repo_name stringlengths 7 90 | path stringlengths 5 191 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 976 581k | license stringclasses 15
values |
|---|---|---|---|---|---|
tim777z/opencog | opencog/python/spatiotemporal/demo.py | 33 | 1221 | __author__ = 'sebastian'
from spatiotemporal.temporal_events.trapezium import TemporalEventTrapezium
from spatiotemporal.temporal_events.relation_formulas import FormulaCreator
from spatiotemporal.temporal_events.composition.non_linear_least_squares import DecompositionFitter
import matplotlib.pyplot as plt
all_rel... | agpl-3.0 |
phoebe-project/phoebe2-docs | 2.2/tutorials/requiv.py | 1 | 3172 | #!/usr/bin/env python
# coding: utf-8
# Equivalent Radius
# ============================
#
# Setup
# -----------------------------
# Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the lates... | gpl-3.0 |
dgketchum/MT_Rsense | obspnts/obsio/providers/ghcnd.py | 1 | 21917 | from .. import LOCAL_DATA_PATH
from ..util.misc import download_if_new_ftp, open_remote_file
from .generic import ObsIO
from ftplib import FTP
from multiprocessing.pool import Pool
import gc
import numpy as np
import os
import pandas as pd
import sys
import tarfile
_NETWORK_CODE_TO_SUBPROVIDER = {'0': '', '1': 'CoCoRa... | apache-2.0 |
henningjp/CoolProp | dev/scripts/ideal_curves.py | 2 | 5121 | import numpy as np
import matplotlib.pyplot as plt
import CoolProp, scipy.optimize
class CurveTracer(object):
def __init__(self, backend, fluid, p0, T0):
"""
p0 : Initial pressure [Pa]
T0 : Initial temperatrure [K]
"""
self.P = [p0]
self.T = []
self.AS = Co... | mit |
paulsbrookes/cqed_sims_qutip | monte/stochastic_solve.py | 1 | 3579 | import numpy as np
from qutip import *
from pylab import *
from scipy.fftpack import fft
import matplotlib.pyplot as plt
from datetime import datetime
import os
# runfile('C:/Users/User/Documents/Python Scripts/untitled3.py',
# wdir='C:/Users/User/Documents/Python Scripts',
# args="5450... | apache-2.0 |
chaowu2009/stereo-vo | tools/testPoseEstimation.py | 1 | 2875 | #!/usr/bin/env python
import cv2
import numpy as np
from matplotlib import pylab as plt
# Camera internals
focal_length = 718.856
center = (607.1928, 185.2157)
camera_matrix = np.array(
[[focal_length, 0, center[0]],
[0, focal_length, center[1]],
... | mit |
pypot/scikit-learn | sklearn/linear_model/tests/test_passive_aggressive.py | 121 | 6117 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal, assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.base import ClassifierMixin
from skle... | bsd-3-clause |
openDAQ/opendaq-gui | daq_control/main.py | 1 | 42392 | #!/usr/bin/env python
# Copyright 2012
# Adrian Alvarez <alvarez@ingen10.com> and Juan Menendez <juanmb@ingen10.com>
#
# This file is part of opendaq.
#
# opendaq is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free So... | lgpl-3.0 |
nasa/mplStyle | mplStyle/MplFontStyle.py | 1 | 5937 | #===========================================================================
#
# Copyright (c) 2014, California Institute of Technology.
# U.S. Government Sponsorship under NASA Contract NAS7-03001 is
# acknowledged. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modific... | bsd-3-clause |
caisq/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimator.py | 14 | 62939 | # 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 |
walterreade/scikit-learn | examples/gaussian_process/plot_gpr_noisy_targets.py | 45 | 3680 | """
=========================================================
Gaussian Processes regression: basic introductory example
=========================================================
A simple one-dimensional regression example computed in two different ways:
1. A noise-free case
2. A noisy case with known noise-level per ... | bsd-3-clause |
wkentaro/chainer | examples/vae/train_vae.py | 5 | 7083 | #!/usr/bin/env python
"""Chainer example: train a VAE on MNIST
"""
import argparse
import os
import warnings
import matplotlib.pyplot as plt
import numpy as np
import chainer
from chainer import training
from chainer.training import extensions
import chainerx
import net
import matplotlib
matplotlib.use('Agg')
def... | mit |
CasperTheCat/D3D11Wrapper | Tools/GenTimingGraph.py | 1 | 3044 | import sys
import matplotlib.pyplot
import pandas
import numpy
import struct
import os
data = None
with open(sys.argv[1], "rb") as f:
data = f.read()
data = struct.unpack("{}Q".format(len(data) // 8), data)
data = numpy.array(data, dtype=numpy.uint64)[1:]
data = numpy.array([x for x in data if x < 100000])
rt ... | apache-2.0 |
Jimmy-Morzaria/scikit-learn | examples/svm/plot_svm_kernels.py | 329 | 1971 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
SVM-Kernels
=========================================================
Three different types of SVM-Kernels are displayed below.
