repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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celliern/triflow | examples/notebooks/advection_diffusion.ipynb | gpl-3.0 | import numpy as np
import pylab as pl
import triflow as trf
from scipy.signal import gaussian
%matplotlib inline
"""
Explanation: The convection diffusion equation
End of explanation
"""
model = trf.Model("k * dxxU - c * dxU", "U", ["k", "c"])
"""
Explanation: The convection–diffusion equation is a combination of ... |
adityaka/misc_scripts | python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/04_01/Begin/.ipynb_checkpoints/Create-checkpoint.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
"""
Explanation: Creating Data Frames
documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html
DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it
like a spreadsheet or SQL table, o... |
phoebe-project/phoebe2-docs | 2.3/examples/minimal_GPs.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
import matplotlib.pyplot as plt
plt.rc('font', family='serif', size=14, serif='STIXGeneral')
plt.rc('mathtext', fontset='stix')
import phoebe
import numpy as np
logger = phoebe.logger('warning')
# we'll set the random seed so that the noise model is reproducible
np.random.seed(1... |
ryneches/SuchTree | docs/slides1.ipynb | bsd-3-clause | %pylab inline
import seaborn
from SuchTree import SuchTree
"""
Explanation: SuchTree
I didn't want to write this. I really didn't.
End of explanation
"""
from ete2 import Tree, TreeStyle, NodeStyle, TextFace
from numpy import linspace
ts = TreeStyle()
ts.mode = 'r'
ts.show_leaf_name = True
ts.branch_vertical_margi... |
dendisuhubdy/tensorflow | tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb | apache-2.0 | # In the tf.keras.layers package, layers are objects. To construct a layer,
# simply construct the object. Most layers take as a first argument the number
# of output dimensions / channels.
layer = tf.keras.layers.Dense(100)
# The number of input dimensionss is often unnecessary, as it can be inferred
# the first time ... |
eyurtsev/fcsparser | doc/fcsparser_example.ipynb | mit | %pylab inline
import fcsparser
path = fcsparser.test_sample_path # path to a test data file that is included with the package
"""
Explanation: Working with the fcs parser
ATTENTION The parser returns the data exactly as recorded in the file. This means that no transformations or compensation have been applied to thi... |
kubeflow/kfserving-lts | docs/samples/pipelines/kfs-pipeline-v1alpha2.ipynb | apache-2.0 | !pip3 install kfp --upgrade
import kfp.dsl as dsl
import kfp
from kfp import components
import json
# Create kfp client
# Note: Add the KubeFlow Pipeline endpoint below if the client is not running on the same cluster.
client = kfp.Client('kfserving_endpoint')
EXPERIMENT_NAME = 'KFServing Experiments'
experiment = cl... |
Vvkmnn/books | AutomateTheBoringStuffWithPython/lesson49.ipynb | gpl-3.0 | import pyautogui
"""
Explanation: Lesson 49:
Controlling the Keyboard with Python
Python can be used to control the keyboard and mouse, which allows us to automate any program that uses these as inputs.
Graphical User Interface (GUI) Automation is particularly useful for repetative clicking or keyboard entry. The pro... |
sainathadapa/fastai-courses | deeplearning1/nbs-custom-mine/lesson6_01.ipynb | apache-2.0 | from theano.sandbox import cuda
%matplotlib inline
import utils; reload(utils)
from utils import *
from __future__ import division, print_function
"""
Explanation: Table of Contents
<p><div class="lev2 toc-item"><a href="#Setup" data-toc-modified-id="Setup-01"><span class="toc-item-num">0.1 </span>Setup</a... |
motasay/twitter-sentiment | src/analysis.ipynb | gpl-3.0 | np.random.seed(142)
print 'Loading the word2vec model...'
sys.stdout.flush()
w2v = load_word2vec()
print 'Building the word2vec sentence features...'
sys.stdout.flush()
vec_function = lambda sentence: get_word2vec_features(w2v, sentence)
num_features = w2v.layer1_size
w2v_X_train, w2v_Y_train, w2v_X_test , w2v_Y_tes... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/n08_simple_q_learner_1000_states.ipynb | mit | # Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
from multiprocessing import Pool
%matplotlib inline
%pylab inline
pylab.rcPar... |
JackDi/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."""
