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 |
|---|---|---|---|---|---|
andaag/scikit-learn | sklearn/metrics/pairwise.py | 104 | 42995 | # -*- coding: utf-8 -*-
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Robert Layton <robertlayton@gmail.com>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Philippe Gervais <philippe.gervais@inria.fr>
# Lars Buitinck ... | bsd-3-clause |
jcasner/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/ticker.py | 69 | 37420 | """
Tick locating and formatting
============================
This module contains classes to support completely configurable tick
locating and formatting. Although the locators know nothing about
major or minor ticks, they are used by the Axis class to support major
and minor tick locating and formatting. Generic t... | agpl-3.0 |
louispotok/pandas | pandas/tests/test_multilevel.py | 1 | 107731 | # -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101,W0141
from warnings import catch_warnings
import datetime
import itertools
import pytest
import pytz
from numpy.random import randn
import numpy as np
from pandas.core.index import Index, MultiIndex
from pandas import Panel, DataFrame, Series, notna, isna, Tim... | bsd-3-clause |
SSJohns/osf.io | scripts/analytics/addons.py | 18 | 2173 | # -*- coding: utf-8 -*-
import os
import re
import matplotlib.pyplot as plt
from framework.mongo import database
from website import settings
from website.app import init_app
from .utils import plot_dates, oid_to_datetime, mkdirp
log_collection = database['nodelog']
FIG_PATH = os.path.join(settings.ANALYTICS_PATH... | apache-2.0 |
GbalsaC/bitnamiP | venv/lib/python2.7/site-packages/sklearn/tests/test_preprocessing.py | 2 | 18316 | import numpy as np
import numpy.linalg as la
import scipy.sparse as sp
from numpy.testing import assert_almost_equal
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from nose.tools import assert_raises, assert_true, assert_false
... | agpl-3.0 |
wegamekinglc/Finance-Python | PyFin/tests/Math/Accumulators/testAccumulatorsArithmetic.py | 2 | 31296 | # -*- coding: utf-8 -*-
u"""
Created on 2015-7-27
@author: cheng.li
"""
import unittest
import copy
import tempfile
import pickle
import os
import math
import numpy as np
import pandas as pd
from scipy.stats import norm
from PyFin.Math.Accumulators.IAccumulators import Identity
from PyFin.Math.Accumulators.IAccumulat... | mit |
bobmyhill/burnman | examples/example_seismic.py | 2 | 6866 | # This file is part of BurnMan - a thermoelastic and thermodynamic toolkit for the Earth and Planetary Sciences
# Copyright (C) 2012 - 2015 by the BurnMan team, released under the GNU
# GPL v2 or later.
"""
example_seismic
---------------
Shows the various ways to input seismic models
(:math:`V_s, V_p, V_{\\phi}, \\... | gpl-2.0 |
azvoleff/pyabm | pyabm/rcsetup.py | 1 | 29166 | # Copyright 2009-2013 Alex Zvoleff
#
# This file is part of the pyabm agent-based modeling toolkit.
#
# pyabm 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 optio... | gpl-3.0 |
unnikrishnankgs/va | venv/lib/python3.5/site-packages/IPython/lib/tests/test_latextools.py | 4 | 3811 | # encoding: utf-8
"""Tests for IPython.utils.path.py"""
# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
from unittest.mock import patch
import nose.tools as nt
from IPython.lib import latextools
from IPython.testing.decorators import onlyif_cmds_exist, skipif_not_m... | bsd-2-clause |
meetshah1995/pytorch-semseg | ptsemseg/loader/camvid_loader.py | 1 | 4256 | import os
import collections
import torch
import numpy as np
import scipy.misc as m
import matplotlib.pyplot as plt
from torch.utils import data
from ptsemseg.augmentations import Compose, RandomHorizontallyFlip, RandomRotate
class camvidLoader(data.Dataset):
def __init__(
self,
root,
spl... | mit |
uber-common/deck.gl | bindings/pydeck/examples/icon_layer.py | 1 | 1113 | """
IconLayer
=========
Location of biergartens in Germany listed on OpenStreetMap as of early 2020.
