repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
psiq/gdsfactory | notebooks/02_components.ipynb | mit | import pp
c = pp.c.mzi()
pp.qp(c)
c.ports
c = pp.c.ring_single_bus()
pp.qp(c)
"""
Explanation: Components
We store our component functions inside the pp.components module. Each
function there returns a Component object
You can use dir or help over the pp.c module to see the all available
components.
Some of which a... |
phuongxuanpham/SelfDrivingCar | CarND-Camera-Calibration/camera_calibration.ipynb | gpl-3.0 | import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
%matplotlib qt
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*8,3), np.float32)
objp[:,:2] = np.mgrid[0:8, 0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpo... |
philmui/datascience2016fall | lecture04.data.wrangling/ch06.ipynb | mit | from __future__ import division
from numpy.random import randn
import numpy as np
import os
import sys
import matplotlib.pyplot as plt
np.random.seed(12345)
plt.rc('figure', figsize=(10, 6))
from pandas import Series, DataFrame
import pandas as pd
np.set_printoptions(precision=4)
%pwd
"""
Explanation: Data loading, s... |
0x4a50/udacity-0x4a50-deep-learning-nanodegree | image-classification/dlnd_image_classification.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifar10... |
mne-tools/mne-tools.github.io | 0.23/_downloads/84d68dbced84793d122fec3a2cf0cde5/source_power_spectrum.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, compute_source_psd
print(__doc__)
"""
Explanation: Compute source power spectral d... |
ctroupin/OceanData_NoteBooks | PythonNotebooks/PlatformPlots/Read_drifter_data_3.ipynb | gpl-3.0 | year = 2015
month = 7
%matplotlib inline
import glob
import os
import netCDF4
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from matplotlib import colors
"""
Explanation: Now that we know how to read the data, we will add some processing to generate a gridded field.<br /... |
siva82kb/siva82kb.github.io | .old/notebooks/2018-09-10-Least-Square-Estimation-of-AR-Models-And-Whitening-part-ii.ipynb | gpl-2.0 | # AR parameters
p = 4
a = 1.0 * np.random.rand(p) - 0.5
print "Original AR parameters:\n", a
# Time series data
N = 1000
n = np.arange(0, N)
# Input white noise
eparam = (0, 1.0)
e = np.sqrt(eparam[1]) * np.random.randn(N) + eparam[0]
# Generate AR time series.
y = genARProcess(p, a, eparam, e)
"""
Explanation: Lea... |
mas-dse-greina/neon | dicom/Sanity Check HDF5 DICOM.ipynb | apache-2.0 | from neon.data import HDF5Iterator # Neon's HDF5 data loader
from neon.backends import gen_backend
be = gen_backend(backend='cpu', batch_size=1)
outFilename = 'dicom_out.h5' # The name of our HDF5 data file
train_set = HDF5Iterator(outFilename)
train_set.get_description()
train_set.ndata # Number of patients... |
dimonaks/siman | tutorials/surface_MC.ipynb | gpl-2.0 | from siman import header
from siman.calc_manage import add, res, smart_structure_read
from siman.geo import create_surface2, ortho_vec, create_supercell
from siman.header import db
from siman.analysis import suf_en
from siman.classes import CalculationVasp
from siman.set_functions import read_vasp_sets
header.PATH2PO... |
agushman/coursera | src/cours_2/week_4/bagging_and_rand_forest.ipynb | mit | from sklearn import datasets, tree, cross_validation, ensemble
from sklearn.model_selection import cross_val_score
from math import sqrt
import numpy as np
digits = datasets.load_digits()
X = digits.data
y = digits.target
def write_answer(cross_val_score, file_name):
with open(file_name, "w") as fout:
... |
materialsvirtuallab/matgenb | notebooks/2017-09-03-Analyze and plot band structures.ipynb | bsd-3-clause | from pymatgen.ext.matproj import MPRester
from pymatgen.electronic_structure.core import Spin
#This initiliazes the Rest connection to the Materials Project db. Put your own API key if needed.
a = MPRester()
#load the band structure from mp-3748, CuAlO2 from the MP db
bs = a.get_bandstructure_by_material_id("mp-3748")
... |
tpin3694/tpin3694.github.io | machine-learning/find_nearest_neighbors.ipynb | mit | # Load libraries
from sklearn.neighbors import NearestNeighbors
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
import numpy as np
"""
Explanation: Title: Find Nearest Neighbors
Slug: find_nearest_neighbors
Summary: Find a observation's nearest neighbors in scikit-learn.
