repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
nyoungb2/CLdb | doc/examples/Ecoli/DR_consensus.ipynb | gpl-2.0 | # directory where you want the spacer blasting to be done
## CHANGE THIS!
workDir = "/home/nyoungb2/t/CLdb_Ecoli/DR_consensus/"
"""
Explanation: Description:
This notebook goes through the creation and assessment of direct repeat (DR) consensus sequences
Before running this notebook:
run the Setup notebook
User-de... |
mayankjohri/LetsExplorePython | Section 1 - Core Python/Chapter 09 - Classes & OOPS/02_02_Single_Inheritance.ipynb | gpl-3.0 | class Pen():
def __init__(self, size, name):
self.name = name
self.size = size
def set_name(self, name):
self.name = name
class BallPen(Pen):
def __init__(self, size, name, color):
self.color = color
super().__init__(size, name)
def set_color(self, col... |
banyh/ShareIPythonNotebook | Gensim - Corpora and Vector.ipynb | gpl-3.0 | corpus = [[(0, 1.0), (1, 1.0), (2, 1.0)],
[(2, 1.0), (3, 1.0), (4, 1.0), (5, 1.0), (6, 1.0), (8, 1.0)],
[(1, 1.0), (3, 1.0), (4, 1.0), (7, 1.0)],
[(0, 1.0), (4, 2.0), (7, 1.0)],
[(3, 1.0), (5, 1.0), (6, 1.0)],
[(9, 1.0)],
[(9, 1.0), (10, 1.0)],
... |
JaviMerino/lisa | ipynb/tutorial/UseCaseExamples_SchedTuneAnalysis.ipynb | apache-2.0 | res_dir = '../../results/SchedTuneAnalysis/'
!tree {res_dir}
noboost_trace = res_dir + 'trace_noboost.dat'
boost15_trace = res_dir + 'trace_boost15.dat'
boost25_trace = res_dir + 'trace_boost25.dat'
# trace_file = noboost_trace
trace_file = boost15_trace
# trace_file = boost25_trace
"""
Explanation: Experiments coll... |
hsaghir/hsaghir.github.io | _posts/_code/RBC-DeepLearning-Notebooks/02 - LinearAlgebra.ipynb | mit | %matplotlib notebook
# Note that this is the usual way that I import Numpy and Matplotlib
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Attribution:
Most material based on Sam Roweis' Linear Algebra Review
End of explanation
"""
a = np.array([1, 2, 3])
b = np.ones(3,)
print a
print b
print a... |
springcoil/PyDataLondonTutorial | .ipynb_checkpoints/Statistics-checkpoint.ipynb | mit | def generate_data(no_samples,
treatment_proportion=0.1,
treatment_mu=1.2,
control_mu=1.0,
sigma=0.4):
"""
Generate sample data from the experiment.
"""
rnd = np.random.RandomState(seed=12345)
treatment = rnd.binomial(1, treatm... |
emmaqian/DataScientistBootcamp | DS_HW2_HuiminQian_060117.ipynb | mit | %%javascript
$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')
# import the necessary package at the very beginning
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import sklearn
"""
Explanation: 数据应用学院 Data Scientist Program Hw2
<h1... |
sbu-python-summer/python-tutorial | day-2/python-functions.ipynb | bsd-3-clause | def my_fun(i):
print("in the function, i = {}".format(i))
my_fun(10)
my_fun(5)
a = my_fun(0)
print(a)
"""
Explanation: functions
Functions are used to organize program flow, especially to allow us to easily do commonly needed tasks over and over again. We've already used a lot of functions, such as those th... |
ucbrise/clipper | examples/image_query/pytorch_on_docker_example.ipynb | apache-2.0 | !conda list |grep -i torch
"""
Explanation: Prerequisites
pip install clipper_admin==0.4.1 \
torch==1.0.1 \
torchvision==0.2.2 \
numpy==1.16.4 \
pandas==0.24.2 \
pillow==6.0.0
End of explanation
"""
from torchvision.models.resnet import resnet50
model = re... |
quole/gensim | docs/notebooks/gensim_news_classification.ipynb | lgpl-2.1 | import os
import re
import operator
import matplotlib.pyplot as plt
import warnings
import gensim
import numpy as np
warnings.filterwarnings('ignore') # Let's not pay heed to them right now
import nltk
nltk.download('stopwords') # Let's make sure the 'stopword' package is downloaded & updated
nltk.download('wordnet')... |
pligor/predicting-future-product-prices | 04_time_series_prediction/35_price_history_autoencoder_dyn_rnn_with_diff.ipynb | agpl-3.0 | factors(689)
max_seq_len = 682
#full_train_size = 55820
#train_size = 55800
#small_train_size = 6000 #just because of performance reasons, no statistics behind this decision
#test_size = 6200
data_path = '../../../../Dropbox/data'
phae_path = data_path + '/price_hist_autoencoder'
csv_in = '../price_history_03_seq_... |
drincruz/PyGotham-2016 | POS_Example.ipynb | mit | import nltk
abstract = """
It's morning, you settle in, check your dashboards and it looks like there is an increase of load coming through on some of your web server logs. What happened? You're about to deploy code that will hopefully fix some issues; how will you know that things worked well? The design team is thin... |
whitead/numerical_stats | unit_9/lectures/lecture_1.ipynb | gpl-3.0 | import this
"""
Explanation: Advanced Python
Unit 9, Lecture 1
Numerical Methods and Statistics
Prof. Andrew White, March 30, 2020
Becoming Pythonic
End of explanation
"""
my_list = ['hi', 'this', 'is', 'my', 'list']
for i,e in enumerate(my_list):
print(i, e)
"""
Explanation: Programming Idioms To Make Life E... |
cdpetty/Pixal | VidAnalysis/old_hw_reference_for_panda.ipynb | apache-2.0 | # this is a comment
# you can execute this cell by hitting shift+enter
# the output will appear immediately below
print 'Hello world!'
