repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
StingraySoftware/notebooks | Simulator/Lag Analysis.ipynb | mit | import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
"""
Explanation: Contents
This notebook analyses lag-frequency spectrums of the light curves simulated through impulse response approach. First, a simple case with delta impulse response is covered. Subsequently, an energy-dependent impulse res... |
wbinventor/openmc | examples/jupyter/tally-arithmetic.ipynb | mit | import glob
from IPython.display import Image
import numpy as np
import openmc
"""
Explanation: This notebook shows the how tallies can be combined (added, subtracted, multiplied, etc.) using the Python API in order to create derived tallies. Since no covariance information is obtained, it is assumed that tallies are... |
karst87/ml | dev/pyml/datacamp/kaggle-python-tutorial-on-machine-learning/02_predicting-with-decision-trees.ipynb | mit | # Import the Pandas library
import pandas as pd
kaggle_path = "http://s3.amazonaws.com/assets.datacamp.com/course/Kaggle/"
# Load the train and test datasets to create two DataFrames
train_url = kaggle_path + "train.csv"
train = pd.read_csv(train_url)
test_url = kaggle_path + "test.csv"
test = pd.read_csv(test_url)
... |
zzsza/TIL | python/bayesian-optimization.ipynb | mit | !pip3 install bayesian-optimization
"""
Explanation: 베이지안 최적화
gaussian process를 사용한 bayesian global optimization library
최대한 적은 iteration으로 최대값을 구하는 것이 목표
원리
최적화하려는 함수를 가장 살 설명하는 함수의 사후 분포(가우시안 프로세스)를 구성해 작동
관측치가 많아지면 사후 분포가 개선되고 파라미터 공간에서 탐색할 가치가 있는 영역과 그렇지 않은 영역이 더 명확해짐
<img src="https://github.com/fmfn/Bayesia... |
InsightLab/data-science-cookbook | 2019/09-clustering/cl_AlefCarneiro.ipynb | mit | # import libraries
# linear algebra
import numpy as np
# data processing
import pandas as pd
# data visualization
from matplotlib import pyplot as plt
# sys - to get maximum float value
import sys
# load the data with pandas
url = 'https://raw.githubusercontent.com/InsightLab/data-science-cookbook/master/2019/09-c... |
yafeunteun/wikipedia-spam-classifier | notebooks/feature_engineering.ipynb | mit | import sys
sys.path.append("/usr/local/lib/python3.4/dist-packages/")
sys.path.append("/usr/local/lib/python3.4/dist-packages/revscoring/")
sys.path.append("/usr/local/lib/python3.4/dist-packages/more_itertools/")
sys.path.append("/usr/local/lib/python3.4/dist-packages/deltas/")
!sudo pip3 install dependencies deltas
... |
vsoch/singularity-python | examples/shub/paper/compare_builds.ipynb | agpl-3.0 | import numpy
import os
import pickle
container_names = ['vsoch/singularity-hello-world',
'researchapps/quantum_state_diffusion',
'vsoch/pe-predictive']
base = "/home/vanessa/Documents/Work/singularity/hub"
results = pickle.load(open('%s/results.pkl' %base,'rb'))
df = results['df'... |
adamamiller/PS1_star_galaxy | PS1casjobs/WeightsComparison.ipynb | mit | import numpy as np
import pandas as pd
from astropy.io import fits
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import describe, iqr
%matplotlib notebook
hst_data = fits.getdata("HST_COSMOS_features_adamamiller.fit")
star = np.where(hst_data['MU_CLASS'] == 2)
gal = np.where(hst_data['MU_CLASS... |
KolesovDmitry/remote_sensing | slides/kazan-dsm/RemoteSensingIntro.ipynb | unlicense | import numpy as np
import pandas as pd
points = pd.read_csv('rand.txt')
points.tail()
y = points["class"]
X = points[['r1', 'r2', 'r3', 'r4', 'r5', 'r6', 'r7', 'r8', 'r9', 'r10', 'r11']]
# Разбиваем на обучающее и тестовое множества:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_tes... |
mldbai/mldb | container_files/tutorials/Using pymldb Tutorial.ipynb | apache-2.0 | from pymldb import Connection
mldb = Connection("http://localhost")
"""
Explanation: Using pymldb Tutorial
Interactions with MLDB occurs via a REST API. Interacting with a REST API over HTTP from a Notebook interface can be a little bit laborious if you're using a general-purpose Python library like requests directly,... |
YeEmrick/learning | cs231/assignment/assignment2/FullyConnectedNets.ipynb | apache-2.0 | # As usual, a bit of setup
from __future__ import print_function
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solv... |
jpilgram/phys202-2015-work | assignments/assignment11/OptimizationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Optimization Exercise 1
Imports
End of explanation
"""
# YOUR CODE HERE
#raise NotImplementedError()
def hat(x, a, b):
return -a*x**2+b*x**4
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(0.0, 1.0, 1.0... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_dics_source_power.ipynb | bsd-3-clause | # Author: Roman Goj <roman.goj@gmail.com>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
from mne.time_frequency import csd_epochs
from mne.beamformer import dics_source_power
print(__doc__)
data_path = sample.data_path()
raw_fname = data_pa... |
phungkh/phys202-2015-work | days/day19/FittingModels.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy import optimize as opt
from IPython.html.widgets import interact
"""
Explanation: Fitting Models
Learning Objectives: learn to fit models to data using linear and non-linear regression.
