repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
mit-eicu/eicu-code | notebooks/patient.ipynb | mit | # Import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import psycopg2
import getpass
import pdvega
# for configuring connection
from configobj import ConfigObj
import os
%matplotlib inline
# Create a database connection using settings from config file
config='../db/config.ini'
#... |
Jim00000/Numerical-Analysis | 2_Systems_Of_Equations.ipynb | unlicense | # Import modules
import sys
import numpy as np
import numpy.linalg
import scipy
import sympy
import sympy.abc
from scipy import linalg
from scipy.sparse import linalg as slinalg
"""
Explanation: CHAPTER 2 - Systems Of Equations
End of explanation
"""
def naive_gaussian_elimination(matrix):
"""
A simple gauss... |
gaufung/Data_Analytics_Learning_Note | Scikit_Learning/User_Guide/Generalized_Linear_Models.ipynb | mit | from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit([[0, 0], [1, 2], [2,2]], [0, 1, 2])
reg.coef_
"""
Explanation: Generailized Linear Models
In mathematical notion.
$$\hat{y}(\omega, x)=\omega_0 + \omega_1x_1 + \ldots +\omega_px_p$$
We designate the vector $\omega=(\omega_1,\ldots,\omeg... |
kylemede/DS-ML-sandbox | KaggelChallenges/titanic/explore.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")
# machine learning
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import Ran... |
utensil/julia-playground | packages/galgebra.py.ipynb | mit | from sympy import solve,sqrt
g = '0 # #,# 0 #,# # 1'
necl = Ga('X Y e',g=g)
(X,Y,e) = necl.mv()
X
Y
e
(X^Y)*(X^Y)
L = X^Y^e
L
B = (L*e).expand().blade_rep()
B
Bsq = B*B
Bsq
BsqScalar = Bsq.scalar()
BsqScalar
(Bsq - BsqScalar).simplify() == 0
BeBr = B*e*B.rev()
BeBr
B*B
L*L
(s,c,Binv,M,S,C,alpha) = symbols... |
julienchastang/unidata-python-workshop | notebooks/Bonus/Siphon_XARRAY_Cartopy_HRRR.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
# Resolve the latest HRRR dataset
from siphon.catalog import get_latest_access_url
hrrr_catalog = "http://thredds.ucar.edu/thredds/catalog/grib/NCEP/HRRR/CONUS_2p5km/catalog.xml"
latest_hrrr_ncss = get_latest_access_url(hrrr_catalog, "NetcdfSubset"... |
Duke-GCB/cwl-freezer | cwl-freezing.ipynb | mit | workflow = parse('/Users/dcl9/Code/python/mmap-cwl/mmap.cwl')
"""
Explanation: Questions
Could this be a CWL compiler?
WIll it take a root document and return the whole structure?
Can I find the dockerRequirement anywhere in the doc?
Can I find the dockerRequirement using the schema?
1. CWL Docker Compiler
What does... |
rcrehuet/Python_for_Scientists_2017 | notebooks/Pandas_Github_Day3.ipynb | gpl-3.0 | data_file ='usagov_bitly_data2012-03-16-1331923249.txt'
file = open(data_file)
file.readline()
"""
Explanation: Title: Data Analysis with Python: Overview of Pandas
Author: Fermín Huarte Larrañaga
Created: 2015
Version: 2.0
Date: June 2017
Bibliography
This IPython Notebook is based almost completely on:
"Python for ... |
ds-modules/LINGUIS-110 | FormantsUpdated/Assignment.ipynb | mit | # DON'T FORGET TO RUN THIS CELL
import math
import numpy as np
import pandas as pd
import seaborn as sns
import datascience as ds
import matplotlib.pyplot as plt
sns.set_style('darkgrid')
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
"""
Explanation: Linguistics 110: Vowel Formants
Professor S... |
StephenHarrington/bitcoin-examples | Regtest_RPC.ipynb | mit | #!/bin/bash
#regtest_start_network.sh
import os
import shutil
#os.system("killall --regex bitcoin.*")
idir = os.environ['HOME']+'/regtest'
if os.path.isdir(idir):
shutil.rmtree(idir)
os.mkdir(idir)
connects = {'17591' : '17592', '17592' : '17591'}
for port in connects.keys():
adir = idir+'/'+port
os.mkd... |
ceos-seo/data_cube_notebooks | notebooks/general/Shapefile_Masking.ipynb | apache-2.0 | import sys
import os
sys.path.append(os.environ.get('NOTEBOOK_ROOT'))
import matplotlib.pyplot as plt
%matplotlib inline
