repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
hiteshagrawal/python | udacity/nano-degree/ipython_notebook_tutorial (1).ipynb | gpl-2.0 | # Hit shift + enter or use the run button to run this cell and see the results
print 'hello world'
# The last line of every code cell will be displayed by default,
# even if you don't print it. Run this cell to see how this works.
2 + 2 # The result of this line will not be displayed
3 + 3 # The result of this line... |
megatharun/basic-python-for-researcher | .ipynb_checkpoints/Tutorial 7 - Data Visualization and Plotting-checkpoint.ipynb | artistic-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: <span style="color: #B40486">BASIC PYTHON FOR RESEARCHERS</span>
by Megat Harun Al Rashid bin Megat Ahmad
last updated: April 14, 2016
<span style="color: #29088A">7. Data Visualization and Plotting</span>
The <span style="color: #0000FF">$Matplotli... |
materialsproject/MPContribs | mpcontribs-portal/notebooks/contribs.materialsproject.org/get_started.ipynb | mit | name = "your-project-name"
apikey = "your-api-key" # profile.materialsproject.org
"""
Explanation: MPContribs
Walkthrough
start with a materials detail page on MP with user contributions
navigate to https://mpcontribs.org and explore
apply for project on https://workshop-contribs.materialsproject.org/contribute (wai... |
axbaretto/beam | examples/notebooks/documentation/transforms/python/elementwise/map-py.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License")
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you u... |
vadim-ivlev/STUDY | handson-data-science-python/DataScience-Python3/SimilarMovies.ipynb | mit | import pandas as pd
r_cols = ['user_id', 'movie_id', 'rating']
ratings = pd.read_csv('e:/sundog-consult/udemy/datascience/ml-100k/u.data', sep='\t', names=r_cols, usecols=range(3), encoding="ISO-8859-1")
m_cols = ['movie_id', 'title']
movies = pd.read_csv('e:/sundog-consult/udemy/datascience/ml-100k/u.item', sep='|',... |
postBG/DL_project | intro-to-rnns/Anna_KaRNNa.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is base... |
probml/pyprobml | notebooks/book1/13/mlp_cifar_pytorch.ipynb | mit | import sklearn
import scipy
import scipy.optimize
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
import itertools
import time
from functools import partial
import os
import numpy as np
fr... |
compsocialscience/summer-institute | 2018/materials/boulder/day2-digital-trace-data/BoulderSICSS.ipynb | mit | # Install tweepy
# !pip install tweepy
# Import the libraries we need
import tweepy
import json
import time
import networkx
import os
import matplotlib.pyplot as plt
from collections import Counter
# Authenticate!
auth = tweepy.OAuthHandler("Consumer Key", "Consumer Secret")
auth.set_access_token("Access Token", "Acc... |
dvklopfenstein/PrincetonAlgorithms | notebooks/ElemSymbolTbls.ipynb | gpl-2.0 | # Setup for running examples
import sys
import os
sys.path.insert(0, '{GIT}/PrincetonAlgorithms/py'.format(GIT=os.environ['GIT']))
from AlgsSedgewickWayne.BST import BST
# Function to convert keys to key-value pairs where
# 1. the key is the letter and
# 2. the value is the index into the key list
get_kv = lambda ... |
opengeostat/pygslib | pygslib/Ipython_templates/backtr_raw.ipynb | mit | #general imports
import matplotlib.pyplot as plt
import pygslib
from matplotlib.patches import Ellipse
import numpy as np
import pandas as pd
#make the plots inline
%matplotlib inline
"""
Explanation: Testing the back normalscore transformation
End of explanation
"""
#get the data in gslib format into a pa... |
ES-DOC/esdoc-jupyterhub | notebooks/nerc/cmip6/models/hadgem3-gc31-hm/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'hadgem3-gc31-hm', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: NERC
Source ID: HADGEM3-GC31-HM
Topic: Atmos
Sub-Topics: Dynamical Core, Radia... |
kimkipyo/dss_git_kkp | 통계, 머신러닝 복습/160607화_12일차_(확률론적)선형 회귀 분석 Linear Regression Analysis/5.patsy 패키지 소개.ipynb | mit | from patsy import dmatrix, dmatrices
np.random.rand(5)
np.random.seed(0)
