repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_point_spread.ipynb | bsd-3-clause | import os.path as op
import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, apply_inverse
from mne.simulation import simulate_stc, simulate_evoked
"""
Explanation: Corrupt known signal with point spread
The aim of this tutorial is to demonstrate how to put ... |
ebmdatalab/openprescribing | notebooks/measure-calculations.ipynb | mit | import pandas as pd
import requests
"""
Explanation: Measure calculations
This notebook describes how we perform measure calculations.
A measure is the compuation of a ratio between two values (a numerator and a denominator).
For instance:
The proportion of prescribing (either items or quantity) for a chemical that i... |
mne-tools/mne-tools.github.io | dev/_downloads/b36af73820a7a52a4df3c42b66aef8a5/source_power_spectrum_opm.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
# Luke Bloy <luke.bloy@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
import os.path as op
from mne.filter import next_fast_len
import mne
print(__doc__)
data_path = mne.datasets.opm.data_path()
subject = 'OPM_sam... |
AbnerZheng/Titanic_Kaggle | scikit-learn/03_getting_started_with_iris.ipynb | mit | from IPython.display import HTML
HTML('<iframe src=http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data width=300 height=200></iframe>')
"""
Explanation: Getting started in scikit-learn with the famous iris dataset
From the video series: Introduction to machine learning with scikit-learn
Agenda
Wha... |
rnder/data-science-from-scratch | notebook/ch16_logistic_regression.ipynb | unlicense | from collections import Counter
from functools import partial, reduce
from linear_algebra import dot, vector_add
from gradient_descent import maximize_stochastic, maximize_batch
from working_with_data import rescale
from machine_learning import train_test_split
from multiple_regression import estimate_beta, predict
im... |
ES-DOC/esdoc-jupyterhub | notebooks/bnu/cmip6/models/bnu-esm-1-1/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bnu', 'bnu-esm-1-1', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: BNU
Source ID: BNU-ESM-1-1
Sub-Topics: Radiative Forcings.
Properties: 85 (4... |
KitwareMedical/TubeTK | docs/examples/MergeAdjacentImages/MergeAdjacentImages.ipynb | apache-2.0 | import os
import sys
import numpy
# Path for TubeTK libs
#Values takend from TubeTK launcher
sys.path.append("C:/src/TubeTK_Python_ITK/TubeTK-build/lib/")
sys.path.append("C:/src/TubeTK_Python_ITK/TubeTK-build/lib/Release")
# Setting TubeTK Build Directory
TubeTK_BUILD_DIR=None
if 'TubeTK_BUILD_DIR' in os.environ:... |
dsacademybr/PythonFundamentos | Cap10/Notebooks/DSA-Python-Cap10-Mini-Projeto3.ipynb | gpl-3.0 | # Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
# Instala o TensorFlow
!pip install -q tensorflow==2.5
# Instala o Pydot
!pip install -q pydot
# Imports
import numpy as np
import pandas as pd
import matplotlib.pyplot... |
sdpython/ensae_teaching_cs | _doc/notebooks/2a/seance_5_prog_fonctionnelle.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 2A.i - programmation fonctionnelle
Itérateur, générateur, programmation fonctionnelle, tout pour éviter de charger l'intégralité des données en mémoire et commencer les calculs le plus vite possible.
End of explanation
"""
import pyensa... |
Vincibean/machine-learning-with-tensorflow | advanced-mnist-with-tensorflow.ipynb | apache-2.0 | from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
"""
Explanation: Advanced MNIST with TensorFlow
Abstract
Just like programming has "Hello World", machine learning has MNIST. MNIST is a simple computer vision dataset. It consists of images of hand... |
tensorflow/agents | docs/tutorials/8_networks_tutorial.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... |
TomAugspurger/engarde | examples/Trains.ipynb | mit | import pandas as pd
import engarde.decorators as ed
pd.set_option('display.max_rows', 10)
dtypes = dict(
price1=int,
price2=int,
time1=int,
time2=int,
change1=int,
change2=int,
comfort1=int,
comfort2=int
)
@ed.is_shape((-1, 11))
@ed.has_dtypes(items=dtypes)
def unload():
url = "ht... |
aburgasser/splat | tutorials/spectral_database_query.ipynb | mit | # main splat import
import splat
import splat.database as spdb
# other useful imports
import astropy.units as u
import copy
import numpy as np
import pandas
import matplotlib.pyplot as plt
# make sure this is at least 2021.07.22
splat.VERSION
"""
Explanation: SPLAT Tutorials: Database Query Tools
Authors
Adam Burgas... |
Xero-Hige/Notebooks | Algoritmos I/2018-1C/clase-09-04.ipynb | gpl-3.0 | Image(filename='./clase-09-04_images/i1.jpg')
"""
Explanation: La Magia de la television
Capitulo 1: La television argentina es un template gigante
Parte 0: Repaso general de secuencias
| |Cadenas|Tuplas|Listas|
|:---|:---|:---|:---|
|Acceso por indice|Si|Si|Si|
|Recorrer por indices|Si|Si|Si|
|Recorrer por elemento|S... |
marfeljoergsen/crypto-trading-scripts | testing/single_stock_example.ipynb | mit | import pyfolio as pf
%matplotlib inline
# silence warnings
import warnings
warnings.filterwarnings('ignore')
"""
Explanation: Single stock analysis example in pyfolio
Here's a simple example where we produce a set of plots, called a tear sheet, for a single stock.
