repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
kubeflow/examples | github_issue_summarization/notebooks/Training.ipynb | apache-2.0 | # Ensure that the github-issues-data volume is mounted in /mnt
!ls -la /mnt
# Set path for data dir
%env DATA_DIR=/mnt/github-issues-data
# Download the github-issues.zip training data to /mnt/github-issues-data
!wget --directory-prefix=${DATA_DIR} https://storage.googleapis.com/kubeflow-examples/github-issue-summari... |
jsub10/MLCourse | Notebooks/Systematic-Approach-to-Visualizing-Data.ipynb | mit | # We keep plotting simple and use common packages and defaults
import matplotlib.pyplot as plt
import seaborn as sns
# Set the aesthetics for Seaborn visuals
sns.set(context='notebook',
style='whitegrid',
palette='deep',
font='sans-serif',
font_scale=1.3,
color_codes=True,
... |
dimonaks/siman | tutorials/create_vasp_calculation.ipynb | gpl-2.0 | from siman import header
from siman.header import db
from siman.SSHTools import SSHTools
from siman.calc_manage import add, res
from siman.database import write_database, read_database
from siman.set_functions import read_vasp_sets
from siman.calc_manage import smart_structure_read
%matplotlib inline
read_database() # ... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-3/cmip6/models/sandbox-1/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-3', 'sandbox-1', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: TEST-INSTITUTE-3
Source ID: SANDBOX-1
Sub-Topics: Radiative Forcin... |
timothydmorton/usrp-sciprog | day4/Newton-Method.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
# two parabolae
f = lambda x, c: c*x**2
c = 1, 0.5
# initial point
x_ = -0.75
y_ = [ f(x_, ci) for ci in c ]
# compute gradient and hessian
g = [ 2*c[i]*x_ for i in range(2) ]
H = [ 2*c[i] for i in range(2) ]
# Newton step
x__ = [ x_ - 1/H[i]*g[i... |
johntanz/ROP | Old Code/EyeDrops160223.ipynb | gpl-2.0 | from ROPini import *
#Takes a little bit, wait a while.
Hour1, Minute1, Hour2, Minute2, Hour3,
Minute3 = [int(x) for x in raw_input("Enter times for eye drops here: ").split()]
#Syntax should be "HH MM HH MM HH MM" First time, second time, third time all in one line.
#No commas or colons.
W1 = datetime(Year, Month... |
DiXiT-eu/collatex-tutorial | unit8/unit8-collatex-and-XML/CollateX and XML, Part 1.ipynb | gpl-3.0 | from collatex import *
from lxml import etree
import json,re
"""
Explanation: CollateX and XML, Part 1
David J. Birnbaum (djbpitt@gmail.com, http://www.obdurodon.org), 2015-06-29
This is the first part of multi-part tutorial on processing... |
AllenDowney/ThinkBayes2 | notebooks/chap13.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... |
rileyrustad/pdxapartmentfinder | analysis/Fourth_Analysis.ipynb | mit | import numpy as np
import pandas as pd
from pandas import DataFrame, Series
import json
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: This is my fourth attempt at creating a model using sklearn alogithms
In this iteration of analysis we'll be looking... |
hanezu/cs231n-assignment | assignment1/.ipynb_checkpoints/features-checkpoint.ipynb | mit | import random
import numpy as np
from cs231n.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 extenrnal modu... |
dnc1994/MachineLearning-UW | ml-classification/module-8-boosting-assignment-2-solution.ipynb | mit | import graphlab
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Boosting a decision stump
The goal of this notebook is to implement your own boosting module.
Brace yourselves! This is going to be a fun and challenging assignment.
Use SFrames to do some feature engineering.
