repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
samstav/scipy_2015_sklearn_tutorial | notebooks/04.3 Analyzing Model Capacity.ipynb | cc0-1.0 | import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import Pipeline
from sklearn.svm import SVR
from sklearn import cross_validation
np.random.seed(0)
n_samples = 200
kernels = ['linear', 'poly', 'rbf']
true_fun = lambda X: X ** 3
X = np.sort(5 * (np.random.rand(n_samples) - .5))
y = true_fun(X)... |
tdhoang0412/python-class | Monday_2017-04-24/06_Homework_1.ipynb | gpl-3.0 | # do not forget to put the following '%matplotlib inline'
# within Jupyter notebooks. If you forget it, external
# windows are opened for the plot but we would like to
# have the plots integrated in the notebooks
# The line only needs to be give ONCE per notebook!
%matplotlib inline
# Verification of scipys Bessel func... |
mcocdawc/chemcoord | Tutorial/Cartesian.ipynb | lgpl-3.0 | import chemcoord as cc
from chemcoord.xyz_functions import get_rotation_matrix
import numpy as np
import time
water = cc.Cartesian.read_xyz('water_dimer.xyz', start_index=1)
small = cc.Cartesian.read_xyz('MIL53_small.xyz', start_index=1)
middle = cc.Cartesian.read_xyz('MIL53_middle.xyz', start_index=1)
"""
Explanatio... |
weleen/mxnet | example/notebooks/moved-from-mxnet/class_active_maps.ipynb | apache-2.0 | # -*- coding: UTF-8 –*-
import matplotlib.pyplot as plt
%matplotlib inline
from IPython import display
import os
ROOT_DIR = '.'
import sys
sys.path.insert(0, os.path.join(ROOT_DIR, 'lib'))
import cv2
import numpy as np
import mxnet as mx
import matplotlib.pyplot as plt
"""
Explanation: This demo shows the method pro... |
BYUFLOWLab/BYUFLOWLab.github.io | onboarding/PythonPrimer.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Why Python
For this comparison I'm going to assume most of you are primarily Matlab users. Matlab is great, especially in a university environment. It's an easy to use, interpreted, high-level language with automatic memory manag... |
wutienyang/facebook_fanpage_analysis | Facebook粉絲頁分析三部曲-分析和輸出報表篇.ipynb | mit | import math
import datetime
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
# 讀取粉絲頁posts
page_id = "appledaily.tw"
path = 'post/'+page_id+'_post.csv'
df = pd.read_csv(path, encoding = 'utf8')
"""
Explanation: 如何分析Facebook粉絲頁資料並匯出excel報表？
接下來會以前面爬下來的蘋果日報粉絲頁當作本文範例
使用套件
p... |
ES-DOC/esdoc-jupyterhub | notebooks/cas/cmip6/models/fgoals-f3-l/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cas', 'fgoals-f3-l', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: CAS
Source ID: FGOALS-F3-L
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Ra... |
quasars100/Resonance_testing_scripts | python_tutorials/Checkpoints.ipynb | gpl-3.0 | import rebound
rebound.add(m=1.)
rebound.add(m=1e-6, a=1.)
rebound.add(a=2.)
rebound.save("checkpoint.bin")
"""
Explanation: Checkpoints
You can easily save and load particle positions to a binary file with REBOUND. The binary file includes the masses, positions and velocities of all particles, as well as the current ... |
microsoft/dowhy | docs/source/example_notebooks/dowhy_mediation_analysis.ipynb | mit | import numpy as np
import pandas as pd
from dowhy import CausalModel
import dowhy.datasets
# Warnings and logging
import warnings
warnings.filterwarnings('ignore')
"""
Explanation: Mediation analysis with DoWhy: Direct and Indirect Effects
End of explanation
"""
# Creating a dataset with a single confounder an... |
sdpython/ensae_teaching_cs | _doc/notebooks/exams/interro_rapide_20_minutes_2014_12.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 1A.e - Correction de l'interrogation écrite du 14 novembre 2014
dictionnaires
End of explanation
"""
def make_squares(n):
squares = [i**2 for i in range(n)]
"""
Explanation: Enoncé 1
Q1
Le code suivant produit une erreur. Laquelle ... |
srikarpv/CV_PA1 | PA1-Q3-1.ipynb | mit | import math
from scipy import ndimage
from PIL import Image
from numpy import *
from matplotlib import pyplot as plt
from pylab import *
import cv2
import time
# input image
# x vertex of corner
# y vertex of corner
def plott (I,x,y):
plt.figure()
plt.imshow(I,cmap = cm.gray) # plots the image ... |
chengsoonong/didbits | Estimation/SVM_rbf_gamma.ipynb | apache-2.0 | import itertools
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn import datasets
from sklearn.model_selection import train_test_split
%matplotlib inline
"""
Explanation: Picking the gamma value for a SVM with a Radial Basis Function kernel
End of explanation
"""
iris = datase... |
UoS-SNe/LSST_tools | opsimout/notebooks/OpSim_basics_notebook.ipynb | gpl-3.0 | from __future__ import print_function ## Force python3-like printing
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import time
import sqlite3
from sqlalchemy import create_engine
opsimdbpath = os.environ.get('OPSIMDBPATH')
print(opsimdbpath)
engine = create_eng... |
