repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
rcrehuet/Python_for_Scientists_2017 | notebooks/solutions/6_1_linear_regression.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
x = np.arange(10.)
y = 5*x+3
np.random.seed(3)
y+= np.random.normal(scale=10,size=x.size)
plt.scatter(x,y);
def lin_reg(x,y):
"""
Perform a linear regression of x vs y.
x, y are 1 dimensional numpy arrays
returns alpha and beta for ... |
nick-youngblut/SIPSim | ipynb/bac_genome/n1147/validation_rep3.ipynb | mit | workDir = '/home/nick/notebook/SIPSim/dev/bac_genome1147/validation_rep3/'
genomeDir = '/var/seq_data/ncbi_db/genome/Jan2016/bac_complete_spec-rep1_rn/'
R_dir = '/home/nick/notebook/SIPSim/lib/R/'
figureDir = '/home/nick/notebook/SIPSim/figures/bac_genome_n1147/'
bandwidth = 0.8
DBL_scaling = 0.5
subsample_dist = 'log... |
tensorflow/docs-l10n | site/zh-cn/lattice/tutorials/custom_estimators.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
Jackporter415/phys202-2015-work | assignments/assignment05/InteractEx02.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 2
Imports
End of explanation
"""
#CHANGE TICK MARKERS
def plot_sine1(a,b):
x = np.linspace(0,4*np.pi,20... |
jennybrown8/python-notebook-coding-intro | lesson4exercises.ipynb | apache-2.0 | def hokeypokey(limb):
print("You put your " + limb +" in,")
print("You put your " + limb + " out,")
print("You put your " + limb +" in,")
print("and you shake it all about.")
print("You do the hokey pokey and you turn yourself around,")
print("and that's what it's all about!")
print("")
... |
tensorflow/text | docs/tutorials/text_similarity.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... |
stitchfix/d3-jupyter-tutorial | bar_chart_with_update.ipynb | mit | from IPython.core.display import display, HTML
from string import Template
import pandas as pd
import json, random
HTML('<script src="lib/d3/d3.min.js"></script>')
"""
Explanation: D3 Bar Chart with Update
This example is based on the canonical simple bar chart example, but with a minor twist - a data update is added... |
Xilinx/PYNQ | boards/Pynq-Z1/base/notebooks/video/opencv_filters_webcam.ipynb | bsd-3-clause | from pynq.overlays.base import BaseOverlay
from pynq.lib.video import *
base = BaseOverlay("base.bit")
"""
Explanation: OpenCV Filters Webcam
In this notebook, several filters will be applied to webcam images.
Those input sources and applied filters will then be displayed either directly in the notebook or on HDMI out... |
chengsoonong/crowdastro | notebooks/56_nonlinear_astro_features.ipynb | mit | import h5py, numpy, sklearn.linear_model, sklearn.cross_validation, sklearn.metrics
with h5py.File('../data/training.h5') as f:
raw_astro_features = f['features'][:, :4]
dist_features = f['features'][:, 4]
image_features = f['features'][:, 5:]
w1_w2 = raw_astro_features[:, 0] - raw_astro_features[... |
chapman-phys227-2016s/hw-7-ChinmaiRaman | hw7.ipynb | mit | xp = np.linspace(0, np.pi, 5)
yp = np.sin(xp)
#Lagrange's interpolation polynomial
p_L = p1.LagrangeInterpolation(xp, yp)
x = 1.2
print 'p_L(%g) = %g' % (x, p_L(x))
print 'sin(%g) = %g' % (x, np.sin(x))
p_L.plot()
"""
Explanation: Chinmai Raman
Homework 7
7.8 Wrapping functions in a class
Visualizes $p_L(x)$ for x be... |
sebastiandres/mat281 | clases/Unidad1-IntroduccionYProyectos/Clase01-IntroduccionYReglas/IntroduccionYReglas.ipynb | cc0-1.0 | from matplotlib import pyplot as plt
import numpy as np
x = np.linspace(1E-8,np.pi/6,1000)
y = x*np.sin(1./x)
plt.plot(x, y)
plt.plot(x, x)
plt.plot(x, -x)
plt.show()
"""
Explanation: <header class="w3-container w3-teal">
<img src="images/utfsm.png" alt="" height="100px" align="left"/>
<img src="images/mat.png" alt=""... |
abhinav-upadhyay/intro-to-python | notebooks/Conditionals in Python.ipynb | bsd-2-clause | import random
toss = random.random() # returns a random value between 0 and 1
if toss > 0.5:
print 'I won'
"""
Explanation: Conditions in Python
Python has a very natural looking syntax for conditionals and boolean operations
if statment in Python
if True:
do something
End of explanation
"""
toss = ran... |
neothemachine/rawpy-notebooks | simple-convert/simple-convert.ipynb | unlicense | %matplotlib inline
import matplotlib as mpl
mpl.rcParams['figure.figsize'] = 5, 10
from matplotlib.pyplot import imshow
"""
Explanation: Converting a RAW file to a standard image format
In this notebook we read and process a RAW image and save it in a standard image format. The sample image used here can be found on r... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/n08_simple_q_learner_full_training.ipynb | mit | # Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
from multiprocessing import Pool
%matplotlib inline
%pylab inline
pylab.rcPar... |
510908220/python-toolbox | books/files/question.ipynb | mit | import os
os.listdir('question/one')
