repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
spencer2211/deep-learning | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
elenduuche/deep-learning | seq2seq/sequence_to_sequence_implementation.ipynb | mit | import helper
source_path = 'data/letters_source.txt'
target_path = 'data/letters_target.txt'
source_sentences = helper.load_data(source_path)
target_sentences = helper.load_data(target_path)
"""
Explanation: Character Sequence to Sequence
In this notebook, we'll build a model that takes in a sequence of letters, an... |
pbutenee/ml-tutorial | source/1/machine_learning.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (13.0, 8.0)
%matplotlib inline
import pickle
import sklearn
import sklearn.linear_model
import sklearn.preprocessing
import sklearn.gaussian_process
import sklearn.ensemble
"""
Explanation: Machine Learning 101++ in Python
Created b... |
mjabri/holoviews | doc/Tutorials/Showcase.ipynb | bsd-3-clause | import holoviews as hv
from holoviews.operation import contours, threshold, gradient
import numpy as np
%reload_ext holoviews.ipython
"""
Explanation: HoloViews encapsulates your continuous or discrete data into storable, composable, sliceable objects, leveraging matplotlib for visualization and IPython Notebook for ... |
AllenDowney/ThinkBayes2 | examples/changepoint.ipynb | mit | # If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install empiricaldist
# Get utils.py
from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename... |
atremblay/deep-learning | tv-script-generation/dlnd_tv_script_generation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scrip... |
IIPBC/Material | IntroPandas/Intro-Pandas-PBCII.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Introduction to data manipulation and analysis with Pandas
The objective of this notebook is to make a brief overview of the pandas module for data analysis and manipulation. To present the different pandas tools,w e use as an exam... |
jseabold/statsmodels | examples/notebooks/stationarity_detrending_adf_kpss.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
"""
Explanation: Stationarity and detrending (ADF/KPSS)
Stationarity means that the statistical properties of a time series i.e. mean, variance and covariance do not change over time. Many statistical... |
ucsd-ccbb/jupyter-genomics | notebooks/networkAnalysis/network_viz_interactive_widget/network_viz_interactive_widget.ipynb | mit | import numpy as np
from pandas import *
import networkx as nx
import matplotlib.pyplot as plt
% matplotlib inline
"""
Explanation: This notebook will create an interactive widget to illustrate network interconnections
End of explanation
"""
# set file name and directory: network format from GeneMania
file... |
kinverarity1/lasio | notebooks/set logging level for lasio.ipynb | mit | import logging
import lasio
"""
Explanation: lasio uses the logging module to log warnings and other information when manipulating LAS files.
End of explanation
"""
l = lasio.read("../tests/examples/sample.las")
"""
Explanation: Sometimes you may want more or less information shown to you when you are reading LAS f... |
AllenDowney/ThinkBayes2 | examples/blaster_soln.ipynb | mit | # Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
import math
import numpy as np
from thinkbayes2 import Hist, Pmf, Suite, Beta
import thinkplot
"""
Expl... |
arborh/tensorflow | tensorflow/lite/g3doc/performance/post_training_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... |
fugufisch/hu_bp_python_course | 05_dataio_matplotlib/dataio_matplotlib_exercise.ipynb | mit | import cPickle as pickle
with open('GPD1_seq.fasta', 'r') as f:
lines = f.readlines()
a = 0
t = 0
g = 0
c = 0
for line in lines:
if line.startswith('>'):
continue
else:
a = a + line.count('A')
t = t + line.count('T')
g = g + line.count('G')
c = c + line.count('... |
JAmarel/Phys202 | Fitting/.ipynb_checkpoints/FittingModelsEx02-checkpoint.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Fitting Models Exercise 2
Imports
End of explanation
"""
data = np.load('decay_osc.npz')
tdata = data['tdata']
ydata = data['ydata']
dy = data['dy']
plt.figure(figsize=(8,6))
plt.plot(tdata, ydata)
... |
MartyWeissman/Python-for-number-theory | PwNT Notebook 5.ipynb | gpl-3.0 | 23 % 5 # What is the remainder after dividing 23 by 5? What is the natural representative of 23 modulo 5?
