repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
daniel-koehn/Theory-of-seismic-waves-II | 05_2D_acoustic_FD_modelling/6_fdac2d_marmousi_model_exercise.ipynb | gpl-3.0 | # Execute this cell to load the notebook's style sheet, then ignore it
from IPython.core.display import HTML
css_file = '../style/custom.css'
HTML(open(css_file, "r").read())
"""
Explanation: Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2018 by D. Koehn, heterogeneou... |
tensorflow/lucid | notebooks/feature-visualization/regularization.ipynb | apache-2.0 | # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the L... |
jamesjia94/BIDMach | tutorials/CreateModels.ipynb | bsd-3-clause | import BIDMat.{CMat,CSMat,DMat,Dict,IDict,FMat,FND,GDMat,GMat,GIMat,GLMat,GSDMat,GSMat,
HMat,IMat,Image,LMat,Mat,SMat,SBMat,SDMat}
import BIDMat.MatFunctions._
import BIDMat.SciFunctions._
import BIDMat.Solvers._
import BIDMat.JPlotting._
import BIDMach.Learner
import BIDMach.models.{FM,GLM,KMeans,KMeans... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/ml_ops/stage6/get_started_with_matching_engine_twotowers.ipynb | apache-2.0 | import os
# The Vertex AI Workbench Notebook product has specific requirements
IS_WORKBENCH_NOTEBOOK = os.getenv("DL_ANACONDA_HOME")
IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(
"/opt/deeplearning/metadata/env_version"
)
# Vertex AI Notebook requires dependencies to be installed with '--user'
USER_FLAG = ... |
Krekelmans/Train_prediction_kaggle | BDS_Lab09_FILL_IN-plots.ipynb | mit | import os
os.getcwd()
%matplotlib inline
%pylab inline
import pandas as pd
import numpy as np
from collections import Counter, OrderedDict
import json
import matplotlib
import matplotlib.pyplot as plt
import re
from scipy.misc import imread
from sklearn.linear_model import LogisticRegression
from sklearn.model_select... |
pligor/predicting-future-product-prices | 02_preprocessing/exploration04-price_history_dfa.ipynb | agpl-3.0 | # -*- coding: UTF-8 -*-
from __future__ import division
import numpy as np
import pandas as pd
import sys
import math
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import re
import os
import csv
from helpers.outliers import MyOutliers
from skroutz_mobile import SkroutzMobile
from sklearn.ensemble import... |
arcyfelix/Courses | 18-03-07-Deep Learning With Python by François Chollet/Chapter 6.2 - Understanding recurrent neural networks.ipynb | apache-2.0 | from keras.models import Sequential
from keras.layers import Embedding, SimpleRNN
model = Sequential()
model.add(Embedding(10000, 32))
model.add(SimpleRNN(32))
model.summary()
"""
Explanation: Chapter 6.2 - Understanding recurrent neural networks
Simple RNN
SimpleRNN layer takes input of shape (batch_size, timesteps,... |
metpy/MetPy | v1.1/_downloads/87fd6ee8be4ea1587fa2ad7f4206407a/Combined_plotting.ipynb | bsd-3-clause | import xarray as xr
from metpy.cbook import get_test_data
from metpy.plots import ContourPlot, ImagePlot, MapPanel, PanelContainer
from metpy.units import units
# Use sample NARR data for plotting
narr = xr.open_dataset(get_test_data('narr_example.nc', as_file_obj=False))
"""
Explanation: Combined Plotting
Demonstra... |
JarnoRFB/qtpyvis | notebooks/tensorflow/train.ipynb | mit | from IPython.display import clear_output, Image, display, HTML
# Helper functions for TF Graph visualization
def strip_consts(graph_def, max_const_size=32):
"""Strip large constant values from graph_def."""
strip_def = tf.GraphDef()
for n0 in graph_def.node:
n = strip_def.node.add()
n.Mer... |
karst87/ml | dev/pyml/datacamp/kaggle-python-tutorial-on-machine-learning/01_getting-started-with-python.ipynb | mit | #Compute x = 4 * 3 and print the result
x = 4 * 3
print(x)
#Compute y = 6 * 9 and print the result
y = 6 * 9
print(y)
"""
Explanation: getting-started-with-python
https://campus.datacamp.com/courses/kaggle-python-tutorial-on-machine-learning/getting-started-with-python?ex=1
1. How it works
https://campus.datacamp.com... |
philippgrafendorfe/stackedautoencoders | ROBO_SAE_Comments.ipynb | mit | IPython.display.Image("images/robo1_nn.png")
"""
Explanation: Title of Database: Wall-Following navigation task with mobile robot SCITOS-G5
The data were collected as the SCITOS G5 navigates through the room following the wall in a clockwise
direction, for 4 rounds. To navigate, the robot uses 24 ultrasound sensors ar... |
TimofeyBalashov/MagnetizationTunneling | Using the code.ipynb | mit | %pylab inline
from ipywidgets import interact
from pyatoms.J.SingleAtom import SingleAtom
import mpmath as mp
"""
Explanation: Calculation of atomic spectra in crystal field
This notebook introduces the code for calculating the energy spectrum of single magnetic atoms in environments of various symmetry.
