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 |
|---|---|---|---|---|---|
cdiazbas/LMpyMilne | milne.py | 1 | 6532 | # Author: cdiazbas@iac.es
# Code: Zeeman pattern
# ADDED: velocidad LOS
# ADDED: signo a eta_blue y eta_red
# ADDED: normalization factor in p,b,r eta + rho profiles
from math import pi, sin, cos
from numpy import sqrt, arange
# from zeeman import *
# from fvoigt import fvoigt
from copy import deepcopy
from mutils2 i... | mit |
zeromon77/EMtools | convert_tot_V01.py | 1 | 4418 | #!/usr/bin/env python3
## This script convert 'dm3' image files from Gatan and 'ser' image files from FEI
## Prerequsits: hyperspy, numpy, matplotlib
# Script information for the file.
__author__ = "Daesung Park"
__email__ = "zeromon.park@gmail.com"
__version__ = "0.1"
__copyright__ = "Copyright (c) 2017 Daesung Par... | gpl-2.0 |
victorbergelin/scikit-learn | sklearn/tests/test_learning_curve.py | 225 | 10791 | # Author: Alexander Fabisch <afabisch@informatik.uni-bremen.de>
#
# License: BSD 3 clause
import sys
from sklearn.externals.six.moves import cStringIO as StringIO
import numpy as np
import warnings
from sklearn.base import BaseEstimator
from sklearn.learning_curve import learning_curve, validation_curve
from sklearn.u... | bsd-3-clause |
andyfaff/scipy | scipy/signal/wavelets.py | 16 | 14046 | import numpy as np
from scipy.linalg import eig
from scipy.special import comb
from scipy.signal import convolve
__all__ = ['daub', 'qmf', 'cascade', 'morlet', 'ricker', 'morlet2', 'cwt']
def daub(p):
"""
The coefficients for the FIR low-pass filter producing Daubechies wavelets.
p>=1 gives the order of... | bsd-3-clause |
zorojean/scikit-learn | examples/linear_model/lasso_dense_vs_sparse_data.py | 348 | 1862 | """
==============================
Lasso on dense and sparse data
==============================
We show that linear_model.Lasso provides the same results for dense and sparse
data and that in the case of sparse data the speed is improved.
"""
print(__doc__)
from time import time
from scipy import sparse
from scipy ... | bsd-3-clause |
mendes-jose/xde-sim | src/aiv/scripts/table2chart.py | 1 | 21339 | import numpy as np
import numpy.linalg as LA
import matplotlib as mpl
import matplotlib.pyplot as plt
import os
import csv
import xml.etree.ElementTree as ET
from optparse import OptionParser
#C:\Users\JM246044\workspace\dev\xde\xde\xde\xde\build\out\Release\bin\..\..\..\..\src\aiv\
def wrapTo2Pi(angle):
""" Map a... | gpl-3.0 |
ejhumphrey/icml16-dml | dml/driver.py | 1 | 3025 | """Primary training driver for the NLSE models.
Example
-------
$ python dml/driver.py \
data/nlse_config.yaml \
--output_file training_config.yaml
"""
from __future__ import print_function
import argparse
import copy
import optimus
import os
import pandas as pd
import yaml
import dml.data as D
import dml.m... | mit |
wpreimes/gldas | docs/conf.py | 1 | 9151 | # -*- coding: utf-8 -*-
#
# 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 configuration values have a default; values that are commented out
# serve to show the default.
import os
impo... | bsd-3-clause |
tomsilver/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backend_bases.py | 69 | 69740 | """
Abstract base classes define the primitives that renderers and
graphics contexts must implement to serve as a matplotlib backend
:class:`RendererBase`
An abstract base class to handle drawing/rendering operations.
:class:`FigureCanvasBase`
The abstraction layer that separates the
:class:`matplotlib.fi... | gpl-3.0 |
chenyyx/scikit-learn-doc-zh | examples/en/gaussian_process/plot_gpc_iris.py | 100 | 2269 | """
=====================================================
Gaussian process classification (GPC) on iris dataset
=====================================================
This example illustrates the predicted probability of GPC for an isotropic
and anisotropic RBF kernel on a two-dimensional version for the iris-dataset.
