repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
Vvkmnn/books | ThinkBayes/13_Simulation.ipynb | gpl-3.0 | # time between discharge and diagnosis, in days
interval = 3291.0
# doubling time in linear measure is doubling time in volume * 3
dt = 811.0 * 3
# number of doublings since discharge
doublings = interval / dt
# how big was the tumor at time of discharge (diameter in cm)
d1 = 15.5
d0 = d1 / 2.0 ** doublings
"""
Ex... |
sdpython/ensae_teaching_cs | _doc/notebooks/td1a_home/2020_numpy.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
"""
Explanation: Tech - calcul matriciel avec numpy
numpy est la librairie incontournable pour faire des calculs en Python. Ces fonctionnalités sont disponibles dans tous les langages et utilisent les optimisations processeurs. Il est ... |
zephirefaith/AI_Fall15_Assignments | A6/hmm_notebook.ipynb | mit | def part_1_a(): #(20 pts)
# TODO: Fill in below !
# Fill in the matrix below with state probabilities at each time step, P(High) being the value at the 0th index
part_a_avalanche_trellis = [[0.4,0], [0.096,0.016], [0.00576,0.01536], [0.0006,0.003], [0.00012,0.0006], [0.000096,0.00003], [0.000023,0.000... |
salma1601/aspp2015 | Advanced NumPy Patterns.ipynb | bsd-3-clause | gene0 = [100, 200, 50, 400]
gene1 = [50, 0, 0, 100]
gene2 = [350, 100, 50, 200]
expression_data = [gene0, gene1, gene2]
"""
Explanation: Intro
Juan Nunez-Iglesias
Victorian Life Sciences Computation Initiative (VLSCI)
University of Melbourne
Quick example: gene expression, without numpy
| | Cell type A | Cell... |
tensorflow/text | docs/tutorials/bert_glue.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... |
AaronCWong/phys202-2015-work | assignments/assignment04/TheoryAndPracticeEx02.ipynb | mit | from IPython.display import Image
"""
Explanation: Theory and Practice of Visualization Exercise 2
Imports
End of explanation
"""
# Add your filename and uncomment the following line:
Image(filename='Assignment04b.png')
"""
Explanation: Violations of graphical excellence and integrity
Find a data-focused visualizat... |
AMICI-developer/AMICI | python/examples/example_petab/petab.ipynb | bsd-2-clause | from amici.petab_import import import_petab_problem
from amici.petab_objective import simulate_petab
import petab
import os
"""
Explanation: Using PEtab
This notebook illustrates how to use PEtab with AMICI.
End of explanation
"""
!git clone --depth 1 https://github.com/Benchmarking-Initiative/Benchmark-Models-PEta... |
JackDi/phys202-2015-work | assignments/assignment10/ODEsEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy.integrate import odeint
from IPython.html.widgets import interact, fixed
"""
Explanation: Ordinary Differential Equations Exercise 1
Imports
End of explanation
"""
def solve_euler(derivs, y0, x):
"""Solve a 1d ... |
krishnan-r/sparkmonitor | notebooks/Testing Extension.ipynb | apache-2.0 | print(conf.toDebugString()) #Instance of SparkConf with options set by the extension
"""
Explanation: Testing SparkMonitor Extension
The configuration object SparkConf is provided by the extension, added to the namespace as 'conf'.
The user passes this to the SparkContext
End of explanation
"""
conf.setAppName('Exte... |
othersite/document | machinelearning/deep-learning-book/code/model_zoo/saving-and-reloading-models.ipynb | apache-2.0 | %load_ext watermark
%watermark -a 'Sebastian Raschka' -v -p tensorflow
"""
Explanation: Accompanying code examples of the book "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python" by Sebastian Raschka. All code examples are released under the MIT license. If you... |
tensorflow/docs | site/en/guide/sparse_tensor.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... |
sns-chops/multiphonon | tests/notebooks/getdos2.ipynb | mit | workdir = '/SNS/users/lj7/reduction/ARCS/getdos-demo-test'
!mkdir -p {workdir}
%cd {workdir}
"""
Explanation: Density of States Analysis Example
Given sample and empty-can data, compute phonon DOS
To use this notebook, first click jupyter menu File->Make a copy
Click the title of the copied jupyter notebook and chang... |
CamDavidsonPilon/lifelines | examples/Modelling time-lagged conversion rates.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
from matplotlib import pyplot as plt
import autograd.numpy as np
from autograd.scipy.special import expit, logit
import pandas as pd
plt.style.use('bmh')
N = 200
U = np.random.rand(N)
T = -(logit(-np.log(U) / 0.5) - np.random.exponential(2, N) - 6.00)... |
VictorQuintana91/Thesis | notebooks/000_data_inspection.ipynb | mit | import cufflinks as cf
print(cf.__version__)
import pandas as pd
import numpy as np
import gzip
# Configure cufflings
cf.set_config_file(offline=False, world_readable=True, theme='pearl')
"""
Explanation: Plotly & Cufflinks
At this point you will need to isntall cufflinks. Cufflinks binds Plotly directly to pandas d... |
hongguangguo/shogun | doc/ipython-notebooks/multiclass/KNN.ipynb | gpl-3.0 | import numpy as np
from scipy.io import loadmat, savemat
from numpy import random
from os import path
mat = loadmat('../../../data/multiclass/usps.mat')
Xall = mat['data']
Yall = np.array(mat['label'].squeeze(), dtype=np.double)
# map from 1..10 to 0..9, since shogun
# requires multiclass labels to be
# 0,... |
jdsanch1/SimRC | 02. Parte 2/09. Clase 9/09Class NB.ipynb | mit | #importar los paquetes que se van a usar
import pandas as pd
import pandas_datareader.data as web
import numpy as np
import datetime
from datetime import datetime
import scipy.stats as stats
import scipy as sp
import scipy.optimize as scopt
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.covariance... |
ozorich/phys202-2015-work | assignments/assignment05/InteractEx01.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 01
Import
End of explanation
"""
def print_sum(a, b):
"""Print the sum of the arguments a and b."""