# %load question/one/main.py
from . import m
# %load question/one/m.py
import sys
import os
print('cwd', os.getcwd())
print('__file__:', __file__)
print('argv[0]:', sys.argv[0])
print('__name__:', __name__)
"""
Explanation: 文件相关的问题整理
问题一:difference between __file__ and sys.argv[... |
lparolin/state_estimation | notebook/Example 1.ipynb | mit | #Import base libraries
import numpy as np
import matplotlib.pyplot as plt
import random
from matplotlib import animation, rc
from IPython.display import HTML
import importlib
# Import libraries for the examples
import os
import sys
module_path = os.path.abspath(os.path.join('../python'))
if module_path not in sys.path... |
tpin3694/tpin3694.github.io | regex/match_any_of_a_list_of_symbols.ipynb | mit | # Load regex package
import re
"""
Explanation: Title: Match Any Of A List Of Characters
Slug: match_any_of_a_list_of_symbols
Summary: Match Any Of A List Of Characters
Date: 2016-05-01 12:00
Category: Regex
Tags: Basics
Authors: Chris Albon
Based on: Regular Expressions Cookbook
Preliminaries
End of explanation
"""... |
statsmodels/statsmodels.github.io | v0.13.2/examples/notebooks/generated/markov_autoregression.ipynb | bsd-3-clause | %matplotlib inline
from datetime import datetime
from io import BytesIO
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import requests
import statsmodels.api as sm
# NBER recessions
from pandas_datareader.data import DataReader
usrec = DataReader(
"USREC", "fred", start=datetime(1947, 1,... |
agile-geoscience/striplog | docs/tutorial/02_Basic_objects.ipynb | apache-2.0 | import striplog
striplog.__version__
# If you get a lot of warnings here, just run it again.
"""
Explanation: Basic objects
A striplog depends on a hierarchy of objects. This notebook shows the objects and their basic functionality.
Lexicon: A dictionary containing the words and word categories to use for rock descr... |
asarnow/pubs-analysis | analysis.ipynb | agpl-3.0 | # First, we must perform the incantations.
%pylab inline
import pandas as pd
# Parse data file.
proteins = pd.read_table('data/pubs2015/proteinGroups.txt', low_memory=False)
# Find mass spec intensity columns.
intensity_cols = [c for c in proteins.columns if 'intensity '
in c.lower() and 'lfq' not in c.lo... |
LucaCanali/Miscellaneous | Spark_Physics/CMS_Higgs_opendata/H_ZZ_4l_analysis_basic_monte_carlo_signal.ipynb | apache-2.0 | # Run this if you need to install Apache Spark (PySpark)
# !pip install pyspark
# Install sparkhistogram
# Note: if you cannot install the package, create the computeHistogram
# function as detailed at the end of this notebook.
!pip install sparkhistogram
# Run this to download the dataset
# See further details at... |
m2dsupsdlclass/lectures-labs | labs/05_conv_nets_2/ConvNets_for_Classification_and_Localization_rendered.ipynb | mit | from __future__ import division
import numpy as np
import xml.etree.ElementTree as etree
import os
import os.path as op
# Parse the xml annotation file and retrieve the path to each image,
# its size and annotations
def extract_xml_annotation(filename):
z = etree.parse(filename)
objects = z.findall("./object")... |
maxalbert/tohu | notebooks/v5/Primitive_generators.ipynb | mit | import tohu
from tohu.v5.primitive_generators import *
from tohu.v5.utils import print_generated_sequence
print(f'Tohu version: {tohu.__version__}')
"""
Explanation: Primitive generators
This notebook contains tests for tohu's primitive generators.
End of explanation
"""
g = Constant('quux')
print_generated_sequen... |
jupyter-widgets/ipywidgets | docs/source/examples/Widget List.ipynb | bsd-3-clause | import ipywidgets as widgets
"""
Explanation: Index - Back - Next
Widget List
End of explanation
"""
widgets.IntSlider(
value=7,
min=0,
max=10,
step=1,
description='Test:',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='d'
)
"... |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/sandbox-1/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'sandbox-1', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: SANDBOX-1
Topic: Ocean
Sub-Topics: Tim... |
mauriciogtec/PropedeuticoDataScience2017 | Alumnos/Alejandro_Hdz/Tarea 2 - Alejandro Hernández Farías.ipynb | mit | from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
im = Image.open("/Users/alex/PropedeuticoDataScience2017/foto.jpg")
im = im.convert('LA')
col,ren = im.size
data = np.zeros((ren*col, 5))
mat = np.array(list(im.getdata(band=0)), float)
mat.shape = (im.size[1], im.size[0])
mat = np.matrix(mat)
p... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch6-Example_6-05d_gen.ipynb | unlicense | %pylab notebook
"""
Explanation: Electric Machinery Fundamentals 5th edition
Chapter 6 (Code examples)
Example 6-5 (d) - for generator and motor
Creates a plot of the torque-speed curve of the induction motor as depicted in Figure 6-62.
