repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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sz2472/foundations-homework | homework_2/Homework_2_Shengying_Zhao.ipynb | mit | import pg8000
conn = pg8000.connect(database="homework2")
"""
Explanation: Homework 2: Working with SQL (Data and Databases 2016)
This homework assignment takes the form of an IPython Notebook. There are a number of exercises below, with notebook cells that need to be completed in order to meet particular criteria. Yo... |
CopernicusMarineInsitu/INSTACTraining | PythonNotebooks/PlatformPlots/Read_CORA_dataset.ipynb | mit | datafile = (
'~/CMEMS_INSTAC/INSITU_GLO_TS_OA_REP_OBSERVATIONS_013_002_b/'
'CORIOLIS-GLOBAL-CORA04.1-OBS_FULL_TIME_SERIE/data/2013/OA_CORA4.1_20131215_dat_PSAL.nc'
)
"""
Explanation: Salinity from CORA dataset
The data can be obtained from Coriolis FTP at ftp://ftp1.ifremer.fr/Core/INSITU_GLO_TS_REP_OBSERVATIO... |
eaton-lab/eaton-lab.github.io | slides/fundamentals2019/session-3-tree-think/notebooks/nb-3.3-assignment.ipynb | mit | import toytree
"""
Explanation: Notebook 3.3: Newick Assignment
Complete the notebook then download as an HTML file (toolbar -> File -> Download as) and submit your assignment by emailing to Natalie (natalie.niepoth@columbia.edu).
End of explanation
"""
newick = "((a,b),(c, d));"
tre = toytree.tree(newick)
... |
guyk1971/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... |
chuckberry1974/DataScience | Iris data set (full walk thru).ipynb | mit | from sklearn.linear_model import LogisticRegression
# instantiate the model
logreg = LogisticRegression()
#fit the model
logreg.fit(x,y)
# predict the response value
logreg.predict(x)
y_pred = logreg.predict(x)
len(y_pred)
"""
Explanation: Logistic Regression
End of explanation
"""
from sklearn import metrics
p... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/06_structured/labs/1_explore.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# change these to try this notebook out
BUCKET = 'cloud-training-demos-ml' # CHANGE this to a globally unique value. Your project name is a good option to try.
PROJECT = 'cloud-training-demos' # CHANGE this to your project name
REGION = 'us-centr... |
quantopian/research_public | notebooks/lectures/Universe_Selection/notebook.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from quantopian.pipeline.classifiers.fundamentals import Sector
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.research import run_pipeline
from quantopian.pipeline.data import... |
mjuric/LSSTC-DSFP-Sessions | Session4/Day1/LSSTC-DSFP4-Juric-FrequentistAndBayes-03-Credibility.ipynb | mit | import numpy as np
N = 5
Nsamp = 10 ** 6
sigma_x = 2
np.random.seed(0)
x = np.random.normal(0, sigma_x, size=(Nsamp, N))
mu_samp = x.mean(1)
sig_samp = sigma_x * N ** -0.5
print("{0:.3f} should equal {1:.3f}".format(np.std(mu_samp), sig_samp))
"""
Explanation: Frequentism and Bayesianism III: Confidence, Credibilit... |
desihub/desisim | doc/nb/bgs-redshift-efficiency.ipynb | bsd-3-clause | import os
import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table
from astropy.io import fits
import seaborn as sns
import multiprocessing
nproc = multiprocessing.cpu_count() // 2
from desispec.io.util import write_bintable
from desiutil.log import get_logger
log = get_logger()
%matplotli... |
ituethoslab/navcom-2017 | exercises/Week 3-What are Digital Methods/Exercises week 3.ipynb | gpl-3.0 | import pandas as pd
%matplotlib inline
"""
Explanation: Exercises week 3: What are Digital Methods?
1. Install Tableau Desktop
<img src="https://cdns.tblsft.com/sites/default/files/pages/answerdeeperquestions.png" style="width: 50%; float: right;"></img>
Students are given a license for this software.
2. Open the DAMD... |
davofis/computational_seismology | lambs_problem/lambs_problem_solution.ipynb | gpl-3.0 | # Import all necessary libraries, this is a configuration step for the exercise.