... |
vitojph/kschool-nlp | notebooks-py2/word2vec.ipynb | gpl-3.0 | import gensim, logging, os
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
"""
Explanation: Ejemplo de word2vec con gensim
En la siguiente celda, importamos las librerías necesarias y configuramos los mensajes de los logs.
End of explanation
"""
class Corpus(object):
'... |
4DGenome/Chromosomal-Conformation-Course | Notebooks/A3-Compare_and_merge_Hi-C_experiments.ipynb | gpl-3.0 | from pytadbit.mapping.analyze import eig_correlate_matrices, correlate_matrices
from pytadbit import load_hic_data_from_reads
from cPickle import load
from matplotlib import pyplot as plt
reso = 200000
base_path = 'results/fragment/{0}/03_filtering/valid_reads12_{0}.tsv'
bias_path = 'results/fragment/{1}/04_normalizin... |
wuafeing/Python3-Tutorial | 01 data structures and algorithms/01.01 unpack sequence into separate variables.ipynb | gpl-3.0 | p = (4, 5)
x, y = p
x
y
data = ["ACME", 50, 91.1, (2012, 12, 21)]
name, shares, price, date = data
name
date
name, shares, price, (year, mon, day) = data
name
year
mon
day
"""
Explanation: Previous
1.1 解压序列赋值给多个变量
问题
现在有一个包含 N 个元素的元组或者是序列，怎样将它里面的值解压后同时赋值给 N 个变量？
解决方案
任何的序列(或者是可迭代对象)可以通过一个简单的赋值语句解压并赋值给多个变量。 唯... |
ffmmjj/intro_to_data_science_workshop | solutions/03-Delimitação de grupos de flores.ipynb | apache-2.0 | import pandas as pd
iris = pd.read_csv('../datasets/iris_without_classes.csv') # Carregue o arquivo 'datasets/iris_without_classes.csv'
# Exiba as primeiras cinco linhas usando o método head() para checar que não existe mais a coluna "Class"
iris.head()
"""
Explanation: Suponha que não soubéssemos quantas espécies ... |
cmawer/pycon-2017-eda-tutorial | notebooks/2-Aquastat-EDA/5-Aquastat-Multivariate.ipynb | mit | # must go first
%matplotlib inline
%config InlineBackend.figure_format='retina'
# plotting
import matplotlib as mpl
from matplotlib import pyplot as plt
import seaborn as sns
sns.set_context("poster", font_scale=1.3)
import folium
# system packages
import os, sys
import warnings
warnings.filterwarnings('ignore')
... |
robertoalotufo/ia898 | master/tutorial_numpy_1_5a.ipynb | mit | # download image from github: -q quiet mode; -N overwrite on the next download
!wget -q -N https://github.com/robertoalotufo/ia898/raw/830a0f5f6e6a1ddd459127631bf9c0c750bf1f58/data/cameraman.tif
!wget -q -N https://github.com/robertoalotufo/ia898/raw/830a0f5f6e6a1ddd459127631bf9c0c750bf1f58/data/keyb.tif
!wget -q -N ht... |
LSSTDESC/LSSTDarkMatter | stronglens/SubstructureLikelihood.ipynb | mit | # General imports
%matplotlib inline
import logging
import numpy as np
import pylab as plt
from scipy import stats
from scipy import integrate
from scipy.integrate import simps,trapz,quad,nquad
from scipy.interpolate import interp1d
from scipy.misc import factorial
"""
Explanation: Dark Matter Substructure from Stron... |
throx66/deep-learning | dcgan-svhn/DCGAN.ipynb | mit | %matplotlib inline
import pickle as pkl
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat
import tensorflow as tf
!mkdir data
"""
Explanation: Deep Convolutional GANs
In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a De... |
mne-tools/mne-tools.github.io | 0.24/_downloads/9e70404d3a55a6b6d1c1877784347c14/mixed_source_space_inverse.ipynb | bsd-3-clause | # Author: Annalisa Pascarella <a.pascarella@iac.cnr.it>
#
# License: BSD-3-Clause
import os.path as op
import matplotlib.pyplot as plt
from nilearn import plotting
import mne
from mne.minimum_norm import make_inverse_operator, apply_inverse
# Set dir
data_path = mne.datasets.sample.data_path()
