repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
paolorivas/homeworkfoundations | 05/homework_5_Paolo_Rivas_Legua.ipynb | mit | #Question 2 awnser.
for artist in artists:
print(artist['name'], artist['popularity'])
if len(artist['genres']) == 0:
print("no genres listed")
else:
genres = ", ".join(artist['genres'])
print("Genres list: ", genres)
"""
Explanation: Question 2: What genres are most represented in... |
junhwanjang/DataSchool | Lecture/05. 기초 선형 대수 1 - 행렬의 정의와 연산/4) NumPy를 활용한 선형대수 입문.ipynb | mit | x = np.array([1, 2, 3, 4])
x
x = np.array([[1], [2], [3], [4]])
x
"""
Explanation: NumPy를 활용한 선형대수 입문
선형대수(linear algebra)는 데이터 분석에 필요한 각종 계산을 위한 기본적인 학문이다.
데이터 분석을 하기 위해서는 실제로 수많은 숫자의 계산이 필요하다. 하나의 데이터 레코드(record)가 수십개에서 수천개의 숫자로 이루어져 있을 수도 있고 수십개에서 수백만개의 이러한 데이터 레코드를 조합하여 계산하는 과정이 필요할 수 있다.
선형대수를 사용하는 첫번째 장점은 이러한... |
dataDogma/Computer-Science | Courses/DAT-208x/DAT 208x - Week 4 - Section 4 - Lab.ipynb | gpl-3.0 | """Since the data is unavailabe from data camp, let's create
some of our own"""
# Import numpy
import numpy as np
from numpy import random
from numpy import column_stack
# np_baseball is un-available, so let's generate some random distribution!
height = np.round( np.random.normal( 5.50, 5.0, 1015 ), 2 )
weight = np... |
FractalFlows/DAOResearch | notebooks/predictive.ipynb | mit | import numpy as np
import math
from scipy.interpolate import interp1d
from scipy.special import erf
import matplotlib.pylab as pl
%matplotlib inline
# illustration of what a predictive market would look like.
guess = np.array([
# year, n_articles, std
[ 0.0, 0., 0.0001 ],
[ 1.0, 2., 3. ... |
florent-leclercq/borg_sdss_data_release | borg_sdss_origami/borg_sdss_origami.ipynb | gpl-3.0 | import numpy as np
origami = np.load('borg_sdss_origami.npz')
"""
Explanation: BORG SDSS data products
borg_sdss_origami package
Authors: Florent Leclercq, Jens Jasche, Guilhem Lavaux, Benjamin Wandelt, Will Percival
Last update: 09/10/2018
This package contains the maps obtained by Leclercq et al. (2017), who perfo... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_time_frequency_mixed_norm_inverse.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
from mne.inverse_sparse import tf_mixed_norm
from mne.viz import plot_sparse_source_estimates
print(__doc__)
... |
initialized/tensorflow-tutorial | inception-inference/Inception Inference.ipynb | mit | # Location of checkpoint directory from training
CHECKPOINT_DIR = "/tmp/mnist-inception"
# Number of classes (MNIST = 10, 1 for each digit)
NUM_CLASSES = 10
# Number of test images to evaluate at a time. Adjust to smaller if you're
# evaluation on a memory-constrained box
BATCH_SIZE = 20
from __future__ import absol... |
roebius/deeplearning_keras2 | nbs/statefarm-sample.ipynb | apache-2.0 | from __future__ import division, print_function
%matplotlib inline
# path = "data/state/"
path = "data/state/sample/"
from importlib import reload # Python 3
import utils; reload(utils)
from utils import *
from IPython.display import FileLink
batch_size=64
#batch_size=1
"""
Explanation: Enter State Farm
End of expla... |
arcyfelix/Courses | 17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/03-General Pandas/08-Data-Input-and-Output.ipynb | apache-2.0 | import numpy as np
import pandas as pd
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Typically we will just be either reading csv files directly or using pandas-datareader or quandl. Consider this lecture just a quick overview of what is possible with pandas (we wo... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch9-Problem_9-05.ipynb | unlicense | %pylab notebook
%precision %.4g
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 9
Problem 9-5
End of explanation
"""
V = 220 # [V]
p = 6
R1 = 1.3 # [Ohm]
R2 = 1.73 # [Ohm]
X1 = 2.01 # [Ohm]
X2 = 2.01 # [Ohm]
Xm = 105.0 # [Ohm]
s = 0.05
Prot = 291.0 # [W]
"""
Explanation: Desc... |
vzg100/Post-Translational-Modification-Prediction | .ipynb_checkpoints/Lysine Acetylation -XGB -dbptm -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:
print("y", i)
y = Predictor()
y.load_data(file="Data/Trainin... |
Linlinzhao/linlinzhao.github.io | _drafts/.ipynb_checkpoints/多种方法实现一个两层神经网络-checkpoint.ipynb | mit | import numpy as np
#先定义网络结构: batch_size, Input Dimension, Hidden Dimension, Output Dimension
N, D_in, D_hidden, D_out = 10, 20, 30, 5
#随机生成输入和输出数据
x = np.random.randn(N, D_in)
y = np.random.randn(N, D_out)
#对输入层和输出层的参数进行初始化
w1 = np.random.randn(D_in, D_hidden)
w2 = np.random.randn(D_hidden, D_out)
learning_rate ... |
Automating-GIS-processes/Lesson-7-Automating-Raster-Data-Processing | Python-and-Gdal.ipynb | mit | mkdir LandsatData
tar -zxvf LC81910182016153-SC20161208043748.tar.gz -C LandsatData/
"""
Explanation: Raster data processing with Python and GDAL
In this lesson we will learn basic techniques for automating raster data processing with python and the Geospatial Data Abstraction Library GDAL.