Import pyfolio and matplotlib
End of explanation
"""
... |
mnschmit/LMU-Syntax-nat-rlicher-Sprachen | 04-notebook.ipynb | apache-2.0 | grammar1 = """
S -> NP VP
NP -> DET N
DET -> "der" | "die" | "das"
N -> "Mann" | "Frau" | "Buch"
VP -> V NP NP
V -> "gibt" | "schenkt"
"""
"""
Explanation: Übungsblatt 4
Präsenzaufgaben
Aufgabe 1 Eine erste (Phrasenstruktur-)Grammatik
Werfen Sie einen Blick auf die folgende s... |
tensorflow/docs-l10n | site/en-snapshot/lattice/tutorials/custom_estimators.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... |
tensorflow/docs-l10n | site/zh-cn/addons/tutorials/optimizers_conditionalgradient.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is d... |
SamLau95/nbinteract | docs/notebooks/tutorial/tutorial_interact.ipynb | bsd-3-clause | from ipywidgets import interact
"""
Explanation: A Simple Webpage
In this section, you will create and publish a simple interactive webpage!
In Jupyter, create a notebook and name it tutorial.ipynb. Type or paste in the code from this tutorial into the notebook.
Using Interact
The ipywidgets library provides the simpl... |
rishuatgithub/MLPy | torch/PYTORCH_NOTEBOOKS/00-Crash-Course-Topics/01-Crash-Course-Pandas/04-Groupby.ipynb | apache-2.0 | import pandas as pd
# Create dataframe
data = {'Company':['GOOG','GOOG','MSFT','MSFT','FB','FB'],
'Person':['Sam','Charlie','Amy','Vanessa','Carl','Sarah'],
'Sales':[200,120,340,124,243,350]}
df = pd.DataFrame(data)
df
"""
Explanation: <a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_label_from_stc.ipynb | bsd-3-clause | # Author: Luke Bloy <luke.bloy@gmail.com>
# Alex Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.minimum_norm import read_inverse_operator, apply_inverse
from mne.datasets import sample
print(__doc__)
data_pa... |
ellisztamas/faps | docs/tutorials/03_paternity_arrays.ipynb | mit | import faps as fp
import numpy as np
print("Created using FAPS version {}.".format(fp.__version__))
"""
Explanation: Paternity arrays
Tom Ellis, March 2017, updated June 2020
End of explanation
"""
np.random.seed(27) # this ensures you get exactly the same answers as I do.
allele_freqs = np.random.uniform(0.3,0.5, 5... |
google-research/google-research | graph_sampler/molecule_sampling_demo.ipynb | apache-2.0 | # Install graph_sampler
!git clone https://github.com/google-research/google-research.git
!pip install google-research/graph_sampler
from rdkit import Chem
import rdkit.Chem.Draw
from graph_sampler import molecule_sampler
from graph_sampler import stoichiometry
import numpy as np
"""
Explanation: <a href="https://col... |
cliburn/sta-663-2017 | notebook/00_Jupyter.ipynb | mit | %lsmagic
%%file hello.txt
Hello, world
This is thing number 1
This is thing number 2
This is thing number 3
%cat hello.txt
"""
Explanation: Notes on using Jupyter
Keyboard Shortcuts
General
See Help menu for list of keyboard shortcuts
For Windows users, the Ctrl key is the equivalent of the Cmd key
Cmd-Shift-P: Bri... |
UWSEDS/LectureNotes | PreFall2018/07-Exceptions.ipynb | bsd-2-clause | X = [1, 2, 3
a = 4
y = 4*x + 3
def f():
return GARBAGE
"""
Explanation: When Things Go Wrong:
Exceptions and Errors
Today we'll cover perhaps one of the most important aspects of using Python: dealing with errors and bugs in code.
