardir = rootdir + '/example/1/ppm' # rgb data, .ppm format
obsdir = rootdir + '/example/0/ppm' # rgb data, .ppm format
svdir = CSSV.glob.glob(rootdir + '/example/0/sv/*') # set of dire... |
alexandrnikitin/algorithm-sandbox | courses/DAT256x/Module03/03-01-Vectors.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
# We'll use a numpy array for our vector
v = np.array([2,1])
# and we'll use a quiver plot to visualize it.
origin = [0], [0]
plt.axis('equal')
plt.grid()
plt.ticklabel_format(style='sci', axis='both', scilimits=(0,0))
plt.quiver(*origin, *v, scal... |
gsorianob/fiuba-python | Clase 09 - Introducción POO.ipynb | apache-2.0 | class Mesa(object):
cantidad_de_patas = None
color = None
material = None
mi_mesa = Mesa()
mi_mesa.cantidad_de_patas = 4
mi_mesa.color = 'Marrón'
mi_mesa.material = 'Madera'
print 'Tendo una mesa de {0.cantidad_de_patas} patas de color {0.color} y esta hecha de {0.material}'.format(mi_mesa)
"""
Expla... |
mfouesneau/pyphot | examples/sandbox_QuickStart.ipynb | mit | %matplotlib inline
import pylab as plt
import numpy as np
import sys
sys.path.append('../')
from pyphot import sandbox as pyphot
"""
Explanation: pyphot - A tool for computing photometry from spectra
Some examples are provided in this notebook
Full documentation available at http://mfouesneau.github.io/docs/pyphot/
... |
duncanwp/python_for_climate_scientists | course_content/notebooks/python_introduction.ipynb | gpl-3.0 | # The obligatory hello world...
print("Hello world!")
an_integer = 9
a_float = 2.5
a_string = "blah"
a_complex_number = 2.0 + 2.3j
# Note that variables are case-sensitive
a = 1
A = 2
print(a, A)
# And can be dynamically reassigned
an_integer = 7
print(an_integer)
"""
Explanation: Python 101
This introduction is b... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/sdk/sdk_custom_tabular_regression_online.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 SDK: Custom training tabular regression model for online prediction
<table align="left">... |
loredanacirstea/ai-algos | DNN Speech Recognizer/vui_notebook.ipynb | gpl-3.0 | from data_generator import vis_train_features
# extract label and audio features for a single training example
vis_text, vis_raw_audio, vis_mfcc_feature, vis_spectrogram_feature, vis_audio_path = vis_train_features()
"""
Explanation: Artificial Intelligence Nanodegree
Voice User Interfaces
Project: Speech Recognition... |
ledeprogram/algorithms | class7/donow/ronga_paul_donow_7.ipynb | gpl-3.0 | import pandas as pd
%matplotlib inline
import numpy as np
from sklearn.linear_model import LogisticRegression
"""
Explanation: Apply logistic regression to categorize whether a county had high mortality rate due to contamination
1. Import the necessary packages to read in the data, plot, and create a logistic regressi... |
inakic/matsoft | IPython-parallel.ipynb | unlicense | from IPython.parallel import Client
cli = Client()
"""
Explanation: IPython parallel
Jupyter prilično jednostavno omogućava paralelizaciju koda. Dokumentaciju možete naći ovdje: Using IPython for parallel computing.
