repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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pmgbergen/porepy | tutorials/meshing_of_fractures.ipynb | gpl-3.0 | import numpy as np
import porepy as pp
# Point coordinates
point_coordinates = np.array([[0, 2, 1, 1],
[0, 0, 0, 1]])
# Point connections as a 2 x num_frac arary
point_indices = np.array([[0, 2],
[1, 3]])
# Define a rectangular domain in terms of range in the... |
seblabbe/MATH2010-Logiciels-mathematiques | ipynb-cours/16-04-12.ipynb | gpl-3.0 | from IPython.display import Image
Image('../NotesDeCours/images/bart_simpson.jpg')
range(100, 109)
for i in range(100, 109):
print(i, "I will not do this again")
for i in range(10):
print(i)
"""
Explanation: 13 Boucles for et while
End of explanation
"""
s = 0
"""
Explanation: 13.1 La boucle for
Syntaxe ... |
mbakker7/ttim | pumpingtest_benchmarks/9_test_of_texas_hill.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from ttim import *
"""
Explanation: Leaky Aquifer Test
This example is taken from AQTESOLV examples.
End of explanation
"""
Q = 24464.06 #constant discharge in m^3/d
b1 = 6.096 #overlying aquitard thickness in m
b2 = 15.24 #aqu... |
meteotest/hurray-py | docs/notebooks/tutorial.ipynb | bsd-3-clause | import os
import sys
import logging
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
sys.path.append(module_path)
import hurraypy as hurray
import numpy as np
hurray.__version__
"""
Explanation: Tutorial
End of explanation
"""
logger = logging.getLogger('hurraypy')
# console =... |
AssembleSoftware/IoTPy | examples/ExamplesOfMap.ipynb | bsd-3-clause | import os
import sys
sys.path.append("../")
from IoTPy.core.stream import Stream, run
from IoTPy.agent_types.op import map_element
from IoTPy.helper_functions.recent_values import recent_values
"""
Explanation: Examples of @map_e, @fmap_e and map_element
This notebook had examples of map from IoTPy/IoTPy/agent_types/... |
JAmarel/Phys202 | LaTeX/.ipynb_checkpoints/InteractEx05-checkpoint.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 SVG
from IPython.display import display
"""
Explanation: Interact Exercise 5
Imports
Put the standard imports for Matplotlib, Numpy and the IPython widgets ... |
jesford/cluster-lensing | fitting_a_model.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import seaborn; seaborn.set()
from clusterlensing import ClusterEnsemble
import emcee
import corner
% matplotlib inline
import matplotlib
matplotlib.rcParams["axes.labelsize"] = 20
matplotlib.rcParams["legend.fontsize"] = 12
"""
Explanation: Model fitting with clu... |
joshnsolomon/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... |
gtrichards/QuasarSelection | SpIESHighzCandidateSelection2.ipynb | mit | # Read in training data file
%matplotlib inline
from astropy.table import Table
import numpy as np
import matplotlib.pyplot as plt
data = Table.read('GTR-ADM-QSO-ir-testhighz_findbw_lup_2016_starclean.fits')
# X is in the format need for all of the sklearn tools, it just has the colors
X = np.vstack([ data['ug'], data... |
bjshaw/phys202-2015-work | days/day06/Matplotlib.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Visualization with Matplotlib
Learning Objectives: Learn how to make basic plots using Matplotlib's pylab API and how to use the Matplotlib documentation.
This notebook focuses only on the Matplotlib API, rather that the broader que... |
wbinventor/openmc | examples/jupyter/post-processing.ipynb | mit | %matplotlib inline
from IPython.display import Image
import numpy as np
import matplotlib.pyplot as plt
import openmc
"""
Explanation: This notebook demonstrates some basic post-processing tasks that can be performed with the Python API, such as plotting a 2D mesh tally and plotting neutron source sites from an eigen... |
maxis42/ML-DA-Coursera-Yandex-MIPT | 3 Unsupervised learning/Homework/1 banners placing/Размещение баннеров.ipynb | mit | import numpy as np
import pandas as pd
from sklearn.cluster import MeanShift
"""
Explanation: Размещение баннеров
Представим, что международное круизное агентство Carnival Cruise Line решило себя разрекламировать с помощью баннеров и обратилось для этого к вам. Чтобы протестировать, велика ли от таких баннеров польза,... |
dwhswenson/openpathsampling | examples/misc/custom_strategy_repex_shoot_repex.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import matplotlib
import openpathsampling as paths
import numpy as np
import openpathsampling.visualize as ops_vis
from IPython.display import SVG
matplotlib.rcParams.update({'font.size': 16,
'xtick.labelsize': 12,
... |
pagutierrez/tutorial-sklearn | notebooks-spanish/04-entrenando_y_generalizando.ipynb | cc0-1.0 | from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
iris = load_iris()
X, y = iris.data, iris.target
classifier = KNeighborsClassifier()
"""
Explanation: Entrenamiento y test
Para evaluar que tal generalizan nuestros modelos supervisados, podemos dividir los datos en un conjunto... |
abhi1509/deep-learning | weight-initialization/weight_initialization.ipynb | mit | %matplotlib inline
import tensorflow as tf
import helper
from tensorflow.examples.tutorials.mnist import input_data
print('Getting MNIST Dataset...')
