repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
timothydmorton/isochrones | notebooks/batch-demo.ipynb | mit | import numpy as np
from isochrones import get_ichrone
bands = ['J', 'H', 'K', 'G', 'BP', 'RP']
mist = get_ichrone('mist', bands=bands)
from itertools import product
primary_masses = [0.8, 1.0]
mass_ratios = [0.5, 0.9]
feh_grid = [-0.25, 0.0]
age = 9.7
distance = 500
AV = 0.
m1, m2, feh, name = zip(*[(m, q*m, f, f'... |
phoebe-project/phoebe2-docs | development/tutorials/distributions.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
import phoebe
logger = phoebe.logger()
b = phoebe.default_binary()
b.add_dataset('lc', compute_phases=phoebe.linspace(0,1,101))
"""
Explanation: Distributions
Distributions are mostly useful when using samplers (which we'll see in the next tutorial on solving the inverse problem)... |
tensorflow/neural-structured-learning | g3doc/tutorials/adversarial_keras_cnn_mnist.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 u... |
mne-tools/mne-tools.github.io | 0.24/_downloads/d12911920e4d160c9fd8c97cffdda6b7/time_frequency_erds.ipynb | bsd-3-clause | # Authors: Clemens Brunner <clemens.brunner@gmail.com>
# Felix Klotzsche <klotzsche@cbs.mpg.de>
#
# License: BSD-3-Clause
"""
Explanation: Compute and visualize ERDS maps
This example calculates and displays ERDS maps of event-related EEG data. ERDS
(sometimes also written as ERD/ERS) is short for event-relat... |
Kaggle/learntools | notebooks/deep_learning_intro/raw/tut5.ipynb | apache-2.0 | #$HIDE_INPUT$
# Setup plotting
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
# Set Matplotlib defaults
plt.rc('figure', autolayout=True)
plt.rc('axes', labelweight='bold', labelsize='large',
titleweight='bold', titlesize=18, titlepad=10)
import pandas as pd
red_wine = pd.read_csv('../inpu... |
WenboTien/Crime_data_analysis | exploratory_data_analysis/.ipynb_checkpoints/UCIrvine_Crime_data_analysis-checkpoint.ipynb | mit | df = pd.read_csv('../datasets/UCIrvineCrimeData.csv');
df = df.replace('?',np.NAN)
features = [x for x in df.columns if x not in ['state', 'community', 'communityname', 'county'
, 'ViolentCrimesPerPop']]
"""
Explanation: Read the CSV
We use pandas read_csv(path/to/csv) me... |
nehal96/Deep-Learning-ND-Exercises | Transfer-Learning/Transfer_Learning.ipynb | mit | from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_s... |
mne-tools/mne-tools.github.io | dev/_downloads/33d5dd5786fed13908838e94d55ac785/90_compute_covariance.ipynb | bsd-3-clause | import os.path as op
import mne
from mne.datasets import sample
"""
Explanation: Computing a covariance matrix
Many methods in MNE, including source estimation and some classification
algorithms, require covariance estimations from the recordings.
In this tutorial we cover the basics of sensor covariance computations... |
AtmaMani/pyChakras | udemy_ml_bootcamp/Machine Learning Sections/Support-Vector-Machines/Support Vector Machines with Python.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Support Vector Machines with Python
Welcome to the Support Vector Machines with Python Lecture Notebook! Rem... |
MTG/sms-tools | notebooks/E10-1-Music-piece.ipynb | agpl-3.0 | import sys, os
sys.path.append('../software/models/')
import utilFunctions as UF
# read sounds chosen and perform the analysis
### your code here
"""
Explanation: Exercise 10-1: Music piece combining sound transformations
The aim of this exercise is to extend what you did in Exercise 8 by having no limitations on ... |
DSSatPitt/katz-python-workshop | intro-to-python/participant.ipynb | cc0-1.0 | # open the source CSV file
csv = open("cars.csv")
