repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
VVard0g/ThreatHunter-Playbook | docs/notebooks/windows/05_defense_evasion/WIN-190510202010.ipynb | mit | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: WDigest Downgrade
Metadata
| Metadata | Value |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2019/05/10 |
| modification date | 2020/09/20 |
| playbook related | [] |
Hypothe... |
liganega/Gongsu-DataSci | previous/y2017/Wextra/GongSu26_Pandas_Introduction_2.ipynb | gpl-3.0 | from GongSu24_Pandas_Introduction_1 import *
"""
Explanation: Pandas 소개 2
GonsSu24 내용에 이어서 Pandas 라이브러리를 소개한다.
먼저 GongSu24를 임포트 한다.
End of explanation
"""
s6 = Series(range(3), index=['a', 'b', 'c'])
s6
"""
Explanation: 색인(Index) 클래스
Pandas에 정의된 색인(Index) 클래스는 Series와 DataFrame 자료형의 행과 열을 구분하는 이름들의 목록을 저장하는 데에 사용된... |
tensorflow/tfx | docs/tutorials/mlmd/mlmd_tutorial.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... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/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', 'ncc', 'sandbox-3', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: NCC
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: Transport, Emissions, Concent... |
google/starthinker | colabs/bigquery_run_query.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: BigQuery Query Run
Run query on a project.
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 a copy of the License at... |
ES-DOC/esdoc-jupyterhub | notebooks/messy-consortium/cmip6/models/sandbox-1/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'sandbox-1', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: MESSY-CONSORTIUM
Source ID: SANDBOX-1
Topic: Landice
Sub-Topics: Gla... |
tensorflow/docs | site/en/tutorials/interpretability/integrated_gradients.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... |
statsmodels/statsmodels.github.io | v0.12.2/examples/notebooks/generated/statespace_fixed_params.ipynb | bsd-3-clause | %matplotlib inline
from importlib import reload
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from pandas_datareader.data import DataReader
"""
Explanation: Estimating or specifying parameters in state space models
In this notebook we show how to fix specific val... |
mauriciogtec/PropedeuticoDataScience2017 | Alumnos/FedericoRiveroll/Tarea2_entregable.ipynb | mit | import numpy as np
from numpy import *
Eq = np.array([[1, 1, -1, 9],[0, 1, 3, 3],[-1, 0, -2, 2]])
A = Eq[:,0:3] # As
b = Eq[:,3] # Resultados 9, 3, 2
# Las soluciones son: [0.666666666666667, 7.0, -1.3333333333333333]
U,s,V = linalg.svd(A) # descomposición SVD de A
# inversa usando pinv
pinv = linalg.pinv(A)
# inv... |
QuantScientist/Deep-Learning-Boot-Camp | day03/1.2 Introduction - Tensorflow.ipynb | mit | # A simple calculation in Python
x = 1
y = x + 10
print(y)
import tensorflow as tf
# The ~same simple calculation in Tensorflow
x = tf.constant(1, name='x')
y = tf.Variable(x+10, name='y')
print(y)
"""
Explanation: <img src="imgs/tensorflow_head.png" />
Tensorflow
TensorFlow (https://www.tensorflow.org/) is a softw... |
kingb12/languagemodelRNN | old_comparisons/noing6_LSTM_v_BOW.ipynb | mit | report_files = ["/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing6_200_512_04drb/encdec_noing6_200_512_04drb.json", "/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing6_bow_200_512_04drb/encdec_noing6_bow_200_512_04drb.json"]
log_files = ["/Users/bking/IdeaProjects/Lang... |
ES-DOC/esdoc-jupyterhub | notebooks/snu/cmip6/models/sandbox-3/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'snu', 'sandbox-3', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: SNU
Source ID: SANDBOX-3
Topic: Ocnbgchem
Sub-Topics: Tracers.
Properties: 6... |
linsalrob/PhiSpy | jupyter_notebooks/metrics_vs_genomes.ipynb | mit | import os, sys
import itertools
import re
import json
%matplotlib inline
from random import randint
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import gzip
from math import log, e
from scipy import stats
from math import sqrt
"""
Explanation: Metrics vs Predictions
In P... |
statsmodels/statsmodels.github.io | v0.12.2/examples/notebooks/generated/statespace_dfm_coincident.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
np.set_printoptions(precision=4, suppress=True, linewidth=120)
from pandas_datareader.data import DataReader
# Get the datasets from FRED
start = '1979-01-01'
end = '2014-12-01'
indprod = DataReade... |
Kaggle/learntools | notebooks/deep_learning/raw/ex8_dropout_strides.ipynb | apache-2.0 | import numpy as np
from sklearn.model_selection import train_test_split
from tensorflow import keras
# Set up code checking
from learntools.core import binder
binder.bind(globals())
from learntools.deep_learning.exercise_8 import *
print("Setup Complete")
img_rows, img_cols = 28, 28
num_classes = 10
def prep_data(ra... |
harmsm/pythonic-science | labs/03_molecular-structure/03_structure-files_key.ipynb | unlicense | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
"""
Explanation: Parsing and manipulating PDB files
End of explanation
"""
def get_R(pdb_file):
f = open(pdb_file,"r")
lines = f.readlines()
f.close()
for l in lines:
if l.startswith("REM... |
M0nica/python-foundations-hw | 07/.ipynb_checkpoints/billionaires-checkpoint.ipynb | mit | df['citizenship'].value_counts().head()
us_pop = 318.9 #billion (2014)
us_bill = df[df['citizenship'] == 'United States']
print("There are", us_pop/len(us_bill), "billionaires per billion people in the United States.")
