repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
cyang019/blight_fight | Final_Report.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import Image
%matplotlib inline
"""
Explanation: Study of Correlation Between Building Demolition and Associated Features
Capstone Project for Data Science at Scale on Coursera
Repo is located here
Chen Yang yangcnju@gmail.co... |
mattpitkin/corner.py | docs/_static/notebooks/quickstart.ipynb | bsd-2-clause | import corner
import numpy as np
ndim, nsamples = 2, 10000
np.random.seed(42)
samples = np.random.randn(ndim * nsamples).reshape([nsamples, ndim])
figure = corner.corner(samples)
"""
Explanation: Getting started
The only user-facing function in the module is corner.corner and, in its simplest form, you use it like th... |
tbphu/fachkurs_bachelor | tellurium/tellurium_introduction_empty.ipynb | mit | import tellurium as te; te.setDefaultPlottingEngine('matplotlib')
%matplotlib inline
antimony_model = '''J0: -> y; -x;J1: -> x; y;x = 1.0;y = 0.2;'''
r = te.loada(antimony_model)
r.simulate(0,100,1000)
r.plot()
"""
Explanation: Tellurium Introduction:
Motivation ... a minimal example!
Just a few lines of code allow a... |
cranndarach/namegen | explore_names.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde
"""
Explanation: Exploring the names database
I might like to try some sort of frequency-weighting for namegen, but instead of including each name the same number of times that it appears in the corpus, I will t... |
phoebe-project/phoebe2-docs | 2.2/tutorials/reflection_heating.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Reflection and Heating
For a comparison between "Horvat" and "Wilson" methods in the "irad_method" parameter, see the tutorial on Lambert Scattering.
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you... |
Diyago/Machine-Learning-scripts | statistics/CreditScore.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import math
from scipy.stats import chisquare
from statsmodels.stats.descriptivestats import sign_test
from statsmodels.sandbox.stats.multicomp import multipletests
import scipy
import scipy as sc
from statsmodels.stats.weightstats import *
import pandas as pd
import statsmod... |
tolaoniyangi/dmc | notebooks/week-2/04 - Lab 2 Assignment.ipynb | apache-2.0 | import random
"""
Explanation: Lab 2 assignment
This assignment will get you familiar with the basic elements of Python by programming a simple card game. We will create a custom class to represent each player in the game, which will store information about their current pot, as well as a series of methods defining ho... |
barjacks/foundations-homework | 07/.ipynb_checkpoints/Animal_Panda_Homework_7_Skinner-checkpoint.ipynb | mit | import pandas as pd
"""
Explanation: *1. Import pandas with the right name
End of explanation
"""
%matplotlib inline
"""
Explanation: *2. Set all graphics from matplotlib to display inline
End of explanation
"""
#for encoding the command would look smth like this:
#df = pd.read_csv("XXXXXXXXXXXXXXXXX.csv", encodi... |
dlsun/symbulate | docs/common_joint.ipynb | mit | from symbulate import *
%matplotlib inline
"""
Explanation: Symbulate Documentation
Common Joint Distributions
Introduction to joint distributions
BivariateNormal
MultivariateNormal
< Methods for common discrete and continuous distributions | Contents | Common random processes >
Be sure to import Symbulate using the... |
spencerkclark/aospy | aospy/examples/tutorial.ipynb | apache-2.0 | import os # Python built-in package for working with the operating system
import aospy
rootdir = os.path.join(aospy.__path__[0], 'test', 'data', 'netcdf')
"""
Explanation: aospy Tutorial
This notebook closely follows the descriptions, objects created, and code executed in the examples page in the documentation.
Pre... |
feststelltaste/software-analytics | notebooks/Finding tested code with jQAssistant.ipynb | gpl-3.0 | import py2neo
import pandas as pd
graph = py2neo.Graph()
query = """
MATCH
(testMethod:Method)
-[:ANNOTATED_BY]->()-[:OF_TYPE]->
(:Type {fqn:"org.junit.Test"}),
(testType:Type)-[:DECLARES]->(testMethod),
(type)-[:DECLARES]->(method:Method),
(testMethod)-[i:INVOKES]->(method)
WHERE
NOT type.name E... |
whitead/numerical_stats | unit_6/lectures/lecture_3.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
import matplotlib
"""
Explanation: Unit 6, Lecture 3
Numerical Methods and Statistics
Prof. Andrew White, Feb 22 2020
Lecture Goals
Know what a python function is and be able to define one
Be able to call a function and understand how arguments ar... |
agile-geoscience/welly | docs/_userguide/Quick_start.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import welly
welly.__version__
"""
Explanation: Quick start
Welcome to the Quick start guide! This should help you get started using welly.
First some preliminaries...
