repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
beangoben/HistoriaDatos_Higgs | Dia1/.ipynb_checkpoints/Intro a Matplotlib-checkpoint.ipynb | gpl-2.0 | import numpy as np # modulo de computo numerico
import matplotlib.pyplot as plt # modulo de graficas
import pandas as pd # modulo de datos
# esta linea hace que las graficas salgan en el notebook
%matplotlib inline
"""
Explanation: Intro a Matplotlib
Matplotlib = Libreria para graficas cosas matematicas
Que es Matplot... |
google/starthinker | colabs/smartsheet_to_bigquery.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: SmartSheet Sheet To BigQuery
Move sheet data into a BigQuery table.
License
Copyright 2020 Google LLC,
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... |
andrewzwicky/puzzles | FiveThirtyEightRiddler/2018-04-06/vandal_dates.ipynb | mit | import datetime
from collections import Counter
start = datetime.date(2001, 1, 1)
end = datetime.date(2100, 1, 1) - datetime.timedelta(days=1)
d = start
anarchy_dates = []
delta = datetime.timedelta(days=1)
while d <= end:
if d.day * d.month == d.year % 100:
anarchy_dates.append(d)
d += delta
ana... |
zambzamb/zpic | python/R-L Waves.ipynb | agpl-3.0 | import em1ds as zpic
electrons = zpic.Species( "electrons", -1.0, ppc = 64, uth=[0.005,0.005,0.005])
sim = zpic.Simulation( nx = 1000, box = 100.0, dt = 0.0999, species = electrons )
sim.emf.solver_type = 'PSATD'
#Bx0 = 0.5
#Bx0 = 1.0
Bx0 = 2.0
sim.emf.set_ext_fld('uniform', B0= [Bx0, 0.0, 0.0])
"""
Explanation: ... |
tyler-abbot/PyShop | session2/PyShop_session2_notes.ipynb | agpl-3.0 | import numpy as np #This is the traditional way to import NumPy
a = np.array([[1.,0],[0,1]]) #Create an identity matrix by hand NOTE: What are the data types of entries?
b = np.eye(2) # Creat an identity matrix using built in funciton
print(a)
print(b)
"""
Explanation: PyShop Session 2
This session focuses on so... |
Benedicto/ML-Learning | Clustering_5_lda_blank.ipynb | gpl-3.0 | import graphlab as gl
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
'''Check GraphLab Create version'''
from distutils.version import StrictVersion
assert (StrictVersion(gl.version) >= StrictVersion('1.8.5')), 'GraphLab Create must be version 1.8.5 or later.'
# import wiki data
wiki = gl.SFr... |
fja05680/pinkfish | examples/245.double-7s-ave-portfolio/strategy.ipynb | mit | import datetime
import matplotlib.pyplot as plt
import pandas as pd
import pinkfish as pf
import strategy
# Format price data.
pd.options.display.float_format = '{:0.2f}'.format
%matplotlib inline
# Set size of inline plots
'''note: rcParams can't be in same cell as import matplotlib
or %matplotlib inline
... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/migration/UJ5 AutoML for vision with Vertex AI Video Classification.ipynb | apache-2.0 | ! pip3 install -U google-cloud-aiplatform --user
"""
Explanation: Vertex AI AutoML Image Object Detection
Installation
Install the latest (preview) version of Vertex SDK.
End of explanation
"""
! pip3 install google-cloud-storage
"""
Explanation: Install the Google cloud-storage library as well.
End of explanation
... |
Hyperparticle/graph-nlu | notebooks/dynamic_memory_3.ipynb | mit | import pandas as pd
import numpy as np
import nltk
from sklearn.metrics import accuracy_score
from neo4j.v1 import GraphDatabase, basic_auth
from collections import defaultdict
refs_utts = pd.read_pickle('resources/utts_refs.pkl')
props = pd.read_pickle('resources/restaurants_props.pkl')
len(refs_utts), len(props)
re... |
xdnian/pyml | assignments/ex06_ch1113_xdnian.ipynb | mit | %load_ext watermark
%watermark -a '' -u -d -v -p numpy,pandas,matplotlib,scipy,sklearn
%matplotlib inline
# Added version check for recent scikit-learn 0.18 checks
from distutils.version import LooseVersion as Version
from sklearn import __version__ as sklearn_version
"""
Explanation: Assignment 6
This assignment has... |
mintcloud/deep-learning | sentiment-rnn/.ipynb_checkpoints/Sentiment RNN Solution-checkpoint.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... |
dtamayo/rebound | ipython_examples/EscapingParticles.ipynb | gpl-3.0 | import rebound
import numpy as np
def setupSimulation():
sim = rebound.Simulation()
sim.add(m=1., hash="Sun")
sim.add(x=0.4,vx=5., hash="Mercury")
sim.add(a=0.7, hash="Venus")
sim.add(a=1., hash="Earth")
sim.move_to_com()
return sim
sim = setupSimulation()
sim.status()
"""
Explanation: Esc... |
laurajchang/NPTFit | examples/Example6_Manual_nonPoissonian_Likelihood.ipynb | mit | # Import relevant modules
%matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import healpy as hp
import matplotlib.pyplot as plt
from NPTFit import nptfit # module for performing scan
from NPTFit import create_mask as cm # module for creating the mask
from NPTFit import psf_correction as pc # m... |
UWashington-Astro300/Astro300-A17 | Python_Introduction.ipynb | mit | print("Hello World!")