germ_pop = 0.08062 #(2013)
germ_bill = df[df['citizenship'] == 'Germany']
print("There are", ger... |
dougsweetser/ipq | q_notebooks/space-time_reversal.ipynb | apache-2.0 | %%capture
%matplotlib inline
import numpy as np
import sympy as sp
import matplotlib.pyplot as plt
# To get equations the look like, well, equations, use the following.
from sympy.interactive import printing
printing.init_printing(use_latex=True)
from IPython.display import display
# Tools for manipulating quaternion... |
laowantong/algo_magic | doc/instructions.ipynb | mit | !pip install algo_magic
"""
Explanation: This set of IPython magic extensions is provided to the first year students enrolled in the algorithmics course at ISFATES (University of Lorraine).
Installation
In a Jupyter Notebook cell, simply paste this in a new cell and run it (shift-enter).
End of explanation
"""
%load... |
bjshaw/phys202-2015-work | assignments/assignment06/ProjectEuler17.ipynb | mit | def number_to_words(n):
"""Given a number n between 1-1000 inclusive return a list of words for the number."""
x = []
a = {1:'one',2:'two',3:'three',4:'four',5:'five',6:'six',7:'seven',8:'eight',9:'nine',10:'ten',
11:'eleven',12:'twelve',13:'thirteen',14:'fourteen',15:'fifteen',16:'sixteen',17:'sev... |
changhoonhahn/centralMS | centralms/notebooks/notes_assemblybias.ipynb | mit | # random step (NO ASSEMBLY BIAS)
t_NOabias = theta.copy()
t_NOabias['sfh'] = {'name': 'random_step', 'dt_min': 0.5, 'dt_max': 0.5, 'sigma': 0.3}
SHcat_NOabias = EvoWrap(t_NOabias)
testEvo.EvolverQAplots(SHcat_NOabias, t_NOabias)
plt.show()
"""
Explanation: If there's no assembly bias then it should be the same as ran... |
zhuwei05/ml-basic | learn_pandas.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: 学习 pandas
Reference
10 Minutes to pandas
Cookbook
简介
Python Data Analysis Library
pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python prog... |
IS-ENES-Data/submission_forms | test/Templates/form_retrieval.ipynb | apache-2.0 | from dkrz_forms import form_widgets
form_widgets.show_status('form-retrieval')
"""
Explanation: Retrieve your DKRZ data form
Via this form you can retrieve previously generated data forms and make them accessible via the Web again for completion.
Additionally you can get information on the data ingest process status r... |
johnhw/sqlexperiment | notebooks/design-record-plot.ipynb | mit | factors = [('F0', ['a', 'b']), ('F1', ['x', 'y'])]
levels = [np.array(['a', 'b']), np.array(['x', 'y'])]
e = ExperimentLog(":memory:", ntp_sync=False)
if e.meta.stage == 'init':
# needed?
e.create('SESSION', 'data', description='')
e.create('USER', 'user', description='user session')
for factor in fa... |
BayesianTestsML/tutorial | slides/case-against-nhst.ipynb | gpl-3.0 | import numpy as np
scores_a = np.array([ 95.95, 71.4 , 83.34, 49.99, 76.17, 86.22, 84.45, 81.87, 52.81, 75.04, 71.94, 50.12, 72.03, 60. , 83.69])
scores_b = np.array([ 97.88, 71.66, 82.87, 50.71, 74.17, 86.68, 85.46, 82.02, 60.08, 75.83, 74.53, 45.76, 72.65, 60. , 84.31])
sum(scores_a > sc... |
UCSBarchlab/PyRTL | ipynb-examples/introduction-to-hardware.ipynb | bsd-3-clause | import pyrtl
"""
Explanation: Introduction to Hardware Design
This code works through the hardware design process with the the
audience of software developers more in mind. We start with the simple
problem of designing a fibonacci sequence calculator (http://oeis.org/A000045).
End of explanation
"""
def software_fi... |
tpin3694/tpin3694.github.io | machine-learning/encode_days_of_the_week.ipynb | mit | # Load library
import pandas as pd
"""
Explanation: Title: Encode Days Of The Week
Slug: encode_days_of_the_week
Summary: How to the days of the week for dates and times for machine learning in Python.
