repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
kgrodzicki/machine-learning-specialization | course-3-classification/module-3-linear-classifier-learning-assignment-blank.ipynb | mit | import graphlab
"""
Explanation: Implementing logistic regression from scratch
The goal of this notebook is to implement your own logistic regression classifier. You will:
Extract features from Amazon product reviews.
Convert an SFrame into a NumPy array.
Implement the link function for logistic regression.
Write a f... |
ajgpitch/qutip-notebooks | development/development-ssesolve-tests.ipynb | lgpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
from qutip import *
import numpy as np
"""
Explanation: Development notebook: Tests for QuTiP's stochastic Schrödinger equation solver
Copyright (C) 2011 and later, Paul D. Nation & Robert J. Johansson
In this notebook we test the qutip stochastic Schrödinger equatio... |
aba1476/ds-for-wall-street | ds-for-ws-solutions.ipynb | apache-2.0 | %matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import seaborn as sns
"""
Explanation: Loading and Cleaning the Data
Turn on inline matplotlib plotting and import plotting dependencies.
End of explanation
"""
import numpy as np
import pandas as pd
import... |
mcocdawc/chemcoord | Tutorial/Transformation.ipynb | lgpl-3.0 | import chemcoord as cc
import time
import pandas as pd
water = cc.Cartesian.read_xyz('water_dimer.xyz', start_index=1)
zwater = water.get_zmat()
small = cc.Cartesian.read_xyz('MIL53_small.xyz', start_index=1)
"""
Explanation: Transformation between internal and cartesian coordinates
End of explanation
"""
zwater.lo... |
NathanYee/ThinkBayes2 | code/chap05mine.ipynb | gpl-2.0 | % matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import numpy as np
from thinkbayes2 import Pmf, Cdf, Suite, Beta
import thinkplot
"""
Explanation: Think Bayes: Chapter 5
This notebook presents code and exercises from Think Bayes, second edition.
Copyright 2016 Allen B. Downey
MIT License: https... |
akchinSTC/systemml | projects/breast_cancer/MachineLearning.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
import os
import matplotlib.pyplot as plt
import numpy as np
from pyspark.sql.functions import col, max
import systemml # pip3 install systemml
from systemml import MLContext, dml
plt.rcParams['figure.figsize'] = (10, 6)
ml = MLContext(sc)
"""
Explanation: Pre... |
mahieke/maschinelles_lernen | a1/excercise1.ipynb | mit | import pandas as pd
import numpy as np
%matplotlib inline
"""
Explanation: Aufgabe 1: Explorative Analyse und Vorverarbeitung
End of explanation
"""
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data'
cols =["CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS","RAD","TAX","PTRATIO","B... |
kunalj101/scipy2015-blaze-bokeh | 1.2 Plotting - Timeseries.ipynb | mit | import pandas as pd
from bokeh.plotting import figure, show, output_notebook
# Get data
# Process data
# Output option
# Create your plot
# Show plot
"""
Explanation: <img src=images/continuum_analytics_b&w.png align="left" width="15%" style="margin-right:15%">
<h1 align='center'>Bokeh tutorial</h1>
1.2 Plotting... |
herberthamaral/curso-python-sec2015 | Mini curso Python - SEC 2015.ipynb | mit | print "Olá Mundo!" #Este é o nosso hello world e este é um comentário :)
"""
Explanation: Mini-curso de Python - SEC 2015
Autor: Herberth Amaral - herberthamaral@gmail.com
Montando o ambiente para este mini-curso
$ sudo apt-get install build-essential python-dev libblas-dev liblapack-dev gfortran python-pip git libzmq... |
ML4DS/ML4all | U_lab1.Clustering/Lab_ShapeSegmentation_draft/LabSessionClustering_student.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.... |
JarronL/pynrc | notebooks/Exoplanet_Filter_Selection.ipynb | mit | def get_planet_counts(bp, age, masses=[0.1,1,10], dist=10, model='linder', file=None,
return_mags=False):
if 'linder' in model.lower():
tbl = nrc_utils.linder_table(file)
res = nrc_utils.linder_filter(tbl, bp.name, age, dist=dist,
con... |
bdestombe/flopy-1 | examples/Notebooks/flopy3_WatertableRecharge_example.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import print_function
import os
import sys
import platform
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import flopy
print(sys.version)
print('numpy version: {}'.format(np.__version__))
print('matplotlib version: {}'.format(mpl.__version__))
print('flop... |