# Please run it before the simulation code!
import numpy as np
import matplotlib.pyplot as plt
import os
from ricker import ricker
# Show the plots in the Notebook.
plt.switch_backend("nbagg")
# Compile the source code (needs gfortran!)
... |
rongchuhe2/workshop_data_analysis_python | Introduction_to_Python.ipynb | mit | 4
2 + 2
50 - 5*6
(50-5)*6
8/5
8//5 # Floor division discards the fractional part
8%5 # The % operator return the remainder of the division
"""
Explanation: Using Python as a Calculator
Let's try some simple python commands
Numbers
The interpreter acts as a simple calculator: you can type an expression at it an... |
GoogleCloudPlatform/mlops-on-gcp | immersion/supplemental/solutions/text2hub.ipynb | apache-2.0 | import os
import tensorflow as tf
import tensorflow_hub as hub
"""
Explanation: Custom TF-Hub Word Embedding with text2hub
Learning Objectives:
1. Learn how to deploy AI Hub Kubeflow pipeline
1. Learn how to configure the run parameters for text2hub
1. Learn how to inspect text2hub generated artifacts and word ... |
giacomov/3ML | examples/obsolete/gbm_lle_catalog_demo.ipynb | bsd-3-clause | %matplotlib inline
%matplotlib notebook
from astropy.time import Time
from threeML import *
get_available_plugins()
"""
Explanation: GBM, LAT LLE and Swift Catalogs
Using 3ML's catalog and data downloading tools, it is easy to build an analysis for either a single or multiple GRBs from start to finish.
Here, we de... |
sylvchev/coursMLpython | 3b-RegressionLineaire-Salaires.ipynb | unlicense | % matplotlib inline
from numpy import zeros, zeros_like, ones, vstack, mod, loadtxt
import matplotlib.pyplot as plt
from numpy.linalg import pinv
"""
Explanation: Regression linéaire avec les moindres carrés
End of explanation
"""
def h(theta, x):
y_estimated = 0.
for theta_i, x_i in zip(theta, x):
y... |
keras-team/keras-io | examples/keras_recipes/ipynb/sample_size_estimate.ipynb | apache-2.0 | import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow import keras
import tensorflow_datasets as tfds
from tensorflow.keras import layers
# Define seed and fixed variables
seed = 42
tf.random.set_seed(seed)
np.random.seed(seed)
AUTO = tf.data.AUTOTUNE
"""
Explanation: Estimating r... |
Hvass-Labs/TensorFlow-Tutorials | 04_Save_Restore.ipynb | mit | from IPython.display import Image
Image('images/02_network_flowchart.png')
"""
Explanation: TensorFlow Tutorial #04
Save & Restore
by Magnus Erik Hvass Pedersen
/ GitHub / Videos on YouTube
WARNING!
This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being update... |
iannesbitt/ml_bootcamp | Python-Crash-Course/Python Crash Course Exercises .ipynb | mit | 7**4
"""
Explanation: Python Crash Course Exercises
This is an optional exercise to test your understanding of Python Basics. If you find this extremely challenging, then you probably are not ready for the rest of this course yet and don't have enough programming experience to continue. I would suggest you take anothe... |
minyoungg/selfconsistency | demo.ipynb | apache-2.0 | # Arg: quality and num_per_dim -> tradeoffs between quality and time spent running
# quality affects dense=False, and num_per_dim affects dense=True
ckpt_path = './ckpt/exif_final/exif_final.ckpt'
exif_demo = demo.Demo(ckpt_path=ckpt_path, use_gpu=0, quality=3.0, num_per_dim=30)
"""
Explanation: Initialize Demo Solve... |
ernestyalumni/MLgrabbag | theano_RNN_LSTM.ipynb | mit | %matplotlib inline
from collections import namedtuple
import matplotlib.pyplot as plt
import sklearn
from sklearn import datasets
import pandas as pd
import theano
from theano import function, config, sandbox, shared
import theano.tensor as T
import numpy as np
import scipy
import time
print( theano.config.devic... |
tensorflow/text | docs/tutorials/bert_orbit.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... |
seniosh/StatisticalMethods | notes/InferenceSandbox.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
import scipy.stats
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 5.0)
# the model parameters
a = np.pi
b = 1.6818
# my arbitrary constants
mu_x = np.exp(1.0) # see definitions above
tau_x = 1.0
s = 1.0
N = 50 # number of data points
# get some x's and y... |
bjshaw/phys202-2015-work | assignments/assignment04/MatplotlibEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Matplotlib Exercise 1
Imports
End of explanation
"""
import os
assert os.path.isfile('yearssn.dat')
"""
Explanation: Line plot of sunspot data
Download the .txt data for the "Yearly mean total sunspot number [1700 - now]" from th... |
tensorflow/docs-l10n | site/ko/guide/migrate/logging_stop_hook.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... |
edwardd1/phys202-2015-work | assignments/assignment06/ProjectEuler17.ipynb | mit | #First I define a dictionary of all the necessary words to make numbers
numbers = {1:'one', 2:'two', 3:'three', 4:'four', 5:'five', 6:'six', 7:'seven', 8:'eight', 9:'nine',
10:'ten', 11:'eleven', 12:'twelve', 13:'thirteen', 14:'fourteen', 15:'fifteen',
16:'sixteen', 17:'seventeen', 18:'eighteen', ... |
ajhenrikson/phys202-2015-work | assignments/assignment12/FittingModelsEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Fitting Models Exercise 2
Imports
End of explanation
"""
def modl(t,A,o,l,d):
return A*np.exp(-1*t)*np.cos(o*t)+d
thetabest,thetacov=opt.curve_fit(modl,tdata,ydata,np.array((6,1,1,0)),dy,absolute_... |
Benedicto/ML-Learning | Linear_Regression_2_multiple_regression_assignment_1.ipynb | gpl-3.0 | import graphlab
"""
Explanation: Regression Week 2: Multiple Regression (Interpretation)
The goal of this first notebook is to explore multiple regression and feature engineering with existing graphlab functions.
