repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
steinam/teacher | jup_notebooks/data-science-ipython-notebooks-master/analyses/churn.ipynb | mit | from __future__ import division
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
from sklearn.cross_validation import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import train_test_split
from sklearn.svm import SVC
from sklearn.ensemble import R... |
rdhyee/webtech-learning | notebooks/js_notes.ipynb | apache-2.0 | %%javascript
// version of jQuery
element.text($.fn.jquery)
%%javascript
{
// incorrect attempt to compute element independently...
// just use element: http://stackoverflow.com/a/20020566/
var _e = IPython.notebook.get_selected_cell().output_area.element;
var _element = _e.find(".rendered_html");
element.text... |
udacity/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... |
GoogleCloudPlatform/python-docs-samples | notebooks/tutorials/bigquery/BigQuery query magic.ipynb | apache-2.0 | %%bigquery
SELECT name, SUM(number) as count
FROM `bigquery-public-data.usa_names.usa_1910_current`
GROUP BY name
ORDER BY count DESC
LIMIT 10
"""
Explanation: BigQuery query magic
Jupyter magics are notebook-specific shortcuts that allow you to run commands with minimal syntax. Jupyter notebooks come with many built-... |
astroferreira/galclean | Galclean Example.ipynb | gpl-2.0 | %matplotlib inline
import numpy as np
from galclean import galclean, plot_result, galshow
from matplotlib import pyplot as plt
from astropy.io import fits
"""
Explanation: Refer to the requirements.yml file to reproduce the same conda env used in this Notebook. Using latest versions of Astropy, photutils, matplotlib... |
SamLau95/nbinteract | docs/notebooks/recipes/recipes_interactive_questions.ipynb | bsd-3-clause | import nbinteract as nbi
"""
Explanation: Interactive Questions
nbinteract also provides built-in methods to easily create multiple choice and short answer questions.
End of explanation
"""
nbi.multiple_choice(question="What is 10 + 2 * 5?",
choices=['12', '60', '20'],
answers... |
cmflannery/MAE170 | jupyter/lab5/Lab5-camerons_updates.ipynb | mit | temp1 = np.array([30,35,40,45,50,55,60,65,70,75,80,85,90])
temp2 = np.array([0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90])
measured = np.array([1.142,1.280,1.475,1.698,1.882,2.10,2.26,2.53,2.70,2.93,3.11,3.32,3.42])
expected = np.array([0,.198,.397,.597,.798,1,1.203,1.407,1.612,1.817,2.023,2.230,2.436,2.644,... |
bmeaut/python_nlp_2017_fall | course_material/05_Decorators_Packaging/05_Decorators_lab.ipynb | mit | import functools
from datetime import datetime
def timer(func):
@functools.wraps(func)
def timed_func(*args, **kwargs):
start = datetime.now()
ret = func(*args, **kwargs)
tm = datetime.now() - start
print("Running time: {}".format(tm.total_seconds()))
return ret
retu... |
gschivley/ERCOT_power | Raw Data/Merging EPA and EIA.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import os
import glob
import re
import cPickle as pickle
import gzip
import seaborn as sns
"""
Explanation: TODO:
Verify that the merge result is as expected. I noticed that when I checked the length of the EPA for a 6-month period, the length w... |
christophebertrand/ada-epfl | HW02-Data_from_the_Web/bachelor_data_analysis.ipynb | mit | select = soupe.find_all('select')
select_name = [s.attrs['name'] for s in select]
select_name
"""
Explanation: Collect the data
Get the name of the 4 fields we have to select
End of explanation
"""
select_field = [soupe.find('select',{'name': name}) for name in select_name]
"""
Explanation: Get the select field c... |
ES-DOC/esdoc-jupyterhub | notebooks/niwa/cmip6/models/sandbox-1/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'niwa', 'sandbox-1', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: NIWA
Source ID: SANDBOX-1
Sub-Topics: Radiative Forcings.
