repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
tritemio/multispot_paper | LCOS_pattern/Pattern Roto-Translation Documentation.ipynb | mit | def rotate(x, y, angle):
"""Rotate the point (x, y) (or array of points) with respect to the origin.
Arguments:
x, y (floats or arrays): input coordinates to be transformed.
angle (float): rotation angle in degrees. When the Y axis points
up and the X axis points right, a positi... |
probml/pyprobml | notebooks/book2/07/advi_beta_binom_jax.ipynb | mit | try:
import jax
except ModuleNotFoundError:
%pip install -qqq jax jaxlib
import jax
import jax.numpy as jnp
from jax import lax
try:
from tensorflow_probability.substrates import jax as tfp
except ModuleNotFoundError:
%pip install -qqq tensorflow_probability
from tensorflow_probability.substra... |
raschuetz/foundations-homework | Data_and_Databases_homework/03/.ipynb_checkpoints/homework_3_schuetz-checkpoint.ipynb | mit | from bs4 import BeautifulSoup
from urllib.request import urlopen
html_str = urlopen("http://static.decontextualize.com/widgets2016.html").read()
document = BeautifulSoup(html_str, "html.parser")
"""
Explanation: Homework assignment #3
These problem sets focus on using the Beautiful Soup library to scrape web pages.
Pr... |
jdeitmerg/PlainReflow | design/PT100 optimization.ipynb | mit | from scipy.optimize import curve_fit
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
eps = np.finfo(float).eps
A = 3.9083e-3
B = -5.775e-7
term1 = (A/B/2)*(A/B/2)
term2 = (1-96/100)/B
print(term1, ' ', term2)
# PT100 formula: R = 100*(1+A*T+B*T²)
# with A = 3.9083e-3
# B = -5.775e-... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_point_spread.ipynb | bsd-3-clause | import os.path as op
import numpy as np
from mayavi import mlab
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, apply_inverse
from mne.simulation import simulate_stc, simulate_evoked
"""
Explanation: Corrupt known signal with point spread
The aim of this tutorial is to... |
kscottz/PyBay2017 | MovieTime.ipynb | apache-2.0 | # Basemap Mosaic (v1 API)
mosaicsSeries = 'global_quarterly_2017q1_mosaic'
# Planet tile server base URL (Planet Explorer Mosaics Tiles)
mosaicsTilesURL_base = 'https://tiles0.planet.com/experimental/mosaics/planet-tiles/' + mosaicsSeries + '/gmap/{z}/{x}/{y}.png'
# Planet tile server url
mosaicsTilesURL = mosaicsTiles... |
ericmjl/Network-Analysis-Made-Simple | notebooks/05-casestudies/01-gameofthrones.ipynb | mit | from nams import load_data as cf
books = cf.load_game_of_thrones_data()
"""
Explanation: Introduction
In this chapter, we will use Game of Thrones as a case study to practice our newly learnt skills of network analysis.
It is suprising right? What is the relationship between a fatansy TV show/novel and network science... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/06_structured/5_train_keras.ipynb | apache-2.0 | # change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
os.environ['TFVERSION'] = '2.0' # not used in this notebook
%%bash
gcloud config set... |
fluffy-hamster/A-Beginners-Guide-to-Python | A Beginners Guide to Python/19. For-Loops.ipynb | mit | a_string = "12345"
a_list = list(range(1,6))
a_range_object = range(1, 6)
for num in a_range_object:
print(num, num*num) # prints num and num**2.
for num in a_list:
print(num, "is {}even".format("not " if num % 2 != 0 else "")) # returns num and whether it is/isnot even
for num in a_string:
print(int... |
adityaka/misc_scripts | python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/02_07/Final/Input and Output.ipynb | bsd-3-clause | file_name_string = 'C:/Users/Charles Kelly/Desktop/Exercise Files/02_07/Final/EmployeesWithGrades.xlsx'
employees_df = pd.read_excel(file_name_string, 'Sheet1', index_col=None, na_values=['NA'])
employees_df
"""
Explanation: read from an Excel file
documentation: http://pandas.pydata.org/pandas-docs/stable/generated/... |
jrmontag/Data-Science-45min-Intros | clustering-algos/Clustering algorithm comparison.ipynb | unlicense | import time
import hdbscan
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set(context='poster', style='darkgrid')
sns.set_color_codes()
from sklearn import cluster
from sklearn import datasets
"""
Explanation: Comparing clustering algorithms
2017-09-08, Josh Montagu... |
mne-tools/mne-tools.github.io | 0.20/_downloads/d9e2f27df3a137317d331d3be6f3814d/plot_dics_source_power.ipynb | bsd-3-clause | # Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
# Roman Goj <roman.goj@gmail.com>
# Denis Engemann <denis.engemann@gmail.com>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import mne
from mne.datasets import somato
from... |
mne-tools/mne-tools.github.io | 0.18/_downloads/ec24ebcf066d9fa611749fe52d13e07b/plot_sensor_permutation_test.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne import io
from mne.stats import permutation_t_test
from mne.datasets import sample
print(__doc__)
"""
Explanation: Permutation T-test on sensor data
One tests if the signal sign... |
theandygross/TCGA_differential_expression | Notebooks/Exploratory/sigmoid_updown.ipynb | mit | paired_bp_tn_split(matched_rna.ix['WASF3'], codes)
"""
Explanation: Inactivation of the WASF3 gene in prostate cancer cells leads to suppression of tumorigenicity and metastases.
