repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
dfm/george | docs/_static/notebooks/mixture.ipynb | mit | import george
george.__version__
"""
Explanation: Mixtures of GPs
This notebook was made with the following version of george:
End of explanation
"""
import numpy as np
import matplotlib.pyplot as plt
from george import kernels
np.random.seed(42)
N = 256
t = np.sort(np.random.uniform(0, 10, N))
theta = np.random.u... |
joverbee/electromagnetism_course | vector_example.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import scipy.signal as signal
%matplotlib inline
"""
Explanation: Example of fields and operators acting on them
Simple script to show a 2D vector field and apply curl and divergence operator on it.
can you predict from looking at the vectorfield what the qualitative... |
ajgpitch/qutip-notebooks | examples/homodyned-Jaynes-Cummings-emission.ipynb | lgpl-3.0 | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import numpy as np
from qutip import *
"""
Explanation: QuTiP example: Homodyned Jaynes-Cummings emission
K.A. Fischer, Stanford University
This Jupyter notebook demonstrates how to simulate quantum statistics of homody... |
ResearchComputing/xsede_2015 | 04a_exploratory_pandas.ipynb | mit | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Getting Started with Exploratory Data Analysis
3 important Python packages
1. NumPy for efficient computation on arrays
2. Pandas for data analysis for small and medium data
3. Matplotlib for plotting i... |
qinwf-nuan/keras-js | notebooks/layers/convolutional/Cropping2D.ipynb | mit | data_in_shape = (3, 5, 4)
L = Cropping2D(cropping=((1,1),(1, 1)), data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(250)
data_in = 2 * np.random.random(data_in_shap... |
rcurrie/tumornormal | genesets.ipynb | apache-2.0 | import os
import json
import numpy as np
import pandas as pd
import tensorflow as tf
import keras
import matplotlib.pyplot as plt
# fix random seed for reproducibility
np.random.seed(42)
# See https://github.com/h5py/h5py/issues/712
os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
"""
Explanation: Gene Sets and Expres... |
qingshuimonk/STA663 | docs/vae.ipynb | mit | import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(0)
tf.set_random_seed(0)
# Load MNIST data in a format suited for tensorflow.
# The script input_data is available under this URL:
# https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/e... |
python-control/python-control | examples/kincar-fusion.ipynb | bsd-3-clause | import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import control as ct
import control.optimal as opt
import control.flatsys as fs
from IPython.display import Image
# Define line styles
ebarstyle = {'elinewidth': 0.5, 'capsize': 2}
xdstyle = {'color': 'k', 'linestyle': '--', 'linewidth': 0.5,
... |
CELMA-project/CELMA | MES/singleOperators/subConvergenceDDXOverJ.ipynb | lgpl-3.0 | %matplotlib notebook
from IPython.display import display
from sympy import Function, S, Eq
from sympy import symbols, init_printing, simplify, Limit
from sympy import sin, cos, tanh, exp, pi, sqrt
from boutdata.mms import x
# Import common
import os, sys
# If we add to sys.path, then it must be an absolute path
comm... |
chusine/dlnd | intro-to-rnns/Anna_KaRNNa_Exercises.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, we'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is bas... |
jmausolf/jmausolf.github.io | courses/cfss/hw05-python-data-analysis.ipynb | apache-2.0 | # Import libraries
import pandas as pd
import numpy as np
# Turn off notebook package warnings
import warnings
warnings.filterwarnings('ignore')
# print graphs in the document
%matplotlib inline
"""
Explanation: title: "Homework 05: Analyzing the gapminder dataset using Python"
output: html_document
Overview
Due be... |
calroc/joypy | docs/Advent of Code 2017 December 6th.ipynb | gpl-3.0 | from notebook_preamble import D, J, V, define
J('[0 2 7 0] dup max')
from joy.library import SimpleFunctionWrapper
from joy.utils.stack import list_to_stack
@SimpleFunctionWrapper
def index_of(stack):
'''Given a sequence and a item, return the index of the item, or -1 if not found.
E.g.:
[a b c] a ... |
quantopian/research_public | notebooks/lectures/Introduction_to_Volume_Slippage_and_Liquidity/notebook.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
num_shares = np.asarray([30000, 60000, 10000])
prices = np.asarray([30, 31, 33])
np.dot(num_shares, prices)
"""
Explanation: Introduction to Volume, Slippage, and Liquidity
by Delaney Mackenzie and Maxwell Margenot
Part of the Quantopian Lecture... |
turbomanage/training-data-analyst | courses/fast-and-lean-data-science/01_MNIST_TPU_Keras.ipynb | apache-2.0 | import os, re, time, json
import PIL.Image, PIL.ImageFont, PIL.ImageDraw
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
print("Tensorflow version " + tf.__version__)
#@title visualization utilities [RUN ME]
"""
This cell contains helper functions used for visualization
and downloads on... |
mne-tools/mne-tools.github.io | 0.20/_downloads/05c57a644672d33707fd1264df7f5617/plot_time_frequency_global_field_power.ipynb | bsd-3-clause | # Authors: Denis A. Engemann <denis.engemann@gmail.com>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import somato
from mne.baseline import rescale
from mne.stats import boots... |
tbereau/auto_martini | examples/01_our_first_molecule.ipynb | gpl-2.0 | import auto_martini as am
import rdkit
from rdkit import Chem
from rdkit.Chem import Draw
from rdkit.Chem import AllChem
print(rdkit.__version__)
"""
Explanation: Tutorial 1: Parametrizing our first molecule
Let's parametrize our first molecule with auto_martini.
