repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
alexmojaki/flower | docs/api.ipynb | bsd-3-clause | from celery import Celery
from time import sleep
celery = Celery()
celery.config_from_object({
'BROKER_URL': 'amqp://localhost',
'CELERY_RESULT_BACKEND': 'amqp://',
'CELERYD_POOL_RESTARTS': True, # Required for /worker/pool/restart API
})
@celery.task
def add(x, y):
return x + y
@celery.task
def s... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/05_artandscience/b_hyperparam.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==2.5
import os
PROJECT = 'cloud-training-demos' # REPLACE WITH YOUR PROJECT ID
BUCKET = 'cloud-training-demos-ml' # REPLACE WITH YOUR BUCKET NAME
REGION = 'us-central... |
lilleswing/deepchem | examples/tutorials/14_Modeling_Protein_Ligand_Interactions_With_Atomic_Convolutions.ipynb | mit | !curl -Lo conda_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py
import conda_installer
conda_installer.install()
!/root/miniconda/bin/conda info -e
!pip install --pre deepchem
import deepchem
deepchem.__version__
import deepchem as dc
import os
from deepchem.utils impo... |
flowmatters/veneer-py | doc/training/7_Parallel_Processing_and_Veneer_Command_Line.ipynb | isc | from veneer.manage import create_command_line
help(create_command_line)
veneer_install = 'D:\\src\\projects\\Veneer\\Compiled\\Source 4.1.1.4484 (public version)'
source_version = '4.1.1'
cmd_directory = 'E:\\temp\\veneer_cmd'
veneer_cmd = create_command_line(veneer_install,source_version,dest=cmd_directory)
veneer_c... |
jmhsi/justin_tinker | data_science/courses/learning_dl_packages/models/research/syntaxnet/examples/dragnn/interactive_text_analyzer.ipynb | apache-2.0 | def _parse_tree_explorer(): # put stuff in a function to not pollute global scope
text = widgets.Text("John is eating pizza with anchovies")
# Also try: John is eating pizza with a fork
display.display(text)
html = widgets.HTML()
display.display(html)
def handle_submit(sender):
del sen... |
tensorflow/docs | site/en/tutorials/generative/autoencoder.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... |
darioizzo/optimal_landing | examples/2 - Training.ipynb | lgpl-3.0 | import matplotlib.pyplot as plt
%matplotlib inline
import sys
sys.path.append('..')
import numpy as np
import deep_control as dc
"""
Explanation: Training deep neural networks
@cesans
End of explanation
"""
# The time column is automatically discarded
# For free landing we drop the 'x' column
col_names = ['t', '... |
TUW-GEO/pygeogrids | docs/examples/subsetting_grid_objects_with_shape_files.ipynb | mit | import pygeogrids.grids as grids
import pygeogrids.shapefile as shapefile
import numpy as np
import os
testgrid = grids.genreg_grid(0.1, 0.1)
"""
Explanation: Subsetting grids using shapefiles.
End of explanation
"""
austria = shapefile.get_gad_grid_points(
testgrid, os.path.join('/home', os.environ['USER'], 'D... |
ES-DOC/esdoc-jupyterhub | notebooks/nasa-giss/cmip6/models/giss-e2-1h/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'giss-e2-1h', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: GISS-E2-1H
Topic: Seaice
Sub-Topics: Dynamics, Thermody... |
Ccaccia73/NLAAS-Labs | lab02_membrane.ipynb | mit | import FEM_utilities as FEM
"""
Explanation: Module for computations
End of explanation
"""
a = 1000. # lenght [mm]
b = 200. # width [mm]
t = 1.5 # thickness [mm]
"""
Explanation: Parameters:
End of explanation
"""
nx = 40
ny = 8
nodes, elements, fixed, loaded = FEM.Nodes(nx, ny, a, b)
#nodes
#elements
#... |
pybel/pybel-notebooks | algorithms/Generating Candidate Mechanisms.ipynb | apache-2.0 | import logging
import itertools as itt
import os
import time
from collections import defaultdict
from operator import itemgetter
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib_venn import venn2
import pybel
import pybel_tools as pbt
from pybel.canonicalize... |
XinyiGong/pymks | notebooks/stress_homogenization_2D.ipynb | mit | %matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Effective Stiffness
Introduction
This example uses the MKSHomogenizationModel to create a homogenization linkage for the effective stiffness. This example starts with a brief background of the ho... |
eneskemalergin/OldBlog | _oldnotebooks/Biology_with_Python.ipynb | mit | # Here is the genetic code of the amino acids defined as dictionaries
STANDARD_GENETIC_CODE = {'UUU':'Phe', 'UUC':'Phe', 'UCU':'Ser', 'UCC':'Ser',
'UAU':'Tyr', 'UAC':'Tyr', 'UGU':'Cys', 'UGC':'Cys',
'UUA':'Leu', 'UCA':'Ser', 'UAA':None, 'UGA':None,
... |
YuriyGuts/kaggle-quora-question-pairs | notebooks/preproc-nn-sequences-fasttext.ipynb | mit | from pygoose import *
