repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
herruzojm/udacity-deep-learning | tv-script-generation/.ipynb_checkpoints/dlnd_tv_script_generation-checkpoint.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scrip... |
mbatchkarov/ExpLosion | notebooks/effect_of_adding_noise_to_vectors.ipynb | bsd-3-clause | def plot(d):
experiments = Experiment.objects.filter(**d).order_by('expansions__noise')
e = [x.id for x in experiments if x.expansions.entries_of is None]
print('experiments are', e)
for eid in e:
print('id %d noise %2.2f, acc %2.2f, macrof1 %2.2f'%(eid,
... |
LimeeZ/phys292-2015-work | days/day13/ODEs.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
"""
Explanation: Ordinary Differential Equations
Learning Objectives: Understand the numerical solution of ODEs and use scipy.integrate.odeint to solve and explore ODEs numerically.
Imports
End of explanation
"""
tmax = 10.0 ... |
google/physics-math-tutorials | colabs/Binomial Proportion Confidence with Coin Flipping.ipynb | apache-2.0 | #@title Interesting Tweet
class Tweet(object):
def __init__(self, embed_str=None):
self.embed_str = embed_str
def _repr_html_(self):
return self.embed_str
s = ("""
<blockquote class="twitter-tweet"><p lang="en" dir="ltr">Without doing the math or looking it up, approximately how many coin flip... |
henchc/Rediscovering-Text-as-Data | 04-Stylometry/01-Ad-Hoc-Stylometry.ipynb | mit | ["þæt", "wearð", "underne"]
"""
Explanation: Stylometry
This notebook is designed to reproduce several findings from Emily Thornbury's chapter "The Poet Alone" in her book Becoming a Poet in Anglo-Saxon England. In particular, Fig. 4.5 on page 170.
First, however, we're going to think about what we might do with lists... |
xray/xray | doc/examples/visualization_gallery.ipynb | apache-2.0 | import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import xarray as xr
%matplotlib inline
"""
Explanation: Visualization Gallery
This notebook shows common visualization issues encountered in Xarray.
End of explanation
"""
ds = xr.tutorial.load_dataset('air_temperature')
"""
Explanation: Load example datase... |
weichetaru/weichetaru.github.com | notebook/machine-learning/deep_learning-linear-regression-gradient-decent.ipynb | mit | import numpy
import matplotlib.pyplot as plt
%matplotlib inline
numpy.random.seed(seed=1)
x = numpy.random.uniform(0, 1, 20)
# real model
def f(x): return x * 2
noise_variance = 0.2 # Variance of the gaussian noise
# Gaussian noise error for each sample in x
noise = numpy.random.randn(x.shape[0]) * noise_varianc... |
martinggww/lucasenlights | MachineLearning/DataScience-Python3/SVC.ipynb | cc0-1.0 | import numpy as np
#Create fake income/age clusters for N people in k clusters
def createClusteredData(N, k):
pointsPerCluster = float(N)/k
X = []
y = []
for i in range (k):
incomeCentroid = np.random.uniform(20000.0, 200000.0)
ageCentroid = np.random.uniform(20.0, 70.0)
for j i... |
icrtiou/coursera-ML | ex1-linear regression/2- batch gradient decent.ipynb | mit | %reload_ext autoreload
%autoreload 2
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import sys
sys.path.append('..')