from datacube.utils.aws import configure_s3_access
configure_s3_access(requester_pays=True)
# Import Data Cube API
import utils.data_cube_utilities.data_access_api as dc_api
api = dc_api.DataAcc... |
zzsza/TIL | python/image processing.ipynb | mit | from PIL import Image
import numpy as np
def average_hash(fname, size = 16):
img = Image.open(fname)
img = img.convert('L') # 1을 지정하면 이진화, RGB, RGBA, CMYK 등의 모드도 지원
img = img.resize((size, size), Image.ANTIALIAS)
pixel_data = img.getdata()
pixels = np.array(pixel_data)
pixels = pixels.reshape((... |
gVallverdu/cookbook | matplotlibrc.ipynb | gpl-2.0 | import matplotlib
import matplotlib.style as mpl_style
"""
Explanation: Matplotlib style sheets
This notebook presents how to change the style or appearance of matplotlib plots. In additiopn to the rcParams dictionnary, the matplotlib.style module provides facilities for style sheets utilisation with matplotlib. Look ... |
FluVigilanciaBR/fludashboard | Notebooks/Brazilian_epiweek.ipynb | gpl-3.0 | from episem import episem
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a data-toc-modified-id="Using-Brazilian-epidemiological-week-definition-1" href="#Using-Brazilian-epidemiological-week-definition"><span class="toc-item-num">1 </span>Using Brazilian epidemiological week definition</... |
xunilrj/sandbox | courses/IMTx-Queue-Theory/Week2_Lab_MM1.ipynb | apache-2.0 | %matplotlib inline
from pylab import *
lambda_ = 4
mu = 5
###################
# Write a function that computes the probability Pa that the next event
# is an arrival (when the system is not empty)
def Pa(lambda_,mu):
return lambda_/(mu+lambda_)
###################
V1 = Pa(lambda_,mu)
... |
AllenDowney/ThinkBayes2 | soln/chap02.ipynb | mit | import pandas as pd
table = pd.DataFrame(index=['Bowl 1', 'Bowl 2'])
"""
Explanation: Bayes's Theorem
Think Bayes, Second Edition
Copyright 2020 Allen B. Downey
License: Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
In the previous chapter, we derived Bayes's Theorem:
$$P(A|B) = \frac{P(A) ... |
ProfessorKazarinoff/staticsite | content/code/matplotlib_plots/plot_bond_energy.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
# if using a Jupyter notebook, include:
%matplotlib inline
"""
Explanation: Atoms in solid materials like steel and aluminum are held together with chemical bonds. Atoms of solid materials are more stable when they are chemically bonded together, and it takes energy t... |
tpin3694/tpin3694.github.io | python/function_basics.ipynb | mit | def print_max(x, y):
# if a is larger than b
if x > y:
# then print this
print(x, 'is maximum')
# if a is equal to b
elif x == y:
# print this
print(x, 'is equal to', y)
# otherwise
else:
# print this
print(y, 'is maximum')
"""
Explanation: Title:... |
mathnathan/notebooks | .ipynb_checkpoints/semantic_similarity_with_tf_hub_universal_encoder-checkpoint.ipynb | mit | # Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
GoogleCloudPlatform/healthcare | datathon/nusdatathon18/tutorials/image_preprocessing.ipynb | apache-2.0 | from google.colab import files
from io import BytesIO
# Display images.
from IPython.display import display
from PIL import Image, ImageEnhance
"""
Explanation: Copyright 2018 Google Inc.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. Yo... |
MLIME/12aMostra | src/Keras Tutorial.ipynb | gpl-3.0 | import util
import numpy as np
import keras
from keras.utils import np_utils
X_train, y_train, X_test, y_test = util.load_mnist_dataset()
y_train_labels = np.array(util.get_label_names(y_train))
# Converte em one-hot para treino
y_train = np_utils.to_categorical(y_train, 10)
y_test = np_utils.to_categorical(y_test, 1... |
AstroHackWeek/AstroHackWeek2016 | notebook-tutorial/notebooks/07-Some_basics.ipynb | mit | # Create a [list]
days = ['Monday', # multiple lines
'Tuesday', # acceptable
'Wednesday',
'Thursday',
'Friday',
'Saturday',
'Sunday',
] # trailing comma is fine!
