repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
martinjrobins/hobo | examples/sampling/transformation-with-and-without-jacobian.ipynb | bsd-3-clause | import pints
import numpy as np
import matplotlib.pyplot as plt
# Set some random seed so this notebook can be reproduced
np.random.seed(10)
"""
Explanation: Need for Jacobian adjustment in parameter transformation
This example illustrates the importance of including a Jacobian term when sampling from a transformed p... |
jhnphm/xbs_xbd | Getting_Started.ipynb | gpl-3.0 | # %load startup.ipy
#! /usr/bin/env python3
import sys
sys.path.append('./python')
import logging.config
import os
import xbx.database as xbxdb
import xbx.util as xbxu
import xbx.config as xbxc
import xbx.build as xbxb
import xbx.run as xbxr
logging.config.fileConfig("logging.ini", disable_existing_loggers=False)
... |
shoyer/xray | examples/xarray_seasonal_means.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import pandas as pd
import xarray as xr
from netCDF4 import num2date
import matplotlib.pyplot as plt
print("numpy version : ", np.__version__)
print("pandas version : ", pd.__version__)
print("xarray version : ", xr.__version__)
"""
Explanation: <h1>Table of Contents<span class... |
ML4DS/ML4all | R_lab2_GP/Pract_regression_professor.ipynb | mit | # Import some libraries that will be necessary for working with data and displaying plots
# To visualize plots in the notebook
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.io # To read matlab files
from scipy import spatial
import pylab
pylab.rcParams['fi... |
jaeoh2/self-driving-car-nd | CarND-LaneLines-P1/P1.ipynb | mit | #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... |
HUDataScience/StatisticalMethods2016 | notebooks/Exo8_SNeIa_HubbleDiagram.ipynb | apache-2.0 | import warnings
# No annoying warnings
warnings.filterwarnings('ignore')
# Because we always need that
# plot within the notebook
%matplotlib inline
import numpy as np
import matplotlib.pyplot as mpl
"""
Explanation: Exercise 8 – Fit an Hubble Diagram
The SN Ia Science in short
The Type Ia Supernova event is the therm... |
josealber84/deep-learning | weight-initialization/weight_initialization.ipynb | mit | %matplotlib inline
import tensorflow as tf
import helper
from tensorflow.examples.tutorials.mnist import input_data
print('Getting MNIST Dataset...')
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
print('Data Extracted.')
"""
Explanation: Weight Initialization
In this lesson, you'll learn how to fin... |
chemiskyy/simmit | Examples/ODF/.ipynb_checkpoints/ODF-checkpoint.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from simmit import smartplus as sim
from simmit import identify as iden
import os
dir = os.path.dirname(os.path.realpath('__file__'))
"""
Explanation: Orientation density functions
End of explanation
"""
x = np.arange(0,182,2... |
arcyfelix/Courses | 18-05-28-Complete-Guide-to-Tensorflow-for-Deep-Learning-with-Python/03-Convolutional-Neural-Networks/02-CNN-Project-Exercise.ipynb | apache-2.0 | # Put file path as a string here
CIFAR_DIR = './data./cifar-10-batches-py/'
"""
Explanation: CNN-Project-Exercise
We'll be using the CIFAR-10 dataset, which is very famous dataset for image recognition!
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 500... |
AllenDowney/ModSimPy | soln/chap03soln.ipynb | mit | # Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim library
from modsim import *
# set the random number generator
np.ran... |
darkomen/TFG | medidas/04082015/.ipynb_checkpoints/Untitled-checkpoint.ipynb | cc0-1.0 | #Importamos las librerías utilizadas
import numpy as np
import pandas as pd
import seaborn as sns
#Mostramos las versiones usadas de cada librerías
print ("Numpy v{}".format(np.__version__))
print ("Pandas v{}".format(pd.__version__))
print ("Seaborn v{}".format(sns.__version__))
#Abrimos el fichero csv con los datos... |
tpin3694/tpin3694.github.io | python/functions_vs_generators.ipynb | mit | # Create a function that
def function(names):
# For each name in a list of names
for name in names:
# Returns the name
return name
# Create a variable of that function
students = function(['Abe', 'Bob', 'Christina', 'Derek', 'Eleanor'])
# Run the function
students
"""
Explanation: Title: Func... |
MedievalSure/ToStudy | notebook/02_01_1DConvection.ipynb | mit | import numpy
from matplotlib import pyplot
%matplotlib inline
from matplotlib import rcParams
rcParams['font.family'] = 'serif'
rcParams['font.size'] = 16
"""
Explanation: Content under Creative Commons Attribution license CC-BY 4.0, code under MIT license (c)2014 L.A. Barba, G.... |