Date: 2017-09-11 12:00
Category: Machine Learning
Tags: Preprocessing Dates And Times
Authors: Chris Albon
Preli... |
Kaggle/learntools | notebooks/pandas/raw/ex_4.ipynb | apache-2.0 | import pandas as pd
reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)
from learntools.core import binder; binder.bind(globals())
from learntools.pandas.data_types_and_missing_data import *
print("Setup complete.")
"""
Explanation: Introduction
Run the following cell to load your da... |
Alexoner/mooc | cs231n/2016/assignment2/FullyConnectedNets.ipynb | apache-2.0 | # As usual, a bit of setup
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.solver import Solver
%matplotlib inline
... |
jordanopensource/data-science-bootcamp | session4/L3_Conclusion.ipynb | mit | #your code here
"""
Explanation: Conclusion
I hope you enjoyed the lecture and could do crazy bayesian stuff in your next job as data scientist !
Check list
Understand what machine learning is in terms of probabilites
Can get started in constructing probabilistic graphical models using pgmpy
Can get started in build... |
BayesianTestsML/tutorial | Python/Hierarchical test.ipynb | gpl-3.0 | import numpy as np
scores = np.loadtxt('Data/diffNbcHnb.csv', delimiter=',')
names = ("HNB", "NBC")
print(scores)
"""
Explanation: Bayesian Hierarchical Test
Module hierarchical in bayesiantests compares the performance of two classifiers that have been assessed by m-runs of k-fold cross-validation on q datasets. It r... |
ES-DOC/esdoc-jupyterhub | notebooks/cnrm-cerfacs/cmip6/models/cnrm-esm2-1-hr/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'cnrm-esm2-1-hr', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: CNRM-ESM2-1-HR
Topic: Atmoschem
Sub-Top... |
awadalaa/DataSciencePractice | kaggle/titanic/TitanicPrediction2.ipynb | mit | import csv as csv
import numpy as np
import pandas as pd
# We can use the pandas library in python to read in the csv file.
# This creates a pandas dataframe and assigns it to the titanic variable.
titanic = pd.read_csv("data/train.csv")
# Print the first 5 rows of the dataframe.
print(titanic.head(5))
print(titanic... |
mne-tools/mne-tools.github.io | 0.18/_downloads/1e53d1b1e265859ba0683a6745c36ed7/plot_stats_cluster_spatio_temporal.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import os.path as op
import numpy as np
from numpy.random import randn
from scipy import stats as stats
import mne
from mne.epochs import equalize_epoch_counts
from mne.... |
kraemerd17/kraemerd17.github.io | courses/python/material/ipynbs/Case Studies and Applications.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
%matplotlib inline
"""
Explanation: Financial and Economic Data Ap... |
SchwaZhao/networkproject1 | 02_Analysis_of_Twitter_Social_Network.ipynb | mit | #load tweets
import json
filename = 'AI2.txt'
tweet_list = []
with open(filename, 'r') as fopen:
# each line correspond to a tweet
for line in fopen:
if line != '\n':
tweet_list.append(json.loads(line))
"""
Explanation: Analysis of a Twitter Social Network
In this section we a... |
jhillairet/scikit-rf | doc/source/examples/networktheory/IEEEP370 Deembedding.ipynb | bsd-3-clause | import skrf as rf
import matplotlib.pyplot as plt
from skrf.calibration import IEEEP370_SE_NZC_2xThru
from skrf.calibration import IEEEP370_MM_NZC_2xThru
from skrf.calibration import IEEEP370_SE_ZC_2xThru
from skrf.calibration import IEEEP370_MM_ZC_2xThru
from skrf.media import MLine
import numpy as np
rf.stylely()
""... |
4dsolutions/Python5 | Emod Angles.ipynb | mit | import numpy as np
import pandas as pd
from math import atan, tan, degrees, radians, sqrt
"""
Explanation: Central Angles of the E-Module
By David Koski (trig) & Kirby Urner (python)
The above Fig. 986.411B shows the "plane net" or template for an E or T module.
If you cut it out with scissors and fold it up, rever... |
google-research/google-research | aav/model_and_dataset_analysis/200609_figure3_and_tables_shared.ipynb | apache-2.0 | import os