Raster files are commonly ... |
mne-tools/mne-tools.github.io | 0.18/_downloads/a489b01e2c31005c72c56777df4ed4f7/plot_mixed_norm_inverse.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.inverse_sparse import mixed_norm, make_stc_from_dipoles
from mne.minimum_norm import m... |
bjshaw/phys202-2015-work | assignments/assignment12/FittingModelsEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Fitting Models Exercise 1
Imports
End of explanation
"""
a_true = 0.5
b_true = 2.0
c_true = -4.0
"""
Explanation: Fitting a quadratic curve
For this problem we are going to work with the following mod... |
AllenDowney/ThinkBayes2 | notebooks/chap06.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... |
zerothi/ts-tbt-sisl-tutorial | presentations/01/run.ipynb | gpl-3.0 | chain = sisl.Geometry([0]*3, sisl.Atom(1, R=1.), sc=[1, 1, 10])
chain.set_nsc([3, 3, 1])
# Transport along y-direction
chain = chain.tile(20, 0)
He = sisl.Hamiltonian(chain)
He.construct(([0.1, 1.1], [0, -1]))
Hd = He.tile(20, 1)
He.write('ELEC.nc')
Hd.write('DEVICE.nc')
with open('RUN.fdf', 'w') as f:
f.write(""... |
spectralDNS/shenfun | binder/Shenfun-introduction.ipynb | bsd-2-clause | from shenfun import *
print('hello world')
"""
Explanation: <center>
Shenfun - High-Performance Computing platform for the Spectral Galerkin method
<div><img src="https://rawcdn.githack.com/spectralDNS/spectralutilities/f3419a3e6c40dad55be5dcca51f6e0e21713dd90/figures/Chebyshev_Polynomials_of_the_First_Kind.svg" width... |
U302/Stuff | Bitcoin_Trading_Bot/Saved Predictions.ipynb | gpl-3.0 | def plot_results(predicted_data, true_data):
fig=plt.figure(figsize=(18, 12), dpi= 80, facecolor='w', edgecolor='k')
ax = fig.add_subplot(111)
ax.plot(true_data, label='True Data')
plt.plot(predicted_data, label='Prediction')
plt.legend()
plt.show()
def predict_sequences_multiple(model, data, w... |
rasbt/python-machine-learning-book | code/bonus/softmax-regression.ipynb | mit | %load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p matplotlib,numpy,scipy
# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
%matplotlib inline
"""
Explanation: Sebastian Raschka, 2016
https://github.com/rasbt/p... |
aschaffn/phys202-2015-work | assignments/assignment06/ProjectEuler17.ipynb | mit | def number_to_words(n):
"""Given a number n between 1-1000 inclusive return a list of words for the number."""
num19 = ["", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten",\
"eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen", "seventeen", \
"ei... |
empet/PSCourse | Birthday-problem-ro.ipynb | bsd-3-clause | from __future__ import division
def prob_theor(n):
if n==2:
return (1-1/365)
else:
return prob_theor(n-1)*(1-(n-1)/365)
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot') # setam stilul de afisare a plot-urilor.
# Incepand cu matplotlib 1.4... |
tensorflow/docs | site/en/tutorials/audio/transfer_learning_audio.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... |
tpin3694/tpin3694.github.io | python/queues_and_stacks.ipynb | mit | from collections import deque
"""
Explanation: Title: Queues And Stacks
Slug: queues_and_stacks
Summary: Queues And Stacks in Python.
