repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
mne-tools/mne-tools.github.io | 0.20/_downloads/243172b1ef6a2d804d3245b8c0a927ef/plot_60_maxwell_filtering_sss.ipynb | bsd-3-clause | import os
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, verbose=False)
raw.crop(tmax=60).load_data()
"""
Explanation: ... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/image_classification/labs/1_mnist_linear.ipynb | apache-2.0 | import os
import shutil
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard
from tensorflow.keras.layers import Dense, Flatten, Softmax
print(tf.__version__)
!python3 -m pip freeze | gre... |
mne-tools/mne-tools.github.io | stable/_downloads/87ced9add160fc358769ef662f31e446/45_projectors_background.ipynb | bsd-3-clause | import os
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # noqa
from scipy.linalg import svd
import mne
def setup_3d_axes():
ax = plt.axes(projection='3d')
ax.view_init(azim=-105, elev=20)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
ax.... |
jorgemauricio/INIFAP_Course | ejercicios/Seaborn/4_Graficas_Regresion.ipynb | mit | import seaborn as sns
%matplotlib inline
tips = sns.load_dataset('tips')
tips.head()
"""
Explanation: Regression Plots
Seaborn has many built-in capabilities for regression plots, however we won't really discuss regression until the machine learning section of the course, so we will only cover the lmplot() function ... |
robertoalotufo/ia898 | dev/2017-01-20-RAL-Experimentando conversar página do Adessowiki.ipynb | mit | import numpy as np
a = np.arange(6)
b = a
print "a =\n",a
print "b =\n",b
b.shape = (2,3) # mudança no shape de b,
print "\na shape =",a.shape # altera o shape de a
b[0,0] = -1 # mudança no conteúdo de b
print "a =\n",a ... |
dato-code/tutorials | strata-sj-2016/time-series/time_series_data_object.ipynb | apache-2.0 | import datetime as dt
import graphlab as gl
sf = gl.SFrame.read_csv('raw_data/global_earthquakes.csv', verbose=False)
"""
Explanation: 1. The data: global earthquake events
I pulled the data from the USGS Advanced National Seismic System Comprehensive Earthquake Catalog.
- http://earthquake.usgs.gov/data/
- http://ea... |
kringen/IOT-Back-Brace | web_api/webServiceToKafka.ipynb | apache-2.0 | from flask import Flask, Response, request, json, render_template
from kafka import KafkaProducer
import uuid
import datetime
app = Flask(__name__)
producer = KafkaProducer(bootstrap_servers='localhost:9092')
"""
Explanation: REST API for IoT Back Brace - WebServiceToKafka.py
This script creates a Flask web api runn... |
sujitpal/intro-dl-talk-code | src/05-alice-rnn-langmodel.ipynb | unlicense | from __future__ import division, print_function
from keras.layers.recurrent import SimpleRNN
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.utils.visualize_util import plot
import numpy as np
%matplotlib inline
"""
Explanation: Learning to speak like Alice
A generative charac... |
GoogleCloudPlatform/training-data-analyst | quests/data-science-on-gcp-edition1_tf2/05_bqnotebook/exploration.ipynb | apache-2.0 | a = 3
b = a + 5
print("a={} b={}".format(a,b))
"""
Explanation: <h1> 5. Interactive Data Analysis </h1>
This notebook introduces carrying out interactive data analysis of data in BigQuery using Cloud Datalab.
This cell, for example, is a mark-down cell. Which is why you are seeing text. The cell that follows is a Py... |
susantabiswas/Natural-Language-Processing | Notebooks/Testing_Training_Quadgram.ipynb | mit | from nltk.util import ngrams
from collections import defaultdict
from collections import OrderedDict
import string
import time
import gc
start_time = time.time()
"""
Explanation: Word prediction using Quadgram
This program reads the corpus line by line.This reads the corpus one line at a time loads it into the memory... |
phoebe-project/phoebe2-docs | 2.1/tutorials/intens_weighting.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
"""
Explanation: Intensity Weighting
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
"""
%matplotlib inline
... |
ds-modules/LINGUIS-110 | Intro/Intro.ipynb | mit | print("Hello, World!")
"""
Explanation: Linguistics 110
Professor Susan Lin
This introductory notebook will familiarize you with some of the basic strategies for data analysis that will be useful to you throughout the course. It will cover an overview of our software and an introduction to programming.
Table of Conten... |
apozas/BIST-Python-Bootcamp | 2_Introduction_to_Python.ipynb | gpl-3.0 | print('Hello, world!')