"""
Explanation: CSCE 489 :: Data Science and Analytics :: Texas A&M University :: Fall 2016
Homework 0: Welcome to the Thunderdome
100 points [5% of your final grade]
Due: Wednesday, ... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/production_ml/labs/custom_model_training.ipynb | apache-2.0 | USER_FLAG = "--user"
!pip3 install {USER_FLAG} google-cloud-aiplatform==1.7.0 --upgrade
!pip3 install {USER_FLAG} kfp==1.8.9 google-cloud-pipeline-components==0.2.0
"""
Explanation: Running custom model training on Vertex AI Pipelines
In this lab, you will learn how to run a custom model training job using the Kubefl... |
tensorflow/docs-l10n | site/en-snapshot/tensorboard/hyperparameter_tuning_with_hparams.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... |
datactive/bigbang | examples/datatracker/Working Group Affiliations.ipynb | mit | window = {
'since' : "2018-01-01T00:00:00",
'until' : "2018-01-10T00:00:00"
}
dt = DataTracker()
def extract_data(doc):
data = {}
## TODO: Add document UID?
data['title'] = doc.title
data['time'] = doc.time
data['group-acronym'] = dt.group(doc.group).acronym
data['type'] = doc.type.uri... |
jgomezc1/FEM-Notes | notebooks/gauss_integration.ipynb | mit | from __future__ import division
from sympy.utilities.codegen import codegen
from sympy import *
from sympy import init_printing
from IPython.display import Image
init_printing()
r, s, t, x, y, z = symbols('r s t x y z')
k, m, n = symbols('k m n', integer=True)
rho, nu, E = symbols('rho, nu, E')
"""
Explanation: Gaus... |
dereneaton/ipyrad | testdocs/analysis/cookbook-raxml-ipcoal.ipynb | gpl-3.0 | # conda install ipyrad -c conda-forge -c bioconda
# conda install raxml -c conda-forge -c bioconda
# conda install ipcoal -c conda-forge
import ipyrad.analysis as ipa
import toytree
import ipcoal
"""
Explanation: <span style="color:gray">ipyrad-analysis toolkit:</span> RAxML
RAxML is one of the most popular tools for... |
leosartaj/scipy-2016-tutorial | tutorial_exercises/Advanced-Solvers.ipynb | bsd-3-clause | from sympy import *
from sympy.solvers.solveset import solveset
init_printing()
"""
Explanation: Solvers
End of explanation
"""
a, b, c, d, x, y, z, t = symbols('a b c d x y z t')
f, g, h = symbols('f g h', cls=Function)
"""
Explanation: For each exercise, fill in the function according to its docstring.
End of exp... |
kkhenriquez/python-for-data-science | Week-7-MachineLearning/European Soccer Regression Analysis using scikit-learn.ipynb | mit | import sqlite3
import pandas as pd
from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from math import sqrt
"""
Explanation: <p style="font-family: Arial; font-size:2.75em;... |
harshnisar/badfish | Getting Started With Badfish.ipynb | mit | import pandas as pd
import badfish as bf
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.read_csv('data/airquality.csv', index_col=0)
"""
Explanation: Getting Started with Badfish - A library for missing data
For the purposes of this tutorial, I am using R's Airquality dataset. (Attach Link).
Let's do a q... |
NeuroDataDesign/pan-synapse | pipeline_3/background/OTSU_Notebook.ipynb | apache-2.0 | import sys
import scipy.io as sio
import glob
import numpy as np
import matplotlib.pyplot as plt
from skimage.filters import threshold_otsu
sys.path.append('../code/functions')
import qaLib as qLib
sys.path.append('../../pipeline_1/code/functions')
import connectLib as cLib
from IPython.display import Image
"""
Explan... |
nikkirubinstein/Intro-to-programming-concepts | examples/fileIO.ipynb | apache-2.0 | import numpy as np
import pandas as pd
#Go up one directory as this is where the 'current' directory assumed in the course notes
cd ..
