repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
phoebe-project/phoebe2-docs | development/examples/sun.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
"""
Explanation: Sun (single rotating star)
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
"""
import phoebe
from phoebe import u # units
import numpy a... |
tensorflow/model-remediation | docs/min_diff/guide/integrating_min_diff_without_min_diff_model.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... |
mne-tools/mne-tools.github.io | dev/_downloads/141ddce18e923e8220337b357ba3dc45/ssd_spatial_filters.ipynb | bsd-3-clause | # Author: Denis A. Engemann <denis.engemann@gmail.com>
# Victoria Peterson <victoriapeterson09@gmail.com>
# License: BSD-3-Clause
import matplotlib.pyplot as plt
import mne
from mne import Epochs
from mne.datasets.fieldtrip_cmc import data_path
from mne.decoding import SSD
"""
Explanation: Compute Spectro-Spa... |
tensorflow/ranking | docs/tutorials/quickstart.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... |
landlab/landlab | notebooks/tutorials/flow_direction_and_accumulation/the_FlowAccumulator.ipynb | mit | %matplotlib inline
# import plotting tools
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import matplotlib as mpl
# import numpy
import numpy as np
# import necessary landlab components
from landlab im... |
quanhua92/learning-notes | libs/scipy/scipy-lectures/13_Math_Optimization.ipynb | apache-2.0 | from scipy import optimize
import numpy as np
def f(x):
return -np.exp(-(x - 0.7) ** 2)
optimize.minimize_scalar(f)
"""
Explanation: Chapter 13: Mathematical optimization: finding minima of functions
13.1 Knowing your problem
Dimensionality of the problem: The scale of an optimization problem is pretty much set ... |
mroberge/hydrofunctions | docs/notebooks/USGS_Statistics_Service.ipynb | mit | import hydrofunctions as hf
print(hf.__version__)
"""
Explanation: Requesting Statistics from the USGS Statistics Service
The USGS calculates various types of statistics for its data and provides these values through a web service. You can access this service through the stats function.
Learn more about the USGS Stati... |
osamoylenko/YSDA_deeplearning17 | Seminar1/Classwork_ru.ipynb | mit | !wget https://github.com/goto-ru/Unsupervised_ML/raw/20779daf2aebca80bfe38401bc87cf41fc7b493d/03_zebrafish/zebrafish.npy -O zebrafish.npy
#alternative link: https://www.dropbox.com/s/hhep0wj4c11qibu/zebrafish.npy?dl=1
"""
Explanation: Чем думает рыба?
End of explanation
"""
import numpy as np
data = np.load("zebrafi... |
ejolly/pymer4 | docs/auto_examples/example_03_posthoc.ipynb | mit | # import basic libraries and sample data
import os
import pandas as pd
from pymer4.utils import get_resource_path
from pymer4.models import Lmer
# IV3 is a categorical predictors with 3 levels in the sample data
df = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv"))
# # We're going to fit a multi-leve... |
ShubhamDebnath/Coursera-Machine-Learning | Course 4/Convolution model Application v1.ipynb | mit | import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy
from PIL import Image
from scipy import ndimage
import tensorflow as tf
from tensorflow.python.framework import ops
from cnn_utils import *
%matplotlib inline
np.random.seed(1)
"""
Explanation: Convolutional Neural Networks: Appli... |
cathalmccabe/PYNQ | boards/Pynq-Z1/base/notebooks/microblaze/microblaze_python_libraries.ipynb | bsd-3-clause | from pynq.overlays.base import BaseOverlay
from pynq.lib import MicroblazeLibrary
base = BaseOverlay('base.bit')
lib = MicroblazeLibrary(base.PMODA, ['i2c', 'pmod_grove'])
"""
Explanation: Microblaze Python Libraries
In addition to using the pynqmb libraries from C it is also possible to create Python wrappers for th... |
jinntrance/MOOC | coursera/ml-classification/assignments/module-6-decision-tree-practical-assignment-blank.ipynb | cc0-1.0 | import graphlab
"""
Explanation: Decision Trees in Practice
In this assignment we will explore various techniques for preventing overfitting in decision trees. We will extend the implementation of the binary decision trees that we implemented in the previous assignment. You will have to use your solutions from this pr... |
ajgpitch/qutip-notebooks | examples/control-pulseoptim-symplectic.ipynb | lgpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import datetime
from qutip import Qobj, identity, sigmax, sigmay, sigmaz, tensor
from qutip.qip import hadamard_transform
