repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
TurkuNLP/BINF_Programming | exercises/week-1-exercises.ipynb | gpl-2.0 | def trim(s):
# implement this function
pass
# test case
import Bio.Seq as BS
s = BS.Seq("ACGCGGCGTG")
print(s, "has length", len(s))
# write a piece of code here which will
# print the translated sequence 'TRR'
# without any errors
"""
Explanation: Exercise 1.1 Trim sequence to multiples of three characters
... |
PWhiddy/kbmod | notebooks/kbmod_demo.ipynb | bsd-2-clause | import numpy as np
import matplotlib.pyplot as plt
from searchImage import searchImage
from analyzeImage import analyzeImage
%matplotlib inline
%load_ext autoreload
%autoreload 2
"""
Explanation: KBMOD Demo
The purpose of this demo is to showcase how KBMOD can be used to search through images for moving objects. The i... |
Diegoparrape/testgit-Diego | Parcial_1SimMatDiegoP.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
def theta_t(theta_0, theta_0_dot, g, l, t):
omega_0 = np.sqrt(g/l)
return theta_0 * np.cos(omega_0 * t) + theta_0_dot * np.sin(omega_0 * t)/omega_0
"""
Explanation: <font color = blue>Primer examen parcial </font>
<font color= #8A0829> Simu... |
minrk/launchabel | pub-mpi.ipynb | bsd-2-clause | from ipyparallel import Client, error
cluster = Client()
view = cluster[:]
"""
Explanation: Load IPython support for working with MPI tasks
End of explanation
"""
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Let's also load the plotting and numerical libraries so we have th... |
synthicity/synthpop | demos/non_census_synthesis.ipynb | bsd-3-clause | hh_marginal_file = 'input_data/hh_marginals.csv'
person_marginal_file = 'input_data/person_marginals.csv'
hh_sample_file = 'input_data/household_sample.csv'
person_sample_file = 'input_data/person_sample.csv'
"""
Explanation: Specify sample data csv paths. See the files listed here for expected structure. Marginal tab... |
ES-DOC/esdoc-jupyterhub | notebooks/csir-csiro/cmip6/models/sandbox-2/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csir-csiro', 'sandbox-2', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: CSIR-CSIRO
Source ID: SANDBOX-2
Topic: Ocean
Sub-Topics: Timestepping Framewor... |
DavidVargasMora/SimulationLabs | 160002934JuanVargas.ipynb | cc0-1.0 | def generadorCongruencial(n):
m = 91
a = 11
b = 4
Xo = 17
lastXn = Xo
Xi = []
Ui = []
ls = 1
first = 0
print "Used parameters: "
print "a = "+str(a)+" b = "+str(b)+" m = "+str(m)
print "Random numbers generated: "
for i in range(n):
Xn = float((a*lastXn +... |
AdityaSoni19031997/Machine-Learning | Coursera_DL/Logistic+Regression+with+a+Neural+Network+mindset+v4.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
from lr_utils import load_dataset
%matplotlib inline
"""
Explanation: Logistic Regression with a Neural Network mindset
Welcome to your first (required) programming assignment! You will build a ... |
arne-cl/alt-mulig | information-status/tuebadz-data-extraction.ipynb | gpl-3.0 | import os
import discoursegraphs as dg
import discourseinfostat as di
TUEBADZ8_FILE = dg.corpora.TUEBADZ_PATH
corpus = dg.read_exportxml(TUEBADZ8_FILE, debug=False)
doc = corpus.next()
debug_corpus = dg.read_exportxml(TUEBADZ8_FILE, debug=True)
debug_doc = debug_corpus.next()
edg = dg.readwrite.exportxml.ExportXML... |
SHDShim/pytheos | examples/3_calculate_thermal_terms_ConstQ.ipynb | apache-2.0 | %config InlineBackend.figure_format = 'retina'
"""
Explanation: For high dpi displays.
