repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
dcavar/python-tutorial-for-ipython | notebooks/Document Classification Tutorial.ipynb | apache-2.0 | data = open('data/corpus').read()
labels, texts = [], []
for line in data.split("\n"):
content = line.split(' ', 1)
labels.append(content[0])
texts.append(content[1])
print(texts[:3])
"""
Explanation: Document Classification Tutorial 1
(C) 2019 by Damir Cavar
Amazon Reviews
See for more details the sourc... |
deculler/DataScienceTableDemos | Census.ipynb | bsd-2-clause | # HIDDEN
from datascience import *
%matplotlib inline
import matplotlib.pyplot as plots
plots.style.use('fivethirtyeight')
"""
Explanation: Wrangling data in Tables
David Culler
This notebook illustrates several of the datascience tables methods for wrangling typical data.
Here we use some simple census data. Even th... |
aKumpan/hse-shad-ml | 03-svm_and_logistic_regression/statement-svm/statement-svm.ipynb | apache-2.0 | df_train = pd.read_csv('../data/svm-data.csv', header=None)
X_train = df_train[df_train.columns[1:3]]
y_train = df_train[df_train.columns[0]]
"""
Explanation: 1. Загрузите выборку из файла svm-data.csv. В нем записана двумерная выборка (целевая переменная указана в первом столбце, признаки — во втором и третьем).
End... |
planet-os/notebooks | api-examples/PlanetOS_WAve_Models.ipynb | mit | %matplotlib notebook
import urllib.request
import numpy as np
import simplejson as json
import pandas as pd
from netCDF4 import Dataset, date2num, num2date
import ipywidgets as widgets
from IPython.display import display, clear_output
import dateutil.parser
import matplotlib.pyplot as plt
from mpl_toolkits.basemap impo... |
eggie5/UCSD-MAS-DSE230 | hmwk3/1.PCA_analysis-HW-Copy.ipynb | mit | %pylab inline
data_dir = "./Data/Weather/"
"""
Explanation: Homework 3
You will have to submit the following two completed ipython notebooks for this homework.
PCA_analysis
Reconstruction
End of explanation
"""
!curl -o $data_dir/STAT.pickle http://mas-dse-open.s3.amazonaws.com/Weather/STAT.pickle
"""
Explanation:... |
pjpmarques/Modelling-the-World | Archimedes and Pi.ipynb | mit | from math import sqrt, pi
def side_next(side):
return sqrt(2. - sqrt(4. - side**2.0))
"""
Explanation: Archimedes and Pi
by Paulo Marques, 2014/03/09 (Adapted in 2018/10/15 to Python from Julia)
Since high school I've been fascinated with $\pi$ -- this infinite non-repeating irrational transcendent number. In fa... |
mne-tools/mne-tools.github.io | dev/_downloads/cc2f4b498fc65366ac39d017e939eec5/xdawn_denoising.ipynb | bsd-3-clause | # Authors: Alexandre Barachant <alexandre.barachant@gmail.com>
#
# License: BSD-3-Clause
from mne import (io, compute_raw_covariance, read_events, pick_types, Epochs)
from mne.datasets import sample
from mne.preprocessing import Xdawn
from mne.viz import plot_epochs_image
print(__doc__)
data_path = sample.data_path(... |
amirziai/learning | machine-learning/decision-trees.ipynb | mit | import numpy as np
"""
Explanation: Decision Trees
End of explanation
"""
def entropy(x):
_, counts = np.unique(x, return_counts=True)
p = counts / np.sum(counts)
return -np.sum(p * np.log2(p))
def entropy_binary(x):
trues = np.sum(x)
percentage_true = trues / len(x)
if percentage_true == 1:... |
JAmarel/Phys202 | Numpy/NumpyEx01.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import antipackage
import github.ellisonbg.misc.vizarray as va
"""
Explanation: Numpy Exercise 1
Imports
End of explanation
"""
def checkerboard(size):
"""Return a 2d checkboard of 0.0 and 1.0 as a NumPy array"""
arr... |
planetlabs/notebooks | jupyter-notebooks/udm2/udm2_clouds_aoi.ipynb | apache-2.0 | import datetime
import json
import os
import time
from planet import api
from planet.api import filters
import rasterio
from rasterio.plot import show
"""
Explanation: Usable Data Map (UDM2) Cloud Detection within an AOI
This guide is a follow up to the UDM2 Cloud Detection notebook. Please refer to that notebook for... |
astroumd/GradMap | notebooks/Lectures2021/Lecture1/L1_challenge_problem_stars_instructor.ipynb | gpl-3.0 | # These are your stellar temperatures, you're welcome!
