repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
GoogleCloudPlatform/training-data-analyst | blogs/explainable_ai/AI_Explanations_on_CAIP.ipynb | apache-2.0 | import os
PROJECT_ID = "michaelabel-gcp-training"
os.environ["PROJECT_ID"] = PROJECT_ID
"""
Explanation: AI Explanations: Explaining a tabular data model
Overview
In this tutorial we will perform the following steps:
Build and train a Keras model.
Export the Keras model as a TF 1 SavedModel and deploy the model on ... |
rainyear/pytips | Tips/2016-04-13-Iterator-Tools.ipynb | mit | from itertools import cycle, count, repeat
print(count.__doc__)
counter = count()
print(next(counter))
print(next(counter))
print(list(map(lambda x, y: x+y, range(10), counter)))
odd_counter = map(lambda x: 'Odd#{}'.format(x), count(1, 2))
print(next(odd_counter))
print(next(odd_counter))
print(cycle.__doc__)
cyc =... |
H4ml3t/wmarchive-examples | How to write results into HDFS - example.ipynb | mit | # is SparkContext already loaded?
sc
# Make sure you have a HiveContext
sqlContext
# Which is the version?
sc.version
# load a dataframe from Avro files
df = sqlContext.read.format("com.databricks.spark.avro").load("/cms/wmarchive/test/avro/2016/01/01/")
df.printSchema()
%%time
df.count()
"""
Explanation: How to ... |
gammapy/PyGamma15 | tutorials/analysis-stats/Tutorial.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Tutorial about statistical methods
The following contains a sequence of simple exercises, designed to get familiar with using Minuit for maximum likelihood fits and emcee to determine parameters by MCMC. Commands are generally comme... |
georgetown-analytics/machine-learning | examples/bbengfort/bikeshare/bikeshare.ipynb | mit | import os
import sys
sys.path.append("/Users/benjamin/Repos/ddl/yellowbrick")
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_context('notebook')
sns.set_style('whitegrid')
"""
Explanation: Bikeshare Ridership
Notebook to predict the number of riders per day ... |
JustinNoel1/ML-Course | exercise-sessions/Session-11/Session-11.ipynb | apache-2.0 | import numpy as np
import pandas as pd
"""
Explanation: Problem Set 11
First the exercise:
* What is the maximum depth of a decision tree trained on $N$ samples?
The decision tree must make a proper split at each node, so the size of each node must reduce by at least one as we move down one level. So the maximum depth... |
yunqu/PYNQ | boards/Pynq-Z1/base/notebooks/arduino/arduino_grove_gesture.ipynb | bsd-3-clause | from pynq.overlays.base import BaseOverlay
base = BaseOverlay("base.bit")
"""
Explanation: Grove Gesture Example
This example shows how to use the
Grove gesture sensor on the board.
The gesture sensor can detect 10 gestures as follows:
| Raw value read by sensor | Gesture |
|--------------------------|---... |
relf/smt | tutorial/SMT_Noise.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import KRG
# defining the training data
xt = np.array([0.0, 1.0, 2.0, 2.5, 4.0])
yt = np.array([0.0, 1.0, 1.5, 1.1, 1.0])
# defining the models
sm_noise_free = KRG() # noise-free Kriging model
sm_noise_fixed = KRG(noise0=[1e-6]) # noisy Krig... |
dwaithe/ONBI_image_analysis | day2_colocalisation/.ipynb_checkpoints/2015 Correlation and Colocalisation practical-checkpoint.ipynb | gpl-2.0 | #This line is very important: (It turns on the inline visuals!)
%pylab inline
a = [2,9,32,12,14,6,9,23,4,5,13,6,7,92,21,45];
b = [7,21,4,2,92,9,9,6,13,12,45,5,6,23,14,32];
#Please calculate the dot product of the vectors 'a' and 'b'.
#You may use any method you like. If get stuck. Check:
#http://docs.scipy.org/doc/num... |
schoolie/bokeh | examples/howto/charts/deep_dive-attributes.ipynb | bsd-3-clause | from bokeh.charts.attributes import AttrSpec, ColorAttr, MarkerAttr
"""
Explanation: Bokeh Charts Attributes
One of Bokeh Charts main contributions is that it provides a flexible interface for applying unique attributes based on the unique values in column(s) of a DataFrame.
Internally, the bokeh chart uses the AttrSp... |
Diyago/Machine-Learning-scripts | DEEP LEARNING/NLP/LSTM RNN/Toxic multiclass prediction Glove + Bidirection LSTM.ipynb | apache-2.0 | EMBEDDING_FILE = f'glove.6B.50d.txt'
TRAIN_DATA_FILE = f'train.csv'
TEST_DATA_FILE = f'test.csv'
"""
Explanation: We include the GloVe word vectors in our input files. To include these in your kernel, simple click 'input files' at the top of the notebook, and search 'glove' in the 'datasets' section.