x1 = np.random.rand(5) + 10
x2 = np.random.rand(5) * 10
x1, x2
dmatrix("x1")
"""
Explanation: patsy 패키지 소개
회귀 분석 전처리 패키지
encoding/transform/design matrix 기능
R-style formula 문자열 지원
design matrix
dmatrix(fomula[, data])
R-style formula 문자열을 받... |
enakai00/jupyter_NikkeiLinux | No4/Figure3 - Basic Animations.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from numpy.random import randint
%matplotlib nbagg
"""
Explanation: [1-1] 動画作成用のモジュールをインポートして、動画を表示可能なモードにセットします。
End of explanation
"""
fig = plt.figure(figsize=(6,2))
subplot = fig.add_subplot(1,1,1)
subplot.set_xlim(0,50)
... |
batfish/pybatfish | docs/source/notebooks/interacting.ipynb | apache-2.0 | import pandas as pd
from pybatfish.client.session import Session
from pybatfish.datamodel import *
from pybatfish.datamodel.answer import *
from pybatfish.datamodel.flow import *
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
# Prevent rendering text between '$' as MathJax expre... |
santipuch590/deeplearning-tf | dl_tf_BDU/3.RNN/ML0120EN-3.2-Review-LSTM-basics.ipynb | mit | import numpy as np
import tensorflow as tf
tf.reset_default_graph()
sess = tf.InteractiveSession()
"""
Explanation: <a href="https://www.bigdatauniversity.com"><img src = "https://ibm.box.com/shared/static/jvcqp2iy2jlx2b32rmzdt0tx8lvxgzkp.png" width = 300, align = "center"></a>
<h1 align=center><font size = 5>RECURR... |
anthonyng2/FX-Trading-with-Python-and-Oanda | Oanda v1 REST-oandapy/05.00 Trade Management.ipynb | mit | from datetime import datetime, timedelta
import pandas as pd
import oandapy
import configparser
config = configparser.ConfigParser()
config.read('../config/config_v1.ini')
account_id = config['oanda']['account_id']
api_key = config['oanda']['api_key']
oanda = oandapy.API(environment="practice",
a... |
SylvainCorlay/bqplot | examples/Marks/Object Model/Pie.ipynb | apache-2.0 | data = np.random.rand(3)
pie = Pie(sizes=data, display_labels='outside', labels=list(string.ascii_uppercase))
fig = Figure(marks=[pie], animation_duration=1000)
fig
"""
Explanation: Basic Pie Chart
End of explanation
"""
n = np.random.randint(1, 10)
pie.sizes = np.random.rand(n)
"""
Explanation: Update Data
End of ... |
nwjs/chromium.src | third_party/tensorflow-text/src/docs/guide/subwords_tokenizer.ipynb | bsd-3-clause | #@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... |
roaminsight/roamresearch | BlogPosts/Translation_scaling_invariance_regression/Translation_and_scaling_invariance_in_regression_models.ipynb | apache-2.0 | __author__ = 'Adam Foster and Nick Dingwall'
"""
Explanation: Translation and scaling invariance in regression models
End of explanation
"""
from centering_and_scaling import *
%matplotlib inline
# A dataset:
data = np.random.multivariate_normal(
mean=[4, 0], cov=[[5, 2], [2, 3]], size=250)
X, y = data[:, 0], ... |
jon-young/cell-line-clust | doc/Biclustering.ipynb | gpl-2.0 | dfFile = os.path.join('..', 'data', 'siRNA_dataframe.csv')
RNAiDf = pd.read_csv(dfFile, index_col=0)
RNAiDf.tail()
"""
Explanation: 2015 December 4-6
Loading and exploring UTSW RNAi dataset...
End of explanation
"""
rplVals = np.nanmedian(RNAiDf.values, axis=0)
for i,col in enumerate(RNAiDf.columns):
RNAiDf[c... |
dereneaton/RADmissing | sims_nb_simulations.ipynb | mit | ## standard Python imports
import glob
import itertools
from collections import OrderedDict, Counter
## extra Python imports
import rpy2 ## required for tree plotting
import ete2 ## used for tree manipulation
import egglib ## used for coalescent simulations
import numpy as np
impor... |
nicoguaro/AdvancedMath | notebooks/pde.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib import rcParams
"""
Explanation: Partial differential equations
End of explanation
"""
%matplotlib notebook
rcParams['mathtext.fontset'] = 'cm'
rcParams['font.size'] = 14
red = "#e41a1c"
blue = "#377eb8"
gra... |
satishgoda/learning | python/jupyter/tutorial/jupyter_notebook.ipynb | mit | from IPython.display import FileLink, FileLinks
"""
Explanation: About
This Jupyter notebook demonstrates the features of the Notebook!!