Three Classes of Errors
Types of bugs/errors in code, from the easiest to the most... |
geoscixyz/computation | docs/case-studies/PF/TKC_Mag.ipynb | mit | ## First we need to load all the libraries and set up the path
## for the input files. Same files as used by the online tutorial
%matplotlib notebook
import scipy as sp
import numpy as np
import time as tm
import os
import shutil
import matplotlib.colors as colors
import matplotlib.pyplot as plt
from SimPEG import Mesh... |
raschuetz/foundations-homework | 05/.ipynb_checkpoints/Spotify-API-checkpoint.ipynb | mit | import requests
response = requests.get('https://api.spotify.com/v1/search?query=artist:lil&type=artist&market=us&limit=50')
data = response.json()
artists = data['artists']['items']
for artist in artists:
print(artist['name'], artist['popularity'])
"""
Explanation: 1) With "Lil Wayne" and "Lil Kim" there are ... |
drphilmarshall/SpaceWarps | analysis/make_lens_catalog.ipynb | mit | import pandas as pd
import swap
base_collection_path = '/nfs/slac/g/ki/ki18/cpd/swap/pickles/15.09.02/'
base_directory = '/nfs/slac/g/ki/ki18/cpd/swap_catalog_diagnostics/'
annotated_catalog_path = base_directory + 'annotated_catalog.csv'
cut_empty = True
stages = [1, 2]
categories = ['ID', 'ZooID', 'location', 'mea... |
lwahedi/CurrentPresentation | talks/MDI5/Scraping+Lecture (5).ipynb | mit | import pandas as pd
import numpy as np
import pickle
import statsmodels.api as sm
from sklearn import cluster
import matplotlib.pyplot as plt
%matplotlib inline
from bs4 import BeautifulSoup as bs
import requests
import time
# from ggplot import *
"""
Explanation: Collecting and Using Data in Python
Laila A. Wahedi, P... |
zzsza/Datascience_School | 15. 선형 회귀 분석/01. 회귀 분석용 가상 데이터 생성 방법.ipynb | mit | from sklearn.datasets import make_regression
X, y, c = make_regression(n_samples=10, n_features=1, bias=0, noise=0, coef=True, random_state=0)
print("X\n", X)
print("y\n", y)
print("c\n", c)
plt.scatter(X, y, s=100)
plt.show()
"""
Explanation: 회귀 분석용 가상 데이터 생성 방법
Scikit-learn 의 datasets 서브 패키지에는 회귀 분석 시험용 가상 데이터를 생성하... |
carichte/pyasf | pyasf/examples/GeTe switching.ipynb | gpl-3.0 | %matplotlib notebook
import pylab as pl
import pyasf
import sympy as sp # symbolic computing
from IPython.display import display, Math
print_latex = lambda x: display(Math(sp.latex(x)))
# sp.init_printing()
ls
GeTe = pyasf.unit_cell("ICSD_188458.cif") # initialize crystal structure object
"""
Explanation: Germanium... |
rhiever/scipy_2015_sklearn_tutorial | notebooks/04.2 Model Complexity and GridSearchCV.ipynb | cc0-1.0 | from figures import plot_kneighbors_regularization
plot_kneighbors_regularization()
"""
Explanation: Parameter selection, Validation & Testing
Most models have parameters that influence how complex a model they can learn. Remember using KNeighborsRegressor.
If we change the number of neighbors we consider, we get a sm... |
kabrapratik28/Stanford_courses | cs231n/assignment3/StyleTransfer-TensorFlow.ipynb | apache-2.0 |
%load_ext autoreload
%autoreload 2
from scipy.misc import imread, imresize
import numpy as np
from scipy.misc import imread
import matplotlib.pyplot as plt
# Helper functions to deal with image preprocessing
from cs231n.image_utils import load_image, preprocess_image, deprocess_image
%matplotlib inline
def get_ses... |
jrmontag/Data-Science-45min-Intros | concurrency/enriching_the_enricher.ipynb | unlicense | DT_FORMAT_STR = "%Y-%m-%dT%H:%M:%S.%f"
def stream_of_tweets(n=10):
# generator function to generate sequential tweets
for i in range(n):
time.sleep(0.01)
tweet = {
'body':'I am tweet #' + str(i),
'postedTime':datetime.datetime.now().strftime(DT_FORMAT_STR)
... |
nickkunz/smogn | examples/smogn_example_1_beg.ipynb | gpl-3.0 | ## suppress install output
%%capture
## install pypi release
# !pip install smogn
## install developer version
!pip install git+https://github.com/nickkunz/smogn.git
"""
Explanation: SMOGN (0.1.0): Usage
Example 1: Beginner
Installation
First, we install SMOGN from the Github repository. Alternatively, we could ins... |
tata-antares/tagging_LHCb | Stefania_files/track-based-tagging-OS.ipynb | apache-2.0 | import pandas
import numpy
from folding_group import FoldingGroupClassifier
from rep.data import LabeledDataStorage
from rep.report import ClassificationReport
from rep.report.metrics import RocAuc
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_curve, roc_auc_score
from utils impo... |
sarvex/PythonMachineLearning | Chapter 2/Support Vector Machines.ipynb | isc | from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data / 16., digits.target % 2, random_state=2)