!head -3 "data/titanic.csv"
!tail -3 "data/titanic.csv"
"""
Explanation: File IO
These notebooks are used to check certain examples in the course notes, hence they are mainly for ... |
agile-geoscience/notebooks | Linear_sampling.ipynb | apache-2.0 | area = 120000.0 # km^2, area covered by transects
population = 120 # Total number of features (guess)
no_lines = 250 # Total number of transects
line_length = 150 # km, mean length of a transect
feature_width = 0.5 # km, width of features
density = population / area
length = no_lines * line_length
... |
JohnGriffiths/ConWhAt | docs/examples/downloading_conwhat_atlases.ipynb | bsd-3-clause | from conwhat.utils.fetchers import fetch_conwhat_atlas
import glob
"""
Explanation: Downloading ConWhAt Atlases
Import the fetcher function
End of explanation
"""
atlas_dir = '/scratch/hpc3230/Data/conwhat_atlases'
"""
Explanation: Define the output directory
End of explanation
"""
atlas_name = 'CWL2k8Sc33Vol3d10... |
fdion/stemgraphic | notebooks/Quickstart with number.ipynb | mit | %matplotlib inline
import pandas as pd
from stemgraphic import stem_graphic
"""
Explanation: stemgraphic quickstart with numbers
Import stem_graphic from stemgraphic (shortcut) or explicitely from stemgraphic.num.
End of explanation
"""
df = pd.read_csv('../iris.csv')
df.describe()
"""
Explanation: Load a data fra... |
luofan18/deep-learning | batch-norm/Batch_Normalization_Exercises.ipynb | mit | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
"""
Explanation: Batch Normalization – Practice
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a con... |
wasit7/cs439_python | week07/Titanic Clustering.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
plt.style.use('ggplot')
# Anaconda on Windows will get warning
"""
Explanation: Titanic Survival Prediction
Data Dictionary
Here we got the data dictionary. This useful info will help you to understand the csv.
|Variable |Definition |Key|
|:--... |
JorisBolsens/PYNQ | Pynq-Z1/notebooks/examples/video_filters.ipynb | bsd-3-clause | from pynq import Overlay
from pynq.drivers import Frame, HDMI
from IPython.display import Image
Overlay('base.bit').download()
hdmi=HDMI('in')
hdmi.start()
"""
Explanation: Software Grayscale and Sobel filters on HDMI input
This example notebook will demonstrate two image filters using a snapshot from the HDMI input:... |
PythonFreeCourse/Notebooks | week05/1_Modules.ipynb | mit | import random
"""
Explanation: <img src="images/logo.jpg" style="display: block; margin-left: auto; margin-right: auto;" alt="לוגו של מיזם לימוד הפייתון. נחש מצויר בצבעי צהוב וכחול, הנע בין האותיות של שם הקורס: לומדים פייתון. הסלוגן המופיע מעל לשם הקורס הוא מיזם חינמי ללימוד תכנות בעברית.">
<span style="text-align: ri... |
girpas-ulg/nb_tseries | Drift_Fourier_Analysis_Bootstrap_CCL4_NYA.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
%matplotlib inline
sns.set_context('notebook')
"""
Explanation: Trend analysis with bootstrap resampling
Python / Numpy implementation of the trend analysis presented in Gardiner et al., 2008
The following model is used to fi... |
endgameinc/youarespecial | BSidesLV -- your model isn't that special -- (3) MalwaResNet.ipynb | mit | # for this demo, will slurp in only the first 256K (2**18) bytes of the file
max_file_length = int(2**18)
file_chunks = 16 # break file into this many chunks
file_chunk_size = max_file_length // file_chunks
batch_size = 4
# That this is very long running cell, and
# it may appear that the output is truncated before ... |
mne-tools/mne-tools.github.io | 0.19/_downloads/352c73e605c2040fbc0559050b15d343/plot_read_noise_covariance_matrix.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
from os import path as op
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
fname_cov = op.join(data_path, 'MEG', 'sample', 'sample_audvis-cov.fif')
fname_evo = op.join(data_path, 'MEG', 'sam... |
dtamayo/reboundx | ipython_examples/StochasticForces.ipynb | gpl-3.0 | import rebound
sim = rebound.Simulation()
sim.add(m=1.) # Star
sim.add(m=1e-3, a=1) # Jupiter mass planet
sim.move_to_com()
"""
Explanation: Adding stochastic forces
In this example, we add a stochastic force to a planet. This can be used simulate migration in a turbulent disk. See e.g. Rein (2012) and Rein and Papal... |
interactionlab/imui-tutorial | 03_Model-Training-CNN.ipynb | gpl-3.0 | # If GPU is not available:
# GPU_USE = '/cpu:0'
# config = tf.ConfigProto(device_count = {"GPU": 0})
# If GPU is available:
config = tf.ConfigProto()
config.log_device_placement = True
config.allow_soft_placement = True
config.gpu_options.allocator_type = 'BFC'
# Limit the maximum memory used
config.gpu_options.pe... |
amanahuja/adaptive_resonance_networks | ipynb/ART1_demo_simple.ipynb | mit | import numpy as np
