repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
phoebe-project/phoebe2-docs | 2.1/tutorials/animations.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
"""
Explanation: Advanced: Animations
NOTE: this tutorial may take a while to load in a browser as there are many embedded animations.
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installa... |
lionell/university-labs | eco_systems/lab2.ipynb | mit | X = np.array([[500, 300], [150, 200]])
"""
Explanation: Diversified economy
Matrix $X$ - production needs, where $x_{ij}$ is how much of $i$-th product is needed to make $j$-th product
End of explanation
"""
y = np.array([900, 500])
"""
Explanation: Vector $y$ - consumer needs, where $y_i$ shows how much of $i$-th ... |
marioberges/F16-12-752 | homeworks/homework1/exercise0.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import sys
print('Python version:')
print(sys.version)
print('Numpy version:')
print(np.__version__)
import sklearn
print('Sklearn version:')
print(sklearn.__version__)
"""
Explanation: 12-752: Data-Driven Building Energy Management
Fall 2016, Car... |
xpharry/Udacity-DLFoudation | tutorials/autoencoder/Convolutional_Autoencoder_Solution.ipynb | mit | %matplotlib inline
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', validation_size=0)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
"""
Explanation: C... |
gururajl/deep-learning | intro-to-rnns/Anna_KaRNNa.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is base... |
DBWangGroupUNSW/COMP6714 | BackProp-example.ipynb | mit | import torch
import numpy as np
from math import exp, log
device = torch.device('cpu')
"""
Explanation: Back Propagation Example
We do backprop in some concrete examples.
Import the package and set device
End of explanation
"""
def _acti(z):
return 1.0 / (1.0 + exp(-z))
def acti(zs):
return 1.0/(1.0 + np... |
keylime1/courses_12-752 | projects/kenanz/Final_Project.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import datetime as dt
%matplotlib inline
"""
Explanation: 12-752 Course Project
Xiaowen Gu (xiaoweng), Kenan Zhang (kenanz)
End of explanation
"""
gatesDateConverter = lambda d : dt.datetime.strptime(d,'%m/%d/%Y %H:%M')
gates_elect = np.genfromtxt('pointData_gates.c... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/05_artandscience/labs/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... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/sdk/sdk_automl_text_classification_online.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: AutoML training text classification model for online prediction
<table align="left"... |
Yu-Group/scikit-learn-sandbox | jupyter/backup_deprecated_nbs/19_refined_combined_run_through.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from sklearn.datasets import load_breast_cancer
import numpy as np
from functools import reduce
# Import our custom utilities
from imp import reload
from utils import irf_jupyter_utils
from utils import irf_utils
reload(irf_jupyter_utils)
reload(irf_utils)
"""
Expla... |
mitdbg/modeldb | client/workflows/demos/census-experiment-versioning-registry.ipynb | mit | # restart your notebook if prompted on Colab
try:
import verta
except ImportError:
!pip install verta
"""
Explanation: Logistic Regression (scikit-learn) Experiment Versioning & Registry
<a href="https://colab.research.google.com/github/VertaAI/modeldb/blob/master/client/workflows/demos/census-experiment-versi... |
brettavedisian/phys202-2015-work | assignments/assignment09/IntegrationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy import integrate
"""
Explanation: Integration Exercise 1
Imports
End of explanation
"""
def trapz(f, a, b, N):
"""Integrate the function f(x) over the range [a,b] with N points."""