import zipfile
from IPython.display import display
from matplotlib import pyplot
import numpy
import pandas
import scipy.spatial.distance as distance
import scipy.stats
import seaborn
# The canonical single-letter code residue alphabet.
RESIDUES = tuple('ACDEFGHIKLMNPQRSTVWY')
# Residues sorted by physico... |
jmlon/PythonTutorials | pandas/Apache log analyzer with Pandas.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from dateutil.parser import *
"""
Explanation: Building an Apache log analyzer with Pandas
Apache access logs are text files that record the activity of a web site. The analysis of log files provides useful insights for web masters and site owners.... |
YeEmrick/learning | cs231/assignment/assignment2/ConvolutionalNetworks.ipynb | apache-2.0 | # As usual, a bit of setup
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.cnn import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient
from cs231n.layers import *
fro... |
sthuggins/phys202-2015-work | assignments/assignment05/InteractEx02.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 2
Imports
End of explanation
"""
def plot_sine1(a, b):
x=range(0, 4*np.pi)
y= np.sin(a*x + b)
plt.pl... |
danijel3/PyHTK | python-notebooks/ResamplingTest.ipynb | apache-2.0 | import sys
sys.path.append('../python')
from HTKFeat import MFCC_HTK
import numpy as np
%matplotlib inline
import matplotlib.pyplot as P
"""
Explanation: Resampling from 16kHz to 8kHz
This notebook demonstrates resampling from 16kHz to 8kHz using the scipy.signal.resample method. This isn't theonly or the best metho... |
KristianHolsheimer/tensorflow_training | text_data_representation.ipynb | gpl-3.0 | import tensorflow as tf
import numpy as np
import pandas as pd
%matplotlib inline
"""
Explanation: Sparse and dense representations for text data
Before we can start training we need to prepare our input data in a way that our model will understand it.
End of explanation
"""
from utils import SentenceEncoder
sents ... |
kinshuk4/MoocX | misc/deep_learning_notes/Ch4_Recurrent_Networks/000_Multi-layer_Perceptron_intro_to_edf_framework/Simple_Multi-layer_Perceptron_MNIST_Example.ipynb | mit | sigmoid = lambda x: 1/(1 + np.exp(-x))
xs = np.linspace(-5, 5, 100)
plt.plot(xs, sigmoid(xs), linewidth=4, alpha=0.4)
plt.ylim(-.5, 1.5);
"""
Explanation: Plain sigmoid activation function
sigmoid function $$\sigma(x) = \frac{1}{1 + e^{-x}}$$ looks like:
End of explanation
"""
########### we use sigmoid to demonstra... |
ES-DOC/esdoc-jupyterhub | notebooks/messy-consortium/cmip6/models/sandbox-3/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'sandbox-3', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: MESSY-CONSORTIUM
Source ID: SANDBOX-3
Sub-Topics: Radiative Forcin... |
flohorovicic/pynoddy | docs/notebooks/Pynoddy_parallel_MC.ipynb | gpl-2.0 | %matplotlib inline
# here the usual imports. If any of the imports fails,
# make sure that pynoddy is installed
# properly, ideally with 'python setup.py develop'
# or 'python setup.py install'
import sys, os
import matplotlib.pyplot as plt
import numpy as np
# adjust some settings for matplotlib
from matplotlib imp... |
tpin3694/tpin3694.github.io | machine-learning/.ipynb_checkpoints/imbalanced_classes_in_svm-checkpoint.ipynb | mit | # Load libraries
from sklearn.svm import SVC
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
import numpy as np
"""
Explanation: Title: Imbalanced Classes In SVM
Slug: imbalanced_classes_in_svm
Summary: How to handle imbalanced classes in support vector machines in Scikit-Learn
Date: ... |
pdamodaran/yellowbrick | examples/bbengfort/cluster.ipynb | apache-2.0 | import sys
sys.path.append("../..")
import numpy as np
import yellowbrick as yb
import matplotlib.pyplot as plt
from functools import partial
from sklearn.datasets import make_blobs as sk_make_blobs
from sklearn.datasets import make_circles, make_moons
# Helpers for easy dataset creation
N_SAMPLES = 1000
N_FEAT... |
JingJunYin/tensorflow | tensorflow/tools/docker/notebooks/3_mnist_from_scratch.ipynb | apache-2.0 | from __future__ import print_function
from IPython.display import Image
import base64
Image(data=base64.decodestring("iVBORw0KGgoAAAANSUhEUgAAAMYAAABFCAYAAAARv5krAAAYl0lEQVR4Ae3dV4wc1bYG4D3YYJucc8455yCSSIYrBAi4EjriAZHECyAk3rAID1gCIXGRgIvASIQr8UTmgDA5imByPpicTcYGY+yrbx+tOUWpu2e6u7qnZ7qXVFPVVbv2Xutfce+q7hlasmTJktSAXrnn8... |
mohanprasath/Course-Work | numpy/numpy_exercises_from_kyubyong/String_operations.ipynb | gpl-3.0 | from __future__ import print_function
import numpy as np
author = "kyubyong. https://github.com/Kyubyong/numpy_exercises"
np.__version__
"""
Explanation: String operations
End of explanation
"""
x1 = np.array(['Hello', 'Say'], dtype=np.str)
x2 = np.array([' world', ' something'], dtype=np.str)
"""
Explanation: Q... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_object_evoked.ipynb | bsd-3-clause | import os.path as op
import mne
"""
Explanation: The :class:Evoked <mne.Evoked> data structure: evoked/averaged data
The :class:Evoked <mne.Evoked> data structure is mainly used for storing
averaged data over trials. In MNE the evoked objects are usually created by
averaging epochs data with :func:mne.Epo... |
BrentDorsey/pipeline | gpu.ml/notebooks/01b_Explore_Numba.ipynb | apache-2.0 | import math
def hypot(x, y):
x = abs(x);
y = abs(y);
t = min(x, y);
x = max(x, y);
t = t / x;
return x * math.sqrt(1+t*t)
%%timeit
hypot(3.0, 4.0)
"""
Explanation: Explore Numba - aka. Numpy for GPU
Create and Run a Custom Python Function
Note the slow execution time.
