repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
julienchastang/unidata-python-workshop | notebooks/Time_Series/Basic Time Series Plotting.ipynb | mit | from siphon.simplewebservice.ndbc import NDBC
data_types = NDBC.buoy_data_types('46042')
print(data_types)
"""
Explanation: <a name="top"></a>
<div style="width:1000 px">
<div style="float:right; width:98 px; height:98px;">
<img src="https://raw.githubusercontent.com/Unidata/MetPy/master/metpy/plots/_static/unidata_... |
datactive/bigbang | examples/organizations/Using Domain Entropy to Identify Organizations.ipynb | mit | arx = Archive("httpbisa",mbox=True)
"""
Explanation: Preparing the data
Open a mailing list archive.
End of explanation
"""
email_regex = r'[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+'
domain_regex = r'[@]([a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+)$'
email = re.search(email_regex, "Gerald Oskoboiny <gerald@w3.org>")[0]
re.s... |
boompieman/iim_project | Poem_Segmentation_Demo_Python/khan_segmentation.ipynb | gpl-3.0 | # Import required libraries
import os
import csv
import segeval as se
import numpy as np
import matplotlib.pyplot as plt
import itertools as it
from collections import defaultdict
from decimal import Decimal
from hcluster import linkage, dendrogram, fcluster
"""
Explanation: An initial study of topical poetry segmenta... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/machine_learning_in_the_enterprise/solutions/sdk_custom_xgboost.ipynb | apache-2.0 | # import necessary libraries
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: Migrating Custom XGBoost Model with Pre-built Training Container
L... |
Arn-O/kadenze-deep-creative-apps | session-0/session-0.ipynb | apache-2.0 | 4*2
"""
Explanation: Session 0: Preliminaries with Python/Notebook
<p class="lead">
Parag K. Mital<br />
<a href="https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info">Creative Applications of Deep Learning w/ Tensorflow</a><br />
<a href="https://www.kadenze.com/partners/kadenze... |
csaladenes/csaladenes.github.io | present/mcc2/PythonDataScienceHandbook/05.08-Random-Forests.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
"""
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... |
gabicfa/RedesSociais | encontro02/.ipynb_checkpoints/1-introducao-checkpoint.ipynb | gpl-3.0 | import sys
sys.path.append('..')
import socnet as sn
"""
Explanation: Encontro 02, Parte 1: Revisão de Grafos
Este guia foi escrito para ajudar você a atingir os seguintes objetivos:
formalizar conceitos básicos de teoria dos grafos;
usar funcionalidades básicas da biblioteca da disciplina.
Grafos não-dirigidos
Um ... |
kit-cel/wt | sigNT/tutorial/taxi_problem.ipynb | gpl-2.0 | # importing
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# showing figures inline
%matplotlib inline
# plotting options
font = {'size' : 30}
plt.rc('font', **font)
plt.rc('text', usetex=True)
matplotlib.rc('figure', figsize=(30, 15) )
"""
Explanation: Content and Objective
Show result ... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/introduction_to_tensorflow/labs/imbalanced_data.ipynb | apache-2.0 | # Import necessary libraries.
import tensorflow as tf
from tensorflow import keras
import os
import tempfile
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn
from sklearn.metrics import confusion_matrix
from sklearn.model_selection i... |
Kaggle/learntools | notebooks/pandas/raw/tut_4.ipynb | apache-2.0 | #$HIDE_INPUT$
import pandas as pd
reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)
pd.set_option('max_rows', 5)
reviews.price.dtype
"""
Explanation: Introduction
In this tutorial, you'll learn how to investigate data types within a DataFrame or Series. You'll also learn how to fin... |
pagutierrez/tutorial-sklearn | notebooks-spanish/12-caso_estudio_deteccion_spam_SMS.ipynb | cc0-1.0 | import os
with open(os.path.join("datasets", "smsspam", "SMSSpamCollection")) as f:
lines = [line.strip().split("\t") for line in f.readlines()]
text = [x[1] for x in lines]
y = [int(x[0] == "spam") for x in lines]
text[:10]
y[:10]
print('Número de mensajes de ham/spam:', np.bincount(y))
type(text)
type(y)
... |
ntoll/poem-o-matic | poem-o-matic.ipynb | mit | from os import listdir
from os.path import isfile, join
mypath = 'sources'
filenames = [join(mypath, f) for f in listdir(mypath) if isfile(join(mypath, f))]
print(filenames)
"""
Explanation: Poem-O-Matic
This is a description, in both code and prose, of how to generate original poetry on demand using a computer and ... |
TomAugspurger/engarde | examples/Basics.ipynb | mit | # This will take a few minutes
r = requests.get("http://www.transtats.bts.gov/Download/On_Time_On_Time_Performance_2015_1.zip",
stream=True)
with open("otp-1.zip", "wb") as f:
for chunk in r.iter_content(chunk_size=1024):
f.write(chunk)
f.flush()
r.close()
z = zipfile.ZipFile("otp... |
Stargator/gregreda-jekylified | content/notebooks/cohort-analysis.ipynb | mit | df['OrderPeriod'] = df.OrderDate.apply(lambda x: x.strftime('%Y-%m'))
df.head()
"""
Explanation: 1. Create a period column based on the OrderDate
Since we're doing monthly cohorts, we'll be looking at the total monthly behavior of our users. Therefore, we don't want granular OrderDate data (right now).
