repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
sns-chops/multiphonon | tests/notebooks/getdos.ipynb | mit | # where am I now?
!pwd
# create a new working directory and change into it
workdir = '~/reduction/ARCS/getdos-demo'
!mkdir -p {workdir}
%cd {workdir}
# Data to reduce. Change the IPTS number and run numbers to suit your need
samplenxs = "/SNS/ARCS/IPTS-17327/data/ARCS_83914_event.nxs"
mtnxs = "/SNS/ARCS/IPTS-17327/da... |
mkuron/espresso | doc/tutorials/11-ferrofluid/11-ferrofluid_part2.ipynb | gpl-3.0 | import espressomd
espressomd.assert_features('DIPOLES', 'LENNARD_JONES')
from espressomd.magnetostatics import DipolarP3M
from espressomd.magnetostatic_extensions import DLC
import numpy as np
"""
Explanation: Ferrofluid - Part II
Table of Contents
Applying an external magnetic field
Magnetization curve
Remark: Th... |
ES-DOC/esdoc-jupyterhub | notebooks/awi/cmip6/models/sandbox-3/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'awi', 'sandbox-3', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: AWI
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: Transport, Emissions, Concent... |
retnuh/deep-learning | tensorboard/Anna_KaRNNa_Name_Scoped.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is base... |
dato-code/tutorials | notebooks/fraud-detection.ipynb | apache-2.0 | import graphlab as gl
data = gl.SFrame('fraud_detection.sf')
data.head(3)
len(data)
data.show()
"""
Explanation: Detecting Credit Card Fraud
In this notebook we will use GraphLab Create to identify a large majority of fraud cases in real-world data from an online retailer. Starting by a simple fraud classifier we ... |
flowersteam/naminggamesal | notebooks/6_Intro_Experiment_Database.ipynb | agpl-3.0 | import naminggamesal.ngdb as ngdb
import naminggamesal.ngsimu as ngsimu
"""
Explanation: Experiment Database
End of explanation
"""
xp_cfg={
'pop_cfg':{
'voc_cfg':{
'voc_type':'pandas',
#'voc_type':'sparse_matrix',
#'M':5,
#'W':10
},
'st... |
ForestClaw/forestclaw | applications/clawpack/advection/2d/filament/filament.ipynb | bsd-2-clause | !filament
"""
Explanation: Filament
Scalar advection problem with swirling velocity field.
Run code in serial mode (will work, even if code is compiled with MPI)
End of explanation
"""
!mpirun -n 4 filament
"""
Explanation: Or, run code in parallel mode (command may need to be customized, depending your on MPI in... |
liufuyang/ManagingBigData_MySQL_DukeUniv | notebooks/MySQL_Exercise_01_Looking_at_Your_Data.ipynb | mit | %load_ext sql
"""
Explanation: Copyright Jana Schaich Borg/Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
MySQL Exercise 1: Welcome to your first notebook!
Database interfaces vary greatly across platforms and companies. The interface you will be using here, called Jupyter, is a web application designed f... |
n-witt/MachineLearningWithText_SS2017 | tutorials/9 Kernel Density Estimation.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np
"""
Explanation: Kernel Density Estimation
In this section we will discuss probability density estimation
A density estimator is an algorithm which takes a $D$-dimensional dataset and produces an estimate of the $D$... |
ga7g08/ga7g08.github.io | _notebooks/2015-07-17-Plotting-country-level-data-for-the-UK.ipynb | mit | %%bash
cd ../data_sets/
wget http://census.edina.ac.uk/ukborders/easy_download/prebuilt/shape/England_ct_2011.zip -O zipdata.zip -q
unzip -o zipdata.zip
rm zipdata.zip
"""
Explanation: Plotting county level data for the UK
In this post I wanted to give a simple example of plotting country level data. This uses the [ON... |
cydcowley/Imperial-Visualizations | visuals_mechanics/mechanics_pulse_at_interface/pulse_at_interface.ipynb | mit |
# import libraries/packages to be used
import plotly.figure_factory as ff
from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot
import plotly.graph_objs as go
init_notebook_mode(connected=True)
import numpy as np
# Variables
# x-coordinates
n = 200
x = np.linspace(-10, 10, n)
# Initialise inciden... |
ARCHER-CSE/ci-monitoring | monitoring/io/analysis/benchioPerformanceAnalysis.ipynb | gpl-3.0 | import sys
import os.path
import re
from glob import glob
from datetime import datetime
"""
Explanation: Analysis of performance variation of parallel I/O
This notebook analyses the performance variation for write the single shared file (SSF) and file per process (FPP) on ARCHER.
