repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
phobson/mpl-probscale | docs/tutorial/closer_look_at_viz.ipynb | bsd-3-clause | %matplotlib inline
import warnings
warnings.simplefilter('ignore')
import numpy
from matplotlib import pyplot
import seaborn
import probscale
clear_bkgd = {'axes.facecolor':'none', 'figure.facecolor':'none'}
seaborn.set(style='ticks', context='talk', color_codes=True, rc=clear_bkgd)
# load up some example data from... |
phoebe-project/phoebe2-docs | 2.2/tutorials/apsidal_motion.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Apsidal Motion
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
"""
%matplotlib inline
impor... |
DataPilot/notebook-miner | summary_of_work/server_notebooks/bottom_up/Bottom Up Random Forest.ipynb | apache-2.0 | import sys
home_directory = '/dfs/scratch2/fcipollone'
sys.path.append(home_directory)
import numpy as np
from nbminer.notebook_miner import NotebookMiner
hw_filenames = np.load('../homework_names_jplag_combined_per_student.npy')
hw_notebooks = [[NotebookMiner(filename) for filename in temp[:59]] for temp in hw_filena... |
arasdar/DL | uri-dl/uri-dl-hw-2/assignment2/BatchNormalization.ipynb | unlicense | # As usual, a bit of setup
from __future__ import print_function
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solv... |
rueedlinger/python-snippets | rss/feedparser.ipynb | mit | import feedparser
url = 'https://medium.com/feed/tag/machine-learning'
resp = feedparser.parse(url)
"""
Explanation: Parse a RSS feed with feedparser
In this Python snippet we use the feedparser package to parse a RSS feed from 'Medium'.
- https://medium.com/feed/tag/machine-learning
Let's get the RSS feed and pars... |
paulcon/active_subspaces | tutorials/test_functions/piston/piston_example.ipynb | mit | import active_subspaces as ac
import numpy as np
%matplotlib inline
# The piston_functions.py file contains two functions: the piston function (piston(xx))
# and its gradient (piston_grad(xx)). Each takes an Mx7 matrix (M is the number of data
# points) with rows being normalized inputs; piston returns a column vector... |
ealogar/curso-python | best-practices/1_Duck_Typing.ipynb | apache-2.0 | # Compare
def let_duck_swim_and_quack(d):
if hasattr(d, "swim") and hasattr(d, "quack"):
d.swim()
d.quack()
else:
print "It does not look like a duck"
raise AttributeError
def let_duck_swim_and_quack(d):
try:
d.swim()
d.quack()
except AttributeError:
... |
biosustain/marsi | notebooks/1.4 Converting Gene or Reaction Knockouts into Metabolite Knockouts.ipynb | apache-2.0 | model = models.bigg.iJO1366
model.reactions.EX_o2_e.lower_bound = 0
"""
Explanation: Design a D-lactate producing strain
In this example we are going to create strain designs using OptGene.
| Model | Medium | Aerobicity | Target |
|------- |---------|-------------|-------------|
| iJO1366| Glucose | Anaerobi... |
hglanz/phys202-2015-work | assignments/assignment04/MatplotlibEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import math
"""
Explanation: Matplotlib Exercise 2
Imports
End of explanation
"""
!head -n 30 open_exoplanet_catalogue.txt
"""
Explanation: Exoplanet properties
Over the past few decades, astronomers have discovered thousands of extrasolar planet... |
wittawatj/kernel-gof | ipynb/gof_mmd_test.ipynb | mit | %load_ext autoreload
%autoreload 2
%matplotlib inline
#%config InlineBackend.figure_format = 'svg'
#%config InlineBackend.figure_format = 'pdf'
import freqopttest.tst as tst
import kgof
import kgof.data as data
import kgof.density as density
import kgof.goftest as gof
import kgof.mmd as mgof
import kgof.kernel as ker
... |
ecervera/ga-nb | nao/Crawling.ipynb | mit | import sys
sys.path.append('c:\\udk\\naoqi-sdk-1.12.5-win32-vs2010\\lib')
from Nao import Nao
"""
Explanation: Evolution of a Crawling Behavior for a Humanoid Robot Using Genetic Algorithms
<img src="files/img/crawling.png" />
Controlling a biped robot with several degrees of freedom is a challenging task. For a human... |
GoogleCloudPlatform/professional-services | examples/cloudml-support-routing/Notebooks/EDA.ipynb | apache-2.0 | query = """
SELECT
date_received,
product, subproduct,
issue, subissue,
consumer_complaint_narrative,
company_public_response,
company_name,
state
FROM
`bigquery-public-data.cfpb_complaints.complaint_database`
"""
query_job = client.query(... |
ajfriend/stacklog | docs/tutorial.ipynb | isc | import stacklog
import time
def comp(t=.1):
time.sleep(t)
"""
Explanation: stacklog
Nested benchmarking and timing code.
Stacklog uses a Logger object to capture timing output, so you can use multiple logging objects at once.
The simplest interface is at the module level, and uses the 'default' Logger.