"""
Explanation: Part 5: Modular arithmetic and primality testing
Python, like most programming languages, comes with a "mod operation" % to compute remainders. This makes basic modular arithmetic straightforw... |
quantumlib/Cirq | docs/tutorials/azure-quantum/getting_started_honeywell.ipynb | apache-2.0 | !pip install azure-quantum[cirq] --quiet
"""
Explanation: Getting started with Honeywell and Cirq on Azure Quantum
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://quantumai.google/cirq/tutorials/azure-quantum/getting_started_honeywell"><img src="https://quantumai.google/si... |
ledeprogram/algorithms | class3/homework/born_mathias_2_2.ipynb | gpl-3.0 | import random
"""
Explanation: Assignment 2
Implement the search algorithm you came up with in pseudocode with Python
Test the search algorithm with a list of 10,100,1000 random numbers (sorted with your sorting algorithm) and compare the result using the %time to time your code and submit your results in code commen... |
Cyb3rWard0g/ThreatHunter-Playbook | docs/notebooks/windows/08_lateral_movement/WIN-201009173318.ipynb | gpl-3.0 | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: Remote WMI Wbemcomn DLL Hijack
Metadata
| | |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2020/10/09 |
| modification date | 2020/10/09 |
| playbook related | ['WI... |
asweigart/pyautogui | docs/simplified-chinese.ipynb | bsd-3-clause | import pyperclip
import pyautogui
# PyAutoGUI中文输入需要用粘贴实现
# Python 2版本的pyperclip提供中文复制
def paste(foo):
pyperclip.copy(foo)
pyautogui.hotkey('ctrl', 'v')
foo = u'学而时习之'
# 移动到文本框
pyautogui.click(130,30)
paste(foo)
"""
Explanation: PyAutoGUI——让所有GUI都自动化
本教程译自大神Al Sweigart的PyAutoGUI项目，Python自动化工具，更适合处理GUI任务，网页... |
ekwan/PyQuiver | scripts/pyquiver.ipynb | apache-2.0 | # import the necessary package elements
import numpy as np
import sys
sys.path.append("../src")
from kie import KIE_Calculation
"""
Explanation: PyQuiver
This is an IPython Notebook interface for the PyQuiver package. The code below will guide you through using PyQuiver through a native Python interface. The same step... |
lichao890427/lichao890427.github.io | jupyter/machine_learning_1.ipynb | mit | # coding: utf-8
from sklearn import datasets
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Perceptron
from sklearn.metrics import accuracy_score
iris = datasets.load_iris() # 加载鸢尾花数据库
X = iris.data[:, [2, 3]] # 选... |
Kreiswolke/gensim | docs/notebooks/online_w2v_tutorial.ipynb | lgpl-2.1 | from gensim.corpora.wikicorpus import WikiCorpus
from gensim.models.word2vec import Word2Vec, LineSentence
from pprint import pprint
from copy import deepcopy
from multiprocessing import cpu_count
"""
Explanation: Online word2vec tutorial
So far, word2vec cannot increase the size of vocabulary after initial training. ... |
rishuatgithub/MLPy | nlp/UPDATED_NLP_COURSE/07-BONUS-NOTEBOOKS-NO-VIDEOS/Working with Stopwords.ipynb | apache-2.0 | import spacy
nlp = spacy.load('en_core_web_sm')
stopwords = nlp.Defaults.stop_words
len(stopwords)
type(stopwords)
print(sorted(list(stopwords)))
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Working with Stop Words
In the previous sections we've seen how to acces... |
alasdairtran/mclearn | projects/weiyen/notebooks/knfst.ipynb | bsd-3-clause | def nullspace(A, eps=1e-12):
u, s, vh = svd(A)
null_mask = (s <= eps)
null_space = sp.compress(null_mask, vh, axis=0)
return sp.transpose(null_space)
"""
Explanation: Kernel Null Foley-Sammon Transform for Novelty Detection
The following function calculates the basis for the nullspace of a given matrix... |
feststelltaste/software-analytics | notebooks/Structural Test Case Similarity.ipynb | gpl-3.0 | import pandas as pd
invocations = pd.read_csv("datasets/test_code_invocations.csv", sep=";")
invocations.head()
"""
Explanation: Introduction
In big and old legacy systems, tests are often a mess. Especially end-to-end-tests with UI testing frameworks like Selenium quickly become a PITA aka unmaintainable. They are r... |
hparik11/Deep-Learning-Nanodegree-Foundation-Repository | autoencoder/Simple_Autoencoder.ipynb | mit | %matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
"""
Explanation: A Simple Autoencoder
We'll start off by building a simple autoencoder to compres... |
mne-tools/mne-tools.github.io | 0.23/_downloads/c129e399417e02679103f64ce31da37e/mne_inverse_envelope_correlation.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
# Sheraz Khan <sheraz@khansheraz.com>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.connectivity import envelope_correlation
from mn... |