h=(b-a)/N
k=np.arange(1,N)
return h*(0.... |
thalesians/tsa | src/jupyter/python/foundations/linear-algebra-1.ipynb | apache-2.0 | # Copyright (c) Thalesians Ltd, 2018-2019. All rights reserved
# Copyright (c) Paul Alexander Bilokon, 2018-2019. All rights reserved
# Author: Paul Alexander Bilokon <paul@thalesians.com>
# Version: 3.0 (2019.05.27)
# Previous versions: 1.0 (2018.08.03), 2.0 (2019.04.19)
# Email: education@thalesians.com
# Platform: T... |
statsmodels/statsmodels.github.io | v0.13.2/examples/notebooks/generated/discrete_choice_example.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import statsmodels.api as sm
from scipy import stats
from statsmodels.formula.api import logit
print(sm.datasets.fair.SOURCE)
print(sm.datasets.fair.NOTE)
dta = sm.datasets.fair.load_pandas().data
dta["affair"] = (dta["affair... |
yucelkilic/robotics | python3/python3_2.ipynb | gpl-3.0 | a = 5
b = 6
a > b
"""
Explanation: Koşul Durumları
Hiç kuşkusuz, koşula bağlı durumlar Python’daki en önemli konulardan biridir. Giriş bölümünde bahsettiğimiz koşullu işlemleri yapabilmek için ‘koşul deyimleri’ adı verilen birtakım araçlardan yararlanacağız. Gelin şimdi bu araçların neler olduğunu görelim (Özgül, Pyt... |
lorserker/bridgesim | notebooks/Test_BBO_HandViewer.ipynb | mit | from IPython.display import IFrame
IFrame('http://www.bridgebase.com/tools/handviewer.html?s=saj874ha986daca53&d=n&a=3cp?', width=250, height=250)
"""
Explanation: A Simulation Tutorial for Better Decisionmaking at Bridge
This article describes how computers can be used to help with the analysis of decisions made at ... |
computational-class/computational-communication-2016 | code/.ipynb_checkpoints/0-Introduction-to-Non-Personalized-Recommenders-checkpoint.ipynb | mit | from IPython.core.display import Image
Image(filename='./imgs/recsys_arch.png')
"""
Explanation: Introduction to Non-Personalized Recommenders
The recommendation problem
Recommenders have been around since at least 1992. Today we see different flavours of recommenders, deployed across different verticals:
Amazon
Ne... |
ES-DOC/esdoc-jupyterhub | notebooks/cas/cmip6/models/fgoals-f3-h/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cas', 'fgoals-f3-h', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: CAS
Source ID: FGOALS-F3-H
Topic: Ocean
Sub-Topics: Timestepping Framework, Advecti... |
adityaka/misc_scripts | python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/02_11/Final/Resampling.ipynb | bsd-3-clause | # min: minutes
my_index = pd.date_range('9/1/2016', periods=9, freq='min')
my_index
"""
Explanation: Resampling
documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html
For arguments to 'freq' parameter, please see Offset Aliases
create a date range to use as an index
End of... |
mtchem/Twitter-Politics | EDA.ipynb | mit | # load federal document data from pickle file
fed_reg_data = r'data/fed_reg_data.pickle'
fed_data = pd.read_pickle(fed_reg_data)
# load twitter data from csv
twitter_file_path = r'data/twitter_01_20_17_to_3-2-18.pickle'
twitter_data = pd.read_pickle(twitter_file_path)
len(fed_data)
"""
Explanation: The following data... |
johnpfay/environ859 | 06_WebGIS/Notebooks/05-Scraping-Data-With-Requests-and-BeautifulSoup.ipynb | gpl-3.0 | # Import the requests package; install if necessary
try:
import requests
except:
import pip
pip.main(['install','requests'])
import requests
# Import BeautifulSoup from the bs4 package; install bs4 if necessary
try:
from bs4 import BeautifulSoup
except:
import pip
pip.main(['install','bs4']... |
apache/climate | RCMES/CORDEX/CORDEX.ipynb | apache-2.0 | domain = 'NAM-44'
# The output directory
cwd = os.getcwd()
workdir = cwd +'/evaluation_result'
# Location of obs4Mips files
obs_dir = '/mnt/efs/obs4mips'
# Location of CORDEX files
models_dir = '/mnt/efs/'+domain+'/*'.format(domain=domain)
# Extract metadata from model and obs files, pairing up files with the same
... |
zzsza/Datascience_School | 15. 선형 회귀 분석/08. patsy 패키지 소개.ipynb | mit | from patsy import dmatrix, dmatrices
np.random.seed(0)
x1 = np.random.rand(5) + 10
x2 = np.random.rand(5) * 10
x1, x2
dmatrix("x1")
"""
Explanation: patsy 패키지 소개
회귀 분석 전처리 패키지
encoding/transform/design matrix 기능
R-style formula 문자열 지원
design matrix
dmatrix(fomula[, data])
R-style formula 문자열을 받아서 X matrix 생성
자동으로... |
awhite40/pymks | notebooks/Fiber Homogenization.ipynb | mit | import numpy as np
%matplotlib inline
%load_ext autoreload
%autoreload 2
import matplotlib.pyplot as plt
"""
Explanation: Homogenization with fiber-like structures
Introduction
This example demonstrates the use of the homogenization model from pyMKS on a set of fiber-like structures. These structures are simulated t... |
gurbanics/pytech-vis | notebooks/MainSlides.ipynb | mit | # importing matplotlib as usual
import numpy as np
%config InlineBackend.print_figure_kwargs = {'bbox_inches': None}
%matplotlib inline
import matplotlib.pyplot as plt
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('pdf', 'png', 'svg')
sigma = 10
mu = 5
normal_random = np.random.randn(1000) ... |