We will focus on phenylmethanimine. Feeding only its S... |
keras-team/keras-io | examples/nlp/ipynb/question_answering.ipynb | apache-2.0 | !pip install git+https://github.com/huggingface/transformers.git
!pip install datasets
!pip install huggingface-hub
"""
Explanation: Question Answering with Hugging Face Transformers
Author: Matthew Carrigan and Merve Noyan<br>
Date created: 13/01/2022<br>
Last modified: 13/01/2022<br>
Description: Question answering ... |
esa-as/2016-ml-contest | MandMs/Facies_classification-M&Ms_SVM_rbf_kernel_optimal.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable
from sklearn import preprocessing
from sklearn.metrics import f1_score, accuracy_score, make_scorer
from sklearn.model_selection imp... |
ZhiangChen/deep_learning | Tutorials/Udacity/3_regularization.ipynb | mit | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
"""
Explanation: Deep Learning
Assignment 3
Previously in 2_fullyconnected.ipynb, you tra... |
streamreasoning/rsplab | collector/lab/streamapp/StreamApp - Part 1 - RDF Stream Processing with the CSPARQL engine.ipynb | apache-2.0 | from IPython.display import Image
from IPython.core.display import HTML
from rsplib.processing import RSPSource, RSPEngine
"""
Explanation: Part 1 : RDF Stream Processing with CSPARQL Engine
In this part of the tutorial, we will see how to query RDF Streams using an RDF Stream Processing (RSP) engine.
The CSPARQL e... |
analysiscenter/dataset | examples/experiments/profiling/profiling.ipynb | apache-2.0 | %%time
%run ./tf_model.py
"""
Explanation: Comparison of batchflow performance with tf and torch
Models
Firstly, we comapre torch and tf versions of VGG16. Train them on MNIST.
TensorFlow model
End of explanation
"""
%%time
%run ./torch_model.py
"""
Explanation: ... then restart kernel to clear GPU
Torch model
En... |
weinbe58/QuSpin | examples/notebooks/GPE.ipynb | bsd-3-clause | from quspin.operators import hamiltonian # Hamiltonians and operators
from quspin.basis import boson_basis_1d # Hilbert space boson basis
from quspin.tools.evolution import evolve
import numpy as np # generic math functions
from six import iteritems # loop over elements of dictionary
import matplotlib.pyplot as plt
#
#... |
indranilsinharoy/PyZDDE | Examples/IPNotebooks/01 Notes on ipzCaptureWindow functions.ipynb | mit | import os
import matplotlib.pyplot as plt
import pyzdde.zdde as pyz
%matplotlib inline
l = pyz.createLink() # create a DDE link object for communication
"""
Explanation: Using ipzCaptureWindow and ipzCaptureWindow2 for embedding graphic analysis windows into notebook
<img src="https://raw.githubusercontent.com/indran... |
UltronAI/Deep-Learning | CS231n/assignment3/GANs-PyTorch.ipynb | mit | import torch
import torch.nn as nn
from torch.nn import init
from torch.autograd import Variable
import torchvision
import torchvision.transforms as T
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.utils.data import sampler
import torchvision.datasets as dset
import numpy as np
import ... |
sytays/openanalysis | doc/Langauge/09-Inbuilt Data Structures 4 - Dictionaries.ipynb | gpl-3.0 | x = {'eight':8,'nine':9}
x['eight']
x[0]
"""
Explanation: Dictionaries
Dictionary is a set of key-value pairs, where value is any hashable object. As Lists are indexed by integers, Dictionries are indexed by keys.
Creating Dictionaries
Creating an Empty Dictionary
python
x = {} # {} denotes dictionary ... |
kabrapratik28/Stanford_courses | cs231n/assignment2/ConvolutionalNetworks.ipynb | apache-2.0 | # As usual, a bit of setup
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.cnn import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient
from cs231n.layers import *
fro... |
Honestpuck/charming | Notebooks/JavaScript Notebook Extensions.ipynb | artistic-2.0 | ## you can inspect the autosave code to see what it does.
%autosave??
