repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15
values |
|---|---|---|---|---|---|
togawa28/mousestyles | mousestyles/classification/clustering.py | 3 | 7621 | from __future__ import (absolute_import, division,
print_function, unicode_literals)
from scipy.cluster.hierarchy import linkage
from sklearn.cluster import AgglomerativeClustering, KMeans
from sklearn import metrics
import numpy as np
from scipy.cluster.hierarchy import cophenet
from scipy.spa... | bsd-2-clause |
tomlyscan/Ordenacao | sort.py | 1 | 2277 | import argparse
import pandas as pd
import bib
parser = argparse.ArgumentParser()
parser.add_argument('-f', action='store', dest='list_integers', help='Arquivo com os números a serem ordenados')
parser.add_argument('-s', action='store_const', dest='sort_method', const='selection', help='Algoritmo selection sort sel... | gpl-3.0 |
sumspr/scikit-learn | sklearn/tests/test_cross_validation.py | 27 | 41664 | """Test the cross_validation module"""
from __future__ import division
import warnings
import numpy as np
from scipy.sparse import coo_matrix
from scipy import stats
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_equal
from sklearn... | bsd-3-clause |
verdverm/pypge | test/test_odeint.py | 1 | 1852 | #!/usr/bin/env python
"""
Program to plot the motion of a "springy pendulum".
(kindly taken from: http://julianoliver.com/share/free-science-books/comp-phys-python.pdf [page 102-103])
We actually have FOUR parameters to track, here:
L, L dot, theta, and theta dot.
So instead of the usual Nx2 array, make it Nx4.
Each ... | mit |
StuartLittlefair/astropy | astropy/visualization/wcsaxes/patches.py | 3 | 6772 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
from matplotlib.patches import Polygon
from astropy import units as u
from astropy.coordinates.representation import UnitSphericalRepresentation
from astropy.coordinates.matrix_utilities import rotation_matrix, matrix_product
__all_... | bsd-3-clause |
GGoussar/scikit-image | doc/examples/filters/plot_entropy.py | 9 | 2234 | """
=======
Entropy
=======
In information theory, information entropy is the log-base-2 of the number of
possible outcomes for a message.
For an image, local entropy is related to the complexity contained in a given
neighborhood, typically defined by a structuring element. The entropy filter can
detect subtle variat... | bsd-3-clause |
alistairlow/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimator_input_test.py | 72 | 12865 | # Copyright 2016 The TensorFlow 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 applica... | apache-2.0 |
spallavolu/scikit-learn | examples/decomposition/plot_sparse_coding.py | 247 | 3846 | """
===========================================
Sparse coding with a precomputed dictionary
===========================================
Transform a signal as a sparse combination of Ricker wavelets. This example
visually compares different sparse coding methods using the
:class:`sklearn.decomposition.SparseCoder` esti... | bsd-3-clause |
eleweek/dataisbeautiful | rbugs_statistics.py | 1 | 2922 | import os
from datetime import datetime
from datetime import date
from collections import defaultdict
import json
import praw
from praw.helpers import submissions_between
import seaborn as sns
import matplotlib.patches as mpatches
def get_flair_stats():
user = os.environ['REDDIT_USERNAME']
user_agent = 'Cal... | mit |
nitlev/deepforest | deepforest/models.py | 1 | 3738 | import numpy as np
from sklearn import clone
from sklearn.model_selection import KFold
class Models(object):
"""
A class abstracting away a bunch of models. predict_proba and predict_proba
returns all the individual predictions stacked on a new dimension.
"""
def __init__(self, models):
s... | mit |
ChanderG/scikit-learn | examples/decomposition/plot_pca_vs_lda.py | 182 | 1743 | """
=======================================================
Comparison of LDA and PCA 2D projection of Iris dataset
=======================================================
The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour
and Virginica) with 4 attributes: sepal length, sepal width, petal length
a... | bsd-3-clause |
jeffery-do/Vizdoombot | doom/lib/python3.5/site-packages/dask/array/numpy_compat.py | 6 | 13707 | from __future__ import absolute_import, division, print_function
from ..compatibility import builtins
import numpy as np
import warnings
try:
isclose = np.isclose
except AttributeError:
def isclose(*args, **kwargs):
raise RuntimeError("You need numpy version 1.7 or greater to use "
... | mit |
lbdreyer/iris | lib/iris/tests/test_plot.py | 3 | 31896 | # Copyright Iris contributors