bpgc-cte/python2017 | Week 4/Lecture_9_Inheritance_Overloading_Overidding.ipynb | mit | class Student():
def __init__(self, name, id_no=None):
self.name = name
self.id_no = id_no if id_no is not None else "Not Allocated"
def __str__(self):
s = self.name
return s + "\n" + "Name : " + self.name + " , ID : " + self.id_no
def __add__(self, a):
return s... |
dmittov/misc | Kinder Surprise.ipynb | apache-2.0 | def expect_value(k, p):
steps = [k / p / (k - i) for i in range(k)]
return sum(steps)
k = 10
ps = [1., .5, .33, .25, .2, .1]
count = np.vectorize(lambda p: expect_value(k, p), otypes=[np.float])(ps)
plt.scatter(ps, count)
plt.xlabel('Lion probability')
plt.ylabel('Purchase count')
count
"""
Explanation: Колл... |
mohsinhaider/pythonbootcampacm | Errors and Exceptions/Errors and Exceptions.ipynb | mit | # Producing an Error
print("hey there)
"""
Explanation: Errors and Exceptions
Running into errors and exceptions is inevitable, and debugging is a huge part of modern-day product development. As of now, we've worked with the roots of Python syntax, and have encountered many types of errors that are built-in. Of course... |
mattilyra/gensim | docs/notebooks/Poincare Tutorial.ipynb | lgpl-2.1 | % cd ../..
%load_ext autoreload
%autoreload 2
import os
import logging
import numpy as np
from gensim.models.poincare import PoincareModel, PoincareKeyedVectors, PoincareRelations
logging.basicConfig(level=logging.INFO)
poincare_directory = os.path.join(os.getcwd(), 'docs', 'notebooks', 'poincare')
data_directo... |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/cmip6/models/sandbox-2/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'sandbox-2', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: MOHC
Source ID: SANDBOX-2
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbul... |
physion/ovation-python | examples/file-upload.ipynb | gpl-3.0 | import ovation.core as core
from ovation.session import connect
from ovation.upload import upload_revision, upload_file, upload_folder
from ovation.download import download_revision
from pprint import pprint
from getpass import getpass
from tqdm import tqdm_notebook as tqdm
"""
Explanation: File (Revision) upload e... |
GoogleCloudPlatform/bigquery-notebooks | notebooks/official/template_notebooks/visualizing_bigquery_public_data.ipynb | apache-2.0 | %%bigquery
SELECT
source_year AS year,
COUNT(is_male) AS birth_count
FROM `bigquery-public-data.samples.natality`
GROUP BY year
ORDER BY year DESC
LIMIT 15
"""
Explanation: Vizualizing BigQuery data in a Jupyter notebook
BigQuery is a petabyte-scale analytics data warehouse that you can use to run SQL queries ... |
jamesjia94/BIDMach | tutorials/NVIDIA/BIDMat_Scala_Features.ipynb | bsd-3-clause | import BIDMat.{CMat,CSMat,DMat,Dict,IDict,FMat,FND,GMat,GDMat,GIMat,GLMat,GSMat,GSDMat,
HMat,IMat,Image,LMat,Mat,ND,SMat,SBMat,SDMat}
import BIDMat.MatFunctions._
import BIDMat.SciFunctions._
import BIDMat.Solvers._
import BIDMat.JPlotting._
Mat.checkMKL
Mat.checkCUDA
Mat.setInline
if (Mat.hasCUDA > 0) ... |
bonadio/bike-share-rnn | .ipynb_checkpoints/original-nn-checkpoint.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... |
tensorflow/federated | docs/openmined2020/openmined_conference_2020.ipynb | apache-2.0 | #@title Upgrade tensorflow_federated and load TensorBoard
#@test {"skip": true}
!pip install --quiet --upgrade tensorflow-federated
!pip install --quiet --upgrade nest-asyncio
import nest_asyncio
nest_asyncio.apply()
%load_ext tensorboard
import sys
if not sys.warnoptions:
import warnings
warnings.simplefil... |
JeffAbrahamson/MLWeek | practicum/teste_installation.ipynb | gpl-3.0 | import logging
import time
"""
Explanation: Confirmer l'installation de python
Le seul but de ce notebook est de vous permettre de confirmer la bonne installation de python. Les exercises étaient testées avec python 2.7.12 et ipython 5.1.0. Il y a toute raison à croire que n'importe quelle version de python 2.7 suff... |
morganics/BayesPy | examples/notebook/iris_anomaly_detection.ipynb | apache-2.0 | %matplotlib notebook
import pandas as pd
import sys
sys.path.append("../../../bayespy")
import bayespy
from bayespy.network import Builder as builder
import logging
import os
import matplotlib.pyplot as plt
from IPython.display import display
logger = logging.getLogger()
logger.addHandler(logging.StreamHandler())
l... |
ananswam/bioscrape | inference examples/Stochastic Inference.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = "retina"
from matplotlib import rcParams
rcParams["savefig.dpi"] = 100
rcParams["figure.dpi"] = 100
rcParams["font.size"] = 20
%matplotlib inline
import bioscrape as bs
from bioscrape.types import Model
from bioscrape.simulator import py_simulate_model
import ... |
RedHatInsights/insights-core | docs/notebooks/Insights Core Tutorial.ipynb | apache-2.0 | import sys
sys.path.insert(0, "../..")