import qutip.logging_utils as logging
logger = logging.get_logger()
#Set this to None or logging.WARN for 'quiet' execution
log... |
sgratzl/ipython-tutorial-VA2015 | 03_Plotting_solution.ipynb | cc0-1.0 | #disable some annoying warning
import warnings
warnings.filterwarnings('ignore', category=FutureWarning)
#plots the figures in place instead of a new window
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
#use a standard dataset of heterogenous data
ca... |
maartenbreddels/vaex | docs/source/tutorial_ml.ipynb | mit | import vaex
vaex.multithreading.thread_count_default = 8
import vaex.ml
import numpy as np
import pylab as plt
"""
Explanation: Machine Learning with vaex.ml
If you want to try out this notebook with a live Python kernel, use mybinder:
<a class="reference external image-reference" href="https://mybinder.org/v2/gh/vae... |
jmhsi/justin_tinker | data_science/lendingclub_bak/csv_dl_preparation/clean_pmt_history_2.ipynb | apache-2.0 | import dir_constants as dc
from tqdm import tqdm_notebook
def find_dupe_dates(group):
return pd.to_datetime(group[group.duplicated('date')]['date'].values)
def merge_dupe_dates(group):
df_chunks = []
dupe_dates = find_dupe_dates(group)
df_chunks.append(group[~group['date'].isin(dupe_dates)])
... |
metpy/MetPy | v1.0/_downloads/8c91fa5ab51e12860cfa1e679eaa746d/xarray_tutorial.ipynb | bsd-3-clause | import numpy as np
import xarray as xr
# Any import of metpy will activate the accessors
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.units import units
"""
Explanation: xarray with MetPy Tutorial
xarray <http://xarray.pydata.org/>_ is a powerful Python package that provides N-di... |
ykLIU1982/dental | dental_plan_comp.ipynb | mit | def nonPremCost(maxY, unitCostPrev, rPrev, prevDeduct, costBase, rBase, baseDeduct):
paidPrev = 0
paidBase = 0
# first, calculate the preventive services, suppose that 2 units per year
totalCostPrev = unitCostPrev * 2
coveredPrev = min(maxY, max(0, totalCostPrev - prevDeduct) * rPrev)
#prin... |
kaleoyster/nbi-data-science | Deterioration Curves/(West) Deterioration+Curves+and+Classification+of+Bridges+in+the+West+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... |
christinawlindberg/LtaP | LtaP.ipynb | mit | !pip install nxpd
%matplotlib inline
import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd
import numpy as np
from operator import truediv
from collections import Counter
import itertools
import random
import collaboratr
#from nxpd import draw
#import nxpd
#reload(collaboratr)
"""
Explanation... |
statsmodels/statsmodels | examples/notebooks/wls.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import statsmodels.api as sm
from scipy import stats
from statsmodels.iolib.table import SimpleTable, default_txt_fmt
np.random.seed(1024)
"""
Explanation: Weighted Least Squares
End of explanation
"""
nsample = 50
x = np.linspace(0, 20, nsample... |
lknelson/DH-Institute-2017 | 03-Operationalizing/Operationalizing.ipynb | bsd-2-clause | # Let's assign a string to a new variable
# Using the triple quotation mark, we can simply paste a passage in between
# and Python will treat it as a continuous string
first_sonnet = """From fairest creatures we desire increase,
That thereby beauty's rose might never die,
But as the riper should by time decease,
His t... |
ledeprogram/algorithms | class7/homework/radhikapc_Homework7.ipynb | gpl-3.0 | from sklearn import datasets
import pandas as pd
%matplotlib inline
from sklearn import datasets
from pandas.tools.plotting import scatter_matrix
import matplotlib.pyplot as plt
from sklearn import tree
iris = datasets.load_iris()
iris
iris.keys()
iris['target']
iris['target_names']
iris['data']
iris['feature_n... |
astarostin/MachineLearningSpecializationCoursera | course6/week6/SentimentAnalysisContest.ipynb | apache-2.0 | import scrapy
# to run:
# scrapy crawl reviews -o reviews.json
class ReviewsSpider(scrapy.Spider):
name = "reviews"
start_urls = ['https://market.yandex.ru/catalog/54726/list?how=opinions&deliveryincluded=0&onstock=1']
def __init__(self):
self.count = 0
self.LIMIT = 5
def parse(s... |
tensorflow/docs-l10n | site/ja/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... |
johnnyliu27/openmc | examples/jupyter/mdgxs-part-ii.ipynb | mit | %matplotlib inline
import math
import matplotlib.pyplot as plt
import numpy as np
import openmc
import openmc.mgxs
"""