subject = 'sample'
da... |
tpin3694/tpin3694.github.io | regex/match_dates.ipynb | mit | # Load regex package
import re
"""
Explanation: Title: Match Dates
Slug: match_dates
Summary: Match Dates
Date: 2016-05-01 12:00
Category: Regex
Tags: Basics
Authors: Chris Albon
Based on: Regular Expressions Cookbook
Preliminaries
End of explanation
"""
# Create a variable containing a text string
text = 'My birt... |
astroumd/GradMap | notebooks/Lectures2020/Lecture2/Lecture2_Instructor.ipynb | gpl-3.0 | ourList = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
"""
Explanation: Review from the previous lecture
In the previous lecture we covered basic mathematical operations, variables, and lists. We also introduced you to conditional statements, loops, and basic plotting using matplotlib. Before we move forward, we'll do a quick revie... |
hchauvet/beampy | doc-src/auto_tutorials/first_slide.ipynb | gpl-3.0 | from beampy import *
# We first create a new document for our presentation
# Remove quiet=True to see Beampy compiler output
doc = document(quiet=True)
# Then we create a new slide with the title "My first new slide"
with slide('My first slide title'):
# All the slide contents are functions added inside the with... |
ksooklall/deep_learning_foundation | reinforcement/Q-learning-cart.ipynb | mit | import gym
import tensorflow as tf
import numpy as np
"""
Explanation: Deep Q-learning
In this notebook, we'll build a neural network that can learn to play games through reinforcement learning. More specifically, we'll use Q-learning to train an agent to play a game called Cart-Pole. In this game, a freely swinging p... |
wedelljd/wedelljd.github.io | billboard post_files/billboard post.ipynb | mit | #importing packages and libraries needed
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime
import seaborn as sns
import matplotlib.cm as cm
sns.set_palette(sns.color_palette(None))
sns.set_style("darkgrid")
%matplotlib inline
billboard = pd.read_csv("./billboard.csv") #import da... |
nickmckay/LiPD-utilities | Examples/.ipynb_checkpoints/Welcome LiPD - Quickstart-checkpoint.ipynb | gpl-2.0 | import lipd
"""
Explanation: <div class="clearfix" style="padding: 10px; padding-left: 0px; padding-top: 40px">
<img src="https://www.dropbox.com/s/y8dd1z3sl4uofep/lipd_logo.png?raw=1" width="700px" class="pull-right" style="display: inline-block; margin: 0px;">
</div>
Welcome to the LiPD Quickstart Notebook!
This No... |
nick-youngblut/SIPSim | ipynb/bac_genome/fullCyc/trimDataset/.ipynb_checkpoints/rep3-checkpoint.ipynb | mit | import os
import glob
import re
import nestly
%load_ext rpy2.ipython
%load_ext pushnote
%%R
library(ggplot2)
library(dplyr)
library(tidyr)
library(gridExtra)
library(phyloseq)
"""
Explanation: Goal
Simulating a fullCyc control gradient
Not simulating incorporation (all 0% isotope incorp.)
Don't know how much true i... |
csaladenes/csaladenes.github.io | present/mcc/jupyter/1-DimensionalityReduction-PCA.ipynb | mit | from __future__ import print_function, division
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
plt.style.use('seaborn')
"""
Explanation: Dénes Csala, MCC, Kolozsvár, 2021
<small><i>This notebook was put together by Jake Vanderplas. Source and license info is on GitHub.<... |
arnaldog12/Manual-Pratico-Deep-Learning | Rede Neural_Intuição.ipynb | mit | import numpy as np
"""
Explanation: Neste notebook, vamos codificar Redes Neurais de forma manual para tentar entender intuitivamente como elas são implementadas na prática.
Sumário
Exemplo 1
Exemplo 2
O que precisamos para implementar uma Rede Neural?