In this notebook you will use data on house sales in King County to predict prices using multiple regressi... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_parcellation.ipynb | bsd-3-clause | # Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
from surfer import Brain
import mne
subjects_dir = mne.datasets.sample.data_path() + '/subjects'
mne.datasets.fetch_hcp_mmp_parcellation(subjects_dir=subjects_dir,
verbose=True)
labels = mne.read_label... |
opengeostat/pygslib | pygslib/Ipython_templates/.ipynb_checkpoints/backtr_raw-checkpoint.ipynb | mit | #general imports
import matplotlib.pyplot as plt
import pygslib
from matplotlib.patches import Ellipse
import numpy as np
import pandas as pd
#make the plots inline
%matplotlib inline
"""
Explanation: Testing the back normalscore transformation
End of explanation
"""
#get the data in gslib format into a pa... |
mne-tools/mne-tools.github.io | 0.23/_downloads/8fc13fc21872d78f6b3678c81e192a76/decoding_xdawn_eeg.ipynb | bsd-3-clause | # Authors: Alexandre Barachant <alexandre.barachant@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import StratifiedKFold
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import c... |
lithiumdenis/MLSchool | 1. Визуализация.ipynb | mit | #Выберем для наглядности часть таблицы, где только атакующие и защитники
attDef = df[ ['attacker_king', 'defender_king'] ]
#Отсортируем сначала по attacker_king, внутри attacker_king - по defender_king
attDef = attDef.sort_values(by=['attacker_king', 'defender_king'], ascending=[False, False]);
attDef.attacker_king.v... |
mbakker7/ttim | notebooks/well_near_river_or_wall.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from ttim import *
"""
Explanation: Comparison of HeadLineSinkString and LeakyLineDoubletString vs. image well
End of explanation
"""
ml1 = ModelMaq(kaq=10, z=[20, 0], Saq=[0.1], phreatictop=True, tmin=0.001, tmax=100)
w1 = Well(ml1, 0, 0, rw=0.3,... |
ES-DOC/esdoc-jupyterhub | notebooks/cas/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', 'cas', 'sandbox-2', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: CAS
Source ID: SANDBOX-2
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbulen... |
theJollySin/python_for_scientists | classes/12_matplotlib/2_points_and_errorbars.ipynb | gpl-3.0 | import numpy
from matplotlib import pyplot
%matplotlib inline
### generate some random data
xdata = numpy.arange(15)
ydata = numpy.random.randn(15) + xdata
### initialize the "figure" and "axes" objects
fig, ax = pyplot.subplots()
points_plot = ax.plot(xdata, ydata, marker='o')
"""
Explanation: Scatter Plots
Perh... |
JJINDAHOUSE/deep-learning | dcgan-svhn/DCGAN.ipynb | mit | %matplotlib inline
import pickle as pkl
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat
import tensorflow as tf
!mkdir data
"""
Explanation: Deep Convolutional GANs
In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a De... |
alvaroing12/CADL | session-3/lecture-3.ipynb | apache-2.0 | # imports
%matplotlib inline
# %pylab osx
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
# Some additional libraries which we'll use just
# to produce some visualizations of our training
from libs.utils import montage
from libs i... |
sassoftware/sas-viya-machine-learning | Python-integration/Viya 2020 Example.ipynb | apache-2.0 | # Packages for Python Basics
import sys
import numpy as np
import pandas as pd
# Packages for Building Model Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
import xgboost as xgb
# Pac... |
wanderer2/pymc3 | docs/source/notebooks/lasso_block_update.ipynb | apache-2.0 | %pylab inline
from matplotlib.pylab import *
from pymc3 import *
import numpy as np
d = np.random.normal(size=(3, 30))
d1 = d[0] + 4
d2 = d[1] + 4
yd = .2*d1 +.3*d2 + d[2]
"""
Explanation: Sometimes, it is very useful to update a set of parameters together. For example, variables that are highly correlated are ofte... |
ondrejiayc/StatisticalMethods | examples/Cepheids/FirstLook.ipynb | gpl-2.0 | from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (15.0, 8.0)
"""
Explanation: A First Look at the Periods and Luminosities of Cepheid Stars
Cepheids are stars whose brightness oscillates with a stable period that appears to ... |
ShiroJean/Breast-cancer-risk-prediction | .ipynb_checkpoints/SVM Classification-checkpoint.ipynb | mit | #load libraries
