repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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JamesSample/icpw | toc_trends_oct_2018_part2.ipynb | mit | # User input
# Specify projects of interest
proj_list = ['ICPW_TOCTRENDS_2018',]
# Specify results folder
res_fold = (r'../../update_autumn_2018/results')
"""
Explanation: TOC trends - October 2018 (Part 2: Chemistry trend analysis)
The previous notebook created a new dataset for the trends analysis spanning ther per... |
mintcloud/deep-learning | gan_mnist/Intro_to_GANs_Exercises.ipynb | mit | %matplotlib inline
import pickle as pkl
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')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
waltervh/BornAgain-tutorial | talks/day_1/gui_basics_1_M/GUI_solutions.ipynb | gpl-3.0 | width = 981 * 0.172
print("Width = ", width, " mm")
height = 1043 * 0.172
print("Height = ", height, " mm")
"""
Explanation: Exercise 1: Set up GALAXI instrument
Beam parameters
Pay attention, that Wavelength must be given in nm. Thus, $\lambda = 0.134$ nm.
Detector parameters
Considering pixel size 0.172 mm, calcul... |
smorton2/think-stats | code/chap06exmine.ipynb | gpl-3.0 | from __future__ import print_function, division
%matplotlib inline
import numpy as np
import brfss
import thinkstats2
import thinkplot
"""
Explanation: Examples and Exercises from Think Stats, 2nd Edition
http://thinkstats2.com
Copyright 2016 Allen B. Downey
MIT License: https://opensource.org/licenses/MIT
End of ... |
fazzolini/fast_ai | deeplearning1/nbs/lesson1.ipynb | apache-2.0 | from __future__ import division, print_function
%matplotlib inline
"""
Explanation: Using Convolutional Neural Networks
Welcome to the first week of the first deep learning certificate! We're going to use convolutional neural networks (CNNs) to allow our computer to see - something that is only possible thanks to dee... |
yuyu2172/image-labelling-tool | Image labeller notebook.ipynb | mit | %matplotlib inline
import math
from matplotlib import pyplot as plt
from IPython.display import display, Javascript
from image_labelling_tool import labelling_tool, labelling_tool_jupyter
"""
Explanation: Image labeller with persistence (example)
This notebook will demonstrate the follwing:
using the image labell... |
oditorium/blog | iPython/BayesVsFreq-PartI-Intro.ipynb | agpl-3.0 | N = 20
P = 0.6
l = list(map (pf(P), np.random.uniform(size=N)))
ls = ft.reduce(lambda acc, n: acc + str(n), l, "")
K = sum(l)
l2 = list(map (pf(P), np.random.uniform(size=N*10)))
ls2 = ft.reduce(lambda acc, n: acc + str(n), l, "")
K2 = sum(l2)
print ("N = %i (%i)" % (N,10*N))
print ("P = %.2f" % P)
print ("K = %i (%i)"... |
SylvainCorlay/ipywidgets | docs/source/examples/Using Interact.ipynb | bsd-3-clause | from __future__ import print_function
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
"""
Explanation: Using Interact
The interact function (ipywidgets.interact) automatically creates user interface (UI) controls for exploring code and data interactively. It is the eas... |
NAMD/corpushash | demos/benchmark.ipynb | lgpl-3.0 | import os
import sys
import shutil
import copy
import numpy as np
import matplotlib.pyplot as plt
from timeit import default_timer as timer
import nltk
from nltk.corpus import words
from nltk.corpus import reuters
import corpushash as ch
"""
Explanation: benchmarking the corpushash library
corpushash is a simple libra... |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'ec-earth3-cc', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: EC-EARTH3-CC
Topic: Ocean
Sub-Topic... |
espressomd/espresso | doc/tutorials/error_analysis/error_analysis_part1.ipynb | gpl-3.0 | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})
import sys
import logging
logging.basicConfig(level=logging.INFO, stream=sys.stdout)
np.random.seed(43)
def ar_1_process(n_samples, c, phi, eps):
'''
Generate a correlated random sequence with the AR(1... |
hasecbinusr/pysal | pysal/network/Network_Usage.ipynb | bsd-3-clause | %pylab inline
import pysal as ps
"""
Explanation: Basic Tutorial for pysal.network
updated 07/2016
End of explanation
"""
ntw = ps.Network(ps.examples.get_path('streets.shp'))
"""
Explanation: Instantiate a network
End of explanation
"""
# Crimes
ntw.snapobservations(ps.examples.get_path('crimes.shp'), 'crimes', ... |
4dsolutions/Python5 | Vectors in Space.ipynb | mit | class Vector:
"A point in space"
pass
"""
Explanation: Oregon Curriculum Network <br />
Discovering Math with Python
Chapter 6: VECTORS IN SPACE
A point vector is simply an object that points, from the origin to a specific location. We usually represent such an object with an arrow, with its tail at (0,0) or w... |
IanHawke/maths-with-python | 05-classes-oop.ipynb | mit | p_normal = (12, -14, 0, 2)
"""
Explanation: Classes and Object Oriented Programming
We have looked at functions which take input and return output (or do things to the input). However, sometimes it is useful to think about objects first rather than the actions applied to them.