jhillairet/scikit-rf | doc/source/examples/metrology/SOLT Calibration Standards Creation.ipynb | bsd-3-clause | import skrf
from skrf.media import DefinedGammaZ0
import numpy as np
freq = skrf.Frequency(1, 9000, 1001, "MHz")
ideal_medium = DefinedGammaZ0(frequency=freq, z0=50)
"""
Explanation: SOLT Calibration Standards Creation
Introduction
In scikit-rf, a calibration standard is treated just as a regular one-port or
two-port... |
jobovy/misc-notebooks | inference/open-cluster-ABC-w-lack-of-CCSNe.ipynb | bsd-3-clause | def sftime_ABC(n=100,K=1,tccsn=4.,tmax=20.):
out= []
for ii in range(n):
while True:
# Sample from prior
tsf= numpy.random.uniform()*tmax
# Sample K massive-star formation times
stars= numpy.random.uniform(size=K)*tsf
# Only accept if all go CC... |
JanetMatsen/plotting_python | plot_groupby_and_subplots.ipynb | apache-2.0 | df = pd.DataFrame({'age':[1.,2,3,4,5,6,7,8,9],
'height':[4, 4.5, 5, 6, 7, 8, 9, 9.5, 10],
'gender':['M','F', 'F','M','M','F', 'F','M', 'F'],
#'hair color':['brown','black', 'brown', 'blonde', 'brown', 'red',
# 'brown', 'brown', 'b... |
CityofToronto/bdit_congestion | here_map_change/hereanalysis.ipynb | gpl-3.0 | %sql select count(*) from (select distinct(link_dir) from here.ta_201710) oct
"""
Explanation: Total distinct link_dir in Oct:
End of explanation
"""
%sql select count(*) from (select distinct(link_dir) from here.ta_201711) nov
"""
Explanation: Total distinct link_dir in Nov:
End of explanation
"""
%%sql
select ... |
google-aai/sc17 | cats/step_5_to_6_part1.ipynb | apache-2.0 | # Enter your username:
YOUR_GMAIL_ACCOUNT = '******' # Whatever is before @gmail.com in your email address
import cv2
import numpy as np
import os
import pickle
import shutil
import sys
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from random import random
from scipy import stats
from sklearn impor... |
sfegan/calin | examples/simulation/toy event simulation for mst nectarcam array.ipynb | gpl-2.0 | %pylab inline
import calin.math.hex_array
import calin.provenance.system_info
import calin.simulation.vs_optics
import calin.simulation.geant4_shower_generator
import calin.simulation.ray_processor
import calin.simulation.tracker
import calin.simulation.detector_efficiency
import calin.simulation.atmosphere
import cali... |
ToqueWillot/M2DAC | FDMS/TME3/Model_V5.ipynb | gpl-2.0 | # from __future__ import exam_success
from __future__ import absolute_import
from __future__ import print_function
# Standard imports
%matplotlib inline
import os
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import random
import pandas as pd
import scipy.stats as stats
# ... |
CDIPS-AI-2017/pensieve | Notebooks/emo.ipynb | apache-2.0 | import pensieve as pv
import pandas as pd
pd.options.display.max_rows = 6
import numpy as np
import re
from tqdm import tqdm_notebook as tqdm
%matplotlib notebook
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
"""
Explanation: Extract sentiment vectors from text using NRC lexicon
End of explan... |
shareactorIO/pipeline | source.ml/jupyterhub.ml/notebooks/zz_old/TensorFlow/TFSlim/slim_walkthrough.ipynb | apache-2.0 | import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
import math
import numpy as np
import tensorflow as tf
import time
from datasets import dataset_utils
# Main slim library
slim = tf.contrib.slim
"""
Explanation: TF-Slim Walkthrough
This notebook will walk you through the basics of using TF-Slim to... |
kanhua/pypvcell | legacy/Mechanical stack 2J III-V-Si.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
from scipy.interpolate import interp2d
import matplotlib.pyplot as plt
from scipy.io import savemat
from iii_v_si import calc_2j_si_eta, calc_2j_si_eta_direct
from detail_balanced_MJ import calc_1j_eta
def vary_top_eg(top_cell_qe,n_s=1):
topcell_eg = np.linspace(0.9, 3, num=10... |
dacr26/CompPhys | .ipynb_checkpoints/01_01_euler-checkpoint.ipynb | mit | T0 = 10. # initial temperature
Ts = 83. # temp. of the environment
r = 0.1 # cooling rate
dt = 0.05 # time step
tmax = 60. # maximum time
nsteps = int(tmax/dt) # number of steps
T = T0
for i in range(1,nsteps+1):
new_T = T - r*(T-Ts)*dt
T = new_T
print i,i*dt, T
# we can also do t = t - r*(t-t... |
datapolitan/lede_algorithms | class2_1/EDA_Review.ipynb | gpl-2.0 | df = pd.read_csv('data/ontime_reports_may_2015_ny.csv')
df.describe()
"""
Explanation: Loading data
Simple stuff. We're loading in a CSV here, and we'll run the describe function over it to get the lay of the land.