"""
import pydeck as pdk
import pandas as pd
# Data from OpenStreetMap, accessed via osmpy
DATA_URL = "https://raw.githubusercontent.com/ajduberstein/geo_datasets/master/biergartens.json"
ICON_URL = "https://upload.... | mit |
DR08/mxnet | example/speech_recognition/stt_utils.py | 44 | 5892 | # 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 u... | apache-2.0 |
evelynmitchell/smc | src/smc_sagews/smc_sagews/graphics.py | 4 | 25186 | ###############################################################################
#
# SageMathCloud: A collaborative web-based interface to Sage, IPython, LaTeX and the Terminal.
#
# Copyright (C) 2014, William Stein
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of... | gpl-3.0 |
gofortargets/CNN_brandsafety | knx/text/kte/extract.py | 1 | 98594 | #!/usr/bin/python
# -*- coding: UTF-8 -*-
import matplotlib
matplotlib.use('Agg')
import logging
import argparse
import cPickle as pickle
import gc
from itertools import combinations
import json
import math
# import matplotlib.pyplot as plt
import multiprocessing as mp
import numpy as np
import os
import re
import sci... | apache-2.0 |
okadate/romspy | romspy/tplot/tplot_station.py | 1 | 2385 | # coding: utf-8
# (c) 2015-11-28 Teruhisa Okada
import netCDF4
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter
import numpy as np
import pandas as pd
import romspy
def resample(date, var, **kw):
rule = kw.pop('resample', 'D')
if rule == 'H':
loffset = '-30min'
elif rule... | mit |
python-visualization/folium | tests/plugins/test_fast_marker_cluster.py | 2 | 2514 | # -*- coding: utf-8 -*-
"""
Test FastMarkerCluster
----------------------
"""
import folium
from folium.plugins import FastMarkerCluster
from folium.utilities import normalize
from jinja2 import Template
import numpy as np
import pandas as pd
import pytest
def test_fast_marker_cluster():
n = 100
np.rand... | mit |
sasdelli/lc_predictor | lc_predictor/savgol.py | 1 | 3104 | import numpy as np
# This is Thomas Haslwanter's implementation at:
# http://wiki.scipy.org/Cookbook/SavitzkyGolay
def savitzky_golay(y, window_size, order, deriv=0):
r"""Smooth (and optionally differentiate) data with a Savitzky-Golay filter.
The Savitzky-Golay filter removes high frequency noise from data.
... | gpl-3.0 |
Clyde-fare/scikit-learn | benchmarks/bench_multilabel_metrics.py | 276 | 7138 | #!/usr/bin/env python
"""
A comparison of multilabel target formats and metrics over them
"""
from __future__ import division
from __future__ import print_function
from timeit import timeit
from functools import partial
import itertools
import argparse
import sys
import matplotlib.pyplot as plt
import scipy.sparse as... | bsd-3-clause |
anurag313/scikit-learn | examples/cluster/plot_lena_segmentation.py | 271 | 2444 | """
=========================================
Segmenting the picture of Lena in regions
=========================================
This example uses :ref:`spectral_clustering` on a graph created from
voxel-to-voxel difference on an image to break this image into multiple
partly-homogeneous regions.
This procedure (spe... | bsd-3-clause |
lthurlow/Network-Grapher | proj/external/matplotlib-1.2.1/build/lib.linux-i686-2.7/matplotlib/backends/backend_gtk3agg.py | 6 | 3144 | import cairo
import numpy as np
import sys
import warnings
import backend_agg
import backend_gtk3
from matplotlib.figure import Figure
from matplotlib import transforms
if sys.version_info[0] >= 3:
warnings.warn("The Gtk3Agg backend is not known to work on Python 3.x.")
class FigureCanvasGTK3Agg(backend_gtk3.Fi... | mit |
kashif/scikit-learn | examples/calibration/plot_compare_calibration.py | 82 | 5012 | """
========================================
Comparison of Calibration of Classifiers
========================================
Well calibrated classifiers are probabilistic classifiers for which the output
of the predict_proba method can be directly interpreted as a confidence level.
For instance a well calibrated (bi... | bsd-3-clause |
Edu-Glez/Bank_sentiment_analysis | env/lib/python3.6/site-packages/pandas/indexes/category.py | 7 | 22750 | import numpy as np
import pandas.index as _index
from pandas import compat
from pandas.compat.numpy import function as nv
from pandas.types.generic import ABCCategorical, ABCSeries
from pandas.types.common import (is_categorical_dtype,
_ensure_platform_int,
... | apache-2.0 |
vaishaksuresh/udacity_data_analyst | P2/ProblemSets_2_to_4/P4_01.py | 1 | 1593 | from pandas import *
from ggplot import *
def plot_weather_data(turnstile_weather):
'''
You are passed in a dataframe called turnstile_weather.
Use turnstile_weather along with ggplot to make a data visualization
focused on the MTA and weather data we used in assignment #3.
