repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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ramezquitao/pyoptools | doc/notebooks/basic/SimpleComponents.ipynb | gpl-3.0 | from pyoptools.all import *
from numpy import pi,sqrt
"""
Explanation: Creating components with pyOpTools
To be able to simulate an optical system, the first step is to use the predefined surfaces to create components. In this notebook it will be shown how to create some simple components.
End of explanation
"""
S0=... |
WilliamHPNielsen/broadbean | docs/Subsequences.ipynb | mit | %matplotlib notebook
import broadbean as bb
from broadbean.plotting import plotter
sine = bb.PulseAtoms.sine
ramp = bb.PulseAtoms.ramp
"""
Explanation: Subsequences
This notebook describes the use of subsequences.
Subsequences can be useful in a wide range of settings.
End of explanation
"""
# Uncompressed
SR = 1... |
gregunz/ada2017 | exam/data_cluedo/2-icecream.ipynb | mit | # Run the following to import necessary packages and import dataset. Do not use any additional plotting libraries.
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
matplotlib.style.use('ggplot')
datafile = "dataset/icecream.csv"
df = pd.read_csv(datafile)
df
"""
Explanation: <h1... |
bataeves/kaggle | sber/Model-0.31434.ipynb | unlicense | # train_raw = pd.read_csv("data/train.csv")
train_raw = pd.read_csv("data/train_without_noise.csv")
test = pd.read_csv("data/test.csv")
macro = pd.read_csv("data/macro.csv")
train_raw.head()
def preprocess_anomaly(df):
df["full_sq"] = map(lambda x: x if x > 10 else float("NaN"), df["full_sq"])
df["life_sq"] = ... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_compute_mne_inverse_raw_in_label.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse_raw, read_inverse_operator
print(__doc__)
data_path = sample.data_path()
fname_inv = data_path + '/... |
anhaidgroup/py_entitymatching | notebooks/guides/step_wise_em_guides/Performing Blocking Using Built-In Blockers (Overlap Blocker).ipynb | bsd-3-clause | # Import py_entitymatching package
import py_entitymatching as em
import os
import pandas as pd
"""
Explanation: Introduction
This IPython notebook illustrates how to perform blocking using Overlap blocker.
First, we need to import py_entitymatching package and other libraries as follows:
End of explanation
"""
# Ge... |
ComputationalModeling/spring-2017-danielak | past-semesters/spring_2016/day-by-day/day23-Econophysics/STUDENT-Notebook-for-Econophysics.ipynb | agpl-3.0 | # Use Python to make a filled-in plot
# from the data that got reported out
"""
Explanation: Econophysics
Names of group members
// put your names here!
Goals of this assignment
Witness what we call "emergent behavior"; large patterns manifesting from the simple interactions of tiny agents
Develop a graphical way t... |
fggp/ctcsound | cookbook/03-threading.ipynb | lgpl-2.1 | import ctcsound
cs = ctcsound.Csound()
csd = '''
<CsoundSynthesizer>
<CsOptions>
-d -o dac -m0
</CsOptions>
<CsInstruments>
sr = 48000
ksmps = 100
nchnls = 2
0dbfs = 1
instr 1
idur = p3
iamp = p4
icps = cpspch(p5)
irise = p6
idec = p7... |
zzsza/TIL | python/crawling-google-play.ipynb | mit | import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import json
import re
from bs4 import BeautifulSoup
import warnings
from konlpy.tag import Twitter
from sklearn.feature_extraction.text import CountVectorizer
warnings.filterwarnings('ignore')
%matplotlib inline
%config InlineBackend.figure_fo... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/miroc-es2h/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'miroc-es2h', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: MIROC
Source ID: MIROC-ES2H
Topic: Ocean
Sub-Topics: Timestepping Framework, Advec... |
quantopian/research_public | notebooks/lectures/Spearman_Rank_Correlation/answers/notebook.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import math
"""
Explanation: Exercises: Spearman Rank Correlation
Lecture Link
This exercise notebook refers to this lecture. Please use the lecture for explanations and sample code.