Loading the l... |
wheeler-microfluidics/teensy-minimal-rpc | teensy_minimal_rpc/notebooks/dma-examples/Example - Multi-channel ADC using DMA.ipynb | gpl-3.0 | from arduino_rpc.protobuf import resolve_field_values
from teensy_minimal_rpc import SerialProxy
import teensy_minimal_rpc.DMA as DMA
import teensy_minimal_rpc.ADC as ADC
# Disconnect from existing proxy (if available)
try:
del proxy
except NameError:
pass
proxy = SerialProxy()
"""
Explanation: Overview
Use... |
empet/Math | Joukowski-airfoil.ipynb | bsd-3-clause | import numpy as np
import numpy.ma as ma
import matplotlib.pyplot as plt
%matplotlib inline
def Juc(z, lam):#Joukowski transformation
return z+(lam**2)/z
def circle(C, R):
t=np.linspace(0,2*np.pi, 200)
return C+R*np.exp(1j*t)
def deg2radians(deg):
return deg*np.pi/180
plt.rcParams['figure.figsize'] ... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/vertex_endpoints/tf_hub_obj_detection/deploy_tfhub_object_detection_on_vertex_endpoints.ipynb | apache-2.0 | import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG = "--user"
! pip install {U... |
phoebe-project/phoebe2-docs | 2.1/tutorials/pitch_yaw.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
"""
Explanation: Misalignment (Pitch & Yaw)
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 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
"""
%matplotlib i... |
spectralDNS/shenfun | binder/sphere-helmholtz.ipynb | bsd-2-clause | from shenfun import *
from shenfun.la import SolverGeneric1ND
import sympy as sp
"""
Explanation: Spherical coordinates in shenfun
The Helmholtz equation is given as
$$
-\nabla^2 u + \alpha u = f.
$$
In this notebook we will solve this equation on a unitsphere, using spherical coordinates. To verify the implementation... |
phockett/ePSproc | notebooks/plottingDev/ITK_tests_070320.ipynb | gpl-3.0 | import numpy as np
from itkwidgets import view
"""
Explanation: ITK widgets tests
See also pyVista_tests_070320.ipynb
End of explanation
"""
number_of_points = 3000
gaussian_1_mean = [0.0, 0.0, 0.0]
gaussian_1_cov = [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 0.5]]
point_set_1 = np.random.multivariate_normal(gau... |
agarwal-shubham/ACNN | deep_learning_for_3D_shape_analysis_anisotropic.ipynb | mit | import sys
import os
import numpy as np
import scipy.io
import time
import theano
import theano.tensor as T
import theano.sparse as Tsp
import lasagne as L
import lasagne.layers as LL
import lasagne.objectives as LO
from lasagne.layers.normalization import batch_norm
sys.path.append('..')
from icnn import aniso_util... |
kinnala/sp.fem | learning/Example 1 - Stokes equations.ipynb | agpl-3.0 | import sys
sys.path.append('../')
import numpy as np
import matplotlib.pyplot as plt
from spfem.geometry import GeometryMeshPyTriangle
%matplotlib inline
"""
Explanation: Problem statement
The Stokes problem is a classical example of a mixed problem.
Initialize
End of explanation
"""
g = GeometryMeshPyTriangle(np.a... |
WomensCodingCircle/CodingCirclePython | Lesson12_TabularData/Tabular Data.ipynb | mit | import csv
"""
Explanation: Using Tabular Data in Python
csv module
Python has a csv reader/writer as part of its built in library. It is called csv. This is the simplest way to read tabular data (data in table format). The type of data you used to use excel to process (hopefully you will try out python now). It must ... |
chetnapriyadarshini/deep-learning | batch-norm/Batch_Normalization_Exercises.ipynb | mit | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
"""
Explanation: Batch Normalization – Practice
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a con... |
laic/gensim | docs/notebooks/doc2vec-IMDB.ipynb | lgpl-2.1 | import locale
import glob
import os.path
import requests
import tarfile
import sys
import codecs
dirname = 'aclImdb'
filename = 'aclImdb_v1.tar.gz'
locale.setlocale(locale.LC_ALL, 'C')
if sys.version > '3':
control_chars = [chr(0x85)]
else:
control_chars = [unichr(0x85)]
# Convert text to lower-case and stri... |
BYUFLOWLab/MDOnotebooks | MonteCarlo.ipynb | mit | def func(x):
return x[0]**2 + 2*x[1]**2 + 3*x[2]**2
def con(x):
return x[0] + x[1] + x[2] - 3.5 # rewritten in form c <= 0
x = [1.0, 1.0, 1.0]
sigma = [0.00, 0.06, 0.2]
"""
Explanation: Monte Carlo
This is the simple Monte Carlo example you worked on in class.