Think about a polynomial, such as the cub... |
AtmaMani/pyChakras | stats_101/02_data_description.ipynb | mit | import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
sales_data = pd.read_csv('datasets_csv/CH03/ex3-14.txt')
sales_data.columns = ['Year', 'Month', 'Sales'] #strip out the double quotes
sales_data.head()
"""
Explanation: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul c... |
jonathf/chaospy | docs/user_guide/fundamentals/probability_distributions.ipynb | mit | import chaospy
normal = chaospy.Normal(mu=2, sigma=2)
normal
"""
Explanation: Probability distributions
Probability distribution is the backbone of uncertainty quantification.
Creating a probability distribution in chaospy is done by as follows:
End of explanation
"""
samples = normal.sample(4, seed=1234)
samples
... |
quantumlib/Cirq | docs/tutorials/educators/neutral_atom.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... |
oyamad/game_theory_models | test_logitdyn.ipynb | bsd-3-clause | u = coo_payoffs
beta = 1.0
P = np.zeros((2,2))
"""
Explanation: test_simulate_LLN
End of explanation
"""
P[0,0] = np.exp(u[0,0] * beta) / (np.exp(u[0,0] * beta) + np.exp(u[1,0] * beta))
P[0,0]
P[1,0] = np.exp(u[1,0] * beta) / (np.exp(u[0,0] * beta) + np.exp(u[1,0] * beta))
P[1,0]
P[0,1] = np.exp(u[0,1] * beta) / (... |
EBIvariation/eva-cttv-pipeline | data-exploration/complex-events/notebooks/hgvs-follow-up.ipynb | apache-2.0 | def try_to_vep(hgvs, long=False):
"""Attempts to get consequences from VEP for HGVS expression."""
safe_hgvs = urllib.parse.quote(hgvs)
vep_url = f'https://rest.ensembl.org/vep/human/hgvs/{safe_hgvs}?content-type=application/json'
resp = requests.get(vep_url).json()
if 'error' in resp:
print... |
iagapov/ocelot | demos/ipython_tutorials/7_lattice_design.ipynb | gpl-3.0 | # the output of plotting commands is displayed inline within
# frontends, directly below the code cell that produced it.
%matplotlib inline
from time import time
# this python library provides generic shallow (copy)
# and deep copy (deepcopy) operations
from copy import deepcopy
# import from Ocelot main modules... |
napsternxg/blog | content/Convex Optimization using CVXPY.ipynb | gpl-3.0 | %matplotlib inline
import cvxpy as cvx
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
import seaborn as sns
sns.set_context("paper")
sns.set_style("ticks")
np.random.seed(1337)
"""
Explanation: $\DeclareMathOperator*{\argmin}{arg\,min}$
I got introduced to the concept of convex optimi... |
aselle/tensorflow | tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb | apache-2.0 | !pip install unidecode
"""
Explanation: Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License").
Text Generation using a RNN
<table class="tfo-notebook-buttons" align="left"><td>
<a target="_blank" href="https://colab.research.google.com/github/tensorflow/tensorflow/blob/... |
rnder/data-science-from-scratch | notebook/ch24_mapreduce.ipynb | unlicense | from __future__ import division
import math, random, re, datetime
from collections import defaultdict, Counter
from functools import partial
from naive_bayes import tokenize
def word_count_old(documents):
"""맵리듀스를 사용하지 않고 단어의 빈도 수를 세어 줌"""
return Counter(word
for document in documents
for wor... |
hyzhak/mle | mle.ipynb | mit | import numpy as np
from scipy.stats import bernoulli
np.random.seed(123456789)
p_true = 1/2 # this is the value we will try to estimate from the observed data
fp = bernoulli(p_true)
def sample(n=10):
"""
simulate coin flipping
"""
return fp.rvs(n) # flip it n times
xs = sample(100) # generate some ... |
SteveDiamond/cvxpy | examples/notebooks/WWW/clock_mesh.ipynb | gpl-3.0 | import cvxpy as cp