amandalouparker/graphviz-sitemaps | Notebook/sitemap_visualization_tool.ipynb | mpl-2.0 | url = 'https://www.sportchek.ca/sitemap.xml'
page = requests.get(url)
print('Loaded page with: %s' % page)
sitemap_index = BeautifulSoup(page.content, 'html.parser')
print('Created %s object' % type(sitemap_index))
"""
Explanation: How to visualize an XML sitemap using Python
<img src="static/sitemap_graph_2_layer.pn... |
ml4a/ml4a-guides | examples/info_retrieval/audio-tsne.ipynb | gpl-2.0 | %matplotlib inline
from matplotlib import pyplot as plt
import matplotlib.cm as cm
import fnmatch
import os
import numpy as np
import librosa
import matplotlib.pyplot as plt
import librosa.display
from sklearn.manifold import TSNE
import json
"""
Explanation: Audio t-SNE
This notebook will show you how to create a t-S... |
ttesileanu/twostagelearning | spiking_simulations.ipynb | mit | %matplotlib inline
import matplotlib as mpl
import matplotlib.ticker as mtick
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
plt.rc('text', usetex=True)
plt.rc('font', family='serif', serif='cm')
plt.rcParams['figure.titlesize'] = 10
plt.rcParams['axes.labelsize'] = 8
plt.rcParams['axes.... |
bert9bert/statsmodels | examples/notebooks/distributed_estimation.ipynb | bsd-3-clause | import numpy as np
from statsmodels.base.distributed_estimation import DistributedModel
def _exog_gen(exog, partitions):
"""partitions exog data"""
n_exog = exog.shape[0]
n_part = np.ceil(n_exog / partitions)
ii = 0
while ii < n_exog:
jj = int(min(ii + n_part, n_exog))
yield exog[... |
vsporeddy/bigbang | examples/Auditing Fernando.ipynb | gpl-2.0 | from bigbang.archive import Archive
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
"""
Explanation: This note will show you how to use BigBang to investigate a particular project participant's activity.
We will focus on Fernando Perez's role within the IPython community.
First, imports.
End of ... |
aidiary/notebooks | pytorch/180212-dqn-tutorial.ipynb | mit | import gym
import math
import random
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from collections import namedtuple
from itertools import count
from copy import deepcopy
from PIL import Image
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from to... |
woobe/h2o_tutorials | introduction_to_machine_learning/py_02_data_manipulation.ipynb | mit | # Start and connect to a local H2O cluster
import h2o
h2o.init(nthreads = -1)
"""
Explanation: Machine Learning with H2O - Tutorial 2: Basic Data Manipulation
<hr>
Objective:
This tutorial demonstrates basic data manipulation with H2O.
<hr>
Titanic Dataset:
Source: https://www.kaggle.com/c/titanic/data
<hr>
Ful... |
chapmanbe/utah_highschool_airquality | introducing_python/working_with_air_quality_data.ipynb | apache-2.0 | %matplotlib inline
import os
import pandas as pd
import datetime
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import YouTubeVideo
#!pip install xlrd
"""
Explanation: Examining Weather Data and Air Quality Data
In this notebook we are going to learn how to read tabular data (e.g. spreadshee... |
markovmodel/adaptivemd | examples/tutorial/4_example_advanced_tasks.ipynb | lgpl-2.1 | from adaptivemd import Project, File, PythonTask, Task
"""
Explanation: AdaptiveMD
Example 4 - Custom Task objects
0. Imports
End of explanation
"""
project = Project('tutorial')
"""
Explanation: Let's open our test project by its name. If you completed the first examples this should all work out of the box.
Open a... |
qinjian623/dlnotes | cs231n/assignments/assignment1/.ipynb_checkpoints/svm-checkpoint.ipynb | gpl-3.0 | # Run some setup code for this notebook.
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
# This is a bit of magic to make matplotlib figures appear inline in the
# notebook rather than in a new window.
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.... |
lowks/micromeritics | documentation/BET.ipynb | gpl-3.0 | %matplotlib inline
from micromeritics import bet, util, isotherm_examples as ex, plots
s = ex.carbon_black() # example isotherm of Carbon Black with N2.
min = 0.05 # 0.05 to 0.30 range for BET
max = 0.3
Q,P = util.restrict_isotherm(s.Qads, s.Prel, min, max)
plots.plotIsotherm(s.Qads, s.Prel, s.descr[... |
barronh/GCandPython | PNC_03DC3Eval.ipynb | gpl-3.0 | # Prepare my slides
%pylab inline
%cd working
"""
Explanation: ICARTT Evaluation
Author: Barron H. Henderson
This presentation will teach basics of spatial interpolation and statistical evaluation using Python with PseudoNetCDF, numpy, and scipy.