End of explanation
"""
import numpy as np
import matplotlib.pyplot as plt
import uncertainties as uct
from uncertainties import unumpy as unp
import pandas as pd
import pytheos as eos
"""
Explanation: 0. General note
This noteboo... |
danlamanna/scratch | notebooks/02_Raster_Data.ipynb | apache-2.0 | %matplotlib inline
from matplotlib import pylab as plt
"""
Explanation: Loading raster data on to the map
In this notebook we'll take a look at using the built in tile server to render raster data to the map. The tile server used is based on KTile a fork of TileStache and is directly integrated into the Jupyter Notebo... |
dato-code/tutorials | notebooks/linear_regression_benchmark.ipynb | apache-2.0 | import graphlab as gl
"""
Explanation: <h2>How fast are Out-of-Core Algorithms? A Linear Regression Benchmark</h2>
In this notebook we demonstrate the capabilities of GraphLab Create on the basic task of linear regression, comparing it to the widely used machine learning package scikit-learn. Scikit-learn contains a ... |
tuanavu/coursera-university-of-washington | machine_learning/2_regression/assignment/week5/week-5-lasso-assignment-2-blank.ipynb | mit | import graphlab
"""
Explanation: Regression Week 5: LASSO (coordinate descent)
In this notebook, you will implement your very own LASSO solver via coordinate descent. You will:
* Write a function to normalize features
* Implement coordinate descent for LASSO
* Explore effects of L1 penalty
Fire up graphlab create
Make... |
ga7g08/ga7g08.github.io | _notebooks/2016-09-07-Example-of-zero-padding-using-Scipy.ipynb | mit | N = 600
T = 1.0 / 800.0
f = 50.0
x = np.linspace(0.0, N*T, N)
y = np.sin(f * 2.0*np.pi*x)
yf = fft(y)
xf = np.linspace(0.0, 1.0/(2.0*T), N/2)
fig, (ax1, ax2) = plt.subplots(nrows=2)
ax1.plot(x, y)
ax2.plot(xf, 2.0/N * np.abs(yf[0:N/2]))
ax2.set_xlim(0, )
plt.show()
"""
Explanation: Example of zero-padding using Scip... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/explainable_ai/sdk_custom_tabular_regression_online_explain.ipynb | apache-2.0 | import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex SDK: Custom training tabular regression model for online prediction with explainabilty
<... |
pdamodaran/yellowbrick | examples/pbwitt/testing.ipynb | apache-2.0 | %matplotlib inline
import os
import json
import time
import pickle
import requests
import numpy as np
import pandas as pd
import yellowbrick as yb
import matplotlib.pyplot as plt
df=pd.read_csv("/Users/pwitt/Documents/machine-learning/examples/pbwitt/Dataset/Training/Features_Variant_1.csv")
# Fetch the data if ... |
Mashimo/datascience | 01-Regression/multipleLogRegSky.ipynb | apache-2.0 | import pandas as pd
sdss = pd.read_csv('../datasets/Skyserver_SQL2_27_2018 6_51_39 PM.csv', skiprows=1)
sdss.head(2)
"""
Explanation: Sloan Digital Sky Survey Classification
In this notebook - through a logistic regression classifier - we classify observations of space objects to be either stars, galaxies or quasars... |
DawesLab/LabNotebooks | control-pulseoptim-QFT.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import datetime
from qutip import Qobj, identity, sigmax, sigmay, sigmaz, tensor, mesolve
from qutip.qip.algorithms import qft
import qutip.logging_utils as logging
logger = logging.get_logger()
#Set this to None or logging.WARN for 'quiet' executio... |
harmsm/pythonic-science | labs/02_regression/02_model-fitting.ipynb | unlicense | def first_order(t,A,k):
"""
First-order kinetics model.
"""
return A*(1 - np.exp(-k*t))
def first_order_r(param,t,obs):
"""
Residuals function for first-order model.
"""
return first_order(t,param[0],param[1]) - obs
def fit_model(t,obs,param_guesses=(1,1)):
"""
Fit the fi... |
alkamid/The-Analytics-Edge-in-IPython | Week 3/3.1 Modelling the Expert.ipynb | gpl-2.0 | 98/(98+33)
"""
Explanation: The baseline model has the accuracy of
End of explanation
"""
from sklearn.cross_validation import train_test_split
train, test = train_test_split(quality, train_size=0.75, random_state=1)
qualityTrain = pd.DataFrame(train, columns=quality.columns)
qualityTest = pd.DataFrame(test, colum... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/06_structured/5_train_keras.ipynb | apache-2.0 | # Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.1
"""
Explanation: <h1>Training Keras model on Cloud AI Platform</h1>
This notebook illustrates distributed training on Cloud AI Platform. This uses Keras and requires TensorFlow 2.1
End of explanation
"""
# change these to try ... |
GustavoRP/IA369Z | dev/DTI_open_01-05-17_GRP.ipynb | gpl-3.0 | # import modules and libs
import io, os, sys, types
import numpy as np
# image and graphic
from IPython.display import Image
from IPython.display import display
import matplotlib.pyplot as plt
%matplotlib
#import specific modules
sys.path.append('C:/iPython/DTIlib')
import DTIlib as DTI
"""
Explanation: Openig DTI d... |
WomensCodingCircle/CodingCirclePython | Lesson04_Iteration/Iterations.ipynb | mit | count = 5
count = count + 10
count = count - 3
print(count)
"""
Explanation: Iterations
Updating Variables
You don't need to make a new variable name every time you want to update its value (this is part of why it is called a 'variable', because its contents are variable). It is common to update a single variable.
