repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
drcgw/bass | Single Wave- Interactive.ipynb | gpl-3.0 | from bass import *
"""
Explanation: Welcome to BASS!
Version: Single Wave- Interactive Notebook.
BASS: Biomedical Analysis Software Suite for event detection and signal processing.
Copyright (C) 2015 Abigail Dobyns
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Gen... |
jsnajder/StrojnoUcenje | notebooks/SU-2015-7-LogistickaRegresija.ipynb | cc0-1.0 | import scipy as sp
import scipy.stats as stats
import matplotlib.pyplot as plt
import pandas as pd
%pylab inline
"""
Explanation: Sveučilište u Zagrebu<br>
Fakultet elektrotehnike i računarstva
Strojno učenje
<a href="http://www.fer.unizg.hr/predmet/su">http://www.fer.unizg.hr/predmet/su</a>
Ak. god. 2015./2016.
Bilje... |
g-weatherill/notebooks | gmpe-smtk/ConditionalFields-Training.ipynb | agpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm, Normalize
import smtk.hazard.conditional_simulation as csim
import smtk.sm_database_builder as sdb
from smtk.residuals.gmpe_residuals import Residuals
from smtk.residuals.residual_plotter import ResidualPlot, Re... |
citxx/sis-python | crash-course/if-and-logical-expressions.ipynb | mit | print("Чему равно 2 * 2?")
a = int(input())
if a == 4:
print("Правда")
else:
print("Ложь")
"""
Explanation: <h1>Содержание<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Условный-оператор-if" data-toc-modified-id="Условный-оператор-if-1">Условный оператор if</a></sp... |
piyushbhattacharya/machine-learning | python/Consumer Complains.ipynb | gpl-3.0 | ld_train, ld_test = train_test_split(cd_train, test_size=0.2, random_state=2)
x80_train = ld_train.drop(['Consumer disputed?','Complaint ID'],1)
y80_train = ld_train['Consumer disputed?']
x20_test = ld_test.drop(['Consumer disputed?','Complaint ID'],1)
y20_test = ld_test['Consumer disputed?']
"""
Explanation: Optimi... |
mne-tools/mne-tools.github.io | 0.22/_downloads/dfd4175ec1a2c7f21de3596573c74301/plot_multidict_reweighted_tfmxne.ipynb | bsd-3-clause | # Author: Mathurin Massias <mathurin.massias@gmail.com>
# Yousra Bekhti <yousra.bekhti@gmail.com>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import os.path as op
import mne
from mne.datasets import somato... |
ericmjl/influenza-reassortment-analysis | 13 Where are human-adapted mutations found?.ipynb | mit | # Open the sequences
sequences = SeqIO.to_dict(SeqIO.parse('20150312_PB2_CDS_from_Whole_Genomes.fasta', 'fasta'))
# sequences
data = pd.read_csv('20150312_PB2_CDS_from_Whole_Genomes.csv', parse_dates=['Collection Date'])
data['Strain Name'] = data['Strain Name'].str.split('(').str[0]
data['Sequence Accession'] = data[... |
Chipe1/aima-python | games4e.ipynb | mit | from collections import namedtuple, Counter, defaultdict
import random
import math
import functools
cache = functools.lru_cache(10**6)
class Game:
"""A game is similar to a problem, but it has a terminal test instead of
a goal test, and a utility for each terminal state. To create a game,
subclass this ... |
google/trax | trax/layers/intro.ipynb | apache-2.0 | # Copyright 2018 Google LLC.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, softwa... |
berlemontkevin/Jupyter_Notebook | PyDSTool/PyDSTool_Introduction.ipynb | apache-2.0 | from PyDSTool import *
"""
Explanation: PyDSTool : An introduction
This Notebook is inspired by : http://www2.gsu.edu/~matrhc/FrontPage.html
The description of PyDSTool is :"With PyDSTool we aim to provide a powerful suite of computational tools for the development, simulation, and analysis of dynamical systems that a... |
tpin3694/tpin3694.github.io | machine-learning/imputing_missing_class_labels.ipynb | mit | # Load libraries
import numpy as np
from sklearn.preprocessing import Imputer
"""
Explanation: Title: Imputing Missing Class Labels
Slug: imputing_missing_class_labels
Summary: How to impute missing class labels for machine learning in Python.
Date: 2016-09-06 12:00
Category: Machine Learning
Tags: Preprocessing Stru... |
M-R-Houghton/euroscipy_2015 | scikit_image/lectures/solutions/adv3_panorama-stitching-solution.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
def compare(*images, **kwargs):
"""
Utility function to display images side by side.
Parameters
----------
image0, image1, image2, ... : ndarrray
Images to display.
labels : list
Labels for the different images.