jplourenco/bokeh | examples/interactions/interactive_bubble/gapminder.ipynb | bsd-3-clause | fertility_df, life_expectancy_df, population_df_size, regions_df, years, regions = process_data()
sources = {}
region_color = regions_df['region_color']
region_color.name = 'region_color'
for year in years:
fertility = fertility_df[year]
fertility.name = 'fertility'
life = life_expectancy_df[year]
li... |
trangel/Data-Science | .ipynb_checkpoints/MNIST-dimensionality-reduction-checkpoint.ipynb | gpl-3.0 | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.tools as tls
import seaborn as sns
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
i... |
mattilyra/gensim | docs/notebooks/doc2vec-IMDB.ipynb | lgpl-2.1 | import locale
import glob
import os.path
import requests
import tarfile
import sys
import codecs
import smart_open
dirname = 'aclImdb'
filename = 'aclImdb_v1.tar.gz'
locale.setlocale(locale.LC_ALL, 'C')
if sys.version > '3':
control_chars = [chr(0x85)]
else:
control_chars = [unichr(0x85)]
# Convert text to l... |
enury/collation-viz | pycoviz.ipynb | mit | #python modules
import json
import sys
import re
from IPython.display import display,HTML
from datetime import datetime
#ipywidgets modules
from __future__ import print_function
from ipywidgets import *
import ipywidgets as widgets
"""
Explanation: Exploring the collation of Calpurnius Flaccus
In this notebook, I pre... |
MotokiShiga/stem-nmf | python/demo_libnmf.ipynb | mit | %matplotlib inline
import numpy as np
import scipy.io as sio
from libnmf import NMF, NMF_SO, NMF_ARD_SO
"""
Explanation: Demo of NMF-SO and NMF-ARD-SO
[1] Motoki Shiga, Kazuyoshi Tatsumi, Shunsuke Muto, Koji Tsuda, Yuta Yamamoto, Toshiyuki Mori, Takayoshi Tanji, "Sparse Modeling of EELS and EDX Spectral Imaging Data b... |
FZJ-IEK3-VSA/tsam | examples/aggregation_method_showcase.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
%matplotlib inline
"""
Explanation: tsam - 3. Example
Examples of the different methods used in the time series aggregation module (tsam)
Date: 04.01.2019
Author: Leand... |
rusucosmin/courses | lds/DSLab_week1_CCPython.ipynb | mit | v = [1,2,3,4]
# Overwrite the function moving_avg
def moving_avg():
avg = None
_sum = 0
_cnt = 0
while True:
term = yield avg
_cnt += 1
_sum += term
avg = _sum / _cnt
# Testing your function
ma = moving_avg()
next(ma)
mavg = []
for value in v:
mavg.append(ma.send(v... |
mwegrzyn/mindReading2017 | content/_004_Kreuzvalidierung.ipynb | gpl-3.0 | import os
imgList = ['../training/%s'%x for x in os.listdir('../training/')]; imgList.sort()
imgList
"""
Explanation: Diesmal machen wir da weiter, wo wir letztes Mal aufgehört haben: Wir berechnen wieder Ähnlichkeiten (als Korrelationen), vergleichen diesmal aber unabhängige Daten. Dazu teilen wir unseren Datensatz... |
curiouspal/python-lessons | test2-pal.ipynb | cc0-1.0 | import numpy as np
import pandas as pd
import matplotlib
import scipy
import scipy.stats
import json
import requests
from pprint import pprint
from ggplot import *
import csv
"""
Explanation: Learning Python
End of explanation
"""
"string "*4
'String'[4]
len('strings')
'Search String'.find('r')
'Search String'.f... |
rickiepark/python-tutorial | tutorial-2/word-histogram.ipynb | mit | import re
import random
import json
"""
Explanation: 필요한 모듈을 임포트 합니다.
End of explanation
"""
hist = {}
with open('jungle-book.txt') as f:
for line in f.readlines():
lines = re.findall('[a-zA-Z0-9]+', line)
for word in lines:
word = word.lower()
hist[word] = hist.get(word, ... |
tensorflow/quantum | docs/tutorials/noise.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... |
hongsups/insightfl_shin | project/clean_zipcodes.ipynb | mit | import requests
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
import csv
import seaborn as sns
font = {'family' : 'Arial',
'weight' : 'bold',
'size' : 25}
matplotlib.rc('font', **font)
"""
Explanation: Clean zip code data by convertin sha... |
phoebe-project/phoebe2-docs | 2.1/examples/animation_binary_complete.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
"""
Explanation: Complete Binary Animation
NOTE: animating within Jupyter notebooks can be very resource intensive. This script will likely run much quicker as a Python script.
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this ... |
fwmeng88/Machine_Learning_at_McMaster_University | Introduction_to_Python3.ipynb | apache-2.0 | # set a variable to an argument
# Variable types don’t need to be declared
x = 5
print(x)
y = x * 3
# print(y)
y
# assign a string
x = 'This is a math for chem class.'
print(x)
x = 'Apple'
y = 'Orange'
z = x + ' ' + y
print(z)
# Keywords can not be used for variable names
import keyword
keyword.kwlist
# for number... |
hunterherrin/phys202-2015-work | assignments/assignment07/AlgorithmsEx01.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
"""
Explanation: Algorithms Exercise 1
Imports
End of explanation
"""
s="""
APRIL--this is the cruellest month, breeding
Lilacs out of the dead land, mixing
Memory and desire, stirring
Dull roots with spring rain.