End of explanation
"""
paired_bp_tn_split(matched_rna.ix['SGEF'], codes)
"""
Explanation: SGEF is overexpressed in prostate cancer and co... |
carltoews/tennis | results/DI_plot1.ipynb | gpl-3.0 | from IPython.display import display, HTML
display(HTML('''<img src="image1.png",width=800,height=600>'''))
"""
Explanation: Plot 1: The predictive potential of rank difference
End of explanation
"""
import numpy as np # numerical libraries
import pandas as pd # for data analysis
import matplotlib as mpl # a big libr... |
jhillairet/scikit-rf | doc/source/examples/vectorfitting/vectorfitting_ex3_Agilent_E5071B.ipynb | bsd-3-clause | import skrf
import numpy as np
import matplotlib.pyplot as mplt
"""
Explanation: Ex3: Fitting spiky responses
The Vector Fitting feature is demonstrated using a 4-port example network copied from the scikit-rf tests folder. This network is a bit tricky to fit because of its many resonances in the individual response. ... |
georgetown-analytics/machine-learning | archive/notebook/energy_efficiency.ipynb | mit | %matplotlib inline
import os
import requests
import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import scatter_matrix
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import me... |
mne-tools/mne-tools.github.io | 0.23/_downloads/772492bca9aff751a357f5e3e0163e67/50_cluster_between_time_freq.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.time_frequency import tfr_morlet
from mne.stats import permutation_cluster_test
from mne.datasets import sample
print(__doc__)
"""
Explanation: Non-parametri... |
rrozewsk/OurProject | CDO_Tranches.ipynb | mit | R=0.4
PD= 0.05 # Probability of default per year
PD = np.array([0.05*x for x in range(1,100) ]) # Probability of default per year
beta = 0.3
CT = norm.ppf(PD)
x = 0.5
def AA(CT,x,R):
AAA = CT - np.sqrt(1-beta*beta)*norm.ppf(x/(1-R))
AAA[AAA < -10]=-10
AAA[AAA > 10] = 10
return AAA
"""
Explanation: Qu... |
tensorflow/federated | docs/tutorials/working_with_client_data.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... |
KEHANG/AutoFragmentModeling | ipython/1. frag_mech_generation/generate_fragment_mechanism.ipynb | mit | import os
from tqdm import tqdm
from rmgpy import settings
from rmgpy.data.rmg import RMGDatabase
from rmgpy.kinetics import KineticsData
from rmgpy.rmg.model import getFamilyLibraryObject
from rmgpy.data.kinetics.family import TemplateReaction
from rmgpy.data.kinetics.depository import DepositoryReaction
from rmgpy.d... |
jrg365/gpytorch | examples/07_Pyro_Integration/Pyro_GPyTorch_High_Level.ipynb | mit | import math
import torch
import pyro
import tqdm
import gpytorch
from matplotlib import pyplot as plt
%matplotlib inline
"""
Explanation: Predictions with Pyro + GPyTorch (High-Level Interface)
Overview
In this example, we will give an overview of the high-level Pyro-GPyTorch integration - designed for predictive mod... |
anhaidgroup/py_entitymatching | notebooks/guides/end_to_end_em_guides/Basic EM Workflow DBLP ACM.ipynb | bsd-3-clause | # Import libraries
import py_entitymatching as em
import pandas as pd
import os, sys
print('python version: ' + sys.version )
print('pandas version: ' + pd.__version__ )
print('magellan version: ' + em.__version__ )
"""
Explanation: This quickstart guide explains how to match two tables using Magellan. Our goal is to... |
AlfiyaZi/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC3.ipynb | mit | %matplotlib inline
import numpy as np
from IPython.core.pylabtools import figsize
import matplotlib.pyplot as plt
figsize( 12.5, 5 )
sample_size = 100000
expected_value = lambda_ = 4.5
poi = np.random.poisson
N_samples = range(1,sample_size,100)
for k in range(3):
samples = poi( lambda_, sample_size )
... |
Gleiwer/kaggle_house_prices | 3_Data_preparation.ipynb | gpl-3.0 | import nltk
import pandas as pd
import math
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import gridspec
from sklearn import datasets, linear_model
import numpy as np
from numbers import Number
from sklearn import preprocessing
def correlation_matrix(df,figsize=(15,15)):
f... |
tensorflow/docs-l10n | site/zh-cn/quantum/tutorials/hello_many_worlds.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... |
huizhuzhao/jupyter_notebook | examples/keras_preprocessing_image.ipynb | mit | n_samples = 50
batch_size = 14
iterator = image.Iterator(N=n_samples, batch_size=batch_size, shuffle=True, seed=123)
for ii in range(5):
data = next(iterator.index_generator)
print ('current_batch_size: {0}'.format(data[-1]), data)
"""
Explanation: class Iterator()
生成一个 index_generator，通过调用 next() 方法该生成器可以产生一串... |
mauroalberti/gsf | docs/notebooks/Basic spatial data.ipynb | gpl-3.0 | %load_ext autoreload
%autoreload 1
"""
Explanation: Basic spatial data
March 2018, June 2019. Mauro Alberti.