Date: 2016-01-23 12:00
Category: Python
Tags: Basics
Authors: Chris Albon
Interesting in learning more? Check out Fluent Python
Preliminaries
End of explanation
"""
# Option 1: ... |
tensorflow/probability | spinoffs/oryx/examples/notebooks/probabilistic_programming.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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, sof... |
kingmolnar/DataScienceProgramming | 06-Descriptive-Statistics/CrimeData_orig.ipynb | cc0-1.0 | import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
# load data set
df = pd.read_csv('/home/data/APD/COBRA-YTD2017.csv.gz')
print "Shape of table: ", df.shape
"""
Explanation: Atlanta Police Department
The Atlanta Police Department provides Part 1 crime data at http://www.atlant... |
fonnesbeck/PyMC3_Oslo | notebooks/4. Approximation Methods.ipynb | cc0-1.0 | # %load ../data/melanoma_data.py
from numpy import reshape, sum
melanoma_data = reshape([1.57808, 0.00000, 2, 1.27, 35.9945, 1, 1.48219,
0.00000, 2, 0.76, 41.9014, 1, 0.0, 7.33425, 1, 35.00, 70.2164, 2, 2.23288,
0.00000, 1, 1.70, 33.7096, 1, 0.0, 9.38356, 2, 1.00, 47.9726, 1, 3.27671,
0.00000, 1, 11.00,... |
wheeler-microfluidics/teensy-minimal-rpc | teensy_minimal_rpc/notebooks/dma-examples/Example - Multi-channel ADC multiple samples using DMA.ipynb | gpl-3.0 | from arduino_rpc.protobuf import resolve_field_values
from teensy_minimal_rpc import SerialProxy
import teensy_minimal_rpc.DMA as DMA
import teensy_minimal_rpc.ADC as ADC
# Disconnect from existing proxy (if available)
try:
del proxy
except NameError:
pass
proxy = SerialProxy()
dma_channel_scatter = 0
dma_c... |
mit-eicu/eicu-code | notebooks/allergy.ipynb | mit | # Import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import psycopg2
import getpass
import pdvega
# for configuring connection
from configobj import ConfigObj
import os
%matplotlib inline
# Create a database connection using settings from config file
config='../db/config.ini'
#... |
empet/Plotly-plots | kdeplot-2d-Plotly.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import scipy.stats as st
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: 2D kernel density estimation and its Plotly plot
We have two Excel files with two columns. We read the files into two pandas dataframes and plot... |
florianwittkamp/FD_ACOUSTIC | JupyterNotebook/1D/FD_1D_DX4_DT2_fast.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import time as tm
import matplotlib.pyplot as plt
"""
Explanation: FD_1D_DX4_DT2_fast 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 wav... |
zaqwes8811/micro-apps | self_driving/deps/Kalman_and_Bayesian_Filters_in_Python_master/13-Smoothing.ipynb | mit | from __future__ import division, print_function
%matplotlib inline
#format the book
import book_format
book_format.set_style()
"""
Explanation: Table of Contents
Smoothing
End of explanation
"""
import matplotlib.pyplot as plt
data = [10.1, 10.2, 9.8, 10.1, 10.2, 10.3,
10.1, 9.9, 10.2, 10.0, 9.9, 11.4]
pl... |
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies | ex23-CO2 and Global Temperature Anomaly.ipynb | mit | import pandas as pd
from matplotlib import pyplot as plt
%matplotlib inline
# Set some parameters to apply to all plots. These can be overridden
import matplotlib
# Plot size to 14" x 7"
matplotlib.rc('figure', figsize = (14, 7))
# Font size to 14
matplotlib.rc('font', size = 14)
# Do not display top and right frame ... |
GoogleCloudPlatform/analytics-componentized-patterns | retail/recommendation-system/bqml-scann/ann02_run_pipeline.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
"""
Explanation: Low-latency item-to-item recommendation system - Orchestrating with TFX
Overview
This notebook is a part of the series that describes the process of implementing a Low-latency item-to-item recommendation system.
This notebook demonstrates how to use TFX and AI Platfo... |
probml/pyprobml | notebooks/misc/haiku_intro.ipynb | mit | %%capture
!pip install git+https://github.com/deepmind/dm-haiku
import haiku as hk
%%capture
!pip install git+git://github.com/deepmind/optax.git
import optax
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
"""
Explanation: <a href="https://colab.research.google.com/github/probml/probml-note... |
davofis/computational_seismology | 05_pseudospectral/cheby_elastic_1d.ipynb | gpl-3.0 | # This is a configuration step for the exercise. Please run it before calculating the derivative!