... | gpl-3.0 |
elkingtonmcb/cloudbiolinux | installed_files/ipython_config.py | 15 | 14156 | # Configuration file for ipython.
c = get_config()
c.InteractiveShell.autoindent = True
c.InteractiveShell.colors = 'Linux'
c.InteractiveShell.confirm_exit = False
c.AliasManager.user_aliases = [
('ll', 'ls -l'),
('lt', 'ls -ltr'),
]
#------------------------------------------------------------------------------
#... | mit |
canard0328/malss | malss/app/learning_curve_base.py | 1 | 2255 | # coding: utf-8
from PyQt5.QtWidgets import QSizePolicy
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
from .content import Content
class LearningCurveBase(Content):
def __init__(self, parent=None, title='', params=None):
su... | mit |
panda0881/pycharmtesting | testing2.py | 1 | 6320 | import datetime
import numpy as np
import matplotlib.colors as colors
import matplotlib.finance as finance
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib.font_manager as font_manager
startdate = datetime.date(2015, ... | apache-2.0 |
GuessWhoSamFoo/pandas | pandas/core/indexes/range.py | 2 | 24595 | from datetime import timedelta
import operator
from sys import getsizeof
import warnings
import numpy as np
from pandas._libs import index as libindex, lib
import pandas.compat as compat
from pandas.compat import get_range_parameters, lrange, range
from pandas.compat.numpy import function as nv
from pandas.util._deco... | bsd-3-clause |
BigTone2009/sms-tools | software/transformations_interface/sineTransformations_function.py | 25 | 5018 | # function call to the transformation functions of relevance for the sineModel
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import get_window
import sys, os
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../models/'))
sys.path.append(os.path.join(os.path.dirname(os.p... | agpl-3.0 |
trustedanalytics/spark-tk | regression-tests/sparktkregtests/testcases/frames/frame_group_by_test.py | 13 | 10492 | # 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 |
jdgwartney/boundary-api-cli | boundary/measurement_plot.py | 4 | 4824 | #
# Copyright 2015 BMC Software, 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 ... | apache-2.0 |
rosswhitfield/mantid | scripts/HFIR_4Circle_Reduction/mpl2dgraphicsview.py | 3 | 12888 | # Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2018 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 +
#pyl... | gpl-3.0 |
ARudiuk/mne-python | examples/realtime/plot_compute_rt_decoder.py | 4 | 3602 | """
=======================
Decoding real-time data
=======================
Supervised machine learning applied to MEG data in sensor space.
Here the classifier is updated every 5 trials and the decoding
accuracy is plotted
"""
# Authors: Mainak Jas <mainak@neuro.hut.fi>
#
# License: BSD (3-clause)
import numpy as np... | bsd-3-clause |
nvoron23/scikit-learn | sklearn/covariance/robust_covariance.py | 198 | 29735 | """
Robust location and covariance estimators.
Here are implemented estimators that are resistant to outliers.
"""
# Author: Virgile Fritsch <virgile.fritsch@inria.fr>
#
# License: BSD 3 clause
import warnings
import numbers
import numpy as np
from scipy import linalg
from scipy.stats import chi2
from . import empir... | bsd-3-clause |
sarahgrogan/scikit-learn | examples/plot_johnson_lindenstrauss_bound.py | 127 | 7477 | r"""
=====================================================================
The Johnson-Lindenstrauss bound for embedding with random projections
=====================================================================
The `Johnson-Lindenstrauss lemma`_ states that any high dimensional
dataset can be randomly projected i... | bsd-3-clause |
heplesser/nest-simulator | pynest/examples/sinusoidal_gamma_generator.py | 8 | 11885 | # -*- coding: utf-8 -*-
#
# sinusoidal_gamma_generator.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 ... | gpl-2.0 |
ryfeus/lambda-packs | LightGBM_sklearn_scipy_numpy/source/sklearn/utils/tests/test_utils.py | 27 | 9605 | from itertools import chain, product
import warnings
import numpy as np
import scipy.sparse as sp
from scipy.linalg import pinv2
from scipy.sparse.csgraph import laplacian
from sklearn.utils.testing import (assert_equal, assert_raises, assert_true,
assert_almost_equal, assert_array_... | mit |
tmadl/semisup-learn | examples/plotutils.py | 1 | 1768 | import matplotlib.pyplot as plt
import numpy as np
cols = [np.array([1,0,0]),np.array([0,1,0])] # colors
def evaluate_and_plot(model, Xs, ys, ytrue, lbl, subplot = None, block=False):
if subplot != None:
plt.subplot(2,2,subplot)
# predict, and evaluate
pred = model.predict(Xs)
acc = ... | mit |
JosmanPS/scikit-learn | examples/linear_model/plot_ridge_path.py | 254 | 1655 | """
===========================================================
Plot Ridge coefficients as a function of the regularization
===========================================================
Shows the effect of collinearity in the coefficients of an estimator.
