repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
PYPIT/PYPIT | doc/nb/FluxSpec.ipynb | gpl-3.0 | %matplotlib inline
# import
from importlib import reload
import os
from matplotlib import pyplot as plt
import glob
import numpy as np
from astropy.table import Table
from pypeit import fluxspec
from pypeit.spectrographs.util import load_spectrograph
"""
Explanation: Fluxing with PYPIT [v2]
End of explanation
"""
... |
gevero/py_gmm | examples/Chirality.ipynb | gpl-3.0 | #------Library loading------
# numpy for matrix computations
import numpy as np; import numpy.ma as ma
# system libraries
import sys
# plotting libraries
%matplotlib inline
import matplotlib.pylab as plt
# Generalized Multiparticle Mie import
sys.path.append('../')
import py_gmm
"""
Explanation: # Circular dichroi... |
mbakker7/ttim | pumpingtest_benchmarks/2_test_of_dalem.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from ttim import *
"""
Explanation: Leaky Aquifer Test
This example is taken from Kruseman and de Ridder (1970)
End of explanation
"""
H = 37 #aquifer thickness [m]
zt = - 8 #top boundary of aquifer
zb = zt - H
Q = 761 #constan... |
fluffy-hamster/A-Beginners-Guide-to-Python | A Beginners Guide to Python/20. Functions & Namespaces.ipynb | mit | def zero_args():
# code goes here
pass
def one_arg(a):
# code goes here
pass
def two_args(a, b):
# code goes here
pass
def optional_arg(a, b=0): # <--- please note, optional arguments are listed LAST
# code goes here
pass
def two_options(a=True, b=False):
# code goes here
... |
stereoboy/Study | Issues/algorithms/Arrays and Strings.ipynb | mit | import random
#STR = random.uniform(('a').encode('ascii'), int('Z'))
#print(ord('A'))
#print(ord('z'))
#lowercase = [ chr(char) for char in range(ord('a'), ord('z') + 1)]
#uppercase = [ chr(char) for char in range(ord('A'), ord('Z') + 1)]
#string_seed = lowercase + uppercase
#print(string_seed)
def gen_randstr():
... |
beyondvalence/biof509_wtl | Wk03-OOP/Wk03-Paradigms_wl.ipynb | mit | primes = []
i = 2
while len(primes) < 25:
for p in primes:
if i % p == 0:
break
else:
primes.append(i)
i += 1
print(primes)
"""
Explanation: Week 3 - Programming Paradigms
Learning Objectives
List popular programming paradigms
Demonstrate object oriented programming
Compare pr... |
hainm/mdtraj | examples/centroids.ipynb | lgpl-2.1 | from __future__ import print_function
%matplotlib inline
import mdtraj as md
import numpy as np
"""
Explanation: Finding centroids
In this example, we're going to find a "centroid" (representitive structure) for a group of conformations. This group might potentially come from clustering, using method like Ward hierarc... |
MIT-LCP/mimic-code-sharing | notebooks/vancomycin-dosing.ipynb | mit | # Import libraries
from __future__ import print_function
import numpy as np
import pandas as pd
import psycopg2
import socket
import sys
import os
import getpass
from collections import OrderedDict
import matplotlib
import matplotlib.pyplot as plt
# colours for prettier plots
import colorsys
def gg_color_hue(n):
... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a_ml/td2a_cenonce_session_4A.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 2A.ml - Machine Learning et Marketting
Prédire la souscription d'un contrat sur le jeu de données Bank Marketing Data Set .
End of explanation
"""
url = "https://archive.ics.uci.edu/m... |
phoebe-project/phoebe2-docs | 2.3/tutorials/LC_estimators.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
import phoebe
from phoebe import u # units
import numpy as np
logger = phoebe.logger()
"""
Explanation: Advanced: LC estimators
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 cola... |
mne-tools/mne-tools.github.io | 0.18/_downloads/d9e7f23ac267ddfa6023c7da2df2a984/plot_stats_cluster_time_frequency_repeated_measures_anova.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.time_frequency import tfr_morlet
from mne.sta... |
mne-tools/mne-tools.github.io | 0.17/_downloads/d876d0aad8948c7dc203ef1e5037106a/plot_decoding_xdawn_eeg.ipynb | bsd-3-clause | # Authors: Alexandre Barachant <alexandre.barachant@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import StratifiedKFold
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import c... |
WNoxchi/Kaukasos | pytorch/practice-mnist.ipynb | mit | # %reload_ext autoreload
# %autoreload 2
%matplotlib inline
import torch
import torchvision
import numpy as np
# import mnist_loader
# train, valid, test = mnist_loader.load_data(path='data/mnist/')
"""
Explanation: PyTorch practice with MNIST data
WNixalo - 2018/2/27
0. Imports
End of explanation
"""
# torchvi... |
jorisvandenbossche/geopandas | doc/source/gallery/create_geopandas_from_pandas.ipynb | bsd-3-clause | import pandas as pd
import geopandas
import matplotlib.pyplot as plt
"""
Explanation: Creating a GeoDataFrame from a DataFrame with coordinates
This example shows how to create a GeoDataFrame when starting from
a regular DataFrame that has coordinates either WKT
(well-known text)
format, or in
two columns.