days
# Simple for-loop
for day in days:
print(day)
# Double for-loop
for day in days:
fo... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_decoding_unsupervised_spatial_filter.ipynb | bsd-3-clause | # Authors: Jean-Remi King <jeanremi.king@gmail.com>
# Asish Panda <asishrocks95@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.decoding import UnsupervisedSpatialFilter
from sklearn.decomposition import PCA, FastI... |
oscarmore2/deep-learning-study | intro-to-rnns/Anna_KaRNNa_Exercises.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, we'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is bas... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_covariance_whitening_dspm.ipynb | bsd-3-clause | # Author: Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import os
import os.path as op
import numpy as np
from scipy.misc import imread
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import spm_face
from mne.minimum_norm import apply_inverse, make_inverse_o... |
NlGG/MachineLearning | .ipynb_checkpoints/nn-checkpoint.ipynb | mit | def example1(x_1, x_2):
z = x_1**0.5*x_2*0.5
return z
fig = pl.figure()
ax = Axes3D(fig)
X = np.arange(0, 1, 0.1)
Y = np.arange(0, 1, 0.1)
X, Y = np.meshgrid(X, Y)
Z = example1(X, Y)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1)
pl.show()
"""
Explanation: コブ・ダクラス型生産関数と課題文で例に出された関数を用いる。
いずれも定義域は0≤x≤1である。
... |
lin99/NLPTM-2016 | 4.Docs/word2vec.ipynb | mit | ## Loading the model with `gensim`
# import wrod2vec model from gensim
from gensim.models.word2vec import Word2Vec
# load Google News pre-trained network
model = Word2Vec.load_word2vec_format('GNvectors.bin', binary=True)
"""
Explanation: Playing with word2vec
Fabio A. González, Universidad Nacional de Colombia
Goog... |
jshudzina/keras-tutorial | notebooks/02-Yellowstone-visitors-part1.ipynb | apache-2.0 | # load and plot dataset
from pandas import read_csv
from pandas import datetime
from matplotlib import pyplot
# load dataset
def parser(x):
return datetime.strptime(x, '%Y-%m-%d')
series = read_csv('../data/yellowstone-visitors.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)
# summar... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/noresm2-lm/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-lm', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: NCC
Source ID: NORESM2-LM
Topic: Landice
Sub-Topics: Glaciers, Ice.
Properties:... |
girving/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... |
mikekestemont/lot2016 | Chapter 9 - Text analysis.ipynb | mit | ls data/arabian_nights
"""
Explanation: Chapter 9: What we have covered so far (and a bit more)
In this chapter, we will work our way through a concise review of the Python functionality we have covered so far. Throughout this chapter, we will work with a interesting, yet not too large dataset, namely the well-known ... |
vikasgorur/notebooks | deep-learning/3_regularization.ipynb | mit | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
"""
Explanation: Deep Learning
Assignment 3
Previously in 2_fullyconnected.ipynb, you tra... |
GoogleCloudPlatform/mlops-on-gcp | immersion/kubeflow_pipelines/pipelines/labs/lab-02_vertex.ipynb | apache-2.0 | from google.cloud import aiplatform
REGION = 'us-central1'
PROJECT_ID = !(gcloud config get-value project)
PROJECT_ID = PROJECT_ID[0]
# Set `PATH` to include the directory containing KFP CLI
PATH=%env PATH
%env PATH=/home/jupyter/.local/bin:{PATH}
"""
Explanation: Continuous Training with Kubeflow Pipeline and Verte... |
phoebe-project/phoebe2-docs | 2.0/tutorials/fti.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.0,<2.1"
"""
Explanation: Finite Time of Integration (fti)
Setup
Let's first make sure we have the latest version of PHOEBE 2.0 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
"""
%matplo... |
nbateshaus/chem-search | inchi-split/notebooks/Layer Stats SQL.ipynb | bsd-3-clause | %sql postgresql://localhost/inchi_split \
select count(*) from chembl_export_nonstandard;
"""
Explanation: Our test set here includes the 1.3 million molecules from ChEMBL20 with MW < 600 that could be successfully processed by the RDKit.
We use the Standard InChI that comes with ChEMBL and a non-standard InChI (o... |
xiaoxiaoyao/MyApp | PythonApplication1/deeplearning/examples/gan_pytorch.ipynb | unlicense | # Generative Adversarial Networks (GAN) example in PyTorch.
