repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
a-pagano/BigDive5 | DataScience/Essai_PySpark.ipynb | mit | wre = re.compile('\w')
print wre.sub('', 'This. Is, 22 A Text{}')
notwre = re.compile('[^\w]')
print notwre.sub('', 'This. Is, 22 A Text{}')
notwre = re.compile('([^\w\s]|\d+)')
print notwre.sub('', 'This. Is, 22 A Text{}').lower()
text.map(lambda line: line.replace('--', ' ').lower()) \
.flatMap(lambda line: line... |
farfan92/SpringBoard- | data_wrangling_xml/chrisfarfan_xml_exercise.ipynb | mit | document = ET.parse( './data/mondial_database.xml' )
import pandas as pd
root = document.getroot()
"""
Explanation: XML exercise
Using data in 'data/mondial_database.xml', the examples above, and refering to https://docs.python.org/2.7/library/xml.etree.elementtree.html, find
10 countries with the lowest infant mor... |
tensorflow/docs-l10n | site/zh-cn/agents/tutorials/9_c51_tutorial.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... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch4-Problem_4-09.ipynb | unlicense | %pylab notebook
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 4
Problem 4-9
End of explanation
"""
n_nl1 = 3650.0 # [r/min]
n_fl1 = 3570.0 # [r/min]
p_1 = 2
n_nl2 = 1800.0 # [r/min]
n_fl2 = 1780.0 # [r/min]
p_2 = 4
S_1 = 250e3 # [VA]
S_2 = 250e3 # [VA]
PF_1 = 0.8
PF_2 = 0.85
Pload = 300e3 # [W]... |
bryanwweber/thermostate | docs/diesel-cycle-example.ipynb | bsd-3-clause | from thermostate import State, Q_, units
from thermostate.plotting import IdealGas
%matplotlib inline
import matplotlib.pyplot as plt
from numpy import arange
"""
Explanation: Diesel Cycle Example
Imports
End of explanation
"""
substance = 'air'
T_1 = Q_(25.0, 'degC')
p_1 = Q_(95.0, 'kPa')
V_1 = Q_(3.0, 'L')
r = Q_(... |
makism/dyfunconn | tutorials/EEG - 2 - Static Connectivity.ipynb | bsd-3-clause | import numpy as np
import scipy
from scipy import io
eeg = np.load("data/eeg_eyes_opened.npy")
num_trials, num_channels, num_samples = np.shape(eeg)
eeg_ts = np.squeeze(eeg[0, :, :])
"""
Explanation: In this short tutorial, we will compute the static connectivity of the EEG singals.
Load data
End of explanation
"... |
zrhans/python | topicos/calibracao-temperatura.ipynb | gpl-2.0 | %matplotlib notebook
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
import pandas as pd
df=pd.read_table('./data/temperatura.txt',sep='\s',header=0, engine='python')
df.head()
fig, ax1 = plt.subplots()
ax1.plot(df.Xi, 'b')
ax1.plot(df.Y1, 'y')
#ax1.set_xlabel('time (s)')
# Make the y-axis... |
hbwzhsh/pyDataScienceToolkits_Base | NumPy/.ipynb_checkpoints/(4)common_functions2——stock_analysis-checkpoint.ipynb | mit | import numpy as np
"""
Explanation: 内容索引
计算股票收益率----
diff做差分、std求标准差、where函数返回满足条件的数组索引
日期分析例子----
载入文件对日期的处理、where和take函数、argmax和argmin函数
周汇总例子----
apply_along_axis函数会调用一个自定义函数，作用于每一个数组元素上
线性模型预测价格----
linalg.lstsq函数
绘制趋势线例子----
ndarray补充----
clip函数，修剪；
compress函数，压缩；
prod函数，相乘；
cumprod函数，累积乘积。
上一小节，介绍了... |
GuillaumeDec/machine-learning | deep-lstm-rnn-anomaly-detector/deep-lstm-time-series-ndim-OI.ipynb | gpl-3.0 | from __future__ import print_function
import mxnet as mx
from mxnet import nd, autograd
import numpy as np
from collections import defaultdict
mx.random.seed(1)
# ctx = mx.gpu(0)
ctx = mx.cpu(0)
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import scipy.... |
estepona/PERCEIVE-freddie | TopicFlow_topicSimilarityTest_Zhang_08-31.ipynb | mit | # read a test version (w/o JS codes) of TopicSimilarity.json
file = 'testSim.json'
with open(file) as train_file:
dict_train = json.load(train_file)
dict_train
len(dict_train['links']), len(dict_train['nodes'])
links = pd.DataFrame(dict_train['links'])
nodes = pd.DataFrame(dict_train['nodes'])
links.head(2)
li... |
boffi/boffi.github.io | dati_2016/04/Constant_Acceleration.ipynb | mit | m = 1.00
k = 4*pi*pi
wn = 2*pi
T = 1.0
z = 0.02
wd = wn*sqrt(1-z*z)
c = 2*z*wn*m
"""
Explanation: Constant Acceleration
Define the Dynamical System
End of explanation
"""
NSTEPS = 200 # steps per second
h = 1.0 / NSTEPS
def load(t):
return np.where(t<0, 0, np.where(t<5, sin(0.5*wn*t)**2, 0))
t = np.linspace(-1,... |
ToqueWillot/M2DAC | FDMS/TME3/Submit_V0.ipynb | gpl-2.0 | # from __future__ import exam_success
from __future__ import absolute_import
from __future__ import print_function
%matplotlib inline
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import random
import pandas as pd
import scipy.stats as stats
# Sk cheats
from sklearn.cross_val... |
gfeiden/Notebook | Projects/pleiades_colors/isochrone_interp.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Interpolate Isochrones for Praesepe
Instead of interpolating boundary condition tables and computing a new set of models, we can interpolate between two metallicities to calculate new isochrones for Praesepe at metallicities between... |