from gensim.models.wrappers.fasttext import FastText
"""
Explanation: Preprocessing: FastText Sequences & Embeddings
Based on the tokenized questions and a pre-built word embedding database, build fixed-length (padded) sequences of word indices for each question, as well as a lookup matrix that ... |
ellamil/bubblepopper | bubblepopper_4recommendations.ipynb | mit | from bubble_popper_model import twitter_profile,twitter_links,twitter_articles
from bubble_popper_model import clean_articles,article_topics,publication_scores
from bubble_popper_model import define_bubble,burst_bubble
import tweepy
from sqlalchemy import create_engine
from sqlalchemy_utils import database_exists, cre... |
jve2kor/machine-learning-nanodegree | projects/titanic_survival_exploration/titanic_survival_exploration.ipynb | gpl-3.0 | # Import libraries necessary for this project
import numpy as np
import pandas as pd
from IPython.display import display # Allows the use of display() for DataFrames
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the dataset
in_file... |
SJSlavin/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 ... |
HUDataScience/StatisticalMethods2016 | notebooks/GlobalWarming.ipynb | apache-2.0 | # == Basic import == #
# plot within the notebook
%matplotlib inline
# No annoying warnings
import warnings
warnings.filterwarnings('ignore')
import numpy as np
"""
Explanation: Correction Python part of the Exo3:
Exercise 3 — Global Warming
Get the data in the “BerkeleyEarth GlobalLandTemperature.dat” from the gith... |
FZJ-IEK3-VSA/tsam | examples/aggregation_segment_period_animation.ipynb | mit | %load_ext autoreload
%autoreload 2
import copy
import os
import pandas as pd
import matplotlib.pyplot as plt
import tsam.timeseriesaggregation as tsam
import tsam.hyperparametertuning as tune
import tqdm
import numpy as np
from matplotlib.animation import FuncAnimation
%matplotlib inline
import matplotlib
import subpro... |
google/learned_optimization | docs/notebooks/Part6_custom_learned_optimizers.ipynb | apache-2.0 | import flax
from typing import Any
import jax.numpy as jnp
import jax
from learned_optimization.learned_optimizers import base as lopt_base
from learned_optimization.optimizers import base as opt_base
"""
Explanation: Part 6: Custom learned optimizer architectures
In Part 1 we introduced the LearnedOptimizer abstract... |
wmfschneider/CHE30324 | Resources/Python_Tutorial.ipynb | gpl-3.0 | import numpy as np #handles arrays of values similar to MATLAB
from scipy import linalg #contains certain operators you may need for class
import matplotlib.pyplot as plt #contains everything you need to create plots
import sympy as sy
from sympy.functions import ln
import math
"""
Explanatio... |
NEONScience/NEON-Data-Skills | tutorials/Python/Hyperspectral/intro-hyperspectral/Intro_NEON_AOP_HDF5_Reflectance_Tiles_py/Intro_NEON_AOP_HDF5_Reflectance_Tiles_py.ipynb | agpl-3.0 | import numpy as np
import h5py
import gdal, osr, os
import matplotlib.pyplot as plt
"""
Explanation: syncID: 61ad1fc43ddd45b49cad1bca48656bbe
title: "NEON AOP Hyperspectral Data in HDF5 format with Python - Tiled Data"
description: "Learn how to read NEON AOP hyperspectral flightline data using Python and develop ski... |
Alexoner/skynet | notebooks/FullyConnectedNets.ipynb | mit | # As usual, a bit of setup
import time
import numpy as np
import matplotlib.pyplot as plt
from skynet.neural_network.classifiers.fc_net import *
from skynet.utils.data_utils import get_CIFAR10_data
from skynet.utils.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from skynet.solvers.solver... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/migration/UJ15 AutoML for vision with Vertex AI Video Object Tracking.ipynb | apache-2.0 | ! pip3 install -U google-cloud-aiplatform --user
"""
Explanation: Vertex SDK: AutoML video object tracking model
Installation
Install the latest (preview) version of Vertex SDK.
End of explanation
"""
! pip3 install google-cloud-storage
"""
Explanation: Install the Google cloud-storage library as well.
End of expla... |
phoebe-project/phoebe2-docs | 2.2/examples/single_spots.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Single Star with Spots
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 to the latest release).
End of explanation
"""
import phoebe
fro... |
wmvanvliet/neuroscience_tutorials | eeg-erp/beginner.ipynb | bsd-2-clause | x = 1
y = 2
z = x + y
z * 3
"""
Explanation: <img src="images/charmander.png" alt="Beginner" width="200">
<img src="images/coffee_machine.jpeg" width="300" style="float: right; margin-top: 30px">
Introduction to programming
Welcome!