End of explanatio... |
qutip/qutip-notebooks | docs/guide/Eseries.ipynb | lgpl-3.0 | %matplotlib inline
import numpy as np
from pylab import *
from qutip import *
"""
Explanation: Eseries Class
Contents
Exponential-Series Representation of Quantum Objects
Applications of Exponential-Series
End of explanation
"""
es1 = eseries(sigmax(), 1j)
"""
Explanation: <a id='exponential'></a>
Exponential-Seri... |
sujitpal/intro-dl-talk-code | src/01-nonlinearity.ipynb | unlicense | from __future__ import division, print_function
from sklearn.cross_validation import train_test_split
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout
from keras.utils import np_utils
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
def read_dataset(fil... |
marcinofulus/teaching | Python4physicists_SS2017/Python4hum-Jupyter_intro-from0.ipynb | gpl-3.0 | for i in range(4):
print(i)
%matplotlib notebook
import matplotlib.pyplot as plt
import numpy as np
X = np.linspace(-np.pi, np.pi, 656)
F = np.sin(1/(X**2+0.07))
plt.plot(X,F)
"""
Explanation: Jupyter notebook
Sposoby interakcji z programem komputerowym:
terminal tekstowy
GUI
notatnik (NEW!)
Jupyter
Środowisko... |
shngli/Data-Mining-Python | Mining massive datasets/MapReduce SVM.ipynb | gpl-3.0 | from collections import defaultdict
import math
# determine if an integer n is a prime number
def isPrime(n):
if n == 2:
return True
if n%2 == 0 or n <= 1:
return False
sqr = int(math.sqrt(n)) + 1
for divisor in range(3, sqr, 2):
if n%divisor == 0:
return False
r... |
d00d/quantNotebooks | Notebooks/quantopian_research_public/notebooks/lectures/p-Hacking_and_Multiple_Comparisons_Bias/notebook.ipynb | unlicense | import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
"""
Explanation: p-Hacking and Multiple Comparisons Bias
By Delaney Mackenzie and Maxwell Margenot.
Part of the Quantopian Lecture Series:
www.quantopian.com/lectures
github.com/quantopian/research_public
Notebook rele... |
probml/pyprobml | notebooks/misc/GCP_CC_TPU_Pod_Slice_JAX.ipynb | mit | # Hints from :
# https://medium.com/analytics-vidhya/how-to-access-files-from-google-cloud-storage-in-colab-notebooks-8edaf9e6c020
# https://stackoverflow.com/questions/57772453/login-on-colab-with-gcloud-without-service-account
"""
Explanation: <a href="https://colab.research.google.com/github/probml/probml-notebooks... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch3-Example_3-01.ipynb | unlicense | %pylab notebook
"""
Explanation: Electric Machinery Fundamentals 5th edition
Chapter 3 (Code examples)
Example 3-1
Calculate the net magetic field produced by a three-phase stator.
Import the PyLab namespace (provides set of useful commands and constants like $\pi$)
End of explanation
"""
bmax = 1 # Normal... |
yevheniyc/C | 1d_Biopython_Cookbook/Chapter_1.ipynb | mit | p = (4, 5, 6, 7)
x, y, z, w = p # x -> 4
data = ['ACME', 50, 91.1, (2012, 12, 21)]
name, _, price, date = data # name -> 'ACME', data -> (2012, 12, 21)
s = 'Hello'
a, b, c, d, e = s # a -> H
p = (4, 5)
x, y, z = p # "ValueError"
"""
Explanation: Chapter 1 - Data Structures and Algorithms
1.1 Unpacking a Sequence ... |
kyledef/jammerwebscraper | Scrape Newsday.ipynb | mit | # Import dependencies (i.e. packages that extend the standard language to perform specific [advance] functionality)
import urllib
import urllib2
from datetime import datetime, date, timedelta
from bs4 import BeautifulSoup
"""
Explanation: Web Scraping in Python Series
Introduction
The system will provide a simple exam... |
Danghor/Algorithms | Python/Chapter-09/Union-Find-OO.ipynb | gpl-2.0 | class UnionFind:
def __init__(self, M):
self.mParent = { x: x for x in M }
self.mHeight = { x: 1 for x in M }
"""
Explanation: An Object-Oriented Implementation of the Union-Find Algorithm
The class UnionFind maintains three member variables:
- mParent is a dictionary that assigns each node to it... |
tensorflow/probability | tensorflow_probability/examples/jupyter_notebooks/Bayesian_Gaussian_Mixture_Model.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, sof... |
balarsen/pymc_learning | StateSpace/Bayesian state space estimation in Python via Metropolis-Hastings.ipynb | bsd-3-clause |
%matplotlib inline
import numpy as np
import pandas as pd
import pymc as mc
from scipy import signal
import statsmodels.api as sm
import matplotlib.pyplot as plt
np.set_printoptions(precision=4, suppress=True, linewidth=120)
"""
Explanation: Bayesian state space estimation in Python via Metropolis-Hastings