End of explanat... |
guruucsd/EigenfaceDemo | python/Neural Network Tricks.ipynb | mit | %pycat neural_network.py
from sklearn.decomposition import PCA
from sklearn.cross_validation import train_test_split, ShuffleSplit
from sklearn.preprocessing import OneHotEncoder
from neural_network import NeuralNetwork
# The classifier network
class ClassifierNetwork(NeuralNetwork):
"""Neural network with class... |
taylorwood/Kaggle.HomeDepot | ProjectSearchRelevance.Python/Home Depot Product Search Relevance Features.ipynb | mit | import graphlab as gl
"""
Explanation: Home Depot Product Search Relevance
The challenge is to predict a relevance score for the provided combinations of search terms and products. To create the ground truth labels, Home Depot has crowdsourced the search/product pairs to multiple human raters.
LabGraph Create
This not... |
karlstroetmann/Artificial-Intelligence | Python/Set.ipynb | gpl-2.0 | class Set:
def __init__(self):
self.mKey = None
self.mLeft = None
self.mRight = None
self.mHeight = 0
"""
Explanation: Sets implemented as AVL Trees
This notebook implements <em style="color:blue;">sets</em> as <a href="https://en.wikipedia.org/wiki/AVL_tree">AVL trees</a>. T... |
VVard0g/ThreatHunter-Playbook | docs/notebooks/windows/08_lateral_movement/WIN-200902020333.ipynb | mit | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: Remote WMI ActiveScriptEventConsumers
Metadata
| Metadata | Value |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2020/09/02 |
| modification date | 2020/09/20 |
| playbook rel... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_sensor_connectivity.ipynb | bsd-3-clause | # Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
import numpy as np
from scipy import linalg
import mne
from mne import io
from mne.connectivity import spectral_connectivity
from mne.datasets import sample
print(__doc__)
"""
Explanation: Compute all-to-all connectivity in sensor spa... |
SamLau95/nbinteract | docs/notebooks/examples/examples_probability_distribution_plots.ipynb | bsd-3-clause | # Although this function doesn't appear necessary, the scipy stats functions
# don't explicitly require n and p as args which causes issues with interaction
def binom_pmf(xs, n, p):
return stats.binom.pmf(xs, n, p)
options = {
'xlabel': 'X',
'ylabel': 'probability',
'ylim': (0, 1),
}
nbinteract.bar(np... |
coryandrewtaylor/conll10 | CoNLL10 output with SpaCy.ipynb | gpl-3.0 | import spacy
"""
Explanation: Dependency parsing with spaCy
This script takes Unicode plain text and outputs its dependencies in CoNLL10 format. It was originally written to prepare input files for named/non-named entity extraction with xrenner.
For installation instructions for spaCy, see https://spacy.io/docs#gettin... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/statespace_varmax.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
dta = sm.datasets.webuse('lutkepohl2', 'https://www.stata-press.com/data/r12/')
dta.index = dta.qtr
dta.index.freq = dta.index.inferred_freq
endog = dta.loc['1960-04-01':'1978-10-01', ['dln_inv', 'dl... |
google/applied-machine-learning-intensive | content/03_regression/04_polynomial_regression/colab.ipynb | apache-2.0 | # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the L... |
IIPBC/Material | Notebooks/Python BootCamp 2017 - A example of a notebook.ipynb | mit | import numpy
x = numpy.arange(0, 100, 0.1)
y = numpy.cos(x)
"""
Explanation: Sample Notebook
This Jupyter Notebook is intended to be an example with some references. Jupyter uses Markdown Syntax and accept LaTeX and HTML codes.
With this, one can easily write bold or italic words. One can also type some code inline or... |
mne-tools/mne-tools.github.io | 0.14/_downloads/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'
f... |
turi-code/tutorials | webinars/product-reviews/text_demo.ipynb | apache-2.0 | import graphlab as gl
from graphlab.toolkits.text_analytics import trim_rare_words, split_by_sentence, extract_part_of_speech, stopwords, PartOfSpeech
def nlp_pipeline(reviews, title, aspects):
print(title)
print('1. Get reviews for this product')
reviews = reviews.filter_by(title, 'name')
prin... |
cgpotts/cs224u | rel_ext_01_task.ipynb | apache-2.0 | __author__ = "Bill MacCartney and Christopher Potts"
__version__ = "CS224u, Stanford, Spring 2022"
"""
Explanation: Relation extraction using distant supervision: task definition
End of explanation
"""
import random
import os
from collections import Counter, defaultdict
import rel_ext
import utils
# Set all the ran... |
beangoben/quantum_solar | Dia1/3_Graficame_Espectro_Solar.ipynb | mit | import numpy as np # modulo de computo numerico
import matplotlib.pyplot as plt # modulo de graficas
import pandas as pd # modulo de datos
import seaborn as sns
# esta linea hace que las graficas salgan en el notebook
%matplotlib inline
"""
Explanation: Intro a Matplotlib
Matplotlib = Libreria para graficas cosas mate... |
tensorflow/docs-l10n | site/en-snapshot/guide/estimator.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... |
tensorflow/docs-l10n | site/en-snapshot/guide/keras/writing_a_training_loop_from_scratch.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... |
gertingold/lit2015 | pugmuc2015.ipynb | mit | for n in range(3):
print("The IPython notebook is great.")
