repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
csaladenes/csaladenes.github.io | test/eis-metadata-validation/Planon metadata validation5b.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: EIS metadata validation script
Used to validate Planon output with spreadsheet input
1. Data import
End of explanation
"""
planon=pd.read_excel('EIS Assets v2.xlsx',index_col = 'Code')
#master_loggerscontroller... |
massimo-nocentini/on-python | calculus-I/numbers.ipynb | mit | from sympy import *
init_printing()
x = symbols('x')
x**2
"""
Explanation: Numeri
In questa pagina introdurremo diversi insiemi di numeri, facendosi aiutare da un sistema di calcolo simbolico:
End of explanation
"""
eq = Eq(x + 3, 2, evaluate=False)
eq
"""
Explanation: $\mathbb{N}$
L'insieme dei Naturali $\mathb... |
phoebe-project/phoebe2-docs | 2.3/tutorials/intens_weighting.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
"""
Explanation: Intensity Weighting
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
"""
import phoebe
from phoebe import u # units
import numpy as np
im... |
iRipVanWinkle/ml | Data Science UA - September 2017/Lecture 01 - Introduction/US_Baby_Names-2010.ipynb | mit | %pwd
"""
Explanation: MIE1624H Introductory Example
US Baby Names 2010
End of explanation
"""
import pandas as pd
names2010 = pd.read_csv('/resources/yob2010.txt', names=['name', 'sex', 'births'])
names2010
"""
Explanation: http://www.ssa.gov/oact/babynames/limits.html
Load file into a DataFrame
End of explanation... |
michaelaye/scipy2015 | 2015-05-22 Basic calibration.ipynb | bsd-2-clause | import pandas as pd
import glob
folders = glob.glob('/Volumes/Data/ciss/opus/N*')
len(folders)
def process_folder(folder):
import glob
from pysis.exceptions import ProcessError
from pyciss import pipeline as p
# done = glob.glob(folder+'/*.map.cal.cub')
# if done:
# return folder,True
... |
mdiaz236/DeepLearningFoundations | sentiment_network/.ipynb_checkpoints/Sentiment Classification - Mini Project 4-checkpoint.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()... |
Merinorus/adaisawesome | Homework/01 - Pandas and Data Wrangling/.ipynb_checkpoints/Intro+to+Pandas (Katia)-checkpoint.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
pd.options.mode.chained_assignment = None # default='warn'
"""
Explanation: Table of Contents
<p><div class="lev1"><a href="#Introduction-to-Pandas"><span class="toc-item-num">1 </span>Introduction to Pandas</a></div><div class="lev2"><a href="#Pandas-Data-Structures"... |
rcrehuet/Python_for_Scientists_2017 | notebooks/7_0_matplotlib_scatter_plots.ipynb | gpl-3.0 | from IPython.display import Image
Image('../data/scatter_plot.png')
"""
Explanation: Matplotlib scatter plots
The aim of this tutorial is to reproduce the following figure. You can imagine this figure as measuring A and B from a set of samples numbered (0,1,2,3, and 4).
End of explanation
"""
import numpy as np
impo... |
fabiansinz/pipeline | jupyter/tutorial/pipeline_eyetracking.ipynb | lgpl-3.0 | %pylab inline
pylab.rcParams['figure.figsize'] = (6, 6)
%matplotlib inline
import datajoint as dj
from pipeline import vis, preprocess
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
"""
Explanation: Eye traces in pipeline
End of explanation
"""
(dj.ERD.from_sequence([preprocess.EyeTracking... |
adrianoglemos/end_of_day_two | notes/DefensiveProgrammingNotes.ipynb | mit | def cell_volume(X, Y, Z):
# Return the volume of a unit cell
# described by lattice vectors X, Y and Z
# The volume is given by the determinant of
# the matrix formed by sticking the three
# vectors together. i.e.
#
# | X[0] Y[0] Z[0] |
# V = | X[1] Y[1] Z[1] |
# | X[2] Y[2... |
tsarouch/data_science_references_python | core/Confidence_Prediction_Intervals.ipynb | gpl-2.0 | # This is our distribution (we assume gaussian)
data = np.random.normal(240, 25, 1000)
"""
Explanation: To keep in mind.
- When a $\alpha=95%$ C.I. is asked => The values at the 2.5 ($(1-\alpha)/2)$) and 97.5 ($ \alpha+((1-\alpha)/2)$) percentiles are selected.