# lines that begin with a # are treated as comment lines and not executed
# print("This line is not printed")
print("This line is printed")
"""
Explanation: A jupyter notebook is a browser-based environment that integrates:
A Kernel (python)
Text
Executable code
Plots and images
Rendered math... |
deot95/Tesis | Proyecto de Grado Ingeniería Electrónica/Workspace/RL/TowardsRL/.ipynb_checkpoints/reinforcement_q_learning-checkpoint.ipynb | mit | import gym
import math
import random
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from collections import namedtuple
from itertools import count
from copy import deepcopy
from PIL import Image
from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.optim ... |
phobson/statsmodels | examples/notebooks/pca_fertility_factors.ipynb | bsd-3-clause | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels as sm
from statsmodels.multivariate.pca import PCA
"""
Explanation: Statsmodels Principal Component Analysis
Key ideas: Principal component analysis, world bank data, fertility
In this notebook, we use princip... |
josef-pkt/statsmodels | examples/notebooks/markov_regression.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
# NBER recessions
from pandas_datareader.data import DataReader
from datetime import datetime
usrec = DataReader('USREC', 'fred', start=datetime(1947, 1, 1), end=datetime(2013, 4, 1))
"""
Explanatio... |
metpy/MetPy | v0.9/_downloads/7dd7941230ab04d65d899c66ed400ef4/xarray_tutorial.ipynb | bsd-3-clause | import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import xarray as xr
# Any import of metpy will activate the accessors
import metpy.calc as mpcalc
from metpy.testing import get_test_data
"""
Explanation: xarray with MetPy Tutorial
xarray <http://xarray.pydata.org/>_ ... |
mne-tools/mne-tools.github.io | 0.18/_downloads/012b7ba30b03ebda4c3419b2e4f5161a/plot_ssp_projs_sensitivity_map.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
from mne import read_forward_solution, read_proj, sensitivity_map
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
... |
csc-training/python-introduction | notebooks/examples/3 - Functions.ipynb | mit | def my_function(arg_one, arg_two, optional_1=6, optional_2="seven"):
return " ".join([str(arg_one), str(arg_two), str(optional_1), str(optional_2)])
print(my_function("a", "b"))
print(my_function("a", "b", optional_2="eight"))
#go ahead and try out different components
"""
Explanation: Functions
Functions and fu... |
M-R-Houghton/euroscipy_2015 | scikit_image/lectures/3_morphological_operations.ipynb | mit | import numpy as np
from matplotlib import pyplot as plt, cm
import skdemo
plt.rcParams['image.cmap'] = 'cubehelix'
plt.rcParams['image.interpolation'] = 'none'
image = np.array([[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 0,... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/end-to-end-structured/labs/3b_bqml_linear_transform_babyweight.ipynb | apache-2.0 | %%bigquery
-- LIMIT 0 is a free query; this allows us to check that the table exists.
SELECT * FROM babyweight.babyweight_data_train
LIMIT 0
%%bigquery
-- LIMIT 0 is a free query; this allows us to check that the table exists.
SELECT * FROM babyweight.babyweight_data_eval
LIMIT 0
"""
Explanation: LAB 3b: BigQuery ML... |
adrn/GaiaPairsFollowup | notebooks/Full reduction.ipynb | mit | # Standard library
from os.path import join
import sys
if '/Users/adrian/projects/longslit/' not in sys.path:
sys.path.append('/Users/adrian/projects/longslit/')
# Third-party
from astropy.constants import c
import numpy as np
import matplotlib.pyplot as plt
import astropy.units as u
from astropy.io import fits
im... |
lcharleux/numerical_analysis | doc/ODE/.ipynb_checkpoints/ODE-checkpoint.ipynb | gpl-2.0 | tmax = .2
t = np.linspace(0., tmax, 1000)
x0, y0 = 0., 0.
vx0, vy0 = 1., 1.
g = 10.