End of exp... |
ComputationalModeling/spring-2017-danielak | past-semesters/fall_2016/homework/HW5/Homework_5.ipynb | agpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import math
def calc_accel(velocity, parachute_open=False, use_resistance=True):
'''
This function calculates and returns the acceleration vector
for a sky diver, given an input velocity. You can indicate whether the
parachute is... |
isendel/machine-learning | ml-regression/week1-2/numpy-tutorial.ipynb | apache-2.0 | import numpy as np # importing this way allows us to refer to numpy as np
"""
Explanation: Numpy Tutorial
Numpy is a computational library for Python that is optimized for operations on multi-dimensional arrays. In this notebook we will use numpy to work with 1-d arrays (often called vectors) and 2-d arrays (often cal... |
wtbarnes/loops-workshop-2017-talk | notebooks/time_average_intensities.ipynb | mit | import os
from scipy.interpolate import interp1d,splev,splrep
import numpy as np
import astropy.units as u
import astropy.constants as const
from synthesizAR.instruments import InstrumentHinodeEIS
from synthesizAR.util import EISCube
base1 = '/data/datadrive1/ar_forward_modeling/systematic_ar_study/noaa1109_tn{}'
ba... |
phoebe-project/phoebe2-docs | development/examples/diher_misaligned.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
"""
Explanation: DI Her: Misaligned Binary
In this example, we'll reproduce Figure 8 in the misalignment release paper (Horvat et al. 2018).
<img src="horvat+18_fig8.png" alt="Figure 8" width="400px"/>
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (u... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_rereference_eeg.ipynb | bsd-3-clause | # Authors: Marijn van Vliet <w.m.vanvliet@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
from matplotlib import pyplot as plt
print(__doc__)
# Setup for reading the raw data
data_path = sample.data_path()
raw_f... |
michaelneuder/image_quality_analysis | bin/calculations/human_data/old/human_data_sample.ipynb | mit | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import matplotlib.image as mpimg
from PIL import Image
import progressbar
human_df = pd.read_csv('human_data.csv')
human_df.head()
"""
Explanation: human judgement data
examining some of the human judgement data
End of explanation
"""
# data sa... |
blua/deep-learning | gan_mnist/Intro_to_GANs_Exercises.ipynb.BASE.17033.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... |
scikit-optimize/scikit-optimize.github.io | 0.8/notebooks/auto_examples/plots/partial-dependence-plot.ipynb | bsd-3-clause | print(__doc__)
import sys
from skopt.plots import plot_objective
from skopt import forest_minimize
import numpy as np
np.random.seed(123)
import matplotlib.pyplot as plt
"""
Explanation: Partial Dependence Plots
Sigurd Carlsen Feb 2019
Holger Nahrstaedt 2020
.. currentmodule:: skopt
Plot objective now supports optiona... |
temmeand/scikit-rf | doc/source/examples/vectorfitting/vectorfitting_ex2_190ghz_active.ipynb | bsd-3-clause | import skrf
import numpy as np
import matplotlib.pyplot as mplt
"""
Explanation: Ex2: Measured 190 GHz Active 2-Port
The Vector Fitting feature is demonstrated using a 2-port S-matrix of an active circuit measured from 140 GHz to 220 GHz. Additional explanations and background information can be found in the Vector Fi... |
RadoslawDryzner/LeRepoDuGuerrier | Homework04/Homework 4.ipynb | mit | def show_hist(data, title, xlabel, ylabel):
plt.hist(data, range=[0, 40000])
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.show()
def show_box_plot(data1, data2, legend1, legend2):
plt.boxplot([data1, data2])
plt.xticks([1, 2], [legend1, legend2])
plt.show()
def show_bar_p... |
neto71/courses-1 | deeplearning1/nbs/lesson3.ipynb | apache-2.0 | from theano.sandbox import cuda
%matplotlib inline
import utils; reload(utils)
from utils import *
from __future__ import division, print_function
#path = "data/dogscats/sample/"
path = "data/dogscats/"
model_path = path + 'models/'
if not os.path.exists(model_path): os.mkdir(model_path)
batch_size=64
"""
Explanati... |
K3D-tools/K3D-jupyter | examples/snapshots.ipynb | mit | plot = get_plot()
plot.display()
data = plot.get_binary_snapshot()
with open('binary_snapshot.k3d', 'wb') as f:
f.write(data)
"""
Explanation: Create and save to *.k3d file
End of explanation
"""
plot2 = k3d.plot()
with open('binary_snapshot.k3d', 'rb') as f:
plot2.load_binary_snapshot(f.read())
plot2.displ... |
nick-youngblut/SIPSim | ipynb/bac_genome/fullCyc/trimDataset/rep4_DBL-comm_bw_HR.ipynb | mit | import os
import glob
import re
import nestly
%load_ext rpy2.ipython
%load_ext pushnote
%%R
library(ggplot2)
library(dplyr)
library(tidyr)
library(gridExtra)
library(phyloseq)
"""
Explanation: Goal
Trying varying levels of bandwidth and DBL scaling with pre-fractionation abundances ('DBL-comm')
Varying parameters
b... |
martinggww/lucasenlights | MachineLearning/DataScience-Python3/ConditionalProbabilitySolution.ipynb | cc0-1.0 | from numpy import random
random.seed(0)
totals = {20:0, 30:0, 40:0, 50:0, 60:0, 70:0}
purchases = {20:0, 30:0, 40:0, 50:0, 60:0, 70:0}
