repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
BinRoot/TensorFlow-Book | ch09_cnn/Concept03_cnn.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import cifar_tools
import tensorflow as tf
learning_rate = 0.001
names, data, labels = \
cifar_tools.read_data('./cifar-10-batches-py')
"""
Explanation: Ch 09: Concept 03
Convolution Neural Network
Load data from CIFAR-10.
End of explanation
"""
x = tf.place... |
ueapy/enveast_python_course_materials | Day_1/04-Semantics-Operators.ipynb | mit | # addition, subtraction, multiplication
(4 + 8) * (6.5 - 3)
# True division
print(11 / 2)
# Floor division
print(11 // 2)
"""
Explanation: Basic Python Semantics: Operators
Arithmetic Operations
| Operator | Name | Description |
|--------------|---------------... |
tpin3694/tpin3694.github.io | machine-learning/.ipynb_checkpoints/describe_a_matrix-checkpoint.ipynb | mit | # Load library
import numpy as np
"""
Explanation: Title: Describe An Array
Slug: describe_a_matrix
Summary: How to describe an array.
Date: 2017-09-03 12:00
Category: Machine Learning
Tags: Vectors Matrices Arrays
Authors: Chris Albon
Preliminaries
End of explanation
"""
# Create matrix
matrix = np.array([[1,... |
numerical-mooc/assignment-bank-2015 | mjorisch/Orbital_Perturbations.ipynb | mit | from matplotlib import pyplot
import numpy
from numpy import linalg
%matplotlib inline
from matplotlib import rcParams
rcParams['font.family'] = 'serif'
rcParams['font.size'] = 16
def Kepler_eqn(e, M):
"""Takes the eccentricity and mean anomaly of an orbit to solve Kepler's equation
Parameters:
------... |
atcemgil/notes | KNN.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pylab as plt
df = pd.read_csv(u'data/iris.txt',sep=' ')
df
X = np.hstack([
np.matrix(df.sl).T,
np.matrix(df.sw).T,
np.matrix(df.pl).T,
np.matrix(df.pw).T])
print X[:5] # sample view
c = np.matrix(df.c).T
print c[:5]
"""
Exp... |
sameersingh/ml-discussions | week5/kaggle_comp.ipynb | apache-2.0 | # Import all required libraries
from __future__ import division # For python 2.*
import numpy as np
import matplotlib.pyplot as plt
import mltools as ml
np.random.seed(0)
%matplotlib inline
"""
Explanation: In this week's homework assignment we want you to start playing around with different classifiers and try to s... |
mzszym/oedes | examples/ac/unipolar-prl100.ipynb | agpl-3.0 | %matplotlib inline
import matplotlib.pylab as plt
import oedes
import numpy as np
import scipy.constants # for epsilon_0
oedes.init_notebook()
"""
Explanation: AC simulation of asymmetric single carrier devices
This shows calculation of simulated capacitance-voltage (C-V) curves using AC analysis functionality.
It re... |
mne-tools/mne-tools.github.io | 0.20/_downloads/ad29ebf74c7706fc10eea18e56a6fec7/plot_brainstorm_phantom_ctf.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import fit_dipole
from mne.datasets.brainstorm import bst_phantom_ctf
from mne.io import read_raw_ctf
print(__doc__)
"""
Explanation: Brainstorm CT... |
mjuenema/ipython-notebooks | easysnmp.ipynb | bsd-2-clause | import easysnmp
session = easysnmp.Session(hostname='localhost', community='public', version=2,
timeout=1, retries=1, use_sprint_value=True)
# IMPORTANT: use_sprint_value=True for proper formatting of values
location = session.get('sysLocation.0')
location.oid, location.oid_index, location... |
google/physics-math-tutorials | colabs/Introduction to Abstract Algebra, Exercise Session_ the RSA cryptosystem.ipynb | apache-2.0 | # Exercise: Implement the shift cipher for strings composed of capital letters.
# Input: a string whose characters are either capital letters or spaces, and
# a shift length n.
# Output: the encrypted string
def rot_n(plaintext, n):
# TODO: Your implementation here
pass
"""
Explanation: Copyright 2021 Google LLC
L... |
pyemma/deeplearning | assignment1/two_layer_net.ipynb | gpl-3.0 | # A bit of setup
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.neural_net import TwoLayerNet
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
# for auto-reloadi... |
clauwag/WikipediaGenderInequality | notebooks/Network - 01 - Biography Connectivity.ipynb | mit | from __future__ import print_function, unicode_literals
from dbpedia_utils import iter_entities_from
from collections import defaultdict, Counter
import pandas as pd
import numpy as np
import json
import gzip
import dbpedia_config
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
source_folder... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/nicam16-7s/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'nicam16-7s', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: MIROC
Source ID: NICAM16-7S
Sub-Topics: Radiative Forcings.