x = vx0 * t
y = -g * t**2/2. + vy0 * t
fig = plt.figure()
ax.set_aspect("equal")
plt.plot(x, y, label = "Exact solution")
plt.grid()
plt.xlabel("x")
plt.ylabel("y")
plt.legend()
plt.show()
"""
Explanation: Ordinary differential ... |
ES-DOC/esdoc-jupyterhub | notebooks/mri/cmip6/models/mri-esm2-0/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mri', 'mri-esm2-0', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: MRI
Source ID: MRI-ESM2-0
Topic: Ocean
Sub-Topics: Timestepping Framework, Advection... |
csaladenes/csaladenes.github.io | present/mcc2/PythonDataScienceHandbook/05.13-Kernel-Density-Estimation.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np
"""
Explanation: <!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; t... |
Chipe1/aima-python | notebooks/chapter19/Learners.ipynb | mit | import os, sys
sys.path = [os.path.abspath("../../")] + sys.path
from deep_learning4e import *
from notebook4e import *
from learning4e import *
"""
Explanation: Learners
In this section, we will introduce several pre-defined learners to learning the datasets by updating their weights to minimize the loss function. wh... |
MTgeophysics/mtpy | examples/workshop/Workshop Exercises Core.ipynb | gpl-3.0 | # import required modules
from mtpy.core.mt import MT
# Define the path to your edi file
edi_file = "C:/mtpywin/mtpy/examples/data/edi_files_2/Synth00.edi"
# Create an MT object
mt_obj = MT(edi_file)
"""
Explanation: Introduction
This workbook contains some examples for reading, analysing and plotting processed MT d... |
graphistry/pygraphistry | demos/demos_by_use_case/logs/malware-hypergraph/Malware Hypergraph.ipynb | bsd-3-clause | import pandas as pd
import graphistry as g
# To specify Graphistry account & server, use:
# graphistry.register(api=3, username='...', password='...', protocol='https', server='hub.graphistry.com')
# For more options, see https://github.com/graphistry/pygraphistry#configure
df = pd.read_csv('./barncat.1k.csv', encod... |
quantopian/research_public | notebooks/lectures/CAPM_and_Arbitrage_Pricing_Theory/notebook.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels import regression
import matplotlib.pyplot as plt
"""
Explanation: The Capital Asset Pricing Model and Arbitrage Pricing Theory
by Beha Abasi, Maxwell Margenot, and Delaney Granizo-Mackenzie
Part of the Quantopian Lecture Series:
www... |
reidmcy/MACS30200proj | ProblemSets/PS3/PS3.ipynb | gpl-2.0 | import numpy as np
import pandas
import statsmodels
import statsmodels.formula.api
import statsmodels.stats.api
import statsmodels.stats
import statsmodels.stats.outliers_influence
import statsmodels.graphics.regressionplots
import sklearn.preprocessing
import matplotlib.pyplot as plt
import seaborn
%matplotlib inline... |
cosmostatschool/MACSS2017 | pre-school/NumCompTools/Seb_MACSS2017_python.ipynb | mit | a = 1
b = 2.67
c = "Vamos PSG"
d = True
print type(a), type(b), type(c), type(d)
"""
Explanation: Generalities
Author : Sebastien Fromenteau
context : Mexican AstroCosmology Statistics School 2017
This notebook is partially based on the notebook wrote by Iván Rodríguez Montoya for MACSS 2016
I will not focus on list ... |
benfred/implicit | examples/tutorial_lastfm.ipynb | mit | from implicit.datasets.lastfm import get_lastfm
artists, users, artist_user_plays = get_lastfm()
"""
Explanation: Tutorial - Recommending Music with the last.fm 360K dataset.
This tutorial shows the major functionality of the implicit library by building a music recommender system using the the last.fm 360K dataset.
... |
gwsb-istm-6212-fall-2016/syllabus-and-schedule | scripts/20161129-exporting-csv-from-datanotebook.ipynb | cc0-1.0 | !echo 'redspot' | sudo -S service postgresql restart
%load_ext sql
!createdb -U dbuser test
%sql postgresql://dbuser@localhost:5432/test
"""
Explanation: Exporting CSV data from the server
This process is slightly cumbersome because of Unix permissions. Remember - nine times out of ten, on Unix, it's probably a pe... |
marioberges/F16-12-752 | projects/gvizcain/ApplianceClassifier_v3.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import pickle, time, seaborn, random, json, os
%matplotlib inline
from sklearn import tree
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier, RandomForestClassifier
f... |
mjlong/openmc | docs/source/pythonapi/examples/tally-arithmetic.ipynb | mit | %load_ext autoreload
%autoreload 2
import glob
from IPython.display import Image