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
print('Data Extracted.')
"""
Explanation: Weight Initialization
In this lesson, you'll learn how to fin... |
vadim-ivlev/STUDY | coding/Cracking the code interview.ipynb | mit | # from IPython.core.debugger import set_trace
def has_unique_characters(str=''):
'''
Checks if a string has all unique characters
>>> has_unique_characters('abc')
True
>>> has_unique_characters()
True
>>> has_unique_characters('abac')
False
'''
# 1/ time: 1.74 µs ± 14.5 ns ... |
ES-DOC/esdoc-jupyterhub | notebooks/awi/cmip6/models/sandbox-3/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'awi', 'sandbox-3', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: AWI
Source ID: SANDBOX-3
Topic: Ocean
Sub-Topics: Timestepping Framework, Advection, ... |
CalPolyPat/Python-Workshop | Python Workshop/Scipy.ipynb | mit | import scipy.optimize as opt
import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate as inter
%matplotlib inline
#Let's find out what the scipy.optimize.curve_fit function does and how to use it.
opt.curve_fit?
x = np.linspace(0,10, 200)
y = 34*np.sin(x)*np.random.rand(200)
plt.plot(x,y)
func = ... |
QuantCrimAtLeeds/PredictCode | examples/Chicago/Simulate spatial location.ipynb | artistic-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
import geopandas as gpd
import os
import numpy as np
import pandas as pd
#filename = os.path.join("/media", "disk", "tl_2013_17031_edges", "tl_2013_17031_edges.shp")
filename = os.path.join("..", "..", "..", "..", "..", "Data", "tl_2013_17031_edges", "tl_2013_17031_ed... |
ece579/ece579_f17 | recitation4/problems/summary.ipynb | mit | raw.take(1)
import numpy as np
def parse_kdd(line):
split = line.split(",")
# we will keep just numeric and logical values
# discard any string values
symbolic_indexes = [1,2,3,41]
clean_split = [item for i,item in enumerate(split) if i not in symbolic_indexes]
return np.array([float(x) for x ... |
malminhas/udacity-deep-learning | 5_word2vec.ipynb | apache-2.0 | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import collections
import math
import numpy as np
import os
import random
import tensorflow as tf
import zipfile
from matplotlib import pylab
from six.moves import range
fro... |
pastas/pastas | examples/notebooks/01_basic_model.ipynb | mit | import matplotlib.pyplot as plt
import pandas as pd
import pastas as ps
ps.show_versions()
"""
Explanation: A Basic Model
In this example application it is shown how a simple time series model can be developed to simulate groundwater levels. The recharge (calculated as precipitation minus evaporation) is used as the... |
sdpython/pyquickhelper | _unittests/ut_helpgen/data/rst_notebooks/exemple_of_fix_menu.ipynb | mit | <div style="position:absolute; top:10px; right:5px; width:100px; height:90px; margin:10px;">
[Section 1](#Section-1) -- [Section 2](#Section-2) -- [This is the end](#This-is-the-end.)