Note: You should first click on "Cell → Run All" in order that the plots get... |
mediagit2016/workcamp-maschinelles-lernen-grundlagen | 20-10-14-ml-workcamp/wc-arbeiten-tf-11-aufgabe.ipynb | gpl-3.0 | # Definition der Funktion umrechnung
def umrechnung(C):
F = #Ihr Code hier#
return #Ihr Code hier#
#
# Rufen Sie die definierte Funktion mit unterschiedlichen Werten (8,12.5,23,44.6)
# einmal auf. Sie sollten die Ergebnisse (46.4, 54.5, 73.4, 112.28) erhalten.
# Aufruf - Ersetzen Sie #Wert# durch Ihre Zahl - B... |
ngcm/training-public | FEEG6016 Simulation and Modelling/04-Molecular-Dynamics-Lab-2.ipynb | mit | from IPython.core.display import HTML
css_file = 'https://raw.githubusercontent.com/ngcm/training-public/master/ipython_notebook_styles/ngcmstyle.css'
HTML(url=css_file)
"""
Explanation: Molecular Dynamics: Lab 2
In part based on Fortran code from Furio Ercolessi and the CHEM126 course of Kalju Khan.
End of explanatio... |
wgong/open_source_learning | learn_stem/fun_with_mypets.ipynb | apache-2.0 | HTML("<img src=../images/office-suite.jpg>")
"""
Explanation: Fun with MyPETS
Table of Contents
Motivation
Introduction
Problem Statement
Import packages
History of Open Source Movement
How to learn STEM (or MyPETS)
References
Contributors
Appendix
Motivation <a class="anchor" id="hid_why"></a>
Current Cho... |
Jackporter415/phys202-2015-work | assignments/assignment11/OptimizationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Optimization Exercise 1
Imports
End of explanation
"""
def hat(x,a,b):
V = -a*x**2 + b*x**4
return V
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(1.0, 10.0, 1.0)==-... |
lwahedi/CurrentPresentation | talks/MDI4/MDI_FA18_pt1.ipynb | mit | import pickle
mydata = [1,2,3,4,5,6,7,8,9,10]
pickle.dump(mydata, open('mydata.p','wb'))
"""
Explanation: Collecting and Using Data in Python
Laila A. Wahedi, PhD
Massive Data Institute Postdoctoral Fellow <br>McCourt School of Public Policy<br>
Follow along:
Slides: http://Wahedi.us, Tutorial
Interactive Notebook: h... |
kabrapratik28/Stanford_courses | cs231n/assignment3/NetworkVisualization-TensorFlow.ipynb | apache-2.0 | # As usual, a bit of setup
from __future__ import print_function
import time, os, json
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from cs231n.classifiers.squeezenet import SqueezeNet
from cs231n.data_utils import load_tiny_imagenet
from cs231n.image_utils import preprocess_image, deproc... |
teuben/astr288p | notebooks/01-intro.ipynb | mit | a = 1.0
b = 2
c = "3"
# let's pause and remember python has dynamic types.
# float, integer and string are determined automatically from the way we enter it
print(a,b,c)
"""
Explanation: Python Intro
Notebooks
What you are reading here is text in a cell in a notebook. A notebook can have many cells. This is a text (... |
BONSAMURAIS/bonsai | legacy-examples/Overdetermined system resolution - sugar in soft drinks and spirits.ipynb | bsd-3-clause | from sklearn import linear_model
import matplotlib.pyplot as plt
import numpy as np
## without removing outliers
## wo extension = with outliers
sugarwo = np.array([2.62E7,1.46E8,1.02E7,1.03E8,6.82E6,9.00E7,8.78E6,7.22E7,5.90E7,7.54E6,8.03E7,5.49E8,3.40E8,6.18E6])
wine = np.array([4.70E7,9.13E8,0,3.34E9,0,5.11E9,0,4.... |
enakai00/jupyter_ml4se_commentary | Solutions/02-Matrix and Vector-solution.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import Series, DataFrame
"""
Explanation: NumPyによるベクトルと行列の計算
End of explanation
"""
m = np.array([[2,3],[4,5]])
v = np.array([[6],[7]])
w = np.dot(m, v)
m
v
w
"""
Explanation: 練習問題
(1) 次の (2,2)行列 m と(2,1)行列（縦ベクトル）v をarrayオブジェクトとして... |
AhmetHamzaEmra/Deep-Learning-Specialization-Coursera | Convolutional Neural Networks/Convolution+model+-+Step+by+Step+-+v2.ipynb | mit | import numpy as np
import h5py
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
%load_ext autoreload
%autoreload 2
np.random.seed(1)
"""
Explanation: Convolut... |
giacomov/fermi_school_like | Summer_school_2016.ipynb | bsd-3-clause | # Load the software ("import the module" in python jargon)
from fermi_school_like import *
# Import matplotlib for plotting
from matplotlib import pyplot as plt
# This show the plots inline in the notebook
%matplotlib inline
"""
Explanation: Likelihood primer
These are some quick exercises to familiarize yourself ... |