You should feel free ... | gpl-2.0 |
BorisJeremic/Real-ESSI-Examples | analytic_solution/test_cases/Contact/Coupled_Contact/Steady_State_Single_Foundation_Sysytem_Under_Compression/CoupledHardContact_NonLinHardSoftShear/n_1/Plot_Results.py | 15 | 3553 | #!/usr/bin/env python
#!/usr/bin/python
import h5py
from matplotlib import pylab
import matplotlib.pylab as plt
import sys
from matplotlib.font_manager import FontProperties
import math
import numpy as np
#!/usr/bin/python
import h5py
import matplotlib.pylab as plt
import matplotlib as mpl
import sys
import numpy as ... | cc0-1.0 |
vibhorag/scikit-learn | examples/neighbors/plot_classification.py | 287 | 1790 | """
================================
Nearest Neighbors Classification
================================
Sample usage of Nearest Neighbors classification.
It will plot the decision boundaries for each class.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColorm... | bsd-3-clause |
has2k1/plotnine | plotnine/positions/position_stack.py | 1 | 2953 | from warnings import warn
import numpy as np
import pandas as pd
from ..exceptions import PlotnineWarning
from ..utils import remove_missing
from .position import position
class position_stack(position):
"""
Stack plotted objects on top of each other
The objects to stack are those that have
an over... | gpl-2.0 |
FYP-DES5/deepscan-core | plot.py | 1 | 6984 | from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
import cv2, sys
class EdgeImprover:
def __init__(self, voxels, gridsize, minX, minY, maxX, maxY):
self.voxels = voxels
self.gridsize = gridsize
self.mask = np.zeros((3 + maxX - minX, 3 + maxY - minY), dtype=np.uint8)
se... | mit |
catalyst-cooperative/pudl | src/pudl/extract/epacems.py | 1 | 8422 | """
Retrieve data from EPA CEMS hourly zipped CSVs.
This modules pulls data from EPA's published CSV files.
"""
import logging
from pathlib import Path
from typing import NamedTuple
from zipfile import ZipFile
import pandas as pd
from pudl.workspace.datastore import Datastore
logger = logging.getLogger(__name__)
#... | mit |
mwmuni/LIGGGHTS_GUI | networkx/drawing/nx_pylab.py | 10 | 30226 | """
**********
Matplotlib
**********
Draw networks with matplotlib.
See Also
--------
matplotlib: http://matplotlib.org/
pygraphviz: http://pygraphviz.github.io/
"""
# Author: Aric Hagberg (hagberg@lanl.gov)
# Copyright (C) 2004-2016 by
# Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colg... | gpl-3.0 |
Becksteinlab/PDB_Ion_Survey | src/pdbionsurvey/bulkcoord.py | 1 | 6385 |
m __future__ import division
import MDAnalysis as mda
import datreant as dtr
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pdbionsurvey.coordination
import json
import seaborn as sb
import scipy
import mmtf
import pdbionsurvey.collection
from matp... | gpl-3.0 |
davidgbe/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 |
Galithil/status | status/clusters_per_lane.py | 2 | 2547 | import tornado.web
import json
import cStringIO
import matplotlib.gridspec as gridspec
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
from status.util import SafeHandler
class ClustersPerLaneHandler(SafeHandler):
""" Serves a page with a plot of distribution of l... | mit |
karstenw/nodebox-pyobjc | examples/Extended Application/matplotlib/examples/lines_bars_and_markers/fill_betweenx_demo.py | 1 | 2297 | """
==================
Fill Betweenx Demo
==================
Using ``fill_betweenx`` to color between two horizontal curves.
"""
import matplotlib.pyplot as plt
import numpy as np
# nodebox section
if __name__ == '__builtin__':
# were in nodebox
import os
import tempfile
W = 800
inset = 20
siz... | mit |
sinamoeini/mapp4py | doc/src/conf.py | 1 | 3581 | from __future__ import division, absolute_import, print_function
import sys, os, re, numpydoc
import sphinx
if sphinx.__version__ < "1.0.1":
raise RuntimeError("Sphinx 1.0.1 or newer required")
needs_sphinx = '1.0'
# -----------------------------------------------------------------------------
# General configu... | mit |
jiafeimaowudi/KnowlegeableCNN | structureTestOnMonster2ShareHighLevelOnlyLogistic.py | 1 | 10737 | from theano import tensor as T, printing
import theano
import numpy
from mlp import HiddenLayer
from logistic_sgd_lazy import LogisticRegression
from DocEmbeddingNN import DocEmbeddingNN
# from DocEmbeddingNNPadding import DocEmbeddingNN
from knoweagebleClassifyFlattenedLazy import CorpusReader
import cPickle
import os... | gpl-2.0 |
workflo/dxf2gcode | python_examples/Kegelstump_Abwicklung_2dxf.py | 1 | 12174 | #!/usr/bin/python
# -*- coding: cp1252 -*-
#
#Kegelstump_Abwicklung_2dxf.py
#Programmer: Christian Kohlöffel
#E-mail: n/A
#
#Copyright 2008 Christian Kohlöffel
#
#Distributed under the terms of the GPL (GNU Public License)
#
#dxf2gcode is free software; you can redistribute it and/or modify
#it under the terms of t... | gpl-3.0 |
kntem/webdeposit | modules/bibauthorid/lib/bibauthorid_tortoise.py | 3 | 15730 | # -*- coding: utf-8 -*-
