repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
sot/aca_stats | fit_acq_prob_model-2018-03-sota.ipynb | bsd-3-clause | from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table
from astropy.time import Time
import tables
from scipy import stats
import tables3_api
%matplotlib inline
"""
Explanation: Fit the flight acquisition probability model in 2018-03
Fit values here were co... |
ES-DOC/esdoc-jupyterhub | notebooks/nasa-giss/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', 'nasa-giss', 'sandbox-3', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: Transport, Emissi... |
nicoa/showcase | pydatabln_2018_schedule2cal/pydatabln2018_filter_and_overview.ipynb | mit | import requests as rq
import pandas as pd
import matplotlib.pyplot as mpl
import bs4
import os
from tqdm import tqdm_notebook
from datetime import time
%matplotlib inline
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Query-Data" data-toc-modified-id="Query-Data-1"><span class="toc-item-... |
kevinsung/OpenFermion | docs/fqe/guide/introduction.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... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_object_evoked.ipynb | bsd-3-clause | import os.path as op
import mne
"""
Explanation: The :class:Evoked <mne.Evoked> data structure: evoked/averaged data
End of explanation
"""
data_path = mne.datasets.sample.data_path()
fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif')
evokeds = mne.read_evokeds(fname, baseline=(None, 0), pro... |
pwer21c/pwer21c.github.io | python/pythoncodes/20022021.ipynb | mit | i=1
while i<100:
if i%7==0:
print(i)
i=i+1
"""
Explanation: 1에서 100까지의 수중에서 7의 배수 multiples de 7, multiples of 7를 출력할때 입니다.
End of explanation
"""
i=1
multiplesof7=[]
while i<100:
if i%7==0:
multiplesof7.append(i)
i=i+1
print(multiplesof7)
"""
Explanation: 그런데 7의 배수를 list 변... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/noresm2-mh/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-mh', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: NCC
Source ID: NORESM2-MH
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radi... |
benvanwerkhoven/kernel_tuner | tutorial/diffusion_opencl.ipynb | apache-2.0 | nx = 1024
ny = 1024
"""
Explanation: Tutorial: From physics to tuned GPU kernels
This tutorial is designed to show you the whole process starting from modeling a physical process to a Python implementation to creating optimized and auto-tuned GPU application using Kernel Tuner.
In this tutorial, we will use diffusion ... |
scotthuang1989/Python-3-Module-of-the-Week | concurrency/multiprocessing/Passing_Messages_to_Processes.ipynb | apache-2.0 | import multiprocessing
class MyFancyClass:
def __init__(self, name):
self.name = name
def do_something(self):
proc_name = multiprocessing.current_process().name
print('Doing something fancy in {} for {}!'.format(
proc_name, self.name))
def worker(q):
obj = q.get()
... |
kanhua/pypvcell | demos/metpv_data_reader_demo.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os
from pypvcell.solarcell import SQCell,MJCell,TransparentCell
from pypvcell.illumination import Illumination
from pypvcell.spectrum import Spectrum
from pypvcell.metpv_reader import N... |
gfeiden/MagneticUpperSco | notes/equipartition_B_strength.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate as scint
"""
Explanation: Equipartition Surface Magnetic Field Strengths
Computing equipartition magnetic field strengths using PHOENIX stellar atmosphere models (Hauschildt et al. 1999).
End of explanation
"""
iso_10 = np... |
xaibeing/cn-deep-learning | tutorials/sentiment-rnn/Sentiment_RNN_Solution.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural... |
RogueAstro/RV_PS2017 | notebooks/HIP67620_example.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import astropy.units as u
from radial import estimate, dataset
%matplotlib inline
"""
Explanation: The orbital parameters of the binary solar twin HIP 67620
radial is a simple program designed to do a not very trivial task: simulate ra... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session13/Day0/TooBriefVisualization.ipynb | mit | from sklearn.datasets import load_linnerud
linnerud = load_linnerud()
chinups = linnerud.data[:,0]
"""
Explanation: Introduction to Visualization:
Density Estimation and Data Exploration
Version 0.2
There are many flavors of data analysis that fall under the "visualization" umbrella in astronomy. Today, by way of exa... |
abeschneider/algorithm_notes | Heapsort.ipynb | mit | def build_heap(lst):
# last non-leaf node
nonleaf_nodes = len(lst)/2
# start at bottom work up for each node
for i in range(nonleaf_nodes-1, -1, -1):
percolate_down(lst, i, len(lst))
"""
Explanation: Heap Sort
Summary
| Performance | Complexity |
|--------------------... |
timzhangau/ml_nano | student_intervention/student_intervention.ipynb | mit | # Import libraries
import numpy as np
import pandas as pd
from time import time
from sklearn.metrics import f1_score
# Read student data
student_data = pd.read_csv("student-data.csv")
print "Student data read successfully!"