tleonhardt/CodingPlayground | python/networkx/2 using Networkx to Explore Pathfinding Specific Graphs.ipynb | mit | #image below using <img src="files/wgraph5.png">
"""
Explanation: 2 Using Networkx to Explore Pathfinding Specific graphs Drawing and creating the graph in networkX
Setup of simple examples using NetworkX
- take an example and create networkx graph
- steps involved
- running dijkstra and A* on the graph
End of expla... |
owlas/magpy | docs/source/notebooks/.archive/magpy-equilibrium-tests-combine0.ipynb | bsd-3-clause | # dipole interaction energy
def dd(t1, t2, nu):
return -nu*(3*np.cos(t1)*np.cos(t2) - np.cos(t1-t2))
# anisotropy energy
def anis(t1, t2, sigma):
return -sigma*(np.cos(t1)**2 + np.cos(t2)**2)
# total energy
def tot(t1, t2, nu, sigma):
return dd(t1, t2, nu) + anis(t1, t2, sigma)
# numerator of the Boltzma... |
smorton2/think-stats | code/chap10soln.ipynb | gpl-3.0 | from __future__ import print_function, division
%matplotlib inline
import numpy as np
import random
import thinkstats2
import thinkplot
"""
Explanation: Examples and Exercises from Think Stats, 2nd Edition
http://thinkstats2.com
Copyright 2016 Allen B. Downey
MIT License: https://opensource.org/licenses/MIT
End of... |
whitead/numerical_stats | unit_5/hw_2019/Homework_5_key.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
import numpy as np
#set parameter
p = 0.2
x=[] #days
y=[] #probanilities
#init sum for expetation
#for loop over sample space
for i in range(1, 32):
# P(i) * i
x.append(i)
y.append((1 - p)**(i - 1) * p)
print(x,y)
plt.plot(x,y, 'o')
plt.xlabel('Days')
plt.ylabel('Probabil... |
SciTools/courses | course_content/iris_course/2.Loading_and_Saving.ipynb | gpl-3.0 | import iris
"""
Explanation: Iris introduction course
2. Loading and Saving
Learning Outcome: by the end of this section, you will be able to use Iris to load datasets from disk as Iris cubes and save Iris cubes back to disk.
Duration: 30 minutes.
Overview:<br>
2.1 Iris Load Functions<br>
2.2 Saving Cubes<br>
2.3 Exer... |
Eric89GXL/vispy | examples/ipynb/colormaps.ipynb | bsd-3-clause | import numpy as np
from vispy.color import (get_colormap, get_colormaps, Colormap)
from IPython.display import display_html
for cmap in get_colormaps():
display_html('<h3>%s</h3>' % cmap, raw=True)
display_html(get_colormap(cmap))
"""
Explanation: VisPy colormaps
This notebook illustrates the colormap API pro... |
sbenthall/bigbang | examples/experimental_notebooks/MultiGitRepos.ipynb | agpl-3.0 | 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... |
samirma/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... |
CompPhysics/MachineLearning | doc/Programs/ProjectsData/project_solution.ipynb | cc0-1.0 | import numpy as np # we'll be using this shorthand for the NumPy library throughout
dataset = np.load("../data/images/project_data.npy")
n_samples = dataset.shape[0]
print("Data shape: ", dataset.shape)
"""
Explanation: Image classification of simulated AT-TPC events
Welcome to this project in applied machine learni... |
lcharleux/numerical_analysis | doc/Traitement_signal/signal_processing.ipynb | gpl-2.0 |
import numpy as np
import matplotlib.pyplot as plt
def signal(t, f = 1.):
return np.cos(2. * np.pi * f * t)
D = 3.2 # duration
t = np.linspace(0., D, 1000)
x = signal(t)
fs = 10. # sampling rate
tn = np.linspace(0., D, fs * D)
xn = signal(tn)
plt.plot(t, x, "k-", label = "Analog")
plt.plot(tn, xn, "bo--", l... |
rebeccabilbro/viz | bokeh/gapminder.ipynb | mit | import numpy as np
import pandas as pd
from bokeh.embed import file_html
from bokeh.io import output_notebook, show
from bokeh.layouts import layout
from bokeh.models import (
ColumnDataSource, Plot, Circle, Range1d, LinearAxis, HoverTool,
Text, SingleIntervalTicker, Slider, CustomJS)
from bokeh.palettes impo... |
ueapy/ueapy.github.io | content/notebooks/2016-02-05-ipywidgets-interact.ipynb | mit | import warnings
warnings.filterwarnings('ignore')
"""
Explanation: Today's meeting opened the topic of building interactive figures in Python. This notebook will show an example of using ipywidgets module, more specifically interact() function. The full documentation can be found on the ipywidgets website, but beware:... |
DillmannFrench/Intro-PYTHON | Cours03_DILLMANN_ISEP2016.ipynb | gpl-3.0 | print(type(42))
"""
Explanation: 1 Variables
1.1 Qu'est ce qu'une Variable ?
1.1.1 Contenu
Le contenu d'une variable est une valeur. Il s'agit forcément d'une instance d'un objet , stockée en mémoire. Celle-ci est donc reliée à une classe. Le type de l'instance est le nom de la classe.