Date: 2017-09-19 ... |
amueller/scipy-2017-sklearn | notebooks/23.Out-of-core_Learning_Large_Scale_Text_Classification.ipynb | cc0-1.0 | from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(min_df=1)
vectorizer.fit([
"The cat sat on the mat.",
])
vectorizer.vocabulary_
"""
Explanation: Out-of-core Learning - Large Scale Text Classification for Sentiment Analysis
Scalability Issues
The sklearn.feature_extraction... |
root-mirror/training | SummerStudentCourse/2019/Exercises/ROOTBooks/FittingExample.ipynb | gpl-2.0 | import ROOT
"""
Explanation: Fitting Example
End of explanation
"""
%%cpp -d
//Define functions for fitting
// Quadratic background function
double background(double *x, double *par) {
return par[0] + par[1]*x[0] + par[2]*x[0]*x[0];
}
// Lorenzian Peak function
double lorentzianPeak(double *x, double *par) {
r... |
jasag/Phytoliths-recognition-system | code/notebooks/Prototypes/Classifiers_and_HoG/4_Test.ipynb | bsd-3-clause | #Imports
%matplotlib inline
import matplotlib.pyplot as plt
from skimage.exposure import rescale_intensity
from skimage import io, feature
from skimage.transform import rescale
from skimage.color import rgb2gray
import windows
"""
Explanation: Probando con nuevas imágenes
Realizamos los imports correspondientes
End of... |
evanmiltenburg/python-for-text-analysis | Assignments-colab/ASSIGNMENT_4b_BA.ipynb | apache-2.0 | %%capture
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Data.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/images.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Extra_Material.zip
!unzip Data.zip -d ../
!unzip images.zip -d ./
!unzip Ext... |
mtat76/atm-py | examples/instruments_POPS_calibration.ipynb | mit | data = """130 1.73
140 1.85
150 1.99
173 2.28
200 2.53
233 2.77
270 2.96
315 3.04
365 3.165
420 3.213
490 3.355
570 3.56
660 3.72
770 3.795
890 3.86
1040 4.0
1200 4.05
1400 4.1
1600 4.18
1880 4.32
2180 4.44
2500 4.5"""
cal = calibration.read_str(data)
"""
Explanation: How to use it
Read and save Data
This section sho... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_dics_beamformer.ipynb | bsd-3-clause | # Author: Roman Goj <roman.goj@gmail.com>
#
# License: BSD (3-clause)
import mne
import matplotlib.pyplot as plt
import numpy as np
from mne.datasets import sample
from mne.time_frequency import csd_epochs
from mne.beamformer import dics
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path + '/MEG/s... |
dsacademybr/PythonFundamentos | Cap06/Notebooks/DSA-Python-Cap06-09-Mini-Projeto1.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 6</font>
Download: http://github.com/dsacademybr
Mini-Projeto 1 - Guia de Análise... |
tensorflow/docs-l10n | site/ko/tutorials/distribute/input.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... |
jakobkolb/MayaSim | Evaluation_Notebooks/X8_long_term_dynamics.ipynb | gpl-3.0 | %pylab inline
pylab.rcParams['figure.figsize'] = (14, 6)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
d_start = 150
d_length = 20
testing = False
location = '/home/jakob/Project_MayaSim/Python/output_data/X8_longterm_dynamics/results/trajectory'
if testing:
loc... |
AaronCWong/phys202-2015-work | assignments/midterm/AlgorithmsEx03.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact
"""
Explanation: Algorithms Exercise 3
Imports
End of explanation
"""
def char_probs(s):
l = len(s)
dic = {l}
for i in l:
prob = i/l
"""Find the probabilitie... |
min2bro/WebScraping | .ipynb_checkpoints/WebScrapingnDataAnalysis-checkpoint.ipynb | mit | %matplotlib inline
from selenium import webdriver
import time,re,json,numpy as np
import pandas as pd
from collections import defaultdict,Counter
import matplotlib.pyplot as plt
"""
Explanation: Web Scraping & Data Analysis with Selenium and Python
Vinay Babu
Github: https://github.com/min2bro/WebScraping/blob/master... |
Elixeus/NLP | deep_learning/word2vec.ipynb | mit | import numpy as np
import tensorflow as tf
"""
Explanation: I followed along Aneesh Joshi's blog post on word2vec in this notebook.