End of explanation
"""
df.sort('ARR_DELAY', ascending=False).head(1)
"""
Explanation: In journalism,... |
vzg100/Post-Translational-Modification-Prediction | .ipynb_checkpoints/Phosphorylation Sequence Tests -MLP -dbptm+ELM -EnzymeBenchmarks-VectorAvr.-checkpoint.ipynb | mit | from pred import Predictor
from pred import sequence_vector
from pred import chemical_vector
"""
Explanation: Template for test
End of explanation
"""
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
benchmarks = ["Data/Benchmarks/phos_CDK1.csv", "Data/Benchmarks/phos_CK2.csv", "Data/B... |
bashtage/statsmodels | examples/notebooks/statespace_local_linear_trend.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
"""
Explanation: State space modeling: Local Linear Trends
This notebook describes how to extend the statsmodels statespace classes to create and estimate a custom model.... |
deculler/MachineLearningTables | Chapter3-1.ipynb | bsd-2-clause | # HIDDEN
# For Tables reference see http://data8.org/datascience/tables.html
# This useful nonsense should just go at the top of your notebook.
from datascience import *
%matplotlib inline
import matplotlib.pyplot as plots
import numpy as np
from sklearn import linear_model
plots.style.use('fivethirtyeight')
plots.rc('... |
epicf/ef | examples/ribbon_beam_in_magnetic_field_contour/Ribbon Beam Contour in Uniform Magnetic Field.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from sympy import *
init_printing()
Ex, Ey, Ez = symbols("E_x, E_y, E_z")
Bx, By, Bz, B = symbols("B_x, B_y, B_z, B")
x, y, z = symbols("x, y, z")
vx, vy, vz, v = symbols("v_x, v_y, v_z, v")
t = symbols("t")
q, m = symbols("q, m")
c, eps0 = symbols("c, epsilon_0")
"... |
quantopian/research_public | notebooks/lectures/Introduction_to_Futures/questions/notebook.ipynb | apache-2.0 | # Useful Libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Exercises: Introduction to Futures Contracts
By Christopher van Hoecke, Maxwell Margenot, and Delaney Mackenzie
Lecture Link :
https://www.quantopian.com/lectures/introduction-to-futures
IMPORTANT NOTE:
This lect... |
amkatrutsa/MIPT-Opt | Spring2021/acc_grad.ipynb | mit | import numpy as np
n = 100
# Random
# A = np.random.randn(n, n)
# A = A.T.dot(A)
# Clustered eigenvalues
A = np.diagflat([np.ones(n//4), 10 * np.ones(n//4), 100*np.ones(n//4), 1000* np.ones(n//4)])
U = np.random.rand(n, n)
Q, _ = np.linalg.qr(U)
A = Q.dot(A).dot(Q.T)
A = (A + A.T) * 0.5
print("A is normal matrix: ||AA*... |
VVard0g/ThreatHunter-Playbook | docs/tutorials/jupyter/notebooks/01_intro_to_python.ipynb | mit | for x in list(range(5)):
print("One number per loop..")
print(x)
if x > 2:
print("The number is greater than 2")
print("----------------------------")
"""
Explanation: Introduction to Python
Goals:
Learn basic Python operations
Understand differences in data structures
Get familiarized wi... |
MDAnalysis/cellgrid | tutorials/CellGrid_Tutorial.ipynb | mit | import numpy as np
import cellgrid
"""
Explanation: CellGrid tutorial
CellGrids are an object than contains coordinates, which are split into Cells
End of explanation
"""
coords = np.random.random(30000).reshape(10000, 3).astype(np.float32) * 10.0
box = np.ones(3).astype(np.float32) * 10.0
"""
Explanation: We'll st... |
awadalaa/DataSciencePractice | practice/05.LinearRegression.ipynb | mit | # conventional way to import pandas
import pandas as pd
# read CSV file directly from a URL and save the results
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
# display the first 5 rows
data.head()
"""
Explanation: Data science pipeline: pandas, seaborn, scikit-learn¶
Agenda
... |
dsacademybr/PythonFundamentos | Cap11/DSA-Python-Cap11-Machine-Learning.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 11</font>
Download: http://github.com/dsacademybr
End of explanation
"""
from I... |
elektrobohemian/courses | InformationRetrieval.ipynb | mit | # This cell has to be run to prepare the Jupyter notebook
# The %... is an Jupyter thing, and is not part of the Python language.
# In this case we're just telling the plotting library to draw things on
# the notebook, instead of on a separate window.
%matplotlib inline
# See all the "as ..." contructs? They're just a... |
metpy/MetPy | v0.9/_downloads/3aec65fc693ccd0216a40e663bc10ddb/Hodograph_Inset.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import numpy as np
import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, Hodograph, SkewT
from metpy.units import units
"""
Explanation: Hodograph Inset
... |
Chipe1/aima-python | notebooks/chapter22/Parsing.ipynb | mit | psource(Chart)
"""
Explanation: Parsing
Overview
Syntactic analysis (or parsing) of a sentence is the process of uncovering the phrase structure of the sentence according to the rules of grammar.