import numpy as np
import openmc
from openmc.statepoint import StatePoint
from openmc.summary import Summary
%matplotlib inline
"""
Explanation: This notebook shows the how tallies can be combined (added, subtracted, multiplied, etc.) ... |
philmui/datascience2016fall | lecture03.numpy.pandas/lecture03.web.scaping.ipynb | mit | import requests
from lxml import html
"""
Explanation: Import needed libraries
End of explanation
"""
response = requests.get('http://news.ycombinator.com/')
response
response.content
"""
Explanation: We used the library "request" last time in getting Twitter data (REST-ful). We are introducing the new "lxml" lib... |
ulf1/overgang | examples/internal data format of ctmc_fit.ipynb | mit | data = [([0, 1, 2, 1], [2.2, 3.35, 9.4, 1.3]),
([1, 0, 1], [4.0, 1.25, 1.7])]
"""
Explanation: The Internal Data Format for ctmc_fit
The function ctmc_fit expect the data to be structured as follows
End of explanation
"""
import numpy as np
numstates = 3
statetime = np.zeros(numstates, dtype=float)
transcou... |
gigjozsa/HI_analysis_course | chapter_01_somename/01_01_somename2.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
"""
Explanation: Content
Glossary
1. Somename
Next: 1.2 Somename 3
Import standard modules:
End of explanation
"""
pass
"""
Explanation: Import section specifi... |
jseabold/statsmodels | examples/notebooks/gee_score_test_simulation.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
from scipy.stats.distributions import norm, poisson
import statsmodels.api as sm
import matplotlib.pyplot as plt
"""
Explanation: GEE score tests
This notebook uses simulation to demonstrate robust GEE score tests. These tests can be used in a GEE analysis to compare nested hypo... |
landlab/landlab | notebooks/tutorials/network_sediment_transporter/network_sediment_transporter.ipynb | mit | import warnings
warnings.filterwarnings("ignore")
import matplotlib.pyplot as plt
import numpy as np
from landlab.components import FlowDirectorSteepest, NetworkSedimentTransporter
from landlab.data_record import DataRecord
from landlab.grid.network import NetworkModelGrid
from landlab.plot import graph
from landlab.... |
maniacalbrain/Cluster-vinb-tweets | #vinb - Cluster Tweets.ipynb | mit | from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(use_idf=True, ngram_range =(1,3))
train_data_features = vectorizer.fit_transform(clean_tweets)
terms = vectorizer.get_feature_names()
from sklearn.cluster import KMeans
num_clusters = 15
km = KMeans(n_clusters=num_clusters)
k... |
nbokulich/short-read-tax-assignment | ipynb/mock-community/taxonomy-assignment-trimmed-dbs.ipynb | bsd-3-clause | from os.path import join, expandvars
from joblib import Parallel, delayed
from glob import glob
from os import system
from tax_credit.framework_functions import (parameter_sweep,
generate_per_method_biom_tables,
move_results_to_rep... |
wuafeing/Python3-Tutorial | 01 data structures and algorithms/01.12 determine most freqently items in seq.ipynb | gpl-3.0 | words = [
'look', 'into', 'my', 'eyes', 'look', 'into', 'my', 'eyes',
'the', 'eyes', 'the', 'eyes', 'the', 'eyes', 'not', 'around', 'the',
'eyes', "don't", 'look', 'around', 'the', 'eyes', 'look', 'into',
'my', 'eyes', "you're", 'under'
]
from collections import Counter
word_counts = Counter(words)
# ... |
lukin155/skola-programiranja | 02-Fajlovi-komentari-racun.ipynb | mit | print("This is the first line.")
print("This is the second line.")
print("This is the third line.")
"""
Explanation: Komande u konzoli
Funkcija <i>print</i> služi za prikaz sadržaja na ekranu.<br />
Pokrenite Pajton iz konzole (kao u lekciji Uvod). U konzoli otkucajte sledeće komande (ovde možete videti i komande i ... |
ledeprogram/algorithms | class7/homework/benzaquen_mercy_assignment_7_1.ipynb | gpl-3.0 | !pip install pydotplus
import pandas as pd
%matplotlib inline
import pydotplus
from pandas.tools.plotting import scatter_matrix
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn import tree
from sklearn.externals.six import StringIO
from sklearn.cross_validation import train_test_split
from... |
gaufung/PythonStandardLibrary | FileSystem/tempfile.ipynb | mit | import os
import tempfile
print('Building a filename with PID:')
filename = '/tmp/guess_my_name.{}.txt'.format(os.getpid())
with open(filename, 'w+b') as temp:
print('temp:')
print(' {!r}'.format(temp))
print('temp.name:')
print(' {!r}'.format(temp.name))