2 + 2
import numpy
"""
Explanation: 2 Introduction to Python
A series of commands to introduce beginners to Python.
Does it work?
End of explanation
"""
a_integer = 5
a_float = 1.41421356237
a_integer + a_float
a_number = a_integer + a_float
print(a_number)
a_string = 'How you doi... |
cliburn/sta-663-2017 | notebook/Extras_02_Functional_Word_Counting.ipynb | mit | text = ''''Twas brillig, and the slithy toves
Did gyre and gimble in the wabe;
All mimsy were the borogoves,
And the mome raths outgrabe.
'Beware the Jabberwock, my son!
The jaws that bite, the claws that catch!
Beware the Jubjub bird, and shun
The frumious Bandersnatch!'
H... |
SlipknotTN/udacity-deeplearning-nanodegree | sentiment-rnn/Sentiment RNN Solution With Logs.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment_network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment_network/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... |
diging/methods | 1.7. Latent Dirichlet Allocation/1.7.2 Finding concepts in texts - Hierarchical Dirichlet Process.ipynb | gpl-3.0 | import nltk
from tethne.readers import zotero
import matplotlib.pyplot as plt
from nltk.corpus import stopwords
import gensim
import networkx as nx
import pandas as pd
wordnet = nltk.WordNetLemmatizer()
stemmer = nltk.SnowballStemmer('english')
stoplist = stopwords.words('english')
text_root = '../data/EmbryoProject... |
ES-DOC/esdoc-jupyterhub | notebooks/niwa/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', 'niwa', 'sandbox-2', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: NIWA
Source ID: SANDBOX-2
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balan... |
tensorflow/docs-l10n | site/ja/guide/tf_numpy.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... |
OpenTire/OpenTire | examples/Moment_Diagram_Example.ipynb | mit | from opentire import OpenTire
from opentire.Core import TireState
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: OpenTire Moment Method Example
Draws a constant velocity force-moment diagram of a bicycle using the OpenTire library
Import OpenTire and other libraries used in this demonstration
End... |
mpschr/mutex | MutEx and CoOc test in python.ipynb | mit | import numpy as np
import pandas as pd
import scipy.sparse as sparse
import random
"""
Explanation: Permuation based Mutual Exclusive and Co-occurrence for group of items
End of explanation
"""
row, col = 100, 100
np.random.seed(77)
df = pd.DataFrame(sparse.random(row, col, density=0.15).A).apply(np.ceil)
df.loc[0]... |
brianray/puppy_dec_2015 | .ipynb_checkpoints/Feature Engineering for Text Data-checkpoint.ipynb | apache-2.0 | reviews = gl.SFrame.read_csv('../data/yelp/yelp_training_set_review.json', header=False)
reviews
reviews[0]
"""
Explanation: SFrame -- Scalable Dataframe
Powerful unstructured data processing: read straight up json
End of explanation
"""
reviews=reviews.unpack('X1','')
reviews
"""
Explanation: Unpack to extract st... |
rob-dalton/dsi-alumni-lectures | web_scraping/Web Scraping.ipynb | mit | # url = 'https://www.tripadvisor.com/Attraction_Review-g60878-d3184389-Reviews-Chihuly_Garden_and_Glass-Seattle_Washington.html#REVIEWS'
def get_reviews(response):
# response = requests.get(url)
soup = BeautifulSoup(response, 'html.parser')
entries = soup.findAll('div', {'class': 'entry'})
reviews = [e... |
tensorflow/docs-l10n | site/ko/hub/tutorials/text_classification_with_tf_hub_on_kaggle.ipynb | apache-2.0 | # Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
mne-tools/mne-tools.github.io | 0.19/_downloads/063df3a44a4ac9d23978d7b307e69a4e/plot_read_evoked.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
from mne import read_evokeds
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
# Reading
condition = 'Left Auditory'
evoked = read_evokeds(fname... |
raschuetz/foundations-homework | 09/homework-9-schuetz.ipynb | mit | earthquake = {
'rms': '1.85',
'updated': '2014-06-11T05:22:21.596Z',
'type': 'earthquake',
'magType': 'mwp',
'longitude': '-136.6561',
'gap': '48',
'depth': '10',
'dmin': '0.811',
'mag': '5.7',
'time': '2014-06-04T11:58:58.200Z',
'latitude': '59.0001',
'place': '73km WSW of Haines, Alaska',
'net': 'us',
'... |
neilpquinn/2016-02-notifications-exploration | Notifications research.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.axes as ax
%matplotlib inline
notifs = pd.read_table("./notifications_per_user.tsv")