The polynomial and RBF are especially useful when the
data-points are not linearly sep... | bsd-3-clause |
vibhorag/scikit-learn | sklearn/cross_decomposition/cca_.py | 209 | 3150 | from .pls_ import _PLS
__all__ = ['CCA']
class CCA(_PLS):
"""CCA Canonical Correlation Analysis.
CCA inherits from PLS with mode="B" and deflation_mode="canonical".
Read more in the :ref:`User Guide <cross_decomposition>`.
Parameters
----------
n_components : int, (default 2).
numb... | bsd-3-clause |
rgommers/pywt | demo/wp_scalogram.py | 3 | 1476 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import pywt
x = np.linspace(0, 1, num=512)
data = np.sin(250 * np.pi * x**2)
wavelet = 'db2'
level = 4
order = "freq" # other option is "normal"
interpolation = 'nearest'
cmap = plt.cm.cool
# Construct wavelet packet... | mit |
lucabaldini/ximpol | ximpol/examples/cena_c2x.py | 1 | 8614 | #!/usr/bin/env python
#
# Copyright (C) 2016, the ximpol team.
#
# 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.
#
# Thi... | gpl-3.0 |
liberatorqjw/scikit-learn | examples/decomposition/plot_ica_vs_pca.py | 43 | 3343 | """
==========================
FastICA on 2D point clouds
==========================
This example illustrates visually in the feature space a comparison by
results using two different component analysis techniques.
:ref:`ICA` vs :ref:`PCA`.
Representing ICA in the feature space gives the view of 'geometric ICA':
ICA... | bsd-3-clause |
UCSC-MedBook/MedBook_ | tools/old-external-tools/shazam/layoutCytoscapeWeb.py | 1 | 16284 | #!/usr/bin/env python
"""layoutCytoscapeWeb.py
Author: Sam Ng
Last Updated: 2014-06-13
"""
import math, os, re, shutil, sys
from copy import deepcopy
import pandas
import pandas
from PATHMARK import Pathway
from optparse import OptionParser
html_link = """<a href="%s">%s</a>"""
html_head = """<!DOCTYPE html PUBLIC... | bsd-3-clause |
liangz0707/scikit-learn | examples/svm/plot_svm_regression.py | 249 | 1451 | """
===================================================================
Support Vector Regression (SVR) using linear and non-linear kernels
===================================================================
Toy example of 1D regression using linear, polynomial and RBF kernels.
"""
print(__doc__)
import numpy as np
... | bsd-3-clause |
potash/scikit-learn | benchmarks/bench_plot_omp_lars.py | 28 | 4471 | """Benchmarks of orthogonal matching pursuit (:ref:`OMP`) versus least angle
regression (:ref:`least_angle_regression`)
The input data is mostly low rank but is a fat infinite tail.
"""
from __future__ import print_function
import gc
import sys
from time import time
import numpy as np
from sklearn.linear_model impo... | bsd-3-clause |
yonglehou/scikit-learn | sklearn/metrics/tests/test_classification.py | 83 | 49782 | from __future__ import division, print_function
import numpy as np
from scipy import linalg
from functools import partial
from itertools import product
import warnings
from sklearn import datasets
from sklearn import svm
from sklearn.datasets import make_multilabel_classification
from sklearn.preprocessing import la... | bsd-3-clause |
thekingofkings/chicago-crime | python/preliminary_study.py | 2 | 2212 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Preliminary study on the relation between taxi flow and crime rate difference.
Created on Sat May 20 16:30:33 2017
@author: hxw186
"""
from FeatureUtils import retrieve_crime_count, generate_corina_features
from taxiFlow import getTaxiFlow
import matplotlib.pyplot... | mit |
arokem/pyAFQ | examples/plot_callosal_tract_profile.py | 2 | 17674 | """
==============================
Plotting Novel Tract Profiles:
==============================
The following is an example of tractometry for a novel bundle and plotting the
resulting FA tract profile. We will run tractometry for the *anterior forceps*
using waypoint ROIs.