End of explanation
"""
# load data
with open('darksouls_training.txt', 'r') as fh:
training = [sent.replace('.','').replace('\n', '').lower() for sent in fh.readlines()]
# with open... |
linglaiyao1314/maths-with-python | 02-programs.ipynb | mit | import math
x = math.sin(1.2)
"""
Explanation: Programs
Using the Python console to type in commands works fine, but has serious drawbacks. It doesn't save the work for the future. It doesn't allow the work to be re-used. It's frustrating to edit when you make a mistake, or want to make a small change. Instead, we wan... |
JAmarel/QLab | ZeemanEffect/DataAnalysis.ipynb | mit | Currents = np.array([0, 0.49, 0.98, 1.5, 1.98, 2.48, 2.99, 3.61, 3.97, 4.48, 5.02, 5.49, 6.04, 6.44, 6.98, 7.46, 8.02]) #Amps
Fields = np.array([0.3, 149, 302.3, 470.4, 623.2, 789, 949.2, 1143.5,
1245.8, 1408.2, 1571.5, 1679, 1802.8, 1879.4, 1963.5, 2028.2, 2096.2]) #Gauss
"""
Explanation: Determin... |
tiagotaveiragomes/ode | Ordinary Differential Equations.ipynb | apache-2.0 | def rungekutta(fn, y0, ti=0, tf=10, h=0.01):
h = np.float(h)
x = np.arange(ti, tf, h)
Y = np.zeros((len(x), len(y0)))
Y[0] = y0
for i in range(0, len(x)-1):
yi = Y[i]
xi = x[i]
k1 = h * fn(xi, yi)
k2 = h * fn(xi + 0.5 * h, yi + 0.5 * k1)
... |
zchq88/MyUdacityDeepLearningProject | 3/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... |
perwin/s4g_barfractions | s4gbars_main.ipynb | bsd-3-clause | import numpy as np
import scipy
import datautils as du
import plotutils as pu
import s4gutils
# paths for locating data, saving plots, etc.
dataDir = "./data/"
simDir = dataDir
fbarLitDir = dataDir + "f_bar_trends-from-literature/"
# change the following if you want to save the figures somewhere convenient
baseDir = ... |
maartenbreddels/vaex | docs/source/example_graphql.ipynb | mit | import vaex.ml
df = vaex.ml.datasets.load_titanic()
df
result = df.graphql.execute("""
{
df {
min {
age
fare
}
mean {
age
fare
}
max {
age
fare
... |
dsager/research | projects/keyword-corpus/Keyword corpus for company names.ipynb | mit | import os
from sqlalchemy import create_engine
host = os.environ.get('KW_DB_HOST', 'localhost')
user = os.environ.get('KW_DB_USER', 'me')
password = os.environ.get('KW_DB_PASS', 'somepassword')
engine = create_engine('postgresql://{}:{}@{}/neo_production'.format(user, password, host))
"""
Explanation: Keyword corpus... |
ES-DOC/esdoc-jupyterhub | notebooks/nerc/cmip6/models/ukesm1-0-ll/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'ukesm1-0-ll', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: NERC
Source ID: UKESM1-0-LL
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, ... |
BrainIntensive/OnlineBrainIntensive | resources/matplotlib/AnatomyOfMatPlotLib/AnatomyOfMatplotlib-Part0-Intro2NumPy.ipynb | mit | import numpy as np
"""
Explanation: NumPy
The NumPy package provides the "ndarray" object. The NumPy array is used to contain data of uniform type with an arbitrary number of dimensions. NumPy then provides basic mathematical and array methods to lay down the foundation for the entire SciPy ecosystem. The following im... |
ML4DS/ML4all | TM3.Topic_Models_with_MLlib/ExB3_TopicModels/.ipynb_checkpoints/TM_Exam_Solution-checkpoint.ipynb | mit | %matplotlib inline
import nltk
import time
import matplotlib.pyplot as plt
import pylab
# import nltk
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from nltk.corpus import stopwords
#from test_helper import Test
import collections
from pyspark.mllib.clustering import LDA, LDAMode... |
kidpixo/multibinner | examples/iris_multibinner.ipynb | mit | import matplotlib
from matplotlib import pyplot as plt
%matplotlib inline
import numpy as np
import pandas as pd
import collections
import multibinner as mb
"""
Explanation: Multibinner
The problem I want to solve is:
I have a huge pandas.DataFrame and I want to rebin some columns and get the oupt of custom function... |
robertoalotufo/ia898 | src/gshow.ipynb | mit | import numpy as np
def gshow(f, X1=None, X2=None, X3=None, X4=None, X5=None, X6=None):
if f.dtype == np.bool:
f = np.where(f,255,0).astype('uint8')
r = f
g = f
b = f
if X1 is not None: # red 1 0 0
if (X1.dtype != np.bool):
raise Exception('X1 must be bina... |
musketeer191/job_analytics | preprocess.ipynb | gpl-3.0 | import my_util as my_util; from my_util import *
"""
Explanation: Preparations
Import libraries:
End of explanation
"""
HOME_DIR = 'd:/larc_projects/job_analytics/'
DATA_DIR = HOME_DIR + 'data/clean/'
# job descriptions (JDs)
init_posts = pd.read_csv(DATA_DIR + 'jd_df.csv')
skill_df = pd.read_csv(DATA_DIR + 'skil... |
igmhub/pyLyA | tutorials/QA_plots.ipynb | gpl-3.0 | import os
import glob
import h5py
import fitsio
import healpy
import matplotlib.pyplot as plt
from matplotlib.pyplot import rcParams
import scipy as sp
import scipy.stats
from scipy.interpolate import interp1d
from picca import wedgize
%matplotlib inline
rcParams['figure.figsize'] = 10, 6
rcParams['lines.linewidth'] ... |
ageron/ml-notebooks | 07_ensemble_learning_and_random_forests.ipynb | apache-2.0 | # To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot ... |
sytays/openanalysis | doc/Langauge/02-Formatting Output.ipynb | gpl-3.0 | '8 = %d , 8.5 = %.1f, name = %s, 3 = %04d' % (8, 8.5, 'Ravi', 3)
"""
Explanation: Formatting Output
In C Language, we had printf() function to display a formatted string to stdout. We also had format specifiers like %s for string, %d for integers and so on...