# create a list with the column names. we assume the first row contiains them.
# we strip the carriage return (if there is one) from the line, then split values on the commas.
# Note: this uses a nifty python feature called 'list comprehension' to do it in one line
col... |
akutuzov/webvectors | preprocessing/rusvectores_tutorial.ipynb | gpl-3.0 | import wget
udpipe_url = 'https://rusvectores.org/static/models/udpipe_syntagrus.model'
text_url = 'https://rusvectores.org/static/henry_sobolya.txt'
modelfile = wget.download(udpipe_url)
textfile = wget.download(text_url)
"""
Explanation: RusVectōrēs: семантические модели для русского языка
Елизавета Кузьменко, Анд... |
femtotrader/barchart-ondemand-client-python | notebooks/example.ipynb | bsd-3-clause | #barchart.API_KEY = 'YOURAPIKEY'
"""
Explanation: API key setup
End of explanation
"""
import datetime
import requests_cache
session = requests_cache.CachedSession(cache_name='cache',
backend='sqlite', expire_after=datetime.timedelta(days=1))
#session = None # pass a None session to avoid caching queries
"""
Ex... |
aleph314/K2 | Foundations/Python CS/Activity 08.ipynb | gpl-3.0 | def function_plot(ω0=1, ω1=1):
# Define x axis range
x = np.linspace(-4*np.pi, 4*np.pi, 100)
# Add labels to x and y axis
plt.xlabel('$x$')
plt.ylabel('$\exp(x/10) \cdot \sin(\omega_{1}x) \cdot \cos(\omega_{0}x)$')
# Limit x axis between start and end point of the range
plt.xlim(x[0], x[-1])... |
sorig/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... |
huajianmao/learning | coursera/deep-learning/4.convolutional-neural-networks/week2/.ipynb_checkpoints/pa.1.Keras - Tutorial - Happy House v1-checkpoint.ipynb | mit | import numpy as np
from keras import layers
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.models import Model
from keras.preprocessing import im... |
chengjun/iching | iching_intro.ipynb | mit | from iching import iching
iching.ichingDate(1985052620150704)
"""
Explanation: iching is a packge developed by Cheng-Jun Wang. It employs the method of Shicao prediction to reproce the prediction of I Ching--the Book of Exchanges. The I Ching ([î tɕíŋ]; Chinese: 易經; pinyin: Yìjīng), also known as the Classic of Cha... |
opalytics/opalytics-ticdat | examples/expert_section/notebooks/pandas_and_ticdat.ipynb | bsd-2-clause | import ticdat.testing.testutils as tdu
from ticdat import TicDatFactory
tdf = TicDatFactory(**tdu.netflowSchema())
dat = tdf.copy_tic_dat(tdu.netflowData())
"""
Explanation: pandas and ticdat
pandas is arguably the most successful data library in history, not just for Python but across all languages. That said, in th... |
statsmodels/statsmodels.github.io | v0.13.1/examples/notebooks/generated/statespace_chandrasekhar.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from pandas_datareader.data import DataReader
"""
Explanation: State space models - Chandrasekhar recursions
End of explanation
"""
cpi_apparel = DataReader('CPIAPPNS', 'fred', start='1986')
cpi_a... |
zrhans/python | exemplos/manipulacao-estatistica-de-dados-meteorologicos.ipynb | gpl-2.0 | import pandas as pd
from pandas import DataFrame
import datetime
import pandas.io.data ## Vamos utilizar para acesso à API do Yahoo finance e importação de dados
import matplotlib.pyplot as plt
csna3 = pd.io.data.get_data_yahoo('CSNA3.SA',
start = datetime.datetime(2000,10,1),
... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_decoding_csp_space.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Romain Trachel <romain.trachel@inria.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
"""
... |
zhouqifanbdh/liupengyuan.github.io | chapter1/homework/localization/201621198175.ipynb | mit | def product_sum(end):
i = 1
total_n = 1
while i < end:
i += 1
total_n *= i
return total_n
m = int(input("请输入第1个整数，以回车结束:"))
n = int(input("请输入第2个整数，以回车结束:"))
k = int(input("请输入第3个整数，以回车结束:"))
print("最终的和是：",product_sum(m)+product_sum(n)+product_sum(k))
"""
Explanation: 练习 1：仿照求 ∑... |
patrickmineault/xcorr-notebooks | notebooks/Multi-armed bandit as a Markov decision process.ipynb | mit | import itertools
import numpy as np
from pprint import pprint
def sorted_values(dict_):
return [dict_[x] for x in sorted(dict_)]
def solve_bmab_value_iteration(N_arms, M_trials, gamma=1,
max_iter=10, conv_crit = .01):
util = {}
# Initialize every state to utility 0.
... |
enbanuel/phys202-2015-work | assignments/assignment04/MatplotlibEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Matplotlib Exercise 2
Imports
End of explanation
"""
!head -n 30 open_exoplanet_catalogue.txt
"""
Explanation: Exoplanet properties
Over the past few decades, astronomers have discovered thousands of extrasolar planets. The follo... |
kubeflow/kfp-tekton-backend | samples/core/lightweight_component/lightweight_component.ipynb | apache-2.0 | # Install the SDK
#!pip3 install 'kfp>=0.1.31.2' --quiet
import kfp
import kfp.components as comp
"""
Explanation: Lightweight python components
Lightweight python components do not require you to build a new container image for every code change.
They're intended to use for fast iteration in notebook environment.