ES-DOC/esdoc-jupyterhub | notebooks/mpi-m/cmip6/models/sandbox-3/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mpi-m', 'sandbox-3', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: MPI-M
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: Transport, Emissions, Con... |
rvuduc/cse6040-ipynbs | 01--intro-py.ipynb | bsd-3-clause | x = 5
x_squared = x * x
x_to_the_4th = x_squared * x_squared
x_to_the_4th
"""
Explanation: CSE 6040, Fall 2015 [01]: Intro to Python
In this class, you will do a lot of development in the Python language. Python is great for rapid prototyping, meaning you can get started quickly. Yet, it's much more than a toy languag... |
blua/deep-learning | embeddings/.ipynb_checkpoints/Skip-Gram word2vec-checkpoint.ipynb | mit | import time
import numpy as np
import tensorflow as tf
import utils
"""
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language p... |
googledatalab/notebooks | samples/ML Toolbox/Image Classification/Coast/Local End to End.ipynb | apache-2.0 | import mltoolbox.image.classification as model
from google.datalab.ml import *
worker_dir = '/content/datalab/tmp/coast'
preprocessed_dir = worker_dir + '/coast300'
model_dir = worker_dir + '/model300'
train_set = BigQueryDataSet('SELECT image_url, label FROM coast.train WHERE rand() < 0.04')
model.preprocess(train_s... |
arcyfelix/Courses | 18-05-28-Complete-Guide-to-Tensorflow-for-Deep-Learning-with-Python/02-Tensorflow-Basics/06-Regression-Exercise.ipynb | apache-2.0 | import pandas as pd
df = pd.read_csv('./data/cal_housing_clean.csv')
df.head()
df.describe().T
y = df['medianHouseValue']
x = df.drop('medianHouseValue', axis = 1)
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(x, y,
... |
tensorflow/text | docs/guide/decoding_api.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... |
microsoft/dowhy | docs/source/example_notebooks/dowhy_refutation_testing.ipynb | mit | import dowhy
from dowhy import CausalModel
import pandas as pd
import numpy as np
# Config dict to set the logging level
import logging.config
DEFAULT_LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'loggers': {
'': {
'level': 'WARN',
},
}
}
logging.config.dict... |
PourroyJean/ProgrammingNote | notebook/Note python/Data Analysis.ipynb | apache-2.0 | import numpy as np
import xlrd #With pandas
import matplotlib.pyplot as plt
import pandas as pd
"""
Explanation: PYTHON PROGRAMMING FOR DATA ANALYSIS
From basics to more sophisticated command for data visualisation
0. Requirement
1. Basics python command
1. Lists
2. Numpy
3. Matplotlib
3. Dictionnaires
1. Pandas lib
1... |
rocketproplab/Guides | Guides/python/betterGraphs-bokeh.ipynb | mit | import numpy as np
from scipy.integrate import odeint
from bokeh.plotting import figure, show
from bokeh.io import output_notebook
"""
Explanation: Create Better Graphs with Bokeh
This guide walks through the process of creating better graphs with bokeh. The coolest thing about bokeh is that it lets you create interac... |
alexandrejaguar/strata-sv-2015-tutorial | 03 - Data 1.ipynb | bsd-3-clause | # Import Pandas and then load the data.
from pandas import read_csv
df = read_csv('SFDeptPurchases.csv')
"""
Explanation: SF Purchases Example
In this example, interact is used to build a UI for exploring San Francisco department purchases by city agency data.
End of explanation
"""
df[:5]
"""
Explanation: You can ... |
tommyogden/maxwellbloch | docs/examples/mbs-two-sech-6pi.ipynb | mit | import numpy as np
SECH_FWHM_CONV = 1./2.6339157938
t_width = 1.0*SECH_FWHM_CONV # [τ]
print('t_width', t_width)
mb_solve_json = """
{
"atom": {
"fields": [
{
"coupled_levels": [[0, 1]],
"rabi_freq_t_args": {
"n_pi": 6.0,
"centre": 0.0,
"width": %f
},
... |
arne-cl/alt-mulig | python/scraping_list_of_countries.ipynb | gpl-3.0 | xpath_result = tree.xpath('/html/body/div[3]/div[3]/div[4]/div/table[2]')
table = xpath_result[0]
for elem in table:
print(elem)
"""
Explanation: xpath always returns a list of results, but there's only one, so we'll use that:
End of explanation
"""
def print_outline(tree, indent=0):
"""print the outline of... |
tylere/docker-tmpnb-ee | notebooks/1 - IPython Notebook Examples/IPython Project Examples/Interactive Widgets/File Upload Widget.ipynb | apache-2.0 | class FileWidget(widgets.DOMWidget):
_view_name = Unicode('FilePickerView', sync=True)
value = Unicode(sync=True)
filename = Unicode(sync=True)
def __init__(self, **kwargs):
"""Constructor"""
widgets.DOMWidget.__init__(self, **kwargs) # Call the base.