"""
Explanation: Machine Learning Engineer Nanodegree
Supervised Learning
Project: Building a ... |
chetnapriyadarshini/deep-learning | reinforcement/Q-learning-cart.ipynb | mit | import gym
import tensorflow as tf
import numpy as np
"""
Explanation: Deep Q-learning
In this notebook, we'll build a neural network that can learn to play games through reinforcement learning. More specifically, we'll use Q-learning to train an agent to play a game called Cart-Pole. In this game, a freely swinging p... |
eyadsibai/rep | howto/00-intro_ipython.ipynb | apache-2.0 | %pylab inline
from IPython.display import YouTubeVideo
YouTubeVideo("qb7FT68tcA8", width=600, height=400, theme="light", color="blue")
# You can ignore this, it's just for aesthetic purposes
matplotlib.rcParams['figure.figsize'] = (8,5)
rcParams['savefig.dpi'] = 100
"""
Explanation: Intro into IPython notebooks
End ... |
wilkeraziz/notebooks | nlp2/fsa_permutations.ipynb | apache-2.0 | import fst
"""
Explanation: Permutations
End of explanation
"""
# Let's see the input as a simple linear chain FSA
def make_input(srcstr, sigma = None):
"""
converts a nonempty string into a linear chain acceptor
@param srcstr is a nonempty string
@param sigma is the source vocabulary
"""
ass... |
laa-1-yay/SDC1-DetectLaneLines | .ipynb_checkpoints/Laav_Lane_Lines_Detection-checkpoint.ipynb | gpl-3.0 | #importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
"""
Explanation: Self-Driving Car Engineer Nanodegree
Project: Finding Lane Lines on the Road
In this project, you will use the tools you learned about in the lesson to ide... |
sorig/shogun | doc/ipython-notebooks/structure/FGM.ipynb | bsd-3-clause | %pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
import numpy as np
import scipy.io
dataset = scipy.io.loadmat(os.path.join(SHOGUN_DATA_DIR, 'ocr/ocr_taskar.mat'))
# patterns for training
p_tr = dataset['patterns_train']
# patterns for testing
p_ts = dataset['pat... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/robust_models_0.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import statsmodels.api as sm
"""
Explanation: Robust Linear Models
End of explanation
"""
data = sm.datasets.stackloss.load()
data.exog = sm.add_constant(data.exog)
"""
Explanation: Estimation
Load data:
End of explanation
"""
huber_t = sm.RLM... |
lyndond/Analyzing_Neural_Time_Series | chapter33.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
from scipy.stats import norm
from scipy.signal import convolve2d
import skimage.measure
"""
Explanation: Chapter 33. Nonparametric permutation testing
End of explanation
"""
x = np.arange(-5,5, .01)
pdf = norm.pdf(x)
data = np.random.randn(1000)
... |
eds-uga/csci4360-fa17 | workshops/w8/CSCI+6360-Data+Science-Workshops.ipynb | mit | # Autoendoer using H2o
#CSCI6360 H2O WORKSHOP
from IPython.display import Image,display
from IPython.core.display import HTML
import matplotlib.pyplot as plot
from h2o.estimators.deeplearning import H2ODeepLearningEstimator
from h2o.grid.grid_search import H2OGridSearch
#sp... |
masterfish2015/my_project | python/demo1/scipy-advanced-tutorial-master/Part2/Exercise 2.ipynb | mit | %%javascript
delete requirejs.s.contexts._.defined.CustomViewModule;
define('CustomViewModule', ['jquery', 'widgets/js/widget'], function($, widget) {
var CustomView = widget.DOMWidgetView.extend({
});
return {CustomView: CustomView};
});
from IPython.html.widgets import DOMWidget
from IPython.display imp... |
daniel-koehn/Theory-of-seismic-waves-II | 05_2D_acoustic_FD_modelling/lecture_notebooks/5_fdac2d_heterogeneous.ipynb | gpl-3.0 | # Execute this cell to load the notebook's style sheet, then ignore it
from IPython.core.display import HTML
css_file = '../../style/custom.css'
HTML(open(css_file, "r").read())
"""
Explanation: Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2018 by D. Koehn, heterogen... |
BrainIntensive/OnlineBrainIntensive | resources/matplotlib/Examples/lineplots.ipynb | mit | %load_ext watermark
%watermark -u -v -d -p matplotlib,numpy
"""
Explanation: Sebastian Raschka
back to the matplotlib-gallery at https://github.com/rasbt/matplotlib-gallery
Link the matplotlib gallery at https://github.com/rasbt/matplotlib-gallery
End of explanation
"""
%matplotlib inline
"""
Explanation: <font si... |
M-R-Houghton/euroscipy_2015 | scikit_image/lectures/adv3_panorama-stitching.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
def compare(*images, **kwargs):
"""
Utility function to display images side by side.