from insights.core import dr
# Here's our component type with the clever name "component."
# Insights Core provides several types that we'll come to later.
class component(dr.ComponentType):
pass
"""
Explanation: Red Hat Insights Core
Insights Core is a framework for collec... |
phasedchirp/Assorted-Data-Analysis | exercises/SlideRule-DS-Intensive/UD120/SVM.ipynb | gpl-2.0 | import sys
from sklearn.svm import SVC
from time import time
sys.path.append("../tools/")
from email_preprocess import preprocess
"""
Explanation: Udacity Machine Learning mini-project 2
Prep stuff
End of explanation
"""
features_train, features_test, labels_train, labels_test = preprocess()
"""
Explanation: Traini... |
machinelearningnanodegree/stanford-cs231 | solutions/vijendra/assignment2/Dropout.ipynb | mit | # As usual, a bit of setup
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solver import Solver
%matplotlib inline
... |
MBARIMike/oxyfloat | notebooks/explore_cached_oxyfloat_data.ipynb | mit | import sys
sys.path.insert(0, '../')
from oxyfloat import ArgoData
ad = ArgoData()
"""
Explanation: Explore locally cached Argo oxygen float data - second in a series of Notebooks
Use the oxyfloat module to get data and Pandas to operate on it for testing ability to easily perform calibrations
(See build_oxyfloat_cac... |
janesjanes/sketchy | code/Retrieval_Example.ipynb | mit | import numpy as np
from pylab import *
%matplotlib inline
import os
import sys
"""
Explanation: This script is for retrieving images based on sketch query
End of explanation
"""
#TODO: specify your caffe root folder here
caffe_root = "X:\caffe_siggraph/caffe-windows-master"
sys.path.insert(0, caffe_root+'/python')
i... |
adukic/nd101 | first-neural-network/dlnd-your-first-neural-network.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... |
mne-tools/mne-tools.github.io | 0.24/_downloads/fcc5782db3e2930fc79f31bc745495ed/60_ctf_bst_auditory.ipynb | bsd-3-clause | # Authors: Mainak Jas <mainak.jas@telecom-paristech.fr>
# Eric Larson <larson.eric.d@gmail.com>
# Jaakko Leppakangas <jaeilepp@student.jyu.fi>
#
# License: BSD-3-Clause
import os.path as op
import pandas as pd
import numpy as np
import mne
from mne import combine_evoked
from mne.minimum_norm import ... |
metpy/MetPy | v0.10/_downloads/7dd7941230ab04d65d899c66ed400ef4/xarray_tutorial.ipynb | bsd-3-clause | import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import xarray as xr
# Any import of metpy will activate the accessors
import metpy.calc as mpcalc
from metpy.testing import get_test_data
from metpy.units import units
"""
Explanation: xarray with MetPy Tutorial
xarray <h... |
daniel-koehn/DENISE-Black-Edition | par/pythonIO/DENISE-python_IO.ipynb | gpl-2.0 | # Import Python libaries
# ----------------------
import numpy as np # NumPy library
from denise_IO.denise_out import * # "DENISE" library
"""
Explanation: Creating input files for DENISE Black-Edition
Jupyter notebook for the definition of FD model and modelling/FWI/RTM parameters of DENISE Black... |
cmorgan/toyplot | docs/canvas-layout.ipynb | bsd-3-clause | import numpy
y = numpy.linspace(0, 1, 20) ** 2
import toyplot
toyplot.plot(y, width=300);
"""
Explanation: .. _canvas-layout:
Canvas Layout
In Toyplot, axes (including :ref:cartesian-axes, :ref:table-axes, and others) are used to map data values into canvas coordinates. The axes range (the area on the canvas that th... |
PyDataMallorca/WS_Introduction_to_data_science | ml_miguel/quien_es_quien.ipynb | gpl-3.0 | import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
matplotlib.rcParams['figure.figsize'] = (15.0, 6.0)
import numpy as np
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
from IPython.display import Image
"""
Explanation: Cual es la mejor estrategia para adivinar?