data = np.array([" O ",
" O O",
" O",
" O O",
" O",
" O O",
" O",
" OO O",
" OO ",
" OO O",
" OO ",
"OOO ",
"OO ",
"O ",
"OO ",
"OOO ",
"OOOO ",
... |
quantumlib/Cirq | docs/tutorials/shor.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... |
raghakot/keras-vis | examples/mnist/attention.ipynb | mit | from __future__ import print_function
import numpy as np
import keras
from keras.datasets import mnist
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Flatten, Activation, Input
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
batch_size = 128
num_class... |
lmoresi/UoM-VIEPS-Intro-to-Python | Notebooks/Numpy+Scipy/7 - Scipy Optimize.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: scipy.optimize
See the following page for a detailed list of routines: scipy.optimize reference docs
The module contains:
Unconstrained and constrained minimization of multivariate scalar functions (minimize) using a variety of al... |
rahulremanan/python_tutorial | Machine_Vision/07_Bayesian_deep_learning/notebook/Monte_Carlo_Dropout_MNIST_Example.ipynb | mit | ! wget https://media.githubusercontent.com/media/rahulremanan/python_tutorial/master/Machine_Vision/07_Bayesian_deep_learning/weights/bayesianLeNet.h5 -O ./bayesianLeNet.h5
"""
Explanation: Monte Carlo Dropout -- Example Notebook
Launch this notebook in Google CoLab
This notebook is a modified fork of the Bayesian MNI... |
batfish/pybatfish | jupyter_notebooks/Safely refactoring ACLs and firewall rules.ipynb | apache-2.0 | # The ACL before refactoring
original_acl = """
ip access-list acl
10 deny icmp any any redirect
20 permit udp 117.186.185.0/24 range 49152 65535 117.186.185.0/24 eq 3784
30 permit udp 117.186.185.0/24 range 49152 65535 117.186.185.0/24 eq 3785
40 permit tcp 11.36.216.170/32 11.36.216.169/32 eq bgp
50 permit ... |
InsightLab/data-science-cookbook | 2019/12-spark/12-spark-intro/Transformations.ipynb | mit | data = sc.parallelize(range(1, 11))
def duplicar(x): return x*x
# data é um rdd
res = data.map( duplicar )
print (res.collect())
"""
Explanation: map(func)
Retorna um novo RDD formado pela passagem de cada elemento do RDD de origem através de uma da função func.
Exemplo:
End of explanation
"""
data = sc.parallel... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/gapic/custom/showcase_local_image_classification_online.ipynb | apache-2.0 | import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex client library: Local image classification model for online prediction
<table align=... |
fartashh/fartashh.github.io | static/data/post/how-calculate-mrr/MRR-Report.ipynb | mit | import csv
import pandas as pd
import numpy as np
from datetime import timedelta, date
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
"""
Explanation: Load required libraries
End of explanation
"""
#loading data
df = pd.read_csv('sample-subscription.csv')
report_start_date = date(2015,1,1)
repo... |
juneeseo/foundations-homework | 05/spotify_api.ipynb | mit |
items = data['artists']['items']
for item in items:
if len(item['genres'])==0:
print("No genres listed")
else:
print(','.join(item['genres']))
# AGGREGATION PROBLEM
all_genres = []
# THE LOOP
for item in items:
print ("All genres so far: ", all_genres)
# THE CONDITIONAL
print ("C... |
xjwangsjtu/xjwangsjtu.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... |
turbomanage/training-data-analyst | blogs/ghcn/ghcn_on_bq.ipynb | apache-2.0 | %bash
echo 'Y' | apt-get install python-mpltoolkits.basemap
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import datalab.bigquery as bq
import matplotlib as mpl
import seaborn as sns
import pandas as pd
import numpy as np
import shutil
"""
Explanation: <h2> Global Historical Climate Network... |
sjqgithub/rquestions | Construct Dataset.ipynb | mit | # extract all the code part
temp_code = questions['Body'].str.extractall(r'(<code>[^<]+</code>)')
temp_code.head()
# unstack and convert into a single column for cleaning
test = temp_code.unstack('match')
test.columns = test.columns.droplevel()
# put all columns together
code = pd.DataFrame(test.apply(lambda x: x.s... |
probml/pyprobml | notebooks/book1/08/opt_jax.ipynb | mit | import sklearn
import scipy
import scipy.optimize
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
import itertools
import time
from functools import partial
import os
import numpy as np
# np.set_printoptions(precision=3)
np.set_printoptions(formatter={"float": lambda x: "{0:0.3f}".f... |
ProfHoekstra/bluesky | docs/python_demo.ipynb | gpl-3.0 | import numpy as np
import bluesky as bs
from bluesky.simulation import ScreenIO
"""
Explanation: Bluesky interfacing demo
This Jupyter Notebook demonstrates the use of Bluesky as a Python package and how it can be used to generate aircraft trajectories.