This pos... |
ling7334/tensorflow-get-started | mnist/TensorFlow_Mechanics_101.ipynb | apache-2.0 | data_sets = input_data.read_data_sets(FLAGS.train_dir, FLAGS.fake_data)
"""
Explanation: TensorFlow运作方式入门
代码：tensorflow/examples/tutorials/mnist/
本篇教程的目的，是向大家展示如何利用TensorFlow使用（经典）MNIST数据集训练并评估一个用于识别手写数字的简易前馈神经网络（feed-forward neural network）。我们的目标读者，是有兴趣使用TensorFlow的资深机器学习人士。
因此，撰写该系列教程并不是为了教大家机器学习领域的基础知识。
在学习本教程之前，请确... |
ocelot-collab/ocelot | demos/ipython_tutorials/9_thz_source.ipynb | gpl-3.0 | # To activate interactive matplolib in notebook
# %matplotlib notebook
from ocelot import *
from ocelot.gui import *
import time
#Initial Twiss parameters
tws0 = Twiss()
tws0.beta_x = 29.171
tws0.beta_y = 29.171
tws0.alpha_x = 10.955
tws0.alpha_y = 10.955
tws0.gamma_x = 4.148367385417024
tws0.gamma_y = 4.14836738541... |
hanezu/cs231n-assignment | 17-assignment2/TensorFlow.ipynb | mit | import tensorflow as tf
import numpy as np
import math
import timeit
import matplotlib.pyplot as plt
%matplotlib inline
from cs231n.data_utils import load_CIFAR10
def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=10000):
"""
Load the CIFAR-10 dataset from disk and perform preprocessing to... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/custom/sdk-custom-image-classification-online.ipynb | apache-2.0 | import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG = "--user"
! pip install {U... |
IS-ENES-Data/submission_forms | dkrz_forms/Templates/ESGF_replication_submission_form.ipynb | apache-2.0 | # Evaluate this cell to identifiy your form
from dkrz_forms import form_widgets, form_handler, checks
form_infos = form_widgets.show_selection()
# Evaluate this cell to generate your personal form instance
form_info = form_infos[form_widgets.FORMS.value]
sf = form_handler.init_form(form_info)
form = sf.sub.entity_o... |
w4zir/ml17s | lectures/lec07-logistic-regression.ipynb | mit | from IPython.display import Image
Image(filename='images/06_03.jpg', width=1000)
"""
Explanation: CSAL4243: Introduction to Machine Learning
Muhammad Mudassir Khan (mudasssir.khan@ucp.edu.pk)
Lecture 7: Logistic Regression
Overview
Logistic Regression
Resources
Credits
<br>
<br>
K - Nearest Neighbor Classifier
End o... |
mne-tools/mne-tools.github.io | 0.23/_downloads/f574d1e7527e4460eb09a16f6f836e35/60_maxwell_filtering_sss.ipynb | bsd-3-clause | import os
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import mne
from mne.preprocessing import find_bad_channels_maxwell
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
... |
dlsun/symbulate | labs/Lab 3 - Discrete Distributions.ipynb | mit | from symbulate import *
%matplotlib inline
"""
Explanation: Symbulate Lab 3 - Discrete Distributions
This Jupyter notebook provides a template for you to fill in. Read the notebook from start to finish, completing the parts as indicated. To run a cell, make sure the cell is highlighted by clicking on it, then press ... |
zegnus/self-driving-car-machine-learning | p13-final-project/ros/src/tl_detector/light_classification/scripts/visualize_bosch.ipynb | mit | import os, yaml
import glob
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas.io.json import json_normalize
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Visualize the Bosch Small Traffic Lights Dataset
The Bosch small traffic lights datase... |
agushman/coursera | src/cours_2/week_5/task_3.ipynb | mit | from sklearn import datasets
digits = datasets.load_digits()
X = digits.data
y = digits.target
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, random_state=0)
def write_answer(data, file_name):
with open(file_name, 'w') as fout:
... |
QuantStack/quantstack-talks | 2018-11-14-PyParis-widgets/notebooks/3.ipyleaflet.ipynb | bsd-3-clause | from ipyleaflet import Map, basemaps, basemap_to_tiles
center = (52.204793, 360.121558)
m = Map(
layers=(basemap_to_tiles(basemaps.NASAGIBS.ModisTerraTrueColorCR, "2018-11-12"), ),
center=center,
zoom=4
)
m
"""
Explanation: <center><img src="src/ipyleaflet.svg" width="50%"></center>
Repository: https://... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/art_and_science_of_ml/labs/export_data_from_bq_to_gcs.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
%pip install google-cloud-bigquery==1.25.0
"""
Explanation: Exporting data from BigQuery to Google Cloud Storage
In this notebook, we export BigQuery data to GCS so that we can reuse our Keras model that was developed on CSV data.