from helper import linear_regression as lr # my own module
from helper import general as general
data = pd.read_csv('ex1data1.txt', n... |
Danghor/Algorithms | Python/Chapter-09/Dijkstra-Heap.ipynb | gpl-2.0 | %run Heap-Array.ipynb
def shortest_path(source, Edges):
Distance = { source: 0 }
Visited = { source } # this set is only needed for visualization
Fringe = [] # priority queue, organized as array based heap
insert(Fringe, (0, source))
while Fringe != []:
display(heapToDot(Fringe... |
moble/MatchedFiltering | GW150914/HybridizeNR_GW151226.ipynb | mit | metadata = read_metadata_into_object(data_dir + '/metadata.txt')
m1 = metadata.relaxed_mass1
m2 = metadata.relaxed_mass2
chi1 = np.array(metadata.relaxed_spin1) / m1**2
chi2 = np.array(metadata.relaxed_spin2) / m2**2
# I guess(...) that the units on the metadata quantity are just those of M*Omega, so I'll divide by M... |
sassoftware/sas-viya-programming | python/karate-club/Zachary's Karate Club Social Network Analysis using CAS HyperGroup.ipynb | apache-2.0 | import swat
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
# Also import networkx used for rendering a network
import networkx as nx
%matplotlib inline
"""
Explanation: A simple pipeline using hypergroup to perform com... |
Nikolay-Lysenko/dsawl | docs/stacking_demo.ipynb | mit | from sklearn.datasets import load_boston
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import RandomForestRegressor
from dsawl.stacking import Stacking... |
adrn/AASAbstractSorter | notebooks/AAS abstract similarity.ipynb | mit | # Standard lib
import re
import pickle
from collections import OrderedDict
from datetime import datetime
# Third-party
from sqlalchemy import create_engine
import numpy as np
import matplotlib.pyplot as pl
%matplotlib inline
from sklearn.feature_extraction import text
from sklearn.utils.extmath import cartesian
impor... |
me-surrey/dl-gym | 10_introduction_to_artificial_neural_networks.ipynb | apache-2.0 | # To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
def reset_graph(seed=42):
tf.reset_default_graph()
tf.set_random_seed(seed)
np.random.seed(seed)
# To... |
haltaro/predicting-comic-end | 0_obtain_comic_data_j.ipynb | mit | import json
import urllib.request
from time import sleep
"""
Explanation: 0. Web APIを用いた目次情報の取得
文化庁メディア芸術データベース マンガ分野 WebAPIを用いて，分析に必要なデータを入手します．なお，python3を使ったweb APIの利用については，Python3でjsonを返却するwebAPIにアクセスして結果を出力するまでを参考にさせて頂きました．
環境構築
bash
conda env create -f env.yml
準備
End of explanation
"""
def search_magazine(key='... |
elsdrm/shared_note | Parallel Monte Carlo Option Pricing.ipynb | mit | %pylab inline
import sys
import time
from IPython.parallel import Client
import numpy as np
"""
Explanation: Parallel Monto-Carlo options pricing
This notebook shows how to use IPython.parallel to do Monte-Carlo options pricing in parallel. We will compute the price of a large number of options for different strike p... |
jeffsilverm/presentation | SeaGL-2018/3d_scatter.ipynb | gpl-2.0 | import plotly.plotly as py
import plotly.graph_objs as go
import numpy as np
x, y, z = np.random.multivariate_normal(np.array([0,0,0]), np.eye(3), 200).transpose()
trace1 = go.Scatter3d(
x=x,
y=y,
z=z,
mode='markers',
marker=dict(
size=12,
line=dict(
color='rgba(217, 21... |
jhseu/tensorflow | tensorflow/lite/g3doc/performance/post_training_integer_quant.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... |
jpilgram/phys202-2015-work | days/day13/ODEs.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
"""
Explanation: Ordinary Differential Equations
Learning Objectives: Understand the numerical solution of ODEs and use scipy.integrate.odeint to solve and explore ODEs numerically.
Imports
End of explanation
"""
tmax = 10.0 ... |
open2c/bioframe | docs/guide-io.ipynb | mit | import bioframe
"""
Explanation: Reading genomic dataframes
End of explanation
"""
df = bioframe.read_table(
'https://www.encodeproject.org/files/ENCFF001XKR/@@download/ENCFF001XKR.bed.gz',
schema='bed9'
)
display(df[0:3])
df = bioframe.read_table(
"https://www.encodeproject.org/files/ENCFF401MQL/@@dow... |
oresat/oresat-ground-station | eb-ground-station/structure/independent-structure-design/loadAnalysis.ipynb | gpl-3.0 | import numpy as np
import sys
import matplotlib.pyplot as plt
import sympy as sym
import pandas as pd
import magnitude as mag
from magnitude import mg
mag.new_mag('lbm', mag.Magnitude(0.45359237, kg=1))
mag.new_mag('lbf', mg(4.4482216152605, 'N'))
mag.new_mag('mph', mg(0.44704, 'm/s'))
mag.new_mag('slug', mg(1,'lbf')/m... |
Adamage/python-training | Lesson_03_loops_flow_control_exceptions.ipynb | apache-2.0 | for i in range(0,100):
pass
"""
Explanation: Python Training - Lesson 3 - loops, flow control and exceptions
Now that we have seen some basics in action, let's summarize what we should already know by this point:
- types and their methods
- classes and objects
- simple condition checks with "if"
- using importe... |
IIPBC/Material | machine_learning_Nina/Exercise3-1.ipynb | mit | import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
# draw N random points in the [0,1]x[0,1] square
N = 100
x1 = np.random.rand(N)
x2 = np.random.rand(N)
X = np.vstack(zip(np.ones(N),x1, x2))
print X.shape
# use cosine to define positive and negative classes
y = np.array([1 if np.cos(2*np.pi*X[i,1... |
brettavedisian/phys202-2015-work | assignments/assignment06/DisplayEx01.ipynb | mit | from IPython.display import display
from IPython.display import Image
assert True # leave this to grade the import statements
"""
Explanation: Display Exercise 1
Imports
Put any needed imports needed to display rich output the following cell:
End of explanation
"""
Image(url='http://easyscienceforkids.com/wp-conten... |
tensorflow/docs-l10n | site/ko/hub/tutorials/image_enhancing.ipynb | apache-2.0 | # Copyright 2019 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... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/tfx_pipelines/cicd/solutions/tfx_cicd.ipynb | apache-2.0 | import yaml