End of explanation
"""
from PseudoNetCDF import PNC
"""
Explanation: ... |
ueapy/ueapy.github.io | content/notebooks/2017-05-05-matplotlib-subplots.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Today we covered a very important part of matplotlib - how to create multiple subplots in one figure.
We touched upon the following topics:
<p><div class="lev1 toc-item"><a href="#Matplotlib-Axes" data-toc-modified-id="Matplotlib-A... |
mne-tools/mne-tools.github.io | 0.21/_downloads/20f35983ef279d1b30aa970c81aafe26/plot_read_events.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Chris Holdgraf <choldgraf@berkeley.edu>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
fname = data_path + '/MEG/sample/sample_audvis_raw-eve.fi... |
ES-DOC/esdoc-jupyterhub | notebooks/fio-ronm/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', 'fio-ronm', 'sandbox-1', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: FIO-RONM
Source ID: SANDBOX-1
Topic: Ocean
Sub-Topics: Timestepping Framework, A... |
teoguso/sol_1116 | cumulant-to-pdf.ipynb | mit | !gvim data/SF_Si_bulk/invar.in
"""
Explanation: Best report ever
Everything you see here is either markdown, LaTex, Python or BASH.
The spectral function
It looks like this:
\begin{equation}
A(\omega) = \mathrm{Im}|G(\omega)|
\end{equation}
GW vs Cumulant
Mathematically very different:
\begin{equation}
G^{GW} (\omeg... |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/ec-earth3-aerchem/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'ec-earth3-aerchem', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: EC-EARTH3-AERCHEM
Topic: Land
Su... |
retnuh/deep-learning | tensorboard/Anna_KaRNNa.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, I'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 base... |
ES-DOC/esdoc-jupyterhub | notebooks/inpe/cmip6/models/besm-2-7/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inpe', 'besm-2-7', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: INPE
Source ID: BESM-2-7
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radiat... |
ethen8181/machine-learning | projects/kaggle_rossman_store_sales/rossman_data_prep.ipynb | mit | from jupyterthemes import get_themes
from jupyterthemes.stylefx import set_nb_theme
themes = get_themes()
set_nb_theme(themes[3])
# 1. magic for inline plot
# 2. magic to print version
# 3. magic so that the notebook will reload external python modules
# 4. magic to enable retina (high resolution) plots
# https://gist... |
pyqg/pyqg | docs/examples/barotropic.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import pyqg
"""
Explanation: Barotropic Model
Here will will use pyqg to reproduce the results of the paper: <br />
J. C. Mcwilliams (1984). The emergence of isolated coherent vortices in turbulent flow. Journal of Fluid Mechanics, 146, pp 21-43 do... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/time_series_prediction/solutions/1_optional_data_exploration.ipynb | apache-2.0 | import os
PROJECT = !(gcloud config get-value core/project)
PROJECT = PROJECT[0]
BUCKET = PROJECT
os.environ["PROJECT"] = PROJECT
os.environ["BUCKET"] = BUCKET
import numpy as np
import pandas as pd
import seaborn as sns
from google.cloud import bigquery
from IPython import get_ipython
from IPython.core.magic import... |
5hubh4m/CS231n | Assignment2/ConvolutionalNetworks.ipynb | mit | # As usual, a bit of setup
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 *
from cs231n.fast_layers import *
from cs... |
ptosco/rdkit | Docs/Notebooks/MolStandardize.ipynb | bsd-3-clause | import rdkit
from rdkit import Chem
from rdkit.Chem.Draw import IPythonConsole
from rdkit.Chem.MolStandardize import rdMolStandardize
"""
Explanation: MolStandardize
This is a demonstration on how to use the rdMolStandardize module within RDKit. The structure and capabilities remain largely the same as MolVS (https://... |
materialsvirtuallab/matgenb | notebooks/2019-01-11-How to plot and evaluate output files from Lobster.ipynb | bsd-3-clause | from pymatgen.electronic_structure.plotter import CohpPlotter
from pymatgen.electronic_structure.cohp import CompleteCohp
%matplotlib inline
"""
Explanation: Introduction
This notebook was written by Janine George (E-mail: janine.george@uclouvain.be Université catholique de Louvain, https://jageo.github.io/).