Referências
Imports e Configurações
End of explanation
"""
def... |
paulu/deepart | LFWDMT.ipynb | mit | from glob import glob
import csv
import dmt
import numpy as np
import time
from IPython.display import Image
"""
Explanation: Deep Manifold Traversal with LFW
This Python notebook describes how to run Deep Manifold Traversal to age Aaron Eckhart (as an example). If you have already cloned the deepmanifold github repos... |
dismalpy/dismalpy | doc/notebooks/dfm_coincident.ipynb | bsd-2-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import dismalpy as dp
import matplotlib.pyplot as plt
np.set_printoptions(precision=4, suppress=True, linewidth=120)
from pandas.io.data import DataReader
# Get the datasets from FRED
start = '1979-01-01'
end = '2014-12-01'
indpr... |
mbakker7/timml | notebooks/timml_notebook4_sol.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from timml import *
figsize = (6, 6)
z = [20, 15, 10, 8, 6, 5.5, 5.2, 4.8, 4.4, 4, 2, 0]
ml = Model3D(kaq=10, z=z, kzoverkh=0.1)
ls1 = LineSinkDitch(ml, x1=-100, y1=0, x2=100, y2=0, Qls=10000, order=5, layers=6)
ls2 = HeadLineSinkString(ml, [(200, -... |
wutienyang/facebook_fanpage_analysis | Facebook粉絲頁分析三部曲-爬取篇(comments).ipynb | mit | # 載入python 套件
import requests
import datetime
import time
import pandas as pd
"""
Explanation: 如何爬取Facebook粉絲頁資料 (comments) ？
基本上是透過 Facebook Graph API 去取得粉絲頁的資料,但是使用 Facebook Graph API 還需要取得權限,有兩種方法 :
第一種是取得 Access Token
第二種是建立 Facebook App的應用程式,用該應用程式的帳號,密碼當作權限
兩者的差別在於第一種會有時效限制,必須每隔一段時間去更新Access Token,才能使用
Access To... |
GoogleCloudPlatform/ml-pipeline-generator-python | examples/getting_started_notebook.ipynb | apache-2.0 | # Use the latest major GA version of the framework.
! pip install --upgrade ml-pipeline-gen PyYAML
"""
Explanation: End to End Workflow with ML Pipeline Generator
<table align="left">
<td>
<a href="https://colab.sandbox.google.com/github/GoogleCloudPlatform/ml-pipeline-generator-python/blob/master/examples/getti... |
massimo-nocentini/simulation-methods | notes/matrices-functions/riordan-arrays-ctors.ipynb | mit | from sympy import *
from sympy.abc import n, i, N, x, lamda, phi, z, j, r, k, a, t, alpha
from sequences import *
init_printing()
m = 8
d_fn, h_fn = Function('d'), Function('h')
d, h = IndexedBase('d'), IndexedBase('h')
"""
Explanation: <p>
<img src="http://www.cerm.unifi.it/chianti/images/logo%20unifi_positivo.jp... |
brian-rose/env-415-site | notes/EBM_notes.ipynb | mit | # We start with the usual import statements
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import climlab
# create a new model with all default parameters (except the grid size)
mymodel = climlab.EBM_annual(num_lat = 30)
# What did we just do?
print mymodel
"""
Explanation: Using climlab... |
csieber/yt-dataset | notebooks/avg_quality.ipynb | mit | import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
"""
Explanation: Average video quality
The basic example shows how to plot the shaping to average quality level plot from the IFIP Networking 2016 publication.
Reading the dataset with pandas
Remove warnings and show plots inline:
End of explanation
... |
opencobra/cobrapy | documentation_builder/dfba.ipynb | gpl-2.0 | import numpy as np
from tqdm import tqdm
from scipy.integrate import solve_ivp
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Dynamic Flux Balance Analysis (dFBA) in COBRApy
The following notebook shows a simple, but slow example of implementing dFBA using COBRApy and scipy.integrate.solve_ivp. ... |
JJINDAHOUSE/deep-learning | embeddings/Skip-Grams-Solution.ipynb | mit | import time
import numpy as np
import tensorflow as tf
import utils
"""
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language p... |
root-mirror/training | SoftwareCarpentry/09-rdataframe-advanced.ipynb | gpl-2.0 | import numpy
import ROOT
np_dict = {colname: numpy.random.rand(100) for colname in ["a","b","c"]}
df = ROOT.RDF.MakeNumpyDataFrame(np_dict)
print(f"Columns in the RDataFrame: {df.GetColumnNames()}")
co = df.Count()
m_a = df.Mean("a")
fil1 = df.Filter("c < 0.7")
def1 = fil1.Define("d", "a+b+c")
h = def1.Histo1D("d"... |
dfm/KeplerHack | keplerhack.ipynb | mit | import os
import requests
import numpy as np
import pandas as pd
from io import BytesIO # Python 3 only!
import matplotlib.pyplot as pl
def get_catalog(name, basepath="data"):
"""
Download a catalog from the Exoplanet Archive by name and save it as a
Pandas HDF5 file.