Bu... |
tensorflow/docs-l10n | site/en-snapshot/model_optimization/guide/quantization/training_comprehensive_guide.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... |
luchorivera/Prueba | Kaggle_Panda_Curso.ipynb | mit | import pandas as pd
"""
Explanation: <a href="https://colab.research.google.com/github/luchorivera/Prueba/blob/master/Kaggle_Panda_Curso.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
https://www.kaggle.com/residentmario/creating-reading-and-writin... |
vipmunot/Data-Science-Course | Data Visualization/Lab 8/w08_lab_Vipul_Munot.ipynb | mit | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import scipy.stats as ss
import warnings
warnings.filterwarnings("ignore")
sns.set_style('white')
%matplotlib inline
"""
Explanation: W8 Lab Assignment
End of explanation
"""
x = np.array([1, 1, 1,1, 10, 100, 1000])
y = np... |
jamesmarva/maths-with-python | 10-generators.ipynb | mit | def naivesum_list(N):
"""
Naively sum the first N integers
"""
A = 0
for i in list(range(N + 1)):
A += i
return A
"""
Explanation: Iterators and Generators
In the section on loops we introduced the range function, and said that you should think about it as creating a list of numbers. In Python 2.X th... |
ellisonbg/talk-2014 | Jupyter and IPython.ipynb | mit | from IPython.display import display, Image, HTML
from talktools import website, nbviewer
"""
Explanation: Projects Jupyter and IPython
End of explanation
"""
import ipythonproject
ipythonproject.core_devs()
"""
Explanation: Overview
Jupyter and IPython are a pair of open source projects that together offer an open... |
JAmarel/Phys202 | Matplotlib/MatplotlibEx03.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Matplotlib Exercise 3
Imports
End of explanation
"""
def well2d(x, y, nx, ny, L=1.0):
"""Compute the 2d quantum well wave function."""
return (2/L)*np.sin(nx * np.pi * x/L)*np.sin(ny * np.pi * y/L)
psi = well2d(np.linspac... |
3DGenomes/tadbit | doc/notebooks/install.ipynb | gpl-3.0 | %%bash
wget -nv https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh
"""
Explanation: Installing TADbit on GNU/Linux
TADbit requires python2 >= 2.6 or python3 >= 3.6 as well as several dependencies that are listed below.
Dependencies
Conda
Conda (http://conda.pydata.org/docs/index.htm... |
andher/labs | Modeling Oscellation.ipynb | gpl-3.0 | %matplotlib inline
"""
Explanation: Esteban Martinez, Andres Heredia
Introduction
The purpose of this lab is to find out the effects of mass on the oscillation of a spring scale. We will put several weights on the scale and record the oscillation time five times, after which we will take the average.
Procedure
$$Osci... |
UChicagoPhysics/SampleExercises | exercises/electricityAndMagnetism/Poynting Vector of Half-Wave Antenna.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pylab as plt
"""
Explanation: Poynting Vector of Half-Wave Antenna
PROGRAM: Poynting vector of half-wave antenna
CREATED: 5/30/2018
Import packages.
End of explanation
"""
#Define constants - permeability of free space, speed of light, current amplitude.
u_0 = 1.26 * 10**(-6)
c... |
robertoalotufo/ia898 | master/tutorial_ti_2.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import sys,os
ia898path = os.path.abspath('/etc/jupyterhub/ia898_1s2017/')
if ia898path not in sys.path:
sys.path.append(ia898path)
import ia898.src as ia
"""
Explanation: Table of Contents
<p><div class="lev1 to... |
huazhisong/race_code | kaggle_ws/titanic_ws/Titanic Data Science Solutions.ipynb | gpl-3.0 | # data analysis and wrangling
import pandas as pd
import numpy as np
import random as rnd
# visualization
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
# machine learning
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import ... |
rsignell-usgs/notebook | HOPS/hops_velocity3.ipynb | mit | from netCDF4 import Dataset
#url = ('http://geoport.whoi.edu/thredds/dodsC/usgs/data2/rsignell/gdrive/'
# 'nsf-alpha/Data/MIT_MSEAS/MSEAS_Tides_20160317/mseas_tides_2015071612_2015081612_01h.nc')
url = ('/usgs/data2/rsignell/gdrive/'
'nsf-alpha/Data/MIT_MSEAS/MSEAS_Tides_20160317/mseas_tides_2015071612_20... |
sauravrt/signal-processing | ipynb/BeamformingFFT.ipynb | gpl-2.0 | from IPython.display import YouTubeVideo
YouTubeVideo('DVi1TC24_BY')
"""
Explanation: Beamforming and FFT
This notebook explains the relation between spatial beamforming and the time domain FFT operation and show how beamforming can be implemented using FFT. The content presented below is loosely based on a tutorial b... |
maartenbreddels/vaex | examples/healpix_plotting.ipynb | mit | # Make sure you have healpy installed by running either command
#!conda install -c conda-forge healpy
#!pip install healpy
import vaex as vx
import healpy as hp
%matplotlib inline
tgas = vx.datasets.tgas.fetch()
"""
Explanation: Healpix plotting
End of explanation
"""
level = 2
factor = 34359738368 * (4**(12-level... |
MLWave/kepler-mapper | docs/notebooks/KeplerMapper-Newsgroup20-Pipeline.ipynb | mit | # from kmapper import jupyter
import kmapper as km
import numpy as np
from sklearn.datasets import fetch_20newsgroups
from sklearn.cluster import AgglomerativeClustering
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import TruncatedSVD
from sklearn.manifold import Isomap
from s... |
martinjrobins/hobo | examples/interfaces/statsmodels-state-space.ipynb | bsd-3-clause | import pints
import pints.toy as toy
import pints.plot
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Interface to statsmodels: state space time series models
This notebook provides a short exposition of how it is possible to interface with the cornucopia of time series models provided by the stat... |
ngcm/training-public | FEEG6016 Simulation and Modelling/01-Monte-Carlo-Lab-1.ipynb | mit | from IPython.core.display import HTML
css_file = 'https://raw.githubusercontent.com/ngcm/training-public/master/ipython_notebook_styles/ngcmstyle.css'
HTML(url=css_file)
"""
Explanation: Monte Carlo Methods: Lab 1
Take a look at Chapter 10 of Newman's Computational Physics with Python where much of this material is dr... |
tpin3694/tpin3694.github.io | machine-learning/handling_missing_values_in_time_series.ipynb | mit | # Load libraries
import pandas as pd
import numpy as np
"""
Explanation: Title: Handling Missing Values In Time Series
Slug: handling_missing_values_in_time_series
Summary: How to handle the missing values in time series in pandas for machine learning in Python.