This material is licensed under the MIT license and... |
jackkwong/python-yellow-page-scraping | marketing scraping project.ipynb | mit | from IPython.core.display import display, HTML
import urllib2
import bs4
import urlparse
import pandas as pd
import numpy as np
"""
Explanation: Requirements
From this url http://www.5metal.com.hk/ajax/pager/company_fea?view_amount=36&page=1,
We get a list of urls for detail pages like http://www.5metal.com.hk/won... |
travc/paper-Predicted-MF-Quarantine-Length-Data-and-Code | code/Batch fetch and process temperatures.ipynb | mit | # boilerplate includes
import sys
import os
import numpy as np
import matplotlib as mpl
#mpl.use('nbagg')
import matplotlib.pyplot as plt
#from mpl_toolkits.mplot3d import Axes3D
#import mpld3 # for outputting interactive html figures
import pandas as pd
import seaborn as sns
import ish_parser
import gzip
import ft... |
kimkipyo/dss_git_kkp | 통계, 머신러닝 복습/160627월_21일차_PCA Principal Component Analysis/2.고유분해와 특이값 분해.ipynb | mit | w, V = np.linalg.eig(np.array([[1, -2], [2, -3]]))
w
V
"""
Explanation: 고유분해와 특이값 분해
정방 행렬 $A$에 대해 다음 식을 만족하는 단위 벡터 $v$, 스칼라 $\lambda$을 여러 개 찾을 수 있다.
$$ Av = \lambda v $$
$ A \in \mathbf{R}^{M \times M} $ 정방행렬
$ \lambda \in \mathbf{R} $
$ v \in \mathbf{R}^{M} $
이러한 실수 $\lambda$를 고유값(eigenvalue), 단위 벡터 $v$ ... |
DKolmas/Python-NeuralNet-SimpleEx | Simple_Neural_Network_example.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... |
chagaz/ma2823_2016 | lab_notebooks/Lab 2 2016-09-21 Introduction to scikit-learn.ipynb | mit | import numpy as np
# Load the gene expression data
X = np.loadtxt('data/small_Endometrium_Uterus.csv', delimiter=',',
skiprows=1, usecols=range(1, 3001))
# Load the labels (Python )
y = np.loadtxt('data/small_Endometrium_Uterus.csv', delimiter=',',
skiprows=1, usecols=[3001], dtype='s... |
briennakh/BIOF509 | Wk09/Wk09_Dataset-preprocessing.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
"""
Explanation: Week 9 - Dataset preprocessing
Before we utilize machine learning algorithms we must first prepare our dataset. This can often take a significant amount of time and can have a large impact on the performance of ... |
UWashington-Astro300/Astro300-A17 | Python_Astroplan_Constraints.ipynb | mit | import numpy as np
from astropy.table import QTable
import astropy.units as u
from astropy.time import Time
from astropy.coordinates import SkyCoord, EarthLocation
import pytz
from astroplan import Observer, FixedTarget
import warnings
warnings.filterwarnings('ignore', category=Warning)
"""
Explanation: Planning ob... |
fujii-team/GPinv | notebooks/Abel_inversion.ipynb | apache-2.0 | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import sys
# In ../testing/ dir, we prepared a small script for generating the above matrix A
sys.path.append('../testing/')
import make_LosMatrix
# Import GPinv
import GPinv
"""
Explanation: An example of the Nonlinear infe... |
statsmodels/statsmodels.github.io | v0.13.2/examples/notebooks/generated/statespace_sarimax_stata.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
from scipy.stats import norm
import statsmodels.api as sm
import matplotlib.pyplot as plt
from datetime import datetime
import requests
from io import BytesIO
# Register converters to avoid warnings
pd.plotting.register_matplotlib_converters()
plt.rc("figure", ... |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/Statements Assessment Test.ipynb | apache-2.0 | st = 'Print only the words that start with s in this sentence'
#Code here
"""
Explanation: Statements Assessment Test
Lets test your knowledge!