totalPurchases = 0
for _ in range(100000):
ageDecade = random.choice([20, 30, 40, 50, 60, 70])
purchaseProbability = 0.4
totals[ageDecade] += 1
if (random.random() < pu... |
whitead/numerical_stats | unit_12/lectures/lecture_review.ipynb | gpl-3.0 | plt.plot(pop_x, pop_y, 'o')
plt.xlabel('Year')
plt.ylabel('Population [Millions]')
plt.show()
"""
Explanation: Unit 12, Review
Numerical Methods and Statistics
Prof. Andrew White, April 25 2017
We've seen the following types of regression:
Ordinary least squares with 1 x dimension and 1 y dimension (OLS-1D). Solve w... |
tarunchhabra26/fss16dst | code/8/WS4/tchhabr_performance.ipynb | apache-2.0 | %matplotlib inline
# All the imports
from __future__ import print_function, division
import pom3_ga, sys
import pickle
# TODO 1: Enter your unity ID here
__author__ = "tchhabr"
"""
Explanation: Workshop 4 - Performance Metrics
In this workshop we study 2 performance metrics(Spread and Inter-Generational Distance) on... |
CELMA-project/CELMA | MES/singleOperators/1-DDXCylinder/calculations/exactSolutions.ipynb | lgpl-3.0 | %matplotlib notebook
from sympy import init_printing
from sympy import S
from sympy import sin, cos, tanh, exp, pi, sqrt
from boutdata.mms import x, y, z, t
from boutdata.mms import DDX
import os, sys
# If we add to sys.path, then it must be an absolute path
common_dir = os.path.abspath('./../../../../common')
# Sys... |
regardscitoyens/consultation_an | exploitation/analyse_quanti_theme2.ipynb | agpl-3.0 | def loadContributions(file, withsexe=False):
contributions = pd.read_json(path_or_buf=file, orient="columns")
rows = [];
rindex = [];
for i in range(0, contributions.shape[0]):
row = {};
row['id'] = contributions['id'][i]
rindex.append(contributions['id'][i])
if (withsexe... |
martintb/pe_optimization_tutorial | 03-numba.ipynb | mit | %load_ext memory_profiler
%load_ext snakeviz
%load_ext cython
import holoviews as hv
hv.extension('bokeh','matplotlib')
from IPython.core import debugger
ist = debugger.set_trace
"""
Explanation: Numba
End of explanation
"""
import numpy as np
pos = np.loadtxt('data/positions.dat')
box = np.loadtxt('data/box.dat')
... |
ML4DS/ML4all | R5.Bayesian_Regression/.ipynb_checkpoints/Bayesian_regression_student-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
"""... |
wikistat/Ateliers-Big-Data | Cdiscount/Part1-2-AIF-PythonNltkGensim-FeatureExtraction-Cdiscount.ipynb | mit | #Importation des librairies utilisées
import time
import pandas as pd
import numpy as np
import collections
import itertools
import os
import warnings
warnings.filterwarnings('ignore')
from sklearn.cross_validation import train_test_split
"""
Explanation: Ateliers: Technologies de l'intelligence Artificielle
<center... |
tolaoniyangi/dmc | notebooks/week-5/01-CNN in keras for mnist.ipynb | apache-2.0 | import numpy as np
np.random.seed(1337) # for reproducibility
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
from ker... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/end_to_end_ml/labs/prepare_data_babyweight.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip install --user google-cloud-bigquery==1.25.0
"""
Explanation: Prepare babyweight dataset.
Learning Objectives
Setup up the environment
Preprocess natality dataset
Augment natality dataset
Create the train and eval tables in BigQuery
Export data ... |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/.ipynb_checkpoints/Errors and Exceptions Homework - Solution-checkpoint.ipynb | apache-2.0 | try:
for i in ['a','b','c']:
print i**2
except:
print "An error ocurred!"
"""
Explanation: Errors and Exceptions Homework - Solution
Problem 1
Handle the exception thrown by the code below by using try and except blocks.
End of explanation
"""
x = 5
y = 0
try:
z = x/y
except ZeroDivisionError:
... |
GoogleCloudPlatform/training-data-analyst | blogs/babyweight/train_deploy.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip install --user google-cloud-bigquery==1.25.0
"""
Explanation: <h1> Structured data prediction using Cloud AI Platform </h1>
This notebook illustrates:
<ol>
<li> Create a BigQuery Dataset and Google Cloud Storage Bucket
<li> Export from BigQuery... |
CalebBell/fluids | docs/Data/Sieve Sizes for Particle Size Distributions.ipynb | mit | from fluids.particle_size_distribution import ASTM_E11_sieves, ISO_3310_1_sieves, Sieve
import pandas as pd
pd.options.display.max_rows = 100000
"""
Explanation: Sieve Sizes for Particle Size Distributions
End of explanation
"""
# All units in SI, meters.
dat = {}
for name in Sieve.__slots__:
vals = []
for s... |
necromuralist/machine_learning_studies | machine_learning/udacity/project_1/boston_housing_exploration.ipynb | mit | # Importing a few necessary libraries
# python standard library
import warnings
# third party
import numpy as np
import numpy
import matplotlib.pyplot as pl
from matplotlib import pylab
import pandas
import seaborn
from sklearn import datasets
from sklearn.tree import DecisionTreeRegressor
# Make matplotlib show our ... |
dtamayo/rebound | ipython_examples/Checkpoints.ipynb | gpl-3.0 | import rebound
sim = rebound.Simulation()
sim.add(m=1.)