.. currentmodule:: sklearn.linear_model
:class:`Ridge` Regressi... | bsd-3-clause |
mikeengland/fireant | fireant/queries/builder/dimension_latest_query_builder.py | 1 | 2059 | import pandas as pd
from fireant.dataset.fields import Field
from fireant.utils import (
alias_for_alias_selector,
immutable,
)
from .query_builder import QueryBuilder, QueryException, add_hints
from ..execution import fetch_data
from ..sql_transformer import make_latest_query
class DimensionLatestQueryBuild... | apache-2.0 |
gef756/statsmodels | statsmodels/datasets/statecrime/data.py | 3 | 3141 | #! /usr/bin/env python
"""Statewide Crime Data"""
__docformat__ = 'restructuredtext'
COPYRIGHT = """Public domain."""
TITLE = """Statewide Crime Data 2009"""
SOURCE = """
All data is for 2009 and was obtained from the American Statistical Abstracts except as indicated below.
"""
DESCRSHORT = """State ... | bsd-3-clause |
xwolf12/scikit-learn | examples/cluster/plot_lena_compress.py | 271 | 2229 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Vector Quantization Example
=========================================================
The classic image processing example, Lena, an 8-bit grayscale
bit-depth, 512 x 512 sized image, is used here to illustrate
how ... | bsd-3-clause |
flash-center/PROBLEM | examples/donut/plot_images.py | 1 | 1378 | import numpy as np
import matplotlib.pyplot as plt
import problem.deflect
# Load the plasma coordinates.
X = np.loadtxt('x.dat', delimiter=',')
Y = np.loadtxt('y.dat', delimiter=',')
# Checkpoint interval.
interval = 1000
# Plasma parameters.
ri = 1.3 # Distance from proton source to plasma.
li = 0.2 # Distance acro... | mit |
aewhatley/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 |
iohannez/gnuradio | gr-filter/examples/fir_filter_ccc.py | 7 | 4023 | #!/usr/bin/env python
#
# Copyright 2013 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 option)
# ... | gpl-3.0 |
hydroffice/hyo_soundspeed | hyo2/soundspeed/profile/ray_tracing/tracedprofile.py | 1 | 8365 | import numpy as np
import math
import logging
from scipy.interpolate import interp1d
logger = logging.getLogger(__name__)
class TracedProfile:
def __init__(self, ssp, half_swath=65, avg_depth=10000, tss_depth=None, tss_value=None):
# PyDateTime_IMPORT
self.avg_depth = avg_depth
self.half... | lgpl-2.1 |
openfisca/reformator | openfisca_reformator/econometrics.py | 1 | 1376 | # -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
def get_cumulative(revs):
revs = sorted(revs)
cum_revs = []
current = 0
for rev in revs:
current = current + rev
cum_revs.append(current)
return cum_revs
def draw_ginis(old_revenus, new_revenus):
fig = plt.figure()
ax... | agpl-3.0 |
conversationai/conversationai-crowdsource | constructiveness_toxicity_crowdsource/common/data_visualization_functions.py | 1 | 1535 | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import krippendorff
from krippendorff import alpha
# create and save plots
def plot_donut_chart(size, labels, colors, title = 'Test', dpi = 300):
'''
:param size: (list of floats) Sizes for each slice in the donut plot
:param label... | apache-2.0 |
rhattersley/iris | docs/iris/example_tests/extest_util.py | 4 | 3110 | # (C) British Crown Copyright 2010 - 2016, Met Office
#
# This file is part of Iris.
#
# Iris 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 Software Foundation, either version 3 of the License, or
# (at your option) any l... | lgpl-3.0 |
EricCline/CEM_inc | env/lib/python2.7/site-packages/IPython/core/tests/test_pylabtools.py | 15 | 7752 | """Tests for pylab tools module.