Explanation: This IPython Notebook illustrates the use of the openmc.mgxs.Library class. The Library class is designed to automate the calculation of multi-group cross sections for use cases with on... |
stargaser/advancedviz2016 | Linking_and_brushing.ipynb | mit | import bokeh
import numpy as np
from astropy.table import Table
sdss = Table.read('data/sdss_galaxies_qsos_50k.fits')
sdss
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure, gridplot, output_notebook, output_file, show
umg = sdss['u'] - sdss['g']
gmr = sdss['g'] - sdss['r']
rmi = sdss['... |
lo-co/atm-py | examples/SMPS Algorithm Test.ipynb | mit | # Instantiate a DMA object based on the NOAA wide DMA dimensions
noaa = dma.NoaaWide()
# We also need a gas object that will represent the carrier gas of interest: air
air = atm.Air()
# Set the conditions to something representative of the conditions in the Boulder DMA
air.t = 23
air.p = 820
d = noaa.v2d(50, air, 5,... |
ohgodscience/Python | mousetrackerdata/post2.ipynb | gpl-2.0 | import pandas as pd
import re
data = pd.read_csv("mousetrackercorrected.csv")
data.columns.values
data.iloc[0:4, 0:19]
"""
Explanation: Overview
I'm going to be looking at some pilot data that some colleagues and I collected using Jon Freeman's Mousetracker (http://www.mousetracker.org/). As the name suggests, mouse... |
ewulczyn/talk_page_abuse | src/data_generation/crowdflower_analysis/src/Crowdflower Analysis (Experiment v. 2).ipynb | apache-2.0 | %matplotlib inline
from __future__ import division
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.set_option('display.max_colwidth', 1000)
# Download data from google drive (Respect Eng / Wiki Collab): wikipdia data/v2_annotated
dat = pd.read_csv('../data/exp2_annotated_1k_no_admin_blocked_... |
mne-tools/mne-tools.github.io | 0.23/_downloads/299b3deaa8eb66e88d34f06090d06628/evoked_ers_source_power.ipynb | bsd-3-clause | # Authors: Luke Bloy <luke.bloy@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import mne
from mne.cov import compute_covariance
from mne.datasets import somato
from mne.time_frequency import csd_morlet
from mne.beamformer import (make_di... |
quoniammm/mine-tensorflow-examples | fastAI/deeplearning1/nbs/lesson4.ipynb | mit | ratings = pd.read_csv(path+'ratings.csv')
ratings.head()
len(ratings)
"""
Explanation: Set up data
We're working with the movielens data, which contains one rating per row, like this:
End of explanation
"""
movie_names = pd.read_csv(path+'movies.csv').set_index('movieId')['title'].to_dict()
users = ratings.userId.... |
nadvamir/deep-learning | autoencoder/Simple_Autoencoder.ipynb | mit | %matplotlib inline
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', validation_size=0)
"""
Explanation: A Simple Autoencoder
We'll start off by building a simple autoencoder to compres... |
google/earthengine-api | python/examples/ipynb/Earth_Engine_REST_API_compute_image.ipynb | apache-2.0 | # INSERT YOUR PROJECT HERE
PROJECT = 'your-project'
!gcloud auth login --project {PROJECT}
"""
Explanation: <table class="ee-notebook-buttons" align="left"><td>
<a target="_blank" href="http://colab.research.google.com/github/google/earthengine-api/blob/master/python/examples/ipynb/Earth_Engine_REST_API_compute_imag... |
zhuanxuhit/deep-learning | batch-norm/my_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)
"... |
wso2/product-apim | modules/recommendation-engine/repository/resources/Word2vec_Model/Build_Word2vec_model.ipynb | apache-2.0 | model = gensim.models.Word2Vec (dataset, size=300, window=10, min_count=5, workers=10)
model.train(dataset,total_examples=len(dataset),epochs=15)
"""
Explanation: The 'Dataset.txt' file consists of API descriptions of over 15,000 APIs.
Using the 'Dataset_PW.txt' file, a dataset which consists of sentences, is created.... |
simonsfoundation/CaImAn | demos/notebooks/demo_VST.ipynb | gpl-2.0 | get_ipython().magic('load_ext autoreload')
get_ipython().magic('autoreload 2')
import logging
import matplotlib.pyplot as plt
import numpy as np
import os
import timeit
logging.basicConfig(format=
"%(relativeCreated)12d [%(filename)s:%(funcName)20s():%(lineno)s] [%(process)d] %(message)s",
... |
kevntao/ThinkStats2 | code/chap03ex.ipynb | gpl-3.0 | kids = resp['numkdhh']
kids
"""
Explanation: Make a PMF of <tt>numkdhh</tt>, the number of children under 18 in the respondent's household.
End of explanation
"""
pmf = thinkstats2.Pmf(kids)
thinkplot.Pmf(pmf, label='PMF')
thinkplot.Show(xlabel='# of Children', ylabel='PMF')
"""
Explanation: Display the PMF.