##
## This file is part of Invenio.
## Copyright (C) 2011, 2012 CERN.
##
## Invenio 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 (at your opt... | gpl-2.0 |
ngvozdiev/ctr-base | python/plot_link_paths.py | 1 | 3124 | from collections import defaultdict
from scipy import interpolate
import numpy as np
import matplotlib.pylab as plt
import parser_wrapper
import glob
import itertools
import matplotlib.patches as mpatches
import argparse
import matplotlib
matplotlib.rcParams.update({'font.size': 14})
parser = argparse.ArgumentParser(... | mit |
vybstat/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 |
JT5D/scikit-learn | examples/ensemble/plot_forest_importances.py | 7 | 1742 | """
=========================================
Feature importances with forests of trees
=========================================
This examples shows the use of forests of trees to evaluate the importance of
features on an artificial classification task. The red bars are the feature
importances of the forest, along wi... | bsd-3-clause |
rspavel/spack | var/spack/repos/builtin/packages/lbann/package.py | 1 | 11820 | # Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
import os
import sys
from spack import *
class Lbann(CMakePackage, CudaPackage):
"""LBANN: Livermore Big Artificial ... | lgpl-2.1 |
ernfrid/skll | skll/data/featureset.py | 1 | 15821 | # License: BSD 3 clause
"""
Classes related to storing/merging feature sets.
:author: Dan Blanchard (dblanchard@ets.org)
:organization: ETS
"""
from __future__ import absolute_import, print_function, unicode_literals
from copy import deepcopy
import numpy as np
import scipy.sparse as sp
from six import iteritems
fr... | bsd-3-clause |
yueranyuan/vector_edu | wavelet_analysis.py | 1 | 4265 | import numpy as np
import matplotlib.pyplot as plt
from learntools.emotiv.data import segment_raw_data, gen_wavelet_features
from learntools.emotiv.filter import filter_data
from learntools.libs.wavelet import signal_to_wavelet
def show_raw_wave(eeg):
for channel in xrange(14):
plt.plot(eeg[:, channel])
... | mit |
nelson-liu/scikit-learn | sklearn/feature_extraction/image.py | 19 | 17614 | """
The :mod:`sklearn.feature_extraction.image` submodule gathers utilities to
extract features from images.
"""
# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Olivier Grisel
# Vlad Niculae
# License: BSD 3 clause
fro... | bsd-3-clause |
mpld3/mpld3_rewrite | test_plots/test_plot_w_html_tooltips.py | 1 | 1286 | """Plot to test HTML tooltip plugin
As a data explorer, I want to add rich information to each point in a
scatter plot, as details-on-demand"""
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np, pandas as pd
from mpld3_rewrite import plugins
css = """
table
{
border-collapse... | bsd-3-clause |
shusenl/scikit-learn | examples/ensemble/plot_partial_dependence.py | 249 | 4456 | """
========================
Partial Dependence Plots
========================
Partial dependence plots show the dependence between the target function [1]_
and a set of 'target' features, marginalizing over the
values of all other features (the complement features). Due to the limits
of human perception the size of t... | bsd-3-clause |
zangsir/sms-tools | lectures/04-STFT/plots-code/sine-spectrum.py | 24 | 1563 | import matplotlib.pyplot as plt
import numpy as np
from scipy.fftpack import fft, ifft
N = 256
M = 63
f0 = 1000
fs = 10000
A0 = .8
hN = N/2
hM = (M+1)/2
fftbuffer = np.zeros(N)
X1 = np.zeros(N, dtype='complex')
X2 = np.zeros(N, dtype='complex')
x = A0 * np.cos(2*np.pi*f0/fs*np.arange(-hM+1,hM))
plt.figure(1, figsi... | agpl-3.0 |
herilalaina/scikit-learn | sklearn/ensemble/tests/test_partial_dependence.py | 365 | 6996 | """
Testing for the partial dependence module.
"""
import numpy as np
from numpy.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import if_matplotlib
from sklearn.ensemble.partial_dependence import partial_dependence
from sklearn.ensemble.partial_dependence... | bsd-3-clause |
micksi/datamining_exam | omer/content_base_filtering.py | 1 | 2632 | import sys
import numpy as np
import pandas as pd
from sklearn import preprocessing
from sklearn.neighbors import NearestNeighbors
pd.set_option('display.max_colwidth', 300)
file_name = "anime.csv"
file_name_rating = 'rating.csv'
def normalize(df_user_profiles):
x = df_user_profiles.iloc[:,1:].values #returns a n... | gpl-2.0 |
xiaoxiamii/scikit-learn | sklearn/utils/fixes.py | 133 | 12882 | """Compatibility fixes for older version of python, numpy and scipy
If you add content to this file, please give the version of the package
at which the fixe is no longer needed.