End of explanation
"""
project = welly.read_las('https://geocomp.s3.amazonaws.com/data/P-129.LAS'... |
ComputationalPhysics2015-IPM/floating-points | Floating_Points.ipynb | gpl-2.0 | a = 0
dx = 10**-9
for i in range(10**9):
a += dx
print(a)
"""
Explanation: Floating Points
Lets start with a simple example:
$10^9 \times 10^{-9} = ?$
It is supposed to be 1.
End of explanation
"""
a = 0
dx = 2**-30
for i in range(2**30):
a += dx
print(a)
"""
Explanation: It is not! lets try anoth... |
mne-tools/mne-tools.github.io | 0.18/_downloads/635035741daf88c18928d17907998cb3/plot_run_ica.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.preprocessing import ICA, create_ecg_epochs
from mne.datasets import sample
print(__doc__)
"""
Explanation: Compute ICA components on epochs
ICA is fit to MEG raw data.
We assume that the non-stationary EOG artifacts... |
cliburn/sta-663-2017 | notebook/15B_ResamplingAndSimulation.ipynb | mit | np.random.seed(123)
"""
Explanation: Resampling and Monte Carlo Simulations
Broadly, any simulation that relies on random sampling to obtain results falls into the category of Monte Carlo methods. Another common type of statistical experiment is the use of repeated sampling from a data set, including the bootstrap, ja... |
vinhqdang/my_mooc | coursera/advanced_machine_learning_spec/4_nlp/natural-language-processing-master/week4/week4-seq2seq.ipynb | mit | import random
def generate_equations(allowed_operators, dataset_size, min_value, max_value):
"""Generates pairs of equations and solutions to them.
Each equation has a form of two integers with an operator in between.
Each solution is an integer with the result of the operaion.
allo... |
Parsl/parsl_demos | Bash-Tutorial.ipynb | apache-2.0 | # Import Parsl
import parsl
from parsl import *
print(parsl.__version__) # The version should be v0.2.1+
"""
Explanation: Parsl Bash Tutorial
This tutorial will show you how to run Bash scripts as Parsl apps.
Load parsl
Import parsl, and check the module version. This tutorial requires version 0.2.0 or above.
End of... |
machow/siuba | docs/key_features.ipynb | mit | # this is a hidden cell
print("""
<div class="output_area rendered_html docutils container">
{table}
</div>
""".format(table = table.replace('\n', "")))
"""
Explanation: Key features
End of explanation
"""
import pandas as pd
from siuba import _, mutate
my_data = pd.DataFrame({
'g': ['a', 'a', 'b'],
'x':... |
bismayan/MaterialsMachineLearning | notebooks/old_ICSD_Notebooks/Parsing unique entries and the element information.ipynb | mit | from __future__ import division, print_function
import pylab as plt
import matplotlib.pyplot as mpl
from pymatgen.core import Element, Composition
%matplotlib inline
"""
Explanation: In this notebook we shall try to remove duplicates from the icsd csv file and then store the Elements(and their frequencies) for each... |
lilleswing/deepchem | examples/tutorials/16_Learning_Unsupervised_Embeddings_for_Molecules.ipynb | mit | !curl -Lo conda_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py
import conda_installer
conda_installer.install()
!/root/miniconda/bin/conda info -e
!pip install --pre deepchem
import deepchem
deepchem.__version__
"""
Explanation: Tutorial Part 16: Learning Unsupervised... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/tensorflow/b_estimator.ipynb | apache-2.0 | # Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.6
import tensorflow as tf
import pandas as pd
import numpy as np
import shutil
print(tf.__version__)
"""
Explanation: <h1> Machine Learning using tf.estimator </h1>
In this notebook, we will create a machine learning model using... |
aurix/lammps-induced-dipole-polarization-pair-style | python/examples/pylammps/interface_usage_bonds.ipynb | gpl-2.0 | from lammps import IPyLammps
L = IPyLammps()
# 2d circle of particles inside a box with LJ walls
import math
b = 0
x = 50
y = 20
d = 20
# careful not to slam into wall too hard
v = 0.3
w = 0.08
L.units("lj")
L.dimension(2)
L.atom_style("bond")
L.boundary("f f p")
L.lattice("hex", 0.85)
L.region("... |
landlab/landlab | notebooks/tutorials/overland_flow/overland_flow_driver.ipynb | mit | from landlab.components.overland_flow import OverlandFlow
from landlab.plot.imshow import imshow_grid
from landlab.plot.colors import water_colormap
from landlab import RasterModelGrid
from landlab.io.esri_ascii import read_esri_ascii
from matplotlib.pyplot import figure
import numpy as np
from time import time
%matpl... |
bashtage/statsmodels | examples/notebooks/statespace_sarimax_pymc3.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc3 as pm
import statsmodels.api as sm
import theano
import theano.tensor as tt
from pandas.plotting import register_matplotlib_converters
from pandas_datareader.data import DataReader
plt.style.use("seaborn")
register_m... |
philmui/datascience2016fall | lecture03.numpy.pandas/lecture03.numpy.ipynb | mit | import numpy
dir(numpy)
help(numpy.zeros)
a = numpy.zeros( (3,5) )
a
a[(2,2)] = 3
a
import numpy as np
"""
Explanation: Numpy
NumPy, short for Numerical Python, is the fundamental package required for high performance scientific computing and data analysis.