Results are from the benchio benchmark... |
trsherborne/learn-python | lesson4.ipynb | mit | # Remember we declare an empty list as so
my_list = []
# Add new elements to the end of a previous list
my_list.append(1)
my_list.append('Hello')
my_list.append(0.05)
# Delete specific elements
del my_list[-1] # Remove by index
my_list.remove('Hello') # Remove by value
# Replace elements
my_list[0... |
AllenDowney/ProbablyOverthinkingIt | trivers12.ipynb | mit | from __future__ import print_function, division
import thinkstats2
import thinkplot
import pandas as pd
import numpy as np
import statsmodels.formula.api as smf
%matplotlib inline
"""
Explanation: Does Trivers-Willard apply to people?
This notebook contains a "one-day paper", my attempt to pose a research question... |
atlury/deep-opencl | DL0110EN/2.5.1_training_and_validation_v2.ipynb | lgpl-3.0 | # Import libraries we need for this lab, and set the random seed
from torch import nn
import torch
import numpy as np
import matplotlib.pyplot as plt
from torch import nn,optim
"""
Explanation: <a href="http://cocl.us/pytorch_link_top">
<img src="https://cocl.us/Pytorch_top" width="750" alt="IBM 10TB Storage" />
... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/sdk/sdk_automl_forecasting_evaluating_a_model.ipynb | apache-2.0 | %%capture
!pip3 uninstall -y google-cloud-aiplatform
!pip3 install google-cloud-aiplatform
import IPython
app = IPython.Application.instance()
app.kernel.do_shutdown(True)
import sys
if "google.colab" in sys.modules:
from google.colab import auth
auth.authenticate_user()
"""
Explanation: Vertex AI Model B... |
Naereen/notebooks | Generating_permutations_with_Python.ipynb | mit | from __future__ import print_function, division # Python 2 compatibility if needed
"""
Explanation: Generating permutations, several approaches with Python
This small notebook implements, in Python 3, several algorithms aiming at a simple task:
given a certain list, generate all the permutations of the list.
For inst... |
mjirik/lisa | examples/liver_left_right_segmentation.ipynb | bsd-3-clause | # 6, 11, 16 nefunguje
ircad_id = "10"
ircad_id = "12"
ircad_id = "11"
ircad_id = "20"
pth_left = f"C:/Users/Jirik/lisa_data/ircad{ircad_id}_left.pklz"
pth_left = f"G:\Můj disk\data\medical\orig\ircad_left_right/patient_dicom_seg{ircad_id}.pklz"
pth_liver = f"C:/Users/Jirik/data/medical/orig/3Dircadb1.{ircad_id}/MASKS_... |
parklab/PaSDqc | examples/04_example-dispersion_estimation/Dispersion_comparisons.ipynb | mit | import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pathlib
import sys
import scipy.stats
import pathlib
import PaSDqc
%matplotlib inline
sns.set_style('ticks', {'ytick.minor.size': 0.0, 'xtick.minor.size': 0.0})
sns.set_context('poster')
"""
Explanation: Introduction... |
ZuckermanLab/NMpathAnalysis | test/Clustering_test.ipynb | gpl-3.0 | import sys
sys.path.append("../")
sys.path.append("../nmpath/")
from test.tools_for_notebook import *
%matplotlib inline
from nmpath.auxfunctions import *
from nmpath.mfpt import *
from nmpath.mappers import rectilinear_mapper
from nmpath.clustering import *
#from nmpath.mappers import voronoi_mapper
"""
Explanation: ... |
cuttlefishh/emp | code/02-sequence-processing/sequence_length.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
import re
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: author: lukethompson@gmail.com<br>
date: 23 Oct 2017<br>
language: Python 3.5<br>
conda enviroment: emp-py3<br>
license: BSD3<br>
sequence_length.ipynb
Distribution of study sequen... |
ES-DOC/esdoc-jupyterhub | notebooks/nerc/cmip6/models/hadgem3-gc31-hh/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'hadgem3-gc31-hh', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: NERC
Source ID: HADGEM3-GC31-HH
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, ... |
kimkipyo/dss_git_kkp | Python 복습/13일차.목_pandas + SQL/13일차_2T_Pandas로 배우는 SQL 시작하기 (2) - JOIN ON.ipynb | mit | import pymysql
db = pymysql.connect(
"db.fastcamp.us",
"root",
"dkstncks",
"world",
charset='utf8',
)
df = pd.read_sql("SELECT * FROM Country;", db)
#cursor
cursor = db.cursor()
# 1. 실제로 명령을 수행하는 부분 - 서버
cursor.execute("SELECT * FROM Country;")
# 2. 데이터를 가져오는 부분 - 서버 => 클라이언트
curso... |
evanmiltenburg/python-for-text-analysis | Chapters/Chapter 17 - Data formats II (JSON).ipynb | apache-2.0 | dict_doe_family = {
"John": {
"first name": "John",
"last name": "Doe",
"gender": "male",
"age": 30,
"favorite_animal": "panda",
"married": True,
"children": ["James", "Jennifer"],
"hobbies": ["photography", "sky diving", "reading"]},
"Jan... |
google-research/recsim | recsim/colab/RecSim_Overview.ipynb | apache-2.0 | # @title Install
!pip install --upgrade --no-cache-dir recsim
!pip install -q tf-nightly-2.0-preview
# Load the TensorBoard notebook extension
%load_ext tensorboard
#@title Importing generics
import numpy as np
import tensorflow as tf
"""
Explanation: Copyright 2019 The RecSim Authors.