End of... |
ultiyuan/test0 | lessons/VortexPanelMethod.ipynb | gpl-2.0 | import numpy
# velocity component functions
def get_u( x, y, S, gamma ):
return gamma/(2*numpy.pi)*(numpy.arctan((x-S)/y)-numpy.arctan((x+S)/y))
def get_v( x, y, S, gamma ):
return gamma/(4*numpy.pi)*(numpy.log(((x+S)**2+y**2)/((x-S)**2+y**2)))
# vortex panel class
class Panel:
# save the inputs and ... |
nehal96/Deep-Learning-ND-Exercises | Sentiment Analysis/Sentiment Analysis with Andrew Trask/4-reducing-noise.ipynb | mit | def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()... |
armgilles/presentation | EPSI/I5/Projet Big Data/Cours 2 Big Data.ipynb | mit | # Importer les lib python
import pandas as pd
"""
Explanation: On va débuter step by step
Inscription et récupération des données :
Aller sur le site Kaggle et inscrivez-vous
Ensuite aller sur le contest du Titanic
Télécharger les données train.csv et test.csv dans l'onglet data
Mettez ces données dans un répertoire ... |
HsKA-ThermalFluiddynamics/NSS-1 | PythonTutorial.ipynb | mit | from __future__ import print_function
"""
Explanation: Python Tutorial
End of explanation
"""
# Output "Hello World!"
print("Hello, World!")
print("Hello World!", 10.0)
"""
Explanation: Hello World!
End of explanation
"""
# define a variable
s = "Hello World!"
x = 10.0
i = 42
# define 2 variables at once
a,b = 1... |
aboSamoor/compsocial | Word_Tracker/3rd_Yr_Paper/Grants.ipynb | gpl-3.0 | NIH_df = GetNIH()
NSF_df = GetNSF()
NIH_df.head()
NSF_df.head()
!mkdir data/processed
"""
Explanation: Merge CSV files
Each cvs file represent a specific word results obtained from the NSF and NIH websites.
End of explanation
"""
NSF_df.to_csv("data/Grants/processed/nsf_combined.csv", encoding='utf-8')
NIH_df.to_... |
SubhankarGhosh/NetworkX | 3. Hubs and Paths (Instructor).ipynb | mit | # Load the sociopatterns network data.
G = cf.load_sociopatterns_network()
"""
Explanation: Load Data
We will load the sociopatterns network data for this notebook. From the Konect website:
This network describes the face-to-face behavior of people during the exhibition INFECTIOUS: STAY AWAY in 2009 at the Science G... |
ldiary/marigoso | notebooks/handling_select2_controls_in_selenium_webdriver.ipynb | mit | import os
from marigoso import Test
request = {
'firefox': {
'capabilities': {
'marionette': False,
},
}
}
"""
Explanation: Handling Select2 Controls in Selenium WebDriver
Select2 is a jQuery based replacement for select boxes. This article will demonstrate how Selenium webdriver ca... |
tyamamot/h29iro | codes/1_Try_Notebook.ipynb | mit | print ("Hello" + ", World")
print(10 + 4)
"""
Explanation: 第1回 Jupyter notebookに慣れる
参考文献: IPython データサイエンスクックブック，オライリー社
1. IPythonとJupyterとは
IPython
IPythonはPythonを対話的に動作させるためのプラットフォームです．たとえばある文を入力した直後に結果を確認するといったように，インタラクティブに結果を確認しながらプログラミングしていくことができます．
Jupyter notebookとは
IPythonをWebベースで実行するためのプラットフォームです．ソースコードを書くだ... |
topgate/training-gcp | CPB102/tensorflow/tfclassic.ipynb | apache-2.0 | import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
print(tf.__version__)
"""
Explanation: TensorFlow Low-Level API
高レベル API を使わない、いわゆる生の TensorFlow でコードを書いてみましょう。
End of explanation
"""
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
"""
Explanation... |
kingb12/languagemodelRNN | report_notebooks/encdec_noing_250_512_040dr.ipynb | mit | report_file = '/Users/bking/IdeaProjects/LanguageModelRNN/reports/encdec_noing_250_512_040dr_2.json'
log_file = '/Users/bking/IdeaProjects/LanguageModelRNN/logs/encdec_noing_250_512_040dr_2.json'
import json
import matplotlib.pyplot as plt
with open(report_file) as f:
report = json.loads(f.read())
with open(log_fi... |
quantumlib/ReCirq | docs/benchmarks/rabi_oscillations.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 unde... |
tOverney/ADA-Project | preprocessing/process_path.ipynb | apache-2.0 | columns = [
'agency_id',
'service_date_id', 'service_date_date',
'route_id', 'route_short_name', 'route_long_name',
'trip_id', 'trip_headsign', 'trip_short_name',
'stop_time_id', 'stop_time_arrival_time', 'stop_time_departure_time', 'stop_time_stop_sequence',
'stop_id', 'stop_stop_id', 'stop_n... |
rjenc29/numerical | notebooks/principal_component_analysis.ipynb | mit | from sklearn.datasets import load_iris
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from pprint import pprint
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
import ipywidgets as widgets
from scipy.optimize import fmin... |
tanghaibao/goatools | notebooks/report_depth_level.ipynb | bsd-2-clause | # Get http://geneontology.org/ontology/go-basic.obo
from goatools.base import download_go_basic_obo
obo_fname = download_go_basic_obo()
"""
Explanation: Report counts of GO terms at various levels and depths
Reports the number of GO terms at each level and depth.