End of expl... |
bomboradata/bombora-tutorials | notebooks/topic-interest-score/topic-interest-result-data-schema.ipynb | mit | !ls -lh ../../data/topic-interest-score/
"""
Explanation: Bombora Topic Interest Datasets
Explaining Bombora topic interest score datasets.
0. Surge vs Interest?
As a matter of clarification, topic surge as a product is generated from topic interest models. In technical discussions, we'll refer to both the product and... |
dshean/iceflow | VisualizingDEMData.ipynb | mit | #do not run
import sys
#update path until georaster is installed with the make file
sys.path.insert(0,'/Users/jessica/Classes/Geohackweek2016/iceflow/georaster')
#do not run
import geoutils
import gdal
import pandas
from matplotlib import pyplot as plt
import mpl_toolkits.basemap
from ipyleaflet import (Map,
Mark... |
PhonologicalCorpusTools/PyAnnotationGraph | examples/tutorial/tutorial_3_query.ipynb | mit | from polyglotdb import CorpusContext
"""
Explanation: Tutorial 3: Getting information out
First we begin with the standard import:
End of explanation
"""
with CorpusContext('pg_tutorial') as c:
q = c.query_graph(c.syllable)
q = q.filter(c.syllable.stress == '1')
q = q.filter(c.syllable.begin == c.syllab... |
OpenWeavers/openanalysis | doc/OpenAnalysis/06 - Tree Growth Based Graph Algorithms.ipynb | gpl-3.0 | import openanalysis.tree_growth as TreeGrowth
"""
Explanation: Tree Growth based Graph Algorithms
These class of algorithms takes a Graph as input, and generates Tree, which consists of some of edges of input Graph, which are selected according to particular criteria. Some examples are
DFS
BFS
Minimum Spanning Tree... |
adammenges/ml-muse | numpy-cnn/numpy-cnn.ipynb | mit | import keras
from keras.datasets import mnist
from keras.models import Model
from keras.layers import Dense, Dropout, Flatten, Input, Conv2D, MaxPooling2D
from keras import backend as K
(x_train, y_train), (x_test, y_test) = mnist.load_data()
from PIL import Image
Image.fromarray(x_train[0]).resize((256,256))
y_trai... |
tensorflow/docs-l10n | site/zh-cn/tutorials/keras/text_classification_with_hub.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... |
csaladenes/csaladenes.github.io | present/bi2/2020/ubb/az_en_jupyter2_mappam/sklearn_tutorial/04.1-Dimensionality-PCA.ipynb | mit | from __future__ import print_function, division
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
plt.style.use('seaborn')
"""
Explanation: <small><i>This notebook was put together by Jake Vanderplas. Source and license info is on GitHub.</i></small>
Dimensionality Reducti... |
Jackporter415/phys202-2015-work | assignments/assignment05/MatplotlibEx03.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Matplotlib Exercise 3
Imports
End of explanation
"""
def well2d(x, y, nx, ny, L=1.0):
"""Compute the 2d quantum well wave function."""
answer = np.array(2/L * np.sin(nx*np.pi*x/L)*np.sin(ny*np.pi*y/L))
return answer
p... |
aukintux/business_binomial_analysis | business_analysis.ipynb | mit | # Numpy
import numpy as np
# Scipy
from scipy import stats
from scipy import linspace
# Plotly
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly.graph_objs as go
init_notebook_mode(connected=True) # Offline plotting
"""
Explanation: Business Feasibility Overview
The purpose of... |
wzxiong/DAVIS-Machine-Learning | labs/lab4.ipynb | mit | # %load ../standard_import.txt
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.linear_model as skl_lm
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminant... |
heyengel/kaggle-titanic | code/kaggle-titanic.ipynb | mit | test.info()
train.describe()
# train.Cabin.str.split().str.get(-1).str[0]
# train.Cabin.str.split(expand=True)
# train.Ticket.str.split().str.get(0).str.extract
train.Ticket.str.split()[0:].str[0].head()
print train[train['Survived']==1]["Age"].mean(),
print train[train['Survived']==0]["Age"].mean(),
print test.Age... |
sysid/nbs | cnn/tw_fromScratch.ipynb | mit | %matplotlib inline
"""
Explanation: Using Convolutional Neural Networks
This is running on theano!