#
# This file is part of Iris and is released under the LGPL license.
# See COPYING and COPYING.LESSER in the root of the repository for full
# licensing details.
# import iris tests first so that some things can be initialised before
# importing anything else
import iris.tests as tests
... | lgpl-3.0 |
bdmckean/MachineLearning | fall_2017/hw3/CNN.py | 2 | 4626 |
import argparse
import pickle
import gzip
from collections import Counter, defaultdict
import keras
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import Dense
from keras.layers import MaxPool2D
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.c... | mit |
h2oai/h2o-3 | h2o-py/tests/testdir_algos/gbm/pyunit_PUBDEV_7269_multinomial_auc_large.py | 2 | 6184 | from __future__ import print_function
import sys
sys.path.insert(1,"../../../")
import h2o
from tests import pyunit_utils
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from tests.pyunit_utils import roc_auc_score
def multinomial_auc_prostate_gbm():
data = h2o.import_file(pyunit_utils.locate("smallda... | apache-2.0 |
ahnitz/pycbc | pycbc/results/scatter_histograms.py | 4 | 29832 | # Copyright (C) 2016 Miriam Cabero Mueller, Collin Capano
#
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# option) any later version.
#
# This pro... | gpl-3.0 |
boland1992/SeisSuite | build/lib/ambient/ant/psspectrum.py | 8 | 7434 | """
Module related to Fourier spectrum of trace
"""
import psutils
import numpy as np
from numpy.fft import rfft
import matplotlib.pyplot as plt
import itertools as it
class FreqAmplSpectrum:
"""
Frequence-amplitude spectrum
"""
def __init__(self, trace):
"""
@type trace: L{obspy.co... | gpl-3.0 |
mblue9/tools-iuc | tools/vsnp/vsnp_build_tables.py | 2 | 17888 | #!/usr/bin/env python
import argparse
import multiprocessing
import os
import queue
import re
import pandas
import pandas.io.formats.excel
from Bio import SeqIO
INPUT_JSON_AVG_MQ_DIR = 'input_json_avg_mq_dir'
INPUT_JSON_DIR = 'input_json_dir'
INPUT_NEWICK_DIR = 'input_newick_dir'
# Maximum columns allowed in a Libre... | mit |
sunzuolei/robot | Codes.python/P2/P2.py | 1 | 1166 | #-------by HYH -------#
import numpy as np
import matplotlib.pyplot as plt
world=np.array(['green','red','red','green','green'])
p=np.array([0.2,0.2,0.2,0.2,0.2])
z='green'
pHit=0.6
pMiss=0.2
def plot_P2():
plt.figure(figsize=(8,10), dpi=80)
plt.subplot(411)
plt.bar(x=(0,1,2,3,4),height=p,color='red')
plt.xlabel('P... | mit |
luis-rr/nest-simulator | topology/examples/test_3d_exp.py | 13 | 2956 | # -*- coding: utf-8 -*-
#
# test_3d_exp.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or... | gpl-2.0 |
duncanmmacleod/gwsumm | gwsumm/tests/test_config.py | 1 | 8714 | # -*- coding: utf-8 -*-
# Copyright (C) Duncan Macleod (2013)
#
# This file is part of GWSumm.
#
# GWSumm is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) ... | gpl-3.0 |
untom/scikit-learn | sklearn/setup.py | 225 | 2856 | import os
from os.path import join
import warnings
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
from numpy.distutils.system_info import get_info, BlasNotFoundError
import numpy
libraries = []
if os.name == 'posix':
libraries.appe... | bsd-3-clause |
cshallue/models | research/lexnet_nc/learn_path_embeddings.py | 3 | 7726 | #!/usr/bin/env python
# Copyright 2017, 2018 Google, Inc. 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 re... | apache-2.0 |
biokit/biokit | biokit/rtools/pyper.py | 1 | 18769 | #!/usr/bin/env python
"""FORK of PyperR version 1.1.2 from pypi
Reason for the fork. Code are written on top of pyper inside biokit.
Yet, PypeR is failing sometimes (see examples below) and we may need to
hack it. It had also Python 3 compat issues.
Examples that fail:
installed.packages() --> numpy.array faili... | bsd-2-clause |
Achuth17/scikit-learn | examples/mixture/plot_gmm_selection.py | 248 | 3223 | """
=================================
Gaussian Mixture Model Selection
=================================
This example shows that model selection can be performed with
Gaussian Mixture Models using information-theoretic criteria (BIC).