Use for, split(), and if to create a Statement that will print out words that start with 's':
End of explanation
"""
#Code Here
"""
Explanation: Use range() to print all ... |
dereneaton/ipyrad | tests/cookbook-raxml-pedicularis.ipynb | gpl-3.0 | ## conda install ipyrad -c ipyrad
## conda install toytree -c eaton-lab
## conda install raxml -c bioconda
"""
Explanation: Cookbook: RAxML analyses in a notebook
As part of the ipyrad.analysis toolkit we've created convenience functions for easily running common RAxML commands. This can be useful when you want to run... |
CloverHealth/pycon2017 | bayesian_analysis/em_upcoding/Pycon 2017 - EM Upcoding.ipynb | bsd-3-clause | import os
import sys
import numpy as np
import pandas as pd
import sqlalchemy as sa
from random import randint
import statsmodels.formula.api as sm
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.lines as mlines
matplotlib.rcParams['figure... |
jorisvandenbossche/DS-python-data-analysis | notebooks/case4_air_quality_processing.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: <p><font size="6"><b> CASE - air quality data of European monitoring stations (AirBase)</b></font></p>
© 2021, Joris Van den Bossche and Stijn Van Hoey (jorisvandenbo... |
ecervera/mindstorms-nb | nxt/motors/index.ipynb | mit | from functions import connect, forward, backward, stop, left, right, disconnect
from time import sleep
robot = 12 # canvieu pel número del vostre robot
connect(robot) # i executeu, polsant Majúscules + Enter
"""
Explanation: Moviments bàsics del robot
<img src="img/lego_motor.jpg" align="right">
El robot té dos... |
jason-neal/equanimous-octo-tribble | Notebooks/JobLib/Joblib_for_Daniel.ipynb | mit | import time
import tempfile
import shutil
import os
import numpy as np
from joblib import Parallel, delayed
from joblib import load, dump
def griddata(gridpoints, tlayer, teff_logg_feh, method='linear', rescale=True):
""" Do what ever it does"""
# put a short wait.
time.sleep(0.5)
return np.sum(tlayer... |
mne-tools/mne-tools.github.io | 0.23/_downloads/d8a6d02146c5c075611a652218e020ad/30_reading_fnirs_data.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import mne
"""
Explanation: Importing data from fNIRS devices
MNE includes various functions and utilities for reading NIRS
data and optode locations.
fNIRS devices consist of light sources and light detectors. A channel is formed
by source-detector pairs. MNE stores the location... |
awagner-mainz/notebooks | gallery/TextProcessing_Solorzano.ipynb | mit | # This is the path to our file
bigsourcefile = 'Solorzano/Sections_I.1_TA.txt'
# We use a variable 'input' for keeping its contents.
input = open(bigsourcefile, encoding='utf-8').readlines()
# Just for information, let's see the first 10 lines of the file.
input[0:10] # actually, since python starts counting with '0... |
giacomov/astromodels | examples/Model_tutorial.ipynb | bsd-3-clause | from astromodels import *
"""
Explanation: Model tutorial
In this tutorial we show how to build a simple model with two point sources, how to save it for later use, and re-load it back. We will also plot the spectra of the two point sources, with their components.
See the documents about creating and getting informati... |
ageron/ml-notebooks | extra_tensorflow_reproducibility.ipynb | apache-2.0 | from IPython.display import IFrame
IFrame(src="https://www.youtube.com/embed/Ys8ofBeR2kA", width=560, height=315, frameborder="0", allowfullscreen=True)
"""
Explanation: TensorFlow Reproducibility
This notebook explains how to get fully reproducible code with TensorFlow.
<table align="left">
<td>
<a target="_bla... |
microsoft/dowhy | docs/source/example_notebooks/dowhy_multiple_treatments.ipynb | mit | import numpy as np
import pandas as pd
import logging
import dowhy
from dowhy import CausalModel
import dowhy.datasets
import econml
import warnings
warnings.filterwarnings('ignore')
data = dowhy.datasets.linear_dataset(10, num_common_causes=4, num_samples=10000,
num_instruments=0... |
phanrahan/magmathon | notebooks/tutorial/coreir/Add.ipynb | mit | import ast_tools
from ast_tools.transformers.loop_unroller import unroll_for_loops
from ast_tools.passes import begin_rewrite, end_rewrite, loop_unroll
@m.circuit.combinational
def full_adder(A: m.Bit, B: m.Bit, C: m.Bit) -> (m.Bit, m.Bit):
return A ^ B ^ C, A & B | B & C | C & A # sum, carry
@m.circuit.combinat... |
Mayreck/Lawl | Mayreck_style_transfer.ipynb | gpl-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... |
google/intelligent_annotation_dialogs | exp1_fixed_strategies_IAD_prob.ipynb | apache-2.0 | import matplotlib.pyplot as plt
import numpy as np
from __future__ import division
from __future__ import print_function
import math
import gym
from gym import spaces
import pandas as pd
from sklearn import linear_model, ensemble, neural_network, model_selection, ensemble
from sklearn.neural_network import MLPClassif... |
phoebe-project/phoebe2-docs | development/examples/mesh_wd.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
"""
Explanation: Wilson-Devinney Style Meshing
NOTE: Wilson-Devinney Style meshing requires developer mode in PHOEBE and is meant to be used for testing, not used for science.
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if runn... |
tjmahr/DeepLearning | FirstSteps.ipynb | gpl-2.0 | %load_ext version_information
%version_information theano, numpy
"""
Explanation: First Steps With Theano
In which I practice some numpy and learn the boilerplate for writing Theano functions.