Consider the following objective and constraint... |
miaecle/deepchem | examples/notebooks/deepchem_tensorflow_eager.ipynb | mit | import tensorflow as tf
import tensorflow.contrib.eager as tfe
"""
Explanation: TensorGraph Layers and TensorFlow eager
In this tutorial we will look at the working of TensorGraph layer with TensorFlow eager.
But before that let's see what exactly is TensorFlow eager.
Eager execution is an imperative, define-by-run i... |
ComputationalModeling/spring-2017-danielak | past-semesters/spring_2016/day-by-day/day22-traveling-salesman-problem/TravelingSalesman_Problem_SOLUTIONS.ipynb | agpl-3.0 | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
from IPython.display import display, clear_output
def calc_total_distance(table_of_distances, city_order):
'''
Calculates distances between a sequence of cities.
Inputs: N x N table containing distances between each pair of the N
... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_object_evoked.ipynb | bsd-3-clause | import os.path as op
import mne
"""
Explanation: The :class:Evoked <mne.Evoked> data structure: evoked/averaged data
End of explanation
"""
data_path = mne.datasets.sample.data_path()
fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif')
evokeds = mne.read_evokeds(fname, baseline=(None, 0), pro... |
hannorein/rebound | ipython_examples/HybridIntegrationsWithMercurius.ipynb | gpl-3.0 | import math
import rebound, rebound.data
%matplotlib inline
sim = rebound.Simulation()
rebound.data.add_outer_solar_system(sim) # add some particles for testing
for i in range(1,sim.N):
sim.particles[i].m *= 50.
sim.integrator = "WHFast" # This will end badly!
sim.dt = sim.particles[1].P * 0.002 # Timestep a small ... |
kcyu1993/ML_course_kyu | labs/ex01/solutions/taskB.ipynb | mit | np.random.seed(10)
p, q = (np.random.rand(i, 2) for i in (4, 5))
p_big, q_big = (np.random.rand(i, 80) for i in (100, 120))
print(p, "\n\n", q)
"""
Explanation: Data Generation
End of explanation
"""
def naive(p, q):
result = np.zeros((p.shape[0], q.shape[0]))
for i in range(p.shape[0]):
for j in ra... |
mayankjohri/LetsExplorePython | Section 1 - Core Python/Chapter 06 - Functions/1. Functions.ipynb | gpl-3.0 | def caps(val):
"""
caps returns double the value of the provided value
"""
return val*2
a = caps("TEST ")
print(a)
print(caps.__doc__)
"""
Explanation: Functions
Functions are blocks of code identified by a name, which can receive ""predetermined"" parameters or not ;).
In Python, functions:
return o... |
sbenthall/bigbang | examples/experimental_notebooks/Single Word Trend.ipynb | agpl-3.0 | df = pd.DataFrame(columns=["MessageId","Date","From","In-Reply-To","Count"])
for row in archives[0].data.iterrows():
try:
w = row[1]["Body"].replace("'", "")
k = re.sub(r'[^\w]', ' ', w)
k = k.lower()
t = nltk.tokenize.word_tokenize(k)
subdict = {}
count = 0
... |
sysid/nbs | LP/Introduction-to-linear-programming/Introduction to Linear Programming with Python - Part 6.ipynb | mit | def make_io_and_constraint(y1, x1, x2, target_x1, target_x2):
"""
Returns a list of constraints for a linear programming model
that will constrain y1 to 1 when
x1 = target_x1 and x2 = target_x2;
where target_x1 and target_x2 are 1 or 0
"""
binary = [0,1]
assert target_x1 in binary
a... |
JannesKlaas/MLiFC | Week 1/Ch. 5 - Multiclass Regression.ipynb | mit | # Package imports
# Matplotlib is a matlab like plotting library
import matplotlib
import matplotlib.pyplot as plt
# Numpy handles matrix operations
import numpy as np
# SciKitLearn is a useful machine learning utilities library
import sklearn
# The sklearn dataset module helps generating datasets
import sklearn.datase... |
NYUDataBootcamp/Projects | UG_F16/Qian-DevEconCorrelation .ipynb | mit | import pandas as pd # data package
import matplotlib.pyplot as plt # graphics
import seaborn as sns # seaborn graphics package
import numpy as np # foundation for pandas
import sys # system module
import datetime as dt ... |
bloomberg/bqplot | examples/Tutorials/Brush Interval Selector.ipynb | apache-2.0 | import numpy as np
from ipywidgets import Layout, HTML, VBox
import bqplot.pyplot as plt
"""
Explanation: Linking Plots Using Brush Interval Selector
Details on how to use the brush interval selector can be found in this notebook.