Last version: 2021-04-04
Last running version: 2021-04-24
Developement code:
End of explanation
"""
%matplotlib inline
"""
Explanation: 1. Introduction
gsf is a library for the processing of geometric and ge... |
noppanit/machine-learning | pizza-franchise/Pizza Franchise.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
"""
Explanation: Introduction
We're going to explore Pizza Franshise data set from http://college.cengage.com/mathematics/brase/understandable_statistics/7e/students/datasets/slr/frames/frame.html
We want ... |
lukasmerten/CRPropa3 | doc/pages/example_notebooks/basics/basics.v4.ipynb | gpl-3.0 | from crpropa import *
# simulation: a sequence of simulation modules
sim = ModuleList()
# add propagator for rectalinear propagation
sim.add(SimplePropagation())
# add interaction modules
sim.add(PhotoPionProduction(CMB()))
sim.add(ElectronPairProduction(CMB()))
sim.add(NuclearDecay())
sim.add(MinimumEnergy(1 * EeV)... |
shngli/Data-Mining-Python | IMDB movie data visualization/IMDB visualization.ipynb | gpl-3.0 | ratingsFile = open('ratings.list','r')
countriesFile = open('countries.list','r')
output = open('countryRating.txt','w')
# Start readline() at the appropriate line
while True:
if countriesFile.readline() == "COUNTRIES LIST\n":
break;
countriesFile.readline()
while True:
if ratingsFile.readline() == "M... |
empet/PSCourse | Monty-Hall.ipynb | bsd-3-clause | from IPython.display import YouTubeVideo
YouTubeVideo('mhlc7peGlGg#t=15')
"""
Explanation: <center> Monty-Hall</center>
Problema cunoscuta sub numele Monty Hall sau Let's make a deal este o problema de probabilitati a carei solutie
pare nefireasca. Ea este legata de emisiunile/spectacolele TV cu aceste nume.
Formulare... |
BrownDwarf/ApJdataFrames | notebooks/BCAH2002.ipynb | mit | %pylab inline
import seaborn as sns
sns.set_context("notebook", font_scale=1.5)
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
"""
Explanation: ApJdataFrames BCAH2002
Title: Evolutionary models for low-mass stars and brown dwarfs: uncertainties and limits at very young ages
Authors: BCAH
Data... |
afedynitch/MCEq | examples/Basic_flux.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
#import solver related modules
from MCEq.core import MCEqRun
import mceq_config as config
#import primary model choices
import crflux.models as pm
"""
Explanation: Simplest possible example
Compute the fluxes of atmospheric leptons for a standard set of models at a f... |
agdestine/machine-learning | notebook/Visualization.ipynb | mit | %matplotlib inline
import os
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
# Setup context and style
sns.set_context('talk')
sns.set_style('whitegrid')
IRIS = os.path.join("..", "data", "iris.csv")
data = pd.read_csv(IRIS)
"""
Explanation: Multi-Dimension Vis... |
mcc-petrinets/formulas | spot/tests/python/automata.ipynb | mit | a = spot.translate('(a U b) & GFc & GFd', 'BA', 'complete'); a
"""
Explanation: To build an automaton, simply call translate() with a formula, and a list of options to characterize the automaton you want (those options have the same name as the long options name of the ltl2tgba tool, and they can be abbreviated).