Note: Make sure that you have Bluesky installed as a package in... |
gully/starfish-demo | demo3/notebooks/MCMC_step_size_02-autocorrelation_function.ipynb | mit | import h5py
f = h5py.File('../data/mc.hdf5', mode='r')
list(f.keys())
d = f['samples']
list(d.attrs)
d.attrs['acceptance']
"""
Explanation: Understanding the MCMC chains.
gully
February 2016
In this Notebook we pretty much just look at the Autocorrelation function, and try some experiments, most of which fizzled... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/launching_into_ml/solutions/bigquery.ipynb | apache-2.0 | try:
# Use the Colab's preinstalled TensorFlow 2.x
%tensorflow_version 2.x
except:
pass
# Install the required packages
!pip install fastavro
!pip install tensorflow-io==0.9.0
"""
Explanation: End-to-End Example for BigQuery TensorFlow Reader
Overview
In this lab, you will use BigQuery TensorFlow reader and Ke... |
ekergy/jupyter_notebooks | curso/4-Python.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
from sklearn import linear_model
from matplotlib import pylab as plt
plt.style.use('bmh')
%matplotlib notebook
wine = pd.read_csv('data/winequality-white.csv',delimiter=';')
wine.describe()
fig = plt.figure(2)
ax = [fig.add_subplot(3,4,i) for i in range(1,12)]
models = [linear_... |
mikarubi/notebooks | worker/notebooks/thunder/tutorials/regression.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from thunder import Colorize
image = Colorize.image
tile = Colorize.tile
sns.set_context('notebook')
sns.set_style('darkgrid')
"""
Explanation: Regression
Linear regression is a simple and powerful tool for describing relationships between a res... |
opensanca/trilha-python | 01-python-intro/aula-02/Aula 02.ipynb | mit | 20 + 13
5 * 8 - 10
(5 * 8 - 10) / 3
8 / 5 # o resultado da divsião sempre é um float
"""
Explanation: [Py-Intro] Aula 02
Tipos básicos e estruturas de controles
O que você vai aprender nesta aula?
Após o término da aula você terá aprendido:
Números e expressões aritméticas
Strings
Estruturas de controles de fluxo... |
sergey-tomin/workshop | 1_introduction.ipynb | mit | from IPython.display import Image
#Image(filename='gui_example.png')
"""
Explanation: This notebook was created by Sergey Tomin for Workshop: Designing future X-ray FELs. Source and license info is on GitHub. August 2016.
An Introduction to Ocelot
Ocelot is a multiphysics simulation toolkit designed for studying FEL ... |
smorton2/think-stats | code/chap04ex.ipynb | gpl-3.0 | from __future__ import print_function, division
%matplotlib inline
import numpy as np
import nsfg
import first
import thinkstats2
import thinkplot
"""
Explanation: Examples and Exercises from Think Stats, 2nd Edition
http://thinkstats2.com
Copyright 2016 Allen B. Downey
MIT License: https://opensource.org/licenses/... |
eecs445-f16/umich-eecs445-f16 | handsOn_lecture17_clustering-mixtures-em/handsOn_lecture17_clustering-mixtures-em.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt;
import matplotlib as mpl;
import numpy as np;
"""
Explanation: $$ \LaTeX \text{ command declarations here.}
\newcommand{\R}{\mathbb{R}}
\renewcommand{\vec}[1]{\mathbf{#1}}
\newcommand{\X}{\mathcal{X}}
\newcommand{\D}{\mathcal{D}}
\newcommand{\G}{\mathcal{G}}
\n... |
ledeprogram/algorithms | class9/homework/Skinner_Barnaby_9_2.ipynb | gpl-3.0 | iris = datasets.load_iris()
iris.keys()
X = iris.data[:,2:]
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42, test_size=0.25,train_size=0.75)
#What is random_state?
#What is stratify?
#What is this doing in the moon example exactly?