# Clean up the temporary file yourself.
os.r... |
arasdar/DL | uri-dl/uri-dl-hw-2/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... |
turbomanage/training-data-analyst | courses/ai-for-finance/solution/aapl_regression_scikit_learn.ipynb | apache-2.0 | %%bash
bq mk -d ai4f
bq load --autodetect --source_format=CSV ai4f.AAPL10Y gs://cloud-training/ai4f/AAPL10Y.csv
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn import linear_model
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_scor... |
snowicecat/umich-eecs445-f16 | lecture10_bias-variance-tradeoff/lecture10_bias-variance-tradeoff.ipynb | mit | %pylab inline
import numpy as np
import seaborn as sns
import pandas as pd
from Lec08 import *
"""
Explanation: $$ \LaTeX \text{ command declarations here.}
\newcommand{\R}{\mathbb{R}}
\renewcommand{\vec}[1]{\mathbf{#1}}
\newcommand{\X}{\mathcal{X}}
\newcommand{\D}{\mathcal{D}}
\newcommand{\vx}{\mathbf{x}}
\newcommand... |
OceanPARCELS/parcels | parcels/examples/tutorial_sampling.ipynb | mit | # Modules needed for the Parcels simulation
from parcels import Variable, FieldSet, ParticleSet, JITParticle, AdvectionRK4
import numpy as np
from datetime import timedelta as delta
# To open and look at the temperature data
import xarray as xr
import matplotlib as mpl
import matplotlib.pyplot as plt
"""
Explanation... |
datamicroscopes/release | examples/gamma_poisson.ipynb | bsd-3-clause | import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
sns.set_context('talk')
import csv
import urllib2
import StringIO
%matplotlib inline
"""
Explanation: Count Data and Ordinal Data with the Gamma-Poisson Distribution
Typically, we model count data, or integer valued data, wit... |
bje-/NEMO | doc/guide.ipynb | gpl-3.0 | import nemo
from nemo import scenarios
c = nemo.Context()
scenarios._one_ccgt(c)
print(c.generators)
"""
Explanation: NEMO User's Guide: a Jupyter notebook
Note that this is a Jupyter notebook that uses some magic IPython commands (starting with %). It may not work in other notebooks like the one included with Pycharm... |
dchandan/rebound | ipython_examples/FourierSpectrum.ipynb | gpl-3.0 | import rebound
import numpy as np
sim = rebound.Simulation()
sim.units = ('AU', 'yr', 'Msun')
sim.add("Sun")
sim.add("Jupiter")
sim.add("Saturn")
"""
Explanation: Fourier analysis & resonances
A great benefit of being able to call rebound from within python is the ability to directly apply sophisticated analysis tools... |
byronknoll/tensorflow-compress | nncp-splitter.ipynb | unlicense | batch_size = 96 #@param {type:"integer"}
#@markdown >_Set this to the same value that will be used in tensorflow-compress._
mode = 'split' #@param ["split", "join"]
num_parts = 4 #@param {type:"integer"}
#@markdown >_This is the number of parts the file should be split to._
http_path = '' #@param {type:"string"}
#@mark... |
morganics/bayesianpy | examples/notebook/iris_cluster_count.ipynb | apache-2.0 | import pandas as pd
import logging
import sys
sys.path.append("../../../bayesianpy")
import bayesianpy
import matplotlib.pyplot as plt
import os
logger = logging.getLogger()
bayesianpy.jni.attach(logger)
db_folder = bayesianpy.utils.get_path_to_parent_dir('')
iris = pd.read_csv(os.path.join(db_folder, "data/iris... |
Lasagne/Recipes | examples/spatial_transformer_network.ipynb | mit | !wget -N https://s3.amazonaws.com/lasagne/recipes/datasets/mnist_cluttered_60x60_6distortions.npz
def load_data():
data = np.load(mnist_cluttered)
X_train, y_train = data['x_train'], np.argmax(data['y_train'], axis=-1)
X_valid, y_valid = data['x_valid'], np.argmax(data['y_valid'], axis=-1)
X_test, y_te... |
metpy/MetPy | v1.0/_downloads/4211928bfede6cdca0afdb2d06bea2d1/Find_Natural_Neighbors_Verification.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
from scipy.spatial import Delaunay
from metpy.interpolate.geometry import circumcircle_radius, find_natural_neighbors
# Create test observations, test points, and plot the triangulation and points.
gx, gy = np.meshgrid(np.arange(0, 20, 4), np.arange(0, 20, 4))
pts = ... |
BinRoot/TensorFlow-Book | ch08_rl/Concept01_rl.ipynb | mit | %matplotlib inline
from yahoo_finance import Share
from matplotlib import pyplot as plt
import numpy as np
import random
import tensorflow as tf
import random
"""
Explanation: Ch 08: Concept 01
Reinforcement learning
The states are previous history of stock prices, current budget, and current number of shares of a sto... |
tensorflow/docs-l10n | site/ko/tutorials/reinforcement_learning/actor_critic.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... |
iktakahiro/ipython-notebook-sample | pymook/pymook_reading_20150723.ipynb | mit | import pandas as pd
import numpy as np
"""
Explanation: このNotebookについて
2015年07月23日（木）に開催された、「Pythonエンジニア養成読本」読書会 03 - connpassの登壇時、追加資料として利用したものです。
Author: Takahiro Ikeuchi - @iktakahiro
End of explanation
"""
# 商品データと購買ログの2つのデータを読み込みます。
master = pd.read_csv('./data/master.csv')