"""
stop_words='the is'
s=s.... |
atlury/deep-opencl | cs480/05 Fitting Gaussians.ipynb | lgpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
def normald(X, mu, sigma):
""" normald:
X contains samples, one per row, N x D.
mu is mean vector, D x 1.
sigma is covariance matrix, D x D. """
D = X.shape[1]
detSigma = sigma if D == 1 else np.linalg.det(sigma)
... |
lfairchild/PmagPy | PmagPy_iodp_HOLE_template.ipynb | bsd-3-clause | # import a bunch of packages for use in the notebook
import pmagpy.pmag as pmag # a bunch of PmagPy modules
import pmagpy.pmagplotlib as pmagplotlib
import pmagpy.ipmag as ipmag
import pmagpy.contribution_builder as cb
from pmagpy import convert_2_magic as convert # conversion scripts for many lab formats
from pmagpy i... |
lamahechag/clubes_de_ciencia | Dia_1_monitor/Dia_1.ipynb | mit | 2+3
"""
Explanation: Opreaciones Matematicas
Suma : $2+3$
End of explanation
"""
2*3
"""
Explanation: Multiplicación: $2x3$
End of explanation
"""
2/3
"""
Explanation: División: $\frac{2}{3}$
End of explanation
"""
2**3
"""
Explanation: Potencia: $ 2^{3}$
End of explanation
"""
# Importar una libreria en Py... |
AllenDowney/ThinkBayes2 | examples/hospital3.ipynb | mit | # If we're running on Colab, install libraries
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install pymc3
!pip install arviz
!pip install empiricaldist
# PyMC generates a FutureWarning we don't need to deal with yet
import warnings
warnings.filterwarnings("ignore", category=Futur... |
ncullen93/pyBN | examples/Drawing.ipynb | mit | from pyBN import *
bn = read_bn('data/cancer.bif')
"""
Explanation: Drawing (Plotting) Bayesian Networks in pyBN
The importance of drawing a Bayesian network can not be understated, especially in a research setting. Being able to visualize a network's random variables and the dependencies between them is critical to ... |
phoebe-project/phoebe2-docs | development/tutorials/atm_passbands.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
"""
Explanation: Atmospheres & Passbands
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 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 n... |
KMFleischer/PyEarthScience | Tutorial/03_Xarray_PyNIO_basics.ipynb | mit | import numpy as np
fname = './data/tsurf.nc'
"""
Explanation: 3. Xarray & PyNIO
To read and write scientific data xarray and PyNIO are very efficient, and easy to use because the internal data structure is netCDF like. Xarray can read netCDF and Grib files, and handle the metadata following the netCDF CF-convention. ... |
tensorflow/lucid | notebooks/feature-visualization/neuron_diversity.ipynb | apache-2.0 | # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the L... |
enthought/distarray | examples/julia_set/julia_set.ipynb | bsd-3-clause | from __future__ import print_function
import numpy
import matplotlib.pyplot as plt
# DistArray imports
from distarray.globalapi import Context, Distribution
from distarray.globalapi.distarray import DistArray
# inline plots
%matplotlib inline
# bigger figures
plt.rcParams.update({'figure.figsize': (10, 10)})
"""
E... |
jiumem/tuthpc | mpi.ipynb | bsd-3-clause | %%file hellompi.py
"""
Parallel Hello World
"""
from mpi4py import MPI
import sys
size = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
name = MPI.Get_processor_name()
sys.stdout.write(
"Hello, World! I am process %d of %d on %s.\n"
% (rank, size, name))
"""
Explanation: MPI and cluster computi... |
google-coral/tutorials | retrain_classification_qat_tf1.ipynb | apache-2.0 | # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the L... |
marc-moreaux/Deep-Learning-classes | notebooks/Intro_to_RNN.ipynb | mit | with open('./fables.txt') as f:
text = f.read()
print text
"""
Explanation: Text generation with RNN
In this lab, we are going to generate text with RNNs.
We'll try to have a RNN learning the fables de la Fontaine.
