repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
Saxafras/Spacetime | 1-D CA and TransducerTests.ipynb | bsd-3-clause | A = 2
r = 1
table = lookup_table(18, 2, 1)
R = 2*r + 1
scan = tuple(np.arange(0,A)[::-1])
for a in product(scan, repeat = R):
print a, table[a]
x = [0,1,1,0]
print neighborhood(x, 1)
for item in neighborhood(x, 1):
print item
print max_rule(2,1)
print example.current_state()
example.evolve(1)
print example.c... |
kpei/cs-rating | wl_model/wlbet_team_model_all.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import datetime as dt
from scipy.stats import norm, bernoulli
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from spcl_case import *
plt.style.use('fivethirtyeight')
"""
Explanation: Win/Loss Betting Model
Same as other one but I now filter by teams that... |
ebellm/ztf_summerschool_2015 | notebooks/SNe_Ia_with_PTF.ipynb | bsd-3-clause | # execute this cell
SNlcs = Table.read("../data/Firth14Tbl2.txt", format = 'ascii')
SNlcs
"""
Explanation: Hands-On Exercise 6: Determining $H_0$ with Type Ia SNe from PTF
Version 0.1
Today we learned about a variety of different explosive, extragalactic transients. While the lectures focused on recently discovered, ... |
junghao/fdsn | examples/GeoNet_FDSN_demo_event.ipynb | mit | from obspy import UTCDateTime
from obspy.clients.fdsn import Client as FDSN_Client
from obspy import read_inventory
"""
Explanation: GeoNet FDSN webservice with Obspy demo - Event Service
This demo introduces some simple code that requests data using GeoNet's FDSN webservices and the obspy module in python. This noteb... |
billzhao1990/CS231n-Spring-2017 | assignment2/TensorFlow.ipynb | mit | import tensorflow as tf
import numpy as np
import math
import timeit
import matplotlib.pyplot as plt
%matplotlib inline
from cs231n.data_utils import load_CIFAR10
def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=10000):
"""
Load the CIFAR-10 dataset from disk and perform preprocessing to... |
agile-geoscience/welly | docs/_userguide/Wells.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import welly
welly.__version__
"""
Explanation: Wells
Wells are one of the fundamental objects in welly.
Well objects include collections of Curve objects. Multiple Well objects can be stored in a Project.
On this page, we take a closer look at the Well.
First, some ... |
kmunve/TSanalysis | Test/new_snow_problem.ipynb | mit | # -*- coding: utf-8 -*-
%matplotlib inline
from __future__ import print_function
import pylab as plt
import datetime
import numpy as np
plt.rcParams['figure.figsize'] = (14, 6)
"""
Explanation: APS - new snow
Imports
End of explanation
"""
# New snow amount last 24 h 0-60 cm [10 cm intervals]
new_snow_24h_cat = np.a... |
jameskermode/f90wrap | examples/callback_print_function_issue93/callback_print_output_python.ipynb | lgpl-3.0 | import CBF
"""
Explanation: Setting a callback message to trap print output
print * statements in Fortran do print to the Python console stdandard output, but there is no satisfactory way to do the same with Jupyter notebooks (so far as I know).
This document illustrates how to do so once you've built generated the m... |
quantopian/research_public | notebooks/lectures/Instability_of_Estimates/answers/notebook.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from statsmodels.stats.stattools import jarque_bera
# Set a seed so we can play with the data without generating new random numbers every time
np.random.seed(321)
"""
Explanation: Exercises: Instability of Parameter Estimates - Answer Key
Lecture... |
catarinaqmcosta/Scenarios_Network | ExposureEntireNetwork.ipynb | gpl-3.0 | %matplotlib inline
import utilsNetwork
"""
Explanation: Exposure model: Complete highway network
This IPython notebook processes a shapefile, and creates an exposure model.
Each asset has a typology associated, that will be defined based on an external .csv file or on the length of the asset.
End of explanation
"""
... |
cuttlefishh/papers | vibrio-fischeri-transcriptomics/code/ipynb/vibrio_pathways.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
import seaborn as sns
from sys import argv
"""
Explanation: Function imports
End of explanation
"""
gene_dict = {
'Phosphate': ['VF_A1087', 'VF_A1090', 'VF_1611', 'VF_A1057', 'VF_1610', 'VF_1... |
studentofdata/qcew | vmfiles/IPNB/Examples/b Graphics/30 Seaborn.ipynb | bsd-3-clause | # The imports
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="darkgrid")
"""
Explanation: Seaborn graphics
Seaborn is a Python library with "a high-level interface for drawing attractive statistical graphics". This notebook includes some ... |
chengsoonong/crowdastro | notebooks/17_training_with_features.ipynb | mit | import itertools
import sys
import bson
import h5py
import keras.layers
import keras.models
import matplotlib.pyplot
import numpy
import pandas
import sklearn.cross_validation
import sklearn.dummy
import sklearn.linear_model
import sklearn.metrics
sys.path.insert(1, '..')