# Set `PATH` to include the directory containing TFX CLI.
PATH = %env PATH
%env PATH=/home/jupyter/.local/bin:{PATH}
!python -c "import tfx; print('TFX version: {}'.format(tfx.__version__))"
"""
Explanation: CI/CD for TFX pipelines
Learning Objectives
Develop a CI/CD workflow with Cloud Build to build a... |
chemelnucfin/tensorflow | tensorflow/contrib/autograph/examples/notebooks/algorithms.ipynb | apache-2.0 | !pip install -U -q tf-nightly-2.0-preview
import tensorflow as tf
tf = tf.compat.v2
tf.enable_v2_behavior()
"""
Explanation: AutoGraph: examples of simple algorithms
This notebook shows how you can use AutoGraph to compile simple algorithms and run them in TensorFlow.
It requires the nightly build of TensorFlow, whi... |
eshlykov/mipt-day-after-day | optimizaion/kaggle/eshlykov-kaggle.ipynb | unlicense | # Выделяем outdoor'ы и indoor'ы.
sample_out = sample[result[:, 0] == 1]
sample_in = sample[result[:, 1] == 1]
result_out = result[result[:, 0] == 1]
result_in = result[result[:, 1] == 1]
# Считаем размер indoor- и outdoor-частей в train'е.
train_size_in = int(sample_in.shape[0] * 0.75)
train_size_out = int(sample_out.... |
ES-DOC/esdoc-jupyterhub | notebooks/nuist/cmip6/models/sandbox-1/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nuist', 'sandbox-1', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: NUIST
Source ID: SANDBOX-1
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Bal... |
ComputationalModeling/spring-2017-danielak | past-semesters/fall_2016/day-by-day/day08-modeling-viral-load-day1/viral_load_model_STUDENT.ipynb | agpl-3.0 | # some code to set up the problem.
# Make plots inline
%matplotlib inline
# Make inline plots vector graphics instead of raster graphics
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('pdf', 'svg')
# import modules for plotting and data analysis
import matplotlib.pyplot as plt
import numpy... |
awjuliani/DeepRL-Agents | Q-Exploration.ipynb | mit | from __future__ import division
import gym
import numpy as np
import random
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline
import tensorflow.contrib.slim as slim
"""
Explanation: Simple Reinforcement Learning: Exploration Strategies
This notebook contains implementations of various action... |
oditorium/blog | iPython/MCRisk1-LargePoolCap.ipynb | agpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: iPython Cookbook - Monte Carlo Risk Analysis - Large Pool Capital Model
Looking at the Large Pool Capital Model and idiosyncratic risks on top of it
Set-up
End of explanation
"""
from scipy.stats import norm
from functools import partial
from oper... |
jinntrance/MOOC | coursera/deep-neural-network/quiz and assignments/NLP and Word Embedding/Emojify+-+v2.ipynb | cc0-1.0 | import numpy as np
from emo_utils import *
import emoji
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Emojify!
Welcome to the second assignment of Week 2. You are going to use word vector representations to build an Emojifier.
Have you ever wanted to make your text messages more expressive? You... |
harpolea/CMG_testing_workshop | Testing-presentation.ipynb | mit | def normalise(v):
norm = numpy.sqrt(numpy.sum(v**2))
return v / norm
normalise(numpy.array([0,0]))
"""
Explanation: <center> bit.ly/2nDtVj6 </center>
Testing Scientific Codes
Why do we test?
In the experimental Sciences, new theories are developed by applying the Scientific method
Perform tests to demon... |
JoseGuzman/myIPythonNotebooks | Stochastic_systems/NaiveBayesanClassifier.ipynb | gpl-2.0 | %pylab inline
import pandas as pd
# first row contains units
df = pd.read_excel(io='../data/Cell_types.xlsx', sheetname='PFC', skiprows=1)
del df['CellID'] # remove column with cell IDs
df.head() # show first elements
"""
Explanation: <H1> Naive Bayesan classifier</H1>
<H2>Bayesan theorem</H2>
We will try to comp... |
Aniruddha-Tapas/Applied-Machine-Learning | Machine Learning using GraphLab/Recommender Systems using Affinity Analysis.ipynb | mit | ratings_filename = "data/ml-100k/u.data"
import pandas as pd
all_ratings = pd.read_csv(ratings_filename, delimiter="\t", header=None, names = ["UserID", "MovieID", "Rating", "Datetime"])
all_ratings["Datetime"] = pd.to_datetime(all_ratings['Datetime'],unit='s')
all_ratings[:5]
"""
Explanation: Recommender Systems us... |
DiXiT-eu/collatex-tutorial | unit6/Normalization.ipynb | gpl-3.0 | from collatex import *
collation = Collation()
collation.add_plain_witness('A', 'Look, a koala!')