Lets load into variable the Fables:
End of explanation
"""
import numpy as np
vocab = sorted(set... |
tuanavu/coursera-university-of-washington | machine_learning/2_regression/lecture/week5/Overfitting_Demo_Ridge_Lasso.ipynb | mit | import sys
sys.path.append('C:\Anaconda2\envs\dato-env\Lib\site-packages')
import graphlab
import math
import random
import numpy
from matplotlib import pyplot as plt
%matplotlib inline
"""
Explanation: Overfitting demo
Create a dataset based on a true sinusoidal relationship
Let's look at a synthetic dataset consist... |
keras-team/autokeras | docs/ipynb/customized.ipynb | apache-2.0 |
input_node = ak.ImageInput()
output_node = ak.Normalization()(input_node)
output_node1 = ak.ConvBlock()(output_node)
output_node2 = ak.ResNetBlock(version="v2")(output_node)
output_node = ak.Merge()([output_node1, output_node2])
output_node = ak.ClassificationHead()(output_node)
auto_model = ak.AutoModel(
inputs=... |
saashimi/code_guild | wk2/extras/linked_lists/palindrome/palindrome_challenge.ipynb | mit | %run ../linked_list/linked_list.py
%load ../linked_list/linked_list.py
class MyLinkedList(LinkedList):
def is_palindrome(self):
# TODO: Implement me
pass
"""
Explanation: <small><i>This notebook was prepared by Donne Martin. Source and license info is on GitHub.</i></small>
Challenge Notebook
Pro... |
weikang9009/pysal | notebooks/explore/pointpats/process.ipynb | bsd-3-clause | from pysal.explore.pointpats import PoissonPointProcess, PoissonClusterPointProcess, Window, poly_from_bbox, PointPattern
import pysal.lib as ps
from pysal.lib.cg import shapely_ext
%matplotlib inline
import numpy as np
#import matplotlib.pyplot as plt
"""
Explanation: Point Processes
Author: Serge Rey sj... |
STREAM3/pyisc | docs/pyISC_multivariable_example.ipynb | lgpl-3.0 | import pyisc;
import numpy as np
from scipy.stats import poisson, norm
%matplotlib inline
from pylab import plot
"""
Explanation: pyISC Example: MultivariableAnomaly Detection
In this example, we extend the simple example with one Poisson distributed variable to the multivariate case with three variables, two Poisson ... |
ydluo/qdyn | examples/notebooks/RSF_spring-block.ipynb | gpl-3.0 | # Make plots interactive in the notebook
%matplotlib notebook
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
# Add QDYN source directory to PATH
# Go up in the directory tree
upup = [os.pardir]*2
qdyn_dir = os.path.join(*upup)
# Get QDYN src directory
src_dir = os.path.abspath(
os.path.j... |
ML4DS/ML4all | R2.kNN_Regression/regression_knn_student.ipynb | mit | # Import some libraries that will be necessary for working with data and displaying plots
# To visualize plots in the notebook
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pylab
# Packages used to read datasets
import scipy.io # To read matlab files
import pan... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_otp.ipynb | bsd-3-clause | # Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import mne
import numpy as np
from mne import find_events, fit_dipole
from mne.datasets.brainstorm import bst_phantom_elekta
from mne.io import read_raw_fif
print(__doc__)
"""
Explanation: Plot sensor denoising using ov... |
graphistry/pygraphistry | demos/demos_databases_apis/hypernetx/hypernetx.ipynb | bsd-3-clause | # ! pip install hypernetx -q
# ! pip install graphistry -q
"""
Explanation: HyperNetX + Graphistry = 💪💪💪
You can quickly explore HyperNetX graphs using Graphistry through the below sample class.
PNNL's HyperNetX is a new Python library for manipulating hypergraphs: graphs where an edge may connect any number of nod... |
paris-saclay-cds/python-workshop | Day_1_Scientific_Python/03-matplotib_seaborn.ipynb | bsd-3-clause | %matplotlib inline
"""
Explanation: <p style="margin-top: 3em; margin-bottom: 3em;"><font size="7"><b>Matplotlib & Seaborn: Introduction </b></font></p>
End of explanation
"""
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Matplotlib
Matplotlib is a Python package used widely throughout the sci... |
mdeff/cnn_graph | trials/1_learning_filters.ipynb | mit | import time
import numpy as np
import scipy.sparse, scipy.sparse.linalg, scipy.spatial.distance
import matplotlib.pyplot as plt
%matplotlib inline
tol = 1e-10
"""
Explanation: Trial 1: learning graph filters
Random kNN graph: $W \in \mathbb{R}^{M \times M} \rightarrow L \rightarrow U, \Lambda$.
Random graph signals: ... |
tensorflow/tensorflow | tensorflow/lite/g3doc/models/modify/model_maker/text_searcher.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... |
gschivley/ghgforcing | Example/Example notebook.ipynb | mit | tstep = 0.01
time = np.linspace(0, 100, 100/tstep+1)
"""
Explanation: Example uses of ghgforcing
Define the time axis along which all emissions and calculations take place, from year 0 to 100. The time-step (tstep) is set to 0.01 years to improve the accuracy of calcuations.