:param name: the table name... |
frazer-lab/cardips-ipsc-eqtl | notebooks/RNA-Seq Analysis.ipynb | mit | import copy
import cPickle
import os
import subprocess
import cdpybio as cpb
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.linalg import svd
import scipy.stats as stats
import seaborn as sns
import statsmodels.formula.api as smf
import cardipspy as cpy
import ciepy
%matplotlib inl... |
ES-DOC/esdoc-jupyterhub | notebooks/messy-consortium/cmip6/models/emac-2-53-aerchem/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'emac-2-53-aerchem', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: MESSY-CONSORTIUM
Source ID: EMAC-2-53-AERCHEM
Topic: Atmos
Sub-T... |
ekergy/jupyter_notebooks | curso/5-Pandas.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
trends = pd.read_csv('./data/20160819_OlympicSportsByCountries.csv',
header=1)
trends.head()
trends[trends.Country == "Spain"].sort_values(by="Search Interest",
ascending=False)
trends[trends.Sport == "Tennis"].... |
csaladenes/csaladenes.github.io | present/bi/2020/jupyter/6_pdf_ocr_excel.ipynb | mit | !pip install Pillow
!pip install pdf2image
"""
Explanation: PDF táblázatok pandas-ba való alakítása. Olyan PDF-ekre, amelyek képekből vannak - tehát fényképek, szkennelések, vagy hasonló. Ez magában foglalja a sima fényképek (JPG, PNG) szövegfelismerését is. Az átalakítási folyamat három lépéses:
1. PDF oldalainak k... |
duttashi/Data-Analysis-Visualization | scripts/general/Taarifa_EDA.ipynb | mit | sub1.describe()
"""
Explanation: Distribution Analysis of the data
Now that we have familarity with the basic characterstics, lets look at the distribution of various variables starting with the continuous variable
Distribution analysis of continuous variable using the describe()
End of explanation
"""
sub1['extract... |
LDSSA/learning-units | units/05-summary-statistics/practice/Exercises Summary Statistics.ipynb | mit | import pandas as pd
import numpy as np
from IPython.display import display, HTML
CSS = """
.output {
flex-direction: row;
}
"""
patient_data = pd.read_csv("../data/Exercises_Summary_Statistics_Data.csv")
patient_data.head()
"""
Explanation: Summary Statistics - Exercises
In these exercises you'll use a real lif... |
sz2472/foundations-homework | homework 11/11-homework-data/zhao_shengying_homework 11.ipynb | mit | df.dtypes #dtype: Data type for data or columns
print("The data type is",(type(df['Plate ID'][0])))
"""
Explanation: 1. I want to make sure my Plate ID is a string. Can't lose the leading zeroes!
End of explanation
"""
df['Vehicle Year'] = df['Vehicle Year'].replace("0","NaN") #str.replace(old, new[, max])
df.head(... |
aattaran/Machine-Learning-with-Python | MNIST/0410 - MNIST Project 6 - The ROC Curve/MNIST.ipynb | bsd-3-clause | import numpy as np
from sklearn.datasets import fetch_mldata
mnist = fetch_mldata('MNIST original')
mnist
len(mnist['data'])
"""
Explanation: Classification Based Machine Learning Algorithm
An introduction to machine learning with scikit-learn
Scikit-learn Definition:
Supervised learning, in which the data comes wi... |
adrienhenry/characteristicTimesNetwork | time_real_networks.ipynb | mit | from imp import reload
import re
import numpy as np
from scipy.integrate import ode
import NetworkComponents
"""
Explanation: Characteristic times in real networks
End of explanation
"""
chassagnole = NetworkComponents.Network("chassagnole2002")
chassagnole.readSBML("./published_models/Chassagnole2002.xml")
chassag... |
LucaCanali/Miscellaneous | Spark_Physics/LHCb_opendata/LHCb_OpenData_Spark_CERNSWAN_Version.ipynb | apache-2.0 | # Start the Spark Session
# When Using Spark on CERN SWAN, use this and do not select to connect to a CERN Spark cluster
# If you want to use a cluster, please copy the data to a cluster filesystem first
from pyspark.sql import SparkSession
spark = (SparkSession.builder
.appName("LHCb opendata")
.mas... |
cgre-aachen/gempy | notebooks/Getting_started.ipynb | lgpl-3.0 | # Importing GemPy
import gempy as gp
# Importing aux libraries
from ipywidgets import interact
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
# Embedding matplotlib figures in the notebooks
%matplotlib qt5
"""
Explanation: Getting started
Importing used libraries
End of explanati... |
PyLadiesCZ/pyladies.cz | original/v1/s003-looping/ostrava/Feedback k domácím projektům 2.ipynb | mit | for radek in range(4):
radek += 1
for value in range(radek):
print('X', end=' ')
print('')
"""
Explanation: Feedback k domácím projektům
Jde tento kód napsat jednodušeji, aby ale dělal úplně totéž?
End of explanation
"""
for radek in range(1, 5):
print('X ' * radek)
"""
Explanation: Ano, lze :-)
End of ... |
smorton2/think-stats | code/chap13soln.ipynb | gpl-3.0 | from __future__ import print_function, division
%matplotlib inline
import warnings
warnings.filterwarnings('ignore', category=FutureWarning)
import numpy as np
import pandas as pd
import random
import thinkstats2
import thinkplot
"""
Explanation: Examples and Exercises from Think Stats, 2nd Edition
http://thinkst... |
mdbloice/Machine-Learning-for-Health-Informatics | Assignment2.ipynb | mit | import urllib2
import csv
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
%matplotlib inline
url_X_train = 'http://statweb.stanford.edu/~tibs/ElemStatLearn/datasets/14cancer.xtrain'
url_y_train = 'http://statweb.stanford.edu/~tibs/ElemStatLearn/datasets/14cancer.ytrain'
u... |
Danghor/Algorithms | Python/Chapter-05/Stack.ipynb | gpl-2.0 | class Stack:
pass
S = Stack()
S
"""
Explanation: Implementing a Stack Class
First, we define an empty class Stack.