Date: 2017-09-11 12:00
Category: Machine Learning
Tag... |
geektoni/shogun | doc/ipython-notebooks/distributions/KernelDensity.ipynb | bsd-3-clause | import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
%matplotlib inline
import os
import shogun as sg
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
# generates samples from the distribution
def generate_samples(n_samples,mu1,sigma1,mu2,sigma2):
samples1 = np.random.normal(... |
amirziai/learning | deep-learning/fully-convolutional-networks.ipynb | mit | import numpy as np
import tensorflow as tf
import collections
"""
Explanation: Fully Convolutional Networks (FCN)
Notes from Udacity's Self-Driving Car Nanodegree
- Encoder extracts features that the decoder uses layer
Pieces:
- Pre-train encoder on VGG/ResNet
- Do a 1x1 convolution
- Tansposed convolutions to upsampl... |
mwickert/SP-Comm-Tutorial-using-scikit-dsp-comm | tutorial_part1/IIR Filter Design and C Headers.ipynb | bsd-2-clause | fs = 48000
f_pass = 5000
f_stop = 8000
b_but,a_but,sos_but = iir_d.IIR_lpf(f_pass,f_stop,0.5,60,fs,'butter')
b_cheb1,a_cheb1,sos_cheb1 = iir_d.IIR_lpf(f_pass,f_stop,0.5,60,fs,'cheby1')
b_cheb2,a_cheb2,sos_cheb2 = iir_d.IIR_lpf(f_pass,f_stop,0.5,60,fs,'cheby2')
b_elli,a_elli,sos_elli = iir_d.IIR_lpf(f_pass,f_stop,0.5,60... |
nansencenter/nansat-lectures | notebooks/12 Nansat Use Case 03.ipynb | gpl-3.0 | # download sample files
!wget -P data -nc ftp://ftp.nersc.no/nansat/test_data/obpg_l2/A2015121113500.L2_LAC.NorthNorwegianSeas.hdf
!wget -P data -nc ftp://ftp.nersc.no/nansat/test_data/obpg_l2/A2015122122000.L2_LAC.NorthNorwegianSeas.hdf
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import Im... |
sebp/scikit-survival | doc/user_guide/boosting.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
from sklearn.model_selection import train_test_split
from sksurv.datasets import load_breast_cancer
from sksurv.ensemble import ComponentwiseGradientBoostingSurvivalAnalysis
from sksurv.ensemble import GradientBoostingSurvivalAna... |
phobson/statsmodels | examples/notebooks/regression_diagnostics.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import print_function
from statsmodels.compat import lzip
import statsmodels
import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
import statsmodels.stats.api as sms
import matplotlib.pyplot as plt
# Load data
url = 'http://vincentarelbundock.github.io/Rdatas... |
mne-tools/mne-tools.github.io | 0.14/_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... |
VokhmintcevKirill/ti-nic_competition2 | Titanic_3.0.ipynb | mit | train.info()
test.info()
"""
Explanation: В ходе решения Titanic_2.0 мы получили baseline- 0.76077, проведем глубокое исследование данных и попытаемся улучшить эти значения
Только 2 непрерывных признака, остальные дискретные. Возмодно можно будет понастраивать кодирование признаков
End of explanation
"""
train.Surv... |
dahlend/Physics77Fall17 | Workshop 3 - Practice Makes Perfect.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
import numpy as np
# Base Python range() doesn't allow decimal numbers
# numpy improved and made thier own:
t = np.arange(0.0, 1., 0.01)
y = t**3.