guyk1971/deep-learning | batch-norm/Batch_Normalization_Exercises_MySol.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 – Practice
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a con... |
rhyswhitley/fire_limitation | notebooks/data_viz.ipynb | bsd-3-clause | # data munging and analytical libraries
import re
import os
import numpy as np
import pandas as pd
# graphical libraries
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
# default plot settings for notebook
from IPython.core.pylabtools import figsize
figsize(11, 9)
plt.style.use('ggplot')
%matplotli... |
sysid/nbs | LP/Introduction-to-linear-programming/Introduction to Linear Programming with Python - Part 4.ipynb | mit | import pulp
# Instantiate our problem class
model = pulp.LpProblem("Cost minimising blending problem", pulp.LpMinimize)
"""
Explanation: Introduction to Linear Programming with Python - Part 4
Real world examples - Blending Problem
We're going to make some sausages!
We have the following ingredients available to us:
... |
trsherborne/learn-python | lesson7.ipynb | mit | # Run this if you are using a Mac machine or have multiple versions of Python installed
!pip3 install numpy==1.11.1 pandas==0.19.1 matplotlib==1.5.3 seaborn==0.7.1 --upgrade
# Run this if you are using a Windows machine
!pip install numpy==1.11.1 pandas==0.19.0 matplotlib==1.5.3 seaborn==0.7.1 --upgrade
# Everyone ru... |
Serulab/Py4Bio | notebooks/Chapter 18 - Calculating Melting Temperature from a Set of Primers.ipynb | mit | !pip install biopython
!curl https://raw.githubusercontent.com/Serulab/Py4Bio/master/samples/samples.tar.bz2 -o samples.tar.bz2
!mkdir samples
!tar xvfj samples.tar.bz2 -C samples
"""
Explanation: Python for Bioinformatics
This Jupyter notebook is intented to be used alongside the book Python for Bioinformatics
Chap... |
isb-cgc/examples-Python | notebooks/TCGA Annotations.ipynb | apache-2.0 | import gcp.bigquery as bq
annotations_BQtable = bq.Table('isb-cgc:tcga_201607_beta.Annotations')
"""
Explanation: TCGA Annotations
The goal of this notebook is to introduce you to the TCGA Annotations BigQuery table. You can find more detail about Annotations on the TCGA Wiki, but the key things to know are:
* an ann... |
StudyExchange/Udacity | MachineLearning(Advanced)/p3_creating_customer_segments/.ipynb_checkpoints/customer_segments-checkpoint.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, ... |
dcavar/python-tutorial-for-ipython | notebooks/Linear Algebra.ipynb | apache-2.0 | x = (1, 2, 3, 4)
y = (5, 6, 7, 8)
n = len(x)
if n == len(y):
result = 0
for i in range(n):
result += x[i] * y[i]
print(result)
"""
Explanation: Linear Algebra
(C) 2018-2019 by Damir Cavar
Version: 1.1, September 2019
Download: This and various other Jupyter notebooks are available from my GitHub re... |
FiryZeplin/deep-learning | sentiment-rnn/Sentiment_RNN_Solution.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural... |
darcamo/pyphysim | apps/ia/IA Results 3x3(2).ipynb | gpl-2.0 | %pylab inline
"""
Explanation: Simulation Results for varying number of maximum iterations
This notebook shows BER and Sum Capacity results for different IA
algorithms when the maximum number of allowed iterations is limited. Note
that the algorithm might run less iterations than the allowed maximum if
the precoders ... |
DJCordhose/ai | notebooks/workshops/d2d/cnn-train-augmented.ipynb | mit | import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
import matplotlib.pylab as plt
import numpy as np
from distutils.version import StrictVersion
import sklearn
print(sklearn.__version__)
assert StrictVersion(sklearn.__version__ ) >= StrictVersion('0.18.1')
import tensorflow as tf
t... |
jasonfeiwang/data-512-a1 | hcds-a1-data-curation.ipynb | mit | # import all python libraries needed in this analysis
import requests
import json
import pandas as pd
from pandas.io.json import json_normalize
import matplotlib.pyplot as plt
from datetime import datetime
# provide my credential for API access
headers={'User-Agent' : 'https://github.com/jasonfeiwang', 'From' : 'fwang... |
mne-tools/mne-tools.github.io | 0.17/_downloads/a271bc382505fca1eb3f2c32f85b865f/spm_faces_dataset.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
# sphinx_gallery_thumbnail_number = 10
import matplotlib.pyplot as plt
import mne
from mne.datasets import spm_face
from mne.preprocessing import ICA, create_eog_ep... |
PythonFreeCourse/Notebooks | week02/7_Summary.ipynb | mit | # כתבו את הפונקציה שלכם כאן
"""
Explanation: <img src="images/logo.jpg" style="display: block; margin-left: auto; margin-right: auto;" alt="לוגו של מיזם לימוד הפייתון. נחש מצויר בצבעי צהוב וכחול, הנע בין האותיות של שם הקורס: לומדים פייתון. הסלוגן המופיע מעל לשם הקורס הוא מיזם חינמי ללימוד תכנות בעברית.">