# Allow the us... |
google/starthinker | colabs/dv360_targeter.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: DV360 Bulk Targeting Editor
Allows bulk targeting DV360 through Sheets and BigQuery.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the Licens... |
roatienza/Deep-Learning-Experiments | versions/2020/MLP/code/tf.keras/linear.ipynb | mit | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# numpy package
import numpy as np
# for plotting
import matplotlib.pyplot as plt
# keras modules
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.... |
HrantDavtyan/Data_Scraping | 2018/Text_mining_-_Clustering.ipynb | apache-2.0 | import time
import requests
import numpy as np
import pandas as pd
from itertools import chain
from bs4 import BeautifulSoup
import matplotlib.pyplot as plt
from textblob import TextBlob
from gensim.models import word2vec
from scipy.cluster.hierarchy import ward, dendrogram
from sklearn.metrics.pairwise import cosine... |
napjon/krisk | notebooks/waterfall-barline.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import krisk.plot as kk
from krisk import init_notebook; init_notebook()
np.random.seed(0)
df = pd.DataFrame({'val': -1 + 10 * np.random.randn(10)})
df.head()
kk.waterfall(df['val'])
"""
Explanation: Starting from 0.3, krisk support waterfall chart and barline chart.
End of ex... |
poppy-project/community-notebooks | tutorials-education/poppy_4dof_arm_mini_pousser_de_cube.ipynb | lgpl-3.0 | from poppy.creatures import Poppy4dofArmMini
mini_dof = Poppy4dofArmMini(simulator='vrep')
"""
Explanation: Activité : déplacer un objet à l'aide d'un bras robotisé
Compétences visées par cette activité :
Résoudre un problème par l'expérimentation. Comparer l'approche par l'expérimentation avec avec l'approche par l... |
lwcook/horsetail-matching | notebooks/Targets.ipynb | mit | from horsetailmatching import HorsetailMatching, GaussianParameter
from horsetailmatching.demoproblems import TP3
from scipy.optimize import minimize
import numpy as np
import matplotlib.pyplot as plt
def plotHorsetail(theHM, c='b', label=''):
(q, h, t), _, _ = theHM.getHorsetail()
plt.plot(q, h, c=c, label=l... |
geography-munich/sciprog | material/scipy_intro.ipynb | apache-2.0 | from scipy import stats
import numpy as np
"""
Explanation: SciPy introduction
The SciPy framework builds on top of the low-level NumPy framework for multidimensional arrays, and provides a large number of higher-level scientific algorithms. Some of the topics that SciPy covers are:
Special functions (scipy.special)
... |
kfollette/AST337-Fall2017 | Labs/Lab2/Lab2.ipynb | mit | # Load packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: AST 337 In-Class Lab #2
Wednesday, September 13, 2017
Names: [insert your names here]
In this lab, you will (1) work directly with published astronomical data from the VizieR database, (2) conti... |
jpzhangvincent/MobileAppMarketAnalysis | notebooks/Correlation between app size and app quality.ipynb | mit | rating_df = app[["name","size","overall_rating", "current_rating", 'num_current_rating', "num_overall_rating"]].dropna()
rating_cleaned = {'1 star':1, "1 and a half stars": 1.5, '2 stars': 2, '2 and a half stars':2.5, "3 stars":3, "3 and a half stars":3.5, "4 stars": 4,
'4 and a half stars': 4.5, "5 s... |
LaurelOak/hydro-teaching-resources | Assignment6StreamflowOrovilleDam.ipynb | bsd-3-clause | # Import numerical tools
import numpy as np
#Import pandas for reading in and managing data
import pandas as pd
# Import pyplot for plotting
import matplotlib.pyplot as plt
#Import seaborn (useful for plotting)
import seaborn as sns
# Magic function to make matplotlib inline; other style specs must come AFTER
%matp... |
scotthuang1989/Python-3-Module-of-the-Week | data_structure/Counter — Count Hashable Objects.ipynb | apache-2.0 | import collections
print(collections.Counter(['a', 'b', 'c', 'a', 'b', 'b']))
print(collections.Counter({'a': 2, 'b': 3, 'c': 1}))
print(collections.Counter(a=2, b=3, c=1))
"""
Explanation: A Counter is a container that keeps track of how many times equivalent values are added. It can be used to implement the same al... |
fjaviersanchez/McQ_Workshop | McQ_workshop.ipynb | mit | science_image_path_g = 'data/seo_m66_g-band_180s_apagul_1.fits' #Type the path to your image
sci_g = fits.open(science_image_path_g)
sci_im_g = fits.open(science_image_path_g)[0].data