There are two main approaches to parsing. Top-down, start with the starting symbol and build a parse tree with the given w... |
dkirkby/quantum-demo | jupyter/PerturbTheory.ipynb | mit | %pylab inline
"""
Explanation: Time-Independent Perturbation Theory
End of explanation
"""
def plot_1d_unperturbed(a=1, nmax=3, ngrid=100):
x = np.linspace(0, a, ngrid)
fig, ax = plt.subplots(1, 2, figsize=(12, 5))
for n in range(1, nmax + 1):
color = 'krgb'[n - 1]
psi = np.sqrt(2 / a) * ... |
stijnvanhoey/course_gis_scripting | _solved/02-scientific-python-introduction.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('seaborn-white')
"""
Explanation: <p><font size="6"><b>Scientific Python essentials</b></font></p>
Introduction to GIS scripting
May, 2017
© 2017, Stijn Van Hoey (stijnvanhoey@gm&... |
google/iree | samples/colab/tensorflow_hub_import.ipynb | apache-2.0 | #@title Licensed under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
"""
Explanation: Copyright 2021 The IREE Authors
End of explanation
"""
%%capture
!python -m pip install iree-compiler iree-runtim... |
luofan18/deep-learning | intro-to-rnns/Anna_KaRNNa.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... |
gully/PyKE | docs/source/tutorials/ipython_notebooks/motion-correction/Replicate_Vanderburg_2014_K2SFF.ipynb | mit | from pyke import KeplerTargetPixelFile
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Replicate Vanderburg & Johnson 2014 K2SFF Method
In this notebook we will replicate the K2SFF method from Vanderburg and Johnson 2014. The paper ... |
adelle207/pyladies.cz | original/v1/s011-dicts/simple-api.ipynb | mit | import requests
# Stažení stránky
stranka = requests.get('https://python.cz/')
# Ověření, že se vše povedlo
stranka.raise_for_status()
# Vypsání obsahu stránky
print(stranka.text)
"""
Explanation: Ukázka práce s API
Instalace knihovny requests
Aktivujte si virtuální prostředí a v něm spusťte následující příkaz
(ven... |
aymeric-spiga/mcd-python | tutorial/mcd-python_tutorial.ipynb | gpl-2.0 | from mcd import mcd
# This line configures matplotlib to show figures embedded in the notebook.
%matplotlib inline
"""
Explanation: python package for the Mars Climate Database: how to use?
NB: to learn how to directly wrap within python the Fortran routine call_mcd compiled with f2py, look at the folder test_mcd
Gen... |
SciTools/cube_browser | doc/browsing_cubes/four_axes.ipynb | bsd-3-clause | import iris
import iris.plot as iplt
import matplotlib.pyplot as plt
from cube_browser import Contour, Browser, Contourf, Pcolormesh
"""
Explanation: Four Axes
This notebook demonstrates the use of Cube Browser to produce multiple plots using only the code (i.e. no selection widgets).
End of explanation
"""
air_pot... |
kingmolnar/MSA8150 | Lecture Notes/03-InformationBased/03-InformationBased.ipynb | cc0-1.0 | ### define function information gain IG(d_feature, D_set)
def IG(d_feature, D_set):
# compute the information gain by dividing with feature d
return 0.0
def splitDataSet(d_feature, D_set):
Left_set = []
Right_set = []
return (Left_set, Right_set)
def myID3(list_of_features, D_set):
if len(list... |
tmolteno/TART | doc/calibration/positions/site_survey.ipynb | lgpl-3.0 | import numpy as np
from scipy.optimize import minimize
x0 = [0,0]
x1 = [0, 2209]
"""
Explanation: Antenna Position Measurement
Author: Tim Molteno. tim@elec.ac.nz.
The antennas are laid out on tiles, and these tiles are placed on site. Once this is done, a survey is needed to refine the positions of each antenna in ... |
JarronL/pynrc | docs/tutorials/HR8799_DMS_Level1b.ipynb | mit | # Import the usual libraries
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
#import matplotlib.patches as mpatches
# Enable inline plotting
%matplotlib inline
# Progress bar
from tqdm.auto import trange, tqdm
import pynrc
from pynrc.simul.ngNRC import create_level1b_FITS
# Disable informationa... |
timnon/pyschedule | example-notebooks/readme-notebook.ipynb | apache-2.0 | pip install pyschedule
"""
Explanation: pyschedule - resource-constrained scheduling in python
pyschedule is the easiest way to match tasks with resources. Do you need to plan a conference or schedule your employees and there are a lot of requirements to satisfy, like availability of rooms or maximal allowed working ... |
shikhar413/openmc | examples/jupyter/post-processing.ipynb | mit | %matplotlib inline
from IPython.display import Image
import numpy as np
import matplotlib.pyplot as plt
import openmc
"""
Explanation: Post Processing
This notebook demonstrates some basic post-processing tasks that can be performed with the Python API, such as plotting a 2D mesh tally and plotting neutron source site... |
eds-uga/csci1360-fa16 | assignments/A5/A5_Q2.ipynb | mit | assert Movie
m = Movie(title = "Avengers: Infinity War", year = 2018, stars = 4.9, genre = "Action/Adventure", "Chris Evans", "Robert Downey, Jr.", "Scarlett Johansson")
assert m.title == "Avengers: Infinity War"
assert m.year == 2018
assert set(m.starring) == set(["Chris Evans", "Robert Downey, Jr.", "Scarlett Johans... |
Brunel-Visualization/Brunel | python/src/examples/.ipynb_checkpoints/Brunel Cars-checkpoint.ipynb | apache-2.0 | import pandas as pd
import ibmcognitive
cars = pd.read_csv("data/Cars.csv")
cars.head(6)
"""
Explanation: Demo of Brunel on Cars Data
The Data
We read the data into a pandas data frame. In this case we are grabbing some data that represents cars.