Parameters
----------
image0, image1, image2, ... : ndarrray
Images to display.
labels : list
Labels for the different images.
"""
... |
jamesjia94/BIDMach | tutorials/BIDMat_intro.ipynb | bsd-3-clause | import BIDMat.{CMat,CSMat,DMat,Dict,IDict,FMat,GMat,GIMat,GSMat,GSDMat,HMat,IMat,Image,LMat,Mat,SMat,SBMat,SDMat}
import BIDMat.MatFunctions._
import BIDMat.SciFunctions._
import BIDMat.Solvers._
import BIDMat.JPlotting._
Mat.checkMKL
Mat.checkCUDA
Mat.setInline
if (Mat.hasCUDA > 0) GPUmem
"""
Explanation: Introducti... |
quantopian/research_public | case_studies/traditional_value/traditional_value_notebook.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.research import run_pipeline
from quantopian.pipeline.data import morningstar
from quantopian.pipeline.factors import CustomFactor
... |
tensorflow/workshops | extras/archive/01_linear_regression_low_level.ipynb | apache-2.0 | # The next three imports help with compatability between
# Python 2 and 3
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import pylab
import tensorflow as tf
# A special command for IPython Notebooks that
# intructs Matplotlib... |
tpin3694/tpin3694.github.io | blog/aisle_seat_probabilities.ipynb | mit | # Import required modules
import pandas as pd
import numpy as np
# Set plots to display in the iPython notebook
%matplotlib inline
"""
Explanation: Title: What Is The Probability An Economy Class Seat Is An Aisle Seat?
Slug: aisle_seat_probabilities
Summary: What Is The Probability An Economy Class Seat Is An Aisle S... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a/td2a_correction_session_5_donnees_non_structurees_et_programmation_fonctionnelle_corrige.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 2A.i - Données non structurées, programmation fonctionnelle - correction
Calculs de moyennes et autres statistiques sur une base twitter au format JSON avec de la programmation fonctionnelle (module cytoolz).
End of explanation
"""
impo... |
hankcs/HanLP | plugins/hanlp_demo/hanlp_demo/zh/srl_stl.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/srl_mtl.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Ope... |
jart/tensorflow | tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb | apache-2.0 | !pip install unidecode
"""
Explanation: Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License").
Text Generation using a RNN
<table class="tfo-notebook-buttons" align="left"><td>
<a target="_blank" href="https://colab.research.google.com/github/tensorflow/tensorflow/blob/... |
a301-teaching/a301_code | notebooks/layertops_demo_solution.ipynb | mit | import glob
import h5py
import numpy as np
import glob
from a301lib.cloudsat import get_geo
from a301utils.a301_readfile import download
from matplotlib import pyplot as plt
lidar_name='2006303212128_02702_CS_2B-GEOPROF-LIDAR_GRANULE_P2_R04_E02.h5'
download(lidar_name)
"""
Explanation: Reading the Lidar LayerTops var... |
BadWizard/Inflation | Disaggregated-Data/weather-like-plot-HICP-by-country.ipynb | mit | df_infl_ctry['min'] = df_infl_ctry.apply(min,axis=1)
df_infl_ctry['max'] = df_infl_ctry.apply(max,axis=1)
df_infl_ctry['mean'] = df_infl_ctry.apply(np.mean,axis=1)
df_infl_ctry['mode'] = df_infl_ctry.quantile(q=0.5, axis=1)
df_infl_ctry['10th'] = df_infl_ctry.quantile(q=0.10, axis=1)
df_infl_ctry['90th'] = df_infl_ctry... |
ntftrader/ntfdl | examples/notebooks/Historical data.ipynb | mit | %matplotlib inline
%pylab inline --no-import-all
pylab.rcParams['figure.figsize'] = (18, 10)
from ntfdl import Dl
stl = Dl('STL', exchange='OSE', download=False)
history = stl.get_history()
history.tail()
fig, ax = plt.subplots()
ax.tick_params(labeltop=False, labelright=True)
history.close.plot()
plt.grid()
# An... |
bourneli/deep-learning-notes | DAT236x Deep Learning Explained/.ipynb_checkpoints/Lab6_TextClassification_with_LSTM-checkpoint.ipynb | mit | from __future__ import print_function # Use a function definition from future version (say 3.x from 2.7 interpreter)
import requests
import os
def download(url, filename):
""" utility function to download a file """
response = requests.get(url, stream=True)
with open(filename, "wb") as handle:
for ... |
tommyogden/maxwellbloch | docs/examples/mbs-two-weak-square-decay.ipynb | mit | mb_solve_json = """
{
"atom": {
"decays": [
{
"channels": [[0, 1]],
"rate": 1.0
}
],
"energies": [],
"fields": [
{
"coupled_levels": [[0, 1]],
"detuning": 0.0,
"rabi_freq": 1.0e-3,
"rabi_freq_t_args": {
"ampl": 1.0,
... |
sthuggins/phys202-2015-work | assignments/assignment03/.ipynb_checkpoints/NumpyEx01-checkpoint.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import antipackage