temp = [5809, 16589, 4698, 1869, 37809, 8634]
"""
Explanation: Stellar Classification
Background
The
Harvard Spectral Classification system for stars
classifies stars based on their spectral type - where the type of a star is designated as a letter that correspon... |
moble/PostNewtonian | PNTerms/Variables_Q.ipynb | mit | # Make sure division of integers does not round to the nearest integer
from __future__ import division
import sys
sys.path.insert(0, '..') # Look for modules in directory above this one
# Make everything in python's symbolic math package available
from sympy import * # Make sure sympy functions are used in preference... |
azhurb/deep-learning | sentiment-rnn/Anna_KaRNNa.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is base... |
qinwf-nuan/keras-js | notebooks/layers/convolutional/UpSampling2D.ipynb | mit | data_in_shape = (3, 3, 3)
L = UpSampling2D(size=(2, 2), 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(250)
data_in = 2 * np.random.random(data_in_shape) - 1
res... |
corochann/chainer-hands-on-tutorial | src/02_mnist_mlp/mnist_dataset_introduction.ipynb | mit | import numpy as np
import chainer
# Load the MNIST dataset from pre-inn chainer method
train, test = chainer.datasets.get_mnist()
"""
Explanation: MNIST dataset introduction
MNIST dataset
MNIST (Mixed National Institute of Standards and Technology) database is dataset for handwritten digits, distributed by Yann Lecun... |
softEcon/course | lectures/basics/cloud_computing/lecture.ipynb | mit | # standard library
import os
import time
import shutil
# Load Python SDK
from azure import *
from azure.servicemanagement import *
from azure.storage import *
# Subscription details
subscription_id = '1a61650c-ada5-4173-a8da-2a4ffcfab747'
certificate_path = 'mycert.pem'
# Initialize connection
sms = ServiceManageme... |
ES-DOC/esdoc-jupyterhub | notebooks/ncar/cmip6/models/sandbox-2/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncar', 'sandbox-2', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: NCAR
Source ID: SANDBOX-2
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balan... |
SJSlavin/phys202-2015-work | assignments/assignment09/IntegrationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy import integrate
"""
Explanation: Integration Exercise 1
Imports
End of explanation
"""
def trapz(f, a, b, N):
"""Integrate the function f(x) over the range [a,b] with N points."""
h = (b-a)/N
xvals = np.linspace(a, b, N+1)
... |
astroumd/GradMap | notebooks/Haiti2016/spectrum.ipynb | gpl-3.0 | %matplotlib inline
# python 2-3 compatibility
from __future__ import print_function
"""
Explanation: Spectra in (optical) Astronomy
Here we introduce a simple spectrum, the example taken from the optical, where it all started.
To quote Roger Wesson, the author of this particular spectrum:
The sample spectrum was extr... |
kubeflow/pipelines | samples/contrib/mnist/03_Reusable_Components.ipynb | apache-2.0 | import kfp
import kfp.gcp as gcp
import kfp.dsl as dsl
import kfp.compiler as compiler
import kfp.components as comp
import datetime
import kubernetes as k8s
# Required Parameters
PROJECT_ID='<ADD GCP PROJECT HERE>'
GCS_BUCKET='gs://<ADD STORAGE LOCATION HERE>'
"""
Explanation: Reusable components
This tutorial desc... |
pmorissette/bt | examples/PTE.ipynb | mit | names = ['foo','bar','rf']
dates = pd.date_range(start='2015-01-01',end='2018-12-31', freq=pd.tseries.offsets.BDay())
n = len(dates)
rdf = pd.DataFrame(
np.zeros((n, len(names))),
index = dates,
columns = names
)
np.random.seed(1)
rdf['foo'] = np.random.normal(loc = 0.1/252,scale=0.2/np.sqrt(252),size=n)
r... |
DistrictDataLabs/yellowbrick | examples/zjpoh/stacked_feature_importance.ipynb | apache-2.0 | import os
import sys
sys.path.insert(0, "../..")
import importlib
import numpy as np
import pandas as pd
import yellowbrick
import yellowbrick as yb
from yellowbrick.features.importances import FeatureImportances
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn import manifold, datasets
from skle... |
marc-moreaux/Deep-Learning-classes | notebooks/Intro_to_SNN.ipynb | mit | # Download the dataset in this directory (does that work on Windows OS ?)
! wget http://deeplearning.net/data/mnist/mnist.pkl.gz
import cPickle, gzip, numpy
import numpy as np
# Load the dataset
f = gzip.open('mnist.pkl.gz', 'rb')
train_set, valid_set, test_set = cPickle.load(f)
f.close()
def to_one_hot(y, n_classes... |
hktxt/MachineLearning | PageRank.ipynb | gpl-3.0 | # Before we begin, let's load the libraries.