</div>
"""
Explanation: Example of a notebook with a javascript menu
It is sometimes difficult to navigate through long notebooks. Re... |
nadvamir/deep-learning | tensorboard/Anna_KaRNNa_Summaries.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is base... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch1-Problem_1-05.ipynb | unlicense | %pylab notebook
%precision %.4g
from scipy import constants as c # we like to use some constants
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 1
Problem 1-5
End of explanation
"""
phi = 0.005 # Wb
"""
Explanation: Description
A ferromagnetic core is shown in Figure P1-2:
<img src="figs/Fi... |
bhattacharjee/courses | CourseraDeepLearningSpecialization/2.HyperparameterTrainingRegularizationAndOptimization/Week1/Exercises/Initialization.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import sklearn
import sklearn.datasets
from init_utils import sigmoid, relu, compute_loss, forward_propagation, backward_propagation
from init_utils import update_parameters, predict, load_dataset, plot_decision_boundary, predict_dec
%matplotlib inline
plt.rcParams['f... |
jgarciab/wwd2017 | class8/class8_dimensionality_reduction.ipynb | gpl-3.0 | ##Some code to run at the beginning of the file, to be able to show images in the notebook
##Don't worry about this cell
#Print the plots in this screen
%matplotlib inline
#Be able to plot images saved in the hard drive
from IPython.display import Image
#Make the notebook wider
from IPython.core.display import dis... |
naru-T/2017FOSS4GHOKKAIDO | FOSS4G_Hokkaido.ipynb | mit | from IPython.display import Image, display, HTML
%matplotlib inline
from pylab import *
import datetime
import math
import time
import ee
ee.Initialize()
"""
Explanation: Global-scale MODIS NDVI time series analysis (with interpolation)
A material for the presentation in FOSS4G-Hokkaido on 1st July 2017.
Copyright ... |
vtsatskin/Hot-Car-Data-Collection | IRIS Dataset.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
import os
from skimage import data
from skimage import io
from skimage.transform import resize
from skimage.color import rgb2gray
from scipy.misc import bytescale
%matplotlib inline
WINDOW_WIDTH = 18
WINDOW_HEIGHT = 26
sample_iris_frame = data.imread('external_data/i... |
mjones01/NEON-Data-Skills | code/Python/neonUtilities/neonUtilitiesPython.ipynb | agpl-3.0 | pip install rpy2
"""
Explanation: syncID: 5be9f80592394af3bc09cf8e469fef6e
title: "Using neonUtilities in Python"
description: "Use the neonUtilities R package in Python, via the rpy2 library."
dateCreated: 2018-5-10
authors: Claire K. Lunch
contributors:
estimatedTime: 20 minutes
packagesLibraries: rpy2
topics: data... |
cchuang2009/DyLabIPy | DiffEq/SIR.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from numpy import log
from scipy import integrate
from scipy.optimize import fsolve
import scipy.linalg as la
import scipy.integrate as spi
from IPython.html.widgets import interact, interactive, fixed
from IPython.html import widgets
#from matplot... |
helgako/cms-dqm | notebooks/correlations.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import numpy as np
import pandas as pd
import collections
import re
DATA_PATH = 'merged.pickle'
LABELS_PATH = './quality_2010/labels_v2.pickled'
import cPickle as pickle
with open(DATA_PATH, 'r') as f:
data = pickle.load(f)
with open(LA... |
Jackporter415/phys202-2015-work | assignments/assignment04/MatplotlibEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Matplotlib Exercise 2
Imports
End of explanation
"""
!head -n 30 open_exoplanet_catalogue.txt
"""
Explanation: Exoplanet properties
Over the past few decades, astronomers have discovered thousands of extrasolar planets. The follo... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/production_ml/labs/samples/core/kubeflow_tf_serving/kubeflow_tf_serving.ipynb | apache-2.0 | # Set your output and project. !!!Must Do before you can proceed!!!
project = 'Your-Gcp-Project-ID' #'Your-GCP-Project-ID'
model_name = 'model-name' # Model name matching TF_serve naming requirements
import time
ts = int(time.time())
model_version = ... |
kemerelab/NeuroHMM | StateClustering.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sys
sys.path.insert(0, 'helpers')
from efunctions import * # load my helper function(s) to save pdf figures, etc.
from hc3 import load_data, get_sessions
from hmmlearn import hmm # see https://github.com/ckemere/hmmlea... |
magenta/ddsp | ddsp/colab/tutorials/3_training.ipynb | apache-2.0 | # Copyright 2021 Google LLC. 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 applicable law or a... |
steinam/teacher | jup_notebooks/.ipynb_checkpoints/Subselects_11FI3-checkpoint.ipynb | mit | %load_ext sql
%sql mysql://steinam:steinam@localhost/versicherung_complete
"""
Explanation: Subselect / Unterabfragen)
Zur Durchführung einer Abfrage werden Informationen benötigt, die erst durch eine eigene Abfrage geholt werden müssen.
Sie können stehen
als Vertreter für einen Wert
als Vertreter für eine Liste
als... |
lknelson/text-analysis-2017 | 04-Dictionaries/00.1-DictionaryMethod_AdditionalExercises.ipynb | bsd-3-clause | import pandas as pd
import nltk
import matplotlib.pyplot as plt
#read in our data
df = ...
"""
Explanation: Additional Exercises for Dictionary Method
Ex.