"""
... |
teuben/astr288p | notebooks/wrapup.ipynb | mit | %matplotlib inline
import numpy as np
import numpy.ma as ma
import matplotlib.pyplot as plt
from astropy.io import fits
from scipy.optimize import curve_fit
"""
Explanation: Some final thoughts
In the lectures you should have learned:
Unix (Linux or Mac) terminals and how to work with directories and files
basic com... |
josiahdavis/python_data_analysis | .ipynb_checkpoints/python_data_analysis-checkpoint.ipynb | mit | # Import the pandas and numpy libraries
import pandas as pd
import numpy as np
# Read a file with an absolute path
ufo = pd.read_csv('/Users/josiahdavis/Documents/GitHub/python_data_analysis/ufo_sightings.csv')
# Alterntively, read the the file using a relative path
ufo = pd.read_csv('ufo_sightings.csv')
# Alterntiv... |
gojomo/gensim | docs/src/auto_examples/core/run_corpora_and_vector_spaces.ipynb | lgpl-2.1 | import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
"""
Explanation: Corpora and Vector Spaces
Demonstrates transforming text into a vector space representation.
Also introduces corpus streaming and persistence to disk in various formats.
End of explanation
"""
... |
SJSlavin/phys202-2015-work | assignments/assignment08/InterpolationEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
sns.set_style('white')
from scipy.interpolate import griddata
"""
Explanation: Interpolation Exercise 2
End of explanation
"""
# YOUR CODE HERE
x = np.hstack((np.arange(-5, 6), np.full(10, 5), np.arange(-5, 5), np.full(9, -5... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_read_and_write_raw_data.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
raw = mne.io.read_raw_fif(fname)
# Set up pick list: MEG + STI 014 - b... |
ES-DOC/esdoc-jupyterhub | notebooks/cams/cmip6/models/sandbox-3/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cams', 'sandbox-3', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: CAMS
Source ID: SANDBOX-3
Sub-Topics: Radiative Forcings.
Properties: 85 (42 ... |
deep-learning-indaba/practicals2017 | practical4.ipynb | mit | class FeedForwardModel():
# ...
def act_fn(self, x):
return np.tanh(x)
def forward(self, x):
'''One example of a FFN.'''
# Compute activations on the hidden layer.
hidden_layer = self.act_fn(np.dot(self.W_xh, x))
# Compute the (linear) outp... |
ModestoCabrera/IS360Project_1 | project_1.ipynb | gpl-2.0 | import pandas as pd
from pandas import DataFrame
import matplotlib.pyplot as plt
data = pd.read_csv('project_1.csv')
"""
Explanation: IS360 Project 1 - Data Analysis of Formed Flights Database
<img src="stats_keyboard.jpg" align=right>
<i>Before I get started I need to import the tools and data into my Environmen... |
geography-munich/sciprog | material/sub/jrjohansson/Lecture-5-Sympy.ipynb | apache-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: Sympy - Symbolic algebra in Python
J.R. Johansson (jrjohansson at gmail.com)
The latest version of this IPython notebook lecture is available at http://github.com/jrjohansson/scientific-python-lectures.
The other notebooks in this lecture series are i... |
eds-uga/csci1360e-su17 | assignments/A6/A6_BONUS.ipynb | mit | c = count_datasets("submission_partial.json")
assert c == 4
c = count_datasets("submission_full.json")
assert c == 9
try:
c = count_datasets("submission_nonexistent.json")
except:
assert False
else:
assert c == -1
"""
Explanation: BONUS
This bonus will do a very deep dive into dictionaries and lists, and... |
km-Poonacha/python4phd | Session 2/ipython/Lesson 5- Crawl and scrape.ipynb | gpl-3.0 | import requests
url = 'http://www.tripadvisor.com/'
response = requests.get(url)
print(response.status_code)
#print(response.headers)
"""
Explanation: Lesson 5 - Crawl and Scrape
Making the request
Using 'requests' module
Use the requests module to make a HTTP request to http://www.tripadvisor.com
- Check the status... |
xaibeing/cn-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... |
chrlttv/Teaching | Session5/1_MachineLearning_Regression_keys.ipynb | mit | # Write code to import required libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# For visualzing plots in this notebook
%matplotlib inline
"""
Explanation: Machine Learning: Regression
Supervised Machine Learning algorithms consist of target / outcome variable (or dependent variabl... |
calico/basenji | tutorials/archive/genes.ipynb | apache-2.0 | import os, subprocess
if not os.path.isfile('data/hg19.ml.fa'):
subprocess.call('curl -o data/hg19.ml.fa https://storage.googleapis.com/basenji_tutorial_data/hg19.ml.fa', shell=True)
subprocess.call('curl -o data/hg19.ml.fa.fai https://storage.googleapis.com/basenji_tutorial_data/hg19.ml.fa.fai', shell=True) ... |
AllenDowney/ThinkBayes2 | examples/beta.ipynb | mit | trials = 174
successes = 173
failures = trials-successes
failures
"""
Explanation: Jeffreys interval
Copyright 2020 Allen Downey
MIT License
Suppose you have run 174 trials and 173 were successful. You want to report an estimate of the probability of success and a confidence interval for the estimate.