Level refers to the length of the shortest path fro... |
highb/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... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/text_classification/solutions/text_classification.ipynb | apache-2.0 | # Import necessary libraries
import matplotlib.pyplot as plt
import os
import re
import shutil
import string
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import losses
# Print the TensorFlow version
print(tf.__version__)
"""
Explanation: Basic Text Classification
Overview
This n... |
nathanshammah/pim | doc/notebooks/piqs_superradiance.ipynb | mit | import matplotlib as mpl
from matplotlib import cm
import matplotlib.pyplot as plt
from qutip import *
from piqs import *
#TLS parameters
N = 6
ntls = N
nds = num_dicke_states(ntls)
[jx, jy, jz, jp, jm] = jspin(N)
w0 = 1
gE = 0.1
gD = 0.01
h = w0 * jz
#photonic parameters
nphot = 20
wc = 1
kappa = 1
ratio_g = 2
g = r... |
thehackerwithin/berkeley | code_examples/python_mayavi/mayavi_intermediate.ipynb | bsd-3-clause | # setup parameters for Lorenz equations
sigma=10
beta=8/3.
rho=28
def lorenz(x, t, ):
dx = np.zeros(3)
dx[0] = -sigma*x[0] + sigma*x[1]
dx[1] = rho*x[0] - x[1] - x[0]*x[2]
dx[2] = -beta*x[2] + x[0]*x[1]
return dx
# solve for a specific particle
# initial condition
y0 = np.ones(3) + .01
# time ste... |
minxuancao/shogun | doc/ipython-notebooks/multiclass/KNN.ipynb | gpl-3.0 | import numpy as np
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from scipy.io import loadmat, savemat
from numpy import random
from os import path
mat = loadmat(os.path.join(SHOGUN_DATA_DIR, 'multiclass/usps.mat'))
Xall = mat['data']
Yall = np.array(mat['label'].squeeze(), dtype=n... |
tensorflow/docs-l10n | site/ja/tutorials/load_data/text.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... |
mne-tools/mne-tools.github.io | 0.19/_downloads/f01121873dbae065a1740e6c0c20d1d5/plot_eeg_no_mri.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Joan Massich <mailsik@gmail.com>
#
# License: BSD Style.
import os.path as op
import mne
from mne.datasets import eegbci
from mne.datasets import fetch_fsaverage
# Download fsaverage files
fs_dir = fetch_fsaverage(verbose=True)
subjects_dir = op.... |
fionapigott/Data-Science-45min-Intros | matplotlib-201/matplotlib-201.ipynb | unlicense | import matplotlib.pyplot as plt
%matplotlib inline
plt.plot([1,2,3,4])
plt.ylabel('some numbers')
plt.show()
"""
Explanation: Understanding just enough of matplotlib...
(or, Why a State Machine within a State Machine is a Shitty Idea)
<br>
So, you want to plot something in Python. Perhaps you've typed
python
import... |
debsankha/network_course_python | exercises/01-exercise-python.ipynb | gpl-2.0 | numbers = [[1,2,3],[4,5,6],[7,8,9]]
words = ['if','i','could','just','go','outside','and','have','an','ice','cream']
"""
Explanation: 1. Party game: squeezed
One guessing game, called “squeezed”, is very common in parties. It consists of a player,
the chooser, who writes down a number between 00–99. The other players ... |
fsilva/deputado-histogramado | notebooks/Deputado-Histogramado-2.ipynb | gpl-3.0 | %matplotlib inline
import pylab
import matplotlib
import pandas
import numpy
dateparse = lambda x: pandas.datetime.strptime(x, '%Y-%m-%d')
sessoes = pandas.read_csv('sessoes_democratica_org.csv',index_col=0,parse_dates=['data'], date_parser=dateparse)
"""
Explanation: Deputado Histogramado
expressao.xyz/deputado/
Co... |
bpgc-cte/python2017 | Week 5/Lecture_11_Decorators_Multiple_Inheritance.ipynb | mit | def func2(func1):
return func1 + 1
def func3(func2, arg):# Here func2 is being passed as a parameter.
return func2(arg)
print(func2(2))
print(func3(func2, 3))
def user_defined_decorator(function1):
def wrapper():
print("This statement is being printed before the passed function is called.")