http://jupyter-notebook.readthedocs.io
End of explanation
"""
!ls -1rt *.png
"""
Explanation: Notebook Format
JSON
Viewing Notebooks
http://nbviewer.jupyter.org
Github suppo... |
kratzert/RRMPG | examples/model_api_example.ipynb | mit | # Imports and Notebook setup
from timeit import timeit
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from rrmpg.models import CemaneigeGR4J
from rrmpg.data import CAMELSLoader
from rrmpg.tools.monte_carlo import monte_carlo
from rrmpg.utils.metrics import calc_nse
"""
Explanation: Model API ... |
mne-tools/mne-tools.github.io | 0.21/_downloads/2212671cb1d04d466a35eb15470863da/plot_forward_sensitivity_maps.ipynb | bsd-3-clause | # Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
import matplotlib.pyplot as plt
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-... |
Cristianobam/UFABC | Unidade5-Atividades.ipynb | mit | # Faça aqui o programa usando for
n = int(input("De o valor de n: "))
total = 0
for n in range(1, n + 1):
num = int(input('Número a ser somado: '))
total = total + (num**2)
print(total)
# Faça aqui o programa usando while
teto = int(input('Número a serem somados: '))
total1 = 0
r = 0
while(r < teto):
nume ... |
kubeflow/kfserving-lts | docs/samples/explanation/alibi/moviesentiment/movie_review_explanations.ipynb | apache-2.0 | !pygmentize moviesentiment.yaml
!kubectl apply -f moviesentiment.yaml
CLUSTER_IPS=!(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
CLUSTER_IP=CLUSTER_IPS[0]
print(CLUSTER_IP)
SERVICE_HOSTNAMES=!(kubectl get inferenceservice moviesentiment -o jsonpath='{.s... |
jepegit/cellpy | dev_utils/easyplot/EasyPlot_Dev.ipynb | mit | files = [f1, f2]
names = [f1.name, f2.name]
ezplt = easyplot.EasyPlot(files, names, figtitle="Test1")
ezplt.plot()
"""
Explanation: Checking standard usage
End of explanation
"""
easyplot.EasyPlot(
files,
names,
figtitle="Test2",
galvanostatic_normalize_capacity=True,
all_in_one=True,
dqdv_... |
fortyninemaps/karta | doc/source/geointerface.ipynb | mit | from karta.examples import greenland
from karta.vector.read import from_shape
import shapely.geometry
"""
Explanation: Example using __geo_interface__
The __geo_interface__ specification suggested by Sean Gilles (gist) vastly expands the capabilities of Karta by making data interchange with external modules simple. Th... |
tensorflow/docs-l10n | site/en-snapshot/guide/keras/save_and_serialize.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... |
xmnlab/notebooks | DSP/phase/Phase-Difference.ipynb | mit | from matplotlib import pyplot as plt
from numpy.fft import fft, ifft
import numpy as np
import pandas as pd
%matplotlib inline
def sine_signal(
t: np.array, A: float, f: float, φ: float
) -> pd.Series:
"""
φ input in degree unit
:param t:
:type t:
:param A:
:type A:
:param f:
:typ... |
ES-DOC/esdoc-jupyterhub | notebooks/dwd/cmip6/models/sandbox-3/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'dwd', 'sandbox-3', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: DWD
Source ID: SANDBOX-3
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balance... |
tuanavu/coursera-university-of-washington | machine_learning/4_clustering_and_retrieval/assigment/week3/.ipynb_checkpoints/module-5-decision-tree-assignment-1-blank-Graphlab-checkpoint.ipynb | mit | import graphlab
graphlab.canvas.set_target('ipynb')
"""
Explanation: Identifying safe loans with decision trees
The LendingClub is a peer-to-peer leading company that directly connects borrowers and potential lenders/investors. In this notebook, you will build a classification model to predict whether or not a loan pr... |
hanhanwu/Hanhan_Data_Science_Practice | AI_Experiments/digit_recognition_Pytorch.ipynb | mit | %pylab inline
import os
import numpy as np
import pandas as pd
import imageio as io
from sklearn.metrics import accuracy_score
import torch
# Get data from here: https://datahack.analyticsvidhya.com/contest/practice-problem-identify-the-digits/
seed = 10
rng = np.random.RandomState(seed)
train = pd.read_csv('Train_... |
dusenberrymw/incubator-systemml | samples/jupyter-notebooks/DML Tips and Tricks (aka Fun With DML).ipynb | apache-2.0 | from systemml import MLContext, dml, jvm_stdout
ml = MLContext(sc)
print (ml.buildTime())
"""
Explanation: Replace NaN with mode
Use sample builtin function to create sample from matrix
Count of Matching Values in two Matrices/Vectors
Cross Validation
Value-based join of two Matrices
Filter Matrix to include only Fre... |
schatzlab/biomedicalresearch | lectures/05.BinomialExponential/BinomialDistribution.ipynb | mit | import random
results = []
for trial in xrange(10000):
heads = 0
for i in xrange(100):
flip = random.randint(0,1)
if (flip == 0):
heads += 1
results.append(heads)
print results[1:10]
import matplotlib.pyplot as plt
plt.figure()
plt.hist(results)
plt.show()
## Plot the histogram... |
mne-tools/mne-tools.github.io | 0.24/_downloads/78ad76ea5b03c29b4b851b8b64f74b68/linear_model_patterns.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Romain Trachel <trachelr@gmail.com>
# Jean-Remi King <jeanremi.king@gmail.com>
#
# License: BSD-3-Clause
import mne
from mne import io, EvokedArray
from mne.datasets import sample
from mne.decoding import Vectorizer, get_coef
from sklearn... |
nilmtk/nilmtk | docs/manual/user_guide/nilmtk_api_tutorial.ipynb | apache-2.0 | from nilmtk.api import API
import warnings
warnings.filterwarnings("ignore")
"""
Explanation: NILMTK Rapid Experimentation API
This notebook demonstrates the use of NILMTK's ExperimentAPI - a new NILMTK interface which allows NILMTK users to focus on which experiments to run rather than on the code required to run... |
jonasluz/mia-cg | Exercises/Exercícios#2.ipynb | unlicense | # Demonstração algébrica, sem código.