According to ou... |
chinapnr/python_study | Python 基础课程/Python Basic Lesson 16 - 函数式编程.ipynb | gpl-3.0 | # 将函数作为值返回
def lazy_sum(*args):
def sum():
ax = 0
for n in args:
ax = ax + n
return ax
return sum
f = lazy_sum(1, 3, 5, 7, 9)
print(f())
"""
Explanation: Lesson 16
v1.1, 2020.5, 2020.6 edit by David Yi
本次内容
闭包：将函数作为值返回
偏函数
高阶函数 map/reduce/filte
End of explanat... |
ethen8181/machine-learning | python/pivot_table/pivot_table.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)
import numpy as np
import pandas as pd
# 1. magic... |
moonbury/pythonanywhere | github/MasteringMatplotlib/mmpl-interaction.ipynb | gpl-3.0 | import matplotlib
matplotlib.use('nbagg')
"""
Explanation: Event Handling and Interactive Plots
In the following sections of this IPython Notebook we be looking at the following:
matplotlib's event loop support
Basic Event Handling
List of supported events
Mouse events
Limitations of the IPython Notebook backend
Keyb... |
mattmcd/PyBayes | scripts/qfe_20220221.ipynb | apache-2.0 | import sympy as sp
from sympy.interactive import printing
printing.init_printing(use_latex=True)
from sympy.stats import Bernoulli, LogNormal, density, sample, P as Prob, E as Expected, variance
"""
Explanation: Utility Functions
Date: 2022-02-21
Author: Matt McDonnell @mattmcd
Looking at 'Quantitative Financial Econ... |
ENCODE-DCC/pyencoded-tools | jupyter_notebooks/keenan/pyencoded_tools_skills_lab_2017.ipynb | mit | !encode explore
"""
Explanation: pyencoded-tools
https://github.com/ENCODE-DCC/pyencoded-tools
Skills Lab 2017
Purpose
Tools for programmatically interacting with metadata on the portal
Metadata includes
Origin of raw data (donor, experimental conditions)
Processing steps (align reads to assembly)
Relation betwe... |
EducationalTestingService/rsmtool | rsmtool/notebooks/evaluation.ipynb | apache-2.0 | markdown_str = ("The tables in this section show the standard association metrics between "
"*observed* human scores and different types of machine scores. "
"These results are computed on the evaluation set. `raw_trim` scores "
"are truncated to [{}, {}]. `raw_trim_round... |
NekuSakuraba/my_capstone_research | subjects/em/multivariate t - draft03 - EM Unknown degrees of freedom.ipynb | mit | def log_of_psi():
n = len(X)
u_ = [(_-mu).T.dot(inv(cov).dot(_-mu)) for _ in X]
return -.5*n*p*log(2 * np.pi) -.5*n*log(np.linalg.det(cov)) - .5 * sum(u_)
def az():
n = len(X)
u_ = [(_-mu).T.dot(inv(cov).dot(_-mu)) for _ in X]
return -n*log(gamma(df/2.)) + .5*n*df*log(df/2.) + .5*df*(log(u_) ... |
abigailStev/power_spectra | scripts/TimmerKoenig.ipynb | mit | import numpy as np
from scipy import fftpack
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
import matplotlib.font_manager as font_manager
import itertools
## Shows the plots inline, instead of in a separate window:
%matplotlib inline
## Sets the font size for plotting
font_prop = font_... |
rbondesan/ssd | reinforcement_q_learning.ipynb | mit | import gym
import math
import random
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from collections import namedtuple
from itertools import count
from copy import deepcopy
from PIL import Image
import torch
import torch.nn as nn
import torch.optim as optim
import torch.autograd as autograd
impor... |
vadim-ivlev/STUDY | handson-data-science-python/DataScience-Python3/LinearRegression.ipynb | mit | %matplotlib inline
import numpy as np
from pylab import *
pageSpeeds = np.random.normal(3.0, 1.0, 1000)
purchaseAmount = 100 - (pageSpeeds + np.random.normal(0, 0.1, 1000)) * 3
scatter(pageSpeeds, purchaseAmount)
"""
Explanation: Linear Regression
Let's fabricate some data that shows a roughly linear relationship be... |
mined-gatech/pymks_overview | notebooks/stress_homogenization_2D.ipynb | mit | %matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Effective Stiffness
Introduction
This example uses the MKSHomogenizationModel to create a homogenization linkage for the effective stiffness. This example starts with a brief background of the hom... |
grokkaine/biopycourse | day3/DL2_CNN.ipynb | cc0-1.0 | from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000... |
eigendreams/TensorFlow-Tutorials | 08_Transfer_Learning.ipynb | mit | from IPython.display import Image, display
Image('images/08_transfer_learning_flowchart.png')
"""
Explanation: TensorFlow Tutorial #08
Transfer Learning
by Magnus Erik Hvass Pedersen
/ GitHub / Videos on YouTube
Introduction
We saw in the previous Tutorial #07 how to use the pre-trained Inception model for classifying... |
mne-tools/mne-tools.github.io | 0.17/_downloads/c92e443909d938b06abf63b902dac687/plot_epochs_to_data_frame.ipynb | bsd-3-clause | # Author: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
import matplotlib.pyplot as plt
import numpy as np
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
event_fname = data_path + '... |
xgcm/xgcm | doc/grid_metrics.ipynb | mit | import xarray as xr
import numpy as np
from xgcm import Grid
import matplotlib.pyplot as plt
%matplotlib inline
# hack to make file name work with nbsphinx and binder
import os
fname = '../datasets/mitgcm_example_dataset_v2.nc'
if not os.path.exists(fname):
fname = '../' + fname
ds = xr.open_dataset(fname)
ds... |
nicolas998/wmf | Examples/Ejemplo_Cuencas_Basico_1.ipynb | gpl-3.0 | #Paquete Watershed Modelling Framework (WMF) para el trabajo con cuencas.