End ... |
lmoresi/UoM-VIEPS-Intro-to-Python | Notebooks/Numpy+Scipy/2 - Application - The Game of Life.ipynb | mit | %pylab inline
import numpy as np
import matplotlib.pyplot as plt
start = np.array([[1,0,0,0,0,0],
[0,0,0,1,0,0],
[0,1,0,1,0,0],
[0,0,1,1,0,0],
[0,0,0,0,0,0],
[0,0,0,0,0,1]])
"""
Explanation: The game of life (John Conway)
The u... |
diegocavalca/Studies | programming/Python/tensorflow/exercises/Math_Part3_Solutions.ipynb | cc0-1.0 | from __future__ import print_function
import tensorflow as tf
import numpy as np
from datetime import date
date.today()
author = "kyubyong. https://github.com/Kyubyong/tensorflow-exercises"
tf.__version__
np.__version__
sess = tf.InteractiveSession()
"""
Explanation: Math Part 3
End of explanation
"""
_X = np.a... |
chinapnr/python_study | Python 基础课程/Python Basic Lesson 10 - 函数参数和匿名函数.ipynb | gpl-3.0 | # 函数默认参数
def cal_0(money, rate=0.1):
return money + money * rate
print(cal_0(100))
print(cal_0(100,0.2))
print(cal_0(rate=0.3,money=100))
# 函数默认参数
def cal_1(money, bonus=1000, month=12，a=1, b=2):
i = money * month + bonus
return i
print(cal_1(5000))
print(cal_1(5000, 2000))
print(cal_1(5000, 2000, 10... |
google/starthinker | colabs/floodlight_monitor.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: CM360 Floodlight Monitor
Monitor floodlight impressions specified in sheet and send email alerts.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance wi... |
facaiy/book_notes | Reinforcement_Learing_An_Introduction/Multi-armed_Bandits/note.ipynb | cc0-1.0 | reward_funcs = [
lambda size: np.random.normal(0.2, 1, size),
lambda size: np.random.normal(-0.8, 1, size),
lambda size: np.random.normal(1.5, 1, size),
lambda size: np.random.normal(0.4, 1, size),
lambda size: np.random.normal(1.2, 1, size),
lambda size: np.random.normal(-1.3, 1, size),
lam... |
vierth/chinese_stylometry | Stanford DH Asia Python Basics.ipynb | gpl-3.0 | # You can store integers
x = 10
# You can store strings
y = "Hi, my name is Paul"
# A variable can be as long as you like. It is best to use variable names
# that express what the variable is.
long_variable_names_work_too = 1.3
hi = 'hello'
"""
Explanation: Digital Humanities Asia Workshop
Stylometerics and Genre R... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/06_structured/1_explore.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
%%bash
if ! gsuti... |
gnu-user/mcsc-6030-assignments | Assignment-03/question-1/Numerical-Quadrature-with-the-Trapezoid-Rule.ipynb | mit | import numpy as np
import matplotlib.pyplot as mpl
%matplotlib inline
def f1(x):
return 1. + x**3
a1 = 0.
b1 = 2.
int_true1 = (b1-a1) + (b1**4 -a1**4) / 4.
print "true integral: %22.14e" % int_true1
"""
Explanation: Numerical Quadrature with the trapezoid rule
Numerical quadrature refers to approximating a defin... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/structured/solutions/6_serving_babyweight.ipynb | apache-2.0 | %%bash
# Check your project name
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT
import os
os.environ["BUCKET"] = "your-bucket-id-here" # Recommended: use your project name
"""
Explanation: LAB 6: Serving baby weight predictions
Learning O... |
crowd-course/datascience | evaluating estimator performance using cross-validation.ipynb | mit | import numpy as np
from sklearn import cross_validation
from sklearn import datasets
from sklearn import svm
iris = datasets.load_iris()
iris.data.shape, iris.target.shape
((150, 4), (150,))
"""
Explanation: Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: ... |
gururajl/deep-learning | intro-to-tensorflow/intro_to_tensorflow.ipynb | mit | import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfile import ZipFile
print('All m... |
whitead/numerical_stats | unit_7/lectures/lecture_2.ipynb | gpl-3.0 | %matplotlib inline
import random
import numpy as np
import matplotlib.pyplot as plt
from math import sqrt, pi
import scipy
import scipy.stats
plt.style.use('seaborn-whitegrid')
!pip install --user pydataset
"""
Explanation: 1D Data Analysis, Histograms, Boxplots, and Violin Plots
Unit 7, Lecture 2
Numerical Methods ... |
watsonyanghx/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... |
adelavega/neurosynth-mfc | profiles.ipynb | mit | # Load a neurosynth dataset. If you generate your own dataset, you can try this with fewer or greater number of topics
from neurosynth.base.dataset import Dataset
dataset = Dataset.load("data/neurosynth_60_0.4.pkl")
from sklearn.naive_bayes import GaussianNB
from classification import RegionalClassifier
from sklearn.m... |