Modify the decision tree... |
fastai/fastai | nbs/20a_distributed.ipynb | apache-2.0 | #|export
@patch
def reset(self: DataParallel):
"Patch required `reset` call into `DataParallel`"
if hasattr(self.module, 'reset'): self.module.reset()
#|export
class ParallelTrainer(Callback):
"Wrap a model `DataParallel` automatically"
run_after,run_before = TrainEvalCallback,Recorder
def __init__... |
computational-class/cjc | code/17.networkx.ipynb | mit | %matplotlib inline
import networkx as nx
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import networkx as nx
G=nx.Graph() # G = nx.DiGraph() # 有向网络
# 添加（孤立）节点
G.add_node("spam")
# 添加节点和链接
G.add_edge(1,2)
print(G.nodes())
print(G.edges())
# 绘制网络
nx.draw(G, with_labels = True)
"""
Explanation: 网络科学理论
... |
eds-uga/cbio4835-sp17 | lectures/Lecture15.ipynb | mit | from Bio.Seq import Seq # the submodule structure of biopython is a little awkward
s = Seq("GATTACA")
print(s)
"""
Explanation: Lecture 15: BioPython
CBIO (CSCI) 4835/6835: Introduction to Computational Biology
Overview and Objectives
BioPython is a versatile Python package for computational biology, particularly if ... |
jasontlam/snorkel | tutorials/crowdsourcing/Crowdsourced_Sentiment_Analysis.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
import os
import numpy as np
from snorkel import SnorkelSession
session = SnorkelSession()
"""
Explanation: Training a Sentiment Analysis LSTM Using Noisy Crowd Labels
In this tutorial, we'll provide a simple walkthrough of how to use Snorkel to resolve conflicts i... |
CartoDB/cartoframes | docs/guides/06-Data-Services.ipynb | bsd-3-clause | from cartoframes.auth import set_default_credentials
set_default_credentials('creds.json')
"""
Explanation: Data Services
You can connect to CARTO Data Services API directly from CARTOframes. This API consists of a set of location-based functions that can be applied to your data to perform geospatial analyses without... |
wangzexian/summrerschool2015 | rnn_tutorial/simple_rnn.ipynb | bsd-3-clause | %matplotlib inline
from synthetic import mackey_glass
import matplotlib.pyplot as plt
import theano
import theano.tensor as T
import numpy
floatX = theano.config.floatX
"""
Explanation: This notebook demonstrates how to implement RNNs in Theano
First, we import some dependencies:
End of explanation
"""
class Simple... |
arborh/tensorflow | tensorflow/compiler/xla/g3doc/tutorials/autoclustering_xla.ipynb | apache-2.0 | import tensorflow as tf
# Check that GPU is available: cf. https://colab.research.google.com/notebooks/gpu.ipynb
assert(tf.test.is_gpu_available())
tf.keras.backend.clear_session()
tf.config.optimizer.set_jit(False) # Start with XLA disabled.
def load_data():
(x_train, y_train), (x_test, y_test) = tf.keras.dataset... |
agile-geoscience/striplog | docs/tutorial/14_Merge_overlapping_striplogs.ipynb | apache-2.0 | from striplog import Striplog
csv = """Top,Base,Comp Time,Comp Depth
100,200,2,a
110,120,1,b
150,325,3,c
210,225,1,d
300,400,2,e
350,375,3,f
"""
s = Striplog.from_csv(text=csv)
"""
Explanation: Merge overlapping striplogs
Imagine we have a Striplog with overlapping Interval. We would like to be able to merge the Int... |
European-XFEL/h5tools-py | docs/lpd_data.ipynb | bsd-3-clause | from karabo_data import RunDirectory, by_index
run = RunDirectory('fxe_example_run/')
# Using only the first three trains to keep this example light:
run = run.select_trains(by_index[:3])
run.instrument_sources
"""
Explanation: Accessing LPD data
The Large Pixel Detector (LPD) is made of 16 modules which record data... |
NeuroDataDesign/seelviz | jon/algorithms/Timing+Pipeline.ipynb | apache-2.0 | import analysis3 as a3
reload(a3)
import time
def time_function(fun, *args):
start = time.time();
result = fun(*args);
run_time = time.time() - start;
minutes = run_time / 60;
print('RUN TIME: %f s (%f m)' % (run_time, minutes));
return result;
"""
Explanation: Running on 4 cores with 30 gb RA... |
jdamiani27/DataSciUF-Tutorial-Instructor | DataSciUF Python II - Instructor.ipynb | mit | def download_file(url, local_filename):
import requests
# stream = True allows downloading of large files; prevents loading entire file into memory
r = requests.get(url, stream = True)
with open(local_filename, 'wb') as f:
for chunk in r.iter_content(chunk_size=1024):
if chunk:... |
mne-tools/mne-tools.github.io | 0.17/_downloads/5d2190fa4d2e36b882cc913879fb6f3b/plot_decoding_time_generalization_conditions.ipynb | bsd-3-clause | # Authors: Jean-Remi King <jeanremi.king@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.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 Stan... |
h-mayorquin/time_series_basic | presentations/2016-03-20(Wall Street Columns Nexa - Visualization of Code Vectors).ipynb | bsd-3-clause | import numpy as np
import h5py
%matplotlib inline
import sys
sys.path.append("../")
"""
Explanation: Wall Street Columns Nexa - Visualization of Code Vectors
In this notebook we will analyse the distribution of code vectors. What we want to analyse is whether we are able to increase that difference by making proper u... |
gautam1858/tensorflow | tensorflow/lite/g3doc/examples/jax_conversion/overview.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... |
smharper/openmc | examples/jupyter/unstructured-mesh-part-i.ipynb | mit | from IPython.display import Image
import openmc
import openmc.lib
assert(openmc.lib._dagmc_enabled())
"""
Explanation: Unstructured Mesh Tallies in OpenMC
In this example we'll look at how to setup and use unstructured mesh tallies in OpenMC. Unstructured meshes are able to provide results over spatial regions of a p... |
jsharpna/DavisSML | lectures/lecture4/lecture4.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Convex Optimization
StatML: Lecture 4
Prof. James Sharpnack
Reading: "Convex Optimization" Boyd and Vandenberghe Ch. 1
End of explanation
"""
T = np.linspace(0.01,2*np.pi,100)
plt.plot(T,-np.log(T),label='Conv')
plt.plot(T,3*np.sin(T),label='Nonconv... |
michaelaye/planetpy | notebooks/planetary constants.ipynb | bsd-3-clause | from planetarypy.constants import planets_pretty
planets_pretty
planets_pretty.dtypes
"""
Explanation: Quick show on how to use the planetary constants module
planets_pretty shows a pretty table with the units included.