Properties: 85 (42 ... |
OSHI7/Learning1 | MatplotLib Pynotebooks/AnatomyOfMatplotlib-Part5-Artists.ipynb | mit | """
Show examples of matplotlib artists
http://matplotlib.org/api/artist_api.html
Several examples of standard matplotlib graphics primitives (artists)
are drawn using matplotlib API. Full list of artists and the
documentation is available at
http://matplotlib.org/api/artist_api.html
Copyright (c) 2010, Bartosz Telen... |
Cyberface/nrutils_dev | review/notebooks/dev_bam_extraction_radius.ipynb | mit | # Setup ipython environment
%load_ext autoreload
%autoreload 2
# %matplotlib auto
%matplotlib inline
# Import useful things
from nrutils import scsearch,gwylm
# Setup plotting backend
import matplotlib as mpl
from mpl_toolkits.mplot3d import axes3d
mpl.rcParams['lines.linewidth'] = 0.8
mpl.rcParams['font.family'] = 'se... |
seg/2016-ml-contest | Anjum48/Anjum48 Facies Classification.ipynb | apache-2.0 | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable
from pandas import set_option
set_option("display.max_rows", 10)
pd.options.mode.chained_assignment = None
filen... |
ColeLab/informationtransfermapping | EmpiricalResults/Demo_Network2NetworkInformationTransfer.ipynb | gpl-3.0 | import sys
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import statsmodels.sandbox.stats.multicomp as mc
import multiprocessing as mp
%matplotlib inline
import os
os.environ['OMP_NUM_THREADS'] = str(1)
import warnings
warnings.filterwarnings('ignore')
import networkinformationtransfer... |
bjedwards/NetworkXTutorial | III. Graph Generators.ipynb | bsd-3-clause | import networkx as nx
"""
Explanation: Graph Generators
NetworkX comes with a large number of builtin graph generators. These can be especially useful for testing out new measures, metrics, and dynamics, on already well tested algorithms. In this notebook, we'll go through some of the different classes of graph genera... |
nbortolotti/tensorflow-code-experiences | colaboratory/5steps_object_detection.ipynb | apache-2.0 | !git clone https://github.com/tensorflow/models.git
!apt-get -qq install libprotobuf-java protobuf-compiler
!protoc ./models/research/object_detection/protos/string_int_label_map.proto --python_out=.
!cp -R models/research/object_detection/ object_detection/
!rm -rf models
"""
Explanation: <a href="https://colab.resea... |
mne-tools/mne-tools.github.io | 0.18/_downloads/4702564ec4882e1756a75c62d2a92ee4/plot_stats_cluster_1samp_test_time_frequency.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.time_frequency import tfr_morlet
from mne.stats import permutation_cluster_1samp_test
from mne.datasets import sample
print(__doc__)
"""
Explanat... |
mwickert/SP-Comm-Tutorial-using-scikit-dsp-comm | tutorial_part0/Speech Processing.ipynb | bsd-2-clause | Image('images/PyAudio_RT_flow@300dpi.png',width='80%')
pah.available_devices()
"""
Explanation: Introduction
Without getting too detailed Lab is about a simple technique for either increasing or decreasing the playing time of a sound array, without altering the sound's pitch. Increasing the rate refers to decreasing ... |
jason-neal/spectrum_overload | Notebooks/Tutorial.ipynb | mit | from __future__ import division, print_function
from spectrum_overload import Spectrum
from astropy.io import fits
import copy
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Tutorial for spectrum_overload
Jason Neal
September 2016
Spectrum is a module based around a class called Spectrum. It a... |
fonnesbeck/scipy2015_tutorial | notebooks/4. Resampling Methods and Missing Data.ipynb | cc0-1.0 | salmon = pd.read_table("../data/salmon.dat", delim_whitespace=True, index_col=0)
plt.scatter(x=salmon.spawners, y=salmon.recruits)
"""
Explanation: Resampling Methods
Resampling is the process of repeatedly drawing subsamples from a training dataset, and fitting a model to each sample with the goal of discovering addi... |
gedman4b/MachineLearning | Python/Personalized Medicine.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: <b>Objective</b>
Develop algorithms to classify genetic mutations based on clinical evidence (text)
End of explanation
"""
training_variants_df = pd.read_csv('data/training_variants.csv')
training_variants_df.... |
google/jax | docs/jax-101/03-vectorization.ipynb | apache-2.0 | import jax
import jax.numpy as jnp
x = jnp.arange(5)
w = jnp.array([2., 3., 4.])
def convolve(x, w):
output = []
for i in range(1, len(x)-1):
output.append(jnp.dot(x[i-1:i+2], w))
return jnp.array(output)
convolve(x, w)
"""
Explanation: Automatic Vectorization in JAX
Authors: Matteo Hessel
In the previou... |
rvuduc/cse6040-ipynbs | 12--pagerank.ipynb | bsd-3-clause | import sqlite3 as db
import pandas as pd
def get_table_names (conn):
assert type (conn) == db.Connection # Only works for sqlite3 DBs
query = "SELECT name FROM sqlite_master WHERE type='table'"
return pd.read_sql_query (query, conn)
def print_schemas (conn, table_names=None, limit=0):
assert type (con... |
ES-DOC/esdoc-jupyterhub | notebooks/nims-kma/cmip6/models/sandbox-2/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nims-kma', 'sandbox-2', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: NIMS-KMA
Source ID: SANDBOX-2
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Ener... |
GoogleCloudPlatform/dfcx-scrapi | examples/google_sheets_series/cx_to_sheets_by_flow.ipynb | apache-2.0 | #If you haven't already, make sure you install the `dfcx-scrapi` library
!pip install dfcx-scrapi
"""
Explanation: Introduction
In this notebook, we will demonstrate how Google Sheets can be used as a simple medium for managing, updating, and evaluating Intents and Training Phrases in Dialogflow CX.