Licensed under the Apache Licen... |
jasonfrowe/Kepler | example/PythonExample.ipynb | gpl-3.0 | #Import module
import transitfit5 as tf #import transitfit5 modules
%matplotlib inline
"""
Explanation: Using Python Wrappers
A quick example to use python to read in three-column photometry files, model files and TTVs and then do a transit model fit to the data.
Currently there are binds for the transitfit5 tool su... |
neurodata/ndparse | examples/isbi2012_train.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
import sys, os, copy, logging, socket, time
import numpy as np
import pylab as plt
#from ndparse.algorithms import nddl as nddl
#import ndparse as ndp
sys.path.append('..'); import ndparse as ndp
try:
logger
except:
# do this precisely once
logger = ... |
agile-geoscience/striplog | docs/tutorial/12_Calculate_sand_proportion.ipynb | apache-2.0 | text = """top,base,comp number
24.22,24.17,20
24.02,23.38,19
22.97,22.91,18
22.67,22.62,17
21.23,21.17,16
19.85,19.8,15
17.9,17.5,14
17.17,15.5,13
15.18,14.96,12
14.65,13.93,11
13.4,13.05,10
11.94,11.87,9
10.17,10.11,8
7.54,7.49,7
6,5.95,6
5.3,5.25,5
4.91,3.04,4
2.92,2.6,3
2.22,2.17,2
1.9,1.75,1"""
"""
Explanation: Ca... |
nsrchemie/code_guild | wk0/notebooks/challenges/pins/pins_challenge.ipynb | mit |
## Constants used by this program
CONSONANTS = "bcdfghjklmnpqrstvwyz"
VOWELS = "aeiou"
def convert_pin(pin):
pin1 = pin.pop()
pass
pin1 = pi
pin = '2363'
pin1 = list(pin)
def remove_end(g):
"""
Explanation: <small><i>This notebook was prepared by Thunder Shiviah. Source and license info is on Git... |
IsacLira/data-science-cookbook | 2016/logistic-regression/plot_iris_logistic.ipynb | mit | print(__doc__)
# Code source: Gaël Varoquaux
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model, datasets
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first... |
WNoxchi/Kaukasos | FADL1/custom_model.ipynb | mit | %reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.transforms import *
from fastai.conv_learner import *
from fastai.model import *
from fastai.dataset import *
from fastai.sgdr import *
from fastai.plots import *
import pandas as pd
import numpy as np
path = 'data/gloc/'
model_path = path + 'results... |
omoju/Fundamentals | Data/data_EDA_1_preprocessData.ipynb | gpl-3.0 | %pylab inline
# Import libraries
from __future__ import absolute_import, division, print_function
# Ignore warnings
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
from sklearn.externals import joblib
# Graphing Libraries
import matplotlib.pyplot as pyplt
import seaborn as s... |
ercius/openNCEM | ncempy/notebooks/example_peak_find.ipynb | gpl-3.0 | %matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
# Import these from ncempy.algo
from ncempy.algo import gaussND
from ncempy.algo import peak_find
"""
Explanation: Example of how to find peaks in a synthetic image
Create a set of 2D Gaussians
Find the center of the Guassian to integer accurac... |
xaibeing/cn-deep-learning | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: 语言翻译
在此项目中，你将了解神经网络机器翻译这一领域。你将用由英语和法语语句组成的数据集，训练一个... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/production_ml/labs/sdk_metric_parameter_tracking_for_custom_jobs.ipynb | apache-2.0 | import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG = "--user"
# Install additi... |
metpy/MetPy | v1.1/_downloads/8532b75251585046a16f04a9afaef079/Advanced_Sounding.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, SkewT
from metpy.units import units
"""
Explanation: Advanced Sounding
Plot a sounding using MetPy with more advanced features.
Beyond just plotting data, this ... |
QuantCrimAtLeeds/PredictCode | examples/Prospective HotSpot.ipynb | artistic-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import open_cp
import open_cp.prohotspot as phs
# Generate some random data
import datetime
times = [datetime.datetime(2017,3,10) + datetime.timedelta(days=np.random.randint(0,10)) for _ in range(20)]
times.sort()
xc = np.random.random(size=20) * 5... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/statespace_news.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
macrodata = sm.datasets.macrodata.load_pandas().data
macrodata.index = pd.period_range('1959Q1', '2009Q3', freq='Q')
"""
Explanation: Forecasting, updating datasets, and the "news"
In this notebook,... |
jinntrance/MOOC | coursera/ml-classification/assignments/module-2-linear-classifier-assignment-blank.ipynb | cc0-1.0 | from __future__ import division
import graphlab
import math
import string
"""
Explanation: Predicting sentiment from product reviews
The goal of this first notebook is to explore logistic regression and feature engineering with existing GraphLab functions.