unreads = pd.read_table("./unread_notifications_per_user.tsv")
wikis = set(notifs["wiki"])
notifs.tail()
def filter_by_wiki( df, wiki ):
r... |
ES-DOC/esdoc-jupyterhub | notebooks/messy-consortium/cmip6/models/sandbox-2/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'sandbox-2', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: MESSY-CONSORTIUM
Source ID: SANDBOX-2
Topic: Seaice
Sub-Topics: Dynami... |
yunqu/PYNQ | docs/source/jupyter_notebooks_advanced_features.ipynb | bsd-3-clause | import random
the_number = random.randint(0, 10)
guess = -1
name = input('Player what is your name? ')
while guess != the_number:
guess_text = input('Guess a number between 0 and 10: ')
guess = int(guess_text)
if guess < the_number:
print(f'Sorry {name}, your guess of {guess} was too LOW.\n')
... |
mclaughlin6464/pearce | notebooks/Pearce wt integral tests.ipynb | mit | from pearce.mocks import cat_dict
import numpy as np
from os import path
from astropy.io import fits
import matplotlib
#matplotlib.use('Agg')
from matplotlib import pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set()
"""
Explanation: I've implemented the integral of wt in pearce. This notebook verifies i... |
kingsgeocomp/code-camp | notebook-12-packages.ipynb | mit | from urllib.request import urlopen
print("dir(urlopen) returns:\n")
print(dir(urlopen))
print("\n\n")
print("help(urlopen) returns:\n")
print(help(urlopen)) # Notice!
"""
Explanation: Notebook-12: Introduction to Packages
Functions vs. Packages
We saw in a previous Code Camp Lesson how functions are a useful programmi... |
mdeff/ntds_2017 | projects/reports/brain_network/2_2_ntds_project_network_WeightedSpectralGraphTheory.ipynb | mit | ##force not printing
%%capture
%matplotlib inline
!pip install h5py
import numpy as np
import numpy.ma as ma
import h5py
from scipy import sparse
import IPython.display as ipd
import matplotlib.pyplot as plt
import matplotlib.colors as col
from mpl_toolkits.mplot3d import Axes3D
import re
import networkx as nx
import... |
brandoncgay/deep-learning | autoencoder/Convolutional_Autoencoder.ipynb | mit | %matplotlib inline
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', validation_size=0)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
"""
Explanation: C... |
jagarzone6/cmos | notebooks/.ipynb_checkpoints/Cmos - Transistors (variant KP property)-checkpoint.ipynb | mit | from IPython.core.display import Image, display
display(Image(url='images/TipicalValuesLongChannel.png'))
"""
Explanation: 02 - Transistors Model for not uniform wafer (variant KP property)
Definicion de variables para calcular el Kp que varia sobre la oblea
End of explanation
"""
%matplotlib inline
import math
impo... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/gapic/custom/showcase_custom_text_binary_classification_online_container.ipynb | apache-2.0 | import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex client library: Custom training text binary classification model with custom contain... |
infilect/ml-course1 | keras-notebooks/advanced/8.3-neural-style-transfer.ipynb | mit | from keras.preprocessing.image import load_img, img_to_array
# This is the path to the image you want to transform.
target_image_path = '/home/ubuntu/data/portrait.png'
# This is the path to the style image.
style_reference_image_path = '/home/ubuntu/data/popova.jpg'
# Dimensions of the generated picture.
width, heig... |
mapio/programming-with-escher | notebook.ipynb | gpl-3.0 | f
"""
Explanation: Programming with Escher
This notebook is a collection of preliminary notes about a "code camp" (or a series of lectures) aimed at young students inspired by the fascinating Functional Geometry paper of Peter Henderson.
In such work the Square Limit woodcut by Maurits Cornelis Escher is reconstructed... |
CalPolyPat/phys202-2015-work | assignments/project/NeuralNetworks.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from IPython.html.widgets import interact
from sklearn.datasets import load_digits
digits = load_digits()
print(digits.data.shape)
def show_digit(i):
plt.matshow(digits.images[i]);
interact(show_digit, i=(0,100));
"""
Explanation: Neural Networks
This project w... |
DaveBackus/Data_Bootcamp | Code/Lab/MEPS_data_experiment_Brian.ipynb | mit | import pandas as pd #The data package
import sys #The code below wont work for any versions before Python 3. This just ensures that (I guess).