import numpy as np
import matplotlib.pyplot as plt
from ricker import ricker
# Show the plots in the Notebook.
plt.switch_backend("nbagg")
"""
Explanation: <div style='background-image: url("../../share/images/header.sv... |
morganics/bayesianpy | examples/notebook/iris_univariate_joint_pdf_plot.ipynb | apache-2.0 | %matplotlib inline
import pandas as pd
import sys
sys.path.append("../../../bayesianpy")
import bayesianpy
from bayesianpy.network import Builder as builder
import logging
import os
import math
import numpy as np
import scipy.stats as ss
import matplotlib.pyplot as plt
import seaborn as sns
logger = logging.getLogger... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_artifacts_detection.ipynb | bsd-3-clause | import numpy as np
import mne
from mne.datasets import sample
from mne.preprocessing import create_ecg_epochs, create_eog_epochs
# getting some data ready
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
raw = mne.io.read_raw_fif(raw_fname, preload=True)
"""
Explanation: In... |
avicennax/Bayesian-Notes | Gaussian Process.ipynb | mit | import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from scipy.optimize import minimize
from scipy.spatial.distance import pdist, squareform
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
# Defining Data
n = 10
a = 3
b = .5
sigma = 5
X... |
rmalouf/learning | Person marking.ipynb | mit | from itertools import combinations, combinations_with_replacement
referents = []
for i in xrange(1, len(entities) * 2):
for combo in combinations_with_replacement(entities, i):
# choral we is impossible
if combo.count('self') > 1:
continue
# only singular ... |
tcstewar/testing_notebooks | Cosine tuning curves.ipynb | gpl-2.0 | # How to compute intercept range given an angle range
import numpy as np
angle_range_degrees = np.array([15.0, 35.0])
angle_range = angle_range_degrees * np.pi / 180
print np.cos(angle_range)
"""
Explanation: First, here is a quick way to determine what the intercepts should be, given a desired angle that you want t... |
timgasser/bcycle-austin | notebooks/bcycle_bikes.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import folium
import seaborn as sns
from bcycle_lib.utils import *
%matplotlib inline
plt.rc('xtick', labelsize=14)
plt.rc('ytick', labelsize=14)
# for auto-reloading external modules
# see http://stackoverflow.com/questions/1907993/autoreload-... |
tkoyama010/getfem_presentation | demo/linear-dynamic-2D.ipynb | cc0-1.0 | import numpy as np
import getfem as gf
import pylab as plt
from scipy import io
from scipy.sparse import linalg
"""
Explanation: Getfem++を使用した地盤の線形時刻歴応答解析
Getfem++を使用して地盤の２次元線形時刻歴応答解析を行いました。
End of explanation
"""
# S波速度(m/sec2)
Vs = 2.000e+03
# ポアソン比(-)
Nu = 4.800e-01
# 質量密度(kg/m3)
rho = 1.800e+01
# ラメの弾性定数
Mu ... |
edinburghlivinglab/D4I | viz/python/Simplify_Bin_Sensor_Data.ipynb | apache-2.0 | import pandas as pd
table = pd.read_csv("../data/binsensors.csv")
"""
Explanation: How to get a simple dataset from a big CSV file
This notebook goes through some steps to convert a large unwieldy file from the Council's Litter Bin Sensor Project into a simpler dataset for visualisation. We use a number of functions p... |
t-makaro/nbconvert_pdf_template | example/test.ipynb | mit | next_paragraph = """
Aenean vitae diam consectetur, tempus arcu quis, ultricies urna. Vivamus venenatis sem
quis orci condimentum, sed feugiat dui porta.
"""
def identity_dec(ob):
return ob
@identity_dec
def nifty_print(text):
"""Used to test syntax highlighting"""
print(text * 2)
nifty_print(next_... |
garibaldu/boundary-seekers | gated_perceptron.ipynb | mit | %matplotlib inline
import numpy.random as rng
import pylab as pl
import autograd.numpy as np
from autograd import grad
def sigmoid(phi):
return 1.0/(1.0 + np.exp(-phi))
def calc_outputs(params):
# Sigmoid perceptron ('logistic regression')
XX = X - params['m']
phi = np.dot(XX, params['w'])
psi = ... |
mne-tools/mne-tools.github.io | 0.24/_downloads/e7de7baffeeb4beff147bad8657b46dc/opm_data.ipynb | bsd-3-clause | import os.path as op
import numpy as np
import mne
data_path = mne.datasets.opm.data_path()
subject = 'OPM_sample'
subjects_dir = op.join(data_path, 'subjects')
raw_fname = op.join(data_path, 'MEG', 'OPM', 'OPM_SEF_raw.fif')
bem_fname = op.join(subjects_dir, subject, 'bem',
subject + '-5120-5120-5... |
Kaggle/learntools | notebooks/python/raw/tut_2.ipynb | apache-2.0 | help(round)
"""
Explanation: You've already seen and used functions such as print and abs. But Python has many more functions, and defining your own functions is a big part of python programming.
In this lesson, you will learn more about using and defining functions.
Getting Help
You saw the abs function in the previo... |
CompPhysics/MachineLearning | doc/src/Optimization/GradientMethods.ipynb | cc0-1.0 | #Program that solves exercise 1b.
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
def gradient_descent(xk,dx_f,gamma):
return xk-gamma*dx_f
def quadratic(a,b,c,x):
return a*x**2+b*x+c
def dx_quadratic(a,b,x):
return 2*a*x+b
#One variable examples
a,b,c = 1,0,1
x = np.linspa... |
tensorflow/docs | site/en/r1/tutorials/keras/basic_classification.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
pierre-rouanet/dtw | examples/speech-recognition.ipynb | gpl-3.0 | %matplotlib inline
import os
import glob
import librosa
import numpy as np
"""
Explanation: Speech recognition
Simple sppeech recognition system can be implemented using DTW + MFCC.