This no... |
windj007/tablex-dataset | dataset_from_latex.ipynb | apache-2.0 | # frequent_errors = collections.Counter(err
# for f in glob.glob('./data/arxiv/err_logs/*.log')
# for err in {line
# for line in open(f, 'r', errors='replace')
# ... |
statsmodels/statsmodels.github.io | v0.12.1/examples/notebooks/generated/stationarity_detrending_adf_kpss.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
"""
Explanation: Stationarity and detrending (ADF/KPSS)
Stationarity means that the statistical properties of a time series i.e. mean, variance and covariance do not change over time. Many statistical... |
ibmkendrick/streamsx.health | samples/HealthcareJupyterDemo/notebooks/HealthcareDemo-Distributed.ipynb | apache-2.0 | !pip install --upgrade streamsx
!pip install --upgrade "git+https://github.com/IBMStreams/streamsx.health.git#egg=healthdemo&subdirectory=samples/HealthcareJupyterDemo/package"
"""
Explanation: Healthcare Python Streaming Application Demo
This application demonstrates how users can develop Python Streaming Application... |
tensorflow/docs-l10n | site/zh-cn/tutorials/load_data/unicode.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... |
datosgobar/pydatajson | samples/caso-uso-1-pydatajson-xlsx-justicia-valido.ipynb | mit | import arrow
import os, sys
sys.path.insert(0, os.path.abspath(".."))
from pydatajson import DataJson #lib y clase
from pydatajson.readers import read_catalog # lib, modulo ... metodo Lle el catalogo -json o xlsx o (local o url) dicc- y lo transforma en un diccionario de python
from pydatajson.writers import write_json... |
nd1/women_in_tech_summit_DC2017 | workshop_notebooks/workshop_api_notebook.ipynb | mit | import json
import urllib.request
data = json.loads(urllib.request.urlopen('http://www.omdbapi.com/?t=Game%20of%20Thrones&Season=1').read().\
decode('utf8'))
"""
Explanation: APIs
Let's start by looking at OMDb API.
The OMDb API is a free web service to obtain movie information, all content and imag... |
evanmiltenburg/python-for-text-analysis | Chapters/Chapter 07 - Sets.ipynb | apache-2.0 | a_set = {1, 2, 3}
a_set
empty_set = set() # you have to use set() to create an empty set! (we will see why later)
print(empty_set)
"""
Explanation: Chapter 7 - Sets
This chapter will introduce a different kind of container: sets. Sets are unordered lists with no duplicate entries. You might wonder why we need differe... |
bourneli/deep-learning-notes | DAT236x Deep Learning Explained/.ipynb_checkpoints/Lab1_MNIST_DataLoader-checkpoint.ipynb | mit | # Import the relevant modules to be used later
from __future__ import print_function
import gzip
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import os
import shutil
import struct
import sys
try:
from urllib.request import urlretrieve
except ImportError:
from urllib im... |
jni/aspp2015 | Introduction to Cython.ipynb | bsd-3-clause | def f(x):
y = x**4 - 3*x
return y
def integrate_f(a, b, n):
dx = (b - a) / n
dx2 = dx / 2
s = f(a) * dx2
for i in range(1, n):
s += f(a + i * dx) * dx
s += f(b) * dx2
return s
"""
Explanation: Intro to Cython
Why Cython
Outline:
Speed up Python code
Interact with NumPy ar... |
ES-DOC/esdoc-jupyterhub | notebooks/nuist/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', 'nuist', 'sandbox-1', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: NUIST
Source ID: SANDBOX-1
Topic: Ocean
Sub-Topics: Timestepping Framework, Advecti... |
PmagPy/2017_MagIC_Workshop_PmagPy_Tutorial | PmagPy_structure_notebook.ipynb | bsd-3-clause | def fshdev(k):
"""
Generate a random draw from a Fisher distribution with mean declination
of 0 and inclination of 90 with a specified kappa.
Parameters
----------
k : kappa (precision parameter) of the distribution
Returns
----------
dec, inc : declination and inclination of rand... |
honux77/practice | python/csvgen/simple csv generator.ipynb | mit | import random
import string
def genID(n):
str = ''.join(random.choices(string.ascii_uppercase + string.digits, k=n))
return str
l = "김이박정최정강조윤장임한호서신권황"
m = "동해물과백두산이마르고도록하느님이보우하사우리나라만세무궁화삼천리화려강산대한사람"
def genName():
return random.choice(l) + "".join(random.choices(m, k=2))
import random
import time
def s... |
INM-6/elephant | doc/tutorials/granger_causality.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
from elephant.causality.granger import pairwise_granger, conditional_granger