<p style="tex... |
Merinorus/adaisawesome | Homework/03 - Interactive Viz/.ipynb_checkpoints/HW3_Interactive_Viz-checkpoint.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
# We will read json files, for instance API keys stored in our computers for using Google Maps API, so they're not publicly visible
import json
# Geolocation
import geopy
from geopy.geocoders import geonames
import math
import logging
p3_grant_export_data = pd.read_csv("P3_GrantE... |
heprom/pymicro | examples/SampleDataUserGuide/SampleData_Quick_Reference_Sheet.ipynb | mit | from pymicro.core.samples import SampleData as SD
"""
Explanation: SampleData Quick Reference Sheet
Notation
In the code lines constituting this reference sheet, the following notation conventions are used in the SampleData method arguments:
node_name, group_name, data_item_name are the Name, Indexname, Path or Alias... |
computational-class/cjc2016 | code/05.PythonCrawler_tianya_threads.ipynb | mit | import requests
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.... |
tensorflow/docs | site/en/guide/tf_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... |
marcinofulus/teaching | ML_SS2017/numpy_rachunek_rozniczkowy.ipynb | gpl-3.0 | import numpy as np
x = np.linspace(0, 10, 10)
f = np.sin(x)
f1 = np.cos(x)
df = f[1:] - f[:-1]
dx = x[1:] - x[:-1]
x2 = (x[1:] + x[:-1])/2
fp = df/dx
fp.shape, x.shape
%matplotlib inline
import matplotlib.pyplot as plt
plt.plot(x2, np.cos(x2), 'o-')
plt.plot(x2, fp, 'ro-')
"""
Explanation: Pochodna (różniczkow... |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/.ipynb_checkpoints/Statements Assessment Test-checkpoint.ipynb | apache-2.0 | st = 'Print only the words that start with s in this sentence'
#Code here
"""
Explanation: Statements Assessment Test
Lets test your knowledge!
Use for, split(), and if to create a Statement that will print out words that start with 's':
End of explanation
"""
#Code Here
"""
Explanation: Use range() to print all ... |
PBrockmann/ipython_ferretmagic | notebooks/ferretmagic_01_Discovering.ipynb | mit | %load_ext ferretmagic
"""
Explanation: <hr>
Patrick BROCKMANN - LSCE (Climate and Environment Sciences Laboratory)<br>
<img align="left" width="40%" src="http://www.lsce.ipsl.fr/Css/img/banniere_LSCE_75.png" ><br><br>
<hr>
Updated: 2019/11/13
Install the ferret extension
ferretmagic extension is now a package you can... |
mne-tools/mne-tools.github.io | 0.23/_downloads/8ea2bfc401dbdff70c284d271d62fa8c/label_from_stc.ipynb | bsd-3-clause | # Author: Luke Bloy <luke.bloy@gmail.com>
# Alex Gramfort <alexandre.gramfort@inria.fr>
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.minimum_norm import read_inverse_operator, apply_inverse
from mne.datasets import sample
print(__doc__)
data_path = sample.... |
sthuggins/phys202-2015-work | assignments/assignment03/NumpyEx01.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import antipackage
import github.ellisonbg.misc.vizarray as va
"""
Explanation: Numpy Exercise 1
Imports
End of explanation
"""
import numpy as np
def checkerboard(size):
"""Return a 2d checkboard of 0.0 and 1.0 as a Nu... |
brandoncgay/deep-learning | 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... |
mikekestemont/ruzicka | code/Quickstart.ipynb | mit | ls ../data/2014/du_essays/train
"""
Explanation: Ružička: Authorship Verification in Python
In this notebook, we offer a quick tutorial as to how you could use the code in this repository. While the package is very much geared towards our own work in authorship verification, you might some of the more general function... |
niazangels/CADL | session-5/lecture-5.ipynb | apache-2.0 | # First check the Python version
import sys
if sys.version_info < (3,4):
print('You are running an older version of Python!\n\n',
'You should consider updating to Python 3.4.0 or',
'higher as the libraries built for this course',
'have only been tested in Python 3.4 and higher.\n')
... |
DickJC123/mxnet | example/multi-task/multi-task-learning.ipynb | apache-2.0 | import logging
import random
import time
import matplotlib.pyplot as plt
import mxnet as mx
from mxnet import gluon, np, npx, autograd
import numpy as onp
"""
Explanation: Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work... |
ES-DOC/esdoc-jupyterhub | notebooks/inm/cmip6/models/sandbox-1/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inm', 'sandbox-1', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: INM
Source ID: SANDBOX-1
Topic: Ocean
Sub-Topics: Timestepping Framework, Advection, ... |
CalebVuorinen/summerstudentProtoWork | functional/Example_01.ipynb | mit | import ROOT
from PyTreeReader import PyTreeReader
from functional import DataFrame
from ROOT import TFile
"""
Explanation: DataFrame: Functional Chains for TTrees in Python.