Basic setup
End of explanation
"""
#path = "data/dogscats/"
path = "data/dogscats/sample/"
"""
Explanation: Define path to data: (It's a good idea to put it in a subdirectory of your notebooks folder, and then exclude... |
mwickert/SP-Comm-Tutorial-using-scikit-dsp-comm | hardware_configure/RTL_SDR_Test.ipynb | bsd-2-clause | # Code for performing the capture
import rtlsdr
import numpy as np
def capture(Tc,fo=88.7e6,fs=2.4e6,gain=40,device_index=0):
# Setup SDR
sdr = rtlsdr.RtlSdr(device_index) #create a RtlSdr object
#sdr.get_tuner_type()
sdr.sample_rate = fs
sdr.center_freq = fo
#sdr.gain = 'auto'
sdr.gain = g... |
km-Poonacha/python4phd | Session 2/ipython/.ipynb_checkpoints/Lesson 4 - Web API -checkpoint.ipynb | gpl-3.0 | import requests
url = 'http://www.github.com/ibm'
response = requests.get(url)
print(response.status_code)
"""
Explanation: Lesson 4 - Web API
Requesting information from the web
Python 'requests' module.
This module provides functions to send a HTTP request and get the response from the server
Requests is a third... |
moble/PostNewtonian | Waveforms/SphericalHarmonicTensors.ipynb | mit | from __future__ import division, print_function
import sympy
from sympy import *
from sympy import Rational as frac
import simpletensors
from simpletensors import Vector, xHat, yHat, zHat
from simpletensors import TensorProduct, SymmetricTensorProduct, Tensor
init_printing()
var('vartheta, varphi')
var('nu, m, delta,... |
quasars100/Resonance_testing_scripts | python_tutorials/FourierSpectrum.ipynb | gpl-3.0 | import rebound
rebound.add("Sun")
rebound.add("Jupiter")
rebound.add("Saturn")
"""
Explanation: Fourier Analysis & Resonances
A great benefit of being able to call rebound from within python is the ability to directly apply sophisticated analysis tools from scipy and other python libraries. Here we will do a simple F... |
jinzishuai/learn2deeplearn | deeplearning.ai/C1.NN_DL/week2/Logistic+Regression+with+a+Neural+Network+mindset+v4.ipynb | gpl-3.0 | 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 ... |
JoaoRodrigues/pypdb | demos/demos.ipynb | mit | %pylab inline
from IPython.display import HTML
from pypdb.pypdb import *
import pprint
"""
Explanation: pypdb demos
This is a set of basic examples of the usage and outputs of the various individual functions included in. There are generally two types of functions:
Functions that perform searches and return lists o... |
gaufung/Data_Analytics_Learning_Note | python-statatics-tutorial/basic-theme/scipy_basic/details.ipynb | mit | import numpy as np
from scipy import io as spio
a = np.ones((3,3))
spio.savemat('file.mat',{'a':a})
data = spio.loadmat('file.mat',struct_as_record=True)
data['a']
"""
Explanation: 模块使用
1 scipy.io
读取矩阵数据
End of explanation
"""
from scipy import misc
misc.imread('fname.png')
import matplotlib.pyplot as plt
plt.imrea... |
astarostin/MachineLearningSpecializationCoursera | course4/week2 - Двухвыборочные непараметрические критерии (независимые выборки) - demo.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import itertools
from scipy import stats
from statsmodels.stats.descriptivestats import sign_test
from statsmodels.stats.weightstats import zconfint
from statsmodels.stats.weightstats import *
%pylab inline
"""
Explanation: Непараметрические критерии
Критерий | Одновыборочный |... |
vanheck/blog-notes | QuantTrading/time-series-analyze_2-visualisation.ipynb | mit | MY_VERSION = 1,0
import sys
import datetime
import numpy as np
import pandas as pd
import pandas_datareader as pdr
import pandas_datareader.data as pdr_web
import quandl as ql
from matplotlib import __version__ as matplotlib_version
from seaborn import __version__ as seaborn_version
# Load Quandl API key
import json
... |
esa-as/2016-ml-contest | EvgenyS/Facies_classification_ES.ipynb | apache-2.0 | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable
from pandas import set_option
set_option("display.max_rows", 20)
pd.options.mode.chained_assignment = None
filen... |
madsenmj/ml-introduction-course | Class13/Class13.ipynb | apache-2.0 | import numpy as np
# fix random seed for reproducibility
np.random.seed(23)
# load data
def load_data(path='Class13_mnist.pkl.gz'):
import gzip
from six.moves import cPickle
import sys
#path = get_file(path, origin='https://s3.amazonaws.com/img-datasets/mnist.pkl.gz')
if path.endswith('.gz'):
... |
bongsoos/pythontools | examples/Principal Component Analysis.ipynb | mit | import numpy as np
import arraytools as arry
import statstools as stats
import plottools as pt
%matplotlib inline
"""
Explanation: Demo of pythontools library and Principal Component Analysis
Load libraries
Import pythontools libraries.
End of explanation
"""
tempA = arry.concate([2*np.random.randn(100,1)-3, 1*np.ra... |
tuanavu/coursera-university-of-washington | machine_learning/1_machine_learning_foundations/assignment/week2/ipynb_checkpoints/Predicting house prices-checkpoint.ipynb | mit | import graphlab
"""
Explanation: Fire up graphlab create
End of explanation
"""
sales = graphlab.SFrame('home_data.gl/')
sales
"""
Explanation: Load some house sales data
Dataset is from house sales in King County, the region where the city of Seattle, WA is located.
