repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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bjshaw/phys202-2015-work | assignments/assignment02/ProjectEuler6.ipynb | mit | lst = range(101)
"""
Explanation: Project Euler: Problem 6
https://projecteuler.net/problem=6
The sum of the squares of the first ten natural numbers is,
$$1^2 + 2^2 + ... + 10^2 = 385$$
The square of the sum of the first ten natural numbers is,
$$(1 + 2 + ... + 10)^2 = 552 = 3025$$
Hence the difference between the su... |
manifoldai/merf | notebooks/Real World MERF Examples.ipynb | mit | sleep_df = pd.read_csv('../data/sleepstudy.csv')
fig, ax = plt.subplots(figsize=(15,12))
for label, group in sleep_df.groupby('Subject'):
group.plot(x='Days', y='Reaction', ax=ax, label=label)
plt.legend()
plt.grid('on')
plt.ylabel('Reaction')
sleep_df.head()
train, test = train_test_split(sleep_df, test_size=0.... |
mryab/askme | L2 - Nets.ipynb | mit | import numpy as np
from sklearn.model_selection import train_test_split
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from __future__ import print_function
from keras.datasets import mnist
from keras.models import Sequential, Model
from keras.layers.co... |
alexandrnikitin/workshops | automated-feature-engineering-selection/notebooks/4-feature-selection.ipynb | mit | import numpy as np
import pandas as pd
from IPython.display import Image
"""
Explanation: Automated feature selection
Reasons to have:
Some automatically created features are garbage
Reduces complexity
Trains faster
Improves accuracy
Reduce overfitting
Methods:
1. Filter methods
2. Wrapper Methods
3. Embedded Metho... |
ES-DOC/esdoc-jupyterhub | notebooks/cccma/cmip6/models/sandbox-2/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccma', 'sandbox-2', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: CCCMA
Source ID: SANDBOX-2
Topic: Aerosol
Sub-Topics: Transport, Emissions, Con... |
superbobry/pymc3 | pymc3/examples/posterior_predictive.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
import numpy as np
import pymc3 as pm
import seaborn as sns
import matplotlib.pyplot as plt
from collections import defaultdict
"""
Explanation: Posterior Predictive Checks in PyMC3
PPCs are a great way to validate a model. The idea is to generate data sets from t... |
Heerozh/deep-learning | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
facaiy/book_notes | machine_learning/logistic_regression/demo.ipynb | cc0-1.0 | names = [("x", k) for k in range(8)] + [("y", 8)]
df = pd.read_csv("./res/dataset/pima-indians-diabetes.data", names=names)
df.head(3)
"""
Explanation: 逻辑回归算法简介和Python实现
0. 实验数据
End of explanation
"""
x = np.linspace(-1.5, 1.5, 1000)
y1 = 0.5 * x + 0.5
y2 = sp.special.expit(5 * x)
pd.DataFrame({'linear': y1, 'logi... |
lamahechag/clubes_de_ciencia | Dia_1_monitor/.ipynb_checkpoints/Dia_1-checkpoint.ipynb | mit | 2+3
"""
Explanation: Opreaciones Matematicas
Suma : $2+3$
End of explanation
"""
2*3
"""
Explanation: Multiplicación: $2x3$
End of explanation
"""
2/3
"""
Explanation: División: $\frac{2}{3}$
End of explanation
"""
2**3
"""
Explanation: Potencia: $ 2^{3}$
End of explanation
"""
# Importar una libreria en Py... |
daniel-severo/dask-ml | docs/source/examples/hyperparameter-search.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
from time import time
from scipy.stats import randint as sp_randint
from scipy import stats
from distributed import Client
import distributed.joblib
from sklearn.externals import joblib
from sklearn.datasets import load_digits
from sklearn.linear_model import LogisticRegression... |
AlekseyLobanov/gotohack | Analysis-1.ipynb | mit | import pymongo, json, matplotlib
client2 = pymongo.MongoClient('goto.reproducible.work')
pazans = json.loads(open('/home/oleg/coding/go-to-hack-main/share/pazan_publs.json').read())
users = {}
st = set()
for s in pazans.items():
st.add(s[0])
for l in open('/home/oleg/coding/go-to-hack-main/share/source_data/users.j... |
ES-DOC/esdoc-jupyterhub | notebooks/nerc/cmip6/models/sandbox-1/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'sandbox-1', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: NERC
Source ID: SANDBOX-1
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balan... |
tensorflow/docs-l10n | site/ko/tutorials/estimator/premade.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... |
RafaelNH/Free-water-elimination-DTI | notebook/supplementary_notebook_4.ipynb | bsd-3-clause | from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
import time
%matplotlib inline
# Import Dipy's procedures to process diffusion tensor
import dipy.reconst.dti as dti
# Import Dipy's functions that load and read CENIR data
from dipy.data import fetch_cenir_multib
from dipy.data ... |
ES-DOC/esdoc-jupyterhub | notebooks/nuist/cmip6/models/sandbox-3/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nuist', 'sandbox-3', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: NUIST
Source ID: SANDBOX-3
Topic: Ocean
Sub-Topics: Timestepping Framework, Advecti... |
zakandrewking/cobrapy | documentation_builder/deletions.ipynb | lgpl-2.1 | import pandas
from time import time
import cobra.test
from cobra.flux_analysis import (
single_gene_deletion, single_reaction_deletion, double_gene_deletion,
double_reaction_deletion)
cobra_model = cobra.test.create_test_model("textbook")
ecoli_model = cobra.test.create_test_model("ecoli")
"""
Explanation: S... |
tensorflow/docs-l10n | site/zh-cn/addons/tutorials/tqdm_progress_bar.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... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb | apache-2.0 | import sys
if "google.colab" in sys.modules:
USER_FLAG = ""
else:
USER_FLAG = "--user"
! pip3 install -U tensorflow==2.8 $USER_FLAG
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
"""
Explanation: <table align="left">
<td>
<a href="https://colab.research.google.com/github/GoogleCloudPlatfo... |
zerothi/sisl | docs/visualization/viz_module/showcase/WavefunctionPlot.ipynb | mpl-2.0 | import sisl
import sisl.viz
"""
Explanation: WavefunctionPlot
The WavefunctionPlot class will help you very easily generate and display wavefunctions from a Hamiltonian or any other source. If you already have your wavefunction in a grid, you can use GridPlot.