# YOUR CODE HERE
psi=(2.0/L)*np.sin(nx*np.pi*x/L)*np.sin(ny*np.pi*y/L)
return p... |
tensorflow/docs-l10n | site/ja/guide/keras/preprocessing_layers.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... |
csampez/analisis-numerico-computo-cientifico | MNO/proyecto_final/MNO_2017/proyectos/equipos/equipo_6/avance_22_05_2017/code/Clase_SVD_Imagen.ipynb | apache-2.0 | #SE IMPORTAN LIBRERIAS PARA GRAFICAR, PARA COMPUTO DE MATRICES Y PARA LEER IMAGENES
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
#SE LEE UNA IMAGEN, SE LEE Y SE GUARDA COMO BLANCO Y NEGRO
img = Image.open('GTR.jpg')
#img.rotate(45).show() #Para rotar la imagen cierto no.... |
tensorflow/docs-l10n | site/en-snapshot/guide/keras/custom_layers_and_models.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... |
rahulkgup/deep-learning-foundation | transfer-learning/Transfer_Learning.ipynb | mit | from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_s... |
biolink/ontobio | notebooks/Intro_Local_Ontologies.ipynb | bsd-3-clause | from ontobio.ontol_factory import OntologyFactory
ofactory = OntologyFactory()
ont = ofactory.create("nucleus.json") ## interpreted as a json local handle
"""
Explanation: Load ontology
First load an ontology
Loading a local ontology
Assume we have an ontology on disk in obographs-json format, e.g. nucleus.json.
Firs... |
cgivre/oreilly-sec-ds-fundamentals | Notebooks/Intro/One Dimensional Data Worksheet-Python.ipynb | apache-2.0 | import pandas as pd
import numpy as np
"""
Explanation: One Dimensional Data Worksheet
This worksheet reviews the concepts discussed about 1 dimensional data. The goal for these exercises is getting you to think in terms of vectorized computing. This worksheet should take 20-30 minutes to complete.
End of explanatio... |
ageron/ml-notebooks | 03_classification.ipynb | apache-2.0 | # To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot ... |
karlstroetmann/Algorithms | Python/Chapter-06/Calculator.ipynb | gpl-2.0 | def toInt(s):
try:
return int(s)
except ValueError:
return s
toInt('123')
toInt('**')
"""
Explanation: The Shunting Yard Algorithm (Operator Precedence Parsing)
The function $\texttt{toInt}(s)$ tries to convert the string $s$ to an integer. If this works out, the integer is returned. Oth... |
afeiguin/comp-phys | 00_02_numbers_in_python.ipynb | mit | import sys
sys.float_info
"""
Explanation: Manipulating numbers in Python
Disclaimer: Much of this section has been transcribed from <a href="https://pymotw.com/2/math/">https://pymotw.com/2/math/</a>
Every computer represents numbers using the <a href="https://en.wikipedia.org/wiki/IEEE_floating_point">IEEE floati... |
VictorQuintana91/Thesis | notebooks/sample_scripts/test_working_with_strings.ipynb | mit | import nltk
# nltk.download()
text="Welcome readers. I hope you find it interesting. please fo reply."
from nltk.tokenize import sent_tokenize
sent_tokenize(text)
"""
Explanation: Tokenisation of Text into sentences
Code from Mastering Natural Langiage Processing with Python.
End of explanation
"""
tokenizer=nltk... |
barjacks/foundations-homework | 08/H2B_Applications/H-2B_Visa_Programme_2015.ipynb | mit | import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.read_excel("H-2B_Disclosure_Data_FY15_Q4.xlsx")
df.head()
#df.info()
"""
Explanation: Exploring the H-2B Visa Programme
H-2B visas are nonimmigrant visas, which allow foreign nationals to enter the U.S. temporarily and engage in nonagric... |
msschwartz21/craniumPy | experiments/templates/TEMP-landmarks.ipynb | gpl-3.0 | import deltascope as ds
import deltascope.alignment as ut
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import normalize
from scipy.optimize import minimize
import os
import tqdm
import json
import time
"""
Explanation: Introduction: Landmarks
End of explanation
"... |
flutter/codelabs | tfrs-flutter/step5/backend/retrieval/retrieval.ipynb | bsd-3-clause | #@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... |
kgullikson88/keras_notebooks | notebooks/MNIST_cnn.ipynb | mit | # Import some stuff
from __future__ import print_function, absolute_import, division
import numpy as np
np.random.seed(1337) # for reproducibility
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, M... |
EderSantana/agnez | examples/MNIST.ipynb | bsd-3-clause | %matplotlib inline
'''
This code was modified from: https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py
Train a simple convnet on the MNIST dataset.
Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python mnist_cnn.py
Get to 99.25% test accuracy after 12 epochs (there is still a lot of... |
AtmaMani/pyChakras | udemy_ml_bootcamp/Python-for-Data-Visualization/Matplotlib/Matplotlib Exercises - Solutions.ipynb | mit | import numpy as np
x = np.arange(0,100)
y = x*2
z = x**2
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Matplotlib Exercises - Solutions
Welcome to the exercises for reviewing matplotlib! Take your time with these, Matplotlib can be tricky to understand at first. The... |
apryor6/apryor6.github.io | visualizations/seaborn/notebooks/.ipynb_checkpoints/heatmap-checkpoint.ipynb | mit | %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
plt.rcParams['figure.figsize'] = (20.0, 10.0)
plt.rcParams['font.family'] = "serif"
df = pd.pivot_table(data=sns.load_dataset("flights"),
index='month',
values='pass... |
milancurcic/lunch-bytes | Spring_2019/LB26/Pandas_dataframe_tutorial.ipynb | cc0-1.0 | import pandas as pd
"""
Explanation: Data Analysis with Pandas Dataframe
Pandas is a popular library for manipulating vectors, tables, and time series. We will frequently use Pandas data structures instead of the built-in python data structures, as they provide much richer functionality. Also, Pandas is fast, which ma... |
asengsta/DataScience | machine_learning_template.ipynb | gpl-3.0 | # Import Libraries
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
import io
import boto3
import sagemaker
from sagemaker import KMeans
from sagemaker import get_execution_role
import sagemaker.amazon.common as smac
from sagemaker.amazon.amazon_... |
kit-cel/lecture-examples | mloc/ch4_Deep_Learning/pytorch/function_approximation_with_MLP.ipynb | gpl-2.0 | import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.preprocessing import MinMaxScaler
%matplotlib inline
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print("We are using the following device fo... |
google/grr | colab/examples/demo.ipynb | apache-2.0 | %load_ext grr_colab.ipython_extension
import grr_colab
"""
Explanation: GRR Colab
End of explanation
"""
grr_colab.flags.FLAGS.set_default('grr_http_api_endpoint', 'http://localhost:8000/')
grr_colab.flags.FLAGS.set_default('grr_admin_ui_url', 'http://localhost:8000/')
grr_colab.flags.FLAGS.set_default('grr_auth_ap... |
pydataberlin/conf2017slides | tutorial_notebooks/topic_modelling.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
import gensim
import numpy as np
import spacy
from gensim.models import CoherenceModel, LdaModel, LsiModel, HdpModel
from gensim.models.wrappers import LdaMallet
from gensim.corpora import Dictionary
import pyLDAvis.gensim
import os, re, operator, warnings
warnings.filterwarnings('igno... |
gojomo/gensim | docs/src/auto_examples/tutorials/run_word2vec.ipynb | lgpl-2.1 | import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
"""
Explanation: Word2Vec Model
Introduces Gensim's Word2Vec model and demonstrates its use on the Lee Evaluation Corpus
<https://hekyll.services.adelaide.edu.au/dspace/bitstream/2440/28910/1/hdl_28910.pdf>... |
kmunve/APS | aps/similar_forecast/Find_previous_forecast_knn.ipynb | mit | import pandas as pd
import numpy as np
import json
from sklearn import preprocessing
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.simplefilter('ignore')
"""
Explanation: Find forecast for a similar conditions
The nearest neighbor model should retrieve the region Id a... |
JarnoRFB/qtpyvis | notebooks/pytorch/inference.ipynb | mit | # input batch size for training (default: 64)
batch_size = 64
# input batch size for testing (default: 1000)
test_batch_size = 1000
# number of epochs to train (default: 10)
epochs = 10
# learning rate (default: 0.01)
lr = 0.01
# SGD momentum (default: 0.5)
momentum = 0.5
# disables CUDA training
no_cuda = True
#... |
tuanvu216/udacity-course | intro_to_statistics/Lesson 2 - Programming Charts.ipynb | mit | %%writefile plotting.py
from matplotlib import pyplot
from numpy import arange
import bisect
def scatterplot(x,y):
pyplot.plot(x,y,'b.')
pyplot.xlim(min(x)-1,max(x)+1)
pyplot.ylim(min(y)-1,max(y)+1)
pyplot.show()
def barplot(labels,data):
pos=arange(len(data))
pyplot.xticks(pos+0.4,labels)
... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session10/Day1/MetropolisHastings.ipynb | mit | np.random.seed(212)
m_true = # complete
b_true = # complete
x = np.random.uniform(low=0, high=100, size=40)
y_true = # complete
y_obs = y_true + np.random.normal(0,50,size=40)
y_unc = np.ones_like(y_obs)*50
"""
Explanation: Construction Zone: Building a Metropolis-Hastings Sampler From Scratch
Version 0.1
By AA Mille... |
pydataberlin/conf2017slides | Introductory statistical data analysis with pandas PyData Berlin 2017.ipynb | gpl-3.0 | #import the packages we will use
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
#use simplest tool available
import statsmodels.formula.api as smf
import statsmodels.stats.multicomp as multi
import statsmodels.api as sm
import scipy.stats
from sklearn.cross_validation impor... |
Vooban/Decision-Trees-For-Knowledge-Discovery | Decision-Trees-For-Knowledge-Discovery-With-Scikit-Learn.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn import tree
import pydot
from io import StringIO
import os
"""
Explanation: Discovering structure behind data
Let's understand and model the hidden structure behind data wi... |
DAInamite/programming-humanoid-robot-in-python | joint_control/learn_posture.ipynb | gpl-2.0 | %pylab inline
import pickle
from os import listdir, path
import numpy as np
from sklearn import svm, metrics
ROBOT_POSE_DATA_DIR = 'robot_pose_data'
classes = listdir(ROBOT_POSE_DATA_DIR)
print classes
def load_pose_data(i):
'''load pose data from file'''
data = []
target = []
# YOUR CODE HERE
... |
ctzhu/lifemodels | examples/Intro_example.ipynb | mit | df = pd.read_csv('mckay.txt', sep=' ')
df['Time1'] = df.Time0
#Time-to-event data, Time0 is the left bound, Time1 is the right bound.