<hr style="border-top-width: 4px; border-top-color: #359C38;">
The DataFrame class brings the feature called functional chains with caching to tr... |
rflamary/POT | notebooks/plot_otda_color_images.ipynb | mit | # Authors: Remi Flamary <remi.flamary@unice.fr>
# Stanislas Chambon <stan.chambon@gmail.com>
#
# License: MIT License
import numpy as np
from scipy import ndimage
import matplotlib.pylab as pl
import ot
r = np.random.RandomState(42)
def im2mat(I):
"""Converts an image to matrix (one pixel per line)"""... |
clustbench/network-tests2 | clustering/by_topology/lom2-agnostic-code.ipynb | lgpl-2.1 | nodes = pd.read_pickle("cachenodes.pkl")
edges = pd.read_pickle("edges.pkl")
comp_nodes = pd.read_pickle("comp_nodes.pkl")
"""
Explanation: Load data
End of explanation
"""
def build_topology(nodes, edges):
topology = nx.Graph()
# add all nodes
for index, row in nodes.iterrows():
node_name =... |
LukasDrude/jupyter_search | index.ipynb | mit | import nbformat
"""
Explanation: Dependencies:
- whoosh
- yattag
- hurry.filesize
End of explanation
"""
from whoosh.index import create_in
from whoosh.fields import *
from whoosh.qparser import QueryParser
"""
Explanation: nbformat doc: http://nbformat.readthedocs.org/en/latest/api.html
End of explanation
"""
... |
ES-DOC/esdoc-jupyterhub | notebooks/niwa/cmip6/models/sandbox-3/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'niwa', 'sandbox-3', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: NIWA
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: Transport, Emissions, Conce... |
getsmarter/bda | module_5/M5_NB2_BandicootMachineLearning.ipynb | mit | # Load libraries and set options.
import matplotlib.pyplot as plt
import bandicoot as bc
from tqdm import tqdm_notebook as tqdm # Interactive progress bar.
import glob
import os
import pandas as pd
import numpy as np
from scipy.stats import norm
%matplotlib inline
plt.rcParams['figure.figsize'] = (15, 9)
plt.rcParams... |
betatim/BlackBox | examples/interruptible-optimization.ipynb | bsd-3-clause | import numpy as np
np.random.seed(777)
"""
Explanation: Interruptible optimization runs with checkpoints
Christian Schell, Mai 2018
End of explanation
"""
from skopt import gp_minimize
from skopt import callbacks
from skopt.callbacks import CheckpointSaver
noise_level = 0.1
def obj_fun(x, noise_level=noise_level):... |
KTH-dESA/PyOnSSET-jupyter | pyonsset.ipynb | mit | country = 'Malawi'
#Dependencies
from IPython.display import display, Markdown, HTML
import seaborn as sns
import matplotlib.pylab as plt
import folium
import branca.colormap as cm
import json
%matplotlib inline
%run onsset.py
"""
Explanation: Welcome to OnSSET Jupyter Interface¶
This page will guide you through the ... |
nickcdryan/hep_ml | notebooks/DemoReweighting.ipynb | apache-2.0 | %pylab inline
figsize(16, 8)
import root_numpy
import pandas
from hep_ml import reweight
"""
Explanation: Demonstration of distribution reweighting
hep_ml.reweight contains methods to reweight distributions.