Properties: 85 ... |
barjacks/pythonrecherche | Kursteilnehmer/Thierry Seiler/06 /03 Python Functions, 10 Übungen.ipynb | mit | def test(element):
element = element * 2
return element
"""
Explanation: 03 Python Functions, 10 Übungen
Hier nochmals zur Erinnerung, wie Funktionen geschrieben werden.
End of explanation
"""
test(5)
"""
Explanation: Multipliziert Integers oder Floats mit 2
End of explanation
"""
def highestNumber(lst):
... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_read_epochs.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Matti Hamalainen <msh@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
import mne
from mne import io
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
"""
Explanation: Reading epochs from a raw FIF file
Th... |
bennyrowland/suspect | docs/notebooks/tut1_intro.ipynb | mit | import suspect
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
"""
Explanation: Introduction to MRS processing with Suspect
Welcome to the first in our series of tutorials in how to use Suspect for working with MR spectroscopy data. In this introduction, we will present some of the basic fea... |
Kaggle/learntools | notebooks/sql/raw/ex5.ipynb | apache-2.0 | # Set up feedback system
from learntools.core import binder
binder.bind(globals())
from learntools.sql.ex5 import *
print("Setup Complete")
"""
Explanation: Introduction
You are getting to the point where you can own an analysis from beginning to end. So you'll do more data exploration in this exercise than you've don... |
jdhp-docs/python-notebooks | signal_processing_wavelet_transform_fr.ipynb | mit | %matplotlib inline
import matplotlib
matplotlib.rcParams['figure.figsize'] = (7, 7)
from astropy.io import fits
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import PIL.Image as pil_img # PIL.Image is a module not a class...
"""
Explanation: Transformée en ondelettes
... |
GoogleCloudPlatform/tf-estimator-tutorials | 08_Text_Analysis/06 - Part_4 - Text Classification - Hacker News - DNNClassifier with TF.IDF.ipynb | apache-2.0 | import os
class Params:
pass
# Set to run on GCP
Params.GCP_PROJECT_ID = 'ksalama-gcp-playground'
Params.REGION = 'europe-west1'
Params.BUCKET = 'ksalama-gcs-cloudml'
Params.PLATFORM = 'local' # local | GCP
Params.DATA_DIR = 'data/news' if Params.PLATFORM == 'local' else 'gs://{}/data/news'.format(Params.BUCKE... |
joommf/tutorial | workshops/2017-04-24-Intermag2017/tutorial4_current_induced_dw_motion.ipynb | bsd-3-clause | # Definition of parameters
L = 500e-9 # sample length (m)
w = 20e-9 # sample width (m)
d = 2.5e-9 # discretisation cell size (m)
Ms = 5.8e5 # saturation magnetisation (A/m)
A = 15e-12 # exchange energy constant (J/)
D = 3e-3 # Dzyaloshinkii-Moriya energy constant (J/m**2)
K = 0.5e6 # uniaxial anisotropy constant... |
antoniomezzacapo/qiskit-tutorial | community/games/quantum_awesomeness.ipynb | apache-2.0 | device = 'ibmq_16_melbourne'
"""
Explanation: <img src="../../images/qiskit-heading.gif" alt="Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook" width="500 px" align="left">
Quantum Awesomeness
The latest version of this notebook is available on https://github.co... |
intuinno/gatherplot | data/airline.ipynb | mit | import pandas as pd
import numpy as np
alldata = pd.read_csv('2008.csv')
alldata.shape
"""
Explanation: Gatherplot IPython Demo Using Airline Data
This notebook shows how we can use the gatherplot with ipython
Data Source
2008 Airline Data from http://stat-computing.org/dataexpo/2009/
Gatherplot Gatherplot.or... |
mercybenzaquen/foundations-homework | foundations_hw/05/Homework_5_Spotify.ipynb | mit | for every_lil in Lil_info:
print(every_lil['name'], every_lil['popularity'],"Genre:",(", ".join(every_lil['genres'])))
if every_lil['genres'] == []:
print("No genres listed")
"""
Explanation: 1# Do a search and print a list of 50 that are playable in the USA (or the country of your choice), along with ... |
billbrod/spatial-frequency-preferences | notebooks/How-to-use-model.ipynb | mit | # import necessary packages
%matplotlib inline
%load_ext autoreload
%autoreload 2
import sys
sys.path.append('..')