import crowdastro.data
import crowdastro.show... |
google-research/google-research | kws_streaming/colab/conv_batch_norm_folding.ipynb | apache-2.0 | import tensorflow as tf
import numpy as np
np.random.seed(123)
"""
Explanation: Copyright 2019 Google LLC
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
... |
nouiz/summerschool2015 | scan_tutorial/scan_tutorial.ipynb | bsd-3-clause | import theano
import theano.tensor as T
import numpy as np
vector1 = T.vector('vector1')
vector2 = T.vector('vector2')
"""
Explanation: Scan
In short
Mechanism to perform loops in a Theano graph
Supports nested loops and reusing results from previous iterations
Highly generic
Implementation
You've previous seen th... |
rorimac/Tools-of-a-Math-Student | Chapter2.ipynb | mit | 1 + 1
"""
Explanation: Using python
Python is an interpreted language. This means that there is a "python program" that reads your input and excecutes it. If you open your ipython interpreter, you'll see the following message (or something very similar):
Python 2.7.6 (default, Mar 22 2014, 22:59:56)
Type "copyright",... |
ES-DOC/esdoc-jupyterhub | notebooks/ipsl/cmip6/models/sandbox-2/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ipsl', 'sandbox-2', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: IPSL
Source ID: SANDBOX-2
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbul... |
cmorgan/toyplot | docs/tick-locators.ipynb | bsd-3-clause | import numpy
x = numpy.arange(20)
y = numpy.linspace(0, 1, len(x)) ** 2
import toyplot
canvas, axes, mark = toyplot.plot(x, y, width=300)
"""
Explanation: .. _tick-locators:
Tick Locators
When you create a figure in Toyplot, you begin by creating a :class:canvas<toyplot.canvas.Canvas>, add :mod:axes<toyplot.... |
GoogleCloudPlatform/training-data-analyst | bootcamps/imagereco/fashion_cnn_lab.ipynb | apache-2.0 | import os
PROJECT = 'my-project-id' # REPLACE WITH YOUR PROJECT ID
BUCKET = 'my-bucket-name' # REPLACE WITH YOUR BUCKET NAME
REGION = 'us-central1' # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
MODEL_TYPE='cnn' # 'dnn' or 'cnn'
# do not change these
os.environ['PROJECT'] = PROJECT
os.environ['BUCKET'] = BUCKET
... |
therealAJ/python-sandbox | data-science/learning/ud1/DataScience/PCA.ipynb | gpl-3.0 | from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
import pylab as pl
from itertools import cycle
iris = load_iris()
numSamples, numFeatures = iris.data.shape
print numSamples
print numFeatures
print list(iris.target_names)
"""
Explanation: Principal Component Analysis
PCA is a dimensionali... |
jsmanrique/grimoirelab-personal-utils | Light Github index generator.ipynb | mit | import utils
utils.logging.basicConfig(level=utils.logging.INFO)
settings = utils.read_config_file('settings.yml')
es = utils.establish_connection(settings['es_host'])
"""
Explanation: Github Repositories Index Generator
This notebook generates two ElasticSearch (ES) indexes with information about:
* git (commits, fil... |
georgetown-analytics/machine-learning | examples/bbengfort/occupancy/occupancy.ipynb | mit | %matplotlib notebook
import os
import csv
import pickle
import numpy as np
import pandas as pd
from datetime import datetime
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelEncoder
from sklearn.feature_extraction import DictVectorizer
from sklearn.base import BaseEstimator, TransformerM... |
Vvkmnn/books | ThinkBayes/09_Two_Dimensions.ipynb | gpl-3.0 | import thinkbayes
class Paintball(thinkbayes.Suite, thinkbayes.Joint):
def __init__(self, alphas, betas, locations):
self.locations = locations
pairs = [(alpha, beta)
for alpha in alphas
for beta in betas]
thinkbayes.Suite.__init__(self, pairs)
"""
Expl... |
gfrubi/FM2 | Notebooks/Ejemplo-MSV-Difusion-Calor-2D-rectangulo.ipynb | gpl-3.0 | %matplotlib inline
from numpy import *
from matplotlib.pyplot import *
from mpl_toolkits.mplot3d import axes3d
from ipywidgets import interact
style.use('classic')
"""
Explanation: Ecuación de Difusión del calor 2D
Discutiremos la solución de la ecuación de difusión del calor bidimensional,
$$
\frac{\partial^2 \psi}{... |
M0nica/python-foundations-hw | 11/.ipynb_checkpoints/Parking Data-checkpoint.ipynb | mit | violations_df = violations_df[violations_df['Vehicle Year'] != 0]
# print(violations_df['Vehicle Year'].head())
"""
Explanation: I don't think anyone's car was built in 0AD. Discard the '0's as NaN.