Model selection concerns both the covariance type
and the number of components in th... | bsd-3-clause |
crichardson17/emgtemp | Metals_Sims/z_u_sim_plots.py | 2 | 17136 | import matplotlib.pyplot as plt
import numpy as np
import urllib
import matplotlib.cm as cm
Low_Temp_Color = 'k'
Mid_Temp_Color = 'g'
High_Temp_Color = 'r'
#Temp_Color = 0.5
Cloudy_Sim_Color = 'cyan'
markersize = 40
SDSS_File = '/Users/compastro/jenkins/emgtemp/data/4363_gr_5_0_err_dered.csv'
SDSS_Data = np.genfromtxt(... | mit |
jorge2703/scikit-learn | sklearn/metrics/cluster/tests/test_unsupervised.py | 230 | 2823 | import numpy as np
from scipy.sparse import csr_matrix
from sklearn import datasets
from sklearn.metrics.cluster.unsupervised import silhouette_score
from sklearn.metrics import pairwise_distances
from sklearn.utils.testing import assert_false, assert_almost_equal
from sklearn.utils.testing import assert_raises_regexp... | bsd-3-clause |
rahuldhote/scikit-learn | examples/plot_multilabel.py | 236 | 4157 | # Authors: Vlad Niculae, Mathieu Blondel
# License: BSD 3 clause
"""
=========================
Multilabel classification
=========================
This example simulates a multi-label document classification problem. The
dataset is generated randomly based on the following process:
- pick the number of labels: n ... | bsd-3-clause |
0asa/scikit-learn | sklearn/metrics/tests/test_pairwise.py | 2 | 21138 | import numpy as np
from numpy import linalg
from scipy.sparse import dok_matrix, csr_matrix, issparse
from scipy.spatial.distance import cosine, cityblock, minkowski
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_al... | bsd-3-clause |
nmartensen/pandas | pandas/tests/io/test_stata.py | 1 | 57819 | # -*- coding: utf-8 -*-
# pylint: disable=E1101
import datetime as dt
import os
import struct
import sys
import warnings
from datetime import datetime
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import pytest
from pandas import compat
from pandas.... | bsd-3-clause |
q1ang/scikit-learn | doc/sphinxext/gen_rst.py | 106 | 40198 | """
Example generation for the scikit learn
Generate the rst files for the examples by iterating over the python
example files.
Files that generate images should start with 'plot'
"""
from __future__ import division, print_function
from time import time
import ast
import os
import re
import shutil
import traceback
i... | bsd-3-clause |
wadda/Bari | bariscroller.py | 1 | 8327 | # coding=utf-8
import sys
import matplotlib
import numpy as np
from PyQt4 import QtCore
from PyQt4 import QtGui
matplotlib.use("Qt4Agg")
from matplotlib.figure import Figure
from matplotlib.animation import TimedAnimation
from matplotlib.lines import Line2D
from matplotlib.backends.backend_qt4agg import FigureCanvasQ... | mit |
PhonologicalCorpusTools/CorpusTools | corpustools/visualize.py | 1 | 3419 | import re
import numpy as np
import pdb
from scipy.cluster.hierarchy import linkage, dendrogram
from matplotlib import pyplot as plt
from matplotlib.collections import LineCollection
import seaborn as sns
from sklearn.decomposition import PCA
def organize_data(reader, visualization_method, value_column, segment_co... | bsd-3-clause |
Jimmy-Morzaria/scikit-learn | examples/gaussian_process/gp_diabetes_dataset.py | 223 | 1976 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
========================================================================
Gaussian Processes regression: goodness-of-fit on the 'diabetes' dataset
========================================================================
In this example, we fit a Gaussian Process model onto... | bsd-3-clause |
agrimaldi/metaseq | metaseq/colormap_adjust.py | 1 | 5162 | """
Module to handle custom colormaps.
`cmap_powerlaw_adjust`, `cmap_center_adjust`, and
`cmap_center_adjust` are from
https://sites.google.com/site/theodoregoetz/notes/matplotlib_colormapadjust
"""
import math
import copy
import numpy
import numpy as np
from matplotlib import pyplot, colors, cm
import matplotlib
imp... | mit |
vital-ai/beaker-notebook | plugin/ipythonPlugins/src/dist/python3/beaker_runtime3.py | 1 | 19742 | # Copyright 2014 TWO SIGMA OPEN SOURCE, 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agre... | apache-2.0 |
RNAer/Calour | calour/database.py | 1 | 18564 | '''
database access functions (:mod:`calour.database`)
==================================================
.. currentmodule:: calour.database
Functions
^^^^^^^^^
.. autosummary::
:toctree: generated
add_terms_to_features
enrichment
'''
# ----------------------------------------------------------------------... | bsd-3-clause |
AlexanderFabisch/scikit-learn | sklearn/feature_selection/__init__.py | 33 | 1159 | """
The :mod:`sklearn.feature_selection` module implements feature selection
algorithms. It currently includes univariate filter selection methods and the
recursive feature elimination algorithm.
"""
from .univariate_selection import chi2
from .univariate_selection import f_classif
from .univariate_selection import f_... | bsd-3-clause |
moble/sympy | sympy/plotting/plot.py | 55 | 64797 | """Plotting module for Sympy.