from sklearn.svm import LinearSVC, SVC
linear_svc = LinearSVC(loss="hinge").fit(X_train, y_tra... |
mspieg/principals-appmath | PCA_EOF_example.ipynb | cc0-1.0 | %matplotlib inline
import numpy as np
import scipy.linalg as la
import matplotlib.pyplot as plt
import csv
"""
Explanation: <table>
<tr align=left><td><img align=left src="./images/CC-BY.png">
<td>Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT... |
kdestasio/online_brain_intensive | nipype_tutorial/notebooks/basic_workflow.ipynb | gpl-2.0 | %pylab inline
import nibabel as nb
# Let's create a short helper function to plot 3D NIfTI images
def plot_slice(fname):
# Load the image
img = nb.load(fname)
data = img.get_data()
# Cut in the middle of the brain
cut = int(data.shape[-1]/2) + 10
# Plot the data
imshow(np.rot90(data[...,... |
cwhite1026/Py2PAC | examples/Creating_AngularCatalogs_and_ImageMasks.ipynb | bsd-3-clause | import AngularCatalog_class as ac
import ImageMask_class as imclass
from astropy.io import fits
from astropy.io import ascii
import numpy as np
import numpy.random as rand
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10, 6)
"""
Explanation: Creating AngularCatalogs and ImageMa... |
georgetown-analytics/yelp-classification | data_munging/.ipynb_checkpoints/filter_user_reviews-checkpoint.ipynb | mit | biguser = []
for obj in users.find({'review_count':{'$gt':500}}):
biguser.append(obj['user_id'])
"""
Explanation: The business ID field has already been filtered for only restaurants
We want to filter the users collection for the following:
1. User must have at least 20 reviews
2. For users with 20 review... |
ShubhamDebnath/Coursera-Machine-Learning | Course 5/Emojify v2.ipynb | mit | import numpy as np
from emo_utils import *
import emoji
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Emojify!
Welcome to the second assignment of Week 2. You are going to use word vector representations to build an Emojifier.
Have you ever wanted to make your text messages more expressive? You... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/noresm2-lmec/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-lmec', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: NCC
Source ID: NORESM2-LMEC
Topic: Landice
Sub-Topics: Glaciers, Ice.
Propert... |
aburrell/davitpy | docs/notebook/maps.ipynb | gpl-3.0 | %pylab inline
from davitpy.pydarn.radar import *
from davitpy.pydarn.plotting import *
from davitpy.utils import *
import datetime as dt
"""
Explanation: Mapping utilities and options
This notebook illustrate how to map SuperDARN radars and FoVs
End of explanation
"""
figure(figsize=(15,10))
# Plot map
subplot(121)... |
morganics/bayesianpy | examples/notebook/iris_anomaly_detection.ipynb | apache-2.0 | %matplotlib notebook
import pandas as pd
import sys
sys.path.append("../../../bayesianpy")
import bayesianpy
from bayesianpy.network import Builder as builder
import logging
import os
import matplotlib.pyplot as plt
from IPython.display import display
logger = logging.getLogger()
logger.addHandler(logging.StreamHan... |
mangecoeur/pineapple | data/examples/python3.5/Execution.ipynb | gpl-3.0 | def f(x):
return 1.0 / x
def g(x):
return x - 1.0
f(g(1.0))
"""
Explanation: Executing Code
In this notebook we'll look at some of the issues surrounding executing
code in the notebook.
Backtraces
When you interrupt a computation, or if an exception is raised but not
caught, you will see a backtrace of what ... |
GoogleCloudPlatform/mlops-on-gcp | immersion/kubeflow_pipelines/walkthrough/labs/lab-01_vertex.ipynb | apache-2.0 | !pip freeze | grep google-cloud-aiplatform || pip install google-cloud-aiplatform
import os
import time
from google.cloud import aiplatform
from google.cloud import bigquery
import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import SGDClassifier
from sklearn.pipeline import Pi... |
AEW2015/PYNQ_PR_Overlay | Pynq-Z1/notebooks/examples/usb_wifi.ipynb | bsd-3-clause | # Make sure the base overlay is loaded
from pynq import Overlay
from pynq.drivers import Usb_Wifi
Overlay("base.bit").download()
port = Usb_Wifi()
"""
Explanation: USB Wifi Example
In this notebook, a wifi dongle has been plugged into the board. Specifically a RALink wifi dongle commonly used with Raspberry Pi kits i... |
sdpython/teachpyx | _doc/notebooks/pandas/pandas_groupby.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: Pandas et groupby
Petit tour de passe passe autour d'un groupby et des valeurs manquantes qui ne sont plus prises en compte depuis les dernières versions.