Typically we use reweighting of monte-carlo to fight drawbacks of simulation, though there are many applicati... |
mari-linhares/tensorflow-workshop | code_samples/RNN/sentiment_analysis/SentimentAnalysis-Word2Vec.ipynb | apache-2.0 | # Tensorflow
import tensorflow as tf
print('Tested with TensorFlow 1.2.0')
print('Your TensorFlow version:', tf.__version__)
# Feeding function for enqueue data
from tensorflow.python.estimator.inputs.queues import feeding_functions as ff
# Rnn common functions
from tensorflow.contrib.learn.python.learn.estimators i... |
pombredanne/https-gitlab.lrde.epita.fr-vcsn-vcsn | doc/notebooks/automaton.standard.ipynb | gpl-3.0 | import vcsn
b = vcsn.context('lal_char, z')
a = b.expression('a').standard() | b.expression('b').standard()
a
a = a.standard()
a
"""
Explanation: automaton.standard
Turn an automaton into a standard automaton.
Preconditions:
- None
Postconditions:
- result.is_standard()
See also:
- automaton.is_standard
- expression.... |
mathinmse/mathinmse.github.io | Lecture-19-Spectral-Solutions.ipynb | mit | import sympy as sp
x, y, z, t = sp.symbols('x y z t')
k, m, n = sp.symbols('k m n', integer=True)
f, g, h = sp.symbols('f g h', cls=sp.Function)
sp.var('a_k, phi, c', cls=sp.Function);
sp.var('L', real=True);
sp.init_printing();
"""
Explanation: Lecture 19: Numerical Solutions to the Diffusion Equation (Spectral Met... |
adrn/tutorials | notebooks/User-Defined-Model/User-Defined-Model.ipynb | cc0-1.0 | import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
from astropy.modeling import models, fitting
from astropy.modeling.models import custom_model
from astropy.modeling import Fittable1DModel, Parameter
from astroquery.sdss import SDSS
"""
Explanation: Modeling 2: Create a User Defined Model ... |
agushman/coursera | src/cours_2/week_5/task_nn.ipynb | mit | # Выполним инициализацию основных используемых модулей
%matplotlib inline
import random
import matplotlib.pyplot as plt
from sklearn.preprocessing import normalize
import numpy as np
"""
Explanation: Нейронные сети: зависимость ошибки и обучающей способности от числа нейронов
В этом задании вы будете настраивать двус... |
darkomen/TFG | medidas/01072015/Analisis.ipynb | cc0-1.0 | %pylab inline
#Importamos las librerías utilizadas
import numpy as np
import pandas as pd
import seaborn as sns
#Mostramos las versiones usadas de cada librerías
print ("Numpy v{}".format(np.__version__))
print ("Pandas v{}".format(pd.__version__))
print ("Seaborn v{}".format(sns.__version__))
#Abrimos el fichero csv... |
Saumya-Suvarna/machine-learning | Fuel_prediction/Fuel prediction and co2 emission.ipynb | apache-2.0 | #importing the necessacary packages in python
import matplotlib.pyplot as plt
import pandas as pd
import pylab as pl
import numpy as np
import statsmodels.formula.api as smf
%matplotlib inline
elec = pd.read_csv("fuel consumption dataset\MY2012-2017 Battery Electric Vehicles.csv")
elec.head() #display the column names... |
jmschrei/pomegranate | examples/hmm_example.ipynb | mit | import random
from pomegranate import *
random.seed(0)
"""
Explanation: Hidden Markov Model Example
authors:<br>
Jacob Schreiber [<a href="mailto:jmschreiber91@gmail.com">jmschreiber91@gmail.com</a>]<br>
Nicholas Farn [<a href="mailto:nicholasfarn@gmail.com">nicholasfarn@gmail.com</a>]
A simple example highlighting h... |
dnstanciu/masters-project | sources/notebooks/classification.ipynb | gpl-3.0 | import numpy as np
import random
random.seed(20)
import matplotlib
# Set backend to pgf
matplotlib.use('pgf')
import matplotlib.pyplot as plt
# Some nice default configuration for plots
plt.rcParams['figure.figsize'] = 10, 7.5
plt.rcParams['axes.grid'] = True
#plt.gray()
%matplotlib inline
from scipy.io import lo... |
mayank-johri/LearnSeleniumUsingPython | Section 3 - Machine Learning/ThirdParty-scikit-learn-videos-master/04_model_training.ipynb | gpl-3.0 | from IPython.display import IFrame
IFrame('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', width=300, height=200)
"""
Explanation: Training a machine learning model with scikit-learn
From the video series: Introduction to machine learning with scikit-learn
Agenda
What is the K-nearest neighb... |
angelmtenor/data-science-keras | text_generator.ipynb | mit | %matplotlib inline
import os
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import keras
import helper
helper.reproducible(seed=9)
sns.set()
"""
Explanation: English sequence generator
Creating an English language sequence generator capable of building semi-coherent English sentences from s... |
km-Poonacha/python4phd | Session 1/ipython/Excercise - Working With CSV-Worksheet.ipynb | gpl-3.0 | from google.colab import drive
from google.colab import files
drive.mount('/content/drive/')
uploaded = files.upload()
"""
Explanation: Excercise - Working With CSV
Using the CSV module
A CSV file is often used exchange format for spreadsheets and databases.