End of explanation
"""
co2_emission = np.... |
GoogleCloudPlatform/cloudml-samples | notebooks/scikit-learn/20_newsgroups_automl.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import csv
from sklearn.datasets import fetch_20newsgroups
"""
Explanation: Prepare data for Google Cloud AutoML Natural Language from scikit-learn
Overview
This notebook demonstrates how to prepare text data available in scikit-learn (or other libraries), so that it can be used... |
mtat76/atm-py | examples/timeseries.ipynb | mit | from atmPy.instruments.POPS import housekeeping
from atmPy.instruments.piccolo import piccolo
%matplotlib inline
"""
Explanation: Introduction
The timeseries module provides the TimeSeries class which deals with all 1D timeseries (sizedistribution timeseries are considered 2D timeseries and are delt with in a differen... |
shikhar413/openmc | examples/jupyter/mgxs-part-iii.ipynb | mit | import math
import pickle
from IPython.display import Image
import matplotlib.pyplot as plt
import numpy as np
import openmc
import openmc.mgxs
from openmc.openmoc_compatible import get_openmoc_geometry
import openmoc
import openmoc.process
from openmoc.materialize import load_openmc_mgxs_lib
%matplotlib inline
"""... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/tfx_pipelines/guided_projects/guided_project_3_nlp_starter/reusable_embeddings.ipynb | apache-2.0 | import os
import pandas as pd
from google.cloud import bigquery
%load_ext google.cloud.bigquery
"""
Explanation: Reusable Embeddings
Learning Objectives
1. Learn how to use a pre-trained TF Hub text modules to generate sentence vectors
1. Learn how to incorporate a pre-trained TF-Hub module into a Keras model
1. Lea... |
WomensCodingCircle/CodingCirclePython | Lesson03_Functions/Functions.ipynb | mit | my_string = "Howdy, welcome to my ranch?"
print(len(my_string))
print(max(my_string))
print(min(my_string))
"""
Explanation: Functions
A function is a set of statements that you store and can run multiple times. We have already used some functions.
type("I've been using functions since the beginning?")
This is a bui... |
catalyst-cooperative/pudl | notebooks/work-in-progress/better-heatrates.ipynb | mit | %load_ext autoreload
%autoreload 2
# Standard libraries
import logging
import os
import pathlib
import sys
# 3rd party libraries
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sqlalchemy as sa
# Local libraries
import pudl
import pudl.cons... |
jerkos/cobrapy | documentation_builder/getting_started.ipynb | lgpl-2.1 | from __future__ import print_function
import cobra.test
model = cobra.test.create_test_model("textbook") # "ecoli" and "salmonella" are also valid arguments
"""
Explanation: Getting Started
To begin with, cobrapy comes with bundled models for Salmonella and E. coli, as well as a "textbook" model of E. coli core meta... |
ComputationalModeling/spring-2017-danielak | past-semesters/spring_2016/day-by-day/day18-kinematics-terminal-velocity-of-a-skydiver/Day_18_pre_class_notebook-SOLUTIONS.ipynb | agpl-3.0 | from IPython.display import YouTubeVideo
# WATCH THE VIDEO IN FULL-SCREEN MODE
YouTubeVideo("JXJQYpgFAyc",width=640,height=360) # Numerical integration
"""
Explanation: Day 18 Pre-class assignment
Goals for today's pre-class assignment
In this pre-class assignment, you are going to learn how to:
Numerically integ... |
4dsolutions/Python5 | Martian Volume.ipynb | mit | from qrays import Qvector, Vector
a = Qvector((1,0,0,0))
a.length()
b = Qvector((0,1,0,0))
(a-b).length()
"""
Explanation: Martian Volume
Whoever heard of a linear algebra with four independent basis vectors in 3-space, which basis vectors are not unit length?
This all sounds absurd.
Welcome to Quadrays.