https://www.quantopian.com/lectures#S... |
seblabbe/MATH2010-Logiciels-mathematiques | Devoirs/devoir2-solutions.ipynb | gpl-3.0 | def somme(A, B):
C = []
for i in range(4):
Ai = A[i]
Bi = B[i]
row = [Ai[j]+Bi[j] for j in range(4)]
C.append(row)
return C
X = [[56, 39, 3, 41],
[23, 78, 11, 62],
[61, 26, 65, 51],
[80, 98, 9, 68]]
Y = [[51, 52, 53, 15],
[ 1, 71, 46, 31],
[99, 7,... |
amitkaps/weed | 4-Model.ipynb | mit | # Load the libraries
import numpy as np
import pandas as pd
from scipy import stats
from sklearn import linear_model
# Load the data again!
df = pd.read_csv("data/Weed_Price.csv", parse_dates=[-1])
df.sort(columns=['State','date'], inplace=True)
df1 = df[df.State=="California"].copy()
df1.set_index("date", inplace=Tru... |
jakobrunge/tigramite | tutorials/tigramite_tutorial_basics.ipynb | gpl-3.0 | # Imports
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
%matplotlib inline
## use `%matplotlib notebook` for interactive figures
# plt.style.use('ggplot')
import sklearn
import tigramite
from tigramite import data_processing as pp
from tigramite.toymodels import structural_causal_proce... |
phoebe-project/phoebe2-docs | 2.1/tutorials/20_21_meshes.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
"""
Explanation: 2.0 - 2.1 Migration: Meshes
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
"""
import phoebe
b = ... |
cdalzell/ds-for-wall-street | ds-for-ws-student.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... |
keras-team/keras-io | guides/ipynb/keras_cv/coco_metrics.ipynb | apache-2.0 | import keras_cv
# import all modules we will need in this example
import tensorflow as tf
from tensorflow import keras
# only consider boxes with areas less than a 32x32 square.
metric = keras_cv.metrics.COCORecall(class_ids=[1, 2, 3], area_range=(0, 32**2))
"""
Explanation: Using KerasCV COCO Metrics
Author: lukewo... |
phobson/statsmodels | examples/notebooks/tsa_filters.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import print_function
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
dta = sm.datasets.macrodata.load_pandas().data
index = pd.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3'))
print(index)
dta.index = index
del dta['year']
del dta['qu... |
M0nica/python-foundations-hw | 05/NYT_graded.ipynb | mit | import config
import requests
#imports key from config file
nyt_articles_api = config.nyt_articles_api
nyt_books_api = config.nyt_books_api
nyt_movie_api = config.nyt_movie_api
response = requests.get('https://api.nytimes.com/svc/search/v2/articlesearch.json?api-key=' + nyt_articles_api)
data = response.json()
# pr... |
liganega/Gongsu-DataSci | previous/y2017/W08-numpy-hypothesis_test/.ipynb_checkpoints/GongSu19-Statistical_Hypothesis_Test-checkpoint.ipynb | gpl-3.0 | import numpy as np
from __future__ import print_function, division
"""
Explanation: 자료 안내: 여기서 다루는 내용은 아래 사이트의 내용을 참고하여 생성되었음.
https://github.com/rouseguy/intro2stats
가설검정
End of explanation
"""
import sympy as sp
sp.factorial(5)
"""
Explanation: 오늘의 주요 예제: 동전던지기
동전을 30번 던져서 앞면(Head)이 24번 나왔을 때, 정상적인 동전이라 할 수 있을까?... |
ozorich/phys202-2015-work | assignments/assignment04/MatplotlibExercises.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Visualization 1: Matplotlib Basics Exercises
End of explanation
"""
y=np.random.randn(30)
x=np.random.randn(30)
plt.scatter(x,y, color="r",s=50, marker='x',alpha=.9)
plt.xlabel('Random Values for X')
plt.ylabel('Randome Values fo... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/04_advanced_preprocessing/labs/taxicab_traffic/deploy.ipynb | apache-2.0 | !gsutil cp -r $MODEL_PATH/* gs://$BUCKET/taxifare/model/
"""
Explanation: Deploy for Online Prediction
To get our predictions, in addition to the features provided by the client, we also need to fetch the latest traffic information from BigQuery. We then combine these and invoke our tensorflow model. This is visualize... |
ES-DOC/esdoc-jupyterhub | notebooks/nasa-giss/cmip6/models/sandbox-3/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'sandbox-3', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: SANDBOX-3
Topic: Seaice
Sub-Topics: Dynamics, Thermodyna... |
lenovor/notes-on-dirichlet-processes | 2015-09-02-fitting-a-mixture-model.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
from scipy import stats
from collections import namedtuple, Counter
"""
Explanation: Fitting a Mixture Model with Gibbs Sampling
End of explanation
"""
data = pd.Series.from_csv("clusters.csv")
_=data.hist(bins=2... |
espressomd/espresso | doc/tutorials/ferrofluid/ferrofluid_part2.ipynb | gpl-3.0 | import espressomd
import espressomd.magnetostatics
espressomd.assert_features(['DIPOLES', 'DP3M', 'LENNARD_JONES'])
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})
import numpy as np
import tqdm
"""
Explanation: Ferrofluid - Part 2
Table of Contents
Applying an external mag... |
tritemio/pybroom | doc/notebooks/pybroom-example-multi-datasets.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format='retina' # for hi-dpi displays
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from lmfit import Model
import lmfit
print('lmfit: %s' % lmfit.__version__)
sns.set_style('whitegrid')
import pybroom as br
"""
Explanati... |
statsmodels/statsmodels.github.io | v0.13.2/examples/notebooks/generated/tsa_arma_0.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import statsmodels.api as sm
from scipy import stats
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.graphics.api import qqplot
"""
Explanation: Autoregressive Moving Average (ARMA): Sunspots data
End of explanat... |
Heroes-Academy/OOP_Spring_2016 | notebooks/giordani/Python_3_OOP_Part_3__Delegation__composition_and_inheritance.ipynb | mit | class Door:
colour = 'brown'
def __init__(self, number, status):
self.number = number
self.status = status
@classmethod
def knock(cls):
print("Knock!")