End of explanation
"""
DATABASE_PATH = '/Users/pierrerouanet/Downloads/data-speech_commands_v0.02'
labels = {'cat', 'dog', 'house', '... |
oroszl/topins | RM.ipynb | gpl-2.0 | # The usual imports
%pylab inline
from ipywidgets import *
# Some extra imports for 3D
from mpl_toolkits.mplot3d import *
# These are only needed to make things pretty..
# they are mostly refered to in the formatting part of the figures
# and enshure us to have the figures also present in the book.
from matplotlib.pa... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/text_classification/labs/text_classification.ipynb | apache-2.0 | # Import necessary libraries
import matplotlib.pyplot as plt
import os
import re
import shutil
import string
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import losses
# Print the TensorFlow version
print(tf.__version__)
"""
Explanation: Basic Text Classification
Overview
This n... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/09_sequence/reusable-embeddings.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip install --user google-cloud-bigquery==1.25.0
"""
Explanation: Using pre-trained embedding with Tensorflow Hub
Learning Objectives
1. How to instantiate a Tensorflow Hub module
1. How to find pretrained Tensorflow Hub module for variety of purpose... |
mne-tools/mne-tools.github.io | 0.18/_downloads/c1aa88a2be3f4bc4a4552ce39a81e4e1/plot_morph_volume_stc.ipynb | bsd-3-clause | # Author: Tommy Clausner <tommy.clausner@gmail.com>
#
# License: BSD (3-clause)
import os
import nibabel as nib
import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse, read_inverse_operator
from nilearn.plotting import plot_glass_brain
print(__doc__)
"""
Explanation: Morph volumetric s... |
fastai/course-v3 | nbs/dl2/12c_ulmfit.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
#export
from exp.nb_12a import *
"""
Explanation: ULMFit
End of explanation
"""
path = datasets.untar_data(datasets.URLs.IMDB)
ll = pickle.load(open(path/'ll_lm.pkl', 'rb'))
bs,bptt = 128,70
data = lm_databunchify(ll, bs, bptt)
vocab = ll.train.proc_x[1].voc... |
CompPhysics/MachineLearning | doc/Programs/IsingModel/logisticIsing.ipynb | cc0-1.0 | import pickle
import os
import glob
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.model_selection as skms
import sklearn.linear_model as skl
import sklearn.metrics as skm
import tqdm
import copy
import time
from IPython.display import display
%matplotlib in... |
erdewit/ib_insync | notebooks/bar_data.ipynb | bsd-2-clause | from ib_insync import *
util.startLoop()
ib = IB()
ib.connect('127.0.0.1', 7497, clientId=14)
"""
Explanation: Bar data
End of explanation
"""
contract = Stock('TSLA', 'SMART', 'USD')
ib.reqHeadTimeStamp(contract, whatToShow='TRADES', useRTH=True)
"""
Explanation: Historical data
To get the earliest date of avail... |
GustavoRP/IA369Z | deliver/DTI_open_01-05-17_GRP.ipynb | gpl-3.0 | # import modules and libs
import io, os, sys, types
import numpy as np
# image and graphic
from IPython.display import Image
from IPython.display import display
import matplotlib.pyplot as plt
%matplotlib
#import specific modules
sys.path.append('C:/iPython/DTIlib')
import DTIlib as DTI
"""
Explanation: Openig DTI d... |
LDSSA/learning-units | units/08-data-cleaning/examples/01 - Eliminating Outliers.ipynb | mit | import pandas as pd
import numpy as np
% matplotlib inline
from matplotlib import pyplot as plt
"""
Explanation: 01 - Example - Eliminating Outliers
This notebook presents how to eliminate the diagnosed outliers (in the previous Learning Unit).
By: Hugo Lopes
Learning Unit 08
Some inital imports:
End of explanation
"... |
Applied-Groundwater-Modeling-2nd-Ed/Chapter_4_problems-1 | P4.1_FlopyIntro-Toth_Flowsystem.ipynb | gpl-2.0 | %matplotlib inline
import sys
import os
import shutil
import numpy as np
from subprocess import check_output
# Import flopy
import flopy
"""
Explanation: <img src="AW&H2015.tiff" style="float: left">
<img src="flopylogo.png" style="float: center">
Problem P4.1 Flopy Background and Toth (1962) Flow System
Pages 171-17... |
ES-DOC/esdoc-jupyterhub | notebooks/mpi-m/cmip6/models/icon-esm-lr/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mpi-m', 'icon-esm-lr', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: MPI-M
Source ID: ICON-ESM-LR
Sub-Topics: Radiative Forcings.