End of explanation
"""
graphlab.canvas.set_ta... |
ES-DOC/esdoc-jupyterhub | notebooks/thu/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', 'thu', 'sandbox-2', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: THU
Source ID: SANDBOX-2
Topic: Ocean
Sub-Topics: Timestepping Framework, Advection, ... |
GoogleCloudPlatform/mlops-on-gcp | immersion/guided_projects/guided_project_3_nlp_starter/reusable_embeddings.ipynb | apache-2.0 | import os
from google.cloud import bigquery
import pandas as pd
%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... |
vadim-ivlev/STUDY | handson-data-science-python/DataScience-Python3/KFoldCrossValidation.ipynb | mit | import numpy as np
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn import datasets
from sklearn import svm
iris = datasets.load_iris()
"""
Explanation: K-Fold Cross Validation
End of explanation
"""
# Split the iris data into train/test data sets with 40% reserved for testing
X_t... |
phievo/phievo | Examples/AnalyzeNetwork.ipynb | lgpl-3.0 | %matplotlib notebook
import matplotlib.pyplot as plt
import numpy as np
from ipywidgets import widgets
from ipywidgets import interact, interactive, fixed
from IPython.display import display,HTML,clear_output
import os
HTML('''<script>code_show=true;function code_toggle() {if (code_show){$('div.input').hide();} else ... |
antoniomezzacapo/qiskit-tutorial | community/terra/qis_adv/two-qubit_state_quantum_random_access_coding.ipynb | apache-2.0 | # useful math functions
from math import pi, cos, acos, sqrt
# importing the QISKit
from qiskit import Aer, IBMQ
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, execute
# import basic plot tools
from qiskit.tools.visualization import plot_histogram
# useful additional packages
from qiskit.wra... |
jinzishuai/learn2deeplearn | deeplearning.ai/C4.CNN/week4_SpecialApps/hw/Neural Style Transfer/Art Generation with Neural Style Transfer - v1.ipynb | gpl-3.0 | import os
import sys
import scipy.io
import scipy.misc
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
from PIL import Image
from nst_utils import *
import numpy as np
import tensorflow as tf
%matplotlib inline
"""
Explanation: Deep Learning & Art: Neural Style Transfer
Welcome to the second assi... |
wmorning/StatisticalMethods | examples/XrayImage/Inference.ipynb | gpl-2.0 | # import cluster_pgm
# cluster_pgm.inverse()
from IPython.display import Image
Image(filename="cluster_pgm_inverse.png")
"""
Explanation: Inferring Cluster Model Parameters from an X-ray Image
Forward modeling is always instructive: we got a good sense of the parameters of our cluster + background model simply by g... |
ethen8181/machine-learning | dim_reduct/PCA.ipynb | mit | from jupyterthemes import get_themes
from jupyterthemes.stylefx import set_nb_theme
themes = get_themes()
set_nb_theme(themes[1])
# 1. magic for inline plot
# 2. magic to print version
# 3. magic so that the notebook will reload external python modules
# 4. magic to enable retina (high resolution) plots
# https://gist... |
samsammurphy/ee-atmcorr-timeseries | ee-atmcorr-timeseries.ipynb | apache-2.0 | # standard modules
import os
import sys
import ee
import colorsys
from IPython.display import display, Image
%matplotlib inline
ee.Initialize()
# custom modules
# base_dir = os.path.dirname(os.getcwd())
# sys.path.append(os.path.join(base_dir,'atmcorr'))
from atmcorr.timeSeries import timeSeries
from atmcorr.postProce... |
nicolas998/wmf | Examples/Simula_Salgar_Celdas.ipynb | gpl-3.0 | %matplotlib inline
from wmf import wmf
from fwm import utils
import numpy as np
import pylab as pl
"""
Explanation: Simulador de la Cuenca de Salgar
El siguiente codigo se encarga de simular la cuenca de salgar a partir de la informacion de radar obtenida por Julian, para el evento de Mayo 20.