Brush interval selectors can be used where continuous updates are not desirable (for ex... |
DawesLab/LabNotebooks | Timescales in QuTiP.ipynb | mit | from qutip import *
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Timescales in QuTiP
Andrew M.C. Dawes — 2016
An overview to one frequently asked question about QuTiP.
Introduction
QuTiP is a python package, if you are new to QuTiP, you should first read the tutorial materials... |
denglert/manuals | python/modules/matplotlib/legend/notebooks/legend_outside_figure.ipynb | mit | import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib.lines as mlines
import numpy as np
%matplotlib inline
x = np.linspace(0.0, 2.0*np.pi, 100)
y = np.sin(x)
"""
Explanation: Legend outside the axis
References:
- https://matplotlib.org/examples/pylab_examples/figlegend_demo.html
End... |
heatseeknyc/data-science | src/bryan analyses/Hack for Heat #6.ipynb | mit | #Like before, we're going to select the relevant columns from the database:
connection = psycopg2.connect('dbname= threeoneone user=threeoneoneadmin password=threeoneoneadmin')
cursor = connection.cursor()
cursor.execute('''SELECT createddate, closeddate, borough FROM service;''')
data = cursor.fetchall()
data = pd.Da... |
seg/2016-ml-contest | MandMs/03_Facies_classification-MandMs_RandomForest_EngineeredFeatures_SFSelection_ValidationCurves.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import scipy as sp
from scipy.stats import randint as sp_randint
from scipy.signal import argrelextrema
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import preprocessing
from sklearn.metrics import f1_score, make_scorer
from sklearn.mod... |
scheib/chromium | third_party/tensorflow-text/src/docs/tutorials/uncertainty_quantification_with_sngp_bert.ipynb | bsd-3-clause | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
griffinfoster/fundamentals_of_interferometry | 3_Positional_Astronomy/3_1_equatorial_coordinates.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
from IPython.display import HTML
HTML('../style/code_toggle.html')
import healpy as hp
%pylab inline
pylab.rcParams['figure.figsize'] = (15, 10)
import matplotlib
impor... |
piskvorky/gensim | docs/src/auto_examples/tutorials/run_annoy.ipynb | lgpl-2.1 | LOGS = False # Set to True if you want to see progress in logs.
if LOGS:
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
"""
Explanation: Fast Similarity Queries with Annoy and Word2Vec
Introduces the Annoy library for similarity queries on top of vec... |
me-surrey/dl-gym | 05_support_vector_machines.ipynb | apache-2.0 | # To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
# To plot pretty figures
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
... |
pucdata/pythonclub | sessions/05-astropy/Astropy Explored.ipynb | gpl-3.0 | #Preamble. These are some standard things I like to include in IPython Notebooks.
import astropy
from astropy.table import Table, Column, MaskedColumn
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
from astropy import units as u
from astropy.coordinates import SkyCoord, Angle
import astr... |
russellclarke82/CV | Pi/String formatting for printing.ipynb | apache-2.0 | print('This is a String {}'.format('INSERTED'))
print('This is an example of MultiIndex insertions {} {} {}'.format('INSERTION1', 'INSERTION2', 'INSERTION3'))
a = 'I1'
b = 'I2'
c = 'I3'
print('This is me jumping the gun and testing a theory {} {} {}'.format(a, b, c))
print('Now testing MultiIndexed Insertions witho... |
GoogleCloudPlatform/rad-lab | modules/data_science/scripts/build/notebooks/Quantum_Simulation_qsimcirq.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... |
tsaqib/bike-sharing-time-series-nn-numpy | cnn-tensorflow/cnn-tensorflow.ipynb | mit | from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
import helper
import numpy as np
from sklearn.preprocessing import LabelBinarizer
import pickle
import tensorflow as tf
import random
%matplotlib inline
%config InlineBackend.... |
sdpython/ensae_teaching_cs | _doc/notebooks/sklearn_ensae_course/01_data_manipulation.ipynb | mit | # Start pylab inline mode, so figures will appear in the notebook
%matplotlib inline
"""
Explanation: 2A.ML101.1: Introduction to data manipulation with scientific Python
In this section we'll go through the basics of the scientific Python stack for data manipulation: using numpy and matplotlib.
Source: Course on mach... |
jpn--/larch | larch/doc/example/107_latent_class.ipynb | gpl-3.0 | import larch
import pandas
from larch.roles import P,X
"""
Explanation: 107: Latent Class Models
In this example, we will replicate the latent class example model
from Biogeme.