import pandas as pd
import numpy as np
#Supervised learning
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
#Load data set
from sklearn.datasets import load_breast_cancer
cancer = load_breast_cancer()
cancer =pd.DataFrame(cancer.data)
cancer.head()
#Split data set ... |
matthewfeickert/fellowship-project | Notebooks/HistFactory_Examples/One-Bin.ipynb | mit | import ROOT
ROOT.RooMsgService.instance().setGlobalKillBelow(5)
%jsroot on
import sys
import os
# Don't require pip install to test out
sys.path.append(os.getcwd() + '/../../src')
from dfgmark import histfactorybench as hfbench
#import rootpy
#from rootpy.stats.histfactory import utils as hfutils
"""
Explanation: Ex... |
studentofdata/qcew | vmfiles/IPNB/Examples/b Graphics/20 mpld3.ipynb | bsd-3-clause | # first the imports: the matplotlib usuals, plus mpld3
import numpy as np
import matplotlib.pyplot as plt
import mpld3
plt.style.use('bmh')
"""
Explanation: mpld3
mpld3 is a Python package that adds interactivity to Matplotlib graphics, for enhanced visualization in browsers. It does so by producing [D3.js]( out of t... |
eshlykov/mipt-day-after-day | labs/term-4/lab-1-4.ipynb | unlicense | import numpy as np
import scipy as ps
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Работа 1.4. Исследование вынужденной прецессии гироскопа
Цель работы: исследовать вынужденную прецессию уравновешенного симметричного гироскопа; установить зависимость угловой скорости вынужден... |
ajgeers/frw | frw.ipynb | bsd-2-clause | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from ipywidgets import interact, IntSlider, FloatSlider
%matplotlib inline
"""
Explanation: Flow rate waveform transformation
Arjan Geers
An artery's flow rate waveform (FRW) can be characterized in many ways. Three common descriptors ar... |
statsmaths/stat665 | lectures/lec16/.ipynb_checkpoints/notebook16-checkpoint.ipynb | gpl-2.0 | %pylab inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD, RMSprop
from keras.utils import np_utils
from keras.regularizers import l... |
NeuroDataDesign/pan-synapse | pipeline_1/background/Cluster_Components_Class_Algorithms.md.ipynb | apache-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
import sys
sys.path.insert(0,'../code/functions/')
import tiffIO as tIO
import connectLib as cLib
import plosLib as pLib
import time
import scipy.ndimage as ndimage
import numpy as np
"""
Explanation: Algorithm
Description
The Cluster Components class takes in a binar... |
ricklupton/beamfe | theory/FE element matrices.ipynb | mit | xi, l, rho = symbols('xi, l, rho')
# Shape functions
S = Matrix(np.zeros((4, 12)))
x2 = (1 - xi)
S[0, 0 ] = x2 # extension
S[0, 6 ] = xi
S[1, 1 ] = x2**2 * (3 - 2*x2) # y-deflection
S[1, 7 ] = xi**2 * (3 - 2*xi)
S[1, 5 ] = -x2**2 * (x2 - 1) * l
S[1, 11] = xi**2 * (xi - 1) * l
S[2, 2 ] ... |
OSGeoLabBp/tutorials | english/python/pylint.ipynb | cc0-1.0 | !python -m pip install pylint -q
"""
Explanation: <a href="https://colab.research.google.com/github/OSGeoLabBp/tutorials/blob/master/english/python/pylint.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Pylint
A tool to check your Python code. Pylin... |
unnikrishnankgs/va | venv/lib/python3.5/site-packages/matplotlib/backends/web_backend/nbagg_uat.ipynb | bsd-2-clause | import matplotlib
reload(matplotlib)
matplotlib.use('nbagg')
import matplotlib.backends.backend_nbagg
reload(matplotlib.backends.backend_nbagg)
"""
Explanation: UAT for NbAgg backend.
The first line simply reloads matplotlib, uses the nbagg backend and then reloads the backend, just to ensure we have the latest modi... |
qutip/qutip-notebooks | examples/landau-zener-stuckelberg.ipynb | lgpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from qutip import *
from qutip.ui.progressbar import TextProgressBar as ProgressBar
"""
Explanation: QuTiP example: Landau-Zener-Stuckelberg inteferometry
J.R. Johansson and P.D. Nation
For more information about QuTiP see http://qutip.org
End of... |
rashikaranpuria/Machine-Learning-Specialization | Regression/Assignment_two/week-2-multiple-regression-assignment-1-blank.ipynb | mit | import graphlab
"""
Explanation: Regression Week 2: Multiple Regression (Interpretation)
The goal of this first notebook is to explore multiple regression and feature engineering with existing graphlab functions.
