repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
NORCatUofC/rain | sewer-overflows/notebooks/River Reversals vs. Rainfall.ipynb | mit | # Get River reversals
reversals = pd.read_csv('data/lake_michigan_reversals.csv')
reversals['start_date'] = pd.to_datetime(reversals['start_date'])
reversals.head()
# Create rainfall dataframe. Create a series that has hourly precipitation
rain_df = pd.read_csv('data/ohare_hourly_20160929.csv')
rain_df['datetime'] = ... |
csdms/coupling | docs/demos/ku.ipynb | mit | # Load standard Python modules
import numpy as np
import matplotlib.pyplot as plt
# Load PyMT model(s)
import pymt.models
ku = pymt.models.Ku()
"""
Explanation: Kudryavtsev Model
Link to this notebook: https://github.com/csdms/pymt/blob/master/docs/demos/ku.ipynb
Install command: $ conda install notebook pymt_permam... |
nick-youngblut/SIPSim | ipynb/sandbox/sandbox.ipynb | mit | from scipy import stats
import numpy as np
# making kde
values = np.arange(10)
kde = stats.gaussian_kde(values)
np.median(kde.resample(100000))
def KDE_make_means(kde, size=10):
func = lambda x : np.random.randint(0, x.n, size=x.d)
kde.means = [kde.dataset[:, func(kde)] for i in xrange(size)]
kde.means_n... |
deepmind/abcei_mab | notebooks/paper_experiments.ipynb | apache-2.0 | # Copyright 2022 DeepMind Technologies Limited
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... |
tleonhardt/machine_learning | SL9_Bayseian_Learning.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Naive Bayes Classifiers
Naive Bayes classifiers are a family of classifiers that are quite similar to the linear models discussed previously. However, they tend to be even faster in training. The price paid for this efficiency is th... |
jason-neal/eniric | docs/Notebooks/spectral_gradient.ipynb | mit | import matplotlib
matplotlib.rcParams["text.usetex"] = False
matplotlib.rcParams["text.latex.unicode"] = True
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
from eniric.utilities import load_aces_spectrum
# from eniric.precision import slope, slope_grad
def slope(wavelength, flux):
"""O... |
tensorflow/examples | courses/udacity_intro_to_tensorflow_lite/tflite_c04_exercise_convert_model_to_tflite_solution.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... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_stats_cluster_spatio_temporal_repeated_measures_anova.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Eric Larson <larson.eric.d@gmail.com>
# Denis Engemannn <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
from numpy.random import randn
import matplotlib.pyplot as plt
import mne
f... |
GoogleCloudPlatform/vertex-ai-samples | community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/mlops_pipeline_tf_agents_bandits_movie_recommendation.ipynb | apache-2.0 | import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG = "--user"
! pip3 install {... |
mne-tools/mne-tools.github.io | dev/_downloads/f61f2c2f6132bd3b1076dab7ac194713/20_events_from_raw.ipynb | bsd-3-clause | import os
import numpy as np
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
raw.crop(tmax=60).load_data()
"""
Explanati... |
wmfschneider/CHE30324 | Homework/HW2-soln.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
m = 32/1000/(6.022*10**23) #mass of O2 (kg/molecule)
k = 1.38064852*10**-23 # Boltzmann Constant (m^2 kg s^-2 K^-1)
v = np.linspace(0,1500,1000) #velocity (m/s)
for T in [200,400,600]:
P = (m*v/k/T)*np.exp(-m*v**2/(2*k*T)) #Maxwell-Boltzman speed distribution for a... |
CompPhysics/MachineLearning | doc/Programs/ANN/Ann2.ipynb | cc0-1.0 | # %load partSix.py