%pylab notebook
import numpy as np
import numpy.linalg as la
from readonly.PageRankFunctions import *
np.set_printoptions(suppress=True)
"""
Explanation: PageRank
In this notebook, you'll build on your knowledge of eigenvectors and eigenvalues by exploring the PageRank algo... |
subutai/htmresearch | projects/grid_cell_learning/1D CAN Learning.ipynb | agpl-3.0 | # Imports and notebook statements
%load_ext autoreload
%autoreload 2
%matplotlib notebook
from htmresearch.frameworks.grid_cell_learning.CAN import CAN1DNetwork
import numpy as np
from scipy import stats
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRegr... |
AllenDowney/ThinkBayes2 | notebooks/chap12.ipynb | mit | # If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install empiricaldist
# Get utils.py
from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename... |
plipp/informatica-pfr-2017 | nbs/2/4-ADVANCED-Primer-Idiomatic Pandas.ipynb | mit | import pandas as pd
df = pd.read_csv('../../data/census.csv')
df[:5]
"""
Explanation: Primer: Idiomatic Pandas: Making Code Pandorable
Original Source: Coursera Introduction to Data Science in Python: Week 3
Good Follow up/Exercises can be found in 5 Tips To Write Idiomatic Pandas Code
End of explanation
"""
# good ... |
menome/neo4j-data-integration-meetup | pp-viz-master/jupyter/PP_Viz.ipynb | mit | # !pip install neo4j-driver
# !pip install pandas
# !pip install bokeh
from neo4j.v1 import GraphDatabase
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
plt.figure(dpi=300)
"""
Explanation: Visualing The Paradise Papers With Python And Neo4j
Connect to Neo4j from Python
Create some Pandas Da... |
hhain/sdap17 | notebooks/.ipynb_checkpoints/02_Classification-checkpoint.ipynb | mit | # imports
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
import time
import matplotlib.pyplot as plt
import seaborn as sns
"""
Explanation: Aufgabe 2: Classification
A short test to examine the performance gain when using multiple cores on sklearn's esemble classif... |
gururajl/deep-learning | sentiment-rnn/Sentiment_RNN.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 analy... |
google/applied-machine-learning-intensive | content/06_other_models/07_xgboost/colab.ipynb | apache-2.0 | # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the L... |
google/starthinker | colabs/email_cm_to_bigquery.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: CM360 Report Emailed To BigQuery
Pulls a CM Report from a gMail powered email account into BigQuery.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance... |
crocha700/qg_vertical_modes | eady_problem/wave_structure.ipynb | mit | from __future__ import division
import numpy as np
from numpy import pi, sqrt,cos
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 25, 'legend.handlelength' : 1.25})
%matplotlib inline
import seaborn as sns
#sns.set(style="darkgrid")
sns.set_context("paper", font_scale=5, rc={"lines.linewidth": 1.5... |
sgrid/pysgrid | notebooks/SGrid_velocity_interpolation_demo.ipynb | bsd-3-clause | from netCDF4 import Dataset
import numpy as np
import pysgrid
url = ('http://geoport-dev.whoi.edu/thredds/dodsC/clay/usgs/users/zdefne/run076/his/00_dir_roms_display.ncml')
nc = Dataset(url)
sgrid = pysgrid.load_grid(nc)
sgrid # We need a better __repr__ and __str__ !!!
"""
Explanation: Scenario: Velocity heat/vect... |
js1972/CarND-Behavioral-Cloning | data_processing.ipynb | mit | from keras.preprocessing.image import img_to_array, load_img
plt.rcParams['figure.figsize'] = (12, 6)
i = 0
for camera in ["left", "center", "right"]:
image = load_img("data/"+data_frame.iloc[1090][camera].strip())
image = img_to_array(image).astype(np.uint8)
plt.subplot(1, 3, i+1)
plt.imshow(image)
... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a_eco/TD2A_eco_API_SNCF_corrige.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 2A.eco - Exercice API SNCF corrigé
Manipulation d'une API REST, celle de la SNCF est prise comme exemple. Correction d'exercices.
End of explanation
"""