collation.add_plain_witness('B', 'Look, Koala!')
alignment_table = collate(collation, segmentation=False)
print(alignment_table)
"""
Explanation: Normalization
At the alignment stage, CollateX identifies the tokens to ali... |
arasdar/DL | udacity-dl/RNN/tv-script-generation/dlnd_tv_script_generation.ipynb | unlicense | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scrip... |
JoseGuzman/myIPythonNotebooks | tests/Multivariate regression.ipynb | gpl-2.0 | %pylab inline
import pandas as pd
mypath = 'Cell_types.xlsx'
xls = pd.read_excel(mypath)
xls.head()
xls.InputR
xls['Vrest'].mean()
xls['Vrest'].unique() # get NumPy array
"""
Explanation: <H1>Multivariate regression</H1>
End of explanation
"""
x = xls[['InputR', 'SagRatio','mbTau']]
y = xls[['Vrest']]
# impor... |
stinebuu/nest-simulator | doc/userdoc/model_details/noise_generator.ipynb | gpl-2.0 | import sympy
sympy.init_printing()
x = sympy.Symbol('x')
sympy.series((1-sympy.exp(-x))/(1+sympy.exp(-x)), x)
"""
Explanation: The NEST noise_generator
Hans Ekkehard Plesser, 2015-06-25
This notebook describes how the NEST noise_generator model works and what effect it has on model neurons.
NEST needs to be in your PY... |
ozorich/phys202-2015-work | assignments/assignment10/ODEsEx03.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy.integrate import odeint
from IPython.html.widgets import interact, fixed
"""
Explanation: Ordinary Differential Equations Exercise 3
Imports
End of explanation
"""
g = 9.81 # m/s^2
l = 0.5 # length of pendulum... |
ES-DOC/esdoc-jupyterhub | notebooks/pcmdi/cmip6/models/sandbox-3/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'pcmdi', 'sandbox-3', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: PCMDI
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: Transport, Emissions, Con... |
google/applied-machine-learning-intensive | content/03_regression/08_regression_with_tensorflow/colab.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... |
bkimo/discrete-math-with-python | lab1-truth_table.ipynb | mit | for p in (True, False):
for q in (True, False):
print("%10s %10s %10s" %(p, q, (p and q)))
"""
Explanation: Content provided under a Creative Commons Attribution license, CC-BY 4.0. Bong-Sik Kim. (25 September, 2016)
Fundamentals of Logic
The connection between logic, proofs and programming is a very rich ... |
ES-DOC/esdoc-jupyterhub | notebooks/cams/cmip6/models/sandbox-1/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cams', 'sandbox-1', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: CAMS
Source ID: SANDBOX-1
Topic: Aerosol
Sub-Topics: Transport, Emissions, Conce... |
ddemidov/mba | python/layered.ipynb | mit | def foo(c):
return sin(c[0]/100) + sin(c[1]*3)
cmin = [0.0, 0.0]
cmax = [1000.0, 10.0]
C = mgrid[0:cmax[0]:1e-1,0:cmax[1]:1e-1]
F = foo(C)
coo = uniform(cmin, cmax, (128,2))
val = foo(coo.transpose())
figure(figsize=(13,4))
pcolormesh(C[0], C[1], F)
scatter(coo[:,0], coo[:,1], c='k', s=1)
xlim([cmin[0], cmax[0]... |
Amarchuk/2FInstability | notebooks/2f/photometry_tests.ipynb | gpl-3.0 | mu_eff = 18.37
r_eff = 8.8
n = 2.3
MyTest.test2 = lambda self: self.assertAlmostEqual(mu_bulge(10., mu_eff=mu_eff, r_eff=r_eff, n=n),
mu_bulge2(10., mu_eff=mu_eff, r_eff=r_eff, n=n), places=3)
"""
Explanation: <div class="alert alert-success">
<h5>TEST:</h5>Тест ... |
rajuniit/udacity | my_first_neural_network_project.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
h-mayorquin/time_series_basic | presentations/2016-03-11(Nexa Wall Street Columns High Resolution - Visualizing Receptive Fields and Data Clusters).ipynb | bsd-3-clause | import h5py
import sys
sys.path.append("../")
import matplotlib.pyplot as plt
%matplotlib inline
from visualization.data_clustering import visualize_data_cluster_text_to_image_columns
"""
Explanation: Nexa Well Street Columns High Resolution (30 x 30). Visualizing Receptive Fields and Data Clusters.