import numpy as np
import scipy as scipy
import matplotlib.pyplot as plt
# Show plots inline in ipython.
%matplotlib inline
# Plot properties.
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
font = {'weight' : 'normal',
'size' : 16}
plt.rc('font', **font)
"""
Explanation: Sizi... |
vicente-gonzalez-ruiz/YAPT | 04-concurrency/sharing_a_variable.ipynb | cc0-1.0 | import threading
import time
a = ''
def task_1():
global a
for i in range(10):
print('o', end='', flush=True)
a += 'o'
print(a)
time.sleep(1) # Blocking -> yield to other thread
def task_2():
global a
for i in range(20):
print('O', end='', flush=True)
... |
JJ/ciencia-reproducible | app/Investigadores-UGR.ipynb | mit | %matplotlib inline
from BeautifulSoup import BeautifulSoup
import urllib2
from IPython.display import (
display, HTML
)
import matplotlib.pyplot as plt
url = 'http://investigacion.ugr.es/ugrinvestiga/static/BuscadorRanking/*/buscar?tipo=&rama_c=&disciplina_c=TELE_D&especialidad_c=&indicador=&periodo='
response ... |
vbsteja/code | Python/ML_DL/DL/Neural-Networks-Demystified-master/.ipynb_checkpoints/Part 7 Overfitting, Testing, and Regularization-checkpoint.ipynb | apache-2.0 | from IPython.display import YouTubeVideo
YouTubeVideo('S4ZUwgesjS8')
"""
Explanation: <h1 align = 'center'> Neural Networks Demystified </h1>
<h2 align = 'center'> Part 7: Overfitting, Testing, and Regularization </h2>
<h4 align = 'center' > @stephencwelch </h4>
End of explanation
"""
%pylab inline
from partSix imp... |
david-abel/simple_rl | simple_rl/tasks/navigation/Usage_NavigationWorldMDP.ipynb | apache-2.0 | # Other Imports.
from simple_rl.tasks import NavigationWorldMDP
from simple_rl.agents import QLearningAgent
from simple_rl.planning import ValueIteration
from simple_rl.tasks.grid_world.GridWorldStateClass import GridWorldState
from simple_rl.tasks.navigation.NavigationStateClass import NavigationWorldState
from simple... |
chinapnr/python_study | Python 基础课程/Python Basic Lesson 13 - 完整的 for 语句结构和 pprint.ipynb | gpl-3.0 | # for continue 举例
for l in 'Python':
if l == 'h':
continue
print('letter:', l)
# for continue 举例
# 去除输入的内容中的元音字母
list1 = input('Input:')
list2 = []
for s in list1:
if s in ['a','e','i','o','u']:
continue
list2.append(s)
# 将列表转换为字符串
s2 = ''.join(list2)
print('Output:', s2)
# for br... |
DOV-Vlaanderen/pydov | docs/notebooks/search_hydrogeologische_stratigrafie.ipynb | mit | %matplotlib inline
import os, sys
import inspect
import pydov
"""
Explanation: Example of DOV search methods for hydrogeological interpretations
Use cases:
Select interpretations in a bbox
Select interpretations in a bbox with selected properties
Select interpretations in a municipality
Get interpretations using in... |
akchinSTC/systemml | samples/jupyter-notebooks/Deep_Learning_Image_Classification.ipynb | apache-2.0 | !pip show systemml
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.cross_validation import train_test_split # module deprecated in 0.18
#from sklearn.model_selection import train_test_split # use this module for >=0.18
from sklearn ... |
fastai/fastai | nbs/45_collab.ipynb | apache-2.0 | #|export
class TabularCollab(TabularPandas):
"Instance of `TabularPandas` suitable for collaborative filtering (with no continuous variable)"
with_cont=False
"""
Explanation: Collaborative filtering
Tools to quickly get the data and train models suitable for collaborative filtering
This module contains all t... |
gardenermike/finding-lane-lines | P1.ipynb | mit | #importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
"""
Explanation: Self-Driving Car Engineer Nanodegree
Project: Finding Lane Lines on the Road
In this project, you will use the tools you learned about in the lesson to ide... |
TiKeil/Master-thesis-LOD | notebooks/TEST_VCLOD_Coefficient_1.ipynb | apache-2.0 | import os
import sys
import numpy as np
import scipy.sparse as sparse
import random
import csv
%matplotlib notebook
import matplotlib.pyplot as plt
from visualize import drawCoefficient
from data import *
from gridlod import interp, coef, util, fem, world, linalg, femsolver
import pg_rand, femsolverCoarse, buildcoef... |
Hyperparticle/deep-learning-foundation | materials/keyboard-shortcuts.ipynb | mit | # mode practice
"""
Explanation: Keyboard shortcuts
In this notebook, you'll get some practice using keyboard shortcuts. These are key to becoming proficient at using notebooks and will greatly increase your work speed.