In Python, there are many ways to format a string, We shall... |
tuanavu/python-cookbook-3rd | notebooks/ch01/02_unpacking_elements_from_iterables_of_arbitrary_length.ipynb | mit | record = ('Dave', 'dave@example.com', '773-555-1212', '847-555-1212')
name, email, *phone_numbers = record
print (name)
print (email)
print (phone_numbers)
"""
Explanation: Unpacking Elements from Iterables of Arbitrary Length
Problem
You need to unpack N elements from an iterable, but the iterable may be longer tha... |
mrcslws/nupic.research | projects/archive/dynamic_sparse/notebooks/ExperimentAnalysis-ReplicateHSD-8x.ipynb | agpl-3.0 | %load_ext autoreload
%autoreload 2
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import tabulate
import pprint
import click
import numpy as np
import pandas as pd
from ray.tune.commands import *
from nupic.research.frameworks.dynamic... |
yevheniyc/C | 1t_DataAnalysisMLPython/1j_ML/DS_ML_Py_SBO/DataScience/1_Mean_Midean_Mode/MeanMedianMode.ipynb | mit | import numpy as np
incomes = np.random.normal(27000, 15000, 10000) # (center around, stdev, population )
np.mean(incomes) # calculate mean
"""
Explanation: Mean, Median, Mode, and introducing NumPy
Mean vs. Median
Let's create some fake income data, centered around 27,000 with a normal distribution and standard devia... |
davidzchen/tensorflow | tensorflow/lite/g3doc/performance/post_training_quant.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... |
ES-DOC/esdoc-jupyterhub | notebooks/cmcc/cmip6/models/sandbox-2/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cmcc', 'sandbox-2', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: CMCC
Source ID: SANDBOX-2
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radi... |
GoogleCloudPlatform/data-analytics-golden-demo | notebooks/BigQuery-Demo-Notebook.ipynb | apache-2.0 | from google.cloud import bigquery
import pandas
client = bigquery.Client()
"""
Explanation: Use Cases:
- Data Scientists need access to data in your data warehouse. BigQuery makes this extremely easy.
Description:
- BigQuery provides a magic keyword ```%%bigquery``` used for executing queries to view the results or ... |
madmax983/h2o-3 | h2o-py/demos/turbofan_phm_gtkerror_NOPASS.ipynb | apache-2.0 | import sys
import h2o
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from h2o.h2o import _locate
import numpy as np
import pandas as pd
import seaborn as sns
import pykalman as pyk
sns.set()
doGridSearch = True
doKalmanSmoothing = False #unrel... |
jvitria/DeepLearningBBVA2016 | 1. Basic Concepts I.ipynb | mit | import warnings
warnings.filterwarnings('ignore')
import numpy as np
import seaborn as sns
import bokeh.plotting as bp
import matplotlib.pyplot as plt
from sklearn.datasets.samples_generator import make_regression
from scipy import stats
from bokeh.models import WheelZoomTool, ResetTool, PanTool
%matplotlib inline
... |
sudhanshuptl/Machine-Learning | Kaggle/Titanic/Life_Predictor.ipynb | gpl-2.0 | import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import normalize
import random
test=pd.read_csv("test.csv")
test.head()
mData=pd.read_csv("train.csv")
mData.head()
mData = mData.drop(["PassengerId","Name"... |
drericstrong/Blog | 20161207_AbalonePart1.ipynb | agpl-3.0 | import pandas as pd
import numpy as np
import seaborn as sns
from scipy import stats
import matplotlib.pyplot as plt
from sklearn import linear_model
from sklearn.metrics import r2_score, mean_absolute_error
from sklearn.model_selection import train_test_split
%matplotlib inline
#Load the data from the CSV file
abalone... |
mdiaz236/DeepLearningFoundations | gan_mnist/Intro_to_GANs_Exercises.ipynb | mit | %matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
whitead/numerical_stats | unit_11/hw_2018/Homework_11_Key.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Homework 11 Key
CHE 116: Numerical Methods and Statistics
4/12/2018
Homework Requirements:
Write all equations in $\LaTeX$
Simplify all expressions
Put comments in your Python code
Explain or show your... |
noammor/noammor.github.io | ipy/str-join-benchmark.ipynb | apache-2.0 | from time import time
import numpy as np
import matplotlib.pyplot as plt
from collections import deque
import random
%matplotlib inline
def benchmark(counts):
def times(f):
def ret():
timings = []
for c in counts:
start = time()
f(c)
t... |
no-fire/line-follower | line-follower/src/v1_recurrent_postprocessing/LSTM Postprocessor v1.ipynb | mit | #Create references to important directories we will use over and over
import os, sys