assert sys.version_info >= (3,5)
"""
The MEPS dataset is is compressed in a .zip file in the link above. The code here downloads the .zip file, unzips
and saves t... |
Cyberface/nrutils_dev | examples/core_ipython_example.ipynb | mit | # Setup enviroment to autoreload packages
%load_ext autoreload
%autoreload 2
# Turn on inline plotting with matplotlib, and import needed thigs from nrutils
%matplotlib inline
from nrutils import scsearch,gwylm
"""
Explanation: Core example for nrutils: Find a simulation, load and plot waveform data
Let's start by imp... |
gtatiya/FlashVocab | docs/week4/TextProcessing.ipynb | gpl-3.0 | #INPUT
#folder = 'sample' # create this folder and place the exported file
folder = 'all'
#export_filename = 'English_Vocabulary_Sample.csv'
export_filename = 'English_Vocabulary3.csv'
"""
Generate a dictionary from Anki export file.
Note:
Only GRE words have proper Serial No. Rest words have 99999 as Serial No.
"""
... |
ethen8181/machine-learning | model_selection/auc/auc.ipynb | mit | # code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', '..', 'notebook_format'))
from formats import load_style
load_style(plot_style=False)
os.chdir(path)
# 1. magic for inline plot
# 2. magic to print vers... |
Jackporter415/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... |
bMzi/ML_in_Finance | 0101_GettingStartedWithPython.ipynb | mit | from IPython.display import YouTubeVideo
from datetime import timedelta
YouTubeVideo('jZ952vChhuI')
"""
Explanation: Getting Started With Python
Installation
There are various ways to install Python. Assuming the reader is not (yet) well versed in programming, I suggest to download the Anaconda distribution, which wo... |
scoyote/RHealthDataImport | .ipynb_checkpoints/ImportAppleHealthXML-checkpoint.ipynb | mit | import xml.etree.ElementTree as et
import pandas as pd
import numpy as np
from datetime import *
import matplotlib.pyplot as plt
import re
import os.path
import zipfile
%matplotlib inline
plt.rcParams['figure.figsize'] = 16, 8
"""
Explanation: Download, Parse and Interrogate Apple Health Export Data
The first part... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/03_model_performance/b_feature_engineering_wd.ipynb | apache-2.0 | import tensorflow as tf
import numpy as np
import shutil
print(tf.__version__)
"""
Explanation: More Feature Engineering - Wide and Deep models
Learning Objectives
* Build a Wide and Deep model using the appropriate Tensorflow feature columns
Introduction
In this notebook we'll use what we learned about feature col... |
malogrisard/NTDScourse | algorithms/02_sol_clustering.ipynb | mit | # Load libraries
# Math
import numpy as np
# Visualization
%matplotlib notebook
import matplotlib.pyplot as plt
plt.rcParams.update({'figure.max_open_warning': 0})
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy import ndimage
# Print output of LFR code
import subprocess
# Sparse matrix
import ... |
SSQ/Coursera-UW-Machine-Learning-Classification | Programming Assignment 6/Decision Trees in Practice.ipynb | mit | import numpy as np
import pandas as pd
import json
"""
Explanation: Decision Trees in Practice
In this assignment we will explore various techniques for preventing overfitting in decision trees. We will extend the implementation of the binary decision trees that we implemented in the previous assignment. You will have... |
jason-neal/companion_simulations | Notebooks/chi2_parameter_broadcasting.ipynb | mit | import numpy as np
import scipy as sp
from scipy.stats import chisquare
import matplotlib.pyplot as plt
% matplotlib inline
"""
Explanation: Interpolate wavelength on multiple dimensions
Jason Neal - 19th July 2017
To try and interpolate N-D data along the first axis.
This is to be able to perfrom chisquare analsysis... |
WNoxchi/Kaukasos | misc/MeanShift_tutorial_1_sentdex.ipynb | mit | centers = [[1,1],[5,5]]
X, y = make_blobs(n_samples=500, centers=centers, cluster_std=1)
plt.scatter(X[:,0], X[:,1], s = 10)
plt.show()
"""
Explanation: make_blobs is going to make blobs of data based on std dev. & some randomness around a specified point. You can specify how many points to generate in the blob, and t... |
sthiele/pyasp | pyasp_demo.ipynb | gpl-3.0 | from urllib.request import urlretrieve
urlretrieve('http://sthiele.github.io/data/queens.lp','queens.lp')
urlretrieve('http://sthiele.github.io/data/facts.lp','facts.lp')
"""
Explanation: PyASP basics
In the following I try to explain the basics on how to use the PyASP library. I assume that pyasp is already installed... |
gonmolina/CCE_ProblemasResueltos | ProbsVVEE/Levitador Magnetico/Problema Levitador Magnético-Python.ipynb | mit | import numpy as np
import control as ctrl
import matplotlib.pylab
%matplotlib inline
A=np.matrix("-20 100 2000;1 0 0;0 1 0")
B=np.matrix("1;0;0")
C=np.matrix("0 0 -40")
D=np.matrix("0");
sys=ctrl.ss(A,B,C,D)
"""
Explanation: Selección de Polos del Controlador
Para selecccionar del sistema a lazo cerrado debemos tener... |
kit-cel/wt | sigNT/signals_transforms/sampling.ipynb | gpl-2.0 | import numpy as np
from scipy import signal
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=True)
matplotlib.rc('figure', figsize=(18, 6) )
"""
Explanation: Content and Objective
Show application of sinc ... |
keras-team/keras-io | examples/vision/ipynb/cct.ipynb | apache-2.0 | !pip install -U -q tensorflow-addons
"""
Explanation: Compact Convolutional Transformers
Author: Sayak Paul<br>
Date created: 2021/06/30<br>
Last modified: 2021/06/30<br>
Description: Compact Convolutional Transformers for efficient image classification.