End of explanation
"""
def stack(S):
S.mStackElements = []
"""
Explanation: Next we define a constructor for this class. The function stack(S) takes an uninitialized, empty object S
and init... |
besser82/shogun | doc/ipython-notebooks/neuralnets/neuralnets_digits.ipynb | bsd-3-clause | %pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from scipy.io import loadmat
from shogun import features, MulticlassLabels, Math
# load the dataset
dataset = loadmat(os.path.join(SHOGUN_DATA_DIR, 'multiclass/usps.mat'))
Xall = dataset['data']
# the usps dataset... |
Diyago/Machine-Learning-scripts | general studies/task_nn.ipynb | apache-2.0 | # 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... |
bureaucratic-labs/yargy | docs/ref.ipynb | mit | from yargy.tokenizer import RULES
RULES
"""
Explanation: Справочник
Токенизатор
Токенизатор в Yargy реализован на регулярных выражениях. Для каждого типа токена есть правило с регуляркой:
End of explanation
"""
from yargy.tokenizer import Tokenizer
text = 'a@mail.ru'
tokenizer = Tokenizer()
list(tokenizer(text))... |
adriaanvuik/solid_state_physics | semiconductor_dos_numerics.ipynb | bsd-2-clause | fermi_gas_1D
"""
Explanation: Free electron model
By Anton Akhmerov (also it's my very first lecture ever today!)
This lecture:
* Fermi surface
* Fermi energy
* Fermi velocity
* Electron heat capacitance
Next lecture:
Scattering and magnetic field
Electrons
Q: In which ways are electrons different from phonons?
They ... |
jonathanmorgan/msu_phd_work | analysis/step-2-filter-network-relations-dev.ipynb | lgpl-3.0 | me = "filter-network-relations-dev"
"""
Explanation: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#notes-and-questions" data-toc-modified-id="notes-and-questions-1"><span class="toc-item-num">1 </span>notes and questions</a></span></li><li>... |
tpin3694/tpin3694.github.io | machine-learning/preprocessing_categorical_features.ipynb | mit | from sklearn import preprocessing
from sklearn.pipeline import Pipeline
import pandas as pd
"""
Explanation: Title: Preprocessing Categorical Features
Slug: preprocessing_categorical_features
Summary: Preprocessing Categorical Features
Date: 2016-11-01 12:00
Category: Machine Learning
Tags: Preprocessing Structured Da... |
olifre/root | bindings/pyroot/cppyy/cppyy/doc/tutorial/GSLPythonizationTutorial.ipynb | lgpl-2.1 | import cppyy
"""
Explanation: GSL Pythonization Tutorial
(Hat tip to Neil Dhir for the idea.)
This tutorial introduces pythonizations and how they can be used to solve low-level problems.
The setup: imagine you want to use numpy, but are given a C or C++ library that is based on the GNU Scientific Library (GSL). How d... |
andijcr/andijcr.github.io | assets/hashcode/HashCode Integer Programming Solution.ipynb | mit | #size is the capacity of the cache in Mb, ID is an integer
class Cache:
def __init__(self, size, ID):
self.size = size
self.ID = ID
#like Cache, size is dimension in Mb, ID is an integer
class Video:
def __init__(self, size, ID):
self.size = size
self.ID = ID
#Endpoint represen... |
PyLadiesCZ/pyladies.cz | original/v1/s003-looping/ostrava/Feedback k domácím projektům.ipynb | mit | for radek in range(4):
radek += 1
for value in range(radek):
print('X', end=' ')
print('')
"""
Explanation: Feedback k domácím projektům
Jde tento kód napsat jednodušeji, aby ale dělal úplně totéž?
End of explanation
"""
for radek in range(1, 5):
print('X ' * radek)
"""
Explanation: Ano, lze :-)
End of ... |
syednasar/datascience | deeplearning/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'
text1 = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text1[81:]
print(text1[:81])
print(text[:1000])
"""
Explanation: TV Script Generation
In this project, yo... |
lvphj/mappy | map_postcodes_to_shp_file.ipynb | mit | %matplotlib inline
import pandas as pd
import epydemiology as epy
import geopandas as gpd
from pathlib import Path
import glob
import matplotlib.pyplot as plt
import os
from shapely.geometry import Point
"""
Explanation: Code to map postcodes to shp files
May 2020
Postcode definition file (version 02-2020) downloaded... |
mikelseverson/Udacity-Deep_Learning-Nanodegree | gan_mnist/Intro_to_GANs_Exercises.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... |
wasat/JupyTEPIDE | notebooks/grass/bash/import.ipynb | apache-2.0 | !v.in.ascii input=points.txt output=test_ascii separator=comma x=1 y=2
"""
Explanation: Import and export of data from different sources in GRASS GIS
GRASS GIS Location can contain data only in one coordinate reference system (CRS)
in order to have full control over reprojection
and avoid issues coming from on-the-fly... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_label_from_stc.ipynb | bsd-3-clause | # Author: Luke Bloy <luke.bloy@gmail.com>
# Alex Gramfort <alexandre.gramfort@telecom-paristech.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_pa... |
sebastiandres/mat281 | clases/Unidad4-MachineLearning/Clase05-Clasificacion-RegresionLogistica/ClasificacionRegresionLogistica.ipynb | cc0-1.0 | %%bash
cat data/Challenger.txt
"""
Explanation: <header class="w3-container w3-teal">
<img src="images/utfsm.png" alt="" height="100px" align="left"/>
<img src="images/mat.png" alt="" height="100px" align="right"/>
</header>
<br/><br/><br/><br/><br/>
MAT281
Aplicaciones de la Matemática en la Ingeniería
Sebastián Flor... |
tensorflow/docs | site/en/guide/advanced_autodiff.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... |
petebachant/ALM-turbulence-injection | notebook.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pxl.styleplot import set_sns
set_sns()
import os
from scipy.interpolate import interp1d
import scipy.stats
dataset_name = "NACA0021_2.0e+05.csv"
dataset_url = "https://raw.githubusercontent.com/petebachant/NACAFoil-OpenFOAM/... |
michael-isaev/cse6040_qna | PythonQnA_5_pythonic_code.ipynb | apache-2.0 | # Task: Concatenate a list of strings into a single string