Use-Case: CI for normal distribution - Bootstrap
The siz... |
folivetti/BIGDATA | Spark/Lab4a_regressao_linear.ipynb | mit | # carregar base de dados
from test_helper import Test
import os.path
baseDir = os.path.join('Data')
inputPath = os.path.join('Aula04', 'millionsong.txt')
fileName = os.path.join(baseDir, inputPath)
numPartitions = 2
rawData = sc.textFile(fileName, numPartitions)
# EXERCICIO
numPoints = rawData.<COMPLETAR>
print numPo... |
Timonzimm/CS-401 | project/report/report.ipynb | mit | import json
from urllib.request import urlopen
from urllib.parse import quote_plus
import pandas as pd
import numpy as np
from tqdm import tqdm_notebook
from multiprocessing import Pool, cpu_count
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import pandas as pd
import sqlite3
import warnings
warnings.filter... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_tf_lcmv.ipynb | bsd-3-clause | # Author: Roman Goj <roman.goj@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne import compute_covariance
from mne.datasets import sample
from mne.event import make_fixed_length_events
from mne.beamformer import tf_lcmv
from mne.viz import plot_source_spectrogram
print(__doc__)
data_path = sample.data_path... |
debsankha/network_course_python | exercises/03-exercise-graphs-medium.ipynb | gpl-2.0 | G=nx.read_graphml("../data/visualization/small_graph.xml")#Load the graph
"""
Explanation: E2.1 Shortest Paths and Cycles
End of explanation
"""
# function checks if graph G has K (5) or K (3 ,3) as minors ,
# returns True / False on planarity
import itertools as it
from networkx.algorithms import bipartite
def is_... |
rvperry/phys202-2015-work | assignments/assignment11/OptimizationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Optimization Exercise 1
Imports
End of explanation
"""
def hat(x,a,b):
V=-a*x**2+b*x**4
return V
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(1.0, 10.0, 1.0)==-9.0
... |
ES-DOC/esdoc-jupyterhub | notebooks/nerc/cmip6/models/sandbox-1/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'sandbox-1', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: NERC
Source ID: SANDBOX-1
Sub-Topics: Radiative Forcings.
Properties: 85 (42 ... |
prasants/pyds | 05.Booleans_True_or_False.ipynb | mit | mybool_1 = True
print(mybool_1)
mybool_2 = False
print(mybool_2)
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Booleans" data-toc-modified-id="Booleans-1"><span class="toc-item-num">1 </span>Booleans</a></div><div class="lev2 toc-item"><a href="#Not-True-/-Not-False?" data-toc-... |
AllenDowney/ProbablyOverthinkingIt | bear16.ipynb | mit | from __future__ import print_function, division
from thinkbayes2 import Pmf, Cdf, Beta
import thinkbayes2
import thinkplot
import numpy as np
from scipy import stats
%matplotlib inline
"""
Explanation: When will I win the Great Bear Run?
This notebook presents an application of Bayesian inference to predicting the... |
pligor/predicting-future-product-prices | ARIMA/ShampooSales.ipynb | agpl-3.0 | from __future__ import division
import pandas as pd
from pandas import read_csv
from pandas import datetime
from matplotlib import pyplot as plt
from pandas.plotting import autocorrelation_plot
from statsmodels.tsa.arima_model import ARIMA
from pandas import DataFrame
from sklearn.metrics import mean_squared_error
from... |
kit-cel/wt | nt1/vorlesung/1_quellencodierung/Uniform_Quantization_Audio.ipynb | gpl-2.0 | %matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import wavfile
import IPython.display as ipd
from ipywidgets import interactive
import ipywidgets as widgets
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=matplotlib.checkdep... |
ajgpitch/qutip-notebooks | examples/stinespring.ipynb | lgpl-3.0 | from __future__ import division, print_function
"""
Explanation: QuTiP Example: Stinespring Dilation of Quantum Channels
Christopher Granade <br>
University of Sydney
$\newcommand{\ket}[1]{\left|#1\right\rangle}$
$\newcommand{\bra}[1]{\left\langle#1\right|}$
$\newcommand{\cnot}{{\scriptstyle \rm CNOT}}$
$\newcommand{\... |
ES-DOC/esdoc-jupyterhub | notebooks/inpe/cmip6/models/sandbox-1/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inpe', 'sandbox-1', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: INPE
Source ID: SANDBOX-1
Topic: Aerosol
Sub-Topics: Transport, Emissions, Conce... |
Neyt/First-Neural-Network | original.ipynb | mit | %matplotlib inline
#commands that start with "%" are called "magic words" and are used in Jupyter
%config InlineBackend.figure_format = 'retina'
import numpy as np #is a library that helps to manage arrays www.numpy.org/
import pandas as pd #a library to analyze and show data. http://pandas.pydata.org/pandas-docs/stab... |
ianozsvald/word2vec_sentences_vs_target_sentence_demo | word2vec similarity example.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import pprint
from gensim.models import word2vec
%matplotlib inline
if False:
# start an interactive console
%qtconsole style=monokai
# %install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark... |
diging/tethne-notebooks | 6. Words and topic modeling.ipynb | gpl-3.0 | from tethne.readers import dfr
"""
Explanation: Introduction to Tethne: Words and Topic Modeling
In this workbook we'll start working with word-based FeatureSets, and use the model.corpus.mallet module to fit a Latent Dirichlet Allocation (LDA) topic model.