No modifikacija Jupyter notebook u Sagemath oblaku to ne omogućuje. No u Sagemath oblaku možemo pokret... |
sharynr/notebooks | Exploring Heating Problems in Manhattan.ipynb | apache-2.0 | import os
import matplotlib.pyplot as plt
import pandas as pd
os.environ['BRUNEL_CONFIG'] = "locjavascript=/data/jupyter2/static-file-content-delivery-network/nbextensions/brunel_ext"
import brunel
# Brunel will be used to visualize data later on
!pip install --user brunel
"""
Explanation: Exploring Heating Problems ... |
philippgrafendorfe/stackedautoencoders | .ipynb_checkpoints/MNIST_advanced-checkpoint.ipynb | mit | from keras.layers import Input, Dense, Dropout
from keras.models import Model
from keras.datasets import mnist
from keras.models import Sequential, load_model
from keras.optimizers import RMSprop
from keras.callbacks import TensorBoard
from __future__ import print_function
from IPython.display import SVG, Image
from ke... |
eduardojvieira/Curso-Python-MEC-UCV | 4-matplotlib.ipynb | mit | %matplotlib inline
"""
Explanation: <table width="100%" border="0">
<tr>
<td><img src="./images/ing.png" alt="" align="left" /></td>
<td><img src="./images/ucv.png" alt="" align="center" height="100" width="100" /></td>
<td><img src="./images/mec.png" alt="" align="right"/></td>
</tr>
</table>
<br>
<h1 ... |
tensorflow/hub | examples/colab/semantic_similarity_with_tf_hub_universal_encoder.ipynb | apache-2.0 | # Copyright 2018 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... |
yuvrajsingh86/DeepLearning_Udacity | 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... |
datactive/bigbang | examples/git-analysis/MultiGitRepos.ipynb | mit | org_name = "glass-bead-labs"
repo = repo_loader.get_org_multirepo(org_name)
full_info = repo.commit_data
handled = pd.DataFrame(full_info.groupby(["Repo Name", "Committer Name"]).size())
repo_size_df = pd.DataFrame(full_info.groupby("Repo Name").size())
author_commits_df = pd.DataFrame(full_info.groupby("Committer Nam... |
dipanjank/ml | simple_implementations/LDA.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
%pylab inline
pylab.style.use('ggplot')
"""
Explanation: <h1 align="center">Linear Discriminant Analysis</h1>
End of explanation
"""
import seaborn as sns
iris_df = sns.load_dataset('iris')
iris_df = iris_df.set_index('species')
iris_df.head()
"""
Explanation: The goal of a... |
yunqu/PYNQ | boards/Pynq-Z1/base/notebooks/microblaze/microblaze_programming.ipynb | bsd-3-clause | from pynq.overlays.base import BaseOverlay
base = BaseOverlay('base.bit')
"""
Explanation: Programming Microblaze Subsystems from Jupyter
In the Base I/O overlays that accompany the PYNQ release Microblazes are used to control peripherals attached to the various connectors. These can either be programmed with existin... |
fabiencampillo/systemes_dynamiques_agronomie | Partie5_Controle_PID.ipynb | gpl-3.0 | # -*- coding: utf-8 -*-
from IPython.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>
Pour afficher le code python, cliquer sur le bouton:
... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_objects_from_arrays.ipynb | bsd-3-clause | # Author: Jaakko Leppakangas <jaeilepp@student.jyu.fi>
#
# License: BSD (3-clause)
import numpy as np
import neo
import mne
print(__doc__)
"""
Explanation: Creating MNE objects from data arrays
In this simple example, the creation of MNE objects from
numpy arrays is demonstrated. In the last example case, a
NEO fil... |
oasis-open/cti-python-stix2 | docs/guide/ts_support.ipynb | bsd-3-clause | import stix2
stix2.Indicator()
"""
Explanation: Technical Specification Support
How imports work
Imports can be used in different ways depending on the use case and support levels.