Properties: 8... |
Joozty/FIT-VUT | 2. Semester/ISJ - Scripting Languages/1. Project/xharag01.ipynb | gpl-3.0 | from __future__ import division, print_function
import re
import itertools
def words(text): return set(text.split())
winners = words('''washington adams jefferson jefferson madison madison monroe
monroe adams jackson jackson van-buren harrison polk taylor pierce buchanan
lincoln lincoln grant grapartnt haye... |
rasbt/algorithms_in_ipython_notebooks | ipython_nbs/essentials/recursion-examples.ipynb | gpl-3.0 | def factorial(x):
if x <= 1:
return x
else:
return x * factorial(x-1)
factorial(0)
factorial(1)
"""
Explanation: Examples using Recursion
Important Note
For most cases, using function recursion should be avoided in Python are better be implemented using for/while loops most of the time (altho... |
QuantEcon/QuantEcon.notebooks | ddp_ex_MF_7_6_4_py.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
from scipy import sparse
import matplotlib.pyplot as plt
import quantecon as qe
from quantecon.markov import DiscreteDP, backward_induction, sa_indices
T = 0.5 # Time expiration (years)
vol = 0.2 # Annual volatility
r = 0.05 # Annual interest rate
strike = 2.1 # S... |
rbreu/birdcam | talk/.ipynb_checkpoints/Vogelkamera-checkpoint.ipynb | mit | import glob
import numpy
from matplotlib import image
def rgb2gray(rgb):
return numpy.dot(rgb[...,:3], [0.299, 0.587, 0.144])
oldimg = None
for infile in glob.glob('*.JPG'):
img = rgb2gray(image.imread(infile))
if oldimg is not None:
diff = numpy.linalg.norm(img - oldimg)
# ... do someth... |
GoogleCloudPlatform/training-data-analyst | blogs/ncaa/ncaa_feateng_solution.ipynb | apache-2.0 | %%bigquery df1
SELECT
team_code,
AVG(SAFE_DIVIDE(fgm + 0.5 * fgm3,fga)) AS offensive_shooting_efficiency,
AVG(SAFE_DIVIDE(opp_fgm + 0.5 * opp_fgm3,opp_fga)) AS opponents_shooting_efficiency,
AVG(win) AS win_rate,
COUNT(win) AS num_games
FROM lab_dev.team_box
WHERE fga IS NOT NULL
GROUP BY team_code
"""
Expl... |
yunqu/PYNQ | boards/Pynq-Z1/base/notebooks/arduino/arduino_ardumoto.ipynb | bsd-3-clause | from pynq.overlays.base import BaseOverlay
base = BaseOverlay("base.bit")
"""
Explanation: Ardumoto Example
This example shows how to use the
Ardumoto on the board.
Ardumoto supports two DC motor driving.
There are also instructions
on how to hook up the shield.
Motor A and Motor B are connected as below to the ard... |
tensorflow/docs-l10n | site/en-snapshot/hub/tutorials/senteval_for_universal_sentence_encoder_cmlm.ipynb | apache-2.0 | # Copyright 2021 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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 app... |
rishuatgithub/MLPy | torch/PYTORCH_NOTEBOOKS/00-Crash-Course-Topics/01-Crash-Course-Pandas/01-Series.ipynb | apache-2.0 | import numpy as np
import pandas as pd
"""
Explanation: <a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>
<center><em>Copyright Pierian Data</em></center>
<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>
Series
The fi... |
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies | ex24-Visualize CO2 Time Series with Python.ipynb | mit | import pandas as pd
import statsmodels.api as sm
from matplotlib import pyplot as plt
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
# Set some parameters to apply to all plots. These can be overridden
import matplotlib
# Plot size to 12" x 7"
matplotlib.rc('figure', figsize = (12, 7))
# Font s... |
mne-tools/mne-tools.github.io | 0.19/_downloads/4395f5fd5291fa48208a82dab72d1893/plot_sensor_regression.ipynb | bsd-3-clause | # Authors: Tal Linzen <linzen@nyu.edu>
# Denis A. Engemann <denis.engemann@gmail.com>
# Jona Sassenhagen <jona.sassenhagen@gmail.com>
#
# License: BSD (3-clause)
import pandas as pd
import mne
from mne.stats import linear_regression, fdr_correction
from mne.viz import plot_compare_evokeds
from mne.da... |
rhiever/scipy_2015_sklearn_tutorial | notebooks/01.3 Data Representation for Machine Learning.ipynb | cc0-1.0 | from sklearn.datasets import load_iris
iris = load_iris()
"""
Explanation: Representation and Visualization of Data
Machine learning is about creating models from data: for that reason, we'll start by
discussing how data can be represented in order to be understood by the computer. Along
with this, we'll build on our... |
sergey-tomin/workshop | 6_Genesis_postprocessor.ipynb | mit | # the output of plotting commands is displayed inline within frontends,
# directly below the code cell that produced it
%matplotlib inline
from __future__ import print_function
# this python library provides generic shallow (copy) and deep copy (deepcopy) operations
from copy import deepcopy
# import from Ocelot gra... |
ContinuumIO/cube-explorer | notebooks/Colocated_cube_pair.ipynb | bsd-3-clause | from IPython.display import Image as IPImage
IPImage(url="http://www.wetterzentrale.de/wz/pics/Recm1201.gif", width=500)
import iris
import numpy as np
import holoviews as hv
import holocube as hc
from cartopy import crs
from cartopy import feature as cf
hv.notebook_extension()
iris.FUTURE.strict_grib_load = True
%ou... |
rsignell-usgs/notebook | Siphon/.ipynb_checkpoints/casestudy-checkpoint.ipynb | mit | import xray
ds = xray.open_dataset('https://motherlode.ucar.edu/repository/opendap/41f2b38a-4e70-4135-8ff8-dbf3d1dcbfc1/entry.das',
decode_times=False)
print(ds)
"""
Explanation: netCDF File Visualization Case Study
I was asked by a colleague to visualize data contained within this netCDF file ... |
lesley2958/lesley2958.github.io | blog/2018/aq-india-map.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
"""
Explanation: Mapping location of air quality sensing in India
In this notebook, I'll show a quick example of how to use Folium (which internally uses LeafletJS) for visualising the location of air quality monitors in India. T... |
Vvkmnn/books | AutomateTheBoringStuffWithPython/lesson40.ipynb | gpl-3.0 | import bs4
"""
Explanation: Lesson 40:
Parsing HTML with the BeautifulSoup Module
HTML stands for 'Hyper Text Markup Language', which is text that loads elements of a webpage.