In this notebook you will use data on house sales in King County to predict prices using multiple regressi... |
ernestyalumni/CompPhys | crack/BigO.ipynb | apache-2.0 | def sumOfN(n):
theSum = 0
for i in range(1,n+1):
theSum = theSum + i
return theSum
print(sumOfN(10))
def foo(tom):
fred = 0
for bill in range(1,tom+1):
barney = bill
fred = fred + barney
return fred
print(foo(10))
import time
def sumOfN2(n):
s... |
atulsingh0/MachineLearning | python_DC/ST_Python_01b.ipynb | gpl-3.0 | # import
import pandas as pd
import numpy as np
import seaborn as sns
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Statistical Thinking in Python (Part 1)
Thinking probabilistically
End of explanation
"""
np.random.seed(42)
random_numbers = np.empty(1000... |
microsoft/dowhy | docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb | mit | # Creating some simulated data for our example
import pandas as pd
import numpy as np
num_users = 10000
num_months = 12
signup_months = np.random.choice(np.arange(1, num_months), num_users) * np.random.randint(0,2, size=num_users) # signup_months == 0 means customer did not sign up
df = pd.DataFrame({
'user_id': n... |
astroumd/GradMap | notebooks/Lectures2021/Lecture3/Preview_from2020Lecture3_Instructor.ipynb | gpl-3.0 | import numpy as np
"""
Explanation: Review from the previous lecture
In yesterday's Lecture 2, you learned how to use the numpy module, how to make your own functions, and how to import and export data. Below is a quick review before we move on to Lecture 3.
Remember, to use the numpy module, first it must be imported... |
paultheastronomer/OAD-Data-Science-Toolkit | Teaching Materials/Machine Learning/Supervised Learning/Courses/Astrophysical Machine Learning/Part 1/Exercise 1.ipynb | gpl-3.0 | import numpy as np
# Define your function
def softmax(x):
# This is where you write your code!
vector = "This is only psudo code.\nYou will have to write this function yourself!"
return vector # Replace this with the new array
# Test it out on an array
test=[1,3,2]
print(softmax(test))
# The result should... |
melissawm/oceanobiopython | Notebooks/Aula_2.ipynb | gpl-3.0 | minhalista = "Como fazer uma list comprehension".split()
"""
Explanation: List Comprehensions
End of explanation
"""
minhalista
"""
Explanation: Observe que na linha acima aplicamos o método split diretamente a uma string, sem precisarmos nomear uma variável com o conteúdo da string!
End of explanation
"""
minhal... |
trolldbois/python-haystack-reverse | docs/Haystack_reverse_CLI.ipynb | gpl-3.0 | !haystack-reverse --help
"""
Explanation: Usage reference guide for haystack-reverse
this is an example of every haystack-reverse commands.
The zeus.vmem.856.dump is there https://dl.dropboxusercontent.com/u/10222931/HAYSTACK/zeus.vmem.856.dump.tgz
It was extracted from pid 856 from the zeus.img image from http://malw... |
mjbommar/cscs-530-w2016 | notebooks/basic-space/003-basic_network.ipynb | bsd-2-clause | %matplotlib inline
# Imports
import networkx as nx
import numpy
import matplotlib.pyplot as plt
import pandas
import seaborn; seaborn.set()
seaborn.set_style("darkgrid")
# Import widget methods
from IPython.html.widgets import *
"""
Explanation: CSCS530 Winter 2016
Complex Systems 530 - Computer Modeling of Complex... |
tensorflow/docs-l10n | site/ko/model_optimization/guide/clustering/clustering_comprehensive_guide.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... |
isendel/machine-learning | ml-regression/week3-4/week-4-ridge-regression-assignment-1-blank.ipynb | apache-2.0 | import graphlab
import numpy as np
"""
Explanation: Regression Week 4: Ridge Regression (interpretation)
In this notebook, we will run ridge regression multiple times with different L2 penalties to see which one produces the best fit. We will revisit the example of polynomial regression as a means to see the effect of... |
PMEAL/OpenPNM | examples/tutorials/geometry/stick_and_ball.ipynb | mit | import openpnm as op
%config InlineBackend.figure_formats = ['svg']
import matplotlib.pyplot as plt
pn = op.network.Cubic(shape=[20, 20, 20], spacing=100)
"""
Explanation: The Stick and Ball Geometry
The SpheresAndCylinders class contains an assortment of pore-scale models that generate geometrical information assumi... |
bgruening/EDeN | examples/Sequence_example.ipynb | gpl-3.0 | %matplotlib inline
"""
Explanation: Example
Consider sequences that are increasingly different. EDeN allows to turn them into vectors, whose similarity is decreasing.