End of explanation
"... |
arcyfelix/Courses | 17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/02-NumPy/2-Numpy-Indexing-and-Selection.ipynb | apache-2.0 | import numpy as np
#Creating sample array
arr = np.arange(0, 11)
#Show
arr
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
<center>Copyright Pierian Data 2017</center>
<center>For more information, visit us at www.pieriandata.com</center>
NumPy Indexing and Selectio... |
NYUDataBootcamp/Projects | MBA_S16/Ahmad-Shah-NBA Contract Analysis.ipynb | mit | import sys # system module
import pandas as pd # data package
import matplotlib.pyplot as plt # graphics module
import datetime as dt # date and time module
import numpy as np # foundation for Pandas
%matplotlib inline ... |
r1rajiv92/data-512-a1 | hcds-a1-data-curation.ipynb | mit | import requests
import pandas
endpoint = 'https://wikimedia.org/api/rest_v1/metrics/pageviews/aggregate/{project}/{access}/{agent}/{granularity}/{start}/{end}'
headers={'User-Agent' : 'https://github.com/r1rajiv92', 'From' : 'rajiv92@uw.edu'}
yearMonthCombinations = { '2015' : [ 7, 8, 9, 10, 11, 12],
... |
sdpython/ensae_teaching_cs | _doc/notebooks/td1a/td1a_cenonce_session2.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 1A.1 - Variables, boucles, tests
Répétitions de code, exécuter une partie plutôt qu'une autre.
End of explanation
"""
i = 3 # entier = type numérique (type int)
r = 3.3 # réel = type numérique (type... |
steinam/teacher | jup_notebooks/data-science-ipython-notebooks-master/aws/aws.ipynb | mit | !ssh -i key.pem ubuntu@ipaddress
"""
Explanation: This notebook was prepared by Donne Martin. Source and license info is on GitHub.
Amazon Web Services (AWS)
SSH to EC2
Boto
S3cmd
s3-parallel-put
S3DistCp
Redshift
Kinesis
Lambda
<h2 id="ssh-to-ec2">SSH to EC2</h2>
Connect to an Ubuntu EC2 instance through SSH with ... |
pastas/pasta | examples/notebooks/07_non_linear_recharge.ipynb | mit | import pandas as pd
import pastas as ps
import matplotlib.pyplot as plt
ps.show_versions(numba=True)
ps.set_log_level("INFO")
"""
Explanation: Non-linear recharge models
R.A. Collenteur, University of Graz
This notebook explains the use of the RechargeModel stress model to simulate the combined effect of precipitatio... |
ES-DOC/esdoc-jupyterhub | notebooks/cmcc/cmip6/models/cmcc-esm2-sr5/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cmcc', 'cmcc-esm2-sr5', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: CMCC
Source ID: CMCC-ESM2-SR5
Topic: Ocean
Sub-Topics: Timestepping Framework, A... |
efoley/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 analysis.... |
mmadsen/experiment-seriation-classification | analysis/sc-1-3/sc-1-seriation-feature-engineering.ipynb | apache-2.0 | import numpy as np
import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import cPickle as pickle
from copy import deepcopy
%matplotlib inline
plt.style.use("fivethirtyeight")
sns.set()
all_graphs = pickle.load(open("train-cont-graphs.pkl",'r'))
all_labels = pickle.load(open(... |
pushpajnc/models | predicting-house-prices/housing-project-V1.ipynb | mit | # Import libraries necessary for this project
import numpy as np
import pandas as pd
import visuals as vs # Supplementary code
from sklearn.cross_validation import ShuffleSplit
from IPython.display import display
# Pretty display for notebooks
%matplotlib inline
# Load the Boston housing dataset
data = pd.read_csv('h... |
dsacademybr/PythonFundamentos | Cap06/Notebooks/DSA-Python-Cap06-08-Retornando Dados do MongoDB.ipynb | gpl-3.0 | # Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
"""
Explanation: <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 6</font>
Download: http://github.com/dsacademybr
End of explanation
"""
# Impor... |
stephenpardy/PythonNotebooks | astro/IntroIllustrisNotebook.ipynb | gpl-2.0 | !pip install astropy
import numpy as np
import matplotlib.pyplot as plt
import h5py
import astropy.table as atpy
import requests
import os
%matplotlib inline
#input your own api key; your key is listed here after login: http://www.illustris-project.org/data/
apikey=
def get(path, params=None):
# make HTTP GE... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/structured/labs/5b_deploy_keras_ai_platform_babyweight.ipynb | apache-2.0 | import os
"""
Explanation: LAB 5b: Deploy and predict with Keras model on Cloud AI Platform.