plt.imshow(sci_im_g,cmap='gray', vmax=1800, norm=matplotlib.colors.LogNorm())
plt.colorbar()
"""
Explanation: Let's take a look at the ... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/feature_engineering/solutions/3_keras_basic_feat_eng.ipynb | apache-2.0 | # Run the chown command to change the ownership
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Install Sklearn
# scikit-learn simple and efficient tools for predictive data analysis
# Built on NumPy, SciPy, and matplotlib
!python3 -m pip install --user sklearn
# You can use any Python source fil... |
napsternxg/DataMiningPython | NLP checks.ipynb | gpl-3.0 | nltk.download("movie_reviews")
nltk.download("punkt")
from nltk.corpus import movie_reviews
movie_reviews.categories()
movie_reviews.words()
sents = movie_reviews.sents()
len(sents)
sents[0]
for i, s in enumerate(sents[:10]):
print "S[%s]:\t%s" % (i, " ".join(s))
"""
Explanation: NLTK checks
More details at... |
jmhsi/justin_tinker | data_science/courses/deeplearning2/pytorch-tut.ipynb | apache-2.0 | x = torch.Tensor(5, 3); x
x = torch.rand(5, 3); x
x.size()
y = torch.rand(5, 3)
x + y
torch.add(x, y)
result = torch.Tensor(5, 3)
torch.add(x, y, out=result)
# anything ending in '_' is an in-place operation
y.add_(x) # adds x to y in-place
# standard numpy-like indexing with all bells and whistles
x[:,1]
"""
... |
ES-DOC/esdoc-jupyterhub | notebooks/snu/cmip6/models/sandbox-1/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'snu', 'sandbox-1', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: SNU
Source ID: SANDBOX-1
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbulen... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/03_tensorflow/labs/c_dataset.ipynb | apache-2.0 | from google.cloud import bigquery
import tensorflow as tf
import numpy as np
import shutil
print(tf.__version__)
"""
Explanation: <h1> 2c. Loading large datasets progressively with the tf.data.Dataset </h1>
In this notebook, we continue reading the same small dataset, but refactor our ML pipeline in two small, but si... |
InsightSoftwareConsortium/SimpleITK-Notebooks | Python/66_Registration_Demons.ipynb | apache-2.0 | import SimpleITK as sitk
import numpy as np
# If the environment variable SIMPLE_ITK_MEMORY_CONSTRAINED_ENVIRONMENT is set, this will override the ReadImage
# function so that it also resamples the image to a smaller size (testing environment is memory constrained).
%run setup_for_testing
import registration_utilitie... |
csaladenes/csaladenes.github.io | present/mcc2/PythonDataScienceHandbook/05.12-Gaussian-Mixtures.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np
"""
Explanation: <!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; t... |
kpn-advanced-analytics/modelFactoryPy | Template/Template_postgres.ipynb | mit | main.getConnection()
# this will also create main.engine variable
model_id = 'titanic_training'
#main.addModelId('titanic_training','Training on titanic data','passengerid')
main.getSessionId(model_id)
# this will also create main.session_id variable
"""
Explanation: 1. Get connection; define model_id, get session i... |
rasbt/python-machine-learning-book | code/ch10/ch10.ipynb | mit | %load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,sklearn,seaborn
"""
Explanation: Copyright (c) 2015, 2016 Sebastian Raschka
https://github.com/rasbt/python-machine-learning-book
MIT License
Python Machine Learning - Code Examples
Chapter 10 - Predicting Continuous Target Varia... |
tpin3694/tpin3694.github.io | machine-learning/variance_thresholding_for_feature_selection.ipynb | mit | from sklearn import datasets
from sklearn.feature_selection import VarianceThreshold
"""
Explanation: Title: Variance Thresholding For Feature Selection
Slug: variance_thresholding_for_feature_selection
Summary: How to select the best features for machine learning using variance thresholding in Python.
Date: 2017-09... |
NYUDataBootcamp/Materials | Code/notebooks/bootcamp_web_apis.ipynb | mit | import pandas as pd # data package
import matplotlib.pyplot as plt # graphics
import datetime as dt # date tools, used to note current date
import sys
# these are new
import requests
%matplotlib inline
print('\nPython version: ', sys.version)
print('Pandas version: ', pd.__version__)
prin... |
graphistry/pygraphistry | demos/more_examples/simple/MarvelTutorial.ipynb | bsd-3-clause | import graphistry, igraph, pandas as pd