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2)
# Indirect causal influence diagram
node1 = plt.Circle((0.2, 0.2), 0.1, color='red')
node2 = plt.Circle((0.5, 0.6), 0.1, color='red')
node3 = ... |
dbouquin/AstroHackWeek2015 | day3-machine-learning/05 - Cross-validation.ipynb | gpl-2.0 | from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
from sklearn.cross_validation import cross_val_score
from sklearn.svm import LinearSVC
cross_val_score(LinearSVC(), X, y, cv=5)
cross_val_score(LinearSVC(), X, y, cv=5, scoring="f1_macro")
"""
Explanation: Cross-Validation
<img... |
mne-tools/mne-tools.github.io | dev/_downloads/ed1a04dd775648ca869bfcffae26faca/30_mne_dspm_loreta.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
"""
Explanation: Source localization with MNE, dSPM, sLORETA, and eLORETA
The aim of this tutorial is to teach you how to compute and apply a linear
minimum-n... |
phanrahan/magmathon | notebooks/advanced/coreir.ipynb | mit | import magma as m
# default mantle target is coreir, so no need to do this unless you want to be explicit
# m.set_mantle_target("coreir")
from mantle import Counter
from loam.boards.icestick import IceStick
icestick = IceStick()
icestick.Clock.on()
icestick.D5.on()
N = 22
main = icestick.main()
counter = Counter(N)... |
Aniruddha-Tapas/Applied-Machine-Learning | Machine Learning using GraphLab/Document Retrieval using GraphLab Create.ipynb | mit | import graphlab
"""
Explanation: Document retrieval from wikipedia data
Import GraphLab Create
End of explanation
"""
people = graphlab.SFrame('people_wiki.gl/')
"""
Explanation: Load some text data - from wikipedia, pages on people
End of explanation
"""
people.head()
len(people)
"""
Explanation: Data contain... |
phoebe-project/phoebe2-docs | 2.3/tutorials/pblum.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
import phoebe
from phoebe import u # units
import numpy as np
logger = phoebe.logger()
b = phoebe.default_binary()
"""
Explanation: Passband Luminosity
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online note... |
IS-ENES-Data/submission_forms | test/prov/old/prov-test1.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
from prov.model import ProvDocument
d1 = ProvDocument()
d1.deserialize?
"""
Explanation: Representation of data submission workflow components based on W3C-PROV
End of explanation
"""
from IPython.display import display, Image
Image(filename='key-concepts.png')
from dkrz_forms ... |
dnc1994/MachineLearning-UW | ml-classification/module-6-decision-tree-practical-assignment-solution.ipynb | mit | import graphlab
"""
Explanation: Decision Trees in Practice
In this assignment we will explore various techniques for preventing overfitting in decision trees. We will extend the implementation of the binary decision trees that we implemented in the previous assignment. You will have to use your solutions from this pr... |
Olsthoorn/TransientGroundwaterFlow | exercises_notebooks/Strip-of-land-both-sides-equal-rise.ipynb | gpl-3.0 | # import modules we need
import matplotlib.pyplot as plt
import numpy as np
from scipy.special import erfc
"""
Explanation: <figure>
<IMG SRC="../logo/logo.png" WIDTH=250 ALIGN="right">
</figure>
Strip of land with same rise at both sides
@Theo Olsthoorn
2019-12-21
This exercise was done in class on 2019-01-10
End ... |
AllenDowney/ThinkStats2 | solutions/chap03soln.ipynb | gpl-3.0 | from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename):
from urllib.request import urlretrieve
local, _ = urlretrieve(url, filename)
print("Downloaded " + local)
download("https://github.com/AllenDowney/ThinkStats2/raw/master/code/th... |
yangliuy/yangliuy.github.io | markdown_generator/talks.ipynb | mit | import pandas as pd
import os
"""
Explanation: Talks markdown generator for academicpages
Takes a TSV of talks with metadata and converts them for use with academicpages.github.io. This is an interactive Jupyter notebook (see more info here). The core python code is also in talks.py. Run either from the markdown_gener... |
vitojph/kschool-nlp | notebooks-py2/nltk-pos.ipynb | gpl-3.0 | from __future__ import print_function
from __future__ import division
import nltk
"""
Explanation: Resumen NLTK: Etiquetado morfológico (part-of-speech tagging)
Este resumen se corresponde con el capítulo 5 del NLTK Book Categorizing and Tagging Words. La lectura del capítulo es muy recomendable.