#X, y = make_moons(n_samples=... |
rvernagus/data-science-notebooks | Data Science From Scratch/13 - Naive Bayes.ipynb | mit | def tokenize(message):
message = message.lower()
all_words = re.findall('[a-z0-9]+', message)
return set(all_words)
from collections import defaultdict
def count_words(training_set):
"""training set consists of pairs (message, is_spam)"""
counts = defaultdict(lambda: [0, 0])
for message, is_sp... |
patonelli/map2110-modelagem | .ipynb_checkpoints/ArchiPi-checkpoint.ipynb | cc0-1.0 | import decimal
def ArchPi(precision=99):
# x: circumference of the circumscribed (outside) regular polygon
# y: circumference of the inscribed (inside) regular polygon
decimal.getcontext().prec = precision+1
D=decimal.Decimal
# max error allowed
eps = D(1)/D(10**precision)
# init... |
kubeflow/pipelines | samples/contrib/mnist/04_Reusable_and_Pre-build_Components_as_Pipeline.ipynb | apache-2.0 | import kfp
import kfp.gcp as gcp
import kfp.dsl as dsl
import kfp.compiler as compiler
import kfp.components as comp
import datetime
import kubernetes as k8s
# Required Parameters
PROJECT_ID='<ADD GCP PROJECT HERE>'
GCS_BUCKET='gs://<ADD STORAGE LOCATION HERE>'
"""
Explanation: Composing a pipeline from reusable, pr... |
mabevillar/rmtk | rmtk/vulnerability/model_generator/DBELA_approach/DBELA.ipynb | agpl-3.0 | import DBELA
from rmtk.vulnerability.common import utils
%matplotlib inline
"""
Explanation: Generation of capacity curves using DBELA
This notebook enables the user to generate capacity curves (in terms of spectral acceleration vs. spectral displacement) using the Displacement-based Earthquake Loss Assessment (DBELA)... |
scotthuang1989/Python-3-Module-of-the-Week | data_structure/enum.ipynb | apache-2.0 | import enum
class BugStatus(enum.Enum):
new = 7
incomplete = 6
invalid = 5
wont_fix = 4
in_progress = 3
fix_committed = 2
fix_released = 1
print("member name: {}".format(BugStatus.wont_fix.name))
print("member value: {}".format(BugStatus.wont_fix.value))
"""
Explanation: Creating Enumerat... |
IsaacLab/LaboratorioIntangible | T3/.ipynb_checkpoints/T3.2-Self-Organized-Criticality-checkpoint.ipynb | agpl-3.0 | %%HTML
<iframe src="https://www.openprocessing.org/sketch/434784/embed/" width="450" height="510"></iframe>
"""
Explanation: Self-Organized Criticality
1. Scale invariance
In physics, mathematics, statistics, and economics, scale invariance is a feature of objects or laws that do not change if scales of length, energy... |
mkuron/espresso | doc/tutorials/11-ferrofluid/11-ferrofluid_part1.ipynb | gpl-3.0 | import espressomd
espressomd.assert_features('DIPOLES', 'LENNARD_JONES')
from espressomd.magnetostatics import DipolarP3M
from espressomd.magnetostatic_extensions import DLC
from espressomd.cluster_analysis import ClusterStructure
from espressomd.pair_criteria import DistanceCriterion
import numpy as np
"""
Explan... |
sdpython/ensae_teaching_cs | _doc/notebooks/td1a_algo/td1a_cenonce_session9.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 1A.algo - Optimisation sous contrainte
L'optimisation sous contrainte est un problème résolu. Ce notebook utilise une librairie externe et la compare avec l'algorithme Arrow-Hurwicz qu'il faudra implémenter. Plus de précision dans cet art... |
soccermetrics/project-data | jleague-effective-time/bayesian_regression.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import os
import pymc
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
"""
Explanation: Effective time in 2015 J-League: Club Impact in J1
The objective of this analysis is to determine the impac... |
takanory/python-machine-learning | Pandas guide.ipynb | mit | # numpy と pandas を import する。np, pd と書くのは慣習っぽい
import numpy as np
import pandas as pd
"""
Explanation: Pandas 入門
Pythonを使ったデータ解析入門 3idea | OpenBook を見ながら、Pandasの基本的な操作を写経してなれる
Pandas 基本操作
https://openbook4.me/projects/183/sections/777
End of explanation
"""
# Series
# 軸にラベルを付けた1次元の配列
print(pd.Series([1,2,4]))
# 値... |
Pittsburgh-NEH-Institute/Institute-Materials-2017 | schedule/week_2/collation/3_collation-outputs.ipynb | gpl-3.0 | # import the collatex library
from collatex import *
# create an instance a Collation object
collation = Collation()
# add witnesses to the collateX instance
collation.add_plain_witness( "A", "The quick brown fox jumped over the lazy dog.")
collation.add_plain_witness( "B", "The brown fox jumped over the dog." )
collat... |
graphistry/pygraphistry | demos/demos_databases_apis/sql/postgres.ipynb | bsd-3-clause | import graphistry
#pip install graphistry -q
# To specify Graphistry account & server, use:
# graphistry.register(api=3, username='...', password='...', protocol='https', server='hub.graphistry.com')
# For more options, see https://github.com/graphistry/pygraphistry#configure
"""
Explanation: SQL Example
Get & plot ... |
jonathf/chaospy | docs/user_guide/advanced_topics/stochastic_dependencies.ipynb | mit | import chaospy
dist1 = chaospy.Gamma(1)
dist2 = chaospy.Normal(mu=dist1, sigma=dist1+1)
joint = chaospy.J(dist1, dist2)
joint
"""
Explanation: Dependent random variable
Some times simple stochastically independent random variables are not enough,
and one have to use variables with stochastic dependencies. In chaospy... |
theandygross/HIV_Methylation | PreProcessing/BMIQ_Horvath.ipynb | mit | cd ..