End of explanation
"""
from planetarypy.constants import planets
planets
planets.dtypes
"""
... |
jiarong/SSUsearch | notebooks-pc-linux/.ipynb_checkpoints/pipeline-dependency-installation-checkpoint.ipynb | bsd-3-clause | cd ~/Desktop/SSUsearch/
mkdir -p ./external_tools
cd ./external_tools
mkdir -p ./bin
"""
Explanation: Install dependencies
This pipeline requires:
HMMER3.1
mothur
RDP mcclust
python pandas, numpy, scipy, matplotlib, and screed package.
This tutorial is made on a 64 bit linux (ubuntu) machine. If you have 32 bit m... |
kevinsung/OpenFermion | docs/tutorials/jordan_wigner_and_bravyi_kitaev_transforms.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... |
nikodtbVf/aima-si | games.ipynb | mit | from games import (GameState, Game, Fig52Game, TicTacToe, query_player, random_player,
alphabeta_player, play_game, minimax_decision, alphabeta_full_search,
alphabeta_search, Canvas_TicTacToe)
"""
Explanation: Games or Adversarial search
This notebook serves as supporting mater... |
yassineAlouini/ml-experiments | notebooks/linear_regression.ipynb | mit | import pandas as pd
import numpy as np
import scipy.stats as sps
import matplotlib.pyplot as plt
%matplotlib inline
movies = pd.read_csv('fandango_score_comparison.csv')
movies.describe()
movies.info()
# Check the movie data structure
movies.head()
"""
Explanation: Comparing different movie rating systems
In this ... |
BrownDwarf/ApJdataFrames | notebooks/Muzic2015.ipynb | mit | %pylab inline
import seaborn as sns
sns.set_context("notebook", font_scale=1.5)
#import warnings
#warnings.filterwarnings("ignore")
import pandas as pd
"""
Explanation: ApJdataFrames Muzic2015
Title: SUBSTELLAR OBJECTS IN NEARBY YOUNG CLUSTERS (SONYC) IX: THE PLANETARY-MASS DOMAIN OF CHAMAELEON-I AND UPDATED MASS F... |
statsmodels/statsmodels.github.io | v0.13.2/examples/notebooks/generated/discrete_choice_overview.ipynb | bsd-3-clause | import numpy as np
import statsmodels.api as sm
"""
Explanation: Discrete Choice Models Overview
End of explanation
"""
spector_data = sm.datasets.spector.load()
spector_data.exog = sm.add_constant(spector_data.exog, prepend=False)
"""
Explanation: Data
Load data from Spector and Mazzeo (1980). Examples follow Gree... |
tranlyvu/kaggle | Digit Recognizer/notebook/Digit Recognizer.ipynb | apache-2.0 | import pandas as pd
train_data=pd.read_csv('/train.csv')
train_data.head()
train_data.tail()
train_data.dtypes
train_data.info()
"""
Explanation: Author : Vu Tran. Other info is on github
Kaggle Competition: Digit Recognizer
Info from Competition Site
Description
Evaluation
Data Set
First attempt:
Working with ... |
psychemedia/ou-robotics-vrep | robotVM/notebooks/Demo - Square 3 - Loops.ipynb | apache-2.0 | for count in range(0,3):
print(count)
print("And the final value of `count` is", count)
"""
Explanation: Traverse a Square - Part 3 - Loops
Compared to the original programme, the programme that contains the variables is easier to modify. But it still contains a lot of repetition.