Specifically, we ... |
aldian/tensorflow | tensorflow/lite/g3doc/tutorials/model_maker_question_answer.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/Dropout.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... |
tlapicka/IPythonNotebooks | Matplotlib--popisky,znacky.ipynb | gpl-2.0 | f=logspace(0,4,1000)
C=1e-6
R=3e3
au=(1./(1j*2*pi*f*C)) / (R+(1./(1j*2*pi*f*C)))
fig=figure()
ax=subplot(111)
semilogx(f,20*log10(abs(au)))
grid(True,'both')
text(200,-10,'-20dB/dek',size=20,rotation=-50)
figtext(0.8,0.05,'OSA X')
figtext(0.05,0.8, 'OSA Y',rotation=90)
l = plt.axvline(x= 1/(2*pi*R*C), ymax=0.95, li... |
keii11/TIL | elements_of_programing_python/c5_arrays/c5_bootcamp.ipynb | mit | def even_odd(A):
"""この関数では，左に偶数の値，右に奇数の値を集めていく．
まだ決まっていない部分がそれらのインデックスに挟まれた領域を示すと考え，
3種類のsubarrayから構成されていると見做すこともできる．
Args:
A: list of integers.
Returns:
Sorted list, with the even numbers are at the left side and odd numbers are at the right side.
"""
next_even, next_odd = ... |
Timonzimm/CS-401 | HW02/Homework 2.ipynb | mit | import requests, re, html
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from bs4 import BeautifulSoup
from tqdm import tqdm_notebook
import warnings
warnings.filterwarnings('ignore')
NUM_OBS = 200
"""
Explanation: 02 - Data from the Web
Deadline
Wednesday October 25, 20... |
victor-o-silva/python_subdict | DOCS.ipynb | mit | d = {
'a': 'A',
'b': 'B',
'c': 'C',
'd': {
'x': 'D_X',
'y': 'D_Y',
'z': {
'I': 'D_Z_I',
'II': {
'1': 'D_Z_II_1',
'2': 'D_Z_II_2'
},
'III': 'D_Z_III'
}
}
}
"""
Explanation: python_subdict ... |
zhoujli/zhoujli.github.io | _source/2016-3-11-Spark-II.ipynb | mit | %matplotlib inline
import seaborn as sns
import pandas as pd
user = sc.textFile("E:/Documents/zhoujli.github.io/_source/ml-100k/u.user")
"""
Explanation: In my last post I briefly introduce how I set up Apache Spark on my own laptop. In this post, I will start using Spark and Python to do some data exploration.
Resil... |
therealAJ/python-sandbox | data-science/learning/ud1/DataScience/PolynomialRegression.ipynb | gpl-3.0 | %matplotlib inline
from pylab import *
np.random.seed(2)
pageSpeeds = np.random.normal(3.0, 1.0, 1000)
purchaseAmount = np.random.normal(50.0, 10.0, 1000) / pageSpeeds
scatter(pageSpeeds, purchaseAmount)
"""
Explanation: Polynomial Regression
What if your data doesn't look linear at all? Let's look at some more real... |
DarkEnergyScienceCollaboration/Twinkles | examples/notebooks/MatchingLensGalaxies-SN.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib as mpl
mpl.rcParams['text.usetex'] = False
from matplotlib import pyplot as plt
import os
from lsst.sims.photUtils import Sed, Bandpass, BandpassDict, getImsimFluxNorm
%matplotlib inline
"""
Explanation: Matching Gravitationally Lensed SNe (GLSNe) Lens Galaxies... |
alfkjartan/control-computarizado | state-space/notebooks/Under construction - Output feedback controller for the harmonic oscillator.ipynb | mit | import numpy as np
import sympy as sy
sy.init_printing(use_latex='mathjax', order='lex')
h,omega = sy.symbols('h,omega', real=True, positive=True)
A = sy.Matrix([[0,-omega**2], [1,0]])
B = sy.Matrix([[1],[0]])
Phi = sy.simplify(sy.exp(A*h).rewrite(sy.sin))
Phi
s = sy.symbols('s',real=True, positive=True)
Gamma = sy.... |
intel-analytics/BigDL | python/orca/colab-notebook/quickstart/tf2_keras_lenet_mnist.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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distrib... |
jserenson/Python_Bootcamp | Timing your code - timeit.ipynb | gpl-3.0 | import timeit
"""
Explanation: Timing your code
Sometimes its important to know how long your code is taking to run, or at least know if a particular line of code is slowing down your entire project. Python has a built-in timing module to do this.