# See related blog post at https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f#.sch4xgsa9
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
fro... |
davidbrough1/pymks | notebooks/intro.ipynb | mit | import pymks
%matplotlib inline
%load_ext autoreload
%autoreload 2
"""
Explanation: Meet PyMKS
In this short introduction, we will demonstrate the functionality in PyMKS. We will quantify microstructures using 2-point statistics, predict effective properties using homogenization and predict local properties using lo... |
BuzzFeedNews/2015-07-h2-visas-and-enforcement | notebooks/h2-violation-aggregates.ipynb | mit | import pandas as pd
import sys
sys.path.append("../utils")
import loaders
"""
Explanation: Aggregated H-2 Guest Worker Violations
The Python code below loads all WHISARD violations since 2005 (based on the end-date of the violation period); isolates the violations of laws meant to protect H-2 workers; and provides agg... |
GoogleCloudPlatform/mlops-on-gcp | immersion/tfx_pipelines/01-walkthrough/labs/lab-01.ipynb | apache-2.0 | import absl
import os
import tempfile
import time
import tensorflow as tf
import tensorflow_data_validation as tfdv
import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
import tfx
from pprint import pprint
from tensorflow_metadata.proto.v0 import schema_pb2, statistics_pb2, anomalies_pb2
from t... |
cosmolejo/Fisica-Experimental-3 | Calculo_Error/Poisson/Poisson.ipynb | gpl-3.0 | dado = np.array([5, 3, 3, 2, 5, 1, 2, 3, 6, 2, 1, 3, 6, 6, 2, 2, 5, 6, 4, 2, 1, 3, 4, 2, 2, 5, 3, 3,
2, 2, 2, 1, 6, 2, 2, 6, 1, 3, 3, 3, 4, 4, 6, 6, 1, 2, 2, 6, 1, 4, 2, 5, 3, 6, 6, 3,
5, 2, 2, 4, 2, 2, 4, 4, 3, 3, 1, 2, 6, 1, 3, 3, 5, 4, 6, 6, 4, 2, 5, 6, 1, 4, 5, 4, 3, 5,
... |
TomTranter/OpenPNM | examples/simulations/Fickian Diffusion.ipynb | mit | import numpy as np
import openpnm as op
%matplotlib inline
np.random.seed(10)
ws = op.Workspace()
ws.settings["loglevel"] = 40
np.set_printoptions(precision=5)
"""
Explanation: Fickian Diffusion
One of the main applications of OpenPNM is simulating transport phenomena such as Fickian diffusion, advection diffusion, re... |
Bio204-class/bio204-notebooks | inclass-2016-02-22-Confidence-Intervals.ipynb | cc0-1.0 | %matplotlib inline
import numpy as np
import scipy.stats as stats
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use("bmh")
np.random.seed(20160222) # setting seed insures reproducability
mu, sigma = 10, 2
popn = stats.norm(loc=mu, scale=sigma)
ssizes = [25, 50, 100, 200, 400... |
bjshaw/phys202-2015-work | assignments/assignment11/OptimizationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Optimization Exercise 1
Imports
End of explanation
"""
def hat(x,a,b):
v = -a*x**2+b*x**4
return v
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(1.0, 10.0, 1.0)==-9.... |
no-fire/line-follower | line-follower/src/v1/convnet_regression_circle_and_carpet.ipynb | mit | #Create references to important directories we will use over and over
import os, sys
#import modules
import numpy as np
from glob import glob
from PIL import Image
from tqdm import tqdm
from scipy.ndimage import zoom
from keras.models import Sequential
from keras.metrics import categorical_crossentropy, categorical_a... |
perrette/iis | notebooks/examples.ipynb | mit | from scipy.stats import norm, uniform
from iis import IIS, Model
def mymodel(params):
"""User-defined model with two parameters
Parameters
----------
params : numpy.ndarray 1-D
Returns
-------
state : float
return value (could also be an array)
"""
return params[0] + param... |
blackjax-devs/blackjax | examples/LogisticRegression.ipynb | apache-2.0 | import jax
import jax.numpy as jnp
import jax.random as random
import matplotlib.pyplot as plt
from sklearn.datasets import make_biclusters
import blackjax
%config InlineBackend.figure_format = "retina"
plt.rcParams["axes.spines.right"] = False
plt.rcParams["axes.spines.top"] = False
plt.rcParams["figure.figsize"] = ... |
pbutenee/ml-tutorial | source/1/recommendation_engine.ipynb | mit | import numpy as np
import pandas as pd
import sklearn.metrics.pairwise
"""
Explanation: Recommendation Engine
In this tutorial we are going to build a simple recommender system using collaborative filtering. You'll be learning about the popular data analysis package pandas along the way.
1. The import statements
End o... |
atulsingh0/MachineLearning | MasteringML_wSkLearn/06_Clustering_with_K-Means.ipynb | gpl-3.0 | # import
from sklearn.cluster import KMeans, MiniBatchKMeans
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.utils import shuffle
import mahotas as mh
from mahotas.features import surf
import glob
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial.distan... |
jbn/vaquero | demo/Module_Demo.ipynb | mit | data = [{'user_name': "Jack", 'user_age': "42.0"},
{'user_name': "Jill", 'user_age': 64},
{'user_name': "Jane", 'user_age': "lamp"}]
"""
Explanation: This notebook demonstrates vaquero.