Our goal in this module will be to let you have a taste of what programming is like.
I... |
apryor6/apryor6.github.io | visualizations/seaborn/.ipynb_checkpoints/countplot-checkpoint.ipynb | mit | %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (20.0, 10.0)
plt.rcParams['font.family'] = "serif"
df = pd.read_csv('../../datasets/movie_metadata.csv')
df.head()
"""
Explanation: seaborn.countplot
Bar graphs are useful for displaying rel... |
igabr/Metis_Projects_Chicago_2017 | 03-Project-McNulty/Random_Forest_Parameter_Tuning.ipynb | mit | %matplotlib inline
import pickle
%run helper_functions.py
%run s3.py
%run show_cnf_mtx.py
pd.options.display.max_columns = 1000
plt.rcParams["figure.figsize"] = (15,10)
from datetime import datetime
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV, StratifiedKFold, t... |
rbiswas4/TimingComparisons | timers/UnderstandingTimeitOutputs.ipynb | mit | from __future__ import absolute_import, print_function, division
import timeit
import time
import numpy as np
import pandas
import functools
"""
Explanation: Understanding outputs of Timeit in python
The objective of this notebook is to set up some examples to understand the output of timeit and work towards havin... |
Upward-Spiral-Science/claritycontrol | rough/HW6.ipynb | apache-2.0 | # change working dir
path = "/Users/albertlee/claritycontrol/code/scripts" # use your own path
import os
os.chdir(path)
import numpy as np
import matplotlib.pyplot as plt
import os
import csv
import igraph as ig
%matplotlib inline
# Initializing dataset names
dnames = list(['../data/hist'])
print "Dataset: " + ", "... |
Erotemic/ubelt | docs/notebooks/Ubelt Demo.ipynb | apache-2.0 | import math
import ubelt as ub
timer = ub.Timer('Timer demo!', verbose=1)
with timer:
math.factorial(100000)
"""
Explanation: Timing
Quickly time a single line.
End of explanation
"""
import ubelt as ub
import math
for n in ub.ProgIter(range(7500)):
math.factorial(n)
import ubelt as ub
import math
for n in... |
opesci/devito | examples/seismic/tutorials/02_rtm.ipynb | mit | import numpy as np
%matplotlib inline
from devito import configuration
configuration['log-level'] = 'WARNING'
"""
Explanation: 02 - Reverse Time Migration
This notebook is the second in a series of tutorial highlighting various aspects of seismic inversion based on Devito operators. In this second example we aim to h... |
NikitaLoik/machineLearning_andrewNg | notebooks/4_neural_networks_II.ipynb | mit | file_path = '../course_materials/ex4data1.mat'
data = loadmat(file_path)
eights_file_path = '../course_materials/ex4weights.mat'
weights = loadmat(eights_file_path)
print(weights['Theta1'].shape)
print(weights['Theta2'].shape)
print(type(weights))
"""
Explanation: 0 Data Structure
End of explanation
"""
def get_data... |
tensorflow/docs | site/en/guide/effective_tf2.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... |
GPflow/GPflowOpt | doc/source/notebooks/constrained_bo.ipynb | apache-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
import gpflow
import gpflowopt
import numpy as np
"""
Explanation: Bayesian Optimization with black-box constraints
Joachim van der Herten
Introduction
This notebook demonstrates the optimization of an analytical function using the well known Expected Improvement (EI... |
peastman/deepchem | examples/tutorials/Introduction_to_Graph_Convolutions.ipynb | mit | !pip install --pre deepchem
"""
Explanation: Introduction to Graph Convolutions
In this tutorial we will learn more about "graph convolutions." These are one of the most powerful deep learning tools for working with molecular data. The reason for this is that molecules can be naturally viewed as graphs.
Note how stan... |
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies | ex17-EOF analysis global hgt500.ipynb | mit | % matplotlib inline
import numpy as np
from scipy import signal
import numpy.polynomial.polynomial as poly
from netCDF4 import Dataset
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from eofs.standard import Eof
"""
Explanation: EOF analysis - global hgt500
In statistics and signal processi... |
Dharamsitejas/E4571-Personalisation-Theory-Project | Part2/analysis/feature_extraction_from_api.ipynb | mit | import requests
from goodreads import client
import pandas as pd
# This this is the URL prefix common for each record
url_prefix = 'https://www.goodreads.com/book/isbn_to_id/0441172717,0739467352?key='
# Setting up the GoodReads API client
file = open('goodreads_credentials')
key , secret = [element.strip() for el... |
zingale/hydro_examples | compressible/euler-conserved.ipynb | bsd-3-clause | from sympy.abc import rho
rho, u, c = symbols('rho u c')
A = Matrix([[u, rho, 0], [0, u, rho**-1], [0, c**2 * rho, u]])
A
"""
Explanation: Euler Equations
The Euler equations in primitive variable form, $q = (\rho, u, p)^\intercal$ appear as:
$$q_t + A(q) q_x = 0$$
with the matrix $A(q)$:
$$A(q) = \left ( \begin{arra... |
manoharan-lab/structural-color | tutorial.ipynb | gpl-3.0 | import structcol
# or
import structcol as sc
"""
Explanation: Tutorial for the structural-color python package
Copyright 2016, Vinothan N. Manoharan, Victoria Hwang.