In this notebook... |
jpallas/beakerx | doc/python/TableAPI.ipynb | apache-2.0 | import pandas as pd
from beakerx import *
pd.read_csv('../resources/data/interest-rates.csv')
table = TableDisplay(pd.read_csv('../resources/data/interest-rates.csv'))
table.setAlignmentProviderForColumn('m3', TableDisplayAlignmentProvider.CENTER_ALIGNMENT)
table.setRendererForColumn("y10", TableDisplayCellRenderer.g... |
shreyas111/Multimedia_CS523_Project1 | Style_Transfer_Without_Calculating_Denoising_Loss.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import PIL.Image
"""
Explanation: Style Transfer
Our Changes:
We have modified the code such that the denoise_loss for the mixed image is not calculated. The total loss does not include the denoise loss. The gradient is reduc... |
fonnesbeck/scientific-python-workshop | notebooks/Statistical Data Modeling.ipynb | cc0-1.0 | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
"""
Explanation: Statistical Data Modeling
Pandas, NumPy and SciPy provide the core functionality for building statistical models of our data. We use models to:
Concisely describe the components o... |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/cmip6/models/ukesm1-0-mmh/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'ukesm1-0-mmh', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: MOHC
Source ID: UKESM1-0-MMH
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, ... |
clauwag/WikipediaGenderInequality | notebooks/Notability - 02 - Generate Person Data.ipynb | mit | from __future__ import print_function, unicode_literals
import pandas as pd
import gzip
import csv
import regex as re
import json
import time
import datetime
import requests
import os
import json
import dbpedia_config
from collections import Counter, defaultdict
from cytoolz import partition_all
from dbpedia_utils im... |
tensorflow/hub | examples/colab/text_to_video_retrieval_with_s3d_milnce.ipynb | apache-2.0 | !pip install -q opencv-python
import os
import tensorflow.compat.v2 as tf
import tensorflow_hub as hub
import numpy as np
import cv2
from IPython import display
import math
"""
Explanation: Text-to-Video retrieval with S3D MIL-NCE
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href=... |
Britefury/deep-learning-tutorial-pydata2016 | TUTORIAL 05 - Dogs vs cats with standard learning.ipynb | mit | %matplotlib inline
"""
Explanation: Dogs vs Cats with Standard Learning
In this Notebook we're going to use standard learning to attempt to crack the Dogs vs Cats Kaggle competition.
We are going to downsample the images to 64x64; that's pretty small, but should be enough (I hope). Furthermore, large images means long... |
pdamodaran/yellowbrick | examples/pdamodaran/feature_visualizer.ipynb | apache-2.0 | import os
import sys
# Modify the path
sys.path.append("..")
import pandas as pd
import yellowbrick as yb
import matplotlib.pyplot as plt
g = yb.anscombe()
"""
Explanation: Feature Visualizer
This notebook provides examples of visualizations done in other data studies and modifies them using the Yellowbrick libr... |
teuben/pitp2016 | yt-demo/example3.ipynb | gpl-3.0 | ds = yt.load("../data/virgo_novisc.0054.gdf")
"""
Explanation: <p>1. Load the `"virgo_novisc.0054.gdf"` dataset from the `"data"` directory.</p>
End of explanation
"""
slc = yt.SlicePlot(ds, "y", ["temperature"], width=(0.4, "Mpc"))
slc.set_cmap("temperature", "algae")
slc.annotate_magnetic_field()
"""
Explanation:... |
eblur/AstroHackWeek2015 | day3-machine-learning/05 - Cross-validation.ipynb | gpl-2.0 | from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
from sklearn.cross_validation import cross_val_score
from sklearn.svm import LinearSVC
cross_val_score(LinearSVC(), X, y, cv=5)
cross_val_score(LinearSVC(), X, y, cv=5, scoring="f1_macro")
"""
Explanation: Cross-Validation
<img... |
jegibbs/phys202-2015-work | assignments/assignment11/OptimizationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Optimization Exercise 1
Imports
End of explanation
"""
def hat(x,a,b):
v = -a*(x**2) + b*(x**4)
return v
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(1.0, 10.0, 1.0... |
AllenDowney/ModSimPy | soln/chap14soln.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... |
mne-tools/mne-tools.github.io | stable/_downloads/6965b7b1a563cc32b2b5388d95203d43/60_cluster_rmANOVA_spatiotemporal.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.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
from mne.stats ... |
bjshaw/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... |
mirjalil/DataScience | python-stuff/regular-expression.ipynb | gpl-2.0 | import re
emaildata = open('enron-email-dataset.txt')
for line in emaildata:
line = line.rstrip()
if re.search('^From:', line):
print(line)
x = 'Team A beat team B 38-7. That was the greatest record for team A since 1987.'