Let's say you are processing some html files for users. Someone on your
team already used css selectors to extract a ... |
GoogleCloudPlatform/dfcx-scrapi | examples/template.ipynb | apache-2.0 | # Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
jinntrance/MOOC | coursera/ml-classification/assignments/module-5-decision-tree-assignment-1-blank.ipynb | cc0-1.0 | import graphlab
graphlab.canvas.set_target('ipynb')
"""
Explanation: Identifying safe loans with decision trees
The LendingClub is a peer-to-peer leading company that directly connects borrowers and potential lenders/investors. In this notebook, you will build a classification model to predict whether or not a loan pr... |
arnoldlu/lisa | ipynb/tutorial/02_TestEnvUsage.ipynb | apache-2.0 | import logging
from conf import LisaLogging
LisaLogging.setup()
# Execute this cell to enabled devlib debugging statements
logging.getLogger('ssh').setLevel(logging.DEBUG)
# Other python modules required by this notebook
import json
import time
import os
"""
Explanation: Tutorial goal
This tutorial aims to show how ... |
jserenson/Python_Bootcamp | Lists.ipynb | gpl-3.0 | # Assign a list to an variable named my_list
my_list = [1,2,3]
"""
Explanation: Lists
Earlier when discussing strings we introduced the concept of a sequence in Python. Lists can be thought of the most general version of a sequence in Python. Unlike strings, they are mutable, meaning the elements inside a list can be ... |
aam-at/tensorflow | tensorflow/lite/g3doc/tutorials/model_maker_text_classification.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... |
skorokithakis/pythess-files | 006 - Frank Underwood/schema-presentation/Schema presentation.ipynb | mit | data = {
"operation": "upload", # "upload" or "delete"
"timeout": 3600, # Optional, how long the sig should be valid for.
"md5": "deadbeefetc", # Optional
"files": {
"5gbCtxlvljhx5-al": {
"size": 65536,
"shred_date": "2015-05-02T00:00:00Z" # Must be a date from now up... |
michaelgat/Udacity_DL | intro-to-tflearn/TFLearn_Sentiment_Analysis-MG.ipynb | mit | import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
"""
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by bui... |
eyadsibai/rep | howto/01-howto-Classifiers.ipynb | apache-2.0 | !cd toy_datasets; wget -O MiniBooNE_PID.txt -nc MiniBooNE_PID.txt https://archive.ics.uci.edu/ml/machine-learning-databases/00199/MiniBooNE_PID.txt
import numpy, pandas
from rep.utils import train_test_split
from sklearn.metrics import roc_auc_score
data = pandas.read_csv('toy_datasets/MiniBooNE_PID.txt', sep='\s*', ... |
Danghor/Algorithms | Python/Chapter-04/Radix-Sort.ipynb | gpl-2.0 | %run Counting-Sort.ipynb
"""
Explanation: Radix Sort
As <em style="color:blue">radix sort</em> is based on <em style="color:blue">counting sort</em>, we have to start our implementation of radix sort by defining the function countingSort that we have already discussed previously. The easiest way to do this is by usin... |
erdewit/ib_insync | notebooks/ordering.ipynb | bsd-2-clause | from ib_insync import *
util.startLoop()
ib = IB()
ib.connect('127.0.0.1', 7497, clientId=13)
# util.logToConsole()
"""
Explanation: Ordering
Warning: This notebook will place live orders
Use a paper trading account (during market hours).
End of explanation
"""
contract = Forex('EURUSD')
ib.qualifyContracts(contrac... |
zipeiyang/liupengyuan.github.io | chapter4/python爬虫入门.ipynb | mit | import requests
from bs4 import BeautifulSoup
import re
"""
Explanation: By liupengyuan[at]pku.edu.cn
Project: https://github.com/liupengyuan/
1. 什么是爬虫
简而言之，爬虫就是一段能够获取互联网信息（数据）的程序/工具。
一般需要通过抓取网页来获取互联网的信息与数据。
网页本身就是一个本文文件，只不过这个文本文件是由特定规则和符号标记的(HTML,超文本标记语言)，称为超文本文件，也可称为网页源代码。
这段文本经过浏览器的解析（各类图片视频等在此过程中从网页外部加载），就成为我们日常浏览... |
gdsfactory/gdsfactory | docs/notebooks/01_references.ipynb | mit | import numpy as np
import gdsfactory as gf
gf.config.set_plot_options(show_subports=False)
# Create a blank Component
p = gf.Component("component_with_polygon")
# Add a polygon
xpts = [0, 0, 5, 6, 9, 12]
ypts = [0, 1, 1, 2, 2, 0]
p.add_polygon([xpts, ypts], layer=(2, 0))
# plot the Component with the polygon in it
... |
eaton-lab/toytree | docs/6-treenodes.ipynb | bsd-3-clause | import toytree
import toyplot
import numpy as np
# generate a random tree
tre = toytree.rtree.unittree(ntips=10, seed=12345)
"""
Explanation: TreeNode objects
The .treenode attribute of ToyTrees allows users to access the underlying TreeNode structure directly. This is where you can traverse the tree and query the pa... |
fweik/espresso | doc/tutorials/ferrofluid/ferrofluid_part1.ipynb | gpl-3.0 | import espressomd
espressomd.assert_features('DIPOLES', 'LENNARD_JONES')
from espressomd.magnetostatics import DipolarP3M
from espressomd.magnetostatic_extensions import DLC
from espressomd.cluster_analysis import ClusterStructure