This file is part of the structural-color python package.
This package is free software: you can redistribute it and/or modify it under
the terms of th... |
gagneurlab/concise | nbs/spline_transformation.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from concise.utils.splines import BSpline
from concise.preprocessing import EncodeSplines # uses BSpline
def plot_order(degree, ax):
x = np.linspace(0, 1, 100).reshape((-1,1))
y = EncodeSplines(n_bases=10, degree=degree).fit_transform(x).squeeze()
# alter... |
Jackporter415/phys202-2015-work | assignments/assignment07/AlgorithmsEx02.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
"""
Explanation: Algorithms Exercise 2
Imports
End of explanation
"""
def find_peaks(a):
"""Find the indices of the local maxima in a sequence."""
#empty list and make the parameter into an array
empty =... |
aisaacso/SafeBoss | crimes.ipynb | mit | # Load modules, apply settings
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
import statsmodels.api as sm
import requests
import json
from statsmodels.formula.api import ols
from statsmodels.formula.api import logit
impo... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch6-Problem_6-03.ipynb | unlicense | %pylab notebook
%precision 4
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 6
Problem 6-3
End of explanation
"""
fe = 60.0 # [Hz]
n_noload = 715.0 # [r/min]
n_m = 670.0 # [r/min]
"""
Explanation: Description
A three-phase 60-Hz induction motor runs at 715 r/min at no-load and at 670... |
jdhp-docs/python_notebooks | nb_dev_python/python_scipy_optimize_newton.ipynb | mit | %matplotlib inline
import matplotlib
try:
from ailiib.utils.plot import plot_2d_contour_solution_space, plot_2d_solution_space
from ailib.optimize.functions.unconstrained import rosen2d
except ModuleNotFoundError:
print("Please install ailib:")
print("pip install ailib")
import numpy as np
from scip... |
LinkHS/incubator-mxnet | example/adversary/adversary_generation.ipynb | apache-2.0 | %matplotlib inline
import mxnet as mx
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from mxnet.test_utils import get_mnist_iterator
"""
Explanation: Fast Sign Adversary Generation Example
This notebook demos find adversary example by using symbolic API and integration with Numpy
Refer... |
tkurfurst/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... |
alasdairtran/mclearn | projects/alasdair/notebooks/05_class_proportion_estimation.ipynb | bsd-3-clause | # remove after testing
%load_ext autoreload
%autoreload 2
import pickle
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.ensemble import RandomForestClassifier
from mclearn.classifier import print_classification_result, predict_unlabelled_objects, map_unlabelled... |
InsightLab/data-science-cookbook | 2019/02-python-bibliotecas-manipulacao-dados/garimpagem-de-dados-02-numpy.ipynb | mit | import numpy as np
a = np.array([1, 2, 3])
print(a)
print('')
b = np.array([(1, 2, 3), (4, 5, 6)])
print(b)
a + b
a * b
b - a
a**2
10*np.sin(a) # seno trigonométrico
b<35
print('Axis 1: %s' % b[0])
print(np.average(b))
print(b.sum())
print(b.min())
print(b.max())
b.sum(axis=0) # sum of each column
b.min(axi... |
tylerwmarrs/billboard-hot-100-lyric-analysis | notebooks/exploratory/01-web-scraping.ipynb | mit | # Fetch the Billboard Hot 100 for this week
import requests
from lxml import etree
def get_billboard_hot_100():
hot_100_rss = requests_get('http://www.billboard.com/rss/charts/hot-100')
xml = bytes(bytearray(hot_100_rss.text, encoding='utf-8'))
tree = etree.XML(xml)
songs = []
items = tree.xpath(... |
tensorflow/docs-l10n | site/en-snapshot/tfx/tutorials/tfx/gcp/vertex_pipelines_simple.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... |
guyk1971/deep-learning | tv-script-generation/dlnd_tv_script_generation_orig.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... |
dacr26/CompPhys | 09_02_random_distributions.ipynb | mit | %matplotlib inline
import numpy as np
from matplotlib import pyplot
N = 10000
r = np.random.random(N)
xlambda = 0.1
x = -np.log(r)/xlambda
binwidth=xlambda*5
pyplot.hist(x,bins=np.arange(0.,100., binwidth),normed=True);
pyplot.plot(np.arange(0.,100.,binwidth),xlambda*np.exp(-xlambda*np.arange(0.,100.,binwidth)),ls='... |
arturops/deep-learning | gan_mnist/Intro_to_GANs_Solution.ipynb | mit | %matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
BrunoSanchez/VVV_galaxies | correccion_resto.ipynb | bsd-2-clause | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import display
from astropy.io import ascii
from astropy.table import Table, Column
%matplotlib inline
from astroquery.irsa_dust import IrsaDust
"""
Explanation: Galaxias en VVV
Clasificacion con Machine Learning usando est... |
oasis-open/cti-python-stix2 | docs/guide/memory.ipynb | bsd-3-clause | from stix2 import MemoryStore, Indicator
# create default MemoryStore
mem = MemoryStore()
# insert newly created indicator into memory
ind = Indicator(description="Crusades C2 implant",
pattern_type="stix",
pattern="[file:hashes.'SHA-256' = '54b7e05e39a59428743635242e4a867c932140a999f5... |
dchud/warehousing-course | lectures/week-05-20151006-basic-sql.ipynb | cc0-1.0 | !wget http://files.software-carpentry.org/survey.db
%load_ext sql
%sql sqlite:///survey.db
"""
Explanation: Exercise 1 - Basic SQL DML and DDL
Part 1 - Data manipulation (DML)
Get the survey.db SQLite3 database file from the Software Carpentry lesson and connect to it.