# Desenho da parábola.
#
import math
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid.axislines import SubplotZero
def prep_axis():
"""
Preparação dos eixos do gráfico
"""
fig = plt.figure(1)
ax = Subplot... |
sassoftware/sas-viya-machine-learning | image_recognition/car-damage-analysis/Car-Damage-Image-Analysis-for-Insurance.ipynb | apache-2.0 | # import the required packages
from swat import *
from pprint import pprint
import numpy as np
import matplotlib.pyplot as plt
import cv2
# define the function to display the processed image files.
def imageShow(session, casTable, imageId, nimages):
a = session.table.fetch(sastypes=False,sortby=[{'name':'_id_'}]... |
jphall663/GWU_data_mining | 10_model_interpretability/src/dt_surrogate.ipynb | apache-2.0 | # imports
import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from h2o.estimators.deeplearning import H2ODeepLearningEstimator
from h2o.backend import H2OLocalServer
from IPython.display import Image
from IPython.display import display
import os
import re
import subprocess
from subprocess import Cal... |
metpy/MetPy | v0.12/_downloads/8c91fa5ab51e12860cfa1e679eaa746d/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.cbook import get_test_data
from metpy.units import units
"""
Explanation: xarray with MetPy Tutorial
xarray <htt... |
psas/liquid-engine-analysis | archive/aerobee-150-reconstruction/AJ11-26.ipynb | gpl-3.0 | from math import pi, log
# Physics
g_0 = 9.80665 # kg.m/s^2 Standard gravity
# Chemistry
rho_rfna = 1500.0 # kg/m^3 Density of IRFNA
rho_fa = 1130.0 # kg/m^3 Density of Furfuryl Alcohol
rho_an = 1021.0 # kg/m^3 Density of Aniline
# Data
Isp = 209.0 # s Ave... |
DS-100/sp17-materials | sp17/labs/lab11/lab11.ipynb | gpl-3.0 | !pip install -U sklearn
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn as skl
import sklearn.linear_model as lm
import scipy.io as sio
!pip install -U okpy
from client.api.notebook import Notebook
ok = Notebook('lab11.ok')
"""
Explanatio... |
quoniammm/mine-tensorflow-examples | fastAI/deeplearning1/nbs/statefarm.ipynb | mit | from theano.sandbox import cuda
cuda.use('gpu0')
%matplotlib inline
from __future__ import print_function, division
path = "data/state/"
#path = "data/state/sample/"
import utils; reload(utils)
from utils import *
from IPython.display import FileLink
batch_size=64
"""
Explanation: Enter State Farm
End of explanation... |
astarostin/MachineLearningSpecializationCoursera | course4/week1 - Биномиальный критерий для доли - demo.ipynb | apache-2.0 | import numpy as np
from scipy import stats
%pylab inline
"""
Explanation: Биномиальный критерий для доли
End of explanation
"""
n = 16
n_samples = 1000
samples = np.random.randint(2, size = (n_samples, n))
t_stat = map(sum, samples)
pylab.hist(t_stat, bins = 16, color = 'b', range = (0, 16), label = 't_stat')
pyl... |
arborh/tensorflow | tensorflow/lite/experimental/micro/examples/micro_speech/train_speech_model.ipynb | apache-2.0 | import os
# A comma-delimited list of the words you want to train for.
# The options are: yes,no,up,down,left,right,on,off,stop,go
# All other words will be used to train an "unknown" category.
os.environ["WANTED_WORDS"] = "yes,no"
# The number of steps and learning rates can be specified as comma-separated
# lists t... |
phenology/infrastructure | applications/notebooks/stable/plot_kmeans_clusters-Light.ipynb | apache-2.0 | import sys
sys.path.append("/usr/lib/spark/python")
sys.path.append("/usr/lib/spark/python/lib/py4j-0.10.4-src.zip")
sys.path.append("/usr/lib/python3/dist-packages")
import os
os.environ["HADOOP_CONF_DIR"] = "/etc/hadoop/conf"
import os
os.environ["PYSPARK_PYTHON"] = "python3"
os.environ["PYSPARK_DRIVER_PYTHON"] = "... |
ecell/ecell4-notebooks | en/tests/Reversible.ipynb | gpl-2.0 | %matplotlib inline
from ecell4.prelude import *
"""
Explanation: Reversible
This is for an integrated test of E-Cell4. Here, we test a simple reversible association/dissociation model in volume.