<div class="alert alert-info">
Note
`WavefunctionPlot`... |
gengyj/ml-basic-course | tensorflow_captcha_simple.ipynb | gpl-3.0 | import time
import os
from multiprocessing import Pool
from captcha.image import ImageCaptcha
import numpy as np
import skimage.io as io
import tensorflow as tf
import matplotlib.pylab as plt
%matplotlib inline
"""
Explanation: 验证码识别 简单版本
End of explanation
"""
IMG_H = 64
IMG_W = 160
IMG_CHANNALS = 1
CAPTCHA_SIZE ... |
nikbearbrown/Deep_Learning | NEU/Sai_Raghuram_Kothapalli_DL/CIFAR_10-Keras.ipynb | mit | # Plot ad hoc CIFAR10 instances
from keras.datasets import cifar10
from matplotlib import pyplot
# load data
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
"""
Explanation: Object recognition with CNN
Keras is a Python library for deep learning that wraps the powerful numerical libraries Theano and Tensor... |
MissouriDSA/twitter-locale | twitter/twitter_1.ipynb | mit | import psycopg2
import pandas as pd
# define our query
statement = """SELECT column_name, data_type, is_nullable
FROM information_schema.columns
WHERE table_name = 'tweet';"""
try:
connect_str = "dbname='twitter' user='dsa_ro_user' host='dbase.dsa.missouri.edu'password='readonly'"
# use our connection... |
tensorflow/workshops | tfx_airflow/notebooks/step3.ipynb | apache-2.0 | from __future__ import print_function
!pip install -q papermill
!pip install -q matplotlib
!pip install -q networkx
import os
import tfx_utils
import tensorflow as tf
%matplotlib notebook
tf.get_logger().propagate = False
def _make_default_sqlite_uri(pipeline_name):
return os.path.join(os.environ['HOME'], 'airfl... |
bartleyn/tpot | tutorials/Titanic_Kaggle.ipynb | gpl-3.0 | # Import required libraries
from tpot import TPOT
from sklearn.cross_validation import train_test_split
import pandas as pd
import numpy as np
# Load the data
titanic = pd.read_csv('data/titanic_train.csv')
titanic.head(5)
"""
Explanation: TPOT tutorial on the Titanic dataset
The Titanic machine learning competition... |
retnuh/deep-learning | 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... |
n-witt/MachineLearningWithText_SS2017 | tutorials/0 Python basics 1.ipynb | gpl-3.0 | from IPython.display import Image
Image('images/mem0.jpg')
Image('images/mem1.jpg')
Image('images/C++_machine_learning.png')
Image('images/Java_machine_learning.png')
Image('images/Python_machine_learning.png')
Image('images/R_machine_learning.png')
"""
Explanation: Note: We are using Python here, not Python 2. T... |
aschaffn/phys202-2015-work | assignments/assignment09/IntegrationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy import integrate
"""
Explanation: Integration Exercise 1
Imports
End of explanation
"""
def trapz(f, a, b, N):
"""Integrate the function f(x) over the range [a,b] with N points."""