@classmethod
def paint(cls, colour):
cls.colour = colour
def open(self):
self.status = 'open'
... |
Hebali/learning_machines | tensorflow_tutorials/Tutorial_01_GraphsAndSessions.ipynb | mit | # Create input constants:
X = 2.0
Y = 3.0
# Perform addition:
Z = X + Y
# Print output:
print Z
"""
Explanation: Learning Machines
Taught by Patrick Hebron at NYU/ITP, Fall 2017
TensorFlow Basics: "Graphs and Sessions"
Let's look at a simple arithmetic procedure in pure Python:
End of explanation
"""
# Import Ten... |
mne-tools/mne-tools.github.io | 0.23/_downloads/b99fcf919e5d2f612fcfee22adcfc330/40_autogenerate_metadata.ipynb | bsd-3-clause | from pathlib import Path
import matplotlib.pyplot as plt
import mne
data_dir = Path(mne.datasets.erp_core.data_path())
infile = data_dir / 'ERP-CORE_Subject-001_Task-Flankers_eeg.fif'
raw = mne.io.read_raw(infile, preload=True)
raw.filter(l_freq=0.1, h_freq=40)
raw.plot(start=60)
# extract events
all_events, all_ev... |
markdewing/qmc_algorithms | Variational/Variational_Hydrogen.ipynb | mit | beta = Symbol('beta')
R_T = exp(-r - beta*r*r)
R_T
"""
Explanation: Energy of the Hydrogen Atom
The variational principle states a trial wavefunction will have an energy greater than or equal to the ground state energy.
$$\frac{\int \psi H \psi}{ \int \psi^2} \ge E_0$$
First consider the hydogen atom. Let us use a tr... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_define_target_events.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne import io
from mne.event import define_target_events
from mne.datasets import sample
import matplotlib.pyplot as plt
print(__doc__)
data_path = sample.data_path()
"""
Explanation: ===================================... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/02_generalization/labs/create_datasets.ipynb | apache-2.0 | from google.cloud import bigquery
import seaborn as sns
import pandas as pd
import numpy as np
import shutil
"""
Explanation: <h1> Explore and create ML datasets </h1>
In this notebook, we will explore data corresponding to taxi rides in New York City to build a Machine Learning model in support of a fare-estimation ... |
km-Poonacha/python4phd | Session 2/ipython/.ipynb_checkpoints/Lesson 5- Crawl and scrape-Worksheet-checkpoint.ipynb | gpl-3.0 | <h1 id="HEADING" property="name" class="heading_name ">
<div class="heading_height"></div>
"
Le Jardin Napolitain
"
</h1>
"""
Explanation: Lesson 5 - Crawl and Scrape
Making the request
Using 'requests' module
Use the requests module to make a HTTP request to http://www.tripadvisor.com
- Check the... |
james-prior/euler | euler-204-generalised-hamming-numbers.ipynb | mit | MAX_PRIME = 100
MAX_SMOOTH = 10**9
def is_prime(x):
return all(x % divisor != 0 for divisor in range(2, x))
primes = tuple(x for x in range(2, MAX_PRIME+1) if is_prime(x))
def n_smooth_numbers(n, x, primes, max_smooth):
if not primes:
return n
while True:
n = n_smooth_numbers(n, x, primes... |
geoneill12/phys202-2015-work | assignments/assignment10/ODEsEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy.integrate import odeint
from IPython.html.widgets import interact, fixed
"""
Explanation: Ordinary Differential Equations Exercise 1
Imports
End of explanation
"""
def solve_euler(derivs, y0, x):
"""Solve a 1d ... |
google-research/google-research | gfsa/notebooks/demo_learning_static_analyses.ipynb | apache-2.0 | # 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 the L... |
3upperm2n/notes-deeplearning | tensorboard/tensorboard/Anna KaRNNa Name Scoped.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is base... |
ContinualAI/avalanche | notebooks/how-tos/dataloading_buffers_replay.ipynb | mit | !pip install avalanche-lib
"""
Explanation: description: How to implement replay and data loading
Dataloading, Memory Buffers, and Replay
Avalanche provides several components that help you to balance data loading and implement rehearsal strategies.