from espressomd.pair_criteria import DistanceCriterion
import numpy as np
"""
Explan... |
tarashor/vibrations | py/notebooks/MatricesForPlaneCorrugatedShells1.ipynb | mit | from sympy import *
from geom_util import *
from sympy.vector import CoordSys3D
import matplotlib.pyplot as plt
import sys
sys.path.append("../")
%matplotlib inline
%reload_ext autoreload
%autoreload 2
%aimport geom_util
# Any tweaks that normally go in .matplotlibrc, etc., should explicitly go here
%config InlineBa... |
mne-tools/mne-tools.github.io | 0.19/_downloads/f760cc2f1a5d6c625b1e14a0b05176dd/plot_ecog.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
# Chris Holdgraf <choldgraf@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import loadmat
import mne
from mne.viz import plot_alignment, snapshot_brain_montage
print(__doc__)
"""
Explanation: Working w... |
rustychris/stompy | examples/filtering.ipynb | mit | from stompy import filters, utils
import matplotlib.pyplot as plt
import numpy as np
%matplotlib notebook
# Sample data -- all times in hours
dt=0.1
x=np.arange(0,100,dt)
y=np.random.random(len(x))
target_cutoff=36.0
y_fir=filters.lowpass_fir(y,int(target_cutoff/dt))
y_iir=filters.lowpass(y,dt=dt,cutoff=target_cutof... |
espressomd/espresso | doc/tutorials/error_analysis/error_analysis_part2.ipynb | gpl-3.0 | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})
import sys
import logging
logging.basicConfig(level=logging.INFO, stream=sys.stdout)
np.random.seed(43)
def ar_1_process(n_samples, c, phi, eps):
'''
Generate a correlated random sequence with the AR(1... |
ctralie/TUMTopoTimeSeries2016 | 3DShapes.ipynb | apache-2.0 | import numpy as np
%matplotlib notebook
import scipy.io as sio
from scipy import sparse
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import sys
sys.path.append("pyhks")
from HKS import *
from GeomUtils import *
from ripser import ripser
from persim import plot_diagrams, wasserstein
from skl... |
UWSEDS/short-course | LectureNotes/ExceptionsDebugging.ipynb | mit | X = [1, 2, 3)
y = 4x + 3
"""
Explanation: When Things Go Wrong:
Errors, Exceptions, and Debugging
Today we'll cover perhaps one of the most important aspects of using Python: dealing with errors and bugs in code.
Three Classes of Errors
Types of bugs/errors in code, from the easiest to the most difficult to diagnose:... |
AllenDowney/ThinkBayes2 | workshop/workshop02soln.ipynb | mit | from __future__ import print_function, division
%matplotlib inline
import numpy as np
from thinkbayes2 import Suite
import thinkplot
import warnings
warnings.filterwarnings('ignore')
"""
Explanation: Bayesian Statistics Made Simple
Code and exercises from my workshop on Bayesian statistics in Python.
Copyright 201... |
AllenDowney/ModSimPy | notebooks/chap14.ipynb | mit | # Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim.py module
from modsim import *
"""
Explanation: Modeling and Simulati... |
jepegit/cellpy | dev_utils/easyplot/EasyPlot_Demo.ipynb | mit | from cellpy.utils import easyplot
"""
Explanation: Easyplot user guide
Easyplot is a submodule found in the utils of cellpy. It takes a list of filenames and plots these corresponding to the users input configuration. Please follow the example below to learn how to use it.
1: Import cellpy and easyplot
End of explanat... |
IanHawke/maths-with-python | 03-loops-control-flow.ipynb | mit | from math import pi
def degrees_to_radians(theta_d):
"""
Convert an angle from degrees to radians.
Parameters
----------
theta_d : float
The angle in degrees.
Returns
-------
theta_r : float
The angle in radians.
"""
theta_r = pi / 180.0 *... |
kaleoyster/nbi-data-science | Deterioration Curves/(Southeast) Deterioration+Curves++and+Classification+of+Bridges+in+the+Southeast+United+States.ipynb | gpl-2.0 | import pymongo
from pymongo import MongoClient
import time
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import csv
"""
Explanation: Libraries and Packages
End of explanation
"""
Client = MongoClient("mongodb://bridges:readonly@nbi-mongo.admin/bridge")
db = Client.bridg... |
ecabreragranado/OpticaFisicaII | Experimento de Young/ExperimentoYoung.ipynb | gpl-3.0 | from IPython.display import Image
Image(filename="YoungTwoSlitExperiment.JPG")
"""
Explanation: Experimento de Young
End of explanation
"""
from IPython.display import Image
Image(filename="ExperimentoYoung.jpg")
"""
Explanation: ''The experiments I am about to relate ... may be repeated with great ease,
whenever ... |
mne-tools/mne-tools.github.io | 0.21/_downloads/fa9fcfffc497146dd55d06cee6a5ec68/plot_creating_data_structures.ipynb | bsd-3-clause | import mne
import numpy as np
"""
Explanation: Creating MNE's data structures from scratch
MNE provides mechanisms for creating various core objects directly from
NumPy arrays.