End of explanation
"""
# print(violations_df['Date First Observed'].head())
import datetime
violations_df.head()['Iss... |
ioam/scipy-2017-holoviews-tutorial | notebooks/03-exploration-with-containers.ipynb | bsd-3-clause | import numpy as np
import holoviews as hv
hv.extension('bokeh')
%opts Curve Area [width=600]
"""
Explanation: <a href='http://www.holoviews.org'><img src="assets/hv+bk.png" alt="HV+BK logos" width="40%;" align="left"/></a>
<div style="float:right;"><h2>03. Exploration with Containers</h2></div>
In the first two secti... |
pgroth/prov-wod-analysis | MeasuringPROVProvenanceWebofData.ipynb | apache-2.0 | from IPython.display import HTML
display(HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
We note that the code for our analysis is embeded within this document ... |
mne-tools/mne-tools.github.io | 0.17/_downloads/e33f407965fb2ba1307deaf80b7d794c/plot_brainstorm_auditory.ipynb | bsd-3-clause | # Authors: Mainak Jas <mainak.jas@telecom-paristech.fr>
# Eric Larson <larson.eric.d@gmail.com>
# Jaakko Leppakangas <jaeilepp@student.jyu.fi>
#
# License: BSD (3-clause)
import os.path as op
import pandas as pd
import numpy as np
import mne
from mne import combine_evoked
from mne.minimum_norm impor... |
unpingco/Python-for-Probability-Statistics-and-Machine-Learning | chapters/probability/notebooks/Conditional_expectation_MSE_Ex.ipynb | mit | from __future__ import division
%pylab inline
"""
Explanation: Python for Probability, Statistics, and Machine Learning
End of explanation
"""
import sympy as S
X10,X20,X50 = S.symbols('X10,X20,X50',real=True)
xi = 10*X10+20*X20+50*X50
eta = X10*X20*(1-X50)+X10*(1-X20)*(X50)+(1-X10)*X20*(X50)
num=S.summation(xi*e... |
encima/Comp_Thinking_In_Python | Session_5/5_Homework.ipynb | mit | initial_var = 358645317684531432678
"""
Explanation: What is the difference between a method and a function?
Convert the below variable to a string and assign to string_var. Assign the length of that string to: len_var
End of explanation
"""
name = "Oi, you there"
def update_name():
name = "what?"
print(n... |
zzsza/Datascience_School | 19. 문서 전처리/03. konlpy 한국어 처리 패키지 소개.ipynb | mit | from konlpy.corpus import kolaw
kolaw.fileids()
c = kolaw.open('constitution.txt').read()
print(c[:100])
from konlpy.corpus import kobill
kobill.fileids()
d = kobill.open('1809890.txt').read()
print(d[:100])
"""
Explanation: konlpy 한국어 처리 패키지 소개
konlpy는 한국어 정보처리를 위한 파이썬 패키지이다.
http://konlpy.org/ko/v0.4.4/
https:/... |
jmhsi/justin_tinker | data_science/courses/deeplearning1/nbs/lesson2.ipynb | apache-2.0 | # Rather than importing everything manually, we'll make things easy
# and load them all in utils.py, and just import them from there.
%matplotlib inline
import utils; reload(utils)
from utils import *
"""
Explanation: Linear models with CNN features
End of explanation
"""
%matplotlib inline
from __future__ import ... |
sainathadapa/fastai-courses | deeplearning1/nbs/lesson1.ipynb | apache-2.0 | %matplotlib inline
"""
Explanation: Using Convolutional Neural Networks
Welcome to the first week of the first deep learning certificate! We're going to use convolutional neural networks (CNNs) to allow our computer to see - something that is only possible thanks to deep learning.
Introduction to this week's task: 'Do... |
henridwyer/company-reviews | src/Topic Modeling.ipynb | mit | # words that appear in fewer than MIN_DOC_FREQ documents will be ignored
MIN_DOC_FREQ = 20
# if there are less than MIN_WORDS in a document, it is dropped
MIN_WORDS = 10
from pymongo import MongoClient
import datetime
import reviews_data
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import s... |
PyLCARS/PythonUberHDL | myHDL_DigLogicFundamentals/myHDL_BitOperations/BitwiseBehavior_in_myHDL.ipynb | bsd-3-clause | #This notebook also uses the `(some) LaTeX environments for Jupyter`
#https://github.com/ProfFan/latex_envs wich is part of the
#jupyter_contrib_nbextensions package
from myhdl import *
from myhdlpeek import Peeker
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from sympy im... |
BinRoot/TensorFlow-Book | ch02_basics/Concept10_queue_text.ipynb | mit | import tensorflow as tf
filename_queue = tf.train.string_input_producer(["big.txt"])
"""
Explanation: Ch 02: Concept 10
Queue of text from file
The previous notebook (Concept 09) introduced queues. Let's see how we can use them with real world data.
Download a corpus of text.