# B... |
dancingdan/tensorflow | tensorflow/contrib/eager/python/examples/generative_examples/image_captioning_with_attention.ipynb | apache-2.0 | # Import TensorFlow and enable eager execution
# This code requires TensorFlow version >=1.9
import tensorflow as tf
tf.enable_eager_execution()
# We'll generate plots of attention in order to see which parts of an image
# our model focuses on during captioning
import matplotlib.pyplot as plt
# Scikit-learn includes ... |
italoPontes/Machine-learning | Tarefas/Regressão-Linear-Simples-do-Zero/Task 01.ipynb | lgpl-3.0 | %matplotlib notebook
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#Federal University of Campina Grande (UFCG)
#Author: Ítalo de Pontes Oliveira
#Adapted from: Siraj Raval
#Available at: https://github.com/llSourcell/linear_regression_live
#The optimal values of m and b can be actually calculated with way less effort... |
agutieda/QuantEcon.py | solutions/arellano_solutions.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
import quantecon as qe
from quantecon.models import Arellano_Economy
"""
Explanation: quant-econ Solutions: Default Risk and Income Fluctuations
Solutions for http://quant-econ.net/py/arellano.html
End of explanation... |
ehrlinger/MachineLearningSamples-PredictiveMaintenance | Code/2_feature_engineering.ipynb | mit | ## Setup our environment by importing required libraries
import time
import os
import glob
# Read csv file from URL directly
import pandas as pd
# For creating some preliminary EDA plots.
%matplotlib inline
import matplotlib.pyplot as plt
from ggplot import *
import datetime
from pyspark.sql.functions import to_date... |
josh-gree/maths-with-python | 02-programs.ipynb | mit | import math
x = math.sin(1.2)
"""
Explanation: Programs
Using the Python console to type in commands works fine, but has serious drawbacks. It doesn't save the work for the future. It doesn't allow the work to be re-used. It's frustrating to edit when you make a mistake, or want to make a small change. Instead, we wan... |
statsmodels/statsmodels.github.io | v0.12.2/examples/notebooks/generated/ets.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
from statsmodels.tsa.exponential_smoothing.ets import ETSModel
plt.rcParams['figure.figsize'] = (12, 8)
"""
Explanation: ETS models
The ETS models are a family of time series models with an underlying state space model consistin... |
omoju/udacityUd120Lessons | Validation.ipynb | gpl-3.0 |
import pickle
import sys
sys.path.append("../tools/")
from feature_format import featureFormat, targetFeatureSplit
data_dict = pickle.load(open("../final_project/final_project_dataset.pkl", "r") )
### first element is our labels, any added elements are predictor
### features. Keep this the same for the mini-project,... |
GoogleCloudPlatform/training-data-analyst | quests/vertex-ai/vertex-challenge-lab/vertex-challenge-lab.ipynb | apache-2.0 | # Add installed library dependencies to Python PATH variable.
PATH=%env PATH
%env PATH={PATH}:/home/jupyter/.local/bin
# Retrieve and set PROJECT_ID and REGION environment variables.
# TODO: fill in PROJECT_ID.
PROJECT_ID = ""
REGION = "us-central1"
# TODO: Create a globally unique Google Cloud Storage bucket for art... |
hetaodie/hetaodie.github.io | assets/media/uda-ml/supervisedlearning/bys/垃圾邮件分类/Bayesian_Inference_solution-zh.ipynb | mit | '''
Solution
'''
import pandas as pd
# Dataset from - https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection
df = pd.read_table('smsspamcollection/SMSSpamCollection',
sep='\t',
header=None,
names=['label', 'sms_message'])
# Output printing out first 5 col... |
turbomanage/training-data-analyst | quests/endtoendml/labs/3_keras_dnn.ipynb | apache-2.0 | # change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-east1' #'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
%%bash
if ! gsutil ls | grep -q gs://${BUCKET}/; then
gsutil mb -l ${RE... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/text_classification/solutions/word2vec.ipynb | apache-2.0 | # Use the chown command to change the ownership of repository to user.
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip install -q tqdm
# You can use any Python source file as a module by executing an import statement in some other Python source file.
# The import statement combines two operati... |
Danghor/Algorithms | Python/Chapter-10/Monte-Carlo-Pi.ipynb | gpl-2.0 | import random as rnd
import math
"""
Explanation: Computing <a href="https://en.wikipedia.org/wiki/Pi">$\pi$</a> with the Monte-Carlo-Method
End of explanation
"""
def approximate_pi(n):
k = 0
for _ in range(n):
x = 2 * rnd.random() - 1
y = 2 * rnd.random() - 1
r = x * x + y * y
... |
Jackporter415/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... |
dacr26/CompPhys | 01_00_numerical_differentiation.ipynb | mit | dx = 1.
x = 1.
while(dx > 1.e-10):
dy = (x+dx)*(x+dx)-x*x
d = dy / dx
print("%6.0e %20.16f %20.16f" % (dx, d, d-2.))
dx = dx / 10.