**AFQ Waypoint ROI Tractometry:**
.. note... | bsd-2-clause |
afgaron/rgz-analysis | python/component_distribution.py | 2 | 1504 | # What is the distribution of the number of radio components per image?
import rgz
subjects,classifications = rgz.load_rgz_data()
cc_list = list(subjects.find())
cc_all = [x['metadata']['contour_count'] for x in cc_list]
cc_complete = [x['metadata']['contour_count'] if x['state'] == 'complete' else -999 for x in c... | mit |
siutanwong/scikit-learn | sklearn/utils/tests/test_extmath.py | 130 | 16270 | # Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Denis Engemann <d.engemann@fz-juelich.de>
#
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from scipy import linalg
from scipy import stats
from sklearn.utils.testing import assert_eq... | bsd-3-clause |
jayrambhia/SimpleCV2 | SimpleCV/MachineLearning/ShapeContextClassifier.py | 12 | 6161 | from SimpleCV.base import *
from SimpleCV.Features.Features import Feature, FeatureSet
from SimpleCV.Color import Color
from SimpleCV.ImageClass import Image
from SimpleCV.Features.Detection import ShapeContextDescriptor
import math
import scipy.stats as sps
"""
Classify an object based on shape context
"""
class Sha... | bsd-3-clause |
fovtran/pyoptools | pyoptools/gui/glwindow.py | 9 | 19554 | #!/usr/bin/env python
# This includes the two classes wxGLWindow and wxAdvancedGLWindow
# from OpenGL.TK in the PyOpenGL distribution
# ported to wxPython by greg Landrum
# modified by Y. Wong
# modified by R. Amezquita
# modified by O. Olarte
from OpenGL.GL import *
from OpenGL.GLU import *
from wx import *
from wx.... | bsd-3-clause |
rohitgirdhar-cmu-experimental/fast-rcnn-with3D | lib/fast_rcnn/test.py | 43 | 11975 | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Test a Fast R-CNN network on an imdb (image database)."""
from fast... | mit |
OliverEvans96/rte_matrix | gen_matrices/gen_ddom.py | 1 | 5333 | # File Name: gen_ddmo.py
# Description: Generate diagonally dominant matrix with gen_matrix_2d.py
# Created: Mon Apr 10, 2017 | 10:00am EDT
# Last Modified: Thu May 04, 2017 | 06:40am EDT
#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-
# GNU GPL LICENSE ... | gpl-3.0 |
coolsgupta/machine_learning_nanodegree | Model_Evaluation_and_Validation/evaluation_metrics/precision_vs_recall.py | 1 | 1439 | # As with the previous exercises, let's look at the performance of a couple of classifiers
# on the familiar Titanic dataset. Add a train/test split, then store the results in the
# dictionary provided.
import numpy as np
import pandas as pd
# Load the dataset
X = pd.read_csv('titanic_data.csv')
X = X._get_numeric_d... | mit |
evidation-health/bokeh | bokeh/_legacy_charts/tests/test_legacy_data_adapter.py | 6 | 3293 | """ This is the Bokeh charts testing interface.
"""
#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2014, Continuum Analytics, Inc. All rights reserved.
#
# Powered by the Bokeh Development Team.
#
# The full license is in the file LICENSE.txt, distributed with thi... | bsd-3-clause |
bloyl/mne-python | examples/time_frequency/source_label_time_frequency.py | 20 | 3702 | """
=========================================================
Compute power and phase lock in label of the source space
=========================================================
Compute time-frequency maps of power and phase lock in the source space.
The inverse method is linear based on dSPM inverse operator.
The ex... | bsd-3-clause |
sunzhxjs/JobGIS | lib/python2.7/site-packages/pandas/io/ga.py | 9 | 16202 | """
1. Goto https://code.google.com/apis/console
2. Create new project
3. Goto APIs and register for OAuth2.0 for installed applications
4. Download JSON secret file and move into same directory as this file
"""
from datetime import datetime
import re
from pandas import compat
import numpy as np
from pandas import Data... | mit |
sarahgrogan/scikit-learn | sklearn/utils/__init__.py | 79 | 14202 | """
The :mod:`sklearn.utils` module includes various utilities.
"""
from collections import Sequence
import numpy as np
from scipy.sparse import issparse
import warnings
from .murmurhash import murmurhash3_32
from .validation import (as_float_array,
assert_all_finite,
... | bsd-3-clause |
czhuang/ChordRipple | chord2vec/plot_utilities.py | 1 | 17153 |
from copy import copy
import numpy as np
import pylab as plt
from pylab import setp
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d import proj3d
from music_theory_tools import RELATIVE_MINOR, CIRCLE_OF_FIFTHS_MINOR_DICT, CIRCLE_OF_FIFTHS_MAJOR_DICT
from load_songs_tools import get_raw_data, get_co... | mit |
nvoron23/scikit-learn | examples/decomposition/plot_kernel_pca.py | 353 | 2011 | """
==========
Kernel PCA
==========
This example shows that Kernel PCA is able to find a projection of the data
that makes data linearly separable.
"""
print(__doc__)
# Authors: Mathieu Blondel
# Andreas Mueller
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomp... | bsd-3-clause |
belltailjp/scikit-learn | examples/cluster/plot_birch_vs_minibatchkmeans.py | 333 | 3694 | """
=================================
Compare BIRCH and MiniBatchKMeans
=================================
This example compares the timing of Birch (with and without the global
clustering step) and MiniBatchKMeans on a synthetic dataset having
100,000 samples and 2 features generated using make_blobs.