Dataloaders are used to provide balancing between groups (e.g. tasks/... |
timothydmorton/usrp-sciprog | day2/exercises/Solutions/Python_answers.ipynb | mit | #I don't think this is the code golf winner. Try to beat me.
for i in range(100):
print('FizzBuzz'*(not (i+1)%5)*(not (i+1)%3) or 'Fizz'*(not (i+1)%5) or 'Buzz'*(not (i+1)%3) or str(i+1))
"""
Explanation: Exercises
Rules:
Every variable/function/class name should be meaningful
Variable/function names should be lo... |
DJCordhose/big-data-visualization | code/notebooks/analysis-dask.ipynb | mit | # http://dask.pydata.org/en/latest/dataframe-overview.html
%time lazy_df = dd.read_csv('../../data/raw/2001.csv', encoding='iso-8859-1')
%time len(lazy_df)
# http://dask.pydata.org/en/latest/dataframe-api.html#dask.dataframe.DataFrame.sample
s = 10000 # desired sample size
n = 5967780
fraction = s / n
df = lazy_df.s... |
AllenDowney/ModSim | soln/chap12.ipynb | gpl-2.0 | # install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import exists
filename = 'modsim.py'
if not exists(filename):
from urllib.request import urlretrieve
url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'
... |
readywater/caltrain-predict | .ipynb_checkpoints/01sepEvents-checkpoint.ipynb | mit | # Import necessary libraries
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sys
import re
import random
import operator
from func import *
# inline plot
%matplotlib inline
#%%javascript
#IPython.OutputArea.auto_scroll_threshold = 9999;
#%load 'data/raw-twt2016-01-26-14/21/09.csv'
df = ... |
theideasmith/theideasmith.github.io | _notebooks/.ipynb_checkpoints/Asymptotic Convergence of Gradient Descent for Linear Regression Least Squares Optimization-checkpoint.ipynb | mit | from pylab import *
from numpy import random as random
random.seed(1)
N=1000.
w = array([14., 30.]);
x = zeros((2, int(N))).astype(float32)
x[0,:] = arange(N).astype(float32)
x[1,:] = 1
y = w.dot(x) + random.normal(size=int(N), scale=100.)
"""
Explanation: Supplementary Materials
This code accompanies the paper Asymp... |
TuKo/brainiak | examples/reprsimil/group_brsa_example.ipynb | apache-2.0 | %matplotlib inline
import scipy.stats
import scipy.spatial.distance as spdist
import numpy as np
from brainiak.reprsimil.brsa import GBRSA
import brainiak.utils.utils as utils
import matplotlib.pyplot as plt
import matplotlib as mpl
import logging
np.random.seed(10)
import copy
"""
Explanation: This demo shows how to ... |
AAbercrombie0492/gdelt_distributed_architecture | notebooks/GDELT_Architecture.ipynb | mit | from IPython.display import YouTubeVideo, HTML
HTML('<iframe width="560" height="315" src="https://www.youtube.com/embed/GpCarC_I3Ao?list=PLlRVXVT7h9_gCGCOl_bNYHA7FXbSOIVbs" frameborder="0" allowfullscreen></iframe>')
"""
Explanation: Data Engineering Final Project: GDELT
Global Data on Events, Location, and Tone
"Th... |
celiasmith/syde556 | SYDE 556 Lecture 9 Action Selection.ipynb | gpl-2.0 | %pylab inline
import nengo
model = nengo.Network('Selection')
with model:
stim = nengo.Node(lambda t: [np.sin(t), np.cos(t)])
s = nengo.Ensemble(200, dimensions=2)
Q_A = nengo.Ensemble(50, dimensions=1)
Q_B = nengo.Ensemble(50, dimensions=1)
Q_C = nengo.Ensemble(50, dimensions=1)
Q_D ... |
mortonjt/yummy-octo-duck | ipynb/UniFrac benchmarking.ipynb | bsd-3-clause | import numpy.testing as npt
ids, otu_ids, otu_data, t = get_random_samples(10, tree, True)
fu_mat = make_and_run_pw_distances(unifrac, otu_data, otu_ids=otu_ids, tree=t)
u_mat = pw_distances(unweighted_unifrac, otu_data, otu_ids=otu_ids, tree=t)
fwu_mat = make_and_run_pw_distances(w_unifrac, otu_data, otu_ids=otu_ids... |
gaufung/Data_Analytics_Learning_Note | python-statatics-tutorial/basic-theme/python-language/Function.ipynb | mit | bigx = 10
def double_times(x = bigx):
return x * 2
bigx = 1000
double_times()
"""
Explanation: 函数
1 默认参数
函数的参数中如果有默认参数，那么函数在定义的时候将被计算而不是等到函数被调用的时候
End of explanation
"""
def foo(values, x=[]):
for value in values:
x.append(value)
return x
foo([0,1,2])
foo([4,5])
def foo_fix(values, x=[]):
i... |