from wmf import wmf
"""
Explanation: Ejemplo cuencas
En el siguiente ejemplo se presentan las funcionalidades básicas de la herramienta wmf.Stream y wmf.Basin
dentro de los temas tocados se presenta:
Trazado de corrientes.
Perfil de corrientes.... |
ConnectedSystems/veneer-py | doc/training/6_Model_Setup_and_Configuration.ipynb | isc | existing_models = v.model.link.routing.get_models()
existing_models
"""
Explanation: Session 6 - Model Setup and Reconfiguration
This session covers functionality in Veneer and veneer-py for making larger changes to model setup, including structural changes.
Using this functionality, it is possible to:
Create (and re... |
harmsm/pythonic-science | chapters/00_inductive-python/key/03_conditionals_key.ipynb | unlicense | x = 5
print(x > 2)
x = 5
print(x < 2)
"""
Explanation: Conditional Execution
Conditional execution allows a program to only execute code if some condition is met.
Predict what this code will do.
End of explanation
"""
x = 20
print (x > 2)
# one way
x = 1
print (x > 2)
# another way
x = 20
print (x < 2)
"""
Expl... |
jorisvandenbossche/DS-python-data-analysis | _solved/visualization_02_seaborn.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: <p><font size="6"><b>Visualisation: Seaborn </b></font></p>
© 2021, Joris Van den Bossche and Stijn Van Hoey (jorisvandenbossche@g&#... |
nimish-jose/dlnd | image-classification/dlnd_image_classification.ipynb | gpl-3.0 | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
class DLProgress(tqdm):
last_block = 0
def hoo... |
shareactorIO/pipeline | gpu.ml/notebooks/04_Train_Model_GPU.ipynb | apache-2.0 | import tensorflow as tf
from tensorflow.python.client import timeline
import pylab
import numpy as np
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
tf.logging.set_verbosity(tf.logging.INFO)
tf.reset_default_graph()
num_samples = 100000
from datetime import datetime
version = int(datetime.now(... |
bakerjd99/jacks | numpyjlove/NumPy and J make Sweet Array Love.ipynb | unlicense | import numpy as np
"""
Explanation: NumPy and J make Sweet Array Love
Import NumPy using the standard naming convention
End of explanation
"""
import sys
# local api/python3 path - adjust path for your system
japipath = 'C:\\j64\\j64-807\\addons\\api\\python3'
if japipath not in sys.path:
sys.path.append(japi... |
wuafeing/Python3-Tutorial | 01 data structures and algorithms/01.16 filter sequence elements.ipynb | gpl-3.0 | mylist = [1, 4, -5, 10, -7, 2, 3, -1]
[n for n in mylist if n > 0]
[n for n in mylist if n < 0]
"""
Explanation: Previous
1.16 过滤序列元素
问题
你有一个数据序列，想利用一些规则从中提取出需要的值或者是缩短序列
解决方案
最简单的过滤序列元素的方法就是使用列表推导。比如：
End of explanation
"""
pos = (n for n in mylist if n > 0)
pos
for x in pos:
print(x)
"""
Explanation: 使用列表推导... |
napsternxg/ipython-notebooks | Likelihood+ratio.ipynb | apache-2.0 | def get_likelihood(theta, n, k, normed=False):
ll = (theta**k)*((1-theta)**(n-k))
if normed:
num_combs = comb(n, k)
ll = num_combs*ll
return ll
get_likelihood(0.5, 2, 2, normed=True)
get_likelihood(0.5, 10, np.arange(10), normed=True)
N = 100
plt.plot(
np.arange(N),
get_likelihood... |
fvnts/finitedifference | notebooks/fheat.ipynb | gpl-3.0 | # ----------------------------------------/
%matplotlib inline
# ----------------------------------------/
import numpy as np
import matplotlib.pyplot as plt
from scipy import *
from ipywidgets import *
from scipy import linalg
from numpy import asmatrix
from numpy import matlib as ml
from scipy.sparse import spdiags
... |
elmaso/tno-ai | aind2-cnn/cifar10-classification/cifar10_mlp.ipynb | gpl-3.0 | import keras
from keras.datasets import cifar10
# load the pre-shuffled train and test data
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
"""
Explanation: Artificial Intelligence Nanodegree
Convolutional Neural Networks
In this notebook, we train an MLP to classify images from the CIFAR-10 database.