import NotebookImport
from Setup.Imports import *
import os as os
import pandas as pd
from pandas.rpy.common import convert_to_r_dataframe, convert_robj
import rpy2.robjects as robjects
from IPython.display import clear_output
"""
Explanation: Horvath Normalization Pipeline
Here we run the data normalization p... |
AhmedHani/Kaggle-Machine-Learning-Competitions | Easy/PokerRuleInduction/PokerRuleInduction.ipynb | mit | import pandas as pnd
from sklearn.cross_validation import cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsClassifier
"""
Explanation: Poker Induction Rule Problem
The problem is about the famous Cards Game, Po... |
jph00/part2 | pytorch-tut.ipynb | apache-2.0 | x = torch.Tensor(5, 3); x
x = torch.rand(5, 3); x
x.size()
y = torch.rand(5, 3)
x + y
torch.add(x, y)
result = torch.Tensor(5, 3)
torch.add(x, y, out=result)
# anything ending in '_' is an in-place operation
y.add_(x) # adds x to y in-place
# standard numpy-like indexing with all bells and whistles
x[:,1]
"""
... |
eds-uga/cbio4835-sp17 | lectures/Lecture8.ipynb | mit | matrix = [[ 1, 2, 3],
[ 4, 5, 6],
[ 7, 8, 9] ]
print(matrix)
"""
Explanation: Lecture 8: Advanced Data Structures
CBIO (CSCI) 4835/6835: Introduction to Computational Biology
Overview and Objectives
Before we get to some of the more advanced sequence analysis techniques, we need to cover some crit... |
jhillairet/scikit-rf | doc/source/tutorials/NetworkSet.ipynb | bsd-3-clause | import skrf as rf
rf.read_all(rf.data.pwd, contains='ro')
"""
Explanation: NetworkSet
Introduction
The NetworkSet object represents an unordered set of networks. It
provides methods iterating and slicing the set, sorting by datetime, calculating statistical quantities, and displaying uncertainty bounds on plots.
... |
dsacademybr/PythonFundamentos | Cap05/Notebooks/DSA-Python-Cap05-01-Classes.ipynb | gpl-3.0 | # Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
"""
Explanation: <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 5</font>
Download: http://github.com/dsacademybr
End of explanation
"""
# Crian... |
leopardbruce/FileFun | Horse_or_Human_NoValidation.ipynb | mit | !wget --no-check-certificate \
https://storage.googleapis.com/laurencemoroney-blog.appspot.com/horse-or-human.zip \
-O /tmp/horse-or-human.zip
"""
Explanation: <a href="https://colab.research.google.com/github/leopardbruce/TensorflowforAI-ML-DL/blob/master/Horse_or_Human_NoValidation.ipynb" target="_parent"><i... |
machow/siuba | docs/developer/sql-translators.ipynb | mit | from sqlalchemy import sql
col_names = ['id', 'x', 'y']
sel = sql.select([sql.column(x) for x in col_names])
print(sel)
print(type(sel.columns))
print(sel.columns)
"""
Explanation: SQL translators
The purpose of this vignette is to walk through how expressions like _.id.mean() are converted into SQL.
This process inv... |
farfan92/SpringBoard- | statistics project 2/.ipynb_checkpoints/sliderule_dsi_inferential_statistics_exercise_3-checkpoint.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import bokeh.plotting as bkp
from mpl_toolkits.axes_grid1 import make_axes_locatable
%matplotlib inline
# read in readmissions data provided
hospital_read_df = pd.read_csv('data/cms_hospital_readmissions.csv')
"""
Explanation: Hospital readmission... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/text_models/labs/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... |
jorisvandenbossche/DS-python-data-analysis | notebooks/python_recap/05-numpy.ipynb | bsd-3-clause | import numpy as np
"""
Explanation: Python the basics: numpy
DS Data manipulation, analysis and visualization in Python
May/June, 2021
© 2021, Joris Van den Bossche and Stijn Van Hoey (jorisvandenbossche@gmai... |
muatik/dm | bike-sharing-demand.ipynb | mit | from IPython.core import display
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, cross_validation
from sklearn.ensemble import RandomForestRegressor
from sklearn.grid_search import GridSearchCV
%matplotlib inline
def readCsv(name):
df = pd.read... |
SKA-ScienceDataProcessor/algorithm-reference-library | workflows/notebooks/imaging_serial.ipynb | apache-2.0 | %matplotlib inline
import os
import sys
sys.path.append(os.path.join('..', '..'))
from data_models.parameters import arl_path
results_dir = arl_path('test_results')
from matplotlib import pylab
pylab.rcParams['figure.figsize'] = (8.0, 8.0)
pylab.rcParams['image.cmap'] = 'rainbow'
import numpy
from astropy.coord... |
ceos-seo/data_cube_notebooks | notebooks/DCAL/DCAL_Cloud_Statistics.ipynb | apache-2.0 | # Enable importing of utilities.