End of explanation
"""
pi=3.14... |
Aggieyixin/cjc2016 | code/05.PythonCrawler_tianya_threads.ipynb | mit | import urllib2
from bs4 import BeautifulSoup
"""
Explanation: 数据抓取：
使用Python编写网络爬虫
王成军
wangchengjun@nju.edu.cn
计算传播网 http://computational-communication.com
需要解决的问题
页面解析
获取Javascript隐藏源数据
自动翻页
自动登录
连接API接口
End of explanation
"""
from IPython.display import display_html, HTML
HTML('<iframe src=http://bbs.tianya.cn... |
tbarrongh/cosc-learning-labs | src/notebook/03_interface_configuration_update.ipynb | apache-2.0 | help('learning_lab.03_interface_configuration_update')
"""
Explanation: COSC Learning Lab
03_interface_configuration_update.py
Related Scripts:
* 03_interface_configuration.py
* 03_interface_names.py
* 03_interface_properties.py
Table of Contents
Table of Contents
Documentation
Implementation
Execution
HTTP
Document... |
arasdar/DL | uri-dl/uri-dl-hw-2/assignment2/TensorFlow.ipynb | unlicense | import tensorflow as tf
import numpy as np
import math
import timeit
import matplotlib.pyplot as plt
%matplotlib inline
from cs231n.data_utils import load_CIFAR10
def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=10000):
"""
Load the CIFAR-10 dataset from disk and perform preprocessing to... |
AtmaMani/pyChakras | udemy_ml_bootcamp/Python-for-Data-Visualization/Seaborn/Matrix Plots.ipynb | mit | import seaborn as sns
%matplotlib inline
flights = sns.load_dataset('flights')
tips = sns.load_dataset('tips')
tips.head()
flights.head()
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Matrix Plots
Matrix plots allow you to plot data as color-encoded matrices and... |
hypergravity/cham_hates_python | notebook/cham_hates_python_06_astronomical_data.ipynb | mit | %pylab inline
from astropy.io import fits
# read hdu list
hl = fits.open('./data/lamost_dr2_spectra/spec-55892-F9205_sp09-174.fits')
# show fits info
print hl.info()
# read HDU header
hl[0].header
# read HDU data
hl[0].data
# transform data to WVS coordinates
naxis1 = hl[0].header['NAXIS1']
crval1 = hl[0].header['... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/discrete_choice_example.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import statsmodels.api as sm
from scipy import stats
from statsmodels.formula.api import logit
print(sm.datasets.fair.SOURCE)
print(sm.datasets.fair.NOTE)
dta = sm.datasets.fair.load_pandas().data
dta["affair"] = (dta["affair... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_sensors_time_frequency.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.time_frequency import tfr_morlet, psd_multitaper
from mne.datasets import somato
"""
Explanation: Frequency and time-frequency sensors analysis
The objective is to show you how to explore the spectral content
of your data (frequency and time-frequ... |
ffpenaloza/AstroExp | tarea2-1/.ipynb_checkpoints/Tarea 2.1-checkpoint.ipynb | gpl-3.0 | import kplr
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
client = kplr.API()
star1 = client.star(2305930)
curva1 = star1.get_light_curves(short_cadence=False)
time1, flux1, ferr1, quality1 = [], [], [], []
for lc in curva1:
with lc.open() as f:
hdu_data = f[1].data
times... |
muatik/dm | SingularValueDecomposition.ipynb | mit | A = np.mat([
[4, 5, 4, 1, 1],
[5, 3, 5, 0, 0],
[0, 1, 0, 1, 1],
[0, 0, 0, 0, 1],
[1, 0, 0, 4, 5],
[0, 1, 0, 5, 4],
])
U, S, V = np.linalg.svd(A)
U.shape, S.shape, V.shape
"""
Explanation: SVD is one of the matrix factorization tecniques. It factors a matrix into three parts with which we can r... |
EmuKit/emukit | notebooks/Emukit-tutorial-how-to-write-a-notebook.ipynb | apache-2.0 | # General imports and parameters of figures should be loaded at the beginning of the overview
### General imports
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors as mcolors
### --- Figure config
colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)
LEGEND_SIZE =... |
tensorflow/docs-l10n | site/ja/tutorials/generative/cyclegan.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... |
philippgrafendorfe/stackedautoencoders | IONOSPHERE_Autoencoder.ipynb | mit | # data = np.genfromtxt("data/ionosphere.data")
data = pd.read_csv('data/ionosphere.data', sep=",", header=None)
data.head()
data.describe()
"""
Explanation: Data Set Information
This radar data was collected by a system in Goose Bay, Labrador. This system consists of a phased array of 16 high-frequency antennas with... |
jrg365/gpytorch | examples/02_Scalable_Exact_GPs/SGPR_Regression_CUDA.ipynb | mit | import math
import torch
import gpytorch
from matplotlib import pyplot as plt
# Make plots inline
%matplotlib inline
"""
Explanation: Sparse Gaussian Process Regression (SGPR)
Overview
In this notebook, we'll overview how to use SGPR in which the inducing point locations are learned.