As discussed in the Vision Transformers (ViT) paper,
a Transform... |
planet-os/notebooks | nasa-opennex/Processing Large Datasets with the CSV Module.ipynb | mit | import csv
import urllib2
"""
Explanation: Processing Large Datasets with the CSV Module
This notebook illustrates how the csv module can be used to incrementally process a large subset of OpenNEX DCP30 data in Python.
In this example, we'll identify the minimum and maximum temperatures in the continental United State... |
rawrgulmuffins/presentation_notes | pycon2016/tutorials/computation_statistics/hypothesis.ipynb | mit | %matplotlib inline
from __future__ import print_function, division
import numpy
import scipy.stats
import matplotlib.pyplot as pyplot
from ipywidgets import interact, interactive, fixed
import ipywidgets as widgets
import first
# seed the random number generator so we all get the same results
numpy.random.seed(19)... |
mrcinv/matpy | 01c_kompleksna.ipynb | gpl-2.0 | 1j**2 # 1j je v Pythonu imaginarna enota
type(2+2.2j) #kompleksna števila imajo v Pythonu svoj tip
"""
Explanation: ^ gor: Uvod
Kompleksna števila
V Pythonu so kompleksna števila vgrajena. Imaginarna enota se imenuje j in jo postavimo neposredno za številko.
End of explanation
"""
z = (1-2j)/(1+1j) #Python zlahka o... |
Cyianor/smc2017 | solutions/code/Python/fheld/prep.ipynb | mit | import numpy as np
from scipy.stats import norm
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style()
"""
Explanation: SMC2017: Preparatory exercises
These problems are meant for repetition and to get familiar with some commonly used functions in the chosen language.
Setup
End of e... |
computationalevolutionarydynamics/repeatedmistakes | notebooks/Final_Presentation.ipynb | gpl-3.0 | from repeatedmistakes.strategies import SuspiciousTitForTat, TitForTat
from repeatedmistakes.repeatedgame import RepeatedGame
my_game = RepeatedGame(SuspiciousTitForTat, TitForTat)
simulation_results = my_game.simulate(10)
print("STFT: " + str(simulation_results[SuspiciousTitForTat]))
print("TFT: " + str(simulation_re... |
swirlingsand/deep-learning-foundations | sentiment-network/Sentiment Classification - Mini Project 3.ipynb | mit | def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()... |
aakashm301/Workshop | Refactored_Py_DS_ML_Bootcamp-master/05-Data-Visualization-with-Matplotlib/01-Matplotlib Concepts Lecture.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Matplotlib Overview Lecture
Introduction
Matplotlib is the "grandfather" library of data visualization with Python. It was created by John Hunter. He created it to try to replicate MatLab'... |
xdnian/pyml | assignments/ex04_ch0405_xdnian.ipynb | mit | import pandas as pd
wine_data_remote = 'https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data'
wine_data_local = '../datasets/wine/wine.data'
df_wine = pd.read_csv(wine_data_remote,
header=None)
df_wine.columns = ['Class label', 'Alcohol', 'Malic acid', 'Ash',
... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/image_classification/solutions/3_tf_hub_transfer_learning.ipynb | apache-2.0 | import os
import pathlib
from PIL import Image
import IPython.display as display
import matplotlib.pylab as plt
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import (
Conv2D, Dense, Dropout, Flatten, MaxPooling2D, Softmax)
import tensorflow_hub as h... |
maxis42/ML-DA-Coursera-Yandex-MIPT | 2 Supervised learning/Homework/4 metriks in sklearn/MetricsPA.ipynb | mit | import numpy as np
from matplotlib import pyplot as plt
import seaborn
%matplotlib inline
"""
Explanation: Сравнение метрик качества бинарной классификации
Programming Assignment
В этом задании мы разберемся, в чем состоит разница между разными метриками качества. Мы остановимся на задаче бинарной классификации (с отк... |
karhohs/boardgame-bookie | boardgames/seafall/notebooks/Ship Damage Probability.ipynb | bsd-3-clause | %matplotlib inline
import itertools
import matplotlib
import matplotlib.pyplot
import numpy
import os
import pandas
import scipy.misc