Let's remind ourselves of it... |
lo-co/atm-py | atmPy/for_removal/documentation/HAGiS Analysis.ipynb | mit | import atmPy.instruments.DMA.smps as smps
import atmPy.instruments.DMA.dma as dma
import pandas as pd
from matplotlib.dates import date2num
from matplotlib import colors
import matplotlib.pyplot as plt
from numpy import meshgrid
from matplotlib import rc
from matplotlib import dates
import numpy as np
# Plots will be i... |
ArdenB/SegmentedRESTREND_pub | NCdemo/CombineRuns.ipynb | mit | import numpy as np
import pandas as pd
import xarray as xr
import bottleneck as bn
from scipy import stats
import matplotlib.pyplot as plt
# import statsmodels.stats.multitest as smsM
"""
Explanation: Combining paired C3 and C4 runs
This section describes a methods to combine TSS-RESTREND attribution runs done assumin... |
jmschrei/pomegranate | tutorials/C_Feature_Tutorial_1_Parallelization.ipynb | mit | %matplotlib inline
import numpy
import matplotlib.pyplot as plt
import seaborn; seaborn.set_style('whitegrid')
from pomegranate import *
numpy.random.seed(0)
numpy.set_printoptions(suppress=True)
%load_ext watermark
%watermark -m -n -p numpy,scipy,pomegranate
def create_dataset(n_samples, n_dim, n_classes, alpha=1)... |
VVard0g/ThreatHunter-Playbook | docs/notebooks/windows/08_lateral_movement/WIN-190810201010.ipynb | mit | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: WMI Win32_Process Class and Create Method for Remote Execution
Metadata
| Metadata | Value |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2019/08/10 |
| modification date | 20... |
seblabbe/MATH2010-Logiciels-mathematiques | ipynb-cours/16-02-16.ipynb | gpl-3.0 | 5 / 2
from sympy import Rational
Rational(5,2)
from sympy import S
S?
type(S(5))
S(5)/2
S('13/2') + S(5)/7
"""
Explanation: Chapitres 1 à 4
Quelques ajouts dans les sections déjà vues...
3.1 Nombres rationels
Une autre façon de créer un nombre rationnel
End of explanation
"""
from sympy import arg,re,im,I
a = ... |
graphistry/pygraphistry | demos/demos_databases_apis/neo4j/contributed/Neo4jTwitter.ipynb | bsd-3-clause | # import required dependencies
from neo4j.v1 import GraphDatabase, basic_auth
from pandas import DataFrame
import graphistry
# To specify Graphistry account & server, use:
# graphistry.register(api=3, username='...', password='...', protocol='https', server='hub.graphistry.com')
# For more options, see https://github.... |
LaurelOak/hydro-teaching-resources | Tutorial-ForLoops-Discharge.ipynb | bsd-3-clause | # Import numerical tools
import numpy as np
#Import pandas for reading in and managing data
import pandas as pd
# Import pyplot for plotting
import matplotlib.pyplot as plt
#Import seaborn (useful for plotting)
import seaborn as sns
# Magic function to make matplotlib inline; other style specs must come AFTER
%matp... |
highb/deep-learning | autoencoder/Convolutional_Autoencoder_Solution.ipynb | mit | %matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
"""
Explanation: C... |
thehackerwithin/berkeley | code_examples/keras_introduction/Single_layer_keras.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Keras imports
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import SGD
"""
Explanation: Building and training a single-layer neural network with keras
Keras is a high-leve... |
fonnesbeck/PyMC3_Oslo | notebooks/c. Bayesian Neural Network.ipynb | cc0-1.0 | %matplotlib inline
import pymc3 as pm
import theano.tensor as T
import theano
import sklearn
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
from sklearn import datasets
from sklearn.preprocessing import scale
from sklearn.cross_validation import train_test_split
from skl... |
pagutierrez/tutorial-sklearn | notebooks-spanish/10-caso_estudio_titanic.ipynb | cc0-1.0 | from sklearn.datasets import load_iris
iris = load_iris()
print(iris.data.shape)
"""
Explanation: Caso de estudio - Supervivencia en el Titanic
Extracción de características
Ahora trataremos parte muy importante del aprendizaje automático: la extracción de características cuantitativas a partir de los datos. Con este... |
sylvchev/coursIntroPython | tp/SAE03-TP2_io_if_while.ipynb | gpl-3.0 | # Exécutez cette cellule !
from IPython.core.display import HTML
styles = "<style>\n.travail {\n background-size: 30px;\n background-image: url('https://cdn.pixabay.com/photo/2018/01/04/16/53/building-3061124_960_720.png');\n background-position: left top;\n background-repeat: no-repeat;\n padding-left:... |
Diyago/Machine-Learning-scripts | time series regression/ARIMA/usd-rub predicting.ipynb | apache-2.0 | # возьмем лог, который "penalize higher values more than smaller values"
ts_log = np.log(rub["Adj Close"])
test_stationarity(ts_log)
# далее вычтем скользящее среднее
moving_avg = pd.rolling_mean(ts_log,50)
plt.plot(ts_log)
plt.plot(moving_avg, color='red')
ts_log_moving_avg_diff = ts_log - moving_avg
ts_log_moving_... |
ContinuumIO/nbpresent | notebooks/index.ipynb | bsd-3-clause | from IPython.display import *
"""
Explanation: <i class="fa fa-gift fa-2x text-success"> </i> This is an nbpresent presentation!