"""
Explanation: Embracing web standards
One of the reasons we developed the current notebook web application
was to embrace web technologies.
By being a pure web application using HTML, Javascript and CSS, the Notebook can get
all the web technol... |
Smith42/neuralnet-mcg | CNNs/ECG-CNN-2D.ipynb | gpl-3.0 | import tensorflow as tf
#import tensorflow.contrib.learn.python.learn as learn
import tflearn
import scipy as sp
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from random import shuffle, randint
from sklearn.utils import shuffle as mutualShuf
import os
import pandas as pd
... |
ShubhamDebnath/Coursera-Machine-Learning | Course 4/Keras Tutorial Happy House v2.ipynb | mit | import numpy as np
from keras import layers
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.models import Model
from keras.preprocessing import im... |
arokem/seaborn | doc/docstrings/displot.ipynb | bsd-3-clause | sns.displot(data=penguins, x="flipper_length_mm", kind="ecdf")
"""
Explanation: There are three main plot kinds; in addition to histograms and kernel density estimates (KDEs), you can also draw empirical cumulative distribution functions (ECDFs):
End of explanation
"""
sns.displot(data=penguins, x="flipper_length_mm... |
gwulfs/research_public | lectures/Risk Factor Exposure.ipynb | apache-2.0 | import numpy as np
import statsmodels.api as sm
from statsmodels import regression
import matplotlib.pyplot as plt
import pandas as pd
# Get market cap and book-to-price for all assets in universe
fundamentals = init_fundamentals()
data = get_fundamentals(query(fundamentals.valuation.market_cap,
... |
jamesfolberth/NGC_STEM_camp_AWS | notebooks/20Q/loadMechanicalTurkData.ipynb | bsd-3-clause | # Read in the list of 250 movies, making sure to remove commas from their names
# (actually, if it has commas, it will be read in as different fields)
import csv
movies = []
with open('movies.csv','r') as csvfile:
myreader = csv.reader(csvfile)
for index, row in enumerate(myreader):
movies.append( ' '.j... |
mbuchove/notebook-wurk-b | stats/HW_6_PS.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.special import iv
from scipy.optimize import fmin
from csv import reader
from astropy.io import fits
from __future__ import print_function
"""
Explanation: <h1>Astro 283 Homework 6</h1>
Bijan Pourhamzeh
End of explanation
"""
#Read in... |
eyaltrabelsi/my-notebooks | Lectures/elegent_exception_handling/Exceptions Handling Interactive.ipynb | mit | ! pip install typeguard rollbar returns tenacity icontract > /dev/null 2>&1
import contextlib
import json
import icontract
import logging
import pathlib
import os
from typing import Union
import requests
from typeguard import typechecked
def get_relevant_restaurants(user):
base_url = "https://en.wikipedia.org/wi... |
adrn/globber | notebooks/Proper Motions of MW stuffs.ipynb | mit | import os
# Third-party
from astropy.io import ascii
import astropy.coordinates as coord
import astropy.units as u
import matplotlib as mpl
import matplotlib.pyplot as pl
import numpy as np
%matplotlib inline
import gary.coordinates as gc
import gary.dynamics as gd
import gary.potential as gp
from ophiuchus import g... |
dataworkshop/xgboost | step1.ipynb | mit | import xgboost as xgb
from sklearn.datasets import load_boston
from sklearn.cross_validation import train_test_split
from sklearn.metrics import r2_score, auc
boston = load_boston()
#print(boston.DESCR)
print(boston.data.shape)
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)
model = ... |
linamnt/studyGroup | lessons/python/object_oriented_programming/Object_oriented_programming.ipynb | apache-2.0 | # first we need to creat a class.
# If you like to create an empty class at first, you use the command pass inside the class
class Subject:
pass
# create the objects of that class.
# we create 2 and call them S1 and S2:
S1 = Subject()
S1.fname = "Jane"
S1.lname = "Doe"
S1.ID = 111
S1.date_of_admission = "20... |
tpin3694/tpin3694.github.io | sql/create_index_for_a_table.ipynb | mit | # Ignore
%load_ext sql
%sql sqlite://
%config SqlMagic.feedback = False
"""
Explanation: Title: Create An Index For A Table
Slug: create_index_for_a_table
Summary: Create An Index For A Table in SQL.