La siguiente celda in... |
risantos/schoolwork | Física Computacional/Ficha 4.ipynb | mit | import numpy as np
"""
Explanation: Departamento de Física - Faculdade de Ciências e Tecnologia da Universidade de Coimbra
Física Computacional - Ficha 4 - Sistemas de equações Lineares
Rafael Isaque Santos - 2012144694 - Licenciatura em Física
1 - Resolução de um sistema de equações lineares $Ax = b$ pelo método de e... |
antoniomezzacapo/qiskit-tutorial | community/aqua/optimization/maxcut.ipynb | apache-2.0 | from qiskit_aqua import Operator, run_algorithm, get_algorithm_instance
from qiskit_aqua.input import get_input_instance
from qiskit_aqua.translators.ising import maxcut
import numpy as np
"""
Explanation: Using Qiskit Aqua for maxcut problems
This Qiskit Aqua Optimization notebook demonstrates how to use the VQE quan... |
frankbearzou/Data-analysis | White House/White House.ipynb | mit | position_title = white_house["Position Title"]
title_length = position_title.apply(len)
salary = white_house["Salary"]
from scipy.stats.stats import pearsonr
pearsonr(title_length, salary)
plt.scatter(title_length, salary)
plt.xlabel("title length")
plt.ylabel("salary")
plt.title("Title length - Salary Scatter Plot"... |
Aniruddha-Tapas/Applied-Machine-Learning | Miscellaneous/Plants Clustering.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn import cross_validation, metrics
from sklearn import preprocessing
import matplotlib
import matplotlib.pyplot as plt
cols = ['Class']
for i in range(64):
str = 'f{}'.format(i)
cols.appen... |
TiKeil/Master-thesis-LOD | notebooks/Figure_7.1_Refinement.ipynb | apache-2.0 | import os
import sys
import numpy as np
%matplotlib notebook
import matplotlib.pyplot as plt
from matplotlib import cm
#coarse World
NWorldCoarse = np.array([11,11])
NpCoarse = np.prod(NWorldCoarse+1)
A = np.zeros(NWorldCoarse)
ABase = A.flatten()
aCube = ABase.reshape(NWorldCoarse)
"""
Explanation: Visualizati... |
danielfrg/pelican-ipynb | pelican_jupyter/tests/pelican/markup-incell/content/md-info-in-cell.ipynb | apache-2.0 | a = 1
a
b = 'pew'
b
%matplotlib inline
import matplotlib.pyplot as plt
from pylab import *
x = linspace(0, 5, 10)
y = x ** 2
figure()
plot(x, y, 'r')
xlabel('x')
ylabel('y')
title('title')
show()
import numpy as np
num_points = 130
y = np.random.random(num_points)
plt.plot(y)
"""
Explanation: Title: Notebook... |
ES-DOC/esdoc-jupyterhub | notebooks/fio-ronm/cmip6/models/sandbox-3/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'fio-ronm', 'sandbox-3', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: FIO-RONM
Source ID: SANDBOX-3
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation... |
qinwf-nuan/keras-js | notebooks/layers/recurrent/SimpleRNN.ipynb | mit | data_in_shape = (3, 6)
rnn = SimpleRNN(4, activation='tanh')
layer_0 = Input(shape=data_in_shape)
layer_1 = rnn(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
weights = []
for i, w in enumerate(model.get_weights()):
np.random.seed(3400 + i)
weigh... |
wtbarnes/aia_response | notebooks/sunpy_aia_response_tutorial.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import sunpy.instr.aia
%matplotlib inline
"""
Explanation: Tutorial: Calculating the SDO/AIA Response Functions in SunPy
This notebook gives examples of how to calculate the SDO/AIA wavelength and temperature response functions using SunPy and ChiantiPy. The provided ... |
nick-youngblut/SIPSim | ipynb/bac_genome/n1210/.ipynb_checkpoints/perc_incorp_unif_rep-checkpoint.ipynb | mit | workDir = '/home/nick/notebook/SIPSim/dev/bac_genome1210/'
buildDir = os.path.join(workDir, 'percIncorpUnifRep')
genomeDir = '/home/nick/notebook/SIPSim/dev/bac_genome1210/genomes/'
R_dir = '/home/nick/notebook/SIPSim/lib/R/'
"""
Explanation: Goal
Questions
How is incorporator identification accuracy affected by the ... |
ChadFulton/statsmodels | examples/notebooks/generic_mle.ipynb | bsd-3-clause | from __future__ import print_function
import numpy as np
from scipy import stats
import statsmodels.api as sm
from statsmodels.base.model import GenericLikelihoodModel
"""
Explanation: Maximum Likelihood Estimation (Generic models)
This tutorial explains how to quickly implement new maximum likelihood models in statsm... |
ODM2/ODM2PythonAPI | Examples/WaterQualityMeasurements_RetrieveVisualize.ipynb | bsd-3-clause | import os
import datetime
import matplotlib.pyplot as plt
%matplotlib inline
from shapely.geometry import Point
import pandas as pd
import geopandas as gpd
import folium
from folium.plugins import MarkerCluster
import odm2api
from odm2api.ODMconnection import dbconnection
import odm2api.services.readService as odm2r... |
NekuSakuraba/my_capstone_research | subjects/em/multivariate t - draft04 - Mixtures.ipynb | mit | actual_mu01 = [0,0]
actual_cov01 = [[1,0], [0,1]]
actual_df01 = 15
actual_mu02 = [1,1]
actual_cov02 = [[.5, 0], [0, 1.5]]
actual_df02 = 15
size = 300
x01 = multivariate_t_rvs(m=actual_mu01, S=actual_cov01, df=actual_df01, n=size)
x02 = multivariate_t_rvs(m=actual_mu02, S=actual_cov02, df=actual_df02, n=size)
X ... |
Danghor/Formal-Languages | Ply/Compiler.ipynb | gpl-2.0 | import ply.lex as lex
tokens = [ 'NUMBER', 'ID', 'EQ', 'NE', 'LE', 'GE', 'AND', 'OR',
'INT', 'IF', 'ELSE', 'WHILE', 'RETURN'
]
"""
Explanation: A Simple Compiler for a Fragment of C
This file shows how a simple compiler for a fragment of the programming language C can be implemented using Ply.