"""
Explanation: A primer on numerical differentiation
In order to numerically evaluate a derivative $y'(x)=dy/dx$ at point $x_0$, we approximate is by using finite di... |
alanmitchell/fnsb-benchmark | ddc/siemens_reader.ipynb | mit | import csv
import string
import datetime
import pandas as pd
import numpy as np
# import matplotlib pyplot commands
from matplotlib.pyplot import *
# Show Plots in the Notebook
%matplotlib inline
rcParams['figure.figsize']= (10, 8) # set Chart Size
rcParams['font.size'] = 14 # set Font size in Chart
... |
amitkaps/applied-machine-learning | Module-01a-Frame-Regression.ipynb | mit | #Load the libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#Defualt Variables
%matplotlib inline
plt.rcParams['figure.figsize'] = (16,9)
plt.style.use('fivethirtyeight')
pd.set_option('display.float_format', lambda x: '%.2f' % x)
#Load the dataset
df = pd.read_csv('data/loan_data.csv')... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_mne_dspm_source_localization.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import (make_inverse_operator, apply_inverse,
write_inverse_operator)
"""
Explanation: Source localization with MNE/dSPM/sLORETA
The aim of this tutorials is to teach you h... |
tensorflow/probability | tensorflow_probability/examples/jupyter_notebooks/Learnable_Distributions_Zoo.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, sof... |
ES-DOC/esdoc-jupyterhub | notebooks/awi/cmip6/models/sandbox-3/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'awi', 'sandbox-3', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: AWI
Source ID: SANDBOX-3
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balance... |
weikang9009/pysal | notebooks/explore/giddy/Mobility measures.ipynb | bsd-3-clause | from pysal.explore.giddy import markov,mobility
mobility.markov_mobility?
"""
Explanation: Measures of Income Mobility
Author: Wei Kang weikang9009@gmail.com, Serge Rey sjsrey@gm&... |
DJCordhose/ai | notebooks/tensorflow/tf_low_level_intro.ipynb | mit | # import and check version
import tensorflow as tf
# tf can be really verbose
tf.logging.set_verbosity(tf.logging.ERROR)
print(tf.__version__)
# a small sanity check, does tf seem to work ok?
hello = tf.constant('Hello TF!')
sess = tf.Session()
print(sess.run(hello))
sess.close()
"""
Explanation: <a href="https://co... |
opesci/devito | examples/userapi/03_subdomains.ipynb | mit | from devito import Grid
shape = (10, 10, 10)
grid = Grid(shape=shape, extent=shape)
"""
Explanation: Subdomains tutorial
This tutorial is designed to introduce users to the concept of subdomains in Devito and how to utilize them within simulations for a variety of purposes.
We will begin by exploring the subdomains cr... |
ajgpitch/qutip-notebooks | examples/pulse-wise-two-photon-interference.ipynb | lgpl-3.0 | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import interp2d
from qutip import *
"""
Explanation: QuTiP Lecture: Pulse-wise two-photon interference of emission from a two-level system
K.A. Fischer, Stanford University
Thi... |
Diyago/Machine-Learning-scripts | DEEP LEARNING/image classification/fastai/Dog breeds fastAI.ipynb | apache-2.0 | %reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.imports import *
from fastai.torch_imports import *
from fastai.transforms import *
from fastai.conv_learner import *
from fastai.model import *
from fastai.dataset import *
from fastai.sgdr import *
from fastai.plots import *
torch.cuda.set_device(0... |
kemerelab/NeuroHMM | ModelSelection.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sys
from IPython.display import display, clear_output
sys.path.insert(0, 'helpers')
from efunctions import * # load my helper function(s) to save pdf figures, etc.