Date: 2016-05-01 12:00
Category: SQL
Tags: Basics
Authors: Chris Albon
Note: This tutorial was written using Cat... |
min2bro/WebScraping | .ipynb_checkpoints/FilmfareDataAnalysis-checkpoint.ipynb | mit | %matplotlib inline
from selenium import webdriver
import os,time,json
import pandas as pd
from collections import defaultdict,Counter
import matplotlib.pyplot as plt
"""
Explanation: Web Scraping & Data Analysis with Selenium and Python
Vinay Babu
Github: https://github.com/min2bro/WebScrapingwithSelenium
Twitter: @m... |
manoharan-lab/structural-color | color_mixing_tutorial.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import time
import structcol as sc
import structcol.refractive_index as ri
from structcol import montecarlo as mc
from structcol import detector as det
from structcol import phase_func_sphere as pfs
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.misc import factor... |
magenta/magenta-demos | colab-notebooks/MusicVAE.ipynb | apache-2.0 | #@title Setup Environment
#@test {"output": "ignore"}
import glob
BASE_DIR = "gs://download.magenta.tensorflow.org/models/music_vae/colab2"
print('Installing dependencies...')
!apt-get update -qq && apt-get install -qq libfluidsynth1 fluid-soundfont-gm build-essential libasound2-dev libjack-dev
!pip install -q pyflu... |
tvaught/compintro | 12_camera_explorer.ipynb | bsd-3-clause | from picamera import PiCamera, Color
from time import sleep
camera = PiCamera()
"""
Explanation: Camera Explorer
End of explanation
"""
camera.resolution = (480, 320)
camera.vflip = True
camera.hflip = True
camera.start_preview()
camera.annotate_foreground = Color('white')
camera.annotate_text = "Colorswap Effect"... |
synthicity/activitysim | activitysim/examples/example_estimation/notebooks/20_atwork_subtour_scheduling.ipynb | agpl-3.0 | import os
import larch # !conda install larch -c conda-forge # for estimation
import pandas as pd
"""
Explanation: Estimating At-work Subtour Scheduling
This notebook illustrates how to re-estimate the at-work subtour scheduling component for ActivitySim. This process
includes running ActivitySim in estimation mode... |
mmathioudakis/networkx-tutorial | network_analysis_with_networkx.ipynb | mit | # load image
"""
Explanation: Network analysis with networkx
What is network analysis?
What is networkx?
A simple example
Consider the social network shown in the figure below.
End of explanation
"""
import networkx as nx
G = nx.Graph() # create a Graph object
people = ["Michael", "Jane", "John", "Peter", "An... |
astroumd/GradMap | notebooks/Lectures2021/Lecture2/Lecture2_Student.ipynb | gpl-3.0 | ourList = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
"""
Explanation: Contents
Review
Intro to Numpy Arrays
a. Negative Indexing Exercise
b. Ranges Exercise
c. Indexing & Slicing Exercises
3. Functions in Numpy
a. Numpy Trig Function Exercises
4. User-Defined Functions
a. Path Length Function Exercises
5. Loading and Saving Num... |
landmanbester/fundamentals_of_interferometry | 1_Radio_Science/01_06_synchrotron_emission.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
"""
Explanation: Outline
Glossary
1. Radio Science using Interferometric Arrays
Previous: 1.5 Black body radiation
Next: 1.7 Line emission
Import standard modules:
... |
KUrushi/knocks | 06/英語テキストの処理.ipynb | mit | import pickle
"""
Explanation: 第6章: 英語テキストの処理
英語テキストのnlp.txtに対して, 以下の処理を実行せよ
End of explanation
"""
def separate_sentences(filename):
checks = ['. ', '; ', '? ', '! ']
for sentences in open(filename, 'r'):
sentences = sentences.strip()
sep_flag = False
sep_index = 0
check_sign... |
yevheniyc/Projects | 1m_ML_Security/notebooks/answers/Worksheet 1 - Working with One Dimensional Data - Answers.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: <img src="../../img/logo_white_bkg_small.png" align="left" />
Worksheet 1: Working with One Dimensional Data
This worksheet covers concepts covered in the first half of Module 1 - Exploratory Data Analysis in One Dimension. It s... |
noppanit/linear-regression-basic | Basic Linear Regression.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
from scipy import stats
import collections
import time
from sklearn.linear_model import SGDRegressor
"""
Explanation: Introduction
This is going to be a very basic examples of Linear Regression. Basically, we have generated data of total amount of meals and ti... |
ProfessorKazarinoff/staticsite | content/code/circuit_diagrams/SchemDraw_circuit_diagrams.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
#plt.xkcd() #to make the diagram look hand drawn
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
import SchemDraw as schem
import SchemDraw.elements as e
"""
Explanation: I've tried a bunch of different ways to make circuit diagrams for exams, homework assignments and wor... |
ES-DOC/esdoc-jupyterhub | notebooks/cnrm-cerfacs/cmip6/models/sandbox-1/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'sandbox-1', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: SANDBOX-1
Topic: Land
Sub-Topics: Soil, Snow, Vegetati... |
jsnajder/StrojnoUcenje | notebooks/SU-2015-10-VrednovanjeModela.ipynb | cc0-1.0 | # Učitaj osnovne biblioteke...
import scipy as sp
import sklearn
import pandas as pd
%pylab inline
"""
Explanation: Sveučilište u Zagrebu<br>
Fakultet elektrotehnike i računarstva
Strojno učenje
<a href="http://www.fer.unizg.hr/predmet/su">http://www.fer.unizg.hr/predmet/su</a>
Ak. god. 2015./2016.