A plot is represented by the ``Plot`` class that contains a reference to the
backend and a list of the data series to be plotted. The data series are
instances of classes meant to simplify getting points and meshes from sympy
expressions. ``plot_backends`` is a dictionary with all the bac... | bsd-3-clause |
tristandeleu/switching-kalman-filter | test/test_skf_toyexample.py | 1 | 4797 | import numpy as np
from utils.kalman import SwitchingKalmanState, SwitchingKalmanFilter, KalmanFilter, KalmanState
from utils.kalman.models import NDCWPA, NDBrownian
import matplotlib.pyplot as plt
def angle_between(x,y):
return min(y-x, y-x+2*np.pi, y-x-2*np.pi, key=np.abs)
class ManeuveringTarget(object):
... | mit |
buntyke/Python | Plotting/plot1.py | 1 | 1694 | #!/usr/bin/env
import matplotlib.pyplot as plt
from numpy import genfromtxt
import numpy as np
plt.rcParams.update({'font.size': '18'})
data = genfromtxt('data1',delimiter=',')
xData = data[:,0]
yData = data[:,1]
zData = np.polyfit(xData, yData, 1)
l1 = np.poly1d(zData)
zData = np.polyfit(xData, yData, 2)
l2 = np.... | mit |
dhuppenkothen/stingray | stingray/io.py | 1 | 21680 | from __future__ import (absolute_import, unicode_literals, division,
print_function)
import numpy as np
import logging
import warnings
import os
import stingray.utils as utils
from .utils import order_list_of_arrays, is_string
from .utils import assign_value_if_none
try:
# Python 2
imp... | mit |
alexblaessle/PyFRAP | docs/source/conf.py | 2 | 11634 | # -*- coding: utf-8 -*-
#
# PyFRAP documentation build configuration file, created by
# sphinx-quickstart on Fri Mar 25 21:59:01 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# Al... | gpl-3.0 |
wanghaven/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/cm.py | 70 | 5385 | """
This module contains the instantiations of color mapping classes
"""
import numpy as np
from numpy import ma
import matplotlib as mpl
import matplotlib.colors as colors
import matplotlib.cbook as cbook
from matplotlib._cm import *
def get_cmap(name=None, lut=None):
"""
Get a colormap instance, defaultin... | agpl-3.0 |
nhuntwalker/astroML | book_figures/chapter8/fig_cross_val_D.py | 3 | 3153 | """
Cross Validation Examples: Part 4
---------------------------------
Figure 8.15
The learning curves for the data given by eq. 8.75, with d = 2 and d = 3. Both
models have high variance for a few data points, visible in the spread between
training and validation error. As the number of points increases, it is clear... | bsd-2-clause |
stylianos-kampakis/scikit-learn | doc/sphinxext/gen_rst.py | 106 | 40198 | """
Example generation for the scikit learn
Generate the rst files for the examples by iterating over the python
example files.
Files that generate images should start with 'plot'
"""
from __future__ import division, print_function
from time import time
import ast
import os
import re
import shutil
import traceback
i... | bsd-3-clause |
jayflo/scikit-learn | examples/applications/svm_gui.py | 287 | 11161 | """
==========
Libsvm GUI
==========
A simple graphical frontend for Libsvm mainly intended for didactic
purposes. You can create data points by point and click and visualize
the decision region induced by different kernels and parameter settings.
To create positive examples click the left mouse button; to create
neg... | bsd-3-clause |
MartinSavc/scikit-learn | examples/applications/plot_model_complexity_influence.py | 323 | 6372 | """
==========================
Model Complexity Influence
==========================
Demonstrate how model complexity influences both prediction accuracy and
computational performance.
The dataset is the Boston Housing dataset (resp. 20 Newsgroups) for
regression (resp. classification).
For each class of models we m... | bsd-3-clause |
flavour/Turkey | modules/s3chart.py | 16 | 10983 | # -*- coding: utf-8 -*-
""" S3 Charting Toolkit
@copyright: 2011-15 (c) Sahana Software Foundation
@license: MIT
@requires: U{B{I{NumPy}} <http://www.numpy.org>}
@requires: U{B{I{MatPlotLib}} <http://matplotlib.sourceforge.net>}
Permission is hereby granted, free of charge, to any person
obt... | mit |
HaroldMills/Vesper | scripts/detector_eval/manual/plot_precision_vs_calls.py | 1 | 25972 | """
Script that plots precision vs. number of calls for several detectors.
The plots are made for the archive of the current working directory
using the "Classification" and "Detector Score" annotations created
by the detectors.
"""
from pathlib import Path
import itertools
import sqlite3
from bokeh.models import R... | mit |
ElDeveloper/scikit-learn | examples/tree/plot_tree_regression_multioutput.py | 22 | 1848 | """
===================================================================
Multi-output Decision Tree Regression
===================================================================
An example to illustrate multi-output regression with decision tree.