End of ex... |
vpw/AndroidForBeginners | Ass2.5/END3_MNIST_addnumber_ass2_5.ipynb | apache-2.0 | #pip install --force-reinstall torch==1.2.0 torchvision==0.4.0 -f https://download.pytorch.org/whl/torch_stable.html
%pylab inline
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset
import torch.utils.data.dataloader as dataloader
import torch.opt... |
shevkunov/workout | labs/2.2_Hydrogen_Spectre.ipynb | bsd-3-clause | plt.plot(h1_phi - h2_phi[:3], h2_phi[:3])
plt.show()
"""
Explanation: Подгон (предсказываем по данным h2_phi с чужой устнановки четвёртое значение в h1_phi - на нашей установке)
End of explanation
"""
from sklearn.linear_model import LinearRegression
LR = LinearRegression()
LR.fit(h2_phi[:3].reshape(3, 1), (h1_... |
flohorovicic/pynoddy | docs/notebooks/.ipynb_checkpoints/Feature-Analysis-checkpoint.ipynb | gpl-2.0 | from IPython.core.display import HTML
css_file = 'pynoddy.css'
HTML(open(css_file, "r").read())
import sys, os
import matplotlib.pyplot as plt
# adjust some settings for matplotlib
from matplotlib import rcParams
# print rcParams
rcParams['font.size'] = 15
# determine path of repository to set paths corretly below
rep... |
tensorflow/docs-l10n | site/ko/hub/tutorials/spice.ipynb | apache-2.0 | #@title Copyright 2020 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... |
mommermi/Introduction-to-Python-for-Scientists | notebooks/.ipynb_checkpoints/FileIO_Formatting_Errors_20160923-checkpoint.ipynb | mit | outf = open('table.dat', 'w') # syntax (filename, mode), mode can be 'w' (write), 'r' (read), or 'a' (append)
outf.writelines('# index some_value another_value\n') # table header line
for i in range(20):
outf.write('{:02d} {:4d} {:3d}\n'.format(i, i**3, i+200)) # just some numbers
outf.close()
"""
Exp... |
weikang9009/pysal | notebooks/explore/segregation/inference_wrappers_example.ipynb | bsd-3-clause | %matplotlib inline
import geopandas as gpd
from pysal.explore import segregation
import pysal.lib
import pandas as pd
import numpy as np
from pysal.explore.segregation.inference import SingleValueTest, TwoValueTest
"""
Explanation: Inference Wrappers use cases
This is an example of the PySAL segregation framework to... |
gon1213/SDC | behavioral_cloning/CarND-Keras-Lab/traffic-sign-classification-with-keras-solution.ipynb | gpl-3.0 | from urllib.request import urlretrieve
from os.path import isfile
from tqdm import tqdm
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_size=None):
self.total = total_size
self.update((block_num - self.last_block) * block_size)
self.last_block = b... |
aliparsai/LittleDarwin | notebooks/mutateJavaSourceCode.ipynb | gpl-3.0 | myJavaCode = """
public class MyCode {
void bubbleSort(int array[])
{
int len = array.length;
for (int i = 0; i < len - 1; i++)
for (int j = 0; j < len - i - 1; j++)
if (array[j] > array[j+1])
{
int temp = array[j];
... |
chemiskyy/simmit | Examples/Continuum_Mechanics/constitutive_relations.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from simmit import smartplus as sim
import os
"""
Explanation: constitutive : The Constitutive Library
End of explanation
"""
E = 70000.0
nu = 0.3
L = sim.L_iso(E,nu,"Enu")
print np.array_str(L, precision=4, suppress_small=True)
d = sim.check_sy... |
jsharpna/DavisSML | lectures/lecture10/lecture10p3.ipynb | mit | from lxml import html, etree
import numpy as np
from sklearn import cluster, feature_extraction, metrics, preprocessing, decomposition
import collections
import nltk
import pandas as pd
import plotnine as p9
# nltk.download()
# Download Corpora -> stopwords, Models -> punkt
from nltk.corpus import stopwords
from nltk.... |
Naereen/notebooks | Verification_de_numeros_CB_RIB_NIRPP_IBAN.ipynb | mit | def l_to_c(l):
try:
return str(int(l))
except ValueError:
return str(10 + ord(l.upper()) - ord('A'))
for l in 'ABCDEFGHIJKLMNROPQRSTUVWXYZ':
print("l = {} --> c = {}".format(l, l_to_c(l)))
"""
Explanation: Vérification de numéros de Cartes Bleues, RIB (IBAN) et NIRPP (sécu)
Ce petit notebo... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/feature_engineering/labs/5_tftransform_taxifare.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip install tensorflow==2.3.0 tensorflow-transform==0.24.0 apache-beam[gcp]==2.24.0
"""
Explanation: Exploring tf.transform #
Learning Objectives
1. Preprocess data and engineer new features using TfTransform.
1. Create and deploy Apache Beam pipeli... |
philmui/datascience2016fall | lecture08.ML2/regression.ipynb | mit | import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
%matplotlib inline
"""
Explanation: Regression
This notebook covers univariate & multi-variate "linear regression". We'll be going over how to use the scikit-le... |
statsmaths/stat665 | lectures/lec16/notebook16.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... |
brucefan1983/GPUMD | examples/empirical_potentials/thermal_expansion/Thermal Expansion.ipynb | gpl-3.0 | from pylab import *
from ase.lattice.cubic import Diamond
from thermo.gpumd.io import ase_atoms_to_gpumd
import pandas as pd
"""
Explanation: Thermal Expansion
1. Introduction
A given crystal should have a well defined average lattice constant at a given pressure and temperature. Here we use silicon as an example to ... |
planetlabs/notebooks | jupyter-notebooks/crop-classification/datasets-prepare-cdl.ipynb | apache-2.0 | import os
import pathlib
from subprocess import check_output, STDOUT, CalledProcessError
import tempfile
import rasterio
"""
Explanation: Prepare CDL Data
In this notebook, we prepare the 2016 from the USDA 2016 Crop Data Layer (CDL) for Iowa for use in the crop classification notebooks. This involves taking the CDL ... |
mmatera/qmnotebooks | paqueteviajero.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from IPython.display import HTML
import ipywidgets as widgets
from IPython.display import display
L = 200
dx = 2.