End of explanation
"""
... |
planetlabs/notebooks | jupyter-notebooks/cloud-native-geospatial/intro-to-cogs/introduction-to-cogs-part2.ipynb | apache-2.0 | import requests
import os
from requests.auth import HTTPBasicAuth
import json
import pathlib
from rio_cogeo.cogeo import cog_translate
from rio_cogeo.profiles import cog_profiles
"""
Explanation: For the purpose of this demonstration, we will place a simple order to the Orders API that will return us some Non Cloud Op... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_sensors_time_frequency.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.time_frequency import tfr_morlet, psd_multitaper
from mne.datasets import somato
"""
Explanation: Frequency and time-frequency sensors analysis
The objective is to show you how to explore the spectral content
of your data (frequency and time-frequ... |
Leguark/pygeomod | notebooks_GeoPyMC/PyMC for Geology Tutorial/PyMC geomod_1.ipynb | mit | %matplotlib inline
from IPython.core.display import Image
import numpy as np
import matplotlib.pyplot as plt
import sys, os
import shutil
#import geobayes_simple as gs
import pymc as pm # PyMC 2
from pymc.Matplot import plot
from pymc import graph as gr
import numpy as np
#import daft
from IPython.core.pylabtools imp... |
afunTW/dsc-crawling | 02_selenium/00_selenium_crawling_render_image.ipynb | apache-2.0 | import os
import requests
import re
from bs4 import BeautifulSoup
from selenium import webdriver
from selenium.webdriver.common.by import By
from fake_useragent import UserAgent
from pprint import pprint
url = 'https://afuntw.github.io/Test-Crawling-Website/pages/gallery/index.html'
fu = UserAgent()
"""
Explanation:... |
nreimers/deeplearning4nlp-tutorial | 2015-10_Lecture/Lecture4/code/BrownCorpus/GenreClassification.ipynb | apache-2.0 | import nltk
import gensim
import nltk.corpus
import random
from nltk.corpus import brown
from nltk.stem.porter import *
import numpy as np
np.random.seed(0)
num_max_words = 5000
stopwords = {}
for stopword in nltk.corpus.stopwords.words('english'):
stopwords[stopword.lower()] = True
def preprocessDocumen... |
jphall663/GWU_data_mining | 05_neural_networks/src/py_part_5_MNIST_autoencoder.ipynb | apache-2.0 | # imports and inits
import h2o
from h2o.estimators.deeplearning import H2ODeepLearningEstimator
h2o.init()
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import pandas as pd
"""
Explanation: License
Copyright (C) 2017 J. Patrick Hall, jphall@gwu.edu
Permission is hereby granted, free of charg... |
ildoonet/tf-openpose | tf_pose/slim/nets/mobilenet/mobilenet_example.ipynb | apache-2.0 | !git clone https://github.com/tensorflow/models
from __future__ import print_function
from IPython import display
checkpoint_name = 'mobilenet_v2_1.0_224' #@param
url = 'https://storage.googleapis.com/mobilenet_v2/checkpoints/' + checkpoint_name + '.tgz'
print('Downloading from ', url)
!wget {url}
print('Unpacking')
... |
sueiras/training | tensorflow/02-text/03-word_tagging/01_identify_tags_in_airline_database_LSTM - EXERCISE.ipynb | gpl-3.0 | from __future__ import print_function
import sys
import os
import numpy as np
import tensorflow as tf
print(tf.__version__)
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]="0"
#Show images
import matplotlib.pyplot as plt
%matplotlib inline
# plt configuration
plt.rcParams['figure.f... |
darioizzo/pykep | doc/sphinx/examples/solar_orbiter.ipynb | gpl-3.0 | # Pykep imports
from pykep.trajopt import mga_1dsm, launchers
from pykep.planet import jpl_lp
from pykep import epoch
from pykep.core import lambert_problem, propagate_lagrangian, fb_prop
from pykep import DAY2SEC, DAY2YEAR, AU, RAD2DEG, ic2par
from pykep.trajopt.gym import solar_orbiter_resdsm, solar_orbiter_1dsm
from... |
jenshnielsen/HJCFIT | exploration/Example_MLL_Fit_AChR_1patch.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import sys, time, math
import numpy as np
from dcprogs.likelihood import inv
"""
Explanation: HJCFIT- maximum likelihood fit of single-channel data: a simple example
Some general settings:
End of explanation
"""
from dcpyps.samples import samples
from dcpyps import... |
MTG/essentia | src/examples/python/tutorial_tensorflow_real-time_auto-tagging.ipynb | agpl-3.0 | !pip -q install pysoundcard
"""
Explanation: Real-time music auto-tagging
In this tutorial, we use Essentia's TensorFlow integration to perform auto-tagging in real-time.