from hc3 import load_data, get_sessions
from hmmlearn... |
keras-team/keras-io | examples/vision/ipynb/token_learner.ipynb | apache-2.0 | import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_addons as tfa
from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
import math
"""
Explanation: Learning to tokenize in Vision Transformers
Authors: Aritra Roy Gosthipaty, Sayak Pau... |
mountaindust/Parasitoids | docs/Bayesian_Model.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
a, b = 5,1
plt.figure()
x = np.linspace(0,1,100)
plt.plot(x,stats.beta.pdf(x,a,b),label='beta pdf')
plt.legend(loc='best')
plt.show()
"""
Explanation: Incorporating the Parasitoid Model into a Stochastic Model for Param... |
yangw1234/BigDL | apps/ray/parameter_server/sharded_parameter_server.ipynb | apache-2.0 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import ray
import time
"""
Explanation: This notebook is adapted from:
https://github.com/ray-project/tutorial/tree/master/examples/sharded_parameter_server.ipynb
Sharded Parameter Servers
G... |
ManchesterBioinference/BranchedGP | notebooks/SyntheticData.ipynb | apache-2.0 | import pickle
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from BranchedGP import VBHelperFunctions as bplot
plt.style.use("ggplot")
%matplotlib inline
"""
Explanation: Branching GP Regression on synthetic data
Alexis Boukouvalas, 2017
Branching GP regression with Gaussian noise on th... |
VokhmintcevKirill/ti-nic_competition2 | Titanic 2.0.ipynb | mit | data = pd.concat([train, test], ignore_index = True)
data.head()
data.describe()
data.isnull().sum()
"""
Explanation: Проведем краткое исследование данных, построим бейзлайн, результаты которого попытаемся улучшить
End of explanation
"""
data['Age'] = data['Age'].fillna(np.median(data['Age'].loc[(data['Age'].isnul... |
dipanjank/ml | simple_implementations/Language_Models.ipynb | gpl-3.0 | import nltk
nltk.download('gutenberg')
from nltk.corpus import gutenberg
gutenberg.fileids()
import re
words = gutenberg.words('austen-emma.txt')
# filter out numbers, etc.
words = [w.lower() for w in words if re.match('^[a-zA-Z]+$', w)]
words[:10]
"""
Explanation: <h1 align="center">Language Models</h1>
Before we... |
dspmathguru/af6uyDitDahReader | Usage.ipynb | mit | import ditDahReader as dd
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: AF6UY ditDahReader Library Usage
The AF6UY ditDahReader python3 library is a morse code (CW) library with its final goal of teach the author (AF6UY) morse code by playing IRC streams in morse code. Along the way it will have... |
google/starthinker | colabs/bulkdozer.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: CM360 Bulkdozer Editor
Bulkdozer is a tool that can reduce trafficking time in Campaign Manager by up to 80%% by providing automated bulk editing capabilities.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the... |
diana-hep/carl | examples/Diagnostics for approximate likelihood ratios.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import theano
from scipy.stats import chi2
from itertools import product
np.random.seed(314)
"""
Explanation: Diagnostics for approximate likelihood ratios
Kyle Cranmer, Juan Pavez, Gilles Louppe, March 2016.
This is an extension of the example in... |
hpparvi/ldtk | notebooks/01_Example_basics.ipynb | gpl-2.0 | %pylab inline
from IPython.display import display, Latex
import seaborn as sb
sb.set_context('notebook')
sb.set_style('ticks')
"""
Explanation: LDTk example 1: basics
Last updated: 2.5.2020<br>
LDTk version: 1.1
This first example covers the basics of LDTk. We learn how to set up filters, how to use LDPSetCreator to c... |
henchc/Rediscovering-Text-as-Data | 01-Text-as-Data/Intro.ipynb | mit | %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from collections import Counter
terms = pd.read_csv('data/terms.csv')
counts = Counter(terms['Terms in Attendance'].replace("—", "0")).most_common()
ax = sns.barplot(x=[x[0] for x in counts], y=[x[1] for x in counts], order=... |
CalPolyPat/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... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/introduction_to_tensorflow/labs/4_keras_functional_api.ipynb | apache-2.0 | # Use the chown command to change the ownership of the repository.
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.3.0 || pip install tensorflow==2.3.0
"""
Explanation: Introducing the Keras Functional API on Ve... |
kimkipyo/dss_git_kkp | 통계, 머신러닝 복습/160607화_12일차_(확률론적)선형 회귀 분석 Linear Regression Analysis/1.확률론적 선형 회귀 모형.ipynb | mit | from sklearn.datasets import make_regression
X0, y, coef = make_regression(n_samples=100, n_features=1, noise=20, coef=True, random_state=0)
dfX0 = pd.DataFrame(X0, columns=["X1"])
dfX = sm.add_constant(dfX0)
dfy = pd.DataFrame(y, columns=["y"])
model = sm.OLS(dfy, dfX)
result = model.fit()
print(result.params)
"""... |
GoogleCloudPlatform/training-data-analyst | quests/serverlessml/03_tfdata/labs/input_pipeline.ipynb | apache-2.0 | %%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT
!pip install tensorflow==2.1.0 --user
"""
Explanation: Input pipeline into Keras
In this notebook, we will look at how to read large datasets, datasets that may not fit into memory, usi... |
tpin3694/tpin3694.github.io | sql/edit_tables.ipynb | mit | # Ignore
%load_ext sql
%sql sqlite://
%config SqlMagic.feedback = False
"""
Explanation: Title: Edit Tables
Slug: edit_tables
Summary: Edit tables in SQL.