$ wget http://norvig.com/big.txt
Create ... |
atcemgil/notes | fe588/fe588_introduction.ipynb | mit |
x = 0.1
N = 3
a = 1
b = 0
c = -1
print('f(' + str(x) + ') = ' + str(a*x**2 + b*x + c))
a = 1
b = 1
print(a*b,a*(b+1),a*(b+2),a*(b+3))
a = 2
print(a*b,a*(b+1),a*(b+2),a*(b+3))
a = 3
print(a*b,a*(b+1),a*(b+2),a*(b+3))
a = 4
print(a*b,a*(b+1),a*(b+2),a*(b+3))
"""
Explanation: Install Python.
jup... |
JAmarel/Phys202 | Interact/.ipynb_checkpoints/InteractEx02-checkpoint.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 2
Imports
End of explanation
"""
def plot_sine1(a,b):
labels = ['0','$\pi$.','$2\pi$.','$3\pi$.','$4\pi$... |
akseshina/dl_course | seminar_6/hw_RNN.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
import tensorflow as tf
from lazy import lazy
from attrdict import AttrDict
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import random
family_classification_metadata =... |
tata-antares/tagging_LHCb | for_IML/BtaggingMadness.ipynb | apache-2.0 | %matplotlib inline
from matplotlib import pyplot as plt
import numpy
import root_numpy
# import pandas - no pandas today
from astropy.table import Table
from sklearn.metrics import roc_auc_score
from scipy.special import logit
from decisiontrain import DecisionTrainClassifier
from collections import OrderedDict
# th... |
agiovann/Constrained_NMF | demos/obsolete/demo_caiman_cnmfe.ipynb | gpl-2.0 | try:
if __IPYTHON__:
# this is used for debugging purposes only. allows to reload classes when changed
get_ipython().magic(u'load_ext autoreload')
get_ipython().magic(u'autoreload 2')
except NameError:
print('Not IPYTHON')
pass
import sys
import numpy as np
from time impo... |
QInfer/qinfer-examples | parallelization.ipynb | agpl-3.0 | from __future__ import division
"""
Explanation: Parallelization with QInfer
Setup
We begin by enabling Python 3–style division, as is recommended for use in Python 2.7.
End of explanation
"""
import ipyparallel as ipp
import qinfer as qi
from functools import partial
"""
Explanation: Next, we import the IPython pa... |
kpei/cs-rating | wl_model/wlbet_team_model.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import datetime as dt
from scipy.stats import norm, bernoulli
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from spcl_case import *
plt.style.use('fivethirtyeight')
"""
Explanation: Win/Loss Betting Model
End of explanation
"""
h_matches = pd.read_csv... |
flaviocordova/udacity_deep_learn_project | tv-script-generation/dlnd_tv_script_generation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scrip... |
georgetown-analytics/machine-learning | examples/bbengfort/traffic/Traffic.ipynb | mit | %matplotlib notebook
import os
import shutil
import urllib3
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
"""
Explanation: Time Series Traffic Analysis
End of explanation
"""
URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/00492/Metro_Interstate_Traff... |
quoniammm/happy-machine-learning | Udacity-DL/dlnd_tv_script_generation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scrip... |
kmclaugh/fastai_courses | deeplearning1/nbs/lesson1.ipynb | apache-2.0 | %matplotlib inline
"""
Explanation: Using Convolutional Neural Networks
Welcome to the first week of the first deep learning certificate! We're going to use convolutional neural networks (CNNs) to allow our computer to see - something that is only possible thanks to deep learning.
Introduction to this week's task: 'Do... |
google/jax | docs/notebooks/Custom_derivative_rules_for_Python_code.ipynb | apache-2.0 | import jax.numpy as jnp
from jax import custom_jvp
@custom_jvp
def f(x, y):
return jnp.sin(x) * y
@f.defjvp
def f_jvp(primals, tangents):
x, y = primals
x_dot, y_dot = tangents
primal_out = f(x, y)
tangent_out = jnp.cos(x) * x_dot * y + jnp.sin(x) * y_dot
return primal_out, tangent_out
from jax import jv... |
tleonhardt/CodingPlayground | dataquest/Statistics/Jeopardy.ipynb | mit | import pandas as pd
# Read the dataset into a Pandas DataFrame
jeopardy = pd.read_csv('../data/jeopardy.csv')
# Print out the first 5 rows
jeopardy.head(5)
# Print out the columns
jeopardy.columns
# Remove the spaces from column names
col_names = jeopardy.columns
col_names = [s.strip() for s in col_names]
jeopardy.... |
feststelltaste/software-analytics | prototypes/Reading a Git repo's commit history with Pandas efficiently.ipynb | gpl-3.0 | import git
GIT_REPO_PATH = r'../../spring-petclinic/'
repo = git.Repo(GIT_REPO_PATH)
git_bin = repo.git
git_bin
"""
Explanation: Introduction