First up, switching between edit mode and command mode. Edit mode allows you to type into cells whi... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/04_advanced_preprocessing/taxicab_traffic/train.ipynb | apache-2.0 | import tensorflow as tf
import numpy as np
import shutil
print(tf.__version__)
"""
Explanation: Taxi Fare Prediction Using Realtime Traffic Data
This will be the same as our previous taxi fare model, but with the addition of ‘trips_last_5min’ data as a feature. This is our proxy for traffic
End of explanation
"""
!g... |
PyPSA/PyPSA | examples/notebooks/ac-dc-lopf.ipynb | mit | import pypsa
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
%matplotlib inline
plt.rc("figure", figsize=(8, 8))
network = pypsa.examples.ac_dc_meshed(from_master=True)
# get current type (AC or DC) of the lines from the buses
lines_current_type = network.l... |
LimeeZ/phys292-2015-work | assignments/assignment05/InteractEx02.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 2
Imports
End of explanation
"""
def plot_sin1(a,b):
x = np.arange(0,4*np.pi)
plt.plot(x, np.sin(a*x... |
cpcloud/ibis | docs/tutorial/08-Geospatial-Analysis.ipynb | apache-2.0 | # Launch the postgis container.
# This may take a bit of time if it needs to download the image.
!docker run -d -p 5432:5432 --name postgis-db -e POSTGRES_PASSWORD=supersecret mdillon/postgis:9.6-alpine
"""
Explanation: Geospatial Analysis
One of the most popular extensions to PostgreSQL is PostGIS,
which adds support... |
nwjs/chromium.src | third_party/tensorflow-text/src/docs/guide/tokenizers.ipynb | bsd-3-clause | #@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... |
abhi1509/deep-learning | batch-norm/Batch_Normalization_Exercises.ipynb | mit | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
"""
Explanation: Batch Normalization – Practice
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a con... |
CyberCRI/dataanalysis-herocoli-redmetrics | v1.52.2/Tests/2.2 Google form analysis tests with sessions.ipynb | cc0-1.0 | %run "../Functions/2. Google form analysis.ipynb"
"""
Explanation: 1.2 Google form analysis tests with sessions
End of explanation
"""
allGFormResponders = getAllResponders()
"""
Explanation: (userIdThatDidNotAnswer)
(userId1AnswerEN)
(userIdAnswersEN)
(userId1ScoreEN)
(userIdScoresEN)
(userId1AnswerFR)
(userIdAnsw... |
tensorflow/docs-l10n | site/en-snapshot/guide/migrate/saved_model.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... |
jokedurnez/neuropower_extended | peakdistribution/peakdistributions_with_threshold_applied_to_thresholdfree_peaks.ipynb | mit | % matplotlib inline
import os
import numpy as np
import nibabel as nib
from nipy.labs.utils.simul_multisubject_fmri_dataset import surrogate_3d_dataset
import nipy.algorithms.statistics.rft as rft
from __future__ import print_function, division
import math
import matplotlib.pyplot as plt
import palettable.colorbrewer a... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_gamma_map_inverse.ipynb | bsd-3-clause | # Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# 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 gamma_map, make_stc_from_dipoles
from mne.viz import (plot_sparse_source_estimate... |
ES-DOC/esdoc-jupyterhub | notebooks/ipsl/cmip6/models/ipsl-cm6a-lr/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ipsl', 'ipsl-cm6a-lr', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: IPSL
Source ID: IPSL-CM6A-LR
Sub-Topics: Radiative Forcings.
Properties: 8... |
sorgerlab/belpy | models/ras_pathway/CyJS_Assembler.ipynb | mit | from __future__ import absolute_import, print_function, unicode_literals
from builtins import dict, str
statement_path = 'ras_pathway.txt'
txt = open(statement_path, 'rt').read()
print(txt)
"""
Explanation: CyJS Assembler
Assemble a network from text using existing INDRA functionality
Translate this into a model supp... |
tuanavu/coursera-university-of-washington | machine_learning/2_regression/assignment/week5/.ipynb_checkpoints/week-5-lasso-assignment-1-exercise-checkpoint.ipynb | mit | import sys
sys.path.append('C:\Anaconda2\envs\dato-env\Lib\site-packages')
import graphlab
"""
Explanation: Regression Week 5: Feature Selection and LASSO (Interpretation)
In this notebook, you will use LASSO to select features, building on a pre-implemented solver for LASSO (using GraphLab Create, though you can use ... |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/GUI/5 - Widget Styling.ipynb | apache-2.0 | %%html
<style>
.example-container { background: #999999; padding: 2px; min-height: 100px; }
.example-container.sm { min-height: 50px; }
.example-box { background: #9999FF; width: 50px; height: 50px; text-align: center; vertical-align: middle; color: white; font-weight: bold; margin: 2px;}
.example-box.med { width: 65px... |
stevetjoa/stanford-mir | evaluation_chord.ipynb | mit | y, sr = librosa.load('audio/simple_piano.wav')
# Estimate onsets.
est_onsets = librosa.onset.onset_detect(y=y, sr=sr, units='time')
est_onsets
# Load the reference annotation.
ref_onsets = numpy.array([0.1, 0.21, 0.3])
mir_eval.onset.evaluate(ref_onsets, est_onsets)
"""
Explanation: ← Back to Index
Evaluation... |
jermainewang/mxnet | example/adversary/adversary_generation.ipynb | apache-2.0 | %matplotlib inline
import mxnet as mx
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import os
import sys
sys.path.append(os.path.join(os.getcwd(), "../../tests/python/common"))
from get_data import MNISTIterator
"""
Explanation: Fast Sign Adversary Generation Example
This notebook dem... |
xdnian/pyml | assignments/solutions/ex05_ch0607_sample_solutions.ipynb | mit | # Added version check for recent scikit-learn 0.18 checks
from distutils.version import LooseVersion as Version
from sklearn import __version__ as sklearn_version
"""
Explanation: Assignment 5
This assignment has weighting $1.5$.