Date: 2017-01-16 12:00
Category: SQL
Tags: Basics
Authors: Chris Albon
Note: This tutorial was written using Catherine Devlin's SQL in Jupyter Notebooks l... |
daniestevez/jupyter_notebooks | CE5/CE-5 high-speed frame.ipynb | gpl-3.0 | def load_frames(path):
frame_size = 1024
frames = np.fromfile(path, dtype = 'uint8')
frames = frames[:frames.size//frame_size*frame_size].reshape((-1, frame_size))
# drop ccsds header
frames = frames[:, 4:]
return frames
frames = np.concatenate([load_frames(f) for f in sorted(pathlib.Path('uhf... |
DavidObando/carnd | Term1/Project1/.ipynb_checkpoints/P1-checkpoint.ipynb | apache-2.0 | #importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
#reading in an image
image = mpimg.imread('test_images/solidWhiteRight.jpg')
#printing out some stats and plotting
print('This image is:', type(image), 'with dimesions:', im... |
phungkh/phys202-2015-work | assignments/assignment05/InteractEx02.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 2
Imports
End of explanation
"""
def plot_sinl(a,b):
x=np.linspace(0,4*np.pi,1000)
plt.figure(figsiz... |
agile-geoscience/notebooks | Fastest_dimension_of_array.ipynb | apache-2.0 | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: Which is the fastest axis of an array?
I'd like to know: which axes of a NumPy array are fastest to access?
End of explanation
"""
a = np.arange(9).reshape(3, 3)
a
' '.join(str(i) for i in a.ravel(order='C'))
' '.join(str(i) fo... |
maxlit/pyEdgeworthBox | README.ipynb | mit | #!pip install pyEdgeworthBox
%matplotlib inline
import pyEdgeworthBox as eb
EB=eb.EdgeBox( u1 = lambda x,y: x**0.6*y**0.4
, u2 = lambda x,y: x**0.1*y**0.9
, IE1 = [10,20]
, IE2 = [20,10])
EB.plot()
"""
Explanation: How to use it
pyEdgeworthBox provides with a tool to plot the... |
karlstroetmann/Artificial-Intelligence | Python/1 Search/Bidirectional-A-Star-Search-Slim.ipynb | gpl-2.0 | import heapq
"""
Explanation: Bidirectional A$^*$ First Search
The module <a href="https://docs.python.org/3.7/library/heapq.html">heapq</a> provides
<a href="https://en.wikipedia.org/wiki/Priority_queue">priority queues</a>
that are implemented as
<a ref="https://en.wikipedia.org/wiki/Heap_(data_structure)">heaps<... |
hannorein/rebound | ipython_examples/RemovingParticlesFromSimulation.ipynb | gpl-3.0 | import rebound
import numpy as np
sim = rebound.Simulation()
sim.add(m=1., hash=0)
for i in range(1,10):
sim.add(a=i, hash=i)
sim.move_to_com()
print("Particle hashes:{0}".format([sim.particles[i].hash for i in range(sim.N)]))
"""
Explanation: Removing particles from the simulation
This tutorial shows the differ... |
phoebe-project/phoebe2-docs | 2.2/examples/mesh_wd.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
%matplotlib inline
"""
Explanation: Wilson-Devinney Style Meshing
NOTE: Wilson-Devinney Style meshing requires developer mode in PHOEBE and is meant to be used for testing, not used for science.
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can... |
bennyrowland/suspect | docs/notebooks/tut04_quant.ipynb | mit | import suspect
import numpy as np
from matplotlib import pyplot as plt
%matplotlib nbagg
data = suspect.io.load_rda("/home/jovyan/suspect/tests/test_data/siemens/SVS_30.rda")
"""
Explanation: 4. External Quantification Tools
End of explanation
"""
# create a parameters dictionary to set the basis set to use
params ... |
ljvmiranda921/pyswarms | docs/examples/tutorials/options_handler.ipynb | mit | # Import modules
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import Image
# Import PySwarms
import pyswarms as ps
from pyswarms.utils.functions import single_obj as fx
from pyswarms.utils.plotters import (plot_cost_history, plot_contour)
from pyswarms.backend.handlers import OptionsHandler... |
wil-langford/FishFace2 | lib/jupyter/prioritize tagging.ipynb | gpl-2.0 | ## EXAMPLE: Get all images from experiment 11.