We read it in and call the brunel use method to ensure the names are us... |
chetan51/nupic.research | projects/dynamic_sparse/notebooks/kWinners-backup.ipynb | gpl-3.0 | dataset = Dataset(config=dict(dataset_name='MNIST', data_dir='~/nta/results'))
# build up a small neural network
inputs = []
def init_weights():
W1 = torch.randn((4,10), requires_grad=True)
b1 = torch.zeros(10, requires_grad=True)
W2 = torch.randn((10,3), requires_grad=True)
b2 = torch.zeros(3, requi... |
atavory/ibex | examples/boston_plotting_cv_preds.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
from sklearn import datasets
from sklearn import model_selection
import seaborn as sns
sns.set_style('whitegrid')
sns.despine()
from ibex import trans
from ibex.sklearn import linear_model as pd_linear_model
from ibex.sklearn import decomposition as pd_decomposition
from ibex.skl... |
tensorflow/model-remediation | docs/min_diff/guide/integrating_min_diff_with_min_diff_model.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... |
cahya-wirawan/SDC-LaneLines-P1 | test.ipynb | mit | import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
%matplotlib inline
img = mpimg.imread('test.jpg')
plt.imshow(img)
"""
Explanation: Run all the cells below to make sure everything is working and ready to go. All cells should run without error.
Test Matplotlib and Plotting
End of exp... |
hektor-monteiro/python-notebooks | aula-11_Solucao-EDOs.ipynb | gpl-2.0 | ################################################################
# Queda livre
# definindo o problema
#
# d^2x/dt^2 = −g
#
# y = [x,v] e dy/dt = [v,-g]
#
# definimos o estado do sistema como y
#
def quedalivre(estado, tempo):
g0 = estado[1]
g1 = -9.8
return np.array( [ g0 , g1 ] )
"""
Expl... |
ioam/geoviews | examples/Homepage.ipynb | bsd-3-clause | import geoviews as gv
import geoviews.feature as gf
import xarray as xr
from cartopy import crs
gv.extension('bokeh', 'matplotlib')
(gf.ocean + gf.land + gf.ocean * gf.land * gf.coastline * gf.borders).opts(
'Feature', projection=crs.Geostationary(), global_extent=True, height=325).cols(3)
"""
Explanation: GeoVi... |
chungjjang80/FRETBursts | notebooks/Example - Exporting Burst Data Including Timestamps.ipynb | gpl-2.0 | from fretbursts import *
sns = init_notebook()
"""
Explanation: Exporting Burst Data
This notebook is part of a tutorial series for the FRETBursts burst analysis software.
In this notebook, show a few example of how to export FRETBursts
burst data to a file.
<div class="alert alert-info">
Please <b>cite</b> FRETBu... |
abhi1509/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: Language Translation
In this project, you’re going... |
letsgoexploring/beapy-package | beapyExample.ipynb | mit | import numpy as np
import pandas as pd
import urllib
import datetime
import matplotlib.pyplot as plt
%matplotlib inline
%load_ext autoreload
%autoreload 2
import beapy
apiKey = '3EDEAA66-4B2B-4926-83C9-FD2089747A5B'
bea = beapy.initialize(apiKey =apiKey)
"""
Explanation: beapy
beapy is a Python package for obtaining... |
brianbreitsch/sportstat | notebooks/parsing-example.ipynb | bsd-3-clause | osu_roster_filepath = '../data/osu_roster.csv'
"""
Explanation: Parsing tsv files and populating the database
End of explanation
"""
!head {osu_roster_filepath}
"""
Explanation: Inspecting the first few lines of the file, we get a feel for this data schema.