Before you start
Download the practice dataset from here, a... |
therealAJ/python-sandbox | data-science/learning/ud1/DataScience/TTest.ipynb | gpl-3.0 | import numpy as np
from scipy import stats
A = np.random.normal(25.0, 5.0, 10000)
B = np.random.normal(26.0, 5.0, 10000)
stats.ttest_ind(A, B)
"""
Explanation: T-Tests and P-Values
Let's say we're running an A/B test. We'll fabricate some data that randomly assigns order amounts from customers in sets A and B, with ... |
ANTsX/ANTsPy | tutorials/10minTutorial.ipynb | apache-2.0 | import ants
import numpy as np
"""
Explanation: 10 minute ANTsPy tutorial
ANTsPy is a Python wrapper for the ANTs neuroimage processing library, along with other pythonic tools that make it easy and fun to work with brain images directly in Python.
This tutorial is available at https://github.com/ANTsX/ANTsPy/tree/mas... |
gorayni/fingerprint | 12. MLP/MLP-Readable.ipynb | mit | from __future__ import division
import matplotlib
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import numpy as np
import random
%matplotlib inline
"""
Explanation: Multilayer Perceptron Network
The development of artificial neural networks has been around almost since the beginning of the Articial ... |
SheffieldML/notebook | GPy/basic_gp.ipynb | bsd-3-clause | GPy.plotting.change_plotting_library('plotly')
"""
Explanation: As of Mon 12th of Oct running on devel branch of GPy 0.8.8
End of explanation
"""
import numpy as np
"""
Explanation: Gaussian process regression tutorial
Nicolas Durrande 2013
with edits by James Hensman and Neil D. Lawrence
We will see in this tutori... |
ES-DOC/esdoc-jupyterhub | notebooks/inm/cmip6/models/inm-cm5-0/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inm', 'inm-cm5-0', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: INM
Source ID: INM-CM5-0
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbulen... |
dostrebel/working_place_ds_17 | 03 python II/02 Python II Hausaugaben.ipynb | mit | def double_1(zahl):
if zahl == 5:
zahl = zahl * 2
return zahl
else:
return zahl
double_1(4)
def double(zahl):
zahl = zahl * 2
return zahl
double(4)
"""
Explanation: Python II Hausaufgaben
1.Baue eine Funktion mit dem Namen 'double', der die Zahl 5 vedoppelt:
End o... |
poppy-project/community-notebooks | tutorials-education/poppy-torso__vrep_Prototype d'ininitiation à l'informatique pour les lycéens/dialogue/Dialogue TP2.ipynb | lgpl-3.0 | from poppy.creatures import PoppyTorso
poppy=PoppyTorso(simulator='vrep')
# oui
for i in range(0,3):
poppy.head_y.goto_position(15,0.5,wait=True)
poppy.head_y.goto_position(-15,0.5,wait=True)
print i
poppy.head_y.goto_position(-15,0.1,wait=True)
"""
Explanation: ET – Niveau 1 - Python
TP2
Et si on communi... |
texib/deeplearning_homework | tensor-flow-exercises/4_convolutions.ipynb | mit | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
import cPickle as pickle
import numpy as np
import tensorflow as tf
pickle_file = 'notMNIST.pickle'
with open(pickle_file, 'rb') as f:
save = pickle.load(f)
train_dataset = save['train_dataset']
train_la... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/dev/.ipynb_checkpoints/n01_first_look_at_the_data-checkpoint.ipynb | mit | import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
%matplotlib inline
%pylab inline
pylab.rcParams['figure.figsize'] = (20.0, 10.0)
%load_ext autoreload
%autoreload 2
sys.path.append('../../')
data_df = pd.read_pickle('../.... |
mne-tools/mne-tools.github.io | stable/_downloads/7b89f7dac105a44e25d2fbdd898b911f/vector_mne_solution.ipynb | bsd-3-clause | # Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
#
# License: BSD-3-Clause
import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, apply_inverse
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path / 'subjects'
smoothing_steps = 7
# Read... |
ecervera/mindstorms-nb | task/sound_teacher.ipynb | mit | from functions import connect, sound, forward, stop
connect()
"""
Explanation: <img src="http://cdn.shopify.com/s/files/1/0059/3932/products/Aldebaran_Robotics_Nao_Humanoid_Robot_07_d2010cf6-cff3-468b-82c4-e2c7cfa6df9b.jpg?v=1439318386" align="right" width=200>
Sensor de so (micròfon)
El micròfon del robot detecta el... |
ES-DOC/esdoc-jupyterhub | notebooks/mri/cmip6/models/sandbox-3/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mri', 'sandbox-3', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: MRI
Source ID: SANDBOX-3
Topic: Ocnbgchem
Sub-Topics: Tracers.