adrn/thejoker | docs/examples/Strader-circular-only.ipynb | mit | from astropy.io import ascii
from astropy.time import Time
import astropy.units as u
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import pymc3 as pm
import pymc3_ext as pmx
import exoplanet.units as xu
import exoplanet as xo
from pymc3_ext.distributions import Angle
import corner
import arviz ... |
steinam/teacher | jup_notebooks/data-science-ipython-notebooks-master/numpy/02.07-Fancy-Indexing.ipynb | mit | import numpy as np
rand = np.random.RandomState(42)
x = rand.randint(100, size=10)
print(x)
"""
Explanation: <!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; the content... |
csdms/dakota | examples/hydrotrend-centered-parameter-study.ipynb | mit | from dakotathon import Dakota
"""
Explanation: <img src="http://csdms.colorado.edu/mediawiki/images/CSDMS_high_res_weblogo.jpg">
Centered Parameter Study with HydroTrend
HydroTrend is a numerical model that creates synthetic river discharge and sediment load time series as a function of climate trends and basin morpho... |
kubernetes-client/python | examples/notebooks/intro_notebook.ipynb | apache-2.0 | from kubernetes import client, config
"""
Explanation: Managing kubernetes objects using common resource operations with the python client
Some of these operations include;
create_xxxx : create a resource object. Ex create_namespaced_pod and create_namespaced_deployment, for creation of pods and deployments respecti... |
tuanavu/coursera-university-of-washington | machine_learning/2_regression/assignment/week2/week-2-multiple-regression-assignment-2-blank.ipynb | mit | import graphlab
"""
Explanation: Regression Week 2: Multiple Regression (gradient descent)
In the first notebook we explored multiple regression using graphlab create. Now we will use graphlab along with numpy to solve for the regression weights with gradient descent.
In this notebook we will cover estimating multiple... |
science-of-imagination/nengo-buffer | Project/manipulation_combination_training.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
%matplotlib inline
import nengo
import numpy as np
import scipy.ndimage
import matplotlib.animation as animation
from matplotlib import pylab
from PIL import Image
import nengo.spa as spa
import cPickle
import random
from nengo_extras.data import load_mnist
from nengo_extras.vision impo... |
phoebe-project/phoebe2-docs | 2.3/tutorials/vgamma.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
"""
Explanation: Systemic Velocity
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
"""
import phoebe
from phoebe import u # units
import numpy as np
impo... |
cpcloud/ibis | docs/tutorial/01-Introduction-to-Ibis.ipynb | apache-2.0 | import ibis
"""
Explanation: Introduction to Ibis
Ibis is a Python framework to access data and perform analytical computations from different sources, in a standard way.
In a way, you can think of Ibis as writing SQL in Python, with a focus on analytics, more than simply accessing data. And aside from SQL databases, ... |
VVard0g/ThreatHunter-Playbook | docs/notebooks/windows/02_execution/WIN-190410151110.ipynb | mit | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: Basic PowerShell Execution
Metadata
| Metadata | Value |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2019/04/10 |
| modification date | 2020/09/20 |
| playbook related | [] ... |
ptitjano/bokeh | examples/howto/charts/donut.ipynb | bsd-3-clause | d = Donut([2, 4, 5, 2, 8])
show(d)
"""
Explanation: Generic Examples
Values with implied index
End of explanation
"""
d = Donut(pd.Series([2, 4, 5, 2, 8], index=['a', 'b', 'c', 'd', 'e']))
show(d)
"""
Explanation: Values with Explicit Index
End of explanation
"""
autompg.head()
"""
Explanation: Autompg Data
Take... |
wzhfy/spark | python/docs/source/getting_started/quickstart.ipynb | apache-2.0 | from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
"""
Explanation: Quickstart
This is a short introduction and quickstart for the PySpark DataFrame API. PySpark DataFrames are lazily evaluated. They are implemented on top of RDDs. When Spark transforms data, it does not immediately compu... |
mdalvi/financial-analysis-and-algo-trading | time_series_analysis/time_series_analysis_notes.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import statsmodels.api as sm
# Importing built-in datasets in statsmodels
df = sm.datasets.macrodata.load_pandas().data
df.head()
print(sm.datasets.macrodata.NOTE)
df.head()
df.tail()
# statsmodels.timeseriesanalysis.dateto... |
gvasold/gdp17 | basics/basics_2.ipynb | apache-2.0 | # Im Notebook basics_1.ipynb haben wir die Zeilen aus der Datei