People who want to support the latest version of STIX 2 without having to make changes, can implicitly use the latest version:<div class=... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_tf_dics.ipynb | bsd-3-clause | # Author: Roman Goj <roman.goj@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.event import make_fixed_length_events
from mne.datasets import sample
from mne.time_frequency import csd_fourier
from mne.beamformer import tf_dics
from mne.viz import plot_source_spectrogram
print(__doc__)
data_path = sample.d... |
Population-Transcriptomics/C1-CAGE-preview | OP-WORKFLOW-CAGEscan-short-reads-v2.0.ipynb | cc0-1.0 | import subprocess, os, csv, signal, pysam
"""
Explanation: OP-WORKFLOW-CAGEscan-short-reads-v2.0
This document is an example of how to process a C1 CAGE library with a Jupyter notebook from raw reads to single molecule count. All the steps are described in the tutorial section of this repository. In the following sect... |
CUFCTL/DLBD | Fall2017/Module1_gpu.ipynb | mit | import sys, os
import pickle
import torch
import torch.utils.data as data
import glob
from PIL import Image
import numpy as np
def unpickle(fname):
with open(fname, 'rb') as f:
Dict = pickle.load(f, encoding='bytes')
return Dict
def load_data(batch):
print ("Loading batch:{}".format(batch))
... |
kevinsung/OpenFermion | docs/tutorials/circuits_2_diagonal_coulomb_trotter.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... |
tpin3694/tpin3694.github.io | machine-learning/chi-squared_for_feature_selection.ipynb | mit | # Load libraries
from sklearn.datasets import load_iris
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
"""
Explanation: Title: Chi-Squared For Feature Selection
Slug: chi-squared_for_feature_selection
Summary: How to remove irrelevant features using chi-squared for machine... |
tpin3694/tpin3694.github.io | machine-learning/.ipynb_checkpoints/split_data_into_training_and_test_sets-checkpoint.ipynb | mit | # Load libraries
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
"""
Explanation: Title: Split Data Into Training And Test Sets
Slug: split_data_into_training_and_test_sets
Summary: How to split data into training and test sets for mach... |
tuanavu/coursera-university-of-washington | machine_learning/4_clustering_and_retrieval/assigment/week3/.ipynb_checkpoints/module-5-decision-tree-assignment-2-blank-Graphlab-checkpoint.ipynb | mit | import graphlab
"""
Explanation: Implementing binary decision trees
The goal of this notebook is to implement your own binary decision tree classifier. You will:
Use SFrames to do some feature engineering.
Transform categorical variables into binary variables.
Write a function to compute the number of misclassified e... |
CitrineInformatics/lolo | python/examples/ling-immi-2017.ipynb | apache-2.0 | %matplotlib inline
from matplotlib import pyplot as plt
from matminer.data_retrieval.retrieve_Citrine import CitrineDataRetrieval
from matminer.featurizers.base import MultipleFeaturizer
from matminer.featurizers import composition as cf
from lolopy.learners import RandomForestRegressor
from sklearn.model_selection imp... |
Chipe1/aima-python | nlp_apps.ipynb | mit | from utils import open_data
from text import *
flatland = open_data("EN-text/flatland.txt").read()
wordseq = words(flatland)
P_flatland = NgramCharModel(2, wordseq)
faust = open_data("GE-text/faust.txt").read()
wordseq = words(faust)
P_faust = NgramCharModel(2, wordseq)
"""
Explanation: NATURAL LANGUAGE PROCESSING... |
michaelaye/planetpy | notebooks/planet4_hirise_index_parsing.ipynb | bsd-3-clause | from planetarypy import pdstools as pds
"""
Explanation: Instructions
pip install planetarypy
I wrote highly generalized PDS index readers, that wrap the Python Parameter Value Language module PVL (The syntax for Planetary Data System label files). Relevant functions are in https://github.com/michaelaye/planetarypy/... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/managed_notebooks/pricing_optimization/pricing-optimization.ipynb | apache-2.0 | import os
PROJECT_ID = ""
# Get your Google Cloud project ID from gcloud
if not os.getenv("IS_TESTING"):
shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null
PROJECT_ID = shell_output[0]
print("Project ID: ", PROJECT_ID)
"""
Explanation: Pricing Optimization
Table of contents
Ov... |
jingyany/data-512-a1 | hcds-a1-data-curation.ipynb | mit | import requests
import json
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import matplotlib.dates as mdates
import datetime
"""
Explanation: English Wikipedia page views, 2008 - 2017
End of explanation
"""
endpoint1 = 'https://wikimedia.org/api/rest_v1/metrics/legacy/pagec... |
3upperm2n/notes-deeplearning | word_embedding/embeddings/Skip-Grams-Solution.ipynb | mit | import time
import numpy as np
import tensorflow as tf
import utils
"""
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language p... |
tpin3694/tpin3694.github.io | machine-learning/detect_edges.ipynb | mit | # Load image
import cv2
import numpy as np
from matplotlib import pyplot as plt
"""
Explanation: Title: Detect Edges
Slug: detect_edges
Summary: How to detect edges images using OpenCV in Python.