This module provides a simple way to time small bits of Python code. I... |
CheungChanDevCoder/blog | python/ipynb/人人推荐系统调研综述.ipynb | mit | import numpy as np
import pandas as pd
import os
# 使用pandas加载csv数据
movies = pd.read_csv(os.path.expanduser("~/ml-latest-small/movies.csv"))
ratings = pd.read_csv(os.path.expanduser("~/ml-latest-small/ratings.csv"))
# 去掉无用的维度
ratings.drop(['timestamp'],axis=1,inplace=True)
movies.head()
ratings.head()
# 将movieid替换为mo... |
datactive/bigbang | examples/network_analysis/Show Interaction Graph.ipynb | mit | %matplotlib inline
"""
Explanation: This notebook shows how BigBang can be used to display a graph of interactions in the mailing list over some period of time.
First we'll make the I Python notebook display computed visualizations inline.
End of explanation
"""
from bigbang.archive import Archive
import bigbang.par... |
GoogleCloudPlatform/training-data-analyst | blogs/timeseries/rnn_cloudmle.ipynb | apache-2.0 | !pip install --upgrade tensorflow
import tensorflow as tf
print tf.__version__
import numpy as np
import tensorflow as tf
import seaborn as sns
import pandas as pd
SEQ_LEN = 10
def create_time_series():
freq = (np.random.random()*0.5) + 0.1 # 0.1 to 0.6
ampl = np.random.random() + 0.5 # 0.5 to 1.5
x = np.sin... |
syednasar/datascience | ml-template/Template Machine Learning Notebook.ipynb | mit | import pandas as pd
iris_data = pd.read_csv('iris-data.csv')
iris_data.head()
"""
Explanation: A template for Data Science
By Nasar, S.
It is recommended to view this notebook in nbviewer for the best viewing experience.
Table of contents
Introduction
License
Required libraries
The problem domain
Step 1: An... |
philippgrafendorfe/stackedautoencoders | .ipynb_checkpoints/MNIST_Autoencoder_dim_reduction-checkpoint.ipynb | mit | from keras.layers import Input, Dense, Dropout
from keras.models import Model
from keras.datasets import mnist
from keras.models import Sequential, load_model
from keras.optimizers import RMSprop
from keras.callbacks import TensorBoard
from keras.utils import plot_model
from IPython.display import Image
from matplotlib... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/pipelines/google_cloud_pipeline_components_TPU_model_train_upload_deploy.ipynb | apache-2.0 | import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex AI Pipelines: TPU model train, upload, and deploy using google-cloud-pipeline-component... |
saga-survey/saga-code | ipython_notebooks/Masterlist Generation.ipynb | gpl-2.0 | inedd = ~mastercat0['Dist_edd'].mask
inkk = ~mastercat0['Dist_kk'].mask
sum(inedd), sum(inkk), sum(inedd&inkk)
meths = list(np.unique(kknearby['Meth']))
meths = dict(zip(meths, range(len(meths))))
plt.scatter(kknearby['Dist'], [meths[m] for m in kknearby['Meth']],lw=0,alpha=.1)
plt.yticks(meths.values(), meths.keys())... |
judithyueli/pyFKF | .ipynb_checkpoints/Demo-checkpoint.ipynb | mit | from IPython.html.widgets import interact, interactive, fixed
from IPython.html.widgets import FloatSlider
from CO2simulation import CO2simulation
def plot_CO2plume(time):
import param as param
CO2 = CO2simulation(param)
x = CO2.extract_state(int(time/3))
data = CO2.extract_data(int(time/3))
fig_set... |
saketkc/hatex | 2017_Fall/CSCI-599/Assignment01/Problem_1.ipynb | mit | from __future__ import division
from lib.fully_conn import *
from lib.layer_utils import *
from lib.grad_check import *
from lib.datasets import *
from lib.optim import *
from lib.train import *
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set def... |
xianjunzhengbackup/code | data science/Extending_to_Linear_regression.ipynb | mit | fitted_model.summary()
print (fitted_model.params)
betas = np.array(fitted_model.params)
fitted_values = fitted_model.predict(X)
"""
Explanation: If we had wanted to manage the same result using the StatsModels.formula.api, we should have typed the following:
linear_regression = smf.ols(formula='target ~ RM', dat... |
ioos/notebooks_demos | notebooks/2017-06-12-NCEI_RA_archive_history.ipynb | mit | from owslib.iso import namespaces
# Append gmi namespace to namespaces dictionary.
namespaces.update({"gmi": "http://www.isotc211.org/2005/gmi"})
namespaces.update({"gml": "http://www.opengis.net/gml/3.2"})
"""
Explanation: Using NCEI geoportal REST API to collect information about IOOS Regional Association archived ... |
scottprahl/miepython | docs/02_efficiencies.ipynb | mit | #!pip install --user miepython
import importlib.resources
import numpy as np
import matplotlib.pyplot as plt
try:
import miepython
except ModuleNotFoundError:
print('miepython not installed. To install, uncomment and run the cell above.')
print('Once installation is successful, rerun this cell again.')