# names_short.txt in eine Liste von Zeilen namens clean_names eingelesen.
# Das tun wir noch einmal, weil wir diese Liste weiterhin brauchen.
with open('data/vornamen/names_short.txt', encoding='utf-8') as fh:
clean_names = [line.rstrip() for line in f... |
dsavransky/MAE2030 | Notebooks/3D coords.ipynb | mit | from miscpy.utils.sympyhelpers import *
init_printing()
th,ph,psi,thd,phd,psid = symbols('theta,phi,psi,thetadot,phidot,psidot')
w1,w2,w3 = symbols('omega_1,omega_2,omega_3')
"""
Explanation: 3D Coordinates and Kinematics Derivations
Preamble stuff (can ignore)
End of explanation
"""
cCi = Matrix(([cos(th),sin(th),... |
AllenDowney/ThinkStats2 | solutions/chap14soln.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... |
griffinfoster/fundamentals_of_interferometry | 2_Mathematical_Groundwork/2_14_CLEAN_in_1D.ipynb | gpl-2.0 | import warnings
warnings.filterwarnings('ignore')
import numpy as np
import matplotlib.pyplot as plt
from scipy import optimize as opt
import scipy.signal as scg
%matplotlib inline
from ipywidgets import interact
"""
Explanation: Outline
Glossary
Mathematical Background
Previous: 2.13 Spherical Trigonometry
Next: 2.... |
tanmay987/deepLearning | intro-to-tflearn/TFLearn_Digit_Recognition_Solution.ipynb | mit | # Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
"""
Explanation: Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
This... |
mdenker/elephant | doc/tutorials/parallel.ipynb | bsd-3-clause | import numpy as np
import quantities as pq
from elephant.spike_train_generation import homogeneous_poisson_process
from elephant.statistics import mean_firing_rate, time_histogram
from elephant.parallel import SingleProcess, ProcessPoolExecutor
try:
import mpi4py
from elephant.parallel.mpi import MPIPoolExec... |
JamesSample/icpw | toc_trends_oct_2016.ipynb | mit | # Import custom functions and connect to db
resa2_basic_path = (r'C:\Data\James_Work\Staff\Heleen_d_W\ICP_Waters\Upload_Template'
r'\useful_resa2_code.py')
resa2_basic = imp.load_source('useful_resa2_code', resa2_basic_path)
engine, conn = resa2_basic.connect_to_resa2()
"""
Explanation: TOC trend... |
pycam/python-functions-and-modules | python_fm_3.ipynb | unlicense | import math
print(math.pi, math.e)
"""
Explanation: Working with Python: functions and modules
Session 3: Modules
Importing modules and libraries
Python file library
Exercise 3.1
Using the csv module
Exercise 3.2
Create your own module
Exercise 3.3
Exercise 3.4
Importing modules and libraries
Like other laguages, Py... |
Bastien-Brd/pi-tuner | pitch_detection_from_samples.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Implementing and comparing several pitch detection methods on sample files
For simplicity I am using the Anaconda distribution on my Macbook Pro for this notebook.
The purpose is to first experiment here with s... |
elektrobohemian/courses | hu_ibi/clustering_demo.ipynb | mit | !pip install jellyfish
# The %... is an iPython thing, and is not part of the Python language.
# In this case we're just telling the plotting library to draw things on
# the notebook, instead of on a separate window.
%matplotlib inline
# See all the "as ..." contructs? They're just aliasing the package names.
# That ... |
McIntyre-Lab/ipython-demo | mahalanobis_distance.ipynb | gpl-2.0 | import pandas as pd
import numpy as np
import scipy as sp
import scipy.stats as stats
import matplotlib.pyplot as plt
import cPickle as pickle
import os
%matplotlib inline
"""
Explanation: Mahalanobis Distance
This notebook shows how I think we should do Mahalanobis distance for the SECIM project. From JMP Website:
T... |
QuantCrimAtLeeds/PredictCode | examples/Networks/Case study Chicago/Hotspotting.ipynb | artistic-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.collections
import numpy as np
import open_cp.network
import open_cp.geometry
import open_cp.network_hotspot
import open_cp.logger
open_cp.logger.log_to_true_stdout()
import pickle, lzma
with lzma.open("input_old.pic.xz", "rb") as f:
timed_point... |
amadeuspzs/travelTime | analysis.ipynb | mit | %matplotlib inline
import pandas as pd, matplotlib.pyplot as plt, matplotlib.dates as dates, math
from datetime import datetime
from utils import find_weeks, find_days # custom
from pytz import timezone
from detect_peaks import detect_peaks
from ipywidgets import interact, interactive, fixed, interact_manual
"""
Expla... |
vicente-gonzalez-ruiz/YAPT | 03-IO/19-database_access.ipynb | cc0-1.0 | # Source: Manuel Torres. Universidad de Almería.