End of explanation
"""
SVG("https://www.continuum.io/sites/all/themes/continuum_foundation/images/logos/logo-anaconda.svg")
"""
Explanation: <i class="fa fa-info-circle fa-2x text-info"> ... |
google/starthinker | colabs/dcm.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: CM360 Report
Create a CM report from a JSON definition.
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 obtain a copy of t... |
xpmethod/middlemarch-critical-histories | notebooks/quoted-language.ipynb | gpl-3.0 | from collections import Counter
import pandas as pd
import numpy as np
%matplotlib inline
from pylab import rcParams
import textacy
rcParams['figure.figsize'] = 10, 4
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import spacy
nlp = spacy.load('en')
"""
Explanation: Analysis of quoted language, v2
How is quo... |
machinelearningnanodegree/stanford-cs231 | solutions/vijendra/assignment3/ImageGradients.ipynb | mit | # As usual, a bit of setup
import time, os, json
import numpy as np
import skimage.io
import matplotlib.pyplot as plt
from cs231n.classifiers.pretrained_cnn import PretrainedCNN
from cs231n.data_utils import load_tiny_imagenet
from cs231n.image_utils import blur_image, deprocess_image
%matplotlib inline
plt.rcParams... |
opesci/devito | examples/cfd/06_poisson.ipynb | mit | from examples.cfd import plot_field, init_hat
import numpy as np
%matplotlib inline
# Some variable declarations
nx = 50
ny = 50
nt = 100
xmin = 0.
xmax = 2.
ymin = 0.
ymax = 1.
dx = (xmax - xmin) / (nx - 1)
dy = (ymax - ymin) / (ny - 1)
# Initialization
p = np.zeros((nx, ny))
pd = np.zeros((nx, ny))
b = np.zer... |
google-research/receptive_field | RF_Keras_Applications.ipynb | apache-2.0 | from google.colab import drive
drive.mount('/content/gdrive')
! mkdir gdrive/MyDrive/rf_keras
%cd gdrive/MyDrive/rf_keras
! ls
! git clone https://github.com/google-research/receptive_field.git
! ls
%cd receptive_field/
! ls
! pip install .
! pip install tensorflow
"""
Explanation: <a href="https://colab.rese... |
encima/Comp_Thinking_In_Python | Session_9/9_Sorting Algorithms and Complexity.ipynb | mit | def ins_sort(k):
for i in range(1,len(k)): #since we want to swap an item with previous one, we start from 1
j = i #because we need 2 indexes as one will reduce and we do not want to affect i
while j > 0 and k[j] < k[j-1]: #j>0 since there is no value to th... |
mne-tools/mne-tools.github.io | 0.22/_downloads/bc5044f9d3ef1d29067dd6b7d83ceed2/plot_20_visualize_epochs.ipynb | bsd-3-clause | import os
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, verbose=False).crop(tmax=120)
"""
Explanation: Visualizing epo... |
sz2472/foundations-homework | homework10/homework10_foundation.ipynb | mit | print(data)
data.keys()
data['currently']
temperature=str(data['currently']['temperature'])
summary=data['currently']['summary']
data['daily']
data['daily'].keys()
data['daily']['data']
"""
Explanation: Forecasts Part One: Getting data
Using the Dark Sky Forecast API at https://developer.forecast.io/, generate ... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_dics_beamformer.ipynb | bsd-3-clause | # Author: Roman Goj <roman.goj@gmail.com>
#
# License: BSD (3-clause)
import mne
import matplotlib.pyplot as plt
import numpy as np
from mne.datasets import sample
from mne.time_frequency import csd_epochs
from mne.beamformer import dics
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path + '/MEG/s... |
AllenDowney/ModSim | python/soln/chap07.ipynb | gpl-2.0 | # install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import exists
filename = 'modsim.py'
if not exists(filename):
from urllib.request import urlretrieve
url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'
... |
bocklund/notebooks | pycalphad/Plot activity.ipynb | mit | from pycalphad import Database, equilibrium, variables as v
import numpy as np
# Database from Zhou, C., Guo, C., Li, C. & Du, Z. Thermodynamic assessment of the phase equilibria and prediction of glass-forming ability of the Al–Cu–Zr system. J. Non. Cryst. Solids 461, 47–60 (2017).
dbf = Database('Al-Cu-Zr_Zhou.TDB')... |
DavidObando/carnd | Term3/Labs/ObjectDetection/CarND-Object-Detection-Lab.ipynb | apache-2.0 | import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from PIL import ImageDraw
from PIL import ImageColor
import time
from scipy.stats import norm
%matplotlib inline
plt.style.use('ggplot')
"""
Explanation: CarND Object Detection Lab
Let's get started!