While NumPy by itself does not provide very muc... |
darioizzo/d-CGP | doc/sphinx/notebooks/symbolic_regression_2.ipynb | gpl-3.0 | # Some necessary imports.
import dcgpy
import pygmo as pg
# Sympy is nice to have for basic symbolic manipulation.
from sympy import init_printing
from sympy.parsing.sympy_parser import *
init_printing()
# Fundamental for plotting.
from matplotlib import pyplot as plt
%matplotlib inline
"""
Explanation: Learning const... |
sdpython/ensae_teaching_cs | _doc/notebooks/exams/td_note_2015_rattrapage_enonce.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 1A.e - TD noté 2015 rattrapage (énoncé, écrit et oral)
Questions posées à l'oral autour du jeu 2048 et d'un exercice Google Jam sur le position de carreaux dans un plus grand carré : Problem D. Cut Tiles.
End of explanation
"""
mat = [[... |
godfreyduke/deep-learning | dcgan-svhn/DCGAN.ipynb | mit | %matplotlib inline
import pickle as pkl
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat
import tensorflow as tf
!mkdir data
"""
Explanation: Deep Convolutional GANs
In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a De... |
amirziai/learning | deep-learning/tangent.ipynb | mit | import tangent
import tensorflow as tf
def f(x):
a = x * x
b = x * a
c = a + b
return c
df = tangent.grad(f)
df
df(33)
"""
Explanation: tangent
Source-to-Source Debuggable Derivatives in Pure Python
As a result, you can finally read your automatic derivative code just like the rest of your program... |
Autodesk/molecular-design-toolkit | moldesign/_notebooks/Example 1. Build and simulate DNA.ipynb | apache-2.0 | import moldesign as mdt
from moldesign import units as u
%matplotlib inline
from matplotlib.pyplot import *
# seaborn is optional -- it makes plots nicer
try: import seaborn
except ImportError: pass
"""
Explanation: <span style="float:right"><a href="http://moldesign.bionano.autodesk.com/" target="_blank" title="A... |
dereneaton/RADmissing | emp_nb_Danio.ipynb | mit | ### Notebook 7
### Data set 7 (Danio)
### Authors: McCluskey (20xx)
### Data Location: SRP065811
"""
Explanation: Notebook 7:
This is an IPython notebook. Most of the code is composed of bash scripts, indicated by %%bash at the top of the cell, otherwise it is IPython code. This notebook includes code to download, as... |
tensorflow/docs-l10n | site/en-snapshot/hub/tutorials/cross_lingual_similarity_with_tf_hub_multilingual_universal_encoder.ipynb | apache-2.0 | # Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
fifabsas/talleresfifabsas | python/Extras/Dinamica_no_Lineal/mapas/mapasembebidos.ipynb | mit | from matplotlib import pyplot as plt #basic plotting
from mpl_toolkits.mplot3d import Axes3D #for 3D plots
import numpy as np #vectorial calculus
import os #basic file handling
#inline plotting
%matplotlib inline
#matplotlib font settings
from matplotlib import rc as rc
font = {'family' : 'sans',
'weight' : 'no... |
jegibbs/phys202-2015-work | assignments/midterm/InteractEx06.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import math
from IPython.display import Image
from IPython.html.widgets import interact, interactive, fixed
"""
Explanation: Interact Exercise 6
Imports
Put the standard imports for Matplotlib, Numpy and the IPython widgets in the following cell.
E... |
cathalmccabe/PYNQ | boards/Pynq-Z1/logictools/notebooks/boolean_generator.ipynb | bsd-3-clause | from pynq.overlays.logictools import LogicToolsOverlay
logictools_olay = LogicToolsOverlay('logictools.bit')
"""
Explanation: Boolean Generator
This notebook will show how to use the boolean generator to generate a boolean combinational function. The function that is implemented is a 2-input XOR.