End of explanation
"""
import pandas
data = [{"a":1, "b":2}, {"a":10, "b":20}, {"... |
ZhangXinNan/tensorflow | tensorflow/contrib/eager/python/examples/generative_examples/cvae.ipynb | apache-2.0 | # to generate gifs
!pip install imageio
"""
Explanation: Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License").
Convolutional VAE: An example with tf.keras and eager
<table class="tfo-notebook-buttons" align="left"><td>
<a target="_blank" href="https://colab.research.go... |
lcharleux/numerical_analysis | doc/Optimisation/notebooks/Optimization_practical_work.ipynb | gpl-2.0 | %load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib nbagg
import sys, copy, os
from scipy import optimize
sys.path.append("truss-master")
try:
import truss
print("Truss is correctly installed")
except:
print("Truss is NOT correctly installed !")
"""
Explan... |
yjzhang/uncurl_python | notebooks/Tutorial.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
import uncurl
"""
Explanation: Tutorial
Let's work through an example of single-cell data analysis using Uncurl, using many of its features. For a much briefer example using the same dataset, see examples/zeisel_subset_example.py.
En... |
info-370/classification | knn/INFO370-KNN_Exercise.ipynb | mit | from sklearn.datasets import load_boston
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import scale
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_squared_error
from sklearn.cross_validation import KFold
import matplotlib.pyplot as plt
import numpy ... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/sdk/sdk_automl_image_object_detection_batch.ipynb | apache-2.0 | import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex SDK: AutoML training image object detection model for batch prediction
<table align="lef... |
xpharry/Udacity-DLFoudation | tutorials/gan_mnist/Intro_to_GANs_Solution.ipynb | mit | %matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
kungsik/text_fabric_sample | tutorial_1.ipynb | gpl-3.0 | from tf.fabric import Fabric
"""
Explanation: Text-Fabric Api 활용 예제
본 파일은 Text-Fabric Api를 활용하여 성서 검색 프로그램을 만들기 위한 시범 예제 페이지입니다.
- 라이브러리 불러오기
End of explanation
"""
ETCBC = 'hebrew/etcbc4c'
PHONO = 'hebrew/phono'
TF = Fabric( modules=[ETCBC, PHONO], silent=False )
"""
Explanation: - 데이터베이스 파일 로드 etcbc4c는 히브리어 텍스트, ... |
cgpotts/cs224u | feature_attribution.ipynb | apache-2.0 | __author__ = "Christopher Potts"
__version__ = "CS224u, Stanford, Spring 2022"
"""
Explanation: Feature attribution
End of explanation
"""
!pip install captum
"""
Explanation: Contents
Overview
InputXGradients
Selectivity examples
Simple feed-forward classifier example
Bag-of-words classifier for the SST
BERT exam... |
bio-guoda/guoda-examples | ids_in_bhl/ids_demo.ipynb | mit | idigbio_full = sqlContext.read.parquet("../2016spr/data/idigbio/occurrence.txt.parquet")
bhl_full = sqlContext.read.parquet("../guoda-datasets/BHL/data/bhl-20160516.parquet")
# This replaces the datasets with ones that are a small subset
#bhl = bhl_full.sample(fraction=0.01, withReplacement=False)
#idigbio = idigbio_f... |
woobe/odsc_h2o_machine_learning | py_04a_classification_basics.ipynb | apache-2.0 | # Start and connect to a local H2O cluster
import h2o
h2o.init(nthreads = -1)
"""
Explanation: Machine Learning with H2O - Tutorial 4a: Classification Models (Basics)
<hr>
Objective:
This tutorial explains how to build classification models with four different H2O algorithms.
<hr>
Titanic Dataset:
Source: https:/... |
intellimath/pyaxon | examples/axon_with_python.ipynb | mit | from __future__ import print_function
from axon import loads, dumps
from pprint import pprint
"""
Explanation: AXON is eXtended Object Notation. It's a simple notation of objects,
documents and data. It's also a text based serialization format in first place.
It tries to combine the best of JSON, XML and YAML.