Model tuning and evaluation
End of explanation
"""
import pandas as pd
wdbc_source = '... |
ChadFulton/statsmodels | examples/notebooks/formulas.ipynb | bsd-3-clause | from __future__ import print_function
import numpy as np
import statsmodels.api as sm
"""
Explanation: Formulas: Fitting models using R-style formulas
Since version 0.5.0, statsmodels allows users to fit statistical models using R-style formulas. Internally, statsmodels uses the patsy package to convert formulas and d... |
ThierryMondeel/FBA_python_tutorial | FBA_tutorials/extra_generate_COBRA_model_geenen_2012.ipynb | mit | import cobra
from cobra.solvers import get_solver_name
from cobra import Model, Reaction, Metabolite
from cobra.flux_analysis import parsimonious
import pandas as pd
"""
Explanation: COBRA model for (Geenen, 2012)
This notebook takes the original kinetic model in SBML format and saves a json model that allows for CBM ... |
murphy214/berrl | example_jupyter/example.ipynb | apache-2.0 | import berrl as bl
import numpy as np
import pandas as pd
apikey='pk.eyJ1IjoibXVycGh5MjE0IiwiYSI6ImNpam5kb3puZzAwZ2l0aG01ZW1uMTRjbnoifQ.5Znb4MArp7v3Wwrn6WFE6A'
data=pd.read_csv('wv_traffic_fatals.csv')
#data=data[data.CNTYNAME=='Clay County']
a=bl.make_points(data,list=True)
bl.parselist(a,'fatals.geojson')
# return... |
rescolo/estadistica | Jupyter_notebooks/Ising model simulations.ipynb | unlicense | import numpy as np
SIZE=5
STEPS=10
#----------------------------------------------------------------------#
# Check periodic boundary conditions
#----------------------------------------------------------------------#
def bc(i):
return i%SIZE
#--------------------------------------------------------------------... |
ecervera/UJI_AMR | exercises/Distance.ipynb | mit | import packages.initialization
import pioneer3dx as p3dx
p3dx.init()
"""
Explanation: <img align="right" src="../img/exercise_distance_240.png" />
Exercise: Move the robot forward for a distance.
You are going to program the robot for moving forward from the initial position at the start of the simulation, in the cent... |
ehrlinger/MachineLearningSamples-PredictiveMaintenance | Code/4_operationalization.ipynb | mit | ## setup our environment by importing required libraries
import json
import os
import shutil
import time
from pyspark.ml import Pipeline
from pyspark.ml.classification import RandomForestClassifier
# for creating pipelines and model
from pyspark.ml.feature import StringIndexer, VectorAssembler, VectorIndexer
# setup... |
bennylp/quant.id | Notebooks/100 - Portfolio Construction and Analysis/10012 - Return Distribution Analysis.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from IPython.display import display, Markdown, HTML
%matplotlib inline
FIGSIZE=(8,4)
returns = pd.read_csv('SmallLargeCapReturns.csv', index_col='date', parse_dates=True)
returns.index = returns.index.to_period('M')
returns.head()
"""
Explanation... |
abulbasar/machine-learning | Telecomm Churn Analysis Using XGBoost.ipynb | apache-2.0 | import xgboost as xgb
import pandas as pd
from sklearn import *
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Problem Statement
In this example I will build a classifier for churn prediction using a dataset from telecomm industry. You can find the data set in github in the following links.