import scipy.special
import seaborn
# colormap for data visualizations
cmap = seaborn.cubehelix_palette(n_colors = 20, start = 0.8, rot = 0.75,
gamma = ... |
pmgbergen/porepy | tutorials/get_started.ipynb | gpl-3.0 | import numpy as np
import scipy.sparse as sps
import porepy as pp
"""
Explanation: Get started
In this tutorial we investigate how to solve single-phase flow in PorePy using a two-point flux approximation.<br>
Let is $\Omega$ a regular domain with boundary $\partial \Omega$. The boundary can be divided in two non-over... |
pkimes/benchmark-fdr | datasets/microbiome/explore_schubert_sb_results.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set_style('white')
fpvals = 'schubert-sb-table.txt'
fotu = 'data/cdi_schubert_results/RDP/cdi_schubert.otu_table.100.denovo.rdp_assigned'
fmeta = 'data/cdi_schubert_results/cdi_schubert.metadata.txt'
... |
bmorris3/salter | stats_archive.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = "retina"
from salter import (LightCurve, subtract_add_divide, concatenate_transit_light_curves,
Residuals, lc_archive, planet_props)
import matplotlib.pyplot as plt
import numpy as np
import warnings
from astropy.stats import mad_std
from it... |
joonasfo/python | Assignment_06.ipynb | mit | # Initial import statements
%matplotlib notebook
import matplotlib.pyplot as plt
import numpy as np
import sympy as sy
from matplotlib.pyplot import *
from numpy import *
from numpy.linalg import *
from sympy import *
"""
Explanation: Assignment: 06 Applications
Introduction to Numerical Problem Solving, Spring 2017
... |
gregunz/ada2017 | exam/cheatsheet.ipynb | mit | # data science
import pandas as pd
import numpy as np
import scipy.stats as stats
from sklearn import linear_model # or others
# plot
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
sns.set(rc={"figure.figsize": (15, 6)})
sns.set_palette(sns.color_palette("Set2", 10))
#sns.set_style("whitegri... |
elizabetht/deep-learning | intro-to-rnns/Anna_KaRNNa.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is base... |
onyxfish/journalism | tutorial.ipynb | mit | import agate
"""
Explanation: The Agate Tutorial
The best way to learn to use any tool is to actually use it. In this tutorial we will use agate to answer some basic questions about a dataset.
The data we will be using is a copy of the National Registry of Exonerations made on August 28th, 2015. This dataset lists ind... |
cdawei/digbeta | dchen/tour/generated_data.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import os, pickle, random
import pandas as pd
import numpy as np
import cvxopt
import seaborn as sns
random.seed(1234554321)
np.random.seed(123456789)
cvxopt.base.setseed(123456789)
"""
Explanation: Experiment on Generated Data
The goal of this notebook is to design... |
phanrahan/magmathon | notebooks/signal-generator/solutions/Square.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
x = np.arange(0, 100)
def square(x):
return (x % 50) < 25
plt.plot(x, square(x))
import magma as m
m.set_mantle_target("ice40")
"""
Explanation: Square Wave Generator
A square wave is a periodic waveform that alternates between two discrete ... |
jupyter/nbgrader | nbgrader/tests/preprocessors/files/test_taskcell.ipynb | bsd-3-clause | def squares(n):
"""Compute the squares of numbers from 1 to n, such that the
ith element of the returned list equals i^2.
"""
### BEGIN SOLUTION
if n < 1:
raise ValueError("n must be greater than or equal to 1")
return [i ** 2 for i in range(1, n + 1)]
### END SOLUTION
"""
Exp... |
phoebe-project/phoebe2-docs | development/tutorials/logg.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
"""
Explanation: Surface Gravity (logg)
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
"""
import phoebe
from phoebe import u # units
import numpy as np... |
AlexandruValeanu/365-days-of-algorithms | Day 10 - Karatsuba multiplication.ipynb | gpl-3.0 | import numpy as np # used for generating random numbers
"""
Explanation: Definition(s)
The Karatsuba algorithm is a fast multiplication algorithm.