ES-DOC/esdoc-jupyterhub | notebooks/bnu/cmip6/models/sandbox-1/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bnu', 'sandbox-1', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: BNU
Source ID: SANDBOX-1
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbulen... |
laurajchang/NPTFit | examples/Example7_Galactic_Center_nonPoissonian.ipynb | mit | # Import relevant modules
%matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
from NPTFit import nptfit # module for performing scan
from NPTFit import create_mask as cm # module for creating the mask
from NPTFit import dnds_analysis # module for analysing the output
from NPTFit import psf_corre... |
2php/CodeToolKit | 9.caffe-ssd/examples/inceptionv3.ipynb | mit | # set up Python environment: numpy for numerical routines, and matplotlib for plotting
import numpy as np
import matplotlib.pyplot as plt
# display plots in this notebook
%matplotlib inline
# set display defaults
plt.rcParams['figure.figsize'] = (10, 10) # large images
plt.rcParams['image.interpolation'] = 'nea... |
spectralDNS/shenfun | docs/source/fasttransforms.ipynb | bsd-2-clause | from shenfun import *
from mpi4py_fft import fftw
"""
Explanation: <!-- File automatically generated using DocOnce (https://github.com/doconce/doconce/):
doconce format ipynb fasttransforms.do.txt -->
Demo - Some fast transforms
Mikael Mortensen (email: mikaem@math.uio.no), Department of Mathematics, University of O... |
the-deep-learners/TensorFlow-LiveLessons | notebooks/transfer_learning_in_keras.ipynb | mit | import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
"""
Explanation: Transfer Learning in Keras
In this notebook, we'll cover how to load a pre-trained model (in this case, VGGNet19) and finetune it for a new task: detecting hot dogs.
End of explanation
"""
from keras.ap... |
kikocorreoso/brythonmagic | notebooks/Highcharts (python) tutorial.ipynb | mit | %load_ext brythonmagic
"""
Explanation: First step
In this tutorial we will use Brython, an implementation of Python written in javascript and Python, to access the Highcharts javascript library and to manage the data to be used in the maps. To integrate Brython in the IPython notebook we are using an extension for th... |
jakejhansen/minesweeper_solver | Minesweeper_results.ipynb | mit | # Initialization goes here
import numpy as np
import matplotlib.pyplot as plt
import pickle
import os
import pandas as pd
import math
from matplotlib import rc
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
def smooth(y,factor):
if type(y)!=list:
y = list(y)
return pd.Series(y).rolling(wind... |
JJINDAHOUSE/deep-learning | sentiment-rnn/Sentiment_RNN.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural... |
gabyx/ApproxMVBB | tests/python/PlotTestResults.ipynb | mpl-2.0 | import sys,os,imp,re
import math
import numpy as np
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
from mpl_toolkits.mplot3d import Axes3D
mpl.rcParams['figure.figsize']=(6.0,4.0) #(6.0,4.0)
mpl.rcParams['font.siz... |
jbocharov-mids/W207-Machine-Learning | John_Bocharov_p1.ipynb | apache-2.0 | # This tells matplotlib not to try opening a new window for each plot.
%matplotlib inline
# Import a bunch of libraries.
import time
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
from sklearn.pipeline import Pipeline
from sklearn.datasets import fetch_mldata
from skle... |
georgetown-analytics/machine-learning | archive/notebook/nist_clustering.ipynb | mit | from lxml import html
import requests
from __future__ import print_function
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans, MiniBatchKMeans
from time import time
"""
Explanation: Clustering NIST headlines and descriptions
adapted from https://github.com/star-is-here/open... |
skaae/Recipes | examples/ImageNet Pretrained Network (VGG_S).ipynb | mit | !wget https://s3.amazonaws.com/lasagne/recipes/pretrained/imagenet/vgg_cnn_s.pkl
"""
Explanation: Introduction
This example demonstrates using a network pretrained on ImageNet for classification. The model used was converted from the VGG_CNN_S model (http://arxiv.org/abs/1405.3531) in Caffe's Model Zoo.