... |
dsacademybr/PythonFundamentos | Cap02/Notebooks/DSA-Python-Cap02-02-Variaveis.ipynb | gpl-3.0 | # Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
"""
Explanation: <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 2</font>
Download: http://github.com/dsacademybr
End of explanation
"""
# Atrib... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-1/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', 'test-institute-1', 'sandbox-3', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: TEST-INSTITUTE-1
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: Tra... |
4DGenome/Chromosomal-Conformation-Course | Notebooks/00-Hi-C_quality_check.ipynb | gpl-3.0 | for renz in ['HindIII', 'MboI']:
print renz
! head -n 4 /media/storage/FASTQs/K562_"$renz"_1.fastq
print ''
"""
Explanation: FASTQ format
The file is organized in 4 lines per read:
1 - The header of the DNA sequence with the read id (the read length is optional)
2 - The DNA sequence
3 - The header of th... |
hashiprobr/redes-sociais | encontro02/2-largura.ipynb | gpl-3.0 | import sys
sys.path.append('..')
import socnet as sn
"""
Explanation: Encontro 02, Parte 2: Revisão de Busca em Largura
Este guia foi escrito para ajudar você a atingir os seguintes objetivos:
implementar o algoritmo de busca em largura;
usar funcionalidades avançadas da biblioteca da disciplina.
Primeiramente, vam... |
agile-geoscience/notebooks | Jerk_jounce_etc.ipynb | apache-2.0 | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
"""
Explanation: Jerk, jounce, etc.
This notebook accompanies a blog post on Agile.
First, the usual preliminaries...
End of explanation
"""
data = np.loadtxt('data/tesla_speed.csv', delimiter=',')
"""
Explanation... |
tpin3694/tpin3694.github.io | python/create_a_new_file_and_the_write_to_it.ipynb | mit | # Create a file if it doesn't already exist
with open('file.txt', 'xt') as f:
# Write to the file
f.write('This file now exsits!')
# Close the connection to the file
f.close()
"""
Explanation: Title: Create A New File Then Write To It
Slug: create_a_new_file_and_the_write_to_it
Summary: Create A New Fi... |
tensorflow/docs-l10n | site/ja/xla/tutorials/compile.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/training-data-analyst | blogs/pandas_to_beam/pandas_to_beam.ipynb | apache-2.0 | %pip install --quiet apache-beam[gcp]==2.26.0
import apache_beam as beam
import pandas as pd
print(beam.__version__)
"""
Explanation: Pandas API in Apache Beam
Apache Beam 2.26 onwards supports the Pandas API. This makes it very convenient to write complex pipelines, and execute them at scale, or in a streaming manne... |
dwillis/agate | example.py.ipynb | mit | import agate
table = agate.Table.from_csv('examples/realdata/ks_1033_data.csv')
print(table)
"""
Explanation: Using agate in a Jupyter notebook
First we import agate. Then we create an agate Table by loading data from a CSV file.
End of explanation
"""
kansas_city = table.where(lambda r: r['county'] in ('JACKSON',... |
rtidatascience/connected-nx-tutorial | notebooks/2. Creating Graphs.ipynb | mit | import csv
import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
# Create empty graph
G = nx.Graph()
# Add nodes
G.add_node(1)
G.add_nodes_from([2, 3])
G.add_node(4)
G.nodes()
"""
Explanation: Creating Graphs in NetworkX
Creating a graph object
Adding nodes and edges
Add... |
andre-martins/AD3 | examples/python/parse_example.ipynb | lgpl-3.0 | rng = np.random.RandomState(0)
sentence = ["*", "the", "quick", "fox", "jumps", "."]
# possible edge from every node to every other node OR the root
link_ix = []
link_var = []
fg = ad3.PFactorGraph()
for mod in range(1, len(sentence)):
for head in range(len(sentence)):
if mod == head:
continu... |
kit-cel/wt | mloc/ch7_Evolutionary_Algorithms/Differential_Evolution.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Optimization of Non-Differentiable Functions Using Differential Evolution
This code is provided as supplementary material of the lecture Machine Learning and Optimization in Communications (MLOC).<br>
This code illustrates:
* Use of differential evolu... |
crystalzhaizhai/cs207_yi_zhai | lectures/L9/L9.ipynb | mit | from IPython.display import HTML
"""
Explanation: Lecture 9
Object Oriented Programming
Monday, October 2nd 2017
End of explanation
"""
def Complex(a, b): # constructor
return (a,b)
def real(c): # method
return c[0]
def imag(c):
return c[1]
def str_complex(c):
return "{0}+{1}i".format(c[0], c[1])
... |
rohinkumar/galsurveystudy | old/Parallel Computing with Python public.ipynb | mit | %pylab inline
"""
Explanation: Parallel Computing with Python
Rodrigo Nemmen, IAG USP
This IPython notebook illustrates a few simple ways of doing parallel computing.