Properties: 6... |
tiffanyj41/hermes | notebooks/Hermes.Run.Script.Example.ipynb | apache-2.0 | #This block of code will set up a spark content and sql context if you are running locally
#If you are on cluster or have deployed spark a different way you don't need this
from pyspark import SparkContext, SparkConf
from pyspark.sql import SQLContext
try:
sc = SparkContext()
except:
sc = SparkContext._activ... |
CS109-comment/NYTimes-Comment-Popularity-Prediction | IPython Process Notebook.ipynb | mit | # Import packages, libraries, and modules to be used
from datetime import date, datetime, timedelta
import requests, time, simplejson, sys, re
import numpy as np
import pandas as pd
import sys
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression, LogisticRegression
from sk... |
bayesimpact/bob-emploi | data_analysis/notebooks/datasets/job_offers/recent_job_postings.ipynb | gpl-3.0 |
import datetime
from os import path
import pandas as pd
import seaborn as _
from bob_emploi.data_analysis.lib import plot_helpers
DATA_PATH = '../../../data/'
pd.options.display.max_rows = 999
"""
Explanation: Pôle Emploi Active Job Postings
Author: pascal@bayes.org
Date: 2016-09-05
Skip the run test because it w... |
RTHMaK/RPGOne | scipy-2017-sklearn-master/notebooks/14 Model Complexity and GridSearchCV.ipynb | apache-2.0 | from sklearn.model_selection import cross_val_score, KFold
from sklearn.neighbors import KNeighborsRegressor
# generate toy dataset:
x = np.linspace(-3, 3, 100)
rng = np.random.RandomState(42)
y = np.sin(4 * x) + x + rng.normal(size=len(x))
X = x[:, np.newaxis]
cv = KFold(shuffle=True)
# for each parameter setting do... |
geoneill12/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... |
keras-team/keras-io | examples/audio/ipynb/transformer_asr.ipynb | apache-2.0 |
import os
import random
from glob import glob
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
"""
Explanation: Automatic Speech Recognition with Transformer
Author: Apoorv Nandan<br>
Date created: 2021/01/13<br>
Last modified: 2021/01/13<br>
Description: Training a sequence-t... |
aepton/python-data-wrangling | Data Wrangling with Python.ipynb | apache-2.0 | import agate
table = agate.Table.from_csv('data/contracts_data.csv')
print table
"""
Explanation: Data wrangling
Who we are
Jackie Kazil (@JackieKazil), author of the O'Reilly book Data Wrangling with Python
Abe Epton (@aepton)
What we'll cover
Loading data
Transforming it
Storing it
How we'll do it
We'll be work... |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/sandbox-1/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'sandbox-1', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: SANDBOX-1
Topic: Aerosol
Sub-Topic... |
mdeff/ntds_2016 | project/reports/youtube_fame/Titles_deep_learning.ipynb | mit | import requests
import json
import pandas as pd
from math import *
import numpy as np
import tensorflow as tf
import time
import collections
import os
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from IPython.display import display
from random i... |
physion/ovation-python | examples/activities.ipynb | gpl-3.0 | import ovation as ov
import ovation.activities as activities
import ovation.download as download
from ovation.session import connect
from tqdm import tqdm_notebook
from pprint import pprint
"""
Explanation: Activities
This example shows how to create an Activity record, linking inputs, outputs and related files.
End ... |
ComputationalModeling/spring-2017-danielak | past-semesters/spring_2016/day-by-day/day09-random-walks/Day_9_Pre_Class_Notebook.ipynb | agpl-3.0 | # Imports the functionality that we need to display YouTube videos in a Jupyter Notebook.
# You need to run this cell before you run ANY of the YouTube videos.
from IPython.display import YouTubeVideo
# WATCH THE VIDEO IN FULL-SCREEN MODE
YouTubeVideo("fF841G53fGo",width=640,height=360) # random numbers
"""
Exp... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/structured/solutions/4c_keras_wide_and_deep_babyweight.ipynb | apache-2.0 | import datetime
import os
import shutil
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
print(tf.__version__)
"""
Explanation: LAB 4c: Create Keras Wide and Deep model.