1. ... |
gojomo/gensim | docs/notebooks/Any2Vec_Filebased.ipynb | lgpl-2.1 | import gensim
import gensim.downloader as api
from gensim.utils import save_as_line_sentence
from gensim.models.word2vec import Word2Vec
print(gensim.models.word2vec.CORPUSFILE_VERSION) # must be >= 0, i.e. optimized compiled version
corpus = api.load("text8")
save_as_line_sentence(corpus, "my_corpus.txt")
model = ... |
steinam/teacher | jup_notebooks/datenbanken/Sommer_2015.ipynb | mit | %load_ext sql
%sql mysql://steinam:steinam@localhost/sommer_2015
"""
Explanation: Subselects
End of explanation
"""
%%sql
%sql select count(*) as AnzahlFahrten from fahrten
"""
Explanation: Sommer 2015
Datenmodell
Aufgabe
Erstellen Sie eine Abfrage, mit der Sie die Daten aller Kunden, die Anzahl deren Aufträg... |
mohanprasath/Course-Work | numpy/numpy_exercises_from_kyubyong/Logic_functions.ipynb | gpl-3.0 | import numpy as np
np.__version__
"""
Explanation: Logic functions
End of explanation
"""
x = np.array([1,2,3])
#
x = np.array([1,0,3])
#
"""
Explanation: Truth value testing
Q1. Let x be an arbitrary array. Return True if none of the elements of x is zero. Remind that 0 evaluates to False in python.
End of expla... |
Bio204-class/bio204-notebooks | Introduction-to-Simulation.ipynb | cc0-1.0 | # set the seed for the pseudo-random number generator
# the seed is any 32 bit integer
# different seeds will generate different results for the
# simulations that follow
np.random.seed(20160208)
"""
Explanation: A brief note about pseudo-random numbers
When carrying out simulations, it is typical to use random numb... |
mne-tools/mne-tools.github.io | 0.24/_downloads/31239620dd9631320a99b07ac4a81074/interpolate_bad_channels.ipynb | bsd-3-clause | # Authors: Denis A. Engemann <denis.engemann@gmail.com>
# Mainak Jas <mainak.jas@telecom-paristech.fr>
#
# License: BSD-3-Clause
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
evoked = mne.read_evokeds(fname, ... |
nick-youngblut/SIPSim | ipynb/bac_genome/fullCyc/.ipynb_checkpoints/Day1_default_run-checkpoint.ipynb | mit | import os
import glob
import re
import nestly
%load_ext rpy2.ipython
%%R
library(ggplot2)
library(dplyr)
library(tidyr)
library(gridExtra)
library(phyloseq)
## BD for G+C of 0 or 100
BD.GCp0 = 0 * 0.098 + 1.66
BD.GCp100 = 1 * 0.098 + 1.66
"""
Explanation: Goal
Simulating fullCyc Day1 control gradients
Not simulati... |
joshnsolomon/phys202-2015-work | assignments/assignment03/NumpyEx04.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
"""
Explanation: Numpy Exercise 4
Imports
End of explanation
"""
import networkx as nx
K_5=nx.complete_graph(5)
nx.draw(K_5)
"""
Explanation: Complete graph Laplacian
In discrete mathematics a Graph is a set of vertices or n... |
southpaw94/MachineLearning | TextExamples/3547_08_Code.ipynb | gpl-2.0 | %load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,scikit-learn,nltk
# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
"""
Explanation: Sebastian Raschka, 2015
Python Machine Learning Ess... |
danresende/deep-learning | sentiment_network/Sentiment Classification - Mini Project 1.ipynb | mit | def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()... |
fastai/fastai | nbs/35_tutorial.wikitext.ipynb | apache-2.0 | path = untar_data(URLs.WIKITEXT_TINY)
"""
Explanation: Tutorial - Assemble the data on the wikitext dataset
Using Datasets, Pipeline, TfmdLists and Transform in text
In this tutorial, we explore the mid-level API for data collection in the text application. We will use the bases introduced in the pets tutorial so yo... |
ethen8181/machine-learning | trees/gbm/gbm.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(css_style = 'custom2.css', plot_style = False)
os.chdir(path)
# 1. magic for inline ... |
biosustain/cameo-notebooks | Advanced-SynBio-for-Cell-Factories-Course/Vanillin Production.ipynb | apache-2.0 | from cameo import models
model = models.bigg.iMM904
"""
Explanation: Vanillin production
In 2010, Brochado et al used heuristic optimization together with flux simulations to design a vanillin producing yeast strain.