plt.plot(100 * t, y)
plt.xlabel('Time (% of semester)')
plt.ylabel('Enjoyment of Fridays')
plt.title('Happiness over Time')
plt.show()
"... |
rafburzy/Python_EE | RL_and_RLC_circuit/RLC_circuit_current_v2.ipynb | bsd-3-clause | #importing all required modules
#important otherwise pop-up window may not work
%matplotlib inline
import numpy as np
import scipy as sp
from scipy.integrate import odeint, ode, romb, cumtrapz
import matplotlib as mpl
import matplotlib.pyplot as plt
from math import *
import seaborn
from IPython.display import Image
... |
eford/rebound | ipython_examples/FourierSpectrum.ipynb | gpl-3.0 | import rebound
import numpy as np
sim = rebound.Simulation()
sim.units = ('AU', 'yr', 'Msun')
sim.add("Sun")
sim.add("Jupiter")
sim.add("Saturn")
"""
Explanation: Fourier analysis & resonances
A great benefit of being able to call rebound from within python is the ability to directly apply sophisticated analysis tools... |
vzg100/Post-Translational-Modification-Prediction | .ipynb_checkpoints/Phosphorylation Sequence Tests -isolation_forest -dbptm+ELM-checkpoint.ipynb | mit | from pred import Predictor
from pred import sequence_vector
from pred import chemical_vector
"""
Explanation: Template for test
End of explanation
"""
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
for i in par:
try:
print("y", i)
y = Predictor()
y.load_da... |
mitdbg/modeldb | client/workflows/demos/census-end-to-end-s3-example.ipynb | mit | # restart your notebook if prompted on Colab
try:
import verta
except ImportError:
!pip install verta
"""
Explanation: Logistic Regression with Grid Search (scikit-learn)
<a href="https://colab.research.google.com/github/VertaAI/modeldb/blob/master/client/workflows/demos/census-end-to-end-s3-example.ipynb" tar... |
tuanavu/coursera-university-of-washington | machine_learning/3_classification/assigment/week2/module-3-linear-classifier-learning-assignment-blank.ipynb | mit | import graphlab
"""
Explanation: Implementing logistic regression from scratch
The goal of this notebook is to implement your own logistic regression classifier. You will:
Extract features from Amazon product reviews.
Convert an SFrame into a NumPy array.
Implement the link function for logistic regression.
Write a f... |
dwhswenson/contact_map | examples/custom_plotting.ipynb | lgpl-2.1 | %matplotlib inline
import matplotlib.pyplot as plt
import mdtraj as md
traj = md.load("5550217/kras.xtc", top="5550217/kras.pdb")
from contact_map import ContactFrequency
traj_contacts = ContactFrequency(traj)
frame_contacts = ContactFrequency(traj[0])
diff = traj_contacts - frame_contacts
"""
Explanation: Customizin... |
davek44/Basset | tutorials/prepare_compendium.ipynb | mit | !cd ../data; preprocess_features.py -y -m 200 -s 600 -o er -c genomes/human.hg19.genome sample_beds.txt
"""
Explanation: In this tutorial, we'll walk through downloading and preprocessing the compendium of ENCODE and Epigenomics Roadmap data.
This part won't be very iPython tutorial-ly...
First cd in the terminal over... |
Small-Bodies-Node/pds4-python | notebooks/spectrum-example-hyakutake.ipynb | bsd-3-clause | from urllib.request import urlretrieve # to download the data
from pds4_tools import pds4_read # to read and inspect the data and metadata
import matplotlib.pyplot as plt # for plotting
# for plotting in Jupyter notebooks
%matplotlib notebook
# Download data from PDS SBN
label_fn, headers = urlretrieve('... |
mathnathan/notebooks | dissertation/.ipynb_checkpoints/tests_for_colloquium-checkpoint.ipynb | mit | p = GMM([1.0], np.array([[0.5,0.05]]))
num_samples = 1000
beg = 0.0
end = 1.0
t = np.linspace(beg,end,num_samples)
num_neurons = len(p.pis)
colors = [np.random.rand(num_neurons,) for i in range(num_neurons)]
p_y = p(t)
p_max = p_y.max()
np.random.seed(110)
num_neurons = 1
network = Net(1,1,num_neurons, bias=0.0006, ... |
prk327/CoAca | 6_Grouping_and_Summarising.ipynb | gpl-3.0 | # Loading libraries and files
import numpy as np
import pandas as pd
market_df = pd.read_csv("../global_sales_data/market_fact.csv")
customer_df = pd.read_csv("../global_sales_data/cust_dimen.csv")
product_df = pd.read_csv("../global_sales_data/prod_dimen.csv")
shipping_df = pd.read_csv("../global_sales_data/shipping_... |
tensorflow/docs-l10n | site/zh-cn/federated/tutorials/simulations.ipynb | apache-2.0 | #@test {"skip": true}
!pip install --quiet --upgrade tensorflow-federated-nightly
!pip install --quiet --upgrade nest-asyncio
import nest_asyncio
nest_asyncio.apply()
import collections
import time
import tensorflow as tf
import tensorflow_federated as tff
source, _ = tff.simulation.datasets.emnist.load_data()
d... |
DS-100/sp17-materials | sp17/disc/disc12/disc12.ipynb | gpl-3.0 | import numpy as np
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
import ds100
"""
Explanation: K-means
End of explanation
"""
np.random.seed(13337)
c1 = np.random.randn(25, 2)
c2 = np.array([2, 8]) + np.random.randn(25, 2)
c3 = np.array([8, 4]) + np.random.randn(25, 2)
x1 = np.vstack((c1, c2,... |
Kaggle/learntools | notebooks/deep_learning/raw/ex4_transfer_learning.ipynb | apache-2.0 | # Set up code checking
from learntools.core import binder
binder.bind(globals())
from learntools.deep_learning.exercise_4 import *
print("Setup Complete")
"""
Explanation: Exercise Introduction
The cameraman who shot our deep learning videos mentioned a problem that we can solve with deep learning.