"""
# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# ... | bsd-3-clause |
lgarren/spack | var/spack/repos/builtin/packages/julia/package.py | 3 | 9966 | ##############################################################################
# Copyright (c) 2013-2017, Lawrence Livermore National Security, LLC.
# Produced at the Lawrence Livermore National Laboratory.
#
# This file is part of Spack.
# Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved.
# LLNL-CODE-64... | lgpl-2.1 |
JaviMerino/trappy | trappy/stats/Aggregator.py | 1 | 5544 | # Copyright 2015-2016 ARM Limited
#
# 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 w... | apache-2.0 |
rahuldhote/scikit-learn | sklearn/metrics/cluster/bicluster.py | 359 | 2797 | from __future__ import division
import numpy as np
from sklearn.utils.linear_assignment_ import linear_assignment
from sklearn.utils.validation import check_consistent_length, check_array
__all__ = ["consensus_score"]
def _check_rows_and_columns(a, b):
"""Unpacks the row and column arrays and checks their shap... | bsd-3-clause |
DANA-Laboratory/CoolProp | wrappers/Python/CoolProp/tests/test_plots.py | 4 | 4608 | import numpy as np
import matplotlib.pyplot as plt
def test_back_compatibility():
fluid_ref = 'R290'
def Ts_plot_tests():
from CoolProp.Plots import Ts
Ts(fluid_ref, show=False)
from matplotlib import pyplot
fig = pyplot.figure(2)
ax = fig.gca()
Ts(fluid_ref, s... | mit |
DailyActie/Surrogate-Model | 01-codes/scikit-learn-master/sklearn/cluster/dbscan_.py | 1 | 12153 | # -*- coding: utf-8 -*-
"""
DBSCAN: Density-Based Spatial Clustering of Applications with Noise
"""
# Author: Robert Layton <robertlayton@gmail.com>
# Joel Nothman <joel.nothman@gmail.com>
# Lars Buitinck
#
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from ._dbscan_inner import ... | mit |
mkukielka/oddt | docs/conf.py | 1 | 12238 | # -*- coding: utf-8 -*-
#
# ODDT documentation build configuration file, created by
# sphinx-quickstart on Mon Aug 25 13:49:30 2014.
#
# 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.
#
# All ... | bsd-3-clause |
MJuddBooth/pandas | pandas/tests/io/formats/test_to_excel.py | 3 | 10955 | """Tests formatting as writer-agnostic ExcelCells
ExcelFormatter is tested implicitly in pandas/tests/io/test_excel.py
"""
import pytest
import pandas.util.testing as tm
from pandas.io.formats.css import CSSWarning
from pandas.io.formats.excel import CSSToExcelConverter
@pytest.mark.parametrize('css,expected', [
... | bsd-3-clause |
fyffyt/scikit-learn | examples/covariance/plot_robust_vs_empirical_covariance.py | 248 | 6359 | r"""
=======================================
Robust vs Empirical covariance estimate
=======================================
The usual covariance maximum likelihood estimate is very sensitive to the
presence of outliers in the data set. In such a case, it would be better to
use a robust estimator of covariance to guar... | bsd-3-clause |
dmargala/lyabao | bin/inspect_deltas.py | 1 | 9353 | #!/usr/bin/env python
import argparse
from itertools import chain
import numpy as np
import numpy.ma as ma
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
from scipy.optimize import minimize
import h5py
from tqdm import tqdm
from uniform_grid import get_fiducial_pixel_index_offset
from unifor... | mit |
joernhees/scikit-learn | examples/svm/plot_iris.py | 65 | 3742 | """
==================================================
Plot different SVM classifiers in the iris dataset
==================================================
Comparison of different linear SVM classifiers on a 2D projection of the iris
dataset. We only consider the first 2 features of this dataset:
- Sepal length
- Se... | bsd-3-clause |
xavierwu/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 |
asford/depth | bin/binding_site_prediction_app.py | 1 | 8481 | # This is part of DEPTH.
# DEPTH (Version: 2.0) computes the closest distance of a residue/atom to bulk solvent and predicts small molecule binding site of a protein.
# Copyright (C) 2013, Kuan Pern Tan, Nguyen Thanh Binh, Raghavan Varadarajan and M.S. Madhusudhan
#
# DEPTH is free software: you can redistribute it a... | gpl-3.0 |
bikong2/scikit-learn | benchmarks/bench_tree.py | 297 | 3617 | """
To run this, you'll need to have installed.