Etiquetado morfológic... |
ComputationalPhysics2015-IPM/Python-01 | Python-01.ipynb | gpl-2.0 | 3 + 4
3 * 5
4 / 3
4 // 3
4 % 3
abs(-3)
2**2024+2**1024
2.0**1024
1.0+2
2**-30
1.0e-10
"""
Explanation: Python as a calculator
Python can be used as calculator + - * / () ** // % abs min max, int and float
End of explanation
"""
True
False
2 == 3
3<5
3 < 6 and 2 == 3
True + 2
True * 10
"""
Explanati... |
jdvelasq/ingenieria-economica | 07-impuestos.ipynb | mit | import cashflows as cf
# representación del flujo de fondos
cflo = cf.cashflow(const_value=[1000]*5+[-500]*5,
start='2016',
freq='A')
cf.textplot(cflo)
## cómputo del impuesto
tax_rate = cf.interest_rate(const_value=[30]*10, start=2016, freq='A')
x = cf.after_tax_cashflow(cfl... |
rhiever/crowd-machines | Crowd machines demo.ipynb | mit | import pandas as pd
import numpy as np
breast_cancer_data = pd.read_csv('data/breast-cancer-wisconsin.tsv.gz',
sep='\t',
compression='gzip')
"""
Explanation: Breast cancer data set
End of explanation
"""
from collections import Counter
Counter(breas... |
PyPSA/PyPSA | examples/notebooks/spatial-clustering.ipynb | mit | import pypsa
import re
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import pandas as pd
from pypsa.networkclustering import get_clustering_from_busmap, busmap_by_kmeans
n = pypsa.examples.scigrid_de()
n.lines["type"] = np.nan # delete the 'type' specifications to make this example eas... |
atcemgil/notes | BayesianNetworks.ipynb | mit |
import numpy as np
import scipy as sc
from scipy.special import gammaln
from scipy.special import digamma
%matplotlib inline
from itertools import combinations
import pygraphviz as pgv
from IPython.display import Image
from IPython.display import display
def normalize(A, axis=None):
"""Normalize a probability t... |
jdvelasq/ingenieria-economica | 2016-03/IE-01-calculos-basicos.ipynb | mit | ## cálculo manual
-450 * (1 + 0.07 * 60 / 360)
"""
Explanation: Modelos de Valor del Dinero en el Tiempo
Notas de clase sobre ingeniería economica avanzada usando Python
Juan David Velásquez Henao
jdvelasq@unal.edu.co
Universidad Nacional de Colombia, Sede Medellín
Facultad de Minas
Medellín, Colombia
Software utili... |
suresh/notebooks | Intro to Data Science - Seattle Fremont Bridge.ipynb | mit | url = 'https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD'
df = pd.read_csv(url, parse_dates=True)
df.head()
df.shape
df.index = pd.DatetimeIndex(df.Date)
df.head()
df.drop(columns=['Date'], inplace=True)
df.head()
df['total'] = df['Fremont Bridge East Sidewalk'] + df['Fremont Bridge West S... |
KECB/learn | BAMM.101x/Networks_new.ipynb | mit | import networkx as nx
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
simple_network = nx.Graph()
nodes = [1,2,3,4,5,6,7,8]
edges = [(1,2),(2,3),(1,3),(4,5),(2,7),(1,9),(3,4),(4,5),(4,9),(5,6),(7,8),(8,9)]
simple_network.add_nodes_from(nodes)
simple_network.add_edges_from(edges)
nx.draw(simple_ne... |
abhipr1/DATA_SCIENCE_INTENSIVE | Data_Story_1/Adv_vs_Trade.ipynb | apache-2.0 | est_m=smf.ols(formula='Sales ~ TV', data=adv).fit()
est_m.summary()
"""
Explanation: Determine if TV advertisement has any impact on sell.
End of explanation
"""
# Plot the data and fitted line
x_prime = pd.DataFrame({'TV': np.linspace(adv.TV.min(),
adv.TV.max(),
... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session02/Day2/ModelSelection_ExerciseSolutions.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
# comment out this line if you don't have seaborn installed
import seaborn as sns
sns.set_palette("colorblind")
import numpy as np
"""
Explanation: Model Selection For Machine Learning
In this exercise, we will explore methods to do model selection in a machine lear... |
amitkaps/machine-learning | RF_GBM/notebook/Bank Marketing.ipynb | mit | #Import the necessary libraries
import numpy as np
import pandas as pd
#Read the train and test data
train = pd.read_csv("../data/train.csv")
test = pd.read_csv("../data/test.csv")
"""
Explanation: Frame
The client bank XYZ is running a direct marketing campaign. It wants to identify customers who would potentially b... |
bramacchino/numberSense | GraphStructure.ipynb | mit | from igraph import Graph, summary
g = Graph()
g.add_vertices(3) #0,1,2
#g.add_vertices([0,1,2])
g.add_edges([(0,1), (1,2)])
g.add_vertices(3)
#g.delete_vertices([1,2]) try out, deleting vertices delete also the incident edges
g.add_edges([(2,3),(3,4),(4,5),(5,3)])
g.delete_edges(g.get_eid(2,3)) #Note that edges and... |
sbalanovich/APM115Proj1 | src/Serguei Workspace.ipynb | mit | %matplotlib inline
import csv
import numpy as np
from scipy import interp
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.cross_validation import train_test_split
from sklearn.metrics import roc_curve, auc
from sklearn.cross_validation import StratifiedKFold
from sklearn import preprocess... |