Learning Objectives
Set CSV Columns, label column, and column defaults
Make dataset of features and label from CSV files
... |
meg-simula/solving-hdiv-in-practice | src/What is the fastest way to solve H(div) PDEs in practice? .ipynb | lgpl-3.0 | # Import useful libraries
from dolfin import *
import numpy
import pylab
# Plot inline in this notebook
%matplotlib inline
# Set basic optimization parameters for FEniCS
parameters["form_compiler"]["representation"] = "uflacs"
parameters["form_compiler"]["cpp_optimize"] = True
#parameters["plotting_backend"] = "matpl... |
PG-TUe/tpot | tutorials/Portuguese Bank Marketing/Portuguese Bank Marketing Stratergy.ipynb | lgpl-3.0 | # Import required libraries
from tpot import TPOTClassifier
from sklearn.cross_validation import train_test_split
import pandas as pd
import numpy as np
#Load the data
Marketing=pd.read_csv('Data_FinalProject.csv')
Marketing.head(5)
"""
Explanation: Portuguese Bank Marketing Stratergy- TPOT Tutorial
The data is rela... |
liganega/Gongsu-DataSci | previous/notes2017/old/NB-12-Higher-order_Functions.ipynb | gpl-3.0 | def x2_list(xs):
L = []
for x in xs:
L.append(x**2)
return L
x2_list([0, 0.5, 1, 1.5, 2, 2.5])
"""
Explanation: 고계함수(higher-order functions) 활용법
파이썬은 함수와 메소드를 1급 객체(first-class object)로 다루며, 이는 C, C#, Java 등과 매우 다른 특징 중의 하나이다. 1급 객체는 예를 들어 함수의 인자로 사용될 수 있다. 파이썬을 제외한 기타 언어에서는 함수는 2급 객체로 구분되는 반면에 숫자... |
hackthemarket/gym-trading | gym_trading/envs/TradingEnv.ipynb | mit | import gym
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import interactive
interactive(True)
"""
Explanation: TradingEnv-v0
Open AI 'Gym' for reinforcement-learning based trading algorithms
This gym implements a very simple trading environment for rei... |
ComputationalModeling/spring-2017-danielak | past-semesters/spring_2016/cmse201-spring2016-d2l-export/Day 2 pre-class assignment.ipynb | agpl-3.0 | # The command below this comment imports the functionality that we need to display
# YouTube videos in a Jupyter Notebook. You need to run this cell before you
# run ANY of the YouTube videos.
from IPython.display import YouTubeVideo
"""
Explanation: Day 2 pre-class assignment
Goals for today's pre-class assignment... |
tensorflow/docs-l10n | site/zh-cn/tutorials/generative/cyclegan.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... |
davidparks21/qso_lya_detection_pipeline | docs/nb/Vetting_DR5.ipynb | mit | def lya(z,zem=None):
wvobs = (1+z)*1215.67
print('wvobs = {:g}'.format(wvobs))
if zem is not None:
wvrest = wvobs/(1+zem)
print('wvrest = {:g}'.format(wvrest))
return wvobs
"""
Explanation: Vetting the DR5
v6.1
End of explanation
"""
z=2.22131
zem = 2.569999
lya(z,zem)
"""
Explanatio... |
dnc1994/MachineLearning-UW | ml-classification/module-2-linear-classifier-solution.ipynb | mit | from __future__ import division
import graphlab
import math
import string
"""
Explanation: Predicting sentiment from product reviews
The goal of this first notebook is to explore logistic regression and feature engineering with existing GraphLab functions.
In this notebook you will use product review data from Amazon.... |
OpenBookProjects/ipynb | _data-sci-cases/survival_analysis.ipynb | mit | # You can also install packages from within IPython!
# Install Python Packages
!pip install lifelines
!pip install rpy2
!pip install plotly
!pip install pandas
# Install R libraries
%load_ext rpy2.ipython
%R install.packages("devtools")
%R install_github("ropensci/plotly")
%R install.packages("IOsurv")
%R install.pa... |
qutip/qutip-notebooks | examples/pulse-wise-second-order-coherence-g2.ipynb | lgpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from qutip import *
from IPython.display import display, Math, Latex
"""
Explanation: QuTiP Lecture: Pulse-wise second-order optical coherences of emission from a two-level system
K.A. Fischer, Stanford University
This Jupyter notebook demonstrat... |
christophmark/bayesloop | docs/source/examples/stockmarketfluctuations.ipynb | mit | %matplotlib inline
import numpy as np
import bayesloop as bl
import sympy.stats as stats
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_color_codes() # use seaborn colors
# minute-scale pricing data
prices = np.array(
[ 221.14 , 221.09 , 221.17 , 221.3 , 221.3 ... |
rh01/ml-course-4-cluster-and-retrieval | 4_em-with-text-data_blank.ipynb | agpl-3.0 | import graphlab
'''Check GraphLab Create version'''
from distutils.version import StrictVersion
assert (StrictVersion(graphlab.version) >= StrictVersion('1.8.5')), 'GraphLab Create must be version 1.8.5 or later.'