log = pd.read_csv('./data/log.csv')
... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_sensor_noise_level.ipynb | bsd-3-clause | # Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import mne
data_path = mne.datasets.sample.data_path()
raw_erm = mne.io.read_raw_fif(op.join(data_path, 'MEG', 'sample',
'ernoise_raw.fif'), preload=True)
"""
Explanation: Show nois... |
google-research/google-research | ged_tts/toy_example/toy_ged.ipynb | apache-2.0 | import numpy as np
from scipy.optimize import minimize
import functools
import matplotlib.pyplot as plt
import palettable
"""
Explanation: Toy example to demonstrate the importance of the repulsive term in the energy distance
This notebook reproduces Figure 1 from A Spectral Energy Distance for Parallel Speech Synthes... |
planet-os/notebooks | api-examples/CFSv2_usage_example.ipynb | mit | %matplotlib notebook
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from API_client.python import datahub
from API_client.python.lib import dataset
from API_client.python.lib import variables
"""
Explanation: Using a CFSv2 forecast
CFSv2 is a seasonal forecast system, used for analysing past ... |
squishbug/DataScienceProgramming | 08-Machine-Learning-I/HW08/README.ipynb | cc0-1.0 | # Loading python packages and APD data file (this step does not have to be included in hw6_answers.py)
import pandas as pd
import numpy as np
df = pd.read_csv('/home/data/APD/COBRA-YTD2017.csv.gz')
"""
Explanation: Homework 6
Use this notebook to work on your answers and check solutions. You can then submit your fun... |
wzbozon/statsmodels | examples/notebooks/statespace_sarimax_stata.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
from scipy.stats import norm
import statsmodels.api as sm
import matplotlib.pyplot as plt
from datetime import datetime
import requests
from io import BytesIO
"""
Explanation: SARIMAX: Introduction
This notebook replicates examples from the Stata ARIMA time se... |
lukauskas/bernoulli-mixture-model | notebooks/Proof of Concept (digits dataset).ipynb | gpl-3.0 | import sklearn.datasets
digits_dataset = sklearn.datasets.load_digits()
digits = pd.DataFrame(digits_dataset.data)
labels = pd.Series(digits_dataset.target, index=digits.index, name='label')
THRESHOLD = np.mean(digits.values.reshape(-1))
binary_digits = digits >= THRESHOLD
from sklearn.utils import shuffle
binary_dig... |
scottquiring/Udacity_Deeplearning | intro-to-tflearn/TFLearn_Sentiment_Analysis.ipynb | mit | import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
"""
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network w... |
gerbaudo/fbu | tutorial.ipynb | gpl-2.0 | import fbu
myfbu = fbu.PyFBU()
"""
Explanation: Basic usage
Create an instance of PyFBU
End of explanation
"""
myfbu.data = [100,150]
"""
Explanation: Supply the input distribution to be unfolded as a 1-dimensional list for N bins, with each entry corresponding to the bin content.
End of explanation
"""
myfbu.res... |
ypeleg/Deep-Learning-Keras-Tensorflow-PyCon-Israel-2017 | 2.4 Transfer Learning & Fine-Tuning.ipynb | mit | import numpy as np
import datetime
np.random.seed(1337) # for reproducibility
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras import backe... |
jeiros/Jupyter_notebooks | python/markov_analysis/msmbuilder-API.ipynb | mit | from msmbuilder.dataset import dataset
import numpy as np
import os
from mdtraj.utils import timing
from msmbuilder.featurizer import DihedralFeaturizer
import seaborn as sns; sns.set_style("white"); sns.set_palette("Blues")
with timing("Loading data as dataset object"):
wt_xyz = dataset("/Users/je714/wt_data/*/05... |
elsuizo/Control_de_robots_py | tp3.ipynb | gpl-3.0 | from IPython.core.display import Image
Image(filename='Imagenes/copy_left.png')
Image(filename='Imagenes/dibujo_robot2_tp2.png')
#imports
from sympy import *
import numpy as np
#Con esto las salidas van a ser en LaTeX
init_printing(use_latex=True)
"""
Explanation: Martín Noblía
Tp3
<img src="files/copy_left.png... |
corochann/chainer-hands-on-tutorial | src/01_chainer_intro/dataset_introduction.ipynb | mit | # Initial setup following http://docs.chainer.org/en/stable/tutorial/basic.html
import numpy as np
import chainer
from chainer import cuda, Function, gradient_check, report, training, utils, Variable
from chainer import datasets, iterators, optimizers, serializers
from chainer import Link, Chain, ChainList
import chain... |
giacomov/astromodels | examples/Additional_features_for_scripts_and_applications.ipynb | bsd-3-clause | from astromodels import *
my_model = load_model("my_model.yml")
"""
Explanation: Additional features for scripts and applications
In this document we describe some features of the astromodels package which are useful for non-interactive environment such as scripts or applications
First let's import astromodels and le... |
maxalbert/tohu | notebooks/v4/Custom_generators.ipynb | mit | import tohu