Mongo Considerations:
- can specify categories for validat... |
intel-analytics/BigDL | apps/anomaly-detection-hd/autoencoder-zoo.ipynb | apache-2.0 | from bigdl.dllib.nncontext import *
sc = init_nncontext("Anomaly Detection HD Example")
from scipy.io import arff
import pandas as pd
import os
dataset = "ionosphere" #real world dataset
data_dir = os.getenv("BIGDL_HOME")+"/bin/data/HiCS/"+dataset+".arff"
rawdata, _ = arff.loadarff(data_dir)
data = pd.DataF... |
mariedekou/pymks_overview | notebooks/.ipynb_checkpoints/cahn_hilliard-checkpoint.ipynb | mit | %matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Cahn-Hilliard Example
This example demonstrates how to use PyMKS to solve the Cahn-Hilliard equation. The first section provides some background information about the Cahn-Hilliard equation as wel... |
mne-tools/mne-tools.github.io | 0.24/_downloads/7b89f7dac105a44e25d2fbdd898b911f/vector_mne_solution.ipynb | bsd-3-clause | # Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
#
# License: BSD-3-Clause
import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, apply_inverse
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
smoothing_steps = 7
# Rea... |
tomilsinszki/python_samples | learningPython.ipynb | mit | print("Hello World")
"""
Explanation: Learning Python
Hello World
print is a method that will print some text
End of explanation
"""
x = 1
if x < 0:
print("x is negative")
elif x == 0:
print("x is zero")
elif 0 < x:
print("x is positive")
else:
print("ERROR")
"""
Explanation: Indentation
Instead of... |
yavuzovski/playground | machine learning/google-ml-crash-course/first_steps_with_tf.ipynb | gpl-3.0 | # Load the necessary libraries
import math
from IPython import display
from matplotlib import cm, gridspec, pyplot as plt
import numpy as np
import pandas as pd
from sklearn import metrics
import tensorflow as tf
from tensorflow.python.data import Dataset
tf.logging.set_verbosity(tf.logging.ERROR)
pd.options.display.... |
brillozon-code/pebaystats | examples/aggregation_example.ipynb | apache-2.0 | import numpy as np
import nose.tools as nt
from pebaystats import dstats
"""
Explanation: Demonstrate Aggregation of Descriptive Statistics
Here we create an array of random values and for each row of the array, we create
a distinct pebaystats.dstats object to accumulate the descriptive statistics
for the values ... |
statsmodels/statsmodels.github.io | v0.12.1/examples/notebooks/generated/regression_plots.ipynb | bsd-3-clause | %matplotlib inline
from statsmodels.compat import lzip
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.formula.api import ols
plt.rc("figure", figsize=(16,8))
plt.rc("font", size=14)
"""
Explanation: Regression Plots
End of explanation
"""
prestige = sm.datasets.get... |
jupyter-widgets/ipywidgets | docs/source/examples/Using Interact.ipynb | bsd-3-clause | from __future__ import print_function
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
"""
Explanation: Using Interact
The interact function (ipywidgets.interact) automatically creates user interface (UI) controls for exploring code and data interactively. It is the eas... |
goodwordalchemy/thinkstats_notes_and_exercises | code/chap14_analytical_methods.ipynb | gpl-3.0 | live, firsts, others = first.MakeFrames()
"""
Explanation: Central Limit Theorem if we add up n values for almost any distribution the distribution of the sum converges to normal as n increases.
values have to be drawn independently
values have to come from same distribution (relaxed)
distribution has to have finite ... |
napsternxg/gensim | docs/notebooks/nmf_tutorial.ipynb | gpl-3.0 | import logging
import time
from contextlib import contextmanager
import os
from multiprocessing import Process
import psutil
import numpy as np
import pandas as pd
from numpy.random import RandomState
from sklearn import decomposition
from sklearn.cluster import MiniBatchKMeans
from sklearn.datasets import fetch_olive... |
c22n/ion-channel-ABC | docs/examples/getting_started.ipynb | gpl-3.0 | # Importing standard libraries
import numpy as np
import pandas as pd
"""
Explanation: Getting Started
This notebook gives a whirlwind overview of the ionchannelABC library and can be used for testing purposes of a first installation. The notebook follows the workflow for parameter inference of a generic T-type Ca2+ c... |
sandrofsousa/Resolution | Pysegreg/Pysegreg_notebook_distance.ipynb | mit | # Imports
import numpy as np
np.seterr(all='ignore')
import pandas as pd
from decimal import Decimal
import time
# Import python script with Pysegreg functions
from segregationMetrics import Segreg
# Instantiate segreg as cc
cc = Segreg()
"""
Explanation: Pysegreg run - Distance based
Instructions
For fast processi... |
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies | ex27-Wind Rose.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import matplotlib.cm as cm
import numpy as np
from math import pi
from windrose import WindroseAxes, WindAxes, plot_windrose
from pylab import rcParams
rcParams['figure.figsize'] = 6, 6
"""
Explanation: Wind Rose
A wind ro... |
andreyf/machine-learning-examples | linear_models/Logistic Gradient Descent.ipynb | gpl-3.0 | from sklearn import datasets
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set(style='ticks', palette='Set2')
import pandas as pd
import numpy as np
import math
from __future__ import division
data = datasets.load_iris()
X = data.data[:100, :2]
y = data.target[:100]
X_full = data.data[:1... |
jllanfranchi/playground | test_sqlite/test_sqlite.ipynb | mit | def adapt_array(arr):
out = io.BytesIO()
np.save(out, arr)
out.seek(0)
return sqlite3.Binary(out.read())
def convert_array(text):
out = io.BytesIO(text)
out.seek(0)
return np.load(out)
# Converts np.array to TEXT when inserting
sqlite3.register_adapter(np.ndarray, adapt_array)