sim.add(m=1e-6, a=1.)
sim.add(a=2.)
sim.integrator = "whfast"
sim.save("checkpoint.bin")
sim.status()
"""
Explanation: Checkpoints
You can easily save and load a REBOUND simulation to a binary file. The binary file includes all information about the particles (ma... |
nehal96/Deep-Learning-ND-Exercises | Deep Neural Networks/tf-deep-neural-networks.ipynb | mit | import tensorflow as tf
# Hidden Layer with ReLU activation function
hidden_layer = tf.add(tf.matmul(features, hidden_weights), hidden_biases)
hidden_layer = tf.nn.relu(hidden_layer)
output = tf.add(tf.matmul(hidden_layer, output_weights), output_biases)
"""
Explanation: Deep Neural Networks in TensorFlow
Multilayer... |
relopezbriega/mi-python-blog | content/notebooks/DataViz.ipynb | gpl-2.0 | # <!-- collapse=True -->
# importando modulos necesarios
import numpy as np
import pandas as pd
from pydataset import data
import re
# librerías de visualizaciones
import seaborn as sns
import matplotlib.pyplot as plt
from bokeh.io import output_notebook, show
from bokeh.charts import Histogram, Scatter
import folium... |
csc-training/python-introduction | notebooks/answers/Advanced Object oriented programming.ipynb | mit | class Person(object):
def __init__(self, name):
self.name = name
def say_hi(self):
return "Hi, I'm " + self.name
"""
Explanation: Object-oriented programming
Our first example is an example of composition. We create a Company that consists a list of Persons and an account balance.
Then... |
cubewise-code/TM1py-samples | Samples/time_series_forecasting.ipynb | mit | #import tm1 service module
from TM1py.Services import TM1Service
#import tm1 utils module
from TM1py.Utils import Utils
#import pandas
import pandas as pd
#import matplotlib
import matplotlib.pyplot as plt
#inline plotting for matplotlib
%matplotlib inline
#import statsmodels package
from fbprophet import Prophet
"""
... |
QuantCrimAtLeeds/PredictCode | notebooks/sepp_2_testbed.ipynb | artistic-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: The EM algorithm for Hawkes processes
Here we explore the optimisation algorithm for parameter estimation given in
Mohler et al. "Randomized Controlled Field Trials of Predictive Policing". Journal of the American Statistical Assoc... |
droud/courses | deeplearning1/nbs/lesson5.ipynb | apache-2.0 | from keras.datasets import imdb
idx = imdb.get_word_index()
"""
Explanation: Setup data
We're going to look at the IMDB dataset, which contains movie reviews from IMDB, along with their sentiment. Keras comes with some helpers for this dataset.
End of explanation
"""
idx_arr = sorted(idx, key=idx.get)
idx_arr[:10]
... |
feststelltaste/software-analytics | demos/20190319_JavaLand_Bruehl/DataScienceMeetsSoftwareData.ipynb | gpl-3.0 | import pandas as pd
log = pd.read_csv("../dataset/linux_blame_log.csv.gz")
log.head()
"""
Explanation: Data Science meets Software Data
<b>Markus Harrer</b>, Software Development Analyst
@feststelltaste
<small>JavaLand 2019, Phantasialand, Brühl, 19.03.2019</small>
<img src="../resources/innoq_logo.jpg" width=20% hei... |
andrzejkrawczyk/python-course | part_1/01.Wstęp.ipynb | apache-2.0 | print("Hello world!")
"""
Explanation: Kurs Python Część 1
Zespół Learnetic
Andrzej Krawczyk
Python
Język wysokiego poziomu
Język skryptowy
Język interpretowany
Język dynamiczny
Język szerokiego zastosowania
Cechy Pythona
Dynamiczne typowanie
Automatyczne zarządzanie pamięcią
Garbage Collector
Obiektowo orientowany... |
yw-fang/readingnotes | machine-learning/Hitchhiker-guide-2016/ch04.ipynb | apache-2.0 | import this
"""
Explanation: Writing Great Code
Y.-W. FANG at Kyoto University
这个章节对于 python 初学者真的是极其重要的。
This chapter focuses on writing the great code. You will see it is very useful to help you develop some good manners.