Learning Objectives
Setup up the environment
Deploy trained Keras model to Cloud AI Platform
Online predict from model on Cloud AI Platform
Batch predict from model on Cloud AI Platform
Introduction
In this notebook, we'll ... |
NAU-CFL/Python_Learning_Source | 06_Functions_Lecture.ipynb | mit | def average(n1, n2, n3): # Function Header
# Function Body
res = (n1+n2+n3)/3.0
return res
num1 = 10
num2 = 25
num3 = 16
print(average(num1, num2, num3))
average(100, 90, 29)
average(1.2, 6.7, 8)
def power(n1, n2):
return (n1 ** n2)
print(power(2, 3))
2**3
"""
Explanation: Functions
Sometimes wh... |
drphilmarshall/StatisticalMethods | tutorials/Week3/Metropolis.ipynb | gpl-2.0 | import numpy as np
import statsmodels.api as sm
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import scipy.stats
%matplotlib inline
class SolutionMissingError(Exception):
def __init__(self):
Exception.__init__(self,"You need to complete the solution for this code to work!")
def ... |
d00d/quantNotebooks | Notebooks/quantopian_research_public/notebooks/lectures/Random_Variables/notebook.ipynb | unlicense | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.stats as stats
from statsmodels.stats import stattools
from __future__ import division
"""
Explanation: Discrete and Continuous Random Variables
by Maxwell Margenot
Revisions by Delaney Granizo Mackenzie
Part of the Quantopian Le... |
GoogleCloudPlatform/ml-design-patterns | 03_problem_representation/neutral.ipynb | apache-2.0 | import numpy as np
import pandas as pd
def create_synthetic_dataset(N, shuffle):
# random array
prescription = np.full(N, fill_value='acetominophen', dtype='U20')
prescription[:N//2] = 'ibuprofen'
np.random.shuffle(prescription)
# neutral class
p_neutral = np.full(N, fill_value='Neutral', ... |
pligor/predicting-future-product-prices | 02_preprocessing/exploration03-price_history_standardization.ipynb | agpl-3.0 | stds_threshold = std*3
stds_threshold
min(df_norm_prices.iloc[0])
"""
Explanation: Trust only up to three standard deviations.
Which is expected, ~75 euros difference from the original price is the maximum of what
normally see as a customer
End of explanation
"""
keep_inds = [ii for ii in range(len(df_norm_prices))... |
j-coll/opencga | opencga-client/src/main/python/notebooks/pyopencga_basic_notebook_003-variants.ipynb | apache-2.0 | # Initialize PYTHONPATH for pyopencga
import sys
import os
from pprint import pprint
cwd = os.getcwd()
print("current_dir: ...."+cwd[-10:])
base_modules_dir = os.path.dirname(cwd)
print("base_modules_dir: ...."+base_modules_dir[-10:])
sys.path.append(base_modules_dir)
from pyopencga.opencga_config import ConfigClie... |
RaoUmer/lightning-example-notebooks | images/image-poly.ipynb | mit | from lightning import Lightning
from sklearn import datasets
"""
Explanation: <img style='float: left' src="http://lightning-viz.github.io/images/logo.png"> <br> <br> Image polygon plots in <a href='http://lightning-viz.github.io/'><font color='#9175f0'>Lightning</font></a>
<hr> Setup
En... |
sanabasangare/data-visualization | fin_MPT.ipynb | mit | import numpy as np
import pandas as pd
from pandas_datareader import data as web
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
%matplotlib inline
import warnings; warnings.simplefilter('ignore')
"""
Explanation: Modern Portfolio Theory (MPT) analysis with python
Modern portfolio theory (MPT) also k... |
thunder-project/thunder-docs | tutorials/registration.ipynb | mit | %matplotlib inline
import seaborn as sns
import matplotlib.pyplot as plt
from showit import image, tile
sns.set_style('darkgrid')
sns.set_context('notebook')
import thunder as td
"""
Explanation: Image registration
A common problem when working with collections of images is registering or aligning them, relative to ... |
basp/aya | .ipynb_checkpoints/noise_old-checkpoint.ipynb | mit | v0 = 2
v1 = 5
plt.plot([0, 1], [2, 5], '--')
t = 1.0 / 3
vt = noise.lerp(2, 5, t)
plt.plot(t, vt, 'ro')
"""
Explanation: linear interpolation
We need a function ${f}$ that, given values ${v_0}$ and ${v_1}$ and some interval ${t}$ where $0 \le {t} \le 1$, returns an interpolated value between ${v_0}$ and ${v_1}$.