The :ref:`decision trees <tree>`
is used to predict simultaneously the ... | bsd-3-clause |
thunderhoser/GewitterGefahr | setup.py | 1 | 2199 | """Setup file for GewitterGefahr."""
from setuptools import setup
PACKAGE_NAMES = [
'gewittergefahr', 'gewittergefahr.gg_io', 'gewittergefahr.gg_utils',
'gewittergefahr.deep_learning', 'gewittergefahr.plotting',
'gewittergefahr.scripts', 'gewittergefahr.feature_selection_example',
'gewittergefahr.natu... | mit |
mattcaldwell/zipline | zipline/utils/cli.py | 4 | 6275 | #
# Copyright 2014 Quantopian, Inc.
#
# 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 applicable law or agreed to in wr... | apache-2.0 |
ssalonen/financedatahoarder | financedatahoarder/services/tests/test_data_access_api.py | 1 | 15053 | from functools import partial
from itertools import chain
import pkg_resources
import grequests
from financedatahoarder.services import data_access_api
from financedatahoarder.services.data_access_api import NonCachingAsyncRequestsClient
from nose.tools import eq_
from nose_parameterized import parameterized
from datet... | bsd-3-clause |
kiszk/spark | python/pyspark/sql/group.py | 24 | 12490 | #
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | apache-2.0 |
larrybradley/astropy | astropy/nddata/ccddata.py | 5 | 27852 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""This module implements the base CCDData class."""
import itertools
import numpy as np
from .compat import NDDataArray
from .nduncertainty import (
StdDevUncertainty, NDUncertainty, VarianceUncertainty, InverseVariance)
from astropy.io import fits... | bsd-3-clause |
jforbess/pvlib-python | pvlib/test/test_spa.py | 5 | 14836 | import os
import datetime as dt
import logging
pvl_logger = logging.getLogger('pvlib')
try:
from importlib import reload
except ImportError:
try:
from imp import reload
except ImportError:
pass
import numpy as np
import numpy.testing as npt
import pandas as pd
import unittest
from nose.too... | bsd-3-clause |
nrhine1/scikit-learn | sklearn/manifold/tests/test_spectral_embedding.py | 216 | 8091 | from nose.tools import assert_true
from nose.tools import assert_equal
from scipy.sparse import csr_matrix
from scipy.sparse import csc_matrix
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal
from nose.tools import assert_raises
from nose.plugins.skip import SkipTest
from sk... | bsd-3-clause |
Silmathoron/nest-simulator | pynest/nest/tests/test_get_set.py | 5 | 21303 | # -*- coding: utf-8 -*-
#
# test_get_set.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, o... | gpl-2.0 |
modsim/molyso | molyso/mm/highlevel_interactive_advanced_ground_truth.py | 1 | 28374 | # -*- coding: utf-8 -*-
"""
documentation
"""
from __future__ import division, unicode_literals, print_function
import numpy as np
import time
from .tracking_output import s_to_h
from ..generic.etc import QuickTableDumper
from .fluorescence import FluorescentChannel
import json
import jsonpickle
import jsonpickle.... | bsd-2-clause |
lbishal/scikit-learn | sklearn/manifold/tests/test_mds.py | 324 | 1862 | import numpy as np
from numpy.testing import assert_array_almost_equal
from nose.tools import assert_raises
from sklearn.manifold import mds
def test_smacof():
# test metric smacof using the data of "Modern Multidimensional Scaling",
# Borg & Groenen, p 154
sim = np.array([[0, 5, 3, 4],
... | bsd-3-clause |
lekshmideepu/nest-simulator | pynest/examples/one_neuron_with_noise.py | 8 | 3161 | # -*- coding: utf-8 -*-
#
# one_neuron_with_noise.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the L... | gpl-2.0 |
eshaw2/SoftwareSystems | HFC_ch5/thinkplot.py | 88 | 12565 | """This file contains code for use with "Think Stats",
by Allen B. Downey, available from greenteapress.com
Copyright 2010 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
import math
import matplotlib
import matplotlib.pyplot as pyplot
import numpy as np
# customize some matplotlib attrib... | gpl-3.0 |
racu10/emapy | src/emapy.py | 3 | 50258 |
# coding: utf-8
# In[1]:
#Useful emapy @racu10
import pandas as pd
import numpy as np
import overpy
import overpass
import folium
from osmapi import OsmApi
import math
import geopy
import geopy.distance
import time
import unicodedata
import sys;
reload(sys);
sys.setdefaultencoding("utf8")
MyApi = OsmApi()
apiOver... | bsd-3-clause |
SixTrack/SixTrackLib | examples/python/test_footprint_from_sixtrack_input/003_gen_coord_for_footprint.py | 1 | 2101 | import pickle
import pysixtrack
import numpy as np
import helpers as hp
import footprint
epsn_x = 3.5e-6
epsn_y = 3.5e-6
r_max_sigma = 6.