"""
Explanation: Note to Amazon EC2 users: To conserve memory, make sure to stop all the other notebooks... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_visualize_raw.ipynb | bsd-3-clause | import os.path as op
import numpy as np
import mne
data_path = op.join(mne.datasets.sample.data_path(), 'MEG', 'sample')
raw = mne.io.read_raw_fif(op.join(data_path, 'sample_audvis_raw.fif'),
preload=True)
raw.set_eeg_reference('average', projection=True) # set EEG average reference
"""
Ex... |
M-R-Houghton/euroscipy_2015 | modelling/EuroScipy.ipynb | mit | import scipy
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.cross_validation as cv
# Extra plotting functionality
import visplots
from sklearn import preprocessing, metrics
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.linear_model ... |
pbutenee/ml-tutorial | release/1/machine_learning.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (13.0, 8.0)
%matplotlib inline
import pickle
import sklearn
import sklearn.linear_model
import sklearn.preprocessing
import sklearn.gaussian_process
import sklearn.ensemble
"""
Explanation: Machine Learning 101++ in Python
Created b... |
jinhong211/CarND-LaneLines-P1 | 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... |
kit-cel/wt | qc/speech_coding/first_order_DPCM.ipynb | gpl-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import lfilter
import librosa
import librosa.display
import IPython.display as ipd
# define circular frequency
Omega = np.linspace(0,np.pi,100)
"""
Explanation: Differential Pulse Code Modulation of First Order
This code is provid... |
GoogleCloudPlatform/mlops-on-gcp | workshops/tfx-caip-tf23/lab-04-tfx-metadata/solutions/lab-04.ipynb | apache-2.0 | import os
import json
import ml_metadata
import tensorflow_data_validation as tfdv
import tensorflow_model_analysis as tfma
from ml_metadata.metadata_store import metadata_store
from ml_metadata.proto import metadata_store_pb2
from tfx.orchestration import metadata
from tfx.types import standard_artifacts
from ten... |
cosmicBboy/cosmicBboy.github.io | notebooks/2018-12-28-validating-pandas-dataframes.ipynb | gpl-2.0 | import logging
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from collections import OrderedDict
from IPython.display import display, Markdown
from sodapy import Socrata
logging.disable(logging.WARNING)
# utility function to print python output as markdown snippets
def print_output(s):
... |
usantamaria/ipynb_para_docencia | 10b_libreria_sklearn/sklearn.ipynb | mit | from sklearn import __version__ as vsn
print(vsn)
"""
Explanation: <img src="images/utfsm.png" alt="" width="200px" align="right"/>
USM Numérica
Tema del Notebook
Objetivos
Conocer el funcionamiento de la librerìa sklearn de Machine Learning
Aplicar la librerìa sklearn para solucionar problemas de Machine Learning
S... |
grigorisg9gr/menpo-notebooks | menpo/Transforms/Thin_Plate_Splines.ipynb | bsd-3-clause | import numpy as np
from menpo.transform import ThinPlateSplines
from menpo.shape import PointCloud
"""
Explanation: Thin Plate Splines (TPS) Transforms
End of explanation
"""
# landmarks used in Principal Warps paper
# http://user.engineering.uiowa.edu/~aip/papers/bookstein-89.pdf
src_landmarks = np.array([[3.6929, ... |
giacomov/3ML | docs/notebooks/Minimization_tutorial.ipynb | bsd-3-clause | from threeML import *
import matplotlib.pyplot as plt
%matplotlib inline
from threeML.minimizer.tutorial_material import *
"""
Explanation: Minimization
When using a Maximum Likelihood analysis we want to find the maximum of the likelihood $L(\vec{\theta})$ given one or more datasets (i.e., plugin instances) and o... |
MaayanLab/clustergrammer-widget | tmp-API-check.ipynb | mit | import numpy as np
import pandas as pd
from clustergrammer_widget import *
net = Network(clustergrammer_widget)
"""
Explanation: tmp-API-check
The clustergrammer_widget class is now being loaded into the Network class. The class and widget instance are saved in th Network instance, net. This allows us to load data, cl... |
fdcl-gwu/MAE3134_examples | Second Order Response.ipynb | gpl-3.0 | G1 = signal.TransferFunction([9], [1, 9, 9])
time, resp1 = signal.step(G1)
fig, ax = plt.subplots()
ax.plot(time, resp1)
ax.grid()
plt.show()
"""