End of explanation
"""
# Create some dummy metadata
n_channels = 32
sampling_rate = 200
info = mne.create_info(n_channels, sampling_rate)
pr... |
kaizu/ecell4-lectures | lecture2.ipynb | mit | %matplotlib inline
from ecell4 import *
import matplotlib.pylab as plt
import numpy as np
import seaborn
seaborn.set(font_scale=1.5)
import matplotlib as mpl
mpl.rc("figure", figsize=(6, 4))
"""
Explanation: <p style="text-align:center">Lecture 2. 化学反応回路</p>
<p style="text-align:center;font-size:150%;line-height:15... |
DOV-Vlaanderen/pydov | docs/notebooks/search_gecodeerde_lithologie.ipynb | mit | %matplotlib inline
import inspect, sys
# check pydov path
import pydov
"""
Explanation: Example of DOV search methods for interpretations (gecodeerde lithologie)
Use cases explained below
Get 'gecodeerde lithologie' in a bounding box
Get 'gecodeerde lithologie' with specific properties within a distance from a poin... |
mangeshjoshi819/ml-learn-python3 | Some Advanced Python.ipynb | mit | class Person:
##SCOPE OF CLASS DOWN BELOW
institute="IIT"
def __init__(self,name,department):
self.name=name
self.department=department
def getName(self):
return self.name
p=Person("mangesh","physics")
p.getName()
"""
Explanation: Advance Python
Define class
class variab... |
qdev-dk/Majorana | examples/Qcodes example with Alazar ATS9360.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import qcodes as qc
import qcodes.instrument.parameter as parameter
import qcodes.instrument_drivers.AlazarTech.ATS9360 as ATSdriver
from qdev_wrappers.alazar_controllers.ATSChannelController import ATSChannelController
from qdev_wrappers.alazar_con... |
rcrehuet/Python_for_Scientists_2017 | notebooks/extras/Numpy arrays. Data manipulation.ipynb | gpl-3.0 | !head ../../data/profasi/n0/rt
"""
Explanation: Numpy arrays. Data manipulation
In this notebook we are going to work with some numerical data that we need to re-format.
Profasi is a Monte Carlo code for protein simulation. It can run Parallel Tempering simulations where each replica runs in a processor and exchanges ... |
probml/pyprobml | notebooks/book1/15/cnn1d_sentiment_torch.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import math
from IPython import display
try:
import torch
except ModuleNotFoundError:
%pip install -qq torch
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils import data
import collections
import re
import random
imp... |
joaoandre/algorithms | intro-python-data-science/week2.ipynb | mit | !pip freeze > requirements.txt
import pandas as pd
pd.Series?
animals = ['Tiger', 'Bear', 'Moose']
pd.Series(animals)
numbers = [1, 2, 3]
pd.Series(numbers)
animals = ['Tiger', 'Bear', None]
pd.Series(animals)
numbers = [1, 2, None]
pd.Series(numbers)
import numpy as np
np.nan == None
np.nan == np.nan
np.isnan(... |
arviz-devs/arviz | doc/source/user_guide/numpyro_refitting.ipynb | apache-2.0 | import arviz as az
import numpyro
import numpyro.distributions as dist
import jax.random as random
from numpyro.infer import MCMC, NUTS
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats
import xarray as xr
numpyro.set_host_device_count(4)
"""
Explanation: Refitting NumPyro models with Arv... |
opengeostat/pygslib | pygslib/Ipython_templates/probplt_html.ipynb | mit | #general imports
import pygslib
"""
Explanation: PyGSLIB
PPplot
End of explanation
"""
#get the data in gslib format into a pandas Dataframe
mydata= pygslib.gslib.read_gslib_file('../data/cluster.dat')
true= pygslib.gslib.read_gslib_file('../data/true.dat')
true['Declustering Weight'] = 1
"""
Explanation: Gettin... |
tcstewar/testing_notebooks | Converting non-spiking neurons to spiking neurons.ipynb | gpl-2.0 | class LeakyIntegrator:
def __init__(self, threshold, tau_rc=20):
self.threshold = threshold
self.tau_rc = tau_rc
self.v = 0
def step(self, J):
if self.v > self.threshold:
output = self.v - self.threshold
else:
output = 0
... |
dfm/dfm.io | static/downloads/notebooks/emcee-pymc3.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = "retina"
from matplotlib import rcParams
rcParams["savefig.dpi"] = 100
rcParams["figure.dpi"] = 100
rcParams["font.size"] = 20
"""
Explanation: Title: emcee + PyMC3
Date: 2018-08-21
Category: Data Analysis
Slug: emcee-pymc3
Summary: sampling models defined in P... |