N_r_footp = 10.
N_theta_footp = 10.
n_turns_beta = 150
with open('line.pkl', 'rb') as fid:
line = pysixtrack.Line.from_dict(pickle.load(fid))
with open('particle_on_CO.pkl'... | lgpl-2.1 |
botswana-harvard/edc-rdb | bcpp_rdb/old/rdb/query.py | 1 | 1515 | import os
import pandas as pd
from datetime import datetime
from bcpp_rdb.private_settings import Rdb
from sqlalchemy import create_engine
engine = create_engine('postgresql://{user}:{password}@{host}/{db}'.format(
user=Rdb.user, password=Rdb.password, host=Rdb.host, db=Rdb.name))
class Pims(object):
def _... | gpl-2.0 |
zrhans/pythonanywhere | .virtualenvs/django19/lib/python3.4/site-packages/pandas/tseries/tdi.py | 9 | 31432 | """ implement the TimedeltaIndex """
from datetime import timedelta
import numpy as np
from pandas.core.common import (ABCSeries, _TD_DTYPE, _INT64_DTYPE,
is_timedelta64_dtype, _maybe_box,
_values_from_object, isnull, is_integer, is_float)
from pandas.cor... | apache-2.0 |
grundgruen/powerline | tests/test_history.py | 2 | 15600 | from unittest import TestCase
import numpy as np
import pandas as pd
from zipline.history.history import HistorySpec
from zipline.protocol import BarData
from zipline.finance.trading import TradingEnvironment
from powerline.history.history_container import EpexHistoryContainer
__author__ = 'Max'
class TestHistory(... | apache-2.0 |
neale/CS-program | 434-MachineLearning/final_project/linearClassifier/sklearn/metrics/tests/test_common.py | 31 | 41654 | from __future__ import division, print_function
from functools import partial
from itertools import product
import numpy as np
import scipy.sparse as sp
from sklearn.datasets import make_multilabel_classification
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils.multiclass import type_of_target
fro... | unlicense |
TomAugspurger/pandas | pandas/tests/groupby/test_grouping.py | 1 | 35091 | """ test where we are determining what we are grouping, or getting groups """
import numpy as np
import pytest
import pandas as pd
from pandas import (
CategoricalIndex,
DataFrame,
Index,
MultiIndex,
Series,
Timestamp,
date_range,
)
import pandas._testing as tm
from pandas.core.groupby.gro... | bsd-3-clause |
BonexGu/Blik2D-SDK | Blik2D/addon/tensorflow-1.2.1_for_blik/tensorflow/contrib/learn/python/learn/estimators/estimator_input_test.py | 72 | 12865 | # Copyright 2016 The TensorFlow 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 applica... | mit |
mhdella/scikit-learn | sklearn/decomposition/truncated_svd.py | 199 | 7744 | """Truncated SVD for sparse matrices, aka latent semantic analysis (LSA).
"""
# Author: Lars Buitinck <L.J.Buitinck@uva.nl>
# Olivier Grisel <olivier.grisel@ensta.org>
# Michael Becker <mike@beckerfuffle.com>
# License: 3-clause BSD.
import numpy as np
import scipy.sparse as sp
try:
from scipy.sp... | bsd-3-clause |
ssaeger/scikit-learn | sklearn/linear_model/randomized_l1.py | 9 | 24350 | """
Randomized Lasso/Logistic: feature selection based on Lasso and
sparse Logistic Regression
"""
# Author: Gael Varoquaux, Alexandre Gramfort
#
# License: BSD 3 clause
import itertools
from abc import ABCMeta, abstractmethod
import warnings
import numpy as np
from scipy.sparse import issparse
from scipy import spar... | bsd-3-clause |
mojoboss/scikit-learn | examples/cluster/plot_affinity_propagation.py | 349 | 2304 | """
=================================================
Demo of affinity propagation clustering algorithm
=================================================
Reference:
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007
"""
print(__doc__)
from sklearn.cluster impor... | bsd-3-clause |
bospetersen/h2o-3 | h2o-py/tests/testdir_algos/gbm/pyunit_weightsGBM.py | 1 | 6149 | import sys
sys.path.insert(1, "../../../")
import h2o, tests
import random
import copy
def weights_check(ip,port):
def check_same(data1, data2, min_rows_scale):
gbm1_regression = h2o.gbm(x=data1[["displacement", "power", "weight", "acceleration", "year"]],
y="ec... | apache-2.0 |
zorojean/scikit-learn | examples/linear_model/plot_sgd_loss_functions.py | 249 | 1095 | """
==========================
SGD: convex loss functions
==========================
A plot that compares the various convex loss functions supported by
:class:`sklearn.linear_model.SGDClassifier` .