End of explanation
"""
D = 1
radius = 0.005
N_A = 60
U = 0.5
ka_factor = 0.1 # 0.1 is for reaction-limited
N = 20 # a n... |
eds-uga/csci1360e-su16 | lectures/L3 - Python Variables and Syntax.ipynb | mit | x = 2
"""
Explanation: Lecture 3: Python Variables and Syntax
CSCI 1360E: Foundations for Informatics and Analytics
Overview and Objectives
In this lecture, we'll get into more detail on Python variables, as well as language syntax. By the end, you should be able to:
Define variables of string and numerical types, co... |
vadim-ivlev/STUDY | handson-data-science-python/DataScience-Python3/ItemBasedCF.ipynb | mit | import pandas as pd
r_cols = ['user_id', 'movie_id', 'rating']
ratings = pd.read_csv('e:/sundog-consult/udemy/datascience/ml-100k/u.data', sep='\t', names=r_cols, usecols=range(3), encoding="ISO-8859-1")
m_cols = ['movie_id', 'title']
movies = pd.read_csv('e:/sundog-consult/udemy/datascience/ml-100k/u.item', sep='|',... |
olivierverdier/demo-notebooks | PageRank.ipynb | mit | A1 = array([
[0, 1, 0, 0, 0, 0 ],
[1, 0, 0, 0, 0, 1 ],
[0, 0, 0, 1/3, 1/2, 0 ],
[0, 0, 0, 0, 0, 0 ],
[0, 0, 0, 1/3, 0, 0 ],
[0, 0, 1, 1/3, 1/2, 0 ] ])
brus = 1/6*array([
[1,1,1,1,1,1],
[1,1,1,1,1,1],
[1,1,1,1,1,1],
[1,1,1,1,1,1],
[1,1,1,1,1,1],
[1,1,1,1,1,1]... |
amirfz/pinder | exploration/exploring_the_idea.ipynb | gpl-3.0 | client = MongoClient('localhost:27017')
db = client.arXivDB
db.arXivfeeds.count()
"""
Explanation: connecting to mongodb
End of explanation
"""
print(db.arXivfeeds.find_one().keys())
for item in db.arXivfeeds.find({'published_parsed': 2016}).sort('_id', pymongo.DESCENDING).limit(5):
print(item['title'])
#db.ar... |
ES-DOC/esdoc-jupyterhub | notebooks/nasa-giss/cmip6/models/sandbox-3/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'sandbox-3', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: SANDBOX-3
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, En... |
ES-DOC/esdoc-jupyterhub | notebooks/fio-ronm/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', 'fio-ronm', 'sandbox-1', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: FIO-RONM
Source ID: SANDBOX-1
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation... |
anonyXmous/CapstoneProject | sliderule_dsi_xml_exercise.ipynb | unlicense | from xml.etree import ElementTree as ET
"""
Explanation: XML example and exercise
study examples of accessing nodes in XML tree structure
work on exercise to be completed and submitted
reference: https://docs.python.org/2.7/library/xml.etree.elementtree.html
data source: http://www.dbis.informatik.uni-goettinge... |
ES-DOC/esdoc-jupyterhub | notebooks/fio-ronm/cmip6/models/sandbox-3/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'fio-ronm', 'sandbox-3', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: FIO-RONM
Source ID: SANDBOX-3
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Ener... |
ray-project/ray | doc/source/tune/examples/hebo_example.ipynb | apache-2.0 | # !pip install ray[tune]
!pip install HEBO==0.3.2
"""
Explanation: Running Tune experiments with HEBOSearch
In this tutorial we introduce HEBO, while running a simple Ray Tune experiment. Tune’s Search Algorithms integrate with ZOOpt and, as a result, allow you to seamlessly scale up a HEBO optimization process - with... |
irazhur/StatisticalMethods | examples/SDSScatalog/CorrFunc.ipynb | gpl-2.0 | %load_ext autoreload
%autoreload 2
import numpy as np
import SDSS
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import copy
# We want to select galaxies, and then are only interested in their positions on the sky.
data = pd.read_csv("downloads/SDSSobjects.csv",usecols=['ra','dec','u','g',\
... |
ajdawson/python_for_climate_scientists | course_content/f2py-example/f2py_example.ipynb | gpl-3.0 | import lanczos1
print(dir(lanczos1))
lanczos1.dfiltrq?
"""
Explanation: Calling Fortran code from Python: f2py
f2py
The program f2py is supplied with numpy. It wraps Fortran code into an extension module, allowing the Fortran code to be called directly from Python.
The quick way
First we'll build a Python module fro... |
sdpython/ensae_teaching_cs | _doc/notebooks/td1a_algo/td1a_correction_session7.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 1A.algo - Programmation dynamique et plus court chemin (correction)
Correction.
End of explanation
"""
import pyensae.datasource
pyensae.datasource.download_data("matrix_distance_7398.zip", website = "xd")
import pandas
df = pandas.re... |
Atzingen/curso-IoT-2017 | aula-03-python/Introducao-Python-01.ipynb | mit | print "Hello Python 2.7 !"