Explanation: We want to investigate the relationship between the poles of a second order system, the damping ratio and natural frequency, and the various performance specif... |
DavidNorman/tensorflow | tensorflow/compiler/xla/g3doc/tutorials/xla_compile.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... |
tensorflow/docs | site/en/r1/tutorials/_index.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... |
tritemio/multispot_paper | realtime kinetics/out_notebooks/Simulated Kinetic Curve Fit - Template-out-single-spot-t0_novary.ipynb | mit | sigma = 0.016
time_window = 30
time_step = 5
time_start = -900
time_stop = 900
decimation = 20
t0_vary = True
true_params = dict(
tau = 60, # time constant
init_value = 0.3, # initial value (for t < t0)
final_value = 0.8, # final value (for t -> +inf)
t0 = 0) # time o... |
davewsmith/notebooks | temperature/FoamCoreExperiment.ipynb | mit | %matplotlib inline
import matplotlib
matplotlib.rcParams['figure.figsize'] = (12, 5)
import pandas as pd
df = pd.read_csv('temps-foamcore.csv', header=None, names=['time', 'mac', 'f', 'h'], parse_dates=[0])
per_sensor_f = df.pivot(index='time', columns='mac', values='f')
downsampled_f = per_sensor_f.resample('2T').mea... |
vpenso/scripts | docs/code/python/pandas.ipynb | gpl-3.0 | import glob
import pandas
# read all files from pack/
files = glob.glob('pack/*.json')
packs = []
for file in files:
packs.append(pandas.read_json(file,orient='records'))
# Use Pandas for data analysis
cards = pandas.concat(packs,sort=False)
# use the unique card identifier as index
cards.set_index('code',inplac... |
Kaggle/learntools | notebooks/ethics/raw/ex3.ipynb | apache-2.0 | # Set up feedback system
from learntools.core import binder
binder.bind(globals())
from learntools.ethics.ex3 import *
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
# Get the same results each time
np.random.seed... |
saashimi/code_guild | interactive-coding-challenges/staging/arrays_strings/reverse_words/reverse_words_challenge.ipynb | mit | def reverse_words (S):
#TODO: implement me
pass
"""
Explanation: <small> <i> This notebook was prepared by Marco Guajardo. For license visit github </i> </small>
.
Challenge Notebook
Problem: Given a string of words, return a string with the words in reverse
Constraits
Test Cases
Algorithm
Code
Unit Test
Sol... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/03_model_performance/labs/feature_columns_playground.ipynb | apache-2.0 | # Toy Features Dictionary
features = {"sq_footage": [ 1000, 2000, 3000, 4000, 5000],
"house_type": ["house", "house", "apt", "apt", "townhouse"]}
"""
Explanation: Intro
The tf.feature_column package provides several options for encoding categorical data. This mini-lab gives you an oppurtunity to exp... |
petrs/ECTester | util/plot_dsa.ipynb | mit | %matplotlib notebook
import numpy as np
from scipy.stats import describe
from scipy.stats import norm as norm_dist
from scipy.stats.mstats import mquantiles
from math import log, sqrt
import matplotlib.pyplot as plt
from matplotlib import ticker, colors, gridspec
from copy import deepcopy
from utils import plot_hist, m... |
sequana/resources | coverage/01-virus/virus.ipynb | bsd-3-clause | %pylab inline
matplotlib.rcParams['figure.figsize'] = [10,7]
"""
Explanation: sequana_coverage test case example (Virus)
This notebook creates the BED file provided in
- https://github.com/sequana/resources/tree/master/coverage and
- https://www.synapse.org/#!Synapse:syn10638358/wiki/465309
WARNING: you need an accou... |
ethen8181/machine-learning | python/python3_cookbook/1_data_structure.ipynb | mit | # code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', '..', 'notebook_format'))
from formats import load_style
load_style(plot_style=False)
os.chdir(path)
# magic to print version
%load_ext watermark
%water... |
MonicaGutierrez/PracticalMachineLearningClass | notebooks/02-IntroMachineLearning_update.ipynb | mit | # Import libraries
%matplotlib inline
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
sns.set();
cmap = mpl.colors.ListedColormap(sns.color_palette("hls", 3))
# Create a random set of examples
from sklearn.datasets.samples_generator import make_blobs
X, Y = make_blobs(... |
sraejones/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... |
mlamoureux/PIMS_YRC | PDE_Solve_widget.ipynb | mit | %matplotlib inline
import numpy as np
from matplotlib.pyplot import *
"""