For details o... |
phoebe-project/phoebe2-docs | 2.1/examples/single_spots.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
"""
Explanation: Single Star with Spots
Setup
IMPORTANT NOTE: if using spots on contact systems or single stars, make sure to use 2.1.15 or later as the 2.1.15 release fixed a bug affecting spots in these systems.
Let's first make sure we have the latest version of PHOEBE 2.1 install... |
Upward-Spiral-Science/claritycontrol | code/Advanced Texture Based Clarity Visualization.ipynb | apache-2.0 | import os
PATH="/Users/albertlee/claritycontrol/code/scripts"
os.chdir(PATH)
import clearity as cl # I wrote this module for easier operations on data
import matplotlib.pyplot as plt
import jgraph as ig
import clearity.resources as rs
import csv,gc # garbage memory collection :)
import matplotlib
#import matplotlib.... |
sujitpal/polydlot | src/pytorch/06-echo-sequence-prediction.ipynb | apache-2.0 | from __future__ import division, print_function
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import matplotlib.py... |
jochym/abinitio-workshop | notebooks/FononyDFT.ipynb | cc0-1.0 | # przygotowanie rysunku
t=ase.io.read('FIGs/MnAs_phM06.traj',index=':')
v=view(t,viewer='nglview')
v.custom_colors({'Mn':'green','As':'blue'})
v.view._remote_call("setSize", target="Widget", args=["500px", "500px"])
#v.view.center_view()
v.view.background='#ffc'
v.view.parameters=dict(clipDist=-200)
# wyświetlenie ry... |
Kaggle/learntools | notebooks/game_ai/raw/tut3.ipynb | apache-2.0 | #$HIDE_INPUT$
import random
import numpy as np
# Gets board at next step if agent drops piece in selected column
def drop_piece(grid, col, mark, config):
next_grid = grid.copy()
for row in range(config.rows-1, -1, -1):
if next_grid[row][col] == 0:
break
next_grid[row][col] = mark
re... |
ogoann/StatisticalMethods | notes/InferenceSandbox.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
import scipy.stats
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 5.0)
# the model parameters
a = np.pi
b = 1.6818
# my arbitrary constants
mu_x = np.exp(1.0) # see definitions above
tau_x = 1.0
s = 1.0
N = 50 # number of data points
# get some x's and y... |
glouppe/scikit-optimize | examples/bayesian-optimization.ipynb | bsd-3-clause | import numpy as np
np.random.seed(777)
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (10, 6)
"""
Explanation: Bayesian optimization with skopt
Gilles Louppe, Manoj Kumar July 2016.
End of explanation
"""
noise_level = 0.1
def f(x, noise_level=noise_level):
return np.sin(5 ... |
jmschrei/pomegranate | tutorials/old/Tutorial_6_Markov_Chain.ipynb | mit | from pomegranate import *
%pylab inline
d1 = DiscreteDistribution({'A': 0.10, 'C': 0.40, 'G': 0.40, 'T': 0.10})
d2 = ConditionalProbabilityTable([['A', 'A', 0.10],
['A', 'C', 0.50],
['A', 'G', 0.30],
['A', 'T', 0.10],
... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/feature_engineering/solutions/4_keras_adv_feat_eng.ipynb | apache-2.0 | import datetime
import logging
import os
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow import feature_column as fc
from tensorflow.keras import layers
from tensorflow.keras import models
# set TF error log verbosity
logging.getLogger("tensorflow").setLevel(logging.ERROR)
... |
rasbt/python-machine-learning-book | code/ch05/ch05.ipynb | mit | %load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -p numpy,scipy,matplotlib,sklearn
"""
Explanation: Copyright (c) 2015-2017 Sebastian Raschka
https://github.com/rasbt/python-machine-learning-book
MIT License
Python Machine Learning - Code Examples
Chapter 5 - Compressing Data via Dimensionality Reduction
No... |
mdeff/ntds_2017 | projects/reports/stackoverflow_network/StackOverflowNetworkAnalysis.ipynb | mit | %matplotlib inline
import os
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
from scipy import sparse
# Own modules
import DataProcessing as proc
import DataCleaning as clean
import NetworkAnalysis as analysis
import Classification as classification
import NetworkEvolution as evol
"""
Explan... |
XinyiGong/pymks | notebooks/filter.ipynb | mit | %matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Filter Example
This example demonstrates the connection between MKS and signal
processing for a 1D filter. It shows that the filter is in fact the
same as the influence coefficients and, thus, ap... |
planetlabs/notebooks | jupyter-notebooks/ship-detector/01_ship_detector.ipynb | apache-2.0 | sample_data_file_name = 'data/1056417_2017-03-08_RE3_3A_Visual_clip.tif'
"""
Explanation: Detect ships in Planet data
This notebook demonstrates how to detect and count objects in satellite imagery using algorithms from Python's scikit-image library. In this example, we'll look for ships in a small area in the San Fr... |
nipy/brainx | brainx/notebooks/detect_partition_degeneracy.ipynb | bsd-3-clause | %matplotlib inline
import os
import numpy as np
import networkx as nx
from glob import glob
from matplotlib import pyplot as plt
from brainx.util import threshold_adjacency_matrix
"""
Explanation: Evaluating Partitions for Degeneracy
<br>
This notebook is designed to allow BrainX users to evaluate degeneracies in t... |
google/trax | trax/models/research/examples/hourglass_downsampled_imagenet.ipynb | apache-2.0 | # 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 the Lice... |
edosedgar/xs-pkg | deep_learning/hw1/homework_modules.ipynb | gpl-2.0 | class Module(object):
"""
Basically, you can think of a module as of a something (black box)
which can process `input` data and produce `ouput` data.