The ... |
Condla/notebooks | IPythonMachineLearningIntro.ipynb | gpl-2.0 | %matplotlib inline
from sklearn import datasets
from sklearn import linear_model
from sklearn import cross_validation
import matplotlib.pyplot as plt
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
"""
Explanation: Introduction: Python + Machine Learning
This IPython notebook is public, can be ... |
pycrystem/pycrystem | doc/demos/02 GaAs Nanowire - Phase Mapping - Orientation Mapping.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import diffpy.structure
import pyxem as pxm
import hyperspy.api as hs
accelarating_voltage = 200 # kV
camera_length = 0.2 # m
diffraction_calibration = 0.032 # px / Angstrom
"""
Explanation: Phase/Orientation Mapping
This tutorial demonstrates how to achieve phase and orienta... |
JasonSanchez/w261 | exams/w261mt/Midterm MRjob code.ipynb | mit | %matplotlib inline
import numpy as np
import pylab
size = 1000
x = np.random.uniform(-40, 40, size)
y = x * 1.0 - 4 + np.random.normal(0,5,size)
data = zip(range(size),y,x)
#data = np.concatenate((y, x), axis=1)
np.savetxt('LinearRegression.csv',data,'%i,%f,%f')
data[:10]
"""
Explanation: DATASCI W261: Machine Learn... |
flohorovicic/pynoddy | docs/notebooks/Feature-Analysis.ipynb | gpl-2.0 | from IPython.core.display import HTML
css_file = 'pynoddy.css'
HTML(open(css_file, "r").read())
import sys, os
import matplotlib.pyplot as plt
# adjust some settings for matplotlib
from matplotlib import rcParams
# print rcParams
rcParams['font.size'] = 15
# determine path of repository to set paths corretly below
rep... |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/cmip6/models/hadgem3-gc31-ll/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'hadgem3-gc31-ll', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: MOHC
Source ID: HADGEM3-GC31-LL
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, ... |
poolio/unrolled_gan | Unrolled GAN demo.ipynb | mit | %pylab inline
from collections import OrderedDict
import tensorflow as tf
ds = tf.contrib.distributions
slim = tf.contrib.slim
from keras.optimizers import Adam
try:
from moviepy.video.io.bindings import mplfig_to_npimage
import moviepy.editor as mpy
generate_movie = True
except:
print("Warnin... |
TheKingInYellow/PySeidon | PySeidon_tuto_3.ipynb | agpl-3.0 | %pylab inline
"""
Explanation: PySeison - Tutorial 3: ADCP class
End of explanation
"""
from pyseidon import *
"""
Explanation: 1. PySeidon - ADCP object initialisation
Similarly to the "TideGauge class" and the "Drifter class", the "ADCP class" is a measurement-based object.
1.1. Package importation
As any other l... |
intel-analytics/BigDL | python/orca/colab-notebook/quickstart/autoxgboost_regressor_sklearn_boston.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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed un... |
metpy/MetPy | dev/_downloads/324acb7faa1ec1d6ac5849ea2223364d/Smoothing.ipynb | bsd-3-clause | from itertools import product
import matplotlib.pyplot as plt
import numpy as np
import metpy.calc as mpcalc
"""
Explanation: Smoothing
Using MetPy's smoothing functions.
This example demonstrates the various ways that MetPy's smoothing function
can be utilized. While this example utilizes basic NumPy arrays, these
... |
ioam/scipy-2017-holoviews-tutorial | solutions/07-working-with-large-datasets-with-solutions.ipynb | bsd-3-clause | import pandas as pd
import holoviews as hv
import dask.dataframe as dd
import datashader as ds
import geoviews as gv
from holoviews.operation.datashader import datashade, aggregate
hv.extension('bokeh')
"""
Explanation: <a href='http://www.holoviews.org'><img src="assets/hv+bk.png" alt="HV+BK logos" width="40%;" alig... |
karlstroetmann/Artificial-Intelligence | Python/5 Linear Regression/Simple-Linear-Regression-with-SciKit-Learn.ipynb | gpl-2.0 | import pandas as pd
"""
Explanation: Simple Linear Regression with SciKit-Learn
We import the module pandas. This module implements so called <em style="color:blue;">data frames</em> and is more convenient than the module csv when reading a <tt>csv</tt> file.
End of explanation
"""
cars = pd.read_csv('cars.csv')
ca... |
jamesdj/tobit | tobit.ipynb | mit | rs = np.random.RandomState(seed=10)
ns = 100
nf = 10
x, y_orig, coef = make_regression(n_samples=ns, n_features=nf, coef=True, noise=0.0, random_state=rs)
x = pd.DataFrame(x)
y = pd.Series(y_orig)
n_quantiles = 3 # two-thirds of the data is truncated
quantile = 100/float(n_quantiles)
lower = np.percentile(y, quantile)... |
cucs-numpde/class | Fundamentals.ipynb | bsd-2-clause | %matplotlib notebook
import numpy
from matplotlib import pyplot
pyplot.style.use('ggplot')
def u_n(n):
x = numpy.linspace(0,1,n)
return x, 1 + x + x**2/2 + x**3/6
for n in (40, 20, 10):
x, y = u_n(n)
pyplot.plot(x, y, 'o', label='$u_{%d}(x)$' % n)
pyplot.plot(x, numpy.exp(x), label='$\exp(x)$')
pyplot... |
kvr777/deep-learning | tv-script-generation/.ipynb_checkpoints/dlnd_tv_script_generation-checkpoint.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scrip... |
jdhp-docs/python-notebooks | python_collections_en.ipynb | mit | import collections
"""
Explanation: Import directives
End of explanation
"""
d = collections.OrderedDict()
d["2"] = 2
d["3"] = 3
d["1"] = 1
print(d)
print(type(d.keys()))
print(list(d.keys()))
print(type(d.values()))
print(list(d.values()))
for k, v in d.items():
print(k, v)
"""
Explanation: Ordered dictio... |
oroszl/mezo | 1D.ipynb | gpl-3.0 | %pylab inline
from ipywidgets import *
"""
Explanation: Exploring 1D scattering problems on a lattice
First let us load matplotlib and numpy by evoking pylab and also let us import interactive widgets from ipywidgets. This is a quick and easy way to set up a simple environment for numerical calculations.