End of explanation
"""
from larch import data_warehouse
raw = pandas.read_csv(larch.data_warehouse.example_file('swissmetro.csv.gz'))
"""
E... |
datactive/bigbang | examples/activity/Cohort Visualization.ipynb | mit | url = "6lo"
arx = Archive(url,archive_dir="../archives")
arx.data[:1]
"""
Explanation: One interesting question for open source communities is whether they are growing. Often the founding members of a community would like to see new participants join and become active in the community. This is important for community... |
Zweedeend/interactive-2d-gvdw | gvdw-bokeh.ipynb | mit | import inspect
from math import sqrt, pi
import numpy as np
import pandas as pd
from bokeh.io import show, output_notebook, push_notebook
from bokeh.plotting import figure
from ipywidgets import interact
from scipy.integrate import quad
output_notebook()
"""
Explanation: Equation of State using Generalized van der W... |
AEW2015/PYNQ_PR_Overlay | Pynq-Z1/notebooks/Video_PR/Image_Duotone_Overlay_Filter.ipynb | bsd-3-clause | from pynq.drivers.video import HDMI
from pynq import Bitstream_Part
from pynq.board import Register
from pynq import Overlay
Overlay("demo.bit").download()
"""
Explanation: Don't forget to delete the hdmi_out and hdmi_in when finished
Image Overlay Duotone Color Filter Example
In this notebook, we will overlay an ima... |
WNoxchi/Kaukasos | pytorch/fastai-pytorch-tutorial-scratch-notes.ipynb | mit | from pathlib import Path
import requests
data_path = Path('data')
path = data_path/'mnist'
path.mkdir(parents=True, exist_ok=True)
url = 'http://deeplearning.net/data/mnist/'
filename = 'mnist.pkl.gz'
(path/filename)
if not (path/filename).exists():
content = requests.get(url+filename).content
(path/filenam... |
feststelltaste/software-analytics | prototypes/Complexity over Time.ipynb | gpl-3.0 | import pandas as pd
diff_raw = pd.read_csv(
"../../buschmais-spring-petclinic_fork/git_diff.log",
sep="\n",
names=["raw"])
diff_raw.head(16)
"""
Explanation: The idea
In my previous blog post, we got to know the idea of "indentation-based complexity". We took a static view on the Linux kernel to spot the ... |
mne-tools/mne-tools.github.io | 0.24/_downloads/5f078eabe74f0448d3e1662c12313289/source_space_time_frequency.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, source_band_induced_power
print(__doc__)
"""
Explanation: Compute induced power in t... |
mohanprasath/Course-Work | coursera/python_for_data_science/4.2Writing_and_Saving_Files.ipynb | gpl-3.0 | with open('/resources/data/Example2.txt','w') as writefile:
writefile.write("This is line A")
"""
Explanation: <a href="http://cocl.us/topNotebooksPython101Coursera"><img src = "https://ibm.box.com/shared/static/yfe6h4az47ktg2mm9h05wby2n7e8kei3.png" width = 750, align = "center"></a>
<a href="https://www.bigdataun... |
InsightLab/data-science-cookbook | 2019/04-naive-bayes/Naive_Bayes_Tutorial_01.ipynb | mit | import csv
def loadCsv(filename):
lines = csv.reader(open(filename, "r"))
dataset = list(lines)
for i in range(len(dataset)):
dataset[i] = [float(x) for x in dataset[i]]
return dataset
"""
Explanation: Naive Bayes
Introdução
Neste tutorial iremos apresentar a implentação do algoritmo Naive Ba... |
chemo-wakate/tutorial-6th | beginner/mario/MachineLearning.ipynb | mit | # 数値計算やデータフレーム操作に関するライブラリをインポートする
import numpy as np
import pandas as pd
import scipy as sp
from scipy import stats
# URL によるリソースへのアクセスを提供するライブラリをインポートする。
# import urllib # Python 2 の場合
import urllib.request # Python 3 の場合
# 図やグラフを図示するためのライブラリをインポートする。
%matplotlib inline
import matplotlib.pyplot as plt
# 機械学習関連のライブラ... |
Yangqing/caffe2 | caffe2/python/tutorials/create_your_own_dataset.ipynb | apache-2.0 | # First let's import some necessities
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
%matplotlib inline
import urllib2 # for downloading the dataset from the web.
import numpy as np
from matplotlib import pyplot
from ... |
lmoresi/UoM-VIEPS-Intro-to-Python | Notebooks/SphericalMeshing/SphericalTriangulations/Ex7-Refinement-of-Triangulations.ipynb | mit | import stripy as stripy
import numpy as np
"""
Explanation: Example 7 - Refining a triangulation
We have seen how the standard meshes can be uniformly refined to finer resolution. The routines used for this task are available to the stripy user for non-uniform refinement as well.