Spe... |
lcharleux/numerical_analysis | doc/Python.ipynb | gpl-2.0 | print 'Hello World !'
a = 5.
b = 7.
a + b
"""
Explanation: Python
Python présente plusieurs avantage à l'origine de son choix pour ce cours:
C'est un langage généraliste présent dans de nombreuses domaines: calcul scientifique, web, bases de données, jeu vidéo, graphisme, etc. C'est un outil polyvalent qu'un ingénieu... |
rochefort-lab/fissa | examples/SIMA example.ipynb | gpl-3.0 | # FISSA toolbox
import fissa
# SIMA toolbox
import sima
import sima.segment
# File operations
import glob
# For plotting our results, use numpy and matplotlib
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Using FISSA with SIMA
SIMA is a toolbox for motion correction and cell detection.
Here we... |
Kaggle/learntools | notebooks/pandas/raw/ex_5.ipynb | apache-2.0 | import pandas as pd
reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)
from learntools.core import binder; binder.bind(globals())
from learntools.pandas.renaming_and_combining import *
print("Setup complete.")
"""
Explanation: Introduction
Run the following cell to load your data an... |
jpn--/larch | book/example/000_mtc_data.ipynb | gpl-3.0 | import os, gzip
import numpy as np, pandas as pd, xarray as xr
import larch.numba as lx
"""
Explanation: MTC Work Mode Choice Data
End of explanation
"""
with gzip.open(lx.example_file("MTCwork.csv.gz"), 'rt') as previewfile:
print(*(next(previewfile) for x in range(10)))
"""
Explanation: The MTC sample dataset... |
mdiaz236/DeepLearningFoundations | seq2seq/sequence_to_sequence_implementation.ipynb | mit | import helper
source_path = 'data/letters_source.txt'
target_path = 'data/letters_target.txt'
source_sentences = helper.load_data(source_path)
target_sentences = helper.load_data(target_path)
"""
Explanation: Character Sequence to Sequence
In this notebook, we'll build a model that takes in a sequence of letters, an... |
hcchengithub/project-k | Play with the FORTH kernel on jupyter notebook.ipynb | mit | import projectk as vm # vm means 'Virtual Machine'.
"""
Explanation: A rewritten of: https://github.com/hcchengithub/project-k/wiki/Play-with-the-forth-kernel-on-python<br>
You can play with this article online directly through the jupyter notebook binder: https://mybinder.org/v2/gh/hcchengithub/project-k/master
Pla... |
samuxiii/notebooks | stock/Ethereum_Stock.ipynb | apache-2.0 | import os
import io
import math
import random
import requests
from tqdm import tqdm
import numpy as np
import pandas as pd
import sklearn
import matplotlib.dates as mdates
import datetime as dt
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics imp... |
florianwittkamp/FD_ACOUSTIC | JupyterNotebook/1D/FD_1D_DX4_DT4_ABS_fast.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import time as tm
import matplotlib.pyplot as plt
"""
Explanation: FD_1D_DX4_DT4_ABS_fast 1-D acoustic Finite-Difference modelling
GNU General Public License v3.0
Author: Florian Wittkamp
Finite-Difference acoustic seismic wave simulation
Discretization of the first-order acoustic... |
cgpotts/cs224u | tutorial_jupyter_notebooks.ipynb | apache-2.0 | __author__ = "Lucy Li"
__version__ = "CS224u, Stanford, Spring 2022"
"""
Explanation: Tutorial: Jupyter notebooks
End of explanation
"""
import time
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
print("cats")
# run this cell and notice how both strings appear as outputs
"cheese"
# cut/copy... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/image_classification/solutions/2_mnist_models.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Here we'll show the currently installed version of TensorFlow
import tensorflow as tf
print(tf.__version__)
from datetime import datetime
import os
PROJECT = "your-project-id-here" # REPLACE WITH YOUR PROJECT ID
BUCKET = "your-bucket-id-here" # R... |
scottprahl/miepython | docs/11_performance.ipynb | mit | #!pip install --user miepython
import numpy as np
import matplotlib.pyplot as plt
try:
import miepython.miepython as miepython_jit
import miepython.miepython_nojit as miepython
except ModuleNotFoundError:
print('miepython not installed. To install, uncomment and run the cell above.')