There are multiple reasons for analyzing a version control system like your Git repository. See for example Adam Tornhill's book "Your Code as a Crime Scene" or his upcoming b... |
kinshuk4/MoocX | misc/deep_learning_notes/Ch4_Recurrent_Networks/001_Optimization_Algorithms_and_Hyper-parameter_Search/Optimization_Algorithms_Variations_Hyper-paramter_Search.ipynb | mit | def batch_SGD(batch = 50, eta = 0.37, verbose=True):
# to clear the globals in `edf`
edf.components = []
edf.params = []
######################################### please modify this cell to finish the problem 2.a #######################
# for repeatability
np.random.seed(0)
# Inputs and p... |
martin-hunt/hublib | examples/UI_Demo.ipynb | mit | e1 = ui.Number(
name='E1',
description="Longitudinal Young's Modulus",
units='GPa',
min=0,
max=500,
value=138
)
e1
def my_cb(x, y):
print("CB:", x == e1)
print("New:", y)
def my_cb2(x, y):
print("CB2:", x == e1)
print("New:", y)
"""
Explanation: Number (... |
gte620v/PythonTutorialWithJupyter | exercises/solutions/Ex2-Iris_Dataset_solutions.ipynb | mit | import pandas as pd
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
df = pd.read_csv(url,names=['sepal_length',
'sepal_width',
'petal_length',
'petal_width',
'species'])
df.h... |
neilzim/SCDA | old_notebooks/scda_survey_demo.ipynb | mit | #survey_params = {'Pupil': { 'pm': ['hex1', 'hex2', 'hex3', 'hex4', 'pie08'],
# 'ss': ['X','Cross'],
# 'sst': ['025','100'],
# 'sm': [True, False],
# 'N': 250 },
# 'FPM': { 'rad': 4. },
# 'LS': { 'shape':['ann', 'hex'], 'obs... |
intel-analytics/analytics-zoo | pyzoo/zoo/chronos/use-case/network_traffic/network_traffic_model_forecasting.ipynb | apache-2.0 | def plot_predict_actual_values(date, y_pred, y_test, ylabel):
"""
plot the predicted values and actual values (for the test data)
"""
fig, axs = plt.subplots(figsize=(16,6))
axs.plot(date, y_pred, color='red', label='predicted values')
axs.plot(date, y_test, color='blue', label='actual values')... |
DiracInstitute/kbmod | notebooks/Kbmod_Reference.ipynb | bsd-2-clause | #everything we will need for this demo
from kbmodpy import kbmod as kb
import numpy as np
import matplotlib.pyplot as plt
import os
path = "../data/demo/"
"""
Explanation: KBMOD Reference
This notebook demonstrates a gpu-accelerated image processing framework designed for image stack and time domain analysis, compatib... |
jeffakolb/Data-Science-45min-Intros | python-objects/python_odds_and_ends.ipynb | unlicense | %%HTML
<img src="https://imgs.xkcd.com/comics/bun_alert.png" width=500></img>
"""
Explanation: WTF is this?
or, When is is what you think is is?
Also, rabbit hole alert...
End of explanation
"""
%%HTML
<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Pay no mind.... <a href="https://t.co/mnIP... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/06_structured/3_tensorflow_wd.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.1
# change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'... |
andrewosh/notebooks | worker/notebooks/thunder/tutorials/multi_index.ipynb | mit | from thunder import Series
from numpy import arange, array
data = tsc.loadSeriesFromArray(arange(12))
data.first()
"""
Explanation: Multi-indexing
When dealing with Series data, it is often useful to index each element of the series with multiple labels and then select and aggregrate data based on these indices. For ... |
vzg100/Post-Translational-Modification-Prediction | .ipynb_checkpoints/Phosphorylation Sequence Tests -MLP -dbptm+ELM-VectorAvr.-checkpoint.ipynb | mit | from pred import Predictor
from pred import sequence_vector
from pred import chemical_vector
"""
Explanation: Template for test
End of explanation
"""
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
for i in par:
print("y", i)
y = Predictor()
y.load_data(file="Data/Trainin... |
mabevillar/rmtk | rmtk/vulnerability/derivation_fragility/equivalent_linearization/miranda_2000_firm_soils/miranda_2000_firm_soils.ipynb | agpl-3.0 | import miranda_2000_firm_soils
from rmtk.vulnerability.common import utils
%matplotlib inline
"""
Explanation: Miranda (2000) for firm soils
This methodology, proposed in Miranda (2000), aims to estimate the maximum lateral inelastic displacement demands on a structure based on the maximum lateral elastic displacement... |
ES-DOC/esdoc-jupyterhub | notebooks/pcmdi/cmip6/models/sandbox-2/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'pcmdi', 'sandbox-2', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: PCMDI
Source ID: SANDBOX-2
Sub-Topics: Radiative Forcings.