s_df = lms.s_models.survt_df(df)
df.head()
"""
Explanation: lifemodels
What is it?
lifemodels is a Python package for fitting lifespan data to a number of models using MLE. It also ... |
fsbr/se3-path-planner | modularPlanner/.ipynb_checkpoints/Cusp Plots (Daily)-checkpoint.ipynb | mit | import tsyganenko as tsyg
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from spacepy import coordinates as coord
import spacepy.time as spt
from spacepy.time import Ticktock
import datetime as dt
from mpl_toolkits.mplot3d import Axes3D
import sys
#adding the year data here so I don't have to c... |
planet-os/notebooks | api-examples/hrrr_smoke_demo.ipynb | mit | %matplotlib notebook
%matplotlib inline
import numpy as np
import dh_py_access.lib.datahub as datahub
import xarray as xr
import matplotlib.pyplot as plt
import ipywidgets as widgets
from mpl_toolkits.basemap import Basemap,shiftgrid
import dh_py_access.package_api as package_api
import matplotlib.colors as colors
impo... |
aasensio/SolarnetGranada | notebooks/Asymmetries.ipynb | mit | lambda0 = 6301.5080
JUp = 2.0
JLow = 2.0
gUp = 1.5
gLow = 1.833
lambdaStart = 6300.8
lambdaStep = 0.015
nLambda = 100
lineInfo = np.asarray([lambda0, JUp, JLow, gUp, gLow, lambdaStart, lambdaStep])
s = pymilne.milne(nLambda, lineInfo)
stokes = np.zeros((4,nLambda))
BField = 100.0
BTheta = 20.0
BChi = 20.0
VMac = 2... |
Ziqi-Li/bknqgis | pandas/doc/source/style.ipynb | gpl-2.0 | import matplotlib.pyplot
# We have this here to trigger matplotlib's font cache stuff.
# This cell is hidden from the output
import pandas as pd
import numpy as np
np.random.seed(24)
df = pd.DataFrame({'A': np.linspace(1, 10, 10)})
df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE'))],
... |
solowPy/undergraduate-teaching | intro-to-linear-regression.ipynb | mit | # grab the entire Penn World Tables data from the web...
pwt = pypwt.load_pwt_data()
#...this gives us a panel (i.e., two dimensional) data set
pwt
def labor_supply(data, year="1950-01-01"):
"""
Labor supply in a given year is the product of number of employed
persons, 'emp', and the average number of ho... |
DeepThoughtTeam/tensorflow | tensorflow/tools/docker/notebooks/1_hello_tensorflow.ipynb | apache-2.0 | import tensorflow as tf
with tf.Session():
input1 = tf.constant([1.0, 1.0, 1.0, 1.0])
input2 = tf.constant([2.0, 2.0, 2.0, 2.0])
output = tf.add(input1, input2)
result = output.eval()
print result
"""
Explanation: Hello, TensorFlow
A beginner-level, getting started, basic introduction to TensorFlow
TensorFl... |
intel-analytics/BigDL | python/nano/notebooks/pytorch/cifar10/nano-inference-example.ipynb | apache-2.0 | import os
from time import time
import torch
from torch import nn
import torch.nn.functional as F
import torchvision
from pl_bolts.datamodules import CIFAR10DataModule
from pl_bolts.transforms.dataset_normalizations import cifar10_normalization
from pytorch_lightning import LightningModule, seed_everything
from torch.... |
hindersin/randomGraphs | randomGraphs.ipynb | mit | popSize = 4 # This is the number of nodes in the network
update = 'BD' # Either 'BD' or 'DB' for Birth-death or death-Birth updating respectively
direction = 'undirected' # Either 'directed' or 'undirected' graphs are used
stepSize = 0.05 # Step size for the probability for each link in the network to be present indepe... |
rsterbentz/phys202-2015-work | assignments/assignment05/InteractEx03.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 3
Imports
End of explanation
"""
def soliton(x, t, c, a):
"""Return phi(x, t) for a soliton wave with co... |
mne-tools/mne-tools.github.io | 0.17/_downloads/0f794e75f963d5793938890d6f3d2513/plot_receptive_field_mtrf.ipynb | bsd-3-clause | # Authors: Chris Holdgraf <choldgraf@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
# Nicolas Barascud <nicolas.barascud@ens.fr>
#
# License: BSD (3-clause)
# sphinx_gallery_thumbnail_number = 3
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import loadmat
from os.path import jo... |
benbovy/cosmogenic_dating | emcee_test_3params.ipynb | mit | import math
import numpy as np
import pandas as pd
from scipy import stats
from scipy import optimize
import emcee
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
clr_plt = sns.color_palette()
"""
Explanation: Bayesian approach with emcee - Test case - 3 free parameters