DATA_HOME_DIR = '/home/nathan/olin/spring2017/line-follower/line-follower/data'
#import modules
import numpy as np
from glob import glob
from PIL import Image
from tqdm import tqdm
import bcolz
from matplotlib import pyplot as plt
im... |
boffi/boffi.github.io | dati_2018/wt03/Continuous.ipynb | mit | from sympy import *
init_printing(use_latex=True)
from IPython.display import Latex
%matplotlib inline
"""
Explanation: Continuous System
Imports, stuff
End of explanation
"""
x, w2 = symbols('x omega^2')
L, m, EJ = symbols('L m EJ', positive = True)
A, B, C, D, ld, LD = symbols('A B C D lambda Lambda')
f, φ = symbo... |
sripaladugu/sripaladugu.github.io | ipynb/OOP Concepts.ipynb | mit | class Employee:
emp_count = 0 # Class Variable
company = 'Google' # Class Variable
def __init__(self, fname, lname):
self.fname = fname
self.lname = lname
self.email = self.fname + '.' + self.lname + '@' + self.company + '.com'
Employee.emp_count += 1
def get_fu... |
statkraft/shyft-doc | notebooks/grid-pp/gridpp_simple.ipynb | lgpl-3.0 | # first you should import the third-party python modules which you'll use later on
# the first line enables that figures are shown inline, directly in the notebook
%pylab inline
import os
from os import path
import sys
from matplotlib import pyplot as plt
# once the shyft_path is set correctly, you should be able to i... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch6-Problem_6-04.ipynb | unlicense | %pylab notebook
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 6
Problem 6-4
End of explanation
"""
fse = 50.0 # [Hz]
p = 2
Pout = 50e3 # [W]
s = 5e-2
Pfw = 700.0 # [W]
Pcore = 600.0 # [W]
Pmisc = 0.0 # [W]
"""
Explanation: Description
A 50-kW, 460-V, 50-Hz, two-pole induction ... |
DB2-Samples/db2jupyter | Db2 Jupyter Macros.ipynb | apache-2.0 | %run db2.ipynb
"""
Explanation: Db2 Macros
The %sql command also allows the use of macros. Macros are used to substitute text into SQL commands that you execute. Macros substitution is done before any SQL is executed. This allows you to create macros that include commonly used SQL commands or parameters rather than ha... |
lilleswing/deepchem | examples/tutorials/01_The_Basic_Tools_of_the_Deep_Life_Sciences.ipynb | mit | !curl -Lo conda_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py
import conda_installer
conda_installer.install()
!/root/miniconda/bin/conda info -e
!pip install --pre deepchem
"""
Explanation: Tutorial 1: The Basic Tools of the Deep Life Sciences
Welcome to DeepChem's ... |
phoebe-project/phoebe2-docs | development/tutorials/t0s.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
"""
Explanation: Various t0s
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
"""
import phoebe
from phoebe import u # units
import numpy as np
import mat... |
joetrollo/thinkful-library | linear-regression/workshop notes.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
import matplotlib
from matplotlib import pyplot as plt
%matplotlib inline
"""
Explanation: This workshop walks through the assignment for the accompanying course, Linear Regression and a Space Scam, focusing on the background behind linear regression and performing hypothesis tes... |
PMEAL/OpenPNM | examples/reference/data_management/using_and_creating_labels.ipynb | mit | import openpnm as op
%config InlineBackend.figure_formats = ['svg']
import numpy as np
"""
Explanation: Using and Creating Labels
Labels are a way to 'bookmark' certain pores for easier lookup later, such as specifying boundary conditions. When networks are generated they include a set of relevent labels that have be... |
mne-tools/mne-tools.github.io | 0.20/_downloads/e1c688e612e75986f5dc98445428685b/plot_mne_inverse_coherence_epochs.ipynb | bsd-3-clause | # Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import (apply_inverse, apply_inverse_epochs,
read_inverse_operator)
from mne.connectivity import seed_target_indices, spec... |
SBRG/ssbio | docs/notebooks/GEM-PRO - Calculating Protein Properties.ipynb | mit | import sys
import logging
# Import the GEM-PRO class
from ssbio.pipeline.gempro import GEMPRO
# Printing multiple outputs per cell
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
"""
Explanation: GEM-PRO - Calculating Protein Properties
This notebook gives a... |
TeamHG-Memex/eli5 | notebooks/explain_text_prediction.ipynb | mit | show_html_expl(explain_prediction(clf, test['data'][2], vec, target_names=train['target_names']),
force_weights=True)
"""
Explanation: dense_multitarget=True is supported for prediction explanations too, and shows just the top prediction highlighting.