https... |
ericmjl/Network-Analysis-Made-Simple | notebooks/04-advanced/01-bipartite.ipynb | mit | from IPython.display import YouTubeVideo
YouTubeVideo(id="BYOK12I9vgI", width="100%")
"""
Explanation: Introduction
End of explanation
"""
from nams import load_data as cf
G = cf.load_crime_network()
for n, d in G.nodes(data=True):
G.nodes[n]["degree"] = G.degree(n)
"""
Explanation: In this chapter, we will lo... |
mne-tools/mne-tools.github.io | 0.23/_downloads/0602f866d20bff8af56294e10dd6854d/10_preprocessing_overview.ipynb | bsd-3-clause | import os
import numpy as np
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
raw.crop(0, 60).load_data() # just use a fr... |
tgsmith61591/pyramid | benchmarks/Benchmarking Seasonality.ipynb | mit | import pandas as pd
X = pd.read_csv('item_sales_daily.csv.gz')
y = X['sales'].values
X.head()
import pmdarima as pm
import time
from functools import wraps
def timed(func):
"""A decorator to time a result"""
@wraps(func)
def wrapper(*args, **kwargs):
start = time.time()
res = func(*args,... |
dbouquin/AstroHackWeek2015 | day3-machine-learning/02 - First Steps.ipynb | gpl-2.0 | from sklearn.datasets import load_digits
digits = load_digits()
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(digits.data,
digits.target)
"""
Explanation: Get some data to play with
End of explanation
"""
... |
bakanchevn/DBCourseMirea2017 | Неделя 2/Задание в классе/Лабораторная 2-1.ipynb | gpl-3.0 | %%sql
SELECT t
FROM tracks t
INNER JOIN genres g
ON t.genreid = g.genreid
INNER JOIN media_types m
ON m.mediatypeid = t.mediatypeid
ORDER BY t.bytes desc
limit 10
"""
Explanation: Задание 1
Вывести 10 самых больших по размеру треков жанра ROCK и формата MPEG
End of explanation
"""
%%sql
SELECT ... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/autoregressive_distributed_lag.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import seaborn as sns
sns.set_style("darkgrid")
sns.mpl.rc("figure", figsize=(16, 6))
sns.mpl.rc("font", size=14)
"""
Explanation: Autoregressive Distributed Lag (ARDL) models
ARDL Models
Autoregressive Distributed Lag (ARDL) models extend Autoregressive models with lags of expl... |
fsix/mnist | notebooks/Deskewing.ipynb | apache-2.0 | plt.imshow(X_train[0].reshape(28,28)) # This is what the image looks like
from scipy.ndimage import interpolation
def moments(image):
c0,c1 = np.mgrid[:image.shape[0],:image.shape[1]] # A trick in numPy to create a mesh grid
totalImage = np.sum(image) #sum of pixels
m0 = np.sum(c0*image)/totalImage #mu_x
... |
simkovic/simkovic.github.io | _ipynb/The Missing Square.ipynb | mit | %pylab inline
def fibo(n): return np.int32(((1 + 5**0.5)/2)**n/5**0.5+0.5)
x=np.arange(20)
print fibo(x)
"""
Explanation: You should be familiar with the following puzzle.
There is very similar puzzle that has some mathematical background in Fibonacci numbers. Recall Fibonacci numbers are given by $F_n = F_{n-1}+F_{... |
ewulczyn/readers | src/data_generation/QA match EL to Google Forms.ipynb | mit | response_table = 'log.QuickSurveysResponses_15266417'
impression_table = 'log.QuickSurveyInitiation_15278946'
host = 'analytics-store.eqiad.wmnet'
"""
Explanation: QuickSurveysResponses_15266417
QuickSurveyInitiation_15278946
Survey
End of explanation
"""
query = """
SELECT
COUNT(*) as n
FROM log.QuickSurveyInit... |
martadesimone/Protoplanetarydisks | ChamaleonI_Tab.ipynb | gpl-2.0 | def Mdisk(F,d,r,k,Td,lam):
#Eq 2 Andrews et al. 2013
#F is *mm* Flux in *Jy*
#d is the distance in *pc*
#r is the dust to mass ratio *adimensional*
#k is the dust opacity in *cm^2/g*
#lam is the wavelenght in *mm*
c=2.99792458e10 # Light velocity *cm/s*
h=6.6260755e-27 # Planck con... |
hvillanua/deep-learning | intro-to-tensorflow/intro_to_tensorflow.ipynb | mit | import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfile import ZipFile
print('All m... |
jrrembert/cybernetic-organism | dato/clustering/.ipynb_checkpoints/Document retrieval-checkpoint.ipynb | gpl-2.0 | import graphlab
"""
Explanation: Document retrieval from wikipedia data
Fire up GraphLab Create
End of explanation
"""
people = graphlab.SFrame('people_wiki.gl/')
"""
Explanation: Load some text data - from wikipedia, pages on people
End of explanation
"""
people.head()
len(people)
"""
Explanation: Data contain... |
mdietrichstein/vgg16-transfer-learning | vgg16_transfer_learning.ipynb | bsd-2-clause | #reset python environment
%reset -f
import time
default_device = '/gpu:0'
# default_device = '/cpu:0'
num_hidden_neurons = 256
vgg_mean = [103.939, 116.779, 123.68]
classes = [l.strip() for l in open('synset.txt').readlines()]
training_dataset_dir = './datasets/dogs-vs-cats-redux-kernels-edition/train/'