"""
#-----------------------------------------------------------------------------
# Copyright (c) 2011, the IPython Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#---------... | mit |
dhaitz/CalibFW | plotting/plotbase.py | 1 | 20971 | # -*- coding: utf-8 -*-
"""Basic plot formatting and helper module.
This module contains all the often used plotting tools
"""
import os
import os.path
import sys
import copy
import matplotlib.pyplot as plt
from time import clock
import inspect
import math
import matplotlib
import pkgutil
from matplotlib.backends.ba... | gpl-2.0 |
bigdataelephants/scikit-learn | examples/svm/plot_iris.py | 62 | 3251 | """
==================================================
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 |
ChinaQuants/zipline | zipline/utils/tradingcalendar.py | 6 | 11182 | #
# Copyright 2013 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 |
shaunstanislaus/BDA_py_demos | demos_ch3/demo3_3.py | 19 | 7882 | """Bayesian Data Analysis, 3rd ed
Chapter 3, demo 3
Visualise the marginal distribution of mu as a mixture of normals.
"""
from __future__ import division
import os, threading
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
# import from utilities
im... | gpl-3.0 |
SimeonFritz/aima-python | submissions/VanderKallen/myNN.py | 13 | 3398 | import traceback
from submissions.VanderKallen import slavery
from sklearn.neural_network import MLPRegressor
from sklearn.neural_network import MLPClassifier
class DataFrame:
data = []
feature_names = []
target = []
target_names = []
slaveTot = DataFrame()
slaveAS = DataFrame()
slaveBoth = DataFrame(... | mit |
ryfeus/lambda-packs | Sklearn_scipy_numpy/source/sklearn/linear_model/tests/test_sgd.py | 30 | 44274 | import pickle
import unittest
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing ... | mit |
vladcalin/eventer | quartz/lib/utilscripts/generate_chart.py | 1 | 1594 | import json
import argparse
import random
import datetime
import matplotlib.pyplot as plt
def parse_data(data):
with open(data, "r") as f:
return json.load(f)
def make_line_chart(args):
data = parse_data(args.data)
plt.plot(range(len(data)), data)
plt.title(args.title)
plt.savefig(args.... | mit |
robcarver17/pysystemtrade | sysobjects/instruments.py | 1 | 8540 | from syscore.objects import arg_not_supplied
from syscore.genutils import flatten_list
from dataclasses import dataclass
import pandas as pd
EMPTY_INSTRUMENT = ""
class futuresInstrument(object):
def __init__(self, instrument_code: str):
self._instrument_code = instrument_code
@property
def inst... | gpl-3.0 |
kastnerkyle/speech_density | speech_mel_lstmrbm.py | 1 | 18503 | # Author: Kratarth Goel
# BITS Pilani (2014)
# LSTM-RBM for music generation
import sys
import os
import tables
import tarfile
import fnmatch
import random
import numpy
import numpy as np
from scipy.io import wavfile
import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
f... | bsd-3-clause |
MohammedWasim/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 |
costypetrisor/scikit-learn | sklearn/feature_selection/tests/test_from_model.py | 244 | 1593 | import numpy as np
import scipy.sparse as sp
from nose.tools import assert_raises, assert_true
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_greater
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGD... | bsd-3-clause |
crisbarros/trading-with-python | nautilus/nautilus.py | 77 | 5403 | '''
Created on 26 dec. 2011
Copyright: Jev Kuznetsov
License: BSD
'''
from PyQt4.QtCore import *
from PyQt4.QtGui import *
from ib.ext.Contract import Contract
from ib.opt import ibConnection
from ib.ext.Order import Order
import tradingWithPython.lib.logger as logger
from tradingWithPython.lib.eve... | bsd-3-clause |
noxer-org/noxer | examples/vae_gaussian.py | 2 | 1702 | """
Simplistic example usage of Variational Auto Encoder
for generation of artificial data.
"""
import numpy as np
X = np.zeros((1024, 1))
Y = np.random.randn(len(X), 3)
from sklearn.preprocessing import RobustScaler, StandardScaler
from sklearn.base import clone
from sklearn.pipeline import make_pipeline
from skle... | mit |
jeffsilverm/presentation | SeaGL-2018/Plotting_the_results_of_measure2.sh.py | 1 | 7102 | #! /usr/bin/python3
# coding: utf-8
# From http://pandas.pydata.org/pandas-docs/version/0.23/visualization.html
# http://pandas.pydata.org/pandas-docs/version/0.23/visualization.html
# #scatter-matrix-plot
# From https://pythonprogramming.net/matplotlib-3d-scatterplot-tutorial/
# This does NOT work in a jupyter noteboo... | gpl-2.0 |
alexeyum/scikit-learn | examples/exercises/plot_iris_exercise.py | 323 | 1602 | """
================================
SVM Exercise
================================
A tutorial exercise for using different SVM kernels.