"""
Explanation: Working with the IPython notebook
Gert-Ludwig Ingold
<div style="margin-top:10ex;font-size:smaller">source: `git clone https://github.com/gertingold/lit2015`</div>
<div style="font-size:smaller">static view: http://nbviewer.ipython.org/g... |
BBN-Q/Auspex | doc/examples/Example-Filter-Pipeline.ipynb | apache-2.0 | from QGL import *
cl = ChannelLibrary(":memory:")
# Create five qubits and supporting hardware
for i in range(5):
q1 = cl.new_qubit(f"q{i}")
cl.new_APS2(f"BBNAPS2-{2*i+1}", address=f"192.168.5.{101+2*i}")
cl.new_APS2(f"BBNAPS2-{2*i+2}", address=f"192.168.5.{102+2*i}")
cl.new_X6(f"X6_{i}", address=0)
... |
jnarhan/Breast_Cancer | src/img_processing/RemoveArtifacts.ipynb | mit | __version__ = '0.1.0'
__status__ = 'Development'
__date__ = '2017-March-21'
__author__ = 'Jay Narhan'
import os
import cv2
import copy
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
from IPython.display import clear_output
import time
"""
Explanation: <h1>Removing Artifacts ... |
psas/composite-propellant-tank | Analysis/Calculations/.ipynb_checkpoints/Shrink Fit and Liner as Gasket Analysis-checkpoint.ipynb | gpl-3.0 | # Import packages here:
import math as m
import numpy as np
from IPython.display import Image
import matplotlib.pyplot as plt
# Properties of Materials (engineeringtoolbox.com, Cengel, Tian, DuPont, http://www.dtic.mil/dtic/tr/fulltext/u2/438718.pdf)
# Coefficient of Thermal Expansion
alphaAluminum = 0.0000131 # in... |
sdpython/ensae_teaching_cs | _doc/notebooks/td1a_home/2020_carte.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
"""
Explanation: Tech - carte
Faire une carte, c'est toujours compliqué. C'est simple jusqu'à ce qu'on s'aperçoive qu'on doit récupérer la description des zones administratives d'un pays, fournies parfois dans des coordonnées autres qu... |
eecs445-f16/umich-eecs445-f16 | handsOn_lecture12_bagging-boosting/handsOn12.ipynb | mit | import pandas as pd
df = pd.read_csv('forest-cover-type.csv')
df.head()
"""
Explanation: Recall: Boosting
AdaBoost Algorithm
An iterative algorithm for "ensembling" base learners
Input: ${(\mathbf{x}i, y_i)}{i = 1}^n, T, \mathscr{F}$, base learner
Initialize: $\mathbf{w}^{1} = (\frac{1}{n}, ..., \frac{1}{n})$
For $t... |
dsacademybr/PythonFundamentos | Cap07/DesafioDSA/Missao3/missao3.ipynb | gpl-3.0 | # Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
"""
Explanation: <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 7</font>
Download: http://github.com/dsacademybr
End of explanation
"""
class G... |
c22n/ion-channel-ABC | docs/examples/human-atrial/nygren_isus_original.ipynb | gpl-3.0 | import os, tempfile
import logging
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from ionchannelABC import theoretical_population_size
from ionchannelABC import IonChannelDistance, EfficientMultivariateNormalTransition, IonChannelAcceptor
from ionchannelABC.experimen... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/feature_engineering/labs/sdk-feature-store.ipynb | apache-2.0 | import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG = "--user"
# Install necess... |
csc-training/python-introduction | notebooks/examples/2 - Control Structures.ipynb | mit | value = 4
value = value + 1
if value < 5:
print("value is less than 5")
elif value > 5:
print("value is more than 5")
else:
print("value is precisely 5")
# go ahead and experiment by changing the value
"""
Explanation: Conditional statements
The most common conditional statement in Python is the if-elif-e... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch4-Problem_4-13.ipynb | unlicense | %pylab notebook
%precision 1
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 4
Problem 4-13
End of explanation
"""
Sbase = 25e6 # [VA]
Vbase = 12.2e3 # [V]
PF = 0.9
Ra = 0.6 # [Ohm]
"""
Explanation: Description
A 25-MVA, 12.2-kV, 0.9-PF-lagging, three-phase, two-pole, Y-connected, 60-Hz ... |
nathawkins/PHY451_FS_2017 | Diode Laser Spectroscopy/20171003_morning/Interference with SAS no Dopple/.ipynb_checkpoints/Interferometer with SAS No Doppler Analysis-checkpoint.ipynb | gpl-3.0 | get_peak_data(ch2, [0.025, 0.030]);
get_peak_data(ch2, [0.030, 0.035]);
get_peak_data(ch2, [0.0350,0.045]);
get_peak_data(ch2, [0.049, 0.0517]);
maximum_time_positions = [0.028124, 0.03266, 0.042744, 0.05052]
maximum_voltage_positions = [0.738, 0.53, 0.716, 0.48]
# Two subplots, unpack the axes array immediately
f... |
scikit-optimize/scikit-optimize.github.io | dev/notebooks/auto_examples/optimizer-with-different-base-estimator.ipynb | bsd-3-clause | print(__doc__)
import numpy as np
np.random.seed(1234)
import matplotlib.pyplot as plt
from skopt.plots import plot_gaussian_process
from skopt import Optimizer
"""
Explanation: Use different base estimators for optimization
Sigurd Carlen, September 2019.