End of explanation
"""
show_html_expl(explain_pred... |
nick-youngblut/SIPSim | ipynb/theory/standard_vs_our_conditions.ipynb | mit | import numpy as np
%load_ext rpy2.ipython
%%R
library(ggplot2)
library(dplyr)
"""
Explanation: Description
Determining how differences in our isopycnic cfg conditions vary in meaningful ways from those of Clay et al., 2003. Eur Biophys J
Needed to determine whether the Clay et al., 2003 function describing diffusio... |
mne-tools/mne-tools.github.io | 0.22/_downloads/3927e2933ae8d1b19effcbd5c5341bd0/plot_20_visualize_evoked.ipynb | bsd-3-clause | import os
import numpy as np
import mne
"""
Explanation: Visualizing Evoked data
This tutorial shows the different visualization methods for
~mne.Evoked objects.
:depth: 2
As usual we'll start by importing the modules we need:
End of explanation
"""
sample_data_folder = mne.datasets.sample.data_path()
sample_data... |
balarsen/pymc_learning | 2014_07_18b_pymc_nonlinear_model_example.ipynb | bsd-3-clause | # set random seed for reproducibility
np.random.seed(12345)
x = np.arange(5,400,10)*1e3
# Parameters for gaussian
amp_true = 0.2
size_true = 1.8
ps_true = 0.1
# Gaussian function
gauss = lambda x,amp,size,ps: amp*np.exp(-1*(np.pi**2/(3600.*180.)*size*x)**2/(4.*np.log(2.)))+ps
f_true = gauss(x=x,amp=amp_true, size=si... |
SunPower/pvfactors | docs/tutorials/Run_fast_simulations.ipynb | bsd-3-clause | # Import external libraries
import os
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
import pandas as pd
import warnings
# Settings
%matplotlib inline
np.set_printoptions(precision=3, linewidth=300)
warnings.filterwarnings('ignore')
# Paths
LOCAL_DIR = os.getcwd()
DATA_DIR = os.path.j... |
google-research/google-research | dvrl/main_corrupted_sample_discovery.ipynb | apache-2.0 | # Uses pip3 to install necessary package (lightgbm)
!pip3 install lightgbm
# Resets the IPython kernel to import the installed package.
import IPython
app = IPython.Application.instance()
app.kernel.do_shutdown(True)
import os
from git import Repo
# Current working directory
repo_dir = os.getcwd() + '/repo'
if no... |
wittawatj/interpretable-test | ipynb/ume_test.ipynb | mit | %load_ext autoreload
%autoreload 2
%matplotlib inline
import autograd.numpy as np
import matplotlib
import matplotlib.pyplot as plt
import freqopttest.util as util
import freqopttest.data as data
import freqopttest.kernel as kernel
import freqopttest.tst as tst
import freqopttest.glo as glo
# font options
font = {
... |
tensorflow/federated | docs/tutorials/federated_select.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... |
tensorflow/recommenders | docs/examples/dcn.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... |
bryantbiggs/movie_torrents | Test_Analysis2.ipynb | mit | df = pd.read_csv('data/test_data2.csv', encoding='latin-1')
print(len(df))
df.head()
"""
Explanation: Read in TRAIN data set and select pertinent columns
End of explanation
"""
df['Released'] = pd.to_datetime(df['Released'])
df['Year'] = pd.DatetimeIndex(df['Released']).year
df['Month'] = pd.DatetimeIndex(df['Releas... |
dwiel/deepfreq | backup.ipynb | mit | y.shape[0], 44100 * 60
"""
Explanation: y is roughly 44100 samples/second * 60 seconds. I'm not sure why its off by 16 ...
End of explanation
"""
# roughly matches 5169 above
print D.shape
print len(y)/2048. * 4.
# windows are roughly 11ms long
1000/(5169/60.)
# D has twice as many values as y
float(D.shape[0] * ... |
Upward-Spiral-Science/the-fat-boys | docs/Team Fatboys 5 Updates Fixed Report Part 2.ipynb | apache-2.0 | import pickle
raw_data = pickle.load(open('raw_data'))
import statsmodels.api as sm
synapsin1_raw = np.array(raw_data[1])[0:1119259]
synapsin2_raw = np.array(raw_data[2])[0:1119259]
sub_sample = np.random.permutation(len(synapsin2_raw))[1:1000]
filter1 = synapsin1 > 15
synapsin1_raw_out = synapsin1_raw[filter1]
densi... |
Zsailer/PyCourses | notebooks/Lesson 1.ipynb | bsd-3-clause | from matplotlib.pyplot import *
"""
Explanation: Lesson 1: Let's load, plot, and analyze your data!