# Neural Networks Demystified
# Part 6: Training
#
# Supporting code for short YouTube series on artificial neural networks.
#
# Stephen Welch
# @stephencwelch
## ----------------------- Part 1 ---------------------------- ##
import numpy as np
# X = (hours sleeping, hours studying), y = Score on ... |
tensorflow/docs-l10n | site/ja/r1/tutorials/keras/save_and_restore_models.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... |
gururajl/deep-learning | dcgan-svhn/DCGAN_Exercises.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... |
google/jax-md | notebooks/flocking.ipynb | apache-2.0 | #@title Imports & Utils
# Imports
!pip install -q git+https://www.github.com/google/jax-md
import numpy as onp
from jax.config import config ; config.update('jax_enable_x64', True)
import jax.numpy as np
from jax import random
from jax import jit
from jax import vmap
from jax import lax
vectorize = np.vectorize
fr... |
subutai/htmresearch | projects/sparse_rl/Plot_SparseRL_Results.ipynb | agpl-3.0 | # !python sparse_dqn.py --sparse --timesteps 100000 --results_path results.sparse
# !python sparse_dqn.py --dense --timesteps 100000 --results_path results.dense
sparse = pd.read_csv('results.sparse/SeaquestDeterministic-v4.train.csv')
dense = pd.read_csv('results.dense/SeaquestDeterministic-v4.train.csv')
"""
Explan... |
borja876/Thinkful-DataScience-Borja | Household+Electricity+Consumption+1990-2014.ipynb | mit | #Europe
df5 = df4.loc[df4.index.isin(['Austria', 'Belgium', 'Bulgaria','Croatia', 'Cyprus', 'Czechia','Denmark', 'Estonia','Finland','France','Germany','Greece','Hungary','Ireland','Italy','Latvia','Lithuania','Luxembourg','Malta','Netherlands','Poland','Portugal','Romania','Slovakia', 'Slovenia','Spain', 'Sweden', 'Un... |
junhwanjang/DataSchool | Lecture/05. 기초 선형 대수 1 - 행렬의 정의와 연산/1) NumPy 소개.ipynb | mit | import numpy as np
a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
print(type(a))
a
"""
Explanation: NumPy 소개
NumPy(보통 "넘파이"라고 발음한다)는 2005년에 Travis Oliphant가 발표한 수치해석용 Python 패키지이다. 다차원의 행렬 자료구조인 ndarray 를 지원하여 벡터와 행렬을 사용하는 선형대수 계산에 주로 사용된다. 내부적으로는 BLAS 라이브러리와 LAPACK 라이브러리에 기반하고 있어서 C로 구현된 CPython에서만 사용할 수 있으며 Jython, Ir... |
AllenDowney/CompStats | sampling_soln.ipynb | mit | from __future__ import print_function, division
import numpy
import scipy.stats
import matplotlib.pyplot as pyplot
from ipywidgets import interact, interactive, fixed
import ipywidgets as widgets
# seed the random number generator so we all get the same results
numpy.random.seed(18)
# some nicer colors from http:/... |
uber/pyro | tutorial/source/RSA-hyperbole.ipynb | apache-2.0 | #first some imports
import torch
torch.set_default_dtype(torch.float64) # double precision for numerical stability
import collections
import argparse
import matplotlib.pyplot as plt
import pyro
import pyro.distributions as dist
import pyro.poutine as poutine
from search_inference import HashingMarginal, memoize, Se... |
flohorovicic/pynoddy | docs/notebooks/4-Create-model.ipynb | gpl-2.0 | from matplotlib import rc_params
from IPython.core.display import HTML
css_file = 'pynoddy.css'
HTML(open(css_file, "r").read())
import sys, os
import matplotlib.pyplot as plt
# adjust some settings for matplotlib
from matplotlib import rcParams
# print rcParams
rcParams['font.size'] = 15
# determine path of reposito... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/sandbox-3/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'sandbox-3', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: NCC
Source ID: SANDBOX-3
Topic: Landice
Sub-Topics: Glaciers, Ice.
Properties: 3... |
cathywu/flow | tutorials/tutorial10_controllers.ipynb | mit | import numpy as np
from flow.controllers.base_controller import BaseController
"""
Explanation: Tutorial 10: Custom Controllers
This tutorial walks through the process of defining controllers for the lateral and longitudinal movement of human-driven vehicles within a network. Such controllers may be necessary in orde... |
catarinaqmcosta/Scenarios_Network | OQResultsPath-withDamage_RT_Correlation.ipynb | gpl-3.0 | import utils
import utilsPlot
%matplotlib inline
from time import gmtime, strftime
"""
Explanation: OpenQuake results for a specific path of the network
This IPython notebook allows the user to analyse the results obtained from a scenario damage analysis on a specific path of the transportation network.
The notebook... |
mayankjohri/LetsExplorePython | Section 1 - Core Python/Chapter 01 - Introduction/01_03. Starting Point.ipynb | gpl-3.0 | print("!!! स्वागतम् !!!")
print("!!! Welcome !!!")
print("!!! Willkommen !!!")
print("Hello", ",", "What are you doing")
"""
Explanation: Starting Point
In this section we will cover few items, without which out training can't continue. We will be covering only briefly those topics, as they will be covered in detail... |
wcmckee/lcacoffee | lcacoffee.ipynb | mit | import json
import os
import pandas
import getpass
theuser = getpass.getuser()
"""
Explanation: <h1>lcacoffee</h1>
script that displays coffees sold by hour at lca2015.
Currently it opens a .json file and converts it into a python dict.