y = re.findall('[0-9]+', x)
y
"""
Explanation: Regular Expression
| ... |
ecabreragranado/OpticaFisicaII | Interferencia Múltiples Ondas/.ipynb_checkpoints/InterferenciaMultiplesOndas-checkpoint.ipynb | gpl-3.0 | from IPython.core.display import Image
Image("http://upload.wikimedia.org/wikipedia/commons/thumb/8/89/Multiple_beam_interference.png/580px-Multiple_beam_interference.png")
"""
Explanation: Interferencia por haces múltiples. Filtros interferenciales.
El siguiente notebook explica la irradiancia obtenida en transmisión... |
GoogleCloudPlatform/tf-estimator-tutorials | 03_Clustering/03.0 - TF k-means - Experiment API.ipynb | apache-2.0 | train_data_files = ['data/train-data.csv']
test_data_files = ['data/test-data.csv']
model_name = 'clust-model-02'
resume = False
train = True
preprocess_features = False
extend_feature_colums = False
"""
Explanation: Steps to use the TF Experiment API
Define dataset metadata
Define data input function to read the d... |
google-research/google-research | group_agnostic_fairness/data_utils/CreateLawSchoolDatasetFiles.ipynb | apache-2.0 | from __future__ import division
import pandas as pd
import numpy as np
import json
import os,sys
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import numpy as np
"""
Explanation: Copyright 2020 Google LLC.
Licensed under the Apache License, Version 2.0 (the ... |
SylvainCorlay/ipywidgets | docs/source/examples/Widget List.ipynb | bsd-3-clause | import ipywidgets as widgets
"""
Explanation: Index - Back - Next
Widget List
End of explanation
"""
widgets.IntSlider(
value=7,
min=0,
max=10,
step=1,
description='Test:',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='d'
)
"... |
JoaoFelipe/snowballing | snowballing/example/Progress.ipynb | mit | import database
from datetime import datetime
from snowballing.operations import load_work, reload
from snowballing.jupyter_utils import work_button, idisplay
reload()
"""
Explanation: Index
Work
WorkOk
WorkSnowball
Forward Snowballing
Other
WorkUnrelated
WorkNoFile
WorkLang
End of explanation
"""
reload()
query = ... |
AEW2015/PYNQ_PR_Overlay | Pynq-Z1/notebooks/examples/pmod_grove_pir.ipynb | bsd-3-clause | from time import sleep
from pynq import Overlay
from pynq.board import LED
from pynq.iop import Grove_PIR
from pynq.iop import PMODA
from pynq.iop import PMOD_GROVE_G1
ol1 = Overlay("base.bit")
ol1.download()
pir = Grove_PIR(PMODA,PMOD_GROVE_G1)
"""
Explanation: PMOD Grove PIR Motion Sensor
This examples shows how t... |
netodeolino/TCC | TCC 02/Resultados/Abril/Abril.ipynb | mit | all_crime_tipos.head(10)
all_crime_tipos_top10 = all_crime_tipos.head(10)
all_crime_tipos_top10.plot(kind='barh', figsize=(12,6), color='#3f3fff')
plt.title('Top 10 crimes por tipo (Abr 2017)')
plt.xlabel('Número de crimes')
plt.ylabel('Crime')
plt.tight_layout()
ax = plt.gca()
ax.xaxis.set_major_formatter(ticker.StrM... |
fonnesbeck/scientific-python-workshop | notebooks/Scikit Learn.ipynb | cc0-1.0 | from sklearn.datasets import load_iris
iris = load_iris()
iris.keys()
n_samples, n_features = iris.data.shape
n_samples, n_features
iris.data[0]
"""
Explanation: Introduction to Scikit-learn
The scikit-learn package is an open-source library that provides a robust set of machine learning algorithms for Python. It i... |
bourneli/deep-learning-notes | DAT236x Deep Learning Explained/Lab2_LogisticRegression.ipynb | mit | # Figure 1
Image(url= "http://3.bp.blogspot.com/_UpN7DfJA0j4/TJtUBWPk0SI/AAAAAAAAABY/oWPMtmqJn3k/s1600/mnist_originals.png", width=200, height=200)
"""
Explanation: Lab 2 - Logistic Regression (LR) with MNIST
This lab corresponds to Module 2 of the "Deep Learning Explained" course. We assume that you have successfully... |
kadrlica/skymap | tutorial/chapter2_skymap_subclasses.ipynb | mit | # Basic notebook imports
%matplotlib inline
import matplotlib
import pylab as plt
import numpy as np
import healpy as hp
"""
Explanation: <center>
Go back to the Index
</center>
Chapter 2: Skymap Subclasses
In this chapter we introduce the subclasses of skymap.Skymap and explore some of their features for astronom... |