print('Once inst... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_compute_mne_inverse_raw_in_label.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse_raw, read_inverse_operator
print(__doc__)
data_path = sample.data_path()
fname_inv = data_path + '/... |
CloverHealth/pycon2017 | bayesian_analysis/data/generate_data.ipynb | bsd-3-clause | def create_patients():
"""Creating a table of patient and ids"""
ids = list(range(1, 11))
doctor_ids = ['dr' + str((i % 2) + 1) for i in ids]
names = ['john', 'jeremy', 'mark', 'leslie', 'sam', 'matt', 'judy', 'parth', 'kevin', 'joshua']
patients = {
'patient_id': ids,
'doctor_id': ... |
unpingco/Python-for-Probability-Statistics-and-Machine-Learning | chapters/statistics/notebooks/Confidence_Intervals.ipynb | mit | from __future__ import division
%pylab inline
"""
Explanation: Python for Probability, Statistics, and Machine Learning
End of explanation
"""
from scipy import stats
import numpy as np
b= stats.bernoulli(.5) # fair coin distribution
nsamples = 100
# flip it nsamples times for 200 estimates
xs = b.rvs(nsamples*200)... |
mne-tools/mne-tools.github.io | 0.18/_downloads/e759d6d5e3879a95fca2c0cf44006e74/plot_stats_cluster_spatio_temporal_2samp.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import os.path as op
import numpy as np
from scipy import stats as stats
import mne
from mne import spatial_src_connectivity
from mne.stats import spatio_temporal_cluster... |
batfish/pybatfish | jupyter_notebooks/Analyzing public and hybrid cloud networks.ipynb | apache-2.0 | # Import packages
%run startup.py
bf = Session(host="localhost")
def show_first_trace(trace_answer_frame):
"""
Prints the first trace in the answer frame.
In the presence of multipath routing, Batfish outputs all traces
from the source to destination. This function picks the first one.
"""
... |
HazyResearch/snorkel | tutorials/intro/Intro_Tutorial_3.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
import os
# TO USE A DATABASE OTHER THAN SQLITE, USE THIS LINE
# Note that this is necessary for parallel execution amongst other things...
# os.environ['SNORKELDB'] = 'postgres:///snorkel-intro'
from snorkel import SnorkelSession
session = SnorkelSession()
"""
E... |
jldinh/multicell | examples/01 - Creating a simple tissue.ipynb | mit | %matplotlib notebook
"""
Explanation: In this example, we will show how to create a very simple tissue structure comprised of cubic cells and visualize it using Multicell.
Preparation
Visualizations rely on the matplotlib module. In order for visualizations to work interactively in this Jupyter notebook, we need to ru... |
krondor/nlp-dsx-pot | Operationalizing Models with WML and Scikit-Learn.ipynb | gpl-3.0 | !pip install wget --user
"""
Explanation: <table style="border: none" align="left">
<tr style="border: none">
<th style="text-align: left;border: none"><font face="verdana" size="5" color="black"><b>Train and deploy a heart disease prediction model using XGBoost and IBM Watson Machine Learning APIs</b></th>
... |
agmarrugo/sensors-actuators | notebooks/Ex2-10-errors-in-sensing.ipynb | mit | span = 80-(-30) #input span or input full scale (IFS)
e_input = 0.5 # error as input
e = (e_input/span) *100
## Error as % IFS
print('The error as percentange of the input span is e = %2.3f %%' % (e))
"""
Explanation: Errors in Sensing
Andrés Marrugo, PhD
A thermistor is used to measure temperatures between $-30^{\c... |
postBG/DL_project | first-neural-network/Your_first_neural_network.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... |
y2ee201/Deep-Learning-Nanodegree | 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... |
hannorein/rebound | ipython_examples/Forces.ipynb | gpl-3.0 | import rebound
sim = rebound.Simulation()
sim.integrator = "whfast"
sim.add(m=1.)
sim.add(m=1e-6,a=1.)
sim.move_to_com() # Moves to the center of momentum frame
"""
Explanation: Additional forces
REBOUND is a gravitational N-body integrator. But you can also use it to integrate systems with additional, non-gravitatio... |
afeiguin/comp-phys | 14_02_multilayer-networks.ipynb | mit | %matplotlib inline
from matplotlib import pyplot
pyplot.rcParams['image.cmap'] = 'jet'
import numpy as np
x0 = -1.4
y0 = 0.5
x = [x0] # The algorithm starts at x0, y0
y = [y0]
eta = 0.1 # step size multiplier
precision = 0.00001
def f(x,y):
f1 = x**2/2-y**2/4+3
f2 = 2*x+1-np.exp(y)
return np.sin(f1)*np.... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/structured/solutions/5b_deploy_keras_ai_platform_babyweight.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
import os
"""
Explanation: LAB 5b: Deploy and predict with Keras model on Cloud AI Platform.