Reformatted by Holger Nahrstaedt 2020
.. currentmodule:: skopt... |
tpin3694/tpin3694.github.io | python/pandas_make_new_columns_using_functions.ipynb | mit | # Import modules
import pandas as pd
# Example dataframe
raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'],
'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2... |
TheKingInYellow/PySeidon | PySeidon_tuto_4.ipynb | agpl-3.0 | %pylab inline
"""
Explanation: PySeison - Tutorial 4: TideGauge class
End of explanation
"""
from pyseidon import *
"""
Explanation: 1. PySeidon - TideGauge object initialisation
Similarly to the "ADCP class" and the "Drifter class", the "TideGauge class" is a measurement-based object.
1.1. Package importation
As a... |
awsteiner/o2sclpy | doc/static/examples/buchdahl.ipynb | gpl-3.0 | import o2sclpy
import matplotlib.pyplot as plot
import ctypes
import numpy
import sys
plots=True
if 'pytest' in sys.modules:
plots=False
"""
Explanation: Buchdahl equation of state example for O$_2$sclpy
See the O$_2$sclpy documentation at https://neutronstars.utk.edu/code/o2sclpy for more information.
End of exp... |
atulsingh0/MachineLearning | BMLSwPython/01_GettingStarted_withPython.ipynb | gpl-3.0 | start = timeit.timeit()
X = range(1000)
pySum = sum([n*n for n in X])
end = timeit.timeit()
print("Total time taken: ", end-start)
"""
Explanation: Comparing the time
End of explanation
"""
# reading the web data
data = sp.genfromtxt("data/web_traffic.tsv", delimiter="\t")
print(data[:3])
print(len(data))
"""... |
freedomofpress/fingerprint-securedrop | notebooks/data_crawling_status.ipynb | agpl-3.0 | import os
import pandas as pd
import sqlalchemy
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
with open(os.environ["PGPASS"], "rb") as f:
content = f.readline().decode("utf-8").replace("\n", "").split(":")
engine = sqlalchemy.create_engine("postgresql://{user}:{... |
mdeff/ntds_2017 | projects/reports/movie_success/YouTube_analytics.ipynb | mit | %matplotlib inline
import configparser
import os
import requests
from tqdm import tqdm
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import sparse, stats, spatial
import scipy.sparse.linalg
from sklearn import preprocessing, decomposition
import librosa
import IPython.display as ip... |
gcrahay/otx_misp | src/otx_misp/otx/howto_use_python_otx_api.ipynb | apache-2.0 | pulses = otx.getall()
len(pulses)
"""
Explanation: Replace YOUR_KEY with your OTX API key. You can find it in your settings page https://otx.alienvault.com/settings
The getall() method downloads all the OTX pulses and their assocciated indicators of compromise (IOCs) from your account. This includes all of the follow... |
noppanit/social-network-analysis | Centralities.ipynb | mit | %matplotlib inline
import networkx as nx
import matplotlib.pyplot as plt
import operator
import timeit
g_fb = nx.read_edgelist('facebook_combined.txt', create_using = nx.Graph(), nodetype = int)
print nx.info(g_fb)
print nx.is_directed(g_fb)
"""
Explanation: Centralities
In this section, I'm going to learn how Cent... |
kraemerd17/kraemerd17.github.io | courses/python/material/ipynbs/Time Series.ipynb | mit | from __future__ import division
from pandas import Series, DataFrame
import pandas as pd
from numpy.random import randn
import numpy as np
pd.options.display.max_rows = 12
np.set_printoptions(precision=4, suppress=True)
import matplotlib.pyplot as plt
plt.rc('figure', figsize=(12, 4))
%matplotlib inline
"""
Explanati... |
eds-uga/csci1360-fa16 | assignments/A2/A2_Q2.ipynb | mit | def return_ordinals(numbers):
out_list = []
### BEGIN SOLUTION
### END SOLUTION
return out_list
inlist = [5, 6, 1, 9, 5, 5, 3, 3, 9, 4]
outlist = ["5th", "6th", "1st", "9th", "5th", "5th", "3rd", "3rd", "9th", "4th"]
for y_true, y_pred in zip(outlist, return_ordinals(inlist)):
assert... |
bradleypallen/fb15k-akbc | FB15K-237 Evaluation.ipynb | mit | import pandas as pd
import numpy as np
from operator import itemgetter
from CFModel import CFModel
"""
Explanation: Import packages
End of explanation
"""
TEST_CSV_FILE = 'fb15k_test.csv'
CVSC_ENTITIES_CSV_FILE = 'fb15k_cvsc_entities.csv'