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
def modified_huber_loss(y_true, y_pred):
z ... | bsd-3-clause |
lazywei/scikit-learn | sklearn/feature_selection/rfe.py | 137 | 17066 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Vincent Michel <vincent.michel@inria.fr>
# Gilles Louppe <g.louppe@gmail.com>
#
# License: BSD 3 clause
"""Recursive feature elimination for feature ranking"""
import warnings
import numpy as np
from ..utils import check_X_y, safe_sqr
fro... | bsd-3-clause |
luminescence/PolyLibScan | Analysis/plotting.py | 1 | 19133 | from matplotlib.artist import setp
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import numba as nb
import pymc as mc
import numerics as num_
class Project(object):
def scatter_plot(self, subset=None, with_errors=False, with_labels=False, with_crossva... | mit |
B3AU/waveTree | sklearn/linear_model/tests/test_omp.py | 5 | 8953 | # Author: Vlad Niculae
# Licence: BSD 3 clause
import warnings
import numpy as np
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_... | bsd-3-clause |
kipohl/ncanda-data-integration | scripts/redcap/scoring/brief/__init__.py | 2 | 9447 | #!/usr/bin/env python
##
## See COPYING file distributed along with the ncanda-data-integration package
## for the copyright and license terms
##
import os
import time
import datetime
import pandas
#
# Behavior Rating Inventory of Executive Function (BRIEF)
#
input_fields = { 'youthreport2' : [ 'youthreport2_bri... | bsd-3-clause |
tracierenea/gnuradio | gnuradio-runtime/examples/volk_benchmark/volk_plot.py | 78 | 6117 | #!/usr/bin/env python
import sys, math
import argparse
from volk_test_funcs import *
try:
import matplotlib
import matplotlib.pyplot as plt
except ImportError:
sys.stderr.write("Could not import Matplotlib (http://matplotlib.sourceforge.net/)\n")
sys.exit(1)
def main():
desc='Plot Volk performanc... | gpl-3.0 |
eramirem/astroML | examples/datasets/plot_LIGO_spectrum.py | 3 | 2513 | """
Plot the power spectrum of LIGO
-------------------------------
This compares the power spectrum computed using the raw FFT, and using
Welch's method (i.e. overlapping window functions that reduce noise).
The top panel shows the raw signal, which is the measurements of the
change in baseline length. The bottom pan... | bsd-2-clause |
qiime2/q2-diversity | q2_diversity/tests/test_alpha.py | 2 | 20675 | # ----------------------------------------------------------------------------
# Copyright (c) 2016-2021, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | bsd-3-clause |
PG-TUe/tpot | tests/test_config_sparse.py | 4 | 1422 | # -*- coding: utf-8 -*-
"""This file is part of the TPOT library.
TPOT was primarily developed at the University of Pennsylvania by:
- Randal S. Olson (rso@randalolson.com)
- Weixuan Fu (weixuanf@upenn.edu)
- Daniel Angell (dpa34@drexel.edu)
- and many more generous open source contributors
TPOT is f... | lgpl-3.0 |
UNR-AERIAL/scikit-learn | doc/sphinxext/numpy_ext/docscrape_sphinx.py | 408 | 8061 | import re
import inspect
import textwrap
import pydoc
from .docscrape import NumpyDocString
from .docscrape import FunctionDoc
from .docscrape import ClassDoc
class SphinxDocString(NumpyDocString):
def __init__(self, docstring, config=None):
config = {} if config is None else config
self.use_plots... | bsd-3-clause |
aetilley/scikit-learn | examples/model_selection/plot_roc.py | 146 | 3697 | """
=======================================
Receiver Operating Characteristic (ROC)
=======================================
Example of Receiver Operating Characteristic (ROC) metric to evaluate
classifier output quality.
ROC curves typically feature true positive rate on the Y axis, and false
positive rate on the X a... | bsd-3-clause |
0x0all/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/units.py | 70 | 4810 | """
The classes here provide support for using custom classes with
matplotlib, eg those that do not expose the array interface but know
how to converter themselves to arrays. It also supoprts classes with
units and units conversion. Use cases include converters for custom
objects, eg a list of datetime objects, as we... | gpl-3.0 |
annaritz/fly-interactome | interactome/weighted-interactome/parameter-optimize.py | 1 | 4407 | from __future__ import print_function
import sys
import os
## NetworkX imports
import numpy as np
import networkx as nx
from graph import Graph
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import glob
A_PARAMS = [0.05, 0.1, 0.5, 1.0, 1.5, 2.0]
W_PARAMS = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, ... | gpl-3.0 |
savoirfairelinux/odoo | addons/resource/faces/timescale.py | 170 | 3902 | ############################################################################