test_datas... |
anilcs13m/MachineLearning_Mastering | songrecommender/.ipynb_checkpoints/Song recommender-checkpoint.ipynb | gpl-2.0 | import graphlab
"""
Explanation: Building a song recommender
Fire up GraphLab Create
End of explanation
"""
song_data = graphlab.SFrame('song_data.gl/')
"""
Explanation: Load music data
End of explanation
"""
song_data.head(5)
"""
Explanation: Explore data
Music data shows how many times a user listened to a son... |
GetsDrawn/getsdrawndotcom | GetsDrawnDotCom.ipynb | mit | import os
import requests
from bs4 import BeautifulSoup
import re
import json
import time
import praw
import dominate
from dominate.tags import *
from time import gmtime, strftime
#import nose
#import unittest
import numpy as np
import pandas as pd
from pandas import *
from PIL import Image
from pprint import pprint
... |
SimonBiggs/planningbook | Testing on a patient beam.ipynb | agpl-3.0 | from glob2 import glob
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from rtplanchecking.pull_mephisto import *
from rtplanchecking.scatter_factors import *
from scipy.interpolate import interp1d
"""
Explanation: An example of basic plan checking using mephisto data file... |
opengeostat/pygslib | doc/source/Ipython_templates/bicalib_raw.ipynb | mit | #general imports
import matplotlib.pyplot as plt
import pygslib
import numpy as np
import pandas as pd
#make the plots inline
%matplotlib inline
"""
Explanation: PyGSLIB
Bicalib
The GSLIb equivalent parameter file is
```
Parameters for BICALIB
****
START OF PARAMETERS:
... |
antoniomezzacapo/qiskit-tutorial | community/algorithms/bernstein_vazirani.ipynb | apache-2.0 | #initialization
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
# importing Qiskit
from qiskit import IBMQ, Aer
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister
from qiskit import available_backends, execute, register, get_backend
from qiskit.wrapper.jupyter import *
# impor... |
neeasthana/ML-SQL | Clustering/Iris/Iris.ipynb | gpl-3.0 | #Libraries and Imports
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn import preprocessing
"""
Explanation: Iris dataset (Clustering)
Authors
Written by: Neeraj Asthana (under Professor Robert Brunner)
University of Illinois ... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/introduction_to_tensorflow/labs/feat.cols_tf.data.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import tensorflow as tf
from tensorflow import feature_column
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
print("TensorFlow version: ",tf.version.VERSION)
"""... |
jorisvandenbossche/2015-EuroScipy-pandas-tutorial | 03 - Indexing and selecting data.ipynb | bsd-2-clause | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
try:
import seaborn
except ImportError:
pass
# redefining the example objects
# series
population = pd.Series({'Germany': 81.3, 'Belgium': 11.3, 'France': 64.3,
'United Kingdom': 64.9, 'Netherla... |
NYUDataBootcamp/Projects | MBA_S16/Gammal-MLB_Injuries.ipynb | mit | '''Data were imported from referenced sources and stored locally'''
import sys # system module
import pandas as pd # data package
import matplotlib.pyplot as plt # graphics module
import datetime as dt # date and time module
import numpy as np ... |
ecell/ecell4-notebooks | en/tutorials/tutorial01.ipynb | gpl-2.0 | %matplotlib inline
from ecell4.prelude import *
"""
Explanation: 1. Brief Tour of E-Cell4 Simulations
First of all, you have to load the E-Cell4 library:
End of explanation
"""
with reaction_rules():
A + B > C | 0.01 # equivalent to create_binding_reaction_rule
C > A + B | 0.3 # equivalent to create_unbi... |
phoebe-project/phoebe2-docs | 2.0/tutorials/vgamma.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.0,<2.1"
%matplotlib inline
"""
Explanation: Systemic Velocity
Setup
Let's first make sure we have the latest version of PHOEBE 2.0 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
"""
im... |
GitYiheng/reinforcement_learning_test | test05_machine_learning/Code snippets.ipynb | mit | import pandas as pd
from sklearn import tree
X = [[0, 0], [1, 2]]
y = [0, 1]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, y)
clf.predict([[2., 2.]])
clf.predict_proba([[2. , 2.]])
clf.predict([[0.4, 1.2]])
clf.predict_proba([[0.4, 1.2]])
clf.predict_proba([[0, 0.2]])
"""
Explanation: Decision Tree
CAR... |
rigetticomputing/grove | examples/DeutschJosza.ipynb | apache-2.0 | from itertools import product
from mock import patch
from grove.deutsch_jozsa.deutsch_jozsa import DeutschJosza
"""
Explanation: This notebook is a brief sketch of how to use the Deutsch-Jozsa algorithm.
We start by declaring all necessary imports.