Practical examples included:
Parallel function mapping to a list of arguments (multiprocessing module)
Parallel execution of array function (scatter/ga... |
Kaggle/learntools | notebooks/geospatial/raw/ex1.ipynb | apache-2.0 | import geopandas as gpd
from learntools.core import binder
binder.bind(globals())
from learntools.geospatial.ex1 import *
"""
Explanation: Introduction
Kiva.org is an online crowdfunding platform extending financial services to poor people around the world. Kiva lenders have provided over $1 billion dollars in loans ... |
ethen8181/machine-learning | trees/lightgbm.ipynb | mit | # code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', 'notebook_format'))
from formats import load_style
load_style(css_style='custom2.css', plot_style=False)
os.chdir(path)
# 1. magic for inline plot
# 2. ... |
DylanGification/PhleepGG | data/MultipleLinearRegression.ipynb | mit | import pandas as pd
import numpy as np
import statsmodels.api as sm
data = pd.read_json("Overwatch090317.json")
rank=[x for x in data['rank']]
level=[x for x in data['level']]
wins=[x for x in data.get('comp', {})]
# winsdata=[x for x in wins.index[x]]
# wins=[x for x in data['comp'] if x <1E100]
a=wins[0]
b=a.get... |
rebeccabilbro/machine-learning | notebook/Wheat Classification.ipynb | mit | %matplotlib inline
import os
import json
import time
import pickle
import requests
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/00236/seeds_dataset.txt"
def fetch_data(fname='seeds_dataset.txt'):
"""
Helper method to... |
SJSlavin/phys202-2015-work | assignments/assignment07/AlgorithmsEx01.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
"""
Explanation: Algorithms Exercise 1
Imports
End of explanation
"""
file = open("mobydick_chapter1.txt")
mobydick = file.read()
mobydick = mobydick.splitlines()
mobydick = " ".join(mobydick)
punctuation = ["-", ",", "."]
mobydick = list(... |
julienchastang/unidata-python-workshop | notebooks/Surface_Data/Surface Data with Siphon and MetPy.ipynb | mit | from siphon.catalog import TDSCatalog
# copied from the browser url box
metar_cat_url = ('http://thredds.ucar.edu/thredds/catalog/'
'irma/metar/catalog.xml?dataset=irma/metar/Metar_Station_Data_-_Irma_fc.cdmr')
# Parse the xml
catalog = TDSCatalog(metar_cat_url)
# what datasets are here?
print(list(... |
jrieke/mhbf | 4/4.ipynb | mit | from __future__ import division, print_function
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Solution from Johannes Rieke and Alex Moore¶
End of explanation
"""
from scipy.integrate import odeint
def step(x):
return int(x >= 0)
x = np.linspace(-10, 10, 1000)
plt.plot(x... |
bsipocz/AstroHackWeek2015 | inference/straightline.ipynb | gpl-2.0 | %load_ext autoreload
%autoreload 2
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (8.0, 8.0)
plt.rcParams['savefig.dpi'] = 100
from straightline_utils import *
"""
Explanation: Fitting a Straight Line
Phil Marshall, Danie... |
flaxandteal/python-course-lecturer-notebooks | Python Course - 002a - And so we begin.ipynb | mit | import datetime
print(datetime.date.today())
"""
Explanation: ... and so we begin
Critical information
First steps
Order of the day
Learn to use Jupyter / iPython Notebook
Get familiar with basic Python
Start with Spyder, a traditional editor
Fundamental Python-in-Science skills
What is Jupyter
(previously i... |
trudake/Notebooks | PixieApp+for+Outlier+Detection.ipynb | mit | !pip install --user --upgrade pixiedust
!pip install --user --upgrade scikit-learn
import pixiedust
import numpy as np
import matplotlib.pyplot as plt
import sklearn.ensemble
import pandas as pd
from sklearn import svm
from pyspark.mllib.stat import Statistics
from pyspark.mllib.clustering import *
import pyspark.sql.... |
zomansud/coursera | ml-classification/week-3/module-5-decision-tree-assignment-2-blank.ipynb | mit | import graphlab
"""
Explanation: Implementing binary decision trees
The goal of this notebook is to implement your own binary decision tree classifier. You will:
Use SFrames to do some feature engineering.
Transform categorical variables into binary variables.