# !!!!! Attention à bien mettre votre token ici !!!!!
token_auth = 'XXXXXXXXXXXXX... |
tensorflow/docs-l10n | site/en-snapshot/hub/tutorials/wav2vec2_saved_model_finetuning.ipynb | apache-2.0 | #@title Copyright 2021 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 ... |
jinzishuai/learn2deeplearn | deeplearning.ai/C1.NN_DL/week2/Python+Basics+With+Numpy+v3.ipynb | gpl-3.0 | ### START CODE HERE ### (≈ 1 line of code)
test = "Hello World"
### END CODE HERE ###
print ("test: " + test)
"""
Explanation: Python Basics with Numpy (optional assignment)
Welcome to your first assignment. This exercise gives you a brief introduction to Python. Even if you've used Python before, this will help fami... |
WormLabCaltech/mprsq | src/2 Predicting Interactions.ipynb | mit | # important stuff:
import os
import pandas as pd
import numpy as np
import genpy
import gvars
import morgan as morgan
# stats
import sklearn.decomposition
import statsmodels.api as stm
# network graphics
import networkx as nx
# Graphics
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
... |
stefanbuenten/nanodegree | p3/P3_Analysis_and_Report.ipynb | mit | # file size of original OSM file: 376 MB
!ls -lh cologne_germany.osm
# !/usr/bin/env python
# -*- coding: utf-8 -*-
#
#import xml.etree.ElementTree as ET # Use cElementTree or lxml if too slow
#
#OSM_FILE = "cologne_germany.osm" # Replace this with your osm file
#SAMPLE_FILE = "cologne_germany_sample.osm"
#
#k = 10 ... |
hankcs/HanLP | plugins/hanlp_demo/hanlp_demo/zh/ner_restful.ipynb | apache-2.0 | pip install hanlp_restful -U
"""
Explanation: <h2 align="center">点击下列图标在线运行HanLP</h2>
<div align="center">
<a href="https://colab.research.google.com/github/hankcs/HanLP/blob/doc-zh/plugins/hanlp_demo/hanlp_demo/zh/ner_restful.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.sv... |
james-prior/cohpy | 20180129-cohpy-or-operator.ipynb | mit | # meld is a great visual difference program
# http://meldmerge.org/
# the following command relies on the directory structure on my computer
# tdd-demo comes from https://github.com/james-prior/tdd-demo/
!cd ~/projects/tdd-demo;git difftool -t meld -y 389df2a^ 389df2a
"""
Explanation: "or" operator
This presentation... |
ccwang002/2015Talk-DeepLearn-CNN | code/01_softmax.ipynb | mit | import random
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.data_utils import load_CIFAR10
from cs231n.gradient_check import grad_check_sparse
# plotting setting
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation']... |
JamesSample/icpw | correct_toc_elev.ipynb | mit | # Create db connection
r2_func_path = r'C:\Data\James_Work\Staff\Heleen_d_W\ICP_Waters\Upload_Template\useful_resa2_code.py'
resa2 = imp.load_source('useful_resa2_code', r2_func_path)
engine, conn = resa2.connect_to_resa2()
# Get example data
sql = ("SELECT * FROM resa2.water_chemistry_values2 "
"WHERE sample_... |
mne-tools/mne-tools.github.io | 0.23/_downloads/511c644e95ae48be8cf394cec24a1865/80_brainstorm_phantom_elekta.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import find_events, fit_dipole
from mne.datasets.brainstorm import bst_phantom_elekta
from mne.io import read_raw_fif
print(__doc__)
"""
Explanatio... |
srcole/qwm | burrito/Burrito_Coordinates.ipynb | mit | %config InlineBackend.figure_format = 'retina'
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sns.set_style("white")
"""
Explanation: San Diego Burrito Analytics: Coordinates
Determine the longitude and latitude for each restaurant b... |
mne-tools/mne-tools.github.io | 0.20/_downloads/12e915738bb5b40ffcb1157f1c2dee72/plot_receptive_field.ipynb | bsd-3-clause | # Authors: Chris Holdgraf <choldgraf@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.decoding import ReceptiveField, TimeDelayingRidge
from scipy.stats import multivariate_normal
from scipy.io import loadm... |
ltiao/project-euler | problem-3-largest-prime-factor.ipynb | unlicense | from six.moves import filter, map, range, reduce
factors = lambda n: list(filter(lambda x: not n % x, range(1, n+1)))
factors(24)
"""
Explanation: The prime factors of 13195 are 5, 7, 13 and 29.
What is the largest prime factor of the number 600851475143 ?
Version 1 - Inefficient Trial Division
First a function to g... |
ml4a/ml4a-guides | examples/reinforcement_learning/q_learning.ipynb | gpl-2.0 | class Environment():
def __init__(self, grid):
self.grid = grid
self.n_rows = len(grid)
self.n_cols = len(grid[0])
self.positions = self._positions()
self.starting_positions = [p for p in self.positions
if not self.is_terminal_state(p)]
... |
GoogleCloudPlatform/practical-ml-vision-book | 09_deploying/09c_changesig.ipynb | apache-2.0 | import tensorflow as tf
print('TensorFlow version' + tf.version.VERSION)
print('Built with GPU support? ' + ('Yes!' if tf.test.is_built_with_cuda() else 'Noooo!'))
print('There are {} GPUs'.format(len(tf.config.experimental.list_physical_devices("GPU"))))
device_name = tf.test.gpu_device_name()
if device_name != '/devi... |
AllenDowney/ModSimPy | notebooks/variables_r_strange.ipynb | mit | from modsim import System
# If this doesn't work, move this file into your /code folder.
# It needs to be in the same folder as modsim.py.
"""
Explanation: Hey friends.