Read in the childrens_lit.csv.bz2 file from the data folder.
Come up with a hypothesis on what you think the sentiment ratings is for children's literature.
Do ... |
franciscodominguezmateos/DeepLearningNanoDegree | sentiment-network/Sentiment_Classification_Projects.ipynb | mit | def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()... |
jbwhit/2017-05-PyCon-EDA-Tutorial | notebooks/2017-02-24-djm-redcard-eda.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%%time
df = pd.read_csv('../data/redcard/crowdstorm_disaggregated.csv.gz', compression='gzip')
"""
Explanation: Are soccer referees more likely to give red cards to dark skin toned players than light skin toned players?
End of... |
phoebe-project/phoebe2-docs | 2.2/tutorials/meshes.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Accessing and Plotting Meshes
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... |
chunweixu/Deep-Learning | image-classification/dlnd_image_classification.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifar10... |
GoogleCloudPlatform/ml-design-patterns | 04_hacking_training_loop/overfitting.ipynb | apache-2.0 | import os
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow import keras
from tensorflow import feature_column as fc
from tensorflow.keras import layers, models, Model
%matplotlib inline
df = pd.read_csv("./data/babyweight_train.csv")
"""
Explanation: Ove... |
rahulkgup/deep-learning-foundation | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
natematias/research_in_python | instrumental_variables_estimation/Instrumental-Variables Estimation.ipynb | mit | # THINGS TO IMPORT
# This is a baseline set of libraries I import by default if I'm rushed for time.
import codecs # load UTF-8 Content
import json # load JSON files
import pandas as pd # Pandas handles dataframes
import numpy as np # Numpy handles... |
tallakahath/pymatgen | examples/Basic functionality.ipynb | mit | import pymatgen as mg
"""
Explanation: Introduction
This notebook demostrates the core functionality of pymatgen, including the core objects representing Elements, Species, Lattices, and Structures.
By convention, we import pymatgen as mg.
End of explanation
"""
si = mg.Element("Si")
print("Atomic mass of Si is {}"... |
Abjad/intensive | day-2/1-rhythms-by-hand.ipynb | mit | %load_ext abjadext.ipython
import abjad
import random
durations = []
numerators = range(16)
denominators = [2**x for x in range(1, 5)]
for x in range(10):
numerator = random.choice(numerators)
denominator = random.choice(denominators)
duration_token = (numerator, denominator)
duration = abjad.Duration(... |
phoebe-project/phoebe2-docs | 2.3/tutorials/constraints.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_binary()
"""
Explanation: Constraints
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if r... |
probml/pyprobml | notebooks/misc/poly_regression_torch.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
np.random.seed(seed=1)
import math
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils import data
from IPython import display
!mkdir figures # for saving plots
import warnings
warnings.filterwarnings("ignore")
# For reproducin... |
LedaLima/incubator-spot | spot-oa/oa/flow/ipynb_templates/Advanced_Mode_master.ipynb | apache-2.0 | import datetime
import pandas as pd
import numpy as np
import linecache, bisect
import os
spath = os.getcwd()
path = spath.split("/")
date = path[len(path)-1]
"""
Explanation: Apache Spot's Ipython Advanced Mode
Netflows
This guide provides examples about how to request data, show data with some cool libraries like p... |
grlurton/orbf_data_validation | notebooks/reporting/Algorithm AEDES.ipynb | mit | import sys
sys.path.insert(0, '../../src/monitoring_algorithms/')
from aedes_algorithm import *
%matplotlib inline
store = pd.HDFStore('../../data/processed/orbf_benin.h5')
tarifs = store['tarifs']
data = store['data']
store.close()
data = data[data.period.apply(str).str[0:4] == '2016']
data = data[data.date < '2016... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session07/Day4/LetsGetNetworking.ipynb | mit | # install software on the backend, which is located at