Properties: 85 (4... |
Avsecz/concise | nbs/PWM_initialization.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
# RBP PWM's
from concise.data import attract
dfa = attract.get_metadata()
dfa
# TF PWM's
from concise.data import encode
dfe = encode.get_metadata()
dfe
# TF PWM's
from concise.data import hocomoco
dfh = hocomoco.get_metadata()
dfh
"""
Explanation: Initializing ... |
analog-rl/Easy21 | Joe #3 TD Learning in Easy21/easy21 tests.ipynb | mit | import matplotlib.pyplot as plt
%matplotlib notebook
plt.figure(1)
values = []
for i in xrange(0,100000):
values.append(Card().absolute_value)
# values.append(random.randint(1,10))
plt.title('Test; Each draw from the deck results in a value between 1 and 10 (uniformly distributed)')
plt.hist(values)
... |
pagutierrez/tutorial-sklearn | notebooks-spanish/03-representacion_datos_aa.ipynb | cc0-1.0 | from sklearn.datasets import load_iris
iris = load_iris()
"""
Explanation: Representación y visualización de datos
El aprendizaje automático trata de ajustar modelos a los datos; por esta razón, empezaremos discutiendo como los datos pueden ser representados para ser accesibles por el ordenador. Además de esto, nos ba... |
google/jax | docs/jax-101/04-advanced-autodiff.ipynb | apache-2.0 | import jax
f = lambda x: x**3 + 2*x**2 - 3*x + 1
dfdx = jax.grad(f)
"""
Explanation: Advanced Automatic Differentiation in JAX
Authors: Vlatimir Mikulik & Matteo Hessel
Computing gradients is a critical part of modern machine learning methods. This section considers a few advanced topics in the areas of automatic d... |
transcranial/keras-js | notebooks/layers/pooling/MaxPooling2D.ipynb | mit | data_in_shape = (6, 6, 3)
L = MaxPooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(270)
data_in = 2 * np.... |
xiaoxiaoyao/MyApp | jupyter_notebook/loans-tree.ipynb | unlicense | #为了让Python能够高效率处理表格数据，我们使用一个非常优秀的数据处理框架Pandas。
import pandas as pd
#然后我们把loans.csv里面的内容全部读取出来，存入到一个叫做df的变量里面。
df = pd.read_csv('loans.csv')
#我们看看df这个数据框的前几行，以确认数据读取无误。因为表格列数较多，屏幕上显示不完整，我们向右拖动表格，看表格最右边几列是否也正确读取。
df.tail()
#统计一下总行数，看是不是所有行也都完整读取进来了。
df.shape
"""
Explanation: 如何用Python和机器学习帮你决策？
贷还是不贷：
每一行数据，都代表了之前的一次... |
OSHI7/Learning1 | MatplotLib Pynotebooks/AnatomyOfMatplotlib-Part3-HowToSpeakMPL.ipynb | mit | %load exercises/3.1-colors.py
t = np.arange(0.0, 5.0, 0.2)
plt.plot(t, t, , t, t**2, , t, t**3, )
plt.show()
t = np.arange(0.0, 5.0, 0.2)
plt.plot(t, t, , t, t**2, , t, t**3, )
plt.show()
"""
Explanation: How to speak "MPL"
In the previous parts, you learned how matplotlib organizes plot-making by figures and axes.... |
alexandrnikitin/algorithm-sandbox | courses/DAT256x/Module01/01-05-Polynomials.ipynb | mit | from random import randint
x = randint(1,100)
(x**3 + 2*x**3 - 3*x - x + 8 - 3) == (3*x**3 - 4*x + 5)
"""
Explanation: Polynomials
Some of the equations we've looked at so far include expressions that are actually polynomials; but what is a polynomial, and why should you care?
A polynomial is an algebraic expression ... |
GoogleCloudPlatform/mlops-on-gcp | workshops/tfx-caip-tf23/lab-03-tfx-cicd/labs/lab-03.ipynb | apache-2.0 | import yaml