CVSC_PAIRS_CSV_FILE = 'fb15k_cvsc_pairs.csv'
MODEL_WEIGHTS_FILE = 'test_weight... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_xdawn_denoising.ipynb | bsd-3-clause | # Authors: Alexandre Barachant <alexandre.barachant@gmail.com>
#
# License: BSD (3-clause)
from mne import (io, compute_raw_covariance, read_events, pick_types, Epochs)
from mne.datasets import sample
from mne.preprocessing import Xdawn
from mne.viz import plot_epochs_image
print(__doc__)
data_path = sample.data_pa... |
mjasher/gac | original_libraries/flopy-master/examples/Notebooks/lake_example.ipynb | gpl-2.0 | %matplotlib inline
import os
import numpy as np
import matplotlib.pyplot as plt
import flopy.modflow as mf
import flopy.utils as fu
workspace = os.path.join('data')
#make sure workspace directory exists
if not os.path.exists(workspace):
os.makedirs(workspace)
"""
Explanation: Lake Example
First set the path and i... |
NEONScience/NEON-Data-Skills | tutorials-in-development/Python/neon_api/neon_api_06_stacking_py.ipynb | agpl-3.0 | import requests
import json
import pandas as pd
SERVER = 'http://data.neonscience.org/api/v0/'
SITECODE = 'TEAK'
PRODUCTCODE = 'DP1.10003.001'
"""
Explanation: syncID:
title: "Stacking and Joining NEON Data with Python"
description: ""
dateCreated: 2020-05-07
authors: Maxwell J. Burner
contributors: Donal O'Leary
es... |
thushear/MLInAction | kaggle/titanic_sklearn.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
data_train = pd.read_csv('./input/titanic/train.csv')
data_test = pd.read_csv('./input/titanic/test.csv')
data_train.sample(20)
"""
Explanation: 熟悉Pandas Sklearn
CSV to DataFrame
End of explanation
"""
s... |
ddtm/dl-course | Seminar4/bonus/Bonus-advanced-cnn.ipynb | mit | import numpy as np
from cifar import load_cifar10
X_train,y_train,X_val,y_val,X_test,y_test = load_cifar10("cifar_data")
class_names = np.array(['airplane','automobile ','bird ','cat ','deer ','dog ','frog ','horse ','ship ','truck'])
print X_train.shape,y_train.shape
import matplotlib.pyplot as plt
%matplotlib inl... |
neurodata/ndmg | tutorials/Qa_skullstrip.ipynb | apache-2.0 | #import packages
import warnings
warnings.simplefilter("ignore")
import sys
import nibabel as nib
import numpy as np
import os
from PIL import Image, ImageDraw,ImageFont
import matplotlib.pyplot as plt
from m2g.stats.qa_skullstrip import gen_overlay_pngs
"""
Explanation: Tutorial for QA of Skull Strip
This tutorial i... |
synthicity/activitysim | activitysim/examples/example_estimation/notebooks/09_school_tour_scheduling.ipynb | agpl-3.0 | import os
import larch # !conda install larch -c conda-forge # for estimation
import pandas as pd
"""
Explanation: Estimating School Tour Scheduling
This notebook illustrates how to re-estimate the mandatory tour scheduling component for ActivitySim. This process
includes running ActivitySim in estimation mode to r... |
grcanosa/code-playground | scrum/pandasCSV/csvRedminePandas1.ipynb | mit | from IPython.display import HTML
from IPython.display import display
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_togg... |
DaveBackus/Data_Bootcamp | Code/IPython/bootcamp_indicators.ipynb | mit | # import packages
import pandas as pd # data management
import matplotlib.pyplot as plt # graphics
import numpy as np # numerical calculations
# IPython command, puts plots in notebook
%matplotlib inline
# check Python version
import datetime as dt
import sys
print('To... |
cshankm/rebound | ipython_examples/ParticleIDsAndRemoval.ipynb | gpl-3.0 | import rebound
import numpy as np
def setupSimulation(Nplanets):
sim = rebound.Simulation()
sim.integrator = "ias15" # IAS15 is the default integrator, so we don't need this line
sim.add(m=1.,id=0)
for i in range(1,Nbodies):
sim.add(m=1e-5,x=i,vy=i**(-0.5),id=i)
sim.move_to_com()
return... |
bjackman/lisa | ipynb/releases/ReleaseNotes_v17.03.ipynb | apache-2.0 | from test import LisaTest
print LisaTest.__doc__
"""
Explanation: Documentation
Documentation of many LISA modules has got a big improvement with the usage of Sphinx and the refresh of docstrings for many existing methods.