End of explanation
"""
bit_value = '0'
bit = ("0", "1")
constant_b... |
WilliamHPNielsen/broadbean | docs/Filter compensation.ipynb | mit | %matplotlib notebook
import matplotlib.pyplot as plt
import numpy as np
import sys
from broadbean.ripasso import applyRCFilter, applyInverseRCFilter, applyCustomTransferFunction
def squarewave(npts, periods=5):
periods = int(periods)
array = np.zeros(npts)
for n in range(periods):
array[int(n*npt... |
rnwatanabe/projectPR | ExampleNotebooks/AntidromicStimulationofMNandRC.ipynb | gpl-3.0 | import sys
sys.path.insert(0, '..')
import time
import matplotlib.pyplot as plt
%matplotlib notebook
plt.rcParams['figure.figsize']= 7,7
import numpy as np
from Configuration import Configuration
from MotorUnitPool import MotorUnitPool
from InterneuronPool import InterneuronPool
from SynapsesFactory import SynapsesFa... |
KJE2001/seminars | 03_planck_distribution.ipynb | mit | from scipy.constants import *
print("The Planck constant h:", h)
"""
Explanation: <figure>
<IMG SRC="gfx/Logo_norsk_pos.png" WIDTH=100 ALIGN="right">
</figure>
The Planck distribution
Roberto Di Remigio, Luca Frediani
In this set of exercises we will work with the Planck distribution. The first exercise is numerica... |
ES-DOC/esdoc-jupyterhub | notebooks/noaa-gfdl/cmip6/models/sandbox-3/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'sandbox-3', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: NOAA-GFDL
Source ID: SANDBOX-3
Topic: Seaice
Sub-Topics: Dynamics, Thermodyna... |
dit/dit | examples/PID.ipynb | bsd-3-clause | import dit
from dit.pid.helpers import compare_measures
from dit.pid.distributions import bivariates, trivariates
dit.ditParams['print.exact'] = dit.ditParams['repr.print'] = True
dit.ditParams['text.font'] = 'linechar'
"""
Explanation: Partial Information Decomposition
The partial information decomposition is a metho... |
nicoguaro/FEM_resources | Analytic FEM.ipynb | mit | from sympy import *
init_session()
def const_model(E, nu, const="plane_stress"):
if const == "plane_stress":
fac = E/(1 - nu**2)
C = fac*Matrix([
[1, nu, 0],
[nu, 1, 0],
[0, 0, (1 - nu)/2]])
elif const == "plane_strain":
fac = E*(1 - nu)/((1 - 2*nu)*(... |
ES-DOC/esdoc-jupyterhub | notebooks/csir-csiro/cmip6/models/sandbox-3/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csir-csiro', 'sandbox-3', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: CSIR-CSIRO
Source ID: SANDBOX-3
Topic: Landice
Sub-Topics: Glaciers, Ice. ... |
michrawson/nyu_ml_lectures | notebooks/03.4 Methods - Text Feature Extraction.ipynb | cc0-1.0 | X = ["Some say the world will end in fire,",
"Some say in ice."]
len(X)
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer()
vectorizer.fit(X)
vectorizer.vocabulary_
X_bag_of_words = vectorizer.transform(X)
X_bag_of_words.shape
X_bag_of_words
X_bag_of_words.toarray()
... |
dineshpackt/Fast-Data-Processing-with-Spark-2 | extras/010-Linear-Regression.ipynb | mit | from pyspark.context import SparkContext
print "Running Spark Version %s" % (sc.version)
from pyspark.conf import SparkConf
conf = SparkConf()
print conf.toDebugString()
import datetime
from pytz import timezone
print "Last run @%s" % (datetime.datetime.now(timezone('US/Pacific')))
df_cars = sqlContext.read.load('ca... |
dbkinghorn/blog-jupyter-notebooks | ML-LR-part2.ipynb | gpl-3.0 | import pandas as pd # data handeling
import numpy as np # numeriacal computing
import matplotlib.pyplot as plt # plotting core
import seaborn as sns # higher level plotting tools
%matplotlib inline
pd.set_option('display.float_format', lambda x: '%.2f' % x)
pd.set_option('display.max_columns', 21)
pd.set_option(... |
UWashington-Astro300/Astro300-W17 | StyleGuide.ipynb | mit | # Good - Full credit
mass_particle = 10.0
velocity_particle = 20.0
kinetic_energy = 0.5 * mass_particle * (velocity_particle ** 2)
print(kinetic_energy)
# Bad - Half credit at best
x = 10.0
y = 20.0
print(0.5*x*y**2)
# Really bad - no credit
print(0.5*10*20**2)
"""
Explanation: The Astro 300 Python programming... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_compute_mne_inverse_raw_in_label.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse_raw, read_inverse_operator
print(__doc__)
data_path = sample.data_path()
fname_inv = data_path + '/... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/chi2_fitting.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import statsmodels.api as sm
"""
Explanation: Least squares fitting of models to data
This is a quick introduction to statsmodels for physical scientists (e.g. physicists, astronomers) or engineers.
Why is this needed?
Because most of statsmodels was written by statisticians and ... |
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