Variables r weird. Shift+Enter if you agree.
by Jane Sieving
End of explanation
"""
def func1(input1, input2):
print("Input 1 = ", input1)
p... |
JanetMatsen/meta4_bins_janalysis | ipython_notebooks/160226_reads_mapped_to_genome_bins.ipynb | bsd-2-clause | mpl.rcParams.update({
'font.size': 16, 'axes.titlesize': 17, 'axes.labelsize': 15,
'xtick.labelsize': 10, 'ytick.labelsize': 13,
#'font.family': 'Lato',
'font.weight': 600,
'axes.labelweight': 600, 'axes.titleweight': 600
#'figure.autolayout': True
})
regen_plots = False
if regen_plots:
... |
DSSatPitt/katz-python-workshop | intro-to-apis/index.ipynb | cc0-1.0 | import requests
"""
Explanation: Introduction to APIs
This tutorial introduces Application Programming Interfaces (APIs) using the Yelp API as an example.
Key Terminology
API: An Application Programming Interface is the mechanism by which computers can talk to other computers and share information.
REST API: This is... |
sot/image_reconstruction | asol_upper_limit_trend.ipynb | bsd-2-clause | from mica.vv import get_rms_data
from astropy.table import Table
import matplotlib.pyplot as plt
from Ska.Matplotlib import plot_cxctime
from astropy.time import Time
%matplotlib inline
# Get the mica table of V&V data
dat = Table(get_rms_data())
# Filter table to select only useful and final data
datm = dat['obsid i... |
keras-team/keras-io | examples/graph/ipynb/gat_node_classification.ipynb | apache-2.0 | import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
import pandas as pd
import os
import warnings
warnings.filterwarnings("ignore")
pd.set_option("display.max_columns", 6)
pd.set_option("display.max_rows", 6)
np.random.seed(2)
"""
Explanation: Graph attention ne... |
mintcloud/deep-learning | 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... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-2/cmip6/models/sandbox-1/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-2', 'sandbox-1', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: TEST-INSTITUTE-2
Source ID: SANDBOX-1
Sub-Topics: Radiative Forcin... |
ahkab/ahkab | doc/examples/PZ_Example.ipynb | gpl-2.0 | %pylab inline
figsize = (10, 7)
# libraries we need
import ahkab
print "We're using ahkab %s" % ahkab.__version__
"""
Explanation: Pole-Zero (PZ) simulation example
In this short example we will simulate a simple RLC circuit with the ahkab simulator.
In particular, we consider a series resonant RLC circuit. If you ne... |
chseifert/tutorials | data-science/DBSCAN_Clustering.ipynb | apache-2.0 | import sklearn.metrics as sm
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn.metrics as sm
from sklearn import datasets
from sklearn.cluster import DBSCAN
iris = datasets.load_iris()
x = pd.DataFrame(iris.data)
x.columns = ['SepalLength','SepalWidth','PetalLength','PetalWidth']
... |
bgroveben/python3_machine_learning_projects | airline_flight_data/predicting_flight_delays.ipynb | mit | import datetime, warnings, scipy
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.patches import ConnectionPatch
from collections import OrderedDict
from matplotlib.gridspec import GridSpec
# https... |
harishkrao/DSE200x | Week-7-MachineLearning/European Soccer Regression Analysis using scikit-learn.ipynb | mit | import sqlite3
import pandas as pd
from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from math import sqrt
"""
Explanation: <p style="font-family: Arial; font-size:2.75em;... |
jhillairet/scikit-rf | doc/source/tutorials/Plotting.ipynb | bsd-3-clause | %matplotlib inline
import skrf as rf
"""
Explanation: Plotting
Introduction
This tutorial describes skrf's plotting features. If you would like to use skrf's matplotlib interface with skrf styling, start with this
End of explanation
"""
from skrf import Network
ring_slot = Network('data/ring slot.s2p')
ring_slot.... |
Phlya/adjustText | figures/Examples.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt # Matplotlib 2.0 shown here
from adjustText import adjust_text
import numpy as np
import pandas as pd
"""
Explanation: Examples
Some usage examples and ways to make the figure better than what default options yield.