End of explanation
"""
import numpy as np
my_matrix = np.asarray([[1., 2], [3, 4], [5, 6]])
my_matrix
"""
Explanation: NumP... |
BrainIntensive/OnlineBrainIntensive | resources/nipype/nipype_tutorial/notebooks/basic_import_workflows.ipynb | mit | from nipype.workflows.fmri.fsl.preprocess import create_susan_smooth
smoothwf = create_susan_smooth()
"""
Explanation: Reusable workflows
Nipype doesn't just allow you to create your own workflows. It also already comes with predefined workflows, developed by the community, for the community. For a full list of all wo... |
dsblank/ProgLangBook | Chapter 05 - Interpreting Scheme in Python.ipynb | gpl-3.0 | def tokenizer(string):
"""
Takes a string and segments it into parts.
We break strings up by brackets, and whitespace.
Returns a Python list of strings.
"""
retval = []
current = ""
for i in range(len(string)):
if string[i] in ["(", "[", ")", "]"]:
if current:
... |
midnighteuler/projecteuler | src/Prob2.ipynb | gpl-2.0 | # The values of a and b:
a = (1.0 + sqrt(5.0))/2.0
b = (1.0 - sqrt(5.0))/2.0
print "a =",a, "b =",b, "\n"
def fib_ith(i):
return (a**i - b**i)/sqrt(5.0)
#for i in range(1,10):
# print fib_ith(i)
def fib_even_sum_brute(N):
s = 0
i = 3
i_f = fib_ith(i)
while i_f < N:
s += i_f
#pri... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/production_ml/labs/post_training_quant.ipynb | apache-2.0 | # Importing necessary modules
import logging
logging.getLogger("tensorflow").setLevel(logging.DEBUG)
import tensorflow as tf
from tensorflow import keras
import numpy as np
import pathlib
"""
Explanation: Post-training dynamic range quantization
Learning Objectives
1. We will learn how to train a TensorFlow model.
... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/building_production_ml_systems/solutions/3_kubeflow_pipelines.ipynb | apache-2.0 | !pip3 install --user kfp --upgrade
"""
Explanation: Kubeflow pipelines
Learning Objectives:
1. Learn how to deploy a Kubeflow cluster on GCP
1. Learn how to create a experiment in Kubeflow
1. Learn how to package you code into a Kubeflow pipeline
1. Learn how to run a Kubeflow pipeline in a repeatable and trac... |
aemerick/galaxy_analysis | physics_data/UVB/grackle_tables/photo.ipynb | mit | import h5py
from matplotlib import pyplot
%matplotlib inline
import numpy as np
LW_model = 'Qin2020'
pyplot.rcParams['figure.figsize'] = (10, 6)
pyplot.rcParams['font.size'] = 14
from make_table import k31_JW2012, k31_RFT14, k31_Qin2020
def interpvals(xtab, ytab, x, log=True):
i = np.digitize(x, xtab) - 1
i... |
linglaiyao1314/maths-with-python | 07-sympy.ipynb | mit | import sympy
sympy.init_printing()
"""
Explanation: In standard mathematics we routinely write down abstract variables or concepts and manipulate them without ever assigning specific values to them. An example would be the quadratic equation
\begin{equation}
a x^2 + b x + c = 0
\end{equation}
and its roots $x_{\pm}$... |
kimkipyo/dss_git_kkp | Python 복습/15일차.목_serialize, SQL실습/15일차_5T_데이터 분석을 위한 SQL 실습 (4) - SQL Advanced.ipynb | mit | import pymysql
db = pymysql.connect(
"db.fastcamp.us",
"root",
"dkstncks",
"sakila",
charset='utf8',
)
rental_df = pd.read_sql("SELECT * FROM rental;", db)
inventory_df = pd.read_sql("SELECT * FROM inventory;", db)
film_df = pd.read_sql("SELECT * FROM film;", db)
film_category_df = pd.read_sql("SE... |
doingmathwithpython/moremathwithpython | misc/fibonacci_miles_km/Fibonacci Miles and Kilometers.ipynb | mit |
def plot_fibo_ratio(series):
ratios = []
for i in range(len(series)-1):
ratios.append(series[i+1]/series[i])
plt.plot(ratios, 'b*')
plt.ylabel('Ratio')
plt.xlabel('No.')