Date: 2017-09-11 12:00
Category: Machine Learning
Tags: Preprocessing Images
Authors: Chris Albon
Preliminaries
End of... |
hadibakalim/deepLearning | 01.neural_network/01.first_neural_net-linear_regression_1/01.linear_regression_1.ipynb | mit | import pandas as pd
from sklearn import linear_model
import matplotlib.pyplot as plt
"""
Explanation: First Neural Networks
We have a dataset of measurement of different animals of brain weight and body weight. We want to predict an animal's body weight given its brain weight.
Since our data is labeled this will be Su... |
MSeeker/Notebook-Collections | Numerical Solution to the 1D Steady State Schrödinger Equation.ipynb | mit | import numpy as np
import scipy
import matplotlib
from scipy import linalg
from scipy.integrate import quad, ode
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: 1. Introduction
Apart from a few worked examples in the textbook, it is quite difficult to obtain analytic solutions to the Schrödinger Eq... |
xpharry/Udacity-DLFoudation | tutorials/batch-norm/Batch_Normalization_Solutions.ipynb | mit | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
"""
Explanation: Batch Normalization – Solutions
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a co... |
bhattacharjee/courses | CourseraDeepLearningSpecialization/1.NeuralNetworksAndDeepLearning/Week4/Exercises/Deep+Neural+Network+-+Application+v3.ipynb | mit | import time
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy
from PIL import Image
from scipy import ndimage
from dnn_app_utils_v2 import *
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams[... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/end_to_end_ml/labs/preproc.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip install --user google-cloud-bigquery==1.25.0
"""
Explanation: <h1> Preprocessing using Dataflow </h1>
This notebook illustrates:
<ol>
<li> Creating datasets for Machine Learning using Dataflow
</ol>
<p>
While Pandas is fine for experimenting, fo... |
jasag/Phytoliths-recognition-system | code/notebooks/Prototypes/Classifiers_and_HoG/3_validation.ipynb | bsd-3-clause | import pickle
path = '../../rsc/obj/'
# Todas las rutas
svm_path = path + 'svm_clf.sav'
svm2_path = path + 'svm2_clf.sav'
gtb_path = path + 'gtb_clf.sav'
rf_path = path + 'rf_clf.sav'
# Serizalización
svm_clf = pickle.load(open(svm_path, 'rb'))
svm2_clf = pickle.load(open(svm2_path, 'rb'))
gtb_clf = pickle.load(open... |
tensorflow/cloud | g3doc/tutorials/distributed_training_nasnet_with_tensorflow_cloud.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... |
colour-science/colour-ipython | notebooks/colorimetry/lightness.ipynb | bsd-3-clause | import colour
colour.utilities.filter_warnings(True, False)
sorted(colour.LIGHTNESS_METHODS.keys())
"""
Explanation: !!! D . R . A . F . T !!!
Lightness
Lightness is defined as the brightness of an area judged relative to the brightness of a similarly illuminated area that appears to be white or highly transmitting.... |
mne-tools/mne-tools.github.io | 0.24/_downloads/e41b6a898e7a75f8a9f1a6c00ca73857/20_visualize_epochs.ipynb | bsd-3-clause | import os
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, verbose=False).crop(tmax=120)
"""
Explanation: Visualizing epo... |
learnleapfly/gamecamp | Take notes in a notebook.ipynb | mit | !ls -la
"""
Explanation: Bonus points for taking notes in a notebook
The best way to document what you're doing is in a jupyter notebook. If you've never used notebooks before, make sure you have it installed in whatever virtual environment you're using.
If you're using anaconda, from your kivy environment you'll wan... |
balarsen/pymc_learning | Logit/Reeves_Denton_Dst_MinL-10nTbins-pymc3.ipynb | bsd-3-clause | # http://onlinelibrary.wiley.com/doi/10.1002/2016JA022652/epdf
import pymc3
import tqdm
from pprint import pprint
import numpy as np
import matplotlib.pyplot as plt
import spacepy.plot as spp
import seaborn as sns
sns.set(font_scale=1.5)
"""
Explanation: Is there a cutoff in Dst that allows slot filling?