End of explanation
"""
%%sql
SELECT personal,... |
mikedewar/RealTimeStorytelling | 4/relationships.ipynb | apache-2.0 | time = np.arange(0,20,0.1)
A = [np.sin(x)**2 for x in time]
sns.tsplot(A,interpolate=False)
B = [np.sin(x)**2 + np.random.exponential(0.2) for x in time]
sns.tsplot(B,interpolate=False)
phi, p = scipy.stats.pearsonr(A,B)
print phi # correlation is between -1 and 1. -1 means that one series goes up when the other goe... |
dhimmel/hetio | test/data/create-subgraphs.ipynb | cc0-1.0 | import random
import hetnetpy.readwrite
import hetnetpy.pathtools
import hetnetpy.stats
# Read Hetionet v1.0
url = 'https://github.com/hetio/hetionet/raw/{}/{}'.format(
'00bf0b6f8886821d91cfdf00eadad145a7a1b6da',
'hetnet/json/hetionet-v1.0.json.bz2',
)
graph = hetnetpy.readwrite.read_graph(url)
metagraph = gr... |
GoogleCloudPlatform/training-data-analyst | quests/bq-teradata/02_teradata_bq_sql_translation/labs/teradata_bq_sql_translation.ipynb | apache-2.0 | !bq head -n 5 --selected_fields rental_id,duration,bike_id,end_date,end_station_id,start_date,start_station_id bigquery-public-data:london_bicycles.cycle_hire
"""
Explanation: Teradata to BigQuery SQL Translation
Introduction
Both BigQuery and Teradata Database conform to the ANSI/ISO SQL:2011 standard. In addition, ... |
swirlingsand/deep-learning-foundations | gans/dcgan-svhn/.ipynb_checkpoints/DCGAN_Exercises-checkpoint.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... |
elsuizo/Charlas_presentaciones | Python_scientific/numpy_matplot_ejemplos.ipynb | gpl-2.0 | #Importamos el modulo numpy con el alias np
import numpy as np
#Creo un array
a = np.array([1,0,0])
a
type(a)
"""
Explanation: <img src="files/Images/Numpylogo.png" style="float: left;"/>
<div style="clear: both;">
## Numpy
### Biblioteca(modulo) numérico de Python
* Arrays multimensionales (ndarray)
* Más cerca... |
ingJSNA/IS-assigment2 | assign2-a.ipynb | mit | class Directions:
NORTH = 'North'
SOUTH = 'South'
EAST = 'East'
WEST = 'West'
STOP = 'Stop'
"""
Explanation: Intelligent Systems Assignment 2
Bayes' net inference
Names:
IDs:
End of explanation
"""
def getMapa():
mapa = [[0] * 6 for i in range(1, 6)]
mapa[1][1] = 1
mapa[1][3] = 1
... |
AnoshZahir/Greengraph | Section_2_Notebook_Student_Version.ipynb | mit | num_respondents = 1156
dates = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'June', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec', 'Jan2020']
data = {}
data['approve'] = [37, 40, 42, 43, 43, 43, 40, 39, 41, 42, 43, 43]
data['disapprove'] = [57, 55, 51, 52, 52, 52, 54, 55, 57, 54, 53, 53]
data['no_opinion'] = [7, 5, 8, 5, 5, 5, 6, 6, 2, 4... |
katyhuff/berkeley | sklearn/sklearn_intro.ipynb | bsd-3-clause | import sklearn
import numpy as np
%matplotlib
import matplotlib.pyplot as plt
"""
Explanation: Messing Around with Scikit-Learn
End of explanation
"""
# Code source: Gaël Varoquaux
# Andreas Müller
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause
from matplotlib.colors import List... |
ketch/PseudoSpectralPython | PSPython_02-pseudospectral-collocation.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from matplotlib import animation
from IPython.display import HTML
font = {'size' : 15}
matplotlib.rc('font', **font)
"""
Explanation: Content provided under a Creative Commons Attribution license, CC-BY 4.0; code under MIT Licens... |
gregcaporaso/short-read-tax-assignment | ipynb/mock-community/taxonomy-assignment-qiime1.ipynb | bsd-3-clause | from os.path import join, expandvars
from joblib import Parallel, delayed
from glob import glob
from os import system
from tax_credit.framework_functions import (parameter_sweep,