End of explanation
"""
import u... |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/cmip6/models/hadgem3-gc31-hm/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'hadgem3-gc31-hm', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: MOHC
Source ID: HADGEM3-GC31-HM
Sub-Topics: Radiative Forcings.
Propert... |
tanghaibao/goatools | notebooks/semantic_similarity.ipynb | bsd-2-clause | %load_ext autoreload
%autoreload 2
from goatools.obo_parser import GODag
godag = GODag("go-basic.obo")
go_id3 = 'GO:0048364'
go_id4 = 'GO:0032501'
print(godag[go_id3])
print(godag[go_id4])
"""
Explanation: Computing basic semantic similarities between GO terms
Adapted from book chapter written by Alex Warwick Vesztr... |
mne-tools/mne-tools.github.io | 0.22/_downloads/d9e2f27df3a137317d331d3be6f3814d/plot_dics_source_power.ipynb | bsd-3-clause | # Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
# Roman Goj <roman.goj@gmail.com>
# Denis Engemann <denis.engemann@gmail.com>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import mne
from mne.datasets import somato
from... |
jdossgollin/CWC_ANN | Week02/01-GettingStartedSequentialModel.ipynb | mit | from keras.models import Sequential # sequential model
from keras.layers import Dense, Activation # layers
import numpy as np
"""
Explanation: Keras Sequential Model
Today we're going to look at Keras, which is a high-level language for building neural networks in TensorFlow (or other languages -- but we'll focus on T... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/migration/UJ4 Vertex SDK AutoML Tabular Binary Classification.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: Vertex AI Migration: AutoML Tabular Binary Classification
<table align="left">
<td... |
4dsolutions/Python5 | DinoDataLab.ipynb | mit | dinos.assign(Decimal = dinos.sha256.apply(lambda x: int(x, base=16)))
"""
Explanation: LAB
Add a column to dinos that contains the decimal equivalent of the sha256 hash. Hint.
End of explanation
"""
dinos.sort_values(by='sha256').head(10)
"""
Explanation: LAB
Sort dinos by the column sha256 -- this will be an alph... |
KirstieJane/BrainsForPublication | notebooks/show_glass_brain.ipynb | mit | %matplotlib inline
"""
Explanation: Visualize Glass Brain
End of explanation
"""
stats_file = '../test_data/ALL_N95_Mean_cope2_thresh_zstat1.nii.gz'
view = 'ortho'
colormap = 'RdBu_r'
threshold = '2.3'
black_bg
"""
Explanation: 1. Upload all statistical maps into the data folder
The data folder can be found in the... |
ethen8181/machine-learning | python/cython/cython.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 th... |
dewitt-li/deep-learning | embeddings/Skip-Gram_word2vec.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... |
guyrt/teaching | 2017/Com597I/visualization/VisualizationNotebook_workedinclass.ipynb | mit | # This section imports some important libraries that we'll need.
# the most important is the second line. After you run this, "plt" will be
# the plotting library in python.
import numpy
import matplotlib.pyplot as plt
%pylab inline
"""
Explanation: Welcome to the Python Plotting Tutorial for Com597I!
This is intend... |
CPernet/LanguageDecision | notebooks/exploratory/2017-06-11-ddm-pilot-data.ipynb | gpl-3.0 | def parse_condition(stim_num):
if stim_num == '1':
return 'SS'
if stim_num == '2':
return 'CP'
if stim_num == '3':
return 'CS'
if stim_num == '4':
return 'US'
import csv
import glob
csv_dir = '../data/pilot_subjects/'
subjects = []
for csv_file in glob.glob(csv_dir +... |
hetaodie/hetaodie.github.io | assets/media/uda-ml/deep/shensd/keras录取学生/StudentAdmissionsKeras-zh.ipynb | mit | # Importing pandas and numpy
import pandas as pd
import numpy as np
# Reading the csv file into a pandas DataFrame
data = pd.read_csv('student_data.csv')
# Printing out the first 10 rows of our data
data[:10]
"""
Explanation: 利用神经网络的 Kera 预测学生录取情况
在该 notebook 中，我们基于以下三条数据预测了加州大学洛杉矶分校的研究生录取情况：
GRE 分数（测试）即 GRE Score... |
nellore/omfgene | omfgene_submit.ipynb | mit | import sevenbridges as sbg
from sevenbridges.errors import SbgError
from sevenbridges.http.error_handlers import *
import re
import datetime
import binpacking
print("SBG library imported.")