Write a function to compute the number of misclassified e... |
JackDi/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
"""
# YOUR CODE HERE
def hat(x,a,b):
v=-1*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... |
sraejones/phys202-2015-work | assignments/midterm/AlgorithmsEx03.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact
"""
Explanation: Algorithms Exercise 3
Imports
End of explanation
"""
def char_probs(s):
"""Find the probabilities of the unique characters in the string s.
Parameters
----------
... |
eaton-lab/toytree | docs/10-treestyles.ipynb | bsd-3-clause | import toytree
# generate a random tree
tree = toytree.rtree.unittree(ntips=10, seed=123)
"""
Explanation: Using built-in Tree Styles
The built-in treestyle or ts drawing options in toytree provide a base layer for styling drawings that can make it easier to achieve a desired style using fewer options. Additional sty... |
jeicher/cobrapy | documentation_builder/io.ipynb | lgpl-2.1 | import cobra.test
import os
from os.path import join
data_dir = cobra.test.data_directory
print("mini test files: ")
print(", ".join(i for i in os.listdir(data_dir)
if i.startswith("mini")))
textbook_model = cobra.test.create_test_model("textbook")
ecoli_model = cobra.test.create_test_model("ecoli"... |
robblack007/clase-dinamica-robot | Practicas/practica1/inicio.ipynb | mit | 2 + 3
2*3
2**3
sin(pi)
"""
Explanation: Práctica 1 - Introducción a Jupyter lab y libreria robots
Introducción a Jupyter y el lenguaje de programación Python
Expresiones aritmeticas y algebraicas
Empezaremos esta práctica con algo de conocimientos previos de programación. Se que muchos de ustedes no han tenido la o... |
cfjhallgren/shogun | doc/ipython-notebooks/structure/FGM.ipynb | gpl-3.0 | %pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
import numpy as np
import scipy.io
dataset = scipy.io.loadmat(os.path.join(SHOGUN_DATA_DIR, 'ocr/ocr_taskar.mat'))
# patterns for training
p_tr = dataset['patterns_train']
# patterns for testing
p_ts = dataset['pat... |
GeosoftInc/gxpy | examples/jupyter_notebooks/Tutorials/Geosoft Databases.ipynb | bsd-2-clause | from IPython.display import Image
import numpy as np
import geosoft.gxapi as gxapi
import geosoft.gxpy.gx as gx
import geosoft.gxpy.gdb as gxdb
import geosoft.gxpy.utility as gxu
gxc = gx.GXpy()
url = 'https://github.com/GeosoftInc/gxpy/raw/9.3.1/examples/data/'
gxu.url_retrieve(url + 'mag_data.csv')
"""
Explanation:... |
arnoldlu/lisa | ipynb/tutorial/04_ExecutorUsage.ipynb | apache-2.0 | import logging
from conf import LisaLogging
LisaLogging.setup()
# Execute this cell to enabled executor debugging statements
logging.getLogger('Executor').setLevel(logging.DEBUG)
"""
Explanation: Tutorial Goal
This tutorial aims to show how to configure and run a predefined set of
synthetic workload using the executo... |
mgorenstein/multiplot | tutorial.ipynb | mit | import pandas as pd
import numpy as np
import scipy.signal as signal
from multiplot import PandasPlot, NumpyPlot
%matplotlib inline
"""
Explanation: multiplot tutorial
Although the forthcoming inline plots are static, running this code in a Python shell will produce interactive matplotlib windows.
End of explanation
"... |
CompPhysics/MachineLearning | doc/src/NeuralNet/notes/.ipynb_checkpoints/mlp-checkpoint.ipynb | cc0-1.0 | # import necessary packages
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
#ensure the same random numbers appear every time
np.random.seed(0)
# display images in notebook
%matplotlib inline
plt.rcParams['figure.figsize'] = (10,10)
# download MNIST dataset
digits = datasets.load_di... |
melissawm/oceanobiopython | exemplos/exemplo_5/CTD_Data.ipynb | gpl-3.0 | import pandas as pd
"""
Explanation: Exemplo: manipulação de NaNs e limpeza de dados
Neste exemplo, usaremos um arquivo com muitos dados ausentes (representados por NaNs) para explorar o conceito de filtros. Além disso, vamos fazer um gráfico simples para representar os dados. Para isso, vamos usar duas bibliotecas im... |
BinRoot/TensorFlow-Book | ch02_basics/Concept08_TensorBoard.ipynb | mit | import tensorflow as tf
import numpy as np
raw_data = np.random.normal(10, 1, 100)
"""
Explanation: Ch 02: Concept 08
Using TensorBoard
TensorBoard is a great way to visualize what's happening behind the code.
In this example, we'll loop through some numbers to improve our guess of the average value. Then we can vis... |
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