It's missing monday data.
sale by hour is: key - the hour (24hr). Value is tota... |
bramkaarga/transcrit | 01_example_metric_calculation.ipynb | bsd-3-clause | %matplotlib inline
from matplotlib import pyplot as plt
import networkx as nx
import pandas as pd
import geopandas as gp
from __future__ import division
import os
import sys
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
sys.path.append(module_path)
#Modules developed for this p... |
AllenDowney/ThinkBayes2 | examples/voter.ipynb | mit | import thinkbayes2
"""
Explanation: Think Bayes
Example problem related to Think Bayes
The MIT License
Copyright 2016 Allen Downey
End of explanation
"""
prior = thinkbayes2.Suite({'State 1': 0.4, 'State 2': 0.25, 'State 3': 0.35})
prior.Print()
"""
Explanation: The following problem was submitted to my blog, Proba... |
AllenDowney/ThinkBayes2 | soln/chap13redux.ipynb | mit | # If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install empiricaldist
# Get utils.py
from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename... |
dolittle007/dolittle007.github.io | notebooks/rugby_analytics.ipynb | gpl-3.0 | !date
import numpy as np
import pandas as pd
try:
from StringIO import StringIO
except ImportError:
from io import StringIO
%matplotlib inline
import pymc3 as pm, theano.tensor as tt
"""
Explanation: A Hierarchical model for Rugby prediction
@Author: Peadar Coyle
@email: peadarcoyle@googlemail.com
@date: 31/... |
napsternxg/pytorch-practice | Pytorch Gradient reversal.ipynb | apache-2.0 | import torch
import tensorflow as tf
from torch.autograd import Variable
import numpy as np
def f(X):
return X*X
def g(X):
return X**3
X = np.random.randn(10)
X
"""
Explanation: Gradient reversal pytorch
Inspired from the following tweets:
https://twitter.com/mat_kelcey/status/932149793765261313
https://t... |
zerothi/sisl | docs/visualization/viz_module/showcase/FatbandsPlot.ipynb | mpl-2.0 | import sisl
import sisl.viz
"""
Explanation: FatbandsPlot
End of explanation
"""
# First, we create the geometry
BN = sisl.geom.graphene(atoms=["B", "N"])
# Create a hamiltonian with different on-site terms
H = sisl.Hamiltonian(BN)
H[0, 0] = 2
H[1, 1] = -2
H[0, 1] = -2.7
H[1, 0] = -2.7
H[0, 1, (-1, 0)] = -2.7
H[... |
molgor/spystats | notebooks/.ipynb_checkpoints/Spatial Model Fitting using GLS-Copy1-checkpoint.ipynb | bsd-2-clause | # Load Biospytial modules and etc.
%matplotlib inline
import sys
sys.path.append('/apps')
sys.path.append('..')
sys.path.append('../spystats')
import django
django.setup()
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
## Use the ggplot style
plt.style.use('ggplot')
import tools
"""
Explanatio... |
srcole/qwm | yelp/Analyze - food by cities.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import glob
import os
import scipy as sp
from scipy import stats
from tools.plt import color2d #from the 'srcole/tools' repo
from matplotlib import cm
"""
Explanation: https://docs.google.com/document/d/15GMxvJYAUO-b96c18QmfcF27... |
zzsza/Datascience_School | 12. 추정 및 검정/03. SciPy를 사용한 기초적인 검정.ipynb | mit | N = 10
theta_0 = 0.5
np.random.seed(0)
x = sp.stats.bernoulli(theta_0).rvs(N)
n = np.count_nonzero(x)
n
sp.stats.binom_test(n, N)
"""
Explanation: SciPy를 사용한 기초적인 검정
SciPy 파이썬 패키지는 다음과 같은 다양한 검정 명령을 제공한다.
이항 검정 (Binomial test)
카이 제곱 검정 (Chi-square test)
단일 표본 z-검정 (One-sample z-test)
단일 표본 t-검정 (One-sample t-test)
독... |
dotsdl/msmbuilder | examples/uncertainty.ipynb | lgpl-2.1 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as pp
from mdtraj.utils import timing
from msmbuilder.cluster import NDGrid
from msmbuilder.example_datasets import QuadWell
from msmbuilder.msm import BayesianMarkovStateModel
from msmbuilder.msm import ContinuousTimeMSM
"""
Explanation: This example demo... |
rdhyee/nypl50 | rtc50_checklist.ipynb | apache-2.0 | from __future__ import print_function
import gspread
import json
# rtc50_settings.py holds URL related to the Google spreadsheet
from rtc50_settings import (g_name, g_url, g_key)
OFFICIAL_NAME_KEY = "Name in rtc/books.json, Official Name"
"""
Explanation: Jennifer 8. Lee et al have been using a Google spreadsheet ... |
leosartaj/scipy-2016-tutorial | tutorial_exercises/03-Integrals.ipynb | bsd-3-clause | # Indefinite integral
integrate(x**2, x)
"""
Explanation: Integrals
In the last section we learned symbolic differentiation with diff. Here we'll cover symbolic integration with integrate.