End of explanation
""... |
ltiao/project-euler | problem-12-highly-divisible-triangular-number.ipynb | unlicense | from six.moves import map, range, reduce
def triangle_num(n):
"The nth triangle number"
return n*(n+1)>>1
triangle_num(7)
list(map(triangle_num, range(1, 11)))
"""
Explanation: The sequence of triangle numbers is generated by adding the natural numbers. So the 7th triangle number would be 1 + 2 + 3 + 4 + 5 ... |
ES-DOC/esdoc-jupyterhub | notebooks/cmcc/cmip6/models/sandbox-2/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cmcc', 'sandbox-2', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: CMCC
Source ID: SANDBOX-2
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balan... |
kkkddder/dmc | notebooks/week-4/02-tensorflow ANN for classification.ipynb | apache-2.0 | %matplotlib inline
import math
import random
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_boston
'''Since this is a classification problem, we will need to
represent our targets as one-hot encoding vectors (see previous lab).
To do this we will use sciki... |
cliburn/sta-663-2017 | notebook/04_Strings.ipynb | mit | import requests
try:
with open('looking_glass.txt') as f:
text = f.read()
except IOError:
url = 'http://www.gutenberg.org/cache/epub/12/pg12.txt'
res = requests.get(url)
text = res.text
with open('looking_glass.txt', 'w') as f:
f.write(str(text))
"""
Explanation: Strings
The proces... |
rflamary/POT | docs/source/auto_examples/plot_barycenter_1D.ipynb | mit | # Author: Remi Flamary <remi.flamary@unice.fr>
#
# License: MIT License
import numpy as np
import matplotlib.pylab as pl
import ot
# necessary for 3d plot even if not used
from mpl_toolkits.mplot3d import Axes3D # noqa
from matplotlib.collections import PolyCollection
"""
Explanation: 1D Wasserstein barycenter demo
... |
macieksmuga/server | scripts/ga4gh_example.ipynb | apache-2.0 | baseURL = "http://localhost:8000"
client = ga4gh.client.HttpClient(baseURL)
"""
Explanation: GA4GH IPython Example Notebook
This notebook provides an overview of how to call a the GA4GH reference server from an iPython notebook. Before running this notebook:
git clone https://github.com/ga4gh/server.git -b develop
D... |
wuafeing/Python3-Tutorial | 01 data structures and algorithms/01.05 implement a priority queue.ipynb | gpl-3.0 | import heapq
class PriorityQuene:
def __init__(self):
self._queue = []
self._index = 0
def push(self, item, priority):
heapq.heappush(self._queue, (-priority, self._index, item))
self._index += 1
def pop(self):
return heapq.heappop(self._queue)[-1] ... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_temporal_whitening.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
import mne
from mne.time_frequency import fit_iir_model_raw
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
r... |
matousc89/python-web-tutorials | Unittests.ipynb | mit | def sum_together1(a, b):
return a + b
"""
Explanation: Catching errors and unit tests
In this tutorial are few examples how catch error and how to perform unit tests in Python.
When you code in Python, keep in mind:
Errors should never pass silently. Unless explicitly silenced. (<a href="https://www.python.org/dev... |
rflamary/POT | notebooks/plot_OT_L1_vs_L2.ipynb | mit | # Author: Remi Flamary <remi.flamary@unice.fr>
#
# License: MIT License
import numpy as np
import matplotlib.pylab as pl
import ot
import ot.plot
"""
Explanation: 2D Optimal transport for different metrics
2D OT on empirical distributio with different gound metric.
Stole the figure idea from Fig. 1 and 2 in
https://... |
gaoshuming/udacity | face-generation/dlnd_face_generation.ipynb | mit | data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
"""
Explanation: 人脸生成（Face Generation）
在该项目中，你将使用生成式对抗网络（Generative Adversa... |
GoogleCloudPlatform/ml-design-patterns | 02_data_representation/mixed_representation.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import Model
from tensorflow.keras.layers import Dense, Embedding, Input, Flatten, Conv2D, MaxPooling2D
from google.cloud import bigquery
"""
Explanation: Mixed Input Representations Design Pattern
This ... |
karlstroetmann/Artificial-Intelligence | Python/3 Games/Tic-Tac-Toe-Bitboard.ipynb | gpl-2.0 | gPlayers = [0, 1]
"""
Explanation: Tic-Tac-Toe via Bitboards
This notebook defines the game tic-tac-toe.