An example of applyin... |
mcquay239/cg-lectures | convex_hull.ipynb | mit | %matplotlib
import numpy as np
import matplotlib.pyplot as plt
from pyhull.convex_hull import ConvexHull
X, Y = np.random.uniform(1, 9, size=(2, 30))
ch = ConvexHull(np.transpose([X, Y]))
plt.plot(X, Y, "ro")
for v in ch.vertices:
plt.plot(X[v], Y[v], "g")
plt.axis([0, 10, 0, 10])
plt.show()
"""
Explanati... |
mjirik/imtools | examples/Trainer3d.ipynb | mit | def externfv(data3d, voxelsize_mm): # scale
f0 = scipy.ndimage.filters.gaussian_filter(data3d, sigma=3).reshape(-1, 1)
f1 = scipy.ndimage.filters.gaussian_filter(data3d, sigma=1).reshape(-1, 1) - f0
fv = np.concatenate([
f0, f1
], 1)
return fv
"""
Explanation: Training
Define feature... |
faheemziker/FZValidationTextField | covid.ipynb | mit |
!pip install elasticsearch
!pip install google-cloud-datastore
"""
Explanation: <a href="https://colab.research.google.com/github/faheemziker/FZValidationTextField/blob/master/covid.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Monthly Cases of C... |
fonnesbeck/scientific-python-workshop | notebooks/Matplotlib.ipynb | cc0-1.0 | %matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: Scientific Plotting with Matplotlib
We require plots, charts and other statistical graphics for the written communication of quantitative ideas.
They allow us to more easily convey relationships and reveal deviations from patterns.
Gelman and Unwin 20... |
rajuniit/udacity | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
petspats/pyhacores | under_construction/fsk_demodulator/doc.ipynb | apache-2.0 | from pyha.components.fsk_modulator import FSKModulator
samples_per_symbol = 64 # this is so high to make stuff plottable
symbols = [1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0]
data = []
for x in symbols:
data.extend([x] * samples_per_symbol)
plt.plot(data... |
laurent-george/protolab_sound_recognition | notebooks/confusion matrix - final.ipynb | mit | files = glob.glob('/mnt/protolab_innov/data/sounds/dataset_demo/*/*.wav')
files = glob.glob('/home/lgeorge/Downloads/dataset/*/*.wav')
_class = [os.path.basename(f).split('-')[0] for f in files]
df = pd.DataFrame(zip(_class, files), columns=['classname', 'filename'])
mask_to_remove = df.filename.str.contains('BlowNos... |
metpy/MetPy | v1.1/_downloads/9041777e133eed610f5b243c688e89f9/surface_declarative.ipynb | bsd-3-clause | from datetime import datetime, timedelta
import cartopy.crs as ccrs
import pandas as pd
from metpy.cbook import get_test_data
import metpy.plots as mpplots
"""
Explanation: Surface Analysis using Declarative Syntax
The MetPy declarative syntax allows for a simplified interface to creating common
meteorological analy... |
Olsthoorn/TransientGroundwaterFlow | Syllabus_in_notebooks/Sec5_3_1_tide_damping_effect_frequency.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Section 5.3.1
Dependence of the inland effect of a wave frequency
Relates to exercise 5 on page 47.
@olsthoorn, 2012-12-30
The solution for the partial differential equation
$$ kD \frac {\partial^2 s} {\partial x} = S \frac {\partial s} {\partial t} $... |
BrandonSmithJ/tensorflow-double-DQN | Double-DQN/tensorflow-deepq/notebooks/karpathy_game.ipynb | mit | g.plot_reward(smoothing=100)
"""
Explanation: Average Reward over time
End of explanation
"""
g.__class__ = KarpathyGame
np.set_printoptions(formatter={'float': (lambda x: '%.2f' % (x,))})
x = g.observe()
new_shape = (x[:-2].shape[0]//g.eye_observation_size, g.eye_observation_size)
print(x[:-2].reshape(new_shape))
p... |
serenejiang/q2-permutationFDR | demo/small example of using Q2-DSFDR.ipynb | bsd-3-clause | !qiime tools import \
--input-path ../data/deblur-feature-table.biom \
--type 'FeatureTable[Frequency]' \
--source-format BIOMV210Format \
--output-path ../data/dblr_haddad.qza
"""
Explanation: This notebook is show how to use Q2-DSFDR in command line interface
convert feature table to qiime2 qza artifact
End of expla... |
kanishkamisra/something-like | recommender-system.ipynb | mit | import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.metrics.pairwise import linear_kernel
"""
Explanation: Simple Product Recommendations
By leveraging text frequency metrics and vector similarity, products can be matched with one another.