# Google's Super Secret Sky Server in an alternate universe.
# The backend is called a 'hosted runtime' if it is on their server.
# A local runtime would start a colab notebook on your machine locally.
# Think of google colab as a Google Docs version of Jupyter N... |
hannorein/variations | Figure6.ipynb | gpl-3.0 | import rebound
import numpy as np
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from timeit import Timer
print(rebound.__build__)
"""
Explanation: Figure 6
This notebook recreates Figure 6 in Rein & Tamayo 2016. The figure illustrates the scaling of second o... |
prodicus/dabble | nltk/.ipynb_checkpoints/text_classification-checkpoint.ipynb | mit | movie_reviews.categories()
"""
Explanation: There are a thousand movie reviews for both
positive and
negetive
reviews
End of explanation
"""
documents = [(list(movie_reviews.words(fileid)), category)
for category in movie_reviews.categories()
for fileid in movie_reviews.fileids(category)
... |
skkandrach/foundations-homework | Homework6_Soma.ipynb | mit |
#api KEY = c9d64e80aa02ca113562a075e57256d7
https://api.forecast.io/forecast/c9d64e80aa02ca113562a075e57256d7/10.4806,66.9036
import requests
response = requests.get("https://api.forecast.io/forecast/c9d64e80aa02ca113562a075e57256d7/10.4806,66.9036")
forecast = response.json()
print(forecast.keys())
print(forecast... |
deflaux/linkage-disequilibrium | datalab/Tag_variant_identification.ipynb | apache-2.0 | import gcp.bigquery as bq
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
"""
Explanation: <!-- Copyright 2015 Google Inc. 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. -->
<... |
GSimas/EEL7045 | Problemas - Cap1.ipynb | mit | print("1.1")
carga_eletron = 1.602*10**(-19) #coulombs
n_eletrons = 1/carga_eletron
a = 6.482*10**17
b = 1.24*10**18
c = 2.46*10**19
d = 1.628*10**20
carga_a = -a / n_eletrons
carga_b = -b / n_eletrons
carga_c = -c / n_eletrons
carga_d = -d / n_eletrons
print("Carga (a):", carga_a, "C")
print("Carga (b):", carga_b, "C"... |
awhite40/pymks | notebooks/MD.ipynb | mit | %matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
from pymks_share import DataManager
manager = DataManager('pymks.me.gatech.edu')
manager.list_datasets()
X = manager.fetch_data('Molecular Dynamics')
"""
Explanation: Authors
Alexander M. Lohse, 2nd year PhD S... |
dbouquin/AstroHackWeek2015 | inference/straightline.ipynb | gpl-2.0 | %load_ext autoreload
%autoreload 2
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (6.0, 6.0)
plt.rcParams['savefig.dpi'] = 100
from straightline_utils import *
"""
Explanation: Bayesian Inference II: Fitting a Straight Li... |
ES-DOC/esdoc-jupyterhub | notebooks/cams/cmip6/models/cams-csm1-0/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cams', 'cams-csm1-0', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: CAMS
Source ID: CAMS-CSM1-0
Topic: Ocean
Sub-Topics: Timestepping Framework, Advec... |
ES-DOC/esdoc-jupyterhub | notebooks/awi/cmip6/models/sandbox-1/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'awi', 'sandbox-1', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: AWI
Source ID: SANDBOX-1
Topic: Landice
Sub-Topics: Glaciers, Ice.
Properties: 3... |
Cyb3rWard0g/ThreatHunter-Playbook | docs/notebooks/windows/06_credential_access/WIN-180815210510.ipynb | gpl-3.0 | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: Active Directory Replication From Non-Domain-Controller Accounts
Metadata
| | |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2018/08/15 |
| modification date | 2020/... |
roatienza/Deep-Learning-Experiments | versions/2020/MLP/code/tf.keras/mlp-mnist.ipynb | mit | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.utils import to_categorical, plot_model
from tensorflow.keras.... |
dpshelio/2015-EuroScipy-pandas-tutorial | solved - 04 - Groupby operations.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
pd.options.display.max_rows = 10
"""
Explanation: Groupby operations
Some imports:
End of explanation
"""
df = pd.DataFrame({'key':['A','B','C','A','B','C','A','B','C'],
... |
Jackporter415/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... |
tien-le/uranus | tools_numpy.ipynb | mit | from __future__ import division, print_function, unicode_literals
"""
Explanation: Tools - NumPy
NumPy is the fundamental library for scientific computing with Python. NumPy is centered around a powerful N-dimensional array object, and it also contains useful linear algebra, Fourier transform, and random number functi... |
espressomd/espresso | doc/tutorials/constant_pH/constant_pH.ipynb | gpl-3.0 | # ionization degree alpha calculated from the Henderson-Hasselbalch equation for an ideal system
def ideal_alpha(pH, pK):
return 1. / (1 + 10**(pK - pH))
"""
Explanation: The constant-pH ensemble method for acid-base reactions
Expected prior knowledge
To work with this tutorial, you should be familiar with the fol... |
yongtang/tensorflow | tensorflow/lite/g3doc/tutorials/model_maker_object_detection.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... |
planet-os/notebooks | api-examples/swan-oahu-api.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import dateutil.parser
import datetime
from urllib.request import urlopen, Request
import simplejson as json
"""
Explanation: Plotting Data from the Oahu Simulating Waves Nearshore (SWAN) Regional Wave Model
Note: this notebook requires python3.