Code style
Pythonistas (vesteran Python developers)总是对 python 这一预言引以为豪的，因为即使不懂 python 的人，阅读一些简单... |
lukechen526/deep-learning | image-classification/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'
class DLProgress(tqdm):
last_block = 0
def hoo... |
fabiansinz/pipeline | jupyter/tutorial/pipeline_vis.ipynb | lgpl-3.0 | %pylab inline
import datajoint as dj
from pipeline.vis import *
matplotlib.rcParams['figure.figsize'] = (10.0, 9.0)
"""
Explanation: Previous: pipeline_experiment | Next: pipeline_preprocess
Schema vis
Schema pipeline.vis is upstream from preprocess and it contains information about the visual stimulus entered by the ... |
abevieiramota/data-science-cookbook | 2016/decision-tree/DecisionTree.ipynb | mit | import os
import subprocess
import pandas as pd
import math
import numpy as np
from sklearn.tree import DecisionTreeClassifier, export_graphviz
headers = ["buying", "maint", "doors", "persons","lug_boot", "safety", "class"]
data = pd.read_csv("carData.csv", header=None, names=headers)
data = data.take(np.random.permu... |
longyangking/ML | Notes/Ensemble/ID3 Decision Tree.ipynb | lgpl-3.0 | from math import log
def calcShannonEnt(dataSet):
numEntries = len(dataSet)
labelCounts = dict()
for featVec in dataSet:
currentLabel = featVec[-1]
if currentLabel not in labelCounts.keys():
labelCounts[currentLabel] = 0
labelCounts[currentLabel] += 1
shannonEnt = 0.0... |
mavillan/SciProg | 04_jit/04_jit.ipynb | gpl-3.0 | %matplotlib inline
%load_ext memory_profiler
import numpy as np
import numexpr as ne
import numba
import math
import random
import matplotlib.pyplot as plt
import scipy as sp
import sys
"""
Explanation: <h1 align="center">Scientific Programming in Python</h1>
<h2 align="center">Topic 4: Just in Time Compilation: Numb... |
reubano/meza | examples/usage.ipynb | mit | # Create a csv file
text = (
'col1,col2,col3\n'
'hello,5/4/82,1\n'
'one,1/1/15,2\n'
'happy,7/4/92,3\n')
f = StringIO(text)
f.seek(0)
# Load the file
records = io.read_csv(f)
# Records are an iterator over the rows
row = next(records)
row
# Replace first row so as not to loose any data
records = pr.p... |
jlawman/jlawman.github.io | content/sklearn/Datasets - Generating Random Data.ipynb | mit | #Import pandas and plotting libraries to visualize data
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
"""
Explanation: Scikit-learn has a number of methods to generate data in its datasets module. Below are some examples of the options available.
End of explanation
"""
... |
PollyP/trend-friend-or-foe | notebooks/Looking at trend investing, part 1.ipynb | mit | %matplotlib inline
from datetime import date
import IPython.html.widgets as widgets
import IPython.display as display
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import pandas as pd
import pandas.io.data as web
pd.set_option('display.float_format', lambda x: '%.4f' % ... |
tensorflow/examples | courses/udacity_intro_to_tensorflow_for_deep_learning/l09c02_nlp_padding.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... |
tpin3694/tpin3694.github.io | sql/drop_rows.ipynb | mit | # Ignore
%load_ext sql
%sql sqlite://
%config SqlMagic.feedback = False
"""
Explanation: Title: Drop Rows
Slug: drop_rows
Summary: Drop rows in SQL.
Date: 2017-01-16 12:00
Category: SQL
Tags: Basics
Authors: Chris Albon
Note: This tutorial was written using Catherine Devlin's SQL in Jupyter Notebooks library... |
mjames-upc/python-awips | examples/notebooks/Watch_and_Warning_Polygons.ipynb | bsd-3-clause | from awips.dataaccess import DataAccessLayer
from awips.tables import vtec
from datetime import datetime
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
from cartopy.feature import ... |
gaufung/Data_Analytics_Learning_Note | python-statatics-tutorial/basic-theme/python-language/Collections.ipynb | mit | from collections import *
"""
Explanation: Collections module
End of explanation
"""
c1 = Counter()
c2 = Counter('gaufung')
c3 = Counter({'red':4,'blue':10})
c4 = Counter(cats=4,dogs=5)
"""
Explanation: 1 Counter
A Counter is a dict subclass for counting hashable objects. It is an unordered collection where element... |
pkreissl/espresso | doc/tutorials/polymers/polymers.ipynb | gpl-3.0 | def solvent_langevin(system, kT, gamma):
'''
Implicit solvation model based on Langevin dynamics (Rouse model).
'''
system.thermostat.set_langevin(kT=kT, gamma=gamma, seed=42)
def solvent_lbm(system, kT, gamma):
'''
Lattice-based solvation model based on the LBM (Zimm model).