santanche/java2learn | notebooks/pt/c04components/s03message-bus/1.iot-devices.ipynb | gpl-2.0 | publisher = IoT_mqtt_publisher("localhost", 1883)
"""
Explanation: Instanciando Componente de Publicação de Mensagens no MQTT
End of explanation
"""
sensor_1 = IoT_sensor("1", "temperature", "°C", 20, 26, 2)
sensor_2 = IoT_sensor("2", "umidade", "%", 50, 60, 3)
sensor_3 = IoT_sensor("3", "temperature", "°C", 28... |
4DGenome/Chromosomal-Conformation-Course | Notebooks/01-Mapping.ipynb | gpl-3.0 | from pytadbit.mapping.full_mapper import full_mapping
"""
Explanation: Table of Contents
Iterative vs fragment-based mapping
Advantages of iterative mapping
Advantages of fragment-based mapping
Mapping
Iterative mapping
Fragment-based mapping
Iterative vs fragment-based mapping
Iterative mapping first proposed b... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_decoding_csp_space.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Romain Trachel <romain.trachel@inria.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
"""
... |
dariox2/CADL | session-0/session-0.ipynb | apache-2.0 | 4*2
import numpy as np
print(np.sin(.5))
print(np.random.random(3))
"""
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/ Tenso... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a_ml/td2a_timeseries.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
"""
Explanation: 2A.ml - Séries temporelles
Prédictions sur des séries temporelles et autres opérations classiques.
End of explanation
"""
import pyensae.datasource as ds
ds.download_data('xavierdupre_sessions.csv',
... |
satishkt/ML-Foundations-Coursera | Week4-Clustering/Document retrieval.ipynb | bsd-2-clause | import graphlab
"""
Explanation: Document retrieval from wikipedia data
Fire up GraphLab Create
End of explanation
"""
people = graphlab.SFrame('people_wiki.gl/')
"""
Explanation: Load some text data - from wikipedia, pages on people
End of explanation
"""
people.head()
len(people)
"""
Explanation: Data contain... |
DataReply/persistable | examples/Persistable.ipynb | gpl-3.0 | # Persistable Class:
from persistable import Persistable
# Set a persistable top path:
from pathlib import Path
LOCALDATAPATH = Path('.').absolute()
"""
Explanation: Introduction:
This material has been used in the past to teach colleagues in our group how to use persistable.
The persistable package provides a genera... |
gregmedlock/Medusa | docs/machine_learning.ipynb | mit | import medusa
from medusa.test import create_test_ensemble
ensemble = create_test_ensemble("Staphylococcus aureus")
"""
Explanation: Applying machine learning to guide ensemble curation
An ensemble of models can be though of as a set of feasible hypotheses about how a system behaves. From a machine learning perspecti... |
abotero/text-mining-amazon-reviews | final-project-part3and4.ipynb | mit | import numpy as np
import pandas as pd
import gzip
import json
import gzip
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
matplotlib.style.use('ggplot')
pd.set_option('display.max_colwidth', -1)
#Some functions to handle files
def parse(path):
with open(path) as data_file:
for d... |
phungkh/phys202-2015-work | assignments/assignment12/FittingModelsEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Fitting Models Exercise 1
Imports
End of explanation
"""
a_true = 0.5
b_true = 2.0
c_true = -4.0
"""
Explanation: Fitting a quadratic curve
For this problem we are going to work with the following mod... |
DanielFGomez/Tarea2MetodosAvanzados | Punto2.ipynb | mit |
fig,ax=subplots(3,3,figsize=(10, 10))
n=1
for i in range(3):
for j in range(3):
ax[i,j].scatter(X[:,0],X[:,n],c=Y)
n+=1
Xnorm=sklearn.preprocessing.normalize(X)
pca=sklearn.decomposition.PCA()
pca.fit(Xnorm)
fig,ax=subplots(1,3,figsize=(16, 4))
ax[0].scatter(pca.transform(X)[:,0],Y,c=Y)
ax[0].s... |
ministryofjustice/opg-digi-deps-notebooks | notebooks/Digital Deputyship traffic distribution.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Digital Deputyship traffic distribution
As we don't have enough data per day to see usage pattern for the site, then we need to be creative.
What if we import data from last month, and group it ... |
aam-at/tensorflow | tensorflow/python/ops/numpy_ops/g3doc/TensorFlow_NumPy_Text_Generation.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... |
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