End of explanation
"""
import random
def make_data(size):
text = ''.join([str(unichr(97+i)) for i in range(26)])
seqs = []
def swap_two_... |
mne-tools/mne-tools.github.io | 0.21/_downloads/ee17e3e8df43ce4f0119faeeeccc374f/plot_sensors_time_frequency.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
# Richard Höchenberger <richard.hoechenberger@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.time_frequenc... |
hetland/python4geosciences | materials/ST_singular_value_decomposition.ipynb | mit |
# The tranformation matrix
M = np.array([[ 0.50, 0.75],
[-0.25, 1.50]])
# Generate some random points scattered around the origin
N = 10
x = np.random.randn(2, N)
# Transform the points into the new coordinate system
x_trans = np.dot(M, x)
fig, axs = plt.subplots(2, 3, sharex=True, sharey=True, squeez... |
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies | ex09-Read SST and visualize in different projections.ipynb | mit | %matplotlib inline
import numpy as np
from netCDF4 import Dataset # http://unidata.github.io/netcdf4-python/
import matplotlib.pyplot as plt # to generate plots
from mpl_toolkits.basemap import Basemap # plot on map projections
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 15, 6
""... |
DavidPowell/openmodes-examples | Using and creating geometric shapes.ipynb | gpl-3.0 | import openmodes
import os
import os.path as osp
os.listdir(openmodes.geometry_dir)
"""
Explanation: Working with the included and custom geometries
For convenience, a number of common meta-atom geometries are included with OpenModes. The code below shows a list of those which are currently available
End of explanati... |
thehackerwithin/berkeley | code_examples/keras_introduction/Multi_layer_keras.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import SGD
%matplotlib inline
"""
Explanation: Building and training a mutli-layer network with Keras
End of explanation
"""
# Load data
df = pd.... |
qqwjq/lightFM | examples/movielens/learning_schedules.ipynb | apache-2.0 | import numpy as np
import data
%matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from lightfm import LightFM
train, test = data.get_movielens_data()
train.data = np.ones_like(train.data)
test.data = np.ones_like(test.data)
from sklearn.metrics import roc_auc_score
def preci... |
dhuppenkothen/BayesPSD | docs/Demo.ipynb | bsd-2-clause | %matplotlib inline
import matplotlib.pyplot as plt
## this is just to make plots prettier
## comment out if you don't have seaborn
import seaborn as sns
sns.set()
########################################
import numpy as np
"""
Explanation: How To Search for QPOs with BayesPSD
This notebook is a demonstration for h... |
nvenayak/impact | docs/source/create_new_feature.ipynb | gpl-3.0 | from impact.core.features import BaseAnalyteFeature, BaseAnalyteFeatureFactory
"""
Explanation: Creating a feature
Features are derived from multiple analytes within a single trial. For example, product yield is a function of the substrate consumed, and product produced. Features are registered to the SingleTrial clas... |
Dharamsitejas/E4571-Personalisation-Theory-Project | Part2/analysis/tree_based_ann.ipynb | mit | data = pd.read_csv('../created_datasets/Combine.csv')
rows = data.user_id.unique()
cols = data['isbn'].unique()
print("Sparsity :", 100 - (data.shape[0]/(len(cols)*len(rows)) * 100))
idict = dict(zip(cols, range(len(cols))))
udict = dict(zip(rows, range(len(rows))))
data.user_id = [
udict[i] for i in data.user... |
tknapen/FIRDeconvolution | test/pupil_preprocess_python.ipynb | mit | from __future__ import division
import numpy as np
import scipy as sp
import matplotlib
import matplotlib.pyplot as pl
%matplotlib inline
import seaborn as sn
sn.set(style="ticks")
# extra dependencies of this notebook, for data loading and fitting of kernels
import pandas as pd
from lmfit import minimize, Paramet... |
ES-DOC/esdoc-jupyterhub | notebooks/pcmdi/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', 'pcmdi', 'sandbox-1', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: PCMDI
Source ID: SANDBOX-1
Sub-Topics: Radiative Forcings.