generate_per_method_biom_tables,
move_results_to_rep... |
rmoehn/cartpole | notebooks/IntegrationExperiments.ipynb | mit | mc_env = gym.make("MountainCar-v0")
mc_n_weights, mc_feature_vec = fourier_fa.make_feature_vec(
np.array([mc_env.low, mc_env.high]),
n_acts=3,
order=2)
mc_experience = linfa.init(lmbda=0.9,
init_alp... |
ga7g08/ga7g08.github.io | _notebooks/2015-01-30-Applying-python-functions-in-moving-windows.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
# The user defined function we want to apply in a moving window
mad = lambda x: np.fabs(x - x.mean()).mean()
# Some random data
N = 1000
t = np.linspace(0, 10, N)
x = np.random.randn(N).cumsum()
# The moving window parameters
... |
davidfumo/intro-to-machine-learning | linear regression/linear-regression-nb.ipynb | mit | import numpy as np
import pandas as pd
data_path = "data/Advertising.csv"
# or load the dataset directly from the link
# # data_link = "http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv"
data = pd.read_csv(data_path, index_col=0)
# display the first 5 rows
data.head()
"""
Explanation: TODO:
Place the Advertisi... |
mcc-petrinets/formulas | spot/tests/python/stutter-inv.ipynb | mit | f = spot.formula('a U b')
print(f.is_syntactic_stutter_invariant())
f = spot.formula('a U Xb')
print(f.is_syntactic_stutter_invariant())
"""
Explanation: Stutter-invariant languages
A language $L$ is said to be stutter-invariant iff $\ell_0\ldots\ell_{i-1}\ell_i\ell_{i+1}\ldots\in L \iff \ell_0\ldots\ell_{i-1}\ell_i\... |
mattiasostmar/GAR_Syria_2016-06 | create_metatdata_textfiles.ipynb | mit | def strip(text):
try:
return text.strip()
except AttributeError:
return text
it_converters = {"Folder":strip, "Nome foto":strip, "Anno":strip, "Luogo":strip, "Nome monumento":strip, "Descrizione":strip, "Nome autore":strip}
metadata_it = pd.read_excel("./data/COH_GAR_metadata.xlsx",sheetnam... |
metpy/MetPy | v0.7/_downloads/isentropic_example.ipynb | bsd-3-clause | import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
from netCDF4 import Dataset, num2date
import numpy as np
import metpy.calc as mcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo
from metpy.units import units
"""
Explanation: Isentropic Analy... |
oditorium/blog | iPython/ReportLab1-Intro.ipynb | agpl-3.0 | from io import BytesIO
from IPython.core import display
from PIL import Image
def display_pil_image(im):
"""Displayhook function for PIL Images, rendered as PNG."""
b = BytesIO()
im.save(b, format='png')
data = b.getvalue()
ip_img = display.Image(data=data, format='png', embed=True)
return ip_img.... |
rgarcia-herrera/sistemas-dinamicos | Leslie Model.ipynb | gpl-3.0 | # el modelo de Leslie
L = np.array([[0, 1, 2],
[0.8, 0, 0],
[0, 0.7, 0]])
# las cuatro clases de edad tienen 100 pobladoras al inicio
poblacion_inicial = [100,100,100]
# lista para contener un vector de tamaños de población por clase para
# cada unidad de tiempo transcurrida en ... |
ClaudioVZ/Metodos_numericos_I | 01_Raices_de_ecuaciones_de_una_variable/02_Falsa_posicion.ipynb | gpl-2.0 | def raiz(f, x_l, x_u):
x_r = x_u - (f(x_u)*(x_u - x_l))/(f(x_u) - f(x_l))
return x_r
def intervalo_de_raiz(f, x_l, x_u):
x_r = raiz(f, x_l, x_u)
if f(x_l)*f(x_r) < 0:
x_u = x_r
if f(x_l)*f(x_r) > 0:
x_l = x_r
return x_l, x_u
"""
Explanation: Método de la falsa posición
El métod... |
Kaggle/learntools | notebooks/ml_intermediate/raw/ex7.ipynb | apache-2.0 | # Set up code checking
from learntools.core import binder
binder.bind(globals())
from learntools.ml_intermediate.ex7 import *
print("Setup Complete")
"""
Explanation: Most people find target leakage very tricky until they've thought about it for a long time.