x = np.linspace(a,b,N+1)
h = np.diff(x)[1]
... |
ES-DOC/esdoc-jupyterhub | notebooks/bcc/cmip6/models/sandbox-3/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bcc', 'sandbox-3', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: BCC
Source ID: SANDBOX-3
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radiat... |
jsnajder/MachineLearningTutorial | Machine Learning Tutorial.ipynb | cc0-1.0 | import scipy as sp
import scipy.stats as stats
import matplotlib.pyplot as plt
from numpy.random import normal
from SU import *
%pylab inline
"""
Explanation: Basics of Machine Learning
Tutorial held at University of Zurich, 23-24 March 2016
(c) 2016 Jan Šnajder (jan.snajd
... |
atulsingh0/MachineLearning | python_DC/IntoductionToDataBase_#1.5.ipynb | gpl-3.0 | # Import create_engine, MetaData
from sqlalchemy import create_engine , MetaData
# Define an engine to connect to chapter5.sqlite: engine
engine = create_engine('sqlite:///chapter5.sqlite')
# Initialize MetaData: metadata
metadata = MetaData()
"""
Explanation: Case Study
Import create_engine and MetaData from sqlalc... |
jotterbach/Data-Exploration-and-Numerical-Experimentation | Numerical-Experimentation/t-SNE and the KL Divergence.ipynb | cc0-1.0 | import pymc
import seaborn as sns
import scipy.stats as stats
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
def calculate_single_kl_value(p, q):
return p * (np.log(p) - np.log(q))
def single_bernoulli_draw(p, n_bern):
bernoulli = pymc.Bernoulli('bern', p, size = n_bern)
return flo... |
AC209ConsumerConfidence/AC209ConsumerConfidence.github.io | Applying SentiWordNet to News Data.ipynb | gpl-3.0 | DATA_DIR = "./data/"
all_data_list = []
for year in range(1990,2017):
data = pd.read_csv(DATA_DIR + '{}_Output.csv'.format(year), header=None, encoding="utf-8")
all_data_list.append(data) # list of dataframes
data = pd.concat(all_data_list, axis=0)
data.columns = ['id','date','headline', 'lead']
# Drop dupes ... |
mne-tools/mne-tools.github.io | 0.21/_downloads/78dfec6019dc9e7214e1efd97200f1c4/plot_10_overview.ipynb | bsd-3-clause | import os
import numpy as np
import mne
"""
Explanation: Overview of MEG/EEG analysis with MNE-Python
This tutorial covers the basic EEG/MEG pipeline for event-related analysis:
loading data, epoching, averaging, plotting, and estimating cortical activity
from sensor data. It introduces the core MNE-Python data struct... |
Luke035/dlnd-lessons | batch-norm/Batch_Normalization_Lesson.ipynb | mit | # Import necessary packages
import tensorflow as tf
import tqdm
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Import MNIST data so we have something for our experiments
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
"... |
yangw1234/BigDL | apps/variational-autoencoder/using_variational_autoencoder_and_deep_feature_loss_to_generate_faces.ipynb | apache-2.0 | from bigdl.dllib.nn.layer import *
from bigdl.dllib.nn.criterion import *
from bigdl.dllib.optim.optimizer import *
from bigdl.dllib.feature.dataset import mnist
import datetime as dt
from glob import glob
import os
import numpy as np
from utils import *
import imageio
image_size = 148
Z_DIM = 100
ENCODER_FILTER_NUM =... |
tatjanus/cianparser | cian_dataprep_visualization.ipynb | bsd-2-clause | import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
plt.style.use('bmh')
%matplotlib inline
import random
random.seed(42)
np.random.seed(42)
districts = {1: 'NW', 4: 'C', 5:'N', 6:'NE', 7:'E', 8:'SE', 9:'S', 10:'SW', 11:'W'}
data = pd.read_csv('cian_full_data.csv')
dat... |
datahac/jup | candidates results/Bugrov_test.ipynb | apache-2.0 | path = 'task_data/Sessions_Page.json'
path2 = 'task_data/Goal1CompletionLocation_Goal1Completions.json'
with open(path, 'r') as f:
sessions_page = json.loads(f.read())
with open(path2, 'r') as f:
goals_page = json.loads(f.read())
"""
Explanation: .загружаем файлы .json
End of explanation
"""
type (sessions... |
jbwhit/jupyter-best-practices | notebooks/08-More_basics.ipynb | mit | names = ['alice', 'jonathan', 'bobby']
ages = [24, 32, 45]
ranks = ['kinda cool', 'really cool', 'insanely cool']
for (name, age, rank) in zip(names, ages, ranks):
print(name, age, rank)
for index, (name, age, rank) in enumerate(zip(names, ages, ranks)):