import sfp
import pandas as pd
import torch
import numpy as np
import matplotlib.pyplot as plt
from tqdm.auto import tqdm
import seaborn as sns
"""
Explanation: This notebook presents the model developed... |
Upward-Spiral-Science/grelliam | code/test_assumptions.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import os
import csv
import igraph as ig
%matplotlib inline
font = {'weight' : 'bold',
'size' : 18}
import matplotlib
matplotlib.rc('font', **font)
# Initializing dataset names
dnames = list(['../data/desikan/KKI2009'])
print "Dataset: " + ", ".join(dname... |
qiu997018209/MachineLearning | 七月在线机器学习在bat工业中应用项目实战/随机森林/随机森林.ipynb | apache-2.0 | %pylab inline
# 导入训练集、验证集和测试集
import pandas as pd
samtrain = pd.read_csv('samtrain.csv')
samval = pd.read_csv('samval.csv')
samtest = pd.read_csv('samtest.csv')
# 使用 sklearn的随机森林模型，其模块叫做 sklearn.ensemble.RandomForestClassifier
# 在这里我们需要将标签列 ('activity') 转换为整数表示，
# 因为Python的RandomForest package需要这样的格式。
# 其对应关系如下：... |
weleen/mxnet | example/notebooks/basic/ndarray.ipynb | apache-2.0 | import mxnet as mx
# create a 1-dimensional array with a python list
a = mx.nd.array([1,2,3])
# create a 2-dimensional array with a nested python list
b = mx.nd.array([[1,2,3], [2,3,4]])
{'a.shape':a.shape, 'b.shape':b.shape}
"""
Explanation: NDArray Tutorial
In MXNet, NDArray is the core datastructure for all mathem... |
tschinz/iPython_Workspace | 02_WP/General/VarData_SpeedCalculation_Comparison.ipynb | gpl-2.0 | import math
import datetime
def varData_speedCalc_comparison(test_case, test_title, test_description, test_time=None, cups_speed=250.0, substrate_size_mm=None, substrate_size_px=None, dpi_image=[360.0,360.0]):
inch2mm = 25.4 # mm/inch
bpp = 4.0 # bit/px
if test_time == None:
test_... |
tensorflow/neural-structured-learning | research/neural_clustering/examples/ncp_demo_with_mog_data.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 u... |
nishantsbi/pattern_classification | clustering/hierarchical/clust_complete_linkage.ipynb | gpl-3.0 | %load_ext watermark
%watermark -a "Sebastian Raschka" -d -v
"""
Explanation: [<- back to pattern_classification]
End of explanation
"""
import pandas as pd
import numpy as np
np.random.seed(123)
variables = ['X', 'Y', 'Z']
labels = ['ID_0','ID_1','ID_2','ID_3','ID_4']
X = np.random.random_sample([5,3])*10
df = p... |
SergioSantGre/project_portfolio | P1_SSG.ipynb | lgpl-3.0 | #importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
"""
Explanation: Self-Driving Car Engineer Nanodegree
Project: Finding Lane Lines on the Road
In this project, you will use the tools you learned about in the lesson to ide... |
googledatalab/notebooks | samples/ML Toolbox/Regression/Census/2 Service Preprocess.ipynb | apache-2.0 | import google.datalab as datalab
import google.datalab.ml as ml
import mltoolbox.regression.dnn as regression
import os
"""
Explanation: Data Preparation and Preprocessing with BigQuery
This notebook is the first of a set of steps to run machine learning on the cloud. This step is about data preparation and preprocess... |
metpy/MetPy | v0.10/_downloads/ef4bfbf049be071a6c648d7918a50105/Simple_Sounding.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, SkewT
from metpy.units import units
# Change default to be better for skew-T
plt.rcParams['figure.figsize'] = (9, 9)
# Upper air data can be... |
e-sr/SDWirkungNi | DSP/auswertungLS.ipynb | cc0-1.0 | %reset -f
%matplotlib notebook
%load_ext autoreload
%autoreload 1
%aimport functions
# %qtconsole
import numpy as np
import acoustics
from functions import *
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
mpl.rcParams['lines.linewidth']=0.5
"""
Explanation: Auswertung der Lichtschrankes... |
tinenbruno/aes_classifier | jupyter_notebooks/AVA Dataset Notebook.ipynb | mit | import pandas as pd
import numpy as np
import seaborn
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: AVA dataset explorer
This study aims to explore and select a chunk of the AVA dataset to be used in preliminary tests of a aesthetic classifier.
End of explanation
"""
ava_header = ["row_number"... |
oneyb/eaternity | importing-agribalyse-1.3-with-ecoinvent-3.2-cutoff.ipynb | gpl-3.0 | from brightway2 import *
"""
Explanation: Import Agribalyse and link to ecoinvent 3.2 cutoff
Agribalyse is a French LCI database of agricultural products. It builds on top of ecoinvent 3.2. It was exported from SimaPro, so the names of ecoinvent processes are mangled, and need to be fixed back to standard ecoinvent.