First import stuff
End of explanation
"""
np.rando... |
pyoceans/python-ctd | notebooks/quick_intro.ipynb | bsd-3-clause | import io
import requests
from pathlib import Path
def download_demo_file(url):
if not Path("CTD-spiked-unfiltered.cnv.bz2").exists():
response = requests.get(url, allow_redirects=True)
try:
response.raise_for_status()
except requests.exceptions.HTTPError as err:
ra... |
cosmodesi/desibgsdev | specsim/spectral-simulations-paper1.ipynb | bsd-3-clause | import os
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
from astropy.io import fits
from astropy.table import Table
import yaml
import desispec.io
import desisim.io
from desisim.scripts import quickgen
from desispec.scripts import group_spectra
from desispec.io.util import write_bintable, makep... |
GoogleCloudPlatform/ai-platform-samples | notebooks/samples/optimizer/ai_platform_optimizer_conditional_parameters.ipynb | apache-2.0 | ! pip install -U google-cloud
! pip install -U google-cloud-storage
! pip install -U requests
# Automatically restart kernel after installs
import IPython
app = IPython.Application.instance()
app.kernel.do_shutdown(True)
"""
Explanation: <table align="left">
<td>
<a href="https://colab.research.google.c... |
FederatedAI/FATE | doc/tutorial/pipeline/pipeline_tutorial_match_id.ipynb | apache-2.0 | !pipeline --help
"""
Explanation: Pipeline Match ID Tutorial
Starting at version 1.7, FATE distinguishes sample id(sid) and match id. Sid are unique to each sample entry, while match id corresponds to individual sample source identity. This adaption allows FATE to perform private set intersection on samples with repea... |
legacysurvey/obiwan | doc/nb/dr5_qa.ipynb | bsd-3-clause | # To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
import pandas as pd
# To plot pretty figures
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xt... |
metpy/MetPy | v0.12/_downloads/7fd39302ff9f3fa4a7870d3c31b04722/cross_section.ipynb | bsd-3-clause | import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.interpolate import cross_section
"""
Explanation: Cross Section Analysis
The MetPy function metpy.interpolat... |
KristianHolsheimer/tensorflow_training | build_your_own_lstm_cell.ipynb | gpl-3.0 | n_features = 7
# input data (one single data point)
x = np.random.random([1, n_features])
print x
"""
Explanation: Exercise: Build an LSTM cell
In the sequence of transformations (see fig. above), $x$ is the input vector at the current timestep. Notice that the memory cell state vectors $m$ and $c$ are updated at e... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_clickable_image.ipynb | bsd-3-clause | # Authors: Christopher Holdgraf <choldgraf@berkeley.edu>
#
# License: BSD (3-clause)
from scipy.ndimage import imread
import numpy as np
from matplotlib import pyplot as plt
from os import path as op
import mne
from mne.viz import ClickableImage, add_background_image # noqa
from mne.channels import generate_2d_layout ... |
gaufung/Data_Analytics_Learning_Note | DesignPattern/StrategyPattern.ipynb | mit | class customer:
def setPhone(self,phone):
self.phone=phone
def setEmail(self,mail):
self.email=mail
def getPhone(self):
return self.phone
def getEmail(self):
return self.email
def setInfo(self,info):
self.info=info
def setName(self,name):
self.cust... |
joseerlang/PySpark_docker | notebook/.ipynb_checkpoints/Contador de palabras-checkpoint.ipynb | apache-2.0 | fileName='book.txt'
"""
Explanation: Leer texto
Lo primero que tenemos que hacer es cargar el texto. Para nuestro ejemplo, cargaremos una obra del proyecto
Gutenberg.
End of explanation
"""
import re
def removePunctuation(text):
return re.sub('[^a-z| |0-9]', '', text.strip().lower())
"""
Explanation: Ahora vam... |
qutip/qutip-notebooks | examples/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb | lgpl-3.0 | %pylab inline
import contextlib
import time
import numpy as np
from qutip import *
from qutip.nonmarkov.heom import HEOMSolver, HSolverDL, BosonicBath, UnderDampedBath
def cot(x):
""" Vectorized cotangent of x. """
return 1. / np.tan(x)
def coth(x):
""" Vectorized hyperbolic cotangent of x. """
ret... |
szitenberg/ReproPhyloVagrant | notebooks/Tutorials/Basic/3.3 Reading data.ipynb | mit | from reprophylo import *
pj = unpickle_pj('outputs/my_project.pkpj', git=False)
"""
Explanation: See quick reference at the bottom
See full module reference section for full details
This part will show methods by which to read data into the ReproPhylo Project
3.3.1 Reading data from GenBank or EMBL files
GenBank or EM... |
letsgoexploring/linearsolve-package | examples/nk_model.ipynb | mit | # Import numpy, pandas, linearsolve, matplotlib.pyplot
import numpy as np
import pandas as pd
import linearsolve as ls
import matplotlib.pyplot as plt
plt.style.use('classic')
%matplotlib inline
"""
Explanation: A New-Keynesian Model
Consider the new-Keynesian business cycle model from Walsh (2017), chapter 8 expresse... |
NathanYee/ThinkBayes2 | code/.ipynb_checkpoints/report03-checkpoint.ipynb | gpl-2.0 | from __future__ import print_function, division
% matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import math
import numpy as np
from thinkbayes2 import Pmf, Cdf, Suite, Joint
import thinkplot
"""
Explanation: Report03 - Nathan Yee
This notebook contains report03 for computational baysian statis... |
marco-olimpio/ufrn | EEC2006/1_CrimeProject/Finding crime patterns in Montgomery County-Copy1.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
from bokeh.io import push_notebook, show, output_notebook
from bokeh.layouts import row
from bokeh.plotting import figure
from bokeh.models import (
GMapPlot, GMapOptions, ColumnDataSource, Circle, DataRange1d, PanTool, WheelZoomTool, BoxSelectTool
)
output_notebook()
"""
Ex... |
minxuancao/shogun | doc/ipython-notebooks/gaussian_process/gaussian_processes.ipynb | gpl-3.0 | %matplotlib inline
# import all shogun classes
from modshogun import *
import random
import numpy as np
import matplotlib.pyplot as plt
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from math import exp
"""
Explanation: Gaussian Processes in Shogun
By Heiko Strathmann - <a href="mailto:heiko.... |
tensorflow/docs-l10n | site/ko/guide/random_numbers.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... |
arsenovic/clifford | docs/tutorials/cga/interpolation.ipynb | bsd-3-clause | from clifford.g3c import *
blades
"""
Explanation: This notebook is part of the clifford documentation: https://clifford.readthedocs.io/.