He offers a servi... |
cxhernandez/msmbuilder | examples/advanced/hmm-and-msm.ipynb | lgpl-2.1 | from __future__ import print_function
import os
%matplotlib inline
from matplotlib.pyplot import *
from msmbuilder.featurizer import SuperposeFeaturizer
from msmbuilder.example_datasets import AlanineDipeptide
from msmbuilder.hmm import GaussianHMM
from msmbuilder.cluster import KCenters
from msmbuilder.msm import Mark... |
getsmarter/bda | module_2/M2_NB3_CollectYourOwnData.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib
import os
import bandicoot as bc
from IPython.display import IFrame
%matplotlib inline
matplotlib.rcParams['figure.figsize'] = (10, 8)
"""
Explanation: <div align="right">Python 3.6 Jupyter Notebook</div>
Collect your own data
Your completion of the notebook ex... |
tien-le/kaggle-titanic | Applying Machine Learning Techniques-Regression.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import random
"""
Explanation: Applying Machine Learning Techniques-Regression
Homepage: https://github.com/tien-le/kaggle-titanic
Updating later ...
End of explanation
"""
#Training Corpus
trn_corpus_aft... |
vinhqdang/my_mooc | coursera/advanced_machine_learning_spec/4_nlp/natural-language-processing-master/week1/week1-MultilabelClassification.ipynb | mit | import sys
sys.path.append("..")
from download_utils import download_week1_resources
download_week1_resources()
"""
Explanation: Predict tags on StackOverflow with linear models
In this assignment you will learn how to predict tags for posts from StackOverflow. To solve this task you will use multilabel classificatio... |
google/starthinker | colabs/trends_places_to_sheets_via_value.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: Trends Places To Sheets Via Values
Move using hard coded WOEID values.
License
Copyright 2020 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 obt... |
p-chambers/Python_OOP_Workshop | soln/02-Classes_pt2.ipynb | mit | from IPython.core.display import HTML
def css_styling():
sheet = '../css/custom.css'
styles = open(sheet, "r").read()
return HTML(styles)
css_styling()
"""
Explanation: Python OOP 2: Inheritance and Magic Methods
The purpose of this exercise is to test your new found knowledge of inheritance using the cla... |
as595/AllOfYourBases | Nuclear/PoissonMLRealData.ipynb | gpl-3.0 | def gauss_fn(p0, x):
amp,mu,sigma,gamma = p0
model = SkewedGaussianModel()
#amp*=sigma*np.sqrt(2*np.pi)
# set initial parameter values
params = model.make_params(amplitude=amp, center=mu, sigma=sigma, gamma=gamma)
ymod = model.eval(params=params,x=x)
return ymod
def lnlike(p... |
gsorianob/fiuba-python | Clase 04 - Excepciones, funciones lambda, búsquedas y ordenamientos.ipynb | apache-2.0 | lista_de_numeros = [1, 6, 3, 9, 5, 2]
lista_ordenada = sorted(lista_de_numeros)
print lista_ordenada
"""
Explanation: <!--
27/10
Ordenamientos y búsquedas.
Excepciones. Funciones anónimas.(Pablo o Andres)
-->
Ordenamiento de listas
Las listas se pueden ordenar fácilmente usando la función sorted:
End of explanation
"... |
DavidLeoni/relmath | bq-examples/Marks/Image.ipynb | apache-2.0 | import ipywidgets as widgets
import os
image_path = os.path.abspath('../data_files/trees.jpg')
with open(image_path, 'rb') as f:
raw_image = f.read()
ipyimage = widgets.Image(value=raw_image, format='jpg')
ipyimage
"""
Explanation: The Image Mark
Image is a Mark object, used to visualize images in standard forma... |
doingmathwithpython/pycon-us-2016 | notebooks/.ipynb_checkpoints/slides-checkpoint.ipynb | mit | As I will attempt to describe in the next slides, Python is an amazing way to lead to a more fun learning and teaching
experience.
It can be a basic calculator, a fancy calculator and
Math, Science, Geography..