You can access documentation either interactively in Notebooks, using the standard TAB completi... |
akloster/amplicon_classification | notebooks/amplicon_classification.ipynb | isc | %load_ext autoreload
%autoreload 2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import re
import pysam
import random
import feather
import h5py
%matplotlib inline
training_data = feather.read_dataframe("amplicon_training_metadata.feather")
test_data = feather.read_dataframe("amplicon_test_met... |
OpenBookProjects/ipynb | XKCD-style/XKCD_plots_zh_cn-by-ZQ.ipynb | mit | from IPython.display import Image
Image('http://jakevdp.github.com/figures/xkcd_version.png')
"""
Explanation: Matplotlib 实现 XKCD 样图表
This notebook originally appeared as a blog post at Pythonic Perambulations by Jake Vanderplas.
<!-- PELICAN_BEGIN_SUMMARY -->
Update: the matplotlib pull request has been merged! See
... |
zambzamb/zpic | python/Morse and Nielsen 1971.ipynb | agpl-3.0 | import em1ds as zpic
import numpy as np
import matplotlib.pyplot as plt
# Thermal velocity
uth = [0.05,0.25,0.0]
electrons = zpic.Species( "electrons", -1.0, 200, uth = uth )
sim = zpic.Simulation( 150, box = 15.0, dt = 0.08, species = electrons )
"""
Explanation: Numerical Simulation of the Weibel Instability in ... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/kubeflow_pipelines/pipelines/labs/kfp_pipeline_vertex_automl_online_predictions.ipynb | apache-2.0 | from google.cloud import aiplatform
REGION = "us-central1"
PROJECT = !(gcloud config get-value project)
PROJECT = PROJECT[0]
# Set `PATH` to include the directory containing KFP CLI
PATH = %env PATH
%env PATH=/home/jupyter/.local/bin:{PATH}
"""
Explanation: Continuous Training with AutoML Vertex Pipelines
Learning O... |
aitatanit/metatlas | 4notebooks/ISTD Assessment.ipynb | bsd-3-clause | import sys
sys.path.insert(0,'/project/projectdirs/metatlas/projects/ms_monitor_tools' )
import warnings
warnings.filterwarnings('ignore')
import ms_monitor_util as mtools
%matplotlib notebook
"""
Explanation: Assess and Monitor QCs, Internal Standards, and Common Metabolites
This notebook will guide people to
Ident... |
IanHawke/Southampton-PV-NumericalMethods-2016 | solutions/01-Integration.ipynb | mit | from __future__ import division
import numpy
data_southampton_2005 = numpy.loadtxt('../data/irradiance/southampton_2005.txt')
"""
Explanation: Integration
How much solar power was available to be collected in Southampton in 2005?
To answer this, we need to integrate the solar irradiance data, to get the insolation,
\b... |
vadim-ivlev/STUDY | handson-data-science-python/DataScience-Python3/ConditionalProbabilityExercise.ipynb | mit | from numpy import random
random.seed(0)
totals = {20:0, 30:0, 40:0, 50:0, 60:0, 70:0}
purchases = {20:0, 30:0, 40:0, 50:0, 60:0, 70:0}
totalPurchases = 0
for _ in range(100000):
ageDecade = random.choice([20, 30, 40, 50, 60, 70])
purchaseProbability = float(ageDecade) / 100.0
totals[ageDecade] += 1
if ... |
AntonelliLab/seqcap_processor | docs/notebook/subdocs/align_contigs.ipynb | mit | %%bash
source activate secapr_env
secapr align_sequences -h
"""
Explanation: Align contigs
We can use SECAPR to produce Multiple Sequence Alignments (MSAs) from the contig data. The alignment function align_sequences looks as follows:
End of explanation
"""
from IPython.display import Image, display
img1 = Image("..... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/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', 'ncc', 'sandbox-2', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: NCC
Source ID: SANDBOX-2
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balance... |
google/physics-math-tutorials | colabs/Multivariate Calculus for ML, 1 of 2.ipynb | apache-2.0 | #@title Python imports
import collections
import datetime
from functools import partial
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from scipy import stats
import seaborn as sns
from sklearn.datasets import make_regression
from sklearn.model_se... |
mwickert/SP-Comm-Tutorial-using-scikit-dsp-comm | hardware_configure/Pyaudio_Test.ipynb | bsd-2-clause | Audio('c_major.wav')
"""
Explanation: Playback Using the Notebook Audio Widget
This interface is used often when developing algorithms that involve processing signal samples that result in audible sounds. You will see this in the tutorial. Processing is done before hand as an analysis task, then the samples are writte... |
davebshow/DH3501 | graph_dbs.ipynb | mit | %matplotlib inline
%load_ext gremlin
import asyncio
import aiogremlin
import networkx as nx
"""
Explanation: Graph Databases and the Humanities
End of explanation
"""
g = nx.scale_free_graph(10)
nx.draw_networkx(g)
"""
Explanation: What's a graph?