This exercise is used in the :ref:`using_kernels_tut` part of the
:ref:`supervised_learning_tut` section of the :ref:`stat_learn_tut_index`.
"""
print(__doc__)
import numpy as np
i... | bsd-3-clause |
stylianos-kampakis/scikit-learn | examples/linear_model/plot_lasso_coordinate_descent_path.py | 254 | 2639 | """
=====================
Lasso and Elastic Net
=====================
Lasso and elastic net (L1 and L2 penalisation) implemented using a
coordinate descent.
The coefficients can be forced to be positive.
"""
print(__doc__)
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
import num... | bsd-3-clause |
josl/ThinkStats2 | code/hinc_soln.py | 67 | 4296 | """This file contains code used in "Think Stats",
by Allen B. Downey, available from greenteapress.com
Copyright 2014 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
from __future__ import print_function
import numpy as np
import pandas
import hinc
import thinkplot
import thinkstats2
""... | gpl-3.0 |
hvanwyk/quadmesh | tests/test_plot.py | 1 | 6466 | '''
Created on Feb 24, 2017
@author: hans-werner
'''
import unittest
from plot import Plot
from mesh import Mesh
from fem import System, QuadFE
import matplotlib.pyplot as plt
#from mpl_toolkits.mplot3d import * # @UnresolvedImport
import numpy as np
class TestPlot(unittest.TestCase):
def test_plot_mesh(self)... | mit |
terkkila/scikit-learn | sklearn/feature_extraction/dict_vectorizer.py | 234 | 12267 | # Authors: Lars Buitinck
# Dan Blanchard <dblanchard@ets.org>
# License: BSD 3 clause
from array import array
from collections import Mapping
from operator import itemgetter
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..ext... | bsd-3-clause |
pianomania/scikit-learn | sklearn/decomposition/pca.py | 7 | 28653 | """ Principal Component Analysis
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Denis A. Engemann <denis-alexander.engemann@inria.fr>
# Michael Eickenberg <michael.eickenberg@inria.fr>... | bsd-3-clause |
Adai0808/scikit-learn | examples/mixture/plot_gmm.py | 248 | 2817 | """
=================================
Gaussian Mixture Model Ellipsoids
=================================
Plot the confidence ellipsoids of a mixture of two Gaussians with EM
and variational Dirichlet process.
Both models have access to five components with which to fit the
data. Note that the EM model will necessari... | bsd-3-clause |
jetuk/pywr | examples/two_reservoir_moea.py | 1 | 8924 | """
This example shows the trade-off (pareto frontier) of deficit against cost by altering a reservoir control curve.
Two types of control curve are possible. The first is a monthly control curve containing one value for each
month. The second is a harmonic control curve with cosine terms around a mean. Both Parameter... | gpl-3.0 |
jreinhardt/manual-labour | src/manuallabour/exporters/gantt.py | 1 | 2764 | # Manual labour - a library for step-by-step instructions
# Copyright (C) 2014 Johannes Reinhardt <jreinhardt@ist-dein-freund.de>
#
# This library 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 Software Foundation; eith... | lgpl-2.1 |
nesterione/scikit-learn | sklearn/gaussian_process/gaussian_process.py | 83 | 34544 | # -*- coding: utf-8 -*-
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# (mostly translation, see implementation details)
# Licence: BSD 3 clause
from __future__ import print_function
import numpy as np
from scipy import linalg, optimize
from ..base import BaseEstimator, RegressorMixin
from ..metrics... | bsd-3-clause |
shaharkadmiel/pySW4 | pySW4/utils/spectral.py | 2 | 9858 | """
Python module for spectral analysis.