Tools that will help us in that quest are:
"""
Explanation: <center> Doing Math with Python </center>
<c... |
takashi-suehiro/rtmtools | rtc_handle_example/script/basic.ipynb | mit | #!/usr/bin/env python
# -*- Python -*-
import sys
import time
import subprocess
"""
Explanation: rtc_handle.py（basic）
this ipnb shows a basic usage of rtc_handle.py
precondition: rtcs(cin and cout) are prelaunched separetely
you can monitor the behavior of the system with openrtp
you can access and control rtcs of ... |
akloster/table-cleaner | docs/source/Tutorial.ipynb | bsd-2-clause | import numpy as np
import pandas as pd
from IPython import display
import table_cleaner as tc
"""
Explanation: Tutorial
This tutorial will show you how to use the Table-Cleaner validation framework.
First, let's import the necessary modules. My personal style is to abbreviate
the scientific python libraries with two l... |
retnuh/deep-learning | embeddings/Skip-Gram_word2vec.ipynb | mit | import time
import numpy as np
import tensorflow as tf
import utils
from collections import Counter
import random
"""
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... |
jacquerie/senato.py | cirinna.ipynb | mit | import os
import re
from itertools import combinations
import xml.etree.ElementTree as ET
from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
%matplotlib inline
DATA_FOLDER = 'data/cirinna'
NAMESPACE = {'an': 'http://docs.oasis-open.org/legaldocml/ns/akn/3.0/CSD03'}
ALPHANUM_... |
crystalzhaizhai/cs207_yi_zhai | homeworks/HW6/HW6_finished.ipynb | mit | from enum import Enum
class AccountType(Enum):
SAVINGS = 1
CHECKING = 2
"""
Explanation: Homework 6
Due: Tuesday, October 10 at 11:59 PM
Problem 1: Bank Account Revisited
We are going to rewrite the bank account closure problem we had a few assignments ago, only this time developing a formal class for a Bank ... |
google/applied-machine-learning-intensive | content/04_classification/06_images_and_video/02-video_processing.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... |
darshanbagul/ComputerVision | FourierTransforms/FourierTransforms.ipynb | gpl-3.0 | %matplotlib inline
import cv2
import numpy as np
import matplotlib.pyplot as plt
import cmath
"""
Explanation: Fourier Transform
Problem 1.
In this problem, given an image we perform the following tasks:
1. Compute its Fourier Transform
2. Try to reconstruct the image by applying Inverse Fourier Transform to the Four... |
zzsza/Datascience_School | 11. 기초 확률론4 - 상관 관계/02. 확률 밀도 함수의 독립.ipynb | mit | np.set_printoptions(precision=4)
pmf1 = np.array([[0, 1, 2, 3, 2, 1],
[0, 2, 4, 6, 4, 2],
[0, 4, 8,12, 8, 4],
[0, 2, 4, 6, 4, 2],
[0, 1, 2, 3, 2, 1]])
pmf1 = pmf1/pmf1.sum()
pmf1
sns.heatmap(pmf1)
plt.xlabel("x")
plt.ylabel("y")
plt.title("Joint Proba... |
alfkjartan/control-computarizado | polynomial-design/notebooks/Polynomial design exercise.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import control
import sympy as sy
"""
Explanation: Effect of cancelling a process zero
The following exercise is taken from Åström & Wittenmark (problem 5.3)
Consider the system with pulse-transfer function
$$ H(z) = \frac{z+0.7}{z^2 - 1.8z + 0.81}.$$
Use polynomial ... |
steven-murray/halomod | devel/using_angular_corr.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import simps
from halomod.integrate_corr import AngularCF, angular_corr_gal, flat_z_dist, dxdz
from hmf.cosmo import Cosmology
from mpmath import gamma as Gamma
"""
Explanation: Using the Angular Correlation Function
End of exp... |
CRPropa/CRPropa3 | doc/pages/example_notebooks/Diffusion/DiffusionValidationI.v4.ipynb | gpl-3.0 | %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.stats import chisquare
from scipy.integrate import quad
from crpropa import *
#figure settings
A4heigth = 29.7/2.54
A4width = 21./2.54
"""
Explanation: Diffusion Validation I
This notebook simul... |
7deeptide/Design-Optimization | Homeworks/ME596_Homework_4.ipynb | gpl-3.0 | import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from __future__ import division
%config InlineBackend.figure_formats=['svg']
%matplotlib inline
plt.rc('pdf',fonttype=3) # for proper subsetting of fonts
plt.rc('axes',linewidth=0.5) # thin axes; the default for lines is 1pt
al = np.lins... |
espressomd/espresso | doc/tutorials/lattice_boltzmann/lattice_boltzmann_poiseuille_flow.ipynb | gpl-3.0 | import logging
import sys
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})
import numpy as np
import tqdm
import espressomd
import espressomd.lb
import espressomd.lbboundaries
import espressomd.shapes
logging.basicConfig(level=logging.INFO, stream=sys.stdout)
espressomd.asse... |
privong/pythonclub | sessions/07-pandas/01 - Pandas tutorial.ipynb | gpl-3.0 | import numpy as np