def plot_km_miles_ratio(kms):
miles_km = [1.6094*km/km for km in kms]
plt.plot(miles_km, 'ro')
num = 100
series... |
peterwittek/pelican-ipynb | tests/pelican/content/with-meta-file.ipynb | apache-2.0 | a = 1
a
b = 'pew'
b
%matplotlib inline
import matplotlib.pyplot as plt
from pylab import *
x = linspace(0, 5, 10)
y = x ** 2
figure()
plot(x, y, 'r')
xlabel('x')
ylabel('y')
title('title')
show()
import numpy as np
num_points = 130
y = np.random.random(num_points)
plt.plot(y)
"""
Explanation: Lorem ipsum dol... |
mapattacker/cheatsheets | python/network_basics.ipynb | mit | import psycopg2
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
conn = psycopg2.connect(database="postgres", user="postgres", password="***", host="127.0.0.1", port="5432")
query = """SELECT fromnode, tonode, distance from edges"""
df = pd.read_sql_qu... |
superliaoyong/plist-forsource | python 第五课课件.ipynb | apache-2.0 | import pandas as pd
%matplotlib inline
"""
Explanation: 人生苦短，我用python
泰坦尼克数据处理与分析
<img src='https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1502440065892&di=51db15bf76374068735a690806ad66a2&imgtype=0&src=http%3A%2F%2Fwww.pp3.cn%2Fuploads%2F201607%2F20160708007.jpg'>
End of explanation
"""
# 如果不... |
pyrdr/charlas | intro_text_mining/notebooks/Modelado de Topicos 101.ipynb | mit | from prep import helpers
from prep import clustering
df = helpers.get_mcc_data()
df.head()[['mcc', 'irs_description','desc']]
"""
Explanation: Minería de Texto: Clustering de Códigos y Tipos de Comercio
<br/>
<div align='center'>
<img src="../images/merchant-category-code-instabill.jpg" width="640px">
</div>
Cl... |
zommiommy/FourierSeriesApproximation | FourierSeriesApproximation.ipynb | mit | import numpy as np
from scipy import linalg
"""
Explanation: Foruier Series Approximation for discrete time serires
Dependancy:
End of explanation
"""
#the function to calculate the coefficent
def approx(x, y, n, w):
x = np.matrix(x).transpose()
y = np.matrix(y).transpose()
f, b = x.shape
c, d = y.s... |
gdsfactory/gdsfactory | docs/notebooks/09_pdk_import.ipynb | mit | import gdsfactory as gf
c = gf.components.mzi()
c
"""
Explanation: Import PDK
To import a PDK in gdsfactory you need 2 things:
GDS file with all the cells that you want to import in the PDK (or separate GDS files, one per cell)
Klayout layer properties files, to define the Layers that you can use when creating new c... |
nadvamir/deep-learning | autoencoder/Convolutional_Autoencoder.ipynb | mit | %matplotlib inline
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', validation_size=0)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
"""
Explanation: C... |
zomansud/coursera | ml-regression/week-2/numpy-tutorial.ipynb | mit | import numpy as np # importing this way allows us to refer to numpy as np
"""
Explanation: Numpy Tutorial
Numpy is a computational library for Python that is optimized for operations on multi-dimensional arrays. In this notebook we will use numpy to work with 1-d arrays (often called vectors) and 2-d arrays (often cal... |
eramirem/numerical-methods-pdes | reaction-diffusion_demo.ipynb | cc0-1.0 | %matplotlib inline
import numpy
import matplotlib.pyplot as plt
import scipy.sparse as sparse
import scipy.sparse.linalg as linalg
"""
Explanation: <table>
<tr align=left><td><img align=left src="https://i.creativecommons.org/l/by/4.0/88x31.png">
<td>Text provided under a Creative Commons Attribution license, CC-BY.... |
kdestasio/online_brain_intensive | nipype_tutorial/notebooks/basic_interfaces_caching.ipynb | gpl-2.0 | from nipype.caching import Memory
mem = Memory(base_dir='/output/workingdir')
"""
Explanation: Memory caching
In Workflow notebook you learnt about Workflows that specify processing by an execution graph and offer efficient recomputing. However, sometimes you might want to use Interfaces that gives better control of t... |
mne-tools/mne-tools.github.io | 0.21/_downloads/ab8b138d73a5774a0442141d84ed9689/plot_10_epochs_overview.ipynb | bsd-3-clause | import os
import mne
"""
Explanation: The Epochs data structure: discontinuous data
This tutorial covers the basics of creating and working with :term:epoched
<epochs> data. It introduces the :class:~mne.Epochs data structure in
detail, including how to load, query, subselect, export, and plot data from an
:clas... |
tensorflow/docs-l10n | site/ja/tutorials/images/data_augmentation.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... |
brettavedisian/phys202-2015-work | days/day19/FittingModels.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy import optimize as opt
from IPython.html.widgets import interact
"""
Explanation: Fitting Models
Learning Objectives: learn to fit models to data using linear and non-linear regression.
This material is licensed under the MIT license and... |
quinngroup/bsec-2015 | BSEC2015 Thunder large-scale image SVD demo.ipynb | apache-2.0 | # IPython Notebook preliminaries.
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context("notebook")
from thunder import Colorize
image = Colorize.image
"""
Explanation: thunder-python for large-scale SVD of images
Bioimage analysis is central to researchers in biology and biomedical... |
dhimmel/obo | examples/go-obonet.ipynb | cc0-1.0 | import networkx
import obonet
"""
Explanation: obonet tutorial: import and analyze the Gene Ontology in Python
This tutotial shows:
How to read the Gene Ontology OBO export into networkx using the obonet package.
Simple tasks you can do with the networkx.MultiDiGraph data structure.