T... |
tensorflow/docs-l10n | site/en-snapshot/model_optimization/guide/pruning/pruning_with_sparsity_2_by_4.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
tensorflow/tfx | docs/tutorials/tfx/penguin_tft.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... |
Britefury/deep-learning-tutorial-pydata2016 | INTRO ML 01 - Machine learning - a very basic introduction.ipynb | mit | %matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
import seaborn as sns
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.ensemble import RandomForestClassifier
"""
Explanation: Machine Learning - a very ba... |
ilanman/gdi | prework/00_Week0_I.ipynb | mit | my_variable = 2
my_variable
my_variable = 2
my_variable # no output
print "hello"
my_variable = 2
print my_variable
type(my_variable)
my_variable = 2
print my_variable
print type(my_variable) # printing a variable is different from the output of its value
my_variable = my_variable + 1
my_variable
my_varia... |
NAU-CFL/Python_Learning_Source | 03_Data_and_Expressions_Lecture.ipynb | mit | type(1.0)
3+6
type(1)
# Limit of Precision
1.0/3
5.0//3
3*(1.0/3)
# Built-in Format Function
12.0/5
a = format(12.0/5, '.2f')
a
type(a) # It's string careful.
float(a)
int('123')
str(13)
a
5.0/7
2*a
float(format(5.0/7, '.2f'))
"""
Explanation: Python Basics
Data and Expressions
A literal is a sequence... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session03/Day4/ANTARES/miniAntaresSolutions_serial.ipynb | mit | import numpy as np
import scipy.stats as spstat
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib notebook
"""
Explanation: Challenges of Streaming Data:
Building an ANTARES-like Pipeline for Data Management and Discovery
Version 0.1
By AA Miller 2017 Apr 10
Edited by Gautham Narayan, 2017 Apr 26
As ... |
mjbrodzik/ipython_notebooks | charis/modice_area_by_country_and_year.ipynb | apache-2.0 | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%pylab inline
filename = 'modice_v4_3strikes_by_country_by_yr.txt'
df3 = pd.read_csv( filename, delim_whitespace=True, index_col=0 )
df3
"""
Explanation: MODICE v04 area by country, 2000-2014
End of explanation
"""
sorte... |
vascotenner/holoviews | doc/Tutorials/Containers.ipynb | bsd-3-clause | import numpy as np
import holoviews as hv
hv.notebook_extension()
"""
Explanation: This notebook serves as a reference for all the container types in HoloViews, with an extensive list of small, self-contained examples wherever possible, allowing each container type to be understood and tested independently. The contai... |
WNoxchi/Kaukasos | research/autocsv-dev.ipynb | mit | %matplotlib inline
%reload_ext autoreload
%autoreload 2
from pathlib import Path
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from cifar_utils import count_files
PATH = Path('data/cifar10')
"""
Explanation: AutoCSV Dev
WNixalo –– 2018/6/2-3
End of explanation
"""
with open(PATH... |
ghvn7777/ghvn7777.github.io | content/feature_engineering/2017_04_22_predict_ibm_attrition.ipynb | apache-2.0 | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
# Import statements required for Plotly
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
i... |
google-research/evoflow | notebooks/onemax.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 & Creative Common licence 4.0
# EvoFlow and its tutorials are released under the Apache 2.0 licence
# its documentaton is licensed under the Creative Common licence 4.0
"""
Explanation: Copyright 2020 The EvoFlow Authors.
End of explanation
"""
# installing the ... |
amkatrutsa/MIPT-Opt | Spring2020/acc_grad.ipynb | mit | import numpy as np
n = 100
# Random
# A = np.random.randn(n, n)
# A = A.T.dot(A)
# Clustered eigenvalues
A = np.diagflat([np.ones(n//4), 10 * np.ones(n//4), 100*np.ones(n//4), 1000* np.ones(n//4)])
U = np.random.rand(n, n)
Q, _ = np.linalg.qr(U)
A = Q.dot(A).dot(Q.T)
A = (A + A.T) * 0.5
print("A is normal matrix: ||AA*... |
nsaunier/CIV8760 | Python/tutoriel-pandas.ipynb | mit | import pandas as pd
import numpy as np
from pandas import DataFrame,read_csv
"""
Explanation: Tutoriel sur Pandas
1. Importer pandas
L'étape la plus importante est d'importer les bibliothèques nécessaires:
End of explanation
"""
#Emplacement des compteurs avec la correspondance pour les noms dans les fichiers de com... |
srodriguex/coursera_data_analysis_tools | Week_3.ipynb | mit | # Import all ploting and scientific library,