Bilježnica 10: Vred... |
tgquintela/temporal_tasks | deliver/Report-ToyProblem.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
from svm import SVM
## Apply test to the code
%run tests.py
"""
Explanation: A toy problem: implement training a Support Vector Machine with SGD
Antonio G. Quintela
SVM
Support Vectors Machines are generalizations of linear decision boundaries for linearly nonsepara... |
jomavera/Work | Center of Gravity with JAX.ipynb | mit | from jax import grad, jit, vmap
import jax.numpy as np
import numpy as np2
#2D coordinates of locations of demand or supply
x_n = np.array([[-0.97,-80.7],
[-1.05, -80.45],
[-2.15, -79.92],
[-1.81, -79.53],
[-1.03, -79.47]])
#Quantities of demand/supply for ... |
appanacca/tutmom | intro.ipynb | bsd-3-clause | import numpy as np
objective = np.poly1d([1.3, 4.0, 0.6])
print(objective)
"""
Explanation: Introduction to optimization
The basic components
The objective function (also called the 'cost' function)
End of explanation
"""
import scipy.optimize as opt
x_ = opt.fmin(objective, [3])
print("solved: x={}".format(x_))
%... |
beyondvalence/biof509_wtl | Wk01-overview/Wk01-Overview.ipynb | mit | import numpy as np
print("Numpy:", np.__version__)
"""
Explanation: Week 1 - Getting Started
End of explanation
"""
location = 'Bethesda'
zip_code = 20892
elevation = 71.9
print("We're in", location, "zip code", zip_code, ", ", elevation, "m above sea level")
print("We're in " + location + " zip code " + str(zip_c... |
jhconning/Dev-II | notebooks/jupyter_notebooks.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import seaborn as sns
from ipywidgets import interact
df = pd.read_stata(".\data\country.dta")
"""
Explanation: Economics with Jupyter Notebooks
Jupyter Notebook is "a web application that allows you to create and share documents that contain li... |
foxan/dataquest | Data Visualization/Challenge - Data Visualization.ipynb | apache-2.0 | # %sh
# wget https://raw.githubusercontent.com/jgoodall/cinevis/master/data/csvs/moviedata.csv
# ls -l
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
hollywood_movies = pd.read_csv('moviedata.csv')
print hollywood_movies.head()
print hollywood_movies['exclude'].value_c... |
JAmarel/Phys202 | LaTeX/.ipynb_checkpoints/ProjectEuler17-checkpoint.ipynb | mit | def round_down(n):
s = str(n)
if n <= 20:
return n
elif n < 100:
return int(s[0] + '0'), int(s[1])
elif n<1000:
return int(s[0] + '00'),int(s[1]),int(s[2])
assert round_down(5) == 5
assert round_down(55) == (50,5)
assert round_down(222) == (200,2,2)
def number_to_word... |
shikhar413/openmc | examples/jupyter/candu.ipynb | mit | %matplotlib inline
from math import pi, sin, cos
import numpy as np
import openmc
"""
Explanation: Modeling a CANDU Bundle
In this example, we will create a typical CANDU bundle with rings of fuel pins. At present, OpenMC does not have a specialized lattice for this type of fuel arrangement, so we must resort to manua... |
sysid/nbs | lstm/CNTK_106A_LSTM_Timeseries_with_Simulated_Data.ipynb | mit | # Figure 1
Image(url="http://www.cntk.ai/jup/sinewave.jpg")
"""
Explanation: CNTK 106: Part A - Time series prediction with LSTM (Basics)
This tutorial demonstrates how to use CNTK to predict future values in a time series using LSTMs.