"""
Explanation: Introdução a linguagem Python (parte 1)
Notebook para o curso de IoT - IFSP Piracicaba
Gustavo Voltani von Atzingen
Python - versão 2.7
Este notebook contém uma introdução aos comandos básicos em python.
Serão cobertos os seguintes tópicos
Print
Comentários
Atribuição de va... |
smousavi05/EQTransformer | docs/source/downloading.ipynb | mit | from EQTransformer.utils.downloader import makeStationList, downloadMseeds
"""
Explanation: Downloading Continuous Data
This notebook demonstrates the use of EQTransformer for downloading continuous data from seismic networks.
End of explanation
"""
help(makeStationList)
"""
Explanation: You can use help() to learn... |
simpeg/tutorials | notebooks/fundamentals/pixels_and_neighbors/mesh.ipynb | mit | %matplotlib inline
import numpy as np
from SimPEG import Mesh, Utils
import matplotlib.pyplot as plt
plt.set_cmap(plt.get_cmap('viridis')) # use a nice colormap!
"""
Explanation: The Mesh: Where do things live?
<img src="images/FiniteVolume.png" width=70% align="center">
<h4 align="center">Figure 3. Anatomy of a fi... |
scraperwiki/databaker | databaker/tutorial/Finding_your_way.ipynb | agpl-3.0 |
# Load in the functions
from databaker.framework import *
# Load the spreadsheet
tabs = loadxlstabs("example1.xls")
# Select the first table
tab = tabs[0]
print("The unordered bag of cells for this table looks like:")
print(tab)
"""
Explanation: Opening and previewing
This uses the tiny excel spreadsheet example1.... |
udibr/flavours-of-physics | sPlot.ipynb | mit | import numpy as np
%matplotlib inline
from matplotlib import pylab as plt
import pandas as pd
import evaluation
folder = '../inputs/'
agreement = pd.read_csv(folder + 'check_agreement.csv', index_col='id')
"""
Explanation: In the kaggle flavours of physics competition the admins wanted to test
if the predictions tha... |
paolorivas/homeworkfoundations | homeworkdata/Homework_3_Paolo_Rivas_Legua.ipynb | mit | from bs4 import BeautifulSoup
from urllib.request import urlopen
html_str = urlopen("http://static.decontextualize.com/widgets2016.html").read()
document = BeautifulSoup(html_str, "html.parser")
"""
Explanation: Homework assignment #3
These problem sets focus on using the Beautiful Soup library to scrape web pages.
Pr... |
anhaidgroup/py_entitymatching | notebooks/guides/step_wise_em_guides/Sampling and Labeling.ipynb | bsd-3-clause | # Import py_entitymatching package
import py_entitymatching as em
import os
import pandas as pd
# Get the datasets directory
datasets_dir = em.get_install_path() + os.sep + 'datasets'
path_A = datasets_dir + os.sep + 'DBLP.csv'
path_B = datasets_dir + os.sep + 'ACM.csv'
path_C = datasets_dir + os.sep + 'tableC.csv'
... |
kirichoi/tellurium | examples/notebooks/core/tesedmlExample.ipynb | apache-2.0 | from __future__ import print_function
import tellurium as te
te.setDefaultPlottingEngine('matplotlib')
%matplotlib inline
import phrasedml
antimony_str = '''
model myModel
S1 -> S2; k1*S1
S1 = 10; S2 = 0
k1 = 1
end
'''
phrasedml_str = '''
model1 = model "myModel"
sim1 = simulate uniform(0, 5, 100)
task1 =... |
olivertomic/hoggorm | examples/PCA/PCA_on_cancer_data.ipynb | bsd-2-clause | import hoggorm as ho
import hoggormplot as hop
import pandas as pd
import numpy as np
"""
Explanation: Principal component analysis (PCA) on cancer data
This notebook illustrates how to use the hoggorm package to carry out principal component analysis (PCA) on a multivariate data set on cancer in men across OECD count... |
google-research/google-research | yoto/colabs/plot_yoto_vae.ipynb | apache-2.0 | import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_datasets as tfds
from io import StringIO, BytesIO
import numpy as np
import IPython.display
import PIL.Image
tf.compat.v1.enable_eager_execution()
tf.compat.v1.enable_v2_behavior()
#@title Plotting utilities
# Plotting utils, taken from
# https://... |
Juanlu001/Charla-PyConES15-poliastro | Going to Mars with Python in 5 minutes.ipynb | mit | %matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import astropy.units as u
from astropy import time
from poliastro import iod