It is played on a $3 \times 3$ board. There are two players, which are called X and O. Player X starts. Player X always puts an 'X' into an empty field on the board, while player O always puts an 'O' in an empty... |
itsjeevs/projectEuler | Euler.ipynb | mit | get_sum = lambda x,y: x+y
get_product = lambda x,y: x*y
get_substract = lambda x,y: x-y
get_division = lambda x,y: x/y
is_bigger_than = lambda x,y: x if x>y else y
is_smaller_than = lambda x,y: x if x<y else y
reduce(get_sum, filter(lambda x: x%3 ==0 or x%5 == 0, range(1,1001)))
"""
Explanation: 1. Multiples of 3 and... |
daniaki/Enrich2 | docs/notebooks/min_count.ipynb | gpl-3.0 | % matplotlib inline
import os.path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from enrich2.variant import WILD_TYPE_VARIANT
import enrich2.plots as enrich_plot
pd.set_option("display.max_rows", 10) # rows shown when pretty-printing
"""
Explanation: Selecting variants by input library count... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/end_to_end_ml/labs/serving_babyweight.ipynb | apache-2.0 | %%bash
# Check your project name
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT
import os
os.environ["BUCKET"] = "your-bucket-id-here" # Recommended: use your project name
"""
Explanation: Building an App Engine app to serve ML prediction
... |
rabernat/pyqg | docs/examples/layered.ipynb | mit | import numpy as np
from numpy import pi
from matplotlib import pyplot as plt
%matplotlib inline
import pyqg
from pyqg import diagnostic_tools as tools
"""
Explanation: Fully developed baroclinic instability of a 3-layer flow
End of explanation
"""
L = 1000.e3 # length scale of box [m]
Ld = 15.e3 # def... |
staeiou/reddit_downvote | games/games-analysis.ipynb | mit | !ls
!pip install bokeh
import pandas as pd
import seaborn as sns
from bokeh.charts import TimeSeries, output_file, show
%matplotlib inline
posts_df = pd.DataFrame.from_csv("reddit_posts_games_201301.csv")
posts_df[0:5]
posts_df['created'] = pd.to_datetime(posts_df.created_utc, unit='s')
posts_df['created_date'] = ... |
ga7g08/ga7g08.github.io | _notebooks/2016-03-21-Using-Scipy-KS-test.ipynb | mit | xvals = np.linspace(0, 0.5, 500)
args = [0.5, 20]
d1 = ss.beta.rvs(args[0], args[1], size=1000)
noise = ss.norm.rvs(0.4, 0.1, size=50)
d2 = np.append(d1[:len(d1)-len(noise)], noise)
fig, (ax1, ax2) = plt.subplots(nrows = 2)
ax1.plot(xvals, ss.gaussian_kde(d1).evaluate(xvals), "-k",
label="d1")
ax1.legend()
... |
kubeflow/kfp-tekton-backend | components/gcp/dataproc/submit_sparksql_job/sample.ipynb | apache-2.0 | %%capture --no-stderr
KFP_PACKAGE = 'https://storage.googleapis.com/ml-pipeline/release/0.1.14/kfp.tar.gz'
!pip3 install $KFP_PACKAGE --upgrade
"""
Explanation: Name
Data preparation using SparkSQL on YARN with Cloud Dataproc
Label
Cloud Dataproc, GCP, Cloud Storage, YARN, SparkSQL, Kubeflow, pipelines, components
... |
econchick/pyladies-data | Creating Graphs with Pandas & matplotlib.ipynb | mit | DATA_DIR = "meetup_data"
MEMBER_JSON = "pug_members.json"
GROUP_DIRS = [d for d in os.listdir(DATA_DIR)]
PYLADIES_GROUPS = []
for group in GROUP_DIRS:
if os.path.isdir(os.path.join(DATA_DIR, group)):
PYLADIES_GROUPS.append(group)
def load_group_data(pyladies_group):
pyladies_dir = os.path.join(DATA_DIR... |
samgoodgame/sf_crime | iterations/.ipynb_checkpoints/Cha_Goodgame_Kao_Moore_W207_Final_Project_updated_08_19_0919-checkpoint.ipynb | mit | # Import relevant libraries:
import time
import numpy as np
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn import preprocessing
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.naive_bayes import BernoulliNB
from sklearn.na... |
samstav/scipy_2015_sklearn_tutorial | notebooks/02.2 Supervised Learning - Regression.ipynb | cc0-1.0 | x = np.linspace(-3, 3, 100)
print(x)
y = np.sin(4 * x) + x + np.random.uniform(size=len(x))
plt.plot(x, y, 'o')
"""
Explanation: Regression
In regression we try to predict a continuous output variable. This can be most easily visualized in one dimension.