print(index, name, age, rank)
# return, esc, shift+ent... |
hparik11/Deep-Learning-Nanodegree-Foundation-Repository | gan_mnist/.ipynb_checkpoints/Intro_to_GANs_Solution-checkpoint.ipynb | mit | %matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
jotterbach/Data-Exploration-and-Numerical-Experimentation | Numerical-Experimentation/The Monty Hall Problem.ipynb | cc0-1.0 | import matplotlib
import matplotlib.pyplot as plt
import random as rd
import numpy as np
from numpy.random import choice
%matplotlib inline
matplotlib.style.use('ggplot')
matplotlib.rc_params_from_file("../styles/matplotlibrc" ).update()
"""
Explanation: The Monty Hall Problem
Introduction
Thinking conditionally is a... |
AbstractGeek/rusmalai-ncbs | 02-perceptron-learning-and-backpropagation.ipynb | mit | # Import libraries
%matplotlib inline
from sklearn import datasets
import matplotlib.pyplot as plt
import numpy as np
from copy import deepcopy
"""
Explanation: Back propagation algorithm
Perceptron learning algorithm (Recap)
<img src="imgs/perceptron.png">
Output is simply:
$$ y(x) = \mathbf{w^Tx} $$
The classifying ... |
NYUDataBootcamp/Projects | UG_S16/Webb-HealthcareSystems.ipynb | mit | #Import pandas & matplotlib Tools
%matplotlib inline
import pandas as pd
import pandas_datareader.data as web
from pandas_datareader import wb
import matplotlib as mpl
import matplotlib.pyplot as plt
#Download necessary data from World Bank
#Private health spending as a percentage of GDP
df1 = wb.download(indicator='... |
davebshow/DH3501 | class15.ipynb | mit | # This sets up the "cell magic" used by ipython-cypher
%load_ext cypher
%matplotlib inline
import networkx as nx
import matplotlib.pyplot as plt
%%cypher
// Cypher comments use two slashes
// A really useful query that clears the database
MATCH (n)
OPTIONAL MATCH (n)-[r]-()
DELETE n, r
"""
Explanation: <div align="le... |
danielgoncalvesti/BIGDATA2017 | Atividade02/Lab/Lab3_AnaliseExploratoria.ipynb | gpl-3.0 | sc = SparkContext.getOrCreate()
import os
import numpy as np
filename = os.path.join("Data","Aula03","train.csv")
CrimeRDD = sc.textFile(filename,8)
header = CrimeRDD.take(1)[0] # o cabeçalho é a primeira linha do arquivo
print "Campos disponíveis: {}".format(header)
"""
Explanation: Análise Exploratória
Esse note... |
AEW2015/PYNQ_PR_Overlay | Pynq-Z1/notebooks/Video_PR/GrayScale_Filter.ipynb | bsd-3-clause | from pynq.drivers.video import HDMI
from pynq import Bitstream_Part
from pynq.board import Register
from pynq import Overlay
Overlay("demo.bit").download()
"""
Explanation: Don't forget to delete the hdmi_out and hdmi_in when finished
Grayscale Filter Example
In this notebook, we will explore the averaging of RGB val... |
MingChen0919/learning-apache-spark | notebooks/03-data-preparation/stringindexer-and-onehotencoder.ipynb | mit | import pandas as pd
pdf = pd.DataFrame({
'x1': ['a','a','b','b', 'b', 'c'],
'x2': ['apple', 'orange', 'orange','orange', 'peach', 'peach'],
'x3': [1, 1, 2, 2, 2, 4],
'x4': [2.4, 2.5, 3.5, 1.4, 2.1,1.5],
'y1': [1, 0, 1, 0, 0, 1],
'y2': ['yes', 'no', 'no', 'yes', 'yes', 'ye... |
parrt/msan501 | notes/dataframes.ipynb | mit | import pandas as pd
df = pd.read_csv("data/rent.csv", parse_dates=['created'])
df.head(2)
df.head(2).T
"""
Explanation: Sniffing data frames
We're going to use a real kaggle competition data set to explore Pandas dataframes. Grab the rent.csv.zip file and unzip it.
End of explanation
"""
df.info()
df.describe()
d... |
MissouriDSA/twitter-locale | twitter/twitter_3.ipynb | mit | # BE SURE TO RUN THIS CELL BEFORE ANY OF THE OTHER CELLS
import psycopg2
import pandas as pd
# put your code here
# ------------------
statement = """
SELECT DISTINCT iso_language, job_id,COUNT(*)
FROM
(SELECT
DISTINCT ON (from_user, iso_language)
*
FROM (SELECT * FROM twitter.tweet WHERE iso_language != 'und' A... |
Naereen/notebooks | simus/Simulations_du_jeu_de_151.ipynb | mit | import numpy as np
import numpy.random as rn
rn.seed(0) # Pour obtenir les mêmes résultats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(context="notebook", style="darkgrid", palette="hls", font="sans-serif", font_scale=1.4)
"""
Explanation: Simulons le jeu de 151 avec Python !