... |
herruzojm/udacity-deep-learning | sentiment-rnn/Sentiment_RNN_Solution.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... |
napsternxg/Udacity-Deep-Learning | udacity/5_word2vec.ipynb | gpl-3.0 | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
import collections
import math
import numpy as np
import os
import random
import tensorflow as tf
import urllib
import zipfile
from matplotlib import pylab
from sklearn.manifold import TSNE
"""
Explanation: Dee... |
saudijack/unfpyboot | Day_01/03_Homework/Homework_01/00_Homework-Solutions.ipynb | mit | %pylab inline
"""
Explanation: Homework 1
Google Trends is pretty awesome, except that on the site you cannot do more than overlay plots. Here we'll play with search term data downloaded from Google and draw our own conclusions.
Data from:
https://www.google.com/trends/explore#q=spring%20break%2C%20textbooks%2C%20nor... |
arturops/deep-learning | seq2seq/sequence_to_sequence_implementation.ipynb | mit | import numpy as np
import time
import helper
source_path = 'data/letters_source.txt'
target_path = 'data/letters_target.txt'
source_sentences = helper.load_data(source_path)
target_sentences = helper.load_data(target_path)
"""
Explanation: Character Sequence to Sequence
In this notebook, we'll build a model that ta... |
ilanfri/StatsML | Bayesian_bootstrap.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
%matplotlib inline
data = pd.read_csv('american_presidents.csv', header=0, index_col=None)
data
data.describe()
data.plot(x='order',y='height_cm', color='blue')
data.plot('order', kind='hist', color='blue')
import random... |
GoogleCloudPlatform/tf-estimator-tutorials | 07_Image_Analysis/01.0 - CNN with CIFAR-10 binary dataset.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import os
import re
import shutil
import sys
import tarfile
import tensorflow as tf
from six.moves import urllib
print(tf.__version__)
def maybe_download_and_extract(
dest_directory='data',
data_url='http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz'):
... |
volgy/notebook | SNR_SINAD_THD.ipynb | mit | %matplotlib inline
from itertools import combinations
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import get_window
FS = 20e6 # sampling freq
N = int(1e5) # number of samples
FREQ = 3 * FS / 20 # frequency of the signal (avoid harmonics near DC)
N_FFT = 8192
N_HARMONICS = 3 #... |
ecell/ecell4-notebooks | en/tutorials/tutorial09.ipynb | gpl-2.0 | from ecell4.prelude import *
w1 = ode.World(ones())
w2 = gillespie.World(ones())
"""
Explanation: 9. Spatial Gillespie Method
9.1. Spaces in E-Cell4
What the space in E-Cell4 looks like?
End of explanation
"""
w3 = ode.World(Real3(2, 0.5, 1)) # is almost equivalent to 'w1'
w4 = gillespie.World(Real3(2, 2, 0.25)) ... |
jorgemauricio/INIFAP_Course | ejercicios/Seaborn/2_Graficas_Categoricas.ipynb | mit | import seaborn as sns
%matplotlib inline
tips = sns.load_dataset('tips')
tips.head()
"""
Explanation: Categorical Data Plots
Now let's discuss using seaborn to plot categorical data! There are a few main plot types for this:
factorplot
boxplot
violinplot
stripplot
swarmplot
barplot
countplot
Let's go through exampl... |
DarkEnergyScienceCollaboration/Twinkles | examples/notebooks/MatchingLensGalaxies.ipynb | mit | import numpy as np
import matplotlib as mpl
mpl.rcParams['text.usetex'] = False
from matplotlib import pyplot as plt
import corner
import urllib
import os
from sklearn.cross_validation import train_test_split
from astroML.plotting import setup_text_plots
from lsst.sims.photUtils import Sed, Bandpass, BandpassDict, getI... |
kmunve/APS | aps/notebooks/varsom_training_data.ipynb | mit | import sys
import pandas as pd
import numpy as np
import json
from flatten_json import flatten
from pprint import pprint
from pathlib import Path
# make sure the aps module is in the pythonpath
APS_dir = str(Path.cwd().parents[1])
if APS_dir not in sys.path:
sys.path.append(APS_dir)
import aps.aps_io.get_forecasts ... |
JKeun/project-02-watcha | 02_preprocess/05_preprocess_expectation_df(mean, std, skewness).ipynb | mit | df = pd.read_csv('../resource/preprocess_dist_df.csv')
col = df.columns.astype(float).values
df.head()
"""
Explanation: 별점분포의 mean, var, skewness 구하기 - expect_df
$\mu = E[X] = \sum x \cdot p(x)$
$\sigma^2 = E[(X - \mu)^2] = \sum (x - \mu)^2 p(x)$
$\frac{\mu_3}{\sigma^3} = \operatorname{E}\left[\left(\frac{X-\mu}{\sig... |
mne-tools/mne-tools.github.io | 0.21/_downloads/31a362832c6e14bb418b12340a063c91/plot_movement_detection.ipynb | bsd-3-clause | # Authors: Adonay Nunes <adonay.s.nunes@gmail.com>
# Luke Bloy <luke.bloy@gmail.com>
# License: BSD (3-clause)
import os.path as op
import mne
from mne.datasets.brainstorm import bst_auditory
from mne.io import read_raw_ctf
from mne.preprocessing import annotate_movement, compute_average_dev_head_t
# Load d... |
kubeflow/kfp-tekton-backend | components/gcp/ml_engine/deploy/sample.ipynb | apache-2.0 | %%capture --no-stderr
KFP_PACKAGE = 'https://storage.googleapis.com/ml-pipeline/release/0.1.14/kfp.tar.gz'
!pip3 install $KFP_PACKAGE --upgrade
"""
Explanation: Name
Deploying a trained model to Cloud Machine Learning Engine
Label
Cloud Storage, Cloud ML Engine, Kubeflow, Pipeline
Summary
A Kubeflow Pipeline compone... |
ajaybhat/DLND | Project 3/dlnd_tv_script_generation.ipynb | apache-2.0 | import time
import pylab as pl
from IPython import display
"""
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... |
nyoungb2/CLdb | doc/examples/Ecoli/arrayBlast.ipynb | gpl-2.0 | # directory where you want the spacer blasting to be done