Additionally, this serves as an example of TensorFlow inference in streaming mode and can be easily adapted to work offline.
Setup
To install Essent... |
xunzhang/dynet | examples/jupyter-tutorials/RNNs.ipynb | apache-2.0 | # we assume that we have the dynet module in your path.
# OUTDATED: we also assume that LD_LIBRARY_PATH includes a pointer to where libcnn_shared.so is.
import dynet as dy
"""
Explanation: RNNs tutorial
End of explanation
"""
pc = dy.ParameterCollection()
NUM_LAYERS=2
INPUT_DIM=50
HIDDEN_DIM=10
builder = dy.LSTMBuil... |
garibaldu/multicauseRBM | Max/MNIST-ORBM-Inference.ipynb | mit | for key in results:
logging.info("Plotting, win, lose and tie images for the {}".format(key))
results[key].plot_various_images()
"""
Explanation: In the cell below
I have calculated in the previous cell the loglikelyhood score of the partitioned sampling and vanilla sampling technique image-wise. So I have a s... |
UW-Hydro/bmorph | tutorial/bmorph_tutorial.ipynb | mit | %pylab inline
%load_ext autoreload
%autoreload 2
%reload_ext autoreload
import warnings
warnings.filterwarnings('ignore')
import os
import sys
import numpy as np
import xarray as xr
import pandas as pd
import geopandas as gpd
import matplotlib as mpl
import matplotlib.pyplot as plt
from tqdm.notebook import tqdm
from ... |
Olsthoorn/TransientGroundwaterFlow | exercises_notebooks/exercChap6_3+answ.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
from scipy.special import expi
def W(u): return -expi(-u) # Theis well function
"""
Explanation: Exercises chapter 6.3 (Theis)
End of explanation
"""
# sampling times in minutes
t_min = np.array([1, 2, 3, 5, 7, 10, 15, 30, 45, 60, 120, 180, 240, 300, 360,
... |
keras-team/keras-io | guides/ipynb/working_with_rnns.ipynb | apache-2.0 | import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
"""
Explanation: Working with RNNs
Authors: Scott Zhu, Francois Chollet<br>
Date created: 2019/07/08<br>
Last modified: 2020/04/14<br>
Description: Complete guide to using & customizing RNN layers.
Introduction
... |
Featuretools/featuretools | docs/source/getting_started/handling_time.ipynb | bsd-3-clause | import pandas as pd
pd.options.display.max_columns = 200
import featuretools as ft
es = ft.demo.load_mock_customer(return_entityset=True, random_seed=0)
es['transactions'].head()
"""
Explanation: Handling Time
When performing feature engineering with temporal data, carefully selecting the data that is used for any c... |
djfan/why_yellow_taxi | Filter/Sjoin_Pyspark_5_ServiceTime.ipynb | mit | sc
"""
Explanation: df_shuffle.csv
| Day of Week | Index |
| ----------- | ----- |
| Monday | 0 |
| Tuesday | 1 |
| Wednesday | 2 |
| Thursday | 3 |
| Friday | 4 |
| Saturday | 5 |
| Sunday | 6 |
| Hour | Index |
| ------------------- | ----- |
| 00... |
tensorflow/docs-l10n | site/ko/tutorials/images/segmentation.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... |
ES-DOC/esdoc-jupyterhub | notebooks/nasa-giss/cmip6/models/sandbox-3/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'sandbox-3', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: SANDBOX-3
Sub-Topics: Radiative Forcings.
Propertie... |
samuelshaner/openmc | docs/source/pythonapi/examples/post-processing.ipynb | mit | %matplotlib inline
from IPython.display import Image
import numpy as np
import matplotlib.pyplot as plt
import openmc
"""
Explanation: This notebook demonstrates some basic post-processing tasks that can be performed with the Python API, such as plotting a 2D mesh tally and plotting neutron source sites from an eigen... |
rahulkgup/deep-learning-foundation | intro-to-tensorflow/intro_to_tensorflow.ipynb | mit | import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfile import ZipFile
print('All m... |
ozak/CompEcon | notebooks/Faster Computation with Numba.ipynb | gpl-3.0 | from numba import jit, njit, autojit, jitclass
import numba as nb
import math
import warnings
with warnings.catch_warnings():
warnings.simplefilter('ignore', nb.errors.NumbaDeprecationWarning)
"""
Explanation: Faster Computations with Numba
Some notes mostly for myself, but could be useful to you
Altough Python i... |
mbuchove/notebook-wurk-b | stats/astro283_hw5.ipynb | mit | # import modules
import numpy as np
from matplotlib import pyplot
%matplotlib inline
from scipy import optimize, stats, special
"""
Explanation: <h2>HW #5</h2>
Matt Buchovecky
Astro 283
End of explanation
"""
# define the pdf for the Rice distribution as a subclass of rv_continuous
class Rice_dist(stats.rv_contin... |
lfairchild/PmagPy | data_files/notebooks/data_model_conversion.ipynb | bsd-3-clause | from importlib import reload
import pmagpy.contribution_builder as cb
from pmagpy import ipmag
import os
import json
import numpy as np
import sys
import pandas as pd
import numpy as np
from pandas import DataFrame
from pmagpy import builder2 as builder
from pmagpy import validate_upload2 as validate_upload
from pmagp... |
Vvkmnn/books | AutomateTheBoringStuffWithPython/lesson21.ipynb | gpl-3.0 | 'hello ' + 'world!'