Properties: 85 (4... |
mne-tools/mne-tools.github.io | 0.19/_downloads/ef89d1f7daeb4e357098461753c3af0f/plot_source_alignment.ipynb | bsd-3-clause | import os.path as op
import numpy as np
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
raw_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif')
trans_fname = op.join(data_path, 'MEG', 'sample',
... |
Luindil/Glassure | glassure/notebooks/Effect on extrapolation and optimization.ipynb | mit | %matplotlib inline
import os
import sys
import matplotlib.pyplot as plt
sys.path.insert(1, os.path.join(os.getcwd(), '../../'))
from glassure.core.calc import calculate_fr, calculate_sq, optimize_sq, calculate_gr
from glassure.core.utility import extrapolate_to_zero_poly, convert_density_to_atoms_per_cubic_angstrom
fr... |
mne-tools/mne-tools.github.io | dev/_downloads/a9e07affc8c71aa96bb4ffe855ff552c/morph_surface_stc.ipynb | bsd-3-clause | # Author: Tommy Clausner <tommy.clausner@gmail.com>
#
# License: BSD-3-Clause
import os
import os.path as op
import mne
from mne.datasets import sample
print(__doc__)
"""
Explanation: Morph surface source estimate
This example demonstrates how to morph an individual subject's
:class:mne.SourceEstimate to a common r... |
hootnot/oanda-api-v20 | jupyter/historical.ipynb | mit | import json
import oandapyV20
import oandapyV20.endpoints.instruments as instruments
from exampleauth import exampleauth
accountID, access_token = exampleauth.exampleAuth()
client = oandapyV20.API(access_token=access_token)
instrument = "EUR_USD"
params = {
"from": "2017-01-01T00:00:00Z",
"granularity": "H1",... |
phoebe-project/phoebe2-docs | 2.3/tutorials/t0s.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
"""
Explanation: Various t0s
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
import mat... |
halfak/are-the-bots-really-fighting | analysis/main/5-3-reverts-per-page.ipynb | mit | import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import glob
import datetime
import pickle
%matplotlib inline
start = datetime.datetime.now()
"""
Explanation: Section 5.3: Reverts per page (setup and exploratory)
This is a data analysis script used to produce findings in th... |
wikistat/Intro-Python | Cal4-PythonProg.ipynb | mit | for i in range(5):
print (i)
"""
Explanation: <center>
<a href="http://www.insa-toulouse.fr/" ><img src="http://www.math.univ-toulouse.fr/~besse/Wikistat/Images/logo-insa.jpg" style="float:left; max-width: 120px; display: inline" alt="INSA"/></a>
<a href="http://wikistat.fr/" ><img src="http://www.math.univ-toulo... |
ameliecordier/iutdoua-info_algo2015 | 2015-09-14 - TD2 - Variables et conditions.ipynb | cc0-1.0 | age = 33
"""
Explanation: Le concept de variable
Une variable est une "boîte" dans laquelle il est possible de stocker une valeur (et de la changer au fil du temps). Les variables peuvent être de plusieurs types, mais c'est une autre histoire, que l'on verra plus tard.
On peut faire plusieurs opérations sur les vari... |
probml/pyprobml | notebooks/book2/18/bnn_mnist_sgld.ipynb | mit | %%capture
!pip install -qq git+https://github.com/jamesvuc/jax-bayes
!pip install -qq SGMCMCJax
!pip install -qq distrax
import jax.numpy as jnp
from jax.experimental import optimizers
import jax
try:
import jax_bayes
except ModuleNotFoundError:
%pip install -qq jax_bayes
import jax_bayes
try:
impor... |
BrainIntensive/OnlineBrainIntensive | resources/nipype/nipype_tutorial/notebooks/basic_mapnodes.ipynb | mit | from nipype import Function
def square_func(x):
return x ** 2
square = Function(["x"], ["f_x"], square_func)
"""
Explanation: <img src="../static/images/mapnode.png" width="300">
MapNode
If you want to iterate over a list of inputs, but need to feed all iterated outputs afterwards as one input (an array) to the n... |
datactive/bigbang | examples/attendance/IETF Attendance.ipynb | mit | from ietfdata.datatracker import *
from ietfdata.datatracker_ext import *
import pandas as pd
import matplotlib.pyplot as plt
import dataclasses
datatracker = DataTracker()
meetings = datatracker.meetings(meeting_type = datatracker.meeting_type(MeetingTypeURI('/api/v1/name/meetingtypename/ietf/')))
full_ietf_meet... |
kirichoi/tellurium | examples/notebooks/models/yeast_glycolysis.ipynb | apache-2.0 | %matplotlib inline
from __future__ import print_function
import tellurium as te
# load the model
r = te.loadSBMLModel('yeast_glycolysis.xml')
# promote all the local parameters to global parameters
sbml_str = r.getSBML()
sbmlp_str = r.getParamPromotedSBML(sbml_str)
r = te.loads(sbmlp_str)
print(r.getGlobalParameterId... |
karlstroetmann/Algorithms | Python/Chapter-05/Three-Way-Merge-Sort-Array.ipynb | gpl-2.0 | def sort(L):
A = L[:]
mergeSort(L, 0, len(L), A)
"""
Explanation: 3-Way Merge Sort: An Array-Based Implementation
The function $\texttt{sort}(L)$ sorts the list $L$ in place using merge sort.