Por M... |
ghvn7777/ghvn7777.github.io | content/fluent_python/12_inherit.ipynb | apache-2.0 | class DoppelDict(dict):
def __setitem__(self, key, value):
super().__setitem__(key, [value] * 2)
dd = DoppelDict(one=1)
dd # 继承 dict 的 __init__ 方法忽略了我们覆盖的 __setitem__方法，'one' 值没有重复
dd['two'] = 2 # `[]` 运算符会调用我们覆盖的 __setitem__ 方法
dd
dd.update(three=3) #继承自 dict 的 update 方法也不会调用我们覆盖的 __setitem__ 方法
dd
"""... |
lileiting/goatools | notebooks/semantic_similarity.ipynb | bsd-2-clause | %load_ext autoreload
%autoreload 2
import sys
sys.path.insert(0, "..")
from goatools import obo_parser
go = obo_parser.GODag("../go-basic.obo")
go_id3 = 'GO:0048364'
go_id4 = 'GO:0044707'
print(go[go_id3])
print(go[go_id4])
"""
Explanation: Computing basic semantic similarities between GO terms
Adapted from book c... |
tensorflow/docs-l10n | site/en-snapshot/guide/tensor_slicing.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... |
pjbull/data-science-is-software | notebooks/data-science-is-software-talk.ipynb | mit | # install the watermark extension
!pip install watermark
# once it is installed, you'll just need this in future notebooks:
%load_ext watermark
%watermark -a "Peter Bull" -d -v -p numpy,pandas -g
"""
Explanation: <table style="width:100%; border: 0px solid black;">
<tr style="width: 100%; border: 0px solid black... |
wbinventor/openmc | examples/jupyter/nuclear-data.ipynb | mit | %matplotlib inline
import os
from pprint import pprint
import shutil
import subprocess
import urllib.request
import h5py
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm
from matplotlib.patches import Rectangle
import openmc.data
"""
Explanation: In this notebook, we will go through the salien... |
marxav/hello-world | artificial_neural_network_101_tensorflow.ipynb | mit | # To enable Tensorflow 2 instead of TensorFlow 1.15, uncomment the next 4 lines
#try:
# %tensorflow_version 2.x
#except Exception:
# pass
# library to store and manipulate neural-network input and output data
import numpy as np
# library to graphically display any data
import matplotlib.pyplot as plt
# library ... |
ethen8181/Business-Analytics | ab_tests/bayesian_ab_test.ipynb | mit | # Website A had 1055 clicks and 28 sign-ups
# Website B had 1057 clicks and 45 sign-ups
values_A = np.hstack( ( [0] * (1055 - 28), [1] * 28 ) )
values_B = np.hstack( ( [0] * (1057 - 45), [1] * 45 ) )
print(values_A)
print(values_B)
"""
Explanation: A/B Testing with Hierarchical Models
Though A/B testing seems simple... |
intel-analytics/analytics-zoo | docs/docs/colab-notebook/orca/quickstart/pytorch_lenet_mnist_data_creator_func.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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed un... |
EvanBianco/striplog | tutorial/Striplog_object.ipynb | apache-2.0 | %matplotlib inline
import striplog
striplog.__version__
from striplog import Legend, Lexicon, Interval, Component
legend = Legend.default()
lexicon = Lexicon.default()
"""
Explanation: Striplog objects
This notebooks looks at the main striplog object. For the basic objects it depends on, see Basic objects.
First, im... |
bloomberg/bqplot | examples/Marks/Pyplot/Pie.ipynb | apache-2.0 | data = np.random.rand(3)
fig = plt.figure(animation_duration=1000)
pie = plt.pie(data, display_labels="outside", labels=list(string.ascii_uppercase))
fig
"""
Explanation: Basic Pie Chart
End of explanation
"""
n = np.random.randint(1, 10)
pie.sizes = np.random.rand(n)
"""
Explanation: Update Data
End of explanatio... |
tritemio/multispot_paper | out_notebooks/usALEX-5samples-PR-raw-out-DexDem-17d.ipynb | mit | ph_sel_name = "DexDem"
data_id = "17d"
# ph_sel_name = "all-ph"
# data_id = "7d"
"""
Explanation: Executed: Mon Mar 27 11:35:09 2017
Duration: 11 seconds.
usALEX-5samples - Template
This notebook is executed through 8-spots paper analysis.
For a direct execution, uncomment the cell below.