Step 1: Download the... |
survey-methods/samplics | docs/source/tutorial/replicate_weights.ipynb | mit | import numpy as np
import pandas as pd
import samplics
from samplics.datasets import PSUSample, SSUSample
from samplics.weighting import ReplicateWeight
"""
Explanation: Replicate weights
Replicate weights are usually created for the purpose of variance (uncertainty) estimation. One common use case for replication-ba... |
newworldnewlife/TensorFlow-Tutorials | 12_Adversarial_Noise_MNIST.ipynb | mit | from IPython.display import Image
Image('images/12_adversarial_noise_flowchart.png')
"""
Explanation: TensorFlow Tutorial #12
Adversarial Noise for MNIST
by Magnus Erik Hvass Pedersen
/ GitHub / Videos on YouTube
Introduction
The previous Tutorial #11 showed how to find so-called adversarial examples for a state-of-th... |
phoebe-project/phoebe2-docs | development/tutorials/datasets_advanced.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
"""
Explanation: Advanced: Datasets
Datasets tell PHOEBE how and at what times to compute the model. In some cases these will include the actual observational data, and in other cases may only include the times at which you want to compute a synthetic model.
If you're not already f... |
unnikrishnankgs/va | venv/lib/python3.5/site-packages/nbconvert/tests/files/Widget_List.ipynb | bsd-2-clause | import ipywidgets as widgets
widgets.Widget.widget_types
"""
Explanation: Index - Back - Next
Widget List
Complete list
For a complete list of the GUI widgets available to you, you can list the registered widget types. Widget and DOMWidget, not listed below, are base classes.
End of explanation
"""
widgets.IntSlide... |
keras-team/keras-io | examples/nlp/ipynb/nl_image_search.ipynb | apache-2.0 | import os
import collections
import json
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_hub as hub
import tensorflow_text as text
import tensorflow_addons as tfa
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from tqdm impo... |
sysid/nbs | LP/Introduction-to-linear-programming/Introduction to Linear Programming with Python - Part 5.ipynb | mit | import pandas as pd
import pulp
factories = pd.DataFrame.from_csv('csv/factory_variables.csv', index_col=['Month', 'Factory'])
factories
"""
Explanation: Introduction to Linear Programming with Python - Part 5
Using PuLP with pandas and binary constraints to solve a scheduling problem
In this example, we'll be solvin... |
aje/POT | notebooks/plot_otda_color_images.ipynb | mit | # Authors: Remi Flamary <remi.flamary@unice.fr>
# Stanislas Chambon <stan.chambon@gmail.com>
#
# License: MIT License
import numpy as np
from scipy import ndimage
import matplotlib.pylab as pl
import ot
r = np.random.RandomState(42)
def im2mat(I):
"""Converts and image to matrix (one pixel per line)""... |
HydPy/HydPy-meetups | 2020/2020-02-29/MetaProgramming In Python.ipynb | mit | class Test:
pass
a = Test()
a
type(a)
type(Test)
type(type)
"""
Explanation: MetaProgramming In Python
Classes in Python - What is a class in Python?
End of explanation
"""
type?
TestWithType = type('TestWithType', (object,), {})
type(TestWithType)
ins1 = TestWithType()
type(ins1)
type('TestWithType', (... |
fastai/fastai | nbs/70a_callback.tensorboard.ipynb | apache-2.0 | #|export
import tensorboard
from torch.utils.tensorboard import SummaryWriter
from fastai.callback.fp16 import ModelToHalf
from fastai.callback.hook import hook_output
#|export
class TensorBoardBaseCallback(Callback):
order = Recorder.order+1
"Base class for tensorboard callbacks"
def __init__(self): self.... |
sdpython/ensae_teaching_cs | _doc/notebooks/td1a/td1a_cenonce_session3.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 1A.1 - Dictionnaires, fonctions, code de Vigenère
Le dictionnaire est une structure de données très utilisée. Elle est illustrée pour un problème de décryptage.