Thi... |
rishuatgithub/MLPy | torch/PYTORCH_NOTEBOOKS/01-PyTorch-Basics/01-Tensor-Operations.ipynb | apache-2.0 | import torch
import numpy as np
"""
Explanation: <img src="../Pierian-Data-Logo.PNG">
<br>
<strong><center>Copyright 2019. Created by Jose Marcial Portilla.</center></strong>
Tensor Operations
This section covers:
* Indexing and slicing
* Reshaping tensors (tensor views)
* Tensor arithmetic and basic operations
* Dot ... |
sheikhomar/ml | mixtures-of-gaussians.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Mixture of Gaussians
End of explanation
"""
X = np.linspace(-6, 12, 100)
def gaussian(X, mu, sigma):
a = 1 / (sigma * np.sqrt(2 * np.pi))
return a * np.exp(-np.power(X - mu, 2.) / (2 * sigma * sigma))
fig, ax = plt.sub... |
walchko/netscan2 | docs/netscan.ipynb | mit | from __future__ import print_function
from netscan.lib import WhoIs, GetHostName, MacLookup, Commands
import pprint as pp
"""
Explanation: Netscan
Here is a sample of some the capabilities of the netscan library.
End of explanation
"""
print(GetHostName('192.168.1.13').name)
print(GetHostName('127.0.0.1').name)
"""... |
rule-lab/ligfit | notebook/.ipynb_checkpoints/Derivation of Binding Equation-checkpoint.ipynb | gpl-3.0 | #Kd-prime and Kd-doubleprime as expressions of Kd and alpha (cooperativity)
#as well as their concentration ratios
kd, alpha, p, l, pl, plp = symbols('K_{D} alpha [P] [L] [PL] [PLP]')
kd_p = Eq(kd / 2, p * l / pl)
kd_p
kd_pp = Eq(2 * kd / alpha, p * pl / plp)
kd_pp
"""
Explanation: The Binding Model
The binding mod... |
squishbug/DataScienceProgramming | DataScienceProgramming/02-Python/WordFrequencies.ipynb | cc0-1.0 | from urllib.request import urlopen
# from urllib.request import *
# in order to get the help text, we should import the whole subpackage.
import urllib.request
help(urllib.request)
help(urlopen)
"""
Explanation: Word Frequencies
Can we identify different types of text documents based on the frequency of their words... |
anilcs13m/MachineLearning_Mastering | sentiment analysis/Analyzing product sentiment.ipynb | gpl-2.0 | import graphlab
"""
Explanation: Predicting sentiment from product reviews
Load Graph lab
End of explanation
"""
products = graphlab.SFrame('amazon_baby.gl/')
"""
Explanation: Read some product review data
Loading reviews for a set of baby products.
End of explanation
"""
products.head(5)
"""
Explanation: Let's ... |
noppanit/titanic-random-forest | Titanic Learning from Disaster.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
"""
Explanation: Titanic Learning from Disaster
We would like to predict survival on the Titanic.
https://www.kaggle.com/c/titanic
End of explanation
"""
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
train.shape
train.head()
train.Age.his... |
tanghaibao/goatools | notebooks/goea_nbt3102_group_results.ipynb | bsd-2-clause | %run goea_nbt3102_fncs.ipynb
goeaobj = get_goeaobj_nbt3102('fdr_bh')
# Read Nature data from Excel file (~400 study genes)
studygeneid2symbol = read_data_nbt3102()
# Run Gene Ontology Enrichment Analysis using Benjamini/Hochberg FDR correction
geneids_study = studygeneid2symbol.keys()
goea_results_all = goeaobj.run_stu... |
karlstroetmann/Formal-Languages | Python/Equivalence.ipynb | gpl-2.0 | %run Regexp-2-NFA.ipynb
"""
Explanation: Checking the Equivalence of Regular Expressions
In order to check whether two regular expressions $r_1$ and $r_2$ are equivalent, perform the
following steps:
- convert the regular expressions $r_1$ and $r_2$ into non-deterministic FSMs
$F_1$ and $F_2$ such that $L(r_1) = L(... |
bobflagg/deepER | notebook/Twitter-NER.ipynb | apache-2.0 | def read_data(file_path):
tokens = []
tags = []
tweet_tokens = []
tweet_tags = []
for line in open(file_path, encoding='utf-8'):
line = line.strip()
if not line:
if tweet_tokens:
tokens.append(tweet_tokens)
tags.append(tweet_tags)
... |
agvergara/DatatonURJC | Day4/Lab_4.ipynb | gpl-3.0 | #read data using pandas
import pandas as pd
import numpy as np
boston_df = pd.read_csv('boston.csv')
#verify whether ther exisits NaN
print np.sum(boston_df.isnull())
boston_df
boston_df.describe()
#get names
x_var_names = list(boston_df)[:-1]
print x_var_names
y_var_names = list(boston_df)[-1]
print y_var_nam... |
dianafprieto/SS_2017 | .ipynb_checkpoints/05_NB_VTKPython_Scalar-checkpoint.ipynb | mit | %gui qt
import vtk
from vtkviewer import SimpleVtkViewer
#help(vtk.vtkRectilinearGridReader())
"""
Explanation: <img src="imgs/header.png">
Visualization techniques for scalar fields in VTK + Python
Reminder: The VTK pipeline
<img src="imgs/vtk_pipeline.png", align=left>
$~$
Visualizing data within a recilinear grid
T... |
google/tf-quant-finance | tf_quant_finance/examples/jupyter_notebooks/Introduction_to_TensorFlow_Part_3_-_Advanced_Tensor_Manipulation.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, sof... |
danecollins/pyawr | awr_nb/measurement_to_dataframe.ipynb | mit | import os
import pandas as pd
from pyawr import connect, AwrMeas, as_list, meas_from_graph, open_example
# connect to AWRDE
awrde, awrc = connect() # awrde is the pointed to the awrde COM object while awrc is the AWR defined constants
# Open the lumped filter example
open_example(awrde, 'LPF_lumped.emp')
# Let's se... |
parrt/msan692 | notes/dict.ipynb | mit | p = (32,9,9732)
print(type(p))
print(p)
"""
Explanation: Associations and dictionaries
In mathematics, we group numbers or other elements in parentheses, thus, forming a tuple. For example, to represent a three-dimensional Euclidean point, we'd use 3-tuple notation like (32,9,9732). Python uses the same mathematical ... |
nproctor/phys202-2015-work | assignments/assignment06/ProjectEuler17.ipynb | mit | import numpy as np
numbers = {0: "", 1:"one", 2:"two", 3:"three", 4:"four", 5:"five", 6:"six", 7:"seven", 8:"eight", 9:"nine", 10:"ten", 11:"eleven",
12:"twelve", 13:"thirteen", 14:"fourteen", 15:"fifteen", 16:"sixteen", 17:"seventeen", 18:"eighteen", 19:"nineteen",
20:"twenty", 30:"thirty", 40:"fo... |
InsightLab/data-science-cookbook | 2019/02-python-bibliotecas-manipulacao-dados/pandas_basico.ipynb | mit | import pandas as pd
import numpy as np
"""
Explanation: Pandas
Importando o Pandas e o NumPy
End of explanation
"""
""" Apenas a partir dos valores """
obj = pd.Series([4, 7, -5, 3])
obj
obj.values
obj.index
""" A partir dos valores e dos índices """
obj2 = pd.Series([4, 7, -5, 3], index=['d','b','a','c'])
obj2... |
cliburn/sta-663-2017 | worksheet/04_Working_With_Numbers_Solutions.ipynb | mit | def f(n):
x = np.ones((n, n)).astype('int')
for i in range(1, (n+1)//2):
x[i:-i, i:-i] += 1
return x
f(13)
"""
Explanation: 1. Write a function to generate square arrays of size $n \times n$ that increment by one in layers. For example, for n=11, we have
array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
... |
kit-cel/wt | nt2_ce2/vorlesung/ch_4_diversity/ber_fading.ipynb | gpl-2.0 | # importing
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import matplotlib
# showing figures inline
%matplotlib inline
# plotting options
font = {'size' : 30}
plt.rc('font', **font)
#plt.rc('text', usetex=True)
matplotlib.rc('figure', figsize=(30, 12) )
"""
Explanation: Content and... |
Mashimo/datascience | 02-Classification/TensorFlow regularisation .ipynb | apache-2.0 | import tensorflow as tf
print(tf.__version__)
"""
Explanation: Regularisation with Tensorflow
Previously, we have seen how to build in Tensorflow an end-to-end workflow
for training, evaluating and predicting from
deep learning models. Now we are
going to look at adding some important features.