import github.ellisonbg.misc.vizarray as va
"""
Explanation: Numpy Exercise 1
Imports
End of explanation
"""
def checkerboard(size):
"""Return a 2d checkboard of 0.0 and 1.0 as a NumPy array"""
... |
ingmarschuster/distributions | Transforms_demo.ipynb | lgpl-3.0 | from __future__ import division, print_function, absolute_import
import numpy as np
import scipy as sp
import scipy.stats as stats
from numpy import exp, log, sqrt
from scipy.misc import logsumexp
import distributions as dist, distributions.transform as tr
import matplotlib.pyplot as plt
def apply_to_mg(func, *mg)... |
dmitrip/PML | performance_tests/num_clumps.ipynb | apache-2.0 | S = 10_000 # support set size
p = np.ones(S)/S # distribution
# iterate over S unknown, known
S_known_list = [False,True]
# make sample size list n_list
n_min = np.sqrt(S)
n_max = 100*S
num_n_points = 21
n_list = np.logspace(np.log10(n_min), np.log10(n_max), num_n_points).astype(int)
num_trials = 100 # number of tri... |
MingChen0919/learning-apache-spark | notebooks/01-data-strcture/1.1-rdd.ipynb | mit | # from a list
rdd = sc.parallelize([1,2,3])
rdd.collect()
# from a tuple
rdd = sc.parallelize(('cat', 'dog', 'fish'))
rdd.collect()
# from a list of tuple
list_t = [('cat', 'dog', 'fish'), ('orange', 'apple')]
rdd = sc.parallelize(list_t)
rdd.collect()
# from a set
s = {'cat', 'dog', 'fish', 'cat', 'dog', 'dog'}
rdd... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_eeg_erp.ipynb | bsd-3-clause | import mne
from mne.datasets import sample
"""
Explanation: EEG processing and Event Related Potentials (ERPs)
For a generic introduction to the computation of ERP and ERF
see tut_epoching_and_averaging. Here we cover the specifics
of EEG, namely:
- setting the reference
- using standard montages :func:`mne.channels.M... |
aleereza/twitterbot | twitterbot.ipynb | apache-2.0 | import tweepy
import time
import sys
import pickle
import datetime
"""
Explanation: Twitterbot
Here I am going to create a Twitter Bot step by step.
First I should create a Twitter application on https://dev.twitter.com/
Install tweepy: #pip install tweepy
End of explanation
"""
path="./data/"
filename="auth_data"
f... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/machine_learning_in_the_enterprise/solutions/gapic-vizier-multi-objective-optimization.ipynb | apache-2.0 | # Setup your dependencies
import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG = ... |
keras-team/keras-io | examples/vision/ipynb/convmixer.ipynb | apache-2.0 | from tensorflow.keras import layers
from tensorflow import keras
import matplotlib.pyplot as plt
import tensorflow_addons as tfa
import tensorflow as tf
import numpy as np
"""
Explanation: Image classification with ConvMixer
Author: Sayak Paul<br>
Date created: 2021/10/12<br>
Last modified: 2021/10/12<br>
Description... |
slundberg/shap | notebooks/api_examples/plots/waterfall.ipynb | mit | import xgboost
import shap
# train XGBoost model
X,y = shap.datasets.adult()
model = xgboost.XGBClassifier().fit(X, y)
# compute SHAP values
explainer = shap.Explainer(model, X)
shap_values = explainer(X)
"""
Explanation: waterfall plot
This notebook is designed to demonstrate (and so document) how to use the shap.p... |
gregmedlock/Medusa | docs/stats_compare.ipynb | mit | import medusa
from medusa.test import create_test_ensemble
ensemble = create_test_ensemble("Staphylococcus aureus")
import pandas as pd
biolog_base = pd.read_csv("../medusa/test/data/biolog_base_composition.csv", sep=",")
biolog_base
# convert the biolog base to a dictionary, which we can use to set ensemble.base_mo... |
NREL/bifacial_radiance | docs/tutorials/18 - AgriPV - Coffee Plantation with Tree Modeling.ipynb | bsd-3-clause | import bifacial_radiance
import os
from pathlib import Path
import numpy as np
import pandas as pd
testfolder = str(Path().resolve().parent.parent / 'bifacial_radiance' / 'TEMP' / 'Tutorial_18')
if not os.path.exists(testfolder):
os.makedirs(testfolder)
resultsfolder = os.path.join(testfolder, 'results')
""... |
massimo-nocentini/on-python | UniFiCourseSpring2020/introduction.ipynb | mit | __AUTHORS__ = {'am': ("Andrea Marino",
"andrea.marino@unifi.it",),
'mn': ("Massimo Nocentini",
"massimo.nocentini@unifi.it",
"https://github.com/massimo-nocentini/",)}
__KEYWORDS__ = ['Python', 'Jupyter', 'notebooks', 'keynote',]
"""
... |
sangheestyle/ml2015project | howto/model02_linear_models.ipynb | mit | import gzip
import cPickle as pickle
with gzip.open("../data/train.pklz", "rb") as train_file:
train_set = pickle.load(train_file)
with gzip.open("../data/test.pklz", "rb") as test_file:
test_set = pickle.load(test_file)
with gzip.open("../data/questions.pklz", "rb") as questions_file:
questions = pickle... |
cliburn/sta-663-2017 | notebook/11C_IPyParallel.ipynb | mit | import numpy as np
"""
Explanation: Using ipyparallel
Parallel execution is tightly integrated with Jupyter in the ipyparallel package. Install with
bash
conda install ipyparallel
ipcluster nbextension enable
Official documentation
End of explanation
"""
from ipyparallel import Client
"""
Explanation: Starting engi... |
FlorentSilve/Udacity_ML_nanodegree | projects/customer_segments/customer_segments.ipynb | mit | # Import libraries necessary for this project
import numpy as np
import pandas as pd
from IPython.display import display # Allows the use of display() for DataFrames
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the wholesale custo... |
uber/pyro | tutorial/source/contrib_funsor_intro_i.ipynb | apache-2.0 | from collections import OrderedDict
import torch
import funsor
from pyro import set_rng_seed as pyro_set_rng_seed
funsor.set_backend("torch")
torch.set_default_dtype(torch.float32)
pyro_set_rng_seed(101)
"""
Explanation: pyro.contrib.funsor, a new backend for Pyro - New primitives (Part 1)
Introduction
In this tutor... |
ucsdlib/python-novice-inflammation | 3-lists.ipynb | cc0-1.0 | odds = [1,3,5,7]
print('odds are:',odds)
print('first and last:', odds[0], odds[-1])
for number in odds:
print(number)
"""
Explanation: for loop is a way to do many operations
list a way to store many values
Unlike numpy, lits are built into the language so we don't need to load
[] creates a list
End of explan... |
tpin3694/tpin3694.github.io | machine-learning/save_images.ipynb | mit | # Load library
import cv2
import numpy as np
from matplotlib import pyplot as plt
"""
Explanation: Title: Save Images
Slug: save_images
Summary: How to save images using OpenCV in Python.
Date: 2017-09-11 12:00
Category: Machine Learning
Tags: Preprocessing Images
Authors: Chris Albon
Preliminaries
End of explana... |
DS-100/sp17-materials | sp17/hw/hw1/hw1.ipynb | gpl-3.0 | !pip install -U okpy
"""
Explanation: Homework 1: Setup and (Re-)Introduction to Python
Course Policies
Here are some important course policies. These are also located at
http://www.ds100.org/sp17/.
Tentative Grading
There will be 7 challenging homework assignments. Homeworks must be completed
individually and will mi... |
halexan/cs231n | assignment1/features.ipynb | mit | import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
# for auto-reloading extenrnal modu... |
iwansmith/FutureLHCb | notebooks/01_LoadingSmearingPlotting.ipynb | gpl-3.0 | import sys
sys.path.append('../../FourVector')
sys.path.append('../project')
from FourVector import FourVector
from ThreeVector import ThreeVector
from FutureColliderTools import SmearVertex, GetCorrectedMass, GetMissingMass2, GetQ2
from FutureColliderDataLoader import LoadData_KMuNu, LoadData_DsMuNu
from FutureColl... |
iiasa/xarray_tutorial | xarray-tutorial-egu2017.ipynb | bsd-3-clause | # standard imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import xarray as xr
import warnings
%matplotlib inline
np.set_printoptions(precision=3, linewidth=80, edgeitems=1) # make numpy less verbose
xr.set_options(display_width=70)
warnings.simplefilter('ignore') # filter some warnin... |
icrtiou/coursera-ML | ex3-neural network/2- one vs all logistic regression.ipynb | mit | # add intercept=1 for x0
X = np.insert(raw_X, 0, values=np.ones(raw_X.shape[0]), axis=1)
X.shape
# y have 10 categories here. 1..10, they represent digit 0 as category 10 because matlab index start at 1
# I'll ditit 0, index 0 again
y_matrix = []
for k in range(1, 11):
y_matrix.append((raw_y == k).astype(int))
#... |
smharper/openmc | examples/jupyter/mgxs-part-iii.ipynb | mit | import math
import pickle
from IPython.display import Image
import matplotlib.pyplot as plt
import numpy as np
import openmc
import openmc.mgxs
from openmc.openmoc_compatible import get_openmoc_geometry
import openmoc
import openmoc.process
from openmoc.materialize import load_openmc_mgxs_lib
%matplotlib inline
"""... |
jonathf/chaospy | docs/user_guide/main_usage/pseudo_spectral_projection.ipynb | mit | import chaospy
from problem_formulation import joint
gauss_quads = [
chaospy.generate_quadrature(order, joint, rule="gaussian")
for order in range(1, 8)
]
sparse_quads = [
chaospy.generate_quadrature(
order, joint, rule=["genz_keister_24", "clenshaw_curtis"], sparse=True)
for order in range(1,... |
nholtz/structural-analysis | matrix-methods/frame2d/30-test-Beaufait-9-4-1.ipynb | cc0-1.0 | from Frame2D import Frame2D
from Frame2D.Members import Member
# because units are kips, inches
Member.E = 30000. #ksi
Member.G = 11500.