.. module:: spectral
:author:
Shahar Shani-Kadmiel (s.shanikadmiel@tudelft.nl)
:copyright:
Shahar Shani-Kadmiel
:license:
This code is distributed under the terms of the
GNU Lesser General Public License, Version 3
(https://www.gnu.org/copyleft/lesser.htm... | gpl-3.0 |
moonbury/notebooks | github/MasteringMLWithScikit-learn/8365OS_04_Codes/ch42.py | 3 | 1763 | import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import train_test_split
from sklearn.metrics import precision_score, recall_score, roc_auc_score, auc, confusion_matrix
import numpy as np
from scipy.sparse i... | gpl-3.0 |
JackKelly/neuralnilm_prototype | scripts/e536.py | 2 | 7704 | from __future__ import print_function, division
import matplotlib
import logging
from sys import stdout
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
from neuralnilm import (Net, RealApplianceSource)
from neuralnilm.source import (standardise, discretize, fdiff, power_and_fdiff,
... | mit |
jm-begon/scikit-learn | sklearn/preprocessing/tests/test_imputation.py | 213 | 11911 | import numpy as np
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 import assert_false
from sklearn.utils.testing import assert_true
from sklearn.preprocessing.imputa... | bsd-3-clause |
MechCoder/scikit-learn | examples/decomposition/plot_image_denoising.py | 6 | 5958 | """
=========================================
Image denoising using dictionary learning
=========================================
An example comparing the effect of reconstructing noisy fragments
of a raccoon face image using firstly online :ref:`DictionaryLearning` and
various transform methods.
The dictionary is fi... | bsd-3-clause |
statkraft/shyft-doc | notebooks/nea-example/simulation-api.py | 1 | 18932 |
import os
import datetime as dt
import pandas as pd
import sys
from matplotlib import pyplot as plt
from netCDF4 import Dataset
os.environ['SHYFTDATA'] = '/home/johnbur/workspace/shyft_workspace/shyft-data/'
sys.path.insert(0, '/home/johnbur/workspace/shyft_workspace/shyft')
from shyft import api
import shyft
from ... | lgpl-3.0 |
droundy/deft | papers/square-well-fluid/figs/animate-liquid-vapor.py | 1 | 4121 | #!/usr/bin/python2
import matplotlib, sys
if 'show' not in sys.argv:
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy, time, os
matplotlib.rc('text', usetex=True)
import readandcompute
ww = float(sys.argv[1])
#arg ww = [1.3]
ff = float(sys.argv[2])
#arg ff = [0.1, 0.2, 0.3]
lenx = float(sys.arg... | gpl-2.0 |
gfyoung/pandas | pandas/core/array_algos/putmask.py | 2 | 5055 | """
EA-compatible analogue to to np.putmask
"""
from typing import Any
import warnings
import numpy as np
from pandas._libs import lib
from pandas._typing import ArrayLike
from pandas.core.dtypes.cast import convert_scalar_for_putitemlike, maybe_promote
from pandas.core.dtypes.common import is_float_dtype, is_intege... | bsd-3-clause |
shusenl/scikit-learn | sklearn/metrics/tests/test_common.py | 54 | 41186 | 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... | bsd-3-clause |
jwdegee/2017_eLife | hddm_regression.py | 1 | 14598 | #!/usr/bin/env python
# encoding: utf-8
"""
Created by Jan Willem de Gee on 2011-02-16.
Copyright (c) 2011 __MyCompanyName__. All rights reserved.
"""
import os, sys, pickle, time
import datetime
import math
import numpy as np
import scipy as sp
import matplotlib
matplotlib.use('Agg') # Force matplotlib to not use any ... | mit |
Djabbz/scikit-learn | examples/decomposition/plot_incremental_pca.py | 244 | 1878 | """
===============
Incremental PCA
===============
Incremental principal component analysis (IPCA) is typically used as a
replacement for principal component analysis (PCA) when the dataset to be
decomposed is too large to fit in memory. IPCA builds a low-rank approximation
for the input data using an amount of memo... | bsd-3-clause |
wdurhamh/statsmodels | statsmodels/datasets/template_data.py | 31 | 1680 | #! /usr/bin/env python
"""Name of dataset."""
__docformat__ = 'restructuredtext'
COPYRIGHT = """E.g., This is public domain."""