pyaxo... |
quantopian/research_public | notebooks/data/quandl.cboe_vxv/notebook.ipynb | apache-2.0 | # For use in Quantopian Research, exploring interactively
from quantopian.interactive.data.quandl import cboe_vxv as dataset
# import data operations
from odo import odo
# import other libraries we will use
import pandas as pd
# Let's use blaze to understand the data a bit using Blaze dshape()
dataset.dshape
# And h... |
sdpython/ensae_teaching_cs | _doc/notebooks/sklearn_ensae_course/06_unsupervised_dimreduction.ipynb | mit | from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
"""
Explanation: 2A.ML101.6: Unsupervised Learning: Dimensionality Reduction and Visualization
Unsupervised learning is interested in situations in which X is available, but not y: data without labels. A typical use case is to find... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch6-Problem_6-02.ipynb | unlicense | %pylab notebook
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 6
Problem 6-2
End of explanation
"""
fse = 60.0 # [Hz]
p = 2.0
s = 0.025
"""
Explanation: Description
Answer the questions in Problem 6-1 for a 480-V three-phase two-pole 60-Hz induction motor running at a sli... |
flmath-dirty/matrixes_in_erlang | jupyter/results.ipynb | mit | LoadedTable.head()
"""
Explanation: Introduction
Test scenarios descriptions
The LoadedTable contains statistics gathered from running the script that generates a matrix (Width x Height) and then
runs with the fprof following tests for each representation of matrix and sizes:
The tests categories:
one_rows_sums - s... |
DallasTrinkle/Onsager | examples/Garnet.ipynb | mit | import sys
sys.path.extend(['../'])
import numpy as np
import onsager.crystal as crystal
import onsager.OnsagerCalc as onsager
"""
Explanation: Garnet correlation coefficients
Comparing to correlation coefficients from William D. Carlson and Clark R. Wilson, Phys Chem Minerals 43, 363-369 (2016)
doi:10.1007/s00269-016... |
cathalmccabe/PYNQ | pynq/notebooks/common/programming_pybind11.ipynb | bsd-3-clause | from pynq.lib import pybind11
"""
Explanation: Programming C/C++ using Pybind11
In this notebook we will show how to leverage Pybind11
to develop normal C/C++ program in Jupyter environment. This is a unique feature added by the
pynq package.
Compared to the SWIG binding, Pybind11
supports C++ program; therefore it... |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/ec-earth3-veg/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'ec-earth3-veg', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: EC-EARTH3-VEG
Topic: Atmos
Sub-Top... |
alexandrnikitin/algorithm-sandbox | courses/DAT256x/Module01/01-06-Factorization.ipynb | mit | from random import randint
x = randint(1,100)
y = randint(1,100)
(2*x*y**2)*(-3*x*y) == -6*x**2*y**3
"""
Explanation: Factorization
Factorization is the process of restating an expression as the product of two expressions (in other words, expressions multiplied together).
For example, you can make the value 16 by per... |
tensorflow/docs-l10n | site/en-snapshot/agents/tutorials/1_dqn_tutorial.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... |
shawger/uc-dand | P3/Wrangle OpenStreetMap Data.ipynb | gpl-3.0 | #Creates and uses sample file if True
USE_SAMPLE = False
k = 10
inputFile = "calgary_canada.osm"
sampleFile = "calgary_canada_sample.osm"
if USE_SAMPLE:
import createTestFile
createTestFile.createTestFile(inputFile,sampleFile,k)
print '%s created from %s for testing.' % (sampleFile,inputFil... |
DigitalSlideArchive/HistomicsTK | docs/examples/polygon_merger_from_tiled_masks.ipynb | apache-2.0 | import os
CWD = os.getcwd()
import os
import girder_client
from pandas import read_csv
from histomicstk.annotations_and_masks.polygon_merger import Polygon_merger
from histomicstk.annotations_and_masks.masks_to_annotations_handler import (
get_annotation_documents_from_contours, )
"""
Explanation: Merging annotat... |
GoogleCloudPlatform/ai-notebooks-extended | dataproc-hub-example/build/infrastructure-builder/mig/files/gcs_working_folder/examples/Python/bigquery/Visualizing BigQuery public data.ipynb | apache-2.0 | %%bigquery
SELECT
source_year AS year,
COUNT(is_male) AS birth_count
FROM `bigquery-public-data.samples.natality`
GROUP BY year
ORDER BY year DESC
LIMIT 15
"""
Explanation: Vizualizing BigQuery data in a Jupyter notebook
BigQuery is a petabyte-scale analytics data warehouse that you can use to run SQL queries ... |
Xilinx/PYNQ | boards/Pynq-Z1/base/notebooks/pmod/pmod_dac_adc.ipynb | bsd-3-clause | from pynq.overlays.base import BaseOverlay
from pynq.lib import Pmod_ADC, Pmod_DAC
"""
Explanation: DAC-ADC Pmod Examples using Matplotlib and Widget
Contents
Pmod DAC-ADC Feedback
Tracking the IO Error
Error plot with Matplotlib
Widget controlled plot
Pmod DAC-ADC Feedback
This example shows how to use the PmodDA4 ... |
ProfessorKazarinoff/staticsite | content/code/sympy/sympy_solving_equations-polymer-density-problem.ipynb | gpl-3.0 | from sympy import symbols, nonlinsolve
"""
Explanation: Sympy is a Python package used for solving equations using symbolic math.
Let's solve the following problem with SymPy.