# and embed figures in this file.
%pylab inline
# Package to manipulate dataframes.
import pandas as pd
# Nice looking plot functions.
import seaborn as sn
# The Pearson correlation function.
from scipy.stats import pearsonr
# Read the dataset.
df = pd.read_csv('data/gap... |
irazhur/StatisticalMethods | examples/XrayImage/FirstLook.ipynb | gpl-2.0 | import astropy.io.fits as pyfits
import numpy as np
import os.path
import astropy.visualization as viz
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 10.0)
"""
Explanation: A First Look at an X-ray Image Dataset
Images are data. They can be 2D, from cameras, or 1D, from spe... |
ueapy/ueapy.github.io | content/notebooks/2017-01-20-function-quirks.ipynb | mit | def my_super_function():
pass
def even_better():
print('This is executed within a function')
even_better()
type(even_better)
"""
Explanation: Let's review the basics
Functions without arguments
End of explanation
"""
import numpy as np
def uv2wdir(u, v):
"""Calculate horizontal wind direction (meteor... |
tensorflow/docs-l10n | site/en-snapshot/agents/tutorials/9_c51_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... |
zerothi/sids | docs/visualization/viz_module/diy/Building a plot class.ipynb | lgpl-3.0 | from sisl.viz.plotly import Plot
class HappinessPlot(Plot):
pass
"""
Explanation: Building a plot class
Following this guide, you will not only build a very flexible plot class that you will be able to use in a wide range of cases, but also your class will be automatically recognized by the graphical interface. T... |
besser82/shogun | doc/ipython-notebooks/logdet/logdet.ipynb | bsd-3-clause | %matplotlib inline
from scipy.sparse import eye
from scipy.io import mmread
from matplotlib import pyplot as plt
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
matFile=os.path.join(SHOGUN_DATA_DIR, 'logdet/apache2.mtx.gz')
M = mmread(matFile)
rows = M.shape[0]
cols = M.shape[1]
A = M + eye(row... |
nicocardiel/numina | doc/notebooks/Wavelength_Calibration.ipynb | gpl-3.0 | from __future__ import division
from __future__ import print_function
import sys
import numpy as np
from numpy.polynomial import polynomial
from scipy.stats import norm
import scipy.misc
import matplotlib.pyplot as plt
import itertools
ldebug = True # display intermediate information
lplot = True # display interme... |
OSGeoLabBp/tutorials | english/data_processing/lessons/bridge_observations_tps.ipynb | cc0-1.0 | import re # for regular expressions
import numpy as np # for vector/matri operations
import matplotlib.pyplot as plt # for charts
import pandas as pd # for table like data structures
from datetime import datetime, timedelta # for time/date handling
"""
Explanatio... |
wikistat/Ateliers-Big-Data | Cdiscount/Part3-AIF-PythonKeras-TextGeneration-Cdiscount-Copy1.ipynb | mit | #Importation des librairies utilisées
import pandas as pd
import numpy as np
import pickle
import functools
from tqdm import tqdm
import keras.models as km
import keras.layers as kl
"""
Explanation: Ateliers: Technologies de l'intelligence Artificielle
<center>
<a href="http://www.insa-toulouse.fr/" ><img src="http:... |
amcdawes/QMlabs | Chapter 6 - Spin.ipynb | mit | from numpy import sin,cos,pi,sqrt
from qutip import *
pz = Qobj([[1],[0]])
mz = Qobj([[0],[1]])
px = Qobj([[1/sqrt(2)],[1/sqrt(2)]])
mx = Qobj([[1/sqrt(2)],[-1/sqrt(2)]])
py = Qobj([[1/sqrt(2)],[1j/sqrt(2)]])
my = Qobj([[1/sqrt(2)],[-1j/sqrt(2)]])
Sx = 1/2.0*sigmax()
Sy = 1/2.0*sigmay()
Sz = 1/2.0*sigmaz()
py
"""
Ex... |
Kaggle/learntools | notebooks/python/raw/ex_7.ipynb | apache-2.0 | from learntools.core import binder; binder.bind(globals())
from learntools.python.ex7 import *
print('Setup complete.')
"""
Explanation: Try It Yourself
There are only three problems in this last set of exercises, but they're all pretty tricky, so be on guard!