statsmodels/statsmodels.github.io | v0.13.2/examples/notebooks/generated/distributed_estimation.ipynb | bsd-3-clause | import numpy as np
from scipy.stats.distributions import norm
from statsmodels.base.distributed_estimation import DistributedModel
def _exog_gen(exog, partitions):
"""partitions exog data"""
n_exog = exog.shape[0]
n_part = np.ceil(n_exog / partitions)
ii = 0
while ii < n_exog:
jj = int(m... |
amitkaps/applied-machine-learning | Module-05a-ML-Pipeline.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use("ggplot")
%matplotlib` inline
"""
Explanation: Machine Learning Supervised Pipeline
Frame
Supervised Learning - Regression
y: Predict Sale Price
X: Features about the house
score: Mean Squared Error
End of explanation
"""
data = ... |
ScienceStacks/jupyter_scisheets_widget | test_notebooks/20171017_scisheets_widget.ipynb | bsd-3-clause | import json
import numpy as np
import pandas as pd
from jupyter_scisheets_widget import scisheets_widget
"""
Explanation: Demonstration of Use Case
Users can enter step by step explanations of changes made to a SciSheet in a Jupyter notebook
Load necessary packages
End of explanation
"""
income_data = pd.read_cs... |
ml4a/ml4a-guides | examples/fundamentals/convolutional_neural_networks.ipynb | gpl-2.0 | import random
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
"""
Explanation: Convolutional networks
This is an... |
materialsvirtuallab/matgenb | notebooks/2018-03-09-Computing the Reaction Diagram between Two Compounds.ipynb | bsd-3-clause | from pymatgen import MPRester, Composition
from pymatgen.analysis.phase_diagram import PhaseDiagram
from pymatgen.entries.computed_entries import ComputedEntry
from pymatgen.apps.borg.hive import VaspToComputedEntryDrone
from pymatgen.entries.compatibility import MaterialsProjectCompatibility
from pymatgen.analysis.pha... |
PLBMR/cmuDSCWorkshopNotebooks | okCupidInitialAnalysis.ipynb | mit | #imports
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import display, HTML
#constants
%matplotlib inline
sns.set_style("dark")
sigLev = 3
figWidth = figHeight = 5
"""
Explanation: OkCupid: Dataset Analysis
This is a notebook I am using to test out t... |
JanetMatsen/bacteriopop | depreciated/dmd_demo.ipynb | apache-2.0 | import matplotlib as mpl
mpl.use('TkAgg')
import matplotlib.pyplot as plt
%matplotlib inline
import bacteriopop_utils
import feature_selection_utils
import load_data
loaded_data = data = load_data.load_data()
loaded_data.shape
"""
Explanation: for some reason Janet's virtualenv is much happier with this TkAgg thing... |
DJCordhose/ai | notebooks/sklearn/knn.ipynb | mit | import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
import pandas as pd
print(pd.__version__)
"""
Explanation: ML using KNN
End of explanation
"""
df = pd.read_csv('./insurance-customers-300.csv', sep=';')
y=df['group']
df.drop('group', axis='columns', inplace=True)
X = df.as_matr... |
ebellm/ztf_summerschool_2015 | notebooks/Period_Finding.ipynb | bsd-3-clause | # point to our previously-saved data
reference_catalog = '../data/PTF_Refims_Files/PTF_d022683_f02_c06_u000114210_p12_sexcat.ctlg'
outfile = reference_catalog.split('/')[-1].replace('ctlg','shlv')
"""
Explanation: Hands-On Exercise 3: Period Finding
One of the fundamental tasks of time-domain astronomy is determining... |
huangziwei/pyMF3 | pymf3/examples/01_using_nmf_to_factorize_face_images.ipynb | mit | import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
import sys
sys.path.append('/home/Repos/pyMF3/')
import pymf3
from pymf3.datasets import CBCL_faces
from matplotlib import rcParams
rcParams['font.family'] = 'DejaVu Sans'
faces = CBCL_faces.get_CBCL_faces(scale_images=True... |
ktmud/deep-learning | tv-script-generation/dlnd_tv_script_generation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scrip... |
csadorf/signac | doc/signac_104_Modifying_the_Data_Space.ipynb | bsd-3-clause | import numpy as np
def V_vdW(p, kT, N, a=0, b=0):
"""Solve the van der Waals equation for V."""
coeffs = [p, - (kT * N + p * N *b), a * N**2, - a * N**3 * b]
V = sorted(np.roots(coeffs))
return np.real(V).tolist()
"""
Explanation: 1.4 Modifying the Data Space
It is very common that we discover at a la... |
fastai/course-v3 | zh-nbs/Lesson2_download.ipynb | apache-2.0 | from fastai.vision import *
"""
Explanation: Practical Deep Learning for Coders, v3
Lesson 2_download
Creating your own dataset from Google Images
从Google Images创建你自己的数据集
作者: Francisco Ingham 和 Jeremy Howard. 灵感来源于Adrian Rosebrock
In this tutorial we will see how to easily create an image dataset through Google Images... |
analysiscenter/dataset | examples/experiments/stochastic_depth/stochastic_depth.ipynb | apache-2.0 | import sys
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook as tqn
%matplotlib inline
sys.path.append('../../..')