Brochado, A. R., Andrejev, S., Maranas, C. D., & Patil, K. R. (2012). Impact of stoichiometry represe... |
GoogleCloudPlatform/ml-design-patterns | 03_problem_representation/multilabel.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import Model
from tensorflow.keras.layers import Dense, Embedding, Input, Flatten, Conv2D, MaxPooling2D
from sklearn.utils import shuffle
from sklearn.preprocessing import MultiLabelBinarizer
"""
Explana... |
dxl0632/deeplearning_nd_udacity | intro-to-tflearn/TFLearn_Sentiment_Analysis.ipynb | mit | import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
"""
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network w... |
davofis/computational_seismology | 05_pseudospectral/ps_derivative.ipynb | gpl-3.0 | # Import all necessary libraries, this is a configuration step for the exercise.
# Please run it before the simulation code!
import numpy as np
import matplotlib.pyplot as plt
# Show the plots in the Notebook.
plt.switch_backend("nbagg")
"""
Explanation: <div style='background-image: url("../../share/images/header.sv... |
cougarTech2228/Scouting-2016 | notebooks/Obstacle_scatter.ipynb | mit | import matplotlib
import matplotlib.pyplot as plot
import pandas as pd
import numpy as np
%matplotlib inline
"""
Explanation: Scatter Plots of Rank, OPR, and Obstacle Success
End of explanation
"""
a = [3, 4, 5, 6, 7, 15]
b = [4, 5, 6, 7 , 7, 3]
size = [2, 200, 10, 2, 15]
hues = [0.1, 0.2, 0.6, 0.7, 0.9]
plot.scatt... |
ES-DOC/esdoc-jupyterhub | notebooks/mri/cmip6/models/sandbox-2/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mri', 'sandbox-2', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: MRI
Source ID: SANDBOX-2
Topic: Atmoschem
Sub-Topics: Transport, Emissions Co... |
mne-tools/mne-tools.github.io | 0.23/_downloads/9d142d98d094666b7fd2d94155f8b3ec/decoding_unsupervised_spatial_filter.ipynb | bsd-3-clause | # Authors: Jean-Remi King <jeanremi.king@gmail.com>
# Asish Panda <asishrocks95@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.decoding import UnsupervisedSpatialFilter
from sklearn.decomposition import PCA, FastI... |
rastala/mmlspark | notebooks/samples/103 - Before and After MMLSpark.ipynb | mit | import pandas as pd
import mmlspark
from pyspark.sql.types import IntegerType, StringType, StructType, StructField
dataFile = "BookReviewsFromAmazon10K.tsv"
textSchema = StructType([StructField("rating", IntegerType(), False),
StructField("text", StringType(), False)])
import os, urllib
if not... |
tensorflow/docs-l10n | site/ja/tutorials/keras/text_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... |
rvianello/rdkit | Code/GraphMol/ChemReactions/tutorial/EnumerationToolkit.ipynb | bsd-3-clause | from __future__ import print_function
from rdkit.Chem import AllChem
from rdkit.Chem import rdChemReactions
from rdkit.Chem.AllChem import ReactionFromRxnBlock, ReactionToRxnBlock
from rdkit.Chem.Draw import IPythonConsole
IPythonConsole.ipython_useSVG=True
rxn_data = """$RXN
ISIS 090220091539
2 1
$MOL
... |
albahnsen/ML_RiskManagement | notebooks/02-IntroPython_Numpy_Scypy_Pandas.ipynb | mit | import sys
print('Python version:', sys.version)
import IPython
print('IPython:', IPython.__version__)
import numpy
print('numpy:', numpy.__version__)
import scipy
print('scipy:', scipy.__version__)
import matplotlib
print('matplotlib:', matplotlib.__version__)
import pandas
print('pandas:', pandas.__version__)
... |
FFIG/ffig | demos/CppLondon_Aug-2017.ipynb | mit | %%file Tree.hpp
#ifndef FFIG_DEMOS_TREE_H
#define FFIG_DEMOS_TREE_H
#include <memory>
class Tree {
std::unique_ptr<Tree> left_;
std::unique_ptr<Tree> right_;
int data_ = 0;
public:
Tree(int children) {
if(children <=0) return;
left_ = std::make_unique<Tree>(children-1);
right_ = std::make_... |
jbwhit/WSP-312-Tips-and-Tricks | notebooks/01-Tips-and-tricks.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format='retina'
# Add this to python2 code to make life easier
from __future__ import absolute_import, division, print_function
from itertools import combinations
import string
from IPython.display import IFrame, HTML, YouTubeVideo
import matplotlib as mpl
from matplo... |
quantopian/research_public | notebooks/data/eventvestor.issue_equity/notebook.ipynb | apache-2.0 | # import the dataset
from quantopian.interactive.data.eventvestor import issue_equity
# or if you want to import the free dataset, use:
# from quantopian.interactive.data.eventvestor import issue_equity_free
# import data operations
from odo import odo
# import other libraries we will use
import pandas as pd
# Let's ... |
PythonFreeCourse/Notebooks | week08/3_Exceptions.ipynb | mit | counter = 0
while counter < 10
print("Stop it!")