Run the setup code below before working on the questions.... |
donaghhorgan/COMP9033 | labs/08c - Recommender systems.ipynb | gpl-3.0 | %matplotlib inline
import pandas as pd
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import GridSearchCV, KFold, cross_val_predict
from sklearn.neighbors import KNeighborsRegressor
"""
Explanation: Lab 08c: Recommender systems
Introduction
In this lab, you will build a simple movie reco... |
sofroniewn/2P-RAM-pipeline | notebooks/sources-view.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
pathToImage = '/Users/nick/Downloads/neurofinder.02.00/images_summary/mean.tif'
pathToSources = '/Users/nick/Downloads/neurofinder.02.00/regions/regions3.json'
pathToSourcesGT = '/Users/nick/Downloads/neurofinder.02.00/regions/regionsRand.json'
import extraction as ... |
weikang9009/pysal | notebooks/viz/splot/mapping_vba.ipynb | bsd-3-clause | import pysal.lib as lp
from pysal.lib import examples
import geopandas as gpd
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
%matplotlib inline
"""
Explanation: Mapping with splot and PySAL
Imports
End of explanation
"""
link_to_data = examples.get_path('columbus.shp')
gdf ... |
Jay-Oh-eN/data-science-workshops | presenting-data.ipynb | mit | from bokeh.plotting import figure, show, output_notebook
output_notebook()
"""
Explanation: Presenting Data
Your job as a data scientist is to communicate. Often you are communicating insight from distilling lots of data (models, stats, EDA, narative/editorialization) // making data accessible/self-service (run queri... |
sorig/shogun | doc/ipython-notebooks/pca/pca_notebook.ipynb | bsd-3-clause | %pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
# import all shogun classes
from shogun import *
"""
Explanation: Principal Component Analysis in Shogun
By Abhijeet Kislay (GitHub ID: <a href='https://github.com/kislayabhi'>kislayabhi</a>)
This notebook is about... |
ud3sh/coursework | deeplearning.ai/coursera-improving-neural-networks/week3/TensorFlow_Tutorial_v3b.ipynb | unlicense | import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.framework import ops
from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict
%matplotlib inline
np.random.seed(1)
"""
Explanation: TensorFlow Tutorial
Welcome to this w... |
feststelltaste/software-analytics | demos/20210630_WeAreDevelopersWorldCongress/Production Coverage Demo Notebook (complete).ipynb | gpl-3.0 | import pandas as pd
coverage = pd.read_csv("../dataset/jacoco.csv")
coverage = coverage[['PACKAGE', 'CLASS', 'LINE_COVERED' ,'LINE_MISSED']]
coverage['LINES'] = coverage.LINE_COVERED + coverage.LINE_MISSED
coverage.head(1)
"""
Explanation: Context
John Doe remarked in #AP1432 that there may be too much code in our app... |
ereodeereigeo/dataTritiumWS22 | analisis promedios-Copy1.ipynb | gpl-2.0 | import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Importamos las librerías necesarias
End of explanation
"""
import ext_datos as ext
import procesar as pro
import time_plot as tplt
"""
Explanation: Importamos las librerías creadas para trabajar
End of explanation
"""
dia1 = ... |
mirjalil/DataScience | notebooks/computer-vision/.ipynb_checkpoints/face-landmarks-checkpoint.ipynb | gpl-2.0 | import numpy as np
import matplotlib
from matplotlib import pyplot as plt
%matplotlib inline
import pandas
"""
Explanation: Playing with Eigen Faces
In this tutorial article, we want to learn how to combine landmark points together in order to create new faces. This process is used in developing Active Shape Model (... |
adlyons/AWOT | examples/awot_cfad_demo.ipynb | gpl-2.0 | import awot
import os
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: <h2>Demonstrating Frequency Distribution Plots in AWOT</h2>
<h4>Additionally, other RadarUtility tools are shown</h4>
End of explanation
"""
p3f = os.path.join("/Users/guy/data/dynamo/", "111124I", "0705_windsyn.nc")
wcrf = os.... |
rahulremanan/python_tutorial | Fundamentals_of_deep-learning/notebooks/01_Introduction_Building_a_neural_network.ipynb | mit | if input_1 == input_2:
output = 0
else:
output = 1
"""
Explanation: Introduction to Neural Networks:
Author:
Dr. Rahul Remanan
Dr. Jesse Kanter
CEO and Chief Imagination Officer
Moad Computer
Launch this notebook in Google Colab
This is a hands-on workshop notebook on deep-learning using python 3. In this noteboo... |
quantopian/research_public | notebooks/data/eventvestor.shareholder_meetings/notebook.ipynb | apache-2.0 | # import the dataset
from quantopian.interactive.data.eventvestor import shareholder_meetings
# or if you want to import the free dataset, use:
# from quantopian.data.eventvestor import shareholder_meetings_free
# import data operations
from odo import odo
# import other libraries we will use
import pandas as pd
# Le... |
pelodelfuego/word2vec-toolbox | notebook/classification/taxonomy.ipynb | gpl-3.0 | summaryDf = pd.DataFrame([extractSummaryLine(l) for l in open('../../data/learnedModel/taxo/summary.txt').readlines()],