Method
Usin... |
StudyExchange/Udacity | MachineLearning(Advanced)/p3_creating_customer_segments/customer_segments.ipynb | mit | %%time
# 引入这个项目需要的库
import numpy as np
import pandas as pd
import visuals as vs
from IPython.display import display # 使得我们可以对DataFrame使用display()函数
# 设置以内联的形式显示matplotlib绘制的图片（在notebook中显示更美观）
%matplotlib inline
# 载入整个客户数据集
try:
data = pd.read_csv("customers.csv")
data.drop(['Region', 'Channel'], axis = 1, ... |
DaveBackus/Data_Bootcamp | Code/IPython/bootcamp_exam_practice.ipynb | mit | # import packages
import pandas as pd # data management
import matplotlib.pyplot as plt # graphics
# IPython command, puts plots in notebook
%matplotlib inline
# check Python version
import datetime as dt
import sys
print('Today is', dt.date.today())
print('What version of Python are we r... |
cni/psych204a | mrDiffusion.ipynb | gpl-2.0 | %pylab inline
rcParams["figure.figsize"] = (8, 6)
rcParams["axes.grid"] = True
from IPython.display import display, clear_output
from mpl_toolkits.axes_grid1 import make_axes_locatable
from time import sleep
from __future__ import division
def cart2pol(x, y):
theta = arctan2(y, x)
r = sqrt(x ** 2 + y ** 2)
... |
parrt/msan692 | notes/html.ipynb | mit | ! curl https://www.sec.gov/Archives/edgar/data/1318605/000119312510017054/ds1.htm > /tmp/TeslaIPO.html
"""
Explanation: Extracting text from HTML file
There are lots of data sources from which we might want to extract information, such as initial public offerings for various companies. E.g., Tesla's IPO prospectus. On... |
texib/spark_tutorial | 3.AnalysisArticle_HTML.ipynb | gpl-2.0 | def parseRaw(json_map):
url = json_map['url']
content = json_map['html']
return (url,content)
"""
Explanation: Parse Json
End of explanation
"""
import json
import pprint
pp = pprint.PrettyPrinter(indent=2)
path = "./pixnet.txt"
all_content = sc.textFile(path).map(json.loads).map(parseRaw)
"""
Explanati... |
NicolasHug/Surprise | examples/notebooks/KNNBasic_analysis.ipynb | bsd-3-clause | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import pickle
import os
import pandas as pd
from surprise import KNNBasic
from surprise import Dataset
fr... |
skkandrach/foundations-homework | data-databases/Homework2.ipynb | mit | import pg8000
conn = pg8000.connect(database="homework2")
"""
Explanation: Homework 2: Working with SQL (Data and Databases 2016)
This homework assignment takes the form of an IPython Notebook. There are a number of exercises below, with notebook cells that need to be completed in order to meet particular criteria. Yo... |
Danghor/Formal-Languages | Ply/LaTeX2HTML.ipynb | gpl-2.0 | with open('example.tex') as f:
data = f.read()
"""
Explanation: Converting $\LaTeX$ to <span style="font-variant:small-caps;">Html</span>
The purpose of the following exercise is to implement a translator from $\LaTeX$ to
MathML. $\LaTeX$ is a document markup language
that is especially well suited to present te... |
quiltdata/quilt | docs/walkthrough/installing-a-package.ipynb | apache-2.0 | import quilt3
# list local packages
list(quilt3.list_packages())
# list remote packages
list(quilt3.list_packages("s3://quilt-example"))
"""
Explanation: Searching for packages
As explained in "Uploading a Package", packages are managed using registries. There is a one local registry on your machine, and potentially... |
dolittle007/dolittle007.github.io | notebooks/GP-smoothing.ipynb | gpl-3.0 | %pylab inline
figsize(12, 6);
import numpy as np
import scipy.stats as stats
x = np.linspace(0, 50, 100)
y = (np.exp(1.0 + np.power(x, 0.5) - np.exp(x/15.0)) +
np.random.normal(scale=1.0, size=x.shape))
plot(x, y);
xlabel("x");
ylabel("y");
title("Observed Data");
"""
Explanation: Gaussian Process (GP) smooth... |
mayank-johri/LearnSeleniumUsingPython | Section 1 - Core Python/Chapter 12 - Introspection/Chapter14_Introspection.ipynb | gpl-3.0 | trospection or reflection is the ability of software to identify and report their own internal structures, such as types, variabl# Getting some information