In this notebook you will use product review data from Amazon.... |
d-k-b/udacity-deep-learning | intro-to-tflearn/TFLearn_Sentiment_Analysis_Solution.ipynb | mit | import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
"""
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network w... |
rusucosmin/courses | ml/ex01/solution/taskC_detailed.ipynb | mit | %matplotlib inline
import numpy as np
from numpy.random import rand, randn
import matplotlib.pyplot as plt
%load_ext autoreload
%autoreload 2
# Data generation
n, d, k = 100, 2, 2
np.random.seed(20)
X = rand(n, d)
means = [rand(d) * 0.5 + 0.5 , - rand(d) * 0.5 + 0.5] # for better plotting when k = 2
S = np.diag(rand... |
mlund/labs | excess/excess.ipynb | gpl-3.0 | # evaluate this cell to show a movie about MC
from IPython.display import HTML
HTML('<iframe width="560" height="315" src="https://www.youtube.com/embed/xVvUFB5Hk-g?rel=0&controls=1&showinfo=0" frameborder="0" allowfullscreen></iframe>')
"""
Explanation: Monte Carlo Simulation Exercise: Electrolyte Solutions
... |
d00d/quantNotebooks | Notebooks/quantopian_research_public/notebooks/lectures/Arbitrage_Pricing_Theory/notebook.ipynb | unlicense | import numpy as np
import pandas as pd
from statsmodels import regression
import matplotlib.pyplot as plt
"""
Explanation: Arbitrage Pricing Theory
By Evgenia "Jenny" Nitishinskaya, Delaney Granizo-Mackenzie, and Maxwell Margenot.
Part of the Quantopian Lecture Series:
www.quantopian.com/lectures
github.com/quantopia... |
tensorflow/docs | site/en/r1/tutorials/load_data/images.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... |
jforbess/pvlib-python | docs/tutorials/tmy.ipynb | bsd-3-clause | # built in python modules
import datetime
import os
import inspect
# python add-ons
import numpy as np
import pandas as pd
# plotting libraries
%matplotlib inline
import matplotlib.pyplot as plt
try:
import seaborn as sns
except ImportError:
pass
import pvlib
"""
Explanation: TMY tutorial
This tutorial show... |
DavidQiuChao/CS231nHomeWorks | assignment1/knn.ipynb | mit | # Run some setup code for this notebook.
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
from __future__ import print_function
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a new window.
%matplotlib inlin... |
tensorflow/docs | site/en/tutorials/load_data/numpy.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... |
Ruediger-Braun/compana16 | Lektion13.ipynb | gpl-3.0 | from sympy import *
init_printing()
from IPython.display import display
"""
Explanation: Lektion 13
End of explanation
"""
x = Symbol('x')
y = Function('y')
dgl = Eq(y(x).diff(x, 2), -1/x*y(x).diff(x) + 1/x**2*y(x) +4*y(x))
dgl
#dsolve(dgl) # NotImplementedError
#N = 8
N=18
a = [Symbol('a'+str(j)) for j in rang... |
jonathanrocher/pandas_tutorial | climate_timeseries/climate_timeseries-Part1.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.set_option("display.max_rows", 16)
LARGE_FIGSIZE = (12, 8)
# Change this cell to the demo location on YOUR machine
%cd ~/Projects/pandas_tutorial/climate_timeseries/
%ls
"""
Explanation: Last updated: June 29th 2016
Climat... |
brentp/pedagree | data/paper-figures.ipynb | mit | DATA = "/data/" # directory where VCFs are KEPT
%%bash -s $DATA
DATA=$1
echo "$1"
mkdir -p plots/
python -m peddy --prefix plots/ceph1463 --plot ${DATA}/ceph1463.vcf.gz ${DATA}/ceph1463.ped
"""
Explanation: CEPH1463 Setup
We first download the ceph1463 VCF and ped into our data-directory and then run peddy.
We can se... |
karlstroetmann/Artificial-Intelligence | Python/3 Games/Alpha-Beta-Pruning-Pure.ipynb | gpl-2.0 | import random
random.seed(0)
"""
Explanation: Alpha-Beta Pruning
This notebook implements alpha-beta pruning. Memoization techniques are only added in a naive way since adding these techniques in a sophisticated way results in an algorithm that is quite complicated.
Effectively, this notebook is a game solver because ... |
google/starthinker | colabs/dv360_api_to_bigquery.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: 1. Install Dependencies
First install the libraries needed to execute recipes, this only needs to be done once, then click play.