BiG-CZ/notebook_data_demo | notebooks/2017-06-24-odm2api_sample_fromsqlite.ipynb | bsd-3-clause | import os
from odm2api.ODMconnection import dbconnection
odm2db_fpth = os.path.join('data', 'ODM2.sqlite')
session_factory = dbconnection.createConnection('sqlite', odm2db_fpth, 2.0)
"""
Explanation: odm2api demo with Little Bear SQLite sample DB
Largely from https://github.com/ODM2/ODM2PythonAPI/blob/master/Example... |
dataventures/workshops | 0/Pandas Intro.ipynb | mit | # General syntax to import specific functions in a library:
##from (library) import (specific library function)
from pandas import DataFrame, read_csv
# General syntax to import a library but no functions:
##import (library) as (give the library a nickname/alias)
import matplotlib.pyplot as plt
import pandas as pd #... |
awhite40/pymks | notebooks/Ising model.ipynb | mit | from pymks_share import DataManager
import numpy as np
manager = DataManager('pymks.me.gatech.edu')
X = manager.fetch_data('2 phase ising model')
Y = manager.fetch_data('Ising 30%')
Z = manager.fetch_data('ising 10%')
R1 = manager.fetch_data('Ising 40%_Run#1')
R2 = manager.fetch_data('Ising 40%_Run#3')
X.shape, R1.sha... |
ES-DOC/esdoc-jupyterhub | notebooks/fio-ronm/cmip6/models/sandbox-2/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'fio-ronm', 'sandbox-2', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: FIO-RONM
Source ID: SANDBOX-2
Topic: Landice
Sub-Topics: Glaciers, Ice.
Pro... |
science-of-imagination/nengo-buffer | Project/trained_mental_scaling_ens.ipynb | gpl-3.0 | import nengo
import numpy as np
import cPickle
from nengo_extras.data import load_mnist
from nengo_extras.vision import Gabor, Mask
from matplotlib import pylab
import matplotlib.pyplot as plt
import matplotlib.animation as animation
"""
Explanation: Using the trained weights in an ensemble of neurons
On the function... |
tensorflow/docs-l10n | site/en-snapshot/lite/performance/post_training_quant.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... |
google/earthengine-api | python/examples/ipynb/ee-api-colab-setup.ipynb | apache-2.0 | import ee
"""
Explanation: <table class="ee-notebook-buttons" align="left"><td>
<a target="_blank" href="http://colab.research.google.com/github/google/earthengine-api/blob/master/python/examples/ipynb/ee-api-colab-setup.ipynb">
<img src="https://www.tensorflow.org/images/colab_logo_32px.png" /> Run in Google Col... |
quantumlib/ReCirq | docs/quantum_chess/quantum_chess_client.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... |
infilect/ml-course1 | keras-notebooks/RNN/7.2 LSTM for Sentence Generation.ipynb | mit | from keras.optimizers import SGD
from keras.preprocessing.text import one_hot, text_to_word_sequence
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.embeddings import Embedding
from keras.layers.recurrent import LSTM, GRU
fr... |
datactive/bigbang | examples/experimental_notebooks/Testing Power Law Response Time Hypothesis.ipynb | mit | from bigbang.archive import Archive
import pandas as pd
arx = Archive("ipython-dev",archive_dir="../archives")
print(arx.data.shape)
arx.data.drop_duplicates(subset=('From','Date'),inplace=True)
"""
Explanation: An early result in the study of human dynamic systems is the claim that response times to email follow a ... |
peterwittek/ipython-notebooks | Multipartite_entanglement.ipynb | gpl-3.0 | import warnings
from numpy import array, cos, dot, equal, kron, mod, pi, random, real, \
reshape, sin, sqrt, zeros
from qutip import expect, basis, qeye, sigmax, sigmay, sigmaz, tensor
from scipy.optimize import minimize
from ncpol2sdpa import SdpRelaxation, generate_variables, flatten, \
generate_measurements,... |
facaiy/book_notes | Mining_of_Massive_Datasets/Mining_Social_Network_Graphs/note.ipynb | cc0-1.0 | plt.imshow(plt.imread('./res/fig10_1.png'))
"""
Explanation: 10 Mining Social-Network Graphs
how to identify "communities"?
communities: strong connections, usually overlap.
explore efficient algorithms for discovering other properities of graphs.