But :
Simuler numériq... |
NYUDataBootcamp/Materials | Code/notebooks/bootcamp_pandas_adv4-merge-extended.ipynb | mit | %matplotlib inline
import pandas as pd # data package
import matplotlib.pyplot as plt # graphics
import datetime as dt # date tools, used to note current date
# these are new
import os # operating system tools (check files)
import requests, io # internet an... |
tensorflow/tfx | docs/tutorials/tfx/penguin_simple.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... |
harish-garg/Data-Analysis | udacity_intro_data_analysis/udacity_student_data/L1_Starter_Code.ipynb | gpl-3.0 | import unicodecsv
## Longer version of code (replaced with shorter, equivalent version below)
# enrollments = []
# f = open('enrollments.csv', 'rb')
# reader = unicodecsv.DictReader(f)
# for row in reader:
# enrollments.append(row)
# f.close()
def read_csv(filename):
with open(filename, 'rb') as f:
re... |
coolharsh55/advent-of-code | 2016/python3/Day02.ipynb | mit | def numpad_number_from_point(point):
return str(point.y * 3 + point.x + 1)
"""
Explanation: Day 2: Bathroom Security
author: Harshvardhan Pandit
license: MIT
link to problem statement
You arrive at Easter Bunny Headquarters under cover of darkness. However, you left in such a rush that you forgot to use the bathro... |
oasis-open/cti-python-stix2 | docs/guide/datastore.ipynb | bsd-3-clause | from taxii2client import Collection
from stix2 import CompositeDataSource, FileSystemSource, TAXIICollectionSource
# create FileSystemStore
fs = FileSystemSource("/tmp/stix2_source")
# create TAXIICollectionSource
colxn = Collection('http://127.0.0.1:5000/trustgroup1/collections/91a7b528-80eb-42ed-a74d-c6fbd5a26116/'... |
ucsc-astro/coffee | 16_02_03_intro_to_pandas/intro_to_pandas.ipynb | gpl-3.0 | url = "https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/ggplot2/diamonds.csv"
data = np.genfromtxt(url, delimiter=",", dtype=None, names=True)
data
"""
Explanation: What is Pandas?
Pandas provides fast, flexible, and expressive data structures designed to make working with “relational” or “la... |
dataewan/deep-learning | gan_mnist/Intro_to_GANs_Exercises.ipynb | mit | %matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
scotthuang1989/Python-3-Module-of-the-Week | text/text_wrap.ipynb | apache-2.0 | import textwrap
"""
Explanation: The textwrap module can be used to format text for output in situations where pretty-printing is desired. It offers programmatic functionality similar to the paragraph wrapping or filling features found in many text editors and word processors.
End of explanation
"""
sample_text = ''... |
adrn/tutorials | notebooks/units-and-integration/units-and-integration.ipynb | cc0-1.0 | import numpy as np
from scipy import integrate
from astropy.modeling.blackbody import blackbody_lambda, blackbody_nu, BlackBody1D
from astropy import units as u, constants as c
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Using scipy.integrate
Authors
Zach Pace, Lia Corrales, Stephanie T. Dougl... |
kimkipyo/dss_git_kkp | 통계, 머신러닝 복습/160531화_10일차_Scikit-Learn & statsmodels 패키지 소개 Introduction to Scikit-Learn & statsmodels packages/3.Scikit-Learn 패키지의 샘플 데이터 - 회귀 분석용.ipynb | mit | from sklearn.datasets import load_boston
boston = load_boston()
print(boston.DESCR)
dfX = pd.DataFrame(boston.data, columns=boston.feature_names)
dfy = pd.DataFrame(boston.target, columns=["MEDV"])
df = pd.concat([dfX, dfy], axis=1)
df.tail()
df.describe()
cols = ["LSTAT", "NOX", "RM", "MEDV"]
sns.pairplot(df[cols])... |
hainm/scikit-xray-examples | demos/speckle/X-ray_Speckle_Visibility_Spectroscopy.ipynb | bsd-3-clause | import xray_vision
import xray_vision.mpl_plotting as mpl_plot
import skxray.core.speckle as xsvs
import skxray.core.roi as roi
import skxray.core.correlation as corr
import skxray.core.utils as utils
import numpy as np
import os, sys
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker im... |
Bio204-class/bio204-notebooks | Introduction-to-Pandas.ipynb | cc0-1.0 | import numpy as np
import pandas as pd
"""
Explanation: A Quick Introduction to Pandas
Author: Paul M. Magwene
End of explanation
"""
np.random.seed(482010) # seed the pseudo-random number generators
x = np.random.random(15)
y = np.random.binomial(10, x)
df = pd.DataFrame()
df['prob'] = x
df['count'] = y
df.head()... |
NekuSakuraba/my_capstone_research | subjects/em/Expectation Maximization.ipynb | mit | from scipy.interpolate import interp1d
"""
Explanation: https://stackoverflow.com/questions/11808074/what-is-an-intuitive-explanation-of-the-expectation-maximization-technique
End of explanation
"""
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
def estimate_mean(data, weight):
retur... |
ecabreragranado/OpticaFisicaII | TratamientoAntirreflejante/Tratamiento_Antirreflejante_Ejercicio.ipynb | gpl-3.0 | # NO TOCAR. SOLO EJECUTAR (SOLO UNA VEZ)
####################################################################################
%pylab inline
lambda0 = int(np.random.rand()*150 +475)
print "Longitud de onda para la que optimizamos el tratamiento = ",lambda0, " nm"
"""
Explanation: Diseño y caracterización de un tratamie... |
ipython/ipywidgets | docs/source/examples/Output Widget.ipynb | bsd-3-clause | import ipywidgets as widgets
"""
Explanation: Index - Back - Next
Output widgets: leveraging Jupyter's display system
End of explanation
"""
out = widgets.Output(layout={'border': '1px solid black'})
out
"""
Explanation: The Output widget can capture and display stdout, stderr and rich output generated by IPython. ... |
tzoiker/gensim | docs/notebooks/Word2Vec_FastText_Comparison.ipynb | lgpl-2.1 | import nltk
nltk.download('brown')