Here is how we write the indefinite integral
$$ \int x^2 dx = \frac{x^3}{3}$$
End of explanation
"""
# Definite integral
integr... |
ecell/ecell4-notebooks | en/examples/example05.ipynb | gpl-2.0 | %matplotlib inline
from ecell4.prelude import *
"""
Explanation: Glycolysis Model and Metabolic Control Analysis
End of explanation
"""
citation(125616)
citation(168932)
"""
Explanation: A Simple Model of the Glycolysis of Human Erythrocytes
This is a model for the glycolysis of human erythrocytes which takes into... |
kinshuk4/MoocX | misc/machinelearningbootcamp/day2/deep_learning.ipynb | mit | import os
import numpy as np
import matplotlib.pyplot as plt
from scipy import misc
% matplotlib inline
plt.rcParams['figure.figsize'] = (10, 10)
plt.rcParams['image.cmap'] = 'gray'
"""
Explanation: Module 2
Convolutions
First let's have a look at what convolutions do
Learning Activity 1: Load the Python libraries
... |
ceos-seo/data_cube_notebooks | notebooks/machine_learning/Uruguay_Random_Forest/Random_Forests_Uruguay.ipynb | apache-2.0 | import pandas as pd
df = pd.read_csv('data.csv')
df.head()
"""
Explanation: Read in the data
End of explanation
"""
df.groupby("LandUse").size()
from matplotlib import pyplot
import seaborn as sns
fig, ax = pyplot.subplots(figsize=(15,5))
sns.countplot(x="LandUse",data=df, palette="Greens_d");
"""
Explanation: S... |
CSB-book/CSB | python/solutions/Singh2015_solution.ipynb | gpl-3.0 | import csv
"""
Explanation: Solution of 3.8.2, Singh et al. 2015
Compute the mean RecombinantFraction for each Drosophila Line and InfectionStatus. Print the results like:
Line 45 Average Recombination Rate:
W : 0.187
I : 0.191
To read the data, we're going to use the csv module. First, we need to import it:
End of ... |
timothyb0912/pylogit | examples/notebooks/.ipynb_checkpoints/Python Biogeme Benchmark--01Logit-checkpoint.ipynb | bsd-3-clause | # Load the raw swiss metro data
# Note the .dat files are tab delimited text files
swissmetro_wide = pd.read_table("../data/swissmetro.dat", sep='\t')
"""
Explanation: 1. Load the Swissmetro Dataset
End of explanation
"""
# Select obervations whose choice is known (i.e. CHOICE != 0)
# **AND** whose PURPOSE is either... |
hglanz/phys202-2015-work | assignments/assignment05/InteractEx03.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 3
Imports
End of explanation
"""
def soliton(x, t, c, a):
"""Return phi(x, t) for a soliton wave with co... |
Astrohackers-TW/IANCUPythonAdventure | notebooks/notebooks4HowtoSeries/how_to_fit_data.ipynb | mit | from astropy.io import ascii
radio_infrared_NLSy1 = ascii.read('../files4examples/Radio_Infrared_NLSy1.txt',names=['radio luminosity','infrared luminosity'])
radio_infrared_SG = ascii.read('../files4examples/Radio_Infrared_spiral.txt',names=['radio luminosity','infrared luminosity'])
radio_infrared_RG = ascii.read('../... |
ottogroup/dstoolbox | notebooks/Showcase.ipynb | apache-2.0 | from functools import partial
import operator
import random
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pr... |
josephmfaulkner/stoqs | stoqs/contrib/notebooks/explore_BIOM_data_for_SIMZ.ipynb | gpl-3.0 | from campaigns import campaigns
dbs = [c for c in campaigns if 'simz' in c]
print dbs
"""
Explanation: Update BIOM file with data from STOQS
Given a .biom file and multiple STOQS databases, explore Next Generation Sequence and associated STOQS data
Executing this Notebook requires a personal STOQS server. Follow the s... |
crystalzhaizhai/cs207_yi_zhai | homeworks/HW6/HW6.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 ... |
kamujun/exercise_of_deep_larning_from_scratch | notebooks/section3.ipynb | mit | import matplotlib.pyplot as plt
from matplotlib.image import imread
from graphviz import Digraph
f = Digraph(format="png")
f.attr(rankdir='LR')
f.attr('node', shape='circle')
f.node('x1','')
f.node('x2','')
f.node('s1','')
f.node('s2','')
f.node('s3','')
f.node('y1','')
f.node('y2','')
with f.subgraph(name='cluster... |
Hades1996/nummeth | finding roots/tercerPunto.ipynb | mit | import math
def funcion(x):
return (230*math.pow(x,4))+(18*math.pow(x,3))+(9*math.pow(x,2))-(221*x)-9
def biseccion(intA, intB, errorA, noMaxIter):
if(funcion(intA)*funcion(intB)<0):
noIter = 0
errorTmp = 1
intTmp = 0
oldInt = intA
while(noIter<n... |
vladan-jovicic/ComplexNetworks | Notebooks/HowTo.ipynb | mit | import sys, os
# sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath('../src/'))))
sys.path.append('../src/')
import graph
reload(graph)
"""
Explanation: How to use implemented algorithms
Overview
The project has the following structure:
doc contains documentation of the project
src contains all the sour... |
ES-DOC/esdoc-jupyterhub | notebooks/thu/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', 'thu', 'sandbox-2', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: THU
Source ID: SANDBOX-2
Topic: Atmoschem
Sub-Topics: Transport, Emissions Co... |
Alexoner/skynet | notebooks/features.ipynb | mit | import random
import numpy as np
from skynet.utils.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
# for auto-reloading extenrna... |
ueapy/ueapy.github.io | content/notebooks/2015-12-18-meeting-summary.ipynb | mit | !cat ~/anaconda/lib/python2.7/site-packages/rk/config/rk.ini
"""
Explanation: This week one of the group's members asked how to use Jupyter on our UEA's supercomputer cluster Grace. So today we tried to walk through that. We also discussed other Jupyter capabilities and advantages.