## CHANGE THIS!
workDir = "/home/nyoungb2/t/CLdb_Ecoli/"
"""
Explanation: Description:
This notebook goes through the analysis of blasting spacers vs a database containing potential protospacers.
Any blast datbase can be used.
A script is provided to easily bl... |
stephensekula/smu-honors-physics | pi_monte_carlo/Monte Carlo Lecture Code.ipynb | mit | Ntotal = 100
print(Ntotal)
Nin = 55.0
print(Ntotal/Nin)
"""
Explanation: Monte Carlo Methods: Computation of Pi
Basics: variables, values, printing
End of explanation
"""
import random
random.uniform(0.0,1.0)
random.uniform(0.0,1.0)
random.uniform(0.0,1.0)
random.uniform(0.0,1.0)
"""
Explanation: Random Numbers... |
QuantEcon/QuantEcon.notebooks | sci_python_quickstart.ipynb | bsd-3-clause | import sys
print(sys.version)
"""
Explanation: Scientific Python Quickstart
John Stachurski
ANU
This is a fast-paced, hands-on introduction to scientific computing with Python, contained in a Jupyter notebook. The main focus will be on introducing Python's four most important scientific libraries: NumPy, Scipy, Panda... |
tpin3694/tpin3694.github.io | machine-learning/ski-tomasi_corner_detector.ipynb | mit | # Load image
import cv2
import numpy as np
from matplotlib import pyplot as plt
"""
Explanation: Title: Shi-Tomasi Corner Detector
Slug: ski-tomasi_corner_detector
Summary: How to detect corners images using OpenCV in Python with the Shi-Tomasi Corner Detector.
Date: 2017-09-11 12:00
Category: Machine Learning
Tags... |
shaunharker/DSGRN | Tutorials/GettingStarted.ipynb | mit | import DSGRN
"""
Explanation: DSGRN Python Interface Tutorial
This notebook shows the basics of manipulating DSGRN with the python interface.
End of explanation
"""
network = DSGRN.Network("""
X1 : (X1+X2)(~X3)
X2 : (X1)
X3 : (X1)(~X2)""")
DSGRN.DrawGraph(network)
"""
Explanation: Network
The starting point of the... |
NYUDataBootcamp/Projects | UG_S17/Gender Inequality-Shi-Zhou.ipynb | mit | %matplotlib inline
import sys # system module
import numpy as np # scientific computing
import pandas as pd # data package
import matplotlib as mpl
import matplotlib.pyplot as plt # graphics module
"""
Explanation: Data Bootcamp Final Proj... |
splicemachine/splice-community-sample-code | twimlcon-workshop-materials/4 - Model Deployment.ipynb | apache-2.0 | #Begin spark session
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
#Create pysplice context. Allows you to create a Spark dataframe using our Native Spark DataSource
from splicemachine.spark import PySpliceContext
splice = PySpliceContext(spark)
#Initialize our Feature Store API
fr... |
openstreams/wflow | notebooks/openDAP-e2o.ipynb | gpl-3.0 | #!/usr/bin/env python
# we need the netcdf4 library. This allows us to read netcdf from OpeNDAP or files
import netCDF4
# Uncomment these if needed
#from numpy import *
#from scipy import *
# specify an url
rainurl = 'http://wci.earth2observe.eu/thredds/dodsC/ecmwf/met_forcing_v0/2012/Rainf_daily_E2OBS_201201.nc'
# ... |
kubeflow/fairing | examples/lightgbm/simple-training.ipynb | apache-2.0 | from kubeflow import fairing
# Setting up google container repositories (GCR) for storing output containers
# You can use any docker container registry istead of GCR
GCP_PROJECT = fairing.cloud.gcp.guess_project_name()
DOCKER_REGISTRY = 'gcr.io/{}/fairing-job'.format(GCP_PROJECT)
BASE_IMAGE = 'gcr.io/{}/lightgbm:lates... |
squaseghost/WWU-projects | Lab9 - TOOLS.ipynb | gpl-3.0 | import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
"""
Explanation: Lab 4: Curve Fitting
Jacob Skinner
End of explanation
"""
x=np.array([1.1,2.2,3.1,4.0,5.0,5.8,6.9])
y=np.array([2.0,3.0,4.0,5.0,6.0,7.0,8.0])
dely=np.array([0.1,0.1,0.1,0.1,0.1,0.1,0.1])
plt.plot(x,y,'ro')
plt.show()
"""
Ex... |
chusine/dlnd | face_generation/dlnd_face_generation-Original.ipynb | mit | data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
"""
Explanation: Face Generation
In this project, you'll use generative adv... |