# Only the brown corpus is needed in case you don't have it.
# Generate brown corpus text file
with open('brown_corp.txt', 'w+') as f:
for word in nltk.corpus.brown.words():
f.write('{word} '.format(word=word))
# Make sure you set FT_HOME to your fastText directory root... |
kitu2007/dl_class | weight-initialization/weight_initialization.ipynb | mit | %matplotlib inline
import tensorflow as tf
import helper
from tensorflow.examples.tutorials.mnist import input_data
print('Getting MNIST Dataset...')
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
print('Data Extracted.')
"""
Explanation: Weight Initialization
In this lesson, you'll learn how to fin... |
KshitijT/fundamentals_of_interferometry | 1_Radio_Science/1_8_astronomical_radio_sources.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
"""
Explanation: Outline
Glossary
1. Radio Science using Interferometric Arrays
Previous: 1.7 Line emission
Next: 1.9 A brief introduction to interferometry
Section... |
ohbm/brain-hacking-101 | beginner-python/001-arrays.ipynb | apache-2.0 | import numpy as np
# Numpy is a package. To see what's in a package, type the name, a period, then hit tab
#np?
#np.
# Some examples of numpy functions and "things":
print(np.sqrt(4))
print(np.pi) # Not a function, just a variable
print(np.sin(np.pi)) # A function on a variable :)
"""
Explanation: Brain-hacking 10... |
stscieisenhamer/stginga | stginga/examples/ginga_nbinteract.ipynb | bsd-3-clause | webbrowser.open(server.get_viewer_urls()['Main Viewer'])
"""
Explanation: The next cell will open a new window with the same view as above
End of explanation
"""
f = fits.open('https://archive.stsci.edu/pub/hlsp/angst/acs/hlsp_angst_hst_acs-wfc_10210-ugc8760_f814w_v1_ref.fits')
server.load_fits(f)
"""
Explanation: ... |
OlafLee/matplotlib-gallery | ipynb/barplots.ipynb | gpl-3.0 | %load_ext watermark
%watermark -u -v -d -p matplotlib,numpy
"""
Explanation: Sebastian Raschka
back to the matplotlib-gallery at https://github.com/rasbt/matplotlib-gallery
End of explanation
"""
%matplotlib inline
"""
Explanation: <font size="1.5em">More info about the %watermark extension</font>
End of explanati... |
JamesSample/icpw | toc_report_feb_2019_part6.ipynb | mit | # Read stations
stn_path = r'../../../all_icpw_sites_may_2019.xlsx'
stn_df = pd.read_excel(stn_path, sheet_name='all_icpw_stns')
stn_df.head()
nivapy.spatial.quickmap(stn_df,
cluster=True,
popup='station_code')
"""
Explanation: TOC Thematic Report - February 2019 (Part ... |
google/jax | docs/notebooks/thinking_in_jax.ipynb | apache-2.0 | import matplotlib.pyplot as plt
import numpy as np
x_np = np.linspace(0, 10, 1000)
y_np = 2 * np.sin(x_np) * np.cos(x_np)
plt.plot(x_np, y_np);
import jax.numpy as jnp
x_jnp = jnp.linspace(0, 10, 1000)
y_jnp = 2 * jnp.sin(x_jnp) * jnp.cos(x_jnp)
plt.plot(x_jnp, y_jnp);
"""
Explanation: How to Think in JAX
JAX prov... |
metpy/MetPy | v0.10/_downloads/52c3e3d710569bed83f26e14e23bb356/Inverse_Distance_Verification.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
from scipy.spatial import cKDTree
from scipy.spatial.distance import cdist
from metpy.interpolate.geometry import dist_2
from metpy.interpolate.points import barnes_point, cressman_point
from metpy.interpolate.tools import calc_kappa
def draw_circle(ax, x, y, r, m, ... |
kylemede/DS-ML-sandbox | KaggelChallenges/titanic/.ipynb_checkpoints/explore-checkpoint.ipynb | gpl-3.0 | import pandas as pd
from pandas import Series, DataFrame
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set_style("whitegrid")
train_df = pd.read_csv("train.csv",dtype={"Age":np.float64},)
train_df.head()
# find how many ages
train_df['Age'].count()
# how many ages ... |
jaganadhg/data_science_notebooks | WiPBDSCwR/CH4.ipynb | bsd-3-clause | %matplotlib inline
from matplotlib import pylab as plt
plt.rcParams['figure.figsize'] = (15.0, 10.0)
import pandas as pd
import seaborn as sns
"""
Explanation: Chapter 4
Data Visualization
End of explanation
"""
data = pd.read_csv("978-3-319-12065-2/chapter-4/teams.csv")
data.head()
"""
Explanation: 4.1 Introductio... |
tensorflow/docs-l10n | site/zh-cn/guide/mixed_precision.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... |
4dsolutions/Python5 | Polyhedrons 101.ipynb | mit | import sqlite3 as sql
import os
from pprint import pprint
class DB:
backend = 'sqlite3' # default
target_path = os.getcwd() # current directory
db_name = ":file:" # lets work directly with a file
db_name = os.path.join(target_path, 'shapes_lib.db')
@classmethod
def connect(cls)... |
liganega/Gongsu-DataSci | previous/notes2017/W04/GongSu08_Lists.ipynb | gpl-3.0 | record_f = open("Sample_Data/Swim_Records/record_list.txt")
record = record_f.read().decode('utf-8').split('\n')
record_f.close()
for line in record:
print(line)
"""
Explanation: 리스트 활용
주요 내용
파이썬에 내장되어 있는 컬렉션 자료형 중의 하나인 리스트(list)에 대해 알아본다.