Those who are still sceptical or hav... |
jhillairet/scikit-rf | doc/source/examples/networktheory/Time domain reflectometry, measurement vs simulation.ipynb | bsd-3-clause | %matplotlib inline
import skrf
from skrf.media import MLine, DefinedAEpTandZ0
import numpy as np
from numpy import real, log, log10, sum, absolute, pi, sqrt
from scipy.optimize import minimize
import matplotlib.pyplot as plt
from IPython.display import *
skrf.stylely()
"""
Explanation: Time domain reflectometry, measu... |
eds-uga/csci1360e-su17 | assignments/A3/A3_Q1.ipynb | mit | import numpy as np
np.random.seed(7584)
list1 = np.random.randint(-100, 100, 10)
actual1 = list1 + 1
pred1 = add_one(list1.tolist())
assert set(pred1) == set(actual1.tolist())
np.random.seed(68527)
list2 = np.random.randint(-100, 100, 100)
actual2 = list2 + 1
pred2 = add_one(list2.tolist())
assert set(pred2) == set(... |
tensorflow/docs-l10n | site/en-snapshot/datasets/keras_example.ipynb | apache-2.0 | import tensorflow as tf
import tensorflow_datasets as tfds
"""
Explanation: Training a neural network on MNIST with Keras
This simple example demonstrates how to plug TensorFlow Datasets (TFDS) into a Keras model.
Copyright 2020 The TensorFlow Datasets Authors, Licensed under the Apache License, Version 2.0
<table cla... |
Alex-Ian-Hamilton/solarbextrapolation | docs/auto_examples/plot_define_and_run_trivial_preprocessor_and_extrapolator.ipynb | mit | # General imports
import sunpy.map as mp
import numpy as np
from mayavi import mlab # Necessary for visulisation
# Module imports
from solarbextrapolation.preprocessors import Preprocessors
from solarbextrapolation.extrapolators import Extrapolators
from solarbextrapolation.map3dclasses import Map3D
from solarbextrapo... |
mne-tools/mne-tools.github.io | 0.20/_downloads/e807ecd348331c3d789aa11b874576a5/plot_decoding_time_generalization_conditions.ipynb | bsd-3-clause | # Authors: Jean-Remi King <jeanremi.king@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
f... |
Benedicto/ML-Learning | Classifier_4_decision_tree_combat_overfitting_assignment.ipynb | gpl-3.0 | import graphlab
"""
Explanation: Decision Trees in Practice
In this assignment we will explore various techniques for preventing overfitting in decision trees. We will extend the implementation of the binary decision trees that we implemented in the previous assignment. You will have to use your solutions from this pr... |
theandygross/TCGA_differential_expression | Notebooks/DX_screen.ipynb | mit | import NotebookImport
from Imports import *
"""
Explanation: Fraction upregulated screen
Import everything from the imports notebook. This reads in all of the expression data as well as the functions needed to analyse differential expression data.
End of explanation
"""
matched_meth = pd.read_hdf(METH_STORE, 'matche... |
loudinthecloud/pytorch-ntm | notebooks/copy-task-plots.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from glob import glob
import json
import os
import sys
sys.path.append(os.path.abspath(os.getcwd() + "./../"))
%matplotlib inline
"""
Explanation: Copy Task Plots
End of explanation
"""
batch_num = 40000
files = glob("./copy/*-{}.json".format(b... |
andim/pysnippets | random-choice.ipynb | mit | import numpy as np
%load_ext Cython
"""
Explanation: A comparison of methods of random choice
random.choice vs. a random.multinomial based implementation of the same weighted choice. Also compare with a GNU Scientific Library based implementation.