If ``n_clusters... | bsd-3-clause |
anurag313/scikit-learn | sklearn/neighbors/tests/test_kde.py | 208 | 5556 | import numpy as np
from sklearn.utils.testing import (assert_allclose, assert_raises,
assert_equal)
from sklearn.neighbors import KernelDensity, KDTree, NearestNeighbors
from sklearn.neighbors.ball_tree import kernel_norm
from sklearn.pipeline import make_pipeline
from sklearn.dataset... | bsd-3-clause |
stevenzhang18/Indeed-Flask | lib/pandas/sparse/series.py | 9 | 26315 | """
Data structures for sparse float data. Life is made simpler by dealing only
with float64 data
"""
# pylint: disable=E1101,E1103,W0231
from numpy import nan, ndarray
import numpy as np
import warnings
import operator
from pandas.core.common import isnull, _values_from_object, _maybe_match_name
from pandas.core.in... | apache-2.0 |
ajrichards/htsint | htsint/blast/BlastMapper.py | 1 | 16495 | #!/usr/bin/env python
"""
a class to handle results from BLAST searches
the parsed file that BlastMapper handles is a csv file with the following rows
query,hit-identifier,hit-identifier-long,e-score,bit-score
query is specific to the assembly
HitId is a RefSeq
there is an example of this file in the uni... | bsd-3-clause |
emon10005/scikit-image | skimage/filters/_gabor.py | 10 | 6966 | import numpy as np
from scipy import ndimage as ndi
from .._shared.utils import assert_nD
__all__ = ['gabor_kernel', 'gabor_filter']
def _sigma_prefactor(bandwidth):
b = bandwidth
# See http://www.cs.rug.nl/~imaging/simplecell.html
return 1.0 / np.pi * np.sqrt(np.log(2) / 2.0) * \
(2.0 ** b + 1)... | bsd-3-clause |
StefanD986/peregrine | peregrine/sig_gen.py | 3 | 18840 | #!/usr/bin/env python
# Copyright (C) 2012 - 2016 Swift Navigation Inc.
# Contact: Fergus Noble <fergus@swiftnav.com>
#
# This source is subject to the license found in the file 'LICENSE' which must
# be be distributed together with this source. All other rights reserved.
#
# THIS CODE AND INFORMATION IS PROVIDED "AS I... | gpl-3.0 |
Shaswat27/scipy | doc/source/tutorial/examples/newton_krylov_preconditioning.py | 99 | 2489 | import numpy as np
from scipy.optimize import root
from scipy.sparse import spdiags, kron
from scipy.sparse.linalg import spilu, LinearOperator
from numpy import cosh, zeros_like, mgrid, zeros, eye
# parameters
nx, ny = 75, 75
hx, hy = 1./(nx-1), 1./(ny-1)
P_left, P_right = 0, 0
P_top, P_bottom = 1, 0
def get_precon... | bsd-3-clause |
magnunor/hyperspy | hyperspy/drawing/_widgets/scalebar.py | 5 | 5340 | # -*- coding: utf-8 -*-
# Copyright 2007-2016 The HyperSpy developers
#
# This file is part of HyperSpy.
#
# HyperSpy 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... | gpl-3.0 |
dmnfarrell/mhcpredict | epitopepredict/tests.py | 2 | 5628 | #!/usr/bin/env python
"""
MHC prediction unit tests
Created September 2015
Copyright (C) Damien Farrell
"""
from __future__ import absolute_import, print_function
import sys, os
import pandas as pd
import unittest
from . import base, analysis, sequtils, peptutils, mhclearn
from Bio.Seq import Seq
from Bio... | apache-2.0 |
LiaoPan/scikit-learn | examples/ensemble/plot_forest_importances_faces.py | 403 | 1519 | """
=================================================
Pixel importances with a parallel forest of trees
=================================================
This example shows the use of forests of trees to evaluate the importance
of the pixels in an image classification task (faces). The hotter the pixel,
the more impor... | bsd-3-clause |
PhenixI/machine-learning | 20-Applications/1_Text Classification/sentiment_analysis_movie/learn_bigger_data.py | 1 | 3266 | #define a tokenizer function that cleans the unprocessed text data from
#movie_data.csv file that we constructed in the beginning of this chapter
#and separates it into word tokens while removing stop words
import numpy as np
import re
from nltk.corpus import stopwords
stop = stopwords.words('english')
def tokenizer(... | gpl-2.0 |
yanlend/scikit-learn | sklearn/cluster/tests/test_dbscan.py | 176 | 12155 | """
Tests for DBSCAN clustering algorithm
"""
import pickle
import numpy as np
from scipy.spatial import distance
from scipy import sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing im... | bsd-3-clause |
rosmo/ansible | hacking/aws_config/build_iam_policy_framework.py | 25 | 11861 | # Requires pandas, bs4, html5lib, and lxml
#
# Call script with the output from aws_resource_actions callback, e.g.
# python build_iam_policy_framework.py ['ec2:AuthorizeSecurityGroupEgress', 'ec2:AuthorizeSecurityGroupIngress', 'sts:GetCallerIdentity']
#
# The sample output:
# {
# "Version": "2012-10-17",
# "S... | gpl-3.0 |
ky822/Data_Bootcamp | Code/Python/intro_io.py | 1 | 1891 | """
Messing around with the IMF's WEO dataset. The first section is an exploration
of various methods of reading data from a url.