End of explanation
"""
def polynome ( x ) :
x2 = x*x
return x2 + x ... |
timkpaine/lantern | experimental/widgets/6_Widget Asynchronous.ipynb | apache-2.0 | %gui asyncio
"""
Explanation: Index - Back
Asynchronous Widgets
This notebook covers two scenarios where we'd like widget-related code to run without blocking the kernel from acting on other execution requests:
Pausing code to wait for user interaction with a widget in the frontend
Updating a widget in the background... |
ihmeuw/dismod_mr | examples/few_data_types.ipynb | agpl-3.0 | import matplotlib.pyplot as plt, numpy as np
import dismod_mr
models = {}
#iter=101; burn=0; thin=1 # use these settings to run faster
iter=10_000; burn=5_000; thin=5 # use these settings to make sure MCMC converges
"""
Explanation: Consistent models in DisMod-MR without many different types of data
In DisMod-II th... |
malcolmw/seismic-python | jupyter/fd_first_order_1d.ipynb | gpl-3.0 | # Parameterize the propagation domain
c = 100 # Wave speed [m/s]
xmin, xmax = -1000, 1000 # Computational domain [m]
tmin, tmax = 0, 20 # Computational domain [s]
f0 = 20 # Dominant frequency [1/s]
t0 = 4 / f0 # Zero-crossing time [s]
s0 = 0 # S... |
synthicity/activitysim | activitysim/examples/example_estimation/notebooks/14_joint_tour_scheduling.ipynb | agpl-3.0 | import os
import larch # !conda install larch -c conda-forge # for estimation
import pandas as pd
"""
Explanation: Estimating Joint Tour Scheduling
This notebook illustrates how to re-estimate the joint tour scheduling component for ActivitySim. This process
includes running ActivitySim in estimation mode to read h... |
ledeprogram/algorithms | class7/donow/Kromreig_Georgia_7_donow.ipynb | gpl-3.0 | import pandas as pd
%matplotlib inline
import numpy as np
from sklearn.linear_model import LogisticRegression
import statsmodels.formula.api as smf
"""
Explanation: Apply logistic regression to categorize whether a county had high mortality rate due to contamination
1. Import the necessary packages to read in the data... |
gaoshuming/udacity | 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... |
Illumina/interop | docs/src/Tutorial_01_Intro.ipynb | gpl-3.0 | run_folder = r"D:\RTA.Data\InteropData\MiSeqDemo"
"""
Explanation: Using the Illumina InterOp Library in Python
Install
If you do not have the Python InterOp library installed, then you can do the following:
$ pip install -f https://github.com/Illumina/interop/releases/latest interop
You can verify that InterOp is pr... |
jeancochrane/learning | python-machine-learning/code/ch02solutions.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from algos.perceptron import Perceptron
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
y = df.iloc[0:100, 4].values
y = np.where(y == 'Iris-setosa', 1, -1)
X = df.iloc[... |
ES-DOC/esdoc-jupyterhub | notebooks/cnrm-cerfacs/cmip6/models/cnrm-cm6-1/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'cnrm-cm6-1', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: CNRM-CM6-1
Topic: Land
Sub-Topics: Soil, Snow, Vegeta... |
kaleoyster/nbi-data-science | Bridge Life-Cycle Models/CDF+Probability+Reconstruction+vs+Age+of+Bridges+in+the+Northeast+United+States.ipynb | gpl-2.0 | import pymongo
from pymongo import MongoClient
import time
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib.pyplot import *
import matplotlib.pyplot as plt
import folium
import datetime as dt
import random as rnd
import warnings
import datetime as dt
import csv
%matplotlib inline
"""
Explan... |
science-of-imagination/nengo-buffer | Project/trained_mental_rotation_ens_compare.ipynb | gpl-3.0 | import nengo
import numpy as np
import cPickle
from nengo_extras.data import load_mnist
from nengo_extras.vision import Gabor, Mask
from matplotlib import pylab
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import scipy.ndimage
from skimage.measure import compare_ssim as ssim
"""
Explanation... |
mne-tools/mne-tools.github.io | 0.23/_downloads/ed1a04dd775648ca869bfcffae26faca/30_mne_dspm_loreta.ipynb | bsd-3-clause | import os.path as op
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/eLORETA
The aim of this tutorial is to teach you how to compute and apply a l... |
ES-DOC/esdoc-jupyterhub | notebooks/cas/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', 'cas', 'sandbox-1', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: CAS
Source ID: SANDBOX-1
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balance... |
fagonzalezo/is-2016-1 | exam_is.ipynb | mit | def bn_model(data, k):
'''
data: training data as a list of lists
[[x_1, x_2, ..., X_n, A, B]
[x_1, x_2, ..., X_n, A, B]
:
[x_1, x_2, ..., X_n, A, B]
]
k: Laplace's smoothing parameter
returns:
It must return the model as a dictionary with the following form:
For... |
dinrker/PredictiveModeling | Session 2 - Overfitting_Regularization_ModelSelection.ipynb | mit | import numpy as np
import pandas as pd
from IPython.display import Image
"""
Explanation:
End of explanation
"""
def mean_squared_error(y_true, y_pred):
"""
calculate the mean_squared_error given a vector of true ys and a vector of predicted ys
"""
diff = y_true - y_pred
return np.dot(diff, dif... |
Jonestj1/mbuild | docs/tutorials/tutorial_methane.ipynb | mit | import mbuild as mb
class Methane(mb.Compound):
def __init__(self):
super(Methane, self).__init__()
"""
Explanation: Methane: Compounds and bonds