'''
lbf =... |
robertoalotufo/ia898 | 2S2018/05 Histograma da imagem.ipynb | mit | import numpy as np
import matplotlib.image as mpimg
f = mpimg.imread('../data/cameraman.tif')
print(f.min(), f.max())
%matplotlib inline
import matplotlib.pyplot as plt
plt.imshow(f, cmap = 'gray')
plt.colorbar()
nbins = 20
h, bin_edges = np.histogram(f, nbins,(0,255))
print('h=\n',h)
print('bins=\n',bin_edges)
... |
mne-tools/mne-tools.github.io | 0.24/_downloads/7ba58cd4e9bc2622d60527d21fc13577/decoding_spatio_temporal_source.ipynb | bsd-3-clause | # Author: Denis A. Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Jean-Remi King <jeanremi.king@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import... |
FaustineLi/Sta663-Project | examples/Autoencoder.ipynb | mit | import pickle, gzip
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
np.random.seed(0)
with gzip.open('../resources/data/mnist.pkl.gz', 'rb') as f:
train, test, val = pickle.load(f, encoding='latin1')
mnist = train[0]
from vae import Network
"""
Explanation: Autoencoder
Autoencoders ar... |
YeoLab/single-cell-bioinformatics | notebooks/2.7_Apply_dimensionality_reduction_on_Shalek2013_Macaulay2016.ipynb | bsd-3-clause | # Alphabetical order is standard
# We're doing "import superlongname as abbrev" for our laziness - this way we don't have to type out the whole thing each time.
# Python plotting library
import matplotlib.pyplot as plt
# Numerical python library (pronounced "num-pie")
import numpy as np
# Dataframes in Python
import... |
santipuch590/deeplearning-tf | tensorflow_2_tutorials/text/word_embeddings.ipynb | mit | import tensorflow as tf
import tensorflow_datasets as tfds
tfds.disable_progress_bar()
import matplotlib.pyplot as plt
"""
Explanation: Word embeddings Tutorial in TensorFlow 2.0
End of explanation
"""
embedding_layer = tf.keras.layers.Embedding(input_dim=1000, output_dim=5)
"""
Explanation: Embedding Layer
Creat... |
ntftrader/ntfdl | examples/notebooks/Multiple days of intraday data.ipynb | mit | %matplotlib inline
%pylab inline --no-import-all
pylab.rcParams['figure.figsize'] = (18, 10)
from ntfdl import Multi
from matplotlib.finance import candlestick_ohlc
from datetime import datetime, timedelta
# Instantiate multi with instrument FOE from Oslo exchange (OSS)
foe = Multi('FOE', exchange='OSE')
"""
Explana... |
rsignell-usgs/notebook | HOPS/.ipynb_checkpoints/hops_velocity3-checkpoint.ipynb | mit | from netCDF4 import Dataset
#url = ('http://geoport.whoi.edu/thredds/dodsC/usgs/data2/rsignell/gdrive/'
# 'nsf-alpha/Data/MIT_MSEAS/MSEAS_Tides_20160317/mseas_tides_2015071612_2015081612_01h.nc')
url = ('/usgs/data2/rsignell/gdrive/'
'nsf-alpha/Data/MIT_MSEAS/MSEAS_Tides_20160317/mseas_tides_2015071612_20... |
tensorflow/docs-l10n | site/ja/tutorials/images/cnn.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... |
paulhoule/gastrodon | notebooks/remote/Collecting_Counts_From_Ontology2_DBpedia.ipynb | mit | %load_ext autotime
import sys
from os.path import expanduser
from gastrodon import RemoteEndpoint,QName,ttl,URIRef,inline
import pandas as pd
import json
pd.options.display.width=120
pd.options.display.max_colwidth=100
prefixes=inline("""
@prefix dbo: <http://dbpedia.org/ontology/> .
@prefix summary: <http://r... |
nwhidden/ND101-Deep-Learning | image-classification/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... |
grigorisg9gr/menpo-notebooks | menpo/Transforms/Generalized_Procrustes_Analysis.ipynb | bsd-3-clause | %matplotlib inline
from pathlib import Path
from menpo.transform import GeneralizedProcrustesAnalysis
from menpo.shape import PointCloud
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Generalized Procrustes Analysis
Generalized Procrustes Analysis is an iterative technique for optimally rigidly ... |
charlesll/RamPy | examples/Normalisation.ipynb | gpl-2.0 | %matplotlib inline
import sys
sys.path.append("../")
import numpy as np
import scipy
from matplotlib import pyplot as plt
import rampy as rp
from sklearn import preprocessing
"""
Explanation: Use of normalisation function
In this example we normalise a spectrum to different values: area, maximum, or min-max.