A binary mathematical structure consisting of nodes and edges:
$g = ... |
vanheck/blog-notes | QuantTrading/creating_trading_strategy_02-backtest.ipynb | mit | NB_VERSION = 1,0
import sys
import datetime
import numpy as np
import pandas as pd
print('Verze notebooku:', '.'.join(map(str, NB_VERSION)))
print('Verze pythonu:', '.'.join(map(str, sys.version_info[0:3])))
print('---')
import pandas_datareader as pdr
import pandas_datareader.data as pdr_web
from matplotlib import _... |
pagutierrez/tutorial-sklearn | notebooks-spanish/18-arboles_y_bosques.ipynb | cc0-1.0 | %matplotlib widget
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Árboles de decisión y bosques
End of explanation
"""
from figures import make_dataset
x, y = make_dataset()
X = x.reshape(-1, 1)
plt.figure()
plt.xlabel('Característica X')
plt.ylabel('Objetivo y')
plt.scatter(X, y);
from sklear... |
kimkipyo/dss_git_kkp | 통계, 머신러닝 복습/160502월_1일차_분석 환경, 소개/13.pandas 패키지의 소개.ipynb | mit | s = pd.Series([4, 7, -5, 3])
s
s.values
type(s.values)
s.index
type(s.index)
"""
Explanation: pandas 패키지의 소개
pandas 패키지
Index를 가진 자료형인 R의 data.frame 자료형을 Python에서 구현
참고 자료
http://pandas.pydata.org/
http://pandas.pydata.org/pandas-docs/stable/10min.html
http://pandas.pydata.org/pandas-docs/stable/tutorials.htm... |
mne-tools/mne-tools.github.io | stable/_downloads/d12911920e4d160c9fd8c97cffdda6b7/time_frequency_erds.ipynb | bsd-3-clause | # Authors: Clemens Brunner <clemens.brunner@gmail.com>
# Felix Klotzsche <klotzsche@cbs.mpg.de>
#
# License: BSD-3-Clause
"""
Explanation: Compute and visualize ERDS maps
This example calculates and displays ERDS maps of event-related EEG data. ERDS
(sometimes also written as ERD/ERS) is short for event-relat... |
danijel3/ASRDemos | notebooks/MLP_TIMIT_ctx10.ipynb | apache-2.0 | import os
os.environ['CUDA_VISIBLE_DEVICES']='1'
import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Reshape
from keras.optimizers import Adam, SGD
from IPython.display import clear_output
from tqdm import *
"""
Explanation: Using the frame context in the TIMIT MLP... |
obust/Pandas-Tutorial | Case Study - MovieLens.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.set_option('max_columns', 50)
# pass in column names for each CSV
u_cols = ['user_id', 'age', 'sex', 'occupation', 'zip_code']
users = pd.read_csv('data/ml-100k/u.user', sep='|', names=u_cols)
r_cols = ['user_id', 'movie_id', 'rating', 'unix_ti... |
dtamayo/reboundx | ipython_examples/ModifyMass.ipynb | gpl-3.0 | import rebound
import reboundx
import numpy as np
M0 = 1. # initial mass of star
def makesim():
sim = rebound.Simulation()
sim.G = 4*np.pi**2 # use units of AU, yrs and solar masses
sim.add(m=M0)
sim.add(a=1.)
sim.add(a=2.)
sim.add(a=3.)
sim.move_to_com()
return sim
%matplotlib inlin... |
mne-tools/mne-tools.github.io | 0.24/_downloads/b99fcf919e5d2f612fcfee22adcfc330/40_autogenerate_metadata.ipynb | bsd-3-clause | from pathlib import Path
import matplotlib.pyplot as plt
import mne
data_dir = Path(mne.datasets.erp_core.data_path())
infile = data_dir / 'ERP-CORE_Subject-001_Task-Flankers_eeg.fif'
raw = mne.io.read_raw(infile, preload=True)
raw.filter(l_freq=0.1, h_freq=40)
raw.plot(start=60)
# extract events
all_events, all_ev... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/launching_into_ml/labs/first_model.ipynb | apache-2.0 | !pip install --user google-cloud-bigquery==1.25.0
"""
Explanation: First BigQuery ML models for Taxifare Prediction
In this notebook, we will use BigQuery ML to build our first models for taxifare prediction.
BigQuery ML provides a fast way to build ML models on large structured and semi-structured datasets.
Learning ... |
yl565/statsmodels | examples/notebooks/markov_autoregression.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
import requests
from io import BytesIO
# NBER recessions
from pandas_datareader.data import DataReader
from datetime import datetime
usrec = DataReader('USREC', 'fred', start=datetime(1947, 1, 1), en... |
RaspberryJamBe/ipython-notebooks | notebooks/nl-be/Communicatie - Cloud bericht 2 - Bericht ontvangen + LED knipperen.ipynb | cc0-1.0 | APPKEY = "******"
"""
Explanation: APPKEY is de Application Key voor een (gratis) http://www.realtime.co/ "Realtime Messaging Free" subscription.