* scikit-learn
Does two benchmarks
First, we fix a training set, increase the number of
samples to classify and plot number of classified samples as a
function of time.
In the second benchmark, we increase the number of dimensions of the
training set, classify a sam... | bsd-3-clause |
alexeyum/scikit-learn | sklearn/tests/test_metaestimators.py | 57 | 4958 | """Common tests for metaestimators"""
import functools
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.externals.six import iterkeys
from sklearn.datasets import make_classification
from sklearn.utils.testing import assert_true, assert_false, assert_raises
from sklearn.pipeline import Pipeline... | bsd-3-clause |
alextag/Twitter-Sentiment-Analysis | parse_dataset.py | 2 | 5576 | import sys
import string
import numpy as np
from pandas import read_csv
from sklearn.utils import shuffle
from sklearn.cross_validation import train_test_split
STOP_WORDS = np.array([])
FILENAME = 'training_data.csv' # the training data csv
RANDOM_SEED = 1337
def load_stopwords():
"""Loads the stopwords.txt file int... | gpl-3.0 |
nlholdem/icodoom | .venv/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py | 88 | 31139 | # 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... | gpl-3.0 |
ChangLab/FAST-iCLIP | bin/oldscripts/fastclip.py | 2 | 65904 | # -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <codecell>
import os
import cmath
import math
import sys
import numpy as np
import glob
import subprocess
import re
from matplotlib_venn import venn2
import pandas as pd
from collections import defaultdict
from operator import itemgetter
import matplotlib as mpl
i... | gpl-2.0 |
sniemi/SamPy | resolve/misc/findslits.py | 1 | 25091 | '''
Fits slit image to a direct image to find x and y positions.
Currently the script uses a slit image. In the future however
we probably want to use the spectrum itself because there is
no guarantee that a slit confirmation image has always been
taken.
:todo: try to do the minimalization with scipy.optmize or
some ... | bsd-2-clause |
jdmcbr/blaze | blaze/server/tests/test_server.py | 3 | 13833 | from __future__ import absolute_import, division, print_function
import pytest
pytest.importorskip('flask')
from base64 import b64encode
import datashape
import numpy as np
from datetime import datetime
from pandas import DataFrame
from toolz import pipe
from odo import odo
from blaze.utils import example
from blaz... | bsd-3-clause |
poeticcapybara/pythalesians | pythalesians-examples/plotly_examples.py | 1 | 4222 | __author__ = 'saeedamen'
import datetime
from pythalesians.market.loaders.lighttimeseriesfactory import LightTimeSeriesFactory
from pythalesians.market.requests.timeseriesrequest import TimeSeriesRequest
from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs
from pythalesians.graphics.graphs.plotfa... | apache-2.0 |
rneher/FitnessInference | toydata/figure_scripts/Fig3_polarizer.py | 1 | 6419 | import glob,sys
import numpy as np
sys.path.append('../../flu/src')
import test_flu_prediction as test_flu
import matplotlib.pyplot as plt
from matplotlib import cm
import analysis_utils_toy_data as AU
file_formats = ['.svg', '.pdf']
plt.rcParams.update(test_flu.mpl_params)
line_styles = ['-', '--', '-.']
cols = ['b... | mit |
xdnian/pyml | code/optional-py-scripts/ch10.py | 2 | 14374 | # Sebastian Raschka, 2015 (http://sebastianraschka.com)
# Python Machine Learning - Code Examples
#
# Chapter 10 - Predicting Continuous Target Variables with Regression Analysis
#
# S. Raschka. Python Machine Learning. Packt Publishing Ltd., 2015.
# GitHub Repo: https://github.com/rasbt/python-machine-learning-book
#
... | mit |
maxlikely/scikit-learn | examples/ensemble/plot_gradient_boosting_regularization.py | 4 | 2826 | """
================================
Gradient Boosting regularization
================================
Illustration of the effect of different regularization strategies
for Gradient Boosting. The example is taken from Hastie et al 2009.
The loss function used is binomial deviance. Regularization via
shrinkage (``lear... | bsd-3-clause |
DailyActie/Surrogate-Model | 01-codes/scikit-learn-master/sklearn/utils/deprecation.py | 1 | 2418 | import warnings
__all__ = ["deprecated", ]
class deprecated(object):
"""Decorator to mark a function or class as deprecated.
Issue a warning when the function is called/the class is instantiated and
adds a warning to the docstring.