counter += 1
"""
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 ... |
stitchfix/d3-jupyter-tutorial | iris_scatterplot.ipynb | mit | from IPython.core.display import display, HTML
from string import Template
import pandas as pd
import json, random
HTML('<script src="lib/d3/d3.min.js"></script>')
"""
Explanation: Iris Scatterplot
A simple example of using a bl.ock as the basis for a D3 visualization in Jupyter
Using this bl.ocks example as a templa... |
ibm-cds-labs/simple-data-pipe-connector-flightstats | notebook/Flight Predict with Pixiedust.ipynb | apache-2.0 | !pip install --upgrade --user pixiedust
!pip install --upgrade --user pixiedust-flightpredict
"""
Explanation: Flight Delay Predictions with PixieDust
<img style="max-width: 800px; padding: 25px 0px;" src="https://ibm-watson-data-lab.github.io/simple-data-pipe-connector-flightstats/flight_predictor_architecture.png"/... |
danijel3/ASRDemos | notebooks/VoxforgeDataPrep.ipynb | apache-2.0 | import sys
sys.path.append('../python')
from voxforge import *
"""
Explanation: Preparing the Voxforge database
This notebook will demonstrate how to prepare the free Voxforge database for training. This database is a medium sized (~80 hours) database available online for free under the GPL license. A much more comm... |
moble/MatchedFiltering | GW150914/AdjustCoM.ipynb | mit | import scri
import scri.SpEC
import numpy as np
data_dir = '/Users/boyle/Research/Data/SimulationAnnex/Incoming/BBH_SKS_d13.4_q1.23_sA_0_0_0.320_sB_0_0_-0.580/Lev5/'
w_N2 = scri.SpEC.remove_avg_com_motion(data_dir + 'rhOverM_Asymptotic_GeometricUnits.h5/Extrapolated_N2.dir', file_write_mode='w')
w_N3 = scri.SpEC.remo... |
SheffieldML/notebook | compbio/periodic/figure1.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
import GPy
np.random.seed(1)
"""
Explanation: Supplementary materials : Details on generating Figure 1
This document is a supplementary material of the article Detecting periodicities with Gaussian
processes by N. Durrande, J. Hensman, M. Ratt... |
hektor-monteiro/python-notebooks | aula-12_Monte-carlo.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
s = np.random.uniform(8,10., 100000)
count, bins, ignored = plt.hist(s, 30)
#print (count, bins, ignored)
import numpy as np
import matplotlib.pyplot as plt
mean = [0, 0]
cov = [[1, 10], [5, 10]] # covariancia diagonal
x, y = np.random.multivariate_normal(mean... |
Autodesk/molecular-design-toolkit | moldesign/_notebooks/Example 2. UV-vis absorption spectra.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
from matplotlib.pylab import *
try: import seaborn #optional, makes plots look nicer
except ImportError: pass
import moldesign as mdt
from moldesign import units as u
"""
Explanation: <span style="float:right"><a href="http://moldesign.bionano.autodesk.com/" target="_blank" tit... |
lukasmerten/CRPropa3 | doc/pages/example_notebooks/propagation_comparison/Propagation_Comparison_CK_BP.ipynb | gpl-3.0 | def analytical_solution(max_trajectory, p_z, r_g_0, number_steps):
# calculate the time stamps similar to that used in the numerical simulation
t = np.linspace(0, max_trajectory/pc, int(number_steps+1))
# shift the phase so that the analytical solution
# also starts at (0,0,0) with in the direction (p... |
deepchem/deepchem | examples/tutorials/Introducing_JaxModel_and_PINNModel.ipynb | mit | !pip install --pre deepchem[jax]
import numpy as np
import functools
try:
import jax
import jax.numpy as jnp
import haiku as hk
import optax
from deepchem.models import PINNModel, JaxModel
from deepchem.data import NumpyDataset
from deepchem.models.optimizers import Adam
from jax import jacrev
has_ha... |
rnikutta/rhocube | rhocube.ipynb | bsd-3-clause | from rhocube import *
from models import *
import warnings
import pylab as p
import matplotlib
%matplotlib inline
def myplot(images,titles='',interpolation='bicubic'):
if not isinstance(images,list):
images = [images]