# To specify Graphistry account & server, use:
# graphistry.register(api=3, username='...', password='...')
# For more options, see https://github.com/graphistry/pygraphistry#configure
"""
Explanation: PyGraphistry Example: Graphing the Marvel Universe
Plots hero social networ... |
martinjrobins/hobo | examples/sampling/adaptive-covariance-haario-bardenet.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
import pints
import pints.plot
import pints.toy
# Load a forward model
model = pints.toy.LogisticModel()
# Create some toy data
real_parameters = [0.015, 500]
times = np.linspace(0, 1000, 1000)
org_values = model.simulate(real_parameters, times)
# Add noise
noise = ... |
tensorflow/docs-l10n | site/ko/tutorials/generative/adversarial_fgsm.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... |
mpacer/nb_struct2app | nbgrader.ipynb | mit | 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... |
sdpython/pyquickhelper | _unittests/ut_helpgen/notebooks_rst/having_a_form_in_a_notebook.ipynb | mit | from IPython.display import HTML, Javascript, display_html, display_javascript
input_form = """
<div style="background-color:gainsboro; width:500px; padding:10px;">
<label>Variable Name: </label>
<input type="text" id="var_name" value="myvar" size="170" />
<label>Variable Value: </label>
<input type="text" id="var_va... |
zaqwes8811/micro-apps | self_driving/deps/Kalman_and_Bayesian_Filters_in_Python_master/Appendix-A-Installation.ipynb | mit | from __future__ import division, print_function
"""
Explanation: Table of Contents
Installation
End of explanation
"""
print(3+7.2)
"""
Explanation: This book is written in Jupyter Notebook, a browser based interactive Python environment that mixes Python, text, and math. I choose it because of the interactive feat... |
hankcs/HanLP | plugins/hanlp_demo/hanlp_demo/zh/ner_mtl.ipynb | apache-2.0 | !pip install hanlp -U
"""
Explanation: <h2 align="center">点击下列图标在线运行HanLP</h2>
<div align="center">
<a href="https://colab.research.google.com/github/hankcs/HanLP/blob/doc-zh/plugins/hanlp_demo/hanlp_demo/zh/ner_mtl.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Ope... |
googledatalab/notebooks | samples/ML Toolbox/Regression/Census/6 Cleanup.ipynb | apache-2.0 | workspace_path = '/content/datalab/workspace/census'
!rm -fr {workspace_path}
"""
Explanation: Removing files
This is an optional notebook in the census sample series. Run any cell if you want to remove the files or models that were made in previous notebooks. This assumes you did not change the default locations or n... |
ondrolexa/sg2 | 20_Introduction_to_stress_in_geology.ipynb | mit | %pylab inline
S = array([[10, 0], [0, 5]])
tau = []
sn = []
for theta in pi*uniform(size=5000):
n = array([cos(theta), sin(theta)])
sv = dot(S, n)
sn.append(dot(sv, n))
tau.append(norm(sv-n*dot(sv, n)))
plot(sn, tau, 'k.')
axis('equal')
margins(x=0.1, y=0.1)
show()
"""
Explanation: Stress in geology... |
tensorflow/docs-l10n | site/ko/addons/tutorials/average_optimizers_callback.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... |
gwsb-istm-6212-fall-2016/syllabus-and-schedule | lectures/week-06/20161004-lecture-notes.ipynb | cc0-1.0 | %load_ext sql
!createdb week6
%sql postgresql://dchud@localhost:5432/week6
!wget https://raw.githubusercontent.com/gwsb-istm-6212-fall-2016/syllabus-and-schedule/master/projects/project-01/2016q1.csv.zip
!unzip 2016q1.csv.zip
!wc -l 2016q1.csv
!csvcut -n 2016q1.csv
"""
Explanation: SQL Indexes and basic ETL
This... |
steinam/teacher | jup_notebooks/data-science-ipython-notebooks-master/python-data/unit_tests.ipynb | mit | !pip install nose
"""
Explanation: This notebook was prepared by Donne Martin. Source and license info is on GitHub.
Nose Unit Tests with IPython Notebook
Nose
Testing is a vital part of software development. Nose extends unittest to make testing easier.
Install Nose
Run the following command line:
End of explanation... |
HSE-LaMBDA/modern-technologies-for-ml-and-big-data | lecture1/NumPy_crash_course.ipynb | mit | import numpy as np
"""
Explanation: Matplotlib and NumPy crash course
You may install numpy, matplotlib, sklearn and many other usefull package e.g. via Anaconda distribution.