from IPython import display
display.Image('data/Beaufait-9-4-1.d/fig1.jpg')
frame = Frame2D('Beaufait-9-4-1') # Example 9.4.1, p. 460
frame.input_all()
rs = frame.solve()
fra... |
eecs445-f16/umich-eecs445-f16 | handsOn_lecture00_python_tutorial/lecture00_python_tutorial_exercises_with_solutions.ipynb | mit | # 1. With Loops
# OK, not very "Pythonic"
def sum_of_multiples_with_loop(l, max_):
total = 0
# [1, 1000)
for i in range(1, max_):
for j in l:
if i % j == 0:
total += i
break
return total
# With Filter.
# Better (At least more "Pythonic")
def sum_of_m... |
Epidemium/RAMP-1 | epidemium_01_starting_kit.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
pd.set_option('display.max_columns', None)
"""
Explanation: Find this notebook in https://tinyurl.com/epidemium-ramp
<div class="page-header"><h1 class="alert alert-info">Epidemium RAMP: Cancer... |
zhouqifanbdh/liupengyuan.github.io | chapter2/homework/computer/3-29/201611680697-3.29作业.ipynb | mit | def factorial_sum(end):
i = 0
factorial_n = 1
while i < end:
i = i + 1
factorial_n = factorial_n *i
return factorial_n
m= int(input('请输入第1个整数，以回车结束。'))
n= int(input('请输入第2个整数，以回车结束。'))
k = int(input('请输入第3个整数，以回车结束。'))
print('最终的和是：', factorial_sum(m) + factorial_sum(n) + factorial_s... |
xpmanoj/content | HW0.ipynb | mit | x = [10, 20, 30, 40, 50]
for item in x:
print "Item is ", item
"""
Explanation: Homework 0
Due Tuesday, September 10 (but no submission is required)
Welcome to CS109 / STAT121 / AC209 / E-109 (http://cs109.org/). In this class, we will be using a variety of tools that will require some initial configuration. To ... |
Kaggle/learntools | notebooks/feature_engineering/raw/ex2.ipynb | apache-2.0 | # Set up code checking
# This can take a few seconds
from learntools.core import binder
binder.bind(globals())
from learntools.feature_engineering.ex2 import *
"""
Explanation: Introduction
In this exercise you'll apply more advanced encodings to encode the categorical variables ito improve your classifier model. The ... |
liganega/Gongsu-DataSci | previous/y2017/GongSu05_Flow_Control.ipynb | gpl-3.0 | def sum_if_3(k, m):
if (m % 3 == 0) or (str(m).endswith('3')):
return k + m
else:
return k
"""
Explanation: 흐름 제어: 조건문과 반복문(루프) 활용
수정 사항
gcd 함수 위주로 반복문 작성 가능여부 확인
좀 더 실용적인 수학함수 활용 가능
요약
조건문 활용
if문: 불리언 값을 이용하여 조건을 제시하는 방법
반복문(루프) 활용
while 반복문(루프): 특정 조건이 만족되는 동안 동일한 과정을 반복하는 방법
for ... |
sdpython/ensae_teaching_cs | _doc/notebooks/exams/td_note_2022.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 1A - Enoncé 3 novembre 2021
Correction de l'examen du 3 novembre 2021.
End of explanation
"""
import time
def mesure_temps_fonction(fct, N=100):
begin = time.perf_counter()
for i in range(N):
fct()
return (time.perf... |
colour-science/colour-ipython | notebooks/colorimetry/luminance.ipynb | bsd-3-clause | import colour
colour.utilities.filter_warnings(True, False)
sorted(colour.LUMINANCE_METHODS.keys())
"""
Explanation: !!! D . R . A . F . T !!!
Luminance
The Luminance $L_v$ is the quantity defined by the formula: <a name="back_reference_1"></a><a href="#reference_1">[1]</a>
$$
\begin{equation}
L_v=\cfrac{d\Phi_v}{dA... |
BrownDwarf/ApJdataFrames | notebooks/Luhman2004c.ipynb | mit | import warnings
warnings.filterwarnings("ignore")
"""
Explanation: ApJdataFrames 003: Luhman2004c
Title: New Brown Dwarfs and an Updated Initial Mass Function in Taurus
Authors: Luhman K.L.