Each line is called a record and each field within a record... |
vvishwa/deep-learning | gan_mnist/Intro_to_GANs_Solution.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... |
omaclaren/open-learning-material | inverse-problems/2021/code/L02-equation-solving.ipynb | mit | import numpy as np
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('png', 'pdf')
"""
Explanation: Equation solving using pseudo-inverses
Author: OJM.
We saw that inverses are unstable even when exist
Before we deal with stability, let's deal with existence and uniqueness
Same stabilit... |
YaleDHLab/lab-workshops | generative-art/minimal-autoencoder.ipynb | mit | # load special jupyter notebook helpers
%matplotlib inline
# import libraries we'll use below
import matplotlib.pyplot as plt
from scipy.misc import imread
from glob import glob
import numpy as np
import requests
import zipfile
import warnings
import os
warnings.filterwarnings('ignore')
"""
Explanation: Creating Gen... |
brian-rose/ClimateModeling_courseware | Lectures/Lecture06 -- Radiation.ipynb | mit | # Ensure compatibility with Python 2 and 3
from __future__ import print_function, division
"""
Explanation: ATM 623: Climate Modeling
Brian E. J. Rose, University at Albany
Lecture 6: A Brief Review of Radiation
Warning: content out of date and not maintained
You really should be looking at The Climate Laboratory boo... |
parambharat/ML-Programs | P2:_student_intervention/P2 Building a Student Intervention System.ipynb | mit | # Import libraries
import numpy as np
import pandas as pd
from IPython.display import display, HTML
from collections import defaultdict
# Read student data
student_data = pd.read_csv("student-data.csv")
print "Student data read successfully!"
# Note: The last column 'passed' is the target/label, all other are feature... |
unmrds/cc-python | Loops and Functions.ipynb | apache-2.0 | # A list is a mutable, ordered collection
# Collections can be heterogenous: integers, strings, other data structures, etc.
# can all be added to a single list.
# Creating lists:
# Initialize an empty list:
my_movies = []
print(my_movies, type(my_movies))
# Initialize a list with one or more objects in it
unwatch... |
tuanavu/coursera-university-of-washington | machine_learning/4_clustering_and_retrieval/assigment/week2/1_nearest-neighbors-lsh-implementation_blank.ipynb | mit | import numpy as np
import graphlab
from scipy.sparse import csr_matrix
from scipy.sparse.linalg import norm
from sklearn.metrics.pairwise import pairwise_distances
import time
from copy import copy
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Locality Sensitive Hashing
Locality Sensitive Hashing... |
tensorflow/docs-l10n | site/en-snapshot/tensorboard/scalars_and_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... |
jasag/Phytoliths-recognition-system | code/notebooks/Prototypes/Classifiers_and_HoG/1_HoG.ipynb | bsd-3-clause | from sklearn.datasets import fetch_lfw_people
faces = fetch_lfw_people()
positive_patches = faces.images
positive_patches.shape
"""
Explanation: Características de HoG
En este notebook creará un conjunto de imagenes con caras y no caras mediante las que obtendremos las características de HoG que nos servirán como conj... |
sgratzl/ipython-tutorial-VA2015 | 03_Plotting.ipynb | cc0-1.0 | #disable some annoying warning
import warnings
warnings.filterwarnings('ignore', category=FutureWarning)
#plots the figures in place instead of a new window
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
#use a standard dataset of heterogenous data
ca... |
dtamayo/reboundx | ipython_examples/Migration.ipynb | gpl-3.0 | import rebound
import reboundx
import numpy as np
sim = rebound.Simulation()
ainner = 1.
aouter = 10.
e0 = 0.1
inc0 = 0.1
sim.add(m=1.)
sim.add(m=1e-6,a=ainner,e=e0, inc=inc0)
sim.add(m=1e-6,a=aouter,e=e0, inc=inc0)
sim.move_to_com() # Moves to the center of momentum frame
ps = sim.particles
"""
Explanation: Migratio... |
geo-fluid-dynamics/phaseflow-fenics | tutorials/Phaseflow/LidDrivenCavity.ipynb | mit | import phaseflow
import fenics
import matplotlib
%matplotlib inline
"""
Explanation: This tutorial demonstrates how to use FEniCS and the phaseflow.abstract_simulation.AbstractSimulation class as an interface for implementing time-dependent simulations with mixed finite elements and goal-oriented adaptive mesh refinem... |
eblur/clarsach | notebooks/ParTest_ProofOfConcept.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
try:
import seaborn as sns
except ImportError:
print("No seaborn installed. Oh well.")