The primary building block in mBuild is a Compound. Anything you construct will inherit from this class. Let's start with some basic imports and initialization:... |
tensorflow/docs-l10n | site/ja/quantum/tutorials/barren_plateaus.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... |
jrg365/gpytorch | examples/08_Advanced_Usage/Simple_Batch_Mode_GP_Regression.ipynb | mit | import math
import torch
import gpytorch
from matplotlib import pyplot as plt
%matplotlib inline
"""
Explanation: Batch GP Regression
Introduction
In this notebook, we demonstrate how to train Gaussian processes in the batch setting -- that is, given b training sets and b separate test sets, GPyTorch is capable of tr... |
albahnsen/ML_SecurityInformatics | notebooks/01-IntroMachineLearning.ipynb | mit | # Import libraries
%matplotlib inline
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
plt.style.use('ggplot')
# Create a random set of examples
from sklearn.datasets.samples_generator import make_blobs
X, Y = make_blobs(n_samples=50, centers=2,random_state=23, cluster_std=2.90)
plt.scatter... |
mne-tools/mne-tools.github.io | 0.17/_downloads/a35e576fa66929a73782579dc334f91a/plot_time_frequency_mixed_norm_inverse.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
from mne.inverse_sparse impor... |
dennisobrien/PublicNotebooks | fivethirtyeight/2017-06-30 Who steals the most in a town full of theives.ipynb | mit | (999/1000)**999
"""
Explanation: The Riddler
https://fivethirtyeight.com/features/who-steals-the-most-in-a-town-full-of-thieves/
A town of 1,000 households has a strange law intended to prevent wealth-hoarding. On January 1 of every year, each household robs one other household, selected at random, moving all of that... |
RafaelNH/Free-water-elimination-DTI | notebook/run_simulations_1.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import time
import sys
import os
%matplotlib inline
# Change directory to the code folder
os.chdir('..//code')
# Functions to sample the diffusion-weighted gradient directions
from dipy.core.sphere import disperse_charges, HemiSphere
# Function to... |
aakashm301/Workshop | Refactored_Py_DS_ML_Bootcamp-master/09-Geographical-Plotting/02-Choropleth Maps Exercise.ipynb | gpl-3.0 | import plotly.graph_objs as go
from plotly.offline import init_notebook_mode,iplot
init_notebook_mode(connected=True)
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Choropleth Maps Exercise
Welcome to the Choropleth Maps Exercise! In this exercise we will give you ... |
bioe-ml-w18/bioe-ml-winter2018 | homeworks/Week6-DynamicalModels.ipynb | mit | % matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.optimize import least_squares
# 104, 105, 107
V0 = np.array([52., 643., 77.])*1.0e3
c = np.array([3.68, 2.06, 3.09])
delta = np.array([0.5, 0.53, 0.5])
Ttot = np.array([2., 11., 412.])*1.0e3
tdelay = n... |
anukarsh1/deep-learning-coursera | Improving Deep Neural networks- Hyperparameter Tuning - Regularization and Optimization/Initialization.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import sklearn
import sklearn.datasets
from init_utils import sigmoid, relu, compute_loss, forward_propagation, backward_propagation
from init_utils import update_parameters, predict, load_dataset, plot_decision_boundary, predict_dec
%matplotlib inline
plt.rcParams['f... |
drericstrong/Blog | 20170119_Visualizing Dice Distributions.ipynb | agpl-3.0 | import numpy as np
import seaborn as sns
from scipy.stats import norm
import matplotlib.pyplot as plt
from itertools import combinations_with_replacement as cwr
%matplotlib inline
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
def find_hist(num_dice, dice_type):
formula = range(1,dice_type+1)
co... |
theandygross/TCGA_differential_expression | Notebooks/GABA_Receptors_GTEX.ipynb | mit | import NotebookImport
from metaPCNA import *
import GTEX as GTEX
f_win.order().tail()
gabr = [g for g in rna_df.index if g.startswith('GABR')]
f = dx_rna.ix[gabr].dropna()
f.join(f_win).sort(f_win.name)
GTEX.plot_tissues_across_gene('GABRD', log=True)
gtex = np.log2(GTEX.gtex)
meta = GTEX.meta
tissue_type = GTEX.t... |
streety/biof509 | Wk04-Data-retrieval-and-preprocessing.ipynb | mit | # required packages:
import numpy as np
import pandas as pd
import sklearn
import skimage
import sqlalchemy as sa
import urllib.request
import requests
import sys
import json
import pickle
import gzip
from pathlib import Path
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
!pip install pymysql
i... |
phuongxuanpham/SelfDrivingCar | CarND-LaneLines-Project1/P1.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
import pdb
"""
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 le... |
Thylossus/tud-movie-character-insights | Server/Tools/InsightsAutoEncoder/AutoEncoderInsights.ipynb | apache-2.0 | # Tell matplotlib to show the results directly within the notebook instead
# of using popup windows
%matplotlib inline
# Basic keras functionality to define network models
from keras.models import Model
# Needed layer classes for "normal" autoencoders ...
from keras.layers import Input, Dense
# ... and those we need... |
SteveDiamond/cvxpy | examples/notebooks/WWW/nonneg_matrix_fact.ipynb | gpl-3.0 | import cvxpy as cp
import numpy as np
# Ensure repeatably random problem data.
np.random.seed(0)
# Generate random data matrix A.
m = 10
n = 10
k = 5
A = np.random.rand(m, k).dot(np.random.rand(k, n))