End of explanation
"""
np.array(range(10))
np.ndarray(shape=(5, 4))
np.linspace(0, 1, num=20)
np.arange(0, 20)
np.zeros(shape=(5, 4))
np... |
rayxi2dot71828/FluentPython | Chapter 2 - Iterables Consistency.ipynb | mit | couleurs = ["rouge", "orange", "jaune", "vert", "bleu", "indigo", "violet"]
tailles = ["page", "homme", "demi patron", "patron", "grand patron"]
[(couleur, taille) for couleur in couleurs for taille in tailles]
couleurs_et_tailles = ((couleur, taille) for couleur in couleurs for taille in tailles)
couleurs_et_taille... |
sassoftware/sas-viya-programming | deeplearning/twitter_sentiment/RNN_Twitter_Sentiment_Analysis.ipynb | apache-2.0 | import swat
from IPython.display import display
swat.options.cas.exception_on_severity = 2
s = swat.CAS('rdcgrd075.unx.sas.com', 3217,authinfo=r'/u/saleem/.authinfo')
s.loadactionset('deeplearn')
"""
Explanation: Twitter Sentiment Analysis
Businesses and organizations around the world know that the first requiremen... |
Grzego/nn-workshop | 1-neural-networks-intro/neural-networks-completed.ipynb | mit | # skorzystamy z gotowej funkcji do pobrania tego zbioru
from sklearn.datasets import fetch_mldata
mnist = fetch_mldata('MNIST original')
"""
Explanation: Dane treningowe
Ponieważ będziemy potrzebowali na czymś wytrenować naszą sieć neuronową skorzystamy z popularnego zbioru w Machine Learningu czyli MNIST. Zbiór ten z... |
MATH497project/MATH497-DiabeticRetinopathy | ICO Data Object Example.ipynb | mit | import ICO
import os
import pandas as pd
import time
"""
Explanation: ICO Data object example
End of explanation
"""
data = ICO.Data(os.getcwd()+'/data/')
"""
Explanation: The Data object is initialized with the path to the directory of .pickle files. On creation it reads in the pickle files, but does not transform... |
aborgher/Main-useful-functions-for-ML | Python_jupyter_utilities/myFunctions.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import xgboost as xgb
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import precision_recall_curve
df = pd.read_csv("iris.csv")
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Rot... |
birnstiel/disktemperature | tests/test_evolutionary_tracks.ipynb | gpl-3.0 | from disktemperature import pmstracks
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import style
from constants import R_sun,AU,year,L_sun
%matplotlib inline
style.use(['seaborn-dark',{'axes.grid': True,'font.size':18,'figure.figsize':(10,8),'lines.linewidth':2}]);
""... |
tanghaibao/goatools | notebooks/Enrichment_analyses_human_phenotype_ontology.ipynb | bsd-2-clause | fin_study = 'data/hpo/genes.list' # Study genes
fin_pop = 'data/hpo/gobackground.list' # Population genes
fin_obo = 'data/hpo/hp.obo' # DAG containing HPO terms
fin_anno = 'data/hpo/hpo.annotation.tab' # Annotation of genes-to-HPO terms
"""
Explanation: Do enrichment analyses using the Human ... |
mne-tools/mne-tools.github.io | 0.20/_downloads/ef81cbafce45c79dbfcdf37970a37c57/plot_source_space_snr.ipynb | bsd-3-clause | # Author: Padma Sundaram <tottochan@gmail.com>
# Kaisu Lankinen <klankinen@mgh.harvard.edu>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
import numpy as np
import matplotlib.pyplot as plt
print(__doc__)
data_path = s... |
mne-tools/mne-tools.github.io | stable/_downloads/9d142d98d094666b7fd2d94155f8b3ec/decoding_unsupervised_spatial_filter.ipynb | bsd-3-clause | # Authors: Jean-Remi King <jeanremi.king@gmail.com>
# Asish Panda <asishrocks95@gmail.com>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.decoding import UnsupervisedSpatialFilter
from sklearn.decomposition import PCA, FastICA... |
cloudmesh/book | notebooks/opencv/secchi.ipynb | apache-2.0 | import os, sys
from os.path import expanduser
os.path
home = expanduser("~")
sys.path.append('/usr/local/Cellar/opencv/3.3.1_1/lib/python3.6/site-packages/')
sys.path.append(home + '/.pyenv/versions/OPENCV/lib/python3.6/site-packages/')
import cv2
cv2.__version__
! pip install numpy > tmp.log
! pip install matplotlib >... |
MingChen0919/learning-apache-spark | notebooks/01-data-strcture/.ipynb_checkpoints/1.2-dataframe-checkpoint.ipynb | mit | from pyspark import SparkContext
sc = SparkContext(master = 'local')
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.appName("Python Spark SQL basic example") \
.config("spark.some.config.option", "some-value") \
.getOrCreate()
"""
Explanation: DataFrame object
Crea... |
Naereen/notebooks | Trying_the_SnakeViz_Jupyter_extension_for_profiling__27-02-2021.ipynb | mit | !python3 -m pip install snakeviz
%load_ext snakeviz
"""
Explanation: Trying the SnakeViz Jupyter extension for profiling
I want to try the https://jiffyclub.github.io/snakeviz/ SnakeViz Jupyter extension for profiling Python code right inside the Jupyter notebook!
Author: Lilian Besson (@Naereen ;
License: MIT Licen... |
roryyorke/python-control | examples/cruise.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
from math import pi
import control as ct
"""
Explanation: Cruise control
Richard M. Murray and Karl J. Åström
17 Jun 2019
The cruise control system of a car is a common feedback system encountered in everyday life. The system attempts to maintain a constant velocity i... |
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