Goal
We use simulated data set of a continuous function (in our case a sine wave). ... |
phoebe-project/phoebe2-docs | 2.3/tutorials/alternate_backends.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
import phoebe
from phoebe import u # units
logger = phoebe.logger()
b = phoebe.default_binary()
"""
Explanation: Advanced: Alternate Backends
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook sessi... |
southpaw94/MachineLearning | TextExamples/3547_05_Code.ipynb | gpl-2.0 | %load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,scipy,matplotlib,scikit-learn
# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
"""
Explanation: Sebastian Raschka, 2015
Python Machine Learning Essential... |
ES-DOC/esdoc-jupyterhub | notebooks/messy-consortium/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', 'messy-consortium', 'sandbox-3', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: MESSY-CONSORTIUM
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: Tra... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/bigquery/labs/c_extract_and_benchmark.ipynb | apache-2.0 | import pandas as pd
from google.cloud import bigquery
PROJECT = !gcloud config get-value project
PROJECT = PROJECT[0]
%env PROJECT=$PROJECT
"""
Explanation: Extract Datasets and Establish Benchmark
Learning Objectives
- Divide into Train, Evaluation and Test datasets
- Understand why we need each
- Pull data out of ... |
california-civic-data-coalition/python-calaccess-notebooks | calaccess-exploration/campaign_finance_filings.ipynb | mit | %load_ext sql
from django.conf import settings
connection_string = 'postgresql+psycopg2://{USER}:{PASSWORD}@{HOST}:{PORT}/{NAME}'.format(
**settings.DATABASES['default']
)
%sql $connection_string
"""
Explanation: Campaign Finance Disclosure Filings
Setup
End of explanation
"""
%%sql
SELECT cvr."FILING_ID", COUN... |
giacomov/3ML | examples/Photometry_demo.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from threeML import *
# we will need XPSEC models for extinction
from astromodels.xspec import *
# The filter library takes a while to load so you must import it explicitly..
from threeML.plugins.photometry.filter_library import threeML_filter_li... |
widdowquinn/SI_Holmes_etal_2017 | notebooks/03-crossvalidation.ipynb | mit | %pylab inline
import os
import pickle
import warnings; warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import scipy
import seaborn as sns; sns.set_context('notebook')
import tools
"""
Explanation: <img src="images/JHI_STRAP_Web.png" style="width: 150px; float: right;">
Supplementary Informa... |
ES-DOC/esdoc-jupyterhub | notebooks/cas/cmip6/models/sandbox-2/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cas', 'sandbox-2', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: CAS
Source ID: SANDBOX-2
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balance... |
FZJ-IEK3-VSA/tsam | examples/aggregation_example.ipynb | mit | %load_ext autoreload
%autoreload 2
import copy
import os
import pandas as pd
import matplotlib.pyplot as plt
import tsam.timeseriesaggregation as tsam
%matplotlib inline
"""
Explanation: tsam - 1. Example
Example usage of the time series aggregation module (tsam)
Date: 08.05.2017
Author: Leander Kotzur
Import pandas a... |
andreyf/machine-learning-examples | visualization/part1_seaborn.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (10, 6)
"""
Explanation: <center>
<img src="../img/ods_stickers.jpg">
Открытый курс по машинному обучению
</center>
Автор материала: программист-исследователь Mail.ru Group, ... |
NlGG/MachineLearning | NeuralNetwork/nn.ipynb | mit | def example1(x_1, x_2):
z = x_1**0.5*x_2*0.5
return z
fig = pl.figure()
ax = Axes3D(fig)
X = np.arange(0, 1, 0.1)
Y = np.arange(0, 1, 0.1)
X, Y = np.meshgrid(X, Y)
Z = example1(X, Y)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1)
pl.show()
"""
Explanation: コブ・ダクラス型生産関数と課題文で例に出された関数を用いる。
いずれも定義域は0≤x≤1である。
... |
dnc1994/MachineLearning-UW | ml-regression/blank/week-4-ridge-regression-assignment-2-blank.ipynb | mit | import graphlab
"""
Explanation: Regression Week 4: Ridge Regression (gradient descent)
In this notebook, you will implement ridge regression via gradient descent. You will:
* Convert an SFrame into a Numpy array
* Write a Numpy function to compute the derivative of the regression weights with respect to a single feat... |
AMICI-developer/AMICI | python/examples/example_steadystate/ExampleSteadystate.ipynb | bsd-2-clause | # SBML model we want to import
sbml_file = 'model_steadystate_scaled.xml'
# Name of the model that will also be the name of the python module
model_name = 'model_steadystate_scaled'
# Directory to which the generated model code is written
model_output_dir = model_name
import libsbml
import importlib
import amici
impor... |
statsmodels/statsmodels.github.io | v0.12.1/examples/notebooks/generated/mixed_lm_example.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
from statsmodels.tools.sm_exceptions import ConvergenceWarning
"""
Explanation: Linear Mixed Effects Models
End of explanation
"""
data = sm.datasets.get_rdataset('dietox', 'geepack').data
md... |
egodat/chaos_game_fractal | fractal_mandlebrot_julia.ipynb | mit | from PIL import Image, ImageDraw
import math, colorsys, numpy
from matplotlib import colors
from IPython.display import Image as ipythonImage
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Honors-Physics-PHYS1010" data-toc-modified-id="Honors-Physics-PHYS1010-1"><span class="toc-item-num">1... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/markov_regression.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