리스트(lists): 파이썬에서 사용할 수 있는 임의의 값들을 모아서
하나의 값으로 취급하는 자료형
사용 형태: 대괄호 사용
e... |
anandha2017/udacity | nd101 Deep Learning Nanodegree Foundation/DockerImages/09_preparing_for_sirajs_lesson/notebooks/02_intro-to-tflearn/TFLearn_Sentiment_Analysis.ipynb | mit | import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
"""
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network w... |
vipmunot/Data-Science-Course | Data Visualization/Lab 12/w12_lab_Vipul_Munot.ipynb | mit | import pandas as pd
from urllib.request import urlopen
import json
import warnings
warnings.filterwarnings("ignore")
"""
Explanation: W12 lab assignment
End of explanation
"""
pokemon = pd.read_csv('pokemon.csv')
pokemon.head()
"""
Explanation: Choropleth map
Let's make a choropleth map with Pokemon statistics. The... |
DawesLab/LabNotebooks | Cloud Chamber Test Analysis.ipynb | mit | #SG Here is the cloud chamber data collected of brightness vs. time when the brightness of a laser
#incident on the cloud chamber is without any added mist for 5 seconds, then 5 seconds of mist.
plt.plot(cloud_data[1:,0],cloud_data[1:,1])
plt.plot(cloud_data[1:,0],cloud_data[1:,3])
plt.plot(cloud_data[1:,0],cloud_dat... |
neuroidss/nupic.research | projects/modules_math/Grid_Cell_Modules_Math.ipynb | agpl-3.0 | # n = number of cells per module
# m = number of modules
# theta = number of matching modules needed to call two representations equal
# t = exact match threshold used as an intermediate variable
# U = number of representations in union
# s = number of subsampled bits, or the number of synapses that a segment receives ... |
CorySimon/pyIAST | ternary_example/ternary_adsorption_example.ipynb | mit | df_N2 = pd.read_csv("N2.csv", skiprows=1)
N2_isotherm = pyiast.ModelIsotherm(df_N2, loading_key="Loading(mmol/g)",
pressure_key="P(bar)", model='Henry')
pyiast.plot_isotherm(N2_isotherm)
N2_isotherm.print_params()
df_CO2 = pd.read_csv("CO2.csv", skiprows=1)
CO2_isotherm = pyia... |
openradar/AMS-Short-Course-on-Open-Source-Radar-Software | 9b_PyTDA_Demo-AMS_OSRSC.ipynb | bsd-2-clause | from __future__ import division, print_function
import numpy as np
import matplotlib.pyplot as plt
import os
import glob
import pyart
import pytda
%matplotlib inline
"""
Explanation: PyTDA Demo
<b>Author</b><br>
Timothy Lang, NASA MSFC<br>
timothy.j.lang@nasa.gov
<b>Overview</b><br>
PyTDA is a Python module that allow... |
dsg-bielefeld/pentoref | code/ipython_notebooks/PentoRef_Exploration_sqlite_databases_1.ipynb | gpl-3.0 | import sys
sys.path.append("../python/")
import pentoref.IO as IO
import sqlite3 as sqlite
# Create databases if required
if False: # make True if you need to create the databases from the derived data
for corpus_name in ["TAKE", "TAKECV", "PENTOCV"]:
data_dir = "../../../pentoref/{0}_PENTOREF".format(co... |
BeyondTheClouds/enoslib | docs/jupyter/00_setup_and_basics.ipynb | gpl-3.0 | import enoslib as en
"""
Explanation: Setup and basic objects
Get started with EnOSlib on Grid'5000.