This is like applying a function which is called `forward`:
output = module.forward(input)
The module should be able to perfor... |
maxvogel/NetworKit-mirror2 | Doc/Notebooks/NetworKit_UserGuide.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: NetworKit User Guide
About NetworKit
NetworKit is an open-source toolkit for high-performance
network analysis. Its aim is to provide tools for the analysis of large
networks in the size range from thousands to billions of edges. For this
purpose, it ... |
radhikapc/foundation-homework | homework06/Homework06-Dark Sky Forecast API-Radhika.ipynb | mit | #https://api.forecast.io/forecast/APIKEY/LATITUDE,LONGITUDE,TIME
response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/12.971599,77.594563')
data = response.json()
#print(data)
#print(data.keys())
print("Bangalore is in", data['timezone'], "timezone")
timezone_find = data.keys()
#fi... |
yedivanseven/bestPy | examples/05_Recommender.ipynb | gpl-3.0 | import sys
sys.path.append('../..')
"""
Explanation: CHAPTER 5
The Recommender
Now that we got to know bestPy's powerful algorithms, we cant't wait to use them, right? In trying to do so, however, we might realize that they are pretty bare-bone and inconvenient to handle. For example, we need to know the internally us... |
aldian/tensorflow | tensorflow/lite/g3doc/performance/post_training_float16_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... |
halimacc/CS231n-assignments | assignment2/BatchNormalization.ipynb | unlicense | # 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... |
tensorflow/docs-l10n | site/ja/lite/performance/post_training_integer_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... |
AllenDowney/ThinkBayes2 | examples/hockey.ipynb | mit | # If we're running on Colab, install PyMC and ArviZ
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install pymc3
!pip install arviz
# PyMC generates a FutureWarning we don't need to deal with yet
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
import seaborn ... |
spulido99/NetworksAnalysis | alejogm0520/Repaso_Estadistico.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stas
%matplotlib inline
x = np.arange(0.01, 1, 0.01)
values = [(0.5, 0.5),(5, 1),(1, 3),(2, 2),(2, 5)]
for i, j in values:
y = stas.beta.pdf(x,i,j)
plt.plot(x,y)
plt.show()
"""
Explanation: Analisis de Redes: Repaso Estadistico
Ejercici... |
tensorflow/graphics | tensorflow_graphics/notebooks/6dof_alignment.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... |
openmrslab/suspect | docs/notebooks/tut05_hsvd.ipynb | mit | import suspect
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: 5. Water suppression with HSVD
In this tutorial we will take a look at water suppression. Water is present in the body at concentrations thousands of times higher than any of the metabolites we are interested in, so a... |
d00d/quantNotebooks | Notebooks/quantopian_research_public/notebooks/lectures/Futures_Trading_Considerations/notebook.ipynb | unlicense | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from quantopian.research.experimental import continuous_future, history
"""
Explanation: Futures Trading Considerations
by Maxwell Margenot and Delaney Mackenzie
Part of the Quantopian Lecture Series:
www.quantopian.com/lectures
github.com/quantop... |
moonbury/pythonanywhere | github/MasteringMatplotlib/mmpl-preview.ipynb | gpl-3.0 | import matplotlib
matplotlib.use('nbagg')
%matplotlib inline
"""
Explanation: A Preview
A quick look at some examples indicating the sorts of things that will be examined in later notebooks.