End of e... |
pmgbergen/porepy | tutorials/contact_mechanics_with_AD.ipynb | gpl-3.0 | import porepy as pp
import numpy as np
import inspect
model = pp.ContactMechanics({})
print(inspect.getsource(model._assign_equations))
"""
Explanation: Advanced AD application
This notebook documents how the algorithmic (or automatic) differentiation framework may be applied to non-linear equations. For an introduc... |
computational-class/cc2017 | data/17.networkx.ipynb | mit | %matplotlib inline
import networkx as nx
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import networkx as nx
G=nx.Graph() # G = nx.DiGraph() # 有向网络
# 添加（孤立）节点
G.add_node("spam")
# 添加节点和链接
G.add_edge(1,2)
print(G.nodes())
print(G.edges())
# 绘制网络
nx.draw(G, with_labels = True)
"""
Explanation: 网络科学理论
... |
bjmorgan/vasppy | docs/source/examples/rdfs.ipynb | mit | # Create a pymatgen Structure for NaCl
from pymatgen import Structure, Lattice
# Create a pymatgen Structure for NaCl
from pymatgen import Structure, Lattice
a = 5.6402 # NaCl lattice parameter
lattice = Lattice.from_parameters(a, a, a, 90.0, 90.0, 90.0)
lattice
structure = Structure.from_spacegroup(sg='Fm-3m', latt... |
ziky5/F4500_Python_pro_fyziky | lekce_08/N2spec.ipynb | mit | #kod v teto bunce neni soucasti lekce,
#presto ho ale netajime
import massiveOES
import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib import colors as mcolors
colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)
N2 = massiveOES.SpecDB('N2CB.db')
spec_cold = N2.get_spectrum(Trot=300, Tvib=300, wmin... |
sameersingh/uci-statnlp | tutorials/rnn_examples.ipynb | apache-2.0 | !wget https://raw.githubusercontent.com/UniversalDependencies/UD_English/master/en-ud-dev.conllu
!wget https://raw.githubusercontent.com/UniversalDependencies/UD_English/master/en-ud-test.conllu
!wget https://raw.githubusercontent.com/UniversalDependencies/UD_English/master/en-ud-train.conllu
"""
Explanation: Example:... |
MinnowBoard/fishbowl-notebooks | Slideshow.ipynb | mit | import time
import sys
import os
from PIL import Image
from pyDrivers.ada_lcd import *
import pyDrivers.ILI9341 as TFT
import Adafruit_GPIO as GPIO
import Adafruit_GPIO.SPI as SPI
"""
Explanation: Slideshow Project
This complicated project uses all the major components of the other projects to create a system that wil... |
gregcaporaso/short-read-tax-assignment | ipynb/simulated-community/dataset-generation.ipynb | bsd-3-clause | from os.path import join, expandvars
from tax_credit.simulated_communities import generate_simulated_communities
# Project directory
project_dir = expandvars("$HOME/Desktop/projects/tax-credit/")
# Directory containing reference sequence databases
reference_database_dir = join(project_dir, 'data', 'ref_dbs')
# simula... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session12/Day3/NotebookIII_part1_intro_to_3d.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Data, in Python, with THREE Dimensions!
Hi! My name's Matt. I'll be working through some things in python with you that I think are cool, and that relate to visualizing things in three dimensions.
Libraries We'll Use
We're going to use a handful of ... |
WNoxchi/Kaukasos | quantum/qaoa_max_cut.ipynb | mit | %matplotlib inline
%reload_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
import itertools
import networkx as nx
from grove.pyqaoa.qaoa import QAOA
from grove.pyqaoa.maxcut_qaoa import maxcut_qaoa
from pyquil.paulis import PauliSum, PauliTerm
from pyquil.gates import H
from pyquil.ap... |
mne-tools/mne-tools.github.io | 0.24/_downloads/0bf55d4c93021947144bdb72823131e5/read_neo_format.ipynb | bsd-3-clause | import neo
import mne
"""
Explanation: How to use data in neural ensemble (NEO) format
This example shows how to create an MNE-Python ~mne.io.Raw object from data
in the neural ensemble <http://neuralensemble.org/neo/>__ format. For general
information on creating MNE-Python's data objects from NumPy arrays, see... |
jepegit/cellpy | dev_utils/view_file.ipynb | mit | import cellpy
from cellpy.utils import plotutils
"""
Explanation: Have a look at the data
Here are some examples how to get a peak at the data. If we need an interactive plot of the raw-data, we can use the
plotutils.raw_plot function. If we would like to see some statistics for some of the cycles, the plotutils.cycle... |
cwhanse/pvlib-python | docs/tutorials/solarposition.ipynb | bsd-3-clause | import datetime
# scientific python add-ons
import numpy as np
import pandas as pd
# plotting stuff
# first line makes the plots appear in the notebook
%matplotlib inline
import matplotlib.pyplot as plt
# finally, we import the pvlib library
import pvlib
import pvlib
from pvlib.location import Location
"""
Explan... |
Kaggle/learntools | notebooks/sql_advanced/raw/tut3.ipynb | apache-2.0 | from google.cloud import bigquery
# Create a "Client" object
client = bigquery.Client()
# Construct a reference to the "google_analytics_sample" dataset
dataset_ref = client.dataset("google_analytics_sample", project="bigquery-public-data")
# Construct a reference to the "ga_sessions_20170801" table
table_ref = data... |
dsquareindia/gensim | docs/notebooks/WordRank_wrapper_quickstart.ipynb | lgpl-2.1 | from gensim.models.wrappers import Wordrank
wr_path = 'wordrank' # path to Wordrank directory
out_dir = 'model' # name of output directory to save data to
data = '../../gensim/test/test_data/lee.cor' # sample corpus
model = Wordrank.train(wr_path, data, out_dir, iter=11, dump_period=5)
"""
Explanation: WordRank wrap... |
klavinslab/coral | docs/tutorial/introduction/modules.ipynb | mit | import coral as cor # alternative you can import each module by itself e.g. from coral import design
dir(cor) # dir lists everything in a module/object. Ignore the double underscore items.