End of explanation
"""
f... |
ES-DOC/esdoc-jupyterhub | notebooks/uhh/cmip6/models/sandbox-1/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'uhh', 'sandbox-1', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: UHH
Source ID: SANDBOX-1
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radiat... |
joshspeagle/dynesty | demos/Examples -- LogGamma.ipynb | mit | # system functions that are always useful to have
import time, sys, os
import warnings
# basic numeric setup
import numpy as np
# inline plotting
%matplotlib inline
# plotting
import matplotlib
from matplotlib import pyplot as plt
# seed the random number generator
rstate = np.random.default_rng(1028)
# re-definin... |
PLN-FaMAF/DeepLearningEAIA | deep_learning_tutorial_1.ipynb | bsd-3-clause | import numpy
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.datasets import mnist
"""
Explanation: Express Deep Learning in Python - Part 1
Do you have everything ready? Check the part 0!
How fast can you build a MLP?
In this first part we will see how to implement ... |
rubensfernando/mba-analytics-big-data | Python/2016-07-29/aula4-parte3-tratamento-excecoes.ipynb | mit | 10 *(1/0)
4 + spam*3
'2' + 2
"""
Explanation: O básico sobre tratamento de exceções
Erros detectados durante a execução são chamados de exceções e não são necessariamente fatais. A maioria das exceções não são lidadas pelos programas, entretanto, um resultado de mensagens de erros são ilustradas abaixo:
End of expla... |
jenshnielsen/HJCFIT | exploration/CH82.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from dcprogs.likelihood import QMatrix
tau = 1e-4
qmatrix = QMatrix([[ -3050, 50, 3000, 0, 0 ],
[ 2./3., -1502./3., 0, 500, 0 ],
[ 15, 0, -2065, 50, 2000 ],
... |
bashtage/statsmodels | examples/notebooks/ets.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
from statsmodels.tsa.exponential_smoothing.ets import ETSModel
plt.rcParams["figure.figsize"] = (12, 8)
"""
Explanation: ETS models
The ETS models are a family of time series models with an underlying state space model consisti... |
Hexiang-Hu/mmds | week6/.ipynb_checkpoints/Quiz-Week6-checkpoint.ipynb | mit | import numpy as np
p1 = (5, 4)
p2 = (8, 3)
p3 = (7, 2)
p4 = (3, 3)
def calc_wb(p1, p2):
dx = ( p1[0] - p2[0] )
dy = ( p1[1] - p2[1] )
return ( ( float(dy) *2 / float(dy - dx), float(-dx)*2 / float(dy - dx) ),\
(dx*p2[1] - dy * p2[0])*2 / float(dy - dx) + 1) # b = dx*y1 - dy*x1
def cal_margin(... |
ajgpitch/qutip-notebooks | examples/piqs-boundary-time-crystals.ipynb | lgpl-3.0 | from time import clock
from scipy.io import mmwrite
import matplotlib.pyplot as plt
from qutip import *
from qutip.piqs import *
"""
Explanation: Boundary time crystals
Notebook author: Nathan Shammah (nathan.shammah at gmail.com)
We apply the Permutational Invariant Quantum Solver (PIQS) [1], imported in QuTiP as $\... |
squishbug/DataScienceProgramming | 05-Operating-with-Multiple-Tables/HW05/CheckHomework05.ipynb | cc0-1.0 | import pandas as pd
import numpy as np
"""
Explanation: Check Homework HW05
Use this notebook to check your solutions. This notebook will not be graded.
End of explanation
"""
import hw5_answers
reload(hw5_answers)
from hw5_answers import *
"""
Explanation: Now, import your solutions from hw5_answers.py. The follow... |
wmvanvliet/neuroscience_tutorials | conpy-intro/MEG_connectivity_exercise.ipynb | bsd-2-clause | # Don't worry about warnings in this exercise, as they can be distracting.
import warnings
warnings.simplefilter('ignore')
# Import the required Python modules
import mne
import conpy
import surfer
# Import and configure the 3D graphics backend
from mayavi import mlab
mlab.init_notebook('png')
# Tell MNE-Python to b... |
sdpython/ensae_teaching_cs | _doc/notebooks/td1a_soft/td1a_sql.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 1A.soft - Notions de SQL
Premiers pas avec le langage SQL.