Context: random.choice is only available in numpy >= 1.7, so I was tryi... |
lvapeab/nmt-keras | examples/tutorial.ipynb | mit | !pip install update pip
!pip uninstall -y kapre keras tensorflow-probability albumentations datascience h5py keras-nightly # Avoid crashes with pre-installed packages
!git clone https://github.com/lvapeab/nmt-keras
import os
os.chdir('nmt-keras')
!pip install -e .
"""
Explanation: <a href="https://colab.research.goo... |
BrahmsPotato/PlayWithPrettyFat | kaggle/titanic/reference.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%pylab inline
# Get a glimpse of data
titanic_df = pd.read_csv('../input/train.csv')
titanic_df.head()
# Check the information of our data
titanic_df.info()
# Check Cabin column
# print titanic_df['Cabin']
"""
Explanation... |
lisa-1010/smart-tutor | code/data_preprocessing_for_rnn.ipynb | mit | import sys
print sys.executable
%load_ext autoreload
%autoreload 2
%reload_ext autoreload
import sonnet as snt
import tensorflow as tf
import tflearn
import numpy as np
import dataset_utils
"""
Explanation: Sketchbook to experiment with RNN models to model the dynamics. Once tested, the code in this notebook will be... |
mne-tools/mne-tools.github.io | 0.23/_downloads/8de61cd59c9d83353f96a413e8484686/compute_mne_inverse_raw_in_label.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse_raw, read_inverse_operator
print(__doc__)
data_path = sample.data_path()
fname_inv = data_path + '/MEG/sample/s... |
pjpmarques/Modelling-the-World | Lorenz Attractor.ipynb | mit | %matplotlib inline
from scipy.integrate import odeint
from mpl_toolkits.mplot3d.axes3d import Axes3D
from pylab import *
"""
Explanation: Lorenz Attractor
by Paulo Marques, 2013/09/21
This notebook implements the beautiful Lorenz Attractor in Python. The Lorenz Attractor is probably the
most ilustrative example of a... |
DJCordhose/ai | notebooks/tf2/nn-model.ipynb | mit | !pip install -q tf-nightly-gpu-2.0-preview
import tensorflow as tf
print(tf.__version__)
import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
import numpy as np
from tensorflow import keras
!curl -O https://raw.githubusercontent.com/DJCordhose/deep-learning-crash-course-notebooks/master/data/i... |
pfschus/fission_bicorrelation | methods/nn_sum_and_br_subtraction.ipynb | mit | import os
import sys
import matplotlib.pyplot as plt
import matplotlib.colors
import numpy as np
import os
import scipy.io as sio
import sys
import pandas as pd
from tqdm import *
# Plot entire array
np.set_printoptions(threshold=np.nan)
import seaborn as sns
sns.set_style(style='white')
sys.path.append('../scripts/... |
OSGeoLabBp/tutorials | english/data_processing/lessons/rtklib_python.ipynb | cc0-1.0 | !git clone https://github.com/rtklibexplorer/RTKLIB.git
"""
Explanation: <a href="https://colab.research.google.com/github/OSGeoLabBp/tutorials/blob/master/english/data_processing/lessons/rtklib_python.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>... |
jhamman/xray | examples/xarray_multidimensional_coords.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import pandas as pd
import xarray as xr
import cartopy.crs as ccrs
from matplotlib import pyplot as plt
print("numpy version : ", np.__version__)
print("pandas version : ", pd.__version__)
print("xarray version : ", xr.__version__)
"""
Explanation: <h1>Table of Contents<span c... |
AllenDowney/ModSimPy | soln/kitten_soln.ipynb | mit | # Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim.py module
from modsim import *
"""
Explanation: Modeling and Simulati... |
steinam/teacher | jup_notebooks/data-science-ipython-notebooks-master/deep-learning/tensor-flow-exercises/3_regularization.ipynb | mit | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
import cPickle as pickle
import numpy as np
import tensorflow as tf
"""
Explanation: Deep Learning with TensorFlow
Credits: Forked from TensorFlow by Google
Setup
Refer to the setup instructions.