Parsing HTML in Python can be done via the beautifulsoup module. It is a third party module, and must be installed via pip.
End of explanation
... |
fereshteh-razmi/mimic-code | notebooks/elective-surgery.ipynb | mit | # Import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import psycopg2
# used to calculate AUROC/accuracy
from sklearn import metrics
%matplotlib inline
# create a database connection
sqluser = 'postgres'
dbname = 'mimic'
schema_name = 'mimiciii'
# Connect to local postgres versi... |
rethore/FUSED-Wake | examples/Script.ipynb | agpl-3.0 | v80 = wt.WindTurbine('Vestas v80 2MW offshore','V80_2MW_offshore.dat',70,40)
HR1 = wf.WindFarm('Horns Rev 1','HR_coordinates.dat',v80)
WD = range(0,360,1)
"""
Explanation: Verification of the FUSED-Wind wrapper
common inputs
End of explanation
"""
"""
##Fused inputs
inputs = dict(
wind_speed=8.0,
roughness=0... |
juditacs/labor | notebooks/Python_alapok.ipynb | lgpl-3.0 | print("Hello world\nvagy magyarul: Szia világ")
"""
Explanation: Python alapok
Ez a leírás Python 3.5-ös verziójához készült, de bármilyen Python 3.3 feletti verzióhoz használható.
Az anyag elsajátításához nem szükséges Pythont és Jupytert telepíteni, de teljes értékűbb, ha te is ki tudod próbálni, át tudod írni az it... |
dato-code/tutorials | notebooks/model_parameter_search.ipynb | apache-2.0 | import graphlab as gl
data_url = 'https://static.turi.com/datasets/movie_ratings/sample.small'
movie_data = gl.SFrame.read_csv(data_url, delimiter='\t')
"""
Explanation: Model Parameter Search - Automated and Distributed
Back when I was creating a movie recommender the most time consuming part was finding good parame... |
google/eng-edu | ml/testing-debugging/testing-debugging-ml-debugging.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... |
bbglab/adventofcode | 2017/iker/Ferran AoC 2017.ipynb | mit | digits = '91212129'
L = len(digits)
sum([int(digits[i]) for i in range(L) if digits[i] == digits[(i+1) % L]])
def solve(captcha):
captcha = list(map(int, captcha))
prev_val = captcha[-1]
repeated = 0
for v in captcha:
if v == prev_val:
repeated += v
prev_val = v
return ... |
aleph314/K2 | Foundations/Math/linear-algebra_exercise.ipynb | gpl-3.0 | import numpy as np
"""
Explanation: Linear Algebra
Although its name may sound harmless, Linear Algebra is by no means a trivial branch of mathematics, and the deeper you dive into Data Science and Statistics, the more often you will encounter its concepts. In this exercise, we'll start with some basic operations on ... |
hashiprobr/redes-sociais | encontro07/pagerank.ipynb | gpl-3.0 | import sys
sys.path.append('..')
import numpy as np
import socnet as sn
"""
Explanation: Encontro 07: Simulação e Demonstração de PageRank
Importando as bibliotecas:
End of explanation
"""
sn.graph_width = 225
sn.graph_height = 225
"""
Explanation: Configurando a biblioteca:
End of explanation
"""
g = sn.load_gr... |
misken/hillmaker | hillmaker/testing/metamodeling_obs_ldr.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Make the graphs a bit prettier, and bigger
# pd.set_option('display.mpl_style', 'default')
# pd.set_option('display.width', 5000)
# pd.set_option('display.max_columns', 60)
%matplotlib... |
infotranecon/IEtools | Dynamic Equilibrium Examples.ipynb | mit | import numpy as np
import pylab as pl
from IEtools import *
%pylab inline
"""
Explanation: Here are some examples of using IEtools ('Information Equilibrium
tools') for python to investigate a couple of dynamic equilibrium
models. There are two datasets used below, both obtainable from FRED.