End of explanation
"""
from pyensae.datasource import download_data
download_data("td8_velib.zip", website = 'xd')
"""
Explanation: Le langage SQL est utilisé pour manipuler de... |
erickpeirson/statistical-computing | Statistical Learning.ipynb | cc0-1.0 | import numpy as np
from scipy.stats import uniform
f = lambda x: np.log(x)
x = np.linspace(0.1, 5.1, 100)
y = f(x)
Eps = uniform.rvs(-1., 2., size=(100,))
plt.plot(x, y, label='$f(x)$', lw=3)
plt.scatter(x, y + Eps, label='y')
plt.xlabel('x')
plt.legend(loc='best')
plt.show()
"""
Explanation: Statistical Learning
Di... |
tpin3694/tpin3694.github.io | machine-learning/adding_interaction_terms.ipynb | mit | # Load libraries
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
from sklearn.preprocessing import PolynomialFeatures
import warnings
# Suppress Warning
warnings.filterwarnings(action="ignore", module="scipy", message="^internal gelsd")
"""
Explanation: Title: Adding Interac... |
GEMScienceTools/rmtk | notebooks/vulnerability/derivation_fragility/equivalent_linearization/lin_miranda_2008/lin_miranda_2008.ipynb | agpl-3.0 | from rmtk.vulnerability.derivation_fragility.equivalent_linearization.lin_miranda_2008 import lin_miranda_2008
from rmtk.vulnerability.common import utils
%matplotlib inline
"""
Explanation: Lin and Miranda (2008)
This method, described in Lin and Miranda (2008), estimates the maximum inelastic displacement of an exi... |
dtamayo/rebound | ipython_examples/HyperbolicOrbits.ipynb | gpl-3.0 | from io import StringIO
import numpy as np
import rebound
epoch_of_elements = 53371.0 # [MJD, days]
c = StringIO(u"""
# id e q[AU] i[deg] Omega[deg] argperi[deg] t_peri[MJD, days] epoch_of_observation[MJD, days]
168026 12.181214 15.346358 136.782470 37.581438 268.412314 54776.806093 ... |
rjurney/Agile_Data_Code_2 | ch07/Making_Predictions.ipynb | mit | import pyspark.sql.functions as F
import pyspark.sql.types as T
from pyspark.sql import SparkSession
# Initialize PySpark with MongoDB and Elastic support
spark = (
SparkSession.builder.appName("Exploring Data with Reports")
# Load support for MongoDB and Elasticsearch
.config("spark.jars.packages", "org.... |
DJCordhose/speed-limit-signs | notebooks/retrain-cnn-step-3-fine-tuning-bottleneck-layer.ipynb | apache-2.0 | import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
import matplotlib.pylab as plt
import numpy as np
from distutils.version import StrictVersion
import sklearn
print(sklearn.__version__)
assert StrictVersion(sklearn.__version__ ) >= StrictVersion('0.18.1')
import tensorflow as tf
t... |
jpilgram/phys202-2015-work | assignments/assignment12/FittingModelsEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Fitting Models Exercise 1
Imports
End of explanation
"""
a_true = 0.5
b_true = 2.0
c_true = -4.0
"""
Explanation: Fitting a quadratic curve
For this problem we are going to work with the following mod... |
tensorflow/examples | courses/udacity_intro_to_tensorflow_for_deep_learning/l10c02_nlp_multiple_models_for_predicting_sentiment.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... |
vbalderdash/LMAsimulation | LMAsimulation_full.ipynb | mit | %pylab inline
import pyproj as proj4
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import datetime
import time
import simulation_functions as sf
# import read_logs
from mpl_toolkits.basemap import Basemap
from coordinateSystems import TangentPlaneCartesianSystem, GeographicSystem, MapProjecti... |
tensorflow/recommenders | docs/examples/deep_recommenders.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... |
kitu2007/dl_class | autoencoder/Simple_Autoencoder.ipynb | mit | %matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
"""
Explanation: A Simple Autoencoder
We'll start off by building a simple autoencoder to compres... |
211217613/python_meetup | Word frequency python session.ipynb | unlicense | # Lets see how many lines are in the PDF
# We can use the '!' special character to run Linux commands inside of our notebook
!wc -l test.txt
# Now lets see how many words
!wc -w test.txt
import nltk
from nltk import tokenize
# Lets open the file so we can access the ascii contents
# fd stands for file descriptor b... |
VirusTotal/vt-py | examples/jupyter/ransomware_report_usecases1.ipynb | apache-2.0 | #@markdown Please, insert your VT API Key*:
API_KEY = '' #@param {type: "string"}
#@markdown **The API key should have Premium permissions, otherwise some of the use cases might not provide the expected results.*
#@markdown
"""
Explanation: Jupyter Notebook - Ransomware report use cases 1
Copyright © 2021 Googl... |
nproctor/phys202-2015-work | assignments/assignment04/TheoryAndPracticeEx01.ipynb | mit | from IPython.display import Image
"""
Explanation: Theory and Practice of Visualization Exercise 1
Imports
End of explanation
"""
# Add your filename and uncomment the following line:
Image(filename='graphie.JPG')
"""
Explanation: Graphical excellence and integrity
Find a data-focused visualization on one of the fo... |
ymero/pyDataScienceToolkits_Base | Visualization/(3)special_curves_plot.ipynb | mit | %matplotlib inline
import numpy as np
from matplotlib.pyplot import plot
from matplotlib.pyplot import show
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
"""
Explanation: 内容索引
利萨如曲线 --- 使用标准三角函数绘制
绘制方波 --- 利用无穷傅里叶级数表示
绘制锯齿波和三角波
End of explanation
"""
# 为简单起见，令A和B为1
t = np.linspace(-np.pi, n... |
sdpython/ensae_teaching_cs | _doc/notebooks/notebook_eleves/2017-2018/dimensions_reduction.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
import matplotlib.pyplot as plt # traçage de graphiques
import numpy as np # traitement des arrays numériques
import pandas as pd
from sklearn import datasets # datasets classiques
from sklearn import preprocessing # normalisation les... |
igabr/Metis_Projects_Chicago_2017 | 05-project-kojack/.ipynb_checkpoints/Final_Notebook-checkpoint.ipynb | mit | df = unpickle_object("FINAL_DATAFRAME_PROJ_5.pkl")
df.head()
def linear_extrapolation(df, window):
pred_lst = []
true_lst = []
cnt = 0
all_rows = df.shape[0]
while cnt < window:
start = df.iloc[cnt:all_rows-window+cnt, :].index[0].date()
end = df.iloc[cnt:all_rows-window+cnt, :].... |
blehman/Data-Science-45min-Intros | networks-201/network_analysis.ipynb | unlicense | #relatively fast networks package (pip install python-igraph) that I used for these homeworks