# Converts ... |
yunfeiz/py_learnt | sample_code/date_utils.ipynb | apache-2.0 | col_show = ['name', 'open', 'pre_close', 'price', 'high', 'low', 'volume', 'amount', 'time', 'code']
initial_letter = ['HTGD','OFKJ','CDKJ','ZJXC','GXKJ','FHTX','DZJG']
code =[]
for letter in initial_letter:
code.append(df[df['UP']==letter].code[0])
#print(code)
if code != '': #not empty != ''
df_price = t... |
pelodelfuego/word2vec-toolbox | notebook/classification/antonyms.ipynb | gpl-3.0 | summaryDf = pd.DataFrame([extractSummaryLine(l) for l in open('../../data/learnedModel/anto/summary.txt').readlines()],
columns=['bidirectional', 'strict', 'clf', 'feature', 'post', 'precision', 'recall', 'f1'])
summaryDf.sort_values('f1', ascending=False)[:10]
"""
Explanation: Experience
Base... |
AlphaSmartDog/DeepLearningNotes | Note-3 Tensor Ridge Regression/global dimensionality reduction (GDR) algorithm Ver 1.0.ipynb | mit | import numpy as np
import pandas as pd
from scipy import linalg
from scipy import optimize
import functools
import tensorly
from tensorly.decomposition import partial_tucker
from tensorly.decomposition import tucker
tensorly.set_backend('numpy')
"""
Explanation: global dimensionality-reduction (GDR) algorithm 示例
End... |
daniestevez/jupyter_notebooks | Lucy/Lucy frames Bochum 2021-10-24.ipynb | gpl-3.0 | def timestamps(packets):
epoch = np.datetime64('2000-01-01T12:00:00')
t = np.array([struct.unpack('>I', p[ccsds.SpacePacketPrimaryHeader.sizeof():][:4])[0]
for p in packets], 'uint32')
return epoch + t * np.timedelta64(1, 's')
def load_frames(path):
frame_size = 223 * 5 - 2
frames... |
fmfn/BayesianOptimization | examples/basic-tour.ipynb | mit | def black_box_function(x, y):
"""Function with unknown internals we wish to maximize.
This is just serving as an example, for all intents and
purposes think of the internals of this function, i.e.: the process
which generates its output values, as unknown.
"""
return -x ** 2 - (y - 1) ** 2 + 1
... |
rajul/tvb-library | tvb/simulator/demos/surface_deterministic_stimulus.ipynb | gpl-2.0 | from tvb.datatypes.cortex import Cortex
from tvb.simulator.lab import *
"""
Explanation: Demonstrate using the simulator for a surface simulation.
Run time: approximately 2 min (workstation circa 2010).
Memory requirement: ~ 1 GB
End of explanation
"""
LOG.info("Configuring...")
#Initialise a Model, Coupling, and ... |
TomTranter/OpenPNM | examples/tutorials/Intro to OpenPNM - Advanced.ipynb | mit | import warnings
import numpy as np
import scipy as sp
import openpnm as op
%matplotlib inline
np.random.seed(10)
ws = op.Workspace()
ws.settings['loglevel'] = 40
np.set_printoptions(precision=4)
pn = op.network.Cubic(shape=[10, 10, 10], spacing=0.00006, name='net')
"""
Explanation: Tutorial 3 of 3: Advanced Topics and... |
team-hdnet/hdnet | examples/demoValidation.ipynb | gpl-3.0 | true_spikes = hdnet.spikes.Spikes(spikes=hdnet.sampling.sample_from_ising_gibbs(J=Js[0,:15,:15], theta=thetas[0,:15],
num_samples = 10**3, burn_in = 5*10**2, sampling_steps = 10**2))
print(true_spikes._spikes.shape)
"""
Explanation: Generating Spike Train from First Trial, and only first 15 Neurons, of the fitted par... |
MatteusDeloge/opengrid | notebooks/Demo_caching.ipynb | apache-2.0 | import pandas as pd
from opengrid.library import misc
from opengrid.library import houseprint
from opengrid.library import caching
from opengrid.library import analysis
import charts
hp = houseprint.Houseprint()
"""
Explanation: Demo caching
This notebook shows how caching of daily results is organised. First we show ... |
zero323/spark | python/docs/source/getting_started/quickstart_df.ipynb | apache-2.0 | from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
"""
Explanation: Quickstart: DataFrame
This is a short introduction and quickstart for the PySpark DataFrame API. PySpark DataFrames are lazily evaluated. They are implemented on top of RDDs. When Spark transforms data, it does not immedi... |
kbrose/article-tagging | lib/notebooks/explorations.ipynb | mit | print('# total articles :', df.shape[0])
print('# tagged articles :', df.loc[:, 'OEMC':'TASR'].any(1).sum())
print('# not relevant articles:', (~df['relevant']).sum())
print('# w/ no information :', df.shape[0] - df.loc[:, 'OEMC':'TASR'].any(1).sum() - (~df['relevant']).sum())
print('# w/ location info ... |
patryk-oleniuk/emotion_recognition | temp/emotion_recognition_Oleniuk_Galotta_v1.ipynb | gpl-3.0 | import random
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import csv
import scipy.misc
import time
import collections
import os
import utils as ut
import importlib
import copy
importlib.reload(ut)
%matplotlib inline
plt.rcParams['figure.figsize'] = (20.0, 20.0) # set default size of plo... |
mtambos/springleaf | Springleaf - preprocess - string columns.ipynb | mit | %pylab inline
%load_ext autoreload
%autoreload 2
from __future__ import division
from collections import defaultdict, namedtuple
from datetime import datetime, timedelta
from functools import partial
import inspect
import json
import os
import re
import sys
import cPickle as pickle
import numpy as np
import pandas a... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/sandbox-2/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'sandbox-2', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: MIROC
Source ID: SANDBOX-2
Topic: Aerosol
Sub-Topics: Transport, Emissions, Con... |
kubeflow/pipelines | components/gcp/dataflow/launch_flex_template/sample.ipynb | apache-2.0 | %%capture --no-stderr
!pip3 install kfp --upgrade
"""
Explanation: Name
Data preparation by using a Flex template to submit a job to Cloud Dataflow
Labels
GCP, Cloud Dataflow, Kubeflow, Pipeline
Summary
A Kubeflow Pipeline component to prepare data by using a Flex template to submit a job to Cloud Dataflow.