End of explanation
"""
%%time
ur... |
JKeun/project-02-watcha | 01_crawling/01_watcha_api_structure.ipynb | mit | import requests
import json
import pandas as pd
response = requests.get("https://watcha.net/v2/users/jXaIHl0ZtdYZ/movies.json?filter%5Bsorting%5D=time&page=1")
watcha_dict = json.loads(response.text)
watcha_dict
watcha_dict.keys()
"""
Explanation: 왓챠 json구조 분석
뽑아야 할 feature들
End of explanation
"""
watcha_list = ... |
castelao/CoTeDe | docs/notebooks/profile_CTD.ipynb | bsd-3-clause | from bokeh.io import output_notebook, show
from bokeh.layouts import row
from bokeh.plotting import figure
import numpy as np
import cotede
from cotede import datasets, qctests
output_notebook()
"""
Explanation: [WIP] Quality Controlling a CTD profile
Quality control of a shipboard CTD profile (Temperature & Salinit... |
daniestevez/jupyter_notebooks | dslwp/DSLWP-B deorbit.ipynb | gpl-3.0 | utc = 0
sma = 1
ecc = 2
inc = 3
raan = 4
aop = 5
ma = 6
ta = 7
"""
Explanation: Keys for each of the columns in the orbit (Keplerian state) report.
End of explanation
"""
#fig1 = plt.figure(figsize = [15,8], facecolor='w')
fig_peri = plt.figure(figsize = [15,8], facecolor='w')
fig_peri_deorbit = plt.figure(figsize =... |
GoogleCloudPlatform/training-data-analyst | blogs/babyweight_keras/babyweight.ipynb | apache-2.0 | !pip install tensorflow==2.2
"""
Explanation: Structured data prediction using Cloud ML Engine
This notebook illustrates:
Exploring a BigQuery dataset using JupyterLab
Creating datasets for Machine Learning using Dataflow
Creating a model using the feature columns and Keras API
Training on Cloud AI Platform
Deploying... |
turbomanage/training-data-analyst | courses/fast-and-lean-data-science/02_Dataset_playground.ipynb | apache-2.0 | import os, sys, math
import numpy as np
from matplotlib import pyplot as plt
if 'google.colab' in sys.modules: # Colab-only Tensorflow version selector
%tensorflow_version 2.x
import tensorflow as tf
print("Tensorflow version " + tf.__version__)
#@title "display utilities [RUN ME]"
def display_9_images_from_dataset(... |
mcc-petrinets/formulas | spot/tests/python/word.ipynb | mit | aut = spot.translate('!a & G(Fa <-> XXb)'); aut
"""
Explanation: Let's build a small automaton to use as example.
End of explanation
"""
run = aut.accepting_run(); run
"""
Explanation: Build an accepting run:
End of explanation
"""
print(spot.bdd_format_formula(aut.get_dict(), run.prefix[0].label))
print(run.cycl... |
abhi1509/deep-learning | sentiment-tflearn/Sentiment Analysis with TFLearn.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... |
AlCap23/Thesis | Python/FOTD-Design.ipynb | gpl-3.0 | # Import the needed packages, SymPy
import sympy as sp
from sympy import init_printing
init_printing()
# Define the variables
# Complex variable
s = sp.symbols('s')
# FOTD Coeffficients
T1,T2,T3,T4 = sp.symbols('T_1 T_2 T_3 T_4')
K1,K2,K3,K4 = sp.symbols('K_1 K_2 K_3 K_4')
# Time Delay Coefficients
L1,L2,L3,L4 = sp.sy... |
d00d/quantNotebooks | Notebooks/quantopian_research_public/notebooks/tutorials/2_pipeline_lesson8/notebook.ipynb | unlicense | from quantopian.pipeline.data import morningstar
# Since the underlying data of morningstar.share_class_reference.exchange_id
# is of type string, .latest returns a Classifier
exchange = morningstar.share_class_reference.exchange_id.latest
"""
Explanation: Classifiers
A classifier is a function from an asset and a mo... |
corochann/deep-learning-tutorial-with-chainer | src/01_chainer_intro/chainer_module1.ipynb | mit | # Initial setup following
import numpy as np
import chainer
from chainer import cuda, Function, gradient_check, report, training, utils, Variable
from chainer import datasets, iterators, optimizers, serializers
from chainer import Link, Chain, ChainList
import chainer.functions as F
import chainer.links as L
from chai... |
pbeens/ICS-Computer-Studies | Coursera_Introduction_to_Data_Science_with_Python.ipynb | mit | # Jupyter has magic commands!