from __future__ import print_function
import pandas as pd
pd.__version__
"""
Explanation: Reading and manipulating datasets with Pandas
This notebook shows how to create Series and Dataframes with Pandas. Also, how to read CSV files and creaate pivot tables. The first part is based on the chapter... |
pbutenee/ml-tutorial | release/1/anomaly_detection.ipynb | mit | import pickle
with open('data/past_data.pickle', 'rb') as file:
past = pickle.load(file, encoding='latin1')
with open('data/all_data.pickle', 'rb') as file:
all_data = pickle.load(file, encoding='latin1')
print(f'Past data shape = {past.shape}')
print(f'Full data shape = {all_data.shape}')
"""
Explanati... |
sertansenturk/tomato | demos/joint_analysis_demo.ipynb | agpl-3.0 | data_folder = os.path.join('..', 'sample-data')
# score inputs
symbtr_name = 'ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede'
txt_score_filename = os.path.join(data_folder, symbtr_name, symbtr_name + '.txt')
mu2_score_filename = os.path.join(data_folder, symbtr_name, symbtr_name + '.mu2')
# instantiate
audio_mbid ... |
jotterbach/SimpleNeuralNets | examples/Causal_vs_Noncausal_Learning.ipynb | apache-2.0 | %load_ext autoreload
import numpy as np
from numpy.polynomial.polynomial import polyval
import numpy.random as rd
import matplotlib.pyplot as plt
import seaborn as sns
import sys
%matplotlib inline
sys.path.append('./NeuralNetworks')
# Configuration for plots
fig_size = (12,8)
font_size = 14
"""
Explanation: Causa... |
biof-309-python/BIOF309-2016-Fall | Week_08/Week 08 - 02 - Dictionaries.ipynb | mit | dna = "ATCGATCGATCGTACGCTGA"
a_count = dna.count("A")
"""
Explanation: Dictionaries, (Sets, Tuples)
Source: This materials is adapted from Python for Biologists and Learn Python 3 in Y Minutes.
You can read more about dictionaries and tuples in the Python for Everyone book.
Storing paired data
Suppose we want to count... |
johnnyliu27/openmc | examples/jupyter/mg-mode-part-iii.ipynb | mit | import os
import matplotlib.pyplot as plt
import numpy as np
import openmc
%matplotlib inline
"""
Explanation: This Notebook illustrates the use of the the more advanced features of OpenMC's multi-group mode and the openmc.mgxs.Library class. During this process, this notebook will illustrate the following features... |
vipmunot/Data-Science-Course | Data Visualization/Lab 3/lab03_Munot_Vipul.ipynb | mit | import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings('ignore')
"""
Explanation: W3 Lab Assignment
Submit the .ipynb file to Canvas with file name w03_lab_lastname_firstname.ipynb.
In this lab, we will introduce pandas, matplotlib, and seaborn and continue to use the imdb.csv file from the l... |
rdempsey/python-for-sharing | pandas-for-noobs/Three Pandas Tips for Pandas Noobs.ipynb | mit | # Import the Python libraries we need
import pandas as pd
# Define a variable for the accidents data file
f = './data/accidents1k.csv'
# Use read_csv() to import the data
accidents = pd.read_csv(f,
sep=',',
header=0,
index_col=False,
... |
TheMitchWorksPro/DataTech_Playground | PY_Basics/TMWP_DictionaryBasics.ipynb | mit | # Ex39 in Learn Python the Hard Way:
# https://learnpythonthehardway.org/book/ex39.html
# edited, expanded, and made PY3.x compliant by Mitch before inclusion in this notebook
# create a mapping of state to abbreviation
states = {
'Oregon': 'OR',
'Florida': 'FL',
'California': 'CA',
'New York': 'NY',
... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/computer_vision_fun/labs/classifying_images_using_dropout_and_batchnorm_layer.ipynb | apache-2.0 | import tensorflow as tf
print(tf.version.VERSION)
"""
Explanation: Classifying Images using Dropout and Batchnorm Layer
Introduction
In this notebook, you learn how to build a neural network to classify the tf-flowers dataset using dropout and batchnorm layer.
Learning objectives
Define Helper Functions.
Apply dropou... |
dnxbjyj/python-basic | libs/ConfigParser/handout.ipynb | mit | import ConfigParser
cf = ConfigParser.ConfigParser()
cf.read('./sys.conf')
"""
Explanation: 用ConfigParser模块读写conf配置文件
ConfigParser是Python内置的一个读取配置文件的模块，用它来读取和修改配置文件非常方便，本文介绍一下它的基本用法。
数据准备
假设当前目录下有一个名为sys.conf的配置文件，其内容如下：
```bash
[db]
db_host=127.0.0.1
db_port=22
db_user=root
db_pass=root123
[concurrent]
thread = ... |
rsignell-usgs/ipython-notebooks | files/ncSOS_and_OWSlib.ipynb | unlicense | %matplotlib inline
from owslib.sos import SensorObservationService
import pdb
from owslib.etree import etree
import pandas as pd
import datetime as dt
import numpy as np
url = 'http://sdf.ndbc.noaa.gov/sos/server.php?request=GetCapabilities&service=SOS&version=1.0.0'
ndbc = SensorObservationService(url)
# usgs woods... |
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