Zie "104 - Remote deurbel - Een cloud API gebruiken om berichten te sturen" voor meer gedetailleerde info.
End of explanation
"""
import time
import RPi.GPIO as GPIO
GPIO.... |
Kaggle/learntools | notebooks/ml_intermediate/raw/ex4.ipynb | apache-2.0 | # Set up code checking
import os
if not os.path.exists("../input/train.csv"):
os.symlink("../input/home-data-for-ml-course/train.csv", "../input/train.csv")
os.symlink("../input/home-data-for-ml-course/test.csv", "../input/test.csv")
from learntools.core import binder
binder.bind(globals())
from learntools.m... |
ES-DOC/esdoc-jupyterhub | notebooks/ipsl/cmip6/models/sandbox-3/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ipsl', 'sandbox-3', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: IPSL
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: Transport, Emissions, Conce... |
dipanjanS/text-analytics-with-python | New-Second-Edition/Ch08 - Semantic Analysis/Ch08b - Named Entity Recognition.ipynb | apache-2.0 | text = """Three more countries have joined an “international grand committee” of parliaments, adding to calls for
Facebook’s boss, Mark Zuckerberg, to give evidence on misinformation to the coalition. Brazil, Latvia and Singapore
bring the total to eight different parliaments across the world, with plans to send repr... |
4dsolutions/Python5 | SUBPLOTS_PYT_DS_SAISOFT.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-white')
import numpy as np
"""
Explanation: PYT-DS: Subplots in Matplotlib
The VanderPlas Syllabus is one of the more useful and core to this course in many ways.
Jake VanderPlas has been a key player in helping to promote open source. He's a... |
google/data-pills | pills/GA/[DATA_PILL]_[GA360]_Conversion_Blockers.ipynb | apache-2.0 | # Import all necessary libs
from google.colab import auth
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from IPython.display import display, HTML
# Authenticate the user to query datasets in Google BigQuery
auth.authenticate_user()
%matplotlib inline
"""
Explanation: Copyright 2021 Goo... |
linamnt/studyGroup | lessons/python/intro/intro_data_analysis_AE.ipynb | apache-2.0 | 4 + 4
4**2 # 4 to the power of 2
3*5; # semi-colon suppresses output
"""
Explanation: Data analysis in Python
Contributors:
This notebook combines two notebooks (with minor modifications by Amanda Easson) from previous UofT Coders sessions:
Intro Python (authors: Madeleine Bonsma-Fisher, heavily borrowing from Lina... |
melissawm/oceanobiopython | exemplos/exemplo_6/.ipynb_checkpoints/Diagrama TS-checkpoint.ipynb | gpl-3.0 | import gsw
"""
Explanation: Diagrama TS
Vamos elaborar um diagrama TS com o auxílio do pacote gsw [https://pypi.python.org/pypi/gsw/3.0.3], que é uma alternativa em python para a toolbox gsw do MATLAB:
End of explanation
"""
import numpy as np
import matplotlib.pyplot as plt
sal = np.linspace(0, 42, 100)
temp = np.... |
hainm/dask | notebooks/parallelize_image_filtering_workload.ipynb | bsd-3-clause | %pylab inline
from scipy.ndimage import uniform_filter
import dask.array as da
def mean(img):
"ndimage.uniform_filter with `size=51`"
return uniform_filter(img, size=51)
"""
Explanation: Parallelize image filters with dask
This notebook will show how to parallize CPU-intensive workload using dask array. A sim... |
GoogleCloudPlatform/training-data-analyst | blogs/bqml/online_prediction.ipynb | apache-2.0 | !pip install google-cloud # Reset Session after installing
PROJECT = 'cloud-training-demos' # change as needed
"""
Explanation: Online prediction with BigQuery ML
ML.Predict in BigQuery ML is primarily meant for batch predictions. What if you want to build a web application to provide online predictions? Here, I s... |
quantopian/research_public | notebooks/lectures/Plotting_Data/notebook.ipynb | apache-2.0 | # Import our libraries
# This is for numerical processing
import numpy as np
# This is the library most commonly used for plotting in Python.
# Notice how we import it 'as' plt, this enables us to type plt
# rather than the full string every time.
import matplotlib.pyplot as plt
"""
Explanation: Graphical Representat... |
NEAT-project/neat | policy/neat_policy_example.ipynb | bsd-3-clause | property1 = NEATProperty(('low_latency', True), precedence=NEATProperty.IMMUTABLE)
property2 = NEATProperty(('remote_ip', '10.1.23.45'), precedence=NEATProperty.IMMUTABLE)
property3 = NEATProperty(('MTU', {"start":1500, "end":9000}), precedence=NEATProperty.OPTIONAL)
property4 = NEATProperty(('TCP', True)) # OPTIONAL... |
luofan18/deep-learning | image-classification/dlnd_image_classification.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'
# Use Floyd's cifar-10 dataset if present
floyd_cifar10... |
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