phoebe-project/phoebe2-docs | 2.3/examples/rossiter_mclaughlin.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
"""
Explanation: Rossiter-McLaughlin Effect
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
"""
import phoebe
import numpy as np
b = phoebe.default_bina... |
alexwhb/algorithm-practice | jupyter-notebooks/algorithems/Graphs DFS and BFS.ipynb | mit | graph = {'A': set(['B', 'C']),
'B': set(['A', 'D', 'E']),
'C': set(['A', 'F']),
'D': set(['B']),
'E': set(['B', 'F']),
'F': set(['C', 'E'])}
print(graph)
set1 = set(['a', 'b'])
set2 = set(['b', 'c'])
set1.difference(set2)
set1.intersection(set2)
set1.issubset(set2)
set... |
organicmatters/Learnfastai | deeplearning1/nbs/lesson1.ipynb | apache-2.0 | %matplotlib inline
"""
Explanation: Using Convolutional Neural Networks
Welcome to the first week of the first deep learning certificate! We're going to use convolutional neural networks (CNNs) to allow our computer to see - something that is only possible thanks to deep learning.
Introduction to this week's task: 'Do... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/introduction_to_tensorflow/solutions/feat.cols_tf.data.ipynb | apache-2.0 | # You can use any Python source file as a module by executing an import statement in some other Python source file.
# The import statement combines two operations; it searches for the named module, then it binds the results of that search
# to a name in the local scope.
import numpy as np
import pandas as pd
# Import m... |
bloomberg/bqplot | examples/Tutorials/Pyplot.ipynb | apache-2.0 | import bqplot.pyplot as plt
# first, let's create two vectors x and y to plot using a Lines mark
import numpy as np
x = np.linspace(-10, 10, 100)
y = np.sin(x)
# 1. Create the figure object
fig = plt.figure(title="Simple Line Chart")
# 2. By default axes are created with basic defaults. If you want to customize the... |
DominikDitoIvosevic/Uni | STRUCE/2018/.ipynb_checkpoints/SU-2018-LAB03-SVM-i-kNN-checkpoint.ipynb | mit | import numpy as np
import scipy as sp
import pandas as pd
import mlutils
import matplotlib.pyplot as plt
%pylab inline
"""
Explanation: Sveučilište u Zagrebu
Fakultet elektrotehnike i računarstva
Strojno učenje 2018/2019
http://www.fer.unizg.hr/predmet/su
Laboratorijska vježba 3: Stroj potpornih vektora i algoritam... |
javoweb/deep-learning | image-classification/.ipynb_checkpoints/dlnd_image_classification-checkpoint.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'
class DLProgress(tqdm):
last_block = 0
def hoo... |
enbanuel/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 ... |
Diyago/Machine-Learning-scripts | DEEP LEARNING/Pytorch from scratch/MLP/Part 2 - Neural Networks in PyTorch (Solution).ipynb | apache-2.0 | # Import necessary packages
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import torch
import helper
import matplotlib.pyplot as plt
"""
Explanation: Neural networks with PyTorch
Deep learning networks tend to be massive with dozens or hundreds of layers, that's where the ter... |
srnas/barnaba | examples/example_12_escore.ipynb | gpl-3.0 | from barnaba import escore
pdb = "../test/data/1S72.pdb"
Escore = escore.Escore([pdb])
"""
Explanation: eSCORE, a scoring function for RNA structure prediction
With barnaba it is possible to score decoys according to the eSCORE function. Essentially, we train a simple statistical potential using the interactions obser... |
pyannote/pyannote-audio | tutorials/add_your_own_task.ipynb | mit | # this assumes that the AMI corpus has been setup for diarization
# according to https://github.com/pyannote/AMI-diarization-setup
import os
os.environ['PYANNOTE_DATABASE_CONFIG'] = '/Users/bredin/Development/pyannote/pyannote-db/AMI-diarization-setup/pyannote/database.yml'
from pyannote.database import get_protocol, ... |
tpin3694/tpin3694.github.io | python/pandas_create_pipeline.ipynb | mit | import pandas as pd
"""
Explanation: Title: Create A Pipeline In Pandas
Slug: pandas_create_pipeline
Summary: Create a pipeline in pandas.