import sys
import os
sys.path.append(os.environ.get('NOTEBOOK_ROOT'))
import numpy as np
import xarray as xr
import pandas as pd
import matplotlib.pyplot as plt
# Load Data Cube Configuration
import datacube
import utils.data_cube_utilities.data_access_api as dc_api
api = dc_api.Dat... |
ZhiangChen/deep_learning | Tutorials/Udacity/2_fullyconnected.ipynb | mit | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range
"""
Explanation: Deep Learning
Assignment 2
Previously in 1_n... |
tensorflow/docs-l10n | site/en-snapshot/agents/tutorials/2_environments_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... |
foxan/dataquest | Data Exploration/Challenge - Cleaning Data.ipynb | apache-2.0 | # %sh
# wget https://raw.githubusercontent.com/fivethirtyeight/data/master/avengers/avengers.csv
# ls -l
"""
Explanation: https://www.dataquest.io/mission/114/challenge-cleaning-data/
2: Life And Death Of Avengers
The Avengers are a well-known and widely loved team of superheroes in the Marvel universe that were int... |
mjasher/gac | original_libraries/flopy-master/examples/Notebooks/swiex4.ipynb | gpl-2.0 | %matplotlib inline
import os
import platform
import numpy as np
import matplotlib.pyplot as plt
import flopy.modflow as mf
import flopy.utils as fu
"""
Explanation: SWI2 Example 4. Upconing Below a Pumping Well in a Two-Aquifer Island System
This example problem is the fourth example problem in the SWI2 documentation... |
marcelomiky/PythonCodes | Coursera/IDSP/Week+1.ipynb | mit | x = 1
y = 2
x + y
x
"""
Explanation: You are currently looking at version 1.1 of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the Jupyter Notebook FAQ course resource.
The Python Programming Language: Functions
End of explanation
"""
d... |
enram/infrastructure | file_transfer/ad_hoc_management/remove_corrupted_h5_files.ipynb | mit | from file_transfer.creds import URL, LOGIN, PASSWORD
btos = dm.BaltradToS3(URL, LOGIN, PASSWORD, "lw-enram", profile_name="lw-enram")
btos.transfer(name_match="_vp_", overwrite=True,
limit=5, verbose=True)
btos.transferred
s3handle.create_zip_version(btos.transferred)
import shutil
shutil.rmtree(os... |
fastai/fastai | nbs/18b_callback.preds.ipynb | apache-2.0 | #|export
class MCDropoutCallback(Callback):
def before_validate(self):
for m in [m for m in flatten_model(self.model) if 'dropout' in m.__class__.__name__.lower()]:
m.train()
def after_validate(self):
for m in [m for m in flatten_model(self.model) if 'dropout' in m.__class__.__n... |
mph-/lcapy | doc/examples/notebooks/dependent_source_example1.ipynb | lgpl-2.1 | Vx = V('V_x').Voc
I = (cct.V1.V - 4 * Vx) / (cct.R1.Z + cct.R2.Z)
"""
Explanation: Let's determine the current through R1. There are many ways to solve this; the easiest is to combine the sources, combine the resistances, and then use Ohm's law. The result is a function of $V_x$:
End of explanation
"""
I * cct.R1.Z... |
tensorflow/docs-l10n | site/ja/guide/estimator.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... |
NeuroDataDesign/pan-synapse | pipeline_1/background/hungarianAlgorithms.ipynb | apache-2.0 | ######################################
###THIS IS PSEUDOCODE, WILL NOT RUN###
######################################
def hungarian(costMatrix):
p1CostMatrix = costMatrix.copy()
p2CostMatrix = costMatrix.copy().T
#for every point in the first set
for all p1 in p1CostMatrix:
#find its minimum weighted edge
min... |
davebshow/ten_million_books | demos/10_million_books.ipynb | mit | import string
import unidecode
s = "Historia de la Inquisición' "
s = s.decode('utf-8')
s = unidecode.unidecode(s)
s = s.strip()
s = s.strip(string.punctuation)
s = s.lower()
s
"""
Explanation: Ten Million Books
Cleaning
Basic Parsing
Marc21
author -> 100, 110, 111
title -> 244a, 244b
pub. location -> 264a,b,c
Str... |
totalgood/twip | docs/notebooks/10 Learning -- Predicting Impact.ipynb | mit | print(len(dates))
print(len(topics))
print(len(nums))
sum(nums.index == dates.index) == len(dates)
sum(nums.index == topics.index) == len(dates)
disc = LinearDiscriminantAnalysis()
disc
category = (np.ceil(nums.favorite_count ** .13)).astype(np.int8)
disc = LinearDiscriminantAnalysis().fit(topics, category)
predi... |
maubarsom/ORFan-proteins | phage_assembly/5_annotation/asm_v1.2/assembly_homologues/3_parse_d9539_blast_hits.ipynb | mit | blast_file_regex = re.compile(r"(blast[np])_vs_([a-zA-Z_]+).tsv")
blast_cols = ["query_id","subject_id","pct_id","ali_len","mism",
"gap_open","q_start","q_end","s_start","s_end","e_value","bitscore"]
blast_hits = []
for blast_filename in glob("blastn/*.tsv"):
tool_id,db_id = blast_file_regex.search(b... |
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