from poliastro.plotting import plot
from poliastro.bodies import Sun, Earth
from poliastro.twobody import State
from poliastro im... |
egentry/dwarf_photo-z | dwarfz/data/get_data.ipynb | mit | from __future__ import division, print_function
# give access to importing dwarfz
import os, sys
dwarfz_package_dir = os.getcwd().split("dwarfz")[0]
if dwarfz_package_dir not in sys.path:
sys.path.insert(0, dwarfz_package_dir)
import dwarfz
from dwarfz.hsc_credentials import credential
from dwarfz.hsc_release_que... |
metpy/MetPy | v0.7/_downloads/sigma_to_pressure_interpolation.ipynb | bsd-3-clause | import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
from netCDF4 import Dataset, num2date
import metpy.calc as mcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo
from metpy.units import units
"""
Explanation: Sigma to Pressure Interpolation
By ... |
MasterRobotica-UVic/Control-and-Actuators | proportional_control.ipynb | gpl-3.0 | def carSys(n, kp, d0):
return d0*pow(1-0.1*kp,n)
# The system starts at 11m from the wall
d0 = 11
# optimal values of kp [0,10]:
# 0 < kp < 10
# try different cases
kp = 5.0
def interactiveCar(n):
print("Total time: ", n*0.01, " seconds")
print("Distance to wall: ", carSys(n, kp, d0) )
return
inte... |
mehmetcanbudak/JupyterWorkflow | JupyterWorkflow.ipynb | mit | URL = "https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD"
from urllib.request import urlretrieve
urlretrieve(URL, "Fremont.csv")
!head Freemont.csv
import pandas as pd
data = pd.read_csv("Fremont.csv")
data.head()
data = pd.read_csv("Fremont.csv", index_col="Date", parse_dates=True)
data.hea... |
jtwhite79/pyemu | examples/working_stack_demo.ipynb | bsd-3-clause | %matplotlib inline
import os
import shutil
import platform
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import flopy
import pyemu
"""
Explanation: Current working stack for setting up PEST interface
End of explanation
"""
nam_file = "freyberg.nam"
org_model_ws = "freyberg_sfr_update"
m = fl... |
afunTW/dsc-crawling | appendix_ptt/00_parse_article.ipynb | apache-2.0 | import requests
import re
import json
from bs4 import BeautifulSoup, NavigableString
from pprint import pprint
ARTICLE_URL = 'https://www.ptt.cc/bbs/Gossiping/M.1537847530.A.E12.html'
"""
Explanation: 爬取單一文章資訊
你有可能會遇到「是否滿18歲」的詢問頁面
解析 ptt.cc/bbs 裏面文章的結構
爬取文章
爬取留言
URL https://www.ptt.cc/bbs/Gossiping/M.1537847530.A.... |
roatienza/Deep-Learning-Experiments | versions/2020/cnn/code/cnn-siamese.ipynb | mit | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.keras.layers import Dense, Dropout, Input
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten
from tensorflow.keras.models import Model
from tensorflow.keras.la... |
mlamoureux/PIMS_YRC | P_Data_analysis.ipynb | mit | # Get some basic tools
%pylab inline
from pandas import Series, DataFrame
import pandas as pd
#import pandas.io.data as web
#from pandas_datareader import data, web
#import pandas_datareader as pdr
from pandas_datareader import data as pdr
import fix_yahoo_finance
# Here are apple and microsoft closing prices since 2... |
mfouesneau/pyphot | examples/astropy_Sun_Vega.ipynb | mit | %matplotlib inline
import pylab as plt
import numpy as np
import sys
sys.path.append('../')
from pyphot import astropy as pyphot
from pyphot.astropy import Vega, Sun
"""
Explanation: pyphot - A tool for computing photometry from spectra
Some examples are provided in this notebook
Full documentation available at http... |
ddtm/dl-course | Seminar9/Bonus-seminar.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Deep learning for Natural Language Processing
Simple text representations, bag of words
Word embedding and... not just another word2vec this time
1-dimensional convolutions for text
Aggregating several data sour... |
sdpython/ensae_teaching_cs | _doc/notebooks/td1a/texte_langue.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 1A.2 - Deviner la langue d'un texte
Comment deviner la langue d'un texte sans savoir lire la langue ? Ce notebook aborde les dictionnaires, les fichiers et les graphiques.
End of explanation
"""
def read_file(filename):