n = len(images)
if not isinstance(titles,list):
titles = [titles]
ass... |
matthewzhenggong/fiwt | workspace_py/RigFreeRollId-Copy1.ipynb | lgpl-3.0 | %run matt_startup
%run -i matt_utils
button_qtconsole()
#import other needed modules in all used engines
#with dview.sync_imports():
# import os
"""
Explanation: Parameter Estimation of RIG Roll Experiments
Setup and descriptions
Without ACM model
Turn off wind tunnel
Only 1DoF for RIG roll movement
Free in any a... |
fastai/course-v3 | docs/production/lesson-1-export-jit.ipynb | apache-2.0 | %reload_ext autoreload
%autoreload 2
%matplotlib inline
import os
import io
import tarfile
import PIL
import boto3
from fastai.vision import *
path = untar_data(URLs.PETS); path
path_anno = path/'annotations'
path_img = path/'images'
fnames = get_image_files(path_img)
np.random.seed(2)
pat = re.compile(r'/([^/]+)... |
zerothi/ts-tbt-sisl-tutorial | TS_01/run.ipynb | gpl-3.0 | graphene = sisl.geom.graphene(1.44, orthogonal=True)
graphene.write('STRUCT_ELEC_SMALL.fdf')
graphene.write('STRUCT_ELEC_SMALL.xyz')
elec = graphene.tile(2, axis=0)
elec.write('STRUCT_ELEC.fdf')
elec.write('STRUCT_ELEC.xyz')
"""
Explanation: First TranSiesta example.
This example will only create the structures for ... |
vahidpartovinia/pythonworkshop | jupyter/chapter03.ipynb | gpl-2.0 | import numpy as np
n=200
x_tr = np.linspace(0.0, 2.0, n)
y_tr = np.exp(3*x_tr)
import random
mu, sigma = 0,50
random.seed(1)
y = y_tr + np.random.normal(loc=mu, scale= sigma, size=len(x_tr))
import matplotlib.pyplot as plt
%matplotlib inline
plt.plot(x_tr,y,".",mew=3);
plt.plot(x_tr, y_tr,"--r",lw=3);
plt.xlabe... |
davebshow/DH3501 | class5.ipynb | mit | # Usually we import networkx as nx.
import networkx as nx
# Instantiate a graph.
g = nx.Graph()
# Add a node.
g.add_node(1)
# Add a list of nodes.
g.add_nodes_from([2, 3, 4, 5])
# Add an edge.
g.add_edge(1, 2)
# Add a list of edges.
g.add_edges_from([(2, 3), (3, 4)])
# Remove a node.
g.remove_node(5)
"""
Explana... |
mne-tools/mne-tools.github.io | 0.23/_downloads/27d6cff3f645408158cdf4f3f05a21b6/30_eeg_erp.ipynb | bsd-3-clause | import os
import numpy as np
import matplotlib.pyplot as plt
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, pr... |
enakai00/jupyter_NikkeiLinux | No5/Figure11 - derivative_animation.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
%matplotlib nbagg
"""
Explanation: [4-1] 動画作成用のモジュールをインポートして、動画を表示可能なモードにセットします。
End of explanation
"""
def derivative(f, filename):
fig = plt.figure(figsize=(4,4))
images = []
x0, d = 0.5, 0.5
for _ in range... |
google/applied-machine-learning-intensive | content/04_classification/03_classification_with_tensorflow/colab.ipynb | apache-2.0 | # 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 the L... |
axm108/Rydberg | model_fitting/rabi/rabi_fit.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats
from scipy.optimize import curve_fit
%matplotlib inline
"""
Explanation: Rabi model fitting
End of explanation
"""
def rabiModel(time, rabiFreq, T1, Tdec, phi, a0, a1, a2, detuning=0.0):
phi_deg = phi*(np.pi/180)
W = np.sqrt(rabiF... |
transientskp/notebooks | trap movie.ipynb | mit | import matplotlib
# remove this inline statement to stop the previews in the notebook
%matplotlib inline
#matplotlib.use('Agg')
import logging
from tkp.db.model import Image, Extractedsource
from tkp.db import Database
from pymongo import MongoClient
from gridfs import GridFS
from astropy.io import fits
from astropy... |
psychemedia/ou-robotics-vrep | robotVM/notebooks/Demo - Square N - Functions.ipynb | apache-2.0 | import time
def myFunction():
print("Hello...")
#Pause awhile...
time.sleep(2)
print("...world!")
#call the function - note the brackets!
myFunction()
"""
Explanation: Traverse a Square - Part N - Functions
tricky because of scoping - need to think carefully about this....
In the previo... |
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