Example 1 Interpolating Conformal Objects
In this example we will look at a few of the tools provided by the clifford package for (4,1) conformal geometric algebra (CGA) and see how... |
quoniammm/happy-machine-learning | Udacity-DL/image_classification_2/dlnd_image_classification.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifar10... |
chinmaymk/machine-learning-experiments | 01-iris.ipynb | mit | from sklearn import datasets
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.learning_curve import learning_curve
import matplotlib.pyplot as plt
"""
Explanation: The qu... |
keras-team/keras-io | examples/keras_recipes/ipynb/endpoint_layer_pattern.ipynb | apache-2.0 | import tensorflow as tf
from tensorflow import keras
import numpy as np
"""
Explanation: Endpoint layer pattern
Author: fchollet<br>
Date created: 2019/05/10<br>
Last modified: 2019/05/10<br>
Description: Demonstration of the "endpoint layer" pattern (layer that handles loss management).
Setup
End of explanation
"""
... |
McDermott-Group/Simulation | Projects/fluxNoiseMaskDesignV1.ipynb | gpl-2.0 | qubits = []
for i in range(3):
q = qubit.Qubit('Transmon')
q.C_g = 3.87e-15
q.C_q = 75.1e-15
q.C_resToGnd = 79.1e-15
qubits.append(q)
q = qubit.Qubit('OCSQubit')
q.C_g = 2.94e-15
q.C_q = 48.5e-15
q.C_resToGnd = 51.5e-15
qubits.append(q)
"""
Explanation: Flux Noise Mask Design
General Notes:
* Look... |
KartikKannapur/Programming_Challenges | HackerRank/Intro_to_Statistics/Day_04.ipynb | mit | # #Distribution
scipy.stats.norm(30, 4)
scipy.stats.norm(30, 4).cdf(40)
1 - scipy.stats.norm(30, 4).cdf(21)
scipy.stats.norm(30, 4).cdf(35) - scipy.stats.norm(30, 4).cdf(30)
"""
Explanation: Day 4: Normal Distribution #1
Objective
In this challenge, we practice solving problems with normally distributed variables.
... |
openclimatedata/pymagicc | notebooks/MAGICC7.ipynb | agpl-3.0 | # NBVAL_IGNORE_OUTPUT
import pandas as pd
import pymagicc
from pymagicc import MAGICC6, MAGICC7
from pymagicc.io import MAGICCData
from pymagicc import rcp26, rcp45, rcp60, rcp85, scenarios
# NBVAL_IGNORE_OUTPUT
%matplotlib inline
from matplotlib import pyplot as plt
plt.style.use("ggplot")
plt.rcParams["figure.fig... |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/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', 'mohc', 'sandbox-1', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: MOHC
Source ID: SANDBOX-1
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balan... |
massimo-nocentini/oeis-tools | notebooks/oeis-interaction.ipynb | mit | from pprinting import search
"""
Explanation: <p>
<img src="http://www.cerm.unifi.it/chianti/images/logo%20unifi_positivo.jpg"
alt="UniFI logo" style="float: left; width: 20%; height: 20%;">
<div align="right">
Massimo Nocentini<br>
<small>
<br>January 30, 2017: refactoring
<br>September 12, 2016: search hint... |
fadeetch/Mastering-ML-Python | Chapters/Three/Feature Extraction.ipynb | mit | from sklearn.feature_extraction import DictVectorizer
onehot_encoder = DictVectorizer()
instances = [{'city':"New York"},{'city':"San Fransisco"},{'city':"Chapel Hill"}]
print(onehot_encoder.fit_transform(instances).toarray())
"""
Explanation: Walk through techniques to extract features
Extract from categorical valu... |
ES-DOC/esdoc-jupyterhub | notebooks/ipsl/cmip6/models/sandbox-3/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ipsl', 'sandbox-3', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: IPSL
Source ID: SANDBOX-3
Sub-Topics: Radiative Forcings.