So, before trying to think about leakage in the housing pric... |
greg-ashby/deep-learning-nanodegree | first-neural-network/.ipynb_checkpoints/nn-Copy1-checkpoint.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
wikistat/Intro-Python | Cal2-PythonPandas.ipynb | mit | # Exemple de data frame
import pandas as pd
data = {"state": ["Ohio", "Ohio", "Ohio",
"Nevada", "Nevada"],
"year": [2000, 2001, 2002, 2001, 2002],
"pop": [1.5, 1.7, 3.6, 2.4, 2.9]}
frame = pd.DataFrame(data)
# ordre des colonnes
pd.DataFrame(data, columns=["year", "state", "pop"])
# index des lignes ... |
mnishida/RII_Pandas | docs/notebooks/02_RiiDataFrame.ipynb | mit | import riip
ri = riip.RiiDataFrame()
"""
Explanation: RiiDataFrame
Here, a little bit more detail about RiiDataFrame class will be given.
End of explanation
"""
ri.catalog.head(3)
"""
Explanation: RiiDataFrame has an attribute named catalog that is a Pandas DataFrame provinding the catalog of experimental data as s... |
IBM/differential-privacy-library | notebooks/exploration.ipynb | mit | %matplotlib inline
import numpy as np
import diffprivlib as dp
import matplotlib.pyplot as plt
"""
Explanation: Data exploration workflow with diffprivlib
End of explanation
"""
acc = dp.BudgetAccountant(1, 0)
acc.set_default()
# Our default epsilon value for this exploration
eps = 0.04
"""
Explanation: Data and ... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/migration/UJ15 legacy AutoML Vision Video Object Tracking.ipynb | apache-2.0 | ! pip3 install -U google-cloud-automl --user
"""
Explanation: AutoML SDK: AutoML video object tracking model
Installation
Install the latest (preview) version of AutoML SDK.
End of explanation
"""
! pip3 install google-cloud-storage
"""
Explanation: Install the Google cloud-storage library as well.
End of explana... |
mdeff/ntds_2016 | project/reports/fisheries_monitoring/3.Fish_detection.ipynb | mit | import os
import glob
import time
from SimpleCV import *
import scipy
import numpy as np
import tensorflow as tf
import collections
import matplotlib.pyplot as plt
import cv2
import imutils
from skimage.transform import pyramid_gaussian
import argparse
import cv2
from scipy import ndimage
from scipy.ndimage import sum ... |
xesscorp/myhdlpeek | examples/peeker_dataframe.ipynb | mit | from myhdl import *
from myhdlpeek import Peeker # Import the myhdlpeeker module.
def mux(z, a, b, sel):
"""A simple multiplexer."""
@always_comb
def mux_logic():
if sel == 1:
z.next = a # Signal a sent to mux output when sel is high.
else:
z.next = b # Signal b s... |
usantamaria/iwi131 | ipynb/20-Conjuntos/Conjuntos.ipynb | cc0-1.0 | # Utilizando strings
def letras_en_comun(p, r):
n = 0
letras_a_revisar = p + r
letras_vistas = ""
for c in letras_a_revisar:
if (c in p) and (c in r) and not (c in letras_vistas):
n += 1
letras_vistas += c
return n
## Verificación
print(letras_en_comun('murcielagoo', 'g... |
relf/smt | tutorial/SMT_MixedInteger_application.ipynb | bsd-3-clause | !pip install smt
%matplotlib inline
from math import exp
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
from mpl_toolkits.mplot3d import Axes3D
from scipy.stats import norm
from scipy.optimize import minimize
import scipy
import six
from smt.applications import EGO
from smt.surroga... |
tarasane/h2o-3 | h2o-py/demos/H2O_tutorial_medium.ipynb | apache-2.0 | import pandas as pd
import numpy
from numpy.random import choice
from sklearn.datasets import load_boston
import h2o
h2o.init()
# transfer the boston data from pandas to H2O
boston_data = load_boston()
X = pd.DataFrame(data=boston_data.data, columns=boston_data.feature_names)
X["Median_value"] = boston_data.target
X ... |
RafaelNH/Reproduce-Hoy-et-al-2014 | notebook/run_simulations_3.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import time
import sys
import os
%matplotlib inline
# Change directory to the code folder
os.chdir('..//code')
# Functions to sample the diffusion-weighted gradient directions
from dipy.core.sphere import disperse_charges, HemiSphere
# Function to... |
AstroHackWeek/AstroHackWeek2016 | notebook-tutorial/notebooks/08-More_basics.ipynb | mit | names = ['alice', 'jonathan', 'bobby']
ages = [24, 32, 45]
ranks = ['kinda cool', 'really cool', 'insanely cool']
for (name, age, rank) in zip(names, ages, ranks):