Website: https://discovery.gitlabpages.inria.fr/enoslib/index.html
Instant chat: https://framateam.org/enoslib
Source code: https://gitlab.inria.fr/discovery/enoslib
This is the first notebooks of a series that wil... |
tensorflow/docs-l10n | site/ja/agents/tutorials/4_drivers_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... |
lit-mod-viz/middlemarch-critical-histories | notebooks/anthologies-analysis.ipynb | gpl-3.0 | import spacy
import pandas as pd
%matplotlib inline
from ast import literal_eval
import numpy as np
import re
import json
from nltk.corpus import names
from collections import Counter
from matplotlib import pyplot as plt
plt.rcParams["figure.figsize"] = [16, 6]
plt.style.use('ggplot')
nlp = spacy.load('en')
with open... |
Ykharo/notebooks | C elemental, querido Cython..ipynb | bsd-2-clause | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Cython, que no CPython
No, no nos hemos equivocado en el título, hoy vamos a hablar de Cython.
¿Qué es Cython?
Cython son dos cosas:
Por una parte, Cython es un lenguaje de programación (un superconjunto de Python) que une Python c... |
CrowdTruth/CrowdTruth-core | tutorial/notebooks/Free Input Task - Person Annotation in Video.ipynb | apache-2.0 | import pandas as pd
test_data = pd.read_csv("../data/person-video-free-input.csv")
test_data.head()
"""
Explanation: CrowdTruth for Free Input Tasks: Person Annotation in Video
In this tutorial, we will apply CrowdTruth metrics to a free input crowdsourcing task for Person Annotation from video fragments. The workers... |
google/trax | trax/models/reformer/text_generation.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 Licen... |
mroberge/hydrofunctions | docs/notebooks/Graphing.ipynb | mit | import hydrofunctions as hf
import pandas as pd
%matplotlib inline
hf.__version__
pd.__version__
"""
Explanation: Example Plots
This notebook illustrates the different types of graph you can produce with Hydrofunctions.
We have:
hydrograph
flow duration
cycleplot
histogram
We'll start with the usual imports:
End of ... |
Cyb3rWard0g/ThreatHunter-Playbook | docs/notebooks/windows/05_defense_evasion/WIN-191224222300.ipynb | gpl-3.0 | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: Extended NetNTLM Downgrade
Metadata
| | |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2019/12/24 |
| modification date | 2020/09/20 |
| playbook related | [] |
Hyp... |
tzoiker/gensim | docs/notebooks/annoytutorial.ipynb | lgpl-2.1 | # Load the model
import gensim, os
from gensim.models.word2vec import Word2Vec
# Set file names for train and test data
test_data_dir = '{}'.format(os.sep).join([gensim.__path__[0], 'test', 'test_data']) + os.sep
lee_train_file = test_data_dir + 'lee_background.cor'
class MyText(object):
def __iter__(self):
... |
dandtaylor/MetroShare | Analysis.ipynb | mit | import pickle
import pandas as pd
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('ggplot')
%matplotlib inline
print(plt.style.available)
metro_delays = pickle.load( open( "metro_delays.p", "rb" ) )
bikeshare_rides = pickle.load( open( "bikeshare_rides.p... |
jakdot/pyactr | docs/Getting started I.ipynb | gpl-3.0 | import pyactr as actr
playing_memory = actr.ACTRModel()
"""
Explanation: Getting started I
We will explain basics of ACT-R and pyactr on several very simple models/minds that play Memory.
Model 1 - introduction to the goal buffer and production rules
The first model will be a mind that makes just one action - it will... |
graphistry/pygraphistry | demos/demos_databases_apis/alienvault/OTXLockerGoga.ipynb | bsd-3-clause | #!pip install graphistry -q
#!pip install OTXv2 -q
import graphistry
import pandas as pd
from OTXv2 import OTXv2, IndicatorTypes
from gotx import G_OTX
# To specify Graphistry account & server, use:
# graphistry.register(api=3, username='...', password='...', protocol='https', server='hub.graphistry.com')
# For more... |
ESGF/esgf-pyclient | notebooks/examples/download.ipynb | bsd-3-clause | from pyesgf.logon import LogonManager
lm = LogonManager()
lm.logoff()
lm.is_logged_on()
myproxy_host = 'esgf-data.dkrz.de'
lm.logon(username=None, password=None, hostname=myproxy_host)
lm.is_logged_on()
"""
Explanation: Examples of pyesgf download usage
Obtain MyProxy credentials to allow downloading files:
End of ex... |
amorgun/shad-ml-notebooks | notebooks/s1-4/linear.ipynb | unlicense | def get_grid(data, step=0.1):
x_min, x_max = data.x.min() - 1, data.x.max() + 1
y_min, y_max = data.y.min() - 1, data.y.max() + 1
return np.meshgrid(np.arange(x_min, x_max, step),
np.arange(y_min, y_max, step))
from sklearn.cross_validation import cross_val_score
def get_score(X, y,... |
ucsdlib/python-novice-inflammation | 2-loops.ipynb | cc0-1.0 | #example task: print each character in a word
#one way to do is use a series of print statements
word = 'lead'
print(word[0])
print(word[1])
print(word[2])
print(word[3])
"""
Explanation: last lesson we wrote code to plot some values from our inflammation data.
but we have a dozen we want to do same for
how to repeat... |
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