We will look
at how we ... |
djfan/yelp-challenge | data_urbana_champaign/UC01_Data_PreProcess_Uurbana_Champaign.ipynb | mit | import seaborn
import pandas as pd
import pylab as pl
import yaml
%pylab inline
df = pd.read_pickle("../yelp-challenge/data_urbana_champaign/business_urbana_champaign.p")
df.reset_index(drop=True, inplace=True)
print df.shape
print df.columns.values
len(df.business_id.unique())
"""
Explanation: To-Do:
* category [lis... |
ES-DOC/esdoc-jupyterhub | notebooks/uhh/cmip6/models/sandbox-3/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'uhh', 'sandbox-3', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: UHH
Source ID: SANDBOX-3
Topic: Atmoschem
Sub-Topics: Transport, Emissions Co... |
turbomanage/training-data-analyst | blogs/bqml/taxifare_bqml.ipynb | apache-2.0 | %pip install google-cloud-bigquery seaborn
"""
Explanation: <h1> Structured data prediction using BigQuery ML </h1>
This notebook illustrates:
<ol>
<li> Training Machine Learning models using BQML
<li> Predicting with model
<li> Using spatial queries in BigQuery
<li> Building a linear regression model with feature cr... |
ocelot-collab/ocelot | demos/ipython_tutorials/pfs_4_synchrotron_radiation_visualization.ipynb | gpl-3.0 | import numpy as np
import logging
from ocelot import *
from ocelot.rad import *
from ocelot.optics.wave import dfl_waistscan, screen2dfl, RadiationField
from ocelot.gui.dfl_plot import plot_dfl, plot_dfl_waistscan
from ocelot import ocelog
ocelog.setLevel(logging.ERROR) #suppress logger output
# Activate interactive ... |
eng-tools/sfsimodels | examples/saving_and_loading_objects.ipynb | mit | import numpy as np
import json
import sfsimodels as sm
"""
Explanation: Saving and loading objects
End of explanation
"""
# Set the void ratio and specific gravity
sl = sm.Soil()
sl.id = 1 # Must set an id before saving
sl.e_curr = 0.7
assert sl.unit_dry_weight is None
sl.specific_gravity = 2.95
assert np.isclose(... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch6-Problem_6-01.ipynb | unlicense | %pylab notebook
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 6
Problem 6-1
End of explanation
"""
fse = 50 # [Hz]
p = 6
s = 3.5/100
"""
Explanation: Description
A 220-V three-phase six-pole 50-Hz induction motor is running at a slip of 3.5 percent.
End of explanation
"""
n_s... |
UWSEDS/LectureNotes | PreFall2018/Deep-and-Shallow-Copies.ipynb | bsd-2-clause | # BUGGY Code
# Create a list
list1 = [1, 2, 3]
# Make a copy
list2 = list1
# Update the copy
list2.append(4)
# BUG: list1 has changed too!
print(list1, list2)
"""
Explanation: Deep and Shallow Copies
This short lecture addresses a common source of bugs in python (and other languages). This is illustrated below.
End o... |
piscataway/datascience | lab/02 Regression-v1.ipynb | mit | from numpy import *
# y = mx + b
# m is slope, b is y-intercept
# here we are calculating the sum of squared error by using the equation which we have seen.
def compute_error_for_line_given_points(b, m, points):
totalError = 0
for i in range(0, len(points)):
x = points[i, 0]
y = points[i, 1]
... |
henrysky/astroNN | demo_tutorial/NN_uncertainty_analysis/Uncertainty_Demo_MNIST.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format='retina'
from tensorflow.keras.datasets import mnist
from tensorflow.keras import utils
import numpy as np
import pylab as plt
from astroNN.models import MNIST_BCNN
"""
Explanation: Classification Uncertainty Analysis in Bayesian Deep Learning with Dropout Varia... |
sanjayankur31/nest-simulator | doc/userdoc/model_details/siegert_neuron_integration.ipynb | gpl-2.0 | import numpy as np
from scipy.special import erf, erfcx
import matplotlib.pyplot as plt
"""
Explanation: Siegert neuron integration
Alexander van Meegen, 2020-12-03
This notebook describes how NEST handles the numerical integration of the 'Siegert' function.
For an alternative approach, which was implemented in NEST b... |
heprom/pymicro | examples/SampleDataUserGuide/3_SampleData_Image_groups.ipynb | mit | from pymicro.core.samples import SampleData as SD
from config import PYMICRO_EXAMPLES_DATA_DIR # import file directory path
import os
dataset_file = os.path.join(PYMICRO_EXAMPLES_DATA_DIR, 'test_sampledata_ref') # test dataset file path
data = SD(filename=dataset_file)
"""
Explanation: 4 - Creating, getting and visua... |
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