Learning Objectives
Setup up the environment
Deploy trained Keras model to Cloud AI Platform
Online predict from model on Cloud AI Platform
Batch predict fr... |
LogicWang/ml | train/titanic.ipynb | apache-2.0 | # data analysis and wrangling
import pandas as pd
import numpy as np
import random as rnd
# visualization
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
# machine learning
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import ... |
bbengfort/cloudscope | notebooks/traces.ipynb | mit | %matplotlib inline
import os
import re
import csv
import glob
import json
import numpy as np
import pandas as pd
import seaborn as sns
## Load Data
PROPRE = re.compile(r'^trace-(\d+)ms-(\d+)user.tsv$')
TRACES = os.path.join("..", "fixtures", "traces", "trace-*")
def load_trace_data(traces=TRACES, pattern=PROPRE):
... |
aboSamoor/compsocial | Word_Tracker/3rd_Yr_Paper/Google_NYT.ipynb | gpl-3.0 | plot_both(['bicultural', 'biracial', 'biethnic', 'interracial'])
plt.xlim(1910, 2015)
"""
Explanation: monoracial has no data from NYT.
1865, 1905, 1915 (monocultural) NYT
End of explanation
"""
plot_both(['multicultural', 'multiracial', 'multiethnic', 'polycultural', 'polyracial', 'polyethnic'])
plt.xlim(1950, 201... |
mne-tools/mne-tools.github.io | 0.20/_downloads/ecc61038e0082bd1c13f6a49dd4cd752/plot_70_fnirs_processing.ipynb | bsd-3-clause | import os
import numpy as np
import matplotlib.pyplot as plt
from itertools import compress
import mne
fnirs_data_folder = mne.datasets.fnirs_motor.data_path()
fnirs_raw_dir = os.path.join(fnirs_data_folder, 'Participant-1')
raw_intensity = mne.io.read_raw_nirx(fnirs_raw_dir, verbose=True).load_data()
"""
Explanati... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/time_series_prediction/labs/1_optional_data_exploration.ipynb | apache-2.0 | import os
PROJECT = !(gcloud config get-value core/project)
PROJECT = PROJECT[0]
BUCKET = PROJECT
os.environ["PROJECT"] = PROJECT
os.environ["BUCKET"] = BUCKET
import numpy as np
import pandas as pd
import seaborn as sns
from google.cloud import bigquery
from IPython import get_ipython
from IPython.core.magic import... |
mommermi/Introduction-to-Python-for-Scientists | notebooks/.ipynb_checkpoints/Functions_Modules_StandardLibrary-checkpoint.ipynb | mit | def area_circle(radius, pi=3.14):
"""determine area of a circle, given its radius""" # documentation!
return pi*radius*radius
print area_circle(3) # uses the default value of 'pi'
print area_circle(3, pi=3) # uses your own value of 'pi'
print area_circle.__doc__
"""
Explanation: Functions, Modules, and th... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_python_intro.ipynb | bsd-3-clause | a = 3
print(type(a))
b = [1, 2.5, 'This is a string']
print(type(b))
c = 'Hello world!'
print(type(c))
"""
Explanation: .. _tut_intro_pyton:
Introduction to Python
Python is a modern, general-purpose, object-oriented, high-level programming
language. First make sure you have a working python environment and
dependenci... |
NeuPhysics/aNN | ipynb/test.ipynb | mit | # This line configures matplotlib to show figures embedded in the notebook,
# instead of opening a new window for each figure. More about that later.
# If you are using an old version of IPython, try using '%pylab inline' instead.
%matplotlib inline
%load_ext snakeviz
import numpy as np
from scipy.optimize import mi... |
google-research/recsim | recsim/colab/RecSim_Developing_an_Agent.ipynb | apache-2.0 | # @title Install
!pip install --upgrade --no-cache-dir recsim
# @title Imports
# Generic imports
import functools
from gym import spaces
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
# RecSim imports
from recsim import agent
from recsim import document
from recsim import user
from recsim.c... |
miltonsarria/dsp-python | images/2_fullyconnected.ipynb | mit | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range
"""
Explanation: Deep Learning
Assignment 2
Previously in 1_n... |
pymanopt/pymanopt | examples/notebooks/mixture_of_gaussians.ipynb | bsd-3-clause | import autograd.numpy as np
np.set_printoptions(precision=2)
import matplotlib.pyplot as plt
%matplotlib inline
# Number of data points
N = 1000
# Dimension of each data point
D = 2
# Number of clusters
K = 3
pi = [0.1, 0.6, 0.3]
mu = [np.array([-4, 1]), np.array([0, 0]), np.array([2, -1])]
Sigma = [
np.arr... |
chetan51/nupic.research | projects/dynamic_sparse/notebooks/ToyProblem-NewOrganization.ipynb | gpl-3.0 | %load_ext autoreload
%autoreload 2
import sys
sys.path.append(os.path.expanduser("~/nta/nupic.research/projects/"))
# general imports
import os
import numpy as np
# torch imports
import torch
import torch.optim as optim
import torch.optim.lr_scheduler as schedulers
import torch.nn as nn
from torch.utils.data import ... |
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