TITLE = """Title of the dataset"""
SOURCE = """
This section should provide a link to the original dataset if possible and
attribution and correspondance information for the da... | bsd-3-clause |
pyrocko/pyrocko | test/base/test_gmtpy.py | 1 | 11149 | from __future__ import division, print_function, absolute_import
import os
import math
import tempfile
import shutil
import unittest
import numpy as num
from numpy.testing import assert_allclose
from matplotlib import image, pyplot as plt
from pyrocko import util
from pyrocko.plot import gmtpy
from pyrocko.plot.gmtpy ... | gpl-3.0 |
lmccalman/reverend | demo/visualise_filtering.py | 1 | 1882 | # Reverend -- Practical Bayesian Inference with Kernel Embeddings
# Copyright (C) 2013 Lachlan McCalman
# lachlan@mccalman.info
# Reverend 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 t... | gpl-3.0 |
changsiyao/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 |
asimshankar/tensorflow | tensorflow/examples/learn/iris_custom_model.py | 13 | 3469 | # 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 appl... | apache-2.0 |
stevenzhang18/Indeed-Flask | lib/pandas/io/stata.py | 9 | 78805 | """
Module contains tools for processing Stata files into DataFrames
The StataReader below was originally written by Joe Presbrey as part of PyDTA.
It has been extended and improved by Skipper Seabold from the Statsmodels
project who also developed the StataWriter and was finally added to pandas in
a once again improv... | apache-2.0 |
nlpub/russe-evaluation | russe/common.py | 1 | 8514 | import pandas as pd
import codecs
from os.path import splitext
from pandas import read_csv
from collections import defaultdict
import re
from pymystem3 import Mystem
# dependenies to the dsl nlp repository
from nlp.common import wc
from nlp.patterns import re_numbers
_mystem = Mystem()
def get_pos(text):
pos = ... | mit |
rollend/trading-with-python | lib/extra.py | 77 | 2540 | '''
Created on Apr 28, 2013
Copyright: Jev Kuznetsov
License: BSD
'''
from __future__ import print_function
import sys
import urllib
import os
import xlrd # module for excel file reading
import pandas as pd
class ProgressBar:
def __init__(self, iterations):
self.iterations = iterations
... | bsd-3-clause |
boxcontrol/lesAdmin | app/loginMap.py | 1 | 1161 | #import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from geopy.geocoders import GoogleV3
geolocator = GoogleV3()
cities = ['Amsterdam', 'Milan', 'Dallas', 'Los Angeles', 'Montreal', 'Lenoir', 'Nottingham', 'Rotterdam', 'Dusseldorf',
'Falkenstein... | cc0-1.0 |
fabianp/scikit-learn | examples/svm/plot_separating_hyperplane_unbalanced.py | 329 | 1850 | """
=================================================
SVM: Separating hyperplane for unbalanced classes
=================================================
Find the optimal separating hyperplane using an SVC for classes that
are unbalanced.
We first find the separating plane with a plain SVC and then plot
(dashed) the ... | bsd-3-clause |
liyu1990/sklearn | sklearn/utils/tests/test_sparsefuncs.py | 30 | 17391 | import numpy as np
import scipy.sparse as sp
from scipy import linalg
from numpy.testing import (assert_array_almost_equal,
assert_array_equal,
assert_equal)
from sklearn.datasets import make_classification
from sklearn.utils.sparsefuncs import (mean_variance_axis... | bsd-3-clause |
NICTA/dora | setup.py | 1 | 1537 | """ Setup utility for the dora package. """
from setuptools import setup, find_packages
# from setuptools.command.test import test as TestCommand
setup(
name='dora',
version='0.1',
description='Active sampling using a non-parametric regression model.',
url='http://github.com/nicta/dora',
... | apache-2.0 |
rahulreddy1004/pso_multiObj | tesst.py | 2 | 10255 | import numpy as np
import numpy.matlib as matlib
import random
from math import sin, sqrt
import matplotlib.pyplot as plt
''' Problem Definition'''
def MOP2(x):
n=len(x)
z1=1-np.exp(-np.sum(np.power((x-1/sqrt(n)),2))) #ckeck exp for single
z2=1-np.e... | bsd-2-clause |
hsuantien/scikit-learn | examples/mixture/plot_gmm.py | 248 | 2817 | """
=================================
Gaussian Mixture Model Ellipsoids
=================================
Plot the confidence ellipsoids of a mixture of two Gaussians with EM
and variational Dirichlet process.