# delimited by spaces.
list_of_words = ['the', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy', 'dog']
i = 0 # A counter to maintain the current position in the list
new_string = '' # String to hold the output
while i < len(list_of_words): # Iterate ... |
mohanprasath/Course-Work | coursera/python_for_data_science/2.3_Dictionaries.ipynb | gpl-3.0 | Dict={"key1":1,"key2":"2","key3":[3,3,3],"key4":(4,4,4),('key5'):5,(0,1):6}
Dict
"""
Explanation: <a href="http://cocl.us/topNotebooksPython101Coursera"><img src = "https://ibm.box.com/shared/static/yfe6h4az47ktg2mm9h05wby2n7e8kei3.png" width = 750, align = "center"></a>
<a href="https://www.bigdatauniversity.com"><im... |
MingChen0919/learning-apache-spark | notebooks/02-data-manipulation/2.8-sql-functions-to-extend-column-expressions.ipynb | mit | from pyspark.sql import functions as F
"""
Explanation: pyspark.sql.functions functions
pyspark.sql.functions is collection of built-in functions for creating column expressions. These functions largely increase methods that we can use to manipulate DataFrame and DataFrame columns.
There are many sql functions from th... |
SimonBiggs/electroninserts_bundle | Model an insert shape.ipynb | agpl-3.0 | energy = 6
applicator = 10
ssd = 100
x = [0.99, -0.14, -1.0, -1.73, -2.56, -3.17, -3.49, -3.57, -3.17, -2.52, -1.76,
-1.04, -0.17, 0.77, 1.63, 2.36, 2.79, 2.91, 3.04, 3.22, 3.34, 3.37, 3.08, 2.54,
1.88, 1.02, 0.99]
y = [5.05, 4.98, 4.42, 3.24, 1.68, 0.6, -0.64, -1.48, -2.38, -3.77, -4.81,
-5.26, -5.51, -5.... |
mbohlool/client-python | examples/notebooks/create_secret.ipynb | apache-2.0 | from kubernetes import client, config
"""
Explanation: How to create and use a Secret
A Secret is an object that contains a small amount of sensitive data such as a password, a token, or a key. In this notebook, we would learn how to create a Secret and how to use Secrets as files from a Pod as seen in https://kubern... |
Alex-Ian-Hamilton/solarbextrapolation | docs/auto_examples/define_and_run_trivial_preprocessor_and_extrapolator.ipynb | mit | # Define a trivial preprocessor
class PreZeros(Preprocessors):
def __init__(self, map_magnetogram):
super(PreZeros, self).__init__(map_magnetogram)
def _preprocessor(self):
# Adding in custom parameters to the meta
self.meta['preprocessor_routine'] = 'Zeros Preprocessor'
# Crea... |
drphilmarshall/StatisticalMethods | tutorials/Week2/Xray_mock.ipynb | gpl-2.0 | import astropy.io.fits as pyfits
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from astropy.visualization import LogStretch
logstretch = LogStretch()
from io import StringIO # StringIO behaves like a file object
import scipy.stats
class SolutionMissingError(Exception):
def __init__(self):... |
mintcloud/deep-learning | autoencoder/Convolutional_Autoencoder_Solution.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)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
"""
Explanation: C... |
ES-DOC/esdoc-jupyterhub | notebooks/cmcc/cmip6/models/sandbox-1/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cmcc', 'sandbox-1', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: CMCC
Source ID: SANDBOX-1
Topic: Landice
Sub-Topics: Glaciers, Ice.