# ...
re... |
shareactorIO/pipeline | source.ml/jupyterhub.ml/notebooks/zz_old/Spark/Intro/Lab 1 - Hello Spark/Lab 1 - Hello Spark - Instructor.ipynb | apache-2.0 | #Step 1 - sc is Spark Context, Execute Spark Context to see if its active in cluster
#Note: Notice the programming language used
sc
#Step 1 - The spark context has a .version available to return the version of the spark driver application
#Note: Different versions of spark application support additional functionalit... |
calroc/joypy | docs/Zipper.ipynb | gpl-3.0 | from notebook_preamble import J, V, define
"""
Explanation: This notebook is about using the "zipper" with joy datastructures. See the Zipper wikipedia entry or the original paper: "FUNCTIONAL PEARL The Zipper" by Gérard Huet
Given a datastructure on the stack we can navigate through it, modify it, and rebuild it usi... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_dipole_fit.ipynb | bsd-3-clause | from os import path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.forward import make_forward_dipole
from mne.evoked import combine_evoked
from mne.simulation import simulate_evoked
data_path = mne.datasets.sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
fname_ave = op.... |
caseresearch/code-review | tutorials/jupyter_notebook_emcee/emcee_notebook.ipynb | mit | %matplotlib inline
"""
Explanation: The rad-ness of notebooks
I use notebooks more often than I use an executable .py script. This is partially because notebooks were my first major introduction to python, but my continued use relates back to the fact that it allows me to break up problems I'm solving into different b... |
kdmurray91/kwip-experiments | writeups/misc/sklearn-rice/clustering.ipynb | mit | cl = AgglomerativeClustering(16, compute_full_tree=True, affinity='precomputed', linkage='complete')
np.unique(cl.fit_predict(wip_0.data), return_counts=True)
"""
Explanation: sklearn Agglomerative
I can't get this to work properly. The values returned by fit_predict below should essentially be
[[0, 1, 2, 3, 4, 5, 6,... |
gururajl/deep-learning | dcgan-svhn/DCGAN.ipynb | mit | %matplotlib inline
import pickle as pkl
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat
import tensorflow as tf
!mkdir data
"""
Explanation: Deep Convolutional GANs
In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a De... |
gregcaporaso/short-read-tax-assignment | ipynb/mock-community/taxonomy-assignment-template.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... |
nonmean/nonmean.github.io | _notebooks/2020-09-01-fastcore.ipynb | mit | #hide
! pip install -U git+git://github.com/fastai/fastcore@master
! pip install -U git+git://github.com/fastai/nbdev@master
! pip install -U numpy
from fastcore.foundation import *
from fastcore.meta import *
from fastcore.utils import *
from fastcore.test import *
from nbdev.showdoc import *
from fastcore.dispatch im... |
kdestasio/online_brain_intensive | nipype_tutorial/notebooks/basic_iteration.ipynb | gpl-2.0 | from nipype import Node, Workflow
from nipype.interfaces.fsl import BET, IsotropicSmooth
# Initiate a skull stripping Node with BET
skullstrip = Node(BET(mask=True,
in_file='/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz'),
name="skullstrip")
"""
Explanation: <i... |
artfisica/notebooks | september_2018_v-2.0/ATLAS_OpenData_02-simple_python_example_histogram.ipynb | gpl-3.0 | import ROOT
"""
Explanation: <CENTER>
<a href="http://opendata.atlas.cern" class="icons"><img src="../images/opendata-top-transblack.png" style="width:40%"></a>
</CENTER>
A simple introductional notebook to HEP analysis in python
<p> In this notebook you can find an easy set of commands that show the basic computi... |
turi-code/tutorials | notebooks/datapipeline_recsys_intro.ipynb | apache-2.0 | import graphlab
"""
Explanation: Making batch recommendations using GraphLab Create
In this notebook we will show a complete recommender system implemented using GraphLab's deployment tools. This recommender example is common in many batch scenarios, where a new recommender is trained on a periodic basis, with the ge... |
google-research/google-research | group_agnostic_fairness/data_utils/CreateCompasDatasetFiles.ipynb | apache-2.0 | from __future__ import division
import pandas as pd
import numpy as np
import json
import os,sys
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import numpy as np
"""
Explanation: Copyright 2020 Google LLC.
Licensed under the Apache License, Version 2.0 (the ... |
brinkar/real-world-machine-learning | Chapter 2 - Data Processing.ipynb | mit | %pylab inline
"""
Explanation: Chapter 2: Processing data for machine learning
To simplify the code examples in these notebooks, we populate the namespace with functions from numpy and matplotlib:
End of explanation
"""
cat_data = array(['male', 'female', 'male', 'male', 'female', 'male', 'female', 'female'])
def c... |
calroc/joypy | docs/1. Basic Use of Joy in a Notebook.ipynb | gpl-3.0 | from joy.joy import run
from joy.library import initialize
from joy.utils.stack import stack_to_string
from joy.utils.pretty_print import TracePrinter
"""
Explanation: Preamble
First, import what we need.
End of explanation
"""
D = initialize()
S = ()
def J(text):
print stack_to_string(run(text, S, D)[0])
de... |
msampathkumar/kaggle-quora-tensorflow | references/intro-to-rnns/Anna KaRNNa.ipynb | apache-2.0 | 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... |
root-mirror/training | SoftwareCarpentry/04-histograms-and-graphs.ipynb | gpl-2.0 | import ROOT
h = ROOT.TH1D(name="h", title="My histo", nbinsx=100, xlow=-5, xup=5)
h.FillRandom("gaus", ntimes=5000)
"""
Explanation: ROOT histograms
Histogram class documentation
ROOT has powerful histogram objects that, among other features, let you produce complex plots and perform fits of arbitrary functions.
TH1... |
samuxiii/notebooks | titanic/Titanic Survival Kaggle.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline
#load the files
train = pd.read_csv('input/train.csv')
test = pd.read_csv('input/test.csv')
data = pd.concat([train, test]).reset_index(drop=True)
#size of training dataset
train_samples = train.shape[0]
#print some of them
data.head()
... |
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