# Set `PATH` to include the directory containing TFX CLI.
PATH=%env PATH
%env PATH=/home/jupyter/.local/bin:{PATH}
!python -c "import tfx; print('TFX version: {}'.format(tfx.__version__))"
"""
Explanation: CI/CD for TFX pipelines
Learning Objectives
Develop a CI/CD workflow with Cloud Build to build and... |
davidzchen/tensorflow | tensorflow/lite/g3doc/tutorials/model_maker_text_classification.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
GoogleCloudPlatform/professional-services | examples/vertex_pipeline/notebook/batch_prediction_pipeline_development.ipynb | apache-2.0 | !python3 -c "import kfp; print('KFP SDK version: {}'.format(kfp.__version__))"
import os
from functools import partial
import kfp
import pprint
import yaml
from jinja2 import Template
from kfp.v2 import dsl
from kfp.v2.compiler import compiler
from kfp.v2.dsl import Dataset
from kfp.v2.google.client import AIPlatform... |
anhaidgroup/py_entitymatching | notebooks/vldb_demo/.ipynb_checkpoints/Demo_notebook_v6-checkpoint.ipynb | bsd-3-clause | import py_entitymatching as em
import profiler
import pandas as pd
"""
Explanation: |S.No| Package | Comments |
|---|---|---|
|1| pandas | provides data structures (such as DataFrame) to <span style="color:red;">store and manage relational data</span>. Specifically, DataFrame is used to represent input tables. |
... |
eds-uga/csci1360e-su16 | lectures/L8.ipynb | mit | li = ["this", "is", "a", "list"]
print(li)
print(li[1:3]) # Print element 1 (inclusive) to 3 (exclusive)
print(li[2:]) # Print element 2 and everything after that
print(li[:-1]) # Print everything BEFORE element -1 (the last one)
"""
Explanation: Lecture 8: Array Indexing, Slicing, and Broadcasting
CSCI 1360E: Fou... |
JackDi/phys202-2015-work | assignments/assignment10/ODEsEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import odeint
from IPython.html.widgets import interact, fixed
"""
Explanation: Ordinary Differential Equations Exercise 1
Imports
End of explanation
"""
def lorentz_derivs(yvec, t, sigma, rho, beta):
"""Compute the the de... |
ledeprogram/algorithms | class7/homework/bennion_kate_assignment_7.ipynb | gpl-3.0 | import pandas as pd
%matplotlib inline
from sklearn import datasets
from pandas.tools.plotting import scatter_matrix
import matplotlib.pyplot as plt
iris = datasets.load_iris()
from sklearn.cross_validation import train_test_split
x = iris.data[:,2:]
y = iris.target
x_train, x_test, y_train, y_test = train_test_s... |
cesarcontre/Simulacion2017 | Modulo3/Clase20_ProgramaciónLineal.ipynb | mit | import numpy as np
f = np.array([-1, -1])
A = np.array([[50, 24], [30, 33], [-1, 0], [0, -1]])
b = np.array([2400, 2100, -45, -5])
import pyomo_utilities
x, obj = pyomo_utilities.linprog(f, A, b)
x
obj
obj_real = x.sum()-50
obj_real.round(2)
"""
Explanation: Programación lineal
<img style="float: right; margin: ... |
fablln/Deep-Learning | mnist_cnn/TP2-notebook.ipynb | mit | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
X_train, y_train = mnist.train.images, mnist.train.labels
X_validation, y_validation = mnist.validation.images, mnist.validation.labels
X_test, y_test =... |
thewtex/SimpleITK-Notebooks | 63_Registration_Initialization.ipynb | apache-2.0 | import SimpleITK as sitk
import os
import numpy as np
from ipywidgets import interact, fixed
from downloaddata import fetch_data as fdata
import registration_callbacks as rc
import registration_utilities as ru
# Always write output to a separate directory, we don't want to pollute the source directory.
OUTPUT_DIR =... |
kitu2007/dl_class | batch-norm/Batch_Normalization_Solutions.ipynb | mit | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
"""
Explanation: Batch Normalization – Solutions
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a co... |
zhuanxuhit/deep-learning | sentiment-rnn/.ipynb_checkpoints/Sentiment RNN-checkpoint.ipynb | mit | import numpy as np
import tensorflow as tf
with open('reviews.txt', 'r') as f:
reviews = f.read()
with open('labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural network that performs sentiment analysis.... |
tensorflow/docs-l10n | site/en-snapshot/probability/examples/TensorFlow_Probability_Case_Study_Covariance_Estimation.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, sof... |
Diyago/Machine-Learning-scripts | DEEP LEARNING/NLP/Kaggle Quora Insincere Questions Classification/improve-your-score-with-some-text-preprocessing.ipynb | apache-2.0 | import pandas as pd
import numpy as np
import operator
import re
"""
Explanation: Improve your Score with some Text Preprocessing
Updated version :
https://www.kaggle.com/theoviel/improve-your-score-with-text-preprocessing-v2
This kernel is an improved version of @Dieter's work.
https://www.kaggle.com/christofhenk... |
kimkipyo/dss_git_kkp | 통계, 머신러닝 복습/160705화수_25,26일차_뉴럴 네트워크 Neural Network/2.TensorFlow 패키지 소개.ipynb | mit | import tensorflow as tf
x = tf.Variable(1.0)
y = tf.Variable(2.0)
type(x), type(y)
"""
Explanation: TensorFlow 패키지 소개
TensorFlow 패키지는 Theano 패키지와 마찬가지로 선형 대수 심볼 컴파일러(Symbolic Linear Algebra Compiler)이다. Theano에 비해 분산처리 기능이 강화되어 있다.
TensorFlow 기본 사용법
Theano와 유사하게 TensorFlow도 다음과 같은 과정을 거쳐 사용한다.