End of explanation
"""
CLOUD_PROJECT = 'PASTE PROJECT ID HERE'
print("Cloud Project Set To: %s" % CLOUD_PROJECT)
... |
UDST/activitysim | activitysim/examples/example_estimation/notebooks/02_school_location.ipynb | bsd-3-clause | import larch # !conda install larch #for estimation
import pandas as pd
import numpy as np
import yaml
import larch.util.excel
import os
"""
Explanation: Estimating School Location Choice
This notebook illustrates how to re-estimate a single model component for ActivitySim. This process
includes running ActivitySi... |
staeiou/assorted-notebooks | infinite_scream/2017-02-14/infinite_scream.ipynb | mit | !pip install tweepy pandas seaborn
"""
Explanation: Graphing the number of favorites to @infinite_scream over time
By R. Stuart Geiger (@staeiou), Released CC-BY 4.0 & MIT License
Setup
Installing dependencies
End of explanation
"""
import random
import twitter_login # a file containing my API keys
import tweepy
... |
jasonpcasey/ipeds-peers | peer_examples.ipynb | mit | nx.degree(g, 3)
nx.degree(g, 4)
"""
Explanation: A node's degree is the number of connections it has.
End of explanation
"""
nx.clustering(g, 0)
nx.clustering(g, 4)
nx.clustering(g, 1)
"""
Explanation: The local clustering coefficient is the fraction of a node's connections that are also connected.
End of explan... |
joeandrewkey/deep-learning | first-neural-network/Your_first_neural_network.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
CubeBrowser/cube-explorer | notebooks/CubeExplorer_Prototype.ipynb | bsd-3-clause | import iris
import numpy as np
import holoviews as hv
import holocube as hc
from cartopy import crs
from cartopy import feature as cf
hv.notebook_extension()
"""
Explanation: This is an initial prototype for the cube-explorer project to integrate Iris, Cartopy and HoloViews to allow easily exploring Iris Cubes of N-d... |
Olsthoorn/TransientGroundwaterFlow | Syllabus_in_notebooks/Sec6_5_6_superposition_space_Theis-wells.ipynb | gpl-3.0 | from scipy.special import exp1 as W
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib.animation import FuncAnimation
from IPython.display import HTML
import pdb
def newfig(title, xlabel, ylabel, xlim=None, ylim=None, xscale=None, yscale=None,
size_inches=(12, 5), fonts... |
seva100/optic-nerve-cnn | scripts/U-Net, OD cup on DRISHTI-GS, cropped by OD (fold 0).ipynb | mit | %load_ext autoreload
%autoreload 2
import os
import glob
from datetime import datetime
#import warnings
#warnings.simplefilter('ignore')
import scipy as sp
import scipy.ndimage
import numpy as np
import pandas as pd
import tensorflow as tf
import skimage
import skimage.exposure
import skimage.transform
import mahotas ... |
qutip/qutip-notebooks | examples/piqs-open-dicke-model.ipynb | lgpl-3.0 | import matplotlib as mpl
from matplotlib import cm
import matplotlib.pyplot as plt
from qutip import *
from qutip.piqs import *
#TLS parameters
N = 6
ntls = N
nds = num_dicke_states(ntls)
[jx, jy, jz] = jspin(N)
jp = jspin(N, "+")
jm = jp.dag()
w0 = 1
gE = 0.1
gD = 0.01
gP = 0.1
gCP = 0.1
gCE = 0.1
gCD = 0.1
h = w0 *... |
jdvelasq/ingenieria-economica | 02-flujos-de-efectivo-y-tasas.ipynb | mit | # Importa la librería financiera.
# Solo es necesario ejecutar la importación una sola vez.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cashflows as cf
"""
Explanation: Representación de flujos de efectivo y tasas de interés
Juan David Velásquez Henao
jdvelasq@unal.edu.co
Universidad... |
mathLab/RBniCS | tutorials/09_advection_dominated/tutorial_advection_dominated_1_pod.ipynb | lgpl-3.0 | from dolfin import *
from rbnics import *
from problems import *
from reduction_methods import *
"""
Explanation: Tutorial 09 - Advection Dominated problem
Keywords: POD-Galerkin method, SUPG
1. Introduction
This tutorial addresses the POD-Galerkin method to the advection dominated worked problem in a two-dimensional ... |
edosedgar/stm32f0_ARM | labs/09_usart_terminal/notebook/09_usart_terminal.ipynb | mit | import serial
import pyaudio
import numpy as np
import wave
import scipy.signal as signal
import warnings
warnings.filterwarnings('ignore')
"""
Explanation: В данном ноутбуке вам предлагается написать различные обработчики для команд с компьютера.Также можно реализовать свой дополнительный набор команд под свои задачи... |
kaizu/nurgle | notebooks/index.ipynb | gpl-3.0 | from jupyterthemes.stylefx import set_nb_theme
set_nb_theme('grade3')
import os
PREFIX = os.environ.get('PWD', '.')