import pymysql
# Establecemos la conexion con la base de datos
bd = pymysql.connect("localhost", "root", "gebd", "RRHH")
# Preparamos el cursor que nos va a ayudar a realizar las operaciones con la base de datos
cursor = bd.cursor()
# Ejecutamos un query SQL usando... |
gregorjerse/rt2 | 2015_2016/complexes/Alpha shapes vs. Vietoris Rips.ipynb | gpl-3.0 | import dionysus
import math
from random import random
from matplotlib import pyplot
def generate_circle(n, radius, max_noise):
"""
Generate n points on a sphere with the center in the point (0,0)
with the given radius.
Noise is added so that the distance from
the generated point to some poin... |
gxxjjj/QuantEcon.py | solutions/lakemodel_solutions.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from quantecon.models import LakeModel
alpha = 0.012
lamb = 0.2486
b = 0.001808
d = 0.0008333
g = b-d
N0 = 100.
e0 = 0.92
u0 = 1-e0
T = 50
"""
Explanation: Lake Model Solutions
Excercise 1
We begin by initializing the variables and import the nece... |
arjunbazinga/arjunbazinga.github.io | _notebooks/2017-11-12-multi-armed-bandits.ipynb | apache-2.0 | # Importing numpy for math, and matplotlib for plots
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
"""
Explanation: Multi Armed Bandit Problem
An introduction to multi-armed bandits
toc: true
badges: true
comments: true
categories: [AI]
image: images/chart-preview.png
Problem Description
I... |
rahulkgup/deep-learning-foundation | gan_mnist/Intro_to_GANs_Exercises.ipynb | mit | %matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
ljwolf/ucgis_workshop_2016 | notebooks/06 - Spatial Dynamics.ipynb | mit | f.header
"""
Explanation: To determine what is in the file, check the header attribute on the file object:
End of explanation
"""
name = f.by_col('Name')
name
"""
Explanation: Ok, lets pull in the name variable to see what we have
End of explanation
"""
y1929 = f.by_col('1929')
y1929
"""
Explanation: Now obtai... |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/GUI/.ipynb_checkpoints/4 - Widget List-checkpoint.ipynb | apache-2.0 | import ipywidgets as widgets
# Show all available widgets!
widgets.Widget.widget_types.values()
"""
Explanation: Widget List
This lecture will serve as a reference for widgets, providing a list of the GUI widgets available!
Complete list
For a complete list of the GUI widgets available to you, you can list the regist... |
karlstroetmann/Artificial-Intelligence | Python/6 Classification/Digit-Recognition-SVM.ipynb | gpl-2.0 | import gzip
import pickle
import numpy as np
import sklearn.svm as svm
"""
Explanation: Digit Recognition using a Support Vector Machine
End of explanation
"""
def load_data():
with gzip.open('../mnist.pkl.gz', 'rb') as f:
train, validate, test = pickle.load(f, encoding="latin1")
X_train = np.a... |
tmilliman/phenocam_notebooks | python-vegindex_CLI/Using_the_python-vegindex_package.ipynb | cc0-1.0 | %%bash
generate_roi_timeseries --help
"""
Explanation: Using the python-vegindex package command line tools
The python-vegindex package can be used to extract greeness-index values from PhenoCam Network images. In this notebook we'll download some images and a region-of-interest (ROI) mask list and extract the greene... |
ES-DOC/esdoc-jupyterhub | notebooks/inpe/cmip6/models/sandbox-3/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inpe', 'sandbox-3', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: INPE
Source ID: SANDBOX-3
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radi... |
tensorflow/docs-l10n | site/ko/tutorials/keras/classification.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... |
verilylifesciences/site-selection-tool | notebooks/trial_specification_demo.ipynb | bsd-3-clause | import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('ticks')
import functools
import importlib.resources
import numpy as np
import os
import pandas as pd
pd.plotting.register_matplotlib_converters()
import xarray as xr
from IPython.display import display
# bsst imports
from b... |
mne-tools/mne-tools.github.io | 0.20/_downloads/c0b7aacae8a68010257e46e5ec530f2e/plot_compute_mne_inverse_raw_in_label.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse_raw, read_inverse_operator
print(__doc__)
data_path = sample.data_path()
fname_inv = data_path + '/MEG/sample/s... |