# Copyright (C) 2005 by Reithinger GmbH
# mreithinger@web.de
#
# This file is part of faces.
#
# faces is free software; you can redistribute it and/or modify
# ... | agpl-3.0 |
ihmeuw/vivarium | tests/framework/test_state_machine.py | 1 | 5788 | import pandas as pd
import numpy as np
from vivarium import InteractiveContext
from vivarium.framework.randomness import choice
from vivarium.framework.state_machine import Machine, State, Transition
def _population_fixture(column, initial_value):
class PopFixture:
@property
def name(self):
... | gpl-3.0 |
natj/bender | bender.py | 1 | 18533 | import sys
sys.path.append('/Users/natj/projects/arcmancer/lib/')
import pyarcmancer as pyac
import numpy as np
import matplotlib as mpl
#import matplotlib.pyplot as plt
from pylab import *
from matplotlib import cm
#from scipy import interpolate
import scipy.interpolate as interp
from joblib import Parallel, delay... | mit |
blaisb/cfdemUtilities | dem/rdfAnalysis/plotMultRdf.py | 1 | 1745 | # This plots the RDF of a LIGGGHTS post-processed analysis
#
# USAGE : python ./file
#
# Author : Bruno Blais
#Python imports
#----------------
import os
import sys
import numpy
import matplotlib.pyplot as plt
#----------------
#********************************
# OPTIONS AND USER PARAMETERS
#********************... | lgpl-3.0 |
kianho/mpl_preamble | examples/stock_prices.py | 1 | 1615 | #!/usr/bin/env python
# encoding: utf-8
"""
Date:
Sun Feb 15 21:57:25 AEDT 2015
Author:
Kian Ho <hui.kian.ho@gmail.com>
Description:
TODO
Usage:
stock_prices.py
"""
from __future__ import print_function
import datetime
import mpl_preamble
from pandas.io import data
from mpl_preamble import mpl, ... | mit |
pletisan/python-data-viz-cookbook | 3367OS_Code/3367OS_07_Code/ch07/ch07_rec01_understand_log_plot.py | 1 | 1045 | from matplotlib import pyplot as plt
import numpy as np
x = np.linspace(1, 10)
y = [10 ** el for el in x]
z = [2 * el for el in x]
fig = plt.figure(figsize=(10, 8))
ax1 = fig.add_subplot(2, 2, 1)
ax1.plot(x, y, color='blue')
ax1.set_yscale('log')
ax1.set_title(r'Logarithmic plot of $ {10}^{x} $ ')
ax1.set_ylabel(r'$... | mit |
treycausey/scikit-learn | benchmarks/bench_plot_ward.py | 290 | 1260 | """
Benchmark scikit-learn's Ward implement compared to SciPy's
"""
import time
import numpy as np
from scipy.cluster import hierarchy
import pylab as pl
from sklearn.cluster import AgglomerativeClustering
ward = AgglomerativeClustering(n_clusters=3, linkage='ward')
n_samples = np.logspace(.5, 3, 9)
n_features = n... | bsd-3-clause |
magnunor/hyperspy | hyperspy/misc/rgb_tools.py | 5 | 2801 | import numpy as np
from dask.array import Array
rgba8 = np.dtype({'names': ['R', 'G', 'B', 'A'],
'formats': ['u1', 'u1', 'u1', 'u1']})
rgb8 = np.dtype({'names': ['R', 'G', 'B'],
'formats': ['u1', 'u1', 'u1']})
rgba16 = np.dtype({'names': ['R', 'G', 'B', 'A'],
'for... | gpl-3.0 |
btabibian/scikit-learn | examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py | 87 | 3903 | """
==============================================
Feature agglomeration vs. univariate selection
==============================================
This example compares 2 dimensionality reduction strategies:
- univariate feature selection with Anova
- feature agglomeration with Ward hierarchical clustering
Both metho... | bsd-3-clause |
mikewiebe-ansible/ansible | hacking/cgroup_perf_recap_graph.py | 54 | 4384 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# (c) 2018, Matt Martz <matt@sivel.net>
#
# This file is part of Ansible
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the Licens... | gpl-3.0 |
ywcui1990/nupic.research | projects/wavelet_dataAggregation/runDataAggregationExperiment.py | 11 | 21206 | from os.path import isfile, join, exists
import pandas as pd
import numpy as np
from scipy import signal
import numpy.matlib
import csv
import os
import time
os.environ['TZ'] = 'GMT'
time.tzset()
display = True
if display:
import matplotlib.pyplot as plt
plt.close('all')
plt.ion()
def plotWaveletPower(sig, cw... | agpl-3.0 |
jbedorf/tensorflow | tensorflow/lite/experimental/micro/examples/micro_speech/apollo3/captured_data_to_wav.py | 11 | 1442 | # Copyright 2018 The TensorFlow 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 applica... | apache-2.0 |
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