"""
Explanation: Lesson 21:
String Formatting
You can typically combine strings with +.
End of explanation
"""
name = 'Alice'
place = 'Main Street'
time = '6 pm'
food = 'turnips'
print('Hello ' + name + ', you are invited to a party at ' + place + ' at ' + time + '. Please bring ' + food + '.')
... |
google/timesketch | notebooks/MUS2019_CTF.ipynb | apache-2.0 | # Install the TimeSketch API client if you don't have it
!pip install timesketch-api-client
# Import some things we'll need
from timesketch_api_client import config
from timesketch_api_client import search
import pandas as pd
pd.options.display.max_colwidth = 60
"""
Explanation: <a href="https://colab.research.google... |
google/sentencepiece | python/add_new_vocab.ipynb | apache-2.0 | import sentencepiece_model_pb2 as model
m = model.ModelProto()
m.ParseFromString(open("old.model", "rb").read())
"""
Explanation: You can add new special tokens to pre-trained sentencepiece model
Run this code in google/sentencepiece/python/
Load pre-trained sentencepiece model
Pre-trained model is needed
End of expla... |
sarvex/tensorflow | tensorflow/lite/g3doc/examples/super_resolution/overview.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... |
leriomaggio/python-in-a-notebook | 06 Dictionaries.ipynb | mit | dictionary_name = {key_1: value_1, key_2: value_2, key_3: value_3}
"""
Explanation: Dictionaries (Data Structure)
Dictionaries allow us to store connected bits of information. For example, you might store a person's name and age together.
<a name="top"></a>Contents
What are dictionaries?
General Syntax
Example
Exerci... |
cosmolejo/Fisica-Experimental-3 | Fourier/Tarea_Fourier/Ciclo.Solar.ipynb | gpl-3.0 | import numpy as np
import matplotlib
import pylab as plt
import scipy.misc as pim
from scipy import stats
% matplotlib inline
font = {'weight' : 'bold',
'size' : 12}
matplotlib.rc('font', **font)
"""
Explanation: Tarea 04: Análisis de Fourier - ciclos solares
Alejando Mesa y Yennifer Angarita
Ciclos del So... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/introduction_to_tensorflow/labs/bigquery_tensorflow.ipynb | apache-2.0 | %%bash
# create output dataset
bq mk advdata
%%bigquery
CREATE OR REPLACE MODEL advdata.ulb_fraud_detection
TRANSFORM(
* EXCEPT(Amount),
SAFE.LOG(Amount) AS log_amount
)
OPTIONS(
INPUT_LABEL_COLS=['class'],
AUTO_CLASS_WEIGHTS = TRUE,
DATA_SPLIT_METHOD='seq',
DATA_SPLIT_COL='Time',
MODEL_TY... |
machlearn/ipython-notebooks | ML Algorithm - Random Forests.ipynb | mit | from sklearn.ensemble import BaggingClassifier
from sklearn.neighbors import KNeighborsClassifier
bagging = BaggingClassifier(KNeighborsClassifier(), max_samples = 0.5, max_features=0.5)
"""
Explanation: Random Forests belong to the class of ensemble methods. The goal of ensemble methods is to combine the predictions ... |
anshbansal/anshbansal.github.io | udacity_data_science_notes/intro_machine_learning/lesson_01/lesson_01.ipynb | mit | import numpy as np
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
Y = np.array([1, 1, 1, 2, 2, 2])
from sklearn.naive_bayes import GaussianNB
clf = GaussianNB()
clf.fit(X, Y)
print(clf.predict([[-0.8, -1], [4, 1]]))
"""
Explanation: Lesson 01 - Naive Bayes
ML in The Google Self-Driving Car
We wi... |
anhaidgroup/py_entitymatching | notebooks/guides/step_wise_em_guides/Evaluating the Selected Matcher.ipynb | bsd-3-clause | # Import py_entitymatching package
import py_entitymatching as em
import os
import pandas as pd
# Set the seed value
seed = 0
# Get the datasets directory
datasets_dir = em.get_install_path() + os.sep + 'datasets'
path_A = datasets_dir + os.sep + 'dblp_demo.csv'
path_B = datasets_dir + os.sep + 'acm_demo.csv'
path_... |
authman/DAT210x | Module5/Module5 - Lab7.ipynb | mit | import random, math
import pandas as pd
import numpy as np
import scipy.io
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
matplotlib.style.use('ggplot') # Look Pretty
# Leave this alone until indicated:
Test_PCA = False
"""
Explanation: DAT210x - Programming with Python for DS
Module5- Lab... |
sz2472/foundations-homework | data and database/Classnote_06_30.ipynb | mit | from flask import Flask, request, render_template
app=Flask(_name_)
@app.route("/")
def display_form():
return render_template("simplify_home.html")
@app.route("/transformed", methods=["POST"]) #methods=["POST"]:to make a post request
def display_transformation():
return"put transformed text here"
app.run()
@... |
matias-rivera/seminario2 | Comparacion de Documentos.ipynb | mit | import graphlab
"""
Explanation: Importar GraphLab
End of explanation
"""
people = graphlab.SFrame('people_wiki.gl/')
"""
Explanation: Cargar el dataset
End of explanation
"""
people.head()
len(people)
"""
Explanation: Los datos contienen articulos de wikipedia sobre diferentes personas.