It reduces the multiplication of two n-digit numbers to at most ${\displaystyle n^{\log _{2}3}\approx n^{1.585}}$ single-digit multiplications in general.
End of explanat... |
phoebe-project/phoebe2-docs | 2.2/tutorials/passbands.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Adding new passbands to PHOEBE
In this tutorial we will show you how to add your own passband to PHOEBE. Adding a custom passband involves:
downloading and setting up model atmosphere tables;
providing a passband transmission function;
defining and registering pass... |
sfegan/calin | examples/calib/gain estimation from photostatistics.ipynb | gpl-2.0 | %pylab inline
import calin.ix.scripts.compute_diagnostics
import calin.io.sql_transceiver
import calin.diagnostics.waveform
import calin.diagnostics.functional
import calin.plotting
sql = calin.io.sql_transceiver.SQLite3Transceiver("/CTA/diagnostics.sqlite",
calin.io.sql_transceiver.SQLite3Transceiver.READ_ONLY)
dia... |
AllenDowney/ThinkBayes2 | examples/world_cup_mcmc.ipynb | mit | # Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import classes from thinkbayes2
from thinkbayes2 import Pmf, Cdf, Suite
import thinkbayes2
import thin... |
fevangelista/wicked | examples/CoupledClusterEquations.ipynb | mit | import wicked as w
import time
from IPython.display import display, Math, Latex
def latex(expr):
"""Function to render any object that has a member latex() function"""
display(Math(expr.latex()))
"""
Explanation: Automatic generation of coupled cluster equations
In this example we use wick&d to generate arbit... |
raoyvn/deep-learning | intro-to-rnns/Anna KaRNNa.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is base... |
kimkipyo/dss_git_kkp | 통계, 머신러닝 복습/160530월_9일차_추정 및 검정 Estimation and Test/3.SciPy를 사용한 기초적인 검정.ipynb | mit | N = 10
theta_0 = 0.5
np.random.seed(0)
x = sp.stats.bernoulli(theta_0).rvs(N)
n = np.count_nonzero(x)
n
sp.stats.binom_test(n ,N)
"""
Explanation: SciPy를 사용한 기초적인 검정
실제로는 t검정을 주로 쓴다. z검정은 잘 안 쓴다.
등분산은 표준편차로 비교하는 건가?
SciPy 파이썬 패키지는 다음과 같은 다양한 검정 명령을 제공한다.
이항 검정 (Binomial test)
카이 제곱 검정 (Chi-square test)
단일 표본 z-검정 ... |
tclaudioe/Scientific-Computing | SC1/04_roots_of_1D_equations.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
import sympy as sym
%matplotlib inline
from ipywidgets import interact
from ipywidgets import widgets
sym.init_printing()
from scipy import optimize
"""
Explanation: <center>
<h1> ILI285 - Computación Científica I / INF285 - Computación Científica </h1>
<h2> ... |
tensorflow/docs-l10n | site/ko/tutorials/distribute/save_and_load.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... |
ToqueWillot/M2DAC | FDMS/TME3/Submit_V1.ipynb | gpl-2.0 | # from __future__ import exam_success
from __future__ import absolute_import
from __future__ import print_function
%matplotlib inline
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import random
import pandas as pd
import scipy.stats as stats
# Sk cheats
from sklearn.cross_val... |
carriegardner428/cybersecurity_datascience | tutorials/Pandas Intro.ipynb | mit | v = pd.Series(np.random.randn(5))
v
"""
Explanation: Pandas
pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
Pandas Website
This tutorial pulls from the Pandas website and the Handson-ML tutorial: https:/... |
ctroupin/OceanData_NoteBooks | PythonNotebooks/PlatformPlots/Read_CORA_dataset.ipynb | gpl-3.0 | datafile = (
'~/CMEMS_INSTAC/INSITU_GLO_TS_OA_REP_OBSERVATIONS_013_002_b/'
'CORIOLIS-GLOBAL-CORA04.1-OBS_FULL_TIME_SERIE/data/2013/OA_CORA4.1_20131215_dat_PSAL.nc'
)
"""
Explanation: Salinity from CORA dataset
The data can be obtained from Coriolis FTP at ftp://ftp1.ifremer.fr/Core/INSITU_GLO_TS_REP_OBSERVATIO... |
ericfourrier/auto-clean | examples/Influence of imputation of missing values on prediction.ipynb | mit | import pandas as pd
from autoc import DataExploration, PreProcessor, NaImputer
from autoc.utils.getdata import get_dataset
import numpy as np
# skicit learn
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import cross_val_score,train_test_split
from sklearn.ensemble import RandomFore... |
DJCordhose/ai | notebooks/workshops/tss/cnn-standard-architectures.ipynb | mit | import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