import numpy as np
%timeit a = np.random.randn(100, 100)
# magic commands can be assigned to variables
foo = %pwd
print(f'pwd: {foo}')
"""
Explanation: <a href="https://colab.research.google.com/github/pbeens/ICS-Computer-Studies/blob/master/Coursera_Introduction_to_Data_Science_with_Pyt... |
poppy-project/community-notebooks | tutorials-education/poppy_ergo_jr__decouverte_du_robot/Poppy-Ergo-Jr DECOUVRIR VOTRE ROBOT.ipynb | lgpl-3.0 | %pylab inline
from __future__ import print_function
"""
Explanation: Traduction du notebook Discover your Poppy Ergo Jr par Georges Saliba sous licence CC BY SA
Découvrir votre Poppy Ergo Jr
Ce notebook qui permet à la fois d'insérer du code pour faire fonctionner le robot et de le commenter dans le même temps, va ... |
tensorflow/docs | site/en/guide/tensor.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... |
liufuyang/deep_learning_tutorial | course-deeplearning.ai/course5-rnn/Week 1/Jazz improvisation with LSTM/Improvise a Jazz Solo with an LSTM Network - v2.ipynb | mit | from __future__ import print_function
import IPython
import sys
from music21 import *
import numpy as np
from grammar import *
from qa import *
from preprocess import *
from music_utils import *
from data_utils import *
from keras.models import load_model, Model
from keras.layers import Dense, Activation, Dropout, Inp... |
meli-lewis/pycaribbean2016 | pycaribbean.ipynb | unlicense | import numpy as np
import pandas as pd
from IPython.display import display, Image, YouTubeVideo
%matplotlib inline
"""
Explanation: Introduction to data cleaning with Jupyter and pandas
Meli Lewis, data engineer at Simple
melidata.com
End of explanation
"""
?pd.read_csv
"""
Explanation: The case for open source d... |
dennissergeev/classcode | notebooks/01_MODIS_L1B.ipynb | cc0-1.0 | __author__ = 'ATSC-301 UBC'
"""
Explanation: Objectives
The objectives of this notebook is to provide examples about working with AQUA MODIS L1B files, including:
Convert HDF4 format data to HDF5 format;
Read *.HDF5 data through "h5py" module;
Correct the raw data by using scale_factor and offset
Reproject sinusoida... |
HsKA-ThermalFluiddynamics/NSS-1 | NumPyTutorial.ipynb | mit | from __future__ import print_function
from numpy import *
from matplotlib.pylab import *
%pylab --no-import-all inline
"""
Explanation: NumPy and Matplotlib Tutorial
End of explanation
"""
a1 = array([1.0, 2.0, 3.0])
a2 = arange(1.0, 5.0, 0.5)
a3 = linspace(1.0, 10.0, 17)
print(a1)
print(a2)
print(a3)
m1 = array([[... |
vberlier/nbtlib | docs/Usage.ipynb | mit | import nbtlib
nbt_file = nbtlib.load('nbt_files/bigtest.nbt')
nbt_file['stringTest']
"""
Explanation: Using nbtlib
The Named Binary Tag (NBT) file format is a simple structured binary format that is mainly used by the game Minecraft (see the official specification for more details). This short documentation will show... |
YaleATLAS/CERNDeepLearningTutorial | deeplearning_intro.ipynb | mit | import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
%matplotlib inline
"""
Explanation: <center>
<h1>Introduction to Deep Learning with Keras</h1>
<h3>Michela Paganini - Yale University</h3>
<h4>High Energy Phenomenology, Experim... |
Bedrock-py/bedrock-core | examples/RAND2011study/CooperationAnalysis.ipynb | lgpl-3.0 | from bedrock.client.client import BedrockAPI
"""
Explanation: Rand 2011 Cooperation Study
This notebook outlines how to recreate the analysis of the Rand et al. 2011 study "Dynamic social networks promote cooperation in experiments with humans" Link to Paper
This outlines the steps to re-create the analysis using the ... |
VVard0g/ThreatHunter-Playbook | docs/notebooks/windows/08_lateral_movement/WIN-190815181010.ipynb | mit | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: Remote Service creation
Metadata
| Metadata | Value |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2019/08/15 |
| modification date | 2020/09/20 |
| playbook related | ['WIN-... |
quantumlib/Cirq | docs/tutorials/azure-quantum/getting_started_ionq.ipynb | apache-2.0 | !pip install azure-quantum[cirq] --quiet
"""
Explanation: Getting started with IonQ and Cirq on Azure Quantum
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://quantumai.google/cirq/tutorials/azure-quantum/getting_started_ionq"><img src="https://quantumai.google/site-assets/... |
brettavedisian/Liquid-Crystals-Summer-2015 | Smectic/SmAtoSmC.ipynb | mit | def f1_numpy(r, u, c):
return (r*c**2)/2 + (u*c**4)/4
n = 2
T = np.linspace(-n,n,101)
"""
Explanation: u > 0
End of explanation
"""
fig1 = plt.figure(figsize=(11,8))
ax1 = fig1.gca()
plt.plot(T, f1_numpy(1, 1, T))
plt.xlabel('c', fontsize=14)
plt.ylabel('f', rotation='horizontal',verticalalignment='center', f... |
CoreSecurity/pysap | docs/fileformats/SAPCAR.ipynb | gpl-2.0 | from pysap.SAPCAR import *
from IPython.display import display
"""
Explanation: SAP CAR
The following subsections show a graphical representation of the file format portions and how to generate them.
First we need to perform some setup to import the packet classes:
End of explanation
"""
with open("some_file", "w") ... |
ddtm/dl-course | Seminar4/bonus/Bonus-advanced-theano.ipynb | mit | import numpy as np
def sum_squares(N):
return <student.Implement_me()>
%%time
sum_squares(10**8)
"""
Explanation: Theano, Lasagne
and why they matter
got no lasagne?