# NBER recessions
from pandas_datareader.data import DataReader
from datetime import datetime
usrec = DataReader(
"USREC", "fred", start=datetime(1947, 1, 1), end=datetime(2013, 4, 1)
)
"""
Exp... |
alihalabyah/flexx | examples/notebooks/flexx_tutorial_pyscript.ipynb | bsd-2-clause | from flexx.pyscript import py2js, evalpy
"""
Explanation: Tutorial for flexx.pyscript - Python to JavaScript transpilation
End of explanation
"""
js = py2js('for i in range(10): print(i)')
print(js)
"""
Explanation: We can transpile strings of Python code:
End of explanation
"""
def foo(x):
res = []
for i... |
cavaunpeu/willwolf.io-source | content/downloads/notebooks/neural_implicit_mf.ipynb | mit | from collections import namedtuple
from copy import deepcopy
import random
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.manifold import TSNE
from sklearn.metrics.pairwise import cosine_similarity
from sqlalchemy import create_engine
from dotify.config impor... |
leliel12/scikit-criteria | ahp_ext/tuto.ipynb | bsd-3-clause | from skcriteria import Data, MIN, MAX
mtx = [
[1, 2, 3], # alternative 1
[4, 5, 6], # alternative 2
]
mtx
# let's says the first two alternatives are
# for maximization and the last one for minimization
criteria = [MAX, MAX, MIN]
criteria
# et’s asume we know in our case, that the importance of
# the auto... |
fluffy-hamster/A-Beginners-Guide-to-Python | A Beginners Guide to Python/07.2. Philosophy, Part Two.ipynb | mit | def net_force(mass, acceleration):
return mass * acceleration
"""
Explanation: “Explicit is better than implicit”
What does this line of the poem refer to? Well, there are several examples and interpretations one could give, but today I would to interpret this line with reference to the idea of readability.
We ... |
peteWT/cec_apl | IOUdata/.ipynb_checkpoints/ReadFromDB-Copy1-checkpoint.ipynb | mit | import pandas as pd
from sqlalchemy import create_engine
"""
Explanation: This notebook is intended to show how to use pandas, and sql alchemy to upload data into DB2-switch.
Install using pip or any other package manager pandas, sqlalchemy and pg8000. The later one is the driver to connect to the db.
End of explanati... |
dwhswenson/openpathsampling | examples/tests/langevin_integrator_check.ipynb | mit | import openpathsampling.engines.toy as toys
import openpathsampling as paths
import numpy as np
"""
Explanation: Langevin Integrator Check
The toy_dynamics subpackage provides an integrator called LangevinBAOABIntegrator, which is based on a paper by Leimkuhler and Matthews. This notebook uses the toy_dynamics package... |
ES-DOC/esdoc-jupyterhub | notebooks/nims-kma/cmip6/models/sandbox-1/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nims-kma', 'sandbox-1', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: NIMS-KMA
Source ID: SANDBOX-1
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Ener... |
adrn/TriandRRLyrae | notebooks/2016 A13 + GASS observing run.ipynb | mit | css = ascii.read("/Users/adrian/projects/triand-rrlyrae/data/catalina.csv")
print(css.colnames)
linear = ascii.read("/Users/adrian/projects/triand-rrlyrae/data/linear.csv")
print(linear.colnames)
css_c = coord.SkyCoord(ra=css['ra']*u.deg, dec=css['dec']*u.deg, distance=css['helio_dist']*u.kpc)
lin_c = coord.SkyCoord(... |
mne-tools/mne-tools.github.io | stable/_downloads/7ca3f34c286b629113cbb522edf26a21/75_cluster_ftest_spatiotemporal.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
# Jona Sassenhagen <jona.sassenhagen@gmail.com>
# Alex Rockhill <aprockhill@mailbox.org>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_... |
DTOcean/dtocean-core | notebooks/DTOcean Fixed Tidal Scenario Analysis.ipynb | gpl-3.0 | %matplotlib inline
from IPython.display import display, HTML
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (14.0, 8.0)
import numpy as np
from datetime import datetime
from dtocean_core import start_logging
from dtocean_core.core import Core
from dtocean_core.menu import DataMenu, ModuleMenu, Pro... |
StudyExchange/Udacity | MachineLearning(Advanced)/p2_finding_donors/finding_donors.ipynb | mit | # 为这个项目导入需要的库
import numpy as np
import pandas as pd
from time import time
from IPython.display import display # 允许为DataFrame使用display()
# 导入附加的可视化代码visuals.py
import visuals as vs
# 为notebook提供更加漂亮的可视化
%matplotlib inline
# 导入人口普查数据
data = pd.read_csv("census.csv")
# 成功 - 显示第一条记录
display(data.head())
"""
Explanati... |
adicu/AccessibleML | 5. Dimensionality Reduction via Principal Component Analysis.ipynb | mit | from sklearn.decomposition import PCA
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # 3D Plotting
from scipy import stats
import ipywidgets # interactions
%matplotlib inline
"""
Explanation: Dimensionality Reduction via Principal Component An... |
rasbt/pyprind | examples/pyprind_demo.ipynb | bsd-3-clause | %load_ext watermark
%watermark -v -d -u -p pyprind
"""
Explanation: Sebastian Raschka
End of explanation
"""
import pyprind
"""
Explanation: PyPrind demo
<a name='sections'></a>
Sections
Basic Progress Bar
Basic Percentage Indicator
Progress Bar and Percentage Indicator generators
Progress Bar/Percentage Indica... |
mrcinv/matpy | 01b_neenacbe.ipynb | gpl-2.0 | import sympy as sym
sym.init_printing() # lepši izpis formul
"""
Explanation: ^ gor: Uvod
Neenačbe
Podobno kot enačb, se tudi neenačb lotimo s knjižnico Sympy. Bolj konkretno z modulom
solvers.inequalities.