import igraph
# slow-and-steady networks package. fewer bugs, easier drawing
import networkx as nx
# plots!
import matplotlib.pyplot as plt
from matplotlib import style
%matplotlib inline
# other packages
from __future__ imp... |
tolaoniyangi/dmc | notebooks/week-6/01-training a RNN model in Keras.ipynb | apache-2.0 | import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
from keras.callbacks import ModelCheckpoint
from keras.utils import np_utils
from time import gmtime, strftime
import os
import re
import pickle
import random
import sys
... |
geilerloui/deep-learning | tv-script-generation/dlnd_tv_script_generation.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... |
mit-eicu/eicu-code | notebooks/demo/02-demographics-and-severity-of-illness.ipynb | mit | # Import libraries
import pandas as pd
import matplotlib.pyplot as plt
import psycopg2
import os
# Plot settings
%matplotlib inline
plt.style.use('ggplot')
fontsize = 20 # size for x and y ticks
plt.rcParams['legend.fontsize'] = fontsize
plt.rcParams.update({'font.size': fontsize})
# Connect to the database - which i... |
sdpython/ensae_teaching_cs | _doc/notebooks/td1a/td1a_cenonce_session4.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 1A.2 - Modules, fichiers, expressions régulières
Le langage Python est défini par un ensemble de règle, une grammaire. Seul, il n'est bon qu'à faire des calculs. Les modules sont des collections de fonctionnalités pour interagir avec des ... |
DS-100/sp17-materials | sp17/labs/lab06/lab06_solution.ipynb | gpl-3.0 | !pip install ipython-sql
%load_ext sql
%sql sqlite:///./lab06.sqlite
import sqlalchemy
engine = sqlalchemy.create_engine("sqlite:///lab05.sqlite")
connection = engine.connect()
!pip install -U okpy
from client.api.notebook import Notebook
ok = Notebook('lab06.ok')
"""
Explanation: Lab 6: SQL
End of explanation
"... |
dshean/iceflow | Iceflow visualization.ipynb | mit | %matplotlib inline
import os
import matplotlib.pyplot as plt
# The two statements below are used mainly to set up a plotting
# default style that's better than the default from matplotlib
#import seaborn as sns
plt.style.use('bmh')
from shapely.geometry import Point
#import pandas as pd
import geopandas as gpd
from ... |
snegirigens/DLND | embeddings/Skip-Gram_word2vec.ipynb | mit | import time
import numpy as np
import tensorflow as tf
import utils
"""
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language p... |
hadim/public_notebooks | Analysis/MSD_Bayes/notebook.ipynb | mit | %matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import pandas as pd
from scipy import io
from scipy import optimize
import pymc3 as pm
import theano
import theano.tensor as t
import matplotlib.pyplot as plt
"""
Explanation: Classify particle motion from MSD anaysis and bayesian inference (i... |
cdawei/digbeta | dchen/music/aotm2011_subset.ipynb | gpl-3.0 | %matplotlib inline
%load_ext autoreload
%autoreload 2
import os, sys
import gzip
import pickle as pkl
import numpy as np
import pandas as pd
from scipy.optimize import check_grad
from scipy.sparse import lil_matrix, issparse
from collections import Counter
import matplotlib.pyplot as plt
import seaborn as sns
sys.pa... |
cloudmesh/book | notebooks/machinelearning/crossvalidation.ipynb | apache-2.0 | from sklearn.datasets import load_iris
from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
# read in the iris data
iris = load_iris()
# create X (features) and y (response)
X = iris.data
y = iris.target
# use train/test split with diffe... |
karlstroetmann/Formal-Languages | Ply/Html2Text.ipynb | gpl-2.0 | data = \
'''
<html>
<head>
<meta charset="utf-8">
<title>Homepage of Prof. Dr. Karl Stroetmann</title>
<link type="text/css" rel="stylesheet" href="style.css" />
<link href="http://fonts.googleapis.com/css?family=Rochester&subset=latin,latin-ext"
rel="stylesheet" type="text/css">
<link h... |
tleonhardt/LearningCython | Learning_Cython_video/Chapter11/dates/dateobject-withC.ipynb | mit | import numpy as np
import pandas as pd
def make_sample_data(size):
d = dict(
# Years: 1980 - 2015
year=np.random.randint(1980, 2016, int(size)),
# Months 1 - 12
month=np.random.randint(1, 13, int(size)),
# Day number: 1 - 28
day=np.random.randint(1, 28, int(size)),
... |
paix120/DataScienceLearningClubActivities | Activity05/Mushroom Edibility Classification - Naive Bayes.ipynb | gpl-2.0 | #import pandas and numpy libraries
import pandas as pd
import numpy as np
import sys #sys needed only for python version
#import gaussian naive bayes from scikit-learn
import sklearn as sk
#seaborn for pretty plots
import seaborn as sns
#display versions of python and packages
print('\npython version ' + sys.version)
... |
kazzz24/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... |
vravishankar/Jupyter-Books | Lists.ipynb | mit | vowels = ['a','e','i','o','u']
print(vowels)
"""
Explanation: Lists
Lists are constructed with square brackets with elements separated by a comma.