The optional extra argument will be appended to the deprecation mes... | mit |
DonBeo/scikit-learn | sklearn/neighbors/tests/test_kde.py | 13 | 5622 | 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 |
BBN-Q/Auspex | utils/auspex-plot-client.py | 1 | 28017 | #!/usr/bin/env python
# auspex-specific implementation by Graham Rowlands
# (C) 2019 Raytheon BBN Technologies
# Original File:
# embedding_in_qt5.py --- Simple Qt5 application embedding matplotlib canvases
#
# Copyright (C) 2005 Florent Rougon
# 2006 Darren Dale
# 2015 Jens H Nielsen
#
# T... | apache-2.0 |
JosmanPS/scikit-learn | benchmarks/bench_lasso.py | 297 | 3305 | """
Benchmarks of Lasso vs LassoLars
First, we fix a training set and increase the number of
samples. Then we plot the computation time as function of
the number of samples.
In the second benchmark, we increase the number of dimensions of the
training set. Then we plot the computation time as function of
the number o... | bsd-3-clause |
krez13/scikit-learn | examples/bicluster/bicluster_newsgroups.py | 142 | 7183 | """
================================================================
Biclustering documents with the Spectral Co-clustering algorithm
================================================================
This example demonstrates the Spectral Co-clustering algorithm on the
twenty newsgroups dataset. The 'comp.os.ms-windows... | bsd-3-clause |
RyanChinSang/ECNG3020-ORSS4SCVI | Stable/v01/models/VideoStream.py | 2 | 3245 | import cv2
from threading import Thread
class VideoStream(object):
def __init__(self, src=0, height=None, width=None, ratio=None):
cv2.setUseOptimized(True)
self.stream = cv2.VideoCapture(src)
self.config(dim=None, height=height, width=width, ratio=ratio)
(self.grabbed, self.frame)... | gpl-3.0 |
rodrigokuroda/recominer | recominer-extractor/src/main/resources/scripts/classification.py | 1 | 2460 | porco1 = [1, 1, 0]
porco2 = [1, 1, 0]
porco3 = [1, 1, 0]
cachorro4 = [1, 1, 1]
cachorro5 = [0, 1, 1]
cachorro6 = [0, 1, 1]
dados = [porco1, porco2, porco3, cachorro4, cachorro5, cachorro6]
marcacoes = [1, 1, 1, -1, -1, -1]
import numpy as np
f = open("filename.txt")
f.readline() # skip the header
tr... | apache-2.0 |
vadimtk/chrome4sdp | chrome/test/nacl_test_injection/buildbot_nacl_integration.py | 94 | 3083 | #!/usr/bin/env python
# Copyright (c) 2012 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
import os
import subprocess
import sys
def Main(args):
pwd = os.environ.get('PWD', '')
is_integration_bot = 'nacl-chrome' in ... | bsd-3-clause |
lewisc/spark-tk | regression-tests/sparktkregtests/testcases/graph/graph_connected_test.py | 11 | 2429 | # vim: set encoding=utf-8
# Copyright (c) 2016 Intel Corporation
#
# 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 require... | apache-2.0 |
eramirem/astroML | book_figures/chapter5/fig_poisson_likelihood.py | 3 | 4970 | """
Binned Regression: Poisson vs Gaussian
--------------------------------------
Figure 5.15
The left panels show data sets with 50 points, binned in 5 bins (upper panels)
and 40 bins (lower panels). The curves show the input distribution (solid), the
Poisson solution (dashed), and the Gaussian solution (dotted). The... | bsd-2-clause |
atantet/QG4 | plot/plotFP.py | 1 | 5774 | import numpy as np
import matplotlib.pyplot as plt
import pylibconfig2
fs_latex = 'xx-large'
fs_xticklabels = 'large'
fs_yticklabels = fs_xticklabels
configFile = '../cfg/QG4.cfg'
cfg = pylibconfig2.Config()
cfg.read_file(configFile)
dim = cfg.model.dim
L = cfg.simulation.LCut + cfg.simulation.spinup
printStepNum = ... | gpl-3.0 |
anurag313/scikit-learn | examples/preprocessing/plot_function_transformer.py | 161 | 1949 | """
=========================================================
Using FunctionTransformer to select columns
=========================================================
Shows how to use a function transformer in a pipeline. If you know your
dataset's first principle component is irrelevant for a classification task,
you ca... | bsd-3-clause |
nwillemse/nctrader | examples/rsi2.py | 1 | 8242 | #!/usr/bin/env python
"""
RSI2.py
Standard trading system trading the 2-period RSI on SPY
Created on Mon Aug 15 19:20:57 2016
@author: nwillemse
"""
import click
import csv
import pandas as pd
import numpy as np
from datetime import datetime
from os import path, remove
from collections import OrderedDict
from nctr... | mit |
IshankGulati/scikit-learn | sklearn/externals/joblib/__init__.py | 23 | 5101 | """ Joblib is a set of tools to provide **lightweight pipelining in
Python**. In particular, joblib offers:
1. transparent disk-caching of the output values and lazy re-evaluation
(memoize pattern)
2. easy simple parallel computing
3. logging and tracing of the execution
Joblib is optimized to be **fast*... | bsd-3-clause |
google/flax | setup.py | 1 | 2665 | # Copyright 2020 The Flax Authors.