columns=['full', 'strict', 'clf', 'feature', 'post', 'precision', 'recall', 'f1'])
summaryDf = summaryDf[summaryDf['clf'] != 'KNeighborsClassifier']
directDf = summaryDf[summ... |
edhuckle/statsmodels | examples/notebooks/statespace_local_linear_trend.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
from scipy.stats import norm
import statsmodels.api as sm
import matplotlib.pyplot as plt
"""
Explanation: State space modeling: Local Linear Trends
This notebook describes how to extend the Statsmodels statespace classes to create and estimate a custom model.... |
kriete/cie5703_notebooks | week_6_Rotterdam.ipynb | mit | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
%matplotlib inline
plt.style.use('ggplot')
"""
Explanation: Assignment CIE 5703 - week 6
Import libraries
End of explanation
"""
data = pd.read_csv('rotterdam_rg_2003-2014.csv', skipinitialspace=True)
"""
Explanation: Rotterdam rain gauge datase... |
lowks/micromeritics | documentation/thickness.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
from micromeritics import thickness as t, plots
Prel = np.array([0.000000448802,0.000000936935,0.00000148652,0.00000209607,0.00000276863,0.00000351456,
0.00000435161,0.00000526938,0.00000630655,0.00000748296,0.00000884365,0.0000104439,0.0000123147,
... |
anisfeld/MachineLearning | Diagnostic/.ipynb_checkpoints/ML HW0-1-checkpoint.ipynb | mit | import numpy as np
import pandas as pd
import requests
from urllib.parse import quote_plus, urlencode
# Helper Functions
def check_nulls(df, col):
'''
returns df with NaN in specified column
'''
return df.ix[df.ix[:,col].isnull()]
def get_notnulls(df, col):
'''
returns df with NaN in specified... |
dsacademybr/PythonFundamentos | Cap03/Notebooks/DSA-Python-Cap03-05-Metodos.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 3</font>
Download: http://github.com/dsacademybr
End of explanation
"""
# Crian... |
ajgpitch/qutip-notebooks | examples/piqs-entropy_purity.ipynb | lgpl-3.0 | from time import clock
import matplotlib.pyplot as plt
import numpy as np
from qutip import *
from qutip.piqs import *
from scipy.sparse import block_diag
from scipy.sparse.linalg import eigsh, eigs
from scipy import log
"""
Explanation: Calculate Von Neumann Entropy and Purity for Dicke-Basis density matrix in presen... |
BIDS-collaborative/purchasing | script/time_clustering/time_analysis.ipynb | isc | # Parsing date columns allows us to do some nifty things with pandas
date_columns = ['po_closed_date', 'creation_date']
data = pd.read_csv(data_path, parse_dates=date_columns)
data['cost_total'] = data.unit_price * data.quantity
"""
Explanation: Modified data
The first step was to select a subset of useful columns, ... |
dtamayo/MachineLearning | Day1/05_model_evaluation_tts.ipynb | gpl-3.0 | from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
# print the shapes of X and y
print X.shape
print y.shape
# STEP 1: split X and y into training and testing sets
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_... |
Chipe1/aima-python | improving_sat_algorithms.ipynb | mit | from logic import *
"""
Explanation: Propositional Logic
Improving Boolean Satisfiability Algorithms
Introduction
A propositional formula $\Phi$ in Conjunctive Normal Form (CNF) is a conjunction of clauses $\omega_j$, with $j \in {1,...,m}$. Each clause being a disjunction of literals and each literal being either a ... |
pybel/pybel-notebooks | integration/Resolving Names of Complexes.ipynb | apache-2.0 | import itertools as itt
import math
import time
import os
import pybel
import networkx as nx
from pybel.constants import *
import matplotlib.pyplot as plt
import pybel_tools as pbt
from pybel_tools.visualization import to_jupyter
%matplotlib inline
pybel.__version__
pbt.__version__
time.asctime()
"""
Explanation: ... |
tensorflow/examples | courses/udacity_intro_to_tensorflow_for_deep_learning/l10c03_nlp_constructing_text_generation_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... |
FRESNA/powerplantmatching | doc/example.ipynb | gpl-3.0 | import powerplantmatching as pm
import pandas as pd
"""
Explanation: Example of Use
End of explanation
"""
geo = pm.data.GEO()
geo.head()
"""
Explanation: Load open source data published bei the Global Energy Observation, GEO. As you might know, this is not the original format of the database but the standardized ... |
caromedellin/Python-notes | exploratory-data-analysis/Final Project.ipynb | mit | import pandas as pd
import numpy as np
congress_words = pd.read_csv("../data/legislators.csv")
congress_words = pd.DataFrame(congress_words)
congress_words.head()
congress_words.columns.tolist()
"""
Explanation: Congress Words
End of explanation
"""
l_bioGuides = congress_words.bioguide_id.tolist()
print l_bioGuid... |
ES-DOC/esdoc-jupyterhub | notebooks/ipsl/cmip6/models/sandbox-3/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ipsl', 'sandbox-3', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: IPSL
Source ID: SANDBOX-3
Topic: Ocnbgchem
Sub-Topics: Tracers.