fabianrost84/Rost-Rodrigo-Albors-et-al-2016 | calculations/number_quiescent_cells.ipynb | bsd-3-clause | import pandas as pd
import scipy as sp
from scipy import stats
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
exec(open('settings.py').read(), globals())
cell_numbers = pd.read_csv('../data/cell_number_data.csv')
outgrowth = pd.read_csv('../data/outgrowth.csv')
lcell = ... |
mavillan/SciProg | 08_parallelism/08_parallelism.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import sys
import memory_profiler
%load_ext memory_profiler
"""
Explanation: <h1 align="center">Scientific Programming in Python</h1>
<h2 align="center"> Topic 8: Basics of Parallelism </h2>
Notebook created by Martín Villanueva - martin.villanueva@usm.cl - DI UTFSM ... |
brian-rose/env-415-site | notes/RadiativeConvectiveEquilibrium.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import netCDF4 as nc
import climlab
"""
Explanation: ENV / ATM 415: Climate Laboratory
Radiative- and Radiative-Convective Equilibrium with climlab
Thursday March 31, 2016
End of explanation
"""
# Test in a 2-layer atmosphere
col = climlab.GreyRa... |
jo-tez/aima-python | neural_nets.ipynb | mit | from learning import *
from notebook import psource, pseudocode
"""
Explanation: NEURAL NETWORKS
This notebook covers the neural network algorithms from chapter 18 of the book Artificial Intelligence: A Modern Approach, by Stuart Russel and Peter Norvig. The code in the notebook can be found in learning.py.
Execute t... |
Danghor/Algorithms | Python/Chapter-09/Topological-Sorting.ipynb | gpl-2.0 | def topo_sort(T, D):
Parents = { t: set() for t in T } # dictionary of parents
Children = { t: set() for t in T } # dictionary of children
for s, t in D:
Children[s].add(t)
Parents [t].add(s)
Orphans = { t for (t, P) in Parents.items() if len(P) == 0 }
Sorted = []
count = 0... |
bataeves/kaggle | sber/Model.ipynb | unlicense | def align_to_lb_score(df):
# https://www.kaggle.com/c/sberbank-russian-housing-market/discussion/32717
df = df.copy()
trainsub = df[df.timestamp < '2015-01-01']
trainsub = trainsub[trainsub.product_type=="Investment"]
ind_1m = trainsub[trainsub.price_doc <= 1000000].index
ind_2m = trainsub[trai... |
MingChen0919/learning-apache-spark | notebooks/06-machine-learning/classification/decision-tree-classification.ipynb | mit | from pyspark import SparkContext
sc = SparkContext(master = 'local')
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.appName("Python Spark SQL basic example") \
.config("spark.some.config.option", "some-value") \
.getOrCreate()
"""
Explanation: Decision Tree Classifi... |
nud3l/smart-contract-analysis | analysis/contract-analysis.ipynb | mit | import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib as mpl
import pymongo
from pprint import pprint
mpl.rcParams['figure.dpi'] = 300
mpl.rcParams['savefig.dpi'] = 300
mpl.rcParams['axes.titlesize'] = "small"
mpl.rcParams['axes.labelsize'] = "large"
db = pymongo.MongoClient()
"""
Explanat... |
nagordon/mechpy | tutorials/design.ipynb | mit | # setup
import numpy as np
import sympy as sp
import scipy
from pprint import pprint
sp.init_printing(use_latex='mathjax')
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (12, 8) # (width, height)
plt.rcParams['font.size'] = 14
plt.rcParams['legend.fontsize'] = 16
from matplotlib import patches
#ge... |
mjbommar/scotus-uncertainty | simulate_baseline_performance.ipynb | bsd-2-clause | """
Setup the outcome map.
Rows correspond to vote types. Columns correspond to disposition types.
Element values correspond to:
* -1: no precedential issued opinion or uncodable, i.e., DIGs
* 0: affirm, i.e., no change in precedent
* 1: reverse, i.e., change in precent
"""
outcome_map = pandas.DataFrame([[-1, 0,... |
boada/planckClusters | analysis_ir/notebooks/04b. Understand LF.ipynb | mit | cosmo = LambdaCDM(H0=70, Om0=0.3, Ode0=0.7, Tcmb0=2.725)
"""
Explanation: Setup Cosmology
End of explanation
"""
# check to make sure we have defined the bpz filter path
if not os.getenv('EZGAL_FILTERS'):
os.environ['EZGAL_FILTERS'] = (f'{os.environ["HOME"]}/Projects/planckClusters/MOSAICpipe/bpz-1.99.3/FILTER/'... |
quantumlib/ReCirq | docs/qaoa/routing_with_tket.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... |
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