sys.path.append('../../utils')
import utils
from resnet_with_stochastic_depth import StochasticResNet
from batchflow import B,V,F
from batchflow.opensets import MNIST
from batchflow.models.t... |
UCSBarchlab/PyRTL | ipynb-examples/example8-verilog.ipynb | bsd-3-clause | import random
import io
import pyrtl
pyrtl.reset_working_block()
"""
Explanation: Example 8: Interfacing with Verilog.
While there is much more about PyRTL design to discuss, at some point somebody
might ask you to do something with your code other than have it print
pretty things out to the terminal. We provide im... |
diging/tethne-notebooks | 2. Working with data from JSTOR Data-for-Research.ipynb | gpl-3.0 | from tethne.readers import dfr
"""
Explanation: Introduction to Tethne: Working with data from the Web of Science
Now that we have the basics down, in this notebook we'll begin working with data from the JSTOR Data-for-Research (DfR) portal.
The JSTOR DfR portal gives researchers access to
bibliographic data and N-gra... |
campagnucci/api_sof | .ipynb_checkpoints/SOF_Execucao_Orcamentaria_PMSP-checkpoint.ipynb | gpl-3.0 | import pandas as pd
import requests
import json
import numpy as np
TOKEN = '198f959a5f39a1c441c7c863423264'
base_url = "https://gatewayapi.prodam.sp.gov.br:443/financas/orcamento/sof/v2.1.0"
headers={'Authorization' : str('Bearer ' + TOKEN)}
"""
Explanation: Explorando as despesas da cidade de São Paulo
Um tutorial d... |
tensorflow/docs-l10n | site/ko/agents/tutorials/8_networks_tutorial.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... |
computational-class/cjc2016 | code/tba/DeepLearningMovies/Kaggle tutorial Part 1 Natural Language Processing.ipynb | mit | import os
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.ensemble import RandomForestClassifier
from KaggleWord2VecUtility import KaggleWord2VecUtility # in the same folader
import pandas as pd
import numpy as np
"""
Explanation: Deep learning goes to the movies
Kaggle tutorial Part 1: Natu... |
tensorflow/docs-l10n | site/zh-cn/tutorials/distribute/multi_worker_with_keras.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... |
eds-uga/csci1360-fa16 | lectures/L13.ipynb | mit | li = ["this", "is", "a", "list"]
print(li)
print(li[1:3]) # Print element 1 (inclusive) to 3 (exclusive)
print(li[2:]) # Print element 2 and everything after that
print(li[:-1]) # Print everything BEFORE element -1 (the last one)
"""
Explanation: Lecture 13: Array Indexing, Slicing, and Broadcasting
CSCI 1360: Fou... |
zaqwes8811/micro-apps | self_driving/deps/Kalman_and_Bayesian_Filters_in_Python_master/11-Extended-Kalman-Filters.ipynb | mit | from __future__ import division, print_function
%matplotlib inline
#format the book
import book_format
book_format.set_style()
"""
Explanation: Table of Contents
The Extended Kalman Filter
End of explanation
"""
import kf_book.ekf_internal as ekf_internal
ekf_internal.show_linearization()
"""
Explanation: We have ... |
OceanPARCELS/parcels | parcels/examples/tutorial_timestamps.ipynb | mit | from parcels import Field
from glob import glob
import numpy as np
"""
Explanation: Tutorial on how to use timestaps in Field construction
End of explanation
"""
# tempfield = Field.from_netcdf(glob('WOA_data/woa18_decav_*_04.nc'), 't_an',
# {'lon': 'lon', 'lat': 'lat', 'time': 'time'}... |
bgroveben/python3_machine_learning_projects | introduction_to_ml_with_python/4_Data_and_Features.ipynb | mit | import os
# The file has no headers naking the columns, so we pass
# header=None and provide the column names explicitly in "names".
adult_path = os.path.join(mglearn.datasets.DATA_PATH, "adult.data")
print(adult_path)
data = pd.read_csv(adult_path, header=None, index_col=False,
names=['age', 'workclass', 'fnl... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/text_models/solutions/reusable_embeddings_vertex.ipynb | apache-2.0 | import os
import pandas as pd
from google.cloud import bigquery
"""
Explanation: Reusable Embeddings
Learning Objectives
1. Learn how to use a pre-trained TF Hub text modules to generate sentence vectors
1. Learn how to incorporate a pre-trained TF-Hub module into a Keras model
1. Learn how to deploy and use a text m... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.