# Initialize Y randomly.
Y_init = np.random.rand(m, k)
"""
Explanation: Nonnegative matrix factorization
A derivati... |
sysid/nbs | LP/Introduction-to-linear-programming/LaTeX_formatted_ipynb_files/Introduction to Linear Programming with Python - Part 6.ipynb | mit | def make_io_and_constraint(y1, x1, x2, target_x1, target_x2):
"""
Returns a list of constraints for a linear programming model
that will constrain y1 to 1 when
x1 = target_x1 and x2 = target_x2;
where target_x1 and target_x2 are 1 or 0
"""
binary = [0,1]
assert target_x1 in binary
a... |
DiCarloLab-Delft/PycQED_py3 | examples/MeasurementControl.ipynb | mit | import pycqed as pq
import numpy as np
from pycqed.measurement import measurement_control
from pycqed.measurement.sweep_functions import None_Sweep
import pycqed.measurement.detector_functions as det
from qcodes import station
station = station.Station()
"""
Explanation: Tutorial 1. The Measurement Control
This tutor... |
miaecle/deepchem | examples/tutorials/03_Modeling_Solubility.ipynb | mit | %tensorflow_version 1.x
!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py
import deepchem_installer
%time deepchem_installer.install(version='2.3.0')
"""
Explanation: Tutorial Part 3: Modeling Solubility
Computationally predicting molecular solubility t... |
mne-tools/mne-tools.github.io | 0.20/_downloads/fd92a90eaeac818b497ef44c9c13172a/plot_eeg_csd.ipynb | bsd-3-clause | # Authors: Alex Rockhill <aprockhill206@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
"""
Explanation: Transform EEG data using current source density (CSD)
This script shows an exa... |
MotokiShiga/stem-nmf | old/python_ver0.1/demo.ipynb | mit | %matplotlib inline
import numpy as np
import scipy.io as sio
from libnmf import NMF, NMF_SO, NMF_ARD_SO
"""
Explanation: Demo of NMF-SO and NMF-ARD-SO
[1] Motoki Shiga, Kazuyoshi Tatsumi, Shunsuke Muto, Koji Tsuda, Yuta Yamamoto, Toshiyuki Mori, Takayoshi Tanji, "Sparse Modeling of EELS and EDX Spectral Imaging Data b... |
tuanavu/coursera-university-of-washington | machine_learning/2_regression/lecture/week1/.ipynb_checkpoints/PhillyCrime-checkpoint.ipynb | mit | import sys
sys.path.append('C:\Anaconda2\envs\dato-env\Lib\site-packages')
import graphlab
"""
Explanation: Fire up graphlab create
End of explanation
"""
sales = graphlab.SFrame.read_csv('Philadelphia_Crime_Rate_noNA.csv/')
sales
"""
Explanation: Load some house value vs. crime rate data
Dataset is from Philadelp... |
phoebe-project/phoebe2-docs | 2.0/examples/binary_spots.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.0,<2.1"
"""
Explanation: Binary with Spots
Setup
Let's first make sure we have the latest version of PHOEBE 2.0 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
"""
%matplotlib inline
im... |
studentofdata/qcew | vmfiles/IPNB/Examples/a Basic/03 Matplotlib essentials.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
"""
Explanation: Matplotlib
This notebook is (will be) as small crash course on the functionality of the Matplotlib Python module for creating graphs (and embedding it in notebooks). It is of course no substirute for the proper Matplotlib thorough documentation.
First we need to import ... |
dwhswenson/openpathsampling | examples/toy_model_mstis/toy_mstis_3_analysis.ipynb | mit | from __future__ import print_function