Once we've read in the data, we can slice as needed.
Note: data file is labeled xls but it's really tab-delimited text.
Prepared for the NYU Course "Data Bootcamp."
More at https... | mit |
shikhardb/scikit-learn | sklearn/utils/tests/test_extmath.py | 13 | 16336 | # Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Denis Engemann <d.engemann@fz-juelich.de>
#
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from scipy import linalg
from scipy import stats
from sklearn.utils.testing import assert_eq... | bsd-3-clause |
3manuek/scikit-learn | examples/cluster/plot_segmentation_toy.py | 258 | 3336 | """
===========================================
Spectral clustering for image segmentation
===========================================
In this example, an image with connected circles is generated and
spectral clustering is used to separate the circles.
In these settings, the :ref:`spectral_clustering` approach solve... | bsd-3-clause |
olologin/scikit-learn | sklearn/svm/classes.py | 34 | 40599 | import warnings
import numpy as np
from .base import _fit_liblinear, BaseSVC, BaseLibSVM
from ..base import BaseEstimator, RegressorMixin
from ..linear_model.base import LinearClassifierMixin, SparseCoefMixin, \
LinearModel
from ..feature_selection.from_model import _LearntSelectorMixin
from ..utils import check_X... | bsd-3-clause |
exa-analytics/atomic | exatomic/algorithms/neighbors.py | 2 | 14923 | # -*- coding: utf-8 -*-
# Copyright (c) 2015-2020, Exa Analytics Development Team
# Distributed under the terms of the Apache License 2.0
"""
Neighbor Selection Algorithms
###############################
This module provides algoirthms for selecting nearest neighbors, e.g. nearest
solvent molecules to a solute m... | apache-2.0 |
VDBWRAIR/bioframes | bioframes/samframe.py | 1 | 5678 | import re
import pandas as pd
import numpy as np
from itertools import groupby
from func import pmap, psplit, pstrip, compose, compose_all, merge_dicts, fzip, partial2, dictmap, starcompose
from operator import itemgetter
from functools import partial
import operator as op
from operator import add, div
from schema impo... | gpl-2.0 |
mnwhite/HARK | ConsumptionSaving/ConsMedModel.py | 1 | 60754 | '''
Consumption-saving models that also include medical spending.
'''
import sys
sys.path.insert(0,'../')
import numpy as np
from scipy.optimize import brentq
from HARKcore import HARKobject
from HARKutilities import approxLognormal, addDiscreteOutcomeConstantMean, CRRAutilityP_inv,\
CRRAuti... | apache-2.0 |
pv/scikit-learn | sklearn/svm/setup.py | 321 | 3157 | import os
from os.path import join
import numpy
from sklearn._build_utils import get_blas_info
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration('svm', parent_package, top_path)
config.add_subpackage('tests')
# Section L... | bsd-3-clause |
lthurlow/Network-Grapher | proj/external/matplotlib-1.2.1/build/lib.linux-i686-2.7/matplotlib/sankey.py | 2 | 39908 | #!/usr/bin/env python
"""
Module for creating Sankey diagrams using matplotlib
"""
__author__ = "Kevin L. Davies"
__credits__ = ["Yannick Copin"]
__license__ = "BSD"
__version__ = "2011/09/16"
# Original version by Yannick Copin (ycopin@ipnl.in2p3.fr) 10/2/2010, available
# at:
# http://matplotlib.org/examples/api/... | mit |
rosswhitfield/mantid | scripts/Muon/GUI/Common/plot_widget/plotting_canvas/plot_color_queue.py | 3 | 2244 | # Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2020 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
# Se... | gpl-3.0 |
mojoboss/scikit-learn | sklearn/neighbors/nearest_centroid.py | 199 | 7249 | # -*- coding: utf-8 -*-
"""
Nearest Centroid Classification
"""
# Author: Robert Layton <robertlayton@gmail.com>
# Olivier Grisel <olivier.grisel@ensta.org>
#
# License: BSD 3 clause
import warnings
import numpy as np
from scipy import sparse as sp
from ..base import BaseEstimator, ClassifierMixin
from ..met... | bsd-3-clause |
fzalkow/scikit-learn | sklearn/linear_model/tests/test_theil_sen.py | 234 | 9928 | """
Testing for Theil-Sen module (sklearn.linear_model.theil_sen)
"""
# Author: Florian Wilhelm <florian.wilhelm@gmail.com>
# License: BSD 3 clause
from __future__ import division, print_function, absolute_import
import os
import sys
from contextlib import contextmanager
import numpy as np
from numpy.testing import ... | bsd-3-clause |
boneil3/backtest | BoP.py | 1 | 3114 | __author__ = 'brendan'
import main
import pandas as pd
import numpy as np