Properties: 85 (42 ... |
ocefpaf/folium | examples/Plugins.ipynb | mit | import folium
from folium import plugins
m = folium.Map([45, 3], zoom_start=4)
plugins.ScrollZoomToggler().add_to(m)
m
"""
Explanation: Examples of plugins usage in folium
In this notebook we show a few illustrations of folium's plugin extensions. These are available after importing folium.plugins.
ScrollZoomToggle... |
mayankjohri/LetsExplorePython | Section 1 - Core Python/Chapter 11 - Exceptions/Chapter13_Exceptions.ipynb | gpl-3.0 | print (10/0)
"""
Explanation: Chapter 13: Exceptions
When a failure occurs in the program (such as division by zero, for example) at runtime, an exception is generated. If the exception is not handled, it will be propagated through function calls to the main program module, interrupting execution.
End of explanation
... |
anandha2017/udacity | nd101 Deep Learning Nanodegree Foundation/DockerImages/10_sirajs_sentiment_analysis/notebooks/02_Ludos_pure-numpy-feedfowardNN/example/Exploring_XOR_approximation.ipynb | mit | %matplotlib inline
import numpy as np
from matplotlib import cm
import matplotlib.pyplot as plt
import seaborn as sns
from progress_bar import log_progress
import FeedforwardNN
"""
Explanation: Pure numpy feedforward neural network
The NeuralNetwork class in the FeedForwardNN file is a simple neural network class cod... |
Justin-YueLiu/CarND-Projects | CarND-Traffic-Sign-Classifier-P2/.ipynb_checkpoints/Traffic_Sign_Classifier-checkpoint.ipynb | mit | # Load pickled data
import pickle
# TODO: Fill this in based on where you saved the training and testing data
training_file = ?
validation_file=?
testing_file = ?
with open(training_file, mode='rb') as f:
train = pickle.load(f)
with open(validation_file, mode='rb') as f:
valid = pickle.load(f)
with open(test... |
poppy-project/community-notebooks | tutorials-education/poppy_ergo_jr__decouverte_du_robot/TP1_mouvement_et_fonctions_cor_prof.ipynb | lgpl-3.0 | i = 0
pos_init = [0, -90, 30, 0, 60, 0]
for m in poppy.motors:
m.moving_speed = 60
m.compliant = False
m.goal_position = pos_init[i]
i = i + 1
"""
Explanation: Initialisation
Ecrire un jeu d'instructions permettant
d'initialiser la vitesse de chacun des moteurs à 30°/s.
de mettre les moteurs poppy.... |
dpiparo/swanExamples | notebooks/CppFromPython_pycpp.ipynb | lgpl-2.1 | %%python
code='''
#include <iostream>
#include <typeinfo>
/// A trivial class
class A {
public:
A();
~A();
};
/// A trivial function
int CountCharacters(const std::string s);
/// A trivial template
template<class T>
class B {
public:
B()
{
std::cout << "The typeid name of the template argument is "... |
datadryad/dans-bagit | examples/10.5061_dryad.q447c/data/10.5061_dryad.q447c_2/ORIGINAL/Sci-Hub.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
months = ("sep2015", "oct2015", "nov2015", "dec2015", "jan2016", "feb2016")
%mkdir "scihub_data_temp"
"""
Explanation: Sci-Hub server log data analysis
John Bohannon, Science magazine
This Notebook will help you process the 6 months of raw server log data provided by Sci-Hub to... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch2-Example_2-05.ipynb | unlicense | %pylab notebook
"""
Explanation: Electric Machinery Fundamentals 5th edition
Chapter 2 (Code examples)
Example 2-5
Calculate and plot the voltage regulation of a transformer as a function of load for power factors of 0.8 lagging, 1.0, and 0.8 leading.
Import the PyLab namespace (provides set of useful commands and c... |
IST256/learn-python | content/lessons/04-Iterations/Slides.ipynb | mit | i,j,k = 1, 20, 1
while (i<j):
n = k*(j-i)
print(n)
i = i + 1
j = j - 1
k = k * 5
"""
Explanation: IST256 Lesson 04
Iterations
Zybook Ch 4
P4E Ch5
Links
Participation: https://poll.ist256.com
In-Class Questions: Zoom Chat!
Agenda
Exam 1 this week
Go over HW 03
Iterations
Make our code execute... |
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