Let me introduce you to Matplotlib, Python's most popular plotting package.
If you have used or seen Matlab before, you'll notice it has a similar style. It produces high quality 2d plots (and 3D extensions), and can m... |
soloman817/udacity-ml | notebooks/linear_regression.ipynb | mit | %matplotlib inline
import random
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib import animation, rc
from sklearn.linear_model import LinearRegression
from IPython.display import HTML
"""
Explanation: Partial derivative equations
A linear equation is defined as:
$$ y(x_{0},x_{1}... |
JKeun/lecture-statistics | .ipynb_checkpoints/ch14-correlation-analysis-and-regression-basic-checkpoint.ipynb | mit | import matplotlib.pylab as plt
X = [10, 20, 30, 40, 50, 60, 70]
y = [40, 45, 50, 65, 70, 70, 80]
plt.figure(figsize=(4, 2))
plt.scatter(X, y)
plt.xlabel("training time"); plt.ylabel("productivity")
plt.show()
"""
Explanation: Ch14. 상관분석과 회귀분석의 기초
상관분석은 두 변수 간의 관계의 강도, 즉 얼마나 밀접하게 관려되어 있는지를 분석하는 것을 말한다. 그러나 때로는 관련성뿐만 ... |
CivicKnowledge/metatab-packages | sandiego.gov/sandiego.gov-police_calls-2015e/notebooks/San Diego Police Calls For Service.ipynb | mit | %matplotlib inline
import metapack as mp
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import geopandas as gpd
"""
Explanation: San Diego Police Calls For Service
Eric Busboom, 7 Nov 2017
In preparation for the SCALE and Open San Diego Crime ANalysis Presentation, I downloaded the data pac... |
google/earthengine-community | tutorials/intro-to-python-api/index.ipynb | apache-2.0 | import ee
# Trigger the authentication flow.
ee.Authenticate()
# Initialize the library.
ee.Initialize()
"""
Explanation: An Intro to the Earth Engine Python API
Author: guiattard
Within the last decade, a large amount of geospatial data, such as satellite data (e.g. land surface temperature, vegetation) or the outp... |
james-prior/cohpy | 20150223-cohpy-memoization.ipynb | mit | from __future__ import print_function
"""
Explanation: This is based on the previous dojo-20150131-memoization notebook.
End of explanation
"""
def fib(n):
if n == 0:
return 0
elif n == 1:
return 1
else:
return fib(n-1) + fib(n-2)
"""
Explanation: We start with a function that ca... |
nproctor/phys202-2015-work | assignments/assignment05/MatplotlibEx03.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Matplotlib Exercise 3
Imports
End of explanation
"""
def well2d(x, y, nx, ny, L=1.0):
sine1 = np.sin(nx*np.pi*x/L)
sine2 = np.sin(ny*np.pi*y/L)
eq = 2/L * sine1 * sine2
return(eq)
psi = well2d(np.linspace(0,1,10),... |
tommyod/KDEpy | docs/source/introduction.ipynb | gpl-3.0 | import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt
from KDEpy import FFTKDE # Fastest 1D algorithm
np.random.seed(123) # Seed generator for reproducible results
distribution = norm() # Create normal distribution
data = distribution.rvs(32) # Draw 32 random samples
"""
Explanation: In... |
ES-DOC/esdoc-jupyterhub | notebooks/inm/cmip6/models/sandbox-1/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inm', 'sandbox-1', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: INM
Source ID: SANDBOX-1
Topic: Landice
Sub-Topics: Glaciers, Ice.
Properties: 3... |
nicoguaro/FEM_resources | variational/poly_ritz.ipynb | mit | %matplotlib notebook
import numpy as np
from scipy.linalg import eigh
from sympy import (symbols, lambdify, init_printing, cos, sin,
pi, expand, Matrix, diff, integrate, Poly)
from sympy.plotting import plot3d
from sympy.utilities.lambdify import lambdify
import matplotlib.pyplot as plt
from mpl_to... |
srcole/qwm | misc/Slope change by low frequency aliasing.ipynb | mit | %config InlineBackend.figure_format = 'retina'
%matplotlib inline
import numpy as np
import scipy as sp
from scipy import signal
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
"""
Explanation: Low frequency content biases spectral slope only for long windows
Scott
Based on Figure 1 of p... |
boffi/boffi.github.io | dati_2019/05/2DOF.ipynb | mit | k0, m0 = 1.0, 1.0 # ideally, dimensional units...