# PREFIX = "../build/outputs"
import numpy
import pandas
import plotly.graph_objs as go
import plotly.figure_factory as ff
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connec... |
bassio/omicexperiment | doc/03_experiment_filters.ipynb | bsd-3-clause | %load_ext autoreload
%autoreload 2
#Load our data
from omicexperiment.experiment.microbiome import MicrobiomeExperiment
mapping = "example_map.tsv"
biom = "example_fungal.biom"
tax = "blast_tax_assignments.txt"
exp = MicrobiomeExperiment(biom, mapping,tax)
"""
Explanation: Experiment objects filters - the rationale... |
hyunny88/study | regression/LogisticRegression.ipynb | unlicense | import tensorflow as tf
import numpy as np
xy = np.loadtxt('../data/logistic_data.txt',unpack=True, dtype='float32')
x_data = xy[0:-1]
y_data = xy[-1]
"""
Explanation: Logistic Classfication
https://ko.wikipedia.org/wiki/%EB%A1%9C%EC%A7%80%EC%8A%A4%ED%8B%B1_%ED%9A%8C%EA%B7%80
End of explanation
"""
x_data = [ [1,2... |
tommyogden/maxwellbloch | docs/usage/built-in-time-functions.ipynb | mit | from maxwellbloch import t_funcs
tlist = np.linspace(0., 1., 201)
"""
Explanation: Built-in Time Functions
Field profiles are defined as functions of time. A base rabi_freq is multiplied by a time function rabi_freq_t_func and related arguments rabi_freq_t_args. For example, a Gaussian pulse with a peak of $\Omega_0... |
Maluuba/qgen-workshop | notebook.ipynb | mit | import tensorflow as tf
from qgen.embedding import glove
embedding = tf.get_variable("embedding", initializer=glove)
EMBEDDING_DIMENS = glove.shape[1]
"""
Explanation: <img src="assets/Maluuba_Microsoft_Brandmark_Colour.png" width="60%"/>
Deep Learning for Language
A tutorial prepared by Maluuba
Prepared by Justin ... |
zzsza/Datascience_School | 27. 모형 최적화/02. 모형 최적화 분산 처리.ipynb | mit | from ipyparallel import Client
c = Client()
c.ids
dview = c[:]
dview
"""
Explanation: 모형 최적화 분산 처리
ipyparallel
http://ipyparallel.readthedocs.org/en/latest/index.html
Engine <-> Client
Engine: 실제 계산이 실행되는 프로세스
Client: 엔진을 제어하기 위한 인터페이스
$ conda install ipyparallel
Engine 가동/중지
가동
$ ipcluster start -n 4
중지
C... |
mohanprasath/Course-Work | numpy/numpy_exercises_from_kyubyong/Set_routines_Solutions.ipynb | gpl-3.0 | import numpy as np
np.__version__
author = 'kyubyong. longinglove@nate.com'
"""
Explanation: Set routines
End of explanation
"""
x = np.array([1, 2, 6, 4, 2, 3, 2])
out, indices = np.unique(x, return_inverse=True)
print "unique elements =", out
print "reconstruction indices =", indices
print "reconstructed =", out... |
ML4DS/ML4all | U_lab1.Clustering/Lab_ShapeSegmentation_draft/LabSessionClustering.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.misc import imread
"""
Explanation: Lab Session: Clustering algorithms for Image Segmentation
Author: Jesús Cid Sueiro
Jan. 2017
End of explanation
"""
name = "birds.jpg"
name = "Seeds.jpg"
birds = imread("Images/" + name)
birdsG = np.... |
daniel-koehn/Theory-of-seismic-waves-II | 00_Intro_Python_Jupyter_notebooks/0_Jupyter_Python_short_intro.ipynb | gpl-3.0 | # Execute this cell to load the notebook's style sheet, then ignore it
from IPython.core.display import HTML
css_file = '../style/custom.css'
HTML(open(css_file, "r").read())
"""
Explanation: Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2017 L.A. Barba, N.C. Clementi,... |
vzhuang/osim-rl | scripts/train.arm.ipynb | mit | # Derived from keras-rl
import opensim as osim
import numpy as np
import sys
from keras.models import Sequential, Model
from keras.layers import Dense, Activation, Flatten, Input, concatenate
from keras.optimizers import Adam
import numpy as np
from rl.agents import DDPGAgent
from rl.memory import SequentialMemory
f... |
goodwordalchemy/thinkstats_notes_and_exercises | code/chap07_relationships_btw_vars_notes.ipynb | gpl-3.0 | df = df.dropna(subset=['htm3', 'wtkg2'])
bins = np.arange(135, 210, 5)
indices = np.digitize(df.htm3, bins)
groups = df.groupby(indices)
for i, group in groups:
print i, len(group)
heights = [group.htm3.mean() for i, group in groups]
cdfs = [thinkstats2.Cdf(group.wtkg2) for i, group in groups]
for percent in... |
zaqwes8811/micro-apps | self_driving/deps/Kalman_and_Bayesian_Filters_in_Python_master/09-Nonlinear-Filtering.ipynb | mit | from __future__ import division, print_function
%matplotlib inline
#format the book
import book_format
book_format.set_style()
"""
Explanation: Table of Contents
Nonlinear Filtering
End of explanation
"""
import numpy as np
from numpy.random import randn
import matplotlib.pyplot as plt
N = 5000
a = np.pi/2. + (ran... |