심볼 변수 정의
심볼 관계 정의
세션 ... |
ES-DOC/esdoc-jupyterhub | notebooks/niwa/cmip6/models/sandbox-1/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'niwa', 'sandbox-1', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: NIWA
Source ID: SANDBOX-1
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balan... |
Mixpap/MasterThesis | Notebooks/Density Profile.ipynb | mit | #HCG=np.load('../Data/H2CoolingG512.npz')
HCG=np.load('../Data/TabulatedG.npz')
f.quadruple(HCG,np.log10(HCG['RHO']),rows=2,nn=0,tlim=26,Save_Figure='H2CoolGRquad')
f.pprofile(HCG,'RHO',steps=4,itlim=26,tdk='Myrs',Save_Figure='H2CoolGRHOprofile',sc2='log',xlim=[0,20],yprop=256)
f.pprofile(HCG,'Temp',steps=4,itlim=26,td... |
hannorein/rebound | ipython_examples/ChaoticHyperion.ipynb | gpl-3.0 | import rebound
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Chaotic Hyperion
In this example, we simulate the spin of Hyperion. The spin evolution is governed by an ordinary differential equation that is coupled to the moon's orbit.
We start by importing REBOUND, numpy and matplotlib.
End of ex... |
CentreForCorpusResearch/clic | docs/notebooks/Cheshire/Cheshire queries.ipynb | mit | # coding: utf-8
import os
from cheshire3.baseObjects import Session
from cheshire3.document import StringDocument
from cheshire3.internal import cheshire3Root
from cheshire3.server import SimpleServer
session = Session()
session.database = 'db_dickens'
serv = SimpleServer(session, os.path.join(cheshire3Root, 'con... |
locuslab/dreaml | examples/seqnn.ipynb | apache-2.0 | from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
class SimpleNN(ContinuousTransform):
def init_func(self,target_df,X_train_df,y_train_df,X_test_df,y_test_df):
model=Sequential()
model.add(Dense(64, input_dim=784, init='uni... |
ozak/CompEcon | notebooks/DynamicProgramming.ipynb | gpl-3.0 | from __future__ import division
%pylab --no-import-all
%matplotlib inline
from numpy import interp
interp?
x = np.linspace(0, np.pi, 100)
plt.figure(1)
plt.plot(x, np.sin(x), label='Actual Function')
for i in np.arange(3,11,2):
fig1 = plt.figure(1)
xp = np.linspace(0, np.pi, i)
yp = np.sin(xp)
y = in... |
uriba/physical-catalysis | angledSimulation.ipynb | gpl-3.0 | %pylab inline
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import matplotlib.pyplot as plt
from pylab import meshgrid,cm,imshow,contour,clabel,colorbar,axis,title,show
"""
Explanation: Modelling catalysis of izomerization in macro sca... |
crystalzhaizhai/cs207_yi_zhai | lectures/L10/L10.ipynb | mit | from IPython.display import HTML
"""
Explanation: Lecture 10
Object Oriented Programming II
Wednesday, October 4th 2017
End of explanation
"""
class Animal():
def __init__(self, name):
self.name=name
def __repr__(self):
class_name = type(self).__name__
return "{0!s}({1.n... |
akloster/porekit-python | docs/02_Plots.ipynb | isc | df = pd.read_hdf("../examples/data/ru9_meta.h5", "meta")
"""
Explanation: Plotting Data
Visualizing the metadata is very useful to get a first look at the nature and quality of the run.
First we need a DataFrame with the meta data. You can make one with porekit.gather_metadata once, and then load it later from a hdf f... |
stephenbeckr/convex-optimization-class | Demos/AutomaticDifferentiation.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
"""
Explanation: <a href="https://colab.research.google.com/github/stephenbeckr/convex-optimization-class/blob/master/Demos/AutomaticDifferentiation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/c... |
Jay-Oh-eN/data-science-workshops | eda.ipynb | mit | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%pylab inline
# We will use the Inside AirBnB dataset from here on
df = pd.read_csv('data/sf_listings.csv')
df.head()
df.room_type.value_counts().plot.bar()
# Since SF doesn't have many neighborhoods (comparatively) we can also see the raw # ... |
materials-data-facility/forge | docs/tutorials/6-Data_Retrieval_Functions.ipynb | apache-2.0 | from mdf_forge.forge import Forge
mdf = Forge()
"""
Explanation: Part 6 - Data Retrieval Functions
End of explanation
"""
# NBVAL_SKIP
# Running this example will save a file in the current directory.
res = mdf.search("dft.converged:true AND mdf.resource_type:record", limit=10)
mdf.globus_download(res)
"""
Explana... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_lcmv_beamformer.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import numpy as np
import mne
from mne.datasets import sample
from mne.beamformer import lcmv
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_a... |
opesci/devito | examples/userapi/02_apply.ipynb | mit | from devito import Grid, TimeFunction, Eq, Operator
grid = Grid(shape=(4, 4))
u = TimeFunction(name='u', grid=grid, save=3)
op = Operator(Eq(u.forward, u + 1))
"""
Explanation: Introduction to Operator apply and arguments
This tutorial describes two fundamental user APIs:
Operator.apply
Operator.arguments
We will u... |
sheikhomar/ml | tree-based-methods.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
df = pd.read_csv('data/titanic-train.csv')
df.head()
df['Survival State'] = df['Survived'].apply(lambda x: 'Survived' if x == 1 else 'Died')
df['Survival State'].value_counts()
"""
Explanation: Tree-based... |
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