End of explan... |
bosscha/alma-calibrator | notebooks/2mass/11_PCA_combine_test_matchagain.ipynb | gpl-2.0 | #obj = ["3C 454.3", 343.49062, 16.14821, 1.0]
#obj = ["PKS J0006-0623", 1.55789, -6.39315, 1.0]
obj = ["M87", 187.705930, 12.391123, 1.0]
#### name, ra, dec, radius of cone
obj_name = obj[0]
obj_ra = obj[1]
obj_dec = obj[2]
cone_radius = obj[3]
obj_coord = coordinates.SkyCoord(ra=obj_ra, dec=obj_dec, unit=(u.deg,... |
UltronAI/Deep-Learning | CS231n/assignment3/StyleTransfer-TensorFlow.ipynb | mit |
%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... |
sns-chops/multiphonon | tests/notebooks/getdos-multiple-Ei.ipynb | mit | # where am I now?
!pwd
# create a new working directory and change into it
workdir = '~/reduction/ARCS/getdos-multiple-Ei-demo'
!mkdir -p {workdir}
%cd {workdir}
# Data to reduce. Change the IPTS number and run numbers to suit your need
samplenxs = "/SNS/ARCS/IPTS-15398/shared/mantid_reduce/non-radC/non-radC_130p00.n... |
turbomanage/training-data-analyst | courses/machine_learning/cloudmle/cloudmle.ipynb | apache-2.0 | import os
PROJECT = 'cloud-training-demos' # REPLACE WITH YOUR PROJECT ID
REGION = 'us-central1' # Choose an available region for Cloud MLE from https://cloud.google.com/ml-engine/docs/regions.
BUCKET = 'cloud-training-demos-ml' # REPLACE WITH YOUR BUCKET NAME. Use a regional bucket in the region you selected.
# for b... |
radajin/whoscored | recomend_position/recomend_position_1.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_formats = {'png', 'retina'}
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
import pandas as pd
import MySQLdb
from sklearn.tree import export_graphviz
from sklearn.cross_validation import train_test_split
from sklearn.m... |
tbarrongh/cosc-learning-labs | src/notebook/03_management_interface.ipynb | apache-2.0 | help('learning_lab.03_management_interface')
"""
Explanation: COSC Learning Lab
03_management_interface.py
Related Scripts:
* 03_interface_configuration.py
* 01_device_control.py
* 01_inventory_mounted.py
Table of Contents
Table of Contents
Documentation
Implementation
Execution
HTTP
Documentation
End of explanation... |
tensorflow/docs | site/en/guide/migrate/model_mapping.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... |
tridesclous/tridesclous | example/example_locust_dataset.ipynb | mit | %matplotlib inline
import time
import numpy as np
import matplotlib.pyplot as plt
import tridesclous as tdc
from tridesclous import DataIO, CatalogueConstructor, Peeler
"""
Explanation: tridesclous example with locust dataset
Here a detail notebook that detail the locust dataset recodring by Christophe Pouzat.
This ... |
quantopian/alphalens | alphalens/examples/intraday_factor.ipynb | apache-2.0 | %pylab inline --no-import-all
import alphalens
import pandas as pd
import numpy as np
import datetime
import warnings
warnings.filterwarnings('ignore')
"""
Explanation: Alphalens: intraday factor
In this notebook we use Alphalens to analyse the performance of an intraday factor, which is computed daily but the stocks... |
bjackman/lisa | ipynb/examples/wlgen/rtapp_example.ipynb | apache-2.0 | import logging
from conf import LisaLogging
LisaLogging.setup()
# Generate plots inline
%pylab inline
import json
import os
# Support to initialise and configure your test environment
import devlib
from env import TestEnv
# Support to configure and run RTApp based workloads
from wlgen import RTA, Periodic, Ramp, St... |
anonyXmous/CapstoneProject | Mini_Project_Naive_Bayes.ipynb | unlicense | %matplotlib inline
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from six.moves import range
# Setup Pandas
pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('... |
anonyXmous/CapstoneProject | Mini_Project_Clustering.ipynb | unlicense | %matplotlib inline
import pandas as pd
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
# Setup Seaborn
sns.set_style("whitegrid")
sns.set_context("poster")
"""
Explanation: Customer Segmentation using Clustering
This mini-project is based on this blog post by yhat. Please feel free to refer to t... |
oemof/examples | oemof_examples/oemof.solph/v0.4.x/jupyter_tutorials/1_Simple_dispatch_store_results.ipynb | gpl-3.0 | import os
import pandas as pd
from oemof.solph import (Sink, Source, Transformer, Bus, Flow, Model,
EnergySystem, processing, views)
import pickle
"""
Explanation: Energy system optimisation with oemof - how to collect and store results
Import necessary modules
End of explanation
"""
solver... |
gregnordin/ECEn360_Winter2016 | transmission_lines/01c_standingwaveanimation.ipynb | mit | import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation
# Switch to a backend that supports FuncAnimation
plt.switch_backend('tkagg')
print 'Matplotlib graphics backend in use:',plt.get_backend()
"""