10.1 Social Networks as Graphs
10.1.1 What is a Social Netw... |
mrcslws/nupic.research | projects/archive/dynamic_sparse/notebooks/ExperimentAnalysis-MNISTSparser.ipynb | agpl-3.0 | %load_ext autoreload
%autoreload 2
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import tabulate
import pprint
import click
import numpy as np
import pandas as pd
from ray.tune.commands import *
from nupic.research.frameworks.dynamic... |
CINPLA/exdir | tests/benchmarks/benchmarks.ipynb | mit | import exdir
import os
import shutil
import h5py
def setup_exdir():
testpath = "test.exdir"
if os.path.exists(testpath):
shutil.rmtree(testpath)
f = exdir.File(testpath)
return f, testpath
def setup_exdir_no_validation():
testpath = "test.exdir"
if os.path.exists(testpath):
shu... |
phoebe-project/phoebe2-docs | 2.2/examples/minimal_contact_binary.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Minimal Contact Binary System
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
"""
%matplotli... |
topologicalbudapest/topins2 | HgTe_edge_proximity.ipynb | gpl-2.0 | #here we define sympy symbols to be used in the analytic calculations
g,mu,b,D,k=sympy.symbols('gamma mu B Delta k',real=True)
"""
Explanation: HgTe edge in proximity to an s-wave superconductor
End of explanation
"""
# onsite and hopping terms
U=sympy.Matrix([[-mu+b,g,0,D],
[g,-mu-b,-D,0],
... |
fmeynadier/allantools | examples/gradev-demo.ipynb | lgpl-3.0 | %matplotlib inline
import pylab as plt
import numpy as np
import allantools
"""
Explanation: GRADEV: gap robust allan deviation
Notebook setup & package imports
End of explanation
"""
def example1():
"""
Compute the GRADEV of a white phase noise. Compares two different
scenarios. 1) The original data a... |
mne-tools/mne-tools.github.io | 0.23/_downloads/066ec12646ce0d0818ad9b78bc602218/fdr_stats_evoked.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.stats import bonferroni_correction, fdr_correction
print(__doc__)
"""
Explanation:... |
mjones01/NEON-Data-Skills | code/Python/remote-sensing/hyperspectral-data/Calc_NDVI_Extract_Spectra_Masks_Tiles_py.ipynb | agpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings('ignore') #don't display warnings
# %load ../neon_aop_hyperspectral.py
"""
Created on Wed Jun 20 10:34:49 2018
@author: bhass
"""
import matplotlib.pyplot as plt
import numpy as np
import h5py, os, copy
de... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/machine_learning_in_the_enterprise/labs/sdk_custom_tabular_regression_online_explain.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 SDK: Custom Training Tabular Regression Models for Online Prediction and Explainability
... |
goodwordalchemy/thinkstats_notes_and_exercises | code/.ipynb_checkpoints/chap05soln-checkpoint.ipynb | gpl-3.0 | from __future__ import print_function, division
"""
Explanation: Exercise from Think Stats, 2nd Edition (thinkstats2.com)<br>
Allen Downey
End of explanation
"""
import scipy.stats
%matplotlib inline
"""
Explanation: Exercise 5.1
<tt>scipy.stats</tt> contains objects that represent analytic distributions
End of ex... |
tpin3694/tpin3694.github.io | python/cartesian_product.ipynb | mit | # import pandas as pd
import pandas as pd
"""
Explanation: Title: Cartesian Product
Slug: cartesian_product
Summary: Cartesian Product
Date: 2016-05-01 12:00
Category: Python
Tags: Basics
Authors: Chris Albon
Preliminaries
End of explanation
"""
# Create two lists
i = [1,2,3,4,5]
j = [1,2,3,4,5]
"""
Explanation: ... |
Krastanov/cutiepy | examples/Schroedinger_Equation_Solver_Examples.ipynb | bsd-3-clause | from cutiepy import *
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Table of Contents
Rabi Oscillations
Simulating the Full Hamiltonian
With Rotating Wave Approximation
Coherent State in a Harmonic Oscillator
Jaynes-Cummings Revival
Definite Photon State
Coherent State
End o... |
ibm-cds-labs/pixiedust | notebook/PixieDust 3 - Scala and Python.ipynb | apache-2.0 | pythonString = "Hello From Python"
pythonInt = 20
"""
Explanation: Mixing Scala and Python on the same Notebook
Python has a rich ecosystem of modules including plotting with Matplotlib, data structure and analysis with Pandas, Machine Learning or Natural Language Processing. However, data scientists working with Spar... |
mne-tools/mne-tools.github.io | 0.19/_downloads/4a27e3735cff9a10082eb2938ef41c34/plot_sensor_permutation_test.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne import io
from mne.stats import permutation_t_test
from mne.datasets import sample
print(__doc__)
"""
Explanation: Permutation T-test on sensor data
One tests if the signal significantly de... |
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