from datetime import datetime as dt
from matplotlib import pyplot as plt
import random
import itertools
import time
import dateutil
from datetime import timedelta
cols = ['BoP FA Net', 'BoP FA OI Net', 'BoP FA PI Net', 'CA % GDP']
raw_data = pd... | mit |
murali-munna/scikit-learn | sklearn/cluster/tests/test_dbscan.py | 114 | 11393 | """
Tests for DBSCAN clustering algorithm
"""
import pickle
import numpy as np
from scipy.spatial import distance
from scipy import sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing im... | bsd-3-clause |
gsmaxwell/phase_offset_rx | gr-utils/src/python/plot_fft_base.py | 53 | 10449 | #!/usr/bin/env python
#
# Copyright 2007,2008,2011 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio 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, or (at your ... | gpl-3.0 |
lthurlow/Network-Grapher | proj/external/matplotlib-1.2.1/doc/mpl_examples/axes_grid/parasite_simple2.py | 16 | 1245 | import matplotlib.transforms as mtransforms
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.parasite_axes import SubplotHost
obs = [["01_S1", 3.88, 0.14, 1970, 63],
["01_S4", 5.6, 0.82, 1622, 150],
["02_S1", 2.4, 0.54, 1570, 40],
["03_S1", 4.1, 0.62, 2380, 170]]
fig = plt.figure()
... | mit |
kubkon/simplex | examples/one-minimum.py | 1 | 1465 | from simplex.algorithm import NelderMeadSimplex
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
## for Palatino and other serif fonts use:
#rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)
# Defin... | mit |
a-holm/MachinelearningAlgorithms | Clustering/K-MeansClustering/howItWorksKMeans.py | 1 | 3871 | # -*- coding: utf-8 -*-
"""K-Means unsupervised classification for machine learning.
K-means clustering is a unsupervised method to cluser or group the data.
K-means allows you to choose the number (k) of categories/groups and
categorizes it automatically when it has come up with solid categories.
This algorithm is u... | mit |
mne-tools/mne-tools.github.io | mne-realtime/_downloads/31a533964353bc4276f671185a7c5348/plot_rt_feedback_server.py | 1 | 4886 | """
==============================================
Real-time feedback for decoding :: Server Side
==============================================
This example demonstrates how to setup a real-time feedback
mechanism using StimServer and StimClient.
The idea here is to display future stimuli for the class which
is pred... | bsd-3-clause |
lbishal/scikit-learn | examples/svm/plot_svm_kernels.py | 329 | 1971 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
SVM-Kernels
=========================================================
Three different types of SVM-Kernels are displayed below.
The polynomial and RBF are especially useful when the
data-points are not linearly sep... | bsd-3-clause |
megahertz0/tusharedemo | kdj_checker.py | 1 | 7969 | # -*- coding: utf-8 -*-
"""
Created on Sun Feb 05 17:56:27 2017
@author: megahertz
"""
import lxml.html
import matplotlib.pyplot as plt
import matplotlib as mpl
import tushare as ts
import datetime
import time
import talib as ta
import numpy as np
import pandas as pd
from matplotlib.dates import DateFormatter, Weekd... | lgpl-3.0 |
nborggren/zipline | zipline/assets/futures.py | 6 | 7574 | #
# Copyright 2015 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 |
0x0all/kaggle-galaxies | try_convnet_cc_multirotflip_3x69r45_normconstraint.py | 7 | 17800 | import numpy as np
# import pandas as pd
import theano
import theano.tensor as T
import layers
import cc_layers
import custom
import load_data
import realtime_augmentation as ra
import time
import csv
import os
import cPickle as pickle
from datetime import datetime, timedelta
# import matplotlib.pyplot as plt
# plt.i... | bsd-3-clause |
wogsland/QSTK | build/lib.linux-x86_64-2.7/Bin/DataGenerate_SineWave.py | 5 | 2057 | import datetime as dt
import csv
import copy
import os
import pickle
import math
# 3rd party imports
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# QSTK imports
from QSTK.qstkutil import qsdateutil as du
# import qstkutil.DataEvolved as de
def write(ls_symbols, d_data, ldt_timestamps):
... | bsd-3-clause |
mne-tools/mne-python | tutorials/epochs/30_epochs_metadata.py | 10 | 7730 | """
.. _tut-epochs-metadata:
Working with Epoch metadata
===========================
This tutorial shows how to add metadata to `~mne.Epochs` objects, and
how to use :ref:`Pandas query strings <pandas:indexing.query>` to select and
plot epochs based on metadata properties.