It takes advantage of the fact that, in Python, lists are stored internally as arrays.
The function sort is a wrapper f... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_visualize_epochs.ipynb | bsd-3-clause | import os.path as op
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.load_data().filter(None, 9, fir_design='firwin')
raw.set_eeg_reference('average', projection=True) # set EEG average refe... |
quantopian/research_public | notebooks/lectures/VaR_and_CVaR/notebook.ipynb | apache-2.0 | import numpy as np
import pandas as pd
from scipy.stats import norm
import time
import matplotlib.pyplot as plt
"""
Explanation: Portfolio Value at Risk and Conditional Value at Risk
By Jonathan Larkin and Delaney Granizo-Mackenzie.
Part of the Quantopian Lecture Series:
www.quantopian.com/lectures
github.com/quant... |
Xilinx/BNN-PYNQ | notebooks/LFC-BNN_Chars_Webcam.ipynb | bsd-3-clause | import bnn
"""
Explanation: BNN on Pynq
This notebook covers how to use Binary Neural Networks on Pynq.
It shows an example of handwritten character recognition using a binarized neural network composed of 4 fully connected layers with 1024 neurons each, trained on the NIST database of handwritten characters.
In ord... |
adityaka/misc_scripts | python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/04_05/Begin/Panels.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
import datetime
from pandas_datareader import data, wb
pd.set_eng_float_format(accuracy=2, use_eng_prefix=True)
my_first_panel = pd.Panel(np.random.randn(2, 5, 4),
items=['Item01', 'Item02'],
major_axis=pd.date_range('9/6/2016... |
hvillanua/deep-learning | image-classification/dlnd_image_classification.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
class DLProgress(tqdm):
last_block = 0
def hoo... |
JJINDAHOUSE/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... |
bloomberg/bqplot | examples/Interactions/Selectors.ipynb | apache-2.0 | import pandas as pd
import numpy as np
symbol = "Security 1"
symbol2 = "Security 2"
price_data = pd.DataFrame(
np.cumsum(np.random.randn(150, 2).dot([[0.5, 0.4], [0.4, 1.0]]), axis=0) + 100,
columns=[symbol, symbol2],
index=pd.date_range(start="01-01-2007", periods=150),
)
dates_actual = price_data.index... |
tiagoantao/biopython-notebook | notebooks/16 - Supervised learning methods.ipynb | mit | from Bio import LogisticRegression
xs = [[-53, -200.78], [117, -267.14], [57, -163.47], [16, -190.30],
[11, -220.94], [85, -193.94], [16, -182.71], [15, -180.41],
[-26, -181.73], [58, -259.87], [126, -414.53], [191, -249.57],
[113, -265.28], [145, -312.99], [154, -213.83], [147, -380.85],[93, -291.13]... |
whitead/numerical_stats | unit_3/lectures/lecture_2.ipynb | gpl-3.0 | from IPython.display import Math
from math import frexp, pi
import math
#Convert a float into its mantissa and exponent and print as LaTeX
def fprint(x):
m,e = frexp(x)
return Math('{:4} \\times 2^{{{:}}}'.format(m, int(e)))
#Convert a mantissa from decimal to binary and print as LaTeX
def ffrac_ltx(x, te... |
metpy/MetPy | dev/_downloads/7fd39302ff9f3fa4a7870d3c31b04722/cross_section.ipynb | bsd-3-clause | import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.interpolate import cross_section
"""
Explanation: Cross Section Analysis
The MetPy function metpy.interpolat... |
manipopopo/tensorflow | tensorflow/contrib/eager/python/examples/notebooks/eager_basics.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... |
daniel-acuna/python_data_science_intro | notebooks/lab-random_forest_for_predicting_credit_score.ipynb | mit | import pandas as pd
import numpy as np
X = np.array([
[0, 0],
[0, 1],
[1, 0],
[1, 1]])
y = np.array([0, 1, 1, 0])
pd.DataFrame(np.hstack((X, y.reshape(-1, 1))), columns=['x1', 'x2', 'y'])
%matplotlib inline
import matplotlib.pyplot as plt
plt.scatter(X[y==0, 0], X[y==0, 1], c='red')... |
dlegor/Tutorial-Pandas-Python | Code/Capitulo_2-Exploración.ipynb | cc0-1.0 | #Se prepara el entorno de trabajo
%matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
#matplotlib.style.use('ggplot') se puede correr este código para usar gráficos del tipo de ggplot2 en R
plt.rcParams['figure.figsize']=(20,7)
# -*- coding:... |
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