Data is from this paper:
http://iopscience.iop.org/0004-637X/617/2/1216/
End of explanation
"""
import pandas as pd
names = ["... |
vipmunot/Data-Science-Course | Data Visualization/Lab 6/w06_Vipul_Munot.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd
sns.set_style('white')
%matplotlib inline
"""
Explanation: W6 Lab Assignment
Deep dive into Histogram and boxplot.
End of explanation
"""
bins = [0, 1, 3, 5, 10, 24]
data = {0.5: 4300, 2: 6900, 4: 4900, 7: 2000, 15: 2100}... |
rpmunoz/topicos_ingenieria_1 | clase_1/02 - Lectura de datos con Pandas.ipynb | gpl-3.0 | import numpy as np
from __future__ import print_function
import pandas as pd
pd.__version__
"""
Explanation: Lectura y manipulación de datos con Pandas
Autor: Roberto Muñoz <br />
E-mail: rmunoz@uc.cl
This notebook shows how to create Series and Dataframes wit... |
quantopian/research_public | notebooks/data/quandl.fred_usdontd156n/notebook.ipynb | apache-2.0 | # import the dataset
from quantopian.interactive.data.quandl import fred_usdontd156n as libor
# Since this data is public domain and provided by Quandl for free, there is no _free version of this
# data set, as found in the premium sets. This import gets you the entirety of this data set.
# import data operations
from... |
karst87/ml | dev/pyml/datacamp/intro-to-python-for-data-science/02_python-lists.ipynb | mit | fmz = [1.65, 1.45, 1.76]
fmz
fmz2 = [1, 3, 1.2, 'Hello']
fmz2
fmz3= [[23,12],
[99, 1]]
fmz3
"""
Explanation: Python Lists
https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-2-python-lists?ex=1
Learn to store, access and manipulate data in lists: the first step towards efficiently wor... |
bjshaw/phys202-project | galaxy_project/F) Plotting_function.ipynb | mit | def plotter(ic,sol,n=0):
"""Plots the positions of the stars and disrupting galaxy at each t in the time array
Parameters
--------------
ic : initial conditions
sol : solution array
n : integer
Returns
-------------
"""
plt.figure(figsize=(10,10))
y = np.linspa... |
synthicity/synthpop | demos/census_api.ipynb | bsd-3-clause | c = Census(os.environ["CENSUS"])
"""
Explanation: The census api needs a key - you can register for can sign up
http://api.census.gov/data/key_signup.html
End of explanation
"""
income_columns = ['B19001_0%02dE'%i for i in range(1, 18)]
vehicle_columns = ['B08201_0%02dE'%i for i in range(1, 7)]
workers_columns = ['B... |
IsaacLab/LaboratorioIntangible | T4/T4.5-Prisoner's-dilemma.ipynb | agpl-3.0 | from pydilemma.game_play import *
play_with('Nice', 'TitForTat') # These 2 guys get along very well...
#play_with('Nice', 'Naive') # Naive tries to get advantage of what works...
#play_with('Nice', 'NaiveProber') # And Naive Prober tries aggressively...
#play_with('NaiveProber', 'Majority') # But the NaiveProber can'... |
tcstewar/testing_notebooks | semd/sEMD.ipynb | gpl-2.0 | # the facilitation spikes
def stim_1_func(t):
index = int(t/0.001)
if index in [100, 1100, 2100]:
return 1000
else:
return 0
# the trigger spikes
def stim_2_func(t):
index = int(t/0.001)
if index in [90, 1500, 2150]:
return 1000
else:
return 0
# the operatio... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/tfx_pipelines/guided_projects/guided_project_1.ipynb | apache-2.0 | import os
"""
Explanation: Guided Project 1
Learning Objectives:
Learn how to generate a standard TFX template pipeline using tfx template
Learn how to modify and run a templated TFX pipeline
Note: This guided project is adapted from Create a TFX pipeline using templates).
End of explanation
"""
PATH = %env PATH
... |
jmitz/daymetDataExtraction | daymetDataDownload.ipynb | unlicense | import urllib
import os
from datetime import date as dt
"""
Explanation: <h1>Daymet Data Download</h1>
Daymet data can be extracted/downloaded in two ways. The nationwide or localized grid can be downloaded; alternately, the data for particular grid cells can be extracted through a web interface.
<h2>Daymet Data Dow... |
JakeColtman/BayesianSurvivalAnalysis | PyMC Done.ipynb | mit | running_id = 0
output = [[0]]
with open("E:/output.txt") as file_open:
for row in file_open.read().split("\n"):
cols = row.split(",")
if cols[0] == output[-1][0]:
output[-1].append(cols[1])
output[-1].append(True)
else:
output.append(cols)
output = out... |
spulido99/Programacion | Camilo/Taller 2 - Archivos y Bases de Datos.ipynb | mit | import pandas as pd
DF = pd.read_csv('../data/alternative.tsv', sep='\t')
DF
"""
Explanation: Archivos y Bases de datos
La idea de este taller es manipular archivos (leerlos, parsearlos y escribirlos) y hacer lo mismo con bases de datos estructuradas.