espressofiend/NCIL-SOC-2015 | Production fMRI example/parse_pdxn_fMRI_logs.ipynb | mit | validTrials = df[pd.notnull(df['WORD'])]
validTrials[0:5]
"""
Explanation: Select only rows with stimulus words (so, where WORD != NaN)
End of explanation
"""
fMRIruns = {'STUDY0', 'STUDY1', 'TEST0', 'TEST1'}
Conditions = {'P':'Produced', 'S':'SilentView', 'Y':'MotorControl', 'FOIL':'Foil'}
for run in fMRIruns:
... |
mayank-johri/LearnSeleniumUsingPython | Section 3 - Machine Learning/ThirdParty-scikit-learn-videos-master/05_model_evaluation.ipynb | gpl-3.0 | # read in the iris data
from sklearn.datasets import load_iris
iris = load_iris()
# create X (features) and y (response)
X = iris.data
y = iris.target
"""
Explanation: Comparing machine learning models in scikit-learn
From the video series: Introduction to machine learning with scikit-learn
Agenda
How do I choose wh... |
keras-team/keras-io | examples/vision/ipynb/3D_image_classification.ipynb | apache-2.0 | import os
import zipfile
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
"""
Explanation: 3D image classification from CT scans
Author: Hasib Zunair<br>
Date created: 2020/09/23<br>
Last modified: 2020/09/23<br>
Description: Train a 3D convolutional neural n... |
adricnet/dfirnotes | win5mem-jupyter.ipynb | mit | !vol.py --plugins=/home/sosift/f/dfirnotes/ -f /cases/win5mem/winxp_java6-meterpreter.vmem --profile WinXPSP2x86 imageinfo
## Get setup to process memory with Volatility, analyse data with Pandas, chart with matplotlib
## Charting tips from https://datasciencelab.wordpress.com/2013/12/21/beautiful-plots-with-pandas-an... |
ES-DOC/esdoc-jupyterhub | notebooks/snu/cmip6/models/sandbox-3/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'snu', 'sandbox-3', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: SNU
Source ID: SANDBOX-3
Topic: Landice
Sub-Topics: Glaciers, Ice.
Properties: 3... |
brian-rose/ClimateModeling_courseware | Lectures/LectureTemplate.ipynb | mit | # Ensure compatibility with Python 2 and 3
from __future__ import print_function, division
"""
Explanation: ATM 623: Climate Modeling
Brian E. J. Rose, University at Albany
Lecture N: No title
About these notes:
This document uses the interactive Jupyter notebook format. The notes can be accessed in several different... |
c24b/c24b.github.io | cours/graphviz/Tuto_graph_with_python.ipynb | gpl-2.0 | #importer les libraires
#pour afficher
import matplotlib.pyplot as plt
#pour le calcul
import numpy as np
#pour le réseau
import networkx as nx
%matplotlib inline
#instancier le graph
g = nx.Graph()
#ajouter un noeud
#g.add_node("paul")
#ajouter une liste de noeud
g.add_nodes_from(["paul", "matthieu", "jean", "luc", ... |
julianogalgaro/udacity | nd101/c2l8-sentiment-analysis/sentiment_network/Sentiment Classification - Mini Project 5.ipynb | mit | def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()... |
zzsza/TIL | pytorch/Tutorial.ipynb | mit | import torch
x = torch.Tensor(5, 3)
print(x)
len(x)
x.shape
y = torch.rand(5,3)
print(y)
print(x + y)
print(torch.add(x, y))
result = torch.Tensor(5, 3)
print(result)
torch.add(x, y, out=result)
print(result)
print('before y:', y)
y.add_(x)
print('after y:', y)
x.t_()
"""
Explanation: What is pytorch?
gpu... |
Naereen/notebooks | Oraux_CentraleSupelec_PSI__Juin_2019.ipynb | mit | import numpy as np
import matplotlib as mpl
mpl.rcParams['figure.figsize'] = (10, 7)
import matplotlib.pyplot as plt
from scipy import integrate
import numpy.random as rd
import seaborn as sns
sns.set(context="notebook", style="whitegrid", palette="hls", font="sans-serif", font_scale=1.1)
"""
Explanation: Table of Co... |
mit-crpg/openmc | examples/jupyter/search.ipynb | mit | # Initialize third-party libraries and the OpenMC Python API
import matplotlib.pyplot as plt
import numpy as np
import openmc
import openmc.model
%matplotlib inline
"""
Explanation: Criticality Search
This notebook illustrates the usage of the OpenMC Python API's generic eigenvalue search capability. In this Notebo... |
Kaggle/learntools | notebooks/intro_to_programming/raw/tut2.ipynb | apache-2.0 | # Define the function
def add_three(input_var):
output_var = input_var + 3
return output_var
"""
Explanation: Introduction
In this lesson, you will learn how to organize your code with functions. A function is a block of code designed to perform a specific task. As you'll see, functions will let you do rough... |
n-witt/MachineLearningWithText_SS2017 | tutorials/6 Decision Trees and Random Forests.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
"""
Explanation: Decision Trees and Random Forests
We'll take a look at a powerful, non-parametric algorithm called random forests.