End of explanation
"""
... |
xR86/ml-stuff | labs-machine-learning/Intro_Entropy.ipynb | mit | import math
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
#print(plt.style.available)
"""
Explanation: Some introduction to entropy
Entropy (or expected surprisal) is a measure of either information given by probability or of the chaos present in a system (more or less: how much of my dat... |
TomTranter/OpenPNM | examples/simulations/Advection-Diffusion.ipynb | mit | import numpy as np
import openpnm as op
np.random.seed(10)
%matplotlib inline
ws = op.Workspace()
ws.settings["loglevel"] = 40
np.set_printoptions(precision=5)
net = op.network.Cubic(shape=[1, 20, 30], spacing=1e-4)
"""
Explanation: Advection-Diffusion
In this example, we will learn how to perform an advection-diffusi... |
opesci/devito | examples/finance/bs_ivbp.ipynb | mit | from devito import (Eq, Grid, TimeFunction, Operator, solve, Constant,
SpaceDimension, configuration, SubDomain)
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.axis3d import Axis
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import cm
from sympy.stats... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/02_generalization/labs/create_datasets.ipynb | apache-2.0 | !pip install --user google-cloud-bigquery==1.25.0
"""
Explanation: <h1> Explore and create ML datasets </h1>
In this notebook, we will explore data corresponding to taxi rides in New York City to build a Machine Learning model in support of a fare-estimation tool. The idea is to suggest a likely fare to taxi riders s... |
mne-tools/mne-tools.github.io | 0.17/_downloads/c1aa88a2be3f4bc4a4552ce39a81e4e1/plot_morph_volume_stc.ipynb | bsd-3-clause | # Author: Tommy Clausner <tommy.clausner@gmail.com>
#
# License: BSD (3-clause)
import os
import matplotlib.pyplot as plt
import nibabel as nib
import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse, read_inverse_operator
from nilearn.plotting import plot_glass_brain
print(__doc__)
""... |
IST256/learn-python | content/lessons/04-Iterations/LAB-Iterations.ipynb | mit | i = 1
while i <= 3:
print(i,"Mississippi...")
i=i+1
print("Blitz!")
"""
Explanation: Class Coding Lab: Iterations
The goals of this lab are to help you to understand:
How loops work.
The difference between definite and indefinite loops, and when to use each.
How to build an indefinite loop with complex exit c... |
bioinformatica-corso/lezioni | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | cc0-1.0 | import pandas as pd
"""
Explanation: Introduzione a Pandas
Pandas è una libreria, costruita sulla base della libreria numpy, che ha lo scopo di manipolare data frames.
Oggetto di tipo DataFrame = tabella organizzata in righe (records) e colonne intestate.
Pandas offre tre funzionalità principali:
costruzione
interrog... |
probml/pyprobml | notebooks/misc/Superimport.ipynb | mit | !pip install superimport -qqq
!pip install deimport -qqq
import superimport
def try_deimport():
try:
from deimport.deimport import deimport
deimport(superimport, verbose=False)
except Exception as e:
print(e)
"""
Explanation: <a href="https://colab.research.google.com/github/probml/... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session08/Day2/CorrallingUnrulyDataSolutions.ipynb | mit | # Solution 1 - pure python solution with pandas
with open('irsa_catalog_WISE_iPTF14jg_search_results.tbl') as f:
ll = f.readlines()
for linenum, l in enumerate(ll):
if l[0] == '|':
header = l.replace('|', ',').replace(' ', '')
header = list(header[1:-2].split(','))
b... |
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