We will start with a very simple toy example. We will create a... |
WNoxchi/Kaukasos | FAI_old/conv_test_Asus.ipynb | mit | import theano
import os, sys
sys.path.insert(1, os.path.join('utils'))
from __future__ import print_function, division
path = 'data/statefarm/'
import utils; reload(utils)
from utils import *
batch_size=16
vgg = Vgg16()
model = vgg.model
last_conv_idx = [i for i, l in enumerate(model.layers) if type(l) is Convoluti... |
vvishwa/deep-learning | autoencoder/Simple_Autoencoder_Solution.ipynb | mit | %matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
"""
Explanation: A Simple Autoencoder
We'll start off by building a simple autoencoder to compres... |
limpapud/data_science_tutorials_projects | DataScience_Tutorials/AZ/Pandas_DF_Creation.ipynb | mit | import pandas as pd
"""
Explanation: Pandas: DataFreym yaratmağın müxtəlif üsulları
İlk öncə pandas-da verilənlərin 2D əndazəli forma (sadə dil ilə "cədvəl") daxilində saxlanma vasitəsi və forması olan Dataframe yaratmaqdan başlayaq. Bu dərs tam olaraq müxtəlif mənbələrdən və formalarda əldə edilmiş məlumatı Datafreym... |
tpin3694/tpin3694.github.io | machine-learning/effect_of_alpha_on_lasso_regression.ipynb | mit | from sklearn.linear_model import Lasso
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_boston
import pandas as pd
"""
Explanation: Title: Effect Of Alpha On Lasso Regression
Slug: effect_of_alpha_on_lasso_regression
Summary: The effect of alpha values on lasso regression in Scikit... |
jhaber-zz/Charter-school-identities | scripts/analysis_prelim.ipynb | mit | # The keyword categories to help parse website text:
mission = ['mission',' vision ', 'vision:', 'mission:', 'our purpose', 'our ideals', 'ideals:', 'our cause', 'cause:', 'goals', 'objective']
curriculum = ['curriculum', 'curricular', 'program', 'method', 'pedagogy', 'pedagogical', 'approach', 'model', 'system', 'stru... |
deepfield/ibis | docs/source/notebooks/tutorial/8-More-Analytics-Helpers.ipynb | apache-2.0 | import ibis
import os
hdfs_port = os.environ.get('IBIS_WEBHDFS_PORT', 50070)
hdfs = ibis.hdfs_connect(host='quickstart.cloudera', port=hdfs_port)
con = ibis.impala.connect(host='quickstart.cloudera', database='ibis_testing',
hdfs_client=hdfs)
ibis.options.interactive = True
"""
Explanation: A... |
authman/DAT210x | Module4/Module4 - Lab2.ipynb | mit | import math
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
from sklearn import preprocessing
# Look pretty...
# matplotlib.style.use('ggplot')
plt.style.use('ggplot')
"""
Explanation: DAT210x - Programming with Python for DS
Module4- Lab2
End of explanation
"""
def scaleFeaturesDF(df):
... |
DoWhatILove/turtle | programming/python/notebooks/scikit/clustering/document_clustering.ipynb | mit | # Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Lars Buitinck
# License: BSD 3 clause
from __future__ import print_function
from sklearn.datasets import fetch_20newsgroups
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feat... |
carefree0910/MachineLearning | Notebooks/numba/zh-cn/CNN.ipynb | mit | @nb.jit(nopython=True)
def jit_conv_kernel2(x, w, rs, n, n_channels, height, width, n_filters, filter_height, filter_width, out_h, out_w):
for i in range(n):
for j in range(out_h):
for p in range(out_w):
for q in range(n_filters):
for r in range(n_channels):
... |
quantumlib/OpenFermion-Cirq | examples/tutorial_2_diagonal_coulomb_trotter.ipynb | apache-2.0 | import openfermion
# Set parameters of jellium model.
wigner_seitz_radius = 5. # Radius per electron in Bohr radii.
n_dimensions = 2 # Number of spatial dimensions.
grid_length = 2 # Number of grid points in each dimension.
spinless = True # Whether to include spin degree of freedom or not.
n_electrons = 2 # Number of... |
mne-tools/mne-tools.github.io | dev/_downloads/9253528f5ba1c240d3397da64c84a625/90_phantom_4DBTi.ipynb | bsd-3-clause | # Authors: Alex Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import os.path as op
import numpy as np
from mne.datasets import phantom_4dbti
import mne
"""
Explanation: 4D Neuroimaging/BTi phantom dataset tutorial
Here we read 4DBTi epochs data obtained with a spherical phantom
using four different... |
tensorflow/tfx | docs/tutorials/transform/simple.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... |
statsmodels/statsmodels.github.io | v0.12.2/examples/notebooks/generated/statespace_structural_harvey_jaeger.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 IPython.display import display, Latex
"""
Explanation: Detrending, Stylized Facts and the Business Cycle
In an influential article, Harvey and Jaeger (1993) described the use of unobserved comp... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/automl/sdk_automl_tabular_regression_online_bq.ipynb | apache-2.0 | import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex AI SDK: AutoML training tabular regression model for online prediction using BigQuery
<t... |
OSGeoLabBp/tutorials | english/data_processing/lessons/nmea.ipynb | cc0-1.0 | with open('nmea1.txt') as fp:
i = 0
for line in fp:
i += 1
print(i)
with open('nmea1.txt') as fp:
print(len(fp.readlines())) # readlines function reads the whole file into a list line by line
"""
Explanation: <a href="https://colab.research.google.com/github/OSGeoLabBp/tutorials/blob/master/english/data_... |
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