End of explanation
""... |
maxis42/ML-DA-Coursera-Yandex-MIPT | 5 Data analysis applications/Homework/4 project text analysis/Text analysis.ipynb | mit | from __future__ import division, print_function
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import cross_val_score
from sklearn.pipeline im... |
InsightSoftwareConsortium/SimpleITK-Notebooks | Python/55_VH_Resample.ipynb | apache-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
import SimpleITK as sitk
# If the environment variable SIMPLE_ITK_MEMORY_CONSTRAINED_ENVIRONMENT is set, this will override the ReadImage
# function so that it also resamples the image to a smaller size (testing environment is memory constrained).
%run setup_for_test... |
ecell/ecell4-notebooks | en/tutorials/tutorial03.ipynb | gpl-2.0 | from ecell4_base.core import *
"""
Explanation: 3. How to Setup the Initial Condition
Here, we explain the basics of World classes. In E-Cell4, six types of World classes are supported now: spatiocyte.SpatiocyteWorld, egfrd.EGFRDWorld, bd.BDWorld, meso.MesoscopicWorld, gillespie.GillespieWorld, and ode.ODEWorld.
In th... |
eds-uga/csci1360-fa16 | assignments/A2/A2_Q5.ipynb | mit | def urn_to_dict(urn_list):
urn_dict = {}
### BEGIN SOLUTION
### END SOLUTION
return urn_dict
u1 = ["green", "green", "blue", "green"]
a1 = set({("green", 3), ("blue", 1)})
assert a1 == set(urn_to_dict(u1).items())
u2 = ["red", "blue", "blue", "green", "yellow", "black", "black", "green"... |
ankdesh/Udacity-MachineLearning-Nanodegree | projects/boston_housing/boston_housing.ipynb | mit | # Import libraries necessary for this project
import numpy as np
import pandas as pd
import visuals as vs # Supplementary code
from sklearn.cross_validation import ShuffleSplit
# Pretty display for notebooks
%matplotlib inline
# Load the Boston housing dataset
data = pd.read_csv('housing.csv')
prices = data['MEDV']
f... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-1/cmip6/models/sandbox-3/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-1', 'sandbox-3', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: TEST-INSTITUTE-1
Source ID: SANDBOX-3
Topic: Ocnbgchem
Sub-Topic... |
robblack007/clase-cinematica-robot | Practicas/practica3/Practica.ipynb | mit | # Se importan widgets de IPython para interactuar con la funcion
from ipywidgets import interact, fixed
# Si la linea anterior no funciona, se puede quitar el comentario a la siguiente linea
#from IPython.html.widgets import interact, fixed
# Se define la constante τ, la cual representa la cantidad de radianes
# en un... |
tperol/earthquake-clusters | Final.ipynb | mit | %matplotlib inline
import numpy as np
import scipy as sp
import json
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd
from pyquery import PyQuery as pq
import requests
pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('disp... |
linglaiyao1314/maths-with-python | 09-exceptions-testing.ipynb | mit | from __future__ import division
def divide(numerator, denominator):
"""
Divide two numbers.
Parameters
----------
numerator: float
numerator
denominator: float
denominator
Returns
-------
fraction: float
numerator / denominator
""... |
google/tf-quant-finance | tf_quant_finance/examples/jupyter_notebooks/Dates_in_TFF.ipynb | apache-2.0 | #@title (1) Constructing Dates: sequence of `datetime.datetime`
# Use Python's datetime library to construct a date as datetime.date(year, month, day).
dates = [datetime.date(2015, 4, 15), datetime.date(2017, 12, 30)]
# Then, convert this into a date tensor.
date_tensor = tff.datetime.dates_from_datetimes(dates)
date_t... |
zomansud/coursera | ml-clustering-and-retrieval/week-3/2_kmeans-with-text-data_blank.ipynb | mit | import graphlab
import matplotlib.pyplot as plt
import numpy as np
import sys
import os
from scipy.sparse import csr_matrix
%matplotlib inline
'''Check GraphLab Create version'''
from distutils.version import StrictVersion
assert (StrictVersion(graphlab.version) >= StrictVersion('1.8.5')), 'GraphLab Create must be ve... |
mne-tools/mne-tools.github.io | dev/_downloads/2455121b46e43615a45b660a36d0ad93/30_epochs_metadata.ipynb | bsd-3-clause | import os
import numpy as np
import pandas as pd
import mne
kiloword_data_folder = mne.datasets.kiloword.data_path()
kiloword_data_file = os.path.join(kiloword_data_folder,
'kword_metadata-epo.fif')
epochs = mne.read_epochs(kiloword_data_file)
"""
Explanation: Working with Epoch meta... |
csaladenes/blog | deold/3_deoldify.ipynb | mit | #@title Check GPU
#@markdown Run this to connect to a Colab Instance, and see what GPU Google gave you.