Date: 2017-01-16 12:00
Category: Python
Tags: Data Wrangling
Authors: Chris Albon
Pandas' pipeline feature allows you to string together Python functions in order to build a pip... |
antoinecarme/pyaf | docs/PyAF_Introduction.ipynb | bsd-3-clause | import pandas as pd
csvfile_link = "https://raw.githubusercontent.com/antoinecarme/TimeSeriesData/master/ozone-la.csv"
ozone_dataframe = pd.read_csv(csvfile_link);
"""
Explanation: An introduction to time series forecasting with PyAF
This is an introduction to time series forecasting with PyAF.
We describe the basi... |
fugufisch/hu_bp_python_course | 07_modelling/Modelling_Hard.ipynb | mit | # initial values
time_range = range(0,100)
k1 = 0.2
k2 = 0.1
conc_A = 3.0
conc_B = 2.0
conc_C = 2.5
# function to return changes
def eval_change():
# reactions
v1 = k1 * conc_A * conc_B
v2 = k2 * conc_C
dadt = -v1 + v2
dbdt = -v1 + v2
dcdt = v1 - v2
return dadt, dbdt, dcdt
# solutions for ... |
kit-cel/wt | wt/vorlesung/basic_concepts_Python.ipynb | gpl-2.0 | # defining lists
sport_list = [ 'cycling', 'football', 'fitness' ]
first_prime_numbers = [ 2, 3, 5, 7, 11, 13, 17, 19 ]
# getting contents
sport = sport_list[ 2 ]
third_prime = first_prime_numbers[ 2 ]
# printing
print( 'All sports:', sport_list )
print( 'Sport to be done:', sport )
print( '\nFirst primes:', first_p... |
agile-geoscience/xlines | notebooks/00_Synthetic_wedge_model.ipynb | apache-2.0 | import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: X lines of Python
Wedge model
This is part of an Agile blog series called x lines of Python.
We start with the usual preliminaries.
End of explanation
"""
length, depth = 40, 100
model = 1 + np.tri(depth, length, -depth//3, dtype=int)
model[:depth//... |
newsapps/public-notebooks | Crimes by beat.ipynb | mit | from datetime import datetime, timedelta
pivot = datetime.strptime('11/18/2014', '%m/%d/%Y')
today = datetime.strptime('1/18/2016', '%m/%d/%Y')
print today - pivot
period = timedelta(days=426)
print pivot - period
"""
Explanation: First, we need to know how far back to go. We want equal number of days before and afte... |
TomTranter/OpenPNM | examples/paper_recreations/Tranter et al. (2017)/Tranter et al. (2017) - Part A.ipynb | mit | import pickle
import numpy as np
import openpnm as op
from pathlib import Path
import matplotlib.pyplot as plt
from openpnm.models import physics as pm
%matplotlib inline
ws = op.Workspace()
ws.settings["loglevel"] = 50
ws.clear()
np.random.seed(10)
path = Path('../../fixtures/hysteresis_paper_network.pnm')
save_net =... |
mne-tools/mne-tools.github.io | stable/_downloads/58e35821e0f211b843d5ead3e33d8849/20_sensors_time_frequency.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
# Richard Höchenberger <richard.hoechenberger@gmail.com>
#
# License: BSD-3-Clause
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.time_frequency... |
google/tf-quant-finance | tf_quant_finance/examples/jupyter_notebooks/Root_Search.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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, sof... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/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', 'miroc', 'sandbox-2', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: MIROC
Source ID: SANDBOX-2
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Ra... |
Olsthoorn/TransientGroundwaterFlow | exercises_notebooks/WellInLeakyAquifer.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pylab as plt
#%%writefile wells.py # remove first # to write this as file "wells.py" to disk
import pdb
import numpy as np
from scipy.special import expi
def W(u): return -expi(-u) # Theis
def wh(u, rho):
"""Returns Hantush's well function values
Note: works only for... |
NYUDataBootcamp/Projects | UG_S16/Yusef-Shaheen-IPO-Underpricing.ipynb | mit | import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import requests
import re
plt.style.use('ggplot')
import matplotlib
%matplotlib inline
"""
Explanation: Abstract
Companies seeking public funding partner up with investment banks who bear the responsibility of facilitating the private bidding proce... |
danresende/deep-learning | sentiment_network/Sentiment Classification - Mini Project 3.ipynb | mit | def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()... |
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