Explanation: Sinusoidal Steady State Voltage on a Transmission Line
The voltage on a ... |
atulsingh0/MachineLearning | ML_UoW/Course00_MLFoundation/03_Classification_Analyzing_Product_Sentiment-Quiz.ipynb | gpl-3.0 | # ignoring the 3 star rating
data2 = data[data['rating'] != 3 ]
data2['sentiment'] = data2['rating'] > 3
data2.head(5)
# training the classifier model
# first, spliting the data into train and test datasets
train_data, test_data = data2.random_split(0.8, seed=0)
sentiment_model = gl.logistic_classifier.create(trai... |
iRipVanWinkle/ml | Data Science UA - September 2017/Lecture 04 - Overview of Linear Algebra and Matrix Computations/Finding a Root of a Function - Bisection and Newton Methods.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Finding the Root (Zero) of a Function
Finding the root, or zero, of a function is a very common task in exploratory computing. This Notebook presents the Bisection method and Newton's method for finding the root, or 0, of a function... |
fastai/course-v3 | zh-nbs/Lesson5_sgd_mnist.ipynb | apache-2.0 | %matplotlib inline
from fastai.basics import *
"""
Explanation: Practical Deep Learning for Coders, v3
Lesson5_sgd_mnist
End of explanation
"""
path = Config().data_path()/'mnist'
path.ls()
with gzip.open(path/'mnist.pkl.gz', 'rb') as f:
((x_train, y_train), (x_valid, y_valid), _) = pickle.load(f, encoding='la... |
phoebe-project/phoebe2-docs | 2.0/examples/single_spots.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.0,<2.1"
"""
Explanation: Single Star with Spots
Setup
Let's first make sure we have the latest version of PHOEBE 2.0 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
fro... |
ML4DS/ML4all | R5.Bayesian_Regression/.ipynb_checkpoints/Bayesian_regression-checkpoint.ipynb | mit | # Import some libraries that will be necessary for working with data and displaying plots
# To visualize plots in the notebook
%matplotlib inline
from IPython import display
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.io # To read matlab files
import pylab
import time
"""... |
materialsproject/mapidoc | index.ipynb | bsd-3-clause | # We start by importing MPRester, which is available from the root import of pymatgen.
from pymatgen import MPRester
from pprint import pprint
# Initializing MPRester. Note that you can call MPRester. MPRester looks for the API key in two places:
# - Supplying it directly as an __init__ arg.
# - Setting the "MAPI_KEY... |
zomansud/coursera | ml-regression/week-2/week-2-multiple-regression-assignment-1-blank.ipynb | mit | import graphlab
"""
Explanation: Regression Week 2: Multiple Regression (Interpretation)
The goal of this first notebook is to explore multiple regression and feature engineering with existing graphlab functions.
In this notebook you will use data on house sales in King County to predict prices using multiple regressi... |
ChadFulton/statsmodels | examples/notebooks/ols.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import print_function
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.sandbox.regression.predstd import wls_prediction_std
np.random.seed(9876789)
"""
Explanation: Ordinary Least Squares
End of explanation
"""
nsample = 100
x = np.... |
gdementen/larray | doc/source/tutorial/tutorial_aggregations.ipynb | gpl-3.0 | from larray import *
"""
Explanation: Aggregations
Import the LArray library:
End of explanation
"""
# load the 'demography_eurostat' dataset
demography_eurostat = load_example_data('demography_eurostat')
# extract the 'country', 'gender' and 'time' axes
country = demography_eurostat.country
gender = demography_eur... |
darcamo/pyphysim | apps/comp_BD/Block Diagonalization.ipynb | gpl-2.0 | %pylab inline
"""
Explanation: Simulation Results for the Enhanced Block Diagonalization algorithm
Initializations
Here we import some packages and do some initialization.
End of explanation
"""
import sys
sys.path.append("/home/darlan/cvs_files/pyphysim/")
# xxxxxxxxxx Import Statements xxxxxxxxxxxxxxxxxxxxxxxxxxxx... |
akshaybabloo/Car-ND | Term_1/CNN_5/LeNet_8/LeNet_8_2.ipynb | mit | from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("./MNIST_data/", reshape=False)
X_train, y_train = mnist.train.images, mnist.train.labels
X_validation, y_validation = mnist.validation.images, mnist.validation.labels
X_test, y_test = mnist.test.images, ... |
bearing/dosenet-analysis | Programming Lesson Modules/Module 3- Simple Plots and Histograms.ipynb | mit | %matplotlib inline
# Enables IPython matplotlib mode which allows plots to be shown in
# markdown sections. Not necessary in functionality of code.
import csv
import io
import urllib.request
import matplotlib.pyplot as plt
# matplotlib is one of the most frequently used Python extensions for p... |
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