Notebook contents
Uniform meshes
Re... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_stats_cluster_spatio_temporal_2samp.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import os.path as op
import numpy as np
from scipy import stats as stats
import mne
from mne import spatial_tris_connectivity, grade_to_tris
from mne.stats import spatio_... |
GoogleCloudPlatform/mlops-on-gcp | workshops/kfp-caip-sklearn/lab-01-caip-containers/lab-01.ipynb | apache-2.0 | import json
import os
import numpy as np
import pandas as pd
import pickle
import uuid
import time
import tempfile
from googleapiclient import discovery
from googleapiclient import errors
from google.cloud import bigquery
from jinja2 import Template
from kfp.components import func_to_container_op
from typing import N... |
agile-geoscience/xlines | notebooks/05_Read_and_write_SHP.ipynb | apache-2.0 | import numpy as np
import fiona
import matplotlib.pyplot as plt
import folium
import pprint
with fiona.open('../data/offshore_wells_2011_Geographic_NAD27.shp') as src:
pprint.pprint(src[0])
"""
Explanation: x lines of Python
Read and write SHP files
This notebook goes with the blog post of the same name, publishe... |
mcflugen/bmi-tutorial | notebooks/coupled_example.ipynb | mit | %matplotlib inline
import numpy as np
"""
Explanation: <img src="images/csdms_logo.jpg">
Using a BMI: Coupling Waves and Coastline Evolution Model
This example explores how to use a BMI implementation to couple the Waves component with the Coastline Evolution Model component.
Links
CEM source code: Look at the files ... |
rhiever/scipy_2015_sklearn_tutorial | notebooks/02.2 Supervised Learning - Regression.ipynb | cc0-1.0 | x = np.linspace(-3, 3, 100)
print(x)
rng = np.random.RandomState(42)
y = np.sin(4 * x) + x + rng.uniform(size=len(x))
plt.plot(x, y, 'o')
"""
Explanation: Regression
In regression we try to predict a continuous output variable. This can be most easily visualized in one dimension.
We will start with a very simple toy... |
emilhe/tmm | examples.ipynb | mit | from __future__ import division, print_function, absolute_import
from tmm import (coh_tmm, unpolarized_RT, ellips,
position_resolved, find_in_structure_with_inf)
from numpy import pi, linspace, inf, array
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
%matplotlib inline
... |
ivukotic/ML_platform_tests | PerfSONAR/AnomalyDetection/BDT/Testing BDT AD on simulated data.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rc('xtick', labelsize=14)
matplotlib.rc('ytick', labelsize=14)
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import... |
Rotvig/cs231n | Deep Learning/Exercise 1/Q2.ipynb | mit | # As usual, a bit of setup
import numpy as np
import matplotlib.pyplot as plt
from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient
from cs231n.layers import *
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolati... |
yl565/statsmodels | examples/notebooks/statespace_dfm_coincident.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
np.set_printoptions(precision=4, suppress=True, linewidth=120)
from pandas.io.data import DataReader
# Get the datasets from FRED
start = '1979-01-01'
end = '2014-12-01'
indprod = DataReader('IPMAN... |
ysasaki6023/NeuralNetworkStudy | examples/visualization.ipynb | mit | def target(x):
return np.exp(-(x - 2)**2) + np.exp(-(x - 6)**2/10) + 1/ (x**2 + 1)
x = np.linspace(-2, 10, 1000)
y = target(x)
plt.plot(x, y)
"""
Explanation: Target Function
Lets create a target 1-D function with multiple local maxima to test and visualize how the BayesianOptimization package works. The target ... |
folivetti/BIGDATA | Spark/Lab5b_kmeans_quantiza.ipynb | mit | import os
import numpy as np
def parseRDD(point):
""" Parser for the current dataset. It receives a data point and return
a sentence (third field).
Args:
point (str): input data point
Returns:
str: a string
"""
data = point.split('\t')
return (int(data[0]),data[2])
... |
emmaqian/DataScientistBootcamp | DS_HW1_Huimin Qian_052617.ipynb | mit | # import the necessary package at the very beginning
import numpy as np
import pandas as pd
print(str(float(100*177/891)) + '%')
"""
Explanation: 数据应用学院 Data Scientist Program
Hw1
End of explanation
"""
def foolOne(x): # note: assume x is a number
y = x * 2
y -= 25
return y
## Type Your Answer Below ##... |
trungdong/datasets-provanalytics-dmkd | Extra 2.2 - Unbalanced Data - Application 2.ipynb | mit | import pandas as pd
df = pd.read_csv("collabmap/depgraphs.csv", index_col='id')
df.head()
df.describe()
"""
Explanation: Extra 2.1 - Unbalanced Data - Application 1: CollabMap Data Quality
Assessing the quality of crowdsourced data in CollabMap from their provenance
In this notebook, we compared the classification a... |
lneuhaus/pyrpl | docs/source/user_guide/tutorial/tutorial.ipynb | mit | import pyrpl
print pyrpl.__file__
"""
Explanation: Introduction to pyrpl
1) Introduction
The RedPitaya is an affordable FPGA board with fast analog inputs and outputs. This makes it interesting also for quantum optics experiments. The software package PyRPL (Python RedPitaya Lockbox) is an implementation of many devic... |
egentry/lamat-2016-solutions | day5/ODE_practice.ipynb | mit | y_0 = 1
t_0 = 0
t_f = 10
def dy_dt(y):
return .5*y
def analytic_solution_1st_order(t):
return np.exp(.5*t)
dt = .5
t_array = np.arange(t_0, t_f, dt)
y_array = np.empty_like(t_array)
y_array[0] = y_0
for i in range(len(y_array)-1):
y_array[i+1] = y_array[i] + (dt * dy_dt(y_array[i]))
plt.plot(t_ar... |
kit-cel/wt | wt/vorlesung/ch1_3/laplace_hypergeometric.ipynb | gpl-2.0 | # importing
import numpy as np
from scipy import special
import matplotlib.pyplot as plt
import matplotlib
# showing figures inline
%matplotlib inline
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=True)
matplotlib.rc('figure', figsize=(18, 6) )
"""
Explanation: Content an... |
phoebe-project/phoebe2-docs | 2.2/tutorials/optimizing.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
import phoebe
b = phoebe.default_binary()
"""
Explanation: Advanced: Optimizing Performance with PHOEBE
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 ... |
giotta/EUR8217 | labo/tests/test-r-in-python.ipynb | mit | %load_ext rpy2.ipython
"""
Explanation: Jupyter, R et Python
Exemple ci-dessous est le même que celui de test-r.ipynb mais le présent notebook a le kernel Python 3 (les code cells sont interprétées en Python par défaut.