print sbg.__version__
"""
Explanation: Scattering and SBG-FS file stream read notebook
This notebook explores setting up tasks fo... |
computational-class/cjc2016 | code/09.01-machine-learning-with-sklearn.ipynb | mit | import seaborn as sns
sns.set_context("talk", font_scale=1.5)
iris = sns.load_dataset('iris')
iris.head()
"""
Explanation: Introducing Scikit-Learn for Machine Learning
<!--BOOK_INFORMATION-->
This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; the content is available on GitH... |
pyreaclib/pyreaclib | pynucastro/library/tabular/use_tabulated_rate.ipynb | bsd-3-clause | import pynucastro as pyrl
"""
Explanation: Tabulated weak nuclear reaction rates
The reaction rate parameterizations in pynucastro/library/tabular were obtained from:
Toshio Suzuki, Hiroshi Toki and Ken'ichi Nomoto (2016):
ELECTRON-CAPTURE AND beta-DECAY RATES FOR sd-SHELL NUCLEI IN STELLAR ENVIRONMENTS RELEVANT TO HI... |
eegkno/mltoolbox.tutorial | 0.1/binary_classification.ipynb | bsd-3-clause | # Load the iris dataset and randomly permute it
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.model_selection import StratifiedKFold
from mltoolbox.model_selection.classification import MultiClassifier
"""
Explanation: Binary Classification
L... |
terrai/rastercube | doc/notebooks/load_ndvi_glcf.ipynb | mit | import pylab as pl
import numpy as np
import matplotlib.cm as cm
import terrapy
import rastercube.jgrid as jgrid
import rastercube.jgrid.utils as jgrid_utils
import rastercube.regions as regions
import rastercube.datasources.glcf as terra_glcf
%matplotlib inline
np.set_printoptions(precision=5, suppress=True)
glcf_he... |
yannikbehr/spectroscopy | examples/datamodel.ipynb | gpl-3.0 | from IPython.display import Image
Image(filename='../docs/_images/datamodel_simple.png')
"""
Explanation: General structure of the datamodel
End of explanation
"""
d = Dataset.open('../tests/data/minidoas_test.h5')
# Get the first PreferredFlux element
pf = d.elements['PreferredFlux'][0]
pf
# Show the values of th... |
fagonzalezo/is-2017-1 | assign3.ipynb | mit | class Directions:
NORTH = 'North'
SOUTH = 'South'
EAST = 'East'
WEST = 'West'
STOP = 'Stop'
"""
Explanation: Intelligent Systems Assignment 3
Bayes' net inference
Names:
IDs:
End of explanation
"""
def P_1(eps, E_N, E_S):
'''
Calculates: P(X=x|E_{N}=e_{N},E_{S}=e_{S})
Arguments: E_N, ... |
XinyiGong/pymks | notebooks/elasticity_2D_Multiphase.ipynb | mit | %matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
n = 21
n_phases = 3
from pymks.tools import draw_microstructures
from pymks.datasets import make_delta_microstructures
X_delta = make_delta_microstructures(n_phases=n_phases, size=(n, n))
"""
Explanation: Line... |
hasadna/knesset-data-pipelines | jupyter-notebooks/running the pipelines - getting fresh committee data.ipynb | mit | import os
os.chdir('..')
os.getcwd()
"""
Explanation: Running pipelines
The pipelines are run on the server periodically and based on pipeline and data dependencies.
You can also run specific pipelines manually for development or to run custom pipelines.
Change directory to project root
The Jupyter notebooks run in ... |
Atomahawk/flagging-suspicious-blockchain-transactions | lab_notebooks/blockchain-preprocessing.ipynb | mit | import errno
import os
import shutil
import zipfile
import numpy as np
import pandas as pd
# In[22]:
# TARGETDIR = '../btc/graphs_njp.zip'
# In[23]:
# with open(doc, "rb") as zipsrc:
# zfile = zipfile.ZipFile(zipsrc)
# for member in zfile.infolist():
# target_path = os.path.join(TARGETDIR, member.f... |
sangheestyle/ml2015project | howto/model05_Au_Aq_RMSE_linear_model_and_DictVectorizer.ipynb | mit | import gzip
import cPickle as pickle
with gzip.open("../data/train.pklz", "rb") as train_file:
train_set = pickle.load(train_file)
with gzip.open("../data/test.pklz", "rb") as test_file:
test_set = pickle.load(test_file)
with gzip.open("../data/questions.pklz", "rb") as questions_file:
questions = pickle... |
calroc/joypy | docs/Hylo-, Ana-, Cata-, and Para-morphisms - Recursion Combinators.ipynb | gpl-3.0 | def hylomorphism(c, F, P, G):
'''Return a hylomorphism function H.'''