w20 = k0/m0
w0 = np.sqrt(w20)
k1, k2 = 2*k0, 3*k0
m1, m2 = 2*m0, 4*m0
M = np.array(((m1, 0), ( 0, m2)))
K = np.array(((k1+k2, -k2), (-k2, k2)))
p = np.array(( 0.0, 1.0)); w = 2.0*w0
print_mat(M, pre='\\boldsymbol{M}=m\\,', fmt='%d')
print_mat(K, pre='\\boldsymbol{K}... |
tritemio/multispot_paper | out_notebooks/usALEX-5samples-PR-raw-dir_ex_aa-fit-out-AexAem-7d.ipynb | mit | ph_sel_name = "AexAem"
data_id = "7d"
# ph_sel_name = "all-ph"
# data_id = "7d"
"""
Explanation: Executed: Mon Mar 27 11:37:51 2017
Duration: 8 seconds.
usALEX-5samples - Template
This notebook is executed through 8-spots paper analysis.
For a direct execution, uncomment the cell below.
End of explanation
"""
fro... |
cliburn/sta-663-2017 | notebook/13E_SparseMatrices.ipynb | mit | %matplotlib inline
import numpy as np
import pandas as pd
from scipy import sparse
import scipy.sparse.linalg as spla
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context('notebook', font_scale=1.5)
"""
Explanation: Sparse Matrices
End of explanation
"""
A = np.random.poisson(0.2, (5,15)) * np.rand... |
machinelearningnanodegree/stanford-cs231 | solutions/vijendra/assignment2/BatchNormalization.ipynb | mit | # As usual, a bit of setup
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.solver import Solver
%matplotlib inline
... |
machow/siuba | docs/backends.ipynb | mit | # pandas fast grouped implementation ----
from siuba.data import cars
from siuba import _
from siuba.experimental.pd_groups import fast_mutate, fast_filter, fast_summarize
fast_mutate(
cars.groupby('cyl'),
avg_mpg = _.mpg.mean(), # aggregation
hp_per_mpg = _.hp / _.mpg, # elementwise ... |
pligor/predicting-future-product-prices | 02_preprocessing/exploration01-linear_regression.ipynb | agpl-3.0 | def getModel(alpha):
return Ridge(alpha=alpha, fit_intercept=True, normalize=False, copy_X=True, random_state=random_state)
model = getModel(alpha=0.01)
cvs = cross_val_score(estimator=model, X=XX, y=yy, cv=10)
cvs
cv_score = np.mean(cvs)
cv_score
def gpOptimization(n_jobs=n_jobs, cv=10, verbose=True):
... |
zzsza/Datascience_School | 16. 과최적화와 정규화/03. 정규화 선형 회귀.ipynb | mit | np.random.seed(0)
n_samples = 30
X = np.sort(np.random.rand(n_samples))
y = np.cos(1.5 * np.pi * X) + np.random.randn(n_samples) * 0.1
dfX = pd.DataFrame(X, columns=["x"])
dfX = sm.add_constant(dfX)
dfy = pd.DataFrame(y, columns=["y"])
df = pd.concat([dfX, dfy], axis=1)
model = sm.OLS.from_formula("y ~ x + I(x**2) + ... |
HNoorazar/PyOpinionGame | Two_Topics_Coupling_example.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import matplotlib.image as mpimg
from matplotlib import rcParams
import seaborn as sb
"""
Explanation: Opinion Game - Two Coupled Topics in the network
These codes were ... |
Kaggle/learntools | notebooks/data_viz_to_coder/raw/ex3.ipynb | apache-2.0 | import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
print("Setup Complete")
"""
Explanation: In this exercise, you will use your new knowledge to propose a solution to a real-world scenario. To succeed, you will need to import data ... |
mit-crpg/openmc | examples/jupyter/hexagonal-lattice.ipynb | mit | %matplotlib inline
import openmc
fuel = openmc.Material(name='fuel')
fuel.add_nuclide('U235', 1.0)
fuel.set_density('g/cm3', 10.0)
fuel2 = openmc.Material(name='fuel2')
fuel2.add_nuclide('U238', 1.0)
fuel2.set_density('g/cm3', 10.0)
water = openmc.Material(name='water')
water.add_nuclide('H1', 2.0)
water.add_nuclide... |
KhanSuleyman/tuthpc | mpi.ipynb | bsd-3-clause | %%file hellompi.py
"""
Parallel Hello World
"""
from mpi4py import MPI
import sys
size = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
name = MPI.Get_processor_name()
sys.stdout.write(
"Hello, World! I am process %d of %d on %s.\n"
% (rank, size, name))
"""
Explanation: MPI and cluster computi... |
zakandrewking/cobrapy | documentation_builder/gapfilling.ipynb | lgpl-2.1 | import cobra.test
from cobra.flux_analysis import gapfill
model = cobra.test.create_test_model("salmonella")
"""
Explanation: Gapfillling
Model gap filling is the task of figuring out which reactions have to be added to a model to make it feasible. Several such algorithms have been reported e.g. Kumar et al. 2009 and ... |
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