Exercise 3
Pre... |
ES-DOC/esdoc-jupyterhub | notebooks/ipsl/cmip6/models/sandbox-2/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ipsl', 'sandbox-2', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: IPSL
Source ID: SANDBOX-2
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radi... |
florianwittkamp/FD_ACOUSTIC | JupyterNotebook/1D/FD_1D_DX8_DT2.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import time as tm
import matplotlib.pyplot as plt
"""
Explanation: FD_1D_DX8_DT2 1-D acoustic Finite-Difference modelling
GNU General Public License v3.0
Author: Florian Wittkamp
Finite-Difference acoustic seismic wave simulation
Discretization of the first-order acoustic wave equ... |
IBMStreams/streamsx.health | samples/HealthcareJupyterDemo/notebooks/HealthcareDemo-Distributed.ipynb | apache-2.0 | from icpd_core import icpd_util
from streamsx.topology.context import JobConfig
from streamsx.topology import context
streams_instance_name = ## Change this to Streams instance
try:
cfg=icpd_util.get_service_instance_details(name=streams_instance_name, instance_type="streams")
except TypeError:
cfg=icpd_util... |
mne-tools/mne-tools.github.io | 0.19/_downloads/1d463c137037878e3b256b9dc4ee82d3/plot_visualize_evoked.ipynb | bsd-3-clause | import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
"""
Explanation: Visualize Evoked data
In this tutorial we focus on the plotting functions of :class:mne.Evoked.
End of explanation
"""
data_path = mne.datasets.sample.data_path()
fname = op.join(data_path, 'MEG', 'sample', 'sample_a... |
italoPontes/Machine-learning | Tarefas/Predicao-de-CRA-com-Regressao/.ipynb_checkpoints/Ridge and LASSO Regression-checkpoint.ipynb | lgpl-3.0 | features.applymap(np.isreal).apply(pd.value_counts)
"""
Explanation: 1. Data preprocessing
Checking for missing values in data
To search for missing values we first apply a function that checks if each value of our data frame is a real number and return true for those who are and false otherwise. In a second moment, w... |
vanheck/blog-notes | Analyzes/Volatile movements/02 Volatile movements in python and pandas 2.ipynb | mit | import sys
import pandas as pd
import pandas_datareader as pdr
import pandas_datareader.data as web
import matplotlib
import seaborn as sns
import datetime
print('Python', sys.version)
print('Pandas', pd.__version__)
print('Pandas-datareader', pdr.__version__)
print('Matplotlib', matplotlib.__version__)
print('Seabor... |
ES-DOC/esdoc-jupyterhub | notebooks/inpe/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', 'inpe', 'sandbox-3', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: INPE
Source ID: SANDBOX-3
Sub-Topics: Radiative Forcings.
Properties: 85 (42 ... |
nick-youngblut/SIPSim | ipynb/bac_genome/n1147/.ipynb_checkpoints/atomIncorp_taxaIncorp_MW-HR-SIP_preSparsity-checkpoint.ipynb | mit | import os
import glob
import itertools
import nestly
%load_ext rpy2.ipython
%load_ext pushnote
%%R
library(ggplot2)
library(dplyr)
library(tidyr)
library(gridExtra)
"""
Explanation: Goal
Follow-up to: atomIncorp_taxaIncorp
Determining the effect of 'heavy' BD window (number of windows & window sizes) on HR-SIP accu... |
setiQuest/ML4SETI | tutorials/Step_5d_Build_CNN_Tf_DSX.ipynb | apache-2.0 | import requests
import json
#import ibmseti
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import tensorflow as tf
import pickle
import time
#!sudo pip install sklearn
import os
from sklearn.metrics import confusion_matrix
from sklearn import metrics
"""
Explanation: <a href="https://www.cogniti... |
Kidel/In-Codice-Ratio-OCR-with-CNN | Notebooks/04_Mnist-Ensemble.ipynb | apache-2.0 | import os.path
from IPython.display import Image
from util import Util
u = Util()
import numpy as np
# Explicit random seed for reproducibility
np.random.seed(1337)
from keras.callbacks import ModelCheckpoint
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from kera... |
Upward-Spiral-Science/team1 | other/Descriptive and Exploratory_oldversion.ipynb | apache-2.0 | from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import urllib2
def check_condition(row):
if row[-1] == 0:
return False
return True
url = ('https://raw.githubusercontent.com/Upward-Spiral-Science'
'/data/master/syn-density/output.csv... |
Intel-Corporation/tensorflow | tensorflow/lite/g3doc/tutorials/model_maker_question_answer.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... |
ES-DOC/esdoc-jupyterhub | notebooks/cnrm-cerfacs/cmip6/models/sandbox-3/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'sandbox-3', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: SANDBOX-3
Topic: Ocnbgchem
Sub-Topics: Trace... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a_ml/ml_cc_machine_learning_problems2.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 2A.ml - Boosting, random forest, gradient - les features qu'ils aiment
Avantages et inconvénients des méthodes à gradient. Exercice sur la rotation de features avant l'utilisation d'une random forest.