Each notebook will take advantage of the NbAgg backend, and we set that up first:
End of explanation
"""
import matplotlib.pyp... |
fgnt/nara_wpe | examples/WPE_Tensorflow_online.ipynb | mit | channels = 8
sampling_rate = 16000
delay = 3
alpha=0.99
taps = 10
frequency_bins = stft_options['size'] // 2 + 1
"""
Explanation: Example with real audio recordings
The iterations are dropped in contrast to the offline version. To use past observations the correlation matrix and the correlation vector are calculated r... |
Bastien-Brd/pi-tuner | pitch_detection_from_microphone.ipynb | mit | import sounddevice as sd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Tutorial for recording a guitar string stroke and detecting its pitch
I use the python library called sounddevice which allows to easily record audio and represent the result as a numpy ... |
julienchastang/unidata-python-workshop | notebooks/AWIPS/Model_Sounding_Data.ipynb | mit | from awips.dataaccess import DataAccessLayer
import matplotlib.tri as mtri
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from math import exp, log
import numpy as np
from metpy.calc import get_wind_components, lcl, dry_lapse, parcel_profile, dewpoint
from metpy.calc import... |
zephirefaith/AI_Fall15_Assignments | A1/player_notebook.ipynb | mit | from random import randint
class RandomPlayer():
"""Player that chooses a move randomly."""
def move(self, game, legal_moves, time_left):
if not legal_moves: return (-1,-1)
return legal_moves[randint(0,len(legal_moves)-1)]
"""
Explanation: This is the ipython notebook you should use as a templ... |
DawesLab/LabNotebooks | Beam Splitter Leonhart.ipynb | mit | from qutip import *
from numpy import sqrt, pi, cos, sin, exp, array, real, imag, linspace
from numpy import math
factorial = math.factorial
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Beam Splitter QM
AMCDawes
Based on paper (and book) by Ulf Leonhardt
arXiv:quant-ph/0305007v2 4 Jul 2003
Some ... |
otavio-r-filho/AIND-Deep_Learning_Notebooks | autoencoder/Simple_Autoencoder_Solution.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)
"""
Explanation: A Simple Autoencoder
We'll start off by building a simple autoencoder to compres... |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/Milestone Project 1 - Advanced Solution.ipynb | apache-2.0 | # Specifically for the iPython Notebook environment for clearing output.
from IPython.display import clear_output
# Global variables
board = [' '] * 10
game_state = True
announce = ''
"""
Explanation: Tic Tac Toe
This is the solution for the Milestone Project! A two player game made within a Jupyter Notebook. Feel fr... |
rashikaranpuria/Machine-Learning-Specialization | Machine Learning Foundations: A Case Study Approach/Assignment_three/Document retrieval.ipynb | mit | import graphlab
"""
Explanation: Document retrieval from wikipedia data
Fire up GraphLab Create
End of explanation
"""
people = graphlab.SFrame('people_wiki.gl/people_wiki.gl')
"""
Explanation: Load some text data - from wikipedia, pages on people
End of explanation
"""
people.head()
len(people)
"""
Explanatio... |
balarsen/pymc_learning | updating_info/Updating Info.ipynb | bsd-3-clause | # pymc3.distributions.DensityDist?
import matplotlib.pyplot as plt
import matplotlib as mpl
from pymc3 import Model, Normal, Slice
from pymc3 import sample
from pymc3 import traceplot
from pymc3.distributions import Interpolated
from theano import as_op
import theano.tensor as tt
import numpy as np
from scipy import ... |
JKeun/project-02-watcha | 01_crawling/02_api_crawling(raw_df1).ipynb | mit | import requests
import json
import pandas as pd
"""
Explanation: api(json)을 통한 feature 크롤링
y : 'owner_action' key안에 있는 ['rating': 내가준 별점]
X : ['title':영화제목, 'eval_count':평가자수, 'watcha_rating':평균별점, 'filmrate':관람가, 'main_genre':장르, 'nation':국가, 'running_time':상영시간, 'year':제작년도]
$\hat y$ : predicted_rating
추가로 뽑아야... |
santanche/java2learn | notebooks/pt/c04components/s03message-bus/3.iot-dashboard-python.ipynb | gpl-2.0 | from resources.iot.device import IoT_sensor_consumer
from IPython.core.display import display
import ipywidgets as widgets
from resources.iot.device import IoT_mqtt_publisher, IoT_sensor
"""
Explanation: Exemplos de Componentes Visuais para iPython
End of explanation
"""
widgets.FloatProgress(value=30.0, min=0, max=... |
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