Given:
The density of two different polymer samples $\rho_1$ and $\rho_2$ are measured.
$$ \rho_1 = 0.904 \ g/cm^3 $$
$$ \rho_2 = 0.895 \ g/... |
sidaw/mompy | polynomial_optimization.ipynb | mit | # we are dependent on numpy, sympy and cvxopt.
import numpy as np
import cvxopt
import mompy as mp
# just some basic settings and setup
mp.cvxsolvers.options['show_progress'] = False
from IPython.display import display, Markdown, Math, display_markdown
sp.init_printing()
def print_problem(obj, constraints = None, mom... |
jarvis-fga/Projetos | Problema 2/alexandre/Second Project ML.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: 1. Descrição do Problema
A empresa Amazon deseja obter um sistema inteligente para processar os comentários de seus clientes sobre os seus produtos, podendo classificar tais comentários dentre as categorias: positivo ou negativo. P... |
waltervh/BornAgain-tutorial | old/python/tutorial.ipynb | gpl-3.0 | from __future__ import print_function
"""
Explanation: Introduction to Python
Useful links
BornAgain: http://bornagainproject.org
BornAgain tutorial: https://github.com/scgmlz/BornAgain-tutorial
Python official tutorial: https://docs.python.org/3/tutorial/
Anaconda Python: https://www.continuum.io/
PyCharm IDE: https... |
roebius/deeplearning_keras2 | nbs2/babi-memnn.ipynb | apache-2.0 | %matplotlib inline
import importlib, utils2; importlib.reload(utils2)
from utils2 import *
np.set_printoptions(4)
cfg = K.tf.ConfigProto(gpu_options={'allow_growth': True})
K.set_session(K.tf.Session(config=cfg))
"""
Explanation: Babi End to End MemNN
End of explanation
"""
def tokenize(sent):
return [x.strip()... |
tarunchhabra26/fss16dst | code/8/WS4/dndesai_performance.ipynb | apache-2.0 | %matplotlib inline
# All the imports
from __future__ import print_function, division
import pom3_ga, sys
import pickle
# TODO 1: Enter your unity ID here
__author__ = "dndesai"
"""
Explanation: Workshop 4 - Performance Metrics
In this workshop we study 2 performance metrics(Spread and Inter-Generational Distance) on... |
Kaggle/learntools | notebooks/machine_learning/raw/ex6.ipynb | apache-2.0 | # Code you have previously used to load data
import pandas as pd
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
# Path of the file to read
iowa_file_path = '../input/home-data-for-ml-course/train.csv'
home_data = pd.... |
tensorflow/docs-l10n | site/ko/federated/tutorials/building_your_own_federated_learning_algorithm.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... |
gldmt-duke/CokerAmitaiSGHMC | logistic_regression/logistic_regression_simulated.ipynb | mit | def logistic(x):
'''
'''
return 1/(1+np.exp(-x))
def U_logistic(theta, Y, X, phi):
'''
'''
return - (Y.T @ X @ theta - np.sum(np.log(1+np.exp(X @ theta))) - 0.5 * phi * np.sum(theta**2))
def gradU_logistic(theta, Y, X, phi):
'''
'''
n = X.shape[0]
Y_pred = logistic(X @ the... |
epam/DLab | integration-tests/examples/test_templates/deeplearning/template_caffe2.ipynb | apache-2.0 | # We'll also import a few standard python libraries
from matplotlib import pyplot
import numpy as np
import time
# These are the droids you are looking for.
from caffe2.python import core, workspace
from caffe2.proto import caffe2_pb2
# Let's show all plots inline.
%matplotlib inline
"""
Explanation: Caffe2 Basic Co... |
Abjad/intensive | day-3/4-command-classes.ipynb | mit | class IndicatorCommand:
"""
Indicator command.
"""
def __init__(self, indicator, selector):
self.indicator = indicator
self.selector = selector
def __call__(self, music):
for selection in self.selector(music):
indicator = copy.copy(self.indicator)
ab... |
edwardd1/phys202-2015-work | assignments/assignment05/InteractEx01.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 01
Import
End of explanation
"""
def print_sum(a, b):
"""Print the sum of the arguments a and b."""