# If our large test file is available, use it. Otherwise, use file generated
# from toy_mstis_2_run.ipynb. This is so the notebook can be used in testing.
import os
test_file = "../toy_mstis_1k_OPS1.nc"
filename = test_file if os.path.isfile(test_file) else "mstis.nc"
print("Usin... |
probml/pyprobml | notebooks/book2/28/gp_mauna_loa.ipynb | mit | try:
import tinygp
except ImportError:
!pip install -q tinygp
from jax.config import config
config.update("jax_enable_x64", True)
"""
Explanation: <a href="https://colab.research.google.com/github/probml/probml-notebooks/blob/main/notebooks/gp_mauna_loa.ipynb" target="_parent"><img src="https://colab.researc... |
justanr/notebooks | monads.ipynb | mit | x = y = ' Fred\n Thompson '
"""
Explanation: I swore to myself up and down that I wouldn't write one of these. But then I went and hacked up Pynads. And then I wrote a post on Pynads. And then I posted explainations about Monads on reddit. So what the hell. I already fulfilled my "Write about decorators when I und... |
eds-uga/csci1360e-su16 | lectures/L7.ipynb | mit | import random
"""
Explanation: Lecture 7: Vectorized Programming
CSCI 1360E: Foundations for Informatics and Analytics
Overview and Objectives
We've covered loops and lists, and how to use them to perform some basic arithmetic calculations. In this lecture, we'll see how we can use an external library to make these co... |
ModestoCabrera/IS360_Project3 | IS360project_3.ipynb | gpl-2.0 | import pandas as pd
import csv
import matplotlib.pyplot as plt
"""
Explanation: IS-360 Project 3
End of explanation
"""
income_df = pd.read_csv('LifeExpectancyIncome.csv')
income_df
"""
Explanation: Reading CSV File into Pandas DataFrame
READING CSV: I want to read the csv using the Pandas '.read_csv' which return... |
gschivley/Index-variability | Notebooks/Assign NERC region labels.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import os
from os.path import join
import pandas as pd
from sklearn import neighbors, metrics
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split, GridSearchCV
from collections import Counter
from copy import deepcopy
c... |
gregcaporaso/short-read-tax-assignment | ipynb/runtime/analysis.ipynb | bsd-3-clause | from os.path import expandvars
from tax_credit.plotting_functions import (lmplot_from_data_frame, calculate_linear_regress)
import pandas as pd
from os.path import join
import seaborn.xkcd_rgb as colors
"""
Explanation: Evaluate computational runtimes
The purpose of this notebook is to analyze and plot computational r... |
CopernicusMarineInsitu/INSTACTraining | PythonNotebooks/PlatformPlots/plot_CMEMS_mooring_NorthWestShelf.ipynb | mit | %matplotlib inline
import netCDF4
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import rcParams
from matplotlib import colors
from mpl_toolkits.basemap import Basemap
"""
Explanation: The objective of this notebook is to show how to read and plot data from a mooring (time ... |
ColCarroll/ai_talk | talk_code/slides.ipynb | mit | import matplotlib
%matplotlib inline
from bokeh.plotting import figure, show, ColumnDataSource
from bokeh.models import HoverTool
from bokeh.io import output_notebook, save
from clean_data import (get_models, predict, explain_model, LATEST_DATA as results_2016, get_df,
get_features, regression_... |
martinjrobins/hobo | examples/sampling/first-example.ipynb | bsd-3-clause | import pints
"""
Explanation: Sampling: First example
This example shows you how to perform Bayesian inference on a time series, using Adaptive Covariance MCMC.
It follows on from Optimisation: First example
Like in the optimisation example, we start by importing pints:
End of explanation
"""
import pints.toy as toy... |
apache/beam | examples/notebooks/tour-of-beam/reading-and-writing-data.ipynb | apache-2.0 | # Install apache-beam with pip.
!pip install --quiet apache-beam
# Create a directory for our data files.
!mkdir -p data
%%writefile data/my-text-file-1.txt
This is just a plain text file, UTF-8 strings are allowed 🎉.
Each line in the file is one element in the PCollection.
%%writefile data/my-text-file-2.txt
There... |
davidgutierrez/HeartRatePatterns | Jupyter/MimicII/0a Fill Database WaveForm Headers.ipynb | gpl-3.0 | import urllib.request
import wfdb
import psycopg2
from psycopg2.extensions import AsIs
"""
Explanation: Fill Database WaveForm Headers
1) Import de las librerias que utilizaremos
End of explanation
"""
target_url = "https://physionet.org/physiobank/database/mimic2wdb/matched/RECORDS-waveforms"
data = urllib.request.... |
SIMEXP/Projects | NSC2006/labo1/.ipynb_checkpoints/test1_plotly_local_install-checkpoint.ipynb | mit | import plotly.plotly as py
from plotly.graph_objs import *
trace0 = Scatter(
x=[1, 2, 3, 4],
y=[10, 15, 13, 17]
)
trace1 = Scatter(
x=[1, 2, 3, 4],
y=[16, 5, 11, 9]
)
data = Data([trace0, trace1])
py.iplot(data, filename = 'basic-line')
"""
Explanation: Creating an interactive graph inside an IPython... |
Iolaum/ud370 | assignments/2_fullyconnected.ipynb | gpl-3.0 | # 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
from six.moves import range
import os
"""
Explanation: Deep Learning
Assignment 2
Previou... |
waltervh/BornAgain-tutorial | old/python/notebooks/initial-setup.ipynb | gpl-3.0 | print('hello, world!')
"""
Explanation: Anaconda and BornAgain setup
If you do not already have a working Python 2.7 environment, download and install Anaconda at https://www.continuum.io/downloads. Be sure to get Python 2.7 version. You will need numpy and matplotlib. We recommend that you install ipython and jupyte... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.