Both models have access to five components with which to fit the
data. Note that the EM model will necessari... | bsd-3-clause |
eranchetz/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_ps.py | 69 | 50262 | """
A PostScript backend, which can produce both PostScript .ps and .eps
"""
from __future__ import division
import glob, math, os, shutil, sys, time
def _fn_name(): return sys._getframe(1).f_code.co_name
try:
from hashlib import md5
except ImportError:
from md5 import md5 #Deprecated in 2.5
from tempfile im... | agpl-3.0 |
tomhunter-gh/Lean | Algorithm.Python/PythonPackageTestAlgorithm.py | 2 | 6403 | # QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect 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 Li... | apache-2.0 |
johnbachman/rasmodel | ras_gdp_binding.py | 6 | 2813 | from rasmodel.scenarios.default import model
import numpy as np
from matplotlib import pyplot as plt
from pysb.integrate import Solver
from pysb import *
from tbidbaxlipo.util import fitting
# Zero out all initial conditions
for ic in model.initial_conditions:
ic[1].value = 0
# In this first experiment, 0.5 uM ... | mit |
kdebrab/pandas | doc/source/conf.py | 5 | 21946 | # -*- coding: utf-8 -*-
#
# pandas documentation build configuration file, created by
#
# 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 configuration values have a default; values tha... | bsd-3-clause |
shyamalschandra/scikit-learn | sklearn/externals/joblib/__init__.py | 23 | 4764 | """ 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 |
glorizen/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/figure.py | 69 | 38331 | """
The figure module provides the top-level
:class:`~matplotlib.artist.Artist`, the :class:`Figure`, which
contains all the plot elements. The following classes are defined
:class:`SubplotParams`
control the default spacing of the subplots
:class:`Figure`
top level container for all plot elements
"""
impo... | agpl-3.0 |
yavalvas/yav_com | build/matplotlib/lib/mpl_examples/user_interfaces/rec_edit_gtk_custom.py | 9 | 1306 | """
generate an editable gtk treeview widget for record arrays with custom
formatting of the cells and show how to limit string entries to a list
of strings
"""
from __future__ import print_function
import gtk
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.cbook as cbook
import mpl_toolkits.gtktool... | mit |
Barmaley-exe/scikit-learn | sklearn/utils/tests/test_shortest_path.py | 42 | 2894 | from collections import defaultdict
import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.utils.graph import (graph_shortest_path,
single_source_shortest_path_length)
def floyd_warshall_slow(graph, directed=False):
N = graph.shape[0]
#set nonzer... | bsd-3-clause |
ThomasMiconi/htmresearch | 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 |
saiwing-yeung/scikit-learn | sklearn/decomposition/tests/test_incremental_pca.py | 297 | 8265 | """Tests for Incremental PCA."""
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn import datasets
from sklearn.decomposition import PCA, IncrementalPCA
iris = datasets.load... | bsd-3-clause |
jundongl/PyFeaST | skfeature/function/similarity_based/SPEC.py | 3 | 3515 | import numpy.matlib
import numpy as np
from scipy.sparse import *
from sklearn.metrics.pairwise import rbf_kernel
from numpy import linalg as LA
def spec(X, **kwargs):
"""
This function implements the SPEC feature selection
Input
-----
X: {numpy array}, shape (n_samples, n_features)
input... | gpl-2.0 |
yukisakurai/hhana | mva/plotting/classify.py | 5 | 12122 |
# stdlib imports
import os
# local imports
from . import log
from ..variables import VARIABLES
from .. import PLOTS_DIR
from .draw import draw
from statstools.utils import efficiency_cut, significance
# matplotlib imports
from matplotlib import cm
from matplotlib import pyplot as plt
from matplotlib.ticker import Ma... | gpl-3.0 |
google-research/understanding-transfer-learning | libtrain.py | 1 | 8720 | # Copyright 2020 Google 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | apache-2.0 |
stinebuu/nest-simulator | pynest/examples/gif_population.py | 7 | 5050 | # -*- coding: utf-8 -*-
#
# gif_population.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,... | gpl-2.0 |
radiasoft/pypi-shadow3 | examples/primer/primer_ch24_run1.py | 1 | 2535 | #
# example of running shadow under python
#
# corresponds to the example in Chapter 2.4 of the SHADOW Primer:
# http://ftp.esrf.eu/pub/scisoft/shadow3/Shadow3Primer.pdf
#
# Notes:
#
# -This example sets directly the parameters of the sorce and optical
# element for the corresponding case. This is the "direct" ... | gpl-3.0 |
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