import numpy as np
from scipy.special import gammaln as scipy_gammaln
import scipy.stats
import astropy.io.fits as fits
import astropy.modeling.models as models
from a... |
udacity/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... |
LDSSA/learning-units | units/11-validation-metrics/examples/01 - Validation Metrics for Classification.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score, precision_score, \
recall_score, f1_score, roc_auc_score, roc_curve, confusion_matrix
%matplotlib inline
"""
Explanation: Example - Validation Metrics for Classification
Scikit-learn covers extensive... |
GoogleCloudPlatform/tf-estimator-tutorials | 03_Clustering/02.0 - TF k-means - Estimator API.ipynb | apache-2.0 | train_data_files = ['data/train-data.csv']
test_data_files = ['data/test-data.csv']
model_name = 'clust-model-01'
resume = False
train = True
preprocess_features = False
extend_feature_colums = False
"""
Explanation: Steps to use the TF Estimator APIs
Define dataset metadata
Define data input function to read the d... |
pdh21/XID_plus | docs/notebooks/examples/SED_emulator/JAX_CIGALE_emulator-kasia.ipynb | mit |
from astropy.cosmology import WMAP9 as cosmo
import jax
import numpy as onp
import pylab as plt
import astropy.units as u
import scipy.integrate as integrate
%matplotlib inline
import jax.numpy as np
from jax import grad, jit, vmap, value_and_grad
from jax import random
from jax import vmap # for auto-vectorizing fun... |
psiq/gdsfactory | notebooks/03_references.ipynb | mit | from IPython.display import Image
Image("../docs/images/gds.png")
"""
Explanation: References
GDS allows defining the component once in memory and reference to that structure in other components.
End of explanation
"""
import pp
"""
Explanation: Adding a component reference
As we build complex circuit we'll make ci... |
phobson/paramnormal | docs/tutorial/activities.ipynb | mit | %matplotlib inline
import warnings
warnings.simplefilter('ignore')
from numpy.random import seed
from scipy import stats
from matplotlib import pyplot
import seaborn
import paramnormal
clean_bkgd = {'axes.facecolor':'none', 'figure.facecolor':'none'}
seaborn.set(style='ticks', rc=clean_bkgd)
"""
Explanation: Param... |
oturing/pymanas | labs/Viagem a Jupyter.ipynb | gpl-3.0 | def fatorial(n):
return 1 if n < 2 else n * fatorial(n-1)
fatorial(42)
"""
Explanation: Jupyter Notebook é uma ferramenta excelente para:
Exploração interativa de dados;
Análise de dados (i.e. Analytics);
Aprender Python, R, Julia e dezenas de outras linguagens suportadas
O nome Jupyter é mistura de Julia, Pyth... |
dlsun/symbulate | docs/common_discrete.ipynb | mit | from symbulate import *
%matplotlib inline
"""
Explanation: Symbulate Documentation
Common Discrete Distributions
Introduction to discrete distributions and random variables
Defining a general discrete distribution with BoxModel
Discrete Uniform
Bernoulli
Binomial
Hypergeometric
Poisson
Geometric
Negative Binomial (a... |
dtherrick/dataviz_baseball | iPython/2 - Scrape and Plot 2015 Baseball Standings-Copy1.ipynb | mit | import pandas as pd
import matplotlib.pyplot as plt
import ipywidgets
from IPython.display import display
from collections import OrderedDict
%matplotlib inline
pd.options.mode.chained_assignment = None # default='warn'
"""
Explanation: 2. Scrape and Plot 2015 Baseball Standings (matplotlib)
Begin by importing our ... |
relopezbriega/mi-python-blog | content/notebooks/MachineLearningPractica.ipynb | gpl-2.0 | # <!-- collapse=True -->
# Importando las librerías que vamos a utilizar
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder
# graficos incrustados
%matplotlib inline
# parametros esteticos de seaborn
sns.set_palette("deep", de... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session03/Day2/OOP.ipynb | mit | import matplotlib.pyplot as plt
import random
import numpy as np
%matplotlib inline
"""
Explanation: DSFP Object Oriented Programming Notebook
Incorporating classes, objects, and functions into your code will improve its efficiency, readability, and make it easier to extend to other programs down the road. This note... |
sdpython/actuariat_python | _doc/notebooks/sessions/population_recuperation_donnees.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
# le code qui suit n'est pas indispensable, il génère automatiquement un menu
# dans le notebook
from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: Récupération des données
Ce notebook donne quelques exemples de co... |
tensorflow/docs-l10n | site/pt-br/tutorials/keras/regression.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... |
massimo-nocentini/PhD | notebooks/words-avoiding-riordan-patterns.ipynb | apache-2.0 | from sympy import *