import matplotlib.pylab as plt
import numpy as np
from distutils.version import StrictVersion
import sklearn
print(sklearn.__version__)
assert StrictVersion(sklearn.__version__ ) >= StrictVersion('0.18.1')
import tensorflow as tf
t... |
omoju/udacityUd120Lessons | SVM.ipynb | gpl-3.0 | %pylab inline
import sys
from time import time
sys.path.append("../tools/")
from email_preprocess import preprocess
from prep_terrain_data import makeTerrainData
from class_vis import prettyPicture, output_image
import copy
"""
Explanation: Lesson 2 - Support Vector Machines
This is the code to accompany the Less... |
KrisCheng/ML-Learning | archive/MOOC/Deeplearning_AI/ImprovingDeepNeuralNetworks/SettingupyourMachineLearningApplication/Regularization.ipynb | mit | # import packages
import numpy as np
import matplotlib.pyplot as plt
from reg_utils import sigmoid, relu, plot_decision_boundary, initialize_parameters, load_2D_dataset, predict_dec
from reg_utils import compute_cost, predict, forward_propagation, backward_propagation, update_parameters
import sklearn
import sklearn.da... |
BrownDwarf/ApJdataFrames | notebooks/McClure2010.ipynb | mit | import warnings
warnings.filterwarnings("ignore")
from astropy.io import ascii
import pandas as pd
"""
Explanation: ApJdataFrames McClure
Title: THE EVOLUTIONARY STATE OF THE PRE-MAIN SEQUENCE POPULATION IN OPHIUCHUS: A LARGE INFRARED SPECTROGRAPH SURVEY
Authors: McClure et al.
Data is from this paper:
http://iopsci... |
phoebe-project/phoebe2-docs | 2.2/tutorials/constraints.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Constraints
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
"""
import phoebe
from phoebe im... |
murrayrm/python-control | examples/stochresp.ipynb | bsd-3-clause | import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import control as ct
from math import sqrt, exp
# Fix random number seed to avoid spurious figure regeneration
np.random.seed(1)
"""
Explanation: Stochastic Response
Richard M. Murray, 6 Feb 2022
This notebook illustrates the implementation of rand... |
pinga-lab/magnetic-ellipsoid | code/lambda_prolate_ellipsoids.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: $\lambda$ variable for prolate ellipsoids
End of explanation
"""
a = 20.
b = 13.
x = 21.
y = 23.
z = 30.
"""
Explanation: Here, we follow the reasoning presented by Webster (1904) for analyzing the ellipsoidal coordinate $\lamb... |
vsingla2/Self-Driving-Car-NanoDegree-Udacity | Term1-Computer-Vision-and-Deep-Learning/Project2-Traffic-Sign-Classifier/Traffic_Sign_Classifier.ipynb | mit | # Load pickled data
import pickle
# TODO: Fill this in based on where you saved the training and testing data
training_file = r'C:\Users\VINOD\Google Drive\SDCND\CarND-Traffic-Sign-Classifier-Project\pickled_data\train.p'
validation_file = r'C:\Users\VINOD\Google Drive\SDCND\CarND-Traffic-Sign-Classifier-Project\pick... |
wcmckee/wcmckee | numbernerd.ipynb | mit | import random
import getpass
import giphypop
#from IPython.display import Image
#from IPython.core.display import HTML
import webbrowser
"""
Explanation: Number Nerd
Game to test knowledge of numbers.
Code based on:
http://www.nzqa.govt.nz/assets/qualifications-and-standards/qualifications/ncea/NCEA-subject-resourc... |
ES-DOC/esdoc-jupyterhub | notebooks/pcmdi/cmip6/models/sandbox-2/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'pcmdi', 'sandbox-2', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: PCMDI
Source ID: SANDBOX-2
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Ra... |
azhurb/deep-learning | gan_mnist/Intro_to_GANs_Exercises.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... |
aakashsinha19/Aspectus | Image Segmentation/tensorflow_notes-master/upsampling_segmentation.ipynb | apache-2.0 | from __future__ import division
import sys
import os
import numpy as np
from matplotlib import pyplot as plt
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
sys.path.append("/home/dpakhom1/workspace/models/slim")
# A place where you have downloaded a network checkpoint -- look at the previous post
checkpoints_dir = '/home/d... |
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