#
# 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 wri... | apache-2.0 |
ctogle/dilapidator | src/dilap/core/qtgui.py | 1 | 24408 | #import modular4.base as mb
import dilap.geometry.tools as gtl
import dilap.core.base as mb
import dilap.core.plotting as dtl
import os,sys,numpy,matplotlib,six,multiprocessing
matplotlib.rcParams['backend.qt4'] = 'PySide'
matplotlib.rcParams['pdf.fonttype'] = 42
#matplotlib.use('Qt4Agg')
from matplotlib.backends.back... | mit |
Barmaley-exe/scikit-learn | sklearn/covariance/tests/test_graph_lasso.py | 37 | 2901 | """ Test the graph_lasso module.
"""
import sys
import numpy as np
from scipy import linalg
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_less
from sklearn.covariance import (graph_lasso, GraphLasso, GraphLassoCV,
empirical_... | bsd-3-clause |
anewmark/galaxy_dark_matter | call2_age_lum.py | 1 | 10505 | import astropy.table as table
import numpy as np
from defcuts import *
from defflags import *
from halflight_first import *
from def_get_mags import *
from def_halflight_math import *
from def_ages import *
ty='mean'
stax=True
if stax==False:
tag=''
else:
tag='uplim'
txtdist= ''
txtslope=''
outdir='/Users/amand... | mit |
rvraghav93/scikit-learn | examples/applications/plot_outlier_detection_housing.py | 110 | 5681 | """
====================================
Outlier detection on a real data set
====================================
This example illustrates the need for robust covariance estimation
on a real data set. It is useful both for outlier detection and for
a better understanding of the data structure.
We selected two sets o... | bsd-3-clause |
campagnola/acq4 | acq4/analysis/tools/Fitting.py | 3 | 36863 | #!/usr/bin/env python
from __future__ import print_function
"""
Python class wrapper for data fitting.
Includes the following external methods:
getFunctions returns the list of function names (dictionary keys)
FitRegion performs the fitting
Note that FitRegion will plot on top of the current data using MPlots routines... | mit |
cogmission/nupic.research | projects/sequence_prediction/continuous_sequence/nupic_output.py | 13 | 6732 | # ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013-2015, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This p... | agpl-3.0 |
Fireblend/scikit-learn | benchmarks/bench_plot_omp_lars.py | 266 | 4447 | """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 |
loli/sklearn-ensembletrees | examples/plot_learning_curve.py | 250 | 4171 | """
========================
Plotting Learning Curves
========================
On the left side the learning curve of a naive Bayes classifier is shown for
the digits dataset. Note that the training score and the cross-validation score
are both not very good at the end. However, the shape of the curve can be found
in ... | bsd-3-clause |
vortex-ape/scikit-learn | doc/conf.py | 5 | 10156 | # -*- coding: utf-8 -*-
#
# scikit-learn documentation build configuration file, created by
# sphinx-quickstart on Fri Jan 8 09:13:42 2010.
#
# 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.
... | bsd-3-clause |
drericstrong/pyedna | examples/examples.py | 1 | 1220 | # -*- coding: utf-8 -*-
import pandas as pd
import pyedna.ezdna as dna
# The following code will pull snap data from TESTPNT1 over a 30-second interval:
tag = "TESTSITE.TESTSERVICE.TESTPNT1" # format site.service.tag
start = "12/01/16 01:01:01" # format mm/dd/yy hh:mm:ss
end = "01/03/17 01:01:01... | agpl-3.0 |
juhuntenburg/pipelines | src/mindwandering/resting_state_volume_analysis_group_level.py | 2 | 27788 | import nipype.pipeline.engine as pe
import nipype.interfaces.io as nio
import nipype.interfaces.utility as util
import nipype.interfaces.fsl as fsl
import os
import pandas as pd
from CPAC.group_analysis.group_analysis import create_group_analysis
dropbox_root = "/scr/adenauer1/PowerFolder/Dropbox"
regressors_file = dr... | mit |
oesteban/mriqc | mriqc/classifier/sklearn/_split.py | 1 | 4547 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
# @Author: oesteban
# @Date: 2017-06-14 12:47:30
import numpy as np
from sklearn.utils import indexable
from sklearn.model_selection import (LeavePGroupsOut,... | bsd-3-clause |
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