from IPython.display import Markdown, Latex
from oeis import oeis_search
init_printing()
%run ~/Developer/working-copies/programming-contests/competitive-programming/python-libs/oeis.py
"""
Explanation: <p>
<img src="http://www.cerm.unifi.it/chianti/images/logo%20unifi_positivo.jpg"
alt... |
mne-tools/mne-tools.github.io | 0.21/_downloads/7f0f8817512fdcca1eab8f4befa07ff9/plot_metadata_query.ipynb | bsd-3-clause | # Authors: Chris Holdgraf <choldgraf@gmail.com>
# Jona Sassenhagen <jona.sassenhagen@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import os
import pandas as pd
import matplotlib.pyplot as plt
import mne
# First load some data
events = mne.read_events(os.path.join(mne... |
anthonyng2/FX-Trading-with-Python-and-Oanda | Oanda v1 REST-oandapy/10.00 Bonus Materials II.ipynb | mit | import numpy as np
import pandas as pd
import oandapy
import configparser
%matplotlib inline
import seaborn as sns
import matplotlib.pyplot as plt
config = configparser.ConfigParser()
config.read('../config/config_v1.ini')
account_id = config['oanda']['account_id']
api_key = config['oanda']['api_key']
oanda = oandapy... |
Chipe1/aima-python | viterbi_algorithm.ipynb | mit | from probability import *
%psource HiddenMarkovModel
"""
Explanation: Probabilistic Reasoning over Time
Finding the Most Likely Sequence with Viterbi Algorithm
Introduction
An Hidden Markov Model (HMM) network is parameterized by two distributions:
the emission or sensor probabilties giving the conditional probabil... |
MarcKjerland/Worst-Winter-Ever | worst-winter-mpls.ipynb | mit | import pandas as pd
# Read data, sort by year & month
dateparse = lambda x: pd.datetime.strptime(x, '%Y%m%d')
noaa_monthly = pd.read_csv('mpls-noaa.csv', index_col=2,
parse_dates=True, date_parser=dateparse, na_values=-9999)
noaa_monthly = noaa_monthly.groupby([noaa_monthly.index.year, noaa_m... |
lknelson/text-analysis-2017 | 04-Dictionaries/00-DictionaryMethod.ipynb | bsd-3-clause | #import the necessary packages
import pandas
import nltk
from nltk import word_tokenize
import string
#read the Music Reviews corpus into a Pandas dataframe
df = pandas.read_csv("../Data/BDHSI2016_music_reviews.csv", encoding='utf-8', sep = '\t')
#view the dataframe
df
"""
Explanation: Dictionary Method
This is the ... |
betoesquivel/onforums-application | testdataextractor/TestDataExtractor.ipynb | mit | from bs4 import BeautifulSoup
f = open('../test_data/1957284403.ofs.gold.xml', 'r')
article_text = f.read();
soup = BeautifulSoup(article_text, "lxml")
comment = {
"bloggerId": "author",
"sentences": [], # all sentences in a comment,
"parents": [] # the order depends on how beautifulsoup gives me the pare... |
musketeer191/job_analytics | .ipynb_checkpoints/skill_cluster4-checkpoint.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
import sklearn.feature_extraction.text as text_manip
import matplotlib.pyplot as plt
import gc
from sklearn.decomposition import NMF, LatentDirichletAllocation
from time import time
from scipy.sparse import *
# my own modules
from my_util import *
from helpers import *
"""
Expl... |
vascotenner/holoviews | doc/Tutorials/Options.ipynb | bsd-3-clause | import numpy as np
import holoviews as hv
hv.notebook_extension()
x,y = np.mgrid[-50:51, -50:51] * 0.1
image = hv.Image(np.sin(x**2+y**2), group="Function", label="Sine")
coords = [(0.1*i, np.sin(0.1*i)) for i in range(100)]
curve = hv.Curve(coords)
curves = {phase: hv.Curve([(0.1*i, np.sin(phase+0.1*i)) for i in r... |
TheMitchWorksPro/DataTech_Playground | PY_Basics/TMWP_Python_Getting_Names_of_Things.ipynb | mit | from dill.source import getname # run this cell first before any cells below
peg1 = [1,2]
peg2 = [3]
peg3 = [5,4]
# this example shows what at first would appear to be unexpected behavior
# it illustrates some concepts though later cells of this notebook show how to effectively use dill.getname
def move_from(sour... |
steinam/teacher | jup_notebooks/data-science-ipython-notebooks-master/pandas/03.11-Working-with-Time-Series.ipynb | mit | from datetime import datetime
datetime(year=2015, month=7, day=4)
"""
Explanation: <!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub.
Th... |
bioinformatica-corso/lezioni | laboratorio/lezione2-01ott21/lezione2-strutture-controllo.ipynb | cc0-1.0 | my_number = 15
if my_number > 10:
print("My number is bigger than 10")
"""
Explanation: Python - Strutture di controllo dell'esecuzione
Strutture di selezione
Costrutto if
Costrutto if-else
Costrutto if-elif
Strutture iterative
Ciclo while
Costrutto if
if [condition]:
[if_block]
[condition]: espressio... |
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