buttonrunsim=widgets.Button(description="Simulate")
outwdt = widgets.Output()
centropaquete = widgets.FloatSli... |
oscar6echo/ezmarkdown | demo_ezmarkdown.ipynb | mit | # path_img1 = 'data/svgclock.svg'
# path_img2 = 'data/example2.jpg'
# path_img3 = 'data/example4.png'
path_img1 = 'http://upload.wikimedia.org/wikipedia/commons/f/fd/Ghostscript_Tiger.svg'
path_img2 = 'http://upload.wikimedia.org/wikipedia/commons/thumb/3/3e/Einstein_1921_by_F_Schmutzer_-_restoration.jpg/220px-Einstei... |
joelagnel/lisa | ipynb/examples/energy_meter/EnergyMeter_ACME.ipynb | apache-2.0 | import logging
from conf import LisaLogging
LisaLogging.setup()
"""
Explanation: Energy Meter Examples
BayLibre's ACME Cape and IIOCapture
More information can be found at https://github.com/ARM-software/lisa/wiki/Energy-Meters-Requirements#iiocapture---baylibre-acme-cape.
End of explanation
"""
# Generate plots inl... |
a-mt/dev-roadmap | docs/!matplotlib/Basemap.ipynb | mit | # https://rabernat.github.io/research_computing_2018/xarray-tips-and-tricks.html
url = 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2018.nc'
from netCDF4 import Dataset as NetCDFFile
nc = NetCDFFile(url)
nc
# Extract data from NetCDF file (will take a minute)
lats = nc.varia... |
xqding/StatisticsExamples | Hit_Run_Sampler/main.ipynb | gpl-3.0 | %matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
import ternary
## generate order statistics from uniform distribution between 0 and 1
U = [[np.random.uniform(), np.random.uniform()] for i in range(50000)]
for u in U:
u.sort()
## calculate the spacing and plot the sampling results
x_1 = [u[... |
sassoftware/sas-viya-programming | developerTrial/python/Basic+Predictive+Modeling+Example.ipynb | apache-2.0 | import os
import pandas as pd
import swat
import sys
from matplotlib import pyplot as plt
%matplotlib inline
target = "bad"
class_inputs = ["reason", "job"]
class_vars = [target] + class_inputs
interval_inputs = ["im_clage", "clno", "im_debtinc", "loan", "mortdue", "value", "im_yoj", "im_ninq", "derog... |
SKA-ScienceDataProcessor/algorithm-reference-library | processing_library/notebooks/gridding.ipynb | apache-2.0 | %matplotlib inline
import sys, os
sys.path.append(os.path.join('..','..'))
from matplotlib import pylab
import numpy
from matplotlib import pyplot as plt
from processing_library.fourier_transforms.convolutional_gridding import *
from processing_library.fourier_transforms.convolutional_gridding import coordinates
... |
sclamons/murraylab_tools | examples/Echo Setup Usage Examples.ipynb | mit | import murraylab_tools.echo as mt_echo
import os.path
# Relevant input and output files. Check these out for examples of input file format.
txtl_inputs = os.path.join("txtl_setup", "inputs")
txtl_outputs = os.path.join("txtl_setup", "outputs")
stock_file = os.path.join(txtl_inputs, "TX-TL_setup_example_stocks.csv") ... |
slerch/ppnn | nn_postprocessing/notebooks/presentation_notebook.ipynb | mit | # Imports
import numpy as np
import sys
sys.path.append('../') # This is where all the python files are!
from importlib import reload
import utils; reload(utils)
from utils import *
import keras_models; reload(keras_models)
from keras_models import *
from losses import crps_cost_function
from scipy.stats import norm... |
kubeflow/kfserving-lts | docs/samples/drift-detection/alibi-detect/cifar10/cifar10_drift.ipynb | apache-2.0 | !pip install -r requirements_notebook.txt
"""
Explanation: Cifar10 Drift Detection
In this example we will deploy an image classification model along with a drift detector trained on the same dataset. For in depth details on creating a drift detection model for your own dataset see the alibi-detect project and associa... |
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