from tohu.v4.primitive_generators import *
from tohu.v4.derived_generators import *
from tohu.v4.dispatch_generators import *
from tohu.v4.custom_generator import *
from tohu.v4.utils import print_generated_sequence, make_dummy_tuples
print(f'Tohu version: {tohu.__version__}')
"""
Explanation: Custom gene... |
basp/notes | single_value_calculus.ipynb | mit | c1 = lambda x: x + 1
c2 = lambda x: -x + 2
x1 = np.linspace(0.01, 2, 10)
x2 = np.linspace(-2, -0.01, 10)
plt.plot(x1, c1(x1), label=r"$y = x + 1$")
plt.plot(x2, c2(x2), label=r"$y = -x + 2$")
plt.plot(0, 2, 'wo', markersize=7)
plt.plot(0, 1, 'wo', markersize=7)
ax = plt.axes()
ax.set_ylim(0, 4)
plt.legend(loc=3)
"""
E... |
darkomen/TFG | medidas/0107150/.ipynb_checkpoints/Analisis-checkpoint.ipynb | cc0-1.0 | %pylab inline
#Importamos las librerías utilizadas
import numpy as np
import pandas as pd
import seaborn as sns
#Mostramos las versiones usadas de cada librerías
print ("Numpy v{}".format(np.__version__))
print ("Pandas v{}".format(pd.__version__))
print ("Seaborn v{}".format(sns.__version__))
#Abrimos el fichero csv... |
cmmorrow/sci-analysis | docs/using_sci_analysis.ipynb | mit | import warnings
warnings.filterwarnings("ignore")
import numpy as np
import scipy.stats as st
from sci_analysis import analyze
"""
Explanation: Using sci-analysis
From the python interpreter or in the first cell of a Jupyter notebook, type:
End of explanation
"""
%matplotlib inline
import numpy as np
import scipy.st... |
marksibrahim/musings | notebooks/.ipynb_checkpoints/A Neural Network Classifier using Keras-checkpoint.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LogisticRegressionCV
from sklearn import datasets
from keras.models import Sequential
from keras.layers.core import De... |
regardscitoyens/consultation_an | exploitation/analyse_quanti.ipynb | agpl-3.0 | #contributions = pd.read_json(path_or_buf='../data/EGALITE4.brut.json', orient="columns")
def loadContributions(file, withsexe=False):
contributions = pd.read_json(path_or_buf=file, orient="columns")
rows = [];
rindex = [];
for i in range(0, contributions.shape[0]):
row = {};
row['id'] ... |
TimothyHelton/k2datascience | notebooks/HR_Exercise.ipynb | bsd-3-clause | from k2datascience import hr_analytics
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
%matplotlib inline
"""
Explanation: HR Dataset - Statistics Review
Timothy Helton
<br>
<font color="red">
NOTE:
<br>
This notebook uses code found in the
... |
mne-tools/mne-tools.github.io | 0.16/_downloads/make_report.ipynb | bsd-3-clause | # Authors: Teon Brooks <teon.brooks@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
from mne.report import Report
from mne.datasets import sample
from mne import read_evokeds
from matplotlib import pyplot as plt
data_path = sample.data_path()
meg_path = data_path + '/MEG/sampl... |
google/trax | trax/examples/NER_using_Reformer.ipynb | apache-2.0 | #@title
# Copyright 2020 Google LLC.
# 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... |
SheffieldML/notebook | GPy/sparse_gp_regression.ipynb | bsd-3-clause | %matplotlib inline
%config InlineBackend.figure_format = 'svg'
import GPy
import numpy as np
np.random.seed(101)
"""
Explanation: Sparse GP Regression
14th January 2014 James Hensman
29th September 2014 Neil Lawrence (added sub-titles, notes and some references).
This example shows the variational compression effect o... |
Kaggle/learntools | notebooks/pandas/raw/tut_2.ipynb | apache-2.0 | #$HIDE_INPUT$
import pandas as pd
pd.set_option('max_rows', 5)
import numpy as np
reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)
reviews
"""
Explanation: Introduction
In the last tutorial, we learned how to select relevant data out of a DataFrame or Series. Plucking the right dat... |
materialsvirtuallab/matgenb | notebooks/2018-09-25-Structure Prediction using Pymatgen and the Materials API.ipynb | bsd-3-clause | # Imports we need for running structure prediction
from pymatgen.analysis.structure_prediction.substitutor import Substitutor
from pymatgen.analysis.structure_prediction.substitution_probability import SubstitutionPredictor
from pymatgen.analysis.structure_matcher import StructureMatcher, ElementComparator
from pymat... |
jon-young/medicalimage | Liver Interactive.ipynb | mit | import collections
import matplotlib
matplotlib.use('Agg')
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import os
import pickle
import scipy.stats
import SimpleITK as sitk
from os.path import expanduser, join
from scipy.spatial.distance import euclidean
"""
Explanation: Liver Segmen... |
PyladiesMx/Pyladies_ifc | 4. Lops/.ipynb_checkpoints/For Loops-checkpoint.ipynb | mit | #Obtén el cuadrado de 1
#Obtén el cuadrado de 2
#Obtén el cuadrado de 3
#Obtén el cuadrado de 4
#Obtén el cuadrado de 5
#Obtén el cuadrado de 6
#Obtén el cuadrado de 7
#Obtén el cuadrado de 8
#Obtén el cuadrado de 9
#Obtén el cuadrado de 10
"""
Explanation: Bienvenid@ a otra reunión de pyladies!!