def H(a):
if P(a):
result = c
else:
b, aa = G(a)
result = F(b, H(aa))
return result
return H
"""
Explanation: Cf. "Bananas, Lenses, & Barbed Wire"
Hylomorphism
A hylomorphism ... |
DLTK/DLTK | examples/tutorials/03_building_a_model_fn.ipynb | apache-2.0 | import SimpleITK as sitk
import tensorflow as tf
import os
from dltk.io.augmentation import *
from dltk.io.preprocessing import *
tf.logging.set_verbosity(tf.logging.ERROR)
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
# Generate a simple toy dataset
im_width = 256
im_height = 256
num_imgs = 200
def create_test_image(w... |
rgarcia-herrera/sistemas-dinamicos | Glass_Pasternack.ipynb | gpl-3.0 | def g(x, alpha, beta):
assert alpha >= 0 and beta >= 0
return (alpha*x)/(1 + (beta * x))
def plot_cobg(x, alpha, beta):
y = np.linspace(x[0],x[1],300)
g_y = g(y, alpha, beta)
cobweb(lambda x: g(x, alpha, beta), y, g_y)
# configura gráfica interactiva
interact(plot_cobg,
x=widgets.FloatR... |
hetland/python4geosciences | materials/ST_shapefiles.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import cartopy
import cartopy.io.shapereader as shpreader
import shapely.geometry
"""
Explanation: Shapefiles on maps
End of explanation
"""
# how we tell cartopy which data we want, from the list at the end of the maps notebook
shapename = 'adm... |
4dsolutions/Python5 | Clock Arithmetic.ipynb | mit | (5 + 5) % 10 # no remainder
divmod(10, 5) # 5 goes into 10 twice, no remainder
19 % 10 # divide by 10, give the remainder
"""
Explanation: Oregon Curriculum Network <br />
Discovering Math with Python
Chapter 4: CLOCK ARITHMETIC
Calling modulo arithmetic "clock arithmetic" is a way of calling attention to the finite... |
wiheto/sentiment-timelines | notebooks/league_1_watford.ipynb | mit | import praw
import datetime
import pandas as pd
import nltk.sentiment.vader
import matplotlib.pyplot as plt
# Import all relevant packages
from bs4 import BeautifulSoup
from selenium import webdriver
import numpy as np
import os
"""
Explanation: Sentiment analysis of match thread
To run this yourself. Two things have ... |
himkt/notebooks | Hidden Markov Models.ipynb | mit | A = zeros((3, 3))
A[0, 0] = 0.4
A[0, 1] = 0.3
A[0, 2] = 0.3
A[1, 0] = 0.2
A[1, 1] = 0.6
A[1, 2] = 0.2
A[2, 0] = 0.1
A[2, 1] = 0.1
A[2, 2] = 0.8
# this is not probability (observed)
pi = [1, 1, 1]
print(A)
"""
Explanation: A tutorial on hidden Markov models and selected applications in speech recognition.
Rabiner, ... |
bassio/omicexperiment | omicexperiment/docs/03_experiment_transforms.ipynb | bsd-3-clause | %load_ext autoreload
%autoreload 2
#Load our data
from omicexperiment.experiment.microbiome import MicrobiomeExperiment
mapping = "example_map.tsv"
biom = "example_fungal.biom"
tax = "blast_tax_assignments.txt"
exp = MicrobiomeExperiment(biom, mapping,tax)
exp.data_df
exp.mapping_df
"""
Explanation: Experiment tr... |
avicennax/jedi | notebooks/DFORCE.ipynb | mit | import pylab as plt
import numpy as np
%matplotlib inline
from __future__ import division
from scipy.integrate import odeint,ode
from numpy import zeros,ones,eye,tanh,dot,outer,sqrt,linspace,cos,pi,hstack,zeros_like,abs,repeat
from numpy.random import uniform,normal,choice
%config InlineBackend.figure_format = 'retina'... |
csaladenes/csaladenes.github.io | test/eis-metadata-validation/Planon metadata validation4-Copy2.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: EIS metadata validation script
Used to validate Planon output with spreadsheet input
1. Data import
End of explanation
"""
planon=pd.read_excel('EIS Assets.xlsx',index_col = 'Code')
master_loggerscontrollers = ... |
mit-eicu/eicu-code | notebooks/infusiondrug.ipynb | mit | # Import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import psycopg2
import getpass
import pdvega
# for configuring connection
from configobj import ConfigObj
import os
%matplotlib inline
# Create a database connection using settings from config file
config='../db/config.ini'
#... |
HazyResearch/snorkel | tutorials/snark/Snark Tutorial.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
import os
# Remember to specify the SNORKELDB environment variable (with an absolute path if using the file system)
os.environ['SNORKELDB'] = 'sqlite:///' + os.getcwd() + os.sep + '../cdr/snorkel.db'
from snorkel import SnorkelSession
snorkel_session = SnorkelSession()
"""
Explan... |
aleph314/K2 | Foundations/Math/2-derivatives_and_integrals.ipynb | gpl-3.0 | import numpy as np