... |
Adamage/python-training | Lesson_01_variables_and_data_types.ipynb | apache-2.0 | my_name = 'Adam'
print my_name
my_age = 92
your_age = 23
age_difference = my_age - your_age
print age_difference
"""
Explanation: Python Training - Lesson 1 - Variables and Data Types
Variables
A variable refers to a certain value with specific type. For example, we may want to store a number, a fraction, or a name, ... |
tensorflow/docs-l10n | site/ko/tutorials/images/data_augmentation.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... |
h-mayorquin/time_series_basic | presentations/2015-11-10(Letter Frequency).ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
from nltk.book import text7 as text
import nltk
import string
print('Number of words', len(text))
text[0:10]
"""
Explanation: Studying Frequency on Corpus
This is just to check what type of letters come in a determinate corpus and study the frequency distribution. T... |
kubeflow/kfserving-lts | docs/samples/v1alpha2/custom/kfserving-custom-model/kfserving_sdk_custom_image.ipynb | apache-2.0 | # Set this to be your dockerhub username
# It will be used when building your image and when creating the InferenceService for your image
DOCKER_HUB_USERNAME = "your_docker_username"
%%bash -s "$DOCKER_HUB_USERNAME"
docker build -t $1/kfserving-custom-model ./model-server
%%bash -s "$DOCKER_HUB_USERNAME"
docker push ... |
mne-tools/mne-tools.github.io | stable/_downloads/0602f866d20bff8af56294e10dd6854d/10_preprocessing_overview.ipynb | bsd-3-clause | import os
import numpy as np
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
raw.crop(0, 60).load_data() # just use a fr... |
samuelsinayoko/kaggle-housing-prices | research/long_form_featureplots_seaborn.ipynb | mit | import string
import pandas as pd
import numpy as np
import seaborn as sns
"""
Explanation: Distributions of multiple numerical features with Seaborn
When given a set of numerical features, it is desirable to plot all of them using for example violinplots, to get a sense of their respective distributions. Seaborn can... |
andersrmr/JupyterWorkflow | UnsupervisedAnalysis.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn')
import pandas as pd
import numpy as np
from sklearn.decomposition import PCA
from sklearn.mixture import GaussianMixture
"""
Explanation: Unsupervised Analysis of Days of Week
Treating crossings each day of features to learn about the relati... |
tensorflow/docs-l10n | site/ja/tfx/tutorials/data_validation/tfdv_basic.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... |
zhenxinlei/SpringSecurity1 | ex1/ex1.ipynb | epl-1.0 | import csv
import pandas as pd
import numpy as np
from numpy import genfromtxt
data = pd.read_csv('./ex1data1.csv', delimiter=',',
names=['population','profit'])
data.head()
%matplotlib inline
'''
import matplotlib.pyplot as plt
x= data['population']
y= data['profit']
plt.plot(x,y,'rx')
plt.ylabe... |
karlstroetmann/Algorithms | Python/Chapter-06/Stack.ipynb | gpl-2.0 | class Stack:
pass
S = Stack()
S
"""
Explanation: Implementing a Stack Class
First, we define an empty class Stack.
End of explanation
"""
def stack(S):
S.mStackElements = []
"""
Explanation: Next we define a constructor for this class. The function stack(S) takes an uninitialized, empty object S
and init... |
natashabatalha/PandExo | notebooks/JWST_Running_Pandexo_w_ExoMAST.ipynb | gpl-3.0 | import warnings
warnings.filterwarnings('ignore')
import pandexo.engine.justdoit as jdi
import numpy as np
import os
"""
Explanation: Getting Started
Before starting here, all the instructions on the installation page should be completed!
Here you will learn how to:
set planet and star properties using exomast
run... |
rusucosmin/courses | ml/ex01/solution/taskB.ipynb | mit | np.random.seed(10)
p, q = (np.random.rand(i, 2) for i in (4, 5))
p_big, q_big = (np.random.rand(i, 80) for i in (100, 120))
print(p, "\n\n", q)
"""
Explanation: Data Generation
End of explanation
"""
def naive(p, q):
result = np.zeros((p.shape[0], q.shape[0]))
for i in range(p.shape[0]):
for j in ra... |
cni/psych204a | mrImaging.ipynb | gpl-2.0 | %pylab inline
import matplotlib as mpl
mpl.rcParams["figure.figsize"] = (8, 6)
mpl.rcParams["axes.grid"] = True
from IPython.display import display, clear_output
from time import sleep
"""
Explanation: MR Imaging
Class: Psych 204a
Tutorial: MR Imaging
Author: Wandell
Date: 03.15.04
Duration: 90 minutes
C... |
brian-rose/env-415-site | notes/Introducing_CESM.ipynb | mit | from IPython.display import YouTubeVideo
YouTubeVideo('As85L34fKYQ')
"""
Explanation: ENV / ATM 415: Climate Laboratory
Introducing the Community Earth System Model
About the model
We are using a version of the Community Earth System Model (CESM) which is developed and maintained at the National Center for Atmospheri... |
choyichen/omics | examples/biomaRt.ipynb | mit | import pandas as pd
%load_ext rpy2.ipython
%%R
library(biomaRt)
%load_ext version_information
%version_information pandas, rpy2
"""
Explanation: Access Ensembl BioMart using biomart module
We use rpy2 and R magics in IPython Notebook to utilize the powerful biomaRt package in R.
Usage:
Run Setup
Select a mart & dat... |
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