"""
Explanation: coral modules
coral has 7 modules: analysis, constants, database, design, reaction, seqio, and sequence.
The mo... |
SSQ/Coursera-UW-Machine-Learning-Classification | Programming Assignment 2/Implementing logistic regression from scratch.ipynb | mit | products = pd.read_csv('amazon_baby_subset.csv')
"""
Explanation: Load review dataset
End of explanation
"""
products['name'][:10]
"""
Explanation: 1. listing the name of the first 10 products in the dataset.
End of explanation
"""
print (products['sentiment'] == 1).sum()
print (products['sentiment'] == -1).sum()... |
vlad17/vlad17.github.io | assets/2020-03-07-subset-isomorphism/subset-isomorphism.ipynb | apache-2.0 | import numpy as np
np.random.seed(1234)
x = np.random.choice(10, replace=False, size=10)
s = np.argsort(x)
inverse = np.empty_like(s)
inverse[s] = np.arange(len(s), dtype=int)
np.all(x == inverse)
"""
Explanation: Numpy Gems, Part 3
Much of scientific computing revolves around the manipulation of indices. Most formula... |
scikit-optimize/scikit-optimize.github.io | 0.7/notebooks/auto_examples/plots/visualizing-results.ipynb | bsd-3-clause | print(__doc__)
import numpy as np
np.random.seed(123)
import matplotlib.pyplot as plt
"""
Explanation: Visualizing optimization results
Tim Head, August 2016.
Reformatted by Holger Nahrstaedt 2020
.. currentmodule:: skopt
Bayesian optimization or sequential model-based optimization uses a surrogate
model to model the... |
zunio/python-recipes | 01-loading/.ipynb_checkpoints/[Optimization]Pandas Large Data Optimization-checkpoint.ipynb | apache-2.0 | import os
import pandas as pd
import zipfile
!ls
with zipfile.ZipFile("..\data\game_logs.csv","r") as zip_ref:
zip_ref.extractall("..\data")
# Load Data
gl = pd.read_csv('..\data\game_logs.csv')
# Available also at https://data.world/dataquest/mlb-game-logs
# Data Preview
gl.head()
"""
Explanation: Abstract
I... |
chagaz/ma2823_2016 | lab_notebooks/Lab 6 2016-11-04 Tree-based methods.ipynb | mit | import numpy as np
%pylab inline
# Load the data
# TODO
# Normalize the data
from sklearn import preprocessing
X = preprocessing.normalize(X)
# Set up a stratified 10-fold cross-validation
from sklearn import cross_validation
folds = cross_validation.StratifiedKFold(y, 10, shuffle=True)
def cross_validate(design_ma... |
dynaryu/rmtk | rmtk/vulnerability/derivation_fragility/R_mu_T_no_dispersion/dolsek_fajfar/dolsek_fajfar.ipynb | agpl-3.0 | from rmtk.vulnerability.derivation_fragility.R_mu_T_no_dispersion.dolsek_fajfar import DF2004
from rmtk.vulnerability.common import utils
%matplotlib inline
"""
Explanation: Dolsek and Fajfar (2004)
This methodology makes use of work by Dolsek and Fajfar (2004) to estimate the inelastic displacement of a SDOF system ... |
pgmpy/pgmpy_notebook | notebooks/8. Sampling Algorithms.ipynb | mit | from pgmpy.sampling import HamiltonianMC as HMC
"""
Explanation: Sampling In Continuous Graphical Models
As we know inference is asking conditional probability questions to the models, the exact solution of these problems quickly becomes intractable. Sampling algorithms can be used to get approximate inference results... |
gsorianob/fiuba-python | Clase 05 - Manejo de strings y Archivos de texto .ipynb | apache-2.0 | cadena_caracteres = "Hola mundo"
print dir(cadena_caracteres)
"""
Explanation: <!--
29/10
Archivos de texto.
Operaciones con cadenas de caracteres.
String formaters.
CLASE DE LABORATORIO
(Gonzalo)
-->
Manejo de strings
Con los strings podemos hacer muchas operaciones:
End of explanation
"""
print 'Hola mundo'
pri... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/time_series_prediction/labs/3_modeling_bqml.ipynb | apache-2.0 | PROJECT = "your-gcp-project-here" # REPLACE WITH YOUR PROJECT NAME
REGION = "us-central1" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
%env
PROJECT = PROJECT
REGION = REGION
"""
Explanation: Time Series Prediction with BQML and AutoML
Objectives
1. Learn how to use BQML to create a classification time-series m... |
cstrelioff/ARM-ipynb | Chapter3/chptr3.4.ipynb | mit | from __future__ import print_function, division
%matplotlib inline
import matplotlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# use matplotlib style sheet
plt.style.use('ggplot')
# import statsmodels for R-style regression
import statsmodels.formula.api as smf
"""
Explanation: 3.4 Stati... |
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