Random forests are an example of an ensemble method, meaning that it relies on ag... |
ireapps/pycar | completed/basics_complete_notebook.ipynb | mit | # This could just as easily be 'horse' or 'Helen' or 'Agamemnon' or `sand` -- or 'Trojan'
search_term = 'Achilles'
"""
Explanation: Python Basics at PyCAR2020
Let's search some text
You already know the components of programming. You have been exercising the reasoning programming relies on for your entire life, probab... |
rabernat/pyqg | docs/examples/two-layer.ipynb | mit | import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
import pyqg
"""
Explanation: Two Layer QG Model Example
Here is a quick overview of how to use the two-layer model. See the
:py:class:pyqg.QGModel api documentation for further details.
First import numpy, matplotlib, and pyqg:
End of explanati... |
ES-DOC/esdoc-jupyterhub | notebooks/ncar/cmip6/models/sandbox-2/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncar', 'sandbox-2', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: NCAR
Source ID: SANDBOX-2
Topic: Ocnbgchem
Sub-Topics: Tracers.
Properties:... |
seewhydee/ntuphys_nb | jupyter/gradqm/scattering.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
## Function to estimate the first Born contribution to the scattering amplitude f.
## The inputs are:
##
## Vfun -- A function object. The function should take an array [x, y, z]
## and return the value of the potential V(x,y,z).
## ki,... |
hershaw/data-science-101 | course/class2/01-clean/examples/00-kill.ipynb | mit | import pandas as pd
import numpy as np
% matplotlib inline
from matplotlib import pyplot as plt
"""
Explanation: 01-kill
This notebook presents how to eliminate the diagnosed outliers.
Some inital imports:
End of explanation
"""
data = pd.read_csv('../../data/all_data.csv', index_col=0)
print('Our dataset has %d co... |
WNoxchi/Kaukasos | FACLA/L3-notes.ipynb | mit | import torch
import numpy as np
Q = np.eye(3)
print(Q)
print(Q.T)
print(Q @ Q.T)
# construct I matrix
Q = torch.eye(3)
# torch matrix multip
# torch.mm(Q, Q.transpose)
Q @ torch.t(Q)
"""
Explanation: FCLA/FNLA Fast.ai Numerical/Computational Linear Algebra
Lecture 3: New Perspectives on NMF, Randomized SVD
Notes / ... |
planet-os/notebooks | api-examples/rtofs-reftime-vs-time.ipynb | mit | %matplotlib inline
import pandas as pd
import simplejson as json
from urllib.parse import urlencode
from urllib.request import urlopen, Request
import datetime
import numpy as np
"""
Explanation: API Time Selectors - Using Start, End, and Reftime with Forecast Data
Forecast datasets typically have two time dimensions... |
glasperfan/thesis | bach_code/legacy/NLL Curves.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd
pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('display.notebook_repr_html', True)
import seaborn as sns
sns.set_s... |
JeffAbrahamson/MLWeek | practicum/05_regression/regression_logistique.ipynb | gpl-3.0 | # Inspired by https://stackoverflow.com/questions/20045994/how-do-i-plot-the-decision-boundary-of-a-regression-using-matplotlib
# and http://stackoverflow.com/questions/28256058/plotting-decision-boundary-of-logistic-regression
X = np.array(rouge + bleu)
y = [1] * len(rouge) + [0] * len(bleu)
logreg = LogisticRegress... |
hamzamerzic/ml-playground | notebooks/nn-regression.ipynb | mit | # Used to clear up the workspace.
%reset -f
import numpy as np
import pickle
import tensorflow as tf
from sklearn.model_selection import train_test_split
# Load the data.
data = pickle.load(open('../data/data-ant.pkl', 'rb'))
actions = data['actions']
observations = data['observations']
X_train, X_test, y_train, y_t... |
voyageth/udacity-Deep_Learning_Foundations_Nanodegree | 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... |
mF2C/COMPSs | tests/sources/python/9_jupyter_notebook/src/simple.ipynb | apache-2.0 | import pycompss.interactive as ipycompss
"""
Explanation: Test suite for Jupyter-notebook
Sample example of use of PyCOMPSs from Jupyter
First step
Import ipycompss library
End of explanation
"""
ipycompss.start(graph=True, trace=True, debug=True, project_xml='../project.xml', resources_xml='../resources.xml')
"""
... |
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