xp_11_images = all_data_images.filter(xp_id=156)
## EXAMPLE: Get all images from CJRs 140, 158, and 161.
selected_cjrs_images = all_data_images.filter(cjr_id__in=[140,158,161])
## EXAMPLE: Get all images from experiments 11 and 94.
selected_xps_images = all_data_images.f... |
jegibbs/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
"""
decay = np.load('decay_osc.npz')
tdata = decay['tdata']
ydata = decay['ydata']
dy = decay['dy']
plt.errorbar(tdata, ydata, dy,
fmt... |
jerkos/cobrapy | documentation_builder/io.ipynb | lgpl-2.1 | import cobra.test
import os
print("mini test files: ")
print(", ".join([i for i in os.listdir(cobra.test.data_directory) if i.startswith("mini")]))
textbook_model = cobra.test.create_test_model("textbook")
ecoli_model = cobra.test.create_test_model("ecoli")
salmonella_model = cobra.test.create_test_model("salmonella"... |
tensorflow/docs-l10n | site/zh-cn/tutorials/text/text_classification_rnn.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... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/06_structured/5_train_bqml.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.1
# change these to try this notebook out
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] =... |
Olsthoorn/TransientGroundwaterFlow | readthedocs/Course2016_jupyter/docs/source/TheisWellFunction.ipynb | gpl-3.0 | import scipy.special as sp
import numpy as np
from scipy.special import expi
def W(u): return -expi(-u)
def W1(u):
"""Returns Theis' well function axpproximation by numerical intergration
Works only for scalar u
"""
if not np.isscalar(u):
raise ValueError("","u must be a scalar")
... |
kgourgou/stochastic-simulations-class | ipython_notebooks/langevin.ipynb | mit | a = -1;
b = 1;
def U(x,a=-1,b=1):
return (b-a/2)*(x**2-1)**2+a/2*(x+1)
x = np.linspace(-1.5,1.5)
pl.plot(x,U(x),color=pale_red,linewidth=5)
pl.title('The potential $U(x)$ with $a=-1$ and $b=1$',fontsize=20)
"""
Explanation: Overdamped Langevin Equation
The overdamped Langevin equation is defined as
$$
dX_t=-\nab... |
DigNeurosurgeon/seeg | notebooks/2 seeg_predict_implantation_accuracy-regresion.ipynb | gpl-3.0 | # import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
%matplotlib inline
import warnings; warnings.simplefilter('ignore')
%xmode plain; # shorter error messages
# global setting whether to save figures or not
# will save as 300 dpi PNG - all filename... |
undercertainty/ou_nlp | semeval_experiments/.ipynb_checkpoints/Building a dataframe from a core file v.2-checkpoint.ipynb | apache-2.0 | filename='semeval2013-task7/semeval2013-Task7-5way/beetle/train/Core/FaultFinding-BULB_C_VOLTAGE_EXPLAIN_WHY1.xml'
"""
Explanation: A simple (ie. no error checking or sensible engineering) notebook to extract the student answer data from a single xml file.
I'll also export the data to a csv file at the end of this, s... |
tritemio/pybroom | doc/notebooks/pybroom-example-multi-datasets-scipy-robust-fit.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format='retina' # for hi-dpi displays
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.pylab import normpdf
import seaborn as sns
from lmfit import Model
import lmfit
print('lmfit: %s' % lmfit.__version__)
sns.set_style('whitegrid')... |
phoebe-project/phoebe2-docs | 2.3/tutorials/logg.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
"""
Explanation: Surface Gravity (logg)
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... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/01_bigquery/labs/a_sample_explore_clean.ipynb | apache-2.0 | PROJECT = "cloud-training-demos" # Replace with your PROJECT
REGION = "us-central1" # Choose an available region for Cloud MLE
import os
os.environ["PROJECT"] = PROJECT
os.environ["REGION"] = REGION
"""
Explanation: Sample, Explore, and Clean Taxifare Dataset
Learning Objectives
- Practice querying BigQue... |
mcamack/Jupyter-Notebooks | keras/keras202-VGG16FineTuning.ipynb | apache-2.0 | #GBs required for 16 image mini-batch
size = ((15184000 + 3*4096000) * 4 * 2 * 16) / (1024**3)
print(str(round(size,2)) + 'GB')
"""
Explanation: VGG16
This notebook will recreate the VGG16 model from FastAI Lesson 1 (wiki) and FastAI Lesson 2 (wiki)
The Oxford Visual Geometry Group created a 16 layer deep ConvNet whic... |
donaghhorgan/COMP9033 | labs/09a - K-means clustering.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn import cluster
from sklearn import datasets
"""
Explanation: Lab 09a: K-means clustering
Introduction
This lab focuses on $K$-means clustering using the Iris flower data set. At the end of the lab, you should b... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch3-Animation_ThreePhaseFluxes.ipynb | unlicense | %pylab notebook
"""
Explanation: Electric Machinery Fundamentals 5th edition
Chapter 3
Animation: Three-phase fluxes
(based on Example 3-1)
Calculate the net magetic field produced by a three-phase stator (adapted for 50Hz).