Install the bleeding edge version from here: http://lasagne.readthedocs.org/en/latest/user/installation.html
Warming up
Implement a function that c... |
kalfasyan/Data_Science_playground | imdb_best_actordirector.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
%matplotlib inline
"""
Explanation: We will discover the best actor/director according to imdb ratings
First we import all the necessary libraries to process and plot data.
End of explanation
"""
df = pd.read_csv('movie_m... |
intel-analytics/analytics-zoo | pyzoo/zoo/chronos/use-case/network_traffic/network_traffic_autots_forecasting_deprecated.ipynb | apache-2.0 | def get_drop_dates_and_len(df, allow_missing_num=3):
"""
Find missing values and get records to drop
"""
missing_num = df.total.isnull().astype(int).groupby(df.total.notnull().astype(int).cumsum()).sum()
drop_missing_num = missing_num[missing_num > allow_missing_num]
drop_datetimes = df.iloc[dro... |
drvinceknight/cfm | assets/assessment/mock/main.ipynb | mit | ### BEGIN SOLUTION
import sympy as sym
a, b, c = sym.Symbol("a"), sym.Symbol("b"), sym.Symbol("c")
sym.expand((9 * a ** 2 * b * c ** 4) ** (sym.S(1) / 2) / (6 * a * b ** (sym.S(3) / 2) * c))
### END SOLUTION
q1_a_answer = _
feedback_text = """Your output is not a symbolic expression.
You are expected to use sympy fo... |
AlCap23/Thesis | Python/SOTD-Design.ipynb | gpl-3.0 | # Import the needed packages, SymPy
import sympy as sp
from sympy import init_printing
init_printing()
# Define the variables
# Complex variable
s = sp.symbols('s')
# FOTD Coeffficients
T11,T21,T31,T41 = sp.symbols('T_{11} T_{21} T_{31} T_{41}')
T12,T22,T32,T42 = sp.symbols('T_{12} T_{22} T_{32} T_{42}')
K1,K2,K3,K4 =... |
iancze/Starfish | examples/setup.ipynb | bsd-3-clause | import numpy as np
from Starfish.grid_tools import download_PHOENIX_models
ranges = [[5700, 8600], [4.0, 6.0], [-0.5, 0.5]] # T, logg, Z
download_PHOENIX_models(path="PHOENIX", ranges=ranges)
"""
Explanation: Setup
Here I will go over setting up our interfaces and emulators from a raw spectral library to prepare u... |
chbrandt/pynotes | SS82_filtering/SS82_first_observations_filtering.ipynb | gpl-2.0 | from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_toggle()"><input type="submit" value="Click here... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_time_frequency_global_field_power.ipynb | bsd-3-clause | # Authors: Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import somato
from mne.baseline import rescale
from mne.stats import _bootstrap_ci
"""
Explanation: Explore event-related dynamics for specific frequency... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_epoching_and_averaging.ipynb | bsd-3-clause | import os.path as op
import numpy as np
import mne
"""
Explanation: Epoching and averaging (ERP/ERF)
End of explanation
"""
data_path = mne.datasets.sample.data_path()
fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(fname)
raw.set_eeg_reference() # set EEG average ref... |
aryarohit07/machine-learning-with-python | linear_regression/linear_regression_gradient_descent_with_one_variable.ipynb | mit | import pandas as pd
import matplotlib.pyplot as plt
#shape = (97, 2)
data = pd.read_csv('ex1data1.txt', header=None)
plt.scatter(data[0], data[1])
plt.xlabel('population')
plt.ylabel('profit')
plt.close()
"""
Explanation: The first column is the population of a city and the second column is the profit of a food truc... |
mne-tools/mne-tools.github.io | 0.23/_downloads/1242d47b65d952f9f80cf19fb9e5d76e/35_eeg_no_mri.ipynb | bsd-3-clause | import os.path as op
import numpy as np
import mne
from mne.datasets import eegbci
from mne.datasets import fetch_fsaverage
# Download fsaverage files
fs_dir = fetch_fsaverage(verbose=True)
subjects_dir = op.dirname(fs_dir)
# The files live in:
subject = 'fsaverage'
trans = 'fsaverage' # MNE has a built-in fsaverag... |
CPernet/LanguageDecision | notebooks/individuals/pilot.ipynb | gpl-3.0 | # Environment setup
%matplotlib inline
%cd /lang_dec
# Imports
import warnings; warnings.filterwarnings('ignore')
import hddm
import numpy as np
import matplotlib.pyplot as plt
from utils import model_tools, signal_detection
# Import pilot models
pilot_data = hddm.load_csv('/lang_dec/data/pilot_clean.csv')
pilot_model... |
cliburn/sta-663-2017 | exams/Midterm Exams2.ipynb | mit | heart = sm.datasets.heart.load_pandas().data
heart.head(n=6)
"""
Explanation: Q1 (10 points)
The heart dataframe contains the survival time after receiving a heart transplant, the age of the patient and whether or not the survival time was censored
Number of Observations - 69
Number of Variables - 3
Variable name de... |
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