Properties:... |
kimkipyo/dss_git_kkp | 통계, 머신러닝 복습/160518수_5일차_미적분Calculus과 최적화Optimization/4.SciPy 시작하기.ipynb | mit | rv = sp.stats.norm(loc=10, scale=10)
rv.rvs(size=(3, 10), random_state=1)
sns.distplot(rv.rvs(size=10000, random_state=1))
xx = np.linspace(-40, 60, 1000)
pdf = rv.pdf(xx)
plt.plot(xx, pdf)
cdf = rv.cdf(xx)
plt.plot(xx, cdf)
"""
Explanation: SciPy 시작하기
SciPy란
과학기술계산용 함수 및 알고리즘 제공
Home
http://www.scipy.org/
Documen... |
sueiras/training | tensorflow_old/03-text_use_cases/03_word_tagging/00_identify_tags_in_airline_database_embedings - SOLVED.ipynb | gpl-3.0 | from __future__ import print_function
import os
import numpy as np
import tensorflow as tf
print(tf.__version__)
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]="0"
"""
Explanation: Identify tags in airline database
Minimal code
- Read dataset
- transform data
- Minimal model
... |
samstav/scipy_2015_sklearn_tutorial | notebooks/02.3 Unsupervised Learning - Transformations and Dimensionality Reduction.ipynb | cc0-1.0 | from sklearn.datasets import load_iris
iris = load_iris()
X, y = iris.data, iris.target
print(X.shape)
"""
Explanation: Unsupervised Learning
Many instances of unsupervised learning, such as dimensionality reduction, manifold learning and feature extraction, find a new representation of the input data without any add... |
pschragger/big-data-python-class | tutorials/Python_Basics.ipynb | mit | a=5
print ("a")
a
"""
Explanation: Python Tutorial - Some of the basics
Notes on the content of this tutorial
This tutorial is a composite of a number of sources:
[1]Python for Data analysis: Appendix Python Essentials
[2] https://developers.google.com/edu/python/introduction
I reccommend making a copy of this notebo... |
ShinjiKatoA16/UCSY-sw-eng | python-2.ipynb | mit | x = 1
print('x =', x, type(x))
x = 'abc'
print('x =', x, type(x))
"""
Explanation: Python 2nd step: Variables and Data type
In case of C or other compile langueage, variables need to be declared with data type.
In Python, Object have data type, variables just refer Object. Following sequence is valid in Python.
End of... |
dataventureutc/Kaggle-HandsOnLab | Machine Learning - Hands on Lab - Session #3 - Feature Engineering.ipynb | gpl-3.0 | import os
from datetime import datetime
import numpy as np
import pandas as pd
import sklearn as sk
"""
Explanation: Machine Learning - Hands on Lab - Session #1
Lecturer: Jonathan DEKHTIAR
Date: 2017-03-13
<br/><br/>
Contact: contact@jonathandekhtiar.eu
Twitter: @born2data
LinkedIn: JonathanDEKHTIAR
Personal Websi... |
phoebe-project/phoebe2-docs | 2.1/examples/rossiter_mclaughlin.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
%matplotlib inline
"""
Explanation: Rossiter-McLaughlin Effect
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation... |
kellyrowland/openmc | docs/source/pythonapi/examples/tally-arithmetic.ipynb | mit | %load_ext autoreload
%autoreload 2
import glob
from IPython.display import Image
import numpy as np
import openmc
from openmc.statepoint import StatePoint
from openmc.summary import Summary
from openmc.source import Source
from openmc.stats import Box
%matplotlib inline
"""
Explanation: This notebook shows the how ... |
farr/emcee | docs/_static/notebooks/quickstart.ipynb | mit | import emcee
emcee.__version__
"""
Explanation: Quickstart
This notebook was made with the following version of emcee:
End of explanation
"""
import numpy as np
"""
Explanation: The easiest way to get started with using emcee is to use it for a project. To get you started, here’s an annotated, fully-functional exam... |
ctroupin/OceanData_NoteBooks | PythonNotebooks/PlatformPlots/Read_TimeSeries_3.ipynb | gpl-3.0 | %matplotlib inline
import cf
import netCDF4
import matplotlib.pyplot as plt
"""
Explanation: Reading a file using CF module
The main difference with the previous example is the way we will read the data from the file.
Instead of the netCDF4 module, we will use the cf-python package, which implements the CF data model ... |
hetaodie/hetaodie.github.io | assets/media/uda-ml/supervisedlearning/jc/为慈善机构寻找捐助者/finding_donors.ipynb | mit | # TODO：总的记录数
n_records = len(data)
# # TODO：被调查者 的收入大于$50,000的人数
n_greater_50k = len(data[data.income.str.contains('>50K')])
# # TODO：被调查者的收入最多为$50,000的人数
n_at_most_50k = len(data[data.income.str.contains('<=50K')])
# # TODO：被调查者收入大于$50,000所占的比例
greater_percent = (n_greater_50k / n_records) * 100
# 打印结果
print ("To... |
InsightSoftwareConsortium/SimpleITK-Notebooks | Python/02_Pythonic_Image.ipynb | apache-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rc("image", aspect="equal")
import SimpleITK as sitk
# Download data to work on
%run update_path_to_download_script
from downloaddata import fetch_data as fdata
"""
Explanation: Pythonic Syntactic Sugar <a href="https://mybinder.org/v2/g... |
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