print(name, age, rank)
for index, (name, age, rank) in enumerate(zip(names, ages, ranks)):
print(index, name, age, rank)
# return, esc, shift+ent... |
w4zir/ml17s | lectures/.ipynb_checkpoints/lec05-multinomial-regression-checkpoint.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
from sklearn import linear_model
import matplotlib.pyplot as plt
import matplotlib as mpl
# read data in pandas frame
dataframe = pd.read_csv('datasets/house_dataset2.csv', encoding='utf-8')
# check data by printing first few rows
dataframe.head()
from mpl_t... |
edwardd1/phys202-2015-work | assignments/assignment03/NumpyEx03.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import antipackage
import github.ellisonbg.misc.vizarray as va
"""
Explanation: Numpy Exercise 3
Imports
End of explanation
"""
def brownian(maxt, n):
"""Return one realization of a Brownian (Wiener) process with n steps... |
HazyResearch/deepdive | examples/spouse/DeepDive Tutorial - Extracting mentions of spouses from the news.ipynb | apache-2.0 | # PATH needs correct setup to use DeepDive
import os; PWD=os.getcwd(); HOME=os.environ["HOME"]; PATH=os.environ["PATH"]
# home directory installation
%env PATH=$HOME/local/bin:$PATH
# notebook-local installation
%env PATH=$PWD/deepdive/bin:$PATH
!type deepdive
no_deepdive_found = !type deepdive >/dev/null
if no_deepdi... |
kit-cel/lecture-examples | nt1/vorlesung/1_quellencodierung/Huffman.ipynb | gpl-2.0 | # importing
import numpy as np
"""
Explanation: Content and Objectives
Realizing Huffman encoding by a recursive function constructing of the Huffman tree
Application of this function to several examples
Import
End of explanation
"""
# Huffman function
def huffman_recursive( symb_dict, show_steps = 0 ):
'''
... |
WNoxchi/Kaukasos | FAI_old/Lesson6/Theano_RNN_test_4.ipynb | mit | import theano
import os, sys
sys.path.insert(1, os.path.join('../utils'))
from utils import *
# from __future__ import division, print_functions
path = get_file('nietzsche.txt', origin="https://s3.amazonaws.com/text-datasets/nietzsche.txt")
text = open(path).read()
print('corpus length:', len(text))
chars = sorted(lis... |
jmschrei/pomegranate | examples/distributions_test_table.ipynb | mit | from pomegranate import *
import math
"""
Explanation: Distribution Test Tables
This example demonstrates how to create some conditional probability tables and a bayesian network.
End of explanation
"""
c_table = [[0, 0, 0, 0.6],
[0, 0, 1, 0.4],
[0, 1, 0, 0.7],
[0, 1, 1, 0.3],
... |
ES-DOC/esdoc-jupyterhub | notebooks/cnrm-cerfacs/cmip6/models/cnrm-esm2-1/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'cnrm-esm2-1', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: CNRM-ESM2-1
Topic: Atmos
Sub-Topics: Dynamical Cor... |
bMzi/ML_in_Finance | 0102_Pandas.ipynb | mit | # We start by importing the NumPy, Pandas packages
import numpy as np
import pandas as pd
"""
Explanation: Pandas
Pandas Objects
In the previous chapter we discussed the very basics of Python and NumPy. Here we go one step further and introduce the Pandas package and its data structures. At the very basic level, Panda... |
fastai/course-v3 | nbs/dl2/07a_lsuv.ipynb | apache-2.0 | x_train,y_train,x_valid,y_valid = get_data()
x_train,x_valid = normalize_to(x_train,x_valid)
train_ds,valid_ds = Dataset(x_train, y_train),Dataset(x_valid, y_valid)
nh,bs = 50,512
c = y_train.max().item()+1
loss_func = F.cross_entropy
data = DataBunch(*get_dls(train_ds, valid_ds, bs), c)
mnist_view = view_tfm(1,28,... |
monicathieu/cu-psych-r-tutorial | public/tutorials/python/2-datacleaning/index.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context("poster")
sns.set(style="ticks",font="Arial",font_scale=2)
"""
Explanation: title: "Data Cleaning in Python"
subtitle: "CU Psych Scientific Computing Workshop"
weight: 1201
tags: ["core", "python"]
Goals of t... |
elsdrm/shared_note | Tools for High-Performance Computing Applications.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: Lecture 6B - Tools for high-performance computing applications
J.R. Johansson (robert@riken.jp) http://dml.riken.jp/~rob/
The latest version of this IPython notebook lecture is available at http://github.com/jrjohansson/scientific-python-lectures.
The... |
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