Explanation: WaveSolver - slider
M. Lamoureux May 31, 2016. Pacific Insitute for the Mathematical Sciences
Updated June 2017, to remove all reference to Bokeh, cuz it doesn't work now. (deprecated = broken)
(Bokeh = Brokehn)
This code does some... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/introduction_to_tensorflow/labs/1_core_tensorflow.ipynb | apache-2.0 | import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
print(tf.__version__)
"""
Explanation: Getting started with TensorFlow
Learning Objectives
1. Practice defining and performing basic operations on constant Tensors
1. Use Tensorflow's automatic differentiation capability
1. Learn ho... |
blue-yonder/tsfresh | notebooks/examples/05 Timeseries Forecasting.ipynb | mit | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pylab as plt
from tsfresh import extract_features, select_features
from tsfresh.utilities.dataframe_functions import roll_time_series, make_forecasting_frame
from tsfresh.utilities.dataframe_functions import impute
try:
import pandas_dat... |
lukechen526/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... |
a-slide/iPython-Notebook | Notebooks/2016_18_08_Epi2_pilot_datasets_analysis_protein_interaction.ipynb | gpl-2.0 | # Standard lib imports
from glob import glob
from os import remove
# Local lib import
import pycl
import pyBioPlot as pbl
# Third party import
import pandas as pd
import numpy as np
import pylab as pl
import seaborn as sns
import scipy.stats as stats
# Pyplot tweaking
%matplotlib inline
pl.rcParams['figure.figsize']... |
iskhakovt/numeric | coursework/integral.ipynb | mit | %matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import scipy.integrate
import sys
sys.path.append('../numeric-core')
import numeric
"""
Explanation: Численное интегрирование
End of explanation
"""
def estimate_error(derivative_4, derivative_5_roots, a, b, n):
n = (n + 1)... |
rahulkgup/deep-learning-foundation | first-neural-network/bike-sharing_simple-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: Bike Sharing - simple neural network
In this project, I am building simple neural network and use it to predict daily bike rental ridership. Udacity has provided so... |
ceos-seo/data_cube_notebooks | notebooks/training/ardc_training/Training_TaskC_Indices.ipynb | apache-2.0 | import datacube
import utils.data_cube_utilities.data_access_api as dc_api
from datacube.utils.aws import configure_s3_access
configure_s3_access(requester_pays=True)
api = dc_api.DataAccessApi()
dc = datacube.Datacube(app = 'ardc_task_c')
api.dc = dc
"""
Explanation: ARDC Training: Python Notebooks
Task-C: Fracti... |
ayush29feb/cs231n | 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
... |
sympy/scipy-2017-codegen-tutorial | notebooks/55-C-calling-3rd-party-lib.ipynb | bsd-3-clause | import sympy as sym
sym.init_printing()
args = x, y, z = sym.symbols('x y z')
expr = x + sym.exp(2*y + x) + sym.exp(3*z)
expr
"""
Explanation: Generating C code calling a third-party library
In this notebook we will look at how we can customize the CCodePrinter to generate C code which calls functions provided by a 3... |
gabrielcs/nyc-subway-canvass | recommender.ipynb | mit | import pandas as pd
high_schools = pd.read_pickle('pickle/high_schools.p')
print(len(high_schools))
high_schools.head()
stations_locations = pd.read_pickle('pickle/stations_locations.p')
stations_locations.head()
stations_traffic = pd.read_pickle('pickle/stations_traffic.p')
stations_traffic.head(6)
"""
Explanation... |
xzturn/tensorflow | tensorflow/compiler/xla/g3doc/tutorials/autoclustering_xla.ipynb | apache-2.0 | import tensorflow as tf
# Check that GPU is available: cf. https://colab.research.google.com/notebooks/gpu.ipynb
assert(tf.test.is_gpu_available())
tf.keras.backend.clear_session()
tf.config.optimizer.set_jit(False) # Start with XLA disabled.
def load_data():
(x_train, y_train), (x_test, y_test) = tf.keras.dataset... |
uwdata/ipython-vega-lite | notebooks/Widget.ipynb | bsd-3-clause | from vega import VegaLite
VegaLite(spec=spec_with_data)
"""
Explanation: Static Vega Plot
End of explanation
"""
from vega.widget import VegaWidget
widget = VegaWidget(spec=spec_no_data)
values = [
dict(
x=random.gauss(0.0, 1.0),
y=random.gauss(0.0, 1.0),
t=0,
)
for _ in range(... |
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