import sympy
import matplotlib.pyplot as plt
%matplotlib inline
def gradient_descent(func, var, initial_value, lamb = 1e-3, eps = 1e-6):
'''
Calculates gradient descent for the function func in the variable var starting from initial_value
with a step of lamb. Stops if the difference in t... |
PYPIT/COS_REDUX | docs/nb/Redux_Example.ipynb | bsd-2-clause | # imports
import os
import glob
import pdb
#from imp import reload
#from importlib import reload
from astropy.io import fits
from cosredux import utils as cr_utils
from cosredux import trace as cr_trace
from cosredux import darks as cr_darks
from cosredux import io as cr_io
from cosredux import science as cr_science
... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_artifacts_correction_ica.ipynb | bsd-3-clause | import numpy as np
import mne
from mne.datasets import sample
from mne.preprocessing import ICA
from mne.preprocessing import create_eog_epochs, create_ecg_epochs
# getting some data ready
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
raw = mne.io.read_raw_fif(... |
4dsolutions/Python5 | Dot Product.ipynb | mit | import numpy as np
one_dimensional = np.array([1,1,1,2,3,3,3,3,3])
one_dimensional
one_dimensional.shape # not yet rows & columns
one_dimensional.reshape((9,-1)) # let numpy figure out how many columns
one_dimensional # still the same
one_dimensional.ndim
two_dimensional = one_dimensional.reshape(1,9) # recyc... |
cosmicBboy/cosmicBboy.github.io | notebooks/quest-for-an-offensiveness-detector-part-1.ipynb | gpl-2.0 | import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import re
from os import path
from scipy.ndimage import imread
from nltk.util import ngrams
from collections import Counter
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
from IPython.display import display, HTML
... |
psychemedia/ou-robotics-vrep | robotVM/notebooks/Demo - Square 2 - Variables.ipynb | apache-2.0 | # Create the message variable and assign the value "Hello World" to it
message="Hello World"
# Use the variable in a print statement
# The print statement retrieves the value assigned to the variable and displays the value
print(message)
"""
Explanation: Traverse a Square - Part 2 - Variables
In this notebook, we wil... |
jsrois/uoc-summer16 | experiments/1.visualize-rf.ipynb | mit | # set up Python environment: numpy for numerical routines, and matplotlib for plotting
import numpy as np
import matplotlib.pyplot as plt
# display plots in this notebook
%matplotlib inline
# set display defaults
plt.rcParams['figure.figsize'] = (5,5) # large images
plt.rcParams['image.interpolation'] = 'neares... |
walkon302/CDIPS_Recommender | notebooks/07282017_todo_CDIPS.ipynb | apache-2.0 | import pandas as pd
import numpy as np
import os
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
os.chdir('/Users/Walkon302/Desktop/deep-learning-models-master/view2buy')
# Read the preprocessed file, containing the user profile and item features from view2buy folder
df = pd.read_pickle('user_... |
tensorflow/docs-l10n | site/en-snapshot/lite/models/modify/model_maker/audio_classification.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... |
eneskemalergin/Data_Structures_and_Algorithms | Chapter5/5-Searching_and_Sorting.ipynb | gpl-3.0 | theArray = range(0,100)
key = 101
if key in theArray:
print("The key is in the array.")
else:
print("The key is not in the array.")
"""
Explanation: Searching and Sorting
Searching
Process of selecting information from a collection based on specific criteria.
Linear Search
Simplest approach to searching probl... |
hethapu/big-data-python-class | Lectures/Lecture2-Jupyter_and_python/Lecture-2-Introduction-to-Python-Programming 2017.ipynb | mit | ls ..\..\Scripts\hello-world*.py
"""
Explanation: Introduction to Python programming
This crash course on python is take from two souces:
http://github.com/jrjohansson/scientific-python-lectures.
and
Chapter 2 of the Datascience from scratch: First principles with python
Python program files
Python code is usually s... |
Kelvinrr/test_notebook | Black Box Tutorial.ipynb | unlicense | import os
import sys
import argparse
import yaml
import networkx as nx
sys.path.insert(0, os.path.abspath('..'))
from autocnet.examples import get_path
from autocnet.graph.network import CandidateGraph
from autocnet.fileio.io_controlnetwork import to_isis, write_filelist
from IPython.display import display
%pylab inl... |
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