Lists are mutable, meaning the individual items in the list can be changed.
Example 1
End of explanation
"""
list1 = [1,'a',"This is a list",5.25]
print(list1)
"""
Expla... |
pylada/pylada-light | notebooks/IPython high-throughput interface.ipynb | gpl-3.0 | %load_ext pylada
"""
Explanation: Manipulating job-folders
IPython is an ingenious combination of a bash-like terminal with a python shell. It can be used for both bash related affairs such as copying files around creating directories, and for actual python programming. In fact, the
two can be combined to create a tru... |
yandexdataschool/gumbel_lstm | binary_lstm.ipynb | mit | %env THEANO_FLAGS="device=gpu2"
import numpy as np
import theano
import theano.tensor as T
import lasagne
import os
"""
Explanation: Contents
We train an LSTM with gumbel-sigmoid gates on a toy language modelling problem.
Such LSTM can than be binarized to reach signifficantly greater speed.
End of explanation
"""
... |
BoasWhip/Black | Notebook/M269 Unit 4 Notes -- Search.ipynb | mit | def quickSelect(k, aList):
if len(aList) == 1:
return aList[0] # Base case
pivotValue = aList[0]
leftPart = []
rightPart = []
for item in aList[1:]:
if item < pivotValue:
leftPart.append(item)
else:
rightPart.append(item)
if ... |
UWashington-Astro300/Astro300-W17 | 06_PlottingWithPython.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from astropy.table import QTable
"""
Explanation: Plotting and Fitting with Python
matplotlib is the main plotting library for Python
End of explanation
"""
t = np.linspace(0,2,100) # 100 points linearly spaced between 0.0 and 2.0
s... |
cniedotus/Python_scrape | .ipynb_checkpoints/Python3_tutorial-checkpoint.ipynb | mit | width = 20
height = 5*9
width * height
"""
Explanation: <center> Python and MySQL tutorial </center>
<center> Author: Cheng Nie </center>
<center> Check chengnie.com for the most recent version </center>
<center> Current Version: Feb 12, 2016</center>
Python Setup
Since most students in this class use Windows 7, I wil... |
uliang/First-steps-with-the-Python-language | Day 2 - Unit 3.2.ipynb | mit | PRSA.head()
"""
Explanation: 2. Density based plots with matplotlib
In this section, we will be looking at density based plots. Plots like these address a problem with big data: How does one visualise a plot with 10,000++ data points and avoid overplotting.
End of explanation
"""
plt.plot( PRSA.TEMP, PRSA["pm2.5"], ... |
ericmjl/reassortment-simulator | Simulator Notebook.ipynb | mit | hosts = []
n_hosts = 1000
for i in range(n_hosts):
if i < n_hosts / 2:
hosts.append(Host(color='blue'))
else:
hosts.append(Host(color='red'))
"""
Explanation: Agent-Based Model to Dissect Contribution of Host Immunity and Contact Structure to Influenza Reassortment
Eric J. Ma
Runstadler Lab Me... |
stanfordmlgroup/ngboost | examples/user-guide/content/5-dev.ipynb | apache-2.0 | import sys
sys.path.append('/Users/c242587/Desktop/projects/git/ngboost')
"""
Explanation: Developing NGBoost
End of explanation
"""
from scipy.stats import laplace as dist
import numpy as np
from ngboost.distns.distn import RegressionDistn
from ngboost.scores import LogScore
class LaplaceLogScore(LogScore): # will... |
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