En suivant la procédure présentée dans le README.md, on arrive à utiliser R dans ce note book Pytho... |
bjshaw/phys202-2015-work | assignments/assignment10/ODEsEx01.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 1
Imports
End of explanation
"""
def solve_euler(derivs, y0, x):
"""Solve a 1d ... |
pschragger/big-data-python-class | Lectures/Week 2 - Python and Jupyter for Big-Data/Lecture 2 continued.ipynb | mit | import re
print all([
not re.match("a","cat"),
re.search("a","cat"),
not re.search("c","dog"),
3 == len(re.split("[ab]","carbs")),
"R-D-" == re.sub("[0-9]","-","R2D2")
]) # prints true if all are true
"""
Explanation: Lecture 2 continued
regular expressions
Provides a way to search text.
Looking f... |
gwtsa/gwtsa | examples/groundwater_paper/Ex2_monitoring_network/Example2.ipynb | mit | # Import the packages
import pandas as pd
import pastas as ps
import numpy as np
import os
import matplotlib.pyplot as plt
%matplotlib inline
# This notebook has been developed using Pastas version 0.9.9 and Python 3.7
print("Pastas version: {}".format(ps.__version__))
print("Pandas version: {}".format(pd.__version_... |
rashikaranpuria/Machine-Learning-Specialization | Regression/Assignment_six/week-6-local-regression-assignment-blank.ipynb | mit | import graphlab
"""
Explanation: Predicting house prices using k-nearest neighbors regression
In this notebook, you will implement k-nearest neighbors regression. You will:
* Find the k-nearest neighbors of a given query input
* Predict the output for the query input using the k-nearest neighbors
* Choose the be... |
adityaka/misc_scripts | python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/05_06/Final/Data Frame Plots.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
"""
Explanation: Data Frame Plots
documentation: http://pandas.pydata.org/pandas-docs/stable/visualization.html
End of explanation
"""
ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
ts... |
harishkrao/Machine-Learning | Titanic - Machine Learning from Disaster - data analysis and visualization.ipynb | mit | sns.barplot(x='Pclass',y='Survived',data=train, hue='Sex')
"""
Explanation: The plot shows that the number of female survivors were significantly more than the male survivors. There were more survivors overall in first class than in any other class.
There were also less survivors overall in third class than in any oth... |
mne-tools/mne-tools.github.io | 0.19/_downloads/006560919734f06efa76c80dc321a748/plot_object_source_estimate.ipynb | bsd-3-clause | import os
from mne import read_source_estimate
from mne.datasets import sample
print(__doc__)
# Paths to example data
sample_dir_raw = sample.data_path()
sample_dir = os.path.join(sample_dir_raw, 'MEG', 'sample')
subjects_dir = os.path.join(sample_dir_raw, 'subjects')
fname_stc = os.path.join(sample_dir, 'sample_au... |
csadorf/signac | doc/signac_101_Getting_Started.ipynb | bsd-3-clause | import signac
assert signac.__version__ >= '0.8.0'
"""
Explanation: 1.1 Getting started
Prerequisites
Installation
This tutorial requires signac, so make sure to install the package before starting.