End of explanation
"""
from sympy.solvers.inequalities import solve_univariate_inequality
from sympy import Sy... |
Santana9937/Classification_ML_Specialization | Week_1_Predicting_Sentiment_from_Reviews/week_1_lin_classifier_assign.ipynb | mit | import os
import zipfile
import string
import numpy as np
import pandas as pd
from sklearn import linear_model
from sklearn.feature_extraction.text import CountVectorizer
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
%matplotlib inline
"""
Explanation: Predict... |
CNS-OIST/STEPS_Example | user_manual/source/API_2/Interface_Tutorial_1_wm.ipynb | gpl-2.0 | import steps.interface
from steps.model import *
from steps.geom import *
from steps.rng import *
from steps.sim import *
from steps.saving import *
from matplotlib import pyplot as plt
import numpy as np
mdl = Model()
r = ReactionManager()
"""
Explanation: Well-Mixed Reaction System
<div class="admonition note">
... |
tuanavu/python-cookbook-3rd | notebooks/ch01/04_finding_the_largest_or_smallest_n_items.ipynb | mit | import heapq
nums = [1, 8, 2, 23, 7, -4, 18, 23, 42, 37, 2]
print(heapq.nlargest(3, nums)) # Prints [42, 37, 23]
print(heapq.nsmallest(3, nums)) # Prints [-4, 1, 2]
"""
Explanation: Finding the Largest or Smallest N Items
Problem
You want to make a list of the largest or smallest N items in a collection.
Solution
... |
NLeSC/embodied-emotions-scripts | 03_AnalysisCorpora.ipynb | apache-2.0 | # Render our plots inline
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import os
pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier
plt.rcParams['figure.figsize'] = (16, 6)
# adjust to your local directories
embem_data_dir = '/home/jvdzwaan/da... |
dataDogma/Computer-Science | Databases-Content/Intro_to_DB/Stanford - Introduction_to_database - Getting the database ready.ipynb | gpl-3.0 | # importing the sqlalechemy ORM( object realtional mapper )
import sqlalchemy
"""
Explanation: Using SQL in Jupyter via SqlAlchemy ORM
Using the ORM( Object Relational Mappers) for various databases used in this notebook, some of them are:
Sqlite
MySQL
Oracle
PostgreSQL
MongoDB
Neo4j
Sqlite
Table of C... |
mne-tools/mne-tools.github.io | 0.17/_downloads/3ebd331f774b241a9eda870b8410800d/plot_info.ipynb | bsd-3-clause | import mne
import os.path as op
"""
Explanation: The :class:Info <mne.Info> data structure
The :class:Info <mne.Info> data object is typically created
when data is imported into MNE-Python and contains details such as:
date, subject information, and other recording details
the sampling rate
information ab... |
guidesc/DSBA---case-study | RATP.ipynb | mit | import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
# ---- Summary of the twitter accounts -----#
# RER_A
# RERB
# RERC_SNCF --< Infotrafic
# RERD_SNCF --< Infotrafic
# RERE_SNCF --< Infotrafic
# Ligne12_RATP --< from 1 to 14 lines
line_list = ['RER_A', 'RER_B', 'RER_C', 'RER_... |
aKumpan/hse-shad-ml | 02-metric_classification/statement_neighbours/statement_neighbours.ipynb | apache-2.0 | df = pd.read_csv('../data/wine.data')
"""
Explanation: 1. Загрузите выборку Wine
End of explanation
"""
X_train = df[df.columns[1:]]
y_train = df[df.columns[0]]
"""
Explanation: 2. Извлеките из данных признаки и классы. Класс записан в первом столбце (три варианта), признаки — в столбцах со второго по последний. Бо... |
P7h/FutureLearn__Learn_to_Code_for_Data_Analysis | Week#3/Week_3_exercises.ipynb | apache-2.0 | import sys
sys.version
import warnings
warnings.simplefilter('ignore', FutureWarning)
from pandas import *
show_versions()
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Exercise-notebook-3:-Transforming-and-Combining-Data" data-toc-modified-id="Exercise-notebook-3:-Transforming-and-Comb... |
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