# about global objects in the program
from types import ModuleType
def info(n_obj):
# Create a referênce to the object
obj = globals()[n_obj]
# Show... |
psci2195/espresso-ffans | doc/tutorials/08-visualization/08-visualization.ipynb | gpl-3.0 | from matplotlib import pyplot
import espressomd
import numpy
espressomd.assert_features("LENNARD_JONES")
# system parameters (10000 particles)
box_l = 10.7437
density = 0.7
# interaction parameters (repulsive Lennard-Jones)
lj_eps = 1.0
lj_sig = 1.0
lj_cut = 1.12246
lj_cap = 20
# integration parameters
system = esp... |
bharath31/carnd-p1 | lanelines.ipynb | mit | import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
"""
Explanation: Finding Lanes on the Road
The objective of this project is to develop a software pipeline to identify lane lines on the road. The software pipeline is first tested on a series of images an... |
OSGeoLabBp/tutorials | english/data_processing/lessons/text_files.ipynb | cc0-1.0 | from IPython.display import Image
from IPython.core.display import HTML
Image(url= "https://github.com/OSGeoLabBp/tutorials/blob/master/english/data_processing/lessons/images/file_proc.png?raw=true")
"""
Explanation: <a href="https://colab.research.google.com/github/OSGeoLabBp/tutorials/blob/master/english/data_proce... |
fluffy-hamster/A-Beginners-Guide-to-Python | A Beginners Guide to Python/08. Calling Functions.ipynb | mit | # Defining the function...
def boring():
return "ZZZZZZZZ..."
# calling said function...
boring()
"""
Explanation: How to Call Functions...
What are functions...
Functions are an incredibly useful concept and I will cover them in greater detail later on, I promise. But for now let's just say functions are bits of... |
machlearn/ipython-notebooks | ML dataset - Iris.ipynb | mit | from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
y = iris.target
X.shape
y.shape
"""
Explanation: This is the best known dataset for doing classification. It contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other ... |
Xilinx/meta-petalinux | recipes-multimedia/gstreamer/gstreamer-vcu-notebooks/vcu-demo-transcode-to-file.ipynb | mit | from IPython.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 here... |
eramirem/numerical-methods-pdes | 07_ivp.ipynb | cc0-1.0 | %matplotlib inline
import numpy
import matplotlib.pyplot as plt
"""
Explanation: <table>
<tr align=left><td><img align=left src="https://i.creativecommons.org/l/by/4.0/88x31.png">
<td>Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c)... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/tensorflow_extended/solutions/penguin_simple.ipynb | apache-2.0 | try:
import colab
!pip install --upgrade pip
except:
pass
"""
Explanation: TFX Pipeline Tutorial using Penguin dataset
Learning objectives
Prepare example data.
Create a pipeline.
Run the pipeline.
Introduction
In this notebook, you will create and run a TFX pipeline
for a simple classification model.
The pipe... |
relopezbriega/mi-python-blog | content/downloads/PiDay.ipynb | gpl-2.0 | # Pi utilizando el módulo math
import math
math.pi
# Pi utiizando sympy, dps nos permite variar el número de dígitos de Pi
from sympy.mpmath import mp
mp.dps = 33 # número de dígitos
print(mp.pi)
"""
Explanation: El día Pi
Esta notebook fue creada originalmente como un blog post por Raúl E. López Briega en Mi blog s... |
kecnry/autofig | docs/tutorials/3d.ipynb | gpl-3.0 | import autofig
import numpy as np
#autofig.inline()
t = np.linspace(0,10,31)
x = np.random.rand(31)
y = np.random.rand(31)
z = np.random.rand(31)
"""
Explanation: Plotting in 3D with autofig
End of explanation
"""
autofig.reset()
autofig.plot(x, y, z, i=t,
xlabel='x', ylabel='y', zlabel='z')
mplfig = ... |
tensorflow/docs-l10n | site/ja/tutorials/estimator/boosted_trees_model_understanding.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... |
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