For this tutorial we'll use a different dat... | bsd-3-clause |
BonexGu/Blik2D-SDK | Blik2D/addon/tensorflow-1.2.1_for_blik/tensorflow/contrib/learn/python/learn/estimators/multioutput_test.py | 136 | 1696 | # 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 |
florian-wagner/gimli | doc/tutorials/modelling/plot_4-mod-fem-heat-1d.py | 1 | 3955 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Heat equation in 1D
-------------------
Isotropic and homogeneous heat equation in one dimension with test case:
.. math::
\Delta u(t,x) - check(-) \frac{\partial u(t,x)}{\partial t} & = f(t,x)\\
u(0,x) & = \sin(\pi x)\in x=\Omega \\
u(t,x) & = 0 \in x=\... | gpl-3.0 |
hlin117/scikit-learn | sklearn/metrics/cluster/tests/test_bicluster.py | 394 | 1770 | """Testing for bicluster metrics module"""
import numpy as np
from sklearn.utils.testing import assert_equal, assert_almost_equal
from sklearn.metrics.cluster.bicluster import _jaccard
from sklearn.metrics import consensus_score
def test_jaccard():
a1 = np.array([True, True, False, False])
a2 = np.array([T... | bsd-3-clause |
toobaz/pandas | pandas/core/arrays/_ranges.py | 2 | 6835 | """
Helper functions to generate range-like data for DatetimeArray
(and possibly TimedeltaArray/PeriodArray)
"""
from typing import Tuple
import numpy as np
from pandas._libs.tslibs import OutOfBoundsDatetime, Timestamp
from pandas.tseries.offsets import DateOffset, Tick, generate_range
def generate_regular_range... | bsd-3-clause |
SalemAmeen/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | Chapter2_MorePyMC/separation_plot.py | 86 | 1494 | # separation plot
# Author: Cameron Davidson-Pilon,2013
# see http://mdwardlab.com/sites/default/files/GreenhillWardSacks.pdf
import matplotlib.pyplot as plt
import numpy as np
def separation_plot( p, y, **kwargs ):
"""
This function creates a separation plot for logistic and probit classification.
Se... | mit |
Tauranis/super-resolution | DatasetReader.py | 1 | 4010 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# MIT License
# Copyright (c) 2017 Tauranis
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation ... | mit |
mehdidc/scikit-learn | examples/model_selection/plot_underfitting_overfitting.py | 230 | 2649 | """
============================
Underfitting vs. Overfitting
============================
This example demonstrates the problems of underfitting and overfitting and
how we can use linear regression with polynomial features to approximate
nonlinear functions. The plot shows the function that we want to approximate,
wh... | bsd-3-clause |
CarterBain/AlephNull | alephnull/finance/performance/futures_period.py | 1 | 12057 | from __future__ import division
import math
from collections import OrderedDict, defaultdict
import logbook
import numpy as np
import pandas as pd
import alephnull.protocol as zp
from .position import positiondict
try:
from alephtools.connection import get_multiplier
except:
#Replace this with source to mul... | apache-2.0 |
AlexanderFabisch/scikit-learn | sklearn/tests/test_cross_validation.py | 20 | 46586 | """Test the cross_validation module"""
from __future__ import division
import warnings
import numpy as np
from scipy.sparse import coo_matrix
from scipy.sparse import csr_matrix
from scipy import stats
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.test... | bsd-3-clause |
duncanmmacleod/gwpy | gwpy/table/gravityspy.py | 1 | 9062 | # -*- coding: utf-8 -*-
# Copyright (C) Scott Coughlin (2017-2020)
#
# This file is part of GWpy.
#
# GWpy 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 |
faroit/mir_eval | tests/test_display.py | 4 | 11202 | #!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''Unit tests for the display module'''