gpu = !nvidia-smi --query-gpu=gpu_name --format=csv
print(gpu[1])
print("The Tesla T4 and P100 are fast and support hardware encoding. The K80 and P4 are slower.")
print("Sometimes resetting the instance in the 'run... |
HSE-LaMBDA/modern-technologies-for-ml-and-big-data | lecture3/06-howto-neural-nets.ipynb | mit | !cd toy_datasets; wget -O MiniBooNE_PID.txt -nc MiniBooNE_PID.txt https://archive.ics.uci.edu/ml/machine-learning-databases/00199/MiniBooNE_PID.txt
import numpy, pandas
from rep.utils import train_test_split
from sklearn.metrics import roc_auc_score
data = pandas.read_csv('toy_datasets/MiniBooNE_PID.txt', sep='\s*', ... |
joostgp/kaggle_bosch | pre_path_per_sample.ipynb | apache-2.0 | const.TRAIN_FILES
const.TEST_FILES
"""
Explanation: Load lookup table & data
End of explanation
"""
num_data = func.load_data_file(const.TRAIN_FILES[0], ftype='bin')
cat_data = func.load_data_file(const.TRAIN_FILES[1], ftype='bin')
num_data_te = func.load_data_file(const.TEST_FILES[0], ftype='bin')
cat_data_te = fu... |
mne-tools/mne-tools.github.io | stable/_downloads/d7890cef2b07af34d1364209c590db5f/roi_erpimage_by_rt.ipynb | bsd-3-clause | # Authors: Jona Sassenhagen <jona.sassenhagen@gmail.com>
#
# License: BSD-3-Clause
import mne
from mne.event import define_target_events
from mne.channels import make_1020_channel_selections
print(__doc__)
"""
Explanation: Plot single trial activity, grouped by ROI and sorted by RT
This will produce what is sometime... |
pmgbergen/porepy | tutorials/tracer_transport.ipynb | gpl-3.0 | import numpy as np
import scipy.sparse as sps
import porepy as pp
import data.flow_benchmark_2d_geiger_setup as setup
"""
Explanation: Assembly of system with multiple domains, variables and numerics
This tutorial demonstrates how a transient problem may be solved in PorePy.
We consider the advective-diffusive trace... |
ShinjiKatoA16/UCSY-sw-eng | RaspberryPiProgramming.ipynb | mit | import RPi.GPIO as GPIO
GPIO.setmode(GPIO.BCM) # use GPIO numbering, see output of gpio command
#GPIO.setmode(GPIO.BOARD) # use Physical pin numbering
GPIO.setup(18, GPIO.OUT) # PIN18 (Physical:Pin12) : Output
GPIO.output(18, GPIO.HIGH) # Ping18 -> High (3.3V)
"""
Explanation: Raspberry Pi Programming
GPIO digital ou... |
ledrui/week4_Ridge_Regression | Overfitting_Demo_Ridge_Lasso.ipynb | mit | 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 consisting of 30 points drawn from the sinusoid $y = \sin(4x)$:
End of explanation
... |
NYUDataBootcamp/Projects | MBA_S17/170512_PeterMeijer_EconomyAndPolitics.ipynb | mit | import sys
import pandas as pd
import pandas_datareader.data as web
import matplotlib.pyplot as plt
import datetime as dt
import seaborn as sns
import numpy as np
import datetime as dt
from dateutil.relativedelta import relativedelta
%matplotlib inline ... |
yafeunteun/wikipedia-spam-classifier | notebooks/spam_detection_wikipedia.ipynb | mit | ! sh bootstrap.sh
from sklearn.cluster import KMeans
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import random
from sklearn.utils import shuffle
from sklearn.metrics import f1_score
from sklearn.cross_validation import KFold
from sklearn.metrics import recall_score
from sklearn.ensemble impo... |
tensorflow/docs-l10n | site/ko/hub/tutorials/cord_19_embeddings.ipynb | apache-2.0 | # Copyright 2019 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 by app... |
GoogleCloudPlatform/tf-estimator-tutorials | 00_Miscellaneous/tfx/01_tf_estimator_deepdive.ipynb | apache-2.0 | try:
COLAB = True
from google.colab import auth
auth.authenticate_user()
except:
pass
RANDOM_SEED = 19831006
import os
import math
import multiprocessing
import pandas as pd
from datetime import datetime
import tensorflow as tf
print "TensorFlow : {}".format(tf.__version__)
tf.enable_eager_execution()
pri... |
Python4AstronomersAndParticlePhysicists/PythonWorkshop-ICE | notebooks/07_01_scipy.ipynb | mit | import scipy as sp
import numpy as np