End of explanation
"""
from pyquick... |
WNoxchi/Kaukasos | pytorch/nn_practice.ipynb | mit | import torch # PyTorch base
from torch.autograd import Variable # Tensor class w gradients
import torch.nn as nn # modules, layers, loss fns
import torch.nn.functional as F # Conv,Pool,Loss,Actvn,Nrmlz fns from here
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# 1 inpu... |
sailuh/perceive | Parsers/SecLists/Index-Parse.ipynb | gpl-2.0 | import requests
from bs4 import BeautifulSoup
from IPython.display import Pretty
import pprint
pp = pprint.PrettyPrinter(indent=4)
url = 'http://seclists.org/fulldisclosure/2017/Jan'
r = requests.get(url)
raw = r.text
Pretty(raw)
"""
Explanation: SecLists Index parsing
Example: http://seclists.org/fulldisclosure/... |
tpin3694/tpin3694.github.io | statistics/t-tests.ipynb | mit | from scipy import stats
import numpy as np
"""
Explanation: Title: T-Tests
Slug: t-tests
Summary: T-tests in Python.
Date: 2016-02-08 12:00
Category: Statistics
Tags: Basics
Authors: Chris Albon
Preliminaries
End of explanation
"""
# Create a list of 20 observations drawn from a random distribution
# with mean ... |
jdossgollin/CWC_ANN | Week03/Time Series_example_Water Pollution_LSTM.ipynb | mit | # The data set has the date separated into three columns - we want to reformat the date to set it as our index
from datetime import datetime
def parse(x):
return datetime.strptime(x, '%Y %m %d %H')
dataset = read_csv('./raw.csv', parse_dates = [['year', 'month', 'day', 'hour']], index_col=0, date_parser=parse)
da... |
parrt/msan692 | notes/text.ipynb | mit | ! head data/IntroIstanbul.txt
"""
Explanation: Intro to information extraction from text
TODO: spacy.io for text processing. does parts of speech even. Merge with some of the content in html->text lecture. strip punct you dummy.
Most of the data we've looked at so far has been structured, meaning essentially that the ... |
tensorflow/docs-l10n | site/ja/tutorials/interpretability/integrated_gradients.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... |
cscottwatson/end_of_day_two | DefensiveProgramming_3.ipynb | gpl-2.0 | def test_range_overlap():
assert range_overlap([(-3.0, 5.0), (0.0, 4.5), (-1.5, 2.0)]) == (0.0, 2.0)
assert range_overlap([ (2.0, 3.0), (2.0, 4.0) ]) == (2.0, 3.0)
assert range_overlap([ (0.0, 1.0), (0.0, 2.0), (-1.0, 1.0) ]) == (0.0, 1.0)
"""
Explanation: # Defensive programming (2)
We have seen the ba... |
radhikapc/foundation-homework | homework05/Homework05_NYTimes-radhika.ipynb | mit | #What books topped the Hardcover Fiction NYT best-sellers list on Mother's Day in 2009 and 2010? How about Father's Day?
#mothers day in 2010
book_result = data['results']
#print(book_result)
print("The hardcover Fiction NYT best-sellers on mothers day in 2010 are:")
for i in book_result:
#print(i['book_details'... |
jphall663/GWU_data_mining | 02_analytical_data_prep/src/py_part_2_feature_extraction.ipynb | apache-2.0 | import pandas as pd # pandas for handling mixed data sets
import numpy as np # numpy for basic math and matrix operations
import matplotlib.pyplot as plt # pyplot for plotting
# scikit-learn for machine learning and data preprocessing
from sklearn.decomposition import PCA
"""
Explanation:... |
brainiak/brainiak | examples/reconstruct/iem2d_example.ipynb | apache-2.0 | # Load the fMRI data
data = scipy.io.loadmat('AL61_Bilat-V1_attnContrast.mat')
trn_conds = data['trn_conds'] # position in space for 128 trials
# flip to cartesian coordinates to make life easier
trn_conds[:,1] = trn_conds[:,1]*-1
trn = data['trn'] # matrix of (trials, voxels)
"""
Explanation: In this example, we wi... |
neuro-data-science/neuro_data_science | python/modeling/latent_variables_and_dim_reduction.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (12, 9)
plt.rcParams["font.size"] = 20
import numpy as np
from sklearn.decomposition import PCA, FactorAnalysis
"""
Explanation: NOTE: the final analyses don't appear to be correct. Need to debug.
Latent Factor in Neural data
Original... |
ireapps/cfj-2017 | completed/13. Web scraping (Part 3).ipynb | mit | import requests
from bs4 import BeautifulSoup
"""
Explanation: Let's scrape the IRE homepage
Our goal: Print out the headlines from the IRE home page.
requests is a handy third-party library for making HTTP requests. It does the same thing your browser does when you type in a URL and hit enter -- sends a message to a ... |
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