Details
I... |
rkburnside/python_development | notes and useful info/useful info.ipynb | gpl-2.0 | # strings
s = 'Hello World'
print(s[2])
s[:3]
s[:-1] # Grab everything but the last lettesr
s[::1] # Grab everything, but go in steps size of 1
s[::-1] # string backward
s[1:] # Grab everything past the first term all the way to the length of s which is len(s)
s[::2] # Grab everything, but go in step sizes of 2
s + ' c... |
scikit-optimize/scikit-optimize.github.io | 0.8/notebooks/auto_examples/plots/visualizing-results.ipynb | bsd-3-clause | print(__doc__)
import numpy as np
np.random.seed(123)
import matplotlib.pyplot as plt
"""
Explanation: Visualizing optimization results
Tim Head, August 2016.
Reformatted by Holger Nahrstaedt 2020
.. currentmodule:: skopt
Bayesian optimization or sequential model-based optimization uses a surrogate
model to model the... |
machinelearningdeveloper/lc101-kc | December 15, 2016/Covered in class.ipynb | unlicense | import this as t
print(t)
"""
Explanation: Python design patterns
Covering only a small portion of what exists.
import this
creating main functions
Take a look at this guide: http://docs.python-guide.org/en/latest/writing/style/
Take a look at this "Zen of Python" by example: http://artifex.org/~hblanks/talks/2011/p... |
mne-tools/mne-tools.github.io | 0.17/_downloads/93d330ee6c0ab8305ae11bf7f764aeb8/plot_evoked_arrowmap.ipynb | bsd-3-clause | # Authors: Sheraz Khan <sheraz@khansheraz.com>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.datasets.brainstorm import bst_raw
from mne import read_evokeds
from mne.viz import plot_arrowmap
print(__doc__)
path = sample.data_path()
fname = path + '/MEG/sample/samp... |
blowekamp/SimpleITK-Notebook-Answers | ConnectedThresholdAndOtherFilterPerformance.ipynb | apache-2.0 | img = sitk.Image(100,100, sitk.sitkUInt8)
ctFilter = sitk.ConnectedThresholdImageFilter()
ctFilter.SetSeed([0,0])
ctFilter.SetUpper(1)
ctFilter.SetLower(0)
ctFilter.AddCommand(sitk.sitkProgressEvent, lambda: print("\rProgress: {0:03.1f}%...".format(100*ctFilter.GetProgress())))
"""
Explanation: Demonstrate Reporting ... |
wikistat/Ateliers-Big-Data | Cdiscount/Part2-2bis-AIF-PysparkWorkflowPipeline-Cdiscount.ipynb | mit | sc
# Importation des packages génériques et ceux
# des librairie ML et MLlib
##Nettoyage
import nltk
import re
##Liste
from numpy import array
##Temps
import time
##Row and Vector
from pyspark.sql import Row
from pyspark.ml.linalg import Vectors
##Hashage et vectorisation
from pyspark.ml.feature import HashingTF
from... |
karlstroetmann/Artificial-Intelligence | Python/5 Linear Regression/Simple-Linear-Regression-Function.ipynb | gpl-2.0 | import csv
"""
Explanation: Simple Linear Regression
We need to read our data from a <tt>csv</tt> file. The module csv offers a number of functions for reading and writing a <tt>csv</tt> file.
End of explanation
"""
with open('cars.csv') as handle:
reader = csv.DictReader(handle, delimiter=',')
Data = [] ... |
fantasycheng/udacity-deep-learning-project | tv-script-generation/dlnd_tv_script_generation_2.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... |
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