Properties:... |
Quadrocube/rep | howto/04-howto-folding.ipynb | apache-2.0 | %pylab inline
"""
Explanation: About
This notebook demonstrates stacking machine learning algorithm - folding, which physics use in their analysis.
End of explanation
"""
import numpy, pandas
from rep.utils import train_test_split
from sklearn.metrics import roc_auc_score
sig_data = pandas.read_csv('toy_datasets/to... |
mathinmse/mathinmse.github.io | Lecture-10B-Reading-and-Writing-Data.ipynb | mit | x1 = np.linspace(0, 10, 9)
y1 = x1**2
np.savetxt('test_data_001.txt', (x1,y1))
"""
Explanation: A simple function with dependence on only one independent variable might be written to a file like this:
End of explanation
"""
x2 = np.linspace(0, 1, 100)
y2 = np.exp(x)
np.savetxt('test_data_002.txt', (x2,y2))
"""
Expl... |
nadvamir/deep-learning | seq2seq/sequence_to_sequence_implementation.ipynb | mit | import helper
source_path = 'data/letters_source.txt'
target_path = 'data/letters_target.txt'
source_sentences = helper.load_data(source_path)
target_sentences = helper.load_data(target_path)
"""
Explanation: Character Sequence to Sequence
In this notebook, we'll build a model that takes in a sequence of letters, an... |
param411singh/inf1340-2015-notebooks | Week 4.ipynb | mit | x = 1
y = 2
y != x
"""
Explanation: Overview
Hour 1
Boolean Expressions
Logical Operators
Repetition Structures
Hour 2
Repetition Structures continued
Hour 3
Programming Exercise
Lab Exercise 1
The intention is to complete the lab during class
We made an exception for last week, since it was the first one.
Solution... |
unmrds/cc-python | 1.2-The Basics.ipynb | apache-2.0 | # The interpreter can be used as a calculator, and can also echo or concatenate strings.
3 + 3
3 * 3
3 ** 3
3 / 2 # classic division - output is a floating point number
# Use quotes around strings, single or double, but be consistent to the extent possible
'dogs'
"dogs"
"They're going to the beach"
'He said "I... |
ES-DOC/esdoc-jupyterhub | notebooks/noaa-gfdl/cmip6/models/sandbox-3/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'sandbox-3', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: NOAA-GFDL
Source ID: SANDBOX-3
Topic: Landice
Sub-Topics: Glaciers, Ice.
P... |
mdpiper/wunderkammer | jupyter-notebook-widgets.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from ipywidgets import interact
"""
Explanation: Widgets in a Jupyter Notebook
An example of using version 4.x widgets in a Jupyter Notebook.
Reference: https://ipywidgets.readthedocs.io/en/latest/.
Depending on the current state of your Python inst... |
chris1610/pbpython | notebooks/Google-Forms-Data-Analysis.ipynb | bsd-3-clause | import gspread
from oauth2client.client import SignedJwtAssertionCredentials
import pandas as pd
import json
"""
Explanation: Visualizing Google Forms Data with Seaborn
This is the second part of an article from Practical Business Python describing how to retrieve and analyze data from a Google Form.
Please review par... |
catalyst-cooperative/pudl | devtools/harvesting_debug.ipynb | mit | pudl_settings = pudl.workspace.setup.get_defaults()
settings_file_name= 'etl_full.yml'
etl_settings = EtlSettings.from_yaml(
pathlib.Path(pudl_settings['settings_dir'],
settings_file_name))
validated_etl_settings = etl_settings.datasets
datasets = validated_etl_settings.get_datasets()
eia_settings ... |
mikekestemont/ghent1516 | 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 ... |
google-research/google-research | rllim/main_local_dynamics_recovery.ipynb | apache-2.0 | import os
from git import Repo
# Current working directory
repo_dir = os.getcwd() + '/repo'
if not os.path.exists(repo_dir):
os.makedirs(repo_dir)
# Clones github repository
if not os.listdir(repo_dir):
git_url = "https://github.com/google-research/google-research.git"
Repo.clone_from(git_url, repo_dir)
... |
daniaki/Enrich2 | docs/notebooks/unique_barcodes.ipynb | gpl-3.0 | % matplotlib inline
import os.path
from collections import Counter
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from enrich2.variant import WILD_TYPE_VARIANT
import enrich2.plots as enrich_plot
pd.set_option("display.max_rows", 10) # rows shown when pretty-printing
"""
Explanation: Selecting... |
cerrno/neurokernel | notebooks/sensory_int.ipynb | bsd-3-clause | %cd -q ~/neurokernel/examples/sensory_int/data
%run gen_vis_input.py
%run gen_olf_input.py
%run gen_integrate.py
"""
Explanation: Sensory Integration Demo
This notebook demonstrates how to use Neurokernel to integrate multiple independently developed LPUs. In this example, partial models of Drosophila's olfaction and ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.