# we will need to plot stuff later
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10, 8)
plt.rcParams['font.size'] = 16
plt.rcParams['lines.linewidth'] = 2
"""
Explanation: SciPy
Uses numpy as its core
Numerical methods for:
integration
so... |
bjackman/lisa | ipynb/examples/utils/gem5_example.ipynb | apache-2.0 | from conf import LisaLogging
LisaLogging.setup()
# One initial cell for imports
import json
import logging
import os
from env import TestEnv
# Suport for FTrace events parsing and visualization
import trappy
from trappy.ftrace import FTrace
from trace import Trace
# Support for plotting
# Generate plots inline
%mat... |
femtotrader/pyfolio | pyfolio/examples/round_trip_example.ipynb | apache-2.0 | %matplotlib inline
import gzip
import os
import pandas as pd
import pyfolio as pf
transactions = pd.read_csv(gzip.open('../tests/test_data/test_txn.csv.gz'),
index_col=0, parse_dates=0)
positions = pd.read_csv(gzip.open('../tests/test_data/test_pos.csv.gz'),
index_col=0, parse_d... |
google/fhir | examples/gcp_datalab/notebooks/1_train_and_eval_ml_model_to_predict_los.ipynb | apache-2.0 | import os
GCP_PROJECT = 'dp-workspace'
GCS_BUCKET = 'gs://cluster19-bkt'
GCS_REGION = 'us-central1'
os.putenv("REGION", GCS_REGION)
LABELS_JOB = 'bundlesTolabels'
SEQEX_JOB = 'gen_seqex'
STAGING_LOCATION = GCS_BUCKET+'/staging'
TEMP_LOCATION = GCS_BUCKET+'/temp'
RUNNER = 'DirectRunner'
TF_RECORD_BUNDLES = 'gs://cluster... |
grokkaine/biopycourse | day2/stats_scipy.ipynb | cc0-1.0 | %matplotlib inline
import pandas as pd
import numpy as np
from scipy import stats
df = pd.read_csv('data/gex.txt', sep = '\t', index_col = 0)
print(df.head(4))
#df.iloc[:,1:].astype(float)
#print df.dtypes
#print type(df.GSM21712.values)
pmatrix = np.zeros((6,6))# the P-value matrix
i = -1
for ci in df.columns[1:]:
... |
dstuebe/generators | presentation.ipynb | mit | def fib():
a, b = 0, 1
while True:
yield b
a, b = b, a+b
function_result = fib()
print function_result
"""
Explanation: Generators: x = yield 42
And other applications
David Stuebe
<a href="https://www.swipely.com/" target="_blank">www.swipely.com</a>
Get the presentation
On Github IO: <a href... |
whiterd/Tutorial-Notebooks | 2017-03-Presentation-Jupyter-Notebook.ipynb | mit | some_global_variable = 6
def my_function(arg):
"""This is a docstring."""
some_global_variable = 1
return some_global_variable
print(my_function(5))
some_global_variable
"""
Explanation: <img src="resources/jupyter-main-logo.svg" alt="Jupyter" height="200" width="200">
<h1 align="center">Jupyte... |
bhattacharjee/courses | CourseraDeepLearningSpecialization/2.HyperparameterTrainingRegularizationAndOptimization/Week1/Exercises/Regularization.ipynb | mit | # import packages
import numpy as np
import matplotlib.pyplot as plt
from reg_utils import sigmoid, relu, plot_decision_boundary, initialize_parameters, load_2D_dataset, predict_dec
from reg_utils import compute_cost, predict, forward_propagation, backward_propagation, update_parameters
import sklearn
import sklearn.da... |
jstac/quantecon_nyu_2016 | lecture11/overfitting_noises_dcs.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sn
"""
Explanation: Stochastic and Deterministic Noise as Catalysts for Overfitting
Small exercise for showcasing the sources of overfitting. Example taken from the 4th Chapter of the book by Abu-Mostafa Yaser, Learning from data.
... |
xdze2/thermique_appart | Ordre_de_grandeur.ipynb | mit | S = 25.8e-2 * 43.7e-2 # m3
m = 3.2 # kg
# Masse surfacique :
rhoS = m/S
print( rhoS )
"""
Explanation: Ordre de grandeur
Albédo tuile
http://www.ecohabitation.com/actualite/nouvelles/toits-blancs-alternative-diminuer-changements-climatiques#_ftn1
absorption 0.64
https://dumas.ccsd.cnrs.fr/dumas-00469805/document
... |
tensorflow/recommenders | docs/examples/quickstart.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... |
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