The easiest way to do so is using conda:
$ conda config --add channels conda-forge
$ conda install signac
or pip:
pip in... |
deepmind/enn | enn/colabs/epinet_demo.ipynb | apache-2.0 | # Copyright 2022 DeepMind Technologies Limited. 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 ... |
anhquan0412/deeplearning_fastai | deeplearning1/nbs/char-rnn.ipynb | apache-2.0 | path = get_file('nietzsche.txt', origin="https://s3.amazonaws.com/text-datasets/nietzsche.txt")
text = open(path).read().lower()
print('corpus length:', len(text))
!tail -n 25 {path}
chars = sorted(list(set(text)))
vocab_size = len(chars)+1
print('total chars:', vocab_size)
chars.insert(0, "\0")
''.join(chars[1:-6]... |
google-research/google-research | activation_clustering/examples/cifar10/train.ipynb | apache-2.0 | import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
from activation_clustering import ac_model, utils
# The same dataset preprocessing as used in the baseline cifar10 model training.
def input_fn(batch_size, ds, label_key='label'):
dataset = ds.batch(batch_size, drop_remainder=True).pre... |
microsoft/dowhy | docs/source/example_notebooks/do_sampler_demo.ipynb | mit | import os, sys
sys.path.append(os.path.abspath("../../../"))
import numpy as np
import pandas as pd
import dowhy.api
N = 5000
z = np.random.uniform(size=N)
d = np.random.binomial(1., p=1./(1. + np.exp(-5. * z)))
y = 2. * z + d + 0.1 * np.random.normal(size=N)
df = pd.DataFrame({'Z': z, 'D': d, 'Y': y})
(df[df.D ==... |
fdion/infographics_research | nfl_viz.ipynb | mit | !wget http://nflsavant.com/pbp_data.php?year=2015 -O pbp-2015.csv
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sns.set_context("talk")
plt.figure(figsize=(10, 8))
df = pd.read_csv('pbp-2015.csv')
# What do we have?
df.columns
def event_to_datetime(row):
"""Calcula... |
derekjchow/models | research/deeplab/deeplab_demo.ipynb | apache-2.0 | import os
from io import BytesIO
import tarfile
import tempfile
from six.moves import urllib
from matplotlib import gridspec
from matplotlib import pyplot as plt
import numpy as np
from PIL import Image
import tensorflow as tf
"""
Explanation: Overview
This colab demonstrates the steps to use the DeepLab model to pe... |
google/starthinker | colabs/cm360_conversion_upload_from_bigquery.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: CM360 Conversion Upload From BigQuery
Move from BigQuery to CM.
License
Copyright 2020 Google LLC,
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 c... |
broundy/udacity | nanodegrees/deep_learning_foundations/unit_1/lesson_11_handwriting_recognition/handwritten-digit-recognition-with-tflearn-exercise.ipynb | unlicense | # Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
"""
Explanation: Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
This... |
opentraffic/reporter-quality-testing-rig | notebooks/GPS Processing.ipynb | lgpl-3.0 | import pandas as pd
from matplotlib import pyplot as plt
import os
import sys; sys.path.insert(0, os.path.abspath('..'));
import validator.validator as val
import numpy as np
import time as t
import glob
import seaborn as sns
%matplotlib inline
"""
Explanation: Open Traffic Reporter: Speed Error Comparison with Real-W... |
rkburnside/python_development | bootcamp/.ipynb_checkpoints/Functions and Methods Homework-checkpoint.ipynb | gpl-2.0 | def vol(rad):
pass
"""
Explanation: Functions and Methods Homework
Complete the following questions:
Write a function that computes the volume of a sphere given its radius.
End of explanation
"""
def ran_check(num,low,high):
pass
"""
Explanation: Write a function that checks whether a number is in a given ... |
slundberg/shap | notebooks/tabular_examples/neural_networks/Census income classification with Keras.ipynb | mit | from sklearn.model_selection import train_test_split
from keras.layers import Input, Dense, Flatten, Concatenate, concatenate, Dropout, Lambda
from keras.models import Model
from keras.layers.embeddings import Embedding
from tqdm import tqdm
import shap
# print the JS visualization code to the notebook
shap.initjs()
... |
joshspeagle/frankenz | demos/2 - Photometric Inference.ipynb | mit | from __future__ import print_function, division
import sys
import pickle
import numpy as np
import scipy
import matplotlib
from matplotlib import pyplot as plt
from six.moves import range
# import frankenz code
import frankenz as fz
# plot in-line within the notebook
%matplotlib inline
np.random.seed(83481)
# re-de... |
davakian/playground | notebooks/sierpinski_cube_plotly.ipynb | mit | def sierp_cube_iter(x0, x1, y0, y1, z0, z1, cur_depth, max_depth=3, n_pts=10, cur_index=0):
if cur_depth >= max_depth:
x = np.linspace(x0, x1, n_pts)
y = np.linspace(y0, y1, n_pts)
z = np.linspace(z0, z1, n_pts)
xx, yy, zz = np.meshgrid(x, y, z)
rr = np... |
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies | ex01-Read SST NetCDF data, subsample and save.ipynb | mit | %matplotlib inline
import numpy as np
from netCDF4 import Dataset # http://unidata.github.io/netcdf4-python/
"""
Explanation: Read SST NetCDF data, Subsample and Save
This notebook carries out some basic operations:
* Open a data file
* Check variables
* Indexing to subsampe a variable
* Save data
1. Load basic libr... |
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