Ejercicio 1
Baje el archivo de "All associations with added ontol... |
zambzamb/zpic | python/O-X Waves.ipynb | agpl-3.0 | import em1ds as zpic
electrons = zpic.Species( "electrons", -1.0, ppc = 64, uth=[0.005,0.005,0.005])
sim = zpic.Simulation( nx = 1000, box = 100.0, dt = 0.05, species = electrons )
#Bz0 = 0.5
Bz0 = 1.0
#Bz0 = 4.0
sim.emf.set_ext_fld('uniform', B0= [0.0, 0.0, Bz0])
"""
Explanation: Waves in magnetized Plasmas: O-wa... |
regata/dbda2e_py | chapters/4.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import numpy as np
np.random.seed(47405)
N = 500 # Specify the total number of flips, denoted N.
p_heads = 0.5 # Specify underlying probability of heads.
# Flip a coin N times and compute the running proportion of heads at each flip.
# Genera... |
HNoorazar/PyOpinionGame | Famous_Models.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import matplotlib.image as mpimg
from matplotlib import rcParams
import seaborn as sb
"""
Explanation: Famous Opinion Dynamic Models
End of explanation
"""
def converg... |
DB2-Samples/db2jupyter | v1/Db2 11 Time and Date Functions.ipynb | apache-2.0 | %run db2.ipynb
"""
Explanation: <a id="top"></a>
Db2 11 Time and Date Functions
There are plenty of new date and time functions found in Db2 11. These functions allow you to extract portions from a date
and format the date in a variety of different ways. While Db2 already has a number of date and time functions, these... |
IACS-CS-207/cs207-F17 | lectures/L14/L14.ipynb | mit | class SentenceIterator:
def __init__(self, words):
self.words = words
self.index = 0
def __next__(self):
try:
word = self.words[self.index]
except IndexError:
raise StopIteration()
self.index += 1
return word
def __iter_... |
davidbrough1/pymks | notebooks/stats_checker_board.ipynb | mit | import pymks
%matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Checkerboard Microstructure
Introduction - What are 2-Point Spatial Correlations (also called 2-Point Statistics)?
The purpose of this example is to introduce 2-point spatial correl... |
neurodata/ndparse | examples/isbi2012_deploy.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
import sys, os, copy, logging, socket, time
import numpy as np
import pylab as plt
#from ndparse.algorithms import nddl as nddl
#import ndparse as ndp
sys.path.append('..'); import ndparse as ndp
try:
logger
except:
# do this precisely once
logger = ... |
McIntyre-Lab/ipython-demo | r_inside_ipython_pt1.ipynb | gpl-2.0 | # Imports
import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
# Load R magic
%load_ext rmagic
# Make data to plot in python
x = np.random.uniform(0, 1000, size=1000)
y = np.random.normal(1000, size=1000)
# Plot using matplotlib
plt.scatter(x=x, y=y, color='k')... |
chbehrens/pr_bc_connectivity-1 | RBC_subtypes.ipynb | gpl-3.0 | import numpy as np
from scipy.stats import itemfreq
from scipy.io import loadmat
from scipy.spatial import ConvexHull
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from PIL import Image
from PIL import ImageDraw
from sklearn.mixture import GMM
from shapely.geometry import P... |
hschh86/usersong-extractor | documents/Investigationing.ipynb | mit | from __future__ import print_function, division
import itertools
import re
# numpy imports
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
def hexbyte(x):
return "{:02X}".format(x)
def binbyte(x):
return "{:08b}".format(x)
def tohex(by, sep=" "):
return sep.join(hexbyte(x) for x in... |
kubeflow/pipelines | components/gcp/bigquery/query/sample.ipynb | apache-2.0 | %%capture --no-stderr
!pip3 install kfp --upgrade
"""
Explanation: Name
Gather training data by querying BigQuery
Labels
GCP, BigQuery, Kubeflow, Pipeline
Summary
A Kubeflow Pipeline component to submit a query to BigQuery and store the result in a Cloud Storage bucket.
Details
Intended use
Use this Kubeflow compone... |
sidazhang/udacity-dlnd | intro-to-tflearn/TFLearn_Sentiment_Analysis_Solution.ipynb | mit | import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
"""
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network w... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/statespace_dfm_coincident.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
np.set_printoptions(precision=4, suppress=True, linewidth=120)
from pandas_datareader.data import DataReader
# Get the datasets from FRED
start = '1979-01-01'
end = '2014-12-01'
indprod = DataReade... |
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