One is (nominal) GDP div... |
RaspberryJamBe/ipython-notebooks | notebooks/en-gb/Communication - Cloud message 2 - Receive messages and blink LEDs.ipynb | cc0-1.0 | APPKEY = "******"
"""
Explanation: APPKEY is the Application Key for a (free) http://www.realtime.co/ "Realtime Messaging Free" subscription.
See "104 - Remote deurbel - Een cloud API gebruiken om berichten te sturen" voor meer gedetailleerde info.
End of explanation
"""
import time
import RPi.GPIO as GPIO
GPIO.setm... |
Sandor-PRA/pandas_tutorial | demos/climate_timeseries/climate_timeseries.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pandas import set_option
set_option("display.max_rows", 16)
LARGE_FIGSIZE = (12, 8)
# Change this cell to the demo location on YOUR machine
%cd 'D:\\Git\\Pandas_Tutorial\\demos\\climate_timeseries'
%ls
"""
Explanation: L... |
Oaklight/Grade-Sender | send_the_grade.ipynb | apache-2.0 | import gradeSender as gs
gs.DEBUG = True # Switch to False when deployed :D
gs.user_account = "xyz@whatever.com"
gs.user_password = "password/autherization_code" # autherization code if using 163, qq, etc.
gs.user_smtp_server = "smtp.whatever.com" # some may use mail.whatever.com as an alternative
gs.user_smtp_port ... |
sbenthall/bigbang | examples/word_analysis/SpecialWordsAnalysis.ipynb | agpl-3.0 | from bigbang.archive import Archive
from bigbang.archive import load as load_archive
import bigbang.parse as parse
import bigbang.graph as graph
import bigbang.mailman as mailman
import bigbang.process as process
import networkx as nx
import matplotlib.pyplot as plt
import pandas as pd
from pprint import pprint as pp
i... |
daniel-severo/dask-ml | docs/source/examples/joblib-distributed.ipynb | bsd-3-clause | from time import time
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_olivetti_faces
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.externals import joblib
import distributed.joblib
from distributed import Client
"""
Explanation: Distributed Joblib
Scikit-learn already parallelizes... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/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', 'ncc', 'sandbox-2', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: NCC
Source ID: SANDBOX-2
Topic: Atmoschem
Sub-Topics: Transport, Emissions Co... |
m2dsupsdlclass/lectures-labs | labs/09_triplet_loss/Face_Verification_Using_Triplet_Loss.ipynb | mit | import tensorflow as tf
# If you have a GPU, execute the following lines to restrict the amount of VRAM used:
gpus = tf.config.experimental.list_physical_devices('GPU')
if len(gpus) > 1:
print("Using GPU {}".format(gpus[0]))
tf.config.experimental.set_visible_devices(gpus[0], 'GPU')
else:
print("Using CPU"... |
samirma/deep-learning | transfer-learning/Transfer_Learning_Solution.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... |
minesh1291/Practicing-Kaggle | master-class/FeatureTransformation/Feature_transformation.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
%matplotlib inline
"""
Explanation: Hypothesis/ Questions?
When we need log transformation ?
Which log base ? natural, 2, 10 ?
When we need **2 polynomial ?
Distribution with Spike, very low variance/ standard deviation
By observation
STD, Var, Mean
MSE
LIbraries
End of expla... |
adrn/TwoFace | notebooks/figures/Sample K cuts.ipynb | mit | with h5py.File(control_samples_file) as f:
control_K = np.full((len(f.keys()), 256), np.nan)
for i, key in enumerate(f):
n_samples = len(f[key]['K'])
control_K[i, :n_samples] = f[key]['K'][:]
ln_control_K = np.log(control_K)
n_samples = np.sum(np.logical_not(np.isnan(control_K)), axis=... |
dedx/STAR2015 | notebooks/DoubleSlit.ipynb | mit | %pylab inline
import numpy as np
import matplotlib.pyplot as plot
from scipy.integrate import trapz,cumtrapz
from IPython.html.widgets import interact, interactive
def distribute1D(x,prob,N):
"""takes any distribution which is directly proportional
to the number of particles, and returns data that is
sta... |
FlorianWilhelm/distvis | index.ipynb | mit | import warnings
from collections import OrderedDict, defaultdict
from enum import Enum
import numpy as np
from scipy import stats
from bokeh.io import output_notebook, show, push_notebook
from bokeh.plotting import figure
from bokeh.io import show
from ipywidgets import widgets, interact, interactive
from IPython.di... |
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