Sólo para as... |
mne-tools/mne-tools.github.io | 0.17/_downloads/4365eab31ed2fa347de7f294ac9500c3/plot_label_from_stc.ipynb | bsd-3-clause | # Author: Luke Bloy <luke.bloy@gmail.com>
# Alex Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.minimum_norm import read_inverse_operator, apply_inverse
from mne.datasets import sample
print(__doc__)
data_pa... |
jonasluz/mia-cg | Exercises/Exercícios#1.ipynb | unlicense | from typing import List
Vector = List[float]
import numpy as np
import matplotlib.pyplot as plt
def dcos(v: Vector, verbose=False):
"""
Calcula os cosenos diretores do vetor a.
Para cada componente c1, c2, ... cn do vetor v, o cosseno diretor é dado por:
dcosk = ck / ||v||
"""
result = []
... |
Chipe1/aima-python | notebooks/chapter24/Image Segmentation.ipynb | mit | import os, sys
sys.path = [os.path.abspath("../../")] + sys.path
from perception4e import *
from notebook4e import *
import matplotlib.pyplot as plt
"""
Explanation: Segmentation
Image segmentation is another early as well as an important image processing task. Segmentation is the process of breaking an image into gro... |
DwangoMediaVillage/pqkmeans | tutorial/3_billion_scale_clustering.ipynb | mit | import numpy
import pqkmeans
import tqdm
import os
import six
import gzip
import texmex_python
"""
Explanation: Chapter 3: Billion-scale clustering
This chapter contains the followings:
Download the SIFT1B dataset
Encode billion-scale data iteratively
Run clustering
Requisites:
- numpy
- pqkmeans
- tqdm
- six
- os
... |
kingb12/languagemodelRNN | report_notebooks/encdec_noing23_200_512_04drb.ipynb | mit | report_file = '/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing23_200_512_04drb/encdec_noing23_200_512_04drb.json'
log_file = '/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing23_200_512_04drb/encdec_noing23_200_512_04drb_logs.json'
import json
import matplotlib.pyplo... |
t-vi/pytorch-tvmisc | wasserstein-distance/sn_projection_cgan_64x64_143c.ipynb | mit | import matplotlib
try:
%matplotlib inline
except: # if we are not in Jupyter, use the headless frontend
matplotlib.use('Agg')
from matplotlib import pyplot
import IPython
import numpy
import time
import torch
import torchvision
import torch.utils.data
"""
Explanation: Spectral normalization GAN with c... |
tensorflow/examples | courses/udacity_intro_to_tensorflow_lite/tflite_c03_exercise_convert_model_to_tflite.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... |
mjuric/LSSTC-DSFP-Sessions | Session4/Day1/LSSTC-DSFP4-Juric-FrequentistAndBayes-02-Nuisance.ipynb | mit | p_hat = 5. / 8.
freq_prob = (1 - p_hat) ** 3
print("Naïve Frequentist Probability of Bob Winning: {0:.2f}".format(freq_prob))
"""
Explanation: Frequentism and Bayesianism II: When Results Differ
Mario Juric & Jake VanderPlas, University of Washington
e-mail: mjuric@ast... |
mdda/pycon.sg-2015_deep-learning | ipynb/6-RNN-as-Author.ipynb | mit | import numpy
import theano
from theano import tensor
from blocks.bricks import Tanh
from blocks.bricks.recurrent import GatedRecurrent
from blocks.bricks.sequence_generators import (SequenceGenerator, Readout, SoftmaxEmitter, LookupFeedback)
from blocks.graph import ComputationGraph
import blocks.algorithms
from bloc... |
fluffy-hamster/A-Beginners-Guide-to-Python | A Beginners Guide to Python/Homework Solutions/10. Strings (HW).ipynb | mit | # Solution One: Triple Quotes!!
cool_story_bro = """"Ahhh!!!! spiders!", cried the monster."Don't worry" said our hero, "I have a sharp spoon"."""
print(cool_story_bro)
"""
Explanation: Homework:
Name a variable "cool_story_bro" and then assign the the following text as a string:
"Ahhh!!!! spiders!", cried the monst... |
alexweav/Learny-McLearnface | Example1.ipynb | mit | import numpy as np
import LearnyMcLearnface as lml
"""
Explanation: Example 1 - Overfitting Sample Data
Here, we will use a simple model to overfit a set of randomly generated data points.
First, we import Numpy to hold the data, and we import Learny McLearnface.
End of explanation
"""
test_data = np.random.randn(10... |
NewKnowledge/punk | examples/Novelty Detection.ipynb | mit | import punk
help(punk)
"""
Explanation: The goal os punk is to make available sime wrappers for a variety of machine learning pipelines.
The pipelines are termed primitves and each primitive is designed with a functional programming approach in mind.
At the time of this writing, punk is being periodically updated. Any... |
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