# For testing purposes, clobber the rcfile
import matplotlib
matplotlib.use('Agg') # nopep8
import matplotlib.pyplot as plt
import numpy as np
# Import the hacked image comparison module
from mpl_ic import image_comparison
fro... | mit |
bankur16/SU2 | SU2_PY/find_cfl_number.py | 6 | 9093 | #!/usr/bin/env python
## \file find_cfl_number.py
# \author T. Lukaczyk
#
# SU2 Lead Developers: Dr. Francisco Palacios (Francisco.D.Palacios@boeing.com).
# Dr. Thomas D. Economon (economon@stanford.edu).
#
# SU2 Developers: Prof. Juan J. Alonso's group at Stanford University.
# ... | lgpl-2.1 |
fspaolo/scikit-learn | sklearn/utils/tests/test_extmath.py | 4 | 12928 | # Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Denis Engemann <d.engemann@fz-juelich.de>
#
# License: BSD 3 clause
import warnings
import numpy as np
from scipy import sparse
from scipy import linalg
from scipy import stats
from sklearn.utils.testing i... | bsd-3-clause |
e-koch/Phys-595 | project_code/Machine Learning/unsupervised_specs_lsq.py | 1 | 3652 |
'''
Unsupervised learning on SDSS spectral data
'''
import numpy as np
import matplotlib.pyplot as p
from sklearn.cross_validation import train_test_split
from pandas import read_csv, DataFrame, concat
import joblib
from lsanomaly import LSAnomaly
save_models = False
test_params = False
multi = True
learn = True
vi... | mit |
sthyme/ZFSchizophrenia | BehaviorAnalysis/HSMovieAnalysis/common.py | 1 | 6303 | #!/usr/bin/env python
'''
This module contais some common routines used by other samples.
'''
import numpy as np
import cv2
import os
from contextlib import contextmanager
import itertools as it
image_extensions = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.pbm', '.pgm', '.ppm']
class Bunch(object):
de... | mit |
liberatorqjw/scikit-learn | sklearn/metrics/tests/test_classification.py | 8 | 46538 | from __future__ import division, print_function
import numpy as np
from functools import partial
from itertools import product
import warnings
from sklearn import datasets
from sklearn import svm
from sklearn.datasets import make_multilabel_classification
from sklearn.preprocessing import LabelBinarizer, MultiLabelB... | bsd-3-clause |
wmvanvliet/mne-python | examples/inverse/plot_psf_ctf_label_leakage.py | 15 | 8034 | """
============================================================
Visualize source leakage among labels using a circular graph
============================================================
This example computes all-to-all pairwise leakage among 68 regions in
source space based on MNE inverse solutions and a FreeSurfer c... | bsd-3-clause |
AndreasMadsen/course-02456-sparsemax | benchmark/datasets.py | 1 | 8208 |
import collections
import os
import os.path as path
import sys
import io
import shutil
import gzip
import numpy as np
import scipy.io.arff
import rarfile
import idx2numpy
import urllib.request
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBina... | mit |
iskandr/mhcpred | old/train_aa_features.py | 1 | 9943 | import numpy as np
import pandas as pd
from collections import Counter
import cPickle
import math
import pmbec
from epitopes import amino_acid
from parakeet import jit
import scipy.weave
import sklearn.linear_model
from sklearn.linear_model import LassoCV
import sklearn.svm
import sklearn.ensemble
import sklea... | apache-2.0 |
markgw/pimlico | src/python/pimlico/datatypes/sklearn.py | 1 | 1248 | # This file is part of Pimlico
# Copyright (C) 2020 Mark Granroth-Wilding
# Licensed under the GNU LGPL v3.0 - https://www.gnu.org/licenses/lgpl-3.0.en.html
from __future__ import absolute_import
from builtins import object
from pimlico.core.dependencies.python import sklearn_dependency
from pimlico.datatypes import N... | gpl-3.0 |
poojavade/Genomics_Docker | Dockerfiles/gedlab-khmer-filter-abund/pymodules/python2.7/lib/python/scipy/signal/signaltools.py | 2 | 89744 | # Author: Travis Oliphant
# 1999 -- 2002
from __future__ import division, print_function, absolute_import
import warnings
import threading
from . import sigtools
from scipy._lib.six import callable
from scipy._lib._version import NumpyVersion
from scipy import fftpack, linalg
from numpy import (allclose, angle, aran... | apache-2.0 |
BenKaehler/short-read-tax-assignment | tax_credit/eval_framework.py | 4 | 44170 | #!/usr/bin/env python
# ----------------------------------------------------------------------------
# Copyright (c) 2014--, tax-credit development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# --------------------... | bsd-3-clause |
yavalvas/yav_com | build/matplotlib/doc/mpl_examples/widgets/lasso_selector_demo.py | 9 | 2780 | from __future__ import print_function
import numpy as np
from matplotlib.widgets import LassoSelector
from matplotlib.path import Path
try:
raw_input
except NameError:
# Python 3
raw_input = input
class SelectFromCollection(object):
"""Select indices from a matplotlib collection using `LassoSelecto... | mit |
herilalaina/scikit-learn | examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py | 33 | 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 |
mkeilman/sirepo | sirepo/analysis/ml.py | 2 | 1878 | # -*- coding: utf-8 -*-
u"""Machine learning tools
:copyright: Copyright (c) 2018-2019 RadiaSoft LLC. All Rights Reserved.
:license: http://www.apache.org/licenses/LICENSE-2.0.html
"""
import numpy
import sklearn.cluster
import sklearn.metrics.pairwise
import sklearn.mixture
import sklearn.preprocessing
from pykern.... | apache-2.0 |
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