Import the PyLab namespace (provides set of useful commands and constants like $\pi$):
End of ... |
rohinkumar/galsurveystudy | old/healpix_lin_coasting_V02.ipynb | mit | import healpix_util as hu
import astropy as ap
import numpy as np
from astropy.io import fits
from astropy.table import Table
import astropy.io.ascii as ascii
from astropy.constants import c
import matplotlib.pyplot as plt
import math
import scipy.special as sp
"""
Explanation: Healpix pixelization of DR72 SDSS Databa... |
mne-tools/mne-tools.github.io | 0.22/_downloads/34fd5b71616977c61ebac55c010819c1/plot_beamformer_lcmv.ipynb | bsd-3-clause | # Authors: Britta Westner <britta.wstnr@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample, fetch_fsaverage
from mne.beamformer import make_lcmv, apply_lcmv
"""
Explanation: Source reconstruction using an L... |
otavio-r-filho/AIND-Deep_Learning_Notebooks | intro-to-tensorflow/intro_to_tensorflow.ipynb | mit | import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfile import ZipFile
print('All m... |
jorisvandenbossche/DS-python-data-analysis | notebooks/pandas_02_basic_operations.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# redefining the example DataFrame
countries = pd.DataFrame({'country': ['Belgium', 'France', 'Germany', 'Netherlands', 'United Kingdom'],
'population': [11.3, 64.3, 81.3, 16.9, 64.9],
'area': [30510, 671308, 357050, 41526, 244820... |
numeristical/introspective | examples/Ames_Housing_Analysis.ipynb | mit | # "pip install ml_insights" in terminal if needed
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import ml_insights as mli
# To Plot matplotlib figures inline on the notebook
%matplotlib inline
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestReg... |
ecell/ecell4-notebooks | en/tests/Birth_Death.ipynb | gpl-2.0 | %matplotlib inline
from ecell4.prelude import *
"""
Explanation: Birth-Death
This is for an integrated test of E-Cell4. Here, we test a simple birth-death process in volume.
End of explanation
"""
D = 1 # 0.01
radius = 0.005
N = 20 # a number of samples
y0 = {} # {'A': 60}
duration = 3
V = 8
"""
Explanation: Pa... |
james-prior/cohpy | 20181027-ccc-or-operator.ipynb | mit | # meld is a great visual difference program
# http://meldmerge.org/
# the following command relies on the directory structure on my computer
# tdd-demo comes from https://github.com/james-prior/tdd-demo/
#!cd ~/projects/tdd-demo;git difftool -t meld -y 389df2a^ 389df2a
!cd ~/20181027/tdd-demo/;git difftool -t meld -y... |
mne-tools/mne-tools.github.io | 0.20/_downloads/f01121873dbae065a1740e6c0c20d1d5/plot_eeg_no_mri.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Joan Massich <mailsik@gmail.com>
#
# License: BSD Style.
import os.path as op
import mne
from mne.datasets import eegbci
from mne.datasets import fetch_fsaverage
# Download fsaverage files
fs_dir = fetch_fsaverage(verbose=True)
subjects_dir = op.... |
jmhsi/justin_tinker | data_science/courses/deeplearning1/fastai-course-1-pytorch/lesson1-pytorch.ipynb | apache-2.0 | import torch
import torchvision.models as models
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torchvision.utils import make_grid
from PIL import Image
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.optim as optim
import torch.utils.trainer as trainer
impor... |
johntanz/ROP | Old Code/Masimo Analysis - Template150919.ipynb | gpl-2.0 | #the usual beginning
import pandas as pd
import numpy as np
from pandas import Series, DataFrame
from datetime import datetime, timedelta
from pandas import concat
"""
Explanation: Masimo Analysis
For Pulse Ox. Analysis, make sure the data file is the right .csv format:
a) Headings on Row 1
b) Open the csv file throug... |
FordyceLab/AcqPack | notebooks/ExperimentTemplate - Copy.ipynb | mit | # WELL
# all valves closed
st = 'A01'
x1,y1,z1 = locs[st]
Z.move(42)
XY.move_xy(x1,y1)
Z.move(z1)
log.append([time.ctime(time.time()), 'AT '+st])
# ACQUIRE 120 frames 11000 ms
# OPEN Hep_1 + W_1 (tree in + out)
log.append([time.ctime(time.time()), 'OPEN tree in + out'])
# flow 20 min (fill tube + tree)
# ACQUIRE 1... |
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